From 545dccf9b73d1c9794e7f47821a1331c3b18bc86 Mon Sep 17 00:00:00 2001
From: Meet Vyas <87984759+Meet-Vyas-Dev@users.noreply.github.com>
Date: Sat, 12 Jul 2025 18:32:08 +0530
Subject: [PATCH 1/2] Add RT Model simulation notebook and output archive

Introduced RT_Model_simulations.ipynb for microlensing light curve simulations and integration with RTModel, including data download, extraction, and repository setup steps. Added output.tar.xz archive containing simulation data for analysis which can be used to run on any cluster.
---
 .gitattributes                                |    1 +
 .../RT_Model_simulations.ipynb                | 4164 +++++++++++++++++
 group_4_ampel_filter/output.tar.xz            |    3 +
 3 files changed, 4168 insertions(+)
 create mode 100644 .gitattributes
 create mode 100644 group_4_ampel_filter/RT_Model_simulations.ipynb
 create mode 100644 group_4_ampel_filter/output.tar.xz

diff --git a/.gitattributes b/.gitattributes
new file mode 100644
index 0000000..51c7726
--- /dev/null
+++ b/.gitattributes
@@ -0,0 +1 @@
+output.tar.xz filter=lfs diff=lfs merge=lfs -text
diff --git a/group_4_ampel_filter/RT_Model_simulations.ipynb b/group_4_ampel_filter/RT_Model_simulations.ipynb
new file mode 100644
index 0000000..d81a40c
--- /dev/null
+++ b/group_4_ampel_filter/RT_Model_simulations.ipynb
@@ -0,0 +1,4164 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "fd15ca06",
+   "metadata": {},
+   "source": [
+    "### Real-Time Modelling of Simulations using [RTModel](https://github.com/valboz/RTModel) for the LSST Vera C. Rubin Microlensing Subgroup, for the Purpose of Developing a Microlensing Filter using the AMPEL Broker. The RTModel Paper (A&A, 2024) can be accessed [here](https://www.aanda.org/articles/aa/pdf/2024/08/aa50450-24.pdf). \n",
+    "\n",
+    "- This notebook presents microlensing light curve simulations toward the Galactic Bulge, developed using the PyLIMA package (Bachelet et al.). The simulations were originally produced by Daniel Goldiens, Anibal Varela, and members of the LSST Microlensing Subgroup.\n",
+    "\n",
+    "-  The current notebook was developed during the LSST Microlensing Subgroup Sprint Week by Meet J. Vyas, with significant support from Stela Ishitani Silva (for contributions to the notebook implementation and RTModel integration).\n",
+    "\n",
+    "- We gratefully acknowledge Markus Hundertmark for his valuable discussions and guidance on microlensing filter criteria, and the broader microlensing subgroup for their insightful suggestions and feedback throughout the development process."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "34945117",
+   "metadata": {},
+   "source": [
+    "> #### 1. We will first download the tar zip from the google drive link using the python library gdown  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "892b76c5",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Downloading...\n",
+      "From (original): https://drive.google.com/uc?id=1-arXORvd7EGYeI0xA1gz_1O_kzUoCMEk\n",
+      "From (redirected): https://drive.google.com/uc?id=1-arXORvd7EGYeI0xA1gz_1O_kzUoCMEk&confirm=t&uuid=ccef2672-2fde-4ff4-b1ed-61fb4266c0eb\n",
+      "To: c:\\Users\\Meet\\OneDrive\\Desktop\\Summer 2025\\RTModel\\jupyter\\downloaded_file.tar.xz\n",
+      "100%|██████████| 514M/514M [01:39<00:00, 5.16MB/s] \n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Extraction complete!\n"
+     ]
+    }
+   ],
+   "source": [
+    "import gdown\n",
+    "import tarfile\n",
+    "\n",
+    "# Google Drive file ID\n",
+    "file_id = '1-arXORvd7EGYeI0xA1gz_1O_kzUoCMEk'\n",
+    "url = f'https://drive.google.com/uc?id={file_id}'\n",
+    "\n",
+    "# Correct output filename\n",
+    "output = 'downloaded_file.tar.xz'\n",
+    "\n",
+    "# Download\n",
+    "gdown.download(url, output, quiet=False)\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "293a40aa",
+   "metadata": {},
+   "source": [
+    "> #### 2. Once the file is downloaded we then unzip it using tar  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ee1ddd68",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Extract the file \n",
+    "with tarfile.open(output, 'r:xz') as tar_ref:\n",
+    "    tar_ref.extractall('./downloaded_file')\n",
+    "    print(\"Extraction complete!\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4883fcb9",
+   "metadata": {},
+   "source": [
+    "> #### 3. Clone the RT Model Repository"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "712f9cf4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!git clone https://github.com/valboz/RTModel.git"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9369a747",
+   "metadata": {},
+   "source": [
+    "> ##### 3.1 Once the RT Model is cloned, we can now build the library by changing the directory to the RT Model repository and then building the package through python package installer (pip)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "dfbf42f0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!cd RTModel\n",
+    "!pip install -e ."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e08ec23d",
+   "metadata": {},
+   "source": [
+    "> #### 4. Check the size and shape of the data by looking at the simulations"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d03d386d",
+   "metadata": {},
+   "source": [
+    "> #### 4.1 First print the total number of simulations in all the three folders"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "9cf48806",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "FSPL Simulations: 38030\n",
+      "PSPL Simulations: 35787\n",
+      "USBL Simulations: 12767\n",
+      "Total Simulations: 86584\n"
+     ]
+    }
+   ],
+   "source": [
+    "import os\n",
+    "\n",
+    "# --- Define folder paths ---\n",
+    "fspl_dir = 'downloaded_file/microlensing_lightcurves/FSPL'\n",
+    "pspl_dir = 'downloaded_file/microlensing_lightcurves/PSPL'\n",
+    "usbl_dir = 'downloaded_file/microlensing_lightcurves/USBL'\n",
+    "\n",
+    "# --- Count .npz files in each directory ---\n",
+    "def count_npz_files(directory):\n",
+    "    return len([f for f in os.listdir(directory) if f.endswith('.npz')])\n",
+    "\n",
+    "fspl_count = count_npz_files(fspl_dir)\n",
+    "pspl_count = count_npz_files(pspl_dir)\n",
+    "usbl_count = count_npz_files(usbl_dir)\n",
+    "\n",
+    "total_count = fspl_count + pspl_count + usbl_count\n",
+    "\n",
+    "# --- Print results ---\n",
+    "print(f\"FSPL Simulations: {fspl_count}\")\n",
+    "print(f\"PSPL Simulations: {pspl_count}\")\n",
+    "print(f\"USBL Simulations: {usbl_count}\")\n",
+    "print(f\"Total Simulations: {total_count}\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d856daa2",
+   "metadata": {},
+   "source": [
+    "> #### 4.2 We shall look at the contents of a sample file for each folder "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f1b4127a",
+   "metadata": {},
+   "source": [
+    "> #### 4.2.1 Print the keys for a sample FSPL event"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "820f8f57",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Keys in the file: ['event_id', 'fblend_W149', 'fblend_g', 'fblend_i', 'fblend_r', 'fblend_u', 'fblend_y', 'fblend_z', 'fsource_W149', 'fsource_g', 'fsource_i', 'fsource_r', 'fsource_u', 'fsource_y', 'fsource_z', 'ftotal_W149', 'ftotal_g', 'ftotal_i', 'ftotal_r', 'ftotal_u', 'ftotal_y', 'ftotal_z', 'gblend_W149', 'gblend_g', 'gblend_i', 'gblend_r', 'gblend_u', 'gblend_y', 'gblend_z', 'piEE', 'piEN', 'rho', 't0', 'tE', 'u0', 'u_time', 'u_flux', 'u_err_flux', 'u_mag', 'u_err_mag', 'g_time', 'g_flux', 'g_err_flux', 'g_mag', 'g_err_mag', 'r_time', 'r_flux', 'r_err_flux', 'r_mag', 'r_err_mag', 'i_time', 'i_flux', 'i_err_flux', 'i_mag', 'i_err_mag', 'z_time', 'z_flux', 'z_err_flux', 'z_mag', 'z_err_mag', 'y_time', 'y_flux', 'y_err_flux', 'y_mag', 'y_err_mag']\n",
+      "\n",
+      "Key: event_id\n",
+      "Shape: ()\n",
+      "Data Type: <U1\n",
+      "Content Preview:\n",
+      " 7\n",
+      "\n",
+      "Key: fblend_W149\n",
+      "Shape: ()\n",
+      "Data Type: float64\n",
+      "Content Preview:\n",
+      " 144.11694019615493\n",
+      "\n",
+      "Key: fblend_g\n",
+      "Shape: ()\n",
+      "Data Type: float64\n",
+      "Content Preview:\n",
+      " 2096.9941975092306\n",
+      "\n",
+      "Key: fblend_i\n",
+      "Shape: ()\n",
+      "Data Type: float64\n",
+      "Content Preview:\n",
+      " 2186.302306421813\n",
+      "\n",
+      "Key: fblend_r\n",
+      "Shape: ()\n",
+      "Data Type: float64\n",
+      "Content Preview:\n",
+      " 2564.573316468024\n",
+      "\n",
+      "Key: fblend_u\n",
+      "Shape: ()\n",
+      "Data Type: float64\n",
+      "Content Preview:\n",
+      " 368.50993128329395\n",
+      "\n",
+      "Key: fblend_y\n",
+      "Shape: ()\n",
+      "Data Type: float64\n",
+      "Content Preview:\n",
+      " 457.01093585090223\n",
+      "\n",
+      "Key: fblend_z\n",
+      "Shape: ()\n",
+      "Data Type: float64\n",
+      "Content Preview:\n",
+      " 1011.3140533351459\n",
+      "\n",
+      "Key: fsource_W149\n",
+      "Shape: ()\n",
+      "Data Type: float64\n",
+      "Content Preview:\n",
+      " 1888.614479219863\n",
+      "\n",
+      "Key: fsource_g\n",
+      "Shape: ()\n",
+      "Data Type: float64\n",
+      "Content Preview:\n",
+      " 4783.188963921139\n",
+      "\n",
+      "Key: fsource_i\n",
+      "Shape: ()\n",
+      "Data Type: float64\n",
+      "Content Preview:\n",
+      " 2235.506904320879\n",
+      "\n",
+      "Key: fsource_r\n",
+      "Shape: ()\n",
+      "Data Type: float64\n",
+      "Content Preview:\n",
+      " 3544.8469325876968\n",
+      "\n",
+      "Key: fsource_u\n",
+      "Shape: ()\n",
+      "Data Type: float64\n",
+      "Content Preview:\n",
+      " 472.49782278593983\n",
+      "\n",
+      "Key: fsource_y\n",
+      "Shape: ()\n",
+      "Data Type: float64\n",
+      "Content Preview:\n",
+      " 911.9781948498345\n",
+      "\n",
+      "Key: fsource_z\n",
+      "Shape: ()\n",
+      "Data Type: float64\n",
+      "Content Preview:\n",
+      " 1878.0349282239379\n",
+      "\n",
+      "Key: ftotal_W149\n",
+      "Shape: ()\n",
+      "Data Type: float64\n",
+      "Content Preview:\n",
+      " 2032.7314194160178\n",
+      "\n",
+      "Key: ftotal_g\n",
+      "Shape: ()\n",
+      "Data Type: float64\n",
+      "Content Preview:\n",
+      " 6880.183161430369\n",
+      "\n",
+      "Key: ftotal_i\n",
+      "Shape: ()\n",
+      "Data Type: float64\n",
+      "Content Preview:\n",
+      " 4421.809210742692\n",
+      "\n",
+      "Key: ftotal_r\n",
+      "Shape: ()\n",
+      "Data Type: float64\n",
+      "Content Preview:\n",
+      " 6109.420249055721\n",
+      "\n",
+      "Key: ftotal_u\n",
+      "Shape: ()\n",
+      "Data Type: float64\n",
+      "Content Preview:\n",
+      " 841.0077540692338\n",
+      "\n",
+      "Key: ftotal_y\n",
+      "Shape: ()\n",
+      "Data Type: float64\n",
+      "Content Preview:\n",
+      " 1368.9891307007367\n",
+      "\n",
+      "Key: ftotal_z\n",
+      "Shape: ()\n",
+      "Data Type: float64\n",
+      "Content Preview:\n",
+      " 2889.3489815590838\n",
+      "\n",
+      "Key: gblend_W149\n",
+      "Shape: ()\n",
+      "Data Type: float64\n",
+      "Content Preview:\n",
+      " 0.07630828937395728\n",
+      "\n",
+      "Key: gblend_g\n",
+      "Shape: ()\n",
+      "Data Type: float64\n",
+      "Content Preview:\n",
+      " 0.4384092314408936\n",
+      "\n",
+      "Key: gblend_i\n",
+      "Shape: ()\n",
+      "Data Type: float64\n",
+      "Content Preview:\n",
+      " 0.9779895119966028\n",
+      "\n",
+      "Key: gblend_r\n",
+      "Shape: ()\n",
+      "Data Type: float64\n",
+      "Content Preview:\n",
+      " 0.7234651778309411\n",
+      "\n",
+      "Key: gblend_u\n",
+      "Shape: ()\n",
+      "Data Type: float64\n",
+      "Content Preview:\n",
+      " 0.7799187922401147\n",
+      "\n",
+      "Key: gblend_y\n",
+      "Shape: ()\n",
+      "Data Type: float64\n",
+      "Content Preview:\n",
+      " 0.5011204636599379\n",
+      "\n",
+      "Key: gblend_z\n",
+      "Shape: ()\n",
+      "Data Type: float64\n",
+      "Content Preview:\n",
+      " 0.5384958704104338\n",
+      "\n",
+      "Key: piEE\n",
+      "Shape: ()\n",
+      "Data Type: float64\n",
+      "Content Preview:\n",
+      " -0.28604067714016956\n",
+      "\n",
+      "Key: piEN\n",
+      "Shape: ()\n",
+      "Data Type: float64\n",
+      "Content Preview:\n",
+      " -0.07056814983902666\n",
+      "\n",
+      "Key: rho\n",
+      "Shape: ()\n",
+      "Data Type: float64\n",
+      "Content Preview:\n",
+      " 0.02141297288549351\n",
+      "\n",
+      "Key: t0\n",
+      "Shape: ()\n",
+      "Data Type: float64\n",
+      "Content Preview:\n",
+      " 2461694.0456028786\n",
+      "\n",
+      "Key: tE\n",
+      "Shape: ()\n",
+      "Data Type: float64\n",
+      "Content Preview:\n",
+      " 3.7487981119192293\n",
+      "\n",
+      "Key: u0\n",
+      "Shape: ()\n",
+      "Data Type: float64\n",
+      "Content Preview:\n",
+      " 0.01914021517498372\n",
+      "\n",
+      "Key: u_time\n",
+      "Shape: (36,)\n",
+      "Data Type: float64\n",
+      "Content Preview:\n",
+      " [2460465.64860403 2460466.83028755 2460466.83070156 2460493.75879477\n",
+      " 2460503.53838952 2460523.69493153 2460531.5052004  2460857.74139707\n",
+      " 2460877.54840148 2461236.75537376 2461261.63245866 2461262.50606592\n",
+      " 2461262.62285532 2461264.64455532 2461264.66268907 2461265.62281787\n",
+      " 2461265.62927707 2461562.83627317 2461615.75731921 2461621.49879444\n",
+      " 2461939.7807792  2461944.7857523  2461944.78658138 2462001.6403077\n",
+      " 2462213.82353715 2462329.70472409 2462357.67006135 2462678.61450373\n",
+      " 2462711.64897354 2462713.49797855 2463072.66391823 2463072.66434419\n",
+      " 2463447.50551359 2463747.64831016 2463773.57496323 2463801.49867968]\n",
+      "\n",
+      "Key: u_flux\n",
+      "Shape: (36,)\n",
+      "Data Type: float64\n",
+      "Content Preview:\n",
+      " [841.00776825 841.00776826 841.00776826 841.00776859 841.00776872\n",
+      " 841.00776898 841.00776908 841.00777515 841.0077757  841.00779606\n",
+      " 841.00779932 841.00779944 841.00779946 841.00779975 841.00779975\n",
+      " 841.00779989 841.00779989 841.00847168 841.01267543 841.01437747\n",
+      " 841.00787523 841.00786981 841.0078698  841.00783064 841.00779007\n",
+      " 841.00778248 841.00778114 841.00777189 841.00777128 841.00777125\n",
+      " 841.0077667  841.0077667  841.00776398 841.00776253 841.00776242\n",
+      " 841.00776231]\n",
+      "\n",
+      "Key: u_err_flux\n",
+      "Shape: (36,)\n",
+      "Data Type: float64\n",
+      "Content Preview:\n",
+      " [4.0795588  4.10521619 4.10969585 4.11261474 4.10312076 4.07730303\n",
+      " 4.09033006 4.08597602 4.08886292 4.09448409 4.11635761 4.09316503\n",
+      " 4.10945252 4.08690592 4.09458179 4.10355943 4.08641652 4.09638865\n",
+      " 4.09775548 4.08382174 4.09795071 4.09336048 4.1011218  4.11300377\n",
+      " 4.09995122 4.09150339 4.09643748 4.09511905 4.09707213 4.11494836\n",
+      " 4.11519137 4.09135674 4.09204106 4.09765787 4.08783561 4.08431145]\n",
+      "\n",
+      "Key: u_mag\n",
+      "Shape: (36,)\n",
+      "Data Type: float64\n",
+      "Content Preview:\n",
+      " [19.71680564 19.72316515 19.69677866 19.73569498 19.72883082 19.70633419\n",
+      " 19.70079481 19.73014127 19.70736248 19.71290947 19.72693937 19.73887417\n",
+      " 19.72296657 19.72381723 19.7098912  19.70649689 19.7124706  19.72303633\n",
+      " 19.73003446 19.7344937  19.71516746 19.71029883 19.72685484 19.72426744\n",
+      " 19.69107848 19.70538713 19.70852643 19.72625788 19.71717244 19.74493515\n",
+      " 19.71758441 19.70931364 19.71948577 19.72538513 19.70916734 19.73704254]\n",
+      "\n",
+      "Key: u_err_mag\n",
+      "Shape: (36,)\n",
+      "Data Type: float64\n",
+      "Content Preview:\n",
+      " [0.01291529 0.01338006 0.0133064  0.01220111 0.01214567 0.01328843\n",
+      " 0.01153844 0.01323292 0.01212669 0.01284282 0.01181697 0.01258341\n",
+      " 0.01088434 0.01116374 0.01176872 0.01324228 0.01187303 0.01124851\n",
+      " 0.01141447 0.01597578 0.01601421 0.01102777 0.01105686 0.01136399\n",
+      " 0.01436228 0.01463874 0.01144567 0.01399298 0.01394279 0.01154941\n",
+      " 0.01146471 0.01225533 0.01128673 0.01159981 0.01133532 0.01138251]\n",
+      "\n",
+      "Key: g_time\n",
+      "Shape: (191,)\n",
+      "Data Type: float64\n",
+      "Content Preview:\n",
+      " [2460418.79806424 2460418.80078583 2460433.71284195 2460441.74651385\n",
+      " 2460465.67212045 2460466.85307079 2460466.85351946 2460467.7974697\n",
+      " 2460490.62357955 2460495.59384514 2460495.59761297 2460503.56315999\n",
+      " 2460503.57186729 2460503.57418409 2460527.63554942 2460527.63786602\n",
+      " 2460561.56401037 2460576.55255982 2460576.55524567 2460788.75882987\n",
+      " 2460788.76289091 2460790.78268693 2460796.71314532 2460823.86624529\n",
+      " 2460849.62218371 2460849.62354677 2460850.56971478 2460855.52782099\n",
+      " 2460857.76401456 2460871.53827983 2460872.48954202 2460873.48418206\n",
+      " 2460876.71140413 2460877.52367455 2460885.64861052 2460886.67636387\n",
+      " 2460959.52857153 2460961.51936707 2461143.78347778 2461170.70093657\n",
+      " 2461176.89616386 2461176.8966122  2461181.87741912 2461229.79386905\n",
+      " 2461229.79521292 2461234.71028269 2461236.77847573 2461243.75830873\n",
+      " 2461261.65590255 2461262.64568907 2461262.68101539 2461264.62025441\n",
+      " 2461264.68547529 2461265.64549302 2461324.52149255 2461470.83203608\n",
+      " 2461501.8225522  2461536.74890387 2461536.75037104 2461556.63727009\n",
+      " 2461557.82688548 2461557.82733389 2461560.83578503 2461562.85977354\n",
+      " 2461588.76007193 2461613.68163403 2461613.68253326 2461613.71729881\n",
+      " 2461613.71953594 2461616.6742909  2461616.67473835 2461617.73595941\n",
+      " 2461621.5225453  2461643.64371425 2461643.64595216 2461649.61060351\n",
+      " 2461649.62230529 2461673.5376678  2461673.56633357 2461677.50900966\n",
+      " 2461827.84512213 2461856.81960283 2461859.86108598 2461879.71671833\n",
+      " 2461879.78683753 2461879.78818988 2461879.81862565 2461886.74955364\n",
+      " 2461908.6820455  2461908.68249341 2461936.67660319 2461943.74866336\n",
+      " 2461944.6079103  2461944.61874891 2461965.58583809 2462210.78843007\n",
+      " 2462213.84565779 2462214.85126593 2462234.81395447 2462241.76702851\n",
+      " 2462245.71656977 2462276.83761886 2462276.89723078 2462323.70887825\n",
+      " 2462324.8439473  2462325.79644034 2462329.7279996  2462359.48835903\n",
+      " 2462415.54493858 2462561.84234219 2462561.85040194 2462563.85251467\n",
+      " 2462568.84329384 2462590.83294445 2462594.76710458 2462598.80446595\n",
+      " 2462616.71975027 2462616.72110302 2462623.66880314 2462626.72798967\n",
+      " 2462678.63796201 2462711.67256304 2462713.52200426 2462736.63210973\n",
+      " 2462736.63390172 2462741.67763196 2462928.87312971 2462929.87190961\n",
+      " 2462972.73104609 2462980.7666765  2462980.76712505 2462980.81879665\n",
+      " 2462980.81976226 2462984.72025662 2463000.67275068 2463004.69563519\n",
+      " 2463008.85828566 2463008.86434623 2463009.65151592 2463018.84565829\n",
+      " 2463038.74792002 2463041.75255922 2463042.7528633  2463042.75470696\n",
+      " 2463070.6515305  2463072.686948   2463072.68739857 2463072.71968227\n",
+      " 2463072.72012988 2463073.546168   2463089.63814953 2463098.58079336\n",
+      " 2463098.58557761 2463101.58146758 2463116.56003084 2463118.55932794\n",
+      " 2463122.55732566 2463127.52084759 2463128.50430387 2463128.5047713\n",
+      " 2463130.5204437  2463333.77257607 2463383.61549297 2463385.6501021\n",
+      " 2463426.7052787  2463440.54957224 2463473.59126528 2463475.5616384\n",
+      " 2463478.55467315 2463480.5920549  2463710.67902637 2463714.77357256\n",
+      " 2463715.76594609 2463715.76639464 2463745.6424842  2463747.59460411\n",
+      " 2463747.60981669 2463747.67073995 2463773.59925008 2463801.52179624\n",
+      " 2463801.52224816 2463801.54624919 2463804.68084379 2463804.69282563\n",
+      " 2463834.59689076 2463836.61738838 2463836.61783624 2463836.62642661\n",
+      " 2463853.56590676 2463856.54932932 2463859.53295046]\n",
+      "\n",
+      "Key: g_flux\n",
+      "Shape: (191,)\n",
+      "Data Type: float64\n",
+      "Content Preview:\n",
+      " [6880.18329957 6880.18329957 6880.18330124 6880.18330217 6880.183305\n",
+      " 6880.18330514 6880.18330514 6880.18330525 6880.18330808 6880.18330871\n",
+      " 6880.18330871 6880.18330973 6880.18330973 6880.18330973 6880.18331289\n",
+      " 6880.18331289 6880.18331756 6880.1833197  6880.1833197  6880.18335765\n",
+      " 6880.18335765 6880.18335811 6880.18335948 6880.18336602 6880.18337268\n",
+      " 6880.18337268 6880.18337294 6880.18337427 6880.18337488 6880.18337871\n",
+      " 6880.18337898 6880.18337926 6880.18338019 6880.18338042 6880.18338278\n",
+      " 6880.18338308 6880.18340671 6880.18340743 6880.18349902 6880.18351991\n",
+      " 6880.18352515 6880.18352515 6880.1835295  6880.18357823 6880.18357823\n",
+      " 6880.18358406 6880.18358657 6880.18359529 6880.18361954 6880.18362097\n",
+      " 6880.18362102 6880.18362385 6880.18362394 6880.18362536 6880.18373465\n",
+      " 6880.18465154 6880.18535048 6880.18713043 6880.18713054 6880.18935839\n",
+      " 6880.18954518 6880.18954525 6880.19005482 6880.19043052 6880.19924053\n",
+      " 6880.22816726 6880.22816922 6880.22824482 6880.22824969 6880.23531771\n",
+      " 6880.23531888 6880.23820076 6880.25029729 6880.46452026 6880.46456961\n",
+      " 6880.64592508 6880.64640635 6889.49654365 6889.5457966  6900.91853407\n",
+      " 6880.19052774 6880.18698644 6880.18676833 6880.18571703 6880.1857142\n",
+      " 6880.18571415 6880.18571292 6880.18545638 6880.18487068 6880.18487067\n",
+      " 6880.18442448 6880.18434404 6880.18433492 6880.18433481 6880.18414804\n",
+      " 6880.18352853 6880.1835259  6880.18352505 6880.18350892 6880.18350365\n",
+      " 6880.18350073 6880.18347941 6880.18347937 6880.18345218 6880.18345159\n",
+      " 6880.18345109 6880.18344905 6880.18343461 6880.18341146 6880.18336743\n",
+      " 6880.18336743 6880.18336693 6880.18336571 6880.18336046 6880.18335955\n",
+      " 6880.18335862 6880.1833546  6880.1833546  6880.18335308 6880.18335242\n",
+      " 6880.18334182 6880.18333569 6880.18333536 6880.18333135 6880.18333135\n",
+      " 6880.1833305  6880.18330461 6880.18330449 6880.1832996  6880.18329872\n",
+      " 6880.18329872 6880.18329871 6880.18329871 6880.18329829 6880.18329657\n",
+      " 6880.18329614 6880.1832957  6880.1832957  6880.18329562 6880.18329466\n",
+      " 6880.18329262 6880.18329232 6880.18329222 6880.18329222 6880.18328949\n",
+      " 6880.1832893  6880.1832893  6880.18328929 6880.18328929 6880.18328922\n",
+      " 6880.18328771 6880.18328689 6880.18328688 6880.18328661 6880.18328527\n",
+      " 6880.1832851  6880.18328475 6880.18328431 6880.18328423 6880.18328423\n",
+      " 6880.18328405 6880.18326887 6880.18326563 6880.1832655  6880.18326298\n",
+      " 6880.18326215 6880.18326025 6880.18326014 6880.18325998 6880.18325986\n",
+      " 6880.18324867 6880.18324849 6880.18324844 6880.18324844 6880.18324715\n",
+      " 6880.18324707 6880.18324707 6880.18324707 6880.18324598 6880.18324483\n",
+      " 6880.18324483 6880.18324483 6880.18324471 6880.1832447  6880.18324352\n",
+      " 6880.18324345 6880.18324345 6880.18324345 6880.1832428  6880.18324269\n",
+      " 6880.18324257]\n",
+      "\n",
+      "Key: g_err_flux\n",
+      "Shape: (191,)\n",
+      "Data Type: float64\n",
+      "Content Preview:\n",
+      " [11.73001279 11.72890447 11.73822815 11.73674572 11.72192817 11.73159836\n",
+      " 11.72162105 11.74260618 11.73795553 11.722713   11.73834741 11.72668751\n",
+      " 11.72714799 11.73147902 11.71040563 11.71872007 11.72441896 11.73228026\n",
+      " 11.7374614  11.72046074 11.73528014 11.728427   11.73338826 11.7105081\n",
+      " 11.74340666 11.71076428 11.74929785 11.73563803 11.72523774 11.72900678\n",
+      " 11.72757434 11.73872225 11.72849521 11.71755947 11.72953537 11.71930032\n",
+      " 11.72225234 11.74808921 11.72812005 11.74292979 11.73405301 11.72854637\n",
+      " 11.71590372 11.73911411 11.73884151 11.70832183 11.72295185 11.72651696\n",
+      " 11.71685965 11.73964224 11.74396866 11.72743791 11.73055838 11.71774722\n",
+      " 11.73664347 11.71578422 11.7071602  11.70967122 11.71363308 11.72088734\n",
+      " 11.72982523 11.72893857 11.73139378 11.72985933 11.7339337  11.73741028\n",
+      " 11.74212928 11.71223292 11.72707977 11.71464041 11.73817703 11.73195636\n",
+      " 11.72764256 11.71873713 11.72805184 11.72639757 11.73081412 11.72317363\n",
+      " 11.74924678 11.75743169 11.72690923 11.72276418 11.74877015 11.74015332\n",
+      " 11.73360985 11.72044368 11.74676126 11.71624513 11.73057543 11.72771077\n",
+      " 11.72580061 11.73481998 11.71624513 11.72412897 11.7410732  11.72639757\n",
+      " 11.73815999 11.72687512 11.75404611 11.72360013 11.73275756 11.74779979\n",
+      " 11.73693316 11.73047314 11.73584253 11.72549359 11.72329305 11.72342953\n",
+      " 11.71762775 11.74912763 11.74260618 11.71803738 11.73459842 11.72561299\n",
+      " 11.73659235 11.74328744 11.72189404 11.74373024 11.72881921 11.71795204\n",
+      " 11.72078496 11.73744436 11.72653401 11.73054133 11.73079707 11.73950595\n",
+      " 11.72259357 11.73916522 11.72143336 11.73185407 11.73512676 11.7130184\n",
+      " 11.75346757 11.72005119 11.72489659 11.74419005 11.74429223 11.73877336\n",
+      " 11.71728638 11.73727396 11.72566416 11.74582479 11.7202901  11.7367798\n",
+      " 11.72728443 11.71808858 11.73238254 11.70859513 11.72542537 11.73412119\n",
+      " 11.74183972 11.73129149 11.72936486 11.72498188 11.73672868 11.72303715\n",
+      " 11.72059725 11.7340871  11.74044292 11.7249307  11.7340871  11.72846111\n",
+      " 11.74565452 11.74126058 11.73175179 11.71902726 11.73769995 11.73079707\n",
+      " 11.73657531 11.72223528 11.73844964 11.71592079 11.73964224 11.73638786\n",
+      " 11.7266534  11.74149905 11.72684101 11.74441144 11.74047699 11.73079707\n",
+      " 11.73545057 11.74364509 11.71254029 11.73197341 11.74195895 11.72711388\n",
+      " 11.7271821  11.72438485 11.72859753 11.75484581 11.74768062]\n",
+      "\n",
+      "Key: g_mag\n",
+      "Shape: (191,)\n",
+      "Data Type: float64\n",
+      "Content Preview:\n",
+      " [18.78553972 18.79816099 18.78274426 18.78587482 18.79425806 18.78756135\n",
+      " 18.78486052 18.78750635 18.78493131 18.77881816 18.78537545 18.78833871\n",
+      " 18.7940784  18.78455005 18.79250474 18.77936663 18.78322628 18.79337081\n",
+      " 18.78316879 18.78027695 18.78390635 18.79134577 18.79054212 18.79911611\n",
+      " 18.78927093 18.78828077 18.78269538 18.78870792 18.79031678 18.78193264\n",
+      " 18.78656811 18.79438581 18.79335783 18.78572573 18.77293263 18.78219133\n",
+      " 18.78294814 18.78453655 18.79250072 18.79061974 18.78594761 18.79226675\n",
+      " 18.79460303 18.78985005 18.78684908 18.77191753 18.78781019 18.7839577\n",
+      " 18.78471779 18.77592878 18.79950119 18.78701245 18.78797687 18.77836771\n",
+      " 18.77555689 18.78757644 18.79673641 18.78842532 18.78105607 18.78506181\n",
+      " 18.79581444 18.79289936 18.78142061 18.78905735 18.79452674 18.79400176\n",
+      " 18.787382   18.79063735 18.78367496 18.78496127 18.77862718 18.78531908\n",
+      " 18.78377751 18.79197579 18.78430855 18.78196041 18.79065671 18.78074466\n",
+      " 18.78934609 18.79642539 18.79286051 18.79386108 18.79307876 18.7837872\n",
+      " 18.78970437 18.7758703  18.78941321 18.78405689 18.78856238 18.78924741\n",
+      " 18.78052167 18.79038322 18.78969438 18.78756072 18.78522056 18.78168068\n",
+      " 18.79295733 18.79182341 18.7932462  18.77969973 18.78734292 18.79741258\n",
+      " 18.78824081 18.78097171 18.78145154 18.77417084 18.76972264 18.78299771\n",
+      " 18.78153296 18.78582066 18.79125788 18.79324537 18.78213349 18.78639622\n",
+      " 18.78305931 18.77964848 18.78847316 18.79092562 18.78572797 18.7803837\n",
+      " 18.78162592 18.78157184 18.79151355 18.77963554 18.79023731 18.78358694\n",
+      " 18.79514989 18.78221328 18.79362692 18.7834109  18.78724689 18.79271332\n",
+      " 18.77376297 18.78388088 18.79488379 18.78924904 18.79420618 18.77255246\n",
+      " 18.77992513 18.78380978 18.78665212 18.7806987  18.78549433 18.7806283\n",
+      " 18.78386717 18.77581543 18.79090244 18.78270478 18.78154457 18.79517208\n",
+      " 18.779558   18.78153182 18.78528017 18.7890369  18.78211103 18.78254553\n",
+      " 18.77882634 18.79264338 18.79185773 18.78675544 18.77746005 18.78528322\n",
+      " 18.77813153 18.78423288 18.78788281 18.78213327 18.78767728 18.78913814\n",
+      " 18.77882015 18.7781408  18.77723835 18.78186766 18.78845624 18.77594881\n",
+      " 18.7838496  18.787605   18.78764687 18.78631084 18.79420036 18.7945169\n",
+      " 18.78020177 18.77915754 18.78387533 18.78694714 18.78019972 18.78554709\n",
+      " 18.77840036 18.78855924 18.79223753 18.79601278 18.77662193]\n",
+      "\n",
+      "Key: g_err_mag\n",
+      "Shape: (191,)\n",
+      "Data Type: float64\n",
+      "Content Preview:\n",
+      " [0.00580626 0.00563389 0.00561664 0.00573538 0.00559152 0.00560824\n",
+      " 0.00581998 0.00558701 0.00581202 0.00566245 0.00566025 0.00603907\n",
+      " 0.00566326 0.00569897 0.00586177 0.00573911 0.00580173 0.00568\n",
+      " 0.00577455 0.00612215 0.00608847 0.00592016 0.00586894 0.00563652\n",
+      " 0.00571486 0.00566171 0.005619   0.00561969 0.00585147 0.00573878\n",
+      " 0.00578088 0.00566523 0.00599799 0.00564566 0.00591567 0.00567593\n",
+      " 0.00559698 0.00560519 0.00561637 0.00561714 0.00569163 0.00563236\n",
+      " 0.00565718 0.00599507 0.00568251 0.00562963 0.00557009 0.00564555\n",
+      " 0.00579752 0.00570179 0.00565208 0.00560136 0.00560532 0.00558078\n",
+      " 0.00572092 0.00562258 0.00556083 0.00555114 0.00577519 0.00560032\n",
+      " 0.00566864 0.005616   0.00563776 0.00557513 0.00563488 0.00565853\n",
+      " 0.00573869 0.00590513 0.00599293 0.00589297 0.00609618 0.00572489\n",
+      " 0.00562649 0.00572054 0.00557934 0.00573413 0.00555591 0.00569549\n",
+      " 0.00610623 0.00556996 0.00622924 0.00571443 0.00563257 0.00566862\n",
+      " 0.00568436 0.00572506 0.00566214 0.00582281 0.00559801 0.00554006\n",
+      " 0.0057279  0.00575176 0.00558571 0.00559517 0.00566038 0.00562438\n",
+      " 0.00560196 0.00614806 0.00557279 0.00567652 0.00567504 0.00557589\n",
+      " 0.00587319 0.00569203 0.00574217 0.00560137 0.00559481 0.00557406\n",
+      " 0.00581313 0.0056409  0.00573667 0.00564001 0.00566103 0.00556902\n",
+      " 0.00565331 0.00578631 0.00565625 0.00581252 0.00594062 0.00560206\n",
+      " 0.00561002 0.00560586 0.00570692 0.00555326 0.00557131 0.00568465\n",
+      " 0.00575946 0.00577301 0.00570969 0.0056711  0.00562433 0.00565343\n",
+      " 0.0057055  0.00587288 0.0057757  0.00572279 0.00602897 0.00567519\n",
+      " 0.00591461 0.00566018 0.00560736 0.00569163 0.00563332 0.00562235\n",
+      " 0.00563902 0.00572091 0.00573579 0.0056844  0.00564231 0.00562978\n",
+      " 0.00587089 0.00566123 0.00560354 0.00574734 0.00558613 0.00567428\n",
+      " 0.00583877 0.00569402 0.00600669 0.00557383 0.00571574 0.00562374\n",
+      " 0.00557598 0.00571006 0.00566043 0.00562146 0.00615072 0.00560802\n",
+      " 0.00561879 0.0056211  0.00560512 0.00557412 0.00570679 0.00575513\n",
+      " 0.00581941 0.00566891 0.00572103 0.00558689 0.00580736 0.00566937\n",
+      " 0.00558913 0.00575324 0.00561895 0.00577511 0.00564606 0.00572984\n",
+      " 0.0056032  0.00570029 0.00558914 0.00576608 0.00555109]\n",
+      "\n",
+      "Key: r_time\n",
+      "Shape: (197,)\n",
+      "Data Type: float64\n",
+      "Content Preview:\n",
+      " [2460413.76382861 2460413.76607368 2460418.82247789 2460418.82529879\n",
+      " 2460433.73684761 2460434.90687273 2460441.77145105 2460490.64921317\n",
+      " 2460495.61875844 2460503.59636371 2460503.59887201 2460523.67215909\n",
+      " 2460527.65963422 2460527.66184432 2460527.66229365 2460561.60405709\n",
+      " 2460576.57687903 2460576.57956279 2460788.78295204 2460788.78705603\n",
+      " 2460788.79541624 2460788.79587187 2460790.80687245 2460796.73718647\n",
+      " 2460796.75046754 2460823.88578873 2460849.64690643 2460849.7955053\n",
+      " 2460850.59384957 2460855.55175278 2460871.56239695 2460872.51356223\n",
+      " 2460873.50853348 2460876.73602245 2460885.67347311 2460886.70025767\n",
+      " 2460907.67341299 2460907.67833903 2460941.55988616 2460957.54969273\n",
+      " 2460959.55305176 2460961.54310766 2461143.75938446 2461169.87546225\n",
+      " 2461170.72505242 2461176.87183624 2461176.87228712 2461179.84286621\n",
+      " 2461181.90300205 2461225.74303362 2461229.81910993 2461232.78429916\n",
+      " 2461234.73440799 2461240.71618213 2461240.7166297  2461243.78204749\n",
+      " 2461291.66549453 2461294.56040373 2461297.55255901 2461324.54549324\n",
+      " 2461470.85458083 2461498.79942706 2461501.84705698 2461505.7753406\n",
+      " 2461505.83628853 2461528.71779044 2461531.90741685 2461531.90786372\n",
+      " 2461536.77433096 2461536.77537152 2461556.66146978 2461557.85094443\n",
+      " 2461557.85139274 2461560.85980629 2461613.65745438 2461613.65837717\n",
+      " 2461613.74108907 2461613.74332387 2461616.69870009 2461617.7121409\n",
+      " 2461642.61241337 2461649.58668183 2461649.5984766  2461673.51334961\n",
+      " 2461677.53300797 2461827.8693492  2461856.84451192 2461859.88790406\n",
+      " 2461879.74062072 2461879.76247649 2461886.77467408 2461908.65824062\n",
+      " 2461908.65868869 2461936.65225497 2461939.80356461 2461940.79867863\n",
+      " 2461943.77314674 2461944.63238565 2461944.64374776 2461944.80827269\n",
+      " 2461971.56389545 2461973.69036886 2461975.74963286 2461975.75007787\n",
+      " 2461977.67779187 2461999.46924553 2462003.68028832 2462004.47859947\n",
+      " 2462021.58431167 2462025.6025848  2462047.51580243 2462210.81260417\n",
+      " 2462214.82706828 2462234.83818906 2462241.79125516 2462245.74091563\n",
+      " 2462276.86185316 2462276.87183451 2462323.73299449 2462324.82026447\n",
+      " 2462325.82016803 2462359.51317185 2462561.86606582 2462561.87414477\n",
+      " 2462563.87818744 2462568.86749181 2462588.8270359  2462591.80290889\n",
+      " 2462592.86932276 2462594.79098116 2462598.8289129  2462616.74370477\n",
+      " 2462616.74505864 2462626.70355643 2462649.69439079 2462651.68936336\n",
+      " 2462676.62697052 2462677.74755104 2462705.71136079 2462706.7012274\n",
+      " 2462708.51086164 2462713.6757716  2462736.65594642 2462736.65773405\n",
+      " 2462761.56018526 2462928.84846754 2462929.8466335  2462980.79427146\n",
+      " 2462980.79518644 2462984.7440544  2463004.67130737 2463004.71959527\n",
+      " 2463008.83400428 2463008.89284278 2463009.67558743 2463018.82154152\n",
+      " 2463038.77262616 2463042.77677215 2463042.77858309 2463070.67570922\n",
+      " 2463098.60995525 2463101.6055612  2463118.58321206 2463122.58115854\n",
+      " 2463128.52893756 2463128.52938871 2463329.84311676 2463333.79685105\n",
+      " 2463366.67238722 2463383.6393263  2463385.67418988 2463426.7292089\n",
+      " 2463478.57883919 2463480.61581802 2463658.88858824 2463710.70277064\n",
+      " 2463713.92193671 2463713.92235338 2463714.74953131 2463719.87982214\n",
+      " 2463723.74721239 2463723.74766098 2463740.66519836 2463745.61855473\n",
+      " 2463747.5708618  2463747.6335779  2463769.61865135 2463795.51328034\n",
+      " 2463804.65641499 2463804.71673392 2463804.7234461  2463834.62084544\n",
+      " 2463836.59347613 2463836.59392621 2463836.65051239 2463856.57405557\n",
+      " 2463859.50869852]\n",
+      "\n",
+      "Key: r_flux\n",
+      "Shape: (197,)\n",
+      "Data Type: float64\n",
+      "Content Preview:\n",
+      " [6109.42035102 6109.42035102 6109.42035143 6109.42035143 6109.42035267\n",
+      " 6109.42035277 6109.42035336 6109.42035774 6109.4203582  6109.42035896\n",
+      " 6109.42035896 6109.42036091 6109.42036131 6109.42036131 6109.42036131\n",
+      " 6109.42036477 6109.42036635 6109.42036635 6109.42039448 6109.42039448\n",
+      " 6109.42039448 6109.42039448 6109.42039482 6109.42039583 6109.42039584\n",
+      " 6109.42040068 6109.42040562 6109.42040565 6109.42040581 6109.4204068\n",
+      " 6109.42041009 6109.42041029 6109.4204105  6109.42041118 6109.4204131\n",
+      " 6109.42041333 6109.42041803 6109.42041803 6109.4204262  6109.42043031\n",
+      " 6109.42043084 6109.42043137 6109.42049923 6109.42051422 6109.42051474\n",
+      " 6109.4205186  6109.4205186  6109.42052051 6109.42052185 6109.42055448\n",
+      " 6109.42055797 6109.42056056 6109.42056229 6109.42056774 6109.42056774\n",
+      " 6109.42057061 6109.4206242  6109.42062807 6109.42063216 6109.42067391\n",
+      " 6109.42135365 6109.42180373 6109.42187194 6109.42196752 6109.42196907\n",
+      " 6109.42276647 6109.42292298 6109.422923   6109.42319201 6109.42319207\n",
+      " 6109.42484441 6109.42498295 6109.424983   6109.42536097 6109.45356422\n",
+      " 6109.45356571 6109.45369901 6109.45370262 6109.45894985 6109.46098943\n",
+      " 6109.6127314  6109.76247798 6109.76283653 6116.29166653 6124.86923968\n",
+      " 6109.42570483 6109.42308241 6109.42292078 6109.42214231 6109.42214166\n",
+      " 6109.42194922 6109.42151616 6109.42151615 6109.42118533 6109.42115784\n",
+      " 6109.42114947 6109.4211253  6109.42111854 6109.42111846 6109.42111717\n",
+      " 6109.42094841 6109.42093776 6109.42092775 6109.42092775 6109.42091865\n",
+      " 6109.42083097 6109.42081674 6109.42081413 6109.42076378 6109.42075335\n",
+      " 6109.42070379 6109.4205211  6109.42051855 6109.42050657 6109.42050266\n",
+      " 6109.4205005  6109.4204847  6109.4204847  6109.42046453 6109.4204641\n",
+      " 6109.42046371 6109.4204515  6109.42040172 6109.42040172 6109.42040135\n",
+      " 6109.42040044 6109.42039691 6109.42039639 6109.42039621 6109.42039588\n",
+      " 6109.42039519 6109.42039221 6109.42039221 6109.4203906  6109.42038701\n",
+      " 6109.42038671 6109.42038303 6109.42038287 6109.42037899 6109.42037886\n",
+      " 6109.42037862 6109.42037793 6109.42037498 6109.42037498 6109.42037195\n",
+      " 6109.42035517 6109.42035508 6109.4203508  6109.4203508  6109.42035048\n",
+      " 6109.42034889 6109.42034889 6109.42034857 6109.42034856 6109.4203485\n",
+      " 6109.42034779 6109.42034628 6109.42034598 6109.42034598 6109.42034396\n",
+      " 6109.42034203 6109.42034183 6109.4203407  6109.42034044 6109.42034006\n",
+      " 6109.42034006 6109.42032887 6109.42032868 6109.42032708 6109.42032628\n",
+      " 6109.42032618 6109.42032431 6109.42032209 6109.420322   6109.42031542\n",
+      " 6109.42031371 6109.4203136  6109.4203136  6109.42031357 6109.42031341\n",
+      " 6109.42031328 6109.42031328 6109.42031274 6109.42031259 6109.42031252\n",
+      " 6109.42031252 6109.42031184 6109.42031105 6109.42031077 6109.42031077\n",
+      " 6109.42031077 6109.4203099  6109.42030984 6109.42030984 6109.42030984\n",
+      " 6109.42030927 6109.42030919]\n",
+      "\n",
+      "Key: r_err_flux\n",
+      "Shape: (197,)\n",
+      "Data Type: float64\n",
+      "Content Preview:\n",
+      " [11.05589436 11.04536102 11.04787762 11.04700864 11.05017647 11.06146464\n",
+      " 11.04651981 11.0374816  11.05880645 11.06070522 11.06629116 11.04405723\n",
+      " 11.04941627 11.05878836 11.0609222  11.05936707 11.04197446 11.04957918\n",
+      " 11.0628387  11.04690002 11.04143107 11.06012658 11.04709917 11.04583179\n",
+      " 11.05520692 11.05891496 11.0647187  11.05334339 11.04948868 11.02575168\n",
+      " 11.04976018 11.0723078  11.06005425 11.06267599 11.06213361 11.07983754\n",
+      " 11.05936707 11.0579745  11.05679881 11.05556873 11.06175393 11.05155193\n",
+      " 11.0464836  11.06459218 11.04717158 11.04854742 11.03615875 11.05678072\n",
+      " 11.05824579 11.04159409 11.03237055 11.06166353 11.02968721 11.0420288\n",
+      " 11.05024887 11.04146729 11.05811919 11.04570505 11.04485401 11.05929473\n",
+      " 11.04711727 11.05773937 11.04825778 11.04664655 11.04237293 11.05099091\n",
+      " 11.03659368 11.05059274 11.0814259  11.06517058 11.05927665 11.05455562\n",
+      " 11.05057465 11.06504406 11.05857134 11.04738883 11.03719167 11.04793193\n",
+      " 11.07429456 11.05518883 11.05889687 11.06184433 11.05052035 11.06153696\n",
+      " 11.07611845 11.06390528 11.04849311 11.05238436 11.0484026  11.05347004\n",
+      " 11.04995928 11.05889687 11.06014466 11.05006787 11.0438218  11.05779363\n",
+      " 11.04834829 11.07097105 11.07438486 11.03940216 11.0529634  11.05498982\n",
+      " 11.05526119 11.0450713  11.05068324 11.054827   11.05026697 11.04941627\n",
+      " 11.05153383 11.05231198 11.0331863  11.03940216 11.07577537 11.05234817\n",
+      " 11.05095471 11.06301948 11.05196815 11.03440075 11.04994118 11.06226017\n",
+      " 11.05775746 11.04376747 11.04528859 11.03617687 11.05001357 11.06025316\n",
+      " 11.06591162 11.05312625 11.07252455 11.05142525 11.06218785 11.05656366\n",
+      " 11.04079707 11.05095471 11.04498076 11.06582125 11.06873073 11.04322417\n",
+      " 11.05701587 11.0694354  11.04389424 11.05075563 11.06110302 11.08180491\n",
+      " 11.05587627 11.05144334 11.05309007 11.04657413 11.05658175 11.03974637\n",
+      " 11.06444757 11.05591245 11.0541395  11.04450995 11.03559695 11.06139232\n",
+      " 11.05777554 11.0477328  11.04550587 11.02716645 11.0543747  11.06939926\n",
+      " 11.04144918 11.03659368 11.06739355 11.05723293 11.03943839 11.04594043\n",
+      " 11.04561451 11.05256531 11.0453248  11.05542401 11.03800707 11.05341576\n",
+      " 11.06497176 11.08120932 11.06775497 11.04814917 11.06028933 11.04671897\n",
+      " 11.06027124 11.04353204 11.04469103 11.07091685 11.05990958 11.05352432\n",
+      " 11.04762418 11.04450995 11.06050632 11.05927665 11.04981448 11.06484523\n",
+      " 11.06677912 11.04863793 11.04867413 11.05723293 11.05151573]\n",
+      "\n",
+      "Key: r_mag\n",
+      "Shape: (197,)\n",
+      "Data Type: float64\n",
+      "Content Preview:\n",
+      " [18.69793859 18.69723031 18.70154567 18.70064091 18.68931179 18.69717211\n",
+      " 18.67929593 18.68650878 18.69211906 18.69198798 18.69095435 18.70272031\n",
+      " 18.68504877 18.69748799 18.70284324 18.69370636 18.68301551 18.69380652\n",
+      " 18.69225391 18.70314123 18.69616318 18.69147178 18.69448876 18.69526433\n",
+      " 18.70007988 18.6889691  18.69484864 18.69327435 18.68758808 18.70288153\n",
+      " 18.6828162  18.6996878  18.70503    18.69965409 18.68301551 18.69422681\n",
+      " 18.69571724 18.69749855 18.70470487 18.69677335 18.69149063 18.69086081\n",
+      " 18.69113151 18.69416771 18.70129606 18.69771214 18.68968371 18.69536168\n",
+      " 18.6953626  18.69187838 18.69601799 18.68872821 18.69775018 18.695678\n",
+      " 18.70043578 18.70141975 18.69529257 18.69961582 18.6981398  18.69698391\n",
+      " 18.70173619 18.69574784 18.68482306 18.6983178  18.69187694 18.69556394\n",
+      " 18.69704043 18.69943828 18.68129412 18.69902044 18.69548125 18.69806693\n",
+      " 18.69415742 18.6976776  18.6905269  18.69272556 18.69078426 18.70062967\n",
+      " 18.70081762 18.69319487 18.69198681 18.6971027  18.70377174 18.6892105\n",
+      " 18.69385963 18.69864957 18.69171761 18.69974052 18.6955909  18.68666695\n",
+      " 18.69661968 18.68596926 18.69209206 18.69635931 18.69438935 18.69310436\n",
+      " 18.70400634 18.68889241 18.70226239 18.6924382  18.69687675 18.69811022\n",
+      " 18.68952835 18.69746874 18.69062235 18.68552833 18.69139823 18.69524024\n",
+      " 18.6884136  18.70139906 18.69553454 18.69211068 18.70207823 18.69361491\n",
+      " 18.69529274 18.69270771 18.6870801  18.69040149 18.69646063 18.68874121\n",
+      " 18.68709668 18.6993691  18.6902807  18.69794809 18.69369515 18.68895342\n",
+      " 18.69218376 18.69295394 18.70023192 18.68755088 18.69903175 18.69802172\n",
+      " 18.69052852 18.69796741 18.69381914 18.68950653 18.69118766 18.69093174\n",
+      " 18.70287803 18.6957639  18.70067057 18.69612681 18.68918515 18.69170991\n",
+      " 18.69024986 18.71321932 18.70004535 18.69945762 18.69183609 18.70070091\n",
+      " 18.68554454 18.69579746 18.69565784 18.68908319 18.7028283  18.69382834\n",
+      " 18.69679937 18.68765415 18.70256012 18.70034622 18.6940337  18.69151034\n",
+      " 18.68689043 18.69622577 18.68746752 18.70250609 18.70362784 18.68576958\n",
+      " 18.69701336 18.69887134 18.70774728 18.70403236 18.69062122 18.70226134\n",
+      " 18.68986662 18.69903046 18.70400091 18.69082214 18.69388555 18.69737012\n",
+      " 18.69304773 18.68479508 18.68729549 18.69195019 18.6890553  18.69101684\n",
+      " 18.70352573 18.6931151  18.69092914 18.69948662 18.69614237 18.69126208\n",
+      " 18.69517546 18.70293287 18.70115136 18.70268437 18.69638766]\n",
+      "\n",
+      "Key: r_err_mag\n",
+      "Shape: (197,)\n",
+      "Data Type: float64\n",
+      "Content Preview:\n",
+      " [0.00596524 0.00560522 0.00583664 0.00588764 0.00615988 0.00561853\n",
+      " 0.00654831 0.00596258 0.00571743 0.00574181 0.00591082 0.00582918\n",
+      " 0.00586884 0.0057758  0.00614631 0.00572277 0.00605331 0.006055\n",
+      " 0.00656953 0.00598659 0.00578863 0.00566057 0.00576965 0.00622821\n",
+      " 0.00591647 0.0059313  0.00570289 0.00569627 0.00570553 0.00590046\n",
+      " 0.00587452 0.00583425 0.00588434 0.00619565 0.00581364 0.00564045\n",
+      " 0.00569398 0.00577262 0.00593547 0.00587381 0.00565332 0.00564001\n",
+      " 0.00591041 0.00571398 0.00568867 0.00565029 0.00567696 0.00570807\n",
+      " 0.0056943  0.00576493 0.00638055 0.00569654 0.00561644 0.00587325\n",
+      " 0.00576381 0.00576947 0.00560224 0.00585527 0.00594109 0.0059487\n",
+      " 0.00585148 0.0060061  0.00595106 0.00562747 0.00571882 0.00572392\n",
+      " 0.00575026 0.00569195 0.00601557 0.00571348 0.00582252 0.00604112\n",
+      " 0.00591244 0.00599917 0.00561979 0.00567737 0.00577479 0.00586838\n",
+      " 0.00600957 0.00568609 0.00586284 0.00562458 0.00567787 0.00567608\n",
+      " 0.0060823  0.00573334 0.00609204 0.00563725 0.0057685  0.00583313\n",
+      " 0.00570372 0.0059595  0.00629581 0.00568602 0.0057602  0.0056249\n",
+      " 0.00579839 0.00594623 0.00575927 0.0057055  0.00561752 0.00616219\n",
+      " 0.00569345 0.00596791 0.00569494 0.00576582 0.00576023 0.00652253\n",
+      " 0.00598961 0.00571297 0.0058427  0.00576184 0.00574582 0.00568199\n",
+      " 0.00570068 0.00567112 0.00560503 0.00622037 0.0057986  0.00620148\n",
+      " 0.00577082 0.00579067 0.00582225 0.00592128 0.00609025 0.00580907\n",
+      " 0.00597927 0.00599328 0.00562495 0.00573767 0.00565927 0.00597692\n",
+      " 0.00601892 0.00571544 0.0057618  0.00567919 0.00566038 0.00577576\n",
+      " 0.005913   0.00627326 0.0057614  0.00597944 0.00604991 0.00593563\n",
+      " 0.00603587 0.00567638 0.00594852 0.00570292 0.00574615 0.00580761\n",
+      " 0.0057103  0.00612002 0.00578019 0.00619079 0.00575945 0.00620051\n",
+      " 0.00603179 0.00579262 0.00577835 0.00583003 0.00563587 0.00587984\n",
+      " 0.00595156 0.00576776 0.00728184 0.0056545  0.0057823  0.00575376\n",
+      " 0.00565523 0.00625963 0.00596266 0.00594733 0.00651218 0.00589456\n",
+      " 0.00578024 0.0059736  0.00571385 0.00608907 0.00596168 0.00570371\n",
+      " 0.00567575 0.00568275 0.00567396 0.00600668 0.00582394 0.00578024\n",
+      " 0.00634305 0.00614664 0.00583821 0.00580146 0.00572499 0.00566603\n",
+      " 0.00583321 0.00585967 0.00591128 0.00592284 0.00847031]\n",
+      "\n",
+      "Key: i_time\n",
+      "Shape: (195,)\n",
+      "Data Type: float64\n",
+      "Content Preview:\n",
+      " [2460413.78810289 2460413.79035961 2460418.90999502 2460418.92119697\n",
+      " 2460422.79913617 2460434.89513457 2460448.87601338 2460448.87791685\n",
+      " 2460460.87393564 2460476.78726082 2460486.75582001 2460487.73999085\n",
+      " 2460487.75696332 2460519.47723573 2460519.48814042 2460527.6860035\n",
+      " 2460527.68645289 2460531.68564428 2460547.59740502 2460547.59785531\n",
+      " 2460561.62787979 2460569.60057162 2460569.60101762 2460573.52315404\n",
+      " 2460778.82782883 2460788.82019987 2460788.82068242 2460796.7750416\n",
+      " 2460828.80937298 2460829.85238467 2460830.65885534 2460830.66064845\n",
+      " 2460831.60412306 2460849.81941635 2460858.59068279 2460859.75504461\n",
+      " 2460870.4903996  2460870.56897654 2460871.758389   2460897.48073549\n",
+      " 2460897.67763434 2460907.69726057 2460907.70218367 2460924.64417943\n",
+      " 2460932.6032346  2460941.58400897 2460950.50799635 2461105.86210525\n",
+      " 2461127.85192243 2461133.79465237 2461161.75331821 2461169.88693012\n",
+      " 2461179.86685776 2461196.81889545 2461196.83476256 2461225.76730752\n",
+      " 2461240.74022323 2461242.78385968 2461243.48301336 2461297.57647805\n",
+      " 2461308.51428134 2461308.51473197 2461485.7903402  2461511.81359056\n",
+      " 2461513.76581661 2461515.80403285 2461531.89563844 2461531.89608732\n",
+      " 2461542.71339365 2461552.84266896 2461608.49410175 2461608.49589212\n",
+      " 2461608.7321137  2461608.73345288 2461610.49468325 2461625.62668729\n",
+      " 2461626.49023751 2461627.50186057 2461643.58925453 2461643.59069491\n",
+      " 2461671.55226489 2461671.55271389 2461842.86191498 2461863.82346862\n",
+      " 2461863.82436795 2461866.76767644 2461869.77368322 2461894.70803127\n",
+      " 2461905.87423696 2461908.91991003 2461922.85892571 2461935.7879797\n",
+      " 2461937.83081427 2461964.74814683 2461964.74993619 2461964.75038365\n",
+      " 2461973.7144095  2461975.77335803 2461975.77380201 2461987.67382716\n",
+      " 2461992.67806895 2461999.48036034 2462003.70316641 2462003.70406124\n",
+      " 2462004.48988337 2462021.60813674 2462025.62630062 2462047.54029274\n",
+      " 2462223.81537988 2462247.81308814 2462252.72724024 2462252.72768771\n",
+      " 2462290.86018738 2462344.7358523  2462375.59747668 2462375.60196996\n",
+      " 2462398.56300436 2462398.58205832 2462399.55360509 2462571.8822299\n",
+      " 2462582.8459353  2462602.76644879 2462607.76120021 2462608.74694116\n",
+      " 2462608.76529122 2462636.62381063 2462636.62425828 2462643.89135528\n",
+      " 2462645.86930978 2462649.71939621 2462651.71358466 2462671.90653784\n",
+      " 2462677.77175309 2462691.48905004 2462704.54321544 2462705.73520433\n",
+      " 2462706.72517025 2462713.70014444 2462727.48429706 2462728.47987392\n",
+      " 2462747.58681552 2462752.59658005 2462761.58418327 2462764.54726165\n",
+      " 2462962.80905839 2462986.90186725 2463000.89673878 2463015.81561118\n",
+      " 2463015.8160691  2463015.89356991 2463015.89938909 2463016.66865737\n",
+      " 2463016.66910425 2463016.83929307 2463045.57946526 2463046.7569306\n",
+      " 2463048.75025209 2463074.6644475  2463082.47976134 2463084.65476562\n",
+      " 2463084.66296079 2463103.60735177 2463136.51554979 2463140.50398449\n",
+      " 2463140.50678734 2463383.71077841 2463383.86083875 2463402.57059435\n",
+      " 2463437.48728611 2463457.47581157 2463467.58014534 2463467.58341474\n",
+      " 2463502.5195428  2463658.90182235 2463672.87036748 2463693.80013889\n",
+      " 2463695.82042093 2463699.72471469 2463699.72605389 2463719.89128658\n",
+      " 2463722.78413817 2463723.77199937 2463723.77246063 2463740.68938367\n",
+      " 2463752.60886494 2463752.67337732 2463768.75320813 2463784.57203426\n",
+      " 2463792.74710208 2463794.73499679 2463796.73596145 2463822.68378463\n",
+      " 2463841.5597197  2463854.56999726 2463868.49127256]\n",
+      "\n",
+      "Key: i_flux\n",
+      "Shape: (195,)\n",
+      "Data Type: float64\n",
+      "Content Preview:\n",
+      " [4421.80927505 4421.80927505 4421.80927531 4421.80927531 4421.80927551\n",
+      " 4421.80927615 4421.80927691 4421.80927691 4421.80927757 4421.80927847\n",
+      " 4421.80927905 4421.80927911 4421.80927911 4421.80928102 4421.80928102\n",
+      " 4421.80928153 4421.80928153 4421.80928178 4421.8092828  4421.8092828\n",
+      " 4421.80928372 4421.80928425 4421.80928425 4421.80928451 4421.80930141\n",
+      " 4421.80930246 4421.80930246 4421.80930331 4421.80930694 4421.80930707\n",
+      " 4421.80930716 4421.80930716 4421.80930728 4421.8093095  4421.80931061\n",
+      " 4421.80931076 4421.80931215 4421.80931216 4421.80931232 4421.80931584\n",
+      " 4421.80931587 4421.80931731 4421.80931731 4421.80931983 4421.80932105\n",
+      " 4421.80932246 4421.80932389 4421.8093568  4421.80936334 4421.80936522\n",
+      " 4421.80937489 4421.80937797 4421.80938194 4421.8093892  4421.80938921\n",
+      " 4421.80940337 4421.80941173 4421.80941293 4421.80941335 4421.80945236\n",
+      " 4421.80946234 4421.80946234 4421.81003787 4421.81039926 4421.8104367\n",
+      " 4421.81047787 4421.81089663 4421.81089665 4421.81131089 4421.8118517\n",
+      " 4421.82572634 4421.82572767 4421.82590461 4421.82590562 4421.82730509\n",
+      " 4421.84863082 4421.85064574 4421.85317512 4421.94014892 4421.94016369\n",
+      " 4424.87580445 4424.87603759 4421.81160599 4421.81077627 4421.81077624\n",
+      " 4421.81069514 4421.81061876 4421.81016806 4421.8100377  4421.81000728\n",
+      " 4421.80988996 4421.80980612 4421.80979459 4421.80967481 4421.8096748\n",
+      " 4421.8096748  4421.80964499 4421.80963868 4421.80963868 4421.80960562\n",
+      " 4421.80959325 4421.8095777  4421.8095687  4421.8095687  4421.80956707\n",
+      " 4421.80953531 4421.80952873 4421.80949748 4421.80937721 4421.8093686\n",
+      " 4421.80936695 4421.80936695 4421.80935529 4421.80934167 4421.80933508\n",
+      " 4421.80933507 4421.80933065 4421.80933064 4421.80933046 4421.80930587\n",
+      " 4421.80930464 4421.80930248 4421.80930196 4421.80930185 4421.80930185\n",
+      " 4421.80929902 4421.80929902 4421.8092983  4421.80929811 4421.80929774\n",
+      " 4421.80929755 4421.80929566 4421.80929513 4421.80929391 4421.80929278\n",
+      " 4421.80929268 4421.8092926  4421.80929202 4421.80929089 4421.80929081\n",
+      " 4421.8092893  4421.80928892 4421.80928825 4421.80928803 4421.80927584\n",
+      " 4421.80927459 4421.80927389 4421.80927316 4421.80927316 4421.80927315\n",
+      " 4421.80927315 4421.80927311 4421.80927311 4421.80927311 4421.80927174\n",
+      " 4421.80927168 4421.80927159 4421.80927042 4421.80927007 4421.80926998\n",
+      " 4421.80926998 4421.80926916 4421.80926781 4421.80926765 4421.80926765\n",
+      " 4421.80925944 4421.80925944 4421.80925889 4421.8092579  4421.80925736\n",
+      " 4421.80925709 4421.80925709 4421.80925619 4421.8092526  4421.8092523\n",
+      " 4421.80925186 4421.80925182 4421.80925174 4421.80925174 4421.80925133\n",
+      " 4421.80925127 4421.80925125 4421.80925125 4421.80925091 4421.80925067\n",
+      " 4421.80925067 4421.80925035 4421.80925004 4421.80924989 4421.80924985\n",
+      " 4421.80924981 4421.80924933 4421.80924899 4421.80924875 4421.80924851]\n",
+      "\n",
+      "Key: i_err_flux\n",
+      "Shape: (195,)\n",
+      "Data Type: float64\n",
+      "Content Preview:\n",
+      " [9.41305476 9.40189343 9.40614693 9.40848553 9.39450903 9.40372267\n",
+      " 9.39427485 9.41020722 9.39655256 9.41592268 9.424118   9.41490308\n",
+      " 9.4073163  9.41534917 9.39127254 9.40208488 9.38652225 9.39122995\n",
+      " 9.40406295 9.39672283 9.41160985 9.40988842 9.40476475 9.40536017\n",
+      " 9.40029787 9.41428701 9.39629714 9.39546699 9.39174105 9.41721827\n",
+      " 9.40993092 9.40497741 9.4100797  9.39274188 9.40229759 9.40138288\n",
+      " 9.38777929 9.41881097 9.38564862 9.40202106 9.40820918 9.39944679\n",
+      " 9.41296978 9.39799979 9.4005319  9.40419056 9.41713332 9.40548776\n",
+      " 9.409187   9.40010638 9.4064446  9.39653127 9.40652965 9.40457336\n",
+      " 9.40493487 9.39631843 9.39048455 9.38982428 9.3873745  9.40395661\n",
+      " 9.41165235 9.40076593 9.39997872 9.39448775 9.41626253 9.40423309\n",
+      " 9.40295698 9.41498805 9.40034042 9.38277145 9.38400767 9.40746512\n",
+      " 9.38865273 9.40967587 9.41641121 9.39925529 9.40065955 9.41360717\n",
+      " 9.40920826 9.4009574  9.42167713 9.40863433 9.40891067 9.38588302\n",
+      " 9.41101482 9.40914449 9.42021231 9.39653127 9.41653864 9.40240395\n",
+      " 9.39778697 9.40057445 9.40357379 9.3939555  9.40297825 9.40495614\n",
+      " 9.39427485 9.41067479 9.37581996 9.39925529 9.4064446  9.40312714\n",
+      " 9.37741969 9.38601087 9.4057004  9.42210168 9.40738008 9.38347484\n",
+      " 9.41471189 9.40478602 9.38616002 9.40550902 9.39591401 9.4033611\n",
+      " 9.42528514 9.40888941 9.41273605 9.38863142 9.39504125 9.40199979\n",
+      " 9.41392585 9.4028719  9.41641121 9.38645833 9.40782653 9.40199979\n",
+      " 9.41505178 9.40282936 9.40034042 9.40874062 9.40680605 9.40591303\n",
+      " 9.41107858 9.39638228 9.40744386 9.40463715 9.41292728 9.41056853\n",
+      " 9.40984591 9.4287645  9.39506253 9.39691439 9.38326169 9.41120609\n",
+      " 9.39084661 9.39565857 9.40905946 9.40757142 9.40604061 9.39591401\n",
+      " 9.40040425 9.40495614 9.40978214 9.40436069 9.4019147  9.41092982\n",
+      " 9.4059343  9.40484981 9.42337519 9.42273846 9.38304855 9.40484981\n",
+      " 9.41099357 9.39606301 9.39122995 9.40389281 9.40340364 9.38426342\n",
+      " 9.41522172 9.40038297 9.41396834 9.397638   9.37880589 9.42008493\n",
+      " 9.39731877 9.4127573  9.40693361 9.39640357 9.40818792 9.40648712\n",
+      " 9.40793282 9.41339471 9.38875924 9.40416929 9.4007872  9.4075289\n",
+      " 9.40916574 9.40419056 9.39851052 9.41800403 9.40491361 9.40650839\n",
+      " 9.38790712 9.39035676 9.39612686]\n",
+      "\n",
+      "Key: i_mag\n",
+      "Shape: (195,)\n",
+      "Data Type: float64\n",
+      "Content Preview:\n",
+      " [18.74026907 18.72686377 18.72708665 18.71470553 18.73737545 18.74067984\n",
+      " 18.74054819 18.73988591 18.74272842 18.73230507 18.74268734 18.74099402\n",
+      " 18.74324616 18.74720518 18.74360758 18.73010832 18.73366634 18.74073351\n",
+      " 18.72719998 18.73493891 18.73812243 18.73254788 18.72918284 18.73197316\n",
+      " 18.73681221 18.74671025 18.73972973 18.74344967 18.75290406 18.73356334\n",
+      " 18.74158887 18.73840636 18.73452096 18.74097899 18.74612633 18.73070958\n",
+      " 18.73311026 18.72940057 18.73410868 18.73864285 18.73961885 18.74170143\n",
+      " 18.74598141 18.7309585  18.73262468 18.74275587 18.73822806 18.73771599\n",
+      " 18.72335497 18.72772899 18.73920454 18.72741977 18.75285147 18.72935023\n",
+      " 18.74829744 18.74149746 18.74221754 18.74175895 18.73856262 18.74202865\n",
+      " 18.73588033 18.74236826 18.73091198 18.72833367 18.7286673  18.7298333\n",
+      " 18.73792979 18.73255484 18.72390923 18.72918987 18.74401816 18.7433891\n",
+      " 18.72306242 18.72984338 18.72803531 18.74047262 18.74243287 18.73869759\n",
+      " 18.73730989 18.73094197 18.74705307 18.72804497 18.73589792 18.73371259\n",
+      " 18.73882685 18.74098853 18.72925879 18.73232788 18.74708738 18.73128498\n",
+      " 18.73514695 18.72519699 18.74584779 18.74615139 18.72283172 18.75084467\n",
+      " 18.72692769 18.72842733 18.74902082 18.73443523 18.734973   18.73854059\n",
+      " 18.73496487 18.75164928 18.74260781 18.73392801 18.73079678 18.74030289\n",
+      " 18.74319081 18.73045913 18.72304565 18.73173009 18.73521082 18.7332819\n",
+      " 18.7291366  18.73761143 18.7442387  18.73693638 18.73539643 18.7273057\n",
+      " 18.7357193  18.73154285 18.73673163 18.7339549  18.73151855 18.7325133\n",
+      " 18.73871237 18.72899848 18.72632458 18.73903819 18.72821233 18.73762671\n",
+      " 18.73317598 18.74903238 18.73561974 18.74790552 18.73882482 18.74053679\n",
+      " 18.72981133 18.74593475 18.73772098 18.73145329 18.73834786 18.74015432\n",
+      " 18.73580908 18.72563769 18.73748823 18.73911459 18.7221188  18.72487083\n",
+      " 18.73611154 18.72941707 18.74090703 18.7618948  18.73164241 18.73163812\n",
+      " 18.73213739 18.72612374 18.72649183 18.75015235 18.73881769 18.74308367\n",
+      " 18.74424975 18.72168304 18.73681073 18.73866692 18.73998663 18.73594638\n",
+      " 18.73562701 18.73015062 18.72895072 18.72884812 18.74095369 18.74235969\n",
+      " 18.75335119 18.74661977 18.73032939 18.74199419 18.73110621 18.73316505\n",
+      " 18.731378   18.72694496 18.73985958 18.74156151 18.74518565 18.74518999\n",
+      " 18.73233588 18.73186206 18.74901336 18.73332394 18.73785884 18.73468763\n",
+      " 18.74866339 18.72138089 18.73673736]\n",
+      "\n",
+      "Key: i_err_mag\n",
+      "Shape: (195,)\n",
+      "Data Type: float64\n",
+      "Content Preview:\n",
+      " [0.00588922 0.00706744 0.00902093 0.00760264 0.00812811 0.00649089\n",
+      " 0.00809257 0.00944619 0.00818401 0.00665773 0.00669172 0.00909292\n",
+      " 0.00849634 0.0070643  0.00689643 0.00709438 0.00632257 0.00760773\n",
+      " 0.00765852 0.00941883 0.00602157 0.00629192 0.00928728 0.00600181\n",
+      " 0.00613235 0.00646212 0.00787971 0.00850643 0.00703586 0.00716354\n",
+      " 0.00613351 0.00632514 0.00662256 0.00617218 0.00614076 0.00597907\n",
+      " 0.00606012 0.0076841  0.00651158 0.00614353 0.00675663 0.00601746\n",
+      " 0.00587088 0.0071329  0.00717152 0.00769155 0.00592845 0.00591391\n",
+      " 0.0066953  0.00815626 0.00765503 0.00671268 0.0090683  0.00596525\n",
+      " 0.00687077 0.00587191 0.00730282 0.0107204  0.0066806  0.00644485\n",
+      " 0.00611306 0.00613181 0.00627349 0.00591589 0.00675841 0.00675544\n",
+      " 0.00666679 0.00692736 0.00828257 0.00758316 0.00618715 0.0071623\n",
+      " 0.00740879 0.00606324 0.00789376 0.00617949 0.00697985 0.00602569\n",
+      " 0.0121312  0.00733545 0.00651001 0.00679638 0.00594591 0.00668247\n",
+      " 0.00716909 0.00592443 0.00744928 0.00768122 0.0062134  0.00700909\n",
+      " 0.0063269  0.00627232 0.00779655 0.00623006 0.0063434  0.00711503\n",
+      " 0.00671389 0.00779797 0.00627883 0.00613649 0.006173   0.00681012\n",
+      " 0.00604349 0.00606374 0.01324676 0.0060592  0.00670646 0.00622866\n",
+      " 0.00588309 0.00588361 0.01239304 0.00736784 0.00617245 0.00631382\n",
+      " 0.00646657 0.00614679 0.00679262 0.00602525 0.00655833 0.00657834\n",
+      " 0.00615292 0.00632263 0.00587904 0.00590828 0.00689621 0.00876368\n",
+      " 0.00847712 0.00643715 0.00629862 0.00798464 0.0075175  0.00862012\n",
+      " 0.00640695 0.00685957 0.00591417 0.00739238 0.00599802 0.00747347\n",
+      " 0.00595922 0.00750143 0.00602087 0.00659676 0.00599829 0.00816504\n",
+      " 0.00698842 0.00772631 0.00678323 0.0067897  0.00803534 0.00839634\n",
+      " 0.00609496 0.0061117  0.0069008  0.01367044 0.00616226 0.00683869\n",
+      " 0.00623333 0.00618496 0.00652694 0.0101093  0.00604501 0.00697406\n",
+      " 0.005986   0.00613683 0.00789604 0.00625074 0.00773214 0.00685296\n",
+      " 0.0065019  0.00613246 0.00704558 0.00607019 0.0058927  0.00608651\n",
+      " 0.00814885 0.00695637 0.00687491 0.00725697 0.00735151 0.00702997\n",
+      " 0.00643325 0.00618631 0.00714132 0.00631328 0.00636449 0.00675112\n",
+      " 0.0061667  0.00709318 0.00726097 0.00665317 0.0068917  0.00682107\n",
+      " 0.00637661 0.01030502 0.00854853]\n",
+      "\n",
+      "Key: z_time\n",
+      "Shape: (176,)\n",
+      "Data Type: float64\n",
+      "Content Preview:\n",
+      " [2460417.91201631 2460422.82411949 2460423.79523008 2460448.68487232\n",
+      " 2460448.90042318 2460448.9022932  2460460.84999303 2460476.81150077\n",
+      " 2460486.77932986 2460487.76421999 2460519.46642205 2460519.51258333\n",
+      " 2460547.62144448 2460547.62189237 2460720.89184426 2460778.85212861\n",
+      " 2460778.9214835  2460779.82622393 2460828.83267409 2460829.82832743\n",
+      " 2460830.63381057 2460830.6356024  2460831.62810198 2460858.56625865\n",
+      " 2460859.77909464 2460870.51434385 2460870.59345811 2460871.7822899\n",
+      " 2460895.49419955 2460897.46953437 2460924.620362   2460932.62699812\n",
+      " 2460950.53178307 2461104.90441802 2461105.84282623 2461127.82748668\n",
+      " 2461133.7707594  2461154.91950917 2461161.77820546 2461196.7946633\n",
+      " 2461196.85871127 2461216.60166221 2461224.78572891 2461225.80431721\n",
+      " 2461242.75998615 2461243.50727099 2461243.71243728 2461251.66002408\n",
+      " 2461271.62466445 2461305.52100512 2461308.53932317 2461308.53977041\n",
+      " 2461309.52157014 2461485.81421757 2461511.83811516 2461513.79075415\n",
+      " 2461515.82841325 2461515.82937893 2461540.90572179 2461596.76384501\n",
+      " 2461607.70769625 2461608.5182387  2461608.52003549 2461608.70793881\n",
+      " 2461608.70928141 2461610.51895911 2461625.65071397 2461626.51478473\n",
+      " 2461643.61335594 2461643.61476028 2461670.52174669 2461671.57677421\n",
+      " 2461842.84029183 2461863.84794788 2461863.84885255 2461866.79160236\n",
+      " 2461869.79759415 2461894.73208049 2461905.88552881 2461908.93204902\n",
+      " 2461922.83498368 2461935.81208704 2461964.77194727 2461964.77373132\n",
+      " 2461964.77417719 2461987.69753272 2461992.70188259 2462017.59707412\n",
+      " 2462043.55788118 2462049.51846718 2462247.83758961 2462252.75127302\n",
+      " 2462252.75172261 2462290.88445613 2462342.72986238 2462342.73030837\n",
+      " 2462344.71186015 2462347.72319769 2462375.62141751 2462398.53884995\n",
+      " 2462398.60606991 2462399.57753878 2462553.89181534 2462571.89387435\n",
+      " 2462582.86725451 2462602.79048754 2462602.82216525 2462607.78535122\n",
+      " 2462608.72311372 2462608.79002222 2462608.7918361  2462636.64755306\n",
+      " 2462636.64800099 2462643.84443307 2462645.89618671 2462671.88637888\n",
+      " 2462691.51287067 2462698.73614473 2462727.47333397 2462728.46904722\n",
+      " 2462734.48958396 2462752.54647166 2462752.62033999 2462752.64230305\n",
+      " 2462752.64275223 2462754.58416137 2462754.58457803 2462755.55874676\n",
+      " 2462764.57124326 2462962.83348746 2462986.91328609 2463000.90862547\n",
+      " 2463015.83995306 2463015.84040291 2463015.86782909 2463015.87009941\n",
+      " 2463015.91104999 2463016.69259857 2463016.86327149 2463048.77441696\n",
+      " 2463074.68850898 2463082.46876516 2463084.63066043 2463084.68774493\n",
+      " 2463103.58253859 2463112.58949288 2463136.53964233 2463137.56233793\n",
+      " 2463140.4931193  2463140.53097327 2463383.88312411 2463431.71364744\n",
+      " 2463436.54110636 2463437.51147909 2463437.54381421 2463457.48670458\n",
+      " 2463467.60452779 2463467.6077159  2463502.54369976 2463672.88160677\n",
+      " 2463693.77630226 2463695.84462964 2463699.74851421 2463699.74985603\n",
+      " 2463722.75981303 2463726.68797162 2463752.63281657 2463752.64904853\n",
+      " 2463768.77741443 2463784.59641517 2463794.75896344 2463796.71189937\n",
+      " 2463811.66550396 2463811.66595203 2463820.63922958 2463854.54528419]\n",
+      "\n",
+      "Key: z_flux\n",
+      "Shape: (176,)\n",
+      "Data Type: float64\n",
+      "Content Preview:\n",
+      " [2889.34903576 2889.34903597 2889.34903602 2889.34903713 2889.34903714\n",
+      " 2889.34903714 2889.3490377  2889.34903846 2889.34903895 2889.349039\n",
+      " 2889.3490406  2889.3490406  2889.3490421  2889.3490421  2889.34905304\n",
+      " 2889.34905773 2889.34905773 2889.34905781 2889.34906238 2889.34906248\n",
+      " 2889.34906256 2889.34906256 2889.34906266 2889.34906545 2889.34906558\n",
+      " 2889.34906676 2889.34906677 2889.3490669  2889.34906962 2889.34906985\n",
+      " 2889.3490732  2889.34907423 2889.34907662 2889.34910404 2889.34910426\n",
+      " 2889.34910975 2889.34911133 2889.34911736 2889.34911946 2889.34913147\n",
+      " 2889.3491315  2889.34913939 2889.34914294 2889.3491434  2889.34915141\n",
+      " 2889.34915178 2889.34915188 2889.34915597 2889.34916725 2889.34919056\n",
+      " 2889.34919294 2889.34919294 2889.34919373 2889.34967663 2889.34998041\n",
+      " 2889.35001189 2889.35004649 2889.3500465  2889.35067937 2889.35747513\n",
+      " 2889.36237583 2889.3628713  2889.36287243 2889.36299066 2889.36299151\n",
+      " 2889.36419958 2889.38214378 2889.38384053 2889.45918969 2889.45920181\n",
+      " 2891.52035011 2891.93592536 2889.35099477 2889.35029616 2889.35029613\n",
+      " 2889.35022806 2889.35016394 2889.34978553 2889.34967618 2889.34965063\n",
+      " 2889.34955231 2889.34948161 2889.34937135 2889.34937134 2889.34937134\n",
+      " 2889.34931324 2889.34930286 2889.34926    2889.34922678 2889.34922036\n",
+      " 2889.34911417 2889.34911278 2889.34911278 2889.34910298 2889.34909193\n",
+      " 2889.34909193 2889.34909155 2889.34909098 2889.34908601 2889.34908229\n",
+      " 2889.34908228 2889.34908213 2889.34906322 2889.34906147 2889.34906044\n",
+      " 2889.34905863 2889.34905862 2889.34905818 2889.3490581  2889.3490581\n",
+      " 2889.3490581  2889.34905571 2889.34905571 2889.34905512 2889.34905496\n",
+      " 2889.3490529  2889.34905143 2889.3490509  2889.34904889 2889.34904882\n",
+      " 2889.34904842 2889.34904724 2889.34904723 2889.34904723 2889.34904723\n",
+      " 2889.34904711 2889.34904711 2889.34904705 2889.34904648 2889.34903624\n",
+      " 2889.3490352  2889.34903461 2889.34903399 2889.34903399 2889.34903399\n",
+      " 2889.34903399 2889.34903399 2889.34903396 2889.34903395 2889.34903268\n",
+      " 2889.34903169 2889.3490314  2889.34903132 2889.34903132 2889.34903064\n",
+      " 2889.34903032 2889.3490295  2889.34902947 2889.34902937 2889.34902937\n",
+      " 2889.34902246 2889.34902131 2889.3490212  2889.34902118 2889.34902118\n",
+      " 2889.34902072 2889.34902049 2889.34902049 2889.34901973 2889.34901647\n",
+      " 2889.3490161  2889.34901607 2889.349016   2889.349016   2889.3490156\n",
+      " 2889.34901554 2889.3490151  2889.3490151  2889.34901483 2889.34901458\n",
+      " 2889.34901441 2889.34901438 2889.34901415 2889.34901415 2889.34901401\n",
+      " 2889.34901349]\n",
+      "\n",
+      "Key: z_err_flux\n",
+      "Shape: (176,)\n",
+      "Data Type: float64\n",
+      "Content Preview:\n",
+      " [7.59323383 7.60342028 7.58841222 7.61001971 7.60644464 7.57524917\n",
+      " 7.5995263  7.61146504 7.60476167 7.59246995 7.5983156  7.59373426\n",
+      " 7.5849588  7.59447167 7.61511655 7.61049276 7.61149131 7.60520874\n",
+      " 7.59291775 7.60573468 7.61477511 7.6098883  7.60331507 7.60923124\n",
+      " 7.58337656 7.59444534 7.6186613  7.60954664 7.59081023 7.60336767\n",
+      " 7.61587815 7.60378853 7.60039473 7.60789064 7.59289141 7.60512985\n",
+      " 7.59491935 7.57928757 7.59286507 7.60828496 7.61020368 7.6035518\n",
+      " 7.59663083 7.5988157  7.61606197 7.59642021 7.60599763 7.61356684\n",
+      " 7.58928192 7.59439267 7.6109395  7.59357623 7.60799579 7.59352356\n",
+      " 7.60178926 7.60597134 7.59623591 7.60502465 7.5978418  7.61046648\n",
+      " 7.60089468 7.61109716 7.59260166 7.60084206 7.60031578 7.59813135\n",
+      " 7.59178503 7.5923119  7.59320749 7.59981579 7.59196944 7.62577209\n",
+      " 7.57883896 7.61713857 7.60113149 7.61556301 7.60436717 7.61582563\n",
+      " 7.60975689 7.61393459 7.60739114 7.59673614 7.62118101 7.59302311\n",
+      " 7.5968941  7.61159642 7.60873183 7.59091562 7.60202605 7.61453873\n",
+      " 7.5998421  7.59175869 7.59931576 7.59462968 7.5914689  7.60036841\n",
+      " 7.595446   7.60668127 7.60938894 7.61393459 7.61965879 7.57173692\n",
+      " 7.61621953 7.60597134 7.61488017 7.59668349 7.60484056 7.61178034\n",
+      " 7.58664616 7.59491935 7.60486686 7.60515615 7.6091261  7.60689161\n",
+      " 7.61506402 7.59828928 7.61204309 7.6056032  7.60883697 7.59955262\n",
+      " 7.60781177 7.58337656 7.59136351 7.59931576 7.59997368 7.62152216\n",
+      " 7.59997368 7.60336767 7.60320985 7.58424683 7.59531435 7.60510355\n",
+      " 7.61180662 7.6091261  7.59718369 7.60455127 7.61847754 7.60507725\n",
+      " 7.60094731 7.61624579 7.60807466 7.58796415 7.61488017 7.59497202\n",
+      " 7.59009881 7.6126211  7.61312025 7.59128448 7.58121362 7.61264737\n",
+      " 7.60013158 7.59123178 7.6069442  7.60249959 7.60980946 7.60896839\n",
+      " 7.60725969 7.60410415 7.59036231 7.60110518 7.59657818 7.59821032\n",
+      " 7.59391862 7.61750615 7.57865423 7.60641834 7.60523504 7.60168402\n",
+      " 7.60247328 7.5968941  7.58424683 7.59849985 7.59913153 7.58134553\n",
+      " 7.5850379  7.60994087]\n",
+      "\n",
+      "Key: z_mag\n",
+      "Shape: (176,)\n",
+      "Data Type: float64\n",
+      "Content Preview:\n",
+      " [18.83575607 18.80432726 18.80290899 18.79007562 18.80485609 18.81410261\n",
+      " 18.80056298 18.81136186 18.82253119 18.79366319 18.79923675 18.81770898\n",
+      " 18.82117911 18.79141646 18.82616337 18.79846255 18.81400054 18.79439323\n",
+      " 18.79654265 18.80650251 18.81064634 18.80304456 18.80865765 18.82553409\n",
+      " 18.81650996 18.81758586 18.80334284 18.82205987 18.79024177 18.79619222\n",
+      " 18.81441374 18.7927547  18.80221112 18.8152395  18.80446809 18.81268932\n",
+      " 18.79754126 18.81496823 18.79718502 18.81313383 18.79749824 18.80173739\n",
+      " 18.80900129 18.80819371 18.80337983 18.80865629 18.80303169 18.80504637\n",
+      " 18.82400617 18.81517956 18.80731308 18.78830381 18.81605403 18.80933902\n",
+      " 18.81851764 18.80596097 18.79937536 18.81588767 18.798656   18.8267066\n",
+      " 18.81456472 18.81475478 18.81131412 18.81340865 18.82784517 18.82524461\n",
+      " 18.7965536  18.81025611 18.79719468 18.8034982  18.80832151 18.79568668\n",
+      " 18.81626754 18.82048949 18.80599716 18.81386979 18.80907333 18.80335281\n",
+      " 18.80411561 18.7936997  18.80729538 18.79364764 18.81363175 18.80289063\n",
+      " 18.80736758 18.8069335  18.81106812 18.8171664  18.80604748 18.79823556\n",
+      " 18.79170571 18.79399181 18.81343563 18.80371519 18.8001374  18.8065632\n",
+      " 18.80563885 18.81239823 18.81182972 18.80097874 18.81615186 18.81073883\n",
+      " 18.81017674 18.80609787 18.81027666 18.81140297 18.81207977 18.80609918\n",
+      " 18.78981589 18.80716446 18.80976926 18.81080085 18.8044908  18.81522959\n",
+      " 18.81673106 18.82922693 18.80766939 18.79472092 18.80609489 18.81572623\n",
+      " 18.81484809 18.79706605 18.79934728 18.81383726 18.79336565 18.81535173\n",
+      " 18.8086609  18.81049347 18.81714182 18.80613963 18.82939646 18.80513861\n",
+      " 18.80577879 18.79119192 18.80338075 18.79789983 18.80571079 18.8085337\n",
+      " 18.80564182 18.79748418 18.79703604 18.78345364 18.82434271 18.81326679\n",
+      " 18.8187634  18.81457601 18.79934106 18.80513586 18.81201619 18.82097783\n",
+      " 18.82042478 18.79841715 18.81397147 18.8162298  18.81136398 18.8130343\n",
+      " 18.80257067 18.80312917 18.8030325  18.8173322  18.80310651 18.80911712\n",
+      " 18.80114243 18.81021313 18.79513494 18.80354989 18.80974834 18.81468505\n",
+      " 18.81871575 18.81809713 18.80343756 18.80134436 18.80654322 18.80953035\n",
+      " 18.80711598 18.80117603]\n",
+      "\n",
+      "Key: z_err_mag\n",
+      "Shape: (176,)\n",
+      "Data Type: float64\n",
+      "Content Preview:\n",
+      " [0.00893613 0.00895886 0.01094395 0.00941914 0.00893527 0.00897444\n",
+      " 0.01109798 0.00797277 0.00897442 0.01112004 0.00738132 0.00783365\n",
+      " 0.00908258 0.00927347 0.00871978 0.0078213  0.00757879 0.00985379\n",
+      " 0.00828698 0.01181461 0.01017812 0.01162714 0.00782526 0.01129687\n",
+      " 0.01029224 0.0073884  0.00704455 0.00679644 0.01149074 0.00690174\n",
+      " 0.00792985 0.00721393 0.00931118 0.00732349 0.00751003 0.00949513\n",
+      " 0.00860586 0.00888532 0.01224381 0.00837046 0.00906788 0.00689499\n",
+      " 0.00818926 0.00903911 0.00692008 0.00872058 0.00833742 0.0094543\n",
+      " 0.00967106 0.01373087 0.0077614  0.01253807 0.00968461 0.01097361\n",
+      " 0.01195679 0.00706554 0.00730343 0.00774857 0.00831633 0.0074687\n",
+      " 0.0080707  0.00926184 0.00738756 0.00776852 0.01480393 0.01377515\n",
+      " 0.01057791 0.00757593 0.00685972 0.00717811 0.00843116 0.0097573\n",
+      " 0.01019367 0.01045862 0.00965618 0.00780818 0.00942131 0.0078612\n",
+      " 0.01147621 0.00838051 0.00692363 0.00807077 0.01116986 0.00820101\n",
+      " 0.00976266 0.00705959 0.0071913  0.00746684 0.00786252 0.00839734\n",
+      " 0.00764721 0.00748389 0.00726424 0.01191399 0.00685072 0.00677215\n",
+      " 0.00904089 0.00727953 0.00812198 0.0177905  0.01060765 0.00712882\n",
+      " 0.00845989 0.00695684 0.00934625 0.00836254 0.00886404 0.00688315\n",
+      " 0.00833138 0.00777749 0.01041809 0.01075209 0.00745929 0.01381045\n",
+      " 0.00707108 0.00899251 0.00854496 0.0120385  0.00685925 0.00852318\n",
+      " 0.00692659 0.0091798  0.00838687 0.0107539  0.00998989 0.00927885\n",
+      " 0.01082382 0.00733024 0.00936843 0.00914391 0.00844947 0.00825978\n",
+      " 0.00879308 0.00834071 0.0072638  0.00796737 0.00766982 0.00873199\n",
+      " 0.00970215 0.01369179 0.00682031 0.00904221 0.00675592 0.0076263\n",
+      " 0.00755618 0.00815097 0.00774466 0.00893797 0.00875564 0.00830315\n",
+      " 0.00828163 0.00868391 0.01080016 0.00736195 0.00877605 0.00932326\n",
+      " 0.0083414  0.00890449 0.00690628 0.00686743 0.00791615 0.00994167\n",
+      " 0.00993153 0.0085265  0.00883661 0.00839146 0.00786843 0.00830139\n",
+      " 0.00723255 0.00728764 0.00874837 0.00758578 0.00812132 0.00728566\n",
+      " 0.00842535 0.01288684]\n",
+      "\n",
+      "Key: y_time\n",
+      "Shape: (58,)\n",
+      "Data Type: float64\n",
+      "Content Preview:\n",
+      " [2460420.76134779 2460420.76179396 2460420.78412008 2460420.78456951\n",
+      " 2460423.81985597 2460486.80347048 2460513.65683135 2460513.65820054\n",
+      " 2460513.68019187 2460513.68153996 2460779.80150927 2460779.80196064\n",
+      " 2460828.8572206  2460868.74366275 2460868.7661664  2460895.51930775\n",
+      " 2461216.62715323 2461224.80989676 2461243.73634425 2461251.68449148\n",
+      " 2461271.64843791 2461305.54519652 2461607.7317455  2461607.73356517\n",
+      " 2461633.66438614 2461633.68678417 2461949.74297658 2461949.76584707\n",
+      " 2461949.79882656 2461982.51544244 2461982.51680725 2462043.58156984\n",
+      " 2462049.54228129 2462698.71223136 2462698.72482004 2462752.57048706\n",
+      " 2462752.66600928 2462752.66645846 2462754.56001281 2462754.60905943\n",
+      " 2462755.5827591  2463081.67754061 2463081.70056778 2463082.75369168\n",
+      " 2463082.77167399 2463082.77614701 2463112.61386918 2463431.73729341\n",
+      " 2463431.73774063 2463436.56587914 2463510.4908948  2463510.50048868\n",
+      " 2463791.70266858 2463791.70451836 2463791.7261974  2463811.68938784\n",
+      " 2463811.68983407 2463820.66317106]\n",
+      "\n",
+      "Key: y_flux\n",
+      "Shape: (58,)\n",
+      "Data Type: float64\n",
+      "Content Preview:\n",
+      " [1368.98915708 1368.98915708 1368.98915708 1368.98915708 1368.98915715\n",
+      " 1368.98915857 1368.98915923 1368.98915923 1368.98915923 1368.98915923\n",
+      " 1368.98916773 1368.98916773 1368.98916995 1368.98917198 1368.98917198\n",
+      " 1368.98917346 1368.98920735 1368.98920907 1368.98921342 1368.9892154\n",
+      " 1368.98922088 1368.9892322  1368.99564197 1368.9956425  1369.01542243\n",
+      " 1369.01546091 1368.98934456 1368.98934452 1368.98934446 1368.98929736\n",
+      " 1368.98929736 1368.98924977 1368.98924665 1368.98916437 1368.98916437\n",
+      " 1368.98916259 1368.98916259 1368.98916259 1368.98916253 1368.98916253\n",
+      " 1368.9891625  1368.98915492 1368.98915492 1368.9891549  1368.9891549\n",
+      " 1368.9891549  1368.98915438 1368.98915    1368.98915    1368.98914995\n",
+      " 1368.98914916 1368.98914916 1368.98914668 1368.98914668 1368.98914668\n",
+      " 1368.98914652 1368.98914652 1368.98914646]\n",
+      "\n",
+      "Key: y_err_flux\n",
+      "Shape: (58,)\n",
+      "Data Type: float64\n",
+      "Content Preview:\n",
+      " [5.21969348 5.20664959 5.22972275 5.2349212  5.21957853 5.23706024\n",
+      " 5.21735565 5.24480696 5.23595264 5.24637017 5.24461629 5.2271981\n",
+      " 5.2293403  5.23824398 5.2343481  5.24351027 5.22769548 5.22903433\n",
+      " 5.22562915 5.22478708 5.24408238 5.2403435  5.24415866 5.22379173\n",
+      " 5.24057249 5.21908038 5.22631802 5.23094638 5.226892   5.24156465\n",
+      " 5.21992337 5.23748031 5.24450188 5.24179359 5.2401145  5.23098461\n",
+      " 5.23778579 5.22248983 5.23052579 5.25185681 5.2211493  5.23919841\n",
+      " 5.22964626 5.22513158 5.23469197 5.22869008 5.24       5.21321398\n",
+      " 5.22956977 5.24808537 5.24297625 5.22199196 5.23312526 5.22543778\n",
+      " 5.23224617 5.22888133 5.22428943 5.22620321]\n",
+      "\n",
+      "Key: y_mag\n",
+      "Shape: (58,)\n",
+      "Data Type: float64\n",
+      "Content Preview:\n",
+      " [18.82051888 18.85803517 18.88593369 18.80605442 18.83257531 18.82133759\n",
+      " 18.83532369 18.85772925 18.84457094 18.81354384 18.82668436 18.84620144\n",
+      " 18.85420269 18.83894073 18.82321074 18.83630076 18.84680507 18.86075186\n",
+      " 18.84018843 18.85254962 18.82220209 18.82030158 18.84164397 18.85150958\n",
+      " 18.84405026 18.85489461 18.8351601  18.84740542 18.81870687 18.82612016\n",
+      " 18.82758939 18.86511119 18.8316055  18.85312146 18.84980814 18.82542151\n",
+      " 18.85043576 18.87449383 18.82150775 18.76152313 18.80525323 18.83205962\n",
+      " 18.8642753  18.83845999 18.84003709 18.82368051 18.84159342 18.85101906\n",
+      " 18.85026444 18.81542797 18.83134295 18.81130256 18.84404442 18.82684769\n",
+      " 18.8427649  18.83760677 18.84980434 18.82385993]\n",
+      "\n",
+      "Key: y_err_mag\n",
+      "Shape: (58,)\n",
+      "Data Type: float64\n",
+      "Content Preview:\n",
+      " [0.01239091 0.01155766 0.04074531 0.02042496 0.0158342  0.05479528\n",
+      " 0.00998152 0.01138952 0.01282715 0.01281117 0.02640214 0.01332892\n",
+      " 0.01281721 0.02629342 0.01764791 0.01176557 0.01199081 0.01125482\n",
+      " 0.01258856 0.03160934 0.00999402 0.01156343 0.01356354 0.02183233\n",
+      " 0.01497214 0.01586543 0.01578264 0.01327491 0.01321834 0.01560645\n",
+      " 0.02001727 0.0224553  0.02144533 0.01538305 0.01283254 0.01772353\n",
+      " 0.01933818 0.02004411 0.01523689 0.04211821 0.0215603  0.01955515\n",
+      " 0.01498602 0.01485565 0.01817733 0.01437734 0.01592947 0.01247614\n",
+      " 0.02270639 0.019014   0.01397871 0.01770312 0.01830791 0.01543192\n",
+      " 0.01351181 0.0210644  0.01559409 0.02227828]\n"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "\n",
+    "# Load the .npz file\n",
+    "data = np.load('downloaded_file/microlensing_lightcurves/FSPL/FSPL_7.npz')\n",
+    "\n",
+    "# Check the keys (array names) inside the file\n",
+    "print(\"Keys in the file:\", data.files)\n",
+    "\n",
+    "# Inspect each array\n",
+    "for key in data.files:\n",
+    "    print(f\"\\nKey: {key}\")\n",
+    "    print(\"Shape:\", data[key].shape)\n",
+    "    print(\"Data Type:\", data[key].dtype)\n",
+    "    print(\"Content Preview:\\n\", data[key])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b477f9a7",
+   "metadata": {},
+   "source": [
+    "> #### 4.2.2 Print the keys for a sample PSPL event"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "a30b0a92",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Keys in the file: ['event_id', 'fblend_W149', 'fblend_g', 'fblend_i', 'fblend_r', 'fblend_u', 'fblend_y', 'fblend_z', 'fsource_W149', 'fsource_g', 'fsource_i', 'fsource_r', 'fsource_u', 'fsource_y', 'fsource_z', 'ftotal_W149', 'ftotal_g', 'ftotal_i', 'ftotal_r', 'ftotal_u', 'ftotal_y', 'ftotal_z', 'gblend_W149', 'gblend_g', 'gblend_i', 'gblend_r', 'gblend_u', 'gblend_y', 'gblend_z', 'piEE', 'piEN', 't0', 'tE', 'u0', 'g_time', 'g_flux', 'g_err_flux', 'g_mag', 'g_err_mag', 'r_time', 'r_flux', 'r_err_flux', 'r_mag', 'r_err_mag', 'i_time', 'i_flux', 'i_err_flux', 'i_mag', 'i_err_mag', 'z_time', 'z_flux', 'z_err_flux', 'z_mag', 'z_err_mag', 'y_time', 'y_flux', 'y_err_flux', 'y_mag', 'y_err_mag']\n",
+      "\n",
+      "Key: event_id\n",
+      "Shape: ()\n",
+      "Data Type: <U3\n",
+      "Content Preview:\n",
+      " 100\n",
+      "\n",
+      "Key: fblend_W149\n",
+      "Shape: ()\n",
+      "Data Type: float64\n",
+      "Content Preview:\n",
+      " 2.3655040565862606\n",
+      "\n",
+      "Key: fblend_g\n",
+      "Shape: ()\n",
+      "Data Type: float64\n",
+      "Content Preview:\n",
+      " 0.37794436477094373\n",
+      "\n",
+      "Key: fblend_i\n",
+      "Shape: ()\n",
+      "Data Type: float64\n",
+      "Content Preview:\n",
+      " 0.02343107365532937\n",
+      "\n",
+      "Key: fblend_r\n",
+      "Shape: ()\n",
+      "Data Type: float64\n",
+      "Content Preview:\n",
+      " 1.5473835086971925\n",
+      "\n",
+      "Key: fblend_u\n",
+      "Shape: ()\n",
+      "Data Type: float64\n",
+      "Content Preview:\n",
+      " 0.006533789049130573\n",
+      "\n",
+      "Key: fblend_y\n",
+      "Shape: ()\n",
+      "Data Type: float64\n",
+      "Content Preview:\n",
+      " 1.1262864436795763\n",
+      "\n",
+      "Key: fblend_z\n",
+      "Shape: ()\n",
+      "Data Type: float64\n",
+      "Content Preview:\n",
+      " 0.5284418837887133\n",
+      "\n",
+      "Key: fsource_W149\n",
+      "Shape: ()\n",
+      "Data Type: float64\n",
+      "Content Preview:\n",
+      " 4.3531147302230675\n",
+      "\n",
+      "Key: fsource_g\n",
+      "Shape: ()\n",
+      "Data Type: float64\n",
+      "Content Preview:\n",
+      " 0.890291410784644\n",
+      "\n",
+      "Key: fsource_i\n",
+      "Shape: ()\n",
+      "Data Type: float64\n",
+      "Content Preview:\n",
+      " 4.9654138009447975\n",
+      "\n",
+      "Key: fsource_r\n",
+      "Shape: ()\n",
+      "Data Type: float64\n",
+      "Content Preview:\n",
+      " 1.8317083656800357\n",
+      "\n",
+      "Key: fsource_u\n",
+      "Shape: ()\n",
+      "Data Type: float64\n",
+      "Content Preview:\n",
+      " 0.02347165097530041\n",
+      "\n",
+      "Key: fsource_y\n",
+      "Shape: ()\n",
+      "Data Type: float64\n",
+      "Content Preview:\n",
+      " 1.6791471942721399\n",
+      "\n",
+      "Key: fsource_z\n",
+      "Shape: ()\n",
+      "Data Type: float64\n",
+      "Content Preview:\n",
+      " 4.346843017245161\n",
+      "\n",
+      "Key: ftotal_W149\n",
+      "Shape: ()\n",
+      "Data Type: float64\n",
+      "Content Preview:\n",
+      " 6.718618786809328\n",
+      "\n",
+      "Key: ftotal_g\n",
+      "Shape: ()\n",
+      "Data Type: float64\n",
+      "Content Preview:\n",
+      " 1.2682357755555878\n",
+      "\n",
+      "Key: ftotal_i\n",
+      "Shape: ()\n",
+      "Data Type: float64\n",
+      "Content Preview:\n",
+      " 4.988844874600127\n",
+      "\n",
+      "Key: ftotal_r\n",
+      "Shape: ()\n",
+      "Data Type: float64\n",
+      "Content Preview:\n",
+      " 3.3790918743772282\n",
+      "\n",
+      "Key: ftotal_u\n",
+      "Shape: ()\n",
+      "Data Type: float64\n",
+      "Content Preview:\n",
+      " 0.030005440024430983\n",
+      "\n",
+      "Key: ftotal_y\n",
+      "Shape: ()\n",
+      "Data Type: float64\n",
+      "Content Preview:\n",
+      " 2.805433637951716\n",
+      "\n",
+      "Key: ftotal_z\n",
+      "Shape: ()\n",
+      "Data Type: float64\n",
+      "Content Preview:\n",
+      " 4.875284901033874\n",
+      "\n",
+      "Key: gblend_W149\n",
+      "Shape: ()\n",
+      "Data Type: float64\n",
+      "Content Preview:\n",
+      " 0.5434049417909655\n",
+      "\n",
+      "Key: gblend_g\n",
+      "Shape: ()\n",
+      "Data Type: float64\n",
+      "Content Preview:\n",
+      " 0.424517590749133\n",
+      "\n",
+      "Key: gblend_i\n",
+      "Shape: ()\n",
+      "Data Type: float64\n",
+      "Content Preview:\n",
+      " 0.004718856190972645\n",
+      "\n",
+      "Key: gblend_r\n",
+      "Shape: ()\n",
+      "Data Type: float64\n",
+      "Content Preview:\n",
+      " 0.8447761323199038\n",
+      "\n",
+      "Key: gblend_u\n",
+      "Shape: ()\n",
+      "Data Type: float64\n",
+      "Content Preview:\n",
+      " 0.278369385093796\n",
+      "\n",
+      "Key: gblend_y\n",
+      "Shape: ()\n",
+      "Data Type: float64\n",
+      "Content Preview:\n",
+      " 0.6707490847267787\n",
+      "\n",
+      "Key: gblend_z\n",
+      "Shape: ()\n",
+      "Data Type: float64\n",
+      "Content Preview:\n",
+      " 0.12156912078311415\n",
+      "\n",
+      "Key: piEE\n",
+      "Shape: ()\n",
+      "Data Type: float64\n",
+      "Content Preview:\n",
+      " 0.015918263028338393\n",
+      "\n",
+      "Key: piEN\n",
+      "Shape: ()\n",
+      "Data Type: float64\n",
+      "Content Preview:\n",
+      " 0.015246348102087914\n",
+      "\n",
+      "Key: t0\n",
+      "Shape: ()\n",
+      "Data Type: float64\n",
+      "Content Preview:\n",
+      " 2461653.454228777\n",
+      "\n",
+      "Key: tE\n",
+      "Shape: ()\n",
+      "Data Type: float64\n",
+      "Content Preview:\n",
+      " 683.9858183268905\n",
+      "\n",
+      "Key: u0\n",
+      "Shape: ()\n",
+      "Data Type: float64\n",
+      "Content Preview:\n",
+      " 3.934595856987061e-05\n",
+      "\n",
+      "Key: g_time\n",
+      "Shape: (5,)\n",
+      "Data Type: float64\n",
+      "Content Preview:\n",
+      " [2461643.64371425 2461643.64595216 2461649.61060351 2461649.62230529\n",
+      " 2461673.56633357]\n",
+      "\n",
+      "Key: g_flux\n",
+      "Shape: (5,)\n",
+      "Data Type: float64\n",
+      "Content Preview:\n",
+      " [ 63.38959717  63.40376005 159.74275348 160.22650471  29.72929327]\n",
+      "\n",
+      "Key: g_err_flux\n",
+      "Shape: (5,)\n",
+      "Data Type: float64\n",
+      "Content Preview:\n",
+      " [1.13437207 1.13154761 1.77549993 1.78974859 0.7610519 ]\n",
+      "\n",
+      "Key: g_mag\n",
+      "Shape: (5,)\n",
+      "Data Type: float64\n",
+      "Content Preview:\n",
+      " [23.77769818 23.9227165  22.94549056 22.81076031 24.36227543]\n",
+      "\n",
+      "Key: g_err_mag\n",
+      "Shape: (5,)\n",
+      "Data Type: float64\n",
+      "Content Preview:\n",
+      " [0.15993775 0.09933343 0.06933344 0.03640461 0.48835132]\n",
+      "\n",
+      "Key: r_time\n",
+      "Shape: (2,)\n",
+      "Data Type: float64\n",
+      "Content Preview:\n",
+      " [2461649.58668183 2461649.5984766 ]\n",
+      "\n",
+      "Key: r_flux\n",
+      "Shape: (2,)\n",
+      "Data Type: float64\n",
+      "Content Preview:\n",
+      " [327.41273134 328.40355824]\n",
+      "\n",
+      "Key: r_err_flux\n",
+      "Shape: (2,)\n",
+      "Data Type: float64\n",
+      "Content Preview:\n",
+      " [2.56132778 2.56889081]\n",
+      "\n",
+      "Key: r_mag\n",
+      "Shape: (2,)\n",
+      "Data Type: float64\n",
+      "Content Preview:\n",
+      " [21.86477138 21.87695224]\n",
+      "\n",
+      "Key: r_err_mag\n",
+      "Shape: (2,)\n",
+      "Data Type: float64\n",
+      "Content Preview:\n",
+      " [0.02298863 0.02698219]\n",
+      "\n",
+      "Key: i_time\n",
+      "Shape: (4,)\n",
+      "Data Type: float64\n",
+      "Content Preview:\n",
+      " [2461643.58925453 2461643.59069491 2461671.55226489 2461671.55271389]\n",
+      "\n",
+      "Key: i_flux\n",
+      "Shape: (4,)\n",
+      "Data Type: float64\n",
+      "Content Preview:\n",
+      " [349.54652998 349.59680668 182.50375462 182.49910198]\n",
+      "\n",
+      "Key: i_err_flux\n",
+      "Shape: (4,)\n",
+      "Data Type: float64\n",
+      "Content Preview:\n",
+      " [2.64370952 2.62449995 1.90147311 1.91112532]\n",
+      "\n",
+      "Key: i_mag\n",
+      "Shape: (4,)\n",
+      "Data Type: float64\n",
+      "Content Preview:\n",
+      " [21.68165873 21.53051896 22.28727404 22.1039655 ]\n",
+      "\n",
+      "Key: i_err_mag\n",
+      "Shape: (4,)\n",
+      "Data Type: float64\n",
+      "Content Preview:\n",
+      " [0.12909818 0.05895997 0.07781327 0.08978887]\n",
+      "\n",
+      "Key: z_time\n",
+      "Shape: (2,)\n",
+      "Data Type: float64\n",
+      "Content Preview:\n",
+      " [2461643.61335594 2461643.61476028]\n",
+      "\n",
+      "Key: z_flux\n",
+      "Shape: (2,)\n",
+      "Data Type: float64\n",
+      "Content Preview:\n",
+      " [307.24755    307.29067237]\n",
+      "\n",
+      "Key: z_err_flux\n",
+      "Shape: (2,)\n",
+      "Data Type: float64\n",
+      "Content Preview:\n",
+      " [2.49415316 2.46787358]\n",
+      "\n",
+      "Key: z_mag\n",
+      "Shape: (2,)\n",
+      "Data Type: float64\n",
+      "Content Preview:\n",
+      " [21.24573163 21.20854696]\n",
+      "\n",
+      "Key: z_err_mag\n",
+      "Shape: (2,)\n",
+      "Data Type: float64\n",
+      "Content Preview:\n",
+      " [0.03378367 0.03898138]\n",
+      "\n",
+      "Key: y_time\n",
+      "Shape: (1,)\n",
+      "Data Type: float64\n",
+      "Content Preview:\n",
+      " [2463791.70451836]\n",
+      "\n",
+      "Key: y_flux\n",
+      "Shape: (1,)\n",
+      "Data Type: float64\n",
+      "Content Preview:\n",
+      " [2.82734087]\n",
+      "\n",
+      "Key: y_err_flux\n",
+      "Shape: (1,)\n",
+      "Data Type: float64\n",
+      "Content Preview:\n",
+      " [0.24979992]\n",
+      "\n",
+      "Key: y_mag\n",
+      "Shape: (1,)\n",
+      "Data Type: float64\n",
+      "Content Preview:\n",
+      " [18.41408374]\n",
+      "\n",
+      "Key: y_err_mag\n",
+      "Shape: (1,)\n",
+      "Data Type: float64\n",
+      "Content Preview:\n",
+      " [2.1360212]\n"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "\n",
+    "# Load the .npz file\n",
+    "data = np.load('downloaded_file/microlensing_lightcurves/PSPL/PSPL_100.npz')\n",
+    "\n",
+    "# Check the keys (array names) inside the file\n",
+    "print(\"Keys in the file:\", data.files)\n",
+    "\n",
+    "# Inspect each array\n",
+    "for key in data.files:\n",
+    "    print(f\"\\nKey: {key}\")\n",
+    "    print(\"Shape:\", data[key].shape)\n",
+    "    print(\"Data Type:\", data[key].dtype)\n",
+    "    print(\"Content Preview:\\n\", data[key])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f4f4653b",
+   "metadata": {},
+   "source": [
+    "> #### 4.2.3 Print the keys for a sample USBL event"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "1d7650af",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Keys in the file: ['event_id', 'alpha', 'fblend_W149', 'fblend_g', 'fblend_i', 'fblend_r', 'fblend_u', 'fblend_y', 'fblend_z', 'fsource_W149', 'fsource_g', 'fsource_i', 'fsource_r', 'fsource_u', 'fsource_y', 'fsource_z', 'ftotal_W149', 'ftotal_g', 'ftotal_i', 'ftotal_r', 'ftotal_u', 'ftotal_y', 'ftotal_z', 'gblend_W149', 'gblend_g', 'gblend_i', 'gblend_r', 'gblend_u', 'gblend_y', 'gblend_z', 'mass_ratio', 'piEE', 'piEN', 'rho', 'separation', 't0', 'tE', 't_center', 'u0', 'u_center', 'u_time', 'u_flux', 'u_err_flux', 'u_mag', 'u_err_mag', 'g_time', 'g_flux', 'g_err_flux', 'g_mag', 'g_err_mag', 'r_time', 'r_flux', 'r_err_flux', 'r_mag', 'r_err_mag', 'i_time', 'i_flux', 'i_err_flux', 'i_mag', 'i_err_mag', 'z_time', 'z_flux', 'z_err_flux', 'z_mag', 'z_err_mag', 'y_time', 'y_flux', 'y_err_flux', 'y_mag', 'y_err_mag']\n",
+      "\n",
+      "Key: event_id\n",
+      "Shape: ()\n",
+      "Data Type: <U1\n",
+      "Content Preview:\n",
+      " 7\n",
+      "\n",
+      "Key: alpha\n",
+      "Shape: ()\n",
+      "Data Type: float64\n",
+      "Content Preview:\n",
+      " 1.6917346704698597\n",
+      "\n",
+      "Key: fblend_W149\n",
+      "Shape: ()\n",
+      "Data Type: float64\n",
+      "Content Preview:\n",
+      " 890.695373570507\n",
+      "\n",
+      "Key: fblend_g\n",
+      "Shape: ()\n",
+      "Data Type: float64\n",
+      "Content Preview:\n",
+      " 2888.119237000291\n",
+      "\n",
+      "Key: fblend_i\n",
+      "Shape: ()\n",
+      "Data Type: float64\n",
+      "Content Preview:\n",
+      " 1547.697361769669\n",
+      "\n",
+      "Key: fblend_r\n",
+      "Shape: ()\n",
+      "Data Type: float64\n",
+      "Content Preview:\n",
+      " 3020.7182048831964\n",
+      "\n",
+      "Key: fblend_u\n",
+      "Shape: ()\n",
+      "Data Type: float64\n",
+      "Content Preview:\n",
+      " 256.32869633906796\n",
+      "\n",
+      "Key: fblend_y\n",
+      "Shape: ()\n",
+      "Data Type: float64\n",
+      "Content Preview:\n",
+      " 92.00794193002525\n",
+      "\n",
+      "Key: fblend_z\n",
+      "Shape: ()\n",
+      "Data Type: float64\n",
+      "Content Preview:\n",
+      " 964.5541023297019\n",
+      "\n",
+      "Key: fsource_W149\n",
+      "Shape: ()\n",
+      "Data Type: float64\n",
+      "Content Preview:\n",
+      " 1142.0360458455107\n",
+      "\n",
+      "Key: fsource_g\n",
+      "Shape: ()\n",
+      "Data Type: float64\n",
+      "Content Preview:\n",
+      " 3992.0639244300783\n",
+      "\n",
+      "Key: fsource_i\n",
+      "Shape: ()\n",
+      "Data Type: float64\n",
+      "Content Preview:\n",
+      " 2874.111848973023\n",
+      "\n",
+      "Key: fsource_r\n",
+      "Shape: ()\n",
+      "Data Type: float64\n",
+      "Content Preview:\n",
+      " 3088.7020441725244\n",
+      "\n",
+      "Key: fsource_u\n",
+      "Shape: ()\n",
+      "Data Type: float64\n",
+      "Content Preview:\n",
+      " 584.6790577301659\n",
+      "\n",
+      "Key: fsource_y\n",
+      "Shape: ()\n",
+      "Data Type: float64\n",
+      "Content Preview:\n",
+      " 1276.9811887707115\n",
+      "\n",
+      "Key: fsource_z\n",
+      "Shape: ()\n",
+      "Data Type: float64\n",
+      "Content Preview:\n",
+      " 1924.7948792293819\n",
+      "\n",
+      "Key: ftotal_W149\n",
+      "Shape: ()\n",
+      "Data Type: float64\n",
+      "Content Preview:\n",
+      " 2032.7314194160176\n",
+      "\n",
+      "Key: ftotal_g\n",
+      "Shape: ()\n",
+      "Data Type: float64\n",
+      "Content Preview:\n",
+      " 6880.183161430369\n",
+      "\n",
+      "Key: ftotal_i\n",
+      "Shape: ()\n",
+      "Data Type: float64\n",
+      "Content Preview:\n",
+      " 4421.809210742692\n",
+      "\n",
+      "Key: ftotal_r\n",
+      "Shape: ()\n",
+      "Data Type: float64\n",
+      "Content Preview:\n",
+      " 6109.420249055721\n",
+      "\n",
+      "Key: ftotal_u\n",
+      "Shape: ()\n",
+      "Data Type: float64\n",
+      "Content Preview:\n",
+      " 841.0077540692339\n",
+      "\n",
+      "Key: ftotal_y\n",
+      "Shape: ()\n",
+      "Data Type: float64\n",
+      "Content Preview:\n",
+      " 1368.9891307007367\n",
+      "\n",
+      "Key: ftotal_z\n",
+      "Shape: ()\n",
+      "Data Type: float64\n",
+      "Content Preview:\n",
+      " 2889.3489815590838\n",
+      "\n",
+      "Key: gblend_W149\n",
+      "Shape: ()\n",
+      "Data Type: float64\n",
+      "Content Preview:\n",
+      " 0.7799187922401147\n",
+      "\n",
+      "Key: gblend_g\n",
+      "Shape: ()\n",
+      "Data Type: float64\n",
+      "Content Preview:\n",
+      " 0.723465177830941\n",
+      "\n",
+      "Key: gblend_i\n",
+      "Shape: ()\n",
+      "Data Type: float64\n",
+      "Content Preview:\n",
+      " 0.5384958704104337\n",
+      "\n",
+      "Key: gblend_r\n",
+      "Shape: ()\n",
+      "Data Type: float64\n",
+      "Content Preview:\n",
+      " 0.9779895119966028\n",
+      "\n",
+      "Key: gblend_u\n",
+      "Shape: ()\n",
+      "Data Type: float64\n",
+      "Content Preview:\n",
+      " 0.43840923144089367\n",
+      "\n",
+      "Key: gblend_y\n",
+      "Shape: ()\n",
+      "Data Type: float64\n",
+      "Content Preview:\n",
+      " 0.07205113335976146\n",
+      "\n",
+      "Key: gblend_z\n",
+      "Shape: ()\n",
+      "Data Type: float64\n",
+      "Content Preview:\n",
+      " 0.5011204636599378\n",
+      "\n",
+      "Key: mass_ratio\n",
+      "Shape: ()\n",
+      "Data Type: float64\n",
+      "Content Preview:\n",
+      " 6.958430601749931e-05\n",
+      "\n",
+      "Key: piEE\n",
+      "Shape: ()\n",
+      "Data Type: float64\n",
+      "Content Preview:\n",
+      " -0.004064849073902546\n",
+      "\n",
+      "Key: piEN\n",
+      "Shape: ()\n",
+      "Data Type: float64\n",
+      "Content Preview:\n",
+      " -2.8617338727940578e-05\n",
+      "\n",
+      "Key: rho\n",
+      "Shape: ()\n",
+      "Data Type: float64\n",
+      "Content Preview:\n",
+      " 0.0002954435150087589\n",
+      "\n",
+      "Key: separation\n",
+      "Shape: ()\n",
+      "Data Type: float64\n",
+      "Content Preview:\n",
+      " 0.12359493593990661\n",
+      "\n",
+      "Key: t0\n",
+      "Shape: ()\n",
+      "Data Type: float64\n",
+      "Content Preview:\n",
+      " 2462526.9791368325\n",
+      "\n",
+      "Key: tE\n",
+      "Shape: ()\n",
+      "Data Type: float64\n",
+      "Content Preview:\n",
+      " 271.70307773157896\n",
+      "\n",
+      "Key: t_center\n",
+      "Shape: ()\n",
+      "Data Type: float64\n",
+      "Content Preview:\n",
+      " 2462526.9790782304\n",
+      "\n",
+      "Key: u0\n",
+      "Shape: ()\n",
+      "Data Type: float64\n",
+      "Content Preview:\n",
+      " 0.0008455386908556168\n",
+      "\n",
+      "Key: u_center\n",
+      "Shape: ()\n",
+      "Data Type: float64\n",
+      "Content Preview:\n",
+      " 0.0008473134093695858\n",
+      "\n",
+      "Key: u_time\n",
+      "Shape: (36,)\n",
+      "Data Type: float64\n",
+      "Content Preview:\n",
+      " [2460465.64860403 2460466.83028755 2460466.83070156 2460493.75879477\n",
+      " 2460503.53838952 2460523.69493153 2460531.5052004  2460857.74139707\n",
+      " 2460877.54840148 2461236.75537376 2461261.63245866 2461262.50606592\n",
+      " 2461262.62285532 2461264.64455532 2461264.66268907 2461265.62281787\n",
+      " 2461265.62927707 2461562.83627317 2461615.75731921 2461621.49879444\n",
+      " 2461939.7807792  2461944.7857523  2461944.78658138 2462001.6403077\n",
+      " 2462213.82353715 2462329.70472409 2462357.67006135 2462678.61450373\n",
+      " 2462711.64897354 2462713.49797855 2463072.66391823 2463072.66434419\n",
+      " 2463447.50551359 2463747.64831016 2463773.57496323 2463801.49867968]\n",
+      "\n",
+      "Key: u_flux\n",
+      "Shape: (36,)\n",
+      "Data Type: float64\n",
+      "Content Preview:\n",
+      " [ 841.31886842  841.31957257  841.31957282  841.33617565  841.34247182\n",
+      "  841.35590191  841.36127125  841.70870418  841.74190319  842.85594841\n",
+      "  842.99605063  843.001191    843.00187935  843.01383875  843.01394639\n",
+      "  843.01965527  843.01969374  846.29386479  847.46838708  847.61365906\n",
+      "  868.01162361  868.73238682  868.73250811  878.75259764  982.34246495\n",
+      " 1206.13970287 1320.66354201 1403.73305607 1242.77822409 1235.7127717\n",
+      "  874.27871142  874.27863406  847.18075815  843.26575777  843.09498961\n",
+      "  842.92862787]\n",
+      "\n",
+      "Key: u_err_flux\n",
+      "Shape: (36,)\n",
+      "Data Type: float64\n",
+      "Content Preview:\n",
+      " [4.09677922 4.12184425 4.11791209 4.09707213 4.11008516 4.12824418\n",
+      " 4.10199951 4.10799221 4.10429044 4.11917467 4.10102426 4.11669771\n",
+      " 4.10794352 4.10823563 4.11261474 4.10701838 4.10073164 4.09189443\n",
+      " 4.11363586 4.12834107 4.15061441 4.16696532 4.1890333  4.19647471\n",
+      " 4.42470338 4.92162575 5.12909349 5.30935024 4.98080315 4.97059353\n",
+      " 4.16893272 4.18640657 4.10672619 4.10482643 4.11042577 4.12654819]\n",
+      "\n",
+      "Key: u_mag\n",
+      "Shape: (36,)\n",
+      "Data Type: float64\n",
+      "Content Preview:\n",
+      " [19.7558938  19.72452787 19.69984111 19.72853175 19.72254772 19.72334673\n",
+      " 19.70547366 19.70002906 19.74790124 19.71846592 19.73519868 19.7119635\n",
+      " 19.73597967 19.70148897 19.71409519 19.73206495 19.71995814 19.71877185\n",
+      " 19.70626807 19.70584477 19.69701729 19.67891331 19.67847551 19.66317479\n",
+      " 19.54125778 19.32122023 19.23530112 19.16722697 19.28428843 19.30550991\n",
+      " 19.66997615 19.67790856 19.71509257 19.71022666 19.71137236 19.73515246]\n",
+      "\n",
+      "Key: u_err_mag\n",
+      "Shape: (36,)\n",
+      "Data Type: float64\n",
+      "Content Preview:\n",
+      " [0.01291239 0.01337692 0.0133033  0.01219844 0.01214299 0.01328498\n",
+      " 0.01153597 0.01322604 0.01212083 0.01282586 0.01180218 0.01256598\n",
+      " 0.01087281 0.01115112 0.01175396 0.01322252 0.01185786 0.01121459\n",
+      " 0.0113712  0.01588373 0.01564505 0.01086474 0.01089242 0.01112267\n",
+      " 0.01295885 0.01158749 0.0093117  0.01028884 0.01097245 0.00963355\n",
+      " 0.01124529 0.01199234 0.01124673 0.01158387 0.01132156 0.01136968]\n",
+      "\n",
+      "Key: g_time\n",
+      "Shape: (191,)\n",
+      "Data Type: float64\n",
+      "Content Preview:\n",
+      " [2460418.79806424 2460418.80078583 2460433.71284195 2460441.74651385\n",
+      " 2460465.67212045 2460466.85307079 2460466.85351946 2460467.7974697\n",
+      " 2460490.62357955 2460495.59384514 2460495.59761297 2460503.56315999\n",
+      " 2460503.57186729 2460503.57418409 2460527.63554942 2460527.63786602\n",
+      " 2460561.56401037 2460576.55255982 2460576.55524567 2460788.75882987\n",
+      " 2460788.76289091 2460790.78268693 2460796.71314532 2460823.86624529\n",
+      " 2460849.62218371 2460849.62354677 2460850.56971478 2460855.52782099\n",
+      " 2460857.76401456 2460871.53827983 2460872.48954202 2460873.48418206\n",
+      " 2460876.71140413 2460877.52367455 2460885.64861052 2460886.67636387\n",
+      " 2460959.52857153 2460961.51936707 2461143.78347778 2461170.70093657\n",
+      " 2461176.89616386 2461176.8966122  2461181.87741912 2461229.79386905\n",
+      " 2461229.79521292 2461234.71028269 2461236.77847573 2461243.75830873\n",
+      " 2461261.65590255 2461262.64568907 2461262.68101539 2461264.62025441\n",
+      " 2461264.68547529 2461265.64549302 2461324.52149255 2461470.83203608\n",
+      " 2461501.8225522  2461536.74890387 2461536.75037104 2461556.63727009\n",
+      " 2461557.82688548 2461557.82733389 2461560.83578503 2461562.85977354\n",
+      " 2461588.76007193 2461613.68163403 2461613.68253326 2461613.71729881\n",
+      " 2461613.71953594 2461616.6742909  2461616.67473835 2461617.73595941\n",
+      " 2461621.5225453  2461643.64371425 2461643.64595216 2461649.61060351\n",
+      " 2461649.62230529 2461673.5376678  2461673.56633357 2461677.50900966\n",
+      " 2461827.84512213 2461856.81960283 2461859.86108598 2461879.71671833\n",
+      " 2461879.78683753 2461879.78818988 2461879.81862565 2461886.74955364\n",
+      " 2461908.6820455  2461908.68249341 2461936.67660319 2461943.74866336\n",
+      " 2461944.6079103  2461944.61874891 2461965.58583809 2462210.78843007\n",
+      " 2462213.84565779 2462214.85126593 2462234.81395447 2462241.76702851\n",
+      " 2462245.71656977 2462276.83761886 2462276.89723078 2462323.70887825\n",
+      " 2462324.8439473  2462325.79644034 2462329.7279996  2462359.48835903\n",
+      " 2462415.54493858 2462561.84234219 2462561.85040194 2462563.85251467\n",
+      " 2462568.84329384 2462590.83294445 2462594.76710458 2462598.80446595\n",
+      " 2462616.71975027 2462616.72110302 2462623.66880314 2462626.72798967\n",
+      " 2462678.63796201 2462711.67256304 2462713.52200426 2462736.63210973\n",
+      " 2462736.63390172 2462741.67763196 2462928.87312971 2462929.87190961\n",
+      " 2462972.73104609 2462980.7666765  2462980.76712505 2462980.81879665\n",
+      " 2462980.81976226 2462984.72025662 2463000.67275068 2463004.69563519\n",
+      " 2463008.85828566 2463008.86434623 2463009.65151592 2463018.84565829\n",
+      " 2463038.74792002 2463041.75255922 2463042.7528633  2463042.75470696\n",
+      " 2463070.6515305  2463072.686948   2463072.68739857 2463072.71968227\n",
+      " 2463072.72012988 2463073.546168   2463089.63814953 2463098.58079336\n",
+      " 2463098.58557761 2463101.58146758 2463116.56003084 2463118.55932794\n",
+      " 2463122.55732566 2463127.52084759 2463128.50430387 2463128.5047713\n",
+      " 2463130.5204437  2463333.77257607 2463383.61549297 2463385.6501021\n",
+      " 2463426.7052787  2463440.54957224 2463473.59126528 2463475.5616384\n",
+      " 2463478.55467315 2463480.5920549  2463710.67902637 2463714.77357256\n",
+      " 2463715.76594609 2463715.76639464 2463745.6424842  2463747.59460411\n",
+      " 2463747.60981669 2463747.67073995 2463773.59925008 2463801.52179624\n",
+      " 2463801.52224816 2463801.54624919 2463804.68084379 2463804.69282563\n",
+      " 2463834.59689076 2463836.61738838 2463836.61783624 2463836.62642661\n",
+      " 2463853.56590676 2463856.54932932 2463859.53295046]\n",
+      "\n",
+      "Key: g_flux\n",
+      "Shape: (191,)\n",
+      "Data Type: float64\n",
+      "Content Preview:\n",
+      " [ 6882.12778111  6882.12779093  6882.1826602   6882.21309896\n",
+      "  6882.3074794   6882.31228451  6882.31228634  6882.31613714\n",
+      "  6882.41197847  6882.43354608  6882.43356252  6882.46865301\n",
+      "  6882.46869172  6882.46870202  6882.57865828  6882.57866916\n",
+      "  6882.74397849  6882.82094692  6882.82096093  6884.27316252\n",
+      "  6884.27319944  6884.29162065  6884.34636029  6884.60975355\n",
+      "  6884.87976096  6884.87977578  6884.89008114  6884.94453856\n",
+      "  6884.96935104  6885.12566935  6885.13668798  6885.14824015\n",
+      "  6885.18594195  6885.19548423  6885.29211238  6885.30448873\n",
+      "  6886.27431746  6886.30353888  6889.88589991  6890.63237521\n",
+      "  6890.81478     6890.81479335  6890.96444167  6892.54894012\n",
+      "  6892.54898842  6892.72718925  6892.80308908  6893.06329017\n",
+      "  6893.75976685  6893.79954275  6893.80096487  6893.87929497\n",
+      "  6893.88193838  6893.92091591  6896.57039485  6906.23177959\n",
+      "  6909.15400473  6912.98401956  6912.98419376  6915.45718396\n",
+      "  6915.61243352  6915.6124922   6916.00892788  6916.27874547\n",
+      "  6919.96231782  6923.94269218  6923.94284408  6923.94871742\n",
+      "  6923.94909539  6924.45167605  6924.45175266  6924.63390603\n",
+      "  6925.29102886  6929.36286714  6929.36329998  6930.53259879\n",
+      "  6930.53492384  6935.55317849  6935.55952584  6936.44047082\n",
+      "  6986.58656085  7001.95377781  7003.7270414   7016.14774747\n",
+      "  7016.19434712  7016.19524606  7016.21547943  7020.92362295\n",
+      "  7037.23145513  7037.2318115   7061.58497632  7068.44846232\n",
+      "  7069.30349091  7069.31430605  7091.69105158  7824.31475763\n",
+      "  7845.34067822  7852.38599963  8006.76858335  8067.76988322\n",
+      "  8104.29191224  8448.4127583   8449.18262689  9238.13975055\n",
+      "  9262.97539032  9284.07675458  9373.76983865 10216.53941243\n",
+      " 13225.23437665 34056.76820152 34049.61785799 32370.7908026\n",
+      " 28889.16116566 20079.48860919 19114.19917338 18235.98237151\n",
+      " 15316.63265216 15316.45773824 14485.47827895 14158.0212431\n",
+      " 10721.37436696  9622.76355762  9574.52549373  9056.72078178\n",
+      "  9056.68597491  8961.55177347  7411.92895164  7408.57267406\n",
+      "  7285.09623677  7265.83930895  7265.83826398  7265.71790946\n",
+      "  7265.71566076  7256.75493946  7222.53926997  7214.49259224\n",
+      "  7206.3987503   7206.38713507  7204.88263127  7187.90197681\n",
+      "  7154.62249794  7149.98076781  7148.45645301  7148.45365314\n",
+      "  7109.86244585  7107.32160917  7107.32105057  7107.28103036\n",
+      "  7107.28047554  7106.2594388   7087.45620502  7077.84649845\n",
+      "  7077.84150824  7074.7474181   7060.15941737  7058.31797489\n",
+      "  7054.70649019  7050.3507166   7049.50407802  7049.50367688\n",
+      "  7047.78504192  6946.80059378  6934.40920471  6933.96393973\n",
+      "  6925.85859261  6923.47115845  6918.38361871  6918.10522901\n",
+      "  6917.68737939  6917.40637262  6897.46590952  6897.24684133\n",
+      "  6897.19422844  6897.19420471  6895.69526003  6895.60286186\n",
+      "  6895.6021444   6895.59927154  6894.43332008  6893.29758646\n",
+      "  6893.29756903  6893.29664331  6893.17646221  6893.17600556\n",
+      "  6892.09866055  6892.03019004  6892.03017492  6892.02988493\n",
+      "  6891.47603395  6891.38210728  6891.28922315]\n",
+      "\n",
+      "Key: g_err_flux\n",
+      "Shape: (191,)\n",
+      "Data Type: float64\n",
+      "Content Preview:\n",
+      " [11.74034071 11.74005111 11.71146447 11.73608112 11.73297916 11.71680844\n",
+      " 11.72624407 11.73654123 11.73060953 11.72346365 11.71180601 11.7239072\n",
+      " 11.74304901 11.72208173 11.71655239 11.73311553 11.73381438 11.73541648\n",
+      " 11.72973998 11.73850076 11.73877336 11.73471772 11.74325338 11.73224616\n",
+      " 11.73872225 11.73057543 11.73708652 11.72694334 11.72597118 11.73074593\n",
+      " 11.73994889 11.73400187 11.73572324 11.73637082 11.73343939 11.74982553\n",
+      " 11.74582479 11.72566416 11.72622701 11.74807218 11.740971   11.7378533\n",
+      " 11.73926744 11.73902892 11.7343598  11.73769995 11.74253806 11.73996593\n",
+      " 11.7422996  11.74156719 11.73050724 11.74127761 11.74194192 11.72685806\n",
+      " 11.74643776 11.7553222  11.74970638 11.73671163 11.77048852 11.76739563\n",
+      " 11.75462462 11.7637749  11.76601887 11.77274819 11.77463375 11.75756778\n",
+      " 11.78880825 11.7728841  11.75343354 11.77784361 11.76720867 11.78176557\n",
+      " 11.76890819 11.76100336 11.78074701 11.74907656 11.77556793 11.7830726\n",
+      " 11.76926506 11.78303866 11.803796   11.83197363 11.82681699 11.8440027\n",
+      " 11.82806831 11.86175366 11.87019798 11.85286463 11.86168622 11.86556362\n",
+      " 11.88987805 11.87710402 11.88101006 11.89546132 11.92577042 12.51373645\n",
+      " 12.52391313 12.53907493 12.66649123 12.70618747 12.73525814 13.01127204\n",
+      " 13.0040609  13.57426978 13.6161228  13.62566696 13.69440762 14.28810694\n",
+      " 16.27565052 26.10583843 26.10104979 25.44961296 24.02060782 20.01911087\n",
+      " 19.53355062 19.11138927 17.50154279 17.50566765 17.01625106 16.83275379\n",
+      " 14.63896171 13.87776639 13.84489798 13.46314971 13.47332179 13.36666002\n",
+      " 12.18251206 12.17158987 12.06567031 12.05739607 12.04687511 12.06160852\n",
+      " 12.05747901 12.03584646 12.02006656 12.00303295 12.01300961 12.01777018\n",
+      " 11.99881661 11.9843064  11.96023411 11.95347648 11.94735117 11.96820789\n",
+      " 11.90001681 11.91775147 11.92669275 11.91593891 11.9234391  11.92622321\n",
+      " 11.89265319 11.90033613 11.88986123 11.89591527 11.89245139 11.88309724\n",
+      " 11.86662547 11.87710402 11.8643331  11.85763889 11.87794595 11.79894911\n",
+      " 11.76416593 11.77906618 11.77679073 11.75564545 11.76671577 11.76071426\n",
+      " 11.77813228 11.76418293 11.7427765  11.74347478 11.74407084 11.74476905\n",
+      " 11.74727202 11.73069478 11.74144795 11.74006814 11.75219129 11.74779979\n",
+      " 11.74510962 11.75271883 11.75601973 11.75231041 11.75491387 11.73931855\n",
+      " 11.73959113 11.74682936 11.72887036 11.73744436 11.74090286]\n",
+      "\n",
+      "Key: g_mag\n",
+      "Shape: (191,)\n",
+      "Data Type: float64\n",
+      "Content Preview:\n",
+      " [18.78078383 18.78257629 18.78798669 18.7808799  18.79173042 18.80028692\n",
+      " 18.7947396  18.78412207 18.78648883 18.78418491 18.79290935 18.79225005\n",
+      " 18.78678632 18.79104064 18.77843383 18.78603157 18.77244433 18.7721756\n",
+      " 18.77396882 18.78381492 18.77388238 18.77841065 18.78359756 18.7878278\n",
+      " 18.78397709 18.78515201 18.78947663 18.78386603 18.79486858 18.79137416\n",
+      " 18.78791525 18.79197784 18.7839359  18.76786225 18.78661121 18.79593058\n",
+      " 18.77622957 18.79101051 18.78259097 18.77855488 18.77995018 18.77507313\n",
+      " 18.78235701 18.78497361 18.78962655 18.78836535 18.78302554 18.77978767\n",
+      " 18.78420777 18.78187092 18.77886119 18.78581911 18.79171345 18.7833896\n",
+      " 18.78530325 18.78752878 18.78629845 18.78246668 18.78228379 18.78175867\n",
+      " 18.77673393 18.7746101  18.77175005 18.78868363 18.78722607 18.78498199\n",
+      " 18.77753635 18.77943302 18.7845618  18.79072477 18.78154421 18.77675993\n",
+      " 18.7777233  18.78133757 18.77133137 18.78669415 18.77095696 18.77618543\n",
+      " 18.77306311 18.78145226 18.77242199 18.76932528 18.7724123  18.76936335\n",
+      " 18.76622776 18.7632916  18.76300208 18.76165073 18.76518506 18.75741557\n",
+      " 18.75465362 18.76152489 18.75498955 18.74862957 18.74453851 18.64124966\n",
+      " 18.65069403 18.63961931 18.62326843 18.60874066 18.60614802 18.56325336\n",
+      " 18.56544928 18.46401853 18.46485465 18.46049598 18.4511683  18.35694203\n",
+      " 18.08076522 17.05287028 17.05221644 17.10803515 17.22308034 17.61228445\n",
+      " 17.67804654 17.73096904 17.91491221 17.90911989 17.98145144 18.00190818\n",
+      " 18.29813204 18.42316144 18.42774749 18.4865118  18.48493695 18.4916571\n",
+      " 18.69561994 18.70488176 18.73924007 18.72945727 18.71767013 18.73394974\n",
+      " 18.73630179 18.72888504 18.7294476  18.7336506  18.73354841 18.72926525\n",
+      " 18.73851425 18.73704355 18.74878805 18.74731809 18.74072806 18.74148298\n",
+      " 18.76636456 18.76194093 18.74867327 18.74747495 18.74988232 18.76268638\n",
+      " 18.75278466 18.75849722 18.74543067 18.75324566 18.76388024 18.75206934\n",
+      " 18.76744954 18.75049365 18.75532012 18.75488849 18.75825358 18.7764447\n",
+      " 18.77837318 18.78086317 18.77293575 18.77211305 18.78258354 18.775713\n",
+      " 18.78432507 18.78104841 18.77977756 18.78683917 18.78798114 18.77987791\n",
+      " 18.78673981 18.7786028  18.78116701 18.78471173 18.78652889 18.78329483\n",
+      " 18.78207039 18.78549144 18.78204431 18.78580279 18.78334645 18.77956649\n",
+      " 18.79126251 18.77905979 18.7868443  18.77759334 18.79088824]\n",
+      "\n",
+      "Key: g_err_mag\n",
+      "Shape: (191,)\n",
+      "Data Type: float64\n",
+      "Content Preview:\n",
+      " [0.00580596 0.00563368 0.00561644 0.00573511 0.00559132 0.00560803\n",
+      " 0.00581964 0.00558681 0.00581168 0.0056622  0.00565999 0.00603859\n",
+      " 0.005663   0.00569869 0.00586137 0.00573878 0.00580134 0.00567969\n",
+      " 0.00577417 0.0061212  0.00608755 0.00591941 0.00586824 0.00563605\n",
+      " 0.00571426 0.00566118 0.00561852 0.0056192  0.00585069 0.00573812\n",
+      " 0.00578016 0.00566466 0.00599699 0.00564512 0.00591475 0.00567533\n",
+      " 0.00559639 0.00560459 0.00561539 0.00561609 0.00569034 0.00563124\n",
+      " 0.00565597 0.00599258 0.00568104 0.00562832 0.00556898 0.00564415\n",
+      " 0.00579549 0.0057001  0.00565058 0.00560003 0.00560398 0.00557953\n",
+      " 0.0057188  0.00561992 0.00555835 0.00554842 0.0057705  0.00559693\n",
+      " 0.00566458 0.00561244 0.00563395 0.00557191 0.00563069 0.00565365\n",
+      " 0.00573287 0.00589741 0.00598422 0.0058853  0.00608623 0.00571914\n",
+      " 0.00562185 0.00571427 0.00557492 0.00572755 0.00555172 0.0056899\n",
+      " 0.00609577 0.00556684 0.00620206 0.0056992  0.00561978 0.00565329\n",
+      " 0.00566847 0.00570772 0.00564703 0.00580132 0.00558328 0.00552776\n",
+      " 0.00570481 0.00572666 0.00556867 0.00557766 0.00563727 0.00553974\n",
+      " 0.0055206  0.00594974 0.00548717 0.00556012 0.00555606 0.00546225\n",
+      " 0.00566514 0.00548695 0.00551419 0.00543257 0.00542442 0.005377\n",
+      " 0.00535689 0.00511159 0.00511597 0.00511763 0.00513385 0.0051859\n",
+      " 0.00520735 0.00523893 0.00526456 0.00529886 0.00535142 0.00527488\n",
+      " 0.00537378 0.00541841 0.00547493 0.0054159  0.00542659 0.0055002\n",
+      " 0.00569143 0.00570365 0.0056618  0.00562907 0.00558682 0.00561312\n",
+      " 0.00566017 0.00581301 0.00572917 0.00568171 0.00596428 0.00563896\n",
+      " 0.00585921 0.00562708 0.00558141 0.00566026 0.00560614 0.00559593\n",
+      " 0.00561527 0.00569262 0.00570663 0.00565824 0.00561862 0.00560693\n",
+      " 0.00583709 0.00563954 0.00558483 0.00572165 0.00556987 0.00565407\n",
+      " 0.00581157 0.00567381 0.00597314 0.00555907 0.00569489 0.00561694\n",
+      " 0.00557114 0.00570332 0.00565531 0.00561705 0.00614159 0.0056043\n",
+      " 0.00561499 0.00561731 0.00560343 0.00557259 0.00570466 0.00575278\n",
+      " 0.005817   0.00566714 0.00571904 0.00558546 0.00580519 0.00566786\n",
+      " 0.0055879  0.00575143 0.00561763 0.00577324 0.00564476 0.00572828\n",
+      " 0.00560204 0.00569882 0.00558809 0.0057645  0.00555017]\n",
+      "\n",
+      "Key: r_time\n",
+      "Shape: (197,)\n",
+      "Data Type: float64\n",
+      "Content Preview:\n",
+      " [2460413.76382861 2460413.76607368 2460418.82247789 2460418.82529879\n",
+      " 2460433.73684761 2460434.90687273 2460441.77145105 2460490.64921317\n",
+      " 2460495.61875844 2460503.59636371 2460503.59887201 2460523.67215909\n",
+      " 2460527.65963422 2460527.66184432 2460527.66229365 2460561.60405709\n",
+      " 2460576.57687903 2460576.57956279 2460788.78295204 2460788.78705603\n",
+      " 2460788.79541624 2460788.79587187 2460790.80687245 2460796.73718647\n",
+      " 2460796.75046754 2460823.88578873 2460849.64690643 2460849.7955053\n",
+      " 2460850.59384957 2460855.55175278 2460871.56239695 2460872.51356223\n",
+      " 2460873.50853348 2460876.73602245 2460885.67347311 2460886.70025767\n",
+      " 2460907.67341299 2460907.67833903 2460941.55988616 2460957.54969273\n",
+      " 2460959.55305176 2460961.54310766 2461143.75938446 2461169.87546225\n",
+      " 2461170.72505242 2461176.87183624 2461176.87228712 2461179.84286621\n",
+      " 2461181.90300205 2461225.74303362 2461229.81910993 2461232.78429916\n",
+      " 2461234.73440799 2461240.71618213 2461240.7166297  2461243.78204749\n",
+      " 2461291.66549453 2461294.56040373 2461297.55255901 2461324.54549324\n",
+      " 2461470.85458083 2461498.79942706 2461501.84705698 2461505.7753406\n",
+      " 2461505.83628853 2461528.71779044 2461531.90741685 2461531.90786372\n",
+      " 2461536.77433096 2461536.77537152 2461556.66146978 2461557.85094443\n",
+      " 2461557.85139274 2461560.85980629 2461613.65745438 2461613.65837717\n",
+      " 2461613.74108907 2461613.74332387 2461616.69870009 2461617.7121409\n",
+      " 2461642.61241337 2461649.58668183 2461649.5984766  2461673.51334961\n",
+      " 2461677.53300797 2461827.8693492  2461856.84451192 2461859.88790406\n",
+      " 2461879.74062072 2461879.76247649 2461886.77467408 2461908.65824062\n",
+      " 2461908.65868869 2461936.65225497 2461939.80356461 2461940.79867863\n",
+      " 2461943.77314674 2461944.63238565 2461944.64374776 2461944.80827269\n",
+      " 2461971.56389545 2461973.69036886 2461975.74963286 2461975.75007787\n",
+      " 2461977.67779187 2461999.46924553 2462003.68028832 2462004.47859947\n",
+      " 2462021.58431167 2462025.6025848  2462047.51580243 2462210.81260417\n",
+      " 2462214.82706828 2462234.83818906 2462241.79125516 2462245.74091563\n",
+      " 2462276.86185316 2462276.87183451 2462323.73299449 2462324.82026447\n",
+      " 2462325.82016803 2462359.51317185 2462561.86606582 2462561.87414477\n",
+      " 2462563.87818744 2462568.86749181 2462588.8270359  2462591.80290889\n",
+      " 2462592.86932276 2462594.79098116 2462598.8289129  2462616.74370477\n",
+      " 2462616.74505864 2462626.70355643 2462649.69439079 2462651.68936336\n",
+      " 2462676.62697052 2462677.74755104 2462705.71136079 2462706.7012274\n",
+      " 2462708.51086164 2462713.6757716  2462736.65594642 2462736.65773405\n",
+      " 2462761.56018526 2462928.84846754 2462929.8466335  2462980.79427146\n",
+      " 2462980.79518644 2462984.7440544  2463004.67130737 2463004.71959527\n",
+      " 2463008.83400428 2463008.89284278 2463009.67558743 2463018.82154152\n",
+      " 2463038.77262616 2463042.77677215 2463042.77858309 2463070.67570922\n",
+      " 2463098.60995525 2463101.6055612  2463118.58321206 2463122.58115854\n",
+      " 2463128.52893756 2463128.52938871 2463329.84311676 2463333.79685105\n",
+      " 2463366.67238722 2463383.6393263  2463385.67418988 2463426.7292089\n",
+      " 2463478.57883919 2463480.61581802 2463658.88858824 2463710.70277064\n",
+      " 2463713.92193671 2463713.92235338 2463714.74953131 2463719.87982214\n",
+      " 2463723.74721239 2463723.74766098 2463740.66519836 2463745.61855473\n",
+      " 2463747.5708618  2463747.6335779  2463769.61865135 2463795.51328034\n",
+      " 2463804.65641499 2463804.71673392 2463804.7234461  2463834.62084544\n",
+      " 2463836.59347613 2463836.59392621 2463836.65051239 2463856.57405557\n",
+      " 2463859.50869852]\n",
+      "\n",
+      "Key: r_flux\n",
+      "Shape: (197,)\n",
+      "Data Type: float64\n",
+      "Content Preview:\n",
+      " [ 6110.91085386  6110.91086005  6110.92489003  6110.92489791\n",
+      "  6110.96735204  6110.97075229  6110.99090702  6111.14479392\n",
+      "  6111.16147957  6111.18867226  6111.18868089  6111.25933835\n",
+      "  6111.27375776  6111.27376579  6111.27376742  6111.40173685\n",
+      "  6111.46122975  6111.46124058  6112.58489582  6112.58492469\n",
+      "  6112.58498351  6112.58498672  6112.59917856  6112.64153324\n",
+      "  6112.64162894  6112.84530282  6113.0542658   6113.05551671\n",
+      "  6113.06224624  6113.10438167  6113.24453552  6113.25306045\n",
+      "  6113.26200206  6113.29117673  6113.37332972  6113.38289704\n",
+      "  6113.58424486  6113.5842935   6113.93429875  6114.11068594\n",
+      "  6114.13331813  6114.1559201   6116.92686836  6117.48635027\n",
+      "  6117.50545941  6117.64548427  6117.64549466  6117.71429182\n",
+      "  6117.76243961  6118.8759148   6118.98848473  6119.07138755\n",
+      "  6119.12637853  6119.29739863  6119.29741156  6119.38643379\n",
+      "  6120.90517618  6121.005135    6121.1094825   6122.10014655\n",
+      "  6129.57587296  6131.60047528  6131.83721337  6132.14727469\n",
+      "  6132.15212947  6134.07437306  6134.35880143  6134.35884158\n",
+      "  6134.80095556  6134.80105116  6136.71456998  6136.83468736\n",
+      "  6136.83473275  6137.14148938  6143.27430029  6143.27442089\n",
+      "  6143.28523229  6143.28552446  6143.67450064  6143.80908927\n",
+      "  6147.31713748  6148.37246362  6148.37427663  6152.2564507\n",
+      "  6152.95131014  6191.75479871  6203.64659576  6205.01966484\n",
+      "  6214.62977471  6214.64101273  6218.32612038  6230.91552059\n",
+      "  6230.91579638  6249.75487456  6252.09148381  6252.83906206\n",
+      "  6255.10190212  6255.76354313  6255.77231645  6255.89942609\n",
+      "  6278.43985748  6280.40121031  6282.32606165  6282.32648034\n",
+      "  6284.15140594  6306.42220824  6311.09607378  6311.99647374\n",
+      "  6332.44638644  6337.58774826  6368.13032346  6840.03170764\n",
+      "  6861.49210161  6981.23038814  7028.43869232  7056.70370566\n",
+      "  7323.01816996  7323.11790518  7934.20175542  7952.6081943\n",
+      "  7969.74720631  8691.45038778 27119.95448721 27114.41659673\n",
+      " 25816.03767529 23126.51343155 16740.04437563 16129.11894715\n",
+      " 15923.89780975 15570.7454752  14891.64616702 12634.39453391\n",
+      " 12634.25916506 11742.31917477 10263.02491243 10162.12451224\n",
+      "  9147.17517154  9110.27392109  8357.9419655   8336.23939055\n",
+      "  8297.28393586  8190.99790173  7793.0722298   7793.04537252\n",
+      "  7467.5266257   6520.901947    6518.30628206  6407.75675139\n",
+      "  6407.7551025   6400.73609035  6368.11611955  6368.04242477\n",
+      "  6361.85270646  6361.76545998  6360.60816328  6347.53893281\n",
+      "  6321.72703173  6316.95803911  6316.95591161  6287.10202744\n",
+      "  6262.33092941  6259.93760922  6247.22812788  6244.4343516\n",
+      "  6240.40927675  6240.40897725  6161.82100874  6160.95760927\n",
+      "  6154.37986991  6151.37145657  6151.02694722  6144.75651102\n",
+      "  6138.43506743  6138.2177176   6125.16383434  6122.79110381\n",
+      "  6122.65764811  6122.65763093  6122.62358648  6122.41467626\n",
+      "  6122.25971735  6122.2596995   6121.60672186  6121.42300316\n",
+      "  6121.35150064  6121.34921214  6120.57867577  6119.74838563\n",
+      "  6119.47402358  6119.4722449   6119.472047    6118.63876617\n",
+      "  6118.58704488  6118.58703312  6118.58555517  6118.08439291\n",
+      "  6118.01370728]\n",
+      "\n",
+      "Key: r_err_flux\n",
+      "Shape: (197,)\n",
+      "Data Type: float64\n",
+      "Content Preview:\n",
+      " [11.05196815 11.05954791 11.0541214  11.05294531 11.05907772 11.04907236\n",
+      " 11.06705019 11.05211292 11.04939817 11.06591162 11.04623013 11.05716058\n",
+      " 11.05399475 11.05560491 11.08705552 11.05446516 11.04114125 11.05766702\n",
+      " 11.0535967  11.06453795 11.06567666 11.05176909 11.04697244 11.05527928\n",
+      " 11.06151888 11.05428424 11.04094199 11.06372451 11.05603907 11.06690562\n",
+      " 11.06976061 11.06115726 11.06356181 11.05283674 11.04499887 11.06097645\n",
+      " 11.03851439 11.0763893  11.05574964 11.05556873 11.05001357 11.06681526\n",
+      " 11.05551446 11.05198625 11.04905426 11.07032068 11.06965221 11.05893304\n",
+      " 11.0663273  11.0589873  11.04322417 11.07106138 11.05654557 11.06404989\n",
+      " 11.07270518 11.05252912 11.05195005 11.0728858  11.06518866 11.0732651\n",
+      " 11.0821839  11.07832117 11.07113364 11.09345753 11.09814399 11.08961677\n",
+      " 11.0667249  11.06198897 11.07261487 11.08274334 11.06650803 11.09298878\n",
+      " 11.08041515 11.08817388 11.06941733 11.07821285 11.08093859 11.08631589\n",
+      " 11.0901037  11.07277743 11.08635197 11.09039224 11.10727689 11.09419668\n",
+      " 11.08027075 11.11791347 11.13353493 11.13727076 11.15365411 11.15804642\n",
+      " 11.15157388 11.15905014 11.15272164 11.17434562 11.18339841 11.18529392\n",
+      " 11.20321382 11.18799356 11.18266516 11.17669003 11.2087109  11.22158634\n",
+      " 11.20651596 11.21265357 11.20646242 11.22664687 11.24567472 11.23720606\n",
+      " 11.25577185 11.26587768 11.29451194 11.68428004 11.72421426 11.79757602\n",
+      " 11.85448438 11.88156555 12.09189811 12.08339356 12.58845503 12.61573621\n",
+      " 12.61327872 13.198015   23.26868282 23.27956185 22.72779796 21.48876916\n",
+      " 18.30706967 17.95377398 17.82538639 17.64550934 17.23494125 15.90099368\n",
+      " 15.8886878  15.32258464 14.32779118 14.23012298 13.53425284 13.48998147\n",
+      " 12.94200912 12.93157376 12.87857135 12.80195298 12.48725751 12.49287797\n",
+      " 12.20121305 11.40387653 11.40924187 11.31929326 11.33144298 11.31551148\n",
+      " 11.26600195 11.28542423 11.28969442 11.28767469 11.28331512 11.27659523\n",
+      " 11.25650034 11.23174074 11.23432241 11.21939392 11.19807126 11.18885159\n",
+      " 11.19437359 11.17973166 11.16134401 11.17636793 11.10745695 11.10187372\n",
+      " 11.10682673 11.08757864 11.09736906 11.07870028 11.07967509 11.07539616\n",
+      " 11.06137424 11.07066394 11.06603813 11.05301769 11.06289293 11.07767124\n",
+      " 11.04816727 11.04746125 11.06856811 11.09502591 11.06403181 11.0671767\n",
+      " 11.08001805 11.05824579 11.06235056 11.07035681 11.05627424 11.06482716\n",
+      " 11.07759902 11.05676264 11.06303756 11.06871266 11.04036231]\n",
+      "\n",
+      "Key: r_mag\n",
+      "Shape: (197,)\n",
+      "Data Type: float64\n",
+      "Content Preview:\n",
+      " [18.69466853 18.69869799 18.69452174 18.70455116 18.69956239 18.70455845\n",
+      " 18.68808654 18.6740872  18.68815985 18.69505175 18.69139958 18.69646315\n",
+      " 18.69594996 18.69142379 18.69166246 18.69775075 18.69429919 18.68964055\n",
+      " 18.68070736 18.69068349 18.69810101 18.70032111 18.69819284 18.6873955\n",
+      " 18.69229168 18.69580673 18.69383375 18.70133043 18.68502054 18.70388093\n",
+      " 18.69562886 18.69650993 18.69723212 18.69852884 18.69811388 18.70199828\n",
+      " 18.69473559 18.69414215 18.69819019 18.69337098 18.70100957 18.68588341\n",
+      " 18.68811333 18.69506017 18.69006787 18.69683813 18.69576352 18.70806943\n",
+      " 18.69307155 18.68947282 18.70285701 18.69463486 18.69329318 18.68979277\n",
+      " 18.69799747 18.69913513 18.67501256 18.68561884 18.69592593 18.68811146\n",
+      " 18.69224631 18.69210307 18.69994572 18.69296489 18.68065501 18.68626772\n",
+      " 18.70068483 18.69167256 18.68549022 18.69849278 18.69504844 18.69260674\n",
+      " 18.68339434 18.6989477  18.69433569 18.69160689 18.68746598 18.68032731\n",
+      " 18.69452748 18.68858571 18.68399389 18.68947829 18.6866967  18.68287589\n",
+      " 18.6857373  18.67676933 18.67674607 18.67455393 18.67165005 18.68012426\n",
+      " 18.67960632 18.6741459  18.67648691 18.6815982  18.67177003 18.66338021\n",
+      " 18.67930486 18.66788121 18.66631561 18.65966611 18.66849823 18.66972003\n",
+      " 18.67013162 18.66896597 18.6579565  18.67103872 18.65491581 18.65358698\n",
+      " 18.66370462 18.66164257 18.6569877  18.56600289 18.5692992  18.54715831\n",
+      " 18.54394934 18.53411497 18.49777335 18.49804442 18.41805877 18.4063408\n",
+      " 18.4061253  18.31184033 17.06975917 17.07786226 17.12856701 17.2528283\n",
+      " 17.611311   17.64598372 17.65280292 17.67551398 17.72938897 17.90262684\n",
+      " 17.90598773 17.98073746 18.13610652 18.13715919 18.25145406 18.25487301\n",
+      " 18.36062909 18.35603089 18.36847083 18.37273541 18.43968568 18.43366067\n",
+      " 18.47957268 18.61835249 18.64002809 18.64909481 18.63314929 18.64952281\n",
+      " 18.65573115 18.64950698 18.64793597 18.63765327 18.65495075 18.64768785\n",
+      " 18.65862142 18.6483576  18.65693119 18.65358188 18.66605448 18.67192185\n",
+      " 18.67619685 18.66172586 18.66574704 18.66421313 18.6824502  18.68923574\n",
+      " 18.68106826 18.69140822 18.68605509 18.68982268 18.68222598 18.68963174\n",
+      " 18.69675091 18.69070714 18.68444787 18.69095333 18.69141823 18.69608109\n",
+      " 18.69427518 18.68700027 18.70038067 18.68548749 18.68316973 18.68708614\n",
+      " 18.68673177 18.70200067 18.69476717 18.69462113 18.68759774 18.69359686\n",
+      " 18.69429115 18.68456738 18.69857127 18.69484835 18.69784207]\n",
+      "\n",
+      "Key: r_err_mag\n",
+      "Shape: (197,)\n",
+      "Data Type: float64\n",
+      "Content Preview:\n",
+      " [0.00596492 0.00560506 0.00583638 0.00588735 0.00615946 0.00561835\n",
+      " 0.00654771 0.00596221 0.00571718 0.00574155 0.00591046 0.00582886\n",
+      " 0.00586849 0.0057755  0.00614581 0.00572248 0.00605282 0.00605451\n",
+      " 0.00656831 0.00598589 0.00578811 0.00566018 0.00576915 0.00622727\n",
+      " 0.00591582 0.0059306  0.00570239 0.00569578 0.00570503 0.00589974\n",
+      " 0.0058738  0.00583358 0.00588361 0.00619457 0.00581297 0.00563999\n",
+      " 0.00569342 0.00577196 0.00593454 0.00587292 0.00565274 0.00563945\n",
+      " 0.00590892 0.00571284 0.0056876  0.00564929 0.00567589 0.00570692\n",
+      " 0.00569317 0.00576345 0.00637737 0.00569523 0.00561536 0.00587139\n",
+      " 0.00576228 0.0057679  0.00560102 0.00585316 0.00593866 0.00594604\n",
+      " 0.00584783 0.00600108 0.00594635 0.00562488 0.0057156  0.00572039\n",
+      " 0.0057465  0.00568862 0.00600977 0.00570993 0.00581781 0.00603465\n",
+      " 0.00590699 0.00599297 0.00561602 0.00567301 0.00576944 0.00586209\n",
+      " 0.00600182 0.00568157 0.0058559  0.00562022 0.00567289 0.00567146\n",
+      " 0.00607535 0.00572145 0.00606875 0.00562621 0.00575229 0.00581493\n",
+      " 0.00568902 0.00593416 0.00625915 0.00566792 0.00573871 0.005609\n",
+      " 0.00577484 0.0059164  0.00573728 0.00568579 0.00559923 0.00611728\n",
+      " 0.0056709  0.0059318  0.00567208 0.00573612 0.00573018 0.0064503\n",
+      " 0.00594213 0.00568223 0.00579854 0.00566238 0.0056469  0.0055836\n",
+      " 0.00559344 0.00556805 0.00549741 0.00594042 0.00557509 0.00582399\n",
+      " 0.00555493 0.00550977 0.00513865 0.00514444 0.00516384 0.0051646\n",
+      " 0.00526306 0.00527768 0.00522205 0.00524621 0.00524454 0.00537343\n",
+      " 0.00538441 0.00533288 0.00540578 0.00537886 0.00541782 0.00547423\n",
+      " 0.00560355 0.00581232 0.00552304 0.00565863 0.00574957 0.00567585\n",
+      " 0.00578503 0.00562689 0.00586897 0.00566408 0.00570366 0.00576103\n",
+      " 0.00567583 0.00605616 0.0057415  0.00612362 0.00572242 0.00613632\n",
+      " 0.00598403 0.00575981 0.00574638 0.00579988 0.00561845 0.00585202\n",
+      " 0.00592322 0.0057471  0.0072094  0.0056388  0.00577393 0.00574596\n",
+      " 0.00564974 0.00624719 0.00595381 0.00593996 0.00650155 0.00588898\n",
+      " 0.00577771 0.00597069 0.00571199 0.00608575 0.00595886 0.00570192\n",
+      " 0.00567409 0.00568106 0.00567239 0.00600395 0.00582187 0.00577832\n",
+      " 0.00633946 0.00614388 0.00583642 0.00579979 0.00572355 0.00566487\n",
+      " 0.00583159 0.00585798 0.00590945 0.00592108 0.00846303]\n",
+      "\n",
+      "Key: i_time\n",
+      "Shape: (195,)\n",
+      "Data Type: float64\n",
+      "Content Preview:\n",
+      " [2460413.78810289 2460413.79035961 2460418.90999502 2460418.92119697\n",
+      " 2460422.79913617 2460434.89513457 2460448.87601338 2460448.87791685\n",
+      " 2460460.87393564 2460476.78726082 2460486.75582001 2460487.73999085\n",
+      " 2460487.75696332 2460519.47723573 2460519.48814042 2460527.6860035\n",
+      " 2460527.68645289 2460531.68564428 2460547.59740502 2460547.59785531\n",
+      " 2460561.62787979 2460569.60057162 2460569.60101762 2460573.52315404\n",
+      " 2460778.82782883 2460788.82019987 2460788.82068242 2460796.7750416\n",
+      " 2460828.80937298 2460829.85238467 2460830.65885534 2460830.66064845\n",
+      " 2460831.60412306 2460849.81941635 2460858.59068279 2460859.75504461\n",
+      " 2460870.4903996  2460870.56897654 2460871.758389   2460897.48073549\n",
+      " 2460897.67763434 2460907.69726057 2460907.70218367 2460924.64417943\n",
+      " 2460932.6032346  2460941.58400897 2460950.50799635 2461105.86210525\n",
+      " 2461127.85192243 2461133.79465237 2461161.75331821 2461169.88693012\n",
+      " 2461179.86685776 2461196.81889545 2461196.83476256 2461225.76730752\n",
+      " 2461240.74022323 2461242.78385968 2461243.48301336 2461297.57647805\n",
+      " 2461308.51428134 2461308.51473197 2461485.7903402  2461511.81359056\n",
+      " 2461513.76581661 2461515.80403285 2461531.89563844 2461531.89608732\n",
+      " 2461542.71339365 2461552.84266896 2461608.49410175 2461608.49589212\n",
+      " 2461608.7321137  2461608.73345288 2461610.49468325 2461625.62668729\n",
+      " 2461626.49023751 2461627.50186057 2461643.58925453 2461643.59069491\n",
+      " 2461671.55226489 2461671.55271389 2461842.86191498 2461863.82346862\n",
+      " 2461863.82436795 2461866.76767644 2461869.77368322 2461894.70803127\n",
+      " 2461905.87423696 2461908.91991003 2461922.85892571 2461935.7879797\n",
+      " 2461937.83081427 2461964.74814683 2461964.74993619 2461964.75038365\n",
+      " 2461973.7144095  2461975.77335803 2461975.77380201 2461987.67382716\n",
+      " 2461992.67806895 2461999.48036034 2462003.70316641 2462003.70406124\n",
+      " 2462004.48988337 2462021.60813674 2462025.62630062 2462047.54029274\n",
+      " 2462223.81537988 2462247.81308814 2462252.72724024 2462252.72768771\n",
+      " 2462290.86018738 2462344.7358523  2462375.59747668 2462375.60196996\n",
+      " 2462398.56300436 2462398.58205832 2462399.55360509 2462571.8822299\n",
+      " 2462582.8459353  2462602.76644879 2462607.76120021 2462608.74694116\n",
+      " 2462608.76529122 2462636.62381063 2462636.62425828 2462643.89135528\n",
+      " 2462645.86930978 2462649.71939621 2462651.71358466 2462671.90653784\n",
+      " 2462677.77175309 2462691.48905004 2462704.54321544 2462705.73520433\n",
+      " 2462706.72517025 2462713.70014444 2462727.48429706 2462728.47987392\n",
+      " 2462747.58681552 2462752.59658005 2462761.58418327 2462764.54726165\n",
+      " 2462962.80905839 2462986.90186725 2463000.89673878 2463015.81561118\n",
+      " 2463015.8160691  2463015.89356991 2463015.89938909 2463016.66865737\n",
+      " 2463016.66910425 2463016.83929307 2463045.57946526 2463046.7569306\n",
+      " 2463048.75025209 2463074.6644475  2463082.47976134 2463084.65476562\n",
+      " 2463084.66296079 2463103.60735177 2463136.51554979 2463140.50398449\n",
+      " 2463140.50678734 2463383.71077841 2463383.86083875 2463402.57059435\n",
+      " 2463437.48728611 2463457.47581157 2463467.58014534 2463467.58341474\n",
+      " 2463502.5195428  2463658.90182235 2463672.87036748 2463693.80013889\n",
+      " 2463695.82042093 2463699.72471469 2463699.72605389 2463719.89128658\n",
+      " 2463722.78413817 2463723.77199937 2463723.77246063 2463740.68938367\n",
+      " 2463752.60886494 2463752.67337732 2463768.75320813 2463784.57203426\n",
+      " 2463792.74710208 2463794.73499679 2463796.73596145 2463822.68378463\n",
+      " 2463841.5597197  2463854.56999726 2463868.49127256]\n",
+      "\n",
+      "Key: i_flux\n",
+      "Shape: (195,)\n",
+      "Data Type: float64\n",
+      "Content Preview:\n",
+      " [ 4423.19631678  4423.19632257  4423.20954298  4423.2095721\n",
+      "  4423.21970316  4423.25195966  4423.29050218  4423.29050752\n",
+      "  4423.32467404  4423.37158503  4423.40190346  4423.40493595\n",
+      "  4423.40498831  4423.50654022  4423.50657641  4423.53403449\n",
+      "  4423.53403601  4423.54761338  4423.60282861  4423.6028302\n",
+      "  4423.65311968  4423.68237827  4423.68237992  4423.69695764\n",
+      "  4424.6897871   4424.75423485  4424.75423801  4424.80694732\n",
+      "  4425.03235409  4425.04005736  4425.04602971  4425.04604301\n",
+      "  4425.05304782  4425.19210244  4425.26169087  4425.27105889\n",
+      "  4425.35888633  4425.35953892  4425.36943451  4425.59153314\n",
+      "  4425.5932938   4425.68412851  4425.68417374  4425.8433914\n",
+      "  4425.92069063  4426.00988543  4426.10063603  4428.12154207\n",
+      "  4428.50063481  4428.60836056  4429.14766472  4429.31515238\n",
+      "  4429.52753768  4429.90631695  4429.9066824   4430.60855548\n",
+      "  4431.00078323  4431.05591581  4431.07486685  4432.68710493\n",
+      "  4433.05053943  4433.05055468  4441.54178469  4443.41101179\n",
+      "  4443.56042878  4443.71786549  4445.0141515   4445.01418902\n",
+      "  4445.94133362  4446.85250848  4452.69061731  4452.69082999\n",
+      "  4452.71890651  4452.71906577  4452.92935258  4454.80691031\n",
+      "  4454.91798246  4455.04865226  4457.20887067  4457.20907119\n",
+      "  4461.3581444   4461.35821533  4503.91089781  4512.45438173\n",
+      "  4512.45477201  4513.74358531  4515.08397131  4527.21231651\n",
+      "  4533.28282721  4535.01348496  4543.37025575  4551.80518681\n",
+      "  4553.20205749  4573.40004294  4573.40150357  4573.40186883\n",
+      "  4580.93189961  4582.72301837  4582.72340714  4593.54919354\n",
+      "  4598.35335591  4605.13561687  4609.49738765  4609.49832453\n",
+      "  4610.32315387  4629.36841772  4634.15318807  4662.57929496\n",
+      "  5169.10699311  5317.48909694  5352.36804536  5352.37129915\n",
+      "  5691.37048778  6494.28674549  7267.98020995  7268.11887329\n",
+      "  8117.04077409  8117.88258276  8161.16428283 19015.95944442\n",
+      " 15643.34899323 12044.16686349 11426.16712683 11313.41194817\n",
+      " 11311.33963131  8993.78767032  8993.76035523  8579.73220119\n",
+      "  8476.43019358  8285.64131451  8191.82850671  7399.7333932\n",
+      "  7213.4407598   6835.9358689   6538.28607867  6513.62343296\n",
+      "  6493.43297706  6358.31616291  6123.95443152  6108.51883113\n",
+      "  5844.97410912  5784.95236335  5685.30678644  5654.5430483\n",
+      "  4731.56414719  4689.3920087   4667.96385208  4647.29705063\n",
+      "  4647.29644839  4647.19454923  4647.18690028  4646.17844146\n",
+      "  4646.17785718  4645.95547299  4611.89337058  4610.63560507\n",
+      "  4608.52920654  4583.58557972  4576.87927288  4575.07447921\n",
+      "  4575.06772842  4560.40539108  4538.88796629  4536.57608888\n",
+      "  4536.57448467  4460.83451232  4460.81077191  4457.98208131\n",
+      "  4453.34494156  4451.02491441  4449.9352556   4449.9349116\n",
+      "  4446.55128133  4436.45837941  4435.82249934  4434.92709569\n",
+      "  4434.84414049  4434.68550636  4434.68545233  4433.90040868\n",
+      "  4433.79236627  4433.75572838  4433.7557113   4433.14817578\n",
+      "  4432.74201543  4432.73986506  4432.21953237  4431.73710595\n",
+      "  4431.49878405  4431.44193317  4431.38513749  4430.68632884\n",
+      "  4430.2195546   4429.91674234  4429.60874449]\n",
+      "\n",
+      "Key: i_err_flux\n",
+      "Shape: (195,)\n",
+      "Data Type: float64\n",
+      "Content Preview:\n",
+      " [ 9.40416929  9.38526505  9.40712496  9.40419056  9.4009574   9.42335397\n",
+      "  9.38696969  9.41403208  9.38797103  9.40559408  9.39631843  9.41143985\n",
+      "  9.42229271  9.40650839  9.40604061  9.41156735  9.40091485  9.40631703\n",
+      "  9.38716145  9.41061103  9.39987234  9.39095309  9.41838627  9.39942551\n",
+      "  9.38771538  9.40695487  9.41740941  9.40229759  9.42738564  9.4071037\n",
+      "  9.39810619  9.40221251  9.41109983  9.40935704  9.41358593  9.41579524\n",
+      "  9.39876588  9.42210168  9.41745188  9.40895318  9.43550741  9.4115886\n",
+      "  9.41309726  9.41218359  9.4118861   9.41628377  9.41135484  9.40886816\n",
+      "  9.40563661  9.39836156  9.41558283  9.43160644  9.41522172  9.4095696\n",
+      "  9.41757931  9.4028719   9.39710594  9.41190735  9.42335397  9.42971898\n",
+      "  9.40355252  9.42307805  9.41475438  9.42023354  9.42897661  9.44449046\n",
+      "  9.43143679  9.41507302  9.4371182   9.41847121  9.42920994  9.42042462\n",
+      "  9.44190659  9.4444481   9.43970339  9.43565578  9.43141559  9.43130956\n",
+      "  9.43855921  9.45255521  9.45528424  9.44258439  9.47402765  9.50513545\n",
+      "  9.49465113  9.51657501  9.49829458  9.5071552   9.53519795  9.52795886\n",
+      "  9.53607886  9.52567058  9.54796313  9.56299116  9.57333798  9.57289925\n",
+      "  9.56443412  9.57020376  9.56393225  9.5978956   9.59328932  9.5977289\n",
+      "  9.58934826  9.59564485  9.61371936  9.61800395  9.6452268   9.64763183\n",
+      " 10.17259062 10.30945197 10.34168265 10.35159891 10.66255129 11.39373512\n",
+      " 12.05796003 12.0549409  12.73596482 12.74580715 12.78217509 19.49289101\n",
+      " 17.69671156 15.52360783 15.10789198 15.06415613 15.04126324 13.42177336\n",
+      " 13.42394875 13.09413609 13.01632821 12.87374072 12.81147923 12.17223069\n",
+      " 11.99964999 11.70446069 11.44959388 11.41674209 11.40128063 11.27845734\n",
+      " 11.07223555 11.04691812 10.82373318 10.77979592 10.66693958 10.62856528\n",
+      "  9.73416663  9.69822664  9.65933745  9.63188455  9.65113465  9.65921322\n",
+      "  9.65005699  9.66146987  9.66484351  9.63914934  9.59672861  9.59583243\n",
+      "  9.58267186  9.57722298  9.56560505  9.55778217  9.56322121  9.55449632\n",
+      "  9.52766498  9.52407476  9.52048318  9.44836494  9.46934     9.45545345\n",
+      "  9.46158549  9.43287867  9.42290826  9.43911013  9.42157099  9.41711208\n",
+      "  9.43056732  9.42660066  9.42528514  9.41957536  9.42696133  9.40729504\n",
+      "  9.43398113  9.41462692  9.4192144   9.4183438   9.40244649  9.40652965\n",
+      "  9.41411706  9.43822017  9.40180834  9.41602889  9.42148608  9.41173735\n",
+      "  9.40965462  9.42772507  9.40874062]\n",
+      "\n",
+      "Key: i_mag\n",
+      "Shape: (195,)\n",
+      "Data Type: float64\n",
+      "Content Preview:\n",
+      " [18.73537989 18.7306455  18.73776776 18.72844316 18.74058203 18.72790917\n",
+      " 18.74686221 18.75455271 18.74212599 18.73207878 18.72580426 18.7426222\n",
+      " 18.72953301 18.74592888 18.71818953 18.73903113 18.73157765 18.73168297\n",
+      " 18.73044547 18.72273578 18.74330849 18.73814655 18.7204096  18.74093471\n",
+      " 18.73050867 18.73502996 18.73852813 18.71969503 18.7332731  18.74474394\n",
+      " 18.74015299 18.73545231 18.7348644  18.72606639 18.73448898 18.73435128\n",
+      " 18.72368949 18.72363284 18.74715704 18.73867742 18.72303026 18.74172305\n",
+      " 18.7422742  18.73635438 18.73790326 18.73014719 18.73990991 18.73520092\n",
+      " 18.7273207  18.74069834 18.729439   18.72989902 18.72299779 18.73297801\n",
+      " 18.74004415 18.73636207 18.73646146 18.74447861 18.73482249 18.73002166\n",
+      " 18.7365172  18.73509801 18.73748231 18.74751005 18.73342389 18.73516541\n",
+      " 18.73260617 18.73266821 18.71765094 18.73449686 18.72601993 18.72854517\n",
+      " 18.71981849 18.73206972 18.71776203 18.73763945 18.74395923 18.73810578\n",
+      " 18.72531453 18.73161963 18.72153067 18.72903412 18.72227857 18.7076199\n",
+      " 18.7098196  18.71561923 18.7196956  18.70423355 18.72134011 18.71531009\n",
+      " 18.7051631  18.70252216 18.70577799 18.70741635 18.70012657 18.7052519\n",
+      " 18.69513866 18.68659004 18.7003384  18.69254395 18.70451778 18.6884874\n",
+      " 18.69774281 18.6867922  18.68920036 18.68541988 18.67954414 18.68088582\n",
+      " 18.56569938 18.53605846 18.52188797 18.5398089  18.45797475 18.31636754\n",
+      " 18.2045248  18.19676499 18.07686417 18.07163649 18.07241051 17.15422152\n",
+      " 17.37139005 17.63926316 17.71503024 17.71034578 17.71409465 17.96052338\n",
+      " 17.95750717 18.01262897 18.02531973 18.05795597 18.06487623 18.18106728\n",
+      " 18.20344858 18.26659583 18.3123362  18.31442945 18.32666923 18.3329245\n",
+      " 18.38369544 18.38427093 18.42796338 18.45157139 18.46201861 18.47193888\n",
+      " 18.66707478 18.66635549 18.67057106 18.68282309 18.67228916 18.68438688\n",
+      " 18.67913333 18.68061834 18.68233544 18.6904408  18.67968411 18.69216987\n",
+      " 18.68791509 18.70302145 18.70618377 18.70260158 18.70131056 18.70731124\n",
+      " 18.71184917 18.70665587 18.71387813 18.71890163 18.71944009 18.71435524\n",
+      " 18.72666498 18.73113973 18.72235711 18.72730765 18.74101913 18.73873176\n",
+      " 18.74021277 18.74730758 18.73783935 18.72335827 18.73929552 18.73364234\n",
+      " 18.73060755 18.7256336  18.72678787 18.74097873 18.73928788 18.73985948\n",
+      " 18.72295071 18.74340868 18.7317707  18.7407976  18.74198064 18.72854819\n",
+      " 18.73018571 18.73222116 18.74252248]\n",
+      "\n",
+      "Key: i_err_mag\n",
+      "Shape: (195,)\n",
+      "Data Type: float64\n",
+      "Content Preview:\n",
+      " [0.00588888 0.00706649 0.00901907 0.00760142 0.00812663 0.0064902\n",
+      " 0.00809104 0.00944404 0.0081824  0.00665689 0.00669084 0.00909078\n",
+      " 0.00849448 0.00706313 0.00689536 0.00709318 0.00632185 0.0076062\n",
+      " 0.00765692 0.00941624 0.00602103 0.00629117 0.00928465 0.00600126\n",
+      " 0.00613137 0.00646074 0.00787687 0.00850293 0.00703367 0.00716121\n",
+      " 0.0061324  0.00632379 0.00662084 0.00617098 0.00613957 0.0059781\n",
+      " 0.00605901 0.0076809  0.00650985 0.00614223 0.00675444 0.00601632\n",
+      " 0.00586997 0.00713003 0.00716853 0.00768776 0.00592734 0.00591231\n",
+      " 0.00669158 0.00814909 0.00764849 0.00670847 0.00905798 0.00596303\n",
+      " 0.00686576 0.00586984 0.00729574 0.01070362 0.00667551 0.00643984\n",
+      " 0.00610932 0.00612799 0.00626571 0.00591044 0.00674589 0.00674285\n",
+      " 0.00665422 0.0069126  0.00825628 0.00756158 0.00617605 0.00714016\n",
+      " 0.00738402 0.00605364 0.00786382 0.00616774 0.00695826 0.00601586\n",
+      " 0.01205412 0.00730808 0.00649243 0.00677498 0.00592451 0.00663374\n",
+      " 0.0071049  0.00590131 0.00737446 0.00758892 0.00617305 0.0069356\n",
+      " 0.00627784 0.00622261 0.0076774  0.00617482 0.00628178 0.00701215\n",
+      " 0.00662812 0.00765308 0.00621742 0.00608025 0.00611284 0.00670579\n",
+      " 0.00598886 0.00600767 0.01279628 0.00599787 0.00659414 0.00614305\n",
+      " 0.00573252 0.00570867 0.01067107 0.0067795  0.00583697 0.00577496\n",
+      " 0.00572935 0.00559731 0.00573953 0.00547875 0.00565295 0.00521399\n",
+      " 0.00523325 0.00534578 0.00529359 0.00530182 0.00548164 0.00626349\n",
+      " 0.00616698 0.00557164 0.00553784 0.00614229 0.00598849 0.00665246\n",
+      " 0.00571176 0.00598052 0.00557054 0.00633553 0.00561664 0.00643094\n",
+      " 0.00564148 0.00653774 0.00571615 0.0060853  0.0057272  0.00718669\n",
+      " 0.00679947 0.00749779 0.00664768 0.00666437 0.00781904 0.00815545\n",
+      " 0.00602565 0.00604128 0.00676754 0.01311576 0.00609848 0.00672952\n",
+      " 0.00616569 0.00612889 0.0064536  0.00986642 0.00599992 0.00688635\n",
+      " 0.00595392 0.00609867 0.00778786 0.00623577 0.00769673 0.00683099\n",
+      " 0.00648677 0.00612257 0.00702654 0.00606136 0.00588669 0.0060818\n",
+      " 0.00813414 0.00694787 0.00686684 0.00724726 0.00734139 0.00702182\n",
+      " 0.00642779 0.00618199 0.0071328  0.00630863 0.00635979 0.00674484\n",
+      " 0.00616301 0.00708625 0.00725365 0.00664798 0.00688571 0.00681574\n",
+      " 0.00637295 0.01029125 0.00853934]\n",
+      "\n",
+      "Key: z_time\n",
+      "Shape: (176,)\n",
+      "Data Type: float64\n",
+      "Content Preview:\n",
+      " [2460417.91201631 2460422.82411949 2460423.79523008 2460448.68487232\n",
+      " 2460448.90042318 2460448.9022932  2460460.84999303 2460476.81150077\n",
+      " 2460486.77932986 2460487.76421999 2460519.46642205 2460519.51258333\n",
+      " 2460547.62144448 2460547.62189237 2460720.89184426 2460778.85212861\n",
+      " 2460778.9214835  2460779.82622393 2460828.83267409 2460829.82832743\n",
+      " 2460830.63381057 2460830.6356024  2460831.62810198 2460858.56625865\n",
+      " 2460859.77909464 2460870.51434385 2460870.59345811 2460871.7822899\n",
+      " 2460895.49419955 2460897.46953437 2460924.620362   2460932.62699812\n",
+      " 2460950.53178307 2461104.90441802 2461105.84282623 2461127.82748668\n",
+      " 2461133.7707594  2461154.91950917 2461161.77820546 2461196.7946633\n",
+      " 2461196.85871127 2461216.60166221 2461224.78572891 2461225.80431721\n",
+      " 2461242.75998615 2461243.50727099 2461243.71243728 2461251.66002408\n",
+      " 2461271.62466445 2461305.52100512 2461308.53932317 2461308.53977041\n",
+      " 2461309.52157014 2461485.81421757 2461511.83811516 2461513.79075415\n",
+      " 2461515.82841325 2461515.82937893 2461540.90572179 2461596.76384501\n",
+      " 2461607.70769625 2461608.5182387  2461608.52003549 2461608.70793881\n",
+      " 2461608.70928141 2461610.51895911 2461625.65071397 2461626.51478473\n",
+      " 2461643.61335594 2461643.61476028 2461670.52174669 2461671.57677421\n",
+      " 2461842.84029183 2461863.84794788 2461863.84885255 2461866.79160236\n",
+      " 2461869.79759415 2461894.73208049 2461905.88552881 2461908.93204902\n",
+      " 2461922.83498368 2461935.81208704 2461964.77194727 2461964.77373132\n",
+      " 2461964.77417719 2461987.69753272 2461992.70188259 2462017.59707412\n",
+      " 2462043.55788118 2462049.51846718 2462247.83758961 2462252.75127302\n",
+      " 2462252.75172261 2462290.88445613 2462342.72986238 2462342.73030837\n",
+      " 2462344.71186015 2462347.72319769 2462375.62141751 2462398.53884995\n",
+      " 2462398.60606991 2462399.57753878 2462553.89181534 2462571.89387435\n",
+      " 2462582.86725451 2462602.79048754 2462602.82216525 2462607.78535122\n",
+      " 2462608.72311372 2462608.79002222 2462608.7918361  2462636.64755306\n",
+      " 2462636.64800099 2462643.84443307 2462645.89618671 2462671.88637888\n",
+      " 2462691.51287067 2462698.73614473 2462727.47333397 2462728.46904722\n",
+      " 2462734.48958396 2462752.54647166 2462752.62033999 2462752.64230305\n",
+      " 2462752.64275223 2462754.58416137 2462754.58457803 2462755.55874676\n",
+      " 2462764.57124326 2462962.83348746 2462986.91328609 2463000.90862547\n",
+      " 2463015.83995306 2463015.84040291 2463015.86782909 2463015.87009941\n",
+      " 2463015.91104999 2463016.69259857 2463016.86327149 2463048.77441696\n",
+      " 2463074.68850898 2463082.46876516 2463084.63066043 2463084.68774493\n",
+      " 2463103.58253859 2463112.58949288 2463136.53964233 2463137.56233793\n",
+      " 2463140.4931193  2463140.53097327 2463383.88312411 2463431.71364744\n",
+      " 2463436.54110636 2463437.51147909 2463437.54381421 2463457.48670458\n",
+      " 2463467.60452779 2463467.6077159  2463502.54369976 2463672.88160677\n",
+      " 2463693.77630226 2463695.84462964 2463699.74851421 2463699.74985603\n",
+      " 2463722.75981303 2463726.68797162 2463752.63281657 2463752.64904853\n",
+      " 2463768.77741443 2463784.59641517 2463794.75896344 2463796.71189937\n",
+      " 2463811.66550396 2463811.66595203 2463820.63922958 2463854.54528419]\n",
+      "\n",
+      "Key: z_flux\n",
+      "Shape: (176,)\n",
+      "Data Type: float64\n",
+      "Content Preview:\n",
+      " [ 2890.28505006  2890.29363333  2890.29534311  2890.34064449\n",
+      "  2890.34104942  2890.34105294  2890.36384213  2890.39535359\n",
+      "  2890.41565754  2890.41768999  2890.48566036  2890.48576296\n",
+      "  2890.55022564  2890.5502267   2891.05186623  2891.27820911\n",
+      "  2891.27850424  2891.28235986  2891.50763817  2891.51256281\n",
+      "  2891.51655732  2891.51656622  2891.5215011   2891.66097873\n",
+      "  2891.66751368  2891.72633515  2891.72677521  2891.7333995\n",
+      "  2891.87014446  2891.88193867  2892.05052211  2892.10259895\n",
+      "  2892.22311624  2893.56574629  2893.57614071  2893.82993987\n",
+      "  2893.90208415  2894.1718418   2894.2638879   2894.77124596\n",
+      "  2894.77223388  2895.0877671   2895.22516365  2895.2425419\n",
+      "  2895.54107411  2895.55463935  2895.5583698   2895.70492449\n",
+      "  2896.09115025  2896.80980355  2896.87787552  2896.87788565\n",
+      "  2896.90017851  2902.56498319  2903.81697555  2903.91707277\n",
+      "  2904.02249139  2904.02254158  2905.40435242  2909.12151329\n",
+      "  2909.96783184  2910.03220322  2910.03234618  2910.04730299\n",
+      "  2910.0474099   2910.19211308  2911.44963163  2911.52406973\n",
+      "  2913.05840771  2913.05853865  2915.7261625   2915.83752548\n",
+      "  2944.3270052   2950.06123594  2950.06149889  2950.92458166\n",
+      "  2951.82236592  2959.94597648  2964.00720321  2965.16663348\n",
+      "  2970.74846598  2976.41835437  2990.88248845  2990.88346388\n",
+      "  2990.88370767  3004.37837729  3007.59628722  3025.23511529\n",
+      "  3046.92631826  3052.45044823  3489.30015995  3512.66198228\n",
+      "  3512.66417217  3739.75052453  4250.41290917  4250.41880865\n",
+      "  4276.9632401   4318.61166164  4795.92670974  5363.33394288\n",
+      "  5365.32281483  5394.31794672 20404.89361497 12660.07137845\n",
+      " 10400.90471097  7991.92093541  7989.12049848  7578.29990847\n",
+      "  7506.46360218  7501.40330668  7501.26624265  5950.23640833\n",
+      "  5950.21811241  5675.58496078  5603.80874541  4884.11543053\n",
+      "  4505.69416473  4391.07853548  4029.39145029  4019.05143713\n",
+      "  3959.09891652  3802.63794631  3802.06167443  3801.8904248\n",
+      "  3801.88692288  3786.91385907  3786.9106801   3779.51823703\n",
+      "  3714.74769378  3096.76096678  3068.5370959   3054.18732539\n",
+      "  3040.3369207   3040.33652454  3040.3123748   3040.3103759\n",
+      "  3040.27432649  3039.58826372  3039.43893364  3014.37851989\n",
+      "  2997.67654626  2993.2056749   2992.00414728  2991.97265566\n",
+      "  2982.17902128  2977.92337471  2967.74724213  2967.34656307\n",
+      "  2966.21261149  2966.19810257  2915.46599642  2910.94619525\n",
+      "  2910.54585206  2910.4665108   2910.46387342  2908.91395426\n",
+      "  2908.18329704  2908.18307242  2905.91732505  2898.73335738\n",
+      "  2898.13469689  2898.07782058  2897.9716034   2897.97156715\n",
+      "  2897.37471517  2897.27765408  2896.67015442  2896.66979207\n",
+      "  2896.32027274  2895.99721716  2895.79956577  2895.76244263\n",
+      "  2895.48710346  2895.48709544  2895.32921935  2894.77897745]\n",
+      "\n",
+      "Key: z_err_flux\n",
+      "Shape: (176,)\n",
+      "Data Type: float64\n",
+      "Content Preview:\n",
+      " [ 7.59557766  7.58822772  7.61992126  7.60484056  7.60528763  7.59391862\n",
+      "  7.60576097  7.61390833  7.58746334  7.60712824  7.58329744  7.59007246\n",
+      "  7.58282269  7.60126305  7.60652352  7.61419727  7.60273635  7.60760146\n",
+      "  7.6142498   7.59955262  7.60986202  7.61143876  7.61375072  7.59178503\n",
+      "  7.61285754  7.58944003  7.62807971  7.6126211   7.60465647  7.59584097\n",
+      "  7.6186613   7.6318805   7.59828928  7.60739114  7.60507725  7.6029731\n",
+      "  7.58891297  7.61881881  7.61868755  7.61149131  7.6170598   7.60326246\n",
+      "  7.6259819   7.62527377  7.59789445  7.6035518   7.64151817  7.60565579\n",
+      "  7.61640335  7.60597134  7.60760146  7.61963254  7.62223064  7.6215484\n",
+      "  7.62666375  7.62779129  7.63756506  7.61965879  7.60938894  7.61330414\n",
+      "  7.66161863  7.62307025  7.63219497  7.612805    7.62710954  7.61514281\n",
+      "  7.62094482  7.63992147  7.62375236  7.63890044  7.64416117  7.64193693\n",
+      "  7.67557164  7.69256784  7.67166214  7.67359108  7.68832882  7.69085171\n",
+      "  7.69945453  7.69877912  7.69376371  7.72279742  7.7295278   7.73185618\n",
+      "  7.74748992  7.76329827  7.74785132  7.77094589  7.8103265   7.79245789\n",
+      "  8.34357238  8.38200453  8.37914077  8.64435076  9.21049401  9.20612839\n",
+      "  9.24843771  9.2965585   9.80126522 10.32933686 10.34777271 10.3835832\n",
+      " 20.19273137 15.91853008 14.4198613  12.65159279 12.63810112 12.31706134\n",
+      " 12.24509698 12.25538249 12.24282647 10.90609004 10.9083821  10.67052014\n",
+      " 10.58514053  9.89043983  9.49808402  9.36606641  8.96285669  8.98449776\n",
+      "  8.90339261  8.723027    8.7174767   8.72896328  8.72804675  8.7106831\n",
+      "  8.72282064  8.68918868  8.62535796  7.86246781  7.82322184  7.80571585\n",
+      "  7.81329636  7.79758937  7.81511356  7.80412711  7.78177358  7.7934588\n",
+      "  7.79920509  7.75538523  7.73635573  7.73638158  7.7240145   7.74043926\n",
+      "  7.7193523   7.71904139  7.70360954  7.70519305  7.70389512  7.69916879\n",
+      "  7.63641277  7.64251268  7.61438113  7.60681274  7.64403035  7.62120725\n",
+      "  7.63093703  7.6282108   7.62585077  7.6152216   7.59078389  7.60518244\n",
+      "  7.6248541   7.60607652  7.60602393  7.59468235  7.61551049  7.61125482\n",
+      "  7.60276266  7.61624579  7.60754888  7.60505095  7.60612911  7.61968503\n",
+      "  7.61501149  7.60199974]\n",
+      "\n",
+      "Key: z_mag\n",
+      "Shape: (176,)\n",
+      "Data Type: float64\n",
+      "Content Preview:\n",
+      " [18.79321977 18.80803144 18.8171832  18.81143904 18.82455631 18.80271526\n",
+      " 18.81542113 18.8015735  18.82605358 18.8141202  18.80739979 18.80413554\n",
+      " 18.81405967 18.81240905 18.8158716  18.81174322 18.8142772  18.79148019\n",
+      " 18.81467667 18.81349052 18.81075546 18.80399425 18.79343018 18.79755423\n",
+      " 18.81517773 18.81214135 18.80156636 18.81540313 18.80786822 18.80586461\n",
+      " 18.82267872 18.81077463 18.80816091 18.80802496 18.81103557 18.79689033\n",
+      " 18.8036677  18.80462852 18.79699194 18.79859065 18.81023294 18.81058074\n",
+      " 18.80302705 18.81180373 18.80195222 18.7988469  18.82018602 18.8134882\n",
+      " 18.81010303 18.8126702  18.80331553 18.8126029  18.79920687 18.80080961\n",
+      " 18.81472276 18.79437677 18.79860305 18.79116794 18.81711831 18.80398303\n",
+      " 18.78858824 18.7904107  18.80431137 18.80218055 18.80361159 18.81164979\n",
+      " 18.80311389 18.78663898 18.79876688 18.78949015 18.78954928 18.79326185\n",
+      " 18.78462118 18.78859789 18.78701793 18.78435331 18.78142365 18.78948926\n",
+      " 18.76575312 18.77355936 18.78921801 18.77283488 18.76912651 18.78093736\n",
+      " 18.7607463  18.77039387 18.75647024 18.74432796 18.76251325 18.75747146\n",
+      " 18.60263365 18.58754379 18.60367301 18.52367323 18.37952206 18.38218581\n",
+      " 18.38861059 18.3858343  18.25583511 18.14568548 18.15256823 18.11770737\n",
+      " 16.68262342 17.20139586 17.43108105 17.70611603 17.70481416 17.76545153\n",
+      " 17.76179252 17.76284626 17.77191615 18.02609679 18.02670611 18.06948392\n",
+      " 18.08232399 18.23155876 18.33394043 18.36396113 18.43590177 18.45277074\n",
+      " 18.46174499 18.50989277 18.5110623  18.50690718 18.51308893 18.50869282\n",
+      " 18.51396428 18.51239511 18.53487027 18.72189065 18.74999441 18.75791995\n",
+      " 18.76990931 18.75593601 18.74184768 18.75567315 18.74704706 18.76629763\n",
+      " 18.75054697 18.77835314 18.76778355 18.77645257 18.77709243 18.76464055\n",
+      " 18.77161404 18.77053849 18.78069594 18.77355899 18.78416276 18.78619716\n",
+      " 18.79812648 18.79341414 18.81762156 18.79324954 18.79745288 18.79946462\n",
+      " 18.80095937 18.80998239 18.81028166 18.79413555 18.80752294 18.79818908\n",
+      " 18.7961322  18.82000859 18.79399687 18.81408198 18.80193324 18.79813465\n",
+      " 18.81916963 18.80940964 18.80598913 18.80441401 18.82551715 18.79226387\n",
+      " 18.80398622 18.82063167]\n",
+      "\n",
+      "Key: z_err_mag\n",
+      "Shape: (176,)\n",
+      "Data Type: float64\n",
+      "Content Preview:\n",
+      " [0.00893433 0.00895704 0.01094128 0.00941701 0.00893337 0.00897252\n",
+      " 0.01109505 0.00797126 0.00897235 0.01111694 0.00738003 0.00783208\n",
+      " 0.0090802  0.00927097 0.00871669 0.00781865 0.00757638 0.00984926\n",
+      " 0.00828351 0.01180771 0.01017273 0.0116204  0.00782227 0.01129\n",
+      " 0.01028635 0.00738568 0.00704227 0.00679449 0.01148304 0.00689951\n",
+      " 0.00792599 0.00721104 0.00930516 0.00731882 0.00750493 0.00948537\n",
+      " 0.00859785 0.00887618 0.01222728 0.00836154 0.00905715 0.00688999\n",
+      " 0.00818012 0.00902752 0.00691459 0.00870932 0.00832731 0.00944059\n",
+      " 0.00965585 0.01370132 0.00775133 0.01251186 0.00966753 0.0109363\n",
+      " 0.01190999 0.0070515  0.0072874  0.0077291  0.00829047 0.00744541\n",
+      " 0.00804007 0.00921931 0.0073641  0.00774093 0.01471374 0.0136927\n",
+      " 0.01051957 0.00754862 0.00683975 0.007154   0.00839096 0.00970284\n",
+      " 0.01005924 0.01030354 0.00952239 0.00772615 0.00929031 0.00776561\n",
+      " 0.01125467 0.00825884 0.00685348 0.00794466 0.01088425 0.00804838\n",
+      " 0.00953759 0.00695343 0.00707414 0.007314   0.00765612 0.00814292\n",
+      " 0.00703015 0.00689068 0.00672958 0.00983556 0.00611257 0.00607191\n",
+      " 0.00729222 0.00630884 0.00650679 0.01061707 0.00728045 0.00591234\n",
+      " 0.00521335 0.00529335 0.00564631 0.00576599 0.00586408 0.00553767\n",
+      " 0.00583227 0.00571794 0.00631397 0.00699981 0.00589803 0.00841026\n",
+      " 0.00584841 0.00686002 0.00686491 0.00890955 0.00619797 0.00719162\n",
+      " 0.00626562 0.00781833 0.00728521 0.00890561 0.0083744  0.00790321\n",
+      " 0.00897715 0.00660613 0.00804896 0.00875667 0.00816734 0.00801337\n",
+      " 0.00852903 0.00810809 0.0071105  0.0077615  0.00748584 0.00847334\n",
+      " 0.00937927 0.0132189  0.00673398 0.00884548 0.00667754 0.00750108\n",
+      " 0.00744578 0.0080193  0.0076434  0.00879251 0.00861873 0.00818273\n",
+      " 0.00824016 0.00864534 0.01074208 0.00733827 0.00873742 0.00928243\n",
+      " 0.00831085 0.00886885 0.00689179 0.0068594  0.00790372 0.00992092\n",
+      " 0.00991107 0.00851168 0.00882161 0.00837835 0.00785824 0.00828961\n",
+      " 0.00722517 0.0072804  0.00873659 0.00757779 0.00811198 0.00727898\n",
+      " 0.00841536 0.01286715]\n",
+      "\n",
+      "Key: y_time\n",
+      "Shape: (58,)\n",
+      "Data Type: float64\n",
+      "Content Preview:\n",
+      " [2460420.76134779 2460420.76179396 2460420.78412008 2460420.78456951\n",
+      " 2460423.81985597 2460486.80347048 2460513.65683135 2460513.65820054\n",
+      " 2460513.68019187 2460513.68153996 2460779.80150927 2460779.80196064\n",
+      " 2460828.8572206  2460868.74366275 2460868.7661664  2460895.51930775\n",
+      " 2461216.62715323 2461224.80989676 2461243.73634425 2461251.68449148\n",
+      " 2461271.64843791 2461305.54519652 2461607.7317455  2461607.73356517\n",
+      " 2461633.66438614 2461633.68678417 2461949.74297658 2461949.76584707\n",
+      " 2461949.79882656 2461982.51544244 2461982.51680725 2462043.58156984\n",
+      " 2462049.54228129 2462698.71223136 2462698.72482004 2462752.57048706\n",
+      " 2462752.66600928 2462752.66645846 2462754.56001281 2462754.60905943\n",
+      " 2462755.5827591  2463081.67754061 2463081.70056778 2463082.75369168\n",
+      " 2463082.77167399 2463082.77614701 2463112.61386918 2463431.73729341\n",
+      " 2463431.73774063 2463436.56587914 2463510.4908948  2463510.50048868\n",
+      " 2463791.70266858 2463791.70451836 2463791.7261974  2463811.68938784\n",
+      " 2463811.68983407 2463820.66317106]\n",
+      "\n",
+      "Key: y_flux\n",
+      "Shape: (58,)\n",
+      "Data Type: float64\n",
+      "Content Preview:\n",
+      " [1369.61344806 1369.61344858 1369.61347449 1369.61347501 1369.61701128\n",
+      " 1369.69683661 1369.73473561 1369.7347376  1369.73476961 1369.73477157\n",
+      " 1370.27173649 1370.27173776 1370.42134498 1370.55983885 1370.55992148\n",
+      " 1370.66186402 1372.79673636 1372.8878836  1373.10896049 1373.20620532\n",
+      " 1373.46245416 1373.93928026 1382.66971497 1382.66981068 1384.1170222\n",
+      " 1384.11834697 1431.15086526 1431.15840686 1431.16928385 1443.16407288\n",
+      " 1443.16462718 1473.54613308 1477.21170193 2365.53242272 2365.40632083\n",
+      " 1974.77498799 1974.28078578 1974.27846309 1964.58961894 1964.34135956\n",
+      " 1959.44061334 1438.1861937  1438.17759687 1437.78584159 1437.77917622\n",
+      " 1437.77751836 1427.74529118 1383.31621683 1383.31619194 1383.05058352\n",
+      " 1379.67274255 1379.67237709 1373.30760005 1373.30757637 1373.30729879\n",
+      " 1373.06110767 1373.06110237 1372.95637027]\n",
+      "\n",
+      "Key: y_err_flux\n",
+      "Shape: (58,)\n",
+      "Data Type: float64\n",
+      "Content Preview:\n",
+      " [5.22248983 5.23618182 5.21869716 5.24537892 5.22448084 5.2187738\n",
+      " 5.22578224 5.23667834 5.24255663 5.23622001 5.24595082 5.24877128\n",
+      " 5.22562915 5.21129542 5.22199196 5.25265647 5.25859297 5.25935357\n",
+      " 5.24957141 5.2504476  5.25440767 5.24427307 5.26003802 5.26193881\n",
+      " 5.25988593 5.26414286 5.36414019 5.35383974 5.37159194 5.36723392\n",
+      " 5.37717398 5.44224218 5.43694767 6.86769248 6.88952829 6.28206972\n",
+      " 6.26708864 6.3195253  6.27579477 6.27853486 6.24326837 5.35242001\n",
+      " 5.35791004 5.3552591  5.35469887 5.37743433 5.33385414 5.25954371\n",
+      " 5.24983809 5.25779421 5.25608219 5.26148268 5.23862577 5.25197106\n",
+      " 5.24156465 5.2383967  5.23446272 5.23178746]\n",
+      "\n",
+      "Key: y_mag\n",
+      "Shape: (58,)\n",
+      "Data Type: float64\n",
+      "Content Preview:\n",
+      " [18.82838316 18.84400903 18.91256839 18.87002513 18.84559497 18.79259984\n",
+      " 18.82898292 18.83385377 18.83024324 18.85312164 18.82745645 18.85037212\n",
+      " 18.85646087 18.80638583 18.84747494 18.83509907 18.81928886 18.82522628\n",
+      " 18.84461512 18.83083645 18.84468007 18.84781761 18.85870085 18.80140858\n",
+      " 18.82713495 18.8330145  18.78602048 18.78988016 18.7802178  18.7664122\n",
+      " 18.76821688 18.7738756  18.77302629 18.24324973 18.2454501  18.43794027\n",
+      " 18.4507609  18.4586682  18.43174408 18.47391037 18.48181443 18.8228203\n",
+      " 18.77661182 18.77534959 18.7627018  18.79332835 18.79077445 18.83623049\n",
+      " 18.79164549 18.82921614 18.82410688 18.81420267 18.86147229 18.83046298\n",
+      " 18.85637696 18.87421463 18.86043539 18.82536163]\n",
+      "\n",
+      "Key: y_err_mag\n",
+      "Shape: (58,)\n",
+      "Data Type: float64\n",
+      "Content Preview:\n",
+      " [0.01238667 0.0115539  0.04072749 0.02041657 0.01582808 0.05476801\n",
+      " 0.00997816 0.01138515 0.0128218  0.01280583 0.02637904 0.01331915\n",
+      " 0.01280695 0.02626526 0.01763027 0.01175519 0.01196642 0.0112325\n",
+      " 0.01255994 0.03151724 0.00997388 0.01153369 0.01345761 0.02163544\n",
+      " 0.01483723 0.01571934 0.01520596 0.01282549 0.01277181 0.01493462\n",
+      " 0.01909449 0.02099729 0.02001855 0.01012423 0.00884479 0.01303888\n",
+      " 0.01410893 0.01457853 0.01146181 0.02982595 0.01569313 0.01871529\n",
+      " 0.0143914  0.01427146 0.0174141  0.01381988 0.01537616 0.01237885\n",
+      " 0.02249063 0.01884234 0.01389193 0.0175835  0.01825723 0.01539136\n",
+      " 0.01347831 0.02100777 0.01555529 0.02221939]\n"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "\n",
+    "# Load the .npz file\n",
+    "data = np.load('downloaded_file/microlensing_lightcurves/USBL/USBL_7.npz')\n",
+    "\n",
+    "# Check the keys (array names) inside the file\n",
+    "print(\"Keys in the file:\", data.files)\n",
+    "\n",
+    "# Inspect each array\n",
+    "for key in data.files:\n",
+    "    print(f\"\\nKey: {key}\")\n",
+    "    print(\"Shape:\", data[key].shape)\n",
+    "    print(\"Data Type:\", data[key].dtype)\n",
+    "    print(\"Content Preview:\\n\", data[key])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f5226762",
+   "metadata": {},
+   "source": [
+    "> #### 4.3 We can also look at the lightcurves of all three event types by generating random light curves for each event given the criterion that we only select events with more than 10 points and 3 filters"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7a9b0111",
+   "metadata": {},
+   "source": [
+    "> #### 4.3.1 We first generate a random lightcurve for the FSPL events"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "616d5f53",
+   "metadata": {},
+   "source": [
+    "### Code Explanation\n",
+    "\n",
+    "#### Objective\n",
+    "- This notebook visualizes a random FSPL (Finite Source Point Lens) microlensing event from a set of simulation files. The purpose is to:\n",
+    "    - Select a scientifically valid event that meets minimum data quality requirements.\n",
+    "    - Plot the light curve in multiple filters centered on the peak event time.\n",
+    "\n",
+    "- Event Selection Criteria\n",
+    "    - From a directory of .npz files, a random FSPL event is selected that satisfies the following:\n",
+    "        - Contains at least 10 photometric points within ±3 × Einstein timescale (t_E) of the event peak (t₀).\n",
+    "        - Has data available in at least 3 different photometric filters (from u, g, r, i, z, y).\n",
+    "\n",
+    "#### Parameters Used\n",
+    "- $t_{0}$: Time of maximum magnification (event center)\n",
+    "\n",
+    "- $t_{E}$: Einstein crossing time (characteristic duration of the event)\n",
+    "\n",
+    "- $u_{0}$: Minimum impact parameter (source-lens separation in units of Einstein radius)\n",
+    "\n",
+    "#### Plot Description\n",
+    "\n",
+    "- The selected event's light curve is plotted as follows:\n",
+    "    - X-axis: Time in days, relative to t₀, i.e., (t - t₀)\n",
+    "    - Y-axis: Observed magnitude (inverted so that brighter objects appear higher)\n",
+    "    - Markers: Each filter’s photometric measurements are shown with error bars.\n",
+    "    - A vertical dashed line at t = 0 marks the event peak.\n",
+    "    - An annotation box provides the event-specific values of t_E and u₀.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "796e7852",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import os\n",
+    "import random\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# --- Path to your FSPL directory ---\n",
+    "fspl_dir = 'downloaded_file/microlensing_lightcurves/FSPL'\n",
+    "\n",
+    "# --- Create a directory to save the plots ---\n",
+    "save_dir = \"lightcurve_plots/FSPL\"\n",
+    "os.makedirs(save_dir, exist_ok=True)\n",
+    "\n",
+    "# --- List all available .npz files ---\n",
+    "all_files = [f for f in os.listdir(fspl_dir) if f.endswith('.npz')]\n",
+    "if not all_files:\n",
+    "    raise FileNotFoundError(\"No .npz files found in FSPL folder.\")\n",
+    "\n",
+    "# --- Try random files until a valid one is found ---\n",
+    "valid_file = None\n",
+    "while all_files:\n",
+    "    candidate = random.choice(all_files)\n",
+    "    file_path = os.path.join(fspl_dir, candidate)\n",
+    "    data = np.load(file_path)\n",
+    "\n",
+    "    t0 = float(data['t0'])\n",
+    "    tE = float(data['tE'])\n",
+    "    u0 = float(data['u0'])\n",
+    "    zoom_range = 3 * tE\n",
+    "\n",
+    "    filters = ['u', 'g', 'r', 'i', 'z', 'y']\n",
+    "    filter_count = 0\n",
+    "    total_points = 0\n",
+    "\n",
+    "    for f in filters:\n",
+    "        try:\n",
+    "            t = data[f\"{f}_time\"] - t0\n",
+    "            mag = data[f\"{f}_mag\"]\n",
+    "            err_mag = data[f\"{f}_err_mag\"]\n",
+    "            mask = (t >= -zoom_range) & (t <= zoom_range)\n",
+    "            n_points = np.sum(mask)\n",
+    "            if n_points >= 1:\n",
+    "                filter_count += 1\n",
+    "                total_points += n_points\n",
+    "        except KeyError:\n",
+    "            continue\n",
+    "\n",
+    "    if total_points >= 10 and filter_count >= 3:\n",
+    "        valid_file = file_path\n",
+    "        break\n",
+    "    else:\n",
+    "        all_files.remove(candidate)\n",
+    "\n",
+    "if valid_file is None:\n",
+    "    raise RuntimeError(\"No valid FSPL event found with ≥10 points in ≥3 filters.\")\n",
+    "\n",
+    "# --- Plotting ---\n",
+    "data = np.load(valid_file)\n",
+    "t0 = float(data['t0'])\n",
+    "tE = float(data['tE'])\n",
+    "u0 = float(data['u0'])\n",
+    "zoom_range = 3 * tE\n",
+    "\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "\n",
+    "for f in filters:\n",
+    "    try:\n",
+    "        t = data[f\"{f}_time\"] - t0\n",
+    "        mag = data[f\"{f}_mag\"]\n",
+    "        err_mag = data[f\"{f}_err_mag\"]\n",
+    "\n",
+    "        mask = (t >= -zoom_range) & (t <= zoom_range)\n",
+    "        if np.sum(mask) == 0:\n",
+    "            continue\n",
+    "\n",
+    "        t = t[mask]\n",
+    "        mag = mag[mask]\n",
+    "        err_mag = err_mag[mask]\n",
+    "\n",
+    "        plt.errorbar(t, mag, yerr=err_mag, fmt='.', label=f\"{f}-band\")\n",
+    "    except KeyError:\n",
+    "        continue\n",
+    "\n",
+    "plt.axvline(0, color='gray', linestyle='--', label='$t_0$')\n",
+    "plt.xlabel(\"Time [days relative to $t_0$]\")\n",
+    "plt.ylabel(\"Magnitude\")\n",
+    "plt.title(f\"Random FSPL Event: {os.path.basename(valid_file)}\")\n",
+    "plt.gca().invert_yaxis()\n",
+    "plt.grid(True)\n",
+    "plt.legend()\n",
+    "\n",
+    "plt.text(0.02, 0.95, f\"$t_E$ = {tE:.2f} d\\n$u_0$ = {u0:.3f}\", \n",
+    "         transform=plt.gca().transAxes, fontsize=10, va='top', ha='left',\n",
+    "         bbox=dict(boxstyle=\"round\", facecolor=\"white\", alpha=0.8))\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "event_name = os.path.splitext(os.path.basename(valid_file))[0]\n",
+    "save_path = os.path.join(save_dir, f\"{event_name}.png\")\n",
+    "plt.savefig(save_path)\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a423b01f",
+   "metadata": {},
+   "source": [
+    "> #### 4.3.2 Now we generate a random lightcurve for the PSPL events"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e1401c70",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import os\n",
+    "import random\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "pspl_dir = 'downloaded_file/microlensing_lightcurves/PSPL'\n",
+    "\n",
+    "# --- Create a directory to save the plots ---\n",
+    "save_dir = \"lightcurve_plots/PSPL\"\n",
+    "os.makedirs(save_dir, exist_ok=True)\n",
+    "\n",
+    "all_files = [f for f in os.listdir(pspl_dir) if f.endswith('.npz')]\n",
+    "if not all_files:\n",
+    "    raise FileNotFoundError(\"No .npz files found in PSPL folder.\")\n",
+    "\n",
+    "valid_file = None\n",
+    "while all_files:\n",
+    "    candidate = random.choice(all_files)\n",
+    "    file_path = os.path.join(pspl_dir, candidate)\n",
+    "    data = np.load(file_path)\n",
+    "\n",
+    "    try:\n",
+    "        t0 = float(data['t0'])\n",
+    "        tE = float(data['tE'])\n",
+    "        u0 = float(data['u0'])\n",
+    "    except KeyError:\n",
+    "        all_files.remove(candidate)\n",
+    "        continue\n",
+    "\n",
+    "    filters = ['g', 'r', 'i', 'z', 'y']\n",
+    "    zoom_range = 3 * tE\n",
+    "    total_points = 0\n",
+    "    filter_count = 0\n",
+    "\n",
+    "    for f in filters:\n",
+    "        try:\n",
+    "            t = data[f\"{f}_time\"] - t0\n",
+    "            mask = (t >= -zoom_range) & (t <= zoom_range)\n",
+    "            if np.sum(mask) > 0:\n",
+    "                filter_count += 1\n",
+    "                total_points += np.sum(mask)\n",
+    "        except KeyError:\n",
+    "            continue\n",
+    "\n",
+    "    if total_points >= 10 and filter_count >= 3:\n",
+    "        valid_file = file_path\n",
+    "        break\n",
+    "    else:\n",
+    "        all_files.remove(candidate)\n",
+    "\n",
+    "if valid_file is None:\n",
+    "    raise RuntimeError(\"No valid PSPL event found.\")\n",
+    "\n",
+    "# --- Plotting ---\n",
+    "data = np.load(valid_file)\n",
+    "t0 = float(data['t0'])\n",
+    "tE = float(data['tE'])\n",
+    "u0 = float(data['u0'])\n",
+    "zoom_range = 3 * tE\n",
+    "\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "for f in filters:\n",
+    "    try:\n",
+    "        t = data[f\"{f}_time\"] - t0\n",
+    "        mag = data[f\"{f}_mag\"]\n",
+    "        err_mag = data[f\"{f}_err_mag\"]\n",
+    "        mask = (t >= -zoom_range) & (t <= zoom_range)\n",
+    "        if np.sum(mask) == 0:\n",
+    "            continue\n",
+    "        plt.errorbar(t[mask], mag[mask], yerr=err_mag[mask], fmt='.', label=f\"{f}-band\")\n",
+    "    except KeyError:\n",
+    "        continue\n",
+    "\n",
+    "plt.axvline(0, color='gray', linestyle='--', label='$t_0$')\n",
+    "plt.xlabel(\"Time [days relative to $t_0$]\")\n",
+    "plt.ylabel(\"Magnitude\")\n",
+    "plt.title(f\"Random PSPL Event: {os.path.basename(valid_file)}\")\n",
+    "plt.gca().invert_yaxis()\n",
+    "plt.grid(True)\n",
+    "plt.legend()\n",
+    "\n",
+    "# Annotation\n",
+    "plt.text(0.02, 0.95, f\"$t_E$ = {tE:.2f} d\\n$u_0$ = {u0:.3f}\",\n",
+    "         transform=plt.gca().transAxes, fontsize=10, va='top', ha='left',\n",
+    "         bbox=dict(boxstyle=\"round\", facecolor=\"white\", alpha=0.8))\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "event_name = os.path.splitext(os.path.basename(valid_file))[0]\n",
+    "save_path = os.path.join(save_dir, f\"{event_name}.png\")\n",
+    "plt.savefig(save_path)\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f951868e",
+   "metadata": {},
+   "source": [
+    "> #### 4.3.3 Nopw we generate a random lightcurve for the USBL events"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "bf2e7978",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA94AAAJOCAYAAABBfN/cAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjMsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvZiW1igAAAAlwSFlzAAAPYQAAD2EBqD+naQAAm1VJREFUeJzt3Qd4FOXWwPGTSid0QoDQqyBNRRCVJthQxMKHiICI/VpQVFSaDRULKlyxIGAFC0UQES5FRJo0KQIKgrTQe03b7zkvzrqb7IaU2ZLd/+95lp2ZbZPNkN0z57znjXA4HA4BAAAAAAA+EembpwUAAAAAAIrAGwAAAAAAHyLwBgAAAADAhwi8AQAAAADwIQJvAAAAAAB8iMAbAAAAAAAfIvAGAAAAAMCHCLwBAAAAAPAhAm8AAAAAAHyIwBsA4DO9evWSqlWrBno3AAAAAorAGwBCwLhx4yQiIsJ5iY6OlooVK5rAd9euXYHevaAxZMgQ8/4cOHDA4+0NGjSQ1q1bu23bv3+/PPLII1K3bl0pVKiQlCtXTi655BJ56qmn5MSJE8776Xud8XdQuXJl+b//+z/5/fff3Z5z/vz55j7ffPNNjn8G3T/X13G96D4Gg//+97/mmLTjmF6+fLnH26+//vpMJ3X09zF48GDzeyxSpIiULl1aGjdubH5/u3fvznQcWJfIyEipUKGCec4lS5a4Pee2bdvMfV5//XWxU9++fc3z6mtmpD/Ho48+KpUqVZICBQpIvXr15L333st0vwULFsgNN9xgjrOCBQtKfHy8XH311fLLL79k+dpHjhwxx3Fuj0EAQM5F5+IxAIAg9fzzz0u1atXkzJkzJoDQ4GXhwoWybt0688UcOXPo0CG56KKL5NixY3LXXXeZwPbgwYOyZs0aEwjdf//9UrRoUef9NUj66KOPzHJqaqps2bJFRo8eLTNnzjTBd0JCgi37pQHZsGHDMm2Pi4uTYAm8y5QpY05G+EtKSopcccUVsnHjRunZs6f85z//MQHs+vXr5YsvvpCbbrop0/uvv0P9/aWnp8uOHTvkww8/NM+xbNkyE7D7ip5M0P+bnv5PpqWlSceOHc19HnzwQalVq5b8+OOP8sADD8jhw4flmWeecd73jz/+MCcN7rvvPhN06+2fffaZ+Rm+//57E4R7MmjQIDl16pTPfj4AQGYE3gAQQq655hoTKKq7777bBD+vvvqqfPfdd3LbbbcFevfynTFjxsj27dtNBrFly5Zut2kwHhsb67ZNs9x33HGH27ZLL73UZDU1ENIspx00wM74OuFuypQpsmrVKvn888/l9ttvd7tNT0QlJydneswtt9xi/o9YOnfubLLlX3/9tc8Cb4fDIQ8//LDceeedMmfOnEy3T5o0SRYtWmSOPT3Zo/QEj+7rCy+8YP5fa7Za6bJeXGmAXr16dRkxYoTHwFtPwukJBw2+9QIA8A9KzQEghF1++eXmWjOvFg1A9At3s2bNTACnJbl6v3nz5nktsf3ggw+kRo0aJqN78cUXy6+//uox8NGgRbN4ej158mSP+3Ty5El5/PHHTXmsPl+dOnXMa2hA4kpf+6GHHjJBUP369U2Zd4sWLWTt2rXm9vfff19q1qxpXk/Lr3V/7abvW1RUlAmeMypevHi2qgg0E2kF5f6i5cP6/v3000+ZbtP3TW/TAMyiWWIN7EqVKmV+Jj15oydrPJV+60mIfv36SdmyZc2xo5lkLce3aPm3Zpn1ta1SbtfyfX1PXY9Hu1jPedlll2W6TX8m/X0Fw+/q008/Ne/9Sy+95PH2n3/+2VzrEAVXuq4nEKZOnZrl8xcuXNj8brSc3BMtu9ffmfW3ITusoRFfffWV2W+tuND3tF27drJ582a3++rvWv//r1ixwpys0v+3WoWjlR+u9DjxNmRCXw8AQg0ZbwAIYVYwWrJkSbdMrZZDd+vWzWRgjx8/brJrWt7qqcRWy3T1Pvfee6/5Uvzaa69Jly5d5K+//pKYmBhzn1mzZsnNN99sAmQtgdZy7N69e5sv6K40uNYxqRrk9+nTx7yWltH279/fjEV/6623MgUhGgBqya3S59bs8ZNPPmnKma3yW90nzQ7OnTvX1vevSpUqpvRXgyUtX84Oa/y4Pk7fIx0LrmONPY3lzS19bk/j1DXI0WD4uuuuMyXUGihdeeWVbveZOHGiXHDBBSY4Uhoka7CqPQGefvpp83h9nGZ/v/32WxOkudISbj2edCy1Hl+aWdUTJPq8Stf1Pvr6zz77rNlWvnx55+M1WFN2nyjR35X65JNP5LnnnjPHanaGEigtNdfjTzPKGlD6qjpE/x/p8aDl4laQn9HZs2fNyZ6M1RQaUCsNaDNWTuj/aT2hpseE/vwa2LuWpFv0JJZm0zds2JCr9/+VV14xpe1PPPGEHD161Py/6969uyxdutTtfvp/8tprrzXvo/6d0eNJs/b6M1lZfD1OXHskKP3/v3r1avP/BQBCjgMAkO+NHTtW08WO//3vf479+/c7duzY4fjmm28cZcuWdRQoUMCsW1JTUx1nz551e/zhw4cd5cuXd9x1113ObVu3bjXPWbp0acehQ4ec26dOnWq2T5s2zbmtcePGjgoVKjiOHDni3DZr1ixzvypVqji3TZkyxWx78cUX3V7/lltucURERDg2b97s3Kb3033X/bC8//77Znt8fLzj2LFjzu0DBgww213v68ngwYPN/fQ98uSCCy5wXHnllc71PXv2mPdQH1O3bl3Hfffd5/jiiy/cfk5Lz549zf0yXipWrOhYsWKF233nzZtnbvv6668dOaX75+l19HLvvfc679etWzdHuXLlzO/bkpSU5IiMjHQ8//zzzm3t2rVzNGzY0HHmzBnntvT0dEfLli0dtWrVynSMtW/f3txueeyxxxxRUVFu70nG99GVHg+ux4Q31uv9+uuvHm+/7rrr3J7n1KlTjjp16jiPuV69ejnGjBnj2Lt3r9fjIOOlRIkSjpkzZ7rd1/p/MHz4cEdePfHEE45q1ao532vdT/05XL3xxhvm9X7++We37U8//bTZfv3112d63o4dOzp/htjYWHMcnD592u0++v4kJiaa/ys5PQat+9arV8/tb8fbb79ttq9duzbT8ak/h0Ufo38j9HhMTk72+BpfffWVeZzrsQkAoYRScwAIIe3btzdlplrGraXDmr3UjLFr5tk1m6aZPs36aSMwLS9euXJlpufs2rWrW8bcKlHVbK5KSkoyWSrNCLs297rqqqtMBtzVjBkzzOvrGFdXWnqusfYPP/zgtl2zo66dq5s3b26uNbterFixTNutfbKLZmp/++0307xKs3haLqvjh3WMrWZHM5bHa7Z09uzZ5qKZfC3r1syvZv+0EZZd9D2xXsf1op2wXX9v+/btcyvb1RJ0/Z3rbUp/91oloJlJzcZqxlQvWrGgFRB//vlnpq7499xzj1s2WY8HzcD//fff2dp3zbT6YliAZvs186rVE1ZpvFZVaLdyzcBrJjkjzejr+6YVG2PHjpXatWubY0uzwnbT3//bb78tw4cPN0MsvNHjS/8faWZY903fKx3qoRUe6vTp0x4z0fozaOWKDovQ7Lf+n854H21A5ykTnl1axeKaic/4t8CipfpaIWPRx+i6Ho+asc9IGw/qz3vjjTeaagUACEWUmgNACBk1apQJHrQM9OOPPzbTDXn6kj9+/Hh54403zNhe/TJu0bGYGSUmJrqtW0G4BqLKCri0+3JGOn7bNZjX+2pnadegWel0Sa7P5e21rcBeTyx42m7tU15kLFHWwE2bUWngo4GoBtTasE7Hyettrs2t9KSCnvxwpUG3vjcDBgwwgZ4d9IRKxtfJSBtr6fuiJeBWebcua3m/HiNKx+fqyYOBAweaiycaLGkZenaPB3/K+LvSn1fLn/Wix5I2L9P+ASNHjjS3vfjii2731+7frs3V9GSV/q40UPcUIOaFjq3WMc8a2GdFS9D1ZFmPHj2kQ4cOZpuOT3/33XfNyS3XLvoW1+Eh2nSvadOmpqO8NVWYBu8a8OvfB0+Pz67s/u71/7geo66sY073xbVngpbJ69AVPca0TD47QwQAID8i8AaAEKLzS1tdzXWMbqtWrUwGbdOmTc4v3DrdkH4p19s1O6jZWw0Ydfy0p6ZXepsnGbO9vuDttXO7T1YzNE9ZQ6VTLHlrmKYBgQYPetEx1BqgaQftjF2lM9JqAz0BoSdB/ElPuOjvWJvc6UmDvXv3msZoL7/8svM+mv1WOmZXM9yeaAO7QBwPefldWWO+NYuqY9S1y7f+rjIG3hnp/xGtntAGZtoEMGPwmFtaVaBTymnHctdsv2al9efTbdrYzmoApycENIusjQR1Pxo1auSch9wKYL3R7LL2UdAMtz63VgLoSSINbF2bEO7Zs8dca2M83aZBtY7fzoovfvf6t0h/Nu0vkZ0GeACQXxF4A0CIsoLpNm3amIyfNs5SmgXTQESDANfskjbLyktTK80GZ6QBf8b7/u9//zNlza5Zb828uz6Xr1jPr/uVMWuugZzO5WxlGbOi759m+7TMPjs0wMrYSMoftKRcqxs086sNtTRAssrMrZ9DaZO882XQc8KOrKXr78pTB24t3bYaxGVFf0/akd+1i3tWrBJt/X3ZFXjrlHRKM7sZaSm/VppoYzHXoQL6/9c1k63/b1R2fk8acOvvWv+faeCtr6/VDdbv25U2KLSy1iVKlBA7aCCd8cSFNdTCdeiInhzQ2RD0b1HdunVteW0ACFaM8QaAEKYZLs2CawdhnYrINWvlmqXSsbGLFy/O1WtoubUGCBrgaYm7Rcen6tjNjGXXOh5YTwS40qBDgzWdh9yXtORaM4JaOm5ley06jlaDLtd90PdFA4iMNDun46A1k30+GnBo8KhZS3/TIE0zqVpirhc9FlyHE2i1gx4jOhbd00kE12nCckIDLm/TWWV3OjGd7k73TzvwZxyfrcGaBqyuvysdi++p07uWnOtxmJ3flY551/HdWu5tzZVth7Zt25rKg4wX7cegFSq63KlTJ6+P19+DDm+48MIL3QJvHQaQkb7vOqRBTyxZP4Nm+jO+tvYoUDpDgK5bQbKegNITYZ7ey+zS/0d6TFl0zLmu68+rv1frRIKO59bO91qZAQChjow3AIQ4LSe/9dZbTbMpbRKm01pphklLcLVkeuvWraZpmDZCy21WVjPr+lxa2q7lvRrA6JhUnbbK9Tk1uNAMvH7Z1vJWDUa1KZSW9mq2TzOTvqSBiJbd6hd+LefVklydpkmDrS+//NJku10DIJ1GTEuU9b3SgEGDds0c6/h5LXPO2KhKAw4t5Vca2OvPqO+tLnuqKNAAycr2u9KxvBkz8q70BIf1OhnpGF+LZrI1yzphwgRzAkHHO2ek437199awYUMzTZVmRbUsXU/E7Ny50wS0OaXvlZ7c0IBPS9X1fdfgMyfTiel7rfur74XOHa+Zep1matWqVeb91yBUG725nujR91h/pzqGWMvGtVxb76uB+5AhQzK9hlZ/6P30JJRmabU5mdVEL2PWXqsGrJNXrjRoPF/mXcu4M46PVnrMawO/jIGnTgGnc9bre6cl4XpSSP8fTZ8+3a0cXE886FAGLY/X91gz29okTn8Wa3o3pb/fjKzstr63rq+vJ5X0/6i+l57es+zQMd56okB/x1oar/uiDRj157CmINRpxjQQ1yEbGY9lbczoOgUdAISEQLdVBwDkXVZTL6WlpTlq1KhhLjq1lE4F9fLLL5upjHS6riZNmjimT59upsNynZ4pq2mUdLtOyeTq22+/NdMN6XPWr1/fMWnSpEzPqY4fP26moEpISHDExMSYKav0NVynqLJe48EHH3Tb5m2fcjo912effea49NJLHUWKFDH7q1OFDR061G1KLbVmzRpH//79HU2bNnWUKlXKER0dbaZNu/XWWx0rV64873RixYsXN9N16TRvnvbX2yXjVFLZnU7M08f67NmzzXadrs11WjlXW7Zscdx5551mmjb9negUaDptlU5Jd75jzPpZ9Np1GjadJqtYsWLmNtepxbI7nZjlhx9+cLRp08a8l7pvOh1Xv379zBR4rv766y/HoEGDzO9Vp63S35VOBaf7MXfu3PNOJ6bHQosWLcy0Vp6OOW+XTz/91JFbnqYTU/r/o3r16ubY1J/h9ttvN7+jjEaOHOlo1aqVo0yZMs6ft1OnTo4FCxac97W9/Z+xtrv+//Z2X+u90WPDor9rnU5u+fLl5v0sWLCg+Tl1X11l9Z66HksAECoi9J9AB/8AAADI/3TogpapZ3dMPQCEC8Z4AwAAAADgQ4zxBgAAyCUdb+9tyjOLNmsDAIQ3Am8AAIBceuSRR0xH/6wwqg8AENAx3gsWLJDhw4fLihUrzDQmOp2Fa2fNXr16Zfow69ixo8ycOdPrc2oHzqFDh7pt0ylEPHWNBQAAyAudqky7iGfFzjnSAQD5U0Az3jq1iU4lo1PP6HQnnlx99dVmagxLgQIFzvu8On2Nzg9piY4msQ8AAOyn0/DpBQCArAQ0ItX5J/WSFQ20czo2SgNtxlMBAAAAAIJB0KeC58+fL+XKlZOSJUtK27Zt5cUXX5TSpUtn+Zg///xTEhISpGDBgtKiRQsZNmyYJCYmZvs109PTTdlYsWLFJCIiwoafAgAAAAAQSnTU9vHjx03sGRmZ9YRhQTOPtwa4Gcd4T5gwQQoXLizVqlWTLVu2yDPPPCNFixaVxYsXS1RUlMfn+eGHH+TEiRNmXLeOG9fx3rt27TLzSWog7cnZs2fNxaL3p2wMAAAAAHA+O3bskEqVKuXfwDujv/76S2rUqGHGb7dr1y5bz3vkyBGpUqWKvPnmm9KnT59sN2RTH330kQn8AQAAsistLU3Wr1/v7DvjLVkAAMjfTp06JXfffbeJOePi4vJ3qbmr6tWrS5kyZWTz5s3ZDrxLlCghtWvXNo/xZsCAAdKvXz/n+rFjx6Ry5crmJEDx4sVt2fdwlZKSIrNnz5arrrpKYmJiAr07CAEcU7AbxxTslpycbCr11PXXXy9FihQJ9C4hBPC3CnbjmMo7jRs18M7O8OR8FXjv3LlTDh48KBUqVMj2Y7TsXD/8evTokWUDN0/d0vUA5CC0B+8l7MYxBbtxTMEursWEHFewG8cU7MYxlXs5ed+yHgHuYxoUr1692lzU1q1bzfL27dvNbf3795clS5bItm3bZM6cOXLjjTdKzZo1zVzeFs18jxw50rn+xBNPyE8//WQes2jRIrnppptMiVe3bt0C8jMCAIDw+yL2wAMPSL169fgyCwAIfMZ7+fLl0qZNG+e6Ve7ds2dPee+992TNmjUyfvx4UzOvneI6dOggL7zwglt2WrPZBw4ccMuKa5CtmfGyZctKq1atTPCuywAAAL6mJYc61E2/rzA7CgAg4IF369at3cqxMvrxxx/P+xya2XalndABAAAAIFQaNup4bLvpc0ZHR8uZM2fMayAzrVqyq0FmvhrjDQAAEOz0C6wOkdPpSXWZcnMAuaEJyj179pjqX189f3x8vJkKi+oc77SCSd+nvL5HBN4AAAA20mB76dKlzmUAyA0r6C5XrpyZ4tju4Dg9Pd301SpatKhERga09VdQ0hMTOl3Yvn37zHpOGnx7QuANAAAAAEFET9pZQXfp0qV98hoaeOv0hwULFiTw9qJQoULmWoNv/V3kpeycdxgAAAAAgog1plsz3Qgs63eQ13H2BN4AAAAAEIQYex06vwMCbwAAAAAAfIjAGwAAAAAAHyLwBgAAAADY6rHHHpMuXboEejeCBoE3AACAjXTe7r59+0qdOnWYwxtA2Fq2bJlcdNFFgd6NoEHgDQAAYHMjnrJly5ppaGiMBCDc6BRletJx0aJF8uyzz5q/g5deeqmEO+bxBgAAAADYIjo6Wn755Rdp3ry5rF69WsqXL2/mCg93ZLwBAABslJaWJgsWLJCkpCSzDAB2Z5S9XVJTU7N934zzUnu7X05FRkbK7t27pXTp0tKoUSOJj4+XEiVKyPTp080QnFq1aslHH30k4YaMtw+sW7dOvvvuO1m+fLmcOHFCwpnD4ZDDhw/LyJEj/V5up//p9Qxb69at5cYbb5RSpUr59fUBAOFJg+2FCxc6lwHATsOGDfN6mwa1t99+u3P99ddfzxRgW6pUqSKdO3d2rr/99tty6tSpTPcbPHhwjvdx1apVJui2pKamSr9+/WTevHkSFxcnzZo1k5tuuskE5+GCwNtmc+fOlQEDBki5cuXk8ssvN8GeBoDhKj09Xc6cOWPKS/z9Puh/8L/++kvGjBkj06ZNk/fff9+MuQMAAADgO1pi7hp4L1u2TC644AKpWLGiWb/mmmtk1qxZ0q1bNwkXBN420gBz0KBBJsP68ssvS1RUlIQ7DbyPHTsmxYsXD9gJiJ07d0qfPn3k3Xffleeffz4g+wAAQH504vAZObLvtJQoV0iKlmSMJhAMNMnnTcbv20888USWlamnT592rj/yyCM27aHI2rVr5eabb3au79692xl0K13etWuXhBMCbxtp5z4Nvv/zn/8QdAeRSpUqmf/4n3/+uRmnEhsbG+hdAgAg6P3+y26Z/9lGcTi0U7tI6zvqSv3LEgK9W0DYy8l32azuqwky18Dbzu/I+tybNm0yAXeRIkVse978LHxroH1g69atUrJkSRPoIbhoqcvJkyflwIEDgd4VAADyRabbCrqVXs//fKPZDgDn8+KLL8q4ceNMZluXExIS3DLcuqzbwgkZbxtpNrVAgQKB3g14YP1ectOZEQCAcKPl5VbQbXGkixzdd5qScwDndccdd5iLa++ldevWmYBbm6v98MMPMnDgQAknBN4AAABwo2O6tbzcNfiOiBSJK1cokLsFIB/P7f3GG29ImzZtTBn6k08+GVYdzRWBNwAAgM1fMHv16mV6v+hyfqRZbR3TreXlmunWoLt197pkuwHk2g033GAu4Sp/fhoAXmhH+caNG8uIESMCvSsAgDClXYV17GLhwoXz9ZSi2kgtsX4pU16umW6CbgDIvfz7aZAPPfbYY9KlS5eAvPaCBQukU6dO5otARESETJkyJdN9hg0bJhdffLEUK1bMzEPeuXNn040wo1GjRknVqlXN3NzNmzc38/Jl1yuvvGJe/9FHH83x/gEAAP/SYLtinZIE3QCQRwTefqQB6kUXXRSQ19aO3trZW4Nmb3766Sd58MEHZcmSJTJ79mxJSUmRDh06mMdaJk6cKP369ZPBgwfLypUrzXN27NhR9u3bd959+PXXX+X999+XCy+8MFf7BwBAfpCWlmY+S/WzUZcBAKDU3A+0k7bOX6fd/HS817PPPmsyxfqh7C/XXHONuWRl5syZbus6BYBmvlesWCFXXHGF2fbmm29K3759pXfv3mZ99OjR8v3338vHH38sTz/9tNfnPnHihHTv3l0+/PBDM6VAbvbPU7B+//33y6RJk0yW/oknnsjR4wEA8AUNtufOnetcBgCAjLcfaGOVX375xSyvXr1akpKSMgW55/Pyyy9L0aJFs7xs377d1v0+evSouS5VqpTzBIIG4e3bt3feR8eu6frixYuzfK6HHnpIrrvuOrfH5lX//v1Nln7q1Kkya9YsmT9/vsnCAwAAAEAwIePtBxqc7t6927TM13Jqi2aP9VKmTBmzrmObdayzZm8zuu++++S2227L8nXsnIRe2/zrOOzLLrtMGjRoYLYdOHDAnLkvX7682311fePGjV6f69tvv5VVq1aZUnO7aAZ9zJgx8tlnn0m7du3MtvHjx0ulSpVsew0AAAAAsAOBt59o4OkadCudRP7tt9+Wm2+++byP16yzlXn2Bx3rrfu3cOHCPD3Pjh07ZMCAASYjrc3Y7LJlyxaTgdeSfYu+P3Xq1LHtNQAAAADADpSa+4mWmHsKvHXqq2ArNdey8OnTp8u8efPcMsiamY+KipK9e/e63V/X4+PjPT6Xlqbv37/fNJXTknu9aHn4O++8Y5YZ+wYAAAAg1JHx9pO1a9e6ZbYdDoeZqst1mwbX1157rcfH+6PUXPfpP//5j0yePNmMl65WrZrb7bGxsdKsWTOZM2eOmWrMKknXdQ3WPdEycB3fricGrLlMtTFb3bp15amnnjKBfG7UqFFDYmJiZOnSpZKYmGi2HT58WP744w+58sorc/WcAAAAQKg5lZwq9Qf9aJZ/f76jFI4lBAwE3nU/0QBVA20d660dzg8ePGiCTw0csyOvpeY6Jnrz5s3O9a1bt5osvD6nFbhqefkXX3xhmpXpOPM9e/aY7XFxcVKoUCGzrFOJ9ezZ02SwL7nkEhkxYoTpLm51OR85cqQJ3DUYV/o89evXl+LFizsDb/35dby7NXY8u/vnSgP5Pn36mAZr+lzafV27xVuvAQAAACD4bdu2zST8dGhudquB7VS1alXT20ovvkTg7Sc6hZZmeDWrrdNeXX755VK7dm2/vf7y5culTZs2znUNoJUG0TptmHrvvffMdevWrd0eO3bsWOnVq5dZ7tq1qykdHzRokAnM9T+Hdmi3Gq5pAzYdf+2L/cto+PDhJmDv1KmTCfAff/xxZyd2AAACRYdS6RSaOm2oLgMAwKeBn9xxxx3mYtEA3J+BtwbTWkqelfPdbtGycm+l5UOGDDGXrGgZe272z1PW+9NPPzUXi2bAAQAIJK2+qlKliqxfv55KLACAwadBgGhjtQ8++MBkjPXimu0FAAAAALvtOXrGL6XbOhzVVePGjc+bnNPpiVu2bGlmQtIhqdqQ2aINmXWYqZak6xBYnclIZ4dypRW62ofq9ddflwoVKpjhqDqUNiUlxXmfffv2mWpZfQ59rs8//1z8hYx3gOhYagAAEHr0C6IOodKhWbqszUABIFC+XbHTudz+zZ9kWJeG0vXizD2UAq1///4mYNf+UG+++aYJkLXvkwbQ2i9LZ1v6+uuvzfqiRYvknnvuMQG2awNqnZVJt+m19o/SYbIa9Pft29cZnGvPLb1d/zY//PDDJhj3BzLeAAAANtJge9asWbJr1y6mzQQQUElHT8vg79Y719MdIs9MWme2B5uHHnrIzPhUr14903tKGzyPGTPG3KZB8tChQ02DZ81Uax8Nbe781VdfuT1HyZIlTbNnbWJ9/fXXy3XXXeds+qyzH/3www/y4YcfyqWXXmpma9LnP33aP+8FgTcAAAAAhKCtB06aYNtVmsMh2w6cCsj+3HfffaZPk3Vx1aJFC+eyNqbUIHvDhg3ObaNGjTLBctmyZc1jddju9u3b3Z7jggsucJuuWLPfVkZbn0ufV5/DogF6iRIlxB8IvAEAAAAgBFUrU0QiI9y3RUVESNUyhX32mtpUMmPT5JR/xlk///zzZspg65JdEyZMMDND6ThvrSjSx2rGOzk52e1+GYf2REREmDL1YEDgDQAAAAAhqEJcIRl6wwXOdQ3CX+7SwGz3Fc1IJyUlOdePHTtmxmqrcuXKSc2aNZ0XVzoFoyU1NVVWrFhhys7VL7/8YhqvPfDAA9KkSRPz2JxOYazZbet5LZs2bZIjR46IPxB4AwAAAECIurlZJefy//pd6fPGam3btjXT/f7888+ydu1a6dmzp1v5tzdaSj558mTT3Vy7kR8+fFjuuusuc1utWrVM08off/zRjNUeOHCg/PrrrznaL+2EfvXVV8u9994rS5cuNQH43XffbTqc+wOBNwAAAACEgfi4gj5/jQEDBsiVV17pbG6mU3zVqFHjvI975ZVXzKVRo0aycOFC+e6776RMmTLmNg2Wu3TpYrqUN2/eXA4ePGiy3zk1duxYSUhIMPunz6ed0TUL7w9MJwa3s0zDhw+XPXv2mAP+3XfflUsuuSTPj9POgXofb7cPGzZMJk2aZM5u6RknLSN59dVXzVkpAAAAAPlH8eLFzZhsVz179sxy3m9rTHi3bt083qdAgQImaNaLK40jLOPGjcv0uIzzicfHx8v06dPdtvXo0UP8gYw3jIkTJ0q/fv1k8ODBsnLlShMgd+zY8bzz2p3vcXr7c889Z8pBvD3vTz/9ZMpJdFzH7NmzTfOFDh06yMmTJ33+cwMAYDftmqvzyuqUN7oMAACBt5/MnDlTihQp4tZVb926dabT3oEDByTQdJJ6nVheuwPqpPWjR4+WwoULy8cff5ynx+lZpjvvvDPL59X3Riez1/b/Gpjr2SqdGsC18QEAAPmFdvTVxj86B60uA0AgFY6Nlm2vXGcuuozA4NPAT1atWiUNGjRw+wDWNvg6xsAau5BdL7/8stv8d54uGee0y4q24dcgt3379s5tup+6vnjx4lw/zrq9devWOXreo0ePmutSpUpl+2cAAAAAgGDFKQ8/0SBbs7mufvvtN+c2HWvw+OOPm4z4U089ZTrsZTXxvJawZUUD+uzSjHtaWpqUL1/ebbuu67jr3D7Oul2nFMju8+rP/+ijj8pll11mTlQAAJDf6GffmjVrTPMfXc44rywAIPwQePsx4/3www9nCsYvuugiM5+cjpOeN2+eKUtr1qyZ3HTTTVK6dGmPz6WZ4Nxmgz///HPTFdDyww8/ZKvLoL/oWG8twddOhgAA5EcabFvNe3QZAABKzf1Am4TpBO+uGW/N7GowrtuWLVtmxjdXrFjRlIlfc801MmvWLJ+Umt9www0m4LcuGvhrqbvOrbd37163++q6dv7z5nyPs27fv39/tp73oYceMl9U9AREpUr/zjcIAAAAAPkZgbcfbN261QTadevWdW7Tyd+1BE0D7927d5ug26LLu3btyrLU3DV49nTxVmperFgx0/DFuuj0XbGxsSbLPmfOHOf9dH91vUWLFl7343yPs27XruVZPa9OH6BB9+TJk2Xu3LmmCywAAAAAhApKzf1AS8a1e/mvv/4q1157rZk2SwPNggULSu3atWX9+vU5er68lJp7o6XuOr+eZsB1jm3tRq6Zeu1Gbhk5cqQJjl0D7fM9Tsdr67IG2pdeeqnH59Xy8i+++EKmTp1qTgzofN9Ky+71xAAAAAAA5GcE3n5QoUIFeeGFF+SOO+4wgWWbNm3k1ltvNQGslmJrdto1w63LGsT6U9euXU1J+KBBg0zg27hxYzPNl2vjNG2WpiXzOXmc3r5jxw4ZMmSI1+d97733zLVr93M1duxYM80YAAAAgFxKPiny8j/VsM/sFoktEug9CksE3n7y7LPPmosnGmRrQzENuDXLqw3PBg4c6Pd91Cy8XrzR4FkvOX3cPffcI0888YTXuUy11BwAAABAeIqIiDCVtZ07d/b7a2vyT5ODWpnrSwTeQSA6OlreeOMNkwnXMdBPPvmk147mAAAAAID8hcA7SGi3cb0AAID8f0JdpwXV2Ut0GQAAupoDAADYSIdW1atXT0qUKOF1mBUABMSx3T5/iePHj0v37t2lSJEiptfVW2+9Zcq5telyVpKSksy0ytpcuXr16vLNN9+43f7UU0+ZxtSFCxc2t+vQ3JSUFOftOiRWS8Y//fRTqVq1qhnC+3//939mfyza5PnOO+800y/rvmnVsb/waQAAAAAAoWr1F/8uj7pEZOUnPn05nfXol19+ke+++05mz54tP//8s6xcufK8j9NA+uabb5bffvvNBO4aNG/YsMF5uzapHjdunPz+++/y9ttvy4cffmiCelfaCHrKlCkyffp0c9FpjV955RXn7f379zfbdDalWbNmyfz587O1b3Yg8AYAALCR9mvRL4tHjhwxywAQMEd3ifzw5L/rjnSRaY+e2+4Dml0eP368vP7669KuXTtp0KCBmakoLS3tvI/VWZ/uvvtuk9XWGaF0uuJ3333Xeftzzz0nLVu2NNnsTp06mebNX331ldtz6N9cDc71dS+//HLp0aOHcyrkEydOyJgxY5z71rBhQ7Ovqamp4g8E3gAAADbSL3HanXfbtm1++0IHAB4d2nIu2HblSBM59JdPXu6vv/4y5d+uUyNryXedOnXM8ssvv2zKvK3L9u3bnfdr0aKF23PpumvGe+LEiXLZZZdJfHy8eawG4q6PVxqUa2bcouXk+/btc2bDk5OTpXnz5s7bS5Uq5dw3XyPwBgAAAIBQVKqGSESGkC8iSqRU9YDszn333SerV692XhIS/plf/DwWL15sys+vvfZaU0KuzSt1qmYNpF3FxMRkmqYsWCqPCLwBAAAAIBTFVRS55jX3oLvTiHPbfUCbnmnw++uvvzq3HT16VP744w9nhrlmzZrOi+vMD0uWLHF7Ll3XRpVq0aJFUqVKFRNsawl6rVq15O+//87RvtWoUcPs29KlS53bDh8+7Nw3XyPwDnGjRo0yJRcFCxY0ZRXLli2z5THZvY/+59NyEC0Vyc5rAwAAALBR49v/XX5wqUjTO332Ulrm3bNnT9PEbN68ebJ+/Xrp06ePmeFBs89Z+frrr+Xjjz82gfDgwYNN7PDQQw+Z2zTQ1rLyCRMmmJLxd955xwzpyQktT9d90X2bO3eurFu3Tnr16uW32ScIvEOYjoPQroJ64Gq3vkaNGknHjh2d4xxy+5ic3Ee7E2q3wAsvvPC8rw0AAADAh4pnr7Q7L958802TdLv++uulffv2Zlx2vXr1TMIuK0OHDjWBtcYNn3zyiXz55ZdSv359c9sNN9wgjz32mAnEdcowzYBrnJFTw4cPN03XtDmb7lurVq2kWbNm4hcOZHL06FGHvjV6nRP//e9/Hdddd53X2zds2OBo3bq1o0CBAo5atWo5vv/+e/M6q1atcvjCJZdc4njwwQed62lpaY6EhATHsGHD8vSYnNxHbzt8+LAjJSXlvK/tS/oeN2vWzLF169aAvD7sk5yc7JgyZYq5BuzAMQW7nT171jFkyBBzOXHiRKB3ByGCv1Xh5fTp047ff//dXOfZ2RMOx+Di5y66/A/re7pe+5L+HYyLi3N89NFHjlD7XeQkbiTj7ScbN240Jdl6hkVLLl599VUzebuOM7DO5GSUseufp0vGTn4WbTSwYsUKcybHomUUuq7NCXL7GLvuAwAAACD0aOMzzVZrSbhWx2pTNHXjjTdKOPt3NDt86sEHHzQTwj///PPOwf2fffaZ/PnnnxIbG+u1699tt92W5fN66wR44MABM19e+fLl3bbrup4EyO1j7LoPAAChKioqypRY/vbbb2YZAAIqtojIkKN+fUmdK3vTpk0mztFS7p9//lnKlCkj4YzA2w+0454O4NcPYFd6IOr4aG+0659eAABA/qHBto5R3LlzJ4E3gLDTpEkTU/0Kd5Sa+4HOUeeppFw76WUVeOel1FzPKOmH/d69e92267p2Gc/tY+y6DwAAAACECwJvP9DxzVp6rRfLzJkzzxt4Z5xg3tPFW6m5VdYxZ84c5zadPF7Xtctgbh9j130AAAhV+pm3efNmM3etLgMAQKm5H2gQqhnvZ555xoz11oYDTz75pLnNl6XmOp2XzqOnk8xfcsklMmLECDl58qT07t3beZ+RI0eaOfCsIDk7j8nJfZo2bWoy/WPGjMl0HwAAQlFqaqp89dVXzuUCBQoEepcAAAFG4O0HmpX+6KOPZMCAAfLBBx/INddcI3379pW3335bypUr57PX7dq1q+zfv18GDRoke/bsMXPeaabdtemZNkLTjoM5eUxO7jNkyBCv9wEAAACAcECpuZ/ccccdsmPHDjl+/Lg5C75v374ss9120Unmtbnb2bNnZenSpWZKM1caGG/bti1Hj8nJfbZu3WrGdus0Yp7uAwAAAAChjsA7QNasWeOXwBsAAABA+DqVckoajm9oLrqMwCDwDpC1a9eaqUYAAAAAIJxERETIlClTAvLarVu3lkcffdTvr8sY7wDJONUWAAAAACA0kfEGAACAT5w4fEZ2bjpsrgGEp+Tk5EDvQlAg8AYAALBRVFSUdOjQQSpWrGiWw9Xa+Ttl/IBFMvWtVfLJM4vk9192B3qXgLC399Rev5Rya5NlLecuU6aMdOzY0eP9kpKSzGxPhQoVkurVq8s333zjdvtTTz0ltWvXlsKFC5vbBw4cKCkpKW5NonXmpE8//VSqVq0qcXFx8n//93+mmbVFpzO+8847pWjRolKhQgV54403JFAIvAEAAGykwfZFF10kZcuWDdvAWzPcP0/8w7nucIjM/3wjmW8gAL7b8p1zufOUzjLpz0k+f83x48dLbGys/PLLLzJ69GiP99FA+uabb5bffvtNunfvboLmDRs2OG8vVqyYjBs3Tn7//XczDfOHH34ob731lttz6LTIOlZ8+vTp5vLTTz/JK6+84ry9f//+ZtvUqVNl1qxZMn/+fFm5cqUEAoE3AAAAbHVk32kTbLtypIsc3Xc6ULsEhKU9J/fIsKXDnOvpki5DFw81232pVq1a8tprr0mdOnXMxZNbb71V7r77bpPVfuGFF8wJy3fffdd5+3PPPSctW7Y02exOnTrJE088YaZldpWenm6C8wYNGsjll18uPXr0kDlz5pjbTpw4IWPGjJHXX39d2rVrJw0bNjQnBFJTUyUQCLxD1KhRo8xBWrBgQTN/9rJly877mF27dpn5xkuXLm1KPvTgXL58ufP2tLQ0c2aqWrVq5vYaNWqY/ySOjJ+sAACEMf0i+Pfff5tyR10ORyXKFZKICPdtEZEiceUKBWqXgLC0/dh2E2y7Sneky47jO3z6us2aNTPXL7/8sinzLvrPZfv27c77tGjRwu0xuu6a8Z44caJcdtllEh8fbx6rgbjr45XGO5oZt2g5+b59+5zZcB1frrGQpVSpUl5PBPgagXcI0oO0X79+MnjwYFNKofOF69gK6yD05PDhw+bAjomJkR9++MGUdOgYiJIlSzrv8+qrr8p7770nI0eONP8pdF3PZLmemQIAINxpNuXzzz83X/oClVkJtKIlC0rrO+qaYFvpdevudc12AP6TWDxRIjOEfJERkVK5WGWfvm6RIkXM9X333SerV692XhISErL1+MWLF5vy82uvvdaUkK9atUqeffbZTI3aNHbJOE1ZsJ7wZDoxP9EDQMcbjB07Vnbs2GEG/2szAS2NsNubb74pffv2ld69e5t1HVfx/fffy8cffyxPP/20x8doEF25cmWzfxbNbLtatGiR3HjjjXLdddc5zzB9+eWX2cqmAwCA8FL/sgRJrF/KlJdrppugG/C/+CLxMqD5AHlp6UvOoHtwi8Fmuz8CVM0w68WTJUuWmMZnrutNmjRxxh1VqlQxwbZFK4lyQqtzNTBfunSpJCYmOpONf/zxh1x55ZXib2S8/WTYsGEyYcIE+eCDD2TTpk0yefJkueKKKzzeN2NJhqdLxjILi54FWrFihbRv3965LTIy0qzrmSNvvvvuOzOuQsdalCtXzhz02sDAlY6x0DETerAqbYSwcOFCcwIBAAAgIw22K9YpSdANBNANNW5wLk+5cYp0qdVFgsHXX39tEoMaW2ilribztBu6NUZc4x2Nn7R66J133jHxU05ozNSnTx/TYG3u3Lmybt066dWrl4mNAoGMt5/8+OOPpilAmzZtzLqewdFA1hMtybjtttuyfD5vZRoHDhwwY7HLly/vtl3XN27c6PX5/vrrL1NGriXqzzzzjPz666/y8MMPm26EPXv2NPfRbPmxY8ekbt26pkurvs5LL71kykAAAAAABLfyhd1jhEAaOnSoCawfeOABMzZbK2nr169vbrvhhhvkscceM4H42bNnTcWt9prSKcRyYvjw4abJmsZhOhb88ccfl6NHj0ogEHj7iR48OhedNivTrLK2zncdP53dkgxf0VITzXhrtl1pxlvPCmmZuhV4axdBHbP2xRdfyAUXXGDGaej8fHoSwLoPAAAAgPClU3adj+Of5swadHujvaT04kpjD4sG4RkDcb3d9T6a9dZ5vvVi0Qx4IFBq7ifa/l4bkmkre51/rmbNmrJ161bbS811knrNRu/du9dtu65rR0Bv9CyTdYbJUq9ePbfX0YNUs946x552PNd2/XomSsvoAQAAAACekfH2I52j7sknnzQl3MWLFzedwzM2MMtrqbmWhmv7fh2L3blzZ2c2W9etMROeaEdzHXvuSsdbaEm85dSpU5nGRGiQH6ydAwEAAIBwVzimsKztuTbQuxH2CLz9QEskNNt88cUXm8D1/fffN3NlexvjnddScx2nraXfWjp+ySWXyIgRI+TkyZPOLudKpwTTBgXWBPOaudb90Wy7Bv3a3EAbwenFomMjdEy3dgXUUnNt668d1O+6665c7ysAAKFGT0q3bdvW9FbRZQAACLz94MyZMyZg1bJtLRPX7LJ21vM2xjuvunbtKvv375dBgwbJnj17pHHjxjJz5ky3hmvahE07BFr0pIAG4gMGDJDnn3/eZOI1YHdtnKbzdWtTAx2LoXOCa9b93nvvNa8DAADO0WD70ksvlUOHDhF4AwAMAm8/0MDU38GplpVnVVruqRnB9ddfby7eaCdADcb1AgAAAADIHpqrAQAA2Eh7n+zevdv0RqEPCgBAkfEGAACwUWpqqowbN865XKBAgUDvEgAgwMh4AwAAAADgQwTeAAAACA5Hd4lsXXDuGoAt0k+dkg1165mLLiMwCLwBAAAQeCs/ERnRQGR8p3PXug4g32ndurU8+uijXm+PiIiQKVOmSDDumy8xxhsAAACBpRnuaY+IOP5pRqfX0x4VqdFOJK5ioPcOQA5MmjRJYmJiAr0bQYeMNwAAAALr0JZ/g26LI03k0F+B2iMAuVSqVCkzDTHcEXiHuFGjRknVqlWlYMGC0rx5c1m2bJktjznffRYsWCCdOnWSSpUqScmSJQNWTgIAAPKBUjVEIjJ8LY2IEilVPVdPl7Jnj5xcstRcA/hXyt69QVHOnZSUJNdcc40UKlRIqlevLt98843b7U899ZTUrl1bChcubG4fOHCgpKSkOG8fMmSING7cWD799FMTk8TFxcn//d//yfHjx533OXnypNx5551StGhRqVChgrzxxhsSSATeIWzixInSr18/GTx4sKxcuVIaNWokHTt2lH379uXpMdm5jx7ouv3dd9/1+c8JAEAwiYqKklatWkn58uXNMrJBy8k7vX0u2FZ63WlErsrMj3zzjWxu20629+plrnUdCGdHXBJgf113fVD8nxg4cKDcfPPN8ttvv0n37t1N0Lxhwwbn7Zox12kZf//9d3n77bflww8/lLfeesvtObZs2WKSe9OnTzeXn376SV555RXn7f379zfbpk6dKrNmzZL58+eb2CVQCLz9bOHChXLJJZeYTHGZMmXMgeQrb775pvTt21d69+4t9evXl9GjR5uzRh9//HGeHpOd++gZrBdffFFuuukmn/18AAAEIw22r7jiCpNhIfDOgaZ3ijy6VqTn9HPXup5DmuFOGjRYJP2fsvX0dLNO5hvhSo/9vS++9O+GIPk/ceutt8rdd99tstovvPCCXHTRRW4Ju+eee05atmxpstlaRfvEE0/IV1995fYc6enpJjhv0KCBXH755dKjRw+ZM2eOue3EiRMyZswYef3116Vdu3bSsGFDGT9+vKSmpkqgEHj70YwZM0wg+sADD8iaNWvk3nvvlccee0y2bdvm8f4vv/yyKY3I6rJ9+3aPj01OTpYVK1ZI+/btndsiIyPN+uLFi3P9mNw8LwAAQLZohrva5bluqJa87e9/g25Lerok/+35+xIQ6gL5fyJjLLPdJW5p0aKF23113TXjrRW2l112mcTHx5vHaiCeMe7RoNx1LLme7LQqcDUbrnGLDol1HXtep04dCRS6mvvJmTNnTKCtGe7bb7/dbHv++eflnXfeMeOh9cDJ6L777pPbbrsty+dNSEjwuP3AgQOSlpZmytxc6frGjRtz/ZjcPC8AAOHE4XDI/v375fTp02YZ/hNbtYpmBNwDjchIia2SmOfn3nNyj2w/tl0SiydKfJH4PD8fkN//T5xPxlgmwUvckpEm87T8fOjQoWY4q47fnjBhQqYx2hk7p+s0ZZoFD1YBzXhbDbj0l+BtPjc983HDDTeYN7xIkSJy8cUXe83yWr7++mupW7euKefWsgLNNAfa3LlzzQdw165dndu0/Ex/7gIFCnh8jJ6VqVmzZpaX6GjOnQAAEEy0AZCOR9y0aZNbMyD4Xkx8vFR4fui5QENFRpp13Z4Xk/6cJB2/6Sh9ZvUx17oO5Ad67Jd/7tl/N9j0fyI7MsYy0S5xy5IlS9zuq+v16tUzy4sWLZIqVarIs88+a0rQa9WqJX///XeOXrtGjRomMF+6dKlz2+HDh+WPP/6QsAy8rQZc2iHbEy0R0OYkGkTrYHgtz9aB+BpQe6O/qG7dukmfPn1k1apV0rlzZ3NZt26dBNK8efNM5z3XsV6bN282nfeaNGlie6m5jh/X19qboXOhrmvJRm4fk5vnBQAAyPM831sXnLs+jxK33CI1586RxPHjzbWu5zXTPWTREEmXc5k0vdZ13Q7kByU6d3YuV/9+ep7/T9jh66+/Nv2hNBDWhs06Q9JDDz1kbtNAW2MczXJrPKgVwpMnT87R82ucpPGgNljTBKjGgr169TJDZAMloOlSbcClF2/0LMe1114rr732mtvZi6xoKffVV19t3mSlg/Vnz54tI0eONE3AAkVPAug4A1f//e9/pVmzZqapgCd5KTWPjY01z60NBvTEg9LSC123DurcPCY3zwsAAJBrKz8RmfbIuXm+dcox7X5+nsZrms2zK6O3et9qcYj7kAFd/23fbxJfjaQD8peYDMNFA2Xo0KEmsNbeVzo2+8svvzRNm5VWO2sfLI0tzp49K9ddd51JvuoUYjkxfPhw02RNK6x1LPjjjz8uR48elUAJ2jplDea+//57efLJJ01tvwau1apVkwEDBjgDPm9jAnSqK1f6+EDPI637r+O8PvnkEzPIX8/yvPfeeyZDn1V5hl5yS9+Hnj17mhIN7aQ+YsQIU2Wg3cgtekJCzyBZHQCz85js3EcPcs3oW+MstIHc6tWrzc+TmOj7MSUAACAEaIbbCrqVXk97VKRGu1w3YLNNRGBfHghWWqmcFcc/vS806PZGE6+uyVflOje4BuEZA3G93fU+mvXWeb71YrGSs4EQtIG3dqTT4E3nYtNpqV599VWZOXOmdOnSxZRtX3nllR4ft2fPHo+Nv3S7N3omRS+WY8eOmWsdl5WTsVnadEwPpIyD+rVU4tChQ/Ldd9/JM888Y0oqLrzwQjP2XEvtfdUEQNv06/s4aNAg8/Nrqbu+ZtmyZZ2vqc1ftITDWs/OY7JzHy0X0db9Fj3DpHQS+7Fjx4q/6X7p7yanv1MEH+v3x+8RduGYgt1cjyU+d/ImYt8mibaCbosjTVL3/yGOwuX8sg8NSjWQCIlwy3rr+gUlL/Dr75a/VeFFf89WXJHXWMH18Wb5n3UrAPYUv8BzHJFxisic/H+McARJu01tMqaZVyubvXv3bqlYsaIZr/3FF18476elB9pkTcsRPNFSaJ2jTR/nWtKt5QwZxyVb9GyJ3p6Rvq7OT51dP/zwg8nyfpNhUnoNSrVU4q+//sr2c8Fea9euNV3l9SxYxhMzAADYSU/E6+eO0iavzOWdewWTD0mH9Y+ZsNeSLpEy+4I35Uxs7qsCc2r52eUy5fS/1ZOdC3WWiwpc5LfXR/jRRmTaP6ly5comvkHg6HDhHTt2mIRjxnnAT506ZWas0hL24sWL58+Mtzbx0gPOqvW3aLe7hQsXen2cHqA5bfyl5euu5ema8daDvEOHDud9A13pL0QvGR9jZbhz8lyhQs/raAM5HVehJ1cCRU/WFCpUSNq0aeNx6jbkH3pmUfs2XHXVVZmmkQByg2MKvviSZgXebdu2NZ9ByL20qiJRMx6XCEeaOCKiJP3aN6Rt4zv8ug/XyrVy76l7ZcfxHVK5WGUpX9j/J/H5WxV+UxFrXKHl0lk1lg6F7+n54XehccQVV1yR6XdhVUpnR9AG3npmR6cO06k4Mgax2l7eG518Xccru9b36x+pjJO0u9LpvDxN6aV/1HLyh82aHixjtzwd26yBdyC76AWKVbbi6X3xJ31t3Yec/k4RvPhdwm4cU7DzM0f7uWilm35J47jKo4t7i9TuIHLoL4koVV2iAzS2u1JcJXMJNP5WhU/ljPX92VffoYPle3qwyyqOyMn/xYAG3lYDLsvWrVvdGnDp4Hed91rPLmimUsd4T5s2zW3Avo4Z1pL0YcOGmfVHHnnEjP/WCda1A552y1u+fLl88MEHEiiBbuwGAAD8R0/Ea58T7R9DmblNNNgOdDM1AMiDgJ7a0IBY57C25rHWcm9d1qZd6qabbjJTgGlHOx0j9dFHH8m3335r5vZ2bVyWlJTkXG/ZsqUZm62BtjYu0/HWGvg2aNAgAD8hAAAAACDcBTTj3bp1a2c3PW/uuusuc8lJu3rtuq0XAAAAf9PvNkeOHDEZ7yDpYQsACDCK+QEAAGxugqUzqmzYsIGpn/wxz/fWBeeusyllzx45uWSpuQYAfwna5moAAACAVys/EZn2iIjO8x0RKdLpbZGmd2b5kCPffCNJgwY75zEuds3VUrr3XVLowoZ+2mnA/1LOpskHj/xklu95+0qJKUDviUAg4x3iRo0aZabP0q6q2mF12bJlWd5fm9RpN3mdVqBcuXJmXvWMneVz+pwAAAC20gy3FXQrvZ72aJaZb81wuwbd6vgPM2XbbbfJ7qcH+GOvAWQwZMgQady4sQSCDlnWbuU6NMgfCLxD2MSJE03DusGDB8vKlStNs7mOHTvKvn37vD7mp59+kgcffFCWLFlipmHTEjmdz/zkyZO5fk4AAABbHdryb9BtcaSZKce8Sd72t1vQ7erolClyes25udcBwBcIvP1IM8PaUE4nYK9bt65zmrMbbrjBJ6/35ptvSt++faV3795Sv3590yG+cOHC8vHHH3t9jE7Z1qtXL7ngggtMUD1u3DjTOX7FihW5fk4AAABblapxrrzcVUSUSKnqXh8SW7WKTsjr9fZTK1fauYcA4IbA2080g6zzi+vc4mvWrJF69erJ888/L6+++qoMHTrU42NefvllKVq0aJYXDYo9SU5ONsFy+/bt3SZ/1/XFixdne7+PHj1qrnVudbueEwAAIE90Tm8d063BttLrTiOynOs7Jj5eKjzv+TuXKty0qS/2FAgqJ46c9enzb9u2zZRvZ7y0bt06y8e9//77UrlyZZPQu+2225wxiPr111/lqquukjJlykhcXJyJqbTy1pW+hk49rdNR63PUqlVLvvvuO7f7zJgxQ2rXrm2SoG3atDH76k8E3n6i5dk6xVn//v3NgdCtWzf5/vvvTVbZmsc8o/vuu09Wr16d5SUhIcHjYw8cOCBpaWlSvnx5t+26viebXTzT09Pl0Ucflcsuu8zMg27HcwIAANhCG6k9ulak5/Rz1+dprKZK3HKL1Jw/T4q0auW2Pa5zZxqsIWRtXJzkXP5yyBL5/ZfdPnstDZ6TkpKcl1WrVknp0qXliiuu8PqYzZs3y1dffSXTpk0z1bf6mAceeMB5+/Hjx6Vnz56ycOFCk8zUWOraa681211pMlODdk1y6u3du3eXQ4cOmdt27NghXbp0kU6dOpkY6u6775ann35a/Imu5n6wc+dOkxF+/fXXnduio6PN3J7est1WllkvgaJjvdetW2cOcgAAkD1aDda0aVNTlabL8CHNcGeR5faW+U786EMzplvLyzXTTdCNUHXi8Bn5eeIfznWHQ2T+5xslsX4pKRwXa/vrRUVFSXx8vFk+c+aMadTcokUL00TNG73fJ598IhUrnvu//O6775oq4TfeeMM8V9u2bd3ur0N1S5QoYXpTXX/99c7tOlxWk5tW5fA777xjhvpeffXV8t5770mNGjXMc6o6derI2rVrTfWxv/Bp4Ac6j6fSD2GLdgq/5JJLpGFD73/o81JqrqUYeuDv3bvXbbuuW/8ZsvLQQw/J9OnTZd68eVKpUiVbnhMAgHCgJ9f1i55+fuoygpMG26V79SToRkg7su+0CbZdaV/Co/tO+/y177rrLpOV/uKLL8xJSNcYRit7LYmJic6gW2mgrpW31sxKGmtojynNdGupefHixeXEiROZ4qALL7zQuVykSBFzP6sBtMZjOhuTK30df+LTwA90jIIGrDr2QGnJg2a/tcw8K3pAarlEVryVmsfGxkqzZs1kzpw55kyT0gNY1zWo9kaz8P/5z39k8uTJpsV+tWrV8vycAAAAAPyvRLlCoiGIa/CtfQnjyhXy6eu++OKL8uOPP5qMs05TrLTE26JBcXZpmfnBgwfl7bfflipVqkiBAgVM0Kz9p1zFxMS4rWvspbFKsCDw9gOdm07HRr/22mtmnPcjjzxi5sH+/fff5e+//zYHkC9KzXVcuR6oF110kcmujxgxwkwLph3JLSNHjjRBtgbPVnm5npWaOnWq+U9ijd3Ws0vaiCA7zwkAQDjTk9j62ZiammqWgT0n98j2Y9slsXiixBehShD+U7RkQbm8a21ZMOFcubkG4a271zXbfRWUfvvtt6aJ9A8//GDKuy01a9b0eH/NXO/evduZUNRx3Joh13Jw9csvv8h///tfM27bGq+tvadyQhtbZ2y2pq/jT5Sa+4EeZHrw6VkabaSmB9WsWbNMSYWWovlK165dTWZ90KBBJvjXs0zasMC1OZoetFu2bHGu6/gHzdBr58EKFSo4Lzp/d3afEwCAcJaSkmI+87VPii4jvE36c5J0/Laj9JnVx1zrOuBPdVtUcC53G3Kp1L/Mc8WsHfTv3p133ilPPfWUmZ5Yk3h6OfRPkzNPChYsaBJ7v/32m/z888/y8MMPm6pfayirlph/+umnplx86dKlpmmaJgRzQiuJ//zzT9PoWkvYNdGo0yb7E4G3nwwcONAEuadPn5axY8ea7n46NZc1/ttXtARcs+pnz541B2rGsQ3a6MC1lb6emfd00WYF2X1OAAAAnMt0D100VNJ1UK0O0XOky9DFQ812IBCKlijg0+dfvny5nDp1ypSauybxunTpkmWSUm/XjHaHDh3MWG3NcFvGjBkjhw8fNv2yevToYQLzcuXK5Wi/dBy5ZuKnTJlihvuOHj3a9NPyJ0rNAQAAAB/Q8vJ0cS/n1eB7x/EdlJwjJGmyzjVhdz5Dhgxxdjy///77Pd5HK4Z1Lm9Xt9xyi9u6p2E9R44ccVvXDuiuXdCVP4fLkvG2GWO5ghO/FwAA4G86pjtSO1m50PXKxSoHbJ8ABAaBt40KFy5sWuYHU/c8nHPs2DHn7wgAAMAfNKs9uMVgZ/Ct17pOthv+FFMgSh4c3dZcdBmBQam5jbTZmI5p0MYAWhKB4KHdELWpXdmyZQO9KwAAIIx0qdVFWia0NOXlmukm6AbCExlvGzVs2FAqV65sOpj/8ccflDcHAe0mq1OjaSOF6667zjmXOgAAgL9osH1x/MUE3UAYI+NtI51vTufFvvfee+X22283GVadh1u3hys9+aDdz3Wie38HvTp/qs4LeOLECbnmmmukb9++fn19AEB40s99PRm/c+fOsP4OAAD4F4G3zXRu7kmTJplptrT7ngZ94SwtLc3Mmact/KOi/DumJDo6Wi6//HIzJ7lOU0C2GwDgr8+fTp06yYwZM8wyAAB8GvhAbGysCfj0Eu601Fu/eOi8fDExMYHeHQAAAADwO+qfAAAAbB5mlZycbKq+6PcCAFAE3gAAADZXe73++uuydu1aswwAgZRy5oy80fV6c9FlBAaBNwAAAAAgIKpWrSojRowIyGv36tVLOnfu7JfXIvAGAAAAAMCHCLwBAAAAAPAhAm8AAAAACAPHDx306fN/8sknUrp0aTl79qzb9s6dO0uPHj2879fx49KtWzcpUqSImZ551KhRbre/+eab0rBhQ3N75cqV5YEHHnCbtnncuHFSokQJ+fHHH6VevXpStGhRufrqqyUpKcl5H2142a9fP3M/3ccnn3zSrw0wCbwBAAAAIESt/2mOc3lcv/tk7dxZPnutW2+91QS43333nXPbvn375Pvvv5e77rrL6+OGDx8ujRo1klWrVsnTTz8tjzzyiMyePdt5e2RkpLzzzjuyfv16GT9+vMydO9cEzq5OnTplGlt++umnsmDBAtm+fbs88cQTztvfeOMNE6B//PHHsnDhQjl06JBMnjxZ/IXAGwAAAABC0PGDB2Tu2NHOdc3wzv5wpNnuC4UKFZLbb79dxo4d69z22WefSWJiorRu3drr4y677DITcNeuXVv+85//yC233CJvvfWW8/ZHH31U2rRpYxqxtW3bVl588UX56quv3J5DZ5EYPXq0XHTRRdK0aVN56KGHZM6cf086aAO3AQMGSJcuXUxWXO8bFxcn/kLgDQAAYCPNzNStW9d8odNlAAiUw0m7M5VTO9LT5cie3T57zb59+8qsWbNk165dZl2zzL169ZIvvvjClIBbl59//tn5mBYtWrg9h65v2LDBuf6///1P2rVrZ8rQixUrZsrWDx48aLLclsKFC0uNGjWc6xUqVDDZdnX06FFTdt68eXPn7dHR0SZI95dov70SAABAGNAvc5pRmTFjhlkGgEApWSFBIiIi3ILviMhIKRGf4LPXbNKkiSkb1/HeHTp0MOXh33//vRlb7Rr4ahCdHdu2bZPrr79e7r//fnnppZekVKlSplS8T58+kpycbAJuFRMT4/a4jD93oHEaFgAAAABCULHSZaRt7/vcgu6r+j5ktvvS3XffbTLdWnLevn170xBNM9U1a9Z0XrQs3bJkyRK3x+u6loOrFStWSHp6uhmjfemll5py9N27c5ax1wokzYAvXbrUuS01NdU8t78QeAMAACCknDh8RnZuOmyugXB3wZXtnMu93nhPGrbt4PPX1HHeO3fulA8//DDLpmqWX375RV577TX5448/TEfzr7/+2jRYUxqk6/jtd999V/766y/TPE3HZ+eUPt8rr7wiU6ZMkY0bN5rO6EeOHBF/IfAGAACwkZY+vvzyy7J69WqzDP/6/Zfd8skzi2TqW6vMta4DOKdYqdJ+eR3NMN98881mLLdOJXY+jz/+uCxfvtyUqWvjNJ0+rGPHjuY2LVvX9VdffVUaNGggn3/+uQwbNuy8z+npNXRseM+ePc0Ycs3A33TTTeIvDDwCAABASNAM97xPNzrXdXjnvM82SmL9UlK0ZMGA7hsQbrS5Wvfu3aVAgQLnHcN9Po899pi5uHKdF1ybt+nFlQb8rmO8teeGdjbXSyCQ8QYAAEBISNpyNPNGh8ievzxsB+AThw8fNvNjz58/Xx588MFA707QIOMNAACA0BY8jY0Bv4spWFAenzjdb6+n5eIafGtpeJ06dfz2usGOwBsAAAAhoUKNuMwbI0TiPW0H4BPZKR0PR5SaAwAAICToOO42PeqaYNuIEGlzR13GdwMIODLeAAAACBn1L0swzdSO7jstceUK2Rd0H90lcmiLSKkaInEV7XlOAGGDwBsAAMBGkZGRUqNGDdm/f79Zhv9psG1rlnvlJyLTHhFxpItERIp0eluk6Z32PT/gRXp6eqB3Ieyl2/Q7IPAGAACwkU5Z07VrV5kxY4ZZRj6nmW4r6FZ6Pe1RkRrtyHzDZ2JjY82Ju927d0vZsmXNekSENYbCvoAyOTlZzpw5w0lCD3QqMn1/rJOo+jvICz4NAAAAAG+0vNwKui2ONJFDfxF4w2c00KtWrZokJSWZ4NtXgeXp06elUKFCtgf1oaRw4cKSmJiY55MTBN4AAACANzFFvGwv7O89QZjRDKsGfKmpqZKWlmb786ekpMiCBQvkiiuukJiYGNufPxRERUWZyiU7TkwQeAMAANhISxNff/1180W5ffv2fKHN71JOetl+SvKbPSf3yPZj2yWxeKLEF4kP9O4gGzTg078hvvg7okGlBvUFCxbk75QfEHgDAAD4IJOE4HTi8Bk5su+0lMhux3PtYq4N1VzLzSOiREpVl/xk0p+TZOjioZLuSJfIiEgZ3GKwdKnVJdC7BYQNAm8AAACEhbXzd8qCCX8412s0KytNrkqU8lXjvD9Ix3FrF3NtqKZjuzXo7jTC+/juIJx2TDPdQxcNlXQ5d/JAg28NwlsmtCTzDfgJgTcAAADCItP988R/g261ZcV+c6lzaby071Xf+4N16jDtYq4N1TTT7S2gDtJpx7S83Aq6LRp87zi+g8Ab8BP6xgMAACDkaXm5w+H5tk1L9sjebUezfgINtqtdnnWm29O0Y7o9wHRMt5aXu4qQCDl4+qDJhgPwPQJvAAAAhDwd051VY+KkzecJvPMy7ViAaVZbx3RbwbcG3ar/gv7S8duOZvw3AN8i8AYAAEDI00Zqre+o6/X2uHKF8vYCVhM2V0HUhE0bqf1484/y+pWvm3WHONzGe5P5BnyLwBsAAMDm6X907t0iRYrYMvcr7FP/sgTpcPcFHm+LLZDH1kdWEzYNttX5mrAFKPNdskBJZ9Cdcbw3AN+huRoAAICNdD7cO+64Q2bMmMHcuEGoQo04U3LuOt5bE9V5znhn1YQtiDqdW+O9Ndi26HrlYpUDul9AqCPjDQAAgLArObeqwvW6dfe62ZvTOzsyNmHTTucjGoiM73TuWteDaLy3Nac33c0B3yLjDQAAgLArOU+sX0qO7jttMt22Bd3Z7XSuWfEAZr51vLfO4a3l5ZrpJugGfI/AGwAAwEbJyckyYsQIc92+fXvKzYOUBts+C7iz0+k8wCXnGmwTcAP+Q+ANAABgs9OnTwd6FxAMrE7nrsF3EHU6B+A/jPEGAAAAfMGGTucpe/bIySVLJXUP030B+RkZbwAAAMBXvHU6z4Yj33wjSQMHnWvBHhEhxbt0Ebn2Wp/uLgDfIOMNAAAA+FLGTufZzHQ7g27lcEj5b78l8w3kUwTeAAAAQJA5tXKV+2TjWqmu/QN++y1g+wQg9wi8AQAAgGCjUTaAkEHgDQAAYKOIiAipUKGCFCpUyCwDuVG4SRMzrtuVIyJCCjVqFLB9ApB7BN4AAAA20nm7e/fuLXXq1GEOb+RaTHy8VHjheZHIf76uR0bK3i5dJDqeubeB/Iiu5gAAAEAQKnHLLVKkVStJ/nu7RCZUkD9Wrgz0LgHIJQJvAAAAIIgz33pJSUkJ9K4AyANKzQEAAGykAdKoUaNk/fr1BEvwG51+7OSSpeYaQPAh4w0AAGAjh8MhR48edS4Dvnbkm28kadBgkfR0Mxa8wvNDTZk6gOBBxhsAAADIpzTD7Qy6VXq6WSfzDQQXAm8AAAAgn0re9ve/QbclPd00ZAMQPAi8AQAAgHwqtmqVf6ccs0RGSmyVxEDtEgAPCLwBAACA/Dzf9/ND3eb7Lvd4P5MJz2/l5ntO7pFlScvMNRBqaK4GAAAAhMh832fWrZV9b7yZ7xqtTfpzkgxdPFTSHekSGREpg1sMli61ugR6twDbkPEGAACwUUREhJQpU0YKFixolgF/Zb61vNwZdOejRmua4R666FzQrfR6yKIhsnb/2kDvGmAbAm8AAAAbxcTEyD333CN169Y1y4C/5NdGa9uPbZd0cd9vhzik+4zuJhMOhAICbwAAACAMGq1p5vvkkqVBlwFPLJ5oyssz0uBby88Z841QQOANAAAAhGijNV3X7Ue++UY2t20n23v1Mte6Hizii8SbMd0Rknlohpad7zi+IyD7BdiJ5moAAAA2SklJkQ8++EBOnDghV111FeXmCFijNc10a9CtGW4d651x7LfeT28PBtpIrVaJWqa8XDPdFs2EVy5WOaD7BtiBjDcAAICNHA6HHDhwQM6cOWOWAX/TYLpI80ucQbW3sd/HZv4YVGXnDcs2lCEthzjLzq3u5poRB/I7Mt4AAABAOIz9zhB873vlFdn32mtBNeWYZr5bJrQ05eWa6SboRqgg4w0AAACE09hvV0E45ZgG2xfHX0zQjZBC4A0AAACEOM1o15w7R8o9/XTmG/PBlGNAfkfgDQAAAIRJ5rv41R2znHIMgG8QeAMAAABhIqspxwD4Ds3VAAAAbBQRESFxcXFy6tQpswzkhynHAPgWgTcAAICNdN7uBx98UGbMmMEc3ghaGmwTcAP+Q6k5AAAAAAA+ROANAAiMo7tEti44dw0AABDCCLwBAP638hOREQ1Exnc6d63rQIhISUmRsWPHyqZNm8wyAAAE3gAA/9IM97RHRBzp59b1etqjZL4RMhwOhyQlJcnp06fNMgAABN4AAP86tOXfoNviSBM59Feg9ggAAMCnCLwBAP5VqoZIRIaPn4gokVLVA7VHAADk2J6Te2RZ0jJzDZwPgTcAwL/iKop0evtcsK30utOIc9sBAMgHJv05STp+21H6zOpjrnUdyArzeAMA/K/pnSI12p0rL9dMN0E3ACCf0Az30EVDJV3ODZtKd6TLkEVDpFaJWnI69bQkFk+U+CLMkQ53BN4AgMDQYJuAGwCQz2w/tt0ZdFsc4pDuM7qb68iISBncYrB0qdUlYPuI4EOpOQAAgM0KFSokUVH/DKcA8uDE4TOyc9NhOXH4bKB3Bf/QjHaERGTarkG3lQEfungoY7/hhow3AOSAfojqmW7KyAB4ExsbK4899pjMmDHDLAO5tXb+Tlkw4Y9zKxEiJS+IkfwuZc8eSd72t8RWrSIx8Vl8juoUkzoLhjbkDLLqKP3873lBTxm3fpzX+2jwveP4DrfvCnyHCG9kvAEgm2ikAgDwZ6bbGXQrh8jhdQXydeb7yDffyOa27WR7r17mWtc9WvahyFv1RcZ3EhnRQGTlJxJsutfrLpFZhFJabl65WGXnOt8hkOfA+8yZM/bsCQDkh0Yq/8w/TRkZAMCXkrYc9bA1QvZuPSb5NdOdNGiwSPo/Y6PT0826bs+U6f7hyX/X9XN32qPntgcRzVgPbjnYBNhKS8+t8nNrjLeV1eY7BHJdap6eni4vvfSSjB49Wvbu3St//PGHVK9eXQYOHChVq1aVPn368O4CCPlGKp7KyEJJtssBAbhJSUmRzz77TA4ePGiWY2Lyf3kwkFf6eeIMui3p6ZL893b3zxgtL/8nQHVypJ2bBSPISs61eVrLhJbmu4CV3baWXb8bhON3CNiU8X7xxRdl3Lhx8tprr7mNXWrQoIF89NFH2X6eBQsWSKdOnSQhIUEiIiJkypQpme6zYcMGueGGGyQuLk6KFCkiF198sWzfvt3rc+p+6XO5XgoWLJiLnxIA/qXjsayz2t7KyELJ/tGjZXPrNucvBwSQicPhMN9VTp48aZaB3KhQI87DVoeUr1Zc8iM9iSuRGUKPyEiJrZLovk3HdGf4vJWIqHNTTwYhDZwvjr/YXLsuZ/oOIeHzHQI2Bt6ffPKJfPDBB9K9e3e3jp2NGjWSjRs3Zvt59ANJHzNq1CiPt2/ZskVatWoldevWlfnz58uaNWtMVv18gXTx4sUlKSnJefn7779z8NMBgJeSshb/lpRlLCMLJQfHjJEDI97+d4O3ckAAgM8ULVlQ2vSoa5qqWUo2OCtFSxaQ/Eiz2hWeH/pv8B0ZadYzVVRpVrvT2+eCbaXXnUYEXbY7L2Xpgf4OoSXuy5KWyd5TewPy+uEqV6Xmu3btkpo1a3osQdeSquy65pprzMWbZ599Vq699lqTWbfUqFHjvM+rWe54yiIB+LikLBSDbg2u973+RuYbPJUDAgB8qv5lCZJYv5Qc3XdaCpeMkQWL50h+VuKWW6RIq1bm80Qz3d4+U1ISOkhyq08ltliaxNRplq+D7mD7DqFN3YYsGmKmPtMx6TcWulGulWsDsi/hJlcZ7/r168vPP/+cafs333wjTZo0sWO/TBD//fffS+3ataVjx45Srlw5ad68ucdy9IxOnDghVapUkcqVK8uNN94o69evt2WfAMBbGVlIjcHzVBrrqRwQAOCXzHfFOiXzbaY7Iw22izS/xGvQ7ex8/uATsrnXADkye7GEikB/h9BMtxV0K72ecnoKme9gzngPGjRIevbsaTLfGiBPmjRJNm3aZErQp0+fbsuO7du3zwTQr7zyihlT/uqrr8rMmTOlS5cuMm/ePLnyyis9Pq5OnTry8ccfy4UXXihHjx6V119/XVq2bGmC70qVKnl8zNmzZ83FcuzYuW6Rmr3PSQYfmVnvH+8j7MIx5VvpsZ6bQJXUppmlS4fk+84xBbu5Hkt8l4C//lbpNGPH9p+W4mUL5dsgPdVL5/MCzZtLNBVXebYiaYUz6Ha1cs9KKV+4fED2Kb/Lyd/3CEcuu35oxvv555+X3377zQTITZs2NQF5hw4dcvN0pjx88uTJ0rlzZ7O+e/duqVixonTr1k2++OIL5/200Zo2Wfvyyy+z/WbUq1fPPM8LL7zg8T5DhgyRoUOHZtqur1u4cOFc/TwAkB8V2rJFKn/wYabtO+7pK6ezMdQHgEhaWpqsXbvWLDds2NCtHw7gCyd3xJg5vs8NCHeYseBFKue/Ez58BvnW2uS1MvHUxEzbuxbuKg1jGwZkn/K7U6dOye23324SvtpnzPaMt7r88stl9uzZ4itlypSR6OhoU9buSoPohQsXZvt5dAoPLX/fvHmz1/sMGDBA+vXr55bx1jJ1PYlwvjcQ5z/xocfJVVddxXQqYUxLmLYf3y6JxRLzfEaVY8q3NNuw7aMx7lO+REbK5bfeGrLZBo4p2C05Odk0m9UAvG3btiZhAPjqb5Vmur+cuczlnhFyZH1Buea2K/Jd5jscP4P8qdmpZvLVlK/cst46zrt7m+5SKc5zZTCyZlVKZ0euA29f02nKdOowLWF3pXOG6/jtnJ511iZt3hQoUMBcMtI/anwJswfvZfjRcUQ6b+WSpCXy0dqPzB95q4unNhjJK44p34ipXNl0mXWW+v3TdbZQ5dCf8oRjCnbR46h///4yY8YME3RzXMGXf6tOHj6RqTWHrp86nCIlyxWV/CScP4P8QYPrIS2HyNDFQ8084vq97IaCN5jt/J3KnZy8b9kOvEuWLGnKwbPj0KFD2bqflqi7ZqK3bt0qq1evllKlSkliYqL50OratatcccUV0qZNGzPGe9q0aWZqMcudd95pStKHDRtm1rX8/dJLLzVd148cOSLDhw8304ndfffd2f1RAdjQMdP6o+5K13W7dvUM1eZkoSC7XWcBAIFXolwh0a/obsF3hMip48ly4vAZ05wtPwnZz6Cju0QObTk3T3kAu7S7dlevUKiCrJi/ImD7Em6yHXiPGDHCuXzw4EHT8Ey7jbdo0cJsW7x4sfz4449mnu3sWr58uQmoLVa5tzZuGzdunNx0000yevRoE1Q//PDDpnHat99+a+b2tmzfvl0irfkAReTw4cPSt29f2bNnjzlZ0KxZM1m0aFGmknUAvst0D100VNLFPeh2Db71jz2Bd3DTLzoh82UHAEKYBtat76gr8z/fKOZ897lh3jLro/UmINfbdFqy/CTkPoNWfiIy7RExvyCdy1vnKW96Z8B2R7+D6YXGj0EaeGswbLn55ptNZvmhhx5ybtPAeOTIkfK///1PHnvssWw9Z+vWreV8vd3uuusuc/HGNfut3nrrLXMBEBhaXu4t6FaREmnmr8y3guSMNYDglZqaKhMnTpT9+/ebZUo44a/5vvdsOSo/jvl3Gl39mq0Bud6W3zLfIWPnCpHvHj53NkRp8D3tUZEa7fge4WGIYmLxxJBNzuRqHm/NbF999dWZtus2DbwBhC/9g6ljhrx5tNmj+fcP6rIPRd6qLzK+k8iIBufOYANABjrV6pYtW0zTHV0G/EED64LFYp3xnUXjvKP7Tgdqt8Kbfm/4qO2/QbfFkSZy6K9A7VVQDlHs+G1H6TOrj7nW9VCUq8C7dOnSMnXq1EzbdZveBiB8aVCtDdQyBt/aNbNfs37Su0FvybeZ7h+e/HfdOmOt2wEACKLx3q704ziuXCEJZyl79sjJJUvNdcC+N7iKiBIpVd1/+5Ifhig60t36Aen2UJOrruY657U2K9My7+bNm5ttS5cuNc3PPvww89x7QCgLh9KYvDTuKHjmuJw5/JdUrnCRxMc3lnxLy8szNItznrGmVAwAEITjvTXobt29bliXmR/55ptMXdK1gVtAvjcoM8Z7RGC/O1jD5opnf6Yofw5RTA/RfkC5Crx79epl5tN+5513ZNKkSW7za1uBOBAOtBRmyKIhZqoszejqFA12TJUVCkzjjk2zgqqZSJ7omG79GVw/RH1wxvr0mrVyasUKKdysmRS6sKGtzw0ACJ/x3lperpnucA66NcPtDLpVerpZ167pPm/e5ul7gxYb9/mfSKVmEgyN3qIjIiWxslYiXhuwXjaJ/wxRdJ0JR9fzdT8gu+fx1gD7888/t3dvgHyW6baCbqXXus5UWS5/uK2g21szkfzUqEz3T08c6M+gmW4Num0+Y72rf385Nm36vy/ZubMkvHJuqkQAALJLg+1wDrgtydv+/jfotqSnm6nKfB54e/veEMigO8N3swhHujTaPlbSjj0iUrrKuTHpM544d18/JUzi/xmi6Dq3uK6H4nfpXAXeOoVXVnQObiDUrd632hl0W3T9t/2/heQfC9tLs4Nsao1s0f3TEwf6M2im28agWzPdrkG3OjplipS8/XYy3wAA5EJs1SqmvNwt+I6MNPOD5/fvDXZ9N4vUQu/Df4lER3vuZeOH7utdXIYoaqY7VL9H5yrwrlq1qkRk7NzgIi0tLS/7BORvWc+QFz5iivw7mair3avOfficLxserHT/fLCPWl7ucfvKlQTeAADkgma1dUx3xjHeec12awm7ZtM1sD/vc/noe0Puv5u5029pjpjCAe9lE//P3OKhLFeB96pVq9zWdfJ13fbmm2/KSy+9ZNe+AUGtcbnGZly3a9Zb1xuVaxTQ/QoKrqVKGf1viEhcJRqVZaBjuj1ub9rU7/sCIG9iY2PlmWeekRkzZphlAIGjjdR0TLeWl2umO69Bd8Catdkh5WSmTZoiiUg5JVK2tl962YSzXAXejRplDiwuuugiSUhIkOHDh0uXLjSXCqRg77Id7PuXXbrv2kzNTIEg6RIpkTK4ZWiOSbFt+gwrwNY/8/xxd6NZbR3TreXlFl0n2w0AQN5osG3HmG6PzdqeGyjpySlSrG0b348b90HDN/0G6yhZ3S+9bMJdrpureVKnTh359ddf7XxK5KLLdsbmBP7osp3dYDqr/cuPAXmX4yek5Y5dsiM6Uiqnpkv88ROB3qXA8zZ9hkX/kFe+hD/uHmgjNR3TreXlmukm6AYAIMibtYnI3uefl70vvCAVXng+uLPfGYJrR0SU/Fa5lzQonhCcY9JDTK4C72PHjrmtOxwOSUpKkiFDhkitWrXs2jfkdgJ6cZ+A3tddtrMb7Ge1f4t2LwrICYM8Z3a/e1jixSHxqf9sm/Zw/hin7PfpM/7hGmDzx90jDbYJuIH8LTU11Uy3qt+NdDkmJibQuwTAV83aLBoPnWeqshyNDfcVl+9fqcUTZfvC1dIgWMekh5jI3DyoRIkSUrJkSeelVKlSUr9+fVm8eLG899579u8lPNJAdlnSMnOttiet9DoBvS/3wQTTDvdg2tonV5rN9rR/2gU8u88RVHYszdw4zOEQ2bFMwpp1NlWDbKXXVz0v0nO6yKNr3TuX632rXc4feAAhJT09XTZu3ChHjx41ywBCq1mbeGsy/c9UZd7Ghm9u20629+plrnU9YKzvX1amG8Gb8Z43b57bemRkpJQtW1Zq1qwp0dqKHj43YeMEeWnJi+Y/fqREyOCKHaTlknESWSle0l3+GPh6AnpvwbQG+xmz7FpCrvtjBdjW/mnFRHafA/kE2WwAABCCtJS8QO06su222zLf6GWqMo9jw8+THUfoyVXGW6cSu+yyy+TKK680l8svv1zq1q1rbluwYIHd+4gMNBM8bOnLzrNt6eKQoTtnmtLewQcOSaRmXc0v1/cT0FvBtCtvwb7uh+6PdX+rpFy7g+vJA7fnkAifnjCwReXm//SCdBVxbvwyyGYDAICQpEPCKrz4gnvmOyLC61RlHseGZ5EdR2jKVXq6TZs2ZtxSuXLl3LZrSZXexjzevnWupNy9xFmz3DtioqXLiZPS8vQZs1w5Nc3nzb5MMJ1wlQn8dR806B9c8Sqvwb6O225ZrLrsSFoulStcJPHxjc1Y6cEHDsrQ0iX/fY6DhyU+NciPIw0ob3hH5LtHzOkPcx7rhrcJNAEAAMJkmrJTq1ab9cJNGnvNXnscG+4lO47QlavAW0uDNeud0cGDB6VIkcwTs8NeiakpJjh1Kyl3OKRyyrkOX/FpaeZiaNdCXzb7OrpLuvwyRlpGngv8dR/i//5Y5OLHPL/myk8kftojEq/l5pr51rHAJaue6w5+6vS/z5GWT+Z0pqQ6aAVFAxMAABCy9PtF3DVXZ+t+mg3POP+3Xd9P+M4TgoG3NT+3Bt29evWSAgUKOG/TLPeaNWukZcuW9u8l3MRXuMhkhN0yxAcOS3yapw6LPg5g/5k6Kj7tXMD/73YPr6ldwKc98m+3a73WEwN9Zpsg3O2EQX6a05nuj0FHG5Zk/HAL6uk9AABAWGTItbxcM912zSt+aPwncmj8eL7zhFrgHRcX58x4FytWTAoVKuS8LTY2Vi699FLp27ev/XsJd3EVpUubYdJyRr9/54++9k2RcheIfNTOvdO2rwNYT1NHeXtNT/M764mBlFPM6Qzb0MAEAAAEI/0eYtd3EZNkGDjo3Iw6Fr7zhE7gPXbsWHNdtWpVeeKJJygrD6Smd0p8jXYSn7HEWccc+zOAtaaOys5rZhWkaxMuSrZhg6wamPAhBMAfdN5u/Z70448/Moc3AN8lGVyDbgvfeUJrjPfgwYPt3xPYU+IciDHH2X3N8wXplGzDBjQwARBoOiRPKwGjoqI89sQBANuTDBa+8+T/wLtp06YyZ84cKVmypDRp0iTLD5KVK1fatX/IjUAEsNl9TZqRwcd83cAEAADk3onDZ+TIvtNSolwhKVqyYKB3J3SSDIrvPKEReN94443OZmqdO3f25T4h1JHZRj5sYAIA2ZWamirTpk2TnTt3mmXKzYFzfv9lt8z/bKOpkNYcXus76kr9yxICvVv5P8kQESGleveWUnf28Mt3Hrqo+zjwdi0vp9QcQDg1MAlHZCSA3EtPT5e1a9c6lwGc+1yxgm6l1/M/3yiJ9UvxOZOPkgyHvvhC9j7/wrkVL13UCcxtHONtSU5Oln379mX6UElMZFwBAIRCRkK16FJDmnaoEujdAgDkY3oyN2MvMO23e3TfaQLvfJJk0IB674svZdlFPTuBeXaFWgCfq8D7jz/+kD59+siiRYvctus0Yzr2W+f0BgDk/4yEWjxpi2hXjyYE3wCAXNIKKi0vd/180clu4sr9Oz0x8vfMMdkJzLMbZJvp0jL068nv85PnKvDu3bu3REdHy/Tp06VChQp07ASAEM5IqMWTt0iti8uTlQAA5Ip+fuiYbi0v10y3Bt2tu9flcyWEZo7J7ZSuRzIE2eUe7yf73njz3+cKkfnJcxV4r169WlasWCF169a1f48AAAHNSHiiwTjlgACAvNBGajqmWz9PNNPNZ0pozRyTmyldU6w5yV2C7H2vv5F5jvIQmJ88V4F3/fr15cCBA/bvDYCQHNOC/E8LmygHBADklQbbBNyh2dQtN1O6JnvKkltt712D7xCYnzxXgferr74qTz75pLz88svSsGHDTNNkFC9e3K79A8JOKI5pQf4qNfekUftEvigBAIAsm7rltNt6rJcsuVu5eYjMT56rwLt9+/bmul27dm7baa4G5I2ncptQGNOC/N38RjurNWpbKYB7BeQvmpB45JFHZM6cOczhDSDs5KTbeoyXLLkG8MWvu87v06UFXeA9b948+/cEQK6bUgB2ofkNkHeahChSpIhpREsDWiB4Zu3Qqi49wcxnWnAp4SVL7u/p0oIy8L7yyivt3xMgm06vWSunVqyQws2aSaELG0ooyU1TCsBuNL8BAISS33/Z7ZwqU8+F6Qlm/axD8IgJsSDbtsB7zZo1HrfrWd2CBQtKYmKiFChQIK/7hjDmrbnY7qcHyNEpU5zrcZ07S8IrwyRU5KYpBeALNL8Bci81NVVmzpwpO3fuNMuUmwOBzXRbQbfSa63q0hPMfM4h6APvxo0bZ1k6pR8wXbt2lffff98E4oAdzcU00+0adCtdL3n77SGV+c5pUwoAQHBJT0+XlStXOpcBBI6Wl2ecmUqHUnmbIpOSdPhKZG4eNHnyZKlVq5Z88MEHZk5vvehynTp15IsvvpAxY8bI3Llz5bnnnrN/jxGWzcV0u5aXe3Lqny83oUSD7SLNLyHoBgAAsKFpqCvtX+JpikwtSR//9I8y6bWp5lrXgYBmvF966SV5++23pWPHjs5tOq1YpUqVZODAgbJs2TLTVOTxxx+X119/3badRXg3F9Mx3Z4UbtrUPzsH+AFn2gEA8H/TUP38/d+YryXl5P80Jy4pEiH/G7NNEuvfy+cxAhd4r127VqpUqZJpu27T26xy9KSkpLzvIQI6pjqYmovpfumY7oxjvEOpzBzhjeYvAAAEpmno7j93ScrJ2S5bHGY96c/rpdYlNfy6vwhNuSo1r1u3rrzyyiuSnJzs3JaSkmK26W1q165dUr58efv2FD4dU725bTvZ3quXudb1QDcXM8G3ytBcTBupVf3qKyn39NPmOpQaqyG8eWv+otsBAEDeaLBdsU5Jr9nrU0f/9rz9mOftgF8y3qNGjZIbbrjBlJZfeOGFZptmutPS0mT69Olm/a+//pIHHnggN0+PIBhTrc29ApX5Pl9zMc1wk+VGuDd/AQAA9ilYJCZH2wG/BN4tW7aUrVu3yueffy5//PGH2XbrrbfK7bffLsWKFTPrPXr0yM1TI4jGVAey5Dwc5vIDPDV/cQ2+vTV/AQAA9qpYp17mjRERklDbw3bAX4G30gD7vvvuy+3DESSyGlMNIPiavwAIfjqtqlb9zZs3jzm8gXyiWOky0uHeh2XWB++eOwseESEd7vmP2Q4ENPBWv//+u2zfvt1trLfSMnTkD9aY6ozzZpNtBoKz+QuA4BcRESElSpSQAgUKmGUA+UPDth2kaqOmcmTPbikRn0DQjcAH3jp++6abbjLjuvUDxfFPbaT14aJjvZF/nG9MNQD/0WCbgBsAgMDQYJuAG0HT1fyRRx6RatWqyb59+6Rw4cKyfv16WbBggVx00UUyf/58+/cSPqfBdpHmlxB0AwCQR5qAmDNnjpnhhWQEACDXGe/FixfL3LlzpUyZMhIZGWkurVq1kmHDhsnDDz8sq1at4t0FEFA6DZd2CtemZWSQAfiTBttLly51LgOAL/BdJwwCb/0QsbqXa/C9e/duqVOnjlSpUkU2bdpk9z4CQI78/stu55zYOgJGm5bp2Gk+nAAAQKh+19FeMQixwLtBgwby22+/mXLz5s2by2uvvSaxsbHywQcfSPXq1e3fSwDI5pnfpC1HZd5nG0X+mZZLP5B0XTtQ8OEEAABC4fuOFXQrvdZZUTTJ4C25QHY8nwbezz33nJw8edIsDx06VDp16iSXX365lC5dWiZMmGD3PgJAjs78ZuJwxuHZ+nACAAAIVhpAZ/y+o1OR7tlyVGpelPm7DdnxfBx4d+zY0blcq1Yt2bhxoxw6dEhKlizJtBkAAn7m93z0w0mn7CLwBgAA+Y1mrTXkyvi9Z9aY9ZJ8Ns0tqM4qO16gaJSf9zy85Sjwvuuuu7J1v48//ji3+wMAtpz5dTI15hk2RYqZJxsAACC/0cSBZq1dh9Z5q+rzlh3XBES5okX9sr+Uueci8B43bpxpoNakSRPn3N0AEGgxBTzPjHhFt9pS7cIysv33Q+aDSD9oNOhu3b1uWP/hBwAA+ZtmtWMKRMmsj9ZnWdXnKTvu6wTE3m1HZffmo5JQM052/3FEFk3eYk4QhHuZe44C7/vvv1++/PJL2bp1q/Tu3VvuuOMOKVWqlO/2DgCyIeVsusftpeKLmA8e/QOvZ3/1g0g/aAi6AfhSTEyM9O3bVxYsWGCWAcAXKtSIO29QbWXHPSUgUlJSbN+n/437XTYt2ePxNkeY99nxnCbyYtSoUZKUlCRPPvmkTJs2TSpXriy33Xab/Pjjj2TAAQSMdTbXlacPnop1SoblH3oA/qX9bsqWLSuFCunfJnrfAPANK6jW7zzKW1WfJiDufKmldH6sibn2VcZZM93egu6MGfnslqjv3HTYXIdlc7UCBQpIt27dzOXvv/825ecPPPCApKamyvr166Won8YKAEB2zuYCAACEquxW9el2X38v+mPZXnPtSD8u6WmHJTKqpEREFnO/U0T2ytxDsRN7rrqaWyIjI82ZXM12p6Wl2bdXAJBDlJMDCBb6nUjLzLVKUJcpNwfgS/4IqrPFIZJ6dq2knprt3BRd+CqJiqnqDMQbXFnnvPuam3nKQ67UXJ09e9aM877qqqukdu3asnbtWhk5cqRs376dbDeAgKKcHEAw0GB74cKFsnfvXhITAMJGxTrRbkG30vWzRz+SlBPfmOuicVvO+zxZdWIPm4y3lpRPmDDBjO3WqcU0AC9Tpozv9g4IU0y7AAAAgPwk+dQuL7dYUbRDFnz+vtRp0VyKlfYeQwaiE3vQBd6jR4+WxMREqV69uvz000/m4smkSZPs2j8g7Kydv1MWTPjDLIfKmBYAAACEtjMnjp33Po70dDmyZ3eWgXeo9u7JUeB955130p0T8HGm++eJ54LuUBrTAgAAgNBWsGjx894nIjJSSsQnhGXvnhwF3trBHAh0qbVOVbB781FJqBkn5avGSSjJakxLKPzBAQAAQGiqWKfeee9zebeeWWa7g7JpXDB0NQd8IavpA/437ne3+QHrXBov7XvVl1ARqmNaAAAAENo0oO5w78My64N33b/MuihetpyEqxx3NQd8ydv0AbpdM92uQbfSdd0eKqwxLRpsq1AZ0wIAAIDQ17BtB7ln1Fi5tMv/BXpXgg4Zb+SbUut9O457fEzS5qMhVXIeimNaACCcREdHS69evWTRokVmGQDCLfN9YfurZcmkCe43RERIQu3zl6OHKjLeCMpSa1dWqbWO6fakgpft+RnzUQNA/hUZGSkJCQlSuHBhswwA4Vp27vxiHxEhHe75T7bHd4ciTsMiqGQ1fYBedEx3xjHeoZTtBgAAAEKl7Lxqo6Zm+rAS8QlhHXQrAm/kq1JrbaTWsHVFU16umW6CbgBAsElLS5MlS5bIvn37zHJMTEygdwkAAkKD7XAPuC0E3ghKWU0foME2ATcAIFhpsD137lznMgAADDwCAAAAAMCHCLwBAAAAAPAhAm8AAAAAAHyIwBsAAAAAAB8i8AYAAAAAwIcIvAEAAAAA8CECbwAAABtFR0dL9+7dpUaNGmYZAAACbwAAABtFRkZKlSpVpFixYmYZAAA+DQAAAAAA8CECbwAAABulpaXJ8uXLZf/+/WYZAAAGHgEAANhIg+1Zs2Y5lwEAIOMNAAAAAIAPEXgDAAAAAOBDBN4AAAAAAPgQgTcAAAAAAD5E4A0AAAAAgA8ReAMAAAAA4EME3gAAADaKjo6W2267TapVq2aWAQAg8AYAALBRZGSk1KxZU+Li4swyAAB8GgAAAAAA4EME3gAAADZKS0uTNWvWyMGDB80yAAAMPAIAALCRBtvTp093LgMAQMYbAAAAAAAfIvAGAAAAAMCHCLwBAAAAAPAhAm8AAAAAAHyIwBsAAAAAAB8i8AYAAAAAwIcIvAEAAGwUHR0tN910k1StWtUsAwBA4A0AAGCjyMhIqVevnpQoUcIsAwDApwEAAAAAAD5E4A0AAGCj9PR02bBhgxw5csQsAwDAwCMAAAAbpaamyuTJk53LBQoUCPQuAQDCOeO9YMEC6dSpkyQkJEhERIRMmTLF7Xbd5ukyfPjwLJ931KhRpqFJwYIFpXnz5rJs2TIf/yQAAAAAAARh4H3y5Elp1KiRCZQ9SUpKcrt8/PHHJvC++eabvT7nxIkTpV+/fjJ48GBZuXKlef6OHTvKvn37fPiTAAAAAAAQhKXm11xzjbl4Ex8f77Y+depUadOmjVSvXt3rY958803p27ev9O7d26yPHj1avv/+exO0P/300zbuPQAAAAAAIdRcbe/evSaA7tOnj9f7JCcny4oVK6R9+/bObTqNh64vXrzYT3sKAAAAAEA+bK42fvx4KVasmHTp0sXrfQ4cOCBpaWlSvnx5t+26vnHjRq+PO3v2rLlYjh07Zq5TUlLMBblnvX+8j7ALxxTsxjEFu7keS3yXgF34WwW7cUzlXU7eu3wTeGupePfu3U3DNLsNGzZMhg4dmmn7rFmzpHDhwra/XjiaPXt2oHcBIYZjCnbjmIJdNAlgmTt3rkRFRQV0fxBa+FsFu3FM5d6pU6dCK/D++eefZdOmTaZxWlbKlCljPty0LN2VrmccL+5qwIABpiGba8a7cuXK0qFDBylevLgNP0F4nwXS/8xXXXWVxMTEBHp3EAI4pmA3jin4IvCuVKmSrFu3zhxXvkgaIPzwtwp245jKO6tSOmQC7zFjxkizZs1Mh/KsxMbGmvvNmTNHOnfubLalp6eb9Yceesjr43R+TU9zbOoByEFoD95L2I1jCnbjmIJd9Dhq0qSJmZFFg26OK9iJv1WwG8dU7uXkfQtoc7UTJ07I6tWrzUVt3brVLG/fvt3tLMLXX38td999t8fnaNeunYwcOdK5rpnrDz/80IwJ37Bhg9x///1m2jKryzkAAAAAAP4U0Iz38uXLzfRgFqvcu2fPnjJu3DizPGHCBHE4HNKtWzePz7FlyxbTVM3StWtX2b9/vwwaNEj27NkjjRs3lpkzZ2ZquAYAAOALWm23efNmOXr0qFkGACCggXfr1q1NUJ2Ve+65x1y82bZtW6ZtWlaeVWk5AACAr6SmpspXX33lXPY0nA0AEF7yzTzeAAAAAADkRwTeAAAAAAD4EIE3AAAAAAA+ROANAAAAAIAPEXgDAAAAAOBDBN4AAAAAAPgQgTcAAICNoqKipEOHDlKxYkWzDAAAgTcAAICNNNi+6KKLpGzZsgTeAACDwBsAAAAAAB8i8AYAALBRenq6/P3333L8+HGzDABAdKB3AAAAIJSkpqbK559/7lwuUKBAoHcJABBgZLwBAAAAAPAhAm8AAAAAAHyIwBsAAAAAAB8i8AYAAAAAwIcIvAEAAAAA8CECbwAAAAAAfIjAGwAAwEZRUVHStm1bSUhIMMsAADCPNwAAgI002L700kvl0KFDBN4AAIOMNwAAAAAAPkTgDQAAYKP09HTZvXu3nDp1yiwDAECpOQAAgI1SU1Nl3LhxzuUCBQoEepcAAAFGxhsAAAAAAB8i8AYAAAAAwIcIvAEAAAAA8CECbwAAAAAAfIjAGwAAAAAAHyLwBgAAAADAhwi8AQAAbBQVFSWtWrWS8uXLm2UAAJjHGwAAwEYabF9xxRVy4sQJAm8AgEHGGwAAAAAAHyLwBgAAsJHD4ZD9+/fL6dOnzTIAAJSaAwAA2CglJUU+/PBD53JsbGygdwkAEGBkvAEAAAAA8CECbwAAAAAAfIjAGwAAAAAAHyLwBgAAAADAhwi8AQAAAADwIQJvAAAAAAB8iMAbAADARlFRUdK8eXMpW7asWQYAgHm8AQAAbKTBdrt27eTs2bME3gAAg4w3AAAAAAA+ROANAABgI4fDIUeOHDEZb10GAIDAGwAAwEYpKSny3//+VzZs2GCWAQAg8AYAAAAAwIcIvAEAAAAA8CECbwAAAAAAfIjAGwAAAAAAHyLwBgAAAADAhwi8AQAAAADwIQJvAAAAG0VGRkrTpk2lTJkyZhkAAD4NAAAAbBQdHS1XX321VKpUySwDAEDgDQAAAACADxF4AwAA2MjhcMjJkyclNTXVLAMAQOANAABgo5SUFHn77bdl3bp1ZhkAAAJvAAAAAAB8iMAbAAAAAAAfIvAGAAAAAMCHCLwBAAAAAPAhAm8AAAAAAHyIwBsAAAAAAB8i8AYAALBRZGSkNGzYUEqWLGmWAQDg0wAAAMBG0dHR0qlTJ6lSpYpZBgCAwBsAAAAAAB8i8AYAALCRw+GQ5ORkSUtLM8sAABB4AwAA2CglJUVef/11Wbt2rVkGAIDAGwAAAAAAHyLwBgAAAADAhwi8AQAAAADwIQJvAAAAAAB8iMAbAAAAAAAfIvAGAAAAAMCHCLwBAABsFBkZKXXr1pW4uDizDAAAnwYAAAA2io6Oli5duki1atXMMgAABN4AAAAAAPgQgTcAAAAAAD5E4A0AAGCj5ORkefnll2X16tVmGQAAAm8AAAAAAHyIwBsAAAAAAB8i8AYAAAAAwIcIvAEAAAAA8CECbwAAAAAAfIjAGwAAAAAAHyLwBgAAsFFkZKTUqFFDihcvbpYBAODTAAAAwEbR0dHStWtXqV69ulkGAIDAGwAAAAAAHyLwBgAAAADAhwi8AQAAbJScnCzDhw+XNWvWmGUAABh4BAAAYLOUlJRA7wIAIIiQ8QYAAAAQVJKOnpZFWw6YayAUkPEGAAAAEDQ+XbxNBk5db5YjI0SGdWkoXS9ODPRuAXlCxhsAAABAUNAM9+DvzgXdKt0h8sykdWS+ke8ReAMAAAAIClsPnDTBtqs0h0O2HTgVqF0CbEHgDQAAACAoVCtTxJSXu4qKiJCqZQoHapcAWxB4AwAA2CgiIkISExOlSJEiZhlA9lWIK2TGdGuwrfT65S4NzHYgP6O5GgAAgI1iYmLkjjvukBkzZphlADmjjdSuqF3WlJdrppugG6GAwBsAAABAUNFgm4AboYRScwAAAAAAfIjAGwAAwEbJycny1ltvydq1a80yAACUmgMAANjs9GnmHAYA/IuMNwAAAAAAoRp4L1iwQDp16iQJCQlmuo0pU6a43a7bPF2GDx/u9TmHDBmS6f5169b1w08DAAAAAECQBd4nT56URo0ayahRozzenpSU5Hb5+OOPTSB98803Z/m8F1xwgdvjFi5c6KOfAAAAAACAIB7jfc0115iLN/Hx8W7rU6dOlTZt2kj16tWzfN7o6OhMjwUAAAAAIBDyTXO1vXv3yvfffy/jx48/733//PNPU75esGBBadGihQwbNkwSExO93v/s2bPmYjl27Ji5TklJMRfknvX+8T7CLhxTsBvHFOzmeizxXQJ24W8V7MYxlXc5ee8iHA6HQ4KAlpBPnjxZOnfu7PH21157TV555RXZvXu3Cai9+eGHH+TEiRNSp04dU2Y+dOhQ2bVrl6xbt06KFSvmdVy43i+jL774QgoXLpyHnwoAAISb9PR0kwRQtWrVkshIetkCQCg6deqU3H777XL06FEpXrx4aATe2iDtqquuknfffTdHz3vkyBGpUqWKvPnmm9KnT59sZ7wrV64sBw4cOO8biPOfBZo9e7b53cXExAR6dxACOKZgN44p+ALHFezGMQW7cUzlncaNZcqUyVbgnS9KzX/++WfZtGmTTJw4McePLVGihNSuXVs2b97s9T4FChQwl4z0AOQgtAfvJezGMQW7cUzBFziuYDeOqfwl6ehp2XrgpBSJjZKTyWlSrUwRqRBXSIIJx1Tu5eR9yxeB95gxY6RZs2amA3pOadn5li1bpEePHj7ZNwAAAADI6NPF22Tg1PVu2yIjRIZ1aShdL/befwqhKaCDjjQoXr16tbmorVu3muXt27e7pe+//vprufvuuz0+R7t27WTkyJHO9SeeeEJ++ukn2bZtmyxatEhuuukmiYqKkm7duvnhJwIAAOFOyzd1qtT169fTtAgI40z34O/cg26V7hB5ZtI6czvCS0Az3suXLzfTg1n69etnrnv27Cnjxo0zyxMmTBAdhu4tcNZsto7FtuzcudPc9+DBg1K2bFlp1aqVLFmyxCwDAAD4mn5v0fF+1jKA8KPl5Rpke5LmcMi2A6eCruQcIRx4t27d+rwfSPfcc4+5eKOZbVcaqAOAL8ZnBeO4LAAAEHz0O4OWlXsKvqMiIqRqGWZOCjfMbwEA5xmf1WLYXLn9w6Vy2StzZeKv/w6FAQAA8ERP1OtYbg2yXen6y10acCI/DOWL5moAEAzjs6xxWVfULssHJgAAyJI2UNPvDFpWXjg2Uk4lp5tMN98hwhOBNwDkYHwW47IAAEB26fcFvjNAUWoOAOcZn+WKcVkAAADIKQJvAMjm+CzGZQHIjoiICClTpowULFjQLAMAQKk5AGRzfBbjsgBkR0xMjJmRZcaMGWYZAAACbwABk1+m6WJ8FgAAAPKCwBtAwKbpGjj1XMdwHUetJd2aXQYAAABCDWO8AQTNNF26HQDyu5SUFPnggw9k48aNZhkAAAJvAEE1TRcA5HcOh0MOHDggZ86cMcsAABB4A/A7pukCAABAOCHwBuB3TNMFAACAcEJzNQABwTRdAAAACBcE3gAChmm6AAAAEA4oNQcAAAAAwIcIvAEAAGwUEREhcXFxEhMTY5YBACDwBgAAsJEG3A8++KBccMEFZhkAAAJvAAAAAAB8iMAbAAAAAAAfIvAGAACwUUpKiowdO1Y2bdpklgEAYDoxAAAAGzkcDklKSnIuAwBAxhsAAAAAAB8i8A4hSUdPy6ItB8w1AAAAACA4UGoeIj5dvE0GTl1vliMjRIZ1aShdL04M9G4BAAAAQNgj4x0CNMM9+LtzQbdKd4g8M2kdmW8AAAAACAIE3iFg64GTJth2leZwyLYDpwK1SwAAAACAfxB4h4BqZYqY8nJXURERUrVM4UDtEgAAYa1QoUISFRUV6N0AAAQJAu8QUCGukBnTrcG20uuXuzQw2wEAgH/FxsbKY489Jg0bNjTLAADQXC1EaCO1K2qXNeXlmukm6AYAAACA4EDgHUI02CbgBgAAAIDgQuANAABgo5SUFPnss8/k4MGDZjkmJibQuwQACDACbwAAABs5HA7Zvn27cxkAAJqrAQAAAADgQwTeAAAAAAD4EIE3AAAAAAA+ROANAAAAAIAPEXgDAAAAAOBDBN4AAAA20ynEIiP5mgUAOIdPBAAAABvFxsZK//795cILLzTLAAAQeAMAAAAA4EME3gAAAAAA+FC0L58cAAAg3KSmpsrEiRNl//79ZlnHewMAwhuBNwAAgI3S09Nly5YtzmUAACg1BwAAAADAhwi8AQAAAADwIQJvAAAAAAB8iMAbAAAAAAAfIvAGAAAAAMCH6GrugcPhMNfHjh0L9K7keykpKXLq1CnzXjKdCuzAMQW7cUzBbsnJyXLmzBmzrMdVWlpaoHcJIYC/VbAbx1TeWfGiFT9mJcKRnXuFmZ07d0rlypUDvRsAAAAAgCC3Y8cOqVSpUpb3IfD2QOfc3L17txQrVkwiIiICvTv5/iyQnsTQg7F48eKB3h2EAI4p2I1jCr7AcQW7cUzBbhxTeaeh9PHjxyUhIUEiI7MexU2puQf6pp3vjAVyRv8z8x8aduKYgt04puALHFewG8cU7MYxlTdxcXHZuh/N1QAAAAAA8CECbwAAAAAAfIjAGz5VoEABGTx4sLkG7MAxBbtxTMEXOK5gN44p2I1jyr9orgYAAAAAgA+R8QYAAAAAwIcIvAEAAAAA8CECbwAAAAAAfIjAG7a44YYbJDExUQoWLCgVKlSQHj16yO7du93us2bNGrn88svNfSpXriyvvfZapuf5+uuvpW7duuY+DRs2lBkzZvjxp0Aw2bZtm/Tp00eqVasmhQoVkho1apgGIMnJyW7347hCTrz00kvSsmVLKVy4sJQoUcLjfbZv3y7XXXeduU+5cuWkf//+kpqa6naf+fPnS9OmTU1Dmpo1a8q4ceP89BMgPxg1apRUrVrV/M1p3ry5LFu2LNC7hCC1YMEC6dSpkyQkJEhERIRMmTLF7XZtxTRo0CDz3Uo/C9u3by9//vmn230OHTok3bt3N/Mw6981/ew8ceKEn38SBIthw4bJxRdfLMWKFTOfYZ07d5ZNmza53efMmTPy4IMPSunSpaVo0aJy8803y969e3P8WYicIfCGLdq0aSNfffWV+Y/97bffypYtW+SWW25x3n7s2DHp0KGDVKlSRVasWCHDhw+XIUOGyAcffOC8z6JFi6Rbt27mA2PVqlXmD4Ve1q1bF6CfCoG0ceNGSU9Pl/fff1/Wr18vb731lowePVqeeeYZ5304rpBTeuLm1ltvlfvvv9/j7WlpaeaLht5Pj53x48eboFq/+Fq2bt1q7qN/91avXi2PPvqo3H333fLjjz/68SdBsJo4caL069fPnChcuXKlNGrUSDp27Cj79u0L9K4hCJ08edIcI3qyxhM9mfzOO++Yz7+lS5dKkSJFzPGkgZNFg279nJw9e7ZMnz7dBPP33HOPH38KBJOffvrJBNVLliwxx0RKSor5rqTHmuWxxx6TadOmmcSE3l+TZV26dMnRZyFyQbuaA3abOnWqIyIiwpGcnGzW//vf/zpKlizpOHv2rPM+Tz31lKNOnTrO9dtuu81x3XXXuT1P8+bNHffee68f9xzB7LXXXnNUq1bNuc5xhdwaO3asIy4uLtP2GTNmOCIjIx179uxxbnvvvfccxYsXdx5nTz75pOOCCy5we1zXrl0dHTt29MOeI9hdcskljgcffNC5npaW5khISHAMGzYsoPuF4KdfyydPnuxcT09Pd8THxzuGDx/u3HbkyBFHgQIFHF9++aVZ//33383jfv31V+d9fvjhB/MdbNeuXX7+CRCM9u3bZ46Rn376yXkMxcTEOL7++mvnfTZs2GDus3jx4mx/FiLnyHjDdlry9Pnnn5tyzpiYGLNt8eLFcsUVV0hsbKzzfnrGVjPkhw8fdt5HS6hc6X10O6COHj0qpUqVcq5zXMFuelzocITy5cu7HS9aXaEZJes+HFPwRLNDWn3jenxERkaadY4P5JRW1+zZs8fteIqLizPDF6zjSa+1vPyiiy5y3kfvr8edZsgB/e6krO9P+jdKs+Cux5UOx9Mho67H1fk+C5FzBN6wzVNPPWVKoHS8iI4LmTp1qvM2/eBw/c+rrHW9Lav7WLcjvG3evFneffdduffee53bOK5gt7wcU/qF5PTp037cWwSbAwcOmBJN/ubADtYxk9XxpNc6/tZVdHS0CbI45qBD9nQ41GWXXSYNGjQw2/S40IRFxj4nGY+r830WIucIvOHV008/bRp9ZHXRcbgWbbqgY2hnzZolUVFRcuedd5qmIEBejiu1a9cuufrqq83Y3L59+wZs3xE6xxQAAKFOx3prT5sJEyYEelegJ8UCvQMIXo8//rj06tUry/tUr17duVymTBlzqV27ttSrV890mNbGDi1atJD4+PhM3RKtdb3NuvZ0H+t2hOdxpQ0/tImVDl1wbZqmOK6Qm2MqK3pcZOxAnd1jSjsKa9dhhC/9DNQTz/zNgR2sY0aPH+1qbtH1xo0bO++TsXGfdp7WYX8cc+HtoYcecjbbq1SpknO7Hhc6LObIkSNuWW/Xv1PZ+SxEzpHxhldly5Y1Yz6yuriOrc1Y2qLOnj1rrjX41v/4OqbEop0W69SpIyVLlnTeZ86cOW7Po/fR7QjP40oz3a1bt5ZmzZrJ2LFjzZg1VxxXyOvfqoz0uFi7dq3bF1k9XjSorl+/vvM+HFPwRI8z/Xvlenzo56Guc3wgp3Q6TQ1yXI8nHdKiY7et40mvNYDScbuWuXPnmuNOx4Ij/Gi1qQbdkydPNseCHkeu9G+U9mByPa60N44OE3U9rs73WYhcCHR3N+R/S5Yscbz77ruOVatWObZt2+aYM2eOo2XLlo4aNWo4zpw54+ygWL58eUePHj0c69atc0yYMMFRuHBhx/vvv+98nl9++cURHR3teP311013xcGDB5uui2vXrg3gT4dA2blzp6NmzZqOdu3ameWkpCTnxcJxhZz6+++/zd+qoUOHOooWLWqW9XL8+HFze2pqqqNBgwaODh06OFavXu2YOXOmo2zZso4BAwY4n+Ovv/4yx1n//v3NMTVq1ChHVFSUuS+gf4e06/S4ceNMx+l77rnHUaJECbfuwIBF//ZYf4f0a/mbb75plvVvlXrllVfM8aOzxaxZs8Zx4403mtk9Tp8+7XyOq6++2tGkSRPH0qVLHQsXLnTUqlXL0a1btwD+VAik+++/38zaMX/+fLfvTqdOnXLe57777nMkJiY65s6d61i+fLmjRYsW5mLJzmchco7AG3mmHwRt2rRxlCpVynzZqFq1qvkPrcGSq99++83RqlUrc5+KFSuaD5OMvvrqK0ft2rUdsbGxZrqe77//3o8/CYJtuif9EuLp4orjCjnRs2dPj8fUvHnznPfRE4jXXHONo1ChQo4yZco4Hn/8cUdKSorb8+j9GzdubI6p6tWrm+MVsOjJaP1Sq8eHTi+mJ6gBT/Rviae/Sfq3yppSbODAgeYks37O6cnoTZs2uT3HwYMHTaCtJxN1uqfevXs7TyYi/Hj77uT6OaUnbh544AEzJaueSL7pppvcEhvZ/SxEzkToP7nJlAMAAAAAgPNjjDcAAAAAAD5E4A0AAAAAgA8ReAMAAAAA4EME3gAAAAAA+BCBNwAAAAAAPkTgDQAAAACADxF4AwAAAADgQwTeAAAAAAD4EIE3AAAAAAA+ROANAAAAAIAPEXgDAMJS69at5dFHHw3I60ZERJjL6tWrg3If88qu/c6vP3929OrVy3kcTJkyJdC7AwDwMQJvAEDIsQIab5chQ4bIpEmT5IUXXgjI/vXt21eSkpKkQYMGAXn9YOMtwPbH78jO4P6xxx6TLl26ZOu+b7/9tjkGAADhITrQOwAAgN1cA5qJEyfKoEGDZNOmTc5tRYsWNZdAKVy4sMTHx0t+k5ycLLGxsX57vVKlSkl+smzZMrnuuuuydd+4uDhzAQCEBzLeAICQo0GtddHgRrPcrts06M6Y6dT1//znP2ZbyZIlpXz58vLhhx/KyZMnpXfv3lKsWDGpWbOm/PDDD87HpKeny7Bhw6RatWpSqFAhadSokXzzzTe52md9nTvvvNPsW4UKFeSNN97IdJ+ZM2dKq1atpESJElK6dGm5/vrrZcuWLea2Tz75xGw7e/as22M6d+4sPXr0MMu6bw0bNjT7qvdt3769eV1v9D156KGHzHtSpkwZ6dixY65+7qz2W0uuf/rpJ5MBtioStm3b5nx963f0wQcfSEJCgnltVzfeeKPcddddudovb6+t7+HDDz8s5cqVk4IFC5p9//XXX7M8IRETEyOLFi2SZ5991jzPpZde6vX+AIDwQ+ANAMA/xo8fbwJMzVxqEH7//ffLrbfeKi1btpSVK1dKhw4dTBB76tQpc38N8jTgHT16tKxfv96UGt9xxx0mmMup/v37m8dNnTpVZs2aJfPnzzev6UqD5H79+sny5ctlzpw5EhkZKTfddJMJOHU/09LS5LvvvnPef9++ffL999+bwFSrALp162aWN2zYYJ5fy6IdDsd53xPNcv/yyy/m58zNz53VfmvQ26JFC2f5vV4qV66c6Tn05zt48KDMmzfPue3QoUMmqO/evXuu9svbaz/55JPy7bffmp9dfwd6wkVPOujreRIdHW3eH6Xj9vV5dL8AAHByAAAQwsaOHeuIi4vLtP3KK690PPLII27rrVq1cq6npqY6ihQp4ujRo4dzW1JSkkapjsWLFzvOnDnjKFy4sGPRokVuz9unTx9Ht27dvO5PxtdVx48fd8TGxjq++uor57aDBw86ChUqlOm+rvbv32/2Z+3atWb9/vvvd1xzzTXO29944w1H9erVHenp6Y4VK1aY+27bts3r83na1yZNmrhty87P7elnzGq/vd0/4/Ybb7zRcddddznX33//fUdCQoIjLS3Ntt/HiRMnHDExMY7PP//cuS05Odm8zmuvveb1eSZPnuwoXbp0pu3Tpk1z1K5d21GzZk3Hhx9+mOl2fR/0sQCA0MYYbwAA/nHhhRc6l6OiokxZtJZmW7T83Mokb9682WS+r7rqqkxlx02aNMnR62rZtT6uefPmbuOb69Sp43a/P//804xXX7p0qRw4cMBZdr19+3bTqE0ztxdffLHs2rVLKlasKOPGjXN2z9ay63bt2pmfR7O3mr2/5ZZbTFl9Vpo1a+a2npuf+3z7nV2a2daf8b///a8UKFBAPv/8c/m///s/k0G36/ehv4uUlBS57LLLnNu0jPySSy4xlQLerFq1yrzHrlJTU02mX7P0OuRB30vN9OtxBQAILwTeAAC4BFiuNGB13abrSgPHEydOmGUt5dYg15UGhb7QqVMnqVKlihl7bo131sBVg0ulAaYGf1purYG1llvr/lknEmbPnm3GIWsp+7vvvmvGI2swrGOivSlSpIjbem5+7vPtd05+fk0S62vrCYaff/5Z3nrrrVzvl520xDxj4K1DFi644ALn/lxzzTXmvdeSfwBAeCHwBgAgF+rXr28COs3aXnnllXl6rho1apgAX4PgxMREs+3w4cPyxx9/OJ9bxzdrZ3YNXi+//HKzbeHChZme6+6775YRI0aYrLc2T3MdL60nDjSTqxfNQGswPHnyZJOV9dXPnZ391jHkOj79fLTRmY5L10y3Zri1IqBp06a52i9vr62/C2tMu74/SjPg2lwtq2nH1q5dKzfffLPbtt27d7udBNBl/b0AAMIPgTcAALmgXc6feOIJ08BLM7ja+fro0aMmYCtevLj07Nkz28+lncz79OljGqxpGbJ209ZstJZQW7QkXG/T7t7a9VwDzKeffjrTc91+++1mvzTQ1cy3RYN6bWymmXB9fl3fv3+/1KtXz6c/d3b2u2rVqmZ/tKO4vhdaZu/6s2csN9eu6JrN18Zpud2vrF5bm+rp70KX9UTIa6+9ZsrY9Xfkjb6mnmDQYFurBJgqDADgiq7mAADk0gsvvCADBw403bQ1gL366qtNqXNWpdveDB8+3GSEtZxaM9UaOLqOr9ZAdMKECbJixQpTpq0Bpj4mIw34NPOqQaROJWbR4HPBggVy7bXXSu3ateW5554zU5Zp+bMvf+7s7LcGzFoKr1nrsmXLmuDcm7Zt25qAWINcPcmQ2/3K6rVfeeUV8x5qB3vNqGt2/ccff8xyPPyLL75oxtRrVluXlZbVu2a4dVm3AQDCT4R2WAv0TgAAEC50burGjRubcnBf0SZqOrb4nXfe8dlr4Py0uZqeANCp26zmajrG3rW5mpb/a7m/60kSAEDoIeMNAICfaVduzUjruGA76bhwDeI00HvwwQdtfW7knM7vrVUFbdq0MSdbHn/8cWfQfd9995ljAAAQHsh4AwDgR1pufPr0abOs44e1kZdddLyyBt9abq0l1AheOiXdsWPHzLKOfc/YPR4AEFoIvAEAAAAA8CFKzQEAAAAA8CECbwAAAAAAfIjAGwAAAAAAHyLwBgAAAADAhwi8AQAAAADwIQJvAAAAAAB8iMAbAAAAAAAfIvAGAAAAAMCHCLwBAAAAAPAhAm8AAAAAAHyIwBsAAAAAAPGd/wcynx8gHd7fmgAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import os\n",
+    "import random\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "usbl_dir = 'downloaded_file/microlensing_lightcurves/USBL'\n",
+    "\n",
+    "# --- Create a directory to save the plots ---\n",
+    "save_dir = \"lightcurve_plots/USBL\"\n",
+    "os.makedirs(save_dir, exist_ok=True)\n",
+    "\n",
+    "all_files = [f for f in os.listdir(usbl_dir) if f.endswith('.npz')]\n",
+    "if not all_files:\n",
+    "    raise FileNotFoundError(\"No .npz files found in USBL folder.\")\n",
+    "\n",
+    "valid_file = None\n",
+    "while all_files:\n",
+    "    candidate = random.choice(all_files)\n",
+    "    file_path = os.path.join(usbl_dir, candidate)\n",
+    "    data = np.load(file_path)\n",
+    "\n",
+    "    try:\n",
+    "        t0 = float(data['t0'])\n",
+    "        tE = float(data['tE'])\n",
+    "        u0 = float(data['u0'])\n",
+    "    except KeyError:\n",
+    "        all_files.remove(candidate)\n",
+    "        continue\n",
+    "\n",
+    "    filters = ['u', 'g', 'r', 'i', 'z', 'y']\n",
+    "    zoom_range = 3 * tE\n",
+    "    total_points = 0\n",
+    "    filter_count = 0\n",
+    "\n",
+    "    for f in filters:\n",
+    "        try:\n",
+    "            t = data[f\"{f}_time\"] - t0\n",
+    "            mask = (t >= -zoom_range) & (t <= zoom_range)\n",
+    "            if np.sum(mask) > 0:\n",
+    "                filter_count += 1\n",
+    "                total_points += np.sum(mask)\n",
+    "        except KeyError:\n",
+    "            continue\n",
+    "\n",
+    "    if total_points >= 10 and filter_count >= 3:\n",
+    "        valid_file = file_path\n",
+    "        break\n",
+    "    else:\n",
+    "        all_files.remove(candidate)\n",
+    "\n",
+    "if valid_file is None:\n",
+    "    raise RuntimeError(\"No valid USBL event found.\")\n",
+    "\n",
+    "# --- Plotting ---\n",
+    "data = np.load(valid_file)\n",
+    "t0 = float(data['t0'])\n",
+    "tE = float(data['tE'])\n",
+    "u0 = float(data['u0'])\n",
+    "zoom_range = 3 * tE\n",
+    "\n",
+    "mass_ratio = data.get('mass_ratio', None)\n",
+    "separation = data.get('separation', None)\n",
+    "rho = data.get('rho', None)\n",
+    "alpha = data.get('alpha', None)\n",
+    "\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "for f in filters:\n",
+    "    try:\n",
+    "        t = data[f\"{f}_time\"] - t0\n",
+    "        mag = data[f\"{f}_mag\"]\n",
+    "        err_mag = data[f\"{f}_err_mag\"]\n",
+    "        mask = (t >= -zoom_range) & (t <= zoom_range)\n",
+    "        if np.sum(mask) == 0:\n",
+    "            continue\n",
+    "        plt.errorbar(t[mask], mag[mask], yerr=err_mag[mask], fmt='.', label=f\"{f}-band\")\n",
+    "    except KeyError:\n",
+    "        continue\n",
+    "\n",
+    "plt.axvline(0, color='gray', linestyle='--', label='$t_0$')\n",
+    "plt.xlabel(\"Time [days relative to $t_0$]\")\n",
+    "plt.ylabel(\"Magnitude\")\n",
+    "plt.title(f\"Random USBL Event: {os.path.basename(valid_file)}\")\n",
+    "plt.gca().invert_yaxis()\n",
+    "plt.grid(True)\n",
+    "plt.legend()\n",
+    "\n",
+    "# --- Annotation box with USBL parameters ---\n",
+    "annotation = f\"$t_E$ = {tE:.2f} d\\n$u_0$ = {u0:.3f}\"\n",
+    "if mass_ratio is not None:\n",
+    "    annotation += f\"\\n$q$ = {mass_ratio:.3f}\"\n",
+    "if separation is not None:\n",
+    "    annotation += f\"\\n$s$ = {separation:.2f}\"\n",
+    "if rho is not None:\n",
+    "    annotation += f\"\\n$\\\\rho$ = {rho:.3f}\"\n",
+    "if alpha is not None:\n",
+    "    annotation += f\"\\n$\\\\alpha$ = {alpha:.2f}\"\n",
+    "\n",
+    "plt.text(0.02, 0.95, annotation,\n",
+    "         transform=plt.gca().transAxes, fontsize=10, va='top', ha='left',\n",
+    "         bbox=dict(boxstyle=\"round\", facecolor=\"white\", alpha=0.8))\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "event_name = os.path.splitext(os.path.basename(valid_file))[0]\n",
+    "save_path = os.path.join(save_dir, f\"{event_name}.png\")\n",
+    "plt.savefig(save_path)\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "e62106e8",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import os\n",
+    "import random\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "usbl_dir = 'downloaded_file/microlensing_lightcurves/USBL'\n",
+    "\n",
+    "all_files = [f for f in os.listdir(usbl_dir) if f.endswith('.npz')]\n",
+    "if not all_files:\n",
+    "    raise FileNotFoundError(\"No .npz files found in USBL folder.\")\n",
+    "\n",
+    "valid_file = None\n",
+    "while all_files:\n",
+    "    candidate = random.choice(all_files)\n",
+    "    file_path = os.path.join(usbl_dir, candidate)\n",
+    "    data = np.load(file_path)\n",
+    "\n",
+    "    try:\n",
+    "        t0 = float(data['t0'])\n",
+    "        tE = float(data['tE'])\n",
+    "        u0 = float(data['u0'])\n",
+    "    except KeyError:\n",
+    "        all_files.remove(candidate)\n",
+    "        continue\n",
+    "\n",
+    "    filters = ['u', 'g', 'r', 'i', 'z', 'y']\n",
+    "    zoom_range = 3 * tE\n",
+    "    total_points = 0\n",
+    "    filter_count = 0\n",
+    "\n",
+    "    for f in filters:\n",
+    "        try:\n",
+    "            t = data[f\"{f}_time\"] - t0\n",
+    "            mask = (t >= -zoom_range) & (t <= zoom_range)\n",
+    "            if np.sum(mask) > 0:\n",
+    "                filter_count += 1\n",
+    "                total_points += np.sum(mask)\n",
+    "        except KeyError:\n",
+    "            continue\n",
+    "\n",
+    "    if total_points >= 20 and filter_count >= 3:\n",
+    "        valid_file = file_path\n",
+    "        break\n",
+    "    else:\n",
+    "        all_files.remove(candidate)\n",
+    "\n",
+    "if valid_file is None:\n",
+    "    raise RuntimeError(\"No valid USBL event found.\")\n",
+    "\n",
+    "# --- Plotting ---\n",
+    "data = np.load(valid_file)\n",
+    "t0 = float(data['t0'])\n",
+    "tE = float(data['tE'])\n",
+    "u0 = float(data['u0'])\n",
+    "zoom_range = 3 * tE\n",
+    "\n",
+    "mass_ratio = data.get('mass_ratio', None)\n",
+    "separation = data.get('separation', None)\n",
+    "rho = data.get('rho', None)\n",
+    "alpha = data.get('alpha', None)\n",
+    "\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "for f in filters:\n",
+    "    try:\n",
+    "        t = data[f\"{f}_time\"] - t0\n",
+    "        mag = data[f\"{f}_mag\"]\n",
+    "        err_mag = data[f\"{f}_err_mag\"]\n",
+    "        mask = (t >= -zoom_range) & (t <= zoom_range)\n",
+    "        if np.sum(mask) == 0:\n",
+    "            continue\n",
+    "        plt.errorbar(t[mask], mag[mask], yerr=err_mag[mask], fmt='.', label=f\"{f}-band\")\n",
+    "    except KeyError:\n",
+    "        continue\n",
+    "\n",
+    "plt.axvline(0, color='gray', linestyle='--', label='$t_0$')\n",
+    "plt.xlabel(\"Time [days relative to $t_0$]\")\n",
+    "plt.ylabel(\"Magnitude\")\n",
+    "plt.title(f\"Random USBL Event: {os.path.basename(valid_file)}\")\n",
+    "plt.gca().invert_yaxis()\n",
+    "plt.grid(True)\n",
+    "plt.legend()\n",
+    "\n",
+    "# --- Annotation box with USBL parameters ---\n",
+    "annotation = f\"$t_E$ = {tE:.2f} d\\n$u_0$ = {u0:.3f}\"\n",
+    "if mass_ratio is not None:\n",
+    "    annotation += f\"\\n$q$ = {mass_ratio:.3f}\"\n",
+    "if separation is not None:\n",
+    "    annotation += f\"\\n$s$ = {separation:.2f}\"\n",
+    "if rho is not None:\n",
+    "    annotation += f\"\\n$\\\\rho$ = {rho:.3f}\"\n",
+    "if alpha is not None:\n",
+    "    annotation += f\"\\n$\\\\alpha$ = {alpha:.2f}\"\n",
+    "\n",
+    "plt.text(0.02, 0.95, annotation,\n",
+    "         transform=plt.gca().transAxes, fontsize=10, va='top', ha='left',\n",
+    "         bbox=dict(boxstyle=\"round\", facecolor=\"white\", alpha=0.8))\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.savefig(\"random_usbl_lightcurve.png\")\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fcb87528",
+   "metadata": {},
+   "source": [
+    "> #### 5. Microlensing Lightcurve Data Conversion and Organization for running fits on RT Model \n",
+    "\n",
+    "This section of the workflow focuses on processing microlensing lightcurve data stored in `.npz` format. The purpose is to extract photometric data across multiple filters, convert galactic coordinates to equatorial coordinates, and organize the output data into structured folders with accompanying metadata.\n",
+    "\n",
+    "---\n",
+    "\n",
+    "###  **Overview of the Workflow**\n",
+    "\n",
+    "- The input consists of multiple microlensing event models, specifically `FSPL`, `PSPL`, and `USBL`, each contained within its respective folder.\n",
+    "- Each folder contains `.npz` files, where each file represents a distinct microlensing event with photometric data across several filters: `u`, `g`, `r`, `i`, `z`, and `y`.\n",
+    "- The goal is to parse these files, convert the time series data into a human-readable `.dat` format, and write the sky coordinates for each event alongside the data.\n",
+    "\n",
+    "---\n",
+    "\n",
+    "###  **Coordinate Conversion**\n",
+    "\n",
+    "- The script uses galactic coordinates `(l = 0.5°, b = -1.25°)` as input.\n",
+    "- These are converted into equatorial coordinates `(Right Ascension, Declination)` using Astropy's coordinate transformation tools.\n",
+    "- The resulting RA and Dec are formatted in a standardized string format:  \n",
+    "`HH:MM:SS.S +DD:MM:SS.S`\n",
+    "- This coordinate information is saved into a file named `event.coordinates` for each event, ensuring that each dataset includes its precise sky position.\n",
+    "\n",
+    "---\n",
+    "\n",
+    "###  **Directory and File Structure**\n",
+    "\n",
+    "- The output is organized into a main `output` folder within the parent directory.\n",
+    "- For each event (identified by its filename), a subfolder is created following the structure:  \n",
+    "`output/EventName/Data/`\n",
+    "- Inside each `Data` folder:\n",
+    "  - A `event.coordinates` file contains the RA and Dec.\n",
+    "  - Separate `.dat` files exist for each photometric filter (`u`, `g`, `r`, `i`, `z`, `y`), named with the convention:  \n",
+    "  `RubinFilterband.dat`\n",
+    "\n",
+    "---\n",
+    "\n",
+    "###  **Time Conversion**\n",
+    "\n",
+    "- The time data in the `.npz` files is originally in Julian Date (JD).\n",
+    "- To simplify, the script converts it to a relative Heliocentric Julian Date (HJD) by subtracting 2450000 from all time entries.\n",
+    "\n",
+    "---\n",
+    "\n",
+    "###  **Data Format and File Contents**\n",
+    "\n",
+    "- Each output `.dat` file contains three columns:\n",
+    "  1. **Magnitude (Mag)** – The observed brightness in the specific filter.\n",
+    "  2. **Magnitude Error (err)** – The uncertainty associated with each magnitude measurement.\n",
+    "  3. **Time (HJD)** – The observation time in modified Heliocentric Julian Date.\n",
+    "\n",
+    "- A header line `# Mag err HJD` is included at the top of each file to indicate the column meanings.\n",
+    "\n",
+    "---\n",
+    "\n",
+    "###  **Error Handling and Robustness**\n",
+    "\n",
+    "- The script is robust against missing data:\n",
+    "  - If a specific filter's data is not present in a `.npz` file, the script detects it and prints a warning but continues processing the remaining filters.\n",
+    "  - General exceptions such as failed file reads are caught and logged with an error message, ensuring that one problematic file does not halt the entire process.\n",
+    "\n",
+    "---\n",
+    "\n",
+    "###  **Logging and Console Output**\n",
+    "\n",
+    "- Throughout execution, the script provides informative logs:\n",
+    "  - It announces how many files are being processed for each event type.\n",
+    "  - It confirms when coordinate files and photometric `.dat` files are successfully saved.\n",
+    "  - It reports missing filters or any errors encountered during processing.\n",
+    "\n",
+    "---\n",
+    "\n",
+    "###  **Final Directory Example**\n",
+    "\n",
+    "At the end of execution, the output folder looks like this:\n",
+    "\n",
+    "```\n",
+    "microlensing_lightcurves/\n",
+    "├── FSPL/\n",
+    "│   └── *.npz\n",
+    "├── PSPL/\n",
+    "│   └── *.npz\n",
+    "├── USBL/\n",
+    "│   └── *.npz\n",
+    "└── output/\n",
+    "    ├── Event1/\n",
+    "    │   └── Data/\n",
+    "    │       ├── Rubinu.dat\n",
+    "    │       ├── Rubing.dat\n",
+    "    │       ├── ...\n",
+    "    │       └── event.coordinates\n",
+    "    └── Event2/\n",
+    "        └── Data/\n",
+    "            ├── ...\n",
+    "```\n",
+    "\n",
+    "---\n",
+    "\n",
+    "This process converts the microlensing lightcurve data for the RTModel runs by:\n",
+    "\n",
+    "- Converting raw `.npz` datasets into clean, readable `.dat` files.\n",
+    "- Including sky position metadata with each dataset.\n",
+    "- Structuring the outputs in an organized folder hierarchy for easy access and further scientific analysis."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "83db01be",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Event Types:   0%|          | 0/3 [00:00<?, ?it/s]"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Event Types:  33%|███▎      | 1/3 [25:25<50:50, 1525.40s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "FSPL complete.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Event Types:  67%|██████▋   | 2/3 [1:03:17<32:44, 1964.52s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PSPL complete.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Event Types: 100%|██████████| 3/3 [1:16:07<00:00, 1522.64s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "USBL complete.\n",
+      "All folders processed successfully.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "import os\n",
+    "from glob import glob\n",
+    "from astropy.coordinates import SkyCoord\n",
+    "import astropy.units as u\n",
+    "from tqdm import tqdm\n",
+    "\n",
+    "# Set the parent directory path where FSPL, PSPL, USBL folders are located\n",
+    "parent_dir = r\"C:\\Users\\Meet\\OneDrive\\Desktop\\Summer 2025\\RTModel\\jupyter\\downloaded_file\\microlensing_lightcurves\"  # <-- Update your path if needed\n",
+    "\n",
+    "# The microlensing event types (folders)\n",
+    "event_types = ['FSPL', 'PSPL', 'USBL']\n",
+    "\n",
+    "# The filters\n",
+    "filters = ['u', 'g', 'r', 'i', 'z', 'y']\n",
+    "\n",
+    "# Galactic coordinates (example)\n",
+    "galactic_l = 0.5   # degrees\n",
+    "galactic_b = -1.25 # degrees\n",
+    "\n",
+    "# Convert Galactic to Equatorial (RA, Dec)\n",
+    "coord = SkyCoord(l=galactic_l * u.degree, b=galactic_b * u.degree, frame='galactic')\n",
+    "ra_str = coord.icrs.ra.to_string(unit=u.hour, sep=':', precision=1, pad=True)\n",
+    "dec_str = coord.icrs.dec.to_string(unit=u.deg, sep=':', precision=1, alwayssign=True, pad=True)\n",
+    "coordinate_string = f\"{ra_str} {dec_str}\"\n",
+    "\n",
+    "# Output directory\n",
+    "output_dir = os.path.join(parent_dir, 'output')\n",
+    "os.makedirs(output_dir, exist_ok=True)\n",
+    "\n",
+    "# Loop over each event type folder with progress bar\n",
+    "for event_type in tqdm(event_types, desc='Event Types'):\n",
+    "    folder_path = os.path.join(parent_dir, event_type)\n",
+    "    npz_files = glob(os.path.join(folder_path, '*.npz'))\n",
+    "\n",
+    "    # tqdm over files in event type\n",
+    "    for file_path in tqdm(npz_files, desc=f'{event_type} Files', leave=False):\n",
+    "        try:\n",
+    "            data = np.load(file_path)\n",
+    "            filename_base = os.path.splitext(os.path.basename(file_path))[0]\n",
+    "\n",
+    "            # Create event-specific directory: output/EventName/Data\n",
+    "            event_output_dir = os.path.join(output_dir, filename_base, 'Data')\n",
+    "            os.makedirs(event_output_dir, exist_ok=True)\n",
+    "\n",
+    "            # Write the coordinates file\n",
+    "            coord_file = os.path.join(event_output_dir, 'event.coordinates')\n",
+    "            with open(coord_file, 'w') as cf:\n",
+    "                cf.write(coordinate_string + '\\n')\n",
+    "\n",
+    "            # Process each filter\n",
+    "            for f in filters:\n",
+    "                try:\n",
+    "                    mag = data[f'{f}_mag']\n",
+    "                    err_mag = data[f'{f}_err_mag']\n",
+    "                    time = data[f'{f}_time'] - 2450000  # Apply time correction\n",
+    "\n",
+    "                    # Stack as columns: Mag, Error, HJD\n",
+    "                    final_array = np.column_stack([mag, err_mag, time])\n",
+    "\n",
+    "                    # Output filename\n",
+    "                    output_file = os.path.join(\n",
+    "                        event_output_dir, f\"Rubin{f}band.dat\"\n",
+    "                    )\n",
+    "\n",
+    "                    # Save .dat file\n",
+    "                    np.savetxt(\n",
+    "                        output_file,\n",
+    "                        final_array,\n",
+    "                        fmt='%.6f',\n",
+    "                        header='Mag err HJD',\n",
+    "                        comments='# '\n",
+    "                    )\n",
+    "\n",
+    "                except KeyError:\n",
+    "                    continue  # If filter data is missing, skip\n",
+    "\n",
+    "        except Exception:\n",
+    "            continue  # Skip file if error reading\n",
+    "\n",
+    "    print(f'{event_type} complete.')\n",
+    "\n",
+    "print('All folders processed successfully.')\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0a57d4ba",
+   "metadata": {},
+   "source": [
+    "> ##### 5.1 Now we will tar compress this incase someone just wants to use the models on a cluster"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "f73a860c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Compress with maximum efficiency\n",
+    "!tar -cJf output.tar.xz downloaded_file/microlensing_lightcurves/output"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9fe37301",
+   "metadata": {},
+   "source": [
+    "> #### 6. Import RT Model to start working with the model files"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "4f631242",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import RTModel"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2deb84ab",
+   "metadata": {},
+   "source": [
+    "> #### 7. Test the number of processors available to use for RTModel"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "fde4043c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "*********************\n",
+      "****   RTModel   ****\n",
+      "*********************\n",
+      "Number of processors: 16\n"
+     ]
+    }
+   ],
+   "source": [
+    "rtm = RTModel.RTModel()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f70938f6",
+   "metadata": {},
+   "source": [
+    "> #### 8. Run the event model for a sample event to check the fits"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f98c50e0",
+   "metadata": {},
+   "source": [
+    "> #### 8.1 Set the eventname "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "c4fefe10",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "eventname = r\"C:\\Users\\Meet\\OneDrive\\Desktop\\Summer 2025\\RTModel\\jupyter\\downloaded_file\\microlensing_lightcurves\\output\\FSPL_7\" \n",
+    "rtm.set_event(eventname)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "811904d9",
+   "metadata": {},
+   "source": [
+    "> #### 8.2 Run the event "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a63f91fc",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "o Sat Jun 28 23:21:52 2025\n",
+      "- Analyzing event:  C:\\Users\\Meet\\OneDrive\\Desktop\\Summer 2025\\RTModel\\jupyter\\downloaded_file\\microlensing_lightcurves\\output\\FSPL_7\n",
+      "- Launching: Reader\n",
+      "  Pre-processing data...\n",
+      "  OK\n",
+      "o Sat Jun 28 23:21:53 2025\n",
+      "- Launching: InitCond\n",
+      "  Setting initial conditions...\n",
+      "Peaks:  11964.7500  13082.4700  \n",
+      "  OK\n",
+      "o Sat Jun 28 23:21:53 2025\n",
+      "- Single-lens-Single-source fits\n",
+      "Fits completed:  53%|\u001b[32m█████▎    \u001b[0m| 64/120 [40:19<35:16, 37.80s/it]  "
+     ]
+    },
+    {
+     "ename": "KeyboardInterrupt",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
+      "\u001b[31mKeyboardInterrupt\u001b[39m                         Traceback (most recent call last)",
+      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[19]\u001b[39m\u001b[32m, line 1\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m1\u001b[39m \u001b[43mrtm\u001b[49m\u001b[43m.\u001b[49m\u001b[43mrun\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~\\OneDrive\\Desktop\\Summer 2025\\RTModel\\RTModel\\RTModel.py:453\u001b[39m, in \u001b[36mRTModel.run\u001b[39m\u001b[34m(self, event, cleanup)\u001b[39m\n\u001b[32m    451\u001b[39m \u001b[38;5;28;01melif\u001b[39;00m phase%\u001b[32m2\u001b[39m == \u001b[32m1\u001b[39m:\n\u001b[32m    452\u001b[39m     \u001b[38;5;28;01mif\u001b[39;00m(\u001b[38;5;28mself\u001b[39m.InitCond_modelcategories == \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m.modelcodes[phase//\u001b[32m2\u001b[39m-\u001b[32m1\u001b[39m] \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m.InitCond_modelcategories):\n\u001b[32m--> \u001b[39m\u001b[32m453\u001b[39m         \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mlaunch_fits\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mmodelcodes\u001b[49m\u001b[43m[\u001b[49m\u001b[43mphase\u001b[49m\u001b[43m/\u001b[49m\u001b[43m/\u001b[49m\u001b[32;43m2\u001b[39;49m\u001b[43m-\u001b[49m\u001b[32;43m1\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m \n\u001b[32m    454\u001b[39m         \u001b[38;5;28mprint\u001b[39m(\u001b[33m\"\u001b[39m\u001b[33mo \u001b[39m\u001b[33m\"\u001b[39m + time.asctime())\n\u001b[32m    455\u001b[39m     phase += \u001b[32m1\u001b[39m            \n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~\\OneDrive\\Desktop\\Summer 2025\\RTModel\\RTModel\\RTModel.py:351\u001b[39m, in \u001b[36mRTModel.launch_fits\u001b[39m\u001b[34m(self, modelcode)\u001b[39m\n\u001b[32m    349\u001b[39m         pbar.update(finitcond - \u001b[38;5;28mmax\u001b[39m(finitcondold,\u001b[32m0\u001b[39m))\n\u001b[32m    350\u001b[39m         finitcondold =finitcond\n\u001b[32m--> \u001b[39m\u001b[32m351\u001b[39m     \u001b[43mtime\u001b[49m\u001b[43m.\u001b[49m\u001b[43msleep\u001b[49m\u001b[43m(\u001b[49m\u001b[32;43m0.1\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[32m    352\u001b[39m pbar.close()\n\u001b[32m    353\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m(crashes>\u001b[32m0\u001b[39m):\n",
+      "\u001b[31mKeyboardInterrupt\u001b[39m: "
+     ]
+    }
+   ],
+   "source": [
+    "rtm.run()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8105f7a1",
+   "metadata": {},
+   "source": [
+    "> #### 9. Run the model looping through all the possible events with atleast data files for 3 filters and 20 points"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f1b01589",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Skipping FSPL_10000 - only 1 filters available\n",
+      "Skipping FSPL_100000 - only 2 filters available\n",
+      "Skipping FSPL_100010 - only 1 filters available\n",
+      "Skipping FSPL_100018 - only 1 filters available\n",
+      "Skipping FSPL_100019 - only 1 filters available\n",
+      "Skipping FSPL_10002 - only 2 filters available\n",
+      "Skipping FSPL_100023 - only 1 filters available\n",
+      "Skipping FSPL_100028 - only 2 filters available\n",
+      "Skipping FSPL_100035 - only 1 filters available\n",
+      "Skipping FSPL_100042 - only 1 filters available\n",
+      "Skipping FSPL_100047 - only 1 filters available\n",
+      "Processing event: FSPL_100051 with 6 filters\n",
+      "o Sun Jun 29 00:17:13 2025\n",
+      "- Analyzing event:  C:\\Users\\Meet\\OneDrive\\Desktop\\Summer 2025\\RTModel\\jupyter\\downloaded_file\\microlensing_lightcurves\\output\\FSPL_100051\n",
+      "- Launching: Reader\n",
+      "  Pre-processing data...\n",
+      "  OK\n",
+      "o Sun Jun 29 00:17:13 2025\n",
+      "- Launching: InitCond\n",
+      "  Setting initial conditions...\n",
+      "Peaks:  13082.7800  11179.8400  \n",
+      "  OK\n",
+      "o Sun Jun 29 00:17:14 2025\n",
+      "- Single-lens-Single-source fits\n"
+     ]
+    },
+    {
+     "ename": "KeyboardInterrupt",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
+      "\u001b[31mKeyboardInterrupt\u001b[39m                         Traceback (most recent call last)",
+      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[21]\u001b[39m\u001b[32m, line 26\u001b[39m\n\u001b[32m     23\u001b[39m     rtm.set_event(event_folder)\n\u001b[32m     25\u001b[39m     \u001b[38;5;66;03m# Run the model for this event\u001b[39;00m\n\u001b[32m---> \u001b[39m\u001b[32m26\u001b[39m     \u001b[43mrtm\u001b[49m\u001b[43m.\u001b[49m\u001b[43mrun\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m     28\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m     29\u001b[39m     \u001b[38;5;28mprint\u001b[39m(\u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mSkipping \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mos.path.basename(event_folder)\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m - only \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mlen\u001b[39m(dat_files)\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m filters available\u001b[39m\u001b[33m\"\u001b[39m)\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~\\OneDrive\\Desktop\\Summer 2025\\RTModel\\RTModel\\RTModel.py:453\u001b[39m, in \u001b[36mRTModel.run\u001b[39m\u001b[34m(self, event, cleanup)\u001b[39m\n\u001b[32m    451\u001b[39m \u001b[38;5;28;01melif\u001b[39;00m phase%\u001b[32m2\u001b[39m == \u001b[32m1\u001b[39m:\n\u001b[32m    452\u001b[39m     \u001b[38;5;28;01mif\u001b[39;00m(\u001b[38;5;28mself\u001b[39m.InitCond_modelcategories == \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m.modelcodes[phase//\u001b[32m2\u001b[39m-\u001b[32m1\u001b[39m] \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m.InitCond_modelcategories):\n\u001b[32m--> \u001b[39m\u001b[32m453\u001b[39m         \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mlaunch_fits\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mmodelcodes\u001b[49m\u001b[43m[\u001b[49m\u001b[43mphase\u001b[49m\u001b[43m/\u001b[49m\u001b[43m/\u001b[49m\u001b[32;43m2\u001b[39;49m\u001b[43m-\u001b[49m\u001b[32;43m1\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m \n\u001b[32m    454\u001b[39m         \u001b[38;5;28mprint\u001b[39m(\u001b[33m\"\u001b[39m\u001b[33mo \u001b[39m\u001b[33m\"\u001b[39m + time.asctime())\n\u001b[32m    455\u001b[39m     phase += \u001b[32m1\u001b[39m            \n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~\\OneDrive\\Desktop\\Summer 2025\\RTModel\\RTModel\\RTModel.py:351\u001b[39m, in \u001b[36mRTModel.launch_fits\u001b[39m\u001b[34m(self, modelcode)\u001b[39m\n\u001b[32m    349\u001b[39m         pbar.update(finitcond - \u001b[38;5;28mmax\u001b[39m(finitcondold,\u001b[32m0\u001b[39m))\n\u001b[32m    350\u001b[39m         finitcondold =finitcond\n\u001b[32m--> \u001b[39m\u001b[32m351\u001b[39m     \u001b[43mtime\u001b[49m\u001b[43m.\u001b[49m\u001b[43msleep\u001b[49m\u001b[43m(\u001b[49m\u001b[32;43m0.1\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[32m    352\u001b[39m pbar.close()\n\u001b[32m    353\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m(crashes>\u001b[32m0\u001b[39m):\n",
+      "\u001b[31mKeyboardInterrupt\u001b[39m: "
+     ]
+    }
+   ],
+   "source": [
+    "import os\n",
+    "from glob import glob\n",
+    "\n",
+    "# Parent output directory\n",
+    "output_dir = r\"C:\\Users\\Meet\\OneDrive\\Desktop\\Summer 2025\\RTModel\\jupyter\\downloaded_file\\microlensing_lightcurves\\output\"\n",
+    "\n",
+    "# Get all event folders inside output (e.g., FSPL_7, PSPL_3, etc.)\n",
+    "event_folders = [os.path.join(output_dir, d) for d in os.listdir(output_dir) if os.path.isdir(os.path.join(output_dir, d))]\n",
+    "\n",
+    "# Loop through each event\n",
+    "for event_folder in event_folders:\n",
+    "    data_folder = os.path.join(event_folder, 'Data')\n",
+    "\n",
+    "    if os.path.exists(data_folder):\n",
+    "        # Find all .dat files in Data folder\n",
+    "        dat_files = glob(os.path.join(data_folder, 'Rubin*band.dat'))\n",
+    "\n",
+    "        # Check if at least 3 filters are present\n",
+    "        if len(dat_files) >= 3:\n",
+    "            total_points = 0\n",
+    "\n",
+    "            # Count the total number of data points across all filters\n",
+    "            for file in dat_files:\n",
+    "                with open(file, 'r') as f:\n",
+    "                    # Skip header lines and count data lines\n",
+    "                    lines = [line for line in f if line.strip() and not line.startswith(\"#\")]\n",
+    "                    total_points += len(lines)\n",
+    "\n",
+    "            if total_points >= 20:\n",
+    "                print(f\"Processing event: {os.path.basename(event_folder)} with {len(dat_files)} filters and {total_points} data points\")\n",
+    "\n",
+    "                # Loop through the events in the output directory\n",
+    "                rtm.set_event(event_folder)\n",
+    "\n",
+    "                # Run the model for this event\n",
+    "                rtm.run()\n",
+    "            else:\n",
+    "                print(f\"Skipping {os.path.basename(event_folder)} - only {total_points} data points available\")\n",
+    "        else:\n",
+    "            print(f\"Skipping {os.path.basename(event_folder)} - only {len(dat_files)} filters available\")\n",
+    "    else:\n",
+    "        print(f\"Skipping {os.path.basename(event_folder)} - Data folder missing\")\n",
+    "\n",
+    "print(\"Finished processing all valid events.\")\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": ".venv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/group_4_ampel_filter/output.tar.xz b/group_4_ampel_filter/output.tar.xz
new file mode 100644
index 0000000..42403fa
--- /dev/null
+++ b/group_4_ampel_filter/output.tar.xz
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:0fd7c06b68250b2f07f973271d70b0bcf60d42c539db064f536ab1e86d7f91ed
+size 116376132

From 6645e6de2137eb3f242faa46766c5b39e3c339bb Mon Sep 17 00:00:00 2001
From: Meet Vyas <87984759+Meet-Vyas-Dev@users.noreply.github.com>
Date: Fri, 18 Jul 2025 18:17:03 +0530
Subject: [PATCH 2/2] Clarify lightcurve generation and model run criteria

Updated markdown cell descriptions to specify magnification, time window, and minimum data points for FSPL, PSPL, and USBL event lightcurve generation. Also clarified the model run criteria to require at least 20 points across all filters.
---
 group_4_ampel_filter/RT_Model_simulations.ipynb | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/group_4_ampel_filter/RT_Model_simulations.ipynb b/group_4_ampel_filter/RT_Model_simulations.ipynb
index d81a40c..492c2d7 100644
--- a/group_4_ampel_filter/RT_Model_simulations.ipynb
+++ b/group_4_ampel_filter/RT_Model_simulations.ipynb
@@ -3102,7 +3102,7 @@
    "id": "7a9b0111",
    "metadata": {},
    "source": [
-    "> #### 4.3.1 We first generate a random lightcurve for the FSPL events"
+    " > #### 4.3.1 We first generate a random lightcurve for the FSPL events with a magnification and time window of $3*tE$ and a minimum of 10 points across all bands "
    ]
   },
   {
@@ -3263,7 +3263,7 @@
    "id": "a423b01f",
    "metadata": {},
    "source": [
-    "> #### 4.3.2 Now we generate a random lightcurve for the PSPL events"
+    "> #### 4.3.2 Now we generate a random lightcurve for the PSPL events with a magnification and time window of $3*tE$ and a minimum of 10 points across all bands "
    ]
   },
   {
@@ -3382,7 +3382,7 @@
    "id": "f951868e",
    "metadata": {},
    "source": [
-    "> #### 4.3.3 Nopw we generate a random lightcurve for the USBL events"
+    "> #### 4.3.3 Nopw we generate a random lightcurve for the USBL events with a magnification and time window of $3*tE$ and a minimum of 10 points across all bands "
    ]
   },
   {
@@ -4039,7 +4039,7 @@
    "id": "8105f7a1",
    "metadata": {},
    "source": [
-    "> #### 9. Run the model looping through all the possible events with atleast data files for 3 filters and 20 points"
+    "> #### 9. Run the model looping through all the possible events with atleast data files for 3 filters and 20 points across all filters "
    ]
   },
   {