diff --git a/config_clm_baseline_example.yaml b/config_clm_baseline_example.yaml new file mode 100644 index 000000000..411bb47cd --- /dev/null +++ b/config_clm_baseline_example.yaml @@ -0,0 +1,550 @@ +#============================== +#config_clm_baseline_example.yaml + +#This is the main CAM/CLM diagnostics config file +#for doing comparisons of a CAM or CLM run against +#another run, or baseline simulation. + +#Currently, if one is on NCAR's Casper or +#Cheyenne machine, then only the diagnostic output +#paths are needed, at least to perform a quick test +#run (these are indicated with "MUST EDIT" comments). +#Running these diagnostics on a different machine, +#or with a different, non-example simulation, will +#require additional modifications. +# +#Config file Keywords: +#-------------------- +# +#1. Using ${xxx} will substitute that text with the +# variable referenced by xxx. For example: +# +# cam_case_name: cool_run +# cam_climo_loc: /some/where/${cam_case_name} +# +# will set "cam_climo_loc" in the diagnostics package to: +# /some/where/cool_run +# +# Please note that currently this will only work if the +# variable only exists in one location in the file. +# +#2. Using ${.xxx} will do the same as +# keyword 1 above, but specifies which sub-section the +# variable is coming from, which is necessary for variables +# that are repeated in different subsections. For example: +# +# diag_basic_info: +# cam_climo_loc: /some/where/${diag_cam_climo.start_year} +# +# diag_cam_climo: +# start_year: 1850 +# +# will set "cam_climo_loc" in the diagnostics package to: +# /some/where/1850 +# +#Finally, please note that for both 1 and 2 the keywords must be lowercase. +#This is because future developments will hopefully use other keywords +#that are uppercase. Also please avoid using periods (".") in variable +#names, as this will likely cause issues with the current file parsing +#system. +#-------------------- +# +##============================== +# +# This file doesn't (yet) read environment variables, so the user must +# set this themselves. It is also a good idea to search the doc for 'user' +# to see what default paths are being set for output/working files. +# +# Note that the string 'USER-NAME-NOT-SET' is used in the jupyter script +# to check for a failure to customize +# +user: 'wwieder' + + +#This first set of variables specify basic info used by all diagnostic runs: +diag_basic_info: + + #Is this a model vs observations comparison? + #If "false" or missing, then a model-model comparison is assumed: + compare_obs: false + + #Generate HTML website (assumed false if missing): + #Note: The website files themselves will be located in the path + #specified by "cam_diag_plot_loc", under the "/website" subdirectory, + #where "" is the subdirectory created for this particular diagnostics run + #(usually "case_vs_obs_XXX" or "case_vs_baseline_XXX"). + create_html: true + + #Location of observational datasets: + #Note: this only matters if "compare_obs" is true and the path + #isn't specified in the variable defaults file. + obs_data_loc: /glade/campaign/cgd/amp/amwg/ADF_obs + + #Location where CAM climatology files are stored: + cam_climo_loc: /glade/derecho/scratch/${user}/ADF/${diag_cam_climo.cam_case_name}/climo + + #Location where re-gridded and interpolated CAM climatology files are stored: + cam_climo_regrid_loc: /glade/derecho/scratch/${user}/ADF/${diag_cam_climo.cam_case_name}/climo/regrid + + #Location where re-gridded and interpolated timeseries files are stored: + cam_ts_regrid_loc: /glade/derecho/scratch/${user}/ADF/${diag_cam_climo.cam_case_name}/ts/regrid + + + #Overwrite CAM re-gridded files? + #If false, or missing, then regridding will be skipped for regridded variables + #that already exist in "cam_climo_regrid_loc" or "cam_ts_regrid_loc": + cam_overwrite_climo_regrid: false + cam_overwrite_ts_regrid: false + + #Location where diagnostic plots are stored: + cam_diag_plot_loc: /glade/derecho/scratch/${user}/ADF/plots + + #Location of ADF variable plotting defaults YAML file: + #If left blank or missing, ADF/lib/adf_variable_defaults.yaml will be used + #Uncomment and change path for custom variable defaults file + #TODO, make a land default path + defaults_file: /glade/u/home/wwieder/python/adf/lib/ldf_variable_defaults.yaml + + #Vertical pressure levels (in hPa) on which to plot 3-D variables + #when using horizontal (e.g. lat/lon) map projections. + #If this config option is missing, then no 3-D variables will be plotted on + #horizontal maps. Please note too that pressure levels must currently match + #what is available in the observations file in order to be plotted in a + #model vs obs run: + plot_press_levels: [200,850] + + #Longitude line on which to center all lat/lon maps. + #If this config option is missing then the central + #longitude will default to 180 degrees E. + central_longitude: 0 + + #Number of processors on which to run the ADF. + #If this config variable isn't present then + #the ADF defaults to one processor. Also, if + #you set it to "*" then it will default + #to all of the processors available on a + #single node/machine: + num_procs: 8 + + #If set to true, then redo all plots even if they already exist. + #If set to false, then if a plot is found it will be skipped: + redo_plot: false + # TODO, seems to redo plots anyway "NOTE: redo_plot is set to False" plotting continues... + +#This second set of variables provides info for the CAM simulation(s) being diagnosed: +diag_cam_climo: + + # History file list of strings to match + # eg. cam.h0 or ocn.pop.h.ecosys.nday1 or hist_str: [cam.h2,cam.h0] + # Only affects timeseries as everything else uses the created timeseries + # Default: + hist_str: clm2.h0 + + #Calculate climatologies? + #If false, the climatology files will not be created: + calc_cam_climo: true + + #Overwrite CAM climatology files? + #If false, or not prsent, then already existing climatology files will be skipped: + cam_overwrite_climo: true + + #Name of CAM case (or CAM run name): + cam_case_name: b.e30_beta05.BLT1850.ne30_t232_wgx3.123 + + #Case nickname + #NOTE: if nickname starts with '0' - nickname must be in quotes! + # ie '026a' as opposed to 026a + #If missing or left blank, will default to cam_case_name + case_nickname: '123' + + #Location of CAM history (h0) files: + #Example test files + cam_hist_loc: /glade/derecho/scratch/hannay/archive/${diag_cam_climo.cam_case_name}/lnd/hist + + #Location of CAM climatologies (to be created and then used by this script) + cam_climo_loc: /glade/derecho/scratch/${user}/ADF/${diag_cam_climo.cam_case_name}/climo + + # TODO, should we be able to define ts_start_year and climo_start_year independently + #model year when time series files should start: + #Note: Leaving this entry blank will make time series + # start at earliest available year. + start_year: 25 + + #model year when time series files should end: + #Note: Leaving this entry blank will make time series + # end at latest available year. + end_year: 35 + + #Do time series files exist? + #If True, then diagnostics assumes that model files are already time series. + #If False, or if simply not present, then diagnostics will attempt to create + #time series files from history (time-slice) files: + cam_ts_done: false + + #Save interim time series files? + #WARNING: This can take up a significant amount of space, + # but will save processing time the next time + cam_ts_save: true + + #Overwrite time series files, if found? + #If set to false, then time series creation will be skipped if files are found: + cam_overwrite_ts: false + + #Location where time series files are (or will be) stored: + cam_ts_loc: /glade/derecho/scratch/${user}/ADF/${diag_cam_climo.cam_case_name}/ts + + #TEM diagnostics + #TODO, this isn't needed for land, but may be a helpful way to think about looking at CLM h1 files + #--------------- + #TEM history file number + #If missing or blank, ADF will default to h4 + tem_hist_str: cam.h4 + + #Location where TEM files are stored: + #NOTE: If path not specified or commented out, TEM calculation/plots will be skipped! + cam_tem_loc: /glade/derecho/scratch/${user}/${diag_cam_climo.cam_case_name}/tem/ + + #Overwrite TEM files, if found? + #If set to false, then TEM creation will be skipped if files are found: + overwrite_tem: false + + #---------------------- + + #You can alternatively provide a list of cases, which will make the ADF + #apply the same diagnostics to each case separately in a single ADF session. + #All of the config variables below show how it is done, and are the only ones + #that need to be lists. This also automatically enables the generation of + #a "main_website" in "cam_diag_plot_loc" that brings all of the different cases + #together under a single website. + + #Also please note that config keywords cannot currently be used in list mode. + + #cam_case_name: + # - b.e23_alpha17f.BLT1850.ne30_t232.098 + # - b.e23_alpha17f.BLT1850.ne30_t232.095 + + #Case nickname + #NOTE: if nickname starts with '0' - nickname must be in quotes! + # ie '026a' as opposed to 026a + #If missing or left blank, will default to cam_case_name + #case_nickname: + # - cool nickname + # - cool nickname 2 + + #calc_cam_climo: + # - true + # - true + + #cam_overwrite_climo: + # - false + # - false + + #cam_hist_loc: + # - /glade/campaign/cgd/amp/amwg/ADF_test_cases/b.e23_alpha17f.BLT1850.ne30_t232.098 + # - /glade/campaign/cgd/amp/amwg/ADF_test_cases/b.e23_alpha17f.BLT1850.ne30_t232.095 + + #cam_climo_loc: + # - /some/where/you/want/to/have/climo_files/ #MUST EDIT! + # - /the/same/or/some/other/climo/files/location + + #start_year: + # - 10 + # - 10 + + #end_year: + # - 14 + # - 14 + + #cam_ts_done: + # - false + # - false + + #cam_ts_save: + # - true + # - true + + #cam_overwrite_ts: + # - false + # - false + + #cam_ts_loc: + # - /some/where/you/want/to/have/time_series_files + # - /same/or/different/place/you/want/files + + #TEM diagnostics + #--------------- + #TEM history file number + #If missing or blank, ADF will default to h4 + #tem_hist_str: + # - cam.h4 + # - cam.h# + + #Location where TEM files are stored: + #NOTE: If path not specified or commented out, TEM calculation/plots will be skipped! + #cam_tem_loc: + # - /some/where/you/want/to/have/TEM_files/ + # - /same/or/different/place/you/want/TEM_files/ + + #Overwrite TEM files, if found? + #If set to false, then TEM creation will be skipped if files are found: + #overwrite_tem: + # - false + # - true + + #---------------------- + + +#This third set of variables provide info for the CAM baseline climatologies. +#This only matters if "compare_obs" is false: +diag_cam_baseline_climo: + + # History file list of strings to match + # eg. cam.h0 or ocn.pop.h.ecosys.nday1 or hist_str: [cam.h2,cam.h0] + # Only affects timeseries as everything else uses the created timeseries + # Default: + hist_str: clm2.h0 + + #Calculate cam baseline climatologies? + #If false, the climatology files will not be created: + calc_cam_climo: true + + #Overwrite CAM climatology files? + #If false, or not present, then already existing climatology files will be skipped: + cam_overwrite_climo: true + + #Name of CAM baseline case: + cam_case_name: b.e30_beta05.BLT1850.ne30_t232_wgx3.122 + + #Baseline case nickname + #NOTE: if nickname starts with '0' - nickname must be in quotes! + # ie '026a' as opposed to 026a + #If missing or left blank, will default to cam_case_name + case_nickname: '122' + + #Location of CAM baseline history (h0) files: + #Example test files + cam_hist_loc: /glade/derecho/scratch/hannay/archive/${diag_cam_baseline_climo.cam_case_name}/lnd/hist + + #Location of baseline CAM climatologies: + cam_climo_loc: /glade/derecho/scratch/${user}/ADF/${diag_cam_baseline_climo.cam_case_name}/climo + + #model year when time series files should start: + #Note: Leaving this entry blank will make time series + # start at earliest available year. + start_year: 25 + + #model year when time series files should end: + #Note: Leaving this entry blank will make time series + # end at latest available year. + end_year: 35 + + #Do time series files need to be generated? + #If True, then diagnostics assumes that model files are already time series. + #If False, or if simply not present, then diagnostics will attempt to create + #time series files from history (time-slice) files: + cam_ts_done: false + + #Save interim time series files for baseline run? + #WARNING: This can take up a significant amount of space: + cam_ts_save: true + + #Overwrite baseline time series files, if found? + #If set to false, then time series creation will be skipped if files are found: + cam_overwrite_ts: false + + #Location where time series files are (or will be) stored: + cam_ts_loc: /glade/derecho/scratch/${user}/ADF/${diag_cam_baseline_climo.cam_case_name}/ts + + #Location where re-gridded and interpolated CAM climatology files are stored: + cam_climo_regrid_loc: /glade/derecho/scratch/${user}/ADF/${diag_cam_baseline_climo.cam_case_name}/climo/regrid + + #Location where re-gridded and interpolated timeseries files are stored: + cam_ts_regrid_loc: /glade/derecho/scratch/${user}/ADF/${diag_cam_baseline_climo.cam_case_name}/ts/regrid + + #TEM diagnostics + #--------------- + #TEM history file number + #If missing or blank, ADF will default to h4 + tem_hist_str: cam.h4 + + #Location where TEM files are stored: + #NOTE: If path not specified or commented out, TEM calculation/plots will be skipped! + cam_tem_loc: /glade/derecho/scratch/${user}/${diag_cam_baseline_climo.cam_case_name}/tem/ + + #Overwrite TEM files, if found? + #If set to false, then TEM creation will be skipped if files are found: + overwrite_tem: false + + +#This fourth set of variables provides settings for calling the Climate Variability +# Diagnostics Package (CVDP). If cvdp_run is set to true the CVDP will be set up and +# run in background mode, likely completing after the ADF has completed. +# If CVDP is to be run PSL, TREFHT, TS and PRECT (or PRECC and PRECL) should be listed +# in the diag_var_list variable listing. +# For more CVDP information: https://www.cesm.ucar.edu/working_groups/CVC/cvdp/ +diag_cvdp_info: + + # Run the CVDP on the listed run(s)? + cvdp_run: false + + # CVDP code path, sets the location of the CVDP codebase + # CGD systems path = /home/asphilli/CESM-diagnostics/CVDP/Release/v5.2.0/ + # CISL systems path = /glade/u/home/asphilli/CESM-diagnostics/CVDP/Release/v5.2.0/ + # github location = https://github.com/NCAR/CVDP-ncl + cvdp_codebase_loc: /glade/u/home/asphilli/CESM-diagnostics/CVDP/Release/v5.2.0/ + + # Location where cvdp codebase will be copied to and diagnostic plots will be stored + cvdp_loc: /glade/derecho/scratch/${user}/ADF/cvdp/ + + # tar up CVDP results? + cvdp_tar: false + +# This set of variables provides settings for calling NOAA's +# Model Diagnostic Task Force (MDTF) diagnostic package. +# https://github.com/NOAA-GFDL/MDTF-diagnostics +# +# If mdtf_run: true, the MDTF will be set up and +# run in background mode, likely completing after the ADF has completed. +# +# WARNING: This currently only runs on CASPER (not derecho) +# +# The variables required depend on the diagnostics (PODs) selected. +# AMWG-developed PODS and their required variables: +# (Note that PRECT can be computed from PRECC & PRECL) +# - MJO_suite: daily PRECT, FLUT, U850, U200, V200 (all required) +# - Wheeler-Kiladis Wavenumber Frequency Spectra: daily PRECT, FLUT, U200, U850, OMEGA500 +# (will use what is available) +# - Blocking (Rich Neale): daily OMEGA500 +# - Precip Diurnal Cycle (Rich Neale): 3-hrly PRECT +# +# Many other diagnostics are available; see +# https://mdtf-diagnostics.readthedocs.io/en/main/sphinx/start_overview.html + +# +diag_mdtf_info: + # Run the MDTF on the model cases + mdtf_run: false + + # The file that will be written by ADF to input to MDTF. Call this whatever you want. + mdtf_input_settings_filename : mdtf_input.json + + ## MDTF code path, sets the location of the MDTF codebase and pre-compiled conda envs + # CHANGE if you have any: your own MDTF code, installed conda envs and/or obs_data + + mdtf_codebase_path : /glade/campaign/cgd/amp/amwg/mdtf + mdtf_codebase_loc : ${mdtf_codebase_path}/MDTF-diagnostics.v3.1.20230817.ADF + conda_root : /glade/u/apps/opt/conda + conda_env_root : ${mdtf_codebase_path}/miniconda2/envs.MDTFv3.1.20230412/ + OBS_DATA_ROOT : ${mdtf_codebase_path}/obs_data + + # SET this to a writable dir. The ADF will place ts files here for the MDTF to read (adds the casename) + MODEL_DATA_ROOT : ${diag_cam_climo.cam_ts_loc}/mdtf/inputdata/model + + # Choose diagnostics (PODs). Full list of available PODs: https://github.com/NOAA-GFDL/MDTF-diagnostics + pod_list : [ "MJO_suite" ] + + # Intermediate/output file settings + make_variab_tar: false # tar up MDTF results + save_ps : false # save postscript figures in addition to bitmaps + save_nc : false # save netCDF files of processed data (recommend true when starting with new model data) + overwrite: true # overwrite results in OUTPUT_DIR; otherwise results will be saved under a unique name + + # Settings used in debugging: + verbose : 3 # Log verbosity level. + test_mode: false # Set to true for framework test. Data is fetched but PODs are not run. + dry_run : false # Framework test. No external commands are run and no remote data is copied. Implies test_mode. + + # Settings that shouldn't change in ADF implementation for now + data_type : single_run # single_run or multi_run (only works with single right now) + data_manager : Local_File # Fetch data or it is local? + environment_manager : Conda # Manage dependencies + + + +#+++++++++++++++++++++++++++++++++++++++++++++++++++ +#These variables below only matter if you are using +#a non-standard method, or are adding your own +#diagnostic scripts. +#+++++++++++++++++++++++++++++++++++++++++++++++++++ + +#Note: If you want to pass arguments to a particular script, you can +#do it like so (using the "averaging_example" script in this case): +# - {create_climo_files: {kwargs: {clobber: true}}} + +#Name of time-averaging scripts being used to generate climatologies. +#These scripts must be located in "scripts/averaging": +time_averaging_scripts: + - create_climo_files + #- create_TEM_files #To generate TEM files, please un-comment + +#Name of regridding scripts being used. +#These scripts must be located in "scripts/regridding": +regridding_scripts: + - regrid_climo_wrapper + - regrid_ts_wrapper + +#List of analysis scripts being used. +#These scripts must be located in "scripts/analysis": +analysis_scripts: + - lmwg_table + #- aerosol_gas_tables + + #List of plotting scripts being used. +#These scripts must be located in "scripts/plotting": +plotting_scripts: + - global_unstructured_latlon_map + - polar_ux_map + - global_mean_timeseries_lnd + #- global_latlon_vect_map + #- zonal_mean + #- meridional_mean + #- polar_map + #- cam_taylor_diagram + #- qbo + #- ozone_diagnostics + #- tape_recorder + #- tem + #- regional_map_multicase #To use this please un-comment and fill-out + #the "region_multicase" section below + +#List of CAM variables that will be processesd: +#If CVDP is to be run PSL, TREFHT, TS and PRECT (or PRECC and PRECL) should be listed +#TODO, round this out with more variables for alpha land diags +diag_var_list: + - TSA + - PREC + - ELAI + - GPP + - NPP + - TOTVEGC + - FSDS + - ALTMAX + - ET + - TOTRUNOFF + - DSTFLXT + - MEG_isoprene +# +# MDTF recommended variables +# - FLUT +# - OMEGA500 +# - PRECT +# - PS +# - PSL +# - U200 +# - U850 +# - V200 +# - V850 + +# Options for multi-case regional contour plots (./plotting/regional_map_multicase.py) +# region_multicase: +# region_spec: [slat, nlat, wlon, elon] +# region_time_option: # If calendar, will look for specified years. If zeroanchor will use a nyears starting from year_offset from the beginning of timeseries +# region_start_year: +# region_end_year: +# region_nyear: +# region_year_offset: +# region_month: +# region_season: +# region_variables: + +#END OF FILE diff --git a/lib/adf_config.py b/lib/adf_config.py index 61a8ba112..d470ba14d 100644 --- a/lib/adf_config.py +++ b/lib/adf_config.py @@ -21,7 +21,7 @@ import copy #+++++++++++++++++++++++++++++++++++++++++++++++++ -#import non-standard python modules, including ADF +#import non-standard python modules, including ADF: #+++++++++++++++++++++++++++++++++++++++++++++++++ import yaml diff --git a/lib/adf_dataset.py b/lib/adf_dataset.py index 071ddc3fe..3e771b434 100644 --- a/lib/adf_dataset.py +++ b/lib/adf_dataset.py @@ -1,5 +1,6 @@ from pathlib import Path import xarray as xr +import uxarray as ux import warnings # use to warn user about missing files @@ -47,7 +48,7 @@ class AdfData: def __init__(self, adfobj): self.adf = adfobj # provides quick access to the AdfDiag object # paths - self.model_rgrid_loc = adfobj.get_basic_info("cam_regrid_loc", required=True) + self.model_rgrid_loc = adfobj.get_basic_info("cam_climo_regrid_loc", required=True) # variables (and info for unit transform) # use self.adf.diag_var_list and self.adf.self.adf.variable_defaults @@ -185,15 +186,30 @@ def load_climo_da(self, case, variablename): return self.load_da(fils, variablename, add_offset=add_offset, scale_factor=scale_factor) - def load_climo_file(self, case, variablename): - """Return Dataset for climo of variablename""" + def load_climo_dataset(self, case, field): + """Return a data set to be used as reference (aka baseline) for variable field.""" + fils = self.get_climo_file(case, field) + if not fils: + warnings.warn(f"WARNING: Did not find climo file(s) for case: {case}, variable: {field}") + return None + return self.load_dataset(fils) + + def load_climo_file(self, case, variablename, grid='regular'): + """ + Return Dataset for climo of variablename + uses grid flag to determine if reading in a regular or unstructured grid + returns a xarry or uxarray dataset, respectively + """ fils = self.get_climo_file(case, variablename) if not fils: warnings.warn(f"WARNING: Did not find climo file for variable: {variablename}. Will try to skip.") return None - return self.load_dataset(fils) - + if grid == 'regular': + return self.load_dataset(fils) + elif grid == 'unstructured': + return self.load_ux_dataset(fils) + def get_climo_file(self, case, variablename): """Retrieve the climo file path(s) for variablename for a specific case.""" a = self.adf.get_cam_info("cam_climo_loc", required=True) # list of paths (could be multiple cases) @@ -209,6 +225,15 @@ def load_reference_climo_da(self, case, variablename): fils = self.get_reference_climo_file(variablename) return self.load_da(fils, variablename, add_offset=add_offset, scale_factor=scale_factor) + def load_reference_climo_dataset(self, case, field): + """Return a data set to be used as reference (aka baseline) for variable field.""" + fils = self.get_reference_climo_file(self, field) + if not fils: + warnings.warn(f"WARNING: Did not find climo file(s) for case: {case}, variable: {field}") + return None + return self.load_dataset(fils) + + def get_reference_climo_file(self, var): """Return a list of files to be used as reference (aka baseline) for variable var.""" if self.adf.compare_obs: @@ -223,14 +248,13 @@ def get_reference_climo_file(self, var): #------------------ - # Regridded files #------------------ # Test case(s) def get_regrid_file(self, case, field): """Return list of test regridded files""" - model_rg_loc = Path(self.adf.get_basic_info("cam_regrid_loc", required=True)) + model_rg_loc = Path(self.adf.get_basic_info("cam_climo_regrid_loc", required=True)) rlbl = self.ref_labels[field] # rlbl = "reference label" = the name of the reference data that defines target grid return sorted(model_rg_loc.glob(f"{rlbl}_{case}_{field}_regridded.nc")) @@ -264,7 +288,7 @@ def get_ref_regrid_file(self, case, field): else: fils = [] else: - model_rg_loc = Path(self.adf.get_basic_info("cam_regrid_loc", required=True)) + model_rg_loc = Path(self.adf.get_basic_info("cam_climo_regrid_loc", required=True)) fils = sorted(model_rg_loc.glob(f"{case}_{field}_baseline.nc")) return fils @@ -296,6 +320,8 @@ def load_reference_regrid_da(self, case, field): # DataSet and DataArray load #--------------------------- + # TODO, make uxarray options fo all of these fuctions. + # What's the most robust way to handle this? # Load DataSet def load_dataset(self, fils): @@ -310,7 +336,8 @@ def load_dataset(self, fils): if not Path(sfil).is_file(): warnings.warn(f"Expecting to find file: {sfil}") return None - ds = xr.open_dataset(sfil) + mesh = '/glade/campaign/cesm/cesmdata/inputdata/share/meshes/ne30pg3_ESMFmesh_cdf5_c20211018.nc' + ds = ux.open_dataset(mesh, sfil) if ds is None: warnings.warn(f"invalid data on load_dataset") return ds diff --git a/lib/adf_diag.py b/lib/adf_diag.py index e222c7864..52e733a9a 100644 --- a/lib/adf_diag.py +++ b/lib/adf_diag.py @@ -67,6 +67,30 @@ print("Please install module, e.g. 'pip install Cartopy'.") sys.exit(1) +# Check if "uxarray" is present in python path: +try: + import uxarray as ux +except ImportError: + print("uxarray module does not exist in python path.") + print("Please install module, e.g. 'pip install uxarray'.") + sys.exit(1) + +# Check if "esmpy" is present in python path: +try: + import esmpy as esmpy +except ImportError: + print("xesmf module does not exist in python path.") + print("Please install module, e.g. 'pip install esmpy'.") + sys.exit(1) + +# Check if "xesmf" is present in python path: +try: + import xesmf as xesmf +except ImportError: + print("xesmf module does not exist in python path.") + print("Please install module, e.g. 'pip install xesmf'.") + sys.exit(1) + # pylint: enable=unused-import # +++++++++++++++++++++++++++++ @@ -714,19 +738,41 @@ def call_ncrcat(cmd): ts_outfil_str ] - # Step 3: Create the ncatted command to remove the history attribute + # Step 3a: Optional, add additional variables to clm2.h0 files + cmd_add_clm_h0_fields = [ + "ncks", "-A", "-v", "area,landfrac,landmask", + hist_files[0], + ts_outfil_str + ] + + # Step 3b: Optional, add additional variables to clm2.h1 files + cmd_add_clm_h1_fields = [ + "ncrcat", "-A", "-v", "pfts1d_ixy,pfts1d_jxy,pfts1d_itype_veg,lat,lon", + hist_files, + ts_outfil_str + ] + + # Step 3c: Create the ncatted command to remove the history attribute cmd_remove_history = [ "ncatted", "-O", "-h", "-a", "history,global,d,,", ts_outfil_str ] - + + # Add to command list for use in multi-processing pool: # ----------------------------------------------------- # generate time series files list_of_commands.append(cmd) # Add global attributes: user, original hist file loc(s) and all filenames list_of_ncattend_commands.append(cmd_ncatted) + + # TODO, add some logic to control if these are done + # add time invariant information to clm2.h0 fields + list_of_hist_commands.append(cmd_add_clm_h0_fields) + # add time varrying information to clm2.h1 fields + #list_of_hist_commands.append(cmd_add_clm_h1_fields) + # Remove the `history` attr that gets tacked on (for clean up) # NOTE: this may not be best practice, but it the history attr repeats # the files attrs so the global attrs become obtrusive... diff --git a/lib/adf_variable_defaults.yaml b/lib/adf_variable_defaults.yaml index 60743359c..06f46739f 100644 --- a/lib/adf_variable_defaults.yaml +++ b/lib/adf_variable_defaults.yaml @@ -1932,6 +1932,24 @@ RESTOM: pct_diff_contour_levels: [-100,-75,-50,-40,-30,-20,-10,-8,-6,-4,-2,0,2,4,6,8,10,20,30,40,50,75,100] pct_diff_colormap: "PuOr_r" +SNOWDP: + colormap: "Blues" + contour_levels_range: [-150, 50, 10] + diff_colormap: "BrBG" + diff_contour_range: [-20, 20, 2] + scale_factor: 1 + add_offset: 0 + new_unit: "Wm$^{-2}$" + mpl: + colorbar: + label : "Wm$^{-2}$" + obs_file: "CERES_EBAF_Ed4.1_2001-2020.nc" + obs_name: "CERES_EBAF_Ed4.1" + obs_var_name: "toa_cre_sw_mon" + obs_scale_factor: 1 + obs_add_offset: 0 + category: "TOA energy flux" + SWCF: colormap: "Blues" contour_levels_range: [-150, 50, 10] diff --git a/lib/ldf_variable_defaults.yaml b/lib/ldf_variable_defaults.yaml new file mode 100644 index 000000000..4b51084d7 --- /dev/null +++ b/lib/ldf_variable_defaults.yaml @@ -0,0 +1,2612 @@ + +#This file lists out variable-specific defaults +#for plotting and observations. These defaults +#are: +# +# PLOTTING: +# +# colormap -> The colormap that will be used for filled contour plots. +# contour_levels -> A list of the specific contour values that will be used for contour plots. +# Cannot be used with "contour_levels_range". +# contour_levels_range -> The contour range that will be used for plots. +# Values are min, max, and stride. Cannot be used with "contour_levels". +# diff_colormap -> The colormap that will be used for filled contour different plots +# diff_contour_levels -> A list of the specific contour values thta will be used for difference plots. +# Cannot be used with "diff_contour_range". +# diff_contour_range -> The contour range that will be used for difference plots. +# Values are min, max, and stride. Cannot be used with "diff_contour_levels". +# scale_factor -> Amount to scale the variable (relative to its "raw" model values). +# add_offset -> Amount of offset to add to the variable (relatie to its "raw" model values). +# new_unit -> Variable units (if not using the "raw" model units). +# mpl -> Dictionary that contains keyword arguments explicitly for matplotlib +# +# mask -> Setting that specifies whether the variable should be masked. +# Currently only accepts "landmask", which means the variable will be masked +# everywhere that isn't land. +# +# +# OBSERVATIONS: +# +# obs_file -> Path to observations file. If only the file name is given, then the file is assumed to +# exist in the path specified by "obs_data_loc" in the config file. +# obs_name -> Name of the observational dataset (mostly used for plotting and generated file naming). +# If this isn't present then the obs_file name is used. +# obs_var_name -> Variable in the observations file to compare against. If this isn't present then the +# variable name is assumed to be the same as the model variable name. +# +# +# +# WEBSITE: +# +# category -> The website category the variable will be placed under. +# +# +# DERIVING: +# +# derivable_from -> If not present in the available output files, the variable can be derived from +# other variables that are present (e.g. PRECT can be derived from PRECC and PRECL), +# which are specified in this list +# NOTE: this is not very flexible at the moment! It can only handle variables that +# are sums of the constituents. Futher flexibility is being explored. +# +# +# Final Note: Please do not modify this file unless you plan to push your changes back to the ADF repo. +# If you would like to modify this file for your personal ADF runs then it is recommended +# to make a copy of this file, make modifications in that copy, and then point the ADF to +# it using the "defaults_file" config variable. +# +#+++++++++++ + +#+++++++++++++ +# Available Land Default Plot Types +#+++++++++++++ +default_ptypes: ["Tables","LatLon","TimeSeries", + "Arctic","RegionalClimo","RegionalTimeSeries","Special"] + +#+++++++++++++ +# Constants +#+++++++++++++ + +#seconds per day : +spd: 86400 +diff_levs: [-100,-75,-50,-40,-30,-20,-10,-8,-6,-4,-2,0,2,4,6,8,10,20,30,40,50,75,100] + +#+++++++++++++ +# Category: Atmosphere +#+++++++++++++ + +TSA: # 2m air temperature + category: "Atmosphere" + colormap: "coolwarm" + contour_levels_range: [250, 310, 10] + +PREC: # RAIN + SNOW + category: "Atmosphere" + colormap: "managua" + derivable_from: ["RAIN","SNOW"] + scale_factor: 86400 + add_offset: 0 + new_unit: "mm d$^{-1}$" + mpl: + colorbar: + label : "mm d$^{-1}$" + diff_colormap: "BrBG" + pct_diff_contour_levels: [-100,-75,-50,-40,-30,-20,-10,-8,-6,-4,-2,0,2,4,6,8,10,20,30,40,50,75,100] + pct_diff_colormap: "PuOr_r" + +FLDS: # atmospheric longwave radiation + category: "Atmosphere" + colormap: "Oranges" + contour_levels_range: [100, 500, 25] + diff_colormap: "BrBG" + diff_contour_range: [-20, 20, 2] + scale_factor: 1 + add_offset: 0 + new_unit: "Wm$^{-2}$" + mpl: + colorbar: + label : "Wm$^{-2}$" + pct_diff_contour_levels: [-100,-75,-50,-40,-30,-20,-10,-8,-6,-4,-2,0,2,4,6,8,10,20,30,40,50,75,100] + pct_diff_colormap: "PuOr_r" + +FSDS: # atmospheric incident solar radiation + category: "Atmosphere" + pct_diff_contour_levels: [-100,-75,-50,-40,-30,-20,-10,-8,-6,-4,-2,0,2,4,6,8,10,20,30,40,50,75,100] + pct_diff_colormap: "PuOr_r" + +WIND: # atmospheric air temperature + category: "Atmosphere" + +QBOT: # atmospheric specific humidity + category: "Atmosphere" + +TBOT: + category: "Atmosphere" + colormap: "coolwarm" + contour_levels_range: [250, 310, 10] + +TREFMNAV: # daily minimum of average 2m temperature + category: "Atmosphere" + colormap: "coolwarm" + contour_levels_range: [250, 310, 10] + + +TREFMXAV: # daily maximum of average 2m temperature + category: "Atmosphere" + colormap: "cool warm" + contour_levels_range: [250, 310, 10] + + +#+++++++++++ +# Category: Surface fluxes +#+++++++++++ + +ASA: # all-sky albedo:FSR/FSDS + category: "Surface fluxes" + colormap: "RdBu_r" + diff_colormap: "BrBG" + derivable_from: ["FSR", "FSDS"] + pct_diff_contour_levels: [-100,-75,-50,-40,-30,-20,-10,-8,-6,-4,-2,0,2,4,6,8,10,20,30,40,50,75,100] + pct_diff_colormap: "PuOr_r" + +FSA: # absorbed solar radiation + category: "Surface fluxes" + +FSH: # sensible heat + category: "Surface fluxes" + + +ET: # latent heat: FCTR+FCEV+FGEV + category: "Surface fluxes" + derivable_from: ["FCTR","FCEV","FGEV"] + colormap: "Blues" + contour_levels_range: [0, 220, 10] + diff_colormap: "BrBG" + diff_contour_range: [-45, 45, 5] + scale_factor: 1 + add_offset: 0 + new_unit: "Wm$^{-2}$" + mpl: + colorbar: + label : "Wm$^{-2}$" + pct_diff_contour_levels: [-100,-75,-50,-40,-30,-20,-10,-8,-6,-4,-2,0,2,4,6,8,10,20,30,40,50,75,100] + pct_diff_colormap: "PuOr_r" + +DSTFLXT: # total surface dust emission + category: "Surface fluxes" + colormap: "YlOrBr" + diff_colormap: "BrBG_r" + scale_factor: 86400 + add_offset: 0 + new_unit: "kg m$^{-2}$ d$^{-1}" + mpl: + colorbar: + label : "kg m$^{-2}$ d$^{-1}" + pct_diff_contour_levels: [-100,-75,-50,-40,-30,-20,-10,-8,-6,-4,-2,0,2,4,6,8,10,20,30,40,50,75,100] + pct_diff_colormap: "PuOr_r" + scale_factor_table: 0.000365 #days to years, kg/m2 to Pg globally + avg_method: 'sum' + table_unit: "Pg y$^{-1}" + +MEG_isoprene: # total surface dust emission + category: "Surface fluxes" + colormap: "YlOrBr" + diff_colormap: "BrBG_r" + scale_factor: 86400 + add_offset: 0 + new_unit: "kg m$^{-2}$ d$^{-1}" + mpl: + colorbar: + label : "kg m$^{-2}$ d$^{-1}" + pct_diff_contour_levels: [-100,-75,-50,-40,-30,-20,-10,-8,-6,-4,-2,0,2,4,6,8,10,20,30,40,50,75,100] + pct_diff_colormap: "PuOr_r" + scale_factor_table: 0.365 #days to years, kg/m2 to Tg globally + avg_method: 'sum' + table_unit: "Tg y$^{-1}" + + +#+++++++++++ +# Category: Hydrology +#+++++++++++ +FSNO: # fraction of ground covered by snow + category: "Hydrology" + + +H2OSNO: # SNOWICE + SNOWLIQ + category: "Hydrology" + + +SNOWDP: # snow height + category: "Hydrology" + + +TOTRUNOFF: # total liquid runoff + category: "Hydrology" + derivable_from: ["QOVER","QDRAI","QRGWL"] #TODO, check accuracy + colormap: "Blues" + scale_factor: 86400 + add_offset: 0 + new_unit: "mm d$^{-1}$" + mpl: + colorbar: + label : "mm d$^{-1}$" + +#+++++++++++ +# Category: Vegetation +#+++++++++++ +BTRANMN: # Transpiration beta factor + category: "Vegetation" # Or hydrology? + +ELAI: # exposed one-sided leaf area index + category: "Vegetation" + colormap: "gist_earth_r" + contour_levels_range: [0., 7., 1.0] + diff_colormap: "PuOr_r" + diff_contour_range: [-3.,3.,0.5] + + +HTOP: # canopy top height + category: "Vegetation" + + +TSAI: # total one-sided stem area index + category: "Vegetation" + + +#+++++++++++ +# Category: Carbon +#+++++++++++ +GPP: # Gross Primary Production + category: "Carbon" + colormap: "gist_earth_r" + contour_levels_range: [0., 8., 0.5] + diff_colormap: "BrBG" + diff_contour_range: [-4.,4.,0.5] + scale_factor: 86400 + add_offset: 0 + new_unit: "gC m$^{-2} d$^{-1}" + mpl: + colorbar: #TODO make this print correctly + label : "gC ${m^-2 d^-1}" + pct_diff_contour_levels: [-100,-75,-50,-40,-30,-20,-10,-8,-6,-4,-2,0,2,4,6,8,10,20,30,40,50,75,100] + pct_diff_colormap: "PuOr_r" + scale_factor_table: 0.000000365 #days to years, g/m2 to Pg globally + avg_method: 'sum' + table_unit: "PgC y$^{-1}" + +AR: # Autotrophic Respiration + category: "Carbon" + colormap: "gist_earth_r" + contour_levels_range: [0., 3., 0.25] + diff_colormap: "BrBG" + diff_contour_range: [-1.5, 1.5, 0.25] + scale_factor: 86400 + add_offset: 0 + new_unit: "gC m$^{-2} d$^{-1}" + mpl: + colorbar: + label : "gC m$^{-2} d$^{-1}" + pct_diff_contour_levels: [-100,-75,-50,-40,-30,-20,-10,-8,-6,-4,-2,0,2,4,6,8,10,20,30,40,50,75,100] + pct_diff_colormap: "PuOr_r" + scale_factor_table: 0.000000365 #days to years, g/m2 to Pg globally + avg_method: 'sum' + table_unit: "PgC y$^{-1}" + +NPP: # Net Primary Production + category: "Carbon" + colormap: "gist_earth_r" + contour_levels_range: [0., 3., 0.25] + diff_colormap: "PuOr_r" + diff_contour_range: [-1.5, 1.5, 0.25] + scale_factor: 86400 + add_offset: 0 + new_unit: "gC m$^{-2} d$^{-1}" + mpl: + colorbar: + label : "gC m$^{-2} d$^{-1}" + pct_diff_contour_levels: [-100,-75,-50,-40,-30,-20,-10,-8,-6,-4,-2,0,2,4,6,8,10,20,30,40,50,75,100] + pct_diff_colormap: "PuOr_r" + scale_factor_table: 0.000000365 #days to years, g/m2 to Pg globally + avg_method: 'sum' + table_unit: "PgC y$^{-1}" + +TOTECOSYSC_1m: + category: "Carbon" + scale_factor_table: 0.000000001 #g/m2 to Pg globally + avg_method: 'sum' + table_unit: "PgC" + +TOTSOMC_1m: + category: "Carbon" + + +TOTVEGC: + category: "Carbon" + + + + + +#+++++++++++ +# Category: Soils +#+++++++++++ +ALTMAX: # Active Layer Thickness + category: "Soils" + + +SOILWATER_10CM: # soil liquid water + ice in top 10cm of soil + category: "Soils" # or hydrology? + +TSOI_10CM: # Soil temperature, 0-10 cm + category: "Soils" + + +#+++++++++++ +# ADF examples + +AODDUST: + category: "Aerosols" + colormap: "Oranges" + contour_levels_range: [0.01, 0.6, 0.05] + diff_colormap: "PuOr_r" + diff_contour_range: [-0.06, 0.06, 0.01] + scale_factor: 1 + add_offset: 0 + new_unit: "" + pct_diff_contour_levels: [-100,-75,-50,-40,-30,-20,-10,-8,-6,-4,-2,0,2,4,6,8,10,20,30,40,50,75,100] + pct_diff_colormap: "PuOr_r" + +AODVIS: + category: "Aerosols" + colormap: "Oranges" + contour_levels_range: [0.05, 0.6, 0.05] + diff_colormap: "PuOr_r" + diff_contour_range: [-0.1, 0.1, 0.01] + scale_factor: 1 + add_offset: 0 + new_unit: "" + pct_diff_contour_levels: [-100,-75,-50,-40,-30,-20,-10,-8,-6,-4,-2,0,2,4,6,8,10,20,30,40,50,75,100] + pct_diff_colormap: "PuOr_r" + +AODVISdn: + category: "Aerosols" + colormap: "jet" + contour_levels_range: [0.01, 1.01, 0.05] + diff_colormap: "PuOr_r" + diff_contour_range: [-0.4, 0.401, 0.05] + scale_factor: 1 + add_offset: 0 + new_unit: "" + obs_file: "MOD08_M3_192x288_AOD_2001-2020_climo.nc" + obs_name: "MODIS" + obs_var_name: "AOD_550_Dark_Target_Deep_Blue_Combined_Mean_Mean" + pct_diff_contour_levels: [-100,-75,-50,-40,-30,-20,-10,-8,-6,-4,-2,0,2,4,6,8,10,20,30,40,50,75,100] + pct_diff_colormap: "PuOr_r" + +BURDENBC: + category: "Aerosols" + pct_diff_contour_levels: [-100,-75,-50,-40,-30,-20,-10,-8,-6,-4,-2,0,2,4,6,8,10,20,30,40,50,75,100] + pct_diff_colormap: "PuOr_r" + +BURDENDUST: + category: "Aerosols" + pct_diff_contour_levels: [-100,-75,-50,-40,-30,-20,-10,-8,-6,-4,-2,0,2,4,6,8,10,20,30,40,50,75,100] + pct_diff_colormap: "PuOr_r" + +BURDENPOM: + category: "Aerosols" + pct_diff_contour_levels: [-100,-75,-50,-40,-30,-20,-10,-8,-6,-4,-2,0,2,4,6,8,10,20,30,40,50,75,100] + pct_diff_colormap: "PuOr_r" + +BURDENSEASALT: + category: "Aerosols" + pct_diff_contour_levels: [-100,-75,-50,-40,-30,-20,-10,-8,-6,-4,-2,0,2,4,6,8,10,20,30,40,50,75,100] + pct_diff_colormap: "PuOr_r" + +BURDENSO4: + category: "Aerosols" + pct_diff_contour_levels: [-100,-75,-50,-40,-30,-20,-10,-8,-6,-4,-2,0,2,4,6,8,10,20,30,40,50,75,100] + pct_diff_colormap: "PuOr_r" + +BURDENSOA: + category: "Aerosols" + pct_diff_contour_levels: [-100,-75,-50,-40,-30,-20,-10,-8,-6,-4,-2,0,2,4,6,8,10,20,30,40,50,75,100] + pct_diff_colormap: "PuOr_r" + +DMS: + category: "Aerosols" + colormap: "jet" + diff_colormap: "gist_ncar" + pct_diff_contour_levels: [-100,-75,-50,-40,-30,-20,-10,-8,-6,-4,-2,0,2,4,6,8,10,20,30,40,50,75,100] + pct_diff_colormap: "PuOr_r" + scale_factor: 1000000000.0 + new_unit: "ppbv" + +SO2: + category: "Aerosols" + colormap: "jet" + diff_colormap: "gist_ncar" + pct_diff_contour_levels: [-100,-75,-50,-40,-30,-20,-10,-8,-6,-4,-2,0,2,4,6,8,10,20,30,40,50,75,100] + pct_diff_colormap: "PuOr_r" + scale_factor: 1000000000.0 + new_unit: "ppbv" + +SOAG: + category: "Aerosols" + colormap: "jet" + diff_colormap: "gist_ncar" + pct_diff_contour_levels: [-100,-75,-50,-40,-30,-20,-10,-8,-6,-4,-2,0,2,4,6,8,10,20,30,40,50,75,100] + pct_diff_colormap: "PuOr_r" + scale_factor: 1000000000.0 + new_unit: "ppbv" + +BC: + colormap: "RdBu_r" + diff_colormap: "BrBG" + scale_factor: 1000000000 + add_offset: 0 + new_unit: '$\mu$g/m3' + mpl: + colorbar: + label : '$\mu$g/m3' + category: "Aerosols" + derivable_from: ["bc_a1", "bc_a4"] + pct_diff_contour_levels: [-100,-75,-50,-40,-30,-20,-10,-8,-6,-4,-2,0,2,4,6,8,10,20,30,40,50,75,100] + pct_diff_colormap: "PuOr_r" + +POM: + colormap: "RdBu_r" + diff_colormap: "BrBG" + scale_factor: 1000000000 + add_offset: 0 + new_unit: '$\mu$g/m3' + mpl: + colorbar: + label : '$\mu$g/m3' + category: "Aerosols" + derivable_from: ["pom_a1", "pom_a4"] + pct_diff_contour_levels: [-100,-75,-50,-40,-30,-20,-10,-8,-6,-4,-2,0,2,4,6,8,10,20,30,40,50,75,100] + pct_diff_colormap: "PuOr_r" + +SO4: + colormap: "RdBu_r" + diff_colormap: "BrBG" + scale_factor: 1000000000 + add_offset: 0 + new_unit: '$\mu$g/m3' + mpl: + colorbar: + label : '$\mu$g/m3' + category: "Aerosols" + derivable_from: ["so4_a1", "so4_a2", "so4_a3"] + derivable_from_cam_chem: ["so4_a1", "so4_a2", "so4_a3", "so4_a5"] + pct_diff_contour_levels: [-100,-75,-50,-40,-30,-20,-10,-8,-6,-4,-2,0,2,4,6,8,10,20,30,40,50,75,100] + pct_diff_colormap: "PuOr_r" + +SOA: + colormap: "RdBu_r" + diff_colormap: "BrBG" + scale_factor: 1000000000 + add_offset: 0 + new_unit: '$\mu$g/m3' + mpl: + colorbar: + label : '$\mu$g/m3' + category: "Aerosols" + derivable_from: ["soa_a1", "soa_a2"] + derivable_from_cam_chem: ["soa1_a1", "soa2_a1", "soa3_a1", "soa4_a1", "soa5_a1", "soa1_a2", "soa2_a2", "soa3_a2", "soa4_a2", "soa5_a2"] + pct_diff_contour_levels: [-100,-75,-50,-40,-30,-20,-10,-8,-6,-4,-2,0,2,4,6,8,10,20,30,40,50,75,100] + pct_diff_colormap: "PuOr_r" + +DUST: + colormap: "RdBu_r" + contour_levels: [0,0.1,0.25,0.4,0.6,0.8,1.4,2,3,4,8,12,30,48,114,180] + non_linear: True + diff_colormap: "BrBG" + scale_factor: 1000000000 + add_offset: 0 + new_unit: '$\mu$g/m3' + mpl: + colorbar: + label : '$\mu$g/m3' + category: "Aerosols" + derivable_from: ["dst_a1", "dst_a2", "dst_a3"] + pct_diff_contour_levels: [-100,-75,-50,-40,-30,-20,-10,-8,-6,-4,-2,0,2,4,6,8,10,20,30,40,50,75,100] + pct_diff_colormap: "PuOr_r" + +SeaSalt: + colormap: "RdBu_r" + contour_levels: [0,0.05,0.075,0.2,0.3,0.4,0.7,1,1.5,2,4,6,15,24,57,90] + non_linear: True + diff_colormap: "BrBG" + scale_factor: 1000000000 + add_offset: 0 + new_unit: '$\mu$g/m3' + mpl: + colorbar: + label : '$\mu$g/m3' + ticks: [0.05,0.2,0.4,1,2,6,24,90] + diff_colorbar: + label : '$\mu$g/m3' + ticks: [-10,8,6,4,2,0,-2,-4,-6,-8,-10] + category: "Aerosols" + derivable_from: ["ncl_a1", "ncl_a2", "ncl_a3"] + pct_diff_contour_levels: [-100,-75,-50,-40,-30,-20,-10,-8,-6,-4,-2,0,2,4,6,8,10,20,30,40,50,75,100] + pct_diff_colormap: "PuOr_r" + +bc_a1: + category: "Aerosols" + colormap: "jet" + diff_colormap: "gist_ncar" + scale_factor: 1.0e-9 + new_unit: "ug/kg" + pct_diff_contour_levels: [-100,-75,-50,-40,-30,-20,-10,-8,-6,-4,-2,0,2,4,6,8,10,20,30,40,50,75,100] + pct_diff_colormap: "PuOr_r" +bc_a4: + category: "Aerosols" + colormap: "jet" + diff_colormap: "gist_ncar" + scale_factor: 1.0e-9 + new_unit: "ug/kg" + pct_diff_contour_levels: [-100,-75,-50,-40,-30,-20,-10,-8,-6,-4,-2,0,2,4,6,8,10,20,30,40,50,75,100] + pct_diff_colormap: "PuOr_r" +dst_a1: + category: "Aerosols" + colormap: "jet" + diff_colormap: "gist_ncar" + scale_factor: 1.0e-9 + new_unit: "ug/kg" + pct_diff_contour_levels: [-100,-75,-50,-40,-30,-20,-10,-8,-6,-4,-2,0,2,4,6,8,10,20,30,40,50,75,100] + pct_diff_colormap: "PuOr_r" +dst_a2: + category: "Aerosols" + colormap: "jet" + diff_colormap: "gist_ncar" + scale_factor: 1.0e-9 + new_unit: "ug/kg" + pct_diff_contour_levels: [-100,-75,-50,-40,-30,-20,-10,-8,-6,-4,-2,0,2,4,6,8,10,20,30,40,50,75,100] + pct_diff_colormap: "PuOr_r" +dst_a3: + category: "Aerosols" + colormap: "jet" + diff_colormap: "gist_ncar" + scale_factor: 1.0e-9 + new_unit: "ug/kg" + pct_diff_contour_levels: [-100,-75,-50,-40,-30,-20,-10,-8,-6,-4,-2,0,2,4,6,8,10,20,30,40,50,75,100] + pct_diff_colormap: "PuOr_r" +ncl_a1: + category: "Aerosols" + colormap: "jet" + diff_colormap: "gist_ncar" + scale_factor: 1.0e-9 + new_unit: "ug/kg" + pct_diff_contour_levels: [-100,-75,-50,-40,-30,-20,-10,-8,-6,-4,-2,0,2,4,6,8,10,20,30,40,50,75,100] + pct_diff_colormap: "PuOr_r" +ncl_a2: + category: "Aerosols" + colormap: "jet" + diff_colormap: "gist_ncar" + scale_factor: 1.0e-9 + new_unit: "ug/kg" + pct_diff_contour_levels: [-100,-75,-50,-40,-30,-20,-10,-8,-6,-4,-2,0,2,4,6,8,10,20,30,40,50,75,100] + pct_diff_colormap: "PuOr_r" +ncl_a3: + category: "Aerosols" + colormap: "jet" + diff_colormap: "gist_ncar" + scale_factor: 1.0e-9 + new_unit: "ug/kg" + pct_diff_contour_levels: [-100,-75,-50,-40,-30,-20,-10,-8,-6,-4,-2,0,2,4,6,8,10,20,30,40,50,75,100] + pct_diff_colormap: "PuOr_r" +num_a1: + category: "Aerosols" + colormap: "jet" + diff_colormap: "gist_ncar" + scale_factor: 1.0e-9 + new_unit: "ug/kg" + pct_diff_contour_levels: [-100,-75,-50,-40,-30,-20,-10,-8,-6,-4,-2,0,2,4,6,8,10,20,30,40,50,75,100] + pct_diff_colormap: "PuOr_r" +num_a2: + category: "Aerosols" + colormap: "jet" + diff_colormap: "gist_ncar" + scale_factor: 1.0e-9 + new_unit: "ug/kg" + pct_diff_contour_levels: [-100,-75,-50,-40,-30,-20,-10,-8,-6,-4,-2,0,2,4,6,8,10,20,30,40,50,75,100] + pct_diff_colormap: "PuOr_r" +num_a3: + category: "Aerosols" + colormap: "jet" + diff_colormap: "gist_ncar" + scale_factor: 1.0e-9 + new_unit: "ug/kg" + pct_diff_contour_levels: [-100,-75,-50,-40,-30,-20,-10,-8,-6,-4,-2,0,2,4,6,8,10,20,30,40,50,75,100] + pct_diff_colormap: "PuOr_r" +num_a4: + category: "Aerosols" + colormap: "jet" + diff_colormap: "gist_ncar" + scale_factor: 1.0e-9 + new_unit: "ug/kg" + pct_diff_contour_levels: [-100,-75,-50,-40,-30,-20,-10,-8,-6,-4,-2,0,2,4,6,8,10,20,30,40,50,75,100] + pct_diff_colormap: "PuOr_r" +num_a5: + category: "Aerosols" + colormap: "jet" + diff_colormap: "gist_ncar" + scale_factor: 1.0e-9 + new_unit: "ug/kg" + pct_diff_contour_levels: [-100,-75,-50,-40,-30,-20,-10,-8,-6,-4,-2,0,2,4,6,8,10,20,30,40,50,75,100] + pct_diff_colormap: "PuOr_r" +pom_a1: + category: "Aerosols" + colormap: "jet" + diff_colormap: "gist_ncar" + scale_factor: 1.0e-9 + new_unit: "ug/kg" + pct_diff_contour_levels: [-100,-75,-50,-40,-30,-20,-10,-8,-6,-4,-2,0,2,4,6,8,10,20,30,40,50,75,100] + pct_diff_colormap: "PuOr_r" +pom_a4: + category: "Aerosols" + colormap: "jet" + diff_colormap: "gist_ncar" + scale_factor: 1.0e-9 + new_unit: "ug/kg" + pct_diff_contour_levels: [-100,-75,-50,-40,-30,-20,-10,-8,-6,-4,-2,0,2,4,6,8,10,20,30,40,50,75,100] + pct_diff_colormap: "PuOr_r" +so4_a1: + category: "Aerosols" + colormap: "jet" + diff_colormap: "gist_ncar" + scale_factor: 1.0e-9 + new_unit: "ug/kg" + pct_diff_contour_levels: [-100,-75,-50,-40,-30,-20,-10,-8,-6,-4,-2,0,2,4,6,8,10,20,30,40,50,75,100] + pct_diff_colormap: "PuOr_r" +so4_a2: + category: "Aerosols" + colormap: "jet" + diff_colormap: "gist_ncar" + scale_factor: 1.0e-9 + new_unit: "ug/kg" + pct_diff_contour_levels: [-100,-75,-50,-40,-30,-20,-10,-8,-6,-4,-2,0,2,4,6,8,10,20,30,40,50,75,100] + pct_diff_colormap: "PuOr_r" +so4_a3: + category: "Aerosols" + colormap: "jet" + diff_colormap: "gist_ncar" + scale_factor: 1.0e-9 + new_unit: "ug/kg" + pct_diff_contour_levels: [-100,-75,-50,-40,-30,-20,-10,-8,-6,-4,-2,0,2,4,6,8,10,20,30,40,50,75,100] + pct_diff_colormap: "PuOr_r" +soa_a1: + category: "Aerosols" + colormap: "jet" + diff_colormap: "gist_ncar" + scale_factor: 1.0e-9 + new_unit: "ug/kg" + pct_diff_contour_levels: [-100,-75,-50,-40,-30,-20,-10,-8,-6,-4,-2,0,2,4,6,8,10,20,30,40,50,75,100] + pct_diff_colormap: "PuOr_r" +soa_a2: + category: "Aerosols" + colormap: "jet" + diff_colormap: "gist_ncar" + scale_factor: 1.0e-9 + new_unit: "ug/kg" + pct_diff_contour_levels: [-100,-75,-50,-40,-30,-20,-10,-8,-6,-4,-2,0,2,4,6,8,10,20,30,40,50,75,100] + pct_diff_colormap: "PuOr_r" +SAD_TROP: + category: "Aerosols" + colormap: "jet" + diff_colormap: "gist_ncar" + scale_factor: 1.0 + new_unit: "cm2/cm3" + pct_diff_contour_levels: [-100,-75,-50,-40,-30,-20,-10,-8,-6,-4,-2,0,2,4,6,8,10,20,30,40,50,75,100] + pct_diff_colormap: "PuOr_r" +SAD_AERO: + category: "Aerosols" + colormap: "jet" + diff_colormap: "gist_ncar" + scale_factor: 1.0 + new_unit: "cm2/cm3" + pct_diff_contour_levels: [-100,-75,-50,-40,-30,-20,-10,-8,-6,-4,-2,0,2,4,6,8,10,20,30,40,50,75,100] + pct_diff_colormap: "PuOr_r" +SAD_SULFC: + category: "Aerosols" + colormap: "jet" + diff_colormap: "gist_ncar" + scale_factor: 1.0 + new_unit: "cm2/cm3" + pct_diff_contour_levels: [-100,-75,-50,-40,-30,-20,-10,-8,-6,-4,-2,0,2,4,6,8,10,20,30,40,50,75,100] + pct_diff_colormap: "PuOr_r" + + +#+++++++++++++++++ +# Category: Deep Convection +#+++++++++++++++++ + +CAPE: + category: "Deep Convection" + obs_file: "CAPE_ERA5_monthly_climo_197901-202112.nc" + obs_name: "ERA5" + obs_var_name: "CAPE" + +CMFMC_DP: + category: "Deep Convection" + +FREQZM: + category: "Deep Convection" + +#+++++++++++++++++ +# Category: GW +#+++++++++++++++++ + +QTGW: + category: "GW" + +UGTW_TOTAL: + category: "GW" + +UTGWORO: + category: "GW" + +VGTW_TOTAL: + category: "GW" + +VTGWORO: + category: "GW" + + +#+++++++++++++++++ +# Category: Composition +#+++++++++++++++++ +CO: + category: "Composition" + colormap: "jet" + diff_colormap: "gist_ncar" + pct_diff_contour_levels: [-100,-75,-50,-40,-30,-20,-10,-8,-6,-4,-2,0,2,4,6,8,10,20,30,40,50,75,100] + pct_diff_colormap: "PuOr_r" + scale_factor: 1000000000.0 + new_unit: "ppbv" +CO01: + category: "Composition" + colormap: "jet" + diff_colormap: "gist_ncar" + pct_diff_contour_levels: [-100,-75,-50,-40,-30,-20,-10,-8,-6,-4,-2,0,2,4,6,8,10,20,30,40,50,75,100] + pct_diff_colormap: "PuOr_r" + scale_factor: 1000000000.0 + new_unit: "ppbv" +CO02: + category: "Composition" + colormap: "jet" + diff_colormap: "gist_ncar" + pct_diff_contour_levels: [-100,-75,-50,-40,-30,-20,-10,-8,-6,-4,-2,0,2,4,6,8,10,20,30,40,50,75,100] + pct_diff_colormap: "PuOr_r" + scale_factor: 1000000000.0 + new_unit: "ppbv" +CO03: + category: "Composition" + colormap: "jet" + diff_colormap: "gist_ncar" + pct_diff_contour_levels: [-100,-75,-50,-40,-30,-20,-10,-8,-6,-4,-2,0,2,4,6,8,10,20,30,40,50,75,100] + pct_diff_colormap: "PuOr_r" + scale_factor: 1000000000.0 + new_unit: "ppbv" +CO04: + category: "Composition" + colormap: "jet" + diff_colormap: "gist_ncar" + pct_diff_contour_levels: [-100,-75,-50,-40,-30,-20,-10,-8,-6,-4,-2,0,2,4,6,8,10,20,30,40,50,75,100] + pct_diff_colormap: "PuOr_r" + scale_factor: 1000000000.0 + new_unit: "ppbv" +CO05: + category: "Composition" + colormap: "jet" + diff_colormap: "gist_ncar" + pct_diff_contour_levels: [-100,-75,-50,-40,-30,-20,-10,-8,-6,-4,-2,0,2,4,6,8,10,20,30,40,50,75,100] + pct_diff_colormap: "PuOr_r" + scale_factor: 1000000000.0 + new_unit: "ppbv" +CO06: + category: "Composition" + colormap: "jet" + diff_colormap: "gist_ncar" + pct_diff_contour_levels: [-100,-75,-50,-40,-30,-20,-10,-8,-6,-4,-2,0,2,4,6,8,10,20,30,40,50,75,100] + pct_diff_colormap: "PuOr_r" + scale_factor: 1000000000.0 + new_unit: "ppbv" +CO07: + category: "Composition" + colormap: "jet" + diff_colormap: "gist_ncar" + pct_diff_contour_levels: [-100,-75,-50,-40,-30,-20,-10,-8,-6,-4,-2,0,2,4,6,8,10,20,30,40,50,75,100] + pct_diff_colormap: "PuOr_r" + scale_factor: 1000000000.0 + new_unit: "ppbv" +CO08: + category: "Composition" + colormap: "jet" + diff_colormap: "gist_ncar" + pct_diff_contour_levels: [-100,-75,-50,-40,-30,-20,-10,-8,-6,-4,-2,0,2,4,6,8,10,20,30,40,50,75,100] + pct_diff_colormap: "PuOr_r" + scale_factor: 1000000000.0 + new_unit: "ppbv" +CO09: + category: "Composition" + colormap: "jet" + diff_colormap: "gist_ncar" + pct_diff_contour_levels: [-100,-75,-50,-40,-30,-20,-10,-8,-6,-4,-2,0,2,4,6,8,10,20,30,40,50,75,100] + pct_diff_colormap: "PuOr_r" + scale_factor: 1000000000.0 + new_unit: "ppbv" +CO10: + category: "Composition" + colormap: "jet" + diff_colormap: "gist_ncar" + pct_diff_contour_levels: [-100,-75,-50,-40,-30,-20,-10,-8,-6,-4,-2,0,2,4,6,8,10,20,30,40,50,75,100] + pct_diff_colormap: "PuOr_r" + scale_factor: 1000000000.0 + new_unit: "ppbv" +CO11: + category: "Composition" + colormap: "jet" + diff_colormap: "gist_ncar" + pct_diff_contour_levels: [-100,-75,-50,-40,-30,-20,-10,-8,-6,-4,-2,0,2,4,6,8,10,20,30,40,50,75,100] + pct_diff_colormap: "PuOr_r" + scale_factor: 1000000000.0 + new_unit: "ppbv" +CO12: + category: "Composition" + colormap: "jet" + diff_colormap: "gist_ncar" + pct_diff_contour_levels: [-100,-75,-50,-40,-30,-20,-10,-8,-6,-4,-2,0,2,4,6,8,10,20,30,40,50,75,100] + pct_diff_colormap: "PuOr_r" + scale_factor: 1000000000.0 + new_unit: "ppbv" +CO13: + category: "Composition" + colormap: "jet" + diff_colormap: "gist_ncar" + pct_diff_contour_levels: [-100,-75,-50,-40,-30,-20,-10,-8,-6,-4,-2,0,2,4,6,8,10,20,30,40,50,75,100] + pct_diff_colormap: "PuOr_r" + scale_factor: 1000000000.0 + new_unit: "ppbv" +O3: + category: "Composition" + colormap: "jet" + diff_colormap: "gist_ncar" + pct_diff_contour_levels: [-100,-75,-50,-40,-30,-20,-10,-8,-6,-4,-2,0,2,4,6,8,10,20,30,40,50,75,100] + pct_diff_colormap: "PuOr_r" + scale_factor: 1000000000.0 + new_unit: "ppbv" +N2O: + category: "Composition" + colormap: "jet" + diff_colormap: "gist_ncar" + pct_diff_contour_levels: [-100,-75,-50,-40,-30,-20,-10,-8,-6,-4,-2,0,2,4,6,8,10,20,30,40,50,75,100] + pct_diff_colormap: "PuOr_r" + scale_factor: 1000000000.0 + new_unit: "ppbv" +HNO3: + category: "Composition" + colormap: "jet" + diff_colormap: "gist_ncar" + pct_diff_contour_levels: [-100,-75,-50,-40,-30,-20,-10,-8,-6,-4,-2,0,2,4,6,8,10,20,30,40,50,75,100] + pct_diff_colormap: "PuOr_r" + scale_factor: 1000000000.0 + new_unit: "ppbv" +NO: + category: "Composition" + colormap: "jet" + diff_colormap: "gist_ncar" + pct_diff_contour_levels: [-100,-75,-50,-40,-30,-20,-10,-8,-6,-4,-2,0,2,4,6,8,10,20,30,40,50,75,100] + pct_diff_colormap: "PuOr_r" + scale_factor: 1000000000000.0 + new_unit: "pptv" +NO2: + category: "Composition" + colormap: "jet" + diff_colormap: "gist_ncar" + pct_diff_contour_levels: [-100,-75,-50,-40,-30,-20,-10,-8,-6,-4,-2,0,2,4,6,8,10,20,30,40,50,75,100] + pct_diff_colormap: "PuOr_r" + scale_factor: 1000000000000.0 + new_unit: "pptv" +NOX: + category: "Composition" + colormap: "jet" + diff_colormap: "gist_ncar" + pct_diff_contour_levels: [-100,-75,-50,-40,-30,-20,-10,-8,-6,-4,-2,0,2,4,6,8,10,20,30,40,50,75,100] + pct_diff_colormap: "PuOr_r" + scale_factor: 1000000000000.0 + new_unit: "pptv" +NOY: + category: "Composition" + colormap: "jet" + diff_colormap: "gist_ncar" + pct_diff_contour_levels: [-100,-75,-50,-40,-30,-20,-10,-8,-6,-4,-2,0,2,4,6,8,10,20,30,40,50,75,100] + pct_diff_colormap: "PuOr_r" + scale_factor: 1000000000000.0 + new_unit: "pptv" +OH: + category: "Composition" + colormap: "jet" + diff_colormap: "gist_ncar" + pct_diff_contour_levels: [-100,-75,-50,-40,-30,-20,-10,-8,-6,-4,-2,0,2,4,6,8,10,20,30,40,50,75,100] + pct_diff_colormap: "PuOr_r" + scale_factor: 1000000000000.0 + new_unit: "pptv" +BIGALK: + category: "Composition" + colormap: "jet" + diff_colormap: "gist_ncar" + pct_diff_contour_levels: [-100,-75,-50,-40,-30,-20,-10,-8,-6,-4,-2,0,2,4,6,8,10,20,30,40,50,75,100] + pct_diff_colormap: "PuOr_r" + scale_factor: 1000000000.0 + new_unit: "ppbv" +C2H4: + category: "Composition" + colormap: "jet" + diff_colormap: "gist_ncar" + pct_diff_contour_levels: [-100,-75,-50,-40,-30,-20,-10,-8,-6,-4,-2,0,2,4,6,8,10,20,30,40,50,75,100] + pct_diff_colormap: "PuOr_r" + scale_factor: 1000000000.0 + new_unit: "ppbv" +C2H5O2: + category: "Composition" + colormap: "jet" + diff_colormap: "gist_ncar" + pct_diff_contour_levels: [-100,-75,-50,-40,-30,-20,-10,-8,-6,-4,-2,0,2,4,6,8,10,20,30,40,50,75,100] + pct_diff_colormap: "PuOr_r" + scale_factor: 1000000000.0 + new_unit: "ppbv" +C2H5OH: + category: "Composition" + colormap: "jet" + diff_colormap: "gist_ncar" + pct_diff_contour_levels: [-100,-75,-50,-40,-30,-20,-10,-8,-6,-4,-2,0,2,4,6,8,10,20,30,40,50,75,100] + pct_diff_colormap: "PuOr_r" + scale_factor: 1000000000.0 + new_unit: "ppbv" +C2H5OOH: + category: "Composition" + colormap: "jet" + diff_colormap: "gist_ncar" + pct_diff_contour_levels: [-100,-75,-50,-40,-30,-20,-10,-8,-6,-4,-2,0,2,4,6,8,10,20,30,40,50,75,100] + pct_diff_colormap: "PuOr_r" + scale_factor: 1000000000.0 + new_unit: "ppbv" +C2H6: + category: "Composition" + colormap: "jet" + diff_colormap: "gist_ncar" + pct_diff_contour_levels: [-100,-75,-50,-40,-30,-20,-10,-8,-6,-4,-2,0,2,4,6,8,10,20,30,40,50,75,100] + pct_diff_colormap: "PuOr_r" + scale_factor: 1000000000.0 + new_unit: "ppbv" +C3H6: + category: "Composition" + colormap: "jet" + diff_colormap: "gist_ncar" + pct_diff_contour_levels: [-100,-75,-50,-40,-30,-20,-10,-8,-6,-4,-2,0,2,4,6,8,10,20,30,40,50,75,100] + pct_diff_colormap: "PuOr_r" + scale_factor: 1000000000.0 + new_unit: "ppbv" +C3H7O2: + category: "Composition" + colormap: "jet" + diff_colormap: "gist_ncar" + pct_diff_contour_levels: [-100,-75,-50,-40,-30,-20,-10,-8,-6,-4,-2,0,2,4,6,8,10,20,30,40,50,75,100] + pct_diff_colormap: "PuOr_r" + scale_factor: 1000000000.0 + new_unit: "ppbv" +C3H7OOH: + category: "Composition" + colormap: "jet" + diff_colormap: "gist_ncar" + pct_diff_contour_levels: [-100,-75,-50,-40,-30,-20,-10,-8,-6,-4,-2,0,2,4,6,8,10,20,30,40,50,75,100] + pct_diff_colormap: "PuOr_r" + scale_factor: 1000000000.0 + new_unit: "ppbv" +C3H8: + category: "Composition" + colormap: "jet" + diff_colormap: "gist_ncar" + pct_diff_contour_levels: [-100,-75,-50,-40,-30,-20,-10,-8,-6,-4,-2,0,2,4,6,8,10,20,30,40,50,75,100] + pct_diff_colormap: "PuOr_r" + scale_factor: 1000000000.0 + new_unit: "ppbv" +CCL4: + category: "Composition" + colormap: "jet" + diff_colormap: "gist_ncar" + pct_diff_contour_levels: [-100,-75,-50,-40,-30,-20,-10,-8,-6,-4,-2,0,2,4,6,8,10,20,30,40,50,75,100] + pct_diff_colormap: "PuOr_r" + scale_factor: 1000000000.0 + new_unit: "ppbv" +CFC11: + category: "Composition" + colormap: "jet" + diff_colormap: "gist_ncar" + pct_diff_contour_levels: [-100,-75,-50,-40,-30,-20,-10,-8,-6,-4,-2,0,2,4,6,8,10,20,30,40,50,75,100] + pct_diff_colormap: "PuOr_r" + scale_factor: 1000000000.0 + new_unit: "ppbv" +CFC113: + category: "Composition" + colormap: "jet" + diff_colormap: "gist_ncar" + pct_diff_contour_levels: [-100,-75,-50,-40,-30,-20,-10,-8,-6,-4,-2,0,2,4,6,8,10,20,30,40,50,75,100] + pct_diff_colormap: "PuOr_r" + scale_factor: 1000000000.0 + new_unit: "ppbv" +CFC114: + category: "Composition" + colormap: "jet" + diff_colormap: "gist_ncar" + pct_diff_contour_levels: [-100,-75,-50,-40,-30,-20,-10,-8,-6,-4,-2,0,2,4,6,8,10,20,30,40,50,75,100] + pct_diff_colormap: "PuOr_r" + scale_factor: 1000000000.0 + new_unit: "ppbv" +CFC115: + category: "Composition" + colormap: "jet" + diff_colormap: "gist_ncar" + pct_diff_contour_levels: [-100,-75,-50,-40,-30,-20,-10,-8,-6,-4,-2,0,2,4,6,8,10,20,30,40,50,75,100] + pct_diff_colormap: "PuOr_r" + scale_factor: 1000000000.0 + new_unit: "ppbv" +CFC12: + category: "Composition" + colormap: "jet" + diff_colormap: "gist_ncar" + pct_diff_contour_levels: [-100,-75,-50,-40,-30,-20,-10,-8,-6,-4,-2,0,2,4,6,8,10,20,30,40,50,75,100] + pct_diff_colormap: "PuOr_r" + scale_factor: 1000000000.0 + new_unit: "ppbv" +CH2O: + category: "Composition" + colormap: "jet" + diff_colormap: "gist_ncar" + pct_diff_contour_levels: [-100,-75,-50,-40,-30,-20,-10,-8,-6,-4,-2,0,2,4,6,8,10,20,30,40,50,75,100] + pct_diff_colormap: "PuOr_r" + scale_factor: 1000000000.0 + new_unit: "ppbv" +CH3BR: + category: "Composition" + colormap: "jet" + diff_colormap: "gist_ncar" + pct_diff_contour_levels: [-100,-75,-50,-40,-30,-20,-10,-8,-6,-4,-2,0,2,4,6,8,10,20,30,40,50,75,100] + pct_diff_colormap: "PuOr_r" + scale_factor: 1000000000.0 + new_unit: "ppbv" +CH3CCL3: + category: "Composition" + colormap: "jet" + diff_colormap: "gist_ncar" + pct_diff_contour_levels: [-100,-75,-50,-40,-30,-20,-10,-8,-6,-4,-2,0,2,4,6,8,10,20,30,40,50,75,100] + pct_diff_colormap: "PuOr_r" + scale_factor: 1000000000.0 + new_unit: "ppbv" +CH3CHO: + category: "Composition" + colormap: "jet" + diff_colormap: "gist_ncar" + pct_diff_contour_levels: [-100,-75,-50,-40,-30,-20,-10,-8,-6,-4,-2,0,2,4,6,8,10,20,30,40,50,75,100] + pct_diff_colormap: "PuOr_r" + scale_factor: 1000000000.0 + new_unit: "ppbv" +CH3CL: + category: "Composition" + colormap: "jet" + diff_colormap: "gist_ncar" + pct_diff_contour_levels: [-100,-75,-50,-40,-30,-20,-10,-8,-6,-4,-2,0,2,4,6,8,10,20,30,40,50,75,100] + pct_diff_colormap: "PuOr_r" + scale_factor: 1000000000.0 + new_unit: "ppbv" +CH3CO3: + category: "Composition" + colormap: "jet" + diff_colormap: "gist_ncar" + pct_diff_contour_levels: [-100,-75,-50,-40,-30,-20,-10,-8,-6,-4,-2,0,2,4,6,8,10,20,30,40,50,75,100] + pct_diff_colormap: "PuOr_r" + scale_factor: 1000000000.0 + new_unit: "ppbv" +CH3COCH3: + category: "Composition" + colormap: "jet" + diff_colormap: "gist_ncar" + pct_diff_contour_levels: [-100,-75,-50,-40,-30,-20,-10,-8,-6,-4,-2,0,2,4,6,8,10,20,30,40,50,75,100] + pct_diff_colormap: "PuOr_r" + scale_factor: 1000000000.0 + new_unit: "ppbv" +CH3COCHO: + category: "Composition" + colormap: "jet" + diff_colormap: "gist_ncar" + pct_diff_contour_levels: [-100,-75,-50,-40,-30,-20,-10,-8,-6,-4,-2,0,2,4,6,8,10,20,30,40,50,75,100] + pct_diff_colormap: "PuOr_r" + scale_factor: 1000000000.0 + new_unit: "ppbv" +CH3COOH: + category: "Composition" + colormap: "jet" + diff_colormap: "gist_ncar" + pct_diff_contour_levels: [-100,-75,-50,-40,-30,-20,-10,-8,-6,-4,-2,0,2,4,6,8,10,20,30,40,50,75,100] + pct_diff_colormap: "PuOr_r" + scale_factor: 1000000000.0 + new_unit: "ppbv" +CH3COOOH: + category: "Composition" + colormap: "jet" + diff_colormap: "gist_ncar" + pct_diff_contour_levels: [-100,-75,-50,-40,-30,-20,-10,-8,-6,-4,-2,0,2,4,6,8,10,20,30,40,50,75,100] + pct_diff_colormap: "PuOr_r" + scale_factor: 1000000000.0 + new_unit: "ppbv" +CH3O2: + category: "Composition" + colormap: "jet" + diff_colormap: "gist_ncar" + pct_diff_contour_levels: [-100,-75,-50,-40,-30,-20,-10,-8,-6,-4,-2,0,2,4,6,8,10,20,30,40,50,75,100] + pct_diff_colormap: "PuOr_r" + scale_factor: 1000000000.0 + new_unit: "ppbv" +CH3OH: + category: "Composition" + colormap: "jet" + diff_colormap: "gist_ncar" + pct_diff_contour_levels: [-100,-75,-50,-40,-30,-20,-10,-8,-6,-4,-2,0,2,4,6,8,10,20,30,40,50,75,100] + pct_diff_colormap: "PuOr_r" + scale_factor: 1000000000.0 + new_unit: "ppbv" +CH3OOH: + category: "Composition" + colormap: "jet" + diff_colormap: "gist_ncar" + pct_diff_contour_levels: [-100,-75,-50,-40,-30,-20,-10,-8,-6,-4,-2,0,2,4,6,8,10,20,30,40,50,75,100] + pct_diff_colormap: "PuOr_r" + scale_factor: 1000000000.0 + new_unit: "ppbv" +CH4: + category: "Composition" + colormap: "jet" + diff_colormap: "gist_ncar" + pct_diff_contour_levels: [-100,-75,-50,-40,-30,-20,-10,-8,-6,-4,-2,0,2,4,6,8,10,20,30,40,50,75,100] + pct_diff_colormap: "PuOr_r" + scale_factor: 1000000000.0 + new_unit: "ppbv" +CHBR3: + category: "Composition" + colormap: "jet" + diff_colormap: "gist_ncar" + pct_diff_contour_levels: [-100,-75,-50,-40,-30,-20,-10,-8,-6,-4,-2,0,2,4,6,8,10,20,30,40,50,75,100] + pct_diff_colormap: "PuOr_r" + scale_factor: 1000000000.0 + new_unit: "ppbv" +CLO: + category: "Composition" + colormap: "jet" + diff_colormap: "gist_ncar" + pct_diff_contour_levels: [-100,-75,-50,-40,-30,-20,-10,-8,-6,-4,-2,0,2,4,6,8,10,20,30,40,50,75,100] + pct_diff_colormap: "PuOr_r" + scale_factor: 1000000000.0 + new_unit: "ppbv" +CLONO2: + category: "Composition" + colormap: "jet" + diff_colormap: "gist_ncar" + pct_diff_contour_levels: [-100,-75,-50,-40,-30,-20,-10,-8,-6,-4,-2,0,2,4,6,8,10,20,30,40,50,75,100] + pct_diff_colormap: "PuOr_r" + scale_factor: 1000000000.0 + new_unit: "ppbv" +CLOX: + category: "Composition" + colormap: "jet" + diff_colormap: "gist_ncar" + pct_diff_contour_levels: [-100,-75,-50,-40,-30,-20,-10,-8,-6,-4,-2,0,2,4,6,8,10,20,30,40,50,75,100] + pct_diff_colormap: "PuOr_r" + scale_factor: 1000000000.0 + new_unit: "ppbv" +CLOY: + category: "Composition" + colormap: "jet" + diff_colormap: "gist_ncar" + pct_diff_contour_levels: [-100,-75,-50,-40,-30,-20,-10,-8,-6,-4,-2,0,2,4,6,8,10,20,30,40,50,75,100] + pct_diff_colormap: "PuOr_r" + scale_factor: 1000000000.0 + new_unit: "ppbv" +CO2: + category: "Composition" + #contour_levels_range: [300,450,10.0] + colormap: "jet" + diff_colormap: "gist_ncar" + pct_diff_contour_levels: [-100,-75,-50,-40,-30,-20,-10,-8,-6,-4,-2,0,2,4,6,8,10,20,30,40,50,75,100] + pct_diff_colormap: "PuOr_r" + scale_factor: 1000000.0 + new_unit: "ppmv" +E90: + category: "Composition" + colormap: "jet" + diff_colormap: "gist_ncar" + pct_diff_contour_levels: [-100,-75,-50,-40,-30,-20,-10,-8,-6,-4,-2,0,2,4,6,8,10,20,30,40,50,75,100] + pct_diff_colormap: "PuOr_r" + scale_factor: 1000000000.0 + new_unit: "ppbv" +GLYALD: + category: "Composition" + colormap: "jet" + diff_colormap: "gist_ncar" + pct_diff_contour_levels: [-100,-75,-50,-40,-30,-20,-10,-8,-6,-4,-2,0,2,4,6,8,10,20,30,40,50,75,100] + pct_diff_colormap: "PuOr_r" + scale_factor: 1000000000.0 + new_unit: "ppbv" +GLYOXAL: + category: "Composition" + colormap: "jet" + diff_colormap: "gist_ncar" + pct_diff_contour_levels: [-100,-75,-50,-40,-30,-20,-10,-8,-6,-4,-2,0,2,4,6,8,10,20,30,40,50,75,100] + pct_diff_colormap: "PuOr_r" + scale_factor: 1000000000.0 + new_unit: "ppbv" +H2402: + category: "Composition" + colormap: "jet" + diff_colormap: "gist_ncar" + pct_diff_contour_levels: [-100,-75,-50,-40,-30,-20,-10,-8,-6,-4,-2,0,2,4,6,8,10,20,30,40,50,75,100] + pct_diff_colormap: "PuOr_r" + scale_factor: 1000000000.0 + new_unit: "ppbv" +H2O2: + category: "Composition" + colormap: "jet" + diff_colormap: "gist_ncar" + pct_diff_contour_levels: [-100,-75,-50,-40,-30,-20,-10,-8,-6,-4,-2,0,2,4,6,8,10,20,30,40,50,75,100] + pct_diff_colormap: "PuOr_r" + scale_factor: 1000000000.0 + new_unit: "ppbv" +H2SO4: + category: "Composition" + colormap: "jet" + diff_colormap: "gist_ncar" + pct_diff_contour_levels: [-100,-75,-50,-40,-30,-20,-10,-8,-6,-4,-2,0,2,4,6,8,10,20,30,40,50,75,100] + pct_diff_colormap: "PuOr_r" + scale_factor: 1000000000.0 + new_unit: "ppbv" +HCFC141B: + category: "Composition" + colormap: "jet" + diff_colormap: "gist_ncar" + pct_diff_contour_levels: [-100,-75,-50,-40,-30,-20,-10,-8,-6,-4,-2,0,2,4,6,8,10,20,30,40,50,75,100] + pct_diff_colormap: "PuOr_r" + scale_factor: 1000000000.0 + new_unit: "ppbv" +HCFC142B: + category: "Composition" + colormap: "jet" + diff_colormap: "gist_ncar" + pct_diff_contour_levels: [-100,-75,-50,-40,-30,-20,-10,-8,-6,-4,-2,0,2,4,6,8,10,20,30,40,50,75,100] + pct_diff_colormap: "PuOr_r" + scale_factor: 1000000000.0 + new_unit: "ppbv" +HCFC22: + category: "Composition" + colormap: "jet" + diff_colormap: "gist_ncar" + pct_diff_contour_levels: [-100,-75,-50,-40,-30,-20,-10,-8,-6,-4,-2,0,2,4,6,8,10,20,30,40,50,75,100] + pct_diff_colormap: "PuOr_r" + scale_factor: 1000000000.0 + new_unit: "ppbv" +HCL: + category: "Composition" + colormap: "jet" + diff_colormap: "gist_ncar" + pct_diff_contour_levels: [-100,-75,-50,-40,-30,-20,-10,-8,-6,-4,-2,0,2,4,6,8,10,20,30,40,50,75,100] + pct_diff_colormap: "PuOr_r" + scale_factor: 1000000000.0 + new_unit: "ppbv" +HCL_GAS: + category: "Composition" + colormap: "jet" + diff_colormap: "gist_ncar" + pct_diff_contour_levels: [-100,-75,-50,-40,-30,-20,-10,-8,-6,-4,-2,0,2,4,6,8,10,20,30,40,50,75,100] + pct_diff_colormap: "PuOr_r" + scale_factor: 1000000000.0 + new_unit: "ppbv" +HNO3_GAS: + category: "Composition" + colormap: "jet" + diff_colormap: "gist_ncar" + pct_diff_contour_levels: [-100,-75,-50,-40,-30,-20,-10,-8,-6,-4,-2,0,2,4,6,8,10,20,30,40,50,75,100] + pct_diff_colormap: "PuOr_r" + scale_factor: 1000000000.0 + new_unit: "ppbv" +HO2: + category: "Composition" + colormap: "jet" + diff_colormap: "gist_ncar" + pct_diff_contour_levels: [-100,-75,-50,-40,-30,-20,-10,-8,-6,-4,-2,0,2,4,6,8,10,20,30,40,50,75,100] + pct_diff_colormap: "PuOr_r" + scale_factor: 1000000000.0 + new_unit: "ppbv" +HO2NO2: + category: "Composition" + colormap: "jet" + diff_colormap: "gist_ncar" + pct_diff_contour_levels: [-100,-75,-50,-40,-30,-20,-10,-8,-6,-4,-2,0,2,4,6,8,10,20,30,40,50,75,100] + pct_diff_colormap: "PuOr_r" + scale_factor: 1000000000.0 + new_unit: "ppbv" +HOBR: + category: "Composition" + colormap: "jet" + diff_colormap: "gist_ncar" + pct_diff_contour_levels: [-100,-75,-50,-40,-30,-20,-10,-8,-6,-4,-2,0,2,4,6,8,10,20,30,40,50,75,100] + pct_diff_colormap: "PuOr_r" + scale_factor: 1000000000.0 + new_unit: "ppbv" +HYAC: + category: "Composition" + colormap: "jet" + diff_colormap: "gist_ncar" + pct_diff_contour_levels: [-100,-75,-50,-40,-30,-20,-10,-8,-6,-4,-2,0,2,4,6,8,10,20,30,40,50,75,100] + pct_diff_colormap: "PuOr_r" + scale_factor: 1000000000.0 + new_unit: "ppbv" +HYDRALD: + category: "Composition" + colormap: "jet" + diff_colormap: "gist_ncar" + pct_diff_contour_levels: [-100,-75,-50,-40,-30,-20,-10,-8,-6,-4,-2,0,2,4,6,8,10,20,30,40,50,75,100] + pct_diff_colormap: "PuOr_r" + scale_factor: 1000000000.0 + new_unit: "ppbv" +ISOP: + category: "Composition" + colormap: "jet" + diff_colormap: "gist_ncar" + pct_diff_contour_levels: [-100,-75,-50,-40,-30,-20,-10,-8,-6,-4,-2,0,2,4,6,8,10,20,30,40,50,75,100] + pct_diff_colormap: "PuOr_r" + scale_factor: 1000000000.0 + new_unit: "ppbv" +ISOPNO3: + category: "Composition" + colormap: "jet" + diff_colormap: "gist_ncar" + pct_diff_contour_levels: [-100,-75,-50,-40,-30,-20,-10,-8,-6,-4,-2,0,2,4,6,8,10,20,30,40,50,75,100] + pct_diff_colormap: "PuOr_r" + scale_factor: 1000000000.0 + new_unit: "ppbv" +ISOPO2: + category: "Composition" + colormap: "jet" + diff_colormap: "gist_ncar" + pct_diff_contour_levels: [-100,-75,-50,-40,-30,-20,-10,-8,-6,-4,-2,0,2,4,6,8,10,20,30,40,50,75,100] + pct_diff_colormap: "PuOr_r" + scale_factor: 1000000000.0 + new_unit: "ppbv" +ISOPOOH: + category: "Composition" + colormap: "jet" + diff_colormap: "gist_ncar" + pct_diff_contour_levels: [-100,-75,-50,-40,-30,-20,-10,-8,-6,-4,-2,0,2,4,6,8,10,20,30,40,50,75,100] + pct_diff_colormap: "PuOr_r" + scale_factor: 1000000000.0 + new_unit: "ppbv" +MACR: + category: "Composition" + colormap: "jet" + diff_colormap: "gist_ncar" + pct_diff_contour_levels: [-100,-75,-50,-40,-30,-20,-10,-8,-6,-4,-2,0,2,4,6,8,10,20,30,40,50,75,100] + pct_diff_colormap: "PuOr_r" + scale_factor: 1000000000.0 + new_unit: "ppbv" +MACRO2: + category: "Composition" + colormap: "jet" + diff_colormap: "gist_ncar" + pct_diff_contour_levels: [-100,-75,-50,-40,-30,-20,-10,-8,-6,-4,-2,0,2,4,6,8,10,20,30,40,50,75,100] + pct_diff_colormap: "PuOr_r" + scale_factor: 1000000000.0 + new_unit: "ppbv" +MACROOH: + category: "Composition" + colormap: "jet" + diff_colormap: "gist_ncar" + pct_diff_contour_levels: [-100,-75,-50,-40,-30,-20,-10,-8,-6,-4,-2,0,2,4,6,8,10,20,30,40,50,75,100] + pct_diff_colormap: "PuOr_r" + scale_factor: 1000000000.0 + new_unit: "ppbv" +MVK: + category: "Composition" + colormap: "jet" + diff_colormap: "gist_ncar" + pct_diff_contour_levels: [-100,-75,-50,-40,-30,-20,-10,-8,-6,-4,-2,0,2,4,6,8,10,20,30,40,50,75,100] + pct_diff_colormap: "PuOr_r" + scale_factor: 1000000000.0 + new_unit: "ppbv" +N2O5: + category: "Composition" + colormap: "jet" + diff_colormap: "gist_ncar" + pct_diff_contour_levels: [-100,-75,-50,-40,-30,-20,-10,-8,-6,-4,-2,0,2,4,6,8,10,20,30,40,50,75,100] + pct_diff_colormap: "PuOr_r" + scale_factor: 1000000000.0 + new_unit: "ppbv" +NH3: + category: "Composition" + colormap: "jet" + diff_colormap: "gist_ncar" + pct_diff_contour_levels: [-100,-75,-50,-40,-30,-20,-10,-8,-6,-4,-2,0,2,4,6,8,10,20,30,40,50,75,100] + pct_diff_colormap: "PuOr_r" + scale_factor: 1000000000.0 + new_unit: "ppbv" +NH4: + category: "Composition" + colormap: "jet" + diff_colormap: "gist_ncar" + pct_diff_contour_levels: [-100,-75,-50,-40,-30,-20,-10,-8,-6,-4,-2,0,2,4,6,8,10,20,30,40,50,75,100] + pct_diff_colormap: "PuOr_r" + scale_factor: 1000000000.0 + new_unit: "ppbv" +NO3: + category: "Composition" + colormap: "jet" + diff_colormap: "gist_ncar" + pct_diff_contour_levels: [-100,-75,-50,-40,-30,-20,-10,-8,-6,-4,-2,0,2,4,6,8,10,20,30,40,50,75,100] + pct_diff_colormap: "PuOr_r" + scale_factor: 1000000000.0 + new_unit: "ppbv" +O3S: + category: "Composition" + colormap: "jet" + diff_colormap: "gist_ncar" + pct_diff_contour_levels: [-100,-75,-50,-40,-30,-20,-10,-8,-6,-4,-2,0,2,4,6,8,10,20,30,40,50,75,100] + pct_diff_colormap: "PuOr_r" + scale_factor: 1000000000.0 + new_unit: "ppbv" +OCLO: + category: "Composition" + colormap: "jet" + diff_colormap: "gist_ncar" + pct_diff_contour_levels: [-100,-75,-50,-40,-30,-20,-10,-8,-6,-4,-2,0,2,4,6,8,10,20,30,40,50,75,100] + pct_diff_colormap: "PuOr_r" + scale_factor: 1000000000.0 + new_unit: "ppbv" +OCS: + category: "Composition" + colormap: "jet" + diff_colormap: "gist_ncar" + pct_diff_contour_levels: [-100,-75,-50,-40,-30,-20,-10,-8,-6,-4,-2,0,2,4,6,8,10,20,30,40,50,75,100] + pct_diff_colormap: "PuOr_r" + scale_factor: 1000000000.0 + new_unit: "ppbv" +ONITR: + category: "Composition" + colormap: "jet" + diff_colormap: "gist_ncar" + pct_diff_contour_levels: [-100,-75,-50,-40,-30,-20,-10,-8,-6,-4,-2,0,2,4,6,8,10,20,30,40,50,75,100] + pct_diff_colormap: "PuOr_r" + scale_factor: 1000000000.0 + new_unit: "ppbv" +PAN: + category: "Composition" + colormap: "jet" + diff_colormap: "gist_ncar" + pct_diff_contour_levels: [-100,-75,-50,-40,-30,-20,-10,-8,-6,-4,-2,0,2,4,6,8,10,20,30,40,50,75,100] + pct_diff_colormap: "PuOr_r" + scale_factor: 1000000000.0 + new_unit: "ppbv" +POOH: + category: "Composition" + colormap: "jet" + diff_colormap: "gist_ncar" + pct_diff_contour_levels: [-100,-75,-50,-40,-30,-20,-10,-8,-6,-4,-2,0,2,4,6,8,10,20,30,40,50,75,100] + pct_diff_colormap: "PuOr_r" + scale_factor: 1000000000.0 + new_unit: "ppbv" +RO2: + category: "Composition" + colormap: "jet" + diff_colormap: "gist_ncar" + pct_diff_contour_levels: [-100,-75,-50,-40,-30,-20,-10,-8,-6,-4,-2,0,2,4,6,8,10,20,30,40,50,75,100] + pct_diff_colormap: "PuOr_r" + scale_factor: 1000000000.0 + new_unit: "ppbv" +ROOH: + category: "Composition" + colormap: "jet" + diff_colormap: "gist_ncar" + pct_diff_contour_levels: [-100,-75,-50,-40,-30,-20,-10,-8,-6,-4,-2,0,2,4,6,8,10,20,30,40,50,75,100] + pct_diff_colormap: "PuOr_r" + scale_factor: 1000000000.0 + new_unit: "ppbv" +SO3: + category: "Composition" + colormap: "jet" + diff_colormap: "gist_ncar" + pct_diff_contour_levels: [-100,-75,-50,-40,-30,-20,-10,-8,-6,-4,-2,0,2,4,6,8,10,20,30,40,50,75,100] + pct_diff_colormap: "PuOr_r" + scale_factor: 1000000000.0 + new_unit: "ppbv" +SOAE: + category: "Composition" + colormap: "jet" + diff_colormap: "gist_ncar" + pct_diff_contour_levels: [-100,-75,-50,-40,-30,-20,-10,-8,-6,-4,-2,0,2,4,6,8,10,20,30,40,50,75,100] + pct_diff_colormap: "PuOr_r" + scale_factor: 1000000000.0 + new_unit: "ppbv" +TERP: + category: "Composition" + colormap: "jet" + diff_colormap: "gist_ncar" + pct_diff_contour_levels: [-100,-75,-50,-40,-30,-20,-10,-8,-6,-4,-2,0,2,4,6,8,10,20,30,40,50,75,100] + pct_diff_colormap: "PuOr_r" + scale_factor: 1000000000.0 + new_unit: "ppbv" +XO2: + category: "Composition" + colormap: "jet" + diff_colormap: "gist_ncar" + pct_diff_contour_levels: [-100,-75,-50,-40,-30,-20,-10,-8,-6,-4,-2,0,2,4,6,8,10,20,30,40,50,75,100] + pct_diff_colormap: "PuOr_r" + scale_factor: 1000000000.0 + new_unit: "ppbv" +XOOH: + category: "Composition" + colormap: "jet" + diff_colormap: "gist_ncar" + pct_diff_contour_levels: [-100,-75,-50,-40,-30,-20,-10,-8,-6,-4,-2,0,2,4,6,8,10,20,30,40,50,75,100] + pct_diff_colormap: "PuOr_r" + scale_factor: 1000000000.0 + new_unit: "ppbv" + + +#+++++++++++++++++ +# Category: Clouds +#+++++++++++++++++ + +CLDICE: + category: "Clouds" + obs_file: "CLDICE_ERA5_monthly_climo_197901-202112.nc" + obs_name: "ERA5" + obs_var_name: "CLDICE" + pct_diff_contour_levels: [-100,-75,-50,-40,-30,-20,-10,-8,-6,-4,-2,0,2,4,6,8,10,20,30,40,50,75,100] + pct_diff_colormap: "PuOr_r" + +CLDLIQ: + category: "Clouds" + obs_file: "CLDLIQ_ERA5_monthly_climo_197901-202112.nc" + obs_name: "ERA5" + obs_var_name: "CLDLIQ" + pct_diff_contour_levels: [-100,-75,-50,-40,-30,-20,-10,-8,-6,-4,-2,0,2,4,6,8,10,20,30,40,50,75,100] + pct_diff_colormap: "PuOr_r" + +CLDTOT: + colormap: "Oranges" + contour_levels_range: [0.2, 1.1, 0.05] + diff_colormap: "BrBG" + diff_contour_range: [-0.4, 0.4, 0.05] + scale_factor: 1. + add_offset: 0 + new_unit: "Fraction" + obs_file: "ERAI_all_climo.nc" + obs_name: "ERAI" + obs_var_name: "CLDTOT" + category: "Clouds" + pct_diff_contour_levels: [-100,-75,-50,-40,-30,-20,-10,-8,-6,-4,-2,0,2,4,6,8,10,20,30,40,50,75,100] + pct_diff_colormap: "PuOr_r" + +CLDLOW: + colormap: "Oranges" + contour_levels_range: [0, 1.05, 0.05] + diff_colormap: "BrBG" + diff_contour_range: [-0.4, 0.4, 0.05] + scale_factor: 1. + add_offset: 0 + new_unit: "Fraction" + obs_file: "ERAI_all_climo.nc" + obs_name: "ERAI" + obs_var_name: "CLDLOW" + category: "Clouds" + pct_diff_contour_levels: [-100,-75,-50,-40,-30,-20,-10,-8,-6,-4,-2,0,2,4,6,8,10,20,30,40,50,75,100] + pct_diff_colormap: "PuOr_r" + +CLDHGH: + colormap: "Oranges" + contour_levels_range: [0, 1.05, 0.05] + diff_colormap: "BrBG" + diff_contour_range: [-0.4, 0.4, 0.05] + scale_factor: 1. + add_offset: 0 + new_unit: "Fraction" + obs_file: "ERAI_all_climo.nc" + obs_name: "ERAI" + obs_var_name: "CLDHGH" + category: "Clouds" + pct_diff_contour_levels: [-100,-75,-50,-40,-30,-20,-10,-8,-6,-4,-2,0,2,4,6,8,10,20,30,40,50,75,100] + pct_diff_colormap: "PuOr_r" + +CLDMED: + colormap: "Oranges" + contour_levels_range: [0, 1.05, 0.05] + diff_colormap: "BrBG" + diff_contour_range: [-0.4, 0.4, 0.05] + scale_factor: 1. + add_offset: 0 + new_unit: "Fraction" + obs_file: "ERAI_all_climo.nc" + obs_name: "ERAI" + obs_var_name: "CLDMED" + category: "Clouds" + pct_diff_contour_levels: [-100,-75,-50,-40,-30,-20,-10,-8,-6,-4,-2,0,2,4,6,8,10,20,30,40,50,75,100] + pct_diff_colormap: "PuOr_r" + +CLOUD: + colormap: "Blues" + contour_levels_range: [0, 105, 5] + diff_colormap: "BrBG" + diff_contour_range: [-15, 15, 2] + scale_factor: 100 + add_offset: 0 + new_unit: "Percent" + mpl: + colorbar: + label : "Percent" + category: "Clouds" + pct_diff_contour_levels: [-100,-75,-50,-40,-30,-20,-10,-8,-6,-4,-2,0,2,4,6,8,10,20,30,40,50,75,100] + pct_diff_colormap: "PuOr_r" + +CONCLD: + category: "Clouds" + +TGCLDLWP: + colormap: "Blues" + contour_levels_range: [0, 400, 10] + diff_colormap: "BrBG" + diff_contour_range: [-100, 100, 10] + scale_factor: 1000 + add_offset: 0 + new_unit: "g m$^{-2}$" + mpl: + colorbar: + label : "g m$^{-2}$" + category: "Clouds" + obs_file: "TGCLDLWP_ERA5_monthly_climo_197901-202112.nc" + obs_name: "ERA5" + obs_var_name: "TGCLDLWP" + obs_scale_factor: 1000 + obs_add_offset: 0 + pct_diff_contour_levels: [-100,-75,-50,-40,-30,-20,-10,-8,-6,-4,-2,0,2,4,6,8,10,20,30,40,50,75,100] + pct_diff_colormap: "PuOr_r" + +TGCLDIWP: + colormap: "Blues" + contour_levels_range: [0, 100, 5] + diff_colormap: "BrBG" + diff_contour_range: [-50, 50, 5] + scale_factor: 1000 + add_offset: 0 + new_unit: "g m$^{-2}$" + mpl: + colorbar: + label : "g m$^{-2}$" + category: "Clouds" + obs_file: "TGCLDIWP_ERA5_monthly_climo_197901-202112.nc" + obs_name: "ERA5" + obs_var_name: "TGCLDIWP" + obs_scale_factor: 1000 + obs_add_offset: 0 + pct_diff_contour_levels: [-100,-75,-50,-40,-30,-20,-10,-8,-6,-4,-2,0,2,4,6,8,10,20,30,40,50,75,100] + pct_diff_colormap: "PuOr_r" + +CCN3: + category: "Clouds" + +#+++++++++++++++++ +# Category: CLUBB +#+++++++++++++++++ + +RVMTEND_CLUBB: + category: "CLUBB" + +STEND_CLUBB: + category: "CLUBB" + +WPRTP_CLUBB: + category: "CLUBB" + +WPTHLP_CLUBB: + category: "CLUBB" + +#+++++++++++++++++ +# Category: hydrologic cycle +#+++++++++++++++++ + +PRECC: + colormap: "Greens" + contour_levels_range: [0, 20, 1] + diff_colormap: "BrBG" + diff_contour_range: [-10, 10, 0.5] + scale_factor: 86400000 + add_offset: 0 + new_unit: "mm d$^{-1}$" + mpl: + colorbar: + label : "mm/d" + category: "Hydrologic cycle" + pct_diff_contour_levels: [-100,-75,-50,-40,-30,-20,-10,-8,-6,-4,-2,0,2,4,6,8,10,20,30,40,50,75,100] + pct_diff_colormap: "PuOr_r" + +PRECL: + colormap: "Greens" + contour_levels_range: [0, 20, 1] + diff_colormap: "BrBG" + diff_contour_range: [-10, 10, 0.5] + scale_factor: 86400000 + add_offset: 0 + new_unit: "mm d$^{-1}$" + mpl: + colorbar: + label : "mm d$^{-1}$" + category: "Hydrologic cycle" + pct_diff_contour_levels: [-100,-75,-50,-40,-30,-20,-10,-8,-6,-4,-2,0,2,4,6,8,10,20,30,40,50,75,100] + pct_diff_colormap: "PuOr_r" + +PRECSC: + colormap: "Greens" + contour_levels_range: [0, 20, 1] + diff_colormap: "BrBG" + diff_contour_range: [-10, 10, 0.5] + scale_factor: 86400000 + add_offset: 0 + new_unit: "mm d$^{-1}$" + mpl: + colorbar: + label : "mm d$^{-1}$" + category: "Hydrologic cycle" + pct_diff_contour_levels: [-100,-75,-50,-40,-30,-20,-10,-8,-6,-4,-2,0,2,4,6,8,10,20,30,40,50,75,100] + pct_diff_colormap: "PuOr_r" + +PRECSL: + colormap: "Greens" + contour_levels_range: [0, 20, 1] + diff_colormap: "BrBG" + diff_contour_range: [-10, 10, 0.5] + scale_factor: 86400000 + add_offset: 0 + new_unit: "mm d$^{-1}$" + mpl: + colorbar: + label : "mm d$^{-1}$" + category: "Hydrologic cycle" + pct_diff_contour_levels: [-100,-75,-50,-40,-30,-20,-10,-8,-6,-4,-2,0,2,4,6,8,10,20,30,40,50,75,100] + pct_diff_colormap: "PuOr_r" + +PRECT: + colormap: "Blues" + contour_levels_range: [0, 20, 1] + diff_colormap: "seismic" + diff_contour_range: [-10, 10, 0.5] + scale_factor: 86400000 + add_offset: 0 + new_unit: "mm d$^{-1}$" + mpl: + colorbar: + label : "mm d$^{-1}$" + obs_file: "ERAI_all_climo.nc" + obs_name: "ERAI" + obs_var_name: "PRECT" + category: "Hydrologic cycle" + derivable_from: ['PRECL','PRECC'] + pct_diff_contour_levels: [-100,-75,-50,-40,-30,-20,-10,-8,-6,-4,-2,0,2,4,6,8,10,20,30,40,50,75,100] + pct_diff_colormap: "PuOr_r" + +QFLX: + category: "Hydrologic cycle" + +#+++++++++++++++++ +# Category: Surface variables +#+++++++++++++++++ + +PBLH: + category: "Surface variables" + obs_file: "PBLH_ERA5_monthly_climo_197901-202112.nc" + obs_name: "ERA5" + obs_var_name: "PBLH" + +PSL: + colormap: "Oranges" + contour_levels_range: [980, 1052, 4] + diff_colormap: "PuOr_r" + diff_contour_range: [-9, 9, 0.5] + scale_factor: 0.01 + add_offset: 0 + new_unit: "hPa" + mpl: + colorbar: + label : "hPa" + category: "Surface variables" + obs_file: "PSL_ERA5_monthly_climo_197901-202112.nc" + obs_name: "ERA5" + obs_var_name: "PSL" + pct_diff_contour_levels: [-100,-75,-50,-40,-30,-20,-10,-8,-6,-4,-2,0,2,4,6,8,10,20,30,40,50,75,100] + pct_diff_colormap: "PuOr_r" + +PS: + colormap: "Oranges" + contour_levels: [500,600,630,660,690,720,750,780,810,840,870,900,930,960,990,1020,1050] + diff_colormap: "PuOr_r" + diff_contour_range: [-9, 9, 0.5] + scale_factor: 0.01 + add_offset: 0 + new_unit: "hPa" + mpl: + colorbar: + label : "hPa" + category: "Surface variables" + obs_file: "PS_ERA5_monthly_climo_197901-202112.nc" + obs_name: "ERA5" + obs_var_name: "PS" + pct_diff_contour_levels: [-100,-75,-50,-40,-30,-20,-10,-8,-6,-4,-2,0,2,4,6,8,10,20,30,40,50,75,100] + pct_diff_colormap: "PuOr_r" + +TREFHT: + category: "Surface variables" + obs_file: "TREFHT_ERA5_monthly_climo_197901-202112.nc" + obs_name: "ERA5" + obs_var_name: "TREFHT" + +TS: + colormap: "Blues" + contour_levels_range: [220,320, 5] + diff_colormap: "BrBG" + diff_contour_range: [-10, 10, 1] + scale_factor: 1 + add_offset: 0 + new_unit: "K" + mpl: + colorbar: + label : "K" + obs_file: "ERAI_all_climo.nc" + obs_name: "ERAI" + obs_var_name: "TS" + category: "Surface variables" + pct_diff_contour_levels: [-100,-75,-50,-40,-30,-20,-10,-8,-6,-4,-2,0,2,4,6,8,10,20,30,40,50,75,100] + pct_diff_colormap: "PuOr_r" + +SST: + colormap: "Blues" + contour_levels_range: [220,320, 5] + diff_colormap: "BrBG" + diff_contour_range: [-10, 10, 1] + scale_factor: 1 + add_offset: 0 + new_unit: "K" + mpl: + colorbar: + label : "K" + obs_file: "ERAI_all_climo.nc" + obs_name: "ERAI" + obs_var_name: "TS" + category: "Surface variables" + mask: "ocean" + pct_diff_contour_levels: [-100,-75,-50,-40,-30,-20,-10,-8,-6,-4,-2,0,2,4,6,8,10,20,30,40,50,75,100] + pct_diff_colormap: "PuOr_r" + +U10: + category: "Surface variables" + pct_diff_contour_levels: [-100,-75,-50,-40,-30,-20,-10,-8,-6,-4,-2,0,2,4,6,8,10,20,30,40,50,75,100] + pct_diff_colormap: "PuOr_r" + +Surface_Wind_Stress: + category: "Surface variables" + pct_diff_contour_levels: [-100,-75,-50,-40,-30,-20,-10,-8,-6,-4,-2,0,2,4,6,8,10,20,30,40,50,75,100] + pct_diff_colormap: "PuOr_r" + +TAUX: + vector_pair: "TAUY" + vector_name: "Surface_Wind_Stress" + category: "Surface variables" + scale_factor: -1 + add_offset: 0 + pct_diff_contour_levels: [-100,-75,-50,-40,-30,-20,-10,-8,-6,-4,-2,0,2,4,6,8,10,20,30,40,50,75,100] + pct_diff_colormap: "PuOr_r" + +TAUY: + vector_pair: "TAUX" + vector_name: "Surface_Wind_Stress" + category: "Surface variables" + scale_factor: -1 + add_offset: 0 + pct_diff_contour_levels: [-100,-75,-50,-40,-30,-20,-10,-8,-6,-4,-2,0,2,4,6,8,10,20,30,40,50,75,100] + pct_diff_colormap: "PuOr_r" + +ICEFRAC: + category: "Surface variables" + pct_diff_contour_levels: [-100,-75,-50,-40,-30,-20,-10,-8,-6,-4,-2,0,2,4,6,8,10,20,30,40,50,75,100] + pct_diff_colormap: "PuOr_r" + +OCNFRAC: + category: "Surface variables" + pct_diff_contour_levels: [-100,-75,-50,-40,-30,-20,-10,-8,-6,-4,-2,0,2,4,6,8,10,20,30,40,50,75,100] + pct_diff_colormap: "PuOr_r" + +LANDFRAC: + category: "Surface variables" + pct_diff_contour_levels: [-100,-75,-50,-40,-30,-20,-10,-8,-6,-4,-2,0,2,4,6,8,10,20,30,40,50,75,100] + pct_diff_colormap: "PuOr_r" + +#+++++++++++++++++ +# Category: State +#+++++++++++++++++ + +TMQ: + colormap: "Oranges" + contour_levels_range: [0, 75.0, 5.0] + diff_colormap: "BrBG" + diff_contour_range: [-10, 10, 0.5] + scale_factor: 1. + add_offset: 0 + new_unit: "kg m$^{-2}$" + obs_file: "ERAI_all_climo.nc" + obs_name: "ERAI" + obs_var_name: "PREH2O" + category: "State" + pct_diff_contour_levels: [-100,-75,-50,-40,-30,-20,-10,-8,-6,-4,-2,0,2,4,6,8,10,20,30,40,50,75,100] + pct_diff_colormap: "PuOr_r" + +RELHUM: + colormap: "Blues" + contour_levels_range: [0, 105, 5] + diff_colormap: "BrBG" + diff_contour_range: [-15, 15, 2] + scale_factor: 1 + add_offset: 0 + new_unit: "Fraction" + mpl: + colorbar: + label : "Fraction" + obs_file: "ERAI_all_climo.nc" + obs_name: "ERAI" + obs_var_name: "RELHUM" + category: "State" + pct_diff_contour_levels: [-100,-75,-50,-40,-30,-20,-10,-8,-6,-4,-2,0,2,4,6,8,10,20,30,40,50,75,100] + pct_diff_colormap: "PuOr_r" + +U: + colormap: "Blues" + contour_levels_range: [-10, 90, 5] + diff_colormap: "BrBG" + diff_contour_range: [-15, 15, 2] + scale_factor: 1 + add_offset: 0 + new_unit: "ms$^{-1}$" + mpl: + colorbar: + label : "ms$^{-1}$" + obs_file: "U_ERA5_monthly_climo_197901-202112.nc" + obs_name: "ERA5" + obs_var_name: "U" + vector_pair: "V" + vector_name: "Wind" + category: "State" + pct_diff_contour_levels: [-100,-75,-50,-40,-30,-20,-10,-8,-6,-4,-2,0,2,4,6,8,10,20,30,40,50,75,100] + pct_diff_colormap: "PuOr_r" + +V: + colormap: "Blues" + contour_levels_range: [-10, 90, 5] + diff_colormap: "BrBG" + diff_contour_range: [-15, 15, 2] + scale_factor: 1 + add_offset: 0 + new_unit: "ms$^{-1}$" + mpl: + colorbar: + label : "ms$^{-1}$" + obs_file: "V_ERA5_monthly_climo_197901-202112.nc" + obs_name: "ERA5" + obs_var_name: "V" + vector_pair: "U" + vector_name: "Wind" + category: "State" + pct_diff_contour_levels: [-100,-75,-50,-40,-30,-20,-10,-8,-6,-4,-2,0,2,4,6,8,10,20,30,40,50,75,100] + pct_diff_colormap: "PuOr_r" + +Q: + category: "State" + obs_file: "Q_ERA5_monthly_climo_197901-202112.nc" + obs_name: "ERA5" + obs_var_name: "Q" + pct_diff_contour_levels: [-100,-75,-50,-40,-30,-20,-10,-8,-6,-4,-2,0,2,4,6,8,10,20,30,40,50,75,100] + pct_diff_colormap: "PuOr_r" + +T: + category: "State" + obs_file: "T_ERA5_monthly_climo_197901-202112.nc" + obs_name: "ERA5" + obs_var_name: "T" + pct_diff_contour_levels: [-100,-75,-50,-40,-30,-20,-10,-8,-6,-4,-2,0,2,4,6,8,10,20,30,40,50,75,100] + pct_diff_colormap: "PuOr_r" + +OMEGA: + category: "State" + obs_file: "OMEGA_ERA5_monthly_climo_197901-202112.nc" + obs_name: "ERA5" + obs_var_name: "OMEGA" + pct_diff_contour_levels: [-100,-75,-50,-40,-30,-20,-10,-8,-6,-4,-2,0,2,4,6,8,10,20,30,40,50,75,100] + pct_diff_colormap: "PuOr_r" + +OMEGA500: + category: "State" + pct_diff_contour_levels: [-100,-75,-50,-40,-30,-20,-10,-8,-6,-4,-2,0,2,4,6,8,10,20,30,40,50,75,100] + pct_diff_colormap: "PuOr_r" + +PINT: + category: "State" + pct_diff_contour_levels: [-100,-75,-50,-40,-30,-20,-10,-8,-6,-4,-2,0,2,4,6,8,10,20,30,40,50,75,100] + pct_diff_colormap: "PuOr_r" + +PMID: + category: "State" + pct_diff_contour_levels: [-100,-75,-50,-40,-30,-20,-10,-8,-6,-4,-2,0,2,4,6,8,10,20,30,40,50,75,100] + pct_diff_colormap: "PuOr_r" + +Z3: + category: "State" + pct_diff_contour_levels: [-100,-75,-50,-40,-30,-20,-10,-8,-6,-4,-2,0,2,4,6,8,10,20,30,40,50,75,100] + pct_diff_colormap: "PuOr_r" + +Wind: + category: "State" + pct_diff_contour_levels: [-100,-75,-50,-40,-30,-20,-10,-8,-6,-4,-2,0,2,4,6,8,10,20,30,40,50,75,100] + pct_diff_colormap: "PuOr_r" + +#+++++++++++++++++ +# Category: Radiation +#+++++++++++++++++ + +QRL: + category: "Radiation" + pct_diff_contour_levels: [-100,-75,-50,-40,-30,-20,-10,-8,-6,-4,-2,0,2,4,6,8,10,20,30,40,50,75,100] + pct_diff_colormap: "PuOr_r" + +QRS: + category: "Radiation" + pct_diff_contour_levels: [-100,-75,-50,-40,-30,-20,-10,-8,-6,-4,-2,0,2,4,6,8,10,20,30,40,50,75,100] + pct_diff_colormap: "PuOr_r" + +#+++++++++++++++++ +# Category: TOA energy flux +#+++++++++++++++++ + +RESTOM: + colormap: "RdBu_r" + contour_levels_range: [-100, 100, 5] + diff_colormap: "seismic" + diff_contour_range: [-10, 10, 0.5] + scale_factor: 1 + add_offset: 0 + new_unit: "W m$^{-2}$" + mpl: + colorbar: + label : "W m$^{-2}$" + category: "TOA energy flux" + derivable_from: ['FLNT','FSNT'] + pct_diff_contour_levels: [-100,-75,-50,-40,-30,-20,-10,-8,-6,-4,-2,0,2,4,6,8,10,20,30,40,50,75,100] + pct_diff_colormap: "PuOr_r" + +SNOWDP: + colormap: "Blues" + contour_levels_range: [-150, 50, 10] + diff_colormap: "BrBG" + diff_contour_range: [-20, 20, 2] + scale_factor: 1 + add_offset: 0 + new_unit: "Wm$^{-2}$" + mpl: + colorbar: + label : "Wm$^{-2}$" + obs_file: "CERES_EBAF_Ed4.1_2001-2020.nc" + obs_name: "CERES_EBAF_Ed4.1" + obs_var_name: "toa_cre_sw_mon" + obs_scale_factor: 1 + obs_add_offset: 0 + category: "TOA energy flux" + +SWCF: + colormap: "Blues" + contour_levels_range: [-150, 50, 10] + diff_colormap: "BrBG" + diff_contour_range: [-20, 20, 2] + scale_factor: 1 + add_offset: 0 + new_unit: "Wm$^{-2}$" + mpl: + colorbar: + label : "Wm$^{-2}$" + obs_file: "CERES_EBAF_Ed4.1_2001-2020.nc" + obs_name: "CERES_EBAF_Ed4.1" + obs_var_name: "toa_cre_sw_mon" + obs_scale_factor: 1 + obs_add_offset: 0 + category: "TOA energy flux" + pct_diff_contour_levels: [-100,-75,-50,-40,-30,-20,-10,-8,-6,-4,-2,0,2,4,6,8,10,20,30,40,50,75,100] + pct_diff_colormap: "PuOr_r" + +LWCF: + colormap: "Oranges" + contour_levels_range: [-10, 100, 5] + diff_colormap: "BrBG" + diff_contour_range: [-15, 15, 1] + scale_factor: 1 + add_offset: 0 + new_unit: "Wm$^{-2}$" + mpl: + colorbar: + label : "Wm$^{-2}$" + obs_file: "CERES_EBAF_Ed4.1_2001-2020.nc" + obs_name: "CERES_EBAF_Ed4.1" + obs_var_name: "toa_cre_lw_mon" + category: "TOA energy flux" + pct_diff_contour_levels: [-100,-75,-50,-40,-30,-20,-10,-8,-6,-4,-2,0,2,4,6,8,10,20,30,40,50,75,100] + pct_diff_colormap: "PuOr_r" + +FSUTOA: + colormap: "Blues" + contour_levels_range: [-10, 180, 15] + diff_colormap: "BrBG" + diff_contour_range: [-15, 15, 1] + scale_factor: 1 + add_offset: 0 + new_unit: "Wm$^{-2}$" + mpl: + colorbar: + label : "Wm$^{-2}$" + category: "TOA energy flux" + pct_diff_contour_levels: [-100,-75,-50,-40,-30,-20,-10,-8,-6,-4,-2,0,2,4,6,8,10,20,30,40,50,75,100] + pct_diff_colormap: "PuOr_r" + +FSNT: + colormap: "Blues" + contour_levels_range: [120, 320, 10] + diff_colormap: "BrBG" + diff_contour_range: [-20, 20, 2] + scale_factor: 1 + add_offset: 0 + new_unit: "Wm$^{-2}$" + mpl: + colorbar: + label : "Wm$^{-2}$" + obs_file: "CERES_EBAF_Ed4.1_2001-2020.nc" + obs_name: "CERES_EBAF_Ed4.1" + obs_var_name: "fsnt" + category: "TOA energy flux" + pct_diff_contour_levels: [-100,-75,-50,-40,-30,-20,-10,-8,-6,-4,-2,0,2,4,6,8,10,20,30,40,50,75,100] + pct_diff_colormap: "PuOr_r" + +FSNTC: + category: "TOA energy flux" + pct_diff_contour_levels: [-100,-75,-50,-40,-30,-20,-10,-8,-6,-4,-2,0,2,4,6,8,10,20,30,40,50,75,100] + pct_diff_colormap: "PuOr_r" + +FSNTOA: + category: "TOA energy flux" + pct_diff_contour_levels: [-100,-75,-50,-40,-30,-20,-10,-8,-6,-4,-2,0,2,4,6,8,10,20,30,40,50,75,100] + pct_diff_colormap: "PuOr_r" + +FLUT: + category: "TOA energy flux" + pct_diff_contour_levels: [-100,-75,-50,-40,-30,-20,-10,-8,-6,-4,-2,0,2,4,6,8,10,20,30,40,50,75,100] + pct_diff_colormap: "PuOr_r" + +FLNT: + colormap: "Oranges" + contour_levels_range: [120, 320, 10] + diff_colormap: "BrBG" + diff_contour_range: [-20, 20, 2] + scale_factor: 1 + add_offset: 0 + new_unit: "Wm$^{-2}$" + mpl: + colorbar: + label : "Wm$^{-2}$" + obs_file: "CERES_EBAF_Ed4.1_2001-2020.nc" + obs_name: "CERES_EBAF_Ed4.1" + obs_var_name: "toa_lw_all_mon" + category: "TOA energy flux" + pct_diff_contour_levels: [-100,-75,-50,-40,-30,-20,-10,-8,-6,-4,-2,0,2,4,6,8,10,20,30,40,50,75,100] + pct_diff_colormap: "PuOr_r" + +FLNTC: + colormap: "Oranges" + contour_levels_range: [120, 320, 10] + diff_colormap: "BrBG" + diff_contour_range: [-20, 20, 2] + scale_factor: 1 + add_offset: 0 + new_unit: "Wm$^{-2}$" + mpl: + colorbar: + label : "Wm$^{-2}$" + obs_file: "CERES_EBAF_Ed4.1_2001-2020.nc" + obs_name: "CERES_EBAF_Ed4.1" + obs_var_name: "toa_lw_clr_t_mon" + category: "TOA energy flux" + pct_diff_contour_levels: [-100,-75,-50,-40,-30,-20,-10,-8,-6,-4,-2,0,2,4,6,8,10,20,30,40,50,75,100] + pct_diff_colormap: "PuOr_r" + +#+++++++++++++++++ +# Category: Surface energy flux +#+++++++++++++++++ + +FSDS: + category: "Sfc energy flux" + pct_diff_contour_levels: [-100,-75,-50,-40,-30,-20,-10,-8,-6,-4,-2,0,2,4,6,8,10,20,30,40,50,75,100] + pct_diff_colormap: "PuOr_r" + +FSDSC: + category: "Sfc energy flux" + pct_diff_contour_levels: [-100,-75,-50,-40,-30,-20,-10,-8,-6,-4,-2,0,2,4,6,8,10,20,30,40,50,75,100] + pct_diff_colormap: "PuOr_r" + +FSNS: + colormap: "Blues" + contour_levels_range: [-10, 300, 20] + diff_colormap: "BrBG" + diff_contour_range: [-24, 24, 2] + scale_factor: 1 + add_offset: 0 + new_unit: "Wm$^{-2}$" + mpl: + colorbar: + label : "Wm$^{-2}$" + obs_file: "CERES_EBAF_Ed4.1_2001-2020.nc" + obs_name: "CERES_EBAF_Ed4.1" + obs_var_name: "sfc_net_sw_all_mon" + category: "Sfc energy flux" + pct_diff_contour_levels: [-100,-75,-50,-40,-30,-20,-10,-8,-6,-4,-2,0,2,4,6,8,10,20,30,40,50,75,100] + pct_diff_colormap: "PuOr_r" + +FSNSC: + colormap: "Blues" + contour_levels_range: [-10, 300, 20] + diff_colormap: "BrBG" + diff_contour_range: [-24, 24, 2] + scale_factor: 1 + add_offset: 0 + new_unit: "Wm$^{-2}$" + mpl: + colorbar: + label : "Wm$^{-2}$" + obs_file: "CERES_EBAF_Ed4.1_2001-2020.nc" + obs_name: "CERES_EBAF_Ed4.1" + obs_var_name: "sfc_net_sw_clr_t_mon" + category: "Sfc energy flux" + pct_diff_contour_levels: [-100,-75,-50,-40,-30,-20,-10,-8,-6,-4,-2,0,2,4,6,8,10,20,30,40,50,75,100] + pct_diff_colormap: "PuOr_r" + +FLNS: + category: "Sfc energy flux" + pct_diff_contour_levels: [-100,-75,-50,-40,-30,-20,-10,-8,-6,-4,-2,0,2,4,6,8,10,20,30,40,50,75,100] + pct_diff_colormap: "PuOr_r" + +FLNSC: + category: "Sfc energy flux" + pct_diff_contour_levels: [-100,-75,-50,-40,-30,-20,-10,-8,-6,-4,-2,0,2,4,6,8,10,20,30,40,50,75,100] + pct_diff_colormap: "PuOr_r" + +SHFLX: + category: "Sfc energy flux" + pct_diff_contour_levels: [-100,-75,-50,-40,-30,-20,-10,-8,-6,-4,-2,0,2,4,6,8,10,20,30,40,50,75,100] + pct_diff_colormap: "PuOr_r" + + + +#+++++++++++++++++ +# Category: COSP +#+++++++++++++++++ + +CLDTOT_ISCCP: + category: "COSP" + pct_diff_contour_levels: [-100,-75,-50,-40,-30,-20,-10,-8,-6,-4,-2,0,2,4,6,8,10,20,30,40,50,75,100] + pct_diff_colormap: "PuOr_r" + +CLIMODIS: + category: "COSP" + pct_diff_contour_levels: [-100,-75,-50,-40,-30,-20,-10,-8,-6,-4,-2,0,2,4,6,8,10,20,30,40,50,75,100] + pct_diff_colormap: "PuOr_r" + +CLWMODIS: + category: "COSP" + pct_diff_contour_levels: [-100,-75,-50,-40,-30,-20,-10,-8,-6,-4,-2,0,2,4,6,8,10,20,30,40,50,75,100] + pct_diff_colormap: "PuOr_r" + +FISCCP1_COSP: + category: "COSP" + pct_diff_contour_levels: [-100,-75,-50,-40,-30,-20,-10,-8,-6,-4,-2,0,2,4,6,8,10,20,30,40,50,75,100] + pct_diff_colormap: "PuOr_r" + +ICE_ICLD_VISTAU: + category: "COSP" + pct_diff_contour_levels: [-100,-75,-50,-40,-30,-20,-10,-8,-6,-4,-2,0,2,4,6,8,10,20,30,40,50,75,100] + pct_diff_colormap: "PuOr_r" + +IWPMODIS: + category: "COSP" + pct_diff_contour_levels: [-100,-75,-50,-40,-30,-20,-10,-8,-6,-4,-2,0,2,4,6,8,10,20,30,40,50,75,100] + pct_diff_colormap: "PuOr_r" + +LIQ_ICLD_VISTAU: + category: "COSP" + pct_diff_contour_levels: [-100,-75,-50,-40,-30,-20,-10,-8,-6,-4,-2,0,2,4,6,8,10,20,30,40,50,75,100] + pct_diff_colormap: "PuOr_r" + +LWPMODIS: + category: "COSP" + pct_diff_contour_levels: [-100,-75,-50,-40,-30,-20,-10,-8,-6,-4,-2,0,2,4,6,8,10,20,30,40,50,75,100] + pct_diff_colormap: "PuOr_r" + +MEANCLDALB_ISCCP: + category: "COSP" + pct_diff_contour_levels: [-100,-75,-50,-40,-30,-20,-10,-8,-6,-4,-2,0,2,4,6,8,10,20,30,40,50,75,100] + pct_diff_colormap: "PuOr_r" + +MEANPTOP_ISCCP: + category: "COSP" + pct_diff_contour_levels: [-100,-75,-50,-40,-30,-20,-10,-8,-6,-4,-2,0,2,4,6,8,10,20,30,40,50,75,100] + pct_diff_colormap: "PuOr_r" + +MEANTAU_ISCCP: + category: "COSP" + pct_diff_contour_levels: [-100,-75,-50,-40,-30,-20,-10,-8,-6,-4,-2,0,2,4,6,8,10,20,30,40,50,75,100] + pct_diff_colormap: "PuOr_r" + +MEANTB_ISCCP: + category: "COSP" + pct_diff_contour_levels: [-100,-75,-50,-40,-30,-20,-10,-8,-6,-4,-2,0,2,4,6,8,10,20,30,40,50,75,100] + pct_diff_colormap: "PuOr_r" + +MEANTBCLR_ISCCP: + category: "COSP" + pct_diff_contour_levels: [-100,-75,-50,-40,-30,-20,-10,-8,-6,-4,-2,0,2,4,6,8,10,20,30,40,50,75,100] + pct_diff_colormap: "PuOr_r" + +PCTMODIS: + category: "COSP" + pct_diff_contour_levels: [-100,-75,-50,-40,-30,-20,-10,-8,-6,-4,-2,0,2,4,6,8,10,20,30,40,50,75,100] + pct_diff_colormap: "PuOr_r" + +REFFCLIMODIS: + category: "COSP" + pct_diff_contour_levels: [-100,-75,-50,-40,-30,-20,-10,-8,-6,-4,-2,0,2,4,6,8,10,20,30,40,50,75,100] + pct_diff_colormap: "PuOr_r" + +REFFCLWMODIS: + category: "COSP" + pct_diff_contour_levels: [-100,-75,-50,-40,-30,-20,-10,-8,-6,-4,-2,0,2,4,6,8,10,20,30,40,50,75,100] + pct_diff_colormap: "PuOr_r" + +SNOW_ICLD_VISTAU: + category: "COSP" + pct_diff_contour_levels: [-100,-75,-50,-40,-30,-20,-10,-8,-6,-4,-2,0,2,4,6,8,10,20,30,40,50,75,100] + pct_diff_colormap: "PuOr_r" + +TAUTMODIS: + category: "COSP" + pct_diff_contour_levels: [-100,-75,-50,-40,-30,-20,-10,-8,-6,-4,-2,0,2,4,6,8,10,20,30,40,50,75,100] + pct_diff_colormap: "PuOr_r" + +TAUWMODIS: + category: "COSP" + pct_diff_contour_levels: [-100,-75,-50,-40,-30,-20,-10,-8,-6,-4,-2,0,2,4,6,8,10,20,30,40,50,75,100] + pct_diff_colormap: "PuOr_r" + +TOT_CLD_VISTAU: + category: "COSP" + pct_diff_contour_levels: [-100,-75,-50,-40,-30,-20,-10,-8,-6,-4,-2,0,2,4,6,8,10,20,30,40,50,75,100] + pct_diff_colormap: "PuOr_r" + +TOT_ICLD_VISTAU: + category: "COSP" + pct_diff_contour_levels: [-100,-75,-50,-40,-30,-20,-10,-8,-6,-4,-2,0,2,4,6,8,10,20,30,40,50,75,100] + pct_diff_colormap: "PuOr_r" + + +#+++++++++++++++++ +# Category: Other +#+++++++++++++++++ + +H2O: + colormap: "PuOr_r" + diff_colormap: "BrBG" + scale_factor: 1 + add_offset: 0 + new_unit: "mol mol$^{-1}$" + mpl: + colorbar: + label: "mol mol$^{-1}$" + plot_log_pressure: True + pct_diff_contour_levels: [-100,-75,-50,-40,-30,-20,-10,-8,-6,-4,-2,0,2,4,6,8,10,20,30,40,50,75,100] + pct_diff_colormap: "PuOr_r" + +OMEGAT: + colormap: "PuOr_r" + diff_colormap: "coolwarm" + plot_log_pressure: True + pct_diff_contour_levels: [-100,-75,-50,-40,-30,-20,-10,-8,-6,-4,-2,0,2,4,6,8,10,20,30,40,50,75,100] + pct_diff_colormap: "PuOr_r" + +#++++++++++++++ +# Category: TEM +#++++++++++++++ + +uzm: + ylim: [1e3,1] + units: m s-1 + long_name: Zonal-Mean zonal wind + obs_file: "TEM_ERA5.nc" + obs_name: "ERA5" + obs_var_name: "uzm" + +vzm: + ylim: [1e3,1] + units: m s-1 + long_name: Zonal-Mean meridional wind + obs_file: "TEM_ERA5.nc" + obs_name: "ERA5" + obs_var_name: "vzm" + +epfy: + ylim: [1e2,1] + units: m3 s−2 + long_name: northward component of the Eliassen–Palm flux + obs_file: "TEM_ERA5.nc" + obs_name: "ERA5" + obs_var_name: "epfy" + +epfz: + ylim: [1e2,1] + units: m3 s−2 + long_name: upward component of the Eliassen–Palm flux + obs_file: "TEM_ERA5.nc" + obs_name: "ERA5" + obs_var_name: "epfz" + +vtem: + ylim: [1e2,1] + units: m/s + long_name: Transformed Eulerian mean northward wind + obs_file: "TEM_ERA5.nc" + obs_name: "ERA5" + obs_var_name: "vtem" + +wtem: + ylim: [1e2,1] + units: m/s + long_name: Transformed Eulerian mean upward wind + obs_file: "TEM_ERA5.nc" + obs_name: "ERA5" + obs_var_name: "wtem" + +psitem: + ylim: [1e2,1] + units: m3 s−2 + long_name: Transformed Eulerian mean mass stream function + obs_file: "TEM_ERA5.nc" + obs_name: "ERA5" + obs_var_name: "psitem" + +utendepfd: + ylim: [1e2,1] + units: m3 s−2 + long_name: tendency of eastward wind due to Eliassen-Palm flux divergence + obs_file: "TEM_ERA5.nc" + obs_name: "ERA5" + obs_var_name: "utendepfd" + +utendvtem: + ylim: [1e2,1] + units: m3 s−2 + long_name: tendency of eastward wind due to TEM northward wind advection and the coriolis term + obs_file: "TEM_ERA5.nc" + obs_name: "ERA5" + obs_var_name: "utendvtem" + +utendwtem: + ylim: [1e2,1] + units: m3 s−2 + long_name: tendency of eastward wind due to TEM upward wind advection + obs_file: "TEM_ERA5.nc" + obs_name: "ERA5" + obs_var_name: "utendwtem" + +####### + + + +# Plot Specific formatting +########################## + +# Chemistry and Aerosol Budget Tables +#------------------------------------ +budget_tables: + # INPUTS + #list of the gaseous variables to be caculated. + GAS_VARIABLES: ['CH4','CH3CCL3', 'CO', 'O3', 'ISOP', 'MTERP', 'CH3OH', 'CH3COCH3'] + + # list of the aerosol variables to be caculated. + AEROSOL_VARIABLES: ['AOD','SOA', 'SALT', 'DUST', 'POM', 'BC', 'SO4'] + + # For the case that outputs are saved for a specific region. + # i.e., when using fincllonlat in user_nl_cam + ext1_SE: '' + + # Tropospheric Values + # ------------------- + # if True, calculate only Tropospheric values + # if False, all layers + # tropopause is defiend as o3>150ppb. If needed, change accordingly. + Tropospheric: True + + ### NOT WORKING FOR NOW + # To calculate the budgets only for a region + # Lat/Lon extent + limit: (20,20,40,120) + regional: False + + #Dictionary for Molecular weights. Keys must be consistent with variable name + # For aerosols, the MW is used only for chemical loss, chemical production, and elevated emission calculations + # For SO4, we report everything in terms of Sulfur, so we use Sulfur MW here + MW: {'O3':48, + 'CH4':16, + 'CO':28, + 'ISOP':68, + 'MTERP':136, + 'SOA':144.132, + 'SALT':58.4412, + 'SO4':32.066, + 'POM':12.011, + 'BC':12.011 , + 'DUST':168.0456, + 'CH3CCL3':133.4042, + 'CH3OH':32, + 'CH3COCH3':58} + + # Avogadro's Number + AVO: 6.022e23 + # gravity + gr: 9.80616 + # Mw air + Mwair: 28.97 + + # The variables in the list below must be aerosols - do not add AOD and DAOD + # WARNING: no need to change this list, unless for a specific need! + AEROSOLS: ['SOA', 'SALT', 'DUST', 'POM', 'BC', 'SO4'] + +#----------- + + + +# Plot Specific formatting +########################## + +# AOD 4-Panel plots vs MERRA and MODIS +#------------------------------------- +aod_diags: + plot_params: + range_min: -0.4 + range_max: 0.4 + nlevel: 17 + colormap: "bwr" + + plot_params_relerr: + range_max: 100 + range_min: -100 + nlevel: 21 + colormap: "PuOr_r" + +#----------- + +#End of File diff --git a/lib/plot_uxarray_h1.ipynb b/lib/plot_uxarray_h1.ipynb new file mode 100644 index 000000000..21dbe623b --- /dev/null +++ b/lib/plot_uxarray_h1.ipynb @@ -0,0 +1,5190 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "39545902-0870-4a3f-93f1-493e56403d38", + "metadata": {}, + "source": [ + "### test for plotting pft level data on h1 files\n", + "Created by Will Wieder\n", + "Improved by Orhan Eroglu\n", + "March 2025" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "b75e38a9-54ff-438b-91cd-2f72ef3abd95", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/javascript": [ + "(function(root) {\n", + " function now() {\n", + " return new Date();\n", + " }\n", + "\n", + " const force = true;\n", + " const py_version = '3.6.2'.replace('rc', '-rc.').replace('.dev', '-dev.');\n", + " const reloading = false;\n", + " const Bokeh = root.Bokeh;\n", + "\n", + " // Set a timeout for this load but only if we are not already initializing\n", + " if (typeof (root._bokeh_timeout) === \"undefined\" || (force || !root._bokeh_is_initializing)) {\n", + " root._bokeh_timeout = Date.now() + 5000;\n", + " root._bokeh_failed_load = false;\n", + " }\n", + "\n", + " function run_callbacks() {\n", + " try {\n", + " root._bokeh_onload_callbacks.forEach(function(callback) {\n", + " if (callback != null)\n", + " callback();\n", + " });\n", + " } finally {\n", + " delete root._bokeh_onload_callbacks;\n", + " }\n", + " console.debug(\"Bokeh: all callbacks have finished\");\n", + " }\n", + "\n", + " function load_libs(css_urls, js_urls, js_modules, js_exports, callback) {\n", + " if (css_urls == null) css_urls = [];\n", + " if (js_urls == null) js_urls = [];\n", + " if (js_modules == null) js_modules = [];\n", + " if (js_exports == null) js_exports = {};\n", + "\n", + " root._bokeh_onload_callbacks.push(callback);\n", + "\n", + " if (root._bokeh_is_loading > 0) {\n", + " // Don't load bokeh if it is still initializing\n", + " console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n", + " return null;\n", + " } else if (js_urls.length === 0 && js_modules.length === 0 && Object.keys(js_exports).length === 0) {\n", + " // There is nothing to load\n", + " run_callbacks();\n", + " return null;\n", + " }\n", + "\n", + " function on_load() {\n", + " root._bokeh_is_loading--;\n", + " if (root._bokeh_is_loading === 0) {\n", + " console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n", + " run_callbacks()\n", + " }\n", + " }\n", + " window._bokeh_on_load = on_load\n", + "\n", + " function on_error(e) {\n", + " const src_el = e.srcElement\n", + " console.error(\"failed to load \" + (src_el.href || src_el.src));\n", + " }\n", + "\n", + " const skip = [];\n", + " if (window.requirejs) {\n", + " window.requirejs.config({'packages': {}, 'paths': {}, 'shim': {}});\n", + " root._bokeh_is_loading = css_urls.length + 0;\n", + " } else {\n", + " root._bokeh_is_loading = css_urls.length + js_urls.length + js_modules.length + Object.keys(js_exports).length;\n", + " }\n", + "\n", + " const existing_stylesheets = []\n", + " const links = document.getElementsByTagName('link')\n", + " for (let i = 0; i < links.length; i++) {\n", + " const link = links[i]\n", + " if (link.href != null) {\n", + " existing_stylesheets.push(link.href)\n", + " }\n", + " }\n", + " for (let i = 0; i < css_urls.length; i++) {\n", + " const url = css_urls[i];\n", + " const escaped = encodeURI(url)\n", + " if (existing_stylesheets.indexOf(escaped) !== -1) {\n", + " on_load()\n", + " continue;\n", + " }\n", + " const element = document.createElement(\"link\");\n", + " element.onload = on_load;\n", + " element.onerror = on_error;\n", + " element.rel = \"stylesheet\";\n", + " element.type = \"text/css\";\n", + " element.href = url;\n", + " console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n", + " document.body.appendChild(element);\n", + " } var existing_scripts = []\n", + " const scripts = document.getElementsByTagName('script')\n", + " for (let i = 0; i < scripts.length; i++) {\n", + " var script = scripts[i]\n", + " if (script.src != null) {\n", + " existing_scripts.push(script.src)\n", + " }\n", + " }\n", + " for (let i = 0; i < js_urls.length; i++) {\n", + " const url = js_urls[i];\n", + " const escaped = encodeURI(url)\n", + " if (skip.indexOf(escaped) !== -1 || existing_scripts.indexOf(escaped) !== -1) {\n", + " if (!window.requirejs) {\n", + " on_load();\n", + " }\n", + " continue;\n", + " }\n", + " const element = document.createElement('script');\n", + " element.onload = on_load;\n", + " element.onerror = on_error;\n", + " element.async = false;\n", + " element.src = url;\n", + " console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n", + " document.head.appendChild(element);\n", + " }\n", + " for (let i = 0; i < js_modules.length; i++) {\n", + " const url = js_modules[i];\n", + " const escaped = encodeURI(url)\n", + " if (skip.indexOf(escaped) !== -1 || existing_scripts.indexOf(escaped) !== -1) {\n", + " if (!window.requirejs) {\n", + " on_load();\n", + " }\n", + " continue;\n", + " }\n", + " var element = document.createElement('script');\n", + " element.onload = on_load;\n", + " element.onerror = on_error;\n", + " element.async = false;\n", + " element.src = url;\n", + " element.type = \"module\";\n", + " console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n", + " document.head.appendChild(element);\n", + " }\n", + " for (const name in js_exports) {\n", + " const url = js_exports[name];\n", + " const escaped = encodeURI(url)\n", + " if (skip.indexOf(escaped) >= 0 || root[name] != null) {\n", + " if (!window.requirejs) {\n", + " on_load();\n", + " }\n", + " continue;\n", + " }\n", + " var element = document.createElement('script');\n", + " element.onerror = on_error;\n", + " element.async = false;\n", + " element.type = \"module\";\n", + " console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n", + " element.textContent = `\n", + " import ${name} from \"${url}\"\n", + " window.${name} = ${name}\n", + " window._bokeh_on_load()\n", + " `\n", + " document.head.appendChild(element);\n", + " }\n", + " if (!js_urls.length && !js_modules.length) {\n", + " on_load()\n", + " }\n", + " };\n", + "\n", + " function inject_raw_css(css) {\n", + " const element = document.createElement(\"style\");\n", + " element.appendChild(document.createTextNode(css));\n", + " document.body.appendChild(element);\n", + " }\n", + "\n", + " const js_urls = [\"https://cdn.holoviz.org/panel/1.5.4/dist/bundled/reactiveesm/es-module-shims@^1.10.0/dist/es-module-shims.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-3.6.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.6.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.6.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.6.2.min.js\", \"https://cdn.holoviz.org/panel/1.5.4/dist/panel.min.js\"];\n", + " const js_modules = [];\n", + " const js_exports = {};\n", + " const css_urls = [];\n", + " const inline_js = [ function(Bokeh) {\n", + " Bokeh.set_log_level(\"info\");\n", + " },\n", + "function(Bokeh) {} // ensure no trailing comma for IE\n", + " ];\n", + "\n", + " function run_inline_js() {\n", + " if ((root.Bokeh !== undefined) || (force === true)) {\n", + " for (let i = 0; i < inline_js.length; i++) {\n", + " try {\n", + " inline_js[i].call(root, root.Bokeh);\n", + " } catch(e) {\n", + " if (!reloading) {\n", + " throw e;\n", + " }\n", + " }\n", + " }\n", + " // Cache old bokeh versions\n", + " if (Bokeh != undefined && !reloading) {\n", + " var NewBokeh = root.Bokeh;\n", + " if (Bokeh.versions === undefined) {\n", + " Bokeh.versions = new Map();\n", + " }\n", + " if (NewBokeh.version !== Bokeh.version) {\n", + " Bokeh.versions.set(NewBokeh.version, NewBokeh)\n", + " }\n", + " root.Bokeh = Bokeh;\n", + " }\n", + " } else if (Date.now() < root._bokeh_timeout) {\n", + " setTimeout(run_inline_js, 100);\n", + " } else if (!root._bokeh_failed_load) {\n", + " console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n", + " root._bokeh_failed_load = true;\n", + " }\n", + " root._bokeh_is_initializing = false\n", + " }\n", + "\n", + " function load_or_wait() {\n", + " // Implement a backoff loop that tries to ensure we do not load multiple\n", + " // versions of Bokeh and its dependencies at the same time.\n", + " // In recent versions we use the root._bokeh_is_initializing flag\n", + " // to determine whether there is an ongoing attempt to initialize\n", + " // bokeh, however for backward compatibility we also try to ensure\n", + " // that we do not start loading a newer (Panel>=1.0 and Bokeh>3) version\n", + " // before older versions are fully initialized.\n", + " if (root._bokeh_is_initializing && Date.now() > root._bokeh_timeout) {\n", + " // If the timeout and bokeh was not successfully loaded we reset\n", + " // everything and try loading again\n", + " root._bokeh_timeout = Date.now() + 5000;\n", + " root._bokeh_is_initializing = false;\n", + " root._bokeh_onload_callbacks = undefined;\n", + " root._bokeh_is_loading = 0\n", + " console.log(\"Bokeh: BokehJS was loaded multiple times but one version failed to initialize.\");\n", + " load_or_wait();\n", + " } else if (root._bokeh_is_initializing || (typeof root._bokeh_is_initializing === \"undefined\" && root._bokeh_onload_callbacks !== undefined)) {\n", + " setTimeout(load_or_wait, 100);\n", + " } else {\n", + " root._bokeh_is_initializing = true\n", + " root._bokeh_onload_callbacks = []\n", + " const bokeh_loaded = root.Bokeh != null && (root.Bokeh.version === py_version || (root.Bokeh.versions !== undefined && root.Bokeh.versions.has(py_version)));\n", + " if (!reloading && !bokeh_loaded) {\n", + " if (root.Bokeh) {\n", + " root.Bokeh = undefined;\n", + " }\n", + " console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n", + " }\n", + " load_libs(css_urls, js_urls, js_modules, js_exports, function() {\n", + " console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n", + " run_inline_js();\n", + " });\n", + " }\n", + " }\n", + " // Give older versions of the autoload script a head-start to ensure\n", + " // they initialize before we start loading newer version.\n", + " setTimeout(load_or_wait, 100)\n", + "}(window));" + ], + "application/vnd.holoviews_load.v0+json": "(function(root) {\n function now() {\n return new Date();\n }\n\n const force = true;\n const py_version = '3.6.2'.replace('rc', '-rc.').replace('.dev', '-dev.');\n const reloading = false;\n const Bokeh = root.Bokeh;\n\n // Set a timeout for this load but only if we are not already initializing\n if (typeof (root._bokeh_timeout) === \"undefined\" || (force || !root._bokeh_is_initializing)) {\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_failed_load = false;\n }\n\n function run_callbacks() {\n try {\n root._bokeh_onload_callbacks.forEach(function(callback) {\n if (callback != null)\n callback();\n });\n } finally {\n delete root._bokeh_onload_callbacks;\n }\n console.debug(\"Bokeh: all callbacks have finished\");\n }\n\n function load_libs(css_urls, js_urls, js_modules, js_exports, callback) {\n if (css_urls == null) css_urls = [];\n if (js_urls == null) js_urls = [];\n if (js_modules == null) js_modules = [];\n if (js_exports == null) js_exports = {};\n\n root._bokeh_onload_callbacks.push(callback);\n\n if (root._bokeh_is_loading > 0) {\n // Don't load bokeh if it is still initializing\n console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n return null;\n } else if (js_urls.length === 0 && js_modules.length === 0 && Object.keys(js_exports).length === 0) {\n // There is nothing to load\n run_callbacks();\n return null;\n }\n\n function on_load() {\n root._bokeh_is_loading--;\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n run_callbacks()\n }\n }\n window._bokeh_on_load = on_load\n\n function on_error(e) {\n const src_el = e.srcElement\n console.error(\"failed to load \" + (src_el.href || src_el.src));\n }\n\n const skip = [];\n if (window.requirejs) {\n window.requirejs.config({'packages': {}, 'paths': {}, 'shim': {}});\n root._bokeh_is_loading = css_urls.length + 0;\n } else {\n root._bokeh_is_loading = css_urls.length + js_urls.length + js_modules.length + Object.keys(js_exports).length;\n }\n\n const existing_stylesheets = []\n const links = document.getElementsByTagName('link')\n for (let i = 0; i < links.length; i++) {\n const link = links[i]\n if (link.href != null) {\n existing_stylesheets.push(link.href)\n }\n }\n for (let i = 0; i < css_urls.length; i++) {\n const url = css_urls[i];\n const escaped = encodeURI(url)\n if (existing_stylesheets.indexOf(escaped) !== -1) {\n on_load()\n continue;\n }\n const element = document.createElement(\"link\");\n element.onload = on_load;\n element.onerror = on_error;\n element.rel = \"stylesheet\";\n element.type = \"text/css\";\n element.href = url;\n console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n document.body.appendChild(element);\n } var existing_scripts = []\n const scripts = document.getElementsByTagName('script')\n for (let i = 0; i < scripts.length; i++) {\n var script = scripts[i]\n if (script.src != null) {\n existing_scripts.push(script.src)\n }\n }\n for (let i = 0; i < js_urls.length; i++) {\n const url = js_urls[i];\n const escaped = encodeURI(url)\n if (skip.indexOf(escaped) !== -1 || existing_scripts.indexOf(escaped) !== -1) {\n if (!window.requirejs) {\n on_load();\n }\n continue;\n }\n const element = document.createElement('script');\n element.onload = on_load;\n element.onerror = on_error;\n element.async = false;\n element.src = url;\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.head.appendChild(element);\n }\n for (let i = 0; i < js_modules.length; i++) {\n const url = js_modules[i];\n const escaped = encodeURI(url)\n if (skip.indexOf(escaped) !== -1 || existing_scripts.indexOf(escaped) !== -1) {\n if (!window.requirejs) {\n on_load();\n }\n continue;\n }\n var element = document.createElement('script');\n element.onload = on_load;\n element.onerror = on_error;\n element.async = false;\n element.src = url;\n element.type = \"module\";\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.head.appendChild(element);\n }\n for (const name in js_exports) {\n const url = js_exports[name];\n const escaped = encodeURI(url)\n if (skip.indexOf(escaped) >= 0 || root[name] != null) {\n if (!window.requirejs) {\n on_load();\n }\n continue;\n }\n var element = document.createElement('script');\n element.onerror = on_error;\n element.async = false;\n element.type = \"module\";\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n element.textContent = `\n import ${name} from \"${url}\"\n window.${name} = ${name}\n window._bokeh_on_load()\n `\n document.head.appendChild(element);\n }\n if (!js_urls.length && !js_modules.length) {\n on_load()\n }\n };\n\n function inject_raw_css(css) {\n const element = document.createElement(\"style\");\n element.appendChild(document.createTextNode(css));\n document.body.appendChild(element);\n }\n\n const js_urls = [\"https://cdn.holoviz.org/panel/1.5.4/dist/bundled/reactiveesm/es-module-shims@^1.10.0/dist/es-module-shims.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-3.6.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.6.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.6.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.6.2.min.js\", \"https://cdn.holoviz.org/panel/1.5.4/dist/panel.min.js\"];\n const js_modules = [];\n const js_exports = {};\n const css_urls = [];\n const inline_js = [ function(Bokeh) {\n Bokeh.set_log_level(\"info\");\n },\nfunction(Bokeh) {} // ensure no trailing comma for IE\n ];\n\n function run_inline_js() {\n if ((root.Bokeh !== undefined) || (force === true)) {\n for (let i = 0; i < inline_js.length; i++) {\n try {\n inline_js[i].call(root, root.Bokeh);\n } catch(e) {\n if (!reloading) {\n throw e;\n }\n }\n }\n // Cache old bokeh versions\n if (Bokeh != undefined && !reloading) {\n var NewBokeh = root.Bokeh;\n if (Bokeh.versions === undefined) {\n Bokeh.versions = new Map();\n }\n if (NewBokeh.version !== Bokeh.version) {\n Bokeh.versions.set(NewBokeh.version, NewBokeh)\n }\n root.Bokeh = Bokeh;\n }\n } else if (Date.now() < root._bokeh_timeout) {\n setTimeout(run_inline_js, 100);\n } else if (!root._bokeh_failed_load) {\n console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n root._bokeh_failed_load = true;\n }\n root._bokeh_is_initializing = false\n }\n\n function load_or_wait() {\n // Implement a backoff loop that tries to ensure we do not load multiple\n // versions of Bokeh and its dependencies at the same time.\n // In recent versions we use the root._bokeh_is_initializing flag\n // to determine whether there is an ongoing attempt to initialize\n // bokeh, however for backward compatibility we also try to ensure\n // that we do not start loading a newer (Panel>=1.0 and Bokeh>3) version\n // before older versions are fully initialized.\n if (root._bokeh_is_initializing && Date.now() > root._bokeh_timeout) {\n // If the timeout and bokeh was not successfully loaded we reset\n // everything and try loading again\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_is_initializing = false;\n root._bokeh_onload_callbacks = undefined;\n root._bokeh_is_loading = 0\n console.log(\"Bokeh: BokehJS was loaded multiple times but one version failed to initialize.\");\n load_or_wait();\n } else if (root._bokeh_is_initializing || (typeof root._bokeh_is_initializing === \"undefined\" && root._bokeh_onload_callbacks !== undefined)) {\n setTimeout(load_or_wait, 100);\n } else {\n root._bokeh_is_initializing = true\n root._bokeh_onload_callbacks = []\n const bokeh_loaded = root.Bokeh != null && (root.Bokeh.version === py_version || (root.Bokeh.versions !== undefined && root.Bokeh.versions.has(py_version)));\n if (!reloading && !bokeh_loaded) {\n if (root.Bokeh) {\n root.Bokeh = undefined;\n }\n console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n }\n load_libs(css_urls, js_urls, js_modules, js_exports, function() {\n console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n run_inline_js();\n });\n }\n }\n // Give older versions of the autoload script a head-start to ensure\n // they initialize before we start loading newer version.\n setTimeout(load_or_wait, 100)\n}(window));" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/javascript": [ + "\n", + "if ((window.PyViz === undefined) || (window.PyViz instanceof HTMLElement)) {\n", + " window.PyViz = {comms: {}, comm_status:{}, kernels:{}, receivers: {}, plot_index: []}\n", + "}\n", + "\n", + "\n", + " function JupyterCommManager() {\n", + " }\n", + "\n", + " JupyterCommManager.prototype.register_target = function(plot_id, comm_id, msg_handler) {\n", + " if (window.comm_manager || ((window.Jupyter !== undefined) && (Jupyter.notebook.kernel != null))) {\n", + " var comm_manager = window.comm_manager || Jupyter.notebook.kernel.comm_manager;\n", + " comm_manager.register_target(comm_id, function(comm) {\n", + " comm.on_msg(msg_handler);\n", + " });\n", + " } else if ((plot_id in window.PyViz.kernels) && (window.PyViz.kernels[plot_id])) {\n", + " window.PyViz.kernels[plot_id].registerCommTarget(comm_id, function(comm) {\n", + " comm.onMsg = msg_handler;\n", + " });\n", + " } else if (typeof google != 'undefined' && google.colab.kernel != null) {\n", + " google.colab.kernel.comms.registerTarget(comm_id, (comm) => {\n", + " var messages = comm.messages[Symbol.asyncIterator]();\n", + " function processIteratorResult(result) {\n", + " var message = result.value;\n", + " console.log(message)\n", + " var content = {data: message.data, comm_id};\n", + " var buffers = []\n", + " for (var buffer of message.buffers || []) {\n", + " buffers.push(new DataView(buffer))\n", + " }\n", + " var metadata = message.metadata || {};\n", + " var msg = {content, buffers, metadata}\n", + " msg_handler(msg);\n", + " return messages.next().then(processIteratorResult);\n", + " }\n", + " return messages.next().then(processIteratorResult);\n", + " })\n", + " }\n", + " }\n", + "\n", + " JupyterCommManager.prototype.get_client_comm = function(plot_id, comm_id, msg_handler) {\n", + " if (comm_id in window.PyViz.comms) {\n", + " return window.PyViz.comms[comm_id];\n", + " } else if (window.comm_manager || ((window.Jupyter !== undefined) && (Jupyter.notebook.kernel != null))) {\n", + " var comm_manager = window.comm_manager || Jupyter.notebook.kernel.comm_manager;\n", + " var comm = comm_manager.new_comm(comm_id, {}, {}, {}, comm_id);\n", + " if (msg_handler) {\n", + " comm.on_msg(msg_handler);\n", + " }\n", + " } else if ((plot_id in window.PyViz.kernels) && (window.PyViz.kernels[plot_id])) {\n", + " var comm = window.PyViz.kernels[plot_id].connectToComm(comm_id);\n", + " comm.open();\n", + " if (msg_handler) {\n", + " comm.onMsg = msg_handler;\n", + " }\n", + " } else if (typeof google != 'undefined' && google.colab.kernel != null) {\n", + " var comm_promise = google.colab.kernel.comms.open(comm_id)\n", + " comm_promise.then((comm) => {\n", + " window.PyViz.comms[comm_id] = comm;\n", + " if (msg_handler) {\n", + " var messages = comm.messages[Symbol.asyncIterator]();\n", + " function processIteratorResult(result) {\n", + " var message = result.value;\n", + " var content = {data: message.data};\n", + " var metadata = message.metadata || {comm_id};\n", + " var msg = {content, metadata}\n", + " msg_handler(msg);\n", + " return messages.next().then(processIteratorResult);\n", + " }\n", + " return messages.next().then(processIteratorResult);\n", + " }\n", + " }) \n", + " var sendClosure = (data, metadata, buffers, disposeOnDone) => {\n", + " return comm_promise.then((comm) => {\n", + " comm.send(data, metadata, buffers, disposeOnDone);\n", + " });\n", + " };\n", + " var comm = {\n", + " send: sendClosure\n", + " };\n", + " }\n", + " window.PyViz.comms[comm_id] = comm;\n", + " return comm;\n", + " }\n", + " window.PyViz.comm_manager = new JupyterCommManager();\n", + " \n", + "\n", + "\n", + "var JS_MIME_TYPE = 'application/javascript';\n", + "var HTML_MIME_TYPE = 'text/html';\n", + "var EXEC_MIME_TYPE = 'application/vnd.holoviews_exec.v0+json';\n", + "var CLASS_NAME = 'output';\n", + "\n", + "/**\n", + " * Render data to the DOM node\n", + " */\n", + "function render(props, node) {\n", + " var div = document.createElement(\"div\");\n", + " var script = document.createElement(\"script\");\n", + " node.appendChild(div);\n", + " node.appendChild(script);\n", + "}\n", + "\n", + "/**\n", + " * Handle when a new output is added\n", + " */\n", + "function handle_add_output(event, handle) {\n", + " var output_area = handle.output_area;\n", + " var output = handle.output;\n", + " if ((output.data == undefined) || (!output.data.hasOwnProperty(EXEC_MIME_TYPE))) {\n", + " return\n", + " }\n", + " var id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n", + " var toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n", + " if (id !== undefined) {\n", + " var nchildren = toinsert.length;\n", + " var html_node = toinsert[nchildren-1].children[0];\n", + " html_node.innerHTML = output.data[HTML_MIME_TYPE];\n", + " var scripts = [];\n", + " var nodelist = html_node.querySelectorAll(\"script\");\n", + " for (var i in nodelist) {\n", + " if (nodelist.hasOwnProperty(i)) {\n", + " scripts.push(nodelist[i])\n", + " }\n", + " }\n", + "\n", + " scripts.forEach( function (oldScript) {\n", + " var newScript = document.createElement(\"script\");\n", + " var attrs = [];\n", + " var nodemap = oldScript.attributes;\n", + " for (var j in nodemap) {\n", + " if (nodemap.hasOwnProperty(j)) {\n", + " attrs.push(nodemap[j])\n", + " }\n", + " }\n", + " attrs.forEach(function(attr) { newScript.setAttribute(attr.name, attr.value) });\n", + " newScript.appendChild(document.createTextNode(oldScript.innerHTML));\n", + " oldScript.parentNode.replaceChild(newScript, oldScript);\n", + " });\n", + " if (JS_MIME_TYPE in output.data) {\n", + " toinsert[nchildren-1].children[1].textContent = output.data[JS_MIME_TYPE];\n", + " }\n", + " output_area._hv_plot_id = id;\n", + " if ((window.Bokeh !== undefined) && (id in Bokeh.index)) {\n", + " window.PyViz.plot_index[id] = Bokeh.index[id];\n", + " } else {\n", + " window.PyViz.plot_index[id] = null;\n", + " }\n", + " } else if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n", + " var bk_div = document.createElement(\"div\");\n", + " bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n", + " var script_attrs = bk_div.children[0].attributes;\n", + " for (var i = 0; i < script_attrs.length; i++) {\n", + " toinsert[toinsert.length - 1].childNodes[1].setAttribute(script_attrs[i].name, script_attrs[i].value);\n", + " }\n", + " // store reference to server id on output_area\n", + " output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n", + " }\n", + "}\n", + "\n", + "/**\n", + " * Handle when an output is cleared or removed\n", + " */\n", + "function handle_clear_output(event, handle) {\n", + " var id = handle.cell.output_area._hv_plot_id;\n", + " var server_id = handle.cell.output_area._bokeh_server_id;\n", + " if (((id === undefined) || !(id in PyViz.plot_index)) && (server_id !== undefined)) { return; }\n", + " var comm = window.PyViz.comm_manager.get_client_comm(\"hv-extension-comm\", \"hv-extension-comm\", function () {});\n", + " if (server_id !== null) {\n", + " comm.send({event_type: 'server_delete', 'id': server_id});\n", + " return;\n", + " } else if (comm !== null) {\n", + " comm.send({event_type: 'delete', 'id': id});\n", + " }\n", + " delete PyViz.plot_index[id];\n", + " if ((window.Bokeh !== undefined) & (id in window.Bokeh.index)) {\n", + " var doc = window.Bokeh.index[id].model.document\n", + " doc.clear();\n", + " const i = window.Bokeh.documents.indexOf(doc);\n", + " if (i > -1) {\n", + " window.Bokeh.documents.splice(i, 1);\n", + " }\n", + " }\n", + "}\n", + "\n", + "/**\n", + " * Handle kernel restart event\n", + " */\n", + "function handle_kernel_cleanup(event, handle) {\n", + " delete PyViz.comms[\"hv-extension-comm\"];\n", + " window.PyViz.plot_index = {}\n", + "}\n", + "\n", + "/**\n", + " * Handle update_display_data messages\n", + " */\n", + "function handle_update_output(event, handle) {\n", + " handle_clear_output(event, {cell: {output_area: handle.output_area}})\n", + " handle_add_output(event, handle)\n", + "}\n", + "\n", + "function register_renderer(events, OutputArea) {\n", + " function append_mime(data, metadata, element) {\n", + " // create a DOM node to render to\n", + " var toinsert = this.create_output_subarea(\n", + " metadata,\n", + " CLASS_NAME,\n", + " EXEC_MIME_TYPE\n", + " );\n", + " this.keyboard_manager.register_events(toinsert);\n", + " // Render to node\n", + " var props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n", + " render(props, toinsert[0]);\n", + " element.append(toinsert);\n", + " return toinsert\n", + " }\n", + "\n", + " events.on('output_added.OutputArea', handle_add_output);\n", + " events.on('output_updated.OutputArea', handle_update_output);\n", + " events.on('clear_output.CodeCell', handle_clear_output);\n", + " events.on('delete.Cell', handle_clear_output);\n", + " events.on('kernel_ready.Kernel', handle_kernel_cleanup);\n", + "\n", + " OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n", + " safe: true,\n", + " index: 0\n", + " });\n", + "}\n", + "\n", + "if (window.Jupyter !== undefined) {\n", + " try {\n", + " var events = require('base/js/events');\n", + " var OutputArea = require('notebook/js/outputarea').OutputArea;\n", + " if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n", + " register_renderer(events, OutputArea);\n", + " }\n", + " } catch(err) {\n", + " }\n", + "}\n" + ], + "application/vnd.holoviews_load.v0+json": "\nif ((window.PyViz === undefined) || (window.PyViz instanceof HTMLElement)) {\n window.PyViz = {comms: {}, comm_status:{}, kernels:{}, receivers: {}, plot_index: []}\n}\n\n\n function JupyterCommManager() {\n }\n\n JupyterCommManager.prototype.register_target = function(plot_id, comm_id, msg_handler) {\n if (window.comm_manager || ((window.Jupyter !== undefined) && (Jupyter.notebook.kernel != null))) {\n var comm_manager = window.comm_manager || Jupyter.notebook.kernel.comm_manager;\n comm_manager.register_target(comm_id, function(comm) {\n comm.on_msg(msg_handler);\n });\n } else if ((plot_id in window.PyViz.kernels) && (window.PyViz.kernels[plot_id])) {\n window.PyViz.kernels[plot_id].registerCommTarget(comm_id, function(comm) {\n comm.onMsg = msg_handler;\n });\n } else if (typeof google != 'undefined' && google.colab.kernel != null) {\n google.colab.kernel.comms.registerTarget(comm_id, (comm) => {\n var messages = comm.messages[Symbol.asyncIterator]();\n function processIteratorResult(result) {\n var message = result.value;\n console.log(message)\n var content = {data: message.data, comm_id};\n var buffers = []\n for (var buffer of message.buffers || []) {\n buffers.push(new DataView(buffer))\n }\n var metadata = message.metadata || {};\n var msg = {content, buffers, metadata}\n msg_handler(msg);\n return messages.next().then(processIteratorResult);\n }\n return messages.next().then(processIteratorResult);\n })\n }\n }\n\n JupyterCommManager.prototype.get_client_comm = function(plot_id, comm_id, msg_handler) {\n if (comm_id in window.PyViz.comms) {\n return window.PyViz.comms[comm_id];\n } else if (window.comm_manager || ((window.Jupyter !== undefined) && (Jupyter.notebook.kernel != null))) {\n var comm_manager = window.comm_manager || Jupyter.notebook.kernel.comm_manager;\n var comm = comm_manager.new_comm(comm_id, {}, {}, {}, comm_id);\n if (msg_handler) {\n comm.on_msg(msg_handler);\n }\n } else if ((plot_id in window.PyViz.kernels) && (window.PyViz.kernels[plot_id])) {\n var comm = window.PyViz.kernels[plot_id].connectToComm(comm_id);\n comm.open();\n if (msg_handler) {\n comm.onMsg = msg_handler;\n }\n } else if (typeof google != 'undefined' && google.colab.kernel != null) {\n var comm_promise = google.colab.kernel.comms.open(comm_id)\n comm_promise.then((comm) => {\n window.PyViz.comms[comm_id] = comm;\n if (msg_handler) {\n var messages = comm.messages[Symbol.asyncIterator]();\n function processIteratorResult(result) {\n var message = result.value;\n var content = {data: message.data};\n var metadata = message.metadata || {comm_id};\n var msg = {content, metadata}\n msg_handler(msg);\n return messages.next().then(processIteratorResult);\n }\n return messages.next().then(processIteratorResult);\n }\n }) \n var sendClosure = (data, metadata, buffers, disposeOnDone) => {\n return comm_promise.then((comm) => {\n comm.send(data, metadata, buffers, disposeOnDone);\n });\n };\n var comm = {\n send: sendClosure\n };\n }\n window.PyViz.comms[comm_id] = comm;\n return comm;\n }\n window.PyViz.comm_manager = new JupyterCommManager();\n \n\n\nvar JS_MIME_TYPE = 'application/javascript';\nvar HTML_MIME_TYPE = 'text/html';\nvar EXEC_MIME_TYPE = 'application/vnd.holoviews_exec.v0+json';\nvar CLASS_NAME = 'output';\n\n/**\n * Render data to the DOM node\n */\nfunction render(props, node) {\n var div = document.createElement(\"div\");\n var script = document.createElement(\"script\");\n node.appendChild(div);\n node.appendChild(script);\n}\n\n/**\n * Handle when a new output is added\n */\nfunction handle_add_output(event, handle) {\n var output_area = handle.output_area;\n var output = handle.output;\n if ((output.data == undefined) || (!output.data.hasOwnProperty(EXEC_MIME_TYPE))) {\n return\n }\n var id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n var toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n if (id !== undefined) {\n var nchildren = toinsert.length;\n var html_node = toinsert[nchildren-1].children[0];\n html_node.innerHTML = output.data[HTML_MIME_TYPE];\n var scripts = [];\n var nodelist = html_node.querySelectorAll(\"script\");\n for (var i in nodelist) {\n if (nodelist.hasOwnProperty(i)) {\n scripts.push(nodelist[i])\n }\n }\n\n scripts.forEach( function (oldScript) {\n var newScript = document.createElement(\"script\");\n var attrs = [];\n var nodemap = oldScript.attributes;\n for (var j in nodemap) {\n if (nodemap.hasOwnProperty(j)) {\n attrs.push(nodemap[j])\n }\n }\n attrs.forEach(function(attr) { newScript.setAttribute(attr.name, attr.value) });\n newScript.appendChild(document.createTextNode(oldScript.innerHTML));\n oldScript.parentNode.replaceChild(newScript, oldScript);\n });\n if (JS_MIME_TYPE in output.data) {\n toinsert[nchildren-1].children[1].textContent = output.data[JS_MIME_TYPE];\n }\n output_area._hv_plot_id = id;\n if ((window.Bokeh !== undefined) && (id in Bokeh.index)) {\n window.PyViz.plot_index[id] = Bokeh.index[id];\n } else {\n window.PyViz.plot_index[id] = null;\n }\n } else if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n var bk_div = document.createElement(\"div\");\n bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n var script_attrs = bk_div.children[0].attributes;\n for (var i = 0; i < script_attrs.length; i++) {\n toinsert[toinsert.length - 1].childNodes[1].setAttribute(script_attrs[i].name, script_attrs[i].value);\n }\n // store reference to server id on output_area\n output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n }\n}\n\n/**\n * Handle when an output is cleared or removed\n */\nfunction handle_clear_output(event, handle) {\n var id = handle.cell.output_area._hv_plot_id;\n var server_id = handle.cell.output_area._bokeh_server_id;\n if (((id === undefined) || !(id in PyViz.plot_index)) && (server_id !== undefined)) { return; }\n var comm = window.PyViz.comm_manager.get_client_comm(\"hv-extension-comm\", \"hv-extension-comm\", function () {});\n if (server_id !== null) {\n comm.send({event_type: 'server_delete', 'id': server_id});\n return;\n } else if (comm !== null) {\n comm.send({event_type: 'delete', 'id': id});\n }\n delete PyViz.plot_index[id];\n if ((window.Bokeh !== undefined) & (id in window.Bokeh.index)) {\n var doc = window.Bokeh.index[id].model.document\n doc.clear();\n const i = window.Bokeh.documents.indexOf(doc);\n if (i > -1) {\n window.Bokeh.documents.splice(i, 1);\n }\n }\n}\n\n/**\n * Handle kernel restart event\n */\nfunction handle_kernel_cleanup(event, handle) {\n delete PyViz.comms[\"hv-extension-comm\"];\n window.PyViz.plot_index = {}\n}\n\n/**\n * Handle update_display_data messages\n */\nfunction handle_update_output(event, handle) {\n handle_clear_output(event, {cell: {output_area: handle.output_area}})\n handle_add_output(event, handle)\n}\n\nfunction register_renderer(events, OutputArea) {\n function append_mime(data, metadata, element) {\n // create a DOM node to render to\n var toinsert = this.create_output_subarea(\n metadata,\n CLASS_NAME,\n EXEC_MIME_TYPE\n );\n this.keyboard_manager.register_events(toinsert);\n // Render to node\n var props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n render(props, toinsert[0]);\n element.append(toinsert);\n return toinsert\n }\n\n events.on('output_added.OutputArea', handle_add_output);\n events.on('output_updated.OutputArea', handle_update_output);\n events.on('clear_output.CodeCell', handle_clear_output);\n events.on('delete.Cell', handle_clear_output);\n events.on('kernel_ready.Kernel', handle_kernel_cleanup);\n\n OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n safe: true,\n index: 0\n });\n}\n\nif (window.Jupyter !== undefined) {\n try {\n var events = require('base/js/events');\n var OutputArea = require('notebook/js/outputarea').OutputArea;\n if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n register_renderer(events, OutputArea);\n }\n } catch(err) {\n }\n}\n" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.holoviews_exec.v0+json": "", + "text/html": [ + "
\n", + "
\n", + "
\n", + "" + ] + }, + "metadata": { + "application/vnd.holoviews_exec.v0+json": { + "id": "43449c4e-d004-4192-af2d-5e7c000cc8fb" + } + }, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/javascript": [ + "(function(root) {\n", + " function now() {\n", + " return new Date();\n", + " }\n", + "\n", + " const force = false;\n", + " const py_version = '3.6.2'.replace('rc', '-rc.').replace('.dev', '-dev.');\n", + " const reloading = true;\n", + " const Bokeh = root.Bokeh;\n", + "\n", + " // Set a timeout for this load but only if we are not already initializing\n", + " if (typeof (root._bokeh_timeout) === \"undefined\" || (force || !root._bokeh_is_initializing)) {\n", + " root._bokeh_timeout = Date.now() + 5000;\n", + " root._bokeh_failed_load = false;\n", + " }\n", + "\n", + " function run_callbacks() {\n", + " try {\n", + " root._bokeh_onload_callbacks.forEach(function(callback) {\n", + " if (callback != null)\n", + " callback();\n", + " });\n", + " } finally {\n", + " delete root._bokeh_onload_callbacks;\n", + " }\n", + " console.debug(\"Bokeh: all callbacks have finished\");\n", + " }\n", + "\n", + " function load_libs(css_urls, js_urls, js_modules, js_exports, callback) {\n", + " if (css_urls == null) css_urls = [];\n", + " if (js_urls == null) js_urls = [];\n", + " if (js_modules == null) js_modules = [];\n", + " if (js_exports == null) js_exports = {};\n", + "\n", + " root._bokeh_onload_callbacks.push(callback);\n", + "\n", + " if (root._bokeh_is_loading > 0) {\n", + " // Don't load bokeh if it is still initializing\n", + " console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n", + " return null;\n", + " } else if (js_urls.length === 0 && js_modules.length === 0 && Object.keys(js_exports).length === 0) {\n", + " // There is nothing to load\n", + " run_callbacks();\n", + " return null;\n", + " }\n", + "\n", + " function on_load() {\n", + " root._bokeh_is_loading--;\n", + " if (root._bokeh_is_loading === 0) {\n", + " console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n", + " run_callbacks()\n", + " }\n", + " }\n", + " window._bokeh_on_load = on_load\n", + "\n", + " function on_error(e) {\n", + " const src_el = e.srcElement\n", + " console.error(\"failed to load \" + (src_el.href || src_el.src));\n", + " }\n", + "\n", + " const skip = [];\n", + " if (window.requirejs) {\n", + " window.requirejs.config({'packages': {}, 'paths': {}, 'shim': {}});\n", + " root._bokeh_is_loading = css_urls.length + 0;\n", + " } else {\n", + " root._bokeh_is_loading = css_urls.length + js_urls.length + js_modules.length + Object.keys(js_exports).length;\n", + " }\n", + "\n", + " const existing_stylesheets = []\n", + " const links = document.getElementsByTagName('link')\n", + " for (let i = 0; i < links.length; i++) {\n", + " const link = links[i]\n", + " if (link.href != null) {\n", + " existing_stylesheets.push(link.href)\n", + " }\n", + " }\n", + " for (let i = 0; i < css_urls.length; i++) {\n", + " const url = css_urls[i];\n", + " const escaped = encodeURI(url)\n", + " if (existing_stylesheets.indexOf(escaped) !== -1) {\n", + " on_load()\n", + " continue;\n", + " }\n", + " const element = document.createElement(\"link\");\n", + " element.onload = on_load;\n", + " element.onerror = on_error;\n", + " element.rel = \"stylesheet\";\n", + " element.type = \"text/css\";\n", + " element.href = url;\n", + " console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n", + " document.body.appendChild(element);\n", + " } var existing_scripts = []\n", + " const scripts = document.getElementsByTagName('script')\n", + " for (let i = 0; i < scripts.length; i++) {\n", + " var script = scripts[i]\n", + " if (script.src != null) {\n", + " existing_scripts.push(script.src)\n", + " }\n", + " }\n", + " for (let i = 0; i < js_urls.length; i++) {\n", + " const url = js_urls[i];\n", + " const escaped = encodeURI(url)\n", + " if (skip.indexOf(escaped) !== -1 || existing_scripts.indexOf(escaped) !== -1) {\n", + " if (!window.requirejs) {\n", + " on_load();\n", + " }\n", + " continue;\n", + " }\n", + " const element = document.createElement('script');\n", + " element.onload = on_load;\n", + " element.onerror = on_error;\n", + " element.async = false;\n", + " element.src = url;\n", + " console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n", + " document.head.appendChild(element);\n", + " }\n", + " for (let i = 0; i < js_modules.length; i++) {\n", + " const url = js_modules[i];\n", + " const escaped = encodeURI(url)\n", + " if (skip.indexOf(escaped) !== -1 || existing_scripts.indexOf(escaped) !== -1) {\n", + " if (!window.requirejs) {\n", + " on_load();\n", + " }\n", + " continue;\n", + " }\n", + " var element = document.createElement('script');\n", + " element.onload = on_load;\n", + " element.onerror = on_error;\n", + " element.async = false;\n", + " element.src = url;\n", + " element.type = \"module\";\n", + " console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n", + " document.head.appendChild(element);\n", + " }\n", + " for (const name in js_exports) {\n", + " const url = js_exports[name];\n", + " const escaped = encodeURI(url)\n", + " if (skip.indexOf(escaped) >= 0 || root[name] != null) {\n", + " if (!window.requirejs) {\n", + " on_load();\n", + " }\n", + " continue;\n", + " }\n", + " var element = document.createElement('script');\n", + " element.onerror = on_error;\n", + " element.async = false;\n", + " element.type = \"module\";\n", + " console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n", + " element.textContent = `\n", + " import ${name} from \"${url}\"\n", + " window.${name} = ${name}\n", + " window._bokeh_on_load()\n", + " `\n", + " document.head.appendChild(element);\n", + " }\n", + " if (!js_urls.length && !js_modules.length) {\n", + " on_load()\n", + " }\n", + " };\n", + "\n", + " function inject_raw_css(css) {\n", + " const element = document.createElement(\"style\");\n", + " element.appendChild(document.createTextNode(css));\n", + " document.body.appendChild(element);\n", + " }\n", + "\n", + " const js_urls = [\"https://cdn.holoviz.org/panel/1.5.4/dist/bundled/reactiveesm/es-module-shims@^1.10.0/dist/es-module-shims.min.js\"];\n", + " const js_modules = [];\n", + " const js_exports = {};\n", + " const css_urls = [];\n", + " const inline_js = [ function(Bokeh) {\n", + " Bokeh.set_log_level(\"info\");\n", + " },\n", + "function(Bokeh) {} // ensure no trailing comma for IE\n", + " ];\n", + "\n", + " function run_inline_js() {\n", + " if ((root.Bokeh !== undefined) || (force === true)) {\n", + " for (let i = 0; i < inline_js.length; i++) {\n", + " try {\n", + " inline_js[i].call(root, root.Bokeh);\n", + " } catch(e) {\n", + " if (!reloading) {\n", + " throw e;\n", + " }\n", + " }\n", + " }\n", + " // Cache old bokeh versions\n", + " if (Bokeh != undefined && !reloading) {\n", + " var NewBokeh = root.Bokeh;\n", + " if (Bokeh.versions === undefined) {\n", + " Bokeh.versions = new Map();\n", + " }\n", + " if (NewBokeh.version !== Bokeh.version) {\n", + " Bokeh.versions.set(NewBokeh.version, NewBokeh)\n", + " }\n", + " root.Bokeh = Bokeh;\n", + " }\n", + " } else if (Date.now() < root._bokeh_timeout) {\n", + " setTimeout(run_inline_js, 100);\n", + " } else if (!root._bokeh_failed_load) {\n", + " console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n", + " root._bokeh_failed_load = true;\n", + " }\n", + " root._bokeh_is_initializing = false\n", + " }\n", + "\n", + " function load_or_wait() {\n", + " // Implement a backoff loop that tries to ensure we do not load multiple\n", + " // versions of Bokeh and its dependencies at the same time.\n", + " // In recent versions we use the root._bokeh_is_initializing flag\n", + " // to determine whether there is an ongoing attempt to initialize\n", + " // bokeh, however for backward compatibility we also try to ensure\n", + " // that we do not start loading a newer (Panel>=1.0 and Bokeh>3) version\n", + " // before older versions are fully initialized.\n", + " if (root._bokeh_is_initializing && Date.now() > root._bokeh_timeout) {\n", + " // If the timeout and bokeh was not successfully loaded we reset\n", + " // everything and try loading again\n", + " root._bokeh_timeout = Date.now() + 5000;\n", + " root._bokeh_is_initializing = false;\n", + " root._bokeh_onload_callbacks = undefined;\n", + " root._bokeh_is_loading = 0\n", + " console.log(\"Bokeh: BokehJS was loaded multiple times but one version failed to initialize.\");\n", + " load_or_wait();\n", + " } else if (root._bokeh_is_initializing || (typeof root._bokeh_is_initializing === \"undefined\" && root._bokeh_onload_callbacks !== undefined)) {\n", + " setTimeout(load_or_wait, 100);\n", + " } else {\n", + " root._bokeh_is_initializing = true\n", + " root._bokeh_onload_callbacks = []\n", + " const bokeh_loaded = root.Bokeh != null && (root.Bokeh.version === py_version || (root.Bokeh.versions !== undefined && root.Bokeh.versions.has(py_version)));\n", + " if (!reloading && !bokeh_loaded) {\n", + " if (root.Bokeh) {\n", + " root.Bokeh = undefined;\n", + " }\n", + " console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n", + " }\n", + " load_libs(css_urls, js_urls, js_modules, js_exports, function() {\n", + " console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n", + " run_inline_js();\n", + " });\n", + " }\n", + " }\n", + " // Give older versions of the autoload script a head-start to ensure\n", + " // they initialize before we start loading newer version.\n", + " setTimeout(load_or_wait, 100)\n", + "}(window));" + ], + "application/vnd.holoviews_load.v0+json": "(function(root) {\n function now() {\n return new Date();\n }\n\n const force = false;\n const py_version = '3.6.2'.replace('rc', '-rc.').replace('.dev', '-dev.');\n const reloading = true;\n const Bokeh = root.Bokeh;\n\n // Set a timeout for this load but only if we are not already initializing\n if (typeof (root._bokeh_timeout) === \"undefined\" || (force || !root._bokeh_is_initializing)) {\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_failed_load = false;\n }\n\n function run_callbacks() {\n try {\n root._bokeh_onload_callbacks.forEach(function(callback) {\n if (callback != null)\n callback();\n });\n } finally {\n delete root._bokeh_onload_callbacks;\n }\n console.debug(\"Bokeh: all callbacks have finished\");\n }\n\n function load_libs(css_urls, js_urls, js_modules, js_exports, callback) {\n if (css_urls == null) css_urls = [];\n if (js_urls == null) js_urls = [];\n if (js_modules == null) js_modules = [];\n if (js_exports == null) js_exports = {};\n\n root._bokeh_onload_callbacks.push(callback);\n\n if (root._bokeh_is_loading > 0) {\n // Don't load bokeh if it is still initializing\n console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n return null;\n } else if (js_urls.length === 0 && js_modules.length === 0 && Object.keys(js_exports).length === 0) {\n // There is nothing to load\n run_callbacks();\n return null;\n }\n\n function on_load() {\n root._bokeh_is_loading--;\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n run_callbacks()\n }\n }\n window._bokeh_on_load = on_load\n\n function on_error(e) {\n const src_el = e.srcElement\n console.error(\"failed to load \" + (src_el.href || src_el.src));\n }\n\n const skip = [];\n if (window.requirejs) {\n window.requirejs.config({'packages': {}, 'paths': {}, 'shim': {}});\n root._bokeh_is_loading = css_urls.length + 0;\n } else {\n root._bokeh_is_loading = css_urls.length + js_urls.length + js_modules.length + Object.keys(js_exports).length;\n }\n\n const existing_stylesheets = []\n const links = document.getElementsByTagName('link')\n for (let i = 0; i < links.length; i++) {\n const link = links[i]\n if (link.href != null) {\n existing_stylesheets.push(link.href)\n }\n }\n for (let i = 0; i < css_urls.length; i++) {\n const url = css_urls[i];\n const escaped = encodeURI(url)\n if (existing_stylesheets.indexOf(escaped) !== -1) {\n on_load()\n continue;\n }\n const element = document.createElement(\"link\");\n element.onload = on_load;\n element.onerror = on_error;\n element.rel = \"stylesheet\";\n element.type = \"text/css\";\n element.href = url;\n console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n document.body.appendChild(element);\n } var existing_scripts = []\n const scripts = document.getElementsByTagName('script')\n for (let i = 0; i < scripts.length; i++) {\n var script = scripts[i]\n if (script.src != null) {\n existing_scripts.push(script.src)\n }\n }\n for (let i = 0; i < js_urls.length; i++) {\n const url = js_urls[i];\n const escaped = encodeURI(url)\n if (skip.indexOf(escaped) !== -1 || existing_scripts.indexOf(escaped) !== -1) {\n if (!window.requirejs) {\n on_load();\n }\n continue;\n }\n const element = document.createElement('script');\n element.onload = on_load;\n element.onerror = on_error;\n element.async = false;\n element.src = url;\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.head.appendChild(element);\n }\n for (let i = 0; i < js_modules.length; i++) {\n const url = js_modules[i];\n const escaped = encodeURI(url)\n if (skip.indexOf(escaped) !== -1 || existing_scripts.indexOf(escaped) !== -1) {\n if (!window.requirejs) {\n on_load();\n }\n continue;\n }\n var element = document.createElement('script');\n element.onload = on_load;\n element.onerror = on_error;\n element.async = false;\n element.src = url;\n element.type = \"module\";\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.head.appendChild(element);\n }\n for (const name in js_exports) {\n const url = js_exports[name];\n const escaped = encodeURI(url)\n if (skip.indexOf(escaped) >= 0 || root[name] != null) {\n if (!window.requirejs) {\n on_load();\n }\n continue;\n }\n var element = document.createElement('script');\n element.onerror = on_error;\n element.async = false;\n element.type = \"module\";\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n element.textContent = `\n import ${name} from \"${url}\"\n window.${name} = ${name}\n window._bokeh_on_load()\n `\n document.head.appendChild(element);\n }\n if (!js_urls.length && !js_modules.length) {\n on_load()\n }\n };\n\n function inject_raw_css(css) {\n const element = document.createElement(\"style\");\n element.appendChild(document.createTextNode(css));\n document.body.appendChild(element);\n }\n\n const js_urls = [\"https://cdn.holoviz.org/panel/1.5.4/dist/bundled/reactiveesm/es-module-shims@^1.10.0/dist/es-module-shims.min.js\"];\n const js_modules = [];\n const js_exports = {};\n const css_urls = [];\n const inline_js = [ function(Bokeh) {\n Bokeh.set_log_level(\"info\");\n },\nfunction(Bokeh) {} // ensure no trailing comma for IE\n ];\n\n function run_inline_js() {\n if ((root.Bokeh !== undefined) || (force === true)) {\n for (let i = 0; i < inline_js.length; i++) {\n try {\n inline_js[i].call(root, root.Bokeh);\n } catch(e) {\n if (!reloading) {\n throw e;\n }\n }\n }\n // Cache old bokeh versions\n if (Bokeh != undefined && !reloading) {\n var NewBokeh = root.Bokeh;\n if (Bokeh.versions === undefined) {\n Bokeh.versions = new Map();\n }\n if (NewBokeh.version !== Bokeh.version) {\n Bokeh.versions.set(NewBokeh.version, NewBokeh)\n }\n root.Bokeh = Bokeh;\n }\n } else if (Date.now() < root._bokeh_timeout) {\n setTimeout(run_inline_js, 100);\n } else if (!root._bokeh_failed_load) {\n console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n root._bokeh_failed_load = true;\n }\n root._bokeh_is_initializing = false\n }\n\n function load_or_wait() {\n // Implement a backoff loop that tries to ensure we do not load multiple\n // versions of Bokeh and its dependencies at the same time.\n // In recent versions we use the root._bokeh_is_initializing flag\n // to determine whether there is an ongoing attempt to initialize\n // bokeh, however for backward compatibility we also try to ensure\n // that we do not start loading a newer (Panel>=1.0 and Bokeh>3) version\n // before older versions are fully initialized.\n if (root._bokeh_is_initializing && Date.now() > root._bokeh_timeout) {\n // If the timeout and bokeh was not successfully loaded we reset\n // everything and try loading again\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_is_initializing = false;\n root._bokeh_onload_callbacks = undefined;\n root._bokeh_is_loading = 0\n console.log(\"Bokeh: BokehJS was loaded multiple times but one version failed to initialize.\");\n load_or_wait();\n } else if (root._bokeh_is_initializing || (typeof root._bokeh_is_initializing === \"undefined\" && root._bokeh_onload_callbacks !== undefined)) {\n setTimeout(load_or_wait, 100);\n } else {\n root._bokeh_is_initializing = true\n root._bokeh_onload_callbacks = []\n const bokeh_loaded = root.Bokeh != null && (root.Bokeh.version === py_version || (root.Bokeh.versions !== undefined && root.Bokeh.versions.has(py_version)));\n if (!reloading && !bokeh_loaded) {\n if (root.Bokeh) {\n root.Bokeh = undefined;\n }\n console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n }\n load_libs(css_urls, js_urls, js_modules, js_exports, function() {\n console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n run_inline_js();\n });\n }\n }\n // Give older versions of the autoload script a head-start to ensure\n // they initialize before we start loading newer version.\n setTimeout(load_or_wait, 100)\n}(window));" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/javascript": [ + "\n", + "if ((window.PyViz === undefined) || (window.PyViz instanceof HTMLElement)) {\n", + " window.PyViz = {comms: {}, comm_status:{}, kernels:{}, receivers: {}, plot_index: []}\n", + "}\n", + "\n", + "\n", + " function JupyterCommManager() {\n", + " }\n", + "\n", + " JupyterCommManager.prototype.register_target = function(plot_id, comm_id, msg_handler) {\n", + " if (window.comm_manager || ((window.Jupyter !== undefined) && (Jupyter.notebook.kernel != null))) {\n", + " var comm_manager = window.comm_manager || Jupyter.notebook.kernel.comm_manager;\n", + " comm_manager.register_target(comm_id, function(comm) {\n", + " comm.on_msg(msg_handler);\n", + " });\n", + " } else if ((plot_id in window.PyViz.kernels) && (window.PyViz.kernels[plot_id])) {\n", + " window.PyViz.kernels[plot_id].registerCommTarget(comm_id, function(comm) {\n", + " comm.onMsg = msg_handler;\n", + " });\n", + " } else if (typeof google != 'undefined' && google.colab.kernel != null) {\n", + " google.colab.kernel.comms.registerTarget(comm_id, (comm) => {\n", + " var messages = comm.messages[Symbol.asyncIterator]();\n", + " function processIteratorResult(result) {\n", + " var message = result.value;\n", + " console.log(message)\n", + " var content = {data: message.data, comm_id};\n", + " var buffers = []\n", + " for (var buffer of message.buffers || []) {\n", + " buffers.push(new DataView(buffer))\n", + " }\n", + " var metadata = message.metadata || {};\n", + " var msg = {content, buffers, metadata}\n", + " msg_handler(msg);\n", + " return messages.next().then(processIteratorResult);\n", + " }\n", + " return messages.next().then(processIteratorResult);\n", + " })\n", + " }\n", + " }\n", + "\n", + " JupyterCommManager.prototype.get_client_comm = function(plot_id, comm_id, msg_handler) {\n", + " if (comm_id in window.PyViz.comms) {\n", + " return window.PyViz.comms[comm_id];\n", + " } else if (window.comm_manager || ((window.Jupyter !== undefined) && (Jupyter.notebook.kernel != null))) {\n", + " var comm_manager = window.comm_manager || Jupyter.notebook.kernel.comm_manager;\n", + " var comm = comm_manager.new_comm(comm_id, {}, {}, {}, comm_id);\n", + " if (msg_handler) {\n", + " comm.on_msg(msg_handler);\n", + " }\n", + " } else if ((plot_id in window.PyViz.kernels) && (window.PyViz.kernels[plot_id])) {\n", + " var comm = window.PyViz.kernels[plot_id].connectToComm(comm_id);\n", + " comm.open();\n", + " if (msg_handler) {\n", + " comm.onMsg = msg_handler;\n", + " }\n", + " } else if (typeof google != 'undefined' && google.colab.kernel != null) {\n", + " var comm_promise = google.colab.kernel.comms.open(comm_id)\n", + " comm_promise.then((comm) => {\n", + " window.PyViz.comms[comm_id] = comm;\n", + " if (msg_handler) {\n", + " var messages = comm.messages[Symbol.asyncIterator]();\n", + " function processIteratorResult(result) {\n", + " var message = result.value;\n", + " var content = {data: message.data};\n", + " var metadata = message.metadata || {comm_id};\n", + " var msg = {content, metadata}\n", + " msg_handler(msg);\n", + " return messages.next().then(processIteratorResult);\n", + " }\n", + " return messages.next().then(processIteratorResult);\n", + " }\n", + " }) \n", + " var sendClosure = (data, metadata, buffers, disposeOnDone) => {\n", + " return comm_promise.then((comm) => {\n", + " comm.send(data, metadata, buffers, disposeOnDone);\n", + " });\n", + " };\n", + " var comm = {\n", + " send: sendClosure\n", + " };\n", + " }\n", + " window.PyViz.comms[comm_id] = comm;\n", + " return comm;\n", + " }\n", + " window.PyViz.comm_manager = new JupyterCommManager();\n", + " \n", + "\n", + "\n", + "var JS_MIME_TYPE = 'application/javascript';\n", + "var HTML_MIME_TYPE = 'text/html';\n", + "var EXEC_MIME_TYPE = 'application/vnd.holoviews_exec.v0+json';\n", + "var CLASS_NAME = 'output';\n", + "\n", + "/**\n", + " * Render data to the DOM node\n", + " */\n", + "function render(props, node) {\n", + " var div = document.createElement(\"div\");\n", + " var script = document.createElement(\"script\");\n", + " node.appendChild(div);\n", + " node.appendChild(script);\n", + "}\n", + "\n", + "/**\n", + " * Handle when a new output is added\n", + " */\n", + "function handle_add_output(event, handle) {\n", + " var output_area = handle.output_area;\n", + " var output = handle.output;\n", + " if ((output.data == undefined) || (!output.data.hasOwnProperty(EXEC_MIME_TYPE))) {\n", + " return\n", + " }\n", + " var id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n", + " var toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n", + " if (id !== undefined) {\n", + " var nchildren = toinsert.length;\n", + " var html_node = toinsert[nchildren-1].children[0];\n", + " html_node.innerHTML = output.data[HTML_MIME_TYPE];\n", + " var scripts = [];\n", + " var nodelist = html_node.querySelectorAll(\"script\");\n", + " for (var i in nodelist) {\n", + " if (nodelist.hasOwnProperty(i)) {\n", + " scripts.push(nodelist[i])\n", + " }\n", + " }\n", + "\n", + " scripts.forEach( function (oldScript) {\n", + " var newScript = document.createElement(\"script\");\n", + " var attrs = [];\n", + " var nodemap = oldScript.attributes;\n", + " for (var j in nodemap) {\n", + " if (nodemap.hasOwnProperty(j)) {\n", + " attrs.push(nodemap[j])\n", + " }\n", + " }\n", + " attrs.forEach(function(attr) { newScript.setAttribute(attr.name, attr.value) });\n", + " newScript.appendChild(document.createTextNode(oldScript.innerHTML));\n", + " oldScript.parentNode.replaceChild(newScript, oldScript);\n", + " });\n", + " if (JS_MIME_TYPE in output.data) {\n", + " toinsert[nchildren-1].children[1].textContent = output.data[JS_MIME_TYPE];\n", + " }\n", + " output_area._hv_plot_id = id;\n", + " if ((window.Bokeh !== undefined) && (id in Bokeh.index)) {\n", + " window.PyViz.plot_index[id] = Bokeh.index[id];\n", + " } else {\n", + " window.PyViz.plot_index[id] = null;\n", + " }\n", + " } else if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n", + " var bk_div = document.createElement(\"div\");\n", + " bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n", + " var script_attrs = bk_div.children[0].attributes;\n", + " for (var i = 0; i < script_attrs.length; i++) {\n", + " toinsert[toinsert.length - 1].childNodes[1].setAttribute(script_attrs[i].name, script_attrs[i].value);\n", + " }\n", + " // store reference to server id on output_area\n", + " output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n", + " }\n", + "}\n", + "\n", + "/**\n", + " * Handle when an output is cleared or removed\n", + " */\n", + "function handle_clear_output(event, handle) {\n", + " var id = handle.cell.output_area._hv_plot_id;\n", + " var server_id = handle.cell.output_area._bokeh_server_id;\n", + " if (((id === undefined) || !(id in PyViz.plot_index)) && (server_id !== undefined)) { return; }\n", + " var comm = window.PyViz.comm_manager.get_client_comm(\"hv-extension-comm\", \"hv-extension-comm\", function () {});\n", + " if (server_id !== null) {\n", + " comm.send({event_type: 'server_delete', 'id': server_id});\n", + " return;\n", + " } else if (comm !== null) {\n", + " comm.send({event_type: 'delete', 'id': id});\n", + " }\n", + " delete PyViz.plot_index[id];\n", + " if ((window.Bokeh !== undefined) & (id in window.Bokeh.index)) {\n", + " var doc = window.Bokeh.index[id].model.document\n", + " doc.clear();\n", + " const i = window.Bokeh.documents.indexOf(doc);\n", + " if (i > -1) {\n", + " window.Bokeh.documents.splice(i, 1);\n", + " }\n", + " }\n", + "}\n", + "\n", + "/**\n", + " * Handle kernel restart event\n", + " */\n", + "function handle_kernel_cleanup(event, handle) {\n", + " delete PyViz.comms[\"hv-extension-comm\"];\n", + " window.PyViz.plot_index = {}\n", + "}\n", + "\n", + "/**\n", + " * Handle update_display_data messages\n", + " */\n", + "function handle_update_output(event, handle) {\n", + " handle_clear_output(event, {cell: {output_area: handle.output_area}})\n", + " handle_add_output(event, handle)\n", + "}\n", + "\n", + "function register_renderer(events, OutputArea) {\n", + " function append_mime(data, metadata, element) {\n", + " // create a DOM node to render to\n", + " var toinsert = this.create_output_subarea(\n", + " metadata,\n", + " CLASS_NAME,\n", + " EXEC_MIME_TYPE\n", + " );\n", + " this.keyboard_manager.register_events(toinsert);\n", + " // Render to node\n", + " var props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n", + " render(props, toinsert[0]);\n", + " element.append(toinsert);\n", + " return toinsert\n", + " }\n", + "\n", + " events.on('output_added.OutputArea', handle_add_output);\n", + " events.on('output_updated.OutputArea', handle_update_output);\n", + " events.on('clear_output.CodeCell', handle_clear_output);\n", + " events.on('delete.Cell', handle_clear_output);\n", + " events.on('kernel_ready.Kernel', handle_kernel_cleanup);\n", + "\n", + " OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n", + " safe: true,\n", + " index: 0\n", + " });\n", + "}\n", + "\n", + "if (window.Jupyter !== undefined) {\n", + " try {\n", + " var events = require('base/js/events');\n", + " var OutputArea = require('notebook/js/outputarea').OutputArea;\n", + " if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n", + " register_renderer(events, OutputArea);\n", + " }\n", + " } catch(err) {\n", + " }\n", + "}\n" + ], + "application/vnd.holoviews_load.v0+json": "\nif ((window.PyViz === undefined) || (window.PyViz instanceof HTMLElement)) {\n window.PyViz = {comms: {}, comm_status:{}, kernels:{}, receivers: {}, plot_index: []}\n}\n\n\n function JupyterCommManager() {\n }\n\n JupyterCommManager.prototype.register_target = function(plot_id, comm_id, msg_handler) {\n if (window.comm_manager || ((window.Jupyter !== undefined) && (Jupyter.notebook.kernel != null))) {\n var comm_manager = window.comm_manager || Jupyter.notebook.kernel.comm_manager;\n comm_manager.register_target(comm_id, function(comm) {\n comm.on_msg(msg_handler);\n });\n } else if ((plot_id in window.PyViz.kernels) && (window.PyViz.kernels[plot_id])) {\n window.PyViz.kernels[plot_id].registerCommTarget(comm_id, function(comm) {\n comm.onMsg = msg_handler;\n });\n } else if (typeof google != 'undefined' && google.colab.kernel != null) {\n google.colab.kernel.comms.registerTarget(comm_id, (comm) => {\n var messages = comm.messages[Symbol.asyncIterator]();\n function processIteratorResult(result) {\n var message = result.value;\n console.log(message)\n var content = {data: message.data, comm_id};\n var buffers = []\n for (var buffer of message.buffers || []) {\n buffers.push(new DataView(buffer))\n }\n var metadata = message.metadata || {};\n var msg = {content, buffers, metadata}\n msg_handler(msg);\n return messages.next().then(processIteratorResult);\n }\n return messages.next().then(processIteratorResult);\n })\n }\n }\n\n JupyterCommManager.prototype.get_client_comm = function(plot_id, comm_id, msg_handler) {\n if (comm_id in window.PyViz.comms) {\n return window.PyViz.comms[comm_id];\n } else if (window.comm_manager || ((window.Jupyter !== undefined) && (Jupyter.notebook.kernel != null))) {\n var comm_manager = window.comm_manager || Jupyter.notebook.kernel.comm_manager;\n var comm = comm_manager.new_comm(comm_id, {}, {}, {}, comm_id);\n if (msg_handler) {\n comm.on_msg(msg_handler);\n }\n } else if ((plot_id in window.PyViz.kernels) && (window.PyViz.kernels[plot_id])) {\n var comm = window.PyViz.kernels[plot_id].connectToComm(comm_id);\n comm.open();\n if (msg_handler) {\n comm.onMsg = msg_handler;\n }\n } else if (typeof google != 'undefined' && google.colab.kernel != null) {\n var comm_promise = google.colab.kernel.comms.open(comm_id)\n comm_promise.then((comm) => {\n window.PyViz.comms[comm_id] = comm;\n if (msg_handler) {\n var messages = comm.messages[Symbol.asyncIterator]();\n function processIteratorResult(result) {\n var message = result.value;\n var content = {data: message.data};\n var metadata = message.metadata || {comm_id};\n var msg = {content, metadata}\n msg_handler(msg);\n return messages.next().then(processIteratorResult);\n }\n return messages.next().then(processIteratorResult);\n }\n }) \n var sendClosure = (data, metadata, buffers, disposeOnDone) => {\n return comm_promise.then((comm) => {\n comm.send(data, metadata, buffers, disposeOnDone);\n });\n };\n var comm = {\n send: sendClosure\n };\n }\n window.PyViz.comms[comm_id] = comm;\n return comm;\n }\n window.PyViz.comm_manager = new JupyterCommManager();\n \n\n\nvar JS_MIME_TYPE = 'application/javascript';\nvar HTML_MIME_TYPE = 'text/html';\nvar EXEC_MIME_TYPE = 'application/vnd.holoviews_exec.v0+json';\nvar CLASS_NAME = 'output';\n\n/**\n * Render data to the DOM node\n */\nfunction render(props, node) {\n var div = document.createElement(\"div\");\n var script = document.createElement(\"script\");\n node.appendChild(div);\n node.appendChild(script);\n}\n\n/**\n * Handle when a new output is added\n */\nfunction handle_add_output(event, handle) {\n var output_area = handle.output_area;\n var output = handle.output;\n if ((output.data == undefined) || (!output.data.hasOwnProperty(EXEC_MIME_TYPE))) {\n return\n }\n var id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n var toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n if (id !== undefined) {\n var nchildren = toinsert.length;\n var html_node = toinsert[nchildren-1].children[0];\n html_node.innerHTML = output.data[HTML_MIME_TYPE];\n var scripts = [];\n var nodelist = html_node.querySelectorAll(\"script\");\n for (var i in nodelist) {\n if (nodelist.hasOwnProperty(i)) {\n scripts.push(nodelist[i])\n }\n }\n\n scripts.forEach( function (oldScript) {\n var newScript = document.createElement(\"script\");\n var attrs = [];\n var nodemap = oldScript.attributes;\n for (var j in nodemap) {\n if (nodemap.hasOwnProperty(j)) {\n attrs.push(nodemap[j])\n }\n }\n attrs.forEach(function(attr) { newScript.setAttribute(attr.name, attr.value) });\n newScript.appendChild(document.createTextNode(oldScript.innerHTML));\n oldScript.parentNode.replaceChild(newScript, oldScript);\n });\n if (JS_MIME_TYPE in output.data) {\n toinsert[nchildren-1].children[1].textContent = output.data[JS_MIME_TYPE];\n }\n output_area._hv_plot_id = id;\n if ((window.Bokeh !== undefined) && (id in Bokeh.index)) {\n window.PyViz.plot_index[id] = Bokeh.index[id];\n } else {\n window.PyViz.plot_index[id] = null;\n }\n } else if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n var bk_div = document.createElement(\"div\");\n bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n var script_attrs = bk_div.children[0].attributes;\n for (var i = 0; i < script_attrs.length; i++) {\n toinsert[toinsert.length - 1].childNodes[1].setAttribute(script_attrs[i].name, script_attrs[i].value);\n }\n // store reference to server id on output_area\n output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n }\n}\n\n/**\n * Handle when an output is cleared or removed\n */\nfunction handle_clear_output(event, handle) {\n var id = handle.cell.output_area._hv_plot_id;\n var server_id = handle.cell.output_area._bokeh_server_id;\n if (((id === undefined) || !(id in PyViz.plot_index)) && (server_id !== undefined)) { return; }\n var comm = window.PyViz.comm_manager.get_client_comm(\"hv-extension-comm\", \"hv-extension-comm\", function () {});\n if (server_id !== null) {\n comm.send({event_type: 'server_delete', 'id': server_id});\n return;\n } else if (comm !== null) {\n comm.send({event_type: 'delete', 'id': id});\n }\n delete PyViz.plot_index[id];\n if ((window.Bokeh !== undefined) & (id in window.Bokeh.index)) {\n var doc = window.Bokeh.index[id].model.document\n doc.clear();\n const i = window.Bokeh.documents.indexOf(doc);\n if (i > -1) {\n window.Bokeh.documents.splice(i, 1);\n }\n }\n}\n\n/**\n * Handle kernel restart event\n */\nfunction handle_kernel_cleanup(event, handle) {\n delete PyViz.comms[\"hv-extension-comm\"];\n window.PyViz.plot_index = {}\n}\n\n/**\n * Handle update_display_data messages\n */\nfunction handle_update_output(event, handle) {\n handle_clear_output(event, {cell: {output_area: handle.output_area}})\n handle_add_output(event, handle)\n}\n\nfunction register_renderer(events, OutputArea) {\n function append_mime(data, metadata, element) {\n // create a DOM node to render to\n var toinsert = this.create_output_subarea(\n metadata,\n CLASS_NAME,\n EXEC_MIME_TYPE\n );\n this.keyboard_manager.register_events(toinsert);\n // Render to node\n var props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n render(props, toinsert[0]);\n element.append(toinsert);\n return toinsert\n }\n\n events.on('output_added.OutputArea', handle_add_output);\n events.on('output_updated.OutputArea', handle_update_output);\n events.on('clear_output.CodeCell', handle_clear_output);\n events.on('delete.Cell', handle_clear_output);\n events.on('kernel_ready.Kernel', handle_kernel_cleanup);\n\n OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n safe: true,\n index: 0\n });\n}\n\nif (window.Jupyter !== undefined) {\n try {\n var events = require('base/js/events');\n var OutputArea = require('notebook/js/outputarea').OutputArea;\n if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n register_renderer(events, OutputArea);\n }\n } catch(err) {\n }\n}\n" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import os, sys\n", + "import shutil\n", + "import warnings\n", + "warnings.filterwarnings('ignore')\n", + "\n", + "import numpy as np\n", + "import xarray as xr\n", + "import xesmf as xe\n", + "\n", + "# Helpful for plotting only\n", + "import matplotlib\n", + "import matplotlib.pyplot as plt\n", + "import matplotlib.dates as mdates\n", + "import cartopy\n", + "import cartopy.crs as ccrs\n", + "import cartopy.feature as cfeature\n", + "import uxarray as ux #need npl 2024a or later\n", + "import geoviews.feature as gf\n", + "\n", + "#sys.path.append('/glade/u/home/wwieder/python/adf/lib/plotting_functions.py')" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "07650a02-db90-4ee9-8880-e3f4ac140871", + "metadata": {}, + "outputs": [], + "source": [ + "# Load datataset \n", + "# TODO, develop function for this too\n", + "gppfile='/glade/derecho/scratch/wwieder/ADF/b.e30_beta04.BLT1850.ne30_t232_wgx3.121/climo/b.e30_beta04.BLT1850.ne30_t232_wgx3.121_GPP_climo.nc'\n", + "laih1file='/glade/derecho/scratch/wwieder/ctsm53n04ctsm52028_ne30pg3t232_hist.clm2.h1.TLAI.1860s.nc'\n", + "case = 'ctsm53n04ctsm52028_ne30pg3t232_hist'\n", + "\n", + "mesh0 = '/glade/campaign/cesm/cesmdata/inputdata/share/meshes/ne30pg3_ESMFmesh_cdf5_c20211018.nc'\n", + "\n", + "#ux file for plotting\n", + "uxds0 = ux.open_dataset(mesh0, gppfile).max('time')\n", + "uxds1 = ux.open_dataset(mesh0, laih1file).max('time')" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "3500cb23-f87f-44a1-a204-e8d5c2f22c85", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "starting pft 1\n", + "starting pft 2\n", + "starting pft 3\n", + "starting pft 4\n", + "starting pft 5\n", + "starting pft 6\n", + "starting pft 7\n", + "starting pft 8\n", + "starting pft 9\n", + "starting pft 10\n", + "starting pft 11\n", + "starting pft 12\n", + "starting pft 13\n", + "starting pft 14\n", + "starting pft 15\n" + ] + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
<xarray.UxDataset> Size: 32MB\n",
+       "Dimensions:             (npft: 15, hist_interval: 2, n_face: 15962)\n",
+       "Coordinates:\n",
+       "  * n_face              (n_face) int64 128kB 737 738 745 ... 48598 48599 48600\n",
+       "Dimensions without coordinates: npft, hist_interval\n",
+       "Data variables: (12/16)\n",
+       "    time_bounds         (npft, hist_interval, n_face) object 4MB 1869-12-01 0...\n",
+       "    pfts1d_lon          (npft, n_face) float64 2MB 19.5 20.5 ... 136.0 135.0\n",
+       "    pfts1d_lat          (npft, n_face) float64 2MB -34.9 -34.73 ... 36.2 35.74\n",
+       "    pfts1d_ixy          (npft, n_face) float64 2MB 737.0 738.0 ... 4.86e+04\n",
+       "    pfts1d_jxy          (npft, n_face) float64 2MB 1.0 1.0 1.0 ... 1.0 1.0 1.0\n",
+       "    pfts1d_gi           (npft, n_face) float64 2MB 1.0 2.0 ... 1.596e+04\n",
+       "    ...                  ...\n",
+       "    pfts1d_wtcol        (npft, n_face) float64 2MB 0.0 0.0 0.0 ... 1.0 1.0 1.0\n",
+       "    pfts1d_itype_veg    (npft, n_face) float64 2MB 1.0 1.0 1.0 ... 15.0 15.0\n",
+       "    pfts1d_itype_col    (npft, n_face) float64 2MB 1.0 1.0 1.0 ... 215.0 215.0\n",
+       "    pfts1d_itype_lunit  (npft, n_face) float64 2MB 1.0 1.0 1.0 ... 2.0 2.0 2.0\n",
+       "    pfts1d_active       (npft, n_face) float64 2MB nan nan nan ... nan nan nan\n",
+       "    TLAI                (npft, n_face) float32 958kB nan nan nan ... nan nan nan
" + ], + "text/plain": [ + " Size: 32MB\n", + "Dimensions: (npft: 15, hist_interval: 2, n_face: 15962)\n", + "Coordinates:\n", + " * n_face (n_face) int64 128kB 737 738 745 ... 48598 48599 48600\n", + "Dimensions without coordinates: npft, hist_interval\n", + "Data variables: (12/16)\n", + " time_bounds (npft, hist_interval, n_face) object 4MB 1869-12-01 0...\n", + " pfts1d_lon (npft, n_face) float64 2MB 19.5 20.5 ... 136.0 135.0\n", + " pfts1d_lat (npft, n_face) float64 2MB -34.9 -34.73 ... 36.2 35.74\n", + " pfts1d_ixy (npft, n_face) float64 2MB 737.0 738.0 ... 4.86e+04\n", + " pfts1d_jxy (npft, n_face) float64 2MB 1.0 1.0 1.0 ... 1.0 1.0 1.0\n", + " pfts1d_gi (npft, n_face) float64 2MB 1.0 2.0 ... 1.596e+04\n", + " ... ...\n", + " pfts1d_wtcol (npft, n_face) float64 2MB 0.0 0.0 0.0 ... 1.0 1.0 1.0\n", + " pfts1d_itype_veg (npft, n_face) float64 2MB 1.0 1.0 1.0 ... 15.0 15.0\n", + " pfts1d_itype_col (npft, n_face) float64 2MB 1.0 1.0 1.0 ... 215.0 215.0\n", + " pfts1d_itype_lunit (npft, n_face) float64 2MB 1.0 1.0 1.0 ... 2.0 2.0 2.0\n", + " pfts1d_active (npft, n_face) float64 2MB nan nan nan ... nan nan nan\n", + " TLAI (npft, n_face) float32 958kB nan nan nan ... nan nan nan" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# select a single PFT\n", + "## TODO this step is kind of a memory hog\n", + "## OERO NOTE: Almost no memory increase after this code's execution in this UXarray's case\n", + "npft=16\n", + "var='TLAI'\n", + "\n", + "for i in range(1, npft):\n", + " print('starting pft ' + str(i))\n", + " ## OERO NOTE: UxDataset.where() below had an issue that we've fixed last week and the fixed \n", + " ## version is scheduled for release v2025.03.0 today. If you want to check this code out sooner\n", + " ## than the release, run the following command in your conda environment to install UXarray from \n", + " ## the GitHub repository:\n", + " ## pip install git+https://github.com/UXARRAY/uxarray.git\n", + " temp = uxds1.where(uxds1.pfts1d_itype_veg==i, drop=True)\n", + " # TODO, this should be time evolving, but not currently doen\n", + " # Rename coord, since the pft dimension is not meaningful\n", + " temp= temp.rename({'pft': 'n_face'})\n", + " \n", + " # assign values from pfts1d_ixy to n_face\n", + " temp['n_face'] = temp.pfts1d_ixy.astype(int)\n", + " temp.assign_coords({\"npft\": i})\n", + " # combine along PFT variable\n", + " if i == 1:\n", + " uxdsOut = temp\n", + " else:\n", + " uxdsOut = xr.concat([uxdsOut, temp], dim=\"npft\")\n", + "\n", + "## UXARRAY TODO: After Xarray.concatenate call on UXarray objects, the Grid object, ``uxgrid``\n", + "## is being dropped. To get it back, I had to reassign. While being able to run Xarray's builtin \n", + "## function on UXarray objects directly was convenient, and adding this Grid back is not a big deal\n", + "## we may still want to explore an UXarray solution for concatenate\n", + "uxdsOut.uxgrid = temp.uxgrid\n", + "uxdsOut" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "1a00407c-d4bd-4925-9eb3-8c23701b729d", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
<xarray.UxDataArray 'GPP' (n_face: 48600)> Size: 194kB\n",
+       "array([          nan,           nan,           nan, ..., 7.3058734e-05,\n",
+       "       7.4361727e-05, 8.8926041e-05], dtype=float32)\n",
+       "Coordinates:\n",
+       "  * n_face   (n_face) int64 389kB 1 2 3 4 5 6 ... 48596 48597 48598 48599 48600
" + ], + "text/plain": [ + " Size: 194kB\n", + "array([ nan, nan, nan, ..., 7.3058734e-05,\n", + " 7.4361727e-05, 8.8926041e-05], dtype=float32)\n", + "Coordinates:\n", + " * n_face (n_face) int64 389kB 1 2 3 4 5 6 ... 48596 48597 48598 48599 48600" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# align subset pft output with plotting data array\n", + "target = uxds0.GPP\n", + "n_face_coords = np.arange(1,(uxds1.pfts1d_ixy.max().astype(int)+1))\n", + "target = target.assign_coords({'n_face': ('n_face', n_face_coords)})\n", + "target" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "7c32ae56-de87-4255-8688-82d2bfeee9b3", + "metadata": {}, + "outputs": [], + "source": [ + "# Now align the land only output on the target (full) grid\n", + "uxdsOut_align, target = xr.align(uxdsOut, target, join=\"right\")" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "56e648bf-ca0e-4e72-82b9-96d177800489", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
<xarray.UxDataset> Size: 10MB\n",
+       "Dimensions:         (n_face: 48600, npft: 15)\n",
+       "Coordinates:\n",
+       "  * n_face          (n_face) int64 389kB 1 2 3 4 5 ... 48597 48598 48599 48600\n",
+       "Dimensions without coordinates: npft\n",
+       "Data variables:\n",
+       "    GPP             (n_face) float32 194kB nan nan nan ... 7.436e-05 8.893e-05\n",
+       "    area            (n_face) float32 194kB nan nan nan ... 9.519e+03 9.519e+03\n",
+       "    landfrac        (n_face) float32 194kB nan nan nan ... 0.6608 0.2991 0.07713\n",
+       "    landmask        (n_face) float64 389kB nan nan nan nan ... nan 1.0 1.0 1.0\n",
+       "    TLAI            (npft, n_face) float32 3MB nan nan nan nan ... nan nan nan\n",
+       "    pfts1d_wtgcell  (npft, n_face) float64 6MB nan nan nan nan ... 0.0 0.0 0.0
" + ], + "text/plain": [ + " Size: 10MB\n", + "Dimensions: (n_face: 48600, npft: 15)\n", + "Coordinates:\n", + " * n_face (n_face) int64 389kB 1 2 3 4 5 ... 48597 48598 48599 48600\n", + "Dimensions without coordinates: npft\n", + "Data variables:\n", + " GPP (n_face) float32 194kB nan nan nan ... 7.436e-05 8.893e-05\n", + " area (n_face) float32 194kB nan nan nan ... 9.519e+03 9.519e+03\n", + " landfrac (n_face) float32 194kB nan nan nan ... 0.6608 0.2991 0.07713\n", + " landmask (n_face) float64 389kB nan nan nan nan ... nan 1.0 1.0 1.0\n", + " TLAI (npft, n_face) float32 3MB nan nan nan nan ... nan nan nan\n", + " pfts1d_wtgcell (npft, n_face) float64 6MB nan nan nan nan ... 0.0 0.0 0.0" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Copy pft indexed data back to the h0 file\n", + "# This allows us to use area and landfrac on the same dataset \n", + "# Used to calculate weighted sums of LAI and livefrac\n", + "uxds0_plot = uxds0\n", + "uxds0_plot[var] = uxdsOut_align[var]\n", + "uxds0_plot['pfts1d_wtgcell'] = uxdsOut_align['pfts1d_wtgcell']\n", + "uxds0_plot\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "f847e56b-d807-4dab-8be1-e3d1cfeb5b71", + "metadata": {}, + "outputs": [], + "source": [ + "pft_names = ['NET Temperate', 'NET Boreal', 'NDT Boreal',\n", + " 'BET Tropical', 'BET Temperate', 'BDT Tropical',\n", + " 'BDT Temperate', 'BDT Boreal', 'BES Temperate',\n", + " 'BDS Temperate', 'BDS Boreal', 'C3 Grass Arctic',\n", + " 'C3 Grass', 'C4 Grass', 'UCrop UIrr']" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "55ceea85-e3e2-4e2e-a88c-03c1313acf31", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-- wrote pft TLAI figure --\n" + ] + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "transform = ccrs.PlateCarree()\n", + "proj = ccrs.PlateCarree()\n", + "cmap = plt.cm.viridis_r\n", + "cmap.set_under(color='deeppink')\n", + "cmap = cmap.resampled(7)\n", + "levels = [0.1, 1, 2, 3, 4, 5, 6,7]\n", + "\n", + "# create figure object\n", + "fig, axs = plt.subplots(5,3,\n", + " facecolor=\"w\",\n", + " constrained_layout=True,\n", + " subplot_kw=dict(projection=proj) )\n", + "\n", + "axs=axs.flatten()\n", + "\n", + "# Loop over pfts\n", + "for i in range((npft-1)):\n", + " ac = uxds0_plot[var].isel(npft=i).to_polycollection(projection=proj)\n", + " ac.set_cmap(cmap)\n", + " ac.set_antialiased(False)\n", + " ac.set_transform(transform)\n", + " ac.set_clim(vmin=0.1,vmax=6.9)\n", + " axs[i].add_collection(ac)\n", + "\n", + " #Titles, statistics\n", + " wgts = uxds0_plot.area * uxds0_plot.landfrac * uxds0_plot.pfts1d_wtgcell.isel(npft=i)\n", + " wgts = wgts / wgts.sum()\n", + " mean = str(np.round((uxds0_plot[var].isel(npft=i)*wgts).sum().values,2))\n", + " dead = ((uxds0_plot[var].isel(npft=i)<0.1)*wgts).sum()\n", + " live = ((uxds0_plot[var].isel(npft=i)>0.1)*wgts).sum()\n", + " livefrac = str(np.round((live/(live+dead)).values,2))\n", + " axs[i].set_title(pft_names[i], loc='left',size=6)\n", + " axs[i].text(-30, -45,'mean = '+ mean, fontsize=5)\n", + " axs[i].text(-45, -60,'live frac = '+livefrac,fontsize=5)\n", + "\n", + "for a in axs:\n", + " a.coastlines()\n", + " a.set_global()\n", + " a.spines['geo'].set_linewidth(0.1) #cartopy's recommended method\n", + " a.set_extent([-180, 180, -65, 86])\n", + "\n", + "#fig.subplots_adjust(right=0.97)\n", + "cbar_ax = fig.add_axes([0.92, 0.05, 0.02, 0.8])\n", + "fig.colorbar(ac, cax=cbar_ax, pad=0.05, shrink=0.8, aspect=40,\n", + " extend='both')\n", + "fig.suptitle(\"max LAI \"+ case,size='medium')\n", + "fig.set_layout_engine(\"compressed\")\n", + "\n", + "fig.savefig('h1_test', bbox_inches='tight', dpi=300)\n", + "print('-- wrote pft '+var+' figure --')\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1d2c3658-48a9-4ca9-931d-a592c46e1c60", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python [conda env:cupid-analysis]", + "language": "python", + "name": "conda-env-cupid-analysis-py" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.4" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/lib/plot_uxarray_test.ipynb b/lib/plot_uxarray_test.ipynb new file mode 100644 index 000000000..36aaf3538 --- /dev/null +++ b/lib/plot_uxarray_test.ipynb @@ -0,0 +1,3873 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "39545902-0870-4a3f-93f1-493e56403d38", + "metadata": {}, + "source": [ + "### test for using the _plot_unstructured_map_and_save_ function" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "b75e38a9-54ff-438b-91cd-2f72ef3abd95", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/javascript": [ + "(function(root) {\n", + " function now() {\n", + " return new Date();\n", + " }\n", + "\n", + " const force = true;\n", + " const py_version = '3.6.2'.replace('rc', '-rc.').replace('.dev', '-dev.');\n", + " const reloading = false;\n", + " const Bokeh = root.Bokeh;\n", + "\n", + " // Set a timeout for this load but only if we are not already initializing\n", + " if (typeof (root._bokeh_timeout) === \"undefined\" || (force || !root._bokeh_is_initializing)) {\n", + " root._bokeh_timeout = Date.now() + 5000;\n", + " root._bokeh_failed_load = false;\n", + " }\n", + "\n", + " function run_callbacks() {\n", + " try {\n", + " root._bokeh_onload_callbacks.forEach(function(callback) {\n", + " if (callback != null)\n", + " callback();\n", + " });\n", + " } finally {\n", + " delete root._bokeh_onload_callbacks;\n", + " }\n", + " console.debug(\"Bokeh: all callbacks have finished\");\n", + " }\n", + "\n", + " function load_libs(css_urls, js_urls, js_modules, js_exports, callback) {\n", + " if (css_urls == null) css_urls = [];\n", + " if (js_urls == null) js_urls = [];\n", + " if (js_modules == null) js_modules = [];\n", + " if (js_exports == null) js_exports = {};\n", + "\n", + " root._bokeh_onload_callbacks.push(callback);\n", + "\n", + " if (root._bokeh_is_loading > 0) {\n", + " // Don't load bokeh if it is still initializing\n", + " console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n", + " return null;\n", + " } else if (js_urls.length === 0 && js_modules.length === 0 && Object.keys(js_exports).length === 0) {\n", + " // There is nothing to load\n", + " run_callbacks();\n", + " return null;\n", + " }\n", + "\n", + " function on_load() {\n", + " root._bokeh_is_loading--;\n", + " if (root._bokeh_is_loading === 0) {\n", + " console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n", + " run_callbacks()\n", + " }\n", + " }\n", + " window._bokeh_on_load = on_load\n", + "\n", + " function on_error(e) {\n", + " const src_el = e.srcElement\n", + " console.error(\"failed to load \" + (src_el.href || src_el.src));\n", + " }\n", + "\n", + " const skip = [];\n", + " if (window.requirejs) {\n", + " window.requirejs.config({'packages': {}, 'paths': {}, 'shim': {}});\n", + " root._bokeh_is_loading = css_urls.length + 0;\n", + " } else {\n", + " root._bokeh_is_loading = css_urls.length + js_urls.length + js_modules.length + Object.keys(js_exports).length;\n", + " }\n", + "\n", + " const existing_stylesheets = []\n", + " const links = document.getElementsByTagName('link')\n", + " for (let i = 0; i < links.length; i++) {\n", + " const link = links[i]\n", + " if (link.href != null) {\n", + " existing_stylesheets.push(link.href)\n", + " }\n", + " }\n", + " for (let i = 0; i < css_urls.length; i++) {\n", + " const url = css_urls[i];\n", + " const escaped = encodeURI(url)\n", + " if (existing_stylesheets.indexOf(escaped) !== -1) {\n", + " on_load()\n", + " continue;\n", + " }\n", + " const element = document.createElement(\"link\");\n", + " element.onload = on_load;\n", + " element.onerror = on_error;\n", + " element.rel = \"stylesheet\";\n", + " element.type = \"text/css\";\n", + " element.href = url;\n", + " console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n", + " document.body.appendChild(element);\n", + " } var existing_scripts = []\n", + " const scripts = document.getElementsByTagName('script')\n", + " for (let i = 0; i < scripts.length; i++) {\n", + " var script = scripts[i]\n", + " if (script.src != null) {\n", + " existing_scripts.push(script.src)\n", + " }\n", + " }\n", + " for (let i = 0; i < js_urls.length; i++) {\n", + " const url = js_urls[i];\n", + " const escaped = encodeURI(url)\n", + " if (skip.indexOf(escaped) !== -1 || existing_scripts.indexOf(escaped) !== -1) {\n", + " if (!window.requirejs) {\n", + " on_load();\n", + " }\n", + " continue;\n", + " }\n", + " const element = document.createElement('script');\n", + " element.onload = on_load;\n", + " element.onerror = on_error;\n", + " element.async = false;\n", + " element.src = url;\n", + " console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n", + " document.head.appendChild(element);\n", + " }\n", + " for (let i = 0; i < js_modules.length; i++) {\n", + " const url = js_modules[i];\n", + " const escaped = encodeURI(url)\n", + " if (skip.indexOf(escaped) !== -1 || existing_scripts.indexOf(escaped) !== -1) {\n", + " if (!window.requirejs) {\n", + " on_load();\n", + " }\n", + " continue;\n", + " }\n", + " var element = document.createElement('script');\n", + " element.onload = on_load;\n", + " element.onerror = on_error;\n", + " element.async = false;\n", + " element.src = url;\n", + " element.type = \"module\";\n", + " console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n", + " document.head.appendChild(element);\n", + " }\n", + " for (const name in js_exports) {\n", + " const url = js_exports[name];\n", + " const escaped = encodeURI(url)\n", + " if (skip.indexOf(escaped) >= 0 || root[name] != null) {\n", + " if (!window.requirejs) {\n", + " on_load();\n", + " }\n", + " continue;\n", + " }\n", + " var element = document.createElement('script');\n", + " element.onerror = on_error;\n", + " element.async = false;\n", + " element.type = \"module\";\n", + " console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n", + " element.textContent = `\n", + " import ${name} from \"${url}\"\n", + " window.${name} = ${name}\n", + " window._bokeh_on_load()\n", + " `\n", + " document.head.appendChild(element);\n", + " }\n", + " if (!js_urls.length && !js_modules.length) {\n", + " on_load()\n", + " }\n", + " };\n", + "\n", + " function inject_raw_css(css) {\n", + " const element = document.createElement(\"style\");\n", + " element.appendChild(document.createTextNode(css));\n", + " document.body.appendChild(element);\n", + " }\n", + "\n", + " const js_urls = [\"https://cdn.holoviz.org/panel/1.5.4/dist/bundled/reactiveesm/es-module-shims@^1.10.0/dist/es-module-shims.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-3.6.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.6.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.6.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.6.2.min.js\", \"https://cdn.holoviz.org/panel/1.5.4/dist/panel.min.js\"];\n", + " const js_modules = [];\n", + " const js_exports = {};\n", + " const css_urls = [];\n", + " const inline_js = [ function(Bokeh) {\n", + " Bokeh.set_log_level(\"info\");\n", + " },\n", + "function(Bokeh) {} // ensure no trailing comma for IE\n", + " ];\n", + "\n", + " function run_inline_js() {\n", + " if ((root.Bokeh !== undefined) || (force === true)) {\n", + " for (let i = 0; i < inline_js.length; i++) {\n", + " try {\n", + " inline_js[i].call(root, root.Bokeh);\n", + " } catch(e) {\n", + " if (!reloading) {\n", + " throw e;\n", + " }\n", + " }\n", + " }\n", + " // Cache old bokeh versions\n", + " if (Bokeh != undefined && !reloading) {\n", + " var NewBokeh = root.Bokeh;\n", + " if (Bokeh.versions === undefined) {\n", + " Bokeh.versions = new Map();\n", + " }\n", + " if (NewBokeh.version !== Bokeh.version) {\n", + " Bokeh.versions.set(NewBokeh.version, NewBokeh)\n", + " }\n", + " root.Bokeh = Bokeh;\n", + " }\n", + " } else if (Date.now() < root._bokeh_timeout) {\n", + " setTimeout(run_inline_js, 100);\n", + " } else if (!root._bokeh_failed_load) {\n", + " console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n", + " root._bokeh_failed_load = true;\n", + " }\n", + " root._bokeh_is_initializing = false\n", + " }\n", + "\n", + " function load_or_wait() {\n", + " // Implement a backoff loop that tries to ensure we do not load multiple\n", + " // versions of Bokeh and its dependencies at the same time.\n", + " // In recent versions we use the root._bokeh_is_initializing flag\n", + " // to determine whether there is an ongoing attempt to initialize\n", + " // bokeh, however for backward compatibility we also try to ensure\n", + " // that we do not start loading a newer (Panel>=1.0 and Bokeh>3) version\n", + " // before older versions are fully initialized.\n", + " if (root._bokeh_is_initializing && Date.now() > root._bokeh_timeout) {\n", + " // If the timeout and bokeh was not successfully loaded we reset\n", + " // everything and try loading again\n", + " root._bokeh_timeout = Date.now() + 5000;\n", + " root._bokeh_is_initializing = false;\n", + " root._bokeh_onload_callbacks = undefined;\n", + " root._bokeh_is_loading = 0\n", + " console.log(\"Bokeh: BokehJS was loaded multiple times but one version failed to initialize.\");\n", + " load_or_wait();\n", + " } else if (root._bokeh_is_initializing || (typeof root._bokeh_is_initializing === \"undefined\" && root._bokeh_onload_callbacks !== undefined)) {\n", + " setTimeout(load_or_wait, 100);\n", + " } else {\n", + " root._bokeh_is_initializing = true\n", + " root._bokeh_onload_callbacks = []\n", + " const bokeh_loaded = root.Bokeh != null && (root.Bokeh.version === py_version || (root.Bokeh.versions !== undefined && root.Bokeh.versions.has(py_version)));\n", + " if (!reloading && !bokeh_loaded) {\n", + " if (root.Bokeh) {\n", + " root.Bokeh = undefined;\n", + " }\n", + " console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n", + " }\n", + " load_libs(css_urls, js_urls, js_modules, js_exports, function() {\n", + " console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n", + " run_inline_js();\n", + " });\n", + " }\n", + " }\n", + " // Give older versions of the autoload script a head-start to ensure\n", + " // they initialize before we start loading newer version.\n", + " setTimeout(load_or_wait, 100)\n", + "}(window));" + ], + "application/vnd.holoviews_load.v0+json": "(function(root) {\n function now() {\n return new Date();\n }\n\n const force = true;\n const py_version = '3.6.2'.replace('rc', '-rc.').replace('.dev', '-dev.');\n const reloading = false;\n const Bokeh = root.Bokeh;\n\n // Set a timeout for this load but only if we are not already initializing\n if (typeof (root._bokeh_timeout) === \"undefined\" || (force || !root._bokeh_is_initializing)) {\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_failed_load = false;\n }\n\n function run_callbacks() {\n try {\n root._bokeh_onload_callbacks.forEach(function(callback) {\n if (callback != null)\n callback();\n });\n } finally {\n delete root._bokeh_onload_callbacks;\n }\n console.debug(\"Bokeh: all callbacks have finished\");\n }\n\n function load_libs(css_urls, js_urls, js_modules, js_exports, callback) {\n if (css_urls == null) css_urls = [];\n if (js_urls == null) js_urls = [];\n if (js_modules == null) js_modules = [];\n if (js_exports == null) js_exports = {};\n\n root._bokeh_onload_callbacks.push(callback);\n\n if (root._bokeh_is_loading > 0) {\n // Don't load bokeh if it is still initializing\n console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n return null;\n } else if (js_urls.length === 0 && js_modules.length === 0 && Object.keys(js_exports).length === 0) {\n // There is nothing to load\n run_callbacks();\n return null;\n }\n\n function on_load() {\n root._bokeh_is_loading--;\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n run_callbacks()\n }\n }\n window._bokeh_on_load = on_load\n\n function on_error(e) {\n const src_el = e.srcElement\n console.error(\"failed to load \" + (src_el.href || src_el.src));\n }\n\n const skip = [];\n if (window.requirejs) {\n window.requirejs.config({'packages': {}, 'paths': {}, 'shim': {}});\n root._bokeh_is_loading = css_urls.length + 0;\n } else {\n root._bokeh_is_loading = css_urls.length + js_urls.length + js_modules.length + Object.keys(js_exports).length;\n }\n\n const existing_stylesheets = []\n const links = document.getElementsByTagName('link')\n for (let i = 0; i < links.length; i++) {\n const link = links[i]\n if (link.href != null) {\n existing_stylesheets.push(link.href)\n }\n }\n for (let i = 0; i < css_urls.length; i++) {\n const url = css_urls[i];\n const escaped = encodeURI(url)\n if (existing_stylesheets.indexOf(escaped) !== -1) {\n on_load()\n continue;\n }\n const element = document.createElement(\"link\");\n element.onload = on_load;\n element.onerror = on_error;\n element.rel = \"stylesheet\";\n element.type = \"text/css\";\n element.href = url;\n console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n document.body.appendChild(element);\n } var existing_scripts = []\n const scripts = document.getElementsByTagName('script')\n for (let i = 0; i < scripts.length; i++) {\n var script = scripts[i]\n if (script.src != null) {\n existing_scripts.push(script.src)\n }\n }\n for (let i = 0; i < js_urls.length; i++) {\n const url = js_urls[i];\n const escaped = encodeURI(url)\n if (skip.indexOf(escaped) !== -1 || existing_scripts.indexOf(escaped) !== -1) {\n if (!window.requirejs) {\n on_load();\n }\n continue;\n }\n const element = document.createElement('script');\n element.onload = on_load;\n element.onerror = on_error;\n element.async = false;\n element.src = url;\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.head.appendChild(element);\n }\n for (let i = 0; i < js_modules.length; i++) {\n const url = js_modules[i];\n const escaped = encodeURI(url)\n if (skip.indexOf(escaped) !== -1 || existing_scripts.indexOf(escaped) !== -1) {\n if (!window.requirejs) {\n on_load();\n }\n continue;\n }\n var element = document.createElement('script');\n element.onload = on_load;\n element.onerror = on_error;\n element.async = false;\n element.src = url;\n element.type = \"module\";\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.head.appendChild(element);\n }\n for (const name in js_exports) {\n const url = js_exports[name];\n const escaped = encodeURI(url)\n if (skip.indexOf(escaped) >= 0 || root[name] != null) {\n if (!window.requirejs) {\n on_load();\n }\n continue;\n }\n var element = document.createElement('script');\n element.onerror = on_error;\n element.async = false;\n element.type = \"module\";\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n element.textContent = `\n import ${name} from \"${url}\"\n window.${name} = ${name}\n window._bokeh_on_load()\n `\n document.head.appendChild(element);\n }\n if (!js_urls.length && !js_modules.length) {\n on_load()\n }\n };\n\n function inject_raw_css(css) {\n const element = document.createElement(\"style\");\n element.appendChild(document.createTextNode(css));\n document.body.appendChild(element);\n }\n\n const js_urls = [\"https://cdn.holoviz.org/panel/1.5.4/dist/bundled/reactiveesm/es-module-shims@^1.10.0/dist/es-module-shims.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-3.6.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.6.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.6.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.6.2.min.js\", \"https://cdn.holoviz.org/panel/1.5.4/dist/panel.min.js\"];\n const js_modules = [];\n const js_exports = {};\n const css_urls = [];\n const inline_js = [ function(Bokeh) {\n Bokeh.set_log_level(\"info\");\n },\nfunction(Bokeh) {} // ensure no trailing comma for IE\n ];\n\n function run_inline_js() {\n if ((root.Bokeh !== undefined) || (force === true)) {\n for (let i = 0; i < inline_js.length; i++) {\n try {\n inline_js[i].call(root, root.Bokeh);\n } catch(e) {\n if (!reloading) {\n throw e;\n }\n }\n }\n // Cache old bokeh versions\n if (Bokeh != undefined && !reloading) {\n var NewBokeh = root.Bokeh;\n if (Bokeh.versions === undefined) {\n Bokeh.versions = new Map();\n }\n if (NewBokeh.version !== Bokeh.version) {\n Bokeh.versions.set(NewBokeh.version, NewBokeh)\n }\n root.Bokeh = Bokeh;\n }\n } else if (Date.now() < root._bokeh_timeout) {\n setTimeout(run_inline_js, 100);\n } else if (!root._bokeh_failed_load) {\n console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n root._bokeh_failed_load = true;\n }\n root._bokeh_is_initializing = false\n }\n\n function load_or_wait() {\n // Implement a backoff loop that tries to ensure we do not load multiple\n // versions of Bokeh and its dependencies at the same time.\n // In recent versions we use the root._bokeh_is_initializing flag\n // to determine whether there is an ongoing attempt to initialize\n // bokeh, however for backward compatibility we also try to ensure\n // that we do not start loading a newer (Panel>=1.0 and Bokeh>3) version\n // before older versions are fully initialized.\n if (root._bokeh_is_initializing && Date.now() > root._bokeh_timeout) {\n // If the timeout and bokeh was not successfully loaded we reset\n // everything and try loading again\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_is_initializing = false;\n root._bokeh_onload_callbacks = undefined;\n root._bokeh_is_loading = 0\n console.log(\"Bokeh: BokehJS was loaded multiple times but one version failed to initialize.\");\n load_or_wait();\n } else if (root._bokeh_is_initializing || (typeof root._bokeh_is_initializing === \"undefined\" && root._bokeh_onload_callbacks !== undefined)) {\n setTimeout(load_or_wait, 100);\n } else {\n root._bokeh_is_initializing = true\n root._bokeh_onload_callbacks = []\n const bokeh_loaded = root.Bokeh != null && (root.Bokeh.version === py_version || (root.Bokeh.versions !== undefined && root.Bokeh.versions.has(py_version)));\n if (!reloading && !bokeh_loaded) {\n if (root.Bokeh) {\n root.Bokeh = undefined;\n }\n console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n }\n load_libs(css_urls, js_urls, js_modules, js_exports, function() {\n console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n run_inline_js();\n });\n }\n }\n // Give older versions of the autoload script a head-start to ensure\n // they initialize before we start loading newer version.\n setTimeout(load_or_wait, 100)\n}(window));" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/javascript": [ + "\n", + "if ((window.PyViz === undefined) || (window.PyViz instanceof HTMLElement)) {\n", + " window.PyViz = {comms: {}, comm_status:{}, kernels:{}, receivers: {}, plot_index: []}\n", + "}\n", + "\n", + "\n", + " function JupyterCommManager() {\n", + " }\n", + "\n", + " JupyterCommManager.prototype.register_target = function(plot_id, comm_id, msg_handler) {\n", + " if (window.comm_manager || ((window.Jupyter !== undefined) && (Jupyter.notebook.kernel != null))) {\n", + " var comm_manager = window.comm_manager || Jupyter.notebook.kernel.comm_manager;\n", + " comm_manager.register_target(comm_id, function(comm) {\n", + " comm.on_msg(msg_handler);\n", + " });\n", + " } else if ((plot_id in window.PyViz.kernels) && (window.PyViz.kernels[plot_id])) {\n", + " window.PyViz.kernels[plot_id].registerCommTarget(comm_id, function(comm) {\n", + " comm.onMsg = msg_handler;\n", + " });\n", + " } else if (typeof google != 'undefined' && google.colab.kernel != null) {\n", + " google.colab.kernel.comms.registerTarget(comm_id, (comm) => {\n", + " var messages = comm.messages[Symbol.asyncIterator]();\n", + " function processIteratorResult(result) {\n", + " var message = result.value;\n", + " console.log(message)\n", + " var content = {data: message.data, comm_id};\n", + " var buffers = []\n", + " for (var buffer of message.buffers || []) {\n", + " buffers.push(new DataView(buffer))\n", + " }\n", + " var metadata = message.metadata || {};\n", + " var msg = {content, buffers, metadata}\n", + " msg_handler(msg);\n", + " return messages.next().then(processIteratorResult);\n", + " }\n", + " return messages.next().then(processIteratorResult);\n", + " })\n", + " }\n", + " }\n", + "\n", + " JupyterCommManager.prototype.get_client_comm = function(plot_id, comm_id, msg_handler) {\n", + " if (comm_id in window.PyViz.comms) {\n", + " return window.PyViz.comms[comm_id];\n", + " } else if (window.comm_manager || ((window.Jupyter !== undefined) && (Jupyter.notebook.kernel != null))) {\n", + " var comm_manager = window.comm_manager || Jupyter.notebook.kernel.comm_manager;\n", + " var comm = comm_manager.new_comm(comm_id, {}, {}, {}, comm_id);\n", + " if (msg_handler) {\n", + " comm.on_msg(msg_handler);\n", + " }\n", + " } else if ((plot_id in window.PyViz.kernels) && (window.PyViz.kernels[plot_id])) {\n", + " var comm = window.PyViz.kernels[plot_id].connectToComm(comm_id);\n", + " comm.open();\n", + " if (msg_handler) {\n", + " comm.onMsg = msg_handler;\n", + " }\n", + " } else if (typeof google != 'undefined' && google.colab.kernel != null) {\n", + " var comm_promise = google.colab.kernel.comms.open(comm_id)\n", + " comm_promise.then((comm) => {\n", + " window.PyViz.comms[comm_id] = comm;\n", + " if (msg_handler) {\n", + " var messages = comm.messages[Symbol.asyncIterator]();\n", + " function processIteratorResult(result) {\n", + " var message = result.value;\n", + " var content = {data: message.data};\n", + " var metadata = message.metadata || {comm_id};\n", + " var msg = {content, metadata}\n", + " msg_handler(msg);\n", + " return messages.next().then(processIteratorResult);\n", + " }\n", + " return messages.next().then(processIteratorResult);\n", + " }\n", + " }) \n", + " var sendClosure = (data, metadata, buffers, disposeOnDone) => {\n", + " return comm_promise.then((comm) => {\n", + " comm.send(data, metadata, buffers, disposeOnDone);\n", + " });\n", + " };\n", + " var comm = {\n", + " send: sendClosure\n", + " };\n", + " }\n", + " window.PyViz.comms[comm_id] = comm;\n", + " return comm;\n", + " }\n", + " window.PyViz.comm_manager = new JupyterCommManager();\n", + " \n", + "\n", + "\n", + "var JS_MIME_TYPE = 'application/javascript';\n", + "var HTML_MIME_TYPE = 'text/html';\n", + "var EXEC_MIME_TYPE = 'application/vnd.holoviews_exec.v0+json';\n", + "var CLASS_NAME = 'output';\n", + "\n", + "/**\n", + " * Render data to the DOM node\n", + " */\n", + "function render(props, node) {\n", + " var div = document.createElement(\"div\");\n", + " var script = document.createElement(\"script\");\n", + " node.appendChild(div);\n", + " node.appendChild(script);\n", + "}\n", + "\n", + "/**\n", + " * Handle when a new output is added\n", + " */\n", + "function handle_add_output(event, handle) {\n", + " var output_area = handle.output_area;\n", + " var output = handle.output;\n", + " if ((output.data == undefined) || (!output.data.hasOwnProperty(EXEC_MIME_TYPE))) {\n", + " return\n", + " }\n", + " var id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n", + " var toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n", + " if (id !== undefined) {\n", + " var nchildren = toinsert.length;\n", + " var html_node = toinsert[nchildren-1].children[0];\n", + " html_node.innerHTML = output.data[HTML_MIME_TYPE];\n", + " var scripts = [];\n", + " var nodelist = html_node.querySelectorAll(\"script\");\n", + " for (var i in nodelist) {\n", + " if (nodelist.hasOwnProperty(i)) {\n", + " scripts.push(nodelist[i])\n", + " }\n", + " }\n", + "\n", + " scripts.forEach( function (oldScript) {\n", + " var newScript = document.createElement(\"script\");\n", + " var attrs = [];\n", + " var nodemap = oldScript.attributes;\n", + " for (var j in nodemap) {\n", + " if (nodemap.hasOwnProperty(j)) {\n", + " attrs.push(nodemap[j])\n", + " }\n", + " }\n", + " attrs.forEach(function(attr) { newScript.setAttribute(attr.name, attr.value) });\n", + " newScript.appendChild(document.createTextNode(oldScript.innerHTML));\n", + " oldScript.parentNode.replaceChild(newScript, oldScript);\n", + " });\n", + " if (JS_MIME_TYPE in output.data) {\n", + " toinsert[nchildren-1].children[1].textContent = output.data[JS_MIME_TYPE];\n", + " }\n", + " output_area._hv_plot_id = id;\n", + " if ((window.Bokeh !== undefined) && (id in Bokeh.index)) {\n", + " window.PyViz.plot_index[id] = Bokeh.index[id];\n", + " } else {\n", + " window.PyViz.plot_index[id] = null;\n", + " }\n", + " } else if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n", + " var bk_div = document.createElement(\"div\");\n", + " bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n", + " var script_attrs = bk_div.children[0].attributes;\n", + " for (var i = 0; i < script_attrs.length; i++) {\n", + " toinsert[toinsert.length - 1].childNodes[1].setAttribute(script_attrs[i].name, script_attrs[i].value);\n", + " }\n", + " // store reference to server id on output_area\n", + " output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n", + " }\n", + "}\n", + "\n", + "/**\n", + " * Handle when an output is cleared or removed\n", + " */\n", + "function handle_clear_output(event, handle) {\n", + " var id = handle.cell.output_area._hv_plot_id;\n", + " var server_id = handle.cell.output_area._bokeh_server_id;\n", + " if (((id === undefined) || !(id in PyViz.plot_index)) && (server_id !== undefined)) { return; }\n", + " var comm = window.PyViz.comm_manager.get_client_comm(\"hv-extension-comm\", \"hv-extension-comm\", function () {});\n", + " if (server_id !== null) {\n", + " comm.send({event_type: 'server_delete', 'id': server_id});\n", + " return;\n", + " } else if (comm !== null) {\n", + " comm.send({event_type: 'delete', 'id': id});\n", + " }\n", + " delete PyViz.plot_index[id];\n", + " if ((window.Bokeh !== undefined) & (id in window.Bokeh.index)) {\n", + " var doc = window.Bokeh.index[id].model.document\n", + " doc.clear();\n", + " const i = window.Bokeh.documents.indexOf(doc);\n", + " if (i > -1) {\n", + " window.Bokeh.documents.splice(i, 1);\n", + " }\n", + " }\n", + "}\n", + "\n", + "/**\n", + " * Handle kernel restart event\n", + " */\n", + "function handle_kernel_cleanup(event, handle) {\n", + " delete PyViz.comms[\"hv-extension-comm\"];\n", + " window.PyViz.plot_index = {}\n", + "}\n", + "\n", + "/**\n", + " * Handle update_display_data messages\n", + " */\n", + "function handle_update_output(event, handle) {\n", + " handle_clear_output(event, {cell: {output_area: handle.output_area}})\n", + " handle_add_output(event, handle)\n", + "}\n", + "\n", + "function register_renderer(events, OutputArea) {\n", + " function append_mime(data, metadata, element) {\n", + " // create a DOM node to render to\n", + " var toinsert = this.create_output_subarea(\n", + " metadata,\n", + " CLASS_NAME,\n", + " EXEC_MIME_TYPE\n", + " );\n", + " this.keyboard_manager.register_events(toinsert);\n", + " // Render to node\n", + " var props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n", + " render(props, toinsert[0]);\n", + " element.append(toinsert);\n", + " return toinsert\n", + " }\n", + "\n", + " events.on('output_added.OutputArea', handle_add_output);\n", + " events.on('output_updated.OutputArea', handle_update_output);\n", + " events.on('clear_output.CodeCell', handle_clear_output);\n", + " events.on('delete.Cell', handle_clear_output);\n", + " events.on('kernel_ready.Kernel', handle_kernel_cleanup);\n", + "\n", + " OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n", + " safe: true,\n", + " index: 0\n", + " });\n", + "}\n", + "\n", + "if (window.Jupyter !== undefined) {\n", + " try {\n", + " var events = require('base/js/events');\n", + " var OutputArea = require('notebook/js/outputarea').OutputArea;\n", + " if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n", + " register_renderer(events, OutputArea);\n", + " }\n", + " } catch(err) {\n", + " }\n", + "}\n" + ], + "application/vnd.holoviews_load.v0+json": "\nif ((window.PyViz === undefined) || (window.PyViz instanceof HTMLElement)) {\n window.PyViz = {comms: {}, comm_status:{}, kernels:{}, receivers: {}, plot_index: []}\n}\n\n\n function JupyterCommManager() {\n }\n\n JupyterCommManager.prototype.register_target = function(plot_id, comm_id, msg_handler) {\n if (window.comm_manager || ((window.Jupyter !== undefined) && (Jupyter.notebook.kernel != null))) {\n var comm_manager = window.comm_manager || Jupyter.notebook.kernel.comm_manager;\n comm_manager.register_target(comm_id, function(comm) {\n comm.on_msg(msg_handler);\n });\n } else if ((plot_id in window.PyViz.kernels) && (window.PyViz.kernels[plot_id])) {\n window.PyViz.kernels[plot_id].registerCommTarget(comm_id, function(comm) {\n comm.onMsg = msg_handler;\n });\n } else if (typeof google != 'undefined' && google.colab.kernel != null) {\n google.colab.kernel.comms.registerTarget(comm_id, (comm) => {\n var messages = comm.messages[Symbol.asyncIterator]();\n function processIteratorResult(result) {\n var message = result.value;\n console.log(message)\n var content = {data: message.data, comm_id};\n var buffers = []\n for (var buffer of message.buffers || []) {\n buffers.push(new DataView(buffer))\n }\n var metadata = message.metadata || {};\n var msg = {content, buffers, metadata}\n msg_handler(msg);\n return messages.next().then(processIteratorResult);\n }\n return messages.next().then(processIteratorResult);\n })\n }\n }\n\n JupyterCommManager.prototype.get_client_comm = function(plot_id, comm_id, msg_handler) {\n if (comm_id in window.PyViz.comms) {\n return window.PyViz.comms[comm_id];\n } else if (window.comm_manager || ((window.Jupyter !== undefined) && (Jupyter.notebook.kernel != null))) {\n var comm_manager = window.comm_manager || Jupyter.notebook.kernel.comm_manager;\n var comm = comm_manager.new_comm(comm_id, {}, {}, {}, comm_id);\n if (msg_handler) {\n comm.on_msg(msg_handler);\n }\n } else if ((plot_id in window.PyViz.kernels) && (window.PyViz.kernels[plot_id])) {\n var comm = window.PyViz.kernels[plot_id].connectToComm(comm_id);\n comm.open();\n if (msg_handler) {\n comm.onMsg = msg_handler;\n }\n } else if (typeof google != 'undefined' && google.colab.kernel != null) {\n var comm_promise = google.colab.kernel.comms.open(comm_id)\n comm_promise.then((comm) => {\n window.PyViz.comms[comm_id] = comm;\n if (msg_handler) {\n var messages = comm.messages[Symbol.asyncIterator]();\n function processIteratorResult(result) {\n var message = result.value;\n var content = {data: message.data};\n var metadata = message.metadata || {comm_id};\n var msg = {content, metadata}\n msg_handler(msg);\n return messages.next().then(processIteratorResult);\n }\n return messages.next().then(processIteratorResult);\n }\n }) \n var sendClosure = (data, metadata, buffers, disposeOnDone) => {\n return comm_promise.then((comm) => {\n comm.send(data, metadata, buffers, disposeOnDone);\n });\n };\n var comm = {\n send: sendClosure\n };\n }\n window.PyViz.comms[comm_id] = comm;\n return comm;\n }\n window.PyViz.comm_manager = new JupyterCommManager();\n \n\n\nvar JS_MIME_TYPE = 'application/javascript';\nvar HTML_MIME_TYPE = 'text/html';\nvar EXEC_MIME_TYPE = 'application/vnd.holoviews_exec.v0+json';\nvar CLASS_NAME = 'output';\n\n/**\n * Render data to the DOM node\n */\nfunction render(props, node) {\n var div = document.createElement(\"div\");\n var script = document.createElement(\"script\");\n node.appendChild(div);\n node.appendChild(script);\n}\n\n/**\n * Handle when a new output is added\n */\nfunction handle_add_output(event, handle) {\n var output_area = handle.output_area;\n var output = handle.output;\n if ((output.data == undefined) || (!output.data.hasOwnProperty(EXEC_MIME_TYPE))) {\n return\n }\n var id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n var toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n if (id !== undefined) {\n var nchildren = toinsert.length;\n var html_node = toinsert[nchildren-1].children[0];\n html_node.innerHTML = output.data[HTML_MIME_TYPE];\n var scripts = [];\n var nodelist = html_node.querySelectorAll(\"script\");\n for (var i in nodelist) {\n if (nodelist.hasOwnProperty(i)) {\n scripts.push(nodelist[i])\n }\n }\n\n scripts.forEach( function (oldScript) {\n var newScript = document.createElement(\"script\");\n var attrs = [];\n var nodemap = oldScript.attributes;\n for (var j in nodemap) {\n if (nodemap.hasOwnProperty(j)) {\n attrs.push(nodemap[j])\n }\n }\n attrs.forEach(function(attr) { newScript.setAttribute(attr.name, attr.value) });\n newScript.appendChild(document.createTextNode(oldScript.innerHTML));\n oldScript.parentNode.replaceChild(newScript, oldScript);\n });\n if (JS_MIME_TYPE in output.data) {\n toinsert[nchildren-1].children[1].textContent = output.data[JS_MIME_TYPE];\n }\n output_area._hv_plot_id = id;\n if ((window.Bokeh !== undefined) && (id in Bokeh.index)) {\n window.PyViz.plot_index[id] = Bokeh.index[id];\n } else {\n window.PyViz.plot_index[id] = null;\n }\n } else if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n var bk_div = document.createElement(\"div\");\n bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n var script_attrs = bk_div.children[0].attributes;\n for (var i = 0; i < script_attrs.length; i++) {\n toinsert[toinsert.length - 1].childNodes[1].setAttribute(script_attrs[i].name, script_attrs[i].value);\n }\n // store reference to server id on output_area\n output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n }\n}\n\n/**\n * Handle when an output is cleared or removed\n */\nfunction handle_clear_output(event, handle) {\n var id = handle.cell.output_area._hv_plot_id;\n var server_id = handle.cell.output_area._bokeh_server_id;\n if (((id === undefined) || !(id in PyViz.plot_index)) && (server_id !== undefined)) { return; }\n var comm = window.PyViz.comm_manager.get_client_comm(\"hv-extension-comm\", \"hv-extension-comm\", function () {});\n if (server_id !== null) {\n comm.send({event_type: 'server_delete', 'id': server_id});\n return;\n } else if (comm !== null) {\n comm.send({event_type: 'delete', 'id': id});\n }\n delete PyViz.plot_index[id];\n if ((window.Bokeh !== undefined) & (id in window.Bokeh.index)) {\n var doc = window.Bokeh.index[id].model.document\n doc.clear();\n const i = window.Bokeh.documents.indexOf(doc);\n if (i > -1) {\n window.Bokeh.documents.splice(i, 1);\n }\n }\n}\n\n/**\n * Handle kernel restart event\n */\nfunction handle_kernel_cleanup(event, handle) {\n delete PyViz.comms[\"hv-extension-comm\"];\n window.PyViz.plot_index = {}\n}\n\n/**\n * Handle update_display_data messages\n */\nfunction handle_update_output(event, handle) {\n handle_clear_output(event, {cell: {output_area: handle.output_area}})\n handle_add_output(event, handle)\n}\n\nfunction register_renderer(events, OutputArea) {\n function append_mime(data, metadata, element) {\n // create a DOM node to render to\n var toinsert = this.create_output_subarea(\n metadata,\n CLASS_NAME,\n EXEC_MIME_TYPE\n );\n this.keyboard_manager.register_events(toinsert);\n // Render to node\n var props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n render(props, toinsert[0]);\n element.append(toinsert);\n return toinsert\n }\n\n events.on('output_added.OutputArea', handle_add_output);\n events.on('output_updated.OutputArea', handle_update_output);\n events.on('clear_output.CodeCell', handle_clear_output);\n events.on('delete.Cell', handle_clear_output);\n events.on('kernel_ready.Kernel', handle_kernel_cleanup);\n\n OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n safe: true,\n index: 0\n });\n}\n\nif (window.Jupyter !== undefined) {\n try {\n var events = require('base/js/events');\n var OutputArea = require('notebook/js/outputarea').OutputArea;\n if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n register_renderer(events, OutputArea);\n }\n } catch(err) {\n }\n}\n" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.holoviews_exec.v0+json": "", + "text/html": [ + "
\n", + "
\n", + "
\n", + "" + ] + }, + "metadata": { + "application/vnd.holoviews_exec.v0+json": { + "id": "0c5bc51a-1f13-430c-9f2f-e79a4f480c50" + } + }, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/javascript": [ + "(function(root) {\n", + " function now() {\n", + " return new Date();\n", + " }\n", + "\n", + " const force = false;\n", + " const py_version = '3.6.2'.replace('rc', '-rc.').replace('.dev', '-dev.');\n", + " const reloading = true;\n", + " const Bokeh = root.Bokeh;\n", + "\n", + " // Set a timeout for this load but only if we are not already initializing\n", + " if (typeof (root._bokeh_timeout) === \"undefined\" || (force || !root._bokeh_is_initializing)) {\n", + " root._bokeh_timeout = Date.now() + 5000;\n", + " root._bokeh_failed_load = false;\n", + " }\n", + "\n", + " function run_callbacks() {\n", + " try {\n", + " root._bokeh_onload_callbacks.forEach(function(callback) {\n", + " if (callback != null)\n", + " callback();\n", + " });\n", + " } finally {\n", + " delete root._bokeh_onload_callbacks;\n", + " }\n", + " console.debug(\"Bokeh: all callbacks have finished\");\n", + " }\n", + "\n", + " function load_libs(css_urls, js_urls, js_modules, js_exports, callback) {\n", + " if (css_urls == null) css_urls = [];\n", + " if (js_urls == null) js_urls = [];\n", + " if (js_modules == null) js_modules = [];\n", + " if (js_exports == null) js_exports = {};\n", + "\n", + " root._bokeh_onload_callbacks.push(callback);\n", + "\n", + " if (root._bokeh_is_loading > 0) {\n", + " // Don't load bokeh if it is still initializing\n", + " console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n", + " return null;\n", + " } else if (js_urls.length === 0 && js_modules.length === 0 && Object.keys(js_exports).length === 0) {\n", + " // There is nothing to load\n", + " run_callbacks();\n", + " return null;\n", + " }\n", + "\n", + " function on_load() {\n", + " root._bokeh_is_loading--;\n", + " if (root._bokeh_is_loading === 0) {\n", + " console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n", + " run_callbacks()\n", + " }\n", + " }\n", + " window._bokeh_on_load = on_load\n", + "\n", + " function on_error(e) {\n", + " const src_el = e.srcElement\n", + " console.error(\"failed to load \" + (src_el.href || src_el.src));\n", + " }\n", + "\n", + " const skip = [];\n", + " if (window.requirejs) {\n", + " window.requirejs.config({'packages': {}, 'paths': {}, 'shim': {}});\n", + " root._bokeh_is_loading = css_urls.length + 0;\n", + " } else {\n", + " root._bokeh_is_loading = css_urls.length + js_urls.length + js_modules.length + Object.keys(js_exports).length;\n", + " }\n", + "\n", + " const existing_stylesheets = []\n", + " const links = document.getElementsByTagName('link')\n", + " for (let i = 0; i < links.length; i++) {\n", + " const link = links[i]\n", + " if (link.href != null) {\n", + " existing_stylesheets.push(link.href)\n", + " }\n", + " }\n", + " for (let i = 0; i < css_urls.length; i++) {\n", + " const url = css_urls[i];\n", + " const escaped = encodeURI(url)\n", + " if (existing_stylesheets.indexOf(escaped) !== -1) {\n", + " on_load()\n", + " continue;\n", + " }\n", + " const element = document.createElement(\"link\");\n", + " element.onload = on_load;\n", + " element.onerror = on_error;\n", + " element.rel = \"stylesheet\";\n", + " element.type = \"text/css\";\n", + " element.href = url;\n", + " console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n", + " document.body.appendChild(element);\n", + " } var existing_scripts = []\n", + " const scripts = document.getElementsByTagName('script')\n", + " for (let i = 0; i < scripts.length; i++) {\n", + " var script = scripts[i]\n", + " if (script.src != null) {\n", + " existing_scripts.push(script.src)\n", + " }\n", + " }\n", + " for (let i = 0; i < js_urls.length; i++) {\n", + " const url = js_urls[i];\n", + " const escaped = encodeURI(url)\n", + " if (skip.indexOf(escaped) !== -1 || existing_scripts.indexOf(escaped) !== -1) {\n", + " if (!window.requirejs) {\n", + " on_load();\n", + " }\n", + " continue;\n", + " }\n", + " const element = document.createElement('script');\n", + " element.onload = on_load;\n", + " element.onerror = on_error;\n", + " element.async = false;\n", + " element.src = url;\n", + " console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n", + " document.head.appendChild(element);\n", + " }\n", + " for (let i = 0; i < js_modules.length; i++) {\n", + " const url = js_modules[i];\n", + " const escaped = encodeURI(url)\n", + " if (skip.indexOf(escaped) !== -1 || existing_scripts.indexOf(escaped) !== -1) {\n", + " if (!window.requirejs) {\n", + " on_load();\n", + " }\n", + " continue;\n", + " }\n", + " var element = document.createElement('script');\n", + " element.onload = on_load;\n", + " element.onerror = on_error;\n", + " element.async = false;\n", + " element.src = url;\n", + " element.type = \"module\";\n", + " console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n", + " document.head.appendChild(element);\n", + " }\n", + " for (const name in js_exports) {\n", + " const url = js_exports[name];\n", + " const escaped = encodeURI(url)\n", + " if (skip.indexOf(escaped) >= 0 || root[name] != null) {\n", + " if (!window.requirejs) {\n", + " on_load();\n", + " }\n", + " continue;\n", + " }\n", + " var element = document.createElement('script');\n", + " element.onerror = on_error;\n", + " element.async = false;\n", + " element.type = \"module\";\n", + " console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n", + " element.textContent = `\n", + " import ${name} from \"${url}\"\n", + " window.${name} = ${name}\n", + " window._bokeh_on_load()\n", + " `\n", + " document.head.appendChild(element);\n", + " }\n", + " if (!js_urls.length && !js_modules.length) {\n", + " on_load()\n", + " }\n", + " };\n", + "\n", + " function inject_raw_css(css) {\n", + " const element = document.createElement(\"style\");\n", + " element.appendChild(document.createTextNode(css));\n", + " document.body.appendChild(element);\n", + " }\n", + "\n", + " const js_urls = [\"https://cdn.holoviz.org/panel/1.5.4/dist/bundled/reactiveesm/es-module-shims@^1.10.0/dist/es-module-shims.min.js\"];\n", + " const js_modules = [];\n", + " const js_exports = {};\n", + " const css_urls = [];\n", + " const inline_js = [ function(Bokeh) {\n", + " Bokeh.set_log_level(\"info\");\n", + " },\n", + "function(Bokeh) {} // ensure no trailing comma for IE\n", + " ];\n", + "\n", + " function run_inline_js() {\n", + " if ((root.Bokeh !== undefined) || (force === true)) {\n", + " for (let i = 0; i < inline_js.length; i++) {\n", + " try {\n", + " inline_js[i].call(root, root.Bokeh);\n", + " } catch(e) {\n", + " if (!reloading) {\n", + " throw e;\n", + " }\n", + " }\n", + " }\n", + " // Cache old bokeh versions\n", + " if (Bokeh != undefined && !reloading) {\n", + " var NewBokeh = root.Bokeh;\n", + " if (Bokeh.versions === undefined) {\n", + " Bokeh.versions = new Map();\n", + " }\n", + " if (NewBokeh.version !== Bokeh.version) {\n", + " Bokeh.versions.set(NewBokeh.version, NewBokeh)\n", + " }\n", + " root.Bokeh = Bokeh;\n", + " }\n", + " } else if (Date.now() < root._bokeh_timeout) {\n", + " setTimeout(run_inline_js, 100);\n", + " } else if (!root._bokeh_failed_load) {\n", + " console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n", + " root._bokeh_failed_load = true;\n", + " }\n", + " root._bokeh_is_initializing = false\n", + " }\n", + "\n", + " function load_or_wait() {\n", + " // Implement a backoff loop that tries to ensure we do not load multiple\n", + " // versions of Bokeh and its dependencies at the same time.\n", + " // In recent versions we use the root._bokeh_is_initializing flag\n", + " // to determine whether there is an ongoing attempt to initialize\n", + " // bokeh, however for backward compatibility we also try to ensure\n", + " // that we do not start loading a newer (Panel>=1.0 and Bokeh>3) version\n", + " // before older versions are fully initialized.\n", + " if (root._bokeh_is_initializing && Date.now() > root._bokeh_timeout) {\n", + " // If the timeout and bokeh was not successfully loaded we reset\n", + " // everything and try loading again\n", + " root._bokeh_timeout = Date.now() + 5000;\n", + " root._bokeh_is_initializing = false;\n", + " root._bokeh_onload_callbacks = undefined;\n", + " root._bokeh_is_loading = 0\n", + " console.log(\"Bokeh: BokehJS was loaded multiple times but one version failed to initialize.\");\n", + " load_or_wait();\n", + " } else if (root._bokeh_is_initializing || (typeof root._bokeh_is_initializing === \"undefined\" && root._bokeh_onload_callbacks !== undefined)) {\n", + " setTimeout(load_or_wait, 100);\n", + " } else {\n", + " root._bokeh_is_initializing = true\n", + " root._bokeh_onload_callbacks = []\n", + " const bokeh_loaded = root.Bokeh != null && (root.Bokeh.version === py_version || (root.Bokeh.versions !== undefined && root.Bokeh.versions.has(py_version)));\n", + " if (!reloading && !bokeh_loaded) {\n", + " if (root.Bokeh) {\n", + " root.Bokeh = undefined;\n", + " }\n", + " console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n", + " }\n", + " load_libs(css_urls, js_urls, js_modules, js_exports, function() {\n", + " console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n", + " run_inline_js();\n", + " });\n", + " }\n", + " }\n", + " // Give older versions of the autoload script a head-start to ensure\n", + " // they initialize before we start loading newer version.\n", + " setTimeout(load_or_wait, 100)\n", + "}(window));" + ], + "application/vnd.holoviews_load.v0+json": "(function(root) {\n function now() {\n return new Date();\n }\n\n const force = false;\n const py_version = '3.6.2'.replace('rc', '-rc.').replace('.dev', '-dev.');\n const reloading = true;\n const Bokeh = root.Bokeh;\n\n // Set a timeout for this load but only if we are not already initializing\n if (typeof (root._bokeh_timeout) === \"undefined\" || (force || !root._bokeh_is_initializing)) {\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_failed_load = false;\n }\n\n function run_callbacks() {\n try {\n root._bokeh_onload_callbacks.forEach(function(callback) {\n if (callback != null)\n callback();\n });\n } finally {\n delete root._bokeh_onload_callbacks;\n }\n console.debug(\"Bokeh: all callbacks have finished\");\n }\n\n function load_libs(css_urls, js_urls, js_modules, js_exports, callback) {\n if (css_urls == null) css_urls = [];\n if (js_urls == null) js_urls = [];\n if (js_modules == null) js_modules = [];\n if (js_exports == null) js_exports = {};\n\n root._bokeh_onload_callbacks.push(callback);\n\n if (root._bokeh_is_loading > 0) {\n // Don't load bokeh if it is still initializing\n console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n return null;\n } else if (js_urls.length === 0 && js_modules.length === 0 && Object.keys(js_exports).length === 0) {\n // There is nothing to load\n run_callbacks();\n return null;\n }\n\n function on_load() {\n root._bokeh_is_loading--;\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n run_callbacks()\n }\n }\n window._bokeh_on_load = on_load\n\n function on_error(e) {\n const src_el = e.srcElement\n console.error(\"failed to load \" + (src_el.href || src_el.src));\n }\n\n const skip = [];\n if (window.requirejs) {\n window.requirejs.config({'packages': {}, 'paths': {}, 'shim': {}});\n root._bokeh_is_loading = css_urls.length + 0;\n } else {\n root._bokeh_is_loading = css_urls.length + js_urls.length + js_modules.length + Object.keys(js_exports).length;\n }\n\n const existing_stylesheets = []\n const links = document.getElementsByTagName('link')\n for (let i = 0; i < links.length; i++) {\n const link = links[i]\n if (link.href != null) {\n existing_stylesheets.push(link.href)\n }\n }\n for (let i = 0; i < css_urls.length; i++) {\n const url = css_urls[i];\n const escaped = encodeURI(url)\n if (existing_stylesheets.indexOf(escaped) !== -1) {\n on_load()\n continue;\n }\n const element = document.createElement(\"link\");\n element.onload = on_load;\n element.onerror = on_error;\n element.rel = \"stylesheet\";\n element.type = \"text/css\";\n element.href = url;\n console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n document.body.appendChild(element);\n } var existing_scripts = []\n const scripts = document.getElementsByTagName('script')\n for (let i = 0; i < scripts.length; i++) {\n var script = scripts[i]\n if (script.src != null) {\n existing_scripts.push(script.src)\n }\n }\n for (let i = 0; i < js_urls.length; i++) {\n const url = js_urls[i];\n const escaped = encodeURI(url)\n if (skip.indexOf(escaped) !== -1 || existing_scripts.indexOf(escaped) !== -1) {\n if (!window.requirejs) {\n on_load();\n }\n continue;\n }\n const element = document.createElement('script');\n element.onload = on_load;\n element.onerror = on_error;\n element.async = false;\n element.src = url;\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.head.appendChild(element);\n }\n for (let i = 0; i < js_modules.length; i++) {\n const url = js_modules[i];\n const escaped = encodeURI(url)\n if (skip.indexOf(escaped) !== -1 || existing_scripts.indexOf(escaped) !== -1) {\n if (!window.requirejs) {\n on_load();\n }\n continue;\n }\n var element = document.createElement('script');\n element.onload = on_load;\n element.onerror = on_error;\n element.async = false;\n element.src = url;\n element.type = \"module\";\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.head.appendChild(element);\n }\n for (const name in js_exports) {\n const url = js_exports[name];\n const escaped = encodeURI(url)\n if (skip.indexOf(escaped) >= 0 || root[name] != null) {\n if (!window.requirejs) {\n on_load();\n }\n continue;\n }\n var element = document.createElement('script');\n element.onerror = on_error;\n element.async = false;\n element.type = \"module\";\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n element.textContent = `\n import ${name} from \"${url}\"\n window.${name} = ${name}\n window._bokeh_on_load()\n `\n document.head.appendChild(element);\n }\n if (!js_urls.length && !js_modules.length) {\n on_load()\n }\n };\n\n function inject_raw_css(css) {\n const element = document.createElement(\"style\");\n element.appendChild(document.createTextNode(css));\n document.body.appendChild(element);\n }\n\n const js_urls = [\"https://cdn.holoviz.org/panel/1.5.4/dist/bundled/reactiveesm/es-module-shims@^1.10.0/dist/es-module-shims.min.js\"];\n const js_modules = [];\n const js_exports = {};\n const css_urls = [];\n const inline_js = [ function(Bokeh) {\n Bokeh.set_log_level(\"info\");\n },\nfunction(Bokeh) {} // ensure no trailing comma for IE\n ];\n\n function run_inline_js() {\n if ((root.Bokeh !== undefined) || (force === true)) {\n for (let i = 0; i < inline_js.length; i++) {\n try {\n inline_js[i].call(root, root.Bokeh);\n } catch(e) {\n if (!reloading) {\n throw e;\n }\n }\n }\n // Cache old bokeh versions\n if (Bokeh != undefined && !reloading) {\n var NewBokeh = root.Bokeh;\n if (Bokeh.versions === undefined) {\n Bokeh.versions = new Map();\n }\n if (NewBokeh.version !== Bokeh.version) {\n Bokeh.versions.set(NewBokeh.version, NewBokeh)\n }\n root.Bokeh = Bokeh;\n }\n } else if (Date.now() < root._bokeh_timeout) {\n setTimeout(run_inline_js, 100);\n } else if (!root._bokeh_failed_load) {\n console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n root._bokeh_failed_load = true;\n }\n root._bokeh_is_initializing = false\n }\n\n function load_or_wait() {\n // Implement a backoff loop that tries to ensure we do not load multiple\n // versions of Bokeh and its dependencies at the same time.\n // In recent versions we use the root._bokeh_is_initializing flag\n // to determine whether there is an ongoing attempt to initialize\n // bokeh, however for backward compatibility we also try to ensure\n // that we do not start loading a newer (Panel>=1.0 and Bokeh>3) version\n // before older versions are fully initialized.\n if (root._bokeh_is_initializing && Date.now() > root._bokeh_timeout) {\n // If the timeout and bokeh was not successfully loaded we reset\n // everything and try loading again\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_is_initializing = false;\n root._bokeh_onload_callbacks = undefined;\n root._bokeh_is_loading = 0\n console.log(\"Bokeh: BokehJS was loaded multiple times but one version failed to initialize.\");\n load_or_wait();\n } else if (root._bokeh_is_initializing || (typeof root._bokeh_is_initializing === \"undefined\" && root._bokeh_onload_callbacks !== undefined)) {\n setTimeout(load_or_wait, 100);\n } else {\n root._bokeh_is_initializing = true\n root._bokeh_onload_callbacks = []\n const bokeh_loaded = root.Bokeh != null && (root.Bokeh.version === py_version || (root.Bokeh.versions !== undefined && root.Bokeh.versions.has(py_version)));\n if (!reloading && !bokeh_loaded) {\n if (root.Bokeh) {\n root.Bokeh = undefined;\n }\n console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n }\n load_libs(css_urls, js_urls, js_modules, js_exports, function() {\n console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n run_inline_js();\n });\n }\n }\n // Give older versions of the autoload script a head-start to ensure\n // they initialize before we start loading newer version.\n setTimeout(load_or_wait, 100)\n}(window));" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/javascript": [ + "\n", + "if ((window.PyViz === undefined) || (window.PyViz instanceof HTMLElement)) {\n", + " window.PyViz = {comms: {}, comm_status:{}, kernels:{}, receivers: {}, plot_index: []}\n", + "}\n", + "\n", + "\n", + " function JupyterCommManager() {\n", + " }\n", + "\n", + " JupyterCommManager.prototype.register_target = function(plot_id, comm_id, msg_handler) {\n", + " if (window.comm_manager || ((window.Jupyter !== undefined) && (Jupyter.notebook.kernel != null))) {\n", + " var comm_manager = window.comm_manager || Jupyter.notebook.kernel.comm_manager;\n", + " comm_manager.register_target(comm_id, function(comm) {\n", + " comm.on_msg(msg_handler);\n", + " });\n", + " } else if ((plot_id in window.PyViz.kernels) && (window.PyViz.kernels[plot_id])) {\n", + " window.PyViz.kernels[plot_id].registerCommTarget(comm_id, function(comm) {\n", + " comm.onMsg = msg_handler;\n", + " });\n", + " } else if (typeof google != 'undefined' && google.colab.kernel != null) {\n", + " google.colab.kernel.comms.registerTarget(comm_id, (comm) => {\n", + " var messages = comm.messages[Symbol.asyncIterator]();\n", + " function processIteratorResult(result) {\n", + " var message = result.value;\n", + " console.log(message)\n", + " var content = {data: message.data, comm_id};\n", + " var buffers = []\n", + " for (var buffer of message.buffers || []) {\n", + " buffers.push(new DataView(buffer))\n", + " }\n", + " var metadata = message.metadata || {};\n", + " var msg = {content, buffers, metadata}\n", + " msg_handler(msg);\n", + " return messages.next().then(processIteratorResult);\n", + " }\n", + " return messages.next().then(processIteratorResult);\n", + " })\n", + " }\n", + " }\n", + "\n", + " JupyterCommManager.prototype.get_client_comm = function(plot_id, comm_id, msg_handler) {\n", + " if (comm_id in window.PyViz.comms) {\n", + " return window.PyViz.comms[comm_id];\n", + " } else if (window.comm_manager || ((window.Jupyter !== undefined) && (Jupyter.notebook.kernel != null))) {\n", + " var comm_manager = window.comm_manager || Jupyter.notebook.kernel.comm_manager;\n", + " var comm = comm_manager.new_comm(comm_id, {}, {}, {}, comm_id);\n", + " if (msg_handler) {\n", + " comm.on_msg(msg_handler);\n", + " }\n", + " } else if ((plot_id in window.PyViz.kernels) && (window.PyViz.kernels[plot_id])) {\n", + " var comm = window.PyViz.kernels[plot_id].connectToComm(comm_id);\n", + " comm.open();\n", + " if (msg_handler) {\n", + " comm.onMsg = msg_handler;\n", + " }\n", + " } else if (typeof google != 'undefined' && google.colab.kernel != null) {\n", + " var comm_promise = google.colab.kernel.comms.open(comm_id)\n", + " comm_promise.then((comm) => {\n", + " window.PyViz.comms[comm_id] = comm;\n", + " if (msg_handler) {\n", + " var messages = comm.messages[Symbol.asyncIterator]();\n", + " function processIteratorResult(result) {\n", + " var message = result.value;\n", + " var content = {data: message.data};\n", + " var metadata = message.metadata || {comm_id};\n", + " var msg = {content, metadata}\n", + " msg_handler(msg);\n", + " return messages.next().then(processIteratorResult);\n", + " }\n", + " return messages.next().then(processIteratorResult);\n", + " }\n", + " }) \n", + " var sendClosure = (data, metadata, buffers, disposeOnDone) => {\n", + " return comm_promise.then((comm) => {\n", + " comm.send(data, metadata, buffers, disposeOnDone);\n", + " });\n", + " };\n", + " var comm = {\n", + " send: sendClosure\n", + " };\n", + " }\n", + " window.PyViz.comms[comm_id] = comm;\n", + " return comm;\n", + " }\n", + " window.PyViz.comm_manager = new JupyterCommManager();\n", + " \n", + "\n", + "\n", + "var JS_MIME_TYPE = 'application/javascript';\n", + "var HTML_MIME_TYPE = 'text/html';\n", + "var EXEC_MIME_TYPE = 'application/vnd.holoviews_exec.v0+json';\n", + "var CLASS_NAME = 'output';\n", + "\n", + "/**\n", + " * Render data to the DOM node\n", + " */\n", + "function render(props, node) {\n", + " var div = document.createElement(\"div\");\n", + " var script = document.createElement(\"script\");\n", + " node.appendChild(div);\n", + " node.appendChild(script);\n", + "}\n", + "\n", + "/**\n", + " * Handle when a new output is added\n", + " */\n", + "function handle_add_output(event, handle) {\n", + " var output_area = handle.output_area;\n", + " var output = handle.output;\n", + " if ((output.data == undefined) || (!output.data.hasOwnProperty(EXEC_MIME_TYPE))) {\n", + " return\n", + " }\n", + " var id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n", + " var toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n", + " if (id !== undefined) {\n", + " var nchildren = toinsert.length;\n", + " var html_node = toinsert[nchildren-1].children[0];\n", + " html_node.innerHTML = output.data[HTML_MIME_TYPE];\n", + " var scripts = [];\n", + " var nodelist = html_node.querySelectorAll(\"script\");\n", + " for (var i in nodelist) {\n", + " if (nodelist.hasOwnProperty(i)) {\n", + " scripts.push(nodelist[i])\n", + " }\n", + " }\n", + "\n", + " scripts.forEach( function (oldScript) {\n", + " var newScript = document.createElement(\"script\");\n", + " var attrs = [];\n", + " var nodemap = oldScript.attributes;\n", + " for (var j in nodemap) {\n", + " if (nodemap.hasOwnProperty(j)) {\n", + " attrs.push(nodemap[j])\n", + " }\n", + " }\n", + " attrs.forEach(function(attr) { newScript.setAttribute(attr.name, attr.value) });\n", + " newScript.appendChild(document.createTextNode(oldScript.innerHTML));\n", + " oldScript.parentNode.replaceChild(newScript, oldScript);\n", + " });\n", + " if (JS_MIME_TYPE in output.data) {\n", + " toinsert[nchildren-1].children[1].textContent = output.data[JS_MIME_TYPE];\n", + " }\n", + " output_area._hv_plot_id = id;\n", + " if ((window.Bokeh !== undefined) && (id in Bokeh.index)) {\n", + " window.PyViz.plot_index[id] = Bokeh.index[id];\n", + " } else {\n", + " window.PyViz.plot_index[id] = null;\n", + " }\n", + " } else if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n", + " var bk_div = document.createElement(\"div\");\n", + " bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n", + " var script_attrs = bk_div.children[0].attributes;\n", + " for (var i = 0; i < script_attrs.length; i++) {\n", + " toinsert[toinsert.length - 1].childNodes[1].setAttribute(script_attrs[i].name, script_attrs[i].value);\n", + " }\n", + " // store reference to server id on output_area\n", + " output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n", + " }\n", + "}\n", + "\n", + "/**\n", + " * Handle when an output is cleared or removed\n", + " */\n", + "function handle_clear_output(event, handle) {\n", + " var id = handle.cell.output_area._hv_plot_id;\n", + " var server_id = handle.cell.output_area._bokeh_server_id;\n", + " if (((id === undefined) || !(id in PyViz.plot_index)) && (server_id !== undefined)) { return; }\n", + " var comm = window.PyViz.comm_manager.get_client_comm(\"hv-extension-comm\", \"hv-extension-comm\", function () {});\n", + " if (server_id !== null) {\n", + " comm.send({event_type: 'server_delete', 'id': server_id});\n", + " return;\n", + " } else if (comm !== null) {\n", + " comm.send({event_type: 'delete', 'id': id});\n", + " }\n", + " delete PyViz.plot_index[id];\n", + " if ((window.Bokeh !== undefined) & (id in window.Bokeh.index)) {\n", + " var doc = window.Bokeh.index[id].model.document\n", + " doc.clear();\n", + " const i = window.Bokeh.documents.indexOf(doc);\n", + " if (i > -1) {\n", + " window.Bokeh.documents.splice(i, 1);\n", + " }\n", + " }\n", + "}\n", + "\n", + "/**\n", + " * Handle kernel restart event\n", + " */\n", + "function handle_kernel_cleanup(event, handle) {\n", + " delete PyViz.comms[\"hv-extension-comm\"];\n", + " window.PyViz.plot_index = {}\n", + "}\n", + "\n", + "/**\n", + " * Handle update_display_data messages\n", + " */\n", + "function handle_update_output(event, handle) {\n", + " handle_clear_output(event, {cell: {output_area: handle.output_area}})\n", + " handle_add_output(event, handle)\n", + "}\n", + "\n", + "function register_renderer(events, OutputArea) {\n", + " function append_mime(data, metadata, element) {\n", + " // create a DOM node to render to\n", + " var toinsert = this.create_output_subarea(\n", + " metadata,\n", + " CLASS_NAME,\n", + " EXEC_MIME_TYPE\n", + " );\n", + " this.keyboard_manager.register_events(toinsert);\n", + " // Render to node\n", + " var props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n", + " render(props, toinsert[0]);\n", + " element.append(toinsert);\n", + " return toinsert\n", + " }\n", + "\n", + " events.on('output_added.OutputArea', handle_add_output);\n", + " events.on('output_updated.OutputArea', handle_update_output);\n", + " events.on('clear_output.CodeCell', handle_clear_output);\n", + " events.on('delete.Cell', handle_clear_output);\n", + " events.on('kernel_ready.Kernel', handle_kernel_cleanup);\n", + "\n", + " OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n", + " safe: true,\n", + " index: 0\n", + " });\n", + "}\n", + "\n", + "if (window.Jupyter !== undefined) {\n", + " try {\n", + " var events = require('base/js/events');\n", + " var OutputArea = require('notebook/js/outputarea').OutputArea;\n", + " if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n", + " register_renderer(events, OutputArea);\n", + " }\n", + " } catch(err) {\n", + " }\n", + "}\n" + ], + "application/vnd.holoviews_load.v0+json": "\nif ((window.PyViz === undefined) || (window.PyViz instanceof HTMLElement)) {\n window.PyViz = {comms: {}, comm_status:{}, kernels:{}, receivers: {}, plot_index: []}\n}\n\n\n function JupyterCommManager() {\n }\n\n JupyterCommManager.prototype.register_target = function(plot_id, comm_id, msg_handler) {\n if (window.comm_manager || ((window.Jupyter !== undefined) && (Jupyter.notebook.kernel != null))) {\n var comm_manager = window.comm_manager || Jupyter.notebook.kernel.comm_manager;\n comm_manager.register_target(comm_id, function(comm) {\n comm.on_msg(msg_handler);\n });\n } else if ((plot_id in window.PyViz.kernels) && (window.PyViz.kernels[plot_id])) {\n window.PyViz.kernels[plot_id].registerCommTarget(comm_id, function(comm) {\n comm.onMsg = msg_handler;\n });\n } else if (typeof google != 'undefined' && google.colab.kernel != null) {\n google.colab.kernel.comms.registerTarget(comm_id, (comm) => {\n var messages = comm.messages[Symbol.asyncIterator]();\n function processIteratorResult(result) {\n var message = result.value;\n console.log(message)\n var content = {data: message.data, comm_id};\n var buffers = []\n for (var buffer of message.buffers || []) {\n buffers.push(new DataView(buffer))\n }\n var metadata = message.metadata || {};\n var msg = {content, buffers, metadata}\n msg_handler(msg);\n return messages.next().then(processIteratorResult);\n }\n return messages.next().then(processIteratorResult);\n })\n }\n }\n\n JupyterCommManager.prototype.get_client_comm = function(plot_id, comm_id, msg_handler) {\n if (comm_id in window.PyViz.comms) {\n return window.PyViz.comms[comm_id];\n } else if (window.comm_manager || ((window.Jupyter !== undefined) && (Jupyter.notebook.kernel != null))) {\n var comm_manager = window.comm_manager || Jupyter.notebook.kernel.comm_manager;\n var comm = comm_manager.new_comm(comm_id, {}, {}, {}, comm_id);\n if (msg_handler) {\n comm.on_msg(msg_handler);\n }\n } else if ((plot_id in window.PyViz.kernels) && (window.PyViz.kernels[plot_id])) {\n var comm = window.PyViz.kernels[plot_id].connectToComm(comm_id);\n comm.open();\n if (msg_handler) {\n comm.onMsg = msg_handler;\n }\n } else if (typeof google != 'undefined' && google.colab.kernel != null) {\n var comm_promise = google.colab.kernel.comms.open(comm_id)\n comm_promise.then((comm) => {\n window.PyViz.comms[comm_id] = comm;\n if (msg_handler) {\n var messages = comm.messages[Symbol.asyncIterator]();\n function processIteratorResult(result) {\n var message = result.value;\n var content = {data: message.data};\n var metadata = message.metadata || {comm_id};\n var msg = {content, metadata}\n msg_handler(msg);\n return messages.next().then(processIteratorResult);\n }\n return messages.next().then(processIteratorResult);\n }\n }) \n var sendClosure = (data, metadata, buffers, disposeOnDone) => {\n return comm_promise.then((comm) => {\n comm.send(data, metadata, buffers, disposeOnDone);\n });\n };\n var comm = {\n send: sendClosure\n };\n }\n window.PyViz.comms[comm_id] = comm;\n return comm;\n }\n window.PyViz.comm_manager = new JupyterCommManager();\n \n\n\nvar JS_MIME_TYPE = 'application/javascript';\nvar HTML_MIME_TYPE = 'text/html';\nvar EXEC_MIME_TYPE = 'application/vnd.holoviews_exec.v0+json';\nvar CLASS_NAME = 'output';\n\n/**\n * Render data to the DOM node\n */\nfunction render(props, node) {\n var div = document.createElement(\"div\");\n var script = document.createElement(\"script\");\n node.appendChild(div);\n node.appendChild(script);\n}\n\n/**\n * Handle when a new output is added\n */\nfunction handle_add_output(event, handle) {\n var output_area = handle.output_area;\n var output = handle.output;\n if ((output.data == undefined) || (!output.data.hasOwnProperty(EXEC_MIME_TYPE))) {\n return\n }\n var id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n var toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n if (id !== undefined) {\n var nchildren = toinsert.length;\n var html_node = toinsert[nchildren-1].children[0];\n html_node.innerHTML = output.data[HTML_MIME_TYPE];\n var scripts = [];\n var nodelist = html_node.querySelectorAll(\"script\");\n for (var i in nodelist) {\n if (nodelist.hasOwnProperty(i)) {\n scripts.push(nodelist[i])\n }\n }\n\n scripts.forEach( function (oldScript) {\n var newScript = document.createElement(\"script\");\n var attrs = [];\n var nodemap = oldScript.attributes;\n for (var j in nodemap) {\n if (nodemap.hasOwnProperty(j)) {\n attrs.push(nodemap[j])\n }\n }\n attrs.forEach(function(attr) { newScript.setAttribute(attr.name, attr.value) });\n newScript.appendChild(document.createTextNode(oldScript.innerHTML));\n oldScript.parentNode.replaceChild(newScript, oldScript);\n });\n if (JS_MIME_TYPE in output.data) {\n toinsert[nchildren-1].children[1].textContent = output.data[JS_MIME_TYPE];\n }\n output_area._hv_plot_id = id;\n if ((window.Bokeh !== undefined) && (id in Bokeh.index)) {\n window.PyViz.plot_index[id] = Bokeh.index[id];\n } else {\n window.PyViz.plot_index[id] = null;\n }\n } else if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n var bk_div = document.createElement(\"div\");\n bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n var script_attrs = bk_div.children[0].attributes;\n for (var i = 0; i < script_attrs.length; i++) {\n toinsert[toinsert.length - 1].childNodes[1].setAttribute(script_attrs[i].name, script_attrs[i].value);\n }\n // store reference to server id on output_area\n output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n }\n}\n\n/**\n * Handle when an output is cleared or removed\n */\nfunction handle_clear_output(event, handle) {\n var id = handle.cell.output_area._hv_plot_id;\n var server_id = handle.cell.output_area._bokeh_server_id;\n if (((id === undefined) || !(id in PyViz.plot_index)) && (server_id !== undefined)) { return; }\n var comm = window.PyViz.comm_manager.get_client_comm(\"hv-extension-comm\", \"hv-extension-comm\", function () {});\n if (server_id !== null) {\n comm.send({event_type: 'server_delete', 'id': server_id});\n return;\n } else if (comm !== null) {\n comm.send({event_type: 'delete', 'id': id});\n }\n delete PyViz.plot_index[id];\n if ((window.Bokeh !== undefined) & (id in window.Bokeh.index)) {\n var doc = window.Bokeh.index[id].model.document\n doc.clear();\n const i = window.Bokeh.documents.indexOf(doc);\n if (i > -1) {\n window.Bokeh.documents.splice(i, 1);\n }\n }\n}\n\n/**\n * Handle kernel restart event\n */\nfunction handle_kernel_cleanup(event, handle) {\n delete PyViz.comms[\"hv-extension-comm\"];\n window.PyViz.plot_index = {}\n}\n\n/**\n * Handle update_display_data messages\n */\nfunction handle_update_output(event, handle) {\n handle_clear_output(event, {cell: {output_area: handle.output_area}})\n handle_add_output(event, handle)\n}\n\nfunction register_renderer(events, OutputArea) {\n function append_mime(data, metadata, element) {\n // create a DOM node to render to\n var toinsert = this.create_output_subarea(\n metadata,\n CLASS_NAME,\n EXEC_MIME_TYPE\n );\n this.keyboard_manager.register_events(toinsert);\n // Render to node\n var props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n render(props, toinsert[0]);\n element.append(toinsert);\n return toinsert\n }\n\n events.on('output_added.OutputArea', handle_add_output);\n events.on('output_updated.OutputArea', handle_update_output);\n events.on('clear_output.CodeCell', handle_clear_output);\n events.on('delete.Cell', handle_clear_output);\n events.on('kernel_ready.Kernel', handle_kernel_cleanup);\n\n OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n safe: true,\n index: 0\n });\n}\n\nif (window.Jupyter !== undefined) {\n try {\n var events = require('base/js/events');\n var OutputArea = require('notebook/js/outputarea').OutputArea;\n if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n register_renderer(events, OutputArea);\n }\n } catch(err) {\n }\n}\n" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import os, sys\n", + "import shutil\n", + "import warnings\n", + "warnings.filterwarnings('ignore')\n", + "\n", + "import numpy as np\n", + "import xarray as xr\n", + "import xesmf as xe\n", + "\n", + "# Helpful for plotting only\n", + "import matplotlib\n", + "import matplotlib.pyplot as plt\n", + "import matplotlib.dates as mdates\n", + "import cartopy\n", + "import cartopy.crs as ccrs\n", + "import cartopy.feature as cfeature\n", + "import uxarray as ux #need npl 2024a or later\n", + "import geoviews.feature as gf\n", + "\n", + "#sys.path.append('/glade/u/home/wwieder/python/adf/lib/plotting_functions.py')\n", + "from plotting_functions import *" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "07650a02-db90-4ee9-8880-e3f4ac140871", + "metadata": {}, + "outputs": [], + "source": [ + "# Load datataset \n", + "# TODO, develop function for this too\n", + "gppfile='/glade/derecho/scratch/wwieder/ADF/b.e30_beta04.BLT1850.ne30_t232_wgx3.121/climo/b.e30_beta04.BLT1850.ne30_t232_wgx3.121_GPP_climo.nc'\n", + "laih1file='/glade/derecho/scratch/wwieder/ctsm53n04ctsm52028_ne30pg3t232_hist.clm2.h1.TLAI.1860s.nc'\n", + "mesh0 = '/glade/campaign/cesm/cesmdata/inputdata/share/meshes/ne30pg3_ESMFmesh_cdf5_c20211018.nc'\n", + "ds0 = ux.open_dataset(mesh0, gppfile)\n", + "ds1 = ux.open_dataset(mesh0, laih1file)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "029a4caf-2ffc-4a5c-9cfe-ca8fe4692522", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
<xarray.UxDataset> Size: 12MB\n",
+       "Dimensions:   (time: 12, n_face: 48600)\n",
+       "Coordinates:\n",
+       "  * time      (time) int64 96B 1 2 3 4 5 6 7 8 9 10 11 12\n",
+       "Dimensions without coordinates: n_face\n",
+       "Data variables:\n",
+       "    GPP       (time, n_face) float32 2MB ...\n",
+       "    area      (time, n_face) float32 2MB ...\n",
+       "    landfrac  (time, n_face) float32 2MB ...\n",
+       "    landmask  (time, n_face) float64 5MB ...
" + ], + "text/plain": [ + " Size: 12MB\n", + "Dimensions: (time: 12, n_face: 48600)\n", + "Coordinates:\n", + " * time (time) int64 96B 1 2 3 4 5 6 7 8 9 10 11 12\n", + "Dimensions without coordinates: n_face\n", + "Data variables:\n", + " GPP (time, n_face) float32 2MB ...\n", + " area (time, n_face) float32 2MB ...\n", + " landfrac (time, n_face) float32 2MB ...\n", + " landmask (time, n_face) float64 5MB ..." + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ds0" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "97422a27-562e-484c-b018-aa4368fdc457", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
<xarray.UxDataset> Size: 359MB\n",
+       "Dimensions:           (time: 120, hist_interval: 2, n_face: 48600, pft: 710683)\n",
+       "Coordinates:\n",
+       "  * time              (time) object 960B 1860-02-01 00:00:00 ... 1870-01-01 0...\n",
+       "Dimensions without coordinates: hist_interval, n_face, pft\n",
+       "Data variables:\n",
+       "    time_bounds       (time, hist_interval) object 2kB ...\n",
+       "    lon               (n_face) float32 194kB ...\n",
+       "    lat               (n_face) float32 194kB ...\n",
+       "    pfts1d_ixy        (pft) float64 6MB ...\n",
+       "    pfts1d_jxy        (pft) float64 6MB ...\n",
+       "    pfts1d_itype_veg  (pft) float64 6MB ...\n",
+       "    TLAI              (time, pft) float32 341MB ...
" + ], + "text/plain": [ + " Size: 359MB\n", + "Dimensions: (time: 120, hist_interval: 2, n_face: 48600, pft: 710683)\n", + "Coordinates:\n", + " * time (time) object 960B 1860-02-01 00:00:00 ... 1870-01-01 0...\n", + "Dimensions without coordinates: hist_interval, n_face, pft\n", + "Data variables:\n", + " time_bounds (time, hist_interval) object 2kB ...\n", + " lon (n_face) float32 194kB ...\n", + " lat (n_face) float32 194kB ...\n", + " pfts1d_ixy (pft) float64 6MB ...\n", + " pfts1d_jxy (pft) float64 6MB ...\n", + " pfts1d_itype_veg (pft) float64 6MB ...\n", + " TLAI (time, pft) float32 341MB ..." + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ds1" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "c24b54f2-4239-4326-b8ef-583871b50aef", + "metadata": {}, + "outputs": [], + "source": [ + "# Set up data arrays to plot\n", + "# TODO, this should be wrapped into appropriate plotting scripts\n", + "spd = 3600*24 # to get bigger fluxes\n", + "a = ds0.GPP.isel(time=0) * spd\n", + "a.attrs = ds0.GPP.attrs\n", + "a.attrs['units'] = 'gC/m2/d'\n", + "b = ds0.GPP.isel(time=6) * spd\n", + "b.attrs = a.attrs\n", + "c = a-b\n", + "c.attrs = a.attrs\n", + "d = 100*c/b\n", + "d.attrs = a.attrs\n", + "d.attrs['units'] = '%'" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "73f75d1f-1c10-4dcd-8370-1f0c9b4b76d0", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'/glade/derecho/scratch/wwieder/testFig'" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from pathlib import Path\n", + "wks = Path(\"/glade/derecho/scratch/wwieder/testFig\")\n", + "str(wks)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "55ceea85-e3e2-4e2e-a88c-03c1313acf31", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Size: 389kB\n", + "array([ nan, nan, nan, ..., 1.27199839, 1.42239865,\n", + " 2.10174724])\n", + "Coordinates:\n", + " time int64 8B 1\n", + "Dimensions without coordinates: n_face\n", + "Attributes:\n", + " long_name: gross primary production\n", + " units: gC/m2/d\n", + " cell_methods: time: mean\n", + " landunit_mask: unknown\n", + " Size: 194kB\n", + "array([ nan, nan, nan, ..., 4.2150470e-05,\n", + " 1.9083785e-05, 4.9214168e-06], dtype=float32)\n", + "Coordinates:\n", + " time int64 8B 1\n", + "Dimensions without coordinates: n_face\n", + "/glade/derecho/scratch/wwieder/testFig.png made\n" + ] + } + ], + "source": [ + "case_nickname = 'jan'\n", + "base_nickname = 'july'\n", + "case_climo_yrs, baseline_climo_yrs = [10,14],[10,14]\n", + "mdlfld = a\n", + "obsfld = b\n", + "diffld = c\n", + "pctld = d\n", + "area = ds0.area.isel(time=0)\n", + "landfrac = ds0.landfrac.isel(time=0)\n", + "wgt = area * landfrac / (area * landfrac).sum()\n", + "\n", + "plot_unstructured_map_and_save(wks, case_nickname, base_nickname,\n", + " case_climo_yrs, baseline_climo_yrs,\n", + " mdlfld, obsfld, diffld, pctld, wgt,\n", + " projection = 'global')\n", + "print(str(wks) + '.png made')" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "b288e743-1371-466c-8b03-7c3096ec9697", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/glade/derecho/scratch/wwieder/testPolarFig.png made\n" + ] + } + ], + "source": [ + "# Subset data for polar plots\n", + "# set the bounding box\n", + "lon_bounds = (-180, 180)\n", + "lat_bounds = (50, 90)\n", + "# elements include nodes, edge centers, or face centers) \n", + "element = 'face centers'\n", + "\n", + "wks = Path(\"/glade/derecho/scratch/wwieder/testPolarFig\")\n", + "case_climo_yrs, baseline_climo_yrs = [10,14],[10,14]\n", + "mdlfld = a.subset.bounding_box(lon_bounds, lat_bounds, element=element)\n", + "obsfld = b.subset.bounding_box(lon_bounds, lat_bounds, element=element)\n", + "diffld = c.subset.bounding_box(lon_bounds, lat_bounds, element=element)\n", + "pctld = d.subset.bounding_box(lon_bounds, lat_bounds, element=element)\n", + "area = ds0.area.subset.bounding_box(lon_bounds, lat_bounds, element=element).isel(time=0)\n", + "landfrac = ds0.landfrac.subset.bounding_box(lon_bounds, lat_bounds, element=element).isel(time=0)\n", + "wgt = area * landfrac / (area * landfrac).sum()\n", + "\n", + "plot_unstructured_map_and_save(wks, case_nickname, base_nickname,\n", + " case_climo_yrs, baseline_climo_yrs,\n", + " mdlfld, obsfld, diffld, pctld, wgt,\n", + " projection = 'polar')\n", + "print(str(wks) + '.png made')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ded6d0b6-1dca-4170-8a87-039f297773a2", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python [conda env:cupid-analysis]", + "language": "python", + "name": "conda-env-cupid-analysis-py" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.4" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/lib/plotting_functions.py b/lib/plotting_functions.py index aa5af2413..d961cc9ea 100644 --- a/lib/plotting_functions.py +++ b/lib/plotting_functions.py @@ -99,6 +99,7 @@ from mpl_toolkits.axes_grid1.inset_locator import inset_axes from matplotlib.lines import Line2D import matplotlib.cm as cm +import uxarray as ux #need npl 2024a or later from adf_diag import AdfDiag from adf_base import AdfError @@ -155,12 +156,58 @@ def load_dataset(fils): warnings.warn(f"Input file list is empty.") return None elif len(fils) > 1: - return xr.open_mfdataset(fils, combine='by_coords') + ds = xr.open_mfdataset(fils, combine='by_coords') else: - return xr.open_dataset(fils[0]) + ds = xr.open_dataset(fils[0]) + + # assign time to midpoint of interval (even if it is already) + if 'time_bnds' in ds: + t = ds['time_bnds'].mean(dim='nbnd') + t.attrs = ds['time'].attrs + ds = ds.assign_coords({'time':t}) + elif 'time_bounds' in ds: + t = ds['time_bounds'].mean(dim='hist_interval') + t.attrs = ds['time'].attrs + ds = ds.assign_coords({'time':t}) + else: + warnings.warn("Timeseries file does not have time bounds info.") + return xr.decode_cf(ds) + #End if #End def +def load_ux_dataset(fils, mesh=None): + """ + This method exists to get an uxarray Dataset from input file information that can be passed into the plotting methods. + + Parameters + ---------- + fils : list + strings or paths to input file(s) + + Returns + ------- + ux.Dataset + + Notes + ----- + When just one entry is provided, use `open_dataset`, otherwise `open_mfdatset` + """ + if mesh == None: + mesh = '/glade/campaign/cesm/cesmdata/inputdata/share/meshes/ne30pg3_ESMFmesh_cdf5_c20211018.nc' + warnings.warn(f"No mesh file provided, using defaults ne30pg3 mesh file") + + if len(fils) == 0: + warnings.warn(f"Input file list is empty.") + return None + elif len(fils) > 1: + return ux.open_mfdataset(mesh, fils) + else: + return ux.open_dataset(mesh, fils[0]) + #End if +#End def + + def use_this_norm(): """Just use the right normalization; avoids a deprecation warning.""" @@ -408,6 +455,58 @@ def spatial_average(indata, weights=None, spatial_dims=None): return weighted.mean(dim=spatial_dims, keep_attrs=True) +# TODO, maybe just adapt the spatial average above? +# TODO, should there be some unit conversions for this defined in a variable dictionary? +def spatial_average_lnd(indata, weights, spatial_dims=None): + """Compute spatial average. + + Parameters + ---------- + indata : xr.DataArray + input data + weights xr.DataArray + weights (area * landfrac) + spatial_dims : list, optional + list of dimensions to average, see Notes for default behavior + + Returns + ------- + xr.DataArray + weighted average of `indata` + + Notes + ----- + weights are required + + Makes an attempt to identify the spatial variables when `spatial_dims` is None. + Will average over `ncol` if present, and then will check for `lat` and `lon`. + When none of those three are found, raise an AdfError. + """ + import warnings + + #Apply weights to input data: + weighted = indata*weights + + # we want to average over all non-time dimensions + if spatial_dims is None: + if 'lndgrid' in indata.dims: + spatial_dims = ['lndgrid'] + else: + spatial_dims = [dimname for dimname in indata.dims if (('lat' in dimname.lower()) or + ('lon' in dimname.lower()))] + + if not spatial_dims: + #Scripts using this function likely expect the horizontal dimensions + #to be removed via the application of the mean. So in order to avoid + #possibly unexpected behavior due to arrays being incorrectly dimensioned + #(which could be difficult to debug) the ADF should die here: + emsg = "spatial_average: No spatial dimensions were identified," + emsg += " so can not perform average." + raise AdfError(emsg) + + + return weighted.sum(dim=spatial_dims, keep_attrs=True) + def wgt_rmse(fld1, fld2, wgt): """Calculate the area-weighted RMSE. @@ -429,7 +528,8 @@ def wgt_rmse(fld1, fld2, wgt): Notes: ```rmse = sqrt( mean( (fld1 - fld2)**2 ) )``` """ - assert len(fld1.shape) == 2, "Input fields must have exactly two dimensions." + wgt.fillna(0) + assert len(fld1.shape) <= 2, "Input fields must have less than two dimensions." assert fld1.shape == fld2.shape, "Input fields must have the same array shape." # in case these fields are in dask arrays, compute them now. if hasattr(fld1, "compute"): @@ -545,6 +645,12 @@ def seasonal_mean(data, season=None, is_climo=None): if "month" in data.dims: data = data.rename({"month":"time"}) has_time = True + if isinstance(data, ux.UxDataset): + has_time = 'time' in data.dims + if not has_time: + if "month" in data.dims: + data = data.rename({"month":"time"}) + has_time = True if not has_time: # this might happen if a pure numpy array gets passed in # --> assumes ordered January to December. @@ -1377,7 +1483,205 @@ def plot_map_and_save(wks, case_nickname, base_nickname, plt.close() -# +### + +def plot_unstructured_map_and_save(wks, case_nickname, base_nickname, + case_climo_yrs, baseline_climo_yrs, + mdlfld, obsfld, diffld, pctld, wgt, + obs=False, projection='global',**kwargs): + + """This plots mdlfld, obsfld, diffld in a 3-row panel plot of maps. + + Parameters + ---------- + wks : str or Path + output file path + case_nickname : str + short name for case + base_nickname : str + short name for base case + case_climo_yrs : list + list of years in case climatology, used for annotation + baseline_climo_yrs : list + list of years in base case climatology, used for annotation + mdlfld : uxarray.DataArray + input data for case, needs units and long name attrubutes + obsfld : uxarray.DataArray + input data for base case, needs units and long name attrubutes + diffld : uxarray.DataArray + input difference data, needs units and long name attrubutes + pctld : uxarray.DataArray + input percent difference data, needs units and long name attrubutes + wgt : uxarray.DataArray + weights assumed to be (area*landfrac)/(area*landfrac).sum() + kwargs : dict, optional + variable-specific options, See Notes + + Notes + ----- + kwargs expected to be a variable-specific section, + possibly provided by an ADF Variable Defaults YAML file. + Currently it is inspected for: + - colormap -> str, name of matplotlib colormap + - contour_levels -> list of explict values or a tuple: (min, max, step) + - diff_colormap + - diff_contour_levels + - tiString -> str, Title String + - tiFontSize -> int, Title Font Size + - mpl -> dict, This should be any matplotlib kwargs that should be passed along. Keep reading: + + Organize these by the mpl function. In this function (`plot_map_and_save`) + we will check for an entry called `subplots`, `contourf`, and `colorbar`. So the YAML might looks something like: + ``` + mpl: + subplots: + figsize: (3, 9) + contourf: + levels: 15 + cmap: Blues + colorbar: + shrink: 0.4 + ``` + + This is experimental, and if you find yourself doing much with this, you probably should write a new plotting script that does not rely on this module. + When these are not provided, colormap is set to 'coolwarm' and limits/levels are set by data range. + """ + + # prepare info for plotting + wrap_fields = (mdlfld, obsfld, diffld, pctld) + area_avg = [global_average(x, wgt) for x in wrap_fields] + + # TODO Check this is correct, weighted rmse uses xarray weighted function + #d_rmse = wgt_rmse(a, b, wgt) + d_rmse = (np.sqrt(((diffld**2)*wgt).sum())).values.item() + + # We should think about how to do plot customization and defaults. + # Here I'll just pop off a few custom ones, and then pass the rest into mpl. + if 'tiString' in kwargs: + tiString = kwargs.pop("tiString") + else: + tiString = '' + + if 'tiFontSize' in kwargs: + tiFontSize = kwargs.pop('tiFontSize') + else: + tiFontSize = 8 + + #generate a dictionary of contour plot settings: + cp_info = prep_contour_plot(mdlfld, obsfld, diffld, pctld, **kwargs) + + if projection == 'global': + transform = ccrs.PlateCarree() + proj = ccrs.PlateCarree() + figsize= (14, 7) + elif projection == 'arctic': + transform = ccrs.NorthPolarStereo() + proj = ccrs.NorthPolarStereo() + figsize = (8, 8) + + #nice formatting for tick labels + from cartopy.mpl.ticker import LongitudeFormatter, LatitudeFormatter + lon_formatter = LongitudeFormatter(number_format='0.0f', + degree_symbol='', + dateline_direction_label=False) + lat_formatter = LatitudeFormatter(number_format='0.0f', + degree_symbol='') + + # create figure object + fig, axs = plt.subplots(2,2, + figsize=figsize, + facecolor="w", + constrained_layout=True, + subplot_kw=dict(projection=proj), + **cp_info['subplots_opt'] + ) + axs=axs.flatten() + + # Loop over data arrays to make plots + for i, a in enumerate(wrap_fields): + if i == len(wrap_fields)-2: + levels = cp_info['levelsdiff'] + cmap = cp_info['cmapdiff'] + norm = cp_info['normdiff'] + elif i == len(wrap_fields)-1: + levels = cp_info['levelspctdiff'] + cmap = cp_info['cmappct'] + norm = cp_info['pctnorm'] + else: + levels = cp_info['levels1'] + cmap = cp_info['cmap1'] + norm = cp_info['norm1'] + + levs = np.unique(np.array(levels)) + + #configure for polycollection plotting + #TODO, would be nice to have levels set from the info, above + ac = a.to_polycollection(projection=proj) + #ac.norm(norm) + ac.set_cmap(cmap) + ac.set_antialiased(False) + ac.set_transform(transform) + ac.set_clim(vmin=levels[0],vmax=levels[-1]) + axs[i].add_collection(ac) + if i > 0: + cbar = plt.colorbar(ac, ax=axs[i], orientation='vertical', + pad=0.05, shrink=0.8, **cp_info['colorbar_opt']) + #TODO keep variable attributes on dataarrays + #cbar.set_label(wrap_fields[i].attrs['units']) + #Set stats: area_avg + axs[i].set_title(f"Mean: {area_avg[i].item():5.2f}\nMax: {wrap_fields[i].max().item():5.2f}\nMin: {wrap_fields[i].min().item():5.2f}", + loc='right', fontsize=tiFontSize) + + # Custom setting for each subplot + for a in axs: + a.coastlines() + if projection=='global': + a.set_global() + a.spines['geo'].set_linewidth(1.5) #cartopy's recommended method + a.set_xticks(np.linspace(-180, 120, 6), crs=proj) + a.set_yticks(np.linspace(-90, 90, 7), crs=proj) + a.tick_params('both', length=5, width=1.5, which='major') + a.tick_params('both', length=5, width=1.5, which='minor') + a.xaxis.set_major_formatter(lon_formatter) + a.yaxis.set_major_formatter(lat_formatter) + elif projection == 'arctic': + a.set_extent([-180, 180, 50, 90], ccrs.PlateCarree()) + # __Follow the cartopy gallery example to make circular__: + # Compute a circle in axes coordinates, which we can use as a boundary + # for the map. We can pan/zoom as much as we like - the boundary will be + # permanently circular. + theta = np.linspace(0, 2*np.pi, 100) + center, radius = [0.5, 0.5], 0.5 + verts = np.vstack([np.sin(theta), np.cos(theta)]).T + circle = mpl.path.Path(verts * radius + center) + a.set_boundary(circle, transform=a.transAxes) + a.gridlines(draw_labels=False, crs=ccrs.PlateCarree(), + lw=1, color="gray",y_inline=True, + xlocs=range(-180,180,90), ylocs=range(0,90,10)) + + st = fig.suptitle(wks.stem[:-5].replace("_"," - "), fontsize=18) + st.set_y(0.85) + + #Set plot titles + case_title = "$\mathbf{Test}:$"+f"{case_nickname}\nyears: {case_climo_yrs[0]}-{case_climo_yrs[-1]}" + axs[0].set_title(case_title, loc='left', fontsize=tiFontSize) + if obs: + obs_var = kwargs["obs_var_name"] + obs_title = kwargs["obs_file"][:-3] + base_title = "$\mathbf{Baseline}:$"+obs_title+"\n"+"$\mathbf{Variable}:$"+f"{obs_var}" + axs[1].set_title(base_title, loc='left', fontsize=tiFontSize) + else: + base_title = "$\mathbf{Baseline}:$"+f"{base_nickname}\nyears: {baseline_climo_yrs[0]}-{baseline_climo_yrs[-1]}" + axs[1].set_title(base_title, loc='left', fontsize=tiFontSize) + axs[2].set_title("$\mathbf{Test} - \mathbf{Baseline}$", loc='left', fontsize=tiFontSize) + axs[2].set_title(f"RMSE: {d_rmse:.3f}", fontsize=tiFontSize) + axs[3].set_title("Test % Diff Baseline", loc='left', fontsize=tiFontSize,fontweight="bold") + + fig.savefig(wks, bbox_inches='tight', dpi=300) + + #Close plots: + plt.close() + +## End of plot_unstructured_map_and_save + # -- vertical interpolation code -- # @@ -1650,7 +1954,7 @@ def zm_validate_dims(fld): if not has_lat: return None else: - return has_lat, has_lev + return has_lev def _plot_line(axobject, xdata, ydata, color, **kwargs): """Create a generic line plot and check for some ways to annotate.""" @@ -2546,4 +2850,4 @@ def square_contour_difference(fld1, fld2, **kwargs): return fig ##################### -#END HELPER FUNCTIONS \ No newline at end of file +#END HELPER FUNCTIONS diff --git a/lmwg_wish_list.md b/lmwg_wish_list.md new file mode 100644 index 000000000..49972727b --- /dev/null +++ b/lmwg_wish_list.md @@ -0,0 +1,19 @@ +# List of ideas for SEWG hackathon: +### Simple / busy work +- Identify list of default variables in `config_clm_baseline_example.yml` +- Adapt list of variables in `adf/lib/ldf_variable_defaults.yml` (plotting controls for list above) +- Identify list of regions and bounding boxes where we want to make timeseries or climo plots + +### Integration +- Integrate `regrid_se_to_fv` regridding script into ADF workflow. +- Integrate `plot_unstructured_map_and_save` function into `/scripts/plotting/global_unstructured_latlon_map` +- Develop coherent way to handled structured vs. unstructured input data (maybe adapt all to uxarray)? + +### Development +- Seperate time bounds for time series and climo generation. +- Write python function to make regional timeseries or climo plots +- Adapt adf timeseries plots for land +- Handle h1 files for PFT specific results + +# + diff --git a/scripts/analysis/lmwg_table.py b/scripts/analysis/lmwg_table.py new file mode 100644 index 000000000..9d7d814b1 --- /dev/null +++ b/scripts/analysis/lmwg_table.py @@ -0,0 +1,411 @@ +import numpy as np +import xarray as xr +import sys +from pathlib import Path +import warnings # use to warn user about missing files. + +#Import "special" modules: +try: + import scipy.stats as stats # for easy linear regression and testing +except ImportError: + print("Scipy module does not exist in python path, but is needed for lmwg_table.") + print("Please install module, e.g. 'pip install scipy'.") + sys.exit(1) +#End except + +try: + import pandas as pd +except ImportError: + print("Pandas module does not exist in python path, but is needed for lmwg_table.") + print("Please install module, e.g. 'pip install pandas'.") + sys.exit(1) +#End except + +#Import ADF-specific modules: +import plotting_functions as pf + +def lmwg_table(adf): + + """ + Main function goes through series of steps: + - load the variable data + - Determine whether there are spatial dims; if yes, do global average (TODO: regional option) + - Apply annual average (TODO: add seasonal here) + - calculates the statistics + + mean + + sample size + + standard deviation + + standard error of the mean + + 5/95% confidence interval of the mean + + linear trend + + p-value of linear trend + - puts statistics into a CSV file + - generates simple HTML that can display the data + + Description of needed inputs from ADF: + + case_names -> Name(s) of CAM case provided by "cam_case_name" + input_ts_locs -> Location(s) of CAM time series files provided by "cam_ts_loc" + output_loc -> Location to write AMWG table files to, provided by "cam_diag_plot_loc" + var_list -> List of CAM output variables provided by "diag_var_list" + var_defaults -> Dict that has keys that are variable names and values that are plotting preferences/defaults. + + and if doing a CAM baseline comparison: + + baseline_name -> Name of CAM baseline case provided by "cam_case_name" + input_ts_baseline -> Location of CAM baseline time series files provied by "cam_ts_loc" + + """ + + #Import necessary modules: + from adf_base import AdfError + + #Additional information: + #---------------------- + + # GOAL: replace the "Tables" set in AMWG + # Set Description + # 1 Tables of ANN, DJF, JJA, global and regional means and RMSE. + # + # STRATEGY: + # I think the right solution is to generate one CSV (or other?) file that + # contains all of the data. + # So we need: + # - a function that would produces the data, and + # - then call a function that adds the data to a file + # - another function(module?) that uses the file to produce a "web page" + + # IMPLEMENTATION: + # - assume that we will have time series of global averages already ... that should be done ahead of time + # - given a variable or file for a variable (equivalent), we will calculate the all-time, DJF, JJA, MAM, SON + # + mean + # + standard error of the mean + # -- 95% confidence interval for the mean, estimated by: + # ---- CI95 = mean + (SE * 1.96) + # ---- CI05 = mean - (SE * 1.96) + # + standard deviation + # AMWG also includes the RMSE b/c it is comparing two things, but I will put that off for now. + + # DETAIL: we use python's type hinting as much as possible + + # in future, provide option to do multiple domains + # They use 4 pre-defined domains: + # NOTE, this is likely not as critical for LMWG_table, and won't work we'll with unstructured data + domains = {"global": (0, 360, -90, 90), + "tropics": (0, 360, -20, 20), + "southern": (0, 360, -90, -20), + "northern": (0, 360, 20, 90)} + + # and then in time it is DJF JJA ANN + + # within each domain and season + # the result is just a table of + # VARIABLE-NAME, RUN VALUE, OBS VALUE, RUN-OBS, RMSE + #---------------------- + + #Extract needed quantities from ADF object: + #----------------------------------------- + var_list = adf.diag_var_list + var_defaults = adf.variable_defaults + + #Check if ocean or land fraction exist + #in the variable list: + for var in ["OCNFRAC", "LANDFRAC"]: + if var in var_list: + #If so, then move them to the front of variable list so + #that they can be used to mask or vertically interpolate + #other model variables if need be: + var_idx = var_list.index(var) + var_list.pop(var_idx) + var_list.insert(0,var) + #End if + #End if + + #Special ADF variable which contains the output paths for + #all generated plots and tables for each case: + output_locs = adf.plot_location + + #CAM simulation variables (these quantities are always lists): + case_names = adf.get_cam_info("cam_case_name", required=True) + input_ts_locs = adf.get_cam_info("cam_ts_loc", required=True) + + #Check if a baseline simulation is also being used: + if not adf.get_basic_info("compare_obs"): + #Extract CAM baseline variaables: + baseline_name = adf.get_baseline_info("cam_case_name", required=True) + input_ts_baseline = adf.get_baseline_info("cam_ts_loc", required=True) + + case_names.append(baseline_name) + input_ts_locs.append(input_ts_baseline) + + #Save the baseline to the first case's plots directory: + output_locs.append(output_locs[0]) + else: + print("AMWG table doesn't currently work with obs, so obs table won't be created.") + #End if + + #----------------------------------------- + + #Loop over CAM cases: + #Initialize list of case name csv files for case comparison check later + csv_list = [] + for case_idx, case_name in enumerate(case_names): + + #Convert output location string to a Path object: + output_location = Path(output_locs[case_idx]) + + #Generate input file path: + input_location = Path(input_ts_locs[case_idx]) + + #Check that time series input directory actually exists: + if not input_location.is_dir(): + errmsg = f"Time series directory '{input_location}' not found. Script is exiting." + raise AdfError(errmsg) + #Write to debug log if enabled: + adf.debug_log(f"DEBUG: location of files is {str(input_location)}") + + #Notify user that script has started: + print(f"\n Calculating AMWG variable table for '{case_name}'...") + + #Create output file name: + output_csv_file = output_location / f"amwg_table_{case_name}.csv" + + #Given that this is a final, user-facing analysis, go ahead and re-do it every time: + if Path(output_csv_file).is_file(): + Path.unlink(output_csv_file) + #End if + + #Create/reset new variable that potentially stores the re-gridded + #ocean fraction xarray data-array: + ocn_frc_da = None + + #Loop over CAM output variables: + for var in var_list: + + #Notify users of variable being added to table: + print(f"\t - Variable '{var}' being added to table") + + #Create list of time series files present for variable: + ts_filenames = f'{case_name}.*.{var}.*nc' + ts_files = sorted(input_location.glob(ts_filenames)) + + # If no files exist, try to move to next variable. --> Means we can not proceed with this variable, and it'll be problematic later. + if not ts_files: + errmsg = f"Time series files for variable '{var}' not found. Script will continue to next variable." + warnings.warn(errmsg) + continue + #End if + + #TEMPORARY: For now, make sure only one file exists: + if len(ts_files) != 1: + errmsg = "Currently the AMWG table script can only handle one time series file per variable." + errmsg += f" Multiple files were found for the variable '{var}', so it will be skipped." + print(errmsg) + continue + #End if + + #Load model variable data from file: + ds = pf.load_dataset(ts_files) + weights = ds.landfrac * ds.area + data = ds[var] + + #Extract defaults for variable: + var_default_dict = var_defaults.get(var, {}) + scale_factor = var_default_dict.get('scale_factor', 1) + scale_factor_table = var_default_dict.get('scale_factor_table', 1) + add_offset = var_default_dict.get('add_offset', 0) + # could require this for each variable? + avg_method = var_default_dict.get('avg_method', 'mean') + if avg_method == 'mean': + weights = weights/weights.sum() + + # get units for variable (do this before doing math) + data.attrs['units'] = var_default_dict.get("new_unit", data.attrs.get('units', 'none')) + data.attrs['units'] = var_default_dict.get("table_unit", data.attrs.get('units', 'none')) + if hasattr(data, 'units'): + unit_str = data.attrs['units'] + else: + unit_str = '--' + + data = data * scale_factor * scale_factor_table + #Check if variable has a vertical coordinate: + if 'lev' in data.coords or 'ilev' in data.coords: + print(f"\t ** Variable '{var}' has a vertical dimension, "+\ + "which is currently not supported for the AMWG Table. Skipping...") + #Skip this variable and move to the next variable in var_list: + continue + #End if + + #Check if variable should be masked: + if 'mask' in var_default_dict: + if var_default_dict['mask'].lower() == 'ocean': + #Check if the ocean fraction has already been regridded + #and saved: + if ocn_frc_da is not None: + ofrac = ocn_frc_da + # set the bounds of regridded ocnfrac to 0 to 1 + ofrac = xr.where(ofrac>1,1,ofrac) + ofrac = xr.where(ofrac<0,0,ofrac) + + # apply ocean fraction mask to variable + data = pf.mask_land_or_ocean(data, ofrac, use_nan=True) + #data = var_tmp + else: + print(f"OCNFRAC not found, unable to apply mask to '{var}'") + #End if + else: + #Currently only an ocean mask is supported, so print warning here: + wmsg = "Currently the only variable mask option is 'ocean'," + wmsg += f"not '{var_default_dict['mask'].lower()}'" + print(wmsg) + #End if + #End if + + #If the variable is ocean fraction, then save the dataset for use later: + if var == 'OCNFRAC': + ocn_frc_da = data + #End if + + # we should check if we need to do area averaging: + if len(data.dims) > 1: + # flags that we have spatial dimensions + # Note: that could be 'lev' which should trigger different behavior + # Note: we should be able to handle (lat, lon) or (ncol,) cases, at least + # data = pf.spatial_average(data) # changes data "in place" + data = pf.spatial_average_lnd(data,weights) # hard code for land + # TODO, make this optional for lmwg_tables of amwg_table + # In order to get correct statistics, average to annual or seasonal + data = pf.annual_mean(data, whole_years=True, time_name='time') + + # create a dataframe: + cols = ['variable', 'unit', 'mean', 'sample size', 'standard dev.', + 'standard error', '95% CI', 'trend', 'trend p-value'] + + # These get written to our output file: + stats_list = _get_row_vals(data) + row_values = [var, unit_str] + stats_list + + # Format entries: + dfentries = {c:[row_values[i]] for i,c in enumerate(cols)} + + # Add entries to Pandas structure: + df = pd.DataFrame(dfentries) + + # Check if the output CSV file exists, + # if so, then append to it: + if output_csv_file.is_file(): + df.to_csv(output_csv_file, mode='a', header=False, index=False) + else: + df.to_csv(output_csv_file, header=cols, index=False) + + #End of var_list loop + #-------------------- + + # Move RESTOM to top of table (if applicable) + #-------------------------------------------- + try: + table_df = pd.read_csv(output_csv_file) + if 'RESTOM' in table_df['variable'].values: + table_df = pd.concat([table_df[table_df['variable'] == 'RESTOM'], table_df]).reset_index(drop = True) + table_df = table_df.drop_duplicates() + table_df.to_csv(output_csv_file, header=cols, index=False) + + # last step is to add table dataframe to website (if enabled): + adf.add_website_data(table_df, case_name, case_name, plot_type="Tables") + except FileNotFoundError: + print(f"\n\tAMWG table for '{case_name}' not created.\n") + #End try/except + + #Keep track of case csv files for comparison table check later + csv_list.extend(sorted(output_location.glob(f"amwg_table_{case_name}.csv"))) + + #End of model case loop + #---------------------- + + #Start case comparison tables + #---------------------------- + #Check if observations are being compared to, if so skip table comparison... + if not adf.get_basic_info("compare_obs"): + #Check if all tables were created to compare against, if not, skip table comparison... + if len(csv_list) != len(case_names): + print("\tNot enough cases to compare, skipping comparison table...") + else: + #Create comparison table for both cases + print("\n Making comparison table...") + _df_comp_table(adf, output_location, case_names) + print(" ... Comparison table has been generated successfully") + #End if + else: + print(" No comparison table will be generated due to running against obs.") + #End if + + #Notify user that script has ended: + print(" ...AMWG variable table(s) have been generated successfully.") + + +################## +# Helper functions +################## + +def _get_row_vals(data): + # Now that data is (time,), we can do our simple stats: + + data_mean = data.data.mean() + #Conditional Formatting depending on type of float + if np.abs(data_mean) < 1: + formatter = ".3g" + else: + formatter = ".3f" + + data_sample = len(data) + data_std = data.std() + data_sem = data_std / data_sample + data_ci = data_sem * 1.96 # https://en.wikipedia.org/wiki/Standard_error + data_trend = stats.linregress(data.year, data.values) + + stdev = f'{data_std.data.item() : {formatter}}' + sem = f'{data_sem.data.item() : {formatter}}' + ci = f'{data_ci.data.item() : {formatter}}' + slope_int = f'{data_trend.intercept : {formatter}} + {data_trend.slope : {formatter}} t' + pval = f'{data_trend.pvalue : {formatter}}' + + return [f'{data_mean:{formatter}}', data_sample, stdev, sem, ci, slope_int, pval] + +##### + +def _df_comp_table(adf, output_location, case_names): + import pandas as pd + # TODO, make this output an option for LMWG or AMWG table + output_csv_file_comp = output_location / "amwg_table_comp.csv" + + # * * * * * * * * * * * * * * * * * * * * * * * * * * * * + #This will be for single-case for now (case_names[0]), + #will need to change to loop as multi-case is introduced + case = output_location/f"amwg_table_{case_names[0]}.csv" + baseline = output_location/f"amwg_table_{case_names[-1]}.csv" + + #Read in test case and baseline dataframes: + df_case = pd.read_csv(case) + df_base = pd.read_csv(baseline) + + #Create a merged dataframe that contains only the variables + #contained within both the test case and the baseline: + df_merge = pd.merge(df_case, df_base, how='inner', on=['variable']) + + #Create the "comparison" dataframe: + df_comp = pd.DataFrame(dtype=object) + df_comp[['variable','unit','case']] = df_merge[['variable','unit_x','mean_x']] + df_comp['baseline'] = df_merge[['mean_y']] + + diffs = df_comp['case'].values-df_comp['baseline'].values + df_comp['diff'] = [f'{i:.3g}' if np.abs(i) < 1 else f'{i:.3f}' for i in diffs] + + #Write the comparison dataframe to a new CSV file: + cols_comp = ['variable', 'unit', 'test', 'control', 'diff'] + df_comp.to_csv(output_csv_file_comp, header=cols_comp, index=False) + + #Add comparison table dataframe to website (if enabled): + adf.add_website_data(df_comp, "Case Comparison", case_names[0], plot_type="Tables") + +############## +#END OF SCRIPT diff --git a/scripts/averaging/create_climo_files.py b/scripts/averaging/create_climo_files.py index d90bfbe52..5bffd7be4 100644 --- a/scripts/averaging/create_climo_files.py +++ b/scripts/averaging/create_climo_files.py @@ -76,6 +76,7 @@ def create_climo_files(adf, clobber=False, search=None): overwrite = adf.get_cam_info("cam_overwrite_climo") #Extract simulation years: + #TODO, make this an option to be different from the time series start and end year? start_year = adf.climo_yrs["syears"] end_year = adf.climo_yrs["eyears"] @@ -211,10 +212,21 @@ def process_variable(adf, ts_files, syr, eyr, output_file): if 'time_bnds' in cam_ts_data: time = cam_ts_data['time'] # NOTE: force `load` here b/c if dask & time is cftime, throws a NotImplementedError: - time = xr.DataArray(cam_ts_data['time_bnds'].load().mean(dim='nbnd').values, dims=time.dims, attrs=time.attrs) + time = xr.DataArray(cam_ts_data['time_bnds'].load().mean(dim='nbnd').values, + dims=time.dims, attrs=time.attrs) cam_ts_data['time'] = time cam_ts_data.assign_coords(time=time) cam_ts_data = xr.decode_cf(cam_ts_data) + elif 'time_bounds' in cam_ts_data: + time = cam_ts_data['time'] + # NOTE: force `load` here b/c if dask & time is cftime, throws a NotImplementedError: + time = xr.DataArray(cam_ts_data['time_bounds'].load().mean(dim='hist_interval').values, + dims=time.dims, attrs=time.attrs) + cam_ts_data['time'] = time + cam_ts_data.assign_coords(time=time) + cam_ts_data = xr.decode_cf(cam_ts_data) + print(cam_ts_data) + #Extract data subset using provided year bounds: tslice = get_time_slice_by_year(cam_ts_data.time, int(syr), int(eyr)) cam_ts_data = cam_ts_data.isel(time=tslice) @@ -284,4 +296,4 @@ def check_averaging_interval(syear_in, eyear_in): else: eyr = None #End if - return syr, eyr \ No newline at end of file + return syr, eyr diff --git a/scripts/plotting/global_mean_timeseries_lnd.py b/scripts/plotting/global_mean_timeseries_lnd.py new file mode 100644 index 000000000..d19f86fde --- /dev/null +++ b/scripts/plotting/global_mean_timeseries_lnd.py @@ -0,0 +1,316 @@ +"""Use time series files to produce global mean time series plots for ADF web site. + +Includes a minimal Class for bringing CESM2 LENS data +from I. Simpson's directory (to be generalized). + +""" + +from pathlib import Path +from types import NoneType +import warnings # use to warn user about missing files. +import xarray as xr +import matplotlib.pyplot as plt +import plotting_functions as pf + + +def my_formatwarning(msg, *args, **kwargs): + """custom warning""" + # ignore everything except the message + return str(msg) + "\n" + + +warnings.formatwarning = my_formatwarning + + +def global_mean_timeseries_lnd(adfobj): + """ + load time series file, calculate global mean, annual mean + for each case + Make a combined plot, save it, add it to website. + Include the CESM2 LENS result if it can be found. + """ + + #Notify user that script has started: + print("\n Generating global mean time series plots...") + + # Gather ADF configurations + plot_loc = get_plot_loc(adfobj) + plot_type = adfobj.read_config_var("diag_basic_info").get("plot_type", "png") + res = adfobj.variable_defaults # will be dict of variable-specific plot preferences + # or an empty dictionary if use_defaults was not specified in YAML. + + # Loop over variables + for field in adfobj.diag_var_list: + + # Check res for any variable specific options that need to be used BEFORE going to the plot: + if field in res: + vres = res[field] + #If found then notify user, assuming debug log is enabled: + adfobj.debug_log(f"global_mean_timeseries: Found variable defaults for {field}") + else: + vres = {} + + # Extract variables: + # including a simpler way to get a dataset timeseries + baseline_name = adfobj.get_baseline_info("cam_case_name", required=True) + input_ts_baseline = Path(adfobj.get_baseline_info("cam_ts_loc", required=True)) + # TODO hard wired for single case name: + case_name = adfobj.get_cam_info("cam_case_name", required=True)[0] + input_ts_case = Path(adfobj.get_cam_info("cam_ts_loc", required=True)[0]) + + #Create list of time series files present for variable: + baseline_ts_filenames = f'{baseline_name}.*.{field}.*nc' + baseline_ts_files = sorted(input_ts_baseline.glob(baseline_ts_filenames)) + case_ts_filenames = f'{case_name}.*.{field}.*nc' + case_ts_files = sorted(input_ts_case.glob(case_ts_filenames)) + + ref_ts_ds = pf.load_dataset(baseline_ts_files) + weights = ref_ts_ds.landfrac * ref_ts_ds.area + ref_ts_da= ref_ts_ds[field] + + c_ts_ds = pf.load_dataset(case_ts_files) + c_weights = c_ts_ds.landfrac * c_ts_ds.area + c_ts_da= c_ts_ds[field] + + #Extract category (if available): + web_category = vres.get("category", None) + + # get variable defaults + scale_factor = vres.get('scale_factor', 1) + scale_factor_table = vres.get('scale_factor_table', 1) + add_offset = vres.get('add_offset', 0) + avg_method = vres.get('avg_method', 'mean') + if avg_method == 'mean': + weights = weights/weights.sum() + c_weights = c_weights/c_weights.sum() + # get units for variable + ref_ts_da.attrs['units'] = vres.get("new_unit", ref_ts_da.attrs.get('units', 'none')) + ref_ts_da.attrs['units'] = vres.get("table_unit", ref_ts_da.attrs.get('units', 'none')) + units = ref_ts_da.attrs['units'] + + # scale for plotting, if needed + ref_ts_da = ref_ts_da * scale_factor * scale_factor_table + ref_ts_da.attrs['units'] = units + c_ts_da = c_ts_da * scale_factor * scale_factor_table + c_ts_da.attrs['units'] = units + + # Check to see if this field is available + if ref_ts_da is None: + print( + f"\t Variable named {field} provides Nonetype. Skipping this variable" + ) + validate_dims = True + else: + validate_dims = False + # reference time series global average + # TODO, make this more general for land? + ref_ts_da_ga = pf.spatial_average_lnd(ref_ts_da, weights=weights) + c_ts_da_ga = pf.spatial_average_lnd(c_ts_da, weights=c_weights) + + # annually averaged + ref_ts_da = pf.annual_mean(ref_ts_da_ga, whole_years=True, time_name="time") + c_ts_da = pf.annual_mean(c_ts_da_ga, whole_years=True, time_name="time") + + # check if this is a "2-d" varaible: + has_lev_ref = pf.zm_validate_dims(ref_ts_da) + if has_lev_ref: + print( + f"Variable named {field} has a lev dimension, which does not work with this script." + ) + continue + + ## SPECIAL SECTION -- CESM2 LENS DATA: + lens2_data = Lens2Data( + field + ) # Provides access to LENS2 dataset when available (class defined below) + + # Loop over model cases: + case_ts = {} # dictionary of annual mean, global mean time series + # use case nicknames instead of full case names if supplied: + labels = { + case_name: nickname if nickname else case_name + for nickname, case_name in zip( + adfobj.data.test_nicknames, adfobj.data.case_names + ) + } + ref_label = ( + adfobj.data.ref_nickname + if adfobj.data.ref_nickname + else adfobj.data.ref_case_label + ) + + skip_var = False + for case_name in adfobj.data.case_names: + #c_ts_da = adfobj.data.load_timeseries_da(case_name, field) + + if c_ts_da is None: + print( + f"\t Variable named {field} provides Nonetype. Skipping this variable" + ) + skip_var = True + continue + + # If no reference, we still need to check if this is a "2-d" varaible: + if validate_dims: + has_lat_ref, has_lev_ref = pf.zm_validate_dims(c_ts_da) + # End if + + # If 3-d variable, notify user, flag and move to next test case + if has_lev_ref: + print( + f"Variable named {field} has a lev dimension for '{case_name}', which does not work with this script." + ) + + skip_var = True + continue + # End if + + # Gather spatial avg for test case + case_ts[labels[case_name]] = pf.annual_mean(c_ts_da_ga, whole_years=True, time_name="time") + + # If this case is 3-d or missing variable, then break the loop and go to next variable + if skip_var: + continue + + # Plot the timeseries + fig, ax = make_plot( + case_ts, lens2_data, label=adfobj.data.ref_nickname, ref_ts_da=ref_ts_da + ) + + ax.set_ylabel(getattr(ref_ts_da,"unit", units)) # add units + plot_name = plot_loc / f"{field}_GlobalMean_ANN_TimeSeries_Mean.{plot_type}" + + conditional_save(adfobj, plot_name, fig) + + adfobj.add_website_data( + plot_name, + f"{field}_GlobalMean", + None, + season="ANN", + multi_case=True, + plot_type="TimeSeries", + ) + + #Notify user that script has ended: + print(" ... global mean time series plots have been generated successfully.") + + +# Helper/plotting functions +########################### + +def conditional_save(adfobj, plot_name, fig, verbose=None): + """Determines whether to save figure""" + # double check this + if adfobj.get_basic_info("redo_plot") and plot_name.is_file(): + # Case 1: Delete old plot, save new plot + plot_name.unlink() + fig.savefig(plot_name) + elif (adfobj.get_basic_info("redo_plot") and not plot_name.is_file()) or ( + not adfobj.get_basic_info("redo_plot") and not plot_name.is_file() + ): + # Save new plot + fig.savefig(plot_name) + elif not adfobj.get_basic_info("redo_plot") and plot_name.is_file(): + # Case 2: Keep old plot, do not save new plot + if verbose: + print("plot file detected, redo is false, so keep existing file.") + else: + warnings.warn( + f"Conditional save found unknown condition. File will not be written: {plot_name}" + ) + plt.close(fig) +###### + + +def get_plot_loc(adfobj, verbose=None): + """Return the path for plot files. + Contains side-effect: will make the directory and parents if needed. + """ + plot_location = adfobj.plot_location + if not plot_location: + plot_location = adfobj.get_basic_info("cam_diag_plot_loc") + if isinstance(plot_location, list): + for pl in plot_location: + plpth = Path(pl) + # Check if plot output directory exists, and if not, then create it: + if not plpth.is_dir(): + if verbose: + print(f"\t {pl} not found, making new directory") + plpth.mkdir(parents=True) + if len(plot_location) == 1: + plot_loc = Path(plot_location[0]) + else: + if verbose: + print( + f"Ambiguous plotting location since all cases go on same plot. Will put them in first location: {plot_location[0]}" + ) + plot_loc = Path(plot_location[0]) + else: + plot_loc = Path(plot_location) + print(f"Determined plot location: {plot_loc}") + return plot_loc +###### + + +class Lens2Data: + """Access Isla's LENS2 data to get annual means.""" + + def __init__(self, field): + self.field = field + self.has_lens, self.lens2 = self._include_lens() + + def _include_lens(self): + lens2_fil = Path( + f"/glade/campaign/cgd/cas/islas/CESM_DATA/LENS2/global_means/annualmeans/{self.field}_am_LENS2_first50.nc" + ) + if lens2_fil.is_file(): + lens2 = xr.open_mfdataset(lens2_fil) + has_lens = True + else: + warnings.warn(f"Time Series: Did not find LENS2 file for {self.field}.") + has_lens = False + lens2 = None + return has_lens, lens2 +###### + + +def make_plot(case_ts, lens2, label=None, ref_ts_da=None): + """plot yearly values of ref_ts_da""" + field = lens2.field # this will be defined even if no LENS2 data + fig, ax = plt.subplots() + + # Plot reference/baseline if available + if type(ref_ts_da) != NoneType: + ax.plot(ref_ts_da.year, ref_ts_da, label=label) + for c, cdata in case_ts.items(): + ax.plot(cdata.year, cdata, label=c) + if lens2.has_lens: + lensmin = lens2.lens2[field].min("M") # note: "M" is the member dimension + lensmax = lens2.lens2[field].max("M") + ax.fill_between(lensmin.year, lensmin, lensmax, color="lightgray", alpha=0.5) + ax.plot( + lens2.lens2[field].year, + lens2.lens2[field].mean("M"), + color="darkgray", + linewidth=2, + label="LENS2", + ) + # Get the current y-axis limits + ymin, ymax = ax.get_ylim() + # Check if the y-axis crosses zero + if ymin < 0 < ymax: + ax.axhline(y=0, color="lightgray", linestyle="-", linewidth=1) + ax.set_title(field, loc="left") + ax.set_xlabel("YEAR") + # Place the legend + ax.legend( + bbox_to_anchor=(0.5, -0.15), loc="upper center", ncol=min(len(case_ts), 3) + ) + plt.tight_layout(pad=2, w_pad=1.0, h_pad=1.0) + + return fig, ax +###### + + +############## +#END OF SCRIPT diff --git a/scripts/plotting/global_unstructured_latlon_map.py b/scripts/plotting/global_unstructured_latlon_map.py new file mode 100644 index 000000000..7b1c316d4 --- /dev/null +++ b/scripts/plotting/global_unstructured_latlon_map.py @@ -0,0 +1,928 @@ +""" +Generate global maps of 2-D fields + +Functions +--------- +global_latlon_map(adfobj) + use ADF object to make maps +my_formatwarning(msg, *args, **kwargs) + format warning messages + (private method) +plot_file_op + Check on status of output plot file. +""" +#Import standard modules: +import os +from pathlib import Path +import numpy as np +import xarray as xr +import xesmf as xe +import warnings # use to warn user about missing files. + +# Import plotting modules: +import matplotlib as mpl +import matplotlib.pyplot as plt + +import cartopy.crs as ccrs +import cartopy.feature as cfeature +from cartopy.util import add_cyclic_point +from cartopy.mpl.ticker import LongitudeFormatter, LatitudeFormatter +import plotting_functions as pf + +import uxarray as ux #need npl 2024a or later +import geoviews.feature as gf + +# Warnings +import warnings # use to warn user about missing files. +# - Format warning messages: +def my_formatwarning(msg, *args, **kwargs): + """Issue `msg` as warning.""" + return str(msg) + '\n' +warnings.formatwarning = my_formatwarning + +######### + +def global_unstructured_latlon_map(adfobj): + """ + This script/function is designed to generate global + 2-D lat/lon maps of model fields with continental overlays. + + uses uxarray to handle unstructured grids + also set up to read in raw climatology files (ne30 resolution) + + Parameters + ---------- + adfobj : AdfDiag + The diagnostics object that contains all the configuration information + + Returns + ------- + Does not return a value; produces plots and saves files. + + Notes + ----- + + It uses the AdfDiag object's methods to get necessary information. + Makes use of AdfDiag's data sub-class. + Explicitly accesses: + adfobj.diag_var_list + List of variables + adfobj.plot_location + output plot path + adfobj.climo_yrs + start and end climo years of the case(s), `syears` & `eyears` + start and end climo years of the reference, `syear_baseline` & `eyear_baseline` + adfobj.variable_defaults + dict of variable-specific plot preferences + adfobj.read_config_var + dict of basic info, `diag_basic_info` + Then use to check `plot_type` + adfobj.debug_log + Issues debug message + adfobj.add_website_data + Communicates information to the website generator + adfobj.compare_obs + Logical to determine if comparing to observations + + + The `plotting_functions` module is needed for: + pf.get_central_longitude() + determine central longitude for global plots + pf.lat_lon_validate_dims() TODO, remove this, or check for unstructured grid and mesh file + makes sure latitude and longitude are valid + pf.seasonal_mean() + calculate seasonal mean + pf.plot_map_and_save() + send information to make the plot and save the file + pf.zm_validate_dims() TODO, not necessary for land plots, but maybe keep for atmosphere + Checks on pressure level dimension + """ + + #Notify user that script has started: + print("\n Generating lat/lon maps...") + + # + # Use ADF api to get all necessary information + # + var_list = adfobj.diag_var_list + #Special ADF variable which contains the output paths for + #all generated plots and tables for each case: + plot_locations = adfobj.plot_location + + #Grab case years + syear_cases = adfobj.climo_yrs["syears"] + eyear_cases = adfobj.climo_yrs["eyears"] + + #Grab baseline years (which may be empty strings if using Obs): + syear_baseline = adfobj.climo_yrs["syear_baseline"] + eyear_baseline = adfobj.climo_yrs["eyear_baseline"] + + res = adfobj.variable_defaults # will be dict of variable-specific plot preferences + # or an empty dictionary if use_defaults was not specified in YAML. + + #Set plot file type: + # -- this should be set in basic_info_dict, but is not required + # -- So check for it, and default to png + basic_info_dict = adfobj.read_config_var("diag_basic_info") + plot_type = basic_info_dict.get('plot_type', 'png') + print(f"\t NOTE: Plot type is set to {plot_type}") + + # check if existing plots need to be redone + redo_plot = adfobj.get_basic_info('redo_plot') + print(f"\t NOTE: redo_plot is set to {redo_plot}") + #----------------------------------------- + + #Determine if user wants to plot 3-D variables on + #pressure levels: + pres_levs = adfobj.get_basic_info("plot_press_levels") + + weight_season = True #always do seasonal weighting + + #Set seasonal ranges: + seasons = {"ANN": np.arange(1,13,1), + "DJF": [12, 1, 2], + "JJA": [6, 7, 8], + "MAM": [3, 4, 5], + "SON": [9, 10, 11] + } + + # probably want to do this one variable at a time: + for var in var_list: + if var not in adfobj.data.ref_var_nam: + dmsg = f"No reference data found for variable `{var}`, global lat/lon mean plotting skipped." + adfobj.debug_log(dmsg) + print(dmsg) + continue + + #Notify user of variable being plotted: + print("\t - lat/lon maps for {}".format(var)) + + # Check res for any variable specific options that need to be used BEFORE going to the plot: + if var in res: + vres = res[var] + #If found then notify user, assuming debug log is enabled: + adfobj.debug_log(f"global_latlon_map: Found variable defaults for {var}") + + #Extract category (if available): + web_category = vres.get("category", None) + + else: + vres = {} + web_category = None + #End if + + # For global maps, also set the central longitude: + # can be specified in adfobj basic info as 'central_longitude' or supplied as a number, + # otherwise defaults to 180 + vres['central_longitude'] = pf.get_central_longitude(adfobj) + + # load reference data (observational or baseline) + if not adfobj.compare_obs: + base_name = adfobj.data.ref_case_label + else: + base_name = adfobj.data.ref_labels[var] + + # Gather reference variable data + odata = adfobj.data.load_reference_climo_da(base_name, var) + #odata = odata[var] # now just read in the data array, but don't modify anything + + if odata is None: + dmsg = f"No regridded test file for {base_name} for variable `{var}`, global lat/lon mean plotting skipped." + adfobj.debug_log(dmsg) + continue + + o_has_dims = pf.validate_dims(odata, ["lat", "lon", "lev"]) # T iff dims are (lat,lon) -- can't plot unless we have both + if (not o_has_dims['has_lat']) or (not o_has_dims['has_lon']): + print(f"\t = Unstructured grid, so global map for {var} does not have lat and lon") + + #Loop over model cases: + for case_idx, case_name in enumerate(adfobj.data.case_names): + + #Set case nickname: + case_nickname = adfobj.data.test_nicknames[case_idx] + + #Set output plot location: + plot_loc = Path(plot_locations[case_idx]) + + #Check if plot output directory exists, and if not, then create it: + if not plot_loc.is_dir(): + print(" {} not found, making new directory".format(plot_loc)) + plot_loc.mkdir(parents=True) + + #Load climo model files: TODO, this is kind of clunky, but functional + # read in dataset for area & landfrac + mdata = adfobj.data.load_climo_dataset(case_name, var) + area = mdata.area.isel(time=0) + landfrac = mdata.landfrac.isel(time=0) + # now read in mdata as a data array to get scale_factor + mdata = adfobj.data.load_climo_da(case_name, var) + #odata.attrs = mdata.attrs # copy attributes back to base case + + #Skip this variable/case if the climo file doesn't exist: + if mdata is None: + dmsg = f"No climo file for {case_name} for variable `{var}`, global lat/lon mean plotting skipped." + adfobj.debug_log(dmsg) + continue + + #Determine dimensions of variable: + has_dims = pf.validate_dims(mdata, ["lat", "lon", "lev"]) + if (not has_dims['has_lat']) or (not has_dims['has_lon']): + print(f"\t = Unstructured grid, so global map for {var} for case {case_name} does not have lat and lon") + + # TODO Check output file. If file does not exist, proceed. + # If file exists: + # if redo_plot is true: delete it now and make plot + # if redo_plot is false: add to website and move on + doplot = {} + + for s in seasons: + plot_name = plot_loc / f"{var}_{s}_LatLon_Mean.{plot_type}" + doplot[plot_name] = plot_file_op(adfobj, plot_name, var, case_name, s, web_category, redo_plot, "LatLon") + + if all(value is None for value in doplot.values()): + print(f"All plots exist for {var}. Redo is {redo_plot}. Existing plots added to website data. Continue.") + continue + + #Create new dictionaries: + mseasons = {} + oseasons = {} + dseasons = {} # hold the differences + pseasons = {} # hold percent change + + if not has_dims['has_lev']: # strictly 2-d data + + #Loop over season dictionary: + for s in seasons: + plot_name = plot_loc / f"{var}_{s}_LatLon_Mean.{plot_type}" + if doplot[plot_name] is None: + continue + + if weight_season: + mseasons[s] = pf.seasonal_mean(mdata, season=s, is_climo=True) + oseasons[s] = pf.seasonal_mean(odata, season=s, is_climo=True) + else: + #Just average months as-is: + mseasons[s] = mdata.sel(time=seasons[s]).mean(dim='time') + oseasons[s] = odata.sel(time=seasons[s]).mean(dim='time') + #End if + + # difference: each entry should be (lat, lon) + dseasons[s] = mseasons[s] - oseasons[s] + + # percent change + pseasons[s] = (mseasons[s] - oseasons[s]) / np.abs(oseasons[s]) * 100.0 #relative change + + # calculate weights + wts = area * landfrac / (area * landfrac).sum() + + pf.plot_unstructured_map_and_save(plot_name, case_nickname, adfobj.data.ref_nickname, + [syear_cases[case_idx],eyear_cases[case_idx]], + [syear_baseline,eyear_baseline], + mseasons[s], oseasons[s], dseasons[s], pseasons[s], wts, + obs=adfobj.compare_obs, **vres) + + #Add plot to website (if enabled): + adfobj.add_website_data(plot_name, var, case_name, category=web_category, + season=s, plot_type="LatLon") + + else: # => pres_levs has values, & we already checked that lev is in mdata (has_lev) + + for pres in pres_levs: + + #Check that the user-requested pressure level + #exists in the model data, which should already + #have been interpolated to the standard reference + #pressure levels: + if (not (pres in mdata['lev'])) or (not (pres in odata['lev'])): + print(f"plot_press_levels value '{pres}' not present in {var} [test: {(pres in mdata['lev'])}, ref: {pres in odata['lev']}], so skipping.") + continue + + #Loop over seasons: + for s in seasons: + plot_name = plot_loc / f"{var}_{pres}hpa_{s}_LatLon_Mean.{plot_type}" + if doplot[plot_name] is None: + continue + + if weight_season: + mseasons[s] = pf.seasonal_mean(mdata, season=s, is_climo=True) + oseasons[s] = pf.seasonal_mean(odata, season=s, is_climo=True) + else: + #Just average months as-is: + mseasons[s] = mdata.sel(time=seasons[s]).mean(dim='time') + oseasons[s] = odata.sel(time=seasons[s]).mean(dim='time') + #End if + + # difference: each entry should be (lat, lon) + dseasons[s] = mseasons[s] - oseasons[s] + + # percent change + pseasons[s] = (mseasons[s] - oseasons[s]) / np.abs(oseasons[s]) * 100.0 #relative change + + pf.plot_map_and_save(plot_name, case_nickname, adfobj.data.ref_nickname, + [syear_cases[case_idx],eyear_cases[case_idx]], + [syear_baseline,eyear_baseline], + mseasons[s].sel(lev=pres), oseasons[s].sel(lev=pres), dseasons[s].sel(lev=pres), + pseasons[s].sel(lev=pres), + obs=adfobj.compare_obs, **vres) + + #Add plot to website (if enabled): + adfobj.add_website_data(plot_name, f"{var}_{pres}hpa", case_name, category=web_category, + season=s, plot_type="LatLon") + #End for (seasons) + #End for (pressure levels) + #End if (plotting pressure levels) + #End for (case loop) + #End for (variable loop) + + # Check for AOD, and run the 4-panel diagnostics against MERRA and MODIS + if "AODVISdn" in var_list: + print("\tRunning AOD panel diagnostics against MERRA and MODIS...") + aod_latlon(adfobj) + + #Notify user that script has ended: + print(" ...lat/lon maps have been generated successfully.") + + +def plot_file_op(adfobj, plot_name, var, case_name, season, web_category, redo_plot, plot_type): + """Check if output plot needs to be made or remade. + + Parameters + ---------- + adfobj : AdfDiag + The diagnostics object that contains all the configuration information + + plot_name : Path + path of the output plot + + var : str + name of variable + + case_name : str + case name + + season : str + season being plotted + + web_category : str + the category for this variable + + redo_plot : bool + whether to overwrite existing plot with this file name + + plot_type : str + the file type for the output plot + + Returns + ------- + int, None + Returns 1 if existing file is removed or no existing file. + Returns None if file exists and redo_plot is False + + Notes + ----- + The long list of parameters is because add_website_data is called + when the file exists and will not be overwritten. + + """ + # Check redo_plot. If set to True: remove old plot, if it already exists: + if plot_name.is_file(): + if redo_plot: + plot_name.unlink() + return True + else: + #Add already-existing plot to website (if enabled): + adfobj.add_website_data(plot_name, var, case_name, category=web_category, + season=season, plot_type=plot_type) + return False # False tells caller that file exists and not to overwrite + else: + return True +######## + + +def aod_latlon(adfobj): + """ + Function to gather data and plot parameters to plot a panel plot of model vs observation + difference and percent difference. + + Calculate the seasonal means for DJF, MAM, JJA, SON for model and obs datasets + + NOTE: The model lat/lons must be on the same grid as the observations. If they are not, they will be + regridded to match both the MERRA and MODIS observation dataset using helper function 'regrid_to_obs' + + For details about spatial coordiantes of obs datasets, see /glade/campaign/cgd/amp/amwg/ADF_obs/: + - MERRA2_192x288_AOD_2001-2020_climo.nc + - MOD08_M3_192x288_AOD_2001-2020_climo.nc + """ + + var = "AODVISdn" + season_abbr = ['Dec-Jan-Feb', 'Mar-Apr-May', 'Jun-Jul-Aug', 'Sep-Oct-Nov'] + # Define a list of season labels + seasons = ['DJF', 'MAM', 'JJA', 'SON'] + + test_case_names = adfobj.get_cam_info('cam_case_name', required=True) + # load reference data (observational or baseline) + if not adfobj.compare_obs: + base_name = adfobj.data.ref_case_label + case_names = test_case_names + [base_name] + else: + case_names = test_case_names + + #Grab all case nickname(s) + test_nicknames = adfobj.case_nicknames["test_nicknames"] + base_nickname = adfobj.case_nicknames["base_nickname"] + case_nicknames = test_nicknames + [base_nickname] + + res = adfobj.variable_defaults # will be dict of variable-specific plot preferences + # or an empty dictionary if use_defaults was not specified in YAML. + res_aod_diags = res["aod_diags"] + plot_params = res_aod_diags["plot_params"] + plot_params_relerr = res_aod_diags["plot_params_relerr"] + + # Observational Datasets + #----------------------- + # Round lat/lons to 5 decimal places + # NOTE: this is neccessary due to small fluctuations in insignificant decimal places + # in lats/lons between models and these obs data sets. The model cases will also + # be rounded in turn. + obs_dir = adfobj.get_basic_info("obs_data_loc") + file_merra2 = os.path.join(obs_dir, 'MERRA2_192x288_AOD_2001-2020_climo.nc') + file_mod08_m3 = os.path.join(obs_dir, 'MOD08_M3_192x288_AOD_2001-2020_climo.nc') + + if (not Path(file_merra2).is_file()) or (not Path(file_mod08_m3).is_file()): + print("\t ** AOD Panel plots not made, missing MERRA2 and/or MODIS file") + return + + ds_merra2 = xr.open_dataset(file_merra2) + ds_merra2 = ds_merra2['TOTEXTTAU'] + ds_merra2['lon'] = ds_merra2['lon'].round(5) + ds_merra2['lat'] = ds_merra2['lat'].round(5) + + ds_mod08_m3 = xr.open_dataset(file_mod08_m3) + ds_mod08_m3 = ds_mod08_m3['AOD_550_Dark_Target_Deep_Blue_Combined_Mean_Mean'] + ds_mod08_m3['lon'] = ds_mod08_m3['lon'].round(5) + ds_mod08_m3['lat'] = ds_mod08_m3['lat'].round(5) + + ds_merra2_season = monthly_to_seasonal(ds_merra2) + ds_merra2_season['lon'] = ds_merra2_season['lon'].round(5) + ds_merra2_season['lat'] = ds_merra2_season['lat'].round(5) + + ds_mod08_m3_season = monthly_to_seasonal(ds_mod08_m3) + ds_mod08_m3_season['lon'] = ds_mod08_m3_season['lon'].round(5) + ds_mod08_m3_season['lat'] = ds_mod08_m3_season['lat'].round(5) + + ds_obs = [ds_mod08_m3_season, ds_merra2_season] + obs_lat_shape = ds_obs[0]['lat'].shape[0] + obs_lon_shape = ds_obs[0]['lon'].shape[0] + obs_titles = ["TERRA MODIS", "MERRA2"] + + # Model Case Datasets + #----------------------- + ds_cases = [] + + for case in test_case_names: + #Load re-gridded model files: + ds_case = adfobj.data.load_climo_dataset(case, var) + + #Skip this variable/case if the climo file doesn't exist: + if ds_case is None: + dmsg = f"No test climo file for {case} for variable `{var}`, global lat/lon plots skipped." + adfobj.debug_log(dmsg) + continue + else: + # Round lat/lons so they match obs + # NOTE: this is neccessary due to small fluctuations in insignificant decimal places + # that raise an error due to non-exact difference calculations. + # Rounding all datasets to 5 places ensures the proper difference calculation + ds_case['lon'] = ds_case['lon'].round(5) + ds_case['lat'] = ds_case['lat'].round(5) + case_lat_shape = ds_case['lat'].shape[0] + case_lon_shape = ds_case['lon'].shape[0] + + # Check if the lats/lons are same as the first supplied observation set + if case_lat_shape == obs_lat_shape: + case_lat = True + else: + err_msg = "AOD 4-panel plot:\n" + err_msg += f"\t The lat values don't match between obs and '{case}'\n" + err_msg += f"\t - {case} lat shape: {case_lat_shape} and " + err_msg += f"obs lat shape: {obs_lat_shape}" + adfobj.debug_log(err_msg) + print(err_msg) + case_lat = False + # End if + + if case_lon_shape == obs_lon_shape: + case_lon = True + else: + err_msg = "AOD 4-panel plot:\n" + err_msg += f"\t The lon values don't match between obs and '{case}'\n" + err_msg += f"\t - {case} lon shape: {case_lon_shape} and " + err_msg += f"obs lon shape: {obs_lon_shape}" + adfobj.debug_log(err_msg) + print(err_msg) + case_lon = False + # End if + + # Check to make sure spatial dimensions are compatible + if (case_lat) and (case_lon): + # Calculate seasonal means + ds_case_season = monthly_to_seasonal(ds_case) + ds_case_season['lon'] = ds_case_season['lon'].round(5) + ds_case_season['lat'] = ds_case_season['lat'].round(5) + ds_cases.append(ds_case_season) + else: + # Regrid the model data to obs + #NOTE: first argument is the model to be regridded, second is the obs + # to be regridded to + ds_case_regrid = regrid_to_obs(adfobj, ds_case, ds_obs[0]) + + ds_case_season = monthly_to_seasonal(ds_case_regrid) + ds_case_season['lon'] = ds_case_season['lon'].round(5) + ds_case_season['lat'] = ds_case_season['lat'].round(5) + ds_cases.append(ds_case_season) + # End if + # End if + + # load reference data (observational or baseline) + if not adfobj.compare_obs: + + # Get baseline case name + base_name = adfobj.data.ref_case_label + + # Gather reference variable data + ds_base = adfobj.data.load_reference_climo_da(base_name, var) + if ds_base is None: + dmsg = f"No baseline climo file for {base_name} for variable `{var}`, global lat/lon plots skipped." + adfobj.debug_log(dmsg) + else: + # Round lat/lons so they match obs + # NOTE: this is neccessary due to small fluctuations in insignificant decimal places + # that raise an error due to non-exact difference calculations. + # Rounding all datasets to 5 places ensures the proper difference calculation + ds_base['lon'] = ds_base['lon'].round(5) + ds_base['lat'] = ds_base['lat'].round(5) + base_lat_shape = ds_base['lat'].shape[0] + base_lon_shape = ds_base['lon'].shape[0] + + # Check if the lats/lons are same as the first supplied observation set + if base_lat_shape == obs_lat_shape: + base_lat = True + else: + err_msg = "AOD 4-panel plot:\n" + err_msg += f"\t The lat values don't match between obs and '{base_name}'\n" + err_msg += f"\t - {base_name} lat shape: {base_lat_shape} and " + err_msg += f"obs lat shape: {obs_lat_shape}" + adfobj.debug_log(err_msg) + print(err_msg) + base_lat = False + # End if + + if base_lon_shape == obs_lon_shape: + base_lon = True + else: + err_msg = "AOD 4-panel plot:\n" + err_msg += f"\t The lon values don't match between obs and '{base_name}'\n" + err_msg += f"\t - {base_name} lon shape: {base_lon_shape} and " + err_msg += f"obs lon shape: {obs_lon_shape}" + adfobj.debug_log(err_msg) + print(err_msg) + base_lon = False + # End if + + # Check to make sure spatial dimensions are compatible + if (base_lat) and (base_lon): + # Calculate seasonal means + ds_base_season = monthly_to_seasonal(ds_base) + ds_base_season['lon'] = ds_base_season['lon'].round(5) + ds_base_season['lat'] = ds_base_season['lat'].round(5) + ds_cases.append(ds_base_season) + else: + # Regrid the model data to obs + #NOTE: first argument is the model to be regridded, second is the obs + # to be regridded to + ds_base_regrid = regrid_to_obs(adfobj, ds_base, ds_obs[0]) + + ds_base_season = monthly_to_seasonal(ds_base_regrid) + ds_base_season['lon'] = ds_base_season['lon'].round(5) + ds_base_season['lat'] = ds_base_season['lat'].round(5) + ds_cases.append(ds_base_season) + # End if + # End if + # Number of relevant cases + case_num = len(ds_cases) + + # 4-Panel global lat/lon plots + #----------------------------- + # NOTE: This loops over all obs and available cases, so just + # make lists to keepo track of details for each case vs obs matchup + # Plots: + # - Difference of seasonal avg of case minus seasonal avg of observation + # - Percent Difference of seasonal avg of case minus seasonal avg of observation + + # Loop over each observation dataset first + for i_obs,ds_ob in enumerate(ds_obs): + for i_s,season in enumerate(seasons): + # Plot title list + plot_titles = [] + # Calculated data list + data = [] + # Plot parameter list + params = [] + # Plot type list, ie difference or percent difference + types = [] + # Model case name list + case_name_list = [] + + # Get observation short name + obs_name = obs_titles[i_obs] + + # Get seasonal abbriviation + chem_season = season_abbr[i_s] + + # Then loop over each available model case + for i_case,ds_case in enumerate(ds_cases): + case_nickname = case_nicknames[i_case] + + # Difference with obs + case_field = ds_case.sel(season=season) - ds_ob.sel(season=season) + plot_titles.append(f'{case_nickname} - {obs_name}\nAOD 550 nm - ' + chem_season) + data.append(case_field) + params.append(plot_params) + types.append("Diff") + case_name_list.append(case_names[i_case]) + + # Percent difference with obs + field_relerr = 100 * case_field / ds_ob.sel(season=season) + field_relerr = np.clip(field_relerr, -100, 100) + plot_titles.append(f'Percent Diff {case_nickname} - {obs_name}\nAOD 550 nm - ' + chem_season) + data.append(field_relerr) + params.append(plot_params_relerr) + types.append("Percent Diff") + case_name_list.append(case_names[i_case]) + # End for + + # Create 4-panel plot for season + aod_panel_latlon(adfobj, plot_titles, params, data, season, obs_name, case_name_list, case_num, types, symmetric=True) + # End for + # End for + + +######################################## +# Helper functions for AOD 4-panel plots +# ####################################### + +def monthly_to_seasonal(ds,obs=False): + ds_season = xr.Dataset( + coords={'lat': ds.coords['lat'], 'lon': ds.coords['lon'], + 'season': np.arange(4)}) + da_season = xr.DataArray( + coords=ds_season.coords, dims=['lat', 'lon', 'season']) + + # Create a list of DataArrays + dataarrays = [] + # Define a list of season labels + seasons = ['DJF', 'MAM', 'JJA', 'SON'] + + if obs: + for varname in ds: + if '_n' not in varname: + ds_season = xr.zeros_like(da_season) + for s in seasons: + dataarrays.append(pf.seasonal_mean(ds, season=s, is_climo=True)) + else: + for s in seasons: + dataarrays.append(pf.seasonal_mean(ds, season=s, is_climo=True)) + + # Use xr.concat to combine along a new 'season' dimension + ds_season = xr.concat(dataarrays, dim='season') + + # Assign the 'season' labels to the new 'season' dimension + ds_season['season'] = seasons + ds_season = ds_season.transpose('lat', 'lon', 'season') + + return ds_season +####### + + +def aod_panel_latlon(adfobj, plot_titles, plot_params, data, season, obs_name, case_name, case_num, types, symmetric=False): + """ + Function to plot a panel plot of model vs observation difference and percent difference + + This will be a 4-panel plot if model vs model run: + - Top left is test model minus obs + - Top right is baseline model minus obs + - Bottom left is test model minus obs percent difference + - Bottom right is baseline model minus obs percent difference + + This will be a 2-panel plot if model vs obs run: + - Top is test model minus obs + - Bottom is test model minus obs percent difference + + NOTE: Individual plots of the panel plots will be created and saved to plotting location(s) + but will not be published to the webpage (if enabled) + """ + #Set plot details: + # -- this should be set in basic_info_dict, but is not required + # -- So check for it, and default to png + basic_info_dict = adfobj.read_config_var("diag_basic_info") + file_type = basic_info_dict.get('plot_type', 'png') + plot_dir = adfobj.plot_location[0] + + # check if existing plots need to be redone + redo_plot = adfobj.get_basic_info('redo_plot') + + # Save the panel figure + plot_name = f'AOD_diff_{obs_name.replace(" ","_")}_{season}_LatLon_Mean.{file_type}' + plotfile = Path(plot_dir) / plot_name + + # Check redo_plot. If set to True: remove old plot, if it already exists: + if (not redo_plot) and plotfile.is_file(): + adfobj.debug_log(f"'{plotfile}' exists and clobber is false.") + #Add already-existing plot to website (if enabled): + adfobj.add_website_data(plotfile, f'AOD_diff_{obs_name.replace(" ","_")}', None, + season=season, multi_case=True, plot_type="LatLon", category="4-Panel AOD Diags") + + # Exit + return + else: + if plotfile.is_file(): + plotfile.unlink() + # End if + # End if + + # create figure: + fig = plt.figure(figsize=(7*case_num,10)) + proj = ccrs.PlateCarree() + + # LAYOUT WITH GRIDSPEC + plot_len = int(3*case_num) + gs = mpl.gridspec.GridSpec(2*case_num, plot_len, wspace=0.5, hspace=0.0) + gs.tight_layout(fig) + + axs = [] + for i in range(case_num): + start = i * 3 + end = (i + 1) * 3 + axs.append(plt.subplot(gs[0:case_num, start:end], projection=proj)) + axs.append(plt.subplot(gs[case_num:, start:end], projection=proj)) + # End for + + # formatting for tick labels + lon_formatter = LongitudeFormatter(number_format='0.0f', + degree_symbol='', + dateline_direction_label=False) + lat_formatter = LatitudeFormatter(number_format='0.0f', + degree_symbol='') + + # Loop over each data set + for i,field in enumerate(data): + # Set up sub plots for main panel plot + ind_fig, ind_ax = plt.subplots(1, 1, figsize=((7*case_num)/2,10/2),subplot_kw={'projection': proj}) + + lon_values = field.lon.values + lat_values = field.lat.values + + # Get field plot paramters + plot_param = plot_params[i] + + # Define plot levels + levels = np.linspace( + plot_param['range_min'], plot_param['range_max'], + plot_param['nlevel'], endpoint=True) + if 'augment_levels' in plot_param: + levels = sorted(np.append( + levels, np.array(plot_param['augment_levels']))) + # End if + + if field.ndim > 2: + print(f"Required 2d lat/lon coordinates, got {field.ndim}d") + emg = "AOD panel plot:\n" + emg += f"\t Too many dimensions for {case_name}. Needs 2 (lat/lon) but got {field.ndim}" + adfobj.debug_log(emg) + print(f"{emg} ") + return + # End if + + # Get data + field_values = field.values[:,:] + field_values, lon_values = add_cyclic_point(field_values, coord=lon_values) + lon_mesh, lat_mesh = np.meshgrid(lon_values, lat_values) + field_mean = np.nanmean(field_values) + + # Set plot details + extend_option = 'both' if symmetric else 'max' + + if 'colormap' in plot_param: + cmap_option = plot_param['colormap'] if symmetric else plt.cm.turbo + else: + cmap_option = plt.cm.bwr if symmetric else plt.cm.turbo + + img = axs[i].contourf(lon_mesh, lat_mesh, field_values, + levels, cmap=cmap_option, extend=extend_option, + transform_first=True, + transform=ccrs.PlateCarree()) + ind_img = ind_ax.contourf(lon_mesh, lat_mesh, field_values, + levels, cmap=cmap_option, extend=extend_option, + transform_first=True, + transform=ccrs.PlateCarree()) + + axs[i].set_facecolor('gray') + ind_ax.set_facecolor('gray') + axs[i].coastlines() + ind_ax.coastlines() + + # Set plot titles + axs[i].set_title(plot_titles[i] + (' Mean %.2g' % field_mean),fontsize=10) + ind_ax.set_title(plot_titles[i] + (' Mean %.2g' % field_mean),fontsize=10) + + # Colorbar options + cbar = plt.colorbar(img, orientation='horizontal', pad=0.05) + ind_cbar = plt.colorbar(ind_img, orientation='horizontal', pad=0.05) + + if 'ticks' in plot_param: + cbar.set_ticks(plot_param['ticks']) + ind_cbar.set_ticks(plot_param['ticks']) + if 'tick_labels' in plot_param: + cbar.ax.set_xticklabels(plot_param['tick_labels']) + ind_cbar.ax.set_xticklabels(plot_param['tick_labels']) + cbar.ax.tick_params(labelsize=6) + + # Save the individual figure + pbase = f'AOD_{case_name[i]}_vs_{obs_name.replace(" ","_")}_{types[i].replace(" ","_")}' + ind_plotfile = f'{pbase}_{season}_LatLon_Mean.{file_type}' + ind_png_file = Path(plot_dir) / ind_plotfile + ind_fig.savefig(f'{ind_png_file}', bbox_inches='tight', dpi=300) + plt.close(ind_fig) + # End for + + # Save the panel figure + plot_name = f'AOD_diff_{obs_name.replace(" ","_")}_{season}_LatLon_Mean.{file_type}' + plotfile = Path(plot_dir) / plot_name + + # Save figure and add to website if applicable + fig.savefig(plotfile, bbox_inches='tight', dpi=300) + adfobj.add_website_data(plotfile, f'AOD_diff_{obs_name.replace(" ","_")}', None, + season=season, multi_case=True, plot_type="LatLon", category="4-Panel AOD Diags") + + # Close the figure + plt.close(fig) +###### + + +def regrid_to_obs(adfobj, model_arr, obs_arr): + """ + Check if the model grid needs to be interpolated to the obs grid. If so, + use xesmf to regrid and return new dataset + """ + test_lons = model_arr.lon + test_lats = model_arr.lat + + obs_lons = obs_arr.lon + obs_lats = obs_arr.lat + + # Just set defaults for now + same_lats = True + same_lons = True + model_regrid_arr = None + + if obs_lons.shape == test_lons.shape: + try: + xr.testing.assert_equal(test_lons, obs_lons) + except AssertionError as e: + same_lons = False + err_msg = "AOD 4-panel plot:\n" + err_msg += "\t The lons ARE NOT the same" + adfobj.debug_log(err_msg) + try: + xr.testing.assert_equal(test_lats, obs_lats) + except AssertionError as e: + same_lats = False + err_msg = "AOD 4-panel plot:\n" + err_msg += "\t The lats ARE NOT the same" + adfobj.debug_log(err_msg) + else: + same_lats = False + same_lons = False + print("\tThe model lat/lon grid does not match the " \ + "obs grid.\n\t - Regridding to observation lats and lons") + + # QUESTION: will there ever be a scenario where we need to regrid only lats or lons?? + if (not same_lons) and (not same_lats): + # Make dummy array to be populated + ds_out = xr.Dataset( + { + "lat": (["lat"], obs_lats.values, {"units": "degrees_north"}), + "lon": (["lon"], obs_lons.values, {"units": "degrees_east"}), + } + ) + + # Regrid to the obs grid to make altered model grid + regridder = xe.Regridder(model_arr, ds_out, "bilinear", periodic=True) + model_regrid_arr = regridder(model_arr, keep_attrs=True) + + # Return the new interpolated model array + return model_regrid_arr +####### + +############## +#END OF SCRIPT diff --git a/scripts/plotting/polar_ux_map.py b/scripts/plotting/polar_ux_map.py new file mode 100644 index 000000000..8f584802c --- /dev/null +++ b/scripts/plotting/polar_ux_map.py @@ -0,0 +1,766 @@ +""" +Generate arctic maps of 2-D fields +completely redundant with scripts/plotting/global_unstructured_latlon_map.py +here explicit calls projection = 'arctic' + +Functions +--------- +arctic_latlon_map(adfobj) + use ADF object to make maps +my_formatwarning(msg, *args, **kwargs) + format warning messages + (private method) +plot_file_op + Check on status of output plot file. +""" +#Import standard modules: +import os +from pathlib import Path +import numpy as np +import xarray as xr +import xesmf as xe +import warnings # use to warn user about missing files. + +# Import plotting modules: +import matplotlib as mpl +import matplotlib.pyplot as plt +import cartopy.crs as ccrs +import cartopy.feature as cfeature +from cartopy.util import add_cyclic_point +from cartopy.mpl.ticker import LongitudeFormatter, LatitudeFormatter +import plotting_functions as pf + +import uxarray as ux #need npl 2024a or later +import geoviews.feature as gf + +# Warnings +import warnings # use to warn user about missing files. +# - Format warning messages: +def my_formatwarning(msg, *args, **kwargs): + """Issue `msg` as warning.""" + return str(msg) + '\n' +warnings.formatwarning = my_formatwarning + +######### + +def polar_ux_map(adfobj): + """ + This script/function is designed to generate global + 2-D lat/lon maps of model fields with continental overlays. + + uses uxarray to handle unstructured grids + also set up to read in raw climatology files (ne30 resolution) + + Parameters + ---------- + adfobj : AdfDiag + The diagnostics object that contains all the configuration information + + Returns + ------- + Does not return a value; produces plots and saves files. + + Notes + ----- + + It uses the AdfDiag object's methods to get necessary information. + Makes use of AdfDiag's data sub-class. + Explicitly accesses: + adfobj.diag_var_list + List of variables + adfobj.plot_location + output plot path + adfobj.climo_yrs + start and end climo years of the case(s), `syears` & `eyears` + start and end climo years of the reference, `syear_baseline` & `eyear_baseline` + adfobj.variable_defaults + dict of variable-specific plot preferences + adfobj.read_config_var + dict of basic info, `diag_basic_info` + Then use to check `plot_type` + adfobj.debug_log + Issues debug message + adfobj.add_website_data + Communicates information to the website generator + adfobj.compare_obs + Logical to determine if comparing to observations + + + The `plotting_functions` module is needed for: + pf.get_central_longitude() + determine central longitude for global plots + pf.lat_lon_validate_dims() TODO, remove this, or check for unstructured grid and mesh file + makes sure latitude and longitude are valid + pf.seasonal_mean() + calculate seasonal mean + pf.plot_map_and_save() + send information to make the plot and save the file + pf.zm_validate_dims() TODO, not necessary for land plots, but maybe keep for atmosphere + Checks on pressure level dimension + """ + + #Notify user that script has started: + print("\n Generating lat/lon maps...") + + # + # Use ADF api to get all necessary information + # + var_list = adfobj.diag_var_list + #Special ADF variable which contains the output paths for + #all generated plots and tables for each case: + plot_locations = adfobj.plot_location + + #Grab case years + syear_cases = adfobj.climo_yrs["syears"] + eyear_cases = adfobj.climo_yrs["eyears"] + + #Grab baseline years (which may be empty strings if using Obs): + syear_baseline = adfobj.climo_yrs["syear_baseline"] + eyear_baseline = adfobj.climo_yrs["eyear_baseline"] + + res = adfobj.variable_defaults # will be dict of variable-specific plot preferences + # or an empty dictionary if use_defaults was not specified in YAML. + + #Set plot file type: + # -- this should be set in basic_info_dict, but is not required + # -- So check for it, and default to png + basic_info_dict = adfobj.read_config_var("diag_basic_info") + plot_type = basic_info_dict.get('plot_type', 'png') + print(f"\t NOTE: Plot type is set to {plot_type}") + + # check if existing plots need to be redone + redo_plot = adfobj.get_basic_info('redo_plot') + print(f"\t NOTE: redo_plot is set to {redo_plot}") + #----------------------------------------- + + #Determine if user wants to plot 3-D variables on + #pressure levels: + pres_levs = adfobj.get_basic_info("plot_press_levels") + + weight_season = True #always do seasonal weighting + + #Set seasonal ranges: + seasons = {"ANN": np.arange(1,13,1), + "DJF": [12, 1, 2], + "JJA": [6, 7, 8], + "MAM": [3, 4, 5], + "SON": [9, 10, 11] + } + + # probably want to do this one variable at a time: + for var in var_list: + if var not in adfobj.data.ref_var_nam: + dmsg = f"No reference data found for variable `{var}`, global lat/lon mean plotting skipped." + adfobj.debug_log(dmsg) + print(dmsg) + continue + + #Notify user of variable being plotted: + print("\t - lat/lon maps for {}".format(var)) + + # Check res for any variable specific options that need to be used BEFORE going to the plot: + if var in res: + vres = res[var] + #If found then notify user, assuming debug log is enabled: + adfobj.debug_log(f"arctic_latlon_map: Found variable defaults for {var}") + + #Extract category (if available): + web_category = vres.get("category", None) + + else: + vres = {} + web_category = None + #End if + + # For global maps, also set the central longitude: + # can be specified in adfobj basic info as 'central_longitude' or supplied as a number, + # otherwise defaults to 180 + vres['central_longitude'] = pf.get_central_longitude(adfobj) + + # load reference data (observational or baseline) + if not adfobj.compare_obs: + base_name = adfobj.data.ref_case_label + else: + base_name = adfobj.data.ref_labels[var] + + # Gather reference variable data + odata = adfobj.data.load_reference_climo_da(base_name, var) + #odata = odata[var] # now just read in the data array, but don't modify anything + + if odata is None: + dmsg = f"No regridded test file for {base_name} for variable `{var}`, arctic lat/lon mean plotting skipped." + adfobj.debug_log(dmsg) + continue + + o_has_dims = pf.validate_dims(odata, ["lat", "lon", "lev"]) # T iff dims are (lat,lon) -- can't plot unless we have both + if (not o_has_dims['has_lat']) or (not o_has_dims['has_lon']): + print(f"\t = Unstructured grid, so arctic map for {var} does not have lat and lon") + + #Loop over model cases: + for case_idx, case_name in enumerate(adfobj.data.case_names): + + #Set case nickname: + case_nickname = adfobj.data.test_nicknames[case_idx] + + #Set output plot location: + plot_loc = Path(plot_locations[case_idx]) + + #Check if plot output directory exists, and if not, then create it: + if not plot_loc.is_dir(): + print(" {} not found, making new directory".format(plot_loc)) + plot_loc.mkdir(parents=True) + + #Load climo model files: TODO, this is kind of clunky, but functional + # read in dataset for area & landfrac + mdata = adfobj.data.load_climo_dataset(case_name, var) + area = mdata.area.isel(time=0) + landfrac = mdata.landfrac.isel(time=0) + # now read in mdata as a data array to get scale_factor + mdata = adfobj.data.load_climo_da(case_name, var) + #odata.attrs = mdata.attrs # copy attributes back to base case + + #Skip this variable/case if the climo file doesn't exist: + if mdata is None: + dmsg = f"No climo file for {case_name} for variable `{var}`, arctic lat/lon mean plotting skipped." + adfobj.debug_log(dmsg) + continue + + #Determine dimensions of variable: + has_dims = pf.validate_dims(mdata, ["lat", "lon", "lev"]) + if (not has_dims['has_lat']) or (not has_dims['has_lon']): + print(f"\t = Unstructured grid, so arctic map for {var} for case {case_name} does not have lat and lon") + + # Check output file. If file does not exist, proceed. + # If file exists: + # if redo_plot is true: delete it now and make plot + # if redo_plot is false: add to website and move on + doplot = {} + + for s in seasons: + plot_name = plot_loc / f"{var}_{s}_Arctic_Mean.{plot_type}" + doplot[plot_name] = plot_file_op(adfobj, plot_name, var, case_name, s, web_category, redo_plot, "Arctic") + + if all(value is None for value in doplot.values()): + print(f"All plots exist for {var}. Redo is {redo_plot}. Existing plots added to website data. Continue.") + continue + + #Create new dictionaries: + mseasons = {} + oseasons = {} + dseasons = {} # hold the differences + pseasons = {} # hold percent change + + if not has_dims['has_lev']: # strictly 2-d data + + #Loop over season dictionary: + for s in seasons: + plot_name = plot_loc / f"{var}_{s}_Arctic_Mean.{plot_type}" + if doplot[plot_name] is None: + continue + + if weight_season: + mseasons[s] = pf.seasonal_mean(mdata, season=s, is_climo=True) + oseasons[s] = pf.seasonal_mean(odata, season=s, is_climo=True) + else: + #Just average months as-is: + mseasons[s] = mdata.sel(time=seasons[s]).mean(dim='time') + oseasons[s] = odata.sel(time=seasons[s]).mean(dim='time') + #End if + + # difference: each entry should be (lat, lon) + dseasons[s] = mseasons[s] - oseasons[s] + + # percent change + pseasons[s] = (mseasons[s] - oseasons[s]) / np.abs(oseasons[s]) * 100.0 #relative change + + # calculate weights + wts = area * landfrac / (area * landfrac).sum() + # TODO, set plot_name and web_category correctly here + pf.plot_unstructured_map_and_save(plot_name, case_nickname, adfobj.data.ref_nickname, + [syear_cases[case_idx],eyear_cases[case_idx]], + [syear_baseline,eyear_baseline], + mseasons[s], oseasons[s], + dseasons[s], pseasons[s], wts, + obs=adfobj.compare_obs, + projection='arctic', **vres) + + #Add plot to website (if enabled): + adfobj.add_website_data(plot_name, var, case_name, category=web_category, + season=s, plot_type="Arctic") + + else: # => pres_levs has values, & we already checked that lev is in mdata (has_lev) + + for pres in pres_levs: + + #Check that the user-requested pressure level + #exists in the model data, which should already + #have been interpolated to the standard reference + #pressure levels: + if (not (pres in mdata['lev'])) or (not (pres in odata['lev'])): + print(f"plot_press_levels value '{pres}' not present in {var} [test: {(pres in mdata['lev'])}, ref: {pres in odata['lev']}], so skipping.") + continue + + #Loop over seasons: + for s in seasons: + plot_name = plot_loc / f"{var}_{pres}hpa_{s}_Arctic_Mean.{plot_type}" + if doplot[plot_name] is None: + continue + + if weight_season: + mseasons[s] = pf.seasonal_mean(mdata, season=s, is_climo=True) + oseasons[s] = pf.seasonal_mean(odata, season=s, is_climo=True) + else: + #Just average months as-is: + mseasons[s] = mdata.sel(time=seasons[s]).mean(dim='time') + oseasons[s] = odata.sel(time=seasons[s]).mean(dim='time') + #End if + + # difference: each entry should be (lat, lon) + dseasons[s] = mseasons[s] - oseasons[s] + + # percent change + pseasons[s] = (mseasons[s] - oseasons[s]) / np.abs(oseasons[s]) * 100.0 #relative change + + pf.plot_map_and_save(plot_name, case_nickname, adfobj.data.ref_nickname, + [syear_cases[case_idx],eyear_cases[case_idx]], + [syear_baseline,eyear_baseline], + mseasons[s].sel(lev=pres), oseasons[s].sel(lev=pres), dseasons[s].sel(lev=pres), + pseasons[s].sel(lev=pres), + obs=adfobj.compare_obs, **vres) + + #Add plot to website (if enabled): + adfobj.add_website_data(plot_name, f"{var}_{pres}hpa", case_name, category=web_category, + season=s, plot_type="Arctic") + #End for (seasons) + #End for (pressure levels) + #End if (plotting pressure levels) + #End for (case loop) + #End for (variable loop) + + # Check for AOD, and run the 4-panel diagnostics against MERRA and MODIS + if "AODVISdn" in var_list: + print("\tRunning AOD panel diagnostics against MERRA and MODIS...") + aod_latlon(adfobj) + + #Notify user that script has ended: + print(" ...lat/lon maps have been generated successfully.") + + +def plot_file_op(adfobj, plot_name, var, case_name, season, web_category, redo_plot, plot_type): + """Check if output plot needs to be made or remade. + + Parameters + ---------- + adfobj : AdfDiag + The diagnostics object that contains all the configuration information + + plot_name : Path + path of the output plot + + var : str + name of variable + + case_name : str + case name + + season : str + season being plotted + + web_category : str + the category for this variable + + redo_plot : bool + whether to overwrite existing plot with this file name + + plot_type : str + the file type for the output plot + + Returns + ------- + int, None + Returns 1 if existing file is removed or no existing file. + Returns None if file exists and redo_plot is False + + Notes + ----- + The long list of parameters is because add_website_data is called + when the file exists and will not be overwritten. + + """ + # Check redo_plot. If set to True: remove old plot, if it already exists: + if plot_name.is_file(): + if redo_plot: + plot_name.unlink() + return True + else: + #Add already-existing plot to website (if enabled): + adfobj.add_website_data(plot_name, var, case_name, category=web_category, + season=season, plot_type=plot_type) + return False # False tells caller that file exists and not to overwrite + else: + return True +######## + + +def aod_latlon(adfobj): + """ + Function to gather data and plot parameters to plot a panel plot of model vs observation + difference and percent difference. + + Calculate the seasonal means for DJF, MAM, JJA, SON for model and obs datasets + + NOTE: The model lat/lons must be on the same grid as the observations. If they are not, they will be + regridded to match both the MERRA and MODIS observation dataset using helper function 'regrid_to_obs' + + For details about spatial coordiantes of obs datasets, see /glade/campaign/cgd/amp/amwg/ADF_obs/: + - MERRA2_192x288_AOD_2001-2020_climo.nc + - MOD08_M3_192x288_AOD_2001-2020_climo.nc + """ + + var = "AODVISdn" + season_abbr = ['Dec-Jan-Feb', 'Mar-Apr-May', 'Jun-Jul-Aug', 'Sep-Oct-Nov'] + # Define a list of season labels + seasons = ['DJF', 'MAM', 'JJA', 'SON'] + + test_case_names = adfobj.get_cam_info('cam_case_name', required=True) + # load reference data (observational or baseline) + if not adfobj.compare_obs: + base_name = adfobj.data.ref_case_label + case_names = test_case_names + [base_name] + else: + case_names = test_case_names + + #Grab all case nickname(s) + test_nicknames = adfobj.case_nicknames["test_nicknames"] + base_nickname = adfobj.case_nicknames["base_nickname"] + case_nicknames = test_nicknames + [base_nickname] + + res = adfobj.variable_defaults # will be dict of variable-specific plot preferences + # or an empty dictionary if use_defaults was not specified in YAML. + res_aod_diags = res["aod_diags"] + plot_params = res_aod_diags["plot_params"] + plot_params_relerr = res_aod_diags["plot_params_relerr"] + + # Observational Datasets + #----------------------- + # Round lat/lons to 5 decimal places + # NOTE: this is neccessary due to small fluctuations in insignificant decimal places + # in lats/lons between models and these obs data sets. The model cases will also + # be rounded in turn. + obs_dir = adfobj.get_basic_info("obs_data_loc") + file_merra2 = os.path.join(obs_dir, 'MERRA2_192x288_AOD_2001-2020_climo.nc') + file_mod08_m3 = os.path.join(obs_dir, 'MOD08_M3_192x288_AOD_2001-2020_climo.nc') + + if (not Path(file_merra2).is_file()) or (not Path(file_mod08_m3).is_file()): + print("\t ** AOD Panel plots not made, missing MERRA2 and/or MODIS file") + return + + ds_merra2 = xr.open_dataset(file_merra2) + ds_merra2 = ds_merra2['TOTEXTTAU'] + ds_merra2['lon'] = ds_merra2['lon'].round(5) + ds_merra2['lat'] = ds_merra2['lat'].round(5) + + ds_mod08_m3 = xr.open_dataset(file_mod08_m3) + ds_mod08_m3 = ds_mod08_m3['AOD_550_Dark_Target_Deep_Blue_Combined_Mean_Mean'] + ds_mod08_m3['lon'] = ds_mod08_m3['lon'].round(5) + ds_mod08_m3['lat'] = ds_mod08_m3['lat'].round(5) + + ds_merra2_season = monthly_to_seasonal(ds_merra2) + ds_merra2_season['lon'] = ds_merra2_season['lon'].round(5) + ds_merra2_season['lat'] = ds_merra2_season['lat'].round(5) + + ds_mod08_m3_season = monthly_to_seasonal(ds_mod08_m3) + ds_mod08_m3_season['lon'] = ds_mod08_m3_season['lon'].round(5) + ds_mod08_m3_season['lat'] = ds_mod08_m3_season['lat'].round(5) + + ds_obs = [ds_mod08_m3_season, ds_merra2_season] + obs_lat_shape = ds_obs[0]['lat'].shape[0] + obs_lon_shape = ds_obs[0]['lon'].shape[0] + obs_titles = ["TERRA MODIS", "MERRA2"] + + # Model Case Datasets + #----------------------- + ds_cases = [] + + for case in test_case_names: + #Load re-gridded model files: + ds_case = adfobj.data.load_climo_dataset(case, var) + + #Skip this variable/case if the climo file doesn't exist: + if ds_case is None: + dmsg = f"No test climo file for {case} for variable `{var}`, arctic lat/lon plots skipped." + adfobj.debug_log(dmsg) + continue + else: + # Round lat/lons so they match obs + # NOTE: this is neccessary due to small fluctuations in insignificant decimal places + # that raise an error due to non-exact difference calculations. + # Rounding all datasets to 5 places ensures the proper difference calculation + ds_case['lon'] = ds_case['lon'].round(5) + ds_case['lat'] = ds_case['lat'].round(5) + case_lat_shape = ds_case['lat'].shape[0] + case_lon_shape = ds_case['lon'].shape[0] + + # Check if the lats/lons are same as the first supplied observation set + if case_lat_shape == obs_lat_shape: + case_lat = True + else: + err_msg = "AOD 4-panel plot:\n" + err_msg += f"\t The lat values don't match between obs and '{case}'\n" + err_msg += f"\t - {case} lat shape: {case_lat_shape} and " + err_msg += f"obs lat shape: {obs_lat_shape}" + adfobj.debug_log(err_msg) + print(err_msg) + case_lat = False + # End if + + if case_lon_shape == obs_lon_shape: + case_lon = True + else: + err_msg = "AOD 4-panel plot:\n" + err_msg += f"\t The lon values don't match between obs and '{case}'\n" + err_msg += f"\t - {case} lon shape: {case_lon_shape} and " + err_msg += f"obs lon shape: {obs_lon_shape}" + adfobj.debug_log(err_msg) + print(err_msg) + case_lon = False + # End if + + # Check to make sure spatial dimensions are compatible + if (case_lat) and (case_lon): + # Calculate seasonal means + ds_case_season = monthly_to_seasonal(ds_case) + ds_case_season['lon'] = ds_case_season['lon'].round(5) + ds_case_season['lat'] = ds_case_season['lat'].round(5) + ds_cases.append(ds_case_season) + else: + # Regrid the model data to obs + #NOTE: first argument is the model to be regridded, second is the obs + # to be regridded to + ds_case_regrid = regrid_to_obs(adfobj, ds_case, ds_obs[0]) + + ds_case_season = monthly_to_seasonal(ds_case_regrid) + ds_case_season['lon'] = ds_case_season['lon'].round(5) + ds_case_season['lat'] = ds_case_season['lat'].round(5) + ds_cases.append(ds_case_season) + # End if + # End if + + # load reference data (observational or baseline) + if not adfobj.compare_obs: + + # Get baseline case name + base_name = adfobj.data.ref_case_label + + # Gather reference variable data + ds_base = adfobj.data.load_reference_climo_da(base_name, var) + if ds_base is None: + dmsg = f"No baseline climo file for {base_name} for variable `{var}`, arctic lat/lon plots skipped." + adfobj.debug_log(dmsg) + else: + # Round lat/lons so they match obs + # NOTE: this is neccessary due to small fluctuations in insignificant decimal places + # that raise an error due to non-exact difference calculations. + # Rounding all datasets to 5 places ensures the proper difference calculation + ds_base['lon'] = ds_base['lon'].round(5) + ds_base['lat'] = ds_base['lat'].round(5) + base_lat_shape = ds_base['lat'].shape[0] + base_lon_shape = ds_base['lon'].shape[0] + + # Check if the lats/lons are same as the first supplied observation set + if base_lat_shape == obs_lat_shape: + base_lat = True + else: + err_msg = "AOD 4-panel plot:\n" + err_msg += f"\t The lat values don't match between obs and '{base_name}'\n" + err_msg += f"\t - {base_name} lat shape: {base_lat_shape} and " + err_msg += f"obs lat shape: {obs_lat_shape}" + adfobj.debug_log(err_msg) + print(err_msg) + base_lat = False + # End if + + if base_lon_shape == obs_lon_shape: + base_lon = True + else: + err_msg = "AOD 4-panel plot:\n" + err_msg += f"\t The lon values don't match between obs and '{base_name}'\n" + err_msg += f"\t - {base_name} lon shape: {base_lon_shape} and " + err_msg += f"obs lon shape: {obs_lon_shape}" + adfobj.debug_log(err_msg) + print(err_msg) + base_lon = False + # End if + + # Check to make sure spatial dimensions are compatible + if (base_lat) and (base_lon): + # Calculate seasonal means + ds_base_season = monthly_to_seasonal(ds_base) + ds_base_season['lon'] = ds_base_season['lon'].round(5) + ds_base_season['lat'] = ds_base_season['lat'].round(5) + ds_cases.append(ds_base_season) + else: + # Regrid the model data to obs + #NOTE: first argument is the model to be regridded, second is the obs + # to be regridded to + ds_base_regrid = regrid_to_obs(adfobj, ds_base, ds_obs[0]) + + ds_base_season = monthly_to_seasonal(ds_base_regrid) + ds_base_season['lon'] = ds_base_season['lon'].round(5) + ds_base_season['lat'] = ds_base_season['lat'].round(5) + ds_cases.append(ds_base_season) + # End if + # End if + # Number of relevant cases + case_num = len(ds_cases) + + # 4-Panel arctic lat/lon plots + #----------------------------- + # NOTE: This loops over all obs and available cases, so just + # make lists to keepo track of details for each case vs obs matchup + # Plots: + # - Difference of seasonal avg of case minus seasonal avg of observation + # - Percent Difference of seasonal avg of case minus seasonal avg of observation + + # Loop over each observation dataset first + for i_obs,ds_ob in enumerate(ds_obs): + for i_s,season in enumerate(seasons): + # Plot title list + plot_titles = [] + # Calculated data list + data = [] + # Plot parameter list + params = [] + # Plot type list, ie difference or percent difference + types = [] + # Model case name list + case_name_list = [] + + # Get observation short name + obs_name = obs_titles[i_obs] + + # Get seasonal abbriviation + chem_season = season_abbr[i_s] + + # Then loop over each available model case + for i_case,ds_case in enumerate(ds_cases): + case_nickname = case_nicknames[i_case] + + # Difference with obs + case_field = ds_case.sel(season=season) - ds_ob.sel(season=season) + plot_titles.append(f'{case_nickname} - {obs_name}\nAOD 550 nm - ' + chem_season) + data.append(case_field) + params.append(plot_params) + types.append("Diff") + case_name_list.append(case_names[i_case]) + + # Percent difference with obs + field_relerr = 100 * case_field / ds_ob.sel(season=season) + field_relerr = np.clip(field_relerr, -100, 100) + plot_titles.append(f'Percent Diff {case_nickname} - {obs_name}\nAOD 550 nm - ' + chem_season) + data.append(field_relerr) + params.append(plot_params_relerr) + types.append("Percent Diff") + case_name_list.append(case_names[i_case]) + # End for + + # Create 4-panel plot for season + aod_panel_latlon(adfobj, plot_titles, params, data, season, obs_name, case_name_list, case_num, types, symmetric=True) + # End for + # End for + + +######################################## +# Helper functions for AOD 4-panel plots +# ####################################### + +def monthly_to_seasonal(ds,obs=False): + ds_season = xr.Dataset( + coords={'lat': ds.coords['lat'], 'lon': ds.coords['lon'], + 'season': np.arange(4)}) + da_season = xr.DataArray( + coords=ds_season.coords, dims=['lat', 'lon', 'season']) + + # Create a list of DataArrays + dataarrays = [] + # Define a list of season labels + seasons = ['DJF', 'MAM', 'JJA', 'SON'] + + if obs: + for varname in ds: + if '_n' not in varname: + ds_season = xr.zeros_like(da_season) + for s in seasons: + dataarrays.append(pf.seasonal_mean(ds, season=s, is_climo=True)) + else: + for s in seasons: + dataarrays.append(pf.seasonal_mean(ds, season=s, is_climo=True)) + + # Use xr.concat to combine along a new 'season' dimension + ds_season = xr.concat(dataarrays, dim='season') + + # Assign the 'season' labels to the new 'season' dimension + ds_season['season'] = seasons + ds_season = ds_season.transpose('lat', 'lon', 'season') + + return ds_season +####### + + +def regrid_to_obs(adfobj, model_arr, obs_arr): + """ + Check if the model grid needs to be interpolated to the obs grid. If so, + use xesmf to regrid and return new dataset + """ + test_lons = model_arr.lon + test_lats = model_arr.lat + + obs_lons = obs_arr.lon + obs_lats = obs_arr.lat + + # Just set defaults for now + same_lats = True + same_lons = True + model_regrid_arr = None + + if obs_lons.shape == test_lons.shape: + try: + xr.testing.assert_equal(test_lons, obs_lons) + except AssertionError as e: + same_lons = False + err_msg = "AOD 4-panel plot:\n" + err_msg += "\t The lons ARE NOT the same" + adfobj.debug_log(err_msg) + try: + xr.testing.assert_equal(test_lats, obs_lats) + except AssertionError as e: + same_lats = False + err_msg = "AOD 4-panel plot:\n" + err_msg += "\t The lats ARE NOT the same" + adfobj.debug_log(err_msg) + else: + same_lats = False + same_lons = False + print("\tThe model lat/lon grid does not match the " \ + "obs grid.\n\t - Regridding to observation lats and lons") + + # QUESTION: will there ever be a scenario where we need to regrid only lats or lons?? + if (not same_lons) and (not same_lats): + # Make dummy array to be populated + ds_out = xr.Dataset( + { + "lat": (["lat"], obs_lats.values, {"units": "degrees_north"}), + "lon": (["lon"], obs_lons.values, {"units": "degrees_east"}), + } + ) + + # Regrid to the obs grid to make altered model grid + regridder = xe.Regridder(model_arr, ds_out, "bilinear", periodic=True) + model_regrid_arr = regridder(model_arr, keep_attrs=True) + + # Return the new interpolated model array + return model_regrid_arr +####### + +############## +#END OF SCRIPT diff --git a/scripts/regridding/regrid_climo_wrapper.py b/scripts/regridding/regrid_climo_wrapper.py new file mode 100644 index 000000000..0173430d0 --- /dev/null +++ b/scripts/regridding/regrid_climo_wrapper.py @@ -0,0 +1,445 @@ +#Import standard modules: +import xarray as xr + +def regrid_climo_wrapper(adf): + + """ + This funtion regrids the test cases to the same horizontal + grid as the observations or baseline climatology. + + Description of needed inputs from ADF: + + case_name -> Name of CAM case provided by "cam_case_name" + input_climo_loc -> Location of CAM climo files provided by "cam_climo_loc" + output_loc -> Location to write re-gridded CAM files, specified by "cam_climo_regrid_loc" + var_list -> List of CAM output variables provided by "diag_var_list" + var_defaults -> Dict that has keys that are variable names and values that are plotting preferences/defaults. + target_list -> List of target data sets CAM could be regridded to + taget_loc -> Location of target files that CAM will be regridded to + overwrite_regrid -> Logical to determine if already existing re-gridded + files will be overwritten. Specified by "cam_overwrite_climo_regrid" + """ + + #Import necessary modules: + import plotting_functions as pf + + from pathlib import Path + + # regridding + # Try just using the xarray method + # import xesmf as xe # This package is for regridding, and is just one potential solution. + + # Steps: + # - load climo files for model and obs + # - calculate all-time and seasonal fields (from individual months) + # - regrid one to the other (probably should be a choice) + + #Notify user that script has started: + print("\n Regridding CAM climatologies...") + + #Extract needed quantities from ADF object: + #----------------------------------------- + overwrite_regrid = adf.get_basic_info("cam_overwrite_climo_regrid", required=True) + output_loc = adf.get_basic_info("cam_climo_regrid_loc", required=True) + var_list = adf.diag_var_list + var_defaults = adf.variable_defaults + + #CAM simulation variables (these quantities are always lists): + case_names = adf.get_cam_info("cam_case_name", required=True) + input_climo_locs = adf.get_cam_info("cam_climo_loc", required=True) + + #Grab case years + syear_cases = adf.climo_yrs["syears"] + eyear_cases = adf.climo_yrs["eyears"] + + #Check if land fraction exists + #in the variable list: + for var in ["LANDFRAC"]: + if var in var_list: + #If so, then move it to the front of variable list so + #that it can be used to mask + #other model variables if need be: + var_idx = var_list.index(var) + var_list.pop(var_idx) + var_list.insert(0,var) + #End if + #End for + + #Create new variable that potentially stores the re-gridded + #land fraction dataset: + lnd_frc_ds = None + + #Regrid target variables (either obs or a baseline run): + if adf.compare_obs: + + #Set obs name to match baseline (non-obs) + target_list = ["Obs"] + + #Extract variable-obs dictionary: + var_obs_dict = adf.var_obs_dict + + #If dictionary is empty, then there are no observations to regrid to, + #so quit here: + if not var_obs_dict: + print("\t No observations found to regrid to, so no re-gridding will be done.") + return + #End if + + else: + + #Extract model baseline variables: + target_loc = adf.get_baseline_info("cam_climo_loc", required=True) + target_list = [adf.get_baseline_info("cam_case_name", required=True)] + #End if + + #Grab baseline years (which may be empty strings if using Obs): + syear_baseline = adf.climo_yrs["syear_baseline"] + eyear_baseline = adf.climo_yrs["eyear_baseline"] + + #Set attributes dictionary for climo years to save in the file attributes + base_climo_yrs_attr = f"{target_list[0]}: {syear_baseline}-{eyear_baseline}" + + #----------------------------------------- + + #Set output/target data path variables: + #------------------------------------ + rgclimo_loc = Path(output_loc) + if not adf.compare_obs: + tclimo_loc = Path(target_loc) + #------------------------------------ + + #Check if re-gridded directory exists, and if not, then create it: + if not rgclimo_loc.is_dir(): + print(f" {rgclimo_loc} not found, making new directory") + rgclimo_loc.mkdir(parents=True) + #End if + + #Loop over CAM cases: + for case_idx, case_name in enumerate(case_names): + + #Notify user of model case being processed: + print(f"\t Regridding case '{case_name}' :") + + #Set case climo data path: + mclimo_loc = Path(input_climo_locs[case_idx]) + + #Get climo years for case + syear = syear_cases[case_idx] + eyear = eyear_cases[case_idx] + + # probably want to do this one variable at a time: + for var in var_list: + + if adf.compare_obs: + #Check if obs exist for the variable: + if var in var_obs_dict: + #Note: In the future these may all be lists, but for + #now just convert the target_list. + #Extract target file: + tclimo_loc = var_obs_dict[var]["obs_file"] + #Extract target list (eventually will be a list, for now need to convert): + target_list = [var_obs_dict[var]["obs_name"]] + else: + dmsg = f"No obs found for variable `{var}`, regridding skipped." + adf.debug_log(dmsg) + continue + #End if + #End if + + #Notify user of variable being regridded: + print(f"\t - regridding {var} (known targets: {target_list})") + + #loop over regridding targets: + for target in target_list: + + #Write to debug log if enabled: + adf.debug_log(f"regrid_example: regrid target = {target}") + + #Determine regridded variable file name: + regridded_file_loc = rgclimo_loc / f'{case_name}_{var}_regridded.nc' + + #Check if re-gridded file already exists and over-writing is allowed: + if regridded_file_loc.is_file() and overwrite_regrid: + #If so, then delete current file: + regridded_file_loc.unlink() + #End if + + #Check again if re-gridded file already exists: + if not regridded_file_loc.is_file(): + + #Create list of regridding target files (we should explore intake as an alternative to having this kind of repeated code) + # NOTE: This breaks if you have files from different cases in same directory! + if adf.compare_obs: + #For now, only grab one file (but convert to list for use below): + tclim_fils = [tclimo_loc] + else: + tclim_fils = sorted(tclimo_loc.glob(f"{target}*_{var}_climo.nc")) + #End if + + #Write to debug log if enabled: + adf.debug_log(f"regrid_example: tclim_fils (n={len(tclim_fils)}): {tclim_fils}") + + if len(tclim_fils) > 1: + #Combine all target files together into a single data set: + tclim_ds = xr.open_mfdataset(tclim_fils, combine='by_coords') + elif len(tclim_fils) == 0: + print(f"\t - regridding {var} failed, no file. Continuing to next variable.") + continue + else: + #Open single file as new xarray dataset: + tclim_ds = xr.open_dataset(tclim_fils[0]) + #End if + + #Generate CAM climatology (climo) file list: + mclim_fils = sorted(mclimo_loc.glob(f"{case_name}_{var}_*.nc")) + + if len(mclim_fils) > 1: + #Combine all cam files together into a single data set: + mclim_ds = xr.open_mfdataset(mclim_fils, combine='by_coords') + elif len(mclim_fils) == 0: + wmsg = f"\t - Unable to find climo file for '{var}'." + wmsg += " Continuing to next variable." + print(wmsg) + continue + else: + #Open single file as new xarray dataset: + mclim_ds = xr.open_dataset(mclim_fils[0]) + #End if + + #Create keyword arguments dictionary for regridding function: + regrid_kwargs = {} + + #Perform regridding of variable: + rgdata_interp = _regrid(mclim_ds, var, + regrid_dataset=tclim_ds, + **regrid_kwargs) + + #Extract defaults for variable: + var_default_dict = var_defaults.get(var, {}) + + if 'mask' in var_default_dict: + if var_default_dict['mask'].lower() == 'land': + #Check if the land fraction has already been regridded + #and saved: + if lnd_frc_ds: + lfrac = lnd_frc_ds['LANDFRAC'] + # set the bounds of regridded lndfrac to 0 to 1 + lfrac = xr.where(lfrac>1,1,lfrac) + lfrac = xr.where(lfrac<0,0,lfrac) + + # apply land fraction mask to variable + rgdata_interp['LANDFRAC'] = lfrac + var_tmp = rgdata_interp[var] + var_tmp = pf.mask_land(var_tmp,lfrac) + rgdata_interp[var] = var_tmp + else: + print(f"LANDFRAC not found, unable to apply mask to '{var}'") + #End if + else: + #Currently only a land mask is supported, so print warning here: + wmsg = "Currently the only variable mask option is 'land'," + wmsg += f"not '{var_default_dict['mask'].lower()}'" + print(wmsg) + #End if + #End if + + #If the variable is land fraction, then save the dataset for use later: + if var == 'LANDFRAC': + lnd_frc_ds = rgdata_interp + #End if + + #Finally, write re-gridded data to output file: + #Convert the list of Path objects to a list of strings + climatology_files_str = [str(path) for path in mclim_fils] + climatology_files_str = ', '.join(climatology_files_str) + test_attrs_dict = { + "adf_user": adf.user, + "climo_yrs": f"{case_name}: {syear}-{eyear}", + "climatology_files": climatology_files_str, + } + rgdata_interp = rgdata_interp.assign_attrs(test_attrs_dict) + save_to_nc(rgdata_interp, regridded_file_loc) + rgdata_interp.close() # bpm: we are completely done with this data + + else: + print("\t Regridded file already exists, so skipping...") + #End if (file check) + #End do (target list) + #End do (variable list) + #End do (case list) + + #Notify user that script has ended: + print(" ...CAM climatologies have been regridded successfully.") + +################# +#Helper functions +################# + +def _regrid(model_dataset, var_name, regrid_dataset=None, regrid_ofrac=False, **kwargs): + + """ + Function that takes a variable from a model xarray + dataset, regrids it to another dataset's lat/lon + coordinates (if applicable) + ---------- + model_dataset -> The xarray dataset which contains the model variable data + var_name -> The name of the variable to be regridded/interpolated. + + Optional inputs: + + ps_file -> NOT APPLICABLE: A NetCDF file containing already re-gridded surface pressure + regrid_dataset -> The xarray dataset that contains the lat/lon grid that + "var_name" will be regridded to. If not present then + only the vertical interpolation will be done. + + kwargs -> Keyword arguments that contain paths to THE REST IS NOT APPLICABLE: surface pressure + and mid-level pressure files, which are necessary for + certain types of vertical interpolation. + + This function returns a new xarray dataset that contains the regridded + model variable. + """ + + #Import ADF-specific functions: + import numpy as np + import plotting_functions as pf + from regrid_se_to_fv import make_se_regridder, regrid_se_data_conservative + + #Extract keyword arguments: + if 'ps_file' in kwargs: + ps_file = kwargs['ps_file'] + else: + ps_file = None + #End if + + #Extract variable info from model data (and remove any degenerate dimensions): + mdata = model_dataset[var_name].squeeze() + mdat_ofrac = None + #if regrid_lfrac: + # if 'LANDFRAC' in model_dataset: + # mdat_lfrac = model_dataset['LANDFRAC'].squeeze() + + #Regrid variable to target dataset (if available): + if regrid_dataset: + + #Extract grid info from target data: + if 'time' in regrid_dataset.coords: + if 'lev' in regrid_dataset.coords: + tgrid = regrid_dataset.isel(time=0, lev=0).squeeze() + else: + tgrid = regrid_dataset.isel(time=0).squeeze() + #End if + #End if + + # Hardwiring for now + con_weight_file = "/glade/work/wwieder/map_ne30pg3_to_fv0.9x1.25_scripgrids_conserve_nomask_c250108.nc" + + fv_t232_file = '/glade/derecho/scratch/wwieder/ctsm5.3.018_SP_f09_t232_mask/run/ctsm5.3.018_SP_f09_t232_mask.clm2.h0.0001-01.nc' + fv_t232 = xr.open_dataset(fv_t232_file) + + model_dataset[var_name] = model_dataset[var_name].fillna(0) + model_dataset['landfrac']= model_dataset['landfrac'].fillna(0) + model_dataset[var_name] = model_dataset[var_name] * model_dataset.landfrac # weight flux by land frac + + #Regrid model data to match target grid: + # These two functions come with import regrid_se_to_fv + regridder = make_se_regridder(weight_file=con_weight_file, + s_data = model_dataset.landmask.isel(time=0), + d_data = fv_t232.landmask, + Method = 'coservative', # Bug in xesmf needs this without "n" + ) + rgdata = regrid_se_data_conservative(regridder, model_dataset) + + rgdata[var_name] = (rgdata[var_name] / rgdata.landfrac) + + rgdata['lat'] = fv_t232.lat + rgdata['landmask'] = fv_t232.landmask + rgdata['landfrac'] = rgdata.landfrac.isel(time=0) + + # calculate area + area_km2 = np.zeros(shape=(len(rgdata['lat']), len(rgdata['lon']))) + earth_radius_km = 6.37122e3 # in meters + + yres_degN = np.abs(np.diff(rgdata['lat'].data)) # distances between gridcell centers... + xres_degE = np.abs(np.diff(rgdata['lon'])) # ...end up with one less element, so... + yres_degN = np.append(yres_degN, yres_degN[-1]) # shift left (edges <-- centers); assume... + xres_degE = np.append(xres_degE, xres_degE[-1]) # ...last 2 distances bet. edges are equal + + dy_km = yres_degN * earth_radius_km * np.pi / 180 # distance in m + phi_rad = rgdata['lat'].data * np.pi / 180 # degrees to radians + + # grid cell area + for j in range(len(rgdata['lat'])): + for i in range(len(rgdata['lon'])): + dx_km = xres_degE[i] * np.cos(phi_rad[j]) * earth_radius_km * np.pi / 180 # distance in m + area_km2[j,i] = dy_km[j] * dx_km + + rgdata['area'] = xr.DataArray(area_km2, + coords={'lat': rgdata.lat, 'lon': rgdata.lon}, + dims=["lat", "lon"]) + rgdata['area'].attrs['units'] = 'km2' + rgdata['area'].attrs['long_name'] = 'Grid cell area' + else: + #Just rename variables: + rgdata = mdata + #End if + + #Return dataset: + return rgdata + +##### + +def save_to_nc(tosave, outname, attrs=None, proc=None): + """Saves xarray variable to new netCDF file""" + + xo = tosave # used to have more stuff here. + # deal with getting non-nan fill values. + if isinstance(xo, xr.Dataset): + enc_dv = {xname: {'_FillValue': None} for xname in xo.data_vars} + else: + enc_dv = {} + #End if + enc_c = {xname: {'_FillValue': None} for xname in xo.coords} + enc = {**enc_c, **enc_dv} + if attrs is not None: + xo.attrs = attrs + if proc is not None: + xo.attrs['Processing_info'] = f"Start from file {origname}. " + proc + xo.to_netcdf(outname, format='NETCDF4', encoding=enc) + +##### + +def regrid_data(fromthis, tothis, method=1): + """Regrid data using various different methods""" + + if method == 1: + # kludgy: spatial regridding only, seems like can't automatically deal with time + if 'time' in fromthis.coords: + result = [fromthis.isel(time=t).interp_like(tothis) for t,time in enumerate(fromthis['time'])] + result = xr.concat(result, 'time') + return result + else: + return fromthis.interp_like(tothis) + elif method == 2: + newlat = tothis['lat'] + newlon = tothis['lon'] + coords = dict(fromthis.coords) + coords['lat'] = newlat + coords['lon'] = newlon + return fromthis.interp(coords) + elif method == 3: + newlat = tothis['lat'] + newlon = tothis['lon'] + ds_out = xr.Dataset({'lat': newlat, 'lon': newlon}) + regridder = xe.Regridder(fromthis, ds_out, 'bilinear') + return regridder(fromthis) + elif method==4: + # geocat + newlat = tothis['lat'] + newlon = tothis['lon'] + result = geocat.comp.linint2(fromthis, newlon, newlat, False) + result.name = fromthis.name + return result + #End if + +##### diff --git a/scripts/regridding/regrid_conservative.ipynb b/scripts/regridding/regrid_conservative.ipynb new file mode 100644 index 000000000..59f69bf2b --- /dev/null +++ b/scripts/regridding/regrid_conservative.ipynb @@ -0,0 +1,4200 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "3c9c6614-73bd-48e7-aabb-b293041f93e1", + "metadata": {}, + "source": [ + "#### Created weight file The first one (from mesh files) didn't work\n", + "\n", + "```\n", + "ESMF_RegridWeightGen --source /glade/campaign/cesm/cesmdata/inputdata/share/meshes/ne30pg3_ESMFmesh_cdf5_c20211018.nc --destination /glade/campaign/cesm/cesmdata/inputdata/share/meshes/fv0.9x1.25_141008_polemod_ESMFmesh.nc --weight /glade/work/wwieder/map_ne30pg3_to_fv0.9x1.25_nomask_c250108.nc --method conserve\n", + "```\n", + "\n", + "#### This sencond one (from scripgrid files) has the right dimensions for dst_grid_dims (192x288) \n", + "TODO:\n", + "- what's the correct method here?\n", + "- appropriate to use scripgrids\n", + "- provide these for more common resolutions?\n", + "```\n", + "ESMF_RegridWeightGen --source /glade/campaign/cesm/cesmdata/inputdata/share/scripgrids/ne30pg3_scrip_170611.nc --destination /glade/campaign/cesm/cesmdata/inputdata/share/scripgrids/fv0.9x1.25_141008.nc --weight /glade/work/wwieder/map_ne30pg3_to_fv0.9x1.25_scripgrids_nomask_c250108.nc --method conserve2nd --ignore_unmapped --ignore_degenerate --pole none\n", + "```\n", + "\n", + "Trying to get the pole in lat (as in the FV09 grid), didn't work. \n", + "adding pole all required a method other that conserve2nd\n", + "**Currently using this**\n", + "```\n", + "ESMF_RegridWeightGen --source /glade/campaign/cesm/cesmdata/inputdata/share/scripgrids/ne30pg3_scrip_170611.nc --destination /glade/campaign/cesm/cesmdata/inputdata/share/scripgrids/fv0.9x1.25_141008.nc --weight /glade/work/wwieder/map_ne30pg3_to_fv0.9x1.25_scripgrids_conserve_nomask_c250108.nc --method conserve\n", + "```\n", + "\n", + "```\n", + "ESMF_RegridWeightGen --source /glade/campaign/cesm/cesmdata/inputdata/share/scripgrids/ne30pg3_scrip_170611.nc --destination /glade/campaign/cesm/cesmdata/inputdata/share/scripgrids/fv0.9x1.25_141008.nc --weight /glade/work/wwieder/map_ne30pg3_to_fv0.9x1.25_scripgrids_nomask_c250108.nc --method bilinear --pole all --ignore_unmapped\n", + "```\n", + "\n", + "\n", + "#### Also added area and land frac to single variable time series\n", + "```\n", + "ncks -A -v area,landfrac,landmask /glade/derecho/scratch/hannay/archive/b.e30_beta04.BLT1850.ne30_t232_wgx3.121/lnd/hist/b.e30_beta04.BLT1850.ne30_t232_wgx3.121.clm2.h0.0012-10.nc /glade/derecho/scratch/wwieder/ADF/b.e30_beta04.BLT1850.ne30_t232_wgx3.121/climo/b.e30_beta04.BLT1850.ne30_t232_wgx3.121_GPP_climo.nc\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "09508471-56bc-4cc5-8011-49456d42afea", + "metadata": {}, + "outputs": [], + "source": [ + "import os, sys\n", + "import shutil\n", + "import warnings\n", + "warnings.filterwarnings('ignore')\n", + "\n", + "import numpy as np\n", + "import xarray as xr\n", + "import xesmf as xe\n", + "import regrid_se_to_fv\n", + "\n", + "# Helpful for plotting only\n", + "import matplotlib\n", + "import matplotlib.pyplot as plt\n", + "import matplotlib.dates as mdates\n", + "import cartopy\n", + "import cartopy.crs as ccrs\n", + "import cartopy.feature as cfeature\n", + "import uxarray as ux #need npl 2024a or later\n", + "import geoviews.feature as gf" + ] + }, + { + "cell_type": "markdown", + "id": "1ea71ab6-09b4-4a2f-8979-a1532b5df098", + "metadata": {}, + "source": [ + "#### Conservative regridding\n", + "- set missing values to zero\n", + "- Weight fluxes by source landfrac, \n", + "- Regrid, then\n", + "- Divide by regridded landfrac\n", + "- Calculate global and regional sums\n", + "- For plotting add destination landmask to get rid of bloated coastlines\n", + "\n", + "#### At the end of the day we want to write out a destination grid .nc file with:\n", + "- regridded field\n", + "- regridded land frac\n", + "- wall to wall area (currently from CAM history file)\n", + "- destination grid land mask\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "bac26d5c-492e-4b35-9476-b6601de4bb06", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Dummy source file to regrid (for now this can be from climo files made by adf)\n", + "gppfile='/glade/derecho/scratch/wwieder/ADF/b.e30_beta04.BLT1850.ne30_t232_wgx3.121/climo/b.e30_beta04.BLT1850.ne30_t232_wgx3.121_GPP_climo.nc'\n", + "ds_con = xr.open_dataset(gppfile)\n", + "\n", + "# Weighting file needed for regridding, keep this hard coded for now.\n", + "con_weight_file = \"/glade/work/wwieder/map_ne30pg3_to_fv0.9x1.25_scripgrids_conserve_nomask_c250108.nc\"\n", + "\n", + "# dummy destination grid\n", + "fv_t232_file = '/glade/derecho/scratch/wwieder/ctsm5.3.018_SP_f09_t232_mask/run/ctsm5.3.018_SP_f09_t232_mask.clm2.h0.0001-01.nc'\n", + "fv_t232 = xr.open_dataset(fv_t232_file)\n", + "\n", + "# Fill in missing values with zeros\n", + "ds_con['GPP'] = ds_con['GPP'].fillna(0) \n", + "ds_con['landfrac']= ds_con['landfrac'].fillna(0) \n", + "ds_con['GPP'] = ds_con.GPP * ds_con.landfrac # weight flux by land frac\n", + "\n", + "# not used for actually regridding\n", + "#ds_con['test'] = ((ds_con.GPP)*0+1.)\n", + "#ds_con['test'] = ds_con.test * ds_con.landfrac\n", + "\n", + "# These are the calls to regrid the souce data\n", + "regridder = regrid_se_to_fv.make_se_regridder(weight_file=con_weight_file, \n", + " s_data = ds_con.landmask.isel(time=0), \n", + " d_data = fv_t232.landmask,\n", + " Method = 'coservative',\n", + " )\n", + "ds_out_con = regrid_se_to_fv.regrid_se_data_conservative(regridder, ds_con).load()\n", + "\n", + "# Post processing to finish the conversion correctly:\n", + "ds_out_con['GPP'] = (ds_out_con.GPP / ds_out_con.landfrac)\n", + "#ds_out_con['test'] = (ds_out_con.test / ds_out_con.landfrac)\n", + "\n", + "# drop time variables\n", + "ds_out_con['landfrac'] = ds_out_con['landfrac'].isel(time=0) \n", + "ds_out_con['area'] = ds_out_con['area'].isel(time=0) \n", + "ds_out_con['landmask'] = ds_out_con['landmask'].isel(time=0) \n", + "\n", + "# TODO, add a global area and landmask field from the destination grid for calculating sums and plotting.\n", + "# TODO save this as a .nc file\n", + "# TODO, drop the test field from this once integrated into ADF\n", + "# Quick check of results\n", + "ds_out_con.GPP.isel(time=0).plot() ;" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "7f58c441-3f24-4791-8616-e69cbe28ba43", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
<xarray.DataArray 'GPP' (time: 12, lndgrid: 48600)> Size: 2MB\n",
+       "array([[0.0000000e+00, 0.0000000e+00, 0.0000000e+00, ..., 9.2530281e-06,\n",
+       "        4.7339649e-06, 1.7577652e-06],\n",
+       "       [0.0000000e+00, 0.0000000e+00, 0.0000000e+00, ..., 8.1039307e-06,\n",
+       "        4.2056104e-06, 1.5534362e-06],\n",
+       "       [0.0000000e+00, 0.0000000e+00, 0.0000000e+00, ..., 1.0241940e-05,\n",
+       "        5.4556108e-06, 2.0470754e-06],\n",
+       "       ...,\n",
+       "       [0.0000000e+00, 0.0000000e+00, 0.0000000e+00, ..., 3.4669843e-05,\n",
+       "        1.6131389e-05, 4.9520345e-06],\n",
+       "       [0.0000000e+00, 0.0000000e+00, 0.0000000e+00, ..., 2.7128302e-05,\n",
+       "        1.3086953e-05, 3.9950073e-06],\n",
+       "       [0.0000000e+00, 0.0000000e+00, 0.0000000e+00, ..., 1.4469586e-05,\n",
+       "        7.3627734e-06, 2.6298280e-06]], dtype=float32)\n",
+       "Coordinates:\n",
+       "  * time     (time) int64 96B 1 2 3 4 5 6 7 8 9 10 11 12\n",
+       "Dimensions without coordinates: lndgrid
" + ], + "text/plain": [ + " Size: 2MB\n", + "array([[0.0000000e+00, 0.0000000e+00, 0.0000000e+00, ..., 9.2530281e-06,\n", + " 4.7339649e-06, 1.7577652e-06],\n", + " [0.0000000e+00, 0.0000000e+00, 0.0000000e+00, ..., 8.1039307e-06,\n", + " 4.2056104e-06, 1.5534362e-06],\n", + " [0.0000000e+00, 0.0000000e+00, 0.0000000e+00, ..., 1.0241940e-05,\n", + " 5.4556108e-06, 2.0470754e-06],\n", + " ...,\n", + " [0.0000000e+00, 0.0000000e+00, 0.0000000e+00, ..., 3.4669843e-05,\n", + " 1.6131389e-05, 4.9520345e-06],\n", + " [0.0000000e+00, 0.0000000e+00, 0.0000000e+00, ..., 2.7128302e-05,\n", + " 1.3086953e-05, 3.9950073e-06],\n", + " [0.0000000e+00, 0.0000000e+00, 0.0000000e+00, ..., 1.4469586e-05,\n", + " 7.3627734e-06, 2.6298280e-06]], dtype=float32)\n", + "Coordinates:\n", + " * time (time) int64 96B 1 2 3 4 5 6 7 8 9 10 11 12\n", + "Dimensions without coordinates: lndgrid" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ds_con.GPP" + ] + }, + { + "cell_type": "markdown", + "id": "e42505aa-4d41-42d3-8311-497209386c38", + "metadata": {}, + "source": [ + "#### Bilinear regridding\n", + "- Include a mask\n", + "- set `skipna=True, na_thres=1` in xEMSF regridder\n", + "- Weighting fluxes landfrac degrades results\n", + "- destination Mask where destination landfrac > 0 to avoid bloated coastlines" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "72f6f0b2-bb21-47eb-9205-934aadde8d57", + "metadata": {}, + "outputs": [], + "source": [ + "bilin_weight_file = \"/glade/work/wwieder/map_ne30pg3_to_fv0.9x1.25_scripgrids_bilinear_nomask_c250108.nc\"\n", + "ds_bilin = xr.open_dataset(gppfile)\n", + "ds_bilin['test'] = ((ds_bilin.GPP)*0+1.)\n", + "ds_bilin['mask'] = ds_bilin.landmask \n", + "\n", + "# Read in weight file and regrid\n", + "regridder = regrid_se_to_fv.make_se_regridder(weight_file=bilin_weight_file, \n", + " s_data = ds_con.landmask.isel(time=0), \n", + " d_data = fv_t232.landmask,\n", + " Method='bilinear',\n", + " )\n", + "ds_out_bilin = regrid_se_to_fv.regrid_se_data_bilinear(regridder, ds_bilin).load()\n", + "ds_out_bilin['landfrac'] = ds_out_bilin['landfrac'].isel(time=0) \n", + "ds_out_bilin['area'] = ds_out_bilin['area'].isel(time=0) \n", + "ds_out_bilin['landmask'] = ds_out_bilin['landmask'].isel(time=0) " + ] + }, + { + "cell_type": "markdown", + "id": "3d49c3a6-db67-4795-9ceb-ad7e7a8cea1d", + "metadata": {}, + "source": [ + "----\n", + "#### Quick look at g17 vs. t232 masks and regridded results" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "7186b5ff-76df-4af3-a974-fd0f4840af9c", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "mesh0 = '/glade/campaign/cesm/cesmdata/inputdata/share/meshes/ne30pg3_ESMFmesh_cdf5_c20211018.nc'\n", + "ds0 = ux.open_dataset(mesh0, gppfile)\n", + "ds0['test'] = (ds0.GPP)*0+1\n", + "\n", + "mesh1 = '/glade/campaign/cesm/cesmdata/inputdata/share/meshes/fv0.9x1.25_141008_ESMFmesh.nc'\n", + "fv_g17_file = '/glade/derecho/scratch/oleson/ANALYSIS/climo/ctsm53n04ctsm52028_f09_hist/ctsm53n04ctsm52028_f09_hist_annT_1850.nc'\n", + "fv_g17 = xr.open_dataset(fv_g17_file)\n", + "ux_g17 = ux.open_dataset(mesh1, fv_g17_file)\n", + "\n", + "#CLM output already has area masked\n", + "fv_cam_file = '/glade/campaign/cgd/cesm/CESM2-LE/atm/proc/tseries/month_1/AREA/b.e21.BSSP370smbb.f09_g17.LE2-1301.020.cam.h0.AREA.209501-210012.nc'\n", + "fv_cam_area = xr.open_dataset(fv_cam_file)['AREA'].isel(time=0)*1e-6 # convert m2 to km2\n", + "fv_cam_area.attrs['units'] = fv_t232['area'].attrs['units']\n", + "\n", + "# Plot the two masks\n", + "fig, axs = plt.subplots(nrows=2,ncols=1,\n", + " subplot_kw=dict(projection=ccrs.PlateCarree()),\n", + " figsize=(8,7))\n", + "\n", + "# axs is a 2 dimensional array of `GeoAxes`. We will flatten it into a 1-D array\n", + "axs=axs.flatten()\n", + "\n", + "fv_g17.GPP.isel(time=0).plot(ax=axs[0])\n", + "axs[0].set_title('f09-g17 mask')\n", + "\n", + "fv_t232.FPSN.isel(time=0).plot(ax=axs[1])\n", + "axs[1].set_title('f09-t232 mask') ;\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "511aac49-6baa-477c-aa17-4f7c7ad9d617", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# calculate are directly from weight file.\n", + "ds_weight = xr.open_dataset(con_weight_file)\n", + "\n", + "SHR_CONST_REARTH = 6.37122e3 # radius of earth ~ km\n", + "area_raw = ds_weight.area_b * (SHR_CONST_REARTH**2)\n", + "area_shape = xr.DataArray(area_raw.data.reshape(192,288), dims=(\"lat\", \"lon\"))\n", + "fig, axs = plt.subplots(nrows=1,ncols=2,\n", + " subplot_kw=dict(projection=ccrs.PlateCarree()),\n", + " figsize=(16,4))\n", + "axs=axs.flatten()\n", + "(fv_cam_area).plot(ax=axs[0]) ;\n", + "# fv_t232.area.plot(ax=axs[1]) ;\n", + "area_shape.plot(ax=axs[1], x='lon',y='lat');" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "3eb7bc4d-8c25-4273-a739-1c2ff0f9778a", + "metadata": {}, + "outputs": [], + "source": [ + "# add wall to wall area to clm history file\n", + "fv_cam_area['lat'] = fv_t232.lat\n", + "fv_cam_area['lon'] = fv_t232.lon\n", + "fv_t232['area'] = fv_cam_area" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "2527cd61-ebea-4762-a7b3-4cd5e8782285", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# lats are not the same on destination grid, adjusting now\n", + "ds_out_con['lat'] = fv_t232.lat\n", + "ds_out_bilin['lat'] = fv_t232.lat\n", + "\n", + "# Plot the two masks\n", + "fig, axs = plt.subplots(nrows=2,ncols=2,\n", + " subplot_kw=dict(projection=ccrs.PlateCarree()),\n", + " figsize=(16,8))\n", + "\n", + "axs=axs.flatten()\n", + "ds_out_con.GPP.isel(time=0).plot(ax=axs[0],vmin=0,vmax=1e-4)\n", + "axs[0].set_title('Cons. raw')\n", + "\n", + "ds_out_con.GPP.isel(time=0).where(ds_out_con.landfrac > 0).plot(ax=axs[1],vmin=0,vmax=1e-4)\n", + "axs[1].set_title('Cons. regridded mask')\n", + "\n", + "ds_out_bilin.GPP.isel(time=0).plot(ax=axs[2],vmin=0,vmax=1e-4)\n", + "axs[2].set_title('Bilin. raw')\n", + "\n", + "ds_out_bilin.GPP.isel(time=0).where(fv_t232.landfrac>0).plot(ax=axs[3],vmin=0,vmax=1e-4)\n", + "axs[3].set_title('Bilin. dest mask') ;\n", + "\n", + "## Go ahead and apply the mask based on destination grid?\n", + "# Currently conservative only has mask based on remapped landfrac\n", + "# Bilinear with destination landfrac mask\n", + "ds_out_con = ds_out_con.where(ds_out_con.landfrac>0)\n", + "ds_out_bilin = ds_out_bilin.where(fv_t232.landfrac>0)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "9231d764-7083-4af8-a10f-6edbf81a7271", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "lon_bounds = (105, 145)\n", + "lat_bounds = (25, 58)\n", + "fig, axs = plt.subplots(nrows=2,ncols=2,\n", + " subplot_kw=dict(projection=ccrs.PlateCarree()),\n", + " figsize=(12,8))\n", + "axs=axs.flatten()\n", + "\n", + "fv_t232.landfrac \\\n", + " .sel(lon=slice(lon_bounds[0],lon_bounds[1]),lat=slice(lat_bounds[0],lat_bounds[1]))\\\n", + " .plot(ax=axs[0]) \n", + "axs[0].set_title('raw destination grid') ;\n", + "\n", + "ds_out_con.landfrac \\\n", + " .sel(lon=slice(lon_bounds[0],lon_bounds[1]),lat=slice(lat_bounds[0],lat_bounds[1]))\\\n", + " .plot(ax=axs[1]) \n", + "axs[1].set_title('conservative remapped, no mask')\n", + "\n", + "ds_out_con.landfrac.where(fv_t232.landfrac>0) \\\n", + " .sel(lon=slice(lon_bounds[0],lon_bounds[1]),lat=slice(lat_bounds[0],lat_bounds[1]))\\\n", + " .plot(ax=axs[2]) \n", + "axs[2].set_title('conservative remapped, destination mask')\n", + "\n", + "ds_out_bilin.landfrac \\\n", + " .sel(lon=slice(lon_bounds[0],lon_bounds[1]),lat=slice(lat_bounds[0],lat_bounds[1]))\\\n", + " .plot(ax=axs[3]) \n", + "axs[3].set_title('bilinear remapped, destination mask')\n", + "\n", + "for a in axs:\n", + " a.coastlines() ;" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "f4c5ceb6-e10d-410b-b4e6-193afe90e56f", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "768.7117\n", + "766.0552\n" + ] + } + ], + "source": [ + "print(ds_out_con.landfrac.sel(lon=slice(lon_bounds[0],lon_bounds[1]),lat=slice(lat_bounds[0],lat_bounds[1])).sum().values)\n", + "print(ds_out_con.landfrac.where(fv_t232.landfrac>0) \\\n", + " .sel(lon=slice(lon_bounds[0],lon_bounds[1]),lat=slice(lat_bounds[0],lat_bounds[1])).sum().values)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "7edf1061-927a-45b2-b454-88cbb18ddc3d", + "metadata": {}, + "outputs": [], + "source": [ + "# look a grid structure for ne30\n", + "#projection = ccrs.PlateCarree()\n", + "#ds0[\"area\"].plot.polygons(projection=projection)" + ] + }, + { + "cell_type": "markdown", + "id": "eb590654-1ee0-4dc7-9de5-7e3274c4b38e", + "metadata": {}, + "source": [ + "------------\n", + "### Check global sums\n", + "----------" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "358e9579-c679-4907-9f7e-c1f59b4707cc", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "source, ne30 land area = 1790.2597119999998 1e6 km2\n", + "destination, f09_t232 land area = 149.189408\n", + "conservative regridded land area = 149.18937599999998 1e6 km2\n", + "bilinear regridded land area = 151.03866439950346 1e6 km2\n", + "\n", + "orig ne30 GPP = 104.964 Pg C, globally\n", + "conservative regridded GPP, t232 landfrac = 104.92\n", + "conservative regridded GPP, regridded landfrac = 104.963\n", + "bilinear regridded GPP, t232 landfrac = 105.007\n" + ] + } + ], + "source": [ + "# Not the right way to calculate annual mean from monthly climo, but it works\n", + "\n", + "spy = 3600 * 24 * 365\n", + "km2_m2 = 1e6\n", + "g_Pg = 1e-15\n", + "\n", + "print('source, ne30 land area = ' + str(((ds_bilin.area * ds_bilin.landfrac).sum()*1e-6).values)+ ' 1e6 km2')\n", + "print('destination, f09_t232 land area = ' + str(((fv_t232.area * fv_t232.landfrac).sum()*1e-6).values))\n", + "print('conservative regridded land area = ' + str(((fv_t232.area * ds_out_con.landfrac).sum()*1e-6).values)+ ' 1e6 km2')\n", + "print('bilinear regridded land area = ' + str(((fv_t232.area * ds_out_bilin.landfrac).sum()*1e-6).values)+ ' 1e6 km2')\n", + "print()\n", + "\n", + "GPP_sum = ((ds_bilin.GPP * ds_bilin.area * ds_bilin.landfrac).mean('time') * spy * km2_m2).sum() * g_Pg\n", + "GPP_sum_regrid1 = ((ds_out_con.GPP * fv_t232.area * fv_t232.landfrac).mean('time') * spy * km2_m2).sum() * g_Pg\n", + "GPP_sum_regrid1B = ((ds_out_con.GPP * fv_t232.area * ds_out_con.landfrac).mean('time') * spy * km2_m2).sum() * g_Pg\n", + "GPP_sum_regrid2 = ((ds_out_bilin.GPP * fv_t232.area * fv_t232.landfrac).mean('time') * spy * km2_m2).sum() * g_Pg\n", + "\n", + "print('orig ne30 GPP = ' + str(np.round(GPP_sum.values,3))+ ' Pg C, globally')\n", + "print('conservative regridded GPP, t232 landfrac = ' + str(np.round(GPP_sum_regrid1.values,3)))\n", + "print('conservative regridded GPP, regridded landfrac = ' + str(np.round(GPP_sum_regrid1B.values,3)))\n", + "print('bilinear regridded GPP, t232 landfrac = ' + str(np.round(GPP_sum_regrid2.values,3)))\n", + "\n", + "# best results when using regridded flux and destination grid area and landfrac" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "a3a301cf-fb84-4fad-821a-fa991b79aebb", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
<xarray.Dataset> Size: 6MB\n",
+       "Dimensions:   (time: 12, lat: 192, lon: 288)\n",
+       "Coordinates:\n",
+       "  * time      (time) int64 96B 1 2 3 4 5 6 7 8 9 10 11 12\n",
+       "  * lat       (lat) float32 768B -90.0 -89.06 -88.12 -87.17 ... 88.12 89.06 90.0\n",
+       "  * lon       (lon) float64 2kB 0.0 1.25 2.5 3.75 ... 355.0 356.2 357.5 358.8\n",
+       "Data variables:\n",
+       "    GPP       (time, lat, lon) float32 3MB 0.0 0.0 0.0 0.0 ... nan nan nan nan\n",
+       "    area      (lat, lon) float32 221kB 1.236e+04 1.236e+04 1.236e+04 ... nan nan\n",
+       "    landfrac  (lat, lon) float32 221kB 1.0 1.0 1.0 1.0 1.0 ... nan nan nan nan\n",
+       "    landmask  (lat, lon) float64 442kB 1.0 1.0 1.0 1.0 1.0 ... nan nan nan nan\n",
+       "    test      (time, lat, lon) float32 3MB 1.0 1.0 1.0 1.0 ... nan nan nan nan\n",
+       "Attributes:\n",
+       "    regrid_method:  coservative
" + ], + "text/plain": [ + " Size: 6MB\n", + "Dimensions: (time: 12, lat: 192, lon: 288)\n", + "Coordinates:\n", + " * time (time) int64 96B 1 2 3 4 5 6 7 8 9 10 11 12\n", + " * lat (lat) float32 768B -90.0 -89.06 -88.12 -87.17 ... 88.12 89.06 90.0\n", + " * lon (lon) float64 2kB 0.0 1.25 2.5 3.75 ... 355.0 356.2 357.5 358.8\n", + "Data variables:\n", + " GPP (time, lat, lon) float32 3MB 0.0 0.0 0.0 0.0 ... nan nan nan nan\n", + " area (lat, lon) float32 221kB 1.236e+04 1.236e+04 1.236e+04 ... nan nan\n", + " landfrac (lat, lon) float32 221kB 1.0 1.0 1.0 1.0 1.0 ... nan nan nan nan\n", + " landmask (lat, lon) float64 442kB 1.0 1.0 1.0 1.0 1.0 ... nan nan nan nan\n", + " test (time, lat, lon) float32 3MB 1.0 1.0 1.0 1.0 ... nan nan nan nan\n", + "Attributes:\n", + " regrid_method: coservative" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ds_out_con" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "54dcc949-7255-45f7-84a0-33cd2eddffdd", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1.0\n" + ] + }, + { + "data": { + "text/plain": [ + "array(1.00000006)" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAABJkAAAESCAYAAABehQSXAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjkuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/TGe4hAAAACXBIWXMAAA9hAAAPYQGoP6dpAACU60lEQVR4nO3deXgT5doG8DspXdhalkpLlaULiwpWFqGACKilsigitCBHEA4iy/HUsojiRsHtAwUKKogeERGU5SAqR7DUBVwAZS0KCrQFCtKCIFBAsKWZ7490ppmsk2WSSXL/vHJZJpOZN8lM8s6T531enSAIAoiIiIiIiIiIiNyg93UDiIiIiIiIiIjI/zHIREREREREREREbmOQiYiIiIiIiIiI3MYgExERERERERERuY1BJiIiIiIiIiIichuDTERERERERERE5DYGmYiIiIiIiIiIyG0MMhERERERERERkdsYZCIiIiIiIiIiIrcxyERERERERERERG5jkImIiLzq22+/xb333ou4uDjodDp88sknqu7v2rVrePbZZxEfH4+aNWsiISEBM2fOhMFgcHmbL730Erp27YpatWqhXr16nmssERERkR9jP48YZCIiIq+6fPkykpOT8cYbb3hlf7NmzcJbb72FN954A7/++itmz56NV199Fa+//rrNxzRv3hybN2+2eX95eTnS09Mxfvx4FVpMRERE5J/Yz6Mavm4AEREFlz59+qBPnz427y8vL8ezzz6LFStW4Pz582jTpg1mzZqFnj17urS/bdu2YcCAAejXrx8AY8fio48+ws6dO13aHgDMmDEDALB06VKXt0FEREQUaNjPI2YyERGRpowaNQo//PADVq5ciX379iE9PR333HMPDh8+7NL2br/9dnz11Vc4dOgQACA/Px/ff/89+vbt68lmExEREZED7OcFPgaZiDTgww8/RE5OjtX7dDodsrOzvdoeAHj55ZdVH0N94MABZGdn4+jRo6ruh/xHYWEhPvroI6xZswbdu3dHYmIipkyZgttvvx3vvfeeS9t88skn8eCDD6J169YIDQ1Fu3btkJWVhQcffNDDrSciIndkZ2dDp9PhzJkzDtft2bOnReaDeZ9p8+bN0Ol0dofFaNnWrVuRnZ2N8+fPq7qfhQsXMmODvIL9vODAIBORBtgLMm3btg2PPPKIdxsE7wWZZsyYwSATSXbv3g1BENCyZUvUqVNHum3ZsgWFhYUAgKNHj0Kn09m9PfbYY9I2V61aheXLl+PDDz/E7t278f777+O1117D+++/L60zbtw42f6Ki4vRp08fi2VERKQNCxcuxMKFC+2u0759e2zbtg3t27f3Uqs8a+vWrZgxYwaDTBQw2M8LDqzJROSEv/76C7Vq1fLqPlNSUry6PyJfMhgMCAkJwa5duxASEiK7r06dOgCA66+/Hr/++qvd7dSvX1/6+4knnsBTTz2FoUOHAgDatm2LY8eO4ZVXXsHDDz8MAJg5cyamTJkiPaZnz56YNWsWOnfuLC2Li4tz78kREZHH3HTTTQ7XiYyM1Hw/yhd9SyJfYT8vODCTiVTz22+/4cEHH0RMTAzCw8PRtGlTjBgxAn///be0zi+//IIBAwagfv36iIiIwK233iqLOgPVqc4fffQRnnnmGcTFxSEyMhJ33303Dh48KFt3z5496N+/Pxo1aoTw8HDExcWhX79+OHHihLSOIAhYuHAhbr31VtSsWRP169fH4MGDUVRUJNtWz5490aZNG3z77bfSFJb//Oc/cf/996NZs2ZWp8Xs3Lmz7NeyN998E3fccQcaNWqE2rVro23btpg9ezYqKipk+/n8889x7NgxWYReZJr6nZ+fD51Oh3fffddi3xs3boROp8Nnn30mLTt8+DCGDRsmvR433ngj3nzzTavvlymdTofLly/j/fffl9pjmpJeWlqKsWPH4oYbbkBYWBji4+MxY8YMXLt2TbadRYsWITk5GXXq1EHdunXRunVrPP300wCMhfTS09MBAL169ZL2w1/Sglu7du1QWVmJ06dPIykpSXaLjY0FAISGhqJ169Z2bzExMdI2//rrL+j18q+7kJAQ2TncqFEj2b5q1KiB66+/3mIZERGp7/jx43jggQcQGRmJqKgoPPTQQ/jjjz9k61gbLmfO2nC5kSNHok6dOigoKEDfvn1Rp04dNGnSBJMnT5b1UQFjgeIXX3wRrVu3Rnh4OK677jqMGjXKoi2rVq1C79690bhxY9SsWRM33ngjnnrqKVy+fFm2nrjvn3/+Gb1790bdunVx1113WW17dnY2nnjiCQBAfHy81E8yfS6rVq1Cly5dULt2bdSpUwdpaWnYs2ePbDtFRUUYOnQo4uLiEB4ejpiYGNx1113Yu3cvAGOR5P3792PLli3SPpo3b273dSVyFft5wYGvJKkiPz8ft99+O6KjozFz5ky0aNECJSUl+Oyzz1BeXo7w8HAcPHgQXbt2RaNGjbBgwQI0bNgQy5cvx8iRI3Hq1ClMnTpVts2nn34a3bp1w3/+8x+UlZXhySefxL333otff/0VISEhuHz5MlJTUxEfH48333wTMTExKC0txTfffIOLFy9K2xk7diyWLl2KzMxMzJo1C3/++SdmzpyJrl27Ij8/X/ahVVJSgoceeghTp07Fyy+/DL1ej/Pnz2PAgAH4+uuvcffdd0vr/vbbb/jpp5+wYMECaVlhYSGGDRuG+Ph4hIWFIT8/Hy+99BJ+++03LFmyBIAxRfnRRx9FYWEh1q1bZ/d1TU5ORrt27fDee+9h9OjRsvuWLl2KRo0aSUXuDhw4gK5du6Jp06aYM2cOYmNjkZubi8zMTJw5cwbTp0+3uZ9t27bhzjvvRK9evfDcc88BMP4aCBgDTJ06dYJer8fzzz+PxMREbNu2DS+++CKOHj0qjadeuXIlJkyYgH//+9947bXXoNfrUVBQgAMHDgAA+vXrh5dffhlPP/003nzzTSk4l5iYaPc1IP936dIlFBQUSP8+cuQI9u7diwYNGqBly5b4xz/+gREjRmDOnDlo164dzpw5g6+//hpt27Z1qYjjvffei5deeglNmzbFzTffjD179mDu3Ln45z//6fJzKC4uxp9//oni4mJUVlZKnfWkpCTplzgiInLNwIEDkZGRgXHjxmH//v147rnncODAAfz4448IDQ11e/sVFRW47777MHr0aEyePBnffvstXnjhBURFReH5558HYMy4GDBgAL777jtMnToVXbt2xbFjxzB9+nT07NkTO3fuRM2aNQEYf9Tr27cvsrKyULt2bfz222+YNWsWfvrpJ3z99deyfZeXl+O+++7D2LFj8dRTT1n8QCd65JFH8Oeff+L111/Hxx9/jMaNGwOozuB6+eWX8eyzz2LUqFF49tlnUV5ejldffRXdu3fHTz/9JK3Xt29fVFZWYvbs2WjatCnOnDmDrVu3SkPw1q1bh8GDByMqKkoafhgeHu72a0zBi/08gkCkgjvvvFOoV6+ecPr0aZvrDB06VAgPDxeKi4tly/v06SPUqlVLOH/+vCAIgvDNN98IAIS+ffvK1lu9erUAQNi2bZsgCIKwc+dOAYDwySef2Nzntm3bBADCnDlzZMuPHz8u1KxZU5g6daq0rEePHgIA4auvvpKtW1FRIcTExAjDhg2TLZ86daoQFhYmnDlzxuq+KysrhYqKCmHZsmVCSEiI8Oeff0r39evXT2jWrJnVxwEQpk+fLv17wYIFAgDh4MGD0rI///xTCA8PFyZPniwtS0tLE2644QbhwoULsu099thjQkREhGz/1tSuXVt4+OGHLZaPHTtWqFOnjnDs2DHZ8tdee00AIOzfv1/aT7169ezuY82aNQIA4ZtvvrG7HgUW8Zw2v4nHW3l5ufD8888LzZs3F0JDQ4XY2Fhh4MCBwr59+1zaX1lZmfD4448LTZs2FSIiIoSEhAThmWeeEf7++2+bj2nWrJnd4/Lhhx+2+hx4LBMRuW769OkCAGHixImy5StWrBAACMuXL5eW9ejRQ+jRo4dsPfM+k/h9Y/rZLH5+r169WvbYvn37Cq1atZL+/dFHHwkAhLVr18rW27FjhwBAWLhwodXnYDAYhIqKCmHLli0CACE/P99i30uWLLH7OoheffVVAYBw5MgR2fLi4mKhRo0awr///W/Z8osXLwqxsbFCRkaGIAiCcObMGQGAkJOTY3c/N998s8VrSeQq9vOIQSbyuMuXLwshISHCo48+ane9Ro0aWQSOBEEQVq1aJQAQNm7cKAhC9QfVW2+9JVvvt99+EwAIK1euFARBEM6fPy/Ur19faNWqlbBo0SIp2GHqmWeeEXQ6nXDq1CmhoqJCdktJSRE6deokrdujRw+hfv36Vts+efJkISIiQgqEXbt2TWjcuLGQnp4uW2/37t3CvffeKzRo0MDiQ2r79u3Ses4Emc6ePSuEh4cL06ZNk5a9+eabAgDhl19+EQRBEK5cuSJ1Psyf54YNGwQAwoYNG6zuT2QryHT99dcL9957r8V29+/fL+t0LVu2TAAgDB06VPjkk0+EP/74w2JbDDIRCcKWLVuE/v37C40bNxYACOvWrXP4mM2bNwvt27cXwsPDhfj4eGHRokUW6/z3v/8VbrzxRiEsLEy48cYbhY8//liF1hMReY4YZNq5c6dseUVFhVCjRg1h9OjR0jJ3gkw6nU64cuWK7LFPPfWUEBERIf37H//4h1CvXj2hvLzcos9jGsgRBEEoLCwUHnzwQSEmJkbQ6XSy/p7YTxX3DcDiB0BbbAWZ3nnnHQGAsGPHDou2DRkyRGjUqJEgCMaAV2JionD99dcLc+bMEXbv3i1UVlZa7IdBJiLyJNZkIo87d+4cKisrccMNN9hd7+zZs1Lqrymx6NrZs2dlyxs2bCj7t5jKe+XKFQBAVFQUtmzZgltvvRVPP/00br75ZsTFxWH69OlSDaRTp05BEATExMQgNDRUdtu+fbvFlLnW2gcA//znP3H16lWsXLkSAJCbm4uSkhKMGjVKWqe4uBjdu3fH77//jvnz5+O7777Djh07pJpIYrud1aBBA9x3331YtmwZKisrARiHynXq1Ak333wzAONrd+3aNbz++usWz1NMQ1UyPbA1p06dwvr16y22K+5b3O7w4cOxZMkSHDt2DIMGDUKjRo3QuXNn5OXlubRfokB1+fJlJCcn44033lC0/pEjR9C3b190794de/bswdNPP43MzEysXbtWWmfbtm0YMmQIhg8fjvz8fAwfPhwZGRn48ccf1XoaREQeI9ZmEdWoUQMNGza06Bu6qlatWoiIiJAtCw8Px9WrV6V/nzp1CufPn0dYWJhFn6e0tFTq71y6dAndu3fHjz/+iBdffBGbN2/Gjh078PHHHwOw7O/VqlVLKkHgqlOnTgEAbrvtNou2rVq1SmqbTqfDV199hbS0NMyePRvt27fHddddh8zMTFkpCSIiT2JNJvK4Bg0aICQkRFZs25qGDRuipKTEYvnJkycBANHR0U7vu23btli5ciUEQcC+ffuwdOlSzJw5EzVr1sRTTz2F6Oho6HQ6fPfdd1bHm5svMy3Abeqmm25Cp06d8N5772Hs2LF47733EBcXh969e0vrfPLJJ7h8+TI+/vhjNGvWTFoujul1x6hRo7BmzRrk5eWhadOm2LFjBxYtWiTdX79+fYSEhGD48OH417/+ZXUb8fHxLu07Ojoat9xyC1566SWr95vOzDBq1CiMGjUKly9fxrfffovp06ejf//+OHTokOw1IQpmffr0QZ8+fRSv/9Zbb6Fp06bIyckBANx4443YuXMnXnvtNQwaNAgAkJOTg9TUVEybNg0AMG3aNGzZsgU5OTn46KOPPP4ciIg8qbS0FNdff73072vXruHs2bMWPziqKTo6Gg0bNsQXX3xh9f66desCAL7++mucPHkSmzdvRo8ePaT7xZpH5mz1LZ1tGwD897//ddifatasmTRhzKFDh7B69WpkZ2ejvLwcb731ltttISIyxyATeVzNmjXRo0cPrFmzBi+99JLNYNFdd92FdevW4eTJk7LAxLJly1CrVi23ppzV6XRITk7GvHnzsHTpUuzevRsA0L9/f/zf//0ffv/9d2RkZLi8fcAYQBk/fjy+//57rF+/HpMmTZJNxSl2IkwDV4Ig4J133rHYVnh4uFOZTb1798b111+P9957D02bNkVERAQefPBB6f5atWqhV69e2LNnD2655RaEhYU5/fxstal///7YsGEDEhMTZdOH2lO7dm306dMH5eXluP/++7F//340a9bMIhuNSKuuXr2K8vJyxesLgmBxIREeHu6RYqrbtm2TBbQBIC0tDe+++y4qKioQGhqKbdu2YeLEiRbriIEpIiItW7FiBTp06CD9e/Xq1bh27ZrD2eQ8qX///li5ciUqKytl05ybs9bfA4DFixe73QZb/aS0tDTUqFEDhYWF0o8LSrRs2RLPPvss1q5dK/WNxf2wL0Yk9+S/G6CiQsDct875uil+h0EmUsXcuXNx++23o3PnznjqqaeQlJSEU6dO4bPPPsPixYtRt25dTJ8+Hf/73//Qq1cvPP/882jQoAFWrFiBzz//HLNnz0ZUVJRT+/zf//6HhQsX4v7770dCQgIEQcDHH3+M8+fPIzU1FQDQrVs3PProoxg1ahR27tyJO+64A7Vr10ZJSQm+//57tG3bFuPHj1e0vwcffBCTJk3Cgw8+iL///hsjR46U3Z+amoqwsDA8+OCDmDp1Kq5evYpFixbh3DnLD6q2bdvi448/xqJFi9ChQwfo9Xp07NjR5r5DQkIwYsQIzJ07F5GRkXjggQcsXq/58+fj9ttvR/fu3TF+/Hg0b94cFy9eREFBAdavX28x24m1Nm3evBnr169H48aNUbduXbRq1QozZ85EXl4eunbtiszMTLRq1QpXr17F0aNHsWHDBrz11lu44YYbMGbMGNSsWRPdunVD48aNUVpaildeeQVRUVG47bbbAABt2rQBALz99tuoW7cuIiIiEB8f79VfKokcuXr1KuKb1UHp6UrFj6lTpw4uXbokWzZ9+nRkZ2e73Z7S0lLZLJgAEBMTg2vXruHMmTPS+WZtndLSUrf3T0Skto8//hg1atRAamqqNLtccnKy2z8QOmPo0KFYsWIF+vbti8cffxydOnVCaGgoTpw4gW+++QYDBgzAwIED0bVrV9SvXx/jxo3D9OnTERoaihUrViA/P9/tNrRt2xaAsU/38MMPIzQ0FK1atULz5s0xc+ZMPPPMMygqKsI999yD+vXr49SpU/jpp59Qu3ZtzJgxA/v27cNjjz2G9PR0tGjRAmFhYfj666+xb98+PPXUU7L9rFy5EqtWrUJCQgIiIiKkfRMFo/z8fCz4z3noAAx7ZKfd6zKywqcVoSigHThwQEhPTxcaNmwohIWFCU2bNhVGjhwpXL16VVrn559/Fu69914hKipKCAsLE5KTk4X33ntPth2xaOOaNWtky48cOSIAkNb/7bffhAcffFBITEwUatasKURFRQmdOnUSli5datG2JUuWCJ07dxZq164t1KxZU0hMTBRGjBghKzTZo0cP4eabb7b7HIcNGyYAELp162b1/vXr1wvJyclCRESEcP311wtPPPGEsHHjRosilH/++acwePBgoV69elLBSBHMiliKDh06JBWVzMvLs7r/I0eOCP/85z+F66+/XggNDRWuu+46oWvXrsKLL75o93kJgiDs3btX6Natm1CrVi0BgKwg5B9//CFkZmYK8fHxQmhoqNCgQQOhQ4cOwjPPPCNcunRJEARBeP/994VevXoJMTExQlhYmBAXFydkZGRYzByRk5MjxMfHCyEhIbL3k0grLly4IAAQCnY2EU4fbObwVrCziQBAOH78uHDhwgXpZvrZZwsUFP5u0aKF8PLLL8uWff/99wIAoaSkRBAEQQgNDRU+/PBD2TrLly8XwsPDnXvyREReJBb+3rVrl3DvvfcKderUEerWrSs8+OCDwqlTp2TrulP4u3bt2jb3baqiokJ47bXXpL5cnTp1hNatWwtjx44VDh8+LK23detWoUuXLkKtWrWE6667TnjkkUeE3bt3W/RrbO3bnmnTpglxcXGCXq+3eC6ffPKJ0KtXLyEyMlIIDw8XmjVrJgwePFj48ssvBUEQhFOnTgkjR44UWrduLdSuXVuoU6eOcMsttwjz5s0Trl27Jm3n6NGjQu/evYW6desKAGxORkMULFJ71BImjasnPPlYfaFHl5qCwWDwdZP8ik4QBMGrUS0iIiI/UlZWhqioKJQebIrIuo7nyyi7aEBsq2JcuHDB6eKuOp0O69atw/33329znTvuuAPt2rXD/PnzpWXr1q1DRkYG/vrrL4SGhqJp06aYOHGibMjcvHnzkJOTg2PHjjnVJiIiIqJgsWnTJgwd0geHtzWHXg+07HIM773/Cfr37+/rpvkNzi5HRESkgMGJ/9TUpUsXi1kaN23ahI4dOyI0NNTuOl27dlW1bURERET+qrKyEk9MvBfPPN4A9euFICoyBM9NaoCpkwbh2rVrvm6e32CQiYiISIFKQVB8c8alS5ewd+9eaebJI0eOYO/evSguLgZgnBluxIgR0vrjxo3DsWPHMGnSJPz6669YsmQJ3n33XUyZMkVa5/HHH8emTZswa9Ys/Pbbb5g1axa+/PJLZGVluf06EBEREQWi99+4HmUXDZgwqrrW7aPDo1BxTcB/5l1v55FkikEmIiIiBQwQFN+csXPnTrRr1w7t2rUDAEyaNAnt2rXD888/DwAoKSmRAk4AEB8fjw0bNmDz5s249dZb8cILL2DBggWyGYa6du2KlStX4r333sMtt9yCpUuXYtWqVXZnSCIiIiIKVn/99Reen3UWL06LRnh4dZgkLEyHl59uiOxXz1pM6kLWsSYTERGRHWJNpiO/NUZdBTWZLl40IL51iUs1mYiIiIjI+15+OhrrNlzCtg1NoNfrZPcJgoA7BpxAao9ayH71rFPb/fbbb/Hqq69i165dKCkpcVh7EwC2bNmCSZMmYf/+/YiLi8PUqVMxbtw4Z5+SzzCTiYiISAG1MpmIiIiIyHf++OMPzHrjHGY/H20RYAKME7PMei4ary08h9LSUqe2ffnyZSQnJ+ONN95QtP6RI0fQt29fdO/eHXv27MHTTz+NzMxMrF271qn9+pLiTKarV6+ivLxc7fYQEREpFhYWhoiICFX3IWYyHfo1RnEmU8sbTzGTifwK+3lERKQ13ujnAcBjo+vh2Ilr+PT9OLvrDR59EtENQvD2Bxdc2o+SWYSffPJJfPbZZ/j111+lZePGjUN+fj62bdvm0n69rYaSla5evYr4+Hino3ZERERqio2NxZEjR7zSAamAgAoFWUpK1iHSEvbziIhIi7zRzysoKMC7H5ZhxxdNHK77yjPRuPWuYvxrSj7i4+Nl94WHhyM8PNzt9mzbtg29e/eWLUtLS8O7776LiooKaSZhLVMUZCovL0dpaSmOHz/OX2WJiEgTysrK0KRJE5SXl3slyFQpGG9K1iPyJ+znERGR1nirn/fzzz/jphZhuKmV4wBRi4QwtGsTjhdeeMFi+Nr06dORnZ3tdntKS0sRExMjWxYTE4Nr167hzJkzaNy4sdv7UJuiIJMoMjKSnQ8iIgpKhqqbkvWI/BH7eUREFIx0esCgsAen0wEDBgzAkiVLZMs9kcVUvQ/LwuPWlmuVU0EmIiKiYGWADpVw/OVuULAOEREREWmDAAEVQqXidUNDQ1X7USY2NtZi+Prp06dRo0YNNGzYUJV9ehqDTERERAoYBONNyXpERERE5D+UZjKprUuXLli/fr1s2aZNm9CxY0e/qMcEAI6nySEiIiJUVmUyKbkRERERkX8QIKBSUHYTnJzg5dKlS9i7dy/27t0LADhy5Aj27t2L4uJiAMC0adMwYsQIaf1x48bh2LFjmDRpEn799VcsWbIE7777LqZMmeKx56s2ZjIFoVR9usWyPMMaH7REzlq7bLHVXkfb0MLzJCL/pDSAxCATERERkX8xKAweOZuwvnPnTvTq1Uv696RJkwAADz/8MJYuXYqSkhIp4AQA8fHx2LBhAyZOnIg333wTcXFxWLBgAQYNGuTknn2HQSYiIiIFDIIOBkFBTSYF6xARERGRNggAKp0OHynTs2dPqXC3NUuXLrVY1qNHD+zevVuV9ngDg0wBxJlMIE8+1hdcba87z5NZUETBjZlMRORPbnxuHvb/axEAIC0uGTftMnb7t5xMwnX3/iatl3syX/r7nqYd8UXxTgBA0uqxKMqc7LH2dB/4mnH/i95G0uqxFvcXZCyW/k6LS5balRaXjDrfNQIArE3Mk9ZJWj0WiVnbZdsQH9P6h+H4rdsH0uPZhyMiewQAFYKymkxKM56CmU6wF1arUlZWhqioKFy4cIFT22qMvwWHggE7Mv5Pq0NKSc5b303ifr76uSlq13VcyvDyRQPualvM70zyG+zn+ZekV+fi0PC3kBaXDAAozElBg33G4HbdYb/jq5uMBWOfPn0Ldt2qk9bxJGvBJ9PvzrP/a4XzBfU9uk9P8WTgjIjU463vpnXr1mHGjCHY8Hm0ovXvH3gGkyYtw9ChQ1Vrk79jJpOfYDDJf4jvlS+CEkqOE3fbpfRYFPej5rHrzdfYlefBwFRgYSYTEXlT28+mI/b+AwAAw1dNkHfj/wAAMbcORFpcsixw9Octxt+M//wlDkm/mGQJ5ajTtoQFcywXmgayCszuE3SAThu//lttux0MSjnHUNoSgDwbLembkSh68Gmb64rr+7LfdMeAVxHzZBEAYFXCV9DHHvJZW8j7BAiKh8tp45NM2xhk0hgGk8gaTx4X9rbl6MvdmXZ441j2ZUDPHq21hzyjUtCjUnCcyVTJ3gcRuShVny5dmMfebxJI+hlI+tk0eNRIlf0XZU52OgijiLcCTGJNPBf2d2934zDBb5Z1wr55Ez3ZKo9I3TwRf65oCgBo8O5Wq9lpBRmL0e6V8cifr077zYNCItN+jzQUcp4OSaur22h6XInDHHNPqtJMxUyfz4m7xuLEziQAQNLOJADG9jLIGCQE9t88iUEmL2DgKLB5KqDgq+PEmfZbW9cb7fa3oA2H2wUmA3QwwHGQiWP1iUh08vc4PNykm/RvMYBkK0uiMCel+sI8R+3WyakWYPImJ4JLBRmLpUCJ7Du6nacbZZu1rB/zLLVqNwBtjTVjztkY/njvoXvQ6PWtgEpBJtPjNs9G+RpFQZlMy0W2tqemfncMxOffrrO7jmmfzrR+GTOdAosBOlQozET3waHqdxhk8jAGlAJboAQKPPk8AuU1IXKEw+WIyJrU219CYUaEbJl4oX372smyYFF1Zodvgzli+0yDSooDTGoNfXN3uwoen5i1HRV5zQAYgwS+CGyYqg5UpMtqbDnDIqjDK2DFNh6aBWAWAKAo08ZQP1RnEJpmZoX8NQ8HRy5isCmAGJR+/PC3RIcYZHITg0rBwRuBFPN9qHlsaXWYmTPMXx9vBM48OYOjll77QDgevEH5cDn2PogCRVrN4YjdEgYAOJlShimF+43L4w9IF6Qhv/UE0Ep6jOlFv+nsZ6afsan6dJz7ZxcAQP0l26Tl5gEGa8EgV4Rc1iNv+GwAQPMbSqTlpjO6AbA665sFtYa+ubtdBY+fdfRHTD8WCwC4fdBr+H7tFPf26SGmx0aL/5uH5k9vtVjnxNo22N9lBQDj+9TqhcMAAENGSwY6PER8HU2Dj0WZ1f0k00ymtLhkYCT7UIFCgPIfCQX+mOgQZ5dzIBiDSHmGNUH5vB3x9ZeHo/fEmffN18/FHnePPU88Ny0c/2q9R848Ny0fJ4D3Z5dbm98SteuGOFz/8sVKDEo+FJTfmeSfgrmfZ02qPh0lU7oCAK5/fTcOz7rV7vqmQSUxGGQaHDIN5IjZKqYXqwBwz8DhAICCB2u513gzLaYZ92O4csXmZ/qVkngAwM1rHvPovrWuIGOxJoMz/znUHR+O7wcA+HLFEmm5Ftvqim6DX0Py03sBAAvbL5fVRQLk54gWnrNpv0lplpnpUEwOsXOdN2eXey57CD76n7Jadw8/8AemTn6fs8vZwSCTQlq46FSbGtkbgUbrF93+QI3jydn3xV+OaW/NBOgOX54T3g4yrclvjVoKgkx/XaxEevJvTrVr4cKFePXVV1FSUoKbb74ZOTk56N69u9V1R44ciffff99i+U033YT9+41ZFkuXLsWoUaMs1rly5QoiIiIsllNwYz9PLlWf7vSQJS1yNohiKG1pURtI/LvF5pE43HMpAIXZTt7kRrFvFnVWV5/mxtpQ14pPSMtyT+ZLx5Bppp94rKU+aPzu+uqbaaq0STzOAaBGYjwO/jtGlf2ICjIWI2HTPwEAR0c8peq+AOPzc3SO1rj+LzRL32eRXSnSyvWON4NMz2YPwYfrlR0LIwedxpMMMtnF4XIO+MvFqCe48uFi68OJSK3jQStffGQUTGniag2XW7VqFbKysrBw4UJ069YNixcvRp8+fXDgwAE0bdrUYv358+fj//7v/6R/X7t2DcnJyUhPl59zkZGROHjwoGwZA0xEtomfZ6YBJmtZSu6QDX9zIziiRNKqcSgYYswSURJsMq9RZPp34VAAME5BX2RStDlVn46jq4wX7JUlNd1tsm32Xis3Xj/zzDNyn+w8mnRD1dIbpPtN6xpZ6zt89Y3n2yQ/d8d6tah+0uqxCqYM8RzjuW7/s+ra77Vkn3MJC+YAARBYd4cAHcrh+IdEcV2yj0EmIiIiBSqEEFQIjjsgFVXXO2VlZbLl4eHhCA8Pt1h/7ty5GD16NB555BEAQE5ODnJzc7Fo0SK88sorFutHRUUhKipK+vcnn3yCc+fOWWQu6XQ6xMbGOmwvERERUbAzCEprMpEjDDLZwcwcI2Y1GQVDpoYnia+Xp48FW9tTa3++5MpzMT1OrR2zgfT6eFsl9KhU8HtkZVX3o0mTJrLl06dPR3Z2tmxZeXk5du3ahaeekqfQ9+7dG1u3WhZ+tebdd9/F3XffjWbNmsmWX7p0Cc2aNUNlZSVuvfVWvPDCC2jXzotzcxP5EVufjUqzl8wzYaTHmcx4ZpEt42IGTlHmZGXt0gkmw5LSVenH5BnWIDlrHgDgYoIHpjWzlbGkQrZXYtZ29u08zHyoad0i43fmhXblOPrwk17ZvzktDX1NWDBHGiJ4ZnxX7HlzotX1Ju41DsP6vOBmNMvY5/Rxai0Dc37/99Gv1lUAlpmNzORzrvA3OcYgE1mlxXG5vsTXwHmuBjNcnWWPwRMja6+DeeBJC8XV3ZWqT8fNvVo6XtGDDIIeBgXD5QxVw+WOHz8uqyFgLYvpzJkzqKysREyMvA5ATEwMSktLHe6rpKQEGzduxIcffihb3rp1ayxduhRt27ZFWVkZ5s+fj27duiE/Px8tWrRwuF2iYGNanNd0OI9pkMgepcEoTwy5c2UbhTkpqg0Ny8+ZqKxdSoYHqjVzXZXax/T4eU7VhX2m/XVJObF4d2GOvBbQnqcWAXCu4LW148i0vliPfrNxPM1GVrGGAkq2VAe9DFafa9wWASd73CZbX1zPdBIBpa+pGNTql3FVCjoXZBjrUmmhP6cdOkUlEQAOl1PCqSDTgKgRqKELBeD5iwwGNbTLUeaIvfv8/cLfX49FR+eTFs83V4NL5Jgvs8nU/K4o3HPUo9t2xNlMpsjISMWFKnU6eYdFEASLZdYsXboU9erVw/333y9bnpKSgpSU6s52t27d0L59e7z++utYsGCBojYRBQPx4thmoVx3gx46oTq4ArOaTAp4uiYUAGw40gb31CyX/u2pGa8Ss7ZLwTqrr6fKASQlLjfzQLYVySTMn4vEica6XMipDoSkxSVDn+ncsWXrGDceT1X32QowBYiTPWx/9yetHms1SGwobSmdx6Z/m9ZaMg2eJ6021qWyFdDr/m/j+fvDGt9mOA1a/ZHX9iUAMCisnuX7TzLt81kmk72LHncuiGz9Sq/0QocXtmTKH4oaOzpmXbnfF8+X557/s/Yemi9zZRZLW49JbNcc+Fp5+9xlAFCpYLy+M5cw0dHRCAkJschaOn36tEV2kzlBELBkyRIMHz4cYWFhdtfV6/W47bbbcPjwYSdaR8HmlrdeR1hFLQDAoWesD+NwRevseTgwzpjRMOBwGq72MB7vptlDgG+m9xb3WZSpIODkqqrgSsKCOdIFotIhb2pkHz223nzmyeosCXeyG8zfTy0xzQABAmtoUKo+3eqMgOK/1e7TFQx5Cxhi/DtpdUr1+ZPj3HaM558PZi5UuQC/p1n/3DAJwsn+tsNGlmbS6rFAd8t9mc4EGLElFp+2yAUALCmLwSMtv1PWeCckLJiDxFp1PL5dWwRBh3IFdTfFdck+nSA4ngZHnD6wJwa4ncnEC8nA5crFo5ZpObAk0tIMbv76PpP/iqgXhv+d+1D1qW3F78BFu29DzTqOf5u5cukaxrffobhdnTt3RocOHbBw4UJp2U033YQBAwZYLfwt2rx5M3r16oWff/4Zbdq0sbsPQRDQqVMntG3bFkuWLHHYJgounpwmWgzUAC4Ea6o67okTt1nUUREvcA4v7YgWI3cCMA4jURJ8EduUFpeMyl4dAABH7wtD4sRtOLy0IwCgxcidVmu3uJRJpHCInbPbdjYLylVPpn0GABjbaovTj1W7be6KbnkWP93zsq+b4RR3g5/iuVM4rwuKHp/ksXZZ0+PeV1Gz6DwAYMNXa6QgbpdNT6HOPYXGduSkIGn5ZQBAyJkybCx6DYDxeabFJWuqhpLf8nbgzOSzG5BfR/SJGQ8AuPbHGavvrWnwylSeYQ1S/7MYX44Zp3o/b926dXhy+jC8/mm8ovUnpR/Fc1OWYOjQoaq1yd+5HGQyZe+ClBeewUFJUMJfjgWtBpd8+foFWgCRAoO3g0xv7OqsOMj0WIcfFbdr1apVGD58ON566y106dIFb7/9Nt555x3s378fzZo1w7Rp0/D7779j2bJlsscNHz4chw8fxvbtlh20GTNmICUlBS1atEBZWRkWLFiADz74AD/88AM6deqk/MlTUBCP8XOHElCvRaFL2xAvEMUsit5DRqJokGUdMvIctYNOrmRPKQqIKAzCucv04lU8Ln2RLecKsX+VezIfvYeMBADL88mFQILpUDaRM/3ehPlzLfZZlDlZFsg13Z7pcrtDKbXGS8co2VaQsRhlVxJQPyHXa0GmnE8TFa3/RPoRPDflXQaZ7PDIcDkt1nch70rVqzNribf4c9u9gcEkIsAAHQwKij0qWcfUkCFDcPbsWcycORMlJSVo06YNNmzYIM0WV1JSguLiYtljLly4gLVr12L+/PlWt3n+/Hk8+uijKC0tRVRUFNq1a4dvv/2WASay69Z1/4Q+ojpg4UyQQcxAkOp+PKCDapUr/Gx4i1ocBZdcreckBmdSs5zv34tBnIKMlurVuVKgKHOy3xb2Np2hLWl1CjDIc9u2NpQtYcEcmwWlzYOGRY9bBjaNf1dvV7wvMWt7dSHwHLOC+mpzN0jky88WBrgAGI+5j7t77/pMcKrwNzni8ZpM/lDDhtShldo+rrAWKHWlRoxaGOQh8r1KQa+oA6K0k2JqwoQJmDBhgtX7li5darEsKioKf/31l83tzZs3D/PmzXO6HUSmTC8WRYff62h1KnKxJqY0HELNiyQtX4Bp4AIx7qZTFssKMhYrziCxNqTFtB+SezLfYUZQu51DFO3LXYE49bri4WIePM5Mj43ErOprOfNjxplgpex5ePu80PJnhCO+brsGPsNEGV/dD+BHr+xLAFChtCYTZ5dzSLXC37woJnOemDrd1f1aY6stPHaJyJoKIQQ1FHRAKhyPQifyK6YXi0Vpi5GqN9ZEyjOswcKDvQAA46J+B5Bs7eH+wxMZUhq4OPv+7tkWy/Sxh1BkktnjVH0pGOv5VD83xwW+G933Gy6qEGQwDXg6M6mPeTFsAHi+aA8e/my8xbZ99QPpcz8PrPrrdp/sXySe7x4dhmnvvTc97zQU4AhaQfv66xTNIAwwk0kJn80uR0RE5E8M0CvqgCidApeIiIiIrAs9e9Vr+xIAGBRnojOTyREGmYKYmplFvhwm540i5P5eg4qInGcQ9Io6IMo7KUT+J2n1WBScrK7fYsxgMirMSdF2vSRHWRJabLOTbA0fM83mUTpsrihzsiybRazb41LxbBde2/iP/zbu79s90Le/GQCQ60LfS1YrDJDqET38WYrFeoDv+ngrvvFuBpNp4XhvzVxolemxEQDnIHnObZ/sxgGv7Y2ZTJ7EIFMQC5RhYa50BHw1dM8ZWm8fUbCphA6VCn69UrIOkRbtG/dv2Qw+pkV/5UOJqgMZYsDBUNoSnw6chwHrJjrekbOBKE8NoQmCC9i7e7wM3Xd7AMj7OqaBlsSs7fjwxFYAQOf/2p7S3jTgUKNM71RwKc+wRtlMc2aEBuXGx4zY7bFAj3mwzJZ19+cAANpm1PTIfpWQ9fWU1mKqoigwZOfcsSzeTaQtWR+exvvtvbMvQQAqBWX9N1ZFcMypINOnF5ZJnQ9eAJMrTDsMvj6GlBT4dvRYT/P1a0JEtjGTiYKNGFQoyoQ0U5ehtKVU0+bQ251w7JEnpHUHKL1QdTbY40/BIR/XlCkaFA4MMqmpY1pPS8xyMqnN9PUDjXHnx1McbvdaXeefk3j8JGalKy5mrfszDABkNZQ8wXT2NPH4NW/ThIPDAAA/pM7y6L6tMZS2RL+UfsqLfFuhKDDkT+eOr/ljPSgtZ456QMaXA+G9wt86VAjKQiMs/O2Yy5lM5hfZvDgmkTdSjD25D2eOZTWeG88dIv9QCWVZSpXqN4XIZ/Sxh5B7smXVv/IBPGF75QC/ALJKxedqrzD1LeufBwBcOhJl/cGCThaUaJVTAgA4mOU4wAQARY/bznhyxBgwyq/OaLJxMZ+YtR26EOPkCvpMF4bk2WGahZVn8OimFbu7x8vYtGopgKrsLhdeUp8OawMsi3SLfwcCf3we/thmJzSrVw+HvbQvAYBBcfCIQSZHPDZcztkZvEgdwfI+qDlenrWWiPxDYrvmwNfe2x8zmYiMrA2bSlr9IgCzYUZKLoAC7ULVw2SBpUzb6+27d6bFslS9SQaR2et7MKuxxfrWhpVJs7nZ2bcj+thD8u3aeK9zT+a7Vu/JTxQNCndq6KDF403qbSVmbXc+C8oTmTqsn0SiyqrP7hDPHQfmNeXKysoQ9eg4j23fkUqF/Tce+Y6xJlMAsRcc0dIwNZE/1EUiIm1xZ5iruyoFvaIOiNJOClEgKch41rXsCl6o2hR6Qe/WD195hjW4/YHXAAAnezr+5d38/SvKnOxWcMliWzb2I0paPdY4NJNkxEBfalZ18fbck5AXM1fCH881BqE1yfR8lgWzneDWRAIqEFj426MYZPJzrha9BrQRbHI1+MVsI6LgYOtczzOsQVlZGeZHvey1tgjQKUql5lh9Ciatp88DAJQ3VG8MkmnWhpRZA8uaOoHm4HMKiqg78H1VvSWlAUDT13dQxwnYszNR+retmeucZR5wEi82284b75Hta1VBxmKXMpnE47wgYzH6xHcGAORe+QBAEBTrZnDJ58TzMy0u2WqfzFGRf/HxLT8Yh0PD35K25ekhse4SAFQIIQrXZT/PkYALMgVbdozpc9VK4MXVNjkKOGnl+TnibNu1FPQj8hV/OL+ZyURk6bcZ1gMhzT/4P+jPhQIAvn7gNdy5tqp2k4KLxsSs7fLPBNPsFrNMF9PZ0zTPyeFKqfp0/JWbAMD1YtTixR+gLLhhWnDb9IIxvMklaVuezDwwBpyMQaf96tfb9qhUfTrOf94CALCjj0o/eJgdM8bgkspcyR5ixpFyPnytTIffVn82AMmzx6P5wCIAwPrur8uC0uLnQLvvC61u07iucZ0acX/ht9s/MPuMMJ7fBU8AYiEyX9VFs0/nRLkDBpkcUT3IpFaBcKVDwzy9Xy1zNrij5YCcP1xwWmPr9dRiMJDIGmvBXk8Pt/XXc6BCCIFewa9cFYIme09EXnV0+FMm/5qMxInVnx22AkLWZj9zxPTz5ODxOABAn3WeybjxOCcvKgtzUoBfq/6R6toub90x1Kn1bWXa/H28DpKOG+8L9iFt4sV5Yc5YJParGsqG6qFstoJwaXHJKDhZPctdt0ljccpRbNTkmDEdTpiqT4eYY+ZSgNU0eGUe/FRaS80b9ZkCMXjl5eciZhIlfjVK9nlpepz+PFf+GEdZi7K+oMnxd+1kraqMJzca7CMClE3uIq5L9nk9k8ndrA1XL060HFBRg73C2MH0OvgTZjSRr5l/ZthKi7bGmePWWvDKHxgEHQyC4w6IknWIgkmqPh2Li78HANz98RQ0/s64vKR79TrGCyH3gkOtmpys2lZLtwose52CDCfTzAKLTC87xILg8W/Mgc6dCz+TNiYsmOOxoXP+yPTYMg3wpBljnDYvsE3fM0NpS8cBpiqmQxhNA7BuZe+5GyDyVqAkkIJLPiIer0c8cM76VeaoswQnJm7hYelQwA2XIyIiUoMBehgUFIVUsg4RERERaYOx8LfSTCb+mOiI14NM7mRp+Nuv3r6mhYyYYBkmpoXXmshVgXxuelKloEOlgiwlJesQBYMOG58BAJzLScHdH1f/8v3DGmu/qHsuM8Y4DEQjRZHtZSm5OBSoMCcF90T9E5UXLwKwn2F6+E1joeh6B0NQ1sKNVCazNvprRqqanHkt0uKSgRzb9zsqthyQmSSA03XLgo6Tr48zWY+OGEpbojDHfoZoi/fKoG/dwiP78zYBQIVBWWiER6hjXg0yKb0QV+sLy9N1Rcg59obwBRMOZSStcediwZVj1l8/Bzhcjsi+VH11XZq0uGQ0qFreAN4/78XhXAO+fww/74736r5l7F0QunExfeiFm6W/zWeOE597nmGNdJ9bASYrxCBHwoI5uP5r47a/++QJj+5DS+zNnmW+jpLC6Lkn85G02nagSNzG5fQ5su/Z3z8W3/e6DvehabYCrAww2efk6+Ptz92NGz/y6MQA3qVsBmGAmUxKqBZk0vp09Kx/4xuB9MuXO8eO6evAY5C0wpVAsLOfpf587guCXtF4fYGzy1EQSas9AgCwsXA7gGT0TRW/317yYauqfXr7G7in7ygAwKEX2/i4NYEp4rOfAATuj4mp+nRMK3J8USkGoBKzbL8O3x1NAgA8/Nl4u9uSAofdAHQzCUYVO24vAO0XzNZquwKMq7XcrLGXISpm3vlvgKmq8LfC/huPXse8mskUiF885JpA7Yg4iwEm0hpnA8HBEFwSVSocr690TD+RvzOUtoThSjIA28N6tGDDoR8AAEmr20hFlJ0abuSNC3YP7mPA948BMAbYFD1fN/cdsEO3qhTmpOCRzyyfY1HmZIssMnF906wmeRaU/eCSxzCI4x7TjOQAeS3Nj0tX1Im/AAC4dCRKWpaYtR36TP8NLplSnIkeGIeEqlT5udX8oiPPsEazHQ8iIrLkTPAoWD7fDUL1kDn7N+e3vXDhQsTHxyMiIgIdOnTAd999Z3PdzZs3Q6fTWdx+++032Xpr167FTTfdhPDwcNx0001Yt26d8w0jskMfe0j6DNDy50BaXDLS4pJls3SFn3GiC6wTlF9oujpc1nQfgs717QA4eLoRDp5uBMB4YWkRBDLftjPPz/zxZtsylLaULmQDRUHGYtl78mLf1Xix72qrASZR0uqx/jfDIYd6VxPPiQAJMIni/zcG8f8b4/I5eulIFC4dicLou75Bw706NNyr0/RnvzMEQYcKIUTRjcPlHFMlk8mfDjbztjKzhJTikEsix/zp+8ARg8LhcoqnwK2yatUqZGVlYeHChejWrRsWL16MPn364MCBA2jatKnNxx08eBCRkZHSv6+77jrp723btmHIkCF44YUXMHDgQKxbtw4ZGRn4/vvv0blzZ6faR+TvrH0OGUpbImnVOM9fRHpie25uo/xEbQCWtZo8tX3Z4wPsItwaY5Fu4/NMzNqOYZk/AgCeVVBc3vge+EGwKQjeR5usZfIFaAHykL/cyy+pW2h8/LvohYKX36paOsnNVmmDAOWzAzPI5JjXZ5fTOtbIISJynvlnZyAFl0QVgh46BQGkCieDTHPnzsXo0aPxyCOPAABycnKQm5uLRYsW4ZVXXrH5uEaNGqFevXpW78vJyUFqaiqmTZsGAJg2bRq2bNmCnJwcfPTRR061jygQ6WMPYVb/Dnjy82G+bkpA8eeaLLbIvs8y5feZZscF+rBBTfHkEFZr2wjAAJMn5M+faPIvz80IqhWcHdhzGGQinwmUC1JmwxEZ+fN5rISzmUxlZWWy5eHh4QgPD5ctKy8vx65du/DUU0/Jlvfu3Rtbt261u5927drh6tWruOmmm/Dss8+iV69e0n3btm3DxIkTZeunpaUhJyfHYfuJgkV64i4MynA8axjJibPXAcY+j2lwxTR7ynS9QFSUOVkKOnV4dB4Az87gR3YEUxDI2awqBeunxSUj92TLgAwKu0qAshmEjeuSIwwyWcFhUOQOHj8UCAI9YOQKg8IOiDgFbpMmTWTLp0+fjuzsbNmyM2fOoLKyEjExMbLlMTExKC0ttbr9xo0b4+2330aHDh3w999/44MPPsBdd92FzZs344477gAAlJaWOrVNomAlXmT9OPh6dP5vYAz7UIOtgJGt7B3jbFOBHWQCqvt6DQCcczaTyTQYYCsw4O6wrQAd9hVUnB3GZ+d+08w7BpgsKS53wIwnhxhkIiIiUkCATgogOVoPAI4fPy6rmWSexWRKp5NvVxAEi2WiVq1aoVWrVtK/u3TpguPHj+O1116TgkzObpOIiIgoWAlQXu6AYVvHVJldLlDwl3wiClbMxLOkbGa56mynyMhI2c1akCk6OhohISEWGUanT5+2yESyJyUlBYcPH5b+HRsb6/Y2iYLJQ4l3+roJRrZ+IdfqL+eCDgUZi6syl6olrR4bkDPNmRNn73OpHpOSAuqeLNJO/s+F97Moc7J0E49VXuNaEgSdVBbB0c2Vs0qNWYS1jJlMDnDoE7mKReSJAosas8uFhYWhQ4cOyMvLw8CBA6XleXl5GDBggOLt7NmzB40bN5b+3aVLF+Tl5cnqMm3atAldu3ZVvE2iYJJ75QPpO7twXhffXZyrFWxwk62Z6ooenwxxdqmiTEhBpWCpcSUOI7Q5k5+WebJ4tje268808JrEvz5HysU2lLIekzVKstWNnAv6qzGLsNYxyESqclQUm5F0IvIXpllKjtZzxqRJkzB8+HB07NgRXbp0wdtvv43i4mKMGzcOgHFmuN9//x3Lli0DYJw5rnnz5rj55ptRXl6O5cuXY+3atVi7dq20zccffxx33HEHZs2ahQEDBuDTTz/Fl19+ie+//96pthEFk+pslCC/OFZwQXxn15+tLhcvXIsygWCoySQqypys2UCTzUCYWgEPBpcsefk1Ma29JBanL0xfjLS4ZACAPpMBJnMClM8u5+y7qcYswlrHIBN5VaAGlWzNlMdMOPJn4nEbqOetswwKazIp/yXMaMiQITh79ixmzpyJkpIStGnTBhs2bECzZs0AACUlJSguLpbWLy8vx5QpU/D777+jZs2auPnmm/H555+jb9++0jpdu3bFypUr8eyzz+K5555DYmIiVq1ahc6dOzvVNiLSCG8WcLaxH3FIXL9DffCfjku90xZyS2LWdinIYBoIK8hYjLRBIwAAuWuXBU3mWbDIPZkPwLK4txhIZyaTNTrFmeiCAFRUVPh0FmGtY5CJPC7YLkjNA0j+FlBiICx4cUinc9TKZAKACRMmYMKECVbvW7p0qezfU6dOxdSpUx1uc/DgwRg8eLDTbSEiG3w5U5eX9htxWo9fHltksdx4QWrMiNkY65WmaIo0lDInRVZ/SrxQT9WnA67UZbLCNAvFpVpPqA4ImmesVM8SOBl5P4hLn0XiqheN+xtSU9kOfHkuBOKMeR58Tg336mTvu7W6aEmrx6Igg4EmUwKAa4rLHejw6aefYsSIEbKl3pxFWOsYZHKAF2CWrAUlgi2wFCjsBRkYfApMpu8531vnXDPooTM47oBcU7AOEWmLoiBBoF3YmqkOQEy0u16wSdWnA12SpX9LWT+CDtBVDUGzc+zM6GPsTw1vsV264NfHHkLCgjk2A0pTCvcDAMavrw5qOZNt1GPcGADAd58oWz/vh2cBGJ+rosCWs+dCIAaGPMn8tXGyhlN4k0u4YdAvACyvyWwdN0mrx1YNayWR0h8JBQADBgzAkiVLZMu9PYuwljHIRC5jYMnIU9kgvgjq2NsXAxCBxZVhnDzH5dTMZCIi38ozrNFsTR1v4RAa6/IMa6wXNFcSABB06Fv7hPRP89dXHNYk1soBjMGm8etTrDzGzvFpFpSI+Own6S5rbU/M2o7CFe0AAEUPPi0tt/lcre3PmaCRJwNMwRCssvMcpSw183PVYH/905WX0CikDgCg1+hHsOVzxxnRwUScXU4ZHUJDQ2VFuW3x5CzCy5cvV7y+rzHIREQUoFwJEjGwZJsAZfWWgqD7S0T+yEp2REHGYimYkBaXjDwbF6rkHPHCPi0uGQ0fP2FxvxjQFINL9oKc4nK7xcXNghJiNpJxfctgUWFOCvCH8W9DaUvpGHi890bM36QgY8pbgR4NzMqmNXdMeBQA8P3Hytbv/phx/ZI7dGjx5F4AwJYrDDBZ40wmk1JqzSKsdQwyOcAhQ3K8ALXkjWODtXPIWTxXPY+ZTESkiDtDgzw9rMj0It0suAQYsyGkITMcOuMRBRmLpSyTvvt7y4bIiRKztqMwJ0XKZAJMhyvKa0CJ7GXaiY81DRgpZbr+/E19nHqs6oIkuGQa7LWnxbR85F5e5tS2zw77y/jHidrYeORHV5oXFJypySQ4eViqMYuw1jHIREREpACDTERERESBSFkfT1zXGWrMIqx1DDIpFIyZJMyEUI8rGXLMqiNvEI8vnv+WGGQiCmxiEWZXZ/SSuJN94enMDRvbY+0l5Xp9PRnHflGeHZS0ahyKHjf+veHmKDx+0nIdsfZR9XA5y/sBY/aSIeoaAODow0/arK8kZqH1bdkNeFFxU8nHkj4yZhnpMw+hcyfjsLYff2qFHwfPBQB0/u8kAMBr/Yy1eBZlJTm9jwNdjY9NWj1WOm5Y8NuSAHWGy4nUmEVYyxhkItIIR8EjJUWa7c0Ux+AUiewdCwwu2cYgE1FgM72w9xkvzMJlGpQgx85dqeVyANLed6o+9pAsuHTbRmMB7nr9Dkv7MR16Z0ocbmduw6EfkLS6jVNt9AnWWgIAfLHug6q/nsNHKW8b/0wBAHHopPGzaMrnDxn/mePc9k1rctk6lqiKwP6bJzHI5IRgyiThhaa6nD2GlAYF7B2jwXT8km0MMLlOEHQQFHRAlKxDRGSVFy66+VnvnL39XsDCX3sBAMZF2a6dIwaizF9fW0FLsS4WALxzoTF+bFcCAEjKqd6+eVBA/HfuyZZIWm0MMv01yHwmOj+YJdFfg0seDgJXH0tzoP/b2HcwhAtSja1Wr5/CtcIj1QFFQWe1xpepVH069B2qAo3D63isrYFO6eQu4rpkH4NMRG7yhywhrbePnGPagbX13vI99zwDdIo6IEo7KUSkTXZn8aKgNKHVNyb/sn5sOJsJZx6sGlsVWFAylCktLhkFJ41Bqt8HXUR15ovx+BX7ABeHdUHdD7cBMGZhxf5gXKe0m6ImkjkVg2OGcOO2TTMNNx6eXVXMPUXx/m1l2926Yyj23euRpgYoHa4ZlBb+Zj/PEQaZiDzAm1lCSn6B5NA5Is+rNOihU9ABqVTYSSEi7RKzTJydqUvLqjNnJttdj1zjzcCkaV+wx4I5EANf4pAoWV9xeXX7dI+cBgAUpc5yv70c8uZR0uyCdoKM99y+R/o7VZ/u1PDNf7Xc4mrTgoIzNZnIMQaZXBDoF+lMow581t7jQD6mAwXPTd9iTSai4CEWZP7xxFyp+K6/Yz0W7ZICDC4+VhxClRaXbFFEXOTxmjwMLnmFadZaWlwy9Abje1iYY3sYphgcl7/nDC47ombh72DDIBNJeAHrOm8GaMz35WjolNL3NdCDp2Qdz3vlWJOJKHiYfjYmZhm/G92edc4HxDpBAFjs2w1iEKdf1/uArMYe337CgjluBZrEQIKtAJPpOoD4fAInSy8Q2Kq1JK+9atLXN/k8Mq3vBZgGLRlYUkxwov/GKJNDDDK5KNAyQXih6TqtvO9Ka/OYB6X43vsP8b20Vuzd9H5H+J67RlCYycQgE1FgMR0S70+BpsSs7fy89wDx4h8ADqoQYBJZG75mHngSv+cLc1KkAGLuyXwp807p+11aecmdplrS+tA5L8za6O4+bnxnPADg4HP21yuc10XcobSMWYruE6DDNUFhTSbW3nSIQaYgx86H/3N3pjqtBMnIfY5qcZF7BACCgv6jRrvYROSmPMMavygIbmuWM3Kdr2pzmX6n5xnWyIKc4t9Jq7oAOcZvnoQFc2TBxaRX5yL+yW3S48WA2e1rPZzhotXgksgb7XNzHxVRdtLQTBQMeQuAODSS57inGPt4DB55CoNMQYwfTETBgee6Zxigg46zyxEFtfAzxl+6/442SMEcTWQ3aT2TxI+lxSUDOb7Zt+mxlapPlw2Rkpi954U5KdXB0PDqbRiXcYicx3n53FMyNJJcw5qansMgExERkQKsyUREREQUgJyoycR+nmMMMhG5iMPMSIuYtaQeg6CDjrPLEQW1G17cCsC32UvijFGmtYI+uBgNAJixcZCvmkXOcrKGjyYy5sgSswcDggD23zyJQSYP0tLsXOYXmtbaxaLPrtPK+0xE3iMICmsysb9JFLBsFQFv/mkFAODogFC3tp+YtR25J/MByIv53vzUPMTNNga49JmHLO5/ONb4/xkb56Dnz1cAAK/9moYpN+a61Z5gZRrAK8xRaYiZnwcnxKLkSatfhKG0pvd27I0i3hSEdKhUmsmkcksCgbIS6qRYnmGN6oEbcR/W9uWN/QezVH06A0xEQUocLqfk5qyFCxciPj4eERER6NChA7777jub63788cdITU3Fddddh8jISHTp0gW5ufILyaVLl0Kn01ncrl696nTbiMiSGAgSfbliCb5cscTt7eYZ1kAfe8hitqj9/zdRcR/v3a964d2vemFS/SNutydYJa0eK90sCLrqOjyBxoXndeiOZd59TQI0wFSQsRgFGYt93YygplYfLxgxyORnrHUubAWcrK1DrmNwiXyN57FvVRr0im/OWLVqFbKysvDMM89gz5496N69O/r06YPi4mKr63/77bdITU3Fhg0bsGvXLvTq1Qv33nsv9uzZI1svMjISJSUlsltERITLz5+I5EwvChPy/omEvH8i7IbLPm4VuavZktn2V9AJxpsfX2yKWUgWnAjgJCyYg4QFc4wBUfE1McGAiXLj7v7SanCZvEccLqfkFphhTs/icDmVaGnonD3mU6MSkXaJ5yvPVd9Qa7jc3LlzMXr0aDzyyCMAgJycHOTm5mLRokV45ZVXLNbPycmR/fvll1/Gp59+ivXr16Ndu3bScp1Oh9jYWOcaQ0SKyC8G56DFw7sAVPf/fFU/pyhzcvXMYuSUUTtGAQBCLrVR9gA/z6jx1OyICQvm2AhaTQZg41jkbIgyeW3qYCpni/MthX08cV2yj0EmFZmO2yf/xveQiASFM4+InZSysjLZ8vDwcISHh8uWlZeXY9euXXjqqadky3v37o2tW7cqapfBYMDFixfRoEED2fJLly6hWbNmqKysxK233ooXXnhBFoQiIs+5uDEJAPBxYTsAST5tixg8mNCtMxaiurYQsyTse7fJDwCApG0Kg0x+zFDaEnkG4/HgyaBkqj5dGkqatHosIg8bM3uve3Mr2uwOAQDsOxeHUxubAABqngHqLd0GwDgE1erwxAAmnqsAf0j0PR0MijPR/TeL0VsYZAoSDJK4hq+b95lnAZp+2fL9IF9SOhZfXKdJkyay5dOnT0d2drZs2ZkzZ1BZWYmYmBjZ8piYGJSWlipq15w5c3D58mVkZGRIy1q3bo2lS5eibdu2KCsrw/z589GtWzfk5+ejRYsWirZLRMqYZg9NOfgQigyTpcLRvrhotva9WZiTgqJMrzfFbxhKWwZVgCNp9Vi0mm08IBJPljifzWRWfFsKVOWkIGl19baue9P4Y8m1L5ti3q0mwaxeJtuSlTILziy83JP5SFo1ztfNCGrOzC7HRCbHNBFk4pAtdfHCnPwNPweMmA2pLQKUdSzEdY4fP47IyEhpuXkWkymdTt6xEQTBYpk1H330EbKzs/Hpp5+iUaNG0vKUlBSkpFR39Lt164b27dvj9ddfx4IFCxQ8CyJyhjhcKFWfDmSaZg1556LZNJgkaxe/TxUJpgCT6POdXwAwHqu2jh+bbA1zMws+idtrNeIYerR7FQCwZf0TNjcb6MM9TWePTItLlvp5Pe59FUhl6MLXOFzOczQRZDKVqk8PuAtMMTNDrYwMW9sKtNeRiMiXnM1kioyMlAWZrImOjkZISIhF1tLp06ctspvMrVq1CqNHj8aaNWtw9913211Xr9fjtttuw+HDhx22n8jTTPspuSfzA3rYlnnfKzFrOwrndTH+w0P1Z1L16Tjyf11RMHWi9G/xYt70ItZ4n3E4zuGFnXF03BSP7J8CgxhYK8oEBv96GgAwK9feIxSwcYwfnNoMnw6cV/Uv20EmoLpguGYDfy7UkzIdFid+/uWZ1GCqcbECiVlVk3cw49A3FJZEIGU4uxwREZESghM3hcLCwtChQwfk5eXJlufl5aFr1642H/fRRx9h5MiR+PDDD9GvXz/HTRcE7N27F40bN1beOCIiIqIgIf6Y6OjGmkyOaS6TCQjMwmfmz8XVYTCeyPSytW/zf1tbL5DeE/I//jJro7McnVc87zRCYSaTs9NaT5o0CcOHD0fHjh3RpUsXvP322yguLsa4ccb6DNOmTcPvv/+OZcuWATAGmEaMGIH58+cjJSVFyoKqWbMmoqKiAAAzZsxASkoKWrRogbKyMixYsAB79+7Fm2++6VTbiDzB9LM7afVYFGQY6xUFckaTyFjM2DMzzZnWvQGq0yBk3xFmWRC5J8XaUClSnahgeN3JOWOiSgAAs1TafmLWdrTNPKFoXfH4LMiorpNVkLG4OrPJbEieUmKGlOnx7/RMkHb2XZCxWNp2woI58v05yE76avPTyttAqmBNJs/SRJDJ1oVjIA6d8wRng3C21nN0wR6IF/OkXVo5183bofZ5oJXnTY4JCqe3VTymv8qQIUNw9uxZzJw5EyUlJWjTpg02bNiAZs2aAQBKSkpQXFwsrb948WJcu3YN//rXv/Cvf/1LWv7www9j6dKlAIDz58/j0UcfRWlpKaKiotCuXTt8++236NSpk3ONI/IQ0x+uOu8xBlDrvR/4/byfy684tb6tvq8xQOT88KG2OeONf8QZTIYfzUH9n42DGRq8u1UaXtc3NR1f/PyS0/sIOC4GMfyZ2jXETIdwKpUWl4yCk6aBoaq2ufjeWAuuml4LKQo22dm3PvaQFMgFxiItLrlqH043lXyFNZk8RhNBJoAFbl3hi9eK9Z9IDc4cP9bWDfTPDVtZhuRdgkEPQcH0tkrWMTdhwgRMmDDB6n1i4Ei0efNmh9ubN28e5s2b53A9Im/LM6xBwvy5AIB67/u4MV6Q3PQ4BJ3x4lh2fWojkGHrQjctLhnIcX7/+/9vot37E9oaqjOtxhiDWUGf6WT+vmgx6OSoTU7UDTINbBZlTna+CLgCrhxTnurrFGQsRq/Rj9jMJhL3o7jguJ3XVrN1pEgBhdnqpIhmgkzBSkvDfxjoI0e0dLx6EoM2pIRamUxEwabo8UnGPx73bTu85ci/jTPPGUqrh//Yu/i3erFrdsFvHIoz2eG+TQMG4gx45vuITDwPANjdYRXavzgee9+oXsf0MUFLawEmwHGbnGhzYU4KhmwzZheu6vKWlHVkd5inwsCbVPDaB8WsxeFqALDl86mK1lcUJLLzvMXnm3syH/pM5YE12WcDwPPOFxT28UgZzQWZgvFij8EdIt9Q8nnD85MkSot6s5NCRFboYw+hqOpi291p2pVmhtj6nrN+EfuCFGCyvQ4Foo/iv3buAQoDTL68rnMrI8+F7LWBP/zL5efb+ofhLj2OPEtpJrrAwt8OaS7IFMx8kSVi7cMwGAN9pIynj09fHmv+sG8GtrRFUFj4m+nWRORIUeZkqX7LJcNV3PrfIEnrIk1K2TsYAPDTPdXBmcQsJ4tiwyRzCf53PSEbkupC9lr+rgQYEl0rrp/TfhUe+32U0/skzxHgRCYTf0x0iEEmjfFkvRnWTyKt4bFHfo8dCyLyEPFCNBKAacFlMYMoYf5cmxe7qfp0pO0vAwC8vfYe/PbIItk2iZxx5lBDAIDh1uqaXHmGNYoz7sTgkq/7efd23wkAmN/uI7e2k5i13aWaVK4W+35sfXWAiRmEPsQ+nscwyOQHArUODgUPX3c6zGmtPc7izJu+wUwmIlKLtQvLoscn2bzIL8xJwcK8qn9EVs8cV+SD2jcUOIzHkfGYM9Y0clyjqChzsldqLu0enAMAaP/fLJvruBNckvWrMt0f0uoMV4Na5EEC+2+exCCTn7B1QcngE/kb8Zj1ZJDEG4FY8/a6sz+lQSJ79aDUeB3JAdZkIiIvMx1+xItQ8iZrRbBNi2l7O2suUl9TaoM3ZnETn2vSqnGKh89NK9rn9n5T9elS8XVmJnoZ+28ewyATERGRIrqqm5L1iIiIiMhfCAaF/TcGoxxikMnPqZnBYW27zJoge0yPD9PjxxsZd8E6CxyHznkRM5mIyMusfb6bT3fuDrH4OMCsCbJkmblkv16QobSlaseRuN1UfXp1gW4VVT+PuYof80rCLQCAniedfx1M61qJ52XCgjlIXHUFeT8869S2yBVKf0gkJRhkCgCeKhau5DH2LmhNH8+L3uCjNODJY0O5YAuYaZ5BZ7wpWY+ISCX62EMoyPBMoMl0GwUZLZEWlxyU39PeGoIlI+hcmsXMm9ZergsASE/cZXGfGAhJGzRCCoJ4I1CZezIfSasth47Gf1KuTm0onSAF2/Sxh9B1iLFWU2k3K+t2Sa5az7lzyPScM5S2lAqIIwfIXbsMAINMXqHt09GvMMhETgumC99gzY5xhb3XSCsdVrXboWagjRMA+J4gKJveVvEUuERELhIDTYD12jnWiEEBW+snrR6LG/obqoMHJgGnVH31dPYTUjdhyo25brVfa9rtHCJlknit9pXGA0wA8HaLBOP/kY4PT2xF5/9OMrnXeBwlbttu5ZHqMQayLIty5334HoBpquzTdNa4rauqZn80KwxekLEYaVnJTm3X2g/0SavHYtKBzwEAE+otrjoPXW46KaU0W50UYZApQNm7IPXGhWqgFSXmBX5g8PVx6cx+nTnefP28ggaHyxGRhtxz30PGPx6qXmaace5KhvmJu/XVQagckwtpk8DLpPpHXG6zVuX3fwGoupC3OxzRlewjccYqPwgqmTMNuHX+r/Xg27XUjt5qjkQqym3yPs36MwnTYtXZn7Xz586uP+NYp8vSa6SPPeR0MEgs8C1lLlWZu6kfAOCxzMkMMHmT0tnlOAudQ3pfN4ACWyAFZngB7x+UvE9q1jHLM6yRbp7CY08jBJ3yW5D59ttvce3aNYvl165dw7fffuuDFhEFvk0/PY9NPz2PoszJSPrwMpI+NF7wJiyYg4QFc1CYkyLdEhbMQdLqsR4bYmcobSmr5xRIzC/4ZVwJFOkEvwwwKVUjb6fX95kWl2zxPr3z5V1I1aer0seztt3/dFwKwFhLyXQWSKVMM6FM+3mmdbDEc5m8QzAouwUaNfpwzGQKYLaKMHtbIGVZcPicf9DK++TuMR8I50wgUXqdEMDXEjb16tULJSUlaNSokWz5hQsX0KtXL1RWVvqoZUTBQfjpZwBAwSf5UiApMWu7lCnh6XpD1fsIvMkn8gxreGGvgGmham8z7eeZHuNqDHUcc/dXeCfnLrvtcEVR5mRYK6QuO1f9oG5XwBAQlD8SAur04ZjJFCQ8nVnhCvFXAF9f+JN3+fK4U3Pfts4pd49vniMaJjhxCzKCIECns+ycnT17FrVr1/ZBi4iCi/idZKzVtBgFGYtR47po5zbiwgWWaeZUIHE1OyWY5J7MlwI8viIe82oWHP+6TfV3mCf7Z7YyAYsyJ8MQAhhCwACTl4k/Jjq6BVo/T40+HDOZgoySmeG8IZCymyh4KD1PXD2+zbdvvh2tZGgFLaVD4YLol7AHHngAAKDT6TBy5EiEh4dL91VWVmLfvn3o2rWrr5pHFJTEC+6Np0yXKggCuXlBaxpoMoQKODp+ilvb8yXx+/aOr57Aif3OF/oRA1SmQZg7frkfJw/EeKaBPlaQsdgrM8k5QxYU9OAsc3mGNdVF8zFWOs6NmUjO691pJgCg4KGxSLxlonEfPefJ1mnauhQAEJ56tOoYcm1f5KQACx45omYfjkEm8il/DTZ5qhC4VoY0qkEr76lW2mGLK++71p9TwGLhbwtRUVEAjL+C1a1bFzVr1pTuCwsLQ0pKCsaMGeOr5hGRj+grdH7bxzP17V2vwnBz9ZTypsOxHuz5A366NURa3vjG0wCAP8rqWH3O36Ilkg54dtgiVfPEcWZtBsaizMnotu8Bi3VNi+wr3W5aXLLsGCrcd4Pxj57y9b+969WqBypsOHmGIXh+JATU7cMxyETkIiUBIndmeCEijWGQycJ7770HAGjevDmmTJnCoXFEJBEvpu/q+TK+2vy0j1vjOn3sIeSeFIME1ctfMnzsVBBAH3sIiVnG/mBIvXqoPH9elRpC3tAv+S6zTLnAIGZnZfQcjNWbuwAw9uEnHS4CAGR/9RCuXO985Cdp1TjjHznyDoKUfeXBzCtyUQAOg3NEzT4cazIREREpYdApvwWZqVOnysbzHzt2DDk5Odi0aZMPW0VERESkUJDW3lSjD8cgU5DTSpFhLbTBHabT1lubwl7JtPa2HuuP/L39nmLvuNbKuUfKKS0I6UpZk4ULFyI+Ph4RERHo0KEDvvvuO7vrb9myBR06dEBERAQSEhLw1ltvWayzdu1a3HTTTQgPD8dNN92EdevWOd8whQYMGIBly5YBAM6fP49OnTphzpw5GDBgABYtWqTafolI+0L3HfF1E9wmFpd2t58mPvaLP99Rp6+kZk1AQYeizMkoypyMjacC+3P95Ub7pL8Lc1IwqPZFDKp9EXGvbpWWOyp6fk/0o7gn+lHjUDkbnYNA6PMHFLH2pqObC9To53mKGn04BpmCmNYucHnRTYFOrRkWOXOjl6j0C9eqVauQlZWFZ555Bnv27EH37t3Rp08fFBcXW13/yJEj6Nu3L7p37449e/bg6aefRmZmJtauXSuts23bNgwZMgTDhw9Hfn4+hg8fjoyMDPz444/OP28Fdu/eje7duwMA/vvf/yI2NhbHjh3DsmXLsGDBAlX2SUSOaeG74VB2a5szaZGHqTgbWcEQ9S5ytUaswSVKWj0WSavHyoY3mtZtsqbyz3Oo/POc3fW0cH5SNbV+SFSjn+dJavThWJPJz7lS64cfZqSWQP81xleF2u3tK9Bf82Awd+5cjB49Go888ggAICcnB7m5uVi0aBFeeeUVi/XfeustNG3aFDk5OQCAG2+8ETt37sRrr72GQYMGSdtITU3FtGnTAADTpk3Dli1bkJOTg48++sjjz+Gvv/5C3bp1AQCbNm3CAw88AL1ej5SUFBw7dszj+yMix1L16Zqp+ZO0eiyKWHdGHYJO9anutTibnJryDGuQOGcuAODwP95yGFASmRb4FjOdklYrOwc7jjbur/5729B9398AgGejf4M+9hDiXzfOaFeYHlzvg9epdBqp0c/zJDX6cMxkIiKPCLZgB1Ocg48OCn/lqlq/rKxMdvv7778ttlleXo5du3ahd+/esuW9e/fG1q1bLdYHjFlK5uunpaVh586dqKiosLuOrW26KykpCZ988gmOHz+O3Nxcad+nT59GZGSkKvskCiYTdj/k9GOcDjCpOdSKLHjsxyoVA0yJWduRmLU9KAMbhZMnoXDyJIusJnvS4pKlGeTE7Cd7Bhw4iwEHziJVn44/kwX8mSygMCcFS7/ugaVf95D2LfYvdpWXu/WcyA5nSiIIQEVFhU/7eZ6kRh+OmUwBJFBmMDP/0vXn5xLo+N5ojzOdVr5/TlI6Fr9qnSZNmsgWT58+HdnZ2bJlZ86cQWVlJWJiYmTLY2JiUFpaanXzpaWlVte/du0azpw5g8aNG9tcx9Y23fX8889j2LBhmDhxIu6880506WKclWfTpk1o166dKvskCiZHBjREwhRjpkPixG12P7+l7wFng0wqZ8MkLJgj/S3OqsXvIW3j+2N8DUyPXU8xzQ6bmzNHOicKc1JQkLEYAKDPlAf3bmt6VJYtJQaSE7O2y2a05vvmCufqLX366acYMWKEbJk3+3mepEYfjkEm0jyxs8QPTNISpcEcbw9PNe1kOMJzy0lK6y1VrXP8+HHZL0Dh4eE2H2I6qwcACIJgsczR+ubLnd2mOwYPHozbb78dJSUlSE6u/tX3rrvuwsCBA1XZJ1Ew+XzHxupsksftr+vsMB1fEC+OTWs1BVu2TO7JfOvvkReGvymRmLUd4BBHGEpbIjHL+L1mnh149uQNGNqkGwAgr3KV1J8ylLZ0mMWkjz0kCwibBoyQYVzc5ol5+OXVibK2iOdJ653/QOH3sGhXYU6KLBDFPp4TnDjtBgwYgCVLlsiWebuf5ylq9OEYZAoggf4hYuuiOdCfN5E95ueFK0GtQMmCVJ2TQabIyEiHacbR0dEICQmx+DXr9OnTFr9iiWJjY62uX6NGDTRs2NDuOra26QmxsbG4dOkS8vLycMcdd6BmzZq47bbbVAtsEQWTQA3AJK0eK2VtBNt3kfE9tZIh4+MAE7PM5IyzChr/Nq9zdtt/JwLzjH8bSltKw9sWFl9yeX+5J/Ol8/2XV43LijInV907WVrv8KgEYLT1bUgBrhzg9i+nAgBq9j7C99QOHQCdQeHKAhAaGqpoKJla/TxP83QfjkEmP8cPC6aF+kqwv+bWnr+3spbULECupJMfbBcCIqWzijhzfRAWFoYOHTogLy9P9mtRXl4eBgwYYPUxXbp0wfr162XLNm3ahI4dOyI0NFRaJy8vDxMnTpSt07VrV+WNc8LZs2eRkZGBb775BjqdDocPH0ZCQgIeeeQR1KtXD3PmeH6oAREFBtMLYlHCgjlokG+8uNn57iTvN8pLTIdIaUUwfa97UtLqsSg4aQyYvn7+Jvz3/vkAgFvDIqTgk0Um1P9aGf8okG/HtEi+6XWO6bC9gtxVsmwpW0P6Th6oCmTkxEjrmA6voyouzA6shFr9PE9Sow/HIBORRuUZ1nAmwABl/sWu1feZw+nMOJnJpNSkSZMwfPhwdOzYEV26dMHbb7+N4uJijBs3DoBxZrjff/8dy5YtAwCMGzcOb7zxBiZNmoQxY8Zg27ZtePfdd2Wzxj3++OO44447MGvWLAwYMACffvopvvzyS3z//ffONU6hiRMnIjQ0FMXFxbjxxhul5UOGDMHEiRMZZCIKZi4O//oz2fgY0yFCgcZ0iLvVQJOXh87dc/ser+3LH+UZ1kgzvYlvi1Q/KfYQxEyjx2Plj8s9KQ4Lza8uJJ4J7O77IgDbASJz1RlNADBZCkatKrgN/2nZHICygGVhTgpS9enS0Frz80vs/xXmpJjtM8CpdKqp0c/zJDX6cAwyERERKaAzKEulVpxuXWXIkCE4e/YsZs6ciZKSErRp0wYbNmxAs2bNAAAlJSUoLi6W1o+Pj8eGDRswceJEvPnmm4iLi8OCBQtk09p27doVK1euxLPPPovnnnsOiYmJWLVqFTp37uxc4xTatGkTcnNzccMNN8iWt2jRwuXpb4mIiIi8RWk819kBZGr08zxJjT4cg0wUEAJ1+I4zRZy9IZBeWzXYyz6z99opGZbm7GOd3Y67jw/Uc1DGydnlnDFhwgRMmDDB6n1Lly61WNajRw/s3r3b7jYHDx6MwYMHO90WV1y+fBm1atWyWH7mzBm7hTCJyPNavzve+EddJyPeanEzEydp9VjEr3sFAJD30XsBm9VklZeymMRhewsD9fvbgwrTjZlL4lA1MTMpz87pZnrM2lpPzIgyLxiupE+1/VIiCud1qtrOWw6LjgNV2UwPtgcAfPWNvB/nD5MHqEJx/00b/TxPUaMPxyATESnG+leuUxroUfv15TBMN6g0XC4Q3HHHHVi2bBleeOEFAMbZTwwGA1599VX06tXLx60jCi6/jV4EwPJiVfPsDA07MjAMANDqvfE4PM2bjfKOPMMaNF/4GgBAf42TJWiZGDASh6oZMoxD4UwLf1/ZFI+avY8AUNavMw5JmyzbrjN+S28KPFbdPnEbjobhiefVPZGjUDrRWLPxcjMD0uKq2mWoHipnb3hdQBCcK/wdSNTowzHIROQHGBjwH+4W5Tatg+RshpOaBcEd7c+U+b4DJTCpRuHvQPHqq6+iZ8+e2LlzJ8rLyzF16lTs378ff/75J3744QdfN48oqIgXgIlZNur8uEutOkEKtlkRW+H5/WqEEFF1hXspxOv7Fo8T/pjoPNNgrlj4O2l1CpBjLLhtGujxZH2je+o/gi/O/QcAsPHwbLe2dejFNijKrJ4oBFYCXbkn89Hvtj7G/R13a3faFYT9N0CdPhyDTEFIa0OwiMiSJ89PT3YYbW3L2wEun2Amk0116tTB3r17sXjxYoSEhODy5ct44IEH8K9//QsVFYF7UUikdarMXubDSHpRn/8AmOqz/avpSF9jwCBp1TifvcZipgopJwaO4t+YIwWcCjIWW51RLmHBHNQ8qQcA/Jy1SJb59G2bT6T1xO2MuPNb/HBLmNW+14Zfv7XZJjGwVZCx2GpGo2n7xDY6muxFH3tICi4lLJgjfbbYe4y/CcYfCQF1+nAMMgUxZscQqcte4EXtYK+7X/i2Ph9cqb0UML+MKsxkCsYgU3x8PEpKSjBjxgzZ8rNnz+KGG25AZWWlj1pGFLzyDGtgKDUO5fGL2ipenklNa6QhSDrOxumPjjwmn/nNtPbSbaPmAgCmPPORNAtcUtxYIKdqhQPAnXMeAQCE5u6Qli/7+o7qdczYG7JWnTE1GYlZ1TPFidLiki0+Hy4Prs5mczQsrihzspTtpHRmPL+g9OMnwD6m1OjDMchERE7htPauUZIBZMoTwSdb75X5vx0Nv3O2LQEbwGYmk02CYP1JX7p0CREREV5uDRH5JQUBpoCsBaMGsYCxk0E7Y/BBhfYEuR3vTar6axKGmLy+Yl+pMCfFGFyCsQ8lBm7Cz+jx68yJcIfVfmZVgKj6fJqD0tur/rw9BYDyjLYZfdbAULrYbHt+yJmaTAFGjT4cg0xERBpkHqixFbhREjByJpPIU8FD8wBVQAQnGWSyMGmSseOs0+nw/PPPy2YnqaysxI8//ohbb73VR60jokAhDc1xoShyUHIxI0yVGl5kk6xPZHJs26vdZNrPE98vpbWexOCV+fqm/05YMMepYNHwFtux+GAPAMAYtPTvQFOQUbMPxyBTkAu0+kx+fQEbQIJiOnsvcJSB5AwlASrT9Uzvc6feUiBlNbHwt6U9e/YAMP4K9vPPPyMsLEy6LywsDMnJyZgyZYqvmkcU9MQLvoyeg7F6cxcft8Z17EsQGR1+szMAQF9RPQuhkn53qj4dUBBEdKU4+dhWW5x+jCYFUf8NULcPxyATBQR2PrTB3Swa0j5X3suAef+ZyWThm2++AQCMGjUK8+fPR2RkpI9bRETW/N8t/8Xqzf5ZO6UgYzHE6d0DWfL/nqv6q171Qo3WqTp4PA4tQusA8PMhUn6oxb9+BCDPOjMvLu4oUGQoZcaROR2Un2oaPCVdomYfjkEmIiI/pHZ2EIODlpjJZNt7773n6yYQUYAxBpeCJ4hxsaie5UIffKGIxaCtve7ifX3WVQcxijiEUVWG0pbSTHB5hjUuD2nMPZmPpFXGbEbW3rIhCPtvgDp9OAaZiIiIlArSDggRERFRwAriwt9qYJCJ/B6zLbQhUOru+ANvvdZaLtadqk/Hzb1aenenHC5HROQ11dkbPm4ISZJWj/V1E4JKqj4dhTljgRzjv/s0zQKmNHF9g1WZcYU5KcxYt0Zp/439PIcYZCLyA1oO4Gi5bWSfv3cwCvcc9er+OFyOiPyZOPwM8I9gwYlnuvq6CV6jtCizL/lDGwOF2Lc2Hxp3UGGAydqPhGlxyVKwCoJOCjSZr6clj3d7xqv7Y//NcxhkIgDuzR5FwYvHinf56vX292CUxzCTiYj8mGmNncQs6xexXiNUzYxl56ruhpe2Gv94YaIXGkSAMRBpepzIgh1mx0pi1nbjH6zJ5FGJr82F4OZ5KZ7XNvtvVedd7sl8t/ajJmP9rzTv7pT9N49hkIksiB9GDCCQNc4eHwxOaJu/neem7U1s1xz42nv7ZiYTEQUK0+/mhAU+mHVOwQelli+AA0li1nbptXamyDrfH3VUNqqA/rybl+hVQVzTbKXck/lIWi0PXokZjVoq3i62t7LXP/HJDxOwNOoN7+xY6Q+JpAiDTETkEANFvudvwaCAxEwmIgpAYkaKz7KabGBNJisUZIA5y9U+3j33PQQA2PSTx5oS1KRZ/c57YCiryfEhntfmASYIOk3+Kia2tyBjMS5hglf3rbjwt/ZeNs1hkIlsYkYTAbY7H3mGNTw2VMTX1rGZ657E/KiXvbdDBpmIKACJ3/M+yWiyI5h+4DLtU9kN9nkwKKBkuFuNpjfIF1QFuRLWXsGmn573WFsISFo5zviH3kudCJNjKWHBHBRlTvbOfh2obsdkoKzMuztn/81jGGQi8gNaDOg4ak8wdQ49SWvvs8hTxSFtPT+l2zVdr8zLnQ+dQdmvXJwCl4jIPS3f+TPoav14M9hnCFH2vfv59s8BmBSKrwpM6L7fq1bTAt6awg4AgCc/H4Ytg14DAPRYOwXQ+7JVJplUTgyZtCZVn+7S8EstUBrD1anbjIDAIBMRkY9pNbBkTVAXAWcmExGRV1Tu96+LU3/T4t/bgX85Xs8fZiH0N4NqXwQAPImq4JJGiMNTa7R+GhsPuJclLm6rMEc7GVIOsSaTR/k4ZkrkujzDmuC92KWA4U8BJlGqPt3ldvvzOSsW/lZyU8u5c+cwfPhwREVFISoqCsOHD8f58+dtrl9RUYEnn3wSbdu2Re3atREXF4cRI0bg5MmTsvV69uwJnU4nuw0dOlS9J0JEZIWgN978+bvCXQUZi1XfR+7JfBhKW0rZK6Qu8bVOWDAHSavHajJ4V5iTgsKcFNT6z3m3jo08wxrknsxH7sl8rxzLnqJDdca6oxuDUY4xyERERKSE4MRNJcOGDcPevXvxxRdf4IsvvsDevXsxfPhwm+v/9ddf2L17N5577jns3r0bH3/8MQ4dOoT77rvPYt0xY8agpKREui1e7D+dQyIiIiK3+LiPF0g4XI78UjD+wsVC7KQ1rg6d89fzV2mWklqZTL/++iu++OILbN++HZ07dwYAvPPOO+jSpQsOHjyIVq1aWTwmKioKeXl5smWvv/46OnXqhOLiYjRt2lRaXqtWLcTGxqrTeCIiKwoyFpsMrUlBUmZVMerHfNioIJC0eqxU+NuV2fv89XvcV7SYuWTLnp2JSLv9DwCuz+zob7WYRKy15DkMMpFf4Zeaf+D7RAHJyZpM5oXJw8PDER4e7vLut23bhqioKCnABAApKSmIiorC1q1brQaZrLlw4QJ0Oh3q1asnW75ixQosX74cMTEx6NOnD6ZPn466deu63F4iIlukmc0yzPoMQVbs25brdhovd//oqM6vFolZ26XizETmxBkOtTTrnFcwS8ljGGQiTWOwwpK3Zprja09kxskgU5MmTWSLp0+fjuzsbJd3X1paikaNGlksb9SoEUpLSxVt4+rVq3jqqacwbNgwREZGSsv/8Y9/ID4+HrGxsfjll18wbdo05OfnW2RBERF5AvsYtuljD2HbbGM9HLUyYHJP5ivKNhFr6ojtkIKDDAa6RtCpW7iRXOdMTU2+hQ4xyESaxM6H9nkr2EWkFTooS6UW1zl+/LgskGMriyk7OxszZsywu80dO3YYt62zbIEgCFaXm6uoqMDQoUNhMBiwcOFC2X1jxoyR/m7Tpg1atGiBjh07Yvfu3Wjfvr3DbROR/6ou8OudIT2JWdsZpHBAHEKIHM9uNzLxPADlw5nE9RKz0lGjVRI2sn/uHgaYtM3F4YFkiUEmIpI4G9xjMJDEQGNQHAtOZjJFRkbKgky2PPbYYw5ncmvevDn27duHU6dOWdz3xx9/ICYmxu7jKyoqkJGRgSNHjuDrr7922K727dsjNDQUhw8fZpCJKMDdtHi88Y/63rnCCorvCzeJQ9mSVqd4dLt7+73g0uP4nnkIM5k0jW+N5zDIRDYxS4WIqJo0da2C9ZwRHR2N6Ohoh+t16dIFFy5cwE8//YROnToBAH788UdcuHABXbt2tfk4McB0+PBhfPPNN2jYsKHDfe3fvx8VFRVo3Lix8idCRH7pwNhFAPyrOHGgq840mqPsAUJVNiuvkjXJfNihy3wQpBIzHf21mLdTePp4DINMREQ+xGGHfsaHHZAbb7wR99xzD8aMGYPFi40d1kcffRT9+/eXFf1u3bo1XnnlFQwcOBDXrl3D4MGDsXv3bvzvf/9DZWWlVL+pQYMGCAsLQ2FhIVasWIG+ffsiOjoaBw4cwOTJk9GuXTt069bNJ8+ViLzHW8El8UIbCKJCwm5q8cElHB5ex/GKDC5p2s1vVmULXudmtqAP3mdx6KarM835DSdqMvF0c4xBJtIcpuQ6Jr5GDE4QeY9OYQdEzc7HihUrkJmZid69ewMA7rvvPrzxxhuydQ4ePIgLFy4AAE6cOIHPPvsMAHDrrbfK1vvmm2/Qs2dPhIWF4auvvsL8+fNx6dIlNGnSBP369cP06dMREhKi3pMhoqBRkLE4ODIhPCx3x3QkLFCYzWSDWKyb/Wvf+fWFiQCMWUH+ljEYLLMQ6uB8JjrZxiATaQa//JznySwYvv6+w6Chn3CyJpMaGjRogOXLl9vfvVDdgObNm8v+bU2TJk2wZcsWj7SPiMiatLjkwM+E8BUHQ6iCJUjgD9Likj1ezF1tQRUcZoaSxzDIREREpIAWMpmIiIiISAVK+2/s5znEIBMREbksqDLgNJDJRETkbwoyFkOfGUTZEB4mDncrzLEx05yDXzaCKhOFbOPMdvY5UZOJHGOQiTQhqC5UNYavPZEyzGQiInIegxzuEftpztRmYh0mbcozrHGpxpat91Mss2AzAGmKnRPH+BJ5DINM5FP88vMtvv6BwbSmk+l76s81nkzbrpnjlJlMRESKcTY53yjIWIy0rGRfN4OsMJS2BKC88LdUMD/T+v1i/0itguLGczh4zl+dQWEHzkGtS2KQiSjoaOaCndxi7X00X8aC4h7GIBMRBRh3Zy6zhxlMnlWQsVgWSDDUMH7ZFD3wNuI/fwQAcHubw9DHHmKRdY06eu2izfvqJpwHAOzpuEpapvQc0sceQkFGSwDwSLBJDBB74xzu2Wc2Nm+cqvp+lFCa7KVTtxkBgUEm8hkGOzzDUSCBrzN5chZC8+16g9h2Xx/LHC5HRGRbUWbwZDz4gj72EIqsZrRMwbHR3m4NuSLhhhIkZhn7NLkn842zzcG8f/OCS9sWA0JFmU4Oo7Oi9ZLxAIBDT7v0cKcU9wlRfydKKP0hkRRhkIm8xtcXiIGOr6//Mw8GefI9tbYtf8twMh8O6G06g6AolVpxujUREREFFdN+jFoZZ6b7cCVb8dDTEz3ZHIe09GMieQaDTGSVv118EgUKb3zB+vP5bdr2teff9e7OOVyOiAKEsTYM4Ex9GCIKfN6uw5SYtV3KuDKUtsTvlcYhhdeH1AVq7fRaO3QAdEqDfuznOcQgExFRkHG3VpM3AmFarCfF4XJEFCjEYTrI8WkziEhl4sx0jobOVRfq964aifHS36b1pBKztmPteS82hMPlPIpBJiKiIKVWrSZP0GS7mMlERCRdtALAyXU3Y/+AbN81hojsMv1hUMxgTItLRp5hjTSULjFrO/SZvinU//l3n1gEl3yFPxJ6DoNM5HFq1pUhIs8yPT9Nax5pvZB8nmENysrKvLpPZjIRUaCYWvgLAGDseucLA2vle4CInCMWBxdrQUnF+q0WlFeX2M/MPSlfbvr54t1+ngAI7MB5CoNMRERESjCTiYiIiCggKf2RkD8mOsYgE6mCv3IR+R8lw+e8McObJofKgZlMRBQ47qpZ6esmEJEPJSyYI9VhSlo9Vhqm5q1rOLFG1PQ/rnllfw4JgI4fix6j93UDSHu0eoFHRORTghM3IqIAU5Q5uXp4DRH5tcSs7UhaPVZWD8lbqme3BFZ8czsKMhajIGOx7z9fNNDPO3fuHIYPH46oqChERUVh+PDhOH/+vN3HjBw5EjqdTnZLSXF+KLQnMZOJiIh8TklwWwsZksxSIqJA4IsLSyLSjmtfNgV+qf53jcaxqu9TKjw+aAQwxLjMl0XHzWmhjzds2DCcOHECX3zxBQDg0UcfxfDhw7F+/Xq7j7vnnnvw3nvvSf8OCwtTtZ2OMMhEREQSbxf+9pfgEgDoDAJ0Bsc9ECXrEBH5A3E4jVgwmIgCwzd3zsHd2S8BAEq61MLG319XdX+G0pbVwe0h1cu10scD4PPC37/++iu++OILbN++HZ07dwYAvPPOO+jSpQsOHjyIVq1a2XxseHg4YmPVDxQqxSATeYymPiSIyC1qns/ODMnV1OcKC38TUZBJi0sGUD0bFREFji+/fUb1fYjZS+bZkz4fGmdGad1NUUVFhcXsd+Hh4QgPD3e5Ddu2bUNUVJQUYAKAlJQUREVFYevWrXaDTJs3b0ajRo1Qr1499OjRAy+99BIaNWrkclvcxSATSdypxaSpC0Ei8nta/EzRGYw3JesREWnVLeufBxDlcL3ErO2a/CwmIv/QfNFr0FdYDs1NzNoOZPqgQQ4o7b/pBODTTz/FiBEjZMunT5+O7Oxsl/dfWlpqNTDUqFEjlJaW2nxcnz59kJ6ejmbNmuHIkSN47rnncOedd2LXrl1uBb3cwcLfRERESgRwQci///4b//73vxEdHY3atWvjvvvuw4kTJ9R7IkTkdan6dKTq03HpiOMAE6DNYD8R+Y8N/efJ/i0W+NbsZ4sgKLsBGDBgAC5cuCC7TZs2zepms7OzLfph5redO3cCAHQ6nZVmCVaXi4YMGYJ+/fqhTZs2uPfee7Fx40YcOnQIn3/+uQdeFNcwk4kkjmqxEGmBeHxq9guKHPJ23SdPUZpKrWbhSLUKQmZlZWH9+vVYuXIlGjZsiMmTJ6N///7YtWsXQkJCPP9EiMjrxM9WWW0UOxIWzNHckBYi8h83Nfkd0S2fBgBE9T2smQLftjjTfwsNDUVkZKSidR977DEMHTrU7jrNmzfHvn37cOrUKYv7/vjjD8TExChuW+PGjdGsWTMcPnxY8WM8jUEmkjC4RFpneoya/q3VoATZ53fvm8kvWA7XU4FaBSEvXLiAd999Fx988AHuvvtuAMDy5cvRpEkTfPnll0hLS/P8kyEirxO/Nwtz7AeYxGLfaXHJmhzSQoHNdHp7Fpz3fz/d87LxD62XEnAmE93Jbl50dDSio6MdrtelSxdcuHABP/30Ezp16gQA+PHHH3HhwgV07dpV8f7Onj2L48ePo3Hjxs411IM4XI6IiEgBMZNJyQ0AysrKZLe///7brf07Kghpj1gQsmXLlhgzZgxOnz4t3bdr1y5UVFSgd+/e0rK4uDi0adPG4XaJiIiIAoGuUlB0U8uNN96Ie+65B2PGjMH27duxfft2jBkzBv3795f9kNi6dWusW7cOAHDp0iVMmTIF27Ztw9GjR7F582bce++9iI6OxsCBA1VrqyMMMhEA17OY8gxr/C8bgYjIFU7WZGrSpIlUOykqKgqvvPKKW7t3pyDkihUr8PXXX2POnDnYsWMH7rzzTinoVVpairCwMNSvX1/2uJiYGLvbJSL/knsyH7kn86VMJUfYvyNfSFo9VtFwTiKP83HdTQBYsWIF2rZti969e6N379645ZZb8MEHH8jWOXjwIC5cuAAACAkJwc8//4wBAwagZcuWePjhh9GyZUts27YNdevWVbexdnC4HAEwdiQ8OVyOQ5lIDebHKY8t8iZnazIdP35cNl7f1gwf2dnZmDFjht1t7tixw7htFwtCitq0aYOOHTuiWbNm+Pzzz/HAAw/YfJyj7RKRf3J0Ac8hSuRL1XXAWA+MvEdpHw+AqoGmBg0aYPny5fZ3b1KWoWbNmsjNzVWvQS5ikIlcZusC3zxYxULN5Ek8jshXdAYBOoPjnoW4TmRkpKKikL4uCBkbG4vy8nKcO3dOls10+vRpp2oAEJG2VQeP5ri9LbFuTlpcMr+XiSgwKK6pqXI6UwBgkIlclqpPd6pj4ez6RESaojRN2s8KQnbo0AGhoaHIy8tDRkYGAKCkpAS//PILZs+e7dyTISLNM9Q0QH/FvYoZ/e4w1vrIPbnOE00iIvI5NWcHDjasyUQSV+orperTLTKXzLchbpcBJiLyZ84W/vY0tQpCRkVFYfTo0Zg8eTK++uor7NmzBw899BDatm0rzTZHRIHj6Jgn7N5vrW9nbuOhWdh4aBaH1hFRgBCkjHWHNwajHGImE6mCASUiCjgGwXhTsp5KVqxYgczMTGkmuPvuuw9vvPGGbB1rBSGXLVuG8+fPo3HjxujVqxdWrVolKwg5b9481KhRAxkZGbhy5QruuusuLF26FCEhIao9FyLyHbH4t7X6TIU5KQCAhAVzTOrjEBEFMAGAwdeNCBwMMpEFV4qAcygcEQU8lYbLOUOtgpARERF4/fXX8frrr7vdRiLSPjEDqSizunbm7x/fjKvFvpuNiIjIl3SKazKRIwwykVVKA00MLBFRsNBB4exyqreEiMhz2JcjIoLyHwkZi3KIQSayiZ0OIiITgqBs5hH+EkZERETkPwQomkGYlGGQiYiISAGlRb1ZEJKIiIjIzyj+kZAdPUcYZCIiIlJCAzWZiIiIiMizdAB0Sgt/s5/nEINMRERECugqBegUpCnpKtn7ICIiIvIbAljuwIMYZCIiIlJAJwiKZh7h7CREREREfobdN49hkIlUZT5DHYuJE5Hf4nA5IiIiogAkQGdQNl6OtTcdY5CJPM48sGTtPgabiMjvcHY5IiKZ8yebIP2GFADs2xGRHxMAKK3JRA4xyEQ+wWATEfkbzi5HRAQYSltKf7f/bxaKDJOl5frYQ75qFhGRW5SXO2BHzxEGmchl9jKWiIgCDjOZiIjQr0t/HJx4vfTvhAVzAACJWcnIYyYAEfkrpf03dvMcYpCJvIqZS+SvWWzOBFWdfW6m2/a31yWY6AzKprdVPAUuEZGfEANJAICJ16PV3BMAgGvFJ5B7Mh8AoM9kFhMFhlR9evVxzey84CAA4OzAHsMgEznN2QwmXjQHh1R9uuy9dnSc+LoovK32mbfDlYw9d7L83HlsnmGNS48Xn7Ov3xPNYyYTEQUZWXDJxLViY5CJ3xMEyIdQmvLXAE1hTgqSVqdU/Ut+DiRmbQcA5J7Mt/v8th5LAACkhFdfbvvr6xEclM0gXLUqOcAgEznk7rA4f81cIcfMjw01gitKgj5Kji2lbfPnYaCutt3W45hhZYazyxFRgDMNFtxz/0Mo+GQ5ACBp9VjZeoU5KaDgZZrpYzw2xlpdLzHLd9cA4rGctHosCjIWIy0uGQCkdotMl5sf59aIx74xCDUHBRmLpfvkj/+XlUfLA1biY7vmD8Lpg9HSctNAlth+6/tQznwbiVnb2bczx5pMHsMgExERkQI6QdmvXMoLRxIRERGRJrD/5jEMMpHqGCX3D0qHSnk708dXQ8/INlezyfyeQVA2Xt/ATgoR+SdZlsQw21kTOwbPq/prsvqNIp+zGDYpG05mm5j1Y5n5VM00aweoziwCnO9b3Pr5cwCAssJ6kLKrBJ1xnzmo2r9Zu20tF3TG/zuYMtbVzCJ7j5VnS7m3D1v7yj2ZL2V7cRhfFQaZPIZBJrLAC3NtUjKcTMnwpkAcNuZPbfVXztbYMhUoAShmMhFRoCvKnGyzDpOoIGMxWv/HGFw69Kw3WkWeYChtKQUaijKtBwcNpS2lII8nh0QW2glKmQdUxKCP2B7AGHiy1pdI1aebtbOe5Q4cBIlscvVxalAY8HKGadCpIKN6mGxaXLIsICgGAXv+fAXP3Pw/j+1fcwQBOqWFvzV0aGiVThAc94bLysoQFRWFCxcuIDIy0hvtIh9Q60I9UC4wzTlTr8bVC3AGTyhQqPE54K3vJnE/d976FGqEhDtc/1rl3/h67//xO5P8Bvt5wcvyIl0ZW0GKYNZt8GsAgFof/+jWd16/bzPx14vXAwC+WvauFPRxZpuOAoVAdY2eUcXd8d32m1xoKREg6IEjj6nzeeCt76Z169Zh5LAJ6Np8pKL1tx9bjkVLXsXQoUNVa5O/YyZTkPNGECNQCn/be604pIvIPiXHueY/Izi7HBH5MfFzWFbg2FqASUHWRLANs0mYPxcAkDhxGxYW/2D8u0YdKQCk69gWJQ/VNq58R4oU5GnxwSUYdv0CwPi6H712EQAwtuntKJzXBQVD3gJgPpSpGdAf1ctzqtqgIHAEwPj+6Ryv5okhWBS8xCDld1eBVH31kEfT4Y6AH/TtRAKUlztgP88hBpk0wFF9E04rTkTBQOkMgz5jgKKOOwzqNeHcuXPIzMzEZ599BgC477778Prrr6NevXo2H6PTWW/07Nmz8cQTTwAAevbsiS1btsjuHzJkCFauXOmZhhORz1kMS7JFwZCc6uwat5ulSWJAp+U7f+LQmAbSZ39hTgrSPjZ5/XLsb+fw8DrAQ10AmL3uOQAgqBPo0dIwLwpYsmM3R1yWYnFOiOdSYtZ2HF1l/Nw42H2ZNgPUDB55DINMVnhz2m4lU4c7c581SqaA94ZUfbp2LhbtYGYREVmjhZpMw4YNw4kTJ/DFF18AAB599FEMHz4c69evt/mYkpIS2b83btyI0aNHY9CgQbLlY8aMwcyZM6V/16xZ04MtJyKgOgMIAPokGoMOfwxvh53Zi6Tl1gofu9qHEvs0J5/sCjR2qclWebJejy/N/+1uAMC/6xVbrUV0aEwD5RuzlgEm6AIn6GP+XFytE+Qvr4kKdZA8zh/aWKUwJwWo6o4krR6LokzftseSABgCNGruA0EZZLKVGWQtuOBOwEErtXa0EjTxdYBJK68DEfkpHw+X+/XXX/HFF19g+/bt6Ny5MwDgnXfeQZcuXXDw4EG0atXK6uNiY2Nl//7000/Rq1cvJCQkyJbXqlXLYl2iQGY645U3flU3/qJv8uv/K+IfBqtZAdWPAZCTIhsuJQ5VAYC069sBgvHiKM+wRurvFM7pggKpgK9ng0LV+/e/2kymgb75m4yv+3zAYVaSQ35woe8W8+cXCAW17fGHdvpDG6sUZCzGPU07AgA2lX/o49ZYweFyHhUQQSZ7mUdKAgtqBR8Y1NAGvg9E/sXXAWmbnAwylZWVyRaHh4cjPNxx4XBbtm3bhqioKCnABAApKSmIiorC1q1bbQaZTJ06dQqff/453n//fYv7VqxYgeXLlyMmJgZ9+vTB9OnTUbduXZfbS6QVbabOAwA0fm2rPANHNuOVPIDjaBhT/CflGLRoEwBg3U2NpCBP7sl8mwGroszJ1QEgNzOBZO2bV/1nwoI5wDzj8CzoPDscSwws3fjOeM0PlzPv+8lfbwWviacyRMwfr9XMEy20y5kMJy20l5ymyeCSKUGjH2h+yG+CTIE47Xow8kV9KXv1rYhIOzQbXBI5GWRq0qSJbPH06dORnZ3t8u5LS0vRqFEji+WNGjVCaWmpom28//77qFu3Lh544AHZ8n/84x+Ij49HbGwsfvnlF0ybNg35+fnIy8tzub1EnmT6/S1mH5myG0y5wfg/pYEdJYGZI/eH4bXcqurMJkEeY9CqOmD1/n2LMDOhHQDg8Osp0DsTXHJ1WJFKF97S6xJlMA6/gzErSIu1VWTFzV2hVvBCq0EcX7XLtE3i/xlAss+PXh/5LJR+kPXIDCWP8ZsgExERkS/pKgXooKAmU6VxnePHj8um3LWVxZSdnY0ZM2bY3eaOHTuM27ZSxFsQBJvFvc0tWbIE//jHPxARESFbPmbMGOnvNm3aoEWLFujYsSN2796N9u3bK9o2ERERkV8SBA6X8yDNBJk0P6sQuU1pzStPv+fMXiIKPKn6dNzcq6XjFT3JyUymyMhIWZDJlsceewxDhw61u07z5s2xb98+nDp1yuK+P/74AzExMQ7389133+HgwYNYtWqVw3Xbt2+P0NBQHD58mEEm8olOI41Txp9pX3XOmWQAebq+kJoe/my8VOtHX+nkgzWcqXClsXFYSdLqsWi55HkAQOW+X6X7C3NSzLIY1GcobQlD1fSeLVeP9+q+NUOL2Ur2WGuTknZq8bl4ixafu43sKq1mOtrEwt8e41SQaUDUCNTQhVosdzco4MmZ1Mj/8T0nCk7OnvuFe46q0xBbDIKyzp3SX8KqREdHIzo62uF6Xbp0wYULF/DTTz+hU6dOAIAff/wRFy5cQNeuXR0+/t1330WHDh2QnJzscN39+/ejoqICjRt7cDoqCmrWhruZzqIGAB32Gs+d1Zu7AIxt+o1D/4yq+iul+mITgqxQeaNWZ7A97RWLx7rCtHC36TFUmOO5+lNe56kZ17QQgPCj4VzkIWbvtRhgTv7feTS6z/jZ70q8YNDqj9xvmzOYoeQxHslkYlCAiIgCno9nl7vxxhtxzz33YMyYMVi82FiA99FHH0X//v1lRb9bt26NV155BQMHDpSWlZWVYc2aNZgzZ47FdgsLC7FixQr07dsX0dHROHDgACZPnox27dqhW7duqjwX0qZb3nodLZ7aC8CzWcWp+nRZPSQpEylHvl7hZhc2rrULWn+Znl0tNp776YPRSDhY/fmju2Z83xKmbEPRa8ZC5UIN+WPH3P0VAGDaTRtkAStZ4e4c95usCf5+zJieh7aei9bOVVJN9flaDxerPvvl57AyNcorPNgqBwRBeSYTg1EOaWa4HBERkbYpDDIpqNvkqhUrViAzMxO9e/cGANx333144403ZOscPHgQFy5ckC1buXIlBEHAgw8+aLHNsLAwfPXVV5g/fz4uXbqEJk2aoF+/fpg+fTpCQkJUey6kTYVuXBDY5OZManZp7YJVa+3RKDGgZDzerL9m73x5V9X/PXgsyhrhxwFBrbWdQ9woEDB45DEMMhERkV9KbNcc+NqLO/RxJhMANGjQAMuXL3ewe8v9P/roo3j00Uetrt+kSRNs2bLFI+2jIKT17ARfXIxrLQBAclo/ZpXw57YTKdSsXn0c9uYOGWTyGAaZiIjIL/mkJpOSLCUnazIR+TWtX+z6on1af02CXSC8P94KZAZCQE4L3H0dg/R9OHb+nPd2JggQKhXOzsBglEMMMhERESkhGIw3JesREQUSf8vO8rf2Ostbzy2QX0OlPHEseerxah3XgX6+KKX0R0K+VA4xyERERKSEBobLEQWNQPnlPlAu3vztOXgry8ffXhcycubzxZn32NZ2PfV5Zu/x7uyDx7ER+28ewyATERGREpUKM5mUzk5C5K+8EQAy3ba/Xcybttef2k3OUeuCn9Sl5ueJre164zjgseYeAU703/haO8IgExERkRICFGYyqd4SIt/y9sWMvSnRtXhhpcU2kXdZOwa8cbw6s49gDYQF2/MlhViTyZMYZCIiIlKCw+WIiIiIAhP7bx7DIBMREZESBgMADpcj0gxnMxK0mvnkCcGYleLN99MTr6/Whkxp8VjhrHnkK4LAwt8epPd1A4iIiPyCmMmk5EZE6hAvDl1hXufJkwSd57fpDJ0QfBfMaj1fa++jL19f02PL18eZ2rw5a54n9hXI70UwEmcRdnRT0UsvvYSuXbuiVq1aqFevnrJmCwKys7MRFxeHmjVromfPnti/f7+q7XSEQSYiIiIlGGQiChyeusA0zYhwdZvidtQIIAR6UMIVjl5rbwavlDA9tqwdZ3x/fSfYArsBTjAIim5qpjKVl5cjPT0d48ePV/yY2bNnY+7cuXjjjTewY8cOxMbGIjU1FRcvXlStnY4wyERERH4psV1z7+7QICi/EQULbwcxtHRR50pgydbrJW7L08/P0TZN2xJM2TJKX2tPvg7entEsEKgVfCW/0Kxefe/tTDAW/lZ0U/GUmzFjBiZOnIi2bdsqbLaAnJwcPPPMM3jggQfQpk0bvP/++/jrr7/w4YcfqtdQBxhkIiIiv1S456hX9ycIBsU3oqDhbGDEny8W7bVd6YWwtdfLl0EC033by5axR0kgwPw+TwYPlGzH1X0FcgDHH6gVfPUUf/488wPHzp/z2r4ECE4MlzOgoqICZWVlstvff//ttfaKjhw5gtLSUvTu3VtaFh4ejh49emDr1q1eb4/IqcLf7e65CeE1ItRqCxERkSI7835G41aNgO1e3KnSopAcLkd+6vZGsQirVcvXzSAiIsKx06dx2Av7ueWWW3AZZbgklKGOLtLuupeFi7iI89i+fTtGjBghu2/69OnIzs5WsaWWSktLAQAxMTGy5TExMTh27JhX22JKUZApLCwMsbGxmPfFK2q3h4iISJntQGxsLMLCwryzv8pKQFfpeD1BwTpEGiL281aMesTXTSEiIpJ4o5+XmJiIOMSjAD/jVnSzu24BfkFjNMPcuXPxyivy2Eh4eLjVx2RnZ2PGjBl2t7tjxw507NjRuYab0OnkWXWCIFgs8yZFQaaIiAgcOXIE5eXlareHiIhIsbCwMEREeCnDVlBY7JGZTORn2M8jIiIt8lY/b/fpbWjcKA7nhD9QX3ed1XXOC2fwJ07hRMlxhIeH2wwqmXvssccwdOhQu+s0b97c2SYDMAbhAGNGU+PGjaXlp0+ftshu8ibFw+UiIiK815EnIiLSGMFggKBzXG+JNZnIH7GfR0REweq6665Dc7TGYezDbcKdVjODDmMfmqGlFNhRKjo6GtHR0Z5sriQ+Ph6xsbHIy8tDu3btABhnqNuyZQtmzZqlyj6VYOFvIiIiJQRB+Y2IiIiI/Ma+yz/ib1zFKRy3uO80fscV/IX8iz+q2obi4mLs3bsXxcXFqKysxN69e7F3715cunRJWqd169ZYt24dAOMwuaysLLz88stYt24dfvnlF4wcORK1atXCsGHDVG2rPU4V/iYiIgpaBqVTXjPIRERERORPatWqhbfeW4jxo/6FRsL10OtCAAAGwYAC/ILXF89HnTp1VG3D888/j/fff1/6t5id9M0336Bnz54AgIMHD+LChQvSOlOnTsWVK1cwYcIEnDt3Dp07d8amTZtQt25dVdtqj04Q2BsmIiKypaysDFFRUbgzLB01dKEO178mVODr8jW4cOECIiPtz1JCRERERNpQWVmJejUaojGaoZmuJQCgWCjA7yjE+Yo/UaMGc3SU4HA5IiIiBQSDoPimlpdeegldu3ZFrVq1UK9ePWXtFgRkZ2cjLi4ONWvWRM+ePbF//37ZOn///Tf+/e9/Izo6GrVr18Z9992HEydOqPAMiIiIiLQpJCQEa3NX4wh+RYVQjmtCBY7gAFau/5ABJicwyERERKSEYFB+U0l5eTnS09Mxfvx4xY+ZPXs25s6dizfeeAM7duxAbGwsUlNTcfHiRWmdrKwsrFu3DitXrsT333+PS5cuoX///qisrFTjaRARERFpUu/evRGJBjiCX3EUv6EOotCvXz9fN8uvcLgcERGRHeJwuZ66gYqHy20W1qk6XG7p0qXIysrC+fPn7a4nCALi4uKQlZWFJ598EoAxaykmJgazZs3C2LFjceHCBVx33XX44IMPMGTIEADAyZMn0aRJE2zYsAFpaWmqPAciIiIiLcrPz0f7W9sD0OHHHdvRsWNHXzfJrzCTiYiISIFrwt+4ZlBwE/4GYAxOmd7+/vtvr7f5yJEjKC0tRe/evaVl4eHh6NGjB7Zu3QoA2LVrFyoqKmTrxMXFoU2bNtI6RERERMEiOTkZTdACNyCBASYXcGAhERGRHWFhYYiNjcX3pRsUP6ZOnTpo0qSJbNn06dORnZ3t4dbZV1paCgCIiYmRLY+JicGxY8ekdcLCwlC/fn2LdcTHExEREQWTo8Jvvm6C32KQiYiIyI6IiAgcOXIE5eXlih8jCAJ0Op1sWXh4uNV1s7OzMWPGDLvb27Fjh1u/pJm3xVr7zClZh4iIiIjIFINMREREDkRERCAiIkKVbT/22GMYOnSo3XWaN2/u0rZjY2MBGLOVGjduLC0/ffq0lN0UGxuL8vJynDt3TpbNdPr0aXTt2tWl/RIRERFRcGKQiYiIyIeio6MRHR2tyrbj4+MRGxuLvLw8tGvXDoBxhrotW7Zg1qxZAIAOHTogNDQUeXl5yMjIAACUlJTgl19+wezZs1VpFxEREREFJgaZiIiI/ERxcTH+/PNPFBcXo7KyEnv37gUAJCUloU6dOgCA1q1b45VXXsHAgQOh0+mQlZWFl19+GS1atECLFi3w8ssvo1atWhg2bBgAICoqCqNHj8bkyZPRsGFDNGjQAFOmTEHbtm1x9913++qpEhEREZEfYpCJiIjITzz//PN4//33pX+L2UnffPMNevbsCQA4ePAgLly4IK0zdepUXLlyBRMmTMC5c+fQuXNnbNq0CXXr1pXWmTdvHmrUqIGMjAxcuXIFd911F5YuXYqQkBDvPDEiIiIiCgg6QRAEXzeCiIiIiIiIiIj8m97XDSAiIiIiIiIiIv/HIBMREREREREREbmNQSYiIiIiIiIiInIbg0xEREREREREROQ2BpmIiIiIiIiIiMhtDDIREREREREREZHbGGQiIiIiIiIiIiK3MchERERERERERERuY5CJiIiIiIiIiIjcxiATERERERERERG5jUEmIiIiIiIiIiJy2/8D3Es0bWl0EooAAAAASUVORK5CYII=", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Check to see if you get appropriate test values around the coast\n", + "# should be identically 1, but maybe this is within rounding errors for single precision data?\n", + "\n", + "fig, axs = plt.subplots(nrows=1,ncols=2,\n", + " subplot_kw=dict(projection=ccrs.PlateCarree()),\n", + " figsize=(15,3))\n", + "axs=axs.flatten()\n", + "ds_out_con.test.isel(time=0).plot(vmin=1-1e-8, vmax=1+1e-8, ax=axs[0])\n", + "ds_out_bilin.test.isel(time=0).plot(vmin=1-1e-8, vmax=1+1e-8, ax=axs[1])\n", + "axs[0].set_title('conservative test')\n", + "axs[1].set_title('bilinear test') ;\n", + "print(ds_out_con.test.min().values)\n", + "ds_out_bilin.test.max().values" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "c194849b-a9aa-4125-a579-a814dfc36d23", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAmAAAAGwCAYAAAAOvdliAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjkuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/TGe4hAAAACXBIWXMAAA9hAAAPYQGoP6dpAACyx0lEQVR4nOydeXgURfrHv9U9kwMIAeQIaLiRlUsu5VrlvgRZlVVc9ocEEFRU5FoUWZZDEeSKLq4HHqjIru6uouLBIQiKHCIQQUEEBAEh6iIknMlMd/3+6LunZzI9M5lMkvfzPP2kj+rqmq6uStW33nqLcc45CIIgCIIgiLghlHQCCIIgCIIgyhvUACMIgiAIgogz1AAjCIIgCIKIM9QAIwiCIAiCiDPUACMIgiAIgogz1AAjCIIgCIKIM9QAIwiCIAiCiDOekk5AWUOWZZw8eRJpaWlgjJV0cgiCIIgEhnOOc+fOoU6dOhCE4tNELl++jMLCwqjjSUpKQkpKSgxSRFADLMacPHkSmZmZJZ0MgiAIohRx/PhxXHXVVcUS9+XLl9GgXiXk/iJFHVdGRgaOHDlCjbAYQA2wGJOWlgZAKUyVK1cu4dQQBEEQiUx+fj4yMzP1/x3FQWFhIXJ/kfDjzvqonBa5ypZ/Tka9dkdRWFhIDbAYQA2wGKMNO1auXJkaYARBEERYxMNkpVIaQ6W0yJ8jg8xqYgk1wAiCIAiiHCBxGVIUqz9LXI5dYghqgBEEQRBEeUAGh4zIW2DR3EsEQm4oCIIgCIIg4gwpYARBEARRDpAhI5pBxOjuJuxQA4wgCIIgygES55B45MOI0dxLBEJDkARBEARBEHGGFDCCIAiCKAeQEX5iQQ0wgiAIgigHyOCQqAGWMNAQJEEQBEEQRJwhBYwgCIIgygE0BJlYUAOMIAiCIMoBNAsysaAGGEEQBBGS/JN1UbnOsZJOBgAlLcEIJ41O9yfKbyPKF9QAIwiCIIhygKxu0dxPxA5qgBEEQZRTTp+8Kui1K+qcsFwPFTZarqhzIibPifTeSO8zp7s0IEU5CzKae4lAqAFGlAtyf6qDjCtPlnQyCKLEyf2pTkzDRYMMZSp+PJ5VVBoiIdx0J0rdI3Fli+Z+InaQGwqCIAiCIIg4QwpYKeP4T7Udz2deearYnqf1eupfdSrgmhlz70hkCHrNHk88uMw5jp5wfncaWpqL610SRElQ1HcfDlrZcFJAnK7Zy7+GFsZ8XSri3mCqSzjhnNIRSRqcnuH0u0uibnMD2YAlFtQAIwiCIIhygAwGCUFal2HeT8QOaoAlEAdPhGNP4FwADp6ogyZXxd7OIJQaVJqUIl8YFYeP7BuIUo5zHRL9P81QZcPpWlFlKdh1ERw+Hl56wymvTmFEcEhgrn+TU5jiqHOJ8gM1wAiCIAiiHCBzZYvmfiJ2UAMsAdh7XJsCHXxOhKhO/3WSj1tmlq6p0CWBU0/VeO+hz9P7JUoC7TsMVfYDKbl5VaLJRUG4w1yhfpvTtVDPcLrmNnxRlPa6QIpyCDKae4lAaBYkQRAEQRBEnCEFLAFw6lXtOFYfAHBd3aOO50XIaFuXls+IBvt7196thv3dE0S82HWsLiRT9Sya5p9Jar851Pdp/5ZjTTjP1tIs2fr55t9yGYLl2B7efu2yy2u+EHGarzm9X3N4+7nSCilgiQU1wAiCIAiiHCBzBjnMSQ7B7idiBzXAEpRgPUxSZYqPy7K1OHx+tHHQsDfUP1TcySHKOFt+bAiJK8qKyFS1iGtKS1JUcdu/5VgRznfv9Gzt9wHAZe4JOA4V1uk8APggBLw/p2uh4tSuBYvLHAdgrROoDiCipWzoqmFSv359MMYCtvvvvx8AkJWVFXCtY8eOJZxqgiAIgogebQgymo2IHeVKAduxYwckSdKPv/nmG/Tu3Ru33367fq5fv35YtmyZfpyUFF1PlFBYd+Qafb93g/1xf/6aI80CzgmmWVAyGAR4w45P+z3KfUY88fhtG442DeiZmymJ90sUjbkMKCQbuw7T+zWnl0KQBZAD4zPuM3/Lbp1n2suFdi7c70pThrT0Od0XLO0AHN+F+XygWqhcE5kcvFyEcp9gvxZmXqw7ck2pK2sShKjs2aSigxAuKFcNsBo1aliO582bh0aNGqFr1676ueTkZGRkZMQ7aQRBEARRrPAobcA42YDFlHLVADNTWFiIN954AxMnTgRjxke1ceNG1KxZE1WqVEHXrl0xZ84c1KxZM2g8BQUFKCgo0I/z8/OLNd2JzKofWkFWe1eCaVaRci4Zf2iYUyzPfe+H1mGEio+SueqHVri54Z5ifcY5OdXyfgHo711Lg/m8PS+KKx8IZ7T8kJHsUC4UtPNyGbIKCaUO2a9pZdjp/QR86zz4+VDlwh53UXlhP08Qsabcfl3vvvsuzp49i6ysLP1c//79sWLFCmzYsAGLFi3Cjh070KNHD0sDy87cuXORnp6ub5mZmXFIPUEQBEG4g2zAEgvGOS+Xiwv07dsXSUlJWLVqVdAwp06dQr169fDmm2/itttucwzjpIBlZmYiLy8PlStXjnm6E423Dl1nOR7SeEfc0/DPQx3i/kwNzUeQ2Y9QvN6B+d2XxHsnwkPLJ4HJunpjx8kXVTA/WonA0MbbiyVee31SXMQiL2L1DvLz85Genl6s/zO0Z3y8pwEqpkX+PV04J6N/qyPl5v9bcZN4JTsO/Pjjj/jkk09w9913hwxXu3Zt1KtXDwcPHgwaJjk5GZUrV7ZsBEEQBFHe+eyzz3DzzTejTp06YIzh3Xff1a/5fD48/PDDaNmyJSpWrIg6dergrrvuwsmT1mXjCgoK8OCDD6J69eqoWLEiBg0ahBMnrE60z5w5g2HDhukjUcOGDcPZs2ctYY4dO4abb74ZFStWRPXq1TFu3DgUFhYW108Pi3JpA7Zs2TLUrFkTAwYMCBnu9OnTOH78OGrXrh2nlJUuXjvYGRJPLjpgEF75/vcAAmdp3X3155bjl76/oYiYSnamqn3WmPa73CAyGcObbHF1j6Z6vfL97/Hawc6u7yfiwwU5vDJin1HrdvZiPHnp+xsCymkssL8rp5mNjrMgXYYtCvusR+24OH5zPJHBorJrk0NOJw3kwoULuPbaazFixAgMHjzYcu3ixYvYtWsXpk+fjmuvvRZnzpzB+PHjMWjQIHz11Vd6uPHjx2PVqlV48803ccUVV2DSpEkYOHAgdu7cCVEUAQBDhw7FiRMnsHr1agDAmDFjMGzYMH2ES5IkDBgwADVq1MDmzZtx+vRpDB8+HJxzLFmyJOL3ES3lrgEmyzKWLVuG4cOHw+Mxfv758+cxc+ZMDB48GLVr18bRo0fx6KOPonr16rj11ltLMMUEQRAEET3xXoqof//+6N+/v+O19PR0rFu3znJuyZIluP7663Hs2DHUrVsXeXl5ePnll7F8+XL06tULAPDGG28gMzMTn3zyCfr27Yv9+/dj9erV2LZtGzp0UMxRXnzxRXTq1AkHDhxA06ZNsXbtWuzbtw/Hjx9HnTp1AACLFi1CVlYW5syZU2IjV+WuAfbJJ5/g2LFjGDlypOW8KIrYu3cvXn/9dZw9exa1a9dG9+7d8dZbbyEtLa2EUpvYDG+yBc8e6A5AWaLigd9tcHX/OTkFQOBaa09/18sSTuapEEzeqfVZUCHsONxijkt7lv3YCXvazcf2teqcn6v2sGWGZ77roR8DwNimnwa9T3vvSjqTLOecppm7zRsiNjzzXQ+Eo9BqdkZOaxYGswFz+j5lLsS8XJjjNT9HK6cP/e6TmDwLAEZevVl9Z0rZkGXjWzaXlYBz6jdvLj/mciAwHrb7BSebr3DKcnnCPts/OTkZycmRj4Zo5OXlgTGGKlWqAAB27twJn8+HPn366GHq1KmDFi1aYMuWLejbty+2bt2K9PR0vfEFAB07dkR6ejq2bNmCpk2bYuvWrWjRooXe+AIUO/CCggLs3LkT3bsb9Wk8KXcNsD59+sBp3kFqairWrFlTAikiCIIgiOJH4oLr4Vjr/cr/Tvts/xkzZmDmzJnRJA2XL1/GI488gqFDh+qKVG5uLpKSklC1alVL2Fq1aiE3N1cP4+QqqmbNmpYwtWrVslyvWrUqkpKS9DAlQblrgBGx5byUou/P39cfU5p9HPa9F+VAZSDWPfhYEkwNCIYIDgkMomo3ocn3Yph2FPP3GdJ94DtJCbzBAe2ZWlxaerTzU5t9FFY80TLn24GW42nNP4jLc0sas/I4d99N+n6wbyMY5m8u0crGnG8HBijI0XxXF8O0mdOeBbh/J+b3HU5exKucFDeKDVgUi3Gr9x4/ftwybBet+uXz+XDnnXdClmU8++yzRYbnnFv8d5r3owkTbxKrJBMEQRAEkdDYZ/5H0wDz+Xy44447cOTIEaxbt87SsMvIyEBhYSHOnDljueeXX37RFa2MjAz8/PPPAfH++uuvljB2pevMmTPw+XwBylg8IQWslDBtj7MfMsBq9+DEYy1Xxjo5OuEqXtP33hqQjvP+olWcRPGBFK4NSFF5YZ6pZZ+1JZpUNYkLro/DeYad6XtvDWkbM6fVOyF/zyN7/hhwbl6r/wacOyelOJ6PFjflwvz9Td97q2WGnP3bnPnNHzCzxXuu0+N03/S9yiQekXmLHP5xY6eUKDiVjWl7bivy2wmGpoybv2GNYN+9fbaj0+xH87miyoVGNMv2JCJylGtBup0FWRRa4+vgwYP49NNPccUVV1iut2vXDl6vF+vWrcMdd9wBQPHP+c0332D+/PkAgE6dOiEvLw9ffvklrr/+egDA9u3bkZeXh86dO+th5syZg1OnTuleDdauXYvk5GS0a9cupr/JDeXWEWtxEWuneg/t/lPQa/Z/MKJ6LHEGkXFIauUhMo6F174VcRomfz3EchxuXNp99vRohFu5lfSQSzjDjU4NL+032/NFw3wsQI5qerj5/lDPCZYXQHj5EU1eLGm7IuJ7nXBbNuKZF4D6z87hOcHSY6a48yIWhFMuAODpNv+KKP7JXw+JeV4ACHjnTnmR3frNiJ/plng6Yn0zpxkqpIkRx3PxnIQ7W+8LO63nz5/HoUPK4uxt2rTB4sWL0b17d1SrVg116tTB4MGDsWvXLnzwwQcWJapatWpISlIa4vfddx8++OADvPrqq6hWrRomT56M06dPW9xQ9O/fHydPnsQLL7wAQHFDUa9ePYsbitatW6NWrVpYsGABfvvtN2RlZeGWW24hNxQEQRAEQRQvMoS4+gH76quvLDMMJ06cCAAYPnw4Zs6ciffffx8A0Lp1a8t9n376Kbp16wYAyM7OhsfjwR133IFLly6hZ8+eePXVV/XGFwCsWLEC48aN02dLDho0CM8884x+XRRFfPjhhxg7diy6dOmC1NRUDB06FAsXLnT1e2INKWAxJtrezIgdIwDA0mt2QrAVBLsRbDBDcW069vPtlheZlrG7/g+AdYp3sKndAuN4tu0b+rV7dw4Lmh57z70oo1AntSZcwnElURSR5IX2bCf3FkY4XuS7dVLWtPOh8sKuoCRyXiy7bllY94/YMaJE80Lbt/4mI4/sYZzyybgvMD3xzAtzGiIl3LxY2v61iOK/d+ewmOeF+ZpxnzUvIk1vpMRTAftnTouoFbChrb+hpYhiBClgBEEQBFEOkDiLquEebaOfsEIKWIyJtjczZOu9+n5RvdNQBt92JcB+T7BepBvsCk0wu5VgPflI7VyK2zDW6Z0UZ14Age/SLdHmRah7jOvFnxf/6fxckWH+tG1MkWGCvct45YU5znjlRTj3RYo9XyKtp1Z0eNHVc4dtvzumeWE+t7zDSxHHG0viqYC9uvvaqBWwrDZfkwIWIxJnKg1BEARBEEQ5gYYgE4gBn42DtmxJ0B686Xw4YfRztkV+gykB5vtD2bCY4zLH70btMp8L+Ywwnh8t4byPUNeiyYtQzw/HhiVUXE7vJ+j96vmSzIv+nz2Ej298Ouj1mz9/EICzz6GiykZRqlhReaHFEU5eaPFFmxfhhnF6diwIlhdvdXpezQtnguXFrV/cDwBY2eUfYT3/suQN+YxQdo9mZDC83bloB59O/GHzA3jv988EnLf//lU3lNxsunCRuRDV7FmZBsxiCjXACIIgCKIcIEXpB0yKsR+w8g41wBKIC74kMFsv3UkBMZ93CqvtO9k/COCOCphTjz7UDCQNs2pg7nU7qQTh9vadnsMjsKNxg5PqEU1emM8Hvc/0zpzyI1HzAnDOj1jlRbf1k/X9jT2t08TP+4zlq5xmipZUXmhxmtHKmT0vnOIIRyEO9pzizItQOOWFtm/+a2ddt+ywn3HRb1XAinr35uMPb/x72M8JRu+NEwB40fPTifo541neYrdHJco21AAjCIIgiHKAjChdycQuKQSoAZZQXPQlWXqNjHG9d8scepXaOc6ZRa3Re6AoOmwoirKrMKPFa+6NO/X8g4Z1uK7t269bnxtW8orEaT3WovLCHKaovACM/IhXXmj7QNF5YQlbxHV7GCNs6HTt6P+E4/nrPn40rHAA8GmPRei09pGAtAHO+VVUXgQLG4po8wIw8sPNuzZ/f9HmRaRcv1rJK4FxbOu7SD/fcc3UIvMCAL7o/aSr531449/RfcOkoNeDqYKf9ljkFNw167plo8u6h/Vjp/za2mdeTJ4VD6J3xErz9mIJvU2CIAiCIIg4QwpYCdNq1d9MR56AXrhZmSlSHbP06q332W0kQqkwRc2CDNUTd+qRar1xswpTdG/f8khHO5egdmEh1IlQMxoD333keQGYFS/rfU62ecFUmHBmQYZSFO35Fywv7L/DHIeTmmJ/98Hywim9dkIpXk5cLFTsgrT3GpYqHCQvtPSFkxfaPeHkhbZvDhMsLwAjP8JRuYoqG5HmhVPZaPn+DD09Csq/jD03z7aEu+w3/pWEqmPafTwNO/vPCZoGJzQ1S1M+zc+xv9viUKPsql3rD6fr+6VtUqDEhbAWIQ91PxE7qAFGEARBEOUAGSyqCRrxmNxRnqAGWAlw9duPmY68jj1t+2fObL1KZd+4LjBepIJjv0e7L1ICe/+wHTv3zJ2UFc4DFRn92PbcgB5+NL1Qh/ok4D2GuG7ki3HdrgAEjTeB8sJ8T1R54RQohvzundkAjJlxTnmh7Ct/EzEvlHOB+RFM5eJ6eQmdF05hQuXF93+cHvwi7PWU9Z01fWc2DtxmqPeXC7xF5gUAfPuHmSGfGYoCf+h/V7tuejziuN2QM+CxogMlKKSAJRb0NgmCIAiCIOIMKWBxpMGKuQAAxmxrcak9xICZeKZe5OEh0yyXmvzncaOXqd9v6nXaowrRO+Xc+myt9x3smnYcTi8dMDrhlmvcrrQExqUcOKgJYdiHhYvjzDcWOi+cevf2vDCfc5sX5njteVHUNeV80QqWPS+0cwdv/ysa/9tQEtzmhfmeI3+eGngxSnyFniLzQj8fRl5YrhWRF4Dx/YdzTTkXnpqoX3NUIR3uD6ayFVE23OTJ94MNhazhP58IKBeN3lRt9xgHIIbMi6LUtnC4XOgJqy4ighO9I1bSbGIJNcAIgiAIohwg24a6I7mfiB3UAIsjvFAEGLd2UlmQfeUO5y47AH+htcdp7e0jfMUmFAxF2vOEq2JZeqiOPXrbOT18CJuWAIMkl5VDFHkRSn10mxcB9zumFcWaFwDww1DFx9OhO/4KAKi/fJ4tTOi8OJr1MIqT+q8/CUA0HhyG+lhSeQGYVeHAc8pB8LwIpRYr4dyVi6MjphSd4BBo34aSB3YcyobtuOE/n7DEEwn7bpkJALj6vyYbLO05XDkfC6WNIOIFNcAIgiAIohwgRzkESY5YYws1wOLI0RFTUP/5hTalxVk50Tu8tt54/WXz1fMmr18BZYKr9hrBVBlbF9mtqhxqVpdTL96px25e08Ku3HDrORasd+/wTNdEkhcs8L6i8gIoQqlxSk+4hLL7cVQWjetH7wquWh0d9gjqvTzfMS+Ojv5LBAmNkgLBKnJqSotDHnIgvLwAcOT/Au2iNJsn14SRF/r5SMqF6XyR5cIhPdFQf+kCgBkv1TEvAIAhQHFzVs4iw6xyNfyXqqz9KXJlrTwhcwFyFDMZo7mXCITeJkEQBEEQRJwhBSzOCIWmXi0DAnrApl6k0sW1Xtd6lvVfWGhRZLjFvoUp57QjwdoNtvZcnVJp7zYHkQJC2J1w2aEXbu65m1Uus/Bkn1nHWeA52MJbHhymbMGc3kmYeRGguiAwL2C6x/Qzo88L54ChlCwz9V6e7/BgZ+o/txA/3hed7VAscVLd6j9nV5S1v9wxL34cFd7vsdsqGQpOGGXDbbkAAr9zzsIrF05JgkO5iAFHx/wFDZaoayyay4DtO7e/ErtyFktI+XKHBAYpCmeq0dxLBEINMIIgCIIoB9AQZGLBOCcPKrEkPz8f6enpyMvLQ+XKlUOGbbRwsbLj0IPnWg/TdO2Hhybq+w2WLLKGBYxur1ZGnOzImIP4Eay7HGy2l4N64tRDtxzL1vvs4Zm55w/TNe4Ud5Ckh/LK7tRxM93sqESFyAvd1sgprDmMub6yzcKLNi/CVXPc0vDvqsqhJs783SU6ZoXGnhdHx06OOv56L88PPQvSlqGO5QKmc7L1vJOqxWw2kVDjdFTAuOmeIGk6NGVCkMQXTaNFRp1l+fbtNpG28lQceVEaaTw/G4A1D9z8z4gU7RmztvdCSqXIdZfL5/2Y0eGTYk1reYIUsBJEvMzg1PgCjH/6Bx9xriyFQgatM6KPnuk+JuwVoHXYM9A4n9mOgxAw3GfeZ7ZK3xSl9g/E9g+Dycax5V7ZOrwS3Ng42qEW23BwEXmhv1PBGI7Ur9nzAlDee7zyIgY0WrQY4IBgTrAcKhGJhzbEf3iS0mhs+JTSYPhh/KSYxP/jKHUijZloy4V23nxNRmBjSrYO4UdaLpo+lq3vH5jurjGmvdern8i2NMA4g1oulBPm8sQF06es1lNaI/+HcbHJl9JA08eyIZr2AffvP1okRDeMKMUuKQSoAUYQBEEQ5QIagkwsqAFWgogFcFRaAOC7mc49o6vnKD0nQTAZd+tDZNqwGFOdTgbGyzSVxT6EFgHBhkaMa8Z5e2/ffN2xt2+Px3FoJYQKENYPMN3KQhwz+3Vm7f0LcHzXivrFDNGLBckLPWz4ST/yYOyUgybz1G+KM+e8SGCaPmZVYg5Psw6X/jA+9sOnR++dbAx1OlDkcLp2jtvC2q4HVYjtw/LBrpuOg9FsWjb2zXGvwgiFcPjmnVXhwOFKjkN/KT/Kl4ZYgGJRrt1Ai3EnFvQ2CYIgCIIg4gwpYCWIeFndcVJPgiAUajsOBuAC048tNhg2VUY5p9qT2OLQw6tBOQvRmw7oaTM4KWC6zYoc5F7Z1vN3UseclDUU0dO3H9vfrcNv1s4HKGBBVC7F9iUwLwA1P2y9f6e8UOJhluVw7Eb9Wl5o+40WL8bhibFRd7RvinEE5MX+x+Jro+KWeNvQaGgKZKPFi42TTuotEKj8mvcd7CH1e2XTvpM6FkxZg7ty0XJStvWE6Tvcu9B4vy2mGGqjaLPxcvzO1e59gLosMFz9RLblWjBb17JCy8mq/VcJK2AcDHIUNmCc3FDEFGqAEQRBEEQ5gIYgEwtqgJUArR9Uen+iXTUx2U20nJgN8/Rurl4THXqVeg/UVDbMio0eHlAkL3NvFYHhglwKPkVeOxfE9kTr3VtsWuy2RqYwAfYttntDOagM124ptPsIWN4nN53TwurvWnDOC33f9DfQbQhzOGdNY6i80Gy3olUPxALtgda8+PbJsq1KxILDEyfq+QDA+dsMZr9oUoS18uBo9yWbyoX2DG6qNmxqmOVZLu35QjkGNtuscsH0U7XyYv7ubaqwxR4sRD1VVrHbf2n50eb+bEiFlx3vIco+1AAjCIIgiHKAzBnkKGZeRXMvEQg1wOJI+1GLAQaIZmMfu+2EracYzmw7LjhcC9YDVcPrKbCVJ7PNl7Zvtj2yBjb9DCeVy27jZVLEAhQwm80Xsx87xavfz23HCEqgrZc1L/Qw4eSFHgcC88J8TQgSL4x9Znq+Oa32vLDvA8pMwGhsoewK2J7FpHy5QfApfw9Mn6D4d3IoF/q+TfW123UF+87t91qOg5Ql5ZiHrRAHqsLWD/Lrpyeg7b2q7ZZN6XVU5B3KS8A19d5r/mbEG2wGeGmj3RjjWxARPC9YYfwMwyQIkKKYexfNvUQg9DYJgiAIgiDiDClgxUTPkUvg8aZg678Mfzeey1qXxwgXTFlR7IxYYK9ScLhPcI4nmGIT4JcHJpXL9BsYg8XWBHCwY7HbpTj04B1VLdl6HNQ+TOZGj98pXgAwraYVcvaXo02c6VwQpSpYXmj7TnkRLB6nvDBfM6fPKS/MSTb/1mumZ0c8Y9E8CzJnSdlQH+KJWX08MH0CrpmuqDkBNmChVCxulAvLsek+axnhAeXLfp8SlhtpgXG/BadyYTuvodVh5m9en+1rV4zN37293gqlnpURdi6dgI7/Z54l61BPcQC++ClgNASZWFADjCAIgiDKATIEyFEMfEVzLxFIuWqAzZw5E7NmzbKcq1WrFnJzcwEAnHPMmjULS5cuxZkzZ9ChQwf84x//QPPmzV0/y3tRhscr44ZbFhjnVJsKzgwpQ+n9qefNvUa7XZdgv86KDBvMbxUXEOD/K9yOTUBP2qxi2Wd8BbP5kmFRvZjMLb39AAXMEhbWXj23pymIGmb/jcHyQr0WTl4Y+w55ocXpZBNmyws9OcGUiCBEtQqAia//TqpXLNFswnS7LrMtVyibL9t3zmRu2ndQwOxlBgA4DyyH6nmNcMqFcp7j94MXGmEYg8fhO3dU59VyEU5YJ0W+rOC5pGTM5reVxcdvuFVdR9RUhzGf7HgvUfYpd83Z5s2b49SpU/q2d+9e/dr8+fOxePFiPPPMM9ixYwcyMjLQu3dvnDt3rgRTTBAEQRDRI3EW9UbEjnKlgAGAx+NBRkZGwHnOOZ566ilMmzYNt912GwDgtddeQ61atfDPf/4T99xzj7vnXJLg8Slrx9tn2lmUFgbdIRi39AyVqW7mXqTsMXqVYByyCP2aWYWx24Rpfnm0Z4CZBBO7TZh2DlACmWyP1Mcax2b7MK13b/fnBZPNl03xEiTTvWpvX/Bbe/8BvX0OQDIrYNxqTwEYsyIdKDIvAEBkYeWF+Zo9L7R9u02YU17oSbfbhAGBeWH5nabj+JmREEXw7TxFUWw1MduqepnLgN3mS7Z+54IEiwqslQvt2FxmLCqbxPVyoT0nYFZkEPSyYVHOjbpJKxeA9TvXyob2nev1lJooWXRQ7m02Yea6qyyhKV8angtKpffp2ocBAD16zwP8UsB9xQXZgCUWZexzL5qDBw+iTp06aNCgAe6880788MMPAIAjR44gNzcXffr00cMmJyeja9eu2LJlS9D4CgoKkJ+fb9kIgiAIItHgXIAcxcbL0iyJBKBcKWAdOnTA66+/jquvvho///wzHn/8cXTu3BnffvutbgdWq1Ytyz21atXCjz/+GDTOuXPnBtiVAYB4wQ/R47eeVHuUhk2Q2qPUFBORmdQTqzomi4DgY+AC168LJhWGC9zogYpaD1S7XoTNRTAFTCOUAhbEpsVuxyVIgfYtzG9cN/fuBQkWlUu5rh5LHJDNvXvjXtj87IQkWF4AgMDCygvlGnfMC2XflBfqM53yAjCpYcEUMDOkgJUK9iyegNYPZAex1TJ966Zr+nfut9uAGWE1lcu4zpVyAQCyTQHTbCZdlgtA+z5NCphaLpRrpvKhlg1NBdbqKe26YFLutXrqy1cnWh5r9i/W+gFlP+eZsmebqClfGuIFP7jfHyQ0UdYpVw2w/v376/stW7ZEp06d0KhRI7z22mvo2LEjAIDZnA9yzgPOmZk6dSommhZFzs/PR2ZmZoxTThAEQRDRIYFBimKmQzT3EoGUqwaYnYoVK6Jly5Y4ePAgbrnlFgBAbm4uateurYf55ZdfAlQxM8nJyUhOTg44L14ogCjaTjKmKi3qRyyqx3qvUghQw4xeJIMsMsNeTOCQ/VpYVVlRn8dEqyJmncHHHJWXsGYhaYpX0JmO3HZs7AsSBzP39iUOQTL10tV9QeJgfh7Yu9ePZaO3z7mqBpiO7TYu2rG9ER0kL5T3ycLLC/U+p7zQ9rW8AEzqlz0vACMP7IpYMGzq4+5ny55aUBbIeWYC2t+t+oLiCFDD9DIgGeUCUMuBppapZYX5TWXCrnrJshoP18sFoNp8OZULQDkXpFwAqk2Y9p0zZpQLwFENEzyaQsz0cgEAsp/pddMX/zH8IpoRNF9YzFDL2t6bjV3Pl+3vWjx/GVwqKDpgjJB5dHZcssNnREROuR7QLSgowP79+1G7dm00aNAAGRkZWLdunX69sLAQmzZtQufOnUswlQRBEARBlDXKlQI2efJk3Hzzzahbty5++eUXPP7448jPz8fw4cPBGMP48ePxxBNPoEmTJmjSpAmeeOIJVKhQAUOHDnX9LHb+Iphu4GH0GsGYMaQpChYFDIJg9DBFBu4xjrmHQRAFw5bCw8BEY19RwJRjJtqUGIufKh6owjCT2GK3BXPwNWXMbgxUvBx9e0maAmayafGbFDCz4iXJ6rHao/fLisqlHiNAAZON3j1XlTIewq9OEXkBqO8xjLzQ359DXmjxaHmhhA2SF+q71m3N1FcfKi/sykD7uxfjq5esdjVEYqD5BQvw7SWZFTCulwsAgYqXWi6UY1kvF9oxAhQwtQxo6pc+q7iIsiEYChhjTCkXyoFeLgBVrde/eaVscFUB0+op7ZiJxn4wvnpZ+XavH77YohiXZfo3mKiY3MnxVMAUY/po7nfDZ599hgULFmDnzp04deoUVq5cqY82AcA777yDF154ATt37sTp06exe/dutG7d2hJHt27dsGnTJsu5IUOG4M0339SPz5w5g3HjxuH9998HAAwaNAhLlixBlSpV9DDHjh3D/fffjw0bNiA1NRVDhw7FwoULkZSU5Oo3xZJy1QA7ceIE/vSnP+F///sfatSogY4dO2Lbtm2oV68eAGDKlCm4dOkSxo4dqztiXbt2LdLS0ko45QRBEAQRHTIY5CjsuNzee+HCBVx77bUYMWIEBg8e7Hi9S5cuuP322zF69Oig8YwePRqzZ8/Wj1NTUy3Xhw4dihMnTmD16tUAgDFjxmDYsGFYtWoVAECSJAwYMAA1atTA5s2bcfr0aQwfPhyccyxZssTVb4ol5aoBZm4xO8EYw8yZMzFz5syon8XPXQBXu77MprrodheiCAgCmNbLFEUwzXBMFJQep3rMPYKyacqLR4CgzcrzKGG5mptMVP3ymOzFDBXG5uWd8ahswAJmNtrsvJR9dRak3+jtC35DyWJ+GUwyFC+ldy/pYSEZx5Ako3cvq+dtChgP0csvKi+U9yeElRcAwEXBMS+UcEZeKGGZY14o+0ZeAGHYgNkQaCJVwvLla4q603nIIpMqzK2zIP1cLxf6dX1fNsoFAEiSUS7UY6N8yEa5AAIUsCLLhkkBAzO+eb2eUo+ZKOrqmFY29DKh1lPasSAyyOp+t/7zwT3GjMnP3/2L47sCgA53LUZZhp87r/yVC0s4Je6xu1sKZgvdv39/y+Q3O8OGDQMAHD16NOTzKlSo4Oi/EwD279+P1atXY9u2bejQoQMA4MUXX0SnTp1w4MABNG3aFGvXrsW+fftw/Phx1KlTBwCwaNEiZGVlYc6cOahcuXLI5xcXZVzkJQiCIAgCiJ0n/MzMTKSnp+vb3LlzizXdK1asQPXq1dG8eXNMnjzZsjrN1q1bkZ6erje+AKBjx45IT0/XfXhu3boVLVq00BtfANC3b18UFBRg586dxZr2UJQrBSyeyPnnIDOvcqCqLkxgyr6oHQuAx2NRWvQepyiCeTyAR+1xekS1Z6mqMF6jx8k8MrgoQPZqCo6itGz6cIqeni5/VNd0E6z2Yfq6h3Z7IzM2P2CWdedMdl5WT95mGxZV9fKZe/SmHr5PAtMUL7+seIbWvEP7/QE9fC4Z17gsm+xdZPAwbcCC5YXy/sSw8gIAuCg65gUAyF7BOgtSnT3JNXHCNHNV87lk9pqvRBj4E+w+lIKdIxIDrewJ5lmPfm6d9eiTrXZefhnMp6nAklEuALV8+B1VYS5JRrkAVBtJtVwARZYNvVwAgCgo5QIw6imTWq+rYWrZYB6TQqyWC+2YedT0qfWUZjMZiu2vl+1vevX/lqKP907I3Fd04BgRKxuw48ePW1QjJ/UrVvz5z3/WJ8l98803mDp1Kr7++mt9wlxubi5q1qwZcF/NmjV1/565ubkB3gyqVq2KpKQkPUxJQA0wgiAIgiDCpnLlynEbtjPbhrVo0QJNmjRB+/btsWvXLrRt2xYAHH112n14hhMm3lADrJjgkgSu9SShrQlpUl/UYyYUGuEEZqhhHg9YkldXZeARwbxeo5cpiTY1zJhJyEWG9RumWtLzxX+ta5JpdB6ySFHELAqY7YM0+RBiHLqdl309OifVC4Bu78U0BUzt3euql8+kePl8Rg8fAC/0KT16rbdvUrg0tYubndOE6uEDKCov9Gth5AWgqmEOeaG8Aw5usn/hMsAErs8Ik0Ujufp6k5oipitg5PiwtCP6rKoX4GAH6ZMDVS+fSfHSygWglAe1XACq6mVSuLi9jKjni8ZcZyllwygnhXq5AFSVWFOMtbKhKcRqPcUltUxY1DCO9Z9a66byDJckcE0SjwMyolwLMgEcsbZt2xZerxcHDx5E27ZtkZGRgZ9//jkg3K+//qqrXhkZGdi+fbvl+pkzZ+Dz+UL6+SxuqAFWTAgVUiGwMKa3mh0kMobV+cv0w37VRuvG6fAxwOcHvGql5/eCqftckpR//Kpl69ptfws7nVvemoQuty+yDXkF8bbHYVsCxbZvdifhNxkUq0b2+j8Xnzqk4lMtx30+Y9/vVwzpzcMr6rsBoFotakMfzEhyLLDlhY4kgRciMC8AwOtxzAsA4LIILgr6P1nZo0zV1/4XMg+sRvgCwGWtQQhLXmx5y9mBpcYNty7E5yudG9lEySIUmtxJ+E2dEvPkE59klAtA+b58PmNfLReA2uCSTP+0GTNZ84p6uQCKoWzYygUAo2yoy3PB58fHP5XczLLShFChAgReCFyMz/N4lLMgeQI0wL799lv4fD7dYXqnTp2Ql5eHL7/8Etdffz0AYPv27cjLy9N9eHbq1Alz5szBqVOn9PvWrl2L5ORktGvXrmR+CKgBRhAEQRDlAplHqYC5vPf8+fM4dOiQfnzkyBHk5OSgWrVqqFu3Ln777TccO3YMJ0+eBAAcOHAAgKJYZWRk4PDhw1ixYgVuuukmVK9eHfv27cOkSZPQpk0bdOnSBQBwzTXXoF+/fhg9ejReeOEFAIobioEDB6Jp06YAgD59+qBZs2YYNmwYFixYgN9++w2TJ0/G6NGjS2wGJEANsGJDqFwZgqAoYMF6g/1r34+PT/3DcmyGJXnxce6zyrWMscpJ3QVDoT4EwbxeQOJYs3NWRGn94j+T8PvbFqoPtQ1HAtYFt20KGCyuJkxDkJJsGN3ripemgKm9e7OzSP1hyjImumsOVUUMWeyFyI1KNQeWReK0lAugTvsPzAtAGWbiHkFXJsE5uGw4rJQ5Mw3pwOIexI2adeMfFkCQga43zQcAbPpoShF3EPHEUIK5rngJPtmmeElGuQCsDoYBvVwAqpsUk7qeaGWD1K9A+te+3zEvhPTKEOT4KWDx5quvvkL37t31Y23d5OHDh+PVV1/F+++/jxEjRujX77zzTgDAjBkzMHPmTCQlJWH9+vV4+umncf78eWRmZmLAgAGYMWMGRNNafytWrMC4cePQp08fAIoj1meeeUa/LooiPvzwQ4wdOxZdunSxOGItSagBRhAEQRDlgHh7wu/WrRt4sM4rgKysLGRlZQW9npmZGeAF34lq1arhjTfeCBmmbt26+OCDD4qMK55QA6y4qFgBEIuYmptWKeC4fxOTelGpYuC+02qonIP5gxty9u7yuO6iYv3GRx3DbH5HUVxuuGWBaVkiFmCAD24Y11ucrUrcqno52XyZFS8mAB5TYRaC9OPNTiGLwq3BeoiKIeB6sLD2/DDnBeeALOj3ClwEF7leiQmyYBjdcwAC8NmHVseU4fDZe+7vIeKHNvlEczWhnLPZfGmKl2YEX1TZMH/rRX33sS4X9jDhhC/vaHW9U15I8fs3HO8hSCI05IiVIAiCIAgizpACVky8vWtWkcZ9csUUy/HH3z+Jfq1NMxg5R79rpyv7FVNC9zploF/zacq+tpi0bjMi6i3tXl2fcHRT0XWAYj8kmBaE1mbg6TZgmqNVTdExL7CtLp2iu5rQlkvRYmJMn6YOwRi7VwKH6M1rC2QH68WH6kKEcKcRgIO5Cwv1vovIC0s8+nuQANlIsrJKjPr7uOKklSh7GLaQsuF6Rf0m9O/aI0ZULixxWB4aIkFRlgsgsGys/vqxEA8k5LQU5wuMQZbiV+7jvRYkERpqgBEEQRBEOYCGIBMLaoCVIFJaoJ+w1TnGiu+9Oz+OdVv+CgDo0+kx1abICKv1QtfsmAEA6Nt+pnHRYrtlWriXA4CAnt2Vtbu4h4ELzOgUywiYVmXMguTWWZBmv18+GUyWLT1jTYHTHZjqETqoWkKIawyOthMB9qBR2rowe29fX+Rbve6keskhrqms+Wqmvt/3ulkBeaHtb1z3iKvka/ToPQ8bIryXKH7WfaGU4b7XGbOUuciClgvAoWw4XQvirDfW5QIIUjbUYFodRQRHqqjW9Q55IfnDnG1KlDmoAUYQBEEQ5QBSwBILaoCVIJ98Ni3kdSnVyJ61WxVbsJ7dngCgqFKfbLLOaJSTTdlpWUDbpvSYbLPgh9LD1tQqxkMoYKqtl+4HjFvjYkz3ccWZyZ7F1lPnAT160zHTzhme7+1qWNA6IEazvbTZnsaxojxaVS4jDmaOxrRAOTjX1Q8NTa0EFIVTe3/2vHSD5nuNSGy4R3AuFwD0hdjt5UK9Zg7LVc/3ASoxgpSN4iwXREi69XtS2amg1s0OeeH30yzI8grNgiQIgiAIgogzpIAlKDcOWoDP1gfa9Wh+vPSelQlNbenZY67SU+W2WYxAYO/WrNgAAfYmyv2GvROTuSUO3d7EtHAvEKhqab125RqzKlmMWdeiFOzqmPPi1AGdMYfOmRaGOXXWbecClCzbCgA6smH/wrhTWKt9WDC4ac2+Xl2fcK2CaXZ8tLhx6WDt1um6gg29DNiUXrOSpdtDImi5sIfVj80EES04C69cAFY7UO2Y/M8Vjb+CfWarQxhf/HQQUsASC2qAEQRBEEQ5gCM6VxI06BxbqAEWBzoMW6zvm3uc296YGBC285BFyk5K6F6RFOK6lCyqSpX2TIfZkwHqj+qXSGZggrNfK13ZsfTMbbMWbSoXAHDNt5hW7gXF3iWYAubU+9eVtiBqmB7WBQG9f7NiyJnJlstQupTrLKgCxmQY3vE50K3/fN1m7tM1D1seZ7YB7Nl9Lnp2nxu2mtWzx9ywwhGJhZykKiLMVC7UY61cAEHKgL286OXLtO8wYzjqcgEY37gaWcCsSMIRbYURAOj4f4stebF9uVL/5+fnIz19elzSQwpYYkE2YARBEARBEHGGFLBi4sZx/4CYpHo/TtW6p9Yw7e9ebLEv2vHKRGx5a1JY8X/+bnD7i40fTwk4162/4umeyRycM+sMJvsMvxC9Wy4wS7Pd7JPI3qPX/YDZrmmzvfTevrkHb1PLAnv/9n37lM3gabf+ENttnBu9e3W2V4ANmOk6M6lcTGaWmY9MtoVT7+s6YD42fRiYN4DVHqwoevScBwY1L4hShexVVS2R6eUCUD4R8yxILthUX3vZcigj5v2Ylgs1YvMsSEeVjAiJL9WUCSVUdEkBSyyoAUYQBEEQ5QBqgCUW1AArJvwpAE9WD4LNttN6lRGi2ZYxmWPbitDKmVkV63rTfF2xYRxKb0xLRyj1S2QBM7V0FYYZvXpA7bEL1nBc1K6p95kUMbM6Zrmm9e7N6hms18Kd9aXcZAsaJC+UNS+Neyy9f9mucpmvGfYxTFKP1Wub3w+uWsoiw8bVDwe93qPnPOvP0PKCKFVoCujvBy+0lB8umr59wGL3qKljlmtmm0rmXC70fTNuy4ZZFYbxbe96bkIRv5Sw40+1Hl/7UDYAQCq4XAKpIRIBaoARBEEQRDmAFLDEghpgxYSUDCDZdtJuP6HZGkX4DG0WjVu4hwGSeiBDmd2n9XTBA1Uw3VZLs88y2amIxr7ZrksWAxUvs81X0N6+Zvti7sFbZkyaOupOCphLWxd7XuhRmG3AZLsCZth8aeqXsTqA9ZpZEQuFXf3q3vdJJU6newXQ2o+lnM1vT0bHPy8yFDDB9O3DQSG224Sp5QKwKV7q9QAFLAIbMIvne7We2rOYlK9Ikcz/D0z5IQWELD44Z+BRNKKiuZcIhGZBEgRBEARBxBlSwIoJKQWBChgQMLsuwMt6HNBnJ6qJYDID1+Udkx8wGZY15zT1S++li8yigJlVLtkDa+9etPburce2fbvNF3Po/av7AceuXkQQBUzLF7MfMNl6PVDlUhVEyayGAUxiYSlgAUlT37WWF6R4lT3Mdpvt717sYCdp3beowJaZw7AqXkFmSIaNrRxo5/Y9QepXNEgpcMwLKY6ikgwWlSPWaO4lAqEGGEEQBEGUA8gGLLGgBlgxISUDUN2AWWYX2RUv0/HvZmRbZhvtezywx9lyojJzZm8UthhKzzpQBdMTZFZsZADacmaq+qWrXmKgzZd2TTYrXKJyHXpYQxUDbPYsdnsXe+/fpgTo95t/UrgzvRzyAlDPmWYvKt7tA1Uv7f0wrihfAMBEQNC6tDIgCFy/5grG8OlaZ59hRNlD9rKAMmP55m2KGMzHQezBnI7DKhsOvr4OTCf1KxqaPpatzIp3yIt42oARiQU1wIoJOYUDKTz0P3zZ5tzQNsxlp9mj2WBJyn6LKdnGP30Z2JMdXgXZaegiMJFB0B7EoSytozkWNVUQjHOL2wkA1uVSBLVhBYCrQ46y+kXJpsaY0gCz/XMxN8CEwGuWBpnDkCSg/eMxXlSR/2zCyQvtmtOQo7kBZnr3TAa0dciZbPwWJivvS7vWftRifPVyeBMnnJzpEmUXyWsbprcP0ZuOAzolTkP0MSoXh/4S2UQfQqHxk0qHGSnKS3XqLMpxXGGRjPATC2qAEQRBEEQ5gIYgE4uIGmB+vx8bN27E4cOHMXToUKSlpeHkyZOoXLkyKlWqFOs0lkpkLweSDJXJjMXw2zTUBZMxNzjQZG42Dk41lC05yTQMJplUF0kZmhTM1yRA8GvHXFdeuMAAkYNrYRkDZ4YvDEvx4tYTXOtdm4zyDRVLGUKRtWPRUMOUoUlAFqzH+r0OQy9mdUzp4asvyTYcCcZj5IgVpkXHEZAXFtXLPOQoB+aFvi65DMjMdEwGrEQQvv77BLT4Sza+WWCU92v+pqgnugKmfY4irEqwwK1DkGq50I/NRFI2iIiRUwwZPVheyBIpYOUV1w2wH3/8Ef369cOxY8dQUFCA3r17Iy0tDfPnz8fly5fx/PPPF0c6CYIgCIIgygyuG2APPfQQ2rdvj6+//hpXXHGFfv7WW2/F3XffHdPElWZ4sgyebPM/oC7toa97zbmz7RGgG303nq/0gpkMMK9JARNMqou2ryleqr27FpUAhutGKssWCYLyHN1FhMwDVC9jAV4OxgN7PcayQSzQTkUzwvcYKpbikgIBilhQBUwAuMj1eBWbF8NGzVDAuGXKPUxKXpEEyQstGqe8YKpxvd3myzEvAMBv5IUaDUEERfY6Hwe4cRG5zU7S+O51+y+Lq5bg5eLo6ODLY9V/fmEEv4IwIydpFTaMvNCOVbgcv5qBRzkESQpYbHHdANu8eTO++OILJCUlWc7Xq1cPP/30U8wSRhAEQRBE7OCA0emM8H4idrhugMmyDEkKnDh74sQJpKWlxSRRZYJkSdksXR3oygugFgSZGdIIZ+CSsaQNd7I10lQYm+oiMF3AgeDXfajqz9F7PZxD4MzaEwsyK0pRv6yXPjMtKN15iLGUiizalh8SrYpXgCJmV8A0xcumgOn2X7orDG6aDcYV+6qgv8VeXYSRF4C6PFPReQEo+eGUF1Cj0/ICoGUniNDse2ICms5W7b5E4HvV9UPjBYsDFDCLXaQIw+ZLgFEuABwdEcVsWruCT7ii3ksLgGS1DrErkXpVxAGZHFGUV1z/T+jduzeeeuop/ZgxhvPnz2PGjBm46aabYpk2giAIgiBihOYJP5qNiB2uFbDs7Gx0794dzZo1w+XLlzF06FAcPHgQ1atXx7/+9a/iSGOpRPTKEJKsPUiuqi76OLrMLCoMZKbPToTMAMmwsVJsjYylaZjAdKUFqvqlKy9cMQPRFScOfYakfWkf+5A+44bsxSRl5o5uE2YrfPblUBS/YOqxqccuiwD3BNqAGcfcpIapCpemgIkcXOCGXZXAwUyKF7MfB8w00uzZbGkPlhda2DDyAlCf75AXgKpKcsMlU84SY4YbQTjB1TLx/aOm2c9eU7kAlLKhlgtAVV+1fVVlOTos+qWrjo6YgvrL5yn7MYivvFD/tScBACzJNLtcq6e0Y1N1xPzxtQGjWZCJg+sGWJ06dZCTk4N//etf2LVrF2RZxqhRo/DnP/8ZqampxZFGgiAIgiCIMkVEfsBSU1MxcuRIjBw5MtbpKTN4kvwQk5RpicZEO2ZRXTgHuCzoSguXTfKJrCx0rdl8cYkpvSeTx2tNDtZnPWqusrhm96UeC9b7LEv9QBF8zP0abTFpcK54drf9ti5/VGZHbf3vZFyftViPd9cLRq+95cRsQ9XyaJ7x1Z+m2YB5VLsVj9G75x61dy9qvXuub8ax+khBBmOwKWA8pEheVF4AAJfDzAtA9bkUmBeAoX7tfZKULyI8zMqXxg/jJyozEnU7SacyYahhTIidouJJ9scsrvJAgzfmQvAG5oVeT+kKmFFPSf74vWOZM2XlkyjuJ2JHWA2w999/P+wIBw0aFHFiipu5c+finXfewXfffYfU1FR07twZTz75JJo2baqHycrKwmuvvWa5r0OHDti2bVu8k0sQBEEQMcNkYRLx/UTsCKsBdsstt1iOGWPgtpxg6qC20wzJRGHTpk24//77cd1118Hv92PatGno06cP9u3bh4oVK+rh+vXrh2XLlunHdpcb4ZCS7IeY7LN8sNr4uyQz/VjmsqK2AJBlpqswsqTYHWnGRVxgABPAtdl3JnsnQVVgzLMeuWktQ5jWJ/zqJevabl1uXwSA27zxq8+QufIQk4f4G25ZAHgN+ezLV53XiguY5Wi2AfNwRQXzGD166GqZDCaaevQihyByvScpmHr7AlPsKkST/Qszue+224OFkxeAon6FkxcAwCXnvAAUJYwqLCIWHL13Muq/qtoWCVwvF8qxDMGkhgkxdGHv9SZufZ5oNHrzCYhJznmh1VPMdKz8BSSJVMbySlizIGVZ1re1a9eidevW+Pjjj3H27Fnk5eXh448/Rtu2bbF69eriTm9UrF69GllZWWjevDmuvfZaLFu2DMeOHcPOnTst4ZKTk5GRkaFv1apVK6EUEwRBEERs0Dqe0WxE7HBtAzZ+/Hg8//zz+P3vf6+f69u3LypUqIAxY8Zg//79MU1gcZKXlwcAAQ2sjRs3ombNmqhSpQq6du2KOXPmoGbNmo5xFBQUoKCgQD/Oz88HAKR4fPCoSpH20cqq53VNIZFkAbLMIGlKi8wgqfZXgsggSwJkvyatCOBM1pVGcx9XBlPsvjRbMlHdV49ls3d2B8wdZvMsSEiKERMzyTihOtetH8zWZ/qZ17QrivpLF+hqGPMoapfg0d6DDCZwiCYFzL4vwOhNCjYVzE5ReWHeLyovlGcyx7wAlDzYP5vsv4jYwDzqd+/herkAoJQBh/1YsP/WGTGLq6wjeqWgeaHVU4ZSbtRTfo8vbmmkWZCJhesG2OHDh5Genh5wPj09HUePHg15r1sliTGGXbt2oV69eq7uCwfOOSZOnIjf//73aNGihX6+f//+uP3221GvXj0cOXIE06dPR48ePbBz504kJycHxDN37lzMmjUr5ukjCIIgiFhCRviJhesG2HXXXYfx48fjjTfeQO3atQEAubm5mDRpEq6//vqQ9549exZPPfWUYwPODuccY8eOLTabsgceeAB79uzB5s2bLeeHDBmi77do0QLt27dHvXr18OGHH+K2224LiGfq1KmYONGwg8rPz0dmZiYqeAvh8RpqC6DaGcGkeHFlX1Kv+yUBsnrNLwmQBK5LV7IfUB1f6c/SvXNxze5LfY6szDrUJ0NJhg1Y2/uyses5B1VGX//RmAXJtKmU3BzGWgDbj1JmQcpJDNy9qZyCl4OJqqIkcggeGaLa2xcFGR5RhqD+GI8oQ2SmXiUzKWJQepWhbGCKygsAkDgLLy8AwxZMjd+8esD3U6z2cc0ezca+J0gRI8Kj/rL5yo4o4+hdj+DI/00FAFz99mN6uQAAwbQvmsoDER+uWal0wJO9ctC80OopQ6036im/t7AEUk0kAq4bYK+88gpuvfVW1KtXD3Xr1gUAHDt2DFdffTXefffdIu+/8847gw7n2XnwwQfdJi/seN9//3189tlnuOqqq0KGrV27NurVq4eDBw86Xk9OTnZUxgiCIAgikaBZkImF6wZY48aNsWfPHqxbtw7fffcdOOdo1qwZevXqpdsnBUN2uer7uXPn3CYvJJxzPPjgg1i5ciU2btyIBg0aFHnP6dOncfz4cV3tC5eKXh+8XuvK87Kquvg1ZUUWIHHBUF4EAT51XxAE+CVRtxOQGIfMBGPZSC7oWpTMFbsjWbOjkpkyk1HzrWX2WG/Loi/+Mwk3/mGBsZyizBXbLwCQZUBiJkXMem+HYYuBZFO8DGgxJVu5NQnY93iYao8oQ9DtWxT1S+tFekQJHlGGVzAUMa1XKTIZHkHZAEUBM6tfdiUsnLwAFBuwcPICUJeNNOXFD2MnB/2ZpH4Rbgi2jqPXI+nlAgC85jIhyBBZbBSw3hsn6OVD4gI+67kgJvGWJZq/NxNJ6kxRrZ5yygutntIUMK1uEhiHzxtPG7Do7LioARZbInLEyhhDnz590KdPn1inp1i5//778c9//hPvvfce0tLSkJubC0CxX0tNTcX58+cxc+ZMDB48GLVr18bRo0fx6KOPonr16rj11ltLOPUEQRAEQZQVXDfAZs+eHfL63/72t7Dj+v7777Fx40b88ssvAeqYm3jC5bnnngMAdOvWzXJ+2bJlyMrKgiiK2Lt3L15//XWcPXsWtWvXRvfu3fHWW28hLS3N1bMqeArhVd+uzA07I7PqInMGPxdQKClOsCRZgKgupuhjYuC6hiZkML03wmQOblqfUPN0b17vUfd8z4B2o7Ox88UgagznYGpeMIkrTsGCdXsYwLVEah72NaXNxTLvR+96BI3//TgA6OqX16P0KpNECV5RgldQjkVBRpKo9jiZYgNmV8AEkwKg9TRlffZj6LwAgEJJdJUXP9xF6+QR8ePbP8zE9asfhVctB15B0lWXJFGCJ0oFbPCWsQCAdd2ejS6h5QCvR9LrI62ecsoLrZ4yK2BaPeXzxM8GLN6zID/77DMsWLAAO3fuxKlTp7By5UqLX9F33nkHL7zwAnbu3InTp09j9+7daN26tSWOgoICTJ48Gf/6179w6dIl9OzZE88++6zFfOjMmTMYN26c7jR+0KBBWLJkCapUqaKHOXbsGO6//35s2LABqampGDp0KBYuXBiRn89Y4boBtnLlSsuxz+fDkSNH4PF40KhRo7AbTi+++CLuu+8+VK9eHRkZGZbhS8ZYsTTA7M5j7aSmpmLNmjUxfy5BEARBlDQcgUvLub3fDRcuXMC1116LESNGYPDgwY7Xu3Tpgttvvx2jR492jGP8+PFYtWoV3nzzTVxxxRWYNGkSBg4ciJ07d0IUlU7y0KFDceLECd0X6ZgxYzBs2DCsWrUKgOIgfsCAAahRowY2b96M06dPY/jw4eCcY8mSJS5/Vexw3QDbvXt3wLn8/HxkZWW5GqZ7/PHHMWfOHDz88MNuk1AqqCQWIMnDdT9TgDq7ThYhi+qsR1mEnwt6L8nPBTC/dZaMYSsgwtxP4iZZiysOrYxZkCIHl5ihQgmmiYxOHRhTqWSKUZNyIMsAZ7pn/FClV11aUbc7c+v/6tAdfwUAtHx/BvYOmoWOa5QZXx61V5niUbxFe5hVAfMIkt6r9AgSRMbxUvtXgz7n7q+yAATPCy1ep7zIGfCYHk/Td2Zb0k0Q8STJ49dV4RSPX69D3ChgY74aDgBY2t669NrbnUn5CpcUjx8ekxIZLC+0esqjKflavQ6OQrH0zYLU/F1qBJuM1r9/f/Tv3z9oPMOGDQOAoC6s8vLy8PLLL2P58uXo1asXAOCNN95AZmYmPvnkE/Tt2xf79+/H6tWrsW3bNnTo0AGAIvB06tQJBw4cQNOmTbF27Vrs27cPx48fR506dQAAixYtQlZWFubMmYPKlSu7fgexwMVAUXAqV66M2bNnY/r06WHfc+bMGdx+++2xeDxBEARBEEUQK0/4mZmZSE9P17e5c+cWS3p37twJn89nsTevU6cOWrRogS1btgAAtm7divT0dL3xBQAdO3ZEenq6JUyLFi30xhegOJAvKCgIWAknnkRkhO+EtixRuNx+++1Yu3Yt7r333lglIaGo4ClEsifQ/sgnCLodkk8Q4JdFeJiSDYWyqKs5hbKMghCz+Lgs6J7amcgBkYGrsxe5wAJswHQhzryv8tn7f0GP3vO0hyi2XwCYXwYEQfEFBsUvWICLLVu8386Nbqbf3kGKT50kVfFKFv1IEgw7iyRBQpKgqmGCBC+TdVsK834wKorKqgXB8gIAPMxTZF5oM9AIoiRI9fiQJBh2kvq+4IdHkIKqW2YqegqCXgvGrV/cD0CxkyxUy8u6btmu4ykrJHn8SBaV+kirp+x5ARh1k1e3CZP10Q1PHD3hx2oM8vjx4xbVqLhcMeXm5iIpKQlVq1a1nK9Vq5Y+iS43N9fRtVXNmjUtYWrVqmW5XrVqVSQlJelhSgLXDbC///3vlmPOOU6dOoXly5ejX79+Yd/buHFjTJ8+Hdu2bUPLli3h9XotYceNG+c2aQlFJbEAyeo/aa3hJEGAn4uG41VZRAHz6IXUI3sgMOU9CFJg40v2SIa/VI+kLz0ky0xZ0NpmhK83jkK4odDYsE4xJO/T6TFl6BEAJBmQZWMpohBtDu7QsIsGrVJLEf1IEiWkiEollST49UosWa3gNDl/4bVvFRlvJbUBFiwvAKVhFyovrl/9KLweMdqfSBQjDf/5BH4Y+mhJJ6PYWNctWzeWTxF9eqfEy2S9XADA5K+HBC0XWllww8ou/wh67frVyvv+st8TruMtrSSLfqRoDTC1nrLnBWCYR3iYOgQJowHmFePZAItyPUf13sqVK5fYsB2gtDvsduOxCBNvXDfAsrOtvR1BEFCjRg0MHz4cU6dOdXVvpUqVsGnTJmzatMlynjFW6htgBEEQBEFETkZGBgoLC3HmzBmLCvbLL7+gc+fOepiff/454N5ff/1VV70yMjKwfft2y/UzZ87A5/MFKGPxxHUD7MiRIxE/LJp7SxuVxAKkiBIkMH2YS+ICZDD4TO4NPIKEAlnJBtHsRBSBChjn0I3GZVlQhh6ViAFB3QBFCRO47iKCM5Pype53uEtZQmj769blciDZHbEax8bSRPrCOwHxxooUiwLmR6pJAUvWe5gSvEzCnFbvhB2vFnb2N4Mc8wIACmRPyLyItId/9ePZ+P6v5Iw1HpRl9UtDM5a/+6ssXXVJFvx6uQAUw/DZ3wzSHYIKTIaofs9pMRJxb/78QVyWPPiyn/NQZKO35uDwkGmxeViCkWJRwJR6yp4XgFpXmSYMieqQpAgOr3p/PChtnvDbtWsHr9eLdevW4Y477gAAnDp1Ct988w3mz1eW6urUqRPy8vLw5Zdf6sshbt++HXl5eXojrVOnTpgzZw5OnTqlO1Vfu3YtkpOT0a5du/j+KBOujfBHjhzp6KH+woULGDlyZNjxzJ49GxcvXgw4f+nSpSJ9jREEQRAE4Y5YGeGHy/nz55GTk4OcnBwAigiTk5ODY8eOAQB+++035OTkYN++fQCAAwcOICcnx+IkfdSoUZg0aRLWr1+P3bt34//+7//QsmVLfVbkNddcg379+mH06NHYtm0btm3bhtGjR2PgwIFo2rQpAKBPnz5o1qwZhg0bht27d2P9+vWYPHkyRo8eXaJDqa4bYK+99houXboUcP7SpUt4/fXXw45n1qxZOH/+fMD5ixcvYtasWW6TlXCkCgWoIBQgTbiMSqKypal/K3mUrYJYiApCISqIPlQQfUgVfEgVlS1F9Cnqj6AYdXoFCUkeCV51E0VZ3wRRVg3xlY0L3LDJsm9FwFRHrEyWAUn5qxjf8yINOL9ZEDt1R+tZpqjvI1VQtgqiT3lnQiEqiIWu1C8zQfNCzQ+nvIiakjM1IMowL7V/Va9DtHKhfdeWeke8jDThMiqodZO2RcuqG5aENMQvq+oXAL2OMtdT9rzQ80PNizRbXqTGIA8Sla+++gpt2rRBmzZtAAATJ05EmzZtdD+f77//Ptq0aYMBAwYAUNaKbtOmDZ5//nk9juzsbNxyyy2444470KVLF1SoUAGrVq3SfYABwIoVK9CyZUt9hZ5WrVph+fLl+nVRFPHhhx8iJSUFXbp0wR133IFbbrkFCxcujMdrCErYQ5D5+fngnINzjnPnziElJUW/JkkSPvroo7AX2QaCG799/fXXqFatWtjxEARBEAQRBjxKWxGX93br1i2kA/SsrCxkZWWFjCMlJQVLliwJ6TC1WrVqeOONN0LGU7duXXzwwQchw8SbsBtgVapUAWMMjDFcffXVAdcZY2EpV1WrVrXEY26ESZKE8+fPlwnXFGniZaSKHt3uC1BswCQI8HGl5S6Cw8dECLLmlM86zVAG0+9Vlh5ikAR18WhRgl9S9pkAgHHd7QRjUJyvmpciMruLCMGaHTPQ/2rVOa4sAZJoDPzbClKA/VgMSRINNxSKGqU4K0wW/EgRFHswzb4iEioIBY55AQCCzB3zQpt1FqmjSjfLMxGEG+wzHZ/+Thme8TIJIoxFoQXwmC3WTSj1lDZjW6unNBvVFMGn11HJ6r6o1isik/W88JANWLkl7AbYp59+Cs45evTogbffftuiUiUlJaFevXoWJ2fBeOqpp8A5x8iRIzFr1iykp6db4qlfvz46derk8mcQBEEQBEGUHsJugHXt2hWAYkRXt27diH1nDB8+HH6/0uLv1auXZUHNskQqK0QFQYKsql6AsvyNbFFdZPi4J6TzUM2HmF8Q4BcFfdaeKHB90VdJkMEEBi5ojljVGZBhqF6/H7wQm9+ebHuo1Q+YPgtSVhyxfvFfW/hiQJv1aO9VVhALkMKU/QnXrI04/gpCAYY12aYfP/NdD/i4UhyC5YccoXTfMFuZcSqWoL8Zonzx0O8+AQC8drAzhjfZop9/69B1RTorJsJHs/tS9pV6qoLqXy2F+eFlVjVM0BUwrvoCk4EolHzXxHsxSCIkYTXA9uzZgxYtWkAQBOTl5WHv3r1Bw7Zq1aroh3o8GDt2LPbv3x9+SgmCIAiCiJhIZjLa7ydiR1gNsNatW+vu/lu3bg3GmKNhHWMMkhRea75Dhw7YvXs36tWr5y7FpYSKQgEqCKJqA2YoYD7u0XtBgsBxWV1IGwBg8ssjQ4AEBp+gnPQLEgoFUV+A1yeIEFXFSxA5ZIGDmRUvwWb3ZS43tjLUrd+T8J5XemrrNk/Tlx6CLCtLE3FNAUPcjAC0BbUnfz0koFeZLETvOdqsfgFABaFQyQtAyY8QeeGWHyYotnKNnyy/S7YQJYNZ/QKAIY134KMjLSznVv2gdJplCHodpYUlQpMkGDZgWj2lKfTJgg8pTLNX9SOJSboiJjLFzlRkMpgQRwWMSCjCaoAdOXIENWrU0PdjwdixYzFp0iScOHEC7dq1Q8WKFS3Xw1HSCIIgCIJwAQ0jJgxhNcDMKtWPP/6Izp07w+Ox3ur3+7Fly5awFa0hQ4YAsK75qClrbpS0RCWZ+ZDCZIApsx8Bxdu6l0vwqjZglzmHyGV9ZgxkQFZnOcqcQRIY/KrqUiiISFJVMAAQBRmCagPGGFdmQuqe8AEwDpg84QddC5IrQS3Klqy+e0nCxz+UrJ+Uhde+hTnfDtR7lRWEAn0WZCxJEXwQ1cU1Rcgh8yJiSL0nEoCbGnwDANhwVHFSqX2XEhf0OooIj2TBj2RV5UoRfEhhft23mnkWZAorVGZBqvZ3Xhj7UhxtwGgIMrFwvRRR9+7dcerUqQCfX3l5eejevXvYDafytCwRQRAEQZQ4ZISfULhugAVzoHr69OmAYcRQlFXbL40U5lcUMECfBZkECYUQDT88MiDCo69HIKm2RoDaG+XG+oSK+iUjSVTXK5RkwwaMqTKWbvOlzYJUS4vT7Dt7QTIdf3w0sWyVkgWfbveVYrKriCUprFDJCwAQQudFpByaQutAEolDj/oHLMefH22s11FEeHiZpM/Q1uopTaFPYT7rLEhISFLVLsHkm02iWanllrAbYLfddhsAZZgwKysLycnJ+jVJkrBnzx594ctwOXz4MJ566ins378fjDFcc801eOihh9CoUSNX8RAEQRAEURRhrkkX8n4iVoTdANMcpnLOkZaWhtTUVP1aUlISOnbsiNGjR4f94DVr1mDQoEFo3bo1unTpAs45tmzZgubNm2PVqlXo3bu3i5+ReFRgPlRkiooiMcOWSIDJ5ksARM4hycr1FOaDrKpVPkFEMhd1n2GFggdJgh+Fqrd2DzN5txY4BIFD0hUv5Y95uD7Y0D1TJWmWwC6OU5h5NpFULLOzUgQfbmuw23Lume96AAjMi1jRakI29mSTKkYkBimCX6+jIuHur7IAGDOYywPzr/0P5u67CYBRT+l2X4JJAWM2BUz1AyaCg8dTAaMhyIQi7AbYsmXLAAD169fH5MmTXQ03OvHII49gwoQJmDdvXsD5hx9+uNQ3wAiCIAiCIILh2gZsxowZMXnw/v378e9//zvg/MiRI/HUU0/F5BkliZfJ8DLAC0BSe5Q+CErPh1t7PCmq6iUxBq+qcKUwP3zMo/emPEyCh8nwqDMfRcGwARMFWZkJqSpgMnM2+woGMy0Q1v+aqfh4/9ygYXv0nocN6x4JP/IY4DX5z0lhhcXyDCe7MmMGk5IX2a3fjOkzSf0iEonr6h7FrmN1FZvSMMje3wcAcE5KwXkpGR6WAgAYtv1uLO/wUrGlM9GY2uwjAMALB7rCy/x6HaXsm+3BjNEPZZ/r+3GDFLCEwnUDDAD++9//4t///jeOHTuGwkLrP8Rdu3aFFUeNGjWQk5ODJk2aWM7n5OQEzLAkCIIgCCJKOAtujxLu/UTMcN0A+/vf/45p06Zh+PDheO+99zBixAgcPnwYO3bswP333x92PKNHj8aYMWPwww8/oHPnzmCMYfPmzXjyyScxadIkt8lKOJKZjBQGSGAQVXVJZBw+zk1+dyTFT5hqI5Yi+HQv1F7mh1fwwytrxzIExvXeqWBSvJg2A9IyCxLO9pIBfsBUT/xaJ0zm6J8xVrl08RJYhVQgvbKS3vQKQFpSFG8lMu5pugn/OdwOgKFKxRovAuO9p+kmfV+z84gFzR5VZpnue4IUMCKxSGGSUkeFgaZKa/WUpuQIjGPwlrHIL1Qmap0vTMbFQi8A4FJBEnyXPTjy56nFkPqSRaunDOXcp9crXiYjCZL+jkRwCEz566NZkOUW1w2wZ599FkuXLsWf/vQnvPbaa5gyZQoaNmyIv/3tb/jtt9/Cjmf69OlIS0vDokWLMHWqUhjr1KmDmTNnWpyzEgRBEAQRPSZrk4jvJ2KH6wbYsWPHdHcTqampOHfuHABg2LBh6NixI5555pmw4mGMYcKECZgwYYIeR1pamtvkJCxepm7gurbiAyBCMsbRBQCyYX8kQzB6lUxUbJ9U31MeQdn3qL0ljyDrahhjVkVMUcOcPeFrBDXz4ByQlGdwSQKT5IQodbc32gkAWHfkGnx+tDEA4Ib6h2IWf4rqyycYmp1HTFDzotXEbHguKvu7nic1jCh5mmX+pO9/fSxT9wl2WfbgMvfiIldUrXwpBV6m7Gv1lOYnT6unNHtVpW6CaZ+j3ivzAQA/jpwSl98VL25vtBPrjlwDQFHVtXolCRJSmARBfQ9ecIjafjxH9cgGLKEQ3N6QkZGB06dPA1CcqW7bpixqfOTIEccFusMhLS2tTDW+CIIgCIIgQuG6AdajRw+sWrUKADBq1ChMmDABvXv3xpAhQ3DrrbeGHc/PP/+MYcOGoU6dOvB4PBBF0bKVdlKYgBQmwAuGJKZsKYzDyzhSmNIbSlL9wmibl/lN+4Gbh0kQmKxs4OpMSNlQv1Sly90MSGUWpLaBc3Aug3MZkJV9TbdmnGPD+vjOgLTTu8F+/R19fSwzZvFeV/cotv/YANt/bBCzOAmiNHNt3eNKHQUJN9Q/hN4N9iu2qWo95VRHWeopcL2e0uxXtXqKqVv9Z0t2rdniwFyna+8vhUlq3a9sSYzBC6b/n4gbmhF+NBsRM1wPQS5duhSyrEjL9957L6pVq4bNmzfj5ptvxr333ht2PFlZWTh27BimT5+O2rVrOy5vRBAEQRBEbNBWrYvmfiJ2uG6ACYIAQTBa7HfccQfuuOMO1w/evHkzPv/8c7Ru3dr1vaUBDwR4Vb9fsjZwzgEwwybMCxkSJBSqHtZFyLoNmMi88Ko9SQB6D9JjPoYxC5KZ7Cy0dSH1zkrAzEeHBGvDx5wDsravqGCWawmANsso1v5zimuGpZ19cxR7r1YTs8mmgkhorq173HJ8U4NvAADvHG4DkSkzG726Mm/M0PaYj2Gdsc00G1UAYEDD7MX4YcLEOPya+CDovr6MWY+CahMsqpWxlzEIYBDBIvMFFSlkA5ZQhJX3e/bsCTvCVq1ahRUuMzMzYpux0kCV2vtRuXLlgPMnf6oDr/oVy1CmIWvLU/ggQWSacz5JX6oCgL7v5IZCa4hF3D3hMBpXpgYXl9VhSbVBtuarmZHFH2Pa1j1WrPFu/7EBOtQ7UizPMLNnMRneE6UTkXG9w6LVTZqTUa2eEkyNLsH8n9uhnrp6juKaxXPR6KCUVrTJQV8fyzScrYJDBINX7SULYPCqFkACra9YbgmrAda6dWswxopsMDHGIEnhqQhPPfUUHnnkEbzwwguoX79+WPcQBEEQBBEh5Ig1oQirAXbkSOzVgCFDhuDixYto1KgRKlSoAK/Xa7nuxqdYacLLGCRTr0iCjEJN5eLcGIKEF6JqcA9oQ5D2Y8MNhcWEjpm2onBqU6s2fsoQZNl1EvjryStRo85PAecFxrHp6NXoWv/7iOJt/t5MAMDly14cHjItmiQSRELiZX6IUOpsrZ4y1Hn7sWEewexmEQ71VKuJqhp2Adj1QulVw8zDt8d/qg0vY471DfLzAaTHJ1E0BBkVkiQhOzs76EpAbtstYTXA6tWr5yrScCgL6z0SBEEQBFE+mDVrFl566SVMnDgR06dPx7Rp03D06FG8++67+Nvf/uY6vrja/5kZPnx4WOHmzZuHe++9F1WqVCneBMUJew/owPE6uu2EAGOxVt3VhKaOqT1K0dSr1LBM8Q4X83CythwRoKhe+q4S5uMfyt5UcUDJi9yf6gAAMq48qZ+/ru5R3dlruPT8VDEivlCYDIEpyzW5yg+CKEWIMKnxaj0lmtR50aTQa+e0v27KRcc/L8K2FaV/abrMK0+VdBIUSAGLihUrVuDFF1/EgAEDMGvWLPzpT39Co0aN0KpVK2zbts31Kj5xdEASGU888USZHY4kCIIgiLjBY7CVY3Jzc9GyZUsAQKVKlZCXlwcAGDhwID788EPX8SV8A6wsz5QEoC/Iau5BAurMIt2WQlXDzD1K20wjC9HaSZoXDCvj71/j+E+1LceiSxcXTnlx6I6/Rp0ugkhURFWxF011lLme0o6D1lFkz02UMq666iqcOqWomY0bN8batWsBADt27EBycrLr+BK+AUYQBEEQRAwgT/hRceutt2L9+vUAgIceegjTp09HkyZNcNddd2HkyJGu44vIBuzs2bP473//i8OHD+Mvf/kLqlWrhl27dqFWrVq48sorI4myXKP1EEVu85cD6DZhethwNWCnWZHhYLENk7E6f1mYN5ZONNuv4z/VxsETij3YZS5AhFdfVLd3g/0h47h9y30AUoo1nQSRCGhLdQm27z3qeiqY02gANw5aAAD47P2/hJtMIgjkCT865s2bp+//8Y9/xFVXXYUtW7agcePGGDRokOv4XDfA9uzZg169eiE9PR1Hjx7F6NGjUa1aNaxcuRI//vgjXn/9ddeJIAiCIAiCKE107NgRHTt2jPh+10OQEydORFZWFg4ePIiUFKMX1L9/f3z22WcRJySRePbZZ9GgQQOkpKSgXbt2+Pzzz4vtWaJd8WKyunF9SQvtfDDMSxHFYklNLsvgsox18n+ij6yUkHnlKd0WT0PLCzcYecHR5D+PxzqZBFFi7D1+lb6v1VHmeipUeTGXi0jrqJ7dnojsRsKAjPCjZvny5ejSpQvq1KmDH3/8EYDiVuu9995zHZfrBtiOHTtwzz33BJy/8sorkZub6zoBRXHDDTcgNTU15vEG46233sL48eMxbdo07N69GzfccAP69++PY8eKZ/kbgiAIgiASn+eeew4TJ07ETTfdhLNnz+or/1SpUiUi36auhyBTUlKQn58fcP7AgQOoUaNG2PHs2rULXq9Xn9L53nvvYdmyZWjWrBlmzpyJpCTFl9JHH33kNolRsXjxYowaNQp33303AKVlu2bNGjz33HOYO3dusTzTroIFw6yIBZ0B6RJ9TVyT7dea869FHW9pJpz8mPL17QCAs74KEFhqWHnR8KnF8Fxk8FxUjvc9Xnq9fBPlix9O1IYYZn9dgOEPzPw3Usq73VEsMa+DHun95ZklS5bgxRdfxC233GKxB2vfvj0mT57sOj7XCtgf/vAHzJ49Gz6fD4Cy/uOxY8fwyCOPYPDgwWHHc8899+D775WlXn744QfceeedqFChAv7zn/9gypQpbpMVEwoLC7Fz50706dPHcr5Pnz7YsmWL4z0FBQXIz8+3bARBEARBlC2OHDmCNm3aBJxPTk7GhQsXXMfnugG2cOFC/Prrr6hZsyYuXbqErl27onHjxkhLS8OcOXPCjuf7779H69atAQD/+c9/cOONN+Kf//wnXn31Vbz99ttukxUT/ve//0GSJNSqVctyvlatWkGHV+fOnYv09HR9y8zMjEdSi42Pj2aXdBLKDAdvV/yA1X9hIeq/sBA/jJ+I7x+dgH2PTwhL/epfnxQyIvbIuVfrW2mjX2v3y70QJsgNRVQ0aNAAOTk5Aec//vhjNGvWzHV8rocgK1eujM2bN2PDhg3YtWsXZFlG27Zt0atXL1fxcM4hqws9f/LJJxg4cCAAIDMzE//73//cJiumMJuVKOc84JzG1KlTMXHiRP04Pz+/1DfCCIIgiDIILUUUFX/5y19w//334/Lly+Cc48svv8S//vUvzJ07Fy+99JLr+Fw3wF5//XUMGTIEPXr0QI8ePfTzhYWFePPNN3HXXXeFFU/79u3x+OOPo1evXti0aROee+45AIrEZ1eg4kX16tUhimKA2vXLL78ETVNycnJEHnDNSGGOrMsmwVLmDHIMeiNaFDwW0ydLOQ2vUjwcm2d7BWP+tcoM0TFfDQ87Lxo+tRg/jJ9YZDgzfSspa6bKFy9CqFABANnoEdEhZHwf0X1u6ymtTERbT5lv54zpyrD8v9+oLBBxZcSIEfD7/ZgyZQouXryIoUOH4sorr8TTTz+NO++803V8rocgR4wYoa9/ZObcuXMYMWJE2PE89dRT2LVrFx544AFMmzYNjRsrix//97//RefOnd0mKyYkJSWhXbt2WLduneX8unXrSixNBEEQBBETyA1FxPj9frz22mu4+eab8eOPP+KXX35Bbm4ujh8/jlGjRkUUp2sFLNhw3IkTJ5Cenh52PK1atcLevXsDzi9YsACiKLpNVsyYOHEihg0bhvbt26NTp05YunQpjh07hnvvvbdYnmfvVUpcUP8yi+KlnXeCc6Zukadjze5Zkd9cxmiZeQLbf2ygv/N3DrfBOTkVw5s4T8Qwo+VFLPj4aDb6VTY6NUxQ0tO//gTIVStTnhFxw6mekjSFC4J+LhhamYi2njLDBAG9BWU2cnnyWRgN5Ak/cjweD+677z7s36+sjFK9evXo4ww3YJs2bcAYA2MMPXv2hMdj3CpJEo4cOYJ+/fq5erjTkkb79u0r0SWNhgwZgtOnT2P27Nk4deoUWrRogY8++gj16tUrkfQQBEEQBFHydOjQAbt3745ZeyDsBtgtt9wCAMjJyUHfvn1RqVIl/VpSUhLq16/vyg3Fnj170LNnT1SpUiXhljQaO3Ysxo4dG5dnNbnqJL4+phjtS2CQ7T1N2yix/XpQuG2/HPdcIsEpL57+Tplo8tDvPtHPLW1vtUHpuGZqYGRhvvsbBy2AN19x77J+46PWi8z0HagKtOYZ3FfZS+vkEcWOzJmuhMW8nrL/DYV5BEYtF/0qjwC7oirkKmkAAH96cmAZIuJuhP/ZZ59hwYIF2LlzJ06dOoWVK1fqbQlAGVGbNWsWli5dijNnzqBDhw74xz/+gebNm+thunXrhk2bNlniHTJkCN588039+MyZMxg3bhzef/99AMCgQYOwZMkSVKlSRQ9z7Ngx3H///diwYQNSU1MxdOhQLFy4UPc5Gg5jx47FpEmTcOLECbRr1w4VK1a0XG/VqlXYcQEuGmAzZswAANSvXx9DhgyxLEMUCRMnTsSIESMwf/58pKWl6ef79++PoUOHRhU3QRAEQRA24twAu3DhAq699lqMGDHCUaCZP38+Fi9ejFdffRVXX301Hn/8cfTu3RsHDhywtAtGjx6N2bNn68f21XGGDh2KEydOYPXq1QCAMWPGYNiwYVi1ahUAZZRuwIABqFGjBjZv3ozTp09j+PDh4JxjyZIlYf+eIUOGAADGjRsXcI0xpnvGDxfXNmDDhw93e4sjO3bswAsvvBBwvriWNEpkrq17HADw+dHG+jkJAiQuQFbtKmQw3eZCOw46u8hFISHlxJnO9X7AmiOKXxcJQlj2XwCwrW/kqyU45cXq/GX6fr/0kUHv7fjnRSispHwru14g/2FEbJG5ogqb7bwkk+2XzAVd9TJsw0LMgoyFIm9RwgKfceOgBVS/FRN2h+PBvAH0798f/fv3d4yDc46nnnoK06ZNw2233QYAeO2111CrVi3885//tCx5WKFCBWRkZDjGs3//fqxevRrbtm1Dhw4dAAAvvvgiOnXqhAMHDqBp06ZYu3Yt9u3bh+PHj6NOnToAgEWLFiErKwtz5sxB5cqVw/rdR44cCStcuLieBSlJEhYuXIjrr78eGRkZqFatmmULl1gtaUQQBEEQRNFoRvjRbIDir9PsgDySZfqOHDmC3Nxcy8ozycnJ6Nq1a8DKMytWrED16tXRvHlzTJ48GefOndOvbd26Fenp6XrjCwA6duyI9PR0PZ6tW7eiRYsWeuMLAPr27YuCggLs3Lkz7DTXq1cP9erVw4ULF7B//358/fXX+rZnzx7X78C1AjZr1iy89NJLmDhxIqZPn45p06bh6NGjePfdd/G3v4XvpVhb0ujf//43gMiXNCpLyBD0XqXWo5T0XqWg2GA4+NbR/FC5mn1Hfr+KxJwXscCtDzA7q/Ne0ff7tpsRVAFoNTEbexaTCkbEDgkMEgRjxiNs6rxJHdPqKXsdpf2NuJ5iTJEMTPaQTLDZgwkO9xEG0XqzV+89fvy4RTWKxBemNtLltPLMjz/+qB//+c9/RoMGDZCRkYFvvvkGU6dOxddff627i8rNzUXNmjUD4q9Zs6b+jNzc3IDnVK1aFUlJSa5G3H744Qfceuut2Lt3Lxhj4OqUXs0zRLEPQa5YsQIvvvgiBgwYgFmzZuFPf/oTGjVqhFatWmHbtm2OY6NOLFy4EDfddJNlSaPc3Fx06tTJ1ZJGZYmu9b/Hez+0BuAwBMmZ0iizHGtTu2Gd2k0+W2LCTQ2+iUk8R+91v0irKxzq02bTlCWl9s1RGmL1n1uopOW+Yk4LUSbxaR1A9WPzcU/gEKTeyLIfG64n9HrK/LeoeipYe0EQjMaY4NBJYkDH/1sMACisyGhoHoiZDVjlypXDHrYriqJWnhk9erS+36JFCzRp0gTt27fHrl270LZtW8c4nOIJJ0xRPPTQQ2jQoAE++eQTNGzYENu3b8dvv/2GSZMmYeHChWHHo+G6AZabm4uWLVsCACpVqqQ7ZR04cCCmT58edjyxWtKIIAiCIIjShWbTlZubi9q1a+vnQ608AwBt27aF1+vFwYMH0bZtW2RkZODnn38OCPfrr7/q8WRkZGD79u2W62fOnIHP53O18s7WrVuxYcMG1KhRA4IgQBRF/P73v8fcuXMxbtw47N69O+y4gAgaYFdddRVOnTqFunXronHjxli7di3atm2LHTt2RCRD9ujRA507d0ZycrKrlmhZRRti9HFRUcG0IUh130nK16d8RyAt3/iHBfjsPTJULYpnD3RHnqTMvJna7CP9/LDtd+NsYSrOFSrf/sae7ntBEcFYcGXAAVK+iGi4tu5xfH60MXxccZItcWbsq3WTroip9ZSh0NtcukQ6BMagDkOq9zNmDEFq57X/IfSvxJFEcsSqDSuuW7cObdq0AaAsabhp0yY8+eSTQe/79ttv4fP59EZbp06dkJeXhy+//BLXX389AGD79u3Iy8vTV7DRRtZOnTql37d27VokJyejXbt2YadZkiTdBVf16tVx8uRJNG3aFPXq1cOBAwdcvwPXxi233nor1q9fD0CR46ZPn44mTZrgrrvuwsiRwWdp2ZFlGY899hiuvPJKVKpUSZ9dMH36dLz88stuk0UQBEEQRCjivBTR+fPnkZOTg5ycHACK4X1OTg6OHTsGxhjGjx+PJ554AitXrsQ333yDrKwsVKhQQXdFdfjwYcyePRtfffUVjh49io8++gi333472rRpgy5dugAArrnmGvTr1w+jR4/Gtm3bsG3bNowePRoDBw5E06ZNAQB9+vRBs2bNMGzYMOzevRvr16/H5MmTMXr0aFdDqS1atNCN7Tt06ID58+fjiy++wOzZs9GwYUN3LwcRKGDz5s3T9//4xz/iqquuwpYtW9C4cWMMGjQo7Hgef/xxvPbaa5g/f75ljLdly5bIzs6OeG2l0o6PK1kicQE+LgbYfPnNxzAv72Fa4oMzwNzTsRca6h26ZmzTT/X9KV/fri/GvbzDSwCAG9crKmLbj/6KXTc9XuzpWfPVTH3/xj8sAEDuJ4ji5Yb6h/DRkRYAVBswtS7S6imzOu83H4MFLkWkqWBaPeX0j92pnmImlUtgJhswZr0WTlxEsfPVV1+he/fu+vHEicpEpOHDh+PVV1/FlClTcOnSJYwdO1Z3xLp27VrdB1hSUhLWr1+Pp59+GufPn0dmZiYGDBiAGTNmWJYsXLFiBcaNG6fPqBw0aBCeeeYZ/booivjwww8xduxYdOnSxeKI1Q1//etfceHCBQBKG2bgwIG44YYbcMUVV+Ctt95y/X5cN8DsdOzYER07dnR93+uvv46lS5eiZ8+elnUWW7Vqhe+++y7aZBEEQRAEYSbKIUi3Cli3bt30mYJOMMYwc+ZMzJw50/F6ZmZmgBd8J6pVq4Y33ngjZJi6devigw8+KDKuUPTt21ffb9iwIfbt24fffvsNVatWjciEKqIG2Pfff4+NGzfil19+gSzLlmvhuqL46aef0Lhx44DzsizD5/NFkqwyQaFqV+Hjor5px36zIgYGSTbUMK6qXoAy0yjcTyFG60aXKzT1y8xnPRc4hq33ynywy0o+HR2r2GFpMxQ9lwDPRSXcrucjV68+e+8v+mwvgihONIW+0FY3mesqrZ7SHbPKgsUeDKZZ2+FUP7pYxhi4SeViTAAXjH1o1/WwUf/cskecPeGXB9z4P7XjugH24osv4r777kP16tWRkZERMM0z3AZY8+bN8fnnnwcsavmf//xHN8gjCIIgCIIoi7hugD3++OOYM2cOHn744agePGPGDAwbNgw//fQTZFnGO++8gwMHDuD111+PWiYszWg9TL1XKau9SlnZ12zA/JZepc3BoWZjofUyHXo91DuMDz+OnBJ4Us2LWDpL3fZGcCevjRYthpSiPPTo/ZNi9kyi/GEo9B6rAiaL8Kt1lVZP+S0KPUz7Rt1UlL2qYz2ldfpFAUyzAxIFR/svqudskAKWULieBXnmzBncfvvtUT/45ptvxltvvYWPPvpIV87279+PVatWoXfv3lHHTxAEQRCEQayWIiJig2sF7Pbbb8fatWsthvNu8fv9mDNnDkaOHBmWgV15wmfuYcrmXqZg8a1jVrz0HqWpVxl0ZlHAjEiGHr3nYcO6R2L+Wwhn9j0R39mKhydFtwQSQWjc3khZN++FA10NtV6tp3y2GduyZeajWZ2HLk051lMOdRQARS4QYPEDBk0B0/yAaZKCoJzbvpy+fSJxcd0Aa9y4MaZPn45t27ahZcuW8Hq9luvhLEXk8XiwYMECDB8+3O3jCYIgCIIgSj2uG2BLly5FpUqVsGnTpgD1ijEW9lqQvXr1wsaNG5GVleU2CWUaTfG6zD0BM4vMdhWSLECSjVlG5l5mUPUrCJxWIEg4+nR6DEL+Zaz+tnyui0okNj4u4rLNXtWv11VKPSXpdZUxY1urp4L6KAyBNgOSq+s+MlE02YOJynnTLEjCAbIBSyhcN8A0j/XR0r9/f0ydOhXffPMN2rVrh4oVK1quu3HqShAEQRBEaBJpKSIiBo5YI+W+++4DACxeHOi/iDEGSZLinaSE4DJXhnR9sgcF6qYciyiUPcYsSC7onqhlmUGWmcW7NGAtLMEKDmcgL9EJCGeKPUv/zIeUE5cLgBRlvcmPjz8dEL7DsMVk70LEjcvcC59aN2n1lDZjW6untLpK4oJSPwFGPRWkbnKqp/SZjMwoF8oxM2ZBqud15ctUHRJEohJWA2zixIl47LHHULFiRX0pgWA4NaicsDtwJQiCIAiimCEVK2EIqwG2e/du3Tv97t27g4aLxBU/YaVA9qp/FRswv96rFOGXBRRKyrHZBkzmtl6lPgvSqohZiCCrend5HP5KXny6JjofcEQYMKizvNTuvigY+8HCE0ScKJC9hjqv1lOFus9CpZ4y24DJel3EHGZBsuCNAvt3rZULwFomND9gpvCb35kc1W8sk5ANWEIRVgPs008/ddyPhr///e+O5xljSElJQePGjXHjjTdaFtwkCIIgCIIoC5SYDVh2djZ+/fVXXLx4EVWrVgXnHGfPnkWFChVQqVIl/PLLL2jYsCE+/fRTZGZmllQy485lTQHjXosNmNbDlEyzIGXzzCIZgKqIQUZQT/gBNhZubCVofbWY0/ZeZV1IbS3Ibv2eBAB4GQMEARA0T9+isR9mXARRXFyWvSjghlpfIHssar1kmgUpq7O0ARj1lGaB4uAJ36mO0oJCKxeAUh60DrogKuf1WZCx/b1lBTLCTyzCaoDddtttYUf4zjvvhBXuiSeewNKlS/HSSy+hUaNGAIBDhw7hnnvuwZgxY9ClSxfceeedmDBhAv773/+G/XyCIAiCIBygIciEIqwGWHp6ur7POcfKlSuRnp6O9u3bAwB27tyJs2fPumqo/fWvf8Xbb7+tN74AxcnrwoULMXjwYPzwww+YP38+Bg8eHHacZQFN8SqQPCiUPbpdhWYD5jPZWeg2YBIDl4311hgHINvWVzMXnGCepoPQt80M5ba0pCLDEuFz7fhsiGpedLhrMbwXZHi0Hrw6o4tpXr8FwfAAHgxuxPv1U6SCEcXHYy1X6vt3f5WFQpsNmE+trwDVBkzSFDClntLrJtmq0BdVT3HTTEcmmNUwBq5e18ISRKITVgNs2bJl+v7DDz+MO+64A88//7xunyVJEsaOHYvKlSuH/eBTp07B7/cHnPf7/cjNzQUA1KlTB+fOnQs7ToIgCIIgnKEhyMTC9WLcr7zyCiZPnmwxjhdFERMnTsQrr7wSdjzdu3fHPffcY5lVuXv3btx3333o0aMHAGDv3r1o0KCB2ySWWqZ8fTsuSUnKJntxSfKqKpiihPkkw7ZC8y4tyQK4LIDLTLWtYGCyaS3IIiRnzoCNH08Jer3/1Q8DIgNEBi6wkGGJ6NAnszKAC8o7V5QvdbaXut+3/cySTipB6LzU/lX8q+NSXQUz6imm11PcVk8xF/WUY7nQyoZoKhtqHcUFslUNiv19R7IRMcN1A8zv92P//v0B5/fv3+/Kt9fLL7+MatWqoV27dkhOTkZycjLat2+PatWq4eWXXwYAVKpUCYsWLXKbRIIgCIIgiITG9SzIESNGYOTIkTh06BA6duwIANi2bRvmzZuHESNGhB1PRkYG1q1bh++++w7ff/89OOf43e9+h6ZNm+phunfv7jZ5pRpN+QJUGzDJg8t+JYsu+xVP0z7VrsIviUpvEurMIs7A9JlFUGwr1GOL7Kz+3f560V7T+zecDCR7DXsKsquICS2mKLMVxVC9SXXdO93PkSAa+0XlAzee8c18sgUj4oNWV2n1lF/1WchldZY2YNRTZhswk71qkevYauUCUJUv0dg3XyOcISP8hMJ1A2zhwoXIyMhAdnY2Tp06BQCoXbs2pkyZgkmTJrlOQMOGDcEYQ6NGjeDxlJhXjIRAG3YEoDS+JMMI3yeLKPSL8Pk1uzsBkqRO85YEcImBqYauFmkfCCh0u14I85+yKIALgmV5DyIGhPwHY/zlloWHBX2fM4aePebCn6oaPacKQAWbmE0VJRFnPu2hjFZc9/Gj8PlFvX6SJAGyuq/VU0w2O2KFsxG+HXXZNK5PVBHA1E6JXk+Zyg8RCNmAJRauhiD9fj+WL1+Ou+66Cz/99BPOnj2Ls2fP4qeffsKUKVNcOU29ePEiRo0ahQoVKqB58+Y4duwYAGDcuHGYN2+eu19BEARBEERoyAYsoXDVAPN4PLjvvvtQUFAAAKhcubKrmY9mpk6diq+//hobN25ESkqKfr5Xr1546623IoqztPNS+1dxWfKqm6KAFahboToE6ZcE+CVlcVsuKRtk+wbF0JUjYHNVgFTDVm1bv2FqMf3y8kOzv2Zb8sLSI2VMcTWhTqk3v3t7XnBTWG3YJWi8BBFHtHpKlpleTznXUXCsp2D6hgEYQ4vByoWpbHBmKhsEkeC4NsLv0KFDyPUgw+Xdd9/FM888g9///veWNSSbNWuGw4cPRx0/QRAEQRAmSAFLKFwbXY0dOxaTJk3CiRMn0K5dO1SsWNFyvVWrVmHF8+uvv6JmzZoB5y9cuFCuF/XWDFkLZVFXvgDVuaHftBSRX9TtKiBrdhXKIdMM8E2Grtr+1393YZStTu/m5Tg/YkGTuYpBvOADRFNehFSoNIeTmqNJbao9tPPBb3WtdBJEDNHqKUm1V5UlQV8mTaunzHWVNmkIgPNSRGZMjlh1NxTqvrlcbFr1l1j+pDID2YAlFq4bYEOGDAGg2GppMMbAOQdjDJIkhRXPddddhw8//BAPPvigHgcAvPjii+jUqZPbZBEEQRAEQZQaXDfAjhw5EpMHz507F/369cO+ffvg9/vx9NNP49tvv8XWrVuxadOmmDyjNHLRr8yCVOwoRBSqU7l9fsWuQvJrMx+Z3qtUZhbBmFkkw9rLdKmI9G03Q7nPI4CLZE8RKQ2fXgyhEBDM+RJidipgLCKs2bFwUT2h5QVgLLtizxaHeJtPzca3c2PniqL7BmWmszbjjSDsaPWUtvwQNDswQK+n7Gq947JpDmXDbN/FRQZ4BGNfsxEjgkNuKBIK1w2wevXqxeTBnTt3xhdffIGFCxeiUaNGWLt2Ldq2bYutW7eiZcuWMXkGQRAEQRAKNASZWETkeOvw4cN46qmnsH//fjDGcM011+Chhx6yLKwdDi1btsRrr70WSRLKLJqycP3qR1EoGX6/9F6lX/OnI4D71V6lX7UBU0d/7XYWkF26xTHbWDCGdZunRfmryg/1n18I5lPenyAF2uaZ88KxMjP7ARMQkBeAdh5BM5Vx0yUZuOZv2dg/OzYqGClfRFFo9RTXfH/5GZhWV2lqvWRShbUZkVA/62D/5DU/YOYFtzUbSXUh7k/XPlwcP4kgigXXDbA1a9Zg0KBBaN26Nbp06QLOObZs2YLmzZtj1apV6N27d9B78/Pzw35OpO4tCIIgCIJwgIYgEwrXDbBHHnkEEyZMCHCW+sgjj+Dhhx8O2QCrUqVK2DMcwzXmL6sU+BUbML/mTVrtVeoKmN/wfA/Ns7TdrsK8FJG632ZsNnY/G1oN4frMO27sEyGpv3SBsiMJui0e7PkgW/NCmfHF9f0ATJ7wLXnhVIYscbGAvP/dLGUm5nczaGkionjR6iluUr1gWqUD9jJhXorIMkuYBy0XgFJPrcmZWXw/pCxCDbCEwvV/1/3792PUqFEB50eOHIl9+/aFvPfTTz/Fhg0bsGHDBrzyyiuoWbMmpkyZgpUrV2LlypWYMmUKatWqhVdeecVtskJy9OhRjBo1Cg0aNEBqaioaNWqEGTNmoLCw0BKOMRawPf/88zFNC0EQBEEQhGsFrEaNGsjJyUGTJk0s53Nychz9epnp2rWrvj979mwsXrwYf/rTn/RzgwYNQsuWLbF06VIMHz7cbdKC8t1330GWZbzwwgto3LgxvvnmG4wePRoXLlzAwoULLWGXLVuGfv366cfp6ekxS4cbvh74GK5++zFj1qNfWe8RqiLG/Azwa7ZGyuwiQe9xwvA0DWuvsij1C4DiVRpQjC3E8BTLco8tLwAlP/S8AIyev9kPWJAeJWcAF5hjXnDBYQYknOPV7WvUNDR9LBsHppMKRhQfsl9QlGC1TMDPIOj2qaZyAejfplUVNvbt6OUCwNov/1ZMv6DsEsJ0NOz7idjhugE2evRojBkzBj/88AM6d+4Mxhg2b96MJ5980tVi3Fu3bnVUl9q3b4+7777bbbJC0q9fP0ujqmHDhjhw4ACee+65gAZYlSpVkJGREdPnEwRBEESJQ0OQCYXrBtj06dORlpaGRYsWYepUZW3AOnXqYObMmRbnrEWRmZmJ559/HosWWWdVvfDCC8jMzHSbLNfk5eWhWrVqAecfeOAB3H333WjQoAFGjRqFMWPGQAhhB1VQUKCvjQm4m2hQFJJPhKz2IrksWGYTwW4DZupVMtmqxFh6mGGwdut0AECf62frvU0iNPa8UM4ZeaEdB+vtB8z8Ykyd8cX0Y2MfAXZgzKYcWGbASsZkMZlM+ohihvsFi0JvsQHzM71cAKZ6ykGtd3SZwBjWf0pr0kYKuaFILFw3wBhjmDBhAiZMmIBz584BANLS0lw/ODs7G4MHD8aaNWvQsWNHAMC2bdtw+PBhvP32267jc8Phw4exZMmSgMbfY489hp49eyI1NRXr16/HpEmT8L///Q9//etfg8Y1d+5czJo1q1jTSxAEQRBE2SIiP2CAspbjgQMHwBhD06ZNUb16dVf333TTTTh48CCee+457N+/H5xz/OEPf8C9994btgI2c+bMIhs/O3bsQPv27fXjkydPol+/frj99tsDhjrNDa3WrVsDUGzVQjXApk6diokTJ+rH+fn5MVPwpELB6u3eLwBmX1+a3ZEfll4lk6zHexdHZvNDNhbhY88LQJ3dJVmPBad18ABAncFotu3ijBlKl2jsc/2vKQGc6yfMMy8FGeAyjO+GFDCimGE+pZ5iJtVL8KvXJKNcmI8F+zq2gDpcZkgujnaPhDtoCDKhcN0Au3DhAh588EG8/vrrkGWlpIiiiLvuugtLlixBhQoVgt67Z88etGjRQh/Su+qqqzBnzpyg4b/99ls0bdoUHo9zMh944AHceeedIdNbv359ff/kyZPo3r07OnXqhKVLl4a8DwA6duyI/Px8/Pzzz6hVq5ZjmOTkZCQnJxcZF0EQBEGUONSIShhcN8AmTpyITZs2YdWqVejSpQsAYPPmzRg3bhwmTZqE5557Lui9bdq0QW5uLmrUqBHWszp16oScnBw0bNjQ8Xr16tXDVt5++ukndO/eHe3atcOyZctC2nVp7N69GykpKahSpUpYz4g13C8YvUjNzkvrKfqttkaCH2DmXqbZBowodgSfNS8AJT8sCpjNBozJ3GpTYe/tCyav35wZ+4KD+gVm8qXErb7IJOjTl0gBI4ob5lNX4vBb7b4Ao56yKGA2GzAmKx8ys6nDAKlgRNnCdQPs7bffxn//+19069ZNP3fTTTchNTUVd9xxR8gGGOcc06dPD6mSmbH76YqUkydPolu3bqhbty4WLlyIX3/9Vb+mzXhctWoVcnNz0alTJ6SmpuLTTz/FtGnTMGbMGFK4CIIgiFIPGeEnFq4bYBcvXnQcjqtZsyYuXrwY8t4bb7wRBw4cCPtZWmMoWtauXYtDhw7h0KFDuOqqqyzXuNq78nq9ePbZZzFx4kTIsoyGDRti9uzZuP/++6N+fqSwQsHkQRoB6z0KNsVLn22n9jA1uwqi+Dk0RbGza/pYttHbl6y9fd3+yzwL0mwPhsAKTpv5yGQEzEg1hw2wnTHFK8iArH0bDGj212zse5x8gRHFgz7TUXKwi5SMcqEf29atNfuwM8/epn/+MYBswBIK1w2wTp06YcaMGXj99deRkpICALh06RJmzZqFTp06hbx348aNESUyWrKyspCVlRUyjN1XGEEQBEEQRHHh2iLk6aefxpYtW3DVVVehZ8+e6NWrFzIzM7FlyxY8/fTTxZHGcsvReydD8DHTBss+8xub+VgwzTQiO7D4ouWFlh96Xsim3r95jUi1R2peD09DnwXJGLho2nfyAWaPS900P0uWY3+cXgZRrmi0eDEaLV5sqqcQtJ4SJGs9JQQpG/r6qWrZ2PTRlBL9jaUd7V1Gs7nhs88+w80334w6deqAMYZ3333Xcp1zjpkzZ6JOnTpITU1Ft27d8O2331rCFBQU4MEHH0T16tVRsWJFDBo0CCdOnLCEOXPmDIYNG4b09HSkp6dj2LBhOHv2rCXMsWPHcPPNN6NixYqoXr06xo0bFzMzp0hx3QBr0aIFDh48iLlz56J169Zo1aoV5s2bh4MHD6J58+bFkUaCIAiCIKKFx2BzwYULF3DttdfimWeecbw+f/58LF68GM888wx27NiBjIwM9O7dW/cxCgDjx4/HypUr8eabb2Lz5s04f/48Bg4cCEky1IWhQ4ciJycHq1evxurVq5GTk4Nhw4bp1yVJwoABA3DhwgVs3rwZb775Jt5++21Xq/cUB4xz7vKVEqHIz89Heno68vLyULly5ZjF22RetqOvL/O+bhMmK/s5S8jOpyRo/nA2ANX2RYbFVk/wA4KfG9cldd+nnNeuMXVf8KsZLnNAtQGTPQJkDwP3aMdM2bxKUC4yyKpxgXIN4KJ2DOzJpu+CiD1N5infvaOvL8takEY9ptmHaWVE+ea1sFwvFwCw+Z3J8fopcaW4/mc4PaPlqCcgJqVEHI9UeBl7X340orQyxrBy5UrccsstABT1q06dOhg/fjwefvhhAIraVatWLTz55JO45557kJeXhxo1amD58uUYMmQIAGVSXWZmJj766CP07dsX+/fvR7NmzbBt2zZ06NABgOLUvVOnTvjuu+/QtGlTfPzxxxg4cCCOHz+OOnXqAADefPNNZGVl4Zdffim2914UrhWwuXPn4pVXXgk4/8orr+DJJ5+MSaIIgiAIgogtsRqCzM/Pt2zm5fjC5ciRI8jNzUWfPn30c8nJyejatSu2bNkCANi5cyd8Pp8lTJ06ddCiRQs9zNatW5Genq43vgDFh2d6erolTIsWLfTGFwD07dsXBQUF2Llzp+u0xwrXDbAXXngBv/vd7wLON2/e3HFxbSI2aPYUITfNlsI064iIP5b8MPkC0xQvZrPL0nwfBa57p9iBcZPtF1fXiLQE4+r95vh0X3A8IA0EURyEVUeZyoVZ/VLKBQ9aLmgGZIyI0RBkZmambm+Vnp6OuXPnuk5Kbm4uAAR4VahVq5Z+LTc3F0lJSahatWrIMDVr1gyIv2bNmpYw9udUrVoVSUlJepiSwPUsyNzcXNSuXTvgfI0aNXDq1KmYJIoIRCiEdQjSLPHL1muCBOx6joaZSgrLULBl+IXrjWTlOjeuqcuu2I2Ne/ScpwawPoOZKkPOORg3XJSAcQi651VAhnFMBgdEcSGo9szmRpT52LxvXqBeKxeAZojPjWsc+Hxl2Rx6LBFi5Ibi+PHjlmG7aHxlMtukIs55wLmAZNjCOIWPJEy8ca2AZWZm4osvvgg4/8UXX1jkPYIgCIIgyh6VK1e2bJE0wDQn6HYF6pdfftHVqoyMDBQWFuLMmTMhw/z8888B8f/666+WMPbnnDlzBj6fL+gyg/HAtQJ29913Y/z48fD5fOjRowcAYP369ZgyZUqJzygoy4gFsDjxZNyqgAmm5Tx2vUDqV0ki+tQdGRAkHpBPmuE9k4At/w5dZjasfwSASQkDjB6sphSow5KCekEGA0TVuB8M4ABXw3JyzksUE6JqBqTVU/rSWDZ1XisX5muCSSU2HBlzbH6b1K9Ykkie8Bs0aICMjAysW7cObdq0AaCsfrNp0ybdnrxdu3bwer1Yt24d7rjjDgDAqVOn8M0332D+/PkAFN+keXl5+PLLL3H99dcDALZv3468vDx07txZDzNnzhycOnVKH8Fbu3YtkpOT0a5du9j9KJe4boBNmTIFv/32G8aOHav70EhJScHDDz+MqVOnxjyBBEEQBEHEgDh7wj9//jwOHTqkHx85cgQ5OTmoVq0a6tati/Hjx+OJJ55AkyZN0KRJEzzxxBOoUKEChg4dCgBIT0/HqFGjMGnSJFxxxRWoVq0aJk+ejJYtW6JXr14AgGuuuQb9+vXD6NGj8cILLwAAxowZg4EDB6Jp06YAgD59+qBZs2YYNmwYFixYgN9++w2TJ0/G6NGjS2wGJBBBA4wxhieffBLTp0/H/v37kZqaiiZNmtB6icWM4LPZVdhsLHYuJdUrURB8au/eZpvHZMUgfstbESrFZoMBblpwW68V1aVfwMHVVYu5zBUFTNCOI3s0QRSFoCq/FmeqgM1elQfar8rcYjep24DRt1rq+eqrr9C9e3f9eOLEiQCA4cOH49VXX8WUKVNw6dIljB07FmfOnEGHDh2wdu1apKWl6fdkZ2fD4/HgjjvuwKVLl9CzZ0+8+uqrEEVRD7NixQqMGzdOny05aNAgi+8xURTx4YcfYuzYsejSpQtSU1MxdOhQLFy4sLhfQUhcN8A0KlWqhOuuuy6WaSEIgiAIophgnINFMRPH7b3dunVDKFejjDHMnDkTM2fODBomJSUFS5YswZIlS4KGqVatGt54442Qaalbty4++OCDItMcTyJugBHxZU/2BLS9N1vvFZLilbhYlQCu59nWf0VuI6nZggFAj97zAM7AoSkFDJaxAW6oB1yAEla1CYsmDQQRCrMNmL40FqxlwOxeQj+WDJuwzW9Pxg23LAAAfP7uX+KZ/PJBnIcgidC4ngVJEARBEARBRAcpYKWIXc+T6lUa+PK1icUa/4Z1ihrWvY+68gTjlp4tY8bSQ5riwMkxL1HMiAUmVcukwoIHKl76sRrO7OuLlK/iI5FmQRLUACMIgiCI8gENQSYUNARJEKUUffkWmUOQuL6Yt+DnEHzKxnwcQqGsH8eDdqOz4/IcIrHwFGgbh2jaPJetx6LP2AT1+ySI8ggpYARBEARRDqAhyMSCGmAEUUrR7Gj01RHU2pEzBqaNFQjK8acfTolbuna+OAEdhi3G9uXFawtHJBaeS9rUW2NtU8BmD6b6pdO95KvHRJygIciEghpgBEEQBFEOIAUssaAGGEGUUtZ/qiz91bPbE1bFAVCmQgL4ZNOjJZI270UZvx9seJmmNf3KNl1vXgBRU2QdFDB9hi7n6jqRRlhE4RiUIEoz1AAjCIIgiPIADUEmFNQAI4hSzvqNJaNyhcJz0ZjZtvHj+NmfESWD54LfOFBVLctwlaaGycC6zdPimjbCCg0jJg7khoIgCIIgCCLOkAJGEETM8Vz0Fx2IKDOIl8wKGNdn5gK2BZzJ3qtk4Ty6PKD8iynUACMIgiCIcgDNgkwsaAiSIMowfa6fXSLPFc8X6BtR9hEuFlq3y4UQLhVAuFQAdrnQ2C75SjqpBJEwkAJGEARBEOUBmgWZUFADjCDKGD16zgOTFCMcwRs/kbv/1Q/r+2u+fzJuzyVKHnbhsvWEbPtPLarfoUCDLiUJk02rEkR4PxE7qDQQBEEQBEHEGWqAEUQZY8P6R8C9ArhXgJwkxu/B584D587jY1K/yhX9M8YC5y8Y26XLgM8H+P3KxmVlZQbG8PGBeSWd3PINj8FGxAwagiSIMsiGdY/E5Tl9K96l7DCGNedfi8szicSCF/rARBEQmHFSFACvV9n3evDxvidKJnGEBZoFmVhQA4wgCIIgygPkByyhoAYYQRAR0Td1mDK8BGDN5RUlnBqipGBJXsDjATzqcLfXC3hEcK9yzMU4DoMTRCmCGmAEQRAEUQ6gIcjEghpgBEG4pn/GWAiVKoJLUkknhShpKlRQ1C9VAeMeAVwUsWb3rBJOGBEA+QFLKGgWJEEQBEEQRJwhBYwgCPdUqghIElYfWVzSKSFKGF4xGRBFcI/Sn1cUMAG9uiozHz/Z9GhJJo8wQUOQiQU1wAiCIAiiPECzIBMKaoARBOGajw8tKOkkEAmCXDEZXBDAPYofMC4K4ALTj7veNB+bPppSkkkkiISEGmAEQRAEUQ6gIcjEotwY4devXx+MMcv2yCNWb+HHjh3DzTffjIoVK6J69eoYN24cCgsLSyjFBEEQiY+U6oWU6tE3f6oIqYKIT9c8jE/XPEzqVyJBSxElFOVKAZs9ezZGjx6tH1eqVEnflyQJAwYMQI0aNbB582acPn0aw4cPB+ccS5YsKYnkEgRBEARRRilXDbC0tDRkZGQ4Xlu7di327duH48ePo06dOgCARYsWISsrC3PmzEHlypXjmVSCIIiEplv/+QAAnioCIoOsOrznAgMvN2MrpQsagkwsylUxefLJJ3HFFVegdevWmDNnjmV4cevWrWjRooXe+AKAvn37oqCgADt37gwaZ0FBAfLz8y0bQRAEQSQcMo9+I2JGuVHAHnroIbRt2xZVq1bFl19+ialTp+LIkSN46aWXAAC5ubmoVauW5Z6qVasiKSkJubm5QeOdO3cuZs0ij88EQZQv/Klq/51ZVS/ZoxwTCQh5wk8oSrUCNnPmzADDevv21VdfAQAmTJiArl27olWrVrj77rvx/PPP4+WXX8bp06f1+BgLrDQ4547nNaZOnYq8vDx9O378eOx/KEEQBEEQZYpSrYA98MADuPPOO0OGqV+/vuP5jh07AgAOHTqEK664AhkZGdi+fbslzJkzZ+Dz+QKUMTPJyclITk52l3CCIIhSjj9V9fslMFUFg3oMsgFLUBiitAGLWUoIoJQ3wKpXr47q1atHdO/u3bsBALVr1wYAdOrUCXPmzMGpU6f0c2vXrkVycjLatWsXmwQTBEEQRElBnvATilLdAAuXrVu3Ytu2bejevTvS09OxY8cOTJgwAYMGDULdunUBAH369EGzZs0wbNgwLFiwAL/99hsmT56M0aNH0wxIgiAIG9tWTAIAtBudbVHALPsEQQSlXDTAkpOT8dZbb2HWrFkoKChAvXr1MHr0aEyZYjgIFEURH374IcaOHYsuXbogNTUVQ4cOxcKFC0sw5QRBEAQRG8gNRWJRLhpgbdu2xbZt24oMV7duXXzwwQdxSBFBEETZQEoBOINuIMQFkLFQokKzIBMKEooJgiAIgiDiDDXACIIgiIjJWTIBchIgmTeaGJ6QMM6j3txy7tw5jB8/HvXq1UNqaio6d+6MHTt26NezsrIC3EdpXgo0CgoK8OCDD6J69eqoWLEiBg0ahBMnTljCnDlzBsOGDUN6ejrS09MxbNgwnD17NqL3FC+oAUYQBEEQ5QE5BptL7r77bqxbtw7Lly/H3r170adPH/Tq1Qs//fSTHqZfv344deqUvn300UeWOMaPH4+VK1fizTffxObNm3H+/HkMHDgQkiTpYYYOHYqcnBysXr0aq1evRk5ODoYNG+Y+wXGEcU7zSmNJfn4+0tPTkZeXR7MnCYIol/xuRrbevf9uxoSSTUyCE4//GdozbrhxBjyelIjj8fsv4/PPZoWd1kuXLiEtLQ3vvfceBgwYoJ9v3bo1Bg4ciMcffxxZWVk4e/Ys3n33Xcc48vLyUKNGDSxfvhxDhgwBAJw8eRKZmZn46KOP0LdvX+zfvx/NmjXDtm3b0KFDBwDAtm3b0KlTJ3z33Xdo2rRpxL+5OCkXRvgEQRBE/PhuFjW6EpFIhxHN9wMIWPM4mENyv98PSZKQkmJt9KWmpmLz5s368caNG1GzZk1UqVIFXbt2xZw5c1CzZk0AwM6dO+Hz+dCnTx89fJ06ddCiRQts2bIFffv2xdatW5Genq43vgDF2Xp6ejq2bNmSsA0wGoIkCIIgiPIAj8EGIDMzU7e1Sk9Px9y5cx0fl5aWhk6dOuGxxx7DyZMnIUkS3njjDWzfvh2nTp0CAPTv3x8rVqzAhg0bsGjRIuzYsQM9evRAQUEBAGWd5qSkJFStWtUSd61atfR1mnNzc/UGm5maNWuGXMu5pCEFjCAIgiDKAzHyhH/8+HHLEGSo5fiWL1+OkSNH4sorr4Qoimjbti2GDh2KXbt2AYA+rAgALVq0QPv27VGvXj18+OGHuO2220IkxbpOcyRrOZc0pIARBEEQBBE2lStXtmyhGmCNGjXCpk2bcP78eRw/fhxffvklfD4fGjRo4Bi+du3aqFevHg4ePAgAyMjIQGFhIc6cOWMJ98svv+jrNGdkZODnn38OiOvXX38NuZZzSUMNMIIgCIIoB2ie8KPZIqVixYqoXbs2zpw5gzVr1uAPf/iDY7jTp0/j+PHj+prM7dq1g9frxbp16/Qwp06dwjfffIPOnTsDUNZyzsvLw5dffqmH2b59O/Ly8vQwiQgNQRIEQRBEeaAEFuNes2YNOOdo2rQpDh06hL/85S9o2rQpRowYgfPnz2PmzJkYPHgwateujaNHj+LRRx9F9erVceuttwIA0tPTMWrUKEyaNAlXXHEFqlWrhsmTJ6Nly5bo1asXAOCaa65Bv379MHr0aLzwwgsAgDFjxmDgwIEJa4APUAOMIAiCIIhiIi8vD1OnTsWJEydQrVo1DB48GHPmzIHX64Xf78fevXvx+uuv4+zZs6hduza6d++Ot956C2lpaXoc2dnZ8Hg8uOOOO3Dp0iX07NkTr776KkRR1MOsWLEC48aN02dLDho0CM8880zcf68byA9YjCE/YARBEES4xNMPWLcOf43aD9jG7Y/T/7cYQQoYQRAEQZQHSmAIkggOGeETBEEQBEHEGVLACIIgCKI8YHKmGvH9RMygBhhBEARBlANitRQRERtoCJIgCIIgCCLOkAJGEARBEOUBMsJPKKgBRhAEQZQIrVb9DYxxfD3wsZJOSvmAA5CjvJ+IGdQAIwiCIIhyANmAJRbUACMIgiBKhBSvHwDQcc1UAMC2vnNLMjkEEVeoAUYQBEEQ5QGOKG3AYpYSAtQAIwiCIOLE7z+ZAgAQmPKf/Mt+C0oyOeUPMsJPKMgNBUEQBEEQRJwhBYwot9y4/i/6vmDT1jf2XBjv5BBEmaX3xgkAgFSPoX4RJYAMgEV5PxEzqAFGEARBEOUAmgWZWFADjHDN7Vvu0/dltTulKUj/6fxciaQJAPp/9hAA4OMbny4yDABU9Brn7QrYgM/GGdcYx6oblujHN3/+oOWa+a+dlV3+oe8P3jIWAPB252dDngOMd6zFK3Nm2QcC33WwuAiipKnkLbQca9/y4C1j6Xslyi3UACMIgiCI8gAZ4ScUjHN6o7EkPz8f6enpyMvLQ+XKlUs6OVEx/MtRIa+bVRntWGN5h5f0/T9vHw0AWNHhxZimz6zExRInNUtgoY0fQtm12NU1p3vs79It9vtfu/7liOMiiGj507Yx+n6o7zrWdUJpJB7/M7Rn9Gw2GR4xOeJ4/FIB1u9bWCb+vyUCNAuSIAiCIAgiztAQZDnh3p3DAADPt1teZDitx1pR/To0ZUtg3KJymY/t1+7dOQwyV9r3aR4ZMhf0NGjnNWQwvNT+1bB+x4gdI/T9St4QAbW41WcVpWCFQixCmbIrXNqzZC5AYHLQNIR6f9qxk3qgnQ+VF6Hye8xXw7G0/WshfxNBRENFT6Gj8quVh2jKIxEFNASZUFADLEF5aPef8HSbf0V074ScOyEyDkn9hywyjkoe41p26zf1sJO/HmK5t5KLL0KADFkVUSXOLA0V87G2L3Hr/GdzA+Kh3X8K+hxzg62SGH76YkU4/yycGkrab7a/Bw3zsfldRpTGIHkx+eshAWmo7OWYkHNnQBxafkT63RHlF3v5rWgqp/ay4VQXEHGC3FAkFNQAIwiCIIhyALmhSCyoAVZMzPhmGJIreTGv1X+LDPvInj8GnEsVgWl7bgvrWfYepl0lEpkMSVWRRCZj+t5bTWGVLo35uobEhSKP7c/Qrks8tJojh9kDlhLETFEMs+tXlCG9PS/s71AjnHdfVF44PcOJcPOCIMxo9ZYIuUhlmpyvOvPInj+i4LyvpJNBlBDUACMIgiCI8gDZgCUU1AArJiqJl5EiSpjz7cAiw6aF6D3aDbpDIapGrxIYRHBIpsF+MYJl7M0G5OHi9Eyn9ATDbrieSLjJCwD6bza/B+18JM+O5J3YnxlOXszddxOAovNiWvMPXKeHKBvM+Xagpd4KNdnETrh1QWkjnLreTpoIeMU4KmAyB6JRI2VqgMWSxPsvRxAEQRAEUcYhBayYqCD4kCIU4b7AYUp2Ub1IzRbJbBslQoYEwZWdkt2FgfF8Z9cG5nBFnQuGlmYtvfb0ByPUO4qVUubUgw+nRx/qt4STH/b35zYvnO4t7rxQniXj6e96OebFhGvWFvl8Inqy9/cB4Py+Q12LBRWEQtflAkBAPfXMdz3wwO82FEsa4439nZjrrVD1lCD4iz1tOjQEmVBQA4wgCIIgygVRNsAiMJ8ggkMNsGKikngJqWLxvV6zk0MZLOhyN8EQBefZiqJgmzXHEDizURVYLDPsXJh02NMrl3J7kGjzAnB4707nnN67er6k8uLuqz93FZ6IHWniJQDAS9/f4HANQa8Fo6i8NMcVym5Vw+m7speNkVdvDjt9iUKwdxrOO3HCI8ZRASMSinJhA7Zx40Ywxhy3HTt26OGcrj///PMlmHKCIAiCiBHaEGQ0GxEzyoUC1rlzZ5w6dcpybvr06fjkk0/Qvn17y/lly5ahX79++nF6enpEz0xlPlQQYu82OJgtRSgbsJC2Uk6CRzARJIywoWwe7LZRweyQShNO9nehbMDimRehnueURu13hJsXQxtvDytcWeOtQ9dhSOMdjudDMaTxjqBhJAiu36emWP3zUAdX9wWjqHgquCiiwWwh3diqJgJvHbrOkmYJgqv3EBZMinGEIZA5ohpGpFmQMaVcNMCSkpKQkZGhH/t8Prz//vt44IEHwJj1v1aVKlUsYQmCIAiCIGJNuWiA2Xn//ffxv//9D1lZWQHXHnjgAdx9991o0KABRo0ahTFjxkAQgnd5CgoKUFBQoB/n5+cDACoKBaggxGfhQkHtnZnXEhRMvUwZAgTmsxw7hdPDOpwLFjZYGpyeZ09TWcTpN8YyL4Kdd5v35SEvYsWqH1pZ3lsFQTkHWPNCU0aC5cV7P7S2qOL2cObnhOIPDXP0+ACgYinIvtL2vWn5mxKi7o8VTIijAsZlZYvmfiJmlMsG2Msvv4y+ffsiMzPTcv6xxx5Dz549kZqaivXr12PSpEn43//+h7/+9a9B45o7dy5mzZpV3EkmCIIgiOggNxQJBeO89L7RmTNnFtn42bFjh8XO68SJE6hXrx7+/e9/Y/DgwSHvXbRoEWbPno28vLygYZwUsMzMTLz99dWo6DAtJtoZc8FmE8UKp7UDw13fMVIinTkY7Qy+WMxejPS+cAi2jmM46ztGSri/p3eD/cXy/Hix7sg1rsIHKwPFnReA+7Ie63IRizQUhdP3tO7INSXynTl9G9G8B/N9Tnlx4ZyEwdd+j7y8PFSuXNltcsMiPz8f6enp6HXlvfAIyRHH45cL8MlPzxdrWssTpVoBe+CBB3DnnXeGDFO/fn3L8bJly3DFFVdg0KBBRcbfsWNH5Ofn4+eff0atWrUcwyQnJyM5OfIPmiAIgiCI8kepboBVr14d1atXDzs85xzLli3DXXfdBa/XW2T43bt3IyUlBVWqVHGdtgrMhwpxmt1i752HUqycrol2j9ZqJ03igvWa6bzTvcGUM6e4iks1KGnc/Gana27zIti95T0vPj/aGDfUPxRwfsuPDQEAKSyCvAAAFl5emO8PpxwWt8pc0hT1/X1+tHFA2IoCAs53rvdDsaRvy48N9XdfUYhv2eBhrCsbu4fREGQiUaobYG7ZsGEDjhw5glGjRgVcW7VqFXJzc9GpUyekpqbi008/xbRp0zBmzBhSuAiCIIjSD0eUDbCYpYRAOWuAvfzyy+jcuTOuuSZwjN/r9eLZZ5/FxIkTIcsyGjZsiNmzZ+P++++P6FnJgj8mM2ic/Etp5+3+mlKY3xLWfGyPw37Nad02XX2xX2PO9wWLU7vHHNbL5KDrWtrPJQpu8gIw3kc47zzkuwMc33mofHKbF06/I1Re7DhWP+BcpFxX92hM4tlxrL6e5hRBsKRRO+9lkeeFHk+Q7z/YtWDl0BxeC1tUXtifmSiE8oPntp4KlRf2PG1b9xgAYNexugHvq23dY9h1rG5AnHY/ZEoaAvMCMOqp4swLfzH4iyRKB+WqAfbPf/4z6LV+/fpZHLASBEEQRJmChiATinLVAIsnKcwfYGcSDSI4AAmSqYvtDeFROgWG/Zn9HtGkI2vXgoUPdU1Lk9M9TteCpdcpTaF+W0nj5rcB7vPCfI/9vQa7VprzYu/xqyK+15zWFFZ02YgmL+zng10z4pGC5EXgNaf0aue0e8L51koSN79Nw+lbDva7zeG189q342XO11JY8eSF9oxY5IU/njZgsgxEk2Y5Mb+90kriadkEQRAEQRBlHFLAiokUJiPFnfudsPBGYAXpBXfsTQaLL9Qzgl3TnuGk2LhNcyS/saSI5LfFKy/CfUZR6S0tRPrbnL7bUO/WzfMpL9yFL+68cPuMUMQqP+KqgJXAEOS5c+cwffp0rFy5Er/88gvatGmDp59+Gtddd50aJcesWbOwdOlSnDlzBh06dMA//vEPNG/eXI+joKAAkydPxr/+9S9cunQJPXv2xLPPPourrjLU8zNnzmDcuHF4//33AQCDBg3CkiVLIvJiEC9IASMIgiCI8oDWAItmc8ndd9+NdevWYfny5di7dy/69OmDXr164aeffgIAzJ8/H4sXL8YzzzyDHTt2ICMjA71798a5c+f0OMaPH4+VK1fizTffxObNm3H+/HkMHDgQkmQMOw8dOhQ5OTlYvXo1Vq9ejZycHAwbNiz6d1aMlGpP+ImI5nF4z76aSEsrun0rWnwIFX1NDKKqme+1hwn3mpsw0TzH6TcEux7OO4kVkTzXzW9xcy3c+KN9DuVF0deCPTtYuFjmhTlMSeWFOf5QvzfS31LUvbHMi0jTECpMtHlx7pyMVs1+iY8n/Ooj4RGSIo7HLxfik/+9EnZaL126hLS0NLz33nsYMGCAfr5169YYOHAgHnvsMdSpUwfjx4/Hww8/DEBRu2rVqoUnn3wS99xzD/Ly8lCjRg0sX74cQ4YMAQCcPHkSmZmZ+Oijj9C3b1/s378fzZo1w7Zt29ChQwcAwLZt29CpUyd89913aNq0acS/uTihIchiIoUxi1FwOHhDBA91zem6DEPejCbecMMESwPUdLhNfzjPDjddkRDrvACKfg+RvudYpiFU/JQXkYVzCqOVz0jvL4154RQmnPcQy7yINA2hnhFt2n0u/09EhcwRlTMvWbk3Pz/fcjrYijB+vx+SJCElJcVyPjU1FZs3b8aRI0eQm5uLPn36WOLq2rUrtmzZgnvuuQc7d+6Ez+ezhKlTpw5atGiBLVu2oG/fvti6dSvS09P1xhegrGSTnp6OLVu2JGwDjIYgCYIgCKIcwLkc9QYAmZmZSE9P17e5c+c6Pi8tLQ2dOnXCY489hpMnT0KSJLzxxhvYvn07Tp06hdzcXAAIWOqvVq1a+rXc3FwkJSWhatWqIcPUrFkz4Pk1a9bUwyQipIAVE14m6E4fCYIgCMKJ4lQsA+BcV7Eivh/A8ePHLUOQoVaLWb58OUaOHIkrr7wSoiiibdu2GDp0KHbt2qWHYTYVkHMecC4wKdYwTuHDiackoRYCQRAE8f/t3X9MVfX/B/DnBe4FQSTw1+WCMUZIEMQK0y7rh7Ni0EiaTa3Wwmo0K2wsrGWt4VopsWZro7KtptX8jP5IHFsW0je4RKYGUSIxo0mCfUHKISIIF7iv7x+fL6cuv7mce+49l+ejsck95755PX01fN1zzj2XaNaWLFni9DXdABYXFwebzYarV6+io6MDp06dwvDwMGJjY2E2mwFgwlGq7u5u5aiY2WyG3W5HT0/PtPtcvHhxws/+66+/Jhxd8yY8AuYmAfCDUcX51vH/5+39JrnNw3zWG2+69Sd7jjvqUWtNd3FHrXP9u2Uv/stbe+GOetiLmdfWqhdqrqnpP8Iyz2vA5vGevZCQEISEhKCnpweVlZUoKSlRhrCqqirccsstAAC73Q6bzYa33noLAJCWlgaj0Yiqqips2bIFANDZ2YkzZ86gpKQEAGC1WtHb24tTp05h7dq1AICTJ0+it7cX6enprud1Mw5gREREC4HDAcznvmMy9+dWVlZCRJCQkIDff/8dL774IhISEvDEE0/AYDCgoKAAe/bsQXx8POLj47Fnzx4EBwfj0UcfBQCEhYXhqaeeQmFhIZYuXYqIiAjs3LkTKSkpuPfeewEAiYmJyMzMRF5eHj788EMAwNNPP43s7GyvvQAf4ADmNv4GA/xVPPfsr/KrX1fWU7sGrdZWmztqneua7MV/+XovtFhfLeyFi2t68TVKaujt7cWuXbtw4cIFRERE4KGHHsKbb74Jo9EIAHjppZdw7do1PPvss8qNWI8dO4bQ0FBljXfeeQcBAQHYsmWLciPWgwcPwt/fX9nn0KFDeP7555V3S27cuBGlpaXahp0j3gdMZWP3W/nfs9FYMov7gBER0cJ1pc8BS8IFTe4Dds/iRxFgmMd9wMSO/7n6H7fWupDwCJib+Cn/ze3VjSvXe0x13cM/taizlidrcuV6i/E1sBfqreXq36UrP/Pfa3g6tzvXcXUt9kL9dVxdz5VeaPkyXRwOyDxOQYoLpyBpajxEQ0RERKQxHgFzE6PB36X7gPnPvMucnzM6zasW/3E1zuXnT7eu0eBKksm5utJ8K2AvJvJUL1xZg71Q93nzWcPXewG4/vvCqOU1YB58FyRNxAGMiIhoIXAIYOAA5i04gHkxv3+dIXbA9XPv419BqsVd66qVW03sBXvh6XW9sRfAP3UtpF4A6uSmhY0DGBER0UIgAsxnYOQRMFVxAHMTPxicXrHOf72F+X4Jb8ztjTVpwRtze2NNWvDG3N5Ykxbmm9tvPtdkzZE4BDKPU5C8a5W6OIAREREtBOLA/I6A8XSrmhbmSxYiIiIiD+IRMCIiogWApyC9CwcwIiKihYCnIL0KBzCVjb1CuHKV/6MSEdH0xv6t0OLo0giG53Uf1hEMq1cMcQBTW19fHwAg5tY/PFsIERHpRl9fH8LCwtyytslkgtlsRl3X0XmvZTabYTK5/oHe9A+D8KSuqhwOB86ePYukpCR0dHT43CfGX7lyBatWrWI2nWE2fWI2fZpLNhFBX18fLBYL/Pzc9764wcFB2O32ea9jMpkQFBSkQkXEI2Aq8/PzQ1RUFABgyZIlPveLZQyz6ROz6ROz6dNss7nryNe/BQUFcXDyMrwNBREREZHGOIARERERaYwDmBsEBgaiqKgIgYGBni5FdcymT8ymT8ymT76cjdTDi/CJiIiINMYjYEREREQa4wBGREREpDEOYEREREQa4wBGREREpDEOYCp7//33ERsbi6CgIKSlpeG7777zdElztnv3bhgMBqcvs9msbBcR7N69GxaLBYsWLcL69evR3NzswYqnVltbiwceeAAWiwUGgwFHjhxx2j6bLENDQ9ixYweWLVuGkJAQbNy4ERcuXNAwxeRmyrZt27YJfbz99tud9vHWbHv37sVtt92G0NBQrFixAg8++CDOnj3rtI9eezebbHrt3QcffICbb75ZuQGp1WrFV199pWzXa8+AmbPptWfkORzAVPT555+joKAAr776KhobG3HnnXciKysL7e3tni5tzm666SZ0dnYqX01NTcq2kpIS7Nu3D6Wlpfjxxx9hNptx3333KZ+D6U36+/uRmpqK0tLSSbfPJktBQQHKy8tRVlaGuro6XL16FdnZ2RgdHdUqxqRmygYAmZmZTn08etT5s+C8NZvNZsNzzz2HEydOoKqqCiMjI8jIyEB/f7+yj157N5tsgD57Fx0djeLiYtTX16O+vh4bNmxATk6OMmTptWfAzNkAffaMPEhINWvXrpXt27c7PXbjjTfKyy+/7KGKXFNUVCSpqamTbnM4HGI2m6W4uFh5bHBwUMLCwmT//v0aVegaAFJeXq58P5ssly9fFqPRKGVlZco+f/75p/j5+cnXX3+tWe0zGZ9NRCQ3N1dycnKmfI5esomIdHd3CwCx2Wwi4lu9G59NxLd6Fx4eLh999JFP9WzMWDYR3+oZaYNHwFRit9vR0NCAjIwMp8czMjJw/PhxD1XlutbWVlgsFsTGxuLhhx/GuXPnAABtbW3o6upyyhkYGIi7775bdzlnk6WhoQHDw8NO+1gsFiQnJ+sib01NDVasWIHVq1cjLy8P3d3dyjY9Zevt7QUAREREAPCt3o3PNkbvvRsdHUVZWRn6+/thtVp9qmfjs43Re89IW/wwbpX8/fffGB0dxcqVK50eX7lyJbq6ujxUlWvWrVuHTz/9FKtXr8bFixfxxhtvID09Hc3NzUqWyXKeP3/eE+W6bDZZurq6YDKZEB4ePmEfb+9rVlYWNm/ejJiYGLS1teG1117Dhg0b0NDQgMDAQN1kExG88MILuOOOO5CcnAzAd3o3WTZA371ramqC1WrF4OAgFi9ejPLyciQlJSlDhp57NlU2QN89I8/gAKYyg8Hg9L2ITHjM22VlZSl/TklJgdVqRVxcHD755BPlolJfyDnGlSx6yLt161blz8nJyVizZg1iYmLw5ZdfYtOmTVM+z9uy5efn4/Tp06irq5uwTe+9myqbnnuXkJCAn3/+GZcvX8YXX3yB3Nxc2Gw2ZbueezZVtqSkJF33jDyDpyBVsmzZMvj7+094JdPd3T3hFZ/ehISEICUlBa2trcq7IX0h52yymM1m2O129PT0TLmPXkRGRiImJgatra0A9JFtx44dqKioQHV1NaKjo5XHfaF3U2WbjJ56ZzKZcMMNN2DNmjXYu3cvUlNT8e677/pEz6bKNhk99Yw8gwOYSkwmE9LS0lBVVeX0eFVVFdLT0z1UlTqGhobQ0tKCyMhIxMbGwmw2O+W02+2w2Wy6yzmbLGlpaTAajU77dHZ24syZM7rLe+nSJXR0dCAyMhKAd2cTEeTn5+Pw4cP49ttvERsb67Rdz72bKdtk9NS78UQEQ0NDuu7ZVMayTUbPPSONaH7Zvw8rKysTo9EoH3/8sfz6669SUFAgISEh8scff3i6tDkpLCyUmpoaOXfunJw4cUKys7MlNDRUyVFcXCxhYWFy+PBhaWpqkkceeUQiIyPlypUrHq58or6+PmlsbJTGxkYBIPv27ZPGxkY5f/68iMwuy/bt2yU6Olq++eYb+emnn2TDhg2SmpoqIyMjnoolItNn6+vrk8LCQjl+/Li0tbVJdXW1WK1WiYqK0kW2Z555RsLCwqSmpkY6OzuVr4GBAWUfvfZupmx67t2uXbuktrZW2tra5PTp0/LKK6+In5+fHDt2TET02zOR6bPpuWfkORzAVPbee+9JTEyMmEwmufXWW53eWq4XW7dulcjISDEajWKxWGTTpk3S3NysbHc4HFJUVCRms1kCAwPlrrvukqamJg9WPLXq6moBMOErNzdXRGaX5dq1a5Kfny8RERGyaNEiyc7Olvb2dg+kcTZdtoGBAcnIyJDly5eL0WiU66+/XnJzcyfU7a3ZJssFQA4cOKDso9fezZRNz7178sknld9/y5cvl3vuuUcZvkT02zOR6bPpuWfkOQYREe2OtxERERERrwEjIiIi0hgHMCIiIiKNcQAjIiIi0hgHMCIiIiKNcQAjIiIi0hgHMCIiIiKNcQAjIiIi0hgHMCIiIiKNcQAjonlbv349CgoKPF0GEZFucAAjIiIi0hgHMCIiIiKNcQAjIlX19PTg8ccfR3h4OIKDg5GVlYXW1lZl+8GDB3HdddehsrISiYmJWLx4MTIzM9HZ2enBqomItMUBjIhUtW3bNtTX16OiogI//PADRAT3338/hoeHlX0GBgbw9ttv47PPPkNtbS3a29uxc+dOD1ZNRKStAE8XQES+o7W1FRUVFfj++++Rnp4OADh06BBWrVqFI0eOYPPmzQCA4eFh7N+/H3FxcQCA/Px8vP766x6rm4hIazwCRkSqaWlpQUBAANatW6c8tnTpUiQkJKClpUV5LDg4WBm+ACAyMhLd3d2a1kpE5EkcwIhINSIy5eMGg0H53mg0Om03GAxTPpeIyBdxACMi1SQlJWFkZAQnT55UHrt06RJ+++03JCYmerAyIiLvwgGMiFQTHx+PnJwc5OXloa6uDr/88gsee+wxREVFIScnx9PlERF5DQ5gRKSqAwcOIC0tDdnZ2bBarRARHD16dMJpRyKihcwgvPCCiIiISFM8AkZERESkMQ5gRERERBrjAEZERESkMQ5gRERERBrjAEZERESkMQ5gRERERBrjAEZERESkMQ5gRERERBrjAEZERESkMQ5gRERERBrjAEZERESksf8DnGjYNB6L07EAAAAASUVORK5CYII=", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "ds_out_con.area.plot() ;" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "b7a88577-4501-4c0b-8549-b9e1bd1aece9", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fv_t232.area.plot() ;" + ] + }, + { + "cell_type": "markdown", + "id": "c70c59f8-562f-4bf8-b808-78f31a74f15b", + "metadata": {}, + "source": [ + "----\n", + "### Make plots\n", + "---\n", + "\n", + "First we'll look at ocean masks and regridded data\n", + "Using the correct destination land mask gives nicer coastlines " + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "0898c0c8-56bb-4880-a515-bfd091a82007", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 1.0, 'bilinear remapping, dest mask')" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Define the figure and each axis for the 3 rows and 3 columns\n", + "fig, axs = plt.subplots(nrows=2,ncols=2,\n", + " subplot_kw=dict(projection=ccrs.PlateCarree()),\n", + " figsize=(16,7))\n", + "\n", + "# axs is a 2 dimensional array of `GeoAxes`. We will flatten it into a 1-D array\n", + "axs=axs.flatten()\n", + "\n", + "(fv_t232.area*ds_out_con.landfrac).plot(ax=axs[0],vmin=0, vmax=0, cmap='viridis_r') \n", + "axs[0].set_title('area * remapped landfrac')\n", + "\n", + "\n", + "ds_out_con.GPP.mean('time').plot(ax=axs[1],vmin=0, vmax=1e-4)\n", + "axs[1].set_title('conservative remapping, no mask')\n", + "\n", + "ds_out_con.GPP.mean('time').where(fv_t232.landfrac>0).plot(ax=axs[2],vmin=0, vmax=1e-4)\n", + "axs[2].set_title('conservative remapping, dest mask')\n", + "\n", + "# Mask out coasts based on land mask\n", + "ds_out_bilin.GPP.mean('time').plot(ax=axs[3],vmin=0, vmax=1e-4)\n", + "axs[3].set_title('bilinear remapping, dest mask')\n", + "\n", + "#for a in axs:\n", + "# a.coastlines() ;" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "b251911d-2f91-4207-b3ac-aab7c519cd74", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Look at raw data too using uxarray\n", + "transform = ccrs.PlateCarree()\n", + "projection = ccrs.PlateCarree()\n", + "\n", + "#projection = ccrs.Orthographic(central_latitude=90)\n", + "# TODO, calculate time mean with correct weights\n", + "dc = ds0[\"GPP\"].mean('time').to_polycollection(projection=projection, override=True)\n", + "dc.set_antialiased(False)\n", + "dc.set_transform(transform)\n", + "dc.set_antialiased(False)\n", + "dc.set_clim(vmin=0, vmax=1e-4)\n", + "\n", + "fig, ax = plt.subplots(\n", + " 1,\n", + " 1,\n", + " figsize=(5, 5),\n", + " facecolor=\"w\",\n", + " constrained_layout=True,\n", + " subplot_kw=dict(projection=projection),\n", + ")\n", + "\n", + "# add geographic features\n", + "ax.add_feature(cfeature.COASTLINE)\n", + "\n", + "ax.add_collection(dc)\n", + "ax.set_global()\n", + "cbar = plt.colorbar(dc, ax=ax, orientation='horizontal', pad=0.05, shrink=0.8)\n", + "cbar.set_label('Data Values')\n", + "\n", + "plt.title(\"ne30 w/ uxarray\") ;" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "eaa75249-1414-44d5-b061-c98ad3f566d4", + "metadata": {}, + "outputs": [ + { + "ename": "ValueError", + "evalue": "Data Variable must be 1-dimensional, with shape 48600 for face-centered data.", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[18], line 11\u001b[0m\n\u001b[1;32m 8\u001b[0m dc2\u001b[38;5;241m.\u001b[39mset_transform(transform)\n\u001b[1;32m 9\u001b[0m dc2\u001b[38;5;241m.\u001b[39mset_clim(vmin\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0\u001b[39m, vmax\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1e-4\u001b[39m)\n\u001b[0;32m---> 11\u001b[0m dc1 \u001b[38;5;241m=\u001b[39m \u001b[43mds0\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43marea\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mto_polycollection\u001b[49m\u001b[43m(\u001b[49m\u001b[43mprojection\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mprojection\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43moverride\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[1;32m 12\u001b[0m dc1\u001b[38;5;241m.\u001b[39mset_antialiased(\u001b[38;5;28;01mFalse\u001b[39;00m)\n\u001b[1;32m 13\u001b[0m dc0\u001b[38;5;241m.\u001b[39mset_transform(transform)\n", + "File \u001b[0;32m/glade/u/apps/opt/conda/envs/npl-2024b/lib/python3.11/site-packages/uxarray/core/dataarray.py:213\u001b[0m, in \u001b[0;36mUxDataArray.to_polycollection\u001b[0;34m(self, periodic_elements, projection, return_indices, cache, override)\u001b[0m\n\u001b[1;32m 211\u001b[0m \u001b[38;5;66;03m# data is multidimensional, must be a 1D slice\u001b[39;00m\n\u001b[1;32m 212\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mvalues\u001b[38;5;241m.\u001b[39mndim \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[0;32m--> 213\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m 214\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mData Variable must be 1-dimensional, with shape \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39muxgrid\u001b[38;5;241m.\u001b[39mn_face\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 215\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mfor face-centered data.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 216\u001b[0m )\n\u001b[1;32m 218\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_face_centered():\n\u001b[1;32m 219\u001b[0m poly_collection, corrected_to_original_faces \u001b[38;5;241m=\u001b[39m (\n\u001b[1;32m 220\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39muxgrid\u001b[38;5;241m.\u001b[39mto_polycollection(\n\u001b[1;32m 221\u001b[0m override\u001b[38;5;241m=\u001b[39moverride,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 226\u001b[0m )\n\u001b[1;32m 227\u001b[0m )\n", + "\u001b[0;31mValueError\u001b[0m: Data Variable must be 1-dimensional, with shape 48600 for face-centered data." + ] + } + ], + "source": [ + "## Sample subplots with uxarray!\n", + "dc0 = ds0[\"GPP\"].mean('time').to_polycollection(projection=projection, override=True)\n", + "dc0.set_antialiased(False)\n", + "dc0.set_transform(transform)\n", + "dc0.set_clim(vmin=0, vmax=1e-4)\n", + "dc2 = ds0[\"GPP\"].mean('time').to_polycollection(projection=projection, override=True)\n", + "dc2.set_antialiased(False)\n", + "dc2.set_transform(transform)\n", + "dc2.set_clim(vmin=0, vmax=1e-4)\n", + "\n", + "dc1 = ds0[\"area\"].to_polycollection(projection=projection, override=True)\n", + "dc1.set_antialiased(False)\n", + "dc0.set_transform(transform)\n", + "\n", + "fig, axs = plt.subplots(\n", + " 2,\n", + " 2,\n", + " figsize=(16, 8),\n", + " facecolor=\"w\",\n", + " constrained_layout=True,\n", + " subplot_kw=dict(projection=projection),\n", + ")\n", + "axs=axs.flatten()\n", + "\n", + "axs[0].add_collection(dc0)\n", + "axs[0].set_title(ds0.GPP.attrs['long_name']) ;\n", + "\n", + "axs[1].add_collection(dc1)\n", + "axs[1].set_title(ds0.area.attrs['long_name']) ;\n", + "\n", + "axs[2].add_collection(dc2)\n", + "axs[2].set_title(ds0.GPP.attrs['long_name']) ;\n", + "\n", + "cbar1 = plt.colorbar(dc1, ax=axs[1], orientation='vertical', pad=0.05, shrink=0.8)\n", + "cbar1.set_label(ds0.area.attrs['units'])\n", + "cbar2 = plt.colorbar(dc2, ax=axs[2], orientation='horizontal', pad=0.05, shrink=0.8)\n", + "cbar2.set_label(ds0.GPP.attrs['units'])\n", + "\n", + "for a in axs:\n", + " a.set_global()\n", + " a.add_feature(cfeature.COASTLINE)" + ] + }, + { + "cell_type": "raw", + "id": "55cf7674-4113-4da9-b288-beb5f5569dc1", + "metadata": {}, + "source": [ + "# Can't seem to use uxarray for lat-lon data?\n", + "dc = ux_fv[\"GPP\"].mean('time').to_polycollection(projection=projection, override=True)\n", + "dc.set_antialiased(False)\n", + "dc.set_transform(transform)\n", + "dc.set_antialiased(False)\n", + "dc.set_clim(vmin=0, vmax=1e-4)\n", + "\n", + "fig, ax = plt.subplots(\n", + " 1,\n", + " 1,\n", + " figsize=(5, 5),\n", + " facecolor=\"w\",\n", + " constrained_layout=True,\n", + " subplot_kw=dict(projection=projection),\n", + ")\n", + "\n", + "# add geographic features\n", + "ax.add_feature(cfeature.COASTLINE)\n", + "\n", + "ax.add_collection(dc)\n", + "ax.set_global()\n", + "cbar = plt.colorbar(dc, ax=ax, orientation='horizontal', pad=0.05, shrink=0.8)\n", + "cbar.set_label('Data Values')\n", + "\n", + "plt.title(\"ne30 w/ uxarray\") ;" + ] + }, + { + "cell_type": "markdown", + "id": "232aefe3-d7dc-4643-8155-7010009896db", + "metadata": {}, + "source": [ + "---------\n", + "### Subsetting data for Regional plots\n", + "Example at https://uxarray.readthedocs.io/en/latest/user-guide/subset.html\n", + "1. Look at test data, so see how coastlines are handled\n", + "2. Look at regional fluxes and compare raw and regridded data\n", + "3. Since climatologies weren't identical, tried weighting fluxes by source landfrac too, but these results don't look great." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "a6287eac-a56f-4695-8f40-350b8ea779c3", + "metadata": {}, + "outputs": [ + { + "data": { + "application/javascript": [ + "(function(root) {\n", + " function now() {\n", + " return new Date();\n", + " }\n", + "\n", + " var force = true;\n", + " var py_version = '3.4.2'.replace('rc', '-rc.').replace('.dev', '-dev.');\n", + " var reloading = false;\n", + " var Bokeh = root.Bokeh;\n", + "\n", + " if (typeof (root._bokeh_timeout) === \"undefined\" || force) {\n", + " root._bokeh_timeout = Date.now() + 5000;\n", + " root._bokeh_failed_load = false;\n", + " }\n", + "\n", + " function run_callbacks() {\n", + " try {\n", + " root._bokeh_onload_callbacks.forEach(function(callback) {\n", + " if (callback != null)\n", + " callback();\n", + " });\n", + " } finally {\n", + " delete root._bokeh_onload_callbacks;\n", + " }\n", + " console.debug(\"Bokeh: all callbacks have finished\");\n", + " }\n", + "\n", + " function load_libs(css_urls, js_urls, js_modules, js_exports, callback) {\n", + " if (css_urls == null) css_urls = [];\n", + " if (js_urls == null) js_urls = [];\n", + " if (js_modules == null) js_modules = [];\n", + " if (js_exports == null) js_exports = {};\n", + "\n", + " root._bokeh_onload_callbacks.push(callback);\n", + "\n", + " if (root._bokeh_is_loading > 0) {\n", + " console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n", + " return null;\n", + " }\n", + " if (js_urls.length === 0 && js_modules.length === 0 && Object.keys(js_exports).length === 0) {\n", + " run_callbacks();\n", + " return null;\n", + " }\n", + " if (!reloading) {\n", + " console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n", + " }\n", + "\n", + " function on_load() {\n", + " root._bokeh_is_loading--;\n", + " if (root._bokeh_is_loading === 0) {\n", + " console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n", + " run_callbacks()\n", + " }\n", + " }\n", + " window._bokeh_on_load = on_load\n", + "\n", + " function on_error() {\n", + " console.error(\"failed to load \" + url);\n", + " }\n", + "\n", + " var skip = [];\n", + " if (window.requirejs) {\n", + " window.requirejs.config({'packages': {}, 'paths': {}, 'shim': {}});\n", + " root._bokeh_is_loading = css_urls.length + 0;\n", + " } else {\n", + " root._bokeh_is_loading = css_urls.length + js_urls.length + js_modules.length + Object.keys(js_exports).length;\n", + " }\n", + "\n", + " var existing_stylesheets = []\n", + " var links = document.getElementsByTagName('link')\n", + " for (var i = 0; i < links.length; i++) {\n", + " var link = links[i]\n", + " if (link.href != null) {\n", + "\texisting_stylesheets.push(link.href)\n", + " }\n", + " }\n", + " for (var i = 0; i < css_urls.length; i++) {\n", + " var url = css_urls[i];\n", + " if (existing_stylesheets.indexOf(url) !== -1) {\n", + "\ton_load()\n", + "\tcontinue;\n", + " }\n", + " const element = document.createElement(\"link\");\n", + " element.onload = on_load;\n", + " element.onerror = on_error;\n", + " element.rel = \"stylesheet\";\n", + " element.type = \"text/css\";\n", + " element.href = url;\n", + " console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n", + " document.body.appendChild(element);\n", + " } var existing_scripts = []\n", + " var scripts = document.getElementsByTagName('script')\n", + " for (var i = 0; i < scripts.length; i++) {\n", + " var script = scripts[i]\n", + " if (script.src != null) {\n", + "\texisting_scripts.push(script.src)\n", + " }\n", + " }\n", + " for (var i = 0; i < js_urls.length; i++) {\n", + " var url = js_urls[i];\n", + " if (skip.indexOf(url) !== -1 || existing_scripts.indexOf(url) !== -1) {\n", + "\tif (!window.requirejs) {\n", + "\t on_load();\n", + "\t}\n", + "\tcontinue;\n", + " }\n", + " var element = document.createElement('script');\n", + " element.onload = on_load;\n", + " element.onerror = on_error;\n", + " element.async = false;\n", + " element.src = url;\n", + " console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n", + " document.head.appendChild(element);\n", + " }\n", + " for (var i = 0; i < js_modules.length; i++) {\n", + " var url = js_modules[i];\n", + " if (skip.indexOf(url) !== -1 || existing_scripts.indexOf(url) !== -1) {\n", + "\tif (!window.requirejs) {\n", + "\t on_load();\n", + "\t}\n", + "\tcontinue;\n", + " }\n", + " var element = document.createElement('script');\n", + " element.onload = on_load;\n", + " element.onerror = on_error;\n", + " element.async = false;\n", + " element.src = url;\n", + " element.type = \"module\";\n", + " console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n", + " document.head.appendChild(element);\n", + " }\n", + " for (const name in js_exports) {\n", + " var url = js_exports[name];\n", + " if (skip.indexOf(url) >= 0 || root[name] != null) {\n", + "\tif (!window.requirejs) {\n", + "\t on_load();\n", + "\t}\n", + "\tcontinue;\n", + " }\n", + " var element = document.createElement('script');\n", + " element.onerror = on_error;\n", + " element.async = false;\n", + " element.type = \"module\";\n", + " console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n", + " element.textContent = `\n", + " import ${name} from \"${url}\"\n", + " window.${name} = ${name}\n", + " window._bokeh_on_load()\n", + " `\n", + " document.head.appendChild(element);\n", + " }\n", + " if (!js_urls.length && !js_modules.length) {\n", + " on_load()\n", + " }\n", + " };\n", + "\n", + " function inject_raw_css(css) {\n", + " const element = document.createElement(\"style\");\n", + " element.appendChild(document.createTextNode(css));\n", + " document.body.appendChild(element);\n", + " }\n", + "\n", + " var js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.4.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.4.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.4.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.4.2.min.js\", \"https://cdn.holoviz.org/panel/1.4.4/dist/panel.min.js\", \"https://cdn.jsdelivr.net/npm/@holoviz/geoviews@1.12.0/dist/geoviews.min.js\"];\n", + " var js_modules = [];\n", + " var js_exports = {};\n", + " var css_urls = [];\n", + " var inline_js = [ function(Bokeh) {\n", + " Bokeh.set_log_level(\"info\");\n", + " },\n", + "function(Bokeh) {} // ensure no trailing comma for IE\n", + " ];\n", + "\n", + " function run_inline_js() {\n", + " if ((root.Bokeh !== undefined) || (force === true)) {\n", + " for (var i = 0; i < inline_js.length; i++) {\n", + "\ttry {\n", + " inline_js[i].call(root, root.Bokeh);\n", + "\t} catch(e) {\n", + "\t if (!reloading) {\n", + "\t throw e;\n", + "\t }\n", + "\t}\n", + " }\n", + " // Cache old bokeh versions\n", + " if (Bokeh != undefined && !reloading) {\n", + "\tvar NewBokeh = root.Bokeh;\n", + "\tif (Bokeh.versions === undefined) {\n", + "\t Bokeh.versions = new Map();\n", + "\t}\n", + "\tif (NewBokeh.version !== Bokeh.version) {\n", + "\t Bokeh.versions.set(NewBokeh.version, NewBokeh)\n", + "\t}\n", + "\troot.Bokeh = Bokeh;\n", + " }} else if (Date.now() < root._bokeh_timeout) {\n", + " setTimeout(run_inline_js, 100);\n", + " } else if (!root._bokeh_failed_load) {\n", + " console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n", + " root._bokeh_failed_load = true;\n", + " }\n", + " root._bokeh_is_initializing = false\n", + " }\n", + "\n", + " function load_or_wait() {\n", + " // Implement a backoff loop that tries to ensure we do not load multiple\n", + " // versions of Bokeh and its dependencies at the same time.\n", + " // In recent versions we use the root._bokeh_is_initializing flag\n", + " // to determine whether there is an ongoing attempt to initialize\n", + " // bokeh, however for backward compatibility we also try to ensure\n", + " // that we do not start loading a newer (Panel>=1.0 and Bokeh>3) version\n", + " // before older versions are fully initialized.\n", + " if (root._bokeh_is_initializing && Date.now() > root._bokeh_timeout) {\n", + " root._bokeh_is_initializing = false;\n", + " root._bokeh_onload_callbacks = undefined;\n", + " console.log(\"Bokeh: BokehJS was loaded multiple times but one version failed to initialize.\");\n", + " load_or_wait();\n", + " } else if (root._bokeh_is_initializing || (typeof root._bokeh_is_initializing === \"undefined\" && root._bokeh_onload_callbacks !== undefined)) {\n", + " setTimeout(load_or_wait, 100);\n", + " } else {\n", + " root._bokeh_is_initializing = true\n", + " root._bokeh_onload_callbacks = []\n", + " var bokeh_loaded = Bokeh != null && (Bokeh.version === py_version || (Bokeh.versions !== undefined && Bokeh.versions.has(py_version)));\n", + " if (!reloading && !bokeh_loaded) {\n", + "\troot.Bokeh = undefined;\n", + " }\n", + " load_libs(css_urls, js_urls, js_modules, js_exports, function() {\n", + "\tconsole.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n", + "\trun_inline_js();\n", + " });\n", + " }\n", + " }\n", + " // Give older versions of the autoload script a head-start to ensure\n", + " // they initialize before we start loading newer version.\n", + " setTimeout(load_or_wait, 100)\n", + "}(window));" + ], + "application/vnd.holoviews_load.v0+json": "(function(root) {\n function now() {\n return new Date();\n }\n\n var force = true;\n var py_version = '3.4.2'.replace('rc', '-rc.').replace('.dev', '-dev.');\n var reloading = false;\n var Bokeh = root.Bokeh;\n\n if (typeof (root._bokeh_timeout) === \"undefined\" || force) {\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_failed_load = false;\n }\n\n function run_callbacks() {\n try {\n root._bokeh_onload_callbacks.forEach(function(callback) {\n if (callback != null)\n callback();\n });\n } finally {\n delete root._bokeh_onload_callbacks;\n }\n console.debug(\"Bokeh: all callbacks have finished\");\n }\n\n function load_libs(css_urls, js_urls, js_modules, js_exports, callback) {\n if (css_urls == null) css_urls = [];\n if (js_urls == null) js_urls = [];\n if (js_modules == null) js_modules = [];\n if (js_exports == null) js_exports = {};\n\n root._bokeh_onload_callbacks.push(callback);\n\n if (root._bokeh_is_loading > 0) {\n console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n return null;\n }\n if (js_urls.length === 0 && js_modules.length === 0 && Object.keys(js_exports).length === 0) {\n run_callbacks();\n return null;\n }\n if (!reloading) {\n console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n }\n\n function on_load() {\n root._bokeh_is_loading--;\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n run_callbacks()\n }\n }\n window._bokeh_on_load = on_load\n\n function on_error() {\n console.error(\"failed to load \" + url);\n }\n\n var skip = [];\n if (window.requirejs) {\n window.requirejs.config({'packages': {}, 'paths': {}, 'shim': {}});\n root._bokeh_is_loading = css_urls.length + 0;\n } else {\n root._bokeh_is_loading = css_urls.length + js_urls.length + js_modules.length + Object.keys(js_exports).length;\n }\n\n var existing_stylesheets = []\n var links = document.getElementsByTagName('link')\n for (var i = 0; i < links.length; i++) {\n var link = links[i]\n if (link.href != null) {\n\texisting_stylesheets.push(link.href)\n }\n }\n for (var i = 0; i < css_urls.length; i++) {\n var url = css_urls[i];\n if (existing_stylesheets.indexOf(url) !== -1) {\n\ton_load()\n\tcontinue;\n }\n const element = document.createElement(\"link\");\n element.onload = on_load;\n element.onerror = on_error;\n element.rel = \"stylesheet\";\n element.type = \"text/css\";\n element.href = url;\n console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n document.body.appendChild(element);\n } var existing_scripts = []\n var scripts = document.getElementsByTagName('script')\n for (var i = 0; i < scripts.length; i++) {\n var script = scripts[i]\n if (script.src != null) {\n\texisting_scripts.push(script.src)\n }\n }\n for (var i = 0; i < js_urls.length; i++) {\n var url = js_urls[i];\n if (skip.indexOf(url) !== -1 || existing_scripts.indexOf(url) !== -1) {\n\tif (!window.requirejs) {\n\t on_load();\n\t}\n\tcontinue;\n }\n var element = document.createElement('script');\n element.onload = on_load;\n element.onerror = on_error;\n element.async = false;\n element.src = url;\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.head.appendChild(element);\n }\n for (var i = 0; i < js_modules.length; i++) {\n var url = js_modules[i];\n if (skip.indexOf(url) !== -1 || existing_scripts.indexOf(url) !== -1) {\n\tif (!window.requirejs) {\n\t on_load();\n\t}\n\tcontinue;\n }\n var element = document.createElement('script');\n element.onload = on_load;\n element.onerror = on_error;\n element.async = false;\n element.src = url;\n element.type = \"module\";\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.head.appendChild(element);\n }\n for (const name in js_exports) {\n var url = js_exports[name];\n if (skip.indexOf(url) >= 0 || root[name] != null) {\n\tif (!window.requirejs) {\n\t on_load();\n\t}\n\tcontinue;\n }\n var element = document.createElement('script');\n element.onerror = on_error;\n element.async = false;\n element.type = \"module\";\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n element.textContent = `\n import ${name} from \"${url}\"\n window.${name} = ${name}\n window._bokeh_on_load()\n `\n document.head.appendChild(element);\n }\n if (!js_urls.length && !js_modules.length) {\n on_load()\n }\n };\n\n function inject_raw_css(css) {\n const element = document.createElement(\"style\");\n element.appendChild(document.createTextNode(css));\n document.body.appendChild(element);\n }\n\n var js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.4.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.4.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.4.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.4.2.min.js\", \"https://cdn.holoviz.org/panel/1.4.4/dist/panel.min.js\", \"https://cdn.jsdelivr.net/npm/@holoviz/geoviews@1.12.0/dist/geoviews.min.js\"];\n var js_modules = [];\n var js_exports = {};\n var css_urls = [];\n var inline_js = [ function(Bokeh) {\n Bokeh.set_log_level(\"info\");\n },\nfunction(Bokeh) {} // ensure no trailing comma for IE\n ];\n\n function run_inline_js() {\n if ((root.Bokeh !== undefined) || (force === true)) {\n for (var i = 0; i < inline_js.length; i++) {\n\ttry {\n inline_js[i].call(root, root.Bokeh);\n\t} catch(e) {\n\t if (!reloading) {\n\t throw e;\n\t }\n\t}\n }\n // Cache old bokeh versions\n if (Bokeh != undefined && !reloading) {\n\tvar NewBokeh = root.Bokeh;\n\tif (Bokeh.versions === undefined) {\n\t Bokeh.versions = new Map();\n\t}\n\tif (NewBokeh.version !== Bokeh.version) {\n\t Bokeh.versions.set(NewBokeh.version, NewBokeh)\n\t}\n\troot.Bokeh = Bokeh;\n }} else if (Date.now() < root._bokeh_timeout) {\n setTimeout(run_inline_js, 100);\n } else if (!root._bokeh_failed_load) {\n console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n root._bokeh_failed_load = true;\n }\n root._bokeh_is_initializing = false\n }\n\n function load_or_wait() {\n // Implement a backoff loop that tries to ensure we do not load multiple\n // versions of Bokeh and its dependencies at the same time.\n // In recent versions we use the root._bokeh_is_initializing flag\n // to determine whether there is an ongoing attempt to initialize\n // bokeh, however for backward compatibility we also try to ensure\n // that we do not start loading a newer (Panel>=1.0 and Bokeh>3) version\n // before older versions are fully initialized.\n if (root._bokeh_is_initializing && Date.now() > root._bokeh_timeout) {\n root._bokeh_is_initializing = false;\n root._bokeh_onload_callbacks = undefined;\n console.log(\"Bokeh: BokehJS was loaded multiple times but one version failed to initialize.\");\n load_or_wait();\n } else if (root._bokeh_is_initializing || (typeof root._bokeh_is_initializing === \"undefined\" && root._bokeh_onload_callbacks !== undefined)) {\n setTimeout(load_or_wait, 100);\n } else {\n root._bokeh_is_initializing = true\n root._bokeh_onload_callbacks = []\n var bokeh_loaded = Bokeh != null && (Bokeh.version === py_version || (Bokeh.versions !== undefined && Bokeh.versions.has(py_version)));\n if (!reloading && !bokeh_loaded) {\n\troot.Bokeh = undefined;\n }\n load_libs(css_urls, js_urls, js_modules, js_exports, function() {\n\tconsole.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n\trun_inline_js();\n });\n }\n }\n // Give older versions of the autoload script a head-start to ensure\n // they initialize before we start loading newer version.\n setTimeout(load_or_wait, 100)\n}(window));" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/javascript": [ + "\n", + "if ((window.PyViz === undefined) || (window.PyViz instanceof HTMLElement)) {\n", + " window.PyViz = {comms: {}, comm_status:{}, kernels:{}, receivers: {}, plot_index: []}\n", + "}\n", + "\n", + "\n", + " function JupyterCommManager() {\n", + " }\n", + "\n", + " JupyterCommManager.prototype.register_target = function(plot_id, comm_id, msg_handler) {\n", + " if (window.comm_manager || ((window.Jupyter !== undefined) && (Jupyter.notebook.kernel != null))) {\n", + " var comm_manager = window.comm_manager || Jupyter.notebook.kernel.comm_manager;\n", + " comm_manager.register_target(comm_id, function(comm) {\n", + " comm.on_msg(msg_handler);\n", + " });\n", + " } else if ((plot_id in window.PyViz.kernels) && (window.PyViz.kernels[plot_id])) {\n", + " window.PyViz.kernels[plot_id].registerCommTarget(comm_id, function(comm) {\n", + " comm.onMsg = msg_handler;\n", + " });\n", + " } else if (typeof google != 'undefined' && google.colab.kernel != null) {\n", + " google.colab.kernel.comms.registerTarget(comm_id, (comm) => {\n", + " var messages = comm.messages[Symbol.asyncIterator]();\n", + " function processIteratorResult(result) {\n", + " var message = result.value;\n", + " console.log(message)\n", + " var content = {data: message.data, comm_id};\n", + " var buffers = []\n", + " for (var buffer of message.buffers || []) {\n", + " buffers.push(new DataView(buffer))\n", + " }\n", + " var metadata = message.metadata || {};\n", + " var msg = {content, buffers, metadata}\n", + " msg_handler(msg);\n", + " return messages.next().then(processIteratorResult);\n", + " }\n", + " return messages.next().then(processIteratorResult);\n", + " })\n", + " }\n", + " }\n", + "\n", + " JupyterCommManager.prototype.get_client_comm = function(plot_id, comm_id, msg_handler) {\n", + " if (comm_id in window.PyViz.comms) {\n", + " return window.PyViz.comms[comm_id];\n", + " } else if (window.comm_manager || ((window.Jupyter !== undefined) && (Jupyter.notebook.kernel != null))) {\n", + " var comm_manager = window.comm_manager || Jupyter.notebook.kernel.comm_manager;\n", + " var comm = comm_manager.new_comm(comm_id, {}, {}, {}, comm_id);\n", + " if (msg_handler) {\n", + " comm.on_msg(msg_handler);\n", + " }\n", + " } else if ((plot_id in window.PyViz.kernels) && (window.PyViz.kernels[plot_id])) {\n", + " var comm = window.PyViz.kernels[plot_id].connectToComm(comm_id);\n", + " comm.open();\n", + " if (msg_handler) {\n", + " comm.onMsg = msg_handler;\n", + " }\n", + " } else if (typeof google != 'undefined' && google.colab.kernel != null) {\n", + " var comm_promise = google.colab.kernel.comms.open(comm_id)\n", + " comm_promise.then((comm) => {\n", + " window.PyViz.comms[comm_id] = comm;\n", + " if (msg_handler) {\n", + " var messages = comm.messages[Symbol.asyncIterator]();\n", + " function processIteratorResult(result) {\n", + " var message = result.value;\n", + " var content = {data: message.data};\n", + " var metadata = message.metadata || {comm_id};\n", + " var msg = {content, metadata}\n", + " msg_handler(msg);\n", + " return messages.next().then(processIteratorResult);\n", + " }\n", + " return messages.next().then(processIteratorResult);\n", + " }\n", + " }) \n", + " var sendClosure = (data, metadata, buffers, disposeOnDone) => {\n", + " return comm_promise.then((comm) => {\n", + " comm.send(data, metadata, buffers, disposeOnDone);\n", + " });\n", + " };\n", + " var comm = {\n", + " send: sendClosure\n", + " };\n", + " }\n", + " window.PyViz.comms[comm_id] = comm;\n", + " return comm;\n", + " }\n", + " window.PyViz.comm_manager = new JupyterCommManager();\n", + " \n", + "\n", + "\n", + "var JS_MIME_TYPE = 'application/javascript';\n", + "var HTML_MIME_TYPE = 'text/html';\n", + "var EXEC_MIME_TYPE = 'application/vnd.holoviews_exec.v0+json';\n", + "var CLASS_NAME = 'output';\n", + "\n", + "/**\n", + " * Render data to the DOM node\n", + " */\n", + "function render(props, node) {\n", + " var div = document.createElement(\"div\");\n", + " var script = document.createElement(\"script\");\n", + " node.appendChild(div);\n", + " node.appendChild(script);\n", + "}\n", + "\n", + "/**\n", + " * Handle when a new output is added\n", + " */\n", + "function handle_add_output(event, handle) {\n", + " var output_area = handle.output_area;\n", + " var output = handle.output;\n", + " if ((output.data == undefined) || (!output.data.hasOwnProperty(EXEC_MIME_TYPE))) {\n", + " return\n", + " }\n", + " var id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n", + " var toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n", + " if (id !== undefined) {\n", + " var nchildren = toinsert.length;\n", + " var html_node = toinsert[nchildren-1].children[0];\n", + " html_node.innerHTML = output.data[HTML_MIME_TYPE];\n", + " var scripts = [];\n", + " var nodelist = html_node.querySelectorAll(\"script\");\n", + " for (var i in nodelist) {\n", + " if (nodelist.hasOwnProperty(i)) {\n", + " scripts.push(nodelist[i])\n", + " }\n", + " }\n", + "\n", + " scripts.forEach( function (oldScript) {\n", + " var newScript = document.createElement(\"script\");\n", + " var attrs = [];\n", + " var nodemap = oldScript.attributes;\n", + " for (var j in nodemap) {\n", + " if (nodemap.hasOwnProperty(j)) {\n", + " attrs.push(nodemap[j])\n", + " }\n", + " }\n", + " attrs.forEach(function(attr) { newScript.setAttribute(attr.name, attr.value) });\n", + " newScript.appendChild(document.createTextNode(oldScript.innerHTML));\n", + " oldScript.parentNode.replaceChild(newScript, oldScript);\n", + " });\n", + " if (JS_MIME_TYPE in output.data) {\n", + " toinsert[nchildren-1].children[1].textContent = output.data[JS_MIME_TYPE];\n", + " }\n", + " output_area._hv_plot_id = id;\n", + " if ((window.Bokeh !== undefined) && (id in Bokeh.index)) {\n", + " window.PyViz.plot_index[id] = Bokeh.index[id];\n", + " } else {\n", + " window.PyViz.plot_index[id] = null;\n", + " }\n", + " } else if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n", + " var bk_div = document.createElement(\"div\");\n", + " bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n", + " var script_attrs = bk_div.children[0].attributes;\n", + " for (var i = 0; i < script_attrs.length; i++) {\n", + " toinsert[toinsert.length - 1].childNodes[1].setAttribute(script_attrs[i].name, script_attrs[i].value);\n", + " }\n", + " // store reference to server id on output_area\n", + " output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n", + " }\n", + "}\n", + "\n", + "/**\n", + " * Handle when an output is cleared or removed\n", + " */\n", + "function handle_clear_output(event, handle) {\n", + " var id = handle.cell.output_area._hv_plot_id;\n", + " var server_id = handle.cell.output_area._bokeh_server_id;\n", + " if (((id === undefined) || !(id in PyViz.plot_index)) && (server_id !== undefined)) { return; }\n", + " var comm = window.PyViz.comm_manager.get_client_comm(\"hv-extension-comm\", \"hv-extension-comm\", function () {});\n", + " if (server_id !== null) {\n", + " comm.send({event_type: 'server_delete', 'id': server_id});\n", + " return;\n", + " } else if (comm !== null) {\n", + " comm.send({event_type: 'delete', 'id': id});\n", + " }\n", + " delete PyViz.plot_index[id];\n", + " if ((window.Bokeh !== undefined) & (id in window.Bokeh.index)) {\n", + " var doc = window.Bokeh.index[id].model.document\n", + " doc.clear();\n", + " const i = window.Bokeh.documents.indexOf(doc);\n", + " if (i > -1) {\n", + " window.Bokeh.documents.splice(i, 1);\n", + " }\n", + " }\n", + "}\n", + "\n", + "/**\n", + " * Handle kernel restart event\n", + " */\n", + "function handle_kernel_cleanup(event, handle) {\n", + " delete PyViz.comms[\"hv-extension-comm\"];\n", + " window.PyViz.plot_index = {}\n", + "}\n", + "\n", + "/**\n", + " * Handle update_display_data messages\n", + " */\n", + "function handle_update_output(event, handle) {\n", + " handle_clear_output(event, {cell: {output_area: handle.output_area}})\n", + " handle_add_output(event, handle)\n", + "}\n", + "\n", + "function register_renderer(events, OutputArea) {\n", + " function append_mime(data, metadata, element) {\n", + " // create a DOM node to render to\n", + " var toinsert = this.create_output_subarea(\n", + " metadata,\n", + " CLASS_NAME,\n", + " EXEC_MIME_TYPE\n", + " );\n", + " this.keyboard_manager.register_events(toinsert);\n", + " // Render to node\n", + " var props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n", + " render(props, toinsert[0]);\n", + " element.append(toinsert);\n", + " return toinsert\n", + " }\n", + "\n", + " events.on('output_added.OutputArea', handle_add_output);\n", + " events.on('output_updated.OutputArea', handle_update_output);\n", + " events.on('clear_output.CodeCell', handle_clear_output);\n", + " events.on('delete.Cell', handle_clear_output);\n", + " events.on('kernel_ready.Kernel', handle_kernel_cleanup);\n", + "\n", + " OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n", + " safe: true,\n", + " index: 0\n", + " });\n", + "}\n", + "\n", + "if (window.Jupyter !== undefined) {\n", + " try {\n", + " var events = require('base/js/events');\n", + " var OutputArea = require('notebook/js/outputarea').OutputArea;\n", + " if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n", + " register_renderer(events, OutputArea);\n", + " }\n", + " } catch(err) {\n", + " }\n", + "}\n" + ], + "application/vnd.holoviews_load.v0+json": "\nif ((window.PyViz === undefined) || (window.PyViz instanceof HTMLElement)) {\n window.PyViz = {comms: {}, comm_status:{}, kernels:{}, receivers: {}, plot_index: []}\n}\n\n\n function JupyterCommManager() {\n }\n\n JupyterCommManager.prototype.register_target = function(plot_id, comm_id, msg_handler) {\n if (window.comm_manager || ((window.Jupyter !== undefined) && (Jupyter.notebook.kernel != null))) {\n var comm_manager = window.comm_manager || Jupyter.notebook.kernel.comm_manager;\n comm_manager.register_target(comm_id, function(comm) {\n comm.on_msg(msg_handler);\n });\n } else if ((plot_id in window.PyViz.kernels) && (window.PyViz.kernels[plot_id])) {\n window.PyViz.kernels[plot_id].registerCommTarget(comm_id, function(comm) {\n comm.onMsg = msg_handler;\n });\n } else if (typeof google != 'undefined' && google.colab.kernel != null) {\n google.colab.kernel.comms.registerTarget(comm_id, (comm) => {\n var messages = comm.messages[Symbol.asyncIterator]();\n function processIteratorResult(result) {\n var message = result.value;\n console.log(message)\n var content = {data: message.data, comm_id};\n var buffers = []\n for (var buffer of message.buffers || []) {\n buffers.push(new DataView(buffer))\n }\n var metadata = message.metadata || {};\n var msg = {content, buffers, metadata}\n msg_handler(msg);\n return messages.next().then(processIteratorResult);\n }\n return messages.next().then(processIteratorResult);\n })\n }\n }\n\n JupyterCommManager.prototype.get_client_comm = function(plot_id, comm_id, msg_handler) {\n if (comm_id in window.PyViz.comms) {\n return window.PyViz.comms[comm_id];\n } else if (window.comm_manager || ((window.Jupyter !== undefined) && (Jupyter.notebook.kernel != null))) {\n var comm_manager = window.comm_manager || Jupyter.notebook.kernel.comm_manager;\n var comm = comm_manager.new_comm(comm_id, {}, {}, {}, comm_id);\n if (msg_handler) {\n comm.on_msg(msg_handler);\n }\n } else if ((plot_id in window.PyViz.kernels) && (window.PyViz.kernels[plot_id])) {\n var comm = window.PyViz.kernels[plot_id].connectToComm(comm_id);\n comm.open();\n if (msg_handler) {\n comm.onMsg = msg_handler;\n }\n } else if (typeof google != 'undefined' && google.colab.kernel != null) {\n var comm_promise = google.colab.kernel.comms.open(comm_id)\n comm_promise.then((comm) => {\n window.PyViz.comms[comm_id] = comm;\n if (msg_handler) {\n var messages = comm.messages[Symbol.asyncIterator]();\n function processIteratorResult(result) {\n var message = result.value;\n var content = {data: message.data};\n var metadata = message.metadata || {comm_id};\n var msg = {content, metadata}\n msg_handler(msg);\n return messages.next().then(processIteratorResult);\n }\n return messages.next().then(processIteratorResult);\n }\n }) \n var sendClosure = (data, metadata, buffers, disposeOnDone) => {\n return comm_promise.then((comm) => {\n comm.send(data, metadata, buffers, disposeOnDone);\n });\n };\n var comm = {\n send: sendClosure\n };\n }\n window.PyViz.comms[comm_id] = comm;\n return comm;\n }\n window.PyViz.comm_manager = new JupyterCommManager();\n \n\n\nvar JS_MIME_TYPE = 'application/javascript';\nvar HTML_MIME_TYPE = 'text/html';\nvar EXEC_MIME_TYPE = 'application/vnd.holoviews_exec.v0+json';\nvar CLASS_NAME = 'output';\n\n/**\n * Render data to the DOM node\n */\nfunction render(props, node) {\n var div = document.createElement(\"div\");\n var script = document.createElement(\"script\");\n node.appendChild(div);\n node.appendChild(script);\n}\n\n/**\n * Handle when a new output is added\n */\nfunction handle_add_output(event, handle) {\n var output_area = handle.output_area;\n var output = handle.output;\n if ((output.data == undefined) || (!output.data.hasOwnProperty(EXEC_MIME_TYPE))) {\n return\n }\n var id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n var toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n if (id !== undefined) {\n var nchildren = toinsert.length;\n var html_node = toinsert[nchildren-1].children[0];\n html_node.innerHTML = output.data[HTML_MIME_TYPE];\n var scripts = [];\n var nodelist = html_node.querySelectorAll(\"script\");\n for (var i in nodelist) {\n if (nodelist.hasOwnProperty(i)) {\n scripts.push(nodelist[i])\n }\n }\n\n scripts.forEach( function (oldScript) {\n var newScript = document.createElement(\"script\");\n var attrs = [];\n var nodemap = oldScript.attributes;\n for (var j in nodemap) {\n if (nodemap.hasOwnProperty(j)) {\n attrs.push(nodemap[j])\n }\n }\n attrs.forEach(function(attr) { newScript.setAttribute(attr.name, attr.value) });\n newScript.appendChild(document.createTextNode(oldScript.innerHTML));\n oldScript.parentNode.replaceChild(newScript, oldScript);\n });\n if (JS_MIME_TYPE in output.data) {\n toinsert[nchildren-1].children[1].textContent = output.data[JS_MIME_TYPE];\n }\n output_area._hv_plot_id = id;\n if ((window.Bokeh !== undefined) && (id in Bokeh.index)) {\n window.PyViz.plot_index[id] = Bokeh.index[id];\n } else {\n window.PyViz.plot_index[id] = null;\n }\n } else if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n var bk_div = document.createElement(\"div\");\n bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n var script_attrs = bk_div.children[0].attributes;\n for (var i = 0; i < script_attrs.length; i++) {\n toinsert[toinsert.length - 1].childNodes[1].setAttribute(script_attrs[i].name, script_attrs[i].value);\n }\n // store reference to server id on output_area\n output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n }\n}\n\n/**\n * Handle when an output is cleared or removed\n */\nfunction handle_clear_output(event, handle) {\n var id = handle.cell.output_area._hv_plot_id;\n var server_id = handle.cell.output_area._bokeh_server_id;\n if (((id === undefined) || !(id in PyViz.plot_index)) && (server_id !== undefined)) { return; }\n var comm = window.PyViz.comm_manager.get_client_comm(\"hv-extension-comm\", \"hv-extension-comm\", function () {});\n if (server_id !== null) {\n comm.send({event_type: 'server_delete', 'id': server_id});\n return;\n } else if (comm !== null) {\n comm.send({event_type: 'delete', 'id': id});\n }\n delete PyViz.plot_index[id];\n if ((window.Bokeh !== undefined) & (id in window.Bokeh.index)) {\n var doc = window.Bokeh.index[id].model.document\n doc.clear();\n const i = window.Bokeh.documents.indexOf(doc);\n if (i > -1) {\n window.Bokeh.documents.splice(i, 1);\n }\n }\n}\n\n/**\n * Handle kernel restart event\n */\nfunction handle_kernel_cleanup(event, handle) {\n delete PyViz.comms[\"hv-extension-comm\"];\n window.PyViz.plot_index = {}\n}\n\n/**\n * Handle update_display_data messages\n */\nfunction handle_update_output(event, handle) {\n handle_clear_output(event, {cell: {output_area: handle.output_area}})\n handle_add_output(event, handle)\n}\n\nfunction register_renderer(events, OutputArea) {\n function append_mime(data, metadata, element) {\n // create a DOM node to render to\n var toinsert = this.create_output_subarea(\n metadata,\n CLASS_NAME,\n EXEC_MIME_TYPE\n );\n this.keyboard_manager.register_events(toinsert);\n // Render to node\n var props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n render(props, toinsert[0]);\n element.append(toinsert);\n return toinsert\n }\n\n events.on('output_added.OutputArea', handle_add_output);\n events.on('output_updated.OutputArea', handle_update_output);\n events.on('clear_output.CodeCell', handle_clear_output);\n events.on('delete.Cell', handle_clear_output);\n events.on('kernel_ready.Kernel', handle_kernel_cleanup);\n\n OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n safe: true,\n index: 0\n });\n}\n\nif (window.Jupyter !== undefined) {\n try {\n var events = require('base/js/events');\n var OutputArea = require('notebook/js/outputarea').OutputArea;\n if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n register_renderer(events, OutputArea);\n }\n } catch(err) {\n }\n}\n" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.holoviews_exec.v0+json": "", + "text/html": [ + "
\n", + "
\n", + "
\n", + "" + ] + }, + "metadata": { + "application/vnd.holoviews_exec.v0+json": { + "id": "p1009" + } + }, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "\n", + "
\n", + "\n", + "\n", + "\n", + " \n", + " \n", + "\n", + "\n", + "\n", + "\n", + "
\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import holoviews as hv\n", + "plot_opts = {\"width\": 700, \"height\": 350}\n", + "hv.extension(\"bokeh\")\n", + "warnings.filterwarnings(\"ignore\")" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "bccb5c83-1bbe-4e57-9a71-a9a9e0c735ad", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(0.0, 0.0001671932)\n" + ] + } + ], + "source": [ + "plot_opts = {\"width\": 700, \"height\": 400}\n", + "clim = (np.nanmin(ds0[\"GPP\"].values), np.nanmax(ds0[\"GPP\"].values))\n", + "print(clim)\n", + "features = gf.coastline(\n", + " projection=ccrs.PlateCarree(), line_width=1, scale=\"110m\"\n", + ") #* gf.states(projection=ccrs.PlateCarree(), line_width=1, scale=\"110m\")" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "1563ce17-1d11-47bf-9ee1-56f3612b6dfd", + "metadata": {}, + "outputs": [], + "source": [ + "# This takes a long time to plot, we'll skip it for now\n", + "#ds0[\"test\"][0].plot.polygons(\n", + "# title=\"Global Grid\", **plot_opts\n", + "#) * features" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "c1a7ce6e-cdd0-4e3d-b0e9-41777d834241", + "metadata": {}, + "outputs": [ + { + "data": { + "application/javascript": [ + "(function(root) {\n", + " function now() {\n", + " return new Date();\n", + " }\n", + "\n", + " var force = true;\n", + " var py_version = '3.4.2'.replace('rc', '-rc.').replace('.dev', '-dev.');\n", + " var reloading = false;\n", + " var Bokeh = root.Bokeh;\n", + "\n", + " if (typeof (root._bokeh_timeout) === \"undefined\" || force) {\n", + " root._bokeh_timeout = Date.now() + 5000;\n", + " root._bokeh_failed_load = false;\n", + " }\n", + "\n", + " function run_callbacks() {\n", + " try {\n", + " root._bokeh_onload_callbacks.forEach(function(callback) {\n", + " if (callback != null)\n", + " callback();\n", + " });\n", + " } finally {\n", + " delete root._bokeh_onload_callbacks;\n", + " }\n", + " console.debug(\"Bokeh: all callbacks have finished\");\n", + " }\n", + "\n", + " function load_libs(css_urls, js_urls, js_modules, js_exports, callback) {\n", + " if (css_urls == null) css_urls = [];\n", + " if (js_urls == null) js_urls = [];\n", + " if (js_modules == null) js_modules = [];\n", + " if (js_exports == null) js_exports = {};\n", + "\n", + " root._bokeh_onload_callbacks.push(callback);\n", + "\n", + " if (root._bokeh_is_loading > 0) {\n", + " console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n", + " return null;\n", + " }\n", + " if (js_urls.length === 0 && js_modules.length === 0 && Object.keys(js_exports).length === 0) {\n", + " run_callbacks();\n", + " return null;\n", + " }\n", + " if (!reloading) {\n", + " console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n", + " }\n", + "\n", + " function on_load() {\n", + " root._bokeh_is_loading--;\n", + " if (root._bokeh_is_loading === 0) {\n", + " console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n", + " run_callbacks()\n", + " }\n", + " }\n", + " window._bokeh_on_load = on_load\n", + "\n", + " function on_error() {\n", + " console.error(\"failed to load \" + url);\n", + " }\n", + "\n", + " var skip = [];\n", + " if (window.requirejs) {\n", + " window.requirejs.config({'packages': {}, 'paths': {}, 'shim': {}});\n", + " root._bokeh_is_loading = css_urls.length + 0;\n", + " } else {\n", + " root._bokeh_is_loading = css_urls.length + js_urls.length + js_modules.length + Object.keys(js_exports).length;\n", + " }\n", + "\n", + " var existing_stylesheets = []\n", + " var links = document.getElementsByTagName('link')\n", + " for (var i = 0; i < links.length; i++) {\n", + " var link = links[i]\n", + " if (link.href != null) {\n", + "\texisting_stylesheets.push(link.href)\n", + " }\n", + " }\n", + " for (var i = 0; i < css_urls.length; i++) {\n", + " var url = css_urls[i];\n", + " if (existing_stylesheets.indexOf(url) !== -1) {\n", + "\ton_load()\n", + "\tcontinue;\n", + " }\n", + " const element = document.createElement(\"link\");\n", + " element.onload = on_load;\n", + " element.onerror = on_error;\n", + " element.rel = \"stylesheet\";\n", + " element.type = \"text/css\";\n", + " element.href = url;\n", + " console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n", + " document.body.appendChild(element);\n", + " } var existing_scripts = []\n", + " var scripts = document.getElementsByTagName('script')\n", + " for (var i = 0; i < scripts.length; i++) {\n", + " var script = scripts[i]\n", + " if (script.src != null) {\n", + "\texisting_scripts.push(script.src)\n", + " }\n", + " }\n", + " for (var i = 0; i < js_urls.length; i++) {\n", + " var url = js_urls[i];\n", + " if (skip.indexOf(url) !== -1 || existing_scripts.indexOf(url) !== -1) {\n", + "\tif (!window.requirejs) {\n", + "\t on_load();\n", + "\t}\n", + "\tcontinue;\n", + " }\n", + " var element = document.createElement('script');\n", + " element.onload = on_load;\n", + " element.onerror = on_error;\n", + " element.async = false;\n", + " element.src = url;\n", + " console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n", + " document.head.appendChild(element);\n", + " }\n", + " for (var i = 0; i < js_modules.length; i++) {\n", + " var url = js_modules[i];\n", + " if (skip.indexOf(url) !== -1 || existing_scripts.indexOf(url) !== -1) {\n", + "\tif (!window.requirejs) {\n", + "\t on_load();\n", + "\t}\n", + "\tcontinue;\n", + " }\n", + " var element = document.createElement('script');\n", + " element.onload = on_load;\n", + " element.onerror = on_error;\n", + " element.async = false;\n", + " element.src = url;\n", + " element.type = \"module\";\n", + " console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n", + " document.head.appendChild(element);\n", + " }\n", + " for (const name in js_exports) {\n", + " var url = js_exports[name];\n", + " if (skip.indexOf(url) >= 0 || root[name] != null) {\n", + "\tif (!window.requirejs) {\n", + "\t on_load();\n", + "\t}\n", + "\tcontinue;\n", + " }\n", + " var element = document.createElement('script');\n", + " element.onerror = on_error;\n", + " element.async = false;\n", + " element.type = \"module\";\n", + " console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n", + " element.textContent = `\n", + " import ${name} from \"${url}\"\n", + " window.${name} = ${name}\n", + " window._bokeh_on_load()\n", + " `\n", + " document.head.appendChild(element);\n", + " }\n", + " if (!js_urls.length && !js_modules.length) {\n", + " on_load()\n", + " }\n", + " };\n", + "\n", + " function inject_raw_css(css) {\n", + " const element = document.createElement(\"style\");\n", + " element.appendChild(document.createTextNode(css));\n", + " document.body.appendChild(element);\n", + " }\n", + "\n", + " var js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.4.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.4.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.4.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.4.2.min.js\", \"https://cdn.holoviz.org/panel/1.4.4/dist/panel.min.js\", \"https://cdn.jsdelivr.net/npm/@holoviz/geoviews@1.12.0/dist/geoviews.min.js\"];\n", + " var js_modules = [];\n", + " var js_exports = {};\n", + " var css_urls = [];\n", + " var inline_js = [ function(Bokeh) {\n", + " Bokeh.set_log_level(\"info\");\n", + " },\n", + "function(Bokeh) {} // ensure no trailing comma for IE\n", + " ];\n", + "\n", + " function run_inline_js() {\n", + " if ((root.Bokeh !== undefined) || (force === true)) {\n", + " for (var i = 0; i < inline_js.length; i++) {\n", + "\ttry {\n", + " inline_js[i].call(root, root.Bokeh);\n", + "\t} catch(e) {\n", + "\t if (!reloading) {\n", + "\t throw e;\n", + "\t }\n", + "\t}\n", + " }\n", + " // Cache old bokeh versions\n", + " if (Bokeh != undefined && !reloading) {\n", + "\tvar NewBokeh = root.Bokeh;\n", + "\tif (Bokeh.versions === undefined) {\n", + "\t Bokeh.versions = new Map();\n", + "\t}\n", + "\tif (NewBokeh.version !== Bokeh.version) {\n", + "\t Bokeh.versions.set(NewBokeh.version, NewBokeh)\n", + "\t}\n", + "\troot.Bokeh = Bokeh;\n", + " }} else if (Date.now() < root._bokeh_timeout) {\n", + " setTimeout(run_inline_js, 100);\n", + " } else if (!root._bokeh_failed_load) {\n", + " console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n", + " root._bokeh_failed_load = true;\n", + " }\n", + " root._bokeh_is_initializing = false\n", + " }\n", + "\n", + " function load_or_wait() {\n", + " // Implement a backoff loop that tries to ensure we do not load multiple\n", + " // versions of Bokeh and its dependencies at the same time.\n", + " // In recent versions we use the root._bokeh_is_initializing flag\n", + " // to determine whether there is an ongoing attempt to initialize\n", + " // bokeh, however for backward compatibility we also try to ensure\n", + " // that we do not start loading a newer (Panel>=1.0 and Bokeh>3) version\n", + " // before older versions are fully initialized.\n", + " if (root._bokeh_is_initializing && Date.now() > root._bokeh_timeout) {\n", + " root._bokeh_is_initializing = false;\n", + " root._bokeh_onload_callbacks = undefined;\n", + " console.log(\"Bokeh: BokehJS was loaded multiple times but one version failed to initialize.\");\n", + " load_or_wait();\n", + " } else if (root._bokeh_is_initializing || (typeof root._bokeh_is_initializing === \"undefined\" && root._bokeh_onload_callbacks !== undefined)) {\n", + " setTimeout(load_or_wait, 100);\n", + " } else {\n", + " root._bokeh_is_initializing = true\n", + " root._bokeh_onload_callbacks = []\n", + " var bokeh_loaded = Bokeh != null && (Bokeh.version === py_version || (Bokeh.versions !== undefined && Bokeh.versions.has(py_version)));\n", + " if (!reloading && !bokeh_loaded) {\n", + "\troot.Bokeh = undefined;\n", + " }\n", + " load_libs(css_urls, js_urls, js_modules, js_exports, function() {\n", + "\tconsole.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n", + "\trun_inline_js();\n", + " });\n", + " }\n", + " }\n", + " // Give older versions of the autoload script a head-start to ensure\n", + " // they initialize before we start loading newer version.\n", + " setTimeout(load_or_wait, 100)\n", + "}(window));" + ], + "application/vnd.holoviews_load.v0+json": "(function(root) {\n function now() {\n return new Date();\n }\n\n var force = true;\n var py_version = '3.4.2'.replace('rc', '-rc.').replace('.dev', '-dev.');\n var reloading = false;\n var Bokeh = root.Bokeh;\n\n if (typeof (root._bokeh_timeout) === \"undefined\" || force) {\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_failed_load = false;\n }\n\n function run_callbacks() {\n try {\n root._bokeh_onload_callbacks.forEach(function(callback) {\n if (callback != null)\n callback();\n });\n } finally {\n delete root._bokeh_onload_callbacks;\n }\n console.debug(\"Bokeh: all callbacks have finished\");\n }\n\n function load_libs(css_urls, js_urls, js_modules, js_exports, callback) {\n if (css_urls == null) css_urls = [];\n if (js_urls == null) js_urls = [];\n if (js_modules == null) js_modules = [];\n if (js_exports == null) js_exports = {};\n\n root._bokeh_onload_callbacks.push(callback);\n\n if (root._bokeh_is_loading > 0) {\n console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n return null;\n }\n if (js_urls.length === 0 && js_modules.length === 0 && Object.keys(js_exports).length === 0) {\n run_callbacks();\n return null;\n }\n if (!reloading) {\n console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n }\n\n function on_load() {\n root._bokeh_is_loading--;\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n run_callbacks()\n }\n }\n window._bokeh_on_load = on_load\n\n function on_error() {\n console.error(\"failed to load \" + url);\n }\n\n var skip = [];\n if (window.requirejs) {\n window.requirejs.config({'packages': {}, 'paths': {}, 'shim': {}});\n root._bokeh_is_loading = css_urls.length + 0;\n } else {\n root._bokeh_is_loading = css_urls.length + js_urls.length + js_modules.length + Object.keys(js_exports).length;\n }\n\n var existing_stylesheets = []\n var links = document.getElementsByTagName('link')\n for (var i = 0; i < links.length; i++) {\n var link = links[i]\n if (link.href != null) {\n\texisting_stylesheets.push(link.href)\n }\n }\n for (var i = 0; i < css_urls.length; i++) {\n var url = css_urls[i];\n if (existing_stylesheets.indexOf(url) !== -1) {\n\ton_load()\n\tcontinue;\n }\n const element = document.createElement(\"link\");\n element.onload = on_load;\n element.onerror = on_error;\n element.rel = \"stylesheet\";\n element.type = \"text/css\";\n element.href = url;\n console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n document.body.appendChild(element);\n } var existing_scripts = []\n var scripts = document.getElementsByTagName('script')\n for (var i = 0; i < scripts.length; i++) {\n var script = scripts[i]\n if (script.src != null) {\n\texisting_scripts.push(script.src)\n }\n }\n for (var i = 0; i < js_urls.length; i++) {\n var url = js_urls[i];\n if (skip.indexOf(url) !== -1 || existing_scripts.indexOf(url) !== -1) {\n\tif (!window.requirejs) {\n\t on_load();\n\t}\n\tcontinue;\n }\n var element = document.createElement('script');\n element.onload = on_load;\n element.onerror = on_error;\n element.async = false;\n element.src = url;\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.head.appendChild(element);\n }\n for (var i = 0; i < js_modules.length; i++) {\n var url = js_modules[i];\n if (skip.indexOf(url) !== -1 || existing_scripts.indexOf(url) !== -1) {\n\tif (!window.requirejs) {\n\t on_load();\n\t}\n\tcontinue;\n }\n var element = document.createElement('script');\n element.onload = on_load;\n element.onerror = on_error;\n element.async = false;\n element.src = url;\n element.type = \"module\";\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.head.appendChild(element);\n }\n for (const name in js_exports) {\n var url = js_exports[name];\n if (skip.indexOf(url) >= 0 || root[name] != null) {\n\tif (!window.requirejs) {\n\t on_load();\n\t}\n\tcontinue;\n }\n var element = document.createElement('script');\n element.onerror = on_error;\n element.async = false;\n element.type = \"module\";\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n element.textContent = `\n import ${name} from \"${url}\"\n window.${name} = ${name}\n window._bokeh_on_load()\n `\n document.head.appendChild(element);\n }\n if (!js_urls.length && !js_modules.length) {\n on_load()\n }\n };\n\n function inject_raw_css(css) {\n const element = document.createElement(\"style\");\n element.appendChild(document.createTextNode(css));\n document.body.appendChild(element);\n }\n\n var js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.4.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.4.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.4.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.4.2.min.js\", \"https://cdn.holoviz.org/panel/1.4.4/dist/panel.min.js\", \"https://cdn.jsdelivr.net/npm/@holoviz/geoviews@1.12.0/dist/geoviews.min.js\"];\n var js_modules = [];\n var js_exports = {};\n var css_urls = [];\n var inline_js = [ function(Bokeh) {\n Bokeh.set_log_level(\"info\");\n },\nfunction(Bokeh) {} // ensure no trailing comma for IE\n ];\n\n function run_inline_js() {\n if ((root.Bokeh !== undefined) || (force === true)) {\n for (var i = 0; i < inline_js.length; i++) {\n\ttry {\n inline_js[i].call(root, root.Bokeh);\n\t} catch(e) {\n\t if (!reloading) {\n\t throw e;\n\t }\n\t}\n }\n // Cache old bokeh versions\n if (Bokeh != undefined && !reloading) {\n\tvar NewBokeh = root.Bokeh;\n\tif (Bokeh.versions === undefined) {\n\t Bokeh.versions = new Map();\n\t}\n\tif (NewBokeh.version !== Bokeh.version) {\n\t Bokeh.versions.set(NewBokeh.version, NewBokeh)\n\t}\n\troot.Bokeh = Bokeh;\n }} else if (Date.now() < root._bokeh_timeout) {\n setTimeout(run_inline_js, 100);\n } else if (!root._bokeh_failed_load) {\n console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n root._bokeh_failed_load = true;\n }\n root._bokeh_is_initializing = false\n }\n\n function load_or_wait() {\n // Implement a backoff loop that tries to ensure we do not load multiple\n // versions of Bokeh and its dependencies at the same time.\n // In recent versions we use the root._bokeh_is_initializing flag\n // to determine whether there is an ongoing attempt to initialize\n // bokeh, however for backward compatibility we also try to ensure\n // that we do not start loading a newer (Panel>=1.0 and Bokeh>3) version\n // before older versions are fully initialized.\n if (root._bokeh_is_initializing && Date.now() > root._bokeh_timeout) {\n root._bokeh_is_initializing = false;\n root._bokeh_onload_callbacks = undefined;\n console.log(\"Bokeh: BokehJS was loaded multiple times but one version failed to initialize.\");\n load_or_wait();\n } else if (root._bokeh_is_initializing || (typeof root._bokeh_is_initializing === \"undefined\" && root._bokeh_onload_callbacks !== undefined)) {\n setTimeout(load_or_wait, 100);\n } else {\n root._bokeh_is_initializing = true\n root._bokeh_onload_callbacks = []\n var bokeh_loaded = Bokeh != null && (Bokeh.version === py_version || (Bokeh.versions !== undefined && Bokeh.versions.has(py_version)));\n if (!reloading && !bokeh_loaded) {\n\troot.Bokeh = undefined;\n }\n load_libs(css_urls, js_urls, js_modules, js_exports, function() {\n\tconsole.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n\trun_inline_js();\n });\n }\n }\n // Give older versions of the autoload script a head-start to ensure\n // they initialize before we start loading newer version.\n setTimeout(load_or_wait, 100)\n}(window));" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/javascript": [ + "\n", + "if ((window.PyViz === undefined) || (window.PyViz instanceof HTMLElement)) {\n", + " window.PyViz = {comms: {}, comm_status:{}, kernels:{}, receivers: {}, plot_index: []}\n", + "}\n", + "\n", + "\n", + " function JupyterCommManager() {\n", + " }\n", + "\n", + " JupyterCommManager.prototype.register_target = function(plot_id, comm_id, msg_handler) {\n", + " if (window.comm_manager || ((window.Jupyter !== undefined) && (Jupyter.notebook.kernel != null))) {\n", + " var comm_manager = window.comm_manager || Jupyter.notebook.kernel.comm_manager;\n", + " comm_manager.register_target(comm_id, function(comm) {\n", + " comm.on_msg(msg_handler);\n", + " });\n", + " } else if ((plot_id in window.PyViz.kernels) && (window.PyViz.kernels[plot_id])) {\n", + " window.PyViz.kernels[plot_id].registerCommTarget(comm_id, function(comm) {\n", + " comm.onMsg = msg_handler;\n", + " });\n", + " } else if (typeof google != 'undefined' && google.colab.kernel != null) {\n", + " google.colab.kernel.comms.registerTarget(comm_id, (comm) => {\n", + " var messages = comm.messages[Symbol.asyncIterator]();\n", + " function processIteratorResult(result) {\n", + " var message = result.value;\n", + " console.log(message)\n", + " var content = {data: message.data, comm_id};\n", + " var buffers = []\n", + " for (var buffer of message.buffers || []) {\n", + " buffers.push(new DataView(buffer))\n", + " }\n", + " var metadata = message.metadata || {};\n", + " var msg = {content, buffers, metadata}\n", + " msg_handler(msg);\n", + " return messages.next().then(processIteratorResult);\n", + " }\n", + " return messages.next().then(processIteratorResult);\n", + " })\n", + " }\n", + " }\n", + "\n", + " JupyterCommManager.prototype.get_client_comm = function(plot_id, comm_id, msg_handler) {\n", + " if (comm_id in window.PyViz.comms) {\n", + " return window.PyViz.comms[comm_id];\n", + " } else if (window.comm_manager || ((window.Jupyter !== undefined) && (Jupyter.notebook.kernel != null))) {\n", + " var comm_manager = window.comm_manager || Jupyter.notebook.kernel.comm_manager;\n", + " var comm = comm_manager.new_comm(comm_id, {}, {}, {}, comm_id);\n", + " if (msg_handler) {\n", + " comm.on_msg(msg_handler);\n", + " }\n", + " } else if ((plot_id in window.PyViz.kernels) && (window.PyViz.kernels[plot_id])) {\n", + " var comm = window.PyViz.kernels[plot_id].connectToComm(comm_id);\n", + " comm.open();\n", + " if (msg_handler) {\n", + " comm.onMsg = msg_handler;\n", + " }\n", + " } else if (typeof google != 'undefined' && google.colab.kernel != null) {\n", + " var comm_promise = google.colab.kernel.comms.open(comm_id)\n", + " comm_promise.then((comm) => {\n", + " window.PyViz.comms[comm_id] = comm;\n", + " if (msg_handler) {\n", + " var messages = comm.messages[Symbol.asyncIterator]();\n", + " function processIteratorResult(result) {\n", + " var message = result.value;\n", + " var content = {data: message.data};\n", + " var metadata = message.metadata || {comm_id};\n", + " var msg = {content, metadata}\n", + " msg_handler(msg);\n", + " return messages.next().then(processIteratorResult);\n", + " }\n", + " return messages.next().then(processIteratorResult);\n", + " }\n", + " }) \n", + " var sendClosure = (data, metadata, buffers, disposeOnDone) => {\n", + " return comm_promise.then((comm) => {\n", + " comm.send(data, metadata, buffers, disposeOnDone);\n", + " });\n", + " };\n", + " var comm = {\n", + " send: sendClosure\n", + " };\n", + " }\n", + " window.PyViz.comms[comm_id] = comm;\n", + " return comm;\n", + " }\n", + " window.PyViz.comm_manager = new JupyterCommManager();\n", + " \n", + "\n", + "\n", + "var JS_MIME_TYPE = 'application/javascript';\n", + "var HTML_MIME_TYPE = 'text/html';\n", + "var EXEC_MIME_TYPE = 'application/vnd.holoviews_exec.v0+json';\n", + "var CLASS_NAME = 'output';\n", + "\n", + "/**\n", + " * Render data to the DOM node\n", + " */\n", + "function render(props, node) {\n", + " var div = document.createElement(\"div\");\n", + " var script = document.createElement(\"script\");\n", + " node.appendChild(div);\n", + " node.appendChild(script);\n", + "}\n", + "\n", + "/**\n", + " * Handle when a new output is added\n", + " */\n", + "function handle_add_output(event, handle) {\n", + " var output_area = handle.output_area;\n", + " var output = handle.output;\n", + " if ((output.data == undefined) || (!output.data.hasOwnProperty(EXEC_MIME_TYPE))) {\n", + " return\n", + " }\n", + " var id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n", + " var toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n", + " if (id !== undefined) {\n", + " var nchildren = toinsert.length;\n", + " var html_node = toinsert[nchildren-1].children[0];\n", + " html_node.innerHTML = output.data[HTML_MIME_TYPE];\n", + " var scripts = [];\n", + " var nodelist = html_node.querySelectorAll(\"script\");\n", + " for (var i in nodelist) {\n", + " if (nodelist.hasOwnProperty(i)) {\n", + " scripts.push(nodelist[i])\n", + " }\n", + " }\n", + "\n", + " scripts.forEach( function (oldScript) {\n", + " var newScript = document.createElement(\"script\");\n", + " var attrs = [];\n", + " var nodemap = oldScript.attributes;\n", + " for (var j in nodemap) {\n", + " if (nodemap.hasOwnProperty(j)) {\n", + " attrs.push(nodemap[j])\n", + " }\n", + " }\n", + " attrs.forEach(function(attr) { newScript.setAttribute(attr.name, attr.value) });\n", + " newScript.appendChild(document.createTextNode(oldScript.innerHTML));\n", + " oldScript.parentNode.replaceChild(newScript, oldScript);\n", + " });\n", + " if (JS_MIME_TYPE in output.data) {\n", + " toinsert[nchildren-1].children[1].textContent = output.data[JS_MIME_TYPE];\n", + " }\n", + " output_area._hv_plot_id = id;\n", + " if ((window.Bokeh !== undefined) && (id in Bokeh.index)) {\n", + " window.PyViz.plot_index[id] = Bokeh.index[id];\n", + " } else {\n", + " window.PyViz.plot_index[id] = null;\n", + " }\n", + " } else if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n", + " var bk_div = document.createElement(\"div\");\n", + " bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n", + " var script_attrs = bk_div.children[0].attributes;\n", + " for (var i = 0; i < script_attrs.length; i++) {\n", + " toinsert[toinsert.length - 1].childNodes[1].setAttribute(script_attrs[i].name, script_attrs[i].value);\n", + " }\n", + " // store reference to server id on output_area\n", + " output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n", + " }\n", + "}\n", + "\n", + "/**\n", + " * Handle when an output is cleared or removed\n", + " */\n", + "function handle_clear_output(event, handle) {\n", + " var id = handle.cell.output_area._hv_plot_id;\n", + " var server_id = handle.cell.output_area._bokeh_server_id;\n", + " if (((id === undefined) || !(id in PyViz.plot_index)) && (server_id !== undefined)) { return; }\n", + " var comm = window.PyViz.comm_manager.get_client_comm(\"hv-extension-comm\", \"hv-extension-comm\", function () {});\n", + " if (server_id !== null) {\n", + " comm.send({event_type: 'server_delete', 'id': server_id});\n", + " return;\n", + " } else if (comm !== null) {\n", + " comm.send({event_type: 'delete', 'id': id});\n", + " }\n", + " delete PyViz.plot_index[id];\n", + " if ((window.Bokeh !== undefined) & (id in window.Bokeh.index)) {\n", + " var doc = window.Bokeh.index[id].model.document\n", + " doc.clear();\n", + " const i = window.Bokeh.documents.indexOf(doc);\n", + " if (i > -1) {\n", + " window.Bokeh.documents.splice(i, 1);\n", + " }\n", + " }\n", + "}\n", + "\n", + "/**\n", + " * Handle kernel restart event\n", + " */\n", + "function handle_kernel_cleanup(event, handle) {\n", + " delete PyViz.comms[\"hv-extension-comm\"];\n", + " window.PyViz.plot_index = {}\n", + "}\n", + "\n", + "/**\n", + " * Handle update_display_data messages\n", + " */\n", + "function handle_update_output(event, handle) {\n", + " handle_clear_output(event, {cell: {output_area: handle.output_area}})\n", + " handle_add_output(event, handle)\n", + "}\n", + "\n", + "function register_renderer(events, OutputArea) {\n", + " function append_mime(data, metadata, element) {\n", + " // create a DOM node to render to\n", + " var toinsert = this.create_output_subarea(\n", + " metadata,\n", + " CLASS_NAME,\n", + " EXEC_MIME_TYPE\n", + " );\n", + " this.keyboard_manager.register_events(toinsert);\n", + " // Render to node\n", + " var props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n", + " render(props, toinsert[0]);\n", + " element.append(toinsert);\n", + " return toinsert\n", + " }\n", + "\n", + " events.on('output_added.OutputArea', handle_add_output);\n", + " events.on('output_updated.OutputArea', handle_update_output);\n", + " events.on('clear_output.CodeCell', handle_clear_output);\n", + " events.on('delete.Cell', handle_clear_output);\n", + " events.on('kernel_ready.Kernel', handle_kernel_cleanup);\n", + "\n", + " OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n", + " safe: true,\n", + " index: 0\n", + " });\n", + "}\n", + "\n", + "if (window.Jupyter !== undefined) {\n", + " try {\n", + " var events = require('base/js/events');\n", + " var OutputArea = require('notebook/js/outputarea').OutputArea;\n", + " if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n", + " register_renderer(events, OutputArea);\n", + " }\n", + " } catch(err) {\n", + " }\n", + "}\n" + ], + "application/vnd.holoviews_load.v0+json": "\nif ((window.PyViz === undefined) || (window.PyViz instanceof HTMLElement)) {\n window.PyViz = {comms: {}, comm_status:{}, kernels:{}, receivers: {}, plot_index: []}\n}\n\n\n function JupyterCommManager() {\n }\n\n JupyterCommManager.prototype.register_target = function(plot_id, comm_id, msg_handler) {\n if (window.comm_manager || ((window.Jupyter !== undefined) && (Jupyter.notebook.kernel != null))) {\n var comm_manager = window.comm_manager || Jupyter.notebook.kernel.comm_manager;\n comm_manager.register_target(comm_id, function(comm) {\n comm.on_msg(msg_handler);\n });\n } else if ((plot_id in window.PyViz.kernels) && (window.PyViz.kernels[plot_id])) {\n window.PyViz.kernels[plot_id].registerCommTarget(comm_id, function(comm) {\n comm.onMsg = msg_handler;\n });\n } else if (typeof google != 'undefined' && google.colab.kernel != null) {\n google.colab.kernel.comms.registerTarget(comm_id, (comm) => {\n var messages = comm.messages[Symbol.asyncIterator]();\n function processIteratorResult(result) {\n var message = result.value;\n console.log(message)\n var content = {data: message.data, comm_id};\n var buffers = []\n for (var buffer of message.buffers || []) {\n buffers.push(new DataView(buffer))\n }\n var metadata = message.metadata || {};\n var msg = {content, buffers, metadata}\n msg_handler(msg);\n return messages.next().then(processIteratorResult);\n }\n return messages.next().then(processIteratorResult);\n })\n }\n }\n\n JupyterCommManager.prototype.get_client_comm = function(plot_id, comm_id, msg_handler) {\n if (comm_id in window.PyViz.comms) {\n return window.PyViz.comms[comm_id];\n } else if (window.comm_manager || ((window.Jupyter !== undefined) && (Jupyter.notebook.kernel != null))) {\n var comm_manager = window.comm_manager || Jupyter.notebook.kernel.comm_manager;\n var comm = comm_manager.new_comm(comm_id, {}, {}, {}, comm_id);\n if (msg_handler) {\n comm.on_msg(msg_handler);\n }\n } else if ((plot_id in window.PyViz.kernels) && (window.PyViz.kernels[plot_id])) {\n var comm = window.PyViz.kernels[plot_id].connectToComm(comm_id);\n comm.open();\n if (msg_handler) {\n comm.onMsg = msg_handler;\n }\n } else if (typeof google != 'undefined' && google.colab.kernel != null) {\n var comm_promise = google.colab.kernel.comms.open(comm_id)\n comm_promise.then((comm) => {\n window.PyViz.comms[comm_id] = comm;\n if (msg_handler) {\n var messages = comm.messages[Symbol.asyncIterator]();\n function processIteratorResult(result) {\n var message = result.value;\n var content = {data: message.data};\n var metadata = message.metadata || {comm_id};\n var msg = {content, metadata}\n msg_handler(msg);\n return messages.next().then(processIteratorResult);\n }\n return messages.next().then(processIteratorResult);\n }\n }) \n var sendClosure = (data, metadata, buffers, disposeOnDone) => {\n return comm_promise.then((comm) => {\n comm.send(data, metadata, buffers, disposeOnDone);\n });\n };\n var comm = {\n send: sendClosure\n };\n }\n window.PyViz.comms[comm_id] = comm;\n return comm;\n }\n window.PyViz.comm_manager = new JupyterCommManager();\n \n\n\nvar JS_MIME_TYPE = 'application/javascript';\nvar HTML_MIME_TYPE = 'text/html';\nvar EXEC_MIME_TYPE = 'application/vnd.holoviews_exec.v0+json';\nvar CLASS_NAME = 'output';\n\n/**\n * Render data to the DOM node\n */\nfunction render(props, node) {\n var div = document.createElement(\"div\");\n var script = document.createElement(\"script\");\n node.appendChild(div);\n node.appendChild(script);\n}\n\n/**\n * Handle when a new output is added\n */\nfunction handle_add_output(event, handle) {\n var output_area = handle.output_area;\n var output = handle.output;\n if ((output.data == undefined) || (!output.data.hasOwnProperty(EXEC_MIME_TYPE))) {\n return\n }\n var id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n var toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n if (id !== undefined) {\n var nchildren = toinsert.length;\n var html_node = toinsert[nchildren-1].children[0];\n html_node.innerHTML = output.data[HTML_MIME_TYPE];\n var scripts = [];\n var nodelist = html_node.querySelectorAll(\"script\");\n for (var i in nodelist) {\n if (nodelist.hasOwnProperty(i)) {\n scripts.push(nodelist[i])\n }\n }\n\n scripts.forEach( function (oldScript) {\n var newScript = document.createElement(\"script\");\n var attrs = [];\n var nodemap = oldScript.attributes;\n for (var j in nodemap) {\n if (nodemap.hasOwnProperty(j)) {\n attrs.push(nodemap[j])\n }\n }\n attrs.forEach(function(attr) { newScript.setAttribute(attr.name, attr.value) });\n newScript.appendChild(document.createTextNode(oldScript.innerHTML));\n oldScript.parentNode.replaceChild(newScript, oldScript);\n });\n if (JS_MIME_TYPE in output.data) {\n toinsert[nchildren-1].children[1].textContent = output.data[JS_MIME_TYPE];\n }\n output_area._hv_plot_id = id;\n if ((window.Bokeh !== undefined) && (id in Bokeh.index)) {\n window.PyViz.plot_index[id] = Bokeh.index[id];\n } else {\n window.PyViz.plot_index[id] = null;\n }\n } else if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n var bk_div = document.createElement(\"div\");\n bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n var script_attrs = bk_div.children[0].attributes;\n for (var i = 0; i < script_attrs.length; i++) {\n toinsert[toinsert.length - 1].childNodes[1].setAttribute(script_attrs[i].name, script_attrs[i].value);\n }\n // store reference to server id on output_area\n output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n }\n}\n\n/**\n * Handle when an output is cleared or removed\n */\nfunction handle_clear_output(event, handle) {\n var id = handle.cell.output_area._hv_plot_id;\n var server_id = handle.cell.output_area._bokeh_server_id;\n if (((id === undefined) || !(id in PyViz.plot_index)) && (server_id !== undefined)) { return; }\n var comm = window.PyViz.comm_manager.get_client_comm(\"hv-extension-comm\", \"hv-extension-comm\", function () {});\n if (server_id !== null) {\n comm.send({event_type: 'server_delete', 'id': server_id});\n return;\n } else if (comm !== null) {\n comm.send({event_type: 'delete', 'id': id});\n }\n delete PyViz.plot_index[id];\n if ((window.Bokeh !== undefined) & (id in window.Bokeh.index)) {\n var doc = window.Bokeh.index[id].model.document\n doc.clear();\n const i = window.Bokeh.documents.indexOf(doc);\n if (i > -1) {\n window.Bokeh.documents.splice(i, 1);\n }\n }\n}\n\n/**\n * Handle kernel restart event\n */\nfunction handle_kernel_cleanup(event, handle) {\n delete PyViz.comms[\"hv-extension-comm\"];\n window.PyViz.plot_index = {}\n}\n\n/**\n * Handle update_display_data messages\n */\nfunction handle_update_output(event, handle) {\n handle_clear_output(event, {cell: {output_area: handle.output_area}})\n handle_add_output(event, handle)\n}\n\nfunction register_renderer(events, OutputArea) {\n function append_mime(data, metadata, element) {\n // create a DOM node to render to\n var toinsert = this.create_output_subarea(\n metadata,\n CLASS_NAME,\n EXEC_MIME_TYPE\n );\n this.keyboard_manager.register_events(toinsert);\n // Render to node\n var props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n render(props, toinsert[0]);\n element.append(toinsert);\n return toinsert\n }\n\n events.on('output_added.OutputArea', handle_add_output);\n events.on('output_updated.OutputArea', handle_update_output);\n events.on('clear_output.CodeCell', handle_clear_output);\n events.on('delete.Cell', handle_clear_output);\n events.on('kernel_ready.Kernel', handle_kernel_cleanup);\n\n OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n safe: true,\n index: 0\n });\n}\n\nif (window.Jupyter !== undefined) {\n try {\n var events = require('base/js/events');\n var OutputArea = require('notebook/js/outputarea').OutputArea;\n if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n register_renderer(events, OutputArea);\n }\n } catch(err) {\n }\n}\n" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.holoviews_exec.v0+json": "", + "text/html": [ + "
\n", + "
\n", + "
\n", + "" + ] + }, + "metadata": { + "application/vnd.holoviews_exec.v0+json": { + "id": "p1011" + } + }, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "\n", + "
\n", + "\n", + "\n", + "\n", + " \n", + " \n", + "\n", + "\n", + "\n", + "\n", + "
\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": {}, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.holoviews_exec.v0+json": "", + "text/html": [ + "
\n", + "
\n", + "
\n", + "" + ], + "text/plain": [ + ":Overlay\n", + " .Polygons.I :Polygons [x,y] (test)\n", + " .Coastline.I :Feature [Longitude,Latitude]" + ] + }, + "execution_count": 22, + "metadata": { + "application/vnd.holoviews_exec.v0+json": { + "id": "p1013" + } + }, + "output_type": "execute_result" + } + ], + "source": [ + "# set the bounding box\n", + "lon_bounds = (105, 145)\n", + "lat_bounds = (25, 58)\n", + "# elements include nodes, edge centers, or face centers) \n", + "element = 'face centers'\n", + "\n", + "bbox_subset_nodes = ds0[\"test\"][5].subset.bounding_box(\n", + " lon_bounds, lat_bounds, element=element\n", + ")\n", + "bbox_subset_nodes.plot.polygons(\n", + " cmap='viridis',\n", + " title=\"Bounding Box Subset (\"+element+\")\",\n", + " **plot_opts,\n", + ") * features" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "87fb2283-e414-4c5b-99a0-d337f2f40b99", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, axs = plt.subplots(nrows=2,ncols=2,\n", + " subplot_kw=dict(projection=ccrs.PlateCarree()),\n", + " figsize=(12,8))\n", + "axs=axs.flatten()\n", + "# These differences around the coast seem pretty tiny, again within rounding error?\n", + "ds_out_con.test.isel(time=0)\\\n", + " .sel(lon=slice(lon_bounds[0],lon_bounds[1]),lat=slice(lat_bounds[0],lat_bounds[1]))\\\n", + " .plot(vmin=1-1e-8, vmax=1+1e-8, ax=axs[0])\n", + "\n", + "ds_out_bilin.test.isel(time=0)\\\n", + " .sel(lon=slice(lon_bounds[0],lon_bounds[1]),lat=slice(lat_bounds[0],lat_bounds[1]))\\\n", + " .plot(vmin=1-1e-8, vmax=1+1e-8, ax=axs[1]) ;\n", + "\n", + "ds_out_con.test.isel(time=0).where(fv_t232.landfrac>0)\\\n", + " .sel(lon=slice(lon_bounds[0],lon_bounds[1]),lat=slice(lat_bounds[0],lat_bounds[1]))\\\n", + " .plot(vmin=1-1e-8, vmax=1+1e-8, ax=axs[2])\n", + "\n", + "axs[0].set_title('conservative test, no mask')\n", + "axs[1].set_title('bilinear test') ;\n", + "axs[2].set_title('conservative test, destination mask') ;" + ] + }, + { + "cell_type": "markdown", + "id": "7dd34b5d-bd99-405a-a988-5b561bf3d0b0", + "metadata": {}, + "source": [ + "#### Now look at regional fluxes\n", + "- Not sure if bounding boxes are necessarily identical in unstructured and regular grid.\n", + "- Fluxes still don't look the same when focusing on a few islands, but overall not unreasonable\n", + "- What level of difference are we OK tolerating?" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "d94d7a9a-994a-4c0a-ab57-db7bebcc479f", + "metadata": {}, + "outputs": [], + "source": [ + "# elements include nodes, edge centers, or face centers) \n", + "element = 'face centers'\n", + "region = 'Hawaii'\n", + "month = 6\n", + "# set the bounding box\n", + "plot_opts = {\"width\": 700, \"height\": 400}\n", + "\n", + "if region == 'Global':\n", + " lat_bounds = (-90, 90)\n", + " lon_bounds = (-180, 180)\n", + " lon_bounds2 = (0, 360)\n", + "elif region == 'East Asia':\n", + " lat_bounds = (23, 58)\n", + " lon_bounds = (110, 150)\n", + " lon_bounds2 = (110, 150)\n", + "elif region == 'Polar':\n", + " lat_bounds = (60, 90)\n", + " lon_bounds = (-180, 180)\n", + " lon_bounds2 = (0, 360)\n", + "elif region == 'Hawaii':\n", + " lat_bounds = (17, 25)\n", + " lon_bounds = (-162, -153)\n", + " lon_bounds2 = ((360-162), (360-153)) \n", + "elif region == 'Amazon':\n", + " lat_bounds = (-10, 0)\n", + " lon_bounds = (-70, -50)\n", + " lon_bounds2 = ((290), (310)) \n", + "elif region == 'New Zeland':\n", + " lat_bounds = (-50, -33)\n", + " lon_bounds = (160, 179)\n", + " lon_bounds2 = (160, 180)\n", + "elif region == 'South America':\n", + " lat_bounds = (-57, 13)\n", + " lon_bounds = (-85, -30)\n", + " lon_bounds2 = ((360-85), (360-30))\n", + " plot_opts = {\"width\": 700, \"height\": 700} \n", + "\n", + "\n", + "bbox_subset_nodes = ds0[\"GPP\"][month].subset.bounding_box(\n", + " lon_bounds, lat_bounds, element=element\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "e3c6a535-a39a-4c84-8044-3184d5e94a2d", + "metadata": {}, + "outputs": [ + { + "data": {}, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.holoviews_exec.v0+json": "", + "text/html": [ + "
\n", + "
\n", + "
\n", + "" + ], + "text/plain": [ + ":Overlay\n", + " .Polygons.I :Polygons [x,y] (GPP)\n", + " .Coastline.I :Feature [Longitude,Latitude]" + ] + }, + "execution_count": 25, + "metadata": { + "application/vnd.holoviews_exec.v0+json": { + "id": "p3823" + } + }, + "output_type": "execute_result" + } + ], + "source": [ + "#if region != \"New Zeland\" comment out features below \n", + "bbox_subset_nodes.plot.polygons(\n", + " clim=clim, \n", + " cmap='viridis',\n", + " title=region + \" Bounding Box Subset (\"+element+\" Query)\",\n", + " **plot_opts,\n", + ") * features" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "d7fbcc60-32a2-4fef-afad-06e520c47635", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "if region == \"New Zeland\":\n", + " fig, axs = plt.subplots(nrows=2,ncols=2,\n", + " figsize=(12,8))\n", + "else:\n", + " fig, axs = plt.subplots(nrows=2,ncols=2,\n", + " subplot_kw=dict(projection=ccrs.PlateCarree()),\n", + " figsize=(12,8))\n", + "axs=axs.flatten()\n", + "ds_out_con.GPP.isel(time=month)\\\n", + " .sel(lon=slice(lon_bounds2[0],lon_bounds2[1]),lat=slice(lat_bounds[0],lat_bounds[1]))\\\n", + " .plot(vmin=clim[0],vmax=clim[1], ax=axs[0]) \n", + "axs[0].set_title(region + ' conservaitve, no mask')\n", + "\n", + "ds_out_con.GPP.isel(time=month).where(fv_t232.landfrac>0)\\\n", + " .sel(lon=slice(lon_bounds2[0],lon_bounds2[1]),lat=slice(lat_bounds[0],lat_bounds[1]))\\\n", + " .plot(vmin=clim[0],vmax=clim[1], ax=axs[1]) \n", + "axs[1].set_title(region + ' conservaitve, destination mask')\n", + "\n", + "\n", + "ds_out_bilin.GPP.isel(time=month)\\\n", + " .sel(lon=slice(lon_bounds2[0],lon_bounds2[1]),lat=slice(lat_bounds[0],lat_bounds[1]))\\\n", + " .plot(vmin=clim[0],vmax=clim[1], ax=axs[2]) \n", + "axs[2].set_title(region + ' bilinear') ;\n", + "\n", + "if region != \"New Zeland\":\n", + " for a in axs:\n", + " a.coastlines()" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "4659783e-c119-4031-9988-61e43f659583", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "59.7\n", + "721.1\n", + "716.506\n", + "717.332\n", + "701.8\n" + ] + } + ], + "source": [ + "# elements include nodes, edge centers, or face centers) \n", + "element = 'face centers'\n", + "element = 'nodes'\n", + "\n", + "var = 'GPP'\n", + "bbox_var = ds0[var].subset.bounding_box(\n", + " lon_bounds, lat_bounds, element=element)\n", + "\n", + "bbox_area = ds0[\"area\"].subset.bounding_box(\n", + " lon_bounds, lat_bounds, element=element)\n", + "\n", + "bbox_landfrac = ds0[\"landfrac\"].subset.bounding_box(\n", + " lon_bounds, lat_bounds, element=element)\n", + "\n", + "# Area weighting\n", + "bbox_wgt = bbox_area * bbox_landfrac / ((bbox_area * bbox_landfrac).sum())\n", + "y = (bbox_var * bbox_wgt).sum('n_face').values\n", + "x = bbox_var['time'].values\n", + "\n", + "plt.plot(x, y, label = 'raw ne30, ' + str(np.round(y.mean() * spy, 1))) \n", + "\n", + "#repeat for regridded climo\n", + "bbox_area_r = fv_t232['area'].sel(lon=slice(lon_bounds2[0],lon_bounds2[1]),lat=slice(lat_bounds[0],lat_bounds[1])) \n", + "bbox_landfrac_r = fv_t232['landfrac'].sel(lon=slice(lon_bounds2[0],lon_bounds2[1]),lat=slice(lat_bounds[0],lat_bounds[1])) \n", + "bbox_wgt_r = bbox_area_r * bbox_landfrac_r / ((bbox_area_r * bbox_landfrac_r).sum())\n", + "\n", + "# Better with destination area\n", + "bbox_area_rB = ds_out_con['area'].sel(lon=slice(lon_bounds2[0],lon_bounds2[1]),lat=slice(lat_bounds[0],lat_bounds[1])) \n", + "bbox_landfrac_rB = ds_out_con['landfrac'].sel(lon=slice(lon_bounds2[0],lon_bounds2[1]),lat=slice(lat_bounds[0],lat_bounds[1])) \n", + "bbox_landfrac_rC = ds_out_con['landfrac'].where(fv_t232['landmask']==1).sel(lon=slice(lon_bounds2[0],lon_bounds2[1]),lat=slice(lat_bounds[0],lat_bounds[1])) \n", + "bbox_wgt_rB = bbox_area_r * bbox_landfrac_rB / ((bbox_area_r * bbox_landfrac_rB).sum())\n", + "bbox_wgt_rC = bbox_area_r * bbox_landfrac_rC / ((bbox_area_r * bbox_landfrac_rC).sum())\n", + "\n", + "bbox_var_r = ds_out_con[var].sel(lon=slice(lon_bounds2[0],lon_bounds2[1]),lat=slice(lat_bounds[0],lat_bounds[1])) \n", + "y_r = (bbox_var_r * bbox_wgt_r).sum(['lat','lon']).values\n", + "y_rB = (bbox_var_r * bbox_wgt_rB).sum(['lat','lon']).values\n", + "y_rC = (bbox_var_r * bbox_wgt_rC).sum(['lat','lon']).values\n", + "plt.plot(x, y_r, label = 'cons destination mask & landfrac, ' + str(np.round(y_r.mean()* spy,1))) \n", + "plt.plot(x, y_rB, label = 'cons. no mask regridded landfrac ' + str(np.round(y_rB.mean()* spy,1))) \n", + "\n", + "bbox_var_r2 = ds_out_bilin[var].sel(lon=slice(lon_bounds2[0],lon_bounds2[1]),lat=slice(lat_bounds[0],lat_bounds[1])) \n", + "y_r2 = (bbox_var_r2 * bbox_wgt_r).sum(['lat','lon']).values\n", + "plt.plot(x, y_r2,\n", + " label= 'bilinear destination mask & landfrac, ' + str(np.round(y_r2.mean()* spy,1))) \n", + "\n", + "plt.title(region + ' climatology, annual regional integral (gC/y)')\n", + "plt.legend()\n", + "plt.show();\n", + "# Print mean annual flux from region (not time weighted correctly)\n", + "print(np.round(y.mean()* spy,1))\n", + "print(np.round(y_r.mean()* spy,1))\n", + "print(np.round(y_rB.mean()* spy,3))\n", + "print(np.round(y_rC.mean()* spy,3))\n", + "print(np.round(y_r2.mean()* spy,1))\n" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "537d79e9-58a4-4c50-a251-a15e1ab56852", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Size: 4B\n", + "array(1.7618376, dtype=float32)\n" + ] + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
<xarray.DataArray 'landfrac' ()> Size: 4B\n",
+       "array(1.762814, dtype=float32)
" + ], + "text/plain": [ + " Size: 4B\n", + "array(1.762814, dtype=float32)" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "print(bbox_landfrac_r.sum()) #destination land frac sum\n", + "\n", + "bbox_landfrac_rB.sum() #regridded land frac sum\n", + "#bbox_wgt_rC.plot(vmax=0.18,vmin=0.04)" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "293363a0-ffd0-4a02-a503-ba1681e3eb20", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "bbox_wgt_rB.plot(vmax=0.18,vmin=0.04)" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "3158b3ed-a0d4-436e-95ed-c0ebedfbee56", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "(bbox_landfrac_rC*bbox_var_r).sum(['lat','lon']).plot(label='dest mask')\n", + "(bbox_landfrac_rB*bbox_var_r).sum(['lat','lon']).plot(label='no mask')\n", + "plt.legend();" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "5fa74a76-d239-4155-a123-3deadd3cbec5", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "24003.926\n", + "288054.2\n" + ] + } + ], + "source": [ + " print((bbox_area_r * bbox_landfrac_r).sum().values)\n", + " print((bbox_area * bbox_landfrac).sum().values)" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "db8184cd-e3a6-4585-9eec-ead1704ec0f6", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'/glade/campaign/cesm/cesmdata/inputdata/share/meshes/ne30pg3_ESMFmesh_cdf5_c20211018.nc'" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "mesh0" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0a8b6931-dfd1-4c10-aace-fa24f93edf4f", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "NPL 2024b", + "language": "python", + "name": "npl-2024b" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.9" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/scripts/regridding/regrid_se_to_fv.py b/scripts/regridding/regrid_se_to_fv.py new file mode 100644 index 000000000..b2f232d99 --- /dev/null +++ b/scripts/regridding/regrid_se_to_fv.py @@ -0,0 +1,65 @@ +# Regrids unstructured SE grid to regular lat-lon +# Shamelessly borrowed from @maritsandstad with NorESM who deserves credit for this work +# https://github.com/NorESMhub/xesmf_clm_fates_diagnostic/blob/main/src/xesmf_clm_fates_diagnostic/plotting_methods.py + +import xarray as xr +import xesmf +import numpy as np + +def make_se_regridder(weight_file, s_data, d_data, + Method='coservative' + ): + weights = xr.open_dataset(weight_file) + in_shape = weights.src_grid_dims.load().data + + # Since xESMF expects 2D vars, we'll insert a dummy dimension of size-1 + if len(in_shape) == 1: + in_shape = [1, in_shape.item()] + + # output variable shape + out_shape = weights.dst_grid_dims.load().data.tolist()[::-1] + + dummy_in = xr.Dataset( + { + "lat": ("lat", np.empty((in_shape[0],))), + "lon": ("lon", np.empty((in_shape[1],))), + } + ) + dummy_out = xr.Dataset( + { + "lat": ("lat", weights.yc_b.data.reshape(out_shape)[:, 0]), + "lon": ("lon", weights.xc_b.data.reshape(out_shape)[0, :]), + } + ) + # Hard code masks for now, not sure this does anything? + s_mask = xr.DataArray(s_data.data.reshape(in_shape[0],in_shape[1]), dims=("lat", "lon")) + dummy_in['mask']= s_mask + + d_mask = xr.DataArray(d_data.values, dims=("lat", "lon")) + dummy_out['mask']= d_mask + + # do source and destination grids need masks here? + # See xesmf docs https://xesmf.readthedocs.io/en/stable/notebooks/Masking.html#Regridding-with-a-mask + regridder = xesmf.Regridder( + dummy_in, + dummy_out, + weights=weight_file, + # results seem insensitive to this method choice + # choices are coservative_normed, coservative, and bilinear + method=Method, + reuse_weights=True, + periodic=True, + ) + return regridder + +def regrid_se_data_bilinear(regridder, data_to_regrid): + updated = data_to_regrid.copy().transpose(..., "lndgrid").expand_dims("dummy", axis=-2) + regridded = regridder(updated.rename({"dummy": "lat", "lndgrid": "lon"}), + skipna=True, na_thres=1, + ) + return regridded + +def regrid_se_data_conservative(regridder, data_to_regrid): + updated = data_to_regrid.copy().transpose(..., "lndgrid").expand_dims("dummy", axis=-2) + regridded = regridder(updated.rename({"dummy": "lat", "lndgrid": "lon"}) ) + return regridded \ No newline at end of file diff --git a/scripts/regridding/regrid_ts_wrapper.py b/scripts/regridding/regrid_ts_wrapper.py new file mode 100644 index 000000000..740b7881e --- /dev/null +++ b/scripts/regridding/regrid_ts_wrapper.py @@ -0,0 +1,405 @@ +#Import standard modules: +import xarray as xr + +def regrid_ts_wrapper(adf): + + """ + This funtion regrids the test cases to the same horizontal + grid as the observations or baseline timeseries + + Description of needed inputs from ADF: + + case_name -> Name of CAM case provided by "cam_case_name" + input_ts_loc -> Location of CAM ts files provided by "cam_ts_loc" + output_loc -> Location to write re-gridded CAM files, specified by "cam_ts_regrid_loc" + var_list -> List of CAM output variables provided by "diag_var_list" + var_defaults -> Dict that has keys that are variable names and values that are plotting preferences/defaults. + target_list -> List of target data sets CAM could be regridded to + taget_loc -> Location of target files that CAM will be regridded to + overwrite_regrid -> Logical to determine if already existing re-gridded + files will be overwritten. Specified by "cam_overwrite_regrid" + """ + + #Import necessary modules: + import plotting_functions as pf + + from pathlib import Path + + # regridding + # Try just using the xarray method + # import xesmf as xe # This package is for regridding, and is just one potential solution. + + # Steps: + # - load ts files for model and obs + # - calculate all-time and seasonal fields (from individual months) + # - regrid one to the other (probably should be a choice) + + #Notify user that script has started: + print("\n Regridding CAM timeseries...") + + #Extract needed quantities from ADF object: + #----------------------------------------- + overwrite_regrid = adf.get_basic_info("cam_overwrite_ts_regrid", required=True) + output_loc = adf.get_basic_info("cam_ts_regrid_loc", required=True) + var_list = adf.diag_var_list + var_defaults = adf.variable_defaults + + #CAM simulation variables (these quantities are always lists): + case_names = adf.get_cam_info("cam_case_name", required=True) + input_ts_locs = adf.get_cam_info("cam_ts_loc", required=True) + + #Grab case years + #TODO, make different ts_yrs + syear_cases = adf.climo_yrs["syears"] + eyear_cases = adf.climo_yrs["eyears"] + + #Check if land fraction exists + #in the variable list: + for var in ["LANDFRAC"]: + if var in var_list: + #If so, then move it to the front of variable list so + #that it can be used to mask + #other model variables if need be: + var_idx = var_list.index(var) + var_list.pop(var_idx) + var_list.insert(0,var) + #End if + #End for + + #Create new variable that potentially stores the re-gridded + #land fraction dataset: + lnd_frc_ds = None + + #Regrid target variables (either obs or a baseline run): + if adf.compare_obs: + + #Set obs name to match baseline (non-obs) + target_list = ["Obs"] + + #Extract variable-obs dictionary: + var_obs_dict = adf.var_obs_dict + + #If dictionary is empty, then there are no observations to regrid to, + #so quit here: + if not var_obs_dict: + print("\t No observations found to regrid to, so no re-gridding will be done.") + return + #End if + + else: + + #Extract model basic variables: #WW previously baseline, not basic + target_loc = adf.get_cam_info("cam_ts_loc", required=True) + target_list = [adf.get_cam_info("cam_case_name", required=True)] + #End if + + #Grab baseline years (which may be empty strings if using Obs): + syear_baseline = adf.climo_yrs["syear_baseline"] + eyear_baseline = adf.climo_yrs["eyear_baseline"] + + #Set attributes dictionary for ts years to save in the file attributes + base_climo_yrs_attr = f"{target_list[0]}: {syear_baseline}-{eyear_baseline}" + + #----------------------------------------- + + #Set output/target data path variables: + #------------------------------------ + rgts_loc = Path(output_loc) + #------------------------------------ + + #Check if re-gridded directory exists, and if not, then create it: + if not rgts_loc.is_dir(): + print(f" {rgts_loc} not found, making new directory") + rgts_loc.mkdir(parents=True) + #End if + + #Loop over CAM cases: + for case_idx, case_name in enumerate(case_names): + + #Notify user of model case being processed: + print(f"\t Regridding case '{case_name}' :") + + #Set case ts data path: + mts_loc = Path(input_ts_locs[case_idx]) + + #Get ts years for case + syear = syear_cases[case_idx] + eyear = eyear_cases[case_idx] + + # probably want to do this one variable at a time: + for var in var_list: + + #Notify user of variable being regridded: + print(f"\t - regridding {var} (known targets: {target_list})") + + #loop over regridding targets: + for target in target_list: + + #Write to debug log if enabled: + adf.debug_log(f"regrid_example: regrid target = {target}") + + #Determine regridded variable file name: + regridded_file_loc = rgts_loc / f'{case_name}_{var}_regridded.nc' + + #Check if re-gridded file already exists and over-writing is allowed: + if regridded_file_loc.is_file() and overwrite_regrid: + #If so, then delete current file: + regridded_file_loc.unlink() + #End if + + #Check again if re-gridded file already exists: + if not regridded_file_loc.is_file(): + + #Generate timeseries (ts) file list: + mts_fils = sorted(mts_loc.glob(f"{case_name}.*.{var}.*.nc")) + + if len(mts_fils) > 1: + #Combine all cam files together into a single data set: + mts_ds = xr.open_mfdataset(mts_fils, combine='by_coords') + elif len(mts_fils) == 0: + wmsg = f"\t - Unable to find ts file for '{var}'." + wmsg += " Continuing to next variable." + print(wmsg) + continue + else: + #Open single file as new xarray dataset: + mts_ds = xr.open_dataset(mts_fils[0]) + #End if + + #Create keyword arguments dictionary for regridding function: + regrid_kwargs = {} + + #Perform regridding of variable: + rgdata_interp = _regrid(mts_ds, var, + regrid_dataset=None,#tclim_ds, + **regrid_kwargs) + + #Extract defaults for variable: + var_default_dict = var_defaults.get(var, {}) + + if 'mask' in var_default_dict: + if var_default_dict['mask'].lower() == 'land': + #Check if the land fraction has already been regridded + #and saved: + if lnd_frc_ds: + lfrac = lnd_frc_ds['LANDFRAC'] + # set the bounds of regridded lndfrac to 0 to 1 + lfrac = xr.where(lfrac>1,1,lfrac) + lfrac = xr.where(lfrac<0,0,lfrac) + + # apply land fraction mask to variable + rgdata_interp['LANDFRAC'] = lfrac + var_tmp = rgdata_interp[var] + var_tmp = pf.mask_land(var_tmp,lfrac) + rgdata_interp[var] = var_tmp + else: + print(f"LANDFRAC not found, unable to apply mask to '{var}'") + #End if + else: + #Currently only a land mask is supported, so print warning here: + wmsg = "Currently the only variable mask option is 'land'," + wmsg += f"not '{var_default_dict['mask'].lower()}'" + print(wmsg) + #End if + #End if + + #If the variable is land fraction, then save the dataset for use later: + if var == 'LANDFRAC': + lnd_frc_ds = rgdata_interp + #End if + + #Finally, write re-gridded data to output file: + #Convert the list of Path objects to a list of strings + timeseries_files_str = [str(path) for path in mts_fils] + timeseries_files_str = ', '.join(timeseries_files_str) + test_attrs_dict = { + "adf_user": adf.user, + "ts_yrs": f"{case_name}: {syear}-{eyear}", + "timeseries_files": timeseries_files_str, + } + rgdata_interp = rgdata_interp.assign_attrs(test_attrs_dict) + save_to_nc(rgdata_interp, regridded_file_loc) + rgdata_interp.close() # bpm: we are completely done with this data + + else: + print("\t Regridded file already exists, so skipping...") + #End if (file check) + #End do (target list) + #End do (variable list) + #End do (case list) + + #Notify user that script has ended: + print(" ...CAM timeseries have been regridded successfully.") + +################# +#Helper functions +################# + +def _regrid(model_dataset, var_name, regrid_dataset=None, regrid_ofrac=False, **kwargs): + + """ + Function that takes a variable from a model xarray + dataset, regrids it to another dataset's lat/lon + coordinates (if applicable) + ---------- + model_dataset -> The xarray dataset which contains the model variable data + var_name -> The name of the variable to be regridded/interpolated. + + Optional inputs: + + ps_file -> NOT APPLICABLE: A NetCDF file containing already re-gridded surface pressure + regrid_dataset -> The xarray dataset that contains the lat/lon grid that + "var_name" will be regridded to. If not present then + only the vertical interpolation will be done. + + kwargs -> Keyword arguments that contain paths to THE REST IS NOT APPLICABLE: surface pressure + and mid-level pressure files, which are necessary for + certain types of vertical interpolation. + + This function returns a new xarray dataset that contains the regridded + model variable. + """ + + #Import ADF-specific functions: + import numpy as np + import plotting_functions as pf + from regrid_se_to_fv import make_se_regridder, regrid_se_data_conservative + + #Extract keyword arguments: + if 'ps_file' in kwargs: + ps_file = kwargs['ps_file'] + else: + ps_file = None + #End if + + #Extract variable info from model data (and remove any degenerate dimensions): + mdata = model_dataset[var_name].squeeze() + mdat_ofrac = None + #if regrid_lfrac: + # if 'LANDFRAC' in model_dataset: + # mdat_lfrac = model_dataset['LANDFRAC'].squeeze() + + #Regrid variable to target dataset (if available): + if regrid_dataset: + + #Extract grid info from target data: + if 'time' in regrid_dataset.coords: + if 'lev' in regrid_dataset.coords: + tgrid = regrid_dataset.isel(time=0, lev=0).squeeze() + else: + tgrid = regrid_dataset.isel(time=0).squeeze() + #End if + #End if + + # Hardwiring for now + con_weight_file = "/glade/work/wwieder/map_ne30pg3_to_fv0.9x1.25_scripgrids_conserve_nomask_c250108.nc" + + fv_t232_file = '/glade/derecho/scratch/wwieder/ctsm5.3.018_SP_f09_t232_mask/run/ctsm5.3.018_SP_f09_t232_mask.clm2.h0.0001-01.nc' + fv_t232 = xr.open_dataset(fv_t232_file) + + model_dataset[var_name] = model_dataset[var_name].fillna(0) + model_dataset['landfrac']= model_dataset['landfrac'].fillna(0) + model_dataset[var_name] = model_dataset[var_name] * model_dataset.landfrac # weight flux by land frac + + if 'time' in model_dataset.landmask: + model_dataset['landmask'] = model_dataset.landmask.isel(time=0) + + #Regrid model data to match target grid: + # These two functions come with import regrid_se_to_fv + regridder = make_se_regridder(weight_file=con_weight_file, + s_data = model_dataset.landmask, + d_data = fv_t232.landmask, + Method = 'coservative', # Bug in xesmf needs this without "n" + ) + rgdata = regrid_se_data_conservative(regridder, model_dataset) + + rgdata[var_name] = (rgdata[var_name] / rgdata.landfrac) + + rgdata['lat'] = fv_t232.lat + rgdata['landmask'] = fv_t232.landmask + if 'time' in rgdata.landfrac: + rgdata['landfrac'] = rgdata.landfrac.isel(time=0) + + # calculate area + area_km2 = np.zeros(shape=(len(rgdata['lat']), len(rgdata['lon']))) + earth_radius_km = 6.37122e3 # in meters + + yres_degN = np.abs(np.diff(rgdata['lat'].data)) # distances between gridcell centers... + xres_degE = np.abs(np.diff(rgdata['lon'])) # ...end up with one less element, so... + yres_degN = np.append(yres_degN, yres_degN[-1]) # shift left (edges <-- centers); assume... + xres_degE = np.append(xres_degE, xres_degE[-1]) # ...last 2 distances bet. edges are equal + + dy_km = yres_degN * earth_radius_km * np.pi / 180 # distance in m + phi_rad = rgdata['lat'].data * np.pi / 180 # degrees to radians + + # grid cell area + for j in range(len(rgdata['lat'])): + for i in range(len(rgdata['lon'])): + dx_km = xres_degE[i] * np.cos(phi_rad[j]) * earth_radius_km * np.pi / 180 # distance in m + area_km2[j,i] = dy_km[j] * dx_km + + rgdata['area'] = xr.DataArray(area_km2, + coords={'lat': rgdata.lat, 'lon': rgdata.lon}, + dims=["lat", "lon"]) + rgdata['area'].attrs['units'] = 'km2' + rgdata['area'].attrs['long_name'] = 'Grid cell area' + + #Return dataset: + return rgdata + +##### + +def save_to_nc(tosave, outname, attrs=None, proc=None): + """Saves xarray variable to new netCDF file""" + + xo = tosave # used to have more stuff here. + # deal with getting non-nan fill values. + if isinstance(xo, xr.Dataset): + enc_dv = {xname: {'_FillValue': None} for xname in xo.data_vars} + else: + enc_dv = {} + #End if + enc_c = {xname: {'_FillValue': None} for xname in xo.coords} + enc = {**enc_c, **enc_dv} + if attrs is not None: + xo.attrs = attrs + if proc is not None: + xo.attrs['Processing_info'] = f"Start from file {origname}. " + proc + xo.to_netcdf(outname, format='NETCDF4', encoding=enc) + +##### + +def regrid_data(fromthis, tothis, method=1): + """Regrid data using various different methods""" + + if method == 1: + # kludgy: spatial regridding only, seems like can't automatically deal with time + if 'time' in fromthis.coords: + result = [fromthis.isel(time=t).interp_like(tothis) for t,time in enumerate(fromthis['time'])] + result = xr.concat(result, 'time') + return result + else: + return fromthis.interp_like(tothis) + elif method == 2: + newlat = tothis['lat'] + newlon = tothis['lon'] + coords = dict(fromthis.coords) + coords['lat'] = newlat + coords['lon'] = newlon + return fromthis.interp(coords) + elif method == 3: + newlat = tothis['lat'] + newlon = tothis['lon'] + ds_out = xr.Dataset({'lat': newlat, 'lon': newlon}) + regridder = xe.Regridder(fromthis, ds_out, 'bilinear') + return regridder(fromthis) + elif method==4: + # geocat + newlat = tothis['lat'] + newlon = tothis['lon'] + result = geocat.comp.linint2(fromthis, newlon, newlat, False) + result.name = fromthis.name + return result + #End if + +#####