light-curve-analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "d9b76051-958e-4103-9b98-a05778d340d1",
   "metadata": {},
   "source": [
    "Inspect the sizes and visualize light curves from the LSST and Kepler RR Lyrae datasets."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a71e8780-022a-4bd3-901c-5ccdf75bc929",
   "metadata": {},
   "source": [
    "#### Import libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "30e4e043-f1a3-4773-aa2f-24f5f049dfe0",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "import lcanalyzer.views as views"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "01f08b64-6d72-402b-b586-8bd0b148bd6d",
   "metadata": {},
   "source": [
    "#### Read the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "73cfb12f-d7e8-4091-88b4-6730fc20bf46",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['kepler_RRLyr.csv', 'lsst_RRLyr.pkl', 'lsst_RRLyr_protocol_4.pkl']"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "os.listdir(\"data\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "9b5eb5c7-a616-4bc9-a157-da6c588422a0",
   "metadata": {},
   "outputs": [],
   "source": [
    "lc_datasets = {}\n",
    "lc_datasets[\"lsst\"] = pd.read_pickle(os.path.join(\"data\", \"lsst_RRLyr.pkl\"))\n",
    "lc_datasets[\"kepler\"] = pd.read_csv(os.path.join(\"data\", \"kepler_RRLyr.csv\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c7ac836c-39b7-4dc5-9160-612b98e8be1e",
   "metadata": {},
   "source": [
    "#### Inspecting the Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "a00a7ccb-3c96-4696-94c5-e93862aeb12e",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>band</th>\n",
       "      <th>ccdVisitId</th>\n",
       "      <th>coord_ra</th>\n",
       "      <th>coord_dec</th>\n",
       "      <th>objectId</th>\n",
       "      <th>psfFlux</th>\n",
       "      <th>psfFluxErr</th>\n",
       "      <th>psfMag</th>\n",
       "      <th>ccdVisitId2</th>\n",
       "      <th>band2</th>\n",
       "      <th>expMidptMJD</th>\n",
       "      <th>zeroPoint</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>y</td>\n",
       "      <td>1032263018</td>\n",
       "      <td>62.462569</td>\n",
       "      <td>-44.11336</td>\n",
       "      <td>1251384969897480052</td>\n",
       "      <td>-515.183603</td>\n",
       "      <td>1697.218490</td>\n",
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       "      <td>1032263018</td>\n",
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       "      <td>61100.069706</td>\n",
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       "      <th>1</th>\n",
       "      <td>y</td>\n",
       "      <td>1033987172</td>\n",
       "      <td>62.462569</td>\n",
       "      <td>-44.11336</td>\n",
       "      <td>1251384969897480052</td>\n",
       "      <td>3151.738459</td>\n",
       "      <td>1686.955775</td>\n",
       "      <td>22.653625</td>\n",
       "      <td>1033987172</td>\n",
       "      <td>y</td>\n",
       "      <td>61102.068464</td>\n",
       "      <td>30.606100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>u</td>\n",
       "      <td>675163080</td>\n",
       "      <td>62.462569</td>\n",
       "      <td>-44.11336</td>\n",
       "      <td>1251384969897480052</td>\n",
       "      <td>183.449123</td>\n",
       "      <td>209.242045</td>\n",
       "      <td>25.741211</td>\n",
       "      <td>675163080</td>\n",
       "      <td>u</td>\n",
       "      <td>60582.247144</td>\n",
       "      <td>30.469101</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>y</td>\n",
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       "      <td>60215.203585</td>\n",
       "      <td>30.612801</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>u</td>\n",
       "      <td>466722002</td>\n",
       "      <td>62.462569</td>\n",
       "      <td>-44.11336</td>\n",
       "      <td>1251384969897480052</td>\n",
       "      <td>382.472233</td>\n",
       "      <td>278.926670</td>\n",
       "      <td>24.943500</td>\n",
       "      <td>466722002</td>\n",
       "      <td>u</td>\n",
       "      <td>60261.078221</td>\n",
       "      <td>30.461201</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  band  ccdVisitId   coord_ra  coord_dec             objectId      psfFlux  \\\n",
       "0    y  1032263018  62.462569  -44.11336  1251384969897480052  -515.183603   \n",
       "1    y  1033987172  62.462569  -44.11336  1251384969897480052  3151.738459   \n",
       "2    u   675163080  62.462569  -44.11336  1251384969897480052   183.449123   \n",
       "3    y   443055067  62.462569  -44.11336  1251384969897480052  -704.848327   \n",
       "4    u   466722002  62.462569  -44.11336  1251384969897480052   382.472233   \n",
       "\n",
       "    psfFluxErr     psfMag  ccdVisitId2 band2   expMidptMJD  zeroPoint  \n",
       "0  1697.218490        NaN   1032263018     y  61100.069706  30.602301  \n",
       "1  1686.955775  22.653625   1033987172     y  61102.068464  30.606100  \n",
       "2   209.242045  25.741211    675163080     u  60582.247144  30.469101  \n",
       "3  1624.400086        NaN    443055067     y  60215.203585  30.612801  \n",
       "4   278.926670  24.943500    466722002     u  60261.078221  30.461201  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Inspect datasets\n",
    "lc_datasets[\"lsst\"].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "c92d2ea9-afef-45eb-b995-157acced78a1",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>time</th>\n",
       "      <th>flux</th>\n",
       "      <th>flux_err</th>\n",
       "      <th>quality</th>\n",
       "      <th>timecorr</th>\n",
       "      <th>centroid_col</th>\n",
       "      <th>centroid_row</th>\n",
       "      <th>cadenceno</th>\n",
       "      <th>sap_flux</th>\n",
       "      <th>sap_flux_err</th>\n",
       "      <th>...</th>\n",
       "      <th>psf_centr1</th>\n",
       "      <th>psf_centr1_err</th>\n",
       "      <th>psf_centr2</th>\n",
       "      <th>psf_centr2_err</th>\n",
       "      <th>mom_centr1</th>\n",
       "      <th>mom_centr1_err</th>\n",
       "      <th>mom_centr2</th>\n",
       "      <th>mom_centr2_err</th>\n",
       "      <th>pos_corr1</th>\n",
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       "      <td>128</td>\n",
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       "      <td>1105</td>\n",
       "      <td>10129629.0</td>\n",
       "      <td>79.186980</td>\n",
       "      <td>...</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>653.372470</td>\n",
       "      <td>0.000006</td>\n",
       "      <td>51.053028</td>\n",
       "      <td>0.000063</td>\n",
       "      <td>0.011782</td>\n",
       "      <td>-0.010195</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>131.532839</td>\n",
       "      <td>10013518.0</td>\n",
       "      <td>78.233770</td>\n",
       "      <td>128</td>\n",
       "      <td>0.001411</td>\n",
       "      <td>653.372292</td>\n",
       "      <td>51.053872</td>\n",
       "      <td>1106</td>\n",
       "      <td>9949931.0</td>\n",
       "      <td>78.459984</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>653.372292</td>\n",
       "      <td>0.000006</td>\n",
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       "      <td>0.000062</td>\n",
       "      <td>0.011726</td>\n",
       "      <td>-0.010246</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>131.553273</td>\n",
       "      <td>9852474.0</td>\n",
       "      <td>77.673160</td>\n",
       "      <td>128</td>\n",
       "      <td>0.001412</td>\n",
       "      <td>653.372167</td>\n",
       "      <td>51.044559</td>\n",
       "      <td>1107</td>\n",
       "      <td>9783633.0</td>\n",
       "      <td>77.827200</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>653.372167</td>\n",
       "      <td>0.000006</td>\n",
       "      <td>51.044559</td>\n",
       "      <td>0.000062</td>\n",
       "      <td>0.011575</td>\n",
       "      <td>-0.010089</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>131.573707</td>\n",
       "      <td>9722936.0</td>\n",
       "      <td>77.109710</td>\n",
       "      <td>128</td>\n",
       "      <td>0.001413</td>\n",
       "      <td>653.371408</td>\n",
       "      <td>51.045081</td>\n",
       "      <td>1108</td>\n",
       "      <td>9651452.0</td>\n",
       "      <td>77.313590</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>653.371408</td>\n",
       "      <td>0.000006</td>\n",
       "      <td>51.045081</td>\n",
       "      <td>0.000062</td>\n",
       "      <td>0.011366</td>\n",
       "      <td>-0.009939</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>131.594142</td>\n",
       "      <td>9717073.0</td>\n",
       "      <td>77.103550</td>\n",
       "      <td>0</td>\n",
       "      <td>0.001414</td>\n",
       "      <td>653.372167</td>\n",
       "      <td>51.052828</td>\n",
       "      <td>1109</td>\n",
       "      <td>9646289.0</td>\n",
       "      <td>77.262634</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>653.372167</td>\n",
       "      <td>0.000006</td>\n",
       "      <td>51.052828</td>\n",
       "      <td>0.000062</td>\n",
       "      <td>0.011526</td>\n",
       "      <td>-0.010702</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "<p>5 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         time        flux   flux_err  quality  timecorr  centroid_col  \\\n",
       "0  131.512404  10180609.0  78.926155      128  0.001410    653.372470   \n",
       "1  131.532839  10013518.0  78.233770      128  0.001411    653.372292   \n",
       "2  131.553273   9852474.0  77.673160      128  0.001412    653.372167   \n",
       "3  131.573707   9722936.0  77.109710      128  0.001413    653.371408   \n",
       "4  131.594142   9717073.0  77.103550        0  0.001414    653.372167   \n",
       "\n",
       "   centroid_row  cadenceno    sap_flux  sap_flux_err  ...  psf_centr1  \\\n",
       "0     51.053028       1105  10129629.0     79.186980  ...         NaN   \n",
       "1     51.053872       1106   9949931.0     78.459984  ...         NaN   \n",
       "2     51.044559       1107   9783633.0     77.827200  ...         NaN   \n",
       "3     51.045081       1108   9651452.0     77.313590  ...         NaN   \n",
       "4     51.052828       1109   9646289.0     77.262634  ...         NaN   \n",
       "\n",
       "   psf_centr1_err  psf_centr2  psf_centr2_err  mom_centr1  mom_centr1_err  \\\n",
       "0             NaN         NaN             NaN  653.372470        0.000006   \n",
       "1             NaN         NaN             NaN  653.372292        0.000006   \n",
       "2             NaN         NaN             NaN  653.372167        0.000006   \n",
       "3             NaN         NaN             NaN  653.371408        0.000006   \n",
       "4             NaN         NaN             NaN  653.372167        0.000006   \n",
       "\n",
       "   mom_centr2  mom_centr2_err  pos_corr1  pos_corr2  \n",
       "0   51.053028        0.000063   0.011782  -0.010195  \n",
       "1   51.053872        0.000062   0.011726  -0.010246  \n",
       "2   51.044559        0.000062   0.011575  -0.010089  \n",
       "3   51.045081        0.000062   0.011366  -0.009939  \n",
       "4   51.052828        0.000062   0.011526  -0.010702  \n",
       "\n",
       "[5 rows x 25 columns]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lc_datasets[\"kepler\"].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "91f82988-5db9-4592-9165-8f118abc25fc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['time', 'flux', 'flux_err', 'quality', 'timecorr', 'centroid_col',\n",
       "       'centroid_row', 'cadenceno', 'sap_flux', 'sap_flux_err', 'sap_bkg',\n",
       "       'sap_bkg_err', 'pdcsap_flux', 'pdcsap_flux_err', 'sap_quality',\n",
       "       'psf_centr1', 'psf_centr1_err', 'psf_centr2', 'psf_centr2_err',\n",
       "       'mom_centr1', 'mom_centr1_err', 'mom_centr2', 'mom_centr2_err',\n",
       "       'pos_corr1', 'pos_corr2'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lc_datasets[\"kepler\"].columns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a5dba89a-2682-4ce4-b8cd-47b052997c02",
   "metadata": {},
   "source": [
    "#### Plotting the Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "5b89b156-b87f-4608-b5fd-068941175e0a",
   "metadata": {},
   "outputs": [],
   "source": [
    "plot_filter_labels = [\"u\", \"g\", \"r\", \"i\", \"z\", \"y\"]\n",
    "plot_filter_colors = {\n",
    "    \"u\": \"#56b4e9\",\n",
    "    \"g\": \"#008060\",\n",
    "    \"r\": \"#ff4000\",\n",
    "    \"i\": \"#850000\",\n",
    "    \"z\": \"#6600cc\",\n",
    "    \"y\": \"#000000\",\n",
    "}\n",
    "plot_filter_symbols = {\"u\": \"o\", \"g\": \"^\", \"r\": \"v\", \"i\": \"s\", \"z\": \"*\", \"y\": \"p\"}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "d858d326-84ee-4d9e-bc26-f3ebc345ca50",
   "metadata": {},
   "outputs": [],
   "source": [
    "LC = lc_datasets[\"lsst\"][\"objectId\"].unique()[7]\n",
    "LC = lc_datasets[\"lsst\"][lc_datasets[\"lsst\"][\"objectId\"] == LC]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "7420df38-3a80-47fd-bf59-5463cc4ce962",
   "metadata": {},
   "outputs": [],
   "source": [
    "bands = {}\n",
    "for b in plot_filter_labels:\n",
    "    bands[b] = LC[\"band\"] == b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "2da8ce06-5747-4a12-8542-c384d4097891",
   "metadata": {},
   "outputs": [],
   "source": [
    "band = \"z\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "61b05acd-fed8-43c9-8647-37c324d86d0e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "421"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(LC)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "f863da29-2755-442c-a4ba-addebd3cc26c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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w3+0ff/xxwH+eGavwwVw87AUA7eCBovA3adIkEtYw03onw18SK0mHDh1SUVFRwO9kMEKP9pDIAohK3K4EuubMqgVtCXTVglbBqFaCyMDUAj+YWgBEPm5XAl0X6qoFiE5ULegmVvYCokcgnuAGok2oqxYAHWFE1g9GZIHowANFQNdwJwOh0JU8jETWDxJZAEAk68lqdiwTjWCjagEAAPCrp6vZ8eAVwgkjsn4wIgsAiEQVFRUqLi5WS0uLXC6XYmNjFR8fr9LSUs2ZM8fs8ABJPOwFC3M6naqsrJTT6TQ7FACIGG63W4WFhSooKFBTU5NcLpekL1eza2xsVEFBgYqKiuR2u02OFOgaUxPZqqoqzZw5U8nJybLZbHruued82hsaGrRo0SINHTpUCQkJSk9P16OPPtruMZ999lmNHz9e5557rs455xx9/etf11NPPRXEs+g5krcvVVdXa+LEicrJydHEiRNVU1NjdkgAEBHOrAH71Ruxre/LyspUV1cX6tCAHjE1kT19+rTGjBmjkpISv+2LFy/W5s2b9fTTT2vv3r264447tGjRIm3atKnNYw4aNEj//d//rR07dui9995TYWGhCgsLVVlZGazT6JHq6mplZ2crJydH2dnZUZu8VVRUaPTo0dq1a5ckadeuXRo1apQqKipMjgwArK91Nbv2SmdlZWWxmh0sx9REdvr06Vq6dKmuv/56v+1/+9vfNG/ePF1xxRUaMWKEvv/972vMmDHtFlu+4oordP311+uyyy7TRRddpB/96EfKzMzU66+/HqzT6LaKigplZGTI4XBIkhwOhzIyMqIqefvq7a4zNTU1cbsLCEPcRbImasAiEoX1HNns7Gxt2rRJR48elWEY2rZtm/bt26dp06Z1an/DMPTKK6/oww8/bLcmZHNzs+rr631ewcRcpX+rra3t1JKHtbW1oQkIQLu4i2Rdubm58ng8fts8Ho9mz54d4oiAngvrRHbVqlVKT0/X0KFD1atXL+Xk5KikpKTDQuWnTp1S37591atXL82YMUOrVq3Sd7/73Tb7L1u2TAMGDPC+UlJSAn0qPpirBMCKuItkbaxmh0gU1nVkV61apTfeeEObNm3S8OHDVVVVpYULFyo5OVlTp05tc79+/frJ4XCooaFBr7zyihYvXqy0tDRdccUVfvsvWbJEixcv9r6vr68PajLbOlepo/Wqo2Gu0pkFtQPRD0Dgud1uFRcXq7y8XDabzfu95XK55Ha7VVBQoC1btqi0tJTf1TBHDVhEmrCpI2uz2bRx40bNmjVL0pfzIwcMGKCNGzdqxowZ3n7FxcU6cuSINm/e3OljFxcX6/Dhw51+4CsUdWRXrlypu+++2+9tHrvdruXLl+vOO+8Mys8ON8nJyTp27Fib7RdccIE++eSTEEYE4Ey1tbVKSkrqsN+JEyei4h/gAIIrIurIOp1OOZ1O2e2+IcbExLQ5x6ctHo9Hzc3NgQyvx5ir9G/z589vt72wsDA0gQDwiyfeAYQrU6cWNDQ0aP/+/d73NTU1cjgcGjRokIYNG6bJkyfrrrvuUkJCgoYPH65XX31VTz75pB5++GHvPnPnztWFF16oZcuWSfpyvuv48eN10UUXqbm5WS+++KKeeuopPfLIIyE/v/a0zlVqa73qaJmr5Ha79dFHH7XbZ9++fXK73dyyBEyUl5enXbt2tTkdiifeAZjB1KkF27dv15QpU87aPm/ePJWXl+v48eNasmSJ/vKXv+jkyZMaPny4vv/97+vHP/6xd2SgtTRX68NTP//5z/XHP/5RR44cUUJCgkaOHKkf/ehHXfqSDdUStVVVVW3OVerogbZIwS1LwBpqamqUlpbWZnt1dXXU/AMcQHB1JQ8Lmzmy4SRUiSy+dPnll3f44Ft7tYODyel0auvWrbryyisVFxdnSgxAuMjMzPS5i2QYhjwej0aPHq13333X5OgARIqImCOL6BGuRbqplwn4Wr16tRYtWqSCggINHDhQLpdLiYmJ+tnPfmZ2aACiFIksTBeOD75RLxM426RJk/TNb35TGzZs0MmTJyVJJ0+e1IIFC/jdsBhWZ0OkIJGF6cKpSDerrgH+8bsRObjbhEjCHFk/mCMbeuHy4BsPnwH+8bsRGSoqKlRcXKyWlha5XC7FxsYqPj5epaWlmjNnjtnhAZJ42KvbSkpKVFJSIrfbrX379pHIRqlwfvgMMBO/G9bV1upskrzvCwsLu7Q6Gw/DIlh42KubFi5cqD179ujtt982OxSYKFwfPgPMxu+GddXV1XnLVH71HyKt78vKylRXV9ep4zE9AeGCRBb4inB8+AwIB/xuWFcgV2fjYViEExJZ4CvC6eEzIJzwu2FtPR1R784Df1RHQLAxR9YPHvZCuDx8BoQbfjesq6ers3X1gb/q6mrl5+dr586dGj9+vNavX88/dtApPOzVQySyAIBI9NXV2aQvR1o7uzpbZx/4ozoCeqIreVhsiGICAAAmW716dZsj6p2Rl5enXbt2tZnI3nDDDSosLDyrOoLL5ZLb7VZBQYG2bNnSpeoIQHsYkfWDEVkAAM7W0fSE1mkEHaHeMNpD+S0AABBwHT3wN27cuIBVRwA6g6kFAACg0zqantDR9APqDSOQmFpwBlb2AgCgZ3paHQGgakEPMUcWAIDu62l1BEQ3qhYAAADT9LQ6AtBZjMj6wYgsAACAOahaAAAAgIhHIgsAAABLIpEFwojT6VRlZaWcTqfZoQAAEPZIZIEwUV1drezsbOXk5Cg7O1s1NTVmhwQAQFgjkQXCQEVFhTIyMuRwOCRJDodDGRkZqqioMDcwAADCGIksYCK3263CwkIVFBSoqalJLpdLkuRyudTY2KiCggIVFRXJ7XabHGloMcUCANAZJLKAierq6lReXi5JZy3n2Pq+rKxMdXV1oQ7NNEyxAAB0FonsGUpKSpSenq6srCyzQ0GUSExMVFZWlmw2m992m82mrKwsJSYmhjgyczDFAgDQFSSyZ1i4cKH27Nmjt99+2+xQEEXy8vLaTWTz8/NDHFHoMcUCsCamAcFsJLKAyXJzc+XxePy2eTwezZ49O8QRhR5TLADrYRoQwgGJLGCy1NRUpaSk+G1LSUlRampqiCMKPaZYANbCNCCECxJZwEStt9QPHz7st/3w4cNRc0udKRZA+GMaEMKNzfjqfTyovr5eAwYM0KlTp9S/f3+zw0EEq62tVVJSUof9Tpw4EfGjkTU1NUpLS2uzvbq6OipGp4FwxncWQqEreRgjsoCJuKX+b6mpqcrIyJDdbldcXJz3ZbfblZmZSRILhAG+sxBuYs0OAIh2eXl52rVr11kPOUnRd0t99erVeuaZZ87anpuba0I0APzhOwvhhKkFfjC1AKHELXUAVsJ3FoKNqQWAhXBLHYCV8J2FcMLUgjOUlJSopKSEpy0RctxSB2AlfGchXDC1wA+mFgAAAJiDqQUAAACIeCSyAICQczqdqqyslNPpNDsUABZGIgsACKnq6mplZ2crJydH2dnZqqmpMTskABZFIgsACJmKigplZGTI4XBIkhwOhzIyMlRRUXFWX0ZtAXTE1ES2qqpKM2fOVHJysmw2m5577jmf9oaGBi1atEhDhw5VQkKC0tPT9eijj7Z7zNLSUn3nO9/RwIEDNXDgQE2dOlVvvfVWEM8CANARt9utwsJCFRQUqKmpSS6XS5LkcrnU2NiogoICFRUVeavGMGoLoDNMTWRPnz6tMWPGqKSkxG/74sWLtXnzZj399NPau3ev7rjjDi1atEibNm1q85jbt2/XnDlztG3bNu3YsUMpKSmaNm2ajh49GqzTAAB0oK6uTuXl5ZJ01opQre/LyspUV1fXpVFbANEtbMpv2Ww2bdy4UbNmzfJuGz16tPLz83XPPfd4t40bN07Tp0/X0qVLO3Vct9utgQMHavXq1Zo7d26n9qH8FgAE3uWXX66dO3e2ubTp+PHjNWrUKJWXl8tms/n0a31fWFio0tJSxcTEhDJ0ACEUMeW3srOztWnTJh09elSGYWjbtm3at2+fpk2b1uljNDY2yul0atCgQUGMFADQkby8PNlsNr9tNptN11xzTadHbREemMcMs4V1Irtq1Sqlp6dr6NCh6tWrl3JyclRSUqJJkyZ1+hh33323kpOTNXXq1Db7NDc3q76+3ucFAAis3NxceTwev20ej0c333yzsrKy2k12s7KylJiYGMww0UnMY0Y4CPtE9o033tCmTZu0a9cuPfTQQ1q4cKFefvnlTu3/4IMPat26ddq4caN69+7dZr9ly5ZpwIAB3ldKSkqgTgEA8P+lpqYqIyNDdrtdcXFx3pfdbldmZqZSU1M7HLXNz88PcdTwh3nMCBdhO0e2qalJAwYM0MaNGzVjxgxvv+LiYh05ckSbN29u93grV67U0qVL9fLLL2v8+PHt9m1ublZzc7P3fX19vVJSUpgjCwABVlVVpWeeeeas7bm5uZo0aZJqamqUlpbW5v7V1dVKTU0NZohoh9vtVnFxMfOYEVRdmSMbG6KYuszpdMrpdMpu9x00jomJafPWVKvly5frgQceUGVlZYdJrCTFx8crPj6+R/ECCByn06mtW7fqyiuvVFxcnNnhIIAmTZrU7vSw1lHb999//6wkKSMjgyTWZJ2tPrF8+XKmgCAkTJ1a0NDQIIfD4b01UVNTI4fDoUOHDql///6aPHmy7rrrLm3fvl01NTUqLy/Xk08+qeuvv957jLlz52rJkiXe97/5zW90zz33aM2aNRoxYoSOHz+u48ePq6GhIdSnB6AbqqurNXHiROXk5GjixInMu4tCs2bNUkxMjHeKgc1mU0xMjK677jqTI0NiYiLzmBFeDBNt27bNkHTWa968eYZhGMaxY8eM+fPnG8nJyUbv3r2NSy+91HjooYcMj8fjPcbkyZO9/Q3DMIYPH+73mPfee2+n4zp16pQhyTh16lSAzhRAZ6xdu9ZISEjw+d1NSEgw1q5da3ZoCAGXy2XMnz/fkGTYbDafz0Hr+8LCQsPlcpkdalRbsWKFYbfb/f6ttdvtxsqVK80OERbXlTwsbObIhhPqyAKhdea8u7Yw7y7y1dbWKikpqcN+J06cYMTPRMxjRrBFTB1ZANGhtra23SRW+nLeXW1tbWgCgim4bW0Nnak+geCjhu+XwvZhLwDRo7OjrIzGRr68vDzt2rWrzdW/KL8VHlavXt1m9QkEX3V1tfLz87Vz506NHz9e69evj9p/QDC1wA+mFgChl5KSoiNHjrTbfujQoRBGBDNw2xpoX0VFhYqLi9XS0iKXy6XY2FjFx8ertLRUc+bMMTu8gGBqAQDLufnmm9ttv+mmm0IUCczEbWvAP7fbrcLCQhUUFKipqUkul0uS5HK51NjYqIKCAhUVFcntdpscaWgxInuGkpISlZSUyO12a9++fYzIAiHESBxadbRoAhCNoulhyK6MyJLI+sHUAsAcF110kaqrq30WQvF4PEpLS9OBAwdMjAwAzHf55Zdr586dbc4hHz9+vN566y0TIgusiFjZC0D0KSsr4wESAGgDD0OejRFZPxiRBQAA4SZapmDxsBcAAECE4WHIszG1AGGjsbFRv/3tb/XjH/9Yffr0MTscAADCDjV8fTG1wA+mFoTe9u3bNWPGDDU2NqpPnz566aWXeDoZAIAoxNQCWMptt92mKVOmqLGxUdKXI7OTJ0/WbbfdZnJkAAAgnDEi6wcjsqHR0tKiUaNGaf/+/W32ueSSS/T++++rV69eIYwMAACYhRFZWEJNTU27SawkffTRR6qpqQlRRAAAwEpIZGGaiy++WDExMe32iYmJ0cUXXxyiiAAACH9Op1OVlZVyOp1mh2I6EtkzlJSUKD09XVlZWWaHEhXq6uo6XBPa7Xarrq4uRBEBABDeqqurlZ2drZycHGVnZ0f9XUvmyPrBHNnQGTlypD788MN22/fu3RvCiAAACE8VFRUqLi5WS0uLXC6XYmNjFR8fr9LSUs2ZM8fs8AKGObKwjOLi4h61AwAQ6dxutwoLC1VQUKCmpia5XC5JksvlUmNjowoKClRUVNThXc5IxIisH4zIhk60LLcHAEB31dbWKikpqcN+J06cUGJiYggiCi5GZGEZLLcHAED7EhMTlZWVJZvN5rfdZrMpKysrIpLYrmKJWpiO5fYAAGhfXl6edu3aJX830m02m/Lz802IynxMLfCDqQUAACCcRNNUPKYWAAAARBCm4vnH1AIAAAALYCre2Zha4AdTCwB0l9Pp1NatW3XllVcqLi7O7HAAwHKYWtBNrOwFoCdYcQcAQosRWT8YkQXQVdGy4g4ABBsjsgAQIqy4AwDmYUTWD0ZkAXRWtK24AwDBxogsAIQIK+4AgHlIZAGgh/Ly8tpNZKN1xR0ACDYSWQDoodzcXHk8Hr9tHo9Hs2fPDnFEABAdSGQBoIdYcQdAKDmdTlVWVsrpdJodiulY2QsIAIrgo3XFHbfbrSNHjmjo0KGKiYmJ6hV3AARedXW18vPztXPnTo0fP17r16+P6n8sMyIL9BBF8CFJkyZN0o9+9CO9+eabev755/Xmm2/qjjvu0KRJk8wODUCEqKioUEZGhhwOhyTJ4XAoIyNDFRUV5gZmIhLZM7CyF7qKLxW04rMAIFioV9026sj6QR1ZdMTtdqu4uFjl5eWy2Ww689eo9X1hYaFKS0sVExNjYqQINj4LAIIt2upVdyUPI5H1g0QWHYm2LxW0jc8CgFC4/PLLtXPnTvlL22w2m8aPH6+33nrLhMgCjwURgCCjCD5a8VkAEArUq/avW1UL3nvvPb/bbTabevfurWHDhik+Pr5HgQHhLi8vT7t27WrzX8fR+qUSjfgsAAi23Nxc3XXXXX7borledbemFtjt9jb/VSBJcXFxys/P1//93/+pd+/ePQrQDEwtMIfVSljV1NQoLS2tzfbq6uqoLokSTfgsAAiFzMxMffDBBz7z7d1ut0aPHq13333XxMgCK+hTCzZu3KhLLrlEjz32mBwOhxwOhx577DFdeumlWrt2rR5//HFt3bpVP//5z9s9TlVVlWbOnKnk5GTZbDY999xzPu0NDQ1atGiRhg4dqoSEBKWnp+vRRx9t95gffPCBcnNzNWLECNlsNv3P//xPd04RIWbFElYUwUcrPgsAQmH16tVatGiRfvjDH3pfixYt0qpVq8wOzTTdmlrwwAMP6H//93919dVXe7dlZGRo6NChuueee/TWW2/pnHPO0Z133qmVK1e2eZzTp09rzJgxKioq8jskvnjxYm3dulVPP/20RowYob/85S+69dZblZycrGuvvdbvMRsbG5WWlqYbbrhBP/7xj7tzegixiooKFRcXq6WlRdK/yxaVlpZqzpw5JkfXvtYi+F9FEfzow2cBQLBNmjSJ2tRf0a2pBQkJCXrnnXc0cuRIn+3/+Mc/NHbsWDU1NengwYNKT09XY2Nj5wKx2bRx40bNmjXLu2306NHKz8/XPffc4902btw4TZ8+XUuXLu3wmCNGjNAdd9yhO+64o1MxtGJqQWhQtggAAHxV0KcWjBw5Ug8++KB3BE36cn7jgw8+6E1ujx49qsGDB3fn8F7Z2dnatGmTjh49KsMwtG3bNu3bt0/Tpk3r0XERHurq6lReXi5JZz0k0/q+rKxMdXV1oQ4NAGBhTqdTlZWVcjqdZoeCIOvW1IKSkhJde+21Gjp0qDIzMyVJu3fvltvt1p///GdJX855vPXWW3sU3KpVq/T9739fQ4cOVWxsrOx2u0pLSwM+rN7c3Kzm5mbv+/r6+oAeH/61li3qqC4eZYsAAJ1VXV2t/Px87dy5U+PHj9f69euZpx7BupXItj6M84c//EH79u2TJN1www0qKChQv379JEk333xzj4NbtWqV3njjDW3atEnDhw9XVVWVFi5cqOTkZE2dOrXHx2+1bNky/epXvwrY8dB5lC0CAASKlZ+5QPeEzcpeX50j29TUpAEDBmjjxo2aMWOGt19xcbGOHDmizZs3d3jMzs6R9Tcim5KSwhzZEKBsEQCgp3jmIrJ0ZY5st0ZkW+3Zs0eHDh3ymSsrqc2KAl3hdDrldDplt/tO442JiZHH4+nx8c8UHx/PAg4maS1b1FZdPJJYAEBHOvvMxfLly5muFmG6lchWV1fr+uuv1+7du33+5dO6SILb7e7UcRoaGrR//37v+5qaGjkcDg0aNEjDhg3T5MmTdddddykhIUHDhw/Xq6++qieffFIPP/ywd5+5c+fqwgsv1LJlyyRJLS0t2rNnj/e/jx49KofDob59++riiy/uzukiyChbBADoCZ65iGJGN1xzzTXGddddZ5w4ccLo27evsWfPHuO1114zLr/8cqOqqqrTx9m2bZsh6azXvHnzDMMwjGPHjhnz5883kpOTjd69exuXXnqp8dBDDxkej8d7jMmTJ3v7G4Zh1NTU+D3m5MmTOx3XqVOnDEnGqVOnOr0PAAAwz4oVKwy73e43B7Db7cbKlSvNDhGd1JU8rFtzZBMTE7V161ZlZmZqwIABeuutt3TppZdq69atuvPOO/XOO+8EIMU2D3VkAQCwFp65iBxBryPrdru91QkSExP1ySefSJKGDx+uDz/8sDuHBAAA6DaWio5O3ZojO3r0aL377rtKTU3VhAkTtHz5cvXq1UuPPfZYu/8aAiKV0+nU1q1bdeWVVyouLs7scAAgKvHMRfTp1tSCyspKnT59WrNnz9ZHH32kmTNnat++fTrvvPO0bt06XXXVVcGINWSYWoCuoPg2AACB05U8LGB1ZE+ePKmBAwd6KxdYGYksOuvM4tsul0uxsbGKj4+n+DYAAN0UtDqyRUVFneq3Zs2arhw2bJSUlKikpKTT5cMQvdoqvu1yueR2u1VQUKAtW7ZQfBsAgCDq0ois3W7X8OHDNXbsWL912lpt3LgxIMGZhRFZdKS2tlZJSUkd9jtx4gR1CwEA6IKgjcj+8Ic/VEVFhWpqalRYWKibbrpJgwYN6lGwgBVRfBsAAPN1qfxWSUmJjh07pp/+9Kf605/+pJSUFOXl5amysrLdEVogEuXl5bU5J9xmsyk/Pz/EEQEAEF169LDXxx9/rPLycj355JNyuVz64IMP1Ldv30DGZwqmFqAzKL4NAEDgBX1BBO/Odrv3QRcekEK0ofg2AADm6vKCCM3NzXr22We1Zs0avf7667rmmmu0evVq5eTkyG7vUV4MWA7FtwEAME+XphbceuutWrdunVJSUlRUVKQbb7wxIh9mYWoBAACAOYK2IILdbtewYcM0duzYdhc+ePbZZzsfbRgikQUAADBH0MpvzZ07NyJW7gIAAID1dSmRLS8vD1IYAAAAQNfwdNYZSkpKlJ6erqysLLNDAQAAQAd6VEc2UjFHFgAAwBwhqyMLAAAAmIVEFgAAAJZEIgsAAABLIpEFAACAJZHIAgAAwJJIZAEAAGBJJLIAAACwJBJZAAAAWBKJ7BlY2QsAAMA6WNnLD1b2AgAAMAcrewEAACDikcgCAADAkkhkAQAAYEkksgAAALAkElkAAABYEoksAAAALIlEFgAAAJZEIgsAAABLIpEFAACAJZHInoElagEAAKyDJWr9YIlaAAAAc7BELQAAACIeiSwAAAAsiUQWAAAgCjmdTlVWVsrpdJodSreRyAIAAESZ6upqZWdnKycnR9nZ2aqpqTE7pG4xNZGtqqrSzJkzlZycLJvNpueee86nvaGhQYsWLdLQoUOVkJCg9PR0Pfroox0ed8OGDRo5cqR69+6tjIwMvfjii0E6AwAAAGupqKhQRkaGHA6HJMnhcCgjI0MVFRXmBtYNpiayp0+f1pgxY1RSUuK3ffHixdq8ebOefvpp7d27V3fccYcWLVqkTZs2tXnMv/3tb5ozZ44WLFigd955R7NmzdKsWbP0/vvvB+s0AAAAwp7b7VZhYaEKCgrU1NQkl8slSXK5XGpsbFRBQYGKiorkdrtNjrTzwqb8ls1m08aNGzVr1izvttGjRys/P1/33HOPd9u4ceM0ffp0LV261O9x8vPzdfr0af35z3/2bvvmN7+pr3/9650azZUovwUAACJPbW2tkpKSOux34sQJJSYmhiAi/yKm/FZ2drY2bdqko0ePyjAMbdu2Tfv27dO0adPa3GfHjh2aOnWqz7arr75aO3bsaHOf5uZm1dfX+7wAAAAiSWJiorKysmSz2fy222w2ZWVlmZrEdlVYJ7KrVq1Senq6hg4dql69eiknJ0clJSWaNGlSm/scP35cgwcP9tk2ePBgHT9+vM19li1bpgEDBnhfKSkpATsHAACAcJGXl9duIpufnx/iiHom7BPZN954Q5s2bdKuXbv00EMPaeHChXr55ZcD+nOWLFmiU6dOeV+HDx8O6PEBAADCQW5urjwej982j8ej2bNnhziinok1O4C2NDU16Wc/+5k2btyoGTNmSJIyMzPlcDi0cuXKs6YPtBoyZIg+/fRTn22ffvqphgwZ0ubPio+PV3x8fOCCBwAACEOpqanKyMjQBx98oJiYGO92t9ut0aNHKzU11cToui5sE1mn0ymn0ym73XfQOCYmps1/SUjSxIkT9corr+iOO+7wbtuyZYsmTpwYrFABAAAsY/Xq1XrmmWfO2p6bm2tCND1jaiLb0NCg/fv3e9/X1NTI4XBo0KBBGjZsmCZPnqy77rpLCQkJGj58uF599VU9+eSTevjhh737zJ07VxdeeKGWLVsmSfrRj36kyZMn66GHHtKMGTO0bt067dy5U4899ljIzw8AACDcTJo0qd3njazE1PJb27dv15QpU87aPm/ePJWXl+v48eNasmSJ/vKXv+jkyZMaPny4vv/97+vHP/6xd6LyFVdcoREjRqi8vNy7/4YNG/Tzn/9cBw8e1CWXXKLly5frP/7jPzodF+W3AAAAzNGVPCxs6siGExJZAAAAc0RMHVkAAACgLSSyAAAAUcjpdKqyslJOp9PsULqNRBYAACDKVFdXKzs7Wzk5OcrOzlZNTY3ZIXULiSwAAEAUqaioUEZGhhwOhyTJ4XAoIyNDFRUV5gbWDSSyZygpKVF6erqysrLMDgUAACCg3G63CgsLVVBQoKamJrlcLkmSy+VSY2OjCgoKVFRUJLfbbXKknUfVAj+oWgAAACJNbW2tkpKSOux34sQJJSYmhiAi/6haAAAAAB+JiYnKysry1uL/KpvNpqysLFOT2K4ikQUAAIgSeXl57Say+fn5IY6oZ0hkAQAAokRubq48Ho/fNo/Ho9mzZ4c4op4hkQUAAIgSqampSklJ8ds2bNgwpaamhjiiniGRBQAAiAKtVQsOHz7st/3QoUNULYgEVC0AAACRhqoFAAAAsCSqFgAAAMCyqFoQwVjZCwAARDKqFkSwhQsXas+ePXr77bfNDgUAACCg3G637rvvvnb73H///TzsZXU87AUAACIND3sBAADAknjYCwAAAJaVl5fXbjsPewEAACDsuN1uvfnmm2prVqlhGHrjjTeYI2t1zJEFAACRhjmyAAAAsCTmyAIAAMCyWBABAAAAlsSCCAAAALCk1NRUZWRkyG63Ky4uzvuy2+3KzMxUamqq2SF2SazZAYSTkpISlZSUWOppPQAAgK5YvXq1nnnmmbO25+bmmhBNz1C1wA+qFgAAAJiDqgUAAACIeCSyAAAAsCQSWQAAAFgSiSwAAAAsiUQWAAAAlkQiCwAAAEsikQUAAIAlkcgCAADAkkhkz1BSUqL09HRlZWWZHQoAAAA6wMpefrCyFwAAgDlY2QsAAAARj0QWAAAAlkQiCwAAAEsikQUAAIAlmZrIVlVVaebMmUpOTpbNZtNzzz3n026z2fy+VqxY0eYxv/jiC91xxx0aPny4EhISlJ2drbfffjvIZwIAAIBQMzWRPX36tMaMGaOSkhK/7ceOHfN5rVmzRjabTbm5uW0es7i4WFu2bNFTTz2l3bt3a9q0aZo6daqOHj0arNMAAACACcKm/JbNZtPGjRs1a9asNvvMmjVLX3zxhV555RW/7U1NTerXr5+ef/55zZgxw7t93Lhxmj59upYuXdqpWCi/BQAAYI6u5GGxIYqpxz799FO98MILeuKJJ9rs43K55Ha71bt3b5/tCQkJev3119vcr7m5Wc3Nzd739fX1PQ8YAAAAQWWZh72eeOIJ9evXT7Nnz26zT79+/TRx4kTdf//9+uSTT+R2u/X0009rx44dOnbsWJv7LVu2TAMGDPC+UlJSgnEKAAAACCDLJLJr1qzRjTfeeNZo61c99dRTMgxDF154oeLj4/W73/1Oc+bMkd3e9qkuWbJEp06d8r4OHz4c6PABAAAQYJaYWvDaa6/pww8/1B//+McO+1500UV69dVXdfr0adXX1+uCCy5Qfn6+0tLS2twnPj5e8fHxgQwZAAAAQWaJEdnHH39c48aN05gxYzq9zznnnKMLLrhAdXV1qqys1HXXXRfECAEAABBqpiayDQ0NcjgccjgckqSamho5HA4dOnTI26e+vl4bNmxQcXGx32NcddVVWr16tfd9ZWWlNm/erJqaGm3ZskVTpkzRyJEjVVhYGNRzAQAAQGiZOrVg586dmjJlivf94sWLJUnz5s1TeXm5JGndunUyDENz5szxe4wDBw6otrbW+/7UqVNasmSJjhw5okGDBik3N1cPPPCA4uLignciAAAACLmwqSMbTqgjCwAAYI6u5GGWmCMLAAAAfBWJLAAAACyJRBYAAACWRCILAAAASyKRPUNJSYnS09OVlZVldigAAADoAFUL/KBqAQAAgDmoWgAAAICIRyILAAAASyKRBQAAgCWRyAIAAMCSSGQBAABgSSSyAAAAsCQSWQAAAFgSiSwAAAAsiUT2DKzsBQAAYB2s7OUHK3sBQM84nU5t3bpVV155peLi4swOB4CFsLIXAMA01dXVys7OVk5OjrKzs1VTU2N2SAAiFIksgJBxOp2qrKyU0+k0OxQESUVFhTIyMuRwOCRJDodDGRkZqqioMDcwABGJRBZASDBKF9ncbrcKCwtVUFCgpqYmuVwuSZLL5VJjY6MKCgpUVFQkt9ttcqQAIgmJLICgY5Qu8tXV1am8vFyS9NVHL1rfl5WVqa6uLtShAYhgJLIAgoZRuuiRmJiorKws2Ww2v+02m01ZWVlKTEwMcWQAIhmJLICgYZQuuuTl5bWbyObn54c4IgCRjkQWQNAwShddcnNz5fF4/LZ5PB7Nnj07xBEBiHQksgCCilG66JGamqqMjAzZ7XbFxcV5X3a7XZmZmUpNTTU7RAARhgUR/GBBBCBwampqlJaW1mZ7dXU1CU4Eqaqq0jPPPHPW9tzcXE2aNMmEiABYTVfyMBLZM5SUlKikpERut1v79u0jkQUCJDMzUx988IFiYmJkGIZsNpvcbrdGjx6td9991+zwAABhhES2hxiRBQKrqqpKa9as0UsvvaR//vOfOv/88/Uf//EfKiwsZJQOAOCDJWoBhJWjR49qw4YNOnnypCTp5MmT2rBhg44ePWpyZAAAKyORBRA01JEFAAQTUwv8YGoBEBi1tbVKSkrqsN+JEycowQUAkMTUAgBhgjqyAIBgIpEFEFTUkQUABAuJLICgYrUnAECwkMgCCCpWewIABEus2QEAiHyrV69uc7UnAAC6i0T2DGeu7AUgcCZNmsTCBwCAgKP8lh+U3wIAADAH5bcAAAAQ8UhkAQAAYEkksgAAALAkElkAAABYkqmJbFVVlWbOnKnk5GTZbDY999xzPu02m83va8WKFW0e0+1265577lFqaqoSEhJ00UUX6f777xfPtAEAAEQWU8tvnT59WmPGjFFRUZHf1X2OHTvm8/6ll17SggUL2q09+Zvf/EaPPPKInnjiCY0aNUo7d+5UYWGhBgwYoNtvvz3g5wAAAABzmJrITp8+XdOnT2+zfciQIT7vn3/+eU2ZMkVpaWlt7vO3v/1N1113nWbMmCFJGjFihCoqKvTWW28FJmgAAACEBcvMkf3000/1wgsvaMGCBe32y87O1iuvvKJ9+/ZJkt599129/vrr7SbMAAAAsB7LrOz1xBNPqF+/fn6nIJzpv/7rv1RfX6+RI0cqJiZGbrdbDzzwgG688cY292lublZzc7P3fX19fcDiBgAAQHBYZkR2zZo1uvHGG9W7d+92+61fv15/+MMftHbtWv3973/XE088oZUrV+qJJ55oc59ly5ZpwIAB3ldKSkqgwwcAAECAhc0StTabTRs3btSsWbPOanvttdc0adIkORwOjRkzpt3jpKSk6L/+67+0cOFC77alS5fq6aef1j/+8Q+/+/gbkU1JSWGJWgAAgBDryhK1lpha8Pjjj2vcuHEdJrGS1NjYKLvdd6A5JiZGHo+nzX3i4+MVHx/f4zgBAAAQOqZOLWhoaJDD4ZDD4ZAk1dTUyOFw6NChQ94+9fX12rBhg4qLi/0e46qrrtLq1au972fOnKkHHnhAL7zwgg4ePKiNGzfq4Ycf1vXXXx/UcwEA/JvT6VRlZaWcTqfZoQCIYKYmsjt37tTYsWM1duxYSdLixYs1duxY/eIXv/D2WbdunQzD0Jw5c/we48CBA6qtrfW+X7Vqlf7zP/9Tt956qy677DL95Cc/0S233KL7778/uCcDAJAkVVdXKzs7Wzk5OcrOzlZNTY3ZIQGIUGEzRzacdGVuBgDg3yoqKlRcXKzm5ma53W7FxMSod+/eKi0tbXNAAgDO1JU8zDJVCwAA4cvtdquwsFAFBQVqbGyU2+32bj99+rQKCgpUVFTk3Q4AgcCIrB+MyAJA19TW1iopKanDfidOnFBiYmIIIgJgVYzIAgBCauDAgTrvvPPa7ZOYmKiBAweGKCIA0YBEFgDQY3V1dfrss8/a7VNbW6u6uroQRQQgGpDInqGkpETp6enKysoyOxQAsJTExERlZma22yczM5NpBQACikT2DAsXLtSePXv09ttvmx0KAFjOzTff3G773LlzQxQJgGhBIgsAARatiwHk5ua22z579uwQRQIgWpDIAkAARfNiAKmpqcrIyJDdbldcXJz3ZbfblZmZqdTUVLNDBBBhYs0OAAAiRetiAC0tLZIkh8OhjIyMqFoMYPXq1XrmmWfO2t7RaC0AdAd1ZP2gjiyArnC73SouLlZ5eblsNpvO/FptfV9YWKjS0lLFxMSYGCkAhL+u5GEksn6QyALoChYDAIDAYUEEAAihxMREZWVlyWaz+W232WzKysoiiQWAACORBYAAyMvLazeRzc/PD3FEABD5SGQBIAByc3Pl8Xj8tnk8HkpPAUAQkMgCQABQegoAQo/yW2coKSlRSUmJ3G632aEAsCBKTwFAaFG1wA+qFgAAAJiDqgUAAACIeCSyAAAAsCQSWQAAAFgSiSwAAAAsiUQWAAAAlkQiC4SI0+lUZWWlnE6n2aEAABARSGSBEKiurlZ2drZycnKUnZ2tmpoas0MCAMDySGSBIKuoqFBGRoYcDockyeFwKCMjQxUVFeYGBgCAxZHInqGkpETp6enKysoyOxREALfbrcLCQhUUFKipqUkul0uS5HK51NjYqIKCAhUVFbGSHAAA3cTKXn6wshcCoba2VklJSR32O3HihBITE0MQEQAA4Y+VvYAwkJiYqKysLNlsNr/tNptNWVlZJLEAAHQTiSwQRHl5ee0msvn5+SGOCACAyEEii4gTTmWucnNz5fF4/LZ5PB7Nnj07xBEBAMLp7wR6hkQWEaW6uloTJ05UTk6OJk6caHqZq9TUVGVkZMhutysuLs77stvtyszMVGpqqqnxAUC0Cbe/E+gZHvbyg4e9rKmiokILFixQU1OTd1tCQoIef/xxzZkzx7S4qqqq9Mwzz5y1PTc3V5MmTTIhIgCITuH6dwK+upKHkcj6QSJrLW63W8XFxSovL2+zT2FhoUpLSxUTExO6wAAAYYG/E9ZCIttDJLLW8umnn2rIkCEd9jt+/LgGDx4cgogAAOGEvxPWQvktRJXO/uuZf2UDQHTi70TkIpGF5SUmJmro0KHt9klJSaFeKwBEKf5ORC4S2TOwRK113Xzzze2233TTTSGKBAAQjvg7EZmYI+sHc2Stp6amRmlpaW22V1dXU+oKAKIYfyesgzmyiDqpqaneLyi73e59SVJaWhpfTgAQ5fg7EZlizQ4ACJSysrI267UCAMDficjD1AI/mFoAAABgDstMLaiqqtLMmTOVnJwsm82m5557zqfdZrP5fa1YsaLNY44YMcLvPgsXLgzy2QAAACCUTE1kT58+rTFjxqikpMRv+7Fjx3xea9askc1ma/cWwNtvv+2zz5YtWyRJN9xwQ1DOAQAAAOYwdY7s9OnTNX369Dbbv7oKx/PPP68pU6a0+9RhUlKSz/sHH3xQF110kSZPntyzYAEAABBWLFO14NNPP9ULL7ygBQsWdHqflpYWPf300yoqKpLNZgtidAAAAAg1y1QteOKJJ9SvXz/Nnj270/s899xz+vzzzzV//vx2+zU3N6u5udn7vr6+vrthAgAAIEQsMyK7Zs0a3Xjjjerdu3en93n88cc1ffp0JScnt9tv2bJlGjBggPeVkpLS03ABAAAQZJZIZF977TV9+OGHKi4u7vQ+H3/8sV5++eVO7bNkyRKdOnXK+zp8+HBPwgUAAEAIWGJqweOPP65x48ZpzJgxnd6nrKxM559/vmbMmNFh3/j4eMXHx/ckRAAAAISYqSOyDQ0Ncjgccjgckr5cB9nhcOjQoUPePvX19dqwYUObI6tXXXWVVq9e7bPN4/GorKxM8+bNU2ysJXJ1AAAAdJGpWd7OnTs1ZcoU7/vFixdLkubNm6fy8nJJ0rp162QYhubMmeP3GAcOHFBtba3PtpdfflmHDh1SUVFRcAIHAACA6Vii1g+WqAUAADCHZZaoBQAAALqLRBYAAACWxJNQfrTOtmBhBAAAgNBqzb86M/uVRNaPL774QpJYGAEAAMAkX3zxhQYMGNBuHx728sPj8eiTTz5Rv379ZLPZurRvVlaW3n777W7/7J7sb+bPrq+vV0pKig4fPtztB+TMjN/s/Xt6/ax87j3dn89e9/fn2kX3Z49rZ939zb5+wT53wzD0xRdfKDk5WXZ7+7NgGZH1w263a+jQod3aNyYmpkeVDnqyv5k/u1X//v0tG7/Z+0vdv35mx272/hKfvZ7sz7WLzs8e187a+0vmXb9QnHtHI7GteNgrwBYuXGja/mb+7EAwO36z9zfzZ1t9/54yO36z9zfzZ1t9/57ib4Z5P9/q+/dUpHx2mFqAgKD2bs9w/bqPa9d9XLue4fp1H9euZ7h+/8aILAIiPj5e9957r+Lj480OxZK4ft3Htes+rl3PcP26j2vXM1y/f2NEFgAAAJbEiCwAAAAsiUQWAAAAlkQiCwAAAEsikY1Sv/zlL2Wz2XxeI0eO9LYfOHBA119/vZKSktS/f3/l5eXp008/9bZv3779rP1bX61Fjg8ePOi3/Y033vCJZcOGDRo5cqR69+6tjIwMvfjii6G5CD109OhR3XTTTTrvvPOUkJCgjIwM7dy509tuGIZ+8Ytf6IILLlBCQoKmTp2qjz76yOcYJ0+e1I033qj+/fvr3HPP1YIFC9TQ0ODT57333tN3vvMd9e7dWykpKVq+fPlZsVjtGvb02h08eFALFixQamqqEhISdNFFF+nee+9VS0uLT59I/PwF4nM3YsSIs67Lgw8+6NMnEj93Us+vXzR/93V07Z599llNmzZN5513nmw2mxwOx1nH+Ne//qWFCxfqvPPOU9++fZWbm+vzt0WSDh06pBkzZqhPnz46//zzddddd8nlcvn02b59u77xjW8oPj5eF198scrLy4NxygHV0+t38uRJ3Xbbbbr00kuVkJCgYcOG6fbbb9epU6d8+vn77K1bt86njxWvX5sMRKV7773XGDVqlHHs2DHv68SJE4ZhGEZDQ4ORlpZmXH/99cZ7771nvPfee8Z1111nZGVlGW632zAMw2hubvbZ99ixY0ZxcbGRmppqeDwewzAMo6amxpBkvPzyyz79WlpavHH89a9/NWJiYozly5cbe/bsMX7+858bcXFxxu7du0N/Ubrg5MmTxvDhw4358+cbb775plFdXW1UVlYa+/fv9/Z58MEHjQEDBhjPPfec8e677xrXXnutkZqaajQ1NXn75OTkGGPGjDHeeOMN47XXXjMuvvhiY86cOd72U6dOGYMHDzZuvPFG4/333zcqKiqMhIQE4//+7/+8fax2DQNx7V566SVj/vz5RmVlpXHgwAHj+eefN84//3zjzjvv9B4jEj9/gfrcDR8+3Ljvvvt8rktDQ4O3PRI/d4YRmOsXrd99nbl2Tz75pPGrX/3KKC0tNSQZ77zzzlnH+cEPfmCkpKQYr7zyirFz507jm9/8ppGdne1td7lcxujRo42pU6ca77zzjvHiiy8aiYmJxpIlS7x9qqurjT59+hiLFy829uzZY6xatcqIiYkxNm/eHNRr0BOBuH67d+82Zs+ebWzatMnYv3+/8corrxiXXHKJkZub69NPklFWVubz2Tvz99+K1689JLJR6t577zXGjBnjt62ystKw2+3GqVOnvNs+//xzw2azGVu2bPG7T0tLi5GUlGTcd9993m2tX+b+vsxa5eXlGTNmzPDZNmHCBOOWW27p/MmY4O677za+/e1vt9nu8XiMIUOGGCtWrPBu+/zzz434+HijoqLCMAzD2LNnjyHJePvtt719XnrpJcNmsxlHjx41DMMwfv/73xsDBw40mpubfX72pZde6n1vtWsYiGvnz/Lly43U1FTv+0j8/AXq2g0fPtz47W9/2+ZxIvFzZxjB+exFy3dfR9fuTG2d/+eff27ExcUZGzZs8G7bu3evIcnYsWOHYRiG8eKLLxp2u904fvy4t88jjzxi9O/f3/t5/OlPf2qMGjXK59j5+fnG1Vdf3Z1TC4lAXD9/1q9fb/Tq1ctwOp3ebZKMjRs3trmPFa9fe5haEMU++ugjJScnKy0tTTfeeKMOHTokSWpubpbNZvOpT9e7d2/Z7Xa9/vrrfo+1adMmffbZZyosLDyr7dprr9X555+vb3/729q0aZNP244dOzR16lSfbVdffbV27NjR09MLqk2bNmn8+PG64YYbdP7552vs2LEqLS31ttfU1Oj48eM+5zZgwABNmDDBe247duzQueeeq/Hjx3v7TJ06VXa7XW+++aa3z6RJk9SrVy9vn6uvvloffvih6urqvH2sdA0Dce38OXXqlAYNGnTW9kj6/AXy2j344IM677zzNHbsWK1YscLn1m0kfu6k4Hz2ouW7r6Nr1xm7du2S0+n0Oe+RI0dq2LBhPt+LGRkZGjx4sLfP1Vdfrfr6en3wwQfePla6dlJgrp8/rQsixMbG+mxfuHChEhMTdfnll2vNmjUyzqi0asXr1x4S2Sg1YcIElZeXa/PmzXrkkUdUU1Oj73znO/riiy/0zW9+U+ecc47uvvtuNTY26vTp0/rJT34it9utY8eO+T3e448/rquvvlpDhw71buvbt68eeughbdiwQS+88IK+/e1va9asWT5f6MePH/f5wpKkwYMH6/jx48E58QCprq7WI488oksuuUSVlZX64Q9/qNtvv11PPPGEJHnjb+/cjh8/rvPPP9+nPTY2VoMGDfLp4+8YZ/4Mq13DQFy7r9q/f79WrVqlW265xbstEj9/gbp2t99+u9atW6dt27bplltu0a9//Wv99Kc/9bZH4udOCs5nL1q++zq6dp1x/Phx9erVS+eee67P9q9+L3b3s1dfX6+mpqaunlpIBOL6fVVtba3uv/9+ff/73/fZft9992n9+vXasmWLcnNzdeutt2rVqlXeditev/bEdtwFkWj69One/87MzNSECRM0fPhwrV+/XgsWLNCGDRv0wx/+UL/73e9kt9s1Z84cfeMb35Ddfva/fY4cOaLKykqtX7/eZ3tiYqIWL17sfZ+VlaVPPvlEK1as0LXXXhu8kwsBj8ej8ePH69e//rUkaezYsXr//ff16KOPat68eSZHF94Cfe2OHj2qnJwc3XDDDfre977n3R6Jn79AXbszr0tmZqZ69eqlW265RcuWLYvolYIC/dmLpu8+vvN6JtDXr76+XjNmzFB6erp++ctf+rTdc8893v8eO3asTp8+rRUrVuj222/v0TmEK0ZkIUk699xz9bWvfU379++XJE2bNk0HDhzQP//5T9XW1uqpp57S0aNHlZaWdta+ZWVlOu+88zr1BT1hwgTvz5CkIUOGnPXE6qeffqohQ4b08IyC64ILLlB6errPtssuu8w7PaM1/vbObciQIfrnP//p0+5yuXTy5EmfPv6OcebPsNo1DMS1a/XJJ59oypQpys7O1mOPPdbhz7b65y+Q1+5MEyZMkMvl0sGDB73HibTPnRT46xdN330dXbvOGDJkiFpaWvT555/7bP/q92J3P3v9+/dXQkJCp+MJpUBcv1ZffPGFcnJy1K9fP23cuFFxcXHt9p8wYYKOHDmi5uZmSda8fu0hkYUkqaGhQQcOHNAFF1zgsz0xMVHnnnuutm7dqn/+859nfWEbhqGysjLNnTu3w18mSXI4HD4/Y+LEiXrllVd8+mzZskUTJ07swdkE37e+9S19+OGHPtv27dun4cOHS5JSU1M1ZMgQn3Orr6/Xm2++6T23iRMn6vPPP9euXbu8fbZu3SqPx6MJEyZ4+1RVVcnpdHr7bNmyRZdeeqkGDhzo7WOlaxiIayd9ORJ7xRVXaNy4cSorK/N7t+CrrP75C9S1+yqHwyG73e6d6hKJnzspsNcv2r77Orp2nTFu3DjFxcX5nPeHH36oQ4cO+Xwv7t692+cf+Vu2bFH//v29iaDVrp0UmOsnffl5nDZtmnr16qVNmzapd+/eHe7jcDg0cOBA790WK16/dpn8sBlMcueddxrbt283ampqjL/+9a/G1KlTjcTEROOf//ynYRiGsWbNGmPHjh3G/v37jaeeesoYNGiQsXjx4rOO8/LLLxuSjL17957VVl5ebqxdu9bYu3evsXfvXuOBBx4w7Ha7sWbNGm+fv/71r0ZsbKyxcuVKY+/evca9994b1iVoWr311ltGbGys8cADDxgfffSR8Yc//MHo06eP8fTTT3v7PPjgg8a5555rPP/8894SZv7Kb40dO9Z48803jddff9245JJLfMpvff7558bgwYONm2++2Xj//feNdevWGX369DmrDJKVrmEgrt2RI0eMiy++2LjqqquMI0eO+JSZaRWJn79AXLu//e1vxm9/+1vD4XAYBw4cMJ5++mkjKSnJmDt3rvcYkfi5M4zA/d4aRvR993Xm2n322WfGO++8Y7zwwguGJGPdunXGO++84/N7+YMf/MAYNmyYsXXrVmPnzp3GxIkTjYkTJ3rbW8tvTZs2zXA4HMbmzZuNpKQkv+W37rrrLmPv3r1GSUlJ2JePCsT1O3XqlDFhwgQjIyPD2L9/v8/3nsvlMgzDMDZt2mSUlpYau3fvNj766CPj97//vdGnTx/jF7/4hffnWPH6tYdENkrl5+cbF1xwgdGrVy/jwgsvNPLz833q2d19993G4MGDjbi4OOOSSy4xHnroIW+NxDPNmTPHpwbgmcrLy43LLrvM6NOnj9G/f3/j8ssv9ym70mr9+vXG1772NaNXr17GqFGjjBdeeCFwJxpEf/rTn4zRo0cb8fHxxsiRI43HHnvMp93j8Rj33HOPMXjwYCM+Pt646qqrjA8//NCnz2effWbMmTPH6Nu3r9G/f3+jsLDQ+OKLL3z6vPvuu8a3v/1tIz4+3rjwwguNBx988KxYrHYNe3rtysrKDEl+X60i9fPX02u3a9cuY8KECcaAAQOM3r17G5dddpnx61//2vjXv/7lc5xI/NwZRmB+bw0jOr/7Orp2bf1e3nvvvd4+TU1Nxq233moMHDjQ6NOnj3H99df7JLqGYRgHDx40pk+fbiQkJBiJiYnGnXfe6VNeyjAMY9u2bcbXv/51o1evXkZaWppRVlYWrNMOmJ5ev23btrX5vVdTU2MYxpclHL/+9a8bffv2Nc455xxjzJgxxqOPPuqtAd/KitevLTbDOKMmAwAAAGARzJEFAACAJZHIAgAAwJJIZAEAAGBJJLIAAACwJBJZAAAAWBKJLAAAACyJRBYAAACWRCILAAAASyKRBYAI0NLSoosvvlh/+9vf2uxz8OBB2Ww2ORyOkMS0Z88eDR06VKdPnw7JzwMQfUhkASAA5s+fL5vNph/84AdntS1cuFA2m03z58/36T9r1qyz9rfZbIqLi9PgwYP13e9+V2vWrJHH4+nw5z/66KNKTU1VdnZ2IE4nINLT0/XNb35TDz/8sNmhAIhQJLIAECApKSlat26dmpqavNv+9a9/ae3atRo2bFiH++fk5OjYsWM6ePCgXnrpJU2ZMkU/+tGPdM0118jlcrW5n2EYWr16tRYsWBCQ8wikwsJCPfLII+3GDwDdRSILAAHyjW98QykpKXr22We925599lkNGzZMY8eO7XD/+Ph4DRkyRBdeeKG+8Y1v6Gc/+5mef/55vfTSSyovL29zv127dunAgQOaMWOGz/a33npLY8eOVe/evTV+/Hi98847Pu1ut1sLFixQamqqEhISdOmll+p///d/ve1VVVWKi4vT8ePHffa744479J3vfEeS9PHHH2vmzJkaOHCgzjnnHI0aNUovvviit+93v/tdnTx5Uq+++mqH5w8AXUUiCwABVFRUpLKyMu/7NWvWqLCwsNvHu/LKKzVmzBif5PirXnvtNX3ta19Tv379vNsaGhp0zTXXKD09Xbt27dIvf/lL/eQnP/HZz+PxaOjQodqwYYP27NmjX/ziF/rZz36m9evXS5ImTZqktLQ0PfXUU959nE6n/vCHP6ioqEjSl9MmmpubVVVVpd27d+s3v/mN+vbt6+3fq1cvff3rX9drr73W7WsAAG2JNTsAAIgkN910k5YsWaKPP/5YkvTXv/5V69at0/bt27t9zJEjR+q9995rs/3jjz9WcnKyz7a1a9fK4/Ho8ccfV+/evTVq1CgdOXJEP/zhD7194uLi9Ktf/cr7PjU1VTt27ND69euVl5cnSVqwYIHKysp01113SZL+9Kc/6V//+pe3/dChQ8rNzVVGRoYkKS0t7az4kpOTvdcDAAKJEVkACKCkpCTNmDFD5eXlKisr04wZM5SYmNijYxqGIZvN1mZ7U1OTevfu7bNt7969yszM9Nk+ceLEs/YtKSnRuHHjlJSUpL59++qxxx7ToUOHvO3z58/X/v379cYbb0iSysvLlZeXp3POOUeSdPvtt2vp0qX61re+pXvvvddvwp2QkKDGxsaunTQAdAKJLAAEWFFRkcrLy/XEE094b8H3xN69e5Wamtpme2Jiourq6rp83HXr1uknP/mJFixYoL/85S9yOBwqLCxUS0uLt8/555+vmTNnqqysTJ9++qleeukln3MqLi5WdXW1br75Zu3evVvjx4/XqlWrfH7OyZMnlZSU1OX4AKAjJLIAEGA5OTlqaWmR0+nU1Vdf3aNjbd26Vbt371Zubm6bfcaOHat//OMfMgzDu+2yyy7Te++9p3/961/eba2jqq3++te/Kjs7W7feeqvGjh2riy++WAcOHDjr+MXFxfrjH/+oxx57TBdddJG+9a1v+bSnpKToBz/4gZ599lndeeedKi0t9Wl///33O/WwGwB0FYksAARYTEyM9u7dqz179igmJqbT+zU3N+v48eM6evSo/v73v+vXv/61rrvuOl1zzTWaO3dum/tNmTJFDQ0N+uCDD7zbCgoKZLPZ9L3vfU979uzRiy++qJUrV/rsd8kll2jnzp2qrKzUvn37dM899+jtt98+6/hXX321+vfvr6VLl5714Nodd9yhyspK1dTU6O9//7u2bdumyy67zNt+8OBBHT16VFOnTu30dQCAziKRBYAg6N+/v/r3799mu8fjUWys7/O2mzdv1gUXXKARI0YoJydH27Zt0+9+9zs9//zz7SbE5513nq6//nr94Q9/8G7r27ev/vSnP2n37t0aO3as/vu//1u/+c1vfPa75ZZbNHv2bOXn52vChAn67LPPdOutt551fLvdrvnz58vtdp+VULvdbi1cuFCXXXaZcnJy9LWvfU2///3vve0VFRWaNm2ahg8f3mb8ANBdNuPMe1EAgJDIycnRxRdfrNWrVwfkeO+9956++93v6sCBAz7lrwJlwYIFOnHihDZt2tTpfVpaWnTJJZdo7dq1Z01HAIBAYEQWAEKorq5Of/7zn7V9+/aA3m7PzMzUb37zG9XU1ATsmJJ06tQpvf7661q7dq1uu+22Lu176NAh/exnPyOJBRA0jMgCQAhdf/31evvttzVv3jwtXbq03bJa4eCKK67QW2+9pVtuuUW//e1vzQ4HAHyQyAIAAMCSmFoAAAAASyKRBQAAgCWRyAIAAMCSSGQBAABgSSSyAAAAsCQSWQAAAFgSiSwAAAAsiUQWAAAAlkQiCwAAAEv6f8fsD0mjvzrwAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 700x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "views.plotUnfolded(\n",
    "    LC[bands[band]],\n",
    "    time_col=\"expMidptMJD\",\n",
    "    mag_col=\"psfMag\",\n",
    "    color=plot_filter_colors[band],\n",
    "    marker=plot_filter_symbols[band],\n",
    ")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.11.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}