diff --git a/cosipy/ts_map/preliminary/2023.07.19_TSmap_v2.pdf b/cosipy/ts_map/preliminary/2023.07.19_TSmap_v2.pdf new file mode 100644 index 00000000..b23f78f5 Binary files /dev/null and b/cosipy/ts_map/preliminary/2023.07.19_TSmap_v2.pdf differ diff --git a/cosipy/ts_map/preliminary/TSmap_3MLfreeNormalization_mhealpy_miniDC2.ipynb b/cosipy/ts_map/preliminary/TSmap_3MLfreeNormalization_mhealpy_miniDC2.ipynb new file mode 100644 index 00000000..0c20f301 --- /dev/null +++ b/cosipy/ts_map/preliminary/TSmap_3MLfreeNormalization_mhealpy_miniDC2.ipynb @@ -0,0 +1,3968 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "7773e6e7", + "metadata": {}, + "source": [ + "### This notebook documents initial experiments on how to accelerate the TS grid method using HealpixMap.\n", + "\n", + "The following code was developed in July 2023 and organized in March 2024. If anyone in the future is confused or encounters errors when trying to merge the below code into the TSmap class, please contact ak119ka@gmail.com (Chien-You Jason Hunag) and perhaps I can explain my logic to you." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "9c09befd", + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
18:17:07 WARNING   The naima package is not available. Models that depend on it will not be         functions.py:48\n",
+       "                  available                                                                                        \n",
+       "
\n" + ], + "text/plain": [ + "\u001b[38;5;46m18:17:07\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m The naima package is not available. Models that depend on it will not be \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=763327;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/astromodels/functions/functions_1D/functions.py\u001b\\\u001b[2mfunctions.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=211647;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/astromodels/functions/functions_1D/functions.py#48\u001b\\\u001b[2m48\u001b[0m\u001b]8;;\u001b\\\n", + "\u001b[38;5;46m \u001b[0m \u001b[1;38;5;251mavailable \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b[2m \u001b[0m\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
         WARNING   The GSL library or the pygsl wrapper cannot be loaded. Models that depend on it  functions.py:69\n",
+       "                  will not be available.                                                                           \n",
+       "
\n" + ], + "text/plain": [ + "\u001b[38;5;46m \u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m The GSL library or the pygsl wrapper cannot be loaded. Models that depend on it \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=323048;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/astromodels/functions/functions_1D/functions.py\u001b\\\u001b[2mfunctions.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=128371;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/astromodels/functions/functions_1D/functions.py#69\u001b\\\u001b[2m69\u001b[0m\u001b]8;;\u001b\\\n", + "\u001b[38;5;46m \u001b[0m \u001b[1;38;5;251mwill not be available. \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b[2m \u001b[0m\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/numba/core/decorators.py:262: NumbaDeprecationWarning: \u001b[1mnumba.generated_jit is deprecated. Please see the documentation at: https://numba.readthedocs.io/en/stable/reference/deprecation.html#deprecation-of-generated-jit for more information and advice on a suitable replacement.\u001b[0m\n", + " warnings.warn(msg, NumbaDeprecationWarning)\n" + ] + }, + { + "data": { + "text/html": [ + "
         WARNING   The ebltable package is not available. Models that depend on it will not be     absorption.py:36\n",
+       "                  available                                                                                        \n",
+       "
\n" + ], + "text/plain": [ + "\u001b[38;5;46m \u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m The ebltable package is not available. Models that depend on it will not be \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=990433;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/astromodels/functions/functions_1D/absorption.py\u001b\\\u001b[2mabsorption.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=22412;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/astromodels/functions/functions_1D/absorption.py#36\u001b\\\u001b[2m36\u001b[0m\u001b]8;;\u001b\\\n", + "\u001b[38;5;46m \u001b[0m \u001b[1;38;5;251mavailable \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b[2m \u001b[0m\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
         WARNING   We have set the min_value of K to 1e-99 because there was a postive transform   parameter.py:704\n",
+       "
\n" + ], + "text/plain": [ + "\u001b[38;5;46m \u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m We have set the min_value of K to \u001b[0m\u001b[1;37m1e-99\u001b[0m\u001b[1;38;5;251m because there was a postive transform \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=442274;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/astromodels/core/parameter.py\u001b\\\u001b[2mparameter.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=314868;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/astromodels/core/parameter.py#704\u001b\\\u001b[2m704\u001b[0m\u001b]8;;\u001b\\\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
         WARNING   We have set the min_value of K to 1e-99 because there was a postive transform   parameter.py:704\n",
+       "
\n" + ], + "text/plain": [ + "\u001b[38;5;46m \u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m We have set the min_value of K to \u001b[0m\u001b[1;37m1e-99\u001b[0m\u001b[1;38;5;251m because there was a postive transform \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=991103;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/astromodels/core/parameter.py\u001b\\\u001b[2mparameter.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=880065;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/astromodels/core/parameter.py#704\u001b\\\u001b[2m704\u001b[0m\u001b]8;;\u001b\\\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
         WARNING   We have set the min_value of K to 1e-99 because there was a postive transform   parameter.py:704\n",
+       "
\n" + ], + "text/plain": [ + "\u001b[38;5;46m \u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m We have set the min_value of K to \u001b[0m\u001b[1;37m1e-99\u001b[0m\u001b[1;38;5;251m because there was a postive transform \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=912590;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/astromodels/core/parameter.py\u001b\\\u001b[2mparameter.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=87861;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/astromodels/core/parameter.py#704\u001b\\\u001b[2m704\u001b[0m\u001b]8;;\u001b\\\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
         WARNING   We have set the min_value of K to 1e-99 because there was a postive transform   parameter.py:704\n",
+       "
\n" + ], + "text/plain": [ + "\u001b[38;5;46m \u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m We have set the min_value of K to \u001b[0m\u001b[1;37m1e-99\u001b[0m\u001b[1;38;5;251m because there was a postive transform \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=964143;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/astromodels/core/parameter.py\u001b\\\u001b[2mparameter.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=162221;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/astromodels/core/parameter.py#704\u001b\\\u001b[2m704\u001b[0m\u001b]8;;\u001b\\\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
         WARNING   We have set the min_value of F to 1e-99 because there was a postive transform   parameter.py:704\n",
+       "
\n" + ], + "text/plain": [ + "\u001b[38;5;46m \u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m We have set the min_value of F to \u001b[0m\u001b[1;37m1e-99\u001b[0m\u001b[1;38;5;251m because there was a postive transform \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=70473;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/astromodels/core/parameter.py\u001b\\\u001b[2mparameter.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=193152;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/astromodels/core/parameter.py#704\u001b\\\u001b[2m704\u001b[0m\u001b]8;;\u001b\\\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
         WARNING   We have set the min_value of K to 1e-99 because there was a postive transform   parameter.py:704\n",
+       "
\n" + ], + "text/plain": [ + "\u001b[38;5;46m \u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m We have set the min_value of K to \u001b[0m\u001b[1;37m1e-99\u001b[0m\u001b[1;38;5;251m because there was a postive transform \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=226069;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/astromodels/core/parameter.py\u001b\\\u001b[2mparameter.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=784824;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/astromodels/core/parameter.py#704\u001b\\\u001b[2m704\u001b[0m\u001b]8;;\u001b\\\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/numba/core/decorators.py:262: NumbaDeprecationWarning: \u001b[1mnumba.generated_jit is deprecated. Please see the documentation at: https://numba.readthedocs.io/en/stable/reference/deprecation.html#deprecation-of-generated-jit for more information and advice on a suitable replacement.\u001b[0m\n", + " warnings.warn(msg, NumbaDeprecationWarning)\n" + ] + }, + { + "data": { + "text/html": [ + "
18:17:07 INFO      Starting 3ML!                                                                     __init__.py:35\n",
+       "
\n" + ], + "text/plain": [ + "\u001b[38;5;46m18:17:07\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;49mINFO \u001b[0m \u001b[1;38;5;251m Starting 3ML! \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=48584;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/threeML/__init__.py\u001b\\\u001b[2m__init__.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=826747;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/threeML/__init__.py#35\u001b\\\u001b[2m35\u001b[0m\u001b]8;;\u001b\\\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
         WARNING   WARNINGs here are NOT errors                                                      __init__.py:36\n",
+       "
\n" + ], + "text/plain": [ + "\u001b[38;5;46m \u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m WARNINGs here are \u001b[0m\u001b[1;31mNOT\u001b[0m\u001b[1;38;5;251m errors \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=504067;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/threeML/__init__.py\u001b\\\u001b[2m__init__.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=29713;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/threeML/__init__.py#36\u001b\\\u001b[2m36\u001b[0m\u001b]8;;\u001b\\\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
         WARNING   but are inform you about optional packages that can be installed                  __init__.py:37\n",
+       "
\n" + ], + "text/plain": [ + "\u001b[38;5;46m \u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m but are inform you about optional packages that can be installed \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=34540;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/threeML/__init__.py\u001b\\\u001b[2m__init__.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=247547;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/threeML/__init__.py#37\u001b\\\u001b[2m37\u001b[0m\u001b]8;;\u001b\\\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
         WARNING    to disable these messages, turn off start_warning in your config file            __init__.py:40\n",
+       "
\n" + ], + "text/plain": [ + "\u001b[38;5;46m \u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m \u001b[0m\u001b[1;31m to disable these messages, turn off start_warning in your config file\u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=237328;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/threeML/__init__.py\u001b\\\u001b[2m__init__.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=509621;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/threeML/__init__.py#40\u001b\\\u001b[2m40\u001b[0m\u001b]8;;\u001b\\\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
         WARNING   ROOT minimizer not available                                                minimization.py:1345\n",
+       "
\n" + ], + "text/plain": [ + "\u001b[38;5;46m \u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m ROOT minimizer not available \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=124278;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/threeML/minimizer/minimization.py\u001b\\\u001b[2mminimization.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=594600;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/threeML/minimizer/minimization.py#1345\u001b\\\u001b[2m1345\u001b[0m\u001b]8;;\u001b\\\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
         WARNING   Multinest minimizer not available                                           minimization.py:1357\n",
+       "
\n" + ], + "text/plain": [ + "\u001b[38;5;46m \u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m Multinest minimizer not available \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=874;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/threeML/minimizer/minimization.py\u001b\\\u001b[2mminimization.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=830563;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/threeML/minimizer/minimization.py#1357\u001b\\\u001b[2m1357\u001b[0m\u001b]8;;\u001b\\\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
         WARNING   PyGMO is not available                                                      minimization.py:1369\n",
+       "
\n" + ], + "text/plain": [ + "\u001b[38;5;46m \u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m PyGMO is not available \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=409427;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/threeML/minimizer/minimization.py\u001b\\\u001b[2mminimization.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=898328;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/threeML/minimizer/minimization.py#1369\u001b\\\u001b[2m1369\u001b[0m\u001b]8;;\u001b\\\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
         WARNING   The cthreeML package is not installed. You will not be able to use plugins which  __init__.py:94\n",
+       "                  require the C/C++ interface (currently HAWC)                                                     \n",
+       "
\n" + ], + "text/plain": [ + "\u001b[38;5;46m \u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m The cthreeML package is not installed. You will not be able to use plugins which \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=41947;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/threeML/__init__.py\u001b\\\u001b[2m__init__.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=477511;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/threeML/__init__.py#94\u001b\\\u001b[2m94\u001b[0m\u001b]8;;\u001b\\\n", + "\u001b[38;5;46m \u001b[0m \u001b[1;38;5;251mrequire the C/C++ interface \u001b[0m\u001b[1;38;5;251m(\u001b[0m\u001b[1;38;5;251mcurrently HAWC\u001b[0m\u001b[1;38;5;251m)\u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b[2m \u001b[0m\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
         WARNING   Could not import plugin HAWCLike.py. Do you have the relative instrument         __init__.py:144\n",
+       "                  software installed and configured?                                                               \n",
+       "
\n" + ], + "text/plain": [ + "\u001b[38;5;46m \u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m Could not import plugin HAWCLike.py. Do you have the relative instrument \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=296584;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/threeML/__init__.py\u001b\\\u001b[2m__init__.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=478796;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/threeML/__init__.py#144\u001b\\\u001b[2m144\u001b[0m\u001b]8;;\u001b\\\n", + "\u001b[38;5;46m \u001b[0m \u001b[1;38;5;251msoftware installed and configured? \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b[2m \u001b[0m\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
         WARNING   Could not import plugin FermiLATLike.py. Do you have the relative instrument     __init__.py:144\n",
+       "                  software installed and configured?                                                               \n",
+       "
\n" + ], + "text/plain": [ + "\u001b[38;5;46m \u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m Could not import plugin FermiLATLike.py. Do you have the relative instrument \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=503926;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/threeML/__init__.py\u001b\\\u001b[2m__init__.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=142125;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/threeML/__init__.py#144\u001b\\\u001b[2m144\u001b[0m\u001b]8;;\u001b\\\n", + "\u001b[38;5;46m \u001b[0m \u001b[1;38;5;251msoftware installed and configured? \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b[2m \u001b[0m\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
         WARNING   No fermitools installed                                              lat_transient_builder.py:44\n",
+       "
\n" + ], + "text/plain": [ + "\u001b[38;5;46m \u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m No fermitools installed \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=824624;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/threeML/utils/data_builders/fermi/lat_transient_builder.py\u001b\\\u001b[2mlat_transient_builder.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=854513;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/threeML/utils/data_builders/fermi/lat_transient_builder.py#44\u001b\\\u001b[2m44\u001b[0m\u001b]8;;\u001b\\\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
         WARNING   Env. variable OMP_NUM_THREADS is not set. Please set it to 1 for optimal         __init__.py:387\n",
+       "                  performances in 3ML                                                                              \n",
+       "
\n" + ], + "text/plain": [ + "\u001b[38;5;46m \u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m Env. variable OMP_NUM_THREADS is not set. Please set it to \u001b[0m\u001b[1;37m1\u001b[0m\u001b[1;38;5;251m for optimal \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=433242;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/threeML/__init__.py\u001b\\\u001b[2m__init__.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=58134;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/threeML/__init__.py#387\u001b\\\u001b[2m387\u001b[0m\u001b]8;;\u001b\\\n", + "\u001b[38;5;46m \u001b[0m \u001b[1;38;5;251mperformances in 3ML \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b[2m \u001b[0m\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
         WARNING   Env. variable MKL_NUM_THREADS is not set. Please set it to 1 for optimal         __init__.py:387\n",
+       "                  performances in 3ML                                                                              \n",
+       "
\n" + ], + "text/plain": [ + "\u001b[38;5;46m \u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m Env. variable MKL_NUM_THREADS is not set. Please set it to \u001b[0m\u001b[1;37m1\u001b[0m\u001b[1;38;5;251m for optimal \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=414213;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/threeML/__init__.py\u001b\\\u001b[2m__init__.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=756953;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/threeML/__init__.py#387\u001b\\\u001b[2m387\u001b[0m\u001b]8;;\u001b\\\n", + "\u001b[38;5;46m \u001b[0m \u001b[1;38;5;251mperformances in 3ML \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b[2m \u001b[0m\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
         WARNING   Env. variable NUMEXPR_NUM_THREADS is not set. Please set it to 1 for optimal     __init__.py:387\n",
+       "                  performances in 3ML                                                                              \n",
+       "
\n" + ], + "text/plain": [ + "\u001b[38;5;46m \u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m Env. variable NUMEXPR_NUM_THREADS is not set. Please set it to \u001b[0m\u001b[1;37m1\u001b[0m\u001b[1;38;5;251m for optimal \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=858699;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/threeML/__init__.py\u001b\\\u001b[2m__init__.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=147501;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/threeML/__init__.py#387\u001b\\\u001b[2m387\u001b[0m\u001b]8;;\u001b\\\n", + "\u001b[38;5;46m \u001b[0m \u001b[1;38;5;251mperformances in 3ML \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b[2m \u001b[0m\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Import useful packages\n", + "import numpy as np\n", + "from scipy import stats\n", + "import matplotlib.pyplot as plt\n", + "\n", + "import astropy.units as u\n", + "from astropy.coordinates import SkyCoord\n", + "\n", + "from histpy import HealpixAxis, Histogram\n", + "\n", + "from threeML import DataList, Powerlaw, PointSource, Model, JointLikelihood\n", + "\n", + "from mhealpy import HealpixMap\n", + "\n", + "from cosipy import BinnedData\n", + "from cosipy.response import PointSourceResponse\n", + "from cosipy import COSILike" + ] + }, + { + "cell_type": "markdown", + "id": "383af884", + "metadata": {}, + "source": [ + "### Reference\n", + "\n", + "To generate the binned source and background data, please refer to 'spectral_fit_cds_rot.ipynb'. For the source, 'crab_dataIO_config.yml' and 'GalacticScan.inc1.id1.crab2hr.extracted.tra.gz' are needed. For the background, 'bkg_dataIO_config.yml' and 'Cosmic_diffuse_2hr_unbinned_data.hdf5' are needed.\n", + "\n", + "Moreover, 'spectral_fit_cds_rot.ipynb' also demonstrates how to use 'psr_miniDC_crab_2hr_gal.h5' and do the spectrum fitting.\n", + "\n", + "All files mentioned above can be found in https://drive.google.com/drive/folders/1UdLfuLp9Fyk4dNussn1wt7WEOsTWrlQ6?usp=drive_link." + ] + }, + { + "cell_type": "markdown", + "id": "70b5b2ac", + "metadata": {}, + "source": [ + "### Load source and background data" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "39512df7", + "metadata": {}, + "outputs": [], + "source": [ + "# Open binned source data\n", + "crab = BinnedData(\"crab_dataIO_config.yml\")\n", + "crab.load_binned_data_from_hdf5('crab/Crab_2hr_binned_data.hdf5')\n", + "\n", + "crab = crab.binned_data.project(['Em', 'Phi', 'PsiChi'])\n", + "crab.axes['PsiChi'] = HealpixAxis(nside=8, label='PsiChi')" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "7a1ca30f", + "metadata": {}, + "outputs": [], + "source": [ + "# Open binned background data\n", + "bkg = BinnedData(\"bkg_dataIO_config.yml\")\n", + "bkg.load_binned_data_from_hdf5('bkg/Cosmic_diffuse_2hr_binned_data.hdf5')\n", + "\n", + "bkg = bkg.binned_data.project(['Em', 'Phi', 'PsiChi'])\n", + "bkg.axes['PsiChi'] = HealpixAxis(nside=8, label='PsiChi')" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "3dfb5be1", + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(, )" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Create binned data\n", + "data = crab + bkg\n", + "\n", + "# Visualize binned data\n", + "data.slice[{'Em':3, 'Phi':4}].project('PsiChi').plot()" + ] + }, + { + "cell_type": "markdown", + "id": "c5e726bb", + "metadata": {}, + "source": [ + "### Load point source response" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "7471e9f9", + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "psr label : ['NuLambda' 'Ei' 'Em' 'Phi' 'PsiChi' 'SigmaTau' 'Dist']\n", + "psr NuLambda coordsys: \n", + "psr PsiChi coordsys : \n", + "psr unit : cm2 s\n" + ] + } + ], + "source": [ + "# Open point source response\n", + "psr = Histogram.open(\"psr_miniDC_crab_2hr_gal.h5\")\n", + "\n", + "# Check point source response\n", + "print('psr label :', psr.axes.labels)\n", + "print('psr NuLambda coordsys:', psr.axes['NuLambda'].coordsys)\n", + "print('psr PsiChi coordsys :', psr.axes['PsiChi'].coordsys)\n", + "print('psr unit :', psr.unit)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "171df89a", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Extract position of Crab \n", + "coord_crab = SkyCoord.from_name('Crab').galactic\n", + "coord_crab_psr_pix = psr.axes['NuLambda'].ang2pix(coord_crab)\n", + "\n", + "# Visualize point source response for position of Crab\n", + "CDS_cross_section = psr.slice[{'NuLambda':coord_crab_psr_pix, 'Em':2, 'Phi':4}].project('PsiChi')\n", + "ax, plot = CDS_cross_section.plot(ax_kw = {\"coord\":'G'})\n", + "ax.scatter(coord_crab.l.deg, coord_crab.b.deg, transform=ax.get_transform('world'), color='red', s=1)" + ] + }, + { + "cell_type": "markdown", + "id": "d9280aa0", + "metadata": {}, + "source": [ + "### Spectral fit" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "4ddb9228", + "metadata": {}, + "outputs": [], + "source": [ + "# Create COSI 3ML plugin\n", + "cosi = COSILike(\"cosi\", \n", + " psr = psr, \n", + " data = data, \n", + " bkg = bkg, \n", + " nuisance_param = None) " + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "0c240a49", + "metadata": {}, + "outputs": [], + "source": [ + "# Define initial spectrum parameters\n", + "K = 0.00247 / u.cm / u.cm / u.s / u.keV\n", + "index = -2.\n", + "piv = 1000 * u.keV\n", + "\n", + "# Create Powerlaw spectrum with spectrum parameters\n", + "spectrum = Powerlaw()\n", + "spectrum.K.value = K.value\n", + "spectrum.K.unit = K.unit\n", + "spectrum.index.value = index\n", + "spectrum.piv.value = piv.value\n", + "spectrum.piv.unit = piv.unit\n", + "\n", + "# Create PointSource with position of Crab and Powerlaw spectrum\n", + "source = PointSource(\"source\", \n", + " l = coord_crab.l.deg, \n", + " b = coord_crab.b.deg, \n", + " spectral_shape = spectrum)\n", + "\n", + "# Create Model for COSI 3ML plugin\n", + "model = Model(source)\n", + "cosi.set_model(model)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "2e4ebffd", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "Model summary:

\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
N
Point sources1
Extended sources0
Particle sources0
\n", + "


Free parameters (1):

\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
valuemin_valuemax_valueunit
source.spectrum.main.Powerlaw.K0.002470.01000.0keV-1 s-1 cm-2
\n", + "


Fixed parameters (4):

\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
valuemin_valuemax_valueunit
source.position.l184.5551010.0360.0deg
source.position.b-5.787682-90.090.0deg
source.spectrum.main.Powerlaw.piv1000.0NoneNonekeV
source.spectrum.main.Powerlaw.index-2.0-10.010.0
\n", + "


Properties (0):

(none)


Linked parameters (0):

(none)

Independent variables:

(none)

Linked functions (0):

(none)
" + ], + "text/plain": [ + "Model summary:\n", + "==============\n", + "\n", + " N\n", + "Point sources 1\n", + "Extended sources 0\n", + "Particle sources 0\n", + "\n", + "Free parameters (1):\n", + "--------------------\n", + "\n", + " value min_value max_value unit\n", + "source.spectrum.main.Powerlaw.K 0.00247 0.0 1000.0 keV-1 s-1 cm-2\n", + "\n", + "Fixed parameters (4):\n", + "---------------------\n", + "\n", + " value min_value max_value unit\n", + "source.position.l 184.555101 0.0 360.0 deg\n", + "source.position.b -5.787682 -90.0 90.0 deg\n", + "source.spectrum.main.Powerlaw.piv 1000.0 None None keV\n", + "source.spectrum.main.Powerlaw.index -2.0 -10.0 10.0 \n", + "\n", + "Properties (0):\n", + "--------------------\n", + "\n", + "(none)\n", + "\n", + "\n", + "Linked parameters (0):\n", + "----------------------\n", + "\n", + "(none)\n", + "\n", + "Independent variables:\n", + "----------------------\n", + "\n", + "(none)\n", + "\n", + "Linked functions (0):\n", + "----------------------\n", + "\n", + "(none)" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Fix index of Powerlaw spectrum\n", + "source.spectrum.main.Powerlaw.index.fix = True\n", + "\n", + "# Check free and fixed fitting parameters in Model\n", + "model.display(complete=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "ebb4eac0", + "metadata": {}, + "outputs": [], + "source": [ + "# Less verbose workaround\n", + "import warnings\n", + "warnings.simplefilter('ignore', RuntimeWarning)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "87508f8e", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
18:17:49 INFO      set the minimizer to minuit                                             joint_likelihood.py:1042\n",
+       "
\n" + ], + "text/plain": [ + "\u001b[38;5;46m18:17:49\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;49mINFO \u001b[0m \u001b[1;38;5;251m set the minimizer to minuit \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=178257;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py\u001b\\\u001b[2mjoint_likelihood.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=481718;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py#1042\u001b\\\u001b[2m1042\u001b[0m\u001b]8;;\u001b\\\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
18:17:51 WARNING   get_number_of_data_points not implemented, values for statistical        plugin_prototype.py:128\n",
+       "                  measurements such as AIC or BIC are unreliable                                                   \n",
+       "
\n" + ], + "text/plain": [ + "\u001b[38;5;46m18:17:51\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m get_number_of_data_points not implemented, values for statistical \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=345105;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/threeML/plugin_prototype.py\u001b\\\u001b[2mplugin_prototype.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=886787;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/threeML/plugin_prototype.py#128\u001b\\\u001b[2m128\u001b[0m\u001b]8;;\u001b\\\n", + "\u001b[38;5;46m \u001b[0m \u001b[1;38;5;251mmeasurements such as AIC or BIC are unreliable \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b[2m \u001b[0m\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
Best fit values:\n",
+       "\n",
+       "
\n" + ], + "text/plain": [ + "\u001b[1;4;38;5;49mBest fit values:\u001b[0m\n", + "\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
resultunit
parameter
source.spectrum.main.Powerlaw.K(3.846 +/- 0.008) x 10^-61 / (keV s cm2)
\n", + "
" + ], + "text/plain": [ + " result unit\n", + "parameter \n", + "source.spectrum.main.Powerlaw.K (3.846 +/- 0.008) x 10^-6 1 / (keV s cm2)" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
\n",
+       "Correlation matrix:\n",
+       "\n",
+       "
\n" + ], + "text/plain": [ + "\n", + "\u001b[1;4;38;5;49mCorrelation matrix:\u001b[0m\n", + "\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "
1.00
" + ], + "text/plain": [ + "1.00" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
\n",
+       "Values of -log(likelihood) at the minimum:\n",
+       "\n",
+       "
\n" + ], + "text/plain": [ + "\n", + "\u001b[1;4;38;5;49mValues of -\u001b[0m\u001b[1;4;38;5;49mlog\u001b[0m\u001b[1;4;38;5;49m(\u001b[0m\u001b[1;4;38;5;49mlikelihood\u001b[0m\u001b[1;4;38;5;49m)\u001b[0m\u001b[1;4;38;5;49m at the minimum:\u001b[0m\n", + "\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
-log(likelihood)
cosi-9.204917e+08
total-9.204917e+08
\n", + "
" + ], + "text/plain": [ + " -log(likelihood)\n", + "cosi -9.204917e+08\n", + "total -9.204917e+08" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
\n",
+       "Values of statistical measures:\n",
+       "\n",
+       "
\n" + ], + "text/plain": [ + "\n", + "\u001b[1;4;38;5;49mValues of statistical measures:\u001b[0m\n", + "\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
statistical measures
AIC-1.840983e+09
BIC-1.840983e+09
\n", + "
" + ], + "text/plain": [ + " statistical measures\n", + "AIC -1.840983e+09\n", + "BIC -1.840983e+09" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "( value negative_error positive_error \\\n", + " source.spectrum.main.Powerlaw.K 0.000004 -7.662291e-09 7.700203e-09 \n", + " \n", + " error unit \n", + " source.spectrum.main.Powerlaw.K 7.681247e-09 1 / (keV s cm2) ,\n", + " -log(likelihood)\n", + " cosi -9.204917e+08\n", + " total -9.204917e+08)" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Do JointLikelihood fitting\n", + "plugins = DataList(cosi) # If we had multiple instruments, we would do e.g. DataList(cosi, lat, hawc, ...)\n", + "like = JointLikelihood(model, plugins, verbose = False)\n", + "like.fit()" + ] + }, + { + "cell_type": "markdown", + "id": "c6d08a6e", + "metadata": {}, + "source": [ + "### TS grid with HealpixMap" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "fa03a368", + "metadata": {}, + "outputs": [], + "source": [ + "# Define initial global variables\n", + "used_nside = 1 # nside for zero-order HealpixMap and further iteration\n", + "stop_nside = 8 # nside for final HealpixMap\n", + "\n", + "top_number = 8 # the top few pixels of log(likelihood) need to be further split" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "eab59d60", + "metadata": {}, + "outputs": [], + "source": [ + "# Create empty zero-order HealpixMap\n", + "m = HealpixMap(nside=used_nside, scheme='nested', density=True, coordsys='G')" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "7b659b9a", + "metadata": {}, + "outputs": [], + "source": [ + "def fill_empty_value(input_map, empty_arg_list):\n", + " \n", + " # Loop all empty pixels\n", + " for pix_idx, pix in enumerate(empty_arg_list):\n", + " \n", + " # Print progress\n", + " print(f\"\\rRun pix = {1+pix_idx:d}/{len(empty_arg_list)}\")\n", + " \n", + " # Transfer pix to skycoord in input_map\n", + " skycoord = input_map.pix2skycoord(pix)\n", + " \n", + " # Reset source.position\n", + " source.position.l = skycoord.l.deg\n", + " source.position.b = skycoord.b.deg\n", + " \n", + " # Do the Fitting\n", + " like.fit(quiet=True)\n", + " \n", + " # Fill log(likelihood) in corresponding pix\n", + " input_map[pix] = -like._current_minimum\n", + " \n", + " return" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "864abf35", + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Run pix = 1/12\n", + "Change source position\n" + ] + }, + { + "data": { + "text/html": [ + "
18:17:53 WARNING   get_number_of_data_points not implemented, values for statistical        plugin_prototype.py:128\n",
+       "                  measurements such as AIC or BIC are unreliable                                                   \n",
+       "
\n" + ], + "text/plain": [ + "\u001b[38;5;46m18:17:53\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m get_number_of_data_points not implemented, values for statistical \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=362698;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/threeML/plugin_prototype.py\u001b\\\u001b[2mplugin_prototype.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=940095;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/threeML/plugin_prototype.py#128\u001b\\\u001b[2m128\u001b[0m\u001b]8;;\u001b\\\n", + "\u001b[38;5;46m \u001b[0m \u001b[1;38;5;251mmeasurements such as AIC or BIC are unreliable \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b[2m \u001b[0m\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Run pix = 2/12\n", + "Change source position\n" + ] + }, + { + "data": { + "text/html": [ + "
18:17:55 WARNING   get_number_of_data_points not implemented, values for statistical        plugin_prototype.py:128\n",
+       "                  measurements such as AIC or BIC are unreliable                                                   \n",
+       "
\n" + ], + "text/plain": [ + "\u001b[38;5;46m18:17:55\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m get_number_of_data_points not implemented, values for statistical \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=174952;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/threeML/plugin_prototype.py\u001b\\\u001b[2mplugin_prototype.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=83754;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/threeML/plugin_prototype.py#128\u001b\\\u001b[2m128\u001b[0m\u001b]8;;\u001b\\\n", + "\u001b[38;5;46m \u001b[0m \u001b[1;38;5;251mmeasurements such as AIC or BIC are unreliable \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b[2m \u001b[0m\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Run pix = 3/12\n", + "Change source position\n" + ] + }, + { + "data": { + "text/html": [ + "
18:17:56 WARNING   get_number_of_data_points not implemented, values for statistical        plugin_prototype.py:128\n",
+       "                  measurements such as AIC or BIC are unreliable                                                   \n",
+       "
\n" + ], + "text/plain": [ + "\u001b[38;5;46m18:17:56\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m get_number_of_data_points not implemented, values for statistical \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=978885;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/threeML/plugin_prototype.py\u001b\\\u001b[2mplugin_prototype.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=902284;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/threeML/plugin_prototype.py#128\u001b\\\u001b[2m128\u001b[0m\u001b]8;;\u001b\\\n", + "\u001b[38;5;46m \u001b[0m \u001b[1;38;5;251mmeasurements such as AIC or BIC are unreliable \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b[2m \u001b[0m\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Run pix = 4/12\n", + "Change source position\n" + ] + }, + { + "data": { + "text/html": [ + "
18:17:58 WARNING   get_number_of_data_points not implemented, values for statistical        plugin_prototype.py:128\n",
+       "                  measurements such as AIC or BIC are unreliable                                                   \n",
+       "
\n" + ], + "text/plain": [ + "\u001b[38;5;46m18:17:58\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m get_number_of_data_points not implemented, values for statistical \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=164549;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/threeML/plugin_prototype.py\u001b\\\u001b[2mplugin_prototype.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=515947;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/threeML/plugin_prototype.py#128\u001b\\\u001b[2m128\u001b[0m\u001b]8;;\u001b\\\n", + "\u001b[38;5;46m \u001b[0m \u001b[1;38;5;251mmeasurements such as AIC or BIC are unreliable \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b[2m \u001b[0m\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Run pix = 5/12\n", + "Change source position\n" + ] + }, + { + "data": { + "text/html": [ + "
18:17:59 WARNING   get_number_of_data_points not implemented, values for statistical        plugin_prototype.py:128\n",
+       "                  measurements such as AIC or BIC are unreliable                                                   \n",
+       "
\n" + ], + "text/plain": [ + "\u001b[38;5;46m18:17:59\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m get_number_of_data_points not implemented, values for statistical \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=742422;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/threeML/plugin_prototype.py\u001b\\\u001b[2mplugin_prototype.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=378338;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/threeML/plugin_prototype.py#128\u001b\\\u001b[2m128\u001b[0m\u001b]8;;\u001b\\\n", + "\u001b[38;5;46m \u001b[0m \u001b[1;38;5;251mmeasurements such as AIC or BIC are unreliable \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b[2m \u001b[0m\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Run pix = 6/12\n", + "Change source position\n" + ] + }, + { + "data": { + "text/html": [ + "
18:18:01 WARNING   get_number_of_data_points not implemented, values for statistical        plugin_prototype.py:128\n",
+       "                  measurements such as AIC or BIC are unreliable                                                   \n",
+       "
\n" + ], + "text/plain": [ + "\u001b[38;5;46m18:18:01\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m get_number_of_data_points not implemented, values for statistical \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=216869;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/threeML/plugin_prototype.py\u001b\\\u001b[2mplugin_prototype.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=244601;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/threeML/plugin_prototype.py#128\u001b\\\u001b[2m128\u001b[0m\u001b]8;;\u001b\\\n", + "\u001b[38;5;46m \u001b[0m \u001b[1;38;5;251mmeasurements such as AIC or BIC are unreliable \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b[2m \u001b[0m\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Run pix = 7/12\n", + "Change source position\n" + ] + }, + { + "data": { + "text/html": [ + "
18:18:02 WARNING   get_number_of_data_points not implemented, values for statistical        plugin_prototype.py:128\n",
+       "                  measurements such as AIC or BIC are unreliable                                                   \n",
+       "
\n" + ], + "text/plain": [ + "\u001b[38;5;46m18:18:02\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m get_number_of_data_points not implemented, values for statistical \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=284555;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/threeML/plugin_prototype.py\u001b\\\u001b[2mplugin_prototype.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=261585;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/threeML/plugin_prototype.py#128\u001b\\\u001b[2m128\u001b[0m\u001b]8;;\u001b\\\n", + "\u001b[38;5;46m \u001b[0m \u001b[1;38;5;251mmeasurements such as AIC or BIC are unreliable \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b[2m \u001b[0m\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Run pix = 8/12\n", + "Change source position\n" + ] + }, + { + "data": { + "text/html": [ + "
18:18:04 WARNING   get_number_of_data_points not implemented, values for statistical        plugin_prototype.py:128\n",
+       "                  measurements such as AIC or BIC are unreliable                                                   \n",
+       "
\n" + ], + "text/plain": [ + "\u001b[38;5;46m18:18:04\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m get_number_of_data_points not implemented, values for statistical \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=81388;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/threeML/plugin_prototype.py\u001b\\\u001b[2mplugin_prototype.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=575644;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/threeML/plugin_prototype.py#128\u001b\\\u001b[2m128\u001b[0m\u001b]8;;\u001b\\\n", + "\u001b[38;5;46m \u001b[0m \u001b[1;38;5;251mmeasurements such as AIC or BIC are unreliable \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b[2m \u001b[0m\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Run pix = 9/12\n", + "Change source position\n" + ] + }, + { + "data": { + "text/html": [ + "
18:18:05 WARNING   get_number_of_data_points not implemented, values for statistical        plugin_prototype.py:128\n",
+       "                  measurements such as AIC or BIC are unreliable                                                   \n",
+       "
\n" + ], + "text/plain": [ + "\u001b[38;5;46m18:18:05\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m get_number_of_data_points not implemented, values for statistical \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=304958;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/threeML/plugin_prototype.py\u001b\\\u001b[2mplugin_prototype.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=419930;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/threeML/plugin_prototype.py#128\u001b\\\u001b[2m128\u001b[0m\u001b]8;;\u001b\\\n", + "\u001b[38;5;46m \u001b[0m \u001b[1;38;5;251mmeasurements such as AIC or BIC are unreliable \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b[2m \u001b[0m\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Run pix = 10/12\n", + "Change source position\n" + ] + }, + { + "data": { + "text/html": [ + "
18:18:06 WARNING   get_number_of_data_points not implemented, values for statistical        plugin_prototype.py:128\n",
+       "                  measurements such as AIC or BIC are unreliable                                                   \n",
+       "
\n" + ], + "text/plain": [ + "\u001b[38;5;46m18:18:06\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m get_number_of_data_points not implemented, values for statistical \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=631038;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/threeML/plugin_prototype.py\u001b\\\u001b[2mplugin_prototype.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=36254;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/threeML/plugin_prototype.py#128\u001b\\\u001b[2m128\u001b[0m\u001b]8;;\u001b\\\n", + "\u001b[38;5;46m \u001b[0m \u001b[1;38;5;251mmeasurements such as AIC or BIC are unreliable \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b[2m \u001b[0m\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Run pix = 11/12\n", + "Change source position\n" + ] + }, + { + "data": { + "text/html": [ + "
18:18:07 WARNING   get_number_of_data_points not implemented, values for statistical        plugin_prototype.py:128\n",
+       "                  measurements such as AIC or BIC are unreliable                                                   \n",
+       "
\n" + ], + "text/plain": [ + "\u001b[38;5;46m18:18:07\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m get_number_of_data_points not implemented, values for statistical \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=345613;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/threeML/plugin_prototype.py\u001b\\\u001b[2mplugin_prototype.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=208435;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/threeML/plugin_prototype.py#128\u001b\\\u001b[2m128\u001b[0m\u001b]8;;\u001b\\\n", + "\u001b[38;5;46m \u001b[0m \u001b[1;38;5;251mmeasurements such as AIC or BIC are unreliable \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b[2m \u001b[0m\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Run pix = 12/12\n", + "Change source position\n" + ] + }, + { + "data": { + "text/html": [ + "
18:18:09 WARNING   get_number_of_data_points not implemented, values for statistical        plugin_prototype.py:128\n",
+       "                  measurements such as AIC or BIC are unreliable                                                   \n",
+       "
\n" + ], + "text/plain": [ + "\u001b[38;5;46m18:18:09\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m get_number_of_data_points not implemented, values for statistical \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=119945;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/threeML/plugin_prototype.py\u001b\\\u001b[2mplugin_prototype.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=386236;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/threeML/plugin_prototype.py#128\u001b\\\u001b[2m128\u001b[0m\u001b]8;;\u001b\\\n", + "\u001b[38;5;46m \u001b[0m \u001b[1;38;5;251mmeasurements such as AIC or BIC are unreliable \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b[2m \u001b[0m\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: user 16.2 s, sys: 950 ms, total: 17.1 s\n", + "Wall time: 17.3 s\n" + ] + } + ], + "source": [ + "%%time\n", + "\n", + "# Fill up zero-order HealpixMap\n", + "fill_empty_value(m, range(m.npix))" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "1b533e6e", + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAnUAAAG4CAYAAAAjaRGjAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8pXeV/AAAACXBIWXMAABcSAAAXEgFnn9JSAACu5UlEQVR4nOzdd1hUZ8LG4d80eu+CiIoIiIqKomBXsPeWZqpZU3fTd7PJJpvt32azm957sknsvYuCSrchKoKIoqiA9A7DlO8PItGsUYGZOTPDe1+XVwww5zxJDDxz3ibT6/V6BEEQBEEQBIsmlzqAIAiCIAiC0HWi1AmCIAiCIFgBUeoEQRAEQRCsgCh1giAIgiAIVkCUOkEQBEEQBCsgSp0gCIIgCIIVEKVOEARBEATBCohSJwiCIAiCYAVEqRMEQRAEQbACotQJgiAIgiBYAVHqBEEQBEEQrIAodYIgCIIgCFZAKXUAQRC6h9bWVhobG2loaKCpqYnGxsb2X83NzajValpaWlCr1f/z+2s/1trailarRafTXffXq790Ot11HwOQy+XIZLKb/pLL5SgUCpRKJSqVCqVSiY2NTfvfX/119e9tbGywtbXF3t4eOzs77OzssLe3b//7G31cJpNJ/F9BEARrJtPr9XqpQwiCYP70ej0NDQ3U1tZSV1dHbW3tL/6+rq6OhoaG6wpca2trh+95tTxd+8vW1hYbGxtUKhW2trbY2tri5OSEvb09jo6O2Nvbo1AogLYyB6BUKjl8+DBXv93JZDL0ej11dXXU19dTV1dHXV0dzc3NhvsX9jNyuRwnJ6df/OXs7Nz+excXF1xdXXFzc8PNzU0UQkEQbosodYLQjWm1WmpqaqiqqqKysvJ//nrt72tqatqffF1LLpdjZ2fXXkQcHBxQqVQoFIr2J19KpbK9aCmVSuzs7K4rZjY2NpzLLQa9HJlOj6ujI/6BPZDL5GjUWtTqVlqb2361NLXQqtagbtagblHTXN9MbVU9NeU1VF2poa6yAZ1OB3pA31aEptw9kV0/7P0ptKwtt7O7E+4+rrh6ueLi7oSdox0yedvn9eiQyWVtv7/ap2R6mhobyT1+mitXysHFHmRaRsWF0dzcTHNzc/uTx2t/dfXbrI2NTXvB+/kvd3d3vLy88PLywtPTE1dXV1EABaGbEqVOEKyUWq2mvLycsrKy9l9Xrly57u8rKyvbCtA17OzscHNzw9PTE3t7+/ZhR1tbW5RKJXK5HAcHh/ZhxfO5pdRU1lNWXEV5cQ1N9RpkOgUynQL0V/8q/+nvaSsccp2e8H7u+PXxue7+er2e8stVnD54hvrqRoP8u5hy/3h2fb3PINdycnOg/4h+ePm7t5enq99Gi89dISejAAL82r9ejx5kOvQyLcg06OU//VUv08A1fx07M4K6ujpqamqorq6mpqYGtVrdoXwqlQpPT088PT2vK3uenp74+Pjg6+uLj48PNjY2Bvn3IQiC+RClThAsVEtLC8XFxdf9unz5MqWlpZSVlVFVVXXd1zs4OODj49P+NO3q0KVCocDBwQF7e3sqLtdTW9VA0bkrlJyvQN+qQKZTIdMq2/6qUyLTd3x9lRI9kYN74Obt0l6AdBodpzLPcCm/2CD/Pm7GkKXuZgJDexAa3a992BegpryOI3tPoOvhd5NX3lh7IZS3opdrQN7a9nuZpv1jwyf2paKigvLycqqrq2/72p6envj5+eHr64uvry9+fn7tf+/n54eDg0OH8wqCIC1R6gTBTOn1eqqqqigqKuLixYvXFbfi4mIqKyvbv1alUuHn54eHh8d1hU2lUuHs7IyzszPH0s5QcPIitRXNyDQqZFqbtl86ZfvTM0Px9HQkMtK//UmWVqvl6J4TVFyuusUrjcNUpe5GPP3dGTp5YPvws16v50hiDlVKe4PfS8+PBVChvuavauIXR7Y/qS0tLaWlpeWW13J1dSUgIKD9V8+ePdt/L4Z4BcE8iVInCBJrbGzk4sWL7eXtwoUL7b+vr68H2uZ/+fj44OPjc92KSltbW9zc3CjMLSX3WCHVJY3INLbItDbItSowQmG7ERcXO4YNC2gvLtVltRzcnvU/Q7tSkbLU/ZxcLmfE9CF4+LoAoNXqydyVTZ29s0nu3/b0T4Ne0YJOoUavaEGvaCFmWgglJSWUlpZSW1t702s4OTldV/ICAwMJCgqiV69e4gmfIEhIlDpBMJG6ujrOnTt33a+LFy9SVlbW/jVeXl706NEDBweH9nlrbm5uODs7s3N1KpfPVSHX2CLT2CLX2oJOYZLS9j/UrfTv40bQgIC2f7bqRtI3HUKjMY8S93PmVOp+TqmUM2rOcJzd2srQ+ZxLnDp8Dnmgv2SZ9DItekUzOkULemUz0+6O5OLFi1y6dIkrV67c9LW+vr707t2boKAggoKC2n/v4uJiovSC0H2JUicIBtbY2EhhYeH/FLjy8nKgbag0KCgINzc3nJycsLW1xdHRETc3N/ZvOcL502XINXbIW+3anrqZyx7h6lZGxvTGxdMZ9DryDp3lbPYFqVPdFnMudT/Xd3AvQof3BZmcmvJa0rZlSVrwfk6PDr2yGZ2iGb2imelLh1BUVERhYeH/zOO8loeHB0FBQfTt25fg4GCCg4Pp27cvtra2JkwvCNZNlDpB6CS9Xk9FRQX5+fmcPn2a/Px88vPzKS5um/ivUqkIDAxsL2/29va4urpSUljNsfR8mqr0beVNY4dMZ6b7gKtbiR4VhKtX21OWzO1HJZsX1xWWVOqu5envTvS0SABqKurNruD9nF7Wik7ZhF7ZhE7ZxLCJvSgsLPzFp3tyuZyePXvSr18/+vXrR3BwMP369cPLy0vM2ROEThClThBug06n4/Lly+3F7eqvq4sVPDw8CAwMxNnZGUdHR5ydnSm5UE122mmaa2TIW+3bnrzpzbS8Xevq0GpEIAAZW49QWVItbaYustRSdy0PPzdGzhwGwPmTRZIP0XaEXqZFp/ix7KkaiRznz5kzZ6ipqbnh17u6uhIcHExoaChhYWGEhobSo0cPUfQE4RZEqROEGygvLycnJ4dTp06Rk5PD6dOnaWhoQC6XExAQgLe3d/spAB4eHqQlnODCqTLkagfkrfbIdCqp/xE6zM3FlujYvgCcPlTAmaOF0gYyIGsoddfqN7Q3IcP6IpPLSNt2lFpbJ6kjdZgePchb0Skb0akaGD8/lIKCAi5cuHDDTa5dXV0JDQ29ruh5e3uLoicI1xClTuj2GhsbycvLay9wp06dal+8EBgYiK+vb/vebu7u7mz9Ppm6KxrkrT8WOL1C4n+CLlC3MiSyBz5B3tRW1JG8LlPqREZhbaXuWuMWjsTJw4mSwjKOJuVYzNO7X9I2Z68JnaqB6fcNIi8vjzNnzqDRaP7naz08PAgLCyM8PJyBAwcSHh4uVt8K3ZoodUK3otfrKS4uJjs7m+PHj3Py5EkKCwvR6XTtQ6iurq44OTnh4eHB7rWZVF1qQaF2RNZqZz6LFrrIRq5n/ORQZAoFx5JyTLIBsJSsudRdFRDSg8gJA9BrtexdnYnG20vqSAajR9f+RC/+rnDy8vIoLCz8nyd6CoWC4OBgBg4c2P7L19dXPM0Tug1R6gSrptVqOXv2LNnZ2e1Frry8HKVSSd++ffH09MTFxQU3NzcOH8jjfE45CrUjcrWDZT+B+wV+PVwYHBmARq1h73cHzHYLEkPrDqXuKqVSzoS7RmNjZ0PWvhxKNZY3FeB26NGiUzWiU9Uzdl4wJ06cuOGCDC8vr/aCN2jQIEJCQlAqLWBuqyB0gih1glVRq9WcOnWqvcSdOHGChoYGHB0dCQ4Oxs3Nrf3g+Q1f7EdTZ4O8xRG5zrrPwewX6ku/vp6UXSwnY1uW1HFMrjuVumuNnDEE755e5GUVcra8Y2fIWiKdvAWdTT1zHo7kxIkT5Ofn/8/TPAcHBwYPHszQoUMZMmSIKHmCVRGlTrBoWq2W/Px8jhw5wuHDh8nOzqalpQUvLy969eqFu7s7Tk5OFJ4qIedgEYoWJ+RqR6t8Cncjg4YFEeDryLkTRZxMzZM6jmS6a6m7atCYMIIG9OR8XjE5RXVSxzGZtqd5Dehs6hgxNZDs7Oz2U1quEiVPsCai1AkWRa/Xc/78eQ4fPsyRI0fIysqirq4ODw8P+vbti7u7e9tQ6v5cLuZWI29xQt7qIM2pCxKKGNKLwABncjPyrWoVa2d191J3Vb+hvRkwqj/nci51q3J3lR49emUjWptaRs4IJCsr6xdL3vDhw4mOjiYoKEjMyRMshih1gtmrrq4mMzOTzMxMDh8+TEVFBS4uLvTp0wcvLy9cXV3JzjjDhRNVKFpcflzQ0D2/CYdFBNC7txunDxZw+vBZqeOYDVHqrtc/qi/9RwRTcLyI08WNUseRzO2UPG9vb0aMGMGIESMYPnw4rq6uEqUVhFsTpU4wO1qtllOnTpGRkUFGRgZ5eXkolUr69++Pp6cn7u7u5B+/SP6REhQtzm3Dqd20xF3VM9CDQZE9KMg6T07aaanjmB1R6m4sIjaU4Mggju7L4XKzdazs7oqfSl4NkZN8yM7ORq3+aS6iTCYjNDS0veQNHDhQDNUKZkWUOsEsVFZWkpGRQWZmJgcPHqS2tpaAgAB69uyJh4cHzs7ObPwiGVmjE/IWJ6vZWqSrnB1UjJ0cRmlhabdcAHG7RKm7uZEzhuAb5MO+dZnUO7pIHcds6NGhs6ll3mORHDx4kLNnr3/67eDgQHR0NLGxsYwaNQo3NzdpggrCj0SpEySh1+spKCggOTmZlJQU8vLysLGxITQ0FC8vL9zc3EjecYyaizoUzS5Wvzq1w9StTJwajl6nJ+G/B6ROY/ZEqbs9cfeMRSaXkfB9CvKgAKnjmB2dXI3OpoZxi/pw6NAhqqur2z8nk8mIiIggNjaW2NhY+vTpI+biCSYnSp1gMhqNhuzsbJKTk0lOTqakpARfX1969+6Nh4cHjo6ObPw8pa3EqZ26/ZDqLxk0LIjAABf2/pBCfVX9rV8giFLXAU7uTky6azSFORc5eaH7Laa4XXr06JQN3PlcFKmpqeTn51/3eT8/P0aPHk1MTAxDhgzBxka8MRWMT5Q6wagaGxvJzMwkJSWFtLQ0amtr6du3L/7+/nh6enL4QC5XzrSgbHK1yPNSTcnV2Zaxk8PITT9N7sECqeNYFFHqOi5sRDBho/qTuCpNDMneBp28Ba1tNVFTfTly5Mh1c/EcHR2JjY1l/PjxjBw5EltbWwmTCtZMlDrB4BoaGkhJSSExMZGDBw+i0+no379/+xmqe9cfpKXCDkWzS7fZL65L1K2MnRiCQqEg4Tsx1NoZotR1XtzSsWhbtSStyRBDsrdJL9Oitalhyr39SU1NpbKysv1zdnZ2xMTEMG7cOGJiYsRZtYJBiVInGERjYyOpqakkJiaSkZGBXq9nwIAB+Pj4YG9vz44fDiJvcPlxkYMYVr1dvYI8iRzWk/1r0qm4XCV1HIslSl3XePq7M27hSA7vOcElsUq2Q/To0anqmftYBPv27aO0tLT9czY2NowYMYIJEyYQGxuLs7OzhEkFayBKndBpTU1NpKWlkZiYSFpaGgDh4eH4+PhgZ2fHjv8eQtnkjqzVXhS5jlK3Mnn6AGrK60nfcljqNBZPlDrDiJ0ThbOHE7u+TRZP7Trh6jy8RU9FkpSUxKVLl9o/p1QqGTlyJHFxcYwePRo7OzsJkwqWSpQ6oUM0Gg0ZGRns2rWLtLQ0tFot4eHh+Pr6olQqSViRJYpcF/UK8iAyqhe7v9kvFkIYiCh1huPi6cTkpeM4knCci03i//HOuron3p3PR7Fv3z7OnTvX/jl7e3vGjRtHfHw8w4YNE3vhCbdNlDrhlvR6Pbm5uezatYs9e/ZQW1tLeHg4/v7+2NrasvP7QygaPZCLItc16lbGTepPc2MLqRsPSZ3GqohSZ3ixc4dj52BL4qp08dTOAHTKRhY/E8nu3bspKSlp/7i7uzsTJ04kPj6eAQMGiG1ShJsSpU74RaWlpezatYudO3dy4cIF+vbtS69evXB2dmbnikPI69y65bmqxuDuas+4uFD2rUmn/GLlrV8gdIgodcbh3dOTcYtGkrgqnRo7J6njWIWrc/CmLwshMTGRmpqa9s/5+/szdepUpk2bRo8ePSRMKZgrUeqE6zQ2NpKUlMTOnTs5evQo3t7e9OvXDy8vL/ZuOIym0rFt1aoocgYzZGRfPB0V7BSlw2hEqTOuKfePp+xSFdlnq6WOYlXaTrSoYdwdvUhOTqapqan9c1FRUcyYMYNx48aJLVKEdqLUCe3Dq1u2bCEhIQG9Xs/AgQPx9vbmxMECSk+rUTa6i+1HDEymbmXa/CGcySrkVHr+rV8gdJoodcYXPiqE4MjebPs8EVkvf6njWB29TIvWtopBk105fPinxVNOTk7ExcUxY8YMQkNDxfBsNydKXTdWV1fH7t272bJlC2fOnKFfv3706dMHuVzO3pUnUDZ6INeKd4DG4OFmz/j4cLZ9toeG2qZbv0DoElHqTMPRxZ4ZD09m76o0qm0cpY5jtXSKZu58YQg7duy4bv5d3759mTFjBlOnTsXV1VXChIJURKnrZvR6PSdOnGDTpk0kJSWhUqmIiIjAx8eHxE1H0JQ5ib3kjGxgZE98Pe3FcKsJiVJnWlMfmMDlc1fIKRKrt41Jjx6dTS1jlwSwf//+9lMsbGxsmDhxIvPmzROLK7oZUeq6icbGRnbs2MGGDRsoLCwkLCyMoKAgdDod+1blomz0RKYTy+aNbeKMwVzJvcCxfTlSR+lWRKkzvcgJEXgGeLAv7bzUUboFvUzD46+PZ/PmzZw+fbr94yEhIcyfP5/Jkydjb28vYULBFESps3JFRUWsW7eOHTt2ADB48OC2RQ/rD6OtcEGudhRP5UxAodEw+84RpG46xOWC0lu/QDAoUeqk4d/Pj5hZw9jw/m4IFKs1TaFt9WwDE+/pyd69e9uf3jk5OTF16lTmzZtHUFCQxCkFYxGlzgrpdDoyMzNZu3YtGRkZBAUFERISgk6nI3ltPsoGL7HowYTcnG2ZNGMgG97bgbq5Veo43ZIoddKxsVMx78lp7P4uhVp7se2JKellrTz4p5Fs3LjxutMrhg0bxqJFi4iNjUUuF8e+WRNR6qxIfX0927dvZ/369RQXFzNw4ED8/f1J2XWMlhKxFYkUevfxJGyAH1s+2i11lG5NlDrpzXokjhNp+RTVix85ptY2966GEXO8SE1NRafTAdCzZ0+WLFnC1KlTxdCslRClzgqUlZWxZs0aNm7ciFwuZ/DgwTg7O7NnTRaqem/kGnGGoBQGRwXhbAv7VqZJHaXbE6XOPIxfEkNtRT3Z52pu/cWCUejkLSx4OpxNmzZRX9+2kMXFxYW5c+cyf/58vLy8JE4odIUodRbs3LlzrFixgt27d+Pt7c2AAQMAOLA6v23hgxhilcyoCaE0lpRzZPdxqaMIiFJnToZOHoiDswPp2SW3/mLBaPQyLcv/Hsvq1au5fPkyAEqlksmTJ7NkyRJCQkIkTih0hih1Fkav15Odnc0PP/xAamoq/fr1IyQkhKz0XCpPK1A0u4ohVolNmhXJhSOnyc0okDqK8CNR6sxL6IhgeoYGkJRaKHWUbk+PHq1tFWHj7cjOzm7/+PDhw1m6dClDhw4VW6JYEFHqLIROp+PAgQP88MMP5ObmMmjQIHr06EHSlsNQ4YlCLSYgS65FzdR5Q8jen0NR7mWp0wjXEKXO/ASG+TN43AC2fZ6IPChA6jgCoFXVM2aJL0lJSWi1WgAiIiK49957iYmJEeXOAohSZ+a0Wi1JSUl88803FBUVMWTIEDw9Pdmz9giqOl8xX85ctKiZdccIUtZlUHapUuo0ws+IUmeevHt6MnrecDZ+mCCKnRnRKZqZ9qvebNu2rX1LlODgYJYuXcqECRNQKMTUHnMlSp2Z0mg07N27l2+++Ybi4mKioqJwcXEhcU02qjpfZDqV1BGFq1rUzF86il3f7qOmrE7qNMINiFJnvly9nYlbOo517+wQxc7M6OVq5j0VyoYNG2hqajvOMCAggPvuu4/4+HiUSrFhvbkRpc7MaDQaEhIS+OabbygtLWX48OHY2tqSsvE0qjofZHrxP5FZaVGz4P4Ytny0m8a6ZqnTCL9AlDrz5uhiz8zlcax+c5sodmZIL9Nw1+8Hs2bNGmpra4G27VAeeOABJk+eLJ7cmRFR6syETqdj7969fP7555SXlzNs2DBsbGxI3XgGVb2PWMlqhmTqVhbcF8Omj3bTVNckdRzhJkSpM3/2zvbMfjSO1f8Rxc5c6WVa7v9jFD/88AM1NW3b0vTq1YsHH3yQiRMnio2MzYAodRLT6/WkpaXx6aefcuHCBYYPH45SqSR981lR5sxZi5qF98ey6cNdNNWLJ3TmTpQ6y2DvZMfsx+JFsTNzepmWpX8YwooVK6ira5ty0qdPHx566CHGjRsnFlRISJQ6CWVlZfHJJ59w6tQpoqKisLW1JX1zAap6X1HmzFmLmvn3jmLbp3toqBVP6CyBKHWWw9HFnhkPT2bN29tFsTNzV4dlV65cSUNDAwDh4eE89thjDBkyRNpw3ZQodRLIz8/no48+4tChQwwbNgwXFxeSN55CVeeHTCfmzJm1FjWz74pm738PUFMhFkVYClHqLIurpzMT7xrNxg93i2JnAfQyDYueD2fNmjXtCypiYmJYvnw5wcHBEqfrXkSpM6GysjI+++wzduzYwcCBA/Hz8yNxXRaq2h7IdTZSxxNuw8w7R5L8XRIVl6ukjiJ0gCh1lsfT353YuSPYtjNX6ijCbdLL1Uxd3ovNmzej1WqRyWRMmzaNhx56CF9fX6njdQui1JlAY2MjK1asYMWKFfj5+dG/f3+SNh9FUeGHXGsrdTzhNk2eM4STCUe5mFcsdRShg0Sps0wBIX4MiA1l74FzUkcROkCnaGLUYneSkpIAsLGxYcmSJSxduhQHBwdpw1k5UeqMSKvVsn37dj7//HMAhg0bRua+bFqK3MUJEBZmdFw4l7PPkX/4rNRRhE4Qpc5yhQztg1+wH6lHLkkdReggraqOsElKjh07BoCnpyePPPIIU6ZMEStljUSUOiM5fvw4b731FkVFRURHR1NXV0fO7loUTW7ibFYLEzmiN5qqGo4lnpQ6itBJotRZtsgJEciUco6frZE6itBBevT84fM5vP/++1y+3HZ8Ynh4OL/5zW+IiIiQOJ31EaXOwCorK/noo4/YsWMHw4cPR6VScXhbEcp6H2SIdyaWpm+IDz7utiSvzZA6itAFotRZvrELoyk+V865ao3UUYRO0KPj/teG8fXXX7cvppgyZQqPPPII3t7eEqezHqJlGIhGo2H16tXcc8895ObmMmHCBLKSCjm2phFVvZ8odBbI092efn09RKETBDNwYG0m/Yf2xl3dIHUUoRNkyPnmtSw4H86MGTOQyWTs2rWLpUuXsmrVKjQaUdYNQTQNAzh27BjLli3jiy++ICYmhorL9aSvLMG2qrc4o9VCKbUaxk8ZwLbP9kodRRCEH237fC8T74hBfkksVrJUMp0NSV9UYlMWQUREBE1NTbz33ns88sgj5OTkSB3P4olS1wV1dXW8/vrrPPXUU/j4+BAeHs7+VfloC4LEQggLN+fhCax7Z7vUMQRB+Jn17+5g7pPTpI4hdJFC48TZPU688MILODs7k5+fz2OPPca///3v9lMqhI4Tc+o6Qa/Xk5SUxNtvv429vT3BwcGkbsvBpqan2DzYCsTPHcrhTRlcuVAudRTBQMScOuvi3dOToZMHseeAWI1uDfTyVsbf582OHTsAcHd358knnyQuLk4cOdZBotR1UGlpKW+++SYHDx4kJiaGk1lnaDjjjKLFWepoggEMGdGblvJKju8XG55aE1HqrM+gMWEo7WzIPlstdRTBQLSqWvyGNXD+/HkAYmNjee6558RCig4Qw6+3SafTsX79eu677z4qKiqIiooibXM+TSf9RKGzEn6+zrjay0WhEwQLcDw5Fw9fF3zkrVJHEQxE0erClQxfli1bhlKpJDU1lfvvv5+tW7cinj/dHlHqbkNpaSnPP/88H374IaNHj+Z87hWOra/DpjZArGq1EvJWDaPGhbDnu2SpowiCcJv2fJfM6NnDoEgsnLAWMuT88LeTKEsGEBYWRn19Pf/85z957rnnKCkpkTqe2RON5Cb0ej3btm3jgQceoLGxkYEDB7J/dR7yi32Ra+yljicY0KwHxrL5w11SxxAEoYM2f7Sb2Y/GSR1DMDC5xoHzSa489thj2NjYcOjQIe6//342b94sntrdhJhT9wvKy8t54403OHToEGPHjiU54RDykkDkGjupowkGFj22P1dOFXLmaKHUUQQjEXPqrFvwkN549/TkYE6Z1FEEI9Apmug/WU92djYAo0eP5re//S3u7u4SJzM/4kndDRw4cIAHHniA8vJyBg0axP61uSgu9hOFzgr5+DhhJ9OIQicIFqwgqxB7Zzu89M1SRxGMQK61J3+XPU888QQqlYqUlBQeeOAB0tLSpI5mdkSpu0ZLSwv/+c9/+OMf/0hUVBSXz1aQs7UZm7oe4rxWa9SiZvSE/uwV8+gEweIl/pDCuPnR6M5fkjqKYAQyZHz5ymEUxWH06dOHqqoqfve73/HGG2+0HzsmiFLX7uzZsyxfvpy0tDSioqJI3ngK3bkg5K1i7py1mnp3DDu/TJI6hiAIBrLz6ySmPTBe6hiCEck1jpSkebNkyRIANm3axMMPP0xBQYHEycxDty91er2e9evXs3z5cry9vXFzc+PY5sq2jYTFvx6r1T/cj5KT56mtrJc6iiAIBlJbUc+VC+UEe9lIHUUwIhlytrxzgTfffBNvb2+Kiop45JFHxCIKunmpa2ho4NVXX23fquTwvjyK9tqhaHGVOppgREqthtAwXw7vzpY6iiAIBnZ4dzYDRoYguyi2ObF2L9+1kvoTvRg1ahRqtZp//etf/OUvf6GxsVHqaJLptqXu3LlzLF++nDNnzjBgwABS1p3BtrwfMp1K6miCMWm0PPi7mWz+aLfUSQQTCgjpIXUEwYS2fpLAr/5xJ5RXSh1FMDKZXsWxDXoeffRRFAoFCQkJ/OpXv+q2w7HdstQlJCTwyCOP0NTURFBAX3K2N6Kq9xWLIaycp6cj8x8YTfHZUjRqjdRxBBMZPiWS8ycuMDx+oNRRBBNpbWnl8tkrLHgsDk+VTuo4gpHJkPHNa1kor4RdNxy7c+dOqaOZXLcqda2trbz11lv8+c9/prm5mdi5M8haX4tC7SR1NMHIIqP7MCS6D+ve3UlTQ4vUcQQTcXZ3wquHK3u/P4BXgCdOrmLhU3fRXN/M2vd2MXRcGIPDxNmh3YGi1Zn6k0GMHDkStVrN3/72N9577z00mu7zJr7blLrq6mqeeeYZ1q1bB0D0Q4vJ/rpMDLdaO42WqXMiaa1rIOH7FABk8m7zx77bi79vLDu+TARgxxd7ib9vnMSJBFORydtGXnZ/n4pG3crUqeFiOLYbkOmUtJT247777gNg1apVPP/881RXV0sbzES6xU+3goICli9fTnZ2No6OjlTOGkLv8z0pOyd2H7dmLi52LHxwNKlbjnAyNb/943qdGI7pDsYsiCZjy+HrPpa57Shj5g2XKJFgSnrdT6sgT6adIW1bFgufmIKLXi1hKsEU+vf1ZvUHRfzlL3/B3t6eI0eOsHz5cvLz82/9Ygtn9aUuJSWFxx9/nJKSEgL8NHzw13NMGxdH4pqDUkcTjGjAkF7ETAhlzTs7qa0Q25Z0N/7BvsiBS/nXr4C8lF+MXCHHv48YjutuasrrWPPODmKmRRIR7CF1HMGIkrZkER8/gLGhD/P+Xy4QEBBASUkJjz/+OAcOHJA6nlFZbanT6/X88MMPvPTSSzQ1NTE0ooUP/15O/Jml9N6ppbFeHCdjlTRaJk+PQKHTsOPr/Tf8kpamVuydxJFv1ixm1jD2r7nxEUL7V6cRM2c46LQmTiWYir2THc3NN34it+ObAyiUcuImh4rhWCtVX9uMnb2K6TPupm8vDR/95TDR0dG0tLTwhz/8gVWrVlntfnZWWeo0Gg3/+te/+PDDD9Hr9cyJb+BfL1fi4qRnZs+B7Ft3SOqIghE4OqhY+OBojibmkL3v1C9+XU5mAZFxg02YTDClWcvj2P753pt+zY7P9zDrkXgTJRJMLXJiBCcOFv7i54/tz+Vo0kkWPB6Pg7r77mlmzZK2ZDFxYigAzk56/v7MRubOnYter+e9997jrbfessoFFFZX6pqamvjDH/7Ali1bkMvl/ObBGp55uBalsu3zTipb6mvFOXHWJjQigPHTB7HmnZ1UFFff9Gsriqtx9XI2TTDBpIZMjOBs9jkaa2/+g7qhtomzxy8wZHy4iZIJpuTq4URVac1Nv6b8cjVr393JhPnRhPYSG85bm/raZhyuGZFRKuDpuz/i8ccfRyaTsX79el5++WWr26jYqkrd1RWuqamp2NjY8OfnylkwvRHZj9vP3XHyJWr+z/onSnY3E2cPwdFOztbPEm/7NWIFrPXx8HPDt5cnOamnb+vrc1Lz8O3tjbuPKPjW5urK19ux9YskHF3smTAhxIiJBCmczi7imRd/1/73MhksmfAKf/rTn7CxsSEtLY3f/OY3VrUy1mp+sl2+fJknnniCnJwcXJx0/OeVy4wZcf1+ZMFOvhQcLZQmoGBwdrYKFt47ipP7cziccELqOILEJi8dy84vb7/YA+z8MpG4pWKbk+7u8J4TnErPZ+GSKGwb6qSOIxjImZOX6NPT7X8+Pi7sV7z1x8u4ublx+vRpnnzySUpLS00f0AisotSdPXuWxx9/nKKiIny9NLz75woGhrZKHUswor6hfsTNGcLad3dRWii2punuZi2PY8ennTv6becXe5n1q0kGTiRYmpLz5ax9bxdT7hlDsL/YkN7aDQhp5d3XTuPr68uFCxd44oknuHDhgtSxusziS11eXh5PPfUUlZWV9O3Vyvt/rSCo5/9Ofnww74+UvnJSgoSCoY2dNggvdzs2f7K30yuYik6XEDKyv4GTCVIYPiWSgqxz1FU1dOr1tZX1FBw7T1ScOEbMGvQb2ocL58o79Vq9Xs+mj/fg5efGuDF9DZxMkMKxjAJe+surN/xcoL+Wd/+YTa9evbhy5QpPPPEEeXl5Jk5oWBZd6nJycnjmmWeoqakhLFjN269V4OVx441l/WzdKfnZnlWCZVEpZMy/ZyRnD58hY/uxLl0r99BZgiODDJRMkEpASA+c3Ow5lX578+h+yan007h4OuPfV+xfZ+n6DQki9+DZLl0jfXsWBccuMH/xMFR1YjjWkhWfr8DX7ZePB/Tx0vHOq5mEhYVRU1PDU089xfHjx02Y0LAsttQdO3aMZ555hvr6egaGqvn3K5U4O1nnvjMC9OrrzYzFI9j0UQIXT5cY5Joy2e1PphbMj1KlYNSMISStSDHI9RJ/SCZm9nCUCvHnwpJ1ZJHEzVw8U8KmjxKYcf9YgrzEvpbWzM1Fz39+n8TQoUNpbGzk+eeft9hiZ5Gl7siRI7zwwgvtmwq//nIljg43L3Q6xNFQliomLoKeAa5s+HA3Go347yi0mffr6Wx8b4dBr7nxve3M+/V0g15TsFwajY4NHyYQ0NeH2GjxZN9SabW3/rnhYK/nH89uIyoqiqamJostdhZX6o4fP87vf/97mpubiR7SzP/9vhIHu5sXuvtzX6XyNcseJ++O5Do9c++MpiSviJRNh2/9gg46vPckMfNHGfy6gvHF3TuW/atS0LQadvNQTauWA2vTibtnjEGvK5hG7NzhZCbmGvy6KZuPUHzuCvMWDEVeWW3w6wvGlXv0PL/70x9u+XV2tvC3p7cwbNgwiy12FlXq8vLy+O1vf0tTUxMjIlv4y/NV2Nrc+nV+dm5czrts/ICCwfj5uzL3vhi2fbmPs9lFRrlHRXE1bj4uRrm2YDxDJg6kpKCEKxc6Nxn+VkrPl1FaeEVsTGyBXL2cb7npcGedPVHEtq/2Me/Ryfg5WNSPzm7v4rkyfF1/eV7dtexs4e/PbG0vdi+88AInT1rOIkuL+ZN59uxZnnvuORoaGogMb+Evz1feVqED0IkRO4syLKYf4QP9WffuTtS/cH6j0D0Fhvnj4uHAiWTDP4251vEDp3DzdiEwxNeo9xEsS0uTmrXv7iQ8ui/DBvlJHUfoiA7slHC12F2dY/fCCy9w9mzXFt+YikWUuqKiIp599llqa2sJD1HzjxersLOVOpVgcBot0+YNobGihr0r001yy8xdxxl352iT3EvoGntne6ImDWT/6jST3C9pVSpR8ZE4ON7mu0dBUmMXjiRtt2meqOxdmU5TbRPTZ0RAeaVJ7imYVlux205ERAT19fU8//zzFBeb/w4aZl/qKioqeO6556isrKRf71Ze/30lDvYdW+UqToQyf85Otix8aAwpGw9zKuOMye5bXVaLs7ujye4ndN6cRyaz4b3tJr3nhve2M/uxKaDTmvS+Qsc5uztRW1FvsvvlZBaQsvkIi56cgrO25dYvEKTVid0O7O30/OO5PfTp04fy8nKeffZZKivNu8Sbdd1pbGzkxRdfpKSkBH9fDf96WWxbYo3CBgUyOi6MNW/voLbSdN+Ur2pqbMXJ193k9xVu37wnp7L5w12S3HvTh7uY96RYEWvOnD0caVKbvnjXlNex+u0djJk9jLAgN5PfXzA+Fyc9r/8uHT8/Py5dusQLL7xAfb3pf07dLrMtdRqNhtdee428vDxcnXW8/nIl7q5icpy1GT8zEnsb2P7lfskyHFh/iDFzh0t2f+HmJt09msytR2isa5Lk/k11TRzcmcXkO2Mkub9wa6PnjuDA+kOS3X/bl/twcLJlwrh+kmUQjMfbQ8cbv8/Gzc2N/Px8Xn31VTQaw668NxSzLHV6vZ4333yT9PR0bG30/OPFSnr6ieEPa6JSyJi/dBS5KbkcSTghaRadTodSqZA0g3BjI6YNofRsKZcLDLPhdGddyi+muLCMEfGDJM0h3JhCpUAn8Yq4w3tPknuogIVLolDW1kqaRTC8nj20vP5iPnZ2dhw6dIh33nmn08dUGpNZlroVK1awefNm5HI5rzxVxYCQ1i5dT6x+NS+9+ngxY8kINn6wm5JzV6SOA0DWgVxGzo2WOoZwjbDofijkek6mmscekzmpeShUCkKHizNBzcnImUPJSjXdPNybKT5XxoYPdzPrwfEEeojVfGbFAAWsf18Nr7zyCjKZjA0bNrBu3ToDBDMssyt1mZmZfPzxxwA8eX8VY0Z0fQKqWChhPkZODCOwlzsbPth9W7t8m0rxuTJ8Aj2kjiH8qEewLz1DfEnfckTqKNdJ33KYwNAe9OjjJXUU4UfePT0pPlcmdYx2Go2O9R/spld/P6KH9ZQ6jnCVgY6FHB3yEI8++igA7777Lunpptmp4XaZVd25ePEir732GjqdjpmTGpk/rdEg1xVP6syARsusxVGUFRSTvEG6uS83cy7nMmGxYVLH6PYcXR0YOS2ShG+lm2d5Mwnf7mfkjGE4OosnMVILHRHMudOlUse4oeSNhym/VMXsuZFi2xNzYMCh0iUTXmXGjBnodDpee+01zp8/b7Brd5XZlLrGxkZeeukl6uvrieiv5qllNYYq1oLEvLydWPBALAnfp1Bw7ILUcX7RqcwC+g3pLXWMbk2hkDPrV5PY8K5pty7pqA3vbmfWI/EoZOY3p6Y76Te0N6cyC6SO8YsKss+T8EMKC5+YgodSPF2wFjIZPLP0c4YMGUJjYyOvvPIKjY2GeQjVVWZR6vR6PX//+98pLCzEy13Ln5+rwkZluOuL4VfpDBzemyEjerP23V001Zv/Xk7lJTUERPSSOka3teDpGax7e5vUMW7L+ne2s+DpmVLH6Lb8Q/yoKGuQOsYtNdY1s+adHQybEM7AEE+p43RfBn5KpFLCq4/vxNPTk8LCQt544w2zWDhhFnVn3bp17N+/H5VKxZ+fr8LTXbyjsXgaLZOnRyBrbmb3dylSp7ltB3dlEzUpQuoY3dKsR+NJ+DqJ1pauLYwyFXWzmoT/7mfW8slSR+mWouIGk7krW+oYt233dynIgLhJ/cVwrJXwcGsbflUoFCQkJLB+/XqpI0lf6vLy8vjggw8AeOze8i6vdBWkZ6uSs+DB0WTtzyX7gHmsXOyIiwVl9BsRInWMbmXyPWPISsim6opxDmM3lqrSGrL2nmDyXbFSR+lW+g3tw6XzFVLH6LDs5DyyknNZ+MQUbBvMdwNb4fYN8l3UvnDivffeIycnR9I8kpa6xsZG/vSnP9Ha2sro4c3Mn2qcMWkZYnKeqfTq483UBVGse3cX5Rct893osf2niIjtL3WMbmP03OFczL3IxdOXpY7SKRdPX+bi6WJiZw+TOkq3MSAmhKx9p6SO0SllRZWse28X05aOoZeHOFfYVORGnKS/aNwfGD9+PBqNhj//+c+Szq+TtNS9+eabXLx4ER9PLb97vNpoCyNe7vsJuv8MNM7FhXYjxoYS2MuNjR8lmMXcgq44c+wCERPEnxljGxY3iIbqBvIOmu9k99uRd/AMjbVNDBND90Y3cEwoZ3Is8w3AVTqdjg0fJdAr1J8RQwOkjmP1+ob7s+Dpr412fZkMXnhwJb6+vly+fJl3333XaPe6FclK3d69e9m5cydyuZw/PFWFixHPdHXWnSKyh4/Rrt/tabRMnz+U6stlZrtdSUflHjpLyJAgqWNYtfBRIdjYKMhKlPZEEUPJSjyBjZ0N4dHBUkexav2G9Cb34FmpYxjEgQ2HqLlSw4wZA8U8OyPqNyCAXnrjTgVyctDz8ssvI5PJ2Lp1KwcOHDDq/X6JJKWuqqqKN998E4Cl82sYHGb8eXRiCNY4nBxtWPjQGJI3HuL0oXNSxzGoI4mnGL1InPdpDH0GBuIb6EH6lsNSRzGo9C2H8O3tTe9wf6mjWKXR84ZzMClX6hgGlXekkAObDrHwiSk4qM1jWwxrI5Ob5uf/YL/F3HnnnQC8/vrrVFSYft6nJKXurbfeoqamhuCgVu5daJrJonosezjQHAWH+TF+2kDWvL2D2grrm/R7qaAUjx7u2Lg6SR3FqvgGeRE2vC9JK1OljmIUSStSCB/VH19xQolBqexUePT0MavTIwyltqKeNe/sYOLCkfTt4Sh1HKuj15nu5/+Ds/5Dv379qKmpkWSbE5OXusTERBITE5HL9bz4eDUqpWnu+0r/rRT9Wew/ZigxcRF4uduz5bNEqaMY1a5vDzD13jFSx7Aart7OxMwcxvYv9kodxai2f76HmNnDcfVwkDqK1Zj24ER2mukpI4ay5bNEfPzdiY0WUz8MxcHZnsXPmu77jY0KXno0DaVSSUpKCvv27TPZvcHEpa6mpuaaYdd6QvpoTHZvR/V+pgT3Mdn9rJZGy+zFURTnFpG+LUvqNEan0eioKKmhV6T4s9NV9k52TLl3HBveM+/TIgxlw3vbmfLABOztTfTO1Yr1CvOn/EodGo3172Gati2L4nOlzJozWMyzM4DYuAEEaE37ZqBvLw133303AG+//Tb19aYbyTJpqfv000+prq6md6Dphl1/okYpV5j4ntbF1c2BhQ+NYfcPqZw7XiR1HJPJ2CE2JO4qpUrBnMfiWP3GJqmjmNTqNzYz58npKMW3ni4ZFh9Jxo5jUscwmbMnLrJ3ZRqLnpyCm0zs3doVcrkcUJv8vvdM+Sc9e/akoqKCTz75xGT3NVmpy8vLY/PmzQA8s6zWZMOu1xKLJTqvf0QAo8aHsObtHTTVNUsdx+QO781hzJLRUsewSHK5nMXPzGTtf7ZIHUUSa/+zhUXPzUYu5vV2ypgF0RzeZ12LI25HQ20Tq9/ewcgpkfTv5SJ1HItlqkUSP2drA88//zwAGzdu5MQJ06zyN0mp0+l0vPXWW+j1euLGNBE5wPStGeD5oN/T69Nxktzbko2Oj8DFScWOr6x7PsvNXCooxdHNEbdAL6mjWJxFz7Sd56pp1UodRRKaVg3r397Goudmg657/jvoLDcfFxw9XblUUCp1FMns+GY/ru5OjI4R8+w6asy0QcQ99hfJ7j/E/w6mT5+OXq/nnXfeQacz/vQBk5S6HTt2cPLkSeztdDy6tNYUt7whT1kTHs5iB+/bpm5l9pLhXMy5wMFuNPTxSxK+T2HSYnEcVEcsenYmWz7eTUtTi9RRJNXSpGbLx7tZ9OxsqaNYlEl3jWb395ZzdrSxZO7K5tLpEmbPFfPsOsLZ1YEIpbTbxPxq/lc4ODiQm5tLQkKC0e9n9FLX1NTUPp78wOJ6vDyknej6YsgWiv4kVsHeipOdksXLx7Pnh1QKT1yUOo7ZOJacy6h50VLHsAhzn5xGwtdJNNaKvbcAGmsb2fPdfuY+Hi91FIswcuZQslLPSB3DbJzLucSelWksenIqjq1NUscxe44u9sz5jfS7M3i46Vi6dCkAH3/8Mc3Nxp2+ZPRSt2bNGiorK/H31bBgeoOxb3dLzuo9zArtJ3UMs9Y72JvxsyJZ9eZ2GmrFN49rFeZcwtPfEydfd6mjmLWZyyeTuj6D6jLpnsybo6rSGtI2HmLmw5OkjmLWHN0c8erlS2HOJamjmJWGmiZWv72dSQtHEuRtL3UcszZm6iCCtLuljgHAwnF/wtfXl7KyMlatWmXUexm11NXW1vLDDz8A8OCSOkkWR/wvHQq5pEfemrWo2H74+7uy5VPr3kesK3Z8s59p946VOobZmr5sElkJxykrKpc6ilm6UlRO1t4TTHtgvNRRzNa0B8az/ZvuO4f3VjZ9upeefX0YHtlD6ihmS6GQAeaxBY6tDTzyyCMAfPfdd1RVVRntXkZtN9999x319fX07dXK5NHms2LyqZ5/wfcjcfzTddStTJk9mIayalI2WdfRTcZwcM8JsRr2BqbcP56TyTlcOlMsdRSzdulMMTnpp4m7R2xs/XNjF44kc+8pqWOYveRNh6mramTK1HAxz+5noieGM/6RN6WOcZ2JEY8RFhZGU1MTK1euNNp9jFbqysvLWbduHQAP31mHOT0c85KVE+ghjn66SiXTs3DZOA4lnOBUZoHUcSzCxfxSFDYKAsIDpY5iNibdPZr8Q2e4cEoMmd2OCzkXKcgqZPKd4g3mVf4hfsjtbLv1ateOyD1YwOE9J1j4xBSUtWKqw1XePdwYpDCvP0MyGTzwwAMArF+/nurqaqPcx2hVa/Xq1bS0tDAgRE1MlPmtfHuq90co3xwidQzJ+fo6M+vuUax/fxcVl433SNgaJW84TMzsKOT2dlJHkdyEO2K4cPIC545fkDqKRTl3/DwXci8xYdFIqaNITiaXEbtgFAc2HJI6ikUpv1TF+vd3MWfZBHzszejpiUT6Dw5k8iPfSh3jhkb2uZ+QkBCamppYvXq1Ue5hlD8B9fX1bNrUtnP80gX1yMxwz18PbQ5DAryljiGpiMhAIob2Yt17u9BqzWPugaVJWJHK1Pu6996HYxZEU1JQwpmjhVJHsUj5R85RUljG2HnDpY4iqWkPTmDXf5OljmGRtFoda9/bxcBRwUT085A6jqRCBvYkTHZc6hg3dO3TurVr11JrhKerRil1mzdvpqGhgd49Wxk11Pye0l31dO8v4N+DpY4hiTFTIlDJdOz5IVXqKBatrrKB4nNlDJrcPf8cxc4dTuXlSnIzxdYTXZGbmU9FSRUxs4ZKHUUSg8aFcbmomvpqsf1NV+xZkYaNjZKxsd3zrOrgAQFMftR489UMIbbfQ/Tr14/GxkbWr19v8OsbvNSp1er2x4p3zGkwq7l0P+euzWJ4oK/UMUxL3crMRVEUnTjP0cQcqdNYheOppwns36PbnTYxalYUtWW15KTmSR3FKuSknqa+qoFRM7pXsXP3c6XngCCOp4g/R4ZwJDGH87mXmT0vststoAgf2oswzHuhn0wGd955JwAbNmygtdWwZ/savHLt2bOH8vJyvNy1xI0x/z3OfhP0IbZvdo9vovYqOYuWjydpTTqFJ8VkdkPa+W0y8Xd3n5WMI2cMpbG6jhPJYpWiIR0/cIrGukaip3WfJ79x94xl57cHpI5hVc6fukTiqnQW/3oqdk3S7w9rCmFDghi3/DupY9yW8QOewMPDg4qKCpKSkgx6bYOXuqtz6eZPazCTfeluzlN3isie1j+3zq+HC1MWDmfNWzuorxJDHMaQsCKN6Y9MkTqG0Y2YNoTmhmay94tCZwzZ+3JQN7UyPH6Q1FGMbsbDk9m9Kl3qGFaprqqB1W/vYNrSMfg5KqSOY3T9IgIYKLOM4yxVSpg/fz7QdkCDIRm01BUUFHDy5EkUCj3TJpr/U7qrHgv4Cz0+st49x8IHBTAgsicbPtyNXq+XOo7Vqq2o59K5KwyZMkTqKEYTFT8YbYuaY4knpI5i1bIST6DVaBkeN1DqKEYzZOIAigquUFsp3mQai16vZ/0Hu4kYGUx4Hzep4xhN9KRwYpe9I3WMDpk16u8olUpOnTrFmTOGm5Ns0FJ39SndmBHNeLpZzmpKH9kV/h19iLw/WN/u3KMmhOFgp2DPD2lSR+kWTqbm4x3oSY+wAKmjGNywuEHI9DqO7DHPlWXW5uie4yCTMXTCAKmjGJxfHx+8e/txUuyLaRIJP6Ti6GTPqGjr21dToVRw31/PMURZJHWUDnF31TFmTNuUnW3bthnsugYrdWq1mt27285Zmx1nee+8XFu2cNcgK/rm+eMJEeXnSzm0S/wQNqW9K9MZO28kNs4OUkcxmCETBqBUyDi0yzKGN6zFoZ1ZKG2VRI4PkzqKwahsFIxdEsseMexqUgcTjlNeVMnUaQOsagHFlEUj6Ksx/CpSU5ge03ZAw+7duw22YMJgpS4jI4P6+nq8PLQMHag21GVNqJln+66l4f8s/5unQqtl/kNjObj7OPlHCqWO0y1t/GQPcx6NlzqGQQwePwBbexWZ249KHaVbOrj9KPaOdkSODZU6ikHMfXI6Gz8RZ0tLIT/rPIcSjrPg8XjkldVSx+ky/z7eTHpiN2A+x5B2xPDIFry8vKipqSE11TDbixms1CUkJAAwKbYJhRlvY3Iz7poDxPXrLXWMLnG2VzF/2Vg2f7KXyuJqqeN0Wxq1hrRtx5h8/wSpo3TJwDFhODjZkrH1iNRRurX0LYdxdHVgYEyI1FG6ZPI9Y0jbfgyNWiN1lG6r/FIVmz7Zy8In4nHSWmYZuip6XBgR+gSpY3SaQg7x8W1v/hMTEw1yTYPUr8bGxvaWOdkCtjG5mWX+fyToE8s8IaBnoDvjZg5m9ZvbaW0x7N43QseVnC+jsrSWyPhIqaN0SkRMf1zcHUjfLI5tMgepmw7h4uXMgJHBUkfplMHjB1BRVk/xhQqpo3R7rS2trHprOxPmjSDATSV1nE4ZM20QQ+5/U+oYXTZ+0LsApKWl0dLS9cMaDFLqMjMzaWlpIcBPQ/8+lv0OrIesnL8O28uVP/eVOkqHhA3qSUi4H1s+FcMa5iQ7OQ/vnl4EDgqSOkqHhI3sh7uPC6kbD0odRbhG6saDePZwJ2y4ZZ0Y0DPUH+/ePmSnnJY6inCNzZ8lEhIZRHgfd6mjdIiHtwsLX8km2sIWR9xIaHArfn5+NDU1kZGR0eXrGaTUpaW1rawcPbzZLM957SiP1u3Mj7Cc+SsjxoTg5KBg70ox8dgcJa5OJ3raEJx8LeMbZ+iIYLwD3Ele3/VvMILhHViXgXcvb/oP6y11lNvi6ObIiBnDSFyTKXUU4QYSV6Xj5OrAiKGWs2J//KxIQrUbpI5hEDIZjB8/HoD9+/d3+XpdLnU6na691MUMM99zXjvqoR5/JOhT8x+GnTBjMPVl1RzckS11FOEmNnyYwOxfxSG3t5M6yk2FDO1DjyAvDqwRbxDM2YE1afgH+xIypJfUUW5KJpcx54lpYmGEmcvceYyG6gYmjO8ndZRbGj1tMIOXvit1DIOKCf8AaBv11Om6th1cl0tdbm4u1dXVODroGBRmiateb8xPVsZfhu6g7v/M9ImdupWZi4aTf+gMOeniMHVLsPmzvcx7zHxPnAgeEkRgf1+SVhlmFZZgXEkrU+kZGkDwYPPde2z+r6ex6TPDTAAXjOtk+hnOHDvPzDmDzXbLkx69vZj38kFGqs5JHcWgIvqrsbe3p7q6mvz8/C5dq8ul7siRtlVxUQNbUFrAsWAd4dm6hxmh5je3TqHVsmDZWJI3HORiXrHUcYTb1FTfTObubOIfnCR1lP/RZ2AgvcMD2PtDitRRhA5I/CGZ3gN70WeA+Q2dxd83joyEkzTVW/YKy+6kKK+Y5A0HWfjEFBRV1VLH+R8xkyKI0Btuo15zoVJCVFQUAAcPdm0ec5dL3dGjbXtXDYmwnqd017rX+9dEfm0+T1fsbeQseHgcmz7aQ3VZrdRxhA4qPldG8flyRsweLnWUdkEDetIvshd7vhOHqluiPf/dT7+hfegVZj4n4oyYFsnlC5UUF5ZLHUXooOqyOjZ+vIcFj8dj32w+BwlMv3MUIXf9WeoYRjNixAgADh8+3KXrdKnUaTQaTpxoOwMycoB1lroAhZanwj7C+Z0RUkfBy9ORqQuHs+o/22gV+zxZrFOZBdjY2RIysr/UUQgM8ydsRF92f9v1CbqCdHZ/u4/wkSH0DPGVOgohUX1QOdpz6uBZqaMInXR1y5NpS8fgqdJKHYdBI/sx6pH1jLc1z2FhQxgU8BIAOTk5aLWd/3fepVKXn59PU1MTzo46+gRab8nw1Z3k/dGZXPmrdEOxgb08GDE2hPUf7JYsg2A46duzCBnWB9/+0g2b9QzxY2BMCDu/FHOerMHOLxMZNCYM/z7ekmXwCfIiOKof6WLhllVY9/4uouMjCXS3kSyDm48L9/37AiMU1r14K6inBkdHR5qamjh7tvNviLpU6k6fbttzKDxEjdxCT5G4XZ7qLeyeWUnOy34mv3dIuB/9wv3Y9uU+k99bMJ6d3yYzdt4ISbY68evjw+BxYWz/XKxKtCbbP9/LkEkR+AV5mvzejm6OjFscy67vxLxMa7LtyyRChgQR0tPZ9DeXy/nTRjsidCtNf28TU8hhwIC28+ePH+/8ee0GKXWWvuHw7XJr3sj9QyNMes9Bw4Lw9nJi74o0k95XMI31H+xm1sOTUTo6mOyevkFeRMUNZNune0x2T8F0tn26h6gpkfj0NN2bBZWNgtmPT2Xdh5Z7ZJPwy/asSMPb351B/b1Met+Zd44kpPV7k95TSldLXVdWwBqk1IX06S5HUtXySM936PFhrEnuNnxMCCqZjpRNXZs4KZi39R/uZtFT00xyL68AD0ZOH8LWj8UwvjXb+vFuRs2KwsvfzST3m//UDFHorFzK5iPY2KqIivQ3yf1GThrA0Ie/B+pMcj9z0MfjLQAKCgo6fY1OlzqtVsu5c217xfTr3V1KHfTQn+Ufw7eif2OQUe8zJn4ATRW1HE44YdT7CNLTqDXs/C6FeU/PNOp9PHu4M3pOFJs+2GnU+wjmYdMHOxk9dwTuPsYdNpv35FR2rUhDIxZvWb1DCcdpaWhmdKxx55f3HRDArNeOEqvMMup9zE3foLYuVVhY2OlNiDtd6kpLS1Gr1aiUevx8pF8dY0remmT+OymPy3/qbZTrT5o9hOIzxZxIFeckdhe1FfUc2nOSKcsmG+X6bj4ujFsYzcb3dxjl+oJ52vj+DiYsicXV09Eo14+/bxwHE09RW2k+W18IxnU85TQl564waZJxNuZ39nDk0U/riML69qO7lQA/LTY2NjQ3N3P58uVOXaPTpe7ixYsA+PtqUFj5Iokb8WhZy57ZtZx6ybALJ6YuHM6ZQ2c4c6TQoNcVzF/xuTKKTpcwZslog17XxdOJuLtGs/6d7vdNUoD172wjbuk4nF3tDXrdMQuiOZ9fKvai64bOZBVyJquQqdMNO8dcplDwx60uDNR+bdDrWgqFHAIC2nZEkKzU9ezRvZ7SXcujZQ2/GjHYMBdTtzJjYRTHEk9wPueSYa4pWJzTRwtprG9h2LShBrmek5sjU+8fz5o3txjkeoJlWvvmFqYvm4SDk61Brjd08kAaG1s5k11kkOsJluf8qUtk7zvFzNmDoKLaINece18sA1q/Msi1LFWPHm2biBcXd+60qE6XupKSkrYAPt15HkUDS33+RPiXXRwyU7cy+94YMnccpbig1DDRBIt1bP8pHFwcGDC+a++C7Z3smLFsIqvf2GSgZIIlW/XGJmY9Go+9fdfOcxwQ2x97NyeyDuQZKJlgqS6fu0LGjmPMWTa+y8UubsFw+t79IdC9h/L9/NpG/652rI7qdKmrrGzb2dnLo3OT+axFoLycZ8M/6PyKWHUr8x4YQ/KGQ5RdrDJsOMFipW3LwjfIm37RnTt1wtbehrmPxbPqXxsNnEywZKte38icJ6dja9u5YtdvaG98+viKzYWFdmUXKzmw8TDzlk/sdLEbOTmCqCe3EGfbtcPsrYGPw1dA27qFzuhyqfNw696lDsBff5q/Rm3E/q1hHXuhupWFy8axd0UalSU1xgknWKz96w7Sb0gQvQb37tDrlDZK5j85lVX/Ek/ohP+1+l+bmP+b6SiVsg69LjDMn+BhwezfILZYEq5XWVLNnlVpLHh0UoeL3YDhfZjyyiHGK8VerPBTp6quru7U6ztd6ioqKn4M0H3n1F3LT5vOB6NTaP3nwNt7gbqVhb8az45v9lFT0X324RE6Zvf3qUSOH4BPv9vbG0qplLPoqems+temTi+JF6ybTqdj9RubWPTMLJS3+RPAp5cnQ+IGs1tsgi78gtryenZ9l8KiJ+Jvu9gF9fdjyX+KiJGLVflXuTq3fd+uqencg55Ol7r6+noAnJ3ED46rfDQJ/BCXS+XfQm7+hepWFi0fz7bPE6mvbjJNOMFibftyH2Pnj8A14OZHPykUchY9O5M1/94sCp1wU1qtjjX/3syi52ajkOlv+rUuXk6MXRTL1i/3myidYKnqqhrY8kUii5+8dbHz7OHG8i+aiGa1acJZCBepSl1TU1sZsbe7+TeE7sZTvZ4t00soeq3Xjb/gx0K3+eM9NNY1mzacYLE2fJjAtPsnYO/hcsPPy2Qyljw3i7VvbkWjEU/PhVvTaLSsfWsri1+Yg0x/4zcB9k52TH84nvUfiyPlhNvTVNfM5s9uXuwcXBx4fo0dQ3VfmTSbJXB2bPt/sa6ucyN4nS51zc1thcTOVpS6n/Ns+Z6kuY3k/+FnQ2bqVhY+PI6tnyfS3KiWJpxgsda+u5N5j09B5fS/58QueX4m697eSmtL9zndRei61pZW1r+9nSW/nQu6698MKG2UzHt6Jmve2yVROsFSNdU3s+WLRBY9Efc/xU5pq+LVHZ5Eaj+UJpyZs1G1dSqNpnM7i3Sq1Gk0mvYbilJ3Yx7N/yV9ofqnzYnVrSxYNpYdX++nsVY8oRM6Tq/Xs/6D3Sx6eiZye7v2jy95bhab3t9FS5N4oyB0XEtTC5ve38GSF34qdnKFnMUvzGXd+7vR68X3eKHjGmub2f71fhY8Nrm92MkUCv6W6MdgzfvShjNjKlXbX1tbWzv1/16nSt2183Xk3fA0idvTikfzSg4uaibndz2Y98Bodv03mfrq7r0Hj9A1rWoNmz/bw+KnZyC3s2XR09PZ9mkCTfVibqbQeU31zWz7NIGFz8wCvY5Fz89h06d7aRXnuQpdUF/dyO7vU5i/fBJU1/K3/T0YpPkMEFNEfonumh53de1CR3SqksmvaXLiTdzNNOKp3sJns+aQuCqDusoGqQMJVqCpvoVd36XwxxW/IeHb/dRXiz9XQtfVVzeQ8N8DvLb2eXZ+n0JTQ4vUkQQrUFtRz97V6bz61aMM0X5Ld99c+Fbk1+w01Jkh2E6VOpnsp7uKUndzb1a+yPrXkhm7OEbqKIIVGTM3iu//sZGxBj4nVujexi8ZxQ+vb2bcrA7uuSkINzF2bhTffZPK61v/LnUUs6dU/FSq7O07fl5zl5/UabUd28CyO/lnxV849pdSziXlkr4vj5mPxEsdSbACU+4ezZHdWZw+VMDBnceY9ehUqSMJVmDm8jgydx7n9NFzHEk8zpQ7xRtRoetmLZtI+rFizpwuJW/LKf664R2pI5k1teanTmVjY9Ph13f6Sd3VBtnULErdjfzpyqucfaOOssyzAJSX1nI4rYAZj8RJnEywZBMWjSQ38zSX89uOkCk9X07W/lNMf1j8uRI6b/qyyRw7kEdpUdum8pcLrpB3uIAJ84dLnEywZDMfHM+hnFLKfpx6dPl8BZeTCnll1ZsSJzNfTU1tncrBweG6B2i3q9PLHBwc2rZVaGgUpe7nfl/yIpUfabl44PR1Hy+9XE3WwUKmPTxZomSCJYudNYxLeRcpPF503ccv5ZdwMuMMUx6cJFEywZJNfXAiJzPOcOnsles+XnjyIpcLSomdPliiZIIlm37fOI7kllNy5fr91s6dLqEx6wrP//efEiUzb43XlLrO6HSpc3R0BKChSSx/vdavi55F/609Z3advOHniy9UcuLoBaYtEz+Ahds3fPJAakqryMssuOHni3Ivk3+0kLj7J5g2mGDR4u4dR96R8xTll9zw83mHCqirqGf4+DATJxMs2dR7x3L8XBXFV2pv+PmcrPPYnKvn8c/+YuJk5q+xua1TmbzUOTs7A1BbJ0rdVcsKn8B7sw/HNx696dddOl/B8awLTP+VGDITbm3Q6P7oWtUc33/qpl9XePIi505cZNLScSZKJliySXePpTCnmPO5l276ddnJuejQM2hksImSCZZs+v3jOHGuiouXq2/6dUfTCgiolbH0nVdME8xCVNe0dSo3N7dOvb7TjczLywuA8kpR6gCW5D9EeFI/Mr5Pv62vv3y+kqOZZ5n12BQjJxMsWWhUbxydbDi0M/u2vv5s9gUu5pcy4a6xRk4mWLIJS2IpOlNKwYkLt/X1h3cfx9ndgdDBgUZOJliyWcsmkHW6nEvFt3duaXJCDlG2rsz/52+NnMxylFe1dSpPz5uf9f1LulzqyioUnb2E1ZiRs4RJx4aT+Nm+Dr2u9FI1mQdOM/fJaUZKJliy3gMC8O3pQeqGQx16Xf6Rc5SeLxfb6Ag3NG7RKEqKKjlz7HyHXpey6TB+fX3o3d/XSMkESzb3kclkZJdQfKVjZ5bu2niEaT0DmfjKr42UzLJUVLZ1KpOXOh8fHwDKKrt3qZtwYh4L8yez+a3OnY9YVlJLcsIp5j81w8DJBEvm19uLkEE9SVqR2qnX5x4soKKkltELRhk4mWDJRs+Pprykjrwj5zr1+qRVafQf1ocege4GTiZYsvmPx3PgYFH7KteO2vBdGvcOCmfUbx81cDLLU940H5Cg1Pn5tZ1perm0e5e6qd5xbHt/T5euUVleR+K2bBb9di4yh45vNihYF1cvZ6ImhLPzq449+f25U+n51FY1EDMv2kDJBEsWM2cEtVVNnDp4pkvX2fnNfqLiBuHmLr5XCbDo1SUkppyjsqZrRxVuWZXJA5ERBkpluYqK2nY3CAgI6NTrO13qevXqBcCFy8pue6rEX8v+TO7rBTQ3dv0g9ZqqRravymTx09NRODkaIJ1giVS2KiYvjmbzh7sNcr0TyXk01rcwcpbYb6w7GzlzGI0Nak6knb71F9+GzR8nEHf3GGwUYkur7koul3PHa3ewfeMRquu7fqRcU6OazOTTvLbxbQOks0x6PVy8eBH4qWN1VKdLXc+ePZHJZNQ3yKms6X6LJZ67/DsqPmmlPKNzwxg30ljfwqZvU1j09DRs3JwMdl3BMuj1euYvn8C6t7Yb9LrZ+06hbtUyfNpQg15XsAzDp0ai1ujJTs416HXXvr2d+U/EQ2urQa8rmD+VjZIlry1h/apMGgxQ6K4qPl9BfWYJv/nmHwa7piWpqpFTX1+PTCYz/ZM6W1tbevToAcD5i8rOXsYi3Xd2OR6bPTm9/bjBr93S1Mraz/Yz7/EpOPqJeSvdhV6vZ8Gjk9nw3k50Op3Br5+19yTI5URNFcWuO4mKjwS5gqykHINfW6fTsfGjBOY/OVUUu27E0cWeBS8tZPV3aahbOn7g/K2cPHqeHpV67nj7JYNf29wV/tilevToga2tbaeu0aVHbMHBbfsWnT6r6splLMq0nMXEHBlC6redm8B+O7QaLas/SWLqvePw7ONntPsI5mPGA+NIWplmkKH8X3J493EUKiVDJkca7R6C+Rg6aRAKWxsO773xRuiG0FTfzL41GUx/aILR7iGYDw8/N6b+eiYrv01FqzH8m8+r9u86wSTPHox7tXutiL3apfr379/pa3Sp1IWHhwOQV9B9St1kr4lsfdsw851uZf2XB4iZNYyAiM6NrQuWYcKiaI7vz6GiuMro9zq48xh2TnYMnjjQ6PcSpBM5IQJbJ3sO7r69/Q27oqK4ipNppxk/L8ro9xKkExDsS+zSCaxbkWGS+238IZ37IkJNci9zkXdpLgChoZ3/5zZIqcvtJqXunxV/5cjf8tCbcGXI1h/SGTA6lODoEJPdUzCd6KmDKS4o5kLOzXf1N6SMbUdxdHNi4Dix0swaDRwThoO7Mxk7j5nsnudzLlF6oYzoyeLPlDUKHhzIgOnD2bL+sMnuqdfrSdpxkj9tecdk95RaXl4eAGFhnT+Wr0ul7mqbLL6ipLLauhdL/KboWa58pqH2cMc27DSEhHWH6RnqT8QE8Q3TmgyI6Ye2uYVTqfkmv3f6liO4erswYLQ409OaDIgNxdXHnfRtNz+q0BhOpZ9Bp9USMaKvye8tGM+AUSEERIeze7vxn/r+XEVpDQ0HS3nky7+a/N6mVl4p5/Lly8hkMumGX52cnAgJaXuClJVj05VLmbU5p+6hz/4gcjdnSZZh//bjOHq5MmKWGOKwBgH9fPH0dubgDtM9Tfm51E2H8fT3JGxU9xrisFZhI0Pw7OlN6tYjkmU4uCsbT383AoI8JMsgGM6I+ME4BfuzP/Hm504b0/GD5xigsWfK35+RLIMpHPuxQ4WEhODs7Nzp63T58drQoW2r6Y4c79xKDXM37vh8ZuWNI+HDRKmjcDApl2YNjLsjVuooQhc4utozbGwo+1amSR2F5A0H8e3tQ8gIMbxvyfoPD8anty/JGw9KHYV9q9OJmjwIB/vutSuCtRm/MJomB0cy089KHYUd6w+zpE8w0S89JnUUo8k6dx8AQ4YM6dJ1ulzqoqLanhwdOWGdT+piXWM58E2y1DHanTh0jkuXapj28CSpowidoNfrmb50DBs/6Nyxcsawf20GPUN60C8qWOooQieEDOtDQP8ADmyQvtBdtfGj3cxcNklsdWKhpt03jos1Gk6cuCh1lHaJO7K5o1cfqWMYzZEjbU/Yhw0b1qXrdLnUDR48GIVCweVSJReLrevIsF8VPoF8fQuVHTyg2NjO5hZzJP0cC1+YI44VszDzHotj0wc7pY7xP5JWp9NrQCB9h4r5UJYkeEgfAgf0ImmtaVYkdsSmj3Yx7/EpUscQOkAmk7Hw5UUczb1CwbkyqeNcp7KsnurKBu75+FWpoxjchcsKLl26hFKpJDKya1tOdbnUOTo6tg/BJh+06+rlzMbE4wsZejiCY6sPSR3lhspKati2MpMlz0zH3stV6jjCbZh8Zwzpmw4adS+6rkhckUrfwb3pM9h63w1bkz4De9EnsjeJq9OljnJDzY1q0rcdIW6JOHvYEtg72rLktSVs23iE0ooGqePc0KHkfMY6eDHq1celjmJQqYfautOwYcNwdOzaMaEGWbI6ZswYwLpK3QSP8ew2g3l0N9PcqGbNp/uZuWwi3n17SB1HuIkR8YO4mHeR4oIrUke5qT3fJxMS1YdeAwKljiLcRK8BPQkZ0Y89K4y3CbohFJ8r4+KZEkZMDJc6inATXgEezHh6Nqv/m05Tk3kPmW9fd4h7gzu/OtQcpWa3dajY2K7PlzdIqRs9ejQAJ0+rrGJrk8cvPE3NfytQN5v3H25oO6pn7ef7iZ4xhL5iTpRZChkShFyvk2Trks7Y/d9kBsSG0TNcFDtz1DPUn7BRYez+PkXqKLflVPoZFEoF/QZ27ixLwbj6DurFyDvGsHZFhlGOKDQ0dYuG8wVXWPbln6WOYhAV1XJOnDgB/NSlusIgDczX15ewsDD0ehlJaZb9tG7yiQUMyAghb7N0W010xrYVGQRGBBIZN1jqKMI13Hxc6DvAn7TNptu00xB2fr2PQWPDCegvfhCbk4B+fgwcF8Hu7w5IHaVD0rYeoV9kEO4eDlJHEa4ROTaMwFFhbN1o+n0Nu+L4oUKGK1yJ/qPlr4bdm2KHTqcjIiICX1/fLl/PYI/VpkxpmxC7a79lT9wf7TqevZ8kSR2jU/Zvy0bhYMeYxaOkjiLQttJ18qJotn9u3sP4v2THl0kMmTQQv2AxtG8OevT1JTIukp3f7Jc6Sqds/zKJyXeNFitizcTYecOR+3mxb690e9B1xc71R1gaZPlbMe3JGAlAXFycQa5nsFI3efJkFAoFuQU2XLhsmatgHzr3KOp19bSY+ZyCmzmaeobLxbXMWG6YPyBC5819ZDKbPzSfrUs6Y9vniQyfOgSf3l1/Byl0nm8vb6KmDmX7l0lSR+mSLZ/uYc5j8VLH6PZmPjiBy/Vajh4x/QlJhtLS0kp5SQ13fPQHqaN0WtFlBbm5uSgUCiZNMsw2ZQYrde7u7kRHt61y2plkmY/Y+9hGcHy1+ez11FnnckvITM5n0e/monLp2koaoXPGzI3iyO5jZrvStSO2frqHUbOi8O7lI3WUbsk70JORc0aw9QvLfOJ7rab6ZrKSTjJ29lCpo3RLKpWCxa8uJj37MgXnyqWO02UHk/OZ4OgtdYxO25bY1pVGjBiBu7u7Qa5p0FUNM2bMAGBboj1qC3vY9Wrpy5x+q1DqGAZTcaWOjd+kMu+JKXgEiR/GptR/WG+aquu5kHNJ6igGs/njBGLnReMR4Cl1lG7F09+d0fNj2PzpHqmjGMz5nEs01TUROlgsxDElDz835v1+AetXHaSiuknqOAaTmZzP8+vekDpGh6lbYVtS25zl2bNnG+y6Bi11o0ePxsvLi6oaBQcyLGfBRPzJhXgk+VGZWiB1FIPStGpZ8+k+YuZE0Xd4P6njdAtO7g70HeAv6ZmuxrLpw12MXxKLWw9xrqcpePi5MW7JaDZ+vFvqKAZ3cFc2fSN74exsncdLmpvgwb2IuWccq79LR9OqlTqOQZUUVdK7RkHMn5+QOkqH7M+wo6amBm9vb2JiYgx2XYOWOqVSyZw5cwBYv9Nyhv1GOY8l6ZN9Uscwmm0/ZNCjvz/DposhD2PS6/VMvSuWbZ/ulTqK0Wx4byeT7xqDq4+b1FGsmouXCxPuHseGD62v0F217fNEpt43TiycMLKoSRH0iAph6wbLWuHaEbs2HeHOAMs6DWdT0ngAZs2ahVJpuHOSDb6p3KxZs1AoFJzIs+HUGZWhL29w8/OWYrdHS2uLRuooRpW66wRqvYxJS8dKHcVqTb13LLu/tcyViR2x7t0dxN83AWdxkolRuHg4Ef/ARNa/b37HyRnanu+TmXr/OKljWK1JS0ahdnYmef9pqaMYVWuLlubGVuL+/azUUW7LydMqsrOzUSqVBh16BSOUOi8vr/alud+tdzL05Q0u0jGa1M8ta8+nzjp5qJDTuSXMf3a2ODPWwAbG9udS3iWqr9RKHcUk1r69jenLJuHk7ix1FKvi6OrI1IfjWPfuDqmjmERlaQ2XzpQyaKRlPWUxdzKZjPm/X0juxTqOn7Ceub03k7Q9m3meljFP84cd84C2reC8vLwMem2jHP9wzz33IJPJSD5oR+FFwz1WNLQ7ztwP26xnwujtuFRYwY41B1n81DRc/A37h6m7cvNxoUegO8f3W+Z+T521+j9bmbE8DgdX83/zZgkcnO2Z+egU1ry9XeooJnU8OZcefXxwcxdvNA3BxdOJxa8uZvumo1wu7R5vMq+qrmhgxtsvSB3jpgovKklOTkYmk3HXXXcZ/PpGKXW9e/duPw/2hw3mO7cuzDaKg9+kSR3D5Job1az+dB/jFkXTRxwt1mUTF49k51fWOyfzZta8uY3Zj0/B1skytzEyF7YOtsx+cnq3K3RX7fxmP5Pu6Pq5l91d34GBjHtwMqv+m0azBRxzaWgpe3KY4Wrep+Cs3H0fAGPGjCEoKMjg1zfaQa1Lly4FYHeyPcVXzG8z4oX596Dcbfl7iHXF1u/T8e/vz4iZUVJHsViT74wh0cKObDIkvV7Pmv9sZf6vp6OyEysZO0Nlo2L+U7NY/eY29Hq91HEkk7Q6nbgl0VLHsFjR8YPwHx7ClvVHpI4iqbqaRia/aZ5z6y6VKNi1q21D+nvuucco9zBaqQsPD2fEiBHodDK+WGl+wzMD7UaS/kWy1DEkl7r7JHVNrUx9aKLUUSxO30GBVBdXUllcLXUUSel0eta8uZVFz85CoTL/xVHmRKlUsOj5Oax5u3sXOoCK4ipqyusJHiCOpeuoqfeOpVZlS/KBfKmjSG7/zhPMczPPuXVfbrobrVZLdHQ0AwYMMMo9jFbqAJYvXw5AQrI9+YXmM7cuPmcBzqLPtcvLvsiR9AIW/XYuKjcx8f12KBQyIqL7WuV+dJ2h1epY89Y2Fj8/B7lSFLvbIZfLWfTCPNa8sx2tVid1HLOQuSOLiNhQlHrr2kvNWFS2Kha/spjDOSXk5pVKHcdsaFu1jPybee1bl39OSUJCAvBTNzIGo5a60NBQJk+ejF4v45PvXIx5qw6JchhDyufWv/VER5RfqWfjNynMfngiPcLMe06COZjx0ES2fmI9u/wbgqZVy/r3drL4hdkgN78pF+Zm8e/msf6DXVa3GWxXbf1sLzOWGeYcTGvWo7cPs5+fw7qVmZRXN0sdx6wkbs9miZfh56t1xWfrZgEQFxdH//79jXYfo5Y6gF/96lcolUoOHrPlULaNsW93W1RyOepm696XrjM0Gh3rv0pmwOgwBk0cJHUcsxU5Lozc9Dw0avFn6OfUzWo2f5TA4hfmiGJ3E4t/N5/Nn+xB3dy95/XeiEatIfdQAUNixSk4v2TQ6P4MmD6MdSsyxVPeG1C3aFCpzGd08OgJGzIyMlAoFCxbtsyo9zJ6qfP392fu3LkAfPCNCxqJ35Q+felpSr4Wj6lvZu/Goygd7Zhw12ipo5gdla2KgD5e5GVa15FyhtRU38yur/ax4OmZotjdwIJn57Dj63001YunK78k79BZAvr3wEYhkzqK2ZmwKBplDy/27DopdRSzlnfiEg99/1epY6DRwDvfDgdgzpw5BAQYdyTM6KUO4P7778fFxYWzF1Ss3yHt1gfO2j5c3tm99hPrjKz0Agryy5j39CzkjmK7iqum3TeWbZ9Y7zFghlJbWc++NenMeWyK1FHMyuzHprBvbQb1VQ1SRzF7O75IZJo4baKdXC5n3osLKChp5OjRIqnjmL2zucUMaZb+Z9e6HY6cO3cOV1dXoz+lAxOVOjc3Nx599FEAvljpTFmlSW77P+Jz5uGSIt753a6LheXsXHeQBU9OwT3IR+o4khsQ048zhwvQ6cRwx+2oLK7m4K5spv8qXuooZmH6w5M5uOs4lSXVUkexCBqNjjPHzhMxvLfUUSTn4evGwpcXsn1LFkXFNVLHsRg6jY6ofzwu2f3LK+V8udoXgEceeQQXF+OvLTBZu5oxYwYRERE0Ncv54BtpFk2E2sRw+Lt0Se5tqZobW1n7+X5GzhhK6OgwqeNIRqGQERzmz8mUPKmjWJSSwjJOpuUT/0D33jIn/r7xnEw9TenFSqmjWJSTaacJHhzUrVfDho3oy8g7x7D6+3RamrrfhsJdkZaUy3w7f8nu/9Gae2hqaiIiIoIZM2aY5J4mK3VyuZxnn30WuVxOYqo9mVmmXzThpLKhrrrR5Pe1BjtXH8TF150xi2OkjiKJqfeOY+tnYti1M4ryLnP2eBHj7xwjdRRJTFgSS8GJixQVXJE6ikXa+kUiU+4fL3UMSYydNxznfj3ZvkVsndQZtdWNODpKsyl6RpYtCQkJyOVynn76aeRy09Qtk46DhoSEsGDBAgD+9bEb9Y1iKNSSHNqfR1FRFfOentmt5tn17O/HlfNXxGrXLjibfYErRZXEzBspdRSTip0znJKLFZw7eVHqKBZLo9ZQdrGSnn26z1nVV+fPna9UczDznNRxhA6qa5DxxqehACxYsIDQ0FCT3dvkk9t+9atfERAQQFmFgve/Mt0w7LOXnqb0S7HqtasunLnCjrWHWPDkFLz6+kkdxySGjh9A5rajUseweLmZZ2hqaCFq6lCpo5hE1JRIGhtayDtcKHUUi5e5I4thEyOkjmES3gHuLPj9ArZvyuLCpSqp41i83OOXeGilaVfBvv+1C2VlZfTs2dOoGw3fiMlLnb29PS+99BIymYztSQ6kHjLNo1E7TW+uJOSa5F7WrqWpbZ7dsMmDGDhhoNRxjGpE/CCOJoihD0M5lpSD0kZFxBjjHJFjLgaODkNpa8Ox5NNSR7EaR5NOEj3ZuovdwNgQhs6PYc2KDFpaxPw5Q7hQcIXBtXYmu1/qYVt2JDkgk8n4/e9/j52d6e4NEpQ6gEGDBnHHHXcA8K+PXampE8Owlmj3+sOonO2ZeM9YqaMYhUIhw9vflQs5l6SOYlUyd2ThFehJ8NC+UkcxiuDI3nj18iJzV7bUUazKhdzLeAd6WO2iiYmLR6Ls4c2u7celjiJ0UnWtjH9/FgLAHXfcwaBBpt/EX5q9RYBly5YRFBREVY2C1z90o5ufZW2xstIKyMu5zMLnZ6NycZQ6jkFNWTqGXV+J4+SMYf+aDPoN60uPftKtTDOGHn196TesL/vWHZQ6ilXa9c0B4u+zrr3rVDZKFr60kNyLdWRlif3nLJVOB//8fD4VFRX06tXLJHvS3Yhkpc7W1pZXXnkFlUpFyiE71mw1XiG45+y9aLaIVa/GUlxUxabv0pnzSBw9wgOljmMQzh6ONNc3iWOcjGjXN/uJnjEUV183qaMYhLuPKyNmDGPX9ylSR7Fa6mY1LU1qnJ2lWdFoaP59fJj9wlw2rjtM8ZU6qeNYrYqyOqZ89Fuj3mP1VkfS0tKwsbHhtddew9ZWmj+jkpU6gP79+/Pkk08C8NF3zuTkq4xyH39ZJHmrDxnl2kIbTauWdV8eIHxUCEPiI6WO02Xj549gz3+TpY5h9TZ9uJsp90/EzsmyV1PbOdgSd/8ENn8qtr0xtj3fpzBuYbTUMbps6IQBhE9tO79V02qdQ8rmIivzLHFaD6NdPydfxSffuwPw61//mn79pDu3WNJSBzBv3jwmTpyIVivjT2+5UVdv+Pl1CrkCjUacAmAKiZuz0MjlTHnQcjeb9erpTsXFcqljdBtr39rG/N9MR640zps6Y5PL5cx/aiZr390pdZRuo6qkGm8/aTaxN4SpS8eicXFmz25xfqspaNRaVCrjnENd1yDjz+8ORKvVMnHiRObMmWOU+9wuyUudTCbjhRdeICAggNIyJX9/zw1Dn8KkFxP2TOrk4fMcyTjLot/OxcHbTeo4HTZySiQpG8STXVPR6/Wsf28nC5+dBXLjfOM1poXPzWbdB7vF9xkTStl0mOhpljciYO9kx+JXFnE4p4TjJy9LHadbMcb/nlod/O3jeZSUlODv788LL7yATCbtwk/JSx2Ak5MTr732GjY2NqQdsePzFc5SRxK6qOJKHWu/OEDcPaPpO1y6R9Ed1bO/H8VniqWO0e20NKnZ830ysx+zrHNiZz86hT0r08TcSwmUnCsjMNhb6hi3LXhQIPGPTmX19xmU1zRLHUcwgM9+cCY9PR0bGxv+9Kc/4eTkJHUk8yh1AKGhofz2t20TGb/b4MSeZNPu7SIYx+b/puHb15fRC0dJHeW2DI4N5dBOsS+dFKpKazi27xQT77aM1Y2T7hrDsQO5VJWKA9alcGh3NoNGm26n/q4YPTsK38hgNq0/InUUwUD2JNvxw8a2Evfiiy+a9NSImzGbUgcwZcoU7r77bgD++aEbuWcMM8dGjIpIK33vKS5frmbeM7NQOJnvtieuXs7UlFVLHaNbK8orpuxSJSNmDJM6yk2NmDaU0ouVFJ0Rp9RIqa6qATd3e6lj/CKFQsGCF+dzuU5DalqB1HG6NZ0Bi0DeWSX//MgXgHvuuYe4uDiDXburzKrUQdsxYjExMahbZfzhX+6UV3Y9osRD3AJQmH+FHWsOMu/xOHxDA6SOc0OxM4dwYE2G1DG6vZOpp1HZ2hA6sr/UUW4oLLofClsVOZnih7TU9q/NIGaWeb4B6BHkzbzfzWPrpmOcK6qUOk63JzdQEbhSIecP/x6AWq1m1KhRPPzwwwa5rqGYXalTKBS8+uqrBAUFUV6l4Hf/8KC+sWv/McSTOvPQ0tTKui8OEDE6jGFTh0gd5zoKlQKNmBdlNtK2HCEwvCf+Iea1ObF/sB+B4YFk7BBD9OZC26pFKTOvb/LDJkYQPnUoa1dmiuO+zIQhntTVNcj43eujKCsro1evXrz66qsoFOa1uMvsSh2Ao6Mj//znP/Hw8KDgvIo//Msdtfj/wmokbjpKixamPzoFmYN5DJ1MWjyKPWLTWLOy57tkRs6MwtHdPBZOObk6MHJWFAkrUqWOIlwjcVU6E5eYx5xdmUzGjKdm0WzvwN49p6SOIxiQuhVeecOdc+fO4enpyRtvvGEWCyN+zixLHYC/vz+vv/46Dg4OZJ205R9d2OpEDL+an5ysC6TtPcXC30zFJcBT6jgoFHI0ao3UMYSf2fD+TmY9Go9MoZQ0h0wmY+ZjU9nwUYKkOYT/pW5Wo1BK/7TExdOJxX9YRErKGXLySqSOI/xMV4ZfdTr4x/tuZJ20xcHBgddffx0/Pz8DpjMcsy110HbixF//+leUSiWJafa8/41Lp4ZSxfCreaqpbGDtZ/sZO28EoaPDJMsxIKYfuRmnJbu/cHMb39/FgqemS5ph/lMz2CgKndnKO3SWiOG9Jbt/2Ii+jL13Iqu+T6emqkGyHMIv6+zwq14PH3zjTGKqPUqlkr/97W+EhIQYOJ3hmHWpAxg+fDi///3vAVi7zZGvVnf8cad4Umfetq3MxNnbjQl3j5Hk/r369eDssfOS3Fu4tZYmNSmbDjPlAWlOKYm/bwIpmw7T0iTmXJqrs8cvEBgqzfzLiYtH4tjbn62bsyS5v3B7Ovuk7ouVTqzZ9tPWJVFRUYaMZXBmX+oA4uPj+fWvfw3A12uc+W59x7bFEE/qzN/h5NPk55aw8LnZqNxMN4dKJpOhVYsf1uau9Hw5F/JLTL7VyYhpQ7l4ppgrl6pMel+h4/R6PXKt6c5QVdmqWPjSQk5frOPwEfGm0Nx15kndf9c58u26tp9HTz/9NFOmTDF0LIOziFIHsHjxYpYvXw7Apz+4sGqL+e53JnTO5QuVbPoujZkPTaDX4N4mueeYOcNI2XDQJPcSuiYvswBbezv6DuljkvsFRwZh42BD7uFCk9xP6JrUTYcZPW+4Se4VGNqDWc/MYsOaQ1y6UmeSewqmtXqLI5+taDtf+LHHHmPBggUSJ7o9FlPqAJYuXcpDDz0EwAffuLBuh8NtvU4Mv1oOjUbHxm9SCBrUi5FzRhj9fo4udtRXNxr9PoJhpGw6xIDYMFyNfKawi6czYTFhpG7NMup9BMOprazH0cX4q+ljpg+hT+wA1q8+hFZr4IPKBaPpyPDrhp0OvP9NW6FbtmwZd911l7FiGZxFlTqA+++/n3vvvReAd75wva1it7j3PkqXBxk7mmBAyTtPUFHVwNzfzDDaticKhQy1mCdlcbZ+uodpyyaC3HgrHqc/HMe2L5OMdn3BOLQaLUq9cYZgZTIZc5+fR5ka9u8XC6ssib2zHTZ3X76tr1291YG3PncF2h4k3XfffcaMZnAWV+pkMhkPP/xwe3N+5wvXW86xG04CMbHmtYmpcGsFp0rYvfEIC389FZ9+hv/vN3r2MNI3Hzb4dQXj2/zJXuY+MdUo157zxDQ2f7bXKNcWjCt182Fi5hh+CNanlyeLXl7Irp3HOVNYYfDrC8Y1fHQ/nnT+5JZf9+06J97/uq3Q3XXXXfzqV79CZmFDfRZX6qCt2D366KM88MADQNscu0+/d77JgogGnFW2poonGFBTg5q1n+9n8Phwg59CYecohl4tVVNdE9kHchmzKMag1x2zYCTHk3Npqm826HUF06ivbsTB2c6g1xw6IYLBM0aw+ocMmhrFk31L5GBvA/zyVjN6PXz6vTOfr2hbFPHQQw/x6KOPWlyhAwstddBW7B566CEee+wxAL7b4MS7X7p0eoNiwbzt2dh2CsUMA55CoRfLoi1a4cmLaDU6+kUFG+R6/Yb1RavTU5hbbJDrCdLQ6wzz/7VMJmPmM7NQOziQsPukQa4pmB+dDt772oXvNrRtW/L444/zwAMPWGShAwsudVfdddddPPvsswCs2+HIPz9wRXODgwHmBaVy+aFeJk4nGFJO1gWSE06y8NdT8Qjy6dK13HxcqL1SY6BkglTStx5lQGwYDm5dO67Hyc2RAbGhpG8XZ7pautqKetw9bm8R3S9x93Vl0UsL2Z90mpPidAiLZmtvg+LOsht+rlUDf3/PjbXb2qZwPfvss9x5552mjGdwFl/qAObNm8fLL7+MQqFg534HXvw/Dxoar2/Z0Wxh0gRR6ixdXXUTaz/fz4hpQxg8aVCnrxM1cQAZW48aMJkglc0f7WbWI/FdWjgx45F4Nn8q5tFZg4ztWQybPLDTrx88JpSRi0ezekUGdbVNBkwmSCF2Uhi/dn73fz7e0Cjjd//wICHZHoVCwUsvvcS8efNMH9DArKLUAUydOpW///3v2NnZcSjblt/80ZPyymv/8RqxU6gkyycY1q61h8BWxdSHJ3dqOFaukKMTY/VWY+8PqUx9YHynXjvlgYnsWZFm4ESCVHQ6HQpFx3+0yWQypj05A5mHGzu2HzdCMkEKtnYq4Pq50+WVcn7zR0+OHLfF3t6ef/7zn0ybNk2agAZmNaUOICYmhnfeeQd3d3cKzqt4/GUvzhX9dBD43F4HKF/eW7qAgkEdP1hIZnI+C38zFRd/T6njCBKqKK6itKiCgeMiOvS6QWPDKS2qoLKk2jjBBEnI5B2bD+Xs4ciilxaQkXmWYydvb+sLwfw5utijX1R63cfOX1TyxB8HU3BehYeHB++88w7R0dESJTQ8qyp1AGFhYXz44YcEBgZypULBk694cvi4DQDRsnXEjTfNbvSCaVSX17P2s/2MmR9N+Jjw236dpU6CFX7ZsaRTBA0IxNnT5ba+3sXDiV4RvchOzjNyMsHUOrJYIjw6mLH3TmT1ikyqKn55haRgecZNieBp1zfa//7gMRueeLUPpaWlBAYG8sEHHxAaGiphQsOzulIH4O/vzwcffMDAgQNpaJTzwt88WLfDAb1eh0L8MLdK21dmYO/hzOR7bz0EZ2uvoqVezJWxRts+38vUByfe1tdOeWCi2GDYSqmbW7FR3Pp7/eQ7Y7Hr6cO2LWKBjDVq+3GvQ6+HNdsc+N0/vKmvr2fgwIG8//77+Ptb3/61Mr0V7+vQ0tLCG2+8wc6dOwGYNbmR4Ls8OJD9FHkvJkkbTjAKbz8Xxk0bzLZv9tN4pfqGXzNsYjjnj1+grEhsImqNvAM9GTS6P3u+3feLXxO3dBzHDuRSXiJWQFsj3yAvAvr5cfTAjU9+cHSxZ/qTM9i/PZsrtWJPQms0dspACuYf5KOA9bz1mStb97atiJ4+fTrPPfccNjY2Eic0Dqt8UneVra0tL730Eo899hgymYwtexxI/HcNHgFSJxOMpayklrVfJTPpjlj6j+p/w6/x8HUVhc6KlRVVUF/TRN8hfW/4+eDIIOpqGkWhs2Kl58vx8ne/4ef6D+3NpIfjWbMyUxQ6K+bu6ci/3Tfy3F882LrXAblczuOPP86LL75otYUOrLzUQdvcqbvuuov/+7//w9HRkexTtuR/9ikDXjX8UTKC+dj6QzquPTwYf+doqaMIEsjcnsWQSQNRqK5f8a5QKIicNIjMXWJ1Y3c0YdFIXEMC2bxRbGdkzcZOGUh6v5088vJgsk/Z4ujoyP/93/9x5513Wv18aqsvdVfFxMTw0Ucf0bNnT87nVxJQXkTp5Fb0WO3oc7d36MBp8vNKWPDsbGw9nNs/LpN3mz/23dqWT/Yw69H46z4289F4tnyeJE0gwaSu/eFta2/Lwt8v5HRRDQcPF0oXSjA6nQyUY4q59O5xSktL6dmzJx9++CGjRo2SOppJdKufbkFBQXz88ceMGTOGTZu2MC/Agxa3AvQyrdTRBCMpLqpi43/TmHbfePr+eJyUXuxP1y20trRyJus8gye2bUQbOSGCgmPnaW1plTiZYApXp4v3HRjItCens37NIS6Xi9Wt1kyPllbbHLa9vgaNRsO4ceP45JNP6N27t9TRTMaqF0r8Er1ez4oVK/jiiy9wdnamorgBu6oQ5BrDnCkqmKeRk8JQ6nTYKWGXWPXYbcxaPpnkdemMnhfN1i+SpI4jmMiUe8fS3Kyl1dGR9IyzUscRjEwna0RtfwK9vAGFQsEjjzzCHXfcYfXDrT/XrZ7UXXV1nt0bb7yBXq9HYd9Kk/cJWu3LxHCsFcvYm8uFC1UED73xBHrBOm37LJH7X1vCti9/eTWsYH2CBwdxvqJJFDorp0ePRlmM3CsbvbwBT09P3nrrrW4xf+5GuuWTumtVVlby5z//mdzcXBobG1E0emJb0xuZXnnrFwsWqWeQJ4GBbiT/cEDqKIKJTLl3LLu+S5Y6hmAiY+aN4EJFC0ViuNWq6Wml1fY0WtUVAIYOHcqrr76Kp2f3PWGoWz6pu5aHhwf//ve/ufvuu7G1tUXrUEGT93G0NnVSRxOM5OL5CmzdnPAbECR1FEEQDMw3yAuVn5codFZOK6/Grd8ZtKorKBQKli9fzn/+859uXehAlDqgbZuD++67j7feegt/f39UDtDsmYPa+aIYjrVSSVuOMXb+CKljCIJgYGPmRZO078abDguWT4+OVtU5NE7HKCkpISAggPfff5+lS5eiUCikjic5UequERERweeff87EiROxsbWh1fkSzV4n0SnFkVLWKHHbMaY+MkXqGIIgGMjU+8aRtO+M1DEEI9HJGmixP4LGthCdTsfUqVP5/PPPGTBggNTRzIYodT/j6OjIyy+/zIsvvoiTkxNyhxaavI/T6lgsntpZmaryBiprmgkdP1DqKIIgdFFoVF8qWuVUtogtqqyNHj2tqgvo3bPQK+pwcnLilVde4eWXX8bBwUHqeGZFlLpfEBcXx5dffsmwYcOws7dF7XqBZq8cdArx1M6aHEk5Q3hsKHa+HlJHEQShk+wcbAmbMozDWUVSRxEMrG2rkiNobAtQq9WMGjWKb775hvj4+Fu/uBsSpe4mfH19+de//sVvfvObtncDdo00+ZwQT+2szNbvM5i1bJLUMQRB6KSZD09i88YsqWMIBqRHj0ZVhN79KDpFLY6Ojrz44ov885//xMvLS+p4ZkuUuluQyWTMmjWLr7/++sendjY/PrU7iVYpVldZA51OR8a+XCbcP0HqKIIgdNCEJTGkH75IN9+dy6ro5HW02B+m1fYMarWa4cOH89VXXzFjxoxuufdcR4hSd5t8fX3597//zZNPPomjoyMKRzXN3idQu1wQx4xZgcsXKmnWyAgT8+sEwWKEDg+mUWXH5cpGqaMIBqBHS6vNGTTOR9vnzr3wwgv8+9//xtfXV+p4FkGUug6QyWTMmTOHb7/9ltGjR7etkHUqpsn7OBrbaqnjCV10cH8eIdEhuPYW3zwEwdy5eLkQMnEwB49ckDqKYABaRQUtDplobIrQarVMnDiRb775htmzZ4uncx3Q7U+U6IrU1FTefPNNampqaG5uRtHkgU1NEHKdjdTRhC5Y+PA41vxnK7oasQG1tRAnSlgXmUzG4tfuZPWKTKmjCF2klzXTalPQfiqEr68vzz77LDExMRIns0ziSV0XxMbG8vXXXzN37lxsbGzQ2lfS5HMMtdNl9Oikjid00s7Vmcx5ZLLUMQRB+AWzH4tn+9ZsqWMIXdC2ifB58DyKVnUFuVzOkiVL+Prrr0Wh6wLxpM5ATp8+zZtvvsm5c+dobGxEprHDpiYIZYub1NGETgjq501gTzf2fSsOgbcG4kmd9Ri/cBQXKlsoLKmVOorQSVpFBa22+ejlbVuEDRo0iKeeeor+/ftLnMzyiVJnQDqdjl27dvHRRx/R2NjYNiTb7NY2JKu1kzqe0EFDY4JRV9eRvStL6ihCF4lSZx0GjQnDtoc3h09cljqK0Ak6WROttvnolBVA29nrjz/+OPHx8WLenIGI4VcDksvlTJs2je+++4558+Zha2uL3qGWJp/sH1fJaqSOKHTA0bQCvPr2oPeIEKmjCEK3FxQRiHdEH1HoLJCeVlptzqB1PYROWYFCoeDOO+/ku+++Y8qUKaLQGZAodUbg6OjI448/zmeffcbw4cOxs7el1amYRt9jP25cLObbWYp9W48xeOIgPIL9pY4iCN2Wh58rkTNGkLj/tNRRhA7Qo0OjKkLV4xgamyI0Gg0jRozgyy+/5PHHH8fR0VHqiFZHDL8amV6vJz09nY8++ojS0tIf59vZYlMbiKLZAxniHYolWPTweDa+v4PmK1VSRxE6QQy/Wi5bexvm/W4hq1aKla6WQo8enaKMVtsC9PJmAPr06cMTTzxBdHS0xOmsmyh1JqLRaNixYwefffYZLS0tNDQ0IFc7YlPbC4XaRep4wi0oFHIWPjyOVf/ajFZsdWJxRKmzTAqFgiWv3cnqlZlotWKEwxJo5VVobM+iU7QtZPHw8ODhhx9m+vTpKBQKidNZP1HqTKyxsZGVK1eyYsUK9Hr9j4spXFHV9UTR6iR1POEm7BxsmHVHNCv/vErqKEIHiVJnmZY8N5tNe/JpaWmVOopwCzp5La02Z9Ep20Yz7OzsuPPOO7nzzjvbzk4XTEKUOomUl5fz1VdfsXPnTnQ6Ha2trSia3LGp64lcI/4HMFduHg6Mix/A+jc2SR1F6ABR6izPvCemcuBAAZWt4keUOdPJG34sc+VA29PV2bNnc9999+Hl5SVxuu5HlDqJXbp0ia+++oq9e/ei1WrRaXUomjzbyp3YBsUseXk7MXJsPza9vU3qKMJtEqXOssxeHkfGoYuUNYkndOZKJ2tEY1OIzuYKer0euVxOfHw8Dz74IP7+YmGZVESpMxOFhYV88cUXpKSktJc7ZZMXqjp/5Fp7qeMJP9MjwI3IqF5sfX+H1FGE2yBKneWYuWwSWSdLuVzTLHUU4QZ0sgY0NufR25ah07XNcxw/fjzLli2jd+/e0oYTRKkzN3l5eXz++eccOXIErVaLVqNF0eyBTV2AGJY1MwFBngyMDGCbKHZmT5Q6yzDjwYkczyvjYlWT1FGEn9HJ62m1KUSvKudqbRg1ahTLli0jNDRU4nTCVaLUmalTp07xzTffkJmZiV6vR6PRoGhyR1UfgKJV7O1jLvx7eRA5NJAt722XOopwE6LUmb+ZyyZx7NQVLolCZ1baFkCcb58zBzB27Fjuu+8+UebMkCh1Zu7MmTN8++23pKSkoNfr2xZUNLuiqvdHrnYW+9yZAf9Ad4aMCGKzmGNntkSpM2+zl8eRdaJEPKEzE3r06OQ1aGzOo1NWAiCTyZg0aRJLly4lODhY4oTCLxGlzkIUFhby3//+l6SkpPZyJ1c7oqrvITYxNgM+vs7ETAhlnVgVa5ZEqTNfC349jdS0QkobxKIIqenRoVWWoVEVoVe07cepUCiIj49n6dKl9OrVS+KEwq2IUmdhLl68yKpVq9i2bRtyuZzm5mZkGhtUDT1QNnoj04vNHaXi6u5A3JwhrH13p9ig2MyIUmd+5HI5i19Zwu61h6jSih9DUtKjQasqRqO62H4ChI2NDdOmTePuu+8Wq1ktiCh1Fqq6upoNGzawbt06WltbaWhoAJ0CVYMvygZf5DobqSN2Sw5Otsy6J4Z1b2+jpaxa6jjCj0SpMy+29rYs+P0CNmw8SnOjeEInFb2sBY3qIhrVZZBpAHB1dWXBggXMnz8fNzc3aQMKHSZKnYVraWlhx44drFy5kqqqqrZyp5ehaPJA1eCHvNVRDM2amFwuZ+GyMez4IpHqwlKp4wiIUmdOXL2cmfbkTNasPty+JYZgWjp5LRrVRbArR6vVAhAYGMiSJUuYNm0atra2EicUOkuUOiuh0+lISUlh5cqVnD59Go1Gg0ajQa52RNngi7LJExlyqWN2K7PvieHIriwuHCmQOkq3J0qdeQgM9Sdq3ig2bjomdZRuR48WrfIKGtWl9vlyAJGRkdxxxx3ExsYil4ufEZZOlDordOrUKdavX8/evXtRqVRtT++0SlSN3igbfZFrxbswU5kwawil+Zc4kSB+iElJlDrpDRodis+gvuzdd1rqKN2KTtaEVnUZjaoYZG1D3SqViokTJ7Jw4ULCw8MlTigYkih1Vqy6upqtW7eyYcMG6uvrfxyaBUWzG8pGXxQtrmJo1gSixvTDBj3JPxyQOkq3JUqdtMbMG4Ha1p6D2ZekjtIt6NGjU1S2PZVTVbZvFuzr68vcuXOZOXMm7u7uEqcUjEGUum5Aq9WSmprK+vXrOXbsGAqF4sdVs7YoG71RNnoh14mnd8bUt78v4UMC2fLpXnS19VLH6XZEqZOGTCZjzvPzyTlSyJnLNVLHsXp6WQsaZTFaVXH7KlaA4cOHM3/+fGJjY1EoxA4J1kyUum7m/PnzbNiwgZ07dyKXy6mtrW17etfihrLRG0Wzm5h7ZyTObg5MWzyCLR/vpv5S+a1fIBiMKHWm5+TqwKxn5rBt+wnqasWmwsaiR4dOUdG2gtWmun3xiaOjI9OnT2fevHlif7luRJS6bqqlpYV9+/axZcsWTp482f70Dq0SVZM3ygZv5Fp7qWNaHZlMxqy7R3HywEnyU3KljtNtiFJnWv2G9iYifhibt2QjfsQYh07W0La3nLIE5D9tCzN48GBmzpzJhAkTsLcX38O7G1HqBIqKiti2bRvbt29HrVZTX982PChvcULZ5NW2clavlDildRk5IRSFVsOB78U8O1MQpc50xs6PRuvkRNqh81JHsTp6WtEqy9CqitEpats/7uHhwbRp05g5cyaBgYESJhSkJkqd0E6j0ZCWlsbWrVvJzMzEzs6ureDpZW2LK5q8xPCsAQUFezFkZDBbPtuLukLMNzImUeqMT2WjZM7z8ziSfJrCsgap41iNn4ZXS9ApKkDW9iNboVAwatQoZs6cyahRo1AqxRtvQZQ64ReUlZWRkJDArl27KCoqAkCtVoNOgbLJE2WTJ3K1s1g920VKlYKZd43k2J7jFKTnSR3HaolSZ1zBg4OInDmCzVuz0bRqpY5j8fTo0cmr0apK0SrL2k97AOjTpw9Tp05lypQpeHl5SZhSMEei1Am3VFBQwK5du0hISKCxsZGmpiZ0Oh0yjU3b07smD+QaB1HwumDkxDBs5XoSv0qUOopVEqXOeCbeEUuLjZ0Ybu0iPXr08ga0ylK0ylL08pb2z3l7exMXF8eUKVMIDg6WMKVg7kSpE26bVqvl2LFj7Nq1i6SkJGxsbKiurgZAprFD2eQhCl4X+Pi5MHryABJXp1FRUCx1HKsiSp3hefq5MeGhOFJ3n6CkXpzf2hltRa6+bZ6c8gp6+U+rhB0dHZkwYQLx8fFERkaKrUiE2yJKndApLS0tJCcns3fvXjIyMrCzs2vbHgXa9r9r8hQFr5MmzxlKbUkl6WvTpI5iNUSpM6yRM4bi2tef3XvECu6O+qnIXUGrLLuuyKlUKkaOHMmUKVOIiYkRZ7AKHSZKndBljY2NpKamkpiYSEZGBg4ODtc8wbNte4LX7IG81VEUvNvUO9ibyOg+7F2dTvW5EqnjWDxR6gzD1duFuIenkJWaz9krYhPt29VW5OqueSL308bANjY2jBw5kgkTJhAbG4ujo6OESQVLJ0qdYFCNjY2kpaWRmJhIenr69QVPq0LR7N72q8VFrKK9DXHzhlF/pYqUlSlSR7FootR13eg5w3EK9GXXXvF07nbo0aJTVKNVlqNVVMA1c+RsbW0ZNWoUEyZMICYmBgcHBwmTCtZElDrBaK4WvH379pGRkYFKpaKxsRGNRgM6OYoWVxTN7ihb3JDpVFLHNVsBQR5ExfYjc+cxLh07J3UciyRKXef5B/sxcvFoDh/Io6hSnAxxM3qZGq2iAq2yHJ2iCmQ/rQS2t7dvL3KjRo0SGwMLRiFKnWASarWao0ePkpKSQkpKCg0NDeh0urZTLPQgVzu37YXX4oZMYy+GaW9gdHwEjvZKdv03GW1NndRxLIoodR0nl8uZ9uuZ1Nc0cSBDvJm4kbZh1Ua0inJ0ynJ08lqu/dbl5eXF6NGjGT16NEOHDhVz5ASjE6VOMDm9Xs/p06fbC965c+dwdnamqqoKAJnWBkWza9uTvBZXcZrFNRyd7Zg0dyhFx89zeMshqeNYDFHqOmbYpIH0GtGf3XtO0digljqOWdHTik5RhVZZiU5Red3WIwAhISHtRa5///7IZOINqmA6otQJkispKSElJYW0tDSysrKws7P7aZhWD3K1E4oWNxQtrmKxxY+Cw/wYOCSQtG1ZFJ8U+4Pdiih1t6dHHx9i7hjLiUPnyL8kTjmBq0/jatEqKtEpK//naZyNjQ1DhgxhzJgxxMTE4OvrK11YodsTpU4wKy0tLRw7dozMzEwyMjK4fPkyTk5O7U/x0CrbnuCpXdoWW2htu3XJGz11IG5OtiSsSKGxpFLqOGZLlLqbs3e0Jf6x6VRX1HMgvXsPterRo5c1o1NUtT+Ru/ZEB4BevXoxcuRIoqOjiYyMxM7OTqK0gnA9UeoEs1ZSUkJmZiaZmZkcPnwYmUyGTqejsbERAJnGBoXaBXlLW8mT67rfnBW5XE78gmFom1rY/X0Kulqx1cTPiVJ3YzKZjGlPzkQml7EzIRedTid1JEno2ktcNTpF1f8MqTo5OREVFUV0dDTR0dHiaZxgtkSpEyyGRqPh5MmTHD58mCNHjnDy5EmcnZ1pbGxsO5eWtn3x2ubiuaBQu3SrVbUOTnZMmjuEutIaktZmiHJ3DVHqrieTyZj4qyk4uzmQkJhHY33LrV9kRfSyFrTXlbjm6z6vUCgIDw9vL3Lh4eEolWJur2D+RKkTLFZTUxPHjx/nyJEjHD16lLy8PFxdXamvr6e1te3YIpnGDoXa+cd5ec7ItHZWP1zr5unE6CkDqS6uJHnjIVHuEKXuKplMxrgH43DzcmJ/SgHVlQ1SRzK6tuHURnSKmvZf157iAG1Pu0NDQxk6dCjDhg1j4MCBYu84wSKJUidYjfr6erKzs9tLXkFBAa6urjQ1NbVtnQJtc/Kuljy1848LL6xzE2QvP1dGTgynoaKWxNXp3brcdfdSJ5PJmLx8Kg7O9qRmnqOizHq3xNGjRSevu6bE1YLs+rNpZTIZISEh7SVu8ODB4iQHwSqIUidYrbq6Ok6cOMGJEyfIzs7m1KlT2NjYAG0FEAC9rK3gtTohVzsib3Xi/9u79/Co6gP/4+9zJpnM5AIkQIRwCffLQgBbUVlFUFvFlIvKFrGtPmyquz7Wy+7iiu62BXHXgq6oj/u47bobWa206NoKpiC6FlB/WgUrhIsCQYiBEIWEkNvkMjPn98fMOZlJJjcEk4yf1/PMM3O+3/P9nu9cMB/P5XuMgDuu9ual9Utm5pwcmmp8/HH9ezRWVHX3kL5239RQl5jo4jt3XEdiUiLb3j1E9Zn4mjw4tBeuAcusIuiqCoU4sxqM6D9rbrebCRMmkJOT4zzS0tK6adQi549CnXxjNDU1cfDgQfbs2eM86urq6Nu3L6dPnw5NoQKhvXlNqZiNqZhNKbgaUzCs3n9unifZzazvTSUhGOT/FXxEeVFpdw/pa/NNC3UZg/px2c2zCFgWW7cfpL6+qeNGvYBFI0FXNUGzynnGbP3e0tPTmTx5shPgxo0bR2Ji7/83LNIRhTr5xrIsi2PHjrF3714++eQTPvnkE4qKivB6vSQkJDj3rIXQBRihPXopmE3JmE3JvTroXXbNJNKS3RzdX8K+twq7ezjn3Tcl1P3FpWMZdelEqs7U9fqpSUKT/NYQNKuxzGqCrqpWFzRA6KKGUaNGMWHCBCfIDRkyRJP+yjeSQp1IhIaGBoqKivj000+doHf8+HH69u2LZVktgp4b05+MGRn0etm8eWMnZTF67AU0NfrZ9soHNJ6q7O4hnRfxHOoSkxK56sffJdGdQNH+4xwoqezuIXWJPS+cZdaEQ1wNllndaloR27Bhw5g4cSITJkxgwoQJjB07VrffEglTqBPpQHV1tRPyDh48SFFREaWlpfTp08fZo+fM7xV0OQEvFPi8mH5vj7/VmduTwOzcqZjBIJ8fOM6eN3Z195DOqXgMdZMvm8CI6eMIBIL88Z1DNDb4O27UzSz8WGYtQbM2FN7CIS7yxveRBg0axNixY50QN378eJ0LJ9IOhTqRs1BTU8Phw4c5dOgQRUVFHDp0iCNHjuB2u0lJSeHMmTPO3HkARiAxtCfPHwp5PTnsjRx3AeMmDgbgo7f28sUnn3fziL66eAl1mcMHcNENMwA4WFjC4bKeeRWrRRNBs84JcJZZi2XWtbn3LTExkZEjRzJmzBjnMXr0aAU4kS5SqBM5R5qamiguLnaC3pEjRyguLqa8vJzk5GS8Xi+VlZXOHHoARsCN2eTF8HswA57Qs9/TYw7jTp81nvQ+SRimyY4tuzl56Hh3D+ms9OZQNyArnYsXXgZARXkNH3xc0s0jCrEIhq88rcMyfQSNOiyzjqBZC2Zjm+0GDBjAiBEjGD16tBPgsrOzNbmvyDmgUCdynlVXV3P06FGKi4s5cuRIVNhLTU0lOTmZqqoq59ZnAFhG6OKMiKAXCnuebpty5ZKrJtAvLXSPy13b91O65+jXPoaz1dtCXdaoTKbNuwSAyooa3t/ZPXtLm6cMqcMyfOG9b77wcn2rqUMiDRw4kJEjRzJixAhGjBhBdnY2I0aM0N43kfNIoU6km1RXV1NcXMzRo0c5duwYx44do6SkhOPHj2NZFqmpqbjdbk6fPh11KBfLCAW7QBKmPyn0HAg9G/4kjGDieQ9937psDAMyQjPuV3xxhh1bCgnW9Ny7E/T0UGcYBpfcNJOMgX0AOPnFGXbuOf9TzlhYYDQQNOqxzPrwBQvRz+0FN7fbzZAhQxg6dChDhw5l+PDhjBw5kuzsbE3mK9INFOpEephgMMjJkyedkBf5XFpaimmapKSk4PF4aGho4MyZM9E3Yo8MfQF3+LU7FPYiX5+j4HfBkHSmzRiN1RAKnkf2lnBwV3GPuoNFTwt1hsvF+CunMnLC4NDUG6bBn/eU8sWJynO2jVBgawztabOfzYbwsh3aGtoNbQAJCQlkZWU5wW3YsGHO64EDB2Ka8XlHFpHeSKFOpBcJBAKcOnWKsrIyysrKOHHiRNTzl19+GRX6AoFAq4s2ALDACLoxAi2CXjARI5gQKg8vY5ldCoDjpg5jxOhMrIbQSfFln5dT+Pan3bonr7tDneFyceG8S7hgaHpo2TT5rPQMB/Z17RxFCwsIYBlN4cDWFA5sTVGhLVTX2GFgg9A8b5mZmQwaNIjBgwczaNCgqMeAAQN0vptIL6FQJxJH/H5/VOg7deoUJ0+ejHquqKjA5XKRlJSE1+vF5XJRX19PVVVV9B4/W9AMBb1gRNALusJlCRiWC+zXkcvhIDhkxAAmXjgcIyJYHvj4KMWflH5te/O+zlBnuFyMnDGR8dOynbIgsO/QSY5/Xu6UWQQBP5bhB6MJywi/xh8Obf6o0IbzHOM7aoNpmmRkZDBgwADnMXDgQAYOHOgEuAEDBuByuc7dByAi3UahTuQbxu/3c/r0aU6ePOkEvVOnTlFZWcnp06ejHg0NDbjdbjweD263m6SkJAKBADU1NdTV1cUOgbZwGMRyhYOeiRF+nTNtEkOHDoYmC4IGVhCsAOz7qIgTR74gWFOPYRkQDD8sI5RlIsq6svewq6EudOiS0J4u0wLDwjLDy3ZZgkFWzigmfWsMrgSwQgMEl8Wxsi8pLNxPaK9aKLxhBLAIhMOav8252TojKSmJ9PR0+vXr5zz379/fCW39+/dn4MCBpKenay+byDeIQp2ItMnn80WFPDv4VVdXt/moq6vDNE3cbjcJCQkkJiaSkJDgPPx+Pw0NDTQ0NNDU1ITf7ycYDJKQkMDo0aPJyMjANE1n75HL5cLlcmFZFiUlJZSWllJTUwMWoQeEwh72shFRHlq+LvdaNm/a0lxo50Gj7eXUtFSysrLIzg7tcbMsi0AggGVZBINBgsEg5eXlHDp0qPm+wWchJSWF1NRU0tLSSEtLc16npqY6oc0Obvay1+s96+2JSPxSqBORc8rv91NTU+OEPJ/PR11dHT6fL+p1rLLGxkYaGxudwNfY2Og8NzY2OnsG7YBon6Qf+dyyDEJ7tqqrq4n8z5392u7TfrYsC8uyooKay+XC7XaTmJjoPOxlt9vtzENoPzweT6sy+xEZ4FJSUrQnTUTOGYU6Eek1/H5/VNiL3GsW+bD3qkUuG4bhhL5YryOfI4NbQkKCzjkTkV5BoU5EREQkDmiCIREREZE4oFAnIiIiEgcU6kRERETigEKdiIiISBxQqBMRERGJAwp1IiIiInFAoU5EREQkDijUiYiIiMQBhToRERGROKBQJyIiIhIHFOpERERE4oBCnYiIiEgcUKgTERERiQMKdSIiIiJxQKFOREREJA4o1ImIiIjEAYU6ERERkTigUCciIiISBxTqREREROKAQp2IiIhIHFCoExEREYkDCnUiIiIicUChTkRERCQOKNSJiIiIxAGFOhEREZE4kNDdAxCxWZZFfX19dw9DRKRLPB4PhmF09zBEFOqk56ivr+faa6/t7mGIiHTJli1b8Hq93T0MER1+FREREYkH2lMnPZL7w0wMK/z/HIaJYRpgmGAaYBgYpl0XLjcMMA0Mex2nznDaOA+IKDOj60MNnTLLMJr/1yeiD6fcaN5WZJllhLpx6sxQv6Fyw6mz21jhMqcemvsww+vb9URvI6pNePiWGaMuan2ixthcZrSqa9WGyHG0qKeN8jb6a2scrdq0169TbrVuH9HGqY/oywqXE9EuVGdFjCdUb0TWOevadZbTpxG5vmE5dc5PzC63uwuvE/opWM6y3cYML4fqQst2O6fOsDBobmeGy5wHltPONIgqD7UPNrfDXj+Iy24TXm7uK+j054ro30Wo3GX356wbxGX3iT2OYPP6NPcd6jOISWj7obpQf65wmUEQl90+oo0LQu0Ibcf+POzl0Las8GvCdRZm+HNxYWACrvCXbWKEywxchoGJiRH+5poaXSy8fRAiPYlCnfRMASP8n1dCoY5wAAv/tWyuM8BsTjBGKCGFO7H/upu0+qvdnJiik4TdZ6u/8rQoi9wGMcpatqM5zEWEulZlESEscrnlEKPXj9HGbKeurbfRahxtvO326tr6qM62v4g+YwW+8xrqYtXTctly+o4cR+Q2Y9U5IZCIdSLXb9XGirEtK+oRGeqag2L40VYddvALdRkZAO3wB3Y4wwlFkXWhUBdsDkVGZCgKvTYNIxS4ws84rw2nXagfwn3abQm3C5fHqoto4woHUpczTjvUWR2Gusj+XPbnQXSZSeQYI75DkR5Ch19FRERE4oBCnYiIiEgcUKgTERERiQMKdSIiIiJxQKFOREREJA4o1ImIiIjEAYU6ERERkTigeeqkZ3JZWFZowtHQvGtGxLPRYkJg+zniNZFlVsTrTtRFTFrWPEVsW+XNz1bUa6LaWQCWXd7cp4UBFk7byHqnj6jJ1SLHEmPZihpSi8+jjUfLdTszF117dZ3eVifrIjfZbjurgz6tNsbY9uTD0XPLRdQ565795MPN44iYp46zn6fOormdZVjRD0LPoTqiyoOGBUawuU/sbQUj5tMLrxOut4yg0x9R/Yef7W2Fl83wOvYz0KosGPHP2n4dNCBI8zx1wXCZQVvz1BnOhMEumr8ze9kMt2k5913nJx82aJ58ONa/S5HupVAnPVLjxV929xDOD/tv5llqmUlEbJE/rWB3DuSsRaZqHUQSORv6lyMiIiISBwzLsnSvE+kRLMuivr6+u4chHaivr2fBggUAbNiwAY/H080jkrOl7/Lc8Hg8GIb2nUv30+FX6TEMw8Dr9Xb3MKQLPB6PvrM4oe9SpPfT4VcRERGROKBQJyIiIhIHFOpERERE4oBCnYiIiEgc0NWvIiIiInFAe+pERERE4oBCnYiIiEgcUKgTERERiQMKdSIiIiJxQKFOREREJA4o1ImIiIjEAYU6ERERkTigUCciIiISBxTqREREROJAQncPQER6j7q6On7729+yfft2ysrKME2TYcOGcdVVV7Fw4UISExO7e4g93ubNm/nFL37R4Xpr1qzhoosuill3/Phx1q1bx44dO6ioqMDr9TJu3DjmzZvH7NmzO+z7wIEDvPTSS+zatYvKykrS0tKYNGkSN954I9/+9rc7bP/nP/+ZV155hX379lFdXU2/fv2YNm0aixYtYvz48R223759Oxs3buTQoUPU1dWRkZHB9OnTufnmmxk6dGiH7UUkNt0mTEQ6paysjHvuuYeysjIAPB4PwWCQxsZGAMaOHcuTTz5JWlpadw6zx7NDnWma9OvXr831HnroIaZOndqq/P3332f58uXU19cDkJKSgs/nIxgMApCbm8uyZcswDCNmvwUFBTz++OMEAgEAUlNTqa2txf5TsGTJEvLy8tocV35+PmvXrgXAMAxSUlKoqakBwOVysXTpUubOnRuzrWVZrF69mk2bNgFgmiZer5fa2log9Jt66KGHmDFjRpvbF5G2KdSJSIf8fj+33XYbn332Gf379+ef//mfueiiiwgGg2zdupXHHnuMuro6Lr30Uh599NHuHm6PZoe6QYMG8dJLL3WpbWlpKX/913+Nz+cjJyeHBx54gGHDhjl7UO2wdccdd/CDH/ygVfu9e/dy9913EwgEmDlzJvfeey+ZmZmcOXOGZ599lo0bNwKwYsUKrrrqqlbt//jHP7JixQoA5s+fz+23307fvn358ssveeqpp3jnnXdwuVw8/fTTTJ48uVX7devW8ctf/hIIhcfFixeTnJzM559/zqpVq9i7dy9er5fnnnuOrKysLn02IqJz6kSkE15//XU+++wzAB5++GHnsKBpmlx99dXcd999APzpT3/io48+6rZxxrv8/Hx8Ph8ZGRmsWrWKYcOGAZCcnExeXh7z5s0D4IUXXqC6urpV+1/+8pcEAgFGjRrFQw89RGZmJgB9+/blvvvu4+KLL45aL1IgEHAC2SWXXMJ9991H3759AcjMzGTFihWMHDkyar1I1dXVPP/880AoEObl5ZGcnAzA8OHDWb16NRkZGfh8PvLz87/yZyXyTaRQJyIdev311wG48MILY+6Bufrqqxk8eHDUunJu+Xw+tm/fDsD1118f8zD3j370IwBqa2t55513oupKS0spLCwEYPHixSQktD6l2m5fVlbG7t27o+p27drlHHr/4Q9/2KptYmIiixcvBqCwsJDS0tKo+rfffpu6urqo7URKS0tjwYIFQOicO5/P12odEWmfQp2ItKu+vp69e/cCcOmll8ZcxzAMLrnkEgB27NjxtY3tm2TPnj00NDQAOJ91S4MHDyY7Oxto/T1ELrfVPicnx9l71rL9zp07gdBewZycnJjtI38fbbUfMWIEgwYNitneHldDQwN79uyJuY6ItE1Xv4pIu4qLi52T8EeOHNnmenZdRUUFVVVV9OnT52sZX29VWVnJbbfdRklJCYFAgP79+zN58mTmzp3LhRde2Gp9+/A3wKhRo9rsd9SoURQXF3PkyJGocns5PT2d9PT0mG1dLhfDhw/n008/bbN9dnY2LpcrZvv09HT69etHZWUlR48ejTn+9n5Dke/ryJEjzuFgEekc7akTkXadOnXKeT1w4MA21xswYEDMNhJbfX09Bw8eJCEhAcuyOHHiBG+++Sb33nsvq1atwu/3R61vf6ZpaWkkJSW12a/9PZSXl8dsH/k9xWJ/x1+1fcvfgN1fe78hj8dDampqzPYi0jHtqRORdtnnQQHthgmPxxOzjUTr378/S5YsYdasWQwbNgy3200gEGD//v0899xz7Ny5k02bNuHxePi7v/s7p519jlnk5xyLXd/yO7CXO2pvf8fnq317vyG7/5qaGv2GRM6C9tSJiHyNLr74YvLy8hg9ejRutxsIHfbMycnh3/7t37j88ssBePXVVykpKenOoYpIL6NQJyLtsk+cB5wT9WOxJ8Nt2UY6zzRN7rzzTgCCwSDvvfeeU+f1eoHozzkWu77ld2Avd9Te/o7PV/v2fkOR/es3JNJ1CnUi0q7Ic6hOnjzZ5nqR50B1dN6VtG3o0KHO/G+R04LYn2l1dXW7wcj+Hvr37x9Vbrfv6Fw1+zv+qu1b/gbs/tr7DdXX1zt3p9BvSKTrFOpEpF3Z2dmYZug/FS2viIxk12VkZOjK1/Mg8srQyCthW2rrKlN7+fTp01RWVsZsGwgE+Pzzz9ttX1xc3GpiYltk3yNGjIg5/vZ+Q5Hvq72rZEUkNoU6EWmXx+NxJhz+4IMPYq5jWRYffvghANOnT//axhaPjh8/zpkzZwCcCZ0hNIecfZGB/Vm3VFZWRnFxMdD6e4hcbut73LNnj3OBQsv29l1E6urqnHkLW4rst632xcXFfPHFFzHb2+8rKSmpzbnwRKRtCnUi0qE5c+YA8PHHH7N///5W9Vu3bnUOFdrrSmsd3WrbsiyeeeYZIHR+3V/+5V86dV6vl1mzZgGhiyjsw5SR1q1bB4TOR5s5c2ZUXVZWFlOmTAFg/fr1raZMAXjxxRcBGDRoEFOnTo2qmzZtmjNpsL1eJL/fz/r16wGYMmVKq3u3XnHFFSQnJ2NZVsz21dXVbNiwAYBZs2Y55xCKSOcp1IlIh+bMmcOoUaOwLIuf/exnzv1dg8EgW7du5bHHHgNCdwT49re/3Z1D7dHKysr4m7/5GzZs2EBpaakT8oLBIPv27eMf//Efndt7zZ8/n+HDh0e1z8vLw+v1Ul5ezgMPPOBcHevz+Vi7dq0Tim699daYtxH727/9W1wuF0VFRaxYscI5v62qqoo1a9Y4e9ruuOOOVhMMu1wu7rjjDiB0j981a9ZQVVUFhM6TW7FiBYcPH45aL1JaWhq33norABs2bGDt2rXONC0lJSU8+OCDlJeX4/V6ycvL6+pHKyKAYXX0v44iIsCJEye49957nft/ejwegsEgjY2NAIwdO5Ynn3wyZpiQkBMnTnDTTTc5y263G6/Xi8/ncz5HgNzcXO67776Y92d9//33Wb58uXOVaGpqKj6fzznPLTc3l2XLlmEYRswxFBQU8Pjjjzvrp6amUltb6wTMJUuWtBuq8vPzWbt2LRC6PVxKSoqz19DlcrF06VLmzp0bs61lWaxevZpNmzY563u9Xqe9x+PhoYceYsaMGW1uX0TaplAnIp1WV1fHb3/7W7Zv305ZWRmGYTBs2DCuvvpqFi5cSGJiYncPsUdraGigoKCAffv2UVRURGVlJdXV1bjdbgYOHMjkyZP53ve+1+H5ZMePH2fdunXs2LGDiooKvF4vY8eOZf78+cyePbvDcRw4cID169eze/duKisrSUtLY9KkSdx4442d2tP60Ucf8bvf/Y59+/ZRXV1Nv379mDp1KjfddBPjx4/vsP22bdvYuHEjhw4dwufzkZGRwfTp07n55psZOnRoh+1FJDaFOhEREZE4oHPqREREROKAQp2IiIhIHFCoExEREYkDCnUiIiIicUChTkRERCQOKNSJiIiIxAGFOhEREZE4oFAnIiIiEgcU6kRERETigEKdiIiISBxQqBMRERGJAwndPQAREemad999l4KCAj799FOqqqro06cPEydOZMGCBVx66aVn3e+xY8d499132bVrF4cPH6aiogKXy8WAAQOYMmUKN9xwA+PHj++wn+PHj7Nu3Tp27NhBRUUFXq+XcePGMW/ePGbPnt2lMfn9fm677TY+++wzAObMmcM//dM/nc3b65QdO3ZQUFDA/v37OX36NAD9+/dn0qRJzJ8/n2nTpp23bYt8VYZlWVZ3D0JERDoWCAR45JFHePPNNwEwDIPU1FTq6uoIBAIALFy4kHvvvbfLfe/Zs4ef/OQnUWXJyck0NTXR1NQEgGma3HLLLfz4xz9us5/333+f5cuXU19fD0BKSgo+n49gMAhAbm4uy5YtwzCMTo0rPz+ftWvXOsvnK9RZlsXjjz/Oxo0bnbKkpCQAGhoanLJFixZx1113nfPti5wLOvwqItIN8vPzueKKK8jPz+90m//6r/9yAt1f/dVfsXHjRv7whz+wadMm7rzzTlwuF6+88gr/+7//2+Xx+P1+XC4XM2fOZOXKlbz22mu8/vrrvPHGG/zqV79iypQpBINB/ud//oeCgoKYfZSWlrJixQrq6+vJycnhxRdfZPPmzWzatIklS5YAsGnTJn7zm990akyHDx/m17/+NVlZWWRkZHT5PXXF5s2bnUA3e/ZsXnzxRd58803efPNNfv3rX3P55ZcD8NJLL/H222+f17GInC2FOhGRXqCyspKXX34ZgJkzZ3LPPffQt29fALxeL4sXL+amm24CQoGxtra2S/0PGTKE559/nn/9139l9uzZTt8ul4uJEyfyxBNPMHr0aABefPHFmH3k5+fj8/nIyMhg1apVDBs2DAjt8cvLy2PevHkAvPDCC1RXV7c7nkAgwOrVq/H7/SxduhS3292l99NVW7ZsAUKfw89//nNn7ADDhw9n5cqVZGVlAbB169bzOhaRs6VQJyLSC3z00Uc0NjYCsHjx4pjr/OAHPwCgpqaGd955p0v9Z2ZmRgWZlhITE7nmmmuA0DlzLUOZz+dj+/btAFx//fWkpaW16uNHP/oRALW1tR2Ob/369Xz66adce+21TJ8+vUvv5f333+enP/0pN954I1dffTW5ubncddddvPrqq86h5JbKy8sBGDNmDAkJrU83T0hIYMyYMUDovYr0RAp1IiK9wBdffOG8HjFiRMx1+vTpQ3p6OhA64f9ci9xbZp/DZ9uzZ49z7tkll1wSs/3gwYPJzs7ucHwlJSXk5+fTr1+/Lp2/1tDQwM9//nOWLVvG22+/zalTp3C73dTW1lJYWMiaNWu45557Yu4lHDx4MABFRUX4/f5W9X6/n6KiIoBOXSwi0h0U6kREehn7ooP26uyrRc+ljz/+GAhdDWofnrVFbm/UqFFt9mHXHTlyJGa9ZVmsXr2axsZG7rrrrlbbac9jjz3Gtm3byMrK4mc/+xmbN29m8+bNvPHGGzzyyCNkZWWxb98+Vq1a1art9ddfD4T2Qq5cuZJjx445dZ9//jnLly+ntLSUIUOGsGjRok6PSeTrpClNRER6gUGDBjmvjxw5EnNqjfLycs6cOQPAqVOnzun29+7dy7vvvgvA3LlzW129am8vLS3NuWo0lgEDBjhjjeV3v/sdhYWFXHzxxc7h3s7YvXs3b7zxBunp6Tz11FNccMEFTl1SUhKXX34548aN45ZbbuGdd97h0KFDjB071lnnsssu46677uJXv/oV27ZtY9u2bVFXv6ampnL99ddz2223kZKS0ulxiXydtKdORKQX+Na3vuUc/nz++edjrvPCCy84r+vq6s7ZtisrK1m5ciXBYJChQ4dy8803t1rHPs/M4/G025ddH2t8J06c4D//8z/xeDz8wz/8Q5fG+Ic//AGA7373u1GBLlJmZiYXXnghAB9++GGr+kWLFvEv//IvziHshoYG55Cy3+/H5/NRU1PTpXGJfJ20p05E5Dx56623ePrpp2PW2SFo/fr1UXOjRXr44YfJyckBoF+/fixcuJDf/OY37Ny5k4cffphbb72VIUOGUF5ezu9//3t+//vfk5CQgN/v7/Q8cB2pq6vjwQcfpKysjOTkZFauXElycvI56bulxx57DJ/Px5133ulcadpZe/bsAULh7v/+7//aXM8OZZHnKALU19fzi1/8gq1btzJhwgR++tOfOnvyDh06xLPPPsuWLVv405/+xJNPPulcCSzSkyjUiYicJ42NjVRUVLS7js/na/NqypYn7N9+++18+eWXvPXWW84capEmTZrEmDFj2LBhQ8yrT7vK5/OxbNky9u3bh9fr5dFHH3WuAG3J6/UCOJMOt8WubxkMCwoK2LlzJ+PGjeP73/9+l8dqH86tra3t1HQuLcf5zDPPsHXrVoYPH87TTz8ddQh5+vTpTJkyhby8PEpKSnjiiSf493//9y6PUeR8U6gTETlPrrvuOq677rqYdfadEpYsWUJeXl6n+ktISGD58uVcc801bNmyhaKiIhobG8nMzOTKK69kwYIFrF69GqDd6Uk6ww50u3fvxuv1snr1aqZMmdLm+va5ctXV1TQ0NLR5Xp197l3//v2dspqaGp555hlM0+Tuu++OuoODzb75USAQcA7dejweTDN0FpF9gcjSpUtZsGBBl95rXV0dr732GgA33HBDzLEnJSVx44038tRTT1FYWMjp06edw7QiPYVCnYhILzNjxgxmzJgRs+7AgQMATJ48+az7twPdrl278Hg8rF69usN7nkZe8frZZ58xceLEmOvZV8mOHDnSKauurnYOi959993tbidyD+V///d/O4dIMzIyKCsro6ysrP03F0NJSYkzRUt7h32HDh3qvD5x4oRCnfQ4ulBCRCROHDx4kKNHjwJw7bXXnlUfPp+P+++/n127djmHXDtzE/ucnBxnD1esixAAysrKKC4uBujyhMIdsUPse++91+W2kecftjzXLlLkofTzdV6hyFehUCciEgfq6+tZs2YNELp3qT3Jb1fYgS7ykGtnAh2EzqmbNWsWAK+++mrMq0TXrVsHhALRzJkznfLBgwfz9ttvt/uwp3SZM2eOUxY5Jcn8+fOB0HQvr776aofvM/LOEtnZ2U4gLSgoiDn5cCAQcA7RpqWlfeXD2yLng0KdiEgvsX//fl544QWOHj3qhJKmpiY++OADfvKTn7B//34yMzP5+7//+5jtH3nkEa644gquuOKKVnX19fU88MADTqDr7B66SHl5eXi9XsrLy3nggQcoKSkBQiFq7dq1bNiwAYBbb731nFzIEWnatGnk5uYC8MQTT/D0009TWlrq1Dc2NrJv3z7+4z/+g0WLFnH69GmnLikpiblz5wKhvZ0PPvgghw8fJhgMEgwGOXz4MPfffz979+4F4Pvf/z4ul+ucjl/kXNA5dSIivUR5eTnPPvsszz77LIZhkJaWRm1trXM+2MiRI1m1atVZneu1bds2544RgUCA5cuXt7t+5HQrtqysLFasWMHy5cspLCzkhz/8Iampqfh8PmeMubm5Mee5OxeWLl2KaZoUFBTw8ssv8/LLL+P1eklISKC2tjbqThwtp3y54447OHbsGB988IHzsOcFtO+5C/Cd73yHW2655byMX+SrUqgTEeklxo0bx80338zu3bspKyujqqqKPn36MHr0aK688kquu+66mDej7wz76lLo3FQssQ5RQugijueee45169axY8cOKioqSE1NZezYscyfP5/Zs2ef1fg6IzExkfvvv5/c3Fxee+01CgsLOXXqFD6fj379+pGdnc3UqVOZNWsWAwcOjGqblJTEo48+yvbt23njjTc4cOAAlZWVGIZBZmYmEydOJDc3t80LVER6AsOK/JcsIiIiIr2SzqkTERERiQMKdSIiIiJxQKFOREREJA4o1ImIiIjEAYU6ERERkTigUCciIiISBxTqREREROKAQp2IiIhIHFCoExEREYkDCnUiIiIicUChTkRERCQOKNSJiIiIxAGFOhEREZE4oFAnIiIiEgcU6kRERETigEKdiIiISBxQqBMRERGJAwp1IiIiInHg/wPzoNvLmhx/rQAAAABJRU5ErkJggg==", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Visualize zero-order HealpixMap\n", + "m_plot, m_ax = m.plot(ax_kw = {\"coord\":'G'})\n", + "m.plot_grid(plt.gca(), linewidth = .1, color = 'white');" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "230c3258", + "metadata": {}, + "outputs": [], + "source": [ + "def split_map(old_map, threshold_condition):\n", + " \n", + " # Find log(likelihood) threshold for further possible split\n", + " if threshold_condition > 0: # Rank case\n", + " \n", + " # Make sure top_number is an ceiling integer\n", + " top_number = int(np.ceil(threshold_condition))\n", + " \n", + " # Extract top_number_arg_array\n", + " top_number_arg_array = np.argpartition(old_map, -top_number)[-top_number:]\n", + "\n", + " # Define threshold\n", + " threshold = min(old_map[top_number_arg_array])\n", + " \n", + " else: # Ratio case\n", + "\n", + " # Define threshold\n", + " threshold = min(old_map) + ((max(old_map) - min(old_map)) * threshold_condition)\n", + " \n", + " # Extract may_split_arg_array\n", + " may_split_arg_array = np.argwhere(old_map >= threshold)\n", + " \n", + " # Calculate min nside from min uniq\n", + " min_need_split_uniq = min(old_map.uniq[may_split_arg_array])\n", + " min_need_split_k = np.floor(np.log2(min_need_split_uniq/4)/2)\n", + " min_need_split_nside = int(np.sqrt(4**min_need_split_k))\n", + " \n", + " # Calculate next used_nside based on k of min uniq\n", + " used_nside = int(np.sqrt(4**(min_need_split_k+1)))\n", + " \n", + " # Calculate max nside from max uniq\n", + " max_need_split_uniq = max(old_map.uniq[may_split_arg_array])\n", + " max_need_split_k = np.floor(np.log2(max_need_split_uniq/4)/2)\n", + " max_need_split_nside = int(np.sqrt(4**max_need_split_k))\n", + " \n", + " # Decide wait_flag by comparing min and max nside just for checking\n", + " if max_need_split_nside != min_need_split_nside:\n", + " wait_split_flag = True\n", + " else:\n", + " wait_split_flag = False\n", + " \n", + " # Create list for return\n", + " need_split_pixel_list = []\n", + " \n", + " # Loop all uniq which may be split\n", + " for may_split_uniq in old_map.uniq[may_split_arg_array]:\n", + " \n", + " # Calculate old k, p & nside\n", + " may_split_k = np.floor(np.log2(may_split_uniq/4)/2)\n", + " may_split_p = may_split_uniq - 4**(may_split_k+1)\n", + " may_split_nside = int(np.sqrt(4**may_split_k))\n", + " \n", + " # Only split min nside case\n", + " if may_split_nside == min_need_split_nside:\n", + " \n", + " # Calculate new p (only min is enough)\n", + " each_min_need_split_p = int(4 * may_split_p)\n", + "\n", + " # Save into return list\n", + " need_split_pixel_list.append(each_min_need_split_p)\n", + " \n", + " return used_nside, wait_split_flag, need_split_pixel_list" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "2312a29e", + "metadata": {}, + "outputs": [], + "source": [ + "def fill_unchange_value(old_map, new_map):\n", + " \n", + " # Find splitted uniq in old_map\n", + " uniq_only_in_old = np.setdiff1d(old_map.uniq, new_map.uniq)\n", + " \n", + " # Loop all old_map_uniq\n", + " for old_map_uniq_idx, old_map_uniq in enumerate(old_map.uniq):\n", + " \n", + " # Copy value to new_map for un-splitted uniq\n", + " if old_map_uniq not in uniq_only_in_old:\n", + " new_map_uniq_arg = np.argwhere(new_map.uniq==old_map_uniq)\n", + " new_map[new_map_uniq_arg] = old_map[old_map_uniq_idx]\n", + " \n", + " # Create empty list to return\n", + " uniq_arg_only_in_new_list = []\n", + " \n", + " # Find splitted uniq in new_map\n", + " uniq_only_in_new = np.setdiff1d(new_map.uniq, old_map.uniq)\n", + " \n", + " # Loop all new_map_uniq\n", + " for new_map_idx, new_map_uniq in enumerate(new_map.uniq):\n", + " \n", + " # Transfer to arg and save\n", + " if new_map_uniq in uniq_only_in_new:\n", + " uniq_arg_only_in_new_list.append(new_map_idx)\n", + " \n", + " return uniq_arg_only_in_new_list" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "0887de76", + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Run nside = 2/8\n", + "Run pix = 1/32\n", + "Change source position\n" + ] + }, + { + "data": { + "text/html": [ + "
18:18:10 WARNING   get_number_of_data_points not implemented, values for statistical        plugin_prototype.py:128\n",
+       "                  measurements such as AIC or BIC are unreliable                                                   \n",
+       "
\n" + ], + "text/plain": [ + "\u001b[38;5;46m18:18:10\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m get_number_of_data_points not implemented, values for statistical \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=789748;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/threeML/plugin_prototype.py\u001b\\\u001b[2mplugin_prototype.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=716867;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/threeML/plugin_prototype.py#128\u001b\\\u001b[2m128\u001b[0m\u001b]8;;\u001b\\\n", + "\u001b[38;5;46m \u001b[0m \u001b[1;38;5;251mmeasurements such as AIC or BIC are unreliable \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b[2m \u001b[0m\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Run pix = 2/32\n", + "Change source position\n" + ] + }, + { + "data": { + "text/html": [ + "
18:18:11 WARNING   get_number_of_data_points not implemented, values for statistical        plugin_prototype.py:128\n",
+       "                  measurements such as AIC or BIC are unreliable                                                   \n",
+       "
\n" + ], + "text/plain": [ + "\u001b[38;5;46m18:18:11\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m get_number_of_data_points not implemented, values for statistical \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=672613;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/threeML/plugin_prototype.py\u001b\\\u001b[2mplugin_prototype.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=111339;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/threeML/plugin_prototype.py#128\u001b\\\u001b[2m128\u001b[0m\u001b]8;;\u001b\\\n", + "\u001b[38;5;46m \u001b[0m \u001b[1;38;5;251mmeasurements such as AIC or BIC are unreliable \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b[2m \u001b[0m\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Run pix = 3/32\n", + "Change source position\n" + ] + }, + { + "data": { + "text/html": [ + "
18:18:13 WARNING   get_number_of_data_points not implemented, values for statistical        plugin_prototype.py:128\n",
+       "                  measurements such as AIC or BIC are unreliable                                                   \n",
+       "
\n" + ], + "text/plain": [ + "\u001b[38;5;46m18:18:13\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m get_number_of_data_points not implemented, values for statistical \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=559866;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/threeML/plugin_prototype.py\u001b\\\u001b[2mplugin_prototype.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=984274;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/threeML/plugin_prototype.py#128\u001b\\\u001b[2m128\u001b[0m\u001b]8;;\u001b\\\n", + "\u001b[38;5;46m \u001b[0m \u001b[1;38;5;251mmeasurements such as AIC or BIC are unreliable \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b[2m \u001b[0m\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Run pix = 4/32\n", + "Change source position\n" + ] + }, + { + "data": { + "text/html": [ + "
18:18:14 WARNING   get_number_of_data_points not implemented, values for statistical        plugin_prototype.py:128\n",
+       "                  measurements such as AIC or BIC are unreliable                                                   \n",
+       "
\n" + ], + "text/plain": [ + "\u001b[38;5;46m18:18:14\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m get_number_of_data_points not implemented, values for statistical \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=537721;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/threeML/plugin_prototype.py\u001b\\\u001b[2mplugin_prototype.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=130560;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/threeML/plugin_prototype.py#128\u001b\\\u001b[2m128\u001b[0m\u001b]8;;\u001b\\\n", + "\u001b[38;5;46m \u001b[0m \u001b[1;38;5;251mmeasurements such as AIC or BIC are unreliable \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b[2m \u001b[0m\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Run pix = 5/32\n", + "Change source position\n" + ] + }, + { + "data": { + "text/html": [ + "
18:18:16 WARNING   get_number_of_data_points not implemented, values for statistical        plugin_prototype.py:128\n",
+       "                  measurements such as AIC or BIC are unreliable                                                   \n",
+       "
\n" + ], + "text/plain": [ + "\u001b[38;5;46m18:18:16\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m get_number_of_data_points not implemented, values for statistical \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=936115;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/threeML/plugin_prototype.py\u001b\\\u001b[2mplugin_prototype.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=802662;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/threeML/plugin_prototype.py#128\u001b\\\u001b[2m128\u001b[0m\u001b]8;;\u001b\\\n", + "\u001b[38;5;46m \u001b[0m \u001b[1;38;5;251mmeasurements such as AIC or BIC are unreliable \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b[2m \u001b[0m\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Run pix = 6/32\n", + "Change source position\n" + ] + }, + { + "data": { + "text/html": [ + "
18:18:17 WARNING   get_number_of_data_points not implemented, values for statistical        plugin_prototype.py:128\n",
+       "                  measurements such as AIC or BIC are unreliable                                                   \n",
+       "
\n" + ], + "text/plain": [ + "\u001b[38;5;46m18:18:17\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m get_number_of_data_points not implemented, values for statistical \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=145626;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/threeML/plugin_prototype.py\u001b\\\u001b[2mplugin_prototype.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=36236;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/threeML/plugin_prototype.py#128\u001b\\\u001b[2m128\u001b[0m\u001b]8;;\u001b\\\n", + "\u001b[38;5;46m \u001b[0m \u001b[1;38;5;251mmeasurements such as AIC or BIC are unreliable \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b[2m \u001b[0m\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Run pix = 7/32\n", + "Change source position\n" + ] + }, + { + "data": { + "text/html": [ + "
18:18:18 WARNING   get_number_of_data_points not implemented, values for statistical        plugin_prototype.py:128\n",
+       "                  measurements such as AIC or BIC are unreliable                                                   \n",
+       "
\n" + ], + "text/plain": [ + "\u001b[38;5;46m18:18:18\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m get_number_of_data_points not implemented, values for statistical \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=133761;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/threeML/plugin_prototype.py\u001b\\\u001b[2mplugin_prototype.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=528375;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/threeML/plugin_prototype.py#128\u001b\\\u001b[2m128\u001b[0m\u001b]8;;\u001b\\\n", + "\u001b[38;5;46m \u001b[0m \u001b[1;38;5;251mmeasurements such as AIC or BIC are unreliable \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b[2m \u001b[0m\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Run pix = 8/32\n", + "Change source position\n" + ] + }, + { + "data": { + "text/html": [ + "
18:18:20 WARNING   get_number_of_data_points not implemented, values for statistical        plugin_prototype.py:128\n",
+       "                  measurements such as AIC or BIC are unreliable                                                   \n",
+       "
\n" + ], + "text/plain": [ + "\u001b[38;5;46m18:18:20\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m get_number_of_data_points not implemented, values for statistical \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=290322;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/threeML/plugin_prototype.py\u001b\\\u001b[2mplugin_prototype.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=713722;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/threeML/plugin_prototype.py#128\u001b\\\u001b[2m128\u001b[0m\u001b]8;;\u001b\\\n", + "\u001b[38;5;46m \u001b[0m \u001b[1;38;5;251mmeasurements such as AIC or BIC are unreliable \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b[2m \u001b[0m\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Run pix = 9/32\n", + "Change source position\n" + ] + }, + { + "data": { + "text/html": [ + "
18:18:22 WARNING   get_number_of_data_points not implemented, values for statistical        plugin_prototype.py:128\n",
+       "                  measurements such as AIC or BIC are unreliable                                                   \n",
+       "
\n" + ], + "text/plain": [ + "\u001b[38;5;46m18:18:22\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m get_number_of_data_points not implemented, values for statistical \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=704878;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/threeML/plugin_prototype.py\u001b\\\u001b[2mplugin_prototype.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=381464;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/threeML/plugin_prototype.py#128\u001b\\\u001b[2m128\u001b[0m\u001b]8;;\u001b\\\n", + "\u001b[38;5;46m \u001b[0m \u001b[1;38;5;251mmeasurements such as AIC or BIC are unreliable \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b[2m \u001b[0m\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Run pix = 10/32\n", + "Change source position\n" + ] + }, + { + "data": { + "text/html": [ + "
18:18:23 WARNING   get_number_of_data_points not implemented, values for statistical        plugin_prototype.py:128\n",
+       "                  measurements such as AIC or BIC are unreliable                                                   \n",
+       "
\n" + ], + "text/plain": [ + "\u001b[38;5;46m18:18:23\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m get_number_of_data_points not implemented, values for statistical \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=313070;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/threeML/plugin_prototype.py\u001b\\\u001b[2mplugin_prototype.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=427695;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/threeML/plugin_prototype.py#128\u001b\\\u001b[2m128\u001b[0m\u001b]8;;\u001b\\\n", + "\u001b[38;5;46m \u001b[0m \u001b[1;38;5;251mmeasurements such as AIC or BIC are unreliable \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b[2m \u001b[0m\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Run pix = 11/32\n", + "Change source position\n" + ] + }, + { + "data": { + "text/html": [ + "
18:18:24 WARNING   get_number_of_data_points not implemented, values for statistical        plugin_prototype.py:128\n",
+       "                  measurements such as AIC or BIC are unreliable                                                   \n",
+       "
\n" + ], + "text/plain": [ + "\u001b[38;5;46m18:18:24\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m get_number_of_data_points not implemented, values for statistical \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=567766;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/threeML/plugin_prototype.py\u001b\\\u001b[2mplugin_prototype.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=9188;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/threeML/plugin_prototype.py#128\u001b\\\u001b[2m128\u001b[0m\u001b]8;;\u001b\\\n", + "\u001b[38;5;46m \u001b[0m \u001b[1;38;5;251mmeasurements such as AIC or BIC are unreliable \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b[2m \u001b[0m\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Run pix = 12/32\n", + "Change source position\n" + ] + }, + { + "data": { + "text/html": [ + "
18:18:26 WARNING   get_number_of_data_points not implemented, values for statistical        plugin_prototype.py:128\n",
+       "                  measurements such as AIC or BIC are unreliable                                                   \n",
+       "
\n" + ], + "text/plain": [ + "\u001b[38;5;46m18:18:26\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m get_number_of_data_points not implemented, values for statistical \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=698974;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/threeML/plugin_prototype.py\u001b\\\u001b[2mplugin_prototype.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=806797;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/threeML/plugin_prototype.py#128\u001b\\\u001b[2m128\u001b[0m\u001b]8;;\u001b\\\n", + "\u001b[38;5;46m \u001b[0m \u001b[1;38;5;251mmeasurements such as AIC or BIC are unreliable \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b[2m \u001b[0m\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Run pix = 13/32\n", + "Change source position\n" + ] + }, + { + "data": { + "text/html": [ + "
18:18:27 WARNING   get_number_of_data_points not implemented, values for statistical        plugin_prototype.py:128\n",
+       "                  measurements such as AIC or BIC are unreliable                                                   \n",
+       "
\n" + ], + "text/plain": [ + "\u001b[38;5;46m18:18:27\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m get_number_of_data_points not implemented, values for statistical \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=54507;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/threeML/plugin_prototype.py\u001b\\\u001b[2mplugin_prototype.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=997260;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/threeML/plugin_prototype.py#128\u001b\\\u001b[2m128\u001b[0m\u001b]8;;\u001b\\\n", + "\u001b[38;5;46m \u001b[0m \u001b[1;38;5;251mmeasurements such as AIC or BIC are unreliable \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b[2m \u001b[0m\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Run pix = 14/32\n", + "Change source position\n" + ] + }, + { + "data": { + "text/html": [ + "
18:18:28 WARNING   get_number_of_data_points not implemented, values for statistical        plugin_prototype.py:128\n",
+       "                  measurements such as AIC or BIC are unreliable                                                   \n",
+       "
\n" + ], + "text/plain": [ + "\u001b[38;5;46m18:18:28\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m get_number_of_data_points not implemented, values for statistical \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=374560;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/threeML/plugin_prototype.py\u001b\\\u001b[2mplugin_prototype.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=631921;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/threeML/plugin_prototype.py#128\u001b\\\u001b[2m128\u001b[0m\u001b]8;;\u001b\\\n", + "\u001b[38;5;46m \u001b[0m \u001b[1;38;5;251mmeasurements such as AIC or BIC are unreliable \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b[2m \u001b[0m\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Run pix = 15/32\n", + "Change source position\n" + ] + }, + { + "data": { + "text/html": [ + "
18:18:29 WARNING   get_number_of_data_points not implemented, values for statistical        plugin_prototype.py:128\n",
+       "                  measurements such as AIC or BIC are unreliable                                                   \n",
+       "
\n" + ], + "text/plain": [ + "\u001b[38;5;46m18:18:29\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m get_number_of_data_points not implemented, values for statistical \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=487777;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/threeML/plugin_prototype.py\u001b\\\u001b[2mplugin_prototype.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=512551;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/threeML/plugin_prototype.py#128\u001b\\\u001b[2m128\u001b[0m\u001b]8;;\u001b\\\n", + "\u001b[38;5;46m \u001b[0m \u001b[1;38;5;251mmeasurements such as AIC or BIC are unreliable \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b[2m \u001b[0m\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Run pix = 16/32\n", + "Change source position\n" + ] + }, + { + "data": { + "text/html": [ + "
18:18:30 WARNING   get_number_of_data_points not implemented, values for statistical        plugin_prototype.py:128\n",
+       "                  measurements such as AIC or BIC are unreliable                                                   \n",
+       "
\n" + ], + "text/plain": [ + "\u001b[38;5;46m18:18:30\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m get_number_of_data_points not implemented, values for statistical \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=384583;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/threeML/plugin_prototype.py\u001b\\\u001b[2mplugin_prototype.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=935674;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/threeML/plugin_prototype.py#128\u001b\\\u001b[2m128\u001b[0m\u001b]8;;\u001b\\\n", + "\u001b[38;5;46m \u001b[0m \u001b[1;38;5;251mmeasurements such as AIC or BIC are unreliable \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b[2m \u001b[0m\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Run pix = 17/32\n", + "Change source position\n" + ] + }, + { + "data": { + "text/html": [ + "
18:18:32 WARNING   get_number_of_data_points not implemented, values for statistical        plugin_prototype.py:128\n",
+       "                  measurements such as AIC or BIC are unreliable                                                   \n",
+       "
\n" + ], + "text/plain": [ + "\u001b[38;5;46m18:18:32\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m get_number_of_data_points not implemented, values for statistical \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=340580;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/threeML/plugin_prototype.py\u001b\\\u001b[2mplugin_prototype.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=191284;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/threeML/plugin_prototype.py#128\u001b\\\u001b[2m128\u001b[0m\u001b]8;;\u001b\\\n", + "\u001b[38;5;46m \u001b[0m \u001b[1;38;5;251mmeasurements such as AIC or BIC are unreliable \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b[2m \u001b[0m\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Run pix = 18/32\n", + "Change source position\n" + ] + }, + { + "data": { + "text/html": [ + "
18:18:33 WARNING   get_number_of_data_points not implemented, values for statistical        plugin_prototype.py:128\n",
+       "                  measurements such as AIC or BIC are unreliable                                                   \n",
+       "
\n" + ], + "text/plain": [ + "\u001b[38;5;46m18:18:33\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m get_number_of_data_points not implemented, values for statistical \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=840731;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/threeML/plugin_prototype.py\u001b\\\u001b[2mplugin_prototype.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=194323;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/threeML/plugin_prototype.py#128\u001b\\\u001b[2m128\u001b[0m\u001b]8;;\u001b\\\n", + "\u001b[38;5;46m \u001b[0m \u001b[1;38;5;251mmeasurements such as AIC or BIC are unreliable \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b[2m \u001b[0m\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Run pix = 19/32\n", + "Change source position\n" + ] + }, + { + "data": { + "text/html": [ + "
18:18:35 WARNING   get_number_of_data_points not implemented, values for statistical        plugin_prototype.py:128\n",
+       "                  measurements such as AIC or BIC are unreliable                                                   \n",
+       "
\n" + ], + "text/plain": [ + "\u001b[38;5;46m18:18:35\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m get_number_of_data_points not implemented, values for statistical \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=77537;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/threeML/plugin_prototype.py\u001b\\\u001b[2mplugin_prototype.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=199881;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/threeML/plugin_prototype.py#128\u001b\\\u001b[2m128\u001b[0m\u001b]8;;\u001b\\\n", + "\u001b[38;5;46m \u001b[0m \u001b[1;38;5;251mmeasurements such as AIC or BIC are unreliable \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b[2m \u001b[0m\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Run pix = 20/32\n", + "Change source position\n" + ] + }, + { + "data": { + "text/html": [ + "
18:18:36 WARNING   get_number_of_data_points not implemented, values for statistical        plugin_prototype.py:128\n",
+       "                  measurements such as AIC or BIC are unreliable                                                   \n",
+       "
\n" + ], + "text/plain": [ + "\u001b[38;5;46m18:18:36\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m get_number_of_data_points not implemented, values for statistical \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=228271;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/threeML/plugin_prototype.py\u001b\\\u001b[2mplugin_prototype.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=855485;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/threeML/plugin_prototype.py#128\u001b\\\u001b[2m128\u001b[0m\u001b]8;;\u001b\\\n", + "\u001b[38;5;46m \u001b[0m \u001b[1;38;5;251mmeasurements such as AIC or BIC are unreliable \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b[2m \u001b[0m\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Run pix = 21/32\n", + "Change source position\n" + ] + }, + { + "data": { + "text/html": [ + "
18:18:37 WARNING   get_number_of_data_points not implemented, values for statistical        plugin_prototype.py:128\n",
+       "                  measurements such as AIC or BIC are unreliable                                                   \n",
+       "
\n" + ], + "text/plain": [ + "\u001b[38;5;46m18:18:37\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m get_number_of_data_points not implemented, values for statistical \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=255325;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/threeML/plugin_prototype.py\u001b\\\u001b[2mplugin_prototype.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=898832;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/threeML/plugin_prototype.py#128\u001b\\\u001b[2m128\u001b[0m\u001b]8;;\u001b\\\n", + "\u001b[38;5;46m \u001b[0m \u001b[1;38;5;251mmeasurements such as AIC or BIC are unreliable \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b[2m \u001b[0m\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Run pix = 22/32\n", + "Change source position\n" + ] + }, + { + "data": { + "text/html": [ + "
18:18:38 WARNING   get_number_of_data_points not implemented, values for statistical        plugin_prototype.py:128\n",
+       "                  measurements such as AIC or BIC are unreliable                                                   \n",
+       "
\n" + ], + "text/plain": [ + "\u001b[38;5;46m18:18:38\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m get_number_of_data_points not implemented, values for statistical \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=666121;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/threeML/plugin_prototype.py\u001b\\\u001b[2mplugin_prototype.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=117230;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/threeML/plugin_prototype.py#128\u001b\\\u001b[2m128\u001b[0m\u001b]8;;\u001b\\\n", + "\u001b[38;5;46m \u001b[0m \u001b[1;38;5;251mmeasurements such as AIC or BIC are unreliable \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b[2m \u001b[0m\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Run pix = 23/32\n", + "Change source position\n" + ] + }, + { + "data": { + "text/html": [ + "
18:18:40 WARNING   get_number_of_data_points not implemented, values for statistical        plugin_prototype.py:128\n",
+       "                  measurements such as AIC or BIC are unreliable                                                   \n",
+       "
\n" + ], + "text/plain": [ + "\u001b[38;5;46m18:18:40\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m get_number_of_data_points not implemented, values for statistical \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=527932;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/threeML/plugin_prototype.py\u001b\\\u001b[2mplugin_prototype.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=934298;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/threeML/plugin_prototype.py#128\u001b\\\u001b[2m128\u001b[0m\u001b]8;;\u001b\\\n", + "\u001b[38;5;46m \u001b[0m \u001b[1;38;5;251mmeasurements such as AIC or BIC are unreliable \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b[2m \u001b[0m\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Run pix = 24/32\n", + "Change source position\n" + ] + }, + { + "data": { + "text/html": [ + "
18:18:41 WARNING   get_number_of_data_points not implemented, values for statistical        plugin_prototype.py:128\n",
+       "                  measurements such as AIC or BIC are unreliable                                                   \n",
+       "
\n" + ], + "text/plain": [ + "\u001b[38;5;46m18:18:41\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m get_number_of_data_points not implemented, values for statistical \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=926940;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/threeML/plugin_prototype.py\u001b\\\u001b[2mplugin_prototype.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=260433;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/threeML/plugin_prototype.py#128\u001b\\\u001b[2m128\u001b[0m\u001b]8;;\u001b\\\n", + "\u001b[38;5;46m \u001b[0m \u001b[1;38;5;251mmeasurements such as AIC or BIC are unreliable \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b[2m \u001b[0m\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Run pix = 25/32\n", + "Change source position\n" + ] + }, + { + "data": { + "text/html": [ + "
18:18:42 WARNING   get_number_of_data_points not implemented, values for statistical        plugin_prototype.py:128\n",
+       "                  measurements such as AIC or BIC are unreliable                                                   \n",
+       "
\n" + ], + "text/plain": [ + "\u001b[38;5;46m18:18:42\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m get_number_of_data_points not implemented, values for statistical \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=145507;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/threeML/plugin_prototype.py\u001b\\\u001b[2mplugin_prototype.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=237634;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/threeML/plugin_prototype.py#128\u001b\\\u001b[2m128\u001b[0m\u001b]8;;\u001b\\\n", + "\u001b[38;5;46m \u001b[0m \u001b[1;38;5;251mmeasurements such as AIC or BIC are unreliable \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b[2m \u001b[0m\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Run pix = 26/32\n", + "Change source position\n" + ] + }, + { + "data": { + "text/html": [ + "
18:18:43 WARNING   get_number_of_data_points not implemented, values for statistical        plugin_prototype.py:128\n",
+       "                  measurements such as AIC or BIC are unreliable                                                   \n",
+       "
\n" + ], + "text/plain": [ + "\u001b[38;5;46m18:18:43\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m get_number_of_data_points not implemented, values for statistical \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=979220;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/threeML/plugin_prototype.py\u001b\\\u001b[2mplugin_prototype.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=8227;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/threeML/plugin_prototype.py#128\u001b\\\u001b[2m128\u001b[0m\u001b]8;;\u001b\\\n", + "\u001b[38;5;46m \u001b[0m \u001b[1;38;5;251mmeasurements such as AIC or BIC are unreliable \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b[2m \u001b[0m\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Run pix = 27/32\n", + "Change source position\n" + ] + }, + { + "data": { + "text/html": [ + "
18:18:45 WARNING   get_number_of_data_points not implemented, values for statistical        plugin_prototype.py:128\n",
+       "                  measurements such as AIC or BIC are unreliable                                                   \n",
+       "
\n" + ], + "text/plain": [ + "\u001b[38;5;46m18:18:45\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m get_number_of_data_points not implemented, values for statistical \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=418449;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/threeML/plugin_prototype.py\u001b\\\u001b[2mplugin_prototype.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=438081;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/threeML/plugin_prototype.py#128\u001b\\\u001b[2m128\u001b[0m\u001b]8;;\u001b\\\n", + "\u001b[38;5;46m \u001b[0m \u001b[1;38;5;251mmeasurements such as AIC or BIC are unreliable \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b[2m \u001b[0m\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Run pix = 28/32\n", + "Change source position\n" + ] + }, + { + "data": { + "text/html": [ + "
18:18:46 WARNING   get_number_of_data_points not implemented, values for statistical        plugin_prototype.py:128\n",
+       "                  measurements such as AIC or BIC are unreliable                                                   \n",
+       "
\n" + ], + "text/plain": [ + "\u001b[38;5;46m18:18:46\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m get_number_of_data_points not implemented, values for statistical \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=956803;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/threeML/plugin_prototype.py\u001b\\\u001b[2mplugin_prototype.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=580347;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/threeML/plugin_prototype.py#128\u001b\\\u001b[2m128\u001b[0m\u001b]8;;\u001b\\\n", + "\u001b[38;5;46m \u001b[0m \u001b[1;38;5;251mmeasurements such as AIC or BIC are unreliable \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b[2m \u001b[0m\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Run pix = 29/32\n", + "Change source position\n" + ] + }, + { + "data": { + "text/html": [ + "
18:18:47 WARNING   get_number_of_data_points not implemented, values for statistical        plugin_prototype.py:128\n",
+       "                  measurements such as AIC or BIC are unreliable                                                   \n",
+       "
\n" + ], + "text/plain": [ + "\u001b[38;5;46m18:18:47\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m get_number_of_data_points not implemented, values for statistical \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=342268;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/threeML/plugin_prototype.py\u001b\\\u001b[2mplugin_prototype.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=185545;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/threeML/plugin_prototype.py#128\u001b\\\u001b[2m128\u001b[0m\u001b]8;;\u001b\\\n", + "\u001b[38;5;46m \u001b[0m \u001b[1;38;5;251mmeasurements such as AIC or BIC are unreliable \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b[2m \u001b[0m\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Run pix = 30/32\n", + "Change source position\n" + ] + }, + { + "data": { + "text/html": [ + "
18:18:48 WARNING   get_number_of_data_points not implemented, values for statistical        plugin_prototype.py:128\n",
+       "                  measurements such as AIC or BIC are unreliable                                                   \n",
+       "
\n" + ], + "text/plain": [ + "\u001b[38;5;46m18:18:48\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m get_number_of_data_points not implemented, values for statistical \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=744124;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/threeML/plugin_prototype.py\u001b\\\u001b[2mplugin_prototype.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=452235;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/threeML/plugin_prototype.py#128\u001b\\\u001b[2m128\u001b[0m\u001b]8;;\u001b\\\n", + "\u001b[38;5;46m \u001b[0m \u001b[1;38;5;251mmeasurements such as AIC or BIC are unreliable \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b[2m \u001b[0m\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Run pix = 31/32\n", + "Change source position\n" + ] + }, + { + "data": { + "text/html": [ + "
18:18:50 WARNING   get_number_of_data_points not implemented, values for statistical        plugin_prototype.py:128\n",
+       "                  measurements such as AIC or BIC are unreliable                                                   \n",
+       "
\n" + ], + "text/plain": [ + "\u001b[38;5;46m18:18:50\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m get_number_of_data_points not implemented, values for statistical \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=567209;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/threeML/plugin_prototype.py\u001b\\\u001b[2mplugin_prototype.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=193460;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/threeML/plugin_prototype.py#128\u001b\\\u001b[2m128\u001b[0m\u001b]8;;\u001b\\\n", + "\u001b[38;5;46m \u001b[0m \u001b[1;38;5;251mmeasurements such as AIC or BIC are unreliable \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b[2m \u001b[0m\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Run pix = 32/32\n", + "Change source position\n" + ] + }, + { + "data": { + "text/html": [ + "
18:18:51 WARNING   get_number_of_data_points not implemented, values for statistical        plugin_prototype.py:128\n",
+       "                  measurements such as AIC or BIC are unreliable                                                   \n",
+       "
\n" + ], + "text/plain": [ + "\u001b[38;5;46m18:18:51\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m get_number_of_data_points not implemented, values for statistical \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=872711;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/threeML/plugin_prototype.py\u001b\\\u001b[2mplugin_prototype.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=747661;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/threeML/plugin_prototype.py#128\u001b\\\u001b[2m128\u001b[0m\u001b]8;;\u001b\\\n", + "\u001b[38;5;46m \u001b[0m \u001b[1;38;5;251mmeasurements such as AIC or BIC are unreliable \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b[2m \u001b[0m\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Run nside = 4/8\n", + "Run pix = 1/32\n", + "Change source position\n" + ] + }, + { + "data": { + "text/html": [ + "
18:18:52 WARNING   get_number_of_data_points not implemented, values for statistical        plugin_prototype.py:128\n",
+       "                  measurements such as AIC or BIC are unreliable                                                   \n",
+       "
\n" + ], + "text/plain": [ + "\u001b[38;5;46m18:18:52\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m get_number_of_data_points not implemented, values for statistical \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=858999;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/threeML/plugin_prototype.py\u001b\\\u001b[2mplugin_prototype.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=487464;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/threeML/plugin_prototype.py#128\u001b\\\u001b[2m128\u001b[0m\u001b]8;;\u001b\\\n", + "\u001b[38;5;46m \u001b[0m \u001b[1;38;5;251mmeasurements such as AIC or BIC are unreliable \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b[2m \u001b[0m\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Run pix = 2/32\n", + "Change source position\n" + ] + }, + { + "data": { + "text/html": [ + "
18:18:53 WARNING   get_number_of_data_points not implemented, values for statistical        plugin_prototype.py:128\n",
+       "                  measurements such as AIC or BIC are unreliable                                                   \n",
+       "
\n" + ], + "text/plain": [ + "\u001b[38;5;46m18:18:53\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m get_number_of_data_points not implemented, values for statistical \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=27061;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/threeML/plugin_prototype.py\u001b\\\u001b[2mplugin_prototype.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=971239;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/threeML/plugin_prototype.py#128\u001b\\\u001b[2m128\u001b[0m\u001b]8;;\u001b\\\n", + "\u001b[38;5;46m \u001b[0m \u001b[1;38;5;251mmeasurements such as AIC or BIC are unreliable \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b[2m \u001b[0m\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Run pix = 3/32\n", + "Change source position\n" + ] + }, + { + "data": { + "text/html": [ + "
18:18:55 WARNING   get_number_of_data_points not implemented, values for statistical        plugin_prototype.py:128\n",
+       "                  measurements such as AIC or BIC are unreliable                                                   \n",
+       "
\n" + ], + "text/plain": [ + "\u001b[38;5;46m18:18:55\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m get_number_of_data_points not implemented, values for statistical \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=601552;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/threeML/plugin_prototype.py\u001b\\\u001b[2mplugin_prototype.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=580369;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/threeML/plugin_prototype.py#128\u001b\\\u001b[2m128\u001b[0m\u001b]8;;\u001b\\\n", + "\u001b[38;5;46m \u001b[0m \u001b[1;38;5;251mmeasurements such as AIC or BIC are unreliable \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b[2m \u001b[0m\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Run pix = 4/32\n", + "Change source position\n" + ] + }, + { + "data": { + "text/html": [ + "
18:18:56 WARNING   get_number_of_data_points not implemented, values for statistical        plugin_prototype.py:128\n",
+       "                  measurements such as AIC or BIC are unreliable                                                   \n",
+       "
\n" + ], + "text/plain": [ + "\u001b[38;5;46m18:18:56\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m get_number_of_data_points not implemented, values for statistical \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=138882;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/threeML/plugin_prototype.py\u001b\\\u001b[2mplugin_prototype.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=525509;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/threeML/plugin_prototype.py#128\u001b\\\u001b[2m128\u001b[0m\u001b]8;;\u001b\\\n", + "\u001b[38;5;46m \u001b[0m \u001b[1;38;5;251mmeasurements such as AIC or BIC are unreliable \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b[2m \u001b[0m\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Run pix = 5/32\n", + "Change source position\n" + ] + }, + { + "data": { + "text/html": [ + "
18:18:57 WARNING   get_number_of_data_points not implemented, values for statistical        plugin_prototype.py:128\n",
+       "                  measurements such as AIC or BIC are unreliable                                                   \n",
+       "
\n" + ], + "text/plain": [ + "\u001b[38;5;46m18:18:57\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m get_number_of_data_points not implemented, values for statistical \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=141440;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/threeML/plugin_prototype.py\u001b\\\u001b[2mplugin_prototype.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=827743;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/threeML/plugin_prototype.py#128\u001b\\\u001b[2m128\u001b[0m\u001b]8;;\u001b\\\n", + "\u001b[38;5;46m \u001b[0m \u001b[1;38;5;251mmeasurements such as AIC or BIC are unreliable \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b[2m \u001b[0m\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Run pix = 6/32\n", + "Change source position\n" + ] + }, + { + "data": { + "text/html": [ + "
18:18:58 WARNING   get_number_of_data_points not implemented, values for statistical        plugin_prototype.py:128\n",
+       "                  measurements such as AIC or BIC are unreliable                                                   \n",
+       "
\n" + ], + "text/plain": [ + "\u001b[38;5;46m18:18:58\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m get_number_of_data_points not implemented, values for statistical \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=10849;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/threeML/plugin_prototype.py\u001b\\\u001b[2mplugin_prototype.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=632631;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/threeML/plugin_prototype.py#128\u001b\\\u001b[2m128\u001b[0m\u001b]8;;\u001b\\\n", + "\u001b[38;5;46m \u001b[0m \u001b[1;38;5;251mmeasurements such as AIC or BIC are unreliable \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b[2m \u001b[0m\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Run pix = 7/32\n", + "Change source position\n" + ] + }, + { + "data": { + "text/html": [ + "
18:18:59 WARNING   get_number_of_data_points not implemented, values for statistical        plugin_prototype.py:128\n",
+       "                  measurements such as AIC or BIC are unreliable                                                   \n",
+       "
\n" + ], + "text/plain": [ + "\u001b[38;5;46m18:18:59\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m get_number_of_data_points not implemented, values for statistical \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=982510;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/threeML/plugin_prototype.py\u001b\\\u001b[2mplugin_prototype.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=87196;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/threeML/plugin_prototype.py#128\u001b\\\u001b[2m128\u001b[0m\u001b]8;;\u001b\\\n", + "\u001b[38;5;46m \u001b[0m \u001b[1;38;5;251mmeasurements such as AIC or BIC are unreliable \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b[2m \u001b[0m\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Run pix = 8/32\n", + "Change source position\n" + ] + }, + { + "data": { + "text/html": [ + "
18:19:01 WARNING   get_number_of_data_points not implemented, values for statistical        plugin_prototype.py:128\n",
+       "                  measurements such as AIC or BIC are unreliable                                                   \n",
+       "
\n" + ], + "text/plain": [ + "\u001b[38;5;46m18:19:01\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m get_number_of_data_points not implemented, values for statistical \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=457053;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/threeML/plugin_prototype.py\u001b\\\u001b[2mplugin_prototype.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=422438;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/threeML/plugin_prototype.py#128\u001b\\\u001b[2m128\u001b[0m\u001b]8;;\u001b\\\n", + "\u001b[38;5;46m \u001b[0m \u001b[1;38;5;251mmeasurements such as AIC or BIC are unreliable \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b[2m \u001b[0m\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Run pix = 9/32\n", + "Change source position\n" + ] + }, + { + "data": { + "text/html": [ + "
18:19:02 WARNING   get_number_of_data_points not implemented, values for statistical        plugin_prototype.py:128\n",
+       "                  measurements such as AIC or BIC are unreliable                                                   \n",
+       "
\n" + ], + "text/plain": [ + "\u001b[38;5;46m18:19:02\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m get_number_of_data_points not implemented, values for statistical \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=860445;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/threeML/plugin_prototype.py\u001b\\\u001b[2mplugin_prototype.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=57114;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/threeML/plugin_prototype.py#128\u001b\\\u001b[2m128\u001b[0m\u001b]8;;\u001b\\\n", + "\u001b[38;5;46m \u001b[0m \u001b[1;38;5;251mmeasurements such as AIC or BIC are unreliable \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b[2m \u001b[0m\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Run pix = 10/32\n", + "Change source position\n" + ] + }, + { + "data": { + "text/html": [ + "
18:19:03 WARNING   get_number_of_data_points not implemented, values for statistical        plugin_prototype.py:128\n",
+       "                  measurements such as AIC or BIC are unreliable                                                   \n",
+       "
\n" + ], + "text/plain": [ + "\u001b[38;5;46m18:19:03\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m get_number_of_data_points not implemented, values for statistical \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=510317;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/threeML/plugin_prototype.py\u001b\\\u001b[2mplugin_prototype.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=346720;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/threeML/plugin_prototype.py#128\u001b\\\u001b[2m128\u001b[0m\u001b]8;;\u001b\\\n", + "\u001b[38;5;46m \u001b[0m \u001b[1;38;5;251mmeasurements such as AIC or BIC are unreliable \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b[2m \u001b[0m\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Run pix = 11/32\n", + "Change source position\n" + ] + }, + { + "data": { + "text/html": [ + "
18:19:04 WARNING   get_number_of_data_points not implemented, values for statistical        plugin_prototype.py:128\n",
+       "                  measurements such as AIC or BIC are unreliable                                                   \n",
+       "
\n" + ], + "text/plain": [ + "\u001b[38;5;46m18:19:04\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m get_number_of_data_points not implemented, values for statistical \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=199156;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/threeML/plugin_prototype.py\u001b\\\u001b[2mplugin_prototype.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=877820;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/threeML/plugin_prototype.py#128\u001b\\\u001b[2m128\u001b[0m\u001b]8;;\u001b\\\n", + "\u001b[38;5;46m \u001b[0m \u001b[1;38;5;251mmeasurements such as AIC or BIC are unreliable \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b[2m \u001b[0m\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Run pix = 12/32\n", + "Change source position\n" + ] + }, + { + "data": { + "text/html": [ + "
18:19:06 WARNING   get_number_of_data_points not implemented, values for statistical        plugin_prototype.py:128\n",
+       "                  measurements such as AIC or BIC are unreliable                                                   \n",
+       "
\n" + ], + "text/plain": [ + "\u001b[38;5;46m18:19:06\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m get_number_of_data_points not implemented, values for statistical \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=546344;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/threeML/plugin_prototype.py\u001b\\\u001b[2mplugin_prototype.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=295701;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/threeML/plugin_prototype.py#128\u001b\\\u001b[2m128\u001b[0m\u001b]8;;\u001b\\\n", + "\u001b[38;5;46m \u001b[0m \u001b[1;38;5;251mmeasurements such as AIC or BIC are unreliable \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b[2m \u001b[0m\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Run pix = 13/32\n", + "Change source position\n" + ] + }, + { + "data": { + "text/html": [ + "
18:19:07 WARNING   get_number_of_data_points not implemented, values for statistical        plugin_prototype.py:128\n",
+       "                  measurements such as AIC or BIC are unreliable                                                   \n",
+       "
\n" + ], + "text/plain": [ + "\u001b[38;5;46m18:19:07\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m get_number_of_data_points not implemented, values for statistical \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=679667;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/threeML/plugin_prototype.py\u001b\\\u001b[2mplugin_prototype.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=40127;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/threeML/plugin_prototype.py#128\u001b\\\u001b[2m128\u001b[0m\u001b]8;;\u001b\\\n", + "\u001b[38;5;46m \u001b[0m \u001b[1;38;5;251mmeasurements such as AIC or BIC are unreliable \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b[2m \u001b[0m\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Run pix = 14/32\n", + "Change source position\n" + ] + }, + { + "data": { + "text/html": [ + "
18:19:08 WARNING   get_number_of_data_points not implemented, values for statistical        plugin_prototype.py:128\n",
+       "                  measurements such as AIC or BIC are unreliable                                                   \n",
+       "
\n" + ], + "text/plain": [ + "\u001b[38;5;46m18:19:08\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m get_number_of_data_points not implemented, values for statistical \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=363722;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/threeML/plugin_prototype.py\u001b\\\u001b[2mplugin_prototype.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=269126;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/threeML/plugin_prototype.py#128\u001b\\\u001b[2m128\u001b[0m\u001b]8;;\u001b\\\n", + "\u001b[38;5;46m \u001b[0m \u001b[1;38;5;251mmeasurements such as AIC or BIC are unreliable \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b[2m \u001b[0m\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Run pix = 15/32\n", + "Change source position\n" + ] + }, + { + "data": { + "text/html": [ + "
18:19:09 WARNING   get_number_of_data_points not implemented, values for statistical        plugin_prototype.py:128\n",
+       "                  measurements such as AIC or BIC are unreliable                                                   \n",
+       "
\n" + ], + "text/plain": [ + "\u001b[38;5;46m18:19:09\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m get_number_of_data_points not implemented, values for statistical \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=120845;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/threeML/plugin_prototype.py\u001b\\\u001b[2mplugin_prototype.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=590326;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/threeML/plugin_prototype.py#128\u001b\\\u001b[2m128\u001b[0m\u001b]8;;\u001b\\\n", + "\u001b[38;5;46m \u001b[0m \u001b[1;38;5;251mmeasurements such as AIC or BIC are unreliable \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b[2m \u001b[0m\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Run pix = 16/32\n", + "Change source position\n" + ] + }, + { + "data": { + "text/html": [ + "
18:19:11 WARNING   get_number_of_data_points not implemented, values for statistical        plugin_prototype.py:128\n",
+       "                  measurements such as AIC or BIC are unreliable                                                   \n",
+       "
\n" + ], + "text/plain": [ + "\u001b[38;5;46m18:19:11\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m get_number_of_data_points not implemented, values for statistical \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=891427;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/threeML/plugin_prototype.py\u001b\\\u001b[2mplugin_prototype.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=799541;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/threeML/plugin_prototype.py#128\u001b\\\u001b[2m128\u001b[0m\u001b]8;;\u001b\\\n", + "\u001b[38;5;46m \u001b[0m \u001b[1;38;5;251mmeasurements such as AIC or BIC are unreliable \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b[2m \u001b[0m\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Run pix = 17/32\n", + "Change source position\n" + ] + }, + { + "data": { + "text/html": [ + "
18:19:12 WARNING   get_number_of_data_points not implemented, values for statistical        plugin_prototype.py:128\n",
+       "                  measurements such as AIC or BIC are unreliable                                                   \n",
+       "
\n" + ], + "text/plain": [ + "\u001b[38;5;46m18:19:12\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m get_number_of_data_points not implemented, values for statistical \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=348381;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/threeML/plugin_prototype.py\u001b\\\u001b[2mplugin_prototype.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=779382;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/threeML/plugin_prototype.py#128\u001b\\\u001b[2m128\u001b[0m\u001b]8;;\u001b\\\n", + "\u001b[38;5;46m \u001b[0m \u001b[1;38;5;251mmeasurements such as AIC or BIC are unreliable \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b[2m \u001b[0m\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Run pix = 18/32\n", + "Change source position\n" + ] + }, + { + "data": { + "text/html": [ + "
18:19:13 WARNING   get_number_of_data_points not implemented, values for statistical        plugin_prototype.py:128\n",
+       "                  measurements such as AIC or BIC are unreliable                                                   \n",
+       "
\n" + ], + "text/plain": [ + "\u001b[38;5;46m18:19:13\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m get_number_of_data_points not implemented, values for statistical \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=303273;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/threeML/plugin_prototype.py\u001b\\\u001b[2mplugin_prototype.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=764442;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/threeML/plugin_prototype.py#128\u001b\\\u001b[2m128\u001b[0m\u001b]8;;\u001b\\\n", + "\u001b[38;5;46m \u001b[0m \u001b[1;38;5;251mmeasurements such as AIC or BIC are unreliable \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b[2m \u001b[0m\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Run pix = 19/32\n", + "Change source position\n" + ] + }, + { + "data": { + "text/html": [ + "
18:19:14 WARNING   get_number_of_data_points not implemented, values for statistical        plugin_prototype.py:128\n",
+       "                  measurements such as AIC or BIC are unreliable                                                   \n",
+       "
\n" + ], + "text/plain": [ + "\u001b[38;5;46m18:19:14\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m get_number_of_data_points not implemented, values for statistical \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=905004;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/threeML/plugin_prototype.py\u001b\\\u001b[2mplugin_prototype.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=889482;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/threeML/plugin_prototype.py#128\u001b\\\u001b[2m128\u001b[0m\u001b]8;;\u001b\\\n", + "\u001b[38;5;46m \u001b[0m \u001b[1;38;5;251mmeasurements such as AIC or BIC are unreliable \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b[2m \u001b[0m\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Run pix = 20/32\n", + "Change source position\n" + ] + }, + { + "data": { + "text/html": [ + "
18:19:15 WARNING   get_number_of_data_points not implemented, values for statistical        plugin_prototype.py:128\n",
+       "                  measurements such as AIC or BIC are unreliable                                                   \n",
+       "
\n" + ], + "text/plain": [ + "\u001b[38;5;46m18:19:15\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m get_number_of_data_points not implemented, values for statistical \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=963186;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/threeML/plugin_prototype.py\u001b\\\u001b[2mplugin_prototype.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=397636;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/threeML/plugin_prototype.py#128\u001b\\\u001b[2m128\u001b[0m\u001b]8;;\u001b\\\n", + "\u001b[38;5;46m \u001b[0m \u001b[1;38;5;251mmeasurements such as AIC or BIC are unreliable \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b[2m \u001b[0m\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Run pix = 21/32\n", + "Change source position\n" + ] + }, + { + "data": { + "text/html": [ + "
18:19:16 WARNING   get_number_of_data_points not implemented, values for statistical        plugin_prototype.py:128\n",
+       "                  measurements such as AIC or BIC are unreliable                                                   \n",
+       "
\n" + ], + "text/plain": [ + "\u001b[38;5;46m18:19:16\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m get_number_of_data_points not implemented, values for statistical \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=940984;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/threeML/plugin_prototype.py\u001b\\\u001b[2mplugin_prototype.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=756824;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/threeML/plugin_prototype.py#128\u001b\\\u001b[2m128\u001b[0m\u001b]8;;\u001b\\\n", + "\u001b[38;5;46m \u001b[0m \u001b[1;38;5;251mmeasurements such as AIC or BIC are unreliable \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b[2m \u001b[0m\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Run pix = 22/32\n", + "Change source position\n" + ] + }, + { + "data": { + "text/html": [ + "
18:19:17 WARNING   get_number_of_data_points not implemented, values for statistical        plugin_prototype.py:128\n",
+       "                  measurements such as AIC or BIC are unreliable                                                   \n",
+       "
\n" + ], + "text/plain": [ + "\u001b[38;5;46m18:19:17\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m get_number_of_data_points not implemented, values for statistical \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=616065;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/threeML/plugin_prototype.py\u001b\\\u001b[2mplugin_prototype.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=134471;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/threeML/plugin_prototype.py#128\u001b\\\u001b[2m128\u001b[0m\u001b]8;;\u001b\\\n", + "\u001b[38;5;46m \u001b[0m \u001b[1;38;5;251mmeasurements such as AIC or BIC are unreliable \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b[2m \u001b[0m\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Run pix = 23/32\n", + "Change source position\n" + ] + }, + { + "data": { + "text/html": [ + "
18:19:19 WARNING   get_number_of_data_points not implemented, values for statistical        plugin_prototype.py:128\n",
+       "                  measurements such as AIC or BIC are unreliable                                                   \n",
+       "
\n" + ], + "text/plain": [ + "\u001b[38;5;46m18:19:19\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m get_number_of_data_points not implemented, values for statistical \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=327076;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/threeML/plugin_prototype.py\u001b\\\u001b[2mplugin_prototype.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=85373;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/threeML/plugin_prototype.py#128\u001b\\\u001b[2m128\u001b[0m\u001b]8;;\u001b\\\n", + "\u001b[38;5;46m \u001b[0m \u001b[1;38;5;251mmeasurements such as AIC or BIC are unreliable \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b[2m \u001b[0m\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Run pix = 24/32\n", + "Change source position\n" + ] + }, + { + "data": { + "text/html": [ + "
18:19:20 WARNING   get_number_of_data_points not implemented, values for statistical        plugin_prototype.py:128\n",
+       "                  measurements such as AIC or BIC are unreliable                                                   \n",
+       "
\n" + ], + "text/plain": [ + "\u001b[38;5;46m18:19:20\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m get_number_of_data_points not implemented, values for statistical \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=6983;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/threeML/plugin_prototype.py\u001b\\\u001b[2mplugin_prototype.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=575850;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/threeML/plugin_prototype.py#128\u001b\\\u001b[2m128\u001b[0m\u001b]8;;\u001b\\\n", + "\u001b[38;5;46m \u001b[0m \u001b[1;38;5;251mmeasurements such as AIC or BIC are unreliable \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b[2m \u001b[0m\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Run pix = 25/32\n", + "Change source position\n" + ] + }, + { + "data": { + "text/html": [ + "
18:19:21 WARNING   get_number_of_data_points not implemented, values for statistical        plugin_prototype.py:128\n",
+       "                  measurements such as AIC or BIC are unreliable                                                   \n",
+       "
\n" + ], + "text/plain": [ + "\u001b[38;5;46m18:19:21\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m get_number_of_data_points not implemented, values for statistical \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=966877;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/threeML/plugin_prototype.py\u001b\\\u001b[2mplugin_prototype.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=458792;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/threeML/plugin_prototype.py#128\u001b\\\u001b[2m128\u001b[0m\u001b]8;;\u001b\\\n", + "\u001b[38;5;46m \u001b[0m \u001b[1;38;5;251mmeasurements such as AIC or BIC are unreliable \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b[2m \u001b[0m\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Run pix = 26/32\n", + "Change source position\n" + ] + }, + { + "data": { + "text/html": [ + "
18:19:22 WARNING   get_number_of_data_points not implemented, values for statistical        plugin_prototype.py:128\n",
+       "                  measurements such as AIC or BIC are unreliable                                                   \n",
+       "
\n" + ], + "text/plain": [ + "\u001b[38;5;46m18:19:22\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m get_number_of_data_points not implemented, values for statistical \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=901764;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/threeML/plugin_prototype.py\u001b\\\u001b[2mplugin_prototype.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=404930;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/threeML/plugin_prototype.py#128\u001b\\\u001b[2m128\u001b[0m\u001b]8;;\u001b\\\n", + "\u001b[38;5;46m \u001b[0m \u001b[1;38;5;251mmeasurements such as AIC or BIC are unreliable \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b[2m \u001b[0m\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Run pix = 27/32\n", + "Change source position\n" + ] + }, + { + "data": { + "text/html": [ + "
18:19:23 WARNING   get_number_of_data_points not implemented, values for statistical        plugin_prototype.py:128\n",
+       "                  measurements such as AIC or BIC are unreliable                                                   \n",
+       "
\n" + ], + "text/plain": [ + "\u001b[38;5;46m18:19:23\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m get_number_of_data_points not implemented, values for statistical \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=591317;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/threeML/plugin_prototype.py\u001b\\\u001b[2mplugin_prototype.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=841902;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/threeML/plugin_prototype.py#128\u001b\\\u001b[2m128\u001b[0m\u001b]8;;\u001b\\\n", + "\u001b[38;5;46m \u001b[0m \u001b[1;38;5;251mmeasurements such as AIC or BIC are unreliable \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b[2m \u001b[0m\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Run pix = 28/32\n", + "Change source position\n" + ] + }, + { + "data": { + "text/html": [ + "
18:19:25 WARNING   get_number_of_data_points not implemented, values for statistical        plugin_prototype.py:128\n",
+       "                  measurements such as AIC or BIC are unreliable                                                   \n",
+       "
\n" + ], + "text/plain": [ + "\u001b[38;5;46m18:19:25\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m get_number_of_data_points not implemented, values for statistical \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=178521;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/threeML/plugin_prototype.py\u001b\\\u001b[2mplugin_prototype.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=567402;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/threeML/plugin_prototype.py#128\u001b\\\u001b[2m128\u001b[0m\u001b]8;;\u001b\\\n", + "\u001b[38;5;46m \u001b[0m \u001b[1;38;5;251mmeasurements such as AIC or BIC are unreliable \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b[2m \u001b[0m\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Run pix = 29/32\n", + "Change source position\n" + ] + }, + { + "data": { + "text/html": [ + "
18:19:26 WARNING   get_number_of_data_points not implemented, values for statistical        plugin_prototype.py:128\n",
+       "                  measurements such as AIC or BIC are unreliable                                                   \n",
+       "
\n" + ], + "text/plain": [ + "\u001b[38;5;46m18:19:26\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m get_number_of_data_points not implemented, values for statistical \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=283082;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/threeML/plugin_prototype.py\u001b\\\u001b[2mplugin_prototype.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=134291;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/threeML/plugin_prototype.py#128\u001b\\\u001b[2m128\u001b[0m\u001b]8;;\u001b\\\n", + "\u001b[38;5;46m \u001b[0m \u001b[1;38;5;251mmeasurements such as AIC or BIC are unreliable \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b[2m \u001b[0m\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Run pix = 30/32\n", + "Change source position\n" + ] + }, + { + "data": { + "text/html": [ + "
18:19:27 WARNING   get_number_of_data_points not implemented, values for statistical        plugin_prototype.py:128\n",
+       "                  measurements such as AIC or BIC are unreliable                                                   \n",
+       "
\n" + ], + "text/plain": [ + "\u001b[38;5;46m18:19:27\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m get_number_of_data_points not implemented, values for statistical \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=267329;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/threeML/plugin_prototype.py\u001b\\\u001b[2mplugin_prototype.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=94291;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/threeML/plugin_prototype.py#128\u001b\\\u001b[2m128\u001b[0m\u001b]8;;\u001b\\\n", + "\u001b[38;5;46m \u001b[0m \u001b[1;38;5;251mmeasurements such as AIC or BIC are unreliable \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b[2m \u001b[0m\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Run pix = 31/32\n", + "Change source position\n" + ] + }, + { + "data": { + "text/html": [ + "
18:19:28 WARNING   get_number_of_data_points not implemented, values for statistical        plugin_prototype.py:128\n",
+       "                  measurements such as AIC or BIC are unreliable                                                   \n",
+       "
\n" + ], + "text/plain": [ + "\u001b[38;5;46m18:19:28\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m get_number_of_data_points not implemented, values for statistical \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=127537;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/threeML/plugin_prototype.py\u001b\\\u001b[2mplugin_prototype.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=491624;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/threeML/plugin_prototype.py#128\u001b\\\u001b[2m128\u001b[0m\u001b]8;;\u001b\\\n", + "\u001b[38;5;46m \u001b[0m \u001b[1;38;5;251mmeasurements such as AIC or BIC are unreliable \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b[2m \u001b[0m\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Run pix = 32/32\n", + "Change source position\n" + ] + }, + { + "data": { + "text/html": [ + "
18:19:30 WARNING   get_number_of_data_points not implemented, values for statistical        plugin_prototype.py:128\n",
+       "                  measurements such as AIC or BIC are unreliable                                                   \n",
+       "
\n" + ], + "text/plain": [ + "\u001b[38;5;46m18:19:30\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m get_number_of_data_points not implemented, values for statistical \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=397441;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/threeML/plugin_prototype.py\u001b\\\u001b[2mplugin_prototype.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=573455;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/threeML/plugin_prototype.py#128\u001b\\\u001b[2m128\u001b[0m\u001b]8;;\u001b\\\n", + "\u001b[38;5;46m \u001b[0m \u001b[1;38;5;251mmeasurements such as AIC or BIC are unreliable \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b[2m \u001b[0m\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Run nside = 8/8\n", + "Run pix = 1/32\n", + "Change source position\n" + ] + }, + { + "data": { + "text/html": [ + "
18:19:31 WARNING   get_number_of_data_points not implemented, values for statistical        plugin_prototype.py:128\n",
+       "                  measurements such as AIC or BIC are unreliable                                                   \n",
+       "
\n" + ], + "text/plain": [ + "\u001b[38;5;46m18:19:31\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m get_number_of_data_points not implemented, values for statistical \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=122609;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/threeML/plugin_prototype.py\u001b\\\u001b[2mplugin_prototype.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=910886;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/threeML/plugin_prototype.py#128\u001b\\\u001b[2m128\u001b[0m\u001b]8;;\u001b\\\n", + "\u001b[38;5;46m \u001b[0m \u001b[1;38;5;251mmeasurements such as AIC or BIC are unreliable \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b[2m \u001b[0m\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Run pix = 2/32\n", + "Change source position\n" + ] + }, + { + "data": { + "text/html": [ + "
18:19:32 WARNING   get_number_of_data_points not implemented, values for statistical        plugin_prototype.py:128\n",
+       "                  measurements such as AIC or BIC are unreliable                                                   \n",
+       "
\n" + ], + "text/plain": [ + "\u001b[38;5;46m18:19:32\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m get_number_of_data_points not implemented, values for statistical \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=778429;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/threeML/plugin_prototype.py\u001b\\\u001b[2mplugin_prototype.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=93357;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/threeML/plugin_prototype.py#128\u001b\\\u001b[2m128\u001b[0m\u001b]8;;\u001b\\\n", + "\u001b[38;5;46m \u001b[0m \u001b[1;38;5;251mmeasurements such as AIC or BIC are unreliable \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b[2m \u001b[0m\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Run pix = 3/32\n", + "Change source position\n" + ] + }, + { + "data": { + "text/html": [ + "
18:19:34 WARNING   get_number_of_data_points not implemented, values for statistical        plugin_prototype.py:128\n",
+       "                  measurements such as AIC or BIC are unreliable                                                   \n",
+       "
\n" + ], + "text/plain": [ + "\u001b[38;5;46m18:19:34\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m get_number_of_data_points not implemented, values for statistical \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=25740;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/threeML/plugin_prototype.py\u001b\\\u001b[2mplugin_prototype.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=806660;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/threeML/plugin_prototype.py#128\u001b\\\u001b[2m128\u001b[0m\u001b]8;;\u001b\\\n", + "\u001b[38;5;46m \u001b[0m \u001b[1;38;5;251mmeasurements such as AIC or BIC are unreliable \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b[2m \u001b[0m\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Run pix = 4/32\n", + "Change source position\n" + ] + }, + { + "data": { + "text/html": [ + "
18:19:35 WARNING   get_number_of_data_points not implemented, values for statistical        plugin_prototype.py:128\n",
+       "                  measurements such as AIC or BIC are unreliable                                                   \n",
+       "
\n" + ], + "text/plain": [ + "\u001b[38;5;46m18:19:35\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m get_number_of_data_points not implemented, values for statistical \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=279475;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/threeML/plugin_prototype.py\u001b\\\u001b[2mplugin_prototype.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=396333;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/threeML/plugin_prototype.py#128\u001b\\\u001b[2m128\u001b[0m\u001b]8;;\u001b\\\n", + "\u001b[38;5;46m \u001b[0m \u001b[1;38;5;251mmeasurements such as AIC or BIC are unreliable \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b[2m \u001b[0m\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Run pix = 5/32\n", + "Change source position\n" + ] + }, + { + "data": { + "text/html": [ + "
18:19:36 WARNING   get_number_of_data_points not implemented, values for statistical        plugin_prototype.py:128\n",
+       "                  measurements such as AIC or BIC are unreliable                                                   \n",
+       "
\n" + ], + "text/plain": [ + "\u001b[38;5;46m18:19:36\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m get_number_of_data_points not implemented, values for statistical \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=283841;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/threeML/plugin_prototype.py\u001b\\\u001b[2mplugin_prototype.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=986982;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/threeML/plugin_prototype.py#128\u001b\\\u001b[2m128\u001b[0m\u001b]8;;\u001b\\\n", + "\u001b[38;5;46m \u001b[0m \u001b[1;38;5;251mmeasurements such as AIC or BIC are unreliable \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b[2m \u001b[0m\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Run pix = 6/32\n", + "Change source position\n" + ] + }, + { + "data": { + "text/html": [ + "
18:19:37 WARNING   get_number_of_data_points not implemented, values for statistical        plugin_prototype.py:128\n",
+       "                  measurements such as AIC or BIC are unreliable                                                   \n",
+       "
\n" + ], + "text/plain": [ + "\u001b[38;5;46m18:19:37\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m get_number_of_data_points not implemented, values for statistical \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=84864;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/threeML/plugin_prototype.py\u001b\\\u001b[2mplugin_prototype.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=424059;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/threeML/plugin_prototype.py#128\u001b\\\u001b[2m128\u001b[0m\u001b]8;;\u001b\\\n", + "\u001b[38;5;46m \u001b[0m \u001b[1;38;5;251mmeasurements such as AIC or BIC are unreliable \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b[2m \u001b[0m\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Run pix = 7/32\n", + "Change source position\n" + ] + }, + { + "data": { + "text/html": [ + "
18:19:38 WARNING   get_number_of_data_points not implemented, values for statistical        plugin_prototype.py:128\n",
+       "                  measurements such as AIC or BIC are unreliable                                                   \n",
+       "
\n" + ], + "text/plain": [ + "\u001b[38;5;46m18:19:38\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m get_number_of_data_points not implemented, values for statistical \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=641935;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/threeML/plugin_prototype.py\u001b\\\u001b[2mplugin_prototype.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=959493;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/threeML/plugin_prototype.py#128\u001b\\\u001b[2m128\u001b[0m\u001b]8;;\u001b\\\n", + "\u001b[38;5;46m \u001b[0m \u001b[1;38;5;251mmeasurements such as AIC or BIC are unreliable \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b[2m \u001b[0m\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Run pix = 8/32\n", + "Change source position\n" + ] + }, + { + "data": { + "text/html": [ + "
18:19:40 WARNING   get_number_of_data_points not implemented, values for statistical        plugin_prototype.py:128\n",
+       "                  measurements such as AIC or BIC are unreliable                                                   \n",
+       "
\n" + ], + "text/plain": [ + "\u001b[38;5;46m18:19:40\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m get_number_of_data_points not implemented, values for statistical \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=449615;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/threeML/plugin_prototype.py\u001b\\\u001b[2mplugin_prototype.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=33265;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/threeML/plugin_prototype.py#128\u001b\\\u001b[2m128\u001b[0m\u001b]8;;\u001b\\\n", + "\u001b[38;5;46m \u001b[0m \u001b[1;38;5;251mmeasurements such as AIC or BIC are unreliable \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b[2m \u001b[0m\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Run pix = 9/32\n", + "Change source position\n" + ] + }, + { + "data": { + "text/html": [ + "
18:19:41 WARNING   get_number_of_data_points not implemented, values for statistical        plugin_prototype.py:128\n",
+       "                  measurements such as AIC or BIC are unreliable                                                   \n",
+       "
\n" + ], + "text/plain": [ + "\u001b[38;5;46m18:19:41\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m get_number_of_data_points not implemented, values for statistical \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=260670;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/threeML/plugin_prototype.py\u001b\\\u001b[2mplugin_prototype.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=484606;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/threeML/plugin_prototype.py#128\u001b\\\u001b[2m128\u001b[0m\u001b]8;;\u001b\\\n", + "\u001b[38;5;46m \u001b[0m \u001b[1;38;5;251mmeasurements such as AIC or BIC are unreliable \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b[2m \u001b[0m\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Run pix = 10/32\n", + "Change source position\n" + ] + }, + { + "data": { + "text/html": [ + "
18:19:42 WARNING   get_number_of_data_points not implemented, values for statistical        plugin_prototype.py:128\n",
+       "                  measurements such as AIC or BIC are unreliable                                                   \n",
+       "
\n" + ], + "text/plain": [ + "\u001b[38;5;46m18:19:42\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m get_number_of_data_points not implemented, values for statistical \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=978550;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/threeML/plugin_prototype.py\u001b\\\u001b[2mplugin_prototype.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=63909;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/threeML/plugin_prototype.py#128\u001b\\\u001b[2m128\u001b[0m\u001b]8;;\u001b\\\n", + "\u001b[38;5;46m \u001b[0m \u001b[1;38;5;251mmeasurements such as AIC or BIC are unreliable \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b[2m \u001b[0m\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Run pix = 11/32\n", + "Change source position\n" + ] + }, + { + "data": { + "text/html": [ + "
18:19:43 WARNING   get_number_of_data_points not implemented, values for statistical        plugin_prototype.py:128\n",
+       "                  measurements such as AIC or BIC are unreliable                                                   \n",
+       "
\n" + ], + "text/plain": [ + "\u001b[38;5;46m18:19:43\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m get_number_of_data_points not implemented, values for statistical \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=879576;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/threeML/plugin_prototype.py\u001b\\\u001b[2mplugin_prototype.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=241737;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/threeML/plugin_prototype.py#128\u001b\\\u001b[2m128\u001b[0m\u001b]8;;\u001b\\\n", + "\u001b[38;5;46m \u001b[0m \u001b[1;38;5;251mmeasurements such as AIC or BIC are unreliable \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b[2m \u001b[0m\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Run pix = 12/32\n", + "Change source position\n" + ] + }, + { + "data": { + "text/html": [ + "
18:19:44 WARNING   get_number_of_data_points not implemented, values for statistical        plugin_prototype.py:128\n",
+       "                  measurements such as AIC or BIC are unreliable                                                   \n",
+       "
\n" + ], + "text/plain": [ + "\u001b[38;5;46m18:19:44\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m get_number_of_data_points not implemented, values for statistical \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=642924;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/threeML/plugin_prototype.py\u001b\\\u001b[2mplugin_prototype.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=653735;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/threeML/plugin_prototype.py#128\u001b\\\u001b[2m128\u001b[0m\u001b]8;;\u001b\\\n", + "\u001b[38;5;46m \u001b[0m \u001b[1;38;5;251mmeasurements such as AIC or BIC are unreliable \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b[2m \u001b[0m\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Run pix = 13/32\n", + "Change source position\n" + ] + }, + { + "data": { + "text/html": [ + "
18:19:46 WARNING   get_number_of_data_points not implemented, values for statistical        plugin_prototype.py:128\n",
+       "                  measurements such as AIC or BIC are unreliable                                                   \n",
+       "
\n" + ], + "text/plain": [ + "\u001b[38;5;46m18:19:46\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m get_number_of_data_points not implemented, values for statistical \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=376655;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/threeML/plugin_prototype.py\u001b\\\u001b[2mplugin_prototype.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=271071;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/threeML/plugin_prototype.py#128\u001b\\\u001b[2m128\u001b[0m\u001b]8;;\u001b\\\n", + "\u001b[38;5;46m \u001b[0m \u001b[1;38;5;251mmeasurements such as AIC or BIC are unreliable \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b[2m \u001b[0m\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Run pix = 14/32\n", + "Change source position\n" + ] + }, + { + "data": { + "text/html": [ + "
18:19:47 WARNING   get_number_of_data_points not implemented, values for statistical        plugin_prototype.py:128\n",
+       "                  measurements such as AIC or BIC are unreliable                                                   \n",
+       "
\n" + ], + "text/plain": [ + "\u001b[38;5;46m18:19:47\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m get_number_of_data_points not implemented, values for statistical \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=751902;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/threeML/plugin_prototype.py\u001b\\\u001b[2mplugin_prototype.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=422210;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/threeML/plugin_prototype.py#128\u001b\\\u001b[2m128\u001b[0m\u001b]8;;\u001b\\\n", + "\u001b[38;5;46m \u001b[0m \u001b[1;38;5;251mmeasurements such as AIC or BIC are unreliable \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b[2m \u001b[0m\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Run pix = 15/32\n", + "Change source position\n" + ] + }, + { + "data": { + "text/html": [ + "
18:19:48 WARNING   get_number_of_data_points not implemented, values for statistical        plugin_prototype.py:128\n",
+       "                  measurements such as AIC or BIC are unreliable                                                   \n",
+       "
\n" + ], + "text/plain": [ + "\u001b[38;5;46m18:19:48\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m get_number_of_data_points not implemented, values for statistical \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=596691;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/threeML/plugin_prototype.py\u001b\\\u001b[2mplugin_prototype.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=752625;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/threeML/plugin_prototype.py#128\u001b\\\u001b[2m128\u001b[0m\u001b]8;;\u001b\\\n", + "\u001b[38;5;46m \u001b[0m \u001b[1;38;5;251mmeasurements such as AIC or BIC are unreliable \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b[2m \u001b[0m\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Run pix = 16/32\n", + "Change source position\n" + ] + }, + { + "data": { + "text/html": [ + "
18:19:49 WARNING   get_number_of_data_points not implemented, values for statistical        plugin_prototype.py:128\n",
+       "                  measurements such as AIC or BIC are unreliable                                                   \n",
+       "
\n" + ], + "text/plain": [ + "\u001b[38;5;46m18:19:49\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m get_number_of_data_points not implemented, values for statistical \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=876301;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/threeML/plugin_prototype.py\u001b\\\u001b[2mplugin_prototype.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=996641;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/threeML/plugin_prototype.py#128\u001b\\\u001b[2m128\u001b[0m\u001b]8;;\u001b\\\n", + "\u001b[38;5;46m \u001b[0m \u001b[1;38;5;251mmeasurements such as AIC or BIC are unreliable \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b[2m \u001b[0m\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Run pix = 17/32\n", + "Change source position\n" + ] + }, + { + "data": { + "text/html": [ + "
18:19:51 WARNING   get_number_of_data_points not implemented, values for statistical        plugin_prototype.py:128\n",
+       "                  measurements such as AIC or BIC are unreliable                                                   \n",
+       "
\n" + ], + "text/plain": [ + "\u001b[38;5;46m18:19:51\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m get_number_of_data_points not implemented, values for statistical \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=255942;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/threeML/plugin_prototype.py\u001b\\\u001b[2mplugin_prototype.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=750860;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/threeML/plugin_prototype.py#128\u001b\\\u001b[2m128\u001b[0m\u001b]8;;\u001b\\\n", + "\u001b[38;5;46m \u001b[0m \u001b[1;38;5;251mmeasurements such as AIC or BIC are unreliable \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b[2m \u001b[0m\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Run pix = 18/32\n", + "Change source position\n" + ] + }, + { + "data": { + "text/html": [ + "
18:19:52 WARNING   get_number_of_data_points not implemented, values for statistical        plugin_prototype.py:128\n",
+       "                  measurements such as AIC or BIC are unreliable                                                   \n",
+       "
\n" + ], + "text/plain": [ + "\u001b[38;5;46m18:19:52\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m get_number_of_data_points not implemented, values for statistical \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=887419;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/threeML/plugin_prototype.py\u001b\\\u001b[2mplugin_prototype.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=18493;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/threeML/plugin_prototype.py#128\u001b\\\u001b[2m128\u001b[0m\u001b]8;;\u001b\\\n", + "\u001b[38;5;46m \u001b[0m \u001b[1;38;5;251mmeasurements such as AIC or BIC are unreliable \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b[2m \u001b[0m\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Run pix = 19/32\n", + "Change source position\n" + ] + }, + { + "data": { + "text/html": [ + "
18:19:53 WARNING   get_number_of_data_points not implemented, values for statistical        plugin_prototype.py:128\n",
+       "                  measurements such as AIC or BIC are unreliable                                                   \n",
+       "
\n" + ], + "text/plain": [ + "\u001b[38;5;46m18:19:53\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m get_number_of_data_points not implemented, values for statistical \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=944802;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/threeML/plugin_prototype.py\u001b\\\u001b[2mplugin_prototype.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=214585;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/threeML/plugin_prototype.py#128\u001b\\\u001b[2m128\u001b[0m\u001b]8;;\u001b\\\n", + "\u001b[38;5;46m \u001b[0m \u001b[1;38;5;251mmeasurements such as AIC or BIC are unreliable \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b[2m \u001b[0m\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Run pix = 20/32\n", + "Change source position\n" + ] + }, + { + "data": { + "text/html": [ + "
18:19:54 WARNING   get_number_of_data_points not implemented, values for statistical        plugin_prototype.py:128\n",
+       "                  measurements such as AIC or BIC are unreliable                                                   \n",
+       "
\n" + ], + "text/plain": [ + "\u001b[38;5;46m18:19:54\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m get_number_of_data_points not implemented, values for statistical \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=311005;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/threeML/plugin_prototype.py\u001b\\\u001b[2mplugin_prototype.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=94987;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/threeML/plugin_prototype.py#128\u001b\\\u001b[2m128\u001b[0m\u001b]8;;\u001b\\\n", + "\u001b[38;5;46m \u001b[0m \u001b[1;38;5;251mmeasurements such as AIC or BIC are unreliable \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b[2m \u001b[0m\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Run pix = 21/32\n", + "Change source position\n" + ] + }, + { + "data": { + "text/html": [ + "
18:19:55 WARNING   get_number_of_data_points not implemented, values for statistical        plugin_prototype.py:128\n",
+       "                  measurements such as AIC or BIC are unreliable                                                   \n",
+       "
\n" + ], + "text/plain": [ + "\u001b[38;5;46m18:19:55\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m get_number_of_data_points not implemented, values for statistical \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=878006;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/threeML/plugin_prototype.py\u001b\\\u001b[2mplugin_prototype.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=691372;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/threeML/plugin_prototype.py#128\u001b\\\u001b[2m128\u001b[0m\u001b]8;;\u001b\\\n", + "\u001b[38;5;46m \u001b[0m \u001b[1;38;5;251mmeasurements such as AIC or BIC are unreliable \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b[2m \u001b[0m\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Run pix = 22/32\n", + "Change source position\n" + ] + }, + { + "data": { + "text/html": [ + "
18:19:57 WARNING   get_number_of_data_points not implemented, values for statistical        plugin_prototype.py:128\n",
+       "                  measurements such as AIC or BIC are unreliable                                                   \n",
+       "
\n" + ], + "text/plain": [ + "\u001b[38;5;46m18:19:57\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m get_number_of_data_points not implemented, values for statistical \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=565795;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/threeML/plugin_prototype.py\u001b\\\u001b[2mplugin_prototype.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=546673;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/threeML/plugin_prototype.py#128\u001b\\\u001b[2m128\u001b[0m\u001b]8;;\u001b\\\n", + "\u001b[38;5;46m \u001b[0m \u001b[1;38;5;251mmeasurements such as AIC or BIC are unreliable \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b[2m \u001b[0m\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Run pix = 23/32\n", + "Change source position\n" + ] + }, + { + "data": { + "text/html": [ + "
18:19:58 WARNING   get_number_of_data_points not implemented, values for statistical        plugin_prototype.py:128\n",
+       "                  measurements such as AIC or BIC are unreliable                                                   \n",
+       "
\n" + ], + "text/plain": [ + "\u001b[38;5;46m18:19:58\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m get_number_of_data_points not implemented, values for statistical \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=269065;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/threeML/plugin_prototype.py\u001b\\\u001b[2mplugin_prototype.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=854463;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/threeML/plugin_prototype.py#128\u001b\\\u001b[2m128\u001b[0m\u001b]8;;\u001b\\\n", + "\u001b[38;5;46m \u001b[0m \u001b[1;38;5;251mmeasurements such as AIC or BIC are unreliable \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b[2m \u001b[0m\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Run pix = 24/32\n", + "Change source position\n" + ] + }, + { + "data": { + "text/html": [ + "
18:19:59 WARNING   get_number_of_data_points not implemented, values for statistical        plugin_prototype.py:128\n",
+       "                  measurements such as AIC or BIC are unreliable                                                   \n",
+       "
\n" + ], + "text/plain": [ + "\u001b[38;5;46m18:19:59\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m get_number_of_data_points not implemented, values for statistical \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=453293;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/threeML/plugin_prototype.py\u001b\\\u001b[2mplugin_prototype.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=565585;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/threeML/plugin_prototype.py#128\u001b\\\u001b[2m128\u001b[0m\u001b]8;;\u001b\\\n", + "\u001b[38;5;46m \u001b[0m \u001b[1;38;5;251mmeasurements such as AIC or BIC are unreliable \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b[2m \u001b[0m\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Run pix = 25/32\n", + "Change source position\n" + ] + }, + { + "data": { + "text/html": [ + "
18:20:00 WARNING   get_number_of_data_points not implemented, values for statistical        plugin_prototype.py:128\n",
+       "                  measurements such as AIC or BIC are unreliable                                                   \n",
+       "
\n" + ], + "text/plain": [ + "\u001b[38;5;46m18:20:00\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m get_number_of_data_points not implemented, values for statistical \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=898677;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/threeML/plugin_prototype.py\u001b\\\u001b[2mplugin_prototype.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=161928;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/threeML/plugin_prototype.py#128\u001b\\\u001b[2m128\u001b[0m\u001b]8;;\u001b\\\n", + "\u001b[38;5;46m \u001b[0m \u001b[1;38;5;251mmeasurements such as AIC or BIC are unreliable \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b[2m \u001b[0m\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Run pix = 26/32\n", + "Change source position\n" + ] + }, + { + "data": { + "text/html": [ + "
18:20:01 WARNING   get_number_of_data_points not implemented, values for statistical        plugin_prototype.py:128\n",
+       "                  measurements such as AIC or BIC are unreliable                                                   \n",
+       "
\n" + ], + "text/plain": [ + "\u001b[38;5;46m18:20:01\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m get_number_of_data_points not implemented, values for statistical \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=820007;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/threeML/plugin_prototype.py\u001b\\\u001b[2mplugin_prototype.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=837042;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/threeML/plugin_prototype.py#128\u001b\\\u001b[2m128\u001b[0m\u001b]8;;\u001b\\\n", + "\u001b[38;5;46m \u001b[0m \u001b[1;38;5;251mmeasurements such as AIC or BIC are unreliable \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b[2m \u001b[0m\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Run pix = 27/32\n", + "Change source position\n" + ] + }, + { + "data": { + "text/html": [ + "
18:20:02 WARNING   get_number_of_data_points not implemented, values for statistical        plugin_prototype.py:128\n",
+       "                  measurements such as AIC or BIC are unreliable                                                   \n",
+       "
\n" + ], + "text/plain": [ + "\u001b[38;5;46m18:20:02\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m get_number_of_data_points not implemented, values for statistical \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=279548;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/threeML/plugin_prototype.py\u001b\\\u001b[2mplugin_prototype.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=804717;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/threeML/plugin_prototype.py#128\u001b\\\u001b[2m128\u001b[0m\u001b]8;;\u001b\\\n", + "\u001b[38;5;46m \u001b[0m \u001b[1;38;5;251mmeasurements such as AIC or BIC are unreliable \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b[2m \u001b[0m\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Run pix = 28/32\n", + "Change source position\n" + ] + }, + { + "data": { + "text/html": [ + "
18:20:04 WARNING   get_number_of_data_points not implemented, values for statistical        plugin_prototype.py:128\n",
+       "                  measurements such as AIC or BIC are unreliable                                                   \n",
+       "
\n" + ], + "text/plain": [ + "\u001b[38;5;46m18:20:04\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m get_number_of_data_points not implemented, values for statistical \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=346475;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/threeML/plugin_prototype.py\u001b\\\u001b[2mplugin_prototype.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=527181;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/threeML/plugin_prototype.py#128\u001b\\\u001b[2m128\u001b[0m\u001b]8;;\u001b\\\n", + "\u001b[38;5;46m \u001b[0m \u001b[1;38;5;251mmeasurements such as AIC or BIC are unreliable \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b[2m \u001b[0m\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Run pix = 29/32\n", + "Change source position\n" + ] + }, + { + "data": { + "text/html": [ + "
18:20:05 WARNING   get_number_of_data_points not implemented, values for statistical        plugin_prototype.py:128\n",
+       "                  measurements such as AIC or BIC are unreliable                                                   \n",
+       "
\n" + ], + "text/plain": [ + "\u001b[38;5;46m18:20:05\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m get_number_of_data_points not implemented, values for statistical \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=798311;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/threeML/plugin_prototype.py\u001b\\\u001b[2mplugin_prototype.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=725641;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/threeML/plugin_prototype.py#128\u001b\\\u001b[2m128\u001b[0m\u001b]8;;\u001b\\\n", + "\u001b[38;5;46m \u001b[0m \u001b[1;38;5;251mmeasurements such as AIC or BIC are unreliable \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b[2m \u001b[0m\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Run pix = 30/32\n", + "Change source position\n" + ] + }, + { + "data": { + "text/html": [ + "
18:20:06 WARNING   get_number_of_data_points not implemented, values for statistical        plugin_prototype.py:128\n",
+       "                  measurements such as AIC or BIC are unreliable                                                   \n",
+       "
\n" + ], + "text/plain": [ + "\u001b[38;5;46m18:20:06\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m get_number_of_data_points not implemented, values for statistical \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=506956;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/threeML/plugin_prototype.py\u001b\\\u001b[2mplugin_prototype.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=93694;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/threeML/plugin_prototype.py#128\u001b\\\u001b[2m128\u001b[0m\u001b]8;;\u001b\\\n", + "\u001b[38;5;46m \u001b[0m \u001b[1;38;5;251mmeasurements such as AIC or BIC are unreliable \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b[2m \u001b[0m\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Run pix = 31/32\n", + "Change source position\n" + ] + }, + { + "data": { + "text/html": [ + "
18:20:08 WARNING   get_number_of_data_points not implemented, values for statistical        plugin_prototype.py:128\n",
+       "                  measurements such as AIC or BIC are unreliable                                                   \n",
+       "
\n" + ], + "text/plain": [ + "\u001b[38;5;46m18:20:08\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m get_number_of_data_points not implemented, values for statistical \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=361528;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/threeML/plugin_prototype.py\u001b\\\u001b[2mplugin_prototype.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=955433;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/threeML/plugin_prototype.py#128\u001b\\\u001b[2m128\u001b[0m\u001b]8;;\u001b\\\n", + "\u001b[38;5;46m \u001b[0m \u001b[1;38;5;251mmeasurements such as AIC or BIC are unreliable \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b[2m \u001b[0m\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Run pix = 32/32\n", + "Change source position\n" + ] + }, + { + "data": { + "text/html": [ + "
18:20:09 WARNING   get_number_of_data_points not implemented, values for statistical        plugin_prototype.py:128\n",
+       "                  measurements such as AIC or BIC are unreliable                                                   \n",
+       "
\n" + ], + "text/plain": [ + "\u001b[38;5;46m18:20:09\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m get_number_of_data_points not implemented, values for statistical \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=193477;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/threeML/plugin_prototype.py\u001b\\\u001b[2mplugin_prototype.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=129860;file:///Users/jasonpbu/miniconda3/envs/env_cosipy_3_miniCD2/lib/python3.10/site-packages/threeML/plugin_prototype.py#128\u001b\\\u001b[2m128\u001b[0m\u001b]8;;\u001b\\\n", + "\u001b[38;5;46m \u001b[0m \u001b[1;38;5;251mmeasurements such as AIC or BIC are unreliable \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b[2m \u001b[0m\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: user 1min 56s, sys: 3 s, total: 1min 59s\n", + "Wall time: 1min 59s\n" + ] + } + ], + "source": [ + "%%time\n", + "\n", + "# Stop when used_nside > stop_nside\n", + "while True:\n", + " \n", + " # Get used_nside & need_split_pixel_list\n", + " used_nside, _, need_split_pixel_list = split_map(m, top_number) # wait_split_flag is useless, just for checking\n", + " \n", + " if used_nside > stop_nside:\n", + " break\n", + " \n", + " # Print progress\n", + " print(f\"\\rRun nside = {used_nside:d}/{stop_nside}\")\n", + " \n", + " # Split m base on used_nside & need_split_pixel_list\n", + " m_split_temp = m.moc_from_pixels(used_nside, need_split_pixel_list, nest=True, density=True, coordsys='galactic')\n", + " \n", + " # Take the union of m and m_split_temp\n", + " m_split = m * m_split_temp\n", + " \n", + " # Fill the original value in the old pixel\n", + " m_split_empty_arg_list = fill_unchange_value(m, m_split)\n", + " \n", + " # Fill the new value in the new pixel\n", + " fill_empty_value(m_split, m_split_empty_arg_list)\n", + " \n", + " # Update m\n", + " m = m_split" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "92862be8", + "metadata": { + "scrolled": false + }, + "outputs": [], + "source": [ + "# Calculate log_like0\n", + "spectrum.K.value = 1e-10\n", + "log_like0 = cosi.get_log_like()" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "cee9e9f3", + "metadata": {}, + "outputs": [], + "source": [ + "# Calculate TS\n", + "ts = 2*(m - log_like0)" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "fca19913", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Crab position: \n", + " galactic longitude (l) = 184.5551014904497\n", + " galactic latitude (b) = -5.78768203451836\n", + "--------------------------------------------------\n", + "Best fit position: \n", + " galactic longitude (l) = 185.625\n", + " galactic latitude (b) = -9.594068226860458\n", + "Expected significance: inf sigma\n" + ] + } + ], + "source": [ + "# Report best fit result\n", + "print(\"Crab position: \")\n", + "print(f\" galactic longitude (l) = {coord_crab.l.deg}\")\n", + "print(f\" galactic latitude (b) = {coord_crab.b.deg}\")\n", + "print(\"--------------------------------------------------\")\n", + "best_fit_l = ts.pix2skycoord(np.argmax(ts)).l.deg\n", + "best_fit_b = ts.pix2skycoord(np.argmax(ts)).b.deg\n", + "print(\"Best fit position: \")\n", + "print(f\" galactic longitude (l) = {best_fit_l}\")\n", + "print(f\" galactic latitude (b) = {best_fit_b}\")\n", + "print(f\"Expected significance: {stats.norm.isf(stats.chi2.sf(max(ts), df = 2)):.1f} sigma\")" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "15a3410a", + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Visualize TS map\n", + "ts_plot, ts_ax = ts.plot(ax_kw = {\"coord\":'G'})\n", + "ts_fig = plt.gcf()\n", + "\n", + "ts_fig.set_dpi(300)\n", + "\n", + "ts.plot_grid(ts_ax, linewidth = .1, color = 'white')\n", + "\n", + "ts_ax.scatter(best_fit_l, best_fit_b, transform=ts_ax.get_transform('world'), color='blue', s=1, label='best fit')\n", + "ts_ax.scatter(coord_crab.l.deg, coord_crab.b.deg, transform=ts_ax.get_transform('world'), color='red', s=1, label='crab')\n", + "ts_ax.legend(loc='upper right', fontsize=5)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2c4fa117", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python [conda env:env_cosipy_3_miniCD2]", + "language": "python", + "name": "conda-env-env_cosipy_3_miniCD2-py" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +}