{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pan-cancer analysis (ANOVA)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "%pylab inline\n",
    "matplotlib.rcParams['figure.figsize'] = (10,6)\n",
    "from gdsctools import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "gdsc = ANOVA(ic50_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['lung_NSCLC', 'prostate', 'stomach', 'nervous_system', 'skin',\n",
       "       'Bladder', 'leukemia', 'kidney', 'thyroid', 'soft_tissue',\n",
       "       'aero_dig_tract', 'ovary', 'lymphoma', 'myeloma', 'endometrium',\n",
       "       'pancreas', 'breast', 'neuroblastoma', 'large_intestine', 'cervix',\n",
       "       'liver', 'bone', 'lung_SCLC', 'lung', 'biliary_tract',\n",
       "       'urogenital_system_other', 'testis'], dtype=object)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gdsc.tissue_factor.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of drugs: 11\n",
      "Number of cell lines: 988\n",
      "Percentage of NA 0.20656974604343026\n",
      "\n",
      "Genomic features distribution\n",
      "Number of unique tissues 27\n",
      "Here are the first 10 tissues: lung_NSCLC, prostate, stomach, nervous_system, skin, Bladder, leukemia, kidney, thyroid, soft_tissue\n",
      "MSI column: yes\n",
      "MEDIA column: no\n",
      "\n",
      "There are 47 unique features distributed as\n",
      "- Mutation: 47\n",
      "- CNA (gain): 0\n",
      "- CNA (loss): 0\n"
     ]
    }
   ],
   "source": [
    "print(gdsc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are 27 different tissues in this original data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Select sub set of cancers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What about an analysis on only stomach and pancreas:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "gdsc.set_cancer_type(['pancreas', 'stomach'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of drugs: 11\n",
      "Number of cell lines: 61\n",
      "Percentage of NA 0.2414307004470939\n",
      "\n",
      "Genomic features distribution\n",
      "Number of unique tissues 2\n",
      "Here are the tissues: stomach,pancreas\n",
      "MSI column: yes\n",
      "MEDIA column: no\n",
      "\n",
      "There are 24 unique features distributed as\n",
      "- Mutation: 24\n",
      "- CNA (gain): 0\n",
      "- CNA (loss): 0\n"
     ]
    }
   ],
   "source": [
    "print(gdsc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The number of cell lines has decreased significanly, which is expected.\n",
    "Also, the number of features has decreased! \n",
    "\n",
    "Indeed, features that do not have at least 1 positive or 1\n",
    "negative. Note that later, further features may be ignored based on the content of the settings.featFactorPopulationTh"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'PANCAN'"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# because we have more than one tissue, the analysis type is still PANCAN\n",
    "gdsc.settings.analysis_type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "results = gdsc.anova_one_drug_one_feature(1047, 'ACACA_mut', \n",
    "                                     show=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ANOVA_FEATURE_FDR</th>\n",
       "      <th>ANOVA_FEATURE_pval</th>\n",
       "      <th>ANOVA_MEDIA_pval</th>\n",
       "      <th>ANOVA_MSI_pval</th>\n",
       "      <th>ANOVA_TISSUE_pval</th>\n",
       "      <th>ASSOC_ID</th>\n",
       "      <th>DRUG_ID</th>\n",
       "      <th>DRUG_NAME</th>\n",
       "      <th>DRUG_TARGET</th>\n",
       "      <th>FEATURE</th>\n",
       "      <th>...</th>\n",
       "      <th>FEATURE_IC50_effect_size</th>\n",
       "      <th>FEATURE_delta_MEAN_IC50</th>\n",
       "      <th>FEATURE_neg_Glass_delta</th>\n",
       "      <th>FEATURE_neg_IC50_sd</th>\n",
       "      <th>FEATURE_neg_logIC50_MEAN</th>\n",
       "      <th>FEATURE_pos_Glass_delta</th>\n",
       "      <th>FEATURE_pos_IC50_sd</th>\n",
       "      <th>FEATURE_pos_logIC50_MEAN</th>\n",
       "      <th>N_FEATURE_neg</th>\n",
       "      <th>N_FEATURE_pos</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1047</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ACACA_mut</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   ANOVA_FEATURE_FDR  ANOVA_FEATURE_pval  ANOVA_MEDIA_pval  ANOVA_MSI_pval  \\\n",
       "1                NaN                 NaN               NaN             NaN   \n",
       "\n",
       "   ANOVA_TISSUE_pval  ASSOC_ID  DRUG_ID  DRUG_NAME  DRUG_TARGET    FEATURE  \\\n",
       "1                NaN       NaN     1047        NaN          NaN  ACACA_mut   \n",
       "\n",
       "       ...        FEATURE_IC50_effect_size  FEATURE_delta_MEAN_IC50  \\\n",
       "1      ...                             NaN                      NaN   \n",
       "\n",
       "   FEATURE_neg_Glass_delta  FEATURE_neg_IC50_sd  FEATURE_neg_logIC50_MEAN  \\\n",
       "1                      NaN                  NaN                       NaN   \n",
       "\n",
       "   FEATURE_pos_Glass_delta  FEATURE_pos_IC50_sd  FEATURE_pos_logIC50_MEAN  \\\n",
       "1                      NaN                  NaN                       NaN   \n",
       "\n",
       "   N_FEATURE_neg  N_FEATURE_pos  \n",
       "1             50              1  \n",
       "\n",
       "[1 rows x 21 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Nothing happens here, let us look at the content of the result\n",
    "results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "most of the columns are NA. This means that nothing was computed. Most probably because the MSI or \n",
    "feature balance are not good enough"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us try another feature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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4H1J4JzBKeN2eJozUv9HMPuLuk/mDBsL7BfCu6P7b7t464QHuAxn0X0547g2EbxUqCKPQ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\nSar5H1P5CpMgAy8nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb6ee2320f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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n6XhJ8yWdnifHabv2Gs/zi3n8IYR1tn8i6VBJB0pqJEG+JE+Ok+mKz/sdtj8U\nQuhQTHb3lPT3PDlOx7/c9mmKtc+HSPpbtnp15QHT81xSLZj0or1QkkaNGhWmTZvWwFNpXTNmzNBA\neS4DGddpywuh8UGJZsyYoRsPu7Gu7a+66irNmiXts88+PZYbyJZ1LNMD//uAOhU/YjrVqQc6HtC7\n9n9XVy1yymWbdZ7uvvvufn3OP//5L+jAA6U7jrmj2/KB9v4wSN7z1oUQXlprpe23SDpC0ou3RDCN\ntkH+maSRkv5p+1upHe+kTTjufun++soVIYR16krcXlJl29uqLHsy3VdrClAk0zvmC21PtH2O7dmp\nLXFwbDtd7GOHrPgBiufqbyGElVWOUUu1WOel+20a2I8k3Vi5IISwTNJdkoYrfgGQeji3FcuLc3tP\n2sfbbd9i+1O2D7a9dYPxAQA2wzWPXKOg7l8kggJtkTHo2d5J8Rf8d/RQKdlUDdUghxC+aXuRpA9J\n+ojiz/vB9o2SPlm0a61D8bP9/Brri+Xjq6yrNrH6ujrWDSsW2B4v6R+SdlWsxb1Y0uJUdrykj0pq\ny/ZRxPGEGrO0h3i2anBfle2bC0+l+3EV93Wd2xDCetuHS/ovScdK+mpa3277p5I+G0JY0WCsAIAG\nPbLsEa3rXNdt2brOdXp42cMlRQS0jP0lTZJ0S9afYYgk2V6n2AS1qf2lGm5iEUK4WNLFKck8WNKb\nFTup/cn2XpVtk2soEtlajasnV5RrtvcpJsdnVbaRtv1yxQQ5VyS6O6g8VUfJUNc5XFZxX/e5DSEs\nUewE+HHbz1ccgeQDkk5TTKRPqNwJAKC5PnvQZ8sOAWhVf1VXE/zClyVtq9gs98FmH3CTx0EOISwN\nIVwdQjhFscPdBMW2tYX1kmS7Wk1p0ZZ2WuWK1JHtkPTwjsr1TfL8dH9FlXWvqrJsluKIF6+0PaqP\nYurNRnGl4eJeLGmNpKLtdc1zW7G86rkNITwYQvhROt4KxfbiAAAApQghtIcQ5uQ3xcrLFenxmmYf\ns6EE2fYRKYGttF26X5Uteybd71yl/G8VmzS83fbLKtZ9TNJukq6r0kGvWeam+2n5QtsvkbTRV/g0\nhNplirWv51aOk2x7dCNjG2+iE1J8uemKTSp+kTroSdItisPQHWL72Io4j5X0Skn3S7o5LdvV9ouq\nHG8bxWYmG3XeAwAAGMgabWJxmaQ1tm9WTDKtWGt8gGLntuuysn+VdJykK21frZhoPRpCuCSEsML2\nyZJ+JemUvz8TAAAgAElEQVRG27+S9JhiG5PXKrar/cAmP6veXaw4LNx5tg+T9ICkF0g6SnE84+Or\nbHOa4pBopyqOPfwnxSHvdlUc8eIYSTP6MOZrFNve/FKxHXExEsVcSZ8pCoUQgu2TJP1F0uW2fyfp\nPkl7SHqTpHZJJ2Yjcewr6Te2b5c0R7HD4yTFmuNh6mqTDAAA0BJCCO/uy/03miB/RjEZ3E/S6xV/\n2n9U0qcl/U8IIR/+7YeKE4W8TXFA56GKIzFcIkkhhN/ZfoWkM9I+xykmxt9THMrtSfWREMKTtg9V\nHD/4kHT8+xQ7H16nKglyCGGJ7YMVa7iPVxwDeL3iqBQ/VhwNoi99S9JvsuOvUGzackZlu+8Qwsw0\nWcjnFQfSPlpxUpdfKJ7bf2XFb5P0FcUmFUco1hwvVPzC8+0QAt2nAQDAoNLoKBbfU0xg6ym7XjH5\nPaOHMv9Q7ORXz/7erTg7X7V106Xq0+iEEC5STCQrl9+jWOtbTdUpn9IQb2en26bGOqPW/ntT67nU\nKPsv1dG5LoTwuHq4RgAAAIPNpkwUAgAAUIqwfL6WPzZXHe0rtfLx+xT27OsuQBiMSJABAEC/EJbP\n19w7HtbcVXupY8h4Pb7waU25Y7akEWWHhgFmk4d5AwAA2JKWPzY3JccTJA3RuiFjNHfVXr1uBzSK\nGuQW11P7agAYDPKf1NvGjNLYnafIYyf3viEGnI72leoY0n2S3crHQDOQIAMAWlblT+ptS5ZqyqJ7\nNWU/kSQPQm1jRqltydJUg5yWdS7tYQtg09DEAgDQsip/Uu8YMkFzV+2l5Y/NLTs0lGDszlM0ZeS9\nautcLKlTQzvbNWXkvb1uBzSKGmQAQMuq9ZP6s+0rS4oIZfLYyZqynzThsX/p2faVapv0hKbst5uW\naH7ZoWGAIUEGALSsWj+pbz1mVIlRoUweO1nj9o7Na0bNWi2PlUSCjCajiQUAoGVV/qTe1rlYU0be\nq7E7Tyk7NAADGDXIAICWVfmT+tZjRmnszrvRQQ9AnyJBBgC0tPwndQDYEmhiAQAAAGRIkAEAAIAM\nTSwAADWFEMoOAagbr1c0CzXIAAAAQIYEGQAAAMiQIAMAAAAZEmQAAAAgQ4IMAAAAZEiQAQAAgAzD\nvAEA+syZ4cyyQ8AgwOsMzUYNMgAAAJAhQQYAAAAyJMgAAABAhgQZAAAAyJAgAwAAABkSZAAAACBD\nggwAAABkSJABAACADAkyAAAAkCFBBgAAADIkyAAAAEBmaNkBAABQmDXrqrJDaG1PPBnvm3Se5s6d\nq9Wr5zZlX8BAQoIMAGgJBx5YdgT9wIO3x/smnasRI9q1zz7N2RcwkJAgAwBKd/TRR5cdQv9w9Kqm\n7m7MmDGaNm1aU/cJDAS0QQYAAAAyJMgAAABAhgQZAAAAyJAgAwAAABkSZAAAACBDggwAAABkSJAB\nAACADAkyAAAAkCFBBgAAADIkyAAAAECGBBkAAADIkCADAAAAGRJkAAAAIEOCDAAAAGRIkAEAAIAM\nCTIAAACQIUEGAAAAMiTIAAAAQIYEGQAAAMiQIAMAAAAZEmQAAAAgQ4IMAAAAZEiQAQAAgAwJMgAA\nAJAhQQYAAAAyJMgAAABAhgQZAAAAyJAgAwAAABkSZAAAACBDggwAAABkSJABAACADAkyAAAAkCFB\nBgAAADIkyAAAAECGBBkAAADIkCADAAAAGRJkAAAAIEOCDAAAAGRIkAEAAIAMCTIAAACQIUEGAAAA\nMiTIAAAAQIYEGQAAAMiQIAMAAAAZEmQAAAAgQ4IMAAAAZEiQAQAAgAwJMgAAAJAhQQYAAAAyJMgA\nAABAhgQZAAAAyJAgAwAAABkSZAAAACBDggwAAABkSJABAACADAkyAAAAkCFBBgAAADIkyAAAAECG\nBBkAAADIkCADAAAAGRJkAAAAIEOCDAAAAGRIkAEAAIAMCTIAAACQIUEGAAAAMiTIAAAAQIYEGQAA\nAMiQIAMAAAAZEmQAAAAgQ4IMAAAAZEiQAQAAgAwJMgAAAJAhQQYAAAAyJMgAAABAhgQZAAAAyJAg\nAwAAABkSZAAAACBDggwAAABkSJABAACADAkyAAAAkCFBBgAAADJDyw4AAND/XXXVVRstu/vuu9Xe\n3l5CNKhXy12jnx8f799xeblxZI4++uiyQ0AJSJABAE2xatWqbo+HDh260TK0lpa7RhP2ivctEtPI\nkSPLDgElIUEGADTNkUceueHvOXPmaO+99y4xGvSm5a7R0p/G++x1VJZrrrmm7BBQItogAwAAABkS\nZAAAACBDggwAAABkSJABAACADAkyAAAAkCFBBgAAADIkyAAAAECGBBkAAADIkCADAAAAGRJkAAAA\nIEOCDAAAAGRIkAGgRZ3ls3SWzyo7DAAtiPeHvkWCDAAAAGRIkAEAAIAMCTIAAACQIUEGAAAAMiTI\nAAAAQGZo2QEAAACUZcHyNZq7cIVWdqzVqLZhmjJptLYfO7zssFAyEmS0NNuSpBBCyZEAAAaaBcvX\n6P75S7XbhDaNGd6m9jXrdP/8pZLGlx1aSxmMn8UkyAAAYFCau3CFdpvQpnEjhkmSxo0Ypt0mxOUY\n3GiDDAAABqWVHWs1Znj3usIxw4dqZcfakiJCqyBBBgAAg9KotmFqX7Ou27L2Nes0qm1YSRGhVdDE\nAgAADEpTJo1ObZBjzXH7mnV6eHGHJo4eob/PX67FHUvVucM8Td1pG+06aXTZ4WILIkEGAACDUhyt\nYnwcxWLhGo1qG6aJo0fomRWrte3IIXrepJHac+IwzXpogSSRJA8iJMgAAGDQ2n7s8G7Dus18aJF2\nm9CmhVtvJduaNGZrvVjS7HlLSJAHEdogAwAAJNU67k0cPUxLV3WUFBHKQA0ygEHvkYUrNHveEi1d\n1aHxI9tobwgMYtU67j2zYq3Gj2wrKSKUgQQZwKD2yMIVmvXQAr148ihN3GWknlmxlvaGwCBWdNxb\n9ex6DRsWtLD9Wd01f6UOfN72ZYeGLYgEGcCgNnveEr148ihNGrO1JNHeEBjkio57t9/SqfuXrNDo\nZ9bqwOdtz/vBIEOCDGBQW7qqQxN3Gdlt2cTRw7R0HjNpAYPV9mOHa4/JYzVy5Egdvd9OZYeDEtBJ\nD8CgNn5km55Z0X3WLNobAkD0SDbt9u/umNft8UBGggxgUJu60za6a/5KLWx/Vp2hq73h1J22KTs0\nAChV0UejUIwJPRiSZJpYABjUinaFs+ct0dJ5KzR+ZBvtDYF+YsHyNXGSj461GtU2TFMmje42pjE2\nT9FHozCY+miQIDeJ7YsknSRp1xDC3HKjAdCIXSeNHvBv9s3AcHhoJQuWr0nTRLdpzPA2ta9Zp/vn\nL5U0niS5SQZzH42mJsi2Z0h6VQjBzdwvAKBcDIc3cPXXWti5C1dotwltGjdimCRp3Ihh2m1CXN4f\n4u8PBnMfDdogAwB6lQ+HN6SYfnfyKM2et6Ts0LAZlqzp1P3zl2rHsVvppTuN1o5jt9L985dqwfI1\nZYfWq2oz3o0ZPlQrO9bW2AKNKvpoFAZTHw0SZKDFPLJwhX53xzz99OYHB1WPYbS2pas6NHH0sG7L\nmH63/1u0OmyohR1ip1rYNs3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AoOkWLF8T2/92rNWotmGaMmn0oBgej3GGBwYSZAAA0FQD\nbRY7DD60QQYAAE2Vz2I3xE6z2LVp7kJGc0D/QIIMAACaKs5i1/1H6jHDh2plx9qSIgIaQ4IMAACa\nqpjFLtfXs9gBzUSCDAAAmmrKpNF6eHGHlq1eq84QtGz1Wj28uENT6LyGfoJOegAAoKnKmMUOaCYS\nZABA01xzzTUb/n700Uc1b968EqNBb7bUNeqQtPj+OgrOXhDvx1/Tczmgj5EgAwCaYuTIkd0er1u3\nbqNlaC0td40W3xvvWykmDEokyACAzXb00UdvtGzMmDGaNm3alg8GdWu5a3T0qrIjACTRSQ8AAADo\nhgQZAAAAyJAgAwAAABkSZAAAACBDggwAAABkSJABAACADAkyAAAAkCFBBgAAADIkyAAAAECGBBkA\nAADIkCADAAAAGRJkAAAAIEOCDAAAAGRIkAEAAIAMCTIAAACQIUEGAAAAMiTIAAAAQIYEGQAAAMiQ\nIAMAAAAZEmQAAAAgQ4IMAAAAZEiQAQAAgAwJMgAAAJAhQQYAAAAyJMgAAABAhgQZAAAAyJAgAwAA\nABkSZAAAACBDggwAAABkSJABAACADAkyAAAAkCFBBgAAADIkyAAAAECGBBkAAADIkCADAAAAGRJk\nAAAAIEOCDAAAAGRIkAEAAIAMCTIAAACQIUEGAAAAMg4hlB0D+jnbnZJWlx1HkwyVtK7sINArrlP/\nwHVqfVyj/mEwXKcRIYSWqbglQQYytm8LIby07DjQM65T/8B1an1co/6B67TltUymDgAAALQCEmQA\nAAAgQ4IMdHdh2QGgLlyn/oHr1Pq4Rv0D12kLow0yAAAAkKEGGQAAAMiQIAMAAAAZEmQAAAAgQ4KM\nQc32sba/Y/sm28ttB9uXlh0XutieaPt9tn9j+0Hbq20vs32z7ffa5n2sRdj+qu2/2p6XrtNi23fa\nPtP2xLLjQ3W2T0jvfcH2+8qOB5Ltudk1qbw9VXZ8g8HQsgMASvZ5SftKWiHpcUl7lhsOqjhO0v9I\nmi/pBkmPSdpe0lsk/VDSkbaPC/Q4bgUfl3SHpL9IelrSKEkvkzRd0vttvyyEMK+88FDJ9k6SvqP4\nHji65HDQ3TJJ51VZvmJLBzIYkSBjsPu4YmL8oKRXKSZgaC33SzpG0h9DCJ3FQttnSJol6d8Vk+Ur\nygkPmbEhhDWVC22fLekMSZ+V9KEtHhWqsm1JP5H0jKQrJZ1ebkSosDSEML3sIAYrfprEoBZCuCGE\n8AC1j60rhHB9COGqPDlOy5+S9L30cNoWDwwbqZYcJ79M9y/YUrGgLh+RdLik90haWXIsQEuhBhlA\nf7Y23a8rNQr05uh0P7vUKLCB7b0knSPp/BDC32wfXnZM2Eib7XdJ2lnxC8xsSX8LIawvN6zBgQQZ\nQL9ke6ikE9PDa8uMBd3ZPl2xPes4SS+VdIjih/s5ZcaFKP3vXKLYnv+MksNBbc9RvE65R2y/J4Rw\nYxkBDSYkyAD6q3Mk7S3p6hDCn8oOBt2crtiRsnCtpHeHEBaWFA+6+y9JL5F0SAhhddnBoKqfSLpJ\n0j8ltUvaTdJpkt4v6RrbLw8h/F+J8Q14tEEG0O/Y/oikT0i6T9IJJYeDCiGE54QQrFgD9hbFD/c7\nbe9XbmSwfaBirfE3Qgh/LzseVBdCOCv1v1gQQlgVQpgTQjhV0jcljVAcGQZ9iAQZQL9i+8OSzpd0\nj6TDQgiLSw4JNaQP999Ieq2kiZIuLjmkQS1rWnG/pC+UHA42TdEx+ZWlRjEIkCAD6Ddsf0zSBZLm\nKCbHDJjfD4QQHlX8QvMi29uWHc8gNlrS7pL2krQmn3xC0pmpzA/Ssmrj76J8T6f7UaVGMQjQBhlA\nv2D704rtju+S9JoQwqKSQ0Jjnpvu6YFfng5JP6qxbj/Fdsk3S/qXJJpftKaXp/uHS41iECBBBtDy\nbH9B0hcl3S7ptTSraD2291Sc2OCpiuVDJH1J0naS/jeEsKSM+CClDnlVp5K2PV0xQf5pCOGHWzIu\ndGf7RZLmV77P2d5F8Rc0Sbp0iwc2yJAgY1Cz/SZJb0oPn5PuX277ovT3ohACs0uVyPZJisnxesVe\n3R+JE4B1MzeEcNEWDg3dHSHp67b/JukhxdnZtlecoXI3SU9JOqW88IB+4zhJn7F9g6RHFEexeJ6k\nN0gaLulqSeeWF97gQIKMwe7Fkk6qWLZbuknSo2L61bLtmu63kvSxGmVulHTRFokGtVwn6UJJr5C0\nr6TxipMb3K/YMezb1PwDdblB0h6KNfovV2xvvFSx+cslki5h9te+Z84xAAAA0IVRLAAAAIAMCTIA\nAACQIUEGAAAAMiTIAAAAQIYEGQAAAMiQIAMAAAAZEmQAAAAgQ4IMAOiV7Rm2Sx04P8Vwd5q+ut9y\ndJftm8qOBUB1/fpNBgBage1jbX/H9k22l9sOti+tY7uDbV9te7HtVbZn2/6Y7a3q2Na2/5KOFWwP\nra8kZwMAAAjsSURBVFg/JVvX0+3QzXnuW4rtYxWnrT4zhNCZLZ+WnseMHrZ9je2f2X4knefVth+0\nfYntIyvK9nbeLuvhOCfZnmV7he1lKaE/qrJcmgXtTEmHpOcFoMUw1TQAbL7PK06vvELS45L27G0D\n22+UdIWkNZIul7RY0tGSvqU4XfNxveziNEmHpe2HV1m/VNJZNbbdSdLJkp6RNKu3WMtm25K+rDht\n9W8a2G6MpIslvUnxPF0v6UpJaxWnMH+9pHfZ/kYIoXJK+f+T9Nsqu51T41jnSvqE4vX/gaStJb1N\n0lW2/yOEcEFePoTwO9v3Sjrb9hVMHQy0FhJkANh8H1dMjB5UrOW8oafCtscqJlHrJU0LIdyWln9B\nMYk71vbbQghVaytt7yHpq5LOVUzCdqksE0JYKml6je2/kv68OITQ0duTawGvlrSHpM/Vm0imZhi/\nkvQ6xevxrhDCkxVl2iSdKmn3Kru4K4Qwvc5jHayYHD8k6YAQwpK0/OuSbpd0ru0/hBDmVmz6U0nn\nSPo3SdfVcywAWwZNLABgM4UQbgghPNBALeCxkiZJuqxIjtN+1ijWRkvSB6ttmJpSXCLpEcWf6RuS\ntn93enhho9tX2d8Q26fa/kdqWrAy/f3BWm2Fbb/T9h2pqcPTqanDc3to5/zedH95A6G9XTE5flDS\n0ZXJsSSFEDpCCOdL+s8G9lvNqen+7CI5TvufK+m/JbVJek+V7YovQO+tsg5Aif5/e3cfq3VZx3H8\n/UWxwMYxxFBJPdqDIo2FZnq04Rk+TP8oba4cTGNDqD+i/1KZPEytOS3rj2w5Nx9Lzsqh+UcWNWlA\nRbWagAqCJvIwElB8AIItiW9/fL8/+PHj9zucc3O4D26f13bvOvd1Xb+n+97YdV98r++lAbKISPtN\nynJhTdtSYDdwac5wVs0BJgBTW5z9vQ44FVjq7mtaOL7qF8CDwGjgYWLQfQrws2w7iJndCjwJdBIz\nqI8B44C/ACfV9Dfi89ri7q/3476+meX97v6f3jo2fI6nm9m3zOyOLMf3corevs/fVfqUr7sB2Axc\nmc8pIscIhViIiLTfuVm+Wm1w971m9gYxaDwHeKVoM7OLgNnAveWZ534qBo4PtXj8fmY2GZgCLAcm\nuvuurJ8DLAGmmNlz7t6T9ecA9wBvAxe4+6asnwX0EOEiVecSA+7f9OO+jgcuybeLWng0gKvyVT7v\nYuKHycZS3YnAGGCXu79Zc57XsqwL4wD4BxEjPRZY3eK9isgA0wyyiEj7dWT5fkN7Ub9/RtXMhhEz\nsquBu1u5qJl1EvG824kFgkdqWpazisExQM7Y3p5vp5f6TyEmZh4oBsfZ34FZREx21ZlZ1g0+m4wk\nFslBxIb3x27ge8CFwMfzVcSVdwOLclBc6Pd3WbElyzMb2kVkEGiALCJy7Cn+u70cj/sDYkZ5qrt/\n0OJ5ZxD/7j8xQIvzLgD2AYtr2pYQA94Jpbri7z9XO2e4waZqPXBylu/WtDVpOVzB3be5+zx3f8Hd\n38vXUuBq4O/Apzl40N/nUzfUv5PlqBbOKSJHiQbIIiLtV8wqdjS0jyj3M7PLgW8D33f3Fa1cMMMO\nioViR7w4L3UA77j7f6sN7r6XCKXoqPQH2Npwvrr6PVnWpbJrsh0o7mlMP45rlM/zcL6dWGo63Hd5\nuBnmYVnuaWgXkUGgAbKISPutzfKQuNQcyJ4N7AXWZfUEYlb0rurGFRxI8fZB1n2+4ZpfBk4Dlrj7\n2oY+/fU+MNLMhjY8xyhgR6m6+Ht0w/nq6rdleXJNW60czP4t317R1+P64K0s94dYZDjJZuBjZnZa\nzTGfyfKQePNUPNe2hnYRGQQaIIuItN8fs7ympm0iMBxYVgqDeBl4pOFVxP4+mu+3N1yzWJw3ULPH\nEIvzhnDwjGphInAc8EKlP8CXqp3N7CxiA5OqVUSoxmE3X6konvO7Zja8t44N2ULqFAv/1lXqe/s+\nr630qTqPCFN5qY/3ICJtYNq8R0Rk4JhZN7Gga76739TQZwSxqcQI4LLSRiEfJQZSXcDkpo1CKuda\nT8wiD82Z07o+ZxGDuneBMa3EH2cGh8vd3Up1U4D5RCaGbnffnfXDibjki4gNOuZn/aeANcQuf+Us\nFpbnmQxQvka2/xMYD3S4+55KWzfxeS9x9+5S/RDgt0Qu5OeBb1SzTJjZCURc9lh3n5l1FwPLq2Ej\nZjYpz/cR4jtbVmq7lEhTV90opJPYKORE4LzqRiE5MN8BvOTuX0BEjhlK8yYicoTM7HoiVRdEjmGA\nLjN7PP9+u7yVsbvvMLMZwAJgsZn9klis9RUirdkC+rcpxuFMZ2AX5wHg7j25ZfbXgVVm9iyxGO16\nIkzkqWJwnP1fN7N5RKq3lWb2KyJM4yoi88RKYiBc9TSRVWIS8Fwf722fmX2NyPxxHbDOzBYRafP+\nR/youIJIIXd/6dD7gHH5g6DIgDGeA3mM55YHx3mtZWb2Y2LDkRfNbAGRRePGfK7v1OyiB5EV4wQG\nJqOIiAwgzSCLiBwhM7uT3ne12+DunTXHXUbkNe4iFqH9iwiV+Im716U8q7v2enqZQTaz44CNwOnE\nLGZL8cd1M8hZP4TYSW4acH5Wv5LP8aC776s5183EYHIssBP4PXAb8Afgk+5+UqX/J4gMF8+6+42V\ntm5qZpArfa4mdg/sIn7AGPBvIk755+6+sNT3FuCrwOeIGOqhxOLBvwI/dfc/1X9CYGZTgZn5Oewj\nwkt+6O61OZzNrAe4ATjD3RWDLHIM0QBZREQGXYadbAVWuHtXTftDwFSg0923VNs/bHLQvx7ocfdW\n0saJyFGkRXoiItI2ZnZKNetFZrz4ETGL/uuGQ+cRqdtmH907bJs7iFCPuYN9IyJyKMUgi4hIO90A\n3G1mzxNhEyOJjBefBVYAD9Qd5O5bzewmIj54SF3oxodFLkp8E7i5YXtqERlkCrEQEZG2MbMJxKzp\nFzmQA/gN4BngPnffOVj3JiJS0ABZRERERKREMcgiIiIiIiUaIIuIiIiIlGiALCIiIiJSogGyiIiI\niEiJBsgiIiIiIiX/BxMQ/O48su2/AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb7143a4b70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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9RcR/XFn6w7OT5ReWmUJSFR5Gp/hfd3+Pux+pEKD/m0KHozkqszU9OlHdQ0290t3/GFNa\nUqX8s943fpksJHtdFm2RHGgx3/duhaH2slbjbMSFX3mREVbqWPqaPLpaB4mfc8fFh29w99+6+5ac\nYoVeO+vV/cV/ThWqV0z2GpzdS7lsdJOyP0t74+7b3f1H7v4v7v4shZSLjys0UByjMCoN8iBABupH\n1iL8W4VWtmK3LFez0kO+ZYFk0U5SMSf1hJxtyvVFhQ5xsyT9ex/3UUw2Oci1BTp0FXOlwj/V0fHx\nDytVqUQWHLZVYd8V5e4b3P3bCqkRknRCTo5wFoAUy2/MgoSnPIwPnE8pE4WMlfTCfCti4JwNT3ZX\nvjIVVsp5p7LOeueZ2UHqzkeuZnpFX3Umf+c9v/ilLUsFq+YvQdlrxyUtLVAm72sn5u7eGx8uLPO4\n5T6/ubLX4CHx+d5LHAP7pPiw4BCSleLua9z9s5K+GRedWu1jDlYEyEAdiIPYvzI+vNrdW4rdJP1v\nLPvOXlrTypXlST7bzHLHAk69UyGwlaRf9uVAscUs67X/n1b5GZ36kn8sKUwHLek/FQbS/5i7P1jq\nthZmuyr6D9XM5qt7gP57ipUdSFZkRq4oTadJfxLPJgqZXGTbbfH+gDyjDMjM5kj6P70cP3NRgWv8\nrwqt3VL3a7maSjnv1M/V3Qr/Y4X/wdXsnNcfzcnfxc4vC+7PMrNXFymnfM97ibLXjilMCJO738kK\nE30U8v14/xoze3mRcrnKfX5z/SHuw1S4v8UHFfKppTBcXEWU8V7uLbVl2CJABurDvyj89LpTxWd7\ny2RB6SyF1IxK+am6W1uuMrN/N7Psw1tmNtPC1MFZS9hfVUZ+bx5fUWhBnaKcSSv6I07IcJzCP6fF\nfdmHu3/N3f/T3T9X5qa/lvSAmX3MzJ4XW4iyGbX2M7OPqHua7d3qbsmpB+eY2S1mdl4cfkxS1zTC\nr1CYzEEKEyekwfLyeL+PmZ2t/G5RGIFilMKsZ4fEfY80s7MUnqdS0lhaFFq9fmTdU16PNbP3q3so\nrKvdvVBLYyVl532shenhi/IwDNpV8WE23Fk9th5nrcNZp8l3Ffki/g11f8n7pZldbGZdKVoWphd/\nmZl9Rz2nry7H/epO5/iRmR0b921m9lKF11ahUU+kECDfqRDzXGtm/xFH4Mhef3MtjNiSO4tjWc9v\nrtiYkU2Ocq6ZfTPrbBuvywUKv6RJ0o/cfXm+/fTRxWb2ewtTa6fPxxgze6uk8+OiP1TwmEOL18Fg\nzNy41dNNyUQhfdg2G9S9lIlCfpNs91Dc7ldlHOvuuM1vi5Qpa/KPuM0Byb6z21Z1D3if3f6mwpNz\nFJwoJE/Zy2O5bUomM1D5E4X8LNk2mxjgF70cu8dEIWVc+2IThWzJuU4dCrndbXmu6av6+BotZaKQ\ngpN2qMAkEDnPmyt8YdsUzyFb9rikZ+XZ561JmW2SVsfb/0nKnJ+z/2aFoNnjc3h2oddNel4KM6G5\nwk/gmxXymrPt/iFpcjnXpR/rRit0fMqOvSk574UFrv2RSfldCjMK9ufzqpSJQl5Ywntgr88HhS9E\n6WvhiXhuP8opN1Phy3L63G5TeC90Jsu2l1r/POVel/M63KHuiZOaFb40FTzfWMe/J2U6Y/3S184v\ncrYp6flVgYlC4jpT98Q86Ws2nUzoRuWZZEUlTFSiAp8F6jnJSHa9Nuc8H/crzCjY59ffUL7RggxU\nRykThUyVJDN7sbrHEi0nXSEr+8qYolERHtILjlcYju06hU5uYxT+WaxRyJF+o6QFXuK4wL34gsI/\ni4kKaQ35lDJRSPrzbdaK2Z/W7b46SCEf83KF8Wc3K+SNu8LwdjcpTAF7qLtfW4P6FfMrhR7vP1LI\n9dyu8Lw0KwRSH1OYoe6xPNu+SqE18VGFcaMPireu1Bl3v0whN/xvCv+wRykEXZcpdBgqZUxsuftX\nJb1CYdjD7EvIgwqdj17iYVi1qvMwhu3JCjnqjyu8TrPzztvx1MNkGFlLar13zrtY4T15j8J79ACF\nc+vxeePu6xTyaF8n6TcKw5Y1KuSLP6kw5vn7lXRILZe7/1phlJMbFILvkQqd4P5H0nMVXlPFtl+n\n0LfiHIWAdKPCc7RJ4UvVZ5STBtGX5zfPcd3dP6Awass1sc7j4zn8RSFd7UwvMMlKP3xL4cvd1Qrv\njV0Kn0ObFVrcPyhpvofh4JCHxW8aADAkxFzmLHCf4e7bipUHBpKFGRpXK/zcf5LXZ/4xMOzRggxg\nqHmFwqD5iwmOUYc+oPC/9wGCY6B+MYsKgKFmm6RLJd1c64oAKTM7QeGnbSl0UAVQp0ixAACgSuJw\nW/9UyOPPcneXSHqxF58yG0AN0YIMAED1mEKnLil00rxe0kcJjoH6Rgsy6saIESN87Nixta5Gn3V2\ndmrECNL6BwrXe+BxzQce13zgcc0HXmdnp3bt2uXuXjcXnhZk1I2xY8dqx45Kj3QzcBYvXqwFCxbU\nuhrDBtd74HHNBx7XfOBxzQfe4sWLdfLJJ+/sveTAqZtIHQAAAKgHBMgAAABAggAZAAAASBAgAwAA\nAAkCZAAAACBBgAwAAAAkCJABAACABAEyAAAAkCBABgAAABIEyAAAAECCABkAAABIECADAAAACQJk\nAAAAIEGADAAAACQIkAEAAIAEATIAAACQIEAGAAAAEgTIAAAAQIIAGQAAAEgQIAMAAAAJAmQAAAAg\nQYAMAAAAJAiQAQAAgAQBMgAAAJAgQAYAAAASBMgAAABAggAZAAAASBAgAwAAAAkCZAAAACBBgAwA\nAAAkCJABAACABAEyAAAAkCBABgAAABIEyAAAAECCABkAAABIECADAAAACQJkAAAAIEGADAAAACQI\nkAEAAIDEqFpXAAAw+Fx33XV92/Bnbwr3b726cpUZJBYuXFjrKgAoEQEyAKBP7ryzDxs99by4cUWr\nUvfmz691DQCUgwAZANBn8+eX2Sr62FVxw+HTmnrnnX1sbQdQM+QgAwAAAAkCZAAAACBBgAwAAAAk\nCJABAACABAEyAAAAkCBABgAAABIEyAAAAECCABkAAABIECADAAAACQJkAAAAIEGADAAAACQIkAFg\nGLjULtWldmmtq4Ea43UAlIYAGQAAAEgQIAMAAACJUbWuAABg4PmmFdq4fKlaN29T09RJmn7UPNm0\nQ2pdLQCoCwTIADDM+KYVWnrDvVrW/GK1jpippmfW6egnb9O8M0SQDAAixQIAhp2Ny5eG4HjkLMlG\nqHXkLC1rfrE2Ll9a66oBQF0gQAaAYaZ18za1jpjZc9mImdq5eVuNagQA9YUAGQCGmaapk9TUuU7v\nfa/pve+1sKxzncZOnVTjmmEoMDOZWa2rAfQLATIADDPTj5qnoyfe1vW4qWOtjp54m6YfNa+GtQKA\n+kEnPQAYZmzaIZp3Rvfj059zh6Yf9Vw66AFARAsyAAxDaTA846TXDEhw7M1ru/7etvzvPR4DQD0h\nQAYAVJ03r9Xqe1Z2Pb7niWdr9T0rCZIB1CUCZABA1TU/sVqrW4/oetw2YqpWtx6h5idW165SAFAA\nATIAoOratu9Q24jJPZeNmKzd23fUqEYAUBgBMgCg6honjFNj59aeyzq3avSEcTWqEQAURoAMAKi6\niQfO0Zymh7oeN3Zu1pymhzTxwDm1qxQAFMAwbwCAqrOJszTnOElPh8fHHfhPTTzwYNnEWTWtFwDk\nQ4AMABgQaTA86agTalgTACiOFAsAAAAgQYAMAAAAJAiQAQAAgAQBMgAAAJAgQAYAAAASBMhVYGZz\nzMzN7MoytjknbnNO9WoGAACA3pQdIMcgzs2s08wOKVLu5qTsOXnWv8DMfmpmj5tZm5k1m9kKM7vO\nzP7LzMbllF8d9zWn3DqXcE4EpwAA9JNvWtH194ZbftvjMTCY9LUFeY8kk/TufCvN7FBJJ8Vy+da/\nXdIdkt4i6VFJ35H0PUn3SzpG0hckMXo8AACDhG9aoaU33Nv1+MYHX6ilN9xLkIxBqa8B8jpJd0k6\n18zyTTZynkIA/fvcFWbWJOnbklzSqe5+iruf7+4XuPtr3f1ASS+WtLGPdQMAYC/b2rbpsrsvU3Nb\nc62rMiRtXL5Uy5pf3PW4deQsLWt+sTYuX1rDWmGoMLP3mNl9ZrbNzFri3++s1vH6k4P8PUn7Snpl\nutDMGiS9U9Ltkh7Is91RkiZKWu7uf8m3Y3e/3d239qNuWV0ONrMrzOwxM9tpZpvNbJmZXW5m02KZ\nxZJ+GDf5YZIW0pXOYWb7mdknzOw2M3vGzHab2dNm9jMzO6KXOhxuZtfEY+8ws1vN7LQyz+MAM/uW\nma2M6SibzOxaM3tBGfvoyosup05m1mhmF5rZUjNrjakwfzOzNxYo/yoz+4uZrY11fdrMbjGz95dz\nzgBQaYtWLdKKrSu0aNWiWldlSGrdvE2tI2b2XDZipnZu3lajGmGIeVrSf0uar5Bt8ENJ3zezhdU4\nWH+mmv65pK8qtBZfkyx/laSZki6U9Kw8222K9/uZ2Th339GPOhRkZrMk/UMhGP+DpF9LGiNprqR3\nSPpWrMuVkrZKOlvS7yTdl+wmC9JPVDifm+N+WiQdKun1kl5lZi929/vzVGOupL9LWi7puwppI2+S\ntMjM3uruV5dwHsdJ+qOkqZJulPQbSdMlvVrSrWb2Gnf/Q+9XpPw6mdnoeMyTJD2s0PLfFM/7ajM7\n1t0/lpR/T9znM5KuU/gVYB9J8ySdK+n/lVFPAAPEzPq45Qt07bVV+d9UcdvatmnJ2iVyue5Ye4fO\nnHumJjZOHJBjf/rTn1L4d1R7l+gSSf15zntzbo9HTZ3rNHbqpCodC8OJu+dmJXw9tiCfqBBzVFSf\nA2R3325mv5B0jpkd4O5PxlX/KqlZ0i8lfSzPpisVPileIOk2M/ueYmuzu+/ua33yeL1CUHm+u389\nXRE7AHbG87gyflCcLekad78yz75ukjTT3bfn7OcYSbdJ+rykM/Nsd6KkL7v7R5JtvqUQoF5uZovc\nveBvfTF95ZeSxks62d1vSdbtp3Adv29mc9y9rdB++lGnCxSC40WSXuXue2L5SyXdKemjZvZ7d789\nln+vpN2SjnH39TnnMr3AOb5H0nskadSoUVq8eHGJp1F/WlpaBnX9Bxuud99U+potW7asrPJH93G7\n/vrb9r+po7NDktTR2aGr7r5KL53w0gGtw3Cz9pFbddzUJVqxYz89MIjeq3y2DLyWlhZJGmVmdyWL\nr3D3K/KVN7MRkl4u6dkKrcoV158WZCmkWbxb0rskfdLMDpJ0qqTvuntrvm+o7u5m9npJP5K0QKEl\nV5LazexehRbS7xQLHMu0M08dymq1zg32kuX3m9lNkk4zswZ3b88psk3SJ3O2ucvMfqqQhvIahetQ\nyFmSDlEIaG9JV7j702b2RUmXKbxISm1FLqdO71LIFf9wFhzH8uvN7FOS/kfhF4Tbk93tkZR7HeTu\neXPK44v/CkkaN26cL1iwoMTTqD+LFy/WYK7/YMP1Ls8tCh8h+a6Zu5e0j/SaX3fddbrzTunoo48u\nvlGuGBeXvV0/bGvbpkdvf1SdoV1EnerUo22P6u3Pe/uAtCJfdNF/a/58aeHC8lvbK/06v9QulVT6\nc14u37RCI6aHH4/fv3CDph/1atm0ggNe1SU+WwZe/EKyx92fX6ycmR0o6UFJjQrxxgfc/fpq1Klf\nAbK7LzGzZZLeZWafVgiWRigEzsW2e0LSyTF/91RJz1fIKclu7zezBe6+qth+zOx8SZNzFl/j7vdJ\nulbSZyV928xOV0gVuE3Sg96HTwYzO0vS+2Jdp2vvazdd0tqcZffktjpHixWC0eeqeIB8Qrw/yMwu\nybP+0Hh/hEoPkEuqk5lNUEiRecrdH85T/qZ4/9xk2U8lfUXSA2Z2taRbJN3m7htKrBsAVNyiVYvk\n6vmx73ItWrVIbzr8TTWq1dCUBsMzTnpNDWuCIeppSccq/LJ+iqSvmdlT7n5jpQ/U3xZkKQTD35B0\nhkLy0d3ufm/xTQJ3f0jSQ9ljMztc0g8UAsOvKeTZFnO+pINylq2WdJ+7P25m8yVdEuv22rh+jZl9\n2d2/UUodY70+JOnrkrZI+pOkJyS1KrSuvlohWbwxz6brCuzymXjfW2LWtHj/hl7Kje9lfarUOmX3\nuUG/cpboFRn4AAAgAElEQVR3fUFx96+a2UZJ75f0IYXnx83sFkkfcfe79t4NAFTXqm2rtKez56ij\nezr3aOW2lTWqEYC+iL9mPxYf3mdmcyVdqtAIWlGVCJB/ojBu8Xcl7a+cn+/L4e4Pm9k7FE7+ZSWU\nn9PL+ockvSnm8h6j8G3jgwqJ3Tvc/fu9HSNue6lCAHmcu6/NWX9C3g2DmQWW7xvve+vam60/292v\n7a2uJSq1TttyluealVNOkuTuP5b0YzObLOlFCikb75J0o5kdUShdBQCq5aPHf7TWVQBQHSMUBmCo\nyo77JQ7H9itJB0jaoTC6RX9kP/9XrIutu+9x97vd/QsKk5NIPVunO+L9yDybT1doJb09T3A8XtJx\nRQ59XExVyLUg3vfW0n5HvK9kT5KS6hTTMFZI2t/CxC+5To739+Q7iLtvdfc/uPu/KowUMlWVPQ8A\nADBMmNlnzWyBmc01syPM7MMK/eB+XI3j9TtAji5SaCk8vUB+a5d4Yh8ys73SCyz06vt4fPjX/lTI\nzOabWb7W0mxZa7IsG3ruwDzl18eyz4sBcbb/BoW0i7yjM0STJH0ip17Pl/Q2hZbX3xY7B4Vh51ZI\n+oCZvSJfATM7wcLkK6Uqp04/UPii8iUzG5mUn67uXqM/SJafYfknjtkn3rfmWQcAANCbaQoxx0OS\nblVIP323u3+1GgerRIpF1unuiRKLT1IILL9kZrcpjMe7XSGIepmkgxWC0gv6Wa23KgSWtyikbGxR\nGBFioaQ2hdEfMn9XCN7ON7Op6s7T/aa7bzOzbyiMg7zMzH4nabRCC+pUhbGRT1Z+f5V0npkdr9BB\nMBtzeISk9/Y2Uoe7t5vZaxVya643s9sVxmlulTRbYai8g+N+Sw0+y6nTlxWGrztb0v1m9geFcZDf\noPB8fdHdb03K/0LSLjO7VSEX3BRajV8g6W5Jfy6xjgAAAF3c/b0DebyKBMhlekihtfk0SS9UCM6m\nKgR4jymMPHFZBUY++LlCx7kXKaRBjJX0lEIQ9xV3X54VdPctZvY6SRcrdDQcF1ddpdCq+t+SNiiM\n0vHeuOxPCi3nlxapwyqFkS8+H+8bFVISPllqj0t3XxrHW/6wwqyF5yqM4bxWIR3iYpU3LXfJdXL3\n3WZ2ajz2WxXyt/dIul9hfOncdJoLJZ2ucL1fIWmXpMcl/V+Fofv2Gv4NAACg3pQdILt7ybnB7n6R\nQhCZLmtTmHnvmrwbFd7XnDLLL5G0pIzyN0i6ocC6PQqzBuZrxj8n3tLyq9Uzh/rsEo5/pUKubr51\n6xWCzwt7208pYufFXusUy+5S+NLy2RLKXi7p8v7VDgAAoLYqlYMMAAAADAkEyAAAAECCABkAAABI\n1KKTHmogT140AAAA8qAFGQAAAEgQIAMAAAAJAmQAAAAgQYAMAAAAJAiQAQAAgAQBMgAAAJAgQAYA\nAAASBMgAAABAggAZAAAASDCTHgAMU+5e6ypgCOJ1haGAFmQAAAAgQYAMAAAAJAiQAQAAgAQBMgAA\nAJAgQAYAAAASBMgAAABAgmHeAGAYuNgvrnUVUAd4HQCloQUZAAAASBAgAwAAAAkCZAAAACBBgAwA\nAAAkCJABAACABAEyAAAAkCBABgAAABIEyAAAAECCABkAAABIECADAAAACQJkAAAAIDGq1hUAAAxe\nd955XXkbPPV03LDM7QBgABEgAwD6ZP78Pmz02N1x44pWBQAqigAZAFC2hQsX9nHD1spWBACqgBxk\nAAAAIEGADAAAACQIkAEAAIAEATIAAACQIEAGAAAAEgTIAAAAQIIAGQAAAEgQIAMAAAAJAmQAAAAg\nQYAMAAAAJAiQAQAAgAQBMgAAAJAgQAYAAAASBMgAAABAggAZAAAASBAgAwAAAAkCZAAAACBBgAwA\nAAAkCJABAACABAEyAAAAkCBABgAAABIEyAAAAECCABkAAABIECADAAAACQJkAAAAIEGADAAAACQI\nkAEAAIAEATIAAACQIEAGAAAAEgTIAAAAQIIAGQAAAEgQIAMAAAAJAmQAAAAgQYAMAAAAJAiQAQAA\ngAQBMgAAAJAgQAYAAAASBMgAAABAggAZAAAASBAgAwAAAAkCZAAAACBBgAwAAAAkCJABAACABAEy\nAAAAkCBABgAAABIEyAAAAECCABkAAABIECADAAAACQJkAAAAIEGADAAAACQIkAEAAIAEATIAAACQ\nIEAGAAAAEgTIAAAAQIIAGQAAAEgQIAMAAAAJAmQAAAAgQYAMAAAAJAiQAQAAgAQBMgAAAJAgQAYA\nAAASBMgAAABAggAZAAAASBAgAwAAAAkCZAAAACBBgAwAAAAkCJABAACABAEyAAAAkCBABgAAABIE\nyAAAAECCABkAAABIECADAAAACQJkAAAAIEGADAAAACQIkAEAAIAEATIAAACQIEAGAAAAEgTIAAAA\nQIIAGQAAAEgQIAMAAAAJAmQAAAAgMarWFQAAYLC67rrrqrr/ZcuWafv27VU9Rl342ZskSX859msa\n19igCY0N2t7Wrh1t7RrX2KC508dr5qSxVTv8woULq7ZvDE4EyAAA9ENra2vV9j1q1Kiq7r8ebG3d\nrclTj5AkHTOzUeub2/TE+mYdPL1Jz5rcqJ27O/TE+s3qaB+nyU2jK378pqamiu8Tgx8BMgAA/XTm\nmWdWZb/Lly/XUUcdVZV914slKzbq+N0/lyS1nHaa7n5iq04aO0J7NEIHzxgvSdq2s11PNnfo+EOm\nV/TYixYtquj+MHSQgwwAAGpmR1t7j8c7d3do+vgG7d6zp2vZhDGj9ioHVBMBMgAAqJlxjQ09Ho8d\nPVIbW9o1elT3j9zbd+3ZqxxQTQTIAACgZubENApJ6nTXlLENuv+ZnRo9aqQ63bVtZ7tWbm7rUQ6o\nNnKQAQBAzcycOKbr77vWtGhcY4OOOXC6tu1q19Px8WGzJvcoB1QbATIAAKgLL3vOrKrsd13zLq3e\n0NI1bNycGeMJuFEUATIAABiy1jXv0iNrt+rgqY2aMKZR23ft0SNrt0qaXOuqoY4RIAMAgCFr9YYW\nHTy1UZPGhk5+k8Y26OCpYTlQCAEyAAAYsna0tWvCmMYeyyaMGaVnntyuVWubtbltqzr3X6N5s6do\nLh0BEREgAwCAIWtcY4O279rT1YIsSas3tqqtbY+mN43QITOadPi0Bt25Yl0Na4l6wzBvAABgyJoz\nY7xWbm7Ttp3tXcPGLXumRfP2n6Cm0SNlZpoxYbSOnTVOS9dsqXV1USdoQQYAAENWGK1ichjFYsMu\njWts0MQxo7Xf5DF6MCk3bXyDtq5p0aRaVRR1hQAZADBgLrVLJUkX+8U1rgkqqd6HUZs5cUyP+ixZ\nsVHbd+3pUWZTS7smNzVKzf0/3qoNLVq6Zou2trZpclNjwfxm3g/1iwAZAIAhZKCD1WLDqNVTkJya\nM2O8Hlm7Va27O9TQ4NqwfbfuW7tD8w+Zqccf6N++V21o0Z0r1unYWeM07aAmbWpp78pvphPg4EGA\nDADAEFGLYLXYMGr1GiBnaRd339apR7a0aPymds0/ZKbmzhivx/u576VrtujYWeM0Y8JoSQr5zXE5\nAfLgQYAMAMAQUYtgtdAwajs27Op126y1+/jk8UAF1TMnjtGzZ01UU1OTFh43u2L73drapmkHNfVY\nluU3Y/AgQAYAYIjoT7DaV/mGUdu+a4/GNTYU2apna3em3lMzStHe4frNvc+o0zs1cWyDnrPvBI1t\nGBnymzFoMMwbAABDRBaspkoJVvsj3zBqKze3aU4v6QS5rd2SdPDUxkE9w92qDS3q6OjQ5EbppQeN\n1+HTR+vvKzbpz49u1rzZU2pdPZSBFmQAAFT6yAP1LOt8dvDU0HK8fdcerdzcpsNmTa7aMfMNo3bY\nrN5bgWvR2l1tS9ds0YJDJsvkenJLq3bubtc+E0drW/uoQfdaGu4IkAEMe2YmSXL3GtcEtTJURh7o\na7BaieOWe4y+pmbUsyz/eISZpk8IwX+nu65/aPBPQDLcPicJkAEAw95QGnmgL8FqLaSt3dkVrnZr\nd7VNbmrUppb2rteRlIyvjEGFABkAMGhUKw2iEiMP1PtkGfUmbe3ePy4biNbuapo3e0r4JULh9bOp\npb1rfGUMLgTIAICKq0YgW800iP62/A3GyTLqQVdr953djwez7HW4dM0WbV3ToslNjV3jKw9mq5KO\nk7+7Z82gzM8vFwEyAKCiigWy/dHfNIhiQXt/W/4G42QZqI65M8YPqeAxez9nDp/WMCjz88tFgAwA\n6NWqDS26a12HHr/1sV5bhIsFsv3RnzSI3lqf+9vyNxRHZACk7vdzZjDn55eDABkAsJe0tbWjU9q5\ne7eOmDFGxx8xpdfUhmrNJNafNIhSWp/70/I3FEdkGO6GwrB/lTBcZwZkohAAQA9Za+vh0xp01hFT\nNKlhj0Z07NHuDtcIsxBczhpXsEU4C2RTlejJP2/2FN23doc2bN+tTndt2L5b963dUdIEDFtb2zRt\nfM9gddr4Bm1tbetXnTJ9nSwD9Sn3PZClFawaxJOY9FW13s/1jgAZANBD2to6wkxjRkrHHzRejzd3\ndJUpFlz2J5AtZu6M8Zp/yEw9vKld1z+0RQ9vai85DaLa/+RnThyjw2ZN1pPNHbprTYuebO4Y9CMy\nDGe574HevhQOZdn7OVOp93O9I8UCANBD7k+qY0c3qHGEqTWJL4sFl9Xsyd/XNIiBGH6r3PGHGRau\nfg3XtIJ8ct9v5XwxHcwIkAEAPeTm+h4wpUn3PbFZLlene0nBZb315K+34bcYFq6+MeFHT+n75Ozj\nZtewJgOHABkA0ENua6vLtKV9pHbuDqkNtQ4u+6qegnaGhatvTPgBAmQAQA/5WltPO/oAPT7iGS14\nybNqXLuhgWHh6lu9/eKAgUeADADYS77W1sdrVJehqJrDwpHbXBn19IsDBh4BMgAAA2zOjPExBzm0\nHG/ftUcrN7fpsFmT+7Xf4ZzbzBcDVBLDvFWImc0xMzezK8vY5py4zTnVqxkAoN5Ua1i4NLd5hFnM\nbW7U6iE+fm/2xeCAiSP1/NnjdcDEkXpk7VatayZlBX1TUguymXn80yUd6u4rCpS7WdKC+PBcd78y\nZ/0LJJ0v6SWS9pXUJmmDpAcl/U3St919R1J+taSDJM1199Wl1LVUMSj9Yb56AgBQbaUOC1dOy+hw\nzW3ua6fHra27tXTtDm1OplAHpPJSLPbE8u+W9LHclWZ2qKSTknK5698u6UeSTNJNkn4rqUPSXEnP\nl/RKSb+R9FhZZwAAwBC1ZVentpSRMjFcp7wu54tB9oVjffNO3ffEJu0/ZbyOP6BJD6xt1g9uWSdv\nbddBG1rIPx7mygmQ10laK+lcM/uEu+/JWX+eQvD7e0mvTleYWZOkbyu0QJ/m7n/J3bmZvUjSxjLq\nAwxrqza0hB7WrW1dLR98oANDy8adrvkHld4yWq3c5npX6heDNEd7jI3SwVMatXJLq+5avUnP23+C\n5k4ZrUX3hWmmpb0nyegNn8sDw8z+RaHR9UZ3P6OvZYopNwf5ewqpEa/MqUSDpHdKul3SA3m2O0rS\nREnL8wXHkuTut7v71jLrsxczO9jMrjCzx8xsp5ltNrNlZna5mU2LZRYrpFdI0g9jHnB2mxPL7Gdm\nnzCz28zsGTPbbWZPm9nPzOyIXupwuJldE4+9w8xuNbPTyjyPA8zsW2a20szazGyTmV0b01RK3UdX\nXnT8+xdmttHMdpnZXWb2yiLbvsXMbjazLbH8Q2Z2kZnlHSXdzN5mZvfEa77ezH4Sr+HiJEUHFbJq\nQ/gAP3xag846YooOn9agO1es06ohnmcIDDdtnSHQTU0YM0o72trzli81t7m5rVmX3XOZmnc3V63u\nmXXNu7RkxUbd9OBaLVmxsSp5wXNmjNfKzW3atrM9pE08uVWLH92kbTt39zhemorR3tGhSWNHaUbT\nSO1o69D4MSM1rWmUTNanaaX5XB4YZvZsSZ9XSM3tc5nelDuKxc8lfVWhtfiaZPmrJM2UdKGkfINk\nbor3+5nZuDTPuJLMbJakfygE43+Q9GtJYxTSON4h6VuxLldK2irpbEm/k3RfspssSD9R4Xxujvtp\nkXSopNdLepWZvdjd789TjbmS/i5puaTvSpol6U2SFpnZW9396hLO4zhJf5Q0VdKNCqkn0xVa5m81\ns9e4+x96vyJdDpJ0p6SVkn4S9/smSb8zs1Pc/eac439f0rskPRmPvVXSCyV9StLLzezU9BcEM/uI\npC9K2qLwbW2bpFMl3Rb/RoUtXbNFx84a1zXL04wJo3VsXE5rBTB0NI5Q2SkTpeQ2L1q9SCu3rtQN\nq27QG5/9xorVN1elRtXoLQ87/D1Zy9Zs0VObW7TvxEa9cM4UjWkY0eN4aSrG6FGj1L6nU40jpd0d\nnZKkza171NTQt2ml+VyuvthId7Wk/5J0ikKjbdllSlFWgOzu283sF5LOMbMD3P3JuOpfJTVL+qXy\n5CcrBGb/kPQCSbeZ2fcUW5vdfXdfKl7A6xWCv/Pd/evpCjMbJ6kznseVZiaFAPmaAp30bpI00923\n5+znGIXA7/OSzsyz3YmSvuzuH0m2+ZZC0Hy5mS1y94Jf2c1slMJ1HC/pZHe/JVm3n8J1/L6ZzXH3\ntkL7ybFA0iXufmmyr59JukHSRxS+BGTLz1EIjn8r6W3uvjNZd4mkiyV9QNLX47KDJX1WIT3mOHdf\nE5dfKOlnkt5cYh1Rhq2tbZp2UFOPZX35QEdP8XMBVXSJLpE0tK71kUceqTPPzPfvoP+mjzWt3NxW\n0ZSJ5rZmLVm7RC7XHWvv0Blzz9DE0RMrWOtulZgxsNQge+bEMVo9pkGnHD69xxeK9HhpKsb08Y36\nzre/qQceCD98fyg55n/E+3P6ce6lGIrvhyr7qqRl7n6VmZ3SjzK96ss4yN9T6Kj3LkmfNLODFFoL\nv+vurfmeZHd3M3u9QuviAoWWXElqN7N7FVopv1MscCzTztwF5bZau/v6AsvvN7ObJJ1mZg3unvs7\n1zZJn8zZ5i4z+6lCGsprFK5DIWdJOkQhyL4lXeHuT5vZFyVdJunlCq3kpXhc0qdz9nWjmT0haX5O\n2X9X6Gj5rjQ4jj4l6d8kvU0xQJb0VoXX0Tez4Dju32OQ/AZJIwtVzMzeI+k9kjRq1CgtXry4xFOq\nPy0tLQNW/w3rOrRk9zpNHdudJbV5Z6c2bNilxYufLLLl0DGQ1xvozfLly6uy3zHepl3b1+vO9a62\nztCiPH2saUP7Rm3o4z7/1vw3dXR2SJI6Ojv007t+qpdOfGnlKp1YtalDE2c2Km1pckmr1rVp3M5n\nupYdFe/zXcdHt3Ro9qTR2r7JuvYzdrfr9vvX6dApPf+99Ha83bs6deda0+yJozRuNEFpX1Tjc7el\npUWSRpnZXcniK9z9iuyBmb1W0hmSji20n1LKlKrsANndl5jZMknvMrNPK6RbjFAInItt94Skk2P+\n7qkKI1fMT27vN7MF7r6q2H7M7HxJuV+dr3H3+yRdq9Ca+W0zO10hPeE2SQ+6e9l5sGZ2lqT3xbpO\n197Xa7pCx8XUPbmtztFihQD5uSoeIJ8Q7w+KLba5Do33R6j0APk+d+/Is3xNcrysM+UxCq3B5xf4\nRtsWj515bry/Nbeguz9uZmskzSlUsfjiv0KSxo0b5wsWLCh2HnVt8eLFGqj6HxRz3Z41a5ymjW/Q\nppZ2PbF2h173suEzFWo1rncfPiaGlUpc80st/JA1VK71ddddp9bWVh111FG9F+6D5cuX6wUV3Hdz\nW7Me/fuj6gw/qKpTnXp096N622Fvq0or8o4VGzVx4sgeLbpPbtkpG7ND60c0dqVLaFlYl+86rn9w\nrQ6bPV4jkv9Jne5qXtOio54zq9fjbdvZrrkNHTrqkOmSutM11ra1643n/bv2mz5JLz/9FV2d6zY8\n9bhe97LjtXTNFh0+raErZUKSNmzfrYc3tevs42bvVc8sB/nY5HP5vrU7ik5RPZjeD1lMUI3/czHo\n3uPuzy9w7NmSLpe0sECMVVKZcvR1Jr3vSfqGQpR+rqS73f3eUjZ094ckPZQ9NrPDJf1AIVD7mnJG\nwMjjfIWc2tRqhSDwcTObL+mSWLfXxvVrzOzL7v6NUuoY6/UhhVbSLZL+JOkJSa0KX0ZfrRBI5uuw\ntq7ALrOvypN6OfS0eP+GXsqVEwUV6vy4Rz07ak5RGIlkhkIqRSmy8yl03utUJECutqHaozg7h6Vr\ntmjrmhZNbmos+iEMYGjp66xxi1YvUqd39ljW6Z1Vy0XOHVXj6a27dO+TzTpy3/GaM72pK11iZpF9\nlDN0XSmjeKQ52m2PT1RT09ge00ovXvyk5s4Yr7/+85myUtn4XK6q5ynEJrcljXcjJMnM9kg6XtLs\n3sq4+92lHrCvAfJPJH1BoRPa/spJKSiHuz9sZu9QGP/4ZSWUn9PL+ockvSnm8h6jkKD9QUlfN7Md\n7v793o4Rt71UIag9zt3X5qw/Ie+GQaH3eZYk3luntWz92e5+bW91rbDs2Pe6+3ElbpOlxcxU/hFM\nin3uVVWPb/MHNWlTS3ufh+6pR+kHOoDhoz8d31ZtW6WOnB8UO7xDK7etrEpds85zqze0aMeGXdqw\nvU1H7jteB88YJ6k7J7mYcoauyz3euMaGPs9QOLmpUZta2nu0IG9qadfkpryDOUnic7mK/iLp6Jxl\nn1b4Jf99CjHkIyWUKVmfAmR332pmv1IYGWKHwugW/ZE1hVcsISiOsnC3pLvN7HZJf1Vo+c0C5OwT\nIl9+7HSFNI7f5AmOx0sqFjweZ2YT8jTvL4j3vbW03xHvX6qQMjJg3L3FzB6QdKSZTXX3zSVsdq9C\nXvVLFDo2don56Xv/DjVA6FEMYCjqT8e3C+dfOBBV7CFtsb3pwbWaM71nq2yaXrBkxcaCI1SUGvSW\nOkNhb+bNnhIaWaS9UibKMVR/yRxIMabqkaBuZlsljXH3bPmuEsqUrNxxkFMXKQRGp/eW62Fmc83s\nQ2a2V3qBhXbwj8eHf+1HfWRm880s3ys3W9aaLMuGnjswT/n1sezzYkCc7b9BIe1iepFqTJL0iZx6\nPV+hY9s2hdEhivmdpBWSPmBmr8hXwMxOiPnC1fBVSaMl/cDM9vp6bmZT4jB0mZ8ppGp8MOb/ZOVM\n0udUpINetW1tbdO08T1/gps2vkFbW0sd/AMA6k8Yqqz0sZHrSZYukWne2a7VG7tTFra2tOqG+9fo\nzw+s7TF+8cyJY3T8IdP1sufM0vGHTK9IANybuTPGa/4hM/XwpnZd/9AWPbypveyUCcZGHrz6mmKR\ndbp7osTikxQCyy+Z2W0KEf52SfsopFUcrBCUXtDX+kRvVQgsb1FoSt+iMCLEQoXOZZclZf+uEASf\nb2ZT1Z1D+01332Zm31AYB3mZmf1OIWg8WWEYuZvj3/n8VdJ5Zna8QgfBbBzkEZLe29tIHe7eHnth\n3ijp+tj6fV+s62yFofIOjvttLbijPnL3H5jZ8yS9X9IKM7tR4XmeqjDG84kKk6y8L5ZfYWafUOgc\neb+ZXa3ucZCnSrpf0rxK17MUffl5DMNP+o/qd/esoXUHdW8wTyedmy6xetMObW/rzok+dNpoHTGj\nUU82t/dpvORK62/KRKm/ZP7o1sdoXS6Tu59TiTKF9DlALtNDCq3NpylMOPEmheCpVSGQ/ayky9y9\nr6PWZH6u0HHuRQppEGMlPSXpF5K+kjaxu/sWM3udQme0cyWNi6uuUgjw/lvSBoVROt4bl/1JoeW8\nazzhPFYpBI+fj/eNku6R9El3v7GUk3D3pXG85Q8rzFp4rsIYzmsVUhouVhWn5Xb3D5jZIoX6n6KQ\nbrJZIVD+ksI1Sst/zsyejPU9V+HLz40Kg3T/Ud15ygOqUj+PYejKWncyWeuONDTy1DE0DebppHPT\nJZ7c0qYXz50sPRzWTxwzSi5p9Mh2zZrcWNZ4yfWo2Jj16Zfzs46YMuT6yQx2JQXI7l5ybrC7X6QQ\nRKbL2hRm3rsm70aF9zWnzPJLJC0po/wNCpNl5Fu3RyHd4Kt5Vp+jnPHD3X21euZQn13C8a9UmNUv\n37r1Ci3YfU4Yy1On3PULiqz7vaTfl3Gsnyh03uxiZhMVWvDvy7tRldGjGL3JWncy5KljMKhkR7Ra\nSHOEl6zYqDENPbM923Z3avSoUSFtZEPlp6UeSMV+yUynsh5hxudPnRmoFmQMYWY2Q9LWdNKUOBLI\nVxSm+u4t77pq6FGMYpiREINVpTqi1VrWGr5/fLxrd6c2tLZr5qSmQZM2UkyxXzL/+s9n9irP50/9\nIEBGJbxOYVbFPytMPjJVIVf5MIXW42/WsG5AQVnrTqpe8tTp+Y7hIGsNzzzRvFsHTm2SS4MmbaSY\nYr9kLl2z9+dMvXz+gAAZlbFEYSa9E9U90ckqSZ+R9IU8U1YDdSFr3cls2L67LvLUh/oY3kAqbQkf\n2TBGj2xsG3RpI8UU+iVz3uwpXfmHne70k6kzBMjotziL4mt7LQjUmdx/Wn0ZxqkaGMMbw9XxhxQb\nRbV3fZ1hsBbS9/L1D22hn0ydIUAGMKyl/4zOPq5m89r0QG40UL7+zDBYa+98ybNqXQXk6M9EIQCA\nKqjn3GigXqUzDI4wizMMhqHigHIRIANAnZk3e4ruW7tDG7bvVqd7V270vNlTal01oG4NthkGcycp\nYna9+kKKBQDUGcbwHjiMFjJ0DKYZBpmkqP4RIANAHWIM7+pjtJChZTDNMJh1xI0TCNIRtw4RIAMA\nhiVGCxlaBtMMg3TErX8EyACAYYkgZegZLDMM0hG3/tFJDwAwLBGkoFayjrgZOuLWHwJkAMCwxGgh\nqJW5M8b3mDGvXiYpQjdSLAAMe+5e6yqgBhgtBLVUj5MUFTPcPicJkAEAwxajhQDIhxQLAAAAIEGA\nDAAAACQIkAEAAIAEATIAAACQIEAGAAAAEoxiAQAYMBf7xbWuAlA3eD/UL1qQAQAAgAQBMgAAAJAg\nQBDuSL8AABGUSURBVAYAAAASBMgAAABAggAZAAAASDCKBQAAqJp1zbu0ekOLdrS1a1xjg+bMGK+Z\nE8fUulpAUQTIAACgKtY179Ija7fq4KmNmjCmUdt37dEja7dKmkyQjLpGigUAAKiK1RtadPDURk0a\n26ARZpo0tkEHT23U6g0tta4aUBQBMgAAqIodbe2aMKbnj9UTxozSjrb2GtUIKA0BMgAAqIpxjQ3a\nvmtPj2Xbd+3RuMaGGtUIKA0BMgAAqIo5M8Zr5eY2bdvZrk53bdvZrpWb2zRnxvhaVw0oik56AAD0\n06JFi6qy38cff1xr1qypyr4HytbW3bp72y61te9RY8MozZw0Ri0rR/cstHRduJ9cnesIlIsAGQCA\nfmhqaqravvfs2VPV/Q+EpqYm7Te9l0KbH8oKV70+QCkIkAEA6KOFCxdWdf8TJkzQggULqnqMurCw\ntdY1AHogBxkAAABIECADAAAACQJkAAAAIEGADAAAACQIkAEAAIAEATIAAACQIEAGAAAAEgTIAAAA\nQIIAGQAAAEgQIAMAAAAJAmQAAAAgQYAMAAAAJAiQAQAAgAQBMgAAAJAgQAYAAAASBMgAAABAggAZ\nAAAASBAgAwAAAAkCZAAAACBBgAwAAAAkCJABAACABAEyAAAAkCBABgAAABIEyAAAAECCABkAAABI\nECADAAAACQJkAAAAIEGADAAAACQIkAEAAIAEATIAAACQIEAGAAAAEgTIAAAAQIIAGQAAAEgQIAMA\nAAAJAmQAAAAgQYAMAAAAJAiQAQAAgAQBMgAAAJAgQAYAAAASBMgAAABAwty91nUAJElm1ilpZ63r\n0Q+jJO2pdSWGEa73wOOaDzyu+cDjmg+8UZIa3L1uGm4JkIEKMbO73P35ta7HcMH1Hnhc84HHNR94\nXPOBV4/XvG4idQAAAKAeECADAAAACQJkoHKuqHUFhhmu98Djmg88rvnA45oPvLq75uQgAwAAAAla\nkAEAAIAEATIAAACQIEAGAAAAEgTIQB+Y2evN7Jtm9jczazYzN7Oral2voczMppnZeWb2WzN7zMx2\nmtk2M7vVzN5tZnyeVYGZfcHM/mJma+I132xm95rZxWY2rdb1Gw7M7B3xM8bN7Lxa12eoMbPVyfXN\nvT1T6/oNZWb2UjP7tZmtNbO2eP9HM3tFres2qtYVAAapiyQdI6lF0pOSDq9tdYaFN0j6jqS1km6W\n9ISkmZJeK+l/JJ1pZm9weh5X2n9Iukf6/+3de5hdVXnH8e8v3C8Syl0oJMRCoFA0VC4BGmIgqLVI\nkFAFgxEQGyvyoNJKgUBA0VCCLUJrUSyXQB5Qbn0EhBoICRYtKoT7LSRBqoQYSIAQgpC8/WOtQzbb\nfSYzk8nZM2d+n+eZZ81Za+993nMmz+Sddd69Fj8FFgKbAPsDk4DPS9o/Ip6vL7z2JmlH4BLS75pN\naw6nnb0C/GtF/9JWB9JfSDoL+DqwCLiV9Lt9K2AYMBK4vbbgcIJs1l1fJiXGc4CDSQmbrV1PAx8H\nbouIlY1OSWcA9wNHkZLlG+sJr21tFhHLy52SzgfOAP4J+PuWR9UPSBJwBfAScBNwWr0RtbUlETGp\n7iD6C0lHk5Lj6cAnIuK10vh6tQRW4I8kzbohImZExDOerWydiLg7In5cTI5z/wLgP/LDkS0PrM1V\nJcfZD3O7S6ti6YdOAUYBxwOv1xyLWY/I5XAXAMuAY8vJMUBEvNXywEo8g2xm7aDxy/TtWqPoXw7P\n7cO1RtGmJO0OTAYujohZkkbVHVOb20DSOGAn0h8jDwOzImJFvWG1pQOAnYEbgMWSPgbsCSwH7o+I\nn9cZXIMTZDPr0yStC3wmP7yjzljamaTTSDWwA4EPAgeRkojJdcbVjvK/6amkOvszag6nv9iO9J4X\nzZN0fETMrCOgNrZPbl8k3d/wF8VBSbOAsRHx+1YHVuQSCzPr6yaTZh9uj4g76w6mjZ0GnAOcSkqO\n7wAOq/s/sTZ1NulGpc9GxBt1B9MPXAEcQkqSNyElbJcBg4GfSHp/faG1pW1yOwHYCDgUeA/p9/id\nwAjgR/WEtooTZDPrsySdAnwVeBI4ruZw2lpEbBcRIiURnwCGAA9K2rveyNqLpH1Js8YX9ZaPmttd\nRJyb73F4MSKWRcSjETEB+DYpgZtUb4RtZ53cijRTfFdELI2Ix4AjSTfAHyxpeG0R4gTZzPooSV8E\nLgYeBz4UES/XHFK/kJOIm4HDgC2Bq2sOqW0USiueBibWHI6tuvl3RK1RtJ/FuZ0bEQ8VB/InJo1P\nAvdtaVQlTpDNrM+RdCpwKfAoKTn2Yv4tFhHPkf442UPSVnXH0yY2BXYFdgeWFzesIJW3AHw/91Wt\n2Ws9a2FuN6k1ivbzVG6XNBlvJNAbtSCWpnyTnpn1KZK+Rqo7ng2MjohFNYfUn22fW9/p3zPeBH7Q\nZGxvUl3yz0gJhssv1r7GR/xza42i/cwirTi0i6T1I+IPpfE9czu/pVGVOEE2sz5D0kTgPODXpBvE\nXFaxFknajbSBwoJS/wDSIv/bAPdFxOKq861r8sfLlVtJS5pESpCviojLWxlXO5O0B/BC+XeJpEGk\nT6kArml5YG0sIhZJuh74NOmG1LMaY5JGAx8m7WxY66pETpDNukHSGGBMfrhdbodLujJ/vygivOtV\nD5I0npQcrwDuBU5JG429y/yIuLLFobWzjwAX5mWXniXt6LYtaffIIcAC4KT6wjNbY0cDp0uaAcwD\nXgPeB3wM2JC03fGU+sJrW18B9gPOlDSCtBvqINJNeiuAkyKiWQlGSzhBNuueDwDjS31D8hfAc3hb\n2J62c27XIS01VmUmcGVLoukfpgPfAw4E3g9sTtpE4WnSzWTf8Sy+9XEzgKGk2fnhpHrjJaRSlqnA\nVO+Y2vMiYqGk/Uizx0cC+5P+OLkN+FZE/KLO+ADkn7uZmZmZ2SpexcLMzMzMrMAJspmZmZlZgRNk\nMzMzM7MCJ8hmZmZmZgVOkM3MzMzMCpwgm5mZmZkVOEE2MzMzMytwgmxmZj1C0j2Sal1cP8fwSN4O\nu89SMlvSvXXHYtYf9elfIGZmfYWksZIukXSvpFclhaRrOnHeAZJul/SypGWSHpZ0qqR1OnGuJP00\nP1dIWrc0Prgw1tHXX63Ja28VSWNJ22CfExErC/0j8+u4p4NzR0u6VtK8/D6/IWmOpKmSPlo6dnXv\n23UdPM94SfdLWirplZzQ/035uLx72znAQfl1mVkLeatpM7PWOIu0XfNS4P+A3VZ3gqQjgBuB5cD1\nwMvA4cC/kLZ/Pno1lzgZ+FA+f8OK8SXAuU3O3RE4AXgJuH91sdZNkoBvkLbBvrkL570HuBoYQ3qf\n7gZuAt4ibW/+18A4SRdFRHn7+IeAWyou+2iT55oCfJX08/8+sD7wKeDHkr4UEZcWj4+I/5L0BHC+\npBu95bFZ6zhBNjNrjS+TEqM5pFnOGR0dLGkzUhK1AhgZEb/K/RNJSdxYSZ+KiMrZSklDgQuAKaQk\nbFD5mIhYAkxqcv638rdXR8Sbq3txvcChwFDgzM4mkrkM40fAh0k/j3ER8bvSMRsAE4BdKy4xOyIm\ndfK5DiAlx88C+0TE4tx/IfBrYIqkWyNifunUq4DJwCHA9M48l5mtOZdYmJm1QETMiIhnujALOBbY\nGriukRzn6ywnzUYDfKHqxFxKMRWYR/qYvkvy+Z/ND7/X1fMrrjdA0gRJv8ylBa/n77/QrFZY0qcl\nPZBLHRbmUoftO6hzPjG313chtGNIyfEc4PBycgwQEW9GxMXAV7pw3SoTcnt+IznO158P/BuwAXB8\nxXmNP4BOrBgzs7XECbKZWe80Krd3VIzNApYBB+QZzrKzgGHA+G7O/h4BbAfMiognu3F+2VTgu8C2\nwOWkpHtr4N/z2LtI+gfgGmAwaQb1CmAP4H+AzSuOF+n9WhARz3Yhrs/ndkpEvN7RgU3ex+0l/Z2k\nM3K7VweX6Ojn+ZPSMcXnfQ74LXBofp1m1gIusTAz652G5vbp8kBEvC1pHilpHAI80RiTtA9wJjC5\nOPPcRY3E8bJunv8OSccAxwIPAiMiYmnuPwuYCRwr6baImJb7hwDfBBYBe0fE87n/dGAaqVykbCgp\n4b61C3GtC+yfH97VjZcGMDp/Fa97D+kPk98U+jYBdgCWRsQLFdd5JrdVZRwAvyTVSO8OPN7NWM2s\nCzyDbGbWOw3M7StNxhv978yoStqINCP7OHBed55U0mBSPe9LpBsE19QJuT29kRwD5Bnbr+WHnysc\nfyxp8uaSRnKcjw/gdFJNdtlOua1KPpvZgnSTHKTa8K5YBnwd+EvgT/JXo658JHBXToobuvyzLFmQ\n252ajJtZD3OCbGbWNzU+bi/W4/4zaUZ5fES81c3rnkT6v+GqHro5b29gJXBPxdhMUsI7rNDX+P5n\n5YNzucHz5X5gy9wurhhrptvlChGxMCLOjogHImJJ/poFHAb8L/BnvDvp7/Slm/S/nNutunFNM+sG\nJ8hmZr1TY1ZxYJPxzYrHSToY+CLwjYiY3Z0nzGUHjRvF1vjmvGwg8HJE/KE8EBFvk0opBpaOB3ix\nyfWq+t/IbdVSds28BDRi2qEL5zWVX8/l+eGIwtDqfparm2HeKLdvNBk3sx7mBNnMrHd6Krd/VJea\nE9mdgbeBubl7GGlW9NzyxhWsWuLtrdz3gSbPeTjwXmBmRDzV5JiuegXYQtJ6TV7HVsCrhe7G99s2\nuV5V/8LcblkxVikns7/IDw/p7Hmd8PvcvlNikctJfgtsKum9Fefskts/qjfPGq9rYZNxM+thTpDN\nzHqnu3P7kYqxEcDGwH2FMohHgR80+WrU/v5nfvxSk+ds3JzXU7PHkG7OG8C7Z1QbRgDrAA+Ujgc4\nqHywpEGkDUzKHiOVaqx285WSxus8TdLGHR3YZLWQKo0b/+aW+jv6eX60dEzZbqQylUc6GYOZrSF5\nYx4zs9aSNJJ0Q9e1ETGuyTGbkTaV2Aw4sLBRyIakRGo4cEyzjUJK15pPmkVeL8+cVh0ziJTULQZ2\n6E79cV7B4eCIUKHvWOBa0koMIyNiWe7fmFSXvA9pg45rc//7gCdJu/wVV7FQvs4xAMXnyOO/AvYC\nBkbEG6WxkaT3e2ZEjCz0DwBuJ62FPB34THmVCUnrk+qyd4+Ik3PffsCD5bIRSaPy9TYg/czuK4wd\nQFqmrrxRyGDSRiGbALuVNwrJifmrwCMR8UHMrCW8zJuZWQtIGkNaqgvSGsMAwyVdmb9fVNzKOCJe\nlXQScANwj6TrSDdrfZy0rNkNdG1TjNX5HD17cx4AETEtb5n9t8Bjkm4h3Yw2hlQm8sNGcpyPf1bS\n2aSl3h6SdD2pTGM0aeWJh0iJcNmNpFUlRgG3dTK2lZKOJq38cQQwV9JdpGXzVpD+qDiEtITclMKp\nFwB75D8IGitg7MWqdYwnFpPj/Fz3Sfo2acORhyXdQFpF45P5dX2pYhc9SKtirE/PrChiZp3kGWQz\nsxaQNImOd7V7LiIGV5x3IGld4+Gkm9DmkEolvhMRVUueVT33fDqYQZa0DvAbYHvSLGa36o+rZpBz\n/wDSTnInAH+eu5/Ir+O7EbGy4lrHkZLJ3YHXgDuBfwT+G/jTiNi8dPw2pBUubomIT5bGRlIxg1w6\n5jDS7oHDSX/ACPgdqU756oi4o3DsicCRwJ6kGur1SDcP/hy4NCLurX6HQNJ44OT8PqwklZdcGBGV\nazhLmgYcBewYEa5BNmsRJ8hmZtYn5LKTF4HZETG8YvwyYDwwOCIWlMf7mpz0zwemRUR3lo0zs27y\nTXpmZtarSNq6vOpFXvHiItIs+s1NTj2btHTbmWs3wpY5g1TqMbHuQMz6G9cgm5lZb3MUcJ6k6aSy\niS1IK17sCswGLqk6KSJelDSOVB88oKp0o6/INyW+ABzXZHtqM1uLXGJhZma9iqRhpFnTfVm1BvA8\n4Cbggoh4ra7YzKx/cIJsZmZmZlbgGmQzMzMzswInyGZmZmZmBU6QzczMzMwKnCCbmZmZmRU4QTYz\nMzMzK/h/PYLWIOFOo8sAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb6e97135f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "results = gdsc.anova_one_drug_one_feature(1047, 'APC_mut', \n",
    "                                     show=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cancer Specific"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "gdsc.set_cancer_type('stomach')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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WzB/V4zQ3NbKv4/Axifi+jsM0NzUOsNexFs+dOayEWJIknXhMkDVmioRzfjEy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\ngPnAhwAiYjrwVOAHoxmwNJ5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kqcIEWZIkSaowQZYkSZIqTJAlSZKkChNkSZIkqcIEWZIk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\nJEmSNMQCWZIkSRpigSxJkiQN+R87wtmam2vtXgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb6e964c400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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7wnn8hZnNcPe2fNvpQ5nOUQiOF0g6wd23x+XnSbpP0hfM7E/ufndc/hOStkk6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\nWcH/ALUGXz6CL/IbAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb6e9659668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "results = gdsc.anova_one_drug_one_feature(1047, 'APC_mut', \n",
    "                                     show=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "note that there are only 2 boxplots now since the tissue-specific factor has been dropped"
   ]
  }
 ],
 "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": "3.5.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}