{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "159cba26",
   "metadata": {},
   "source": [
    "# ALMA Data Example\n",
    "\n",
    "Trey V. Wenger (c) December 2024\n",
    "\n",
    "Here we test `bayes_cn_hfs` on some real ALMA data and demonstrate a general procedure for determining the carbon isotopic ratio from observations of CN and 13CN."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "4888afb4",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-08-05T18:56:36.353175Z",
     "start_time": "2024-08-05T18:56:36.347266Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pymc version: 5.19.1\n",
      "bayes_spec version: 1.7.2\n",
      "bayes_cn_hfs version: 1.1.1+2.g81bbbdf.dirty\n"
     ]
    }
   ],
   "source": [
    "# General imports   \n",
    "import os\n",
    "import pickle\n",
    "import time\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import arviz as az\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import pymc as pm\n",
    "\n",
    "print(\"pymc version:\", pm.__version__)\n",
    "\n",
    "import bayes_spec\n",
    "print(\"bayes_spec version:\", bayes_spec.__version__)\n",
    "\n",
    "import bayes_cn_hfs\n",
    "print(\"bayes_cn_hfs version:\", bayes_cn_hfs.__version__)\n",
    "\n",
    "# Notebook configuration\n",
    "pd.options.display.max_rows = None"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fa0a6c4a",
   "metadata": {},
   "source": [
    "## Load the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "e46da6a1",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-08-05T18:04:15.932293Z",
     "start_time": "2024-08-05T18:04:15.684181Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "12CN 1021\n",
      "13CN 1021\n"
     ]
    },
    {
     "data": {
      "image/png": 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Pnz8HULhgkc3QRETmw2CRiIpcQkIC0tPTAQDe3t56P07qs8iaRSIi87Go0dCqfv311zz3v/322ybKCREZSqpVdHZ25mhoIqJixmKDxStXrmhsy8rKwh9//IGIiAgGi0TFiBQslitXrkCPc3BwAACkpaUZPU9ERKQfiw0W586dK9/OyMjAqlWrsGjRInTu3BmTJk0yY86IqKBiYmIAAJ6engV6nK2tLQBAqVQaPU9ERKQfiw0WASApKQlLly7FihUr0KtXLxw4cAD+/v7mzhYR/b8NGzYgKSkp3xpDadocd3f3Ah2fwSIRkflZ7ACXqVOnomHDhkhKSsLp06exYMECBopEFmTDhg0YNGgQ3n//fRw6dCjPtAwWiYiKL4sNFmfNmoUXL15g2bJlqF27NsqVK4dy5crB29u7wP2eiMj4Vq5cKd9ev369PNpZm8IGi3Z2dgByuqIQEZF5WGwzdHZ2trmzQEQ6pKSk4NixY/L92NhY7N27F6+//rrW9KxZJCIqviy2ZjE+Ph4PHz7U2P7w4UMkJCSYPkNEJLt8+TKys7Ph4+ODcePGAchpltaFwSIRUfFlscHi6NGjcffuXY3tV69exccff2yGHBGR5OLFiwCAxo0bY8CAAQCAXbt2ITw8XGt6BotERMWXxQaLoaGheOWVVzS29+zZExcuXCjQsebMmYNmzZrBxcUF5cqVQ9++fXHr1i1jZZWo1JF+yNWuXRsNGzZE7dq1kZGRgZEjR0IIoZE+MjISQMHnWWSwSERkfhYbLOY1CW9eHem1OXr0KD766COcOnUKISEhyMzMRHBwMJKTkw3NJlGp9OjRIwBApUqVAADDhw+Hg4MDdu3apTbwRSLVOFaoUKFA55GCRQ5wISIyH4sNFuvXr4/ffvtNY/uGDRtQu3btAh1r7969ePfdd1G3bl00bNgQa9asQVhYGM6fP2+s7BKVKmFhYQD+CxarVq2KqVOnAsjpQnL27Fk5bWZmplyz6OfnV6DzSKOhWbNIRGQ+FjsaevHixXj11VexevVqNGrUCAqFAhcuXEBCQgK2b99u0LHj4+MBAB4eHlr3p6enq9VeSgNqlEqlxX1pSfmxtHwVFyy/wpGCRT8/P7nsPv74Y5w8eRJ///035s6dK//Ye/r0KbKzs2FtbQ13d/cClbVCoQCQU7NYUl8jvgcNw/IzDMvPMOYqP1OfTyG0dTCyIAcPHsT169chhECdOnW09mMsCCEE+vTpg9jYWBw/flxrmunTp2PGjBka2zds2AAnJyeDzk9U3GVlZeH111+HEAJr165F2bJl5X337t3D+PHjYWNjg8WLF6N8+fK4e/cuJkyYAE9PT6xatapA5woNDcWMGTNQuXJlLFiwwMjPhIioeEpJScHAgQMRHx8PV1fXIj+fxQeLxvbRRx9h165d+Oeff3SuCKOtZjEgIABRUVEmeVEKQqlUIiQkBJ07d5b7d5H+WH4FFxERgUqVKsHKygrJycnIzs6Wy9DGxgadO3fGsWPHMHPmTHz++ec4cOAAunfvjnr16hV4cNrhw4fRpUsX1KlTRx6BXdLwPWgYlp9hWH6GMVf5JSQkwMvLy2TBosU2QxeFjz/+GDt27MCxY8fyXDrQ3t4e9vb2GtttbW0t9sNkyXkrDlh++pOmwfHy8oKDg4PcHCKVYbt27XDs2DGEh4fD1tZWHkhWtmzZApexo6MjgJx+jyX99eF70DAsP8Ow/Axj6vIz9WtVKoJFIQQ+/vhjbNu2DUeOHEHlypXNnSWiYuvZs2cAdE+DI414fvr0KYD/+gi7ubkV+FwcDU1EZH7FLliMiIiAh4eH1po/XT766CNs2LABf/31F1xcXOSRmW5ubnLNBRHpRwoWfXx8tO43ZrDI0dBEROZnsVPn6DJkyBDUqlULEyZM0Psxy5YtQ3x8PDp06IDy5cvLf5s3by7CnBKVTOaoWWSwSERkPsWuZvHAgQMAgJs3b+r9mFI2hoeoSD1//hxA/jWLz58/h1KpZLBIRFTMWXTNYl4rtdSqVcuEOSEiSX7N0N7e3rC1tYUQAhEREQwWiYiKOYsNFseOHQt3d3cEBQXh/v37GDNmDN566y2tS4kRkenkFyxaWVnJK7U8ffpUDhYLM70DB7gQEZmfxQaLf//9N6KiorBo0SK0adMG3t7eePPNNxESEoJp06aZO3tEpVZ+wSKg3m8xOjoagO4Vk/LCAS5EROZnscGim5sbnJyc8NJLL8HV1RVfffUV+vXrhw0bNmDXrl3mzh5RqSX1WdQ1wAVQDxb1CS51kWoWhRDIysoq8OOJiMhwFhssvnjxAjt37sTjx49RpkwZebuNjQ0HrBCZSXZ2dr4DXAD1YFGf9LqoTjzL2kUiIvOwmNHQ+/btQ+fOnWFllRO/fvrpp9i6dStmzpyJ+/fvo3Xr1qhZsyZq1qwpN2sRkWnFxsYiMzMTgH41iw8ePJBXfMkrvS6q86mmpaXBwcGhwMcgIiLDWEyw2L17d0RERMhfKOPGjVPbf//+fVy9ehVXr15FmzZtzJFFolJPqiV0c3PLc2J8KViU1nO2trYudJ9FGxsbZGZmIjk5GWXLli3wMYiIyDAWEyzm17RcpUoVVKlSBb179zZRjogoN337H0rB4t27dwHk1CpKrQYFoVAo4OzsjLi4OCQlJRX48UREZDiL7bNIRJZH32DR399f7X5AQEChz+ns7AwADBaJiMzEooLFJUuWYN++fYiKijJ3VohIC31GQgM5waG1tbV8v2LFioU+pxQsJiYmFvoYRERUeBbTDA3krOE8a9YsKBQKVKhQAU2aNEFQUBCaNGmCJk2aoHz58ubOIlGpJgVs+a3GYmtri4oVK+LBgwcADAsWXVxcALBmkYjIXCwqWLx27RoyMzMRGhqKCxcu4MKFC/jll1/w+PFjKBQK+Pj4IDw83NzZJCq1kpOTAUBtOitdKleubJRgkc3QRETmZTHBokKhAAD4+fnBz88PPXr0kPfFxMTg3Llz8shKIjIPKWCTAri8NGrUCIcOHQIAtG7dutDnZLBI+tiyZQsWL16MGzduYNGiRXjnnXfMnSWiEsNi+izmNRraw8MDwcHBmDhxoglzRES5STWL+gSL77//Puzt7VG7dm00bdq00OdksEj5uX37NoYMGYJTp04hPj4eo0ePxuPHj82dLaISw2KCxT179uTbD4qIzEsK2PRphq5RowauXr2KI0eOyC0HhcFgkfKzd+9eZGdno3PnzvDw8EBSUhK+/vprc2eLqMSwmGCxS5cueU7yS0TmV5CaRQCoVq1aoVZuUcXR0JSX+Ph4nDp1CgDw5Zdf4q+//gIA/Pbbb4iLizNjzohKDosJFonI8hWkZtFYOBqa8rJhwwakpKSgVq1aaNOmDdq0aYN69eohJSUF//vf/8ydPaISgcEiEemtoDWLxsBmaMrL5cuXAQCvvvoqrK2toVAoMGrUKAA507HltzoYEeWPwSIR6a0go6GNhcEi5fb48WO0bt0aderUwebNmwEAtWvXlvcPHjwYdnZ2uHXrFm7fvm2ubBKVGAwWiUhvBZln0VgYLFJu48ePx8mTJ3Hjxg35faEaLLq4uKBVq1YAgMOHD5slj0QlCYNFItKbNMjEHDWLHOBCAHDx4kVs2bJFbVvdunVRv359tW0vv/wyAAaLRMbAYJGI9JKZmSmPLvX09DTZeVmzSKrWrVsHAHjjjTdw7949bNmyBdOnT4eVlfrXmRQsHjlyhP0WiQzEYJGI9BIbGyt/6Xp4eJjsvBwNTRIhBLZv3w4AeOutt1ClShX06dMHtra2GmlbtGgBBwcHPH/+HDdv3jRxTolKFgaLRKSXqKgoAIC7uztsbEy3UihrFkly5coV3L9/Hw4ODujSpUueae3t7eVlJo8cOWKC3BGVXAwWiUgvUrDo5eVl0vMyWCSJVKvYuXNnvQZZtW/fHgBw9OjRoswWUYlXKoLFY8eOoVevXvDz84NCoZAvOESkv+joaADmCxZTU1ORlZUlb09JScGWLVs4NUopsmPHDgA5cyrqo0OHDgDYb5HIUKUiWExOTkbDhg2xZMkSc2eFqNgyd80ioF67OHr0aLz55pvo1q0bA4FSIDY2FhcuXAAAdO3aVa/HtGjRAmXKlMGzZ89w6dKlosweUYlWKoLFbt26YdasWejXr5+5s0JUbEnBoilHQgM5fc+sra0BqAeLe/fuBQDcv38fYWFhJs0Tmd7x48chhEDNmjVRvnx5vR5jb2+PTp06AQB2795dlNkjKtFM10u9GElPT0d6erp8PyEhAQCgVCqhVCrNlS2tpPxYWr6KC5af/p4/fw4gZyS0anmZogxdXFwQFxeH2NhYlCtXDlFRUYiIiJD3Hz9+HH5+fkV2/qLE96B+Dh06BABo165dgd5/Xbp0wY4dO7Br1y589tlnRZ/RYobvP8OYq/xMfT4Gi1rMmTMHM2bM0Ni+f/9+ODk5mSFH+QsJCTF3Foo1lp86pVKJ3377DWlpaRg2bBjs7Oxw8eJFADk1jNpqaYqyDKWaxX379uHevXsa/RR37twpT7FTXPE9mLedO3cCyPnhUJD3nzRy/9SpU9i2bRvs7e2LLpPFGN9/hjF1+aWkpJj0fAwWtZg8eTLGjRsn309ISEBAQACCg4Ph6upqxpxpUiqVCAkJQefOnbXONUZ5Y/lpt3btWnkg2JtvvonBgwdjxYoVAIA2bdqge/fuclpTlKGXlxeio6PRqFEjtGvXTm2gC5Az/55qnoqT/MpPqVRi3bp16Natm97NryVNenq63NXggw8+QGBgoLwvv/ITQmDq1Kl49uwZ/Pz80KJFC1Nlu1jgNdAw5io/qcXTVBgsamFvb6/116etra3FfpgsOW/FActP3datW+XbBw4cwNChQxETEwMA8PHx0VpWRVmGUq1hamoqbG1t5f6Tknv37hX7109X+f3444+YMGECqlWrhuvXrxf751kYly5dglKphIeHB6pVqwaFQqGRJq/3X1BQEHbv3o2LFy/ipZdeKursFku8BhrG1OVn6teqVAxwISL9xcbG4sCBA/L9kydPAjDfaGhAc67FZ8+eAYD8xX/79u0SOSL62rVrmDRpEgDg7t27OH78uJlzZB7nz58HkBP0aQsU89O0aVMAwLlz54yaL6LSolQEi0lJSbh48aLc5+rBgwe4ePEiR1ASaXHkyBFkZmbKTZ4PHjxATEyMPMDFHMFi7iX/IiMjAQCtW7eGlZUVEhMT5QCypAgPD0dwcLBak/u+ffvMmCPzkYJFKegrKOlxZ8+eNVqeiEqTUhEsnjt3Do0bN0bjxo0BAOPGjUPjxo0xdepUM+eMyPJIP6q6du2KKlWqAMgZHCD1kfHx8TF5nnLXLErBYsWKFeX+ayVtcu4JEyYgPDwcderUwbJlywAAS5culSdHL02kGsGgoKBCPV7qp3j9+nW1UfREpJ9SESx26NABQgiNv7Vr15o7a0QW5/LlywCAhg0bom7dugD+Wy7N3t4ebm5uJs+TrmZoX19f1KpVC0BOQFtSxMTEYMuWLQCAdevWYeTIkWjQoAGSkpJK3XUrKSlJnlC7ZcuWhTpGuXLl0KxZMwCcb5GoMEpFsEhE+pO+mBs2bCgHYkeOHAGQU6tYmD5jhpKaoaXaTalm0cfHB7179wYA/O9//0N2drbJ81YUtm/fjszMTDRo0ABNmjSBlZUVPvroIwDAzz//XGKepz5Onz6N7OxsVKpUCRUqVCj0cXr16gXgvyl4iEh/DBaJSJaQkIAHDx4AABo0aCAHi2fOnAFgniZoAPKUVbmDRV9fX/Tv3x9ubm64fv06Pv30U7Pkz9h+//13ADnTFkkGDBgAFxcX3LlzB4cPHzZX1kzuxIkTAHL6pxqiZ8+eAHLmy01LSzM4X0SlCYNFIpJJTdD+/v7w8PCQg0WJr6+vObKlFiwmJSUhOTkZQE7wWrZsWSxYsAAA8NNPP+Hp06dmyaOxREdHy6PR33jjDXm7i4sLBg8eDAD47bffzJI3c/j3338BGB4sNmrUCBUqVEBKSopcU05E+mGwSEQy1f6KADSCRWnAi6lJ/STj4+Pl/opOTk5yX8ahQ4fipZdeQlZWFtavX2+WPBrL9u3bkZWVhUaNGqFGjRpq+3r06AHgvwCqpMvIyJCnC2rbtq1Bx1IoFAgODgYA/PPPPwbnjag0YbBIRDLV/opAzjrQ5cqVk/dXr17dLPlSrVlUbYJW7T/59ttvAwA2bNhg+gwakTSwRbUJWiIN8Lh9+7bGxOQl0enTp5GSkgIvLy/Ur1/f4ONJ5Sd1qyAi/TBYJCKZFCw2aNBA3qY6AtVcwaJUs6gaLObuP/naa6/B1tYWly9fxtWrV02eR2PIyMiQR5736dNHY7+np6dc2ytNll6SHTx4EADQqVMnWFkZ/nXVvHlzADnzLZamQUJEhmKwSEQAgOzsbFy5cgXAfzWLAPDqq68CAMqWLYtWrVqZJW9SzaJqM3Tu/pMeHh7y+tDFtXbx/PnzSEtLg5eXF2rXrq01jdR3r6QHi+np6XLfzE6dOhnlmHXr1oWjoyPi4uJw9+5doxyTqDRgsEhEAHLWV05JSYGjo6NaDeKQIUOwZs0aXLp0SZ7CxtS01SxqG2wzYMAAAMBff/1luswZkbRSScuWLXVOUSQFi9Io4ZJq9erVuHv3Lnx8fNQG+hjC1tYWTZo0AVCy5uUkKmoMFokIAOSm2zp16sDa2lrebm1tjXfffRcVK1Y0V9bU+ixKK3Bom8bn5ZdfBpCzUkdcXJzJ8mcsuQcYaSMFi2fOnIFSqTRJvkxNqVTi+++/BwBMmTIFZcuWNdqx27RpA+C/ieaJKH8MFolKKSGE2v1r164BgLxqiyXx9vaGlZUVsrKy5PV9tU3QXK5cOXnEdnFcBzj3ACNtatasCXd3d6SmpsrpS5rZs2fj4cOHKFeuHIYPH27UY0s/KErTXJVEhmKwSFTKPHnyBK+88gpsbW3RqlUr3Lp1C0BObRxgmcGira0tAgICAPwXUFWuXFlrWmlATnHr05eVlSXX7qoOMMrNyspK7jtaEqfQefHiBebOnQsAWLRoERwdHY16/DZt2sDa2hoPHjzAo0ePjHpsopKKwSJRKTN06FAcPHgQWVlZOHXqlDzljBQs1qlTx5zZ0yn3HI+BgYFa00mBVHHr06faZ7RatWp5ppXmHAwJCTFF1kxqwYIFSElJQVBQEPr372/047u4uKBp06YAwMm5ifTEYJGoFLl8+TIOHDgAW1tbbNiwAba2tjhz5gxCQ0Nx8+ZNAMUjWFQoFHJNY27t27cHkNMnLSUlxSR5KywhBNLT0wEA586dA5BTq6jaZ1Qbaem6AwcO4Pnz50WbSROKiYnB4sWLAQBTp04tsnXIpaZoBotE+mGwSFSKSKub9OzZEwMGDJCDju+++w7p6elwdHTUWWNnbh07dpRvN2nSBHZ2dlrT1atXDxUrVkRaWppF90s7dOgQatasCS8vL/zzzz8FWqmkbt26aNasGdLT0zF79uyizqrJLFy4EElJSWjYsCF69epVZOfp0KEDAPZbJNIXg0WiUiI+Ph4///wzAODdd98FAPTt2xcAsHnzZgA5gZYxJj8uCr169ZJrF4cNG6YznUKhkIPgv//+2yR5K6jExEQMHDgQd+7cQXp6OpYuXYqtW7cC+C+QyYtCocCcOXMAAEuXLi0Ry9fFxcVh0aJFAIq2VhHI6bdoY2ODR48e4cGDB0V2HqKSwjK/FYjI6GbMmIGEhATUrl1bDqa6deum9qXcrFkzc2UvXy4uLjh37hz+/fdffPjhh3mmldZQ3r9/vymyVmAzZszAs2fP4OfnB09PT6SkpCA2NhbOzs56T0DdqVMnDBgwAJmZmXjjjTcQHh5exLkuWt999x0SEhJQr149+UdMUXF2dkaLFi0AAO+99x7S0tKK9HxExR2DRaJS4PTp01iwYAEAYObMmXLtobe3t9pyftIcfpbK3d0drVu3zrfWqV27drC1tcWDBw9w//59E+VOPw8fPsTChQsBAL/88guWLl0q73vnnXfg4OCg97FWrlyJevXqITIyEp9//rmxs2oy+/btw3fffQdA/f1ZlObPnw9nZ2ccPnwYP/74Y5Gfj6g4Y7BIVApIAck777yD119/XW3fDz/8gIoVK6Jv37548803zZE9o3N2dpZHRR84cMDMuVG3YsUKZGVloWPHjujWrRteffVVjB07Fh999BFmzZpVoGOVKVMGq1evBgCsW7cOO3fuLIosF6mYmBgMHjwYQgiMHDlSXl6yqDVv3hxLliwB8F+tJhFpx2CRqIRLT0+Xg4ihQ4dq7G/VqhUePnyIbdu2wdbW1tTZKzJSc64lBYsPHz7ETz/9BAD46KOP5O0dOnTAggULCrVSSbNmzTBmzBgAwOTJk5GdnW2UvJrK3LlzERUVhbp168p9Fk1l8ODBqFmzJmJiYtRqeElTVFQUwsLCsGrVKhw6dEhjUn8q2RgsEpVgKSkpGDt2LGJiYlChQgV5qbPcinIwgbm88sorAHJGHZs7gEpLS8PChQvRsmVLJCQkoFmzZkbtlzdjxgy4ubnh6tWr2L59u9GOW9QOHDiA+fPnAwDmzJlToCZ4Y7C2tsaUKVMA5DRLW/pUS+YQFRWFnj17wtvbG5UqVcLw4cPRqVMntGnTBj/++CNSU1PNnUUyAQaLRCXUmjVr4Ofnh+XLlwMApk2bBhsbGzPnynSaNWsGFxcXREdHy3MYmoMQAgMGDMCnn36KZ8+eoU6dOti6datR++WVLVtWHvSzdu1aox23KJ04cQJ9+vRBRkYGevToIQ+6MrW33noLgYGBePHiBVatWmWWPFiq27dvo3Hjxti1a5e8rXLlyrCzs8PJkyfxySefoEuXLjhz5owZc0mmwGCRqAS6efMmRowYgfj4eFSsWBE7d+7EiBEjzJ0tk7K1tUX37t0B/De/pDnMmjUL27dvh42NDWbNmoWTJ0/C39/f6OcZNGgQgJzBItJE35bq0qVL6N69O1JSUtC1a1ds3brVbLXbtra2mDhxIoCcKXvCwsLMkg9LEx4ejuDgYDx58gTVq1fHpUuXkJKSgvv37+P69etyN4rjx4+jRYsWGDhwIGJiYsycayoqDBaJSpikpCSMGTMGWVlZ6Nq1K+7duydPJVPaSH00ly9fjn379pn8/Js3b8bUqVMB5PTNmzJlClxdXYvkXHXr1oWXlxcyMjIQGhpaJOcwVFpaGmbMmIH27dsjPj4ebdq0wZ9//qlzgnVTGT58OJo3b464uDgMGjQIWVlZZs2PucXFxaFr16549OgRqlevjn/++QcNGjSQ1+muWrUqlixZgi1btshTEG3cuBGenp745JNPEB0dbc7sUxFgsEhUgoSFhSEoKAghISGwsbHBDz/8UKqannMLDg5Gp06doFQq0a1bNwwdOhQPHz40ybmzsrLwzTffAAAmTpyIsWPHFun5FAqFPPWRJa6LHR4ejq5du2L69OmIj49H06ZNsXPnTjg5OZk7a/Lyly4uLvjnn39M+j6xNKmpqejduzeuXLkCX19f7Nu3D+XKldOa9vXXX8epU6dw+vRpeHt7AwB+/PFHvPLKK6yhLWFKVbC4dOlSVK5cGQ4ODggKCpKX1yIqrhITEzFz5kxMmDABzZs3R6VKlXD79m34+Phg586dFrvOs6koFArs2rULAwcOhBACa9euRc2aNdGhQwf4+fmhR48eePvtt7FkyRIolUqjnnvevHm4fPkyypQpg0mTJhn12LpI0wWdPHlSbfuzZ8/MNhAhIyMDS5cuRf369XH06FE4Oztj5cqVOHbsWKFGfxeVqlWrYu7cuQBypiGqVasW/vjjDzPnyrRu3bqFV155BcePH4erqyv27t2LypUr5/u45s2b49ChQ/K0RxcvXkSlSpXQtm1bLFu2zOifLTIDUUps2rRJ2NraipUrV4rr16+LTz75RJQpU0Y8evQo38fGx8cLACI+Pt4EOS2YjIwMsX37dpGRkVGgxz19+lScPn1aZGdnF1HOiofClp8luHTpkqhbt64AoPZXrlw5ce3aNZPloziUYVZWlti1a5do166dRnlJf87OzmLMmDFi+fLl4siRIwadLywsTDg5OQkAYtWqVXmmNWb5HTlyRAAQvr6+IjMzU2RlZYkvvvhCWFlZCVdXV9G7d2/x4YcfipCQEJGVlWXw+bTJysoSf/zxh+jdu7fw8/MTVlZWchkHBQWJGzduGPV8xiy/7OxssWXLFtG8eXM5z7179xZnz5616Pe3ITIyMsTPP/8sBg8eLBQKhfxZKOxn4MaNG6JRo0Yan6/mzZuLH3/8UaSnpxv5GZiXua5/po5LFEKUjsmSWrRogSZNmmDZsmXyttq1a6Nv377yGqu6JCQkwM3NDfHx8UXW3yi3lJQUxMXFwc/PD1FRUfDw8EBGRgbS09MRHx+PpUuXIi4uDq1bt8batWtx/vx5tG/fHu7u7njx4gWioqKQmJiINWvWoGXLlkhNTcWMGTNw/PhxxMXF4fr16wCAmjVr4vXXX8fQoUNRtWpVkzy3ovT06VM4OjrCw8NDr/RKpRK7d+9G9+7d85xjMDExEQcOHMCDBw/QoUMHNGnSRK/jp6am4rfffsOhQ4dQp04dfPrppyhTpoxej83t9u3bGD58OK5du6bRkfztt99GixYt0KNHD1SoUMGkTc/6lqElyM7Oxk8//YTQ0FCkpKQgKSkJNjY2OHHiBF68eKGWdsSIEahWrRr69u2LGjVqFOg877//PlasWIG2bdvi6NGjeQ7eMGb5paeno3z58oiNjcVvv/2GlJQUnQObHB0d0ahRI2RlZSEyMhLly5dHz5498cUXX+Q5UjstLQ329vZIS0vDhAkTsG/fPnh5eeHp06fw8fHB/fv3ERsbq/YYFxcXfPHFFxg3bpzR+ycWxfsvIyMDU6dOxQ8//IDMzEx5e/Xq1eXm/l69esHNzQ01a9ZEZmYmAgMD5XTp6em4e/cuKlasiOfPn+N///sffH19kZWVhWbNmsHe3h4RERGwtrZGu3btcO7cOfj4+KBGjRrIzs6GUqlEYmIiUlNT8fjxY4SFhaFSpUpo2rQpHj58iPLly6NMmTJISEhAaGgoypYti/r168Pa2lrr85G+u1Tfh1Lz8bFjx+R1yYGcJUCXLVuGSpUqFbr8UlJScPjwYdy+fRvfffcdnj17Ju+zs7NDjx49ULduXdjY2KB///4oX748rK2t8fDhQzx8+FBegWnXrl3w9vZG27ZtYWVlBSEEFAqF/D2YnZ0NX19ftXPfvHkT3t7e8PT0RGZmJiIiIuDv74/MzEz5uvjkyRM4OzvD3d0daWlpuH//PqpXry6/fzIyMmBrawuFQgEhBOLi4uDu7g4gp5bewcEBCQkJ2LNnD7y8vLB69WrUrl0bEydOhLe3t5zPomTquKRUBIsZGRlwcnLCli1b1FYH+OSTT3Dx4kUcPXpULX16erraaMKEhAQEBAQgKiqqyF6U//3vf4iJiUFCQgL+97//4cmTJ1AoFPDz88PTp08LfVwHBwfMmzcPmzZtwj///KMzXdmyZXH+/HkEBAQU+lxZWVn4/vvv8ccff6B9+/b47LPP4O3tXaSBS0ZGBr755hvs2bMH0dHRCAsLQ9myZfHjjz/irbfeyvfxSqUSISEh6Ny5s8YXjRACV69exYIFC9RG0yoUCnz++ef44osvYG9vr/PYu3fvxoQJE3D37l15W4cOHbB161Y4OzsX6HmmpKSgefPmuH37ttp2Dw8PrF+/Xp5T0BzyKsPiIisrCwcPHsSqVatw7949XL58WW2/n58fPvzwQ0ycOFHrl4DqF1FUVBSqVKmCtLQ0HDx4EG3bts3z3MYuv08//VSe+FsyceJE9OzZExcuXMCFCxewfv16nXNPurm54e233waQM7F59+7dkZ2djT///BMzZszA3bt3Ua5cOTg6OuLBgwc68zF8+HC8+eab8Pb2RsWKFeHi4mLwc9OmKN9/165dw7Rp07Bjx4580zZr1gydOnXCoUOHcOHCBbUgU18VKlRAQkICEhMT9UqbkZEh/8ixt7eHEAJVqlSBu7s7atSoAV9fXxw4cADnz59HtWrVYGdnh3v37sHT01NjLfGgoCAsWrQIzZs3L3C+85KSkoK9e/fixIkTWLFihd7rcEuBkKRu3bp4/Pgx3N3dERUVheTkZADAyy+/jFdeeQUPHjzAjRs38O+//wIAqlWrhpSUFISHh6NSpUqIjIyEq6srkpOTkZKSAjs7OzRt2hShoaFITU2Fra0tgoOD8fjxY1y9ehW+vr5o2bKlPFK/devWiI6Oxs2bN3Xm2dfXFy4uLoiIiMCgQYOwePFiA0oubwkJCfDy8mKwaEzh4eGoUKEC/v33X7W1b7/55hv873//w61bt9TST58+HTNmzNA4zoYNG4qsM/b48eNx7949vdMrFApYW1vLF6SqVavC2toasbGx6NChAxwcHHD48GE8efJEfoyTkxO6dOmCjIwMuLq6olKlSnj06BH27NmDuLg4NGzYEOPGjYObm5v8GCEE/vrrL5w+fRqtW7dGmzZtcOTIEVy6dAnVqlXDG2+8gYiICISEhCAkJESjb4qdnR0GDx6M3r17G1hCmkJDQzF//nydF1Zvb2/07NkT1apVQ+3atQs0r11aWhq+++47jVGl0i9NIKd/2Lhx47R+QZ04cQLff/89gJyLXtu2bXHgwAGkpaXByckJ7733Hjp27KjXr8/U1FT88MMPOHfuHNzd3TFy5Ejs2LED7u7uGDVqVIEDT8qbEAJbt27FyZMnER0drVZLNmHCBLz00ksAcgLMxMRErFu3DocPH0br1q3x0Ucf4c8//8Qff/yBKlWq4IcffjD5lDBxcXGYMmUKnj59CoVCgZdeegljxoxRe5+mpqbizp07iIqKgq2tLR4/fowtW7ZoXZXD398f4eHhWoNLJycnvPPOO7CxsYGXlxeuX7+O8PBwDBw4EOXLly/S52lK8fHxSEhIwM2bN3Hr1i2cP38eZcqUwdOnT/VaycTJyQnVq1fH48ePERMTAycnJ3h5eSEpKQkxMTGwsrLSGbxLP0h1TYdUtmxZxMXFFfg5WVlZoXbt2ihfvjxatmyJxo0b66yZNJaEhASEhYXh8uXLSEpKwtmzZ9Vq9B0cHNSCSakWOiMjo0jzVRiq3wVVqlRBXFycWotP9+7dMXLkyCI7f0pKCgYOHMhg0ZikYPHEiRNyB3AAmD17NtatW6fxS8EcNYuzZs3ClStX8Pz5c/Tu3Rs+Pj7Yt28f/vrrLwwePBjNmzdH7dq1Ua1aNdjb28tTGERFRWHevHn46quvNIIGqSll5cqV8PX1xYYNG9CwYUONc1+5cgXNmzdHVlYW7O3tsW7dOnl1ibVr1+b5hs99kXNwcMDQoUMREhKiVqM2btw4vP7662jatKna45OTkxEdHQ1/f3+5mSEiIgIxMTHw8PBAVFQUqlevDkdHR2RlZSE6Ohre3t44evQoXnvtNSQmJsLDwwPjxo2Di4sLGjRogG3btuGnn35Sm/6iY8eOGDt2LLp06YK0tDRs374d586dQ/Xq1REZGQknJyd07doVWVlZ+OKLL3Du3DnEx8fDysoKjRo1wpQpU9CgQQNUrFgR69atw/vvv4+srCzUqlULPXv2xOjRo+Hn5wchBCZPniyvStGrVy8sXrwYfn5+OHjwIEaOHInHjx/L+5o0aYJ+/fqhdu3aWss3OzsbPXv2xIEDB+Dg4IC9e/eq/eCxBCWhZlEXIQTu3LmDRYsWYeXKlahWrRp2796NPXv24IcfftAY8enh4SF/YWzZsgV9+vTJ9xxFVX5xcXGwtrbWu0YvOzsbx44dw+PHj7F3717s3LlTY1BM+/btsWTJEjx48ADh4eF45ZVXULFiRaPluTDM+f7LyMhAWFgY7O3tsWPHDhw6dAiNGjXC22+/jYCAADx79gzPnj1D9erV5e4nGRkZchCUlZWF27dvo1KlSkhLS8OdO3egUChQvnx5uLq6wsHBAXZ2dlAoFHjw4AHOnDmDnj17QqlU4vTp00hISEDv3r3x/Plz/Pjjjzh9+jR8fHzQuHFjKJVKxMXFwdvbG2+99RYOHz6M06dPw9PTE82bN0eDBg1QtWpVs5ZfdnY2bt68iQoVKsDKygrOzs549OgR/v77b2RlZWHgwIEoU6YMbt68ib1798LPzw/Ozs7yj6CUlBTMmjULSUlJqFy5MhQKBd544w3Y2Njg0qVLEEKgdevW2LNnD1xcXOTuEwMGDMCJEyewY8cOBAUFoXXr1li8eDESEhLg6emJgQMH4vr16zh58iRSUlLw8ssvIzU1VX49goKC4OzsDF9fXyQnJ8tzTW7cuBEKhQJBQUHw8vJCtWrViqzsTF2zWCoGuKSnpwtra2uxdetWte1jxowR7dq1y/fxJWGAS34DWX777TdRrlw5eYBEXFycuH//vvDx8REAhKenp9xRuUWLFqJ9+/bC2dlZ3tauXTuxYcMGkZqaKufr2rVrYsyYMWqdnNeuXSuEEEKpVIolS5aIMmXKCACiZs2aolevXvJ91b8yZcoIf39/YWtrq7Gvffv28jlVRUdHizlz5ghfX1+dAxry+6tYsaI4cOCA1vL666+/hI2NjZzWzc1NvPXWW8LPz0/e1rdvXxEXF6f2uMzMTDFu3Di189jb24tXX31VBAUFiR9++EFs3rxZREVFCSGEmD17tgAgHB0dxcGDB/N8Dc2lOAxwMVRMTIzw9/fX+j5xdXUVU6dOFe7u7vK2QYMG6T14zFLLLykpSXz88cdi5MiR4tSpU+L69esWOSDOUsuvuGD5Gaa0DHApFcGiEEI0b95cfPjhh2rbateuLT7//PN8H1sSgkV9pKenixo1asiBkhQ81qxZU6Snp4t///1XXLp0SU6fnZ0tnjx5IsLDw3UeU6lUilmzZok6derIX6Q9e/YU1atX1ytgc3R01LmvT58+IikpKc/nlJ2dLa5fvy6GDh0qj/QDICpUqCAaNWokatWqpXHcxo0biyNHjuRbppcuXRKzZs2Sy0w1zytXrszzsb/99puoX79+ns/dwcFBvr1ixYo8j2dOpeXL5ubNm6JFixbyj4P58+eLS5cuidjYWCFEzgwD69evF0ePHi1QUFVayq+osPwMw/IzDIPFEkaaOmfVqlXi+vXrYuzYsaJMmTLi4cOH+T62tASLQgixZ88etYClXr164unTpwYfNzMzUzRu3FijxnDWrFkiIiJCfP3112LkyJHi2LFjIi0tTSiVSpGWliaysrLE0aNHxebNm8WDBw9EbGys2Lx5s9i4caNQKpUFysONGzdEaGiouHv3rvxlnpGRIZYsWSISEhJEfHy8uHLlSoGndoiKihJffvmlGDlypPj444/FvXv39H5sdna2+Omnn8TAgQNF3bp1RWBgoEbQOG7cuALlx9RK05dNdna2iI2NLfB7Ly+lqfyKAsvPMCw/w5SWYLHULO3Qv39/REdHY+bMmYiIiEC9evWwe/dug6YHKIm6du2KDz/8EMuWLcNbb72F5cuXqw14KSxra2ts2LABM2bMQK1atVCzZk107NhRXhngyy+/1HiMNLq0Xbt2atvffPPNQuWhVq1aWrf7+/vDwcEBtra2qFevXoGP6+npia+//rpQeVIoFBg1ahRGjRqltj06Ohpbt26Fu7s7XnvttUIdm4xPoVBY1ETSRESmUGqCRQBav5RJ09KlSzFt2jT4+PgY9bi1atXCxo0bjXrMksrT01Pn/HhERESmVKqW+yP9GTtQJCIiouKJwSIRERER6cRgkYiIiIh0YrBIRERERDoxWCQiIiIinRgsEhEREZFOpWrqnMIS/798dkJCgplzokmpVCIlJQUJCQklbl1eU2D5GY5laBiWn2FYfoZh+RnGXOUnxSNSfFLUGCzqITExEQAQEBBg5pwQERER5UhMTDTKwhn5UQhThaXFWHZ2NsLDw+Hi4gKFQmHu7KhJSEhAQEAAHj9+DFdXV3Nnp9hh+RmOZWgYlp9hWH6GYfkZxlzlJ4RAYmIi/Pz8YGVV9D0KWbOoBysrK/j7+5s7G3lydXXlB90ALD/DsQwNw/IzDMvPMCw/w5ij/ExRoyjhABciIiIi0onBIhERERHpxGCxmLO3t8e0adNgb29v7qwUSyw/w7EMDcPyMwzLzzAsP8OUlvLjABciIiIi0ok1i0RERESkE4NFIiIiItKJwSIRERER6cRgkYiIiIh0YrBYxI4dO4ZevXrBz88PCoUC27dvV9u/detWdOnSBV5eXlAoFLh48aLGMd5//31UrVoVjo6O8Pb2Rp8+fXDz5k2t50tPT0ejRo00jrV27VooFAqtf8+fP5fTXblyBe3bt4ejoyMqVKiAmTNnmmztSW0spfwA4OzZs+jUqRPKli0Ld3d3BAcHa6SxtPIDLKsMDx48iNatW8PFxQXly5fHpEmTkJmZqZbG0srQVOUXGBio8dn8/PPP1dKEhYWhV69eKFOmDLy8vDBmzBhkZGSopWH56S6/Tz75BEFBQbC3t0ejRo205pflp738Ll26hAEDBiAgIACOjo6oXbs2Fi1apHEulp/28ouOjkbXrl3h5+cHe3t7BAQEYPTo0fIazxJLKz8Jg8UilpycjIYNG2LJkiU697dp0wbffvutzmMEBQVhzZo1uHHjBvbt2wchBIKDg5GVlaWRduLEifDz89PY3r9/f0RERKj9denSBe3bt0e5cuUA5Cxb1LlzZ/j5+eHs2bNYvHgx5s2bh/nz5xfy2RvOUsovMTERXbp0QcWKFXH69Gn8888/cHV1RZcuXaBUKgFYZvkBllOGly9fRvfu3dG1a1eEhoZi06ZN2LFjh9oF1RLL0JTlN3PmTLXP6Jdffinvy8rKQo8ePZCcnIx//vkHmzZtwp9//onx48fLaVh+ussPyFkibdiwYejfv7/W87D8dJff+fPn4e3tjfXr1+PatWuYMmUKJk+erJYvlp/u8rOyskKfPn2wY8cO3L59G2vXrsWBAwfwwQcfyGkssfxkgkwGgNi2bZvWfQ8ePBAARGhoaL7HuXTpkgAg7t69q7Z99+7dolatWuLatWv5Huv58+fC1tZW/Prrr/K2pUuXCjc3N5GWliZvmzNnjvDz8xPZ2dn55quombP8zp49KwCIsLAwedvly5fVjmPp5SeEectw8uTJomnTpmrpt23bJhwcHERCQoIQwvLLsCjLr1KlSmLBggU6H7N7925hZWUlnj59Km/buHGjsLe3F/Hx8UIIll9e5adq2rRpomHDhhrbWX4LCpSfUaNGiZdfflm+z/JbUKD8LFq0SPj7+8v3Lbn8WLNYzCQnJ2PNmjWoXLkyAgIC5O3Pnj3DiBEjsG7dOjg5OeV7nF9//RVOTk54/fXX5W0nT55E+/bt1SYX7dKlC8LDw/Hw4UOjPg9zKWz51axZE15eXli1ahUyMjKQmpqKVatWoW7duqhUqRKA0lF+QOHLMD09HQ4ODmrbHB0dkZaWhvPnzwMoHWWoq/wA4LvvvoOnpycaNWqE2bNnqzUxnzx5EvXq1VOrte3SpQvS09NZfv8vr/LTB8uvYOUXHx8PDw8P+T7LT//yCw8Px9atW9G+fXt5myWXH4PFYmLp0qVwdnaGs7Mz9u7di5CQENjZ2QHIaVp599138cEHH6Bp06Z6HW/16tUYOHAgHB0d5W2RkZHw8fFRSyfdj4yMNNIzMQ9Dy8/FxQVHjhzB+vXr4ejoCGdnZ+zbtw+7d++GjY0NgJJdfoDhZdilSxecOHECGzduRFZWFp4+fYpZs2YBACIiIgCU7DLMq/yAnP50mzZtwuHDhzF69GgsXLgQo0aNkvdrKxt3d3fY2dnJZcPy011++mD56V9+J0+exO+//473339f3sbyy7/8BgwYACcnJ1SoUAGurq745Zdf5H2WXH4MFouJQYMGITQ0FEePHkX16tXx5ptvIi0tDQCwePFiJCQkYPLkyXod6+TJk7h+/Tree+89jX0KhULtvvj/jrW5txc3hpZfamoqhg0bhjZt2uDUqVP4999/UbduXXTv3h2pqalyupJafoDhZRgcHIy5c+figw8+gL29PWrUqIEePXoAAKytreV0JbUM8yo/APj000/Rvn17NGjQAMOHD8fy5cuxatUqREdHy2m0lYEQQm07y093+emD5Zd/+V27dg19+vTB1KlT0blzZ7V9LL+8y2/BggW4cOECtm/fjnv37mHcuHFq+y21/BgsFhNubm6oXr062rVrhz/++AM3b97Etm3bAACHDh3CqVOnYG9vDxsbG1SrVg0A0LRpU7zzzjsax/rll1/QqFEjBAUFqW339fXV+PUijZTO/WunuDG0/DZs2ICHDx9izZo1aNasGVq2bIkNGzbgwYMH+OuvvwCU7PIDjPMeHDduHOLi4hAWFoaoqCj06dMHAFC5cmUAJbsM8yo/bVq2bAkAuHv3LgDtZRMbGwulUimXDcvvP7nLTx8sv//oKr/r16+jY8eOGDFihMYAIpbff3SVn6+vL2rVqoU+ffrg559/xrJly+SWFUsuPwaLxZQQAunp6QCAH3/8EZcuXcLFixdx8eJF7N69GwCwefNmzJ49W+1xSUlJ+P3337XWKrZq1QrHjh1T62exf/9++Pn5ITAwsOiejBkUtPxSUlJgZWWl9utOup+dnQ2gdJUfUPj3oEKhgJ+fHxwdHbFx40YEBASgSZMmAEpXGaqWnzahoaEAgPLlywPIKZurV6/KXyxATtnY29vLP/xYfv/JXX76YPn9R1v5Xbt2DS+//DLeeecdjc81wPJTpc/7T6o1lI5j0eVn4gE1pU5iYqIIDQ0VoaGhAoCYP3++CA0NFY8ePRJCCBEdHS1CQ0PFrl27BACxadMmERoaKiIiIoQQQty7d09888034ty5c+LRo0fixIkTok+fPsLDw0M8e/ZM6znzGtX1yy+/CAcHBxETE6OxLy4uTvj4+IgBAwaIK1euiK1btwpXV1cxb9484xVIAVlK+d24cUPY29uLDz/8UFy/fl1cvXpVDB48WLi5uYnw8HAhhGWWnxCWU4ZCCPH999+Ly5cvi6tXr4qZM2cKW1tbtdGJlliGpii/EydOyMe9f/++2Lx5s/Dz8xO9e/eW85GZmSnq1asnOnXqJC5cuCAOHDgg/P39xejRo+U0LD/d5SeEEHfu3BGhoaHi/fffFzVq1JDzlZ6eLoRg+eVVflevXhXe3t5i0KBBIiIiQv57/vy5nIblp7v8du3aJVavXi2uXLkiHjx4IHbt2iXq1q0r2rRpI6exxPKTMFgsYocPHxYANP7eeecdIYQQa9as0bp/2rRpQgghnj59Krp16ybKlSsnbG1thb+/vxg4cKC4efOmznPmFSy2atVKDBw4UOdjL1++LNq2bSvs7e2Fr6+vmD59ulmH7FtS+e3fv1+0adNGuLm5CXd3d9GxY0dx8uRJtTSWVn5CWFYZvvzyy8LNzU04ODiIFi1aiN27d2s81tLK0BTld/78edGiRQu5bGrWrCmmTZsmkpOT1fLy6NEj0aNHD+Ho6Cg8PDzE6NGj1abZEILll1f5tW/fXuu5Hjx4IKdh+Wkvv2nTpmk9T6VKldTyy/LTXn6HDh0SrVq1ktNUr15dTJo0ScTGxqrl19LKT6IQwgKmBiciIiIii8Q+i0RERESkE4NFIiIiItKJwSIRERER6cRgkYiIiIh0YrBIRERERDoxWCQiIiIinRgsEhEREZFODBaJiIiISCcGi0REJLt16xZ8fX2RmJho1nxMmDABY8aMMWseiCgHg0UiKrR3330Xffv21bk/NDQUPXv2RLly5eDg4IDAwED0798fUVFRAICHDx9CoVDIf25ubmjZsiX+/vtvteOsXbsWZcuWVdt248YN+Pv7o1+/fkhPT9d6/sDAQLXjKxQK+Pv7G/ScS7opU6bgo48+gouLCwDgyJEjUCgUcHd3R1pamlraM2fOyOUqkdLHxcVpHDswMBALFy7UKx8TJ07EmjVr8ODBg0I/FyIyDgaLRFQknj9/jldeeQVeXl7Yt28fbty4gdWrV6N8+fJISUlRS3vgwAFERETg9OnTaN68OV577TVcvXpV57HPnj2Ltm3bokuXLtiyZQvs7e11pp05cyYiIiLkv9DQUK3plEpl4Z5oCfLkyRPs2LEDQ4cO1djn4uKCbdu2qW1bvXo1KlasWCR5KVeuHIKDg7F8+fIiOT4R6Y/BIhEViRMnTiAhIQG//PILGjdujMqVK6Njx45YuHChRoDh6ekJX19f1KpVC7Nnz4ZSqcThw4e1HvfQoUPo2LEjhg4dilWrVsHa2jrPfLi4uMDX11f+8/b2BgAoFAosX74cffr0QZkyZTBr1iwAwN9//42goCA4ODigSpUqmDFjBjIzM+Xj3blzB+3atYODgwPq1KmDkJAQKBQKbN++HYD2mrWLFy9CoVDg4cOHauXTrl07ODo6IiAgAGPGjEFycrK8PzAwEN988w2GDRsGFxcXVKxYEStWrFB7bk+ePMFbb70FDw8PlClTBk2bNsXp06fx8OFDWFlZ4dy5c2rpFy9ejEqVKkEIobWsfv/9dzRs2FBr7es777yD1atXy/dTU1OxadMmvPPOO3mUvm5r167VqPVVKBSYPn26nKZ3797YuHFjoY5PRMbDYJGIioSvry8yMzOxbds2ncFJbkqlEitXrgQA2Nraauzftm0bevTogSlTpmDu3LkG53HatGno06cPrly5gmHDhmHfvn0YPHgwxowZg+vXr+Pnn3/G2rVrMXv2bABAdnY2+vXrB2tra5w6dQrLly/HpEmTCnzeK1euoEuXLujXrx8uX76MzZs3459//sHo0aPV0v3www9o2rQpQkNDMWrUKHz44Ye4efMmACApKQnt27dHeHg4duzYgUuXLmHixInIzs5GYGAgXnnlFaxZs0bteGvWrMG7776r1mys6tixY2jatKnWfUOGDMHx48cRFhYGAPjzzz8RGBiIJk2aFPj5A0D//v3Vanw3btwIGxsbtGnTRk7TvHlzPH78GI8ePSrUOYjISAQRUSG98847ok+fPjr3f/HFF8LGxkZ4eHiIrl27iu+//15ERkbK+x88eCAACEdHR1GmTBlhZWUlAIjAwEARHR0tp1uzZo2wtrYW1tbW4quvvtI7f5UqVRJ2dnaiTJky8t+iRYuEEEIAEGPHjlVL37ZtW/HNN9+obVu3bp0oX768EEKIffv2CWtra/H48WN5/549ewQAsW3bNiGEEIcPHxYARGxsrJwmNDRUABAPHjwQQggxZMgQMXLkSLXzHD9+XFhZWYnU1FQ574MHD5b3Z2dni3Llyolly5YJIYT4+eefhYuLi1o5qdq8ebNwd3cXaWlpQgghLl68KBQKhZwHbRo2bChmzpyptk31+fTt21fMmDFDCCHEyy+/LBYtWiS2bdsmVL9KpPSqZS79KRQKsWDBAo3z3r17V3h6eorvv/9ebXt8fLwAII4cOaIzz0RU9FizSERFZvbs2YiMjMTy5ctRp04dLF++HLVq1cKVK1fU0m3evBmhoaHYsWMHqlWrhl9++QUeHh5qaRwdHdG5c2esXLkSN27c0DsPn332GS5evCj/vf322/K+3LVo58+fx8yZM+Hs7Cz/jRgxAhEREUhJScGNGzdQsWJFtWbaVq1aFaRI5POsXbtW7TxdunRBdna22oCOBg0ayLcVCgV8fX3x/PlzADlN240bN9YoJ0nfvn1hY2Mj9zNcvXo1Xn75ZQQGBurMV2pqKhwcHHTuHzZsGNauXYv79+/j5MmTGDRokM60x48fVyv3ixcvws/PTyNdfHw8evbsiW7duuGzzz5T2+fo6AgAGn1cici0bMydASIq2Tw9PfHGG2/gjTfewJw5c9C4cWPMmzcP//vf/+Q0AQEBqF69OqpXrw5nZ2e89tpruH79OsqVKyensba2xvbt2/Haa6/h5ZdfxqFDh1CnTp18z+/l5YVq1app3VemTBm1+9nZ2ZgxYwb69eunkdbBwUFrc3ruJl0rq5zf4Kppcw+eyc7Oxvvvv691ahjV/py5m+IVCgWys7MB/BdI6WJnZ4chQ4ZgzZo16NevHzZs2JDvSGQvLy/Exsbq3N+9e3e8//77eO+999CrVy94enrqTFu5cmWNEew2NupfOVlZWejfvz9cXV3l7geqYmJiAEDuZ0pE5sGaRSIyGTs7O1StWlVtIEdu7du3R7169eR+gqrs7e2xdetWNG/eHC+//HKeI6YLo0mTJrh16xaqVaum8WdlZYU6deogLCwM4eHh8mNOnjypdgwpsImIiJC3Xbx4UeM8165d03oeOzs7vfLaoEEDXLx4UQ6otBk+fDgOHDiApUuXQqlUag2CVTVu3BjXr1/Xud/a2hpDhgzBkSNHMGzYML3ymZdPP/0UV65cwbZt27TWaF69ehW2traoW7euweciosJjsEhEBomPj9dobgwLC8POnTsxePBg7Ny5E7dv38atW7cwb9487N69G3369MnzmOPHj8fPP/+Mp0+fauyzs7PDn3/+idatW6Njx44aTdqGmDp1Kn799VdMnz4d165dw40bN7B582Z8+eWXAIBXXnkFNWvWxNtvv41Lly7h+PHjmDJlitoxqlWrhoCAAEyfPh23b9/Grl278MMPP6ilmTRpEk6ePImPPvoIFy9exJ07d7Bjxw58/PHHeud1wIAB8PX1Rd++ffHvv//i/v37+PPPP9WC19q1a6Nly5aYNGkSBgwYkG9tZJcuXXDy5ElkZWXpTPP111/jxYsX6NKli9551WbNmjVYunQpli9fDisrK0RGRiIyMhJJSUlymuPHj6Nt27b55puIihaDRSIyyJEjR9C4cWO1v6lTp6JOnTpwcnLC+PHj0ahRI7Rs2RK///47fvnlFwwZMiTPY/bs2ROBgYFaaxeBnObZ33//He3atUPHjh1x+fJlozyXLl26YOfOnQgJCUGzZs3QsmVLzJ8/H5UqVQKQ08S8bds2pKeno3nz5hg+fLhGHm1tbbFx40bcvHkTDRs2xHfffSdPyyNp0KABjh49ijt37qBt27Zo3LgxvvrqK5QvX17vvNrZ2WH//v0oV64cunfvjvr16+Pbb7/VmErovffeQ0ZGhl41gd27d4etrS0OHDiQ53m9vLx0jqjW19GjR5GVlYXevXujfPny8t+8efPkNBs3bsSIESMMOg8RGU4htHXCISIivSkUCmzbti3P1WzMZfbs2di0aZPeNbBLly7FX3/9hX379hVxzvK2a9cufPbZZ7h8+bJGX0ciMi1+AomISqCkpCTcuHEDixcvxtdff63340aOHInY2FgkJibKS/6ZQ3JyMtasWcNAkcgCsGaRiMhAlliz+O6772Ljxo3o27cvNmzYkO9KN0REujBYJCIiIiKdOMCFiIiIiHRisEhEREREOjFYJCIiIiKdGCwSERERkU4MFomIiIhIJ05gpYfs7GyEh4fDxcXF4FULiIiIiAwhhEBiYiL8/PxgZVX09X4MFvUQHh6OgIAAc2eDiIiISPb48WP4+/sX+XkYLOpBWsXg8ePHcHV1NXNuip5SqcT+/fsRHBwMW1tbc2en2GH5GYblZziWoWFYfoZh+RlGn/JLSEhAQECAyVZZYrCoB6np2dXVtdQEi05OTnB1deUHvRBYfoZh+RmOZWgYlp9hWH6GKUj5maprHAe4EBEREZFODBaJiIiISCcGi0RERESkE4NFIiIiItKJwSIRFTvJycnIzMw0dzaIiEoFBotEVKwkJSUhICAAzZs3N3dWiIhKBU6dQ0TFypkzZxAbG4vY2FgolUpOzUFEVMRYs0hExYq9vb18OyIiwow5ISIqHRgsElGxkpKSIt9+8uSJGXNCRFQ6MFgkomIlMTFRvv306VMz5oSIqHRgsEhExUpSUpJ8+/Dhw/j222+RnJxsxhwREZVsHOBCRMWKarC4bNkyAEB2dja++OILc2WJiKhEY80iERUrqsGi5ObNm2bICRFR6cBgkYiKFdU+i5KaNWuaISdERKUDg0UiKla01Sza2dmZISdERKUDg0UiKla0BYsZGRlmyAkRUenAYJGIipUXL15obGOwSERUdBgsElGx8ujRI41tDBaJiIoOg0UiKlbCwsIAAL169ZK3MVgkIio6DBaJqNhISkpCTEwMAGDdunUYP348AECpVJozW0REJRqDRSIqNiIjIwEAZcqUgZubG9zc3ACwZpGIqCgxWCSiYiMhIQEAULZsWQCAra0tAAaLRERFicEiERUb8fHxAABXV1cA/82vyGCRiKjoMFgkomJDqllksEhEZDoMFomo2NAVLHKACxFR0WGwSETFhtQMLQ1sYc0iEVHRY7BIRMVG7ppFDnAhIip6DBaJqNjYvHkzAPZZJCIyJQaLRFQsPH36FJcvXwYAuLi4AGCwSERkCgwWiahYOHHihHy7c+fOADjAhYjIFBgsElGxcPbsWQDAhx9+iLZt2wJgzSIRkSkwWCSiYiEqKgoAEBAQIG/jABcioqLHYJGIioXU1FQAgJOTk7xNqllMT083S56IiEoDBotEVCxIwaKjo6O8TQocU1JSzJInIqLSgMEiERUL2oJFaXJuabJuIiIyPgaLRFQsaAsWy5YtCyCnGTotLc0c2SIiKvEYLBJRsSA1NasGiy4uLlAoFABYu0hEVFQYLBJRsaCtZtHKykpezYXBIhFR0WCwSETFgrZgEfiv3+LHH3+MqKgovPXWW/j9999Nnj8iopLKxtwZICLSh7apc4D/gsX9+/fD29sbQM4a0m+++aZpM0hEVEKxZpGIigVdNYu57xMRkXExWCSiYkFXsFi1alVzZIeIqNRgsEhEFi8rK0te0i93sLh8+XKtj8nOzi7yfBERlQYMFonI4km1ioBmsOjq6oqDBw9qPCYxMVHn8cLDwzF+/HjcvXvXeJkkIiqhLC5YXLp0KSpXrgwHBwcEBQXh+PHjOtNGRERg4MCBqFmzJqysrDB27Fit6f7880/UqVMH9vb2qFOnDrZt21ZEuSeiopBXsAgAL7/8ssa2vKbSGTRoEObPn4/27dsbJ4NERCWYRQWLmzdvxtixYzFlyhSEhoaibdu26NatG8LCwrSmT09Ph7e3N6ZMmYKGDRtqTXPy5En0798fQ4YMwaVLlzBkyBC8+eabOH36dFE+FSIyIilYtLOzg5WV5mVLoVDgrbfeUtsWFxen83hHjx4FkFPDSEREebOoYHH+/Pl47733MHz4cNSuXRsLFy5EQEAAli1bpjV9YGAgFi1ahLfffluePiO3hQsXonPnzpg8eTJq1aqFyZMno1OnTli4cGERPhMiMiZd0+aosrW1VbufV7CoLeAkIiLtLGaexYyMDJw/fx6ff/652vbg4GCcOHGi0Mc9efIkPv30U7VtXbp0yTNYTE9PR3p6unw/ISEBAKBUKqFUKgudl+JCeo6l4bkWBZafYbSVn/QZdHR01Fmu1tbWavdjYmJ0prWyskJWVpbGeUoKvgcNw/IzDMvPMPqUn6nL1mKCxaioKGRlZcHHx0dtu4+PDyIjIwt93MjIyAIfc86cOZgxY4bG9v379+dZs1HShISEmDsLxRrLzzCq5Xfz5k0AOSOcd+/erTV97s/0iRMn5HWjc1Pdrut4JQHfg4Zh+RmG5WeYvMovJSXFhDmxoGBRkvviLoTQecEvqmNOnjwZ48aNk+8nJCQgICAAwcHB8jq0JZlSqURISAg6d+6s0bRH+WP5GUZb+Tk4OAAAPD090b17d62Py31hrVWrls60tra28lQ8utIUZ3wPGoblZxiWn2H0KT+ptcVULCZY9PLygrW1tUbtwPPnzzVqBgvC19e3wMe0t7eHvb29xnZbW9tS9cYvbc/X2Fh+hlEtP6nJxcnJSWeZ5v7MKpVKnWlV+yyW5NeI70HDsPwMw/IzTF7lZ+pytZhe3nZ2dggKCtKoHQgJCUHr1q0LfdxWrVppHHP//v0GHZOITEvX6i2qcl8882qm4QAXIiL9WUzNIgCMGzcOQ4YMQdOmTdGqVSusWLECYWFh+OCDDwDkNA8/ffoUv/76q/yYixcvAgCSkpLw4sULXLx4EXZ2dqhTpw4A4JNPPkG7du3w3XffoU+fPvjrr79w4MAB/PPPPyZ/fkRUOPqMhrazs9P6GG1UB8PkVQNJREQWFiz2798f0dHRmDlzJiIiIlCvXj3s3r0blSpVApAzCXfuORcbN24s3z5//jw2bNiASpUq4eHDhwCA1q1bY9OmTfjyyy/x1VdfoWrVqti8eTNatGhhsudFRIYpTM1iXsGiap/lmJgYg7q6EBGVdBYVLALAqFGjMGrUKK371q5dq7FNCJHvMV9//XW8/vrrhmaNiMxEalI2VrCoOjXWtWvXGCwSEeWBHXeIyOLpU7OobzN0RkYGkpOT5ftSVxYiItKOwSIRWTxjDnC5f/++WovE/fv3jZBDIqKSi8EiEVk8Y/ZZvH37ttp91VpGIiLSZFCfRaVSicjISKSkpMDb2xseHh7GyhcRkUxawSWvvoX6NkPfvXtX7T6DRSKivBW4ZjEpKQk///wzOnToADc3NwQGBqJOnTrw9vZGpUqVMGLECJw9e7Yo8kpEpVB6ejoOHjwIAOjatavOdPrWLD5+/BhAzmowAINFIqL8FChYXLBgAQIDA7Fy5Up07NgRW7duxcWLF3Hr1i2cPHkS06ZNQ2ZmJjp37oyuXbvizp07RZVvIiolwsLCkJaWBicnJ9StW1dnOn2DxfDwcABAjRo1ADBYJCLKT4GaoU+cOIHDhw+jfv36Wvc3b94cw4YNw7Jly7B69WocPXoU1atXN0pGiah0kmoCK1asmOea7vo2Q6sGiydPnmSwSESUjwIFi1u2bNErXUZGhs65EomICkKaiL9ixYp5ptNnNPSDBw/k40k/ZBksEhHlrcB9FufNm5fn/oSEBAQHBxc6Q0REqgobLOauWVy0aBGqVKkiH69atWoAGCwSEeWnwMHiV199hTVr1mjdl5SUhC5duiAhIcHgjBERATnL8QGAl5dXnun8/PzU7ucOFseOHat2v0KFCgAYLBIR5afAweK6deswatQobN++XW17UlISgoODERMTg8OHDxsrf0RUymVkZAAA7O3t80xXr149tfuqwaJSqdRI7+vrC4DBIhFRfgocLL7++utYvHgxBg4cKAeFSUlJ6Nq1K6KionDkyBGus0pERiMFi7kHsORmZWWF8ePHy/dVg8XIyEi1tPb29ihbtiwAIC0tDc+ePZNrMImISF2hVnAZPnw4pk+fjr59++LIkSPo1q0bIiMjcfjwYZQvX97YeSSiUkzfmkUgp0/18+fPAeTUJmZmZgL4bwS0xMXFBc7OzvJ9X19feHl5ITs721jZJiIqMQq9gsvEiRMRGxuLTp06ITAwEEePHpX7ABERGUt6ejqA/GsWJWXKlJFvp6amwsXFRWuw6ODgAH9/fzx58gQAIIRAamqq2uOJiKgQwWK/fv3U7tva2sLLywtjxoxR275161bDckZEBP2boSUODg7y7StXrmDo0KEatZIuLi4Acvo5SsEikDPdDoNFIiJ1BQ4W3dzc1O4PGDDAaJkhIsqtIM3QQE7fRQcHB6SlpeHrr7/G7du3NdJIwWKjRo2wd+9eebu2uRmJiEq7AgeLuqbNISIqCgVthgYAR0dHpKWl6dwvBZ6jR4/Gt99+K2/XteoLEVFpVqgBLkREplLQZmggJ1gEcvohamNllXPpq1ChAj777DN5O2sWiYg0FShYlFY+0NfTp08LlJ6IKDdDgsWIiAit+1VXg/nuu+/k2wwWiYg0FShYbNasGUaMGIEzZ87oTBMfH4+VK1eiXr16HORCRAaTmqH17bMIQJ4W5/Llyxr76tevj++//16+r1Ao0LBhQwAMFomItClQn8UbN27gm2++QdeuXWFra4umTZvCz88PDg4OiI2NxfXr13Ht2jU0bdoUc+fORbdu3Yoq30RUShSmZrF27dq4dOmSxvb58+dj7NixUCgUatudnJwAMFgkItKmQDWLHh4emDdvHsLDw7Fs2TLUqFEDUVFRuHPnDgBg0KBBOH/+PP79918GikRkFIUJFv39/bVud3Jy0ggUpe0AB7gQEWlTqEm5HRwc0K9fP405F4mIjK2gU+cAwHvvvYd58+ZpbJeCwtykPo6sWSQi0sTR0ERk0QozdU6tWrXQtWtXje26gkVpe1JSUiFySERUsjFYJCKLVphmaEBzAQFAd7AoBYljx44tWOaIiEoBBotEZNEK0wwNADY2mr1sdAWLAQEB8m32WyQiUsdgkYgsWmGaoQEgKytLY5vUNzG3qVOnyrdjYmIKdB4iopKOwSIRWazs7GxkZmYCKHiwKD1Olbe3t9a0fn5+8j4Gi0RE6go1GlrVr7/+muf+t99+29BTEFEpFRUVJd82Rs1i5cqVdab38PDAixcvEBsbW6DzEBGVdAYHi1euXNHYlpWVhT/++AMREREMFomoULKzs9G8eXMAQLVq1eDq6lqgx6vWLI4aNQrDhg3LM727uzsA1iwSEeVmcLA4d+5c+XZGRgZWrVqFRYsWoXPnzpg0aZKhhyeiUurhw4d49OgRAGD58uVaJ9POS7t27fD3338DAH766ad803t4eABgsEhElJvBwSKQM+3E0qVLsWLFCvTq1QsHDhzQuYICEZE+pFaLxo0bo1OnTgV+/JgxY+Dg4IDOnTvrlZ7BIhGRdgYHi1OnTsVvv/2GQYMG4fTp0/D09DRGvoiolLt+/ToAoF69eoV6vJ2dHUaPHq13egaLRETaGRwszpo1C87Ozli2bBmWL18ubxdCQKFQ4Pnz54aegohKobi4OACAr6+vSc7HYJGISDuDg8Xs7Gxj5IOISE1iYiIAwNnZ2STnY7BIRKSdwfMsxsfH4+HDhxrbHz58iISEBEMPT0SllLQEn4uLi0nOx2CRiEg7g4PF0aNH4+7duxrbr169io8//tjQwxNRKSUFi6aqWZSmzuE8i0RE6gwOFkNDQ/HKK69obO/ZsycuXLhg6OGJqJRizSIRkWUwOFhMS0vTuU9a05WIqKBMXbMoBYvR0dEmOR8RUXFhcLBYv359/PbbbxrbN2zYgNq1axt6eCIqpUxds+jm5gYgZ2ANB+4REf3H4NHQixcvxquvvorVq1ejUaNGUCgUuHDhAhISErB9+3YjZJGISiNT1yyWKVNGvp2amqp2n4ioNDO4ZtHf3x9nz57FF198gcDAQFSsWBFffPEFzp07V6hVXJYuXYrKlSvDwcEBQUFBOH78eJ7pjx49iqCgIDg4OKBKlSpqcz0CwNq1a6FQKDT+8mo+JyLzSkpKwpMnTwCYLlh0cnKSbycnJ5vknERExYFBweLu3bsxefJk/Pvvv/jll19QoUIFjBkzRuuAF31s3rwZY8eOxZQpUxAaGoq2bduiW7duCAsL05r+wYMH6N69O9q2bYvQ0FB88cUXGDNmDP7880+1dK6uroiIiFD7c3BwKFQeiajoHTx4UL5tqmZoKysrOWCUajWJiMjAZugVK1Zg7dq16NOnDw4dOoT3338f/fr1K/Tx5s+fj/feew/Dhw8HACxcuBD79u3DsmXLMGfOHI30y5cvR8WKFbFw4UIAQO3atXHu3DnMmzcPr732mpxOoVAUaBWI9PR0tcE50nyRSqUSSqWyME+tWJGeY2l4rkWB5WcYpVIpT8gNAF5eXiYrS2dnZ6SkpCAuLq5Yv358DxqG5WcYlp9h9Ck/U5etQcGin58fypYtiw8++ADW1tZwdHQs9LEyMjJw/vx5fP7552rbg4ODceLECa2POXnyJIKDg9W2denSBatWrYJSqYStrS2AnFqCSpUqISsrC40aNcLXX3+Nxo0b68zLnDlzMGPGDI3t+/fvV2uqKulCQkLMnYVijeVXeBkZGQCAfv36Yc+ePSY7r0KhAJDz2j1+/Nhk5y0qfA8ahuVnGJafYfIqv5SUFBPmxMBgUaoBHDBgAACgR48ehT5WVFQUsrKy4OPjo7bdx8cHkZGRWh8TGRmpNX1mZiaioqJQvnx51KpVC2vXrkX9+vWRkJCARYsWoU2bNrh06RKqV6+u9biTJ0/GuHHj5PsJCQkICAhAcHAwXF1dC/0ciwulUomQkBB07txZDrhJfyw/wyiVSrnvcZ06ddC9e3eTndvb2xvPnj1Dw4YN0alTJ5Od19j4HjQMy88wLD/D6FN+pl4hz6BgsUmTJmr3u3btalBmgP9+2UuEEBrb8kuvur1ly5Zo2bKlvL9NmzZo0qQJFi9ejB9//FHrMe3t7WFvb6+x3dbWtlS98Uvb8zU2ll/hSTWLzs7OJi1DaTBNWlpaiXjt+B40DMvPMCw/w+RVfqYuV4NHQ6vasGFDoR/r5eUFa2trjVrE58+fa9QeSnx9fbWmt7Gxgaenp9bHWFlZoVmzZrhz506h80pERUvqj2PqgWhSsMjR0ERE/zGoZnHq1KnybSEE9u7di4EDBxbqWHZ2dggKCkJISAheffVVeXtISAj69Omj9TGtWrXC33//rbZt//79aNq0qc6oWwiBixcvon79+oXKJxEVPWmAmSH9oAtDChY5GpqI6D8GBYu3b9/Gd999J99/8OCBQZkZN24chgwZgqZNm6JVq1ZYsWIFwsLC8MEHHwDI6Uv49OlT/PrrrwCADz74AEuWLMG4ceMwYsQInDx5EqtWrcLGjRvlY86YMQMtW7ZE9erVkZCQgB9//BEXL17ETz/9ZFBeiajomKtmUZqImzWLRET/MShY/Pzzz1GpUiX5/qxZswzKTP/+/REdHY2ZM2ciIiIC9erVw+7du+VzREREqM25WLlyZezevRuffvopfvrpJ/j5+eHHH39UmzYnLi4OI0eORGRkJNzc3NC4cWMcO3YMzZs3NyivRFR0pD6L5mqGZs0iEdF/DAoWGzVqpHY/MDDQkMMBAEaNGoVRo0Zp3bd27VqNbe3bt8eFCxd0Hm/BggVYsGCBwfkiItORgkVTN0NLNYt//fUXTp06hRUrVqBChQpYsGABmjdvjpdeesmk+SEisgQGrw1NRGRs5h7gIv0AHTRoEMaPH4/x48cD+G+2BSKi0sSoo6GJiIzB3DWLkmPHjuHRo0cmzQMRkaUpsmAxIiJCbck8IiJ9mbvPoqqsrCz5Nq9pRFQaFVmwOGTIENSqVQsTJkwoqlMQUQllKTWLAHDgwAH59osXL0yZHSIii1BkfRalC+zNmzeL6hREVAKlp6dbVM3irl275NsvXryAv7+/Uc/5119/oVatWqhZs6ZRj0tEZCxGqVnMq2mmVq1axjgFEZUCR44cgbu7O9LS0gCYt2axXLlyGvufP39u1POdOnUKffv25XWSiCyawcHi2LFj4e7ujqCgINy/fx9jxozBW2+9hZUrVxojf0RUigwdOhSZmZnyfRcXF5OeX7VmsUaNGhr7nzx5YtTzqS47yv6QRGSpDA4W//77b0RFRWHRokVo06YNvL298eabbyIkJATTpk0zRh6JqJTI3eysrQ9hUVI9X5UqVTT2G7pKVW6urq7yba5XT0SWyuBg0c3NDU5OTnjppZfg6uqKr776Cv369cOGDRvU+voQEeVHtdm5TJkysLIy7exeqsFbUFCQxv579+4Z9Xypqany7atXrxr12ERExmLwlfjFixfYuXMnHj9+rPar3MbGhhPYElGBqAaLpm6CBnJqE99//318/vnnGDp0qMb++/fvG/V8KSkp8u0jR44Y9dhERMZS4NHQ+/btQ+fOneVf/J9++im2bt2KmTNn4v79+2jdujVq1qyJmjVrIjo62ugZJqKSSzVY1DYyuagpFAosX74cgPbVWqKioox6PtVg8eeff4aPjw9mzJhh1HMQERmqwMFi9+7dERERIY8UHDdunNr++/fv4+rVq7h69SratGljnFwSUamg2mfR3t7ejDnJCRwdHBzkkdkAkJCQYNRzqAaLAPDTTz9h+vTpUCgURj0PEZEhChws5te0XKVKFVSpUgW9e/cudKaIqHRSDRBVR0Wbi6Ojo0mCxffeew9r1qxBdHQ0rl27hnr16hn1PEREhuDa0ERkMVSnjzH2NDWFkXuex4yMDKNOcSMFi2XLlkWHDh0AAF27dkVUVBRWrVpl9NHXRESFUagVXJYsWYI2bdogKCgIXl5exs4TEZVSqqODk5OTzZiTHNomBU9ISIC3t7dRji8Fi05OTvjhhx/QuHFjPH36VD6+u7s7YmJijHIuIqLCKlSwuGzZMsyaNQsKhQIVKlRAkyZNEBQUhCZNmqBJkyYoX768sfNJRKWAapPvt99+a8ac5DBlsNioUSP4+Pjg2bNn8v7Y2FijnIeIyBCFChavXbuGzMxMhIaG4sKFC7hw4QJ++eUXPH78GAqFAj4+PggPDzd2XomohJNqFj/99FONwXPmoCtYNBbVYBEArl+/Dk9PT6MdvzR48uQJ4uLi2M+TqAgVOFiURun5+fnBz88PPXr0kPfFxMTg3LlzuHjxotEySESlhxQsWkrAZOpg0cPDA02aNMGFCxeMdo6SLiAgAAAQEREBX19fM+eGqGQq8ACXvEZDe3h4IDg4GBMnTjQoU0RUOknN0HZ2dmbOSQ4piANy+g8CRRssAsCwYcOMdvySTnXey1u3bpkxJ0QlW4GDxT179sDNza0o8kJEpZxUs2gpwaKHh4d829/fH4Bxg0Xp+aoGiyNHjkTfvn2Ndo6STDVAVB0cRUTGVeBgsUuXLmafLJeISiZLCxZVfxgXRbCorWbR1tYWa9aske/fuXPHaOcraVSDRWOvrkNE/+E8i0RkMaRg0dbW1sw5yaG65KA0Arqog8Xc523WrJnRzmdOsbGxmDt3rlHnjlQNELm8LFHRYbBYyn355Zfo27cvsrKyzJ0VKuWysrKgVCoBmH+pP4mLi4t8W6plNGZQoitYtLH5b+xhfHy80c5nTmvWrMHEiRNRpUoV+XU2lOpyiaxZJCo6DBZLudmzZ+Ovv/7CkSNHzJ0VKuVU51i0lGZoaVUVAHB1dQUAo9aO6QoWSyLV6dSMtToPg0Ui02CwWIqp/rrPyMgwY04sl7FqQCh/qgMULKUZum3bttixYwdu3LghB4sA8PPPPxf6mHfu3MGsWbOQkJCgd7CoGhQVJ5cvX8aXX36J27dvqy2TGBcXZ5Tjq67yw6URiYpOoSblppJBtXlLmj+T/nP48GF069YNCxcuxAcffGDu7JR4Us2ira0trK2tzZyb//Tq1QsAEBISIm9TDRwLqmXLloiJicGdO3fk56wtWPz333/Rpk0bADk1cTVq1Cj0Oc2lUaNGEELg2rVrKFu2rLzdWCvTqAbRly5dMsoxybItXrwYLi4uePfdd82dlVKFNYulmGqwqNoESDn69++P9PR0fPjhh+bOSqkg1SxqmwjbEqh+XgxpNpbWet66dWuex2vdujWqV68OAIiMjMS9e/eQnZ1d6POagzQv78mTJ5GUlCRvN0bN4s2bN7F27Vr5fmRkJKKiopCYmIhFixZxwEsJ9PTpU4wZMwZDhw5FZmamubNTqjBYLMVUv/xUL+SmMHHiRMyYMcOk5ywoKyt+PEzJ0oNF1dpEY3xeVI/h4OCgNY2XlxcAYP78+ahWrRq++OILg89rKqoLOCQnJ6s1GRsSLKanpyMqKgotW7bU2BcVFYUOHTpg7NixmDJlSqHPQZYpMTFRvm3MWQkof/w2LGHi4+Oxb98+vUY3F3WweOPGDTRr1gx//fWX2vYHDx5g7ty5mD59ukX3lbSkptCSLisrS65x0xU4mdvw4cPl28b+otL1w0Sarkf6DH333XdGPW9RUu2DmpSUpHaN+f777wt93DfeeAPlypXTOko8ISFBXipRtdsAWYavv/4aFStWxP379/VKHxMTozYwqij6vZJ+GCyWIBkZGfD390fXrl2xY8eOfNOrXmxVf7EZy3vvvYdz585prEYhBQWAegd1S8NgsehFRkbizJkz6NOnD15++WUAlhssOjk5ybXhRfF50UaqWSyoq1evYsOGDSbLpza5P9vPnz+Xb9+6dQthYWGFOu7ff/+tc9nZFy9eyLdr166tsV8IgRkzZmD79u2FOjcV3pkzZzB16lQ8fvwY+/btyze9EAKenp6oUKGC/D42dlcG0h8HuJQgp06dkj9M+vxyK+qaRV1TWeQ+r5ubG44ePYqAgABUq1atUOeKjIzEkiVLMGLECFSqVKlQx8gtd7B48+ZNZGdno06dOkY5vmTSpElIS0vDokWLjHpcS6dUKlG+fHmN7ZbaDA38N+9iQWsWpeCmoAPJpJpFSZkyZfR6XJMmTeTHL1mypEDnNJbcweK9e/fU7p8+fRoVK1Ys0DHzazFR/SGqWgsl2bdvH6ZPnw4AOgNOKhqq70N9+sirfk88evQI9erVU/ueKinzjxYXrFksQS5fvizf1qd515g1i0II7N27V632QNdceaq//pOTkzF58mR07NgRXbt2LfT5+/fvj9mzZ6N3796FPkZuqsFiWloaateujbp16xq0Bm1UVBTef/99nD59GkDOa/b999/jxx9/xLNnzwzOc2E9evQIq1atMmmn8QMHDmjdbsnBotRvsSDBYmZmJlq0aIGuXbtqDVDmz5+v87G5y0JbcJ2XM2fOFCi9MeUOFnO/t86fP1/gY6oGg/nt1zbdkOqPaA6QMK2IiAj5tj6Bnmrzs0KhQEpKitaaxYiICJP3uS+NGCyWIKrBoj7Nu8asWdy4cSO6desmNyUC6nPl7dmzB5s3bwagXuOYlJSEPXv2ANCseSiIY8eOAVAvA12EEHrNW6faj+zp06fybdVgt6AmTJiAFStWyJ3zVftzmrNJPjg4GMOHD8fs2bNNdk5dNc+W2gwNFC5YvHPnDs6ePYv9+/drNJ2VKVMGY8eO1fnYjh07AgCqVKkCIOczm5qaqvd7xZxTYum6pgwePBiAfivhXL9+HYsWLZJrFCMjI7Wmk641qsGi6g/g9PR0ZGVlqf3QM+ePM0slhCiy5l3Vml59Pj+q19zBgwejTJkyeP311+VtcXFxeP78OapWrao2eT4VDQaLJYjqxU+fYEj1A3v79u0Cn2v//v0Aci4wgwYNApBzcZeo1ix2794db731Fp48eYLRo0fL25OTk9XyaoqmoU8//RQeHh64cuVKnulUaxYfP34s3zZkjrhr166p3VcNbgtbY2mMMpNe/+XLl+ebdtiwYWjevLnBg5N0NUVZcs2iVLNXkB82qkHxo0eP1NZ99vLyyjOga9u2LZ4/f45Dhw4ByPnMtmjRAhUqVJADxtu3b2P06NH49ddf0alTJ9y6dUt+vDmDRV0Brb+/P4CceRE/++wzPHnyBDNnztT63pNGNn///fe4ffs2GjRooLbfxcUFwcHB6NOnDwD1AFS6vqWkpMgBhWoAolpzZQnu3LmD3bt3m2XQ37Zt23D27FlMnjwZ5cqVw969e3Wm/eOPPzBt2rQ8rzsnTpzAjBkzcPPmTcTFxSE9PV3t8y5VVMyYMQP16tXT+sNR9fW5ePGixv74+HicOnUKqampuHDhgtZuB7oIIXDr1i35R0h2dja++OILzJkzR+9jlDqC8hUfHy8AiPj4eHNnJU8dO3YUAAQAMXLkyHzTjxgxQk5vbW0tP7+MjAyxfft2kZGRofOxLVq0EADEqlWrxKFDh+TjABCffvqpyMzMFC+99JLadgDi8OHDavd37twpypcvL9/P65x5UT2mvmnfe+89rfsPHjwoli9fLurWrSunXbNmjXz70KFDeR4/r/JTLRMhhKhdu7Z8/8yZM3o8U3WTJk0SgYGB4vnz5wV+rCp9yy87O1tOFxISIm9/8eKFWLhwoUhISND7nIsXL9Z4fwAQr732Wr7vP3NJTEwUVlZWAoBo2LCh+O6778TFixdF7dq1xfr167U+ZtOmTfJz++uvv4SXl5d8v0qVKnqdNzY2VqOc9uzZI4QQws/PT2276nusZcuWRnvuBbVjxw6NPCsUCvHLL7+obatWrZp8OyIiQu0Y0vaqVauKCRMmaBxv8eLFQgghhg8frrHP3d1dCCHEsWPH5G09evRQey100ecaaExZWVmiXLlyAoAYN26c3o+LjY0V586dM+jcv//+uwAgnJ2d87wOREdHi3fffVfr5z83GxsbAUC+ttepU0fUr19ffuzrr78u0tPT5fu9e/fWOMa3336r9fog/U2bNk3MmjVLvn/nzh2tebl7967YuXOn2raff/5ZABBjxowRQgixbNky+TjJyckFKb4ioc/7z9RxCWsWSxDVZh+ptu7SpUuYO3eu1v45qs3QWVlZePjwod7nkvrcLViwQKO2bMGCBfj777+1Pi53n6Pk5GS1GjVdtWvnzp3D0qVL861Fy29NYdVf7X5+flrTdOrUCR988IHa81Ktrcmv31Re7O3t5dvp6elqNbqFWdLtu+++w8OHD/Ps91ZQeQ2OUn19VCeInjhxIsaOHYtXXnlF7/PoqgmoX7++3scwNWdnZ3kllUuXLmHSpElo1KgRbty4gcGDB2PPnj0aTWyqtVmPHj1Sew/qO5enNLBGlbS8Xe4askePHsm3La1m0cfHBx4eHmrb7t69K9/etWuX1mPdv38fnp6eGtul1XW0LQ+ZkJAAIYRajZbqKi+q/avNITU1FStWrMDTp0+RkJAg52fZsmV696ds06YNmjZtiqNHjxY6Hz/99BOAnO8P1dcmJSUFK1euxNWrVwEAX331ldok6LpGsz979kzOv9RP8fr162qfd6lWULJjxw7s3r1bfvyNGzfybWmJi4tTax168OABhBD4/fffcfPmTXl7tWrV0LNnTxw5ckTe9sknnwAAfvzxRwDqLTycv1E7BosliGqwKF2oGzVqhIkTJ2LBggX44Ycf1D4UuTsZa+uLFxcXp9FcqBqwPX36VOsUMzExMVr7LKl2cpbyrPql8s8//yAzMxMzZ86Um7kBoFmzZvjoo4+wc+dOjWOqUm3i00Y1OFNdfiw/T548kW8XJljMysrCtm3b1L6grl69qja605CBM4auVqHa9CutGqKNahO8at6lZqszZ87kG9ALIbBu3Tqt/Us7duyIjz/+WO98m0PuYEdV9+7d0aJFC7z33nvyl6xqMBcZGan2edJ3eiZt6VS7fKhSDZwsoc+iah78/PzyXCrxxo0b8m3VHyNCCLUg++DBgwgPD5dnPjh37pzGsbKyshAfH6/WPUf1c2zuVau++uorvP/+++jSpYtaP8HU1FS9P8/Se2DLli15prt8+TI6deqEEydOaOxTve74+vrKt/v06YORI0eiTZs2ePbsmdoPZkD3dVDqkpSb6vdNZGQk2rdvr7Zf6r/dvHlz1KlTR/786HLu3Dm1KXgePHiAPXv2oH///qhduzb+/fdfzJo1S96vGlDnfu2Lehq5koDBooVTKpXYs2ePXp2OtdUsSiZOnIgJEyagYcOGAHI+ELnnusr9S/vZs2eoXr06HB0d1T64qhcJGxsbrUFhcnKy1g9d7g7qL168gFKplO/36NED9evXx7Rp09ClSxcA6hezK1eu4MWLF/jmm2/kwE81aMmvpkZ1ol5twZmuQEe1T82TJ0+wf//+AvUt2r17N/r166f2Szh3bYDqa5acnFygPjjSr+H79+/rNSF7bqpBdu4vaVWqwaLqBVZ6XwH/XWznz58PV1dXjRG5v/32G95++238+uuvatvff/99HDx4UGstmiXJb6m/mzdvYvXq1ejUqRMA9UA+Nja2UDWLQE6/MlVLliyBu7u7RjrVWilzBovSe0V1Oqz8gkXVWtHc1zypHLt164aOHTuqjQwfNWqU1uPdunVLZw2iFJQdO3ZM6+e+IJ+/wtiwYQOAnH7MuX+4f/bZZ3KQu2PHDpw8eTLP/OU3KGzgwIE4dOiQvNa4KtX3i+rnW5qtICEhAadOnYKNjfpMe5999pnWUc0HDx7UmgfVyght/cUjIiIghJBrLP/888+8nhL+/fdftffIgwcP8M8//8j3e/bsia+++kq+n9ePWNXjMFjUjsGihdu8eTO6d++OatWq5ftLOK9gMbf+/fvLt8uVKwdAM1j86aef5CBJtWlQdbBHSkqKzqBQ2/bcNYtTp07VSKPahJD7flpaGnr06IEpU6bITQmqX75RUVFyc/qNGzfkL58TJ05g6NCh+P3339XynpuuclMNFmfOnIkuXboUaDkx1aZIieqFTfXcycnJ8PDwKNB8jomJifjjjz9QtWpVnb/sc5s/f768PrG291ZISAh8fX3Vfp2rXlRzd2OQxMbGIjIyEuPHj0diYiK+/fZbteNKo99zK+wE1KamGiw2btxYZzrp86T64yr3CNzcAzbyIg3iUKXtR6RqTaYhtdWGkj4zNWvWlLf5+/vrHSzmbumQmqvbtWun8bghQ4Zg8eLFGttXrlyJ8ePHaz1XWloamjRpgvbt22usMnXq1CkMGjRInpOxKKj+SM79Oq5btw6DBw/G7du30adPH7Ru3Vrj8arX4czMTERGRuKtt96Sm3OBnB9+Fy5cyHMAo+o1T9dMD4mJiVprt6WuOkqlEjNnzsxzhaH81jWPiIgoVFccybfffqvWGpW7TC9dugR/f380bdpU47GGBouq17/s7Gxs2LChUD/aLRmDRQsnBUrR0dHyl+yTJ0/w9OlT7Ny5E4GBgTh27BiUSqXeK6O8+uqral/YlStXBgCsWrVKLeDMHdglJydj6dKlal+QycnJ8q/RsWPHonv37gCAb775Rusv+tz9q/KrnRNCqPVpevToEc6ePQsgp+kzLS1N4xgDBw5EVFQU6tSpg8DAQGRnZ6NNmzZYu3atWj+Z3Bemy5cv62zG1nYRnTdvnnyRCQkJweuvv65RZmfOnEF0dLTW1yP3CD8pPxcvXkRGRgbu37+f7wVWEh8fj2+++QYA5CmKgJyl0Tp06KBx4bp16xbGjx+P1157DUqlUqMshBAIDg7G8+fP1fpD5q5ZVCqVEEKo9fOJiYlRm2A8d/OVrj5BxTFYlN7veVH9XKrWrPfv31/uM6UPfWsJVWuKzFFLIr1npWBR6uMJ5HRxyCtYPHv2rHzNyz1CVtqurTbV2toab7zxhsb2VatW6TxXWlqaXIsl/WiSzJkzB5mZmfJnqijkFSwCOTV7qs3yua9zqn0Gnz17hjfeeAObN29G//79IYTA4sWLYW1tjaCgILVz5ab6o09XX8nExESNmkXgvwqK2bNnY9q0afj88891nic/0dHRBvUHB/Keu3P79u14+vSpWhrpOWkLFkNDQ/Hw4UMIIbBs2TJ5erbchg0bBj8/P/n9unz5cgwaNEjnvKrFlcUFi0uXLkXlypXh4OCAoKAgHD9+PM/0R48eRVBQEBwcHFClShWt0y/8+eefqFOnDuzt7VGnTh2N5hxLpvrh2bNnD/bs2YOAgAD4+/ujV69eePToEdq3b48KFSqoPe7ChQtqVfCqVJe66tq1qzwZ9pUrV/Daa69hz549mD17tsYH79mzZ/joo480jif9wvX09ES/fv3yfD65g6n8zJ07Vy1Qu3nzptov3Nu3b2s0F508eRKhoaHyfdXaClUpKSlITU2VBwpMmzZNZz50/eKuVKkS7ty5g+DgYPz555+YO3euvG/btm1o0aIF+vbtqzVYlM4rkWqBVC/YedUMqabT9mWTmZmJP/74A0ePHtV4LVXzc+HCBY1gUrWmUTVAVL3966+/wsHBAS+99JJGn05pABSgXgOi7b7Ezc1N63ZLoxosaqvlyk1XsLhx40a5Vr+omDpYvHfvHsqVK4ehQ4fKnxnVZujy5cvn+zpLAV7uJk5p4JW2YBFQ/7Ghz1ydqp+t3IGQat+9oqohUg3gdM35qPrDKnc/RtVgOiIiQu7ukZSUhPXr12PMmDFaj5m7r7A+XZzCwsK0LiEbGxuL7OxseZCMKtXBfPpITk7WK1g05opE0uuuel1LTEzEypUr0aRJE3Tq1AnHjh3DqFGjNPpYbt26FSNGjMCaNWvw/PlzbNq0CZmZmfL3bp8+fczaDcTYLCpY3Lx5M8aOHYspU6YgNDQUbdu2Rbdu3XSOunrw4AG6d++Otm3bIjQ0FF988QXGjBmj1tfh5MmT6N+/P4YMGYJLly5hyJAhePPNN9W+zCyZ6gUhLCxMrc+dKm3BjGrzoTY1a9bEnj171NZQ3b9/P/r06YOzZ89qdDBWnXBbldSE4+LionaR1aagweKkSZOwYMEC+f6ZM2fULt7x8fFaL3bvvfeefFvXfIopKSno1q0bqlSpgtDQ0DwHG+hqHklISFDre5eWlib/mvz6668B5DQ3a+tzpOscqjUIeTXLqH7ZRUVFaVyYcu/Xdi4AWn+Qqb6fVL9IVS/m165dQ3Z2Nk6cOKHWzB4bG6vW7JWYmKj2mun6cirqPmLGohos5jUYSBo0pC1YtLe3N8kXSV5zghprgIdU87JhwwaMGDEC0dHRWLt2rTwYzcfHB7Vq1QIAdO7cGU5OTvj555/lybklVatWBZDTFw3QvcpH7iUQJaqfX09PT/mcuqg+/9zBoo+Pj3w794+6/Bw6dAhDhgzJN/BRff11NROr/gjLHSyqHj86OlotCNfWvUfSsGFD+bmnp6fr1VUhd1cSSWxsLOLi4rTOk/jOO+/ke1xVSUlJegWLuWdLWLp0aYHOoyotLQ1Xr15V+15KTEyUy+/+/fu4c+eOWnrJa6+9hl9++UW+7+zsjNOnTyMmJgYeHh744IMPCp0vS2RRweL8+fPx3nvvYfjw4ahduzYWLlyIgIAALFu2TGv65cuXo2LFili4cCFq166N4cOHY9iwYZg3b56cZuHChejcuTMmT56MWrVqYfLkyejUqRMWLlxoomdVeLNmzVIb5RYREWHU2fXr1aun9j8/uoJ2iT7BYmFWTVBths6tXbt2an2iJKq1V6pNOapSU1PlQSZNmjTJt0O1LtKEyUDOtBfvvvsusrKy1Kag0WdlGSmA0zaqXRvVC1dUVJRGk4dqQJj7Yq56XKmZ2NraWg6EVNfXVq2l0bWChqrHjx9r9NFMSEjAmjVr0LJlS501vbl/uVsq1dHGbm5uGgPFJI6Ojjhz5oxamUmvSWFXqVFtwlX9XFSpUgWvvvoqmjVrppZeVzCwatUquLm5GeULbdWqVRg1ahQGDRqEw4cPy9ul96OnpyfOnj2LZ8+eybV/I0eOxIQJE9SOI/0YlUb4SsGi6kj9mjVr4qWXXso3TykpKfmuEa/6GcgdLKr+cCnIlGJAztRb69ev19myA+SUjerrouvaqnoNyR0s5q7xV30++eVZOlZh1lceO3Yshg0bBiAnYNXV6qIacKvSNagrJSVFLTjWZty4cWrfV7du3TL4PZw7+NyzZ4/aZ1a13HVdu4Cc7z+pdrdDhw5am+2LM4sJFjMyMnD+/HkEBwerbQ8ODtY63B/IqTXMnb5Lly44d+6cXMWvK42uYwI5F4qEhAS1PyCn2cBUfxkZGRoXm8jISJ1LpEmk/of6mDx5MpRKZYEekxcHBwetc6GZm64ANb+y1Ffu99LGjRvx7NkztWAtr9UipBqR5ORkjb6nUr9A6W///v0YOXIkYmJi1JqosrKy1J6nUqlU+yIICwuTj7Fu3Tq1HyHSF5KTk5PWkcjW1tbyY/O7mAM5zdpATiAlBUVRUVH45ptvtNboT58+HefOnUO1atXk80jPwRL/VGt+HRwc0LZtW63lEBMTgxYtWmjd5+3tXahzb9++Hb6+vli/fr3al7GjoyM2b94szyCg6tmzZxrHmTRpEjIyMvDzzz/j4sWLBpXHxo0b83w/VKtWDfb29nB3d1d7XO5mSumHpvQ5kIIh1eu3VJ668iJJSUlBQEBAnvlS7VNtZWWldhzVH2ydO3dGSkoKlEolTp48KfcRzy8PS5cu1fj8JiUl4cmTJ0hOTlbrj6xr6pv169er5Vf1WKrXr6ioKK2tEAMGDJADO1U9evTArVu3CnwNfO+99zBnzhx52rEpU6bg1Vdf1Ujn6Oio9VqiUCjUugvknh8z95y9uX377bdwcXHBv//+i0uXLqFy5cpGX+NbtZsWALW+infv3tV4nSWpqalykFm+fHmDrzNA/tdAU7KY0DcqKgpZWVkav0Z8fHx01mZERkZqTZ+ZmYmoqCiUL19eZ5q8akjmzJmDGTNmaGzfv39/vtNmGCouLg4vXrzQ+qvsxYsXalXiuVWvXh2TJ0/WenHIrXbt2njy5In85d+3b1+ND4kunp6emDVrFr755hu1Grxbt27p3U/FyspK78EbhlKdiFeVtiWkjEWfoEri4uKCuLg4XLt2Dbt371YbhBMSEqJWS9C3b18AObWPuWtYVAPS3bt3q9VWnDx5En///Tfi4+M13h/SBdra2lrrr/74+Hh89tlnePnll/Od+wz4b8J2Z2dnKBQKpKWlYdeuXTpriKOjo9XeixJdXS7MTXWpP33z2L59e9y9e1eucbW1tVUbtVoQUr/sgwcPom7durh27RqaNGmC3bt3a23K37Fjh1otW3x8vFptyW+//ZZnbV1cXBz++ecfVKtWDTVr1tRoPs/rc9S8eXOtcyBK+VAl1VBlZmZix44d8o8O1WDAxsZGr3JLT0/Pt9ZMtel369atePz4MYYMGQJbW1uN9+qPP/6IwMBADBgwAEBOAKmt/3Zuo0ePVlvPeNKkSbh165Za15q8qLYeHD16VF504N9//1UbeKarC4e/vz8aNWqE9PR0/Pbbb/L2S5cu4f3330fPnj31ygeQM3CwV69e2Ldvn9oPU20tN3Z2dlqvJba2tmrvn/Lly6tdp/IaTe3l5aX22r948ULuIiB9DgqiZs2aGoPvtFE95549e5CZmak1UDtz5ox8vJiYmEJ/vlXldX0xZOR4oZhknRg9PH36VAAQJ06cUNs+a9YsUbNmTa2PqV69uvjmm2/Utv3zzz9qy0bZ2tqKDRs2qKVZv369sLe315mXtLQ0ER8fL/89fvxYABBRUVEiIyOjSP98fX0FALFo0SKtSxyVLVtW5/JH48ePFxkZGXkukZSRkSFOnjwpYmNj1c67f//+PB+n+jdp0iSRkZEhfvrpJ7Xt58+fz/f80p+Pj4/e59P3T6FQaN3es2dPo5/LmH/SMlhvvPGGyMjIENOmTZP37d27V6Snp4uMjAyRmpoqbx86dKg4c+aMzmOmpaWJkydPyvddXFyElZWV1iXTpL/KlSuLOnXq6Nyvupyk9Ne4cWOd6du1ayeqV68uAIgDBw7oTPfrr7+qvReTk5PF9u3bRXJycpF/3grzN2zYMDnv0ra8Xt/atWuLjIwMMX78eHlbv379jJKXhIQEsX79evnzfPHiRY3zHzx4UNy7d0/8/fffIj09XQwZMkRt//Tp08W5c+dEvXr1xJYtWzTO8cknn8hpP/jgA439Dg4OOp/7hx9+qDPv6enpokKFCnJa1WURnz9/Lj788EMBQHz++edi3759YsKECfm+J1TPPW/ePPm2k5OTRt5yL5MIQCxbtkxkZGSIV199VW37li1bxM6dO9W2RUREaJw/OjpaLc0777wj73vx4oW8fdy4cVrL66uvvlL7/Kv+9e3bV2RkZIgZM2bofW05cOCAzvdov379xO7duwUAUaZMGa2PV319VJ9n7mu/tmvJ1q1bxZtvvqm2vWzZsmrLXb711lv5Pofvv/9evPbaa+LMmTM6X/eoqCgxePBgvcsFgPjxxx8LlF7KS2pqqtpnWfpbsmSJ/L5ZtGiRQZ9rfa6BUVFRAiiFy/15eXnB2tpao8bv+fPnOvs++Pr6ak1vY2MjN4fqSqPrmEBO53NXV1e1PyDnV1FR/0l5zf3LU+r8nVefRV9fX63LXvn5+aFPnz5Yv349bG1t0bJlS5QtW1btvHmtZvLHH39gy5YtcHZ2xujRozFt2jTY2triww8/VJsWo0qVKrC1tcX333+v81gSXZ3UJfXq1ZM7uktUJ+HVRld/SUOnY9DFWCuNSPnesmULxowZo9b3qGvXrujTpw9sbW3VfoE7OzvnOUozd3NpYmIisrOz1frz5ubi4pLnVEaqfTMlqpNx5+bu7i6PXM2r76Wzs7PG5wAwzeetMH+qNeKq+dVly5YtsLW1Vetr5efnZ5S8uLi4YNCgQfLnWdvcnPfv30e1atXQq1cvbN++XWMVpG3btmHIkCG4evUq3njjDZ3XJABYvXo1bGxs1PbnNVDGw8NDZ97t7OzkicuBnPlepS4ZGRkZcq2Rh4cHgoODMXfuXDg5OeVZHqpUrzHarh3aWpfCw8O1PqfIyEj88ccfats+/vhj3L9/X+38uacL8/HxkfepNqfr+uzWrl0b06dPl6/3qrZv346srKw8Z21wcnJS6ytXsWJFne9Rb29vubm9SZMmGDlypEaazp07y7dVn2fu2TcAqHVBcnFxgZWVFdavX6822MbBwUGebmrq1Kl6TUrfqFEj/PHHH2jWrJnO193T0xMtW7bM91hATn/su3fvIjAwUGcaJycntc+r9L5MSkrCH3/8gR9++EHjMUrlf12IVF/3wv4B+V8DTcligkU7OzsEBQVpVLuGhIRonZAUAFq1aqWRfv/+/WjatKlckLrS6DqmpcjdQTm/kX3Af53fc0+Z4Ovri+3bt+c5WXNeq2ZUrFgRr7/+OhITE7F48WK5w7lCoUDv3r3ldNJovM8++yzf/kKqQaY269at07ggqU4kro2uKTUK0iQseeWVV7Bp0yatF1DJ7Nmz8z2OrnJVvUiq/nBZvny5RifqvXv3IiYmRq2z+7Nnz/IcxZiUlFTgZgoXFxetX1J5yWtw1OPHj+X3hLZJySUFnWLD3FQHXEh0dU8ZPXo06tatC0D9M5zXj1VDaPsC+fPPP+XBJv3795f7Akqj+C9duqRzEBigHuhnZGTIgdSvv/6KoKCgPPOT3zQ5qv2lXVxcUKZMGQA5TcTSEpJ5zcuYF9XgRVuwqK0bjFRO0mdH+iEXFham1owL5PyIrlWrlhz4CSEwdOhQtTRZWVkYOHAgunXrpjadl64BKNKPdtUf76r9XvMbtOPi4qLWbF+lShX5tmr/RyCniVtqqi9btqzWCoOFCxfigw8+kF8Libb3r+oPR9X5alVfP1dXV6xcuRK3b9/GjBkz5Gb9mjVr6lxXXt/rg+q1qGXLlti0aZPWdHPmzEHVqlXh7++v81jNmzdXy/dbb70FIKfrhK7uNDExMXL3Dkvsu28oiwkWgZyRTr/88gtWr16NGzdu4NNPP0VYWJg82mny5Ml4++235fQffPABHj16hHHjxuHGjRtYvXo1Vq1apTbK7pNPPsH+/fvx3Xff4ebNm/juu+9w4MABjB071tRPL195TeejT7AoBSZz585VCyz0+bDltaayti9HieqHQrUvSqtWreTbmzZtwoIFC9QuXNpm0Zc8e/YMjRo1Ugv+xowZo7UjtSqpP09u2kYaquZF1aBBg3D8+HFs27YN/fv3zzN4cnFxwbp16/LMk7Yv7xo1aqjVeuSuEdV2MTp37pza9A53796V+8f4+flppC9MsOjs7KzXihWqU5Tk9aOgSZMmcsCf13ypRd0P2Ni+/PJL1K1bV23i8XfffVcjXbt27eTpk4CcL9N69eohICAg3/eyIaQR0dLgotxf9EDOyjG5R4HmduPGDbz99tsac3RKAcY777wj9yvUJb9gUXWeSWdnZzlYVB1VrTpPY36k/pw//PCD2rUpv+cqkfr9Sp8daWqkq1evyv3UmjRpovaYM2fOoF+/fujRo4daf2MgZxWsjRs3arwGugIOKWBTLbeyZcvK59S20IFqjVqZMmXUKkJUP6uDBg1SW9c8NTVVbqnSFixaWVnBzc0Ny5Yt0xg4lV+wKL2OgHqwWL58edjZ2cnl2qNHD5w7dw5nzpzR+V7RN1hs3749Nm3ahCtXrshT5mkj5S2vYLFt27Zq/fGlH3zx8fFq76vAwEB5qcmvv/5a7tNdEoNFi+mzKPnpp59EpUqVhJ2dnWjSpIk4evSovO+dd94R7du3V0t/5MgR0bhxY2FnZycCA/+vvTuPi6pe/wD+mWEZRhw2aQQCRCNzKRI1xUwQN8oNL0ouiRpqXJfslpX3arlQLtfMMruGBoLWjSANw+VelRLcuGYJCmJukbhg0sUAN1Dn+f3Bb849hzkzjAzLQM/79ZrXC875cs53HmbOPPM938WPPvnkE4NjfvXVV/TYY4+RnZ0dderUibZu3fpAdSorK2vwvgFxcXFG+0mkpaXRBx98UGt/ih07dkiOqd/ev3//Ws9//fp1o8c9e/as0b8rLy+nkSNH0meffSbZXlJSQn/+85/p6NGjwjZxP8z09HSj56uoqCAiIp1OJ2ybPXs2ERFlZWVRVFQUDR482ODvevfuLfn9hx9+kO2rBICGDh1qsE3fJ0jsiy++MFpPIqJ9+/aZ/J+88847Bv1arl27Rs8884yw7b333pOUcXJyMjjOihUraOnSpbLn6NOnj8G2I0eO0GefffZA/XHGjBlDRNX9hI2Vad26NeXl5Qm/79y502jZX3/9lT7//HPZfUuXLqVly5bR1KlT6fbt25KYV1VV0bZt2wz+F9bsxo0btHr1aoPXR1MoLS2lb775hl599VWj/5sRI0ZQRUWF7L5bt24REZGPj4/s/m+//ZYSEhLMek3Fx8ebrOuWLVuEshcvXiR/f3/J32s0GtLpdA/0/EtKSoiI6KeffhKOI34fe3t7G63vkCFDiIiEvrv6fpP6PtYqlYpGjhz5QO8ruYetra3s9pycHCIiSZ/J559/no4fPy5bfuvWrTRp0iTh94CAADp//jz17t2bUlNTDWJz7NgxyWvA19eXANDLL79MBw4cEPbt37+fLl26ZDTGN2/eNKjLxo0bhZ+feuop4T2ckpIibB83bpzRY4r7AqtUKuHn3NzcB/r/i0VERAjXG/3x9u7dS0TSzxf94+DBg7Rq1SoqKyujmTNnEgAaNGgQffLJJwSA/vSnP9HatWuF8h07dpTtf/rrr7/Wuc5E5l0DGyMvEbO6ZNEaNfQ/xdhFW38xICLatGmTsM3NzU02ERAn1kT/SxafffbZWutw9+5doXzPnj0lxzV10XgQ9+7do1deeYVef/11+v33340+57t37xo8h1mzZkmOJe68rn/069ePvvzyS3JwcBDi9uKLLxqUU6lUkguT/m/l/r+HDh0Syixbtkz4Wa1WExHR/fv3KTo62iBRBUCDBw+WdHh/7bXXhOOK67V582aLPngmTpxoMPBp27ZttGHDhgc6zosvvkhE1ReqWbNm0fbt2w3KrFmzRnKRFX/AiB8zZswgouovIc7OzsL2Vq1a0VdffWUyAWiOyaKe+Lk2paqqKmEgn6n/tdy+c+fOGd33oI9//OMfJuspHvh0/fp1CggIkPx9dHR0nWNw48YN4Ti//vqr8HNkZKTJOv/73/+m9u3bEwAhSdA/tFotTZ06tdbnvXz58jrF6/Lly0RENGXKFGHb9OnTiYhkk1wikgyI8fHxqTUu+s8O8fEmTpxIRETbtm2j/Px8s+Jbsy76wTIAyNfXV3gP/+tf/xK2v/rqq0aPJ/5y88gjjwg/nzp1yqz6yLl//z6Vl5fTlStXhOPt3r1b2J+VlSVsHzBggORvS0pKKDU1lSorK4UvG6GhobRixQrhb959912aP3++JA5qtfqBv+DUZI3JolXdhv6jat26taQ/i5j+Np/4lqybmxt8fX0NyhrrH2eqI6+era0tXn31VdjZ2WH06NGSfXWdQLgmGxsbfPjhh3jvvffg7OxsdGUEuclMa/YvkuuDdPv2bYwdOxYVFRXCsoNyfXycnJwkt37feust7N+/X7ZvlDjOQUFB2LdvH7p16yYM9lAqlUhISMCbb75p8LedOnWS3PYR11k8kXhtE5nXpn379gZdBWoOlDGHviuCnZ0dPv74Y9lpNTp06ACFQoFXXnkFI0aMkHQ3ENPfvndxcZHcDgoMDMSYMWNa1DJYYqZubTW21q1bIyYmRnaf/ta/3PQexiZZrgu5ATdi4tuV4tvQeqYGXNXG0dERFy5cQHFxMbRaLb766isMHz7c4L0aFBSE+fPnC78/99xzwhymNQdwmTN/4+DBg2sdwCfnm2++EbqUiOci1N8ernle/a1hcZce/dKtpuivFeK+3Pqpk8LDw4VbrrX54YcfJNcx8XMWDx4S3142NUhR3NdSHHdT3aBqo1QqodFo4OnpiZCQEGg0Gkk3guDgYFy/fh3r1q1DSkqK5G/d3d0RGRkJe3t74Tns27dPsv71X//6V4PPx3bt2rXI6xsni1aiW7dusv0c9BcIcdLh6uoqmxjW3Jaamornnnuu1mX/9FavXo2bN29i9uzZku2WvFlNMSeJ1as5IEnuQ0jfn0ecbIrjpufs7CxJ0Ez116yZ4PXv3x85OTkGI+/E85zp+wnp+47NmTMH3t7ekuWvxCttWJosqlQqg/9RUVGR0fntjJF7TYlfkwsXLsSwYcMAVHd8T09PN1gi8fz584iLi5MMDBL/n+X+Hy1JSkoK+vbtazVzRBp7j+kHWIlHu+qVlJTIJmlyI2DlDBw4EFlZWdi0aRP69+9vsmz37t3RqVMnhISEwNbW1iBZtHTEp6+vr/D+GjNmDLZv347u3btLrhFDhw7F0qVLheuAQqEQBgF5eXlJ+gTfuHHDZH9roPr6UjNZEPe7k/tSqtVqJYMFxV9S9e8Z8WCgDh06YOfOnUL9N2zYgKysLKOrnYnVvFb4+fk98NJ8ANCjRw9h4If+Oej74oqXW/Xx8RHiYWpgqXhgn7j/eX2tn56RkYHLly9LEnGgOhmfMWOGwXYxucGT8+bNg42NjWyy2BJxsmhFag5UsLe3F94o4g9ZNzc32QtOzW2RkZHYtWvXA3W2tbOzM1i3tiFHq9b2DaygoADx8fGYMGGCZLvcEn9y0wrJPfeOHTuanSza2Njg8OHD2LNnj+xAEj3xVBsFBQV45ZVXEBkZCQBYs2YNioqKJN+8e/XqJfysVqtx+vRpyUoBYs8995yktTc+Ph7Lly9H7969oVAoEBERITtIRNxB25jAwEDhZ7lkUXyhXrJkiez/Sz/Q46OPPkKHDh0QExMjOZb4g8/YiPWWomvXrjh48KDRkZ2NTb+EnpeXl2S1Dn2SL06a9IMWSkpKcPToUYNjmdNqGhUVhYyMDAQHB0sGIxpjb2+P/Px8YUCLOFl0dXXFW2+9VesxHpRSqZS8//WtRmlpaQCqY6W/k+Hq6mqwnKM5yWLNa9FHH32EqKgo7Ny5U/KeGzt2LJYtW2YwMEb8nuncuTOA/81y4e/vj3Pnzgn/Q6VSienTpyM4ONjk+vZ6NZObRYsW1fnuUUREBJ544glMmzYNPj4+2LRpE7Zs2YLly5cLZby9vZGdnY3Tp0+jb9++Ro+lv15269ZNMt1ZfTVW2Nrampz1w5SAgACDbfrXas3Px5aaLFrNCi6s+rZlXl6e8PvDDz8stACIP2SdnZ3RoUMHTJ06FYcPHxamvajrG0HOww8/jEuXLkGlUpl1Aaqr48eP45NPPsG0adPQo0cPg1HKnTt3Fi6WYmq1GtHR0cjPzxfW4xQvf6cnThYXLFiAgwcPIi4uTvLBWduF0titVjHxB0D79u0RGhoqiVvNJKt169ZYu3YtioqK8Mgjj0ChUKBjx4549NFHDVbpef3111FQUCCsXT1mzBg4Oztj9uzZ+O233+Dn5wdPT0+D1VVqW8sbqP7g03eBkEuatVqtyWlVgOoPseeff95oC6n44tnSWxatTdeuXfHjjz/C29vbYBSn3pEjR3DhwgXs2LEDmzdvlrQKifn6+pqcsQGonjbrQYnfJ+I6Xrp0qcFGynt6egq3YfXJov5c+mme7O3toVarDaaHatu2rdCqPm7cOFRUVEj2Ozk5SZaA9PPzw7Bhw4TW9oMHDwpr0o8bN05YlUlMnJjrz9+7d28cPXoUWq3WotucNZMvS+5sBAQESNa912g0GD16tMEKJ8aWvRSbMGECPD090b17d9llK5uSo6MjnnnmGRw8eFCyDYBBd6ra5gNurrhl0YqsXr3a6AerOFl0cXGBQqFAfHy8pJ9FffUtBKoTgJCQkFo/HCz1xBNPYN26dejevTuuXbtm1nJyegkJCbXWT3wb64033kBmZiZ8fHwk3xT1t5ws0a1bN3z77bdGp8SQM3v2bKxcuVJy4Zfr6xQQECBpNdZ/uLVu3Vr40P/oo4/g6+uLf/zjH+jWrRsAw2RRbtJa8Tx5cv1gP/74Yzg4OOBvf/ubyedi6gNHPO1TY08ky6pv9epbiNPS0jB9+nRhOjKgupU7MjLS5G04fTljpkyZguvXr5s9RY0x4qmqGnJKJfF0PPpz6s9H/z/XovjLk741UZ/MjhgxAkOHDpVt9VKpVHjqqadw6NAhXL58GYWFhZJb+FFRUfD29saQIUOM9jEUz0Mrvv3cs2dP2ffpg6jPZLE+KZVKDBw4EK6urvVyTa5v27dvl/yuf30MGDBAst3U3LPNGbcsWhFvb28UFhYKrYniuaxUKhUGDhyIoqIiyQz+TzzxBBYvXgwPD4967VTbsWNH/OUvf2nUD/e6dAqvjbiztrijtY2NDd5++20kJibWOtm3ufQXDUsWeBfHwN7eHkFBQXB3d8fzzz+Pf/7zn8JtxZo6deok9PnJyspCbm6uZFBQ165dkZqaisWLF6OoqAgZGRkApLfza170gOoL3++//25RVwRxEmJqcm7W8EaNGiXbkgXU/v4ztX60QqEwuQqUuaZNm4aVK1fWeqvXUuIvMPpz1UyixMnili1bsHDhQrz22muSMnJf0PW3UI31z+vcuXOtXUQ0Gg0KCwuhUqnMWuXkQdR8ng01QbwlYmNjMWHCBMmXmqbm4uIiWU9a37I4bNgwfPvtt8jNzUVhYaEwuLKl4WTRyogTvpp9hPbu3QudTmdwW9jU8k9/BOHh4fjmm28kg0b02rZtizNnzsj28YyNjUVsbGxjVNFs4q4ExcXFwgewg4ODQd8pY2r2C/z8888xYcIEKBQKJCQkgIgwb948+Pj4IDg4GLNmzcLjjz9utO9mffRZ7dixI86cOYMRI0ZYfCzWMGrrT9q7d2/MnTsX//3vf5GUlCTZJx7JagmtVotLly7V610SOS+99BLWr1+PZ599VjiXqWSxXbt22LRpk8FxxH+jX95y7ty59VLHBxkA+CDEsVUoFA3yJd1S48ePR58+fSxuRa1vHh4eQrKo/0xRKpUYMGCA7JftloSTRStWs1OtQqFo0P6DzVVSUhI2btwoGZknpl8toDkICwsTluVydnauU6tCzQ99JycnyZcQhUIhWb/7448/rmNtzXfo0CEcO3bMagZ+MEO1JYsKhQKrVq1CZWVlgyWLgOkBZ/XFw8PDoJW7ZrJozjKD4sRr1qxZkhV7rJX49r6Li4vsVGXWoKGSZUt06NBB6G/6oEujNnfW+Sr5g9uwYQP27duH6dOnN3VVmgUXFxeD20PN1QsvvICioiK4u7vX+YtBzQ/9+hz4VFfu7u4YMmRIU1eDmWDuSHW5lub6TBabSs1k0ZzBWOK/aYwktz6IBxHp+2cy84jfI8aWjG2pOFm0QtOnT+dE8Q9KoVBIJgiui5rTCpnTQsKYqT6HtfXrvX//fj3XpvHVJVkUtyw2l2RRfLeiJST5jUk8UKmhu0pYG04WGWthxCOcgebzIcaallzL4uHDh3HixAmjXTz0jA2aaU5sbGxgb28vTEhuzvy0zbFlUcySwXh/RDExMcjMzPxD9r3mZJGxFubhhx9GYGAgcnJy4ODgYHWdxJl1EieLU6dOxWuvvYYuXbrIzjOqHxU6f/58BAcHy64E0xyp1WohWWypLYtiDbngQkvk6OiI9PT0pq5Gk+BkkbEWRqFQ4MCBA7hx44ZFfR/ZH4v4NnRkZKTJdZ2zsrJw8OBBhIeHW+0AiboQJ0/mLDPXXFsWN2/ejBkzZiA1NbWpq8KaiZbzLmeMCRwdHQ3W2WXMFBsbG0ybNg1XrlzBwIEDTZZt27atZAnKlqKkpET42ZxkUTyyuDkli1FRUZgwYQJ/kWRm42SRMcYYAODTTz9t6io0Ka1Wi19//RUAzEqkxBPON6dkETDv+TGmx8v9McYYYwDS09OhVqvx17/+1azy4tbH5pYsMvYguGWRMcYYQ/X612VlZQCAXbt21Vq+ObcsMvYguGWRMcYY+392dnZmlxVPeM/JImvJOFlkjDHG6kA8iKzmpN6MtSScLDLGGGN10KtXLwCAl5dXndZxZ6y54D6LjDHGWB04OzujtLSUJ7dmLR4ni4wxxlgdyS2TyFhLw+3mjDHGGGPMKE4WGWOMMcaYUZwsMsYYY4wxozhZZIwxxhhjRnGyyBhjjDHGjOLR0GYgIgBAeXl5E9ekcdy9exe3bt1CeXn5A61mwKpx/CzD8bMcx9AyHD/LcPwsY0789PmIPj9paJwsmqGiogIA4OPj08Q1YYwxxhirVlFRAWdn5wY/j4IaKy1txnQ6Ha5cuQKNRgOFQtHU1Wlw5eXl8PHxwcWLF+Hk5NTU1Wl2OH6W4fhZjmNoGY6fZTh+ljEnfkSEioqKRls9iFsWzaBUKuHt7d3U1Wh0Tk5O/Ea3AMfPMhw/y3EMLcPxswzHzzK1xa8xWhT1eIALY4wxxhgzipNFxhhjjDFmFCeLzIBKpcKiRYugUqmauirNEsfPMhw/y3EMLcPxswzHzzLWGD8e4MIYY4wxxozilkXGGGOMMWYUJ4uMMcYYY8woThYZY4wxxphRnCwyxhhjjDGjOFlshvbv348RI0bAy8sLCoUC27Ztk+wnIixevBheXl5Qq9Xo378/Tp48KSlz9epVREVFwcPDA46OjujevTu2bNki7M/MzIRCoZB9HD16VHKspKQkBAQEwMHBAR4eHpg9e7Zkf15eHkJCQqBWq/Hwww8jNja20dazlGMt8UtKSjJa5tq1a8KxOH7GX39Hjx7FwIED4eLiAldXVwwZMgS5ubmSc3H8jMfv22+/xdNPPw2NRgNPT0/MmzcP9+7dk5zL2uIHNE4MAeDMmTMIDw+Hu7s7nJyc0LdvX+zbt09SpqioCCNGjICjoyPc3d0xZ84cVFVVScpYWwytKX6vvPIKevToAZVKhW7dusnWl+MnH7/jx49j/Pjx8PHxgVqtRufOnbFmzRqD+tZL/Ig1O7t27aIFCxbQ1q1bCQClpaVJ9q9YsYI0Gg1t3bqV8vLyaOzYseTp6Unl5eVCmUGDBtFTTz1FR44cofPnz9M777xDSqWSjh07RkRElZWVVFxcLHlMmzaN/Pz8SKfTCcd5//33ycvLi/75z3/SuXPnKD8/n9LT04X9ZWVl1LZtWxo3bhzl5eXR1q1bSaPR0KpVqxo2SCZYS/xu3bplUCYsLIxCQkKE83D8jMevvLycXF1dacqUKfTTTz9Rfn4+jR49mrRaLVVVVRERx89U/I4fP0729va0ZMkSOnv2LGVmZlKnTp1o7ty5wnmsMX5EjRNDIiJ/f38aOnQoHT9+nM6cOUMzZ86kVq1aUXFxMRER3bt3jx5//HEKDQ2lY8eO0d69e8nLy4tmz54tHMMaY2gt8SMievnll+njjz+mqKgoevLJJw3qyvEzHr+EhAR6+eWXKTMzk86fP0+fffYZqdVqWrt2rXCM+oofJ4vNXM0Xqk6nIw8PD1qxYoWw7c6dO+Ts7ExxcXHCNkdHR9q8ebPkWG5ubhQfHy97nqqqKtJqtRQbGytsKy0tJbVaTRkZGUbrt27dOnJ2dqY7d+4I25YvX05eXl6SpLOpNGX8arp27RrZ2dlJjsvxqyYXv6NHjxIAKioqEradOHGCANC5c+eIiOOnJxe/v/3tb9SzZ09JubS0NHJwcBA+1Kw9fkQNF8OSkhICQPv37xf2l5eXEwDhmrdr1y5SKpV0+fJloUxycjKpVCoqKysjIuuPYVPGT2zRokWyySLHz7z46c2cOZNCQ0OF3+srfnwbuoUpLCzE1atXMWTIEGGbSqVCSEgIDh8+LGx75plnkJKSgtLSUuh0Onz55ZeorKxE//79ZY+bnp6O3377DVOmTBG27d27FzqdDpcvX0bnzp3h7e2N559/HhcvXhTKZGdnIyQkRDK5aFhYGK5cuYJffvml3p53fWnM+NW0efNmtGrVCmPGjBG2cfyqycXvscceg7u7OxISElBVVYXbt28jISEBXbt2Rbt27QBw/PTk4ldZWQkHBwdJObVajTt37uDHH38E0PziB9RfDNu0aYPOnTtj8+bNuHnzJu7du4f169ejbdu26NGjB4Dq+Dz++OPw8vISjhsWFobKyspmG8PGjJ85OH4PFr+ysjK4ubkJv9dX/DhZbGGuXr0KAGjbtq1ke9u2bYV9AJCSkoJ79+6hTZs2UKlUiImJQVpaGh555BHZ4yYkJCAsLAw+Pj7Ctp9//hk6nQ7Lli3Dhx9+iC1btqC0tBSDBw8W+uxcvXpVti7iulqTxoxfTRs3bsSECROgVqsl9eH4ycdPo9EgMzMTn3/+OdRqNVq3bo3du3dj165dsLW1FerD8ZOPX1hYGA4fPozk5GTcv38fly9fxrvvvgsAKC4uFurTnOIH1F8MFQoF9u7di5ycHGg0Gjg4OOCDDz7Av//9b7i4uAjnqnkeV1dX2NvbC+dqbjFszPiZWx+On3nxy87ORmpqKmJiYiT1qY/4cbLYQikUCsnvRCTZ9tZbb+H69evIyMjADz/8gNdeew2RkZHIy8szONalS5ewe/duTJ06VbJdp9Ph7t27+OijjxAWFoagoCAkJyfj7Nmzkk64cnWR225NGiN+YtnZ2SgoKJAtw/GTj9/t27cRHR2Nvn374j//+Q8OHTqErl27YujQobh9+7bJushttyaNEb8hQ4bgvffew5///GeoVCp07NgRw4YNAwDY2NiYrIvcdmtjaQyJCDNnzoRWq8WBAwfw/fffIzw8HMOHDxeSabnzyJ2rOcawseJX17rIbbcmjR2/kydPIjw8HAsXLsTgwYNrrYvcdpPMvmHNrBJq9Jc4f/48AZB0kiUiGjlyJE2aNImIiM6dO0cAKD8/X1Jm4MCBFBMTY3CO2NhYeuihh4RBA3obN24kAHTx4kXJdq1WSxs2bCAioqioKBo5cqRk/7FjxwgA/fzzzw/2ZBtAU8ZPLDo6mrp162awneNnPH7x8fGk1Wrp/v37wrbKykpq1aoVJScnExHHj6j2159Op6PLly/TrVu3qKCggADQ999/T0TWHz+ihothRkYGKZVKoe+hnr+/Py1fvpyIiN5++20KCAiQ7C8tLSUA9N133xGR9cewKeMnZqzPIsev9vidPHmStFotzZ8/36B+9RU/bllsYdq3bw8PDw/s3btX2FZVVYWsrCw8/fTTAIBbt24BAJRK6b/fxsYGOp1Oso2IkJiYiEmTJsHOzk6yr2/fvgCA06dPC9tKS0vx22+/CX3G+vTpg/3790umktizZw+8vLzg5+dn4bOtf40ZP70bN24gNTVVtlWR42c8frdu3YJSqZR8O9b/rj8Ox6/2159CoRCm+EhOToaPjw+6d+8OoPnFD6i/GBoro1QqJa+v/Px8SUvPnj17oFKphH5lzS2GjRk/c3D8TMfv5MmTCA0NxeTJk7F06VKD+tRb/MxOK5nVqKiooJycHMrJySEAtHr1asrJyaELFy4QUfWwfWdnZ/r6668pLy+Pxo8fLxm2X1VVRf7+/tSvXz86cuQInTt3jlatWkUKhYJ27twpOVdGRgYBoIKCAtm6hIeHU9euXenQoUOUl5dHw4cPpy5dugitGL///ju1bduWxo8fT3l5efT111+Tk5NTk057YE3xI6puIXNwcKDS0lKDfRw/4/E7deoUqVQqmjFjBhUUFFB+fj5NnDiRnJ2d6cqVK0TE8avt9bdy5Uo6ceIE5efnU2xsLNnZ2UlaSawxfkSNE8OSkhJq06YNRUREUG5uLp0+fZpef/11srOzo9zcXCL639Q5AwcOpGPHjlFGRgZ5e3tLps6xxhhaS/yIiM6ePUs5OTkUExNDHTt2FOpVWVlJRBw/U/HLz8+nhx56iF544QXJNFnXrl0T6lpf8eNksRnat28fATB4TJ48mYiqbystWrSIPDw8SKVSUXBwMOXl5UmOcebMGYqIiCCtVkutWrWigIAAg2H8RETjx4+np59+2mhdysrKKDo6mlxcXMjNzY3+9Kc/SaYyIaqezqRfv36kUqnIw8ODFi9e3KRTHlhT/IiI+vTpQxMmTDC6n+NnPH579uyhvn37krOzM7m6utKAAQMoOztbUobjZzx+oaGh5OzsTA4ODtS7d2/atWuXQRlrix9R48Xw6NGjNGTIEHJzcyONRkNBQUEGMbpw4QINGzaM1Go1ubm50ezZsyXTlBBZXwytKX4hISGydSksLBTKcPzk47do0SLZerRr105ynPqIn4KoiafiZ4wxxhhjVov7LDLGGGOMMaM4WWSMMcYYY0ZxssgYY4wxxoziZJExxhhjjBnFySJjjDHGGDOKk0XGGGOMMWYUJ4uMMcYYY8woThYZY4wxxphRnCwyxhgTnD59Gh4eHqioqGjSerz++uuYM2dOk9aBMVaNk0XGWJ1NmTIFo0aNMro/JycHw4cPh1arhYODA/z8/DB27Fj89ttvAIBffvkFCoVCeDg7OyMoKAjbt2+XHCcpKQkuLi6SbadOnYK3tzciIiJQWVkpe34/Pz/J8RUKBby9vS16zi3dggULMGvWLGg0GgBAZmYmFAoFXF1dcefOHUnZ77//Xoirnr7877//bnBsPz8/fPjhh2bV480330RiYiIKCwvr/FwYY/WDk0XGWIO4du0aBg0aBHd3d+zevRunTp3Cxo0b4enpiVu3bknKZmRkoLi4GEeOHEGvXr0wevRo5OfnGz320aNH0a9fP4SFheGrr76CSqUyWjY2NhbFxcXCIycnR7bc3bt36/ZEW5BLly4hPT0dL774osE+jUaDtLQ0ybaNGzfC19e3Qeqi1WoxZMgQxMXFNcjxGWPm42SRMdYgDh8+jPLycsTHxyMwMBDt27fHgAED8OGHHxokGG3atIGHhwc6deqEpUuX4u7du9i3b5/scb/77jsMGDAAL774IhISEmBjY2OyHhqNBh4eHsLjoYceAgAoFArExcUhPDwcjo6OePfddwEA27dvR48ePeDg4IAOHTpgyZIluHfvnnC8s2fPIjg4GA4ODujSpQv27t0LhUKBbdu2AZBvWcvNzYVCocAvv/wiiU9wcDDUajV8fHwwZ84c3Lx5U9jv5+eHZcuWITo6GhqNBr6+vtiwYYPkuV26dAnjxo2Dm5sbHB0d0bNnTxw5cgS//PILlEolfvjhB0n5tWvXol27diAi2VilpqbiySeflG19nTx5MjZu3Cj8fvv2bXz55ZeYPHmyiegbl5SUZNDqq1AosHjxYqHMyJEjkZycXKfjM8bqDyeLjLEG4eHhgXv37iEtLc1oclLT3bt38emnnwIA7OzsDPanpaVh2LBhWLBgAd577z2L67ho0SKEh4cjLy8P0dHR2L17NyZOnIg5c+agoKAA69evR1JSEpYuXQoA0Ol0iIiIgI2NDf7zn/8gLi4O8+bNe+Dz5uXlISwsDBEREThx4gRSUlJw8OBBzJ49W1Lu/fffR8+ePZGTk4OZM2dixowZ+OmnnwAAN27cQEhICK5cuYL09HQcP34cb775JnQ6Hfz8/DBo0CAkJiZKjpeYmIgpU6ZIbhuL7d+/Hz179pTdFxUVhQMHDqCoqAgAsHXrVvj5+aF79+4P/PwBYOzYsZIW3+TkZNja2qJv375CmV69euHixYu4cOFCnc7BGKsnxBhjdTR58mQKDw83un/+/Plka2tLbm5u9Oyzz9LKlSvp6tWrwv7CwkICQGq1mhwdHUmpVBIA8vPzo//+979CucTERLKxsSEbGxt6++23za5fu3btyN7enhwdHYXHmjVriIgIAP3lL3+RlO/Xrx8tW7ZMsu2zzz4jT09PIiLavXs32djY0MWLF4X9//rXvwgApaWlERHRvn37CABdv35dKJOTk0MAqLCwkIiIoqKi6KWXXpKc58CBA6RUKun27dtC3SdOnCjs1+l0pNVq6ZNPPiEiovXr15NGo5HESSwlJYVcXV3pzp07RESUm5tLCoVCqIOcJ598kmJjYyXbxM9n1KhRtGTJEiIiCg0NpTVr1lBaWhqJP0r05cUx1z8UCgV98MEHBuc9d+4ctWnThlauXCnZXlZWRgAoMzPTaJ0ZYw2PWxYZYw1m6dKluHr1KuLi4tClSxfExcWhU6dOyMvLk5RLSUlBTk4O0tPT4e/vj/j4eLi5uUnKqNVqDB48GJ9++ilOnTpldh3eeOMN5ObmCo9JkyYJ+2q2ov3444+IjY1F69athcf06dNRXFyMW7du4dSpU/D19ZXcpu3Tp8+DhEQ4T1JSkuQ8YWFh0Ol0kgEdAQEBws8KhQIeHh64du0agOpb24GBgQZx0hs1ahRsbW2FfoYbN25EaGgo/Pz8jNbr9u3bcHBwMLo/OjoaSUlJ+Pnnn5GdnY0XXnjBaNkDBw5I4p6bmwsvLy+DcmVlZRg+fDiee+45vPHGG5J9arUaAAz6uDLGGpdtU1eAMdaytWnTBpGRkYiMjMTy5csRGBiIVatWYdOmTUIZHx8fPProo3j00UfRunVrjB49GgUFBdBqtUIZGxsbbNu2DaNHj0ZoaCi+++47dOnSpdbzu7u7w9/fX3afo6Oj5HedToclS5YgIiLCoKyDg4Ps7fSat3SVyurv4OKyNQfP6HQ6xMTEyE4NI+7PWfNWvEKhgE6nA/C/RMoYe3t7REVFITExEREREfjiiy9qHYns7u6O69evG90/dOhQxMTEYOrUqRgxYgTatGljtGz79u0NRrDb2ko/cu7fv4+xY8fCyclJ6H4gVlpaCgBCP1PGWNPglkXGWKOxt7fHI488IhnIUVNISAgef/xxoZ+gmEqlwtdff41evXohNDTU5IjpuujevTtOnz4Nf39/g4dSqUSXLl1QVFSEK1euCH+TnZ0tOYY+sSkuLha25ebmGpzn5MmTsuext7c3q64BAQHIzc0VEio506ZNQ0ZGBtatW4e7d+/KJsFigYGBKCgoMLrfxsYGUVFRyMzMRHR0tFn1NOXVV19FXl4e0tLSZFs08/PzYWdnh65du1p8LsZY3XGyyBizSFlZmcHtxqKiIuzYsQMTJ07Ejh07cObMGZw+fRqrVq3Crl27EB4ebvKYc+fOxfr163H58mWDffb29ti6dSuefvppDBgwwOCWtiUWLlyIzZs3Y/HixTh58iROnTqFlJQUvPXWWwCAQYMG4bHHHsOkSZNw/PhxHDhwAAsWLJAcw9/fHz4+Pli8eDHOnDmDnTt34v3335eUmTdvHrKzszFr1izk5ubi7NmzSE9Px8svv2x2XcePHw8PDw+MGjUKhw4dws8//4ytW7dKktfOnTsjKCgI8+bNw/jx42ttjQwLC0N2djbu379vtMw777yDkpIShIWFmV1XOYmJiVi3bh3i4uKgVCpx9epVXL16FTdu3BDKHDhwAP369au13oyxhsXJImPMIpmZmQgMDJQ8Fi5ciC5duqBVq1aYO3cuunXrhqCgIKSmpiI+Ph5RUVEmjzl8+HD4+fnJti4C1bdnU1NTERwcjAEDBuDEiRP18lzCwsKwY8cO7N27F0899RSCgoKwevVqtGvXDkD1Lea0tDRUVlaiV69emDZtmkEd7ezskJycjJ9++glPPvkk/v73vwvT8ugFBAQgKysLZ8+eRb9+/RAYGIi3334bnp6eZtfV3t4ee/bsgVarxdChQ/HEE09gxYoVBlMJTZ06FVVVVWa1BA4dOhR2dnbIyMgweV53d3ejI6rNlZWVhfv372PkyJHw9PQUHqtWrRLKJCcnY/r06RadhzFmOQXJdcJhjDFmNoVCgbS0NJOr2TSVpUuX4ssvvzS7BXbdunX45ptvsHv37gaumWk7d+7EG2+8gRMnThj0dWSMNS5+BzLGWAt048YNnDp1CmvXrsU777xj9t+99NJLuH79OioqKoQl/5rCzZs3kZiYyIkiY1aAWxYZY8xC1tiyOGXKFCQnJ2PUqFH44osval3phjHGjOFkkTHGGGOMGcUDXBhjjDHGmFGcLDLGGGOMMaM4WWSMMcYYY0ZxssgYY4wxxoziZJExxhhjjBnFySJjjDHGGDOKk0XGGGOMMWYUJ4uMMcYYY8yo/wNyWkXEy2bQAQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from bayes_spec import SpecData\n",
    "\n",
    "data_12CN = np.genfromtxt(\"data_CN.tsv\")\n",
    "data_13CN = np.genfromtxt(\"data_13CN.tsv\")\n",
    "\n",
    "# estimate noise\n",
    "noise_12CN = 1.4826 * np.median(np.abs(data_12CN[:, 0] - np.median(data_12CN[:, 0])))\n",
    "noise_13CN = 1.4826 * np.median(np.abs(data_13CN[:, 0] - np.median(data_13CN[:, 0])))\n",
    "\n",
    "obs_12CN = SpecData(\n",
    "    1000.0 * data_12CN[:, 0],\n",
    "    data_12CN[:, 1],\n",
    "    noise_12CN,\n",
    "    xlabel=r\"LSRK Frequency (MHz)\",\n",
    "    ylabel=r\"$T_{B,\\,\\rm CN}$ (K)\",\n",
    ")\n",
    "obs_13CN = SpecData(\n",
    "    1000.0 * data_13CN[:, 0],\n",
    "    data_13CN[:, 1],\n",
    "    noise_13CN,\n",
    "    xlabel=r\"LSRK Frequency (MHz)\",\n",
    "    ylabel=r\"$T_{B,\\,^{13}\\rm CN}$ (K)\",\n",
    ")\n",
    "data = {\"12CN\": obs_12CN, \"13CN\": obs_13CN}\n",
    "\n",
    "for label, dataset in data.items():\n",
    "    print(label, len(dataset.spectral))\n",
    "    # HACK: normalize data by noise\n",
    "    dataset._brightness_offset = np.median(dataset.brightness)\n",
    "    dataset._brightness_scale = dataset.noise\n",
    "\n",
    "# subset of 12CN data\n",
    "data_12CN = {\n",
    "    label: data[label]\n",
    "    for label in data.keys() if \"12CN\" in label\n",
    "}\n",
    "\n",
    "# Plot the data\n",
    "fig, axes = plt.subplots(2, layout=\"constrained\")\n",
    "axes[0].plot(data[\"12CN\"].spectral, data[\"12CN\"].brightness, 'k-')\n",
    "axes[1].plot(data[\"13CN\"].spectral, data[\"13CN\"].brightness, 'k-')\n",
    "axes[0].set_xlabel(data[\"12CN\"].xlabel)\n",
    "axes[1].set_xlabel(data[\"13CN\"].xlabel)\n",
    "axes[0].set_ylabel(data[\"12CN\"].ylabel)\n",
    "_ = axes[1].set_ylabel(data[\"13CN\"].ylabel)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7ca3fda5-37a0-43a9-89f8-8e99eae406b4",
   "metadata": {},
   "source": [
    "## Inspecting the CN data\n",
    "\n",
    "Can we constrain the excitation temperature and optical depth? How about the hyperfine anomalies? It seems like there are two cloud components, so let's explore the data with that assumption for now. We assume make the weak LTE assumption such that the kinetic temperature is equal to the mean cloud excitation temperature. We are otherwise unable to constrain the kinetic temperature because non-thermal broadening is important and we have poor spectral resolution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "37d19591-d096-40d4-9c84-a339dc73efd0",
   "metadata": {},
   "outputs": [],
   "source": [
    "from bayes_cn_hfs.cn_model import CNModel\n",
    "from bayes_cn_hfs import supplement_mol_data\n",
    "\n",
    "mol_data_12CN, mol_weight_12CN = supplement_mol_data(\"CN\")\n",
    "\n",
    "n_clouds = 6\n",
    "baseline_degree = 0\n",
    "model = CNModel(\n",
    "    data_12CN,\n",
    "    molecule=\"CN\", # molecule name\n",
    "    mol_data=mol_data_12CN, # molecular data\n",
    "    bg_temp = 2.7, # assumed background temperature (K)\n",
    "    Beff=1.0, # Main beam efficiency\n",
    "    Feff=1.0, # Forward efficiency\n",
    "    n_clouds=n_clouds,\n",
    "    baseline_degree=baseline_degree,\n",
    "    seed=1234,\n",
    "    verbose=True\n",
    ")\n",
    "model.add_priors(\n",
    "    prior_log10_N = [14.0, 0.25], # mean and width of log10 total column density prior (cm-2)\n",
    "    prior_log10_Tkin = None, # ignored because kinetic temperature is fixed\n",
    "    prior_velocity = [-3.0, 5.0], # mean and width of velocity prior (km/s)\n",
    "    prior_fwhm_nonthermal = 1.0, # width of non-thermal broadening prior (km/s)\n",
    "    prior_fwhm_L = 1.0, # width of latent Lorentzian FWHM prior (km/s) TIP: set to typical feature separation\n",
    "    prior_rms = None, # do not infer spectral rms\n",
    "    prior_baseline_coeffs = None, # use default baseline priors\n",
    "    assume_LTE = False, # do not assume LTE\n",
    "    prior_log10_Tex = [0.75, 0.1], # mean and width of excitation temperature prior (K)\n",
    "    assume_CTEX = False, # do not assume CTEX\n",
    "    prior_LTE_precision = 1000.0, # width of LTE precision prior\n",
    "    fix_log10_Tkin = 1.5, # fix the kinetic temperature (K)\n",
    "    ordered = False, # do not assume optically-thin\n",
    ")\n",
    "model.add_likelihood()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "3d09319d-4e43-413d-80fe-541c44babec9",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Sampling: [12CN, LTE_precision, baseline_12CN_norm, fwhm_L_norm, fwhm_nonthermal_norm, log10_N_norm, log10_Tex_ul_norm, velocity_norm, weights]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from bayes_spec.plots import plot_predictive\n",
    "\n",
    "# prior predictive check\n",
    "prior = model.sample_prior_predictive(\n",
    "    samples=100,  # prior predictive samples\n",
    ")\n",
    "_ = plot_predictive(model.data, prior.prior_predictive)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "bb8339a7-ed47-4bd9-a02d-d8400b6d3e75",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "68164ae90cab4c48aed1308b30a972e4",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Finished [100%]: Average Loss = 533.67\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "bb759f2debb04da9a58814bbec63cf6b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Runtime: 2.68 minutes\n"
     ]
    }
   ],
   "source": [
    "start = time.time()\n",
    "model.fit(\n",
    "    n = 20_000, # maximum number of VI iterations\n",
    "    draws = 1_000, # number of posterior samples\n",
    "    rel_tolerance = 0.01, # VI relative convergence threshold\n",
    "    abs_tolerance = 0.05, # VI absolute convergence threshold\n",
    "    learning_rate = 0.02, # VI learning rate\n",
    ")\n",
    "end = time.time()\n",
    "print(f\"Runtime: {(end-start)/60.0:.2f} minutes\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "53c9d772-f8b4-4675-b6ed-611de43f9ba2",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "arviz - WARNING - Shape validation failed: input_shape: (1, 1000), minimum_shape: (chains=2, draws=4)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>sd</th>\n",
       "      <th>hdi_3%</th>\n",
       "      <th>hdi_97%</th>\n",
       "      <th>mcse_mean</th>\n",
       "      <th>mcse_sd</th>\n",
       "      <th>ess_bulk</th>\n",
       "      <th>ess_tail</th>\n",
       "      <th>r_hat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>velocity[0]</th>\n",
       "      <td>-2.841</td>\n",
       "      <td>0.020</td>\n",
       "      <td>-2.876</td>\n",
       "      <td>-2.804</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.000</td>\n",
       "      <td>872.0</td>\n",
       "      <td>907.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>velocity[1]</th>\n",
       "      <td>-6.163</td>\n",
       "      <td>0.003</td>\n",
       "      <td>-6.169</td>\n",
       "      <td>-6.156</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1221.0</td>\n",
       "      <td>944.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>velocity[2]</th>\n",
       "      <td>-7.379</td>\n",
       "      <td>0.006</td>\n",
       "      <td>-7.391</td>\n",
       "      <td>-7.370</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>695.0</td>\n",
       "      <td>861.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>velocity[3]</th>\n",
       "      <td>-4.291</td>\n",
       "      <td>0.005</td>\n",
       "      <td>-4.299</td>\n",
       "      <td>-4.282</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1099.0</td>\n",
       "      <td>981.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>velocity[4]</th>\n",
       "      <td>-2.317</td>\n",
       "      <td>0.004</td>\n",
       "      <td>-2.325</td>\n",
       "      <td>-2.311</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>999.0</td>\n",
       "      <td>832.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>velocity[5]</th>\n",
       "      <td>-2.790</td>\n",
       "      <td>0.005</td>\n",
       "      <td>-2.799</td>\n",
       "      <td>-2.781</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1007.0</td>\n",
       "      <td>919.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fwhm_thermal[0]</th>\n",
       "      <td>0.236</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.236</td>\n",
       "      <td>0.236</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fwhm_thermal[1]</th>\n",
       "      <td>0.236</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.236</td>\n",
       "      <td>0.236</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fwhm_thermal[2]</th>\n",
       "      <td>0.236</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.236</td>\n",
       "      <td>0.236</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fwhm_thermal[3]</th>\n",
       "      <td>0.236</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.236</td>\n",
       "      <td>0.236</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fwhm_thermal[4]</th>\n",
       "      <td>0.236</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.236</td>\n",
       "      <td>0.236</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fwhm_thermal[5]</th>\n",
       "      <td>0.236</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.236</td>\n",
       "      <td>0.236</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fwhm_nonthermal[0]</th>\n",
       "      <td>8.264</td>\n",
       "      <td>0.038</td>\n",
       "      <td>8.183</td>\n",
       "      <td>8.326</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>932.0</td>\n",
       "      <td>967.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fwhm_nonthermal[1]</th>\n",
       "      <td>1.540</td>\n",
       "      <td>0.008</td>\n",
       "      <td>1.527</td>\n",
       "      <td>1.556</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>982.0</td>\n",
       "      <td>870.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fwhm_nonthermal[2]</th>\n",
       "      <td>2.482</td>\n",
       "      <td>0.010</td>\n",
       "      <td>2.461</td>\n",
       "      <td>2.500</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>907.0</td>\n",
       "      <td>983.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fwhm_nonthermal[3]</th>\n",
       "      <td>1.618</td>\n",
       "      <td>0.007</td>\n",
       "      <td>1.606</td>\n",
       "      <td>1.631</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1048.0</td>\n",
       "      <td>942.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fwhm_nonthermal[4]</th>\n",
       "      <td>2.077</td>\n",
       "      <td>0.009</td>\n",
       "      <td>2.062</td>\n",
       "      <td>2.094</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1063.0</td>\n",
       "      <td>973.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fwhm_nonthermal[5]</th>\n",
       "      <td>7.238</td>\n",
       "      <td>0.007</td>\n",
       "      <td>7.227</td>\n",
       "      <td>7.251</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>956.0</td>\n",
       "      <td>983.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fwhm[0]</th>\n",
       "      <td>8.267</td>\n",
       "      <td>0.038</td>\n",
       "      <td>8.187</td>\n",
       "      <td>8.330</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>932.0</td>\n",
       "      <td>967.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fwhm[1]</th>\n",
       "      <td>1.558</td>\n",
       "      <td>0.008</td>\n",
       "      <td>1.545</td>\n",
       "      <td>1.574</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>982.0</td>\n",
       "      <td>870.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fwhm[2]</th>\n",
       "      <td>2.493</td>\n",
       "      <td>0.010</td>\n",
       "      <td>2.473</td>\n",
       "      <td>2.511</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>907.0</td>\n",
       "      <td>983.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fwhm[3]</th>\n",
       "      <td>1.636</td>\n",
       "      <td>0.007</td>\n",
       "      <td>1.623</td>\n",
       "      <td>1.648</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1048.0</td>\n",
       "      <td>942.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fwhm[4]</th>\n",
       "      <td>2.091</td>\n",
       "      <td>0.009</td>\n",
       "      <td>2.075</td>\n",
       "      <td>2.107</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1063.0</td>\n",
       "      <td>973.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fwhm[5]</th>\n",
       "      <td>7.242</td>\n",
       "      <td>0.007</td>\n",
       "      <td>7.230</td>\n",
       "      <td>7.254</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>956.0</td>\n",
       "      <td>983.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>log10_N[0]</th>\n",
       "      <td>14.005</td>\n",
       "      <td>0.002</td>\n",
       "      <td>14.003</td>\n",
       "      <td>14.009</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>977.0</td>\n",
       "      <td>872.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>log10_N[1]</th>\n",
       "      <td>13.503</td>\n",
       "      <td>0.002</td>\n",
       "      <td>13.499</td>\n",
       "      <td>13.507</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1001.0</td>\n",
       "      <td>1015.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>log10_N[2]</th>\n",
       "      <td>13.659</td>\n",
       "      <td>0.001</td>\n",
       "      <td>13.657</td>\n",
       "      <td>13.662</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>939.0</td>\n",
       "      <td>990.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>log10_N[3]</th>\n",
       "      <td>13.385</td>\n",
       "      <td>0.002</td>\n",
       "      <td>13.381</td>\n",
       "      <td>13.389</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>971.0</td>\n",
       "      <td>826.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>log10_N[4]</th>\n",
       "      <td>13.751</td>\n",
       "      <td>0.001</td>\n",
       "      <td>13.748</td>\n",
       "      <td>13.753</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1035.0</td>\n",
       "      <td>942.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>log10_N[5]</th>\n",
       "      <td>13.897</td>\n",
       "      <td>0.000</td>\n",
       "      <td>13.897</td>\n",
       "      <td>13.898</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1101.0</td>\n",
       "      <td>942.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>log10_Tex_ul[0]</th>\n",
       "      <td>0.814</td>\n",
       "      <td>0.087</td>\n",
       "      <td>0.654</td>\n",
       "      <td>0.986</td>\n",
       "      <td>0.003</td>\n",
       "      <td>0.002</td>\n",
       "      <td>947.0</td>\n",
       "      <td>906.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>log10_Tex_ul[1]</th>\n",
       "      <td>0.515</td>\n",
       "      <td>0.054</td>\n",
       "      <td>0.418</td>\n",
       "      <td>0.618</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.001</td>\n",
       "      <td>1052.0</td>\n",
       "      <td>992.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>log10_Tex_ul[2]</th>\n",
       "      <td>0.503</td>\n",
       "      <td>0.062</td>\n",
       "      <td>0.390</td>\n",
       "      <td>0.621</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.001</td>\n",
       "      <td>948.0</td>\n",
       "      <td>896.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>log10_Tex_ul[3]</th>\n",
       "      <td>0.865</td>\n",
       "      <td>0.083</td>\n",
       "      <td>0.713</td>\n",
       "      <td>1.016</td>\n",
       "      <td>0.003</td>\n",
       "      <td>0.002</td>\n",
       "      <td>1024.0</td>\n",
       "      <td>983.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>log10_Tex_ul[4]</th>\n",
       "      <td>0.605</td>\n",
       "      <td>0.070</td>\n",
       "      <td>0.478</td>\n",
       "      <td>0.735</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>996.0</td>\n",
       "      <td>940.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>log10_Tex_ul[5]</th>\n",
       "      <td>0.891</td>\n",
       "      <td>0.093</td>\n",
       "      <td>0.708</td>\n",
       "      <td>1.065</td>\n",
       "      <td>0.003</td>\n",
       "      <td>0.002</td>\n",
       "      <td>934.0</td>\n",
       "      <td>975.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tau_total[0]</th>\n",
       "      <td>2.209</td>\n",
       "      <td>0.016</td>\n",
       "      <td>2.178</td>\n",
       "      <td>2.237</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>986.0</td>\n",
       "      <td>941.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tau_total[1]</th>\n",
       "      <td>1.727</td>\n",
       "      <td>0.017</td>\n",
       "      <td>1.696</td>\n",
       "      <td>1.756</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1053.0</td>\n",
       "      <td>912.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tau_total[2]</th>\n",
       "      <td>3.334</td>\n",
       "      <td>0.013</td>\n",
       "      <td>3.308</td>\n",
       "      <td>3.357</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1059.0</td>\n",
       "      <td>867.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tau_total[3]</th>\n",
       "      <td>0.367</td>\n",
       "      <td>0.005</td>\n",
       "      <td>0.358</td>\n",
       "      <td>0.377</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>961.0</td>\n",
       "      <td>1005.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tau_total[4]</th>\n",
       "      <td>2.665</td>\n",
       "      <td>0.044</td>\n",
       "      <td>2.580</td>\n",
       "      <td>2.743</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>1064.0</td>\n",
       "      <td>1071.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tau_total[5]</th>\n",
       "      <td>-2.528</td>\n",
       "      <td>0.008</td>\n",
       "      <td>-2.543</td>\n",
       "      <td>-2.515</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1012.0</td>\n",
       "      <td>941.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fwhm_L_norm</th>\n",
       "      <td>0.025</td>\n",
       "      <td>0.008</td>\n",
       "      <td>0.012</td>\n",
       "      <td>0.040</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>955.0</td>\n",
       "      <td>779.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>baseline_12CN_norm[0]</th>\n",
       "      <td>-1.471</td>\n",
       "      <td>0.034</td>\n",
       "      <td>-1.531</td>\n",
       "      <td>-1.403</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>1009.0</td>\n",
       "      <td>982.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         mean     sd  hdi_3%  hdi_97%  mcse_mean  mcse_sd  \\\n",
       "velocity[0]            -2.841  0.020  -2.876   -2.804      0.001    0.000   \n",
       "velocity[1]            -6.163  0.003  -6.169   -6.156      0.000    0.000   \n",
       "velocity[2]            -7.379  0.006  -7.391   -7.370      0.000    0.000   \n",
       "velocity[3]            -4.291  0.005  -4.299   -4.282      0.000    0.000   \n",
       "velocity[4]            -2.317  0.004  -2.325   -2.311      0.000    0.000   \n",
       "velocity[5]            -2.790  0.005  -2.799   -2.781      0.000    0.000   \n",
       "fwhm_thermal[0]         0.236  0.000   0.236    0.236      0.000    0.000   \n",
       "fwhm_thermal[1]         0.236  0.000   0.236    0.236      0.000    0.000   \n",
       "fwhm_thermal[2]         0.236  0.000   0.236    0.236      0.000    0.000   \n",
       "fwhm_thermal[3]         0.236  0.000   0.236    0.236      0.000    0.000   \n",
       "fwhm_thermal[4]         0.236  0.000   0.236    0.236      0.000    0.000   \n",
       "fwhm_thermal[5]         0.236  0.000   0.236    0.236      0.000    0.000   \n",
       "fwhm_nonthermal[0]      8.264  0.038   8.183    8.326      0.001    0.001   \n",
       "fwhm_nonthermal[1]      1.540  0.008   1.527    1.556      0.000    0.000   \n",
       "fwhm_nonthermal[2]      2.482  0.010   2.461    2.500      0.000    0.000   \n",
       "fwhm_nonthermal[3]      1.618  0.007   1.606    1.631      0.000    0.000   \n",
       "fwhm_nonthermal[4]      2.077  0.009   2.062    2.094      0.000    0.000   \n",
       "fwhm_nonthermal[5]      7.238  0.007   7.227    7.251      0.000    0.000   \n",
       "fwhm[0]                 8.267  0.038   8.187    8.330      0.001    0.001   \n",
       "fwhm[1]                 1.558  0.008   1.545    1.574      0.000    0.000   \n",
       "fwhm[2]                 2.493  0.010   2.473    2.511      0.000    0.000   \n",
       "fwhm[3]                 1.636  0.007   1.623    1.648      0.000    0.000   \n",
       "fwhm[4]                 2.091  0.009   2.075    2.107      0.000    0.000   \n",
       "fwhm[5]                 7.242  0.007   7.230    7.254      0.000    0.000   \n",
       "log10_N[0]             14.005  0.002  14.003   14.009      0.000    0.000   \n",
       "log10_N[1]             13.503  0.002  13.499   13.507      0.000    0.000   \n",
       "log10_N[2]             13.659  0.001  13.657   13.662      0.000    0.000   \n",
       "log10_N[3]             13.385  0.002  13.381   13.389      0.000    0.000   \n",
       "log10_N[4]             13.751  0.001  13.748   13.753      0.000    0.000   \n",
       "log10_N[5]             13.897  0.000  13.897   13.898      0.000    0.000   \n",
       "log10_Tex_ul[0]         0.814  0.087   0.654    0.986      0.003    0.002   \n",
       "log10_Tex_ul[1]         0.515  0.054   0.418    0.618      0.002    0.001   \n",
       "log10_Tex_ul[2]         0.503  0.062   0.390    0.621      0.002    0.001   \n",
       "log10_Tex_ul[3]         0.865  0.083   0.713    1.016      0.003    0.002   \n",
       "log10_Tex_ul[4]         0.605  0.070   0.478    0.735      0.002    0.002   \n",
       "log10_Tex_ul[5]         0.891  0.093   0.708    1.065      0.003    0.002   \n",
       "tau_total[0]            2.209  0.016   2.178    2.237      0.000    0.000   \n",
       "tau_total[1]            1.727  0.017   1.696    1.756      0.001    0.000   \n",
       "tau_total[2]            3.334  0.013   3.308    3.357      0.000    0.000   \n",
       "tau_total[3]            0.367  0.005   0.358    0.377      0.000    0.000   \n",
       "tau_total[4]            2.665  0.044   2.580    2.743      0.001    0.001   \n",
       "tau_total[5]           -2.528  0.008  -2.543   -2.515      0.000    0.000   \n",
       "fwhm_L_norm             0.025  0.008   0.012    0.040      0.000    0.000   \n",
       "baseline_12CN_norm[0]  -1.471  0.034  -1.531   -1.403      0.001    0.001   \n",
       "\n",
       "                       ess_bulk  ess_tail  r_hat  \n",
       "velocity[0]               872.0     907.0    NaN  \n",
       "velocity[1]              1221.0     944.0    NaN  \n",
       "velocity[2]               695.0     861.0    NaN  \n",
       "velocity[3]              1099.0     981.0    NaN  \n",
       "velocity[4]               999.0     832.0    NaN  \n",
       "velocity[5]              1007.0     919.0    NaN  \n",
       "fwhm_thermal[0]          1000.0    1000.0    NaN  \n",
       "fwhm_thermal[1]          1000.0    1000.0    NaN  \n",
       "fwhm_thermal[2]          1000.0    1000.0    NaN  \n",
       "fwhm_thermal[3]          1000.0    1000.0    NaN  \n",
       "fwhm_thermal[4]          1000.0    1000.0    NaN  \n",
       "fwhm_thermal[5]          1000.0    1000.0    NaN  \n",
       "fwhm_nonthermal[0]        932.0     967.0    NaN  \n",
       "fwhm_nonthermal[1]        982.0     870.0    NaN  \n",
       "fwhm_nonthermal[2]        907.0     983.0    NaN  \n",
       "fwhm_nonthermal[3]       1048.0     942.0    NaN  \n",
       "fwhm_nonthermal[4]       1063.0     973.0    NaN  \n",
       "fwhm_nonthermal[5]        956.0     983.0    NaN  \n",
       "fwhm[0]                   932.0     967.0    NaN  \n",
       "fwhm[1]                   982.0     870.0    NaN  \n",
       "fwhm[2]                   907.0     983.0    NaN  \n",
       "fwhm[3]                  1048.0     942.0    NaN  \n",
       "fwhm[4]                  1063.0     973.0    NaN  \n",
       "fwhm[5]                   956.0     983.0    NaN  \n",
       "log10_N[0]                977.0     872.0    NaN  \n",
       "log10_N[1]               1001.0    1015.0    NaN  \n",
       "log10_N[2]                939.0     990.0    NaN  \n",
       "log10_N[3]                971.0     826.0    NaN  \n",
       "log10_N[4]               1035.0     942.0    NaN  \n",
       "log10_N[5]               1101.0     942.0    NaN  \n",
       "log10_Tex_ul[0]           947.0     906.0    NaN  \n",
       "log10_Tex_ul[1]          1052.0     992.0    NaN  \n",
       "log10_Tex_ul[2]           948.0     896.0    NaN  \n",
       "log10_Tex_ul[3]          1024.0     983.0    NaN  \n",
       "log10_Tex_ul[4]           996.0     940.0    NaN  \n",
       "log10_Tex_ul[5]           934.0     975.0    NaN  \n",
       "tau_total[0]              986.0     941.0    NaN  \n",
       "tau_total[1]             1053.0     912.0    NaN  \n",
       "tau_total[2]             1059.0     867.0    NaN  \n",
       "tau_total[3]              961.0    1005.0    NaN  \n",
       "tau_total[4]             1064.0    1071.0    NaN  \n",
       "tau_total[5]             1012.0     941.0    NaN  \n",
       "fwhm_L_norm               955.0     779.0    NaN  \n",
       "baseline_12CN_norm[0]    1009.0     982.0    NaN  "
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# ignore transition and state dependent parameters\n",
    "var_names = [\n",
    "    param for param in model.cloud_deterministics\n",
    "    if not set(model.model.named_vars_to_dims[param]).intersection(set([\"transition\", \"state\"]))\n",
    "]\n",
    "pm.summary(model.trace.posterior, var_names=var_names + model.hyper_freeRVs + model.baseline_freeRVs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "49ed1a20-2c91-49ae-a600-3d3cddb15905",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Sampling: [12CN]\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "9c0b2b7a61f2462bbb2035561a1614c8",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "posterior = model.sample_posterior_predictive(\n",
    "    thin=100, # keep one in {thin} posterior samples\n",
    ")\n",
    "_ = plot_predictive(model.data, posterior.posterior_predictive)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "29e2d0c0",
   "metadata": {},
   "source": [
    "## Number of cloud components"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "1ac97eea",
   "metadata": {},
   "outputs": [],
   "source": [
    "from bayes_spec import Optimize\n",
    "\n",
    "max_n_clouds = 10\n",
    "baseline_degree = 0\n",
    "opt = Optimize(\n",
    "    CNModel,\n",
    "    data_12CN,\n",
    "    molecule=\"CN\", # molecule name\n",
    "    mol_data=mol_data_12CN, # molecular data\n",
    "    bg_temp = 2.7, # assumed background temperature (K)\n",
    "    Beff=1.0, # Main beam efficiency\n",
    "    Feff=1.0, # Forward efficiency\n",
    "    max_n_clouds=max_n_clouds,\n",
    "    baseline_degree=baseline_degree,\n",
    "    seed=1234,\n",
    "    verbose=True\n",
    ")\n",
    "opt.add_priors(\n",
    "    prior_log10_N = [14.0, 0.25], # mean and width of log10 total column density prior (cm-2)\n",
    "    prior_log10_Tkin = None, # ignored because kinetic temperature is fixed\n",
    "    prior_velocity = [-3.0, 5.0], # mean and width of velocity prior (km/s)\n",
    "    prior_fwhm_nonthermal = 1.0, # width of non-thermal broadening prior (km/s)\n",
    "    prior_fwhm_L = 1.0, # width of latent Lorentzian FWHM prior (km/s) TIP: set to typical feature separation\n",
    "    prior_rms = None, # do not infer spectral rms\n",
    "    prior_baseline_coeffs = None, # use default baseline priors\n",
    "    assume_LTE = False, # do not assume LTE\n",
    "    prior_log10_Tex = [0.75, 0.1], # mean and width of excitation temperature prior (K)\n",
    "    assume_CTEX = False, # do not assume CTEX\n",
    "    prior_LTE_precision = 1000.0, # width of LTE precision prior\n",
    "    fix_log10_Tkin = 1.5, # fix the kinetic temperature (K)\n",
    "    ordered = False, # do not assume optically-thin\n",
    ")\n",
    "opt.add_likelihood()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "a404d86e-130a-4c95-aab9-270430f73021",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Sampling: [12CN, LTE_precision, baseline_12CN_norm, fwhm_L_norm, fwhm_nonthermal_norm, log10_N_norm, log10_Tex_ul_norm, velocity_norm, weights]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from bayes_spec.plots import plot_predictive\n",
    "\n",
    "# prior predictive check\n",
    "prior = opt.models[1].sample_prior_predictive(\n",
    "    samples=100,  # prior predictive samples\n",
    ")\n",
    "_ = plot_predictive(opt.models[1].data, prior.prior_predictive)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0927a008-b905-4385-8220-5fd4e8038d0f",
   "metadata": {},
   "source": [
    "Approximate all models with variational inference."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "894cad4a-6eaf-484b-84cc-744c9fd29654",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Null hypothesis BIC = 9.837e+05\n",
      "Approximating n_cloud = 1 posterior...\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "935da627d38e4ae4bd9b9e91c3889e95",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Finished [100%]: Average Loss = 25,926\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4f11f4d96ef04b8a9e52b0b93dd81fc9",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "n_cloud = 1 BIC = 5.952e+04\n",
      "\n",
      "Approximating n_cloud = 2 posterior...\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "53ed3395161a4ef1ab92b02300c88241",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Finished [100%]: Average Loss = 14,433\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "9f2cff550040424aa0753c0336b08627",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "n_cloud = 2 BIC = 3.122e+04\n",
      "\n",
      "Approximating n_cloud = 3 posterior...\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "89b16dddb0384b7da4fef7ac4c20ab6b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Finished [100%]: Average Loss = 6,979.5\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2cdbd2aeb0414922890237f70e8bfd4c",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "n_cloud = 3 BIC = 1.077e+04\n",
      "\n",
      "Approximating n_cloud = 4 posterior...\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "af1ee609a0ce4d509f54cf6a100d42a0",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Finished [100%]: Average Loss = 5,383.5\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1b64a69a624849f8bf46b78a49d6b8a2",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "n_cloud = 4 BIC = 6.806e+03\n",
      "\n",
      "Approximating n_cloud = 5 posterior...\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "bc06f1ac078f4c52b48c5fb5a082475e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Finished [100%]: Average Loss = 1,669.4\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "af588bddfba64000b39ef1f2ca3b0051",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "n_cloud = 5 BIC = 1.830e+03\n",
      "\n",
      "Approximating n_cloud = 6 posterior...\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7655be0b5be043f7aaf92af57bbba92c",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Finished [100%]: Average Loss = 533.67\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2d84646dcd884b3ba6401201be85be41",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "n_cloud = 6 BIC = -1.968e+00\n",
      "\n",
      "Approximating n_cloud = 7 posterior...\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e13e777f220c42cba99f6f84c2aa1594",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Finished [100%]: Average Loss = 1,500.3\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "23e869a330fd4b1aa140b2ab6a39ae9f",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "n_cloud = 7 BIC = 1.939e+03\n",
      "\n",
      "Approximating n_cloud = 8 posterior...\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7b101af9550847d7950749afbf5a444d",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Finished [100%]: Average Loss = 343.3\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0afd85d8d7e34e8ba3fc86a4bef429f6",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "n_cloud = 8 BIC = -3.888e+02\n",
      "\n",
      "Approximating n_cloud = 9 posterior...\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f3029f7b30b04debb68892b93dbca4f2",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Finished [100%]: Average Loss = -361.63\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "3f7d30e822394dffb922aeaf8d533784",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "n_cloud = 9 BIC = -1.662e+03\n",
      "\n",
      "Approximating n_cloud = 10 posterior...\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "6495ca9c28f64ee7b8f662a72b5f11e2",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Finished [100%]: Average Loss = -521.79\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7ade92038344482e80b6cbf14acd96b8",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "n_cloud = 10 BIC = -1.525e+03\n",
      "\n",
      "Runtime: 25.33 minutes\n"
     ]
    }
   ],
   "source": [
    "start = time.time()\n",
    "fit_kwargs = {\n",
    "    \"n\": 20_000,\n",
    "    \"rel_tolerance\": 0.01,\n",
    "    \"abs_tolerance\": 0.05,\n",
    "    \"learning_rate\": 0.02,\n",
    "}\n",
    "opt.fit_all(**fit_kwargs)\n",
    "end = time.time()\n",
    "print(f\"Runtime: {(end-start)/60.0:.2f} minutes\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "1143d2aa-1ccf-4cab-b912-5668a6ed5b9a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 9.83688523e+05  5.95200559e+04  3.12208929e+04  1.07687710e+04\n",
      "  6.80598056e+03  1.82960053e+03 -1.96767126e+00  1.93949664e+03\n",
      " -3.88770599e+02 -1.66173758e+03 -1.52473788e+03]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "null_bic = opt.models[1].null_bic()\n",
    "n_clouds = np.arange(max_n_clouds+1)\n",
    "bics_vi = np.array([null_bic] + [model.bic(chain=0) for model in opt.models.values()])\n",
    "print(bics_vi)\n",
    "\n",
    "plt.plot(n_clouds[1:], bics_vi[1:], 'ko')\n",
    "plt.xlabel(\"Number of Clouds\")\n",
    "_ = plt.ylabel(\"BIC\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6e2b5803-bc89-40a4-b5eb-843fbd30c357",
   "metadata": {},
   "source": [
    "The strange baseline structure makes it too difficult to identify the optimal number of clouds."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.13.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}