DrJiang_Ray_2020_paper_ROC.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import sys\n",
    "import math\n",
    "from scipy.stats import pearsonr\n",
    "from scipy.stats import spearmanr\n",
    "from scipy.stats import ttest_ind\n",
    "import seaborn as sns\n",
    "from sklearn import metrics\n",
    "from sklearn.cluster import AgglomerativeClustering\n",
    "from sklearn.model_selection import train_test_split, cross_validate\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import RocCurveDisplay, roc_curve, auc, roc_auc_score\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.preprocessing import LabelEncoder, label_binarize, StandardScaler\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import KFold\n",
    "from sklearn.inspection import permutation_importance\n",
    "from sklearn.naive_bayes import GaussianNB, MultinomialNB, BernoulliNB, CategoricalNB\n",
    "from sklearn.neural_network import MLPClassifier"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# lots of changes made here read carefully"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#be careful about using 'find and replace' in this cell\n",
    "#taking excel file data and putting into dataframe, and creating new variable for each cell line, some modifications from Caleb's HTML file in here\n",
    "mm231=pd.read_excel('MulitplexSERS-CellLines_July_2020_RayShared.xlsx',engine='openpyxl',sheet_name=None)['MM231inPBS'].dropna(axis=1,how='all').dropna(axis=0,how='all').iloc[1:].rename(columns=pd.read_excel('MulitplexSERS-CellLines_July_2020_RayShared.xlsx',engine='openpyxl',sheet_name=None)['MM231inPBS'].dropna(axis=1,how='all').dropna(axis=0,how='all').iloc[0]).reset_index(drop=True)\n",
    "#mm231[mm231<0]=0\n",
    "mm231=pd.concat([pd.concat([pd.DataFrame({'trial': np.repeat(1,mm231.shape[0])}),mm231.iloc[:,:4]],axis=1),\n",
    "                pd.concat([pd.DataFrame({'trial': np.repeat(2,mm231.shape[0])}),mm231.iloc[:,4:8]],axis=1),\n",
    "                pd.concat([pd.DataFrame({'trial': np.repeat(3,mm231.shape[0])}),mm231.iloc[:,8:]],axis=1)],axis=0)\n",
    "mm231.insert(0,'cell_line',np.repeat('mm231',mm231.shape[0]))\n",
    "mm231=mm231.reset_index(drop=True)\n",
    "skbr3=pd.read_excel('MulitplexSERS-CellLines_July_2020_RayShared.xlsx',engine='openpyxl',sheet_name=None)['SKBR3inPBS'].dropna(axis=1,how='all').dropna(axis=0,how='all').iloc[1:].rename(columns=pd.read_excel('MulitplexSERS-CellLines_July_2020_RayShared.xlsx',engine='openpyxl',sheet_name=None)['SKBR3inPBS'].dropna(axis=1,how='all').dropna(axis=0,how='all').iloc[0]).reset_index(drop=True)\n",
    "\n",
    "skbr3=pd.concat([pd.concat([pd.DataFrame({'trial': np.repeat(1,skbr3.shape[0])}),skbr3.iloc[:,:4]],axis=1),\n",
    "                pd.concat([pd.DataFrame({'trial': np.repeat(2,skbr3.shape[0])}),skbr3.iloc[:,4:8]],axis=1),\n",
    "                pd.concat([pd.DataFrame({'trial': np.repeat(3,skbr3.shape[0])}),skbr3.iloc[:,8:]],axis=1)],axis=0)\n",
    "skbr3.insert(0,'cell_line',np.repeat('skbr3',skbr3.shape[0]))\n",
    "skbr3=skbr3.reset_index(drop=True)\n",
    "mcf7=pd.read_excel('MulitplexSERS-CellLines_July_2020_RayShared.xlsx',engine='openpyxl',sheet_name=None)['MCF7inPBS'].dropna(axis=1,how='all').dropna(axis=0,how='all').iloc[1:].rename(columns=pd.read_excel('MulitplexSERS-CellLines_July_2020_RayShared.xlsx',engine='openpyxl',sheet_name=None)['MCF7inPBS'].dropna(axis=1,how='all').dropna(axis=0,how='all').iloc[0]).reset_index(drop=True)\n",
    "#taking care of some NaN values in the mcf7 dataframe\n",
    "mcf7=pd.concat([mcf7.iloc[:,:4].fillna(method='bfill'),mcf7.iloc[:,4:]],axis=1).dropna(axis=0,how='any').reset_index(drop=True).astype(object).copy()\n",
    "#mcf7[mcf7<0]=0\n",
    "mcf7=pd.concat([pd.concat([pd.DataFrame({'trial': np.repeat(1,mcf7.shape[0])}),mcf7.iloc[:,:4]],axis=1),\n",
    "                pd.concat([pd.DataFrame({'trial': np.repeat(2,mcf7.shape[0])}),mcf7.iloc[:,4:8]],axis=1)],axis=0)\n",
    "mcf7.insert(0,'cell_line',np.repeat('mcf7',mcf7.shape[0]))\n",
    "mcf7=mcf7.reset_index(drop=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "for trial in mm231.trial.unique():\n",
    "    mm231.drop(mm231[mm231.trial==trial].index.max(),axis=0,inplace=True)\n",
    "for trial in skbr3.trial.unique():\n",
    "    skbr3.drop(skbr3[skbr3.trial==trial].index.max(),axis=0,inplace=True)\n",
    "for trial in mcf7.trial.unique():\n",
    "    mcf7.drop(mcf7[mcf7.trial==trial].index.max(),axis=0,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "mm231=mm231.rename(columns={\"Her2\": \"HER2\"})\n",
    "skbr3=skbr3.rename(columns={\"Her2\": \"HER2\"})\n",
    "mcf7=mcf7.rename(columns={\"Her2\": \"HER2\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>cell_line</th>\n",
       "      <th>trial</th>\n",
       "      <th>EpCAM</th>\n",
       "      <th>HER2</th>\n",
       "      <th>CD44</th>\n",
       "      <th>IGF1R</th>\n",
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       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>mm231</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.0881312</td>\n",
       "      <td>0.104973</td>\n",
       "      <td>0.989479</td>\n",
       "      <td>0.00185108</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>mm231</td>\n",
       "      <td>1</td>\n",
       "      <td>0.131166</td>\n",
       "      <td>-0.156575</td>\n",
       "      <td>1.8554</td>\n",
       "      <td>0.0785188</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>mm231</td>\n",
       "      <td>1</td>\n",
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       "      <td>0.0667256</td>\n",
       "      <td>0.59461</td>\n",
       "      <td>0.0432075</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>mm231</td>\n",
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       "      <td>-0.113893</td>\n",
       "      <td>0.053339</td>\n",
       "      <td>0.68377</td>\n",
       "      <td>0.0239837</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>mm231</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.0187563</td>\n",
       "      <td>0.168379</td>\n",
       "      <td>0.888258</td>\n",
       "      <td>0.0023825</td>\n",
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       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>mm231</td>\n",
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       "      <td>0.089582</td>\n",
       "      <td>0.257064</td>\n",
       "      <td>1.13875</td>\n",
       "      <td>0.0131244</td>\n",
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       "    <tr>\n",
       "      <th>148</th>\n",
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       "      <td>3</td>\n",
       "      <td>0.19277</td>\n",
       "      <td>-0.00378351</td>\n",
       "      <td>1.15843</td>\n",
       "      <td>0.0477611</td>\n",
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       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>mm231</td>\n",
       "      <td>3</td>\n",
       "      <td>0.222644</td>\n",
       "      <td>-0.12995</td>\n",
       "      <td>1.96731</td>\n",
       "      <td>0.0890588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150</th>\n",
       "      <td>mm231</td>\n",
       "      <td>3</td>\n",
       "      <td>0.25218</td>\n",
       "      <td>0.00560481</td>\n",
       "      <td>1.38762</td>\n",
       "      <td>0.0593045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151</th>\n",
       "      <td>mm231</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0499646</td>\n",
       "      <td>0.133947</td>\n",
       "      <td>0.778071</td>\n",
       "      <td>0.0644616</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    cell_line  trial      EpCAM        HER2      CD44       IGF1R\n",
       "0       mm231      1 -0.0881312    0.104973  0.989479  0.00185108\n",
       "1       mm231      1   0.131166   -0.156575    1.8554   0.0785188\n",
       "2       mm231      1  -0.059342   0.0667256   0.59461   0.0432075\n",
       "3       mm231      1  -0.113893    0.053339   0.68377   0.0239837\n",
       "4       mm231      1 -0.0187563    0.168379  0.888258   0.0023825\n",
       "..        ...    ...        ...         ...       ...         ...\n",
       "147     mm231      3   0.089582    0.257064   1.13875   0.0131244\n",
       "148     mm231      3    0.19277 -0.00378351   1.15843   0.0477611\n",
       "149     mm231      3   0.222644    -0.12995   1.96731   0.0890588\n",
       "150     mm231      3    0.25218  0.00560481   1.38762   0.0593045\n",
       "151     mm231      3  0.0499646    0.133947  0.778071   0.0644616\n",
       "\n",
       "[150 rows x 6 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mm231"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>cell_line</th>\n",
       "      <th>trial</th>\n",
       "      <th>EpCAM</th>\n",
       "      <th>HER2</th>\n",
       "      <th>CD44</th>\n",
       "      <th>IGF1R</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>skbr3</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.10999</td>\n",
       "      <td>0.652451</td>\n",
       "      <td>0.039156</td>\n",
       "      <td>0.067491</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>skbr3</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.06618</td>\n",
       "      <td>0.230195</td>\n",
       "      <td>0.023738</td>\n",
       "      <td>-0.00276</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>skbr3</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.07032</td>\n",
       "      <td>0.495735</td>\n",
       "      <td>0.033673</td>\n",
       "      <td>-0.01158</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>skbr3</td>\n",
       "      <td>1</td>\n",
       "      <td>0.283278</td>\n",
       "      <td>0.927984</td>\n",
       "      <td>0.119659</td>\n",
       "      <td>0.079347</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>skbr3</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.00793</td>\n",
       "      <td>1.48431</td>\n",
       "      <td>0.194084</td>\n",
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       "      <th>147</th>\n",
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       "      <td>0.997588</td>\n",
       "      <td>1.40046</td>\n",
       "      <td>0.163618</td>\n",
       "      <td>0.0566113</td>\n",
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       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>skbr3</td>\n",
       "      <td>3</td>\n",
       "      <td>0.757674</td>\n",
       "      <td>0.962522</td>\n",
       "      <td>0.158397</td>\n",
       "      <td>-0.1049</td>\n",
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       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>skbr3</td>\n",
       "      <td>3</td>\n",
       "      <td>-0.0716846</td>\n",
       "      <td>0.925452</td>\n",
       "      <td>0.0734693</td>\n",
       "      <td>0.108263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150</th>\n",
       "      <td>skbr3</td>\n",
       "      <td>3</td>\n",
       "      <td>0.477565</td>\n",
       "      <td>1.31027</td>\n",
       "      <td>0.185903</td>\n",
       "      <td>0.124822</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151</th>\n",
       "      <td>skbr3</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0708446</td>\n",
       "      <td>2.08726</td>\n",
       "      <td>0.289848</td>\n",
       "      <td>0.188467</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    cell_line  trial      EpCAM      HER2       CD44      IGF1R\n",
       "0       skbr3      1   -0.10999  0.652451   0.039156   0.067491\n",
       "1       skbr3      1   -0.06618  0.230195   0.023738   -0.00276\n",
       "2       skbr3      1   -0.07032  0.495735   0.033673   -0.01158\n",
       "3       skbr3      1   0.283278  0.927984   0.119659   0.079347\n",
       "4       skbr3      1   -0.00793   1.48431   0.194084   0.124917\n",
       "..        ...    ...        ...       ...        ...        ...\n",
       "147     skbr3      3   0.997588   1.40046   0.163618  0.0566113\n",
       "148     skbr3      3   0.757674  0.962522   0.158397    -0.1049\n",
       "149     skbr3      3 -0.0716846  0.925452  0.0734693   0.108263\n",
       "150     skbr3      3   0.477565   1.31027   0.185903   0.124822\n",
       "151     skbr3      3  0.0708446   2.08726   0.289848   0.188467\n",
       "\n",
       "[150 rows x 6 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "skbr3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>4</th>\n",
       "      <td>mcf7</td>\n",
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       "      <td>0.199617</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>mcf7</td>\n",
       "      <td>2</td>\n",
       "      <td>0.685291</td>\n",
       "      <td>0.155486</td>\n",
       "      <td>1.28309</td>\n",
       "      <td>0.190166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>mcf7</td>\n",
       "      <td>2</td>\n",
       "      <td>0.400844</td>\n",
       "      <td>0.0256968</td>\n",
       "      <td>0.056572</td>\n",
       "      <td>0.0122257</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>mcf7</td>\n",
       "      <td>2</td>\n",
       "      <td>0.460165</td>\n",
       "      <td>0.0017038</td>\n",
       "      <td>0.111792</td>\n",
       "      <td>-0.0158494</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>mcf7</td>\n",
       "      <td>2</td>\n",
       "      <td>0.551926</td>\n",
       "      <td>0.0454891</td>\n",
       "      <td>0.12484</td>\n",
       "      <td>0.124258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>mcf7</td>\n",
       "      <td>2</td>\n",
       "      <td>1.06003</td>\n",
       "      <td>0.463803</td>\n",
       "      <td>0.144474</td>\n",
       "      <td>0.0490961</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    cell_line  trial     EpCAM       HER2       CD44       IGF1R\n",
       "0        mcf7      1  0.505908  0.0992778   0.349198    0.185024\n",
       "1        mcf7      1      1.21    0.22128   0.305781  0.00655982\n",
       "2        mcf7      1   0.69633  0.0699004   0.156289    0.156549\n",
       "3        mcf7      1  0.309665  0.0092093  0.0707674   0.0658008\n",
       "4        mcf7      1  0.548672   0.116571   0.374886    0.199617\n",
       "..        ...    ...       ...        ...        ...         ...\n",
       "96       mcf7      2  0.685291   0.155486    1.28309    0.190166\n",
       "97       mcf7      2  0.400844  0.0256968   0.056572   0.0122257\n",
       "98       mcf7      2  0.460165  0.0017038   0.111792  -0.0158494\n",
       "99       mcf7      2  0.551926  0.0454891    0.12484    0.124258\n",
       "100      mcf7      2   1.06003   0.463803   0.144474   0.0490961\n",
       "\n",
       "[100 rows x 6 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mcf7"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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CW8JmMcuv+CLA7wCfAQJsA54Avk/VR4FTqnosgLZZLBZPVGHTOyaTmSaaJcVVezhtlSUbZCrAqnocOA4gItcCG1T1UE4bZilg2Adu2ePCKfjzftj1ldmv+wxUvsVRkyzZx68wNFWdH0jxFZEqIvKNiBwXkRMiMl1EfK57LSJ1RWSqiBwRkbMisklEHg+UfZYA4im+eeCB24CYv6j23P+cNsM3TmyGX1sa8Q0pDtd8A1e8YasUFwAy/AuKyDCMe+E/qprk2s8MVdVXMuskIsWA34BzQD/XdV4F5opII1U9ncn4K13j52EysB0HapFxmJzFafKI79czB0SezvVw5C/4rQMknIQSdaDNt2ZpsaVAkNlH6AiMML6ByXA2wodzKpCpAAMPADWAaFXdAiAia4B/MJnURqc3UESCgM+BOap6q8ehuT5c11JI8Zb5LM/ngCjVAIrXgMia0DIGikQ6bZElgGQmwNUBVPW8536A6Ab8kSy+rutsF5FFQHcyEGBMWFxdjFBbLD6Rb8oLxR+B4HAoUhxCisH1v0HoJTbErACSoQCr6s6M9rNJfdJGUwCsI/MMate4XsNF5A+gGRALTAGeVdWzAbPSUuDI07Peo8tgwe1QphW0/tKIblhpp62y5BB+PYQTkcdEpEyArl0aI5qpOQZcksnYiq7Xr4BfgRuANzG+4MnpDRKRB0Vkmf+mWiy5wNbxMOsaOLMLTu8wfl9Lgcbfx6jvAKNE5CdgAvCjQ6Xpkz84vlDV5AeD80QkGHjdVYVjQ+pBqjoWGCsimluGWpwnz1e9SIyHZYNh6zizX/NhaPYOBIc5apYl5/E3G9qNmCoXNwDTgAMi8r6IXJWFa8fifaab3szYk6Ou11mp2pNjna7Igj2WAkqejng4vRNmtTHiGxxuHrQ1/8iKbyHB37L0vwC/iEhxjJ/2HmAgMFBENmFWzE1S1b0+nG4dxg+cmnrAeh/GZkSSD9e3FDAym+nmSd/vxrfh2DKIqGZyOpS2c4fCRJaqIqvqKVWNUdVrMZERwzBLlV/DlCryhRlASxGpkdwgItWA1q5jGfEzJn64U6r2zq5X6+cthGQkvnlu5ptMk9ch+knovNyKbyEk20tpVHWXiExynetJTGl6X/gEGAR8LyIvcDF+eDcwJrmTiEQBW4GXk6srq+pREXkNeFFETmAWZFyJ+SCY4BnaZil85MmZbjLnj8Pfw6HRy1CkhHE7NMso4tJSkMmyAItICUyNuHsws1aAtZiHc5miqqdF5DrgbWAiZgY9B3hCVU95XgoIJu1s/WXgJMYF8hSwHxiFb4tALJbcJ24dLLgNTm6GC8eNv9dSqPG3KnIQ5mv+PcDNmOrIh4H3MDPPVf6cT1V3Abdn0mcHRoRTtytmsYadPhRy8nyUA8DOr+CPeyHxjKlWXP8Fpy2y5AH8nQHvA8oCF4AfMLPdn1U1MdCGWSy+kqejHJIuwMpnYdPbZr9ab2g+1qxwsxR6/BXgncBLwBRVzSxUzGLJVfKc7zcxHuZ2gkO/g4RA07eh9qN2SbHFjb9haC1yyhCLpcARHG4ymJ38B66ZahKoWyweZCkMzWKxpIMqnDt6cb/Ze9B5hRVfi1cyywf8GyY8rJOqJrj2M0NV9fqAWGexpEOefPCWcAb+egiO/gmdlkJoSbOirWgFpy2z5FEyc0HUwKwqE499m0fB4hjpCa/jD99ObjUhZnFrILgYxK6C8u2ctcmS58ksHWW1jPYtlpzClxnutdFliRnQPJcsyoC9/4PFfeBCHETWMkuKSzVw2ipLPsAWlbLkSTJbVpwnhFeT4O+XYe1LZr9yd2g5wbgeLBYf8HchRiLQV1W95twVkZ7AZFUNDoRxFkueCy3z5MAcl/gKNH4V6j0HYp9rW3zH3xlwZgGMNsDR4jd58oGaL1x2A9QfCuXawmXOV3q25D8C7YKoisnPYLH4THri6/iDNW9sn2iWEl/S2Ow3ftVZeyz5mkwFWES6Y4pkJvOgiHTw0rU00AFYGCDbLIWMPO1uSDwPK56Ef/5rqhTftAZCIpy2ypLP8WUG3ATo7/pZgbauLTWngMWYFJOWXGLg7IEs2LvAaTN8Jl+6G87sgQV3wNE/ICjU+Hqt+FoCQKYCrKovYfI/ICJJQJ/0HsJZcp/U4tumUhuHLPHCpDvgn19TNOUrdwPAwXmwqCfEH4JiVaDNNLg0KxW4LJa0+OsDro5JP2nJY/zd72+nTUhLKvGlVkdwmZmn3Q3JbPoAVjwBmgjlrzdl4sPz6AeFJV/ibzKenTlliKUAM+L4xZ+f+59zdvhLWBkjvvWeg0avQpCNrrQElsxyQXyK8fs+qKqJrv3MUFW9LyDWWSy5TcKZi7l6q/WCUvWhVENnbbIUWDKbAffHCPAjQCIXH8ZlhAJWgC35j93T4a+H4dqfoXQz02bF15KDZJYLIiijfYulQJCUAKuHwoY3zf72SRcF2GLJQWwuCEvhJv4wLOoFB38DCYYrRkH0E05bZSkkBESARaQZZiHGAlWND8Q5LZYc58hfsPB2E+cbXg5af21TSFpyFb9cCiLylIj8kKptMvAXMBP4W0TKB9A+iyVnSDgN828y4lumlalaYcXXksv469PtBexK3hGR61xtU4ChwGXAMwGzzmLJKUIi4KoxUOtRuH4eFKvktEWWQoi/LohqwGce+7cA+zGr41REygDdgH8FwjiLJaCc2gGxK6DKbWa/6u1ms1gcwt8ZcARw1mP/OmC2qiaXKVoP2KmEJe+x/1eY2QwW3QVHlzltjcUC+D8D3gs0BBCRKKAeMNrj+CXAucCYZsmPrH7jBhqf/StFWzUnV79pEqx7Dda8CChUvAkiL3fOHovFA38F+AdgoIiEAC0wYuv539UA2BEY0yz5kdTi+1tikzR9ci3xzvnjsOQe2DsDEGg4Ahq8aKtWWPIM/grwy0AjYCBGfJ9Q1YMAIlIUuBUYH1ALLfkTV/6H63DoE/n4evj9Fjj5DxQpBVdPgko3OWGJxZIufk0FVDVWVa8HSgElVHVMqi7tgP/4ej4RqSIi34jIcRE5ISLTRaSqPza5zvOciKiI2GTwFoOEwNkDUKoxdF5mxdeSJ8nSQgxVPeGl7Syw2tdziEgx4DfMTLofJofEq8BcEWmkqqd9PE8N4AXgkK/XLmxcuHCBPXv2EB+fC2tkOn1tXjdsyPlrpUYVxKMsYb3fIagI7DkPOGCPpVAQHBxMqVKlKFOmDEFB/rm3siTAIlILqAVcipdCnKr6uQ+neQCoAUSr6hbXedcA/wAPkfLhXkZ8BEwCorFLq72yZ88eIiMjqVatGiI5Uzd1+5HTnIy/QKOSLpGvWDdHrpMuSefh1DYILW1WtVksuYCqcuHCBQ4ePMiePXuoWtW/L/D+lqUvD0wAbkhu8mYT4IsAdwP+SBZfAFXdLiKLMDXoMhVgEbkbaArcBUz34ZqFkvj4+BwVX4CT8Rdy7NyZcuEUnNoKSReMEIeVsQ/aLLmCiBAaGkqlSpXYtGmT3+P9nTF+gBHfjzDug6N+X/Ei9YHvvbSvA+7IbLCIXAK8DTyjqsdyUlwKAtn9/STPcNOjmhyghJxN93iOoArnDsHpPYBCkUhTMNOKryWX8df1kIy/AnwD8LGqBqLwZmkg1kv7MUw8cWaMAjaTcmVehojIg8CDvva3wJn9mymmp6kOvj+yDSuRgxa50EQ4vRPOHTP74eXNcmIrvpZ8hL8CHIQfD9pyChFpA9wDNPVYhZcpqjoWGCsiPo8p7BTz7VmoIawEXJpLixySxVeCIKIahJXOnetaLAHEXwFeADQO0LVj8T7TTW9m7MkYTLzxHhEp5WoLAYJd+2dVtUCvyMv1cvQVr8i9a/lC0YqQeM6Ib0hRp61hwIABVK9enWHDhjltSoHh6aefJj4+nvfff99pU3IMf7+vDQFuFZFAZDBZh/EDp6YeJqdERtQFHsYIdfLWGmjp+vmRANiXp/EU3zxVij5A9O/fn65du15sUIVzx1i2dCkiwo7dB6BEHXbsOYiIeN1mzpwJwGeffZaivXz58tx8882sW7cuxTVfe+01rrrqKkqUKEHZsmW5+eabWbt2baa2/v3333z33Xc88cQTaY5Nnz6d4OBgevfunebYvHnzEBGOHDmS5li1atV466230vTv2rUrZcqUoWjRotSpU4fBgwezY8eOTG3MDtOmTaNevXqEhYVRr149vv3220zHqCrvvPMOderUISwsjMsuu4znnnsuRZ/z588zbNgwqlevTlhYGFWrVuW9995zH3/mmWeYMGEC27ZtC/g95RX8nQF/BJwCvhaRfcA2TK04T9S1WCMzZgBviUgNVd0GICLVMEL6XEYDgWu9tL0DBAODgS1ejhdIAlWOPr2HbI3ygks1KcGEmF04cdHnCylifmfOnEnjxim/nJUufdEtUaxYMbZu3YqqsnfvXp555hm6dOnC5s2bCQ0NBYzADRw4kKuuugpVZdiwYXTo0IH169enOFdq3n//fW6//XZKlEjr+x43bhzPPPMM7733HrGxsVxyiS+PN9IyZswYBg4cSJ8+fZg6dSrVq1dn7969TJ48mVdffZVx48Zl6byZsWTJEnr27MlLL73EbbfdxvTp07njjjtYtGgRLVq0SHfcv/71L3788UdGjRpFw4YNOX78OPv370/Rp1evXuzZs4exY8dSq1YtDh48yNmzFx/kli1blo4dO/LRRx8xatSoHLk/x1FVnzfMqtLtmW0+nisCI5R/Y8LOumH8y9uA4h79ooAEYFgm55sHLPTx2mpu3XfWR9fR9dF1/BqTkzT4rIE2+KyBT33Xr1+faZ/Vu2O9brp3hdlymX79+mmXLl1UL5xSjV2temSp6rGVunTJXAV0+/btqqq6fft2BXTp0qXpnismJkYjIiJStM2YMUMBXbNmTbrjTp48qUFBQTpjxox0+yQkJGjJkiX1u+++S3Ns9+7dGh4erkeOHNHrrrtO33///RTH584193L48OE0Y6OionTUqFHu84SGhurgwYO92hAbG5uufdnlzjvv1A4dOqRou/7667VXr17pjtm4caOGhIRk+L775ZdftESJEl7v3ZMJEyZopUqV/DPaIdK7Xw+9SaNFfs2AVbWa3wqf/rlOuxK6vw1MxMQUz8Hklzjl0VUwM9u8MBfLdwyI+Yu7o0O4sCfO3dbtg0VZONO+bNuy4/Uu/g1IOg8nNhr3Q0gxKH45hGR/xh8XF8fkyZMBKFKkSLr9Tp48SVJSUoaz1jVr1nD8+HGuvPLKNMdiYmLo2LEjl156KX379uXdd99l0CD/A4imTp3K+fPn03yFT6ZUqVLpjn344Yf54osvMjz/+vXr011AsGTJEgYPHpyirVOnTnzwwQfpnu/777+nRo0azJw5ky5dupCUlES7du0YNWoU5cqZRTLfffcdV111FaNHj+bzzz+naNGi3HjjjYwcOZLixYu7z9W8eXP27t3L1q1bufzygpfFztGVY6q6C8jQn6yqO/C+4CN1v/aBsSp/4Uuqx7ujL8sFSwKIJkHCKWb++hvFq7bB/PnNWyApKcnrkLZt26aJxdy7dy8lS5YE4PTp0xQvXhxV5cyZMwB069aNOnXqpGvG448/TpMmTWjVqlW6fXbu3ImIcNllKX/HqkpMTAxvvmkqLffo0YNHH32UZcuWeRXrjPjnn38oUaIEFStW9GscwMsvv8xTTz2VYZ+MznvgwAHKl09ZZax8+fIcOHAg3THbtm1j586dTJkyxe1/f+qpp7j55ptZsmQJQUFBbNu2jYULFxIWFsa0adOIi4tj8ODB7Nu3j2+++SaNbTt27LACnIzLV9sBKA9MUtUdIhIKVAAOqOr5wJloyS7hRYJoVLmUe9/rTPToVjiXJsXHRXI7CiIpgbatrmDsxx9A2KXu5rVr13Lrrbem6T558mQaNGiQoi0yMtL9c7FixVi1ahUJCQn8/vvvvPXWW4wZkzqX1EWGDBnCwoULWbhwIcHBwen2O3v2LEWKFEkj/nPmzCE2Npabb74ZgOLFi3PLLbcwfvx4vwVYfY+0TEO5cuXcs87cIikpiXPnzjFx4kRq164NwMSJE4mOjmbp0qW0aNGCpKQkRITJkye7PyQ/+OADOnXqxMGDB92iX7SoiXDx9A0XJPwWYBF5AxMNEYzxbSzB+IbDMdELL2AeiFlyAV++1m/wJTFORuKbGwsr4GIyHQmCIpEUK1GGmvVTPuiJi4vzOrRy5crUrFkz3VOLiPt4nTp12L9/P3fddRdz585N0/fJJ59kypQpzJ07lxo1amRocpkyZTh//jxnzpyhWLFi7vZx48YRFxdHRESEx+0pkZGR/N///R/FihVzP7Q7fvw4ZcqUSXOfycJUu3ZtTpw4wb59+/yeBWfXBVGhQgUOHjyYou3gwYNUqFAh3fNddtllhISEuMUXoFatWgQHB7Nr1y5atGjBZZddRqVKldz3CFC3rskfsmvXLrcAHztmHrqWLZtLOaRzGX+rIj8EPA18CHTEwzWgJkPaDODmQBpoucjA2QNpOKEhDSc0zLmLVLwi7ZbTiytUTerI09vNzwASbLYc4sknn2TFihVMn54yhcjjjz/Ol19+yW+//ZaheyKZJk2aAEbEkjl27BjfffcdEyZMYNWqVe5t9erVhIWFub9i16pVi6CgIJYvX57inNu2beP48eNER0cDxn0RGhrK66+/7tWG9D6UwLggPG3wtmUk6q1atWLWrFkp2mbNmsXVV1+d7pjWrVuTkJDA1q1bU9xTYmIiUVFR7j779u3j1KmLj3s2b94M4O4D5htPkSJFaNgwB9/zTuLtyVx6GyZKYZrr50uBJOA6j+PPAXv8OacTG/k0CiI58iF5q/PB7T6N8yUKwqloB01MUD2xxUQ5HFmqev6EqnpEQaRi6dKlXqMgZs6cqfv370+xnTlzRlW9R0Goqg4ZMkQbNGigiYmJqqo6cOBAjYyM1Dlz5qQ4z8mTJzO8haZNm+rbb7/t3n/nnXe0bNmympCQkKbvww8/rG3atHHvP/jgg1q1alX97rvvdNu2bTp//nxt2bKltmzZUpOSktz9PvzwQxURveeee3Tu3Lm6Y8cOXbx4sQ4aNEjvv//+DO3LDosWLdLg4GB97bXXdMOGDTpy5EgNCQnRP/74w93nueee0+uuu869n5iYqE2bNtW2bdvqihUrdMWKFdq2bVtt0aKF+3d98uRJrVy5svbo0UPXrl2rCxcu1Pr162uPHj1SXH/48OEpzp2XyUoUhL/CdRZ4WNMX4PuAeH/O6cSW3wVYVTXq2R816tkffRqXoQAf2XJRfHNbgC+cUY392wjv0RWq52Ldh/wVYG/bJ598oqrpC/DOnTs1JCREJ02apKqa7nmGDx+e4W18/PHHeuWVV7r3GzZsqA888IDXvnPmzFFAN23apKqqZ8+e1REjRmidOnW0aNGiWq1aNX3ggQe8hmfNnj1bb7zxRi1durSGhYVp7dq1ddCgQbpjx44M7csuU6dO1ejoaC1SpIjWqVNHp02bluJ4v379NCoqKkXbvn37tEePHlq8eHEtW7as3n333XrgwIEUfTZu3Kg33HCDFi1aVCtWrKgDBw7UEydOpOhTu3Zt/fLLL3PkvgJNVgRY1A8Hv4jEAq+q6v+JyKXAYaCDqv7mOv4y8KCqpu8gygMk54Lw59431DH+qbobnUvsnex6+Lvf3+7oB199wMn+tTTsW3nx59zM5XAu1uVySILgoqZQZnB47lw7wMTHx1OnTh0mTpxImzYFb1WiU/zvf//j6aefZs2aNYSE5P1U3+n9nyVnIlTVNNFc/t7VX5i6b//n5SLhQF8gK0GmFj/IdpVhbxEPuRnlcD7W5O8FCL3E5HMIyjl/b04THh7O559/7n5gZAkMp0+fJiYmJl+Ib1bx985GAb+IyETgU1dbBRHpBLwEVAbuDqB9lgzwu7pweqFmuRXlkEyRkhAScbF6RQHI5dy2bVunTShw3HnnnU6bkOP4uxJutog8ArzLRaGd6Ho9DzygqksCaJ+FtJnPMnU7TLoD/vn14n6nr2FfqnpwueluAEg4DUFhEBRiwsxK1CkQwmuxZAe/5/aqOlZEZmCqVtTBhKL9A3ytqnsDbJ+FlJnPEk5FZz7AU3xTk9vCqwrnDsOZ3WbmW/xyV6yvFV+LJatVkQ8A74tICNAcqIQpVW8FOBtkluP35AbvcaDpMuK4ed2wIfeLZIJ5wHZ6J5xzVa4KCs19GyyWPEymAiwi7YHbMNEPhzzaqwPfAQ082iao6r0Bt7KA40ty9eSZr99+X6dIPGcetCWccVWtiEqxpNhisfg2A+4PtFLVx1K1fwY0xEQ9/Al0AvqJyHxVnRBIIws6qV0MZ3cP8NrP72xiTnH+uMnfq4kQHObKYlYs83EWSyHDFwFuDqRwKopIHaAN8Lu6spCJyIvASkytNivAWSAjF0O+mfkCXDhuxDe0JERUNw/eLBZLGnz5z6iAecjmSXvM6g53Gn5VPSsikzEVKSzZIN/MdNOjWGWzuCKsjH3YZrFkgC/JeMIwS5A9ucr1Oj9V+26gJJbCRcIZOLHZlA4C4/MNL2vF12LJBF8EeBdpi2deAxxS1d2p2osBcQGwy5JfOHfUVK24cALO7s+8fyEgNjaW8uXLp8gGZsk+zZs3Z9q0aU6bEVB8EeAFwD0i0gBARG4FagE/e+nbEBuKVjjQJDi9C0658jmEXQrFKgXs9GmqImNKBA0fPpwGDRpQrFgxSpcuTbNmzRg5cmSKysLt27f3WiU5OW3j9OnT6dSpE2XLlkVEmDdvXprrV6tWzT0uuQLxqFGjfMofMnLkSG666SavFRy6detGcHBwmhSP6d0zeK+efP78eUaNGsUVV1zh/l20bNmSMWPGcO7cuUxtzCrnzp1j8ODBlClThoiICLp168aePXsyHOP5u/TcunS56Gr7/fff6datG5UqVUJE+Oyzz9Kc58UXX+S5555LtypKfsQXAX4N44ZYLSKHgG8wq95S5IMQkWBMYc2FgTayIJIruX1zisTzcHIzxB8yboaIqiafg+Rc2b7Y2FhatWrF+PHjefLJJ1myZAkrV67kzTffZNOmTXz66acp+g8YMID9+/en2DzLE1199dWMHj06w2sOGzaM/fv3s2HDBp566imef/55xo4dm+GYM2fOMG7cOO677740x/bv38+cOXN48skns1XF+Pz583Tq1In//Oc/DBgwgEWLFrF8+XKGDBlCTEwMS5bk3GLUJ554gmnTpvHll1+yYMECTpw4QdeuXUlMTF0c/SJLly5N8XdYsWIFIpJiqfGpU6do0KAB7777rrsKRmpuuukmTp48yc8/e5v75U8yfQinqttFpB0wHKiJScjzqqquS9X1WuAo8H3ArSyApI77bVOpDT/lVqK1EQ656ZMXhmSB559/nh07drB58+YUCcSjoqK4/vrr08xMixUrlm7Vhr59+wKkmFF6IzIy0n2O+++/n48++ohff/2Vhx56KN0xP/30EyJC69at0xz77LPP6Ny5M4MHD6ZOnTocPXqUSy/1Pzb6nXfeYf78+fz1118pyhtVr16dHj16pEhyHkiOHz/O+PHjiYmJ4YYbbgBMqaGoqChmz55Np06dvI5LXc1i/PjxlChRIoUA33TTTdx0002A+SbgjeDgYG666Sa+/PLLFLPn/IxPUxZVXaaqN6tqXVXtqqp/eOkzW1UbqurswJtZcPm739/83e9v/tvhv06bkmdJSkpiypQp9OnTJ93qDZKDD/xUlXnz5rFhw4YMqygDLFiwgGbNmqWxR1X59NNP6dOnD1FRUbRo0YKJEyemc5aMmTRpEh06dPBaWy4oKMhd6sgbxYsXz3C78cYb0x27fPlyLly4QMeOHd1tVapUoW7duixevNgn21WV8ePH06dPn3RnuhnRvHlz5s9P/ew//2IDNAsj/s5EkxJBL1zM16tJQO7lczh8+DBxcXHuEj3JXH311axZswaANm3apPhqOnbs2BR+xD59+vDxxx/7dd2hQ4cyYsQIzp8/z4ULFwgPD+exx1KvR0rJzp07vX5IzJ8/n2PHjrlnbvfccw9vv/02TzzxhF82gamS3L59e7/HAaxatSrD4xmJ4oEDBwgODk5Tvy6zKsmezJo1i+3bt/PAAw/41D81FStWZO/evSQkJBSINJX5/w4sOUtiPJzcCiRCiXoXs5nlAb766ivOnTvHSy+9xO7dKQNyevbsyfDhw937Gc0K02PIkCHcd999HD58mKFDh9KxY8cMa6GBqd6buow7mCKdd955J6GhJh9Gjx49GDRoEH/++SctWrRI0z8j/CkkkJqMCpfmBp988glXXXUVjRs3ztL4okWLoqrEx8dTvHjxAFuX+1gBLkikTkOZXc7HuaIcEs3sVxNw4i1TtmxZSpUqxcaNG1O0V6lSBYCSJUumEeCSJUtmW2wuvfRSatasSc2aNZk2bRq1atWiRYsWXHvttemOKVOmDLGxsSna4uLimDZtGufPn+eTTz5xtycmJjJu3Di3AJcoUcJr6FpcXBxBQUFERkYCpkqyT5WuvZCZaKX+JuFJhQoVSExM5MiRIyn8ugcPHvSpEsihQ4f4/vvv+fDDD/0z2oNjx44RHh5eIMQX/KyKbMnjeIpvrY7p98sMVTizF05ucS0pvgRK1HWsZFBQUBA9e/bkiy++SCO0ucUll1zCoEGDePLJJzOcgV5xxRUpKiSD8dmWLVuW1atXp6hGPHbsWL766itOnz4NQHR0NOvXr+fs2ZTrnlasWEFUVBRhYWEA3H333cyePZtly5aluX5SUhInTnhJuu8iswrJGUVnNGvWjCJFiqQIoduzZw8bNmzI9JsBmIeQYWFh3HXXXZn2TY+1a9fStGnTLI/Pa1gBLoiMOA69p2ZtbFICnPzn4qKKYpWheA3HSwaNHDmSqlWr0rJlS8aNG8fq1avZunUrM2bMYN68eQQH+27fsWPHWLVqFWvXrgVgy5YtrFq1KlM/5sCBA9m0aRNTp6b/u+3UqRMbNmzg6NGj7rbx48fTo0cPGjRokGLr168fQUFBfPXVVwD07t2bkJAQ7rnnHpYvX86WLVuIiYnhnXfe4emnn3af74knnuCaa67hhhtu4L333mPVqlVs376d6dOnc80117BixYp07Uue0ae3VaqUfix3yZIlue+++3jmmWeYPXs2K1eupG/fvjRq1IgOHTq4+9WpU4cPPvggxVhVZdy4cfTq1cvr7PXUqVPuD4GkpCR27drFqlWr2LVrV4p+CxYsoHPnzunamO/wVqmzoG/kgarInhWOk/Gn0rFXhpcwWyp8KkufzLljpkrxsZWq549n3ZZs4q0q8vHjx3Xo0KFat25dDQ8P1/DwcG3YsKH++9//1oMHD7r7tWvXTh999NF0zx0TE5Np9eOoqCgdNWpUmrEPPPCA1q1b111e3RstW7bUDz74QFVVly9froAuWrTIa9++fftqq1at3PubNm3SW2+9VStWrKgRERHauHFj/eSTT1KUqFdVjY+P19dff10bNWqk4eHhWqpUKW3RooV+/PHHeu7cuXRtyy7x8fE6aNAgLV26tBYtWlS7du2qu3btStEn9e9SVfW3335TQP/880+v5507d67Xv0m/fv3cffbs2aNFihTR3bt3B/q2AkKOV0UONCJSBXgbuAFTWWM28ISq7spk3JXAg0BboCpwBLNi7wVV3e7DdR2viuxZ4TgZfyodeyU5vjdVlEOGVZG9EX8IipSCYJtAPSvMnDmTxx9/nPXr1/s1M7dkzNNPP83x48czXQzjFFmpiuyYC0JEigG/Ycoa9cNUVK4FzBWRiEyG98Lkp3gPuBF4DmgKLHOJusVXkqtWJJy+2BZezopvNujcuTOPPvpopkt0Lf5Rrlw5XnnlFafNCChORkE8ANQAolV1C4CIrMGkvnwIyGid6BuqetizQUQWAdtd5x2WIxYXNBLPmcTpCafhwkkoWd9mMAsQmcULW/zH0w9eUHDyIVw34I9k8QWz7BlTYaN7RgNTi6+rbSdwGFOfzpIZF07AiQ1GfINDoXh1K74WSy7jpADXB9Z6aV8H1PP3ZCJSFygH5FZGhfyJqolwSM7fW6SEWWARkpnXx2KxBBonBbg0EOul/RhwiT8nclVn/hgzAx6fQb8HRSRt8GRh4vQOE+MLUPQyiKxlSwZZLA5RUOKAPwCuBvqoqjdRB0BVx6pq2gwmhYnQUiDBEFnT5O+1bgeLxTGcnPrE4n2mm97M2Csi8jomJK2fqgZwHW728aXcfK6QXCoIzKq2UpF21mux5AGcnAGvI22pIzD+3/Ve2tMgIkOBZ4HHVDVruf1ykMzEt02lzNfPZ4ukBFjxFJzdCxc8csRa8bVY8gRO/ifOAN4SkRqqug1ARKoBrTFxvRkiIo8BrwJDVfWDzPo7iedii1zj7EFY1BMOzYfaHUxWsyIFI4FJXuSff/7hmmuuYfPmze7KG5bsce7cOWrVqsX06dO95j4uCDg5A/4E2AF8LyLdRaQbpprGbmBMcicRiRKRBBEZ5tHWC3gHmAn8JiItPTa/IygKHEf+gJlNjfiGV4Dw8hBeJvNxeYyDBw/y+OOPc/nllxMWFkalSpW48cYb+emnn9x9POuNhYeHU6VKFW699VZ++OGHdM8bHx9P48aNERGvCW3AVMtIrk+WWeUMMBU7Bg4c6FV8GzduTEhICJs3b05zrH379gwaNChN+2effZYmZ8LJkyd58cUXqVevHkWLFqV8+fK0b9+eL7/8MkfrpMXGxtK3b19KlixJyZIl6du3r7u+XnqcOnWKwYMHU7lyZYoWLUp0dDRvv/22+/iOHTu81okTEUaNGgVAWFgYTz/9NM8++2yO3ZvTOCbAqnoauA7YDEwEJmEWUlynqp41VQQIJqWtnV3tnYElqTZHS0tkpdbbgJi/3MuQs4dCyfMwuy2c3Qdlr4EbVziWxSw77Nixg6ZNm/LLL7/w2muvsWbNGmbPnk2XLl14+OGHU/RNrt22efNmpkyZQrVq1bj11lu9ChvAU089ReXKlTO8/oABA2jSpIlPtu7evZvvvvuOAQMGpDn2119/cejQIe655x7Gj083QCdT4uLiaNWqFZ9++ilPP/00y5YtY+HChfTr149XXnklTdKaQHL33XezYsUKZs6cycyZM1mxYoW7rFN6DBkyhP/9739MnDiRDRs2MHToUJ577jl3FZAqVaqkqdn33//+FxGhR48e7vP07t2bhQsXsm5d6gpoBQRvCSIK+kYOJuNJTrKTvD0y65FMxyQn4Yl69kft/6n3ZCU+8XJx1c9RnYTqssdVE88b2/1JxpNHuPHGG7VixYp68uTJNMdiY2PdP6eXNGfMmDEK6G+//Zai/bvvvtN69erp+vXrFdClS5emGfvOO+/oddddp3PmzFFADx8+nKGto0aN0iZNmng99uCDD+qQIUP0999/1woVKuiFCxdSHE8vcVBMTIxGRES49x955BEtVqyY10Q0Z8+e1bNnz2ZoY1ZJ/j0tXLjQ3bZgwQIFdOPGjemOq1+/vg4bNixFW9u2bTNMktShQwe94YYb0rRfe+21OnTo0CxYn7tkJRmPfRqTQ2TF75vlJDzJJAbBgaJwx3ioln7OVacqMfv6Ozl27BgzZ87k1Vdf9Zq6sFSpUpme47777uO5555j2rRp7gTqe/bs4ZFHHuHnn39Ot/TOypUreeONN1i6dCn//POPT/YuWLDAq4/y9OnTTJkyhXnz5tGkSROKFi3Kjz/+yC233OLTeZNJronXu3dvrzP38PD0v+Hs2rWLevUy9splVK5pyZIlFC9ePEW+39atWxMREcHixYvTlIlK5pprruGHH37g/vvvp0qVKixevJhVq1alu5x427ZtzJkzh6+//jrNsYJWB84TK8B+4qt4Bcal4AN7f4Kze6Dmg2b/dJEMxTc/sGXLFlTVvwxuqQgODqZ27dps27YNMNUnevfuzb/+9S8aN27Mjh070ow5ffo0vXr14v3336dSpUo+C/DOnTu9uiumTp1KlSpVuOKKKwAjdOPGjfNbgI8cOUJsbGyWfh8VK1bMtA5cRuWaDhw4QNmyZVMUGRURypUrl2H+5Pfee4+HHnqIqlWrumu3vf/++3Tt2tVr/3HjxlG2bFm6d0+bhaBixYpe/14FASvAOUDCKe+zgoy4Nrps5p080SRY+wr8/ZJZWFGmlc9DHYnK8AMNUIpUVXULx8iRIwkNDWXIkCHp9n/ssce45ppruP322/26ztmzZ73OQseNG5fCV9q3b19GjhzJvn370q3u7I3s/D5CQkIcqQP3/vvvs3jxYmbMmEFUVBS///47Tz31FNWqVUuTUD0hIYGYmBj69evntep00aJF01QJKShYAfaTPCFe52Nhaj2QA8a7dCQY3mmNeS6Z/6lVqxYiwoYNG7j11luzdI7ExEQ2b95M8+bNAZgzZw4LFixI8w/esmVLevbsyaRJk5gzZw67d+9mwoQJwEXhq1ChAs8++yz/+c9/vF7LWx24jRs3smjRIpYsWcLQoUNT2BUTE+NuK1GiBMePp61SHRcX546oSK6Jl5U6cNl1QVSoUIHDhw+n+DBTVQ4dOkSFChW8jjl79iz//ve/mTp1KjfffDMAjRo1YtWqVbz11ltpBPiHH37gwIED3H///V7Pd+zYsRQ16AoSVoDzG7GrYcFtRnwTBfYXhTMef8bs1ILLI5QuXZpOnTrxwQcf8Nhjj6XxA8fFxWXqBx43bhxxcXHuJ+oxMTHu2msA+/bto1OnTkyaNInWrVsD8Ouvv3L+/Hl3n6VLl3Lvvfcyb948atWqle61vNWBGz9+PC1atEhTY23atGl8+umnPP/884gI0dHR/PTTTykEDkwduGT/alBQEL169eLzzz9n2LBhafzA8fHxgHdfcHZdEK1ateLUqVMsWbLE7QdesmQJp0+fTrcO3IULF7hw4UKaZPTBwcFew+U++eQT2rVrR+3atb2er6DVgUuBtydzBX0jD5QkyhJ7flCdUtREOYwPUn2luE/D8mMUxNatW7VChQoaHR2tX3/9tW7cuFE3bNig//3vf7VKlSruflFRUTps2DDdv3+/7tq1SxctWqRPPPGEBgcH66BBg9I9//bt29ONgkgmuUxOZlEQM2bM0EsvvdQd4XD+/HktV66cvvvuu2n67tu3T4OCgnTOnDnu+wwPD9dHH31UV61apRs3btTRo0drSEiI/vTTT+5xR48e1Tp16mjFihX1008/1bVr1+o///yjn3/+udarV0+3b9+eoY3ZoXPnztqgQQNdvHixLl68WBs0aKBdu3Z1H9+zZ49GR0fr9OnT3W3t2rXT+vXr69y5c3Xbtm0aExOj4eHh+t5776U4986dOzUoKEi/+OKLdK8fFRWln3/+eeBvLMBkJQrCcTF0Ysu3Anxym+rUS1SXDFAdEem1/ps38qMAqxqxGjRokFavXl1DQ0P1sssu086dO+vPP//s7hMVFeV+g4eGhmqlSpW0e/fu+v3332d47kAKcEJCglauXFl//NHU85s2bZqKiO7Zs8dr/zZt2uhdd93l3v/rr7+0Y8eOWq5cOS1RooQ2b95cv/322zTj4uLi9Pnnn9fo6GgNCwvTsmXLart27fTLL7/MsEZddjl27Jj27t1bIyMjNTIyUnv37p0iFDD5dxkTE+Nu279/v/bv318rVqyo4eHhGh0draNGjUpT227YsGF6ySWXpBtGt3jxYi1VqpSeOXMmJ24toOS7mnBOkRdqwvlM/BEIu/Ri1rLTu02l4pdKmf0Raf2HqfG7JpzFbz7++GOmTp3KnDlznDalQHHHHXdwxRVX8PzzzzttSqbkq5pwFh84OB9+qg8bLy7hJKKKTSGZB3nggQe4/vrrvT5Qs2SNc+fO0ahRI5588kmnTckxrADnRVRhw2j47XpTofjArybszJJnCQ4O5vnnn7eJeAJIWFgYL774YrqLZgoCNgoir3HhFPx5H+xyrQiq9yw0ehXEflZaLAUNK8B5iRObTIjZ8fUQEgmtPoMqtzltlcViySGsAOcllj5qxLdEXWgzHUrWcdoii8WSg9jvtXmJlp/C5fdDpz+t+FoshQArwE4SfwTW/sc8dAOIqAotPoEikc7aZbFYcgXrgnCKo0thQQ84swtCIqDOE05bZLFYchkrwE6wZRwsexSSzsOlLaFqj8zHWCyWAocV4NwkMR6WDYKtrtI0tR6Bpm9DcJizdlksFkewPuDc4uxBmNXGiG9wOLT8DK76rxXfPEq1atV46623st0nELz11ltUq1Yt034TJkzguuuuy3F7ChMffvihO6VmTmAFOLcILWUSp0dUhxsWQ41+TluU5/GlKrKTLF26lIEDBzptBgDnz59n6NChDB8+PM2xgwcPEh4eTtWqVb2mgxQRvvnmmzTt/fv3T1PBYuvWrdx3331UqVKFsLAwoqKi6NGjB4sXLw7czaQiPj6e/v3706hRI4oUKUL79u19Gnfu3DkGDx5MmTJliIiIoFu3buzZsydFn127dnHzzTcTERFBmTJleOyxx1KkJL3//vtZvnw5CxYsCOQtubECnJNoEiScMT8Hh5nY3s7LoPQVztqVD/CnKrJTlC1blmLFijltBgDffPMNRYsWpV27dmmOTZgwgZtvvpnw8HB++eWXLF9j2bJlNG3alHXr1vHRRx+xfv16fvjhB5o1a8bgwYOzY36GJCYmEh4ezqBBg+jSxfe6iU888QTTpk3jyy+/ZMGCBZw4cYKuXbuSmJjoPm+XLl04efIkCxYs4Msvv+Sbb77hX//6l/scYWFh3H333bz33nsBvy/ApqP0Fb/TUZ6LU53fXXXezapJOZAqcHiJLKejTL6X3N78wdeqyDt37tRbbrlFixcvrsWLF9dbb701ReXg4cOHa/369fWzzz7TqKgoLVasmPbv31/PnTunH374oVauXFlLly6tTz75ZIqUjlFRUTp8+HDt3bu3RkREaPny5dNUX05dkRnQMWPGaI8ePbRYsWJavXp1nThxYooxe/bs0Z49e2qpUqW0VKlSetNNN+nmzZtT9HnjjTe0fPnyGhERoX379tXhw4drVFRUhr+vLl266BNPPOH1WHR0tM6YMUNffvllvf3229McB3Tq1Klp2vv166ddunRRVdWkpCStX7++NmnSRBMSEtL09fyb5CSPPvqotmvXLtN+cXFxWqRIkRR5hnft2qUiojNnzlRV1Z9++klFRHft2uXuM3HiRA0LC9Pjx4+72+bPn6+hoaF6+vTpDK+ZlXSUdgacE8SthV+ugj3fw6EFcHKL0xblK5KrIj/66KMZVkVOSkqie/fuHDx4kLlz5zJ37lz27dvHLbfckiLV6I4dO/j+++/58ccfmT59OlOnTqVbt24sXbqUX3/9lXHjxvH+++/z7bffprjO6NGjqVu3LitWrOCll17i+eefZ/r06Rna/vLLL9O9e3dWr15Nz549uffee9m1axcAZ86c4dprryU8PJz58+ezZMkSLrvsMjp06MCZM+ab0tdff80LL7zASy+95K6KMXr06Ex/ZwsXLvRamXnBggUcPXqUzp0706dPH3788UcOHz6c6flSs2rVKtatW8fTTz+dptIFZFypetKkSRQvXjzDbdKkSX7blBHLly/nwoULdOx4sUJMlSpVqFu3rttdsmTJEurWrUuVKlXcfTp16sS5c+dYvny5u+3KK68kISGBJUuWBNRGsFEQgWfnV/DHvZB4Bko1Mm6HyMudtioFuZ7T2E98rYo8Z84c1qxZw9atW90PqSZPnkzNmjWZM2cOHTp0AC7WYStZsiQNGjSgc+fOzJ8/n7179xIaGkrdunVp3bo1c+fOTVGQs0WLFu7abbVr12bp0qWMHj2a225LPz9H37596dOnDwCvvPIK7777Lr///jt9+vRhypQpqCoxMTHuHLFjxoyhXLly/Pjjj9x5552888479OvXj4ceegiAoUOHMnfuXLZsSf9DPC4ujuPHj3st9Dl+/Hh69uxJkSJFqF69Os2bN2fChAk89dRTGf5uU5NcIToreaW7detGixYtMuxTvnx5v8+bEQcOHCA4OJgyZcqkuU5yNecDBw6kuW6ZMmUIDg5OUfG5WLFilCxZMkcqM1sBDhRJF2Dls7DJlbu3Wh9oPgZC8oaPMD/hOXvNiA0bNlCxYsUUEQI1atSgYsWKrF+/3i3AVatWTZEmsnz58tSuXZvQ0NAUbYcOHUpx/latWqXZz2wG3KhRI/fPISEhlC1b1n3e5cuXs337diIjU650PHPmDFu3bnXfU+rilK1atcpQgJMrBqeuCXfixAmmTp3Kb7/95m7r27cvo0eP9luAff2beCMyMjLNPec3cqoys3VBBIp/PjLiKyHQ7H1o9bkV3yziWRU5q3gWuExdCVlEvLZ5ixDwl4zOm5SURJMmTVi1alWKbfPmze4Zb1a49NJLEZE0lZknT57MmTNnaN26NSEhIYSEhPDII4+4KzYnExkZmWll5uSCmVn5mzjhgqhQoQKJiYkcOXIkRfvBgwfd1ZwrVKjAwYMHUxw/cuQIiYmJaSo+51RlZivAgaLmw1D1DugwD6IH2aoV2cCzKvKpU6fSHI+LiwPM1+F9+/al+Gq4bds29u3bl2kpdl/4448/0uxnp7RT06ZN2bJlC2XKlKFmzZopttKlSwPmnrxdNyNCQ0OpV6+e18rMgwYNSiP4Xbp0Yfz48e5+0dHRKXyeYNw2q1evdldmbtKkCfXq1WPUqFHuKAJPkv8m3ujWrVsaG1Jv3bp1y/Ae/aVZs2YUKVKEWbNmudv27NnDhg0b3NWcW7VqxYYNG1KEps2aNYuwsDCaNWvmbtu6dSvx8fE5U5nZ25O53NqAKsA3wHHgBDAdqOrj2HBgFLAfOAssAdr6ODb7URBJSapbxqvGH/XrPAEjG1EQ+QFfqiInJSVpkyZN9Oqrr9alS5fq0qVLtWXLltqsWTN38cfkKAhPvD1J79mzZ4oIgaioKI2MjNSRI0fq5s2bdezYsRoaGpoiWsBbFETqaALPPqdPn9batWtr27Ztdd68ebpt2zadP3++DhkyxB0JMWXKFA0NDdWxY8fq5s2bdeTIkRoZGZlpFMSQIUO0e/fu7v3Vq1croKtXr07Td8qUKRoREaEnTpxQVdXJkydrWFiYfvDBB7pp0yZduXKl3nvvvVqiRAndv3+/e9yff/6pkZGR2rJlS/3hhx90y5YtumbNGn3jjTe0WbNmGdqXXdatW6crV67Unj17arNmzXTlypW6cuXKFLZFR0frn3/+6W57+OGHtVKlSjpr1ixdsWKFtm/fXhs3buyO4khISNAGDRrotddeqytWrNBZs2ZpxYoV01TTjomJ0Ro1amRqY76qigwUA/4B1gK3AN2Bv4GtQIQP4ycBccADwPUu8T4LNPFhbPYE+MJp1UV9THn43240YpzbFHABVvWtKvLOnTu1e/fu7jC0W265xWsYmie+CvDw4cO1V69eGhERoeXKldPXX389xRh/BVhV9cCBA9q/f38tW7ashoaGarVq1XTAgAEpKi+PHDlSy5YtqxEREXrXXXf5FIa2YcMGDQsL06NHzYRg8ODBWqtWLa99T506pUWLFtUxY8a42yZPnqzNmjXTyMhILV++vN50001exXvz5s3av39/rVSpkhYpUkSrVq2qt99+uy5ZsiRD+7KLZ/Vrzy2Z5ArWc+fOdbfFx8froEGDtHTp0lq0aFHt2rVripAzVfP+6dKlixYtWlRLly6tgwcP1vj4+BR9OnbsqK+99lqmNuarqsgi8jgwGohW1S2utuoYUX5GVdONvRGRxsAq4F5VjXG1hQDrgE2qmuH3mWxVRV76Ayy4HeLWmCxmLcZDVE+fzxMwRrgeKtmqyBYXvXr1on79+rz44otOm1JgWLt2Lddffz2bN2/OtN5ffquK3A34I1l8AVR1O7AIMxvObOwF4CuPsQnAFKCTiORIgoXitU7BzCuN+EbWgo5/OiO+FosX3nzzTUqUKOG0GQWKffv28fnnn+dYsVUnw9DqA997aV8H3OHD2O2qesbL2FCgpuvnwKBKmXaHKdvuqJH9yreYZDqhtgKuJe9QtWpVHn/8cafNKFB4LuTICZwU4NJArJf2Y8Al2RibfDxwiBAUmoQmgRwNg81z4LeqAb2ExWIpfBSqMDQReVBElmVl7KHZ5dgREwWxYUAeCTGr5funs1O+foulMJDV/y8nZ8CxeJ/ppje7TT02Kp2xcHEmnAJVHQuMTX4I5w91N2z0d0ieITg4mAsXLqRY+WWxWALH2bNn0yzC8QUnZ8DrML7c1NQD1ntpTz22uoikXmpWDzgP2Ow3HpQqVYqDBw8GZKWXxWK5iKpy5swZ9u7dS7ly5fwe72QY2hPAW0BtVd3maquGCUN7TlX/L4OxVwArgP6qOsHVFoKJI96iqhmmsM9KGFp+JikpiT179nD69GmnTbFYChxFihShXLly6UagZBSG5qQARwCrMYsnXsAEK78CRAKNVPWUq18UZnHGy6r6ssf4KUAn4GlgO/AI0BW4WlVXZHLtQiXAFovFOfJkHLCqngauAzYDEzEr27YD1yWLrwsBgklr6wAgBngV+B9mWXPnzMTXYrFY8gqOzYCdxM6ALRZLbpEnZ8AWi8VS2LECbLFYLA5RqCtiiM3Za7FYHMTOgC0Wi8UhCuVDuKwiIstUNW3p2XxIQboXKFj3U5DuBez9ZISdAVssFotDWAG2WCwWh7AC7B9jnTYggBSke4GCdT8F6V7A3k+6WB+wxWKxOISdAVssFotDWAG2WCwWhyj0AiwiVUTkGxE5LiInRGS6iPhUb0hEwkVklIjsF5GzIrJERNrmtM2Z2JSl+xGRK0VkrIhsFJEzIrJLRCa5KlU7Qnb+NqnO85yIqIgszAk7/bAjW/cjInVFZKqIHHG93za5qovnOtn8v6kqIhNc77GzIrJZRF51ZUh0BBGpLCLvu/6Hz7jeL9V8HBskIv8WkR0iEi8iq0Xkdp8u7K1WfWHZgGKY/MNrgVsw1Zj/xqS/jPBh/CQgDngAuB6Yjkmv2SS/3Q8mN/MiYCDQDrgb2AAcBarkp3tJdZ4awCngILAwH7/XrgROADNc468FHgSG5Kd7ASIwGRC3A/1c9/GM6//mKwf/Pu1d75GfgF8w6XGr+Tj2P8A54CnX/YwBkoCbMh3r1A3nhQ14HEgEanq0VQcSMntjA41df6QBHm0hwCZgRj68n7Je2qJcb6SX89O9pDrPL65/iHkOC3B2/jZBmCox3zplfwDvpaPr/6ZjqvbXXeOLOXRPQR4/3++rAAPlXOL7Uqr2OcCazMYXdhdEN+APVXWXMFLV7ZiZYHcfxl4AvvIYmwBMATqJSFjgzc2ULN+Pqh720rYTOAxUCrCdvpCdvw0AInI30BT4d45Y6B/ZuZ/2QF1gdI5Z5x/ZuZfkwoQnUrXHYT5oHEnQoqpZrdfVCXNPX6Rq/wJomJkLr7ALcH3M16jUrMPUl8ts7HZVPeNlbChQM/vm+U127icNIlIX8wm/IZt2ZYVs3YuIXAK8DTyjql6LtOYy2bmfa1yv4SLyh4hcEJFDIvKeiBQNqJW+kZ17mY1xX7whIvVEpLiIXIeZVX+splBDfqI+Zgacug7lOtdrhr+Pwi7A6VVgPob3is2+jk0+nttk535S4Kqx9zFmBjw++6b5TXbvZRTG1/hZAG3KDtm5n4qu16+AX4EbgDcxX5UnB8pAP8jyvahqPOYDJQgjUicxX9d/BAYF1sxcoTQQpy6/gwc+6UChTkdpyZAPgKuBLqrq7Z8tzyIibYB7gKZe/jHyI8kTpS9UdZjr53kiEgy8LiJ1VdWJbyl+IyLhmA+SckBfYBfQHBiG8QE/4px1uU9hF+BYvH9ip/cJn3psVDpj4eInYG6SnftxIyKvY56w91PVXwNkm79k517GYGbte0SklKstBAh27Z9V1XMBstNXsnM/R12vs1K1/4p5eHUFuesmys693IfxaddU1a2utt9F5DgwVkQ+VtXVAbM054kFSomIpPqw90kHCrsLYh3Gh5OaepinzpmNrS4ixbyMPU9an1BukJ37AUBEhgLPAo+p6sQA2uYv2bmXusDDmH+O5K010NL1sxOzrOy+1zIiqw+Qskp27qUhEOshvsn85Xqtm03bcpt1QBhwear2ZN9vhr+Pwi7AM4CWIlIjucEVfN3adSwjfgCKAHd4jA0BegK/OjDDguzdDyLyGKbK9FBV/SCnjPSR7NzLtV621ZgHR9cC3+SAvZmRnfv5GfOgp1Oq9s6u12UBstFXsnMvB4BLRCT1Q+oWrte9gTIyl5iJiYbqnaq9D7DWFR2SPk7E3OWVDRMUvgUTRN4dE16zGtgGFPfoF4XxTw1LNX4KZkZ1P2YhxjdAPMb3mK/uB+iFmUn9jJkpem718tO9pHO+eTgbB5zd99pwV/tIoAPwHGbxwmf56V6AapgQtM1cXIjxtKttGR7xuA7cVw/X9hEmDvgR1347jz4JwPhU4153/d8PwbhXPnL9L3XN9JpO3Wxe2YCqwDTXG+Ak8B2pArBdbxoFRqRqL4qJzTzg+gP8CbTPj/eDiRbQdLZ5+ele0jmXowIcgPeauP7Bt2BcXDuBl4Ei+fBe6gFfA7sxHyKbMSsxL3H475Pp+9+1/1mqccHAC66/yTlgDdDDl2vadJQWi8XiEIXdB2yxWCyOYQXYYrFYHMIKsMVisTiEFWCLxWJxCCvAFovF4hBWgC0Wi8UhrABbLAFCREZ4K2UjIk1EZI6IxLqOjxCRask/Z+E6WR5ryVtYAbbkCK4aWVNFZJtLLHZk8TyNReRLEdniqrd1RETWiMgYEbkiwGYHHNfy9GlALeBFTAaw6TlwnVIuYW8f6HNbco7Cng3NknOMxGSCWgGUysoJRKQrZoXVYeBzzCqwUkBtoAsmsffKbFsaOF7FLEv1zANSw7X9Sz3ya4iIYFZSJmThOju9jC2FWa4MZtWfJR9gBdiSU1yuqtsARGQtUDwL53gNs1T1KlXd43lARIKAS7NtZQBRU5IqtaBWcL0eS9VXMcvXs3KdLI+15C2sC8KSIySLbzapBWxKLb6u8yepRx07T7+oiNzlclPEu0qfj3C5AlIgIpeJyEeuPudFZJ+IjBWRcl76lhCR/4jIBtd5j4rIQhHp5dEnhQ9YROYB812HY1zH1GVrun5cEbldROaJSJyrRPomV/mh0NT36tpvj6kyDDDc4zo7RKSc694mefsFi8iHIpKU2m9tyR3sDNiSl9kK1BeRq1V1sY9jumG+8n+ISZLUDfPVPAoYkNxJRKoCSzD1+8a7rlUTkwHrWhG5UlWPu/qWAhZicuB+g8l2FYxJhN4VkxXPG//BFKp8HhgLLHC1HwbKehsgIv9x9V+PqWm3H5Nr9nZM1YjzXoZtAJ509f+Wiz7mU6p6SERmALeJSClVjfO4VjhwNzBbVXekcw+WnMTJ7EN2KxwbJg/vjiyM64FJ66eYDFMfA/fipVw4FzNvJeKRDhSTRexb17GWHu3fA4eAyqnOcyXGjTDCo+2/rvEPermuZznzEaQqZ45JT6hA/3Ts9bxOc1fbb0B4qv4C7uRZ3samafM4llwKfmCq9t6u9judfo8U1s26ICx5FlX9BmiLmXVWAR7CzFa3i8j3IuJtFjlLVVd4nEMxBSwBbgUQkZKYmesMIF5EyiRvwA7Mw76Orr5BmFzJG1R1rBcbA1mNIjmp97/VFK/0vI667iUrzMK4KO5L1X4fptzRd1k8ryWbWAG2OIqIlBaRCp6b53FVXaiqd2BqbEVjSg2twbgWvvBySm+10ZLLwiRXcIjGvPfvw7gDUm/RQHlX3zKY+mersnSD/lELMyMNaE00l3CPA5qKSBMAVzWL9sBEVfXm1rDkAtYHbHGa6UC7VG2SupNLRDYDm0VkAqYWV0cRqaxeHtJlQvL5vwAmpNPnrJ/nDBTJScADzafAS5gPncEYV45ghNniEFaALU7zL7xX2E0XVY0XkVWYGW0lwFOAvRV1TC6QmByZsQUjcqGqOjuTyx3BlJ1q7I+NWWQzcKPrWn9l0jc1GYq2qh4QkR+A3iLyHNAf+FNVMyv4aclBrAvC4iiqulxVZ3tuycdEpLNrwUIKXL7f1piHZf+kOnyDiDT16CvAM67d71zXPAr8hIkMaOnl/JLsX3b5eL8E6olIah8q3uzLBpNdryOTQ878uNYp12vpDPp8gvmw+xjzwWVnvw5jZ8CWHEFE+mJCv8CEXIWKyAuu/Z3qW8n7b4BDIvIjxo+bgJn19sX4aF9W1WOpxqwGfhORDzEhXN0xRSwnquoSj36PYELLfheRzzEr6oJc5++OWXk3wtX3BeA6YJyIdHSNE0wYWojLnmyjqn+JyBvAs8AKEfkKE0pXHRMR0hyIS2fsURHZAvQSka3AQeC0qv7g0e0XzCq6PhjBTi98zpJbOB2GYbeCuWGWw2aryCdwB8Z3uQ7jBriAEZafgdtT9a3mOvcI4C7Mg7pzmMKPXotXYh6wjcJ89Y/HiNvfwLukqgSNWer7JheLYh7FxPXe6dFnBNkIQ/M4dhcmfvgkcBrYCLyDcZmkOxYj0ItcYxQvoX+YfBRKqsq+dnNms0U5LQUC10qu7cBLqjrCWWvyLiLyDPAGcLWm/EZgcQDrA7ZYCgmu5dgPAX9b8c0bWB+wxVLAEZHqQCuMb7sGxsVhyQNYAbZYCj7tgBhMSN3LqmofvuURrA/YYrFYHML6gC0Wi8UhrABbLBaLQ1gBtlgsFoewAmyxWCwOYQXYYrFYHOL/AdWQfgXEeLZHAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 360x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#careful about changing variables here, should try using find and replace \n",
    "rna_roc = ['HER2','IGF1R', 'CD44','all_three_combined']\n",
    "cell_lines_considered=pd.concat([mm231,mcf7],axis=0)\n",
    "fig,ax=plt.subplots(figsize=(5,4))\n",
    "for marker in rna_roc:\n",
    "    if marker=='all_three_combined':\n",
    "        marker=['HER2','IGF1R', 'CD44']\n",
    "        accuracy=[]\n",
    "        X=cell_lines_considered.iloc[:,2:][marker]\n",
    "        y=cell_lines_considered['cell_line']\n",
    "        #Scale data\n",
    "        scaler = StandardScaler()\n",
    "        scaler.fit(X) \n",
    "        X_scaled = pd.DataFrame(scaler.transform(X),columns = X.columns)\n",
    "        #X_train, X_test, y_train, y_test = train_test_split(X_scaled, y, test_size=0.3)\n",
    "        X_train=X_scaled\n",
    "        y_train=y\n",
    "        X_test=X_scaled\n",
    "        y_test=y\n",
    "        classifier = LogisticRegression(random_state=0,multi_class='multinomial',solver='newton-cg')\n",
    "        classifier.fit(X_train, y_train)\n",
    "        y_pred = classifier.predict(X_test)\n",
    "        #    print(\"The classifier Accuracy: \", accuracy_score(y_test,y_pred))\n",
    "        accuracy.append(accuracy_score(y_test,y_pred))\n",
    "        feature_importance = abs(classifier.coef_[0])\n",
    "        feature_importance =feature_importance / feature_importance.max()\n",
    "        sorted_idx = np.argsort(feature_importance)\n",
    "        pos = np.arange(sorted_idx.shape[0]) + .5\n",
    "        #featfig = plt.figure(figsize=(10,8))\n",
    "        #featax = featfig.add_subplot(1, 1, 1)\n",
    "        #featax.barh(pos, feature_importance[sorted_idx], align='center')\n",
    "        #print(np.array(X.columns)[sorted_idx],feature_importance[sorted_idx])\n",
    "        #featax.set_yticks(pos)\n",
    "        #featax.set_yticklabels(np.array(X.columns)[sorted_idx], fontsize=10)\n",
    "        #featax.set_xlabel('Relative Feature Importance')\n",
    "        RocCurveDisplay.from_estimator(classifier, X_test, y_test, alpha=1.0,name='Combined',ax=ax,lw=2)\n",
    "    else:\n",
    "        accuracy=[]\n",
    "        X=cell_lines_considered.iloc[:,2:][[marker]]\n",
    "        y=cell_lines_considered['cell_line']\n",
    "        #Scale data\n",
    "        scaler = StandardScaler()\n",
    "        scaler.fit(X) \n",
    "        X_scaled = pd.DataFrame(scaler.transform(X),columns = X.columns)\n",
    "        #X_train, X_test, y_train, y_test = train_test_split(X_scaled, y, test_size=0.3)\n",
    "        X_train=X_scaled\n",
    "        y_train=y\n",
    "        X_test=X_scaled\n",
    "        y_test=y\n",
    "        classifier = LogisticRegression(random_state=0,multi_class='multinomial',solver='newton-cg')\n",
    "        classifier.fit(X_train, y_train)\n",
    "        y_pred = classifier.predict(X_test)\n",
    "        #    print(\"The classifier Accuracy: \", accuracy_score(y_test,y_pred))\n",
    "        accuracy.append(accuracy_score(y_test,y_pred))\n",
    "        feature_importance = abs(classifier.coef_[0])\n",
    "        feature_importance =feature_importance / feature_importance.max()\n",
    "        sorted_idx = np.argsort(feature_importance)\n",
    "        pos = np.arange(sorted_idx.shape[0]) + .5\n",
    "        #featfig = plt.figure(figsize=(10,8))\n",
    "        #featax = featfig.add_subplot(1, 1, 1)\n",
    "        #featax.barh(pos, feature_importance[sorted_idx], align='center')\n",
    "        #print(np.array(X.columns)[sorted_idx],feature_importance[sorted_idx])\n",
    "        #featax.set_yticks(pos)\n",
    "        #featax.set_yticklabels(np.array(X.columns)[sorted_idx], fontsize=10)\n",
    "        #featax.set_xlabel('Relative Feature Importance')\n",
    "        RocCurveDisplay.from_estimator(classifier, X_test, y_test, alpha=1.0,name=marker,ax=ax,lw=2)\n",
    "        \n",
    "for axis in ['top','bottom','left','right']:\n",
    "    ax.spines[axis].set_linewidth(2)\n",
    "    \n",
    "        \n",
    "ax.plot([0, 1], [0, 1], color='orange', lw=2, linestyle='--')\n",
    "ax.legend(fontsize=14)\n",
    "#xlbl = ax.xaxis.get_label()\n",
    "#ylbl = ax.yaxis.get_label()\n",
    "#plt.setp(xlbl,fontsize=16)\n",
    "#plt.setp(ylbl,fontsize=16)\n",
    "plt.xlabel('1-Specificity',fontsize=18)\n",
    "plt.ylabel('Sensitivity',fontsize=18)\n",
    "plt.setp(ax.get_xticklabels(),fontsize=16)\n",
    "plt.setp(ax.get_yticklabels(),fontsize=16)\n",
    "plt.tight_layout()   \n",
    "#plt.savefig('Ray_roc_logistic_regression.jpg', dpi = 1000, bbox_inches='tight')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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UtmzZIhyuAFCvfwsFowZugSQ1Ec6Mhdu/Kh5H7QOnzgY1SaCjw506dSqHDh3i9evXODk5MWnSJHr27EmJEiXU+hoZGdG6dWuCgoL0ZqygcCDq3+Yy8bchpCc8v6BI+Wrwk3C2+QSdHO7+/fvx8vKib9++NGnSJMv+derUwdvbO9vGCQQCHXmw903K1wtFylezQCguNrDzCzo53CNHjmBnZ6d1fycnJ5ycRKFigSBPePQX/NtJ8X+nrm+qfNka0iLBO+i0Tdm7d2+Cg4MzfP7vv/+mdevWOTZKIBBkg1ItoXRbqLUImm0TzjYfotMK98GDByQkJGT4fGJiIlFRUTk2SiAQaMnTM2DprMg8KGIMrYJEulc+Rq/vTExMDObm5vq8pEAg0IQkQdhPcKgJHOsH8je1TYSzzddkucI9ffo0oaGhyseHDh3i3r17av1iY2PZt28fVapU0a+FAoFAldREODMObv+ieGxTBSS5YW0SaEWWDjc0NJTvv/8eAJlMxsGDBzl48KDGvuXKlWP69On6tVAgEPxH/G0I6QXPz79J+VoLFQYb2iqBlmTpcIcOHUr37t2RJIk2bdowY8YMtY0xmUyGpaUltra2uWWnQCB4sA+OD3yT8uXyJuWrlqGtEuhAlg63WLFiFCtWDIBNmzbh4uKCvb19rhsmEAjeIeaEwtmW6QyNN4kshAKITlkKDRo00OvNHz58iLe3N8eOHUOSJDw9PZkxY4bWqhLh4eH4+voSGhpKYmIiDg4ODBgwgKFDh+rVTkH2MWT9hEJRIUyS/hNxrDEXrCtD+f5ic6yAkqnD/f7775HJZIwZM4YiRYooY7mZIZPJGDduXJb9EhMTGTp0KKampixZsgQAX19fhgwZwq5du7C0tMx0/OXLlxk6dCgNGjRg4cKFWFlZce/evUzT1gR5z7vONi9rJ7zrbAtc9a+nZ+DMeEWBcAsHRfGZCgMNbZUgB2jlcD/55BNMTU316nD9/PyIiIggKCiIcuXKAeDu7k67du3YunUrw4cPz3CsXC7nyy+/pHHjxqxevVrZ3qhRoyzvKzAMhqyfUCArhIWtV2QiyJPh6iKot8rQFgn0QKYON/1UmampqcpjfXD48GFq1aqldLagqK/r4eFBcHBwpg43NDSU8PBwvvnmG73ZIxDkB2Ty13ByBNzeoGhwHQ11lhrWKIHeyNThlilTJtPHOSEsLEzjMWBXV9csK4ydPXsWgKSkJPr06cPVq1extramY8eOfPHFF+LwhYExVNy2wMds4+9Q+f4ISLoJRuZQfy1UHGJoqwR6RKfI+6ZNm3j27JlebhwbG4u1tbVau42NDXFxcZmOffz4MQCTJ0+mSZMmbNiwgZEjR+Lv78+UKVP0Yp8g+xgqbqvJ2RaYuG3yczhQH8ukm2BVET48KZxtIUSnLIVFixbh4+NDixYt6N69Oy1btsTYOEeiEdlCeiMZ0qVLFyZOnAhAw4YNSUtLY9myZYSHh+ukJJy+YtaF7IzJDXJqR27Mo/ibf5+3n6H4F4jKw9dr7gDVCnX55b3KCgernlgaX+du6Xmk3U4FCobdGVFQXndt0NdcdPKW69atY8eOHRw+fJjDhw9jbW1Np06d6Nq1q86ikdbW1hpXshmtfN8m/YCFp6enSnvTpk1ZtmwZ165d08nh1q1bV+u+e7fs0XlMrhC2Psd2nD17NlfmcftNREjf19Y2ZGDw90Zbkp7Bq7tg9+YbgLRG8Z7Uq29Qs/RBbv1uGQJd5pKVY9bJ4TZr1oxmzZrx6tUrgoKC2LFjB1u2bGHLli1UqFCB7t2706VLFz74IOuvca6urty6dUutPTw8HFdX1yzHZkaRIiJHsTCijbMtMCGEZ+cUqgxpCdD+HFiWUeTWivzaQk224gFFixalZ8+e9OzZk6ioKHbu3MmuXbtYvnw5vr6+XLlyJctreHl54ePjQ0REBM7OzgBERkZy7ty5LOOwzZs3x9TUlKNHj+Ll5aVsDwkJAaB69erZmZYgn5HRilZTmleBWlGF/wynx4E8CezqicIz7xE5/jh1dHSkc+fOdOzYkaJFi5KWlqbVuD59+lCmTBnGjh3LX3/9RXBwMGPHjqV06dL07dtX2e/BgwdUrVpVJQe4ePHijBo1ij///JPly5dz/PhxfvrpJ1avXk337t1VUs0EBZcCvQmmibTXEDpS8SNPAtdR0PYoFHU2tGWCPCLbO17x8fHs37+fHTt2cO6cYle6UqVKdO/eXavxlpaWbNy4EW9vb6ZNm4YkSTRu3JgZM2ZQtGhRZT9JkkhLS1NulKUzbtw4ihYtypYtW9iwYQMlS5ZkxIgRjB07NrtTEuRTCuTBhXeJv/Omyte5Nylfa6DiMENbJchjdHK4crmckJAQduzYwd9//83r16+xs7Nj8ODBdO/eXedauI6OjqxalfkJGicnJ27evKnWLpPJGD58eKYHJASCfEN8uKKkYtEKiqO6xWsb2iKBAdB50+zZs2cYGxvTqlUrunfvTvPmzTEyMsot+wSCwkHpNtB0q+Jf0+JZ9xcUSnRyuI6OjowbN46OHTtiY2OTWzYJBAWfpGdwYihUngSl35yoLNvboCYJDI9ODtff3z+37BBkgfeR1Zx/mHX2R0GnwB/Phf9Svl7dhfgw6HBFUelL8N6T98fEBNlCk7Ot41Dw09+ycrAFLishfAOcHvtfylezAOFsBUoydbhDhgxBJpPx888/Y2xszJAhWZ/tlslkbNy4UW8GClTx67smyz5xcXE8fvyYlJSUTPsZGxtz/fp1fZmmJLW14ri1NtduWdWEllVVj+KaGhthW8xM+Vib6+TWXLRGkiD5GaQ6gusOMLYCUzu4nwBob5fB56EnCss8QHUuJiYmlCpVKsvTsBleK7MnIyMjkclkypSsyMjIbN1EkHfExcURHR1NmTJlsLCwQJauFqCBV69eqaTg6YukhyYAmDlkfiIQwDjiBQCVnG1zdM/cmovWvLwFyeYgs4Ci5cCsRLYuY/B56InCMg/4by6SJJGYmMiDBw8AsuV0M3W4hw8fzvSxIP/x+PFjypQpk6VihkDPmH+gONhg5QLG4rUvjKSL5ZYpU4aoqKhsOVxxcLuQkZKSgoWFhaHNKPxIEqS8/O+xiTXYVBPO9j3AwsIiy3BdRujkcKtUqcLu3bszfH7fvn06H34Q6J/MwggCPSBPVWQfxN2E5Nj/2kXhmfeCnPx96ZSl8O7xWl2fFxR+op7EY//m/7fexGcLFakJCmeblgwyY0B8uAm0R69pYVFRUYUmUC7IHq9ep2Kv429VUfMCkp34OgYS7inCCcaWinitkVnW4wSCN2T5m55eySsdPz8/jh8/rtYvNjaWEydO4OGRdzLYgvxNTjMP8g2SHF7dh6QYxWOzEkxf8CNlnJz47LPPDGtbIWLz5s2EhITw448/GtqUXCNLh3vjxg22b98OKGIXp0+f5vTp02r9LC0tqVOnDrNnz9a/lYJCz1dffcXz589Zu3atSvvly5fp1asXwcHBODk5ERkZqVF8FBSKJM2bN2fbtm1Mnz5d2W5vb0/NmjWZMmUKlSpVUravXbuWgwcPcufOHUxNTalduzaff/45bm5uqheWUiElFmQysCzHzXtP+Ss4mL///lvNhoMHDzJx4kQ6dOjAsmXLVJ4LDQ1lyJAhnDhxAjs7O5XnvLy8GDhwIP369VPpv2HDBi5cuEBiYiKOjo40adKE4cOH4+SkmrusTw4cOICvry/379+nbNmyTJ48mbZt22Y6RpIkNm7cyJ9//klkZCQ2NjZ0796dL774AlC8v+l+5G0sLCy4cOECAL1792bNmjWcOXOGevXq6X1e+YEsHe5nn32m/BSvXLky3377LZ07d851wwSCzFi/fj2VK1dWPk5ISMDBwUH52MLCgkOHDiFJEtHR0Xz77beMGjWKoKAgTE1NATh16hQDBgygRo0aSJLEd999x/Dhw9m7d69SxgmAIqaK8IFMBsZF+f13Xz788EOsrKzU7PL392fkyJH89ttvxMbGZrvmyJ9//sm8efPo0qULvr6+ODk5ER0dzZ49e1izZg0LFy7M1nWz4vz580yePJnx48fz4YcfKj9A/vjjD2rVqpXhuMWLF/PPP/8wdepU3N3defz4MfHx8crnv/76azVhgf79+1O//n9yQqampnTu3JlNmza9vw73bYKDg9U+mQUCQ2Bra0vJkiWVj1+9eqV0pKD4Npb+fKlSpRg2bBhjxozhzp07uLu7A/Dzzz+rXNPHx4d69epx7uxZvBq/ybaxdFT8a6Jwrmlpaezfv5/Fixer2fTo0SNCQ0Px8fHh0qVL7N69m0GDBuk8t0ePHrFgwQIGDhzIzJkzle1OTk7UrVs3S1XrnLBx40YaNmzImDFjABgzZgyhoaFs3LiR5cuXaxxz+/Ztfv/9d3bt2qXUErSzs1PZzylWrBjFihVTPj579iwRERH4+PioXMvLy4vhw4eTmJhYKNMbdXK4ZcqUyS07BLmIIQrCeFSwYt5nWZ80ywvi4uLYs0ch/pmZyvSrV6+Qy+VYm76ExChFo5mdomD4G27evMnLly+pUaOG2vjAwECaNGlC8eLF6dq1K5s2bcqWww0KCiIlJYVPPvlE4/OZJdzPnj0709RNgL179+Lo6KjxuQsXLqjZ3LRpUzZv3pzh9dLDPSEhIXz66adIkkSdOnWYMWMG9vb2Gsf4+/tTqVIltT2f6tWrk5aWxoULF2jcuHGm8yiIZOpwp0+fjkwmY/78+RgZGanExTJCJpOxaNEivRkoeH8ICQmhTp06Km1yuWa9r0GDBqmIhUqSREhIiHIVlZCQQJ06dZTHMUGxespMzXnhgm+o4laeOu6OipQvqwoqzhYUmThvr57fvv+2bduYOnUqAO3ateObb77h8uXLGp1zZty9excrKyutxFjfZeLEiYwYMSLTPqVKlcrwuZiYGEqUUD2WXKJECZ48eZLhmIiICKKioti7dy+LFy9GJpPh7e3N6NGj2bp1q5qo68uXL9m/fz+ff/652rUsLCwoVqxYoS0jkKnD3b59OzKZjLlz52JkZKQx6P0uwuHmP+aMbKSxPTfOu9+KeEFZ45hsja1Xrx7z589Xvd6tW4wbN06t77Jly1Q2wBISElTmYmFhwY4dO0hNTeXMmTNs2LCBb775JsN7ey+Yzdmzp/ljzRyMzKwyTPl6/fo1xsbGak7kxIkTxMXFKUVNixYtSuvWrQkICNDZ4eYkn93e3j7DVWVuIUkSycnJ+Pj4UKFCBQDmz59P9+7duXz5slrsd9euXcjlcrp21SydZGZmRlJSUq7bbQgydbg3btzI9LFAoE8sLCzUBEAzild+8MEHKn1fvXql4gRlMpnyeRcXF548ecLnn3/Ob7/9pnatRfNnsm//ITau+hrnilWhaNkMT40VL16clJQUtRijv78/cXFx1K5dW9kmSRJFixblq6++wsLCQrnJFh8fr7YXEhcXp1ydV6hQgfj4eKKjo3Ve5eY0pFCiRAliYlQ/MGNiYtRW9G9TsmRJjI2Nlc4WoGzZshgZGREVFaXmcP38/Pjwww9VNybfIjY2ttDuFRWQjHOBIGcMGzaMX375hYMHD/Lhhx8q2xcsWMD+oMNsWj0Xlyr1wTzzKl/pR9fDwsKUK9cXL17w119/sWTJEqpWrap23wMHDtCtWzfKlStHkSJFuHLlCmXLllX2iYiI4OXLl0qH1a5dO5YuXcq6detUNs3SiYuLyzCOm9OQQu3atTl+/DgjR45Uth0/flwt1PM2Hh4epKamKtPIQFFZMC0tTW3f59KlS9y4cYMZM2ZovNb9+/dJSkpSex0LC3pxuFeuXCE2NpZ69ephZiZO3ghynxcvXqjEFRMSEjAyMsLc3FxjfysrK3r37s2qVato06YNRdISmLdoGTt37mT16tVYl6nIk5cSvHyCpaVlhqEWOzs7qlWrxtmzZ5UOd+fOnVhZWdG5c2c1fb+2bdvi7+9Pt27dlDZ8++23mJqa4u7uzqNHj1i6dCm1a9emXr16yvS26dOnM3/+fF6+fEmPHj1wcnLi8ePH7Nmzh6SkJBYsWKDRvpyGFIYMGcKgQYP46aefaN26NX/99RehoaFs2bJF2WfZsmVcunRJWffa09OTatWqMWPGDKUjnT9/PrVq1aJ6ddUi+Vu3bqV8+fI0bNhQ4/3PnDmDs7Mz5cuXz/Yc8jM6Odyff/6Z06dPq5wEmTJlCvv27QPA2dmZLVu2qAXdBQJ98/YKLJ0FCxbQu3fGumFDhgzht99+Y+/23+ncsqrSiQwbNkyl32effcb48eMzvE6fPn3w9/dXjgsICKBNmzYaxVTbt2/PsGHDuHPnDhUqVODrr79m3bp1LFu2jKioKOzt7WnSpAmTJ09WKYoycOBAKlasyIYNG5gwYQIJCQk4OjrStGlTZcpWbuDh4cHy5ctZuXIl3333Hc7OzqxYsUIlLPDkyRMiIiKUj4sUKcKPP/6oTGUzNzenYcOGzJw5UyXMEx8fz759+xg7dmyG99+7dy99+vTJncnlByQd6N69uzR37lzl4+PHj0vu7u7S559/Lq1du1aqXbu25O3trcslDcqZM2ekM2fO6DRm3ue7pXmf784lizKm95+jpd5/js6y37Vr17S+Znx8fE5M0sj/7j+XXkfdkl5H3dL7tTNDq7mkpUhS3P8kKea04ufVg2zd6/Xr11KrVq2k06dPZ2t8ZuTGe2IIsjOPmzdvSo0bN5bi4uJywaLso2kuGf2dZeVTdFrhPnjwgB49eigfBwcHU7JkSZYuXYpMJuP58+ccPnyYr776Su8fDAJBjkhNgPhwSEsCmRFYVQTT7J0CMzMzY8mSJbx48UK/Nr7nPH78mCVLlqgckChs6ORwExMTVWK0J0+exNPTU/lVyMXFhT/++EO/FgoEOSUpBl7pt8rX20dSBfqhadOmhjYh19GpYvIHH3zA//73P0Cx2g0LC1P5xYuLi1M5XikQGBxJgqSnin/NSoB1ZVFSUWAwdFrhtmrVii1btpCWlsbFixcxNTWlZcuWyudv3boljv8K8hcymSJ8kBIHZnl7IEAgeBedVrjjxo2jbt26bNmyhVu3bjFjxgxlRsLr1685dOhQhukemnj48CETJkygbt26eHh48NlnnxEVFaXbDICffvoJd3d3+vfvr/NYQSEkJQ5ehitWtQBFTISzFeQLdFrh2tjYsHHjRuLj4zEzM8PExETl+d9//53SpUtrda3ExESGDh2KqakpS5YsAcDX15chQ4awa9curVVnIyIiWLNmTZ4fZxT8R/Kzh0hJrwAoa8ijNJIErx9BgkLGmqQYMM/4hJRAkNdk689DUx1Qc3NzlfqkWeHn50dERARBQUHKI5ju7u60a9eOrVu3Mnz4cK2uM3fuXDp37sydO3dITU3V+v6CjIl6Es+r19q/lmWNX6m1yczyVmpJhlyhNZYu6mjhqIjZCgT5iGw53Lt373Lv3j2eP3+u8flu3bpleY3Dhw9Tq1YtlfPwzs7OeHh4EBwcrJXD3b17N1evXmXZsmWZJqoLdEMXZ/s291MVDq6ouTGOduofyrlGagLmSXdASs5xypdAkJvo5HBjYmL48ssvlZpmkoaqRjKZTCuHGxYWplEqxdXVlaCgoCzHx8bG4u3tzdSpUzMsgiHIGdpqkiU9jNGpv15JTYC4GxSR5ELYUZDv0cnhfvPNNxw/fpz+/fvTqFGjHDm62NhYjQU4bGxstKpo7+PjQ/ny5VUOYmSXs2fP5skYfZDVfY2NjXn1Sv0rfkZk1lfb66T/EulyX31iJrNAKmJCsnFpeJ0K5G1oKS4ujh49evDLL7/g7Oyc4+sZ6nXUNzmdx+DBgxk2bFiGGnZ5ybtzSU5OzpYP0MnhHj9+nH79+hlcKPLMmTPs3LmTbdu2qZw/zy5169bVuu/eLXt0HqMXwtZrdd/r169rXeM2w3q4z14AaH2dpDefjzmpratJRDI+Pp4NGzZw8OBBIiIiMDMzw8nJiQ/beNGnbx/s7BVVrwaPWsgpDcKmp0+fxtramoMHD7J161auXr3K8+fP2bRpk1o2jZeXFw8eKDbbzMzMcHR0pFevXowYMSLL37Hvv/+eli1batzDGD16NP/++y/r16+nSZMmWc751atXXLlyRU1sMjk5mU2bNrFnzx7u3LmDmZmZcsHRo0ePXMt/T05OZsmSJcqiOY0aNWLu3LmZbo6//Vq+TYsWLfjpp58A7QQ8P/vsMxYvXkynTp3U6g/nJZr+TkxNTTVqvGW5INLlxnK5XKeNscywtrbWuJLNaOX7NrNnz6Znz56ULl1aeY3U1FTkcjlxcXGYm5uLAxg6oOsmGahmJuQGsbGxDBw4kLi4OMaPH0+NGjUoVqwY929fZ8e2rQT+Eccn475S1K2VyejSpQvTpk1Tuca76g+dO3fmyy+/zPCe48aNo3///iQlJXHixAnmzp2LlZWVipLuuyQmJhIQEMCaNWvUnnv8+DEnT55k2LBh+Pv7qzlcbUlOTmbEiBFcv36dCRMmUK9ePYoVK8bly5f59ddfqVChgk7pmLqwcOFCgoODWb58Oba2tixevJhRo0axbds2jcV6QFHM5+XLl8pMoydPntCjRw8++ugjZR9tBDxbtGjBrFmzOHLkiEq+f0FGJ4dbr149vRUhd3V15datW2rt4eHhuLpmroUVHh5OeHg4f/75p9pz9evXZ/r06WoVoAQZo8nZFjXP/FfjXWer76yE5cuX8+DBA4KCghRFuN+kfJUxt6Vx1VFIxsUUbW8Wn+bm5hkWyU7fU3j27Fmm9yxatKjyGr179+aPP/7g2LFjmTrcf//9F5lMpvGbx/bt22nWrBmDBg3io48+4vnz5xQvXlyL2auyceNGTp8+jb+/v4p6hLOzM+3btychIUHna2rDy5cvCQwMZNGiRcoPCx8fH1q1asXx48dp1qyZxnF2dnaYmZkpV4UBAQFYWVmpONwMBTzPnVOqZhgZGdG8eXP27Nnzfjrcr776iiFDhtCoUSPatWuXoxt7eXnh4+NDRESEMu4VGRnJuXPn1OSU32XTpk1qbYsWLUIulzNz5kw11YD3gXShyC96OmEc8ULluSLBK5E9uJzhWE3rlNfA7RzYY+HigUO/r7M1Vi6Xs2/fPjp37qxwtvJUeHVHJeVLZuGgOEWWC0iSxKlTpwgPD8/yd+nMmTNUq1ZNLewgSRKBgYFMnTqVMmXKUKtWLXbu3JmthcDu3bvx9PTUKNVTpEgRjWma6WRWOBwUIar169drfO7KlSukpKSo1DhwcHDAxcWF8+fPZ+hw30aSJAICAujSpUuGtYrhLQHPd77d1qxZU6UcbEFHJ4c7d+5cihYtyqRJkyhVqhTOzs5qsRWZTKYsTJwZffr0YfPmzYwdO5aJEycik8nw9fWldOnS9O3bV9nvwYMHtG3blrFjx/LZZ58BaPz6ZG1tTWpqaq59tcrv5LUqb27y7Nkz4uLiqFixokqVr36fzuVmeCS8WVG+7Si2bdumVOYF6Ny5c6YaZppYsWIF33//PSkpKaSkpGBmZsbgwYMzHRMVFaVRQeHUqVPExsbSokULALp27crGjRuz5XDv3btHgwYNdB4HsGPHjkyfz8wJxsTEYGRkpLYqt7e3V5PhyYhjx44RGRmZZY3bhQsXUqVKFbUPiFKlShEdHU1qamqmissFBZ1mkK6k6eDgAJCtY7jpWFpasnHjRry9vZk2bRqSJNG4cWNmzJihEqCWJIm0tLQcCeu9T5QqbqmenjVsrsa+mjYDkh6GAWDmkD8kzkl+oSipaGzJCt/VJKcpNqkePXqk0u3DDz9k0qRJyseZrfoyYvjw4fTq1Ytnz56xYsUKmjZtqibj/S5JSUkaC+77+/vTvn175V5C+/btmT9/PhcvXtS42ZIZOfndN/S3PT8/P2rUqJHp3o+3tzdnz57ljz/+UIsLm5ubI0kSSUlJ75/DPXz4sF5v7ujoyKpVqzLt4+TkxM2bN7O8liZxQEHBxM7ODmtra27fvg0WDorDDOYlcbBRfJsqVqyYmsO1srLKsXOxtbWlXLlylCtXjlWrVvHhhx9Ss2ZNGjXSrHqcPiY2NlalLS4ujoMHD5KSkoK/v7+yPS0tDX9/f6XDtbKy4v79+2rXfPnypUqooHz58orXIhvkJKRQokQJ0tLSeP78uYqo49OnT6lXr16W93769CmHDx/ONKtp0aJF7Nu3j40bN2pMqYuNjVWJBxd0Cv5HhqBwkZZMkYT7fNS+Hbt27WLMmDHKb1R5iY2NDYMGDcLb25sdO3ZkmBpWtWpVtm/frtK2e/du7OzslClQ6Vy4cIHFixczY8YMLC0tqVChArt37+b169cqX+2vXr2Ko6OjcnXcqVMnli9fzuXLl9XiuHK5nISEhAxX9DkJKVSvXh0TExOOHTtG586dAXj06BHh4eFZOnJQhHlMTEzo2LGjxucXLFjA/v372bRpEy4uLhr7/O9//ytUgpLZcriRkZGcOHGCmJgYOnfujJOTE8nJycTExFCiRAmRkpUNvI+s5vzDK4Y2w7DIUyHuGshT+XxUL06fOUufPn2YMGECNWrUwNLSklu3bnHq1CmdihW9ePGChw8fKlMI79+/j7W1NSVKlMhU/nvAgAGsW7eO/fv306FDB419mjZtytKlS1UyEAICAmjXrp1KTikoVqo+Pj7s27ePXr160blzZ1avXs20adP49NNPKVasGMeOHWPjxo188cUXynHDhg3j33//5eOPP2b8+PHUr18fKysrrl+/zoYNG5g8eXKGexc5WfUXK1aMnj178u2332Jvb4+trS3e3t64u7vj6emp7Ne+fXsGDRrEoEGDlG3pm2UdO3bUuDqdN2/efwKe1tZKQdB3BTzPnj2r1eZcQUFnh/vtt9/y66+/kpaWhkwmo3bt2kqH27FjRyZOnChSsrKBNs62jkP1LPtoQ3oOrTH/HVowKJKk2BxLjVc4XRNrbItXwN/fn3Xr1vHLL78o9w/KlSuHl5cXQ4cO1fryhw8fZvr06crH6dLjWYlF2tvb07VrV77//nvat2+vMfne3d2dmjVrsm/fPgYOHMjVq1e5du0as2bNUutramqKl5cXAQEB9OrVC2trazZv3syyZcsYM2YM8fHxODk5MX36dHr16qUy7pdffuHXX38lICCApUuXYmZmRoUKFejRo4dWq83s8vXXX2NsbMzkyZN5/fo1jRs3xsfHRyXWeufOHbW6KmfOnOHu3bt8++23Gq+rjYBndHQ058+fz/AaBRGZpENE/s8//2Tu3LkMHjyYVq1a8fHHH/PLL7/QuHFjQKHgGxMTo1WWQn4g/VSILqfGvpmi2AmfvayTXm3ps1WhxOrXVz2BXhPpaWDv4jPSjSpVqmQ6Nn1jLCNkZkUxtcujr/HyVHh1V7E5BoqYrYWjzilfGZ6aywOOHDnCwoUL2bdvX4aHAbTFkPPQJ/qYx5IlS4iPj2f+/Pl6sip7aJrL9evXNf6dZeVTdFrhbtmyhbZt2/L1119rrBTm7u7OaQ1HLAX6R5OzrVflgwz7azoZlmrtYNg/bkkOcTcg7fWbKl8VwNTWcPZkk+bNm3P37l0ePXokFE/0iL29PSNGjDC0GXpFJ4d79+7dTFUVihcvnmHJRkHusHtZV5XH169f19gvt0+GZQtZEYUSQ/LzAl/la8iQIYY2odAxcuRIQ5ugd3RyuGZmZiQmJmb4fFRUVJZ1EAR5h6ZV7dv5tSmGqEolyd/k1VooHpuXBvMPFM5XICjk6PRbXrNmTQ4dOqTxuaSkJHbu3Jllorgg78h3q9q0ZIi7CS9vKv4PilitcLaC9wSdVrgjRoxgxIgRTJ06lZ49ewKK438hISGsWrWK6Oholi1bliuGvu9ktEn2LmmvYtU2xfLFqbGUOIi/rdgkK2IKUiog0gcF7xc6OVxPT0/mzp3LwoULlefW00vimZiYMH/+/FxNUXmf0XaTTEpNVnls8FXtu8KOJtaKzbEiJpmPEwgKITrn4fbt2xcvLy+CgoK4ffs2kiRRvnx5PvroI0VlJ4HWZOeww7ubZBmRL1a1ekr5EggKC9k6aVayZEkGDx5Mamoqly5dIjo6mri4OOFwdeRdZ6uvgw35hrREhbMtwClfAoE+ydLhhoaGcujQIcaMGaNynDIiIoJx48apFBHv1q0b3t7euWNpISajww7axm3zLSbFwKo8GFuBUcZn9gWC94Ust4e3b99OSEiI2tn16dOn87///Y86deowbNgwXF1d2bFjh1ohD0H2edfZZnawIV8gyeHV/f8KhQOYlSiUzvbu3bt4enry8uVLQ5tSaHj69CmNGjVSqwRXmMjS4V66dEml4jsoJG7OnDlD/fr12bJlC19++SX+/v6UK1cuy+pEAt3Zvawru5d1Zc7IjMsEGpz0lK/XjxVxW0mu8yViYmJYsGABbdq0oXr16jRr1oyRI0fy77//Kvt4eXnh7u6Ou7s7NWrUoEWLFowbN06lz7skJSXRpUsX3N3duXxZs/LFs2fPaNasGe7u7llK8YBCAmjAgAFK3bS36dKlC1WrVuXOnTtqzw0ePFhjYfRt27apbTjHx8ezcuVKOnToQM2aNfH09GTw4MHs2bMHuVz311dbYmNjmTp1KnXr1qVu3bpMnTo1SyXtV69eMX/+fJo3b07NmjXp3r07v/76q0qf+/fvM27cOBo1aoSHhwcTJ05UKWRub29Pt27d+O6773JjWvmCLB1uTEyMWsWhU6dOIZPJVApsmJub06lTJ61q1woKGSlxiipfqa8UKV9Wrjrn1kZGRtK9e3eOHj3K559/zq5du/j1119p2bIlc+bMUek7btw4jh49yoEDB1i+fDllypThiy++yFDhYcmSJZmqzALMmDEjyxoU6Tx8+JDg4GB69Oih9tylS5d49uwZXbt2JSAgQKvraSIuLo5+/foRGBjIiBEjCAwMZMuWLXTr1o0ffvghR8X/s2LKlClcu3aN9evXs379eq5du6Ym0Pkuixcv5p9//lFWQxsxYgRLly5VLsASEhL4+OOPkSSJjRs38scff5CSksLo0aNVPjx69OjB7t27efHiRa7Nz5BkGcNNTk5Wq5mZvkp4V/bDwcGB+Ph4PZonyNfoMeVr3rx5AAQGBqrUd3BxcaFLly4qfd8We3R0dKRu3bo4OTmxcOFCPvzwQ5WC4X/99RehoaF89913Ga6CN27cSGJiolLSPCv27dtHpUqVcHR0VHsuICCATp060aZNGyZNmsTkyZOzpVSwfPlyIiMjCQoKUvmwKF++PJ066bdw0tuEh4cTEhLCli1blCvuefPmMXDgQG7fvq2QPdLA+fPn6dq1q/K179SpE7t37+bSpUt069aNc+fOERkZSWBgIDY2NoDig7B+/fqcPHlSWe7Rzc2NUqVKcejQIXr37p1r8zQUWf4mODg4qKnrnj17Fnt7e7XC0K9fv9b4FUtgWAxRa7eOQ3WmNx+nVd8XL14QEhLCpEmTNBbT0ea4eLdu3fj+++85ePCg8o/+0aNHzJ07l/Xr12NmprlOw7Vr11i3bh2BgYHcvXtXK3vPnDlD9erqGSUJCQns3buX3377jSpVqmBmZsY///xDmzZttLpuOm+LaGpamWc0F1Acr8+o4Hc6mem9nT9/HktLS5UTo3Xr1sXS0pLz589n6HA9PDz4+++/6d27Nw4ODly8eJHr168ri88kJycjk8lUbDczM6NIkSKcPXtWpb5uzZo1OXXq1PvpcOvVq8fOnTvp3bs3bm5uHDp0iHv37tG9e3e1vjdv3hSpYQKduX//PpIkZVj1XxuMjIwoX748ERERgELO5osvvuDjjz+mcuXKynq6b5OQkMDkyZOZNWsWH3zwgdYONyoqSmP4ISgoCAcHB6VCQZcuXfD399fZ4T5//pzY2NhsvR6lSpXKch8lM723mJgY7OzsVBQuZDIZdnZ2mQpHzpw5kzlz5tCyZUvlin7mzJm0atUKgNq1a2NpaYmPj4+yuPqyZctIS0tTFh9/ew4ZxdoLOlk63E8//ZTdu3fTtWtXbG1tefHiBSYmJnz88ccq/dLS0jh8+HCO5dMF+iejlWa2apamJv5XeAZAngZFclYDVl8CoZIkKR3Fjz/+iImJCcOHD8+w/4IFC6hbt67Ov7OvX7/WuMr09/dXCX907dqVtWvXEh0drdNCJCevh7GxsUGEI3///XfOnTvHmjVrcHR05NixY/j4+FCmTBmaN2+OnZ0dvr6+zJ07ly1btlCkSBE6duyoUWLe3Nyc169f5/kc8oIsHa6zszO//fYbq1ev5t69e9SsWZMxY8ZQqVIllX6hoaEUL16c1q1b55qxAgMiySEhUpGFUKwSmCricDl1tqBQcZDJZISHh9O2bdtsXSMtLY27d+9Ss2ZNAE6ePMmZM2eoVq2aSr++ffvy0UcfsWzZMk6ePMnDhw+VK8J0R9e0aVM++eQTJk+erPFexYsXVxOODA8P59y5c1y4cIGVK1eq2LVt2zbGjFEUmC9atKjGVLK4uDjlyjNdRDM8PFzn1yGnIYUSJUrw7NkzlQ8vSZJ49uyZRnViUHwALV++nJUrV+Ll5QUo/Mbt27fZsGEDzZs3BxSv619//cWzZ88wNjbG2tqaJk2aqMkXvXjxQkW0sjChVTS/Ro0a/Pjjj5n28fT0ZPfu3XoxSpDPSEuG+HBFFoJMBvIUvV7e1taWpk2b8vvvvzN48GC1VXdcXFyWcdzt27cTFxenXK0uWrRIpZTo48ePlTvn6fHJn3/+mZSU/+Zy+fJlZsyYwaZNmyhfvnyG96pataqaMwwICKBWrVosWLBApf3gwYMEBgYyevRoZDIZFSpU4MiRIyoODRSx5AoVKgBQpEgROnTowM6dOxk3bpxaHDcpKQnQHMvNaUihTp06JCQkcP78eeXrdP78eRISEjKsk5KamkpKSoqa2oWRkZHG9LV0Z3rixAmePn2qdNLp3Lp1i/r162c6h4KKUO0VZM67Vb6sKoJJxn+w2WXOnDn079+fnj17MnHiRNzd3ZEkidDQUH766Sf++ecfZd9Xr17x5MkTUlNTefjwIUFBQfz+++8MGjRImTnzruS2paWlsj3dgaU7uHTSi+dXrFgx0xVW06ZNmT59OqmpqRgbG5OSksLOnTsZPXq0mnCkra0tq1ev5uTJkzRu3JgBAwawefNm5s+fT58+fTA1NeXIkSPs3buXH374QTlu8uTJyo2jSZMmUbNmTUxNTTl//jzr1q1j7dq1ODk5qdmW05CCi4sLzZo1Y86cOcpV8Jw5c2jVqpVywyw6OpqhQ4cyZcoU2rZti5WVFQ0aNGDZsmVYWlri6OjI0aNH2bFjB1OnTlVeOzAwkIoVK2Jvb8/58+dZtGgRw4YNU9mIS0xM5OrVq3z++efZnkN+RjhcgWYkCV5HK8IIkOtVvpydndm2bRtr165l6dKlREdHY2trS+XKldW+/q5evZrVq1djYmKCnZ0d1atXZ+nSpRkq6+qbFi1aYG5uztGjR2nZsiV///03z5490xgLLlWqFB4eHgQEBNC4cWOcnZ35/fff8fX15eOPPyYpKYmKFSvi6+tLixYtePWmKLytrS1+fn6sX7+edevW8eDBA6ysrHB1dWXs2LEaU9L0xbJly5g/f74yw8DLy4vZs2crn09JSeHOnTsqoZHly5ezfPlyvvjiC2JjY3FwcGDixIkqSr537txh+fLlxMbGUqZMGUaPHq0mIhkcHIyDgwP16tXLtfkZEp1EJAsbhhaRzEo4svOUnUDmFcIe/rmQxPBzysevWk/ErZyjVtXCMt00k6dC7FVF+KAAVPnKa/HFP/74g6CgIL0Lpr7vIpK9evVi6NChdO7cOResyh4GE5EU5D/edrbp6KUGbhFjhc6YlCqqfGmgT58+xMbG8vLlS5F7rieePn1Ku3btcvVgh6ExqMN9+PAh3t7eHDt2DEmS8PT0ZMaMGVl+Xbp8+TJ+fn6cPn2ahw8fUrx4cerWrcukSZPUYnfvCxW/DgQUn7zZljhPegryZMWKFnIlVltYMDIyYvTo0YY2o1Bhb2/PJ598YmgzchWDOdzExESGDh2KqakpS5YsAcDX15chQ4awa9cu5SaHJvbt28etW7cYPHgwlSpVIjo6mh9++IFevXqxY8cOtRNwgix4O+ULFPFa44L/tVYgyG8YzOH6+fkRERFBUFCQclfV3d2ddu3asXXr1kwT1j/55BO1XWQPDw9at26Nn58fEydOzFXbCxVpyYoshNR4RYzW0hmMMv6wEwgE2cdgcqmHDx+mVq1aKikszs7OeHh4EBwcnOlYTSk7ZcqUwc7OjujoAlywO48xkie8qfIVr0j5KuYO5qXy9eaYQFCQMdgKNywsTOOpNFdXV4KCgnS+Xnh4OE+fPs3ReXx9Y4iiMVqT9Ayz5HuK/5sUU+TXCmFHgSBXMZjDjY2N1Xh6yMbGJstix++SmprKnDlzsLOzU6nRqy3pqRz6HqONs61o6ZzltTJ7vvg7fYyNjZW5nJkhk4wxl5mQYmRNilFJSEwGkrMcl5/RZt4FATGP/Me7c0lOTs6W3ygUaWHffPMN58+fZ+3atcpam7qgSx7u3i17tB8Tth7IOM82S7ZEZnmv22++DKT3uX79esb5j2mvoYiZMmSQ8KoilkWLYZo96/IV73v+an6jsMwDNM/F1NSUWrVqqfXNygkbLIZrbW2tcSWb0co3I5YuXYqfnx8LFy5UkwISvEXSM4i9BokPlE2S4d7+94rt27czZMgQQ5tRqNi8eXOBTMsz2F+cq6urWmFzUMRiXV2zPiUFsGbNGtatW8fMmTPp1q2bni0sJKQLO8bfVvxfnqI4tpvP+Oqrr5RaZW//9OnTR+drhYaGMmrUKBo2bEjNmjVp37498+fP11gTNzP9sXSbZsyYofbct99+i7u7O6NGjcrUluTkZFasWMFnn32m9lxMTAw1atSgZcuWGou8uLu7a9zP+Oqrr9Tue//+fWbMmEGLFi2oXr06rVq1YsKECZw7p34wRl8kJSXx1Vdf0blzZ6pVq8bgwYO1GpecnMz8+fNp2LAhtWvXZvTo0WrCkVFRUYwePZratWvTsGFDFixYQHLyfyGv3r17c+XKFc6cOaPXOeU2BnO4Xl5eXLx4UVkwGhS6VufOnVOrHqSJTZs2sXLlSiZPnqxyXtuQeB9ZTZ+tY5Q/BictGeL+p8ivlcmgaFkoWj7fZiF4enpy9OhRlZ+ffvpJp2v8+eefDBs2DFtbW3x9fdm3bx8LFy5ELpezZo1qaEcb/TEHBwf2799PQkKCsi01NZWdO3dqVc/gwIEDmJubq8lRgWLl26pVK8zMzAgJCdFpnm9z+fJlunfvTlhYGHPnzmXfvn2sWbOGatWqqVUv0ydpaWmYmpoyaNAgWrRoofW4hQsXKvXoNm/ezKtXrxg1ahRpaWnK644aNYpXr16xefNmli9fTlBQkDJfHxRf6Tt37symTZv0Pq/cxGAx3D59+rB582bGjh3LxIkTkclk+Pr6Urp0afr27avs9+DBA9q2bcvYsWOVq4S9e/eyaNEimjVrRqNGjbhw4YKyf3qBD0OgaZOsjoO6FEuekPLyTZWvFEX2gZVLvj85ZmpqqtQq04S7uzuzZs3iyJEjhIaGYmdnx6RJk+jaVVFr4tGjRyxYsICBAwcyc+ZM5TgnJyfq1q2rFsLSRn/M3d2dx48fs3//fnr27AnAP//8g6mpKfXr189S7HDPnj20bNlS43OBgYFMmzaN69evExgYqJPTSkeSJKZPn46zszN//PGHSonEypUr079/f52vqS2WlpbKwkI3b97USjL+5cuXBAYGsmjRIpo0aQKAj48PrVq14vjx4zRr1oyjR49y69Yt/v77b+UhpqlTpzJz5kwmT56sLC/p5eXF8OHDSUxMxMLCIsN75icM5nAtLS3ZuHEj3t7eTJs2DUmSaNy4MTNmzFAJUEuSRFpamkoV/JCQECRJIiQkRG1l0KBBA3777bdctz+zFWy2N8n0yetHCmdbyFK+Vq1axeTJk5k+fTpBQUF8+eWXVKxYkYoVKxIUFERKSkqGx0Pf3hvQRX+sV69eBAYGKh1uYGAgPXr00BiieJezZ89qLAh+5swZXrx4QbNmzahUqRIdOnTg2bNnmeqVaeL69evcunWLpUuXqtWjhcz14Hbt2qWmiPwu8+bNUxPxzAlXrlwhJSVFZb/FwcEBFxcXzp8/T7Nmzbhw4QIuLi4qJ0abNWtGcnIyV65cUWrWVa9enbS0NC5cuEDjxo31ZmNuYtAsBUdHR1atWpVpHycnJzXp9cWLF7N48eLcNC3bGGxF+y5Fy4NxDJiX5tr8RTw/m3uxPE0Ur+tB1dlf6zQmJCRErcj1gAEDVGqqtm3bln79+gEwZswYQkND2bhxI/PmzePu3btYWVlpJWeji/5Yp06dWLJkCXfv3qVo0aKEhIQwa9Ysvvvuu0zvERcXx8uXLylVqpTacwEBAXTo0AETExOcnZ2pWbMm27dvV85NW9J12LKTf+7l5aVxp/1t7O3tdb5uZsTExGBkZETx4sVV2u3t7ZWaaTExMWr3LV68OEZGRiq6ahYWFhQrVkyrD778QqFICzMEubGKnbf+JGeuZ/Ok3IurcGM5WE9RPC5i8l8RmgJCvXr1mD9/vkrbu5W4ateurfY4Xdpcl0qjuuiP2djY0LZtWwIDAylWrBgNGzbUKn6brsv17qo1Pj5erbRj165d+eWXX3R2uDnBysoqU/WHgoCZmZlSAaMgIBxuPkKTs61XJevVWtGikXCwoUICp7r61+mMVpr5LVfSwsIiR2oFFSpUID4+PkvRRm31x96mZ8+efPnll1haWmpdq8PW1haZTKYWO969ezeJiYlq8dX0r8fpsU1t9M/SpYDCw8OVq3VtMURIoUSJEqSlpfH8+XOVI/pPnz5VFh0vUaKEWnbF8+fPSUtLU9NVi42NLVD6Z8Lh5kMyKziugjwFe7uL2NiEQypQfiAYF+7arBcvXlQ5TXjx4kWlREu7du1YunSpMlXwXdK10bTVH3ubxo0bY2JiwosXL7SWPTc1NcXV1ZWwsDCVDbGAgAAGDRqksjkMCqWFHTt2KB1uhQoVuHr1Kr1791b2SUtL48aNG/To0QOAKlWq4Orqys8//0yHDh3U4riZ6cEZIqRQvXp1TExMOHbsmLLI+KNHjwgPD1eGk2rXrs2aNWt49OiRUg7p2LFjmJqaUr36fyG7+/fvk5SUpPMHjSERDregkhAFx/pgYxOOJMmQ1V8FlcbCjRuGtizbJCcn8+TJE5U2IyMjlRXMwYMHqVGjBg0aNODAgQOcOHECPz8/QLH5Mn36dObPn8/Lly/p0aMHTk5OPH78mD179pCUlMScOXO01h97G5lMxq5duwCFI9WWpk2bcvbsWaVczY0bN7hy5QoLFy5Uu3/Xrl2ZMWMG8fHxWFlZMXz4cKZPn06lSpXw9PTk9evX/Pbbb8TGxiqdtUwmw9vbm2HDhjFgwABGjx6Ni4sLiYmJHDlyhP3797Nt2zaNtukjpBAWFkZKSgrPnz/n1atXXL9+HYCyZcsCitS7adOm4ePjQ82aNSlWrBg9e/bk22+/xd7eHltbW7y9vXF3d8fT01P5mlWqVIlp06bx1Vdf8eLFC3x8fOjTp4+KvWfOnMHZ2TlTwc/8hnC42SRd/sYgxN+Gg57wOprUVHOiHzekjNs4w9mjJ44fP652WvCDDz7gyJEjysfjx4/nwIEDLFiwADs7O7y9valZs6byrPvAgQOpWLEiGzZsYMKECSQkJODo6EjTpk0ZM2aMTvpj75Id59S7d2+6devGixcvsLW1JSAggPLly1O5cmW1vi1btkSSJPbu3Uvfvn2VygcbNmxg2bJlWFhYUK1aNTZv3qySPlezZk2lHtycOXN49uwZJUuWpEaNGipaZLnBp59+yoMH/51eTD+AlB4SSExM5M6dOyoKyl9//TXGxsZMnjyZ169f07hxY3x8fJSrcyMjI9auXcu8efPo378/5ubmdO7cmWnTpqnce+/evdk6GGNIhKYZ2dM0O01arthUr8oHzBnZSK1dVbtMovQHx5HJ5EQ/ro9cbq6i+KBJa0kT+S2GmxXu7u74+vrSvn17tefy81wmT56Mq6sr48Zl/aGYn+ehC7k9j//9738MGzaMAwcO5LrEkdA0ywdoHWfVE69vh2JUJI00uTkgI/pxAyTJCCiChYtHntoi0I2pU6dy6NAhQ5tRqHj8+DFLliwpcHpywuEWBGKvU8bxH+RyU8yHPSw0hxjeFxwdHRk6dKihzShUFNRCVcLh5nfu+UHox5iaviI52RpePwHLrHNACyPvHoARCAoawuHmV+QpcH4a3FwJwMt4Z2Ji6lDhPXW2AkFhQBREzY8kPoRgL4WzlRlD3VU8eVIPSRKfjwJBQUY43PzI3T/gyVGwcIQ2R8D9MyB/llQUCATaI5ZM+ZHKkyAlDiqNAYusj/YKBIKCgVjh5gdSXsKpUZDwpuqRrAjUnCucrUBQyBArXEMTex1CekDcDYi/C14HDG2RQCDIJYTDNST3/eHkx5AaDzZVoV7m9VUFAkHBRoQUDIE8Bc5+Dkf7KJxtuf7wYShYuxvasvcWLy8vfv755xz30Qc///yzVrp+Qg1Y/+S2GrBwuHmNPA3+bgc3V7xJ+fIFz835Xm8sL4iJiWHBggW0adOG6tWr06xZM0aOHKksMG5oAgICGDBggKHNAIQasCYKghqwcLh5TREjcGj3JuXrH3CfkG9VdPOSyMhIunfvztGjR/n888/ZtWsXv/76Ky1btsyySHZeYWdnl2/ECoUasDoFQQ1YONy8QJIUG2LpVJkGHS5BySYGMym/MW/ePEAh0NihQwcqVqyIi4sLgwYNUtahBcUKZdy4cdSpU4c6derw2WefqaxiVq1aRadOndi+fTteXl7Url2b6dOnk5yczObNm2nRogUNGzbE29tbbfWXkJDAF198QZ06dWjSpIla+ODdkIK7uztbt25lwoQJ1K5dm9atW7Nzp2rZzujoaCZPnkz9+vWpX78+n376qVKHLJ1169bRpEkT6tSpw6xZs1Qk2TMiKzXgbt260aVLFwIDA7O8libeVQNu1aoVZcuWpXLlyowaNYpff/01W9fVhnQ14L59+yoLkGdFuhrwtGnTaNKkCdWqVcPHx4ebN29y/PhxAKUasI+PD9WqVaNJkyZMnToVPz8/4uPjldfy8vLi8OHDKiUl9YXYNMttUuIhdAREB0P7s1C0nGJFa6bfSvqZsWX9KcKuP86z+wG4VinFgJHqqy9NvHjxgpCQECZNmqSxpF+6YoFcLmfs2LGYm5srVyDz589n7NixKiuSBw8eEBwczI8//kh0dDQTJkzg8ePHlCpVig0bNnD79m0mTZqEh4eHSl3cX375hU8++YRx48YRGhrKggULcHZ25sMPP8zQ9tWrVzNlyhSmTJlCQEAAX3/9NfXr18fR0ZHExESGDBlCnTp1+O233zAxMWHDhg0MHz6cffv2YWFhwb59+/D19WXmzJk0bNiQXbt28csvv2Bra5vpa6arGrCuMjQ5VQOePXu2mmrG27yvasDC4eYmsTfepHxdV0jfvLylcLgCFe7fv48kSVkqz544cYKbN29y6NAhnJycAIUsTdu2bTl16pRyoyktLQ1vb2+KFSuGm5sbzZo149SpU6xZswZTU1NcXFzw8PAgNDRUxeHWqlVLqWdWoUIFLl++zC+//JKpw+3atStduypKdU6cOJFNmzZx+vRpunbtyt69e5EkCW9vb6Xz+eabb/D09OTvv/+mQ4cObNq0iW7duinFI0eOHMn58+e5f/9+hvfMjhpwuuKEtuRUDbhSpUpYWlpm2Od9VQMWDje3uB8AJ4f/l/LVbJvBshAyWmnml2LX2tbADw8Pp1SpUkpnC+Ds7EypUqW4ffu20uE6ODio1Em1t7enfPnyKtI49vb2PH36VOX6mhSBs6pj6+7+33tqbGyMnZ0dz549A+Dq1atERkbi4aFarzgxMZGIiAjlnN7WaEu/b2YONztqwLo63JxgZWVF2bJl88XvVnbJLTVg4XD1jTwVLnwFN5YpHpfrBw3WiSyETChXrhwymYzw8HDatm2brWu8/fXVxMRE7TlNbZp28HXF2Fj1T+jt68rlcipXrsyKFSvUxtnY2GT7ntlRAz579qxShSAv1IDzOqRQUNSAhcPVN88v/Ffly2MZuI0XWQhZYGtrS9OmTfn9998ZPHiw2sooXXnWxcWFx48fExkZqVzlRkRE8PjxYypUqJBjOy5evKj2OF0RODtUq1aNvXv3Urx48Qxjni4uLhqViDMjO2rAAQEBSoebF2rAeR1SKChqwCJLQd/Y14P6P4iULx1JT/3q2bMn+/fv5/bt24SHh7NlyxblSsjT0xN3d3e++OILLl++zOXLl/niiy+oWrWqxvQoXblw4QJr167l7t27+Pn5sWPHDoYNG5bt63Xu3Bl7e3vGjh3LqVOniIiI4PTp0yxevFgZIx0yZAjbt2/Hz8+Pu3fvsmHDhiwdLvynBpxOuhpw7969cXNzU/np2rUrQUFByp344cOHExgYyObNm7lz5w7Xr19n1qxZGtWA79+/z4ABA/j777+5f/8+N2/eZN26dZm+LukhhXLlymX4k5UgZ1hYGNevX1dRA05XBAaFGnD79u25dOkSgIoa8PHjx7l27RpTp07NUA342rVrHD9+PM/VgA26wn348CHe3t4cO3YMSZLw9PRkxowZODpmXWQ7KSmJlStXsnv3buLi4qhSpQpffPEF9evXzwPL30KS4OZ3YO0Gjh8p2lw/zVsbCgHOzs5K5dmlS5cSHR2Nra0tlStX5ptvvgEUTuCHH35gwYIFyhNWnp6ezJo1K9Ovr9oyfPhwbt68yY8//oiFhQUTJkzQKFipLRYWFmzevJlly5YxceJE5UZXw4YNlavDDh06EBERwYoVK3j9+jXNmzdn+PDhbN++PdNr66oGLJfLC4UacLrqR0FVAzaYam9iYiJdu3bF1NSUSZMmAeDr60tiYiK7du3K9OsIwJQpU/j333+ZNm0azs7ObN68mSNHjrB161atVWtzoto7e1mnNylfI+H+VjC1gy7hYGqr9bV04fbCngBKdd6MKMyqvZlRWOaiyzx0UQPOawrq+6FJDVifqr0GCyn4+fkRERHB6tWradOmDW3atOGHH34gKiqKrVu3Zjr2xo0b7Nmzh+nTp9OnTx8aN27MypUrcXBwwNfXN28mEHsDDjZUOFtjK2iwNtecrUCgialTp2b51VygG7mtBmwwh3v48GFq1apFuXL/5aU6Ozvj4eFBcHBwpmODg4MxMTGhQ4cOyjZjY2M6duzI0aNHVc5G5wZVbI/BgfoQew2sq0C701C2V9YDBQI9ItSA9U/Tpk1p1qxZrl3fYA43LCwMNzc3tfb03desxpYpU0btXLurqyspKSncu3dPr7a+TZPS/vR2WazIry3bB9qdAhv1uJlAIBC8i8E2zWJjYzWmldjY2KjlF2oaqymPMf04ZGxsrF5s1MT9l1VJkZsS+7wycXdS4F+xwhAIBNoh8nBBJb0mK57H2rLh/Hza2oXmokWaSSnpkqWtxsbGvHz5kiJFtPvy8urVK32Yli8oLHMR88h/vD0XuVxOcnKyTn4jHYM5XGtra40r2YxWvu+OfTtlJJ0XL14Aup/i0SVLoW7dupw9e5aKdadl3dkA3L9/nxcvXvDBBx9gYmKSabpUQd1J1kRhmYuYR/4jfS6SJJGSkkJ0dDTFixenbNmyan2zXBDllpFZ4erqyq1bt9Taw8PDcXV1zXLsX3/9RWJiokocNzw8HBMTE5WNuPcNJycnYmJiuHfvHqmpqZn2TU5OVqkvUJApLHMR88h/vD0XY2NjbGxs1I4Ca4vBHK6Xlxc+Pj5ERETg7OwMKIpQnzt3jilTpmQ5dtWqVQQFBdG9e3cAUlNT2bdvH02bNi00b3R2KFKkCKVKldJYSepdzp49S61atfLAqtynsMxFzCP/oc+5GCxLoU+fPpQpU4axY8fy119/ERwczNixYyldurTKWfAHDx5QtWpVvv/+e2Vb1apV6dChA4sWLcLf358TJ07w+eefExkZyfjx4w0xHYFAIMgSg61wLS0t2bhxI97e3kybNg1JkmjcuDEzZsxQif1IkkRaWppaCT9vb29WrFjBypUriYuLo3Llyqxfv55q1arl9VQEAoFAKwyapeDo6MiqVasy7ePk5KQ8P/025ubmTJ8+nenTp+eWeQKBQKBXRLUwgUAgyCMMVrwmP5CdPDqBQCDIinxXvEYgEAjeN97rFa5AIBDkJWKFKxAIBHmEcLgCgUCQRwiHKxAIBHmEcLgCgUCQRwiHKxAIBHmEcLgCgUCQRwiHKxAIBHmEcLgCgUCQRwiH+4aHDx8yYcIE6tati4eHB5999hlRUVFajU1KSmLJkiU0bdqUmjVr0rdvX06fPp3LFmsmu/O4fPkys2bNon379tSqVYuWLVsyZcoUIiIi8sBqzeTkPXmbn376CXd3d/r3758LVmZNTucRHh7OhAkTaNiwITVr1qRdu3Zs3LgxFy3OmJzMJSoqii+//JKWLVsq57FixQoSEhJy2Wp1Hj16xPz58+nbty+1atXC3d2dyMhIrcbK5XLWrl2Ll5cXNWrUoEuXLhw4cECrseKkGZCYmEjXrl0xNTVl0qRJAPj6+pKYmMiuXbuwtLTMdPyUKVP4999/mTZtGs7OzmzevJkjR46wdetWqlSpkgczUJCTeSxZsoTz58/TuXNnKlWqRHR0ND/88APPnj1jx44dODg45NEsFOT0PUknIiKCLl26YGFhQbly5fjjjz9y0Wp1cjqPy5cvM3ToUBo0aECvXr2wsrLi3r17JCQkMHz48DyYwX/kZC4JCQl0796dlJQUxo8fj4ODA5cvX2bVqlV4eXmxcuXKvJnEG0JDQ5k8eTLVqlVDLpdz9OhRgoODcXJyynLsihUr+Pnnn5Xj9+3bh5+fH2vXrqVFixaZD5YE0q+//ipVrlxZunv3rrLt/v37UpUqVaQNGzZkOvb69euSm5ubFBAQoGxLSUmRPvzwQ2nUqFG5ZrMmcjKPp0+fqrVFRkZK7u7u0sqVK/Vua1bkZC5v8/HHH0uzZs2SBg0aJPXr1y83TM2UnMwjLS1N+uijj6SxY8fmtplakZO5hISESG5ublJISIhK+7fffitVqVJFSkhIyBWbMyItLU35fz8/P8nNzU2KiIjIclxMTIxUrVo1ydfXV6V9yJAhUqdOnbIcL0IKwOHDh6lVq5aKFpqzszMeHh4EBwdnOjY4OBgTExM6dOigbDM2NqZjx44cPXqU5OTkXLP7XXIyDzs7O7W2MmXKYGdnR3R0tN5tzYqczCWd3bt3c/XqVT7//PPcMjNLcjKP0NBQwsPD83wlmxE5mUtKSgoAVlZWKu3W1tbI5XI1gYHcRltV63cJCQkhJSWFLl26qLR36dKF//3vf1mG4ITDBcLCwnBzc1Nrd3V1JSwsLMuxZcqUURGzTB+bkpLCvXv39GprVrZkdx6aCA8P5+nTp7i4uOjDPJ3I6VxiY2Px9vZm6tSp2Nra5oKF2pGTeaSXD01KSqJPnz5Uq1aNxo0bs2DBAl6/fp0r9mZGTubi6elJ+fLlWbp0KWFhYbx69YoTJ06wadMm+vXrp3WIyNCEhYVhamqqJlRbqVIlQPE3kxnC4ZKxNLuNjY1GKfd3x2qSZU//I4+NjdWLjdqQk3m8S2pqKnPmzMHOzo5evXrpy0StyelcfHx8KF++PD169MgN87QmJ/N4/PgxAJMnT6ZJkyZs2LCBkSNH4u/vn6XQam6Qk7mYmZmxZcsW5HI5HTt2xMPDg2HDhtGyZUtmz56dWybrnfTXQCaTqbSn+4AXL15kOt6gEjuC/Ms333zD+fPnWbt2rcYPlPzMmTNn2LlzJ9u2bVP7wyhIpH/N7tKlCxMnTgSgYcOGpKWlsWzZMsLDww3y7SM7JCUlMWnSJJ4+fYqPjw+Ojo5cunSJ1atXY2RkxLx58wxtYp4gHC6KOJKmT+iMPtHfHfvgwQO19vRPurx0VjmZx9ssXboUPz8/Fi9eTNOmTfVpotbkZC6zZ8+mZ8+elC5dWnmN1NRU5HI5cXFxmJubY2pqmit2v0tO5pH+LcnT01OlvWnTpixbtoxr167lqcPNyVwCAgI4deoUhw4domzZsgDUr1+fYsWKMWvWLPr370/lypVzxW59kv4aSJKk8mGe/k02q/CVCCmgiEHdunVLrT08PBxXV9csxz548IDExES1sSYmJmqxntwkJ/NIZ82aNaxbt46ZM2fSrVs3PVuoPTmZS3h4OH/++Sf169dX/pw7d44LFy5Qv359tmzZkltmq5HT363MyO7GT3bJyVxu3ryJjY2N0tmmU7NmTeU1CgKVKlUiOTmZ+/fvq7Snx7Cz+gAUDhfw8vLi4sWLKjuMkZGRnDt3Di8vryzHpqSkEBQUpGxLTU1l3759NG3aNM9WUum2ZHceAJs2bWLlypVMnjyZQYMG5aapWZKTuWzatEntp3Llyri5ubFp0ybat2+f2+Yryck8mjdvjqmpKUePHlVpDwkJAaB69er6NzgTcjKXkiVLEhsbq7aJfPHiRQA++OAD/RucCzRr1gwTExN2796t0r5r1y7c3NxwdnbO/AI6pq8VSl69eiW1adNG6tSpk3To0CHpr7/+kjp37ix5eXlJ8fHxyn6RkZFSlSpVpFWrVqmMnzRpklSvXj3Jz89POn78uDR+/HipevXq0pUrVwrMPPbs2SO5u7tLI0aMkM6fP6/yc+vWrTydR07noglD5eHmdB6rVq2SqlSpIi1btkw6duyYtHbtWqlGjRrSl19+mddTydFcIiIipDp16kgffvihtG3bNunEiRPSunXrpDp16kjdu3dXyYvNK/bv3y/t379fmj17tuTm5iZt3rxZ2r9/vxQaGqrsU6VKFWn69Okq47799lupevXq0oYNG6STJ09Ks2fPltzd3aXDhw9neU8RwwUsLS3ZuHEj3t7eTJs2DUmSaNy4MTNmzKBo0aLKfpIkkZaWppYz6O3tzYoVK1i5ciVxcXFUrlyZ9evXU61atQIzj5CQECRJIiQkRLmCSqdBgwb89ttveTYPyPl7kl/I6TzGjRtH0aJF2bJlCxs2bKBkyZKMGDGCsWPH5vVUcjQXJycn/Pz8WLVqFStXruT58+c4ODjQt29fRo8enefhEUC5EZlO+sbd27/vaWlpyOVylX6TJ0/G0tKSTZs28eTJEypUqMDKlStp1apVlvcUR3sFAoEgjxAxXIFAIMgjhMMVCASCPEI4XIFAIMgjhMMVCASCPEI4XIFAIMgjhMMVCASCPEI4XIFAD6xatUqjTMv169cZOnQo9evXx93dnVWrVhEZGan8v67kZKzA8IiDDwK9s3btWq5evcrVq1eJjIykTJkyHD58WOfr3Lhxg59++onLly/z6NEjLC0tKVWqFHXq1KFfv35UrVo1F6zXH6mpqYwfP57U1FQmTpxIsWLFcHd31/t94uLi2LhxIw0aNKBhw4Z6v75Af4iDDwK94+7ujq2tLVWrVuXq1atYWVnp7HD//vtvxo0bh52dHV27dqVcuXLExcVx9+5djhw5wtChQxkxYkQuzUB3UlNTSUtLw9TUVFlF6s6dO7Rv356vvvpKRbVBkiSSk5MxMjLC2Fi3NY+msZGRkbRu3ZrPPvuM8ePH629SAr0jVrgCvfPXX38pi3h06tQpW6qsy5cvx8zMjICAAEqXLq3ynFwuz7LQc15jbGys5jxjYmIA9RKdMpkMMzOzbN0nJ2MFhkfEcAV6J8uKSVpw9+5dKlSooOZsQVGW8G0Ntrfjmnv27KFz587UqFGDli1bsmrVKlJTU9Wu8fjxY+bMmUPLli2pXr06TZs2ZdasWTx9+lStb3x8PCtWrOCjjz6iRo0aNGzYkP79+7N3715ln3djuIMHD1ZWXJs+fTru7u7K5zOLwx44cIDBgwdTr149atWqRbt27ViwYIFSG+/dsaGhobRu3RqA77//XnkfLy8vnj59SvXq1TNUh5g3bx6VK1fWWh5ckHPECleQLylbtixhYWGcO3cODw8PrcYcPnyYiIgIBg4cSIkSJTh8+DDff/89UVFReHt7K/tFRUXRt29fUlJS6NWrF2XLluXevXv88ccfhIaGEhgYSLFixQBFfHTAgAHcunWLdu3a0b9/f+RyOdeuXePvv/+mY8eOGm0ZPXo0Hh4e/Pjjj/Tt25e6desCCrHOZ8+eaRyzYsUKfvzxR1xdXRk2bBglS5bk/v37HDx4kAkTJmgs9eni4sL06dPx9vambdu2tG3bFoCiRYtib2+Pl5cXhw4dIi4uTqVIeFJSEnv27MHT01MraXCBnshBdTOBIEs6duwotWrVSudx+/fvl9zd3SU3NzepU6dO0qxZsyR/f3+NUtYRERGSm5ubVLlyZZWSmHK5XBo7dqzk5uYmnT9/Xtk+evRoqVGjRtLDhw9VrnPp0iWpSpUq0nfffadsmzNnjuTm5ib9+eefavd9u6Tgd999pya1ffLkScnNzU0KDAzUaO/b97l48aLk5uYmDR48WHr9+rVKf7lcLsnl8gzHampLJ12e/Pfff1dp37lzp+Tm5ibt3btXbYwg9xAhBUG+pH379vz++++0a9eOR48esXXrVr7++mtat27NmDFjNK4SPT09VUpiymQyRo4cCcChQ4cAePnyJf/88w9eXl6Ympry7Nkz5U+ZMmUoW7Ysx44dAxSx4n379uHi4kLfvn3V7qfPkoK7du0CYMqUKWoxWplMlm1ttiZNmuDk5ERgYKBKe0BAALa2trRp0yZ7BguyhQgpCAzGixcvSElJUWkrWbKk8v/16tWjXr16SJLE3bt3CQ0NZcuWLRw+fJipU6fy888/q4zVJG+SLv2SrlJw584d5HI5AQEBBAQEaLQrPQb9/PlzYmNjadasWfYnqSX37t1DJpPpXddLJpPRu3dvVqxYwfXr16lSpQoRERGcOnWKIUOG5KkiiUA4XIEBGT9+PKdOnVJpu3nzplo/mUxGhQoVqFChAt27d6djx44cPXqUR48eadxUywzpLSXc7t27a+xjqCyAnKxkM6Nnz56sWrWKgIAAZs2aRUBAAJIk0bt3b73fS5A5wuEKDMaXX36pUQU2M8zMzJSrtOjoaBWHq0mIMF3cL33VWrZsWWQyGSkpKWpquO9SvHhxbGxsuHHjhk42Zofy5ctz5MgRbty4oRRW1JasnHTJkiVp1aoVu3fvZsqUKWzfvp1atWpRqVKlnJgsyAYihiswGNWrV8fT01PlJ50jR45olM159uwZ586dw9jYWE0R+fjx41y9elX5WJIk1q9fD6CMVRYvXpwWLVpw6NAhLly4oHZ9SZKU8eEiRYrQsWNHwsLC8Pf319hXX3Tu3BlQ5B+np4Bpey9LS0vgP6luTfTu3ZvY2FjmzJlDdHS0WN0aCLHCFeidHTt2EBUVBSgcZEpKCj/88AMAjo6OWsmvT5w4ETs7O1q1aoWLiwvGxsZERESwc+dOYmJiGDduHLa2tipjKleuzNChQxk4cCAlS5YkODiY48eP07VrV+rUqaPsN3fuXAYMGMCgQYPo2rUrVatWRS6XExERQXBwMN26dVOe2Jo0aRInT55k5syZHDt2jLp16yJJEtevXyc1NZVvv/1WL69ZzZo1+eSTT1i3bh09evTgo48+omTJkkRGRnLgwAH8/f1V0rrepnjx4pQrV469e/fi7OxMiRIlsLCwUFHSbdasGWXKlGHXrl1YWlrSoUMHvdgt0A3hcAV6JzAwUC026+vrCygE+rRxuIsWLeLIkSOcOHGCnTt3kpCQgI2NDVWrVmXGjBm0a9dObYyXlxcVKlRg7dq13LlzB3t7e8aOHasmuOjg4EBgYCDr1q3j8OHD7Nq1CzMzMxwcHGjVqhUfffSRsq+NjQ1bt27lxx9/5NChQ/z1118ULVoUFxcXvUvJf/HFF1SuXJnff/+d9evXI0kSpUuXpnnz5pibm2c6dunSpSxatIgVK1aQmJhImTJlVBxukSJF6NmzJ9999x0fffSRiuijIO8QtRQEBR5RS0A71q1bx9KlS/nzzz9VVvyCvEPEcAWC94DU1FS2bt2Km5ubcLYGRIQUBIJCTEREBBcuXCA4OJiIiAiWL19uaJPea4TDFQgKMadPn2b69OkUL16ccePGZVj7QZA3iBiuQCAQ5BEihisQCAR5hHC4AoFAkEcIhysQCAR5hHC4AoFAkEcIhysQCAR5hHC4AoFAkEf8HyVuAVYrVAF2AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 360x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#careful about changing variables here, should try using find and replace \n",
    "rna_roc = ['HER2','IGF1R','CD44','EpCAM','all_combined']\n",
    "cell_lines_considered=pd.concat([mm231,mcf7],axis=0)\n",
    "fig,ax=plt.subplots(figsize=(5,4))\n",
    "for marker in rna_roc:\n",
    "    if marker=='all_combined':\n",
    "        marker=['HER2','IGF1R','CD44','EpCAM']\n",
    "        accuracy=[]\n",
    "        X=cell_lines_considered.iloc[:,2:][marker]\n",
    "        y=cell_lines_considered['cell_line']\n",
    "        #Scale data\n",
    "        scaler = StandardScaler()\n",
    "        scaler.fit(X) \n",
    "        X_scaled = pd.DataFrame(scaler.transform(X),columns = X.columns)\n",
    "        X_train, X_test, y_train, y_test = train_test_split(X_scaled, y, test_size=0.3)\n",
    "        #X_train=X_scaled\n",
    "        #y_train=y\n",
    "        #X_test=X_scaled\n",
    "        #y_test=y\n",
    "        classifier = LogisticRegression(random_state=0,multi_class='multinomial',solver='newton-cg')\n",
    "        classifier.fit(X_train, y_train)\n",
    "        y_pred = classifier.predict(X_test)\n",
    "        #    print(\"The classifier Accuracy: \", accuracy_score(y_test,y_pred))\n",
    "        accuracy.append(accuracy_score(y_test,y_pred))\n",
    "        feature_importance = abs(classifier.coef_[0])\n",
    "        feature_importance =feature_importance / feature_importance.max()\n",
    "        sorted_idx = np.argsort(feature_importance)\n",
    "        pos = np.arange(sorted_idx.shape[0]) + .5\n",
    "        #featfig = plt.figure(figsize=(10,8))\n",
    "        #featax = featfig.add_subplot(1, 1, 1)\n",
    "        #featax.barh(pos, feature_importance[sorted_idx], align='center')\n",
    "        #print(np.array(X.columns)[sorted_idx],feature_importance[sorted_idx])\n",
    "        #featax.set_yticks(pos)\n",
    "        #featax.set_yticklabels(np.array(X.columns)[sorted_idx], fontsize=10)\n",
    "        #featax.set_xlabel('Relative Feature Importance')\n",
    "        RocCurveDisplay.from_estimator(classifier, X_train, y_train, alpha=1.0,name='Combined',ax=ax,lw=2)\n",
    "    else:\n",
    "        accuracy=[]\n",
    "        X=cell_lines_considered.iloc[:,2:][[marker]]\n",
    "        y=cell_lines_considered['cell_line']\n",
    "        #Scale data\n",
    "        scaler = StandardScaler()\n",
    "        scaler.fit(X) \n",
    "        X_scaled = pd.DataFrame(scaler.transform(X),columns = X.columns)\n",
    "        X_train, X_test, y_train, y_test = train_test_split(X_scaled, y, test_size=0.3)\n",
    "        #X_train=X_scaled\n",
    "        #y_train=y\n",
    "        #X_test=X_scaled\n",
    "        #y_test=y\n",
    "        classifier = LogisticRegression(random_state=0,multi_class='multinomial',solver='newton-cg')\n",
    "        classifier.fit(X_train, y_train)\n",
    "        y_pred = classifier.predict(X_test)\n",
    "        #    print(\"The classifier Accuracy: \", accuracy_score(y_test,y_pred))\n",
    "        accuracy.append(accuracy_score(y_test,y_pred))\n",
    "        feature_importance = abs(classifier.coef_[0])\n",
    "        feature_importance =feature_importance / feature_importance.max()\n",
    "        sorted_idx = np.argsort(feature_importance)\n",
    "        pos = np.arange(sorted_idx.shape[0]) + .5\n",
    "        #featfig = plt.figure(figsize=(10,8))\n",
    "        #featax = featfig.add_subplot(1, 1, 1)\n",
    "        #featax.barh(pos, feature_importance[sorted_idx], align='center')\n",
    "        #print(np.array(X.columns)[sorted_idx],feature_importance[sorted_idx])\n",
    "        #featax.set_yticks(pos)\n",
    "        #featax.set_yticklabels(np.array(X.columns)[sorted_idx], fontsize=10)\n",
    "        #featax.set_xlabel('Relative Feature Importance')\n",
    "        RocCurveDisplay.from_estimator(classifier, X_train, y_train, alpha=1.0,name=marker,ax=ax,lw=2)\n",
    "        \n",
    "for axis in ['top','bottom','left','right']:\n",
    "    ax.spines[axis].set_linewidth(2)\n",
    "    \n",
    "        \n",
    "ax.plot([0, 1], [0, 1], color='orange', lw=2, linestyle='--')\n",
    "ax.legend(fontsize=14)\n",
    "#xlbl = ax.xaxis.get_label()\n",
    "#ylbl = ax.yaxis.get_label()\n",
    "#plt.setp(xlbl,fontsize=16)\n",
    "#plt.setp(ylbl,fontsize=16)\n",
    "plt.xlabel('1-Specificity',fontsize=18)\n",
    "plt.ylabel('Sensitivity',fontsize=18)\n",
    "plt.setp(ax.get_xticklabels(),fontsize=16)\n",
    "plt.setp(ax.get_yticklabels(),fontsize=16)\n",
    "plt.tight_layout()   \n",
    "#plt.savefig('Ray_roc_logistic_regression.jpg', dpi = 1000, bbox_inches='tight')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "mm231    150\n",
       "mcf7     100\n",
       "Name: cell_line, dtype: int64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,axs = plt.subplots(nrows=1,ncols=3,figsize=(15,4))\n",
    "j=0\n",
    "expression_data=['EpCAM']\n",
    "for cell_line in expression_data:\n",
    "    sns.set(style=\"whitegrid\")\n",
    "    ax=sns.violinplot(data=mcf7.iloc[:,2:],\n",
    "                           scale='width',\n",
    "                         ax=axs[0])\n",
    "    axs[0].set_xticklabels(ax.get_xticklabels(),rotation = 90,fontsize=16)\n",
    "    axs[0].tick_params(axis='y', labelsize=16)\n",
    "    axs[0].set_title(str(mcf7.cell_line.unique()[0]), fontsize=18)\n",
    "    sns.set(style=\"whitegrid\")\n",
    "    ax=sns.violinplot(data=skbr3.iloc[:,2:],\n",
    "                           scale='width',\n",
    "                         ax=axs[1])\n",
    "    axs[1].set_xticklabels(ax.get_xticklabels(),rotation = 90,fontsize=16)\n",
    "    axs[1].tick_params(axis='y', labelsize=16)\n",
    "    axs[1].set_title(str(skbr3.cell_line.unique()[0]), fontsize=18)\n",
    "    sns.set(style=\"whitegrid\")\n",
    "    ax=sns.violinplot(data=mm231.iloc[:,2:],\n",
    "                           scale='width',\n",
    "                         ax=axs[2])\n",
    "    axs[2].set_xticklabels(ax.get_xticklabels(),rotation = 90,fontsize=16)\n",
    "    axs[2].tick_params(axis='y', labelsize=16)\n",
    "    axs[2].set_title(str(mm231.cell_line.unique()[0]), fontsize=18)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#careful about changing variables here, should try using find and replace \n",
    "rna_roc = ['HER2','IGF1R', 'CD44','all_three_combined']\n",
    "cell_lines_considered=pd.concat([mm231,mcf7],axis=0)\n",
    "fig,ax=plt.subplots(figsize=(5,4))\n",
    "for marker in rna_roc:\n",
    "    if marker=='all_three_combined':\n",
    "        marker=['HER2','IGF1R', 'CD44']\n",
    "        accuracy=[]\n",
    "        X=cell_lines_considered.iloc[:,2:][marker]\n",
    "        y=cell_lines_considered['cell_line']\n",
    "        #Scale data\n",
    "        scaler = StandardScaler()\n",
    "        scaler.fit(X) \n",
    "        X_scaled = pd.DataFrame(scaler.transform(X),columns = X.columns)\n",
    "        #X_train, X_test, y_train, y_test = train_test_split(X_scaled, y, test_size=0.3)\n",
    "        X_train=X_scaled\n",
    "        y_train=y\n",
    "        X_test=X_scaled\n",
    "        y_test=y\n",
    "        classifier = RandomForestClassifier(random_state=0)\n",
    "        classifier.fit(X_train, y_train)\n",
    "        y_pred = classifier.predict(X_test)\n",
    "        #    print(\"The classifier Accuracy: \", accuracy_score(y_test,y_pred))\n",
    "        accuracy.append(accuracy_score(y_test,y_pred))\n",
    "#        feature_importance = abs(classifier.coef_[0])\n",
    "#        feature_importance =feature_importance / feature_importance.max()\n",
    "        sorted_idx = np.argsort(feature_importance)\n",
    "        pos = np.arange(sorted_idx.shape[0]) + .5\n",
    "        #featfig = plt.figure(figsize=(10,8))\n",
    "        #featax = featfig.add_subplot(1, 1, 1)\n",
    "        #featax.barh(pos, feature_importance[sorted_idx], align='center')\n",
    "        #print(np.array(X.columns)[sorted_idx],feature_importance[sorted_idx])\n",
    "        #featax.set_yticks(pos)\n",
    "        #featax.set_yticklabels(np.array(X.columns)[sorted_idx], fontsize=10)\n",
    "        #featax.set_xlabel('Relative Feature Importance')\n",
    "        RocCurveDisplay.from_estimator(classifier, X_test, y_test, alpha=1.0,name='Combined',ax=ax,lw=2)\n",
    "    else:\n",
    "        accuracy=[]\n",
    "        X=cell_lines_considered.iloc[:,2:][[marker]]\n",
    "        y=cell_lines_considered['cell_line']\n",
    "        #Scale data\n",
    "        scaler = StandardScaler()\n",
    "        scaler.fit(X) \n",
    "        X_scaled = pd.DataFrame(scaler.transform(X),columns = X.columns)\n",
    "        #X_train, X_test, y_train, y_test = train_test_split(X_scaled, y, test_size=0.3)\n",
    "        X_train=X_scaled\n",
    "        y_train=y\n",
    "        X_test=X_scaled\n",
    "        y_test=y\n",
    "        classifier = RandomForestClassifier(random_state=0)\n",
    "        classifier.fit(X_train, y_train)\n",
    "        y_pred = classifier.predict(X_test)\n",
    "        #    print(\"The classifier Accuracy: \", accuracy_score(y_test,y_pred))\n",
    "        accuracy.append(accuracy_score(y_test,y_pred))\n",
    "#        feature_importance = abs(classifier.coef_[0])\n",
    "#        feature_importance =feature_importance / feature_importance.max()\n",
    "        sorted_idx = np.argsort(feature_importance)\n",
    "        pos = np.arange(sorted_idx.shape[0]) + .5\n",
    "        #featfig = plt.figure(figsize=(10,8))\n",
    "        #featax = featfig.add_subplot(1, 1, 1)\n",
    "        #featax.barh(pos, feature_importance[sorted_idx], align='center')\n",
    "        #print(np.array(X.columns)[sorted_idx],feature_importance[sorted_idx])\n",
    "        #featax.set_yticks(pos)\n",
    "        #featax.set_yticklabels(np.array(X.columns)[sorted_idx], fontsize=10)\n",
    "        #featax.set_xlabel('Relative Feature Importance')\n",
    "        RocCurveDisplay.from_estimator(classifier, X_test, y_test, alpha=1.0,name=marker,ax=ax,lw=2)\n",
    "        \n",
    "for axis in ['top','bottom','left','right']:\n",
    "    ax.spines[axis].set_linewidth(2)\n",
    "    \n",
    "        \n",
    "ax.plot([0, 1], [0, 1], color='orange', lw=2, linestyle='--')\n",
    "ax.legend(fontsize=14)\n",
    "#xlbl = ax.xaxis.get_label()\n",
    "#ylbl = ax.yaxis.get_label()\n",
    "#plt.setp(xlbl,fontsize=16)\n",
    "#plt.setp(ylbl,fontsize=16)\n",
    "plt.xlabel('1-Specificity',fontsize=18)\n",
    "plt.ylabel('Sensitivity',fontsize=18)\n",
    "plt.setp(ax.get_xticklabels(),fontsize=16)\n",
    "plt.setp(ax.get_yticklabels(),fontsize=16)\n",
    "plt.tight_layout()   \n",
    "#plt.savefig('Ray_roc_logistic_regression.jpg', dpi = 1000, bbox_inches='tight')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#careful about changing variables here, should try using find and replace \n",
    "rna_roc = ['HER2','IGF1R']\n",
    "cell_lines_considered=pd.concat([mm231,mcf7],axis=0)\n",
    "fig,ax=plt.subplots(figsize=(5,4))\n",
    "for marker in rna_roc:\n",
    "    if marker=='all_three_combined':\n",
    "        marker=['HER2','IGF1R', 'CD44']\n",
    "        accuracy=[]\n",
    "        X=cell_lines_considered.iloc[:,2:][marker]\n",
    "        y=cell_lines_considered['cell_line']\n",
    "        #Scale data\n",
    "        scaler = StandardScaler()\n",
    "        scaler.fit(X) \n",
    "        X_scaled = pd.DataFrame(scaler.transform(X),columns = X.columns)\n",
    "        #X_train, X_test, y_train, y_test = train_test_split(X_scaled, y, test_size=0.3)\n",
    "        X_train=X_scaled\n",
    "        y_train=y\n",
    "        X_test=X_scaled\n",
    "        y_test=y\n",
    "        classifier = RandomForestClassifier(random_state=0)\n",
    "        classifier.fit(X_train, y_train)\n",
    "        y_pred = classifier.predict(X_test)\n",
    "        #    print(\"The classifier Accuracy: \", accuracy_score(y_test,y_pred))\n",
    "        accuracy.append(accuracy_score(y_test,y_pred))\n",
    "#        feature_importance = abs(classifier.coef_[0])\n",
    "#        feature_importance =feature_importance / feature_importance.max()\n",
    "        sorted_idx = np.argsort(feature_importance)\n",
    "        pos = np.arange(sorted_idx.shape[0]) + .5\n",
    "        #featfig = plt.figure(figsize=(10,8))\n",
    "        #featax = featfig.add_subplot(1, 1, 1)\n",
    "        #featax.barh(pos, feature_importance[sorted_idx], align='center')\n",
    "        #print(np.array(X.columns)[sorted_idx],feature_importance[sorted_idx])\n",
    "        #featax.set_yticks(pos)\n",
    "        #featax.set_yticklabels(np.array(X.columns)[sorted_idx], fontsize=10)\n",
    "        #featax.set_xlabel('Relative Feature Importance')\n",
    "        RocCurveDisplay.from_estimator(classifier, X_test, y_test, alpha=1.0,name='Combined',ax=ax,lw=2)\n",
    "    else:\n",
    "        accuracy=[]\n",
    "        X=cell_lines_considered.iloc[:,2:][[marker]]\n",
    "        y=cell_lines_considered['cell_line']\n",
    "        #Scale data\n",
    "        scaler = StandardScaler()\n",
    "        scaler.fit(X) \n",
    "        X_scaled = pd.DataFrame(scaler.transform(X),columns = X.columns)\n",
    "        #X_train, X_test, y_train, y_test = train_test_split(X_scaled, y, test_size=0.3)\n",
    "        X_train=X_scaled\n",
    "        y_train=y\n",
    "        X_test=X_scaled\n",
    "        y_test=y\n",
    "        classifier = RandomForestClassifier(random_state=0)\n",
    "        classifier.fit(X_train, y_train)\n",
    "        y_pred = classifier.predict(X_test)\n",
    "        #    print(\"The classifier Accuracy: \", accuracy_score(y_test,y_pred))\n",
    "        accuracy.append(accuracy_score(y_test,y_pred))\n",
    "#        feature_importance = abs(classifier.coef_[0])\n",
    "#        feature_importance =feature_importance / feature_importance.max()\n",
    "        sorted_idx = np.argsort(feature_importance)\n",
    "        pos = np.arange(sorted_idx.shape[0]) + .5\n",
    "        #featfig = plt.figure(figsize=(10,8))\n",
    "        #featax = featfig.add_subplot(1, 1, 1)\n",
    "        #featax.barh(pos, feature_importance[sorted_idx], align='center')\n",
    "        #print(np.array(X.columns)[sorted_idx],feature_importance[sorted_idx])\n",
    "        #featax.set_yticks(pos)\n",
    "        #featax.set_yticklabels(np.array(X.columns)[sorted_idx], fontsize=10)\n",
    "        #featax.set_xlabel('Relative Feature Importance')\n",
    "        RocCurveDisplay.from_estimator(classifier, X_test, y_test, alpha=1.0,name=marker,ax=ax,lw=2)\n",
    "        \n",
    "for axis in ['top','bottom','left','right']:\n",
    "    ax.spines[axis].set_linewidth(2)\n",
    "    \n",
    "        \n",
    "ax.plot([0, 1], [0, 1], color='orange', lw=2, linestyle='--')\n",
    "ax.legend(fontsize=14)\n",
    "#xlbl = ax.xaxis.get_label()\n",
    "#ylbl = ax.yaxis.get_label()\n",
    "#plt.setp(xlbl,fontsize=16)\n",
    "#plt.setp(ylbl,fontsize=16)\n",
    "plt.xlabel('1-Specificity',fontsize=18)\n",
    "plt.ylabel('Sensitivity',fontsize=18)\n",
    "plt.setp(ax.get_xticklabels(),fontsize=16)\n",
    "plt.setp(ax.get_yticklabels(),fontsize=16)\n",
    "plt.tight_layout()   \n",
    "#plt.savefig('Ray_roc_logistic_regression.jpg', dpi = 1000, bbox_inches='tight')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#careful about changing variables here, should try using find and replace \n",
    "rna_roc = ['HER2','IGF1R','CD44','EpCAM','all_four_combined']\n",
    "cell_lines_considered=pd.concat([mm231,mcf7],axis=0)\n",
    "fig,ax=plt.subplots(figsize=(5,4))\n",
    "for marker in rna_roc:\n",
    "    if marker=='all_four_combined':\n",
    "        marker=['HER2','IGF1R', 'CD44','EpCAM']\n",
    "        accuracy=[]\n",
    "        X=cell_lines_considered.iloc[:,2:][marker]\n",
    "        y=cell_lines_considered['cell_line']\n",
    "        #Scale data\n",
    "        scaler = StandardScaler()\n",
    "        scaler.fit(X) \n",
    "        X_scaled = pd.DataFrame(scaler.transform(X),columns = X.columns)\n",
    "        X_train, X_test, y_train, y_test = train_test_split(X_scaled, y, test_size=0.3)\n",
    "        #X_train=X_scaled\n",
    "        #y_train=y\n",
    "        #X_test=X_scaled\n",
    "        #y_test=y\n",
    "        classifier = RandomForestClassifier(random_state=0)\n",
    "        classifier.fit(X_train, y_train)\n",
    "        y_pred = classifier.predict(X_test)\n",
    "        #    print(\"The classifier Accuracy: \", accuracy_score(y_test,y_pred))\n",
    "        accuracy.append(accuracy_score(y_test,y_pred))\n",
    "#        feature_importance = abs(classifier.coef_[0])\n",
    "#        feature_importance =feature_importance / feature_importance.max()\n",
    "        sorted_idx = np.argsort(feature_importance)\n",
    "        pos = np.arange(sorted_idx.shape[0]) + .5\n",
    "        #featfig = plt.figure(figsize=(10,8))\n",
    "        #featax = featfig.add_subplot(1, 1, 1)\n",
    "        #featax.barh(pos, feature_importance[sorted_idx], align='center')\n",
    "        #print(np.array(X.columns)[sorted_idx],feature_importance[sorted_idx])\n",
    "        #featax.set_yticks(pos)\n",
    "        #featax.set_yticklabels(np.array(X.columns)[sorted_idx], fontsize=10)\n",
    "        #featax.set_xlabel('Relative Feature Importance')\n",
    "        RocCurveDisplay.from_estimator(classifier, X_test, y_test, alpha=1.0,name='Combined',ax=ax,lw=2)\n",
    "    else:\n",
    "        accuracy=[]\n",
    "        X=cell_lines_considered.iloc[:,2:][[marker]]\n",
    "        y=cell_lines_considered['cell_line']\n",
    "        #Scale data\n",
    "        scaler = StandardScaler()\n",
    "        scaler.fit(X) \n",
    "        X_scaled = pd.DataFrame(scaler.transform(X),columns = X.columns)\n",
    "        X_train, X_test, y_train, y_test = train_test_split(X_scaled, y, test_size=0.3)\n",
    "        #X_train=X_scaled\n",
    "        #y_train=y\n",
    "        #X_test=X_scaled\n",
    "        #y_test=y\n",
    "        classifier = RandomForestClassifier(random_state=0)\n",
    "        classifier.fit(X_train, y_train)\n",
    "        y_pred = classifier.predict(X_test)\n",
    "        #    print(\"The classifier Accuracy: \", accuracy_score(y_test,y_pred))\n",
    "        accuracy.append(accuracy_score(y_test,y_pred))\n",
    "#        feature_importance = abs(classifier.coef_[0])\n",
    "#        feature_importance =feature_importance / feature_importance.max()\n",
    "        sorted_idx = np.argsort(feature_importance)\n",
    "        pos = np.arange(sorted_idx.shape[0]) + .5\n",
    "        #featfig = plt.figure(figsize=(10,8))\n",
    "        #featax = featfig.add_subplot(1, 1, 1)\n",
    "        #featax.barh(pos, feature_importance[sorted_idx], align='center')\n",
    "        #print(np.array(X.columns)[sorted_idx],feature_importance[sorted_idx])\n",
    "        #featax.set_yticks(pos)\n",
    "        #featax.set_yticklabels(np.array(X.columns)[sorted_idx], fontsize=10)\n",
    "        #featax.set_xlabel('Relative Feature Importance')\n",
    "        RocCurveDisplay.from_estimator(classifier, X_test, y_test, alpha=1.0,name=marker,ax=ax,lw=2)\n",
    "        \n",
    "for axis in ['top','bottom','left','right']:\n",
    "    ax.spines[axis].set_linewidth(2)\n",
    "    \n",
    "        \n",
    "ax.plot([0, 1], [0, 1], color='orange', lw=2, linestyle='--')\n",
    "ax.legend(fontsize=14)\n",
    "#xlbl = ax.xaxis.get_label()\n",
    "#ylbl = ax.yaxis.get_label()\n",
    "#plt.setp(xlbl,fontsize=16)\n",
    "#plt.setp(ylbl,fontsize=16)\n",
    "plt.xlabel('1-Specificity',fontsize=18)\n",
    "plt.ylabel('Sensitivity',fontsize=18)\n",
    "plt.setp(ax.get_xticklabels(),fontsize=16)\n",
    "plt.setp(ax.get_yticklabels(),fontsize=16)\n",
    "plt.tight_layout()   \n",
    "#plt.savefig('Ray_roc_logistic_regression.jpg', dpi = 1000, bbox_inches='tight')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#careful about changing variables here, should try using find and replace \n",
    "rna_roc = ['HER2','IGF1R','CD44','EpCAM','all_four_combined']\n",
    "cell_lines_considered=pd.concat([mm231,mcf7],axis=0)\n",
    "fig,ax=plt.subplots(figsize=(5,4))\n",
    "for marker in rna_roc:\n",
    "    if marker=='all_four_combined':\n",
    "        marker=['HER2','IGF1R', 'CD44','EpCAM']\n",
    "        accuracy=[]\n",
    "        X=cell_lines_considered.iloc[:,2:][marker]\n",
    "        y=cell_lines_considered['cell_line']\n",
    "        #Scale data\n",
    "        scaler = StandardScaler()\n",
    "        scaler.fit(X) \n",
    "        X_scaled = pd.DataFrame(scaler.transform(X),columns = X.columns)\n",
    "        X_train, X_test, y_train, y_test = train_test_split(X_scaled, y, test_size=0.3)\n",
    "        #X_train=X_scaled\n",
    "        #y_train=y\n",
    "        #X_test=X_scaled\n",
    "        #y_test=y\n",
    "        classifier = RandomForestClassifier(random_state=0)\n",
    "        classifier.fit(X_train, y_train)\n",
    "        y_pred = classifier.predict(X_test)\n",
    "        #    print(\"The classifier Accuracy: \", accuracy_score(y_test,y_pred))\n",
    "        accuracy.append(accuracy_score(y_test,y_pred))\n",
    "#        feature_importance = abs(classifier.coef_[0])\n",
    "#        feature_importance =feature_importance / feature_importance.max()\n",
    "        sorted_idx = np.argsort(feature_importance)\n",
    "        pos = np.arange(sorted_idx.shape[0]) + .5\n",
    "        #featfig = plt.figure(figsize=(10,8))\n",
    "        #featax = featfig.add_subplot(1, 1, 1)\n",
    "        #featax.barh(pos, feature_importance[sorted_idx], align='center')\n",
    "        #print(np.array(X.columns)[sorted_idx],feature_importance[sorted_idx])\n",
    "        #featax.set_yticks(pos)\n",
    "        #featax.set_yticklabels(np.array(X.columns)[sorted_idx], fontsize=10)\n",
    "        #featax.set_xlabel('Relative Feature Importance')\n",
    "        RocCurveDisplay.from_estimator(classifier, X_test, y_test, alpha=1.0,name='Combined',ax=ax,lw=2)\n",
    "    else:\n",
    "        accuracy=[]\n",
    "        X=cell_lines_considered.iloc[:,2:][[marker]]\n",
    "        y=cell_lines_considered['cell_line']\n",
    "        #Scale data\n",
    "        scaler = StandardScaler()\n",
    "        scaler.fit(X) \n",
    "        X_scaled = pd.DataFrame(scaler.transform(X),columns = X.columns)\n",
    "        X_train, X_test, y_train, y_test = train_test_split(X_scaled, y, test_size=0.3)\n",
    "        #X_train=X_scaled\n",
    "        #y_train=y\n",
    "        #X_test=X_scaled\n",
    "        #y_test=y\n",
    "        classifier = RandomForestClassifier(random_state=0)\n",
    "        classifier.fit(X_train, y_train)\n",
    "        y_pred = classifier.predict(X_test)\n",
    "        #    print(\"The classifier Accuracy: \", accuracy_score(y_test,y_pred))\n",
    "        accuracy.append(accuracy_score(y_test,y_pred))\n",
    "#        feature_importance = abs(classifier.coef_[0])\n",
    "#        feature_importance =feature_importance / feature_importance.max()\n",
    "        sorted_idx = np.argsort(feature_importance)\n",
    "        pos = np.arange(sorted_idx.shape[0]) + .5\n",
    "        #featfig = plt.figure(figsize=(10,8))\n",
    "        #featax = featfig.add_subplot(1, 1, 1)\n",
    "        #featax.barh(pos, feature_importance[sorted_idx], align='center')\n",
    "        #print(np.array(X.columns)[sorted_idx],feature_importance[sorted_idx])\n",
    "        #featax.set_yticks(pos)\n",
    "        #featax.set_yticklabels(np.array(X.columns)[sorted_idx], fontsize=10)\n",
    "        #featax.set_xlabel('Relative Feature Importance')\n",
    "        RocCurveDisplay.from_estimator(classifier, X_test, y_test, alpha=1.0,name=marker,ax=ax,lw=2)\n",
    "        \n",
    "for axis in ['top','bottom','left','right']:\n",
    "    ax.spines[axis].set_linewidth(2)\n",
    "    \n",
    "        \n",
    "ax.plot([0, 1], [0, 1], color='orange', lw=2, linestyle='--')\n",
    "ax.legend(fontsize=14)\n",
    "#xlbl = ax.xaxis.get_label()\n",
    "#ylbl = ax.yaxis.get_label()\n",
    "#plt.setp(xlbl,fontsize=16)\n",
    "#plt.setp(ylbl,fontsize=16)\n",
    "plt.xlabel('1-Specificity',fontsize=18)\n",
    "plt.ylabel('Sensitivity',fontsize=18)\n",
    "plt.setp(ax.get_xticklabels(),fontsize=16)\n",
    "plt.setp(ax.get_yticklabels(),fontsize=16)\n",
    "plt.tight_layout()   \n",
    "#plt.savefig('Ray_roc_logistic_regression.jpg', dpi = 1000, bbox_inches='tight')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#careful about changing variables here, should try using find and replace \n",
    "rna_roc = ['HER2','IGF1R', 'CD44','all_three_combined']\n",
    "cell_lines_considered=pd.concat([mm231,mcf7],axis=0)\n",
    "fig,ax=plt.subplots(figsize=(5,4))\n",
    "for marker in rna_roc:\n",
    "    if marker=='all_three_combined':\n",
    "        marker=['HER2','IGF1R', 'CD44']\n",
    "        accuracy=[]\n",
    "        X=cell_lines_considered.iloc[:,2:][marker]\n",
    "        y=cell_lines_considered['cell_line']\n",
    "        #Scale data\n",
    "        scaler = StandardScaler()\n",
    "        scaler.fit(X) \n",
    "        X_scaled = pd.DataFrame(scaler.transform(X),columns = X.columns)\n",
    "        X_train, X_test, y_train, y_test = train_test_split(X_scaled, y, test_size=0.2)\n",
    "        #X_train=X_scaled\n",
    "        #y_train=y\n",
    "        #X_test=X_scaled\n",
    "        #y_test=y\n",
    "        classifier = LogisticRegression(random_state=0,multi_class='multinomial',solver='newton-cg')\n",
    "        classifier.fit(X_train, y_train)\n",
    "        y_pred = classifier.predict(X_test)\n",
    "        #    print(\"The classifier Accuracy: \", accuracy_score(y_test,y_pred))\n",
    "        accuracy.append(accuracy_score(y_test,y_pred))\n",
    "        feature_importance = abs(classifier.coef_[0])\n",
    "        feature_importance =feature_importance / feature_importance.max()\n",
    "        sorted_idx = np.argsort(feature_importance)\n",
    "        pos = np.arange(sorted_idx.shape[0]) + .5\n",
    "        #featfig = plt.figure(figsize=(10,8))\n",
    "        #featax = featfig.add_subplot(1, 1, 1)\n",
    "        #featax.barh(pos, feature_importance[sorted_idx], align='center')\n",
    "        #print(np.array(X.columns)[sorted_idx],feature_importance[sorted_idx])\n",
    "        #featax.set_yticks(pos)\n",
    "        #featax.set_yticklabels(np.array(X.columns)[sorted_idx], fontsize=10)\n",
    "        #featax.set_xlabel('Relative Feature Importance')\n",
    "        RocCurveDisplay.from_estimator(classifier, X_test, y_test, alpha=1.0,name='Combined',ax=ax,lw=2)\n",
    "    else:\n",
    "        accuracy=[]\n",
    "        X=cell_lines_considered.iloc[:,2:][[marker]]\n",
    "        y=cell_lines_considered['cell_line']\n",
    "        #Scale data\n",
    "        scaler = StandardScaler()\n",
    "        scaler.fit(X) \n",
    "        X_scaled = pd.DataFrame(scaler.transform(X),columns = X.columns)\n",
    "        X_train, X_test, y_train, y_test = train_test_split(X_scaled, y, test_size=0.2)\n",
    "        #X_train=X_scaled\n",
    "        #y_train=y\n",
    "        #X_test=X_scaled\n",
    "        #y_test=y\n",
    "        classifier = LogisticRegression(random_state=0,multi_class='multinomial',solver='newton-cg')\n",
    "        classifier.fit(X_train, y_train)\n",
    "        y_pred = classifier.predict(X_test)\n",
    "        #    print(\"The classifier Accuracy: \", accuracy_score(y_test,y_pred))\n",
    "        accuracy.append(accuracy_score(y_test,y_pred))\n",
    "        feature_importance = abs(classifier.coef_[0])\n",
    "        feature_importance =feature_importance / feature_importance.max()\n",
    "        sorted_idx = np.argsort(feature_importance)\n",
    "        pos = np.arange(sorted_idx.shape[0]) + .5\n",
    "        #featfig = plt.figure(figsize=(10,8))\n",
    "        #featax = featfig.add_subplot(1, 1, 1)\n",
    "        #featax.barh(pos, feature_importance[sorted_idx], align='center')\n",
    "        #print(np.array(X.columns)[sorted_idx],feature_importance[sorted_idx])\n",
    "        #featax.set_yticks(pos)\n",
    "        #featax.set_yticklabels(np.array(X.columns)[sorted_idx], fontsize=10)\n",
    "        #featax.set_xlabel('Relative Feature Importance')\n",
    "        RocCurveDisplay.from_estimator(classifier, X_test, y_test, alpha=1.0,name=marker,ax=ax,lw=2)\n",
    "        \n",
    "for axis in ['top','bottom','left','right']:\n",
    "    ax.spines[axis].set_linewidth(2)\n",
    "    \n",
    "        \n",
    "ax.plot([0, 1], [0, 1], color='orange', lw=2, linestyle='--')\n",
    "ax.legend(fontsize=14)\n",
    "#xlbl = ax.xaxis.get_label()\n",
    "#ylbl = ax.yaxis.get_label()\n",
    "#plt.setp(xlbl,fontsize=16)\n",
    "#plt.setp(ylbl,fontsize=16)\n",
    "plt.xlabel('1-Specificity',fontsize=18)\n",
    "plt.ylabel('Sensitivity',fontsize=18)\n",
    "plt.setp(ax.get_xticklabels(),fontsize=16)\n",
    "plt.setp(ax.get_yticklabels(),fontsize=16)\n",
    "plt.tight_layout()   \n",
    "#plt.savefig('Ray_roc_logistic_regression.jpg', dpi = 1000, bbox_inches='tight')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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