Heatmaps .ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import cm\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "\n",
    "#hiding warning messages\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "import time "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# NB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Reading in grep files\n",
    "grep_nb = pd.read_csv('grep_nb.txt', sep = '\\t')\n",
    "grep_nb = grep_nb.T\n",
    "grep_nb = grep_nb.reset_index()\n",
    "grep_nb = grep_nb.T\n",
    "grep_nb = grep_nb.reset_index(drop = True)\n",
    "\n",
    "#converting to str (NB)\n",
    "grep_nb = grep_nb.astype(str)\n",
    "\n",
    "#splitting scores \n",
    "str_split_nb = grep_nb[1].tolist()\n",
    "i = 0\n",
    "while (i < len(str_split_nb)):\n",
    "    str_split_nb[i] = str_split_nb[i].split()\n",
    "    i = i + 1\n",
    "    \n",
    "#making df\n",
    "scores_nb = pd.DataFrame(str_split_nb)\n",
    "\n",
    "#label df\n",
    "labels_nb = pd.DataFrame(grep_nb[0])\n",
    "labels_nb = labels_nb.rename(columns = {0 : 'labels'})\n",
    "\n",
    "#combining\n",
    "data_nb = pd.concat([labels_nb, scores_nb], axis = 1)\n",
    "\n",
    "#reindexing\n",
    "data_nb = data_nb.rename(columns = {0 : 'ACC', 1 : 'AUC', 2 : 'MCC', 3 : 'SENS', 4 : 'SPHE', 5 : 'BACC',})\n",
    "data_nb = data_nb.set_index('labels')\n",
    "\n",
    "#seperating datasets into Train, Test, Ext1, Ext2, Ext3\n",
    "data_nb = data_nb.iloc[:25]\n",
    "data_nb.loc['output_maccs:Test']\n",
    "subset_nb_train = data_nb.loc[['output_maccs:Train', 'output_top:Train', 'output_ecfp:Train', 'output_fcfp:Train', 'output_pub:Train',], :]\n",
    "subset_nb_test = data_nb.loc[['output_maccs:Test', 'output_top:Test', 'output_ecfp:Test', 'output_fcfp:Test', 'output_pub:Test',], :]\n",
    "subset_nb_ext1 = data_nb.loc[['output_maccs:Ext1', 'output_top:Ext1', 'output_ecfp:Ext1', 'output_fcfp:Ext1', 'output_pub:Ext1',], :]\n",
    "subset_nb_ext2 = data_nb.loc[['output_maccs:Ext2', 'output_top:Ext2', 'output_ecfp:Ext2', 'output_fcfp:Ext2', 'output_pub:Ext2',], :]\n",
    "subset_nb_ext3 = data_nb.loc[['output_maccs:Ext3', 'output_top:Ext3', 'output_ecfp:Ext3', 'output_fcfp:Ext3', 'output_pub:Ext3',], :]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# DT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Reading in grep files\n",
    "grep_dt = pd.read_csv('grep_dt.txt', sep = '\\t')\n",
    "grep_dt = grep_dt.T\n",
    "grep_dt = grep_dt.reset_index()\n",
    "grep_dt = grep_dt.T\n",
    "grep_dt = grep_dt.reset_index(drop = True)\n",
    "\n",
    "#converting to str (dt)\n",
    "grep_dt = grep_dt.astype(str)\n",
    "\n",
    "#splitting scores \n",
    "str_split_dt = grep_dt[1].tolist()\n",
    "i = 0\n",
    "while (i < len(str_split_dt)):\n",
    "    str_split_dt[i] = str_split_dt[i].split()\n",
    "    i = i + 1\n",
    "    \n",
    "#making df\n",
    "scores_dt = pd.DataFrame(str_split_dt)\n",
    "\n",
    "#label df\n",
    "labels_dt = pd.DataFrame(grep_dt[0])\n",
    "labels_dt = labels_dt.rename(columns = {0 : 'labels'})\n",
    "\n",
    "#combining\n",
    "data_dt = pd.concat([labels_dt, scores_dt], axis = 1)\n",
    "\n",
    "#reindexing\n",
    "data_dt = data_dt.rename(columns = {0 : 'ACC', 1 : 'AUC', 2 : 'MCC', 3 : 'SENS', 4 : 'SPHE', 5 : 'BACC',})\n",
    "data_dt = data_dt.set_index('labels')\n",
    "\n",
    "#seperating datasets into Train, Test, Ext1, Ext2, Ext3\n",
    "data_dt = data_dt.iloc[:25]\n",
    "data_dt.loc['output_maccs:Test']\n",
    "subset_dt_train = data_dt.loc[['output_maccs:Train', 'output_top:Train', 'output_ecfp:Train', 'output_fcfp:Train', 'output_pub:Train',], :]\n",
    "subset_dt_test = data_dt.loc[['output_maccs:Test', 'output_top:Test', 'output_ecfp:Test', 'output_fcfp:Test', 'output_pub:Test',], :]\n",
    "subset_dt_ext1 = data_dt.loc[['output_maccs:Ext1', 'output_top:Ext1', 'output_ecfp:Ext1', 'output_fcfp:Ext1', 'output_pub:Ext1',], :]\n",
    "subset_dt_ext2 = data_dt.loc[['output_maccs:Ext2', 'output_top:Ext2', 'output_ecfp:Ext2', 'output_fcfp:Ext2', 'output_pub:Ext2',], :]\n",
    "subset_dt_ext3 = data_dt.loc[['output_maccs:Ext3', 'output_top:Ext3', 'output_ecfp:Ext3', 'output_fcfp:Ext3', 'output_pub:Ext3',], :]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# RF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Reading in grep files\n",
    "grep_rf = pd.read_csv('grep_rf.txt', sep = '\\t')\n",
    "grep_rf = grep_rf.T\n",
    "grep_rf = grep_rf.reset_index()\n",
    "grep_rf = grep_rf.T\n",
    "grep_rf = grep_rf.reset_index(drop = True)\n",
    "\n",
    "#converting to str (rf)\n",
    "grep_rf = grep_rf.astype(str)\n",
    "\n",
    "#splitting scores \n",
    "str_split_rf = grep_rf[1].tolist()\n",
    "i = 0\n",
    "while (i < len(str_split_rf)):\n",
    "    str_split_rf[i] = str_split_rf[i].split()\n",
    "    i = i + 1\n",
    "    \n",
    "#making df\n",
    "scores_rf = pd.DataFrame(str_split_rf)\n",
    "\n",
    "#label df\n",
    "labels_rf = pd.DataFrame(grep_rf[0])\n",
    "labels_rf = labels_rf.rename(columns = {0 : 'labels'})\n",
    "\n",
    "#combining\n",
    "data_rf = pd.concat([labels_rf, scores_rf], axis = 1)\n",
    "\n",
    "#reindexing\n",
    "data_rf = data_rf.rename(columns = {0 : 'ACC', 1 : 'AUC', 2 : 'MCC', 3 : 'SENS', 4 : 'SPHE', 5 : 'BACC',})\n",
    "data_rf = data_rf.set_index('labels')\n",
    "\n",
    "#seperating datasets into Train, Test, Ext1, Ext2, Ext3\n",
    "data_rf = data_rf.iloc[:25]\n",
    "data_rf.loc['output_maccs:Test']\n",
    "subset_rf_train = data_rf.loc[['output_maccs:Train', 'output_top:Train', 'output_ecfp:Train', 'output_fcfp:Train', 'output_pub:Train',], :]\n",
    "subset_rf_test = data_rf.loc[['output_maccs:Test', 'output_top:Test', 'output_ecfp:Test', 'output_fcfp:Test', 'output_pub:Test',], :]\n",
    "subset_rf_ext1 = data_rf.loc[['output_maccs:Ext1', 'output_top:Ext1', 'output_ecfp:Ext1', 'output_fcfp:Ext1', 'output_pub:Ext1',], :]\n",
    "subset_rf_ext2 = data_rf.loc[['output_maccs:Ext2', 'output_top:Ext2', 'output_ecfp:Ext2', 'output_fcfp:Ext2', 'output_pub:Ext2',], :]\n",
    "subset_rf_ext3 = data_rf.loc[['output_maccs:Ext3', 'output_top:Ext3', 'output_ecfp:Ext3', 'output_fcfp:Ext3', 'output_pub:Ext3',], :]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# KNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Reading in grep files\n",
    "grep_knn = pd.read_csv('grep_knn.txt', sep = '\\t')\n",
    "grep_knn = grep_knn.T\n",
    "grep_knn = grep_knn.reset_index()\n",
    "grep_knn = grep_knn.T\n",
    "grep_knn = grep_knn.reset_index(drop = True)\n",
    "\n",
    "#converting to str (knn)\n",
    "grep_knn = grep_knn.astype(str)\n",
    "\n",
    "#splitting scores \n",
    "str_split_knn = grep_knn[1].tolist()\n",
    "i = 0\n",
    "while (i < len(str_split_knn)):\n",
    "    str_split_knn[i] = str_split_knn[i].split()\n",
    "    i = i + 1\n",
    "    \n",
    "#making df\n",
    "scores_knn = pd.DataFrame(str_split_knn)\n",
    "\n",
    "#label df\n",
    "labels_knn = pd.DataFrame(grep_knn[0])\n",
    "labels_knn = labels_knn.rename(columns = {0 : 'labels'})\n",
    "\n",
    "#combining\n",
    "data_knn = pd.concat([labels_knn, scores_knn], axis = 1)\n",
    "\n",
    "#reindexing\n",
    "data_knn = data_knn.rename(columns = {0 : 'ACC', 1 : 'AUC', 2 : 'MCC', 3 : 'SENS', 4 : 'SPHE', 5 : 'BACC',})\n",
    "data_knn = data_knn.set_index('labels')\n",
    "\n",
    "#seperating datasets into Train, Test, Ext1, Ext2, Ext3\n",
    "data_knn = data_knn.iloc[:25]\n",
    "data_knn.loc['output_maccs:Test']\n",
    "subset_knn_train = data_knn.loc[['output_maccs:Train', 'output_top:Train', 'output_ecfp:Train', 'output_fcfp:Train', 'output_pub:Train',], :]\n",
    "subset_knn_test = data_knn.loc[['output_maccs:Test', 'output_top:Test', 'output_ecfp:Test', 'output_fcfp:Test', 'output_pub:Test',], :]\n",
    "subset_knn_ext1 = data_knn.loc[['output_maccs:Ext1', 'output_top:Ext1', 'output_ecfp:Ext1', 'output_fcfp:Ext1', 'output_pub:Ext1',], :]\n",
    "subset_knn_ext2 = data_knn.loc[['output_maccs:Ext2', 'output_top:Ext2', 'output_ecfp:Ext2', 'output_fcfp:Ext2', 'output_pub:Ext2',], :]\n",
    "subset_knn_ext3 = data_knn.loc[['output_maccs:Ext3', 'output_top:Ext3', 'output_ecfp:Ext3', 'output_fcfp:Ext3', 'output_pub:Ext3',], :]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# SVM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Reading in grep files\n",
    "grep_svm = pd.read_csv('grep_svm.txt', sep = '\\t')\n",
    "grep_svm = grep_svm.T\n",
    "grep_svm = grep_svm.reset_index()\n",
    "grep_svm = grep_svm.T\n",
    "grep_svm = grep_svm.reset_index(drop = True)\n",
    "\n",
    "#converting to str (svm)\n",
    "grep_svm = grep_svm.astype(str)\n",
    "\n",
    "#splitting scores \n",
    "str_split_svm = grep_svm[1].tolist()\n",
    "i = 0\n",
    "while (i < len(str_split_svm)):\n",
    "    str_split_svm[i] = str_split_svm[i].split()\n",
    "    i = i + 1\n",
    "    \n",
    "#making df\n",
    "scores_svm = pd.DataFrame(str_split_svm)\n",
    "\n",
    "#label df\n",
    "labels_svm = pd.DataFrame(grep_svm[0])\n",
    "labels_svm = labels_svm.rename(columns = {0 : 'labels'})\n",
    "\n",
    "#combining\n",
    "data_svm = pd.concat([labels_svm, scores_svm], axis = 1)\n",
    "\n",
    "#reindexing\n",
    "data_svm = data_svm.rename(columns = {0 : 'ACC', 1 : 'AUC', 2 : 'MCC', 3 : 'SENS', 4 : 'SPHE', 5 : 'BACC',})\n",
    "data_svm = data_svm.set_index('labels')\n",
    "\n",
    "#seperating datasets into Train, Test, Ext1, Ext2, Ext3\n",
    "data_svm = data_svm.iloc[:25]\n",
    "data_svm.loc['output_maccs:Test']\n",
    "subset_svm_train = data_svm.loc[['output_maccs:Train', 'output_top:Train', 'output_ecfp:Train', 'output_fcfp:Train', 'output_pub:Train',], :]\n",
    "subset_svm_test = data_svm.loc[['output_maccs:Test', 'output_top:Test', 'output_ecfp:Test', 'output_fcfp:Test', 'output_pub:Test',], :]\n",
    "subset_svm_ext1 = data_svm.loc[['output_maccs:Ext1', 'output_top:Ext1', 'output_ecfp:Ext1', 'output_fcfp:Ext1', 'output_pub:Ext1',], :]\n",
    "subset_svm_ext2 = data_svm.loc[['output_maccs:Ext2', 'output_top:Ext2', 'output_ecfp:Ext2', 'output_fcfp:Ext2', 'output_pub:Ext2',], :]\n",
    "subset_svm_ext3 = data_svm.loc[['output_maccs:Ext3', 'output_top:Ext3', 'output_ecfp:Ext3', 'output_fcfp:Ext3', 'output_pub:Ext3',], :]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# NN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Reading in grep files\n",
    "grep_nn = pd.read_csv('grep_nn.txt', sep = '\\t')\n",
    "grep_nn = grep_nn.T\n",
    "grep_nn = grep_nn.reset_index()\n",
    "grep_nn = grep_nn.T\n",
    "grep_nn = grep_nn.reset_index(drop = True)\n",
    "\n",
    "#converting to str (nn)\n",
    "grep_nn = grep_nn.astype(str)\n",
    "\n",
    "#splitting scores \n",
    "str_split_nn = grep_nn[1].tolist()\n",
    "i = 0\n",
    "while (i < len(str_split_nn)):\n",
    "    str_split_nn[i] = str_split_nn[i].split()\n",
    "    i = i + 1\n",
    "    \n",
    "#making df\n",
    "scores_nn = pd.DataFrame(str_split_nn)\n",
    "\n",
    "#label df\n",
    "labels_nn = pd.DataFrame(grep_nn[0])\n",
    "labels_nn = labels_nn.rename(columns = {0 : 'labels'})\n",
    "\n",
    "#combining\n",
    "data_nn = pd.concat([labels_nn, scores_nn], axis = 1)\n",
    "\n",
    "#reindexing\n",
    "data_nn = data_nn.rename(columns = {0 : 'ACC', 1 : 'AUC', 2 : 'MCC', 3 : 'SENS', 4 : 'SPHE', 5 : 'BACC',})\n",
    "data_nn = data_nn.set_index('labels')\n",
    "\n",
    "#seperating datasets into Train, Test, Ext1, Ext2, Ext3\n",
    "data_nn = data_nn.iloc[:25]\n",
    "data_nn.loc['output_maccs:Test']\n",
    "subset_nn_train = data_nn.loc[['output_maccs:Train', 'output_top:Train', 'output_ecfp:Train', 'output_fcfp:Train', 'output_pub:Train',], :]\n",
    "subset_nn_test = data_nn.loc[['output_maccs:Test', 'output_top:Test', 'output_ecfp:Test', 'output_fcfp:Test', 'output_pub:Test',], :]\n",
    "subset_nn_ext1 = data_nn.loc[['output_maccs:Ext1', 'output_top:Ext1', 'output_ecfp:Ext1', 'output_fcfp:Ext1', 'output_pub:Ext1',], :]\n",
    "subset_nn_ext2 = data_nn.loc[['output_maccs:Ext2', 'output_top:Ext2', 'output_ecfp:Ext2', 'output_fcfp:Ext2', 'output_pub:Ext2',], :]\n",
    "subset_nn_ext3 = data_nn.loc[['output_maccs:Ext3', 'output_top:Ext3', 'output_ecfp:Ext3', 'output_fcfp:Ext3', 'output_pub:Ext3',], :]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Combining Dataframes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "#combining auc for tests\n",
    "auc_nb_test = subset_nb_test['AUC']\n",
    "auc_dt_test = subset_dt_test['AUC']\n",
    "auc_rf_test = subset_rf_test['AUC']\n",
    "auc_knn_test = subset_knn_test['AUC']\n",
    "auc_svm_test = subset_svm_test['AUC']\n",
    "auc_nn_test = subset_nn_test['AUC']\n",
    "auc_all_test = pd.concat([auc_nb_test, auc_dt_test, auc_rf_test, auc_knn_test, auc_svm_test, auc_svm_test], axis = 1)\n",
    "auc_all_test.columns = ['Naive Bayes', 'Decision Tree', 'Random Forest', 'K Nearest Neighbors', 'Support Vector Machine', 'Neural Network']\n",
    "auc_all_test['FPs'] = ['Maccs', 'Topological', 'Morgan ECFP', 'Morgan FCFP', 'PubChem']\n",
    "auc_all_test = auc_all_test.set_index('FPs')\n",
    "auc_all_test = auc_all_test.astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "#combining auc for ext1\n",
    "auc_nb_ext1 = subset_nb_ext1['AUC']\n",
    "auc_dt_ext1 = subset_dt_ext1['AUC']\n",
    "auc_rf_ext1 = subset_rf_ext1['AUC']\n",
    "auc_knn_ext1 = subset_knn_ext1['AUC']\n",
    "auc_svm_ext1 = subset_svm_ext1['AUC']\n",
    "auc_nn_ext1 = subset_nn_ext1['AUC']\n",
    "auc_all_ext1 = pd.concat([auc_nb_ext1, auc_dt_ext1, auc_rf_ext1, auc_knn_ext1, auc_svm_ext1, auc_svm_ext1], axis = 1)\n",
    "auc_all_ext1.columns = ['Naive Bayes', 'Decision Tree', 'Random Forest', 'K Nearest Neighbors', 'Support Vector Machine', 'Neural Network']\n",
    "auc_all_ext1['FPs'] = ['Maccs', 'Topological', 'Morgan ECFP', 'Morgan FCFP', 'PubChem']\n",
    "auc_all_ext1 = auc_all_ext1.set_index('FPs')\n",
    "auc_all_ext1 = auc_all_ext1.astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "#combining auc for ext2\n",
    "auc_nb_ext2 = subset_nb_ext2['AUC']\n",
    "auc_dt_ext2 = subset_dt_ext2['AUC']\n",
    "auc_rf_ext2 = subset_rf_ext2['AUC']\n",
    "auc_knn_ext2 = subset_knn_ext2['AUC']\n",
    "auc_svm_ext2 = subset_svm_ext2['AUC']\n",
    "auc_nn_ext2 = subset_nn_ext2['AUC']\n",
    "auc_all_ext2 = pd.concat([auc_nb_ext2, auc_dt_ext2, auc_rf_ext2, auc_knn_ext2, auc_svm_ext2, auc_svm_ext2], axis = 1)\n",
    "auc_all_ext2.columns = ['Naive Bayes', 'Decision Tree', 'Random Forest', 'K Nearest Neighbors', 'Support Vector Machine', 'Neural Network']\n",
    "auc_all_ext2['FPs'] = ['Maccs', 'Topological', 'Morgan ECFP', 'Morgan FCFP', 'PubChem']\n",
    "auc_all_ext2 = auc_all_ext2.set_index('FPs')\n",
    "auc_all_ext2 = auc_all_ext2.astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "#combining auc for ext3\n",
    "auc_nb_ext3 = subset_nb_ext3['AUC']\n",
    "auc_dt_ext3 = subset_dt_ext3['AUC']\n",
    "auc_rf_ext3 = subset_rf_ext3['AUC']\n",
    "auc_knn_ext3 = subset_knn_ext3['AUC']\n",
    "auc_svm_ext3 = subset_svm_ext3['AUC']\n",
    "auc_nn_ext3 = subset_nn_ext3['AUC']\n",
    "auc_all_ext3 = pd.concat([auc_nb_ext3, auc_dt_ext3, auc_rf_ext3, auc_knn_ext3, auc_svm_ext3, auc_svm_ext3], axis = 1)\n",
    "auc_all_ext3.columns = ['Naive Bayes', 'Decision Tree', 'Random Forest', 'K Nearest Neighbors', 'Support Vector Machine', 'Neural Network']\n",
    "auc_all_ext3['FPs'] = ['Maccs', 'Topological', 'Morgan ECFP', 'Morgan FCFP', 'PubChem']\n",
    "auc_all_ext3 = auc_all_ext3.set_index('FPs')\n",
    "auc_all_ext3 = auc_all_ext3.astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "#combining bacc for tests\n",
    "bacc_nb_test = subset_nb_test['BACC']\n",
    "bacc_dt_test = subset_dt_test['BACC']\n",
    "bacc_rf_test = subset_rf_test['BACC']\n",
    "bacc_knn_test = subset_knn_test['BACC']\n",
    "bacc_svm_test = subset_svm_test['BACC']\n",
    "bacc_nn_test = subset_nn_test['BACC']\n",
    "bacc_all_test = pd.concat([bacc_nb_test, bacc_dt_test, bacc_rf_test, bacc_knn_test, bacc_svm_test, bacc_svm_test], axis = 1)\n",
    "bacc_all_test.columns = ['Naive Bayes', 'Decision Tree', 'Random Forest', 'K Nearest Neighbors', 'Support Vector Machine', 'Neural Network']\n",
    "bacc_all_test['FPs'] = ['Maccs', 'Topological', 'Morgan ECFP', 'Morgan FCFP', 'PubChem']\n",
    "bacc_all_test = bacc_all_test.set_index('FPs')\n",
    "bacc_all_test = bacc_all_test.astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "#combining bacc for ext1s\n",
    "bacc_nb_ext1 = subset_nb_ext1['BACC']\n",
    "bacc_dt_ext1 = subset_dt_ext1['BACC']\n",
    "bacc_rf_ext1 = subset_rf_ext1['BACC']\n",
    "bacc_knn_ext1 = subset_knn_ext1['BACC']\n",
    "bacc_svm_ext1 = subset_svm_ext1['BACC']\n",
    "bacc_nn_ext1 = subset_nn_ext1['BACC']\n",
    "bacc_all_ext1 = pd.concat([bacc_nb_ext1, bacc_dt_ext1, bacc_rf_ext1, bacc_knn_ext1, bacc_svm_ext1, bacc_svm_ext1], axis = 1)\n",
    "bacc_all_ext1.columns = ['Naive Bayes', 'Decision Tree', 'Random Forest', 'K Nearest Neighbors', 'Support Vector Machine', 'Neural Network']\n",
    "bacc_all_ext1['FPs'] = ['Maccs', 'Topological', 'Morgan ECFP', 'Morgan FCFP', 'PubChem']\n",
    "bacc_all_ext1 = bacc_all_ext1.set_index('FPs')\n",
    "bacc_all_ext1 = bacc_all_ext1.astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "#combining bacc for ext2s\n",
    "bacc_nb_ext2 = subset_nb_ext2['BACC']\n",
    "bacc_dt_ext2 = subset_dt_ext2['BACC']\n",
    "bacc_rf_ext2 = subset_rf_ext2['BACC']\n",
    "bacc_knn_ext2 = subset_knn_ext2['BACC']\n",
    "bacc_svm_ext2 = subset_svm_ext2['BACC']\n",
    "bacc_nn_ext2 = subset_nn_ext2['BACC']\n",
    "bacc_all_ext2 = pd.concat([bacc_nb_ext2, bacc_dt_ext2, bacc_rf_ext2, bacc_knn_ext2, bacc_svm_ext2, bacc_svm_ext2], axis = 1)\n",
    "bacc_all_ext2.columns = ['Naive Bayes', 'Decision Tree', 'Random Forest', 'K Nearest Neighbors', 'Support Vector Machine', 'Neural Network']\n",
    "bacc_all_ext2['FPs'] = ['Maccs', 'Topological', 'Morgan ECFP', 'Morgan FCFP', 'PubChem']\n",
    "bacc_all_ext2 = bacc_all_ext2.set_index('FPs')\n",
    "bacc_all_ext2 = bacc_all_ext2.astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "#combining sens for tests\n",
    "sens_nb_test = subset_nb_test['SENS']\n",
    "sens_dt_test = subset_dt_test['SENS']\n",
    "sens_rf_test = subset_rf_test['SENS']\n",
    "sens_knn_test = subset_knn_test['SENS']\n",
    "sens_svm_test = subset_svm_test['SENS']\n",
    "sens_nn_test = subset_nn_test['SENS']\n",
    "sens_all_test = pd.concat([sens_nb_test, sens_dt_test, sens_rf_test, sens_knn_test, sens_svm_test, sens_svm_test], axis = 1)\n",
    "sens_all_test.columns = ['Naive Bayes', 'Decision Tree', 'Random Forest', 'K Nearest Neighbors', 'Support Vector Machine', 'Neural Network']\n",
    "sens_all_test['FPs'] = ['Maccs', 'Topological', 'Morgan ECFP', 'Morgan FCFP', 'PubChem']\n",
    "sens_all_test = sens_all_test.set_index('FPs')\n",
    "sens_all_test = sens_all_test.astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "#combining sens for ext1s\n",
    "sens_nb_ext1 = subset_nb_ext1['SENS']\n",
    "sens_dt_ext1 = subset_dt_ext1['SENS']\n",
    "sens_rf_ext1 = subset_rf_ext1['SENS']\n",
    "sens_knn_ext1 = subset_knn_ext1['SENS']\n",
    "sens_svm_ext1 = subset_svm_ext1['SENS']\n",
    "sens_nn_ext1 = subset_nn_ext1['SENS']\n",
    "sens_all_ext1 = pd.concat([sens_nb_ext1, sens_dt_ext1, sens_rf_ext1, sens_knn_ext1, sens_svm_ext1, sens_svm_ext1], axis = 1)\n",
    "sens_all_ext1.columns = ['Naive Bayes', 'Decision Tree', 'Random Forest', 'K Nearest Neighbors', 'Support Vector Machine', 'Neural Network']\n",
    "sens_all_ext1['FPs'] = ['Maccs', 'Topological', 'Morgan ECFP', 'Morgan FCFP', 'PubChem']\n",
    "sens_all_ext1 = sens_all_ext1.set_index('FPs')\n",
    "sens_all_ext1 = sens_all_ext1.astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "#combining sphe for ext1s\n",
    "sphe_nb_ext1 = subset_nb_ext1['SPHE']\n",
    "sphe_dt_ext1 = subset_dt_ext1['SPHE']\n",
    "sphe_rf_ext1 = subset_rf_ext1['SPHE']\n",
    "sphe_knn_ext1 = subset_knn_ext1['SPHE']\n",
    "sphe_svm_ext1 = subset_svm_ext1['SPHE']\n",
    "sphe_nn_ext1 = subset_nn_ext1['SPHE']\n",
    "sphe_all_ext1 = pd.concat([sphe_nb_ext1, sphe_dt_ext1, sphe_rf_ext1, sphe_knn_ext1, sphe_svm_ext1, sphe_svm_ext1], axis = 1)\n",
    "sphe_all_ext1.columns = ['Naive Bayes', 'Decision Tree', 'Random Forest', 'K Nearest Neighbors', 'Support Vector Machine', 'Neural Network']\n",
    "sphe_all_ext1['FPs'] = ['Maccs', 'Topological', 'Morgan ECFP', 'Morgan FCFP', 'PubChem']\n",
    "sphe_all_ext1 = sphe_all_ext1.set_index('FPs')\n",
    "sphe_all_ext1 = sphe_all_ext1.astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "#combining sphe for tests\n",
    "sphe_nb_test = subset_nb_test['SPHE']\n",
    "sphe_dt_test = subset_dt_test['SPHE']\n",
    "sphe_rf_test = subset_rf_test['SPHE']\n",
    "sphe_knn_test = subset_knn_test['SPHE']\n",
    "sphe_svm_test = subset_svm_test['SPHE']\n",
    "sphe_nn_test = subset_nn_test['SPHE']\n",
    "sphe_all_test = pd.concat([sphe_nb_test, sphe_dt_test, sphe_rf_test, sphe_knn_test, sphe_svm_test, sphe_svm_test], axis = 1)\n",
    "sphe_all_test.columns = ['Naive Bayes', 'Decision Tree', 'Random Forest', 'K Nearest Neighbors', 'Support Vector Machine', 'Neural Network']\n",
    "sphe_all_test['FPs'] = ['Maccs', 'Topological', 'Morgan ECFP', 'Morgan FCFP', 'PubChem']\n",
    "sphe_all_test = sphe_all_test.set_index('FPs')\n",
    "sphe_all_test = sphe_all_test.astype(float)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Making Heatmaps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1fabce147f0>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(bacc_all_ext2, annot = True, cmap = cm.YlGnBu, vmin = .50, vmax = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1fabcd0d908>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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t5TXPDJcvlnRzwvw/SloWHtx7QNLt4fwbJV0aPm8taaakBZL+I2kfSY3Def8JDw6eXJPvybp168nN3bN4Oicnm3Xr1u+03iOPTKV793O59dYHufbaPwJwwglHUL9+PY488myOPfYczjnnt+y+e6OajFulvIleeulNDjhgP+rWrbPTsuqWTOalSz9g7dovOPbYLjvMb9t2X2bMmMu2bYWsWrWWJUs+4LPPvqCmVeV93rRpC/ff/xSDB/er8ZyVyVvWewywYMH75OdfQK9eFzFs2AW10iKJY+aSTEr+YTbWzA5JeIwt8XKrgeYJ082ANTvsz2yNmfUxswOBv4TzNiSzbUmRK1KSugBnEPR7/hK4QFLHcHE74G4z6wBsAf4oaVdgPNA3nL8rMKjEazYDbgSOBQ4EjpB0sqTmwFUETdXjw9cvzaMELb1OwK+Az4HNQG8zOwjoDvy9On7/spjt/GWjtG9lZ5yRz/Tp9zNkSH/GjHkcCPrFMzIymD37IWbMeIDx459h1aq1NRm3SnmLLF/+MaNGPcjw4RfWWM5EFWXevn07I0Y8wJVXDthpvb59e5Cbm03fvpdx880PcOCBbcnMrPkPo6q8z3fe+Qj9+/emQYP6NZ6zSFXeY4BOndowdeo9TJ58G/fd9yTff1/znS5xzLyT6j0mNQ/Ik7Rv2CA4HZiSuIKkPSUVvdrVBJ/RAC8Bx0vaIxwwcXw4r9zoUXMU8JSZbTKz/wHPAEeGyz4ys7fC5xPD+QcAy83sg3D+P4CuJV7zMGCmmX1pZluBSeE6RfO/DrsYJ5cME76Re5rZcwBmtsXMNhE0W0dKWgi8DDSXtGfJ7Uu8VnEzeuzYx8tbdSe5uXuydu2XxdPr1q1nr72alLl+fn5Xpk8P3qrnn/8XRx11EHXqZJGdvTsHHXRA8YHdmlKVvABr137J4ME3M3LkZbRo0bRGsxapKPN3321m2bKPOfvsa+jWbQDvvvs+559/I4sWLScrK5NrrjmXZ5+9gzFjruV///uOffb5ecozl5T4Pi9YsIxRox6kW7cBPPTQFO6770kmTnw+pXnLe48T7b9/c+rXr8eyZR/XaN64Zt5JNY7uM7NtwGCC4vIe8ISZLZE0XFKvcLVjgPclLQNygJvCbb8CbiAodPOA4eG8MkXxZN7yOm1LfqWxCtav6DWT7SAurc/0bGA34CAz2yZpNVCv3BcJms1h03lZuf2wJXXokMfKlWtYtWotOTnZTJ06i7/9bcgO66xcuab4g/G11+bTsmXwvGnTnzF37kJ69z6WzZu/Z8GC9+nfv9dO+6hOVcn77bcbGTRoGJdffjYHH1xW47b2Mzdq1IC5cycVT5911tVcccU5dOiQx+bNWzCDXXetxxtvvENmZuZOB9ZTkRnKfp8nTRpZvM6dd05i113rceaZNdprXaX3eNWqtTRt+jOysjL59NPP+eijT9l7771qNG9cM++kmq84YWYvAC+UmDc04flkSvnSHy4bzw8tqwpFsUjNAu6TdCuQSTB08Xfhsn0lHWpm84B+wOvAUoKm535m9iFwJvCvEq/5FnBrOLpkA0HzdBSwiKA1tDvwHdAHmJ+4oZl9LelLST3N7DlJ9QhaoLsBn4cFqgcVjFCpqqysTIYOPY+BA6+jsHA7fft2Jy+vJaNHT6R9+zyOO+4wJk58njlz3iUrK4vGjRsycuSlQNDVc/XVozn55Asxgz59utO27b41GbdKeSdOnMonn3zGPfc8zj33BC3O8eOHk51drQMmf1Tmsqxfv4EBA64jI0Pk5GRzyy2X12jWymQu631Ohaq8x2+/vZT7759MVlYWGRni+uvPo0mT3TxzMmJ8xQmV1t9a6yGk64GNZjYqnL6CoKUCcJ+Z3SmpFfBPYA5wOPBfoL+ZbZZ0PHALQVGbC1xgZgWJQ9AlnQVcQfC/6zkzuzrc1/nAZQSjT5YBa83sOkk3Egwlv11SG+A+oAlQQDC0cjPwXLjP/xA0b7sBa0lqCHrlWlLOuZ+y1lUqM/tcPTXpz5uVI/IjVdIiUaSSERapyeGQ9ep83YZmtlFSHeBZYEzR8aea5UXKOZesKhapv7yQfJG66aRIFakodvfVthskHUNwPGkaULNHjp1zrrZlRqruVEpsipSZrQCqtRUVvu5l1f2azjkXKak6ibgaxKZIOeec+5H8flLOOeciy4uUc865qDLv7nPOORdZUby2UJK8SDnnXLpL4nJHUeVFyjnn0p0fk3LOORdZ8a1RXqSccy7dmbeknHPORZaP7nPOORdZ3pJyzjkXVRm1f8f6auNFyjnn0lyMe/u8SDnnXLrzIuWccy6yFOMq5UXKOefSXIxrlBcp55xLdz5wwjnnXGR5S8o551xkxfg0KS9SzjmX7rwl5ZxzLrK8SDnnnIssH4LunHMusnx0n3POuciKcUPKi5RzzqW7OBep+N743jnnXFIylPwjGZJ+Lel9SSskXVXK8haSXpX0jqSFkk4qZflGSUMqzJ7sL+mccy6epOQfFb+WMoG7gROBdkA/Se1KrHYt8ISZHQicDtxTYvnfgReTye7dfc45l+aqubuvC7DCzD4MXluPAb2BpQnrGNA4fL4bsOaHLPoN8CHwXTI785aUc86luYxMJf2QNEjS/ITHoBIvtzewKmF6dTgv0fXAmZJWAy8AFwFIagBcCQxLNru3pJxzLs1VpiVlZmOBseW9XGmblZjuBzxoZn+T9EvgYUntCYrT381sY7LnbnmRcs65NFfN3X2rgeYJ081I6M4LDQB+DWBmcyTVA/YEDgNOkXQLsDuwXdIWM7urrJ0lVaQk7Q+sNrPvJR0DdAT+YWbfJPc7OeecS5VqvsDsPCBP0r7ApwQDI35fYp1PgOOAByUdANQDvjCzo4pWkHQ9sLG8AgXJH5N6CiiU1AoYB+wLTEpyW+eccylUnaP7zGwbMBh4CXiPYBTfEknDJfUKV/sTcK6kBcCjwB/MrGSXYFKSLVLbw2C/BW43s8uApj9mh+7HmzXrbU444Tx69BjE2LFP7rR83rzF/Pa3l9CuXW+mTXtjh2UHHNCb3r0vpnfviznvvBsikXfChGc46aQL6NnzIvr3/wuffvp58bI1az7nnHP+yoknns9JJ13A6tXraiVzooryF5k27Q3atOnJokXLazHdD35szilTXiv+m+jd+2Latu3Fe+99GNm8q1evo2PHvsV5hw69u8azFolj5kQZmck/kmFmL5hZazPb38xuCucNNbMp4fOlZnaEmXUys85m9nIpr3G9mY2qaF/JHpPaKqkf0B/oGc6rU94GkgyYaGZnhdNZwGfAXDM7Ocn91piw2/JZ4KOE2UPMbLqkXOB24FDge2AlcClQQPDN4f2EbboQNHVvJWj61iU4MHh/deYtLCxk+PB7mTDhBnJysjnllMvp1u0wWrVqUbxO06Y/Y8SISxk//p87bV+vXl2effaO6oxU5bwHHLAfTz11G/Xr12PSpBe49dYJ3H77lQBceeXfOe+80zjiiAP57rvNZNTyDXGSyQ+wceMmHn74OTp1alOr+aojZ69ex9Cr1zEAvP/+Si644EYOOGC/yOYFaNEit1b/jiGemUuK8wVmk21J/R/wS+AmM/so7IucWME23wHtJdUPp3sQfIgnLSxsNWl2WOWLHtMV/N/8J/Ba+C2hHXANkBNu80GJbQrC+Y+bWWfgGOBmSTk77a0KFi5cTsuWTWnePJe6deuQn9+VGTPm7rBOs2Y5tG27b61/oJcmmbyHH96R+vXrAdC5cxvWrl0PwIoVn7BtWyFHHHEgAA0a1C9eL0r5AUaPfoSBA/uwyy7lfmerMdWVc+rUWZx8cteajhub9zVRHDOXVJ3dfbWtwiIl6UCCs4rHmNmjAGb2kZn9vyRe/0UgP3zej6Bvsuh1m0h6JrxkxluSOobzr5c0VtLLwD8k7SrpiXC9xyXNlXRIuO6YcBz/EknDEl57paRhkv4jaZGktkm+HwDHAlvN7N6iGWb2rpnNTmZjM/sc+ABoWYl9VmjduvXk5u5ZPJ2Tk826deuT3v777wvo0+cyTjttCNOnz6nOaKWqbN7Jk1+ha9eDAVi58lMaN27A4ME385vfXMLIkeMpLCys8cyJksm/dOkHrF37Bcce26VWsyWqrpwvvDCb/PyjayxnkarmXb16Hb/5zSWceeZVzJ+/pMbzQjwzl5S2RUrSUOBxoC8wVdK5lXz9x4DTw+GHHYHErx/DgHfMrCNBS+UfCcsOBnqb2e+BC4Cvw/VuCJcV+YuZHRK+9tFFhS70pZkdBIwByro+1FGS3k147A+0B94u53faP2H9nTqYJe0H7AesKGVZ8UlyY8c+Xs4udlbaMcfKNOFffXU8Tz/9d/72tyHcfPMDfPLJZ5Xaf2VVJu+zz77K4sUrGDiwDwDbtm1n/vylXHnlOUyefBurV6/l6adn1GjekirKv337dkaMeIArrxxQm7F2Uh05Fyx4n/r1d6F162r9XlWqquTda68mvPrqeJ55ZjRXXTWQP/1pFBs3bqrRvBDPzCXFuUhV1J32O6CzmW2SlA1MA5I+1mJmCyXtQ9CKeqHE4iMJih9mNlNStqTdwmVTzGxzwnqjw/UWS1qY8BqnhWdDZxEM5GgHFC1/Ovz5NtCnjIizSx4fS+KD/4OwW6+k30k6kuAY1h/N7KuSK+x4ktyySo10yc3dk7VrvyyeXrduPXvt1STp7XNysgFo3jyXLl3as3Tph7RoUXNjX5LN++ab73LvvU8wceII6tatE26bTbt2+9G8eS4Axx13OAsWvL/TtjWpovzffbeZZcs+5uyzrwHgiy++5vzzb2TMmGvp0CEvVjmnTp1Ffn7Nd/VVR96iv5H27VvRokUuH330aY2/33HMXFIEjgD8aBV1920xs00AZrY+ifVLMwUYRUJXX6i8s5a/q2A9wuNiQ4DjwlbWVIKx+EW+D38WUrmTlpewY2stWY+Hx6gOM7OdRy5UUYcOeaxcuYZVq9ZSULCVqVNn0a1bct1MGzZspKBgKwBffbWB//znPVq1al7BVlWTTN6lSz9g6NC7GTPmr2Rn777Dths2bOSrrzYAMHfuwp0OUte0ivI3atSAuXMnMXPmOGbOHEfnzm1qvUBVR87t27czbdobtVakqpL3q682FHf7rlq1lpUr1xR/kfHM5cvKsKQfUVPRh/f+kqaEz1ViGjPrVfpmOxgPbDCzReGIuiKzgDOAG8L5X5rZt6W0ZF4HTgNeDa+02yGc35igmG0IBymcCLyWRJ6KzCQY+HBu0Qg9SYcCuwIfV8Pr/yhZWZkMHXoeAwdeR2Hhdvr27U5eXktGj55I+/Z5HHfcYSxcuIzBg2/m22838uqr87jzzkeYOvUePvhgFddddzeSMDPOPfeUGv/QTybvLbdMYNOmLVxySXB4s2nTn3HvvX8lMzOTK688h/79rwWMX/xif0499fgazftj8kdBVXPOm7eE3Nw9a+2Dsyp5581bzB13PEJmZiaZmRkMG3Yhu+/eyDMnIc4tKZV3fpWkco+kmtm/ytl2o5k1LDHvGIJh3idLagJMIDgxeBMwKOwevJ7gLORR4TYNgIeA1sA7BMeMTjez5ZIeJLjMxocELacpZvagpJXAIWb2ZTjIYpSZHVNKlpJD0G80s8mSfk4wBP1gYAs/DEHfCjxvZu1LvNYfwv0NLu/92lHluvuccz9lratUZvJffj3pz5upxx8ZqZJWUZFqYWaf1GKe0jJkAnXMbEs4sGEG0Dph6HdMeZFyziWrakWq5yuzk/68ea7HUZEqUhV19z0DHAQg6Skz61vzkXayK0FXXx2CLsfz41+gnHOu9sS5u6+iIpX4q9XsqehlMLP/AYekYt/OOZcOstK4SFkZz51zzsVEcJW6eKqoSHWS9C1Bi6p++Jxw2syscdmbOueci4K07e4zsySvieuccy6qfswJrlHhd+Z1zrk0l5HG3X3OOediLm27+5xzzsVfOo/uc845F3Pe3eeccy6yvLvPOedcZPnoPuecc5Hl3X3OOeciy7v7nHPORZaP7nPOORdZ3t3nnHMusry7zznnXGT56D7nnHOR5S0p55xzkZWZEd9jUnFuBTrnnEtCRiUeyZD0a0nvS1oh6apSlv9d0rvhY5mkb10FlEQAACAASURBVBKW3SJpiaT3JN0hqdx2nreknHMuzVXn6D5JmcDdQA9gNTBP0hQzW1q0jpldlrD+RcCB4fNfAUcAHcPFrwNHA6+Vmb3akjvnnIukDCX/SEIXYIWZfWhmBcBjQO9y1u8HPBo+N6AeUBfYBagDrCs3e1KRnHPOxVZlipSkQZLmJzwGlXi5vYFVCdOrw3k7kdQS2BeYCWBmc4BXgc/Cx0tm9l552b27zznn0lxmJdY1s7HA2HJWKa29VVZ/4unAZDMrBJDUCjgAaBYuf0VSVzObVdbOvEg551yay6re0X2rgeYJ082ANWWsezpwYcL0b4G3zGwjgKQXgcOBMouUd/c551yaq+ZjUvOAPEn7SqpLUIimlFxJUhtgD2BOwuxPgKMlZUmqQzBowrv7nHPupyyzGk/mNbNtkgYDLxH0JI43syWShgPzzayoYPUDHjOzxGbcZKAbsIigi3CamT1X3v68SDnnXJqr7itOmNkLwAsl5g0tMX19KdsVAn+szL68SDnnXJrzq6A755yLLL92n3POuciq40XKOedcVHl3n3POuciqztF9tc2LVIzMmvU2N910P9u3b+fUU3swaNCpOyyfMOEZnnzyZTIzM2nSpDE333wJe++9FwADBlzHggXvc/DBB3DffdelIn6F+R999EUmTZpKRkYGu+5ajxtuGEyrVi1ilXHNms/Jz7+QwYP7MWBAn8hn/u9/P+K66+5m48ZNZGRkMHnybeyyS91I5n3jjXf4298eYuvWbdSpk8Wf//x//PKXnWo0a5wzJ4rzMSntOIS9ml9cMmCimZ0VTmcRXK9prpmdXGM7TpKkY4BngY/CWV+aWfdw2dnAFQSXABHBuQCjJD1IcALahnCb8WZ2h6SVwP+A7QQXTDzbzNaWvfdllXrjCwsLOeGE85gw4QZycrI55ZTLue22P+/wAfnWWwvp1Kk19evXY9KkF/j3vxdx++1XAjBnzgI2b/6exx9/MSVFKpn8GzduomHDXQGYMWMukya9wLhxw2KV8aKLbkbKoFOn1rVSpKqSedu2Qn7720u59dbLadt2X77++lsaN25AZmZlLqJTe3mXLv2A7OzdycnJZtmyjxkwYCizZz9UY1mjlbl1lcrMQ8tfSvrzpn/eCZEqaTV9xYnvgPaS6ofTPYBPK/MCYWGrSbPNrHP4KCpQJwKXAseb2S+Ag/ihKAH8OWGbOxLmH2tmnYD5wDXVGXLhwuW0bNmU5s1zqVu3Dvn5XZkxY+4O6xx+eEfq168HQOfObVi7dn3xsl/+shMNGtQnVZLJX/SPHGDz5i2Uf5eZ6GWcPn0OzZrlkpdXe62/qmR+4413aNNmH9q23ReAPfZoXKMFqqp527Xbn5ycbADy8lpQULCVgoKtNZo3rplLqpNhST+ipja6+14E8gnONC66ZPtRAJKaAOOB/YBNwCAzWyjpeuDnwD7Al5IGAg8CbQkuobEPcKGZzZc0BjgUqE9wIcPrwtdeCTwE9CS4HPypZvbfJDNfDQwxszUAZrYFuL8Sv/Ms4OJKrF+hdevWk5u7Z/F0Tk42CxcuK3P9yZNfoWvXg6szQpUkm/+RR6YyYcIzbN26jYceuqk2I1Yp46ZNW7j//qcYP/4Gxo//Zywyf/TRp0gwYMBQvvpqAyed1JVzz+0b2byJXnrpTQ44YD/q1q1To3khnplLivP172oj+2PA6ZLqEdzoKvEryDDgHTPrSNDy+EfCsoOB3mb2e+AC4OtwvRvCZUX+YmaHhK99tKSOCcu+NLODgDHAkDLyHZVwB8m/hPPaA2+X8zvdmrBNh1KWn0xw2Y9qU1q3bFk3tHz22VdZvHgFAwfWzjGRZCSb/4wz8pk+/X6GDOnPmDGP10a0YlXJeOedj9C/f+9ab61WJXNhYSFvv72UW2/9E5MmjWT69DnMmbMgsnmLLF/+MaNGPcjw4RfutF1NiGPmkqr52n21qsaLlJktJGj59KPEZTSAI4GHw/VmAtmSdguXTTGzzQnrPRautxhYmPAap0n6D/AO8AugXcKyp8Ofb4cZSpPY3ZfsV/fE7r7EYvSqpHeBxsCIkhsl3qdl7NjKfQDn5u7J2rVfFk+vW7eevfZqstN6b775Lvfe+wRjxlybkm9sZUk2f5H8/K5Mn/5WbUQrVpWMCxYsY9SoB+nWbQAPPTSF++57kokTn4905tzcPenSpT1NmuxG/fr16Nr1EJYs+SCyeQHWrv2SwYNvZuTIy2jRommNZi0Sx8wleZGq2BRgFD/cnbFIefcl+a6C9ZC0L0EL6biwlTWV4K6PRb4PfxZSua7NJezYWkvWsWHhOtvMvim50MzGmtkhZnbIoEG/q9QLd+iQx8qVa1i1ai0FBVuZOnUW3bp12WGdpUs/YOjQuxkz5q9kZ+/+I+LXnGTyr1z5w9X+X3ttPi1b/jw2GSdNGsnMmeOYOXMc/fv34o9/PJUzz6z5sUFVyXzkkQfx/vsr2bx5C9u2FTJv3mJatWpOTapK3m+/3cigQcO4/PKzOfjgdtSWOGYuKVOW9CNqamsI+nhgg5ktCkfUFZkFnAHcEM7/0sy+LaUp/TpwGkFLpR1Q1MXWmKCYbZCUA5wIvFYNeUcAt0g62czWStoF+GOJQRK1Kisrk6FDz2PgwOsoLNxO377dyctryejRE2nfPo/jjjuMW26ZwKZNW7jkkv8HQNOmP+Pee/8KwO9/fyUffriaTZu20LXrH7jppos56qiDIpV/4sTnmTPnXbKysmjcuCEjR15aa/nikrE6M++2W0P+8IffcMoplyOJrl0P4ZhjDo1s3okTp/LJJ59xzz2Pc889QU/E+PHDa/wLWRwzlxTFFlKyanoI+kYza1hi3jEEgxJODgdOTCC4vXDJgRMbzWxUuE0DgkEQrQm69doDp5vZ8nBI+GHAhwQtpylm9mA4cOIQM/tS0iHAKDM7pqwspWT/P+BPBK04Ixhqflu4v+fNbHKJ9Yv3l9y7U7kh6M65n7KqDUGfuurFpD9v8pufGKmSVqNFqrpIygTqmNkWSfsDM4DWZlaQ4mhV4EXKOZesqhWpaauTL1K/bhatIhWXK07sStDVV4egZXN+vAuUc87VHr92Xw0zs/8Bh6Q6h3POxVGcz5OKRZFyzjn348V54IQXKeecS3N+FXTnnHORlRXBa/Ily4uUc86lOe/uc845F1k+cMI551xk1fZtb6qTFynnnEtzMa5RXqSccy7d+eg+55xzkSW/4oRzzrmoinFDyouUc86luzgPnIjzyETnnHNJUCUeSb2e9GtJ70taIemqUpb/XdK74WOZpG/C+Z0lzZG0RNJCSRXe/dVbUs45l+aq82Te8NZJdwM9gNXAPElTzGxp0TpmdlnC+hcBB4aTm4Czw3sB/hx4W9JLpd3JvDh79UV3zjkXRRlK/pGELsAKM/swvGXSY0DvctbvBzwKYGbLzGx5+HwN8Dnws3KzJxXJOedcbFWmu0/SIEnzEx6DSrzc3sCqhOnV4byd9yu1JLjz+sxSlnUB6gIflJfdu/uccy7NVaa3z8zGAmMr+XJljXE/HZhsZoU7vIDUFHgY6G9m28vL40XKOefSXDVfYHY10Dxhuhmwpox1TwcuTJwhqTEwFbjWzN6qaGfe3eecc2mumkf3zQPyJO0rqS5BIZqy0z6lNsAewJyEeXWBfwL/MLMnk9mZFynnnEtzkiX9qIiZbQMGAy8B7wFPmNkSScMl9UpYtR/wmJklvuhpQFfgDwlD1DuXm33H7V3tWeZvvHMuSa2r1GH34f+eS/rzZr9GPSN16q8fk3LOuTQX5y4zL1LOOZfm4nxZJC9SzjmX5mJco7xIOedcuvOWlHPOucjymx4655yLrBjXKC9SzjmX7vzOvM455yLLW1LOOeciywdOOOeci6wY1ygvUs45l+58dJ9zzrkIi2+VivMlnX5yZs16mxNOOI8ePQYxdmzZV7mfNu0N2rTpyaJFywEoKNjK1VffTs+eg+nV6yLmzl0UibyPPvoiPXsOpnfvi+nX7wpWrPgEgDfeeIc+fS6lZ8/B9OlzKXPmLKiVvMlkLlLyPV69eh0dO/ald++L6d37YoYOvbu2Isfu76Iiyf4+URL1zKrEf1ET+ZaUpEJgEUHW9wju5LipnPVfA4aY2fxSlp0I3AA0IPhq8byZDZH0YPh8cvX/BtWjsLCQ4cPvZcKEG8jJyeaUUy6nW7fDaNWqxQ7rbdy4iYcffo5OndoUz3vyyZcBeO65u1i//hvOPfd6Jk++jYyMmvuOkkzenj2Ppl+/EwGYMWMuI0aMY9y4YeyxR2PGjPkrOTnZLFv2MQMGDGX27IdqLGtlMkPp7zFAixa5PPvsHTWeM1Hc/i4qkuzvEyVxyCzFtz0Sh+SbzayzmbUHCoDzfsyLSGoP3AWcaWYHAO2BD6svZs1auHA5LVs2pXnzXOrWrUN+fldmzJi703qjRz/CwIF92GWXOsXzVqz4hMMP7wRAdvbuNGrUgMWLV6Q8b8OGuxY/37x5S/EIpHbt9icnJxuAvLwWFBRspaBga43mTTYzlP4ep0rc/i4qkuzvEyXxyFzNtz2sRXEoUolmA60k7SNpcdFMSUMkXZ+w3pmS3pS0WFKXcN4VwE1m9l8IbtxlZvckbNM13OZDSackvPafJc2TtFDSsHDePpL+K+mBcB+PSOou6Q1JyxP2WW3WrVtPbu6exdM5OdmsW7d+h3WWLv2AtWu/4Nhjd9x927b7MmPGXLZtK2TVqrUsWfIBn332RXVHrHRegEcemUr37udy660Pcu21f9xp+UsvvckBB+xH3bo1XxCq8h5D0OX3m99cwplnXsX8+UtqPC/E7++iIsn+3URJHDKLjKQfURO9RGWQlAWcSND1V5EGZvYr4AJgfDivPfB2Ods0BY4ETgb+X7jP44E8oAvQGThYUtdw/VbAaKAj0Bb4fbj9EOCaMn6HQZLmS5o/duzjSfwaPyjt5pRKOPlh+/btjBjxAFdeOWCn9fr27UFubjZ9+17GzTc/wIEHtiUzM7NS+6+sivIWOeOMfKZPv58hQ/ozZsyO78ny5R8zatSDDB9+YY3lTFSV93ivvZrw6qvjeeaZ0Vx11UD+9KdRbNxYZq90tYnb30VFkv27iZI4ZJYykn5ETeSPSQH1Jb0bPp8NjAN+XsE2jwKY2SxJjSXtnsR+njGz7cBSSTnhvOPDxzvhdEOCovUJ8JGZLQKQtASYYWYmaRGwT2k7MLOxwNhgqnJ35s3N3ZO1a78snl63bj177dWkePq77zazbNnHnH12UB+/+OJrzj//RsaMuZYOHfK45ppzi9c9/fQ/s88+Fb2FVVNR3pLy87ty/fVjiqfXrv2SwYNvZuTIy2jRommNZi1S1fe4qLXXvn0rWrTI5aOPPqVDh7xIZ67tv4uKVPbvJgrikTlaRbMyolc2d1Z0TKqzmV1kZgXANnbMXq/ENiULgAFLgIPL2c/3Cc+V8HNEwv5bmdm4UtbfnjC9nRoo/h065LFy5RpWrVpLQcFWpk6dRbduP3TfNGrUgLlzJzFz5jhmzhxH585tij+INm/ewqZNW4Bg5FxmZmaNH9StKC/AypVrip+/9tp8WrYMPiC//XYjgwYN4/LLz+bgg9vVaM7KZC7vPf7qqw0UFhYCsGrVWlauXEPz5rmRzpyKv4uKJPN3EzVxyOyj+2rfOmAvSdnARoIuumkJy38HvCrpSGCDmW2QdCvwtKTXzWyZgnbtpWZ2Wzn7eQm4QdIjZrZR0t5AzR/BL0VWViZDh57HwIHXUVi4nb59u5OX15LRoyfSvn0exx13WJnbrl+/gQEDriMjQ+TkZHPLLZdHIu/Eic8zZ867ZGVl0bhxQ0aOvBSAiROn8sknn3HPPY9zzz1BF+D48cPJzk6mQVyzmcsyb95i7rjjETIzM8nMzGDYsAvZffdGNZq3qplT8XdRkbJ+nyiLQ+YoFp9kqbT+1CiRtNHMGpYy/2LgYuAj4FNgpZldHw5BnwMcDTQGzjGzf4fbnAwMA3YlaF1NNbM/lxyCnrhPSZcAA8PdbgTOBArD9duH6xRvL2mfxGVlq1x3n3Pup6x1larMxq2vJf1507DOMZGqaJEvUunLi5RzLllVK1LfbZuV9OdNg6yukSpSce3uc845l6Q4d/d5kXLOubQXhzFypfMi5Zxzac5bUs455yIraicXV4YXKeecS3tepJxzzkWUSO3lrqoivkfTnHPOJUVS0o8kX+/Xkt6XtELSVWWsc5qkpZKWSJqUML+FpJclvRcu36e8fXlLyjnn0l71dfdJygTuBnoAq4F5kqaY2dKEdfKAq4EjzOxrSXslvMQ/CO5I8YqkhgSXkiuTFynnnEtz1XwLji7ACjP7EEDSY0BvYGnCOucCd5vZ1wBm9nm4bjsgy8xeCedvrGhn3t3nnHNpr1pverg3sCphenU4L1FroHV4j723JP06Yf43kp6W9I6kW8OWWZm8JeWcc2kuoxL3iZI0CBiUMGtseJuh4lVK2azkZZeyCG5rdAzQDJgd3h09CzgKOJDglkePA38guAVTqbxIOedc2ku+SO1437tSrQaaJ0w3A9aUss5bZrYV+EjS+wRFazXwTkJX4TPA4ZRTpLy7zznn0lw1309qHpAnaV9JdYHTgSkl1nkGOBZA0p4E3XwfhtvuIeln4Xrd2PFY1k68SDnnXNqrvmNSZrYNGExwv733gCfMbImk4ZJ6hau9BKyXtBR4Ffizma03s0JgCDAjvIu5gPvLTe636kgVv1WHcy5ZVbtVh/Fe0p834oBIXZ7Cj0k551zai2+nmbek0oykQSVG4kRe3DLHLS945toQt7xxEd/y6soyqOJVIidumeOWFzxzbYhb3ljwIuWccy6yvEg555yLLC9S6SeOfeJxyxy3vOCZa0Pc8saCD5xwzjkXWd6Scs45F1lepJxzzkWWFynnKiDp1GTmRZWkPSR1THWOdCPp4FLm9UxFlnTmRSrmJDWQguvwS2otqZekOqnOVR5J9SVdLenecLqVpBNTnascVyc5LzIkvSapsaQmwAJggqTbUp2rIpJaSuoePq8vqVGqM5XjfkkdiiYk9QOuTWGetOSXRYq/WcBRkvYAZgDzgd8BZ6Q0VfnGA4uAI8PpNcCTwIspS1SKsHCeBOwt6Y6ERY2BbalJlbTdzOxbSQOBCWZ2naSFqQ5VHknnEpwQ2wTYn+AWEPcCx6UyVzlOASZLOoPgb/ls4PjURko/3pKKP5nZJqAPcKeZ/RZol+JMFckzs5uBrQBh/khd1DK0hqDobwHeTnhMAU5IYa5kZElqCpwGPJ/qMEm6EDgC+BbAzJYDe6U0UTnCeyKdDjxFULCON7MNqU2VfrwlFX+S9EuCltOAcF7U/78WSKpHeDdPSfsCBamNtDMzWwAskDQpvHkbYYu1uZl9ndp0FRpGcLuE181snqT9gOUpzlSR782sQAq+r0jKYuc7vqZceIuJxFxNgExgriTMzI//VaOof5i5il1KcHzkn+E9XfYjuH9LlA0HpgHNJD0EHM0PBTaKXgnvk5MFvAt8IelfZnZ5inOVSlImQSEt/rAMv/X3TV2qpPxL0jVAfUk9gAuA51KcqTQnpzrAT4mfzJsmJDUws+9SnSNZ4Z05f0XQzfemmX2e4khlkvSOmR0YHt9pXnR8J8rfmCW9ambHpjpHZYQDgAYQHNcRQUvwAYvgh1SYdaGZtU91lnTnx6RiTtIvw7tfvhdOd5J0T4pjJeM4oL2ZPQPsUtpw3giJ4/GdNyXdJekoSQcVPVIdqjxmtt3M7jezU83slPB55AoUBFkJuoJbpDpLuvPuvvi7neAg/hQIjqNI6praSOWTdBdQB+gK3AR8RzCK69BU5irHcIJv9W/E6PjOr8KfwxPmGdAtBVmSIukI4HqgJcFnkwAzs/1SmascTYElkv5N8DcMgJn1KnsTV1lepNKAma0qOtgcKkxVliT9yswOkvQOgJl9JaluqkOVxcyeJBgiXzQd+eM7cevqC40DLiMYQRn1v2EIBqe4GubdffG3StKvAJNUV9IQwq6/CNsa9ukXje7LBranNlLZwpOkZ0haHE53lBTpkzYl7SbpNknzw8ffJO2W6lwV2GBmL5rZ52a2vuiR6lBlMbN/Af8FGoWP98J5rhp5kYq/8wjOL9kbWA10Dqej7G6Cc0t+JmkY8DowMrWRynU/wQjKovO6FhKcHxNl44H/ERxHO43g3KMJKU1UsVcl3RoeZ438cTRJpwH/Bk4leI/nSjoltanSj4/uizlJTczsq1TnqCxJvwC6Exx3mG5mi1McqUyS5pnZoUWj/MJ575pZ51RnK0tp+WKQubRTJ8zMInkcTdICoEfRyNRwxOp0M+uU2mTpxY9Jxd9cSe8SfHOeFtXRUEXCc3j+E/5DXpLqPEn6UtL+/NA9eQrwWWojVWizpCPN7HUoHpSwOcWZyhXD42gZJU6dWI/3TlU7L1Lx15qgRXIOcJekx4EHzWxZamOVzswKJS2VtLeZfZrqPEm6kOCuq20lfQp8RLSvjQhwPvBQeBxKwFdA/9RGKp2kM81soqRST442s6heGHeapJeAR8Pp3wEvpDBPWvIiFXNhy+kVgqsiHAtMBC4IuyKuMrM5KQ1Yuj2B9yTNYcehu31SF6l04QCPQ8ysu6QGBN+e/5fqXBUxs3eBTpIah9PfpjhSeRqEP6N8xfOdmNmfJfUhuLisgLFm9s8Ux0o7fkwq5sKRcWcCZwHrCIbxTiEYQPGkme2bwnilklTqVa3NbEZtZ0mGpFlmFulzz0oK/y6uI/gANYLBKcOjPFoubiSdA8wOL4Traoi3pOJvDvAw8BszW50wf77C+zVFhaSXzez4qBajcrwSDu1/nB1bflEesPIYwW1cis7nOoMgf/eUJapAOPDgXGAfEj6bzOycVGWqwD7AmZJaEpzbNZugaL2b0lRpxltSMSdJUR8sUSRxdFycSPqolNlRvhICkt42s4NLzJtvZoekKlNFJL1J8EG/w8m8ZvZUykIlQVJ9guI6BNjbzDJTHCmteEsq/vaUdAXwC6Be0cyIDtvdLezDL5WZPV2bYZIVxS7TJLwq6XTgiXD6FGBqCvMkY1czuzLVIZIVntB9BNAQeIegSM1Oaag05C2pmJP0MkE3zhCCE3v7A19E8R+7pPXAs5R+g0OLareOpDoEo+WKjku9BtxXdI+pKJH0P4JjUCIYkFB0JY8MYKOZNU5VtopIupHgivixGCEn6T8Ed2ieCvwLeMvMtqQ2VfrxIhVzRd06ibeOCO91dHSqs5Uk6T9mFtkrCJRF0gMEF8R9KJx1FlBoZgNTlyp9lFJYvye4ukfRBWajXFgbEQxOOZLgqhPrzOzI1KZKL97dF39F3+Y/k5RPcMvzZinMU54o3iI+GYeWuIrAzHCIf6RJ2psfrigOgJnNSl2i0plZrIaeF5HUHjiK4KadhwCr8O6+audFKv5uDE/Y/BNwJ9CY4ErSUXRWqgP8SIWS9jezDwDCW3VE+irdkkYSnFy6lB+yGsGIv0iS9FtgppltCKd3B44J7zkWRSMJuvnuAOZFsfs3HXh3n3MVCM/rmgB8SNAabAn8n5mVdq25SJD0PtDRzL5PdZZklXG9wciOCJV0qZndXmLeJWY2OlWZ0pFfZyqmJNWT1F9SLwWulPS8pNGS9kx1vnQSnteVB1wcPtpEuUCFPiQ4jhYnpX0eRbm35+xS5v2htkOkO29JxZSkJwiORzUA9gAWA88RHMDtbGYnpzBeWpB0s5ldEz7vYWavpDpTRSTdSdCttzfQCZhBMBABADO7OEXRKiRpPPANwa1cDLgI2MPM/pDKXCVJ6gf8nuDfWuIxqEYEA2oie8J0HHmRiilJi82svaQsYLWZ5SYsWxDl2wUoJrcJTxyNGJeRiZLKvYismT1U3vJUCq+N+Fd+uIXLy8CNZvZduRvWsvAKE/sCI4CrEhb9D1hoZttSEixNRbkp7cpXAGBm2yStKbEs0gf1id9twmMjykWoImExuqrCFVPMzD4GPgZ+GRasPDObHl55oj5BsXLVxItUfDWTdAfBN86i54TTe6cuVlI2mNmLqQ6RhL3C20co4XmxCN9CAkmLCO9/lWADMJ+gdRK5C82G1+6Ly9VTkHQuMAhoAuxPcOrHvUCpF1B2P44Xqfj6c8Lz+SWWlZyOmlcl3Qo8zY7HS/6Tukilup8fbh+R+DwOXiRopU4Kp08nKLYbgAeBnqmJVa5HCK6ecjIJV09JaaLyXQh0AeYCmNlySXulNlL68WNSrtbF7TbhcSTpDTM7orR5khaZWYdUZStLnK6eAiBprpkdVjRMPjw+/J+i7K56eEvK1boY3iY8jhpKOszM5gJI6kJwIVQIrjcXRXG6egrAvyRdA9SX1AO4gGCEratG3pJytS68QsZ1/HDB1n8R3JBvQ+pSpRdJhwLjCQqTgG+BgcASIN/Mnihn85SQdDLBkO7m/HD1lGFmNiWlwcoQ3rV5AHA8wXv8EvBAXG6dExdepFytk/QUwXldiRds7RTF28fHXfiFQGb2TaqzOPdjeJGKOUmtgTFATnjeVEegl5ndmOJoZSrj8jc7zYuK8BpyZ7PzHWMjd2KspDPNbGLJkYhFojgiMWFkaqmi9j6Hx1TL+uA0M/PRfdXIj0nF3/0EI/3uAzCzhZImAZEtUsBmSUea2etQfHLv5hRnKs8LwFvAIn64P1NUNQh/xmkk4nkELesnCI5DRf1q+UNKmXc4wfD5z2s5S9rzllTMSZpnZocmXogzyq0SAEmdCbr6diP4QPoK+IOZRfL2F3G52kRcScoGTiW4avs2gmHoT5nZ1ykNlgRJRxNcJWMX4OaYnP8XK36B2fj7UtL+hN0Pkk4BPkttpPKZ2bvhZZs6Ah3M7MCoFqjQw5LOldRUUpOiR6pDlUdSa0kzJC0OpzuGtzuPHDNbb2b3hqM+/wDsDiyRFNlbu0g6QdLr8+ic/wAADzhJREFUBAXqJjM7ygtUzfCWVMyF9zYaC/wK+Br4CDgjvHRLpMTxeAmApAuBmwguflr0DyZy1xpMJOlfhN3ACS3sxWbWPrXJyibpIKAf0IPgkll/M7OlqU21M0nzgJ8BtwJzSi6P4EnpsebHpOLvYzPrHl6cM8PMonzdsDgeLwG4HGhlZl+mOkgl7Gpm/5Z2OLwTyfOjJA0juMrEe8BjwNURv0jrd8BG4BSgLzseQzPAT0qvRt6SijlJnwDTCPrxZ/o5GtVP0hTgdDPblOosyZL0IjAYeNLMDvr/7d19sJ1Vdcfx7y93ILyEKKFYeS0UoYpoGJgoMTSkgNaABgsUFKxYYgulraGC5aVaSqHixKEVLAzlpTQog5XxJRWtpQ0QLEKBSJoUaWjRUBjqMEAKAWOA8Osfex/uSXLOufdGvHs/z12fmTs3z3NyZ9Yw4a7n2XvttfIy8HzbcwuHthlJr5DmX3WKZzr/hjvd8aODwwQWSarhcufl95N6sx0E3AJ8uVM5VyNJC0nVh+tICXY6cKbtLxUNrA9JXyc1Pb2d5sxm6rUM/GHbq0vG1UvuJN5XjUvXYfxEkmoRSTsCl5H2pIZKx9NPp/pQ0m8AHyCN7bi91hlY/WY0NWEsRkOWgUPoK/akWiCXwZ4IzAXuA04oG9GIOmPNjwJusv3MJnsnVbG9SNLWwH751irbLw36mVIk9RppTue/r+0bxjWgEH5GkaQaTtKPgOWkg5CfrG2KaR/flPSfpOW+M/IcoZ8WjqkvSXNI57pWk/ZJ9pB0iu07S8bVx4we90RaEt4NiCT1M8pViH1Fdd9rK5b7Gk7SVNvPlY5jrPLS5HO2N0jaDphq+8el4+pF0jLgJNur8vV+pDfAg8tGNpjS69PJwDnAD0jneVaUjao3SUPAItsfLh3LSPqMmumIkTOvsXiTaihJf2x7IXBxr6WyGjf1JR1u+zZJx3bd6/4rXxv/qEZlq06CArD9sKStBv1ASXmu0UeBs0gD+Y7vjr9G+WFlZ0lb236xdDyDxKiZ8RVJqrkeyt+XFY1ibA4DbqP3VFhTb5K6X9J1wBfz9clU+t89HzxeACwB3tuwyrjVwF255P/VZetaD3kDSDoA2J+Nx93HkuprKJb7QhiBpMmkUeGHkvZ37gSutL1+4A8WkM8cPUkau979P3f1Z44kXdDrvu0LxzuW0cjxziElqW+TCpf+1fbxJeNqm0hSDZeLDs5h86e5atfFJX0GWNiZcZT3p86yXWVvuSZpw5kjSTuQEurzpWMZRNJK0hm/B2xPl/SLpKGHvVYKwhaK5b7mu5HUbeJo0siDU0hP0TWba/v8zoXtNZKOAqpKUvmXUN+nuBrfSpqQhPrJS2dfBKbl66eAj9h+sGhg/a2z/YqklyVNJb3BVtvPsakiSTXfTravk7TA9lJgaW4uWrMhSZM7y2W5a8bkwjH18r78/ffz9+49qca0SGqQq4FP2L4dXi39v4bUNaNG9+eBmNeQ9iifB+4tG1L7RJJqvs6h0v+VdDRpaNzuBeMZjS8BSyRdT3pTOZXhUfLV6LyVSJple1bXR+dKugv48zKRtdb2nQQFYPuO3DGjOrm8/5K8ZH2VpO+QjlFUWeLfZDFPqvkulvQ6Urnx2cC1pDZD1eqUzgNvIfXEuyjfq9X2kg7tXEh6F8Md3askacFo7lXmh5I+LWmv/PUpUs/B6uRGzt/oul4dCernIwonQhF5g39f2/+SD/MO1dpfTtLBwN+SJglDmit1as2dBXpNE+6e3lyjXEBzIamKElIV5Z91CmxqI+kK4O9s31c6ljaLJNVQkv50wMe2fdG4BTNGkn4H+F1gmu19JO0LXGX7iMKhDZQ3x2X72dKx9CPpQ8BJpF/03+36aCrwsu0jiwQ2CpJ+0/bNI92rhaQfkPo5Pko611V9mX8TRZJqKEln9bi9PTCfVEwxZZxDGjVJy4F3AP/WNTV2pe23lY2st3xO6jhgL7r2cW1XtyeV31D3Bi4Bzu36aC2wouZhgn3e/ja7V4t+5f5NrrCsURRONJTtSzt/zudKFgC/TZpsemm/n6vEetsvdloi5TY+NT8tLQaeJVVwVXeAt1v+BfmopCMZLpHeD3gzsLJsdL1JmkvqiL+bpMu7PppKpdOEs5r/zbZGJKkGkzSNNNr8ZFJ13EG215SNalSWSjof2FbSu4EzgG8WjmmQ3W2/t3QQY3Qn8Kt5n2cJcD9pnMvJRaPq7QlSfPPYuN3UWuouAvoWKVGJdJB+b2AVqRgovEZiua+hJH0OOJZ0tuSK2k/nd5M0ibQs+R7S/+D/RDqpX+U/RklXA1+wXeWbSC+dZTJJfwhsa3thAwonpgIv2N6Qr4eAybYbcSYtj/A4zfZppWNpk0hSDZV7tK0nLYf06tE2tUhgo5TbOWG79u4YnQ3yN5HKodfTgA1ySQ+Q3lD/Cphv+8Ga9/0AJN0DHNl54JI0BbjVdq2HeTdT8x5aU8VyX0PZbtwZt3wA8gLgD0i/6CVpA+ktpboihC5zSwewBc4EzgO+nhPULwOD5iDVYJvuFQHbz+fjCVWS9Imuy0nAQdTfkqxxGveLLjTamcAsYIbtnWxPA94JzJJU7d6D7UdzQcI60ltr56tatpfangf8db7+YY0zxjbxQvfU23w+bV3BeEayQ9fXZNIe1TFFI2qhWO4L4yYvQb3b9lOb3N+ZtKxT5X6JpHmkisldSU1Efwl4yHa1G+SSZgLXAVNs7ylpOmm/5IzCofUlaQapOvWJfGsX4ETbVc7u6pC0ve0XRv6bYUvEm1QYT1ttmqDg1X2paifdAhcBhwAP294bOAK4q2xII/o88OvA0wC2/x2YXTSiEeTODW8Gfo+0n/aWmhOUpJl5v/KhfD1d0pWFw2qdSFJhPA0aC17zyPCXbD8NTJI0KTdBPbB0UCOx/dgmtzYUCWSU8v7TOcCCXEm5l6T3jfBjJTXuQaCJonAijKfpkp7rcb9zzqRW/5crze4EbpT0JHUfMgV4LDfCtaStgY+Tn/grdj3pnNTMfP04cDNwS7GIRmD7sc6h9KzqB4EmijepMG5sD9me2uNrB9s1L/cdQ5of9UfAd4BHgNqnr55OmoO1G+mX/YEMz8Wq1T65G/5LALbXkR5garXRg4Cks6n/QaBx4k0qhBF0bYq/AizKh0w/SJqKXJ0c32/ZrrG7xCAv5gGYBpC0D3W3oToduIzhB4Fbqf9BoHGiui+EPnIHhM7byD8A/5yvPwkst11tubGkO2zPKR3HWOQWWZ8C9if9wp8FfNT2HSXjCmVFkgqhD0mLgTXA3aSKvh2BrUkb+8tLxjYSSX9Bmn/196QxEgDUPAMLQNJOpEpKAff0qgYtrcljcpooklQIfXS3EcpLaE8Be9Y6nLGbpF7dJWz78HEPZgS5jPtG4Mu2Hykdz0iaPCaniSJJhdDHpn3Yoi/bz0c+aPxB4ATSg8BNwFdsPzHwByvQNSZnPvAV4FLbT5aNql0iSYXQR+4r2FkqE7AtqcqvKU18jyaNjXi1vL/yHolIOoQ0UuQ44L+Bm2xfUzaqzfUYk3NZQ8bkNE4kqRBaSNJVwHbArwHXAscD99qeXzSwUZI0h9TBfX/bkwuHs5Emj8lpokhSIbSQpBW23971fQrwNdvvKR1bP7l334dIb1GrSX38bq6teKLpY3KaJs5JhdBOne7hP5G0K6l1z94F4+lL0mdIS3xrSIlplu3Hy0bVXxPH5DRZJKkQ2ukWSa8HPgd8n/TEf23ZkPpaD8y1/XDpQEJ9YrkvhJaTNJk0UPDZ0rGEMFbx2hpCC0naTtKnJV1jez3whso7iofQUySpENrpetIyWndH8YvLhRPClokkFUI7Na2jOJKWjOZemFiicCKEdmpMR3FJ25DOdP2CpB0ZTqZTgV2LBRaqEEkqhHa6gDT7ag9JN5I7iheNqL/TgDNJCWkZw0nqOeCKUkGFOkR1Xwgt1YSO4h25ge/50UE8bCqSVAgtImnPQZ/b/p/ximWsJN1te+bIfzNMJJGkQmgRSStJ+1DdRRIGdgbeYHuoSGCjIOlCYAWpfVP8YgpAJKkQWk3SXsA5wJHA5ba/UDSgASStJc1l2kBq6xS98EIkqRDaSNK+wJ8A7wQuBRbZfqlsVCGMXSSpEFpE0gGk5PRWYCFpHtOGslGNnqR5wOx8eYftW0rGE8qLJBVCi+RBjY8B3yItm23E9sfHPahRkvRZYAZplDyksR3LbJ9bLqpQWiSpEFpE0imDPre9aLxiGStJK4ADbb+Sr4eAB2y/vWxkoaQ4zBtCi9SchEbp9cAz+c+vKxlIqEMkqRBCLS4BHpB0O6mybzZwXtmQQmmx3BdCqIakXUj7UgD32v5xyXhCedEFPYQWkbT7gM/eP56xbKGZwBzgMIbHjIQJLJJUCO2yJB/g3YikU4HPj3s0YyDpSuB0YCXwH8BpkqLB7AQXy30htIiko4DLgKNs/1e+dx5wEjDX9uMl4xtE0oPAAZ2WSJImASttv7VsZKGkKJwIoUVsf1vSeuAfJX0A+Bhpj2e27TVloxvRKmBP4NF8vQepl1+YwOJNKoQWknQo8A3ge8AJtn9aOKQRSVpKSqj35lszgLuBnwDYnlcotFBQJKkQWiQ3ae10QZ9MGh+/gQY0a5V02KDPbS8dr1hCPSJJhRCqIemNwDtIifa+KEEPUd0XQqiCpI+RlvqOBY4H7slViWECizepEEIVJK0C3mX76Xy9E/A9279SNrJQUrxJhRBq8Tiwtut6Lamje5jA4k0qhFAFSTcAbwMWk/akjiEt/z0MYPsvy0UXSolzUiGEWjySvzoW5+87FIglVCLepEIIIVQr3qRCCFXIIzo2e2q2fXiBcEIlIkmFEGpxdteftwGOA14uFEuoRCz3hRCqJWmp7YGdKEK7xZtUCKEKkqZ1XU4CDgbeWCicUIlIUiGEWixjuO/gy8CPgPlFIwrFxXJfCCGEakXHiRBCUZJm5MayneuPSFos6fJNlgDDBBRJKoRQ2t8ALwJImg18FrgBeBa4umBcoQKxJxVCKG3I9jP5zycCV9v+KvBVScsLxhUqEG9SIYTShiR1HpiPAG7r+iwepCe4+AcQQijtJmCppKeAdcB3ASS9ibTkFyawqO4LIRQn6RBgF+BW2y/ke/sBU2x/v2hwoahIUiGEEKoVe1IhhBCqFUkqhBBCtSJJhRBCqFYkqRBCCNX6f8ntyZPiIeEzAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(sens_all_ext1, annot = True, cmap = cm.YlGnBu, vmin = .65, vmax = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1fabaf3dcf8>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x216 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (9, 3))\n",
    "sns.heatmap(sens_all_test, annot = True, cmap = cm.YlGnBu, vmin = .55, vmax = 1, )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x216 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f = plt.figure(figsize = (9, 3))\n",
    "sns.heatmap(sens_all_test, annot = True, cmap = cm.YlGnBu, vmin = 0.55, vmax = 1)\n",
    "\n",
    "f.savefig(\"sens_test.png\", bbox_inches='tight', dpi=600)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1fabb0e9dd8>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(auc_all_ext1, annot = True, cmap = cm.Reds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1fabb1e8438>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(auc_all_ext2, annot = True, cmap = cm.Reds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1fabb2b5518>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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s8LKIHIJ7Z4lNqloyRb+I3AVs31+BAitSxhjT4NXm3H3eHSGuASbj3tz1RVVdIiL3APNUdQJwI/C8iNyA2xV4if7G2cytSBljTANXk+6+6lDViZS7eayq3hnyeCnQ7wDnuKs617IiZYwxDVx9nnHCipQxxjRw9XjqPitSB8vVQ6442CnUyLipzxzsFGpu7ZqDnUGNbD1l4MFOocbW/1p0sFP4n9Dt1Ct/1/G+etyWsiJljDENXP0tUVakjDGmwbPuPmOMMRGrHtcoK1LGGNPQ1fYQ9LpkRcoYYxq42vwyb12zImWMMQ1cfZ5J3IqUMcY0cNbdZ4wxJmLZ6D5jjDERy7r7jDHGRKx63JCyImWMMQ2dU4/7+6xIGWNMA1d/S5QVKWOMafDEWlLGGGMilX2Z1xhjTMSSelylrEgZY0wDV497+6xIGWNMQ+dYS8rUhW4nHse5jz+E4/Mxa/wrTH7osTL7k7Jacckrz9A4MQHH5+PjW+/ix0lf4IuO5vxnH6dN715oMMi7193Ciukzw57vjCWrePDdzwkEg5zT73BGndS/QsykeUsY9+k0RISurTJ45LKzAdiweSt3vjaBnEL3zq/PXnM+LVMTw54z7bvjnDACxEEXzEC/+bzMbjnuXKRtV3clKgaaxhF89DoAnBHXQcv2sHYVwXefDH+unqg+RxN7zc3gcyj+7CP2vPVSmf2xV40hqlcfd6VRLE5SMkWnDsDXszeNr76pJM5p3Zad99yKf9ZXdZZ702OOIfP/3Yn4HArfeZeCZ8veATqqeQtajn0EJy4e8fnIe+Rhtk+bVmf5VaY+5mwtqd9ARFKAKd5qJhAANnnrfVV1by1cYyZwjaou+A3HXg1sUdU3anhcFJCvqrX6iiqOw3njHuXx40+ncN16bps7jUUTJrJx2fKSmJPvuInv3/2Ir595geaHdOGaie9ze7s/0H/UJQDce+hRxKWlcs2kD/hHn0Goam2mWEYgGOS+tyYy/roLyUiKZ/iDzzP40C50bJFWEpOdW8Dzk2fyxk2XktC0MQVFO0r23fbSR1w+dABHd+vAjt176+adoAjOSSMJvvkYFBXiXHo7unIh5G8sCdEv32Xfsya9h0BmVsm+4LeTIToGp1cd3gbecYi97jZ23HQFuimXZs+8QfHs6QTXrC4J2f3vsSWPY84cga+TW2QDC+axfdRwACQunmav/wf/vG/qNPfmd93Nmosvojgnh/Yffcy2KV+yd9WqkpC0a66m6LOJFL75BjEdO9L6hRdZNfCYusuxIeRM/R7dd9Bmy1DVAlXtqao9gWeAx/at10aBqoX8xtW0QIVT2769yVu1mvxfsgkUFzP37Q849PRTysSoKrHxcQDEJiSwZUMOAM27deWnKdMB2LYpn11bttKm9+FhzXdx9npapyeTlZZETJSPoX26M3XRT2Vi3p/5AyMH9iGhaWMAUuKbArBqwyYCwSBHd+sAQNPYGBrHRIc1XwBatIPNm2BLPgQD6NK5SOeeVYZL9z7okjmlG7J/gj27w59nCF/XHgQ3rEU3rge/n+Kpk4nuN6jK+OghQyme8nmF7VEDj8c/Z1ad5t/4sMPYu2YNxWvXQnExWz/9lLjjji8bpIrTrBkAvrg4/Hm5dZZfZepjzuC2pKq7RJqInNJJRG4WkR+95VpvW0cRWSIir4nIYhF5V0Qae/uOF5EF3vbnRSSmknNe4O3/UUQeCNl+uYisEJFpIjJeRP7lbb9PRK73HncWkakislBEfhCRtiIS7237QUQWiciwcD4nSS2bU7h2Xcn6lnUbSGrZokzMp3c9yJEXDOfBtcu4ZuJ7vHOt25WzbuFiDjv9ZByfj5S2bWh9RE+SslqGM11yC7eRmRRfsp6ZGE9e4bYyMdl5BWTnFnD+wy8y4qHxzFiyqmR7XJNY/vrMO5x1/7M88sEXBILBsOYLQFwium1z6XpRIcRV0SCOT4bEVLcwHUSSmo7m5ZSsBzflIqnplcdmNMdp3gL//DkV9sUMPpHiKZPClmdlojIyKd5Y2kr152wkOiOjTMymxx8n4Ywz6DRzFq1feJGcu++u0xzLq485gzvjRHWXSBNxRUpE+gLnA32Bo4CrRORQb3c3YJyq/gHYDVwuIk2AF4Gzve1NgNHlztkKuA8YDPQC+onIMBHJAm4FjgRO8M5fmbdwW3qHAUcDecAu4HRVPRw4DnisimNrRyW/POW76/qcdw7fvPwGt2UdwlMn/4k/v/YcIsLsF19jy7oN3DZvOuf+6x+snj2HoN8f1nSVSroSy/0IgWCQNXmbefnGixl72dnc+doEinbuJhAI8v3KX7np7BN499ZRrMvfwsff1LjH9jeo5A+0ih5R6d4XXfYDhLHLtFoqe1GpIqfowSdSPP1LKFfwJTkVp31H/HPrsKsPqpgGoWzu8aeexpYP3mdl/378etmltBz76MF9u18fc8ZaUrVtAPCBqu5U1W3Ax8C+T9x/UdVvvceve9sPAVaq6s/e9leB8h3ARwJTVTVfVYuBN72YfdsLvS7G98snIyJJQKqq/gdAVXer6k7cX9eHRGQR8AWQJSKp+/vBRGS0iMwTkXlLqVmPZuG6DSRltSpZT2zVgi0bNpaJ6XfZRXz/7kcA/PLtHKJiG9EsNYVgIMB7f7uN+3v15+kzzqNxYgJ5K38mnDKT4ksGPQDkbCkiPTGuTExGYjxDDutCtM9Hq9Qk2maksiavgMykeA7JyiQrLYkon8Oxh3Vh6a8by1+i9m0rROKSS9fjk2D7lkpDpVu5rr6DRDflIumZJetOWgZasKnS2JghJ1E8tWJXX/TgE/DP/AoC4X3jUp4/J4fo5s1L1qMym1Ocm1cmJvFPf6Jo4kQAds2fjzRqhC85mYOlPuYM4Pik2kukicQitb9nqfxbRD1A/IHOWd3/kcreml4EJACHe5+r5QOx+z2J6nOq2ltVe3ejQo/kfq2Z+z3pndqT0rYNvuho+ow4m0UTJpaJ2fzrOroe635on9m1M9GxsWzblE9048bENGkCwCHHDSbo95cZcBEOPdq0ZE1eAevyC9nrDzBp7hIGH9qlTMyxPbsyZ0U2AIXbd7Imr4Cs1CR6tG1B0c7dbN7mDqT4dnk2HZqnlb9E7duQDcnpkJAKjs8tRCsWVoxLzoDYJrA+vIW+OgI/LcHXsjWS2QKioogeciLFs6dXiHOy2iBx8QSWVPx5ooecxN467uoD2LVoETFt2xLdqhVER5MwbBjbp3xZJsa/cQNNjz4agJgOHZBGjQgUFNR5rvvUx5zBHThR3SXSROIQ9K+BZ0XkEcAHnA4M9/a1E5E+qjoXOA+YCSwFOolIe1VdDVwAlP8r/RZ4xBtRuBUYAYwFFuO2hhKBHcBZwLzQA1W1UETyReRUVf2PiMTiFvcEIE9V/SJyPBDWD3mCgQDvXHMTf538EY7Px+wXX2Pj0p849e7bWTPvBxb9ZxIf3Ph3Lnj+SY694WpUlVcuuRKA+PQ0rp38ERoMsmX9Bl66cPQBrvb7Rfkcbh9+MqOeeJ1gUDnz6J50apHOkxO+onubFgw5rAv9u3Vg9tKfGXbXOHyOw5izjiexmVtMbzr7eC7916uoQvfWzTmn/xFhzxkNEpz8Js5514Mj6MJZkL8BOeY0dOMaWOm+wEv3vujSuRUOdy68GVIyIaYRzrUPE/zsFVi9JLw5BwPseuIfNH34aXAciid9QjD7Zxr9+UoCy5fi9wpW9JCh7K2kFSUZLXDSMgks/D68eVYmECDn7rto/fIriOOw5f332LNyJWnXX8+uxYvZPmUKOQ88QIsHHiDlz5eCKhtuvunA57WcK4jA2lNtEs5hyNVOQuQuYLuqjvXWb8ZtqQA8q6pPikhH4CPgG+CPwE/Axaq6S0ROAB7GLWrfAVep6t7QIegiciFwM27r6T+qept3rSuBG4D1wAogR1X/T0Tuwx1K/i8R6QI8CyQDe4GzcT+T+o93zR+AQcAQIIdqDEG/QuIP/hNfA+OmPnPgoEgzu+6+71Mbtn9ZsfBFuvW/Fh04yPxu3X5e/bvKzOrunav9etN+yYqIKmkR0ZJS1bvKrT+MW3TKC6hqhWaAqn6B+7lQ+e39Qx6/BrxWyTlfU9WnRSQa+AT41Iu/I+TY5bhFqLwjK9kGUAffOjXGmOqpzy2piChSB9m9IjII9/Okz/GKlDHGNBQ+mxYp/FR1FVD1Nyt/+3lvqO1zGmNMJInEARHVVW+KlDHGmN+mHtcoK1LGGNPQ1eciFYnfkzLGGFOLxJFqL9U6n8hJIrJcRFaJyK2V7H/Mm6pugTft3JaQfQ97U9wtE5En5AB9kdaSMsaYBq42W1Ii4gPGAccD64C5IjJBVZfuiwn9rN+bf7WX9/hooB+wb6q7mcBAYFpV17OWlDHGNHA+R6q9VENfYJWqrvamk3sbd9KFqpyHO/8puLP3xAIxQCMgGtjvNPFWpIwxpoGr5WmRWgJrQ9bXUcWMOyLSBmgHTAVQ1W+Ar4CN3jJZVZft72JWpIwxpoGrySzooRNhe0v5CRQqq2RVzWgxAnhfVQNuHtIRd1LwVriFbYiI7PeOkPaZlDHGNHA1+Z6Uqj4HPLefkHVAVsh6K2BDFbEjgKtD1s8EvlXV7V5ek3Cnufu6qotZS8oYYxq4Wr6f1FzcSb3beTeYHQFMqHhN6QIk4c63us+vwEARifKmohsIWHefMcb8L6vNz6RU1Q9cA0zGLTDvquoSEblHRE4LCT0PeFvLzmL+PvAz7h0oFgIL992rryrW3WeMMQ1cbd/MUFUnAhPLbbuz3PpdlRwXAC6vybWsSBljTENnE8waY4yJWPV4XiQrUsYY08DZLOimxh4e3OFgp1AzX//3YGdQY7p168FOoUaaHtrmYKdQYx077D7YKZjqsO4+Y4wxkUp89XcgtxUpY4xp6Ky7zxhjTKSq7i04IpEVKWOMaeisJWWMMSZiWUvKGGNMpLIh6MYYYyKXje4zxhgTqaT+1igrUsYY0+BZd58xxphIZUPQjTHGRC5rSRljjIlY1pIyxhgTqWzuPmOMMZHLuvuMMcZEqvr8Zd5qtQFFpIOINPIeDxKRv4pIYnhTM8YYUyscqf4SYarbUfkBEBCRjsALQDvgzbBlZYwxptaISLWXSFPd7r6gqvpF5EzgX6r6pIjMD2dipiJfn6OJveYmxHHYO/Fj9r71Upn9ja66kaiefbyVWJykZLaddgy+nr2JvWpMSZzTui277r0V/6xp4U24Qw+ck0aC46A/fI3Omlhmt5w4Aml7iLsSHQNN4wk+dDVkZOGcchE0agwaRGd8ii6ZE95c9+XUtRfOGZeC4xD89kt06kdl9jun/xnp2MPLuRHEJRC4/UJISsN3yc3gOODzEZwxEf3mi7rL+axRXs7/Rb/8oGzOZ15WmnNMI2iWQOC2892cL7vVnY7AF0VwxmforM/Dn2/33kSddwU4PgIzJhGc9G6Z/b7hlyNdDnNjYxpBfCLFfz27NCC2CdH3Pk9w/mwCb44Le771NeeyCTb8gRPFInIecDFwqrcten8HiIgCr6vqhd56FLAR+E5Vh/3GfGuNiAwCPgF+Cdk8RlW/FJFM4F9AH2APkA1cD+wFlgHLQ47pC4wEHgHWAzHAY6r6fK0m7Dg0vu5Wdtx0Jbopl6ZPv4F/9nSCa1aXhOz596Ps8R5HnzkCX8cuAAQWzGPH6BHujrh44l6bgH/et7WaXgUiOCdfSPC1sVC0GWfUnejyBZC/oSREJ7+N7gvveyxkerdPL95L8OPxsDkXmiXijP4/dNVi2LMrzDk7OGeNIvDM3bC1AN8NDxNYMhdy15WEBD8pfWMg/U9GWrZzV4oKCTxxGwT8EBOL7+Z/uccWFYY/5z9dTuDf/wdbCvDdOJbA4jmQu7Y0549eKA0fcArSqn1pzo/dUprzrU+4xxZtDmu+UedfTfE/b4PCfKLueJLggm9h468lIYF3ni157Aw5DWndscwpfGdcRHDF4vDlWF59zLmc+vxl3uqW1z8DRwH3q+ovItIOeP0Ax+wAeohIY2/9eNwX8WrzCls4zVDVniHLl+K2dz8CpqlqB1XtBvwdyPCO+bncMXu97e+oak9gEPCAiGRUuNrv4Ovag+D6tejG9eD3Uzx1MlFHD66B2poAACAASURBVKoyPnrISRRPrfiuOPqY4/DPmQV7dtdmehW1bA+b82DLJggG0CVzkK69qgyXHn9Ef/QK5+ZcdwHYvgV2FEHT+PDmC9C6I5q/0b12wE9w/kykR98qw51e/dH5M92VgN9dAKKi6m40VZtO6KYcKPBy/mEG8of95HzEMegPX7srZXKOdluBYSbtuqB5GyA/x813zjScnkdVnW/fwQTnTCs9vk1HiE9Cl3wf9lxLrlkPc65ApPpLhDngb6WI9AK6AU+r6lsAqvqLqv6jGuefBJziPT4PeCvkvMki8rGILBKRb0XkUG/7XSLynIh8AbwqIk1E5F0v7h0R+U5EenuxT4vIPBFZIiJ3h5w7W0TuFpEfRGSxiHSt5vMBMBgoVtVn9m1Q1QWqOqM6B6tqHvAz0KYG1zwgSU0nmJdbep38XJy0tMpjM5rjZLYgMH9uhX3RQ06stHjVurgkNPQdedFmiEuqPDYhBRJT4ZdlFfe1aAe+KLfghZkkpMCWgtINWwqQhOTKg5PSICUDXRny7jgxBd+Yf+K783mCUz8KfyuKfTnnl27YUuBuq0xSGiSno6Hv6BNT8d3yOL67XyD45YfhbUUBJKWghZtK1wvzkaTUymOT05HUDHTZAnddBN+5owm8Nz68OZZXH3Mur6EOnBCRO4F3gLOBz0RkVA3P/zYwQkRigUOB70L23Q3MV9VDcVsqr4bsOwI4XVVHAlcBhV7cvd6+fW5X1d7euQfuK3SefFU9HHgaGEPlBojIgpClA9AD2N9bng4h8RU6l0WkPdAeWFXJvtFeUZ330ob88rv3r7LfHa1kGxA9+ET8X0+BYLDsKZJTcdp1wj/3m5pd+7eo9He98oSlx5Hosnmg5fY3S8A5cxTBT16o8thaVYPnWHr1Rxd+AxryHG8pIDD2bwQeuAqnz2BolhCWNMsmUtnGKp7nwwegC2eXyzmfwEPXEbj3Cpy+gyEu3DlXknD5/3eP03cQwe9nluTrDDqV4OK5EFow6kR9zLmshjxwYjjQU1V3ikgK8DlQ7c9aVHWRiLTFbUVNLLe7P27xQ1WnikiKiOz7C5mgqrtC4h734n4UkUUh5zhXREZ7P0dz3Bbfvv0fev9+D5xVRYozyn8+Vo3/pJ+9br3yhotIf9zPsC5X1QpvSVX1OeA5gKIhvWr0qqub8nDSS3sQJTWDYH7lv/jRg09k9xMVG7rRg47HP3NqaRdPOBUVIvHJpS+X8cmwbUulodK9L8GJ5XqPY2JxRt5A8KsPYf3qSo+rbbqlAEkMaYUkppRtDYZwevYj8GEVfwpFhWjOWqR9N3RReN8QuDmHvKtPTEG3VpHz4QMIvPdspfso2uzl3N0tZOFSmI8khfQAJKWioa3XEE7fgQTeKH0fKB0OwenUA9+gYe6gmqgo2LOLwAcvhi/f+ppzhcQir/hU14GK1G5V3QmgqgUiv+muJBOAsbif1YT2Q+zvfeuOA8ThfS42BuijqoUi8jIQGxKybwxBgJp9aXkJcE4N4vd5R1Wv+Q3HVUvgpyU4LVsjmS3Q/Dyih5zIrvtvqxDnZLVB4uIJLFlYYV/UkJPYM/7JcKVY1vpfICXd7cYrKnQL0YeVvECmZELjprAupOHp+HCGX4sunAVL59VNvgBrVyFpzSE5HbZuxunVn8Brj1WMS2sBTZpBdsj4mYQU2LkNivdC46ZI264Ep08If86/riyb8+EDCLz6aMW49Jbu85z9U9U5t+tK8KtPwpquZi9HMlpCagYUFuD0HUTg+Uo+OchohTRphv68tGRTYPxDBLzHztHHI20718mLfX3MuYI6+LwxXA704t1BRPb9pUm5dVT1tGpc40Vgq6ou9kbU7fM1cD5wr7c9X1WLKmnJzATOBb4SkW7AH7zt8bjFbKs3SGEoMK0a+RzIVNyBD6P2jdATkT5AE2BNLZz/twkG2P3kQzR56N+Iz2HvpE8IZq+m0SVXElixFP/s6YA3YOKryRUOl4zmOOmZBBbW0Ye3GiQ48Q2cC24EcdAFM2DTBmTQGeiGbFjh9tlLjyPRH78rc6h07wttOiNNmiE9+wO4o/1CRqyFRTBI8MPx+Ebf6Q7nnjMFctfinDQCXfszusT9jM85fEDpgIl9OWe0wjnt4tJTTfukzOivsOb8wXP4rrzLG4I+BXLW4gwdia5dhf44p+qcM1u5w+1VQYTg1I9hY5h/xYNB/G+OI/r6B8BxCMz6At2wBt/pFxHMXoEudAfP+I4cRHDu9PDmUl31MefyIrAbr7pEq+hbBRCRgfs7WFWr/B8Rke2q2qzctkG4w7yHiUgy8BLuF4N3AqO97sG7gO2qOtY7pinwCtAZmI/7mdEIVV3ptZ6OBFbjtpwmqOrLIpIN9FbVfG+QxVhVHVRJLuWHoN+nqu+LSAvcIehHALspHYJeDHyqqj3KnesS73rVbknVtLvvYGs6sLIezsimW7ce7BRqJhA4cEyECe4I8yhRA0DM+Mm/q8r4bziz2q83UY99FFEV7UBFqrWq1sHbwaqJiA+IVtXd3sCGKUDnkKHf9ZIVqfCzIhV+VqTqxu8uUn87q/pF6p8fRlSROlB338fA4QAi8oGqnn2A+HBogtvVF43b5XhlfS9QxhhTp+pxd9+BilToT9Y+nIlURVW3Ab0PxrWNMaZB8PkOdga/2YGKlFbx2BhjTH1Rj1tSBxqXeJiIFInINuBQ73GRiGwTkaK6SNAYY8zvVMvTIonISSKyXERWicitlex/LGTSgxUissXb3lNEvvFmCVokIsMPdK39tqRUtf62EY0xxrhqsSXlDWYbhzsf6zpgrohMUNWSL4ip6g0h8dcC+ybu3Alc5I3ObgF8LyKTVbXyb/pT/QlmjTHG1FeOU/3lwPoCq1R1tTeI7W3g9P3El8zbqqorVHWl93gDkAdUPgnpvtSrk5Exxph6rHa7+1oCod+sX+dtq+Sy0gb3u7BTK9nXF/fWRj/v72LhvhWGMcaYg60G0yJ586GODtn0nDfvaElIJYdVNbBuBPC+qpb5EqCINAdeAy5W1WClR3qsSBljTAMnNShSoRNhV2EdkBWy3grYUEXsCODqMrmIxAOfAXeo6gHvvmrdfcYY09DVbnffXKCTiLQTkRjcQlRhNmUR6QIkAd+EbIvBvansq6r6XnUuZkXKGGMaulosUqrqB64BJgPLgHdVdYmI3CMioZOOnwe8rWXn3jsXOAa4JGSI+n7nXLPuPmOMaehq+cu8qjqRcvcIVNU7y63fVclxrwOvl9++P1akjDGmoWvA95MyxhhT31mRMsYYE7Hq8dx9VqQOkvp2f6bg+o0HO4Ua27mifuUcnRp3sFOoseBOu2tOXYj5vSewlpQxxpiIZS0pY4wxEcuKlDHGmIjVgG96aIwxpr6zlpQxxpiIZUXKGGNMxLLRfcYYYyKWtaSMMcZELCtSxhhjIpaN7jPGGBOxxD6TMsYYE6msu88YY0zEstF9xhhjIpa1pIwxxkQs+0zKGGNMxLLRfcYYYyKWdfcZY4yJWNbdZ+pEhx44J40Ex0F/+BqdNbHMbjlxBNL2EHclOgaaxhN86GrIyMI55SJo1Bg0iM74FF0yJ+zpSvcj8A2/EhyH4MzPCX7+bpn9zrmjcboc5q7ENIK4RPzXn1MaENuEqLufI7hgNsG3/h32fAGi+h5N7DW3gM+h+LOP2PPmi2X2x149hqhefdyVRo1xkpIoGjYAX88+NL5mTEmc07odO++5Bf/Mr8Kes9PzSGL+fB04Dv4pn+L/+PUy+6MvuRZf98O9nGORhER2XTwUAEnNIObKW5CUdFBlzwM3oZtywpqv74g/0ujyG8FxKJ78CcXvvVpmf8yoG/AdeoSbX2wskpDEjnOPddfTMmh03e04qRmAsuvOG9C88N+BuT7mXIZjLalKiYgCr6vqhd56FLAR+E5Vh4Xz2tUhIoOAT4BfvE35qnqct+8i4GZAvOVFVR0rIi8DA4Gt3jEvquoTIpINbAOCQC5wkarW3l+7CM7JFxJ8bSwUbcYZdSe6fAHkbygJ0clvo/vC+x4LmW3cleK9BD8eD5tzoVkizuj/Q1cthj27ai29ivk6+EZejf+xv0NhPlF/f4Lgwm9h468lIcF3nyPoPXYGn4a07lDmFM7pF6ErFocvx/Ich9jr/s6OMZejm3Jp9sybFM+aRnDN6pKQ3ePGljyOOfM8fJ26AhBYMJftfxkOgMTF0+yNT/HP/aZOco75y9/Yc88N6OY8Yv8xnsC8mei67JKQ4pefpNh7HDX0bJx2nUt/hmvvoPiDVwgumgexjSEYJKwch0ZX3cyu269B8/No/K9X8H87A137S0nI3ucfK3kcfeq5OB1K84298S72vvMSgflz3Hw1zPnW15zLq8ctqXBnvgPoISKNvfXjgfU1OYFX2MJphqr29JZ9BWoocD1wgqp2Bw6ntCgB3BRyzBMh2wer6mHAPODvtZply/awOQ+2bIJgAF0yB+naq8pw6fFH9Mdv3ZXNue4CsH0L7CiCpvG1ml6F67fr4r5bzM+BgJ/g3Ok4hx1VdXzfQQTnTCvd0LojEp9IcOkPYc0zlK9rD4Lr16Ib14PfT/HUz4nuN6jK+OhjT6J4yqQK26MGHo//u5mwZ3cYs3U5HQ9Bc9aheRvA78c/60t8ffpXGe/rfxz+mf8FQFq1BcfnFiiA3btg757w5tu5O8EN69AcL9+vvyDqqGOqjI8aeAL+6V+4+Wa1A5/PfbHfl++e8OZbX3OuwPFVf4kwdVFeJwGneI/PA97at0NEkkXkYxFZJCLfisih3va7ROQ5EfkCeFVEmojIu17cOyLynYj09mKfFpF5IrJERO4OOXe2iNwtIj+IyGIR6VqDnG8DxqjqBgBV3a2qz9fg+K+BjjWIP7C4JLRoc+l60WaIS6o8NiEFElPhl2UV97VoB74ot+CFU2IKbN5Usqpb8iEppfLY5HQkNRP9aaG7LoLvT6MJvD8+vDmWI2npZbq6gpvykLSMymMzmuM0b4l/fsVu05ghJ1E89fOw5Vkmj+Q0NL/0/1ILNiHJaZXHpmbgpDcn+KNb+J3mWbBzGzE33U/sIy8SfeFVYf/Sp6Skofm5pfnm5yEpVeSbnolktiCw0C2iTqvW6I7txN7+EI2ffI2YS6+tky+p1secK3Ck+kuEqYtn621ghIjEAocC34XsuxuYr6qH4rY8Qjt6jwBOV9WRwFVAoRd3r7dvn9tVtbd37oH7Cp0nX1UPB54GxlC5ASKywFtu97b1AL7fz8/0SMgxf6hk/zCgdvupKv3d0co2Ij2ORJfNAy23v1kCzpmjCH7yQpXH1prKRhOVz8fj9BlI8PsZJd0gzqBh6I9zoDA/jAlWpvo5Rw85ieLpX1boHpPkVJz2HfHPmR2OBCuqwfPs638c/m+mlebs8+F0PYziV8ax+5ZRSEYLfIOGhi9XqCLfykOjjjkB/8yppfk6Pnzde7LnhcfZdd0lOM1bEnVcHXxqUB9zLk+k+kuECXuRUtVFQFvcVtTEcrv7A695cVOBFBFJ8PZNUNVdIXFve3E/AotCznGuiPwAzAe6A91C9n3o/fu9l0NlQrv77q/mjxXa3RdajL4SkQVAPPBg+YNEZLTX6pv3/Lzl1byUp6gQiU8uXY9Phm1bKg2V7n3Rxd+V3RgTizPyBoJffQjrV1d6XK0qzIeQd/SSmApbNlca6vQZiIZ09Un7Q3AGn0bUA6/g+9NfcP54LM6Zfw53xuimXCQtszSvtPQyrZRQMUMq7+qLHnwC/hlTIeAPW56htCAPSU0vWZeUNLSK4h7V71gCs74MOXYTweyVbldhMEBgzgyc9l3Cm29+HpJa2jqV1HQ0pMVdJt+Bx+OfPrnMscGfl7vdbsEA/m+m43QMb771NecKxKn+EmHqKqMJwFhCuvo8lZXtfe9RdhwgDhFph9tCOtZrZX0GxIaE7Ov8DVCzQSJLKNtaq67BXuG6SFUrVBBVfU5Ve6tq71G9a/iLuv4XSEl3u/Ecn1uIls+vGJeSCY2bwrpVpdscH87wa9GFs2DpvJr+TL+JZi9H0ltASgb4otzW0sJvKwZmtIImcejq0q7JwAsP47/1Ivx/v5jAe+MJfjuF4EcvhT3nwPIl+Fq1RjJbQlSU21qaPb1CnJPVBomLI7BkYYV90ccOZe+UuunqAwiu+glpnoWkN4eoKKL6HUdg7qwKcdIiC5rGEVz+Y+mxPy9DmsZBfCIAvh6HlxlwEZZ8VyzFaZGFZLRw8z3mBALfzqiYb8vWSLM4gstK3wMGVy6FZvGl+R7WG/31lwrHWs6VqMfdfXU1BP1FYKuqLvZG1O3zNXA+cK+3PV9Vi6Rik3MmcC5uS6UbsK+LLR63mG0VkQxgKDCtFvJ9EHhYRIapao6INAIuLzdIom5pkODEN3AuuBHEQRfMgE0bkEFnoBuyYcUCwOvq+7FsK0q694U2nZEmzZCe7ofqwY/HQ+7a8OUbDBJ4699EXX+/OwR91hewcQ3OaReia1aiXsFy+g4iOHda+PKoiUCAXY8/SNNHnnaHGk/6mGD2zzT681UEli/B7xWs6GOHsnfq5AqHS2YLnLTMks8j6kQwwN7x/6TRHf90h6BP/Qxd9wvRwy8j+PNPBOa5BSuq//EEZk0pd2yQva8+Rez//QsQgquX4/9yQtjz3fP0IzS+7wn3Of7iPwR/XU3MBaMJrFxG4Dv3xT960In4p/+3Yr4vPE7jB8eBCMGVP1H8+cfhzbe+5lxeBLaQqku0iv7rWjm5yHZVbVZu2yDcQQnDRCQZeAloB+wERqvqIhG5C9iuqmO9Y5oCrwCdcbv1egAjVHWlNyT8SGA1bstpgqq+7A0J762q+d4gi7GqOqiqXCrJ/c/AjbitOMUdav5P73qfqur75eJLrled5yZw95/D/KFQ7Qqur+PvddSCnSvqV87RqXEHO4UaC+7ce7BT+J/QbOKc39XECXzy72q/3vhOvyqimlNhLVK1RUR8QLSq7haRDsAUoLOq1tu/ECtS4WdFKvysSNWN312k/vN09YvUqVdGVJGqLzNONMHt6ovGbdlcWZ8LlDHG1Kl63N1XL4qUqm4Deh/sPIwxpl6KwKHl1VUvipQxxpjfwe7Ma4wxJmLV45ZU/S2vxhhjqqeW5+4TkZNEZLmIrBKRW6uIOVdElnpT1r0Zsr21iHwhIsu8/W33dy1rSRljTENXi9193mjrcbgThq8D5orIBFVdGhLTCXcO1H6qWigi6SGneBW4X1X/KyLNgP1OC28tKWOMaehqd+6+vsAqVV3tjbJ+Gzi9XMwoYJyqFgKoap6bhnQDolT1v9727aq6c38XsyJljDENXQ3m7gudY9RbRpc7W0sgdLqadd62UJ2BziIyy7vDxUkh27eIyIciMl9EHvFaZlWy7j5jjGnoajBwQlWfA57b39kqO6zcehTQCRgEtAJmiEgPb/sAoBfwK/AOcAnwQlUXs5aUMcY0dD5f9ZcDWwdkhay3AjZUEvOJqhar6i/ActyitQ739kyrVdUPfIx7U9kqWZEyxpiGrnZv1TEX6CQi7UQkBhiBe6eLUB8DgwFEJBW3m2+1d2ySiOy7j88QYCn7YUXKGGMaulocOOG1gK4BJgPLgHdVdYmI3CMip3lhk4ECEVkKfIV7D74CVQ3g3l5piogsxu063O9dz+0zKWOMaehqee4+VZ1IuZvYquqdIY8V+Ju3lD/2v7h3Uq8WK1LGGNPAVXKPvnrDipQxxjR0Ngu6McaYiFW9UXsRyYrUQbJjxsKDnUKNxL3y1sFOocaaTXr9YKdQI87x5x7sFGpu946DnYGpDmtJGWOMiVj2mZQxxpiIZS0pY4wxEctaUsYYYyKWtaSMMcZELBvdZ4wxJmJZd58xxpiIZd19xhhjIpa1pIwxxkQsa0kZY4yJVGIDJ4wxxkQsa0kZY4yJWPaZlDHGmIhlLSljjDERy1pSxhhjIpZjLSljjDGRyrHRfcYYYyKVdfeZuuDrczSxV41BHB97J33E3rdfLrO/0ZU3EnVYb3clNhYnMZltZwzEd1hvYq+8sSTOad2WXffdhn/2tLDmO2PO99z/1HiCwQDnnHwCo0eeUyFm0rSZPPXKWwjQpUM7Hr1jDMtWreaufz3Njh07cXwOV5x/LicPHhDWXEty/mUjD05ZQECVcw5tx6gjD6mY809rGTd7CQJ0TU/kkWF/BODR6QuZvnojAFce1Y2hXVvXTc5z53P/0y8RDAY556RjGT3izIo5T5/NU6+9iwh0ad+WR2+7nmU//8JdTzzPjp07cRyHK847m5MH9Qt/vt8v4v7xbxAMBDnnhIGMPmdYxXxnfsdTb33s/l60a82jY65kfV4+1z74BMGg4vf7uWDY8YwYOiTs+dbXnMuyIhU2IhIAFuPmugy4WFV37id+GjBGVedVsm8ocC/QFPd/7VNVHSMiL3uP36/9n6CWOA6Nr72FHbdchW7Kpem41/HPnk7w119KQvY8/Sh7vMfRZwzH17ErAIGF89hxxXnujrh44l75BP/334Y13UAgwD2PP8uLj9xDRloKf7ryRoYc3ZeObUtfuLPXbeC5N9/jzSceIiGuGQWFWwCIbdSIh269gbatWpCbX8A5V/yN/n16Ed+sWXhzDga5778/MP7cgWTENWb4a18yuEMLOqYmlOZcuI3nv1vGGyOHkBAbQ8GO3QBM/3kDS3O38OHFJ7DXH+Tit79iQLvmNGsUHd6cAwHueWo8L/7jTjJSk/nTtbcy5KjedGyTVZrz+o089/aHvPnYfd7zvBXwnuebr6Vty+bkFmzmnKtvpn/vnsQ3axrGfIPc8+yrvHjPzWSkJPOnG+9iSN9edGzdsjTfDTk8996nvPnQHSQ0a0rBliIA0pISefvh/0dMdDQ7du3m1GtvZ3DfXmSkJIUt3/qacwX1uCVVHz5N26WqPVW1B7AXuOK3nEREegBPAReo6iFAD2B17aUZXr4uPQhuWIduXA9+P8XTJhPVb1CV8dGDT6J46ucVtx9zHP65s2DP7jBmC4t+Wknrls3JapFJTHQ0Jw8ZwJTZ35WJee+zyYw8/RQS4tzik5KUCEC7rJa0bdUCgIzUFJITE9js/dGH0+KNm2md1IysxGbE+HwM7dqaqas2lIl5f+FqRvbqSEJsjJtz01gAVhUU0ScrjSjHoUlMFF3SE5nxS07Yc160fBWtW2SS1TzDfZ4H9mPK7LllYt6b+CUjTzsp5Hl2i267Vi1o27I5ABkpye7zvDW8z/Oilatp3TyDrMx0YqKjOHnAkUz57oey+U6ezshTjiXBK5YpifEAxERHERPtFv29xX40GAxrrvU55wpEqr9EmPpQpELNADqKSFsR+XHfRhEZIyJ3hcRdICKzReRHEenrbbsZuF9VfwJQVb+q/jvkmGO8Y1aLSEm/lIjcJCJzRWSRiNztbWsrIj+JyHjvGm+IyHEiMktEVoZcs9ZIahrBvNIXPd2Uh5OSXnlsenOczBYEFsytsC960IkUT51c2+lVkJtfQPP01JL1zNRUcjcVlInJXreB7HXrOe/amxl+9RhmzPm+wnkWLVtBsd9P6xaZ4c95+y4y45qU5hzXmLztu8rmXLiN7M3bOP+NKYx4/Utm/OJ273VNS2TG6o3sKvZTuHMPc37NI2dblQ3+2ss5fzPN00Ke57QUcgs2l8153Qay123kvOtvZ/hfb2PG3PkVzrPop5UUF/tp3TwjvPkWFNI8Nbk039RkcgsKy+a7IYfs9bmcd/O9DB9zDzO+X1Syb+OmAk679nYGX3oDfzn7lDppkdTHnCsQp/pLhIm8jKogIlHAUNyuvwNpqqpHA1cBL3rbegAVXwVLNQf6A8OAf3jXPAHoBPQFegJHiMgxXnxH4HHgUKArMNI7fgzw9yp+htEiMk9E5r20Pr8aP0aZgyvZqJWGRg8+Af+MKVDuXZskp+K064h/3jc1u/ZvoRVzk3I/gz8QYM26jbz62AM8escY7hj7FEXbt5fszyvYzM0PPsYDN/8Vpw6G0Fb+bJYVCCprCrfz8ojBjB32R+78fB5Fu/fSr10mA9o3Z+QbUxnz6bcc1iKFKKcu3pVW43kOBlizfiOvjr2bR2+7njsee5qi7TtK9ucVFHLzw0/ywJirw/88V/f3YmMOrz5wG4+OuZI7nnqxJN/maSlMePJ+Jj/7MB9PnUm+13VpOR+A1GCJMPWhSDUWkQXAPOBX4IVqHPMWgKp+DcSLSGI1jvlYVYOquhTY93byBG+ZD/yAW4w6eft+UdXFqhoElgBTVFVxi2jbyi6gqs+pam9V7f3nlqmVhVRJN+XhpJe2JiQtnWDBpkpjowefWHlX38Dj8c/6CgL+Gl37t8hIS2VjXmkhzsnPJz3k3ShAZloqQ/odSXRUFK2aZ9IuqyVr1rktk+07dnLFbfdw/aXn07Nb17DnC5DZrHGZ1k/Otl2kN2tcJiYjrjFDOrUg2ufQKrEZbZPjWFPoFtYrjurGR5ecwAvnDgSgdVJc2HPOSE1h46aQ53lTAenJZd+pZ6amMOToPt7znEG7Vi1Ysz7kef5/D3D9JSPoeUjnOsg3mY35pS29nPzNpCeX/fPMTE1myJGHu/lmptGuZXPWbMwte56UJDq2bsm8pSss52qpv1WqPhSpfZ9J9VTVa1V1L+CnbO6x5Y4p/9ZHcQvJEfu5zp6QxxLy74Mh1++oqi9UEh8MWQ8ShgEpgeVLcFpmIZkt4P+3d+fhclVV+se/b8IgU5AgIAaQCMEpDEYjQiJgDAiCOICggoIMYosttEIzdNsI4sijPwXxh6AgKqLSDqACggxhFAgQwtRgC0Qik0BICELGt//Yu0jlpu4QTO7ep7I+z1PPvedUFXd5val1zh7WWmklVt7pXcy/YdISrxuy0avRmsNYcM/UJZ5baULnearlYcvXjWLa3x5h+qOPMXfePC6+8lombLftYq+ZOG5bbpqS4pwxcxYPTX+EoRKD3gAAH7tJREFUjTbcgLnz5vHp//oy793lHey60/hBiRdg9IbDmTZjNtOfmc3cBQu45H/+yjs2f9Vir3nnqBHc/NcnUsz/mMO0Gc+y8cvXYMHChTzzfPoTuO+JZ7jv788wbtPlO3QGsOVrN2fa3x5l+qOPp9/zpOuZsN3YxV4zcfu3ctOUNDqefs+PLvo9n/h13jtxR3bdYfvlHivAlqNGMu2Rx5n+2N+ZO28+F197ExO2fdPi8W47hpum3pvinfUsDz3yGBttsD6PPfk0L8yZC8DM2c9x271/ZuSI5T8M3MSYl9DgOanqV/f14nFgfUnrArNJQ3Ttn777AldJGg/MtD1T0inAryRdZ/t+SUOAI21/s4+f8wfgi5LOsz1b0ghg3vL5n9SPhQt44bSvsfpXT0dDhjD30otYOO0BVj3gkyy4/x7m33gNACtP2JV5Vy8556QNNmTIehuwYGpfI57LzkpDh/L5fz2Mg4/5AgsXLGSv3SYyauQmnHrOeYzeYnMmjNuW8WPHcN3kKez+8TTMdPRhB7LO2sO46PKrmDz1bp6Z9Sy//sOVAHzlmCN4/eavWb4xDxnCf0wcw6H/fQ0LF5r3bzmSUa9Ym9Ouu4s3vnIdJmw+gvGbvpIbHnycPc6+lKESR+24NS9fbVXmzF/A/udfBcCaq6zE1969LSsNwhDlSkOH8vlPH8LBx5/MwoUL2etdExi16caceu7PGL3FZkzYbizj37IN1916B7sfcmT6PR/6UdYZthYX/fEaJt95L8/Mms2vL7sagK8cfTiv32zk8o33sI9y8BdOSfFO3IFRm2zEqef9itGbb8qEbccwfsyWXDflLnY//LgU74H7ss6wNbn+9rv42tnnIwnbHPS+3Xjtphv3/0NXwJiXUGHyGSi5w3hrTSTNtr3E2mNJnwE+AzwI/A14yPYX8hL0G4EdgWHAQbZvzu/ZAzgRWJ10d/V720f3XILe/jMlHQEckn/sbGB/YEF+/ej8mhffL2nT9ud6M2vimLp/8T2sde75pUNYagsv+UnpEJbKkJ33KR3C0nvhuf5fE/5peu3b/qks48f+MuDPG71ys6oyWvVJqltFklr+IkkNgkhSg+KfTlKPPzjwJLXByKqSVFOH+0IIIQxUg4f7IkmFEEK3iyQVQgihXs1NUk1Ygh5CCOGfIGnAjwH+93aVdJ+k/5V0bC+v2UfSPZLulvTTtvMH5Mo8f5Z0QH8/K+6kQgih2y3D4T5JQ4HTgZ2B6cAtki7KhRBarxkFHAeMsz1D0vr5/HDgBOAtpBXWt+b3zuj5c1riTiqEELrdsq3d91bgf20/kIsr/Ax4b4/XHAqc3ko+tp/I598FXG776fzc5cCuff2wSFIhhNDtlqLiRHuN0fz4RI//2gjg4bbj6flcuy2ALXLR7T9J2nUp3ruYGO4LIYSuN/DhPttnAmcu5X+s5z6slUh1TncCNgKuze2SBl4pO4s7qRBC6HbLtnbfdKC9ttNGwCMdXnOh7Xm2HwTuIyWtgbx3MZGkQgih2y3bIui3AKMkjZS0CvAh4KIer/kN8A4ASa8gDf89QKqHuoukdSStQ+oy0WeDuxjuCyGEbrcMmxnani/p06TkMhQ42/bdkk4CJtu+iEXJ6B5SrdOjbT8FIOmLpEQHcJLtp5f8KYtEkgohhG63jCtO2L4YuLjHuf9q+97AZ/Oj53vPZlEz2n5FkgohhK7X3IoTkaRCCKHbRe2+EEII1YokFUIIoV7NTVLR9LDLSPpE3ozXGE2LuWnxQsQ8GJoWb1PEPqnu07OESRM0LeamxQsR82BoWryNEEkqhBBCtSJJhRBCqFYkqe7TxDHxpsXctHghYh4MTYu3EWLhRAghhGrFnVQIIYRqRZIKIYRQrUhSIfRD0gcHcq5WuS3CVqXj6DaS3tzh3HtKxNLNIkk1nKQ1pFSHX9IWkvaUtHLpuPoiaTVJx0k6Ix9vLmm30nH14bgBnquGpKslDZM0HLgDOEfSN0vH1R9Jr5Y0MX+/mqS1SsfUh7Mkbdk6kPRh4D8LxtOVoixS810DvD03ELsCmAzsC+xXNKq+nQ3cCYzPx48AFwCXFIuog5w43w2MkHRq21PDgPllohqwtW3PknQIcI7tEyRNLR1UXyQdStoQOxzYjNS19QzgnSXj6sPewH9L2o/0t/wxUhO/sAzFnVTzyfY/gA8Ap9l+P/CGwjH1Z5TtLwPzAHL8NRYXe4SU9F8Abm17XAS8q2BcA7GSpA2BfYDflQ5mgA4HxgGzAGz/GVi/aER9sP0AqSvtL0kJaxfbM8tG1X3iTqr5JGk70p3Twflc7f+/zpX0MsAAkkYCc8uGtCTbdwB3SPqp7XmQ5neAjW3PKBtdv04kdUe9zvYtkl4D/LlwTP2ZY3uucsVuSSuR/0ZqIulOFo9rOKlD7U2SsB3zf8tQ7R9moX9HkuZHfp1bOL8GuKpwTP05CbgU2EjSucCOLEqwNbpc0p6kfy9TgL9LmmR7ia6jNZA0lJRIX/ywzFf9e5WLakAmSToeWE3SzsCngN8WjqmTPUoHsCKJzbxdQtIatp8rHcdASVoP2J40zHeD7ScKh9QrSbfbflOe39m4Nb9T8xWzpKtsv6N0HEsjLwA6mDSvI9Kd4Pdd4YdUjnWq7dGlY+l2MSfVcJK2k3QPcG8+3lrSdwuHNRDvBEbb/g2waqflvBVp4vzODZK+I+ntksa0HqWD6ovthbbPsv1B23vn76tLUJBiJQ0Fb1I6lm4Xw33N9y3SJP5FkOZRJO1QNqS+SfoOsDKwA/Al4DnSKq6xJePqw0mkq/rrGzS/s33+elLbOQMTCsQyIJLGAV8AXk36bBJg268pGVcfNgTulnQz6W8YANt7lgup+0SS6gK2H9bi7aEXlIplgLa3PUbS7QC2n5a0SumgemP7AtIS+dZx9fM7TRvqy34A/BtpBWXtf8OQFqeE5SyG+5rvYUnbA5a0iqSjyEN/FZuXx/Rbq/vWBRaWDal3eZP0FZLuysdbSap606aktSV9U9Lk/PiGpLVLx9WPmbYvsf2E7adaj9JB9cb2JOB/gLXy4958LixDkaSa75Ok/SUjgOnANvm4ZqeT9pasJ+lE4Drga2VD6tNZpBWUrX1dU0n7Y2p2NvAsaR5tH9Leo3OKRtS/qySdkudZq59Hk7QPcDPwQdLv+CZJe5eNqvvE6r6GkzTc9tOl41hakt4ITCTNO/zR9l2FQ+qVpFtsj22t8svnptjepnRsvekUXwNi7rR1wrarnEeTdAewc2tlal6x+kfbW5eNrLvEnFTz3SRpCunK+dJaV0O15D08t+V/yHeXjmeAnpS0GYuGJ/cGHi0bUr+elzTe9nXw4qKE5wvH1KcGzqMN6bF14ilidGqZiyTVfFuQ7kgOAr4j6efAD23fXzaszmwvkHSPpBG2/1Y6ngE6nNR19XWS/gY8SN21EQH+BTg3z0MJeBo4oGxInUna3/ZPJHXcHG271sK4l0r6A3B+Pt4XuLhgPF0pklTD5Tuny0lVEd4B/AT4VB6KONb2jUUD7OwVwL2SbmTxpbsfKBdSZ3mBx1tsT5S0Bunq+dnScfXH9hRga0nD8vGswiH1ZY38teaK50uwfbSkD5CKywo40/avC4fVdWJOquHyyrj9gY8Cj5OW8V5EWkBxge2RBcPrSFLHqta2rxjsWAZC0jW2q9571lP+uziB9AFq0uKUk2peLdc0kg4Crs2FcMNyEndSzXcj8GPgfbant52frNyvqRaSLrO9S63JqA+X56X9P2fxO7+aF6z8jNTGpbWfaz9S/BOLRdSPvPDgUGBT2j6bbB9UKqZ+bArsL+nVpL1d15KS1pSiUXWZuJNqOEmqfbFES/vquCaR9GCH0zVXQkDSrbbf3OPcZNtvKRVTfyTdQPqgX2wzr+1fFgtqACStRkquRwEjbA8tHFJXiTup5nuFpH8H3gi8rHWy0mW7a+cx/I5s/2owgxmoGodMB+AqSR8CfpGP9wZ+XzCegVjd9jGlgxiovKF7HLAmcDspSV1bNKguFHdSDSfpMtIwzlGkjb0HAH+v8R+7pKeAC+nc4NC1DutIWpm0Wq41L3U18L1Wj6maSHqWNAcl0oKEViWPIcBs28NKxdYfSSeTKuI3YoWcpNtIHZp/D0wC/mT7hbJRdZ9IUg3XGtZpbx2Rex3tWDq2niTdZrvaCgK9kfR9UkHcc/OpjwILbB9SLqru0SGxziFV92gVmK05sa5FWpwynlR14nHb48tG1V1iuK/5Wlfzj0randTyfKOC8fSlxhbxAzG2RxWBK/MS/6pJGsGiiuIA2L6mXESd2W7U0vMWSaOBt5Oadr4FeJgY7lvmIkk138l5w+bngNOAYaRK0jX6aOkAXqIFkjaz/ReA3Kqj6irdkr5G2lx6D4tiNWnFX5UkvR+40vbMfPxyYKfcc6xGXyMN850K3FLj8G83iOG+EPqR93WdAzxAuht8NfBx251qzVVB0n3AVrbnlI5loHqpN1jtilBJR9r+Vo9zR9j+dqmYulHUmWooSS+TdICkPZUcI+l3kr4t6RWl4+smeV/XKOAz+fHamhNU9gBpHq1JOn0e1Tza87EO5w4c7CC6XdxJNZSkX5Dmo9YA1gHuAn5LmsDdxvYeBcPrCpK+bPv4/P3Oti8vHVN/JJ1GGtYbAWwNXEFaiACA7c8UCq1fks4GniG1cjHwr8A6tg8sGVdPkj4MfIT0b619Dmot0oKaajdMN1EkqYaSdJft0ZJWAqbbfmXbc3fU3C5ADWkT3r4asSkrEyX1WUTW9rl9PV9Sro34eRa1cLkMONn2c32+cZDlChMjga8Ax7Y99Sww1fb8IoF1qZpvpUPf5gLYni/pkR7PVT2pT/PahDdGzUmoPzkZHdvvCwuzPQ2YBmyXE9Yo23/MlSdWIyWrsIxEkmqujSSdSrribH1PPh5RLqwBmWn7ktJBDMD6uX2E2r5/UcUtJJB0J7n/VZuZwGTS3Ul1hWZz7b6mVE9B0qHAJ4DhwGakrR9nAB0LKIeXJpJUcx3d9v3kHs/1PK7NVZJOAX7F4vMlt5ULqaOzWNQ+ov37JriEdJf603z8IVKynQn8EHhPmbD6dB6pesoetFVPKRpR3w4H3grcBGD7z5LWLxtS94k5qTDomtYmvIkkXW97XKdzku60vWWp2HrTpOopAJJusr1ta5l8nh++rRV7WDbiTioMuga2CW+iNSVta/smAElvJRVChVRvrkZNqp4CMEnS8cBqknYGPkVaYRuWobiTCoMuV8g4gUUFWyeRGvLNLBdVd5E0FjiblJgEzAIOAe4Gdrf9iz7eXoSkPUhLujdmUfWUE21fVDSwXuSuzQcDu5B+x38Avt+U1jlNEUkqDDpJvyTt62ov2Lp1je3jmy5fEMj2M6VjCeGliCTVcJK2AP4/sEHeN7UVsKftkwuH1qteyt8sca4WuYbcx1iyY2x1G2Ml7W/7Jz1XIrbUuCKxbWVqR7X9nvOcam8fnLYdq/uWoZiTar6zSCv9vgdge6qknwLVJingeUnjbV8HL27ufb5wTH25GPgTcCeL+jPVao38tUkrET9JurP+BWkeqvZq+Ud1OPc20vL5JwY5lq4Xd1INJ+kW22PbC3HWfFcCIGkb0lDf2qQPpKeBA21X2f6iKdUmmkrSusAHSVXb55OWof/S9oyigQ2ApB1JVTJWBb7ckP1/jRIFZpvvSUmbkYcfJO0NPFo2pL7ZnpLLNm0FbGn7TbUmqOzHkg6VtKGk4a1H6aD6ImkLSVdIuisfb5XbnVfH9lO2z8irPg8EXg7cLana1i6S3iXpOlKC+pLtt0eCWj7iTqrhcm+jM4HtgRnAg8B+uXRLVZo4XwIg6XDgS6Tip61/MNXVGmwnaRJ5GLjtDvsu26PLRtY7SWOADwM7k0pmfcP2PWWjWpKkW4D1gFOAG3s+X+Gm9EaLOanmm2Z7Yi7OOcR2zXXDmjhfAvBZYHPbT5YOZCmsbvtmabHpnSr3R0k6kVRl4l7gZ8BxlRdpfQ6YDewN7MXic2gGYlP6MhR3Ug0n6a/ApaRx/Ctjj8ayJ+ki4EO2/1E6loGSdAnwaeAC22PyMPDBtncrHNoSJC0k9b9qLZ5p/Q23quNHBYcVWCSphsuVl99Dqs02Bvgd8LPWyrkaSfo6afXh86QEuzVwpO2fFA2sF5J+TSp6ehXN6c3UaRh4f9sPlYyrk1xJvFc1Dl2HwRNJqotIWgf4NmlOamjpeHrTWn0o6f3A+0htO66qtQdWbz2amtAWoyHDwCH0KuakukBeBrsvsBtwC7BP2Yj61Wpr/m7gfNtP95g7qYrtcyWtAmyRT91ne15f7ylFUqeW5rR+v7Z/NKgBhfBPiiTVcJIeBKaQNkIeXVsX0178VtL/kIb7PpX7CL1QOKZeSdqJtK/rIdI8ycaSDrB9Tcm4ejG2wzmRhoRHAJGk/kl5FWKvYnXfshXDfQ0naZjtWaXjWFp5aHKW7QWSVgeG2X6sdFydSLoV+Ijt+/LxFqQ7wDeXjaxvSrdP+wHHAPeQ9vNMLRtVZ5KGAufa3r90LP3ppdVMS7ScWcbiTqqhJP277a8DJ3caKqtxUl/SBNtXSvpA27n2l/xq8KMakJVbCQrA9v2SVu7rDSXlvkYHAp8jNeTbuz3+GuWLlfUkrWJ7bul4+hKtZgZXJKnmujd/vbVoFEtnR+BKOneFNfUmqcmSfgD8OB/vR6W/97zx+AjgCmDXhq2Mewi4Pi/5f3HYutZN3gCSRgNvYPF29zGkugzFcF8I/ZC0KqlV+HjS/M41wHdtz+nzjQXkPUdPkNqut//jrn7PkaQTOp23feJgxzIQOd6dSEnqYtLCpets710yrm4TSarh8qKDY1jyaq7acXFJXwa+3upxlOenPme7ytpyTdINe44krUVKqLNLx9IXSXeS9vjdbntrSRuQmh52GikIL1EM9zXfeaRqE7uTWh4cQLqKrtluto9vHdieIendQFVJKn8I9XoVV+NdSROSUG/y0NmPgeH5+EngY7bvLhpY7563vVDSfEnDSHew1dZzbKpIUs23ru0fSDrC9iRgUi4uWrOhklZtDZflqhmrFo6pkz3y18Pz1/Y5qcaUSGqQM4HP2r4KXlz6fxapakaNJueGmGeR5ihnAzeXDan7RJJqvtam0kcl7U5qGrdRwXgG4ifAFZLOId2pHMSiVvLVaN2VSBpne1zbU8dKuh44qUxkXWuNVoICsH11rphRnby8/yt5yPoMSZeStlFUucS/yaKfVPOdLGlt0nLjo4Dvk8oMVau1dB54Pakm3hfzuVqtIWl860DS9iyq6F4lSUcM5FxlHpD0eUmb5sd/kmoOVicXcv5N2/FDkaCWj1g4EYrIE/yjbP8xb+YdWmt9OUlvBs4mdRKG1FfqoJorC3TqJtzevblGeQHNiaRVlJBWUX6htcCmNpJOB35o+5bSsXSzSFINJem/+njatr84aMEsJUmHAp8AhtveTNIo4Azb7ywcWp/y5LhszywdS28kfRj4COmD/tq2p4YB821PLBLYAEj6oO0L+jtXC0n3kOo5TiPt66p+mX8TRZJqKEmf63B6DeBg0mKKNQc5pAGTNAV4K3BTW9fYO21vWTayzvI+qb2ATWmbx7Vd3ZxUvkMdCXwFOLbtqWeBqTU3E+zl7m+Jc7Xobbl/k1dY1igWTjSU7W+0vs/7So4APk7qbPqN3t5XiTm257ZKIuUyPjVfLV0IzCSt4KpuA2+7/AE5TdJEFi2R3gJ4HXBn2eg6k7QbqSL+CEmntj01jEq7CWc1/812jUhSDSZpOKm1+X6k1XFjbM8oG9WATJJ0PLCapJ2BTwG/LRxTXzayvWvpIJbSNcDb8zzPFcBkUjuX/YpG1dkjpPj2ZPFyU89S9yKg35MSlUgb6UcC95EWA4VlJIb7GkrSKcAHSHtLTq99d347SUNIw5K7kP6B/4G0U7/KP0ZJZwKn2a7yTqST1jCZpH8FVrP99QYsnBgGPGd7QT4eCqxquxF70nILj8NsH1Y6lm4SSaqhco22OaThkE412oYVCWyAcjknbNdeHaM1Qb45aTn0HBowQS7pdtId6v8DDrZ9d83zfgCS/gRMbF1wSVoTuMx2rZt5l1DzHFpTxXBfQ9lu3B63vAHyBODTpA96SVpAukupbhFCm91KB/ASHAkcB/w6J6jXAH31QarBy9pHBGzPztsTqiTps22HQ4Ax1F+SrHEa90EXGu1IYBww1va6tocD2wLjJFU792B7Wl6Q8DzprrX1qJbtSbb3BL6Tjx+oscdYD8+1d73N+9OeLxhPf9Zqe6xKmqN6b9GIulAM94VBk4egdrb9ZI/z65GGdaqcL5G0J2nF5KtIRURfDdxru9oJcknbAT8A1rS9iaStSfMlnyocWq8kjSWtTn0kn9oQ2Nd2lb27WiStYfu5/l8ZXoq4kwqDaeWeCQpenJeqttMt8EXgbcD9tkcC7wSuLxtSv74FvAt4CsD2HcAORSPqR67c8DrgX0jzaa+vOUFJ2i7PV96bj7eW9N3CYXWdSFJhMPXVFrzmluHzbD8FDJE0JBdB3aZ0UP2x/XCPUwuKBDJAef7pGOCIvJJyU0l79PO2khp3IdBEsXAiDKatJc3qcL61z6RWz+SVZtcA50l6gro3mQI8nAvhWtIqwGfIV/wVO4e0T2q7fDwduAD4XbGI+mH74dam9KzqC4EmijupMGhsD7U9rMNjLds1D/e9l9Q/6t+AS4G/ALV3X/0kqQ/WCNKH/TYs6otVq81yNfx5ALafJ13A1GqxCwFJR1H/hUDjxJ1UCP1omxRfCJybN5l+iNQVuTo5vo/arrG6RF/m5gaYBpC0GXWXofok8G0WXQhcRv0XAo0Tq/tC6EWugNC6G7kIuDwfHw1MsV3tcmNJV9veqXQcSyOXyPpP4A2kD/xxwIG2ry4ZVygrklQIvZB0ITADuJG0om8dYBXSxP6UkrH1R9KXSP2vfk5qIwFAzT2wACStS1pJKeBPnVaDltbkNjlNFEkqhF60lxHKQ2hPApvU2pyxnaRO1SVse8KgB9OPvIz7POBntv9SOp7+NLlNThNFkgqhFz3rsEVdtuUjbzT+ELAP6ULgfOAXth/p840VaGuTczDwC+Abtp8oG1V3iSQVQi9yXcHWUJmA1Uir/JpSxHd3UtuIF5f3V14jEUlvI7UU2Qv4X+B822eVjWpJHdrkfLshbXIaJ5JUCF1I0hnA6sA7gO8DewM32z64aGADJGknUgX3N9hetXA4i2lym5wmiiQVQheSNNX2Vm1f1wR+ZXuX0rH1Jtfu+zDpLuohUh2/C2pbPNH0NjlNE/ukQuhOrerh/5D0KlLpnpEF4+mVpC+ThvhmkBLTONvTy0bVuya2yWmySFIhdKffSXo5cApwG+mK//tlQ+rVHGA32/eXDiTUJ4b7QuhyklYlNRScWTqWEJZW3LaG0IUkrS7p85LOsj0HWL/yiuIhdBRJKoTudA5pGK29ovjJ5cIJ4aWJJBVCd2paRXEkXTGQc2HFEgsnQuhOjakoLullpD1dr5C0DouS6TDgVcUCC1WIJBVCdzqB1PtqY0nnkSuKF42od4cBR5IS0q0sSlKzgNNLBRXqEKv7QuhSTago3pIL+B4fFcRDT5GkQugikjbp63nbfx2sWJaWpBttb9f/K8OKJJJUCF1E0p2keaj2RRIG1gPWtz20SGADIOlEYCqpfFN8MAUgklQIXU3SpsAxwETgVNunFQ2oD5KeJfVlWkAq6xS18EIkqRC6kaRRwH8A2wLfAM61Pa9sVCEsvUhSIXQRSaNJyemNwNdJ/ZgWlI1q4CTtCeyQD6+2/buS8YTyIkmF0EVyo8aHgd+Ths0WY/szgx7UAEn6KjCW1EoeUtuOW20fWy6qUFokqRC6iKQD+nre9rmDFcvSkjQV2Mb2wnw8FLjd9lZlIwslxWbeELpIzUlogF4OPJ2/X7tkIKEOkaRCCLX4CnC7pKtIK/t2AI4rG1IoLYb7QgjVkLQhaV4K4Gbbj5WMJ5QXVdBD6CKSNurjufcMZiwv0XbATsCOLGozElZgkaRC6C5X5A28i5F0EPCtQY9mKUj6LvBJ4E7gLuAwSVFgdgUXw30hdBFJ7wa+Dbzb9p/zueOAjwC72Z5eMr6+SLobGN0qiSRpCHCn7TeWjSyUFAsnQugiti+WNAe4RNL7gENIczw72J5RNrp+3QdsAkzLxxuTavmFFVjcSYXQhSSNB34D3ADsY/uFwiH1S9IkUkK9OZ8aC9wI/APA9p6FQgsFRZIKoYvkIq2tKuirktrHL6ABxVol7djX87YnDVYsoR6RpEII1ZD0SuCtpER7SyxBD7G6L4RQBUmHkIb6PgDsDfwpr0oMK7C4kwohVEHSfcD2tp/Kx+sCN9h+bdnIQklxJxVCqMV04Nm242dJFd3DCizupEIIVZD0I2BL4ELSnNR7ScN/9wPY/ma56EIpsU8qhFCLv+RHy4X561oFYgmViDupEEII1Yo7qRBCFXKLjiWumm1PKBBOqEQkqRBCLY5q+/5lwF7A/EKxhErEcF8IoVqSJtnusxJF6G5xJxVCqIKk4W2HQ4A3A68sFE6oRCSpEEItbmVR3cH5wIPAwUUjCsXFcF8IIYRqRcWJEEJRksbmwrKt449JulDSqT2GAMMKKJJUCKG07wFzASTtAHwV+BEwEzizYFyhAjEnFUIobajtp/P3+wJn2v4l8EtJUwrGFSoQd1IhhNKGSmpdML8TuLLtubiQXsHFH0AIobTzgUmSngSeB64FkLQ5acgvrMBidV8IoThJbwM2BC6z/Vw+twWwpu3bigYXiookFUIIoVoxJxVCCKFakaRCCCFUK5JUCCGEakWSCiGEUK3/A1AuK3yvCYNFAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(auc_all_ext3, annot = True, cmap = cm.Reds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}