{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from random import sample\n",
    "\n",
    "from itertools import chain\n",
    "from random import sample \n",
    "import scipy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "d = pd.read_csv('findings_data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "All Labels (14): ['Atelectasis', 'Cardiomegaly', 'Consolidation', 'Edema', 'Effusion', 'Emphysema', 'Fibrosis', 'Infiltration', 'Mass', 'No Finding', 'Nodule', 'Pleural_Thickening', 'Pneumonia', 'Pneumothorax']\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Patient ID</th>\n",
       "      <th>Finding Labels</th>\n",
       "      <th>Patient Age</th>\n",
       "      <th>Patient Gender</th>\n",
       "      <th>Mass_Size</th>\n",
       "      <th>Atelectasis</th>\n",
       "      <th>Cardiomegaly</th>\n",
       "      <th>Consolidation</th>\n",
       "      <th>Edema</th>\n",
       "      <th>Effusion</th>\n",
       "      <th>Emphysema</th>\n",
       "      <th>Fibrosis</th>\n",
       "      <th>Infiltration</th>\n",
       "      <th>Mass</th>\n",
       "      <th>No Finding</th>\n",
       "      <th>Nodule</th>\n",
       "      <th>Pleural_Thickening</th>\n",
       "      <th>Pneumonia</th>\n",
       "      <th>Pneumothorax</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>659</th>\n",
       "      <td>660</td>\n",
       "      <td>660</td>\n",
       "      <td>No Finding</td>\n",
       "      <td>41</td>\n",
       "      <td>M</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>783</th>\n",
       "      <td>784</td>\n",
       "      <td>784</td>\n",
       "      <td>No Finding</td>\n",
       "      <td>62</td>\n",
       "      <td>F</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>834</th>\n",
       "      <td>835</td>\n",
       "      <td>835</td>\n",
       "      <td>No Finding</td>\n",
       "      <td>41</td>\n",
       "      <td>M</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Unnamed: 0  Patient ID Finding Labels  Patient Age Patient Gender  \\\n",
       "659         660         660     No Finding           41              M   \n",
       "783         784         784     No Finding           62              F   \n",
       "834         835         835     No Finding           41              M   \n",
       "\n",
       "     Mass_Size  Atelectasis  Cardiomegaly  Consolidation  Edema  Effusion  \\\n",
       "659        NaN          0.0           0.0            0.0    0.0       0.0   \n",
       "783        NaN          0.0           0.0            0.0    0.0       0.0   \n",
       "834        NaN          0.0           0.0            0.0    0.0       0.0   \n",
       "\n",
       "     Emphysema  Fibrosis  Infiltration  Mass  No Finding  Nodule  \\\n",
       "659        0.0       0.0           0.0   0.0         1.0     0.0   \n",
       "783        0.0       0.0           0.0   0.0         1.0     0.0   \n",
       "834        0.0       0.0           0.0   0.0         1.0     0.0   \n",
       "\n",
       "     Pleural_Thickening  Pneumonia  Pneumothorax  \n",
       "659                 0.0        0.0           0.0  \n",
       "783                 0.0        0.0           0.0  \n",
       "834                 0.0        0.0           0.0  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Here I'm just going to split up my \"Finding Labels\" column so that I have one column in my dataframe\n",
    "# per disease, with a binary flag. This makes EDA a lot easier! \n",
    "\n",
    "all_labels = np.unique(list(chain(*d['Finding Labels'].map(lambda x: x.split('|')).tolist())))\n",
    "all_labels = [x for x in all_labels if len(x)>0]\n",
    "print('All Labels ({}): {}'.format(len(all_labels), all_labels))\n",
    "for c_label in all_labels:\n",
    "    if len(c_label)>1: # leave out empty labels\n",
    "        d[c_label] = d['Finding Labels'].map(lambda finding: 1.0 if c_label in finding else 0)\n",
    "d.sample(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "14"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(all_labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I see here that there are 14 unique types of labels found in my dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Atelectasis           0.093093\n",
       "Cardiomegaly          0.038038\n",
       "Consolidation         0.043043\n",
       "Edema                 0.016016\n",
       "Effusion              0.095095\n",
       "Emphysema             0.018018\n",
       "Fibrosis              0.027027\n",
       "Infiltration          0.134134\n",
       "Mass                  0.035035\n",
       "No Finding            0.575576\n",
       "Nodule                0.041041\n",
       "Pleural_Thickening    0.032032\n",
       "Pneumonia             0.006006\n",
       "Pneumothorax          0.033033\n",
       "dtype: float64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d[all_labels].sum()/len(d)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0, 0.5, 'Number of Images with Label')]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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VJF0xk4Jt39LJj2R7EfALYDNgN6CzxecCYPd6fzfgBNt32b4euBbYfibnjoiImZk2KNS1CZdK2mKQk0iaB2wLXABsUscoOmMVG9eXbQb8pudtN9VjERExJP0MNG8KXCnpQuCOzkHbu/ZzAknrAN8A3mT7dklTvnSSY8vsES3pAOAAgC22GChWRUTEBP0EhcNmWrik1SgB4Su2v1kP3yppU9u3SNoUuK0evwl4cM/bNwdunlim7WOBYwHmz5+/TNCIiIiZW+5As+1zgBuA1er9i4Dl7qWg0iT4HPAL272pt08F9q339wVO6Tm+l6Q1JG0JbAVc2Of3ERERDVhuS0HS/pTumg2Bh1H6+T8NPGs5b30q8DLgckmX1GPvAI4ETpS0H/BrYA8A21dKOpGSdG8xcKDte1b4O4qIiBnrp/voQMosoAsAbF8jaePp3wK2z2fycQKYIqDYPgI4oo86RUREC/pZp3CX7bs7DyTdh0kGgCMiYvbrJyicI+kdwFqSng2cBHy73WpFRMQo9BMUDgZ+D1wOvAb4HvCuNisVERGj0U+W1HvrxjoXULqNrrad7qOIiDHUz+yjXSizjX5FGTjeUtJrbH+/7cpFRMRw9TP76MPAM21fCyDpYcB3gQSFiIgx08+Ywm2dgFBdx5JVyBERMUambClIelG9e6Wk7wEnUsYU9qCsao6IiDEzXffRC3ru3wo8vd7/PbDBsi+PiIjZbsqgYPuVw6xIRESMXj+zj7YE3gDM6319v6mzIyJi9uhn9tG3KNlOvw3c2251IiJilPoJCv+w/f9ar0lERIxcP0Hho5LeC/wAuKtzsLP/ckREjI9+gsJjKfsi7MiS7iPXxxERMUb6CQovBB7amz47IiLGUz8rmi8F1m+7IhERMXr9tBQ2AX4p6SKWHlPIlNSIiDHTT1B4b+u1iIiIlUI/+ymcM4yKRMwV8w7+7gq9/oYjd2mpJhHL6mdF8yKW7Mm8OrAacIft9dqsWEREDF8/LYV1ex9L2h3YvrUaRUTEyPQz+2gptr9F1ihERIylfrqPXtTzcBVgPku6kyIiYoz0M/uod1+FxcANwG6t1CYiIkaqnzGF7KsQETFHTLcd53umeZ9tH95CfSIiYoSmG2i+Y5IvgP2Aty+vYEmfl3SbpCt6jm0o6QxJ19TbDXqeO0TStZKulrTTjL6biIgYyJRBwfaHO1/AscBawCuBE4CH9lH2F4CdJxw7GDjT9lbAmfUxkrYG9gIeXd/zSUmrrti3EhERg5p2Smq9sv8v4DJKV9N2tt9u+7blFWz7XOBPEw7vBiyo9xcAu/ccP8H2XbavB64layEiIoZuyqAg6SjgImAR8Fjbh9r+84Dn28T2LQD1duN6fDPgNz2vu6kei4iIIZqupfAW4EHAu4CbJd1evxZJur3hemiSY5OuhZB0gKSFkhb+/ve/b7gaERFz23RjCqvYXsv2urbX6/lad4C8R7dK2hSg3na6oW4CHtzzus2Bm6eo17G259uev9FGG82wGhERMZl+Fq816VRgX+DIentKz/HjJR1NaZ1sBVw45LpFQ5IFNGL2ai0oSPoq8AzgAZJuouzLcCRwoqT9gF8DewDYvlLSicBVlFXTB9q+Z6bnzj+liIiZaS0o2N57iqeeNcXrjwCOaKs+ERGxfCucJTUiIsZXgkJERHQlKERERFeCQkREdCUoREREV4JCRER0JShERERXgkJERHQlKERERFeCQkREdCUoREREV4JCRER0JShERERXgkJERHQlKERERFeCQkREdA17O87oQ3aOi7kqv/ujl6AwA/nFjYhxlaAQMWZy0RKDSFCIiJglhhHwExQiYs5IK2r5EhQiJsg/jpjLMiU1IiK6EhQiIqIrQSEiIroypjAHzfY+89le/4iVWVoKERHRtdIFBUk7S7pa0rWSDh51fSIi5pKVKihIWhX4BPBcYGtgb0lbj7ZWERFzx8o2prA9cK3t6wAknQDsBlw10lpFRPRhHMa7VqqWArAZ8JuexzfVYxERMQSyPeo6dEnaA9jJ9qvr45cB29t+Q89rDgAOqA8fCVy9Aqd4APCHhqqb8lN+yh9e+bO57itj+Q+xvdFkT6xs3Uc3AQ/uebw5cHPvC2wfCxw7k8IlLbQ9f+bVS/kpP+WPovzZXPfZVv7K1n10EbCVpC0lrQ7sBZw64jpFRMwZK1VLwfZiSa8HTgdWBT5v+8oRVysiYs5YqYICgO3vAd9rqfgZdTul/JSf8kde/myu+6wqf6UaaI6IiNFa2cYUIiJihBIUIiKiK0EhIiK65kxQkLSKpPVGXY+IcSFpkaTbJ3z9RtLJkh466vqNO0kbT3LskYOWu9LNPmqSpOOB1wL3ABcD95N0tO2jGjzHQuA44Hjbf26q3J7yXwR8ANgYUP2y7UYCnKQ1gf2ARwNrdo7bflVD5a8BvBiYR8/vm+33NVR+2/U/iPL5LgI+C2wLHGz7Bw2V3/bnuwdwmu1Fkt4FbAf8l+2fNVD80ZTFpcdT6r0X8EBKloHPA88YpHBJ9wXeAmxhe39JWwGPtP2dQcqdcI7LgYmzbf4KLKT8nP44QNlbAe+nJPfs/d1sKmCeJ+ndtk+s53sL5W9hoCSi495S2Nr27cDulGmuWwAva/gcewEPAi6SdIKknSSpwfI/COxq+36217O9blP/MKovUf6QdwLOoawiX9Rg+adQkhouBu7o+WpK2/V/Vf0deg6wEfBK4MgGy2/78313DQg7UH5GC4BPNVT2zraPsb3I9u0128DzbH8N2KCB8o8D7gKeXB/fBPxXA+X2+j7wXeCl9evbwLnA74AvDFj2cZSf9WLgmcAXKb+vTXkG8DJJJ0k6F3gEJanoYGyP7RdwJbAacBLw9Hrs0pbOtQqwK/BbSlK/w4ANGyj3Ry3/jH5eby+rt6sBP2yw/Ctmef075X4UeGHvORsqf1if7/uBlzRZf+AnwJ71d3+Vev+n9blLGih/4cT6Nv33O9nPv3MMuHzAsi+eWA5wXsP1P5ASLH8NPLWJMse6+wg4BrgBuBQ4V9JDgNubPomkx1GuIJ8HfAP4CrAD8EPg8QMWv1DS14BvUa6aALD9zQHL7fhnvf2LpMdQrpDmNVQ2wI8lPdb25Q2W2avt+l8s6QfAlsAhktYF7m2w/LY/399KOgb4d+ADtTuvqR6Cl1KC5ScpXTA/BfaRtBbw+gbKv7uWZQBJD6PnZ9SQdST9q+0L6jm2B9apzy0esOx/SFoFuKZmavgtpZuwEZLOAG4BHkNpIX9e0rm2/3Ogcmu0mTMk3cf2oB92b3kXA38BPgd8w/ZdPc990/aLBiz/uEkO2831mb+aEsgeR2nurgO8x/anGyr/KuDhwPWUP+hOn/njGiq/7fqvQgns19n+i6T7A5vZvqyh8tv+fO8L7Ey5Wr1G0qbAY93QmEibJD0beBelj/wHwFOBV9g+u8FzPJEy/rEO5XfzduDVlF6GXVz76wco+xfA+sDhwP2AD9r+6aD1ruXvbvtbPY/vAxxi+/CByh3HoCBpH9tflvTmyZ63fXSD53qo66ZAsazaOluG7RuHXZcVIelRtn8pabvJnnczA7Wtq1fXN9m+S9IzKMHzi7b/0kDZGwH7s+wkgkYCWj3H/YEnUf5h/9R2K+mnJd2P8v9w4J/LbDeu3Udr19t12zpBb8CZbFy5qcAjaXPgY5SrJAPnAwfZvqmh8tcHXs6yf9hvbKJ82zdK2gb4t3roPNuXNlE2tFr/N1P27fjwJM8Z2HHA8oH2P19KK2q+pIdTWrOnUmYLPa+Bsk8BzgP+hzLDrxGTBOJb6u0WkrZoMiBPnB3X+Vv2ALPjJH3E9pskfZtlZzZhe9eZlj3hPE+i/O78C7A6JYno32zfb5ByxzIo2D6m3h7W4mlaCzgTHEf5I96jPt6nHnt2Q+V/j9IXfDnN9pUD3Smd+wOdPvIvSzrW9scaOkUr9bd9QL19ZlNlTqHtz/del+zDLwI+Yvtjkn7eUNn3tf32hsrqNVkg7mgsIFenUKagXkxz4xWdGUYfaqi8qXycMvvxJGA+5eJoq4FLbXIkfGX7okz3W48yI+VMys5E+4y6Xiv4PSwzi2OyYwOU/7OW638ZsHbP47WpM3pmSf33ANat999FCW7bzqLP9wJgb+AKYMt6rJEZYZTpoc9r8+ff9ldTP4sR1b0zO+uynmM/HrTcsWwp9HiO7bdJeiFl2tYewFnAl5s6QduLp4A/SNoH+Gp9vDcw4wU1k/iSpP2B77D07Jc/NVS+WLpr4Z56rClt1//dtk/qmef/IeDTwL82VH7bn+8rKQs4j7B9vaQtae73/yDgHZLuoswCa3rh3csnO277i02UX7U2O07SU4FDgYdQemU6P5+mFq/dWTcju0TSByndbGsv5z3LNe5BYbV6+zzgq7b/1Oy6MqA0FX9J+YfxPso0vV80WP6rKM3E/0tpOv+4HmvK3cBRwDtZ0v9poKlf3OOACySdXB/vTunbbkrb9e8EtF2AT9k+RdKhDZUNLX++tq8C3tjz+HoaWnxnu+0u1Cf23F8TeBbwM8oisKbsALxCUhuz4z4H/B9K11RjYy49XkaZXvz6ep4HU8ZHBjKWs486JB1J+Sf0d8pKv/WB79hu6ioPST+3va2ky2w/TtJqwOm2m+z3bI2kXwH/6pZmddRzbEf54xNwru2m+rRbr7+k71Dml/878ATK79KFtrdp43xNayPVwqhmZtUZQl9yQwO1tczWZsdJuqDJ/zUTyl4VWGB7n6bLHuuWgu2DJX0AuN32PZLuoKRcaFIri6ckvc32ByV9jMlnMDQyO4gyH/vOhsrqkrSe7dslbUhZQHhDz3MbNti900r9e+xJmef/IZd1CpsCbx200CF+vscB76W0RJ5J6U4atLn8FsrkgVZnZk3iTpoYSGXJ7yfNpkSZ6CxJR1HGoXq7NgcOmvX/2UaSVrd996Dl9RrroFBtBjy79v13NNn8PFbSBsC7KdP91gHe00C5nS6ohQ2UNZ17KH2SZ7H0L+6g/5SOB55PaTr3/tMTzXfvtFH/Tjl31tbITpJ2okypbWLh17A+37VsnylJ9er3UEnnUQLFjNjev962OjNrwpTOVSitnRkvJptg4u9nb6Bs6vez00qYP6HspoLmDcCPJJ1KTz4xDzgdfqyDgqT3UpJGbU2ZuvhcyjzwxoKC7c/Wu+fQ3D86bH+73r3T9km9z6lkvmzKt+pXo2w/v95u2XTZE7RS/462ptQO8fNtPNVCnd46JTeXoqN3Sudi4EY3tH5jGL+fbQdNSobamykBs7HxnXEfU7gc2IaSUGsbSZsAn7X9ggbPMdmq6b9SkmFd0kD5P7O93fKODXiOtSjpia9uqsyesp9KmWJ5R51lsx1lvvyvGzxHm/W/DHiy7Tvq47WBnzQ0ENn659tGqoWe1BwbA0+h5PiC0j11tgdM7TJskjZjyQwhAGyf20C596O0yJ5WD50DvM/2Xwcte8J51qUMjv+tifLGuqUA/N32vZIWq2ywcxsNXs1X8+tX58pvF+Ai4LWSTrL9wZkUKum5lFlTm0n6fz1Prcfgibp6z/MCyhXZ6sCWkh5P+cVtajDvU8A2dVXz2ygzMr4EPL2JwodQ/1am1A7r87V9Ub37N8p4QhNlvhK6g/Bb276lPt4U+MSg5UtaxCTjLD3nbyy1eB1z/A/gKpZ8zqakzx7U5ynrQ/asj19GGeNpJGjWMcwvARvWx38AXm77ykHKHfegsLCmQfgMpe/wb8CFDZ/j/sB2nShdu6y+Trk6uJiygG4mbqb0N+9ay+lYRJl+1pRDKTOzzgawfUmdy96UxbYtaTfgo7Y/J2nfBss/lHbr/3namVLb6udb+5mn1FDQnNcJCNWtlJz+A+lMdZX0PsrEjS9RAvFLaT6TwO6UjXuazr4K8DDbvVNED5M0cO9Bj2OBN9s+C0Alt9VnKK23GRvroGD7dfXupyWdBqznhrJb9tiCMle+45/AQ2z/vS7qmRGX/ECXSjre9j+X+4aZW2z7rxPWbzTZp7hI0iGU9A1Pq1PpVlvOe1ZEa/WvffEXUJr9nSm1r2xiSu0QPt8nU/b1+Crle2h8gQ5wtqTT6zlMSblwVoPl7zRhSuenJF3AzC+0JnMd5QuLK8MAABMvSURBVPexjaDwd0k72D4ful2pf2+w/LU7AQHA9tm1e3MgYx0UJJ1p+1kAtm+YeKwhxwM/lXRKffwC4Kv1w7mqgfLnSWpzS78rJL0EWLXOaX8jZQFVU/4DeAmwn+3fSdqCstisKa3Vv3Y9ftj2kymLptrQ1uf7QEr+pL0pP//vUhZwDtS10Mv26+ugcyfZ4bG2T57uPSvoHkkvBU6gBJ29aX4R2J2U2Wtn0vzstf8NLKhjCwL+BLyigXI7rpP0bpbkWtqHkqJ+IGM50Fynn96XctXyDJZcJa0HfN/2vzR8view5EryfNuNTTOUdD5L5pm/gDrP3PaMpxROKP++lNXAz6HU/3TgcNv/aKL8trVdf0mHUfI3fdMt/LG0/fnWc6xB+Yd6FGW8palkhK2SNI+yic9T66HzgTd1LvAaOsekXZm2FzR4jvVqmY1u8FWnwh9Gz8JQ4FAPuFf8uAaFg4A3UfZO/i1LgsLtwGdsf7zh8+0AbGX7OJUc8+u4pBNoouyLbT9B0uW2H1uPnWf735b33pXBhEHD1SlN9YHT+05xrlUpTerG/vhq/demXKF2Ao2bGuxs8/OtwWAXSkCYR1lH83nbvx207Fr+i4APUGYhiYZzHw1LW7PX1HJa+raMZfeR7Y8CH5X0hraviurA8nzgkZSZBatREo49dbr3rYBWtvTTFLneO5qaveMJ+XEk7U4Tm4svKe94SsK3eygDtveTdLTtRrqoJta/BW19vgso2zR+HzjM9hWDljmJDwIvsN1krq8utb/XRNuz19pOS/8I4D9ZNugMtDhuLFsKHZIOBL7iuptSbW7tbfuTDZ7jEmBbSgrnbeuxyxqcx97Kln6SOlNCX0Tpf+5kztwbuMH2OwYpfznn/qntJzVU1iW2H1/7np8AvJ2yRqSRn389x4soTXRTVjQ3tlhuks93PeCoBj7fe1myynWZFeVNXM1L+pHtpi5+Jiv/DMqYXW+f+UttN7XXBCrb6e5IWV/R+fvtttoGLLvR9USTlH8pJWPvUgn3bF885Zv6MJYthR772+7Om7b9Z5U0y40FBeDuOuWys7n4wKP/vdqYZ17LPQdA0uG2n9bz1LclNTFHm1p+75zsVSitqiavRFZTSUK4O/Bx2//sfBZNkPRJyh7TndTWr5X0bNsHNlD2qsCett9K85/vKk2VNY2Fkr5GWVHeO0jb1IrmjWz37mH9BUlvaqjsjjZn37Wd1n2x7U81VFbXuAeFVSSpM0BY/whXb/gcJ0o6Bli//gK8ijJXeCDD6t4BNlLPPtN1jv9GDZUNZfC0YzElX0uTSQmPqWVeCpyrkvWyyQG9pwOP6fkdWkDpDhiYS1KzJ/T+js4y61Fm7zyn55hZkhJkUG3vNQHtzr5rJa27SpJJKBdwrwNOpsGgM+7dR0dR+ts+TfkwXgv8xvZbGj7Ps+mZ/WL7jAbKHEr3jqSdKYtgrquH5gGvsX36gOV+wPbbJe1pu6kkZpOdZ1Xb9/Q8FrCq7UZWBUv6JvB/XFMp16BzpO29Gyr/w5TMnyexdFKzpv6xzlp1+vLHKWsuOntNHOQG0lr3nKN39hrADyhjCgOvW1BLad1V9n6YmMSvw4NOZx73oLAK8BrK5hyifOCf7f0n0uC51mPpwZ5GmoiSzp3QvTPpsQHPsQbwqPrwlw39QVxOyXN0Qcv9qtdT/qEe1+SAZ09L7X6UzV4urI//lbLl4b83dJ7jJjlsN7dzX+M0vLTfrZO0n+3PTTh2pO2DGyj7VGAv262kdpe05sSp15MdW1Fj3X3ksvjoC8APm55u1iHpNZQd1/5OmWHQdGroVrp3On/Y9eGu7snUKem/G2iJnEbZE3ttSb3dOU1PW3wcZSXt5+pFwOeBExqYltr2pusdn7X9o94DKitfV2adRZmtpP2WNF3qeds+vMHT/S9J/7D9lXruT9CziHBAraZ1p7ScJl5wTXZshYx7S2FXSp/e6rabnm7WOcc1lCyabe381Vb3TndmxMRZEk3MmpC0hu27JJ1iu+mNjaY659Mo/c/rU/JPHW772mGce6Ym+1m3PWtlUJK+YPsV9f6+bnChVy1zsu7dtSl7od/f9joNnmst6voNSmr9P9luZDBbLS2Mk/RAyj4xX6asVu9dnPtp24+a6r39GOuWAmWl6MRkafMaPsevaHHnL9un1QGwRrt3WLo/cmLfZBN5cn5CuWJpdBXnRHXywC6UmTvzKLuBfYWSeuF7zDBBm6Tzbe+gZTN2NtLSkfRkSuKyjbR0+vX1gFUHKXsIeqf7HgQ0GhRsd3d0U0kLfRDl8z2ByXd7W2E9g7UAr6bMoPoR8D41tDNg08Gyx06UdBmbA70b6iwCBh5rHPegMNl0s6YdAvxYJVFXY01ESTva/qGW3dDkYZKaGIj0FPcnezwTq9crpadM8j00OZB6DSWdyVG2e2eNfL22HGbqpdDq4rXVKbv03YelM3/eDvyvls45a9R/2m+mfA4LKJmIB0rfMEHvjmud213qVyPdvz0DwksZdCC4BpsFkl5s+xuDlDWZcQ8KbSd7gzIl8oc0v2rx6bXcyTYEamLa3za1r1/AWj39/qKZPtXXUv6g12fZ76HJaYuP8xSbiwwYmE+m9s1K+oaXToE8sLpO5JzaFdPYbJoh2VxlDwj13O9q4ILoKMqsu2OBx071+Q7C7e8ICEtvw7kmsAd174OGnCnpaBrexGfcxxRaT/Ym6ce2B8pfPs4mm93RcPkbUbbLnMfSs78Gmr0j6edessK1e78pQ1yH0rip+so7Gugzv5fS6l5MS6uxe87VetaDCec73/YODZX1DcomPp2f98uAbTzgzndjHRSGQdIRwI2UndeaW0Ay+TafXR5wc+629c5ukrRHC7ObOmX9GDiPZZf6D9Ssnm4gvgk961AmVVsS0TLVNCkTjjVyESCp93ems5r/f9veZtCya/mT1X2ZYytqLLuPhnwV9pJ6e0jvKRi8T7LtRGxt24slm6EcQllL0LEzDQyIVfe1/faGyuo1XffawFer+ae/0mgz60HvoHhnNf+ek790RlrZxGcsgwLDm2PeWt+k7cPaKHeI2p7d1PEdSc+z/b0Gy8R2qzOAJJ1oe8+6yG+ywcjGEvrFtE6npKrpzXpwWhMF235mE+VMo5VNfMa++0gt5UrvKX81yofTGew5GzjGDW2xqCGkD27DENZBdKaKijKH/W7KVqgwC3L6S3qQ7ZtV0mYsYxYOPs9KajHrgaRNgP8GHmT7uZK2pqxpanSMTQ1v4jPWQUE9udJbXLz2WcoeCr2DPffYfnVD5beePrgNku6h5PIRsBZL1nIIWNN2k/s0zzqdwCjpS7ZfNur6zETPBcsOlJl3s+KCZVgkfZ+yx8o7bW8j6T7Az91AWu5afiub+Ixr91HHobS/eO2JEwaOfqiS57wpw0gf3Li2u196qcX9Dlo0rHUcbTqOcsGyR328Tz22Ul+wwNC67x5g+0RJh9QyF9eLpaa0sonPuAeFYSxeu0fSw2z/CkDSQ2l2c/FhpA+etdTifgctG9Y6jjbNyguW6qB6+/wWz3GHpPtTg46kJwEDrSGYYE3b085SnIlxDwrDWLz2VuAsSddRukYeQoObpVD2Z/g4ZWN3KEvxV9oMmiPQ2n4HbaozRs6XtLDNdRwtm7UXLLZvqbdtjt28mZJX6WGSfkRJZNnkavVWNvEZ9zGFibnSO4vXmsgd1HueNSh7NIvmchNFH9TyfgfDIOkpLNsv/MWRVahPGsJ+B22rXXcfoOyLLRpeIFfHETr/G65uagJKLftA4AjgL/Rs4jNoGo1xDwpLLZqa6tgMy54qNxHQXJ+wpA8C/0WZf3wasA3wJttfnvaNc4Skc1iy3wH1/k+oA9sr88pgAElfAh4GXMKSbkcPOlgY/ZF0LfACN7gXx4TyWwv4amkTn3HvPpq4aGqqYzPRdm6ijufYfpukFwI3UQb1zmLJTmxz3XS592eD+cDWnkVXZxrufgdtu7XFgDBpwAeaagVeSQsZmscyKEh6LvA8YLMJybrWZclc9oHYfm+9bXL8YDKdqZvPA75q+08tD5zPKp2VwWpp57shuIKy3eoto67ICrhjkmPd/Q6AlT4o9LTwF0r6GiV1dm+/fBMXdW0H/FY28RnLoADcTMmFs2u97XgIDUXWIeYm+rakX1K6j15XE8A1ltBvtpN0AOWfUFs737XtAcBVki5k6T/slbbby0PY72AIelv4d7Jk3BGaa+m3HfC/Vb8aNe5jCqsBj6bkJ9oTuB74hu2PN1D2e+vdR1L6sU+tj18AnNvU4rV6rg2A223fUwfP17P9u6bKn83U8s53bZsqMd7KnhtJy+538FE3u99BqyS9von/A8s5x1nA4ynjXbMi4MOYBgVJj6AkZOtMkfsa8J+2J00pMOC5fgC82Pai+nhd4CTbOzd4jlk5O2UYJJ0GvMgtbY4ey9LS+x18wi3sd9C2NjLfTnKOVgO+WtrEZ1yDwr2UdMr7ue7RK+m6QX9YU5zrl5Qc5nfVx2sAl3rAfVJ7ys/slGlI2payirbRne/apmW3+ew+xUqeu0lD3O+gLW0GBUlrUhYnPpyyZuZzthe3cJ779zzsbuJje6DJF+M6pvBiSkvhrHoleQLNZubs9SXgQkknU/5AXkize9bOutkpQ9bWznetcnvbfLbO9iqjrkMDHqcl6dB7NRHYFlAmtJwHPBfYmiUrqBtje+JCwY9IOp8BZ+SNZUuhQ9LawO6UbqQdKR/WybZ/0FD5omyevRFlo3go4wk/b6L8eo6TgDd2VmDG0pSd72IG1MJuej1lX95JelcXr13YRqtELW3iM64tBQBs3wF8BfhKHRjbAziYkh63ifIt6Vu2nwD8rIkyJzHrZqcM2Vl1BlKjO99FDKA77b0mwWvrPK1s4jPWLYVhkPQJ4Au2L2qp/Fk5O2VY6mDbRAMv9Y/xJukdtv+7j9cdYvv9K1h2J208LJ06flaMuSQoDEjSVcAjKPs0d/YPcEOpdzvn2IQy7RVKU/S2psqOiKkNY5bSTKmlTXzGYcBo1J5LmR20I2WNwvOZPPXFjEjakzLPeQ9K0/ACSU1mWpyVJL2t5/4eE55b7hVgRJ9W5vQBX6Ak+XxQffz/AQOnLk9QGJDtG2tWyL9TZh91vpryTspGPvvafjll06B3N1j+bLVXz/1DJjzX2BqRmPNW5q6UB9g+kTrrrk57HXgvlwSFAUnata6qvR44hzLY8/0GT7HKhO6iP5LPDZa+gpt4NbcyX93F7LIy/y61sonPWM8+GpLDgScB/2N7W0nPpEyBbcppkk5nyUYm/0HZhm+u8xT3J3scMVNNZFRuSyub+GSgeUB156z5Kvsyb2v7XkkX2t5+wHIfDmxi+0dasgexgD8DX3Hd/nOu6pnh0Tu7g/p4TdurTfXeCEkfY5qLh5V9RXxHG5v4pKUwuL9IWgc4l7Ie4jbKnOFBfQR4B3TT+H4TQNL8+lxjg9mzke1VR12HmNUWjroCDdmeJXnRtpM0cF60tBRmqHMlT8lJ9HdKP/9LKem5v2v74mne3k/5V9h+zBTPdVdMRsTc1FZetLQUZu4jwDvqqmkoMwAW1Cv5Qxn8Sn7NaZ5ba8CyIwKo+5O8nZKfqPs3Z3vHkVWqf63kRcsslpmbZ/uyiQdtL6Q05wZ1kaT9Jx6UtB9LbxwUETP3FeAXwJbAYZTZg61kJ2hBZxOfRqX7aIYkXWv74Sv63AqUvwlwMnA3S4LAfGB14IXZZCdicJIutv0ESZd1shBIOsf2pOllViZtbeKT7qOZu0jS/rY/03uwqSt527cCT6lTXDtjC9+1/cNBy46Irs5snVsk7ULZynfzEdZnRRzaRqFpKcxQruQjZj9Jz6fse/Bg4GPAesBhtk+d9o0j1PYmPgkKA5pwJX9lruQjZgdJq1L2Kvm/o67LipD0NZbexOdG241t4pOgEBFzlqSzbD9z1PVYEW1v4pMxhYiYy34s6ePA11iyBwK229o0qwmtbuKTlkJEzFl1Bs9EXpnXKbS9iU+CQkREdKX7KCLmLEnvmey47fcNuy4riwSFiJjL7ui5vyZl58RfjKguK4V0H0VEVJLWAE61vdOo6zIqyX0UEbHEfYGHjroSo5Tuo4iYsyRdzpLNdlal7F42Z8cTIN1HETGHSXpIz8PFwK1NpoyYjdJ9FBFzlu0bKXmPdrT9W2B9SVuOuFojlZZCRMxZkt5LSWT5SNuPkPQg4CTbTx1x1UYmLYWImMteCOxKnZpq+2Zg3ZHWaMQSFCJiLru7bmdpAElrj7g+I5egEBFz2YmSjqGMJewP/A/wmeW8Z6xlTCEi5jRJzwaeQ0kod7rtM0ZcpZFKUIiIiK4sXouIOUfSIso4gliyeA0aSj89m6WlEBERXWkpRMScI2lN4LXAw4HLgM/P9ZXMHWkpRMScI+lrlG0tzwOeC9xo+6DR1mrlkKAQEXOOpMttP7bevw9woe3tRlytlULWKUTEXPTPzp10Gy0tLYWImHMk3cOSXdcErAXcSWYfJShERMQS6T6KiIiuBIWIiOhKUIiIiK4EhYiI6EpQiIiIrv8fAu6LbKdOAAkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = d[all_labels].sum().plot(kind='bar')\n",
    "ax.set(ylabel = 'Number of Images with Label')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Above, I see the relative frequencies of each disease in my dataset. It looks like 'No Finding' is the most common occurrence. 'No Finding' can never appear with any other label by definition, so we know that in 57.5% of this dataset, there is no finding in the image. Beyond that, it appears that 'Infiltration' is the most common disease-related label, and it is followed by 'Effusion' and 'Atelectasis.'\n",
    "\n",
    "Since 'Infiltration' is the most common, I'm going to now look at how frequently it appears with all of the other diseases: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f20b590d050>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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JurEsPCVJkiRJpSw8JUmSJEmlLDwlSZIkSaUsPCVJkiRJpSw8JUmSJEmlLDwlSZIkSaUsPCVJkiRJpSw8JUmSJEmlLDwlSZIkSaUsPCVJkiRJpSw8JUmSJEmlLDwlSZIkSaUsPCVJkiRJpSw8JUmSJEmlLDwlSZIkSaUsPCVJkiRJpSw8JUmSJEmlLDwlSZIkSaUsPCVJkiRJpSw8JUmSJEmlLDwlSZIkSaUsPCVJkiRJpSw8JUmSJEmlLDwlSZIkSaUsPCVJkiRJpSw8JUmSJEmlLDwlSZIkSaUsPCVJkiRJpSw8JUmSJEmlLDwlSZIkSaU2a70Cre1+5MfX+99eeszDJrgmkiRJkrQ0zXnGMyLeHhFXRMQ3xpZtHxGnR8TF/fftaldTkiRJkrRYzWeo7YnAQ6YtOxJYmZl7ASv7x5IkSZIk3cCchWdmfh74v2mLHwWc1P98EnDQhNdLkiRJkrRErO/kQjtl5mUA/fcdZ3tiRBweEasiYtXq1avXM06SJEmStFiVz2qbmSdk5vLMXL5s2bLqOEmSJEnSArO+heflEbEzQP/9ismtkiRJkiRpKVnfwvNUYEX/8wrglMmsjiRJkiRpqZnP7VTeC3wR2DsifhwRTwOOAQ6IiIuBA/rHkiRJkiTdwGZzPSEzD5nlV/tPeF0kSZIkSUtQ+eRCkiRJkqSNm4WnJEmSJKmUhackSZIkqZSFpyRJkiSplIWnJEmSJKmUhackSZIkqZSFpyRJkiSplIWnJEmSJKmUhackSZIkqZSFpyRJkiSplIWnJEmSJKnUZq1XYGO1+5EfX+9/e+kxD5vgmkiSJElSLc94SpIkSZJKWXhKkiRJkkpZeEqSJEmSSll4SpIkSZJKWXhKkiRJkkpZeEqSJEmSSll4SpIkSZJKeR/PjYz3D5UkSZI0NM94SpIkSZJKWXhKkiRJkkpZeEqSJEmSSnmNp8q1uq7U3GFyJUmSpLl4xlOSJEmSVMrCU5IkSZJUysJTkiRJklTKwlOSJEmSVMrJhSStt41tIiUncJIkSVo/nvGUJEmSJJWy8JQkSZIklbLwlCRJkiSV2qBrPCPiIcBxwKbAWzPzmImslSTpeovtmtbFeB2tuQs309xhcjembTV3YWeaO0xui8z1PuMZEZsC/w0cCNwROCQi7ri+/58kSZIkaWnakKG2fwZ8NzMvyczfA+8DHjWZ1ZIkSZIkLRUbUnjeBvjR2OMf98skSZIkSbpeZOb6/cOIg4G/zMyn94+fBPxZZj5v2vMOBw7vH+4NfGc913UH4Ofr+W83RIvcjWlbN7bcjWlbN7bcjWlbN7bcjWlbzV26meYu3Uxzl27mYs3dLTOXTV+4IZML/Ri47djjXYCfTn9SZp4AnLABOQBExKrMXL6h/89iyN2YtnVjy92YtnVjy92YtnVjy92YttXcpZtp7tLNNHfpZi613A0ZavtVYK+I2CMitgCeAJw6mdWSJEmSJC0V633GMzOvjYi/AT5NdzuVt2fmNye2ZpIkSZKkJWGD7uOZmZ8APjGhdZnLBg/XXUS5G9O2bmy5G9O2bmy5G9O2bmy5G9O2mrt0M81dupnmLt3MJZW73pMLSZIkSZI0HxtyjackSZIkSXOy8JQkSZIklbLwlCRJkiSV2qDJhSRpJhFxG2A3xtqYzPx8uzXShoqI7WdYvCYz/zD4yixxEbEpsBNT3z8/bLdGkhaiiHhQZn522rIVmXlSca79wRJVffy2oCcXiohHA8cCOwLRf2VmbrOUMseybw+8iBvu8L8ozFwGPAPYfVrmU6sy+9wW2zp45rTsNwI7ZeY+EXFn4JGZ+S+FmfsBR7F2e0d/y7eryuxzjwUeD3wL+GO/ODPzkcW5WwEvBHbNzGdExF7A3pn5scLMJu1Fi30bEZcCtwV+2edtC1wGXAE8IzPPLcw+GPhUZq6JiH8C7gb8S2aeV5jZpL2IiOcBLwcuB65bG5t3Ls5dA0w/IPgVsAp4YWZeUpDZ6v0zeHvc5zbpb/vsfYA7AluO5b6zMG/w9ngsu1Xftwx4CTd8nSuPaz4PfBP4e+BmwFuBazLzsVWZfe6lNOgPIuJVwL8AvwU+BdwFeH5mvrsir88cvP/pc09j9jb5zZn5u4LM8uO3hV54fhd4RGZ+eylnjmV/HXgTcC5rdzjFB3TnAP87Q+aHqjL73BbbOnjmWPbn6A5i35yZ+/bLvpGZ+xRmXgT8HTfc3l9UZfa53wHunJnXVObMkPt+um19cn8weVPgi5l518LMJu1Fi30bEW8CPpKZn+4fPxh4CHAycFxm3rMw+4LMvHNE3Bf4d+A/gJcWZzZpL/q/qXtWv09nyD0a+CnwP3QHkk8AbgV8B3h2Zj6gILPV+2fw9rjPaNXfvhx4AF1B9AngQODsyuKkRXs8lt2q7/sM8H66IvBZwApgdWa+pDAz6Ar8Z/aL/jkz31uVN5bbpD+IiPMz864R8VfAQXT7+czMvEtFXp85eP/T5x4HLANG+/PxwM+AmwLbZOaTCjLLj98W+lDbyxsUgC0yR67NzDcOnLlVZaO4Di22tUXmyFaZ+ZWuj1i7PsWZv8rMTxZnzOQSYHNg0MIT2DMzHx8RhwBk5m9j2gteoFV70WLfLs/MZ40eZOZnIuLfMvMFEXGT4uzRwePDgDdm5ikRcVRxZqv24kd0n2oP7SHTDqROiIgvZeYrIuKlRZmt3j8t2uNRbov+9rF0Z4a+lplPiYid6M6MVWrRHo+06vtumZlvi4gjMvNzwOf6DzkqbQfcE/gesAuwW0RE1p9VatUfbN5/fyjw3sz8vwH+rFr0PwD7Zub9xh6fFhGfz8z7RcQ3izLLj98WeuG5qv/U7KOMvQiZ+eElljlyWkQ8B/jItOz/K8z8WEQ8NDM/UZgxkxbb2iJz5OcRsSf9sImIeCzdsJRKZ0bEq4EPM3V7S4eHAFcD50fEymm5f1uc+/v+U/XRa7wn9cVvq/aixb79v4h4CfC+/vHjgV/21yNeN/s/m4ifRMSbgQcBx/YHNtWT47VqLy4BzoqIj0/L/c/i3Osi4nHAB/vH42fDqg5iW71/WrTH0K6//W1mXhcR10bENnTDIUuHndKmPR5p1feNrm+8LCIeRjeCYJfizC8Bx2Tm2/vX+1jgC8B9inNb9Qen9We0fws8px/ePPEhp9O06H8AlkXErtlf3x8RuwI79L/7fVFm+fHbQh9q+44ZFmfl9RAtMseyvz9LduV1W2uAren+iEeN5hDX2LTY1sEzx7JvB5xA1xn8Evg+cGhmXlqYeeYMi7PyepM+d8VMy7N+soMDgH+iG072GWA/4LDMPKsws0l70WLfRsQOdNce3pduKObZwNF0Z+d2zczvFmZvRTeM68LMvDgidgb+NDM/U5jZpL3oh0XOFHx0ce7tgOOAe9MVC1+iG8b2E+DumXl2QWar98/g7XGf26q/fQPwUrrh0y8Efg2cn5lPKcwcvD0ey27V9z2cbij1bYHjgW2AozPz1MLM6wuTsWX3y+LJ/Br3B9sBV2XmH/u+YZvM/Flh3uD9T5/7ULrLPb5H9xrvATwHOIvuOtrXFWSWH78t6MJTWmoiYmtgk8xc03pdKkXEFsDt+4ffyYFmuouIWwL3omukv5SZPx8iV7X6syU/zsxrIuIBwJ2Bd2bmlW3XrE5E3JzuYPnXrddlqdpY2uNxEbE73YH6BQNk2R4PoC/E9mLqhEZLchb5aDPRXLP+pz+7ege699BFWTCh0AyZpcdvC7rwjIhd6D412o/uE9izgSMy88dLKXMse3Pg2cBoTPdZdJMflB60R8QjxzNzmFnnBt/WRpkvWNfvK4fPRcQt6D6RHG3v54BXZGbp9WN9w3wScCldY3lbYEVVRxgRd1vX74s7pCbtRYt9G91MoH/PDWfkHGJW6POB5X32p4FT6WbIfGhhZqv2eB/gXcDodgU/p5ugpeqanlHu4DOuDv3+adkej63DYP1tRNwhMy+arY2saBtbtsdj6zBo+xgRL87MV0XE8cwwLL3yMpOIeDpwBN2Q3vPpCv0vDnB2t0l/EG0mmhu8/xnLvg83fI0rZ6N+AMXHbwv9Gs930M2wd3D/+NB+2QFLLHPkjXQX9b6hf/ykftnTqwIj4hjgHsB7+kVHRMR9M/PIqsze4NvaKPPmhf/3XN4OfAN4XP/4SXR/y48uzn0N8ODM/A5c30G9F7h7Yd5sEqjsCFu1Fy327Qfohv28lbGZIgdyXWZeG93tN16XmcdHxNeKM1u0F9ANAX1BZp4J1x8IvIX6a7ZOoRsm+FmG279Dv39atsct+tsXAIczcxtZ1Ta2bI9Hhm4fRxNkrSr6/9flCLq/qS9l5gMj4g50Q16rteoPWkz006L/ISLeBexJ94HC9bc2AcoKT4Y4fsvMBftFdw3CnMsWe+ZYztfns2zCmRfQDTUaPd4UuGCJbuvgmS2/Wv0tz/T3M8Tf1Eb2GrdoG89t+Dp/GTiE7mByj37ZN4ozm7QXDXMH6efmymyxHgNub5P+dmP72pj+roCvjrYPuMlQ29qqPwA+BryZ7rrHbYGbDHDsOHj/02d8m35k6oCvb/nx20I/4/nziDiUtfewOQSovrdZi8yRP0bEnpn5Pbh+AoQhPknaFhjN1HiLAfKgzba2en1Hk2jMNASnchKN3/afpp/dr8N+dDPBVVsVEW+jGy4I8ES6+6mViognz7Q8C4el0K69aLFvW84K/RS6++L9a2Z+PyL2AMpuGN5r1V5cEhEvY+3751C6yW+qtZhxtcn7p1F7PNKiv20xZK9FezzSpO9rNPz0xxGxLd3M0KdHxC/pZtOt1qo/eBzdRD//kZlX9hP9vKg4s0X/A12heyuGmXF7pPz4baFf47kr8HrWzrJ3Dt31Hz9YSplj2fvTDQe5hG5s9W7AU7IfclWUeQhwDHBmn3k/4B8y833r/IcbnttiWwfPHMt+zNjDLYG/An6atdd+3JVurP4t6Lb3/+hmFfx6VWafexPguayd7e7zwBuy8IbEfe7xYw+3BPYHzsvam6Q3aS9a7NtoOCt0C63ai36ikKOZ+v45KjN/WZw7mnH1GroZV4PiGVcbvn8Gb4/73Fb97YxD9or7n8Hb47HsVn3f1+mGn57L2IdUmVn+wWuff3+6bf5UZlbdbmOU1bQ/iIgdmTqZ0g/X8fRFKbrZme8KfIWpxf0jCzPLj98WdOG5Mep3+t6sncGq/L5X/SdG9+gzv5yF01JPy22xrYNnzrIemwCfLf4kdJS1DUBmXlWdtZD0E0y8q7KRbm1j2bcRsRfdRBJ3ZOrBRvWtTRZEe6FaA7fHg/e3EfFt4I7Z8ICvRXs8dPsYEedmZtVcBjPlbUI3DHKfoTJb6yfneg1wa7r70e5K1zbfqTCzVf9z/5mWZ+bnKnOrLcihti1mCGs8K9lfZOYZ/YXL4/aMCLLgZtozzHY3mkXw1hFx6yyaea7Rtg6eOQ970TWYExcRh2bmu2PaDI4RAdTN3BgRJ2fm4yLiQmZ+D925IncdrqZ7nSeuVXvRYt+u4/0zyhzi/fMOulkqXws8kG7oU1QEtWovIuJ1mfn8iDiNmf+mSg7YZ+gLpudWzHzarL+dRVl7DO362zEthuxNV9Yej7Tq+8YMOvw0M6+LiK/HDPfyrLIA+oNX0s3c+9nM3DciHkg3RL/SYP3PuCELzCGP3xZk4UmbGcJazkp2f+AM4BEz/C6Bijdyi9nuoM22tsicoh/GlvTD14CfAS8pitu6/z7TDI6Vn3gf0X9/eGHGrKYdsG9C9+nkyUVxrdqLFvu2+fsHuGlmroyI6IdhHhUR/0t3MDBprbZ3dE3NfxT9/7Np0Re07G+Hbo+hXX87sgPwrYgYcsjekO3xSKu+b2RF/338msMEKs+M7Qx8s9+3v7k+tG7ftu4P/pCZv4iITSJik8w8MyKOLc4csv8hIs7OzPuOtVPX/4q6yx8GO35b0ENtI+LgzPzAXMsWe+ZYzh6Z+f25lk04c8ucdkPamZYV5LbY1sEzW4qI/TLzC3MtK8g9NjNfMteygtzxYSnXAj/I+vtpNmkvWu3bViLiC8CfAx+kO+j5CXBMZu5dmNmkvYiIIzLzuLmWLQUt+9sWGva3gw/Za9Eej2VvNO3jUh2OOZuI+P5ac34AACAASURBVCxwEN3Q1x3ohtveIzPLbjfVov9pZYjjt4VeeJ6XmXeba9liz5wju/SagVbbu4C2tTqz2c20F9i+vaDBUNtyC+w1rm4bbwI8hhvO2PiKqsyx7HvQnSXblm6o1S2AV2XmlwozB28v1pH7tczctzK3zxl65tNB/45btsd9fsvji53ori0F+EpmXlGd2UrDdnlz4Nl0k0YBnAW8OTP/UJw7+L5t1R9ExNbA7+jO/j2Rri94T2aWzYbdov8Zy94U2Impr3HZsOohjt8W5FDbiDgQeChwm4j4r7FfbUP3ydmSyBzLvgNwJ+AW08bNb8PYhcwTzrwVcBvgphGxL2vHq28DbFWR2ee22NbBM8eMhlZtCSwHvk73Wt+Z7t5Q9510YETcm+5m88umXeuyDd1940pExLOB5wC3i4gLxn51c6Dsk+YZhqNMUTEspVV70Wrf9k4BfkU3Y+Ogk+xk5lf7H39Nd31NmVbtRXQznv41sEdEnDr2q5szzC1GBrtZecP+dvD2GNr1t2P5jwNeTVcIBXB8RLwoMz9YkDV4ezyW3bJ9BHgjsDnwhv7xk/plT68KHHLfTtOkP8jM34w9PGmgzMH6n3ER8Ty64byXA9eNVoeuvZp01mDHbwuy8KS7B9Eq4JFMvX/MGuDvllDmyN5046q3Zeq4+TXAM4oy/xI4DNgFGL/gfg3w0qJMaLOtLTIByMwHAkTE+4DDM/PC/vE+dPf7qrAFcDO69/f4tS5XAZVT2f8P8Em6ITBHji1fUzW5AkBm3hwgIl5Bd63Wu1j7aehM1/pMQqv2otW+BdglMx9SnDHFtALsBoquY2rVXpxDN/nLDky9FnANcMGM/2KyljPczKdN3j+N2mNo19+O/CPdcMQrACJiGfBZuqGDE9WoPR5p2T5C9xrfZezxGdHdYqXSYPt2mkH7gxk+0Bhdn1123WOj/mfcEcDelWdzxwx2/LbQh9puXj1EYSFkjmXfOzO/OHDmYzLzQ0Nm9rkttnXwzLHs8zPzrnMtm3DmbjnA/WfXkT/ofbYi4suZec+5lk04s0l70WLfRsQJwPGjg/WBMlcDPwLeS3dGaspMgsXXqDVrL1qIiA8Af5uZg8182vD9M3h73Ge06m8vzMw/HXu8CfD18WUFmYO3x2M5Tfq+iDgPODgzv9c/vh3wweJLIAbft33OoP1BRHyUbmbmDwPvqz6e6DOb9T99/pnAAZlZOupyluyy47eFesZzZPeIGPreOS0yR74WEc+lG+Y1nv3UqsDM/FBEPGyGzOrrtgbf1kaZI9+OiLcC76b7lO5Q1s7sWOXqiHg1N9ze0hkUI+IRdJ/qj+6ztRvdtpbdZ6v3x4h4IvA+utf4EMZu4l2kVXvRYt/eFzgsuhuHX8PaT5orr929FXAA3b78a+DjwHsz85uFmSNN2ouIuBdwPPAndGdwNgV+UzlEsTf4zKe0e/+0aI9b9refiohP0x1AAzye7uxGpRbt8UiTvo9uNtszI+ISuvZxN+qHZs60bz9RnAkD9weZeVB094J9NPCWiNgSeD9dEVo1oqpl/wNwCXBWRHycqW1y2W2Bhjh+W+iFZ4t75zS5X0/vXcBFdMNyXkE3NKW0M4yIN9FdY/JA4K10w1G+UpnZG3xbG2WOPIVu0oHRlNWfp7v2o9J76BrmhwPPopvqfXVxJsC/MPx9tqDrGI7rvwDO7pdVatVetNi3Bxb//zeQmX8EPkV3cHUTur+jsyLiFZl5fHF8q/bi9cATgA/QDX99MvD/Bsg9aoCM6Vq9f1q0x83628x8UUQ8BtiP7vU9ITM/Uhzboj0eadL3ZXfLjb3ohusHcFFmll7/2GjfQpv+4FfAOyLiJLoC+3i6DxZKCrHG/Q/AD/uvLfqvIdQfv2Xmgv0Czu2/Xzi27H+XWuZYztf67xf03zcHzijOvGDa95sBn1mi2zp45rT8LYA/BfYBNh8gb/S3fMHYss8NkLuq//51YJP+568M9ToP+dWqvWi4b+8LPKX/eRmwxwCZN6H7lPsDwFeBlwG3GSC3SXsx9v4Z37fnVOf2ObsBD+p/3gq4eXFey/520PZ4fJ8O3d+O5W8DbD/6Gip36K+G7eOmdNct/y3dvVtfALyg9etRuL2D9gd0E0cdTzcB2uuBPx9gG5v0P9PWYesBs8qP3xb6Gc/f9ePVL46Iv6G7d86OSzBzZHSty5X9ZAc/o5uqutLo/mFXR8St6WZP3KM4E9psa4tMACLiAXQzsF1K96nkbSNiRWZ+vjB2tL2X9cO7fko3uUW1KyPiZnRnEd4TEVdQPDM0QETsQtcp7Uc3tOts4IisvXdcq/Zi8H0bES+nOwO3N92Zqs3phiruV5h5El1h8Eng6Mz8RlXWDFq1F1dHxBbA+RHxKroJh7auDo2IZwCH0xUle9LNwvomYP/C2Cbvn0btMTTqbyPimXRn7X9LNzvmaGKWsiHNjdrjkVZ932l0+/hC1s5CWiq6mbePpXvfBIWT7UzLHbQ/iIhLgSvphm4fTn9MEf0tkrLgVkiN+5/RLM1vo/uAateIuAvwzMx8TmFs/fHbkFX7elTe96B7wXeh+8P+EHCvpZY5lv10YDu6e0BdQje++pnFmS+jm73xMXQHVpcBr1ii2zp45lj2uXSzk40e357+U9nCzIfT3W9qH+DMfh0eOcC2bg1sQjeUfwXdp7+3HCD3dLohdJv1X4cBpxdnNmkvWuxbuk+Zg/5MYL/sguLM6+hm/lxDNzPl6GsNcFVxdpP2gu6s45Z0Z6deTjeM7P8NkHs+3VnA8f17YXFmq/fP4O1xn9Oqv70Y2KE6Z1rm4O3xWHarvq+0PZwl87vAnzTIHbQ/oLtdzJn91xljP59J0UiUlv1Pn/9l4LbTXuNvFGeWH78t2DOe0d009XGZ+SIGundOi8yx7E3o/pB/SfdJQ/lkRn3mysy8EvhQRHwM2DK7cfTVuS22ddDMaTbPzO+MHmTm/xfdzaZL9H/Le2Xmx+jutfXAqqwZck/JzAfRNdqD3Gertywz3zH2+MSIeH5VWKv2otW+BX6fmRkR2a9H+Vm4zNykOmMmrdqLft/+a2YeSnfm5OghcnvXZObvI2K0LpuxjvsxbqiW/S0Dt8fQrr/tfQ+4eoCccYO2xyMN20eAT0bEgzPzMwNmXp6ZQ81VMW7Q/iAzH1D5/8+S2aT/mbYOPxq1yb2yCbqGOn5r/qLOJruLeu8e017xpZY5ln0d8DcNMl8z9viaITrBhts6aOY0qyLibRHxgP7rLUy9f91E9X/L1feYmi336n72uaH9PCIOjYhN+69D6YaylWjVXrTat8DJEfFmYNt+WOZn6SZIWXJatRf9vl3WD7Ud2uci4qXATSPiALprmk6rCmvZ3zJwewzt+tvePwDnRMSbI+K/Rl/FmYO2xyMN20eALwEfiYjfRsRVEbEmIq6qCIqIR/fDbFdFxPsj4pDRsn55tY2mP2joRxFxHyAjYouI+HsKJ7gb6vhtod/H8zXAXnQd4G9GyzPzw0spcyz7ZXTXYLx/WnbVVNFExNF0Nyf/cA74x9BoWwfPHMu+CfBcuovxg+4syhuycMa7iPhXuuFG07d34tdCTMs9mW5WtNOn5f5tce6udBMO3JvuTM05dNcUld3PrVV70XDfHgA8mO5v+NPA54v/hs9kfmfdTszMd044u0l70R/M3Q04dVpu2RT6fe4mwNMY27+Z+ZbizFbvn8Hb4z63VX/7FbprLKdce5iZZSNSWrTHY9mt2sdLgIPohqiX7t+IeMc6fp05wG3ihuwPortty7pe09F1y6/LzIl8qNKy/+nzd6CbFfpBdNv3Gbp7LVceJ5cfvy30wnOmN1bpG6pF5lj292fJrpwAYA3dmO5r6YZ2DXVheottHTyzpb7RnC6z/j6eK2bJnXjD3Fqr9qLFvo2It49vVz8BwSmZWTb5TETsNs+nXjnps0et2ovoJu2YKbf0Xo/R3SLgn8cebwq8MzOfWJjZrL9toWF/e05m3qcyYyFp2Pd9GjiwP7s9iIjYLzO/MNeygtzB+4Ohtex/+vwDM/OT05Y9KzPfNOmssf+//PhtwV7j2XvrTG+oJZg58ieZ+bvxBdHdJLdMZt688v9fh8G3tUXmHJ+YZXEj/bTMvGTa+gxRZG+bmceNL4iII2Z78oaKiH9ex68zM19ZlU279qLFvv1JRLwxM58dEdvR3Uy79IzYEGdH1qFFGwXwrcz8wLTcgwfI3TUi/iEz/70f6vsB4GvFmYO+fxq3xy372zMj4nC6odPjN6Kf+JmTxu3xSKu+7zK6+zx+kqmvc+VohePpRkjMtWzSBu8Phta4/wF4WURck5lnAETEi+muWS4rPBng+G2hn/E8LzPvNteyxZ7ZMjsiVk7vbGdaVpC7UezbiLj7DIvvBbwYuCIz71GYPdP2npuZM61Tde7XMnPforwXzrB4a7phg7fMzJtV5PbZTdqLhvv2WLohbHcHjsnMD1XmjeXeHngR3Yyv139gWnyGdyHt2yFyA3gP3XDMBwKfzMzXFmcOuq0t2+M+v1V/O9jZ+5bt8dg6tGofZxqtQGZOfJKw6G61cR/g+cD4+3Qb4K8y8y6TzpxhHQbvDyLiMcAxDHj7mBb9T5+7A/CxPvshwB2AJ2TmH9b5Dzcss/z4bUGe8Rx7Qy2LiBeM/Wobuhv0LonMsexb0d0z7aYRsS/dG2mUvVVR5pb9/71D/2nVeOatKzL73BbbOnjmSGZeP2FFRNyfbjr9mwDPmj6EYlIi4g7AnYBbxNRJBrahu01DiYg4BPhrYI+IOHXsVzendpKf6yfsiIibA0fQzZD5PsYm85ikVu1Fi307LecrdH/DX6Gb8ODROcD173Rn395E94l62ax+0K69iIgDgYcCt4mpE79sQ+F9cKO/D17vOODNwBfoJhu6W9bcH6/J+6dFe9xnNelvRzJziHtzj7IGb4/H8pr0fSMVBeY6bEF3K6LN6PrYkauAx1aFLoD+4FjgETnsTL6D9T/jMvPnEfFIuombzgUem0VnC4c8fluQhSdt3lBN3sS9v6S7x9UudPdsG1kDvLQo85l0n5TdGhg/sLgK+O+iTGizrS0yrxcRf0nXOP+O7lYJM11/Mkl7093HbFvgEWPL1wDPKMw9h26o0Q5MPcBYQzehRpmI2B54AfBEuinA75bdrTCqtGovWuzbR0x7/DW6m4U/gm7Y4hCF57WZ+cYBcqBde/FT+vsNMnWG1TXA3xXmTi8GfgncsV+eQMWn+s362wbtMbTrbwGIiCfPtHyS121Nyxu6PR5p1fcBsw/lrjgzlpmfo/tw6MTM/EFf5Gdm/nrSWdO07g9a3D5myP5ndC14wvUTJm1Bd0uvx0ZE1dndwY7fFvpQ292GHmPdInMs+zFDDV0by3xeZh4/ZGaf22JbW2R+FVgGvBr44vTfV5xNGMu+d2beIHOpiYhXA48GTgD+e4COdzy7SXuxsezbkYg4CrgC+AjF16iNZQ7eXvS5m2Vm2RnOhWTo90/L9rjPb9XfjmduCewPnJeZEy/yW7bHY+vQpH2cNpR7S+AxdEXLiwsz9wHeBWzfL/o5sCIzv1GV2VJEHAfcCvgoU/uCyrtdHMXA/c9StiALz4g4jXVMYZyZE79HU4vMsewXrOv3FRemxxz3eap6Ezfa1sEzx7LPYu3f1egTrLHoyX8S2h9krOtvueS2JmOf0t3gVxRegxER19F1BtdOyy/LbdVetNq3ffZJdLdDuLJ/vB3wmlxiM363ai8i4kLWvW/vXJE7lv9vwKum7d8XZuY/FWS1ev+cxcDtcZ/bpL+dTXT36XtX0bHU4O3xWHaz9nE2EfG5zLx/4f9/DvCPozP3EfEA4N+yeBbjVv1BtLnbRasZzv8KOCP7GXMjYlvgAZn50YKswY7fFupQ2//YSDJHWsx0N324xLjK4RIttrXVTIJk5gMaxK5qkNlsxsbM3KRBbKv2osm+7d15dJABkJm/7K+BLDfkNWq0ay8e3ih35MDMvH4ocb9/HwpMvPCk0funUXsM7frb2VxNd//UiWvUHo+0bB9HQ4xHNqGbdOdWxbFbjw8Xz8yzImLr4kxo1B9k5lOqM2bIHLL/GffyzPzI2HpcGd0EVhMvPIc8fluQZzwlLT4RsU1mXjWt872ew1IWt4j4Ot2nrb/sH28PfC4z/3SA7M2BZwP36xedBbw5C2f329hExAXAPbK/AXxE3BRYlZl3artm2lDTzjBvQncN78mZeWS7tVp6+jNjozPp1wLfB16RmWcXZn6E7rrhd/WLDgWWZ+ZBVZl9bpP+ICJ2obtdzH50r/XZdGdef1yY2aT/iYgLpo90iYgLK17jIY/fFuQZz4g4OTMfN9vQo4ohRy0yx7JfnJmvmm2YSMXwkIg4NDPfPduwssLhZC22dfDMsexRRzTrU/rfvy4z/2sdz7sxma/LzOfPNpytcNj4/9CdtTmXGYax0V0cP3GzTegwgxNzQpNptGovGu5b6CYcOCciPtg/Phj418K8cW+km8DiDf3jJ/XLnj7poFbtRUScnZn3nWHIU/kQxd67gZX9ULYEnko3MczENXz/DN4e97lN+tsx42eYrwV+UHWg3qI9Hstu2T62OjP2VOBourPmAXyebhbhaq36g3fQHWuM7m18aL/sgMLMwfqfaVZFxH/STUCWwPOYOvHcJA12/LYgC0+62d9g2KFHLTJHvtV/H3KYyOi2AEMPK2uxrS0ygWYd0agzH3o42zH99z/JzN8NmHvYPJ935dxPmbdW7UWrfUtmvjMiVtHNchrAozPzW3P8s0m5R069L90Z/SfuFVq1F0+GpkPWX9UXgvvT7d9XZuani+KavH8aDplr1d8C3QyoEbEbsFf/800j4uaZuaYg7rB5Pm+S7fFIs/YRICK2opvNd9fMPDwi9gL2zsyPVWX2ZxwHv3a1YX+wLDPHr/M8MSKeP+uzJ2PI/mfc8+hm4H4/3Wv8GeC5RVnDHb9l5oL7opttDbqL35ds5lj2u/rvRwyYeWz//eCNYFsHz2z5Bawc38cD5p7bfz+v9WswwLY2aS9a7Ftgm/779jN9DfV6A3uOPb5d1d9Zq/Zi7P2zcsjcFl8t+9tG29ukvx3LfwbwVeB7/eO9luLfWau+byz//cCLgW/0j28KnF+cuZzubOd5dLe9uAC4oDCvaX9Ad0/LQ+nu97tp/3Pp3/KQ/U+rryGP3xbqGc8tImIFcJ+ZZoPLmhngWmSO3L3/NPKpEfFOpp7iJmuujXtoRPwT8A90N8cdSottbZE5RUQ8hu4TpR37/MrhcztHd3P0R0bE+7jh9lbdMuAP/TC9XSLiBsPUsnhGwYi4PfAiYDfGRnNkzUyVrdqLFvt2+hCckdGwxNKZ/XovAs6MiEv63N2oG07Wqr3YpJ844vYzDcnMussfWgzxbdnfDt0eQ7v+duS5wJ8BXwbIzIsjYsfKwIHb45FWfd/Inpn5+Ig4pM/7bUTEXP9oA72H7nW+ELiuOAva9wdPBV4PvLbPO6dfVmnI/qfVkPHBjt8WauH5LLobD0+/CTDUzQDXInPkTcCn6N6w5zLMtXGforvf09YRcdXY8uoOuMW2tsic7ljgETnMjY//GTgS2AWYfrCa1NwQHrrO6EH9/191HcK6fIBuX78F+GNxVqv2YvB9m5kP77+3GqZIZq4cDVuje/9elP0kOAVatRdPAA6i65cHG5KZmfftvw85DLRlfwvDtsfQrr8duSYzfz+qgSJiM+Z3HeaGGLI9HmnV9438vp+UKwEiYk/G7vtYZHVmnlqccb3W/UFm/hAovVZ3hswh+x9YO1HUkEPGBzt+W9Cz2kbE0zLzbUs9cyz7jZn57IEzT8nMRw2Z2ee22NbBM8eyv5CZ+w2c+bLMfOWQmX3uXTJziOsfpueem5l3n/uZE81s0l403Le34YZnMD5fmPcXmXnGTGfF+uzKm4Y3aS8i4sDM/OTQuX32psBOTN2/PyzMa/X+Gbw97nNb9bevorum8sl01409B/hWZv5jYebg7fFYdqv28QC62w/dke56vP2AwzLzrMLM/YFDgJWMFbnVowb67MH6g0YTUzbrf1oZ4vhtQReeABFxH2B3pv5hT3QmtIWQOZY9aMffUottbfX6RsRxdPfz+igDdg5DFwp95jK6a4p2n5ZbfWPpo4ArgI8w9TUuHUrdqr1oUAQeCzyebvKd0RmMLBr2M8o8OjNfHg1uGt7nt2ijbgI8hhv+Tb2iOPd5wMuBy1k7ZC+zcEb3PrdFH9+kPW4lIjYBngY8mO6MzaeBt2bhAWCr9ngsf/C+r8+9JXAvutf5S5n58+K8dwN3AL7J1Pdtdds4aH8QEY/IzNP6Ifo3kJkTn4F7AfQ/+wFHsfbveDRComyU3hDHbwu68IyIdwF7Aucz9Q+78vYXg2eOZf8N3R/ZYB1//0nOsQx3rcsot8W2Dp45lj14wxURx9AN3xusUOhzzwH+l264xvVDrDLzQ8W5359hcXUj3aS9aLFvI+I7dDcNrx46NlP2Hpn5/bmWTTizSXsREZ8CfsUN3z+vKc79LnDPzPxFZc60zFbvn1YHkk362xZatMdj2U36vj576A8ES+7rOI/cJv1BRBycmR+Ya9mEMwfvf/qMi4C/44Z9QVkbPcTx20IvPL8N3LHyU7mFkDmW3aLj/y7DXusynttiWwfNbKlhx3B+Zt51yMxWWrUXLfZtRHySblbOXw+VOZZ9Xmbebdqy0qF8rdqLiPhGZu4zZGafeyZwQGZeO2Bms/62hYb97eBnTlpq2PeNzgJOP/tY+YHgW4DX5nC3thrlNukPZukLbrBsgMzyoeQR8eXMvGdlxgyZ5cdvC3VyoZFv0A2HuWyJZ478iO6T7iFdPnQn2GuxrS0yAYiIXYDj6a75SOBsuts1lNzEu3cJ3U2Phz5D9bGIeGhmfmLI0IjYHHg2cL9+0VnAmzPzD4WxrdqLwfbt2DU1VwPnR8T0a4kqR6DcAbgTcItp19lsA2xZldtr1V6cExF/mpkXDhEWa2fQvQQ4KyI+ztT9WzKbbq/J+6dRewzt+tu3McOZk0qN2uORVn3fQXT37Rwy977Aiv4M8zWs/VChZGRGq/4gIg4EHgrcJqbOuroNUPJhWav+JyJGRe6ZEfFqusnWxl/jytmZy4/fFnrhuQPwrYj4ClNf9MrhEi0yR1p0/Ksi4v0Mf61Li21tkTnyDrppyA/uHx/aLzugMHPwQqF3BPDSiLgG+APDDSd7I93Bxhv6x0/qlz29MLNVezHkvl3Vfz8XGGz2xN7edLPtTZ/9dA3ddSiVWrUX9wUOG+pAkrUz6P6w/9qi/xpCq/dPi/YY2vW3v8rhJ6xq0R6PtOr7WhS8DxkwC9r1Bz/tsx/J1FlX19B9qFKhVf8z/bKK5WM/V8/OXH78ttALz6M2ksyRFh3/NnSN9IPHlg0xnX2LbW2RObIsM8evKzoxIp5fnHkqwxcKQ9+WYdw9MvMuY4/PiIjq2XWPKv7/ZzPkvn1gZh42UNYUmXkKcEpE3DszvzhwfKv24sABswBukpkvHThz5KhGuS3aY2jX37Y4c9KiPR5p0vfRpuAdeph6k/4gu5lWvx4R/zPQWfOW/c/rq+fEmM0Qx28L+hpPaamIiM8CJwLv7RcdAjwlM/dvtlITNjY8ZEbFBzlExHl015x8r398O+CDldd+bAyqr5+Z5zpsSTcr550YG+JUPRnMkCJi+3X9PotmA10I+3doG0N7PK6/fne6zMyyMycbY3s85IyrY5kX0hWfQdc27gF8JzPvVJTXtL2I7n6a/053y5rxvqByEsFB+58Wr/GQx28L8oxnRKxh5k9xyobstcgcyz5tlmygZthRzHIvpLHMqnH6LbZ18MwZPBV4PfDafl3O6ZdN3FhHNKPCIXvrmnVziJt3v4juk/1L6N63uwFPqQhq1V402rdbRcS+dNs2U2bpBwq9dwEXAX8JvAJ4IlByrVzD9uJc1h5A3iAWqDqw2jQitpslt6Tgbdnf9gZrj6Fdfzvm4Cy+rccMBmuPRxr2fUTEQcAy4MLM/HRVznQ5bUbbvoB4ZmFk6/7gHXS3fXot8EC6v6kZ12WCBut/Ghrs+M0zngtARNx/Xb/PzM8VZM74ydxYZskndI22dfDMliJit3X9PjN/MNS6DC26eyDuTdcRXTT0rIbVWuzbvkj4KrMURJVnTcbW4WuZuW9EXJCZd+4nLvl0RfZG2F5cA/yE2ffvkpz5dEgN+9tHAG+nu1brOuBxmXlORdYs+YO2x636voh4A93ZsHOA/YHTMvOVFVnzXJ+yM2at+4PRbLIxdhuZiPjfzPzzwszB+p8+72rguzP9ioFuAVhpQZ7x3Ni0OJCpHPoxR26LbW12oBgRL87MV832iXfFJ91LubCcSUT8RWaeMW3WOYA9I2JJ3RS+0b797hDF5RxG1/RcGRH7AD+ju8H1xC21wnIevpWZ+7ZeiSG0aI/7/7dJfwv8K/DnmXlRRNwTeBWwzg9WNlTL9rhh33c/4C6Z+ceI2IruPoiDFJ5js1IDbALcDVhdGNm6P/hdRGwCXBzdvZZ/Qndf3EqD9T+97zN1MqMlxcKz118DMZ/Tvydm5jsnnP3P83zqWTmhGxFHxJPn+dTzM/OCSWT2uS22dfDMMaPhGKvW+awJiu7m6PP5W/5oZk5sAoZ+Fs515Ub/+9dl5n+t43k31v2BM5i5oS6ZuKNVe9Fq3y4AJ/TDQf+JbtKQmwEvqwhq1V6MvX9mG2pb9f4ZXMP+dvD2GNr1t8C1mXkRQGZ+OSKGmPht8PZ4pGH7+PvM/CNAZl4dEdVDP8eN79NrgY8DTSamGcjzga2Av6Ur7h8IrHNEwQQM1v/0fj/0hyhDHr9ZeK512Dyfd2VB9nz/wCaZvcc8n3fpBDOhzba2yAQgM0/rf7w6Mz8w/ruIOHiGfzIJJ87zeZdOMjQz5/s3NVGZ+fL+x1dk5vfHwoBuxwAAIABJREFUfxcRVet02DyfN+m/qRPn+bxLJ5j5kvk8KSI+lJmPmWDu6P/dBLgqM38JfJ66ax1HmrQXrd4/wHHzeVJEHJ+Zz5tQ5mHzfN6kX+MW7TG06293nHZGbMrjLLg1UKP2eOTEeT7v0gnn3iEiRh8YBN3Z3QsYYGhkZh4NEBFbZ+ZvqnLGNOsPImJTuuHiLwJ+TfE1w33m0P0P8P+zd+bxts/1/n++jiHjoW6qGxkTl3IMqQwZkoqQEmUooUET6saVCtEtUyIqQogMyZVICMdwkOHgIOpXV3Sj4aqLY6gMr98fn886+7vWXnufg/X+fI69vs/HYz/2+n7X2uv13d+1Pp/3Z3gPXDsnL5K086C8KUranzbGcxzyF24R24/UvpaWFzb9Yi5qZ4ebaIxxj6fbXqvWNQ0TnTiYoPe+2vYGs3/lxCKvsr9qwLtgz/VaJkx/NSz9saQDxnu+M2kJ0h6a/rhmXgVJ6wAnkcaqS0uaAnzM9ieiNOfwukLsgaQrgE1ccPIyt9qfF2qf1e549iDpDGB34GlSlsHFJB1p+/AC2i8CtiH5js/6bGwfFKh5Myn5wJl5RacIkvYkZSebCZwIrAHsa/vSQM0a93czYHNgSUlN94TJJLeYMFSpBIWkbYBDSHEXIj7T68qk/3GxnriiyTT+7yDt15CyNy5D93cqOsHCEqQC1sv26NYsLxI5EPi5pM8BZwOzVvUdVGIE6vRRWfdKUpH0eYHbgP/NA5+oIunVKN1+avbHWb+ovY2cWI5Fzf64cQ1FbV/lvApHkbKt/iRfywxJc8MkKcoe3EqqrXkO3bYgMpdDcfszhwzcpbvE+K2deI5mFduPSNoRuIjkVjAdCJ94AucDD2e9Utk4309yV7gpG8WTgUsLrCbtavtoSW8npSDfpaMdqFnj/j5AiifaKut2mAlEDyRrpQA/FNjSdql04ysBWwCL0x1XNJM0OYvkHOA44ATSYlUpziclsLissG4tOgPGTzbORZYYgTp9FMBi2QZ9GDjZ9gENN76JRun2U7M/hsL29llMQO61/fsBydbsjzsUtX21Y+9t/09PWOlEtgkvAf5Kd3mP0Nhh6tifOSGi3wgfv7UTz9HMl1Mlbw0ca/tJSaW29Jey/Y5CWgDY/i3wBUlfIhmL7wHPSPoecHTgik6nl9ycNLiaUSAgv8b9nQHMkHSG7Sdn+weD5dW2t5X0Ltun5t38EvXF/lxw0ont80kroOvYvr6UbuYp298prAmwkO05irUpSGT7/Tfbf+8SS7sakdToowDmlfSvwHbAFwrozSkR/3vR9lO5P65hb+c0Bu48YCATz8r9cYfStu+UOXzdvQHa/yNpXcCS5icl3ZkbakxG9ZUn2u6KgZS0XpBWhxr2Z06IuMfh47d24jma40mdwwzg6uy7XyrG8zpJr7N9RyE9ACStRjJQm5Oyof0AWJ+UmW71INnpki4lJV34fM6290yQVocq9zezrKSvAavQ7foTuWJWOgV4h5slnQ38mMbOcrArDMCtkj5JWdfiCyR9gjRwa/6v0S44F0ra3PZFwTrPhsiJ8HWkMgGzOzdIavRRkHZoLgGm2b5J0vLAbwrozo45SkL0LKnVfmr0x0BZe2s7PPnKONTojzsUtX2uW4Jpd1LbXBL4A8kj45Pj/kUZouzBMYzu9/udGyQ17M+cMEdJiJ4l4eO3NrnQHCBpXtsl4j/uAl5NquHzDwpkRJM0nZQ58CTgXDcKPEv6L9u9tbgGpTuJZGTvsf2QpH8BloxMolHj/ja0pwEHAN8guR/tQmp/4yZ/eJ6aHyYNbFYjuXQtAuxv+7gozax7cp/TLhBbeg7JvWoHGu5VtvcM1Pxdn9OOHsAqFfFeGPgnI4OskDhaSXfQ36WnRP/0CtKA6nTS59pZ4Z0MHGd75UDt4n1UDSRdwDguW7a3CtSu1X6K98dZt4q9ze+/JKNjaQddPqypV7w/bmjXsn2vAb4DvNz2a/Miw1a2vxKpW5Ja9iAnUVqXVE7lG42nJgPvtj0lQLOK/VF3JupROCAbdUM7fPzWTjwzknayffpYH3jkB924hr6Z0SID1yUtb/ueqPfvo7eyUzHrvitFtm8J1C5+fxva022vJekO26/L566x/eZo7WFBOYuepNttr5Zd5i+JSlQyLIzVbjoE9087k0pvvJ7u2oszSTUeI2q0VumjJO1j+zBJx9BnYGd7jyDdDcd7vvJuTgi1+uPS9raheyjwPuAuRuL/HLyoMHT9saSrSMmyjnfO6CrpTtuvDdQsmmiulj3I/dRGpB3e5gLCTOAC2wP3Cqlhf7JutWzUJWhdbUdYOP8uUWC5L7bvU0qF3TF+1+SYlIHTnGD3C1sKnGh/Fvgo8PU+z5nugPGBUvL+9uHveQflN5I+BdxPyhoWhqTFgQ8y2iCFDGAbukuRXF/WI32m04A9bf8hUpcKrsV5MPVxoJPE40rSoCM8fkzSVk1d2xdG6JRYmBlH+1TgVEnb2C5VFL1WH9WJq7l53FcNmJoTy4rtp2h/XNHedtgaWKm5w1qAWqEe1WwfKfb+xp7PONpbrmiiuVr2IPdTV0k6pdQ1VLI/VSeWJcZv7Y7nXIRS+v6PMJKd693Ad20fE6A1oVdU+lHy/vbRXps0sFwcOJjkqnG47V8Eal4H/AK4g0ZsmgdUcHgc3Z8DZ5AyCwLsBOxoe9Ng3Y571etIyR4WAb5k+/hAzROB+YDOPf0A8LTtD0dpZt1DgLVJ8WEA2wPTbe8bqPkmkkH6N2B+YB7gsQj33oZmNZejuQEVrCUtaUWgaNxjxfZTtD+ubW8l/QzY1vajkTo9msX744Z2Ldv3M+BTwDm215T0XmA325sFat5mOyoXx3i6Re1BjZCA2vZHFUrilRi/tRPPHiQdBnwFeAK4GJgC7GX79ALatwPr2H4sHy8MXF8iBrE0krYFLrY9U9IXSUHaB9u+NVCzyv2VNA9wiO29I3X66FYpLtzPEEYbxzxAf6/tH0ZpjKE7oze2pN+5AN3bgdVtP5OP5wFuDY63vJlUDuIckuvRB0nZI8Myr9YcsNfoo7LuqFrSQHgt6RpxjzXaT63+uCaSziWNZS6nO2FIlPt2lf64oV/L9i0PfJcUi/h/pHwSO9m+N1DzK8B1LpxorrQ9qBESMBcsGNXIWxE+fmtdbUfzNtv7SHo3KUPYtsBUUnBxNKLbVeJpCC1RUGVFJfMl2+dIWp9Ua+sIkt/+GwM1i99fANtPS1pLklx2pec0SR8BLqRsxsgHJe0EnJmPtyfV3QrD9jPZZa70QOdpSSvY/m+YNfAoVUNtcaDzWS5WQtD2byXNY/tp4OS8sxCpV9PzokYfBfVqSS9o+/LcT90HHCjpGtJkNIri7adif1zT3v4k/xShYn/coYrty/G7b82L2pNsz4zUy+wJ7CfpHyT35k6SnzBPlA4l7UGNkIC5wPOvRkm88PFbO/EczXz59+bAmbb/1i8mI4iTgRsknZePtyZlv4ukaKHlBp3BxTuB79g+X9KBwZo17m+HW0m1zc4BHuucjApOz/yTNFj9AiMuKiWKHu8KHEvaOTEp7XiJFPo/l/Q54Gy673HkYGNvYKqke0gGfxnmvHbe8+FrpHIFU7PuBsDngzUfV6oTd1v2DPkjI7HxIahSwp1MjT4K6tWSLh6HTr32U6M/hkr2Ng9a5wdek0/9ukAcbY3+uENR2zeWS2Zn7Bjpkmm7Vl6SovZA0g9tb6cxsupGePtUtj9QJ046fPzWutr2kGOntia52r6BtKtwoe3oVe6O/pqkml4Cri7g1lUl85ykC0kDm7cCa5Hu940FXBSL3t+GbvESI5L+G3ij7QejNOYmVK80w4uAlUjfqV+5UAIPSf9KivMUcIPtPwXrLQP8mRTP8xnSLuu3bf82UHML2xcqZRccRWTMVsU+ag/SLucM0qR3aeB0x2dcLR6HnnWLt58a/XHWrWVvNyLF0d5Lus+vAnZ2bDmVKv1x1i5q+2q4ZCrFZB8BrADcDuxt+/5B64yjX9QeSHql7QdUsDpBTfuT9avFSUfSTjz7IOnFwCPZJWchYHLkoE7S5Oxa9ZJ+z0euEEq60fYbJF0NfIK0onJjgcH6QsA7gDts/yYPol9n+9IArWr3t3EN69m+dnbnBqz5E+D9th+P0ujRq7o6KGkB23+f3bkBab3F9hWS+tbdi9o5Ub1SH/MAp9reKeL9x9E9zfYHJO1p++jC2sX6qDm4ltBa0ioc91ir/TT0i/fHWaOWvZ0O7GD71/n4NSSPrrUCNYv1x320i9q+GmQ3+O8DVwNbkfJXhNWB7dEubg+U43Y7NqGQZk37UzROuuT4rXW17c+SwKY5HqPD9wP1zgC2IMXxND9wEe8a+d080f4SKQZkEWD/QD0AbD+eVyXfLuntpNImUQO6mve3wzGk5CSzOzdInia5wUylQEIJKpWDaHAdo+9nv3ODYEPgClISll7MSObkQVOl1EdehFtC0vy2/xmhMQZr5RXuXSV9n56Y7MhFo8J91CwkLUaKq+yUGLmK5Jb5cJSmy8c91mo/HWr0x1DJ3gLzdSadALb/X95tjaRkf9xLadsHzNpJ7zdoj9hJX9T2Cfnx4ZLCaqD3UskezJ93Htftt2AVtFhV0/6UjpMuNn5rJ549ZJeJjUgp5S8CNiPVsQmbeNreIv9eLkpjHO0T88OrKDMBA0CjS5ucLimktEnN+ytpHVKGuyV64kAmk9KPR/Lj/FME2xfkh4/bPqf5nFKG0BAkvYK0WLSgpDUYMQ6TgYUiNJ0zfdouEY/W1P1o/r1xSd3MvcC1eTehGbMVmVL+OFJ28eVJC0dNwx+6aFSyj+rhe8CdwHb5+AOk+PTo3YxicY+12k/l/riavQVulnQSIyUSdiS1p4FToz/uQ1Hb16BZS3kBUsm2B4K0Fui5v133O8r7pcG9lLUHu5O+t4szesEqarGqmv3JFIuTLjl+a11te1AKXJ5CKk0wRdLLgRNt91uZHbT2esBtth9Tyiq1JnCU7d8HavYLin+YVBPwtkDd4qVNKt3fDUkLGbuTOrEOM4ELbP8mSjvrLwgs3VztjkZ9Utn3OzdAvZ2BD5FSujdX62YCp0S67eXJyclZ6wTSd2rf6J0x1SlH1DeOKSJ+qY/2d2x/PFqnR7NW+aXi5YiyRo049KLtZy7oj2vZ2xcBn6SR34AUjzfweNqa/XHPdRS3fX2uYRJwmQNiePNu7lg4QrNHv4o9kLSb7VJJITuaxe1P1i0eJ11i/NZOPHtoxGBMBzYmdZZ32l61gPbtpEnvaqSVyZOA99get37R89Q8g2QgOqsd7wRuAlYmFUE+LEj3DmDtTrxHdmu+yfbrIvSyRvH729BexgHB77PR3JKUfGB+28tJWh04yAGFlrPeZqRs0NuRVug6TCaViHhDhG5Dfxvb50Zq9NGckReo3k4a2H0JODlqkt3Q7SQnWZ+U4fYIYD8XSoJWgxxX9HIanjrBi0bF+6iscz0pUci0fLwecITtdSJ1a1Cx/RTvj7NuFXtbgxr9cUO7qO0b5zpWAn5q+9UldSc6ktYlZXdt2oLIcLji9qc0JcdvravtaG6WtDhp9XU68ChwYyHtp2xb0ruAo22fpDGyaQ2QfwHWtP0ozFrF+hEpvmg6EGUIv0f50ibF76+kC8gxH+pTlifYEB5Iysx8Zda6TVKku/EDpBXureh245pJynoXQnMXod+OQrAraOdD3Zw0YJ6hfh/04Cle6iOvsPeLXwpdWc/anyJ9n/8MPNORJi0iRVGjj4K0G/f9HOsJqRB9tB0oHZ82Szb/LtJ+KvfHUNjeqk4Jipr9cYcDKWv7AJA0k3SfO/kj/kTKUD3hqGUPJJ1GyuR7GyN20ASGw1WyP0j6YL/zQZPsYuO3duLZg+1P5IfHSbqYlNH29kLyMyV9HtgJ2CCvsEQnAFiaVPOqw5PAMrafUCpIPHCy+8kNpDiXjuvPLpFugpka9/eI4Pcfj6dsP9wzwApzcbA9A5gh6QzH14hrUquOGcB0SZcCywGfl7QoI4YpkvslHU8q9XFodqWbFKz5ucbjBYBtgLBMqz3sBaxke6CFrMeiVh+V+6Sd8i7gZADbj0RqNigZn9ahdPup2R9DeXu7Z/69RcB7j0XN/rhDUds3S6BgTc3shjne/9SZ/B5l+5sBl1DLHryetANX0l2zqP1psHbj8QLAJsAtBEyyS47f2olnD5Iut70JgO17e88F8z5gB2A323+StDSpCHIkZwC/kHR+Pt4SODPHM90VIeiUrevr2XWsWCY2Ktxf21dFvv9suFPSDsA8SjW/9iBlFYxmWUlfIyXompUZOiouoUSM4TjsBqwO3OOUBfVfgBIJU7Yjlfo4wvZDSqU+Qkth2O5NRnKtpFLf7/8hMKtrL7X6KOfssvlxqQlnR7vLLVLSmcBlwbJF20/l/hgK21vbf8wPP2G7a+dN0qEE7MZV7o87FLV9GqO8VQcHJPpxhWSJPfq17MGdwCuAP87uhQOkqP3pYPvTzePsBXPaGC8fFOHjtzbGM5PjdxYCppKSDzQzsf3M9r9VurRw8kCns6o/zXZ4OmVJXyYVPf6vwitXRanh6tTQXgj4AvA20md7CSkBTWgdNUnTSOUgvkEaWO1C6mvGLbL9PPSq1Q+VtEG/8w4szJ51lx5DNzLmsVkHdxKwFvBN2ytFaTa0TwJWAn5Kd3mEMLe9Wn2UpK8DKwLh2WVncx3h8Wml20/N/rhxDTXsbb+EIbcHudpWreecr6Go7dNIop8FSDtyM7LuasANtteP0K1JLXuQ7/XqpBC4pi0Ic5OvYX/GuI75gNsj5yMlxm/tjucIHyNtp7+S7rTJjwDfKnEBjfgAgPlJbqCP2l5s7L8aCAsCj9g+Wak203K2+2XTGiSfBRYGnpbUMQa2PTlKsNL93Sv/LunqBKQ6hCTj+4XC0gvavlyScgKPA5WKXYdMPBnZKahRP7S5y7gAKa5oOkH1NBv8lJFYogVIroq/BiKToHXq4IrkUvU70o5VCX6ff+bPPyUo3kdlXgL8le7vUHhty0rxaaXbT7X+uEExeyvp48AngOWVkut1WBS4NkKTuv0x0G37svv6wpELrs7lrSSdBXzU9h35+LV0u6QOHEnbAIcALyO1XVGmn6plDw4soNFLDfvTFZNOmtyvQnxdz/DxW7vj2YOkTzu+TtscIWlr4A229wvUOIC0QreS7ddIeiUpu956UZpzC4Xu7y2215R0mu0PROn0aDY7q1FEJ9CQdC3wZlLSjCuA+4FDolZCO/dW0p62j47QeBbX8irgMNvbF9ZdE/iY7Y+V1G1pGSTR7adGf9yjX9TeZte8F5MyX+/beGqmA2oBZs3q/bFS9uDdSclnpgOLAUfaDg2tUYVSSJJ+C2xp++4ojZY6KJV/6vAUcJ/tPwRrho/f2h3P0TwjaXHbDwFIejGwve1vl74Q2z+WtO/sX/m8eDewBjmOyfYDOcFDOJLeQ3I5MnCN7aIFnwvd3/mVMueum//f3muI2MXoJNB4DykW4vR8vD2p6HM0e5Hc1vcADibtXkRm5VxL0jLArpK+D3RnlAgaYI3BH4DXFtQDUgyRpLVn/8rnTnZf+yypNt5Hc+zUSrYvnM2fPh/N2osoxfsoScsDRwNvyrrXA3tFe6EoZWLZEVjO9sHZnfsVtktldYf49lOjP25S1N7afpgUm7Y9gKSXkXaWF5G0SJBr/tzQH69i+xFJOwIXkXbupxOfM+NuSSeSbK5JiQyjJ4R/rjHpLG0PejzWup4iaIe3tv2xfVVuSyvmxwtKWtT2zEDZ8PFbO/EczUdsz3Kttf1/kj4ChE88ewzhJNLKaPSW9D9tW1InxfzCwXpknW8DrwbOzKd2l7Sp7U8Gata4v7uTBnOLk/zlm4S4z3USaEg62HYzhuoCSaGxh1n/pvzwUcok2jkOuBhYnm43eUj3OLLYcjOOaRIp9mRGlF5Dt1mmYBKwJvC/wbInk+7vuvn4D6Q4xLCJJxWzkNboozJnkMI73p2P3w+cBUTXaP02KaPsW0gDjpnAuXRnVhwoFdpP8f64h1r2dkvgSFIo0V+AZUgTogjX/Gr9cYP5cjzc1sCxtp/s3PNgdgE+zkg24auB7wRr3izpbODHdMcfRn+Xi9oDF8wY3KBqFuw89/goKfxiBWApUvsKS3ZaYvzWTjxHMyn7NncMwzyU8+luGsKnSLtT7wrW/KFSWYbF85d8V1IN02g2BF7buM+nAncEaxa/v05F4KdJutl2iRqATZaQtLztewCU6pgtESVWa3XQKVX8NyV9x/bHIzTGoRnH9BRwpu2o2KkmTSP8FCnmM7pY+wq23ydpewCnEhChNUtdNwtpjT4KUghMM3Ph6Up15KJ5Y3ZDvRVmLbpG276i7adyfwz17O1XSDvol9leQ9LG5F3QQVO5P+5wPMm+zwCuzrtG4Vmibf9d0rdI2aAN/NrxpcUmA4+TEinNuhTiF1GK24PSVLY/AJ8kxb3fAGD7N9lrYeCUHL+1E8/RXEIyDseRPoTdSat3YUg61CnV+c9sRwcOd2H7CEmbkjrllYD9bf+8gPSvSTXN7svHryJlkBw4Ne9vB9snSVoXWJZGu3NMIeAOnwGulHRPPl6WlEQriqouvrY/nheKXk73PR64O5lGSiyt4p4yBZE0YtMeqhA/9U9JC5KNk6QVaKywRyBp/zl86ZUefCbUYn0UdGWJnJpDAM4i3ev3kRYWonkyt5/O57sEQTU1a7WfDpX645r29knbf5U0SdIk21OVyqmEUbI/7sO33KhdKen3wMbRopI2Ak4l2TsBr5K0c0DfNAvbJbyK+lHcHpSmsv0B+Iftf3bm85LmJc5Lr9j4rU0u1INS4fCPkbayBVwKnGj76UDNO0iucje4J+V5KZSKlTeNQ1Tigc6qymIkF64b8/EbgetsvzVAc264v6eRXCVuIyU8gBSXEJpaXtKLgJXz4a9shxsGSVf3uPj2PReg+ylSxrs/MzJgtmNKBtxFcqk6jlQbtjeOKaT2Y9bdDPgJ3WWfOrph8VN5wPxFUma9S4H1gA/ZvjJQc05jS261PZBJYY0+Kut2CsL32zWwg+rgNvR3JE1y1yQNnt8LfNH2OQFaVdpPQ79Kf9zQL2JvG3qXkdxOvwa8lORuu7btdcf9w+enWaw/7qP9O5Lb58kl4x8lTQd2sP3rfPwa0i7+WoGaSwHHkPpjA9OAPR2fhKa4PShNDfvTo38Y8BDwQeDTpAzVd9kOq1RQYvzWTjz7kFdxlu50HgX0Dif5cS9McpmY9RTxJUY+BhwEPEEyDh3NkEGOurN0jSLCtaHm/W1cw92k1f3wBqdcRy0/3rY5cJT0VQdm8c0adwPv7HHxvcjBtXCVsvu90fZfI3Wy1ntJqePXZ3TZANsOKQchaQ/SgH15Ura5rvipApOTfyG57An4he0HI/VqUKOPmluQtDIji66XRw3aa7Wfhn6x/rhHt6i9begunDUnkWJcFwN+ENlXluyP+2gvSoqN3oX0P38POMt2qLut+tRG7XduwJo/J8WFd9zzdwJ2tL1plGZDe8Lbg5rkjbDd6K5He2Jkv1Vi/NZOPHuQtBUp89n8tpeTtDpw0CD9m/tovsj2PySdbzs6prNX+zfAOhO5w6h5fxvXcA6wh+0/FtCaVSxcPYXDe4+D9N8BfBfocvG1fUmw7lRgU9tPRepkrfVsXytpf9sHRes1dJez/buS8VNKpVrGJHp3Kl/Di4BtGO0aWezeRyPpLbavUJ9sqxCXLETdheD76Q58N65W+2noF+uPe3SL29vs7npJ1E79OLrF+uPZXMcGpARhi5NKRBxs+7dBWt8j7Tp2JoE7AvNGusOqcAmXucEelGYY7E+HEuO3NsZzNAeQgnmvBLB9m6RlgzWvJ7k3hQe/9+G/6d4FDEXSNNvra3Rq7Mjdx5r3t8NLgbsk3Uh35rmIBQ2N8bjf8cCxfbFSavWiLr6kjvJKST+l+x4fGaD1TWAtkvtaSePzo6z7moKaXx/nOZOyoEZzPqksxHTi40pr9FEAG5DqpvVmW4XYZCHNQvAdLfJxVBbSWu2nQ8n+uElRewtg+2lJj0tazKm8SilK9sdd5Mn2O0k7nsuS+rAfkOoTXkRc//lxUkKYPUjt52riKyI8KGknRrJvbw9E7jLPDfagNMXsD8wKDxsv0U/YDnqJ8Vs78RzNU7YfVtnkXDVri30euE7SDXQbh6hYlx3z+5dMjV27dhukWJdSeIzH/Y4Hxjg7NitIKnGff59/5ic+E/WTkk4GlpT0zd4nA9vPJKUi9K9Rd0mVju7AB3W2w5NyzAFL2X5HIa0afRTAg1l3F0mr2v5lCVHby5XQ6aFW++lwYPD7j0Vpe9vh78Ad2S3zsUK6JfvjXn4DTAUOt31d4/yP8g5oCHmAfmT+KcWuwLHAN0j2/bp8LoS5xB6UpqT9AdiioBZQdvzWTjxHc6ekHYB58qx/D1JDjqRmbbHjSavsdxCUwbCH80i7j0g61/Y2BTRr124rHRc2RdIjpBXXBfNj8vECgbobUmfHJgnYX458/x62AN5KWt2dXlD3/aRdonnpLqkSRu4HDyfVtLwD+Jzt+0toN7hO0utslyhnUqOPgpEBJCRXvSKJ0JTS8+9H+nxvBw6JjoWjXvsBqsbplra3HX7K6MzIoXFWhfvjXlaz/Wi/JyIm29mteKz7aacMziE4ZQmO3qmfxVxiD0pT0v5g+z5JW5PvcXSYUqbY+K2N8exB0kLAF+gO5j3Y9t8LaO/mwrXFJF3nwMx2ffRutb1G7+NC2jXub6+73qynKJTYaKKjSvVDs/YU25EF78fS3cz2zwppXQN8n+Q2thUpRq1vHGLgNdxFMsK/I+0UddpPRMbiKn1URd2LSZO/q0kTwkVtf6iQdtH2U7s/Lm1vG7p7uqf8Ur9zA9Kq1h83rmEJ4COMjskL2QmU1C9r7ZsX5fsNAAAgAElEQVSAfYC/2F47QHMf24dJOoY+9ztqN3tusAelKWl/st63gVVJm16bABfYPjhCqwbtxHMuQBWzkEr6T1Kdugvodv2JKqcyZuKbKGre32Gin+tnk6jYHtXJlPxD29vlx506sZ3nLrX9trH/+nnpHmV7r/y4a+Ao6ZSICUNvoopS7bbnGpbpd972ff3OP0+t4n1U1roH+HdSFs7DgL2bz0e5qtf4fGu1n9qUtrcN3VGfadTiRo3+uM81XAdcQ1pQmVUKz/a5BbQ3BL4EvAj4atQCoaQtbV+gMUp+2D41SLe6PShNSfuT9e4EpuT47IWAaxxYkidrFhu/ta62mcqrdO8nDTQgxYA066a9g+QGFcUODd0OUQklYHw30KgV55r3d5goHRMHVHObW7HxeFPgPxrHSwTqNuOTdgaaOxZRCQcWkLQGI8lnFmweu0AWw+x6NIWUHASSIY7aKavRRwFcxYjL3NV0uzxFuqpL0osZ+XznaR4HTYpqtZ/aFLW3krbPmstJ+knjqUUJSkBT0Y25yULNxYwSSHo7acL5d+A/bU+N1LN9QX74uHtq7UraNlC6uj0oTWH7A/BP209n7celIklnio3f2onnCEdU1K6WhbR0Ygnb85TUy1TN8jos1IrpkbT/HL70SttXD0h2PFeRSDeS8b7LUfyR7mQZf2ocF8liKGlPkutcZ/J1uqTv2j5m0FqV+igcWHJhNixG2hlqfp86g8eoSVGV9jObWLwmp9j+/qD1S9tbkqveH0lZfJvZSGeS4nkHTqX+uJcLJW1u+6Kg9+9C0k2kBZPDSVn0u8qOBE/GehfTxzo3KKrbg9KUtD+ZlSV12qdICX5uJ9DFt+T4rZ14ZpqrdJIWBJa2/etS8mM87nc8UCTNR0oB3tlJuRI43vaTkbqFqXZ/hxFJSwHHAOuR7u80YE/bfwiSnFN3l4cGqLlQXuWdRPeKr4AFB6jTy6S8GzWp8XjWTlWEoOeOLIa7kYrRPwbJPZM0wIsy/MWRtPQcvvQhDzD5j+1lB/Vez4Ja7edDc/i6QfYVsyhtb7Mr4H3AOhHvPwY1+mOgK4ZXwH6S/gl07m2kt8JjwKPAe0n1HpuLOCGTMUmbAZszOjP0ZCCsdupcYg9KU9r+/FvQ+86WEuO3NsazB0lbknY/57e9nKTVgYOCE5Q8Teq4Oka3U+dLwAK25wvUPhGYD+jEA3wAeNr2h6M0S1Pz/g4jSin7z2CkiPZOwI62N613VYMl75yMSZRxlnQvKRtmv91O245yka+KUl2ztZ2TvElaALjJ9usCtKrsis3uO5XxoHVrUKv91Ka0va2dTKkljuz6uTqpDm5zl3kmMNX2/1W5sAlISftTmxLjt3bi2YOk6aTVqSs9kmHw9qjsVbWRNMP2lNmda2mZU3qTD4x1LkD3RaTV5mXpzmRYo0D9hEHS7xh/Iqb8/FG2R9VkHNA1fJYU03pePrU1aQJ2VIBW30QSfXjI9sOD1i9N4/Ptu5hBgc+3NJJeQ0retAzdfUWom+Aw2dva/bFSPcL1Sd/da2z/uIRuaSTNV9JDbW6wB6UpaX+y3snM2eLnj23/ZPYve1ba4eO31tV2NE/ZfrhMLO9cwdOSVrD93wCSlqeRBa6l5TnwoKSdgDPz8fYEJbLo4XzgYVK82j9m89qWOaRCXFq/azhS0pWkgaSAXWzfGqQ1ylVQ0iRgkUG6uI6HpHmAl9M9YP99hNbc8PlW4BzgOOAEytq7ovZW0mTbj0h6Sb/ng7PpVuuPlcpRvJoRG7S7pE1tfzJIr+ZkbFlJXwNWoVGnO8r7ZRj7i5L2J3PKHL7u3gDt8PFbu+PZg6STgMuBfUmrdXsA89neveqFBSFpE+Bk4B5Sg1qG1KhCM7KVpHZCiWEjx6ody0hc0bWkGIGQ1OMN3TttvzZSo6FVZaAxbLtTNQfOks4AdidNDKaTkvAcafvwKM2s+2ngAODPJLdqCKwZV4PauyaSpju4PMEYukXtraQLbW8xRr8R6ppfsj/uo/1L4LXOA9y8cHSH7VVrXE8kkqaR+otvkDJh70Ia2x9Q9cImAJUXbqpQYvzWTjx7UKqZ8wWgU0fsEuBg2xN2ByW7xKxEMkq/mmj/67C5zg0rkr4LHGP7jtrXMhGRtA1wCPAyRhLBhMaJ9Rk4z3qK+IHzbbZXl7QjsBap5Mf06AmgpN+SElmU8BJo6q4HHMiI+2n4Pa6FpAOBv5Bc54rV08zaxeytpPVtT5O0QCc+rRQ1+2NJ/wV8pjNYzmOAQ2xvX/paouksoki6oxNzKOka22+e3d8+T93i9qA0Ne1P1j8M+ArwBHAxMAXYy/bpkbrRtBPPHiRt6z41kXrPvdCR9BbbV+Q4iFE4qFj53EJp17lholZnKekuknvV70iDybDU48NInhBtafvu2tdSgrxrsjop0cKxtq8qEY+XPTQ2tR2WmXIM3V8BnyHt7s5y/yw9AS5BHkj2EjaQrGVvG5OSW2yvOfu/GKh2tf5Y0lXA2sCN+dTapCykj0NcXfZKi3PXkupL/gi4ArifNMleKUoz6w6VPahBY/Hz3aS40s+QEkeF2aAS47c2xnM0pWsi1WJDUie1ZZ/nIouVV6Of65ykcNe5IeRttvfJneUfgG2BqUD0Kt1mwe8/ilqrvpJWBIrF9WT+XGuQkXfjbrP9WI4/WZPkhhkS95g5nhRDMwO4Ou+alFiouge4UtJP6d6NO3LsPxkID9v+WbBGF7XaT4U4tVr29smcqGQpdZfcSML2HkG6UKE/bjCntUQHzaGUn4ztBSxECgs7GNiYlAgnmmr2oDSV7A+kDNiQyuacaftvBfLPhI/f2h3PjEZqIm0HnN14alFgFdtvrHJhLQOjluvcsCHpl7ZXlXQCcK7ti0tlblRKMd9xMbrG9oxgvSqrvjXieiQdDbwC+DHdE6LwRSql4tlTgNVIad5PAt5je8No7Z7rmDd6J1JS38/QwQW+JR1CqgX7X3R/vrcEatZqP8NQvxpJLwXeSpoQjZqM2T511B8NVr9of9xHfzLdCbpCXaklXWt7vUiNHr15SLube5fSbGhXswelqWV/cp+8NWn38Q3A4sCFkfOREuO3dsdzhAdIu2Bb5d8dlmGk7uOEQSk99JgUWF2vwXx5wLE1yXXuSUntysvguSC77T0BfELSEkB4fJGkPYGPMLJ7cLqk79qOKvIM9VZ9F7R9uSTlOKYDJV1DmoxGMZnUF76tca6Ud8RTti3pXcDRtk+SFLKqL2kn26eP00eG9o2dCaakRdOhH43Ua9AZzLy+eTmk8mJR1Go/3yHtJnw7H38gn4uqp1nF3tp+EDhL0t0VJn01+uOO9kdJu39PMFL32EB0vPLNks6m0GTM9tOS1sp2oPRYpqY9KE0x+9PE9r6SDgUeyZ/1Y8C7gmXDx2/txDOTO+UZkn4ArArsQNr9/B1wbs1rC2LR/HslUvxDpxbQlsDVVa4onlquc0NFpc4SYDdSUpbHAPI1XA9EDnSKDjQa/D3HKf9G0qdIcT0vixS0vUvk+8+GmZI+TypmvUFe6Z9vNn/zXFk4/1503FcFIem1pFX1l+TjB4EP2v5lpK7tjSPffwxqtZ+1e1bwr5AUOTGrbW8fkLQfo2tq7hqoWaM/7rA3sGqeeJekxmTsVuB8SecAj80SDW5Dle1BaUran1lI2ha4OI+jvkhy8f0K8KcozRLjt9bVNqNUUPr9jNSsORv4nO05zYj6gkTSpcA2tmfm40WBc2y/o+6VlaGE69wwImldRg9yQkvVSLqDNKD8ez5eALjJOdNfkObJfU47eECHpLWBu0muNweTSn0cZvsXgZpLkQaN65EGU9NIadb/EKXZ0H4FaTHwJtvXKKV83yj6O1UDSdcBX3AusSFpI+CrttcN1l2MtGPecT+9CjjIgZm+K7afW4Bt3V1P80fRCXhq2dv8nbqG0YmjwhbVa/THDe2LSa6QE85brZeKbaiaPShNLfsj6Xbbq0lan5TT4Qhgv+jQv+jxWzvxzEh6htQx72b7t/ncPcHJOqqTt9SnOKd0V0r1PsP2ynWvbHDMznVugroVV0PSacAKwG2MDHIcnMii4862M6lEAiSX6lNsHxWpOyxI+jkpw+tp+dROwI62N613VXGoXnbmUfE0JWKkJZ0L3Al04v4+QLINfTOxvpBRpfrVtextJ79BpEYfzWr9saQ1SJ/vDXTvpEfboOKTMUnr2b52ducCdIfKHtRA0q2215D0NVId2jM65wI1w8dvravtCNuQdjyn5tWys6BvkfaJxmnAjZLOI3WU72Zk4DFRqOo6N4S8npSQq+iqlu0jJV0JrE9qu7vYvjVSs9aqr6TXk+oNd2ouAuDYRFlL2G6urp8iaa9AvVlImslIHbX5SW5Oj9peLFC2VnbmeyR9ie4BXb/yH4NmBdvbNI6/LOm2SMFa7SfHR69I+frVtezthZI2t31RAS2gTn/c4HhSFuE7SDGepTiZNBnbNh/vlM9FTsaOIblgzu7coKlmD0pTyf4A3C/peHKCsLxQNSlYM3z81k48M7bPA86TtDAj9XJeLuk7wHm2L616gQFIEvB94GeMZJ4raRyKYPv4/Ds0K2TLLO4kZbv7YwkxSZNtPyLpJaQY3nsbz73EsZkMaww0AH5AimMqObB6UCmV/Jn5uBOWEI7trkUjSVuTsvxFUiOVPcCuwJdJcWEixQCWiKd6QtL6tqfBrBICTwRrFm0/Grue5gqSQuPiKtvbPYH9JP0DeJLAsjWV++MOT9keN6FTEMUmY5LWAdYFlujx5ppMyk4dTTV7UJpK9gdSnpl3AEfYfkjSv5LsfiTh47fW1XYccse5LfA+25GZ/aqhXGC69nWUoJbr3LAhaSqwOql4d9PNKapo94W2t1AqCt/s0DqDqzB3+X4ubCXc2iRNs71+pEYfzaWBY4F1SPf5OtLu1H0lr6NxPb+w/abA9y+eyr4mklYn7b4tRmo7fwM+5MBsqKXbj6Qv2z6gYlzchLe3NfvjxjX8J3AfcAHdNii6nMplwCl0T8Z2sb1JgNaGwEak2uTHNZ6aCVxg+zeD1uzRn6vsQWmi7U+P1svortcdVj+0xPitnXgOOZK+RYq7uKn2tUSjkTqe72ZkV3tqdOzUsJEN4ihsX1X6WqIpOdDo0d0ka13OBK+hBtCzQzWJ5A60oe11gnVfzEh2v4WAybZDMgpKOsr2XpIuoHvADsQt3PS5jslZLzzjd632U4vS9lbSuO6WDqzRWpM86e0lfNJbYzImaZlhmezVoqL92Qr4OvBK4C/A0qSwgFUDNcPHb+3Ec8iRdBfwGtLq4GOMrEpGxopVQQUK47YkJL2cVDYA4EbbfymguR5wm+3HsgvQmsBRwauDVVZ9JZ0OrAz8khFX25AdG0n72D5M0jH0nxCFJuzI19DcoXqK5L53QvT3Sqm0ySp0rzaHZDKUtJbt6aUXblQx+VrF9rMnyaV3JnACqa/YNzqkprS9zbsXY+FIT64a/fEwMdYCVYdAD6Pq9qA0Fe3PDFId5cuckgxtDGxv+6PBuqHjtzbGs2Wz2hdQkPDCuC0gaTvgcOBK0sDqGEl72/5RsPR3gCmSpgD7ACeRknn0HcgPgjyIKrIT1cMUFyhLkLk7/765kN4sJB1q+z+An9n+YWHtA0iubKsAF5H6ymmkOL2BY3t6/j1rgpl3XF9l+/YIzcx4yddCV6Yrtp9dbR8t6e2k+re7kCai0bkcitpb16nN2qF4f9yZFOXH29o+p/HcV23vF6lbeDJ2RMB7zgnV7EFpatqfzJO2/yppkqRJtqcq1dgMo8T4rd3xbAHK+pDXpKTr3LCSV+k27ayS5Qn+ZdE7y5Jusb2mpP2B+22f1DkXoFV11Tfv2n/D9l2ROj2aXQO5sc4NWPMO0k7JDRGf4xxoTwFutT0lrwKfaHvLYN0rSZOxeUkp7f8XuMrByVJUsCzDXNB+OvXxjgautH2egssU9OhPeHtbsj/u1ex93O94wLpb2r5A0s79nrc90SoFVLEHpalpf7L+ZaSwsK8BLyW5267twJrOJcZv7Y7nkNPHh3wZ0opWmA95ZZYENlUqZt1hwhWhr8ykHteMvxKfAhxgpqTPkzJjbiBpHkYykw6a2qu+6wM751imf1DGRf7zQO+got+5QXIx8CCwsKRmzGFYVs4GT9h+RtJTOe7xL0CJus6LOWUF/TBwslMynMgdzw4lyzLUbj/TJV0KLAd8XtKiFMgOXdreanSCn1Evyc8fZfubA5Yv2R930BiP+x0PDNsX5IeP95uMRWhK+qHt7fLkqN/iTXS4VA17UJqa9gfgXSSvvM8AO5ISvx0UrBk+fmsnni0HA2+ix4e88jWFUNp1boi5WNIljCQMeR/pfkfzPmAHYDfbf8rxY4dHCNUYaPTwjgIaAEjajFRSZElJzcHpZFK8SyRftL23pPNtvytYq5ebJS1OigGcDjxKyvQXzbxKafO3I9VqDUUVyjLMBe1nN1LmxntsPy7pXyhTsqaovbW9XNR7zwHF+uMGHuNxv+MISk7GOmVatgh47zGpbA9KU9P+YPuxxmGpXfN+47efDVKgnXi2FPchr8h7GXGd26XjOlf5miYMkl4NvDx31O9hpHD49aS6k6Fkl+kjG8e/J35Rocqqr+37JK0PrGj75OwOs0iQ3AOknamtSBOwDjNJK7GRXE/acQvPsNqL7U/kh8dJupjkll9i5/Eg4BJgmu2bJC0PRJZGmJ/03ZmX7jjPR0h9ZiS1dk06pYhWU5narB2Gxt5W6o+n5J0pAQs2dqlEw7V50FSajF1I6hu/YvsDQRr9qGkPSlPF/kiaSZ9SRBTYae0zfvuu7fMGqdFOPFsekrQIqUj5DyT9hYm3atWhluvcsHAUsB/MKuvxXwCSXp+fi46Na3bW85Pcuh61vViAVtVV37x7/3pgJVJSlPmA04H1Bq3lVMdxhqQzbD856PefDfPnuKl11Z3SvnNtYeVjJF3uXNbD9r2956LIO4DnNI7vAbYJ1LsKuErSKS5UlqF2+6G7CPsCpDqt00kZJCOpYm8lbQMcQkqkJAoMYEv2xx1sh+zQzwE1JmNV+sbK9qA0tezP5cArSGOos0rEgDc2Dq7tGb9tIGkF2/89KK124jmkdL5kJB/yJxjxIV8G+HTFS4ukluvcsLBsvx0h2zdLWjZa3HZXVk5JW5MGlBHUXvV9N7AGcAuA7QdynFoky0r6GqPLi0Qu3uxO6pcWZ/TChcnGcZDk+O+FgJcqJSPrbIlNJsXmhaKUur9fzNbAS+X08Likw0nxhs3PN2IyVrX99CaIkvQq4LBoXUbb2xIxWwCHAlvavnu2rxwQhfvjqlSajBXvG3uoYQ9KU+Ue295a0mLAe4ATsk06mzQJ/VuEJo2Ngx4eZ8AbB21W2yFF0oXAfr0Thbw7dUB05sba5IlQKde5oUDSb22/+tk+F3xNv7D9psD3n6/Gqq+kG22/QSOZIxcGro9MKCFpGnAA8A2SEdqFZEMOiNJsaO9m+6Ronay1Jyl+6pXA/YxMPB8h1W47Nli/ubu5AGmR4YECmV4vJQ1uPkcacO0M/K9TOYEozSrtp891CLjdQSWKmrsJPec3IGV8Hdhuwhj619oeuDfEc7iO0P64NpJWJGUgLTYZK9k39uhWswelqXWPs/YkUpzlMcBXHVRXWdKdtl87xnN3DLJvbCeeQ0rJL9ncQj83uRKuc8OCpDOBK2yf0HN+N+Bttt8XrN90hZlEckXd0PY6gZrFBxpZ93PAisCmWX9X4AzbxwRqTre9VrN/kHSN7TcHalapy5ff/9OR9/NZXMckUjKaUDfQxud7e2cBQ9JVtiPrLtZqP80yLpNIiYbutb1TkF7VhV6lsjGvAH5MyoINhLuqF++Pa1NrMiZpXWBZGl6MtkPjaWvYg9JUtj/rkhKPvZmUBPNs29cE6hXbOGhdbYeX8QLtFyx2FQWo7To3ROwFnCdpR0bc515Piu95dwH95uDtKeBekmtbJCczMtDYmDzQCNbE9hGSNiXtwq0E7G/758Gyf8+ToN9I+hRpN/BlwZrvZ8QFsjfpzDvo7xo0KJ6RtLjth4BODeDtbX87ULMfKwJLF9Dp7Dz+UdI7Se6wSwVrVmk/dJdxeQo4s3c3csBUDUMg2brHgbc15Yl1x6zRH9dmQduXS1KOlz5Q0jWk73gIkk4DViDV/H06nzbxiZxq2IPSVLE/ku4FHgLOAj5KjgOXtCaA7VsCZG+S9JExNg6mj/E3z4l24jm8FPuSzQV8jBHXuel0u859q9ZFTTRs/5kUhL8x0NlN/6ntKyJ1JR2a3QF/ZvuHkVp9KD7Q6JAnmtGTzSZ7kRZw9iCVhdiY5I4ZSZW6fJmP2J7VP9j+P0kfAUInnhqd0fBPQJi7a4Ov5Liifye5dU0mPt6yaPtpeLisEulC3IeqC722S5SKAar3x7WpMRl7Pen7XNp9sYY9KE0t+3MvyQa8nbRY1NQyMUnQim0ctBPP4aX27lQxbB8NHD23uM5NdGxPBaYWlNxc0heBfYHSA52iA40+k5IuHJSlUqn4+3a29yYl5So1kK1Zl29SnhAZZt2D+aPEJK2Xd92WsP33KJ0xtOchlea5EHiYNIgsQemB+r9K2hDYStJZ9Aweg3YSoPJCr6SlSIsJ65HazTRgT9t/CJCr2R/XpsZk7E6SG/Ufg3VmUdEelKaK/bG9UdR7j6NZbOOgjfEccnq+ZL+M3p2qiaRPAj+YC1znWgaIUibOjwILk9zJZj1FfMmAtYG7SVnvDiZlqTzM9i+iNLPuQaSdsNNI/+eOwKKdeJQgzSuATUqurEt6GniM9D8uyMjnK2AB2/MFah9Oips6jjTI2B34H9v/HqTXiZm6xfaaERqz0Z9qu9SEs6NZtP1Iei+wG6lG3c09TzsqjlapZvR5wD/ps9DrVPMyDEk/B84g9RcAOwE72t40QKtaf1yTPBk7JE/GSupOJcUo30h3/O5WwbrF7UFpatqfiUw78WwZGiTdZnv1nnO32l6j1jW1PH8kvcj2PySdb3uixxABIOkG22+c3bkBa36dFG94DskYA7EJSmqSd+I+BmxCGmhcCpxo++lx//C56/2CNAl7Jym2pwvHZ7X9T9LE72y6P9+oXcDidHaVJe1vu0QZk179Kgu9Y9i+UecGpDV0/XGHSotzfZN/OdXnjdQdKntQEkm/Y/wdVeXnj7L9zXFe92w0p85Gs8MpzzdxVetq2zJMFHWdaynG9cCapJjdIki6gPFdXkNXm4Gns5v8Wfk6tmcksUQULwH+Snd8SYl6cVWw/YykU0iZmn9dQHIL4K2k+1sjzn7d/Ls5IQuJJ6rYfr4JrAVsTZn6mV1UCEPo8KCknYAz8/H2pLYcQfH+eC7iVuB8ScUmY9ETzHEYKntQEtvLVZD90By+7qHnK9ROPFuGiUuAH0pqus5dXPeSWgbA/JJ2JsUnvKf3ySCjf0T+/R5SfM3p+Xh7UmKAaHYAjs4/Bq7N5yI5sTfzp6TqtQGjkLQVcDhpcWo5SasDBwVOiva2/R+SlrZ9apDGeOxm+57mCUlRZU1qtZ8nJZ0MLClp1E5B1K5yyd2EMdgVOJaUPdjAdflcBDX647mFYpOxceL9S7k0T3h7MBe022Lk5G5dZK+fRWwPdBGpdbVtGRpKu861lEHS+qQYx+2An/Q8bdtRAywkXW17g9mdmwj0iz2sFY9YAknTSQPIKzvu+GrUuAzQu4O0U3RDpRjPfp/vdNtrBWoWbT+SXkraVT4U2L/3+agJv6Rl5vClD9l+OOIaSlGzP65NI0HYuOcmAsNgD2q3W0nbAIeQEq6JMnkrziBtyjxN8rxZDDjS9uGD0mh3PFuGhgqucy0FsD0NmCbpZtsnFZZfQtLynZ0iScsBS0SJSfqh7e3y407Zgs5zl9p+29h//Zw11yG5YS4h6bONpyYD8wxaby7iKdsPSyXKSgLJ++JBYGFJzRXm0MGGpJWBVYHFenaoJjN+GZBBULT92H4QOEvS3bZnROn00S22m9CjsY/twyQdQ5+dm4gd3sr9cW2OIS0eze7cC5Zhsge12m2DQ4Etbd9dQKvDKrYfyaE8F5FKeU0nef8MhEmDeqOWlrmd7Dp3G9m9VtLqknpXZFteYEjaB8D2SZK27Xnuq8HynwGulHSlpCtJ8Vt7Beqt2Hjcm5EyasA+P7AIaaFy0cbPI8B7gzTnBu6UtAMwj6QV8+D9uigx23vbXoyUwn5y42fRYLe5lUjxpYsDWzZ+1gQ+EqgLhduPpB8C2J4h6dCe5y6N0m1onCFpsqSFgbuAX0uKzILaGbDeTBo89v4MnMr9cRUkrSPp38mTscbPgUywyRhDaA8qtNsOfy486QSYT9J8pDj4820/yYBLx7Suti1DQ2nXuZYyNN17el19Srj+SHoRsHI+/JXtf4z3+uepVe1/lbRMvxXgiYqkhYAvMFLA+xLgYAfV2JS0su1f5ccvan6PJL3J8SV61rF9faTGGLol28+tjb6/t/2EZzhXziSbdxPWIu8mRNsgSdvaPmd25wakVbU/roFSZtmNSC6KxzWemglcYPs3Na4rkmGyBxXb7dGkGPgf010qJyxOWtIepP9vBinD+tLA6bbfPCiN1tW2ZZgo7TrXUgaN8bjf8WAEpbfYvqJP8owVJEUahoUkrUHyVlkwP+7EfiwYIahGBtJ+badABt8q2H6cNPH8QiHJMxhxybuebve8bxPkrtd0w5S0fe/zEe6YFdvPeCvtJVbhm7sJx9p+UlIJ3c+Tyl7M7twgKN4f1yZnlr1K0ikTfTI2pPagVrudTKod2gyhCc0c7FSepZl47T6lMlADo514tgwTXa5zwB4Eus61FMNjPO53PCg2BK4guST2u54ow/BH4Mj8+E+Nx53jCI6Y/UsmDqpX6qPWgP3mwPcei1rtp/jCTQ/Hk7L2zgCuzslLImM8NwM2Z3QW38nAU0GyNdWMLO0AACAASURBVPrjqgzZZGyo7EGmaLvtYHuXaI0OknayfXpP3G6TI8c4/+y1WlfblmGhtOtcSxkkPU2qmdYZPD7eeQpYwPZ8ta6t5YWHxijI3sFBdfOG0UWxNErlEcbE9kBX9ucESfPaDpkESpoCrE6qWdrM4jsTmGr7/wI0h64/rtVntNQjst02NJYiJadaj7SwMQ3Y0/YfArQ+Zvt4SQf0e972lwem1U48W1paWp4d46wKAmB7YKuDtVHOpKtU7qNfZswJGyMtaUFg6RJZsCX9BTiLNEB/X35MPt7O9suDdI+yvddYO70RuzXD1H5g9rsJ0f+vpPlykpCWlufFMNmDuaDd/pwUgnFaPrUTsKPt3sSCLyhaV9uWCU9F17mWicuiNUQl/Y7x3dWUnz8qx2oMgk6W0S0G9H4vCCRtSXIrmx9YTtLqwEGB/UUzS2Kv+2ukO2yn8HlJF7oq7aciC+fftf7vZSV9DViFRokc28tXup4JxTBNxhgue1C73S5h++TG8SmSIrPmI+kw4CvAE6QKEFOAvWyfPjCNdsezZaLTusG0tDx3Om6ekk6z/YHa11OKYcmCLely25uopy7sRKTSwk11JE0DDgC+QYqr3YU0/uvrVvc8taYyZ7Gcp9j+/uxfNvcj6ZW2H8ixf6OYSAmHhtUe1EDSZcApwJn51PbALrY3CdTsZPB9NymZ0mdIbvlTBqXR7ni2THiaE8uSrnMtE5+SMRgVmV/SzsC6fbKQhqZ2r0zRLNiSPjiHL73N9u0DlP7XvDi3laSOq+8sbN8yQK0uSrcf28tFvO+cUmI3YQwWtH25JOVJ0IGSriFNRgfNh+bwdQ8FaNfiQlLW6a8MwWRs6OxBxXa7K3AsacHIpGSYuwZrdmKwNwfOtP23QdvAduLZMjRUcJ1rmficTIrB6BRK3ymfC43BkLQNcAjwMkayctr25AC53YEdgcUZnYU0NLV7ZUpnwZ7TSdG9A9bdH9gXWIrRmQtN2vWNokr7qcjbbO+TdxP+QPq/pwLRA9i/S5oE/EbSp4D7SX3HwOm3u5e1F7Edngm0EsM0GRtGe1Cl3dr+PVB6fHqBpF+RJtmfkLQEMNAEnK2rbcvQMCyucy3l6LilzO5cgO5vgS1t3x2p06O5m+2TSunVpicLNoxkwf7H2H/1wkXSl2wfXFizVvspuXDT1P2l7VUlnQCca/tiSTMG6cY2hu7awN2kycLBwGLAYbZ/Eah5BmmS8jQwPWseafvwKM1aSFqfNBnbDvhJz9O2Hb1LVZxhsgel262kfWwfpkaN5SYRtZV79F8MPGL76WwHJ9seWLm2dsezZZgo6jrXMhQ8KGknumMw/lpA988lJ50Atk+StC6wLA3bMVHitPrwTttfIE0+AZC0LXBOpKikm4HvkdycBl7uYixsHyxpSWAZuj/fqwNla7WfQym8cJMJ303oh+2b8sNHSfGdJVjF9iOSdgQuAv6DNAGdcBNP29OAaZJuHpbJ2JDZg9LtttMv1aixDLAksKmkBRrnBva5tjueLUODpJOAy0luZduQXOfms7171QtrecEiaWlSDMY6+dS1pBi10GQSko4GXgH8GJi1Axfp0iXpNGAF4DbSLkaWjF19rYX61M7sdy5A99WkycH7SAOPk4FLHWysJR0CvB+4i+7PN8zVq2L7udb2epEa42iH7ib0aFXL6C7pl6QaomcAx9q+qsTubm2GZTI2hPagWLttaG5r+5zZnRuw5gHARqQM2BcBmwHTbL93YBrtxLNlWBg217mWiYukk/ucDnXpknQ3aRdjQhsNSZuREitsB5zdeGpR0v//xkLXMYlUsuA7wDOkXdCjbf8tSO/XwGrD0B/WWLhpaL+W0WVNQiYmjYzu7yH9v52YtO2Be23vF6Gbtfcg7XLOAN4JLA2cbvvNUZq1GabJ2LDYgw4l221Ds/jip1JJoCnArbanSHo5cKLt3nje50zratsyTFRxnWuZuNTKdme7lLtckztJg9c/VtAuyQMkl8Ct8u8OywCPl7gASauRdj03B84FfgCsD1xB2kWK4B5SRsNiE8+K2SInkz7LtzXOhSdGGWs3gQG6sTXpZHSXdLDtDRpPXSAp0oUap3I0zZI090naOFJzLuD1DM9kbFjsQfF221j8XFJSsw1NBp6K0GzwhO1nJD0laTLwF2Cg9X7biWfLMPF5Rk8y+51raZlTqmS7q1TG5aXAXZJupHuXaEJlhbY9A5gh6QfAqsAOpN3P35EmgaHkJGgPAScB+zZ2IG+QFOke+jhwm6TL6f58I3dramWLrLFwA/BeRnYTdunsJhTQXULS8rbvAZC0HLBEhJCknWyfLumzY7ykN3PyRGJoJmMMiT3IlG63D5DCLHoXP2eS6mpGcrOkxYETsvajwI2DFGgnni0TnnFWjxYFnqxzVS0ThPCaV2NQowzFgYHvPdcg6TWkWMdOopuzSWEppXZrtu1MEHqxPapUwwD5CaMzckZTpf1UWriBArsJY/AZ4EpJne/VssDHgrQWzr8XDXr/uZlhmowdWPsCClK03TYWP8+wXXSMavsT+eFxki4mxbIOsm50O/FsGQqqu861TFiqZKkElrDdjPM8RdJekYIdt70h4FfANaSsp78FkBS9ykxzh6jf5Mt26E6R7VMj338MarWfWvVDw3cT+pHLP6wIrJxP/Soqltf28fn3lyPefy7nwNoXUIohsgdQqd0Cy0r6GqNjS8MmvZIut71J1rm399xANIbDFb2lBSTNRx/XOdvHVr2wlhc0lbLdXQacQncZil0GaRwaWjPpnxmzSO3D0mS3z/cD65LiDs8iJVdYLlj3gPGejxrI52QS42U+Da1zXKn9VKkf2qO3LAG7CT0ab7F9haS+O+XBWbBrxe+2BDJs9qCXEu22oTUNOAD4BrAlKe5ftse1Fc9RawFgIVKow0akzxNSXOnPbP/bwLTaiWfLRGcM17nP2V6m6oW1TAhqpM/vKUNh4DoKlKEYJiQtDGxN6jfeApwKnGf70qoXNmAkjdsPFihtUqP9FFu46dEdtXMw6N2Envf+su0DKmXBvs326nkhZ2uSu+/UiVhOZdgnYxOd0u22oTHd9lqS7rD9unzumojM0JL2BPYCXgncz8jE8xHghEFu0LQTz5YJj6RnSK5zuzVc5+6JdFdoGQ6GKX3+MCPpJSS3zPfZfkuw1gLAbiTvjKZ7VdgkoRa12k/phZuSuwlzC5J+aXtVSSeQPIsu1hDU8WyZONRut5KuBd4M/IiU0fx+4BDbKwVqftr2MVHvD22MZ8twsA1px3NqDpY+i5EOpKXl+VA0fb6kfWwfJukY+qywRwzYJU3tp9WHU6J3qmrhVDvz+PwTzWmkONO3AwcBOwJ3R4nl3bA5+Xx/bHvQyYeqlJ+w/XtSzH8pPsbIbsJ0uncTvhUlOk5mWSA8brhW/G5Ly6Co0m4b7EWa+O4BHAxsDOwcrPmMpMVtPwSzQiG2t/3tQQm0O54tQ8OwuM61lEPSOcAetoukz5e0pe0LJPU1PhEJYmbnitngIdsPD1p/2JB0q+01JN1ue7Ucm35J1E6rpA3n8KX3DnpHsEL7Kb5w06MfvpvQo1clbrihXzx+t6Vl0JRut1lzHtLu5t6FdfvFv99qe41BabQ7ni1Dg+3HSIXYf9BwndsXaCeeLc+VounzbV+QHz5uu6v+rKRt+/zJIDRHTTYkTQIWsf1IhOaQ00mf/5Ck1wJ/IsVAhlA5O2Xp8hOdneObg95/doTvJjSZCzLLLglsml0WO0xIr4iWCU3RdguQF2vWkqTCHiGTmpp5Ajz/IAXaHc+WlpaW58hYu0XRg3lJt9hec3bnBqx5BrA7KRZvOrAYcKTtw6M0hxFJHwbOBVYjlfhYBNjf9nHBusVjSyu2n237Ldz0ngvQDd9NGEO3eN3SvNu6EakUxEXAZsA02++N0mxpiaBiu/06sCJwDvBY53xwNurDSQudx5H6it2B/7H97wPTaCeeLS0tLc8dSS8H1s6HN9r+S6DWZsDmpHJAZzeemkyKlXtDoHYnS+WOwFrAfwDTHVxuo6UM2e31V6RyU7NiS23vGaxbrP00NIsv3GSN24EpPbsJt9teNVj356S6paflUzsBO9oOq1uay/RMAW61PSV/zifa3jJKs6UlgorttkY26kmk2NZNSDGtl5La7dPj/uGzoHW1bWlpaXmOSNoOOBy4ktRJHyNpb9s/CpJ8gOQmuBVp17HDTFK5gkjmy/GGWwPH2n5SUrtyOWDGSAjzMGmSf1ug9KttbyvpXbZPzTvclwTqFW8/jYWbJSV9s/HUZOCpCM0eLgF+KKm5m3BxAd0lbDcHsadI2itY8wnbz0h6StJk4C9Am0m+5YVIrXZ7ou1rmyckrRcpmNvsKcAVtn8dodFOPFtaWlqeO18A1u7s0uTMjZeR0p8PHNszgBmSzrD95Gz/YLAcD9wLzACuzkmH2hjPwfP6/NOJ530ncBOwu6RzbB8WpFs0tjRTtP1Qd+EGkpfAx4CP09hNKKD7oKSd6K5b+tdgzZslLQ6cQLrXjwI3Bmu2tERQq90eA/R6YfQ7NzAkbUVaDJwfWE7S6sBBg4y7b11tW1paWp4jahR2zseTgBnNc0G6KwJfI8VPNePxiu4oSJrXdomdoqFB0iXANrb/f3v3HmxnXd97/P1JDOViUvRIOQM9EChgJR4TlMvoqSJhYAYQKmK4SXtqORRaCoJKRZkxiPbUgrVV0OoJHkC5SFFTwdFAhYAgVUow3ASmCHKslnG8JPECSMjn/PF7VrLY7CSE/fzWk2fvz2tmz9rrWXvn+4PZz97rd/l+v79snr+YMhE7krLruWeluCPPLe3w/pnRwcLNIPZWwE61dhPWE3O4bynAN6nYt3Sc+LMpFW3vGUW8iLaN8r6V9FrgdZR2Kn8/9NIs4MiavXAlLaN0fbh5kMM6qLDeVozseEZEvHBLmonCYCfhGEohjdouARZS/igdALydSr1pJZ1g+/IN9ASs2QtwKtoJ+M3Q86eBnW0/Iemp9XzPhNkerODfwuiORI53/3xtBHFnSxr5ws0odhPG00HfUiTdaPvAJv73x16L6IsO7tstKAt/LwJmDl1fBdQuzrXa9kqpXqv7TDwjIjaRpN2A7W2fJektwB9QJn7/SmnZU9tWtm9syp4/Bpwr6VbKZLRt2zSPMzf4VdGWK4FvSfpy8/xw4CqVPsTfrRW0ORb5x5TjtWvfG9TsbTnO/fN/bC+uFW/IyBZuxlgI7EvJacX28mY3sCpJ5wMfAp6g5KbNBc6wfXmFWFtSmt6/rGk7Mfj/OgvYoe14ESMw0vu2qep9i6RLR3UqYch9ko4Hpjcnq04Hbm8zQI7aRkRsIklfAd439uiYpL2BhbUrN0r6JvB6yhHMm4AfUppNv7xm3BgNSa9h3WTsNtvV+05Kuh34FnAvsGZw3fZlFWINFm7GFs54A/BD299rO+aYOMtsv2b4qK+kW22/vnLcb9veb7gVQ9vH2NYTd1CR+khKcbAzgaU1juxJegfliOAOlN9Lg4nnKmCR7YvajhlR06jvW0nXUYoYjavmCQlJW1Ny7w+m3LvXAx+0/WRbMbLjGRGx6WaPl69k+85R7GBQ3thtTVmN/CAlJ+N/1gw4yl2TYCtgle1LJG0naRfbj1aOuaXt9R2nbts/AO8b5/qvm9dqt9x4sskn/XdJf0mZIP1O5Zgwgt2E9ZjRPB4KXGX7Z7WO0tn+GPAxSafZvrBKkIjRGvV9+5GK//YG2f41ZeJ5Tq0Y2fGMiNhEkh62vdumvtZno9w1mcokLaRUtX257T0k7QBcY7tqGX1JZ1Iqj34FWJtLavtnFWLdZ/uV63ntWQWHapC0D/AAsC1l4ea3gfNtf6ty3Oq7CeuJ+2HKPfsE5cjgtsBXbO9XMeapwBW2VzTPXwIcZ/uTtWJG1NDVfTtKo9xlzcQzImITSbqK0udq0ZjrJwIH2z6mUtwuj+Dcb3uOpEXAF20vkXR3Jp7tkrQc2Au4a8THMU8F/hpYwbqfMdcouDMVF2661kz8Vtl+pnkjPcv24xXjLbc9b8y1tUcVI2J8kv7J9tGS7mWcv/c1/hZI2n9Drzd5p63IUduIiE13BrBY0ttY1w9wb0o1uiMrxh0cwXkL8F+BwTHX4yg9Nmu6TtKDlF2Tv2h6Lk6aFd/NyG9sW5IBmqJCo/BOYDfbPxlBrH+TdNJ6Fm6Wred7JqyrhZsuF4yGvIJSzXf4fd9nK8ab1hQ/G/wcT6f8fozohQ7v2zOaxzdV+vefY3hiWbt1THY8IyJeIEkHAIMjg/fbvmlEcb9h+w0bu1Yh7kh3TaYiSe8GdgcOovRq/VPgytr5cpKuBY5tcnyqkrQ9sJjSNuY5Cze1fqaGVvXHXbixPV7eaZtxx9XmbsJ64n8O+D1gOfDMurD1KhZLuoBSIflTlDfvpwA/sP2uWjEj2tTVfSvpLtuvlvQ5239UI8YGYh9OWeDewnaV1jGZeEZE9IykB4DDbD/SPN8F+KrtV1SO+0qe2/uw5q7JlCTpIIbyiWz/ywhiLgbmAEt5do5nzcnJlFq4aeKMrBH9UMwHgD09wjd8TfGmk4EDKT/HNwAX235mg98YsRka5X0r6T5K39D3A2eNfd32lyrGXkYpVnhzrVSPHLWNiOifM4GbJT3SPJ9NeZNXTVP05o2UiedXgUOA26h7XG9Ksv0vkr5N8zda0ktrFPkZ45+bj5GxvZQy0R217STtOmbhZrvaQYd3ExhNI/qB+yg7vP9ZOc5attdIupSSCz+ySXZE2zq4b08B3kYpAja2wreBahNPYLXtlbWqXkMmnhERvdMU9tkd+P3m0oO2n9rQ97TgrZQWKt+x/fbmuOTFlWNOOZJOBs6j5NKuoewWGWi9yM8w25eNalVf0lI2kDs15NJKO+ojX7hpnMsIG9EPeRnwXUl38Ozd7JrFyI6g7NqMepId0bZzGeF9a/s24DZJd9r+TK0461G9dUwmnhERPSFpvu2bJL1lzEu/J6nqERzgiWYXY7WkWcCPqTwZmqLeDcwZUZGftUa8qv8nz/PrVlSI3dXCDYxgN2E9zh11QGAh3UyyI9rWyX1r+zOSXkdZGHvR0PWap4xOo7SOeQq4kqZ1TJsBMvGMiOiP/YGbeO7xG6h/BOdOSdsCiygFYX4J3FEx3lT1PaB6gZ9xnMtzJwq71Ahk+7Gx15qcwBfbXlUjZhOjy4UbGH0jeqAUQWlOKOzTXLrD9o8rh+1qkh3Rtk7u2/UVBaNuesthts+hTD4H41gAXNNWgBQXioiITdLsXMyyfU/HQ5l0JO0FXAJ8mxEV+Wniftv2fsO9Fmv3D5V0JSWf6RnKYsZvAx+1fUGleB+wvVDSJeO8bNt/WiPuUPzhRvSwrhF91d1WSUdTjr3eTDm6/XrgLNtfqBjzM8CNwNnAUZQ36zNsn1IrZkQNHd63XRQFu8v2qzd2bUIxMvGMiOgHSe/c0Ou2P1ox9o22D9zYtZiYJg/vNuBeSo4nUHIwK8cd+URB0nLb81T64b4GeA+wrOZkt0uSFti+ZmPXKsS9GzhosMup0oP367bnVow5/GZdrHuznt6/0Ssd3rfXAKfbrl4UTNIhwKHA0cDVQy/NpEx+92srVo7aRkT0x8xRB5S0JbA18LKmj+fg7NwsYIdRj2cKWG17gwsMlQzn9lxFhdyeccyQNAN4M3CR7aclVVsN73LhpvFenntkbbxrbZs25mjtT4FpNQM2/WCfdWQvoqe6um9HWRTsR5RTJ0ewrrcywM60nPqRiWdERE/Y/kAHYU8GzqBMMpexbuK5CvhEB+OZ7JZK+jPgOp79ZqNqO5XhiYKk6cA2I9id+jTwfeBu4BuSdqb8XNUy8oUbeNZuwo6SPj5mPE+PYAhLJF1PWVAAOIbSEql1kq5jAxWLU9U2+mIzuG/PHUEMAGzfDdwt6QpKP+fjKbufjwJfbDNWjtpGRPSMpN8FLgT+B+VN3m3AO2z/R8WYp9m+sNa/H4WkR8e5bNtVKwiPOt9yA+N4ke3Vo4xZm6S5wF7AByhN4Qd2Bra3fWqluLs1//43m4JKf0BZOPo5cIXt71WIuf+GXrd9S9sxI2ro6r7tgqQ9gGOB4ygnIq4G3m1759ZjZeIZEdEvkv6FUur8c82lE4C32T6oYsxTKW9WVzTPXwIcZ/uTtWLG6Iwy31LSCbYvX9/R19pHXrtYuGnizmCc3QTbF1WK9xXgfWOLgEnaG1hoe7zq2G3GH0lf2IiaOrhvf8H4pwZEWYScVSHmGuBW4ETbDzfXHqmx4Fn1jH9ERFSxne1LbK9uPi4Ftqsc86TBpBPA9s+BkyrHnHIkzZB0uqQvNB9/2bzxqW043/LLtp9mA0cmJ2ib5nHmej5quwS4lnJ8fEfKsebxKt22QtIekt4P3ANcBPyAsvB/QK03r43Z41Wetn0npTdgNU1f2OXAkub5PEnX1owZ0aau7lvbM23PGudjZo1JZ+Mo4HFKqsciSQeyLq2mVcnxjIjon59IOoF1OVuD4zE1TZOkQWn3Jg9wi8oxp6J/BGYAg53kP2qu/a/KcUeWb2n7081jFznL0CzcDD2/VNIZFeM9SNlNOHxoN+HMivEGttzAa1tVjn0uz+0LO7tyzIg2dXXfjpztxcBiSdtQFh/PBLaX9I/AYts3tBUrE8+IiP75U8oK7N83z7/ZXKvpeuCfJH2KshN2Cs1uRrRqnzFtLm5q2mHU9gnbawtoSPp/wAE1A0o6H/gQ8ATlZ2kucIbty2vGZfQLN0dR8qeWSloCfJ5Kuwlj/Jukk2wvGr4o6USeXbmyhtW2V0qj+M+MqKKT+1bSUp7faZNLbX+2zdi2fwVcAVwh6aXAAkqLrdYmnsnxjIiIjZI0jVLhdnAE5wbgYtvPdDqwSUbSXcCCQeEXSbsCX2izgfd64j5KaQ9wie0HasYaijnIKz2SdavsS2v2l2zi7kRZuHltc+mblBzPxyrHHewmHAfMBy6j5d2EMfG2BxYDv2HdRHNvykmFI20/XiNuE3vkfWEjaujgvn2+BX1W2F5ZYww1ZeIZEdEzXe0UpVhIfU1uzSXAI5QJ/s7A220vrRx3JmV1/+2U+g//F/i87WrtTSTdb3uOpEWUYh1LJN1de+K5ORjaTTjG9vzKsQ4AXtk8vd/2TTXjNTG3prTnObi5dD3wQdtPrf+7IjZvo7xvx8SdBry45u/jUcnEMyKiZ7rYKZJ0BHABsIXtXSTNA85LX772Sfot4OWUieeDo36zLukNlGOo2wJfoEwYHq4Q58OUn98nKPmA2wJfsb1f27HGxO3qiO+UIWmB7Ws2di0ixre5tLhqW6raRkT0z6DK6aHAVbZ/NoKYCymTgxVQioVQuTLmVCJpfvP4FuAwYDfg94DDmmu140+XdISkxcDHgL8DdqVUfP1qjZi2z6Ycd927qaL7K+APa8Qa4+Bm5+BNwH8AewBnjSDuVPLe53ktIsa3Z/N76s2U38E7UYrN9VqKC0VE9M91kh6k7Nj8haTtgCcrx0yxkLr2B24CxuutaOBLleP/O7AUuMD27UPXv9DsgNayI3CQpOEKrK0WzBjHcxZu8nPdDkmHUP6/7ijp40MvzQSe7mZUEb003OLqIttPS+r9MdVMPCMiesb22ZL+Flhl+xlJo9gpuk/S8cB0SbtTioXcvpHviefJ9sLm8e0dDeFVtn853gu2T68RUNJC4I3AnpQV/UOA26g/8exi4Waq+BHlWOARPLty7s7ArzsZUUQ/jazF1SglxzMioockvY5y1HXtAmLbpdXHxBsuFiLWFQvJG/YWSHrnhl63/dHK8bcDTuK5P1PV2vRIupeSX/kd23ObKqwX2x5v17ft2C9h3cLN1sCsmlVep5pmp2YOcDxwNPAopYDURZ0OLKLHJL3I9uquxzER2fGMiOgZSZ+j5P8tpxQegHIcs9rE0/avKRPPc2rFmOJmNo8vB/YBrm2eHw58YwTxv0xplv511v1M1faE7TWSVkuaBfyYklc6Cq8AZksafh9Ue6d10pO0B6U68qA36tWUTY6qPWEjJgtJJ9i+fAOLkVUXIWvLxDMion/2phQeqH5kRdJ1bKCZdaratsP2BwAk3QC82vYvmufnUvpr1ra17feMIM6wOyVtCyyiHMv8JXBH7aBdLNxMIQ9SFjAOH1RClnRmt0OK6JVtmseZG/yqnspR24iInpF0DXC67f8cQaz9N/S67Vtqj2EqaXIP5w5aqDStVe62/fuV434IuN12lQq2zyP+bMpx13tGEOsBRrRwM9U0LZ6OBV5HaVXzecrx6V06HVhEbBYy8YyI6BlJS4F5lN2htT0ea+8+StoK2Mn2QzXjTGWSzqHkxC2m7MIdCVxt+28qxftFE0eUlfbfsK76qG3PqhG3iX2j7QM3dq1C3JEt3ExVkrahVOM8DpgPXAYstn1DpwOL6InJ2m84E8+IiJ5Z3y5kzd1HSYcDHwG2sL2LpHnAeTlq2x6Vnh6/C2wHvL65/A3b3+luVO1rWqdsTWnf8kbKpBdgFvA126+oHL+ThZupStJLgQXAMbbndz2eiD6QtNz2vOYUwZuBM4Gltud2PLQJycQzIqKHmgqg+zRP77D948rxllF2Lm62vVdz7R7br6oZd6qRtMz2azqK/RbgDyg7oLfa/udKcd4BnAHsAPyQdRPPVcCi2pVPu1i4iYjYFJLutz1H0iJKReglku7u+8RzWtcDiIiITSPpaMpuzQLKscxvS3pr5bCrba+sHCPgW5L22fiXtUvSJ4FTgHuB+4BTJH2iRizbH2ty/t5te1fbuzQfc0fRbqOZYD5IKd4xE3ggk86I2MwM+g3vDdw4WfoNZ8czIqJnJN0NHDTY5Wz+IH295kqopM8ANwJnA0cBpwMzbJ9SK+ZUJOm7wB7AY8CvKLuBrr2zLOl+4JWDgjuSpgH32p5TMeapwBW2VzTPXwIcZ/uTtWI2cY4GQ4y9lQAADndJREFULgBupvz/fT1wlu0v1IwbEbEpJmO/4bRTiYjon2ljjtb+lPonWE6j9PB8CrgSuB74YOWYU9EhHcV9CNiJMuEF+G9A7QqzJ9leu6tq++eSTgKqTjwpP8f7jF24ATLxjIjNyY7AQU1e/ECv2z5l4hkR0T9LJF0PXNU8Pwb4WuWYh9k+h/KmHQBJCxhNj8kpw/ZjAJJ+B9hyI1/epv8CPCBp0EdzH+BfJV3bjKtG4Z1pkjS0yzod2KJCnOfE7WDhJiLieZO0kFJ8bU/gq5RFydvo+cQzR20jInpoqBCMKJVPF1eOd5ftV2/sWkyMpCOAv6MU3vkxsDMlB7Hakdcm7sj7tUq6AJgNfIpS0OgU4Ae239V2rHHivopnL9zcY/s9NeNGRDxfku6ltFD5ju25TUHBi20f3vHQJiQTz4iInpC0G7C97W+Ouf4G4Ie2v1ch5iHAoZQiRlcPvTQT2NP2fm3HnMqa/N35lJzdvSQdQMl7/LMRxZ/F0Gko2z+rGGsacDJwIGUB5QbKG6tnKsVbe/+MWbj5OSXXtPX7JyLihZB0h+19m4ryBwC/AO6rvQhZW47aRkT0xz8A7xvn+q+b12qshP4IWAYc0TwO7NzEjXY9bfunkqZJmmZ7qaS/rR1U0p9RcnafANbQFDUCdq0V0/YaSZcCN9l+qFacIWvvH9tfAr4EIGlv6t0/EREvxJ2StgUWUf72/pJSzb7XsuMZEdETku6z/cr1vHav7f9eMfYMYA5wPGX381FKb7Hq7S+mEklfpzQL/xvgZZTjtvvYfl3luP8OvNb2T2rGGRPzCEp12S1s7yJpHnBepXzSTu+fiIgXStJsSkXb2gXfqksyfUREf2yo2MxWNQJK2kPS+ykVTi8CfkBZtDwgk84q/pCyk3wmsAT4HqPZifseo9/BXgjsC6wAsL2ckvNZy8jvn4iIF0LSjYPPbX/f9j3D1/oqR20jIvrj3ySdZHvR8EVJJ/LsY7BtehC4FTjc9sNNvDMrxZqyxsnfXQNc1uTvbkupvFrTe4HbJX2b0jIHANunV4y52vZKSRVDPEsX909ExPPWtE7ZGnhZ08dz8AtyFqXoXK9l4hkR0R9nAIslvY11b5T3prSgOLJSzKOAY4GlkpYAn2fdH8JoTxf5u8M+DdwE3EuZ9I7CfZKOB6ZL2h04Hbi9Yrwu7p+IiE1xMuV31Q6U31ODv7ergE+s75v6IjmeERE901Q6HeSq3W/7phHE3IaSe3gcperqZcBi2zfUjj0VdJ1/KOn22nmk48TcmtIX9mDKm6vrgQ/afrJy3JHfPxERm0LSabYv7HocbcvEMyIiNomklwILgGNsz+96PJOBpIdt77apr7UY/6+Bx4DrePZR22rtVCIiYnySTqW0eVrRPH8JpbXWJ7sd2cRk4hkR0ROSllJaXGzMpbY/W3s80R5JV1HaioyXf3iw7WMqx390nMu23Xo7FUnXsYGf41pVbSMi+kLSctvzxlz7ju29uhpTGzLxjIjoCUk7P88vXWF7ZdXBRKskbQ8sBn7DOPmHth/vamxtk7T/hl63fcuoxhIRsTmSdA8w181ETdJ04B7bc7od2cRk4hkR0WOSpgEvtr2q67HExI06/1DSX9k+v/l8ge1rhl7737bHK3jUZvytgJ1sP1QzTkREn0i6gNJe6lOUEyKnAD+w/a4uxzVRmXhGRPSMpCspf4SeoeyO/TbwUdsXdDqw6B1Jd9l+9djPx3teIfbhwEeALWzvImkecF6O2kbEVNcsKp8MHEgpvnYDcLHtZzod2ASlnUpERP/saXtV0xbiq8B7KBPQTDx7qsP8Xa3n8/Get+1cYF/gZgDbyyXNrhwzImKzZ3uNpEspuf+T5kRIJp4REf0zQ9IMSnuTi2w/LSnHV/rtT57n161oOa7X8/l4z9u22vZKKW1hIyKGSTqCspi8BTBpToRk4hkR0T+fBr4P3A18oyk6lBzPHrP92NhrI8rfnStpFWV3c6vmc5rnW1aMC3CfpOOB6ZJ2B04Hbq8cMyKiDxYyCU+ETOt6ABERsWlsf9z2jrYPdfEYcEDX44qJk3SlpFmStgG+Czwk6axa8WxPtz3L9kzbL2o+HzyfUStu4zRgDqVv6JXASuAdlWNGRPTB6slYnT47nhERPSHpBNuXS3rner7koyMdUNQwlfJ3D7N9DnDO4IKkBcA16/+WiIgpYVKeCMmOZ0REf2zTPM5cz0f033D+7pdtP039XMuuvPd5XouImGqGT4RcRUmnOaPTEbUg7VQiIiI2E5JOp+xy3g0cBuwEXG779Z0OrEWSDgEOBY4Grh56aSZlx3e/TgYWERFVZeIZEdEzks4HPgQ8ASwB5gJn2L6804FFFZJeZHt11+Noi6S5wF7AB4D3D720M7C97VM7GVhERMckXccGTrn0vaptJp4RET0jabnteZKOpBzJPBNYantux0OLF2hj+bu2J13+bnOkeA5wPGX381Hgi7Yv6nRgEREdkbT/hl63fcuoxlJDigtFRPTPoNroocBVtn+WXoi9N5y/O6lJ2gM4FjgO+CnluK1spzJzRExpwxNLSVsBO9l+qMMhtSo7nhERPSPpw5Sdzicofb62Bb6S3LjoA0lrgFuBE20/3Fx7xPau3Y4sImLzIOlw4CPAFrZ3kTQPOK/vR21T1TYiomdsnw28Fti7qXr6K+APux1VtEHS+U0fzxmSbpT0E0kndD2ulh0FPA4slbRI0oFAtuwjItY5l7KwvALA9nJgdofjaUUmnhER/bQjcJSkPwbeChzc8XiiHQfbXgW8CfgPYA/grG6H1C7bi20fA/w+cDMlR3l7Sf8oKT/HERGw2vbKrgfRtkw8IyJ6RtJC4MLm4wDgfKDXx29irefk73Y5mJps/8r2FbbfBPwusBw4u+NhRURsDu6TdDwwXdLuki4Ebu96UBOViWdERP+8FTgQeNz22yntVH6r2yFFS66T9CCwN3CjpO2AJzseU3W2f2b707bndz2WiIjNwGmUqt9PAVcCK4F3dDqiFqS4UEREz0i6w/a+kpZRdjx/Adxne07HQ4sWSHoJsMr2M5K2BmbZfrzrcUVExGhIWmD7mo1d65u0U4mI6J87JW0LLAKWAb8E7uh2SNGiHYGDJG05dO2zXQ0mIiJG7r3A2EnmeNd6JTueERE9Jmk2ZUfsno6HEi1o8nffCOwJfBU4BLjN9lu7HFdERNQn6RBKjv/RlB7HAzOBPfveNi05nhERPSPpxsHntr9v+57ha9Fryd+NiJi6fkQ5yfRk8zj4eBS4s8NxtSJHbSMieqI5erk18LImD3DQ+3AWsENnA4s2PWF7jaTVkmYBPwZ27XpQERFRn+27gbslXUEpLnQ8ZffzUeCLXY6tDZl4RkT0x8nAGZRJ5jLWTTxXAZ/oalDRquTvRkRMUZL2AI4FjgN+SjluK9sHdDqwliTHMyKiZySdZvvCrscRdSV/NyJiapG0BrgVONH2w821R2xPipMvyfGMiOifNc2uGFDab0j6iy4HFO1I/m5ExJR2FPA4sFTSIkkHsu50U+9lxzMiomckLbc9b8y179jeq6sxxcQM5e8upVS1Hc7f/ZrtV3Q0tIiIGDFJ2wBvphy5nQ9cBiy2fUOnA5ug5HhGRPTPNElys3IoaTqwRcdjiolJ/m5ERABg+1fAFcAVkl4KLADOBno98cyOZ0REz0i6AJgNfAowcArwA9vv6nJcMXHJ342IiMkqOZ4REf3zHuAm4M+BU4Ebgb/qdETRluTvRkTEpJQdz4iIHpK0FbCT7Ye6Hku0J/m7ERExWWXHMyKiZyQdASwHljTP50m6tttRRUumSVpbwTD5uxERMVlk4hkR0T8LgX2BFQC2l1NyPqP/rgf+SdKBkuYDV9EsMERERPRZqtpGRPTPatsrhzbGYvJ4D6XC7Z9TKtveAFzc6YgiIiJakIlnRET/3CfpeGC6pN2B04HbOx5TtMD2GkmXAjclfzciIiaTHLWNiOif04A5wFOUo5irKD0go+eSvxsREZNVqtpGRERsJiQtA+YDNw8q2Uq6x/aruh1ZRETExOSobURET0i6DljvaqHtI0Y4nKgj+bsRETEpZeIZEdEfH+l6AFFd8ncjImJSylHbiIgekrQVsFMK0EwukrYGzgEOplS1vR74oO0nOx1YRETEBGXiGRHRM5IOp+x+bmF7F0nzgPNy1DYiIiI2VzlqGxHRP+cC+wI3A9heLml2d8OJiUr+bkRETHaZeEZE9E8K0Ew+yd+NiIhJLRPPiIj+SQGaScb2LYPPk78bERGT0bSuBxAREZvsNGAO8BRwJbASeEenI4pWNPm7y4ElzfN5kq7tdlQRERETl+JCERE9I2mB7Ws2di36R9IyYD5ws+29mmv32H5VtyOLiIiYmOx4RkT0z3uf57Xon9W2V3Y9iIiIiLYlxzMioickHQIcCuwo6eNDL80Enu5mVNGy5O9GRMSklB3PiIj++BGwDHiyeRx8PArc2eG4oj3J342IiEkpOZ4RET0jaQZlcnI8cDRl4vlF2xd1OrCYsOTvRkTEZJWJZ0RET0jaAzgWOA74KXA18G7bO3c6sGiNpLtsv3pj1yIiIvomOZ4REf3xIHArcLjthwEkndntkKINyd+NiIjJLhPPiIj+OIqy47lU0hLg84C6HVK0ZJC/e0TzOLAz8OtORhQREdGiHLWNiOgZSdsAb6YcuZ0PXAYstn1DpwOLCUv+bkRETFaZeEZE9JiklwILgGNsz+96PPHCJH83IiImu0w8IyIiOiZpDSV/98Sh/N1HbO/a7cgiIiLakT6eERER3TsKeJySv7tI0oEkfzciIiaR7HhGRERsJpK/GxERk1UmnhEREZuh5O9GRMRkkolnREREREREVJUcz4iIiIiIiKgqE8+IiIiIiIioKhPPiIiIiIiIqCoTz4iIiIiIiKgqE8+IiIiIiIio6v8DvVZoevhwI3wAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1152x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "##Since there are many combinations of potential findings, I'm going to look at the 30 most common co-occurrences:\n",
    "plt.figure(figsize=(16,6))\n",
    "d[d.Infiltration==1]['Finding Labels'].value_counts()[0:30].plot(kind='bar')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It looks like Infiltration actually occurs alone for the most part, and that its most-common comorbidities are Atelectasis and Effusion. \n",
    "\n",
    "Let's see if the same is true for another label, we'll try Effusion:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f20b58679d0>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "##Since there are many combinations of potential findings, I'm going to look at the 30 most common co-occurrences:\n",
    "plt.figure(figsize=(16,6))\n",
    "d[d.Effusion==1]['Finding Labels'].value_counts()[0:30].plot(kind='bar')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Same thing! Now let's move on to looking at age & gender: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 29.,  51.,  60.,  96., 154., 194., 229., 109.,  58.,  19.]),\n",
       " array([ 6. , 14.1, 22.2, 30.3, 38.4, 46.5, 54.6, 62.7, 70.8, 78.9, 87. ]),\n",
       " <a list of 10 Patch objects>)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,6))\n",
    "plt.hist(d['Patient Age'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 8., 11., 14., 15., 18., 27., 30.,  4.,  4.,  3.]),\n",
       " array([11. , 18.6, 26.2, 33.8, 41.4, 49. , 56.6, 64.2, 71.8, 79.4, 87. ]),\n",
       " <a list of 10 Patch objects>)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,6))\n",
    "plt.hist(d[d.Infiltration==1]['Patient Age'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 2.,  1.,  6.,  7.,  9., 13., 31., 13.,  7.,  6.]),\n",
       " array([11., 18., 25., 32., 39., 46., 53., 60., 67., 74., 81.]),\n",
       " <a list of 10 Patch objects>)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,6))\n",
    "plt.hist(d[d.Effusion==1]['Patient Age'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looks like the distribution of age across the whole population is slightly different than it is specifically for Infiltration and Effusion. Infiltration appears to be more skewed towards younger individuals, and Effusion spans the age range but has a large peak around 55. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f20b35e7110>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAFkCAYAAADbgnvLAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjAsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8GearUAAAOCklEQVR4nO3db6je91nH8c+1dNbhH2xpWkvTmgp5YDvYJqFM9mSuaiuTpSCFTJQ8KPRJhQmCtj4RhUBFEFHsg6LDgLoS2EbDNv+UaBFhrEvndGu70rDONqQ02UR0CpV2lw/ym5yl5/TcTc7Jaa+8XlB+v/t7f+/7vgKHd365z7lPq7sDwCzv2OkBANh64g4wkLgDDCTuAAOJO8BA4g4w0BU7PUCSXHPNNb13796dHgPgbeXJJ5/8ZnfvXu++t0Tc9+7dmxMnTuz0GABvK1X1bxvd520ZgIHEHWAgcQcYSNwBBhJ3gIHEHWAgcQcYSNwBBhJ3gIHEHWAgcQcYSNwBBlop7lX1jar6SlV9uapOLGtXV9VjVfXccrxqzf4HqupkVT1bVXds1/AArO/NXLn/dHe/t7v3L7fvT3K8u/clOb7cTlXdkuRgkluT3JnkoaratYUzA7CJi/mVvweSfHA5P5Lk8SS/uaw/0t2vJHm+qk4muS3J5y/itd4S9t7/2Z0eYZRvPPjhnR4Bxlr1yr2T/F1VPVlV9y5r13X3S0myHK9d1m9I8uKax55a1gC4RFa9cv9Ad5+uqmuTPFZVX3uDvbXOWr9u07m/JO5NkptuumnFMQBYxUpX7t19ejmeSfLpnHub5eWquj5JluOZZfupJDeuefieJKfXec6Hu3t/d+/fvXvd/0sUABdo07hX1Q9U1Q999zzJzyX5apJjSQ4t2w4leXQ5P5bkYFVdWVU3J9mX5ImtHhyAja3ytsx1ST5dVd/d/1fd/TdV9cUkR6vqniQvJLk7Sbr7qao6muTpJK8mua+7X9uW6YH/5xv+W2fCN/s3jXt3fz3Je9ZZ/1aS2zd4zOEkhy96OgAuiE+oAgwk7gADiTvAQOIOMJC4Awwk7gADiTvAQOIOMJC4Awwk7gADiTvAQOIOMJC4Awwk7gADiTvAQOIOMJC4Awwk7gADiTvAQOIOMJC4Awwk7gADiTvAQOIOMJC4Awwk7gADiTvAQOIOMJC4Awwk7gADiTvAQOIOMJC4Awwk7gADiTvAQOIOMJC4Awwk7gADiTvAQOIOMJC4Awwk7gADiTvAQOIOMJC4Awwk7gADiTvAQOIOMJC4Awwk7gADiTvAQOIOMJC4Awy0ctyraldV/XNVfWa5fXVVPVZVzy3Hq9bsfaCqTlbVs1V1x3YMDsDG3syV+8eSPLPm9v1Jjnf3viTHl9upqluSHExya5I7kzxUVbu2ZlwAVrFS3KtqT5IPJ/nTNcsHkhxZzo8kuWvN+iPd/Up3P5/kZJLbtmZcAFax6pX7Hyb5jSTfWbN2XXe/lCTL8dpl/YYkL67Zd2pZA+AS2TTuVfULSc5095MrPmets9brPO+9VXWiqk6cPXt2xacGYBWrXLl/IMlHquobSR5J8qGq+oskL1fV9UmyHM8s+08luXHN4/ckOX3+k3b3w929v7v37969+yL+CACcb9O4d/cD3b2nu/fm3DdK/767fznJsSSHlm2Hkjy6nB9LcrCqrqyqm5PsS/LElk8OwIauuIjHPpjkaFXdk+SFJHcnSXc/VVVHkzyd5NUk93X3axc9KQAre1Nx7+7Hkzy+nH8rye0b7Duc5PBFzgbABfIJVYCBxB1gIHEHGEjcAQYSd4CBxB1gIHEHGEjcAQYSd4CBxB1gIHEHGEjcAQYSd4CBxB1gIHEHGEjcAQYSd4CBxB1gIHEHGEjcAQYSd4CBxB1gIHEHGEjcAQYSd4CBxB1gIHEHGEjcAQYSd4CBxB1gIHEHGEjcAQYSd4CBxB1gIHEHGEjcAQYSd4CBxB1gIHEHGEjcAQYSd4CBxB1gIHEHGEjcAQYSd4CBxB1gIHEHGEjcAQYSd4CBxB1gIHEHGEjcAQbaNO5V9f1V9URV/UtVPVVVv7OsX11Vj1XVc8vxqjWPeaCqTlbVs1V1x3b+AQB4vVWu3F9J8qHufk+S9ya5s6ren+T+JMe7e1+S48vtVNUtSQ4muTXJnUkeqqpd2zE8AOvbNO59zreXm+9c/uskB5IcWdaPJLlrOT+Q5JHufqW7n09yMsltWzo1AG9opffcq2pXVX05yZkkj3X3F5Jc190vJclyvHbZfkOSF9c8/NSyBsAlslLcu/u17n5vkj1Jbquqd7/B9lrvKV63qereqjpRVSfOnj272rQArORN/bRMd/9Hksdz7r30l6vq+iRZjmeWbaeS3LjmYXuSnF7nuR7u7v3dvX/37t0XMDoAG1nlp2V2V9WPLOfvSvIzSb6W5FiSQ8u2Q0keXc6PJTlYVVdW1c1J9iV5YqsHB2BjV6yw5/okR5afeHlHkqPd/Zmq+nySo1V1T5IXktydJN39VFUdTfJ0kleT3Nfdr23P+ACsZ9O4d/e/JnnfOuvfSnL7Bo85nOTwRU8HwAXxCVWAgcQdYCBxBxhI3AEGEneAgcQdYCBxBxhI3AEGEneAgcQdYCBxBxhI3AEGEneAgcQdYCBxBxhI3AEGEneAgcQdYCBxBxhI3AEGEneAgcQdYCBxBxhI3AEGEneAgcQdYCBxBxhI3AEGEneAgcQdYCBxBxhI3AEGEneAgcQdYCBxBxhI3AEGEneAgcQdYCBxBxhI3AEGEneAgcQdYCBxBxhI3AEGEneAgcQdYCBxBxhI3AEGEneAgcQdYCBxBxhI3AEGEneAgTaNe1XdWFX/UFXPVNVTVfWxZf3qqnqsqp5bjletecwDVXWyqp6tqju28w8AwOutcuX+apJf7+6fSPL+JPdV1S1J7k9yvLv3JTm+3M5y38Ektya5M8lDVbVrO4YHYH2bxr27X+ruLy3n/5XkmSQ3JDmQ5Miy7UiSu5bzA0ke6e5Xuvv5JCeT3LbVgwOwsTf1nntV7U3yviRfSHJdd7+UnPsLIMm1y7Ybkry45mGnljUALpGV415VP5jkk0l+rbv/8422rrPW6zzfvVV1oqpOnD17dtUxAFjBSnGvqnfmXNj/srs/tSy/XFXXL/dfn+TMsn4qyY1rHr4nyenzn7O7H+7u/d29f/fu3Rc6PwDrWOWnZSrJnyV5prv/YM1dx5IcWs4PJXl0zfrBqrqyqm5Osi/JE1s3MgCbuWKFPR9I8itJvlJVX17WfivJg0mOVtU9SV5IcneSdPdTVXU0ydM595M293X3a1s+OQAb2jTu3f1PWf999CS5fYPHHE5y+CLmAuAi+IQqwEDiDjCQuAMMJO4AA4k7wEDiDjCQuAMMJO4AA4k7wEDiDjCQuAMMJO4AA4k7wEDiDjCQuAMMJO4AA4k7wEDiDjCQuAMMJO4AA4k7wEDiDjCQuAMMJO4AA4k7wEDiDjCQuAMMJO4AA4k7wEDiDjCQuAMMJO4AA4k7wEDiDjCQuAMMJO4AA4k7wEDiDjCQuAMMJO4AA4k7wEDiDjCQuAMMJO4AA4k7wEDiDjCQuAMMJO4AA4k7wEDiDjCQuAMMJO4AA4k7wECbxr2qPl5VZ6rqq2vWrq6qx6rqueV41Zr7Hqiqk1X1bFXdsV2DA7CxVa7c/zzJneet3Z/keHfvS3J8uZ2quiXJwSS3Lo95qKp2bdm0AKxk07h39z8m+ffzlg8kObKcH0ly15r1R7r7le5+PsnJJLdt0awArOhC33O/rrtfSpLleO2yfkOSF9fsO7WsAXAJbfU3VGudtV53Y9W9VXWiqk6cPXt2i8cAuLxdaNxfrqrrk2Q5nlnWTyW5cc2+PUlOr/cE3f1wd+/v7v27d+++wDEAWM+Fxv1YkkPL+aEkj65ZP1hVV1bVzUn2JXni4kYE4M26YrMNVfWJJB9Mck1VnUry20keTHK0qu5J8kKSu5Oku5+qqqNJnk7yapL7uvu1bZodgA1sGvfu/ugGd92+wf7DSQ5fzFAAXByfUAUYSNwBBhJ3gIHEHWAgcQcYSNwBBhJ3gIHEHWAgcQcYSNwBBhJ3gIHEHWAgcQcYSNwBBhJ3gIHEHWAgcQcYSNwBBhJ3gIHEHWAgcQcYSNwBBhJ3gIHEHWAgcQcYSNwBBhJ3gIHEHWAgcQcYSNwBBhJ3gIHEHWAgcQcYSNwBBhJ3gIHEHWAgcQcYSNwBBhJ3gIHEHWAgcQcYSNwBBhJ3gIHEHWAgcQcYSNwBBhJ3gIHEHWAgcQcYSNwBBhJ3gIHEHWCgbYt7Vd1ZVc9W1cmqun+7XgeA19uWuFfVriR/kuTnk9yS5KNVdct2vBYAr7ddV+63JTnZ3V/v7v9N8kiSA9v0WgCcZ7vifkOSF9fcPrWsAXAJXLFNz1vrrPX3bKi6N8m9y81vV9Wz2zTL5eiaJN/c6SE2U7+30xOwA3xtbq0f2+iO7Yr7qSQ3rrm9J8nptRu6++EkD2/T61/WqupEd+/f6TngfL42L53telvmi0n2VdXNVfV9SQ4mObZNrwXAebblyr27X62qX03yt0l2Jfl4dz+1Ha8FwOtt19sy6e7PJfncdj0/b8jbXbxV+dq8RKq7N98FwNuKXz8AMJC4Awwk7gADiTuwLarqpp2e4XLmG6pvc1X1hp8f6O6PXKpZYK2q+lJ3/+Ry/snu/sWdnulysm0/Cskl81M593t8PpHkC1n/Vz/ATlj7tfjjOzbFZUrc3/5+NMnPJvlokl9K8tkkn/ChMd4CeoNzLgFvywxSVVfmXOR/P8nvdvcf7/BIXMaq6rUk/51zV/DvSvI/370rSXf3D+/UbJcDV+4DLFH/cM6FfW+SP0ryqZ2cCbp7107PcDlz5f42V1VHkrw7yV8neaS7v7rDIwFvAeL+NldV38m5f/om3/u+pn/6wmVM3AEG8iEmgIHEHWAgcQcYSNwBBhJ3gIH+D/5sxfp6h6wcAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(6,6))\n",
    "d['Patient Gender'].value_counts().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f20b350ad90>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(6,6))\n",
    "d[d.Infiltration ==1]['Patient Gender'].value_counts().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f20b2ff9950>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(6,6))\n",
    "d[d.Effusion ==1]['Patient Gender'].value_counts().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Gender distribution seems to be pretty equal in the whole population as well as with Infiltration, with a slight preference towards females in the Effusion distribution. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Finally, let's look at if and how age & gender relate to mass size in individuals who have a mass as a finding:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7f20b2fa0850>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(d['Patient Age'],d['Mass_Size'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.7275663300043572, 7.354553889321959e-07)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mass_sizes = d['Mass_Size'].values\n",
    "mass_inds = np.where(~np.isnan(mass_sizes))\n",
    "ages = d.iloc[mass_inds]['Patient Age']\n",
    "mass_sizes=mass_sizes[mass_inds]\n",
    "scipy.stats.pearsonr(mass_sizes,ages)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above tells us that age and mass size are significantly correlated, with a Pearson's coerrelation coefficient of 0.727"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1735.7"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(d[d['Patient Gender']== 'M']['Mass_Size'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1550.8"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(d[d['Patient Gender']== 'F']['Mass_Size'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ttest_indResult(statistic=-0.6188395721019645, pvalue=0.5402707532656862)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scipy.stats.ttest_ind(d[d['Patient Gender']== 'F']['Mass_Size'],d[d['Patient Gender']== 'M']['Mass_Size'],nan_policy='omit')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above tells us that there is no statistically significant difference between mass size with gender. "
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}