Check_image_features.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Image creation\n",
    "\n",
    "Creating an image based on our matrices to use opencv."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import numpy\n",
    "import pandas\n",
    "import cv2\n",
    "\n",
    "#List of usable patients \n",
    "data_ID=pandas.read_csv(os.path.join('..','List_Patient_HEGP.csv'),sep=\",\")\n",
    "\n",
    "CD3PATH=os.path.join('..','Data_output','cache_HEGP','cd3','PAR170')\n",
    "img_cd3_im=pandas.read_csv(os.path.join(CD3PATH,'Ratio_Cells_per_InvasiveFront'),sep=' ',header=None)\n",
    "img_cd3_ct=pandas.read_csv(os.path.join(CD3PATH,'Ratio_Cells_per_Tumor'),sep=' ',header=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x1ae98bcfcc0>"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "plt.gray()\n",
    "plt.show()\n",
    "plt.imshow(img_cd3_ct, interpolation='nearest')\n",
    "\n",
    "plt.gray()\n",
    "plt.show()\n",
    "plt.imshow(img_cd3_im, interpolation='nearest')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.savefig('CD3_PAR170.jpg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "CD8PATH=os.path.join('..','Data_output','cache_HEGP','cd8','PAR170')\n",
    "img_cd8=pandas.read_csv(os.path.join(CD8PATH,'Ratio_Cells_per_InvasiveFront'),sep=' ',header=None)\n",
    "plt.gray()\n",
    "plt.imshow(img_cd8, interpolation='nearest')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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     ]
    },
    {
     "ename": "KeyError",
     "evalue": "21",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mget_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m   3062\u001b[0m             \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3063\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   3064\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.Int64HashTable.get_item\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.Int64HashTable.get_item\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;31mKeyError\u001b[0m: 21",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-74-148ebb0283e6>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      7\u001b[0m     \u001b[1;32mfor\u001b[0m \u001b[0mj\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmatrix2\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      8\u001b[0m         \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 9\u001b[1;33m         \u001b[1;32mif\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mmatrix1\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mj\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m==\u001b[0m\u001b[0mnumpy\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnan\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0mmatrix2\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mj\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m==\u001b[0m\u001b[0mnumpy\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnan\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mand\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;33m(\u001b[0m \u001b[0mmatrix1\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mj\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m==\u001b[0m\u001b[0mnumpy\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnan\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mmatrix2\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mj\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m==\u001b[0m\u001b[0mnumpy\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnan\u001b[0m\u001b[1;33m)\u001b[0m  \u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     10\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mmatrix1\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mj\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m==\u001b[0m\u001b[0mnumpy\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnan\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     11\u001b[0m                 \u001b[0mrmatrix\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mj\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmatrix2\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mj\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m   2683\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2684\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2685\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_getitem_column\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2686\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2687\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_getitem_column\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\frame.py\u001b[0m in \u001b[0;36m_getitem_column\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m   2690\u001b[0m         \u001b[1;31m# get column\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2691\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mis_unique\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2692\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_item_cache\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2693\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2694\u001b[0m         \u001b[1;31m# duplicate columns & possible reduce dimensionality\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\generic.py\u001b[0m in \u001b[0;36m_get_item_cache\u001b[1;34m(self, item)\u001b[0m\n\u001b[0;32m   2484\u001b[0m         \u001b[0mres\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcache\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2485\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mres\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2486\u001b[1;33m             \u001b[0mvalues\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_data\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2487\u001b[0m             \u001b[0mres\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_box_item_values\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalues\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2488\u001b[0m             \u001b[0mcache\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mitem\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mres\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\internals.py\u001b[0m in \u001b[0;36mget\u001b[1;34m(self, item, fastpath)\u001b[0m\n\u001b[0;32m   4113\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   4114\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0misna\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 4115\u001b[1;33m                 \u001b[0mloc\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   4116\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   4117\u001b[0m                 \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0marange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0misna\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mget_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m   3063\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3064\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3065\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_maybe_cast_indexer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   3066\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3067\u001b[0m         \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_indexer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmethod\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtolerance\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtolerance\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.Int64HashTable.get_item\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.Int64HashTable.get_item\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;31mKeyError\u001b[0m: 21"
     ]
    }
   ],
   "source": [
    "matrix1 = img_cd3_im\n",
    "matrix2 = img_cd3_ct\n",
    "rmatrix = numpy.empty(matrix1.shape)\n",
    "\n",
    "for i in range((matrix2.shape[0]-1)):\n",
    "    \n",
    "    for j in range((matrix2.shape[1]-1)):\n",
    "        print(i,j)\n",
    "        if (matrix1[i][j]==numpy.nan or matrix2[i][j]==numpy.nan) and not ( matrix1[i][j]==numpy.nan and matrix2[i][j]==numpy.nan)  :\n",
    "            if matrix1[i][j]==numpy.nan:\n",
    "                rmatrix[i][j] = matrix2[i][j]\n",
    "            else:\n",
    "                rmatrix[i][j] = matrix1[i][j]\n",
    "        elif matrix1[i][j]==numpy.nan and matrix2[i][j]==numpy.nan:\n",
    "            rmatrix[i][j] = numpy.nan\n",
    "        else:\n",
    "            rmatrix[i][j] = matrix1[i][j] + matrix2[i][j]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Probleme quand on essaye d'accéder à la ligne 21 et plus. Cela pourrait avoir une impact sur l'extraction de features.\n",
    "Je ne sais pas pourquoi."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(26, 21)"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(matrix1)\n",
    "matrix1.shape[0]\n",
    "range(matrix1.shape[0]-1)\n",
    "matrix1[0][0]\n",
    "img_cd3_im.shape\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cd=numpy.add(numpy.nan_to_num(img_cd3_ct.T),numpy.nan_to_num(img_cd3_im.T))\n",
    "plt.gray()\n",
    "plt.imshow(cd, interpolation='nearest')\n",
    "\n",
    "plt.show()\n",
    "cd[cd==0]=numpy.nan\n",
    "plt.gray()\n",
    "plt.imshow(cd, interpolation='nearest')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.savefig('CD8_PAR170.jpg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "sift = cv2.xfeatures2d.SIFT_create()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "sift = cv2.xfeatures2d.SIFT_create()\n",
    "gen = numpy.array(img_cd3_ct ,dtype=numpy.uint8)\n",
    "kp,des=sift.detectAndCompute(gen,None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<KeyPoint 000001AE98ED4720>,\n",
       " <KeyPoint 000001AE98ED40F0>,\n",
       " <KeyPoint 000001AE98ED46C0>,\n",
       " <KeyPoint 000001AE98ED4510>,\n",
       " <KeyPoint 000001AE98ED4540>,\n",
       " <KeyPoint 000001AE98ED47B0>]"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "size_vector_cd3.loc['PAR170']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(7.348757743835449, 12.353886604309082)"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kp[0].pt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(7.896059989929199, 10.239636421203613)"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kp[1].pt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  2.,   0.,   0.,   0.,   0.,   0.,   0.,   5.,   9.,   0.,   0.,\n",
       "          0.,   0.,   0.,  25.,  74.,  32.,   6.,   2.,   0.,   6.,  33.,\n",
       "         88., 115.,   6.,  11.,  34.,  18.,  41.,  79.,  27.,   6.,  37.,\n",
       "          1.,   0.,   0.,   0.,   0.,   0.,   8., 125.,  11.,   1.,   0.,\n",
       "          0.,   0.,  59., 125.,  49.,  11.,   9.,   0.,  28.,  39., 114.,\n",
       "        125.,   3.,   2.,   1.,   0., 125., 125.,  30.,  11.,  37.,   6.,\n",
       "          0.,   0.,   0.,   0.,   1.,   7., 125.,  91.,   6.,   0.,   0.,\n",
       "          0.,   3.,  49.,  56.,  66.,  39.,   1.,   7.,  11.,  28.,  77.,\n",
       "          5.,  11.,   7.,   7.,  35., 125.,  82.,  22.,   7.,   1.,   1.,\n",
       "          0.,   0.,   0.,  15.,  61., 113.,  14.,   9.,   0.,   0.,   0.,\n",
       "         21., 125.,  60.,  14.,  21.,  27.,   3.,   1.,  13., 103.,   5.,\n",
       "          4.,  23.,  43.,  20.,  14.,  13.,  15.],\n",
       "       [  2.,  16.,   2.,   0.,   0.,   0.,   0.,   0.,  62., 130.,  27.,\n",
       "         13.,   8.,   3.,   5.,   8.,  27.,  10.,   2.,   8.,  40., 118.,\n",
       "         60.,  21.,   0.,   0.,   0.,   1., 102., 134.,   8.,   0.,  15.,\n",
       "         15.,   1.,   0.,   3.,  23.,  20.,  13., 134., 128.,  18.,   3.,\n",
       "          1.,   7.,  13.,  61., 107.,  39., 113., 109.,  44.,  15.,   9.,\n",
       "         15.,   0.,   1.,  45.,  64., 134.,  37.,   1.,   0.,   3.,   3.,\n",
       "          5.,  10.,  17.,  27.,  11.,   5.,  66., 116., 134.,  22.,   9.,\n",
       "          8.,   6.,  19.,  12.,  38., 134., 134.,   1.,   0.,   0.,   1.,\n",
       "          0.,   4.,  80.,  47.,   4.,   1.,   0.,   0.,   0.,   0.,   0.,\n",
       "          1.,   0.,   0.,   0.,   0.,   0.,   5.,  24.,   3.,   0.,   0.,\n",
       "          0.,   0.,   0.,   2.,  34.,  18.,   0.,   0.,   0.,   0.,   0.,\n",
       "          0.,   2.,   2.,   0.,   0.,   0.,   0.],\n",
       "       [  0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   9.,  12.,   8.,\n",
       "          4.,   1.,   1.,   2.,  11.,   2.,   9.,  19.,  45.,  10.,   8.,\n",
       "          3.,   3.,   0.,   0.,   0.,   3.,   1.,   1.,   1.,   0.,  29.,\n",
       "          1.,   0.,   0.,   0.,   0.,   0.,  13., 127.,  10.,   3.,   2.,\n",
       "          1.,  22.,  55., 142.,  11.,   5.,   6.,  26.,  20., 142., 142.,\n",
       "         59.,   3.,   0.,   0.,   2.,   2.,  15., 104.,  74.,  60.,   7.,\n",
       "          0.,   0.,   0.,   0.,   0.,   9., 142.,  53.,   2.,   0.,   0.,\n",
       "          6.,  17.,  81.,  94.,  25.,  20.,  14.,  17.,  92., 142.,  36.,\n",
       "         13.,  28.,  23.,  34.,  25.,  26.,  78.,  44.,   6.,   0.,   0.,\n",
       "          0.,   0.,   0.,   0.,   4., 102.,  54.,  43.,   0.,   0.,   0.,\n",
       "          0.,  24.,  28.,  87., 142.,  58.,   7.,   1.,   0.,   1.,   0.,\n",
       "         11.,  99., 114.,  11.,   1.,   0.,   0.],\n",
       "       [  4.,   1.,   0.,   0.,   0.,   0.,   0.,   1., 110.,  18.,   0.,\n",
       "          0.,   0.,   0.,   0., 102.,  80.,  22.,   4.,   0.,   2.,  92.,\n",
       "         50., 124.,   1.,   1.,   1.,   1.,   5., 124., 124.,  42.,   3.,\n",
       "          2.,   0.,   0.,   0.,   0.,   0.,   0., 124., 120.,   0.,   0.,\n",
       "          0.,   0.,   0.,  20., 124., 124.,  11.,   3.,   0.,   8.,   6.,\n",
       "         57.,  33.,   8.,   7.,  28.,  27.,  29.,  60.,  33.,   0.,   0.,\n",
       "          0.,   0.,   0.,   0.,   0.,   0.,  65.,  12.,   1.,   1.,   0.,\n",
       "          0.,   0.,  63., 124.,  68., 113.,  22.,   0.,   0.,   0.,  78.,\n",
       "         19.,  79., 124.,  55.,   5.,   3.,   1.,   3.,   0.,   0.,   0.,\n",
       "          0.,   0.,   0.,   0.,   0.,   2.,   0.,   1.,   1.,   0.,   0.,\n",
       "          0.,   3.,   4.,  30.,  71.,  19.,   0.,   0.,   0.,   4.,   0.,\n",
       "         86., 114.,  40.,   0.,   0.,   0.,   0.],\n",
       "       [  0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,  91.,  30.,   0.,\n",
       "          0.,   0.,   0.,   0.,   0., 138.,  63.,   9.,  15.,  17.,  15.,\n",
       "          6.,   7.,   5.,  19.,  43.,  16.,  18.,  42.,  23.,   9.,   0.,\n",
       "          0.,   0.,   0.,  20.,  36.,   7.,   6., 126.,  14.,   0.,   0.,\n",
       "          0.,   0.,  27.,  52., 138.,  30.,   0.,   1.,   9.,  12.,  13.,\n",
       "         78.,  27.,   2.,   0.,   4.,  36.,  56.,  38.,  27.,  34.,   7.,\n",
       "          9.,  19.,  55.,  38.,  36.,  95.,  36.,  19.,   7.,   1.,   1.,\n",
       "          0., 138., 138., 100.,  31.,   7.,  17.,  11.,   3.,  54., 138.,\n",
       "         24.,  38.,   7.,   4.,  25.,  53.,  26.,  16.,  68.,  81.,  28.,\n",
       "         10.,   3.,   0.,   2.,  24.,  92., 138.,  52.,   3.,   2.,   0.,\n",
       "         16.,  35.,  27.,  54.,  51.,  58.,  41.,   1.,   4.,   9.,   9.,\n",
       "        104.,  70.,  14.,   9.,  10.,   2.,   0.],\n",
       "       [  0.,  28.,  66.,  17.,  20.,  14.,   5.,   0.,  13.,  62.,  44.,\n",
       "         15.,  10.,  22.,  49.,  48.,  26.,  31.,   9.,   1.,   1.,  26.,\n",
       "         43.,  44.,   9.,   8.,   7.,  13.,  17.,  15.,  44.,  27.,   0.,\n",
       "         68.,  96.,   0.,   0.,   0.,   0.,   0.,  29., 133., 133.,   0.,\n",
       "          0.,   1.,   1.,   3., 114., 133.,  58.,   7.,   4.,  11.,  12.,\n",
       "         16.,   8.,   9.,  92.,  48.,  13.,  20.,  18.,  12.,   0.,   4.,\n",
       "          4.,   0.,   0.,   0.,   4.,   0., 133.,  47.,  16.,   0.,   0.,\n",
       "          0.,   3.,  31., 133.,  65.,  35.,  17.,   5.,   9.,   0.,  53.,\n",
       "          9.,  19.,  62.,  52.,  55.,  45.,   7.,   1.,   2.,   0.,   0.,\n",
       "          0.,   0.,   5.,  66.,  11.,  79.,  31.,   3.,   2.,  12.,  37.,\n",
       "         77.,  17.,  91.,  89., 106.,  68.,   4.,   3.,   1.,  12.,   0.,\n",
       "         28., 133.,  94.,  12.,   0.,   0.,   0.]], dtype=float32)"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "des"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "size_vector_cd3=pandas.DataFrame(data=data_ID['OfficialID'])\n",
    "size_vector_cd3[\"Size_Tumor_kp\"]=numpy.nan\n",
    "size_vector_cd3[\"Size_Tumor_des\"]=numpy.nan\n",
    "size_vector_cd3[\"Size_IM_kp\"]=numpy.nan\n",
    "size_vector_cd3[\"Size_IM_des\"]=numpy.nan\n",
    "size_vector_cd3[\"Size_img_h\"]=numpy.nan\n",
    "size_vector_cd3[\"Size_img_w\"]=numpy.nan\n",
    "size_vector_cd3[\"Size_img\"]=numpy.nan\n",
    "size_vector_cd3.set_index('OfficialID',drop=False,inplace=True)\n",
    "\n",
    "size_vector_cd8=pandas.DataFrame(data=data_ID['OfficialID'])\n",
    "size_vector_cd8[\"Size_Tumor_kp\"]=numpy.nan\n",
    "size_vector_cd8[\"Size_Tumor_des\"]=numpy.nan\n",
    "size_vector_cd8[\"Size_IM_kp\"]=numpy.nan\n",
    "size_vector_cd8[\"Size_IM_des\"]=numpy.nan\n",
    "size_vector_cd8[\"Size_img_h\"]=numpy.nan\n",
    "size_vector_cd8[\"Size_img_w\"]=numpy.nan\n",
    "size_vector_cd8[\"Size_img\"]=numpy.nan\n",
    "size_vector_cd8.set_index('OfficialID',drop=False,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "for patient_id in data_ID['OfficialID']:\n",
    "    CD3PATH=os.path.join('..','Data_output','cache_HEGP','cd3',patient_id)\n",
    "    img_cd3=pandas.read_csv(os.path.join(CD3PATH,'Ratio_Cells_per_Tumor'),sep=' ',header=None)\n",
    "    size_vector_cd3.loc[patient_id,'Size_img_h']=img_cd3.shape[0]\n",
    "    size_vector_cd3.loc[patient_id,'Size_img_w']=img_cd3.shape[1]\n",
    "    size_vector_cd3.loc[patient_id,'Size_img']=img_cd3.size\n",
    "    gen = numpy.array(img_cd3 ,dtype=numpy.uint8)\n",
    "    \n",
    "    #kps=extract_features(gen)\n",
    "    kp, des = sift.detectAndCompute(gen,None)\n",
    "    try:\n",
    "        size_vector_cd3.loc[patient_id,'Size_Tumor_kp']=des.shape[0]\n",
    "        size_vector_cd3.loc[patient_id,'Size_Tumor_des']=des.shape[1]\n",
    "    except (AttributeError):\n",
    "        pass\n",
    "    img_cd3=pandas.read_csv(os.path.join(CD3PATH,'Ratio_Cells_per_InvasiveFront'),sep=' ',header=None)\n",
    "    gen = numpy.array(img_cd3 ,dtype=numpy.uint8)\n",
    "    #kps=extract_features(gen)\n",
    "    kp, des = sift.detectAndCompute(gen,None)\n",
    "    try:\n",
    "        size_vector_cd3.loc[patient_id,'Size_IM_kp']=des.shape[0]\n",
    "        size_vector_cd3.loc[patient_id,'Size_IM_des']=des.shape[1]\n",
    "    except (AttributeError):\n",
    "        pass\n",
    "    CD8PATH=os.path.join('..','Data_output','cache_HEGP','cd8',patient_id)\n",
    "    img_cd8=pandas.read_csv(os.path.join(CD8PATH,'Ratio_Cells_per_Tumor'),sep=' ',header=None)\n",
    "    size_vector_cd8.loc[patient_id,'Size_img_h']=img_cd8.shape[0]\n",
    "    size_vector_cd8.loc[patient_id,'Size_img_w']=img_cd8.shape[1]\n",
    "    size_vector_cd8.loc[patient_id,'Size_img']=img_cd8.size\n",
    "    gen = numpy.array(img_cd8 ,dtype=numpy.uint8)\n",
    "    \n",
    "    #kps=extract_features(gen)\n",
    "    kp, des = sift.detectAndCompute(gen,None)\n",
    "    try:\n",
    "        size_vector_cd8.loc[patient_id,'Size_Tumor_kp']=des.shape[0]\n",
    "        size_vector_cd8.loc[patient_id,'Size_Tumor_des']=des.shape[1]\n",
    "    except (AttributeError):\n",
    "        pass\n",
    "    img_cd8=pandas.read_csv(os.path.join(CD8PATH,'Ratio_Cells_per_InvasiveFront'),sep=' ',header=None)\n",
    "    gen = numpy.array(img_cd8 ,dtype=numpy.uint8)\n",
    "    #kps=extract_features(gen)\n",
    "    kp, des = sift.detectAndCompute(gen,None)\n",
    "    try:\n",
    "        size_vector_cd8.loc[patient_id,'Size_IM_kp']=des.shape[0]\n",
    "        size_vector_cd8.loc[patient_id,'Size_IM_des']=des.shape[1]\n",
    "    except (AttributeError):\n",
    "        pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1.0, 128.0]"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[min(size_vector_cd3['Size_Tumor_kp']),min(size_vector_cd3['Size_Tumor_des'])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[14.0, 128.0]"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[max(size_vector_cd3['Size_Tumor_kp']),max(size_vector_cd3['Size_Tumor_des'])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[nan, nan]"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[min(size_vector_cd3['Size_IM_kp']),min(size_vector_cd3['Size_IM_des'])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[nan, nan]"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[max(size_vector_cd3['Size_IM_kp']),max(size_vector_cd3['Size_IM_des'])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1.0, 128.0]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[min(size_vector_cd8['Size_Tumor_kp']),min(size_vector_cd8['Size_Tumor_des'])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[26.0, 128.0]"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[max(size_vector_cd8['Size_Tumor_kp']),max(size_vector_cd8['Size_Tumor_des'])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1.0, 128.0]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[min(size_vector_cd8['Size_IM_kp']),min(size_vector_cd8['Size_IM_des'])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[nan, nan]"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[max(size_vector_cd8['Size_IM_kp']),max(size_vector_cd8['Size_IM_des'])]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Identifier la taille du vector de features\n",
    "Pour les valeurs manquantes les remplacer par la médiane des vecteurs existants\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "OfficialID        PAR170\n",
       "Size_Tumor_kp          6\n",
       "Size_Tumor_des       128\n",
       "Size_IM_kp           NaN\n",
       "Size_IM_des          NaN\n",
       "Size_img_h            21\n",
       "Size_img_w            26\n",
       "Size_img             546\n",
       "Name: PAR170, dtype: object"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "size_vector_cd3.loc['PAR170']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  0,   1,   3,   4,   5,   6,   8,  10,  11,  12,  13,  14,  15,\n",
       "        16,  17,  18,  21,  23,  26,  27,  29,  30,  35,  37,  38,  40,\n",
       "        42,  43,  44,  45,  46,  47,  48,  53,  54,  55,  57,  58,  59,\n",
       "        60,  62,  63,  64,  65,  67,  68,  70,  71,  72,  73,  74,  75,\n",
       "        76,  80,  81,  84,  86,  96,  97,  99, 100, 101, 103, 105, 106,\n",
       "       108, 110, 112, 113, 114, 116, 118, 121, 125, 128, 134, 136, 137,\n",
       "       138, 140, 142, 143, 144, 145, 146, 150, 151, 154, 155, 156, 157,\n",
       "       159, 162, 163, 164, 166, 167, 168, 173, 174, 176, 177, 178, 179,\n",
       "       180, 182, 183, 185, 187, 189, 195, 198, 199, 203, 204, 205, 209,\n",
       "       210, 211, 213, 214, 216, 219, 220, 221, 223, 225, 226, 227, 229,\n",
       "       230, 231, 233, 234, 235, 237, 238, 241, 242, 243, 245, 248, 249,\n",
       "       251, 252, 258, 260, 262, 263, 264, 268, 270, 272, 273, 275, 277,\n",
       "       278, 279, 281, 286, 287, 290, 292, 293, 294], dtype=int64)"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " pandas.isnull(size_vector_cd8).any(1).nonzero()[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(17.3590087890625, 12.233915328979492)"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kp[0].pt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "121"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " len(pandas.isnull(size_vector_cd3).any(1).nonzero()[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "155"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len( pandas.isnull(size_vector_cd8).any(1).nonzero()[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1989b308c88>"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1989b5caa58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1989b271400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "df=pandas.DataFrame(data=size_vector_cd3['Size_img'])\n",
    "df.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1989b521c50>"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1989b5ab470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1989b633438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "df=pandas.DataFrame(data=size_vector_cd3[['Size_img_h','Size_img_w']])\n",
    "df.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1989b33da90>"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Km6FaWBioXrALOZypVrpEXy/TEGaRB4JSAMwLNAPoU21wOSN04kBnuojtHWvgwn/4Kf7zwd7rUC0FBtA5Ckh9dkw/RdW2mzcncB9o0LOBW3NpMVEKgHlA5HnZjgccnmzhl48cxt6xBr52y2PdfzAPcM0MM9WyjjXakBI4OtW7A58LgH595rTW8LEmOu7f1tDjghlAVHA1UFMLqJDDLTiIAfSDCasUAPMAeqGONxPQv930KP7giptx5R278VffvAuTzWjBx+AuMjN9x2Zjr3YzgfsRsePwBbJO4NhxThZevK0g2WhqAfXnve4GY2pb3HEApQCYF9Ai0q+LwXxhqhWjGSWsQ9bCX3+WAcxsDLPJgm1aJqD+ZAA+E1C2HLQTDlqw07a4BjNLPRO42Ps7F5QCYB5gfAD9uRjMF+iF1NUxF8Eh6r5UM9USZ1ML33IC96kPwGsCyoSBqmuPi9Ww24X3A7D/X2rgw15sFlMKgHmAjgLq08WgKFz+s4fx37+4VX+m6+XVMRca7sI90yHETu36OJF4y+e34peP5Od0WE7gPo0CMnkA2TDQxLGpz1dDGCmLWfCKDlNdaPBxLzYL6FoNtMTMoel2ny4GReED39tmfXZDDedTANzz+BiqYYAnb1xhj8G55TNdI0ylyfSHU60I127bh+ecthrnblnj/c1SigLirNT1C7hRQYVFASWcdQBzLZZadKmKhQZ/LRabxZQMIAe3P3YEV93pbVrWFdqp1qeLwXyBXnRaEOfz+v/2ynvw4R/cl9k+Vx8AXYNuYkIVMzushv0eBcS1bl8egOkBnG4vOlOVs44iNN6lxgCklPjMzx/B7qPT+jNhsa+hFAA5+O3/ewPe8eXbM9sPT7Zw3bZ9HX9LZoT2Yov3BQa9mK0F8AE02om18BI46xBi5iYHdxGMeojoaneIArpj51FcecfsFImiwBfdFjcBOQmL85cJnPU7zOl4bHxRnODj1z6I8T6uu/XY4Sm876p78Y4v3wbAZi6lAFhi+Oatu/CWL2xFox3n7hN56PZyhqtJFl3+14fIaW5C4C9UIMSMKXbk+gB6iNhoxvkM4Is3PYoPfX+b+5MFRZzDAEwpCKchTMHloH2hp3MBbwr/w3v24mPXPuBlg/2CIyqnJPI4wxdbRywFwAzRihNImUa63PzwIZzzgWsy2kfRDTX6HW7pi+YC+ADiJPEuJvyeByJ92b535x684H9f35NAdhd+t2mKD60owVAt7UPg+gDiRC66WYjfps79ANLtbj7AXMHPWaQJKG1xmf49Nt1/DEBKiX+67kE8tH8CALBysKq2m31KBrDEwLXchw5M4uBEC0cm7clHi0C/hgQWDWJDtGC0FqBJetp/N7s9SSRe9vQT8c23nQ8BAQnggX3j2Hl4GpPNfNZG0ALAKV8QdzBnpQKgosZl75cKgMWdB3zR5WPJzQPQQrCY80eW2Wnux+M+i5oq/92Pzvf7943jH655AH/+73cAAEYH0zlid2UrBUDfodND4XV+tKnDWYmOt2qgTSfscyF8AHEivSaKKJE4adUAnnPaagjFABpRuvBPdzDb8eMCbOHvQZhzBuDuFys79WIiyTUB2czNFCkrmAEkxfoAuMCi/g+LzbJ8qDm9KUYHFANg2xbbSDAnASCE+DMhxD1CiLuFEF8RQgwIIbYIIW4WQjwohPiaEKKm9q2rz9vV95uLuID5wNEOdJJeknZsnJAurTW1gPpPK5kPkACglzvvvhSJKJZ+E5CUCFRBuEAISAk0WunC38lvw38PZO3gna6lHRsBkIlCSuSiBwNwOdypIUz2c1FOYHOcIvwKfLxVVf2VynHc+ugRNKPuz3kh4M6FkXrKAJZFFJAQ4mQAfwLgHCnl0wCEAF4D4CMAPialPB3AEQCXqJ9cAuCIlPJJAD6m9utLHJ5s5n7HY6dp0rkP0SQT9Z9WMh9oqoXVmL4WwgcgvdpTkkiEggRA+pk0/1kxAMe/4UMrTjCsXm5XE42TxWcA+U5g+9riREJKmREEcz5/wU5guh4pJapK2LfjBIcnW/i9T92A79+1Z87nKAKuAujLYF7qJqAKgEEhRAXAEIA9AF4M4Bvq+88DeJX6+5XqM9T3Fwoh5pgSMj84NNHSf7thhPT+RD0wgH60S84HMgxggaKAfC9PLKUuCU1RQI12Op6ZCIBsaGT+It5sMyewJw8hkYsbEMAX3VbEfQA220mkLHyxBhyhM0dZyB2/CRNW7VhiqhVBSmCiB1/PQiDrD8r2RViyJiAp5W4Afw/gMaQL/xiAWwEclVJSGchdAE5Wf58MYKf6baT2X+seVwhxqRBiqxBi64EDvddkLxKHJ40AcB8QX+Tywh1NJvDxJQCyPoD5ZADZKKBEOYZ1TwABSBgG0IsJyFSutIV4p2uZaEZYNVRT+2cZAJBvo44TiV1HprqOay7ggtLNyk23kUZts4XCisEVaAJKnMWTt7o0Dvz+YN7uM287SoX792JgLiag1Ui1+i0ATgIwDOBiz650hT5tP3P1UspPSynPkVKes379+tkOb044xASA+xD5wtCK/Cagfs8DKFobJROQTgQjE9A8Xn/kMQHRYmBMQMoHMAMB4DZD0WaSDmxushVhlQrxyzqBzXh9+OHde/Frf/8TS+koGrHFADqFgUpLQy+KwFpO4DnOPT53JTteK2ICoE/0Lncu+PoiLHao+FxMQC8B8IiU8oCUsg3gWwDOB7BKmYQAYBMASoPcBeAUAFDfrwTQl13T+cuY0e5ZhmgeA+AZllJKfOWXj80oTjlO/ElOReDoVAtPu+xq3PDQwcKO2XRMYQvDALImIDq/cQKrKCDyAbR6zwPIhETmXIuUEhONCCsGKggDkaH9SRdlYO+xBtqxxOHJJvaMTeOSz92CiYL6KNA94kO3zTFmLgO2Rk3XVgSsMNA5HtM1UXGlw4SH9ocEcJUG3/uw2NUs5iIAHgPwXCHEkLLlXwjgXgA/BvB7ap83Aviu+vtK9Rnq++vlYtdCzQEXAO5D5M21Ka2+EwPYfXQa7/7WXfjRPXt7Pv+z/u5HeNHf/3hWY++GQ5MtTLdjPHaoOLNDw2UACxEFlGSjgOiz7QOYnRPYLQGRF9LajBJEicTIQAWVQHgTwYD8iLAptdhPNmPcsXMM1923H48cmOw6zl7wJ1+9HX/1zTutBZHmbCoc7TG6PoCi+wEUcUxXQPEChG5jm8WGWwzSnwm8uGOddTVQKeXNQohvALgNQATgdgCfBvA9AF8VQnxAbbtC/eQKAF8UQmxHqvm/Zi4Dn09YAsB15DCTgFno7N9zRyJpx01P3Zo8jDcjjM9TNy03UqcIZBjAgkUB+RfbihIAaS0gYHomYaA5JqC8hWu8kT6nFfUKqmHgzQMA8gXIlBrTZDPKNGSZK3YdnsJgLbQTwaIEQqRCMmMCclhVUQLAV4J6tuAFBpOEVaCNZFe2ttDIMgDFtCwzm9nnn69/EKuHa3j9eactyPiAOZaDllJeBuAyZ/PDAM717NsA8Oq5nG+hMNUyC0Vej9l2BxMQryipnYh94g+gMc9EIOWhGgq0YyPkaDIbwTg/10yRIO46qU1AggSAHQU0EwHgtkXME2ZkrhkZqKASZk1APHHQBxJOk62YLWDF3Ld2LFFzBGWUJAiF0PeGm4hcE1A/FoNzx0eHbvelCSgd3Ad/+2m4/GePmL4IbB9uBLnqzj04YeXAggqAMhPYA55I4kbyaBNQlOQ6gbkZgRbcfkkK05E6BQgkMrXQ/XIjXubrmt1YfXd7yHwA0vIB9J4I5iaE5S3g1Pd4pF5FJfAwAHZPrrrzcbz5c7dY30+1In0ctx7/XBElScZU1o7TRDm6N+6CyhfPotbRIovBxc74tNIxSxPQwYkmDk3k5/3k4Z7Hx/CKT/y8Y99rWjvO27IGw/UwE1pM10DgkYULhVIAeNBsM5tlzgsdJTKfAbDyAToruE+SwkgraUdzf7urQTp96H659u75ouK+FwkwL36Q4wNo9JAh6poRuvkAyAQ0XA9RDUWG6fFkq3d8+XZcf99+6/spzQAitm8xc4UYKB9SK0oZAN0bK6rGEQjFmYAK9AE4402YgJ2NCegv/v0O/PW375rxOD78g/tw1+4x/HJHfhwLzYVKECjlQN0HNjy7TlNSyHs5ExzXAqDRjr0SlzOATFQHmYDixCQ8dWAARS64RYAW51Y892SZMCQG0DkhrmjwYmAc9LmSSQTrPQoocoRXp1IQjXaMo1Opv2hFvapMQPkMwAdtAmpGxu9QGAOQHgaQIAyEvje2dl5sxI4eR4HF4LImIK5sdQ4+aLTjjIA+PNnqGIL78IEJfOf23ZntA9U08Y8riy7omVdCgWoovCU2rGcTSau0+ELguBYAl3z+Fnzgqnsz2xvsofoyOwEnDyBn4UsLxs1eq5uPICkaRxHmmUqOCYjgJuQU1bQjjwHQtYUswbwZJVpo9xQFlGcC8iwqZ773h3jbl9ImHyMDFVS5lkfH62JC0gygGWfCMueKKE4QJ0lGywwEdKE8N6xyPmLUCzUBOQ5lLqDptuWZgF71L7/AJ67fbm1rRf7GQoSv/PIxvOtbd2a211UNok51h+gdq4VBGiKso4DMPvxRR0mCdgG+uZnguBYAe8ca2DPWyGxvRjEGqv4ysz77fl4mcKqBzX7BbXTQLmYLt3H7XECX3QsDuPXRI3j2+67B46ot3lyQV6yMXiZtAgpMmCXQqxPYFurdFnDCSF05gTP1X7KLOhfs3AfQS+G5maCdpCYgfpuiWKISBjpJzg37nK8wUPLLzDXCqZPPIkr8Chlh15Fp7Bmz518rTjoGREy1Yu+70gsD0CagMEgjxKgUBOxr0GOJSh/AgiKRfo2k0U505b68MNBWnF8MzkQBJXMKu6TFoUgUGQVEE3yiEWGCLWAEvoDsGZtGlEirztKsz8sSl6zz6TyA9HMghBXR1ZsAoHP4fQF5WDFQQSUIcqOAeA0efqgpbxRQUaaX1DFqZQLHCQIhdJKcLZjsuVwUAY1iqTXmubJa2wcAr88i71VLTb72+bsxgOl2rKOlOEhB7ORXojlTCQUqATcBmX1cB30RwRkzwXEtAPIybhtRrKs7ui8+TeCIOWzy8gD4A51NGOhUD1ErM4UpVT33iUbX+cWbHsXTLru6Y+ZjNAdTWN558zKBQ+WcTgWAEaKdTEB7xxp4zvuvwf17j1nH7qUhTBgI1CuBDovloCHmJUNNtTx5AIUJAJmZ46kPwOQBZDNrze+LykdoxwlqSgDMddplTFYWA/AzQxoDD9wgtKLODICUBncdGKj04gNIf1MNAoQsQiyvHHTqBC4FwIIhTqS3bkiznWBYdXjKNe+wxd19Ubj2qE0uszABTc4jAyjCBORet/vitaIER5SDrcg+wb6MSn7sUJhEMC5EO4WB7joyhUOTLTyksnC7lYPmL/FIvQIhBCphPgPIc66SUJpqRbnhrbMFLXj2ggMrDyBybeqJsVsXFU8fJVI3RykyCsgVYG5GOgfd54wAYMEcPtCccZ9rnRhAp97gjhOYPvPHS0NNlMO+ZAALCFeDAFTceBRrE1DWqZf+306SXCewFQWUzJ4B9NLCcKYoMhO4W9TPD+/ei+d9+Doca7QLbZLDqfTPHzyIa+/dZ20nE5CAEQBCAI0OQo9evGlnEckzy/BrpblSCbIMwBcFxAUAMZSJZpR7rtkiUg5g93iUB5A4EULcCVwNRXGCKJZ6wSzSBJQ6gc13lJTnM9c1tACwv2v3YALy/a5ODKDDbykPoBKkyoE3CojmR1KcYjYTzCkTeKnD93K0ldNsuJ7T4YlFAfWSB2CigGY+8efDBxAVONHy2BFh55EpNNoJxhuRFoBFLG46QkdK/P4VNwMAdnz4ZfrZBKwaKN3DlYNV3RnMB3pmrhaZ5wTm92/FQPoa1SpBppCb23SFHzNJpHb0T7XiQk1AUkqdh+Iu5DW1GKU+AGn9hs5drQSFCaJ2nGBFVTHqAp3A0lHgppp2b2qORouCMbIMoNNlTrc7K3DdGEAlSNlWJRB6kefn46Vl0vEVc897xXHOALITkpw6Q+QDyInq6CkPIDZ5ALOhdvPBAEwewNwEgFtlEkDGfqm16TmGw7ro5gOohFwApGNYM1T9zKnoAAAgAElEQVTDdDvG40en8aEfbNPx+3rs5Bx3ktrykrO4ACAGUAvzw0B9rRi5T2KC5QEUkT/B56C75tYqgc4DsKN+zFyuhkFxTuBEao25WBOQ/e6RydQnZHwmoCSRXR2vzRwfAI2jkxO4HSd6LvJCgdISYmZfoJgM/ZngOBcAWRMQLQAjNb8TmDt427l5AIn+bZu9iDNF0Qxg37GGEUizYADjjbaO5fe9ZO7LoCc3M4V1WwAOjDcRxQn+9sp7cOujR7z7+OKpeTXIgPkAiKKvGqpiuh3jx/fvx//76cN4wf/+saW9uYLRLcvgjptT/xHFAKphkLmvvn4CrSjBwYmm5Z+YahoGUITt3WQwZ1luNQx0HoDrm6BzV1nUylzBncBzFSqm3lPWB0BlGbwMwGPKabM5mXeteb4D2n+qg5LWjk3T+tQ/RALA7OProjdfGfQ+HNcCIE5kZoGniUJRQG70h3mhe8kENv1gZ0PtJguMAtp5eArP/dB1uOGhQwBmp2k8/W9/hF/54LUA/At53jXGSW+msOlWjF/96I/x7dt343M37MBP79/v3c9nSz000cqUg+YdR1cP1dBom5ju8UaEnYdNSey8FzxmwpyDL/QbVwwASDXrPB8AZxDv/tadOOcD12JsOmUhKwerKhN49uZCF9w8mTEBVfx5AJKZhKqVoFgfQKVYJ3A1DDLhmdQKshMD4IoYf4Z5CpF2AudUee0UqBEliREAgXEC+5LtLMG0gCzguBYArhMMMJrdiPIBdHLq0Xd5mcDpwue3PXYaE2G6QAZweLIFKYHHVSLMTBkA0dZGuzdNnoNXRe30u/FGG1OtGPuOpcl5eZFTJg/AfH9gvJmJAgrM+o/Vw6kA4Jo7/ztfAKj/Y3eepAvDe1/+VPz1y54CwM8AfC/4tdtSwXZQ5USsX1HHJIsCKqRxOhMm7vyshiYPwE6sMvOv2CigBHWVOFVUMbjURGWbIYkBdIoCajlMzPe373d50V2dQrWjWOpseZ4kyEenWTIfSykAFgaxzNKtLAOwv/fZbzs5Q6n+TK8N4rn2V6QPgCYwUdaZahkHnQSumWiHPGu6IwOgl5Q6iuWM0ZdQs3+8oRcsXgyOsHqoinYsreqN/EXL0/Bos1vMj4THptWDWKnaQdYqIvPy0hh910Jd4taN1JBIY/Lrda50QieNslYJmQ/Ajk7iPoDCagElJgy0qHLQlVBkBBjdP29yZys77/mzaubUxsqLHqI52LEaKDMBVZkJyFcLiI9rIXMBjm8B4LG3kWaXlwhGiww3z7jvK/8N2cV7ZgCeCZ2HPWPT+P5de7zfTbdifPHGHVYHM8CEys2UATywb9z67GrEnZD6Qog5dA+5o8U1757xF2ndSNqM/cB402ReBlkGQE3beWvOVgetyxRm8zMXGiOZNgCohjA5eQAewUeO6PXKhHRsOn8Bmym4xuoyqVoovD4A/j7w4mVzQZykUXUUBjpX5Vb7KMIgbQhjmYDyBajvPeQFGg9NZIvCcZafp+R1ZACJcQLzdqH88foYYskAFghu6jvAnMA6Csh5odX+vMZMXjQKYGyIbl+BPFgMoIsP4Gu37MQ7vnybl6q/5zt34b3fvUeXq3Un7Ex9EvfvTQUALa40zvUr6l1/yxPi6P9mFOMDV91rLch0r2hxzTMB0bVImdr2AWD/eDNTDlowZ/Co0tKPTvkFQKaMs7Nw5/kAakwA1MIgo73FMvuCEzE5PJmOZe1weg3jzbb3XLOB63TmMD4Atxy0eR/Ixj5X0IJLgnKuwo2OVw2yDIAYs+8cPibOK+Je/PGf4ez3X2P9xg4S8Av2jj4AZgKqqhwRKWUmMc89/kJWDj6uBYDXBNSNAaiPVtewTCaweZiaQvaocXPNeqpLW0iqdOnrNXDVnSkzIApKE4wm7ExrAREDoIgXesn+7CVPxide++yOv22zcFi633fuGsPlP38ENyqnNMAZQNZhx8GfCS2mB8abetHmmcBAujAPKhv0USZwbB+A/xnSdUppC3oaI4U3AqnjNFMKgvmMCLQoHFEMgIRTkb2U+fncipU1VQwuSex7yUtBVMNi8gDo+EUJANKOB2ph5pm4SXwcxgfg9wEReGDAtBMlRvN415EpfV2do4ASKwqIxsaHJz0moJIBFIQbHzrUsQSx+zAAwwAoESxPM+zUNpIv4nlOpNwxWXHNnRlAXoQRL3DlJjLR4Vs9NEfheFiVSND2eVpsAyNk8hAnLBxW/T+mNHHrhVT3nv7PMwFxMxJd1/7xBssEtn0AtYoRAGMs/p9rxq4QpUfKny1fLFs5JqCWU3rBJPqY49P4yOQwqoRqK8fcMBvEnrHycYocJ7CVCFaEKUozgGKcwHQtg9UwvxSE5xQNTzinjwVfu22f+Q3rH/G1W3bi/A9fj8uuvAcXfOTHOKi6iHViADwPIGTM2TIBOWGg/BoXAnMSAEKIVUKIbwgh7hNCbBNCPE8IsUYIcY0Q4kH1/2q1rxBC/JMQYrsQ4k4hxNnFXIIfhyaaeN3lN+Frt+z0fp9n2yUGMJLDAIwpJd8EFCVSa5+6mFTPTmDz8Lv5AHRopbNQ3v6YiZ+n79zrmKkJiOyrjXZsZWCGQVoErRN4OCzdPzL9NJmWpRlAl9BZfi9p4d4/3syEgZIPoF4JMFhLpzpnANwE4NJun+2eP5um1wQkrHGndJ/OZY5DDOWIFgDEAPI12Jmi3cUEpIvBuXkAasC1UBTSj0LXxC+oGBzd94FqCAlbYaIItU55AJ1MYwDwd/9xL96uejzwvJaHD07gwHhTN4eZ1O9Dkvu8okSiEpAT2AgAX0vIpRoG+nEAP5RSngngmQC2AXgXgOuklKcDuE59BoCLAZyu/l0K4JNzPHdH7Dg0CSnhrfcPsCgP17mnGUBeFJCHAXjyAKhaIKWS90rruCLazRaoyzo4x955ZJrtI619CTOlmbQ4JxK60xSQmjO6MQAeBkq/o4WY03DtA+jKANiirI473ogYK7F9ALUw0M/jyGTLaNw9hIH6qk3y33IGQIucr0QItyeTj+KwYiMjzniKMb3ka5TVMPB2BOM+gUpQlAlICUqKAprjMUlhIAaQeO6x1wTUypqA3PtCAQW3KL/ZtJOoBxhW3kuwRjtO9HWTIIgchqhNQB3m4nxi1gJACDEK4IUArgAAKWVLSnkUwCsBfF7t9nkAr1J/vxLAF2SKmwCsEkKcOOuRd8Gjh1Jb3sGchs8669ItBdHuzABo/8kOTuAoMcWvpmcY2sdf3G5mo7z6IceYlmuK0WWF1ExecP4yNKNEm2GCQGiam4c4SbQJiH435hMAjg+gWxQQ32eKJVLpTGC1T60SYKCWCoBjjShjcweyJiAd/8/NOewe+hiA62+xHZRmvpCAOjLZQjUUurb8XOpGuegUVVKvBN5M2jhhTuBK6gSeKwugeVeUD8BiADK9x/QMtA+gQ5a6bWu3zaCveOZJ+IPnnqbvP/cBuKYePm/zIoHS5jsmDwBQJiC2jy9RcKmYgJ4A4ACAzwohbhdCXC6EGAawUUq5BwDU/xvU/icD4PaYXWrbvGCHEgAHxrMCgDeGyAvvG86pBRR7Jof7viYWAzATrx0n2Pyu7+GLN+7IHbf9QnZ+WfJMQMeY38PVvO3f9z7RptuxNqk02rGm8j0xgCSbEX1MC4BswxYTBpod85dufhRX37OXXYNiZO3YYwLK+gAAYIUyuVhOYNcE5CnLYDMAjxNY3QdTJdYcb4rNF20CmmpjsBrq/gX6dwXa3vlx+Th1OWgnD4DmC5ksZiOLpJR4zvuvwb/8eLueY9oEVJgACLTTesDJMvYzgESNLS39PTbVtpr0AMBQLcRgLdTKDn/H3VLivA9AXi5AO5Ha+WsYgN8E1OogsOcTcxEAFQBnA/iklPLZACZhzD0++NTEzJMSQlwqhNgqhNh64MCBWQ/u0UOp09LHAM587w/x+svTKpLZRDDjZBIiq4X7bHa+fgC6YxAzZ5Cm8P6rtuWO2xIAOS/L/mMN3LLjsB6bu5CPWQzALySAmUUCTbdiHUvfjExtn0D0ZgJyBa7xAWQjpmjB8r0In/rpQzrCKT02MQBbKAFpS0hAMQAmAIzTNZ9t+XoBW45VZ2EDjJnDVyKELyBkAhqbbmOoVtHjNc2DijC9ZNmKHqdiAFLai5FkTuG5aOy7jkzj0GQLH736/mwU0AwkSjOK8dCBicy2MBBpprIyAdXZswX8AoCb4C74yI/x/I9cn5lfQ7UKBqth2gUskVb12CwDMN/lMbYoTlANbAbQju3qo4nHBLRUGMAuALuklDerz99AKhD2kWlH/b+f7X8K+/0mAI+7B5VSflpKeY6U8pz169fPenA7tAnI34Lwjp1HAWQX2UYUoxoKhEFawjXPCczhywMgzdA0lDBacCcJ77Nvt6LEmsCX//wR/NEXtrLvXROQmay6Dr9n3EcmWz29kFT3aJUynTTbMbMVC72A5f4+Ma0HI1cAeE1AtBBm79PYlB3VRdfVis09cjOBeRgoYBhARx+Ax0dkhVa2O/kAsr+1TEAsQ3moFupxFsoAPMKKYHwAbjE4M5dJY52NH+DmR1Ib+ukbRhgDoCig3o/zrdt24+KP/8yysTfbCeoVxWCS9DlVA6FZX3qOzgIASIMa3IV2WDEAIJ2DnAG4/bn5vM0T2JYJiHoiO1FAvjDhhSwJPWsBIKXcC2CnEOIMtelCAPcCuBLAG9W2NwL4rvr7SgBvUNFAzwUwRqai+cBjigEcmWpZC0kmscvjBKbFmzvCDow3ccNDB72TK04ktu+fwEs/9lMcnmwhShLGALgJqPuDpYVHCDO29191L/7ws7fofcYbbUw149w6Q8cabb3g6egbz2L6or//CT7504e6jokaqawaqqprMpEPYSgsLdh7TawURNYHwGm2EpDaXuv4XxKJcYdu8+dJkUomD8BvAhqshagEomMeANn7fYW7gHRRFQKW8MuagBgD4CYg9pvBWqgXCc0AZlky+/t37cHdu8fSY3QwAely0Il9TTwstKJNQDNfjH75SJrbsWn1YMYHMBOBcnC8iVaUWCVRWnGifRgUjRYEwopE65QHwOG+N8QAaP9OLUT53Mm7R+0kMSYg9X+UZDu0uWNZEk5ghXcC+JIQ4k4AzwLwvwB8GMCvCyEeBPDr6jMAfB/AwwC2A/hXAH88x3PnYmyqjSNTbZy6ZghSwkrxHm84DTs8YaC0eKcdntKH8W83PYo3ffYWr8YcS4mPXfsAHtg3gZ/cvx+JhKalvJZIL9SOvzA0kXccmtRF3ICUVaQllrMOJCBdXNeo7NK8TFbCD+7uLoNJA1utTUCGAYSiOwPwNcbphQG4L8J4M8qUE06kWYQn1LMl049xAocYqJmpXq8EqFWCnhgAv28f+sE23KZCbJuR0UQJVUbz+TEAvxMYAIZrFf3ZRAEhg8lmhOd/+Hrc9PCh7JcK7/uPe/H5G3aoMfiZC+CUgkjsxYiea20WCzbhlh3pPZpux8YEVJ25SYkct26uSJ3XMpISoeOH6lUAuO/jUN0wgKlW1LGFKA9fzjcBSW0Cov/bseME1nkA3L+0cAJgTh3BpJT/BeAcz1cXevaVAN4+l/P1ChEAf/2bZ2K8EeET12/HgYkmNoyqeitOYphrAuIMIGT1UKZVKWHfw04SiQPHUl8DFQYbqNpOYPfvPOgXkBWPmmxGFoWdbseQktvMXRNQG2tHath9dLprpyGKdnLx2KEprBiopFU0lWa+csg4T2kJ68UJHDPzF2nWVJLB5wPIEwA8usm6hoEKjk6ZXgUV3RQ+/b4WBqip5CcpUwdiVwHgCQO9+p592Lx2GGefuhqtyIT4EaoV2wdgdavKSRwkNgJwAZBdAA5ONLH76DQe3DeO5z5hrfc+NKLYqxTkMQA3EoybhHTi1izWosePpsrKdCvWz53u1Z6xBvYda2Cjeh87gcwufPzNKEa9GiAITBRT6Pih/Cag7IXsH7dDxIdrFa1gNNpxx25fXHHJE5JpIlg6rpCZgPi8IDZgMbYlxAD6EqMDVVz6wifiV5+c+hC4H4Dbx4HsBG+oCQakCwm9UJ0ck7GU2KcmEy3yxolm9uulybt2wlVDVnHQLmPM2wgCHk25EWltnRaTvCJsI/Wqd/sffu6X+Ni1D1jXtJo5gXnWbU9RQIyJSCk7RwHlVGAcyxEA1JKR2J0pB53+T5o60ft6JUQ9IwAcc1NOnsiUrlcUa7s2oU5hoLSQ5zAA/rwsH4A2k2WvkZ5/J8c9V1C4Xdr9TTW0F1BCwnw1c3ECa8WlxRlAeq+u+PkjePe37urpOEYhsEOQayyKKZG9mYB8i/ljh6etz0O10JiAWsncTUBONdB0m+ME9voASgFQCNaNpIXKDrJQ0F4YwID2AZgmDp166cYJdA17MkMMOJEJgBtL759cNgNIzzXRjCxNmSamcTBnNWUqMNbuygCy4wRSDZ0WXCMAUmGx8/CUdrKHPeQBRLFdVbHRNs10ZmICcp+duYaq+t4xATEfAAD9cmsG0OGl0z0dnPnBx1ivdGYAfBG268qYc3EfgAlj9ERstXsTAL7opWw5aOMEpnOmJiHzO9LYn/3+a7D/mD+Z0oeEsYrplvFTcbZ0ZMofmOHCzwCSlAFoH0DK9LqagFoxhmv2XOd1f4DUBzDETECNdoIu1k0AHZzASaIFk50HwPxKEnjW+36Ev//RA3rbkjEB9TvWqUqVPBTU1SJ95aA1AwhFRqPiDycMBFYPVa3m3uSIHKxmZSvXAo9MtnHCyuziG2uNKUBzOtbHbERpCQYhhNaQpz0moFg5SskH4Es0qVcCvZAEOTO8GRnHrW6srhjA//edu617MJM8gCiR1jPwZgLP0ARkGEBbjwmwTUCAEcr1Soia07zFfYmltDNjfWPMCAAnEYxrhlwA83MNMRMQwbeekMLQzNFKY8WyTOKfvWi64xRCYOeRaVyjat9Q/X8y0XHH/vYDE9qE2g08oW6qFRmTEnsfmh5zjA/aB+CagLQPIHVaB0JYAiYvEWx0sGrV19p1ZArVUEAg7eMwVA/1+KeVCWi4Xsn4DQlkRsxjAGk1UNsEFMVpFFCoWm5KKbU5lEyUJQMoCMO1NJafL7y0iPCXzqrymGEAtgbNNasTRgdQCwOd0g+YkrRU34WD24EPTfozlLUAqITaXDKhnJ80Blr4fSYgWgTXqLT2thN+CZgkN8BPjWk7CZaGwwA4Qod+E/iaRotT+ndiC4B21gRE6NkEVHdMQLoURPo9LT7k3E+dwKG1MHpNe0lWAJAwbEVJJvqJ7gPdtzzbcMsyAVV0Ipg5r4cBdDEBuWUkeNivLw8gFGlk2y9VyGZNddii3/FrO5QTSu0DvS9CkAnINikBnRupczQdRphuoyggocNWXRbqs3ZOt2KtKBAOTrRS/5Aa2zCLAqL2ocO1fB25riN78n0ANCeqLAooUY5rwFYSaN3JK4M+H1jWAkAIgaFqaEl9MiMMMjpoFZRiDICkNOCXyptWDyIIBB45OKm3kZ1/lWex5D4At/kEIWIvYBxLy95OWqAxAakyE2zGk4+DTECk0XGtk4dE+tLYozi1JdM1U3jmqsFaZt88BsAzZKPY7gjGNXWfCYiQZQB+TUwzAFVP3/UBkHZIz3ygGmZMQL6cg1hmW4ZOdWAA9NnHADj4gjFYDa28AMAvODQD6CIAfIl/bilyKgfNUQ1Nh7BKIKwx5ZVT8YHm2crBKlpRYkpmhLNgAB4TEIWB8mqm3aKAkiR9j3xKWa0S6Oc2VA+ZCShOz1UNrKgt97d0fB+iJJsHQAzA5AXYx3OZ6XxjWQsAABisVaxFjhaRWs6EabRjLYmrzA7vi83etHoIYSCw64ixJZIWSlmzHNwHkCcAEs0AArVYmkWPXgj6X5eZYCYgEnDksG17FoRhZvf3hbrRwuiagHxCrRIE3jBQrkGmJiCjFdO4Vw5Wvc5tQqcMZw4qpjbhMIAgxwdQrwSoh4HFPnw+Eh8DMGa32BJyQDYPII/J82MO10OEYQ8CQPsA/NoztTR0y38DWXZTc8JXgbSqa6yigEInAXImDIAYJ0XDEeMOA6FZYa8Z6F4nMAsDlTK9XjcbPePXU+dzGQCQPjOaH0PV0MoDoEivvDDnTqGyZD4kE5AuBZHIXAZQVTk1pQmoQAzVQq0pf/6GHfjZg2l5CS5l+QMkJxOg2rh1cKKevHoQoRDWwkXmpoFqqE0ONFF4QsuhiRYOTjQzZg+eOh8ndg9behEoRZ2GzV9wWiRXDlYVg8magAYZrfVFOrjVOLUJaNjHAFKm5b4kXDuOEzsTmO7X6EDFTgTzmIB40kw3J/B4I4IQyCxudP+1D6AHJzCNOy8KyG8Cyq8GmodBVgqCn9eFNgHlaM+03VfGuhnF4LckzQQ2n1/5rJO0UzVK0siVHYcMq50JA6BrJ22bHPPkd6Dx9AI3KIB+S05gKl9NpSEIrkZOx1nBGMAG5R+sqZyQWiVAJQw0S5xuxfoZ5/m46Pn762xJdd2OE1hFAXFGQKio0uolAygQQ7VQv7SXXXkP7tiVZkryl9+uKR7bPoAOJqCTVg5knKi0YFcCoRcmHllAODDRxDkfuBbv+PJt1u9jZjONkkQ7lQHzIrg2VK7dk8Y1Oli1fBj2RDNj9pmA3JaM9AKNejSoUNc7t6cSXxx5ElzESjasHKzaeQA+cxR7ufIYAPl60vrr5trcQmQkAAZYGKiUEjdsP2jNB62dJemCyhfPTk5gtxy0rq3fIVN6qBpmTAy+BaWrCSi2Bb1tApKWSYebgC48cwM+/ppnmw5hcYIwEDhl9aDeP6+cig/cBASYZ1ZhOTUzZQBuFJDuaMbyALgPIC9ya3TQzN8zTxxNx6WExzAzDwJKAMSJEgw5DKBDo3uaA5QHoJMEEwlA6veGKz2tOPH2lZ5PLHsBMFgL/VmA7CYnOQygwk1AHgawcXQgY7+lUgVhIPSCSbSSL7YP7kuLXF27bb/1expWvRIikXBMQLG3pAT/TOcfqVdsAcZMWBtHTR9fnwlIl2OOyAQUW9fBQdfvdj6yGUDCnMBSv9ijjgmoW7p+XhRQEAjtrOO2bTcEcdBlAFGCGx46hNddfrPueAaYlzWWErG0zYWdncDp52Zka+OuoOAYqmV9AL4FRQv/HKd9JydwK04sRaVWETpUlswipFGnDEDg9eedhh//xYtwwZPW5QYs+ED33DUBVZijmwRvNzQ8oa/0fuo8gCQN+7VMQM770fAwgDNPWAEAODTZQq0SYEjNn2qYauHT7VgLm0owcwZgqqraJqA4SZAkKXPmYwPSaD+Xmc43lr0AGK5VMMlqxRP4/Mv4ANRCYZuAsg/lBIcBDFZDiwHQy0W0kpzAJ68axJ27jurfcOgGGmpyuSGTvgWAT5gpLgDCINOJCwA2rBjAAx+4GL//3FP9JiAnDHO6HacvgocKk/265mg6lhM4sRvCUG2h1AdgIpm8VJr5N/IYQBgIzbK4Nu1WouSJYPSicf8Nga4lStK+B3wB52YJVwDUtAlICTs1yXw5IYTBWtYH4FM2OuUB3LVrDHtUqRAfA2iyUt7pOENtjqHeCEGgykMrH0AQCGxZN4y1I7UZmoCkOm4698ls50aK9cICmr4w0DaFgZrqpRknsMsAWiQAGANQAmC8ESkBwGpFVVOrAQn5PB8AndPnBCaFq+ooRm1VDjoQaTkOroCRz6HMAygQg7UQByeaHW8qTRgp02gBqi9eDYV+8fji9Dtnn4xNqwZxxsYV4GviioGKFgBhILRzUpuAlA9g0+pBXTHRdayS9lfXAsDQ72Y78aa08wWDIp6G6qHFAFwTEGk9Xgbg+gBasRU1xUEvR4UJgkbbXhyjWOoXIk6kdr5yJ3CeZsuZy7FGhOFamOmVHAomAHwMgASApvkm2mLfsezilmb4RtoERJ/TcSY4NNHE2HQ7WwrCqQWU9MQAevUBZB2iQLpovOKff85+m52vrdguW1GrBLqUh2EAQvkAbG193Ui9oxNYSomfPXgQzzp1FUYHqsYHoBmAUoice9WMko6CEWBRQE5OAw8DJSdwrZLNBL710cN40oYVhnFaDGDU3I/QEQC1UIeB1pkJyK0O3MkJTO9bxTGRRqoWUCDSSKspZ94vtBN42QuAIWUC6iQAaI1JG3qbtPUwCNBOsrH2W9YO450Xnp7uwxac0cGqTuqohAIrlA9Am4DUwz5lzZAWAESVCW71RJsB+OuT8LFNtSJt16yEfgZTZTZxqn3OmYzRyo0PwGf+AYzJhSY4LZZcANB9pevjPgAyB+Sl3fNxT7dirB6uYbJlp/AHgdAUnl8HXTuNpa7zAEJtAnLrwaTfqxdbZZq6C/hrPn0TptsxXvHMk6ztFeVcdZ3AZ2xcgV1H7DEThmpZH4AvkSkvD+C+vcfs36qv3fLCA1W7aJ1xxCsGINR5pbRs3mtHaphqxZhqRfoec/zjtQ/i49c9iHddfCau27YPv7J5DQBmAmpk827S64gB+MuQEExpEHM/oyQttR6reHpyAnOhlcg0eOJ3P3kjXnD6Olz6wicAsBnAlnXD+u9LLthi12dS7wX5AGhun/eENTgw3sQDynzbqcKp8QHYChKNW4j03XEVsNQHUOYBFAZyAlOY3IvP3ICnn7zS2odeOHq56MFWWBQNfyh8keF/rxioYELFogeCm4DS/8k8c8rqIf2bUUcAuNUYuQBotP0mIP6yTzZjDNVCFZkTWLZ3AlUmJK3HdSobJ7DxAXRjAPSS0L2zEn/YmKkURCBMQlozSnTBucy1RbYmu8ITyx0Kcy2WE5juZZh+x8NAayob2scAdDesJDUxuALgwf0TeMsFW/DCJ2f7VVQZhad59ZtPz+986vMBzCQKiPpamN+axZKPm/tGapVAM9UVWgCYHsFcIK0bTv1FPhYwNt3Gx697EEAqnG/ZcQS3P5aOR0cBTZMJyGEAXXIBImYSpNaNuurypEoAACAASURBVA+z8gFQGKhbDC5OpDZbPbR/wivsBlhm8kvPOgEXs2dEoePNKLbCQC88cyO++/YL9H7EqnwCWzcMCs1aAqh1RKaJckGQZb7pvOwtSqoILHsBMFitYIo1f7jorBNw+oYRax+i6vQwiAHYUTTZKBGAJx2l/gaabNUw0AsclYUg08XJLMIi00xGm4DSMRydshmA31FqjjHVivR501IWSoCx8xAdH2QRDxxuT95GJwYQGHoMMG2b/c8neZQkaEaxCpPNNt8YcgQNNwG148QbiRQEAkP1LAMgJ3a2FhBjAKzODY2ZO/eiRGZs/UDq//GhFhonHg39lDVD3n0BygTuJQ/AbwK63REAJmpNWiYWfo5qGOjoMrLV68SqWKLKtGkqKXJ4suVtqkKg78jPxaOAhEDmGkmgXbdtHz73i0cy19tgTKel2Y96PysmiilW7JWbgBIJ7Ff1v1YO1TJRQGm3P4F/f+vzcNU7zYJOGKwG2gRE4aFAypw5O+rkBKacFFICeT8A8gEEQmQayrtFCucby14ADNVCTDETUK0SZEI3dXiaWrzJB8BrAXEtm2ts3ATCtYpVQ1Wr9gyQLs7VUOBEtnhMt2Ncfc9eTeU7MYBmlHht9hYDaMWWQ9TnFKRJPMiyHjm0DyAyTuBeGUBNM4F0/+F6xRJaxAAGqqFecLlgc8tTW6aMKMkwJiB9BiQYrOY/jkNdC+RaiHolRCtOsJcJALpv3LabJFkGAADrR+qZbUC6SLj9AMIAuPwN5+Cis07I7D9cTxcjvkB2ZABRNwZgnjefjxYDCLMMIAzIB5BkGtYAwLY9x3DWZVdbJieeYUzCgP43JqDIEijmetLn/dGr78f/+dEDmaggLmzca6eWlkA6P1wGAAB7x9LnumqwqsOLiQHQNf3K5jV4mmMNoO/tPAAV2OAkhblO4ChOcPnPHkYzivV7S/OVM4BEQguAaYcJETNdKCx/AVAPIVk4pc+rb0xANgMIWUcwrmVzxq77zoaB1Zt07XBda5zk2J1sppSSh2E22jHe8+278Nmf70jH4jgOx6bbegI227GlGRFsE5BhANXARAFxLYVeSFrwXFbhNmWfauUzgFALAJsBPPvUVTh38xqsG6lZjutIhYEOVEwGZpOZtkYcDT9rAvLlIgi87txTAaSN1vW5yAegXtRXPPMk/NNrn411I3V9f7kJiGzcPL6bt/fkoEKDLmphoMdMC0MgBF7y1I247Leeqvc7d8safPL1Z1sLsL7mOMEP796bSVDk/wOpZvwwK0PCr5mYC83VwJqzwjAA5gROEugwUALdp0cOTiJOJB47ZKKmbN+Tqk/VtCNu4kR64+ibURqBdd/ecYw3IxxgFXsPT7bw0/tNP/CWY/6qV0KtxJHJyg3VJAGweriqTZx0r7s5nwerFRMFFIYWu+XC2nUC377zKD7wvW246eHD2vdBgtB1Agukz8RlVfVKgGY7wUevvg9/9x/3dBxnEVj+AkA97KNKItfCbG0PeoANhwFUA9MRjJeCsExAtABW7LaDq4erWgOj40+1IlQrgdUMo9FOMN6ItPDxCYC1yg6bxwBacYJWlGD7/glMNW0GEHdiAMwEdMNDB3FI2U11+F2cOmjHptuaPp+rnHyZ69dOYOUw27IWX3/r8zBYDbM+gMhlAEkuAyBzipRpVzFfPZdQCJz/pHU4d8sanH3qKr297TCAlYNV/JZy3LoRPEAOA5DSqmRJWJfLAASr608MQOhxEtaP1C27M//uoQOTeOu/3Yp/vn673uarBrrvWCPTIY3XrqqyMh3unHcZgGA9gvm+JPxoQeNsseUoHoAxAdUqgdVZz0WzneD6+0wOzHbW/P0LN+7An//7HeY8HhMQ3a4oVv0AKvY59igBwCPdRpy8nDzoKCBVC6jizO08AUD3ptGOdfTTKBPwoVpPtBM4ME7gVz7rJHzitc9GvRKiGcW4/bGjuEslrc4nlr8AUFrdUVWxs1bJFwBZBsAXUPOm8d8bE5DQE36kXkG9YtrLRWyCVMMAI/UKLjxzA9aN1NNa/1GSyeSkZiPjjUiHijajxOsgimKJD//gPrzkH36KB/eP66SoaiisKqYmLd0Oi5xsRXjdv96MV/+/G/V59LGTtNvZhhWp0Pr6W5+HW97zEnMvnEQwWlhDtvDYPoCUAdSroV5cmlGsaborAEhw6fjyHB8AAHzt0ufiG28937ovgD8M02fXJwFAwoyiTnzCYt1ItiwG/bblmIBojvASFa4Z0rdIfvO2XfpvXx4Ama/WshIdvJl9JTTaqlv8bVKbRdxEsMQK2aQ5TSYNXtDQ9j0pH0DT1NriyVUumlGMGx86pJnCQ/uNAOBsgF8zD9Kg64mSNKDAfUZ7j03r+0DKxWA1VO9pZwEwVA0x0YzQjtNn71b0pCAK1wnMO9qRwOTZx+n7mOiy7pXA+GLe+eLT8YpnnqR9ADwfaT6x7AUALXLkTO0kAFwGMFgziV1c2+EvMteAqYTE6mFFNdVnfjbqZnTFm34Fr3rWSfrFcjM5adEip65QdDHPB3CHSiw7MtXWDlFXgOkXMrAZAJnHKBuWR2gcm25jvBlhPTN5cH9AkMMA6L5UgsDjA4jTgmzVLAMYzvgAbB+M+316rvR/IYTtBHbyADj4Nlrk6P7w8L4kMQzgJOW7qVeC3FaaqQnIzgMIPVq4axVxk8EA4LHDU5kSELy3ArVefOJ6E9TATZaVINDmvpxkVpMIJkwiWMXHAJRGO8XqWXETEC1kZLWqhIEWLr5qms0owZ6xBp65aRWGayEeYpnYR52Ev4wAqIbapBXF2SggwJiA0sU00cy/EgRdGcBQPTQWg4rJBKY5QwLSDQPlHe2OTbdRCYR1LlIOpEwFbho1GFnHqlcDlfBp+3DmC3M+gxAiFELcLoS4Sn3eIoS4WQjxoBDia0KImtpeV5+3q+83z/XcvWDIJwByUu9dBrB2uI5jjTSCyGIAXACwksMksdeoSpy8qBzBXnjM5HDLB9B+063UbzCg6tfnlUvgmjHVNamwuiJRkljb03uT/sbNsOUs4/Gj6YvEBcCAZ0HNCgCoc5mYc6pv1FSTW5uA2ua6VuQ4gbkT34Wr3Zrf2j4ADtomBHDOaalZK2MCUpmm1TDA1X/6QnzhknMBpOYft+CcPq7XCWwixfSYe2AAAHDbo6lg58+ElBFa5J64wcS0R3GC/9p5FA/uH08ZAGWi5oyXngEvBcEZQN1hADzyh0er8KKFdD3UQMjPABIcGG9iw2gdT9wwgrt3j2mWftTpGOYmwXEG0FZlLlw/gxYAMWnTSnsPBQZyAhoIw7WKCTllPkM3pNM1AXGWdqzRxuhg1ZonNfU+UhQQ92fxHJVmlKiy9EuDAfwPANvY548A+JiU8nQARwBcorZfAuCIlPJJAD6m9pt3aAagMmprYZBbflczAPUw1iqaf2Sq5fgAzG+5BkznIlsjJZs89cRRZl4w5+YS3q3nrgVAO05rllfT8sW+TOB2LK2EMlrYKw4DGHTGQGMaY47TP/3q7fjmbbv1591Ky+QCwFcSwn0pqNhVGAi9uA9UQ8SxREOFgZJ2+aEfbMNO1Z814wR2SlP7FpO8eu1RBwZA4XdvuWCLvg90f7QTODE28TNOWKF9MetzHMA0PtcHQItVEGQVB0KeEDug/TIsG1bNgT1jDYzUK1i/wviU4kTiVf/yCzx6aAoV5hx1Bc4fPn8zAMNmdSkI1Q+AQALC+ACYAPA4gQm1SqAbCPmcwI1WjP3jaXP4M09Yga2PHsGr/uUXAOzQ58FqyHwA5nkKbQJKGQA9Mxr6nmOGAUyzPJZaJfB26+MYYuXSeTE4wwBs1qtbYHIT0HSUMVdSjgixJEsAVEyOSjOKrcZU84k5CQAhxCYALwNwufosALwYwDfULp8H8Cr19yvVZ6jvLxR5alSBIHs4LXJcohMyDEDdeLLzHpxo2olgHgZQrQjTWEKd8xmbVuGqd16AP/61J2mTQR4DMLHjVNvEhI1Vw0DF0xtNmV9CO7bDI6nefyUMTD+AROIJ60dw7hYT+kbn5wzgO//1uNXghswMGzoseun122Gg2jcQmPK2g9UQbR0FZJzAd+4aww/u3gPAFwZKyUD5i3meduvrbkV4xTNPwp//+pPx5y89w1QJrfqdwHR8WkTyHMAArHK+NK8qmgFkfUeEPAYwprRhbpajhXDP2DROXDlg5U7waC9eHTUQAt982/n41O8/BwBw2SvOwo4Pv8waj1ROYJ8JyPgAmAmIM4BWlgGsckIgOfYea6AdS2xcUce7Ln4KnrB+WGdLcwEwOlgx/aMZk+QmoLQpvM1AyTnejhOtcKRj6W4C4l3AeA0skyBqlJtAZE1ALcUA3Cz/akWoEufp/eZz3eTNhGjHElOtaEmYgP4RwF8CoJmwFsBRKSXNhl0ATlZ/nwxgJwCo78fU/vMKejmOcCdwJvMy/d9lAGuUxrffyRa1BAD3AaiJxV/Ip528EmFg6B7XYC0BwBhAKITVJrBeCXR0QFPRWbvcsh0eaTMAddw4wcrBKr7+35+nbca0oOUVWQP8DMAH1zFGw3cdiiYPIMAT14/gxWduAJDe4zDIOugyJiAPA8jTIzr5AFYN1fDOC0/HAGsCQosG1+xilhlLC8/6FX4HMJAKB4pAoXnliwJyNXKfDwAwi6HVFEX9vWesgRMcAcArid65a8w693NOW42LnpbNRQBcE1DWZEkZvVPNrBO4Xgks3wCQPvdVHUxAj6mG7BtHB7BmuIbfefbJiJK0bDhvGs9Lhk+3qd92aExASYIwyAYhEDQDUM/4LS/Yglc++2R0Ar+ftUqg57au7MlqA1VUIx2AVy+NVeScIwC0D0AiCOzqpNwHAKT5E77w46IxawEghHg5gP1Sylv5Zs+usofv+HEvFUJsFUJsPXDggOcnM4MxAREDCD09WG0JTtSLTEA8WQhwooDY5KCLcbNZAWBkwI4HBuxwNK41ps4qcw6KMGpGaT/doVrFOg5pFfpcxABYJnPbie9O70V6DNd+y/H40WkEAtr8kQdXAzOdkLjJK9T9AAaqaZTUv77hHACm3pBrzjHdyaR1Ho48ExDdk07F2NJx0YJvCwxqCUnPWAiBVz3rZPzaGRtyj3XmCWndn4lmZPIA9O/5mO3fuXHslUBguGackc3I+HAMA2jgpJWD1jyKEmn5UeiZd+PaQggm8MxYiI0SsbAYAHPMZ7qOhYG3gxyBBMAGlRND2vCRqZZlTlo5WNXHnmiaSDG6HilhOYFrlawCMc0iat7ygid0fH50Pfo6KvlhoGnVVLZ+RNwE1M6ELFOAQJoHYJRCCgwBbMdyvzOA5wP4LSHEDgBfRWr6+UcAq4QQdAc3AXhc/b0LwCkAoL5fCeCwe1Ap5aellOdIKc9Zvz5ba2WmGHJMQGkUkL2PMQGZWiOAqYNCDiWCbctN/6+Fge48NljNRojQS1nLYwDkrFUU3HUc1ytpgaqfPnAAZ5+62nJiuqWUNQNgmcxckyVUgrQk7UQHAbD76DTWjtRzF1l9LAqRcxiAFVNeDXUUEC+5TS//QNWY53icN2Br89/7kwvwqd8/Wx/XIxMs+FgDh2sC0v2UVRgoF2L/8N+ehZd6MnoJT96Ylhl+YN+4cQILXxSQ6wOwjzNYDbFqqMYYgDHzNdsJElXvZsNo3SrSJqVhE6/5lVO8EUg+pAyA8gdcRcHMU64s0Jz1KTyV0JiAfE2HdpIAUP4LUpDc8tyjA1WtHNG506g4WwmrOWYaQjMyQQe9wmIALAyUBECVmYBCITxRQAmONSIrBJR+b5zAZk3gY+b3uq/DQKWU75ZSbpJSbgbwGgDXSylfD+DHAH5P7fZGAN9Vf1+pPkN9f73spSvEHDHkMIBUAHRmAPQQRgfTUr2uAPBR+Woo9ET3MgCvDyDrBI4TFdVgMYDUB3DTw4ewZ6yBlz3jBL3QDlXDVAAwH4X2AbBM4HacZLRMoZxnHQXAkemu9n+6/lRwpZ+1b4QLvEqg+wHw5Cpylg0wBkAsTJuAtBNY4KyTVuIJLPQxz4H66uds0tfZCaTxnbxqED/6sxfigtNTxYMygfOO7wOVGb5/77hxAgfZcWajgOxnU6+GWDlY1eXAm+1Ya5Tfun0X9qoksIFqmJlv060Yb37+Fnz4d59h2as7gXwAPkWBL1BeBuCpEloJhG4hynMDNqxIs7DJTOYyAAoGOEElS64cMj0jJptpy8+hWmgJTM4AOMutqMSrTtVsfcgwAAoD9eS48FBrbgLyMQCq9JnIdE6SCYi/C/Wc9WG+MB9n+CsA/1MIsR2pjf8Ktf0KAGvV9v8J4F3zcO4M6iruXyeChZ1KQSSosuQZIQTWjtSwzykZ7E4+IH24525Jwwl9VSJHtA/A/NhnAoqlnwEMVEMdz3zhUzbqzMfBWogollaUEvcB8H4ArgmI7k8nE9ChyVZHpyehGqY1lkxxuOzCM1ANtZ2XRzjQizBYNWn3ZLrL8wFYzyBncfvI7z4D973/oq5jf/MFW/C6807Fm56/GU/euMLq15p4FsRO2LR6EEO1EPfvHdfMkhcM1GN2hIp7jsFaakKxGUD6XD/7ix3486+nmbL1SpCp09SMEj0/3KzvPISBqQbqRnnxRcnyAURkArKPnTJLoZ2gpGDc/Xe/gV+868X6eKuGqlrZInMIMYOPvvoZ2Pa+i6yQzIlmhJFaqv27fjg3Ugcw5qNOtax8cH0Arn+BN3nhAoCytI81IouxEaqh0D4AIcw1c62fC4OFYACF9AOQUv4EwE/U3w8DONezTwPAq4s430wglLd9TJelFRnti1rI8X7AhLXD9c4mIGIAlQDnP3Edtr3vIu9k8zmB6z4BoKobco2wFpqaRL9+1kaMDlT1cQZraVEzbgIyUUDGB+DadvWxK6FuI0nX4xYjW+NpBu/igtPX6V4EAHMCWwLAXAef3LpKYy1E6FQqdRPBKNrIF4nlIggEBoLuL9FIvYL/9dtPN8dTY05UHkBehE7eOZ+8cQXu3zuO09YOWcdLF67U1JIxxznCeaASYtVQFQ/sm9ClkblGSVE3dQ8DAIyZgoT7eU/oHG9hSkEkmevl89RXCsJNzqO5uVo5gXn4pj5eI8JGFr5KDIAigVYP1VTRvsAyAdG5+BC5CcgVAFOtGFJm3+tO4IymXgmy/i0W6RZyJ7BiKgdVJrPPCTzRjHRPY80A8kxA/ewEXkrg5pc0Bdue4FsfPYI7dx21+gET1o7UsM91AnuofI0tyD74fQDm7yaLHfcxgBsfPgQAeNP5mwGwgm7VijIBZRlAyPoBtJMklwFQ6dowEPiNszZmx+4pv+Di187YgA/9zjMy9m6+sPGFgl87L9JFz4aeg5sHYBhAvjllrqCxx4kRyDPBGRtX4P593ATExurJCeDbCbVKgJWDqQ+AFlq+oJy8Ki0pXg8DvwBQ92mHKt72q09e13HMJJjcKCDANQFlo4BcExD9Ps8JTMd70kZjxiOGvFP5AMh8VKsEmGzFeNFHf4wD402t3AhHATD9KLhiUTWJYDNhADwPIDRmSZ8TOAy4Apk+J+pF4OYB8EQwwa65ZgkAriDO//K87DuCAYxqOTY8wqd++hDu3j2GDaP1TOjV2uGaVWESsNPqjQmo8yJBD5tHS7gmIKnC8DilTY8d4K8uOhNX3vE4zjltdbpNUfyBWohoQjpOYJPwlfa1TaOEfM2ta5VAm8e+/cfnI5HA9+/aa+3jEwC/eNeLMd7Iho+GjumHn5OHklovKivSxTOrycFN94fuBeAPxS0KutY82cRnmK5yxgkr8LWtO3VN+ozCkMiM05dnm7biBFUVRTM23WINTcxz4AELgx4bvFsc7ZmbVmX24UirgWbzAABHAHgygV0BRM9o1aCfOdLYzzrJtGVc4TAAciDT4rjj0BSm27H2DbgKQNUx0wApA2hH6YI7E23ais+vBnj6yStx7uY1mcq32gns1AIiAeC+N9UwrRRLeQB6XWLrgO0DWCImoH7HCkfS+haMyVaEZlTNSN3VHvNHXh5AJ4zUTUE3Qjbm3Tjh3AYeb3vRE/G2Fz3R2gakTuCWcgLXKwHedP5m3XEsDARi5h/wZWTWlYZFx/TVuPF14Uo10MHM9rNOGsU5p622IiUA5RQcMvfSxwB4GGglTOuoTDapMiklddlRQkC+E3i2MAwgzdqcKQOghuP3Pn7MOh5gTBd5PoDUTqzCKAeraMdSx8Xz50DCt14JdMVbDrr/f/Pyp2KyGXmztzlSuSRVETnXB2CO344lrrl3H150xvo0YigUmTwLWiDzmCMVezvrJFOLnxSkx49OWz16uTDaP97UOSyuP4U3bSGMqp7T7STJ+Ck6YaASQog0oqoWBvjNp59odXXjSk4YikxDKVIYXWZUraTCnaqBjjqKKWALg1IAFAR6cToJgOmWSrJyNAVvC0IPpe8qAIgBdBAArTjRAoBPfF8ikxYAtTQKKE4kTlkzhHf/5lOsfSKl1QH+jEx+7Fol0LkPHL2YgAgveepGvOSpxoxEQmf1cC2TE0AgH8BA1TjcwiBIG5JPpouF7u4VmvBRQsEEQN8TaoM4Ex8AADyZBMCeVAD4/BWZKCDmxJxsxahWhDahkAmSMygqzsarznLQIvzmC7b0NGYqBtdOfD4Ae/790Re24r0vf6oSAEFm7ueVn3DBGcCgKvAWJRKnrR3UJp67d5sGNFKC+QC4EmY0f5sBVFioau9zOAgEhqohJlux/93jJiBhAi3cMi1uWRPKEpcytSKQUpgbBdQlf6UIHHc+AMDvNJxqxSphxL4lvvLDoTP5+LHzQIsoz+h0IzNaUaJNDpYPwKO500QfqKWp422P8y7tCGZCRH1aIH9heAlfDl8N/l5BY1qbEQAeBsAapFcCgXUjNa0tGicwRdTMnwnopJUDeMqJo/jSzY/N6vjrRupYN1LD4clW5vc07rxaQCacMfUBAEZjXjNcw/YPXozNa4d0bZ40RyTImpS6JUc4CESaNe4zFfoS6b5886NoRakAyGMAAPCMTSvx1l99ovtzAHZJDQrWAICTVhpm+YbnnWb9hvaxGGBg+gHwsfJ5OxMGAEBX1PWWHgnMMwyDrAnIHSuBfAASdjG4eo4PoK/zAJYSeMYd4DeFTLdjTLXiTESDT/v11XXv6gNgDdAJ7oulBYAbBeRlAOn5hlRyVTtOMtdVVWGg2gTUhQHQ4nPJBVvw/lc9TW+fCQNwQXR51VBVR/gAcMJATR4Ab2CybqSu7akmDyAbBlq0CUgIgUtfuEXXRJrN8c9QLCD9vdlO8yWvGqhONmKZtFSKhLJS65VQl2aoq8CGoVolI8xngiAw7DTjBK5mWddDByZx08OHvQyAf77yHRfgXRefaX3/wd9+WmYbYBSBk1YZAXD+k9ZZoby0kLt1ldxIHX48YGYMADAVdTuxbx0GGvsFgC86KnUCp2OmfAY7DHSJJIItJbje9jwTEO+mpX9b72wC8iU8+UDaPjcBpc2sTXcjEgC8kUfesXkYKJAKMDfMMwwCSGkWzzwfAIHuz3tf/lS8jNk8fWawXkF0eeVg1RJAvKXiqOUDUEI6EFi3oo6DygyTcQJ7QnGLxMufcZJ2OM7UBAQYLTYQjsJAPoAMW7NNGLVKoAXjocmW9V29avw2tHi86Iz1Og8FyDqBu0EIU8QuLxGM+3AA4IH946iFIsNQu7GP1593mpcVaAawasDaPlAN9YLsNQGJNOu4GgoraZHP7eEZRAEB2fagHFYpCO4Edtq1ZhhAhRLBUh8AsZ58BlCagArBqOsD8Gh00+0YE80o47jx96Dlf/cmAHj7Q47VQ1UdD92KY10MrlcfgBYArTiTwk8LPjWR8TXnthmA8P7tM4P1CipL4AqAU9cM6b9X8CggNZwwEFg/Uk9LcceJVXgMmF8TEJDeXyqZPJsw07XKvJFd6P0mIJcB1ELTL0Fn0uqKkVmh/c+vOxuvVX2R0+PN3ASkFYWMAKDQy/TzMzelzlspU8dmlgHM7nmQosYZAIEKy63IyQNYO1LHz/7yxZb/ic/tIU9wQycQ0/AJgAqLAqp4nMBAeg9dhs8bwpBS8JKnbMQ5rM1qXrXg+cJxIQBGnBh834IRJ2nv26G66wTOThxfWd9ulJsceBeeaRei+sKbz8OfvuR0AKlwoGJwobUY55uASGBNtWJvrR/AOKd8182LZ/Hz8L/nwgCEqgE4OlC1GIjtBOY+APOM1q2oQ0rg2m37dPGwhTABEV573ql41imr8JQTV3Tf2QGVEudlxIH8PAC3dWM1FHrhnXAEwEBO2GA31tgJgWAmoBwG8PwnrsXG0Tr+5hVnWeepeha62WBFBwFAyYjD2gfAGUD6/wkrBxzf2dwYgC9pFIBlpiQnsJTSEgBuvaJ0PKYlJB32H/7bs/C684zgtp3AZRRQIeglDBRAVwZAyTL+MNDOi9CqoRpuec9LMlm1Z5ywQtfcp85jM40CAlIBsHHUicZQ+1CGYlcTUI4AcItazQSkvY4OVvXivnHULi0x6vEBVAKB9WoRfeu/3QYgvf96oZxnE1A6riq+8/bnz+q3vmgqgAkAZ8g8+xiA5Vyllp2+gmc8gsStIDsThJwBuGGg6hwnrx7EzX/9EkiVHU39krNRQLNkALom00Dmu9WOALDCQD0mWcBhADP1AdTDXKWuwhTJtISG1DV+3GvhqIapSbYdS29pZMB+BxciEey4YgD1LgIAyE4Urv0OepxhvBREN6xf4a+qSROVO4HzNBkCnc/2AeQxABPnn3fuSiByF9WZFNJyQSU4RgcqejybVg9Z+5y8agjnnLYaz9y0yrKvujWI+AvdSymIxcSanPLZedU56d5Q9nA1DPRzp5h/X7YrnxtzYQBCCM1W8kxAuse1cmAC6Tx0fQBzZQAnrswyAOouNuLxAeTNWz6OGUcB1Sq5kX06DFRFAR2caOHPvvZfAMy74hUA2gwcPeFoUwAAE8NJREFU57JWIYRy7HcvY14EjhMGYPsAOtlH3YnCGcCAig32mYBmO+n5uFpxglhK1IKwaxRQLcMAoozWRxr/R35wH4AcE5AnesLFXBq3Ubji6GBVs4FTVtsv+GAtxDfedj4AYOuOtEJ4ReUBcPB7bNuAZz28ecPanPpJdCvdBUAnn0nTxYyeiWsCshlAmDkG4Gd7nRAII3zyisFxjXS4XsGxRpQ6gZ25M9NzE848YRRnnTSaiZ4BjANaMwDuh/O8j4A9p33H7ISXP+NEnVDpQgcqhKkTeNueY9imcj5WDlYx3Y69AofmbzNKOvZnIAGwAA0Tjw8G4IaBdlowXAYw6AnLsiaf+numYXccNDG+ddtu3ProkbSqpsh+b/8m3Ye0skY7ydUq79g1Zn3moJd7LgKsE45pBlDVaf4uA+DgGrLbhaxmCQD/S98vyKugmscAtAlIMwCRcQL7FmIuDCyz4SzyAHzHsc7LmIcpN9I5DHQmeOP5m/G9P3mB9zsynY54agHlMwDzt69kdSe86IwN+B/KN+eCjhuIbDN6qoA64vGbEVNKBUD+nK1XwwVxAAPHiQBwE8FIu/bZSd0wUP6gyNzi0zjmxADUb799e9qM/f69x6yidb6X+fSNK3DWSSut87qaG2cRm9cO4UkbRuBivgXAn1x4Ok5ZM4hzn7AGv3v2Jpy6ZshyerngncTyqkwC8x8FNFfkVVDNSwRzGUA1TGP+A+FjABQLby/Wc3ICs93z8gB4WCK9U1wADLMaVEUj6wPwmwD5ZdP9EqLYkEpe58pVPrQA6MQA2nHH7PV6JVgQB/D/397ZxspRnXf8/5/Zva/2ffX1xfcN22C7qY2N8a2JTRtjSktIiMiLjRJVLSmlBlGaEpISmg+pFCWSpVCilDaV3KZJm7Yg1LjBkVCbQKFpVJVAWgohSIEkLXESJU5ISqhBDub0w86ZnZk9Z3Zmd3Z2Zuf5SZbvnbsze87uzHnO8w5URAC0OoEbx00Lq6myombU1wDSO4HjiKrQF2+aDV17yBDTfc3qMj73u78YOje6cws+yA///kGcOztufe9e2Rt3r0zjX2+7DBMjdazMjuGLtx00Rnlo/OYp3lweuPUA3ur1cA37AALnFFADsJnU9LhtiWC6rUMwsekFzwnc9AE0/xbcoAS/7/QmoLQagEEADOs+FNnfS3s3zmDvphk/fNjmBA6ZgHzB1BqR0w21gCUh+lnpgAmTxhEyAVndwJ4AyMEBDFREAPiJYH4dGW/Xa3hI42yFvhPYFAXUxQIaXHzfddn5+MjhnQCCFSLtQimkAbT0lW38HtfQpZl5WoxFNNpL+Pz1a3yBUbM86EXUAGz4GkDkdtH3pO6QtWGyEQkz5Dbr4UeFdbRyrWnxSzsuwF4KIpRYpePka/Q3KM2mR9kvK9vOWYt7b9jnCx6bEzjYvUt/XnGbuk6oBTSA6L2nhXW0DhDQXCPOnH011gw9XMvPBFQJJ/BwzcXC5IifYRgMNYwSd7PomuKd5AHEEdwtrsyO+w92U7jYF7igdmCqBQQ0zD/W8wO1Z6Ic2rNkNBv1EpONXKvVwVLa4b7M5REA0Xj/5vHG/9ddsgnuLxFX7mj0HW7kaUTCQL3FIaq1BRfu9LWAAmNp6QegTUDxGkDTLNT77yP48YXLbcMfg36u0jqA29FMBAt/j5vnxnHVzg348reeN0YB6e8vmAhmYrjuhEJKe0klBAAAPPCeAwHbqfkhBOJvFl2dL+wEzs4HAITjx/VDHCdcgtpBVO3XxchMph+NXkxMJos7Du+KG3ZPMAlnLQBeijQX9/MySqTH2u49Xyt1Hbx+R7PpfHCR1wtr0AQUvobZAZoExpmA6nYNIBiuqs0enUYBpSFaDbT5szabNgVTrzQA13FCc/38La/Dv3z9FADzOhLcrMV9QotTo8hp/a+OAAhG99RcuwCIu1lMiWRJG8LEEXyQ5wLmmiTmpXqMBqB/3+M1kTG+d4wG0A/cwMOl0ZnCL/0sKgDoZ04XkRM3X+IXctPoadkitmx1eBoNcjwBYNMAAvdgliag7QsT2Ld5NlTgzq+VU2tmAvfSBBRl23yw2F5z7J4PvRFGGxFMWeH3u2azN3HNoV+oD7AngpnGHOXOay6EykkEVEYABGnaYQ0aQMzN4j+kxmqg2ZiAQhpATBSQf25MFNDb9ixhcqyOX/351jaP0ffuxoSVJXoRCy5mWgM4HdUAPBWgiE5gANhp6MLlWjUAs29gyLDbt/kAwnkAnZuAojv49WtHcPeR14aO+dUyAxpAMDKo10yPD2F+YhjfjwjYoL/E9wGkTAJrh28CcpuRetEQ3fYCwH79dqXls6TjdyK5TPIhkk+TfIrk73nHZ0h+geQz3v/T3nGS/GOSz5J8guRFWU0iLdHG5UFMN8td79iNN16wwX+oaFA/u/nSgjv32fFWDSBucQ6HgbYuKldsPyfe3mgxJ/QL0wI5OWquRaQ/tqJqACZo2XzYfANxAqDFBBQ4N3UpiJAjtf254wF7v74HR+ouRi1N6nvBO/dvAhCut7Q4PYq9G2dwx6FdPdMApseGUHeJ8SHX3wBqrUwnAM5PtJazCD6reSR5JaGbT+YVAO9RSv0HybUAvkLyCwDeCeBBpdRRkrcDuB3A+wBcCWCL9+9iAH/m/Z87toqMrkPjYvumXQt4064F3P6ZJ0LnA8CW9Wuxed248QtPSvBmiIZ1Rks0RLElAiWlaFFAwYYwGlstojhfTlGx7fRtCWLDBg1N7/yjJqBuS0FokoRxjoXyAPRGhfib6/di07p8AgdueN1mbFm/Bpdum/OP1V0H9964D0AzfyJrgXTljnNwweIBTI0N+WuI/i42z63BA7cewHlzhpDrkADIdEgd07EAUEp9D8D3vJ9/SvJpAIsArgZwqfeyvwLwMBoC4GoAf62UUgD+neQUyQ3edXLFVFBsYqQGhXjJ7Kt+gdfsWJzEP7/30p6Ns92DHBcGmgTTDrOf+PZVgxM4iq2wWpHx8wAs5aCjmxKjBqCdspFQwaAG2Ek1UNN1bIwbMoHrroM9587EnZYpjsNQ+ecovgaQeRSQg43rxr2fwwIAgDVyrh5yAhfjps3kqSe5EcBuAI8AmNeLuve/rn+8CODbgdNOesdyx/SwTY7V26qKenFiTmtlzWlttxel3qUG0OtM4LToEMTgXEz2VKCxaDksjjqdBJv/ybQpAYJO+uZxU1x+8BpkerNYXCKYiaAT2L+HCrKJ0OhyKbb7JwscXwNor2Uk9QHkSdefDMk1AD4D4Bal1AsxD6PpDy2ubpJHABwBgJUVe8mAbnAM6vb4UA1n3FdtpwCw79J6RRINIM4JnAR94xbFCdywKTPUwMN2TzkOS2X/B9qXgojOp6kBtNakainC5nQuzMMaQPvzm2GggXaMBbmHNCRx1zsuwoUrrc74rPCdwAkyd221rPpJVwKAZB2Nxf9vlVLHvcPf16YdkhsA/MA7fhLAcuD0JQDfjV5TKXUMwDEAWF1d7UksVM3fKTW/hJG62/bBCdYBz5qjb70AK5GELddp7SoUJbgz7MYHUBQT0HDNxT1H9mHrfFiN/vBbdmDrfLgxixMIwysLtp2+H5gQmY8voI1RQGYncCcLcTibNrkGEGwIU5R7KMgbd25o/6Iu0J9bks+8Pkg+ADZWz08AeFopdWfgTycAXAvgqPf/fYHjN5O8Bw3n7//2w/4PmB1xm+fG/XK4NmwPaRa8fW+rttPQAOLfi2w4rs8YmsInoWh5AIA5b+HXLj635ZjDEmoA1kxg8+bCL/9gdAJHwkANIbSJx2WpKWQjmAg2VndxeM8S9p83m/p9y47vBE6iAdSCAqAY9203GsAlAH4dwJMkH/eOvR+Nhf9ekr8F4DkAh72/3Q/gDQCeBXAawG928d5dYYoCuvOaC6FUvAAYqTu+3TkPak5rrXUTdZc4c7bbKKDiCICkOCxXGQggGLoaPt7OCRx0INo0AH2Nbk1ASYSqrs+/dqQGxyE+0oes8SLg+k7gJD6A5udalH1LN1FAX4I9o/mXDa9XAH6n0/fLEpsa3k4qH15dxvnr13Zka++EJD4AwHO+nTnboQ+guOp7OxzGh8gWEVsimK8ZRBPBDPZ1U2kGIJA53sFnkjYMdH5iBH9/4z5jsluViIaBxjFQJqAy41occe2YnxgJ1WnpNcN1t61ZCohvdt/2XD/OvCB3ZAqirTPLgC0RzFoKom5KBLPkAeiyJB0I89NnGjHzu1em/PaL7VjdmF/IZ1GJZgLHMXBO4LJi0wCKxm1XbMMrCQRAMw47/XxG6i6Ga45fb6dMMEdzXFboNSC6AOieFdFQZG37D0YBjdZdDBm+M8chyM5MQJe/Zh7PPf8SPnT1jsLYp8tAmlIwjkPMjg/hR/93RgRAP6HnPCz64rFjcTLR65rN7tM/+HXXwfGb9mNjTMXQolLGKCBbHsCl29bj+E37sTwTjgQzZWoP1Rz8g+U709njadm9Mo27VuxFAwUzWutKan5dmhnDj7wqvUWgfIbfjHBLGEFiw9cAOpzP9oXJzLMl88DpIOGp35hyUPTvFxkWYFvHNtt35iYMHBCyQTuBk2rfy9ON5kZF2biU76nPiIYGQHzpfQcLl8CSFlOZ6irgOOXVAJKOO22iXs1xOtIAhM5wLRqdDa3hvdom4jAvKi0AXIdYmrZ3yyoLdTe5HXKQKGMegN4oJh122kS9pJFjQrYk/cx1T+Mf/PTlXg4nMZW9U1yHpYsht1HvIgqozAySCchG2kS9mgiAXNFBGok1AG/D+Z0fv9SzMaWhsndKuzLLZULvDvNoxVckGk7gfo8iHWlNQGk1ACdB9riQHT/z+lQn9b8tzzR8AN/5iQiAvuKUIAooKXqX2Ek56DJTThNQOg0gbaKeaAD5ovN0kkYBLUw1BEA3/UOypLI+gFoJk4hsVFYDcABHlWvOtp7ANtK27BQfQL7obmRJv8+66+Du336ttWdA3lRWALgljCCxUfc1gMGYT1KqZAJKkmkKAAuTo1iYKsbusgqcfdUzAaXYfO0rUNG8ygqAqbE6phKmvBedeqVNQP0eRTpsiWA20jbs+fT1ewcmuKEMNDWAkt2IHpUVAJ+49hf8xhplp7ImIAKqZCqArR+1DVM/gCSvF/LhlQ40gCJRWQFQFCdMFgwZ2ihWgcZuuhgJNUnR637StqLT40Mggdk1w70blNAxvhNYNAChX5S5pn83OA7Bcq3/qSvRLk6N4sFbD2DTuvLVaqoC2gRU1s2XCIABoKqJYLuXp0q2/0+fCAYAm+eKETEitPKuy7bgm6dexBXb8ysTnyUiAAYA3wlcUjtkp/zBG17T7yGkJm0UkFBsVmbHcPymS/o9jI6pls1gQPGdwCW1Q1YJN2UegCD0ElkxBoCJkRpqDhPHigv9o9kSss8DEQT0wQRE8vUAPgbABfAXSqmjeY9h0HjbniVsXyxnTf+qcdWuBUyM1qXrllAIcl0xSLoA/hTArwA4CeBRkieUUl/LcxyDxthQzdhMRCgeW+fXYuv82n4PQxAA5G8C2gvgWaXUN5VSZwDcA+DqnMcgCIIgIH8BsAjg24HfT3rHBEEQhJzJWwCYDJ+hUG6SR0g+RvKxU6dO5TQsQRCE6pG3ADgJYDnw+xKA7wZfoJQ6ppRaVUqtzs3N5To4QRCEKpG3AHgUwBaSm0gOAXg7gBM5j0EQBEFAzlFASqlXSN4M4J/QCAP9S6XUU3mOQRAEQWiQe+C4Uup+APfn/b6CIAhCGEkdFQRBqChUqrj1FEmeAvA/XVxiHYAfZjScfiNzKS6DNJ9BmgswWPNJM5dzlVJto2gKLQC6heRjSqnVfo8jC2QuxWWQ5jNIcwEGaz69mIuYgARBECqKCABBEISKMugC4Fi/B5AhMpfiMkjzGaS5AIM1n8znMtA+AEEQBMHOoGsAgiAIggURAIIgCBVFBIAgCEJFEQEgCIJQUUQACIIgVBQRAMLAQ3KJ5H0knyH5DZIf88qRg+TdJJ8g+W6SP0fycZL/SfI8kv/W5rofJHl5m9d8iuQh7+eHSa56P/83ySe9f18j+SGSw1nNWRCSIAJAGGhIEsBxAJ9VSm0BsBXAGgAfJnkOgP1KqZ1KqY8CeDOA+5RSu5VS31BK7Y+7tlLqA0qpB7oY3kGl1AVo9MrejMGKWRdKgAgAYdC5DMDLSqlPAoBS6iyAdwO4DsAXAaz3dv1/COAWANeTfAgASL6oL0LyNm+3/l8kj3rHgrv7D5B8lORXSR7zBE8ilFIvArgRwJtJzmQya0FIQO79AAQhZ7YD+ErwgFLqBZLPAbgWwN8ppS4EfG3hRaXUHcHXk7wSDe3gYqXUacsi/SdKqQ96r/80gKsAfC7pIL0xfQvAFgCPJJ6dIHSBaADCoEMApnR323ETlwP4pFLqNAAopZ43vOYgyUdIPomG1rG9w7EKQm6IABAGnacAhErokpwAsAzgbMJrxAoLkiMAPg7gkGfT/3MAI2kGSXItgI0Avp7mPEHoBhEAwqDzIIAxkr8BACRdAH8E4FMATie8xucBXEdyzLtG1ASkF/sfklwD4FCaAXrnfBwNR/WP05wrCN0gAkAYaFSj2uFbABwm+QwaO+yXAbw/xTX+EcAJAI+RfBzAeyN//wkau/4nAXwWwKMJL/0Qya8C+DKA5wDckHRMgpAFUg1UEAShoogGIAiCUFFEAAiCIFQUEQCCIAgVRQSAIAhCRREBIAiCUFFEAAiCIFQUEQCCIAgVRQSAIAhCRfl/lRV/Wn0tKlUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1989b63dcf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "df=size_vector_cd8['Size_img']\n",
    "df.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1989b4fb4a8>"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1989a1dd2e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1989b2224e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "df=pandas.DataFrame(data=size_vector_cd8[['Size_img_h','Size_img_w']])\n",
    "df.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "gen_bow_features\n",
    "\n",
    "clustering et construire l'histogramme\n",
    "\n",
    "possible différent algo clustering\n",
    "\n",
    "\n",
    "isoler tumeur, IM tres petits\n",
    "\n",
    "voir nombre patients"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [],
   "source": [
    "size_vector_cd3=pandas.DataFrame(data=data_ID['OfficialID'])\n",
    "size_vector_cd3[\"Size_Tumor_kp\"]=numpy.nan\n",
    "size_vector_cd3[\"Size_Tumor_des\"]=numpy.nan\n",
    "\n",
    "size_vector_cd3[\"Size_img_h\"]=numpy.nan\n",
    "size_vector_cd3[\"Size_img_w\"]=numpy.nan\n",
    "size_vector_cd3[\"Size_img\"]=numpy.nan\n",
    "size_vector_cd3.set_index('OfficialID',drop=False,inplace=True)\n",
    "\n",
    "size_vector_cd8=pandas.DataFrame(data=data_ID['OfficialID'])\n",
    "size_vector_cd8[\"Size_Tumor_kp\"]=numpy.nan\n",
    "size_vector_cd8[\"Size_Tumor_des\"]=numpy.nan\n",
    "\n",
    "size_vector_cd8[\"Size_img_h\"]=numpy.nan\n",
    "size_vector_cd8[\"Size_img_w\"]=numpy.nan\n",
    "size_vector_cd8[\"Size_img\"]=numpy.nan\n",
    "size_vector_cd8.set_index('OfficialID',drop=False,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [],
   "source": [
    "for patient_id in data_ID['OfficialID']:\n",
    "    CD3PATH=os.path.join('..','Data_output','cache_HEGP','cd3',patient_id)\n",
    "    img_cd3=pandas.read_csv(os.path.join(CD3PATH,'Ratio_Cells_per_Tumor'),sep=' ',header=None)\n",
    "    size_vector_cd3.loc[patient_id,'Size_img_h']=img_cd3.shape[0]\n",
    "    size_vector_cd3.loc[patient_id,'Size_img_w']=img_cd3.shape[1]\n",
    "    size_vector_cd3.loc[patient_id,'Size_img']=img_cd3.size\n",
    "    gen = numpy.array(img_cd3 ,dtype=numpy.uint8)\n",
    "    \n",
    "    #kps=extract_features(gen)\n",
    "    kp, des = sift.detectAndCompute(gen,None)\n",
    "    try:\n",
    "        size_vector_cd3.loc[patient_id,'Size_Tumor_kp']=des.shape[0]\n",
    "        size_vector_cd3.loc[patient_id,'Size_Tumor_des']=des.shape[1]\n",
    "    except (AttributeError):\n",
    "        pass\n",
    "    \n",
    "    CD8PATH=os.path.join('..','Data_output','cache_HEGP','cd8',patient_id)\n",
    "    img_cd8=pandas.read_csv(os.path.join(CD8PATH,'Ratio_Cells_per_Tumor'),sep=' ',header=None)\n",
    "    size_vector_cd8.loc[patient_id,'Size_img_h']=img_cd8.shape[0]\n",
    "    size_vector_cd8.loc[patient_id,'Size_img_w']=img_cd8.shape[1]\n",
    "    size_vector_cd8.loc[patient_id,'Size_img']=img_cd8.size\n",
    "    gen = numpy.array(img_cd8 ,dtype=numpy.uint8)\n",
    "    \n",
    "    #kps=extract_features(gen)\n",
    "    kp, des = sift.detectAndCompute(gen,None)\n",
    "    try:\n",
    "        size_vector_cd8.loc[patient_id,'Size_Tumor_kp']=des.shape[0]\n",
    "        size_vector_cd8.loc[patient_id,'Size_Tumor_des']=des.shape[1]\n",
    "    except (AttributeError):\n",
    "        pass\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "42"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " len(pandas.isnull(size_vector_cd3).any(1).nonzero()[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "71"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " len(pandas.isnull(size_vector_cd8).any(1).nonzero()[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {
    "scrolled": false
   },
   "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>OfficialID</th>\n",
       "      <th>Size_Tumor_kp</th>\n",
       "      <th>Size_Tumor_des</th>\n",
       "      <th>Size_img_h</th>\n",
       "      <th>Size_img_w</th>\n",
       "      <th>Size_img</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OfficialID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>PAR170</th>\n",
       "      <td>PAR170</td>\n",
       "      <td>6.0</td>\n",
       "      <td>128.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>546.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PAR171</th>\n",
       "      <td>PAR171</td>\n",
       "      <td>5.0</td>\n",
       "      <td>128.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>552.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PAR172</th>\n",
       "      <td>PAR172</td>\n",
       "      <td>5.0</td>\n",
       "      <td>128.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>560.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PAR173</th>\n",
       "      <td>PAR173</td>\n",
       "      <td>2.0</td>\n",
       "      <td>128.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>350.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PAR174</th>\n",
       "      <td>PAR174</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>250.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PAR175</th>\n",
       "      <td>PAR175</td>\n",
       "      <td>1.0</td>\n",
       "      <td>128.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>437.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PAR176</th>\n",
       "      <td>PAR176</td>\n",
       "      <td>2.0</td>\n",
       "      <td>128.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>480.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PAR177</th>\n",
       "      <td>PAR177</td>\n",
       "      <td>5.0</td>\n",
       "      <td>128.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PAR178</th>\n",
       "      <td>PAR178</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>80.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PAR180</th>\n",
       "      <td>PAR180</td>\n",
       "      <td>8.0</td>\n",
       "      <td>128.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>700.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PAR181</th>\n",
       "      <td>PAR181</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>18.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>162.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PAR182</th>\n",
       "      <td>PAR182</td>\n",
       "      <td>1.0</td>\n",
       "      <td>128.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>432.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PAR183</th>\n",
       "      <td>PAR183</td>\n",
       "      <td>1.0</td>\n",
       "      <td>128.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>187.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PAR184</th>\n",
       "      <td>PAR184</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>336.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PAR185</th>\n",
       "      <td>PAR185</td>\n",
       "      <td>6.0</td>\n",
       "      <td>128.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>510.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PAR186</th>\n",
       "      <td>PAR186</td>\n",
       "      <td>6.0</td>\n",
       "      <td>128.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>770.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PAR187</th>\n",
       "      <td>PAR187</td>\n",
       "      <td>12.0</td>\n",
       "      <td>128.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>836.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PAR188</th>\n",
       "      <td>PAR188</td>\n",
       "      <td>5.0</td>\n",
       "      <td>128.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>464.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PAR189</th>\n",
       "      <td>PAR189</td>\n",
       "      <td>4.0</td>\n",
       "      <td>128.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>350.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PAR190</th>\n",
       "      <td>PAR190</td>\n",
       "      <td>5.0</td>\n",
       "      <td>128.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>225.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PAR191</th>\n",
       "      <td>PAR191</td>\n",
       "      <td>7.0</td>\n",
       "      <td>128.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>952.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PAR192</th>\n",
       "      <td>PAR192</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>144.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PAR194</th>\n",
       "      <td>PAR194</td>\n",
       "      <td>2.0</td>\n",
       "      <td>128.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>640.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PAR197</th>\n",
       "      <td>PAR197</td>\n",
       "      <td>5.0</td>\n",
       "      <td>128.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>527.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PAR198</th>\n",
       "      <td>PAR198</td>\n",
       "      <td>7.0</td>\n",
       "      <td>128.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>551.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PAR199</th>\n",
       "      <td>PAR199</td>\n",
       "      <td>4.0</td>\n",
       "      <td>128.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>902.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PAR200</th>\n",
       "      <td>PAR200</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>198.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PAR201</th>\n",
       "      <td>PAR201</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>552.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PAR202</th>\n",
       "      <td>PAR202</td>\n",
       "      <td>3.0</td>\n",
       "      <td>128.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>595.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PAR203</th>\n",
       "      <td>PAR203</td>\n",
       "      <td>1.0</td>\n",
       "      <td>128.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>392.0</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PAR462</th>\n",
       "      <td>PAR462</td>\n",
       "      <td>12.0</td>\n",
       "      <td>128.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>779.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PAR463</th>\n",
       "      <td>PAR463</td>\n",
       "      <td>3.0</td>\n",
       "      <td>128.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>352.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PAR464</th>\n",
       "      <td>PAR464</td>\n",
       "      <td>1.0</td>\n",
       "      <td>128.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>612.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PAR465</th>\n",
       "      <td>PAR465</td>\n",
       "      <td>8.0</td>\n",
       "      <td>128.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>462.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PAR466</th>\n",
       "      <td>PAR466</td>\n",
       "      <td>2.0</td>\n",
       "      <td>128.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>595.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PAR467</th>\n",
       "      <td>PAR467</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>210.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PAR468</th>\n",
       "      <td>PAR468</td>\n",
       "      <td>1.0</td>\n",
       "      <td>128.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>783.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PAR469</th>\n",
       "      <td>PAR469</td>\n",
       "      <td>1.0</td>\n",
       "      <td>128.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>414.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PAR470</th>\n",
       "      <td>PAR470</td>\n",
       "      <td>2.0</td>\n",
       "      <td>128.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>63.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PAR471</th>\n",
       "      <td>PAR471</td>\n",
       "      <td>1.0</td>\n",
       "      <td>128.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>270.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PAR472</th>\n",
       "      <td>PAR472</td>\n",
       "      <td>1.0</td>\n",
       "      <td>128.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>210.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PAR473</th>\n",
       "      <td>PAR473</td>\n",
       "      <td>3.0</td>\n",
       "      <td>128.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>512.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PAR474</th>\n",
       "      <td>PAR474</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>238.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PAR475</th>\n",
       "      <td>PAR475</td>\n",
       "      <td>2.0</td>\n",
       "      <td>128.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>360.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PAR476</th>\n",
       "      <td>PAR476</td>\n",
       "      <td>5.0</td>\n",
       "      <td>128.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>456.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PAR477</th>\n",
       "      <td>PAR477</td>\n",
       "      <td>10.0</td>\n",
       "      <td>128.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>832.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PAR478</th>\n",
       "      <td>PAR478</td>\n",
       "      <td>9.0</td>\n",
       "      <td>128.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>720.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PAR479</th>\n",
       "      <td>PAR479</td>\n",
       "      <td>3.0</td>\n",
       "      <td>128.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>858.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PAR480</th>\n",
       "      <td>PAR480</td>\n",
       "      <td>5.0</td>\n",
       "      <td>128.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>475.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PAR481</th>\n",
       "      <td>PAR481</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>144.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PAR482</th>\n",
       "      <td>PAR482</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>88.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PAR483</th>\n",
       "      <td>PAR483</td>\n",
       "      <td>6.0</td>\n",
       "      <td>128.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1015.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PAR484</th>\n",
       "      <td>PAR484</td>\n",
       "      <td>9.0</td>\n",
       "      <td>128.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>441.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PAR485</th>\n",
       "      <td>PAR485</td>\n",
       "      <td>1.0</td>\n",
       "      <td>128.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>294.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PAR486</th>\n",
       "      <td>PAR486</td>\n",
       "      <td>4.0</td>\n",
       "      <td>128.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>580.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PAR487</th>\n",
       "      <td>PAR487</td>\n",
       "      <td>2.0</td>\n",
       "      <td>128.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>324.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PAR488</th>\n",
       "      <td>PAR488</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>96.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PAR489</th>\n",
       "      <td>PAR489</td>\n",
       "      <td>1.0</td>\n",
       "      <td>128.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>475.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PAR490</th>\n",
       "      <td>PAR490</td>\n",
       "      <td>2.0</td>\n",
       "      <td>128.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>280.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PAR491</th>\n",
       "      <td>PAR491</td>\n",
       "      <td>5.0</td>\n",
       "      <td>128.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>522.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>297 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           OfficialID  Size_Tumor_kp  Size_Tumor_des  Size_img_h  Size_img_w  \\\n",
       "OfficialID                                                                     \n",
       "PAR170         PAR170            6.0           128.0        21.0        26.0   \n",
       "PAR171         PAR171            5.0           128.0        23.0        24.0   \n",
       "PAR172         PAR172            5.0           128.0        16.0        35.0   \n",
       "PAR173         PAR173            2.0           128.0        14.0        25.0   \n",
       "PAR174         PAR174            NaN             NaN        10.0        25.0   \n",
       "PAR175         PAR175            1.0           128.0        19.0        23.0   \n",
       "PAR176         PAR176            2.0           128.0        24.0        20.0   \n",
       "PAR177         PAR177            5.0           128.0        20.0        25.0   \n",
       "PAR178         PAR178            NaN             NaN        10.0         8.0   \n",
       "PAR180         PAR180            8.0           128.0        20.0        35.0   \n",
       "PAR181         PAR181            NaN             NaN        18.0         9.0   \n",
       "PAR182         PAR182            1.0           128.0        18.0        24.0   \n",
       "PAR183         PAR183            1.0           128.0        17.0        11.0   \n",
       "PAR184         PAR184            NaN             NaN        12.0        28.0   \n",
       "PAR185         PAR185            6.0           128.0        17.0        30.0   \n",
       "PAR186         PAR186            6.0           128.0        22.0        35.0   \n",
       "PAR187         PAR187           12.0           128.0        22.0        38.0   \n",
       "PAR188         PAR188            5.0           128.0        16.0        29.0   \n",
       "PAR189         PAR189            4.0           128.0        14.0        25.0   \n",
       "PAR190         PAR190            5.0           128.0        15.0        15.0   \n",
       "PAR191         PAR191            7.0           128.0        28.0        34.0   \n",
       "PAR192         PAR192            NaN             NaN         9.0        16.0   \n",
       "PAR194         PAR194            2.0           128.0        20.0        32.0   \n",
       "PAR197         PAR197            5.0           128.0        17.0        31.0   \n",
       "PAR198         PAR198            7.0           128.0        29.0        19.0   \n",
       "PAR199         PAR199            4.0           128.0        22.0        41.0   \n",
       "PAR200         PAR200            NaN             NaN        11.0        18.0   \n",
       "PAR201         PAR201            NaN             NaN        24.0        23.0   \n",
       "PAR202         PAR202            3.0           128.0        17.0        35.0   \n",
       "PAR203         PAR203            1.0           128.0        28.0        14.0   \n",
       "...               ...            ...             ...         ...         ...   \n",
       "PAR462         PAR462           12.0           128.0        19.0        41.0   \n",
       "PAR463         PAR463            3.0           128.0        22.0        16.0   \n",
       "PAR464         PAR464            1.0           128.0        17.0        36.0   \n",
       "PAR465         PAR465            8.0           128.0        22.0        21.0   \n",
       "PAR466         PAR466            2.0           128.0        17.0        35.0   \n",
       "PAR467         PAR467            NaN             NaN        15.0        14.0   \n",
       "PAR468         PAR468            1.0           128.0        27.0        29.0   \n",
       "PAR469         PAR469            1.0           128.0        18.0        23.0   \n",
       "PAR470         PAR470            2.0           128.0         7.0         9.0   \n",
       "PAR471         PAR471            1.0           128.0        15.0        18.0   \n",
       "PAR472         PAR472            1.0           128.0        21.0        10.0   \n",
       "PAR473         PAR473            3.0           128.0        16.0        32.0   \n",
       "PAR474         PAR474            NaN             NaN        14.0        17.0   \n",
       "PAR475         PAR475            2.0           128.0        15.0        24.0   \n",
       "PAR476         PAR476            5.0           128.0        19.0        24.0   \n",
       "PAR477         PAR477           10.0           128.0        26.0        32.0   \n",
       "PAR478         PAR478            9.0           128.0        20.0        36.0   \n",
       "PAR479         PAR479            3.0           128.0        26.0        33.0   \n",
       "PAR480         PAR480            5.0           128.0        25.0        19.0   \n",
       "PAR481         PAR481            NaN             NaN        12.0        12.0   \n",
       "PAR482         PAR482            NaN             NaN         8.0        11.0   \n",
       "PAR483         PAR483            6.0           128.0        29.0        35.0   \n",
       "PAR484         PAR484            9.0           128.0        21.0        21.0   \n",
       "PAR485         PAR485            1.0           128.0        21.0        14.0   \n",
       "PAR486         PAR486            4.0           128.0        20.0        29.0   \n",
       "PAR487         PAR487            2.0           128.0        18.0        18.0   \n",
       "PAR488         PAR488            NaN             NaN         6.0        16.0   \n",
       "PAR489         PAR489            1.0           128.0        19.0        25.0   \n",
       "PAR490         PAR490            2.0           128.0        10.0        28.0   \n",
       "PAR491         PAR491            5.0           128.0        18.0        29.0   \n",
       "\n",
       "            Size_img  \n",
       "OfficialID            \n",
       "PAR170         546.0  \n",
       "PAR171         552.0  \n",
       "PAR172         560.0  \n",
       "PAR173         350.0  \n",
       "PAR174         250.0  \n",
       "PAR175         437.0  \n",
       "PAR176         480.0  \n",
       "PAR177         500.0  \n",
       "PAR178          80.0  \n",
       "PAR180         700.0  \n",
       "PAR181         162.0  \n",
       "PAR182         432.0  \n",
       "PAR183         187.0  \n",
       "PAR184         336.0  \n",
       "PAR185         510.0  \n",
       "PAR186         770.0  \n",
       "PAR187         836.0  \n",
       "PAR188         464.0  \n",
       "PAR189         350.0  \n",
       "PAR190         225.0  \n",
       "PAR191         952.0  \n",
       "PAR192         144.0  \n",
       "PAR194         640.0  \n",
       "PAR197         527.0  \n",
       "PAR198         551.0  \n",
       "PAR199         902.0  \n",
       "PAR200         198.0  \n",
       "PAR201         552.0  \n",
       "PAR202         595.0  \n",
       "PAR203         392.0  \n",
       "...              ...  \n",
       "PAR462         779.0  \n",
       "PAR463         352.0  \n",
       "PAR464         612.0  \n",
       "PAR465         462.0  \n",
       "PAR466         595.0  \n",
       "PAR467         210.0  \n",
       "PAR468         783.0  \n",
       "PAR469         414.0  \n",
       "PAR470          63.0  \n",
       "PAR471         270.0  \n",
       "PAR472         210.0  \n",
       "PAR473         512.0  \n",
       "PAR474         238.0  \n",
       "PAR475         360.0  \n",
       "PAR476         456.0  \n",
       "PAR477         832.0  \n",
       "PAR478         720.0  \n",
       "PAR479         858.0  \n",
       "PAR480         475.0  \n",
       "PAR481         144.0  \n",
       "PAR482          88.0  \n",
       "PAR483        1015.0  \n",
       "PAR484         441.0  \n",
       "PAR485         294.0  \n",
       "PAR486         580.0  \n",
       "PAR487         324.0  \n",
       "PAR488          96.0  \n",
       "PAR489         475.0  \n",
       "PAR490         280.0  \n",
       "PAR491         522.0  \n",
       "\n",
       "[297 rows x 6 columns]"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "size_vector_cd3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for patient_id in data_ID['OfficialID']:\n",
    "    CD3PATH=os.path.join('..','Data_output','cache_HEGP','cd3',patient_id)\n",
    "    img_cd3=pandas.read_csv(os.path.join(CD3PATH,'Ratio_Cells_per_Tumor'),sep=' ',header=None)\n",
    "    size_vector_cd3.loc[patient_id,'Size_img_h']=img_cd3.shape[0]\n",
    "    size_vector_cd3.loc[patient_id,'Size_img_w']=img_cd3.shape[1]\n",
    "    size_vector_cd3.loc[patient_id,'Size_img']=img_cd3.size\n",
    "    gen = numpy.array(img_cd3 ,dtype=numpy.uint8)\n",
    "    \n",
    "    #kps=extract_features(gen)\n",
    "    kp, des = sift.detectAndCompute(gen,None)\n",
    "    try:\n",
    "        size_vector_cd3.loc[patient_id,'Size_Tumor_kp']=des.shape[0]\n",
    "        size_vector_cd3.loc[patient_id,'Size_Tumor_des']=des.shape[1]\n",
    "    except (AttributeError):\n",
    "        pass\n",
    "    \n",
    "    CD8PATH=os.path.join('..','Data_output','cache_HEGP','cd8',patient_id)\n",
    "    img_cd8=pandas.read_csv(os.path.join(CD8PATH,'Ratio_Cells_per_Tumor'),sep=' ',header=None)\n",
    "    size_vector_cd8.loc[patient_id,'Size_img_h']=img_cd8.shape[0]\n",
    "    size_vector_cd8.loc[patient_id,'Size_img_w']=img_cd8.shape[1]\n",
    "    size_vector_cd8.loc[patient_id,'Size_img']=img_cd8.size\n",
    "    gen = numpy.array(img_cd8 ,dtype=numpy.uint8)\n",
    "    \n",
    "    #kps=extract_features(gen)\n",
    "    kp, des = sift.detectAndCompute(gen,None)\n",
    "    try:\n",
    "        size_vector_cd8.loc[patient_id,'Size_Tumor_kp']=des.shape[0]\n",
    "        size_vector_cd8.loc[patient_id,'Size_Tumor_des']=des.shape[1]\n",
    "    except (AttributeError):\n",
    "        pass\n",
    "\n",
    "img_descs.append(desc)"
   ]
  }
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