{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "87b1c418",
   "metadata": {},
   "source": [
    "# Measures neurites, branches and trunks, with probability map and filtered cells"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b01d9efe",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "aee9835e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.patches as patches\n",
    "from pathlib import Path\n",
    "import numpy as np\n",
    "import os\n",
    "import sys\n",
    "import glob\n",
    "import pandas as pd\n",
    "import xml.etree.ElementTree as et\n",
    "from ims_to_tiff import convert_to_tif\n",
    "import datetime\n",
    "from imageio import volread as imread\n",
    "\n",
    "import tifffile\n",
    "\n",
    "# from pystackreg import StackReg --> don't run this, run this with imlab environment\n",
    "from skimage.filters import threshold_otsu\n",
    "\n",
    "import seaborn as sns\n",
    "#from pystackreg.util import to_uint16\n",
    "\n",
    "from skimage import measure\n",
    "from scipy import stats\n",
    "\n",
    "import umap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "255b969d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/Users/bkang/Desktop/lab/FocalStack_D28'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "os.getcwd()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "aee408ca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'A2': 'PGGT1B',\n",
       " 'AA5': 'MAP4K4',\n",
       " 'C5': 'MINK1',\n",
       " 'C10': 'NCK1',\n",
       " 'C12': 'HSF1',\n",
       " 'D1': 'IER5',\n",
       " 'D3': 'PPP2R2B',\n",
       " 'D4': 'FAN1',\n",
       " 'D7': 'MLH1',\n",
       " 'D8': 'MSH3',\n",
       " 'D10': 'SNX8',\n",
       " 'D11': 'non-target',\n",
       " 'E2': 'PPP2R1A',\n",
       " 'E4': 'HRAS',\n",
       " 'E5': 'SLC25A11',\n",
       " 'E8': 'WASL',\n",
       " 'F5': 'ARPC3',\n",
       " 'F8': 'NCKAP1',\n",
       " 'G8': 'RAPGEF2'}"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "procode_gene_df = pd.read_csv('Procode_Gene.csv')\n",
    "procode_to_gene = {}\n",
    "for cell in procode_gene_df.index:\n",
    "    procode = procode_gene_df.loc[cell,'ProCode ID']\n",
    "    if procode not in procode_to_gene.keys():\n",
    "        procode_to_gene[procode] = procode_gene_df.loc[cell,'Gene Target']\n",
    "procode_to_gene"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "36a3556a",
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>A2</th>\n",
       "      <th>AA5</th>\n",
       "      <th>C10</th>\n",
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       "      <th>NWS</th>\n",
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       "      <td>1.0</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>HSV</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>S</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>0.0</td>\n",
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      "text/plain": [
       "        A2  AA5  C10  C12   C5   D1  D10  D11   D3   D4   D7   D8   E2   E4  \\\n",
       "NWS    1.0  0.0  1.0  0.0  0.0  0.0  0.0  1.0  0.0  1.0  0.0  0.0  0.0  0.0   \n",
       "VSVG   1.0  0.0  1.0  0.0  1.0  0.0  0.0  0.0  0.0  0.0  1.0  0.0  1.0  0.0   \n",
       "FLAG   1.0  1.0  0.0  0.0  1.0  0.0  1.0  0.0  1.0  0.0  0.0  0.0  0.0  1.0   \n",
       "HSV    0.0  0.0  0.0  1.0  0.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0   \n",
       "C      0.0  1.0  1.0  0.0  1.0  1.0  0.0  0.0  0.0  0.0  0.0  1.0  0.0  1.0   \n",
       "S      0.0  1.0  0.0  1.0  0.0  0.0  1.0  1.0  0.0  0.0  0.0  1.0  1.0  0.0   \n",
       "Ollas  0.0  0.0  0.0  1.0  0.0  1.0  0.0  0.0  1.0  1.0  1.0  0.0  0.0  0.0   \n",
       "\n",
       "        E5   E8   F5   F8   G8  \n",
       "NWS    1.0  0.0  0.0  1.0  0.0  \n",
       "VSVG   0.0  1.0  1.0  1.0  0.0  \n",
       "FLAG   0.0  0.0  1.0  0.0  1.0  \n",
       "HSV    1.0  1.0  1.0  1.0  0.0  \n",
       "C      1.0  1.0  0.0  0.0  1.0  \n",
       "S      0.0  0.0  0.0  0.0  0.0  \n",
       "Ollas  0.0  0.0  0.0  0.0  1.0  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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YmXXDxCDXdM3MBk2fZuEpzEnXzIbKZM1ruqpgAgj3ejCzojrOmBdveFjhnLPffedVnqFd0zWzoTLQN9K6dpFZc9o6fnz5MgCefGBxW3EzZ8/tKK7dcsLglLVRzmGPq/r9bPd6zdcc9u9M2XJ2alL1bl5wTdfMhspEvwuQo94TxJuZtWlSxZc8kvaSdIekRZKOyzjuTZJC0ry8c7qma2ZDpVu9FyStCJxBMmvOUmChpPkRcduU49YAPgBcV+S8ruma2VDp4nQ9LwMWRcTiiFgOXADsO81xHwc+Dfy1SPmcdM1sqLTTvNA8iW66NE/qOAe4p+n10nTbUyRtD2wSEYVn0nHzgpkNlXa6jEXEKFBqynJJKwCfA97RTpyTrpkNlYnu9RhbBmzS9HrjdFvDGsALgR8r6aa2ITBf0j4R0TwBxDM46ZrZUOniwxELga0kbU6SbA8BDmvsjIhHgNmN15J+DPzvrIQLbtM1syEz2caSJSLGgSNJJuH9DXBhRNwq6RRJ+5Qtn2u6ZjZUujlFWkQsABZM2XZCi2NfXeScTrpmNlTqPvZCZvOCpC0l7TzN9p0lbdG7YpmZlTPRxtIPeW26nwcenWb7o+k+M7Na6eZjwL2Q17ywQUTcMnVjRNwiabPeFMnMrLy6Ny/kJd21M/at0mpH+lTHCMBZZ53VfqnMzEqqe9LNa14Yk3T41I2S3gPc0CooIkYjYl5EzBsZGWl1mJlZ13Vx7IWeyKvpHg18V9JbeDrJzgNmAfv3sFxmZqUM9MSUEXE/sJOk3UgedwO4JCKu7HnJzMxKqPsg5oX66UbEVcBVPS6LmVnHJms+F64fjjCzoVL3G2lOumY2VOpdz3XSNbMh45qumVmFBrr3gpnZoJmoeQODk66ZDRU3L5iZVchdxszMKlTvlOuka2ZDpu7NC4ro+e+Fuv/iMbP66LjvwTGbHVI455x69wWV93VwTdfMhkrda7qVJN0Zs+a0dfz48mRq+ScfWNxW3MzZcyu9XjeuWfV7U/V76vfl7/XrvRmUuE5Fzf+4dk3XzIaKa7pmZhVylzEzswrVO+U66ZrZkBmvedp10jWzoeIbaWZmFfKNNDOzCrmma2ZWIdd0zcwqNNH7oQ064qRrZkPF/XTNzCrkNl0zswrVvU13haydknaQtGHT67dL+p6k0ySt2/vimZm1Z5IovPRDZtIFzgKWA0h6FfAp4FzgEWC0t0UzM2tftPFfHkl7SbpD0iJJx02z/1hJt0m6WdIVkp6Xd868pLtiRPwpXT8YGI2IiyLiY8CWGQUdkTQmaWx01LnZzKozEVF4ySJpReAM4J+A5wOHSnr+lMNuAuZFxIuAbwP/nle+3KQrqdHuuwdwZdO+lu3BETEaEfMiYt7IyEheGczMuqaLzQsvAxZFxOKIWA5cAOzbfEBEXBURj6cvrwU2zjtp3o2084GfSHoAeAL4KYCkLUmaGMzMaqWLN9LmAPc0vV4K7Jhx/LuBH+adNDPpRsS/SboC2Ai4PJ6eUG0F4P15Jzczq1o7XcYkjQDNf46PRkTbbaKS3grMA3bNOza3y1hEXDvNtjvbLZSZWRXa6ZWQJthWSXYZsEnT643Tbc8g6TXAR4BdI+Jvedd0P10zGypdnOF8IbCVpM1Jku0hwGHNB0jajqSX114R8YciJ3XSNbOhMtGl/rcRMS7pSOAyYEXg7Ii4VdIpwFhEzAc+A6wOfEsSwJKI2CfrvE66ZjZUuvnQQ0QsABZM2XZC0/pr2j2nk66ZDZUuNi/0hJOumQ0VjzJmZlYhjzJmZlYhD2JuZlYhNy+YmVWo7klXFdzpq/c7YGZ1ok5P8PLnvrpwzrn23h93fL12uaZrZkOl7jXdSpLujFlz2jp+fPmyvsQ9+cDituIAZs6eW+k1O71e1eWsOq7u37V+XHNQ/j01PsNOufeCmVmFJqLes6Q56ZrZUPETaWZmFXKbrplZherepps3RxoAklaTtEK6vrWkfSTN7G3RzMzaNxlReOmHQkkXuBpYWdIc4HLgbcDXelUoM7OyujkFey8UTbpKZ7w8APhiRBwEvKB3xTIzK2ciJgsv/VC0TVeSXgG8hWTGS0hGUjczq5V+NRsUVTTpfgA4HvhuOl3FXOCq3hXLzKycut9IK5R0I+JqknbdxuvFwFG9KpSZWVlDUdOVtD7wYZJ23JUb2yNi9x6Vy8yslLrXdIveSPsGcDuwOXAycDfJ9MRmZrUyEROFl34omnTXi4ivAE9GxE8i4l2Aa7lmVjsRUXjph6I30p5M//97Sa8H7gXW7U2RzMzKG5bHgP9V0lrAB4HTgTWBY1odLGkEGAE466yzOi2jmVlhQzHgTUT8IF19BNitwPGjwGjj5b8ceXK50pmZtWmgey9IOp2M6XYiwt3GzKxW6t57Ia+mO1ZJKczMumSgBzGPiHOqKoiZWTcMdJuupO+T3bywT9dLZGbWgYFu0wX+I/3/qsCWJAl4EfBELwtlZlbWQNd0gWuAfwPeBSxJt21CMpbu/+1dsczMyql7P928J9L+HVgH2Dwito+I7YEtgLWAz/S6cGZm7Rr0J9LeAGwdTaWLiEclvZdkLIaje1g2M7O2DXTvBSBiml8HETEhqd51eDN7Vqr7jbS85oXbJL196kZJbyWp6ZqZ1cqgNy+8D/iOpHcBN6Tb5gGrAPv3smBmZmV084k0SXsBXyCZnuy/IuJTU/avBJwLvBR4EDg4Iu7OOmfewxHLgB0l7c7TE1EuiIgrSv0EZmY91q0arKQVgTOAPYGlwEJJ8yPitqbD3g08FBFbSjoE+DRwcOZ5K6hi17uBxczqRJ2eYMasOYVzzvjyZS2vl07Ge1JEvC59fTxARHyy6ZjL0mN+IWkGcB+w/nT3whqKDmLeCbVaJB2Rtb8ucYNUVsfVI26QylqzuI6NL1+mooukEUljTctI06nmAPc0vV6abmO6YyJinGQkxvWyyldF0s0ykn9ILeL6cU3HDXZcP6457HFdFxGjETGvaRnNj+pMv5OumVldLSN5Ardh43TbtMekzQtrkdxQa8lJ18xseguBrSRtLmkWcAgwf8ox84F/TtcPBK7Mas+F4tP19ErZqnzVcf24puMGO64f1xz2uEpFxLikI4HLSLqMnR0Rt0o6BRiLiPnAV4CvS1oE/IkkMWeqoveCmZml3LxgZlYhJ10zswo56ZqZVchJ18ysQpUlXUlrStpimu0vauMcu0g6VtJrCxw7Q9IRki6VdHO6/FDS/5I0MyNuxTTu45J2nrLvo22UdXNJB0japmhMi/Nkxkt6naQzJc1PlzPTQTrKXu+EnP0rSFohXZ8laXtJ65a4zr8UOKbwd2Oa2E0lrZ2ubybpQEkvzInpymffFHNngWNe1LQ+U9JH08/xE5JWzYibK+lsSf8qaXVJX5b0a0nfkrRZG2VcPf0M1y5wbJn3tO2YodfOMGhlF+DNwL3AL4FbgR2a9t2YEXd90/rhafyJwM+B43KueT5wJvBykk7NG6frZwLfzIj7L+A8kgHabwA+V7CsFzet7wvcBXwVuAN4Rwfv3ZKMfZ8HFpB0U9klXQ5Jt32hB9fbD7gf+H36M14HXEHyeOQbM+KOnbJ8EHig8TojbgL4LfBx4Plt/AzHpe//7cB70v9/Jf3uZV2v1Gef7n8MeDRdHkuXicb2jLgbm9Y/SzIV1q7AqcC5GXFXA+9Nf9Zfp+/pJiQDsFyZEffFpvVdSKbhuorkUda9u/melv0chn2p5iJJstwoXX9Z+ubvn76+KSPupqb1hSQDSQCsBtySc807S+67uWl9Bkmfwu8AK7VR1mtIpjgCmA38Kqesp7VYTs/5Bzvtz0HyDPtvM+IebbE8Boxn/YzAhsDm6fH/kG5/Hkm/xVZxjwHfBE4g+aV5IvBQYz3nei8kmadvEfCr9B/yZjnv560kw4+ul167+Xvz625/9k2f4bnABk3b7irwb6P5e/NLYGbTZ3hzwbglrfZNE9ec5K8Ctk/X5+Z8hm2/p2U/h2FfqmpeWDEifg8QEdcDuwEflXQU2aOQrSBpHUnrkfQp/mN6jr8A4znX/JOkgxp/CsNTfxofTPIPvpVZjZWIGI+IEZJ/DFcCq2fENf8cMyLirvQcDwB584e8k6S2csOUZQxYnhH3V0k7TLN9B+CvGXEPA1tFxJpTljVIarEtRcR96c+2JCLuSLf9juymqhek+1cDPhMRJ5MMh3dyup5xufh1RHwkIrYk+WvnOcDPJF2TETcREU+kP+cTpI9lpt+bLGU/eyLiKJJxV8+XdFT6vSvSCX4tSftLehOwUkQ8mZ4vcuInJW2dfv6rSpoHIGlLko78RawZETem11tM9mdY5j0t+zkMtaqeSHtM0hYR8T8AEfF7SbuR1CJekBG3FknyERCSNkpjVyd/RKLG2JZflNRIsmuT/HbPempkTNJeEXFpY0NEnCLpXpKmiVZeLOnRtFwrNZV1Fvn/CBaS/Ob/u0Qi6aSMuHcAZ0pag+RPfEj+xHwk3dfKuSS10/un2XdeVkElrRARkyQzRDe2rUhTwpoqIpYAB0naF/h/kk7Nukbz5aac53rgekkfBF6VEXejpPNIkvwVwDmSLgV2B27LiCv72TeOvUHSa4AjgZ8AK+fFpMftk65fK2mDiLhf0oYkTTCtfBj4Pskv9P2A4yW9GFiT7AFltpF0M8l7u5mkdSLiofSXRMvPkHLvaauYPTJihl4lT6SlX4bHI+K3U7a/EjgnIua2eb5VSP6Mu7vg8esBRETmQBSdkLRVWqafTdm+C8n7/NOM2HWBv0bE4yWvvSFPDzm3LCLuK3OeAtfZgaRZ569Ttm8G7BIR/13gHKsBJwE7RkRW4kTSYRGR+UugRdwM4CCSmuK3SZq0DiNpvzyjipqWpI2A7SJiQa+v1XTN2SR/QUxkHPO8KZvujYgn09hXRcR3WsS1/Z62iDm0KabU933gVd2eAWxHMn373SS1zve3Gb8a8Dbgkg7KsGe344AfANtOs31b4PttXGN90ravDt/nbXoZV7acdY4DPty0ftCUfZ/oRWwf4jYt+b1oO47kZuv7ml5fT3JjbTFwYJlyDMNSzUVga5KbJrcDPwPeD/yujfhZJHOyfYvkBs5XybhbXuB8Le/Ql40DFmbsy7vpJ5La3wMkg2Y8BPwROKFmP2Opck6JeyiNLfTzdXC9E5uuVzTuxunWp3vdrdg+x13Uxvei7TiSXkabNL3+JbAusClwRZnv5zAsVbXp3g78FHhDRCwCkHRMXpCS/riHAq8lqRWfS9Ld7J0FYqcOwfbULjJGdi8bR9Je3MoqGfsAjgF2JvnZ7krLMZekvfaYiJi2DVTSaRllbVmesnFly1k2TtKxHVxvlxJxarE+3etuxfYzrp1mvTJxsyKieeaFn0XEn0hucq/WxrWHSlVJ9wCSm1dXpQ3pF1Bsao5LSZL1Lk3/eL5Q8JqvBN4K/HnKdpG0LXU7bkzS4RHx5WcESe/h6ZmUW3kbSdPFUzdOImKxkqnuLyfpszmdd5L0z/zbNPsOzbhe2biy5RyUuGixPt3rbsXWKS5Lmbh1nnGCiCObXq7fxrWHSiVJNyIuBi5Of7vtS9L5/DmSzgS+GxGXtwjdniRZ/0jSYpJkXbQ7zLUkN+9+MnWHpDt6EHc08F1Jb+GZ09U3mkayzGxOEA0R8UdlPD1H+V4PZePKlnNQ4pp7oKySrpO+zuuJUDa2TnEREWt2Me66FhWRI0jad5+VKh3EPJI7nOcB50lah+TO5v8hqX1Md/wvSdqBjpO0E0ktbKakH5Ik66zBkI8i6dP5DEoe78xqnigVFxH3AzulXeEajzleEhFXZlyrIasvbta+A2nRHzciNu9BXNlyDsS+iCj6C71rsUMedwxJZesw4MZ020tJHjbZr0w5hsHADWKe9if8CMlTSe/OOO4HwPERccuU7duS3N19YzfjOiFpApiuG5OAlSMiq3bWOMf6kNTm2rx24biy5RyUOOsNSbvzdH/8WwtWRIZWv6frKUzSdiQ13TeTdDu5KCdkg6mJEyAiblH2gCBl40orW/uQ1LhLfyTJ00SSNA6cHhGndDtuQGpXHdVYrfvSJPusTrTNaj20o5LHHE+UdDvJOAT3kNTOd4uI/8wJXztjX1ZvgrJx/dDcK2DdiFgH2BHYOad3SNk4M+tQrZMuSVez3Um6mu0SEaeTjNxUxJikw6duLNCboGxcP7wNOLTRswOeeob+rcDbexBnZh2qe/NC2a5mUL43Qdm4fqj67r6ZdajWSbeDrmalexN02AuhalXf+TezDg1i74VGV7ODI2KPfpenn3x332zwDFzSNTMbZHW/kWZmNlScdM3MKuSka2ZWISddM7MKOemamVXo/wP9EIn2xPkrBAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# load codebook, markers, etc\n",
    "# load metadata - load full codebook as well\n",
    "codebook = pd.read_csv('codebook.csv',sep='.',index_col=0) # this is \"legal\" codebook\n",
    "#AllProcodes = pd.read_csv('AllProcodes.csv', sep='.')\n",
    "sns.heatmap(codebook, linewidths = 0.3)\n",
    "markers = pd.read_csv('./markers.csv')\n",
    "codebook"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c8e0d839",
   "metadata": {},
   "source": [
    "## Neurites"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "9c157c80",
   "metadata": {},
   "outputs": [
    {
     "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>label</th>\n",
       "      <th>Barcode_Idx</th>\n",
       "      <th>SUM</th>\n",
       "      <th>Barcode</th>\n",
       "      <th>RAW</th>\n",
       "      <th>P</th>\n",
       "      <th>LOGIT</th>\n",
       "      <th>ENTROPY</th>\n",
       "      <th>X_NORM</th>\n",
       "      <th>Gene</th>\n",
       "      <th>...</th>\n",
       "      <th>Texture_Variance_TOM20_3_02_256</th>\n",
       "      <th>Texture_Variance_TOM20_3_03_256</th>\n",
       "      <th>Texture_Variance_cCasp3_3_00_256</th>\n",
       "      <th>Texture_Variance_cCasp3_3_01_256</th>\n",
       "      <th>Texture_Variance_cCasp3_3_02_256</th>\n",
       "      <th>Texture_Variance_cCasp3_3_03_256</th>\n",
       "      <th>Texture_Variance_pPRS6_3_00_256</th>\n",
       "      <th>Texture_Variance_pPRS6_3_01_256</th>\n",
       "      <th>Texture_Variance_pPRS6_3_02_256</th>\n",
       "      <th>Texture_Variance_pPRS6_3_03_256</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>intensity_mean-2</td>\n",
       "      <td>1.401477</td>\n",
       "      <td>C10</td>\n",
       "      <td>1.185651</td>\n",
       "      <td>0.846001</td>\n",
       "      <td>1.703575</td>\n",
       "      <td>0.755951</td>\n",
       "      <td>0.746494</td>\n",
       "      <td>NCK1</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.121185</td>\n",
       "      <td>-0.192197</td>\n",
       "      <td>13.193644</td>\n",
       "      <td>14.147616</td>\n",
       "      <td>13.349172</td>\n",
       "      <td>13.525736</td>\n",
       "      <td>1.718389</td>\n",
       "      <td>1.696825</td>\n",
       "      <td>1.618599</td>\n",
       "      <td>1.718385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>intensity_mean-9</td>\n",
       "      <td>1.545138</td>\n",
       "      <td>D4</td>\n",
       "      <td>1.251146</td>\n",
       "      <td>0.809731</td>\n",
       "      <td>1.448262</td>\n",
       "      <td>0.903049</td>\n",
       "      <td>0.810684</td>\n",
       "      <td>FAN1</td>\n",
       "      <td>...</td>\n",
       "      <td>3.901309</td>\n",
       "      <td>4.358029</td>\n",
       "      <td>-0.819853</td>\n",
       "      <td>-0.939350</td>\n",
       "      <td>-1.027013</td>\n",
       "      <td>-1.050751</td>\n",
       "      <td>0.889957</td>\n",
       "      <td>0.910505</td>\n",
       "      <td>0.875227</td>\n",
       "      <td>1.038351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>intensity_mean-18</td>\n",
       "      <td>1.878230</td>\n",
       "      <td>G8</td>\n",
       "      <td>0.739110</td>\n",
       "      <td>0.393514</td>\n",
       "      <td>-0.432563</td>\n",
       "      <td>2.237265</td>\n",
       "      <td>0.619890</td>\n",
       "      <td>RAPGEF2</td>\n",
       "      <td>...</td>\n",
       "      <td>6.505616</td>\n",
       "      <td>6.181922</td>\n",
       "      <td>1.306165</td>\n",
       "      <td>1.403712</td>\n",
       "      <td>1.236809</td>\n",
       "      <td>1.389544</td>\n",
       "      <td>-0.159159</td>\n",
       "      <td>0.310605</td>\n",
       "      <td>0.212255</td>\n",
       "      <td>-0.437281</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>intensity_mean-8</td>\n",
       "      <td>1.889894</td>\n",
       "      <td>D3</td>\n",
       "      <td>1.340203</td>\n",
       "      <td>0.709142</td>\n",
       "      <td>0.891221</td>\n",
       "      <td>1.290217</td>\n",
       "      <td>0.953239</td>\n",
       "      <td>PPP2R2B</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.654907</td>\n",
       "      <td>-0.636207</td>\n",
       "      <td>0.235150</td>\n",
       "      <td>0.096587</td>\n",
       "      <td>-0.123112</td>\n",
       "      <td>0.244418</td>\n",
       "      <td>-0.713525</td>\n",
       "      <td>-0.904783</td>\n",
       "      <td>-0.791925</td>\n",
       "      <td>-0.746413</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>13</td>\n",
       "      <td>intensity_mean-17</td>\n",
       "      <td>1.524615</td>\n",
       "      <td>F8</td>\n",
       "      <td>1.253440</td>\n",
       "      <td>0.822135</td>\n",
       "      <td>1.530883</td>\n",
       "      <td>0.833137</td>\n",
       "      <td>0.914237</td>\n",
       "      <td>NCKAP1</td>\n",
       "      <td>...</td>\n",
       "      <td>1.330187</td>\n",
       "      <td>1.284044</td>\n",
       "      <td>15.935914</td>\n",
       "      <td>15.926096</td>\n",
       "      <td>15.850147</td>\n",
       "      <td>16.124994</td>\n",
       "      <td>10.879939</td>\n",
       "      <td>10.843237</td>\n",
       "      <td>10.804308</td>\n",
       "      <td>10.816426</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31961</th>\n",
       "      <td>585</td>\n",
       "      <td>intensity_mean-8</td>\n",
       "      <td>1.539119</td>\n",
       "      <td>D3</td>\n",
       "      <td>0.851739</td>\n",
       "      <td>0.553393</td>\n",
       "      <td>0.214391</td>\n",
       "      <td>1.693448</td>\n",
       "      <td>0.538902</td>\n",
       "      <td>PPP2R2B</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.069720</td>\n",
       "      <td>0.115072</td>\n",
       "      <td>-0.448166</td>\n",
       "      <td>-0.309180</td>\n",
       "      <td>-0.502968</td>\n",
       "      <td>-0.389645</td>\n",
       "      <td>0.037909</td>\n",
       "      <td>0.021434</td>\n",
       "      <td>-0.036310</td>\n",
       "      <td>0.043551</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31965</th>\n",
       "      <td>589</td>\n",
       "      <td>intensity_mean-9</td>\n",
       "      <td>2.057904</td>\n",
       "      <td>D4</td>\n",
       "      <td>0.882562</td>\n",
       "      <td>0.428865</td>\n",
       "      <td>-0.286485</td>\n",
       "      <td>2.144727</td>\n",
       "      <td>0.462669</td>\n",
       "      <td>FAN1</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.659632</td>\n",
       "      <td>-0.652950</td>\n",
       "      <td>0.032669</td>\n",
       "      <td>0.023026</td>\n",
       "      <td>-0.026831</td>\n",
       "      <td>0.110429</td>\n",
       "      <td>-0.661623</td>\n",
       "      <td>-0.650801</td>\n",
       "      <td>-0.650774</td>\n",
       "      <td>-0.648211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31967</th>\n",
       "      <td>591</td>\n",
       "      <td>intensity_mean-2</td>\n",
       "      <td>1.647154</td>\n",
       "      <td>C10</td>\n",
       "      <td>0.816353</td>\n",
       "      <td>0.495614</td>\n",
       "      <td>-0.017543</td>\n",
       "      <td>1.989583</td>\n",
       "      <td>0.441945</td>\n",
       "      <td>NCK1</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.191185</td>\n",
       "      <td>-1.203391</td>\n",
       "      <td>0.987863</td>\n",
       "      <td>1.199380</td>\n",
       "      <td>0.982808</td>\n",
       "      <td>1.114231</td>\n",
       "      <td>-0.819007</td>\n",
       "      <td>-0.822783</td>\n",
       "      <td>-0.826657</td>\n",
       "      <td>-0.807547</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31968</th>\n",
       "      <td>592</td>\n",
       "      <td>intensity_mean-11</td>\n",
       "      <td>2.194014</td>\n",
       "      <td>D8</td>\n",
       "      <td>0.722803</td>\n",
       "      <td>0.329443</td>\n",
       "      <td>-0.710704</td>\n",
       "      <td>2.405019</td>\n",
       "      <td>0.455295</td>\n",
       "      <td>MSH3</td>\n",
       "      <td>...</td>\n",
       "      <td>21.995864</td>\n",
       "      <td>21.634942</td>\n",
       "      <td>-0.731125</td>\n",
       "      <td>-0.652067</td>\n",
       "      <td>-0.759698</td>\n",
       "      <td>-0.676539</td>\n",
       "      <td>-0.555488</td>\n",
       "      <td>-0.534198</td>\n",
       "      <td>-0.590011</td>\n",
       "      <td>-0.563616</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31974</th>\n",
       "      <td>598</td>\n",
       "      <td>intensity_mean-17</td>\n",
       "      <td>1.742791</td>\n",
       "      <td>F8</td>\n",
       "      <td>1.017022</td>\n",
       "      <td>0.583559</td>\n",
       "      <td>0.337402</td>\n",
       "      <td>1.789591</td>\n",
       "      <td>0.579071</td>\n",
       "      <td>NCKAP1</td>\n",
       "      <td>...</td>\n",
       "      <td>6.383522</td>\n",
       "      <td>6.659102</td>\n",
       "      <td>0.357757</td>\n",
       "      <td>0.345598</td>\n",
       "      <td>0.180955</td>\n",
       "      <td>0.226975</td>\n",
       "      <td>-0.406725</td>\n",
       "      <td>-0.374244</td>\n",
       "      <td>-0.425325</td>\n",
       "      <td>-0.417779</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6214 rows × 1860 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       label        Barcode_Idx       SUM Barcode       RAW         P  \\\n",
       "1          2   intensity_mean-2  1.401477     C10  1.185651  0.846001   \n",
       "7          8   intensity_mean-9  1.545138      D4  1.251146  0.809731   \n",
       "8          9  intensity_mean-18  1.878230      G8  0.739110  0.393514   \n",
       "9         10   intensity_mean-8  1.889894      D3  1.340203  0.709142   \n",
       "12        13  intensity_mean-17  1.524615      F8  1.253440  0.822135   \n",
       "...      ...                ...       ...     ...       ...       ...   \n",
       "31961    585   intensity_mean-8  1.539119      D3  0.851739  0.553393   \n",
       "31965    589   intensity_mean-9  2.057904      D4  0.882562  0.428865   \n",
       "31967    591   intensity_mean-2  1.647154     C10  0.816353  0.495614   \n",
       "31968    592  intensity_mean-11  2.194014      D8  0.722803  0.329443   \n",
       "31974    598  intensity_mean-17  1.742791      F8  1.017022  0.583559   \n",
       "\n",
       "          LOGIT   ENTROPY    X_NORM     Gene  ...  \\\n",
       "1      1.703575  0.755951  0.746494     NCK1  ...   \n",
       "7      1.448262  0.903049  0.810684     FAN1  ...   \n",
       "8     -0.432563  2.237265  0.619890  RAPGEF2  ...   \n",
       "9      0.891221  1.290217  0.953239  PPP2R2B  ...   \n",
       "12     1.530883  0.833137  0.914237   NCKAP1  ...   \n",
       "...         ...       ...       ...      ...  ...   \n",
       "31961  0.214391  1.693448  0.538902  PPP2R2B  ...   \n",
       "31965 -0.286485  2.144727  0.462669     FAN1  ...   \n",
       "31967 -0.017543  1.989583  0.441945     NCK1  ...   \n",
       "31968 -0.710704  2.405019  0.455295     MSH3  ...   \n",
       "31974  0.337402  1.789591  0.579071   NCKAP1  ...   \n",
       "\n",
       "       Texture_Variance_TOM20_3_02_256  Texture_Variance_TOM20_3_03_256  \\\n",
       "1                            -0.121185                        -0.192197   \n",
       "7                             3.901309                         4.358029   \n",
       "8                             6.505616                         6.181922   \n",
       "9                            -0.654907                        -0.636207   \n",
       "12                            1.330187                         1.284044   \n",
       "...                                ...                              ...   \n",
       "31961                        -0.069720                         0.115072   \n",
       "31965                        -0.659632                        -0.652950   \n",
       "31967                        -1.191185                        -1.203391   \n",
       "31968                        21.995864                        21.634942   \n",
       "31974                         6.383522                         6.659102   \n",
       "\n",
       "       Texture_Variance_cCasp3_3_00_256  Texture_Variance_cCasp3_3_01_256  \\\n",
       "1                             13.193644                         14.147616   \n",
       "7                             -0.819853                         -0.939350   \n",
       "8                              1.306165                          1.403712   \n",
       "9                              0.235150                          0.096587   \n",
       "12                            15.935914                         15.926096   \n",
       "...                                 ...                               ...   \n",
       "31961                         -0.448166                         -0.309180   \n",
       "31965                          0.032669                          0.023026   \n",
       "31967                          0.987863                          1.199380   \n",
       "31968                         -0.731125                         -0.652067   \n",
       "31974                          0.357757                          0.345598   \n",
       "\n",
       "       Texture_Variance_cCasp3_3_02_256  Texture_Variance_cCasp3_3_03_256  \\\n",
       "1                             13.349172                         13.525736   \n",
       "7                             -1.027013                         -1.050751   \n",
       "8                              1.236809                          1.389544   \n",
       "9                             -0.123112                          0.244418   \n",
       "12                            15.850147                         16.124994   \n",
       "...                                 ...                               ...   \n",
       "31961                         -0.502968                         -0.389645   \n",
       "31965                         -0.026831                          0.110429   \n",
       "31967                          0.982808                          1.114231   \n",
       "31968                         -0.759698                         -0.676539   \n",
       "31974                          0.180955                          0.226975   \n",
       "\n",
       "       Texture_Variance_pPRS6_3_00_256  Texture_Variance_pPRS6_3_01_256  \\\n",
       "1                             1.718389                         1.696825   \n",
       "7                             0.889957                         0.910505   \n",
       "8                            -0.159159                         0.310605   \n",
       "9                            -0.713525                        -0.904783   \n",
       "12                           10.879939                        10.843237   \n",
       "...                                ...                              ...   \n",
       "31961                         0.037909                         0.021434   \n",
       "31965                        -0.661623                        -0.650801   \n",
       "31967                        -0.819007                        -0.822783   \n",
       "31968                        -0.555488                        -0.534198   \n",
       "31974                        -0.406725                        -0.374244   \n",
       "\n",
       "       Texture_Variance_pPRS6_3_02_256  Texture_Variance_pPRS6_3_03_256  \n",
       "1                             1.618599                         1.718385  \n",
       "7                             0.875227                         1.038351  \n",
       "8                             0.212255                        -0.437281  \n",
       "9                            -0.791925                        -0.746413  \n",
       "12                           10.804308                        10.816426  \n",
       "...                                ...                              ...  \n",
       "31961                        -0.036310                         0.043551  \n",
       "31965                        -0.650774                        -0.648211  \n",
       "31967                        -0.826657                        -0.807547  \n",
       "31968                        -0.590011                        -0.563616  \n",
       "31974                        -0.425325                        -0.417779  \n",
       "\n",
       "[6214 rows x 1860 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# load cell data\n",
    "cell_df = pd.read_csv('072622_CP_All_FOV/cp_output/all_RNA.csv', sep=',',index_col=0)\n",
    "cell_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "7769f98d",
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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    {
     "data": {
      "image/png": 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2Vck245n39W2pCNIDbq68kRLTRMZro3W18qpUx5jvoqZ+ZsygVI5nhfSUMXs8ZqQrdZ1b5J/U54Ewc3c+qdsU5frsWrTV56UaY7HH0DI9WDGDUOQV5FSi7otpo20ngjCYFU9bTEdZI7XRznx+6+NFPW9+69fkuaeuJmTUrNdTP+coBuZ837ODZbPEybNII7iUGkAehdqVV5q2BgrtDEudidpiUE6LkQsXxqShc7w2ZvXFufO2x+03ffqPikh4/V4jD9WYUKFVja0xQj839vVAab5BgO3PRkBMC+daedXrIEeNdkevabNFGw3Qj3gWJ6TfUUO9lxJHQneUynV+PW6rGHIsmo4XmE13K6+0BZC9jry9gKJtiWwvnezrGZKhWzdqPA/MYcSOfeT12ZeekGdM66O4jjxbjm9F5l5cow4pg/sEoB5t5Su0/OfaptfHFxO0pZvPum12lF6jDnSjX637YdAXTXllnXdjthEL2co953m23Do+VUg8y71yG8iBmKZ08GpNwwXyVcZHN/VHAzIttjMS0ZGue7Zpsv5e6zFjPq4BbfIoUpBvyqtRPl2DUjSA2qBODuebpAVsaS1brvaDbxJByTZa6MpbjWm5WLfBaKOhV1rrLLSnLW9sj6fEdoOzCx3AQn7a+6Khm7ptAzVl6JjtAjUc7yKlw0HT8bemKQ+WpulctZWjEOv005SWubElRZy9mOaTmmdiBkXOPL9x/R4lPqOmWuVT09bHs9A287+3mLZNv9462WMQ08Jt45mWPKDpWHLRVoehPS35XMtxxAhZvTIC4lkc11a4qGfk8gg3Tddd07GE2jvmHs/jSHfbBmq5AKHHoeV4F3uZ+ggdbXPStH0VeQ0zOZvfY8ttaFn3zUY7OuaUgS3f9ko901afjXhze6T1OY+Y3q3TFG1puvaajiWX2C2YRkwD6NRjXlvfz4astABQx7Y8jjwBOAR1U32kd5/UDV6VzkgzVo5HgWG0TsOzZsobOGe74oqyBB+tdYtvz/RSn3H0e8pSXlrzXkkznjPQMzpwHiIdgPMoR4BOo+9aE2OW82cSAc5SN3hVw6wFxnXes6YHrhBIztmbPURaYqEpL2g6liOhzwoB1qiH07ie4eh6hhtpixLIV8B1lInz1quvAOhBmZwP1zyv2fpxpxu8muXCAjEoF+dsH2gNIL+QeootcOeV4wHXAABgHNwPAABGNVOMm27wajYsxwXqm2kGBNqYNX8dxbRZ0wU4wzXxggkZQDm00YC6aJ8B5RDTgHJmW2W1h8Grgc2euQEAYyGmAXX47iG5vwTOoQwBAEZCTANQ0iOtDwDlaA0gzOIFAIzg7sv3iGkYgtZ7RmBEWssb8QwjYzUxMA/aaJjFLPn8cPDKGPN3jDFfMsb8wupnt4wxP2WM+eXL/990+bkxxvxtY8x9Y8xLxpg/VPLga5klM9SyfqA6QQUjWedl8rVOxDTyZk7rh4AT0zAaX14mn+swe0wjH+ZFPMPIyM+6zR7PRMijOdFGw2i0TnyqJWTl1UdE5J2bn71fRD5hrX2TiHzi8r2IyHeLyJsu/94nIj+S5zDbmj2TrFHhA27UFV34iBDTWh/CkEhXAA18RCaOadS7D+Vsoy2dfqQvRkJ+Vu8jMnE8EyGPlkJMwyhm7o8/HLyy1v4TEfnNzY/fJSIfvXz9URH546uf/6i98s9E5FXGmNdmOlYosJ69kOO9CCIY2czBRStiGvasZ+SdKbc9xjRmIgL9IqZh0VvsAYA14hlcZm2nzHrecJv5Xi/1mVffbK394uXrXxORb758/ToR+dzqdZ+//AyDmbnQ0NEHDIeYhmtmrONnPOeSekzPme/tBkNMw5Roo+EIca47xDM8eF7dbPU79VUZo+Sj5TxmySepg1cPWGutiNjYvzPGvM8Y8/PGmJ//T/LbZw8DqKbHmfW9GCWQoF/EtDnt1emxK7Fyrd5qhbiWX4/5IMbo5zeClJhGPEOvaKOVQ32P1mijzW2p31PbWr3WYb0eN8pbysMsHkv8u39rjHmttfaLl+W5X7r8/Asi8vrV656+/OwGa+2HRORDIiJ33va4vXv33oPfcdPZr7sv3+P6IRl5B40Q0+AcwHJdv+1sp15vIPdmM5Jn8+gxHXvNx7jmVExbx7MnzS1L3TAG2mg4g7yDRrK20Z40t6IHv6DLmbqIOAiRceLZkp9nydepK68+LiLvuXz9HhH5idXP/5y58nYR+epqme8hZkv1j+uH2VEGulQkpi16XYmDuPJca+l+jdVd5NfziAVoKGtM4xm1Y+D6AVe4x+lK9jbacv173i0BaWrEQfITaptlW83DwStjzN8XkX8qIv+lMebzxpj3isjfFJHvMsb8soj815fvRUT+sYj8iojcF5H/n4j8DyEH8dmXnuCGelK1CtnoBRlAmBoxbQ8xbjzba7p8v24U1/hcAPNqFdMwPtpoAGqqFc/WKxWYmDGPXjv42REBIWaoy8zV1rFtPWlu2WfMs60PA5XsLWt0VcjaC+AsSzQRb6QHKL5gn3/RWnun9XH04klzy37li69+8P0IeQBx9mKa5nzgaxRpPm6UEdNI7jF/ENPC0Uaby1G7hi0kMYKR2u/EszjENKz11F9D/MWWq1+95/zhi2mpz7wCku0Vpp4LWE9BDwBQHvEAo2IGKDCuo9jVY2zrvSMHAFBGT7FhpmcbId3I+YTBK3Rn22kyYsEE0DfqJcRoPZtub0CCPAwAiKExjmg4BgBAn1q30dZGHphAPqPmDwav0EzqiiXXloOtOvwAH24wABxZ1xGt6gxW1CDEkj+15hVNnQxAr0ZoowEAcJaGNtpa688HWmHwCs3kqnipwIFzNNyIAbhyVBb3OudzPTfy6P2ABTEDGBdtNEAH2miAHpRF4JwzMY3BK3SFGbV6MJvSrbeOX64hNJmpbgk5V199clTX5Jo934OzHTzrtNK49RXicM0AzIxBjzFwDaHJTPVK6XOdrb17to02QzrN4Mx1fCTfYaCl3jrLU22X7c5y3qWEdnzuYYY+AOSxxLNtfZqrfiVeXglJA9Kpb889dZsGLlSZqU5ZzpWYc96ZNprIHB2iAOrSvG10T5bBGOrpMNt852o3Y2wMXg1ixIovpCMv9ryp5K47Sr/Q35OmAHIZtV5Z32j7YlHJG/LR0nRt3bDZO0/Xz1xpvTQqaagDOGPENprIfp26nCudcuedbaOJjB3zAbQxet2+tAu2k+ZD/y5k4sFsE/KP2mgurr7gdRttZrO1Udk2EF1LXUK693czLd0tgeW8D80WSIDcqEuQyhWLUia7kA8B4Kaj+jGl/qQdBgBoydeGcMWovZ8fPerE9fzi0eNf7Dku6b6eGIN5MXiF7qRU8L5Z2HsBKEfFOEMAAgAca70F64gxrebxaDpvANDuzGQBX0cgdXEc0gsAwhy1K1zttaMJc9v45vqMGerrlHNkUjgWDF6hK+sKL3a7hO3I/d7v1z8/G0C2Fa2WgJS7AajlvGJo64QFMC7f1kp7P3O9/syNe4563zfrsLaUrSf2jNAgKnGfcZQuxFBgbrkmRKzfz/cMkBL1jcZ6TOMx1UYaAGghtN6JrZ9qrazSVHfuDdidbYP2SNM1GQHPvIJaufcxXSqPkI6e3DPhtVRavXfS5aLlegCYT2r9k7uzUMP75Mb+5/mQjgBK2j5XMHYyAZMXxkTsAYB4mmLaciw5B296fDZYjXg2U8xk8ApD23ZkrWdrh1SAOVZfrT+3le25zlTJAUBLKTP0XA+ibT1zTXPsaDko2JIrr5TW+r4GwDj2Ot1Kt9G0WLfRRjknAABKrZrGnBi8whRazsbW0BDxdYbmVGJGRI8BiiXCANbO1L97z2VMlaM+1VC3bdMy56Dc+r17nOVXi4Z8AGAc27p3lhnLPa8Y7jU+alqhAGBs68kJpdsVmmJJiWPpOV6WNEs8Y/AKU/FVeLMEkt70mHY9HjMAhBq5jmMGfJgWA3sMJgJzYHAhvxLpSZwEgHAMvuTDPcJ8GLyaEAX95j7rpEles6fn7OcP1EQdHm60BlPORuB2i+F1npo1j42WXwDtZqxnXKh/8mJA8AppANRDeSOW5UI6us0S3x9rfQCoj4J/HekBAABmufnvFfdrGB15HCWRvwDUNHudw6Mk8mJ3jLkxeAWgiNmDNR2gQB0z1zMxcqTTXr02U/rPHtcAAOgZMRwA+rR+Libmw7aBQGUjV7bs43uFNADQ2np7XN+WeCF8f7P8buTYtjbbueZ4DQD0YIb6bIZzPMJEFAClUc+UQ7rOicEr4MDZm/zl79cdXikdhwAAhNywt7qpnylWzXSuAKCRhnpYwzEAAHRJaYsRTwA3Bq+AA2c7AV3P0IiZrd7rEtnejjeX9XkzMwRALSVWv5acbKExRlBnx60uI70AtJKz/tlONhy1jcazHQFAJ0331NrjhKa0am2WVX488wqowNVQiK1keqmUaBgBQBlnY0mNOBJ6E91LTHOZpbEAAKPLMVmxF0s7radjzmnW8waAUL3UkzPHstkweAVUMmulOltAYdAOQCml6pcS7zvLCtQRYxwTUABgbKPFLQDQYrmHzlXPjtjWyGX2yRgi88RzBq8AFLHeRmOmgDLLeQKoz7cN7Zm6p/Rgheu9qS/1CskTXD8AAADgSol+r/X7zdSvFmpv+17SaDw88wpAUSWewQIAs/ANIOSsX1vU01pX9xCzrpAOAAAAwE0h7ZjcbZ2Qe3Ot7avSlnYx7ZcxsfIKQ9tW3L1WZMywAADM2hhpga3z3LgfAYArtNEAADXRPsFipnsQBq8wrJaV+nqfW1+FEnOMM1VMAICHXLFihMZL7n3hc2LrvOtpMPq5AqhPa/vmaNXz+jVazwEAUE5IvZ87NoTEm3VsKnksQE0MXkG1M42B7d6wtaw/axs4tuey/Z7nggDAeFL34B5hcKp3xN8rpAOAtZEGbPbOxXduDOgDwDjO1Onr+LGNJbnjZMz77b02ZFIGoBWDV1Cth0p0u8rKJWVmxogdlyM1dgEgVEq9F7N1Xe66tfa2eTkesrt3vMSbNCPefwDIp4e6da9Dcm8meuy59HDuKWijAZjRmXrP1X9X4tlLoe/nel3Iiq3SMYA4g1QMXgEnrStfX2dfSkW9fr8SFX2LpcQEKwCz6bXea/Xcp9AZkAywlLGXrjQ2AfRmr84qsTMHbTQAgIY61LcS7EjIa3OsUqNNkcds6fhI6wMASmvRuZXjGVd77+XavzbVMiNkhkrv7sv36OgEAA9NMeFoy6aQ+lzDeZRUIqb53pMYCmAkuSZolJjosY7HM8Qy4guAkWio01Jik68+XgZLYmPS8p7L340e00R0XP/RGGtt62OQJ80t+4x5tvVhYGCtHlbo2lLwzOfn2med/drLiN2LWET/NXjBPv+itfZO6+PoBTENuR3dAJesQ1rdfK9nDab+LeKEpvVe+va0ZSMxLRzxDLW0vifONYN5tpnQI+vhWhLP4hDTUFLrOqPEM7YWqSutUv8WN53tW+5h5ZsvprFtIKbQqnCW+NylwjlTYaGMlC0e18GHawNtWg38I4zm63Fm8OnM3wEA+tM6nuX6/NSOoe3f0GZrL3XrLQCo7aieSq3HUlcWU2/m54pJIf3CPQxcHWHlVWYjZIrSuDm/LvX8Y2c5M0BS1sid/MzqizNKTHPdqDLzqo0WK1tSB5Bybp179jMRJ2e514qYFm6UeCZCGy0EbbTrzp5/6N/Pns5IRzyLM1JMg0616/OSbWzfIMneZ9Ler2PkewZfTOOZVwXUnC3c48zkbUEbteCFSj3/0G17UB7pDoShrKRrGStjVpICgEa1B656jHe00a47e/6hM9ZnT2cAaXI9L67HeKVV7YGrkp+3tz3d+t/6d6nPwwJCMXiVWe4Hq7Z8zgX0i7n+BJPrct3o9fScD8xtya+1Gyi5nveHdvV4yL1IyEN4Sx07+SqPGVZdYW6lngVR+rPQr9Qtl2ZWIr24BhgR/Ttza72afP04E/IhSpv6mVeuZY1aOtrWz89pfSzQa/tckfW2KNvX9CZmu421kL9JTZNtutIYgga+8r6XR0PKTIm9/keolzSolXa+Ou7sqmHf+8bUq6PlI43bQ9IohRaa2kStO43QD/JK/POAUz9j7z1mT3vo58qjNfIu5aNvrfurZ8s7Le5D1+k8c9/j1INXe7Yd/632/p45U+K8bf7R1NiPcXS8OctJaNne21KFDnlodnaQlTw9t5Krpc4OYI2WN1ut5PXtZ7/389HSHfrs3ZOt22i+37c8RsAn5SHrvWrxLOBWnf/AWa6O6VrlZuS6COXMlFda9qe6+h9nSn8RBq+8e3u79vGseVy9DjqgPlYBXYkpK65BvpT3AjRw5VlNjSLMKyQfzHgzvpVj1nnoZ5De6MVeG61lPqbcIMZ64HX9s1GNfG5AKS23fwO2fO2K2doPWs51vU3/bI8vmXrwKnaVhYZRVswnppEzy2y+RcqWgimOZgDHHA/gkzphIWaVYst8ShlBiFmfJVMrpm3fh2ddQRtf3ls33ENeD5RUYzu8GYXeD2tYhQmcRZ6FJiH1L/n1ppi4FfK6I7Ndg6kHr3xmywjAqFzLatdBI7WjlNUkyKnkSt/Wq5+IqZhxG0AAABAv14Ag9xXoAfkUWjCQmm67G0DK5EDfwNbs12aIwavtjPLZLyrG4nsG29Hf4cpeWsTM2KXTFS0cDbrGIo9CK/JmuNiBaFfjJ+XvgFitnh0M1MIWrG3t7SxAfxBKIV9hdOTvfHL211D3iDzS+gBycnXyAb177qnbzmfWABiTL6ZRD6A3s99w53J24Crmc6hncBYr1zGL1ivcZ7W0kdfPFOM6oBTuZQH4lBpk4h5jkJVX28yx99wfAg1GQD5uw/UsMSCn9U0JZR0jIl+ftzeRJeUZecQytLD3oGnqBYyCvNwW7TW0ouXZwgDaovyXM8TglQsZB0BuOToOAZeQB6OybBxAzof8+jr5GGBAKeQpAKWwdSBqIp8BKG32uKZy28D1831mXPo92/kCPZo1aCBdrnhG3gOQk28HA2CNNtq91ocAACiA+h2AdjPvnmGsta2PQZ40t+wz5tnWh6EKM12BPsww++EF+/yL1to7rY+jF8S0m2YoJ0Dv+dzVGOr5nPYQ08IRzwBAL+JZHGIaMKfe22iz8MW0obcN7BkFC+gDZRU4RjnBLEabfDTKeQAAAACYD+2Z/qncNhDAPGbcdgYAMJ7eG0ZsHwgAWNBGAwCMYoR4NsI5pGLlFTC4dQWnsSNK4zEBAPTx3bDHxJJSq6NGWHW1HPvMjSMAQN+xDADQp1Jb/I0Q05ZnXo1wLrFYeQVM5ExnFB1ZbqQNAEBkjIYRMQ0A6shR31Jnu5E2ANCPWQdmcIyVV8DgltH5HO8zq5DZ9ARaACinhw4o7TFgLw21HzMAjCzHLOqZ63HaXwDQVs72BfW538wxj8ErYDIzV3ixtmnlSzvSFAD6QH19HYNaANDOzNsAnUXaAUBb1L/1zJzWbBsITOC5p27fGITBsZmDAwBossQx6uV0rvTb+xn3CQBQzzIIQ90bj/sCAMColnuD2e8PWHkFTISb+zSkGwDoQZ18jmsAa90oIo0BoC7qXQAAsMa9wRVWXgHASQQUAEDviGUAgJEQ1wAA6B+DVwCSzb50dUE6AEDf2I7hytLRR3oAQL+ov6+QDgDQN+pxiDB4BUztbCDw7c++3pt1hoAzy3ki3bZMkF8AfY7KJ2UXAFBajjZajc/RbrslLgCgL9TjD82cDgxeARPLuZWCryJlywZg38w3IIAGvjLoGnAmpgEASsoRZ3yTMJb/iWcAAEA7Bq+AiZ3tON/+/bZzb/kHnRg4qYOVGoBO6w48XPHFdQBAHTnqXVcbbPk5bTS9iLvtcN8D6LEuj+tySTm9MlMaPNb6AI4wyxUo52y5Wv/9jGW19xmLPR+7Zm9+69fk7t17rQ8DwIGYbSjWr13/zWj1KFtzIEbv90GAVpSrc3qvm3o+9t6R9oAe2/K47nOcsb2yPe+Z6iv1g1cA+jBTxbmY8ZwBYCSzNn72kA4AgBHQRkOK3gc9gRnsrR7etmFGLsdL23Xkc9yjbvBquQh725bMdnGAXsxaPunoA4D+LfHLVafvNRKo/zGbWe/1gJ7NXm6J1Qg1e1kBNDt6RjFld3wqnnn15rd+7cHXR3szA9Bhu1x1xsYB9RIA6HB273Nfw2e9XeCMsQ7z8rXR6CwAdKKN9hB1FEIQzwC9lvJ5NNlwFrPWVyoGrz770hOtDwGAw95DEvcqzKMKdNQgM2PggB6jlisgxBKftg/wTfn7mLo89fO0YlAOLrTRgL7QRgPCpdwDAqgrNqZhTIfbBhpjXi8iPyoi3ywiVkQ+ZK39IWPMLRH5MRF5g4j8qoj8SWvtl40xRkR+SES+R0S+JiL/vbX2X/g+481v/ZrIp/3HkSuozPpwM8AlZLb59uvURtD6AYu1cEOKtRoxLcaZvLkdVD77fkBPjraPyPl+oJ7RqGUbrdS9VYv7RECrkHK2LTMhZUdLvKvVRpv1+SC9qdlG267kKIH7Jswq9/On1nFu7732VmSNWuZmjmUhz7z6uoj8RWvtvzDG/D4RedEY81Mi8t+LyCestX/TGPN+EXm/iPxlEfluEXnT5d8zIvIjl/+dPvvSE/KMOT6QHBdq1gsN7PGtpBK5udVEj+Wnx2NGUcVjWi2zbwWDebmej5rbLNtTUJd0S00bLRfu2YBwKX0jmjq+tBwH1KjSRqs5aArM5mz52muPhL4fZW5sh4NX1tovisgXL1//ljHmMyLyOhF5l4h85+VlHxWRn5GrIPIuEflRa60VkX9mjHmVMea1l/cJQgMaKGuvjG07A9cz+FLL5CzPR1h3cI56jqNoEdNccuQT8hp6dHZ1Rcl8n/Le1PtooWUbbX3fQ94H8nO11UTiVlltUV6hVa2YRhkA/M7c2+393fr9jganKJ9wCVl59YAx5g0i8gdF5GdF5JtXgeHX5Gp5r8hVgPnc6s8+f/nZtSBijHmfiLxPRORxYT91wKfEtpkuvqW4oVyvJxhBk1Ix7f/5uqjQWhQdi9Aq1xYSOYXELiZYQaNabTSeOwBcV6qNlqM9hodIu74UbaO9XO64gd6V6DvYmyCPNDNvgxvcw2aM+b0i8g9F5C9Ya//91RazV6y11hhjYz7YWvshEfmQiMiT5ta1v3WNyta4QOvVJzNmCIyNrYGAKyVj2p23PW73Vh2u1X7uW+3PBErJGcNSZ62PGEdHPa8Z1GyjtUTbDCPzPX+YfI+ZFI9pFbfC9aFsYxa18/oMZWvG/p2gwStjzCvkKoD8PWvtP7r8+N8uy3KNMa8VkS9dfv4FEXn96s+fvvwsWosLcWYZPlBKzvzoWsqLfGYImD0rHdP2nhFy5pkEuW5OWuTLGW+scKz18xRLzCgEWmjVRmuBcgaNSudLJtViJsQ0oK0cMaflpN0Wn9fCdleQGc75kaMXmKupDh8Wkc9Ya//W6lcfF5H3XL5+j4j8xOrnf85cebuIfDXHs0FcRux0H/GcAEADzTFteWbaEgPWX+/9PoSWPaSJa/Dx5Y9tnj+Tj5976na2Z82t/81GW3lOqRtHoDmejWq2PAYdZowzmA8xrT5iGvac3Y1h9nZSbT3snpHj+EJWXn2HiPx3IvJpY8zyif8fEfmbIvLjxpj3isi/EZE/efndPxaR7xGR+yLyNRH5f50+ygO+To0eZ333dKwYA3kOE1Ef00IcPew05Oeu98u90nM9GEddM6+jh/W68seZG/KZ8luP97s59dBwK0B9PGu5wrKEEc4BmBX3oeqpj2ki/k7Ykvmr5LOIKBfYIk/0Rfv1ynF8h4NX1tr/U8S5M+yzO6+3IvLnTx5XsG1jtWbDtXRHAYFkTnsN/dZLb7WijCCW5pg2aucrA1hYX/8UZwZmZ7C9Dy6RLr6Bx9Zm3fJbczxbbLc18f0+J9poKME1GDvaIG0OlBHE6iGmifgHfErk+9LxjAEszD4BrgbS+LygZ15p51pttf1Zb884IGPPadvRu/d7XNH2cONRBx/QB803RZSN+ZS83hrzeEm+mLY3uaVUDKwxSIZx1eroc31WT+8Pndb1KwNWftraaEAuvjxc4h6sRnmhTM5p23fAhPl4IeV9u/sI6ZpmiMGrLddMKDJKOs2doqNY58+9AVnSPlxIAAl5HVDbUYdIyIBASodKTFlILT/LuWm4MaYOKOvoGjMxI07oqifSEL1ZxzzybxraCOXttdFI8zShW1mTvtDqKG9yz3YO5b8sXz8BfZDxtmMPexN22e4zjyEHr9aOtqtAnBkLSS2+dC05g3qG67nNt6VWgVDHIIdc+bN02Y6NB65zalkXEdPyiE3HElvQaY9pOY7vqG44m56+9ye+oRStZbYnxLKyaqet9nhW2qznjbH0lo+1xJFZO+ZL2esHa3EMrT47VI4858q7rNTO45HWB1BTy4zSe6OfQpaPpryg/brmSqu98yy9TY2m64z+PPfU7eR86/rbEvZWUu39LnbFWGna676euFZRhebVnLiuV2qWf6B3vefjpb7t/Tw00JKGNe/jUmlJqxiUE8BNU/nQdCwjq7U6sIeYlour/2amNCjFXD3nsK0nzS37jLnxDMbhbEdh1yPQ6yWGvkzNLARAtxFnVrxgn3/RWnun9XH0omRM891khua3s0vZQ250e1g5hvN6mEmHeDHlt8drT0wLN0sbbWuvjQYA2hDP4swW01L6JYh5QB+25XuEsuuLacNvGxirZMdzyJ6iy/97n330ewDtrTvuKavILWUv6qMBBq0DEFqPa3Qx6c616cOIkyqAklxbTVN+AAC92Hsez/p712uhE31LY0q9rrPt+sTKqxXXBW9dQfBgc71Gb8zOHiBTr2+Nm8GaeY9ZfXG0x7QYZ/JX7psoLXXRyPU+DdlxxQ5g1V59RUzTSUs8E9FdP81+v6wR12RsZ2JGjbxRK/8Rz+Joimki7nzcov6izuwD12lMZ6/rXl2SM69oiGmsvFrRureq1uOazXYLke3vRgwiI55TDM3nT72AGs52Fi6vz5FXtdSzGo5hcWZyC/UHfEJijKaygPFp6NxzYXWWHnvtNa7JeLRfU+3Hh7aO6qYW+WeUbcdGxvUZ19mBq9RnpfeEwauNUiOVZ207ETQd22xoCEEL8iBqS9lSLucgCbHvodR0Df070nksqdtR+PJL6PNac3wWsKWtjtJ2PDNxrSzlmgDQyPX4kNZ1FgNYbTFpDGftbQ+aI99oyHtsG5ggZ2P9zDGIMKsM0Ejz1jYp2JIijsaYVqNTuPa+y72XK5G0xmpK2vqeoxnyWsxrxIY0MS2cxnjmo6XTTctxANg3ShklnsXpLaaJ6N9CcJSytFWjj3XUtENdI+Ujtg3MbD1g1KqTejkGZqkCOoTMTgdaqbGqoVU86rV8+bairXE+PaYZ6vKVafIPNNrWp63yKeUDAJCDqw1X47ltrs8KjbO9ttFEru8gEjvJMHRCZ69pA7TA4FUi13ZINVdBzb6V4Pp8Q1fDtQj8GB+DyOhBiW38WhihzvYNXC2/34tpMVv+9X6d0U5I3pntnhP90LQdE+UkbseSvbYdkMPeJCHyGHoRUneGvi5m6/cz7xFyPBptz89VV/jS4SjuUffgrL0B5NHzFYNXJ/k6npbf1zRyhvV18NXergoAsK/kwMloDyMNHSSI0XN69ICtmh8a+Z4T49ibuEG+zWvdUSey/9ypkHsDtsNHbr78RB7DKLZxzpW3Qyd7+34/a7mJHfg7infEOqRy5cUSz7rShMGrDHw34y0aSiNmVJG4Ro9vFgSQ2zrfHa2oAForvQKrxPuOGNNKopM2v5Yr7QGc55roRhk+b9v+CulYOXovIAfKN2YSO3Die4+jvx+1bO1tP+yKba5t3xkAREkhO42NmMeMtbb1MXT54MQzcj0kndnY6Wi01jPyzU2sXvMdDwOOM1JM66UDqafytIi5F0i5Dj2miVYhE2dm1lucJ6aFGymehcj5/MHQctFb+amBNEFtveY54lmc2WIa2mM7XLTUW57zxTRWXjVwtNXg8hof17ObeuloxPi2z2Pb6qkSzWV949I6kLT+fPSBuFLOXtoebfMBnahPgTHkfF7W0UQE6gy0woSMmzR1Lms4BiBE6sqjmbAdbln0M86DwatGjpb0uh7AFvsZ2wEECvKVkP1+cU6u1QMji334Z07kfYRi4KScmHJYertH7NtuCYubiCcYla/e9W1DGPo8gu3PgBrIc25nngVU4xgATXJO+BgZ6VHGuo3mur+avc93pHNn28AO5VihBQbzetL7tdouF1+E7Fcb8trS2JIiDjHtSmpnv++hw8S0687uWY84OWarj9iwD4nRmlacENPCEc/q6f1eNyfSoh+9X6vU49cS04hncYhpeYx4L4sxxdTxvcezPTFbWIu0L9O+mMbgFQD19mZTtK5Ya6Bh1CdiWn65nhU5ou3N5iz1Y2upA6laGgczI6aFI54BiME9SF3EszjEtPy4r8VIiGFt8cwrAF3bBpASAUXjio7Wnw9oQVlwq1E/4qbUdK51fTTGNADA2Eq20YhhgD6US4ykRX5mwCwMg1doioKK0kJWL/EsE2BcxBmM5KgTj3gGIAdiJ2oIGZiK3XodAIAWYrcpFCGehWLwCl41Gi7ciKIUX97yPVOKBjt6QV71W8p5jWfIsdIFpa3zGINUwNyI/+idKw9vnxW8fE1+R0+oo4G59PZ8qd4weAURafM8kb3PpCAjt73ZeqF/A2hG5/W+2HQ5O7Dl+7xckzNoAGMRG9PIN0DfWj3z8bmnbtMuQzHbASrX78l76BV59yYm+mFU27b63vciQlw7gcEreLVqtJzpqKOhhYXvWTCjd/xTDsbHtS0jZbl/7vfd+4zQMk3ZH1fMNeX6A2NwrUwpzTW4QN2CHNb5iDwFzOFopWUNrSaEYGx7/Y57905MSE1nrLWtj0GeNLfsM+bZ1oeBi5IVeo1gQQMLM6mxouwF+/yL1to7SX88IWKaHjk6+HzlpnT5i5mhSGNsPq5Vg703jFLLbeg5E9PCEc90y13WS09+6L1uAmIteb7UihPiWRximk6+2JAzbpSIQbGTHke5V4cffdJpfDGNlVe4wXWDlaMAlq6smXGO2cSsJqN8YAalO/NqrdqMXdW1Z4nnlPnx+PLH2evda56hoYjZ5M7nlBsgL9/zunqNtUCqlDyvvYykTk6kjTY2JgHlx+AVdh110p3tIChV4GLel1nqGE3tznVAqxJlIOU9W8eTmW9wZ1Hi+o6QZ8j7mEVPE5NSt+Tt4dyAWDxXDrNpmc9br7qqcTwYn5Y80yL/sm0gghx12mkpRKmYrYtR5djWjC0p4hDT2tMygJtjBUzIe2mN0XTKIFapsrvOg8S0cMSz/tAhBvQp5Z6JeBaHmKZbqT454iJQTs72vi+mPXL63TGF0Sv7XM8iyUFLpyviaL1uzz11e/jyi7a05v0RLOW3RhkueR3JIwhVcpsNYAbc8wHX9VL/L/d7vRwv9OstL63bPXdfvvfgX473xU295Q/otC2zpbDyqrIRZiGXfkB9L2o9WDIXjceENmJXaTCrL86MMW3h27+7xg1yq891qV3npj4Q3HdvonVlMjGtnRpbepUst4++9j4xLdBM8Ww0M9eRtNEwgpB8QRstDjGtX6n9qCPUr8Q0pNB2HUL6FFh5pcB2FLL0DOeS73/muVI5Z1BodfbcXOmzTruR0w/tkb+w8D330FVPrX9fu85KHbwZxfpcY1ZshbzuTDr68sqZ9yIm1lfrWTQzlVu0tddWGcHMKzg0TBwBzqoxkx39mi1fpKzu0NZ5n8p3DiOcH/LTmPfP7ijzWN7DwZ4l42xHGl2zB44ymq9zzjVAtteAqZWZRw2srvTL1Vgs9VBJEYJcbdrSfeYOjVpGHUTpNd+0yPOtrnfq55aKaesBTdfvfJ8f+l4AEMvXRqtZh5e6TxzhviO3XBMxtpNFtK5Shp/GDr61bR+P5mPtRas+MeSxLhNHfaZc22OlygN1Vhu9pHtK+WTbwEJCt+Xau2ium1/Xex69xvf+e7+PwR60fRi1Iz2nkjc4vTRoXeWZLZbijBTTeh0kOCpnpc9LczlvzZf2sRN3Qv92RiUnwKz1+Dw2Ylq4keLZ2rp8+La69W2lGvu72OPL8T5H779FPQqN6IR+aFt3Ec/ijBTTSpeL0HuvkHv3FuWXeuO8lOtHuh8jjR7a5jHftoEMXlXgauzHNn40DkDk7FBofS5AKb3MgFjEBBHcNFJMG3XwSqTOAFboimuX3uqOWLGTaVIHvpBXi3wZUl5DVwnS2RdupHhWWsiAWG2xx6HluAGEoY0WZ7SYdjTJYmtvtyaXnLGgVWzxfS7xDtBjKY8MXnUmJgi1rHBLdPzlOB8CEVzIG2loGMUZKab1OHh15rmMuYV0pvcQ70uKWZnKaoHreolppVf8h+xSsMbgVbiR4lkOvZW5lGPt5RwxFvJdGtpocUaMaduyczRgIzLXfXOJASzqKxwhj8Q7Wk2sYvDqztsetz939/UPvm+x1zjOCe2YSOkojJ2hHvt3mAf1ShoaRnG2MW2rpzyoefBqb/ZgatpqOk/XrMie8k1pMza+92jfkjbHSrmUcnBUnhm8CreOZxrzGPrCfThcyBtpaKPFGXHwKhZl7UqOdCAtMQJN+dgX0x6pfTAheCB3f5bM/txTt70Z/+j3e2L229VS6ICzqP/GxbWNt9Tv6397vzvz/tpoPCYtasR77eVU+/HlkDqAe3QfinCffemJB+Xt7sv3HvzDPHJec8ofXMgbQB0zljXuXYB9y8BVavmoWa4eq/ZJHkvDaG09gDVjBdujmOsUu8ULs6z16una9HCMa6FpG7NdABAjZG908loZNLLaWKe75u0be9ilwHWvl7J6KvZ8zzTEsE9zXkM5e2108kIfemqj9Sa0HNBG00n7ynXktXeNj/ojyRc6sTNJXutFKLFxrfazXlUMXu0hE84jpYPBdcNBR0Vb3Ajmsy4XR8GATrr+jNCh0POx73GVo5ABvJwoy7rt1cetYl9PZTAmpu2hXAA6rMtyT3XQjPbayFyzPEI7+rb3lqS/DiH9R1yreXCt++Brg3ENz0uZIFgz/VU884q9ZyGS3jHh61ikEquP2RB5xQSEXMFje2PAfupxjp55JbJ/jZiN2ZcWnenkj/JCrqtv8Mr3utnlWH2V4zOJaeFoo8En9r6F+5z2GEjpG220c1JjGnXXuFzXNuSaky/04Fq0kTvdfTFN7corzCd1BZXvtaFBZ/v5SJfSIRXztzPZbl/lS5+cgYPrUJYrfUn3voSsvsk9wBXSmU/H1Dm1VgTt7TE+0/VKmbFOwxTQpfR2aLTR8gttF4e+fla1t0tacD3aIN3Hs5Tb1GubWu65ly2DNG2jZrozeAWVcnb8hWy5hrpyzZ6eQUhZ2Fu2S4O/nfVzHNnqah4tytpeOafM13UmvWeqH86srs/1EOHl+0dfm/R2ADxK3HsSz+qjMzZMzH3Xtg07Y3oBo0mdIE/Zx2hqxTS2DUQXcnTwECj6xraQcXIEkeU92JIiDjENe/ZW2pxFHdifo+s/6jWtPZvfl86PvvY+MS0Q8Qy50XE/NibP1UEbLQ0xDaHO3K8T5/rGCuS61uWFbQPRpRLbLVHx9ItrFyfHwBUwm9KdLqW3EVyjzoQmtbZXInYB5Z2ZUMbuAGPjmgIopdQ9ZM5J0vQ59o/rV09MeWHwCqflqqCPOuFKzFrHOGgIp9umHWUMyKNlWaLxpJPvPmaG6zXDOQJa5I4Due61qQfmwz0JgDNKxTORtOc3up51TD0H7Ds7SMy2gVBhrzGU+oyjkM5Cggrw0NHAMVtSxCGmYU/LgSxiHlL1NjEkZJsXYlo44hlcck9e7KWOAVpw9YsQz+IQ0+DD4BPQFtsGQj1fkIgNICGrR2goAQ+xohEYGzEPZ6VOKKqNFcRAHbnqAc31CaAF5QQoL+djFyizQF4MXmFYoYNYBJa58ADGfXvlhbTBCBi4eYi0GM+ZB0qf+UzteWhvJb/2YwZmRjkFgLloup/McSzceyJUrW0wR8LgFW7QEkRKzOjb6+TJ+YBG6Ba7Gq/kQ0G15jGtxwX0Lvf2t2fMcIM7uhori0KfzaU5pomQxzEO7WXtjO0kqt7qGZyzvr4h13rGNhowmvVz7VvLfQwMZM3tKKaVzG+u4+jdI60PAPqMkrmBreeeuu3M377fbTvwjgbBWsyEB7Cvx/Lmq49yY3u1sSx552weIl8A+vQYz2ItdVfI/TTGkZq3Y9poJY8DQJoZytw6rmEO6/uYknl8pn5HY61tfQw8OBHVxQSOkQo8yggNGr2uduBhwHGIaSildKOnp3oJ5VaOj94QIqaFI55Bg5FmDqM+X0yjjTYXYho0YCXWPNgeMI4vprFtIKYU80BvGkwItZ1RU3p5MIC5lJ61R7zDEfIHgBaIT0gVkm/IWwBqidkiFX3LvTXmzPmFwauOUdmdFzqIxewI+Kzzhaa9mwGMhwEs+OR+4DSAeNSjeR09DwsAgN6s23TEtXFxnfNg8KpTrkYRjaV4MZ2AvocILz/nGoDrD6CkmNXDKUbfkmAUXBtAH9po5ey1v2LSlGsAANBkHZMY4BgX1/Q8Bq8mRKfUda4Gps9e42f5njQFgLLWdfDMMa30INby3rOlK8ZAJwAwtthJg9QFAACt2E4QcDPW2tbHwIMTK3B1bFEpHovpFCQ9oUXqTc/e3/Ew4DjEtPxSB2dmrJNLDmStzZi2aGc7EOWLcUf3vMS0cMSz+ui0SkfaoQc58ynxLA4xrTzf6uM16upjxDT06KiN5pt45Itpj+Q8yNZqddj0hnQB5rO3BP0IdQVG8dxTt6e92Z/1vDG+vYGrddy6+/K9pEEtlEfa+y15l46qc5YOkW29AGiS0kaLfS3QwpJH1zFtHdvW/3BsL6YBmrnaaOvfpebnobYN5HlDN1HRnRezLRP5b25ar//RzAdAM+JYvDM3hqHYkg01rfPZUZ6jztCHNpob6ZJXje100R+t5Sy0jab1+IEFebQM0hQ9OZpAuB3cisnfbBs4CYJJXkcNItJ6DrOUK7akiENMy4ttb89jK8Fx5RxEHGVAci+/r8+JmBaOeIYRzHK/jodmuebEszjEtDJmKW8axN6rj3Jv36Pc5WKGcjbNtoFwGz2T1xYy65cZfzrluC65ry95BUBJbNGBIyPFoe3WNOR9YG41ViPjvJzXiHofqIfyVs9yXxuzKxTXp51ccW2GgasjDF4BiUICAQ0lfc5W+utrOnsAAWrYljNuwtORbuPJEdNy36swgQeANsQ//XLFMyYYAhhdyAAWca+tnH0Wud6n55g21DOvzmAkE6mO9lbf/jxmiS95Up8S14TrDPhRRvJJfVB4CAb329DWMOLaIyfuh5Hb2W2UyJM60UYDMJu9vkZi1BhKXMee8wWDVxc9X0TokHNLip5HxAEAfSi5lVLoxA0aWAB8qB+Q23biIXkMANATV/uJdtU4uI7XHQ5eGWMeF5F/IiKvvLz+eWvt/9cY80YR+ZiIvFpEXhSR/85a+zvGmFeKyI+KyLeKyG+IyJ+y1v5qoeMHVNlWMHudgjEdhQQfIC9iGnBdyZVYa3udhMvPiHXtkO79Ip4B56wHsULqQp4fApRDTAPC+NprOVde0T6DJiHPvPptEXmHtfZtInJbRN5pjHm7iPygiHzAWvstIvJlEXnv5fXvFZEvX37+gcvrACRad+6xIgs4jZgGOKwbKKUaK6Fb7AI4RDwDMii5ChlAMGIaEOCojcaOUBjR4cora60Vkf9w+fYVl39WRN4hIn/m8vOPishfFZEfEZF3Xb4WEXleRP5XY4y5vE8QRngxiqPnYYViWwsgjxYxbQ/PBoJWueKWj28Ai/IAhKGNBuQT0tlH3gfK0dJGA7QLuRdzxbTQOMb9HrQJeuaVMeZRuVqi+y0i8sMi8q9F5CvW2q9fXvJ5EXnd5evXicjnRESstV83xnxVrpb4/vrmPd8nIu8TEXlcnrj2ebULCZ2IKC3nIBZ5FDindkzb2tYDoc8GCkVMQw41BrH2Poc4B4QbvY0mQp2AeujUA9pq3UYrjboDOYTmoZS8trc9LvkWGoRsGyjW2t+11t4WkadF5NtE5A+c/WBr7YestXestXdeIa88fD3LFTGC7ZZM7JsO1KchpvksW4QS96ABcQrQS3s8y4Et3aANeRIoY/SYRt2BltZ9DK58SJsPWgWtvFpYa79ijPmkiHy7iLzKGPPYZRbE0yLyhcvLviAirxeRzxtjHhORb5SrByieVmrrNAooatrLb3sPsPfJvVIDmFHrmBbi7EwnZkohl704tW2EpzTK917PVrlAnNbxrHSZpS6ANuRJoJzWMW2tRFuK9hla2ct3R7u2LO078ixaOlx5ZYx5jTHmVZevf4+IfJeIfEZEPiki33d52XtE5CcuX3/88r1cfv/TOfad3XaaMGMBsyLvA+m0xLQYR2We2VOordYqYuId4KYpni11AGUWs/E9wxFAOE0xbW2JbSk7Y9A+gyaufMcuG+hByMqr14rIRy/7zz4iIj9urf1JY8y/EpGPGWP+uoh8SkQ+fHn9h0Xk7xpj7ovIb4rIu3Md7N5zEdY/B0ZA4x8oqnlMK/UsIVZkoqZaz8QC4NQ8nm2t72GJQZjB3ox0ZqgDSdTFtMW2PFPG0YO9Nhr5Fr0ylSeQ73rS3LLPmGeT/tYXOHI0nghMaC2kY5A8ipJesM+/aK290/o4ehEb01xbsJVCfYFSSuZf8i1yIaaFO9NGK402GrRh4Ba1Ec/itI5pxC30gFiGVnwx7XDbQO2OBq6Wr1O2GmQ2MTQgaABjWy/Vp7yjZ2w7ASAU28BjNMQ/AHsYDEBP1ltBc58GLUK2DeySawY7sx0wIvI1MA62DkXvSmwpSJwDxrEuzymdetQHAICW1ve4IW03V9winkEr8iU0GXbwarHdh5oOQfTozA0RAAAtbJ8BchYzV4GxbNtolG30jue+AXNIec7d3vOJqSfQA+7R0Fr32wYeWQLEdhtBYERa87bW4wK0Crk55AYSPcmZX1tvY0FMA85Zd/BTnjCa9ZZL2vVwjMAolrqBNhx6Q0xDa8MPXi22zxNJzdStO0wwr9AbHY15lBs0IJ6vzKc+I2sdAzXWFRhb7ljQKg8T04A8ts98JCZhJD3kaeIZkEdsWWdnKPRmGy805l1i2riGHrw66vTbs9cRQicfNGG2DjCX0itWjr4HcioRv8izQH/26gJfZ/86fu1tSUo9AI16GMACkG7ZTi30MQ9by9+F1BPUJdCE+Iaahh68EnF39Ps6T0L3ql43lhjcQm1HHYDkRwBbrnrDNWkDKIEBLAAuvsmHex0l2+cbi1AfQBc6+ACIuOuCdXyjDYaeMKketRhrbetjkCfNLfuMebbJZ+d6UPBeQwqo5egGJzY/8kBGrL1gn3/RWnun9XH0okRMO4pVrjogdLDqCPVBH1wNYs1yNtC1nyt0IKaFG6GNBmhyNi9TFrBGPItTuo1WGuUfmpE/cZYvpg2/8upIrsLFVm596312S8gqrJhzJC8DuqzLpGs1cehDgHuv7zC+lBhE3ALGchT3gB6dXYVFWQB0KVUmWYU1ppGvJ/EJJT3W+gA0yVHYKLBoJWS7lNjVGa73IJ8DulFGsdBeZ/u2CItp4LECHgDQg5Kd3cQ+YAyxjz4BNCEeIbfpV14BIuPdCJQ8n9HSCphNShkeeZbYSEZr6PZ87AAAHMm5soKYCcyDthk041mPyI3Bq4IorGgpdjUVgHmEbjG4Rj2h03pbEVYfPUR+BeBC/QAtlnsxtggDEIPBAQAzYdvAgmbuNBpF78td1w+6PrJ+Tc/nDCCOr7xv6469uoT6oq4zW+kterpm22M9e/49nTuAMqgH+td7G23LFetGOkcAee319VBntDdafEq1zp+kB85i5RXgMUolywqLeXDtkNO27tirR3wrf5DfKHEpRY7OPPJoX7heAPaMHgtj227Qj3iGUtY7apDP2mOL/uvWq4sxjtrXk5VXFbGdD1oLvaEhb/aJWS0oJXbwm3yIEnLlK2a094G6BLUwax1aEa/GQDxDLa7+HvJfXTFlfob6YbtKcPTzHV2LPGustVU/cM+T5pZ9xjzb+jCqo+BCC9+AVsv8OUMg78EL9vkXrbV3Wh9HL2aIaXtlM0fnHx2I+9bpzYPdH9qbFHQmfUZIExwjpoWbIZ4BvdDULtJ0LDMjnsUhpqWj7/I86s2bSBOs+WIa2wY2lGs7AJZf+rFE9ZyWaUcgA3TaK5vrLStyvu/M1vErdywbJS7myHeLUdIEgA7UKX6kTxxNW4JxvwbMhW0J0/jSi7QkliAcg1cDoMC7rQPCXsdf6HNaRg8sRx1/PM8GQC085wGhXIOoAKAB9VF5s7VNcq/CBoAYxLU4e3U2q9huIqbhCINXmI5vIGY7w322wDLaeRIEAcBt1DryzADoqGkCANqcmcU/WxttixUQANCPdayaNW75kCY4wuAVhhZTCa4HrmY2SuAotdUWAAC1rSfVENMAjCJlEIZnZFzpeQCLeAZgJKF1GXWeW+9tnV6PuxePtT4AoLSeb+xbWTcI97ZadL1WE63HBQCoIyX+9xA7ejhGAAjlqtNcq6uoAx/a25Kqh/Tp4RgBINS2Lt7WcUy68BshfXo/fu0YvMLwzgxcUQH1a4QACAAlUUfepDF2MAEHwIy01cWa9ZRWGuMsAOTgei4hdR5wDoNXGN565vXRQ24JKjftpcns+8wDAPRLGfTJ+aysUjGSjj8AQK+IXwBGRz03D/pG62DwClPYbh1ExXKO9vRbP+9K+7ECgEvJbW+pG8+nwdG1KRWDuHYAgB7RHgcAtFBikGnd1qPvsSwGrzANKpL5cM0BYF4lBv9abuFH4+gKMxwBoE/U2wCA2mpMKFzancS5Mhi8ApDEVzHHdO4xK708OvqAfpUYgJmpLsjxMPvYmLZ+fY5GzPbvZ7p+Wzz/CwD86DwDAPSk9FbrtWLizLG3dJ8jg1cAkuQYuEIdMwdRYAQ5B7Bmrw9Knz8xEADQki/O8dxnAMBoYmIbEzzKKJ2mDF4ByCI1CIwQPEqewwjpA+C8vRVEKX8/q9TzZzBKl9nzMQDEWtoSobtmjFTP0o4CgHGlrPYhJvSJwSsAwXwNgOXnKSuytr/LFVBqNVhKfgbBFcDauk7w1avUHcdCYoRrxh4AAD04aqMd/a5Ee2qENhoAoK2jOn6vzUZc6BODVwCq8HW4rrfEyhlMCEwARkYd18bZVXAz4xmMAJBfbyuMejpWAEB/9uIibbd+PdL6AADodPfle8VWRIUGkeUYYoNMyt/svYcWmo4FAHqUO6alxqXU+LT9uzPv1cLe8QMA8igxGLSutzXV2ZqOBQBwTok6/Wib3J7aULjC4BWAG2pW5OutLHIPjp0JSlpmBGpsNAJAr2rXpSW2WtqeA/EBAOZRq9OtRBstB03HAgA4Z70LU0nLgNb6Hx7S3p5k8AqAiOw/qHcJJCUqsiVghL639sq0hFEfngwAJfhWN4U+Kyz0c7bvHVtHxxxDyGs1xEjXwBr7zQNAPus2VMm6n5nrAIAacg9gpbzfbPGstzhurLWtj0GeNLfsM+bZ1ocBTGVpkJTaGjCHdaPp6DkZvkpX0zmthexPr2EP+xfs8y9aa+80PYiOENOA+lwxoGT9eXSzH7JFbkpMC/28mo4mW2i61yCmhSOeAfXFtH806OEYS6CN1h9iGtDGOk5oqDt9UmPa9rx6io1arokvpj1W+2AAtLftZNI62r6uQH2V6dHxa13BFHIsmo4XADTSGsO2Rq7Pt43RXq4JAGgT2v7RIuQYe+rECzXSuQBAKS0HRlL6AWOOdX1u235VrTGi13jM4BUwoW1F1VvFtZW6/aDm89Y0Sx0AtNneeLeY6Ua9fN22w5UBLACAyHjxstfOPwAo7aiNVrPerNEW7KV/cbF3PXo4bgavAAxh1NnedAACwE2+mek93IDPwLU1MTENAObWYqvfXLRsrwQAGvnqx9HqTuJBPQxeAYBSe7NVAADjG3FCBjENACDSdxzo+dgBAPkQD+p5pPUBAKijRgeYhk62o5kePXSeMYMDAPzuvnxPRcwpJfTclrgWuz977fQjpgFAOyPHSwCAHrO00UY4x57aZ6y8AiaxfZh6bpoexHh0HNorae3HBwCtla4nNcS0vWdq9PacjV6OEwBamqWNBgAY2+httNL9qtjH4BUwkZIVbOsAAgBALlpjWupxjTA7EABGRRsNAIBjGmIasa0+tg0E0K3SsxRHX/IMANClREzzxTFiHAAgt9IDV8QuAEBNNVaU0f/oxsorAAAAAAAAqMe2TQCAkRDP/Fh5BaBbI26xAQCYT8lZdr5nQRLrAAC51YgtxC8AwChYceXH4BWALtWo3H0dfgAAnMX2EAAAAACgC200PRi8QjeoOLCYMS/MeM7AyCjTc6s5aMUAGYCSqF+wNlt+mO18gdFRprGoMZF9aacxad6PwSvlqDgfojDPa/3wwlnKxPo8ZzlnYCbEtDm54ljtbXBrryzexjTiGnpHHn6IeIZ1vT56ftjGs9HPF5gNZXputdsprXd76qXf8bHWBwA/Kk7gphnKxQznCAAzcTUIZng2SOvPB3IjTwMyzYDV2kznCgAzmXFCQi/nO9XgVcmMuO6Q6OXiA1pty9PsZeq5p26rngWBNkrFtG1em738AWfVXmml3cznjn012mjkO+A82mjXzX7+2FejjUbeA87b3iPOXq40n/80g1elO37XncszjtYCJVCOHiItsFYypi15bbSYNsp5oG/kQeC60nXzNqZRBoF0lB/Ar2RMW7/vSO2akc4FfSHf9WOqZ16VzpjL7KMRCgCrPNDaCOUoF8oj9tSIaaOgUYTWRrk/rIFnYqGEUcogZQMAMEI8E6GNBiCMsda2PgZ50tyyz5hnWx8GHAgoQFuttwh4wT7/orX2TvUP7hQxDQD2HXW8b2NciW1MiWnhiGe60UYDdGhVFolncYhpAODWepcCX0ybZttAxFtuwmgUAe1RDgEAvXM9w/Fo0Gr7c2IiZkYbDdCFsggA6N12q+31z1pj8ApOWjIpMLu9sqgxoAAAkCJ2K7SQATBgVOR1AAAAlLBMNtR0v8ngFQB0bm8ga292uutnPMwcAFCLa/XVWaHv+ehrs380AAAP+Dr9tHUIAgCw5eozbIXBKwCYwLpTb9vBx8O/AQA1lRrAAgCgNd+sdY0z2gEAcAlpt5WOaQxeTWK5QeJGCRjXumzv7Vcbarsai1nqAIDc9lYGA7OhjQaMyVeeKfMAgJ60jlePNP10VNc6wwEoL0cn4N2X79GZCAAojlgD0EYDZrDd6p32FgAAx+1BVl5NggYRMJaj2XwidAgCAPTbi2fEL8yCNhowj215337vin3UEwCAXqSsLL56/X3n7xm8AoBB8UwRAECPGNACAMxg3cl31Nm3jYNs7Q4AaC1lsGq7EvkIg1cAAAAd4nkJmMX6uUAAAMzo5j2fe5Y6gHZoo2EWe3k9JO/vPfvYNyGDwSsMb9vR0SKIELxQw5lOPToFgT5QTjGLbV4n7wPjiZ15W+LzaaOhtiXfP/fUbfIfMAjKNWa0fmRJar5/+HdsG4iJaQgcGo4B81oGpo7yYei+6wAAlELsAebRuo3U+vMxBw2TaQGUlaMTH8C+R0JfaIx51BjzKWPMT16+f6Mx5meNMfeNMT9mjPmGy89fefn+/uX3byh07MPba7zfffkejXoAwUL3UPf97WiIZ+jZqOUSEGHgKgUxrT7aaABiLCsxWJFxjJiG3lHGMaKje9zS+T5m5dX3i8hnROTJy/c/KCIfsNZ+zBjzv4vIe0XkRy7/f9la+y3GmHdfXvenMh7zNPYu/kwVITMWgDglysvVew63nzrxDF0rFRtDOl6Jy4A6xLTKaKPRRgNQDDENAJRpfd8XNHhljHlaRP6IiPwNEfkBY4wRkXeIyJ+5vOSjIvJX5SqIvOvytYjI8yLyvxpjjLXW5jtszKB14QAwHuIZ8FDKKgE6LVHSmecvuvLlyKthiGlogRgAoARiGnDFd++63p7Q9TtgNKErrz4oIn9JRH7f5ftXi8hXrLVfv3z/eRF53eXr14nI50RErLVfN8Z89fL6X1+/oTHmfSLyPhGRx+WJxMMHdFk/pBGASh+UzPFMhJiG/rjiVUhjCSjJ1yh3vTbuNUOtJv6g0EYDAIzhg0JMw+SOJgrS54gZHQ5eGWO+V0S+ZK190Rjznbk+2Fr7IRH5kIjIk+YWsyPQvZFn9gIjKBXPRIhp6EPIA8NdK19oIKG0bWOdPOdHGw0AMApiGnDl6P73zC4FQK8eCXjNd4jIHzPG/KqIfEyulu3+kIi8yhizDH49LSJfuHz9BRF5vYjI5fffKCK/kfGYAXUIHmiFvBeFeIZpnakrGERAaUv+vPvyvaJxbbCYSUwDAg1W9tEB8lw0YhqmF1JvsIU7Wmgd0w5XXllr/4qI/BURkcsMiP/JWvtnjTH/QES+T64Cy3tE5Ccuf/Lxy/f/9PL7n2bfWfRuCRChBfZsQGEpMFy2eYM8Eo54hhbWcUNbea0V0wAXVvmlI6YB163vkbexd/0z2mgogTbaOcQ0tKKlnXPULiP+oIWlfLTOd6HPvNrzl0XkY8aYvy4inxKRD19+/mER+bvGmPsi8psi8u5zh4gZaersW88GDpHjeFufM3Qjf2RHPEMR27iRO7aFNmJaz5QC9rQYuLp6/6GeebWHmIbiNHb2rZ+X59oW9ywN5wydyBvFENNQXImYFvueR898pY5BTdoGS6MGr6y1PyMiP3P5+ldE5Nt2XvMfReRPZDg2ILuYAugbYdZWkDEP8lwexDO01ksc0X58GAP57BxiGnqX0kbbe72WQTUA6YhpqKlEzDgTi4hh0EBbPjyz8gqD0rLqadnewbcFRMp7lngtAECnVjEtZks+kbIxLXRlFnEPNZHfgDha2miLbedcjXiW+v4AAF1aTzbwff7ZthFxCsi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ua1l5dVHJk00jGwAwGmIagJ4sNcwAADpZ6+gDAMDFYjxj5ZVCNQsRz5YC9OP6hAv19wP7AbFW+WHxJi8XZQ0laShHD6fhzV7JgGG1y/FMMQYARmOpDreSTgDYmF55FfMQ6JozvmtU+i074Xq+HwDwsC2mjVK/1nr2zDF/SsSzmM+goQfE6/G8K9hCuXhYqRhDvgJAWzUHrrZta2c063EDeJTZwauYiszS7IdW9vlBMAB0oc6al2tAxlVHUz7ciGdlUc4AXBEzuZB65lHEMkAn6ixsaJ+dI5YB+lmbNG128GoTChQEEQCAdrE3DcS0O758sHTzZQHlDUCOmE5e6he3mWfYA4BWPbYr33+3tZhZKr3ERKCeGjvk1GR68MpSRgMAkKJFjLMaR62mGwCAkKuddcRHACirZ706e50++/EDuPNE7wTkYPT9mi0A0DACgH58dTANpLZmPGagJu7T52VxhjgAAC7b/QxxLQ/3BABKMbvyikoQADAaBq7iWUsvAAAAABtoawCADuYGr5jNCQAYAQ0iAMBIiGsAAECEZ1YBWlm8Xzc3eGXtoWIAAPjsYxqxDQAAAAB0oH0GYCRWB5TNDF5ZzWCLjnlN3gNAWcd6lYZRnph8I4YB/TDpbHza6ljKG4DSqFeA8rTdPwDQS/Xg1b237r/zR9sNg7b0lLQ/NgIKAJSzxbTjv2t+T4wZYtr+ngJAHVxfc9HYRpsJ1xsAlKWpXtWUlhrodwQQS/XgFQAASDPzzX/o2OlgBdpjwHhcDFzVQ74CQB+a6l9NaamN52MB9R0nUVu65hi8mlhMMNzPWAdQ10w3qDNy1aMazrmGNLRCTAPq4boC2qKzDwDKaVGfUmf7EdOAuvbPWre2tTuDVzfWRh1LiTlma4UasGjG+md21KvX5Vw3xDSgjmNDyGLDCPFadjJxj+TH9QXURf0zD+rT/jgHAFzUD15ReQEAgFh0MgBAG7TTAACIR9wEgHSqB6/2MzdbfVes2TvHZj9+oCSup/loa7jsy+Bs5XHWlddALTwLCaWllKfZ6/PZjx8ojXiGnqzX6VdXaVs/fkATy220J3onQBOrJ/GK3GOeMa+AWrie5pB7nlNv2q+Up+2G5vid1spoyo0Z+6sDZVmrL4CRcP0BwLgsdj5fSa+1YwU0s3w9qV55ZcU2a3vWzq9ZjxsAamtdv1obyCl1A7b/HEvHDwAIm7lOn/nYAWAEtFEemP34gZmx8moitWZpWB69BYDRbDf21M15yDcAQEu00QDArtCgSsl6ePssi6uvSpjxmAHcGXbwyhVA9jPKjzMYfL+LYWWm+vG4Zg16AGDFvpGSKqaOPz7r6tXnXzQT0wAA45h54sWMxwwAowjV4Vf63OivA4A7U24buO8M3BpKZw0m12sBIBf1CFrwlbMtprkmNVA2AQA1ubZc3yZP7H8f+1l7x7hmseOPOAwAY9gmBebENIvxy4WYBuAq1SuvrlbcW6DYv9f1b9fMc9dnHV/r+r1mxxVmANo71jciNuoPXHPWuRbDNdAU+91XOvNSXhubptplnmsKaMO30wEQclZGXL8/a6NZRhsN0IPVLigtZQekmPYe5RPAbFStvNrPwrt6875V6DEVe0qHoLVAcRbsaCQBfVmrUxCvVDwTcZeTbZb61TKUEi99Uo+zRJ5YnkwCjIB7yHkc22g9zv1ogzu+NhoxDOiHgSu0kPOYku1vC+WzxKRNAGWMcu+8rOvaOw3y9PLM+vLyyiM/Z1VCefsbMm7OgPYsbgXwqfW119d1fal3OqzYYtrZjULM+c+Jgzl1+5V4W2qyyVXENKC81OvbwjVITIvna6OhPNpoQH/Wrj3iWZpjTLvSLrfSV2mtTAMow+K1H4ppqlZeHVmZWWDJaLMWAUssDlyhjtjzf7Zl7ZXPPr6nx8BVSVxTQF9cg0A+2mhAXxY7+nDNlV0stvdprrddW9KGnocMYByjxTPVg1ctaKukW6RntEIMWKCtrkF9peralEZV63KmZZJJr22sAAB1zFina4inAIB4WttoIg9PijhrK9UaiJsxlgOtHa/vEa+76QevWjcSjgXKtX/88W8AtnEtz6vl4I7mGaOhdF19hgrXF9Cf1roHdvVsowGYD3EMNfUqX8fnZbVOh/bVacAIjitBR4xnT/ROwGxit4DSUMHvlxmPWPgBoJUWdWjPevqsYaIhpon035+eBxgDgE77+thKXU0bDQBwpmecSIlTxDQAPtOvvOolpQK/Ois9Jw2s/gIApCjR2Ki1/Z+GbQVrfX/v4wKA0exnqPeYNR4TT+nkA8rhXgroa8SYRr0ClMPgVUc5y3ZdWw3W2Gppn67eQaRGpyOBBADKKxUvrsS143s1z6C/GotSHsQM5Ii9Vz277gDLjtfAWRusxXa4GmKZhokhQAkariegl60ud10Hrer50a5BV34SL6GB1XLItoEKxM7o67E/7chGnN0BGyh3QFjJa2T0622/Unr0Y4Vu+3tZyiJGFLv17Czlv8ZxEssAoK1Qnbt/7ElO3WzpMSRW0gnkslzGWXmlhJbnb2hU88GSFo4f9u3LsNVgAcQ6brcEv9L5w5a/qCG1PHHdY0Rbg5/yXVePbRoBAGEzxL4akyeJZ2jlODDl6hewfB2z8sqAmgXMcuEFrOF6w4xYNQwAAAAAmAkTMtCCa0WVpVWPMVh5VUluBbVfndFrdt8MlSurYNDDDNcWxmSl7Pr2atfgGNdLxB9iGACkudpG60lLPKtNQ15jLrNcWxiP5bJrOe0piGnoYaSBKxEGr6rJ3Q829b1XHqDo+56RCngIS3kBIE6ruJAT0yw+e6TGthRsT4paXDP5KGOwqmXZpY12DW00AAizENN8ZoppMx0r2vJdl6OVOQavjNs6ELYOP27y0412UQOAVbmd4rPX4/vjnz0vUE9M2aL8AQ/s22hIR30CAHpQJ19D/qGW4/aUI9538syrQeRUhC2XEbouHipvzGTEAALMipiGGbkaRZR7IEz7NTLKg7yBXKNtrQTMjJiG2RyfbTVqTGPl1WSY/QcAGMlIMY0YDQsoo0B5+/q/5TW2fx4jMQgAUEqPeEJMw4yOK69GxMqryfQagT17vlTM6DCzfGEdZRcor8d1tf9OV1xLiWkAAGi4R3SlIXYG76gzfQEAeXo/i/dKTAMsGrl8M3g1sd4dfjE/T32NT8zAF4NjAGCXpoaRLy0lBqzYDgM9+Gb0ce8ElKfpeopNy9V2mqZjBgCMKaY/8Ox1IdwXoxfXc69GKocMXmEasYEKqGGkwAEgj2sAILVuOO5rDQCAZcQz9ET5AyBSpi6gPkFP+76G0coiz7zCFFIuXAayUBplCsDRth977nuB1q6sngeAK3h2CWqgTAEARjNi24zBK0DGvLgBAPpcGbRKRWcfSuN+CUAPLWMnAAA10T4D0jB4BdzQIAIAjIS4htooYwAAAEA87p+BNAxeQaVeMxFqz+pjhgUAzKdn3V8rprGqCwAwCuIZAGAExLN5jfxcbAavPLZOGS78dFfyrFd+c54BjI6Yloc8w9HxHpFrC4AFJdporeu6Vt83amcPAIyqRHwY8f791edfHPK4MLcneidAo+OFPvLoZQ1bZZmSZ1ue57y3hFbf1+v4AMxtH9eog9LlxrTNiPm9xbN9/J6B6x7R9e9Z8gOAHbTRAACjuBLT9p8xov0A1qjHiLmcDl4ty/I3ReQ7ROSL67r+x7efPSMif0dEvk5EfklEvnNd119flmURkb8mIt8uIl8WkT+zruvP10l6Ha4Rai72dKl5tn/96PntOj46vMbGOdWjdUzrPVDkm3XFzWyc3POnJV/36a9RFn2fR0zjGkMbM7XTqFfKoI2Whrp8bJxXPWaKZz69240WXYlpPdVuo4mct9O05AUQI2bbwI+LyLcefvZREfn0uq7vE5FP3/4vIvJtIvK+25+PiMgPpSZI43Z9mtKCuVD2gOI+LpVj2vs/8GXnlmJcz/WVyuPj+bPItyKohZhBLQBFfFwatdN6x7LtubR0tqClrcyFVt0CKOLj0rCNpvFef+T4VjqPtZwzi1z3UuQntDtdebWu688sy/J1hx9/SES++fbvT4jIT4vI999+/iPruq4i8rPLsrxrWZbn1nV9O/Qd7//Al0U+e/fv49LP1rMP2B80HTMhr/PlW2x+umZPzLIkGkjRIqZ97o2nHrkW91usabkWtaSjpKvHNOJKtV5bRrq+JzWm5XxHTdwfQpvaMe3YRtvrFc80xVHtLMcubY55mLpNFecACOvRRtv0vj5nqCNKtdFc/V2j510L5CG0i1l55fLuXWD4VRF59+3f7xGRX9m97vO3nz1iWZaPLMvyc8uy/NwvvPH4Q7/rNXDloyENtZR4cO8xgLhms4xitOOpgTx6GPlhQtGY9jvyWw/9bvYtd1q5GndiGkAaZ2mm6rkiy+V4zjSkac/6+S6NfDDhUkwLtdE2WtpoIyvVRttPnhm1fSait27SdK1ozaNeyA8TqrbR0M7VmHZcLbRfjW0xrp1NbLV2PD2QR4/Scr9Rw+nKqzPruq7LsqwZ7/uYiHxMROTp5Rnn+3tnfO/v1y6mQzZ1pYGmG3yNYlZTaajEXedxv/rEhfMODUaOaTgXOkf7+iumntU8G1BDnDjSmKZNiQb3aEY8phHlxDTN8WzU68nlynHGrBJKjVEz5X0u8hSop2YbDfXVrOu0LX6IpT2t1vITY8sdvPq1bVnusizPicgXbz//goi8sHvde28/M0NzZ1MNtY9zH0hivk97vvdO3zE/te6/7tu+MJQ+tp9ER8PGtJDRrrnjtsM573f92/WamLrWSp72bJhoiVnAYIaMaXSilJXa2ac97zWkz9fu9U0+bJ3mmO87lofR7hVhzpDxLBZx705sHlgdxDrSkn4NaQhJ7WuGbbmDVz8uIh8Wkb98+/vHdj//c8uyfFJEXhaR3zjbdxZAutRns7V+blzL9wEFENMGQT1yp9ag0NWVsxYHq64+C5UyiQ6IaYh2deIHHmW5A/XKs72ACoaOZyM+axdpXHFC23nXHstCu4BpTndpox/v6eDVsix/W+4ekvh7l2X5vIj8JbkLHj+6LMv3iMgvi8h33l7+EyLy7SLypoh8WUT+bIU0VzfyCe/laucP4uwboFbym+sNLc0Y01Ce9jo2NX2hhyCnvP/4Gamfk2r0m3StyHM9iGkogWu6PCvxKWZLeqCF2eLZvo44PrOJ6/Aay/nnW/169rOarExysZBG5FvWtf+2r08vz6wvL6/0TgYq016Z5M4o7zEzxpVWjbP8zjottaQTYZ9aX3t9XdeXeqfDCm0xLXQdjnQNaqr7NEgdNNL6DMUYtc97bj5QHnUipsXTFs9Qh6X4mfosZZE+dXHo+b+aWUgjHiCepSGmQRNffRsbu3rU1xYmPLBy0a5QTMvdNhADaF3ZWao89rPWzyro3oNWxzRoymftqxMAjIF6BgAwCtpofq4Z4L786vk8R03pSWEhjcBIGDDGUeyznFqviLo62NaKlnSgLAavgJvjgJVv4CUUIPYVd8xqh9CsvFE6Y60sMwZmwbU4ritxYx97RopBV7HqCgB08Q1c5bY3Sm3XRdwEkGqG+0X6gh4Vkydbeyw0aBT6jKttmOOgVGgQLTZNQC4Gr1DdsUNMs2M6XY2h/Z7E+w6+1GeF+FZ2xb7fEu3nHRgZ119ZlmLaJvY5VKG4pJm2GX8AYIHFeHZ03H0iFA9it4gv1eEHAGhHe0zLiQ2hVVgxg185rrSrNOR/7+9HHTzzCk2M2rHke/ZUyjNmXLP9SjyrBCiF/dTTENP6aHWjqv2ByqG4FPsMR8tKnxNWXo2HmBaPeDa+UTt5jrG69XGO2vaFLsSzNMS08VmKaTlp1d4OFel7DrQPYCIsFNMYvAIKsPDgQiAXDaM0xDRoETPwEjM7Xcts8ph0lIy/DFyNiZgWj3iGUdCRhRERz9IQ06DZSH2KrQbZfBNFiPk2hWIa2wYCBRw71LRVlq5AqC2NAICycut439ZLvZ6Flbotb0/EVQDQ52xLwV40pgkA0N5IcaD2sRA758PgFVCQ1srz2OGY8lwTrccEAKgn5qG8IvEz61IGna7Endi4FjO7UcuKMwDAdRrbNL40HScZWtgqCgAAl9IT54mD82HwCphEiYcRM8MBALAXGw9KdcIdB55yBphcryW+AQC08MU6EeIVAMAWdn7CVQxeAXhHaGsm18otgg8AINXV2NG6AWRp20IAwBhiV0CL6NuyHgCAo1Y7cWA8DF5NjJvcuZ2d++PvXdsshWbPs70FgBaoa+YUE6NC7/O99mymOwDURhsNqViZBQDQLjUupdwP7WMfK73Gw+AVgOJcg1xaH5QMYBzcpM7N11C5Wi4YuAIAWLGfpHG2bTz3TABqov8HV8Q+p5g+gPE91jsBAGxKfYZWyRsXOhIBALFccSclFqW8lvgEANBg6/Q7rije/zn+/ipiIIDNNqDAoAJy7ctQqCxdbetBP1ZeTYqRaeRwbdMU8zqRB40ZV6MmtSxaKLtcY0BbXG84xpmcMhET12Ima1AeAeSg/kBJKdvEl1iVZaHsco0BQHu9Ygp1/hhYeQWgiVDAGG2W3mjHAwAabTPGfTPHa9XFNIAAAKNxrcoazYjHBADaUNeiNFZeTYqOF/QwS7kbtcEHaLTf9maWOgZlXCkvvtnqlEEAV1CHQIsRy+KIxwRoxfU2r9CzF1vq/f0oh8ErAABgGjel82GCAAAAAADo1LuN3vv7UQ7bBgJQzWoHJYESAOpIiQutYggPpAYAzMRqGw0A0M7xmcRADgavFOFiRg1nzwTRjs5AwBbL9Q30yylXtcsicQoYG/EMNVktX8Q+AMCZbYt/YgauYPAKGFCPB9gDAPULapl9QHTmYweAUTADHQCAcRDP22DwqpNjAd/fyFL4cdXZ9kXH1VijlbnRjgfQLjRYzvUIDSyXQzo7gXZc19k2a5hrEFdt7bN9O20rXzOY5TgBTUbu9wEwBwavlNjfyLKcEqWklCVuZAAAIyPOAchFGw01MUAKoIZtu7b9H+oZANYweNWJq/FDgwg1MIAFAMAYiNNAXbTH0Mu+Y5mBLAAl+PodqWMAWPJE7wQAqO940xK6UTn+zlojfr+9krW0A1ZxrQF10LEAAHNx3VNtscD6/Zb19AOj4FpETce+OF95s9xnN0pctoLBK2BCWwXr6xQ7+71mBA8AgI/lRpIIMQ4AZsRWXwAA66xPlN+znHaLGLwCJnY2A4IKGQDQW8kOO2txjQ5LAIDIg3hgLY4BACBirx0GPRi8AvAIggoAQAttAzitt4kgJgMARIgHAAD4sJXfuB7rnQAAc9DU8QgAsKV2I2R7cPX+DwAAoyPeAQBK6jXxkN2jxsXgVWPcHGI0sR19BBFgLHTyo7WzBklug8VXhkPPhSSmAWMhlmE0sfdoxDNgLMQzjIZ+BzB4NSkuelyVGzxS30NZBXSj06MebtLz7AeXtn+XHtiaEQ1HoD6uL5Swf35xbJmijQaMgbYZRrKPZ9q2kkc7DF5NiAe9oqR9h+A+kIRmrKd+PgB9WH1S17E+ZdDgAVfZc5VFX/k85iPl+E5KGaMsAuXRRkMJx3J07OyjjQYA0MgVn1xtvtnaxTMdq88TvRMwG27y6oqp7GaX+xBDV0No7ziAtQWVGfN/Xw5nPH5AG2sPbw3doM5ar8aKOdeu+BR6/Ux57jrO0LaKZ/kW+x1AbxrKpYY01MTEgXM58SYmnu3baTO30QDoY6k+cqXVUvpbOsaamHyKjU8pnzmC2PbWyHmxrOvaOw3y9PLM+vLySu9k4MZaJ99R7/Rrr0CP+VNjwK/3OdBgpAb6p9bXXl/X9aXe6bCCmKaLtRu6szq65TFYyDtN+TWynIEorc/CJKbFI57pwv31NRbaaPv01ZqUqT0farNwbxOLeJaGmKaLxZjmqqfpd4w3Uv2rkcVrai8U0xi8AgoavTJOnTmBO9bzioZRGmIaSusRW650mlmv8+CWWiYYvLKPeIaRjBybaKPNiXiWhpgGAHeOO2Zt/+4pFNPYNhCooPdFX0vsFkt4WEqHb+57AYzLWj1gLb2Iw3kFYNnIdRhttLpGn6AKAJiHb7thzZNfGLwCCtJ6ocMuzQEEwDyohwAAwIxc2zlyXwSgF/qIcIWv7GguUwxeAchCwCzPlZ9n2y4BQA2z1e/EtPJ8+UleAwCsIn4B6G2meoh2A0QYvAKQKfY5FwSaa8g/AKiPmNYOeQgAAADgDO0GiIg81jsBAMZx7637rBQCAAzjGNOIcQAAi4hfAADAIgavABTj2/buOKhF4wkAoJ0vVhHTAADWHNtprkmHxDMAgAWu+EUbbVxsGwigONdDbQEAsOrV518knimWcm4ef65eOgDACrZiAgBYdYxhxLSxMXg1CR5yh1ZigsioZZHrDGiDZxChhdhGkWvCBuXyGt9glGvlAACgjFFjF200oB2uN2hBOSzveH2H2r4l6wK2DZwADXtotpVPnpcFIAb1BCxwbV1hjba0zzQZBvPQdp0BG8omAAD69IzPx4GrV59/MTjBM7S9Y0p7mZVXALo77ktrsTPKcgNvn+fHf4uwxRIAxLAcB45aH0vqirUtVo2U55iTxXtezGGrYymjAGKw+wAwHtd9QOpqq5hdc+69dT/Y78jg1QQIHtDK1+lk9canVHpjGoqhLZVC7z/L89BrAA1cA61AT2d1ptWy2iPNsdsFWsxPALBipDZayRgc20bL+b6c9hcTDKGJpXoBc3DVq9b7Elqn+zixfft/zHb6ode53/em9/emBq+sFi4Abr5Z01avcw111FnDh5nqGEXvaw0QiVsBRFmth5img4b7HwDlxHZKWVByBVnMZ7jiUkxnaV76/B19ADAz17OZ9v+3GM96Og5itbas69rli/eeXp5ZX15e6Z0MTIRGNko4dlpqmqUuUi49n1pfe31d15eKfNgEiGnAnHydVVaMENNitqUgpsUjnqE12mgowbcl+miIZ2mIacC8LK4c3uzbaBbTHysU00ytvAJKGfmCRzsaZnxTlgFAH6t1s4aYdmVFttV8B3CHaxgl9J6IAQDQw3o80NDv2NtjvRMAAKOYPaAAwKz2jSKrDQwtDbotDwEAyEUcAQBsrMeE2dtHQw5eWewwAGZz7637Zq5VK+kERmapzsBc9uVypHLa+zi2RtrMDbXR9C5TAM5ZimOhdB63DgQAzMtSbPPZjsH6ceQYcttAGrmAfpZmpp/VKdQ5ADCv0MPZrbKefuhEuQL0s3SdsqUs0N/oz+GBfSOUz9mvM1ZeAehm5soXADAmq7HN+n7w0I82GgAAAIAUrLwCAADqEduhGeUTOMd1AgDAWIjtAGobcuUVdGK2JTTb7x1LWQUAWDbznugA0lBPQDPaaACAUdBGyzPkyivc0bYnpqa0AEf78plSVinXQF37GzuuN4zM1YjJLfM579ue3aXt/hFAXVzv0Iw2GqAX94yYQclyzvWSh5VXg2IUF2iDaw0AUEuPGEOjCqiLe0cAAAAbtgl+6IfBKwCm7YNIbkAhEAE6cW0C6Y7XDdcRoAez1DEj2mjAeIhnmM3V7f6utNFmv9bYNhCAOcdtxLYbp5bbK5V4LwAAvu0CrzaOjvFp+7xQ3CKmAQBK69FGAwCglBLxaN93iTQMXgEw51jZ16r8Yzr6AAC4onSMcX3ecZUycQ0AYBVxDABg0fE5jsSzOGwbODAuANQw4/YNMx4zoEHuQ7qBWFe2f7i6dcSZKyuKj59zRFwDgLHMVK9zTwj0x3OAUNsM5Wu7jmY41itYeQUgyUyNhZmOFdCOWUmoIadMhfYrn6GMci0CgD7UywBao95BTbOUr1mO8wpWXg2Kwg+NthkFo8wqGOU4AAuIa9Bg9np/O/7R4jnQCrEMWo1Un490LACAPMSCcbDyCkATrpnq1hvwMennuVnANVw76GmLVdYbPzWvoxHiOQDMasRnIsa20UY4VgDAozthUMePhcGrAXGRQpNjh99WNq13BIqkXWtcl0AeBoDR0r687VcZhYxQNrnOgPq4F4Q2M5fJWY8bAEZ0rNNnreNHjeun2wYuy/LCsiw/tSzLP12W5ReWZfnzt58/syzLTy7L8s9vf3/N7efLsix/fVmWN5dleWNZlj9Y+yAAtOfaOsj1ZzNjMAk9FwV9ENNsmqG+QF/HmJVSX89St89ynFYQzwC4hOrqYztt9vsrtr/Vg5hmD9cOepthG/ORjy1FzMqrr4jIX1zX9eeXZfkPReT1ZVl+UkT+jIh8el3Xv7wsy0dF5KMi8v0i8m0i8r7bn5dF5IdufwMYiGsw6mzm9mgNpLPjGe14B0FMA5Bl9Dp99OMbEPEMwCNCdTn1/MPID1WIaQC8XH2N1OGPGjVPTlderev69rquP3/792+KyC+KyHtE5EMi8onbyz4hIn/89u8PiciPrHd+VkTetSzLc6UTDkCfV59/cdjK8ogZEDYR0wDbesyumym2hcwyMcUK4hkAPIo2mk3ENMC+WvXvtlKYNse8kp55tSzL14nIHxCRfyQi717X9e3br35VRN59+/d7RORXdm/7/O1nbwua4IIe1/7B8a3O89l3Ud5gFTEN0OEYZ3o/Y2rGuBZ7zCXzhi2ryiGe2UB5H5+mNhpgFTHNBuofbGoOLs0e62Y+9r3owatlWX63iPxdEfkL67r+22VZ3vnduq7rsixryhcvy/IREfmIiMiT8lTKW4Gp1ZxNdvxsZjf49cqX2YN3KcQ0OyjvY/JtMxsT4862qA19Ftu96rB/buaGvM9DPAP6i41LVz9/QxvNjzaabcQ0oD9ffVajnqsdPzGGqMGrZVm+Su4CyN9a1/Xv3X78a8uyPLeu69u35blfvP38CyLywu7t77397CHrun5MRD4mIvL08kxSAAJmdLVSJyiMg8bRNcQ0QJetPkudnFG6LqRebcN3nolt6YhngA6sCEbr3VFGREwD+jurx479ijn1XsrkNepUiEQMXi13Ux1+WER+cV3Xv7r71Y+LyIdF5C/f/v6x3c//3LIsn5S7Byb+xm6ZL4BMOZW2q4NoH2yOgYfAoBt7uF9HTAP02selWKHVVSkxjfgHa4hngH2hGMZACGZCTAN0CA3E+7Z5d02UL9VGQx/aFj/ErLz6IyLyp0Tks8uy3L/97L+Qu+Dxo8uyfI+I/LKIfOftdz8hIt8uIm+KyJdF5M+WTDAAv9ROPy0VkTWuYN6igZm7OgEPIaYBCvgGqq7Uc6PHNG2NCHRHPAMMcT3fkecKl9WrjbbhnF1CTAOUyH0W7szbgY824UTbsZwOXq3r+g9FZPH8+hXH61cR+d6L6QIQwCCVPi3zmPOZj5gG6JGz0ir0WSM7zm60fLy+AUrLx9QD8QzQxzVA5UOd1wb5bAMxDdDHNyFAxF+3zljnWm+bWRD1zCsAfeRWgq4tAVEGgQkAyikxgDVDnVxyoE8L7lUAWBXaUmnmmec90UYDgLJc7Q/qWfTA4BWgmCswxM7gmyGoxHZ6le4c8zWOaDQB5dCpPYfYARnXTPaZysYxn7Z4Yz0vrKYbwNx8dRdtgTvaYhPnBSiLa2oePEMYGjB4NRiCyPhS9kofXeyg1fH/V/Ns5jwHAJF6WyHFPKuCOvjOaCuxgJHNfs8+C87xndR80DbYBQBWsVKqvW1CIXldz2O9E4CyuFjmwvmO49un995b99/5k/tZ+8/jfAAY2bHObFHnUa/eiZmskRrPALRDXQb4lbg+aKMBmN02gLL/c6VtQLsi3pbXtMfqYOUV0BE31HWF8rXkqqz9w+c5lwBG4rv53uo6V6PoyvMacR2xCACuoY3Wnq+TtcRqbuIigNGFnsVY8vPg59rm3vd7pGHwaiBULvqN8owKi1K3GPS9JvQ5nE8AlqU8f6r0toHUn3lCK4tdvz++jnwHgAeoG/vKfZYYbTSgLa6ptvbtLl/elz4nnONrXG202HsM+vYfxeDVQCjcOtV67hLKSlmlBaAN6kmdStaJV7ezsKrnfvQz5jcA+MR0CqKvKwNXAGCda0VP7MpS6kg9OA/5GLwCKmCJaB0M/AHz4Dpv72yrgxbfC7/YFcTkJwD4HetK6kwAgBX7Qav9//c/2/+cGNdfygAig41uDF4BhZw9FwTl0EEHAHlq3hBTJ8c7rji7el6OnwUAuNNzpetMaJ8BQHusHq6nVLuZR8dcx+AVcCJUybgaQ1RK9WjYXoqZEACsc9VjvevWGR1nTvb4LGIagBHRmdce+QwA7VDn1lcyj0Mr5ja0y/wYvML0Yjt6XANVropllMpGa8XZez9fjXkCwL7SHW1nsa3kgIkFWmOaSJ2GkUjcOdaaJwCwiY1X+/qMuk2XWjFYc2wHMIbSk9NDOzblxDvY4ppAyhaP5xi8AjxGrDhSbvBHPP6ryBMA2p01eq6u9rFaD1pN9xUaVisDQK6ZdrNgEAYAdCo9aBX6POLAfNi9Kw6DV4DMU0mMfpw1j2/0vAPQ135Q6WonVsx7z7YNpM4bQ2gAi3MMQLOZ6qjRj5VnbQKwrkX7DPOifIQxeAXzrm61RCWBEGZCAught+7Jra+o5wAAAAAAgCaP9U4AUBLb4wAARkFMAwBYtq0mJp6hNMoUAABzYOUVzNlmo7tuWJk5jpJoFAGo7ayeYQ9sAIAF+zYaDx8HAFh33AmDbdaBPhi8gjpnWyX5fkfgAABoc3Vr2yvvA0Qefe4V5QlAjtg22nEACyiN8gXgqph74/09NHUO0A/bBkKdLUCkrHohkAAANDqLT1d/D8SgHAG4KqWNRp0DANDs1edffOdPzOsA9MPKK+NGnXUUc0wjHvesRi3HACASN0DFNhRogXIFtDHqve2IxwQAAAC9WHllHA0IjMD3DDMNuMYAtLCf/Ue9g1q0xlpgNNTjGAVxAwAA9MTKKxQz6gxDtEHZAaAFq6AwKsoyACCFxrihMU0AAOQgpp1j8ApJzmZe8TBDjIRyDIyPGcX6xDxAGenIS2Bc+zaYL65RBwAAgFwsWEAvDF7h1L6COtveTfP2b6Nj4BAAzuXcdHOjDgDQbItRxCudOC8AkMbVr0g92tfW38t5QGsMXiEoJ2BQkfVxdeCQIARgdL6YxqQLXTgnAHDOde/OvbxOV84LbTQAMzjWdWf1HpO3+yC/0QODVwjqWTEdt7+gkjx3JY/IXwCj89Vz+5/XGjQJbelE/fso8gQAwnrVk7TR2iJ/Acwgta4rVTcyCAbox+AV1ArNuvA9D4MGFADMq0QMqBVD9ls6HZ1txwsAgBZnM+NdHYG00QBgXppjQCiOuX7PYBfQHoNXMGk/c50HuwPAmFJXKFmIAaFBLN9rAQCwwhXniGcAMC9rMcC1KwerjYF+HuudAMypxLZMrz7/4iN/gKt4zgrQx/Has3Qt5qSVmAUA0KZk7KWNhpIs3RcCs9B+XZbsd2TgCuiHwasB3XvrvvogQqX/KO3nbBaUTaAtCzHrTG69MXLHnvVzOgpL52GrCyylGRjNiPHoKuokHSibQHu++m//c811ZMl6YxvA0ny8wKgYvBrQqB1hMyAQAhjVWcf07HFrpNjt2tYXfVgpU5ZXXgIY2367egAYUWr7bPv5SO2XGCMcLwNwsIhnXgFKlAiCLGUGoMl+j3Bf589Zo2g2Ixz3CMcwAusPlOZ5OQC0uFoH0UYDoM2+XqJ+mgfnGhax8koZRsFxxVkgomyNjfMLTXwDVdwwA21Yud5iYhfxDRpQDpGLNhoATRhQB2AJg1cKEUTmVbvhQtkaH41faEO9A6AE4ht6I57NizYagJFQ5wCwhG0DO/Ft5UIQmRvnHzlczwqhLNl21kmynV/ftlqx24XV3FaMMgjgTGqHMPENtVHG4EKZALBJiRPEFAAj6L2dO4NXHRHEAACbnIGks4eIu24yXK+/OohFwwxAqtyVDNQ3qImyBaA04pZtx3aSq/3lOr++896yPFD2AFzh6ifq8VxlBq86IYAAKIFtlMaxjwsxg1KpcaRmWSGmAWiJzhgAANCC637j+LPS9yWlOoe5VwJwRaj+azmIpf6ZVymdbbGvvffW/eTPdf0BRkY5B8rKjR+vPv+id9be/rO2f/s+/+ymgsYNRkZM04c6xzYN15OrbaYhXUBNlHF7iHf6Xb2uziYepr7W1/4DRkNMs2tfT9U+j+pXXoUq7NitklJeczargpmemAEBBCjv2FDZx5Or8Sz2tbHbXGhiLb1ATz22cbiiVYMH5ZUqY6FznzPpwkrZB3Jt93KUdaCcq9eVbwKF7zNHuX6pi3AVMQ0xlnVde6dBXvrgk+tn7r3w0M98s8yPr0lp7LqWto3cKdb7gWqAFSMEy5rX+6fW115f1/Wloh86MFdME9HRSWu1nMdONgFCrA3spBrlnjb2WX25x0dMi/f08sz68vJK1GuP5+14b3XlPmWE+7Q92mhAnNGu/dKIZ2liY9qVSRUxnzlimeZaxVUzlKEZjvGKUExTO3i1lzpItX8fAOAaGkZpzmJartyVyDHv126UTnmgJt91MkJDicGrPp5enlm/9Pazzmd7iPjL2AhlDgC0I56liRm8csUvYhqAq0atW0pODg3FNDPbBu4HsFz/tn7CAfThq2ypV9BTatnLneRhxUid8EANZ7OELV87rLzUJaYscX4A1GI5nqG/UPmhbAGoIbR9qPV6p1XaVa+8snwCAeg3+hZSpTCrL03syivXdkqURQA5Rt+OpiRiWrz9LHXrjWsAtlDnnCOepfHFNMoagJqoY+KEYtpjrRMDAIh37637Q6+oAQNXAK6zUn8Q0wAAZ+joQwsx9yTcswC4inh2nfptAwGgJguBxEIakeZ4TjnHAK7SXI8cO3/omLSF8wUAGMEWz1wxzRXriH0A0B8rrwBMy8LNqIU0It1+ph/nGAAAALjDvTF62J4/w2orANBFxeDV5954qncSAAAAAFTATGa7WHUFABjFWTzzrcoCAPSjbttAAgUAYBTENAC4Q30IAAAAAEihYuXVhkYtAMC6bTUxMQ0AYB2xDAAAAEAvagavaBgBAEZBTAMAAAAAAADyqRm8AoAZ8ABYAMAoeLA5AGAExDIAwChGi2kMXgFAY6MFEgAAAAAAAAB9jbYT0BO9EwAAMxktiAAA5kVMAwCMgHgGAIBOrLwCAAAADGP7PgAAAADAaBi8mgidGsADXA8AgBHs4xmDWIA9XLPAA1wPAABgj8GribAUHniA6wGa0FAHkIqBKmAM3JMCD3A9AABGQnvtOgavAEyJAAJNaKgDAAAAAACMg76e64YZvJpt9ulMxwqUVvP6ma0uQh2zlaOZjhWwhusTiMf1AlxTu50GIB7XDHANMa2MYQavXn3+RUYzAUShroB2xDQczXRzimtK1h2UOwDACO69dZ97awBAM8SdcoYZvNqjoQ3KAHoiQAHpqLf9yBv0QLlDaZQpUAbQC+0zAKUR09DLbANjT/ROQA0xJ9D6ibac9hbIH/iUvsE4fh5lD0jDTf856hW05Lomrd83QwfaaCB/EFLj+t9iGmUPQGnUK/BpcT87U3wbcvAqxgwnF8CjXn3+xSb7zlLHAACs8cVHYhpaoawBKGXfeUgbDQDQSo1Ys2+nzRbLph28AoCrYlZdWZ9BDACw4WqsYRUkAGAk+7h4jJG00YBzXCOAHjNfjwxeGcSNFnDdlWsoZatArlUgDtcKkGa/krhkTNt/PgAArZSchHH2WcQ4AIAFjAEweGXS7IUWuOrqNXTcfgJAPmIakK/GaiuuSQCARcQvAMAo2O72gdPBq2VZnhSRnxGRr769/rV1Xf/SsixfLyKfFJFnReR1EflT67r+9rIsXy0iPyIi3ygi/1pEvmtd11+qlH4A6IYgYg8xDQAemC2O3Xvrvjz+XO9UlEE8AwCMgpgGAA+brZ0W8ljEa35LRL5lXdcPisiLIvKty7J8k4j8FRH5gXVdf5+I/LqIfM/t9d8jIr9++/kP3F4HAGqwYmpqxDQAQ7n31n3iWqTBGoHEMwBDIZZNjZgGAHA6Hbxa7/y723+/6vZnFZFvEZHXbj//hIj88du/P3T7v9x+/8qyLEupBAPAFTSKwkbPH00xbfS8BlDfvh7ZBrGoW+agKZ4BAOoaPbZrimmj5zUAWBOz8kqWZXl8WZb7IvJFEflJEfkXIvKldV2/cnvJ50XkPbd/v0dEfkVE5Pb735C7Jb7Hz/zIsiw/tyzLz/2O/NalgwAAlDHYrHQnLTGtZF7TYQ1gj/pgDlriGQBcxQPpw2bIGy0xbYa8BtAGbbIyogav1nX99+u6vigi7xWRPyQiv//qF6/r+rF1XV9a1/Wlr5Kv9r7uOKMU5ANQEp3+89ES09AP5wEzIL6NT0s8o5zdIR8AIJ+WmIZ+OA8YzavPv0i5LiBq8GqzruuXROSnROQPi8i7lmV54var94rIF27//oKIvCAicvv975G7Byhm2c96YAbEHfLhUXTQIMV2Dbm2W8I8ese0UrZyazU29LruNF/v1EeI9erzL55e+5Sn8fWOZ1bjT2nkgxv1D2Jw/WDTO6ahH+3ngXtq5Nj3P1J+8pwOXi3L8rXLsrzr9u/fJSJ/VER+Ue6CyZ+4vezDIvJjt3//+O3/cvv9P1jXdS2YZkBEaAghj/YbItRlLaal3OBYrRN7XJMxHf6AJZTn+ViLZ5jH8X6E+gk56OSbCzENFtCGxBVb+SG+pXvi/CXynIh8YlmWx+VusOtH13X9+8uy/FMR+eSyLP83EfknIvLDt9f/sIj8v5ZleVNE/o2IfHeFdAdZn4WOOMz4BJDBVEzb39yMXM+NfnypyAsAEUzFMxHaaLPg/KIEytF0TMa0mNXvlsuy9fQDGnFNpVs0TE54enlmfXl5pdrn+0Y0LRcYGn9AGVxL5z61vvb6uq4v9U6HFb1imojdcsx1CNTBtfUoYlq82vFMhFU6AJCLeJaGmJaHASwALYRiWszKK/P2FS1L8wDscSMGa1zPbBsFjSOgLK4naDdaGSWOAcC8qP8BoLzTZ16NplQwGbHTEABgxzGe0VgCAMyudxtt2+4XAIAR0MYE0Nt0g1ciZR6yd1zN1bqR4pp5T0MJAOayxbPWjYrS8caV/lYxjdgJAOPQuOOGlnQAAMbWIt4Q0wC0NuXgVSnboFXPmQj7726VDoIVAIwntW6vHQtaxlbiGgCMY/+sNw31O200AEAubXU7K7EAtDbFM69qcw1g7QPM1nAapZIf5TgAAA/sO/liVkKNEAtCxwsAsOlsct+odf9oxwMAiKvbtQ1wAUBJrLy64GyrJtfWfqX12LIQADCmmJhWW8uY1mPLRQBAP9uEwm3CRo2YU/OzAQA4ok0DYGQMXhVw1tlXYxDruLIL6InGOTAOYhoAYAY1O/voSIQGtNGAuRB3AIyIbQMbKR1EWqzqAmJxkwSM7bj1ba2YBgDACEbaMh52UQYBAIB1rLyqJOZ5IbW+BwAAAADwMNpoAAAAgB0MXjVUoxHDyisAQE3EGQDAyGijAQAAADqxbaBxzOoD8P9n7+9ibsnOwzDzrWbLYohMW2pGENgkJ5JhMoEuqGOr4VbgXATqGC3ZjqkLYSzHYxMDAryIAyhwAEeeAJnJXMU3EW2M4AFjGaQGgZWkk0CCocGBWhJgBLAtq+MT0o5gqiM4ENm0GduiIoeAfpKai2/v7jp16v93rVXPAxycc/a3v71XrVq13lq/BXsSZwBgHrETAADWs/LqQGbgAcCzHr/95Kk/AHAUcQcAuomRwNkMXh1ory0pBBMActaOj+IaAEexbSAAdHvtpUdiGnAqg1eZsyUFAABAOnT2AQDAegavADKlU4SSmIwBAEDutNEojQkZwJkMXhVAhx9XcYUbpvsxto+169hd+wDk6ArxHNyncRVXqNP72mhdXPuU5grXOJAug1dsTmBjT1coXwaquDJlnau48nNLXedwvKvWNxyj9PJl5QlX5xoAzvL82QmgPDok2MteZevx20+SKbeppAPOoFHElVwhpgHpUC+wl6vEs5TSAkdS9mG91GJaTqy8AgCS4YYOAAAAAINXBbjytjMcT1kD5ppTb6hjACiBeMaRSixvJjQBUAoxbTnbBiaseQPaV8ib77EEkSPkUMamPki3ec3cf6f9OrBeeztAMQ2mm9Mh2RXTgG1NiWf3n4llHCWXcta+Jtoxrv2zof8D23BtwXKun/0ZvEqYZ3/Acl2d5V2defdAc/+ZwAP7ENNgf64x2N+c+0T3lPCudrsronuQt2tSoTYa7GfqteUahKcNxaiudplraJmqruuz0xAvVC/Wr1Svnp2M7LmhW2/qTErS1tUwunNe53ujfv3Nuq5fPjsduRDT1lMXb0M+lmEopjU5x9OIadOJZ6RCOxeeJZ7NI6ZtQ328njwsk/O6zlBM88yrgux1kVxtFu9rLz1S4WTufv7afwN5cM1uR0zLn1gGdLlSG039B5AG9fF68rA8Bq72ZdtARl3pArzSsV6FTj/Ik2t2PXlYlq6VdFfqvAaepo4HADife7J9GbwCuPGAewBS1RWb+uKVLSMBAADSYpXWfLYNhMS0Z1F3zaqeMtPabOx55BfAOdS/AORGG+0Yj99+Is8ATqDu3YeBq/msvIKE3INDewVQ8/9TA4gKcZ6r55fZH8DWhuKVbe/2JX8BttVe0dpsl93/PeVe2v32PFfPL200YC9d7YRmG0LdQyoMXkFi2gFi7P+wBeUK2ErfgMmcbe/YhvwF2MZYDFPfsgflCthK1ySMPuoeUmLwChIiQABQEnENAADgXNpl5Mozrzhcc7Tfdjakxr7qAPM16877TD4NpPOJacBU6gpSpnwCLKP+TI9zMo/BKw7X3h+ca2s+5yuFTjZlEmAZ9ee7xDQgN3OerUv52s9iPpvyCbCM9sDTUoglzsk8Bq84hQuVO2WhO3imEFABplKXpyuFcyOmQR5SqC9Iw70sXHnQSBsNoCyP337iXqchl5hm8AoylEsFM0ezgXS2o9Mw9gDoFGbvAzBfCjHtLH1xS0wDSlVq3ZZKLNNGA2CNVOLZmZpxq50fqca0589OADBfqRVuqce1xNqgYUYJwHnUv9eerQ9ck7q/fEOdflN/XzkB4CxDj/JJNT4ZvAKyVfKz09YeU4l5AkBexmatA1AebbT9fh8A1sotFhm8ArJUcqMIgGsR0wAogZVFAJRCGy0NBq+AbAkgAJRCTAMgd2IZAKUQ09Lw3NkJAMqy9/MtzHwA4AhHPIhdTAOgBJ5xCEApxLS0GLwCNrNXBX//XAEEgCPsGW/EMgCOtHcbLcIkDADypo2WLtsGAknTKAKgFO1GkbgGwN72iDWebQXAGfbYuaId08S3tBi8AjazRwUvaAB70wFD217lQTkDoATiGbA3bTS66He8HtsGAgCX5mYVAAAgHdpoQESGK69sIQZAKbr2VRbbAAAAzrPH1mQAzJf1yisPU7sO5xq4Ao0jAHL1+O0n7tkvxvkGSvXaS4+0zQASkN3gleBxLUc3iDTAgCOJaddydMeumAYcSUffdWijAVAKbTRIWxLbBn70Y1+Px4+fRMS0jjyNouu4n+sjz7mHQgJrfPRjX4/4wvT3q2+u4+hYdv9bGQOWUofQ5Yw2GsAac9toXIdYBmlLYvAqd/bCLYPZD8BWcn4+Y85p51nOIbBGznWIgbcyOI8AD/Q95k9Mg/mSG7zqG0BI/eJOPX2Ma55DNwXAVtpxLYd6JYc0Msw5BK5OPViG9nnU8Qcs8cXPvy++9pW864+c084D5xDmy+aZVymvilH55C3XAVMgT6k/0F7dl7euspVyeQPSdo9ZXX9gT33xzH0KsNRrLz0SwziFNhosl9zKqyFWwwBQCjGNPShPwJaG6hQDCexJ2QL2cK9btMU4knIGyyUxePXFz7+v90IeGp2+z5q4/xuW6Co7yhNwlK44pw5iS8oTsIf2ltu2d2NvyhOwla76xLN/OZIyBtMkMXg1pD0roskSSwBS056QIVYBULr7pMJm202nDAA58Rx0gPQkP3h1NzSIBQCpGpvV10VjCWA7BlKO0dwVAwBy5r4BIA3ZDF7d9c1m1ygF2Ea74+k9HzgnHSWbujKr/TNxDmCZe30qpu3LhEOAfYlnx9LXCLCvx28/GYxpzx2XlG3ZRhCAq3n89hOxDmAmnU7HswoLYB+vvfRIXAOgGGMxLbuVV3d9s/pstdTPjBFgimaH00Od8daZySmWTr11coppHv4M53v32hPTjvLs/QQA5Ker3SaulcF9CqQv28GrO1tTTKcyBqZSX8A2DFwBV6beAyBnfXHMoEcZnD9IX7bbBg5R+ZTBgCS8u02c66E8YtU6OeSf6xaeJqZB/ly/8C7Xw7Xl0B4BmCrVmJb9yqs7QaMMZqhzRV2ztlINGmzLM0GuJaetDmGJvvu4dj3nOoC8NO9VXb9cWTvOuR6uyT193pw/eNC+FlK9LooZvKIsqV4wsIeu8t7cEtX1cA3t89w3qKU85Oc+SOncUbq+Mq7sQ95cw/DAtQD5cx3Dg1yuBYNXJCWXCweOMvWa0DGer6nPbnR+85PDLCZIjWdIAFAKbbQyWEEOXN2ZbTSDVwCZs+1cGaYOYgEAAHAMg1XA1Z1ZD04evKqq6j0R8YsR8eW6rv9oVVXfHhE/ERHvj4g3I+JP1XX9W1VVfWNE/HhEfFdE/NOI+ON1Xf/DzVMOAAukHs80joArUwfOk3pMA7gyMW0eMQ2AtudmvPeHIuKXGv//CxHxI3Vd/96I+LWI+OTt9U9GxK/dXv+R2/sA2EnXw4K3Wr1T6Cog8QyAUohpAJnQRhslpgHwlEmDV1VVfSgi/khE/JXb/6uI+J6IeP32ls9FxPff/v3x2//j9vNXb+8HYGOP337S2XjZapZfabMFxTMASiGmAaRJG20+MQ2ALlO3Dfx0RPy5iPg/3f7//oj4Wl3Xv3P7/5ci4oO3f38wIn41IqKu69+pqurXb+//J80PrKrqUxHxqYiI98b7FiYf4NpKbLjs7NOxcTyLENMAOMWnQxsNIDnaaIt8OsQ0AFpGV15VVfVHI+KrdV2/ueUX13X9mbquX67r+uVviG/c8qOBAxS8VQENJZ3nveJZhJgGuSuprqNfSedZGw3oUlI9x3WIaUAXMe0axs7zlJVXfzAi/lhVVX84It4bES9ExF+MiG+qqur52yyID0XEl2/v/3JEfDgivlRV1fMR8bvj4QGKQCEev/3EbDJyJJ5RlPtN3lB93L4RVHc/S6OITIlpwDPEeTIlpgHPENOu4eE8v9X789GVV3Vd//m6rj9U1/W3RcQPRsTP1XX9JyPi5yPiB25v+0RE/OTt3z91+3/cfv5zdV3XSxLPtfTtC016BJDrKOlci2cc6ciYdv+u5veJqdO99tKjouo6+pV0nsU0AEohplGavnaY9hnMNzp4NeA/iIg/W1XVW/Gwt+yP3V7/sYh4/+31PxsRP7wuiVzNkZV8V2ffVp+55rO2+AxgMvGMTQ3Fla3r93Zn/JZxo5nWpZ+7xWcAs4hpbE79DZxETOMwW8W6oZ2KXnvp0TvfI7bCNFUKkxNeqF6sX6lePTsZHKS5zVG7Uu/aAqmvQl8ze3ZKkGgGlb7vHvucvvf1pb3r80qaJUye3qhff7Ou65fPTkcuxLTjdMWQo+vMqfFk7P1L071Fo2dK+prva75nzpaFY++HI4hp04ln13OPo13xtK/d1ra2nl/SbhpqVw59ztI2mlhGCsSzecS04+T8mImt6vuhGDnUJ7rG1DZa+3dyPVeUZSimGbwqXN/zMJoNk6Y9K605nV17pGNKJ9qUANP3e32DXQJBua4S6DWM5hHTxh1Vz2/5HUsGibZukExJz9SBqKW/M+dYdPaRIjFtOvHseM2YMeW5hnt875r3rPn+uyPaiVe5h7+qq5xf8WweMW3c3vX8GddlX3w5Iqbt2RZsftcV6jvKNxTTnj86MayzRcXUN8Cy5RLZrs9vv+foCnbKMQ4NUg2l9/6zMxqbADlaEgeOrlvXxsU9t4KYMgFl7kqpvt9pEt8AnjU3pk1Z2XRkrDuig23I1A7FrdInhgE8a00sGPu9I+vdsbbP3vHuyFgunnEFVl5dzB6VdNeWDW1XrVDNgijbVc6vWX3ziGn7mXrNrZlBvtezqEogvlMCMW068ew4e6/SVVc/7Sr38Fd1lfMrns0jpuVh6Ppdsi3eVdjxgtxZecU7tnhO1NgAVdcerlcmwJbr7FmqcDVTr7W5e3xvuSK5tPqgr+NzaOCvtDwA2NtebTT1cbexLeHJmzYa5Kt5/Ypp/drtLnlDyay84ilu8oA2s/rmEdPSMTZzb+m2hXdX6xwZO1aDV+RATJtOPEvHlWINMI14No+Ylg4x7VjymxxYecVkKrRjHPkAR7ZnBhDkYet917t+50rX/5QtPO7/v1K+AOxNnQrjrKaDPLg+j3H1XbAoh8EripXyzet90EowyZPtMFkr5fqJ9KT+rCl1YhmsnANwj5az5vkykQa4OnVgGbTRIp47OwFT3Tv6dY4wRdcs8NTKzpUrnhK89tIj55BFcqifSEdf2UitzKgP8+ccspQ4xlzN8pJa2bnf46sT8+b8AUdwD8Te3JNkNHjVvIlUOQyTN/ncrG5VCW15TSg/8+RS1kiD62s+eZZfPZNSepWfeVI6d+RDG20aefOuZl1Tcr2jjQZ5c931kzcPDCzA/rIZvOqisuym4nzQbhSVnC9bDoKVnE+QMjGt29XrpPvx55APqcXa+zXl2oJjNAexeFZK9SPH0EaDvIlp/dRJwFGyfOaVSpKplJX1NJZgO64l1lB+lpFvcCzXHOxPGw2O4TqDbYlfzJX1yitYw4zsbu38EFQA8iGmAUD5tNEAgCvIcuUVrGXgqptl8QD52aPevn9mCZ1jZvcB5MEkum73Npr8AMhHM6apv98lpjGXwSsuaatKsqvDMPcKOPf0T9XsmBU4gZxtWX+VNIHhKvV6ifciwDVtXXeVNBGjhGOYQhsNKIWY1q+EY5iipHN2JoNXsBGVUV6a58u5AzDjPVfNVdNrzplOQqAUOovypY0G8DQxLV/O2TYMXsEKpVZEOrCm2fMmwjkAjqbOydfac9fcTnmvWaIR55Sxs78fOF6p17r2wfmcA+Bo6pxr2yvu5DQo+tzZCQDSkVPlVbr7TPqStvACIE17xv3mZ58d087+foAltNHm2bOu94xoAEqQwj3F1Hhq8AouqK+CSKHy4mnOCQAAlE8bDQA4Si4TMgxecVk5XKB7WdoAKmkl0FbHsfds9aPzu5TzCwxzrXOkszpelXPIz5WvW4NUeZx/bTRgL651uuy9ovgsU+OpwSsua+4Feh+4uWowaR53CXmwVQW9d17c07nX93R9bvPZJ0C5rh7TSorrWx7DXvnx2kuPdm8clXZe4YqW1BNXv95LqvdyGcDbu40GXFMuK2H2VEo828oRbagjrDmvBq8YpMJ4V0rPbJhi60bM/fjdqD/tqCCy5wMau8qK8wt5Grt2c35uxp4NmdzrvLXnM+dycZf7OYQ5lPen5dQ+2WOg6d6xpdPzeHvFza7JGCUNUsLVTG2jReQ3gLVXTFPflePx20+eOa/3P1PKe1XX9TEpHfBC9WL9SvXq2cmAZ7QDCDTdK9pc9aW/3Yn5Rv36m3Vdv3xcyvImppGarpvBnOsutlXKvU47pvU1gt7zgbfEtInEM1JVSr3FPnJvo02ljTaPmEbKrlJvcT1Ty/ZQTHt+60RByqbOLG6OCkOp+sq3cg95KGG1DGkpqeGc26xVuLop9Y82GlMoH0AOTMRgitzbZ1ukPYmVVy9/53vrX3j84YhwweYm94sIrmpOp7dZffOIafnKcTBIHGYrOZclMW0fL3/ne+tv/sK/dXYyAC5li1nqPEtMy1eObTRgnqGYltQzr1RE+TnynJk9C9vpm7XqOuPKcpzNnVt6SVfOZSnHazcHX/z8+9wXMEoZgW1po+3ji59/39lJYCH3eXBtSQxeffHz77O1B6MEq2253ujiOltPw4gIdSxQBm00xrh33JbrjS6uM9iGOhbyk8Tg1V1fQH789pPBCkblQ8rGyu9Z3ADDfoZmh4lpy+WUN/cO3xzSPCeNORwPsK2he0bxjFylWj610SBNqdYZzFPqpJwSjwnunj87AVM0O4C6bubc4JUjlwcWdgWGvvSWGhyBZUqJaWftPZ7jc3lSSHP7fLXjUvv/YzHt7OMB0jBUJ6gnOMOceAbQNNS+UWf080yq7fX1IWqjcUVZDF5F5F0JnlGBHBE8+jrCcj5XfdrncO4xlpgnwHK51gntCQZHDczvHV+GjmPpd/YNDs35vD2Oe2qn3pBcyy+wj1zrhFI7eUo9rqmufOzAOjnXH6VPLNzye7rasSmc++YxaqPBu7IZvMpZ6RXIFh10d6nmVarpAvKUyyrTPmtuqnOzdUNwSsOrK66u/f4rTDQBmOOMevCITr6xySXqf4CylDppYcrEybnHnXJbKMU0QQoMXhXoHri2nBHfFTSaFWtKwTKltKRAfkDamtfo1Hr7yFW1R353l71Wd521nevcgavm61vkvXgAcI497sn72mjq+vRpo0Gacrw2z0rvEXGtSVsIrqmq6/rsNMQL1Yv1K9Wrh3xXyqPsrJP7SoapUlzefEU53tQu9Ub9+pt1Xb98djpyMRbTtho0aU9SWFMep6TpzEbR2Wk40tVmy4tpaRDT6HJ0G+0qZfCKrnJ+r3KcKbvSORDP5pkT0/rK0dSVP1u2X65UpnNyhfNyhWMkHUMxrYiVV3MuqD2f0eHCPsdZs+dTodxdm/Oflz1WxK6Na1M/x+QP9tAud+q0a3P+r8t5L8/VJp9EbDuxiGVSyXPxrBxj13TXs+C3OPddbTRlKh1XOBdiGndnx7SsV15NmfWwx4zeruC09XfAEDcv3B0RRMzqm2copm092L7lc5iO/k76bbGfe27OviHmfEfd24hp022x8mrovG7dIeL+GLgS8WyeuSuv7qbGlD1i0F4DYwCpKXLl1dwBoy1XXAkW59EofXD28U9dRn92Oktl9kuelg4QHTGQMeeZW67z7fTVpVfM067naZ7tioOIR1OflK/vnO5xvR/5XTxNHqdBG+18roW8relv2uO8dz3zaeg6V/a2IS+flmJ+6BM7RirnPpvBqyUdCO1M3ivDpy7jTeWk52zPbR+Zrq8cOz/Qbc11cdRMu6nPvWq/T2xbLsUBmzOllg+2yoB5pnb6pVbvpZaeHMm/NAy10TiGvM5PMwakfv6G4pV2xXbk5dNSzIcU01SalK6BZAevtnhg+ZGZPNa5mNJJz518TJvzsy+rD8s1ZQXxXgw6n8e1nDbn5xjyOR9DEwpTbKPdv08bDYAuOcSAe6yauuNUDseUOnnI1aV0DTx3dgL6pJRJc4zNdtJBCKwx5aaV9AydsxTO6ZzvPyqt4iWUL4X6j3nu56z5JwfaaADkKqVYK14CR0t25VXEs8/8SKnCXqqEYwBgvtQftDvUgXdGelPMIwDKJOYAUIK945l4CRwt6cGrJhXk+VLtcIUzuB5YKvVyMyV9qR8DME9OMa1vxm8u6Yet5XT9AgAwTU73eHtOgk5220DSYmkwPGheC7lcF4/ffvLOn+b/4apcA/CgHRuAvOTUqQF7yjGGte9H3Z9yda4ByFdzO/H7v7e6nqu6rjf5oDVeqF6sX6lePTsZzNQshM1CqQHFFVypvL9Rv/5mXdcvn52OXIhp+Wpe11e6xlMj74+XY54vTbOYNp14lqd2G+3+Wk7XNzBOPJtHTMuXNhrkZcl951BMy2bbQI41JSC0f7a0caQxBcCepjZymj/X4XeOvtWtzsF+cp3huuVsPsjFkjba/TVtNABSsqaNBlyHwSs6re0QmNLY0eFAztw0QT72jmkGWUjJ3PK4pFM7Je1rO9fjgCnWlO+p17pZ7QAcYU2cmXrvmvM9LuRq62vOM6/otbSwNfe5vGs/a+ceQLrey3xLn2dU+gBi+1lP7dfb/17y+Xu892j2luYKpjZu+lb+dF0j7feLZ+usHWAsuR5rxrOu52OsPfZ7p3Zuedh1zfXlFfDsNdN1j6x9th3PnO3Wlxdd5XHp5+/xXuA8Xe2ysXrVwBV7G4pnW8WXnOPUVnlj5VUGUq5wlyzzZX9zVr2lXL720u6kWzNQu8d799J1rrtuAlNIKxyt6waqeS2MDVS5btZbukLu6jHtrq9xMDU/ctuGL6e0UqaU65slbbSUjydXc7e6up+D0s9F37FtVR5LaKPdX79LIZ1wlvY10tenIaalp/TzMBbPxvoYpn5HTvk4NhFlyXEYvMpAygV07r7p7Od+LqZuERRxnXPSVdm3b2zar02RUwCJyC/owZHmdvBdpf48mgcxd+sbWJoSz0vdyjm3wTbKk3IdVfo9bama7bmrnJO+41yTB7nlXd+x5nQMsKep7bTcrv0ruHrbrm9iQsl5scexVXVdb/6hc71QvVi/Ur16djLY0ZUrK/IyJ5DkHHTmpP2N+vU367p+ed8UlUNMK19fh3mu9QHlmTtbO+fZ3c17zCmzG8W06cSz8mmjkQtttGeJZ/OIadeRcx0AVzUU06y8ImmCDkc7sryNdRjsWf5dV7A9nYCkZu+ymMJAbteqaiuyYJql16o2GkfTRgOmmjqhCUqX8wTFJoNXHOKI/aohdXP3elX+IV1zVrO4lok4tvGw117qQ4NCzYGjszu2XXOwL9cYpWnes2mjQZ6a959D16k2GqW7XwulPAfO4BVJOTuInP395GXNM9+UMchXLtevmJaW5izQFM/NlEb+2O9a8QRlSqHOyrnThWMtKSfKFuRv6nXseqct13uMObtg5Hh8dwavSMoZF1PXxb6m4rr/bq6VH/tTLuAaSolpTeqvbS05N3vcX4x1TLdn7Q19xtDnAHlKIZ6tbV9pm9FH2QD2tsdW2ylMLOFcV3gWZITBK9j0AtZxcy1Tznf7QfLKBbCnPWKaVTXDzqjbt26s3mPUlOd89P1cfAO2tGU9I37RNmebQIC19myjsV7K/XVbpCvF45rD4BVsJNWKbi/tgZvm8Q8N6sx9cGaqA4JT09X1IHnK54aS3LUH3u//LtWUjs2u429vAzjld7Y0FlOnxqqpHbsllwGgTFdrozHtnCsTQK6a/W7qsrxNPYdXP9cGryjOWRf1lSqSdifXWOddU7MjtO/zhr53yfZKfd+91Tm7Qscu0330Y1+PiPdHhJsM8nWVcjtnNv6Sun7K5w/VE2vjXtfrUyaVAGxNG+1YU59VOPX3+lZhr10Nt9f5cQ8OlC7l1UJHmBJH5kzQm9vGW9t+m/q+q57fpiQGrz76sa/H48fXveDYVirlqOQgMuXZF82fz3l/8zu26FBrNrT6Bs22OE+lnmv2Z/CT1DW3lFNOtx/sGcvTdr6vjY9LOzQB1lC3HGtJfo91xt0/d8stjPa4t3DfQp85ZUL5gfQ0r8uxnZ/OnDSjj2dbSQxerSWokJrmSqQrls12EBnLh6HVWEfMJlnbkXfFc8z2Ut0ik2vba1VOCjf0R0+maH7WVqupAJjvqm20prlxeK/Bpq4Yu+bz7+m7+vml29JB3aW/C3vaK5alGCOnPnc3hT6VvkeqLPmcCHVPRERV1/XZaYgXqhfrV6pXV39OihcY16U8Pmvu867GPmtOkNqj4+8q5/eN+vU367p++ex05OLl73xv/QuPPxwR08rI0FZeVyljpGvPBsDZZXztc562jGlTPntvV6lvxLTptmqjAXlZE5+XtNHmrIQZc5VYFiGezaXfkdIoi+eR99sbimlFDV5BaVSI8w3t096Vn0ONoLHZ9lc5NxpG87xQvVh/7Svv321LCvUCRyu1zK0dvDrCkYNYJZ7jLmLadNpo0K3UuLi3PSd9XJF4No+YBpCuSw9ebbkEHUpxlQbXlZZR70nDaB4xDY61dob4mJyuszmDXTkd15bEtOl09MGxrtbGYB3xbB4xDY4lpjHHUEwr4plXfboa8PaMhOuU/72O8yr5R1rENHjW1MGavsZTaddOacdD+XRswLtcC5A3MQ3eNfURCq4Zxjx3dgL25EGdAJRCPINnudeDvI1t0QwAQJm045ii6MGru74Hg5Ke+3lp/w0pUB45W19HvbKZpsdvP3nnz/3/7Ked300aRs9SHknFfQBLmUyfeEaKlEdScb/fHLonBRii3njWJQavIp7ttNCJkZauzr2jl4+qIJhLmeEMyt16ezcoU2isnv39R2sf79WOfwn3wqTEKsp0tWOaNhqpU2Y42z2miWvzndGOOvL71E+MUW886zKDVxECyFpHB5Gjz9NVty254jEvVWLd4fznT1zLwxn7eV/p+nYdrHel8gIl2Xsyxr1+Pes5m9pojBH/oRx739M3JzHe/6hDjiGuLSPfIqq6rs9OQ7xQvVi/Ur16djLgcs5qhJYitfy733iN3YDNvUF7o379zbquX16fwmsQ04h49iYzlXqiZLYLXCfFmHYnpp1DPIPz6FBdJ6X82yst4tk8YhqcK6V6OTcp5d0ZbbQkBq+qqvqNiPgHZ6cjcf9SRPyTsxORMPkzTP4Mkz/D/uW6rr/l7ETkQkwb5XobJn/GyaNh8meYmDZRVVX/S0T8b6E8DXG9DZM/w+TPMPkzTDybQUybxDU3TP4Mkz/D5M+w3piWyuDVL5oxMkweDZM/w+TPMPnDlpSnYfJnmPwZJ4+GyR+2pDwNkz/D5M8w+TNM/rA1ZWqY/Bkmf4bJn2HyZ7lLPfMKAAAAAACAtBm8AgAAAAAAIBmpDF595uwEZEAeDZM/w+TPMPnDlpSnYfJnmPwZJ4+GyR+2pDwNkz/D5M8w+TNM/rA1ZWqY/Bkmf4bJn2HyZ6EknnkFAAAAAAAAEemsvAIAAAAAAACDVwAAAAAAAKTD4BUAAAAAAADJMHgFAAAAAABAMgxeAQAAAAAAkAyDVwAAAAAAACTD4BUAAAAAAADJMHgFAAAAAABAMgxeAQAAAAAAkAyDVwAAAAAAACTD4BUAAAAAAADJMHgFAAAAAABAMgxeAQAAAAAAkAyDVwAAAAAAACTD4BUAAAAAAADJMHgFAAAAAABAMgxeAQAAAAAAkAyDVwAAAAAAACTD4BUAAAAAAADJMHgFAAAAAABAMgxeAQAAAAAAkAyDVwAAAAAAACTD4BUAAAAAAADJMHgFAAAAAABAMgxeAQAAAAAAkAyDVwAAAAAAACTD4BUAAAAAAADJMHgFAAAAAABAMgxeAQAAAAAAkAyDVwAAAAAAACTD4BUAAAAAAADJMHgFAAAAAABAMgxeAQAAAAAAkAyDVwAAAAAAACTD4BUAAAAAAADJMHgFAAAAAABAMgxeAQAAAAAAkAyDVwAAAAAAACTD4BUAAAAAAADJMHgFAAAAAABAMgxeAQAAAAAAkAyDVwAAAAAAACTD4BUAAAAAAADJMHgFAAAAAABAMgxeAQAAAAAAkAyDVwAAAAAAACTD4BUAAAAAAADJMHgFAAAAAABAMgxeAQAAAAAAkAyDVwAAAAAAACTj9MGrqqq+t6qqf1BV1VtVVf3w2ek5Q1VVf7Wqqq9WVfX3Gq+9WFXVz1RV9cu3v7/59npVVdVfuuXX56uq+v3npfwYVVV9uKqqn6+q6n+squrvV1X1Q7fX5VFEVFX13qqqfqGqqv/hlj//8e31b6+q6m/f8uG/qKrqd91e/8bb/9+6/fzbTj2Ag1RV9Z6qqv5uVVV//fZ/+cPmxDQxbYyYNkxMm0ZMY2/i2QMxrZ94Nk5Mm0ZMY29imng2RkwbJp5NI57t49TBq6qq3hMRPxoR3xcR3xERf6Kqqu84M00n+WxEfG/rtR+OiJ+t6/ojEfGzt/9HPOTVR25/PhURf/mgNJ7pdyLi36/r+jsi4rsj4s/cyok8evCbEfE9dV1/Z0Q8iojvrarquyPiL0TEj9R1/Xsj4tci4pO3938yIn7t9vqP3N53BT8UEb/U+L/8YVNi2js+G2LaEDFtmJg2jZjGbsSzp3w2xLQ+4tk4MW0aMY3diGnv+GyIZ0PEtGHi2TTi2Q7OXnn1ByLirbquf6Wu69+KiJ+IiI+fnKbD1XX9NyLin7Ve/nhEfO72789FxPc3Xv/x+sHfiohvqqrqA4ck9CR1XX+lruv//vbv34iHiuCDIY8iIuJ2nP/89t9vuP2pI+J7IuL12+vt/Lnn2+sR8WpVVdUxqT1HVVUfiog/EhF/5fb/KuQP2xPTQkwbI6YNE9PGiWkcQDy7EdP6iWfjxLRxYhoHENNCPBsjpg0Tz8aJZ/s5e/DqgxHxq43/f+n2GhHfWtf1V27//kcR8a23f186z25LKX9fRPztkEfvuC1NfRIRX42In4mI/ykivlbX9e/c3tLMg3fy5/bzX4+I9x+a4ON9OiL+XET8H7f/vz/kD9u7XN0zg/q6g5jWTUwb9ekQ09jX5eqdmdTXLeJZPzFt1KdDTGNfl6x7JlJfdxDTuolnoz4d4tkuzh68YoK6rut4GNG+tKqq/sWI+K8j4t+r6/p/bf7s6nlU1/X/Xtf1o4j4UDzMLPpXz01ROqqq+qMR8dW6rt88Oy2A+vpOTOsnpvUT0yAtV6+vI8SzMWJaPzEN0qG+fiCm9RPP+oln+zp78OrLEfHhxv8/dHuNiH98X3J6+/urt9cvmWdVVX1DPASQ/7yu6//m9rI8aqnr+msR8fMR8a/Fw7Ll528/aubBO/lz+/nvjoh/emxKD/UHI+KPVVX1D+Nhi4DviYi/GPKH7V227plAfd0gpk0jpnUS0zjCZeudidTXN+LZdGJaJzGNI1y67hmhvm4Q06YRzzqJZzs6e/Dq70TER6qq+vaqqn5XRPxgRPzUyWlKxU9FxCdu//5ERPxk4/U/XT347oj49cYS1iLd9v38sYj4pbqu/9PGj+RRRFRV9S1VVX3T7d//QkT8oXjYn/fnI+IHbm9r5889334gIn7uNoOkSHVd//m6rj9U1/W3xUMd83N1Xf/JkD9sT0zrp76+EdOGiWnDxDQOIp4NU1+HeDaFmDZMTOMgYlo/9fWNmDZMPBsmnu2srutT/0TEH46IL8bDXpn/4dnpOSkP/lpEfCUifjse9sD8ZDzsdfmzEfHLEfFGRLx4e28VET96y68vRMTLZ6f/gPz51+Nhae7nI+LJ7c8flkfv5M/HIuLv3vLn70XEf3R7/fdExC9ExFsR8V9FxDfeXn/v7f9v3X7+e84+hgPz6t+IiL8uf/zZ64+YJqZNyB8xbTh/xLTpeSWm+bPbH/HsnXwQ0/rzRjwbzyMxbXpeiWn+7PZHTBPPJuSPmDacP+LZ9LwSzzb+U90yDQAAAAAAAE539raBAAAAAAAA8A6DVwAAAAAAACTD4BUAAAAAAADJMHgFAAAAAABAMnYZvKqq6nurqvoHVVW9VVXVD094/6f2SEdJ5NEw+TNM/gyTPwwR07Ylf4bJn3HyaJj8YYiYti35M0z+DJM/w+QPY8S0bcmfYfJnmPwZJn+W23zwqqqq90TEj0bE90XEd0TEn6iq6jtGfs0JHCePhsmfYfJnmPyhk5i2C/kzTP6Mk0fD5A+dxLRdyJ9h8meY/Bkmf+glpu1C/gyTP8PkzzD5s9AeK6/+QES8Vdf1r9R1/VsR8RMR8fEdvgcA9iamAVAKMQ2AUohpABfw/A6f+cGI+NXG/78UEa+033RbLvepiIgqnvuuF6oX6x3SMuqjH/t6RER88fPvi49+7Ovxxc+/74xkjHpvvC/OyqMcyJ9h8meY/Bn2G/Fr/6Su6285Ox0nOSWmNWPT3N9pSjGmud6GyZ9x8miY/Bkmpg3HtGY8e0+857vOKk8pt8uaXG/D5M8w+TNM/gy7eDyLOCmmLYlPzXZayrHNNTdM/gyTP8Pkz7ChmLbH4NUkdV1/JiI+ExHxQvVi/Ur16jkJ+cLDX1/7ypOIeH+89tKjc9IBkKg36tf/57PTkLqtY9rjx08iIibHpMdvP4lmDHv4f8z6DEjRvSwrx2xFTBuWUhvtleqcrwbIgXg2bpeYtiQ+feHdf4ptlODx20+0z9jUUEzbY/DqyxHx4cb/P3R7LWnti05nCbkSRGBTp8S0uddw+/3NQSwDWQDcZNlOgxJoo8Hmso1p2mcA0+0xePV3IuIjVVV9ezwEjh+MiH97h+/ZTTOQAHBpWce01156JKYBcJd1TAOAhmxjmgErgOk2H7yq6/p3qqr6dyPicUS8JyL+al3Xf3/r79lT1yoswYVcNMuqsgvrlBjTICe2woTtlBDTmtznkhNtNNhWaTENciKmcaRdnnlV1/VPR8RP7/HZZ3ARAlxXaTENgOsqKaZpowFcW0kxDXJk4IojPHd2AlIxZ1ul+3vbzxKB1AgiwFRiGil77aVH7/wBrmNJG23u78HRxDJgDjGNVIlnHMHg1c2cC+7+3nsnikACQO66tmcDgDMtaaPN/T0ASJmYBlyZwavYpqPOjHUAUrAmFjUHsMQ0AM60Ng7dY5l4BsDZxDSAZXZ55lVu1s5iMAsCgFSIaQCUQDwDoBRiGsAyVl4BAAAAAACQDINXAAAAAAAAJCOZwast9221B2wZnEcASiGmATnSRqPNeQQA4CjJDF5tuX/r0s+aciPuIYkADPnox75+6vfPjVNiGgB9cmujAUCq5rbRAEhk8Krd0be08TH3d+7vv//d1aBqf2bzPRpJlEAZhn0dEdPaA1avvfRocifh/X1i2v7k776UYdjWWZMx1rbRoATiGWyrq99xiTW/t6SNtuY7GSdvjyGfWSOJwauI9QV57uyFe+BoN47augLLPeDMCTzMJ2+Bq1q7ampuTBXPjiGPgZx88fPveyqeLBkgnvo7zfesaaOxP/kM5OiLn3/fU/9vxpqp7v2IU97X/rPk+5ppZR/y9hjymTWquq7PTkO8UL1Yv1K9+szrY4Gh3cgBYHtv1K+/Wdf1y2enIxd9MW3Mmlnk4iElm9NREOEaYJiYNp02GkC6xLN5tmijiWkA+xiKac8fnZg5xgKDwAFAKdbENPGQUs2Zoeo6gGNoowFwFWIadJs6wRDWSmbbQGA++8YCUDJbWgKQG200AEqnjcZRDF5BxgQLAACAdGijAQBsw+AVAACHmDIbvflwaxiinAAAwDpT76fdd3MGg1dQCEEEgBy0B6fELwBKJcYBkKN2/PKMK85i8Iqi5d5YmDOjWBCBsl11hUFzoOOqeRCR/vmfcn66ftbVCLo/50pcAyhTyvFsijnpF8ugbLnXZ6xXQhl47aVHzxxHVxsNznCZwasSKhPKp5wCfZqd+akPZGzlCseYu66yOGewUSOINQxy5k89Tw6UU6BP8z5EXUGurKoiZZcZvIJc9M1MdyME8EB9eKyhAaqpvw8ApbjPUNdGAyClQZ/21uxT23ApHQO0PX92AtjPvUK6ciV0xrHPzfehBs+Vzx1wbTqDnnZWPGieh2Z8u3fcdW0x0XZPu5hWHvcwrHXFcmJ2cz7nfahdl8sxAMe5Yr0gpp2jb4u/5rkQuyjFZQavrnyBCibHWjto5VwBU1y9rjBB413NeLJFfnTl7ZR90Ls4PwBPaw7+k56tYypAqZorfNSXx7JdJVdymcGrK2p2NLkJ77Z3kB1ajus8AEtdof6YsqKHZ+PMkgGmsdUzzcGsoS2SrlAuedecbSOVDXha8xmWro9z9E2CcT4ApmvXmeLa8bomxegD5q7vmszpWjV4VTidf93a2yC1O+em/u7Ye5sNUwC2l9NN1xnWxJ+uWDn2fueCLsoGdLMK62ld7aw5+TPnvfIcYHvi2tO23C1kLF/7+iqdj+sq5dwbvLoAAyjPaudJ3zM92v9fkofynT62PYNh7XrX9gjn8nBf5nKdwjgdfe/qivN99wJdebYkD+U9AHvp6lecY8q9dDOObRUbKV9O5cLg1YVcZeR96xl3fdskzf2OkvOcebq2+VI2oFvfFrhNJV9DY8d2xgprHa0sncyjzEC3K7QX5h6bh84DpG3pM3FLsCamzfnsq+Qn58jlvtPg1UWtHf1P1V4XXin5w/mu2PkOa01dcVVaTIuYN8liq++yWoatKFMwT6mDWKUdDwAPup551VZiDNjzeErLK1jL4BVFSbmSTzltZyjxBmaMzjtYr6sz/Gp1SZe5s/mGfn9sL/Ur1t9D2mWx9PyZG8vmPjsNKK8uSfk4Uk7bGUoqd8Dx1B/Hm9qOu6ISJ7fOUcpxP3d2AjhXKQW5zSBB+q42G/sqxwlHyWUrhRSv/TX7oL/20qPk8/wMV4tpU8kPWK7EuladkL6rx7OuxwUA66QYz1zn13Bvu17tfN9j2dhxp3htdjF4RZKFdW3FkuIx0e0K5+pqgRKO0jUIk2KdkmIdcM+rFPPrTEs6rXIZSD3a2Ao/ID/aaNdxhXPVF6eucOwAV3Klev2+gnosnqXYR9HH4BVJulLFkiuz0ralzEN6ps5YGrJFJ4j6dn/tPF6a3zq9HuQysAzM4zrOg3uGaZRnuK6trn/1LakZK9s5bqXomVcUKceLkXJdffsNAMoxNaa5BwM4h/oXgJJ4HuN2csxHK68oksGC/ZlFPV9ffslHOMecFU1d7ztqRZSYtj/18HxiGrCEWAZAKa74PCU4mpVXLLJ01PvIFVE6T0iRcskWrC5dr71FXDMv+xogzfcd3Uhxrvel0bmMcgll0EYD2IYVIsdIKZ9TSUfJ5PG1XWrwKqXKLTd9M84j5lUi8h+Asw3FtCm/e195asCjXO5X4DjaaOtskX/yH2Ab6lOAbV1q8EoQWW6ok84KAIBjqW/T4DyUw7mE87j+1pF/AACU6lKDV2b1reOZGwAAwJa00bZlYiEAOZvS53ifYC/WQfkuNXilUgMAAEiHNtq2mhMO5S0AuRqLYWIcXEOyg1d73GybhbYNK7BgWPvaUOewh2Y5U8aetmfeyGtK1XVf17VttGvg2vZqoylXwBH0CXEEcW2ZJdenvIbyPXd2AvqofNLnHME8BnzZi7LVb23evPbSI/GOWR6//SS7a3LsuaZT3ss17FEfqmP343qFp93v65rXhuuELd0HU3K8HzzCUJ5od0G3q9clya682poZNt36ZtlOJT+5qrHg0b42rh5s2JaY1q+vs72dV3P3SZfXlGpufPKMAbamPHVbu+pRnnJVXXVK+zXXB3tTxrrZFQO2caX752RXXnGM+8yGZoEfmyGiEx7mM4uIraiDh0251u4/79oSDdbKqb5fWv5zOT7Sd6WG91zNdtqUWfziGTyYu+WtOgiOJ2bBNM3VnO3XruIyK68i3JSMmfosK/kIOr053tQVRTwQ0wDSpw6ezoPrYbp2W831wV48G3QZK/lhWHu3nWZcu9p1c5mVV1c7sWv07QNtz15Kdi/bWzzfQ33DHrpWyipr47pWwYhplKxZvsdiWrMxNIU6B87Xvq7FM0o21EYb03XvDFtrr5Jluq78Es8o3dT7tq46ZeoE3dJcZvCK+bouEsGY0vU9q2rt8+FgS+rj9eQhV9BXxpfMRne9QBq00bgizxOGaxDPKNl9teHagdur7QRl8IpJjgogV7r4yIOHY3MWM6n34zqlZMo3sDX3I5ylL6aJdQBc2ZXi4KWeeUX6rnTxkZalD/FtLttVfplq6qoHZQpYYkm9MvSAex3XcG3uRwAA1tm67+cq/ZBZrbzScAZSdYWAwXpzVlMpU8DWxuqf0rcf05bYh3wFoCTiGkA6rLwiWVcZQQauQSPoupY8XwjWuJe5vof8rv2cXD0cx1tnJ6M4pZQPAIgQ14B0XXH3p+RXXjVnqV/lpADnMLgAQO62iGXuuRnjuYzAUdQ17E1Muy7nnaNtUeauNHAVYeUVCbvShcj5rlb5cz7l7Tq6niUEe9iynCm3DBHDgCNoo7E3ZQw4ylb1zdXqrOQHr652QgAok3h2bc4/e+saaFrbQFJuATiTOMTelLFrc/45ioHy5ZIfvAI4giACQK66Bq7ENQAAgHMZuFon+WdeAezFdkiU5L5Xu3JdDueTMX1lROMIgFy59wGgFAau1rvE4JWbH6CL53mQo3ZndVfntYGsMtxvcp1LAOAqtNGA1Kmj4DiXGLyaevOj8oFrcc2TMwMa5TMoSR/lgCtR1uFazFKnZGJaGdp1lHtz7trlQDxb7zLPvOoqLCqWMjXP633gUmXBXbt8AKRo6B5F3YVZ6VyBMl6e+znVRmOIckHJlO/8NWPZnfPKXdc9TleZYboiBq+W3vhq+JepWVE4vwzRaCZHJmMAkIO191nu0crT3A7X+aXJTHUgF+onxrTLiDKzThGDV1sUAgWpLPfz6bzS1hzUVD4ohcH68qirmEs5ITXKJH2UDdqUCQCgSxGDV2u4SYLrcd1TOmW8DM4jQ2y7BQAAQMmSGLz66Me+HvGFs1MBAPnSgQ3X47pnT9poAADAmZ47OwEREV/8/PvOTgIAAAA32mgAAMCZkhi8ghQ8fvuJZ8YAUAQxDQAAAMiZwSsur93Bp7NvG/IRyF2O9ZiYBkAJxLN9yEcgdznWYzmmGVIxOnhVVdVfrarqq1VV/b3Gay9WVfUzVVX98u3vb769XlVV9ZeqqnqrqqrPV1X1+/dMPGzhtZcePfPMCIFlPc/hIEViGnOUUo/dOwCtxoKyiGmUrKuNxnrylBSJZ8yRYz3WTLP2GMwzZeXVZyPie1uv/XBE/Gxd1x+JiJ+9/T8i4vsi4iO3P5+KiL+8JnEuaI7UbiDp5IMifTZOimlwBJ19cCmfjRNimvtjzvDaS4+0z6Bcnw1tNAA6jA5e1XX9NyLin7Ve/nhEfO72789FxPc3Xv/x+sHfiohvqqrqA0sTp/OFM3QNYgFlODOmwZ7mdOiJa1CGs2KaNhpnMUEDyqSNRsnaba/mZAztMhi39JlX31rX9Vdu//5HEfGtt39/MCJ+tfG+L91ee0ZVVZ+qquoXq6r6xd+O31yYDNiPASy4DDGNrC2JUeJa2pwfVlgV08QzcqGehOJpo5G9x28/6XxMyX0yxn0gC+i3dPDqHXVd1xFRL/i9z9R1/XJd1y9/Q3zj2mTALFODgwGsfDg/bEFMI3dLV2CZ/Zce54K1lsQ08Ywzzan3dPjBdWijUYquwSzxDIY9v/D3/nFVVR+o6/ort+W5X729/uWI+HDjfR+6vQbJuAeF+99jW090DWDZrgKKIqaRraGGzj1WDb2n62fN18S788h7FhLTyFKzjTa1/mu309SbUBTxjCI041NfnBprtzV/LtZxNUtXXv1URHzi9u9PRMRPNl7/09WD746IX28s84UkzZ3ht+b32Y8AzgpiGsVp1omeEZIf54sVxDSy1NxCaenvN2mjQfbEM4owJ641Y2F7a0EDV1zV6OBVVVV/LSL+ZkT8K1VVfamqqk9GxH8SEX+oqqpfjoh/8/b/iIifjohfiYi3IuI/i4h/Z5dUwwpjlf1YQ6drSa/GEeRBTKMUS7b4azeCgLyJaVzJlJjXXjmsjQZ5EM8o0ZaDTdpvXFn1sHXsuV6oXqxfqV49OxkAdHijfv3Nuq5fPjsduRDTOMLYlhJbfL4GEiUS06YTzwDSJZ7NI6YBpGsopi3dNhAAgJN0DSxtOdhk4AoAAAA4k8ErAIAMGWACAAAASvX82QkAAGAZA1gAAABAiay8AgAAAAAAIBlWXsGOHr/95J1/mx0PQM7ENABKcI9nYhkAJXj89hMxjWIZvIIdvfbSo6c6+wAgV82Y1hfbtmg06VQEYE/3+KKzD4AS3NtpYholsm0g7KzZOGr+OZIBNAC2MNYgase6uXGv+b6+3ykpppV0LAC5OXuioRgAwFaa7bQz+h5LimklHUsJDF7BAbo6+46sDO8NMxUwAGstndE3JQ699tKjp/70vUc8A2ALZ7aTtNEA2EOzPTUWZ7aKQ9po7MW2gXCiI5f1Wj4MwNnEondp3AGkoblTRvP/R343AOyhHWfabZAt45CYxh4MXsFB+gLGnoGjBF2de/II4FxjjaApv7Pl9+fCgBVAus4cxMqZ56wA5EF9TY5sGwgn6QsYOrYAyM1YI0gjCYBctLda0j57Vt9ETADSZWu/YfeYr+2aFoNXkCDBBAAA4DzN5y8axHravQO0OdAHALlqxjTSYttAOFGzUmze8Kss3yUvAPIgpo2TFwD5aQ7U8K5mfsgbgDyIad3kR7oMXkEiVJQAlEJMA6Ak4hoApRDTyIltA4Gi2LKCUinbAADkyH0spVK2AfZV/OCVQALXYgYJJRPTACiBeAbXoo0GACxR/OCVmyQASiGmAVAC8QyAEohnAPsqfvAKAEqgYQQAAADAVRi8AgAAAAAAIBkGr6Agnh8AQCnENAAAALgug1dQkBy2FdMZCcAUYhoAHEM8A6AUYlpZDF4Bh8qhMxIAphDTACiBeAZAKcS0shi8ArJmRgUAAEA6tNEAgC0YvAKy1pxR8fjtJxpKABRBTAMgV9poAJRITDve82cnAGArlgYDUAoxDYASiGcAlEJMO56VV0ByzGIAAABIhzYaAHA0g1dAcraYyaBxBQAAsA1tNADgaAavgCJZygvAGjrYAGBb2mgArKGNdj2eeQVsph1ENE4AyNVrLz16Kq6JaQDk6PHbT96JYc1/A0Bumm008ewarLwCVusKHPeAsuesCDMuANhTM67tFXPunyumAbCldhvtPnCljQZAzl576dHu8UwbLR1VXddnpyFeqF6sX6lePTsZAHR4o379zbquXz47HbkQ0wDSJaZNJ54BpEs8m0dMA0jXUEzLfuVVc5TVaChsw7UEx7vHM9cfpWiWaeUarkUbDbbnWoJzuJcFOE+Wg1fNhtB9qWCEvS4py5k3R3tdS2744FnNxlAzpgFpE9OgnzYabM+1BMfSRgM4X5aDVxpCXEGp5VtnHzxNTKNUVyjTYhoAuRPLACiFmFaeLAevyIuKAwCu6QozVW0lA+RIvQUwrPR7WCjRay89co9TmGIHrxTUdAj4AMPUk+kyMLGNEst4+5iUExjnOklHifUyy+joA3KjzmKIe5yyFDt4paCeRxChqVke7jPwlRHYxn1gxTUFnKlZF6mPGKKNdh7XJk3t8uDahG2pc/elzmKMa7AcxQ5eDVGA4Th9M9N1cD2QDwDlUJ8/kA8sodzAcbraaK5BACA1lxy84mlb36SaAUGfe1lrlhENpQfyANbboz65wjObWKavrKnPH8gHWEcbjSM173fU37CeOvdd6hSOdO8TcA0+Lefr8JKDVwrw0+QHnMf1B0xhoJ8cKbPM4Z7oafKDs9jmHdiSeMaRuiafiml5x/bnz04A7KVrlQ/nap6LrtVXVz1XVz1u1lN2nlZqfuR6k1m6rvLW3Bq31PLYpR3T268BkJfmKiz1OUA39715ENMe5HrsBq8oVq4XZcT1KtQrHSvAXDnPkoq4VqPuCsc4Rh4ApbpaGy1CnQ4wRB2ZF+crT5fcNhBykHNHJQDb8uwtAAAAtqDPkVxkO3h132as62JL4QL0bArW0En5wDXEVTRjmnJPacQ0uJbU22iwlFj2wHUEkD8xjVxkN3g1pWMvhQswhTQAkL4pHQA6CSA9BpvhXe3nvXW1hVJoH6WQBsid64grGbrXcx8IsL/sBq/ujaHmn1SlnDY409SbPNfQcdx4n2dKOXctQHru96Hqz7QYVDzHPU6JV5AvdSc8ayiuiXmQLjGtHNkNXgGwPTfe52rmv3MBeTGAlZbUJ7cBAAD70kYrx/NnJwC2cq+UdFikzzmCZ7kuAIDSaKPlwzkCAFJj8IpiuNkGAM7gHgSgm/oRADiDe5AyGLwCitZcJixwAZAr8QyAUliRB0AJtNH2Z/AKuARBBICcNfdtf/z2E3ENgGyJYQCUQDzbn8EroGgCCQClENMAAAC4iufOTgDkpLkclDI5xwAAkA/37+VzjgHgmgxewQxmPJfPOQYAgHy4fy+fcwwA12TwiknMdAIAAEiHNhoAACUzeMUkZjoBAACkQxsNAICSGbxikNl8AAAA6dBGAwDgCgxeMei1lx5pHAFQDDENgNxpowFQEjEN6GPwilG2owCgFGIaACUQzwAohZgG9DF4BQAAHMLMWgAAgHSk3EZ7/uwEADCsHUTMSgIgZ824JqYBkCNtNABK0d6SOqWYZvAKIHEpBQ0AWGOrmPb47SfiIwCnEYMAKEmqcc22gcBpUl6Wmjt5C0ApHr/95J0/9/+n2rgCyJ12BAAw5qj7hdHBq6qqPlxV1c9XVfU/VlX196uq+qHb6y9WVfUzVVX98u3vb769XlVV9ZeqqnqrqqrPV1X1+/c+CCBPOp721ezk0wh9IKYB5E08eyCeAXvRRtuPGNZNTAPIT3urwb1i3JSVV78TEf9+XdffERHfHRF/pqqq74iIH46In63r+iMR8bO3/0dEfF9EfOT251MR8Zc3TzWQPTfu+7o3OtuBRL6LabCn5goZdQ5bee2lR890pipb4hmwPXXrvpptNPdLTxHTADLUbKPtNflldPCqruuv1HX939/+/RsR8UsR8cGI+HhEfO72ts9FxPff/v3xiPjx+sHfiohvqqrqA1snHPrkduN31RtWM/r219XZd/V8F9PITQmxoYRjIA1dce2qxDNyk1ss0EZjT/d41vxzZWIauckxNlwxplGG5+e8uaqqb4uI3xcRfzsivrWu66/cfvSPIuJbb//+YET8auPXvnR77SsBO8vxGQi5pXcL94B5xWM/g3zuJqaRuhxj2hWJadO0G8tr8kteP008I3U5xrPc0ruVHM8VZRHTSF2u9WSOaV4j1/PEs6ZsGxgREVVV/YsR8V9HxL9X1/X/2vxZXdd1RNRzvriqqk9VVfWLVVX94m/Hb875VRLXHs0/cnRfxZQH54mziWmkLtdZcV31uzqfXMtzDsQzptJGA1InppEDMY0Spdxem7Tyqqqqb4iHAPKf13X939xe/sdVVX2gruuv3JbnfvX2+pcj4sONX//Q7bWn1HX9mYj4TETEC9WLTwUgo6N5s00ZkLKjYxoskXPszDntbCvlRlAJtNGYQxsNSJk2GsB5Ur4vHF15VVVVFRE/FhG/VNf1f9r40U9FxCdu//5ERPxk4/U/XT347oj49cYyX4CISLti5F2ldTyKaQD7W7qiY++VICXFNPEM2IM2GmcQ04CtiWfTpd5GmrLy6g9GxJ+KiC9UVfXk9tr/PSL+k4j4L6uq+mRE/M8R8X+5/eynI+IPR8RbEfH1iPi/bZlggKs647kqD9/11mHfdwAxDdiUhtEyQzFtrxU+hcU08QyAUohpAHQaHbyq6/q/i4iq58evdry/jog/syZROgGAVGz5gPmt0sByZ8Q0gJQ0Y0oK99x9Ma5vACuFuJwCbTTg6lKJZ7ZUXU8bDbi6Myar37839Rg26ZlXAFd1r8TXDiA1A0JfUJr6HWuDy1CHYOpBC4B19q7nt5xw0Y5LXZ/d13kppgGUbYs6vivO9LXR+r7viLgqngGULcWJ8ltMEpkSZ8cYvAKYYElFPdTJ9tpLj56ptO+vLTXU2Gp/flcjTKMIgCF9MaX5Wkp0+AHQ1tUZN9ZGa/9e09p24lg8FccAGNIXU4biR9cE+7auSYRTdsbo+4xm+ubEtuphte25XqherF+pnlkJDHB5XUHg6AbMG/Xrb9Z1/fKhX5oxMW29dgfC/d9Aerqu17XGrvc1g2Zi2nTiGUC6xLN5xDTgau6DRO2BqhT7VoZimpVXBdHBR6qVEMu1OwOdX450dlzxvDdI31bb6zY/a6v3QQrOjqWcTxsNKIWYBvkY230pFwavuIwrNBpKP74lSjjvuaef/LQ7oUu4jkrjnJCCo1ZaQc6U72FXiGelH98SVzjvUKLUV2+cTb6QgtImwNs2cGMqKkhD30PeXZ/z2ZJintxj2lhn9NHXkLgK2z5nY085pFNMmy73eAakp+8ZvSnFiVyIZ/OIacDWuvoZxbRlbBt4kBKW4kEJuoLFls/iAI4jlnJ1zb3KU2erW1KlXML5+jr0XJMwnXgGaWg/R4r9GLxaqVlYBQ44x9SZDa5RGJbaqitITdc1stV1Mdap1xwYuv87xZl9qaWHa2pfG8olALnS7wjnmTLxwnW5r+fOTkDuFFA4z73jDtjGay89eucPMM09FjX/TPmdoc8aovMCxrk+4FzaaLAdMQ2O0TdRUUw7l8ErIAt7znYHntUcyDKgBfM0Y9bUAa0lK4hdlwCcaehZhzr7AEjVnD5GMe1ctg0EnpHCNkRTt3tp7jFrNjoATe1GxtL4sKSxMhRL574OACkailsmXABwpqFnxM2NSWLYeQxewYUNPQS++VyNLb+vTQceAFsaGmiaE9vWzq4zoQKAJYYmP+zRRpvz/Wd+FgCMGRqwIk8Gr+DChpa+rqnkhwbF5n7GVIISAFPjzlijZk38Eo8AWKNrZ4n2z9YYmlA41gbTRgNgjbFJ7VPi0NDvt3/XJIr8GbwCNtfVyGp3FE59ID0ATGEfcgBKsld7aGyrvyXb3gLAmL74Mme1lDh0Pc+dnQDgXF0zFLYKBq+99Oipz2z/f286MgFYYmmM0pgCgGHaaADXNDQxotlfCE1WXgGDS2xzVtKxADBs6zp/7ufpjAOAcdpoAMBUVl4Bp9FwAa7k8dtPDHAUTEwDoATiGQClENPyZ/CKpE3t6NMhCEAupsY1AEiRNhoAJZkaq8Q0OJ5tA0na1BFyI+kApO4eq6Y0esQ1AFKljQZAScQ1SJeVVwAAB9LoAQAAABhm8IrV5iyvtXXEOiXmnTLB1bkG8uS8rVNq/pV6XEB+tNGOI++gPK7rfDl3y8k7UpTl4JWLKS1dM8jb56j5/xxmnKdYxpppSjF9S5RyHDCFzqF8DcU0ttG8PnLN31zTDVtxDaRlahvttZcevfMndamWsTnbAgPp6bp2c6gTr64rpt3jGsuUmnfic96Sf+ZVV8VT6sVUgvb5ajaIcpJieptpeu2lR89UvimmuU9ug5kA6q1tlZSHGkNckTZaXtptMm20feTeRot4N6bllm5YymBHvu517v38OY/by70NLKaVoarr+uw0xAvVi/Ur1atnJwOADm/Ur79Z1/XLZ6cjF0fHNDdkxzAjE8ogpk2njQaQLvFsnjPaaNoKx5DXkL+hmJbltoFbMEuW3CizAOXIeXu8LVz9+IFu6gVyo8wCXbpWYgIw32UHr4zKp2cosDc7ufa+AUj1BmOvMqsDEfImnqWnXa+2n+e05hmGXZ/V9xkp1+/37apSTR9wDjEtP9poj3b53JRjODCNmLa/rkeXpE4aYZ7LDl5xvq6Ot77Ovq7fZRtuqCB/ruM09A1azf29ofd16Tv/OZSLPdLoHgHgGLZq2o98BRiXY12ZQ5q10UjJ82cnANrGVmBNed+aivaoQJJSY6/9oMu2lNIKcIauGX1b34Av/byhOlpMA2CONbFtz+dDimfPSimtAKQnpTihjcZSBq84xJqGzL1ya//d/tnY98393j0dUWHf82CL70khzwDod1ZDYMtYs6XU0gOQsmbbaWn92Z7kMTTgsva79qKNBpC3oW3Jh7YXbPYxtt+bqxQHilJLD3mo6ro+Ow3xQvVi/Ur16tnJYEd9gWHr72gHnD5nVpglBUOu4Y369Tfrun757HTkQkwr39xZ6Uc/2+nI+JJqJ2SKjTXSIKZNJ55dw5n1pXYRLCeezSOmXc9QjBmLfSn3Kc6RWpsotfSQjqGYZvCKQ6TSMNlzK4sSCCR00TCaR0y7hikNnhQeHnzlOl1Mo4uYNp14xtHU2zCdeDaPmMYSS+KSWAbzDcW0545ODNf02kuPkqi8+2Zc3P9cXQrnCCAHY/Vl++dz37+VK8c2MQ0gL/eVyleOXQCkY8kOGs1YJp7Bep55xeUMBZ8rz5C48rFDrlLdso1ua7amAIArKPG5I2toowGcZ2kd3LcDh/oc5jN4xSW1A0YzmCxpKM0JRkNbF57ZOBl6qLIAC7CvPQe2psa1pVvrHvFcSwCupavjb8/4oo0GQNsW9WzfDlBbff4QsYISXG7wyog3Xe6rsZqrsqZU8kMruJqf3fVdXT9PsUymmCaAKxmbubfFqq0p8aydFoCtaKMxZMlqrLnxa+jnKZbJFNMEwDRH1eFiBSXIdvBqzdLN9iobFzMR71bqSxpD99/r2tO2q4xt2eEIXNeSPbjH5N6BWPo2Q13HdeSxduVvsxyWmu/ANFtsr7PmcyjbnImFfWUq9/scAK5lbdxyT0Xuqrquz05DvFC9WL9SvXp2MjqNXeQlVwIlH9uepjSaICdv1K+/Wdf1y2enIxcpxrRmh83SFaVtZwyYLGFgZbol+Ty0DW/Xz/u+17nhKGLadCnGs7srt9EAIsSzuXKOaXPfd6QU03Qlfe0354TcDMU0g1cT5Dw7q9lh1/73lOdr5Ha8ORjq2JvTwQxH0TCaJ7WYNveGdulA0Z7PbNqKenXcVgOFXT8b+z7nhyOIadOlFs/acr5fbqZ97nHkfNwp00YjN+LZPKnGtFxjQFdf4/3/c35/zu8wXVefbvveo/1zOJPBqx5dlX7fa1vpqij6Ko09Z78vfSg7+xkL3p47wlk0jOZJqWF05qDRltujro3L6svlpsSesfOx5HfGfh+WEtOmS6mNNvTeLWzVRls6IEW+dMByFvFsnpTaaE1r7qPX3pfPWfE1JV2kaenzIp1jjjQU07J95tVWuirhKQ2SsfeNaT8rYurvbFWRqITSM+Uhwp7XBvRJZXXTHtbUd+rJdeZ2xE69V5nzvDaTN1hC4zt/U+qIrc/tlDba1LqQa1jSEQwQMa3OGBo4WruSZur9eN8gmcdk5GFumWjqKmNzPxPWuvzg1d2USrt9cc7ZcmnNg4tVENw1t6souaMa2N9WkzG6Pm8PWzyPieWG8rJrK6W5nbtzzq/7IuayNUpebM9GrpRZYIo59yVTB7eOnujX1SelDixP30r0sdV/ygJTTC0rBq/i3Yttq4trbOnu3O+52kWvohvWV17lG2vp3MvTFlvMTh1EWNJImbN9oLKXryUryqd85tTV8Fd0Zp195vYxc7dZJj9H3tMaDJ9OW2MZ+cYWlKN8dZ27PVcNH0mZvIau89y3+u+1lx4pF4yaW1YuN3h11swAF+80bsqWs8c+S+jky9seA0JDz/ZYOxHjarPz1MfT7ZFPJe/Pf8Y2wmP1zRmDae1j70tjKef9Co6uN5WNacSz5bTR4Nr2vO7VL8vIr22ZBM0cS66/qq7rfVIzw5EPTjS7jiu46gqHqZ1Yd0v2l576OVushtnL3JUMHgY8zxExzWoUruKoAfYcrpu1MW3N56S4tejSOCumTXd0Gy2H6xC2dJVyn1sbLYVzMqVsiGfzHBnTInToU76uuv2K5X2rCZOlTMIbivlDxzIU0y638uout5MPc2z9LJsUrVl+v8X7pjS6xh6u2vU5e9ZNW3V8ci4DV5Cnvk6MtROr1qyu7KpP5nYyTjX0zE51F8oAV3SVVelbb5m25ven/O6Z58SARxmu2onPtd1j2tXK/p6PIIqY1obc4nu2tPXCoUsMXlltxZX1dRTlPKqfwqqmKZ+95BlDe9VXXY2wK95YlMy5pHRznp+2xtYdR+30Lq1797jGx/J0aMBpiaHvaadp7mdM/X3S436EK9t6QsPZSriejzwnfW008pb7NQBLteuwPerMM7coP0PfKuKl6dp7ksQen5vltoHthvSc2TNnFzpI0dIthPa6nq4wA3HM1JnpU89d14M0p55DW1LMMzWm9a1uWNJxe8VrhGvoiwd7dOwsuR6H5LI10VSpdaZ1lQUxbVtL22jtfwPrzGkbHdVGc30P5/WSNtpc4tk8S9tofa9N/V0o2Zxro8/Z18yV+1f6jn1pW3ivmJbVyquuzJuTKVcpfDBX16BG09Ay1T1nn135mu3roB0L+lNW1F05X1PTvoZSWY0BqejaavW1lx7tMjN5bWdFxPTBtRyv26lpPnqQK8e8LE3zurxzXmA7qazOcl0/bWiL3al5JU/T0zXJ03mCblMnRY9Nupj63i1Z5NJ/7HPatEfck2S38mruigFgmbVb8019ZobreJkjA61ZffMsiWlzudHiiuYOEC019PlbDZpd5drdcyDLTPX9LXm4vTYaHKurzbVkpnSb6zh94tk8S2LaEuIgV7R2G7s5cWyLa8x1uo0t+6WGYloyg1df+8r73/n/nsvcFU5YTofdNWkYzdOMaXtv22LgC9Lbzm6I6+5pa86dwav93ePZ3uVWGw22NTSL3UBVOcSzeY6IaeIZzNd33cx9fejz71yf6Spm28A1curYgFStne0uUMC5mtdu7o0rg3DcTVnleyZltJ9n/KXviPx2TmFbc7YZTDFuwl7EG8jH0I4XYwNSBqzKkszg1dSbqKWFbsvCunQ/zq6LZ4tl/lPp6GMra1Z7wBVMiWlr6uKtrsEt4tnQ7+U+QEZ+pjZe9o5Jyv025GN6rtKeEL8oibIMx3CtwXxD100KYwCc77mzE9BnqCPsLI/ffjKp86/rZ+33NBt+Xe/Z4zjbD1KGo+gAgGf1xZS9vmvq+8ZiWl8c7Poz57vnEtOYqqus3F/bowwpl5SsOQCc4sSkdgwaet/Q/9uzffeOaXAGbTSYHjcAOEcyK6/mmDLjb+2N2NzA1fd999e6ljvef6f5e/f3uYkEuIY9Y9rSRljX961p0IlrpKCvDC7Zum7ud0BJjto1Yo72xMBm22uojXb/3a73t3fKSOVYAdjOWTFt6+9auqMGQOqSHrwaalTM+d2ttbecmfpdXY2evll/GkcA5Vg7ELRmufyaAaytP09cI0dd920pdt7Dmc4Y4Gl+19gA1ZC+z+hbeeV6ByjTkXFsq+/omhTffL3vd7ZMA8Cekh686nNUIFnb4dcV+Lq2rWn/XvN1DSRKoAzDsL0nXGy1DcaUFVlbfh+kZk0HOVzBkdfD3oPIrm1Kp4xDGZbEQCuKgVxkNXjVV7GuqXCHtpUY28Kp699d71myQqvvu+Yep2AEkLYjZ/ZtMag0JV72xVUxCbZhxiypObKNNvX775/T97616Zvy/UO/7/oFYKq+dtySWDK0ahkgNVkNXi19VsGUz1y6FeCUz16z1WHfQNbSgTAArqPr+R1HrYraI6YB2wxCwxG2bKPNNSW+rJl1ro0GwJH6JonsQRsNljE5aR9ZDV4d4d6I6XvWxxx7VPguAljGDRhXNBTTtrB0hfGdLQZhPtcNVzW0NXvb1Otkq/tC95ewnM4+rmiL/omxFcpr2mjAfLbj3Ec2g1dH758+9RkeXcGi/b4tl/c2P9PFAJCfrhiRyjOv2jdbQ7+3dsukrt8T22C9NdtNj31mDg2yreompjnrGVdd2g+tHyqve5Xj1K8PAPodOTmo6zn3zS1z567kdf8Dedj6XnFJvXGGNXVUNoNXRxva7qhrG6b2+/YOeikXSEjR0ddM3wCAa5cuKdxodMW0LZ+VBexvz4Gr9mtbfP5euxRYbV2+KVvT9r12f32LnTa2/n24oiOvG200hpyxur3rvmVJR7QyDOnba+AqIv1Jhl0D9lNlP3h1xomZ8n1DHX5bBkSNc0jLHh0hR0o52LG/FM5/CmmAXG094NzVsbdkG+2xdG193a9pHLGNHPJ+z/Rpo0F62vWS65NUjE2+ODumnv39wLO6rsscrtMlg2xVXdf7pWiiF6oX61eqV3f/npQrXA0cyENXPbLVgFVXJ+GRy//7OhXf84G33qzr+uXdvrgwU2LaFtvJzo1pU8rp1jNQl8a2s2NiyvcLcDdWToe2S2trd5AM/Xxq2ro+Y84Wplvo+j4xbbqj2mgAezn7nnJL7bj+Rv26eDZDzjHNKsEH2miUYG053nIHjKG24t7X2pyYlv3KqzlSXULXDERrC8nS49uycJZ0gwgR/c+xm7M9zdBnT+1cnJqGpc7YJuGKtsrjpfuhr33PmC2O735sW8e05n7QzZ93zcS1bzypGxu4uv89pW5fO6A+pVNFfCF3a9qRa+KZ+APLbHn9DMWwrgkge1y37Xj+ng9s/hUkKqU4cGZ/X6p9unCEI669I7fWnXM9j668qqrqvRHxNyLiG+NhsOv1uq7/H1VVfXtE/EREvD8i3oyIP1XX9W9VVfWNEfHjEfFdEfFPI+KP13X9D4e+Y+4MiFRGKdeaegN0hK5OujkFdYtVBHCmNZ1qS1bLjH3f2dsCNNNR0qy+M2NaO/YMDY5MXfmQ0mSBs2PaUR3jKeQ1jEktpu2ZhjHttJWy8irFNtpaqbTRItKLr80GvoEwrs4qlAfaaGJaieQbpevqF9q6zA/1PQ1NNDzz2lu78uo3I+J76rr+51VVfUNE/HdVVf3/IuLPRsSP1HX9E1VV/X8i4pMR8Zdvf/9aXde/t6qqH4yIvxARf3xJwpuNhrFZ0ks+++wKcevnEqQohXyGPms6JpaW6/vvpXxdpJy2DZwW0yLW1fd9exqvWaG0pSNW7u1xnHPTnUJeQ5e119+aWNi3WnFKmtr3w66vyU6NZ6VLrRyu7awvub1J+ayGvwQx7QCpt2P6HtGQcpphrrmLRZZ+x9D/p/xOSkYHr+qHpVn//Pbfb7j9qSPieyLi3769/rmI+H/GQxD5+O3fERGvR8T/u6qqql7xcK2ubfXWSG1rrCMKbp+1zySAXM2pV1KuxJnnjJg2tT6d8r4r37w3t0GLGF+pNsVV85LyzLkG9ir3U7bSHeqAdD3Ok0Ib7W6rgUfbAfVbOsjbNQG0/ZmQknYZVSdcQ0oxrWSpX0t995LqAXLX1wbS9z7NpGdeVVX1nnhYovt7I+JHI+J/ioiv1XX9O7e3fCkiPnj79wcj4lcjIuq6/p2qqn49Hpb4/pPWZ34qIj4VEfHeeN9T37eko29uRZZaxbdXerryaKuLoy//zZwlB2Z6X9fRMW1LUzqHz3Rk59gWsezeELL6Co5x1vMRnvbW4WnYy1lttKFYtLZ+VLc+a+4g79D2gvKX1Cmz13VGG22sL2Cow3msbF6t7E7pV2k/T2dqfhrAInd9ZVeZnmbS4FVd1/97RDyqquqbIuK/jYh/de0X13X9mYj4TMTD3rN972tXUn2dS1epyObsdd7Oq7mzcpfm9RXOA/lrdlpzLWfGtDVyKqtz4tPZx2W2EyWY2ulyf+3s645tHB3PpjS8z9xRIid7PHugPSFD3gM5OaONNjaBrW8Fufr1WVMmy/fdL6zJT/GO3KlXxk0avLqr6/prVVX9fET8axHxTVVVPX+bBfGhiPjy7W1fjogPR8SXqqp6PiJ+dzw8QHGyodk2QxVi+xlZpZ349pZJY5ZsqTSn86P98ymdlaWdE/Kks5qI42PaUEdV7mVy7g1Xs7EytZNz6erhpVvhmjRDLqauhul7D/k7Kp5NdbXnVSx5Hs/a93Rd00vzuPTzA+Tl6JiW+y5Oezjy2eBrV26X2v/LdSi740YHr6qq+paI+O1bAPkXIuIPxcPDEH8+In4gIn4iIj4RET95+5Wfuv3/b95+/nN77Ds71PGXeyfgVHNWYfUNZPXNdpgyWNX3+ZA65fW6Uo5pucSuPdM5paE0d+BvaXqnbCmooUQKtuooJy+pxrMupa/G6mtD7REjhp5h1U4LQC5yimlXcMTkp3aM3KI9rG0GZarG6veqqj4WDw9GfE9EPBcR/2Vd1/+vqqp+TzwEkBcj4u9GxP+1ruvfrKrqvRHx/42I3xcR/ywifrCu618Z+o4XqhfrV6pXVx9MX8VaUgXW1UDZOqAMbdM4d1k1kL836tffrOv65bPTsYUcYlpqHVFzt5zd6jPXbh+xFXEPylJKTMshno0pqY3WNOc5Hks/d+g9TSXmL/CglHgWkU9MSzludbVXUk3r1uwwAPkbimmjg1dH2LthdJQzA9nRWyaOdeSlHNSBeUpqGB1hq4ZRxDk34HMGlrYe2Or7vFQGstrEOciPmDZdKW20VJzRPuracUPsgjKIZ/OUHNOOqttTiCEmaUCZhmLarGde0W3KloVHVahHbTs1ZYtBsx8A5kuhUbCHs46rK14NbWGVy/aNAIxLIaae2SbqepZ0CnkCkJuz684UdoLYY+enpWkQx+A6DF5tZGxboebzqZrv36rC3WNrirnf3X7tfqwCC8B0OdSXew3w9A007fH5fZ/btce7AS2A/LTbX01HxtrU4noKnY8AuTm7vuz7/in9kEekIwX6HqFMtg3cwRadXCpcruDo7S5ZxpYU85QU07YatCnxGi/teFivbzUfaRHTpispnnUZi3GuY65KGy0P4tk8V4xpYwNeJV7jpR0P21Au0mfbwINtMUv76If7whmmrIIAzrPk2VZdmrPf117rKc0WF18B8jVWf3uuBleljQb5mXOttndu2qpNk0Lb6OzvJ00lDtReicGrncy5IPq2tNiqk6/rM86aHWxWMkB+2vV111a4zZ912epmMZWYdj/+s7ejIh3NjoChawTIQ1+8OeJ6PqODRX0FcC33+n7rx5mkQlyjj4GsvBi8SkA7YDQ7wtZeUKl08nV9n0BCF+UC0jfU0BkayNrj+j7zeVR9K9PUY7RpIEEZ9p65e2b86HrmI9wpF0Bu2ivMuDZlIF/PnZ2APeT+YPWuGe7tAa0lUsuX9gxlrsv5h34515FD+6w3/2zpjLwa20++/TfXYLU5PK2UOnBo5e0S7bri7PrinoZSzhfLKQMwLPdrJPf0TyWmcdfcIYN8VHVdn52GTR+cWGJHQV8le8QzsY7cGuPO6qy8OEfl8zDgea4a07qeDTKnkbDVs7C2+Ky1xo777PTRr7TnsvEsMW26rR9uX2pnwV7XfApttKO/i+2IReUTz+a5ckzrqw+ObD/llF+kR/kp31BMK3bwqrRCvfcAVgo8FBnSpGE0z1UHr+7WzGrL5RjHTM2DUo4XciKmTbd1R1/pSu5YKbWNDTkTz+bZI6blVu8P9bnldixrXOlYIReXGrwqXd/M9tIq3hw7bKFUGkbziGlPWzKYlXO9f4/Jc48752OGnIhp04lny5TYNmsq/fggF+LZPGLasPYkhdLq+q7j6XvtrqTjh9QNxbQin3kVUe7ere190Et9joYgAVCGdtyaUr+3n4eVW4xbkt7cjhFgiSvUdaU/W6P04wO4mq5BnK2f75iarnbmPR88+xHSYuVV5kqfFWCLCjifWX3ziGnD1jYCUo4HS1dblR7LISVi2nTi2TraMcCexLN5xLRhQyuTSmmr9B2HeA3nu+TKq4jrzOxrzgrY+5iPztOujj2AKyqlHmyvxJr6s7ut8mGP/JyS/nYa2ukoeYYjQEQ58WxMyW20s78XIBWl1INDbZgj+x33/OzmcbRfPyM9wDRFD15dddS8tI4vA1jnku+QhtJi2tyBnqaUB7Ai1h3bnbp3H/IVzldaPBtT8vZDpR5XDuQ7pKHkmNZVx2/Rzpn7nUcQz84l7xlS9ODV1Qw9aPAMewyinX1McBWuNY7StWXD0d87xZKYVnJjFnIippGCVDrGtk6DWHcO+X5NKdQhXEtfXZPKpPkU0sB6Yto1Tb1+PfOqYCnsSzt179i575vyXkhB197Rqeq7vuynPo+Ytt6cRsiR19eamLakYZVL3cF15HQf1ne9imnTiWf7SeH+cEoa5sS9s48HSqWNtg0xbT8pxICtY9r9fSkcG5RkSRvN4FXhcupkiBgPJl2dfzkcF+na82Zk6KYntZugoWtPw2geMW0bS2fRpXxdGcBKU0kPad7zWMbKbwr5136wuJi2jni2n9TuA8ds2SkIU5zVRsuJeDaPmLavnK6lobQ2fyausZUjYlpE3mXV4NWF5dDR0DYlQBjEOk+pAXzucfUFiL7P6bsWuzrZpnYQrGlwde1d3UfDaB4xbXtbbgdxdN115QGsHG6kS4xpa46pa6JF+7OmluEp37+2jAylpe/zxLTpxLP95VBPNs25R+V4Jeb93GMaa4uNTZKd0q47wpTvFs/mEdOOkdO9dXugqivNpdardykfW4l5v9SUNlrX+9rv7/qdo4ydT4NXF5fjAFbEslVYd6keUwlSuBnZIw1zK/OuwaM5ZXZscKtrlVZ7RnnzvWOBYM3vahjNI6btZ6tBrLM6IZY+7yTFmDZ1okmKaW9KIabtYemEjLHOvqn3XlNi6txrYek2nLYNXE48o0sp9T/b2yumzvncrrK3tjwOTVrs22HjiPSKZ/OIaek6e6D4apMySjuenGzVRrv/bMnuTmv6LdcweMWoKY383CqvXAflSNPcjra5MwC3KI9TyvyUmRbt3xmjYTSPmLavLQawzpxttFQqMW1OXaJhdK4523xFDMe+rsGrted2yxWVQwxeLSeeHau0OjOFWb5cx5LO7y2vua7y3kzT1FUdc9Ikns0jph1r6k4vKcS+qROBS1Ha8ZQolYlCa/oR258z9jsGr4iIbbd56fv8IyvAKVvZpFwht29w2ze3ffaoJBiW4sz8uYNUbVNWXN1pGM0jpu1vbad3CtdyLiuw9h5gmDvrbMnvninFGDy1Q3nNTPIlaRpbkbyUwavlxLPzbN2pnkI9lEo6lsg57aRtya4Yd+LZPGLaMeas/sipXs0tvUOGjqWk48zRkvw/q7+yr63YNnWnKoNXvGPvAawzDG3FlOJx7N3pOjQolrK+bRfOSsvdmWlZmhdrtmLq+l4No3nEtH2VMHB1l/oA1hErY+bEtKHf3XLSx1yprwRfOhu9+Tt7bbk09Xe29J4PvCWmTSSenSuFe2IenBljeFcqbbQ1xibbDh2jNto6YtoxSh0YyTntbUODDqUcYw6W9tW2d8U4u8/ybk4br+u9Bq/olXqHyxR9S/TPTvsW20PNHZBLZYZ829rBlCvao6Nv6efo6JvniJiWy6D0XvoG6cekuLXd0nr77AGZreQY0yLSH3g8ytS66KjBqyn1gZg2nTZaWs6OV0us2RLtCFtNfpy7TesScwY55piSB1e/72w6e1Z8c8KneDbPUW0018nT2h3tEfnWJamd37Pzc2l/5dAOQlvVk0NSOodnW3svsWQRQt/5GYppz89OGZeSWuXcpauj4owVPFt0srXTu9cWOktsMXA1p5N5SymV46lBeUl6UzlGOFLzmhqrX7pWk/CsPfNlTj11VswYszRNW8SirRp2Z8y4nHs+x1bW9aUz1XIDa6WyQ8EaKRzDHvXdlM/Zqv7f6vPunznUibjld5Xi7Dba09fQW5t9LttIoY47W1/f3P3f9/dMGfC/cj6OSSF/psSPrvcP3ddvsdvD3MkkZ+fjmD0HKc849v7v7I9pBq8ubkplk0KlOFVXWo+YjTClk2SL758y4NG3Em3Jd21x7vsG5LbsWJoyg2Prz59brnLqSNPxd76x8nXlc9R3szknP1KIc/fvXHout5ot3pWmNboaqFtaW/a3jsV7XId9HavN1+d2vnalszlTbs5nDX1mn/vnLs1/A87wILeO0ZTSe/YM9bX2iD1D95m56JuMkdMxzFXysZXA+Xm2violT1KJaSmkocvUdA29Z+v+x62/p69vcMsVY1t89lGxcG0bbyrbBtIpxZHoqRft1Jv5vs/aYrnj0OeznbFzNTajo+9ne0jxmmoaykv7qc+zNqYtGQxP9QZ2T1t3Yp8xeNX83r075dfGz1TK19F191JLBoTGGhlLVy21P2PLxtXY921hbMC6b9JSX7rEtOm00dKVe9xfkv6lbbSc8ylXS85V3/2Q89dPPJvnjJiWe129xtrJUWflW6qrws7OlyvbYjXY3RHn7+x6Z2xxwZJ+x+JXXp190nKV4gzXuSPoU7aP6vrMObMp+maAC2THmLISLaK/s/PI+mHv1QhrpHSdM02z7Dp/116JNtWa+i6lOivlurRpyfU5d3V113dNjYtrnX0ehlba36VcPmCtVGZ/L7Uk/XPfm0L+5BCv9rDmvF4tr4ZctfykznmZbqiN1vf62fl69vcPSTltJTtqNdhWjvy+rgkofYNWzffNVfTg1T1zUrhxzV0uAbp5rqd0HE352d7LTpdaO2PxStdF33GWHETGtMt3SmljmqsP1rSX0+fmjPTv2VG4pbF05lRfbZ3W1PLliO9sd3z0bRF1dLrgbKWW8706Po7S1R6d8ztXaqMxTDlIk2t1mrFJBLnn3RXP/xWPmWFjO2Lcf7a23BS3bWCqo/e5y2HrhSNWIvTN8k01T7aQw7ln2NqtwGxJMc8WMW1JHeb6zD/fzhiEm7JNT0lxoKRjKdHYffzYNSKmbcu2genLuSNp7vU8dyu6XPNlqiu0Q69i6b2JeDbPXjFtaBtj12d5zopFQ59VQlkT0/LSNZlwykDWkEtvG8g1NFfZHfVdXa+VWNGWeExXsUUnH+ewDd4yuefbGekf2sqjnZ4S6owSjuGK1k7CIC0l3zcf7ewtPNdY28kxRa55M0WJx3RFJXQ6X92UiWCUYckuFluUi9IHriJcLyXY856ruJVXEbYPSdXWM1JS66BUxjjK3LK/dt9/s/rmOWI1cRd10LH2uDlLLa51Uc7Yw5F1nZg2nZVX19H3HLm1nWxbxsop6SmlE48y7N0vJZ7NI6ZdR+qxYG76hrbTTfk4KcveE4KGYtpzu3zjyVy8aXrtpUebzTyY8l1HuB+TMkeKmmVTGc3T1PrF+T3e1tvU5jBwBVPMLcsG6eF8W7fRHr/95J1OtSMneagjONu97DfLvz4DONbWu1ks+ayxXQO0/TjamjK3xz3dHMVuG+jGIF1rV4FM/byhZbVQMvVfefpucJ3rczUHsLaMaUOOeL4jrLF1vaSeg2Ns1Ubba/u/KbFPfcHZlEFIw1YxbclnjP3OFtuzW3XFXEvLSwplrdjBK9K2dQCZu+9s1+cdpR2ozq4EON/cma7KzzW1H4hJOo5sFPXFvqPKRLvuKe2ZWKw3d6BdTIN0nNHJN/cz956gqD4CKMNZMW1Kf86U9/VJYTABjmTwiizs2Sm3dunk3ZTZ8s1/Czb52vv8Tfl85efanP/8bT3oc/RqLA+lpmltubN9CuRtzwGfveOLeFYObWxgC3vFtCXPuur699TfUx/SJcfyYfCKTay5UezrwDtii6wp205MDRJj6c2pYmBYCufSFnKQpqs9v6eEY+B89+tGJzJsa21n/tDvNzs/9toq8P75U4xtsdx3LOqZcqRwLpvl1mAa5OOImDb1c6c8X2goRqp36JLjoNWdwSs2sabwNxsaZ8+6Har4p+6vboVVOc46f1NX8ZmpDuk587qcMxkEUqF8wn62eH5V3zV69i4EY7/Ttd2gtlkZUj6PJq9Curo674/q0N/rmbAmfjHV0rKRwv2TwSuycMRFMuc5Q21DAe/si5zljj5vS57dZgAL8pTj1kqwhHIJeUtxIldb18BV1+vNn6mb8pXqudPuh7Tl8MzeJfVIasdAOVJZTVzVdX3KFze9UL1Yv1K9enYyYNTcFS9nzOiAtdpl+z0feOvNuq5fPic1+RHTuN/Ypby955aD3qkcE+uNlYux7cOmvn/K729VrsS05cQzctHsUOkbyLrrGswSx8jRG/Xr4tkMYhpXIraRm6GYZuUVTLTmYYn3/WqtkClfrjcJyiZsp7kyMlVrY1J79lWudd/VzS0DQ8+MWbNtSd/KiDXENbierjpqbLBK7LqGEu5Tnp2McU46gPRpo1ESK69ghimrp87YFx6WmtK5Z5b6PGIaueraQmNOTEt5Cw66LR3g2ev89jWw56xeHzsmMW068YyczOmgsyMGJbDyah4xjasygMVaR5QhK6+4tC0vsimfs+S7NKCeVkIH6NnHMFTu5255GfHWFkkCEte3omYJDw9+19nxYA97NWCWDlxZZQX52boeWbPaE1Km4xlYI4dnfZG2s8vLc6d+e8vjt59ofHI5S7cjJD3Nh0Ofbc3A1Zz30e0ez+QjW0u5TN23yO2ScrqvKpdzMlSuIvI5jpzJY67KvdzTSsiPXI6h2Zbw+IHtaKNxZfd76rMHIlKRe12Qc9rnSm7bwKHMd4ExR46zvq3AepDb7LIc6q32LJs5q69sSTFPe0uKHMoH6RPT8pViTNuisXPmMa1Jv20Dp+vaYsmsXbaQY3xIsS5nmpzKWzutQ+VOG22evm0DcyofwLXlXl8NtSOGYlpyg1d3Wz83KPcTzDIa2NdzRsOyq75KqbzN6eRrD2y99tIjDaOZxDT2kuMAFuvsEdNyHrjaIu0Gr6Ybez7IFrFIPLsug0HXcmYbLZdyNjePtNHmEdPYk5jG3nIvY2OPNhlqoyX7zKu+B4UvPVk5n2DgWM3tGeZugZVKXZNz52SJxDTgDKVsJ1HKcZRmysqAqZ8BlG2La31OXZNbJ99YuzOnY8lVu72mjQakJMf6ZasFJckOXt0NPbi57+dw11xFUvJNX+orf4601XHPeX5VKnk9t3NvSrofZkAsSw/PGoppqZQj0tVeGVlaXLPF5rNSPO6j0mTAKh/tMlFa3QRLaaNta2rbJYdnurT7tIYmTm4xUYDp5DE8yw4g58ml7u/q15rSRnj3tbd6Pzv5wasuOZw00nCVjo/mNXGVY05BSnXR1PPetwKo/Z7cH16Zk5TKEWnruiZzuZld6yrHuaWr1+FT4h37cK0yZsouByUo+djO1FducilPzXR2PZe47xjak5hMMIR0lB7TTFQ6Vm6LdpaWh6m/l+XgFdewxWqE5g1eDhf8Fq5ynHsFyzmdXF0BJZVnbt31pcuAJxxr65hWoq5GUd/PGLd1eUltxdXYtr79Kx76Z/UB47a4173/vrq9PHu2habeS3W976yO1rnfOxbbnv65eAZrbVk3XCmmXelY9zIU03LI32b6p6a3/b6pv2fwimRt1emSw0XPfKmd15S2qGhvlznl/X3v1dEHnKX0wbq95Zh37vsgfaXPLmedPQeuxj67XTab/05t4GrupCbXG+xDTOMMOZe5MxaIGLwieWZe7+NqK9LG9K2i6tJe0Xdm4Om7PtqdvlPSpyzAfvq2h1nyOVeqv6ceY455csTs9K2klq+ppQeuKMd6Nwfy9Vlz4uXQszaONHWwDTjf1dpXR5Kvz8px4OrsfvmqruvDv7TtherF+pXq1d0+P8eCQbdUBgxyVUKeDQ0orf2s3PNmL2/Ur79Z1/XLZ6cjF2IaU3mQ+3WNDS6tjWlDv983Kz1Fe1wjYtp04hlTaaOtU1KebdW+KilP9iKezSOmMZU2Gk1bDMCpH8YNxbQiV17luE0K03hI4Do55tWeD3TOMT+4HjGtXEtWSEKf3GalT5F6+phPnVemrtX/zu90OeZVVxvNs2O4IitLyuNcXtsek9yVqXWKHLxqUkDK5dxeQ/tBu30/gytQ5svjnNKkPFAyAxrX4TyXr/ksKZ33XJlyD2VxTaenyMErBQ3K47rmqpqdAjoHoAyuYa5IuYcyubYBgL1kNXhlmwngSsxQpo9BLCA37uPL5dwCV6KNBkBJUu9fymrwKtVMBNhKszGkzgMgV33P6xPbyuOcAleizqPJYCaQo+aAVep12HNnJ2Av7S2Wjnjg/RHfAZSjWWc8fvvJM3VIV53S9T6uS1kAUnKPUV3PqOxqGIlp16WNBuRAG40xzS3eAVLVbKfdB937Bq1Si2lZrbya6sjGUNOeI5Vmc0A52oNW96DR1dnX9XvqgmtJ6aYBoEtX/Bqru8S06zp6lbk2GjBVexvUruu7q42mHri2+32PcgCkaE79lGJdVsTg1ZRtSVLL+LlyTz9cUbPSn9JJN3adqweuwWAVkIOx+28xjb77oJLOfUnHAlcxpY0259pWD1zD0PMdU+zsBa6pGdeWTBZMsS4rYvCqmbE6/YBUNBtFKQYA0mTrCSB197imrmJISRMJgXIMtdG03+ijPACpa0/OKCWmFTF41TTnZOR+8oA0teuWNTP52p+79jMoS9fkDWUD2NsWW76JafTRRgP2MNZGa5pbB7kPv5Y5ZQngKH31Uu4x7bmzE3CmVE4CUJY96hadfNdgFQNwBWIaQ5QJYA971S1jD74nf2PPpgYoRYoxbfLKq6qq3hMRvxgRX67r+o9WVfXtEfETEfH+iHgzIv5UXde/VVXVN0bEj0fEd0XEP42IP17X9T/cPOUAF6GTb1spx7M523BttaIP4EipzeTLXcoxDaBk4tn2Uo1p7Taacw6UJuWYNmfl1Q9FxC81/v8XIuJH6rr+vRHxaxHxydvrn4yIX7u9/iO398EurFLgCu6zHlIMIplKOp6NbWGiLJRLTOMK1GGbSzqmcU3iGVcgnu0i2ZimHXZdYhpXkHL9NmnwqqqqD0XEH4mIv3L7fxUR3xMRr9/e8rmI+P7bvz9++3/cfv7q7f2wuVQvLCBNucSzZuNIQ+k6nGNgjlxiGtcjngFziWmkSkyDc01defXpiPhzEfF/3P7//oj4Wl3Xv3P7/5ci4oO3f38wIn41IuL281+/vf8pVVV9qqqqX6yq6hd/O35zWeoBdvD47Sdm15Tr07FxPIsQ06BkuceD3NPPoE+HNhpwEdpoxft0iGnARLnHg9zTf6TRwauqqv5oRHy1rus3t/ziuq4/U9f1y3Vdv/wN8Y1bfjRwklIaFFa5lGmveBYhpkGJSopplEcbDZijlHgmppVJTAPmePz2k+zjQe7pP9KUlVd/MCL+WFVV/zAeHpT4PRHxFyPim6qqev72ng9FxJdv//5yRHw4IuL2898dDw9QBBI11JiZ09DRoCBx4hkUbmzAaUnnnbhGosQ0KNyW8UwsI3FiGlyYmMaQ0cGruq7/fF3XH6rr+tsi4gcj4ufquv6TEfHzEfEDt7d9IiJ+8vbvn7r9P24//7m6rutNUw0FSG32Wzs99//3BYVSZqSnQD4eQzzjCOrG8zTz/X4e2udjTkwzIYOUiWmwj5Ri+FAMEp/2l1JZKJ2YBuUbqlPFNIZMfeZVl/8gIv5sVVVvxcPesj92e/3HIuL9t9f/bET88LokcgVXuzFM6Xj7OvvG6NTbztn5mFJ5PMlp8Uzel8Og1fmG6tKpcQ0KoI3GZq4W11Lbhqgr/692Ts50dllwriPipJgm72F7r730yLXFIlUKkxNeqF6sX6leHX3f2EoQ8nJvHEyZEV2aHBpGdymlk3O8Ub/+Zl3XL5+djlyIadfUVY86t8stvTcQzxgjpk0nnpFam+WKulYFQ4R4NtecmOY6g/24xugyFNOyGryiHCV2Ls2tgLve35cvY5871KiZky6NI7poGM0jpm0jp5vaEmPaHFvtUb5VPi6NpVyDmDadeHZNzfhrYHI/Od3nkCbxbB4xDbaxRfwSA2kbimnPd70IeytpuWh7272IeKbBd3+t77lSU77j/vtTBqaar88JCF3fAXCWEuqj3I5haAXZVnF77/iv0xVguWZ7IOf6M5XdPfrSkXPeAnC83NqVsBWDVxyulEGru65OvWaH2f3fa4+7/fl9QWttMMs9GKbSUIWSHH2j3FWftvUN3E+1xfHMnYCQgzUTLVKTS553sRIaOFMJE9r6JvwNHVdfO6JvMsSUPMo5D7dmUgnkb6vY0DfRe+v6oaR76rE4NmcS4pp82OIeIefzoM/xeAav2FSOFdjYlnv316Y2etq6Pm+u9vf3raq6YsXZDhxXzAOYY+7NVsoDF3M6jYY6pNQbTztqdfTaAcgSldTABtKRY6ybMsg0tQNtasfoUPzr+r3c8vRIXfdd8gvGzX3sw9HX1RbPrZ+6o9BaXd9Tgr7Hjwy1rZb0Zc5JwxWYgHEeg1cXskWA6dK3/UFfJZnSloHN7fi6Xhva2mFKpd/Og76tjObm7dD7rkTwgDJNiRPtVa1zJhSMrSwaG1TZI45t9ZymJYMfRzZ8585QX5u2nDpsDVwBe5nSRmtKoa3W10E61EYbuyeY0umq7l0vp9gLOTvrOlsz8NT1/rX3+l2fMbY66ei827qNNrV/N9W6OLc4kVt6S1PVdX12GuLl73xv/c1f+LcG33N2QZlbwZ29jPCMBseaGRdL8ndO5+GRQWtJWR0rL2eX/6W26ozlXB4GPM/L3/ne+hcef/ip1+bMjtrb0hvntiV19xIpxbOh2DMWk4byaO4xHlFuxtLUNRljaKuK9mfmFgeWnlvSI6ZNN6WNlqtc763v9t4aaGn+tHeqGErbGR2HayZwltRGu1u6swhpEM/meaF6sX6levXsZAxaW09FTF+RupW+idlrdlhYO5lt6kTzLQbN7s5q12+18u0utzggdpVjKKYls/JqSiV2Zmff3O/smomwR4Ww90zwKdYcy9SVRV2mdqwt/flSXedh7oqALWeinKl5PFZJcWV99dXZN71z6qaIp9O7R7xJYab3kKlxZc5xzHnvGeVjTqydUk5yjAFTVwGefWwab+yh7xoeG6xOXdc96v31LfS10ZbEiS57z7Je+hl99/wp1JNr2iVDK7lypY0G70plktKauvduj/vBKfkzJc5tcXxD9q7Pch7k6ZN7+2FosDb3Y+NdyQxejcl5VPiexhzS2qWrMti6Elg6ODg2uy8FYzP4m+9LveN2qXZHZirnZ0mZKaEjlvP1xbTU9a0g2+u7jsibuR2JUwYi9zi/qQxsTo1pU3+PbS1ptKd6/8T5vvj59/XW+0MzqnMpR3u20fomouWWR0t0HVsqxztlRUJfGy2VY9hK+z4rtWNMLT2Uq7SydkRMm/Keo/N0r/OY6n3y1OPtmxxf0v1IX79BSsdWUn6fIYltA9cu3021MjlT36BORPfAD9sZm8WWQ6U1tKJvaYdYUyrHPucaaG+NVWpDtostKebp2jawaazMiGndxuqTvfNtKK72WTIo3nXzfVY5SCUdS3WtqugyteG35vePMKcR2yeVY9mTmDbdFlss5XDfCxF5ltWrtEXGXDUfxLN5mjFtbf/GFctbTrrqhC3aiqnUNbmXw75tC3M9ni31rV6/Qt4MxbQiBq/urnJCSU/qZa9r4GXsvU1LZ4umni9T5N55uwUNo3nGBq8irlmOcjOlLhyT62B/10BdSmV2Tpqmnse5n5VSfkw1ZUZiCXF7jJg2XQ7PB4Ehqddpc9po7d+5/979tZSPcy8p3qMcSTybR0wry9j1327PrK0nrlrPzjG3jdYX+66Y13PKc6kuM3hFOY68MLcKZE25VypXaghcfQb6FBpG87xQvVh/7SvvH3yPspW2rjp9yj7u7d/JWSlxYGzAqbSGwJSYlvsg3Fpi2nTaaLTl1ka7f87dFeu8nPWtoHYeH4hn84hp5Zg6MFXafX5Emcd0JUPx7Or3K0MxLZtnXo1xAedhyo3n3OCz9txvXW5KKIclHMNUU7aUgrn66rapxLS0tM/dVQauSjA2mz33c9VU0nmjHOJZOXJtoyl/+brSNu3kQXnMR6mPeijpWK5kykpr57ZfMYNXTvKDlGZKjz3D4/6edlrnpj2FY6UcyhN7WfL8I46x5Pl3W30e+yt5z/C5g+IR4hzHUt4elHD95dhGK63OvyLnj5Qoj3lo1v0lDWCVchxdSjy2qSupSrhH3FvW2wY6wefao4N1aMbe2UsoS6xMr0R9sZwtKeZZG9MilNMj7TlYmMJNqq1RyzS2kt09Sz8xbTpttPzNiXFd9cjQz67+vCU4m3g2z9ptA9V151v73OEp59B5Zgt95Uj56lfEtoEaQenYsqNv7Nkh7UbRWRf6PX3KYf4MEAAR569wO2LL0rHPF9PK0X4uiHPKUbrKnPKXjr227TNwxVa67lWUKVLi3upcW/bfTPl955k1muW1fY/knmm5rFdecZwpHWxzZuvlxA01V2dW3zxiWvrmzNobm1yRqjWz7clbiZ0sWzb2xLTpxLM8THmmcMl0BnFV4tk8Ylo+rlqvl9KHSr+rlu0pilh5xbn6Vkjl3LE35uxZ+bCGoAjrNPdJh9wot3AdV73etdXIkTYaDLvqNWKVzjU4p8sYvGKWrgvtiK2PjlDCMcCdoAjztBsLV6JxVBbnEriCse3nIUViNAy74jXSt9uTNho8MHjFJnKpULuW4U7dElHgADjWXlsnjH1WznX93E68nI+V61BOoWxT21ld9wXqBwByNNQ/+dpLj8Q3uDF4xWVdqYOvxOdfANdiAsGz1s42z3ViRo5pBqDflHhUYt2vjQZQpqkxq/2e5iBWbrEhxzSTh+fOTgDkYK/tKI7a5sKsDSA3XfVj+7XHbz95588VbVGv5xgbckwzAMPm1u17xn9tNIB1rtxGi5j/eJX2e3OMDTmmmTxYeVWgvbZZmvOdpYy49x1D3560W30+wBVNiR3t9xxRj5YS0yK686uExhJALo6OKTmu7umLS8282/N4csorgL2149aUOKYeHV9FNaVNW1I7FJYyeFWQs2c1bPnQ3L0q6KlpvA/A6dAD2F+7Y22sjr7HiL3q5Pvnnx1Xp1gzq09MAzjWGQNXXc/R2OLz9jLWyTdle8Hm+wFYrqvOTaF+zamu72uv5ZB2SEFV1/XZaYgXqhfrV6pXz05GlqYMwuz53e1KOJfKd4sOybkzTyBXb9Svv1nX9ctnpyMXV4lpWw3ur6mPt653c2tUbD24lvKxwlbEtOmuEs+OcEZbIcc2WsSyVdhr3wc5Es/mEdPmGxogOqt+La1eb+ZxaccGcwzFNCuvMnbWwFXze3NrFA11TM7pBBybeZJT0MkprcD59liNtHSV01b1Vw4rrNoMXAHk4ch77avsGjG1I1UbDWCZFOugFNM0ZmyFWN+q4hyPFfZi8KpAR1RwOa64OrJzMof8uMsprcC5hurRnLZuuMtp0CqntAJwvK6Or5zMuY/o2m54yu/ldI+SU1qB9Kwd/JgST3Js/x2hq690zvkwcAVPM3iVqKl7eR+paybf3unoa4QNPczwru/3+tI8p7EnmAB0m9uIWRtL2vX+1s+r6jqerZ4bcoSpeSGuAYxTVz6Ymw9Dkx67nmM59PntON/8v3MDXNWe28+dtT1g+/tzWE27RZqswIKnGbxK1FjlNNQZ1bc13tK9wbs+r/3aXg/O7drSb0rAGurAXLJNYF+6AK5ibp05p97farCp2ZG1lbGYsaRBsVX6htK2NN4C0G9JXTnUHtnDEZMj1sSYKXFzbOBqizQBlCSVOnDN5Iam0lbTTmHrQOhW1XV9dhou8eDEuY2INZV93x7gW2t3mo0NKg0NIm2V5qHPmTuA1/68rt+9yr7yXJuHAc9TYkzrWt001dJ6cSi27GFKTJv6+0fF4b2JaZRITJuuxHjWNne3iz3qxRQm1O21xdHQRJbm7yydZDn02p1YRqnEs3lKjWlD9e9V6r+hOLo2D5bm4xaTC23LyJUMxTSDVzs7ooNv63RMNZbeOYNXR5naQJrTUDWARek0jOYpKaZtUT+n1tHXZ0pMS80RcVRMozRi2nQlxbMlSu34mzsoFbE8Rg5NDJk6ELU0TUvfD7kQz+YpNaatmaB9BUsnM+wxCWLqxP+htGyZHkjJUEyzbeBOUuvkas6UP6uiG9riYestnqbOkJ/yGUu3tehTakMY4ChrV3M1P2OLGe9nx/yxrX6XfqZnZQFX0DfhrtS6bc5xTY1zW7WX9tgmau3qMYBUzRn0uGr9NjW+Dw0QbRUfuibTz9lK1zMeuSqDV4k4ouI5unJb0jBq2mOrxTmV/BEr4aYs7xaYgKvZq6HV9VlLYsIR2/VO6TDsiiVzB9T60j7n+WF7btUBsKe++2x119O2nHV+/6wUO+CG0qONBuREHfWsoQGjobwaildLBpSWbM87lkYomcGrBKiAuu2VL1MeCLlFoJ+zx23qDTmAIzVX/aQ4Y3DqzPClA1hzB6C2muyxxwqy9gCb+AakwsBVGo6OC30TULritjYakLKh9sZedZZ68F1dMeL+7ym/u/Q74WoMXq0wVDFN7bS6eqW/xbYcfR2bazv/9gr0Xa+1A93WWykCjNljm6SrxLihlVh9sWhoAKnE+r/EYwLSNPUe/ioxaqm1baGpk0/GOl/nfNYUc2e5G8ACzjRnW7kjtOvsHOrDLdq5XbGqb/XWnL7gJauvtKu4mqqu67PTcPiDE+c+0LDL2Kj6lNU9VzO2EmnPADj0QOClWyvNeWjiVttH3T9ri5kcZ5RFjb08eRjwPGfEtIjxa3pqTGt/1trVQ2uk3DCauoq3/dqe352Tocbd0m0Yz5i5n1q5ZJyYNl3KbbQpMW/q9m9XN6ceTmUG/5ZpmbtqYav6P6X+AtdEnsSzeUqMaWO/e9VrO+U2ZJc552lNDNqrPDQ/d84OVNA0FNMuu/Jqy5lkYxfqnQv2aV0z2daYuw/5lO9tn+cp339Ep+LU2TdHLyFva3+HawDS1LfydM6AV/P1reRWZ/TNvlur3UhZM1CZiqE0dsUnswyBuyX3sbl1ZB1paofo0nzbo80xVgbGOvemlIc57cD7e+bOnt/ic5bQRgNKdlSdtlV9PbY44v5dQz+f+z3t19eYkn5Y43Irr4ZuVNd0irhA58thFsiWK6a2/vzU845ymNU3T0orr7aoY4ZWmZpZlZ+t49oRA0oplikNtHyJadMdHc8ils0+bnJNrpdDG22OtQOXU1Y8NCf9lJR3pE08myf1mDb0GRHLVsSqk2hbuooP9nbplVdTt4wwm/d4OVSM7RkEXasRtthea0l6UtpqYioBEbbVtwq4/bO1n931+msvPdp1NYzBgfR0nYsjY2BKrASDddYMUrVXjIgX2yotH/c6nr52Yo5yTjvkqKs/KZf+HTG3TEP9nHC24gev9uRivoauLRX6zv3cTryxZbtdW3mNSbnxkWq6IBdTZ9cdtX3pnp+tvtjH0Aq9rVfvbSXVRnJq6YESTXnmkGuRswy12brahamW1VTTBSWaUz/0bV961jWrjZa3sXZdTnGLa8lu8GrJLL2pHS9TVmGlfOGqXNIy5Tx0rf7rm00653v3vKnY8rOVWZhuTkxbsxomhRXJZtIfayiP5z6/Y86EjLlSKxN9x5pKwz6VdMAaUyaMwdaa9eeUsjY2MaS9veAetm6jucZgH+1ra8p9+NBEjvZrXf1JW13P6oW8ze1L7ypzygBneO7sBMzR7Li7/2n+7IrPD+rqzJyaFynMpr66pY2hIUdsXThHTmUxlXRsqcRjKkUzlvU1Prr+Pfc79nTvkNkzhs6Jacp7mu7nZquycuZ5HitnWwzQKcdc1dROkRzabV0rcaZe2+qA860tY837o+a93p5ld8s2WmrXWInXRInHVJIzzs/SCYfN3z/i2h1qvw69n3PNuS9JLQZMkfvOMwyr6ro+Ow2zHpy4R2Hp2q+6/fMjLQk6za2icqxomKZrpVafFMtBarPm21w/3TwMeJ4Xqhfrr33l/ZNXX25tbNVV36qm9mt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zPZRK2yiVdFyBfAbWSr0eST19rGNC57G0l5cxeNWhWYBSaSSlWKhTTFNuplZc8hpYI7Wb0vaWd7k/V/EsKW0duCQdqZTHUqRSFmAv7TZaCnVIMw0ppCcinXQAAOlLra+gVO0JT0z33NkJALo7blVm25OnXFlqN6P3bXZS7PjLSQp5NvRcq77tlFJId8ruD5kfi1t977ONFcBy7XpVGw2AnIld5zI4uI6VVwMULvbUFTyUt3naKzf6ttTsa3CevZ8zQMmadWpq22qlamrDcsr7NFIplXqEPd3Ll619jqWNBrAfdejx3Edsx+DVAIWLPTUbRUMzpt2s9xvKw7uhzru+bcvkN8A2UnlO55iUnxW1ZIsJA1eULIXrknI1J110xTBthmmm5k8u9wkAuTOYsj+PRthHVdf12WmIF6oX61eqV89OBpyqawBLo2jYlM7GoQbRUBCR7+96o379zbquXz47HbkQ07iirrq2K66laKxBcVS65zRsujpVxbRpxLTpxDN4mrbZdubmpbx/lng2j5gG+Ultx6q+WDQWo9qrusWzZw3FNCuvIBHtDj836MPGVlRNCQxDA1ryH2C6nJ9r1d4eKnXt9PY16rrek8s5AUiRyYXzdeXXkjyU9wDXlNpWsksH1Jr9lGcfQ24MXkGC+p7ZtEfllmOlOfVZH0uOK7e8IH06jVkqtZlmW9nrmli7FcbcFbpbmvM9c2Kg7UEAtpVaJ1qKmnF+y8kt8hlIkTgwbss8OjOvp7YX222w9vbDBrDmee7sBADj5m6pMPX1sQ6wqR1k9z9bW/qZfQ0lOMJHP/b1Xa8LrqHvWYhXK1NHHm/zut1jgO3ImNZ8ZgsA+5laz859Pu/U5/amSFsMKN3VJ4jNnXiXch5N6Rcd6ttpDkY1XxuKhSnnR4qsvIJMzHng7dTfH/vMubPQ53b2De0Xu5QgAORg7vONSrK2o2+Lz57yHVsNYJ3ZyZh6ByfAFQx13C3p2BpqP7W3ol8Sw6a00fo+N/VOSgDWmVvPL40JR7RjphzLlPS3n0E89jti5TxVXddnp2HwwYm2W2KJlCqCUsvw0KyDOb9fWr7kYs414mHA87z8ne+tf+Hxh0ffp+yX6ajBgrP3+S6p/M6d+DH0GWvyZUpcnZvWM2Jt6vFdTJtu7OH2pdUFXI8y3E2+nEcbbT/3mDZ2r67sl2vO9ZX6/WyXK9TdZ5+XnPP47LwbMxTTklx51bVPf84FhGOc8VyKKaPppVq73/sVt786U/v8qE+BuzkzxErUF4/mPBx+izp1Slxcun3gWs1786HPdL9+Dc06wzknFXO3cFJ2+8mX42ijHct1f21z+qDaK2m6fra3OYMNVynbzfNyxvGmlsdT8yH38pHE4NVHP/b1ePz4yTOvz+lE2eMkDJ3cq+9vuoWuQco5xmYi73VxHrE8NkdLjnXt9oJbOqsy36suSX1WxRWlHNO4NnGt/57i6OPdamLHljFgTt3RTv+Wg2dbfh7TtNto9/wf62g98xrSRltvTRvtzPJw1uD+lZXeRttjgN79+LmWbqO55vdJy9ytVbsmbR8lpf6y1OR6vFu3aZa00XKMaUlsG9jeYimVQjh3hU0q6c5ZO8+H9tsemgnR/AwYc/b1u0cn45bHZEuKeVKMaRpd55i6veoRz3bagnKUh5Ji2h7EtOnGtg08izZaHlKvC0hfSdfvHscins2TekwrpaznZMmOAqnWS8pR+lIoOymkoU/y2wZ+8fPvS3oLsaO2i+HZpbl9+dp+Xf6XK+XKdStL6r++a6RrVjTXlmpsvYI5q1CmTMiY8jl7ulK9coXYs5eU7+kpg9WieZD3ZRMnu4210aBN2TjHklVXKcsprTBXEiuvUpwB4WYM9uP6yotZffOkFtPMwjpHu/N+6opIq5vOdea+9qVIvc4R06ZLLZ4B+9JGy4t4No+YRkR3W0vdxxWkXs6TX3mVopRPKGzFQw7hGlxzx2uvPpla31q1kp7UB2NSI58A1tFGA9hH13Nu1X356JogyjQ555XBK7iwPSuvPRtdqc8YgJS4Vs6zNO+ds/MYPATgbNpoAPtTX+Unl2eQsa3nzk4AcK4pnXRLn8e0F8EJgC5bxJ/XXnr01B8AONpebbQ945qYCUCXPfoIDVxdh8ErIB6//WQwmMwJCGasA3CmPeKQhhEAR5q61fBU2mgAnGWPtpT22XUYvIKL26rCvw+Ama0OwFmaD15eyz74AJxtq3imfQbA2UykYAmDV8BmNIgAONtWsUhMA+BsW8Qi8QyAFIhHLPH82QkAzqdRBEBJxCQAAADI2+jKq6qq/mpVVV+tqurvNV57saqqn6mq6pdvf3/z7fWqqqq/VFXVW1VVfb6qqt+/Z+IBYA4xDYBSiGkAlEA8A6DPlG0DPxsR39t67Ycj4mfruv5IRPzs7f8REd8XER+5/flURPzlbZIJAJv4bIhpAJThsyGmAZC/z4Z4BkCH0cGruq7/RkT8s9bLH4+Iz93+/bmI+P7G6z9eP/hbEfFNVVV9YKO0AsAqYhoApRDTACiBeAZAnykrr7p8a13XX7n9+x9FxLfe/v3BiPjVxvu+dHvtGVVVfaqqql+squoXfzt+c2EyAGA1MQ2AUqyKaeIZAInQRgNg8eDVO+q6riOiXvB7n6nr+uW6rl/+hvjGtcnY1eO3n5ydBAAOIKYBUIolMU08AyA12mgA1/X8wt/7x1VVfaCu66/clud+9fb6lyPiw433fej2WtZee+nR2UnIyuO3nySVZ82bgJTSBSRDTOMdfQ3HFPKtnbYU0gQk5zIxTR2Yt3tMcx6BHpeJZxHqQoA+S1de/VREfOL2709ExE82Xv/T1YPvjohfbyzzpWCP337yzh9BF8iMmMY7xDAgc2IaWXjtpUfx2kuPrDYA+ohnPKXZ7whcx+jgVVVVfy0i/mZE/CtVVX2pqqpPRsR/EhF/qKqqX46If/P2/4iIn46IX4mItyLiP4uIf2eXVJO01ALJvWEUkV7agGOJaexl7/jS9fliGlybmMYcqcYMA1iAeMYU9769OZMNxRfIX/Wwdey5XqherF+pXj07Gaxk6wco0xv162/Wdf3y2enIhZjGnmwdCOuIadOJZwDpEs/mEdMA0jUU05Y+8wqesUUHWtesCB1zAORo74Em8RGAI3iGMAAlMfke8rH0mVf8/9n735hbsvsu8P2t7uPYRNyO0yaK3LY1CYoRsnTJAVppo8wL5L6ok/DH0VUGghBYI0vW1c2VgmDEhDvSjJDmBXlDDFJuRr4Y2RkhDDSMYkVcHaUdjxDSkOAmB4ckStJEINvH4EmwQxg0IYa6L55d3XXq1K5dtXf9WWvV5yM9OufZz7/aq1atb61aq1YdSNuo99eXXWOt2aHbgN3muy/lD9RkzQzr62faEn/zmuUyAKjPnn00/YP92QdATbbso0V4vAiUxJ1XjHrw6OHrDXr/QtmSF866f6fPBbr9je0fgFKMZdpWtKX7kmdADXLoowHAEnLoowH5cucVo7YKjlwDygyMNygLoHS5Zg3b2mpGJ8Ba9NEe7r0JACwk16wB8uDOK7L10nP3X7/AlMMM+SM+j6v/nq0LDHCbvdvRsQt+tbftMg1gGXu3m/2/f8Rncg31U4/y3gFqc8TrjUPc8c0Qg1cUY++T8iM+h0to7GvvOg+sZ4/j+yjZdY517QGWl8OFpn4/be/t2drR3u/e9NGgLkMTIrbuow39vSO2Ne1NDEd6z1yWmqbZexvimfRs80J6ce/NIFNbzaQ74ow9mOKV5uVXm6Z5fu/tKIVMY4xMg33JtOnkGWO2zJkjXsCDS+TZPDKNS7YaNJFp8KSxTPPMK4pxbcM+dYb1S8/dFx4AZE2mAZCLW7Jmzl2wMg2AnMk0WI/BK6o3NxRuDZGjPwj+yO8dYE3XzAaUacdhPwFbu6XdceFuWzICYB17LHOnTedIDF5RhHbd065zjXV7oe3c95/7+lKO/kyNkjuic+sUwFK67cy5j3Nrocs0Wv06c+n7AK51bub4pfPpc9+/Vbt01Lav5D7aGHkGLGXOdcfu1861r2u2TfpodbI/hxm8InttozynczTUmRp68N+aF/v6f0cjlK9L+6fWzh6wrWtn5Y0tLSHTGDN2ngSwpXNZ1u2jtf/f4oJfhItEuZuyfyy/BdzqmiyY0q8bGgxbmkwrw5yl/3mSwSuqM3awdwfCtjjR7c/EcMEvT1PqgY4RsLSl7praun2SaXWQacAarrlY122Ptu6jybJ8bXHhF2DMubuxpubUlu2YTKNWBq8oTr8xnrs0xdD3raXfESN/gh5YQ3dZpGt//tLPbt05kmtlkGvAli5d1JvaJm3ZR5Nn+bJvgD2NLd0+9P8hW08y1G7mxzKPtzF4RZW6S0/kJMdt4g32D5CjOSe5W7dh2kwAptrjofYAMGbK6k1DrxmIYC53FF/H4BXFGXqW1aXvmfP7uM4eDfCSD3hWD4C1XLN80h4P/j2i7tKN/Y+pP7fktgz9H6AW+mjHIM+AEs29m6r7zEbqtXSmqS/zGbyieDmfHM+9GFaKoTV/L33/0PcsUR61lClQn2vb/VzbtVozba6hDFxyEsW1v+dcR0gHCdhKexFvbl9hDzlu063W6m/N1X3uigwCStEdkJpCG7euXHK620ezv/dxb+8NgDlyWkv2yOZcYBsaXFzqVln7G6jZUFur3Vve3E7q2m7Zx/06o74AW+le1JmyUsbeum1/jtt3jbmTIM69/yUu0NVSpsCxnJuEUYKaBlf6++Ha97VkztdStiUyeEWWhhrdfniUsM5sqaE3VX8fDO23of1Yc5kAx7F0W3Zp6Yk9Tpin3Flb8on8WE6X/N5K3W4gb3PaxRLa0Ny371rd9zWW47W+f4Br9CdhXBr42LsN7W/f3tuzpP57mnpO0f2+Es5DmMbgFVmqqYGp6b0McUcAcERrDMLneIGpxpnpt+iXh8kYwJGMPY9RRuTn2v1iXwJHM9Ze5ppxOW4TrMHgFdk4d2fV0Nf63+fC2n7mlHmuoQ8wx1oDFudmie3dbu7999c2dxCqv28MYAFHNdYH00fbX013yQGs5VwbuMaSqlxnarnbP3UyeMVullyKSANVBvsJKNmagxQ5DVYd0dAg1KX9MPf7AUoypY271O5pF8tgPwGlG+qnzV1m7hJtZRlMLKyPwSuycG7mchskU+/CAoA1yJ76zb1LwF1XQK3OPX/YhTsAcnMun85lWZdcq093NRPqYPCKzVxqOKZeBBIuAOxhrYkUci0fc/dFe+5iHwI10aYBUIKx8/D+Ncb+xPgpvwPYn8ErduWOKgCgdDq9QMn6F/cAoAb9a47XLIFLmUwyrIfBK3ZzaeDqlgZGAwXAktaYbCGn6mA/AqU7144tPblQHw2AJY1lSj9zDGYcj31dB4NXbGas0Vj6oqAGCoAl3ZpNQ8tWAEDOln7ulewDYGn9bGo/P3eXlQEsKMtTe28A9Hm4HgC5WaKD0/4OnSUASjT1GcUAsJU2m9qPS30tWQZlcecVWXJhD4DcXNvR6f6cfGNp7ugDtqSdASA312aTPhprcpffMgxeAQBMdM1SE2b2sabu4KgOEgAAjLN8IFtQv5Zh2UA4uPbWagCmmXsSarnA9XWXCjlapvXf79HeP0CttOcA69E3W99R+2csy51XwKFC2/JKwB60N+vqPi/zSGWtIzjPg0cP4+m3770VANMcKc8ijpfhALXTpsu2JbjzCg7uaI3oS8/df+wipwt/APU4YqYBQA3aZbz00QCAlsEroDhLdGi6F/x0jgAoVXdSBgDsZek+GgCUrjt5nusYvAKK0F/ub07Hpvsg+/7vGvs+ACiVLANgbWv00c79HrkGQKkMYl0vNU2z9zbEM+nZ5oX04t6bAWA92gGvNC+/2jTN83tvRylkGkCe7p559ZpMm0ieAbnQR3uSPto8Mg0gX2OZ5s4rgA6dIgAAgHzoowHAMRm8AugZe1CwW3wBKM2l5XIBIHdjz9SSZwDUQqY9zuAVUJwlHgY8prvOutAAoHQvPXd/9ewE4Ni27KMNfQ0AaiDTHmfwCg6gtotVcx8GfOvfGvscgG3Vlml7OlqmHe39AnmrLc+27KMBkJfaMm1Lym6cwSuACwQJwLrmtLMujN1GpgHkQZ5dR44BbEMfbRvKbpzBKyjMWHic+5qGcJp++bXlpvwA1tFtd/vLDbk4Nc3U53+0M+JlGsDyrumjMc25PhoA62jb3aH2VqbdRvnNd2/vDYBcPHj0sIgT4fa5FSyvhP0PUJt+psm4eTz/A6hZDX20ErY/Z8oPYFsybT3Kb74qB69KOcElL6XUmW6AtIHS/Zd1jM08AWA52tn1yTT24FyVa5RSZ/p9NLYhzwDWoV3dnkwbVuXglZ3MHDk3DkOd/O7n3a/nuP01Ub5ACXLOtCGlbGdtlDt7UO+YK9cBzzl9NNalnIESyAWmUEeGeeYVZOxSw6VhA6BPNgBQupwv9OmjATCHXIDrGbzi8PZ8eHk7Q97zPQBYwp4dI1kGwFJyyDO5BkDpZBmlq3LZQMhdPzzW7pzlPHMRgLKVtlQhAAzp55k+GgAlkzPUwJ1XMGDNmQndTtEWHSIAjm2LLNiyUyTbAFjDVitymPQBwBb00aiBwSuIu0a229C+9Nz9J15bqiHecpnCrWYNApCvoUxb+vdvSaYBHMOU7CrxYpkcA6A2so21GLyCGB5QuvQ5AJRChgFQmimT/uQbAEC9DF7tpMQZYkdUe2dIPXzcUHkoI7jMcVKGmjNtzTvLSlXj3QoAR6B9fpw+GkCZtNVP0kebz+DVjlTIbXQvaLX/735c83vaz0tX84XMa0xZWmus7uRUJ66p43CNfvvKOi5l163HfOn7bssleUtxKdO6Xx/6vpzqhExjK+rYPm5pf/TR6teWx5R9m3sfLSK/7aFezp/Ymzx70pQ7yku57hixzfakpmlW/yOXPJOebV5IL+69GZt68Oihg3gFlw6athHof37tvrAfj6v/oOXc6kL/WLhl215pXn61aZrnb9ui46g908ba2VvbVB53y4mgfcAU57Iit+NYpu2j9jxjO/3z5nPfs9R5RG5tGNvp7/vc6kK3nkfIsy0dLdNyPxaA+XI9jtfONINXVGnuBb9zB9gSByDl6gbDNSHRrT9LdsiXMGc7dIzmqSnTrhk8yaF+l6LfxixpaD/ItGObm2lDFz0iovgLITJtupryjDxdm0vy7NjW6KPlQh9tPTVl2lg9Gbvjnmmm9tGmnCcPfe/Q37v0PdRriT5aDXVnLNPubb0xcKtLDfs1FwCHDvZaGgCu193/U+rC0ElO+/mW4aLu7m/JuwRKcpT3uaStbvvXLjA307rGzr1yqFs5bAMw7txxOncSoeOdW/Ks/3Nb3nGs7rKUsYFXdex2/fKdUqZDg11zryFxTEtkWl8u9Wqp7fDMK4ozNgOiPTDGQqb9ev/j3N+ZIrc1R9nXtevuL7Ue9ZyTK7ZR81rjY+0ot7uUaXN+D8wxdL6V63Nl1G/Iw7l24ZqLF/porKGtE0MTzcbqiz4aublUZ7nNNeXbvR459Vh2zDNXt482lGn95Wn3tFT9tmzgytz+uZ6pt1N2XbMfbrn9l+PK/W6+OW2TJSnmef5b39L89IN3DX4tl/0/1ZTnCHLZnLyasqTEObfM7supfYK5ZNo6au6jsZ6peXJrP9mdWVyjhD6aZQPX0c+0nPb7rWp6Lzm6Zim3Nf4GlGapTHPn1Ur6s+xzGPEsyZTymjKSfGt4nOsUudOAS3KfKarubq/EMh/b5hLfzx6mHsu3LHuzRB7Zn6xJppErfbR5luqjtd93y8zgJe7M4nhyrzO5zJY/gpz2O/tYsp+mLwZPqu7Oq6988W3VHKwekFgHd1fBHbP65qnpzqsImbaEXGbS5bIdsCeZNl1tfbQu7SFQOnk2T3vnldWRAPJTzJ1X7Z0u3Y8S5T6b5+im1rFzywTCnobqbcntJfmTX7fLpQzN5iU3Mg1ozb0OoJ0gJ+fqrXqal+4ziZxvkBN1kZzklmn3dvmrB+ACUb7mXERs92MuFx6h+3BG7QwwlzwjJ0PPHSE/SzxDNifO7fM0d5/Yh+Sk30fr/588dPdHbftGfSubfUdOcntGdhaDV7/r9/yHeOm5uh6cWMN7yE2/TLcqY/uRHLX1cqx+aofyUfp+6HbCAdYyJdNk27b2OPdeUw3vITfKFN4wZXDEMQNAaS5l2prZlsWygb/02a/dexMW52TkOmO3bvfLVBlzjgvsb7AcwvZ+6bNf+/pyFOceJl6qmt7LFhx7sCzHFLeSY9eZ00eDc7ThdyxZB/ty7MFytphceHHwKqX0rpTSp1NKP59S+rmU0vefXn82pfQTKaVfPv379afXU0rpr6WUXkspfTal9PvmblSJJ8Aav2XUdqEX9jTl7qyj2SPTOC7HHixHpj1OH20afbRl6KPBshxTj9NHu51HCkzn2INlrX1MTbnz6qsR8eebpnlPRLw3Ir4vpfSeiPiBiPhU0zTvjohPnT6PiPjOiHj36eNDEfEji291hjR+kA/H4xuUxRNk2kJ0joCtXZNp7ez2Cme5y7MJnAdBPhyPjJBpC6nsXAc4gEvt1sXBq6Zpvtg0zT89/f83IuIXIuIdEfH+iPj46ds+HhHfffr/+yPiR5s7/zgi3ppSevs1Gw/Ado5woivTluMCBJCz2jNNngEcQ+15FiHTltAu26WPBpSinVx4qd2a9cyrlNI3RcTvjYifiohvbJrmi6cv/euI+MbT/98REZ/r/NjnT6/1f9eHUkqfSSl95rfiN+dsxmxHCHuAW3XXXz9Cu1lqpu1NpwgowdDyObW2XaXm2RHONQBu1WbXUdrMUjNtb7We4wD1mnptafLgVUrpt0fE34uIP9s0zb/rfq1pmiYimjkb2DTNR5qmeb5pmuffFG+e86OzacQB5qu5g1RypgEwzRH6ACXn2RH2D8BSuhMNa1VypgGwjntTviml9Ka4C5C/2TTN3z+9/G9SSm9vmuaLp9tzv3R6/QsR8a7Oj7/z9BoAmTvChSSZBnAcNeeaPAM4Fpkm0wCO5uKdVymlFBEfjYhfaJrmr3S+9MmI+MDp/x+IiB/rvP5n0p33RsSvd27z3UXNM1OokzoL6yg902qfbUl91FlYR+l5FuF8l/Kos7AOmQbAOVOWDfz2iPjTEfG+lNLD08d3RcRfjog/lFL65Yj4v50+j4j4BxHxKxHxWkT8fyPi/7n8ZkP9nPzAKmQabOhoz2mADRWfZzXfQQDALDINNqZ/RikuLhvYNM0/ioh05ssvDnx/ExHfd+N2LUqIAEd3lIfWX1J6ph11vwHH1GbXpUHQI7aNpecZlOiIbQ3r0ke7I9Nge+1z9I7a7nC9sT5aW6+6X7/VpGde5cyBRo3UaW4x1C52Q0X9ytPSAQ+5UKdZSr8uybR82TcAjxvro5EvfTRqpl5zjUtZtnS9mrJsYFHc9ggc3VhQTA0Rz6nZ3tCsFfsAOLKXnrsv0yqh/IGjW+JinjzbXnsuoo8GsKypmVb8nVdmrgBH1M7cW2tms7Z0H8odOKK1l03Stm5PHw04In20enXL3n4AjmTvTKvuzisYY4YMNVGf66djBNROlgHaAQCAvLR3Ru19Xar4O69gjr0POLiVh/oCUKIpF6dlGhyTY58adC/wqdMAlKjbZ8slywxeAWRo7CJfLgECALfcMSHPAChRd3nAiMvPaASA3HQzrJVjlhm8AsjEuQuAOYYHAFwzcCXTACjR0Gx0mQZArsZWbsphOcCpDF4BZGDoAmApQQLAsbV55a5hAGpU0kU+ADiXW+3rJWXaU3tvAOSutgcI1/Z+Stc+ALGvpCCBHGjbplFOLGlqVsk0YGm15Vlt76d03f3R/t8+gvkcN9MoJ7ZSYr/MnVc9Q7eCm2VzbPY9txpaR/ac7trpcK2x28NrdYT3uATlxK3mZNotPwNd+mj02ffcaqwN6T/PCpZ0pLp1hPe4BOXE0mqqU4e/86q966H9GFq7uKYd7qI4bG9sOaVum2PtdG7RzbJWW69qbPvP3bUIrKufaVOWDIS5+nc66KMBS7uUX6Utq0TehvpoAFzmzquOI1w4rvm9MY06sI+xcrdPWFOtF8QcN7CfoeOv1oFy9tHWp1oHq/pqfm9Mow7sR9mzJudGQC5KXbXg8HdelbjTAKCvP4OvfyHZnUqco16wlHMzic0wBgCOaOwcyDk4wGWHH7zqEhwA1GJsmUpoOfdhDUe5W4Z1tZMwtFMAlK6faaXeAcF2nP+wtFLbHINXcfvO06AAkIuxB09f4iIhsBR3W7EE9QiAWnSfRyzbuEQdgTsGr05u7Ri52McW1DNgin6eTc23Sw+uhiWpZwDUQJ4BcxiUIGcyjdzc23sDaiB42Iq6Bkx1bXuhnTkW+xsAbiNLAaiFTCM37ryCypk1AUCOdIwAAACAcwxeVcQgxTH09/Ol/e7iIFAiz986BvsYqJ127hjm9tEASqSPBmzNsoGFGnrAY/vgx/5r1Kfdzx5iXRYPZoXp2kzr5prjp07dTAMomT7acXX3sz5aWfTRYLqhZyQ7fupj/5KT1DTN3tsQz6RnmxfSi5v9PRdJgGsdsXPzSvPyq03TPL/3dpRiy0xzUgkwj0ybbus+GsC19NG4RKYB5Gss0w63bKCBK2Cq9q4PAwQAlG4o0yAn6iYwhz4auZNrALc71OCV4ACm0hkidzINmMqzWMiZyYXAXEe804pytJOF1FGA2x1q8Kpde1qAHIPZxVxr6CKKukRu2vop1+rnjhmuNVZv1CdyIcOOR/vDNYbqjbpEjvTPgDFDEwv198871OAVwCXtDKn+wJWTT3KkXh5Dvz1yYssl3UkYLqAAULp+f+yl5+7rowFZaM+79c+4ZGy1gbbPph496d7eGwBraQ96J7XM0a8r6g4cW//kca82Yc6JrCW4ju3ceY8LfUAutEPMdS7XAPbWXRFljD4aU/a9+vEkd15Rtamzjc1iPzb7HjinnyF7theWimSKc3XDwBVQGn20Y7PvgZrowzFEH+0yg1cMOlpHoYYQOdo+W1Lp+x64rJb2cepsrdLbNZkG8KSjtY015FlEPecgW6th3wPjtI/lsc+WJesus2wgg9qDZ+ptrUPLKrWjx0aRt6GMAc7rLrs3N9PYnkxbnjKF8nX7aFNXl+j+XPdn9dG2o5wBhs1pH4f6aNrX7SlztubOKyaZeyGv+/0atvW4wHod5Qb7WnrmePf3Tfm9l75HbgGQs6nPQew+/Lv/LGBZtx59jesoNzimqcd+m2ndD6B+qWmavbchnknPNi+kF/feDM5Y8mH1ZvhBeV5pXn61aZrn996OUiyRabXNKjvXIVniPd1yoaPkMgXme/DoYTz99tdk2kT6aPlbqm+ljwbl0UebZ6k+Wk1tZf/u3CXf39zriLWVLTDdpT6aO68yt8a65t2Zd1O+d8prUwkj9mQ2H+xjqO3PIQ+0CQBcY438mNNHW1oOmcxxOR+jFFPvei1F/+7cJbOgf4dUTeUGbKuqZ17VOFK/RpB0Z1Vcev6HkKEmtbUPwJPPaASAta3xzKih51LtsR2wNfUX9rf2cXjp2qJ2AI7r7vh/7ezXqxm8qvWi1Zrva+oFv37ILB0qOlywH8+ny0etOdbqZskade3aQaytM+jS3/M8Lrjemm0M13OuP9+c8jKABbCeJR+jcXRTym6vPJv6d7vf51oKbKOawSuuN7WRXesurDXW1BUcMN/QmtcREU+/fZ/tOZqxNlabNt8tA0RrunYQTycJHjd2Man2yQDsz2DRPAaWgVJpt5aR+x3FU/po7XYN9cecF8Djljz3y2Lw6nf9nv8Q8bN7b0Vecut07zEr/drG3+xDmOZcO5Nb+3MktS6B138/ubXRW2xP92+MdYgusZwvPCmn9qQm+mhPOvIgzLVZmVvmQwlMVmILW7bPJdTjS5Mfx/pyrkPC45Y8FlLTNIv9sms9k55tXkgv7r0ZTLTHEksR11f8/uwInS64MzRzqNV9/em3v/Zq0zTPb7t15ZJpTzpXx/a01xIgU7JoLHNczIA7/WNh6qCuTJtOnjFGHw22cS7v5Nk8Mo0xS+SJTIInj4Oh42LotVeal89mWhZ3XsGY9gTtmiBoT+y6naJbZg12twlq5s4OlpZbu9m/2L1VZ2PKRfap27HEHQBz37csJDfqIezjlqWShgaubslhFwyp2fmLgOcfbg+l27pdX+Jvrf2MZyhBv+6P3anYNfa4EndeMVtpnYNbt/fWOwaE13HUuq/HZkDwJJlWlhzvCptj7sz1oWfbTX04cV9J5cSxjA0Qm6k+nTyjZqX1abmePhoRMq1ER2qnj/ReYchYpj219cbA1oYCoL2Ta+rzRdp/24+p+rP6qdvc+gHkqaT2+ppnYHXbqWsHrsZeB4BrTe2jLfW3qJ8+GpC7JdoomUatDF5xlRoaxSknsUvN0rpmKY0ayhigVLW2w9fk2ZSsHCurUsqyhG1kGhcpoUztzPOpfbRbaSsAWMuWfYtzd27p31ADywYyW2nPu1jq2SK3bsMtz9mydi57siTFPDKtLFNP6LvL7A29vqWxbe4/F6H/2hZ/99LPjmXaHktm3PK+KIcH3M8nz8pSYl9haCnbVs7vo5tVlnpiL/po88g0tjB1qfWls8NzIyldFcsGGi3Oz62N21b79Nx25nbB8Zaf2/P4sDTi45QB1Onc3UPta1se+7cs9bfGNkzZnqFBtfb/5wa2ttRdGljnrU727Tqc9+SltGWH2uOyzYK92uFrlt/tbnOOHJuPUx5QpluP3a2P/X6fYqhvs0Z23NKH6mZaTitm5LId7K+4O69yPkE8irkPhT/HfqxHDnci5KDW9smsvnlkWn7GyrmmpYe2mLG+ZHuf4wz7c/Uhl+3bUo77ZwkybTp5VpZbZ11H1HWsc8e+rZc8m8edV+Wa2pfLtZ1rt3+Lc6UjnI/JtTpVc+eVUde8XLM/2sZFI7O8vY4PxyXMJ9O2s8WdUblk2hYz1ufecTXld439nhyOk1z275ZyKHfKcYQLJTUZuvvVnYnryaE9Pfq+zWEfUBZ1ZjtTyvrc3Ut73rE7x5bXQbcqhz2vP+a+v7dypHbq3t4bMJcKmqc5I99brPd6RMoP4ElrLkN31HZ3yfd96XcdtYz3dG55TPuCIepGeeY8S9f+vd1e5WdmOlCTfp9O27avvcrffr9ztPOzYgav9nw+UY4VYs1tm/qAwf52DC0lNDVccixjprHv7vTv7lAujNnjuT5HqJtD73HouXxL3TFEfXSMzztCG8J8e16Yz7EubnWcDP2dsf7a0NeunXRIGey7N8wZsIWIbetJrnmWo7acjnTHCVDQ4NVecr2I0d+OJQJv7nMezNSD89R/LjETd1lDS0nANWo7NpZWaxtCObZ8dsRca/TRur/r3N+Z+3tyKzdYW/eit/oPZXMMc3Rjk6FrPD6KeebV2s7t9P7JTa4Xw9qKe+32LTVwtcWzRSBH/ZmvNQYGdamlnR7KvjWXCXRsUzt3Y5CTcytCdAewcrZEH22J53rkOBkTtqL+k7MSsmwJR3iPsIXuRMKhjxod/s6rseWExpZhyHGE85a/Pednxwa0up2zWg8ayrNlnZzzN8z8Y2lzln1d8mdzdukO6qnLT9RQFtRjy/yY04Y4TtjKUH8tIs8s26qPdul36KORm1zrpDwjN6UPwA5Ncs/1Dmq4xdp1+prfX8NxdujBq1tG/od2fIkzCc5V4rmVuzsb8Jq7r0o/kMjbFvVr7vGf85I3lGnsDuIh5wZxLv1caeZ29s4NZsk0cpLrwJVMY2+1179r+2gR1z0rpPbyZF+5ZVmEPGMbQ8+LH1NqXTz3POIljrHuz08pT8c0W1i7jl07cFV6/U9N0+y9DfFMerZ5Ib24+d/N8e6pLeQwIzHXWVbXqOm9cKf0mU1Le6V5+dWmaZ7feztKkWumlX7Ccs5ad0xNycoazyNkWn1k2uNk2nR75VlEPZ1t9qX+1MU5yuPk2Tx799HU2ztLtctHa9/VI2o3lmmeedVTQ0PQXbrv3MW37lqYe7zn2tbirOm98OTMIChZ6Se6Uwan1ljneUpO1ra2dOl1hctkGqWosR261EdjeTXWoyPrrvTiGKIUNfUVuq49BvtLujNNrfXo6BwL0xz6zqtWTTNSL80+ONrshC3VVI+Yr+Zjy6y+eXLKtIiy26MllpOIuK0Mjjqgc9T3XZsc7rbPjUybbu88i6j7/KrrKO9zLzLtuGo+tuTZPDlkGm9Y4tis+fgec9T3Td3GMs3gFVzh3ECVEKFGOkbzyLT9rdEW196+D72/2t8zdZgyY7Fbj2XadPKM0pwbpJJn1EiezSPT6lR7+177+4OWZQNhYWPLylnGgL0tWf/UZXhDze370DI8luYhB+fq35y6qR7DMYwtK6cNYG/qICyv9v7KWKbV+p4pw9z619bZ7sdU7rzCSP6ClCU16IfI029/zay+GWQawDrmdpKGnqsg06aTZ/vSr1iOsqRG7ryaR6YB7OPceVj39bFMu7fmxlEGJ/KATj0AObtm4ApKpg4Dfd0sfPrt+20HAEx1buCq++9Yphm82pkLxnWxLymNW80ByN21WSXjuJY+Wl3sS2rxeF1+ba/NAICLxp5F+uTr5zPN4NXOnEjDnf5zVljepYt4/QB548KNjhHAXOdO1pnm1oGnoZ+XaUzluIU3yLPtne+XAXArbep6uvnVLeNbrvkavAKyJVCWMfUCYL+slT3APO70uc0a5SfLAK431B/TR1vH2IU95Q1wO5Mx1jV2fnBLmRu8AnZ3bmaZQJlnKIivHbgC4Hba1mkMWgHkqd+v0Edbj3IFWI+JF+tas3wNXgG7OTe7TKBc56Xn7seDRw9nXQRU1sBRLTnzrt/ualunW3LgSrkD3O7cBSht7PXO3cEWoVwButYaBNHWLmcov9YsX4NXwCxLn2QLkP0oeyiTWWPLaAf81/i9zNO98/rW3wFwRAZC8jZ0zmFfATxp6bZR3/k658ptj/K8OHiVUnpLRPzDiHjz6ftfbprmf0gpfXNEfCIi3hYRr0bEn26a5j+mlN4cET8aEb8/In4tIv5E0zT/cqXth0mG7vDRgF1nqTLb6rkgNXXkxsrs0vtrO0w1lMMtZBqla9uBmtq2PS1919Xa+6OG/T6UZdeeE5RcDreSZ9Ske47qfPU6S/bRtij/GvLsGkd7v1PJNGAtW7W7zl/WM+XOq9+MiPc1TfPvU0pvioh/lFL6/0XEn4uIH2qa5hMppf8pIj4YET9y+vfLTdN8S0rpeyPiByPiT6y0/TCJZQ/ysuUD7WvYz1PKa8ryHjWUxQJkGkVzHB9bqft/qdwv9f2vRJ5RDeer+diq/Gvbz+6quplMA4pWYrtfyoDbU5e+obnz70+fvun00UTE+yLi5dPrH4+I7z79//2nz+P09RdTSmmpDQbK1z7ot4RGMkdTyk75DpNpwNJk2nlrPc9qy0kwuZJnAHloL/51P9rnEMuraWQawHamZtNQlq219P6YSc+8Sik9HXe36H5LRPxwRPyLiPhK0zRfPX3L5yPiHaf/vyMiPhcR0TTNV1NKvx53t/j+au93figiPhQR8Zb42tveBYsqZeQV9rLWMTInAIZm9+kcTSPTANbXX+JyjqFM6y4x5Tz1jjw7Fn00GLf2MTL0+6e81h3c4jyZBrCtoecOt6+NZVv/59bOt0mDV03T/KeIuJ9SemtE/C8R8btv/cNN03wkIj4SEfFMera59fdBCaznzhIuPV9FvRon02AZcow1eKj9dPLsWBwHsL8pEwgdq9eRaQDb6GfXlNxqf2aPpZ4nDV61mqb5Skrp0xHxByLirSmle6dZEO+MiC+cvu0LEfGuiPh8SuleRHxd3D1AkUI42VqPu2PqsNcxMmWpQKaTaXCdbo4d9YHr3Onu/1vOb/r1R32aR54BrJsdYxN29NGWJdPgNiYYcsm1dWSvenVx8Cql9A0R8VunAPltEfGH4u5hiJ+OiO+JiE9ExAci4sdOP/LJ0+f/2+nrP9k0jRkOcCJEiLi8jJI79NYh02BZ2qcyLDnIOJRfcweu1JvbyTOAbeiPrU+mwTJMlueSEjNtyp1Xb4+Ij5/Wn30qIv5O0zQ/nlL6+Yj4RErpf4yIn4mIj56+/6MR8T+nlF6LiH8bEd+7wnYDmSqxIdzanBMKZbk4mQY36t5l486rsly7v5bqCKsni5JnwGT6aNdRbpuRabAAk6AZU2q9SDlMTngmPdu8kF7cezOIcisyrGXpC7NT77jKySvNy682TfP83ttRCpmWDwMr61PGeTuXOVP215IzN3OqHzJtOnkGHEX/2dQReWXXEHk2j0wDcrTGdfihflzJmfbU1hsDlGvoOSe18SwXqIvjd10vPXe/2DI+QqYNubS/Hjx6uOidVqXWD8jJkdoo5jtqni2tzT/ZBbCfI2Xa0u+vn2O15NmUZQM5CHddMUV/VlqNdaYfILcuszT1QfY1liXsyTHFJUfItCnW6BgetSxhaUdum5im7Wu0F6lqrTO33E3c/z39n6mxvABKdIRM619jXOI9jv2O0svP4BWvK70ys75uHbnleRkl1LUpy0a04XDpot/Q93U/9/wYgO3dkmkR5bTX3e1b8q6qOX8z9zKCnDl+mOLaTOv2U3Kva2tt39h77/fjci8jgBosce0x5/Z66QkU12R4CbnfMnhFNUo68I6o5E7R2DZPvQg4FKD9AbISygeAcmyVKWN5Jtfg2Jzf5u3IbfWlC5z6aADlKbW9vnbC/7XXJEsqp9Q0zd7b4MGJQHbOdVKunbFeUjD0eRjwPDINyE0/05Z8plVpDwSWadPJM6BEQ/24Ggeg5Nk8Mg3IzZQ7fy9936XfH1HGzQRjmebOK4ANlLQkBwD16XZepj6LcervbMk3ANY0pS81NeM89xKAXM1dsvbShMKSM87gFcBGdIoA2NuSz7ySaQDkZmwFjf6FvBKejQJAnaZkz5x8qjXLDF4Bmyhp4KY/C2+q/s+U8n6hNu4GgTtLDlSd+/2OL4ByldSOT9nWse+Z+2xjAFjbUjlc8/XIp/begFyt3dnP2ZHfO+vJpeEcq98PHj18bHm/OWq5HRducfT8OPr7r9Xc/ZpLPVhiO9olBse+DlvK5fiCWuTSjt/aR+t+D0CXc4c6ldpH6xvKtlsy7VL/rUQGr86obUfnLNcGhNtNXWt8a5f+Zvv1W7ZNG8LRdI+bI7frR37vtZrTnu+5/9tjb4ljsN/paT/vf8DW1LvtyLN65bhvxwamhlbEGLrLXvsAw3I75rfenqWe9Upeamnzzz2rqpb3twSDVzxmjwbdAZm3LQZw9jh56f7d/oW+uWvKqsPwpJw6CFsNqF2z3CjcYqnBKoA+57d5W7uPtnW2DC3bfq6P5g4rmM/5IkeWa26025XjtuXE4BWDBButJRrRobrUrWP9v7FW/Rt7SG//+7rOrY++ZcA4HiF/uZ4Us7z+7O+xTOt//xrbsCR1GPJksJqutfpo7etDfZ01699YH22JVTHWkuM2wZAjXyDXRzuOS3fwTv3+tVnqdp57e29ARMTv+j3/IeJn996K7eT6IPkaH+pGHrp1aq/63+309P/muUGsnJ5jtfffZ7qjZNrYid7e9bV/TG+1VET7vvd+/6zv3F12pQ1cqauMOUqedeXcH8pxmyjb3Dq11vnUuQt4JdT5EraRO0fJtEvXNXLQtiXdf9f+exzD0LXHvff/0B3Ee29TabIYvPqlz35tvJD23or95DDa2p/ZtPf2ULexpbVyeQaWYwDG5bxE3tADT7v/Or45MvWfqY7YR8t1WbLctoe6jU3EWLoejs2SV+9hvpKOGcc4a7p0TrdF/VPHl2HZwB3kfrtuzttG+YaWhFhrBt+5JSbGnm+V+/GZ40ABj/ulz37t3puwqdyPGVjTlsve3sqxCtPldKy48MEebnkm8Jy/MZZNJdV7fbT8Ha2PVoKSjnHKtfZ1R9aXxZ1XR7XmrCUhQKmWvpujO0vw3IzBko6XkraVY7m1bm65DKELgSypvzzF1KVp57q0FGY/P9VxKJ+8Yi/nlvFbsk7WVLdrei/QtWYOOWdlK2tl2l53dB2NwatKDN1Zcs3B4gBja2N1bulGX/2GMs1pC8x+Yk9bP8dx6O/KOsiPC3QAlGjoWcLXMuGe2vWvW6jny7Bs4ALOLU225t8ae23unSuXbtefsg2whP7JzLUN/dAxsdZtwlCrrY+XS8f71O2Z0m4MPcgV1rRUR3/o9+oUwbAc+mjtR3usztmWuZO45BlrWfKCc/+41EeDefY4XtoM6/eh5m7L0O85R7vAFpZePUa/bB3uvFpAWzm3qKRDF9z6g1XXBIjbGsnB0nWwP2iljsN0exwvSy0bOmfbtQuUSt2Fcbn00W75nfpo5GCpOtitz+o3XCeXY2bt7cjlfcI5Mmw7Bq8KtuRze1zoo0RjFwe2vGABLMtxyxFduuDtuIAyjB2rc49jfTRKde4ZkGOfA0COpmQa6zF4dSW3BkK+HI/XMXPkuGQa5MvxeB2ZdkzyDPan/V2W8iRCPYC9OPaWdU15GrxawLlnZ6jcsA1hsgxlSMQ6Sx8B0+2dabWcy5a87Vzv3H6vpV5DCbrPd3O83U4ZHptjCfaVW6bt3Ve81TXbbvDqSlOXdMmpgneVXtkhIp/jKtfjfA5tAhFPnph1nxuX20kb1CaH46qWh2PLNPr00WBbOdTjXI/zObQJ9J8JPPR5DXUdcpbDsdXmQQ7bcq1rM83g1cpyrFS1XJi4lYAvQwkn7N0TxtyYacwU3bpx7m7iXNvMXLcLhpSQaa3cttNycCwp1/qT63ZtSa6XoYQ8y7mPFqGuM0+/j9YdxMqxDqnfsJycs2yOa9sDg1cHJDzuKIdy5H7ik+sJ4xxt567098GySqgPJWwjdOWeaTWQaVAuxy1LyTkHpm6bPGPIuYmHOcl1u6BEuU/GWJvBK2B3pZ+QdzsVucwMH7rjqv9av9xL3gcAuTiXaTl2OPoDad0cG9rePXLiXLmde737XgC43lgfrYRBlaFt3Hubz01g6S8Bp48GsKxL7f/e+dA3lAU5buNUt2y3wSsgC5cGW9rP926ox7YzpwuTYwNW/dcBWNa5trf/PXtnWmvqtuS0zUNy3jaA0ow9S6ffp9i7/T23Hf0M3ns7Lz07PfecBSjVWB7klmnnlJJpSzvc4JWTASjDLXcwrXWcj91dtecM9UuzIvufawfrYV9CfqYs67D3cVvKBIap27l3eXI7eQZ5Ojeocs3xunYf7dzf2HMljLl/WztYB5kGeRo7LnM4Zi+tltS+noMt27nUNM0mf2jMM+nZ5oX04t6bAWRo7xO/c52hqctCXbvtY0tHjG3fLX/znFeal19tmub5RX9pxWQaMCSHWXJzM3WpiRlTfs+lZQCXItOmk2dArub20Zb8uxH6aCWSaUCu9rruOaV/utXdaGOZ9tSqfxmg48Gjh7Nne+cyqyBi2oNRb9nefnCMhUe/43TpZ6A217QncHS3zgC/NmPafJr6LLCx7weArrWfD6WPBvPoo1GSHOrrXhnR7XP1r6/klGFZ3Hn1/Le+pfn6n/2je29G9fa+g4VjW+qOpFxMnWlXA7P65pFp26jtOKMcey4Ty+1k2nTyDI4h92d8LKW2c0d5No9MA8hX9nde/dJnv3bvTTiEmk7U2N4tsxFymMmwpUvH2tHK42hk2jZkGnvQfnMk8gzyt0Qu5TCregv6aMcm0wDKlMXgFZC/W5/dtMTvysESMxNLev86cQB3tnomE+uRaUBtbumj1bQE81KDeKWoZb8BLKWmTDuaS/vN4NUBOIDZU9sJqOX5FVtufw7Hben7i/rINPZy6SG25E+mAdyppW/W0kcDOLbus5soy6VMM3h1ADWdlFIm9e86Wy5tIeAphUxjT7Vd7KuRAW4A1qSPBgDz3NJHu7fspgCwlSUvnroQCzBtadj+Sbf2My/2BwB70kcD2I92M0+37BeDV8BNuhfxhAQAperm2YNHD89mmqwDAACA9Vk2ELiaZQyWZ7kjAGoi0wC2p+1dlj4awL60w8sqqSzdeVWpsRnDsISSGrqSOG4BtjeUac6llqEM4Q3aFdY2Zflb5lOeANuz0tN6SipPg1eVKqkSUgd1DoCayDVgadoV1qaOAVCLNtNMnj82ywZWxm2UrCmXurXFduTyXuHIZBpHoJ5D/RzjrCmX+qWPBsfg3JUjUM/z4c4rYLLuTL7+rL4tl0HZ4u+YtQj7cxyylT1n9annUD/HOWsaq1/6aMDSHIdsbY86p57nw51XFXKAsQf1DgAAnmTmLnvRRwMASnbIwSudByLUg1LZb+e5rRmOy7G/jJeeu//6B7At7RgR6gH10UeD43Lsw+0OOXhVwgWJaxu4Et7bJVue3AmScjjpv8wFV8iPTGNIWy/sM3hDCecwR+6jbeWl5+5rGwsiyy7TR4M8absYItfyc8jBqxIc9eSm20Cs3Vg4iSxHicFR4jYDy9o60yhDifmgEwd3jtrWdgfct2gLjlrOJWr71KVkRCnbCayrfR6gPhpdbb0oab8dIdfu7b0BOdryoaYlbMeW+u93KFCOVibXqqXMjngcAHUYyrRL37Pk36uJTANyOX5y2Y4t6aMtq4Y61H8PpbyfUrYTWFfbFpwbwNJWTFNDnnWV+F5K3Oa5DF4NyGXH57Ide+oGSUkzulhGyfvb8Qv0ybFjK71zV/K2U4dc6mAu27GnNs+UxXVqKLca3gNAhPYMcmfwiuyVOKMrF6WXV+nbD+QhpwtsuWxHiUovu5K3v+RtB9ahXbhOTuckAHALecYWPPOKJ3iuQR2ECEDZd3Byx4U+AHlWC3kGADCdwauFbfkg2zV1T6pLfy8AXKf0TOtud/v/Ut/LkbnQB9yq5Cw7p7b3A8Bx9HNZpgHnWDbwBv3G9aXn7ldzgaW2h+/OmbVthjdTXVNXpvxM/yHY/cFk9ZM1jHUYSq1z7XZ3B65KfS9dU99HbVlOnqbmWqt/XKqbLE0frRy15DJ5WbuPttTfhClqa/db/fdSw3ub2w5oN1jTnD7a0A0sOdXN1DTN3tsQz6RnmxfSi3tvxllzLhLltHOZL8eDlPyteex3B7K6F/z6f2/NbXilefnVpmmeX+WXVyj3TOM4ZBo5GRog7+db9/tk2v5yzzN9tGOxH5lriz7aVn9viDybJ/dM41hkGjk5N9Fwy4n0Y5lm2cAJpu4cDQ8c09rH/qVb6C2HBvRpD8jNuRm2Y7PYYYw+GjBmzWO/nXwxtZ8G0DJwRW7aVQq69XKo77ZXplk2EDoECLkZqpNjt9j3Z6zXuswAcJklR8nNlDo4JdPUZTgexz25mbLs2blzMX00OKat72aBJV3KtLXqssErgMpMGfACjkOniFLV+DwEAI5pbEY7cAwGrqjFlplm2UDYiCUD2EL/Vt6aHlIOTLf2yeSUpXIAALjjvAnoWvOZfFATg1ewsm5weDYRazN7B1ibDAOgdN1+mT4aa9NHA7agraFGBq9gA931P4fWuIUlnFtHHQAAeEO3XzbUR4Ol6KMBW5Fj1Mgzr2BF/ZPToU6RcGEp1lEH1tTPNO0MALWQaaxFHw0ArufOK1iRk1MAAAAAYC3u7KRWBq8KoAEqV3e5wPYDAErjXATgcdrFcnX7aABQOkvfUjPLBhagvwa3k+089ffLlA6tgAHgVkudF8y5ECu7gKPTR6uXPhoAt9oqS2QWtZt851VK6emU0s+klH789Pk3p5R+KqX0Wkrpb6eUvub0+ptPn792+vo3rbTth9JfJ1nDtJxbZk22P9v9HQ8ePZz8O7v70exN2IY8oxb9vJmTP0O/CyiPTNuXPlp5puSdPhrsQ6YB0Dfnzqvvj4hfiIhnTp//YET8UNM0n0gp/U8R8cGI+JHTv19umuZbUkrfe/q+P7HgNlOxbudgy85fO1Nh6O9f6rBM7dCMzYYwUwI2Jc/YxBqZNvWu3jX17zYAdiXTWN0ebf61f7Ofgf3f0e/z6aNBVmQamyitfXd3N0c26c6rlNI7I+IPR8RfP32eIuJ9EfHy6Vs+HhHfffr/+0+fx+nrL56+H7LT77j0v7bUBcBzwSiAYFvyjFItmUm3GrrrGNieTGMr7YBPju1+u13tR/dZw+f6WN33o48GeZBpbGVo8vqtv2/p33Uu24Ys+V4gR1PvvPpwRPyFiPi/nD5/W0R8pWmar54+/3xEvOP0/3dExOciIpqm+WpK6ddP3/+r3V+YUvpQRHwoIuIt8bVXbj612WI295qN+pznXXW/H9jMh2PhPIuQaQybmwmXfleOnZLSZi1CZT4c+mhsJLe2/tYBprGfy+29wkF8OGQaG2jb+KXa+ksTIqa65eeH+oqyjFpcHLxKKf2RiPhS0zSvppT+4FJ/uGmaj0TERyIinknPNkv9Xso3pYE9dwFvbObcUqZs36XvESqwvbXyLEKmMW5uJpz7ubUGsPp/51LHqf/1vZb8hSPTRyNX3YyYeyHuUp50B6zWyhuZBtuTaeRqaqb1+0bX5NStmTPUp1vqd8Oeptx59e0R8cdSSt8VEW+Ju7Vn/2pEvDWldO80C+KdEfGF0/d/ISLeFRGfTyndi4ivi4hfW3zLObSxC3hrLfOw5O/rbr8Qgc3IM7I0NdO2GLg699rY1z0DC3Yh08hSv5/TX57pljuetsgZWQa7kGlkaSzTzk2yyCVHctkOuFVqmumTD04zIP6bpmn+SErp70bE3+s8OPGzTdP8f1JK3xcR/9emaf4fpwcn/t+bpvnjY7/3mfRs80J68fp3AQPmLt039v0afY7sleblV5umeX7v7VjSWnkWcV2mmRXFmD3uIIZayTR9NPJgIh/cpsY8i5BplEeewe3GMu2pG37vfxsRfy6l9FrcrS370dPrH42It51e/3MR8QM3/A242pSZe93vOfdg360CKMdnmcBByDOyNieHLi1RoVMF1ZNpFGHNpf+WpI8Gu9o907QBXFJKnkGpZt15tRaz1LnG1Ntx1ZV15HQ7NOuqdVbfWmQaaxrqQKszt5NpxyHTprt2lroZyMemPd2X8j8OeTaPO6+gPDLtOMYybcozr7KlAjP0LJC97pw6GuUKy3CRj4jp9UA9WYdyheU4no7N/t+X8gegFjKNiIIHr1Rguroz0Y3MAyXRXtHlzioASueOcgAAllDs4BV1O9fh6c5Mb++26t51NfTz/d8BNTFYC/mbs1b+pUxzvFMymQVl6/a9rn2+okzjCOQd5G/Oo0iGsk+eUYvcM8vgFdkZu8h3bknA/sW+nA86mGKsPneDRV2HfVzz3MWI88ds+33uvKJGY+d2zt+gHFMGrqb8DiiVPhrUY+zxI61uH80jSqjNWP3P6fEWBq+oRg4HFCzl0mzWnIIEjmTOMrVT77g6932Ob2oxNdOA/MkmjmxKnjlGoCyeOcxRTbmLPodcM3jFJqbOJB/6Phfp4UndIOl+Dqzj3MX17jE45QK8gSp4Uk6dIwC4lj4alOXSRETHMEeXwzFg8IpVmUkL68ohSAB5B0uQaQDUQJ5B2QxcQT6e2nsDqEd/KaUp399+zP39wJ3+c3IcJ3C7bj7NySkAKMW1+SYX4TJ9NNjeNfl0bvUnA1eQj9Q0zd7bEM+kZ5sX0ot7bwZX6DbqY0shrXmyttTa0h4YDk968OhhPP32115tmub5vbelFFtkmqVI1pXLBYZb9q9MgyfJtHn00crV76P1+2NTl2/vmvOMx6Vzx3kPPOmV5mV5NoNMq8+564BLLNO+Vu7IM3jSpT6aZQO5yZYP2e427kN3eS05O8JMi7zYH/u5K/fX9t4MOnIZWKlVjeWrDYU7Mo2j6E7umzrR75ac6P/s0oNZMixPzi+APVwaAFqiXRqbpL9EXmo/4Q2X+mjV33ll5vE2lm7Qp/7NNf+GMIE7ZvXNs1WmRci1tYwNYM2dtJHDPjLDD94g06Zbe5a6c+066aPBNuTZPGv30QxKrK+2sq3t/cAtxjKt+mdetbPNNAi36a4dO2Ud2S3K2z59Q453CuS4TVCDtXJtqG2v9Tju370795mNfWvO+ltSKfuzlO0cUvK2w9ZyayNLlGNu269vyGF/AOvrtnv6aNfL4Trj1kralyVta1/J284B7rxiGZfurKp1xsBSz9I6t+Rha4myu3Vbl/r5sfd3rixqrDs1Matvnue/9S3N1//sH73592x15/CUZ2DUdoyWdufUrbrt81rrtnflUGa35Ev3Z6e+P3f6l0OmTaePVoYjtj9r9tG6bX8OfbSl/v7Y3ft7byPXkWfzLNVH29LYsVnrcXtpBQzmq/W62xHPf2o2lmnVDV5NbcA9H+k6R3qva1j6dvKc6nE/OMYullr6rCw6RvMMZdrcOr/VgMDUE9kaTwxrPYnvWzO3cx24irh9/86p8zKtLDJtutL7aDDV0vUupz5a+zsipvXRHH/lkGfz7JFpW8hpW8iHeqEMSjOWafe23pi5rrmwNsXYnSBzOlbdpfSmbGfpan9/a1v6dvKc9sfUZbMECEczN5+uvaX93M+NHW9TB5bnPuepBEdph9Z8n7nXiyUepnyJTINpph4nR5thTh6WrltL/r61+oxTXwNus/a1wrVWV6BsR68Pjom6ZHHn1fPf+pbmpx+86+zXpyxjdO2tpUMza6eEi1tZyclaDfOcY+HW7yFfZvXNcynTbnHLRI6xC4Jz/qYTQUoli4iQaXM8k55tvvLFtz3x+qVzvqWWoO0fs+7eojRr9tGmnhPqo9VLns1zTaZFLHft79ocNKEKOIKilg2cskzLpXVf+6Z2sKb87NQZxkKDrax9kuIkCB2jedqO0Vp3pVw7saL7s2PPqLv0N7UJlOzI9bfG5T+vIdOmm7vE0qXj69oL5dfU3SMf6+RBHWRt8myec5l2y3Ol5h7n1/w+Ezegfo7f8Ux7auuNueSl5+6//rGUB48ezlpOKbcKk/OSPOxv7fqa2/Gwpf6yoDBVd7m9pZ3LtCn1tPs9t9RrxwSlOnKmwd7aPl6bY2N9tHM/n9NEQlnIGHmzHn00ljSUS1Pzqf+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\n",
      "text/plain": [
       "<Figure size 2160x10800 with 76 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from skimage.filters import meijering, sato, frangi, hessian\n",
    "from skimage.morphology import skeletonize\n",
    "fig, axs = plt.subplots(19,4, figsize=(30,150))\n",
    "im = imread('072622_CP_ALL_FOV/cp_prob/F000_merged_max_cp_prob.tif')\n",
    "im = im[1:]\n",
    "im_enh = np.zeros(im.shape)\n",
    "\n",
    "for ch in range(19): # for each gene\n",
    "    raw = np.where(im[ch]<0.1, 0, im.argmax(0)==ch)\n",
    "    im_enh[ch] = meijering(raw, black_ridges=False)\n",
    "    enh_bin = np.where(im_enh[ch]<0.1, 0, im_enh.argmax(0)==ch)\n",
    "    raw_skel = skeletonize(raw)\n",
    "    enh_skel = skeletonize(enh_bin) # this needs to be converted into binary\n",
    "       \n",
    "    raw_len = np.sum(raw_skel)\n",
    "    enh_len = np.sum(enh_skel)\n",
    "        \n",
    "    print(raw_len)\n",
    "    print(enh_len)\n",
    "    \n",
    "    axs[ch][0].matshow(raw[:500,:500])\n",
    "    axs[ch][1].matshow(enh_bin[:500,:500])\n",
    "    axs[ch][2].matshow(raw_skel[:500,:500])\n",
    "    axs[ch][3].matshow(enh_skel[:500,:500])\n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "efc3476a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['PGGT1B', 'MAP4K4', 'NCK1', 'HSF1', 'MINK1', 'IER5', 'SNX8', 'non-target', 'PPP2R2B', 'FAN1', 'MLH1', 'MSH3', 'PPP2R1A', 'HRAS', 'SLC25A11', 'WASL', 'ARPC3', 'NCKAP1', 'RAPGEF2']\n",
      "Reading... 072622_CP_All_FOV/cp_prob/F000_merged_max_cp_prob.tif\n",
      "Reading... 072622_CP_All_FOV/cp_prob/F001_merged_max_cp_prob.tif\n",
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      "Reading... 072622_CP_All_FOV/cp_prob/F005_merged_max_cp_prob.tif\n",
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      "Reading... 072622_CP_All_FOV/cp_prob/F015_merged_max_cp_prob.tif\n",
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      "Reading... 072622_CP_All_FOV/cp_prob/F094_merged_max_cp_prob.tif\n",
      "Reading... 072622_CP_All_FOV/cp_prob/F095_merged_max_cp_prob.tif\n",
      "Reading... 072622_CP_All_FOV/cp_prob/F096_merged_max_cp_prob.tif\n",
      "Reading... 072622_CP_All_FOV/cp_prob/F097_merged_max_cp_prob.tif\n",
      "Reading... 072622_CP_All_FOV/cp_prob/F098_merged_max_cp_prob.tif\n",
      "Reading... 072622_CP_All_FOV/cp_prob/F099_merged_max_cp_prob.tif\n"
     ]
    }
   ],
   "source": [
    "# load all score map, filter, sum\n",
    "# try two versions: raw_skel vs enh_skel\n",
    "from skimage.filters import meijering, sato, frangi, hessian\n",
    "from skimage.morphology import skeletonize\n",
    "genes = [procode_to_gene[x] for x in codebook.columns]\n",
    "print(genes)\n",
    "IM_NUM = 100\n",
    "RAW_SKEL = pd.DataFrame(index = list(range(IM_NUM)), columns = genes) \n",
    "ENH_SKEL = pd.DataFrame(index = list(range(IM_NUM)), columns = genes) \n",
    "DATA_DIR = '072622_CP_All_FOV/cp_prob'\n",
    "\n",
    "# image num as index, barcode as columns\n",
    "for im_num in range(IM_NUM):\n",
    "    FOV = str(im_num).zfill(3)\n",
    "    print(\"Reading...\", f'{DATA_DIR}/F{FOV}_merged_max_cp_prob.tif')\n",
    "    im = imread(f'{DATA_DIR}/F{FOV}_merged_max_cp_prob.tif') # ch1 is nuclei\n",
    "    im = im[1:]\n",
    "    \n",
    "    im_enh = np.zeros(im.shape)\n",
    "    \n",
    "    for ch in range(len(genes)):\n",
    "        raw = np.where(im[ch]<0.1, 0, im.argmax(0)==ch)\n",
    "        im_enh[ch] = meijering(raw, black_ridges=False)\n",
    "        enh_bin = np.where(im_enh[ch]<0.1, 0, im_enh.argmax(0)==ch)\n",
    "        raw_skel = skeletonize(raw)\n",
    "        enh_skel = skeletonize(enh_bin) # this needs to be converted into binary\n",
    "        \n",
    "        raw_len = np.sum(raw_skel)\n",
    "        enh_len = np.sum(enh_skel)\n",
    "        \n",
    "        RAW_SKEL.loc[im_num, genes[ch]] = raw_len\n",
    "        ENH_SKEL.loc[im_num, genes[ch]] = enh_len"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9155d0d2",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 258,
   "id": "2e8ca85e",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PGGT1B</th>\n",
       "      <th>MAP4K4</th>\n",
       "      <th>NCK1</th>\n",
       "      <th>HSF1</th>\n",
       "      <th>MINK1</th>\n",
       "      <th>IER5</th>\n",
       "      <th>SNX8</th>\n",
       "      <th>non-target</th>\n",
       "      <th>PPP2R2B</th>\n",
       "      <th>FAN1</th>\n",
       "      <th>MLH1</th>\n",
       "      <th>MSH3</th>\n",
       "      <th>PPP2R1A</th>\n",
       "      <th>HRAS</th>\n",
       "      <th>SLC25A11</th>\n",
       "      <th>WASL</th>\n",
       "      <th>ARPC3</th>\n",
       "      <th>NCKAP1</th>\n",
       "      <th>RAPGEF2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>29685</td>\n",
       "      <td>55930</td>\n",
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       "      <td>80544</td>\n",
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       "      <td>61191</td>\n",
       "      <td>53977</td>\n",
       "      <td>25909</td>\n",
       "      <td>39985</td>\n",
       "      <td>38183</td>\n",
       "      <td>54470</td>\n",
       "      <td>20194</td>\n",
       "      <td>56621</td>\n",
       "      <td>26694</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>15663</td>\n",
       "      <td>48149</td>\n",
       "      <td>96056</td>\n",
       "      <td>74466</td>\n",
       "      <td>32356</td>\n",
       "      <td>69179</td>\n",
       "      <td>46300</td>\n",
       "      <td>39701</td>\n",
       "      <td>51578</td>\n",
       "      <td>51997</td>\n",
       "      <td>56275</td>\n",
       "      <td>47984</td>\n",
       "      <td>22838</td>\n",
       "      <td>38655</td>\n",
       "      <td>46151</td>\n",
       "      <td>51823</td>\n",
       "      <td>16606</td>\n",
       "      <td>41071</td>\n",
       "      <td>21678</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>33659</td>\n",
       "      <td>76930</td>\n",
       "      <td>107500</td>\n",
       "      <td>118332</td>\n",
       "      <td>38751</td>\n",
       "      <td>76438</td>\n",
       "      <td>86516</td>\n",
       "      <td>67365</td>\n",
       "      <td>99739</td>\n",
       "      <td>85739</td>\n",
       "      <td>58965</td>\n",
       "      <td>52143</td>\n",
       "      <td>32657</td>\n",
       "      <td>45039</td>\n",
       "      <td>45508</td>\n",
       "      <td>47697</td>\n",
       "      <td>22540</td>\n",
       "      <td>48435</td>\n",
       "      <td>29291</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>19838</td>\n",
       "      <td>62446</td>\n",
       "      <td>80340</td>\n",
       "      <td>83471</td>\n",
       "      <td>26480</td>\n",
       "      <td>72624</td>\n",
       "      <td>59445</td>\n",
       "      <td>45306</td>\n",
       "      <td>69097</td>\n",
       "      <td>55840</td>\n",
       "      <td>51157</td>\n",
       "      <td>59858</td>\n",
       "      <td>20224</td>\n",
       "      <td>44940</td>\n",
       "      <td>45379</td>\n",
       "      <td>47106</td>\n",
       "      <td>13787</td>\n",
       "      <td>46296</td>\n",
       "      <td>27345</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>29581</td>\n",
       "      <td>67267</td>\n",
       "      <td>95784</td>\n",
       "      <td>103077</td>\n",
       "      <td>36146</td>\n",
       "      <td>71678</td>\n",
       "      <td>76996</td>\n",
       "      <td>35700</td>\n",
       "      <td>77616</td>\n",
       "      <td>58634</td>\n",
       "      <td>44951</td>\n",
       "      <td>45504</td>\n",
       "      <td>19458</td>\n",
       "      <td>54878</td>\n",
       "      <td>45143</td>\n",
       "      <td>50082</td>\n",
       "      <td>18523</td>\n",
       "      <td>51599</td>\n",
       "      <td>29787</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>43970</td>\n",
       "      <td>113625</td>\n",
       "      <td>143596</td>\n",
       "      <td>111288</td>\n",
       "      <td>56030</td>\n",
       "      <td>79726</td>\n",
       "      <td>83887</td>\n",
       "      <td>64437</td>\n",
       "      <td>98846</td>\n",
       "      <td>121239</td>\n",
       "      <td>72740</td>\n",
       "      <td>59546</td>\n",
       "      <td>29815</td>\n",
       "      <td>57628</td>\n",
       "      <td>63246</td>\n",
       "      <td>57357</td>\n",
       "      <td>21274</td>\n",
       "      <td>41337</td>\n",
       "      <td>28994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>45569</td>\n",
       "      <td>114356</td>\n",
       "      <td>143005</td>\n",
       "      <td>116929</td>\n",
       "      <td>60253</td>\n",
       "      <td>70007</td>\n",
       "      <td>83311</td>\n",
       "      <td>66981</td>\n",
       "      <td>113726</td>\n",
       "      <td>114984</td>\n",
       "      <td>71874</td>\n",
       "      <td>61500</td>\n",
       "      <td>27646</td>\n",
       "      <td>68976</td>\n",
       "      <td>76504</td>\n",
       "      <td>53329</td>\n",
       "      <td>24153</td>\n",
       "      <td>43814</td>\n",
       "      <td>29945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>27289</td>\n",
       "      <td>81414</td>\n",
       "      <td>129077</td>\n",
       "      <td>99618</td>\n",
       "      <td>35376</td>\n",
       "      <td>75554</td>\n",
       "      <td>58422</td>\n",
       "      <td>55709</td>\n",
       "      <td>78140</td>\n",
       "      <td>111414</td>\n",
       "      <td>79841</td>\n",
       "      <td>53122</td>\n",
       "      <td>28221</td>\n",
       "      <td>40133</td>\n",
       "      <td>72345</td>\n",
       "      <td>58741</td>\n",
       "      <td>14663</td>\n",
       "      <td>50266</td>\n",
       "      <td>26707</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>23712</td>\n",
       "      <td>79028</td>\n",
       "      <td>136017</td>\n",
       "      <td>114932</td>\n",
       "      <td>42469</td>\n",
       "      <td>64739</td>\n",
       "      <td>69402</td>\n",
       "      <td>46377</td>\n",
       "      <td>96531</td>\n",
       "      <td>100398</td>\n",
       "      <td>78941</td>\n",
       "      <td>46346</td>\n",
       "      <td>23612</td>\n",
       "      <td>37050</td>\n",
       "      <td>51682</td>\n",
       "      <td>54599</td>\n",
       "      <td>13500</td>\n",
       "      <td>31821</td>\n",
       "      <td>25831</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>35530</td>\n",
       "      <td>87594</td>\n",
       "      <td>112594</td>\n",
       "      <td>108073</td>\n",
       "      <td>44015</td>\n",
       "      <td>74460</td>\n",
       "      <td>71978</td>\n",
       "      <td>38343</td>\n",
       "      <td>97277</td>\n",
       "      <td>105302</td>\n",
       "      <td>75505</td>\n",
       "      <td>54386</td>\n",
       "      <td>23960</td>\n",
       "      <td>41174</td>\n",
       "      <td>50306</td>\n",
       "      <td>57570</td>\n",
       "      <td>17958</td>\n",
       "      <td>31837</td>\n",
       "      <td>26017</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 19 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   PGGT1B  MAP4K4    NCK1    HSF1  MINK1   IER5   SNX8 non-target PPP2R2B  \\\n",
       "0   29685   55930  102483  104250  35145  71533  62049      52624   80544   \n",
       "1   15663   48149   96056   74466  32356  69179  46300      39701   51578   \n",
       "2   33659   76930  107500  118332  38751  76438  86516      67365   99739   \n",
       "3   19838   62446   80340   83471  26480  72624  59445      45306   69097   \n",
       "4   29581   67267   95784  103077  36146  71678  76996      35700   77616   \n",
       "..    ...     ...     ...     ...    ...    ...    ...        ...     ...   \n",
       "95  43970  113625  143596  111288  56030  79726  83887      64437   98846   \n",
       "96  45569  114356  143005  116929  60253  70007  83311      66981  113726   \n",
       "97  27289   81414  129077   99618  35376  75554  58422      55709   78140   \n",
       "98  23712   79028  136017  114932  42469  64739  69402      46377   96531   \n",
       "99  35530   87594  112594  108073  44015  74460  71978      38343   97277   \n",
       "\n",
       "      FAN1   MLH1   MSH3 PPP2R1A   HRAS SLC25A11   WASL  ARPC3 NCKAP1 RAPGEF2  \n",
       "0    75766  61191  53977   25909  39985    38183  54470  20194  56621   26694  \n",
       "1    51997  56275  47984   22838  38655    46151  51823  16606  41071   21678  \n",
       "2    85739  58965  52143   32657  45039    45508  47697  22540  48435   29291  \n",
       "3    55840  51157  59858   20224  44940    45379  47106  13787  46296   27345  \n",
       "4    58634  44951  45504   19458  54878    45143  50082  18523  51599   29787  \n",
       "..     ...    ...    ...     ...    ...      ...    ...    ...    ...     ...  \n",
       "95  121239  72740  59546   29815  57628    63246  57357  21274  41337   28994  \n",
       "96  114984  71874  61500   27646  68976    76504  53329  24153  43814   29945  \n",
       "97  111414  79841  53122   28221  40133    72345  58741  14663  50266   26707  \n",
       "98  100398  78941  46346   23612  37050    51682  54599  13500  31821   25831  \n",
       "99  105302  75505  54386   23960  41174    50306  57570  17958  31837   26017  \n",
       "\n",
       "[100 rows x 19 columns]"
      ]
     },
     "execution_count": 258,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "RAW_SKEL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "844ed5cf",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PGGT1B</th>\n",
       "      <th>MAP4K4</th>\n",
       "      <th>NCK1</th>\n",
       "      <th>HSF1</th>\n",
       "      <th>MINK1</th>\n",
       "      <th>IER5</th>\n",
       "      <th>SNX8</th>\n",
       "      <th>non-target</th>\n",
       "      <th>PPP2R2B</th>\n",
       "      <th>FAN1</th>\n",
       "      <th>MLH1</th>\n",
       "      <th>MSH3</th>\n",
       "      <th>PPP2R1A</th>\n",
       "      <th>HRAS</th>\n",
       "      <th>SLC25A11</th>\n",
       "      <th>WASL</th>\n",
       "      <th>ARPC3</th>\n",
       "      <th>NCKAP1</th>\n",
       "      <th>RAPGEF2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>41307</td>\n",
       "      <td>69253</td>\n",
       "      <td>123739</td>\n",
       "      <td>115903</td>\n",
       "      <td>42434</td>\n",
       "      <td>82026</td>\n",
       "      <td>69099</td>\n",
       "      <td>63663</td>\n",
       "      <td>85166</td>\n",
       "      <td>81595</td>\n",
       "      <td>64084</td>\n",
       "      <td>47593</td>\n",
       "      <td>21233</td>\n",
       "      <td>34909</td>\n",
       "      <td>32996</td>\n",
       "      <td>44405</td>\n",
       "      <td>14602</td>\n",
       "      <td>48872</td>\n",
       "      <td>21676</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>18879</td>\n",
       "      <td>55541</td>\n",
       "      <td>108573</td>\n",
       "      <td>83760</td>\n",
       "      <td>42595</td>\n",
       "      <td>80883</td>\n",
       "      <td>54285</td>\n",
       "      <td>48541</td>\n",
       "      <td>58652</td>\n",
       "      <td>60325</td>\n",
       "      <td>62464</td>\n",
       "      <td>46160</td>\n",
       "      <td>19351</td>\n",
       "      <td>33467</td>\n",
       "      <td>42075</td>\n",
       "      <td>43576</td>\n",
       "      <td>12355</td>\n",
       "      <td>35173</td>\n",
       "      <td>18351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>43914</td>\n",
       "      <td>97159</td>\n",
       "      <td>137484</td>\n",
       "      <td>136192</td>\n",
       "      <td>48798</td>\n",
       "      <td>90884</td>\n",
       "      <td>96976</td>\n",
       "      <td>76109</td>\n",
       "      <td>103944</td>\n",
       "      <td>89969</td>\n",
       "      <td>60014</td>\n",
       "      <td>43991</td>\n",
       "      <td>25981</td>\n",
       "      <td>36817</td>\n",
       "      <td>38028</td>\n",
       "      <td>37545</td>\n",
       "      <td>15851</td>\n",
       "      <td>41585</td>\n",
       "      <td>20927</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>26649</td>\n",
       "      <td>73679</td>\n",
       "      <td>99503</td>\n",
       "      <td>94831</td>\n",
       "      <td>34883</td>\n",
       "      <td>85317</td>\n",
       "      <td>67486</td>\n",
       "      <td>54817</td>\n",
       "      <td>73925</td>\n",
       "      <td>61520</td>\n",
       "      <td>56128</td>\n",
       "      <td>54665</td>\n",
       "      <td>16694</td>\n",
       "      <td>40537</td>\n",
       "      <td>41104</td>\n",
       "      <td>38790</td>\n",
       "      <td>9805</td>\n",
       "      <td>45578</td>\n",
       "      <td>22385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>39740</td>\n",
       "      <td>81070</td>\n",
       "      <td>122077</td>\n",
       "      <td>113819</td>\n",
       "      <td>44281</td>\n",
       "      <td>84688</td>\n",
       "      <td>89208</td>\n",
       "      <td>40826</td>\n",
       "      <td>81974</td>\n",
       "      <td>65537</td>\n",
       "      <td>46690</td>\n",
       "      <td>42622</td>\n",
       "      <td>14756</td>\n",
       "      <td>48076</td>\n",
       "      <td>40528</td>\n",
       "      <td>41556</td>\n",
       "      <td>11914</td>\n",
       "      <td>49213</td>\n",
       "      <td>24705</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>58336</td>\n",
       "      <td>126818</td>\n",
       "      <td>174469</td>\n",
       "      <td>132244</td>\n",
       "      <td>68824</td>\n",
       "      <td>96788</td>\n",
       "      <td>91378</td>\n",
       "      <td>70372</td>\n",
       "      <td>99398</td>\n",
       "      <td>127102</td>\n",
       "      <td>72206</td>\n",
       "      <td>50595</td>\n",
       "      <td>23273</td>\n",
       "      <td>45506</td>\n",
       "      <td>49195</td>\n",
       "      <td>42120</td>\n",
       "      <td>12452</td>\n",
       "      <td>31388</td>\n",
       "      <td>18247</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>60135</td>\n",
       "      <td>131494</td>\n",
       "      <td>176138</td>\n",
       "      <td>138793</td>\n",
       "      <td>65492</td>\n",
       "      <td>85789</td>\n",
       "      <td>88369</td>\n",
       "      <td>71325</td>\n",
       "      <td>114816</td>\n",
       "      <td>121176</td>\n",
       "      <td>72165</td>\n",
       "      <td>53868</td>\n",
       "      <td>20209</td>\n",
       "      <td>53179</td>\n",
       "      <td>62304</td>\n",
       "      <td>37484</td>\n",
       "      <td>13836</td>\n",
       "      <td>31404</td>\n",
       "      <td>20421</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>33281</td>\n",
       "      <td>86359</td>\n",
       "      <td>146123</td>\n",
       "      <td>110095</td>\n",
       "      <td>44352</td>\n",
       "      <td>89155</td>\n",
       "      <td>64220</td>\n",
       "      <td>61129</td>\n",
       "      <td>82446</td>\n",
       "      <td>119236</td>\n",
       "      <td>84049</td>\n",
       "      <td>44134</td>\n",
       "      <td>21457</td>\n",
       "      <td>32839</td>\n",
       "      <td>62013</td>\n",
       "      <td>44559</td>\n",
       "      <td>9661</td>\n",
       "      <td>37272</td>\n",
       "      <td>22281</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>29675</td>\n",
       "      <td>86867</td>\n",
       "      <td>150953</td>\n",
       "      <td>124288</td>\n",
       "      <td>46151</td>\n",
       "      <td>75198</td>\n",
       "      <td>75406</td>\n",
       "      <td>47821</td>\n",
       "      <td>98841</td>\n",
       "      <td>107863</td>\n",
       "      <td>80061</td>\n",
       "      <td>37471</td>\n",
       "      <td>17249</td>\n",
       "      <td>29667</td>\n",
       "      <td>44218</td>\n",
       "      <td>43061</td>\n",
       "      <td>8366</td>\n",
       "      <td>23163</td>\n",
       "      <td>20289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>46338</td>\n",
       "      <td>101437</td>\n",
       "      <td>136330</td>\n",
       "      <td>123751</td>\n",
       "      <td>47148</td>\n",
       "      <td>85017</td>\n",
       "      <td>77415</td>\n",
       "      <td>40296</td>\n",
       "      <td>97323</td>\n",
       "      <td>114371</td>\n",
       "      <td>76638</td>\n",
       "      <td>43751</td>\n",
       "      <td>18162</td>\n",
       "      <td>32675</td>\n",
       "      <td>42611</td>\n",
       "      <td>44772</td>\n",
       "      <td>11466</td>\n",
       "      <td>22585</td>\n",
       "      <td>18965</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 19 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   PGGT1B  MAP4K4    NCK1    HSF1  MINK1   IER5   SNX8 non-target PPP2R2B  \\\n",
       "0   41307   69253  123739  115903  42434  82026  69099      63663   85166   \n",
       "1   18879   55541  108573   83760  42595  80883  54285      48541   58652   \n",
       "2   43914   97159  137484  136192  48798  90884  96976      76109  103944   \n",
       "3   26649   73679   99503   94831  34883  85317  67486      54817   73925   \n",
       "4   39740   81070  122077  113819  44281  84688  89208      40826   81974   \n",
       "..    ...     ...     ...     ...    ...    ...    ...        ...     ...   \n",
       "95  58336  126818  174469  132244  68824  96788  91378      70372   99398   \n",
       "96  60135  131494  176138  138793  65492  85789  88369      71325  114816   \n",
       "97  33281   86359  146123  110095  44352  89155  64220      61129   82446   \n",
       "98  29675   86867  150953  124288  46151  75198  75406      47821   98841   \n",
       "99  46338  101437  136330  123751  47148  85017  77415      40296   97323   \n",
       "\n",
       "      FAN1   MLH1   MSH3 PPP2R1A   HRAS SLC25A11   WASL  ARPC3 NCKAP1 RAPGEF2  \n",
       "0    81595  64084  47593   21233  34909    32996  44405  14602  48872   21676  \n",
       "1    60325  62464  46160   19351  33467    42075  43576  12355  35173   18351  \n",
       "2    89969  60014  43991   25981  36817    38028  37545  15851  41585   20927  \n",
       "3    61520  56128  54665   16694  40537    41104  38790   9805  45578   22385  \n",
       "4    65537  46690  42622   14756  48076    40528  41556  11914  49213   24705  \n",
       "..     ...    ...    ...     ...    ...      ...    ...    ...    ...     ...  \n",
       "95  127102  72206  50595   23273  45506    49195  42120  12452  31388   18247  \n",
       "96  121176  72165  53868   20209  53179    62304  37484  13836  31404   20421  \n",
       "97  119236  84049  44134   21457  32839    62013  44559   9661  37272   22281  \n",
       "98  107863  80061  37471   17249  29667    44218  43061   8366  23163   20289  \n",
       "99  114371  76638  43751   18162  32675    42611  44772  11466  22585   18965  \n",
       "\n",
       "[100 rows x 19 columns]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ENH_SKEL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "6327e3c0",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>N_Cells</th>\n",
       "      <th>RAW_SKEL</th>\n",
       "      <th>ENH_SKEL</th>\n",
       "      <th>RAW_SKEL_NORM</th>\n",
       "      <th>ENH_SKEL_NORM</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>PGGT1B</th>\n",
       "      <td>52</td>\n",
       "      <td>2553181</td>\n",
       "      <td>3256884</td>\n",
       "      <td>49099.634615</td>\n",
       "      <td>62632.384615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MAP4K4</th>\n",
       "      <td>281</td>\n",
       "      <td>8521536</td>\n",
       "      <td>9534403</td>\n",
       "      <td>30325.75089</td>\n",
       "      <td>33930.259786</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NCK1</th>\n",
       "      <td>880</td>\n",
       "      <td>10918226</td>\n",
       "      <td>13078420</td>\n",
       "      <td>12407.075</td>\n",
       "      <td>14861.840909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HSF1</th>\n",
       "      <td>647</td>\n",
       "      <td>8918217</td>\n",
       "      <td>10492218</td>\n",
       "      <td>13783.952087</td>\n",
       "      <td>16216.720247</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MINK1</th>\n",
       "      <td>98</td>\n",
       "      <td>3952207</td>\n",
       "      <td>4739187</td>\n",
       "      <td>40328.642857</td>\n",
       "      <td>48359.05102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IER5</th>\n",
       "      <td>437</td>\n",
       "      <td>6891973</td>\n",
       "      <td>8223578</td>\n",
       "      <td>15771.105263</td>\n",
       "      <td>18818.256293</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SNX8</th>\n",
       "      <td>410</td>\n",
       "      <td>7155080</td>\n",
       "      <td>8099218</td>\n",
       "      <td>17451.414634</td>\n",
       "      <td>19754.190244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>non-target</th>\n",
       "      <td>135</td>\n",
       "      <td>5104420</td>\n",
       "      <td>5932905</td>\n",
       "      <td>37810.518519</td>\n",
       "      <td>43947.444444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PPP2R2B</th>\n",
       "      <td>723</td>\n",
       "      <td>7931087</td>\n",
       "      <td>8491695</td>\n",
       "      <td>10969.691563</td>\n",
       "      <td>11745.082988</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FAN1</th>\n",
       "      <td>638</td>\n",
       "      <td>8558547</td>\n",
       "      <td>9546736</td>\n",
       "      <td>13414.65047</td>\n",
       "      <td>14963.53605</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MLH1</th>\n",
       "      <td>489</td>\n",
       "      <td>5794390</td>\n",
       "      <td>6176066</td>\n",
       "      <td>11849.468303</td>\n",
       "      <td>12629.99182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSH3</th>\n",
       "      <td>141</td>\n",
       "      <td>5639512</td>\n",
       "      <td>5074479</td>\n",
       "      <td>39996.539007</td>\n",
       "      <td>35989.212766</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PPP2R1A</th>\n",
       "      <td>55</td>\n",
       "      <td>2269088</td>\n",
       "      <td>1834737</td>\n",
       "      <td>41256.145455</td>\n",
       "      <td>33358.854545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HRAS</th>\n",
       "      <td>293</td>\n",
       "      <td>4842703</td>\n",
       "      <td>4065604</td>\n",
       "      <td>16527.996587</td>\n",
       "      <td>13875.78157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SLC25A11</th>\n",
       "      <td>202</td>\n",
       "      <td>5465382</td>\n",
       "      <td>4779324</td>\n",
       "      <td>27056.346535</td>\n",
       "      <td>23660.019802</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WASL</th>\n",
       "      <td>180</td>\n",
       "      <td>5347995</td>\n",
       "      <td>4319068</td>\n",
       "      <td>29711.083333</td>\n",
       "      <td>23994.822222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ARPC3</th>\n",
       "      <td>80</td>\n",
       "      <td>1814218</td>\n",
       "      <td>1214920</td>\n",
       "      <td>22677.725</td>\n",
       "      <td>15186.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NCKAP1</th>\n",
       "      <td>243</td>\n",
       "      <td>3891164</td>\n",
       "      <td>3279267</td>\n",
       "      <td>16013.020576</td>\n",
       "      <td>13494.925926</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RAPGEF2</th>\n",
       "      <td>230</td>\n",
       "      <td>2493857</td>\n",
       "      <td>1962712</td>\n",
       "      <td>10842.856522</td>\n",
       "      <td>8533.530435</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           N_Cells  RAW_SKEL  ENH_SKEL RAW_SKEL_NORM ENH_SKEL_NORM\n",
       "PGGT1B          52   2553181   3256884  49099.634615  62632.384615\n",
       "MAP4K4         281   8521536   9534403   30325.75089  33930.259786\n",
       "NCK1           880  10918226  13078420     12407.075  14861.840909\n",
       "HSF1           647   8918217  10492218  13783.952087  16216.720247\n",
       "MINK1           98   3952207   4739187  40328.642857   48359.05102\n",
       "IER5           437   6891973   8223578  15771.105263  18818.256293\n",
       "SNX8           410   7155080   8099218  17451.414634  19754.190244\n",
       "non-target     135   5104420   5932905  37810.518519  43947.444444\n",
       "PPP2R2B        723   7931087   8491695  10969.691563  11745.082988\n",
       "FAN1           638   8558547   9546736   13414.65047   14963.53605\n",
       "MLH1           489   5794390   6176066  11849.468303   12629.99182\n",
       "MSH3           141   5639512   5074479  39996.539007  35989.212766\n",
       "PPP2R1A         55   2269088   1834737  41256.145455  33358.854545\n",
       "HRAS           293   4842703   4065604  16527.996587   13875.78157\n",
       "SLC25A11       202   5465382   4779324  27056.346535  23660.019802\n",
       "WASL           180   5347995   4319068  29711.083333  23994.822222\n",
       "ARPC3           80   1814218   1214920     22677.725       15186.5\n",
       "NCKAP1         243   3891164   3279267  16013.020576  13494.925926\n",
       "RAPGEF2        230   2493857   1962712  10842.856522   8533.530435"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# plot total neurite area normalized by number of cells\n",
    "neurite_count = pd.DataFrame(index = genes, columns = ['N_Cells','RAW_SKEL','ENH_SKEL','RAW_SKEL_NORM','ENH_SKEL_NORM'])\n",
    "for gene in genes:\n",
    "    neurite_count.loc[gene, 'N_Cells'] = len(cell_df[cell_df['Gene']==gene])\n",
    "    neurite_count.loc[gene, 'RAW_SKEL'] = RAW_SKEL[gene].sum()\n",
    "    neurite_count.loc[gene, 'ENH_SKEL'] = ENH_SKEL[gene].sum()\n",
    "\n",
    "    # normalize by cell number\n",
    "    neurite_count.loc[gene, 'RAW_SKEL_NORM'] = neurite_count.loc[gene, 'RAW_SKEL']/neurite_count.loc[gene, 'N_Cells']\n",
    "    neurite_count.loc[gene, 'ENH_SKEL_NORM'] = neurite_count.loc[gene, 'ENH_SKEL']/neurite_count.loc[gene, 'N_Cells']\n",
    "neurite_count    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "a9d19adb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# add log values\n",
    "df = neurite_count.copy()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "ea63684a",
   "metadata": {},
   "outputs": [
    {
     "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>N_Cells</th>\n",
       "      <th>RAW_SKEL</th>\n",
       "      <th>ENH_SKEL</th>\n",
       "      <th>RAW_SKEL_NORM</th>\n",
       "      <th>ENH_SKEL_NORM</th>\n",
       "      <th>log_RAW_SKEL</th>\n",
       "      <th>log_ENH_SKEL</th>\n",
       "      <th>log_RAW_SKEL_NORM</th>\n",
       "      <th>log_ENH_SKEL_NORM</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>PGGT1B</th>\n",
       "      <td>52.0</td>\n",
       "      <td>2553181.0</td>\n",
       "      <td>3256884.0</td>\n",
       "      <td>49099.634615</td>\n",
       "      <td>62632.384615</td>\n",
       "      <td>21.283864</td>\n",
       "      <td>21.635061</td>\n",
       "      <td>15.583425</td>\n",
       "      <td>15.934621</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MAP4K4</th>\n",
       "      <td>281.0</td>\n",
       "      <td>8521536.0</td>\n",
       "      <td>9534403.0</td>\n",
       "      <td>30325.750890</td>\n",
       "      <td>33930.259786</td>\n",
       "      <td>23.022682</td>\n",
       "      <td>23.184711</td>\n",
       "      <td>14.888256</td>\n",
       "      <td>15.050285</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NCK1</th>\n",
       "      <td>880.0</td>\n",
       "      <td>10918226.0</td>\n",
       "      <td>13078420.0</td>\n",
       "      <td>12407.075000</td>\n",
       "      <td>14861.840909</td>\n",
       "      <td>23.380235</td>\n",
       "      <td>23.640685</td>\n",
       "      <td>13.598875</td>\n",
       "      <td>13.859325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HSF1</th>\n",
       "      <td>647.0</td>\n",
       "      <td>8918217.0</td>\n",
       "      <td>10492218.0</td>\n",
       "      <td>13783.952087</td>\n",
       "      <td>16216.720247</td>\n",
       "      <td>23.088324</td>\n",
       "      <td>23.322816</td>\n",
       "      <td>13.750702</td>\n",
       "      <td>13.985194</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MINK1</th>\n",
       "      <td>98.0</td>\n",
       "      <td>3952207.0</td>\n",
       "      <td>4739187.0</td>\n",
       "      <td>40328.642857</td>\n",
       "      <td>48359.051020</td>\n",
       "      <td>21.914227</td>\n",
       "      <td>22.176208</td>\n",
       "      <td>15.299517</td>\n",
       "      <td>15.561498</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IER5</th>\n",
       "      <td>437.0</td>\n",
       "      <td>6891973.0</td>\n",
       "      <td>8223578.0</td>\n",
       "      <td>15771.105263</td>\n",
       "      <td>18818.256293</td>\n",
       "      <td>22.716486</td>\n",
       "      <td>22.971335</td>\n",
       "      <td>13.944996</td>\n",
       "      <td>14.199845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SNX8</th>\n",
       "      <td>410.0</td>\n",
       "      <td>7155080.0</td>\n",
       "      <td>8099218.0</td>\n",
       "      <td>17451.414634</td>\n",
       "      <td>19754.190244</td>\n",
       "      <td>22.770536</td>\n",
       "      <td>22.949351</td>\n",
       "      <td>14.091056</td>\n",
       "      <td>14.269871</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>non-target</th>\n",
       "      <td>135.0</td>\n",
       "      <td>5104420.0</td>\n",
       "      <td>5932905.0</td>\n",
       "      <td>37810.518519</td>\n",
       "      <td>43947.444444</td>\n",
       "      <td>22.283316</td>\n",
       "      <td>22.500307</td>\n",
       "      <td>15.206500</td>\n",
       "      <td>15.423492</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PPP2R2B</th>\n",
       "      <td>723.0</td>\n",
       "      <td>7931087.0</td>\n",
       "      <td>8491695.0</td>\n",
       "      <td>10969.691563</td>\n",
       "      <td>11745.082988</td>\n",
       "      <td>22.919087</td>\n",
       "      <td>23.017621</td>\n",
       "      <td>13.421235</td>\n",
       "      <td>13.519769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FAN1</th>\n",
       "      <td>638.0</td>\n",
       "      <td>8558547.0</td>\n",
       "      <td>9546736.0</td>\n",
       "      <td>13414.650470</td>\n",
       "      <td>14963.536050</td>\n",
       "      <td>23.028934</td>\n",
       "      <td>23.186576</td>\n",
       "      <td>13.711522</td>\n",
       "      <td>13.869164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MLH1</th>\n",
       "      <td>489.0</td>\n",
       "      <td>5794390.0</td>\n",
       "      <td>6176066.0</td>\n",
       "      <td>11849.468303</td>\n",
       "      <td>12629.991820</td>\n",
       "      <td>22.466225</td>\n",
       "      <td>22.558257</td>\n",
       "      <td>13.532535</td>\n",
       "      <td>13.624566</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSH3</th>\n",
       "      <td>141.0</td>\n",
       "      <td>5639512.0</td>\n",
       "      <td>5074479.0</td>\n",
       "      <td>39996.539007</td>\n",
       "      <td>35989.212766</td>\n",
       "      <td>22.427139</td>\n",
       "      <td>22.274828</td>\n",
       "      <td>15.287588</td>\n",
       "      <td>15.135277</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PPP2R1A</th>\n",
       "      <td>55.0</td>\n",
       "      <td>2269088.0</td>\n",
       "      <td>1834737.0</td>\n",
       "      <td>41256.145455</td>\n",
       "      <td>33358.854545</td>\n",
       "      <td>21.113681</td>\n",
       "      <td>20.807142</td>\n",
       "      <td>15.332321</td>\n",
       "      <td>15.025782</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HRAS</th>\n",
       "      <td>293.0</td>\n",
       "      <td>4842703.0</td>\n",
       "      <td>4065604.0</td>\n",
       "      <td>16527.996587</td>\n",
       "      <td>13875.781570</td>\n",
       "      <td>22.207381</td>\n",
       "      <td>21.955038</td>\n",
       "      <td>14.012624</td>\n",
       "      <td>13.760281</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SLC25A11</th>\n",
       "      <td>202.0</td>\n",
       "      <td>5465382.0</td>\n",
       "      <td>4779324.0</td>\n",
       "      <td>27056.346535</td>\n",
       "      <td>23660.019802</td>\n",
       "      <td>22.381891</td>\n",
       "      <td>22.188375</td>\n",
       "      <td>14.723679</td>\n",
       "      <td>14.530164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WASL</th>\n",
       "      <td>180.0</td>\n",
       "      <td>5347995.0</td>\n",
       "      <td>4319068.0</td>\n",
       "      <td>29711.083333</td>\n",
       "      <td>23994.822222</td>\n",
       "      <td>22.350567</td>\n",
       "      <td>22.042289</td>\n",
       "      <td>14.858714</td>\n",
       "      <td>14.550436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ARPC3</th>\n",
       "      <td>80.0</td>\n",
       "      <td>1814218.0</td>\n",
       "      <td>1214920.0</td>\n",
       "      <td>22677.725000</td>\n",
       "      <td>15186.500000</td>\n",
       "      <td>20.790916</td>\n",
       "      <td>20.212430</td>\n",
       "      <td>14.468988</td>\n",
       "      <td>13.890502</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NCKAP1</th>\n",
       "      <td>243.0</td>\n",
       "      <td>3891164.0</td>\n",
       "      <td>3279267.0</td>\n",
       "      <td>16013.020576</td>\n",
       "      <td>13494.925926</td>\n",
       "      <td>21.891770</td>\n",
       "      <td>21.644942</td>\n",
       "      <td>13.966958</td>\n",
       "      <td>13.720129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RAPGEF2</th>\n",
       "      <td>230.0</td>\n",
       "      <td>2493857.0</td>\n",
       "      <td>1962712.0</td>\n",
       "      <td>10842.856522</td>\n",
       "      <td>8533.530435</td>\n",
       "      <td>21.249947</td>\n",
       "      <td>20.904417</td>\n",
       "      <td>13.404457</td>\n",
       "      <td>13.058927</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            N_Cells    RAW_SKEL    ENH_SKEL  RAW_SKEL_NORM  ENH_SKEL_NORM  \\\n",
       "PGGT1B         52.0   2553181.0   3256884.0   49099.634615   62632.384615   \n",
       "MAP4K4        281.0   8521536.0   9534403.0   30325.750890   33930.259786   \n",
       "NCK1          880.0  10918226.0  13078420.0   12407.075000   14861.840909   \n",
       "HSF1          647.0   8918217.0  10492218.0   13783.952087   16216.720247   \n",
       "MINK1          98.0   3952207.0   4739187.0   40328.642857   48359.051020   \n",
       "IER5          437.0   6891973.0   8223578.0   15771.105263   18818.256293   \n",
       "SNX8          410.0   7155080.0   8099218.0   17451.414634   19754.190244   \n",
       "non-target    135.0   5104420.0   5932905.0   37810.518519   43947.444444   \n",
       "PPP2R2B       723.0   7931087.0   8491695.0   10969.691563   11745.082988   \n",
       "FAN1          638.0   8558547.0   9546736.0   13414.650470   14963.536050   \n",
       "MLH1          489.0   5794390.0   6176066.0   11849.468303   12629.991820   \n",
       "MSH3          141.0   5639512.0   5074479.0   39996.539007   35989.212766   \n",
       "PPP2R1A        55.0   2269088.0   1834737.0   41256.145455   33358.854545   \n",
       "HRAS          293.0   4842703.0   4065604.0   16527.996587   13875.781570   \n",
       "SLC25A11      202.0   5465382.0   4779324.0   27056.346535   23660.019802   \n",
       "WASL          180.0   5347995.0   4319068.0   29711.083333   23994.822222   \n",
       "ARPC3          80.0   1814218.0   1214920.0   22677.725000   15186.500000   \n",
       "NCKAP1        243.0   3891164.0   3279267.0   16013.020576   13494.925926   \n",
       "RAPGEF2       230.0   2493857.0   1962712.0   10842.856522    8533.530435   \n",
       "\n",
       "            log_RAW_SKEL  log_ENH_SKEL  log_RAW_SKEL_NORM  log_ENH_SKEL_NORM  \n",
       "PGGT1B         21.283864     21.635061          15.583425          15.934621  \n",
       "MAP4K4         23.022682     23.184711          14.888256          15.050285  \n",
       "NCK1           23.380235     23.640685          13.598875          13.859325  \n",
       "HSF1           23.088324     23.322816          13.750702          13.985194  \n",
       "MINK1          21.914227     22.176208          15.299517          15.561498  \n",
       "IER5           22.716486     22.971335          13.944996          14.199845  \n",
       "SNX8           22.770536     22.949351          14.091056          14.269871  \n",
       "non-target     22.283316     22.500307          15.206500          15.423492  \n",
       "PPP2R2B        22.919087     23.017621          13.421235          13.519769  \n",
       "FAN1           23.028934     23.186576          13.711522          13.869164  \n",
       "MLH1           22.466225     22.558257          13.532535          13.624566  \n",
       "MSH3           22.427139     22.274828          15.287588          15.135277  \n",
       "PPP2R1A        21.113681     20.807142          15.332321          15.025782  \n",
       "HRAS           22.207381     21.955038          14.012624          13.760281  \n",
       "SLC25A11       22.381891     22.188375          14.723679          14.530164  \n",
       "WASL           22.350567     22.042289          14.858714          14.550436  \n",
       "ARPC3          20.790916     20.212430          14.468988          13.890502  \n",
       "NCKAP1         21.891770     21.644942          13.966958          13.720129  \n",
       "RAPGEF2        21.249947     20.904417          13.404457          13.058927  "
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# add log values\n",
    "neurite_count = neurite_count.astype('float')\n",
    "for col in neurite_count.columns[1:]:\n",
    "    logcol = f'log_{col}'\n",
    "    neurite_count[logcol] = np.log2(neurite_count[col].values)\n",
    "neurite_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "a766779a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(19, 9)"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "neurite_count.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "54e98e9d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(0.9377283700046944, 3.1522765669803747e-09)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calculate correlation, plot RAW vs ENH\n",
    "print(stats.pearsonr(neurite_count['log_RAW_SKEL_NORM'], neurite_count['log_ENH_SKEL_NORM']))\n",
    "fig, axs = plt.subplots(figsize=(10,10))\n",
    "sns.scatterplot(data=neurite_count, x = 'log_RAW_SKEL_NORM',y='log_ENH_SKEL_NORM',\n",
    "               hue = neurite_count.index, ax = axs, s=50, cmap='tab20')\n",
    "axs.legend(bbox_to_anchor=(1.02, 1), loc='upper left', borderaxespad=0)\n",
    "# add annotations one by one with a loop\n",
    "for line in range(0,neurite_count.shape[0]):\n",
    "     axs.text(neurite_count.log_RAW_SKEL_NORM[line]+0.02, neurite_count.log_ENH_SKEL_NORM[line], \n",
    "              horizontalalignment='left', size='small', color='black', weight='semibold', s = neurite_count.index[line])\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "9f2a0b62",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(-0.5858698625984715, 0.008392223628474124)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x373f13040>"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot neurite/cell numbers\n",
    "# Calculate correlation, plot RAW vs ENH\n",
    "print(stats.pearsonr(neurite_count['N_Cells'], neurite_count['ENH_SKEL_NORM']))\n",
    "fig, axs = plt.subplots(figsize=(5,5))\n",
    "sns.scatterplot(data=neurite_count, x = 'N_Cells',y='ENH_SKEL_NORM',\n",
    "               hue = neurite_count.index, ax = axs, s=50, cmap='tab20')\n",
    "axs.legend(bbox_to_anchor=(1.02, 1), loc='upper left', borderaxespad=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "c026cc60",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(0.8486950232671349, 4.367004402411101e-06)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x37412a020>"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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pRowYEWAwGBATExP6wgsvdD5+/LizTCaL2rp1q3P9Y1obpeoghJBqlkrVUXh2izTv+Cq5oSxXInTw0Lr3mKR27TqSVkVvAKXqIISQFuTadWQ+BSXLoNt9hBBCrBYFKUIIIVaLghQhhBCrRUGKEEKI1aIgRQghxGpRkCKEkDbA3t6+a+3tFStWuE+YMMEXAM6dO2cTFxcXqlAolJ07dw4fN26cHwDs2rXLycnJqYtCoVAqFArlI488EgIAv/zyi6NSqQwTiUQxpjQe1oqmoBNCSBv3xhtv+E6ZMiV7/PjxhQBw8uRJO9O+2NjY0oMHD6bVrt+5c2ftd999d23RokW3rahubShIEUKIhakTt0ivn1kl15bnSiT2Hlq/mElqeUTzPTeVk5Mj9vPzq1l2KS4uruJO9UNDQ7UAIBBY/800ClKEEGJB6sQt0su/L/UzVq+Eri3PlVz+fakfADQlUGk0GoFCoVCatouKioQDBgwoAoA33ngj+6mnngrp2rVrWb9+/YreeOONPA8PDwMAnD592tF03LBhw/I/+uijrKZcX0ujIEUIIRZ0/cwqubFeqg6jQSu4fmaVvClBysbGxqhSqZJN2ytWrHA/ffq0AwBMnTo1b9iwYcU7duxw3rlzp+v333/vmZycnAyYv93Xllh/X48QQtoQbbn5lBwNlVuKv7+/7h//+Efeb7/9dlkkEuH06dN2dz/K+lGQIoQQC5LYm0/J0VC5JWzZssVZo9EwAEhPTxcVFhYKa49RtWUUpAghxIL8YiapBfVSdQiEEqNfjOVSddS3Z88e59DQ0PDQ0FDlgAEDQv71r39l+vr66huqf+jQIXuZTBa1e/dut7ffftsvKCgovLna1lSUqoMQQqpZKlVHS8/ua+soVQchhLQgecTIfApKlkG3+wghhFgtClKEEEKsFgUpQgghVouCFCFtlLY8D5qyXLTFyU+ENBZNnCCkjdGU3ULulYPIOLcW3KhHp4hRkAU/BVunjq3dNEIsjnpShLQhBr0G6X+txaWjH6Gy5AY0ZTm4euILXDq6BDpNSWs3jzQjxljMsGHDAkzbOp0Obm5u0X369AkC6qbumDZtWic7O7uuarW6piNSO9VH7debNm1y8ff3j7h48aLEGlN4UJAipA2pLLmJG4mbbivPu3YQlSU3WqFFpKXY2dkZU1NT7UpLSxkAbN++3Vkmk+kaqu/q6qpfsGDBHVNx/Pjjj04zZszw+fnnny+FhIRoTSk8hgwZkmfp9t8vClKEtCFGXTm40fxCAnptaQu3hjQkTbVZ+tPGJyITvusW89PGJyLTVJulljhv//79izZv3uwKABs2bJCOGDGiwWexxo0bl/fTTz9Js7Ozheb2//LLL45vvPGG/48//pgWHh6uAapSeHTv3r3CmlJ4WE9LCCF3JbaXQmLnflu5QGQLG3vPVmgRqS9NtVn618mlfpUVVQvKVlbkSv46udTPEoHqhRdeyN+0aZNbeXk5S0lJse/Ro0dZQ3UdHR0N48aNyzWX2FCr1bKxY8cGbd26Na1r166VTW1Xc6IgRUgbYuvYEaF954EJ6s55Cu39PmydvFqnUaSO5L/Mp+pI/muVvKnn7t69e0VmZqbNqlWrpP379y+6W/133303JyEhwb2goKBOe8RiMe/WrVvpV1995dHUNjU3mt1HSBvjJn8IMSN/QHH2BXCjDs6yaNi7+UEgFLd20wiqek73Un6vBg4cWDh37lyfX3/9NTUnJ+eO3+EeHh6G4cOH5y9evLhD7XLGGH766acrvXr1Cnn33Xc7Llq0yGoTIVKQIqSNEQjFcHQPgqN7UGs3hZhha+ehNReQbO0sk6rjtddey3V1dTXExcVV7Nq1y+lu9d9///3s2NjYMIPBwGqXOzk5Gffu3XupZ8+eCplMpn/77bcbvYBuS6LbfYQQYkHKLuZTdSi7WCZVR2BgoO6DDz7IaWx9Ly8v/aBBgwq0Wi2rv08mkxn27NlzccmSJV4//PCDizWm8KBUHYQQUs1SqTrSVJulyX+tkldW5Eps7Ty0yi6T1EGKUbQqegNaLVUHY+xbAE8DyOGcR5jZzwAsB/AUgHIAL3LO/2zONhFCSHMLUozKp6BkGc19u+97AAPvsH8QgODqv8kAvmzm9hBCCGlDmjVIcc4PA7jTr4lhAOJ5lT8AuDLGaB4tIYQQAK0/cUIOIKPWdmZ1GSGEENLqQarRGGOTGWOnGWOnb9261drNIYQQ0gJaO0ipAfjU2vauLrsN5/wbznks5zzW05OWfyGEkAdBaz/M+xOANxljGwF0B1DEOb/Zym0ihBCrY29v37W8vPxsamqqJDo6OsLf379mzb0333wz+80338yTy+WRDg4OBgBwcXEx/PDDD1dDQkK0AGDaJxAIIBKJeGJiYkprXcu9aO4p6BsAPA7AgzGWCWAuADEAcM6/ArAbVdPP01A1Bf2l5mwPIYS0Bz4+PhqVSpVsbt+hQ4cuenl56d9+++1Oc+bM8dq4ceP1+vtarqVN16xBinM+7i77OYA3mrMNhBDS0s5f2iw9eWGVvKwyV+Jg66GNi5ykjgpu2eemevbsWfrZZ591uHtN69baY1KklXCDHrwwF7woF21x1ZEHUblOh8vF+biQl4MbZSX0uVmp85c2Sw+fWepXVlm1fl9ZZa7k8JmlfucvWSanFABkZGTYKBQKpelvz549jvXr7N6922XIkCGFtcv69esXHB4eHrZkyRKrX/3cpLXHpEgrMOZnw/D7ThhO7QUEIogeGw5ht75gLm3mv9sHTk55Gb5MOY3d6WngAJzFEsyN6Y2HO8ghFprNaUdayckLq+QGY91UHQajVnDywiq5pXpTd7rd17t375DCwkKRvb29cenSpTUT0Y4ePaoKCAjQqdVqUd++fUPCw8MrBw0aZPWZMqkn9YDh5SXQbV0Ow5HtQGU5UF4M/Z410B3cDK6zyCLNxMI45/glIw0/VwcoACjWaTHzxH5cL71rSiHSwkw9qMaWW9qhQ4cuqtXq8+Hh4eUzZszoZCoPCAjQAYBcLtcPHjy48Pjx4w4t0Z6moiD1gOH5WeBp524rN574Bbyg0QsrkxaUW1mOTVdu/9Fs4BwphVaZXeGB5mBrPiVHQ+XNQSwWY+XKlRlbt251z87OFhYXFwtMiQ+Li4sFBw8edI6KiqpoqfY0BQWpBwzXNvDfpdEA6Kw6izQxh8alrE5c5CS1UFA3VYdQIDHGRVomVQdw+5jUggULbpsg4efnpxs6dGj+kiVLOmRmZooefvhhRWhoqLJbt25hTzzxROHIkSOLLdWe5kRjUg8Y5uwBiG0AnabuDldPMCeLjesSC/KwtcfozmFYmXymTrkADGFu9GC7tTGNO1l6dl95eflZAAgNDdVWVlaazRahVqsv1N5es2ZNzbJzqampZsewrB0FqQcMc5NBNGYa9D98BPDqH3siMcRjpoM5U5CyRowxDPIJwpXiQuzNvAwOwEkswZxuj8HP0aW1m0fMiAoeld/SU87bKwpSDxgmFEIYFgfBPz6D8eZVMKEIrKM/mAet62vNZPaOeLdrT0wIiUK5XgcPW3t42TuiKiUbIe0XBakHEBNJwDr6Q9DRv7WbQu6BvUiMIBfq7ZIHC02cIIQQYrUoSBFCCLFaFKQIIYRYLQpShBDSBsyaNatjUFBQeEhIiFKhUCgPHDjgEBcXFxoRERFmqnP48GH7uLi4UABYs2aNa48ePUJM+/bu3euoUCiUOp0OeXl5wr59+waFhoYqg4KCwpcvX+7eGtfUGBSkCCHEyu3fv99h7969rhcuXEi+ePFi8sGDBy927txZCwB5eXmihIQE5/rHTJw4sVAikRi/+uorqUajYW+99ZbvZ599dl0sFmPx4sWeoaGhFampqcmHDx9OnTNnjk9lZaVVThWl2X2EEGJhh69slv6sWiUvrsyVONt6aAcrJqkf63z/z02p1WqxVCrV29nZcQConRPqzTffzP7oo4+8Ro8efdsKEl9++WX6E088EZqUlGTXpUuXsgEDBpQBVc/elZSUCI1GI4qLiwUuLi56sVhslcuXUE+KEEIs6PCVzdLN55f6FVcvKFtcmSvZfH6p3+Er95+q45lnnim+ceOGxN/fP2L8+PG+P//8c01qjl69epWKxWK+c+dOp/rHKZVK7bBhw/K///57z+XLl9csyzRz5sycS5cu2cpksqhu3bqFf/zxxxlCK11Nn4IUIYRY0M+qVXJ9vVQdeqNW8LNq1X0/Me/i4mJMTExM/vzzz697enrqJ06cGLhixYqacaT33nvv5sKFC73qH6fX63Ho0CFnOzs7Y1paWs0q7Dt27HCJiIioyM7OPn/y5Mnk6dOn++bn51tlPLDKRhFCSFtV3EBKjobKG0skEuHpp58uWbZs2Y3Fixen79ixw820b+jQoSWVlZWCo0eP1km/8dFHH3VQKBQVK1euvPbmm2/6Go1VS6GtWbPGfdSoUQUCgQAREREaHx8fzblz52yb0r7mQkGKEEIsyLmBlBwNlTfGuXPnbC5cuGBj2j579qydt7d3nfO9++67Nz/77LOOpu309HTRypUrZStWrMgcOXJkcceOHXXLli3zAAC5XK799ddfnQEgIyNDdOXKFVuFQmGVCeUoSBFCiAUNVkxSi+ql6hAJJMbBivtP1VFcXCycMGFCQGBgYHhISIhSpVLZffTRRzdq1xkzZkyRVCqtmVDxxhtv+EyZMiWrU6dOegD44osv0j/55BOv7Oxs4YcffnjzxIkTDiEhIcq+ffuGzps3L7P2ZAxrwngbzEcTGxvLT58+3drNIIS0M4yxM5zz2Prl586duxYdHd3oDJOWnt3X3p07d84jOjra39w+moJOCCEW9ljnUfkUlCyDghQhpI5yvRY3ygpRptfC3cYBXg4uEDIaGSCtg4IUIaRGTkUxvkg+hN0ZiQAAe5EE73UZiMc7hsBGJG7l1pEHEf08IoTU2K9W1QQooKpX9c/TP+FaaV4rtoo8yChIEUIAAPmVZUi4cua2cg7gbF5GyzeIEFCQIoSYMDSYjp7BKtceJQ8AClKEEACA1MYBzwU+dFu5AAxd3X1aoUWkJaxYscL92rVrzT7gmJqaKvnqq6/uef1CClKkzSktz0FaxgHs/X0OTib+F7mFaeDcePcDyV318QrByIBuEFb3qJzFtlgUNxx+Tlabbog00bp16zzS09PvKUjpdLp7fp9Lly7ZbNq06Z6DFM3uI21KWUUefjv5b1y7caym7GTSaozqvxoyd2Urtqx98LBzwtTwvhgdEIMyvRZSG3t42bs0eBuQmLfl2gHpqos75LmaIomHjYt2Usgz6pH+fZv03FRqaqpk0KBBwXFxcaWnT592lMlk2r1796adP3/e9rXXXvOrqKgQ+Pn5adavX3/N09PTEBcXFxoTE1N69OhR55KSEuFXX311beDAgaW1z/ndd9+5JSYm2k+YMKGzra2t8fTp0ynz5s3ruGfPHleNRiOIjY0t/eGHH64LBAJUJ1gsP3nypOOIESPy+/btWzJp0iR/gUCA3r17Fx84cMDl0qVLSXq9Hm+88Yb3sWPHnLRaLZs0aVLOjBkzct9//3159fJLynHjxuXOnTs3pzHX3aSeFGNsU1OOJ6QhBRXZyCq5isKKW3XLi6/WCVAAYDBocOyvFdBoy1qyie2WrUiMAGcPREg7oZODKwWoe7Tl2gHp0qT1frmaIgkA5GqKJEuT1vttuXbgvlN1mKSnp9tOmTIlJy0tLcnFxcUQHx/v9uKLLwYsXLgw8+LFi8nh4eEVs2bN6mSqr9fr2YULF1I++uijjPnz53eqf76XXnqpICIiojw+Pv6KSqVKdnR05DNmzMhJTExMuXTpUlJFRYVg48aNLqb6Wq2WJSYmpvzrX//KfuWVVwJWrlx5XaVSJQuFwpqliz799FMPFxcXQ2JiYsq5c+dS1qxZ46lSqSQffvihOjY2tlSlUiU3NkABTb/d16OJxxNSR6WuHKcz92LRwRfwr30jsOTQS0jKOgadQQMAyC++Zva4m7nnodWVtGBLCTFv1cUdcq1RV+e7VWvUCVZd3HHfqTpM5HK55pFHHqkAgK5du5ZfvnzZpqSkRDh48OBSAJg0aVLeH3/8UZNratSoUQUA8Mgjj5RlZmY2ahX2X375xSkqKkoREhKi/P33350SExPtTPvGjRuXDwC5ubnCsrIyQf/+/csAYOLEiTW9xP379zsnJCS4KxQKZdeuXcMKCgpEycnJ973CerOPSTHGBjLGUhljaYyxd83s92WMHWSMnWWMnWeMPdXcbSLWK70wGatPzkZxZdUyaXnlN/DF71Nxs/gyAMDF0fz/51KXzhCL7VusnYQ0xNSDamz5vZBIJDU9FqFQyAsLC+84ZGNra8uBqjQfBoOBAcDIkSP9FQqFsnfv3kH165eXl7Pp06f7bdu27fLFixeTx48fn1tZWVkTJ5ycnO46+Ms5Z0uXLk1XqVTJKpUqWa1WX3j22WdvyxrcWHcNUoyxbg38xQC442AbY0wI4AsAgwAoAYxjjNUfOPgAQALnvCuAsQBW3teVkDZPb9DhQNr628o5jDiZsQdAVTDycA2ps58xAXp1/QdsJc4t0k5C7sTDxsVsyouGypvCxcXF4OzsbNizZ48jAKxevdq9R48epXc6ZsuWLddUKlXyoUOH0gDA0dHRUFRUJASA8vJyAQB07NhRX1RUJNi5c6ebuXN4eHgYHBwcjAcOHHAAgLVr19bcyhwwYEDRl19+6anRaBgAnD9/3qY6Rb2htLT0ntP/NmbixNI77FPd5dg4AGmc8ysAwBjbCGAYgORadTgA07eLC4A6y8+TB4eRG1CqLTS7r0RTteKBk70MTz+2FJczD+LitT1wdvRGN8XztwUuQlrLpJBn1EuT1vvVvuUnEYiNk0Keue9UHXfy3XffXX3ttdf8pkyZIvD19dVs2LDh2r0cP2HChNy33nrLb8aMGcbTp0+nPP/887fCwsLCPT099dHR0Q0O9H799dfXXn31VT+BQIAePXqUODk5GQDg7bffzr127ZpNZGRkGOecSaVS3e7duy/HxcVVCIVCHhoaqnzuuecaPXGiWVN1MMZGAhjIOX+levsFAN0552/WquMF4FcAbgAcAPTnnN/+2HstlKqj/Tp+fSfiz8y9rfyNR1YgouOjdcq02jIIhRIIhbSmHLEMS6XqaI7ZfdamqKhI4OLiYgSA9957r+PNmzfF33333X0tTdKkVB2MsZmc84+rX4/inG+utW8h5/y9+2lULeMAfM85X8oY6wFgLWMsgtd78IUxNhnAZADw9fVt4lsSa6XoEIcuXn3w182DNWWPBYyEr2vYbXUlEofbygixBiP9++a3t6BUX0JCgsvSpUu9DAYDk8vlmvXr119rjve5a0+KMfYn57xb/dfmts0c2wPAPM75k9XbswGAc/6fWnWSUNXbyqjevgLgYc55g11B6km1b6XaQuSWqlGqKYCzrTs8HLxhL3Fq7WaRB4ClelLk3jQ16SFr4LW57fpOAQhmjAUAUKNqYsRz9eqkA+gH4HvGWBgAWwC3QB5YjhJXOEpdW7sZhBAr0Jgp6LyB1+a26+7kXA/gTQB7AaSgahZfEmNsPmNsaHW16QAmMcbOAdgA4EXeFnPaE0IIsbjG9KSiGWPFqOo12VW/RvX2XR/Q4pzvBrC7XtmcWq+TAfRsdIsJIYQ8MO4apDjnDc5rZ4zZWLY5hBBCyP9rzMO8cxood0bVbTxCCCHNTCgUxigUCmVwcHD4oEGDOpeUlAjutTwtLU3cvXv3kMDAwPCgoKDwf//73x1M5x8xYoS/XC6PVCgUytDQUOWPP/5YM1tp6NChAf7+/hHBwcHho0aN8jc9qLtixQp3Nze3aIVCoQwKCgofOHBgzftbSmNO9ihj7MPaBYwxGYDDAA6aP4QQQogl2djYGFUqVfKlS5eSxGIxX7p0qee9lovFYixdujTz8uXLSadOnUpZvXp1hzNnztQM2yxYsCBTpVIlL1myJGPKlCl+pvLnn38+/8qVK4mpqalJlZWV7NNPP/Uw7RsyZEiBSqVKTktLSxKLxfzbb781u0rF/WrMmNRQAFsYY59wzqcxxoIB/AJgCef8K0s2hhBC2oOtV/+U/ld1TJ6rKZV42DhqX1H0VI8I6Gax56YeffTR0vPnz9vda7mfn5/Oz89PBwBubm7GwMDAivT0dElMTExl7fr9+vUrzcnJqXlKfsyYMUWm17GxsWYXq9XpdCgvLxdIpVJDU6+vtrv2pDjnlQCGA/BnjG0AsB/ADApQhBByu61X/5R+cmG/X66mtDpVR6nkkwv7/bZe/bPJqTqAqmCwd+9e58jIyIqmlKempkqSk5Pte/fufdtaf1u3bnXp379/Yf1yjUbDNm3a5D548OCaoLVz5043hUKh7NixY3RhYaFo3Lhxtx3XFI0Zk5oG4C0AJwA8AeAsgADG2LTqfYQQQqr9V3VMrjUa6qXqMAj+qzrWpFQdGo1GoFAolJGRkUpvb2/t1KlTc++nHKha0ujZZ58NXLRoUYZUKq1Z3eeDDz7w9vf3j5g8eXLAe++9l1W/DRMnTvR9+OGHS2snTzTd7rt169a5sLCwijlz5nRsynXW15jbfbUf9V9hpowQQkg1Uw+qseWNZRpjamq5RqNhgwcPDhw1alT+xIkTC2vvW7BgQeZLL71U8OGHH3Z45ZVX/JOSklJM+6ZPn+6Vm5sr2rt372Vz7RMIBBg6dGjhF1980cHc/vvVmCno/7LkGxJCSHvmYeOoNReQPGwcLZ6q414ZjUaMHTvWLyQkpHLevHnZDdWbPXt2zrp16zy2bt3qPGLEiOJPPvnE48CBAy5HjhxJFQobzrZx5MgRJ39/f40l29yY232TqidLgFX5ljFWVJ2gsKslG0MIIW3dK4qeaolAWGeBbIlAaHxF0bNZUnXci3379jnu2LHD/ejRo04KhUKpUCiUmzZtcqlfTyAQYNasWTeWLFnSEQBmzpzpl5ubK4qNjQ1TKBTKd955x8tU1zQmFRISojx//rzdwoULb1qyzY1ZYDYRQFfOuY4x9hyqljF6AkBXAHM5570s2aDGoAVmCSHNwVILzDb37L72pqkLzOo557rq108DiOec5wHYzxj72EJtJISQdmNEQLd8CkqW0ZiHeY2MMS/GmC2qVivfX2vfbfPxCSGEEEtpTE9qDoDTAIQAfuKcJwEAY6w3gCvN2DZCCCEPuMbM7tvFGPMD4MQ5L6i16zSAMaYNxtgAzvm+ZmgjIYSQB1SjFgLknOvrBShwzss457WfVP7Ioi0jhBDywLPkarV3y9JLCCGE3BNLBinKpksIIc3ElHrD9JeamioBgPnz53ewsbHplpeXV/OU7a5du5wYYzHr16+veQaqT58+Qbt27XICgIULF3r6+vpGMMZibt682Zi5Ca3Gonk/CCGENA/TMkemv9DQUC0AbNmyRRoREVG2bt0619r1ZTKZ7qOPPvIyd67evXuX7tu372KnTp1afRWMu7FkkLpmwXMRQkibtfVKivSp3esj47avjnlq9/rIrVdSLLICen1JSUk25eXlwvnz56sTEhLqvEdYWFi5k5OTYfv27c71j+vZs2eFKchZu7t28xhjz95pP+d8W/U/71iPEEIeBFuvpEiXXTjhZ1oJPVdTIVl24YQfAIzoHHbfD/iaVjUHAB8fH82+ffsux8fHuw0fPjx/4MCBpZMmTbLNyMgQ+fj46E3HvP/++zfnzJkjHz58eHFTr6u1NKYnNaTW3zf1tp9uvqYRQkjbs1p11myqjtWqs01K1VH7dt++ffsuA8C2bdvcJ0yYkC8UCvHUU08VrF27tk5W3EGDBpUCwN69ex2b8t6tqTHPSb1kes0YO1t7mxBCSF25mooGUnWYL79fJ0+etLt+/brNwIEDQwBAp9Mxb29v7XvvvXerdr3Zs2ffXLBggZdIJGqTk9vudUyqTV4kIYS0FA8bO7NjPQ2V36/4+Hjp9OnTb6jV6gtqtfpCTk7O+ezsbPHFixfrBMNnn322uKioSKhSqdrkMnY0u48QQizoZUVXs6k6XlZ0tWiqjh07dkhHjx5dWLts0KBBBWvWrLltksasWbNuZmVl1QSvBQsWdJDJZFHZ2dmS6Oho5ZgxY/ws2TZLakyqjp34/x7UYwAO197POR/aPE1rGKXqIIQ0B4ul6riSIl2tOivP1VRIPGzstC8ruqqbMmmivWtqqo4ltV4vtUiLCCGkHRvROSyfgpJlNGbixKGWaAghhBBSX2OekzqIhidMcM55P8s2iRBCCKnSmNt975gpexjATAA5lm0OIYQQ8v8ac7vvjOl1daLDfwKwBfAq5/yXZmwbIc2mWKtFsVYLG6EQnnZtcmYuIQ+ERq1+yxh7EsAHADQAPuScH2zWVhHSTDjnSCkowCfnzuFCfj48bW3xVlQUHpHJ4CSx6LOWhBALuOtzUoyxUwC+BrARVbf4ihhj3Ux/zd1AQizpWkkJXjt8GBfyqyZe3aqsxJyTJ/FXXl4rt4yQO2OMxQwbNizAtK3T6eDm5hbdp0+fIABYsWKF+4QJE3zrHyeXyyNrp+PYtWuXk+mYs2fP2nbp0kUhkUi6zZkzR9YS13GvGtOTKgNQCmBk9V9tHEBfSzeKkOZyLi8PlQbDbeWrk5PRxd2delPEatnZ2RlTU1PtSktLmaOjI9++fbuzTCbTNeWcHTp00C9fvjx9y5Ytbnev3Tru2pPinD/OOe/TwN9dAxRjbCBjLJUxlsYYe7eBOqMZY8mMsSTG2Pr7uRBCGiO3osJseaFWC63RaHYfIfdq25Ur0sE//xzZfevWmME//xy57coVi6Tq6N+/f9HmzZtdAWDDhg3SESNGNOlZLLlcru/du3e5WCy22iXvGnO7b2at16Pq7Vt4l2OFAL4AMAiAEsA4xpiyXp1gALMB9OSchwP4R2Mbb03yNEXIKs+D1tCkHzakmcV4epotf9LXF642Ni3cGtIebbtyRbrs3Dm/3MpKCQDkVlZKlp0752eJQPXCCy/kb9q0ya28vJylpKTY9+jRo6wxx/Xu3TvElNH39ddft9olkMxpzNp9Y2u9nl1v38C7HBsHII1zfoVzrkXVuNawenUmAfiCc14AAJzzNjWtvVBTgl0Zx/DikfkY9b/3sPD897heerO1m0Ua0NnZGc8FB9cpC3J2xhA/PwgZa6VWkfZkdUqKXGs01kvVYRSsTklpUqoOAOjevXtFZmamzapVq6T9+/cvauxxhw4dumhK87Fy5crrTW1HS2rMmBRr4LW57frkADJqbWcC6F6vTggAMMaOARACmMc539OIdlmFg1lnsOD8dzXbOzOP4s+8VHzTczY62rm3YsuIOS42NvibQoEnfXxws7wcLhIJfBwdaRo6sRhTD6qx5fdq4MCBhXPnzvX59ddfU3Nycho1Q7sta8wF8gZem9u+3zYEA3gcgDeAw4yxSM55Ye1KjLHJACYDgK/vbRNYWkV2RT5WqrbeVq6uuIX00mwKUlbKSSKBQiKBws1qx4pJG+Zha6s1F5A8bG0tkqrjtddey3V1dTXExcVV7Nq1y8kS57RmjbndF80YK2aMlQCIqn5t2o68y7FqAD61tr2ry2rLBPAT51zHOb8K4CKqglYdnPNvOOexnPNYzwbGFVqa3qhHvtZ8VuYSXXkLt4YQYg1eDgtTSwSCeqk6BMaXw8IskqojMDBQ98EHH5gdFtmyZYu7TCaLMv1dvnxZfKdzpaeni2QyWdQ333wjW7ZsmZdMJovKz8+3qhROd03V0aSTMyZCVdDph6rgdArAc5zzpFp1BgIYxzmfyBjzAHAWQBfOeYMPrlhLqo5SXQVmnF6Bk7nJt+1b/9h8hLq0qfFJQh54lkrVse3KFenqlBR5bmWlxMPWVvtyWJj62c6daVX0BjQ1Vcd945zrGWNvAtiLqvGmbznnSYyx+QBOc85/qt73BGMsGYABwIw7BShr4ii2w7TwcZj8+yIU6/5/ks0bipHwtu/Qii0jhLSmZzt3zqegZBnNPujGOd8NYHe9sjm1XnMA06r/2pxgZ1/E95qLtOJMFGpLoXDxhY+DDA5iGognhJCmavczQ1qCj4MMPg5WuaIIIYS0aVY1QEYIIYTURkGKEEKI1aIgRQghxGpRkCKEkDbgbqk6MjIyRH369AkKDQ1VBgYGhvfu3TsIAFJTUyXBwcHhtc81bdq0TqbUHFOnTu0UEhKiVCgUyp49ewZfu3btjs9WtTQKUoQQ0gbUTtUBAPVTdcyaNUvet2/f4tTU1OTLly8nffzxx416eHju3LlZFy9eTFapVMmDBg0qeu+997ya6xruBwUpQgixsG2XM6WDdx6NfHjzgZjBO49Gbruc2eypOrKyssQ+Pj41Sy91797dfF6aeqRSac3qGGVlZQJmZQstU5AihBAL2nY5U/rpX2l+eZVaCQDkVWoln/6V5meJQHWnVB1vvPFGzltvveXfvXv3kFmzZnWsfdsuIyPDxpSqQ6FQKOPj4+usLffWW2/JO3bsGLVlyxb3xYsX32hqOy2JghQhhFjQ6uRr5lN1JF9r1lQdI0aMKE5LS7vw0ksv5aamptrFxMQob9y4IQIAHx8fjSlVh0qlSp4wYcKt2sd+9tln6qysrPMjR47MW7x4sVUtl0NBihBCLMjUg2ps+b0ypeqYMGHCbcsuyWQyw6uvvpq/Y8eOq1FRUWW//vqr472c+29/+1v+rl27rCo9AAUpC9IYDMgoKUd6STkq9PrWbg4hpBW420rMpuRoqPxevfbaa7nvvPPOjbi4uDpjTj/99JNTSUmJAAAKCgoE169ftwkICLjre164cKEmJXVCQoJrYGBgo8ayWgoti2QhN8oqsDrpKvakZ8PIOXp18sBbUUHwcbJv7aYRQlrQy0p/9ad/pfnVvuUnEQiMLyv9mzVVx6lTp+zffvttX6FQyDnn7IUXXsjt3bt3eWpq6h17cO+88473lStXbBlj3NvbW7t69WqrytzbrKk6mou1pOowKdfp8a+TyTh0o+5K/go3Jyx7NBputhbp5RNCmpnFUnVczpSuTr4mz6vUStxtJdqXlf7qZwO9aVX0BrRaqo4HRVZ55W0BCgBUBSW4WV5BQYqQB8yzgd75FJQsg8akLEBnbLg3qjW0vZ4qIYRYCwpSFuBhJ4GP4+35o5wlInSwtzFzBCGEkMagIGUB7rY2mN89HM6S/797aicU4j89IuFlb3tf5zQYOMpKjaioMN69MiGEtFM0JtUAraYElRW5YAIR7B1kEArvPK4UJnXG9/0egrqsAkYAnRxsIXeww/0sMVJYYMRfp7W4dtkAeweGhx6RoJO3EDY21rVcCSGENDcKUmYUF17Bn8cXISfrNAQCEfyDhyEs6mU4OHa843GdHO3Qycxtv3tRUmTErm2VKCutGsvSaDh+3aXBgKds0DmYPi5CyIOFbvfVU16Wg8P73kJOVtUUd6NRjyupW5FybhUMBk2zv39+vrEmQNV24nctysvp1h8hDyqhUBijUCiUwcHB4YMGDepsenD3XsrT0tLE3bt3DwkMDAwPCgoK//e//12zBNKIESP85XJ5pEKhUIaGhip//PFHJ9O+hQsXevr6+kYwxmJu3rx526/l/v37B0ZHRyua47opSNVTVpKJ8tKbt5VfvbQT5aVZzf7+FeXmZwOWlXAYDc3+9oQQK2VjY2NUqVTJly5dShKLxXzp0qWe91ouFouxdOnSzMuXLyedOnUqZfXq1R3OnDlTM3C+YMGCTJVKlbxkyZKMKVOm+JnKe/fuXbpv376LnTp1um0Fi9zcXGFiYqJDSUmJMDk52eLP29D9o3qMRp3Zcs71MLZAlHBzN/+7wTdACBtbGpMipC3YnpYv/TbxljyvUi9xtxVp/xbhqR4eJLXYc1OPPvpo6fnz528bW7hbuZ+fn87Pz08HAG5ubsbAwMCK9PR0SUxMTGXt+v369SvNycmpWUW9Z8+eDS6VtG7dOtf+/fsXymQyXXx8vHTRokUW/TVPPal67B29IBTePiPP3TMatvYezf7+rq4MEV3q/nawtQNiu4shFlOQIsTabU/Lly7/M8svr1JfnapDL1n+Z5bf9rR8i+SU0ul02Lt3r3NkZGRFU8pTU1MlycnJ9r179y6t/x5bt2516d+/f2Fj2pOQkCAdP358/sSJE/O3bdtmkWusjXpS9Tg4yvFI34/x+4GZMBiqflzY2XdAbM/3YWPj3Ozvb2MrQEycGIEhItzKNsLBAXD3FMLFlX5PENIWfJt4S6418nqpOrjg28Rb8qb0pjQajUChUCgBoHv37iVTp07NvZ9yACgqKhI8++yzgYsWLcqonfTwgw8+8P7Xv/4lz87OFh84cEB1tzZlZGSIrl+/bvvEE0+UCgQCiEQifurUKduHHnqo8m7HNhYFqXoEAiFknR7GE8M2oqxUDYFADEdnb9g73HlmnyXZ2gnQ0Q7o6CVssfckhFiGqQfV2PLGMo0xNbVco9GwwYMHB44aNSp/4sSJhbX3LViwIPOll14q+PDDDzu88sor/klJSSl3alN8fLy0uLhY6OPjEwkApaWlwvj4ePeHHnrIIovpAnS7zyyBQAQnF190lPdAB6/YFg1QhJC2zd1W1ECqDvPlLcloNGLs2LF+ISEhlfPmzctuqN7s2bNzjEYj27p16x1vH23ZskW6ffv2S2q1+oJarb5w4sSJ5B07dlg0HxUFqVZUWWhE4VUDCq8aUFlI08sJaQ/+FuGplghYnf+hJQJm/FuEp8V6F/dr3759jjt27HA/evSokymV/KZNm1zq1xMIBJg1a9aNJUuWdASABQsWdJDJZFHZ2dmS6Oho5ZgxY/xSU1MlarVa0rdv35oU9gqFQuvk5GQ4cOCAg6XaTKk6WklJpgHJP1RCX161LbIHlONt4SSnW3yEtBZLpepo7tl97Q2l6rAymmIjVAmamgAFAPpyIHWTBpGv2MLGmTq4hLRlw4Ok+RSULIO+DVuBtphDW3R7D1ZTxKEtbns9W0IIaS4UpFoBu8MdvTvtI4SQBw0FqVZg48Lg6HP7v3onXwFsXFrngd2iCiPURUbkltEEDkKI9aAxqVYgthcgZLgNrv6iQcElI8AAt2AhAgZJILZv2d8NBiNHao4BXx/XQl3E4WILvBArwUO+IjhIaIULQkjravZvRMbYQMZYKmMsjTH27h3qjWCMccbYbTNr2iM7dwFCRtmi65u26PqGLUJG2sBO2vId28xCI+b/qoG6eoysqBL4/KgWqTm0mi0hpPU167ciY0wI4AsAgwAoAYxjjCnN1HMCMBXAieZsj7UR2TDYewph7ymEqJUSGp5VG6A3c4dv+wUdKrQ0iYMQa2Fvb9+19vaKFSvcJ0yY4AsA06ZN69ShQ4cohUKhDAwMDP/666/rrKG3du1aV8ZYzNmzZ2sWJjUYDHjxxRd9goODw0NCQpQRERFhKpXK4quYN1Vz/3SPA5DGOb/COdcC2AhgmJl6/wbwEQCLrfdEGqe40nwgKtVw6I0UpAhpK1599dVslUqVvGPHjrTp06f7aTSaml++GzdulHbr1q00Pj6+Jnj997//lWZlZYlVKlXSxYsXk3/88cc0d3d3q7uF0txBSg4go9Z2ZnVZDcZYNwA+nPOfm7ktxIwYH/PDkn2DRXCypXk1hNyPvSqtdNKm8siR35fFTNpUHrlXpbX46uANiYyM1Nja2hpzc3OFQNVisqdOnXL87rvvrm3fvr2mHTdv3hTLZDKdUFg1pTgwMFDn6en5wAWpO2KMCQB8AmB6I+pOZoydZoydvnXrVvM37gHh68owLKJuoArvKMDDfjSnhpD7sVellX5/SudXUMElAFBQwSXfn9L5NTVQmVY1N/395z//6WSu3tGjR+39/Pwq5XK5HgDWr1/v+vjjjxdFRUVp3Nzc9EeOHLEHgBdeeCF///79rgqFQjlp0iTvY8eO3ZaHyho09zeRGoBPrW3v6jITJwARAP7HGAOAjgB+YowN5ZzXWfeIc/4NgG+AqmWRmrPRDxInWwFGREnwaIAI+eUcjjYMHZ0YXOyoF0XI/dhyTi/XGep2AHQGCLac08ufVEjuexWK+quar1ixwv306dM1a+R99dVXsvXr13tcu3bNZuPGjWmm8oSEBOmUKVNyAGDEiBH5a9eulfbq1as8MDBQl5aWlrhz506n3377zfmpp54KjY+Pvzxs2LCS+21jc2juIHUKQDBjLABVwWksgOdMOznnRQBqMgkyxv4H4J36AYo0L3sJQ4C7EAHurd0SQto+Uw+qseWW8uqrr2bPnz8/+4cffnB5/fXX/Z966qkLJSUlgj/++MMpNTXV7s0334TBYGCMMW40GjMFAgHs7Oz46NGji0ePHl0sk8l027Ztc32gghTnXM8YexPAXgBCAN9yzpMYY/MBnOac/9Sc708IIS3NzY5pzQUkNzvWIqk6nn/++aJvv/227IsvvnBnjGH48OH569evv27a/9BDD4Xu3bvX0cnJyejt7a3z9/fXGQwGXLhwwa5+9l5r0OwDD5zz3QB21yub00Ddx5u7PYQQ0pxGRovU35/S+dW+5ScWwjgyWtRiqTrmzZt384UXXugsk8m0M2bMyKq9b9iwYQXr1q2TDh8+vPDvf/+7n1arFQBAly5dyt59992clmpjY1GqDkIIqWapVB17VVrplnN6eUEFl7jZMe3IaJG6KeNR7R2l6iCEkBb0pEKST0HJMmgKFyGEEKtFQYoQQojVoiBFCCHEalGQIoQQYrXa/cQJXlAMY3EpmFgE5uYCZmfT2k2yOG7k4DcroT9TCH5TA2E3FwgCHSCQWt2CxoQQck/abU+KG40wpKVD8+la6Javg3bJ99Bt+BnG/KLWbprFGdMrULnwEvS7cmA4UwTtqnRov02HsVDX2k0jhFjIrFmzOgYFBYWHhIQoFQqF8sCBAw5xcXGhhw8ftq9f9+DBg/axsbGh/v7+EWFhYcoxY8b4lZSUCL788ktpSEiIMiQkRNm1a1fF8ePHa9brk8vlkaZzR0REhNU+n06ng5ubW/Trr79eZ4HwhQsXevr6+kYwxmJu3rxZ0+k5e/asbZcuXRQSiaTbnDlzZE257nbbk+K5hdCt2gLo9DVlxsQ0GNxcwIY8DiYStmLrLIdXGqDbcRPQ1X3ezXixDPxmJeAqbqWWEUIsZf/+/Q579+51vXDhQrKdnR2/efOmqHYqjtoyMjJEzz//fGB8fPyV/v37lwHAd99951ZYWCgICgrSHDt2LNXT09OQkJDg/Pe//93v/PnzKtOxhw4duujl5aWvf87t27c7BwQEaHbu3On2+eefqwWCqv5N7969S0eMGFHUt2/f0Nr1O3TooF++fHn6li1b3Jp67e23J3WroE6AMjEc/wu8uLQVWtQ8eIUBxqvlZvcZb1B6LkJaQ9J5rXTtf8sjv15eFrP2v+WRSeebtgK6Wq0WS6VSvZ2dHQcALy8vvb+/v9lbJUuXLu0wevToPFOAAoCXXnqpwMfHRz9gwIAyUzqOPn36lGVlZTVqTGDDhg3S119/PbtTp07a3377rWZR2549e1aEhobettyTXC7X9+7du1wsFjd5tYh2G6SAtreSxv1gtgII5LZm9wlk7W/8jRBrl3ReKz1+WOdXXla1fl95GZccP6zza0qgeuaZZ4pv3Lgh8ff3jxg/frzvzz//7NhQ3eTkZLvY2Fjzv1xr+eyzzzz69OlTZ/yjX79+weHh4WFLliypWfi7vLycHTt2zHns2LGFo0aNyl+3bl2L5cYC2nGQYp5ugJlbeoLuUWAuDX6+bQ6zE0H8rNdtnyTztgVrIHgRQprPnyf1ckO9VB0GAwR/ntTLGzrmblxcXIyJiYnJn3/++XVPT0/9xIkTA1esWHHfeQt27tzptG7dOo/ly5dnmsqOHj2qSk5OTvn1118vrVq1qsMvv/ziCACbNm1yffjhh0scHR35+PHjC/bu3eum199+l6q5tN8g5eEG8SsjAYf/z+MlUHSGqE8cmLB9jEeZCPzsYTM7GMIebhB0tod4bCfYvO4PgRvN7iOkpZl6UI0tbyyRSISnn366ZNmyZTcWL16cvmPHDrPjPWFhYRWnT5++bTKFyYkTJ+xef/11vx07dqR17NixJhNvQECADqi6VTd48ODC48ePOwBVqeePHTvmLJfLI2NiYpRFRUXCnTt3OjflWu5Fu504wQQCCEP8wKZNBC8qqZqCLnUBs2t/vQsmFkDoZw/BBB/AwMEk7fa3ByFWz96Bac0FJHuH+0/Vce7cORuBQIDIyEgNAJw9e9bO29tbq1Kpbsum+8477+R07949bOjQoUV9+/YtA4A1a9a49u3bt7SyslIwatSowG+//fZqVFSUxnRMcXGxwGAwwM3NzVhcXCw4ePCg8/vvv38jPz9fcOrUKUe1Wn3eNB62fPly9/Xr10uHDx9efL/Xcy/abZAyEbg5A24tFvRbFRMyQGh2wg8hpIV0ixOpjx/W+dW+5ScUwtgt7v5TdRQXFwunTJniW1xcLBQKhdzf31+zZs2a68OGDQscPnx4sEgk4gDQrVu30l9++eVKfHz8lRkzZnjn5eWJBQIBf/jhh0ufffbZ4ldeeUVeWFgoeuutt/wAQCQS8cTExJTMzEzR8OHDgwDAYDCwESNG5I0cObL4s88+c3/kkUdKTAEKAMaOHVs4b94874qKCrZ06VLPzz77rGNeXp44Ojpa2adPn6JNmzZdT09PFz300EPKsrIyIWOMf/3117KUlJREqVRqvNdrp1QdhBBSzVKpOpLOa6V/ntTLy8u4xN6BabvFidThUbQqekMoVQchhLSg8ChJPgUly6DBC0IIIVaLghQhhBCrRUGKEEKI1aIgRQghxGpRkCKEEGK1KEgRQoiVe/nll33mz5/fwbT96KOPBo8ZM8bPtD1p0iTvefPmyRpKqbFhwwaXsLAwZWhoqDIwMDB88eLFHgAwbdq0Tk1NpdHcKEgRQoiVe/TRR0v/+OMPRwAwGAwoKCgQpaam1qw2cerUKcdevXqV1k6pYTRWPTer0WjY1KlT/Xbt2nUpNTU1OTExMfmJJ54oaaVLuWcUpAghxMJuntJKTy4ujzw2tyzm5OLyyJunmpaqo0+fPqV//vmnIwCcOXPGLjQ0tMLBwcFw69YtYUVFBbt8+bJtz549y82l1CgsLBTo9Xomk8n0AGBnZ8ejo6M1d3o/a0IP8xJCiAXdPKWVXt2j8+P6qk6ArpRLru7R+QGA10P394Cvv7+/TigU8kuXLkkOHTrk8PDDD5ep1WrxgQMHHN3c3PQhISEVRqMRx44dc167du31wsJC4bp166QDBgwok8lkhgEDBhT6+vpG9ezZs/ipp54qmjx5cr6wjSy0TT0pQgixoIz/6eWmAGXC9RBk/O/+U3UAQExMTOnBgwcdjh8/7tirV6/SRx55pOzYsWMOR44ccezevXvpnVJqbNq06fqePXsuxsbGlq1YsaLj6NGj/ZvSlpZEPSlCCLEgXan5lBwNlTfWI488Uvr77787qlQqu4ceeqiic+fO2k8//VTm6OhoePHFF3Pj4+M9Tp8+7SiXyyMBwJRSw7RaeVxcXEVcXFzF5MmT84OCgiIBXGtKe1oK9aQIIcSCxI7mU3I0VN5Yjz32WOn+/ftdXV1dDSKRCDKZzFBcXCw8e/asY1xcXMWpU6ccMzMzz6vV6gtqtfrCokWL0tevXy8tKioS7Nq1y8l0nhMnTth16tSpSW1pSRSkCCHEgnweF6mZCHVSUjARjD6P33+qDqCqJ1RYWCiKjY0tNZUpFIoKR0dHw549e5zMpdTYv3+/i16vZ4sXL5b5+/tHKBQK5fz58+WrV6++aqq3bNkyL5lMFmX6a0obmwOl6iCEkGqWStVx85RWmvE/vVxXyiViR6b1eVykvt9JEw8CStVBCCEtyOshST4FJcto9tt9jLGBjLFUxlgaY+xdM/unMcaSGWPnGWO/Mcb8zJ2HEELIg6dZgxRjTAjgCwCDACgBjGOMKetVOwsglnMeBWALgI+bs03tBa/QwZBZBENaLoy3SsEN95yVmRBCrF5z3+6LA5DGOb8CAIyxjQCGAUg2VeCcH6xV/w8A45u5TW2esaAcuu2JMP55o6pAIoT4ua4QRnUEk9AdXEJI+9Hct/vkADJqbWdWlzXkZQC/NGuL2jjOOQynMv8/QAGA1gDdmtPgOaUNH0gIIW2Q1fzsZoyNBxALoHcD+ycDmAwAvr6+jT5vXoUOaYWVOJNTDrmjGF08HeDnbGOJJrcKXqyB4fBVMzsAw9V8CLxdW7xNhBDSXJq7J6UG4FNr27u6rA7GWH8A7wMYyjk3u/Ah5/wbznks5zzW09OzUW+eX6nHsj+z8PahdKxLycVHp25i0r4rSCuovPcrsRYMgJCZ3yWgx94Iac/Wrl3ryhiLOXv2rC0ApKamSmxtbbspFAplYGBg+PDhw/01Gg0DgF27djk5OTl1USgUys6dO4dPnz7dy3SegwcP2sfGxob6+/tHhIWFKceMGeNXUlIiWLdunWtISIhSoVAoIyIiwvbu3evYWtdq0tzfaqcABDPGAhhjEgBjAfxUuwJjrCuAr1EVoHIs+ebXizU4kFFcp6xUZ8TqpBxU6AyWfKsWI3C2hbBPoJkdDAJ/t5ZvECGkxWzcuFHarVu30vj4+JpV1X18fDQqlSo5NTU16ebNm5Jvv/225osgNja2VKVSJf/1118pW7ZscT969Kh9RkaG6Pnnnw9ctGhR5rVr1xJTUlKSBw4cWFxYWCgYMmRIsUqlSlapVMmrV6++9uqrr7b6bOtmvd3HOdczxt4EsBeAEMC3nPMkxth8AKc55z8BWAzAEcBmxhgApHPOh1ri/dMKzfeYTmeXoVhnhJ24bawCXJ+wayfwnFIYjl4DjBxwlEAyvhuYrNV/9BBCAFQe0Eo1P+rlvIhLmAvT2gwTqW37Nu25qaKiIsGpU6cc9+/fnzp06NDgZcuW3ai9XyQSoVu3bmVqtVpc/1hnZ2djZGRkuUqlstmyZYvr6NGj8/r3719m2v/SSy8V1D+mpKREUP2d3KqafUyKc74bwO56ZXNqve7fXO/t5WB+PUcfJwnsRG331pjAxQ7i4REQPRYArjGAOdlAILVv7WYRQlAVoCo36Pygq7pTxYu4pHJDVaqOpgSq9evXuz7++ONFUVFRGjc3N/2RI0fsO3TooDftLy8vZ2fOnHFYsWJFRv1js7KyhGfPnnWYN2/ejYSEBOmECRPyGnqf+Ph417lz58rz8/PFW7duvXS/7bWUtvtN3QhBrjbo5HDbjwq8FiWDs6Rt9qJMmFgIQUdnCP3cKEARYkU0P+rlpgBVQweB5sempepISEiQjhs3rgAARowYkb927VopAGRkZNgoFAqlTCaL7tChg6579+4VpmNOnz7tGBYWpuzXr1/I1KlTs2JjY+86ID9hwoTCq1evJm3cuDFtzpw5TWqzJbTrINXRQYJlj/vheYU75I4SxMoc8EVff0R42N39YEIIuQ+8yHxKjobKGyM7O1v4xx9/OL3xxht+crk88vPPP++4c+dON855zZjUxYsXEy9cuODwww8/uJiOi42NLU1JSUlOSkpKmTlz5i0ACAsLqzh9+vRdf9kOGjSoND093ebmzZutOgu8XQcpAPBxssHfo2RY1T8Ai3r5omsHB9iJ2nYvihBivZiL+ZQcDZU3xtq1a92GDx+ef+PGjQtqtfpCVlbWeW9vb+3Vq1drAp+Xl5d+/vz5mYsXL/a607neeeednISEBPcDBw44mMrWrFnjmpGRIUpMTLQxGqtWrzl69Ki9VqutSTvfWtp9kAIAkYDB1VYE+zY8DkUIaRtshonUENdN1QExjDbD7j9Vx+bNm6XPPvtsnckNw4YNK/jPf/5TJyCNHz++sKKiQrBnz54GZ1H5+Pjo4+Pjr8yYMcPb398/onPnzuF79+51cXV1NW7YsMEtJCQkXKFQKN98803ftWvXXhG08qMtlKqDEEKqWSpVR3PM7mvPKFUHIYS0INu+knwKSpZB978IIYRYLQpShBBCrNYDf7vPoDZCd0YP/XUjxF2FECmEEHpQ7CaEEGvwQAcpQ4YBJR9WAtWPt+nPGCDwY3CcYguBOwUqQghpbQ/sNzHXclT+oqsJUCbG6xz6a5TllhBCrMGDG6QqOQyXzAcjwxUKUoQQ68IYi5k0aZK3aXvOnDmyadOmdTJtf/755+7BwcHhISEhyrCwMOWcOXNkADBixAj/7777zg2oWrkiLCxMuXz5cnfTcX/72998OnToEGUw/H9miBUrVri7ublFm1KALF261AMAzp49a9ulSxeFRCLpZjp/c3tggxSzYRD4mr98oW/rr/xLCCG1SSQSvnv3bjdzyxQlJCQ4r1y5ssO+ffsuXrx4MfnPP/9McXFxqZOPKC8vT9ivX7+QF1988dbUqVPzAMBgMGDPnj2uXl5e2t27dzvVrj9kyJAClUqVfPjw4dQFCxbIMzIyRB06dNAvX748/e9//3t2817t/3ugg5TtEDFQb/1ZQQcGYWdaNokQcv90h3Kl5TOSIssnn4spn5EUqTuUK737UXcmFAr5hAkTbi1cuPC2HszHH3/stWjRokx/f38dANjZ2fHp06fXPHxcUlIi6N+/f/CoUaPyZs2adctU/vPPPzsFBwdXvPLKK7fWr19vto1yuVzv6+urSUtLk8jlcn3v3r3LxWJxi60C8cAGKQAQ+grg+E9bSPoIIQwSwHa0GA7TbSH0fKD/tRBCmkB3KFeqS7jhhyJ91bp6RXqJLuGGnyUC1YwZM3K2bdsmzcvLq/NL+tKlS3Y9e/Ysb+i4f/7znz7du3cvnTt3bp3EsuvXr5eOHj06//nnny/47bffXExZfWtLTk6WZGRk2CiVSrNZ05vbA/1tzAQMIl8h7F6wgeNMW9g+JYFQ9kD/KyGENJFuV7YcOl4vVQcX6HZlNznthVQqNY4aNSpv0aJFHe7luB49ehTv2bPHVa1W19wqrKysZAcOHHB57rnnCqVSqbFLly5l27Ztczbt37lzp5tCoVCOHTu286effnpdJpO1SjrzB3oKugkTMOC+F9EnhJBaTD2oxpbfo9mzZ2d369ZNOXbs2JrbeUFBQRXHjh2zHzp0aIm5Y8aMGVOQnp5e+uSTTwYfOXIk1c3Nzbht2zbnkpISYURERDgAVFRUCGxtbY3jxo0rAqrGpOLj49Mt0eamoG4DIYRYkovIfEqOhsrvkUwmMwwZMqRg/fr1HqaymTNnZs2ePds7PT1dBFT1kj755BOP2sfNnTs3p1evXsWDBw8OrKysZBs2bJB++umn19Vq9QW1Wn3h2rVrF44ePepcUlJiVXHBqhpDCCFtnfhpmRpiVi9VBzOKn5bdd6qO+t5///2swsLCmjthY8aMKZo8eXJOv379QoOCgsIjIyOVxcXFt80A+/LLL9WdOnXSDR8+PODw4cMuo0aNKjTtc3Z2NsbGxpZu3LjRpf5xJunp6SKZTBb1zTffyJYtW+Ylk8mi8vPzmzWOUKoOQgipZqlUHbpDuVLdrmw5ivQSuIi04qdlanFvD1oVvQGUqoMQQlqQuLdHPgUly6DbfYQQQqwWBSlCCCFWi4IUIYQQq9Umx6TOnDmTyxi73trtaAEeABo9WNtOPIjXDDyY122N1+zX2g0gdbXJIMU592ztNrQExthpczON2rMH8ZqBB/O6H8RrJveObvcRQkgbIBQKYxQKhTI4ODi8b9++Qbm5uXWeg1IoFMqnn366c+2yESNG+Mvl8kiFQqFUKpVh+/fvdzDtmzdvniwgICA8JCREGRoaqnzllVe8TWv3yeXyyJCQEKVCoVAqFArliy++6FP/fAqFQrlgwYIOJSUlgscffzwoICAgPCgoKPz1119v8vJPtVGQIoSQNsDGxsaoUqmSL126lOTq6qpfvHhxzR2lP//809ZoNOLkyZOOxcXFdb7XFyxYkKlSqZIXLFigfv311/0A4OOPP/b87bffnE+dOqW6ePFi8rlz51I6dOigLysrq1lg9tChQxdVKlWySqVK/v777zPqn0+lUiV/8MEHOQAwffr07KtXryYlJiYmnzhxwjEhIcEZFtImb/c9QL5p7Qa0ggfxmoEH87rb7TXrjlyV6n9RyVGskcDZRisapFCLewVY7Lmphx9+uOz8+fN2pu34+Hjp6NGj81Qqld369etdX3311dvea+DAgSXPP/+8DQB88sknXv/73/9UHh4eBgCwtbXlCxcuzLqftjg5ORmHDBlSYjpPVFRUeUZGhsVWQ6WelBXjnLfb/4kb8iBeM/BgXnd7vWbdkatS/dYLfijWVH1RF2sk+q0X/HRHrjY5VQcA6PV6HDx40OmZZ54pNJXt2LFDOnHixILnnnsuPyEhwez7bNy40TU4OLgiPz9fUF5eLlAoFHdcS7B3794hptt6//rXv2pWXf/ggw+8TeUnT560q31Mbm6ucN++fa6DBg0qbuJl1qCeFCGEWJD+F5UcemPdDoDeKND/opI3pTel0WgECoVCmZ2dLQ4MDKx85plnigHg8OHD9lKpVB8cHKwNCAjQvvbaa/7Z2dlCU2qNDz74wPujjz7ykkqlutWrV1+rf96tW7c6v//++94lJSXC77///sqAAQPKgKrbfV5eXvr69RcsWJD50ksvFdQv1+l0ePbZZztPnjw5W6lUWmQxXYB6Uq2GMebDGDvIGEtmjCUxxqZWl0sZY/sYY5eq/+lWXc4YYysYY2mMsfOMsW6tewX3jzEmZIydZYztqt4OYIydqL62TYwxSXW5TfV2WvV+/1ZteBMwxlwZY1sYYyrGWApjrEd7/6wZY29X/7edyBjbwBizfRA+65oeVGPLG8k0JpWenn6Bcw5TTqm1a9dKr1y5YiuXyyP9/Pwiy8rKhOvWrXMzHWcaQ/r9998vPfTQQ5VSqdRob29vVKlUEgAYMWJEsUqlSg4JCanQaDT3HROee+45/86dO1fOmTMn5+61G4+CVOvRA5jOOVcCeBjAG4wxJYB3AfzGOQ8G8Fv1NgAMAhBc/TcZwJct32SLmQogpdb2RwCWcc6DABQAeLm6/GUABdXly6rrtVXLAezhnCsARKPq+tvtZ80YkwOYAiCWcx4BQAhgLB6Ez9rZxnwvoqHye+Tk5GRcsWJF+sqVK2WVlZVs586d0r/++ivJlHJjw4YNaZs3b77jrcV//OMfNydNmuRnmiFoNBrRlAA1ZcqUTsXFxcLVq1dn3L32vaEg1Uo45zc5539Wvy5B1ZeWHMAwAGuqq60B8Ez162EA4nmVPwC4Msa8WrbVTccY8wYwGMB/q7cZgL4AtlRXqX/Npn8XWwD0q67fpjDGXAA8BmA1AHDOtZzzQrTzzxpVwwl2jDERAHsAN9HOP2sAEA1SqCES1E3VIRIYRYMUFkvV0bNnzwqFQlHx/vvvd5TJZFp/f3+dad+gQYNK0tLS7K5fvy5u6PiZM2feevzxx4tjY2PDQkJClDExMYrIyMjyHj161KSgrz0mNXz4cP+GznX58mXxZ5995nXp0iXb8PBwpUKhUNbPZdUUNCZlBapvbXQFcAKAjHN+s3pXFgBZ9Ws5gNq/UjKry26ibfkUwEwATtXb7gAKOeeme9+m6wJqXTPnXM8YK6qub22rFNxNAIBbAL5jjEUDOIOq3mS7/aw552rG2BIA6QAqAPyKqutu7581TONOlp7dV15efrb29oEDB9KqX9b570IkEiE3N/ccAGzduvWauXMJBAL8+9//zv73v/+dbW6/Wq2+YK7c3PkCAwN1nPMzd72A+0RBqpUxxhwBbAXwD855ce0fj5xzzhhrewm/GsAYexpADuf8DGPs8VZuTksSAegG4C3O+QnG2HL8/609AO3ys3ZDVe8oAEAhgM0ABrZmm1qSuFdAviWnnD/I6HZfK2KMiVEVoH7gnG+rLs423dqp/qdpEFINwKfW4d7VZW1JTwBDGWPXAGxE1a2f5ai6nWX6wVT7umquuXq/C4C8lmywhWQCyOScn6je3oKqoNWeP+v+AK5yzm9xznUAtqHq82/vnzWxMApSraT6fvtqACmc809q7foJwMTq1xMB/FirfEL1zK+HARTVulXUJnDOZ3POvTnn/qgaRD/AOX8ewEEAI6ur1b9m07+LkdX121xvg3OeBSCDMRZaXdQPQDLa8WeNqtt8DzPG7Kv/Wzddc7v+rInl0e2+1tMTwAsALjDG/qouew/AIgAJjLGXAVwHMLp6324ATwFIA1AO4KUWbW3zmgVgI2NsAYCzqJ5gUP3PtYyxNAD5qApsbdVbAH6onnJ9BVWfnwDt9LOuvq25BcCfqJrJehZVK0z8jLb5WRuNRiMTCAQUOC3MaDQyAMaG9jP6sUIIIXd27ty5nzp27Kj09PQsokBlOUajkd26dcslKysrOTo6eqi5OtSTIoSQu9Dr9a9kZWX9NysrKwI0TGJJRgCJer3+lYYqUE+KEEKI1aJfBIQQQqwWBSlCCCFWi4IUIYQQq0VBihBCiNWiIEWsFmOMM8aW1tp+hzE27y7HTKhODXGhOh3IO3epP89UhzH2PWNs5J3qE0JaFgUpYs00AJ5ljDVqRWXG2CAA/wDwBOc8ElUpUIqar3mEkOZGQYpYMz2qVil4u5H1ZwN4h3N+AwA45xrO+SoAYIwFMsb2MMbOMMaOMMYUdzoRY2wRq0pIeb56NW9CSCugh3mJtfsCwHnG2MeNqBuBqnQQ5nwD4FXO+SXGWHcAK1G1wO1tGGPuAIYDUFSvTu56780mhFgCBSli1arTl8SjKstrxf2cozodyiMANtdKhWJzh0OKAFQCWM2qUtzvup/3JYQ0Hd3uI23Bp6hKL+5wl3pJAGLMlAtQlWyvS62/sIZOUp2ULw5VKTWeBrDnvlpNCGkyClLE6nHO8wEkoCpQ3cl/ACxmjHUEAMaYhDH2Cue8GMBVxtio6nJWnSHXrOqelwvnfDeqxsMarEsIaV4UpEhbsRTAHWf5VQeVzwHsZ4wloSpNhHP17ucBvMwYO4eqHtewO5zKCcAuxth5AEcBTGti2wkh94kWmCWEEGK1qCdFCCHEatHsPtLmMMbeBzCqXvFmzvmHrdEeQkjzodt9hBBCrBbd7iOEEGK1KEgRQgixWhSkCCGEWC0KUoQQQqwWBSlCCCFW6/8AKUjUu92SgPwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot neurite/cell numbers\n",
    "# Calculate correlation, plot RAW vs ENH\n",
    "print(stats.pearsonr(neurite_count['N_Cells'], neurite_count['ENH_SKEL']))\n",
    "fig, axs = plt.subplots(figsize=(5,5))\n",
    "sns.scatterplot(data=neurite_count, x = 'N_Cells',y='ENH_SKEL',\n",
    "               hue = neurite_count.index, ax = axs, s=50, cmap='tab20')\n",
    "axs.legend(bbox_to_anchor=(1.02, 1), loc='upper left', borderaxespad=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "id": "65fe929e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/Users/bkang/Desktop/lab/FocalStack_D28'"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "os.getcwd()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "id": "3151c755",
   "metadata": {},
   "outputs": [],
   "source": [
    "neurite_count.to_csv('072622_CP_All_FOV/neurite_count.csv', sep=',')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "964f947d",
   "metadata": {},
   "source": [
    "## Is particular genotype clustered in one part of image?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "0db19822",
   "metadata": {},
   "outputs": [
    {
     "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>PGGT1B</th>\n",
       "      <th>MAP4K4</th>\n",
       "      <th>NCK1</th>\n",
       "      <th>HSF1</th>\n",
       "      <th>MINK1</th>\n",
       "      <th>IER5</th>\n",
       "      <th>SNX8</th>\n",
       "      <th>non-target</th>\n",
       "      <th>PPP2R2B</th>\n",
       "      <th>FAN1</th>\n",
       "      <th>MLH1</th>\n",
       "      <th>MSH3</th>\n",
       "      <th>PPP2R1A</th>\n",
       "      <th>HRAS</th>\n",
       "      <th>SLC25A11</th>\n",
       "      <th>WASL</th>\n",
       "      <th>ARPC3</th>\n",
       "      <th>NCKAP1</th>\n",
       "      <th>RAPGEF2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>29685</td>\n",
       "      <td>55930</td>\n",
       "      <td>102483</td>\n",
       "      <td>104250</td>\n",
       "      <td>35145</td>\n",
       "      <td>71533</td>\n",
       "      <td>62049</td>\n",
       "      <td>52624</td>\n",
       "      <td>80544</td>\n",
       "      <td>75766</td>\n",
       "      <td>61191</td>\n",
       "      <td>53977</td>\n",
       "      <td>25909</td>\n",
       "      <td>39985</td>\n",
       "      <td>38183</td>\n",
       "      <td>54470</td>\n",
       "      <td>20194</td>\n",
       "      <td>56621</td>\n",
       "      <td>26694</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>15663</td>\n",
       "      <td>48149</td>\n",
       "      <td>96056</td>\n",
       "      <td>74466</td>\n",
       "      <td>32356</td>\n",
       "      <td>69179</td>\n",
       "      <td>46300</td>\n",
       "      <td>39701</td>\n",
       "      <td>51578</td>\n",
       "      <td>51997</td>\n",
       "      <td>56275</td>\n",
       "      <td>47984</td>\n",
       "      <td>22838</td>\n",
       "      <td>38655</td>\n",
       "      <td>46151</td>\n",
       "      <td>51823</td>\n",
       "      <td>16606</td>\n",
       "      <td>41071</td>\n",
       "      <td>21678</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>33659</td>\n",
       "      <td>76930</td>\n",
       "      <td>107500</td>\n",
       "      <td>118332</td>\n",
       "      <td>38751</td>\n",
       "      <td>76438</td>\n",
       "      <td>86516</td>\n",
       "      <td>67365</td>\n",
       "      <td>99739</td>\n",
       "      <td>85739</td>\n",
       "      <td>58965</td>\n",
       "      <td>52143</td>\n",
       "      <td>32657</td>\n",
       "      <td>45039</td>\n",
       "      <td>45508</td>\n",
       "      <td>47697</td>\n",
       "      <td>22540</td>\n",
       "      <td>48435</td>\n",
       "      <td>29291</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>19838</td>\n",
       "      <td>62446</td>\n",
       "      <td>80340</td>\n",
       "      <td>83471</td>\n",
       "      <td>26480</td>\n",
       "      <td>72624</td>\n",
       "      <td>59445</td>\n",
       "      <td>45306</td>\n",
       "      <td>69097</td>\n",
       "      <td>55840</td>\n",
       "      <td>51157</td>\n",
       "      <td>59858</td>\n",
       "      <td>20224</td>\n",
       "      <td>44940</td>\n",
       "      <td>45379</td>\n",
       "      <td>47106</td>\n",
       "      <td>13787</td>\n",
       "      <td>46296</td>\n",
       "      <td>27345</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>29581</td>\n",
       "      <td>67267</td>\n",
       "      <td>95784</td>\n",
       "      <td>103077</td>\n",
       "      <td>36146</td>\n",
       "      <td>71678</td>\n",
       "      <td>76996</td>\n",
       "      <td>35700</td>\n",
       "      <td>77616</td>\n",
       "      <td>58634</td>\n",
       "      <td>44951</td>\n",
       "      <td>45504</td>\n",
       "      <td>19458</td>\n",
       "      <td>54878</td>\n",
       "      <td>45143</td>\n",
       "      <td>50082</td>\n",
       "      <td>18523</td>\n",
       "      <td>51599</td>\n",
       "      <td>29787</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>43970</td>\n",
       "      <td>113625</td>\n",
       "      <td>143596</td>\n",
       "      <td>111288</td>\n",
       "      <td>56030</td>\n",
       "      <td>79726</td>\n",
       "      <td>83887</td>\n",
       "      <td>64437</td>\n",
       "      <td>98846</td>\n",
       "      <td>121239</td>\n",
       "      <td>72740</td>\n",
       "      <td>59546</td>\n",
       "      <td>29815</td>\n",
       "      <td>57628</td>\n",
       "      <td>63246</td>\n",
       "      <td>57357</td>\n",
       "      <td>21274</td>\n",
       "      <td>41337</td>\n",
       "      <td>28994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>45569</td>\n",
       "      <td>114356</td>\n",
       "      <td>143005</td>\n",
       "      <td>116929</td>\n",
       "      <td>60253</td>\n",
       "      <td>70007</td>\n",
       "      <td>83311</td>\n",
       "      <td>66981</td>\n",
       "      <td>113726</td>\n",
       "      <td>114984</td>\n",
       "      <td>71874</td>\n",
       "      <td>61500</td>\n",
       "      <td>27646</td>\n",
       "      <td>68976</td>\n",
       "      <td>76504</td>\n",
       "      <td>53329</td>\n",
       "      <td>24153</td>\n",
       "      <td>43814</td>\n",
       "      <td>29945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>27289</td>\n",
       "      <td>81414</td>\n",
       "      <td>129077</td>\n",
       "      <td>99618</td>\n",
       "      <td>35376</td>\n",
       "      <td>75554</td>\n",
       "      <td>58422</td>\n",
       "      <td>55709</td>\n",
       "      <td>78140</td>\n",
       "      <td>111414</td>\n",
       "      <td>79841</td>\n",
       "      <td>53122</td>\n",
       "      <td>28221</td>\n",
       "      <td>40133</td>\n",
       "      <td>72345</td>\n",
       "      <td>58741</td>\n",
       "      <td>14663</td>\n",
       "      <td>50266</td>\n",
       "      <td>26707</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>23712</td>\n",
       "      <td>79028</td>\n",
       "      <td>136017</td>\n",
       "      <td>114932</td>\n",
       "      <td>42469</td>\n",
       "      <td>64739</td>\n",
       "      <td>69402</td>\n",
       "      <td>46377</td>\n",
       "      <td>96531</td>\n",
       "      <td>100398</td>\n",
       "      <td>78941</td>\n",
       "      <td>46346</td>\n",
       "      <td>23612</td>\n",
       "      <td>37050</td>\n",
       "      <td>51682</td>\n",
       "      <td>54599</td>\n",
       "      <td>13500</td>\n",
       "      <td>31821</td>\n",
       "      <td>25831</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>35530</td>\n",
       "      <td>87594</td>\n",
       "      <td>112594</td>\n",
       "      <td>108073</td>\n",
       "      <td>44015</td>\n",
       "      <td>74460</td>\n",
       "      <td>71978</td>\n",
       "      <td>38343</td>\n",
       "      <td>97277</td>\n",
       "      <td>105302</td>\n",
       "      <td>75505</td>\n",
       "      <td>54386</td>\n",
       "      <td>23960</td>\n",
       "      <td>41174</td>\n",
       "      <td>50306</td>\n",
       "      <td>57570</td>\n",
       "      <td>17958</td>\n",
       "      <td>31837</td>\n",
       "      <td>26017</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 19 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   PGGT1B  MAP4K4    NCK1    HSF1  MINK1   IER5   SNX8 non-target PPP2R2B  \\\n",
       "0   29685   55930  102483  104250  35145  71533  62049      52624   80544   \n",
       "1   15663   48149   96056   74466  32356  69179  46300      39701   51578   \n",
       "2   33659   76930  107500  118332  38751  76438  86516      67365   99739   \n",
       "3   19838   62446   80340   83471  26480  72624  59445      45306   69097   \n",
       "4   29581   67267   95784  103077  36146  71678  76996      35700   77616   \n",
       "..    ...     ...     ...     ...    ...    ...    ...        ...     ...   \n",
       "95  43970  113625  143596  111288  56030  79726  83887      64437   98846   \n",
       "96  45569  114356  143005  116929  60253  70007  83311      66981  113726   \n",
       "97  27289   81414  129077   99618  35376  75554  58422      55709   78140   \n",
       "98  23712   79028  136017  114932  42469  64739  69402      46377   96531   \n",
       "99  35530   87594  112594  108073  44015  74460  71978      38343   97277   \n",
       "\n",
       "      FAN1   MLH1   MSH3 PPP2R1A   HRAS SLC25A11   WASL  ARPC3 NCKAP1 RAPGEF2  \n",
       "0    75766  61191  53977   25909  39985    38183  54470  20194  56621   26694  \n",
       "1    51997  56275  47984   22838  38655    46151  51823  16606  41071   21678  \n",
       "2    85739  58965  52143   32657  45039    45508  47697  22540  48435   29291  \n",
       "3    55840  51157  59858   20224  44940    45379  47106  13787  46296   27345  \n",
       "4    58634  44951  45504   19458  54878    45143  50082  18523  51599   29787  \n",
       "..     ...    ...    ...     ...    ...      ...    ...    ...    ...     ...  \n",
       "95  121239  72740  59546   29815  57628    63246  57357  21274  41337   28994  \n",
       "96  114984  71874  61500   27646  68976    76504  53329  24153  43814   29945  \n",
       "97  111414  79841  53122   28221  40133    72345  58741  14663  50266   26707  \n",
       "98  100398  78941  46346   23612  37050    51682  54599  13500  31821   25831  \n",
       "99  105302  75505  54386   23960  41174    50306  57570  17958  31837   26017  \n",
       "\n",
       "[100 rows x 19 columns]"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "RAW_SKEL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "4662eda6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# plot heatmap of total neurite area/cell by image\n",
    "Overview = {}\n",
    "grid = pd.read_csv('../grid_10by10.csv', sep=',', index_col = None, header=None)\n",
    "for gene in genes:\n",
    "    Overview[gene] = grid.copy()\n",
    "\n",
    "# fill in \n",
    "for gene in genes:\n",
    "    for row in grid.index:\n",
    "        for col in grid.columns:\n",
    "            im_num = grid.loc[row,col]\n",
    "            cell_num = neurite_count.loc[gene, 'N_Cells']\n",
    "            Overview[gene].loc[row,col] = np.log2(RAW_SKEL.loc[im_num, gene].astype('float'))/cell_num                        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "050598b8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 3600x2880 with 38 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(4,5,figsize = (50,40))\n",
    "axs=axs.ravel()\n",
    "for i in range(len(genes)):\n",
    "    gene = genes[i]\n",
    "    sns.heatmap(Overview[gene], ax=axs[i])\n",
    "    axs[i].set_title(gene, fontsize=25)\n",
    "fig.delaxes(axs[-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "6a8657b5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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hzI5N/Ud3v2KjFeaDjA8MlPm+LXICAEdIVV25qu6Q2Qz3BwaFPHk+8GOr7Y6rqtskeVmSm84XP+PAxTzbbHtsVd20qv4yyQ/MF7/qEC6qPrD9/0zyk/OX/6e737XNJodz3myzDFfPrLfsS/LuzAbbbuWYfOV5vquse+/4+evjd5IBABidrcb4HJMk8wlt7j9f/5nrN+7u9yT518zGA93vUD+0u1+a5F8yOye32fEtYMIM1oWt/Xp31zaP92+x/ZWy8f/sfzDJN291a8Du/r/d/Ufd/YHuvmS+7PPd/aIk35rkv+er/t4OZ8UDACaiuy/u7l9Ncu0k98lspt3/THLRfJVrJHlkZjP23/oQ9vcPSf5h/vJ3dpOpqo7NV540uU5mA0622+76mc3u9n+SvDCz2zNfMcl1MztQ0pnNrPvaA7dqBgAG9YYt3vvw/Ov6YyW3nH9944GZZA/W3Z9KcsZB6x/sjVvMpLbR5y7EfNDLH1fVW6rqs1W1VlVdVZ3kj+ernbzFLt7Y3Rdt8t6BmVk+2N3/vdEK3X1+Lp1J+GC3m3+98VYzzST51fl619siJwBwGA70g3lH+FRmx11uMX/7Wdn8wpz/Xrfd+Un+LbNbOGe+j5/d4mNftW7bLyQ5M5feeeA/coh3Iaiq70jyF/OX/5RLu8NWdn3ebJMMx2V2Qdb1Mvs5/Eh3X7DNZq85+FxfZoNzvyfJWzK7WPzfq+qGO8kCAIzHNmN8zpyv9t2ZTZ73uczOOx3swMXg99/gva0cmPjmjvMLsQC+zGBdWKz7H/Q/+t+V2VU0103yF7sdSDK/GvrA7aevl9kseQAAG+ruz3T3s7r7wd19s8x6yR2T/M18lasleeF8IO12Hp3ZwNhvr6r/sYs4v5Xk6zM7+PGa+bI/r6orb7ZBVR2V5CVJvinJM7v7Xt39pvmtoD/U3c/I7ITKFzM7ofVLu8gFABxZ52/x3sXzr8esW3aN+ddzttnv2Qetf7ifm6p60SYDVnd66+gD+3toZndM+qnM+stxST6T5KPzx4HByFfYYjfnbfHe1edfP7zFOsnmP8sDM/Udm61nmjkwe+/lt/kcAGD3Prru8cHMOsRTk3x3d9/nwGQuG/j4uu3OzmyQ6bOS3D3J93b357b4zE+t2/acJO9I8oLMLvT+1u7+6Hahq+q2md0t4HKZnfe623xm/u0csfNmVXWFeYbbJLkgyfd3938e6vbrdfdnu/sfk3x/kg9kdnH5Znc5AABWwwPnX1+8ycU+f5XZHaC+tqq+81B32t2vy6Xn355g8j1gPYN1YY/M/0f/1ZnNHPf2zA4e/OGWG23t39Y9v8Fh7AcAmJjuvrC7/6G775pLrww+OcmdDmHbNyV5/vzlb1fVIf8/xXzGlUfMX/5Sknsm+WRmA0b+YItNvzezgS5J8sRNcr0jl95S+ocPNRMAQGazum00WPXqW220kaq6cWa9Zl9mnenWSY7t7hO6+5rdfc0kP3dg9S12tdnAnPU2m0F4O0fNvz7vEO4oVd19yi4/BwDYxoF+MH9cr7tv0d0P6u5XbbPprdZtd53uvul8cO9LtrjLwAE/tG7bk7v7G7v7R+YXen9pu8zzgbovz+yOR/+W5M6HMJvtVzmc82brBurePrMLwn9gPjDmsMzvTnBgdt/vqqqTDnefAMD4VNUJSX5w/vLe6+92cNBdDw5McvPADXe0uccmWcvszlA/ckRCAyvBYF3YY939+Vx6+6H7VtW3DZkHAJi8/eue3+gQt/nlzGamu0lms65sa34S5S8y+3+Qf0ryx939kSQPm69y36r6gU02/4Z1z9+7xce8e/71+oeSCQAYlQMzyZ68zXoH3t9q5tkd6e5Tj+Ag1XtkNhj2rCT37O43dvdFB61zzcOM/LH512ttuVZy7U2Wf2T+9XqHmQMAmJj5Oa1XZDYD/78l+b75ANdd2+l5s3UDdb8zyeczG6j72sPJcJAPrHt+yhHcLwAwHj+eSwfiHop7VNWVtl9tprvfluSZ85e/WVVH7yQcsLoM1oUBzK+IPnDb5yfscje3Wff8vw8vEQAwYetnPvnioWzQ3e9J8ufzl49PctlD2Ox3k3xNZremfsCBWV66+9lJ/nq+zv6quvIG266te77VoJIT518P6yQRADCIM+Zfb1lVx2+0wrwn3HL+8o17EeogBzrJVjPiXmf+9T+7e22Tdb7nMHO8ef71elV1ykYrzG8hfYtNtv+X+ddb7HK2uEP5OQAAK2Y+iHb9jLp3OtyBugcc6nmz+UDdl2U2UPdzSb6/u1+z2fq7tP7isc8d4X0DAONwYKbcJ2fWbTZ7HJ/ZRdOXS3KvHX7G4zI773bDJA8+/MjAKjBYF4bzW/Ov31FVd1z/RlVtebKjqq6S2bT5SfKhJP9x5OMBAMusqq5fVV93CKved93zN2+61ld7fGYnLK6b5Ge2yXKHJD81f/lz3f2Bg1b5ySSfyGx2uCdvsIv1uX5qg/dTVdfMpbcs+rctkwMAY/TCzGbuPzbJL22yzmMzu0joS/P199pn51+vvMU6n5l//aaNju9U1Z2TnHqYOV65LstjN1nnkUkuv8l7z0/y6STHJHnSVsehqmrfBhdTHcrPAQBYIQcN1P3XzGbU/ezWW+3YpufN5hkODNS9fRY0ULeqjk3yQ/OXFyR515HcPwAwvKr6liQ3m798TndfsMXjs0leNF/3gRvucBPd/cEkfzR/+avZ/DgNMCEG68JAuvv0XDoLzOMPevveVfWiqvrhqrrGgYVVdbmquntmA1BuMF/8C1vM1AIATNc3Jjmrqv6uqn5i/axrVXVMVd28qv4iyc/NF/97ktcd6s67+yNJfn/+8n9stt78tkBPy2zmtZd391M22NdHc+ntDn+iqu5y0Cr/nOQ/588fWlVPqqprzfd/bFXdKclrM7vCuZM86VC/DwBgHLr7nFx60c6jq+rXDwwSraorV9VvJPmF+ftP6u5zB4j5tvnX76iqr99knZfPv35jkj+aX3CdqrpCVT0kyQsyu0hp17r7c0l+Z/7ywVX1u+s+54pV9UtJfi3JpzbZ/tNJHjF/ec8kf1dV31pV++b72FdVN66qn0/y9iQHd7MDP4dv3O421QDA8quq2+TSgbr/kiM4o+56W503q6rLJ/nbzAbqXpDkzt392iP5+VX1DUmem+RG80V/1N2HdBcqAGCpHBh0+4HufsMhrP9X86+3qqqb7PCzfiuzC7uvmeRWW61YVcdX1dUOPDK7yDpJLrt++WZ3pAKWg8G6sLVHVdVHtnm8aPvdbOq3519vU1U/sG75UZnNDPeCJB+tqguq6uOZ3dL5xUm+LrPp8n+mu593GJ8PAKyuL2XW978/yTOS/HdVfbGqPpFZj3hzkvvN131zkh/cxQVA/yfbDzZ5Umaz7346yYM2W6m7n5NLr07eX1UnrHtvLckPJ3lfZoN+H5nknKo6P7OZVP4+s9sIXZLkEQu4/SEAsDcem9kJkMpsxpFPVNUnM+sb/2u+znOS/Mow8fLCzG59eEJmF0V9rKreP3/cJkm6+x8zG+SRzO4I8Imq+lRmJ2b+NMlZmQ2kPVy/m9lxo2Q2iPlj85/VpzK7dfSzk/zN/P0LD964u58xz3dRkjsneX2Sz8+PP12Y5B1Jnpjk6zO7GGq9V2c2y9xRSf6lqj657udwjyPwvQEA4/LbmQ3UTZJvSPLuLc6ZvXGL/RzqZyVffd7sHrn07gRHJ3n+Nufutrqg6Ns2WP9zmV2kdLf5Os/LcJ0TAFiQ+Sz6PzZ/+YKt1l3nNUnOmz/f6ey6n8zsGM6heElmx50OPA70mXsetPwlO8kAjIvBurC1KyQ5cZvHVQ5j/y/J7H/+k6+8SvhVSX45s6uE35vZYJvjM7vN4Bszmz3lxt39x4fx2QDACuvuV2Q2gPXhmd3q+KzMBuleOcnnk7w7s8Ew90xyq+7+8C4+47O59BaFX2V+m+cDBy4ePp8xbys/leTjSU7KpTPrHfis9yb55swG6r56vt6xmQ0meWeSP0vyLd39f3f6fQAA49DdF3X3/8xsMMbfZzZI94rzr3+f5Ie6+8e6+0sD5ftUZrO5PTfJOZkdq7ne/HHsulV/PLOZa9+SWf86Kslbkzwmye0ymw3ucLNcnORHM7sY6t+TfCGzgStnJHlQd/9EZr0vmV00tdE+/jSzmeOemNldDA50xQvm+/n/ktwxswHSB3/2HZI8Jcl/Z3b87MDP4bjD/d4AgNFZfz75hGx9zuzqh/lZm503W5/h2G0ynJjkMlt8xjEbrL8vs17z3Mxm7b3nUJ0TAFioH86lx0v+aov1vqy7L8mlk83cu6q26hkb+YMkQ9whChih6j54YgQAAAAAAJZVVVWSDyY5OclPdPf/GzgSAAAAAMCkmVkXAAAAAGC13CezgboXJ/mHgbMAAAAAAEyewboAsEtV9fCqeltVvb2qHjF0HgAAAKajqp5TVfeoqqutW3ZiVT06yZ/PFz2zu91qEY6QqrpyVb2gqt5ZVWdV1W2HzgQAsGhV9bSqOq+q3rZu2c2q6vVVdWZVnVFVtx4yIwDAkVRV16mqV1XVO+Zjgh4+X/5rVXXOvAOdWVXfv6P9dvdiEgPACquqmyR5bpJbJ7koycuT/GR3v2fQYAAAAExCVX06yfHzl59P8qV1r5Pkn5Pcpbs/u8fRYGVV1TOS/HN3P6WqLpPk8t396YFjAQAsVFXdPskFmV0MeJP5slcm+f3u/vv5IJVf7O5TB4wJAHDEVNVJSU7q7jdX1RWTvCnJ3ZP8aJILuvuJu9mvmXUBYHdunOQN3f357r44yWuS/NDAmQAAAJiOh2V2Eem7knwxyeWTfCzJ6UkemOQOBurCkVNVxye5fZKnJkl3X2SgLgAwBd392iSfPHhxkivNnx+f5MN7GgoAYIG6+9zufvP8+flJzkpy7cPdr5l1AWAXqurGSV6S5LZJvpDkH5Oc0d0/O2gwAAAAAI64qrpZkv1J3pHkppnNqPLw7v7ckLkAAPZCVZ2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      "text/plain": [
       "<Figure size 3600x2880 with 38 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# cell numbers?\n",
    "Overview_cell = {}\n",
    "grid = pd.read_csv('../grid_10by10.csv', sep=',', index_col = None, header=None)\n",
    "for gene in genes:\n",
    "    Overview_cell[gene] = grid.copy()\n",
    "\n",
    "# fill in \n",
    "for gene in genes:\n",
    "    for row in grid.index:\n",
    "        for col in grid.columns:\n",
    "            im_num = grid.loc[row,col]\n",
    "            cell_num = len(cell_df[(cell_df.Gene == gene) & (cell_df.ImageNumber == im_num+1)])\n",
    "            Overview_cell[gene].loc[row,col] = cell_num\n",
    "\n",
    "fig, axs = plt.subplots(4,5,figsize = (50,40))\n",
    "axs=axs.ravel()\n",
    "for i in range(len(genes)):\n",
    "    gene = genes[i]\n",
    "    sns.heatmap(Overview_cell[gene], ax=axs[i])\n",
    "    axs[i].set_title(gene, fontsize=25)\n",
    "fig.delaxes(axs[-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "4269a6b0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['PGGT1B',\n",
       " 'MAP4K4',\n",
       " 'NCK1',\n",
       " 'HSF1',\n",
       " 'MINK1',\n",
       " 'IER5',\n",
       " 'SNX8',\n",
       " 'non-target',\n",
       " 'PPP2R2B',\n",
       " 'FAN1',\n",
       " 'MLH1',\n",
       " 'MSH3',\n",
       " 'PPP2R1A',\n",
       " 'HRAS',\n",
       " 'SLC25A11',\n",
       " 'WASL',\n",
       " 'ARPC3',\n",
       " 'NCKAP1',\n",
       " 'RAPGEF2']"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# save dataframes - for each image and aggregates\n",
    "genes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "eb1affd0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<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>ImageNumber</th>\n",
       "      <th>ObjectNumber</th>\n",
       "      <th>FileName_CP</th>\n",
       "      <th>PathName_CP</th>\n",
       "      <th>AreaShape_Area</th>\n",
       "      <th>AreaShape_BoundingBoxArea</th>\n",
       "      <th>AreaShape_BoundingBoxMaximum_X</th>\n",
       "      <th>AreaShape_BoundingBoxMaximum_Y</th>\n",
       "      <th>AreaShape_BoundingBoxMinimum_X</th>\n",
       "      <th>AreaShape_BoundingBoxMinimum_Y</th>\n",
       "      <th>...</th>\n",
       "      <th>Texture_Variance_TOM20_3_02_256</th>\n",
       "      <th>Texture_Variance_TOM20_3_03_256</th>\n",
       "      <th>Texture_Variance_cCasp3_3_00_256</th>\n",
       "      <th>Texture_Variance_cCasp3_3_01_256</th>\n",
       "      <th>Texture_Variance_cCasp3_3_02_256</th>\n",
       "      <th>Texture_Variance_cCasp3_3_03_256</th>\n",
       "      <th>Texture_Variance_pPRS6_3_00_256</th>\n",
       "      <th>Texture_Variance_pPRS6_3_01_256</th>\n",
       "      <th>Texture_Variance_pPRS6_3_02_256</th>\n",
       "      <th>Texture_Variance_pPRS6_3_03_256</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>F000_merged_max_cp_feat.tif</td>\n",
       "      <td>/media/imaging/Expansion/062722_D28_38pool_Rei...</td>\n",
       "      <td>788</td>\n",
       "      <td>1178</td>\n",
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       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>277.151345</td>\n",
       "      <td>259.547618</td>\n",
       "      <td>387.415288</td>\n",
       "      <td>250.842554</td>\n",
       "      <td>300.501669</td>\n",
       "      <td>279.833227</td>\n",
       "      <td>101.925284</td>\n",
       "      <td>100.215049</td>\n",
       "      <td>96.074928</td>\n",
       "      <td>95.175248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>F000_merged_max_cp_feat.tif</td>\n",
       "      <td>/media/imaging/Expansion/062722_D28_38pool_Rei...</td>\n",
       "      <td>747</td>\n",
       "      <td>1152</td>\n",
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       "      <td>37</td>\n",
       "      <td>327</td>\n",
       "      <td>5</td>\n",
       "      <td>...</td>\n",
       "      <td>379.478440</td>\n",
       "      <td>359.285768</td>\n",
       "      <td>5698.620622</td>\n",
       "      <td>5817.425551</td>\n",
       "      <td>5743.406372</td>\n",
       "      <td>5700.192301</td>\n",
       "      <td>1024.788596</td>\n",
       "      <td>1002.713360</td>\n",
       "      <td>992.403059</td>\n",
       "      <td>1016.025387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>F000_merged_max_cp_feat.tif</td>\n",
       "      <td>/media/imaging/Expansion/062722_D28_38pool_Rei...</td>\n",
       "      <td>758</td>\n",
       "      <td>1240</td>\n",
       "      <td>402</td>\n",
       "      <td>50</td>\n",
       "      <td>362</td>\n",
       "      <td>19</td>\n",
       "      <td>...</td>\n",
       "      <td>52.002482</td>\n",
       "      <td>58.333000</td>\n",
       "      <td>1317.605601</td>\n",
       "      <td>1471.934383</td>\n",
       "      <td>1180.449058</td>\n",
       "      <td>1438.377492</td>\n",
       "      <td>1285.113086</td>\n",
       "      <td>1159.169961</td>\n",
       "      <td>1318.793109</td>\n",
       "      <td>1200.508438</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>F000_merged_max_cp_feat.tif</td>\n",
       "      <td>/media/imaging/Expansion/062722_D28_38pool_Rei...</td>\n",
       "      <td>796</td>\n",
       "      <td>1215</td>\n",
       "      <td>234</td>\n",
       "      <td>61</td>\n",
       "      <td>207</td>\n",
       "      <td>16</td>\n",
       "      <td>...</td>\n",
       "      <td>204.805731</td>\n",
       "      <td>138.247854</td>\n",
       "      <td>184.816400</td>\n",
       "      <td>84.040400</td>\n",
       "      <td>154.652893</td>\n",
       "      <td>118.985254</td>\n",
       "      <td>60.450945</td>\n",
       "      <td>57.448673</td>\n",
       "      <td>57.521123</td>\n",
       "      <td>58.411025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>F000_merged_max_cp_feat.tif</td>\n",
       "      <td>/media/imaging/Expansion/062722_D28_38pool_Rei...</td>\n",
       "      <td>1121</td>\n",
       "      <td>2080</td>\n",
       "      <td>940</td>\n",
       "      <td>90</td>\n",
       "      <td>888</td>\n",
       "      <td>50</td>\n",
       "      <td>...</td>\n",
       "      <td>1351.746821</td>\n",
       "      <td>1371.729765</td>\n",
       "      <td>120.510404</td>\n",
       "      <td>129.624400</td>\n",
       "      <td>151.524375</td>\n",
       "      <td>197.748724</td>\n",
       "      <td>2195.331158</td>\n",
       "      <td>2160.502670</td>\n",
       "      <td>2366.247919</td>\n",
       "      <td>2147.058441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31972</th>\n",
       "      <td>100</td>\n",
       "      <td>596</td>\n",
       "      <td>F099_merged_max_cp_feat.tif</td>\n",
       "      <td>/media/imaging/Expansion/062722_D28_38pool_Rei...</td>\n",
       "      <td>610</td>\n",
       "      <td>1024</td>\n",
       "      <td>928</td>\n",
       "      <td>1992</td>\n",
       "      <td>896</td>\n",
       "      <td>1960</td>\n",
       "      <td>...</td>\n",
       "      <td>4695.246565</td>\n",
       "      <td>4435.336904</td>\n",
       "      <td>200.304610</td>\n",
       "      <td>222.544813</td>\n",
       "      <td>215.732308</td>\n",
       "      <td>298.154894</td>\n",
       "      <td>653.448503</td>\n",
       "      <td>656.909937</td>\n",
       "      <td>667.611172</td>\n",
       "      <td>655.362364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31973</th>\n",
       "      <td>100</td>\n",
       "      <td>597</td>\n",
       "      <td>F099_merged_max_cp_feat.tif</td>\n",
       "      <td>/media/imaging/Expansion/062722_D28_38pool_Rei...</td>\n",
       "      <td>600</td>\n",
       "      <td>918</td>\n",
       "      <td>1090</td>\n",
       "      <td>2001</td>\n",
       "      <td>1063</td>\n",
       "      <td>1967</td>\n",
       "      <td>...</td>\n",
       "      <td>158.478987</td>\n",
       "      <td>160.625029</td>\n",
       "      <td>304.775699</td>\n",
       "      <td>322.083119</td>\n",
       "      <td>280.159028</td>\n",
       "      <td>304.727119</td>\n",
       "      <td>638.719395</td>\n",
       "      <td>628.092775</td>\n",
       "      <td>671.029600</td>\n",
       "      <td>685.594082</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31974</th>\n",
       "      <td>100</td>\n",
       "      <td>598</td>\n",
       "      <td>F099_merged_max_cp_feat.tif</td>\n",
       "      <td>/media/imaging/Expansion/062722_D28_38pool_Rei...</td>\n",
       "      <td>1043</td>\n",
       "      <td>1935</td>\n",
       "      <td>1562</td>\n",
       "      <td>2016</td>\n",
       "      <td>1519</td>\n",
       "      <td>1971</td>\n",
       "      <td>...</td>\n",
       "      <td>2232.853546</td>\n",
       "      <td>2308.103016</td>\n",
       "      <td>743.152477</td>\n",
       "      <td>705.842136</td>\n",
       "      <td>676.394589</td>\n",
       "      <td>670.654250</td>\n",
       "      <td>229.768968</td>\n",
       "      <td>237.366700</td>\n",
       "      <td>224.300741</td>\n",
       "      <td>222.522901</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31975</th>\n",
       "      <td>100</td>\n",
       "      <td>599</td>\n",
       "      <td>F099_merged_max_cp_feat.tif</td>\n",
       "      <td>/media/imaging/Expansion/062722_D28_38pool_Rei...</td>\n",
       "      <td>372</td>\n",
       "      <td>676</td>\n",
       "      <td>832</td>\n",
       "      <td>2002</td>\n",
       "      <td>806</td>\n",
       "      <td>1976</td>\n",
       "      <td>...</td>\n",
       "      <td>411.952046</td>\n",
       "      <td>241.160290</td>\n",
       "      <td>382.511255</td>\n",
       "      <td>406.874096</td>\n",
       "      <td>417.107591</td>\n",
       "      <td>378.168930</td>\n",
       "      <td>823.336505</td>\n",
       "      <td>810.712851</td>\n",
       "      <td>790.108624</td>\n",
       "      <td>770.341149</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31976</th>\n",
       "      <td>100</td>\n",
       "      <td>600</td>\n",
       "      <td>F099_merged_max_cp_feat.tif</td>\n",
       "      <td>/media/imaging/Expansion/062722_D28_38pool_Rei...</td>\n",
       "      <td>520</td>\n",
       "      <td>945</td>\n",
       "      <td>832</td>\n",
       "      <td>2025</td>\n",
       "      <td>805</td>\n",
       "      <td>1990</td>\n",
       "      <td>...</td>\n",
       "      <td>420.184939</td>\n",
       "      <td>417.595006</td>\n",
       "      <td>251.009885</td>\n",
       "      <td>252.772857</td>\n",
       "      <td>237.174023</td>\n",
       "      <td>233.857707</td>\n",
       "      <td>1845.766201</td>\n",
       "      <td>1828.820751</td>\n",
       "      <td>1924.267331</td>\n",
       "      <td>1891.935363</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>31977 rows × 2491 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       ImageNumber  ObjectNumber                  FileName_CP  \\\n",
       "0                1             1  F000_merged_max_cp_feat.tif   \n",
       "1                1             2  F000_merged_max_cp_feat.tif   \n",
       "2                1             3  F000_merged_max_cp_feat.tif   \n",
       "3                1             4  F000_merged_max_cp_feat.tif   \n",
       "4                1             5  F000_merged_max_cp_feat.tif   \n",
       "...            ...           ...                          ...   \n",
       "31972          100           596  F099_merged_max_cp_feat.tif   \n",
       "31973          100           597  F099_merged_max_cp_feat.tif   \n",
       "31974          100           598  F099_merged_max_cp_feat.tif   \n",
       "31975          100           599  F099_merged_max_cp_feat.tif   \n",
       "31976          100           600  F099_merged_max_cp_feat.tif   \n",
       "\n",
       "                                             PathName_CP  AreaShape_Area  \\\n",
       "0      /media/imaging/Expansion/062722_D28_38pool_Rei...             788   \n",
       "1      /media/imaging/Expansion/062722_D28_38pool_Rei...             747   \n",
       "2      /media/imaging/Expansion/062722_D28_38pool_Rei...             758   \n",
       "3      /media/imaging/Expansion/062722_D28_38pool_Rei...             796   \n",
       "4      /media/imaging/Expansion/062722_D28_38pool_Rei...            1121   \n",
       "...                                                  ...             ...   \n",
       "31972  /media/imaging/Expansion/062722_D28_38pool_Rei...             610   \n",
       "31973  /media/imaging/Expansion/062722_D28_38pool_Rei...             600   \n",
       "31974  /media/imaging/Expansion/062722_D28_38pool_Rei...            1043   \n",
       "31975  /media/imaging/Expansion/062722_D28_38pool_Rei...             372   \n",
       "31976  /media/imaging/Expansion/062722_D28_38pool_Rei...             520   \n",
       "\n",
       "       AreaShape_BoundingBoxArea  AreaShape_BoundingBoxMaximum_X  \\\n",
       "0                           1178                             297   \n",
       "1                           1152                             363   \n",
       "2                           1240                             402   \n",
       "3                           1215                             234   \n",
       "4                           2080                             940   \n",
       "...                          ...                             ...   \n",
       "31972                       1024                             928   \n",
       "31973                        918                            1090   \n",
       "31974                       1935                            1562   \n",
       "31975                        676                             832   \n",
       "31976                        945                             832   \n",
       "\n",
       "       AreaShape_BoundingBoxMaximum_Y  AreaShape_BoundingBoxMinimum_X  \\\n",
       "0                                  33                             259   \n",
       "1                                  37                             327   \n",
       "2                                  50                             362   \n",
       "3                                  61                             207   \n",
       "4                                  90                             888   \n",
       "...                               ...                             ...   \n",
       "31972                            1992                             896   \n",
       "31973                            2001                            1063   \n",
       "31974                            2016                            1519   \n",
       "31975                            2002                             806   \n",
       "31976                            2025                             805   \n",
       "\n",
       "       AreaShape_BoundingBoxMinimum_Y  ...  Texture_Variance_TOM20_3_02_256  \\\n",
       "0                                   2  ...                       277.151345   \n",
       "1                                   5  ...                       379.478440   \n",
       "2                                  19  ...                        52.002482   \n",
       "3                                  16  ...                       204.805731   \n",
       "4                                  50  ...                      1351.746821   \n",
       "...                               ...  ...                              ...   \n",
       "31972                            1960  ...                      4695.246565   \n",
       "31973                            1967  ...                       158.478987   \n",
       "31974                            1971  ...                      2232.853546   \n",
       "31975                            1976  ...                       411.952046   \n",
       "31976                            1990  ...                       420.184939   \n",
       "\n",
       "       Texture_Variance_TOM20_3_03_256  Texture_Variance_cCasp3_3_00_256  \\\n",
       "0                           259.547618                        387.415288   \n",
       "1                           359.285768                       5698.620622   \n",
       "2                            58.333000                       1317.605601   \n",
       "3                           138.247854                        184.816400   \n",
       "4                          1371.729765                        120.510404   \n",
       "...                                ...                               ...   \n",
       "31972                      4435.336904                        200.304610   \n",
       "31973                       160.625029                        304.775699   \n",
       "31974                      2308.103016                        743.152477   \n",
       "31975                       241.160290                        382.511255   \n",
       "31976                       417.595006                        251.009885   \n",
       "\n",
       "       Texture_Variance_cCasp3_3_01_256  Texture_Variance_cCasp3_3_02_256  \\\n",
       "0                            250.842554                        300.501669   \n",
       "1                           5817.425551                       5743.406372   \n",
       "2                           1471.934383                       1180.449058   \n",
       "3                             84.040400                        154.652893   \n",
       "4                            129.624400                        151.524375   \n",
       "...                                 ...                               ...   \n",
       "31972                        222.544813                        215.732308   \n",
       "31973                        322.083119                        280.159028   \n",
       "31974                        705.842136                        676.394589   \n",
       "31975                        406.874096                        417.107591   \n",
       "31976                        252.772857                        237.174023   \n",
       "\n",
       "       Texture_Variance_cCasp3_3_03_256  Texture_Variance_pPRS6_3_00_256  \\\n",
       "0                            279.833227                       101.925284   \n",
       "1                           5700.192301                      1024.788596   \n",
       "2                           1438.377492                      1285.113086   \n",
       "3                            118.985254                        60.450945   \n",
       "4                            197.748724                      2195.331158   \n",
       "...                                 ...                              ...   \n",
       "31972                        298.154894                       653.448503   \n",
       "31973                        304.727119                       638.719395   \n",
       "31974                        670.654250                       229.768968   \n",
       "31975                        378.168930                       823.336505   \n",
       "31976                        233.857707                      1845.766201   \n",
       "\n",
       "       Texture_Variance_pPRS6_3_01_256  Texture_Variance_pPRS6_3_02_256  \\\n",
       "0                           100.215049                        96.074928   \n",
       "1                          1002.713360                       992.403059   \n",
       "2                          1159.169961                      1318.793109   \n",
       "3                            57.448673                        57.521123   \n",
       "4                          2160.502670                      2366.247919   \n",
       "...                                ...                              ...   \n",
       "31972                       656.909937                       667.611172   \n",
       "31973                       628.092775                       671.029600   \n",
       "31974                       237.366700                       224.300741   \n",
       "31975                       810.712851                       790.108624   \n",
       "31976                      1828.820751                      1924.267331   \n",
       "\n",
       "       Texture_Variance_pPRS6_3_03_256  \n",
       "0                            95.175248  \n",
       "1                          1016.025387  \n",
       "2                          1200.508438  \n",
       "3                            58.411025  \n",
       "4                          2147.058441  \n",
       "...                                ...  \n",
       "31972                       655.362364  \n",
       "31973                       685.594082  \n",
       "31974                       222.522901  \n",
       "31975                       770.341149  \n",
       "31976                      1891.935363  \n",
       "\n",
       "[31977 rows x 2491 columns]"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "AllFeat = pd.read_csv('072622_CP_All_FOV/cp_output/072622_CP_AllFeat/AllFeat_RNA_.csv')\n",
    "AllFeat"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "017ed3e5",
   "metadata": {},
   "source": [
    "## Trunks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "7906d8fd",
   "metadata": {},
   "outputs": [],
   "source": [
    "# cell_label = 1\n",
    "# coord_data = AllFeat\n",
    "def get_cell_coords(cell_label, coord_data):\n",
    "    \n",
    "    x2 = coord_data.loc[cell_label, 'AreaShape_BoundingBoxMaximum_X']\n",
    "    y2 = coord_data.loc[cell_label, 'AreaShape_BoundingBoxMaximum_Y']\n",
    "    x1 = coord_data.loc[cell_label, 'AreaShape_BoundingBoxMinimum_X']\n",
    "    y1 = coord_data.loc[cell_label, 'AreaShape_BoundingBoxMinimum_Y']\n",
    "    \n",
    "    return round(x1),round(x2),round(y1),round(y2)\n",
    "\n",
    "#def draw_boundary():\n",
    "    \n",
    "    \n",
    "AllFeat = pd.read_csv('072622_CP_All_FOV/cp_output/072622_CP_AllFeat/AllFeat_RNA_.csv')\n",
    "AllFeat = AllFeat.loc[cell_df.index.tolist()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "id": "2889660b",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x864 with 24 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Trunk = Cyto mask, skeletonize it\n",
    "from skimage.draw import disk, circle_perimeter\n",
    "from skimage.segmentation import find_boundaries\n",
    "\n",
    "IMNUM=55 # FOV054\n",
    "gene = 'MAP4K4'\n",
    "\n",
    "# Sholl 3 level\n",
    "labels = cell_df[(cell_df.ImageNumber == IMNUM)&(cell_df.Gene == gene)].label.values # image number 1 -> F000\n",
    "ImFeat = AllFeat[AllFeat.ImageNumber==IMNUM]\n",
    "ImFeat = ImFeat.set_index('ObjectNumber')\n",
    "FOV = str(IMNUM-1).zfill(3)\n",
    "soma = imread(f'072622_CP_All_FOV/cp_output/072622_CP_AllFeat/F{FOV}_merged_max_cp_feat_Soma.tiff')\n",
    "\n",
    "# load probs image\n",
    "im = imread(f'072622_CP_All_FOV/cp_prob/F{FOV}_merged_max_cp_prob.tif')\n",
    "im = im[1:] # this is probability map\n",
    "\n",
    "# load \n",
    "#seg = imread(f'072622_CP_All_FOV/cp_seg/F{FOV}_merged_max_cp_seg.tif')\n",
    "    ## skeleton -> use RAW? \n",
    "fig, axs = plt.subplots(len(labels), 6, figsize = (18,3*len(labels)))\n",
    "\n",
    "R = 20\n",
    "\n",
    "for i in range(len(labels)): # for each cell soma\n",
    "    label = labels[i]\n",
    "    #gene = cell_df[cell_df.label==label]['Gene'].values[0] # correct this line:\n",
    "\n",
    "    x1,x2,y1,y2 = get_cell_coords(label,ImFeat)\n",
    "    #im_to_show = soma == label # to invert, run below instead\n",
    "    # im_to_show = ~(soma == label)\n",
    "    \n",
    "    ## soma\n",
    "    im_to_show = np.zeros((soma == label).shape)\n",
    "    center = (round((y2-y1)/2), round((x2-x1)/2)) # find center point\n",
    "    radius = round(np.sqrt(center[0]**2 + center[1]**2)) # define radius of initial sholl 1\n",
    "    \n",
    "    R1 = 0 # \n",
    "    R2 = 10\n",
    "    rr1,cc1 = circle_perimeter(y1+center[0], x1+center[1], radius+R1) # sholl 1\n",
    "    rr2, cc2 = circle_perimeter(y1+center[0], x1+center[1], radius + R2) # sholl 2\n",
    "    im_to_show[rr,cc] = 1\n",
    "    \n",
    "    _bound = (soma == label) # ROI\n",
    "    bound = skeletonize(find_boundaries(_bound)).astype('uint16') # Trunk\n",
    "    bound_plot = bound.copy() # for plot\n",
    "    \n",
    "    ### Remove this line when quantifying - overlaying\n",
    "    bound_plot[rr1,cc1]=1 # Sholl 1\n",
    "    bound_plot[rr2,cc2]=1 # Sholl 2\n",
    "    \n",
    "    # plot boundary, sholl 1 and sholl 2\n",
    "    axs[i][0].matshow(bound_plot[y1-1-R:y2+1+R, x1-1-R:x2+1+R])\n",
    "    #axs[i][0].imshow(im_to_show[y1-1-R:y2+1+R, x1-1-R:x2+1+R])\n",
    "    axs[i][0].set_title(gene)\n",
    "    axs[i][0].axis('off')\n",
    "\n",
    "    # Find channel index\n",
    "    ch_df = cell_df[(cell_df.ImageNumber == IMNUM)&(cell_df.Gene == gene)]\n",
    "    ch = int(ch_df['Barcode_Idx'].values[0].split('-')[1]) # channel number that corresponds to the barcode\n",
    "    raw = np.where(im[ch]<0.1, 0, im.argmax(0)==ch)\n",
    "    raw_skel = skeletonize(raw)\n",
    "    axs[i][1].matshow(raw_skel[y1-1-R:y2+1+R, x1-1-R:x2+1+R]) # skeleton\n",
    "    axs[i][1].axis('off')\n",
    "    #axs[i][1].matshow(im_to_show[y1-1-R:y2+1+R, x1-1-R:x2+1+R])\n",
    "    \n",
    "    ## Calculate intersection\n",
    "    # not soma & circle & skel ==> sum of all pixel is trunk\n",
    "    inv_soma= ~(soma == label)\n",
    "    \n",
    "    circle1 = np.zeros(inv_soma.shape)\n",
    "    circle2 = np.zeros(inv_soma.shape)\n",
    "    circle1[rr1,cc1] = 1 # re define circle\n",
    "    circle2[rr2,cc2] = 1 # re define circle\n",
    "    inv_soma = inv_soma[y1-1-R:y2+1+R, x1-1-R:x2+1+R]\n",
    "    intersection1 = raw_skel[y1-1-R:y2+1+R, x1-1-R:x2+1+R]*inv_soma * circle1[y1-1-R:y2+1+R, x1-1-R:x2+1+R]\n",
    "    intersection2 = raw_skel[y1-1-R:y2+1+R, x1-1-R:x2+1+R]*inv_soma * circle2[y1-1-R:y2+1+R, x1-1-R:x2+1+R]\n",
    "    \n",
    "    # boundary\n",
    "    intersection_bound = raw_skel[y1-1-R:y2+1+R, x1-1-R:x2+1+R]*bound[y1-1-R:y2+1+R, x1-1-R:x2+1+R]\n",
    "    axs[i][2].matshow(intersection_bound)\n",
    "    axs[i][2].set_title(\"Trunk\")\n",
    "    axs[i][2].axis('off')\n",
    "    \n",
    "    # sholl 1\n",
    "    axs[i][3].matshow(intersection1)\n",
    "    axs[i][3].set_title(\"Sholl_1\")\n",
    "    axs[i][3].axis('off')\n",
    "    \n",
    "    # sholl 2\n",
    "    axs[i][4].matshow(intersection2)\n",
    "    axs[i][4].set_title(\"Sholl_2\")\n",
    "    axs[i][4].axis('off')\n",
    "    \n",
    "    ## max prob\n",
    "    prob = im[ch]\n",
    "    axs[i][5].imshow(prob[y1-1-R:y2+1+R, x1-1-R:x2+1+R])\n",
    "    axs[i][5].set_title('Prob')\n",
    "    axs[i][5].axis('off')\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "id": "654ae333",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'NCK1'"
      ]
     },
     "execution_count": 212,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Trunk = Cyto mask, skeletonize it\n",
    "from skimage.draw import disk, circle_perimeter\n",
    "from skimage.segmentation import find_boundaries\n",
    "\n",
    "IMNUM=55 # FOV054\n",
    "gene = 'MAP4K4'\n",
    "\n",
    "# Sholl 3 level\n",
    "labels = cell_df[(cell_df.ImageNumber == IMNUM)&(cell_df.Gene == gene)].label.values # image number 1 -> F000\n",
    "ImFeat = AllFeat[AllFeat.ImageNumber==IMNUM]\n",
    "ImFeat = ImFeat.set_index('ObjectNumber')\n",
    "FOV = str(IMNUM-1).zfill(3)\n",
    "soma = imread(f'072622_CP_All_FOV/cp_output/072622_CP_AllFeat/F{FOV}_merged_max_cp_feat_Soma.tiff')\n",
    "\n",
    "# load probs image\n",
    "im = imread(f'072622_CP_All_FOV/cp_prob/F{FOV}_merged_max_cp_prob.tif')\n",
    "im = im[1:] # this is probability map\n",
    "\n",
    "# load \n",
    "#seg = imread(f'072622_CP_All_FOV/cp_seg/F{FOV}_merged_max_cp_seg.tif')\n",
    "    ## skeleton -> use RAW? \n",
    "fig, axs = plt.subplots(len(labels), 6, figsize = (18,3*len(labels)))\n",
    "\n",
    "R = 20\n",
    "\n",
    "for i in range(len(labels)): # for each cell soma\n",
    "    label = labels[i]\n",
    "    #gene = cell_df[cell_df.label==label]['Gene'].values[0] # correct this line:\n",
    "\n",
    "    x1,x2,y1,y2 = get_cell_coords(label,ImFeat)\n",
    "    #im_to_show = soma == label # to invert, run below instead\n",
    "    # im_to_show = ~(soma == label)\n",
    "    \n",
    "    ## soma\n",
    "    im_to_show = np.zeros((soma == label).shape)\n",
    "    center = (round((y2-y1)/2), round((x2-x1)/2)) # find center point\n",
    "    radius = round(np.sqrt(center[0]**2 + center[1]**2)) # define radius of initial sholl 1\n",
    "    \n",
    "    R1 = 0 # \n",
    "    R2 = 10\n",
    "    rr1,cc1 = circle_perimeter(y1+center[0], x1+center[1], radius+R1) # sholl 1\n",
    "    rr2, cc2 = circle_perimeter(y1+center[0], x1+center[1], radius + R2) # sholl 2\n",
    "    im_to_show[rr,cc] = 1\n",
    "    \n",
    "    _bound = (soma == label) # ROI\n",
    "    bound = skeletonize(find_boundaries(_bound)).astype('uint16') # Trunk\n",
    "    bound_plot = bound.copy() # for plot\n",
    "    \n",
    "    ### Remove this line when quantifying - overlaying\n",
    "    bound_plot[rr1,cc1]=1 # Sholl 1\n",
    "    bound_plot[rr2,cc2]=1 # Sholl 2\n",
    "    \n",
    "    # plot boundary, sholl 1 and sholl 2\n",
    "    axs[i][0].matshow(bound_plot[y1-1-R:y2+1+R, x1-1-R:x2+1+R])\n",
    "    #axs[i][0].imshow(im_to_show[y1-1-R:y2+1+R, x1-1-R:x2+1+R])\n",
    "    axs[i][0].set_title(gene)\n",
    "    axs[i][0].axis('off')\n",
    "\n",
    "    # Find channel index\n",
    "    ch_df = cell_df[(cell_df.ImageNumber == IMNUM)&(cell_df.Gene == gene)]\n",
    "    ch = int(ch_df['Barcode_Idx'].values[0].split('-')[1]) # channel number that corresponds to the barcode\n",
    "    raw = np.where(im[ch]<0.1, 0, im.argmax(0)==ch)\n",
    "    raw_skel = skeletonize(raw)\n",
    "    axs[i][1].matshow(raw_skel[y1-1-R:y2+1+R, x1-1-R:x2+1+R]) # skeleton\n",
    "    axs[i][1].axis('off')\n",
    "    #axs[i][1].matshow(im_to_show[y1-1-R:y2+1+R, x1-1-R:x2+1+R])\n",
    "    \n",
    "    ## Calculate intersection\n",
    "    # not soma & circle & skel ==> sum of all pixel is trunk\n",
    "    inv_soma= ~(soma == label)\n",
    "    \n",
    "    circle1 = np.zeros(inv_soma.shape)\n",
    "    circle2 = np.zeros(inv_soma.shape)\n",
    "    circle1[rr1,cc1] = 1 # re define circle\n",
    "    circle2[rr2,cc2] = 1 # re define circle\n",
    "    inv_soma = inv_soma[y1-1-R:y2+1+R, x1-1-R:x2+1+R]\n",
    "    intersection1 = raw_skel[y1-1-R:y2+1+R, x1-1-R:x2+1+R]*inv_soma * circle1[y1-1-R:y2+1+R, x1-1-R:x2+1+R]\n",
    "    intersection2 = raw_skel[y1-1-R:y2+1+R, x1-1-R:x2+1+R]*inv_soma * circle2[y1-1-R:y2+1+R, x1-1-R:x2+1+R]\n",
    "    \n",
    "    # boundary\n",
    "    intersection_bound = raw_skel[y1-1-R:y2+1+R, x1-1-R:x2+1+R]*bound[y1-1-R:y2+1+R, x1-1-R:x2+1+R]\n",
    "    axs[i][2].matshow(intersection_bound)\n",
    "    axs[i][2].set_title(\"Trunk\")\n",
    "    axs[i][2].axis('off')\n",
    "    \n",
    "    # sholl 1\n",
    "    axs[i][3].matshow(intersection1)\n",
    "    axs[i][3].set_title(\"Sholl_1\")\n",
    "    axs[i][3].axis('off')\n",
    "    \n",
    "    # sholl 2\n",
    "    axs[i][4].matshow(intersection2)\n",
    "    axs[i][4].set_title(\"Sholl_2\")\n",
    "    axs[i][4].axis('off')\n",
    "    \n",
    "    ## max prob\n",
    "    prob = im[ch]\n",
    "    axs[i][5].imshow(prob[y1-1-R:y2+1+R, x1-1-R:x2+1+R])\n",
    "    axs[i][5].set_title('Prob')\n",
    "    axs[i][5].axis('off')\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "id": "9e487f27",
   "metadata": {},
   "outputs": [
    {
     "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>label</th>\n",
       "      <th>Barcode_Idx</th>\n",
       "      <th>SUM</th>\n",
       "      <th>Barcode</th>\n",
       "      <th>RAW</th>\n",
       "      <th>P</th>\n",
       "      <th>LOGIT</th>\n",
       "      <th>ENTROPY</th>\n",
       "      <th>X_NORM</th>\n",
       "      <th>Gene</th>\n",
       "      <th>...</th>\n",
       "      <th>Texture_Variance_TOM20_3_02_256</th>\n",
       "      <th>Texture_Variance_TOM20_3_03_256</th>\n",
       "      <th>Texture_Variance_cCasp3_3_00_256</th>\n",
       "      <th>Texture_Variance_cCasp3_3_01_256</th>\n",
       "      <th>Texture_Variance_cCasp3_3_02_256</th>\n",
       "      <th>Texture_Variance_cCasp3_3_03_256</th>\n",
       "      <th>Texture_Variance_pPRS6_3_00_256</th>\n",
       "      <th>Texture_Variance_pPRS6_3_01_256</th>\n",
       "      <th>Texture_Variance_pPRS6_3_02_256</th>\n",
       "      <th>Texture_Variance_pPRS6_3_03_256</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>intensity_mean-2</td>\n",
       "      <td>1.401477</td>\n",
       "      <td>C10</td>\n",
       "      <td>1.185651</td>\n",
       "      <td>0.846001</td>\n",
       "      <td>1.703575</td>\n",
       "      <td>0.755951</td>\n",
       "      <td>0.746494</td>\n",
       "      <td>NCK1</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.121185</td>\n",
       "      <td>-0.192197</td>\n",
       "      <td>13.193644</td>\n",
       "      <td>14.147616</td>\n",
       "      <td>13.349172</td>\n",
       "      <td>13.525736</td>\n",
       "      <td>1.718389</td>\n",
       "      <td>1.696825</td>\n",
       "      <td>1.618599</td>\n",
       "      <td>1.718385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>intensity_mean-9</td>\n",
       "      <td>1.545138</td>\n",
       "      <td>D4</td>\n",
       "      <td>1.251146</td>\n",
       "      <td>0.809731</td>\n",
       "      <td>1.448262</td>\n",
       "      <td>0.903049</td>\n",
       "      <td>0.810684</td>\n",
       "      <td>FAN1</td>\n",
       "      <td>...</td>\n",
       "      <td>3.901309</td>\n",
       "      <td>4.358029</td>\n",
       "      <td>-0.819853</td>\n",
       "      <td>-0.939350</td>\n",
       "      <td>-1.027013</td>\n",
       "      <td>-1.050751</td>\n",
       "      <td>0.889957</td>\n",
       "      <td>0.910505</td>\n",
       "      <td>0.875227</td>\n",
       "      <td>1.038351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>intensity_mean-18</td>\n",
       "      <td>1.878230</td>\n",
       "      <td>G8</td>\n",
       "      <td>0.739110</td>\n",
       "      <td>0.393514</td>\n",
       "      <td>-0.432563</td>\n",
       "      <td>2.237265</td>\n",
       "      <td>0.619890</td>\n",
       "      <td>RAPGEF2</td>\n",
       "      <td>...</td>\n",
       "      <td>6.505616</td>\n",
       "      <td>6.181922</td>\n",
       "      <td>1.306165</td>\n",
       "      <td>1.403712</td>\n",
       "      <td>1.236809</td>\n",
       "      <td>1.389544</td>\n",
       "      <td>-0.159159</td>\n",
       "      <td>0.310605</td>\n",
       "      <td>0.212255</td>\n",
       "      <td>-0.437281</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>intensity_mean-8</td>\n",
       "      <td>1.889894</td>\n",
       "      <td>D3</td>\n",
       "      <td>1.340203</td>\n",
       "      <td>0.709142</td>\n",
       "      <td>0.891221</td>\n",
       "      <td>1.290217</td>\n",
       "      <td>0.953239</td>\n",
       "      <td>PPP2R2B</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.654907</td>\n",
       "      <td>-0.636207</td>\n",
       "      <td>0.235150</td>\n",
       "      <td>0.096587</td>\n",
       "      <td>-0.123112</td>\n",
       "      <td>0.244418</td>\n",
       "      <td>-0.713525</td>\n",
       "      <td>-0.904783</td>\n",
       "      <td>-0.791925</td>\n",
       "      <td>-0.746413</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>13</td>\n",
       "      <td>intensity_mean-17</td>\n",
       "      <td>1.524615</td>\n",
       "      <td>F8</td>\n",
       "      <td>1.253440</td>\n",
       "      <td>0.822135</td>\n",
       "      <td>1.530883</td>\n",
       "      <td>0.833137</td>\n",
       "      <td>0.914237</td>\n",
       "      <td>NCKAP1</td>\n",
       "      <td>...</td>\n",
       "      <td>1.330187</td>\n",
       "      <td>1.284044</td>\n",
       "      <td>15.935914</td>\n",
       "      <td>15.926096</td>\n",
       "      <td>15.850147</td>\n",
       "      <td>16.124994</td>\n",
       "      <td>10.879939</td>\n",
       "      <td>10.843237</td>\n",
       "      <td>10.804308</td>\n",
       "      <td>10.816426</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31961</th>\n",
       "      <td>585</td>\n",
       "      <td>intensity_mean-8</td>\n",
       "      <td>1.539119</td>\n",
       "      <td>D3</td>\n",
       "      <td>0.851739</td>\n",
       "      <td>0.553393</td>\n",
       "      <td>0.214391</td>\n",
       "      <td>1.693448</td>\n",
       "      <td>0.538902</td>\n",
       "      <td>PPP2R2B</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.069720</td>\n",
       "      <td>0.115072</td>\n",
       "      <td>-0.448166</td>\n",
       "      <td>-0.309180</td>\n",
       "      <td>-0.502968</td>\n",
       "      <td>-0.389645</td>\n",
       "      <td>0.037909</td>\n",
       "      <td>0.021434</td>\n",
       "      <td>-0.036310</td>\n",
       "      <td>0.043551</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31965</th>\n",
       "      <td>589</td>\n",
       "      <td>intensity_mean-9</td>\n",
       "      <td>2.057904</td>\n",
       "      <td>D4</td>\n",
       "      <td>0.882562</td>\n",
       "      <td>0.428865</td>\n",
       "      <td>-0.286485</td>\n",
       "      <td>2.144727</td>\n",
       "      <td>0.462669</td>\n",
       "      <td>FAN1</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.659632</td>\n",
       "      <td>-0.652950</td>\n",
       "      <td>0.032669</td>\n",
       "      <td>0.023026</td>\n",
       "      <td>-0.026831</td>\n",
       "      <td>0.110429</td>\n",
       "      <td>-0.661623</td>\n",
       "      <td>-0.650801</td>\n",
       "      <td>-0.650774</td>\n",
       "      <td>-0.648211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31967</th>\n",
       "      <td>591</td>\n",
       "      <td>intensity_mean-2</td>\n",
       "      <td>1.647154</td>\n",
       "      <td>C10</td>\n",
       "      <td>0.816353</td>\n",
       "      <td>0.495614</td>\n",
       "      <td>-0.017543</td>\n",
       "      <td>1.989583</td>\n",
       "      <td>0.441945</td>\n",
       "      <td>NCK1</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.191185</td>\n",
       "      <td>-1.203391</td>\n",
       "      <td>0.987863</td>\n",
       "      <td>1.199380</td>\n",
       "      <td>0.982808</td>\n",
       "      <td>1.114231</td>\n",
       "      <td>-0.819007</td>\n",
       "      <td>-0.822783</td>\n",
       "      <td>-0.826657</td>\n",
       "      <td>-0.807547</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31968</th>\n",
       "      <td>592</td>\n",
       "      <td>intensity_mean-11</td>\n",
       "      <td>2.194014</td>\n",
       "      <td>D8</td>\n",
       "      <td>0.722803</td>\n",
       "      <td>0.329443</td>\n",
       "      <td>-0.710704</td>\n",
       "      <td>2.405019</td>\n",
       "      <td>0.455295</td>\n",
       "      <td>MSH3</td>\n",
       "      <td>...</td>\n",
       "      <td>21.995864</td>\n",
       "      <td>21.634942</td>\n",
       "      <td>-0.731125</td>\n",
       "      <td>-0.652067</td>\n",
       "      <td>-0.759698</td>\n",
       "      <td>-0.676539</td>\n",
       "      <td>-0.555488</td>\n",
       "      <td>-0.534198</td>\n",
       "      <td>-0.590011</td>\n",
       "      <td>-0.563616</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31974</th>\n",
       "      <td>598</td>\n",
       "      <td>intensity_mean-17</td>\n",
       "      <td>1.742791</td>\n",
       "      <td>F8</td>\n",
       "      <td>1.017022</td>\n",
       "      <td>0.583559</td>\n",
       "      <td>0.337402</td>\n",
       "      <td>1.789591</td>\n",
       "      <td>0.579071</td>\n",
       "      <td>NCKAP1</td>\n",
       "      <td>...</td>\n",
       "      <td>6.383522</td>\n",
       "      <td>6.659102</td>\n",
       "      <td>0.357757</td>\n",
       "      <td>0.345598</td>\n",
       "      <td>0.180955</td>\n",
       "      <td>0.226975</td>\n",
       "      <td>-0.406725</td>\n",
       "      <td>-0.374244</td>\n",
       "      <td>-0.425325</td>\n",
       "      <td>-0.417779</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6214 rows × 1860 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       label        Barcode_Idx       SUM Barcode       RAW         P  \\\n",
       "1          2   intensity_mean-2  1.401477     C10  1.185651  0.846001   \n",
       "7          8   intensity_mean-9  1.545138      D4  1.251146  0.809731   \n",
       "8          9  intensity_mean-18  1.878230      G8  0.739110  0.393514   \n",
       "9         10   intensity_mean-8  1.889894      D3  1.340203  0.709142   \n",
       "12        13  intensity_mean-17  1.524615      F8  1.253440  0.822135   \n",
       "...      ...                ...       ...     ...       ...       ...   \n",
       "31961    585   intensity_mean-8  1.539119      D3  0.851739  0.553393   \n",
       "31965    589   intensity_mean-9  2.057904      D4  0.882562  0.428865   \n",
       "31967    591   intensity_mean-2  1.647154     C10  0.816353  0.495614   \n",
       "31968    592  intensity_mean-11  2.194014      D8  0.722803  0.329443   \n",
       "31974    598  intensity_mean-17  1.742791      F8  1.017022  0.583559   \n",
       "\n",
       "          LOGIT   ENTROPY    X_NORM     Gene  ...  \\\n",
       "1      1.703575  0.755951  0.746494     NCK1  ...   \n",
       "7      1.448262  0.903049  0.810684     FAN1  ...   \n",
       "8     -0.432563  2.237265  0.619890  RAPGEF2  ...   \n",
       "9      0.891221  1.290217  0.953239  PPP2R2B  ...   \n",
       "12     1.530883  0.833137  0.914237   NCKAP1  ...   \n",
       "...         ...       ...       ...      ...  ...   \n",
       "31961  0.214391  1.693448  0.538902  PPP2R2B  ...   \n",
       "31965 -0.286485  2.144727  0.462669     FAN1  ...   \n",
       "31967 -0.017543  1.989583  0.441945     NCK1  ...   \n",
       "31968 -0.710704  2.405019  0.455295     MSH3  ...   \n",
       "31974  0.337402  1.789591  0.579071   NCKAP1  ...   \n",
       "\n",
       "       Texture_Variance_TOM20_3_02_256  Texture_Variance_TOM20_3_03_256  \\\n",
       "1                            -0.121185                        -0.192197   \n",
       "7                             3.901309                         4.358029   \n",
       "8                             6.505616                         6.181922   \n",
       "9                            -0.654907                        -0.636207   \n",
       "12                            1.330187                         1.284044   \n",
       "...                                ...                              ...   \n",
       "31961                        -0.069720                         0.115072   \n",
       "31965                        -0.659632                        -0.652950   \n",
       "31967                        -1.191185                        -1.203391   \n",
       "31968                        21.995864                        21.634942   \n",
       "31974                         6.383522                         6.659102   \n",
       "\n",
       "       Texture_Variance_cCasp3_3_00_256  Texture_Variance_cCasp3_3_01_256  \\\n",
       "1                             13.193644                         14.147616   \n",
       "7                             -0.819853                         -0.939350   \n",
       "8                              1.306165                          1.403712   \n",
       "9                              0.235150                          0.096587   \n",
       "12                            15.935914                         15.926096   \n",
       "...                                 ...                               ...   \n",
       "31961                         -0.448166                         -0.309180   \n",
       "31965                          0.032669                          0.023026   \n",
       "31967                          0.987863                          1.199380   \n",
       "31968                         -0.731125                         -0.652067   \n",
       "31974                          0.357757                          0.345598   \n",
       "\n",
       "       Texture_Variance_cCasp3_3_02_256  Texture_Variance_cCasp3_3_03_256  \\\n",
       "1                             13.349172                         13.525736   \n",
       "7                             -1.027013                         -1.050751   \n",
       "8                              1.236809                          1.389544   \n",
       "9                             -0.123112                          0.244418   \n",
       "12                            15.850147                         16.124994   \n",
       "...                                 ...                               ...   \n",
       "31961                         -0.502968                         -0.389645   \n",
       "31965                         -0.026831                          0.110429   \n",
       "31967                          0.982808                          1.114231   \n",
       "31968                         -0.759698                         -0.676539   \n",
       "31974                          0.180955                          0.226975   \n",
       "\n",
       "       Texture_Variance_pPRS6_3_00_256  Texture_Variance_pPRS6_3_01_256  \\\n",
       "1                             1.718389                         1.696825   \n",
       "7                             0.889957                         0.910505   \n",
       "8                            -0.159159                         0.310605   \n",
       "9                            -0.713525                        -0.904783   \n",
       "12                           10.879939                        10.843237   \n",
       "...                                ...                              ...   \n",
       "31961                         0.037909                         0.021434   \n",
       "31965                        -0.661623                        -0.650801   \n",
       "31967                        -0.819007                        -0.822783   \n",
       "31968                        -0.555488                        -0.534198   \n",
       "31974                        -0.406725                        -0.374244   \n",
       "\n",
       "       Texture_Variance_pPRS6_3_02_256  Texture_Variance_pPRS6_3_03_256  \n",
       "1                             1.618599                         1.718385  \n",
       "7                             0.875227                         1.038351  \n",
       "8                             0.212255                        -0.437281  \n",
       "9                            -0.791925                        -0.746413  \n",
       "12                           10.804308                        10.816426  \n",
       "...                                ...                              ...  \n",
       "31961                        -0.036310                         0.043551  \n",
       "31965                        -0.650774                        -0.648211  \n",
       "31967                        -0.826657                        -0.807547  \n",
       "31968                        -0.590011                        -0.563616  \n",
       "31974                        -0.425325                        -0.417779  \n",
       "\n",
       "[6214 rows x 1860 columns]"
      ]
     },
     "execution_count": 218,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cell_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "id": "e4df577a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Measure All images, all neurites    \n",
    "AllFeat = pd.read_csv('072622_CP_All_FOV/cp_output/072622_CP_AllFeat/AllFeat_RNA_.csv')\n",
    "AllFeat = AllFeat.loc[cell_df.index.tolist()]\n",
    "\n",
    "for cell in cell_df.index:\n",
    "    IMNUM = cell_df.loc[cell, \"ImageNumber\"]\n",
    "    gene = cell_df.loc[cell, \"Gene\"]\n",
    "    label = cell_df.loc[cell, \"label\"]\n",
    "    ImFeat = AllFeat[AllFeat.ImageNumber==IMNUM]\n",
    "    ImFeat = ImFeat.set_index('ObjectNumber')\n",
    "    FOV = str(IMNUM-1).zfill(3)\n",
    "    soma = imread(f'072622_CP_All_FOV/cp_output/072622_CP_AllFeat/F{FOV}_merged_max_cp_feat_Soma.tiff')\n",
    "    \n",
    "    # load probs image\n",
    "    im = imread(f'072622_CP_All_FOV/cp_prob/F{FOV}_merged_max_cp_prob.tif')\n",
    "    im = im[1:] # this is probability map\n",
    "\n",
    "    ## Input Params\n",
    "    R = 20\n",
    "    x1,x2,y1,y2 = get_cell_coords(label,ImFeat)\n",
    "    \n",
    "    im_to_show = np.zeros((soma == label).shape)\n",
    "    center = (round((y2-y1)/2), round((x2-x1)/2)) # find center point\n",
    "    radius = round(np.sqrt(center[0]**2 + center[1]**2)) # define radius of initial sholl 1\n",
    "    \n",
    "    R1 = 0 # \n",
    "    R2 = 10\n",
    "    rr1,cc1 = circle_perimeter(y1+center[0], x1+center[1], radius+R1) # sholl 1\n",
    "    rr2, cc2 = circle_perimeter(y1+center[0], x1+center[1], radius + R2) # sholl 2\n",
    "    \n",
    "    \n",
    "        \n",
    "        \n",
    "    \n",
    "    im_to_show[rr,cc] = 1\n",
    "    \n",
    "    _bound = (soma == label) # ROI\n",
    "    bound = skeletonize(find_boundaries(_bound)).astype('uint16') # Trunk\n",
    "#    bound_plot = bound.copy() # for plot\n",
    "\n",
    "    ch_df = cell_df[(cell_df.ImageNumber == IMNUM)&(cell_df.Gene == gene)]\n",
    "    ch = int(ch_df['Barcode_Idx'].values[0].split('-')[1]) # channel number that corresponds to the barcode\n",
    "    raw = np.where(im[ch]<0.1, 0, im.argmax(0)==ch)\n",
    "    raw_skel = skeletonize(raw)\n",
    "    \n",
    "    inv_soma= ~(soma == label)\n",
    "    # Trunks\n",
    "    intersection_bound = raw_skel[y1-1-R:y2+1+R, x1-1-R:x2+1+R]*bound[y1-1-R:y2+1+R, x1-1-R:x2+1+R]\n",
    "    \n",
    "    \n",
    "    out_of_bound = (max(rr1)>soma.shape[0]-1)|(max(rr2)>soma.shape[0]-1)|(max(cc1)>soma.shape[1]-1)|(max(cc2)>soma.shape[1]-1)\n",
    "    if out_of_bound:\n",
    "        cell_df.loc[cell,'Trunks'] = np.sum(intersection_bound)\n",
    "        cell_df[cell,'Sholl_1'] = np.nan\n",
    "        cell_df[cell,'Sholl_2'] = np.nan\n",
    "    \n",
    "    else:\n",
    "    \n",
    "        # sholl 1 and 2\n",
    "        circle1 = np.zeros(inv_soma.shape)\n",
    "        circle2 = np.zeros(inv_soma.shape)\n",
    "        circle1[rr1,cc1] = 1 # re define circle\n",
    "        circle2[rr2,cc2] = 1 # re define circle\n",
    "        inv_soma = inv_soma[y1-1-R:y2+1+R, x1-1-R:x2+1+R]\n",
    "        intersection1 = raw_skel[y1-1-R:y2+1+R, x1-1-R:x2+1+R]*inv_soma * circle1[y1-1-R:y2+1+R, x1-1-R:x2+1+R]\n",
    "        intersection2 = raw_skel[y1-1-R:y2+1+R, x1-1-R:x2+1+R]*inv_soma * circle2[y1-1-R:y2+1+R, x1-1-R:x2+1+R]\n",
    "        \n",
    "        \n",
    "        \n",
    "        cell_df.loc[cell,'Trunks'] = np.sum(intersection_bound)\n",
    "        cell_df[cell,'Sholl_1'] = np.sum(intersection1)\n",
    "        cell_df[cell,'Sholl_2'] = np.sum(intersection2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "id": "65c703ea",
   "metadata": {},
   "outputs": [],
   "source": [
    "cell_df.to_csv('072622_CP_All_FOV/cell_trunk_cout.csv', sep=',')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3b133b79",
   "metadata": {},
   "outputs": [],
   "source": [
    "neurite_count = pd.read_csv('072622_CP_All_FOV/neurite_count.csv', sep=',', index_col=0)\n",
    "# append "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "id": "994db38d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "    }\n",
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       "        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>label</th>\n",
       "      <th>Barcode_Idx</th>\n",
       "      <th>SUM</th>\n",
       "      <th>Barcode</th>\n",
       "      <th>RAW</th>\n",
       "      <th>P</th>\n",
       "      <th>LOGIT</th>\n",
       "      <th>ENTROPY</th>\n",
       "      <th>X_NORM</th>\n",
       "      <th>Gene</th>\n",
       "      <th>...</th>\n",
       "      <th>(31961, Sholl_1)</th>\n",
       "      <th>(31961, Sholl_2)</th>\n",
       "      <th>(31965, Sholl_1)</th>\n",
       "      <th>(31965, Sholl_2)</th>\n",
       "      <th>(31967, Sholl_1)</th>\n",
       "      <th>(31967, Sholl_2)</th>\n",
       "      <th>(31968, Sholl_1)</th>\n",
       "      <th>(31968, Sholl_2)</th>\n",
       "      <th>(31974, Sholl_1)</th>\n",
       "      <th>(31974, Sholl_2)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>intensity_mean-2</td>\n",
       "      <td>1.401477</td>\n",
       "      <td>C10</td>\n",
       "      <td>1.185651</td>\n",
       "      <td>0.846001</td>\n",
       "      <td>1.703575</td>\n",
       "      <td>0.755951</td>\n",
       "      <td>0.746494</td>\n",
       "      <td>NCK1</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>intensity_mean-9</td>\n",
       "      <td>1.545138</td>\n",
       "      <td>D4</td>\n",
       "      <td>1.251146</td>\n",
       "      <td>0.809731</td>\n",
       "      <td>1.448262</td>\n",
       "      <td>0.903049</td>\n",
       "      <td>0.810684</td>\n",
       "      <td>FAN1</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>intensity_mean-18</td>\n",
       "      <td>1.878230</td>\n",
       "      <td>G8</td>\n",
       "      <td>0.739110</td>\n",
       "      <td>0.393514</td>\n",
       "      <td>-0.432563</td>\n",
       "      <td>2.237265</td>\n",
       "      <td>0.619890</td>\n",
       "      <td>RAPGEF2</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>intensity_mean-8</td>\n",
       "      <td>1.889894</td>\n",
       "      <td>D3</td>\n",
       "      <td>1.340203</td>\n",
       "      <td>0.709142</td>\n",
       "      <td>0.891221</td>\n",
       "      <td>1.290217</td>\n",
       "      <td>0.953239</td>\n",
       "      <td>PPP2R2B</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>13</td>\n",
       "      <td>intensity_mean-17</td>\n",
       "      <td>1.524615</td>\n",
       "      <td>F8</td>\n",
       "      <td>1.253440</td>\n",
       "      <td>0.822135</td>\n",
       "      <td>1.530883</td>\n",
       "      <td>0.833137</td>\n",
       "      <td>0.914237</td>\n",
       "      <td>NCKAP1</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31961</th>\n",
       "      <td>585</td>\n",
       "      <td>intensity_mean-8</td>\n",
       "      <td>1.539119</td>\n",
       "      <td>D3</td>\n",
       "      <td>0.851739</td>\n",
       "      <td>0.553393</td>\n",
       "      <td>0.214391</td>\n",
       "      <td>1.693448</td>\n",
       "      <td>0.538902</td>\n",
       "      <td>PPP2R2B</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31965</th>\n",
       "      <td>589</td>\n",
       "      <td>intensity_mean-9</td>\n",
       "      <td>2.057904</td>\n",
       "      <td>D4</td>\n",
       "      <td>0.882562</td>\n",
       "      <td>0.428865</td>\n",
       "      <td>-0.286485</td>\n",
       "      <td>2.144727</td>\n",
       "      <td>0.462669</td>\n",
       "      <td>FAN1</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31967</th>\n",
       "      <td>591</td>\n",
       "      <td>intensity_mean-2</td>\n",
       "      <td>1.647154</td>\n",
       "      <td>C10</td>\n",
       "      <td>0.816353</td>\n",
       "      <td>0.495614</td>\n",
       "      <td>-0.017543</td>\n",
       "      <td>1.989583</td>\n",
       "      <td>0.441945</td>\n",
       "      <td>NCK1</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31968</th>\n",
       "      <td>592</td>\n",
       "      <td>intensity_mean-11</td>\n",
       "      <td>2.194014</td>\n",
       "      <td>D8</td>\n",
       "      <td>0.722803</td>\n",
       "      <td>0.329443</td>\n",
       "      <td>-0.710704</td>\n",
       "      <td>2.405019</td>\n",
       "      <td>0.455295</td>\n",
       "      <td>MSH3</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31974</th>\n",
       "      <td>598</td>\n",
       "      <td>intensity_mean-17</td>\n",
       "      <td>1.742791</td>\n",
       "      <td>F8</td>\n",
       "      <td>1.017022</td>\n",
       "      <td>0.583559</td>\n",
       "      <td>0.337402</td>\n",
       "      <td>1.789591</td>\n",
       "      <td>0.579071</td>\n",
       "      <td>NCKAP1</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6214 rows × 14289 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       label        Barcode_Idx       SUM Barcode       RAW         P  \\\n",
       "1          2   intensity_mean-2  1.401477     C10  1.185651  0.846001   \n",
       "7          8   intensity_mean-9  1.545138      D4  1.251146  0.809731   \n",
       "8          9  intensity_mean-18  1.878230      G8  0.739110  0.393514   \n",
       "9         10   intensity_mean-8  1.889894      D3  1.340203  0.709142   \n",
       "12        13  intensity_mean-17  1.524615      F8  1.253440  0.822135   \n",
       "...      ...                ...       ...     ...       ...       ...   \n",
       "31961    585   intensity_mean-8  1.539119      D3  0.851739  0.553393   \n",
       "31965    589   intensity_mean-9  2.057904      D4  0.882562  0.428865   \n",
       "31967    591   intensity_mean-2  1.647154     C10  0.816353  0.495614   \n",
       "31968    592  intensity_mean-11  2.194014      D8  0.722803  0.329443   \n",
       "31974    598  intensity_mean-17  1.742791      F8  1.017022  0.583559   \n",
       "\n",
       "          LOGIT   ENTROPY    X_NORM     Gene  ...  (31961, Sholl_1)  \\\n",
       "1      1.703575  0.755951  0.746494     NCK1  ...               2.0   \n",
       "7      1.448262  0.903049  0.810684     FAN1  ...               2.0   \n",
       "8     -0.432563  2.237265  0.619890  RAPGEF2  ...               2.0   \n",
       "9      0.891221  1.290217  0.953239  PPP2R2B  ...               2.0   \n",
       "12     1.530883  0.833137  0.914237   NCKAP1  ...               2.0   \n",
       "...         ...       ...       ...      ...  ...               ...   \n",
       "31961  0.214391  1.693448  0.538902  PPP2R2B  ...               2.0   \n",
       "31965 -0.286485  2.144727  0.462669     FAN1  ...               2.0   \n",
       "31967 -0.017543  1.989583  0.441945     NCK1  ...               2.0   \n",
       "31968 -0.710704  2.405019  0.455295     MSH3  ...               2.0   \n",
       "31974  0.337402  1.789591  0.579071   NCKAP1  ...               2.0   \n",
       "\n",
       "       (31961, Sholl_2)  (31965, Sholl_1)  (31965, Sholl_2)  (31967, Sholl_1)  \\\n",
       "1                   2.0               9.0              13.0               6.0   \n",
       "7                   2.0               9.0              13.0               6.0   \n",
       "8                   2.0               9.0              13.0               6.0   \n",
       "9                   2.0               9.0              13.0               6.0   \n",
       "12                  2.0               9.0              13.0               6.0   \n",
       "...                 ...               ...               ...               ...   \n",
       "31961               2.0               9.0              13.0               6.0   \n",
       "31965               2.0               9.0              13.0               6.0   \n",
       "31967               2.0               9.0              13.0               6.0   \n",
       "31968               2.0               9.0              13.0               6.0   \n",
       "31974               2.0               9.0              13.0               6.0   \n",
       "\n",
       "       (31967, Sholl_2)  (31968, Sholl_1)  (31968, Sholl_2)  (31974, Sholl_1)  \\\n",
       "1                  16.0               3.0               2.0               NaN   \n",
       "7                  16.0               3.0               2.0               NaN   \n",
       "8                  16.0               3.0               2.0               NaN   \n",
       "9                  16.0               3.0               2.0               NaN   \n",
       "12                 16.0               3.0               2.0               NaN   \n",
       "...                 ...               ...               ...               ...   \n",
       "31961              16.0               3.0               2.0               NaN   \n",
       "31965              16.0               3.0               2.0               NaN   \n",
       "31967              16.0               3.0               2.0               NaN   \n",
       "31968              16.0               3.0               2.0               NaN   \n",
       "31974              16.0               3.0               2.0               NaN   \n",
       "\n",
       "       (31974, Sholl_2)  \n",
       "1                   NaN  \n",
       "7                   NaN  \n",
       "8                   NaN  \n",
       "9                   NaN  \n",
       "12                  NaN  \n",
       "...                 ...  \n",
       "31961               NaN  \n",
       "31965               NaN  \n",
       "31967               NaN  \n",
       "31968               NaN  \n",
       "31974               NaN  \n",
       "\n",
       "[6214 rows x 14289 columns]"
      ]
     },
     "execution_count": 235,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cell_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e93f74f6",
   "metadata": {},
   "source": [
    "## Also consider when sholl radius exceeds lefthand corner"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 250,
   "id": "889d9b63",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>N_Cells</th>\n",
       "      <th>RAW_SKEL</th>\n",
       "      <th>ENH_SKEL</th>\n",
       "      <th>RAW_SKEL_NORM</th>\n",
       "      <th>ENH_SKEL_NORM</th>\n",
       "      <th>log_RAW_SKEL</th>\n",
       "      <th>log_ENH_SKEL</th>\n",
       "      <th>log_RAW_SKEL_NORM</th>\n",
       "      <th>log_ENH_SKEL_NORM</th>\n",
       "      <th>Trunk</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>PGGT1B</th>\n",
       "      <td>52.0</td>\n",
       "      <td>2553181.0</td>\n",
       "      <td>3256884.0</td>\n",
       "      <td>49099.634615</td>\n",
       "      <td>62632.384615</td>\n",
       "      <td>21.283864</td>\n",
       "      <td>21.635061</td>\n",
       "      <td>15.583425</td>\n",
       "      <td>15.934621</td>\n",
       "      <td>9.395652</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MAP4K4</th>\n",
       "      <td>281.0</td>\n",
       "      <td>8521536.0</td>\n",
       "      <td>9534403.0</td>\n",
       "      <td>30325.750890</td>\n",
       "      <td>33930.259786</td>\n",
       "      <td>23.022682</td>\n",
       "      <td>23.184711</td>\n",
       "      <td>14.888256</td>\n",
       "      <td>15.050285</td>\n",
       "      <td>9.395652</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NCK1</th>\n",
       "      <td>880.0</td>\n",
       "      <td>10918226.0</td>\n",
       "      <td>13078420.0</td>\n",
       "      <td>12407.075000</td>\n",
       "      <td>14861.840909</td>\n",
       "      <td>23.380235</td>\n",
       "      <td>23.640685</td>\n",
       "      <td>13.598875</td>\n",
       "      <td>13.859325</td>\n",
       "      <td>9.395652</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HSF1</th>\n",
       "      <td>647.0</td>\n",
       "      <td>8918217.0</td>\n",
       "      <td>10492218.0</td>\n",
       "      <td>13783.952087</td>\n",
       "      <td>16216.720247</td>\n",
       "      <td>23.088324</td>\n",
       "      <td>23.322816</td>\n",
       "      <td>13.750702</td>\n",
       "      <td>13.985194</td>\n",
       "      <td>9.395652</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MINK1</th>\n",
       "      <td>98.0</td>\n",
       "      <td>3952207.0</td>\n",
       "      <td>4739187.0</td>\n",
       "      <td>40328.642857</td>\n",
       "      <td>48359.051020</td>\n",
       "      <td>21.914227</td>\n",
       "      <td>22.176208</td>\n",
       "      <td>15.299517</td>\n",
       "      <td>15.561498</td>\n",
       "      <td>9.395652</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IER5</th>\n",
       "      <td>437.0</td>\n",
       "      <td>6891973.0</td>\n",
       "      <td>8223578.0</td>\n",
       "      <td>15771.105263</td>\n",
       "      <td>18818.256293</td>\n",
       "      <td>22.716486</td>\n",
       "      <td>22.971335</td>\n",
       "      <td>13.944996</td>\n",
       "      <td>14.199845</td>\n",
       "      <td>9.395652</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SNX8</th>\n",
       "      <td>410.0</td>\n",
       "      <td>7155080.0</td>\n",
       "      <td>8099218.0</td>\n",
       "      <td>17451.414634</td>\n",
       "      <td>19754.190244</td>\n",
       "      <td>22.770536</td>\n",
       "      <td>22.949351</td>\n",
       "      <td>14.091056</td>\n",
       "      <td>14.269871</td>\n",
       "      <td>9.395652</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>non-target</th>\n",
       "      <td>135.0</td>\n",
       "      <td>5104420.0</td>\n",
       "      <td>5932905.0</td>\n",
       "      <td>37810.518519</td>\n",
       "      <td>43947.444444</td>\n",
       "      <td>22.283316</td>\n",
       "      <td>22.500307</td>\n",
       "      <td>15.206500</td>\n",
       "      <td>15.423492</td>\n",
       "      <td>9.395652</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PPP2R2B</th>\n",
       "      <td>723.0</td>\n",
       "      <td>7931087.0</td>\n",
       "      <td>8491695.0</td>\n",
       "      <td>10969.691563</td>\n",
       "      <td>11745.082988</td>\n",
       "      <td>22.919087</td>\n",
       "      <td>23.017621</td>\n",
       "      <td>13.421235</td>\n",
       "      <td>13.519769</td>\n",
       "      <td>9.395652</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FAN1</th>\n",
       "      <td>638.0</td>\n",
       "      <td>8558547.0</td>\n",
       "      <td>9546736.0</td>\n",
       "      <td>13414.650470</td>\n",
       "      <td>14963.536050</td>\n",
       "      <td>23.028934</td>\n",
       "      <td>23.186576</td>\n",
       "      <td>13.711522</td>\n",
       "      <td>13.869164</td>\n",
       "      <td>9.395652</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MLH1</th>\n",
       "      <td>489.0</td>\n",
       "      <td>5794390.0</td>\n",
       "      <td>6176066.0</td>\n",
       "      <td>11849.468303</td>\n",
       "      <td>12629.991820</td>\n",
       "      <td>22.466225</td>\n",
       "      <td>22.558257</td>\n",
       "      <td>13.532535</td>\n",
       "      <td>13.624566</td>\n",
       "      <td>9.395652</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSH3</th>\n",
       "      <td>141.0</td>\n",
       "      <td>5639512.0</td>\n",
       "      <td>5074479.0</td>\n",
       "      <td>39996.539007</td>\n",
       "      <td>35989.212766</td>\n",
       "      <td>22.427139</td>\n",
       "      <td>22.274828</td>\n",
       "      <td>15.287588</td>\n",
       "      <td>15.135277</td>\n",
       "      <td>9.395652</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PPP2R1A</th>\n",
       "      <td>55.0</td>\n",
       "      <td>2269088.0</td>\n",
       "      <td>1834737.0</td>\n",
       "      <td>41256.145455</td>\n",
       "      <td>33358.854545</td>\n",
       "      <td>21.113681</td>\n",
       "      <td>20.807142</td>\n",
       "      <td>15.332321</td>\n",
       "      <td>15.025782</td>\n",
       "      <td>9.395652</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HRAS</th>\n",
       "      <td>293.0</td>\n",
       "      <td>4842703.0</td>\n",
       "      <td>4065604.0</td>\n",
       "      <td>16527.996587</td>\n",
       "      <td>13875.781570</td>\n",
       "      <td>22.207381</td>\n",
       "      <td>21.955038</td>\n",
       "      <td>14.012624</td>\n",
       "      <td>13.760281</td>\n",
       "      <td>9.395652</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SLC25A11</th>\n",
       "      <td>202.0</td>\n",
       "      <td>5465382.0</td>\n",
       "      <td>4779324.0</td>\n",
       "      <td>27056.346535</td>\n",
       "      <td>23660.019802</td>\n",
       "      <td>22.381891</td>\n",
       "      <td>22.188375</td>\n",
       "      <td>14.723679</td>\n",
       "      <td>14.530164</td>\n",
       "      <td>9.395652</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WASL</th>\n",
       "      <td>180.0</td>\n",
       "      <td>5347995.0</td>\n",
       "      <td>4319068.0</td>\n",
       "      <td>29711.083333</td>\n",
       "      <td>23994.822222</td>\n",
       "      <td>22.350567</td>\n",
       "      <td>22.042289</td>\n",
       "      <td>14.858714</td>\n",
       "      <td>14.550436</td>\n",
       "      <td>9.395652</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ARPC3</th>\n",
       "      <td>80.0</td>\n",
       "      <td>1814218.0</td>\n",
       "      <td>1214920.0</td>\n",
       "      <td>22677.725000</td>\n",
       "      <td>15186.500000</td>\n",
       "      <td>20.790916</td>\n",
       "      <td>20.212430</td>\n",
       "      <td>14.468988</td>\n",
       "      <td>13.890502</td>\n",
       "      <td>9.395652</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NCKAP1</th>\n",
       "      <td>243.0</td>\n",
       "      <td>3891164.0</td>\n",
       "      <td>3279267.0</td>\n",
       "      <td>16013.020576</td>\n",
       "      <td>13494.925926</td>\n",
       "      <td>21.891770</td>\n",
       "      <td>21.644942</td>\n",
       "      <td>13.966958</td>\n",
       "      <td>13.720129</td>\n",
       "      <td>9.395652</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RAPGEF2</th>\n",
       "      <td>230.0</td>\n",
       "      <td>2493857.0</td>\n",
       "      <td>1962712.0</td>\n",
       "      <td>10842.856522</td>\n",
       "      <td>8533.530435</td>\n",
       "      <td>21.249947</td>\n",
       "      <td>20.904417</td>\n",
       "      <td>13.404457</td>\n",
       "      <td>13.058927</td>\n",
       "      <td>9.395652</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            N_Cells    RAW_SKEL    ENH_SKEL  RAW_SKEL_NORM  ENH_SKEL_NORM  \\\n",
       "PGGT1B         52.0   2553181.0   3256884.0   49099.634615   62632.384615   \n",
       "MAP4K4        281.0   8521536.0   9534403.0   30325.750890   33930.259786   \n",
       "NCK1          880.0  10918226.0  13078420.0   12407.075000   14861.840909   \n",
       "HSF1          647.0   8918217.0  10492218.0   13783.952087   16216.720247   \n",
       "MINK1          98.0   3952207.0   4739187.0   40328.642857   48359.051020   \n",
       "IER5          437.0   6891973.0   8223578.0   15771.105263   18818.256293   \n",
       "SNX8          410.0   7155080.0   8099218.0   17451.414634   19754.190244   \n",
       "non-target    135.0   5104420.0   5932905.0   37810.518519   43947.444444   \n",
       "PPP2R2B       723.0   7931087.0   8491695.0   10969.691563   11745.082988   \n",
       "FAN1          638.0   8558547.0   9546736.0   13414.650470   14963.536050   \n",
       "MLH1          489.0   5794390.0   6176066.0   11849.468303   12629.991820   \n",
       "MSH3          141.0   5639512.0   5074479.0   39996.539007   35989.212766   \n",
       "PPP2R1A        55.0   2269088.0   1834737.0   41256.145455   33358.854545   \n",
       "HRAS          293.0   4842703.0   4065604.0   16527.996587   13875.781570   \n",
       "SLC25A11      202.0   5465382.0   4779324.0   27056.346535   23660.019802   \n",
       "WASL          180.0   5347995.0   4319068.0   29711.083333   23994.822222   \n",
       "ARPC3          80.0   1814218.0   1214920.0   22677.725000   15186.500000   \n",
       "NCKAP1        243.0   3891164.0   3279267.0   16013.020576   13494.925926   \n",
       "RAPGEF2       230.0   2493857.0   1962712.0   10842.856522    8533.530435   \n",
       "\n",
       "            log_RAW_SKEL  log_ENH_SKEL  log_RAW_SKEL_NORM  log_ENH_SKEL_NORM  \\\n",
       "PGGT1B         21.283864     21.635061          15.583425          15.934621   \n",
       "MAP4K4         23.022682     23.184711          14.888256          15.050285   \n",
       "NCK1           23.380235     23.640685          13.598875          13.859325   \n",
       "HSF1           23.088324     23.322816          13.750702          13.985194   \n",
       "MINK1          21.914227     22.176208          15.299517          15.561498   \n",
       "IER5           22.716486     22.971335          13.944996          14.199845   \n",
       "SNX8           22.770536     22.949351          14.091056          14.269871   \n",
       "non-target     22.283316     22.500307          15.206500          15.423492   \n",
       "PPP2R2B        22.919087     23.017621          13.421235          13.519769   \n",
       "FAN1           23.028934     23.186576          13.711522          13.869164   \n",
       "MLH1           22.466225     22.558257          13.532535          13.624566   \n",
       "MSH3           22.427139     22.274828          15.287588          15.135277   \n",
       "PPP2R1A        21.113681     20.807142          15.332321          15.025782   \n",
       "HRAS           22.207381     21.955038          14.012624          13.760281   \n",
       "SLC25A11       22.381891     22.188375          14.723679          14.530164   \n",
       "WASL           22.350567     22.042289          14.858714          14.550436   \n",
       "ARPC3          20.790916     20.212430          14.468988          13.890502   \n",
       "NCKAP1         21.891770     21.644942          13.966958          13.720129   \n",
       "RAPGEF2        21.249947     20.904417          13.404457          13.058927   \n",
       "\n",
       "               Trunk  \n",
       "PGGT1B      9.395652  \n",
       "MAP4K4      9.395652  \n",
       "NCK1        9.395652  \n",
       "HSF1        9.395652  \n",
       "MINK1       9.395652  \n",
       "IER5        9.395652  \n",
       "SNX8        9.395652  \n",
       "non-target  9.395652  \n",
       "PPP2R2B     9.395652  \n",
       "FAN1        9.395652  \n",
       "MLH1        9.395652  \n",
       "MSH3        9.395652  \n",
       "PPP2R1A     9.395652  \n",
       "HRAS        9.395652  \n",
       "SLC25A11    9.395652  \n",
       "WASL        9.395652  \n",
       "ARPC3       9.395652  \n",
       "NCKAP1      9.395652  \n",
       "RAPGEF2     9.395652  "
      ]
     },
     "execution_count": 250,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "neurite_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 253,
   "id": "c298b0c0",
   "metadata": {},
   "outputs": [
    {
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       "      <th>log_ENH_SKEL</th>\n",
       "      <th>log_RAW_SKEL_NORM</th>\n",
       "      <th>log_ENH_SKEL_NORM</th>\n",
       "      <th>Trunk</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>PGGT1B</th>\n",
       "      <td>52.0</td>\n",
       "      <td>2553181.0</td>\n",
       "      <td>3256884.0</td>\n",
       "      <td>49099.634615</td>\n",
       "      <td>62632.384615</td>\n",
       "      <td>21.283864</td>\n",
       "      <td>21.635061</td>\n",
       "      <td>15.583425</td>\n",
       "      <td>15.934621</td>\n",
       "      <td>9.884615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MAP4K4</th>\n",
       "      <td>281.0</td>\n",
       "      <td>8521536.0</td>\n",
       "      <td>9534403.0</td>\n",
       "      <td>30325.750890</td>\n",
       "      <td>33930.259786</td>\n",
       "      <td>23.022682</td>\n",
       "      <td>23.184711</td>\n",
       "      <td>14.888256</td>\n",
       "      <td>15.050285</td>\n",
       "      <td>9.868327</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NCK1</th>\n",
       "      <td>880.0</td>\n",
       "      <td>10918226.0</td>\n",
       "      <td>13078420.0</td>\n",
       "      <td>12407.075000</td>\n",
       "      <td>14861.840909</td>\n",
       "      <td>23.380235</td>\n",
       "      <td>23.640685</td>\n",
       "      <td>13.598875</td>\n",
       "      <td>13.859325</td>\n",
       "      <td>9.085227</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HSF1</th>\n",
       "      <td>647.0</td>\n",
       "      <td>8918217.0</td>\n",
       "      <td>10492218.0</td>\n",
       "      <td>13783.952087</td>\n",
       "      <td>16216.720247</td>\n",
       "      <td>23.088324</td>\n",
       "      <td>23.322816</td>\n",
       "      <td>13.750702</td>\n",
       "      <td>13.985194</td>\n",
       "      <td>8.497682</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MINK1</th>\n",
       "      <td>98.0</td>\n",
       "      <td>3952207.0</td>\n",
       "      <td>4739187.0</td>\n",
       "      <td>40328.642857</td>\n",
       "      <td>48359.051020</td>\n",
       "      <td>21.914227</td>\n",
       "      <td>22.176208</td>\n",
       "      <td>15.299517</td>\n",
       "      <td>15.561498</td>\n",
       "      <td>12.846939</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IER5</th>\n",
       "      <td>437.0</td>\n",
       "      <td>6891973.0</td>\n",
       "      <td>8223578.0</td>\n",
       "      <td>15771.105263</td>\n",
       "      <td>18818.256293</td>\n",
       "      <td>22.716486</td>\n",
       "      <td>22.971335</td>\n",
       "      <td>13.944996</td>\n",
       "      <td>14.199845</td>\n",
       "      <td>9.508009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SNX8</th>\n",
       "      <td>410.0</td>\n",
       "      <td>7155080.0</td>\n",
       "      <td>8099218.0</td>\n",
       "      <td>17451.414634</td>\n",
       "      <td>19754.190244</td>\n",
       "      <td>22.770536</td>\n",
       "      <td>22.949351</td>\n",
       "      <td>14.091056</td>\n",
       "      <td>14.269871</td>\n",
       "      <td>9.885366</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>non-target</th>\n",
       "      <td>135.0</td>\n",
       "      <td>5104420.0</td>\n",
       "      <td>5932905.0</td>\n",
       "      <td>37810.518519</td>\n",
       "      <td>43947.444444</td>\n",
       "      <td>22.283316</td>\n",
       "      <td>22.500307</td>\n",
       "      <td>15.206500</td>\n",
       "      <td>15.423492</td>\n",
       "      <td>9.014815</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PPP2R2B</th>\n",
       "      <td>723.0</td>\n",
       "      <td>7931087.0</td>\n",
       "      <td>8491695.0</td>\n",
       "      <td>10969.691563</td>\n",
       "      <td>11745.082988</td>\n",
       "      <td>22.919087</td>\n",
       "      <td>23.017621</td>\n",
       "      <td>13.421235</td>\n",
       "      <td>13.519769</td>\n",
       "      <td>9.435685</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FAN1</th>\n",
       "      <td>638.0</td>\n",
       "      <td>8558547.0</td>\n",
       "      <td>9546736.0</td>\n",
       "      <td>13414.650470</td>\n",
       "      <td>14963.536050</td>\n",
       "      <td>23.028934</td>\n",
       "      <td>23.186576</td>\n",
       "      <td>13.711522</td>\n",
       "      <td>13.869164</td>\n",
       "      <td>8.631661</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MLH1</th>\n",
       "      <td>489.0</td>\n",
       "      <td>5794390.0</td>\n",
       "      <td>6176066.0</td>\n",
       "      <td>11849.468303</td>\n",
       "      <td>12629.991820</td>\n",
       "      <td>22.466225</td>\n",
       "      <td>22.558257</td>\n",
       "      <td>13.532535</td>\n",
       "      <td>13.624566</td>\n",
       "      <td>7.946830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSH3</th>\n",
       "      <td>141.0</td>\n",
       "      <td>5639512.0</td>\n",
       "      <td>5074479.0</td>\n",
       "      <td>39996.539007</td>\n",
       "      <td>35989.212766</td>\n",
       "      <td>22.427139</td>\n",
       "      <td>22.274828</td>\n",
       "      <td>15.287588</td>\n",
       "      <td>15.135277</td>\n",
       "      <td>9.439716</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PPP2R1A</th>\n",
       "      <td>55.0</td>\n",
       "      <td>2269088.0</td>\n",
       "      <td>1834737.0</td>\n",
       "      <td>41256.145455</td>\n",
       "      <td>33358.854545</td>\n",
       "      <td>21.113681</td>\n",
       "      <td>20.807142</td>\n",
       "      <td>15.332321</td>\n",
       "      <td>15.025782</td>\n",
       "      <td>8.181818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HRAS</th>\n",
       "      <td>293.0</td>\n",
       "      <td>4842703.0</td>\n",
       "      <td>4065604.0</td>\n",
       "      <td>16527.996587</td>\n",
       "      <td>13875.781570</td>\n",
       "      <td>22.207381</td>\n",
       "      <td>21.955038</td>\n",
       "      <td>14.012624</td>\n",
       "      <td>13.760281</td>\n",
       "      <td>10.095563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SLC25A11</th>\n",
       "      <td>202.0</td>\n",
       "      <td>5465382.0</td>\n",
       "      <td>4779324.0</td>\n",
       "      <td>27056.346535</td>\n",
       "      <td>23660.019802</td>\n",
       "      <td>22.381891</td>\n",
       "      <td>22.188375</td>\n",
       "      <td>14.723679</td>\n",
       "      <td>14.530164</td>\n",
       "      <td>9.529703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WASL</th>\n",
       "      <td>180.0</td>\n",
       "      <td>5347995.0</td>\n",
       "      <td>4319068.0</td>\n",
       "      <td>29711.083333</td>\n",
       "      <td>23994.822222</td>\n",
       "      <td>22.350567</td>\n",
       "      <td>22.042289</td>\n",
       "      <td>14.858714</td>\n",
       "      <td>14.550436</td>\n",
       "      <td>9.900000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ARPC3</th>\n",
       "      <td>80.0</td>\n",
       "      <td>1814218.0</td>\n",
       "      <td>1214920.0</td>\n",
       "      <td>22677.725000</td>\n",
       "      <td>15186.500000</td>\n",
       "      <td>20.790916</td>\n",
       "      <td>20.212430</td>\n",
       "      <td>14.468988</td>\n",
       "      <td>13.890502</td>\n",
       "      <td>8.950000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NCKAP1</th>\n",
       "      <td>243.0</td>\n",
       "      <td>3891164.0</td>\n",
       "      <td>3279267.0</td>\n",
       "      <td>16013.020576</td>\n",
       "      <td>13494.925926</td>\n",
       "      <td>21.891770</td>\n",
       "      <td>21.644942</td>\n",
       "      <td>13.966958</td>\n",
       "      <td>13.720129</td>\n",
       "      <td>8.576132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RAPGEF2</th>\n",
       "      <td>230.0</td>\n",
       "      <td>2493857.0</td>\n",
       "      <td>1962712.0</td>\n",
       "      <td>10842.856522</td>\n",
       "      <td>8533.530435</td>\n",
       "      <td>21.249947</td>\n",
       "      <td>20.904417</td>\n",
       "      <td>13.404457</td>\n",
       "      <td>13.058927</td>\n",
       "      <td>9.395652</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            N_Cells    RAW_SKEL    ENH_SKEL  RAW_SKEL_NORM  ENH_SKEL_NORM  \\\n",
       "PGGT1B         52.0   2553181.0   3256884.0   49099.634615   62632.384615   \n",
       "MAP4K4        281.0   8521536.0   9534403.0   30325.750890   33930.259786   \n",
       "NCK1          880.0  10918226.0  13078420.0   12407.075000   14861.840909   \n",
       "HSF1          647.0   8918217.0  10492218.0   13783.952087   16216.720247   \n",
       "MINK1          98.0   3952207.0   4739187.0   40328.642857   48359.051020   \n",
       "IER5          437.0   6891973.0   8223578.0   15771.105263   18818.256293   \n",
       "SNX8          410.0   7155080.0   8099218.0   17451.414634   19754.190244   \n",
       "non-target    135.0   5104420.0   5932905.0   37810.518519   43947.444444   \n",
       "PPP2R2B       723.0   7931087.0   8491695.0   10969.691563   11745.082988   \n",
       "FAN1          638.0   8558547.0   9546736.0   13414.650470   14963.536050   \n",
       "MLH1          489.0   5794390.0   6176066.0   11849.468303   12629.991820   \n",
       "MSH3          141.0   5639512.0   5074479.0   39996.539007   35989.212766   \n",
       "PPP2R1A        55.0   2269088.0   1834737.0   41256.145455   33358.854545   \n",
       "HRAS          293.0   4842703.0   4065604.0   16527.996587   13875.781570   \n",
       "SLC25A11      202.0   5465382.0   4779324.0   27056.346535   23660.019802   \n",
       "WASL          180.0   5347995.0   4319068.0   29711.083333   23994.822222   \n",
       "ARPC3          80.0   1814218.0   1214920.0   22677.725000   15186.500000   \n",
       "NCKAP1        243.0   3891164.0   3279267.0   16013.020576   13494.925926   \n",
       "RAPGEF2       230.0   2493857.0   1962712.0   10842.856522    8533.530435   \n",
       "\n",
       "            log_RAW_SKEL  log_ENH_SKEL  log_RAW_SKEL_NORM  log_ENH_SKEL_NORM  \\\n",
       "PGGT1B         21.283864     21.635061          15.583425          15.934621   \n",
       "MAP4K4         23.022682     23.184711          14.888256          15.050285   \n",
       "NCK1           23.380235     23.640685          13.598875          13.859325   \n",
       "HSF1           23.088324     23.322816          13.750702          13.985194   \n",
       "MINK1          21.914227     22.176208          15.299517          15.561498   \n",
       "IER5           22.716486     22.971335          13.944996          14.199845   \n",
       "SNX8           22.770536     22.949351          14.091056          14.269871   \n",
       "non-target     22.283316     22.500307          15.206500          15.423492   \n",
       "PPP2R2B        22.919087     23.017621          13.421235          13.519769   \n",
       "FAN1           23.028934     23.186576          13.711522          13.869164   \n",
       "MLH1           22.466225     22.558257          13.532535          13.624566   \n",
       "MSH3           22.427139     22.274828          15.287588          15.135277   \n",
       "PPP2R1A        21.113681     20.807142          15.332321          15.025782   \n",
       "HRAS           22.207381     21.955038          14.012624          13.760281   \n",
       "SLC25A11       22.381891     22.188375          14.723679          14.530164   \n",
       "WASL           22.350567     22.042289          14.858714          14.550436   \n",
       "ARPC3          20.790916     20.212430          14.468988          13.890502   \n",
       "NCKAP1         21.891770     21.644942          13.966958          13.720129   \n",
       "RAPGEF2        21.249947     20.904417          13.404457          13.058927   \n",
       "\n",
       "                Trunk  \n",
       "PGGT1B       9.884615  \n",
       "MAP4K4       9.868327  \n",
       "NCK1         9.085227  \n",
       "HSF1         8.497682  \n",
       "MINK1       12.846939  \n",
       "IER5         9.508009  \n",
       "SNX8         9.885366  \n",
       "non-target   9.014815  \n",
       "PPP2R2B      9.435685  \n",
       "FAN1         8.631661  \n",
       "MLH1         7.946830  \n",
       "MSH3         9.439716  \n",
       "PPP2R1A      8.181818  \n",
       "HRAS        10.095563  \n",
       "SLC25A11     9.529703  \n",
       "WASL         9.900000  \n",
       "ARPC3        8.950000  \n",
       "NCKAP1       8.576132  \n",
       "RAPGEF2      9.395652  "
      ]
     },
     "execution_count": 253,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# make image number by genotype \n",
    "for gene in neurite_count.index:\n",
    "    df = cell_df[cell_df['Gene'] == gene].Trunks\n",
    "    neurite_count.loc[gene,'Trunk'] = np.mean(df)\n",
    "neurite_count\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 257,
   "id": "6df36613",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(-0.042811176193047924, 0.8618498734064775)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# scatter plot\n",
    "# plot neurite vs trunk\n",
    "print(stats.pearsonr(neurite_count['Trunk'], neurite_count['log_RAW_SKEL']))\n",
    "fig, axs = plt.subplots(figsize=(10,10))\n",
    "sns.scatterplot(data=neurite_count, x = 'Trunk',y='log_RAW_SKEL',\n",
    "               hue = neurite_count.index, ax = axs, s=50, cmap='tab20')\n",
    "axs.legend(bbox_to_anchor=(1.02, 1), loc='upper left', borderaxespad=0)\n",
    "for line in range(0,neurite_count.shape[0]):\n",
    "     axs.text(neurite_count.Trunk[line]+0.02, neurite_count.log_RAW_SKEL[line], \n",
    "              horizontalalignment='left', size='small', color='black', weight='semibold', s = neurite_count.index[line])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "id": "7a042675",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1922"
      ]
     },
     "execution_count": 230,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# scatter plot\n",
    "# plot neurite vs trunk\n",
    "print(stats.pearsonr(neurite_count['Trunk'], neurite_count['log_RAW_SKEL']))\n",
    "fig, axs = plt.subplots(figsize=(10,10))\n",
    "sns.scatterplot(data=neurite_count, x = 'Trunk',y='log_RAW_SKEL',\n",
    "               hue = neurite_count.index, ax = axs, s=50, cmap='tab20')\n",
    "axs.legend(bbox_to_anchor=(1.02, 1), loc='upper left', borderaxespad=0)\n",
    "for line in range(0,neurite_count.shape[0]):\n",
    "     axs.text(neurite_count.Trunk[line]+0.02, neurite_count.log_RAW_SKEL[line], \n",
    "              horizontalalignment='left', size='small', color='black', weight='semibold', s = neurite_count.index[line])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6ab11833",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.9.15"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}