viabPred_importanceAnalysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Cell 33342 Radial Mean</th>\n",
       "      <th>Cell 33342 Radial Mean Ratio SER-Bright</th>\n",
       "      <th>Cell 33342 Radial Mean Ratio SER-Dark</th>\n",
       "      <th>Cell 33342 Radial Mean Ratio SER-Hole</th>\n",
       "      <th>Cell 33342 Radial Relative Deviation SER-Dark</th>\n",
       "      <th>Cell 33342 Radial Relative Deviation SER-Hole</th>\n",
       "      <th>Cell 488 Axial Length Ratio</th>\n",
       "      <th>Cell 488 Axial Length Ratio SER-Bright</th>\n",
       "      <th>Cell 488 Axial Length Ratio SER-Dark</th>\n",
       "      <th>Cell 488 Radial Mean Ratio SER-Bright</th>\n",
       "      <th>...</th>\n",
       "      <th>Nucleus Mito Profile 5/5 SER-Dark</th>\n",
       "      <th>Nucleus Mito Profile 5/5 SER-Hole</th>\n",
       "      <th>Nucleus Mito Profile 5/5 SER-Saddle</th>\n",
       "      <th>Nucleus Width [µm]</th>\n",
       "      <th>Ring Region Alexa 488 SER Bright 0 px</th>\n",
       "      <th>Ring Region Alexa 488 SER Dark 0 px</th>\n",
       "      <th>Ring Region Alexa 488 SER Saddle 0 px</th>\n",
       "      <th>Ring Region Alexa 647 SER Bright 0 px</th>\n",
       "      <th>Ring Region Alexa 647 SER Dark 0 px</th>\n",
       "      <th>Ring Region Alexa 647 SER Hole 0 px</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>NCAP</th>\n",
       "      <td>0.002132</td>\n",
       "      <td>0.001084</td>\n",
       "      <td>0.080699</td>\n",
       "      <td>0.020221</td>\n",
       "      <td>0.000476</td>\n",
       "      <td>7.679680e-03</td>\n",
       "      <td>0.002880</td>\n",
       "      <td>1.438133e-03</td>\n",
       "      <td>2.644322e-03</td>\n",
       "      <td>0.012201</td>\n",
       "      <td>...</td>\n",
       "      <td>3.457775e-04</td>\n",
       "      <td>2.980966e-04</td>\n",
       "      <td>6.723604e-04</td>\n",
       "      <td>0.001430</td>\n",
       "      <td>0.001460</td>\n",
       "      <td>0.004169</td>\n",
       "      <td>0.001394</td>\n",
       "      <td>0.000817</td>\n",
       "      <td>1.478272e-04</td>\n",
       "      <td>0.000853</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ATOR</th>\n",
       "      <td>0.016815</td>\n",
       "      <td>0.009030</td>\n",
       "      <td>0.000824</td>\n",
       "      <td>0.005102</td>\n",
       "      <td>0.026072</td>\n",
       "      <td>1.732542e-05</td>\n",
       "      <td>0.000007</td>\n",
       "      <td>7.666867e-06</td>\n",
       "      <td>3.855783e-06</td>\n",
       "      <td>0.017078</td>\n",
       "      <td>...</td>\n",
       "      <td>4.969729e-06</td>\n",
       "      <td>5.186137e-06</td>\n",
       "      <td>2.496142e-06</td>\n",
       "      <td>0.000078</td>\n",
       "      <td>0.008251</td>\n",
       "      <td>0.000703</td>\n",
       "      <td>0.009182</td>\n",
       "      <td>0.000007</td>\n",
       "      <td>9.142231e-18</td>\n",
       "      <td>0.004991</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CERI</th>\n",
       "      <td>0.000214</td>\n",
       "      <td>0.000119</td>\n",
       "      <td>0.000011</td>\n",
       "      <td>0.001237</td>\n",
       "      <td>0.001110</td>\n",
       "      <td>8.238367e-18</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.798372e-18</td>\n",
       "      <td>1.925053e-19</td>\n",
       "      <td>0.000195</td>\n",
       "      <td>...</td>\n",
       "      <td>7.392858e-18</td>\n",
       "      <td>6.479111e-20</td>\n",
       "      <td>6.333862e-07</td>\n",
       "      <td>0.002930</td>\n",
       "      <td>0.000026</td>\n",
       "      <td>0.000134</td>\n",
       "      <td>0.000337</td>\n",
       "      <td>0.000224</td>\n",
       "      <td>2.125160e-20</td>\n",
       "      <td>0.000001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CLOF</th>\n",
       "      <td>0.000379</td>\n",
       "      <td>0.001459</td>\n",
       "      <td>0.001250</td>\n",
       "      <td>0.003368</td>\n",
       "      <td>0.001219</td>\n",
       "      <td>7.449322e-04</td>\n",
       "      <td>0.001137</td>\n",
       "      <td>3.606348e-03</td>\n",
       "      <td>2.387139e-03</td>\n",
       "      <td>0.006485</td>\n",
       "      <td>...</td>\n",
       "      <td>5.829646e-03</td>\n",
       "      <td>2.491714e-03</td>\n",
       "      <td>7.126712e-03</td>\n",
       "      <td>0.000419</td>\n",
       "      <td>0.000464</td>\n",
       "      <td>0.000336</td>\n",
       "      <td>0.003511</td>\n",
       "      <td>0.000829</td>\n",
       "      <td>1.406368e-03</td>\n",
       "      <td>0.000265</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>COLC</th>\n",
       "      <td>0.014861</td>\n",
       "      <td>0.013557</td>\n",
       "      <td>0.000030</td>\n",
       "      <td>0.000103</td>\n",
       "      <td>0.004605</td>\n",
       "      <td>2.653465e-05</td>\n",
       "      <td>0.000154</td>\n",
       "      <td>5.326079e-03</td>\n",
       "      <td>3.230103e-05</td>\n",
       "      <td>0.015932</td>\n",
       "      <td>...</td>\n",
       "      <td>1.102978e-03</td>\n",
       "      <td>3.655330e-04</td>\n",
       "      <td>7.343757e-05</td>\n",
       "      <td>0.000235</td>\n",
       "      <td>0.000014</td>\n",
       "      <td>0.000101</td>\n",
       "      <td>0.000383</td>\n",
       "      <td>0.000084</td>\n",
       "      <td>1.849633e-04</td>\n",
       "      <td>0.000124</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 310 columns</p>\n",
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      "text/plain": [
       "      Cell 33342 Radial Mean   Cell 33342 Radial Mean Ratio SER-Bright  \\\n",
       "NCAP                 0.002132                                 0.001084   \n",
       "ATOR                 0.016815                                 0.009030   \n",
       "CERI                 0.000214                                 0.000119   \n",
       "CLOF                 0.000379                                 0.001459   \n",
       "COLC                 0.014861                                 0.013557   \n",
       "\n",
       "      Cell 33342 Radial Mean Ratio SER-Dark  \\\n",
       "NCAP                               0.080699   \n",
       "ATOR                               0.000824   \n",
       "CERI                               0.000011   \n",
       "CLOF                               0.001250   \n",
       "COLC                               0.000030   \n",
       "\n",
       "      Cell 33342 Radial Mean Ratio SER-Hole  \\\n",
       "NCAP                               0.020221   \n",
       "ATOR                               0.005102   \n",
       "CERI                               0.001237   \n",
       "CLOF                               0.003368   \n",
       "COLC                               0.000103   \n",
       "\n",
       "      Cell 33342 Radial Relative Deviation SER-Dark  \\\n",
       "NCAP                                       0.000476   \n",
       "ATOR                                       0.026072   \n",
       "CERI                                       0.001110   \n",
       "CLOF                                       0.001219   \n",
       "COLC                                       0.004605   \n",
       "\n",
       "      Cell 33342 Radial Relative Deviation SER-Hole  \\\n",
       "NCAP                                   7.679680e-03   \n",
       "ATOR                                   1.732542e-05   \n",
       "CERI                                   8.238367e-18   \n",
       "CLOF                                   7.449322e-04   \n",
       "COLC                                   2.653465e-05   \n",
       "\n",
       "      Cell 488 Axial Length Ratio   Cell 488 Axial Length Ratio SER-Bright  \\\n",
       "NCAP                      0.002880                            1.438133e-03   \n",
       "ATOR                      0.000007                            7.666867e-06   \n",
       "CERI                      0.000000                            5.798372e-18   \n",
       "CLOF                      0.001137                            3.606348e-03   \n",
       "COLC                      0.000154                            5.326079e-03   \n",
       "\n",
       "      Cell 488 Axial Length Ratio SER-Dark  \\\n",
       "NCAP                          2.644322e-03   \n",
       "ATOR                          3.855783e-06   \n",
       "CERI                          1.925053e-19   \n",
       "CLOF                          2.387139e-03   \n",
       "COLC                          3.230103e-05   \n",
       "\n",
       "      Cell 488 Radial Mean Ratio SER-Bright  ...  \\\n",
       "NCAP                               0.012201  ...   \n",
       "ATOR                               0.017078  ...   \n",
       "CERI                               0.000195  ...   \n",
       "CLOF                               0.006485  ...   \n",
       "COLC                               0.015932  ...   \n",
       "\n",
       "      Nucleus Mito Profile 5/5 SER-Dark  Nucleus Mito Profile 5/5 SER-Hole  \\\n",
       "NCAP                       3.457775e-04                       2.980966e-04   \n",
       "ATOR                       4.969729e-06                       5.186137e-06   \n",
       "CERI                       7.392858e-18                       6.479111e-20   \n",
       "CLOF                       5.829646e-03                       2.491714e-03   \n",
       "COLC                       1.102978e-03                       3.655330e-04   \n",
       "\n",
       "      Nucleus Mito Profile 5/5 SER-Saddle  Nucleus Width [µm]  \\\n",
       "NCAP                         6.723604e-04            0.001430   \n",
       "ATOR                         2.496142e-06            0.000078   \n",
       "CERI                         6.333862e-07            0.002930   \n",
       "CLOF                         7.126712e-03            0.000419   \n",
       "COLC                         7.343757e-05            0.000235   \n",
       "\n",
       "      Ring Region Alexa 488 SER Bright 0 px  \\\n",
       "NCAP                               0.001460   \n",
       "ATOR                               0.008251   \n",
       "CERI                               0.000026   \n",
       "CLOF                               0.000464   \n",
       "COLC                               0.000014   \n",
       "\n",
       "      Ring Region Alexa 488 SER Dark 0 px  \\\n",
       "NCAP                             0.004169   \n",
       "ATOR                             0.000703   \n",
       "CERI                             0.000134   \n",
       "CLOF                             0.000336   \n",
       "COLC                             0.000101   \n",
       "\n",
       "      Ring Region Alexa 488 SER Saddle 0 px  \\\n",
       "NCAP                               0.001394   \n",
       "ATOR                               0.009182   \n",
       "CERI                               0.000337   \n",
       "CLOF                               0.003511   \n",
       "COLC                               0.000383   \n",
       "\n",
       "      Ring Region Alexa 647 SER Bright 0 px  \\\n",
       "NCAP                               0.000817   \n",
       "ATOR                               0.000007   \n",
       "CERI                               0.000224   \n",
       "CLOF                               0.000829   \n",
       "COLC                               0.000084   \n",
       "\n",
       "      Ring Region Alexa 647 SER Dark 0 px  Ring Region Alexa 647 SER Hole 0 px  \n",
       "NCAP                         1.478272e-04                             0.000853  \n",
       "ATOR                         9.142231e-18                             0.004991  \n",
       "CERI                         2.125160e-20                             0.000001  \n",
       "CLOF                         1.406368e-03                             0.000265  \n",
       "COLC                         1.849633e-04                             0.000124  \n",
       "\n",
       "[5 rows x 310 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# TO USE IN PAPER\n",
    "# analysis of feature importance for viability prediction in tubes / blasts, CellProfiler/Harmony, \n",
    "# 26/10/2023, Roman Lambert\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "#onedrive_path = \"C:/Users/lambr/OneDrive - National University of Ireland, Galway/\"\n",
    "onedrive_path = \"E:/OneDrive - National University of Ireland, Galway/\"\n",
    "\n",
    "# feature importances from RF models\n",
    "#path = onedrive_path + \"CellProfiler\\\\Blasts\\\\importance_df.csv\"\n",
    "path = onedrive_path + \"ViabilityPred-models\\\\Harmony\\\\Blasts\\\\importance_df.csv\"\n",
    "#path = onedrive_path + \"ViabilityPred-models\\\\Harmony\\\\Tubes\\\\importance_df.csv\"\n",
    "\n",
    "importance_df = pd.read_csv(path, index_col=0)\n",
    "importance_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Top20Frequency</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Intensity Nucleus Alexa 555 Mean</th>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cell Contact Area with Neighbors [%]</th>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cell 488 Radial Mean Ratio SER-Dark</th>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cytoplasm 33342 Profile 3/5 SER-Spot</th>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nucleus Alexa 555 SER Dark 0 px</th>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cell 555 Symmetry 05</th>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cytoplasm 33342 Profile 5/5 SER-Saddle</th>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cytoplasm 33342 Profile 5/5 SER-Dark</th>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cell 568 Symmetry 03 SER-Dark</th>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ring Region Alexa 647 SER Hole 0 px</th>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
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       "</table>\n",
       "<p>239 rows × 1 columns</p>\n",
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      ],
      "text/plain": [
       "                                        Top20Frequency\n",
       "Intensity Nucleus Alexa 555 Mean                  10.0\n",
       "Cell Contact Area with Neighbors [%]               9.0\n",
       "Cell 488 Radial Mean Ratio SER-Dark                9.0\n",
       "Cytoplasm 33342 Profile 3/5 SER-Spot               9.0\n",
       "Nucleus Alexa 555 SER Dark 0 px                    8.0\n",
       "...                                                ...\n",
       "Cell 555 Symmetry 05                               1.0\n",
       "Cytoplasm 33342 Profile 5/5 SER-Saddle             1.0\n",
       "Cytoplasm 33342 Profile 5/5 SER-Dark               1.0\n",
       "Cell 568 Symmetry 03 SER-Dark                      1.0\n",
       "Ring Region Alexa 647 SER Hole 0 px                1.0\n",
       "\n",
       "[239 rows x 1 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import_df_T = importance_df.T.copy()\n",
    "# keep only the 10 largest values per column, and class the features by their importance\n",
    "import_df_T = import_df_T.apply(lambda s, n: pd.Series(s.nlargest(n).index), axis=0, n=20)\n",
    "# count the number of times a feature is in the top 10 of the whole dataset\n",
    "import_df_T = import_df_T.apply(pd.value_counts)\n",
    "import_df_T.fillna(0, inplace=True)\n",
    "importance_sums_df = pd.DataFrame(import_df_T.sum(axis=1), index= import_df_T.index, columns=['Top20Frequency']).sort_values(by=['Top20Frequency'], ascending=False)\n",
    "importance_sums_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "from matplotlib.colors import LinearSegmentedColormap\n",
    "\n",
    "#clustermap of binary top 20 feature importance\n",
    "import_df_T['Top20Frequency'] = importance_sums_df['Top20Frequency']\n",
    "import_df_T = import_df_T.sort_values(by=['Top20Frequency'], ascending=False).drop(columns=['Top20Frequency'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>NCAP</th>\n",
       "      <th>ATOR</th>\n",
       "      <th>CERI</th>\n",
       "      <th>CLOF</th>\n",
       "      <th>COLC</th>\n",
       "      <th>DAPT</th>\n",
       "      <th>DEXA</th>\n",
       "      <th>DOXO</th>\n",
       "      <th>EZET</th>\n",
       "      <th>HYCQ</th>\n",
       "      <th>...</th>\n",
       "      <th>WURS</th>\n",
       "      <th>CISP</th>\n",
       "      <th>IMAT</th>\n",
       "      <th>OLAN</th>\n",
       "      <th>SUNI</th>\n",
       "      <th>TEBU</th>\n",
       "      <th>ZIDO</th>\n",
       "      <th>sum_of_scaled_importances</th>\n",
       "      <th>median_rank</th>\n",
       "      <th>top_20_frequency</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Intensity Nucleus Alexa 555 Mean</th>\n",
       "      <td>0.069259</td>\n",
       "      <td>5.966818e-04</td>\n",
       "      <td>1.285944e-03</td>\n",
       "      <td>0.003826</td>\n",
       "      <td>0.001107</td>\n",
       "      <td>0.010038</td>\n",
       "      <td>0.069669</td>\n",
       "      <td>7.919985e-05</td>\n",
       "      <td>6.345250e-05</td>\n",
       "      <td>0.009493</td>\n",
       "      <td>...</td>\n",
       "      <td>8.900952e-03</td>\n",
       "      <td>0.000237</td>\n",
       "      <td>9.000704e-07</td>\n",
       "      <td>0.003198</td>\n",
       "      <td>2.157955e-07</td>\n",
       "      <td>0.001470</td>\n",
       "      <td>0.001359</td>\n",
       "      <td>0.608270</td>\n",
       "      <td>43.5</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cell 568 Radial Mean Ratio SER-Dark</th>\n",
       "      <td>0.004682</td>\n",
       "      <td>7.872251e-02</td>\n",
       "      <td>6.093962e-02</td>\n",
       "      <td>0.001649</td>\n",
       "      <td>0.008503</td>\n",
       "      <td>0.001310</td>\n",
       "      <td>0.004475</td>\n",
       "      <td>8.160253e-05</td>\n",
       "      <td>5.174633e-04</td>\n",
       "      <td>0.004376</td>\n",
       "      <td>...</td>\n",
       "      <td>1.496420e-03</td>\n",
       "      <td>0.004878</td>\n",
       "      <td>5.288007e-07</td>\n",
       "      <td>0.000053</td>\n",
       "      <td>1.292887e-01</td>\n",
       "      <td>0.001247</td>\n",
       "      <td>0.000565</td>\n",
       "      <td>0.388030</td>\n",
       "      <td>49.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cytoplasm 568 Profile 4/5</th>\n",
       "      <td>0.003274</td>\n",
       "      <td>1.401008e-02</td>\n",
       "      <td>1.274923e-02</td>\n",
       "      <td>0.001394</td>\n",
       "      <td>0.000009</td>\n",
       "      <td>0.004071</td>\n",
       "      <td>0.000283</td>\n",
       "      <td>6.174638e-04</td>\n",
       "      <td>1.244406e-02</td>\n",
       "      <td>0.065218</td>\n",
       "      <td>...</td>\n",
       "      <td>2.551329e-04</td>\n",
       "      <td>0.003337</td>\n",
       "      <td>6.831089e-04</td>\n",
       "      <td>0.000376</td>\n",
       "      <td>2.165313e-04</td>\n",
       "      <td>0.006907</td>\n",
       "      <td>0.000346</td>\n",
       "      <td>0.184558</td>\n",
       "      <td>54.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nucleus Alexa 555 SER Dark 0 px</th>\n",
       "      <td>0.005983</td>\n",
       "      <td>2.321553e-04</td>\n",
       "      <td>6.383577e-05</td>\n",
       "      <td>0.000558</td>\n",
       "      <td>0.000508</td>\n",
       "      <td>0.001294</td>\n",
       "      <td>0.011641</td>\n",
       "      <td>1.442938e-04</td>\n",
       "      <td>7.787227e-04</td>\n",
       "      <td>0.000877</td>\n",
       "      <td>...</td>\n",
       "      <td>7.549316e-16</td>\n",
       "      <td>0.011314</td>\n",
       "      <td>1.326905e-06</td>\n",
       "      <td>0.009321</td>\n",
       "      <td>2.652436e-07</td>\n",
       "      <td>0.026293</td>\n",
       "      <td>0.005519</td>\n",
       "      <td>0.297151</td>\n",
       "      <td>56.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cytoplasm 33342 Profile 3/5 SER-Bright</th>\n",
       "      <td>0.003897</td>\n",
       "      <td>1.700374e-05</td>\n",
       "      <td>2.032836e-03</td>\n",
       "      <td>0.001848</td>\n",
       "      <td>0.019915</td>\n",
       "      <td>0.031095</td>\n",
       "      <td>0.000408</td>\n",
       "      <td>1.331652e-03</td>\n",
       "      <td>1.536339e-02</td>\n",
       "      <td>0.014208</td>\n",
       "      <td>...</td>\n",
       "      <td>9.624268e-03</td>\n",
       "      <td>0.000557</td>\n",
       "      <td>1.930664e-04</td>\n",
       "      <td>0.001129</td>\n",
       "      <td>1.257379e-03</td>\n",
       "      <td>0.000005</td>\n",
       "      <td>0.000370</td>\n",
       "      <td>0.212931</td>\n",
       "      <td>62.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cytoplasm 488 Profile 2/5</th>\n",
       "      <td>0.003155</td>\n",
       "      <td>1.569662e-02</td>\n",
       "      <td>9.855803e-03</td>\n",
       "      <td>0.004102</td>\n",
       "      <td>0.000119</td>\n",
       "      <td>0.002403</td>\n",
       "      <td>0.003505</td>\n",
       "      <td>4.254592e-03</td>\n",
       "      <td>2.677322e-02</td>\n",
       "      <td>0.004106</td>\n",
       "      <td>...</td>\n",
       "      <td>2.356938e-04</td>\n",
       "      <td>0.000106</td>\n",
       "      <td>3.197068e-05</td>\n",
       "      <td>0.215042</td>\n",
       "      <td>4.293040e-06</td>\n",
       "      <td>0.038738</td>\n",
       "      <td>0.004044</td>\n",
       "      <td>0.586511</td>\n",
       "      <td>62.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cell Contact Area with Neighbors [%]</th>\n",
       "      <td>0.006087</td>\n",
       "      <td>3.376059e-03</td>\n",
       "      <td>1.696221e-02</td>\n",
       "      <td>0.003638</td>\n",
       "      <td>0.024756</td>\n",
       "      <td>0.001647</td>\n",
       "      <td>0.003719</td>\n",
       "      <td>1.010195e-03</td>\n",
       "      <td>4.216135e-03</td>\n",
       "      <td>0.002823</td>\n",
       "      <td>...</td>\n",
       "      <td>5.414299e-04</td>\n",
       "      <td>0.000165</td>\n",
       "      <td>1.051916e-05</td>\n",
       "      <td>0.000038</td>\n",
       "      <td>3.573937e-04</td>\n",
       "      <td>0.000003</td>\n",
       "      <td>0.001785</td>\n",
       "      <td>0.274758</td>\n",
       "      <td>71.0</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nucleus Alexa 555 SER Bright 0 px</th>\n",
       "      <td>0.010249</td>\n",
       "      <td>4.583849e-03</td>\n",
       "      <td>3.210023e-05</td>\n",
       "      <td>0.003400</td>\n",
       "      <td>0.000638</td>\n",
       "      <td>0.002644</td>\n",
       "      <td>0.001052</td>\n",
       "      <td>1.452051e-03</td>\n",
       "      <td>1.011731e-04</td>\n",
       "      <td>0.001120</td>\n",
       "      <td>...</td>\n",
       "      <td>1.719757e-04</td>\n",
       "      <td>0.000276</td>\n",
       "      <td>1.018304e-06</td>\n",
       "      <td>0.002499</td>\n",
       "      <td>5.865214e-02</td>\n",
       "      <td>0.001887</td>\n",
       "      <td>0.000961</td>\n",
       "      <td>0.214902</td>\n",
       "      <td>74.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cell 555 Profile 5/5</th>\n",
       "      <td>0.003924</td>\n",
       "      <td>9.233154e-03</td>\n",
       "      <td>5.752520e-03</td>\n",
       "      <td>0.001480</td>\n",
       "      <td>0.000032</td>\n",
       "      <td>0.003693</td>\n",
       "      <td>0.001156</td>\n",
       "      <td>1.155113e-04</td>\n",
       "      <td>2.508944e-04</td>\n",
       "      <td>0.002474</td>\n",
       "      <td>...</td>\n",
       "      <td>1.559529e-01</td>\n",
       "      <td>0.001225</td>\n",
       "      <td>5.865807e-06</td>\n",
       "      <td>0.000511</td>\n",
       "      <td>6.021116e-05</td>\n",
       "      <td>0.000859</td>\n",
       "      <td>0.002877</td>\n",
       "      <td>0.250130</td>\n",
       "      <td>76.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cell 555 Threshold Compactness 50%</th>\n",
       "      <td>0.001219</td>\n",
       "      <td>5.062461e-03</td>\n",
       "      <td>7.688018e-03</td>\n",
       "      <td>0.000716</td>\n",
       "      <td>0.000147</td>\n",
       "      <td>0.005397</td>\n",
       "      <td>0.000847</td>\n",
       "      <td>2.862064e-04</td>\n",
       "      <td>4.267643e-05</td>\n",
       "      <td>0.002892</td>\n",
       "      <td>...</td>\n",
       "      <td>5.523068e-02</td>\n",
       "      <td>0.000293</td>\n",
       "      <td>1.436033e-02</td>\n",
       "      <td>0.052913</td>\n",
       "      <td>3.797844e-04</td>\n",
       "      <td>0.003159</td>\n",
       "      <td>0.001784</td>\n",
       "      <td>0.226225</td>\n",
       "      <td>81.0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Intensity Cytoplasm Alexa 568 Mean</th>\n",
       "      <td>0.013316</td>\n",
       "      <td>5.664326e-06</td>\n",
       "      <td>1.411591e-05</td>\n",
       "      <td>0.001024</td>\n",
       "      <td>0.000766</td>\n",
       "      <td>0.000632</td>\n",
       "      <td>0.000355</td>\n",
       "      <td>1.123636e-02</td>\n",
       "      <td>5.046302e-04</td>\n",
       "      <td>0.001921</td>\n",
       "      <td>...</td>\n",
       "      <td>3.073107e-04</td>\n",
       "      <td>0.000191</td>\n",
       "      <td>1.018811e-06</td>\n",
       "      <td>0.001028</td>\n",
       "      <td>3.624342e-05</td>\n",
       "      <td>0.004368</td>\n",
       "      <td>0.000852</td>\n",
       "      <td>0.107764</td>\n",
       "      <td>81.5</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Intensity Nucleus HOECHST 33342 Mean</th>\n",
       "      <td>0.000281</td>\n",
       "      <td>2.749893e-06</td>\n",
       "      <td>9.258018e-04</td>\n",
       "      <td>0.008235</td>\n",
       "      <td>0.000126</td>\n",
       "      <td>0.000432</td>\n",
       "      <td>0.140311</td>\n",
       "      <td>5.755259e-03</td>\n",
       "      <td>1.398156e-03</td>\n",
       "      <td>0.006328</td>\n",
       "      <td>...</td>\n",
       "      <td>8.380744e-05</td>\n",
       "      <td>0.001503</td>\n",
       "      <td>5.017040e-03</td>\n",
       "      <td>0.000292</td>\n",
       "      <td>5.233858e-04</td>\n",
       "      <td>0.000058</td>\n",
       "      <td>0.003803</td>\n",
       "      <td>0.396727</td>\n",
       "      <td>84.5</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cytoplasm Alexa 488 SER Bright 0 px</th>\n",
       "      <td>0.000442</td>\n",
       "      <td>8.324026e-03</td>\n",
       "      <td>1.026361e-04</td>\n",
       "      <td>0.001526</td>\n",
       "      <td>0.024548</td>\n",
       "      <td>0.000239</td>\n",
       "      <td>0.000420</td>\n",
       "      <td>1.450861e-03</td>\n",
       "      <td>2.103880e-16</td>\n",
       "      <td>0.000519</td>\n",
       "      <td>...</td>\n",
       "      <td>7.583675e-04</td>\n",
       "      <td>0.000491</td>\n",
       "      <td>1.949338e-07</td>\n",
       "      <td>0.000783</td>\n",
       "      <td>1.377133e-01</td>\n",
       "      <td>0.022504</td>\n",
       "      <td>0.002214</td>\n",
       "      <td>0.306371</td>\n",
       "      <td>84.5</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nucleus 33342 Profile 5/5</th>\n",
       "      <td>0.003009</td>\n",
       "      <td>1.687751e-03</td>\n",
       "      <td>2.377685e-02</td>\n",
       "      <td>0.003556</td>\n",
       "      <td>0.000107</td>\n",
       "      <td>0.031203</td>\n",
       "      <td>0.000090</td>\n",
       "      <td>1.449777e-03</td>\n",
       "      <td>1.372205e-01</td>\n",
       "      <td>0.002508</td>\n",
       "      <td>...</td>\n",
       "      <td>6.287657e-03</td>\n",
       "      <td>0.001170</td>\n",
       "      <td>2.053441e-04</td>\n",
       "      <td>0.000019</td>\n",
       "      <td>1.978381e-06</td>\n",
       "      <td>0.001409</td>\n",
       "      <td>0.002082</td>\n",
       "      <td>0.288135</td>\n",
       "      <td>86.5</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cytoplasm 488 Profile 4/5 SER-Edge</th>\n",
       "      <td>0.006480</td>\n",
       "      <td>4.687099e-06</td>\n",
       "      <td>3.048739e-02</td>\n",
       "      <td>0.000626</td>\n",
       "      <td>0.000105</td>\n",
       "      <td>0.000588</td>\n",
       "      <td>0.001948</td>\n",
       "      <td>1.578526e-01</td>\n",
       "      <td>8.028016e-04</td>\n",
       "      <td>0.000844</td>\n",
       "      <td>...</td>\n",
       "      <td>1.224877e-03</td>\n",
       "      <td>0.001463</td>\n",
       "      <td>2.591288e-07</td>\n",
       "      <td>0.011296</td>\n",
       "      <td>2.446881e-05</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>0.003294</td>\n",
       "      <td>0.344450</td>\n",
       "      <td>86.5</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cell 488 Threshold Compactness 60%</th>\n",
       "      <td>0.002853</td>\n",
       "      <td>1.630552e-03</td>\n",
       "      <td>8.979103e-05</td>\n",
       "      <td>0.000249</td>\n",
       "      <td>0.000097</td>\n",
       "      <td>0.007836</td>\n",
       "      <td>0.001840</td>\n",
       "      <td>2.143602e-04</td>\n",
       "      <td>8.379633e-04</td>\n",
       "      <td>0.002948</td>\n",
       "      <td>...</td>\n",
       "      <td>3.729677e-04</td>\n",
       "      <td>0.014213</td>\n",
       "      <td>1.749323e-04</td>\n",
       "      <td>0.000958</td>\n",
       "      <td>3.222321e-06</td>\n",
       "      <td>0.000008</td>\n",
       "      <td>0.000779</td>\n",
       "      <td>0.083656</td>\n",
       "      <td>87.5</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cytoplasm 33342 Profile 3/5 SER-Spot</th>\n",
       "      <td>0.003846</td>\n",
       "      <td>6.004386e-03</td>\n",
       "      <td>5.053289e-04</td>\n",
       "      <td>0.001637</td>\n",
       "      <td>0.037406</td>\n",
       "      <td>0.008982</td>\n",
       "      <td>0.008877</td>\n",
       "      <td>4.582515e-04</td>\n",
       "      <td>6.406879e-02</td>\n",
       "      <td>0.013784</td>\n",
       "      <td>...</td>\n",
       "      <td>9.867377e-03</td>\n",
       "      <td>0.001149</td>\n",
       "      <td>2.948308e-05</td>\n",
       "      <td>0.000089</td>\n",
       "      <td>1.099902e-02</td>\n",
       "      <td>0.000005</td>\n",
       "      <td>0.000746</td>\n",
       "      <td>0.222980</td>\n",
       "      <td>90.0</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cytoplasm 33342 Profile 1/5</th>\n",
       "      <td>0.003819</td>\n",
       "      <td>6.896486e-03</td>\n",
       "      <td>7.292464e-03</td>\n",
       "      <td>0.000413</td>\n",
       "      <td>0.000194</td>\n",
       "      <td>0.001389</td>\n",
       "      <td>0.000345</td>\n",
       "      <td>7.020700e-19</td>\n",
       "      <td>5.283937e-04</td>\n",
       "      <td>0.009410</td>\n",
       "      <td>...</td>\n",
       "      <td>1.312187e-03</td>\n",
       "      <td>0.000254</td>\n",
       "      <td>5.152939e-03</td>\n",
       "      <td>0.011395</td>\n",
       "      <td>1.616052e-06</td>\n",
       "      <td>0.000775</td>\n",
       "      <td>0.003128</td>\n",
       "      <td>0.175986</td>\n",
       "      <td>90.5</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nucleus 33342 Radial Relative Deviation SER-Saddle</th>\n",
       "      <td>0.002789</td>\n",
       "      <td>4.223371e-03</td>\n",
       "      <td>3.394002e-04</td>\n",
       "      <td>0.003690</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>0.006398</td>\n",
       "      <td>0.000032</td>\n",
       "      <td>8.666750e-04</td>\n",
       "      <td>3.221609e-05</td>\n",
       "      <td>0.001324</td>\n",
       "      <td>...</td>\n",
       "      <td>6.047187e-03</td>\n",
       "      <td>0.003409</td>\n",
       "      <td>2.669001e-04</td>\n",
       "      <td>0.012256</td>\n",
       "      <td>3.304411e-06</td>\n",
       "      <td>0.000545</td>\n",
       "      <td>0.002174</td>\n",
       "      <td>0.103738</td>\n",
       "      <td>91.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cell 488 Threshold Compactness 60% SER-Edge</th>\n",
       "      <td>0.001545</td>\n",
       "      <td>1.014556e-02</td>\n",
       "      <td>6.589361e-04</td>\n",
       "      <td>0.002344</td>\n",
       "      <td>0.000119</td>\n",
       "      <td>0.007476</td>\n",
       "      <td>0.000627</td>\n",
       "      <td>4.407808e-04</td>\n",
       "      <td>2.479943e-03</td>\n",
       "      <td>0.003889</td>\n",
       "      <td>...</td>\n",
       "      <td>3.250006e-03</td>\n",
       "      <td>0.005844</td>\n",
       "      <td>4.744491e-04</td>\n",
       "      <td>0.000288</td>\n",
       "      <td>6.344714e-02</td>\n",
       "      <td>0.000028</td>\n",
       "      <td>0.002993</td>\n",
       "      <td>0.269897</td>\n",
       "      <td>91.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cell Mito Symmetry 03 SER-Saddle</th>\n",
       "      <td>0.002822</td>\n",
       "      <td>8.934781e-04</td>\n",
       "      <td>2.598088e-07</td>\n",
       "      <td>0.004286</td>\n",
       "      <td>0.000074</td>\n",
       "      <td>0.006235</td>\n",
       "      <td>0.001365</td>\n",
       "      <td>1.047847e-03</td>\n",
       "      <td>1.271068e-16</td>\n",
       "      <td>0.001582</td>\n",
       "      <td>...</td>\n",
       "      <td>3.556850e-04</td>\n",
       "      <td>0.000560</td>\n",
       "      <td>7.235690e-04</td>\n",
       "      <td>0.000165</td>\n",
       "      <td>2.160443e-05</td>\n",
       "      <td>0.000778</td>\n",
       "      <td>0.001662</td>\n",
       "      <td>0.063839</td>\n",
       "      <td>91.5</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cell 568 Symmetry 03</th>\n",
       "      <td>0.005591</td>\n",
       "      <td>1.287867e-05</td>\n",
       "      <td>3.903261e-03</td>\n",
       "      <td>0.002750</td>\n",
       "      <td>0.000533</td>\n",
       "      <td>0.002052</td>\n",
       "      <td>0.003079</td>\n",
       "      <td>1.554245e-04</td>\n",
       "      <td>7.608382e-04</td>\n",
       "      <td>0.002294</td>\n",
       "      <td>...</td>\n",
       "      <td>4.835994e-04</td>\n",
       "      <td>0.001734</td>\n",
       "      <td>3.876868e-04</td>\n",
       "      <td>0.000319</td>\n",
       "      <td>3.027619e-07</td>\n",
       "      <td>0.164620</td>\n",
       "      <td>0.001123</td>\n",
       "      <td>0.311335</td>\n",
       "      <td>92.5</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cell 488 Radial Relative Deviation SER-Hole</th>\n",
       "      <td>0.004323</td>\n",
       "      <td>2.338512e-06</td>\n",
       "      <td>1.344680e-05</td>\n",
       "      <td>0.001955</td>\n",
       "      <td>0.000051</td>\n",
       "      <td>0.002047</td>\n",
       "      <td>0.008299</td>\n",
       "      <td>2.498666e-06</td>\n",
       "      <td>3.304580e-04</td>\n",
       "      <td>0.000010</td>\n",
       "      <td>...</td>\n",
       "      <td>6.055466e-03</td>\n",
       "      <td>0.000715</td>\n",
       "      <td>4.787706e-04</td>\n",
       "      <td>0.001038</td>\n",
       "      <td>3.159805e-05</td>\n",
       "      <td>0.000735</td>\n",
       "      <td>0.000160</td>\n",
       "      <td>0.093371</td>\n",
       "      <td>94.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cell 555 Symmetry 03</th>\n",
       "      <td>0.002540</td>\n",
       "      <td>4.768146e-03</td>\n",
       "      <td>4.124288e-18</td>\n",
       "      <td>0.000296</td>\n",
       "      <td>0.000056</td>\n",
       "      <td>0.001760</td>\n",
       "      <td>0.000180</td>\n",
       "      <td>6.042472e-06</td>\n",
       "      <td>1.509324e-03</td>\n",
       "      <td>0.000364</td>\n",
       "      <td>...</td>\n",
       "      <td>3.405900e-03</td>\n",
       "      <td>0.015209</td>\n",
       "      <td>4.729278e-03</td>\n",
       "      <td>0.009392</td>\n",
       "      <td>5.610291e-05</td>\n",
       "      <td>0.090950</td>\n",
       "      <td>0.002864</td>\n",
       "      <td>0.163793</td>\n",
       "      <td>94.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cytoplasm 488 Profile 3/5 SER-Bright</th>\n",
       "      <td>0.001593</td>\n",
       "      <td>2.137571e-06</td>\n",
       "      <td>7.132501e-18</td>\n",
       "      <td>0.000309</td>\n",
       "      <td>0.000022</td>\n",
       "      <td>0.005158</td>\n",
       "      <td>0.005545</td>\n",
       "      <td>1.718145e-03</td>\n",
       "      <td>8.811762e-04</td>\n",
       "      <td>0.000012</td>\n",
       "      <td>...</td>\n",
       "      <td>8.171641e-04</td>\n",
       "      <td>0.006633</td>\n",
       "      <td>5.089387e-04</td>\n",
       "      <td>0.001252</td>\n",
       "      <td>3.536955e-05</td>\n",
       "      <td>0.000012</td>\n",
       "      <td>0.001795</td>\n",
       "      <td>0.072386</td>\n",
       "      <td>95.5</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cell 488 Symmetry 03 SER-Hole</th>\n",
       "      <td>0.001442</td>\n",
       "      <td>8.713565e-06</td>\n",
       "      <td>9.771195e-19</td>\n",
       "      <td>0.001153</td>\n",
       "      <td>0.000282</td>\n",
       "      <td>0.003042</td>\n",
       "      <td>0.000465</td>\n",
       "      <td>5.588665e-05</td>\n",
       "      <td>1.125359e-16</td>\n",
       "      <td>0.000168</td>\n",
       "      <td>...</td>\n",
       "      <td>1.057855e-03</td>\n",
       "      <td>0.001196</td>\n",
       "      <td>7.256780e-04</td>\n",
       "      <td>0.004499</td>\n",
       "      <td>1.265610e-04</td>\n",
       "      <td>0.000015</td>\n",
       "      <td>0.003742</td>\n",
       "      <td>0.093704</td>\n",
       "      <td>98.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nucleus 33342 Radial Mean SER-Hole</th>\n",
       "      <td>0.008512</td>\n",
       "      <td>2.179742e-17</td>\n",
       "      <td>1.205395e-03</td>\n",
       "      <td>0.000948</td>\n",
       "      <td>0.032325</td>\n",
       "      <td>0.001158</td>\n",
       "      <td>0.000531</td>\n",
       "      <td>4.261162e-04</td>\n",
       "      <td>1.731078e-03</td>\n",
       "      <td>0.001003</td>\n",
       "      <td>...</td>\n",
       "      <td>3.018494e-04</td>\n",
       "      <td>0.002020</td>\n",
       "      <td>5.041212e-03</td>\n",
       "      <td>0.000979</td>\n",
       "      <td>4.462663e-04</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>0.000585</td>\n",
       "      <td>0.204540</td>\n",
       "      <td>98.5</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cell 488 Symmetry 03 SER-Edge</th>\n",
       "      <td>0.001966</td>\n",
       "      <td>3.680526e-06</td>\n",
       "      <td>2.832429e-04</td>\n",
       "      <td>0.000633</td>\n",
       "      <td>0.004448</td>\n",
       "      <td>0.006373</td>\n",
       "      <td>0.002292</td>\n",
       "      <td>4.079614e-03</td>\n",
       "      <td>1.355940e-03</td>\n",
       "      <td>0.000843</td>\n",
       "      <td>...</td>\n",
       "      <td>2.233095e-04</td>\n",
       "      <td>0.000819</td>\n",
       "      <td>2.718900e-04</td>\n",
       "      <td>0.000254</td>\n",
       "      <td>6.153843e-06</td>\n",
       "      <td>0.003813</td>\n",
       "      <td>0.001759</td>\n",
       "      <td>0.066122</td>\n",
       "      <td>100.5</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nucleus 33342 Threshold Compactness 30%</th>\n",
       "      <td>0.001771</td>\n",
       "      <td>1.334568e-03</td>\n",
       "      <td>2.902619e-03</td>\n",
       "      <td>0.008669</td>\n",
       "      <td>0.000012</td>\n",
       "      <td>0.006616</td>\n",
       "      <td>0.005312</td>\n",
       "      <td>1.185663e-04</td>\n",
       "      <td>1.435315e-03</td>\n",
       "      <td>0.000633</td>\n",
       "      <td>...</td>\n",
       "      <td>3.434321e-03</td>\n",
       "      <td>0.000184</td>\n",
       "      <td>1.556893e-06</td>\n",
       "      <td>0.000656</td>\n",
       "      <td>1.575395e-06</td>\n",
       "      <td>0.000003</td>\n",
       "      <td>0.000175</td>\n",
       "      <td>0.108585</td>\n",
       "      <td>101.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cell 488 Radial Mean Ratio SER-Dark</th>\n",
       "      <td>0.002167</td>\n",
       "      <td>1.144452e-02</td>\n",
       "      <td>7.468521e-02</td>\n",
       "      <td>0.002503</td>\n",
       "      <td>0.004652</td>\n",
       "      <td>0.001414</td>\n",
       "      <td>0.001797</td>\n",
       "      <td>8.383902e-05</td>\n",
       "      <td>1.696564e-02</td>\n",
       "      <td>0.025248</td>\n",
       "      <td>...</td>\n",
       "      <td>5.856416e-02</td>\n",
       "      <td>0.016468</td>\n",
       "      <td>2.006600e-06</td>\n",
       "      <td>0.000418</td>\n",
       "      <td>2.954205e-02</td>\n",
       "      <td>0.000009</td>\n",
       "      <td>0.001549</td>\n",
       "      <td>0.302021</td>\n",
       "      <td>102.5</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>30 rows × 33 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                        NCAP          ATOR  \\\n",
       "Intensity Nucleus Alexa 555 Mean                    0.069259  5.966818e-04   \n",
       "Cell 568 Radial Mean Ratio SER-Dark                 0.004682  7.872251e-02   \n",
       "Cytoplasm 568 Profile 4/5                           0.003274  1.401008e-02   \n",
       "Nucleus Alexa 555 SER Dark 0 px                     0.005983  2.321553e-04   \n",
       "Cytoplasm 33342 Profile 3/5 SER-Bright              0.003897  1.700374e-05   \n",
       "Cytoplasm 488 Profile 2/5                           0.003155  1.569662e-02   \n",
       "Cell Contact Area with Neighbors [%]                0.006087  3.376059e-03   \n",
       "Nucleus Alexa 555 SER Bright 0 px                   0.010249  4.583849e-03   \n",
       "Cell 555 Profile 5/5                                0.003924  9.233154e-03   \n",
       "Cell 555 Threshold Compactness 50%                  0.001219  5.062461e-03   \n",
       "Intensity Cytoplasm Alexa 568 Mean                  0.013316  5.664326e-06   \n",
       "Intensity Nucleus HOECHST 33342 Mean                0.000281  2.749893e-06   \n",
       "Cytoplasm Alexa 488 SER Bright 0 px                 0.000442  8.324026e-03   \n",
       "Nucleus 33342 Profile 5/5                           0.003009  1.687751e-03   \n",
       "Cytoplasm 488 Profile 4/5 SER-Edge                  0.006480  4.687099e-06   \n",
       "Cell 488 Threshold Compactness 60%                  0.002853  1.630552e-03   \n",
       "Cytoplasm 33342 Profile 3/5 SER-Spot                0.003846  6.004386e-03   \n",
       "Cytoplasm 33342 Profile 1/5                         0.003819  6.896486e-03   \n",
       "Nucleus 33342 Radial Relative Deviation SER-Saddle  0.002789  4.223371e-03   \n",
       "Cell 488 Threshold Compactness 60% SER-Edge         0.001545  1.014556e-02   \n",
       "Cell Mito Symmetry 03 SER-Saddle                    0.002822  8.934781e-04   \n",
       "Cell 568 Symmetry 03                                0.005591  1.287867e-05   \n",
       "Cell 488 Radial Relative Deviation SER-Hole         0.004323  2.338512e-06   \n",
       "Cell 555 Symmetry 03                                0.002540  4.768146e-03   \n",
       "Cytoplasm 488 Profile 3/5 SER-Bright                0.001593  2.137571e-06   \n",
       "Cell 488 Symmetry 03 SER-Hole                       0.001442  8.713565e-06   \n",
       "Nucleus 33342 Radial Mean SER-Hole                  0.008512  2.179742e-17   \n",
       "Cell 488 Symmetry 03 SER-Edge                       0.001966  3.680526e-06   \n",
       "Nucleus 33342 Threshold Compactness 30%             0.001771  1.334568e-03   \n",
       "Cell 488 Radial Mean Ratio SER-Dark                 0.002167  1.144452e-02   \n",
       "\n",
       "                                                            CERI      CLOF  \\\n",
       "Intensity Nucleus Alexa 555 Mean                    1.285944e-03  0.003826   \n",
       "Cell 568 Radial Mean Ratio SER-Dark                 6.093962e-02  0.001649   \n",
       "Cytoplasm 568 Profile 4/5                           1.274923e-02  0.001394   \n",
       "Nucleus Alexa 555 SER Dark 0 px                     6.383577e-05  0.000558   \n",
       "Cytoplasm 33342 Profile 3/5 SER-Bright              2.032836e-03  0.001848   \n",
       "Cytoplasm 488 Profile 2/5                           9.855803e-03  0.004102   \n",
       "Cell Contact Area with Neighbors [%]                1.696221e-02  0.003638   \n",
       "Nucleus Alexa 555 SER Bright 0 px                   3.210023e-05  0.003400   \n",
       "Cell 555 Profile 5/5                                5.752520e-03  0.001480   \n",
       "Cell 555 Threshold Compactness 50%                  7.688018e-03  0.000716   \n",
       "Intensity Cytoplasm Alexa 568 Mean                  1.411591e-05  0.001024   \n",
       "Intensity Nucleus HOECHST 33342 Mean                9.258018e-04  0.008235   \n",
       "Cytoplasm Alexa 488 SER Bright 0 px                 1.026361e-04  0.001526   \n",
       "Nucleus 33342 Profile 5/5                           2.377685e-02  0.003556   \n",
       "Cytoplasm 488 Profile 4/5 SER-Edge                  3.048739e-02  0.000626   \n",
       "Cell 488 Threshold Compactness 60%                  8.979103e-05  0.000249   \n",
       "Cytoplasm 33342 Profile 3/5 SER-Spot                5.053289e-04  0.001637   \n",
       "Cytoplasm 33342 Profile 1/5                         7.292464e-03  0.000413   \n",
       "Nucleus 33342 Radial Relative Deviation SER-Saddle  3.394002e-04  0.003690   \n",
       "Cell 488 Threshold Compactness 60% SER-Edge         6.589361e-04  0.002344   \n",
       "Cell Mito Symmetry 03 SER-Saddle                    2.598088e-07  0.004286   \n",
       "Cell 568 Symmetry 03                                3.903261e-03  0.002750   \n",
       "Cell 488 Radial Relative Deviation SER-Hole         1.344680e-05  0.001955   \n",
       "Cell 555 Symmetry 03                                4.124288e-18  0.000296   \n",
       "Cytoplasm 488 Profile 3/5 SER-Bright                7.132501e-18  0.000309   \n",
       "Cell 488 Symmetry 03 SER-Hole                       9.771195e-19  0.001153   \n",
       "Nucleus 33342 Radial Mean SER-Hole                  1.205395e-03  0.000948   \n",
       "Cell 488 Symmetry 03 SER-Edge                       2.832429e-04  0.000633   \n",
       "Nucleus 33342 Threshold Compactness 30%             2.902619e-03  0.008669   \n",
       "Cell 488 Radial Mean Ratio SER-Dark                 7.468521e-02  0.002503   \n",
       "\n",
       "                                                        COLC      DAPT  \\\n",
       "Intensity Nucleus Alexa 555 Mean                    0.001107  0.010038   \n",
       "Cell 568 Radial Mean Ratio SER-Dark                 0.008503  0.001310   \n",
       "Cytoplasm 568 Profile 4/5                           0.000009  0.004071   \n",
       "Nucleus Alexa 555 SER Dark 0 px                     0.000508  0.001294   \n",
       "Cytoplasm 33342 Profile 3/5 SER-Bright              0.019915  0.031095   \n",
       "Cytoplasm 488 Profile 2/5                           0.000119  0.002403   \n",
       "Cell Contact Area with Neighbors [%]                0.024756  0.001647   \n",
       "Nucleus Alexa 555 SER Bright 0 px                   0.000638  0.002644   \n",
       "Cell 555 Profile 5/5                                0.000032  0.003693   \n",
       "Cell 555 Threshold Compactness 50%                  0.000147  0.005397   \n",
       "Intensity Cytoplasm Alexa 568 Mean                  0.000766  0.000632   \n",
       "Intensity Nucleus HOECHST 33342 Mean                0.000126  0.000432   \n",
       "Cytoplasm Alexa 488 SER Bright 0 px                 0.024548  0.000239   \n",
       "Nucleus 33342 Profile 5/5                           0.000107  0.031203   \n",
       "Cytoplasm 488 Profile 4/5 SER-Edge                  0.000105  0.000588   \n",
       "Cell 488 Threshold Compactness 60%                  0.000097  0.007836   \n",
       "Cytoplasm 33342 Profile 3/5 SER-Spot                0.037406  0.008982   \n",
       "Cytoplasm 33342 Profile 1/5                         0.000194  0.001389   \n",
       "Nucleus 33342 Radial Relative Deviation SER-Saddle  0.000002  0.006398   \n",
       "Cell 488 Threshold Compactness 60% SER-Edge         0.000119  0.007476   \n",
       "Cell Mito Symmetry 03 SER-Saddle                    0.000074  0.006235   \n",
       "Cell 568 Symmetry 03                                0.000533  0.002052   \n",
       "Cell 488 Radial Relative Deviation SER-Hole         0.000051  0.002047   \n",
       "Cell 555 Symmetry 03                                0.000056  0.001760   \n",
       "Cytoplasm 488 Profile 3/5 SER-Bright                0.000022  0.005158   \n",
       "Cell 488 Symmetry 03 SER-Hole                       0.000282  0.003042   \n",
       "Nucleus 33342 Radial Mean SER-Hole                  0.032325  0.001158   \n",
       "Cell 488 Symmetry 03 SER-Edge                       0.004448  0.006373   \n",
       "Nucleus 33342 Threshold Compactness 30%             0.000012  0.006616   \n",
       "Cell 488 Radial Mean Ratio SER-Dark                 0.004652  0.001414   \n",
       "\n",
       "                                                        DEXA          DOXO  \\\n",
       "Intensity Nucleus Alexa 555 Mean                    0.069669  7.919985e-05   \n",
       "Cell 568 Radial Mean Ratio SER-Dark                 0.004475  8.160253e-05   \n",
       "Cytoplasm 568 Profile 4/5                           0.000283  6.174638e-04   \n",
       "Nucleus Alexa 555 SER Dark 0 px                     0.011641  1.442938e-04   \n",
       "Cytoplasm 33342 Profile 3/5 SER-Bright              0.000408  1.331652e-03   \n",
       "Cytoplasm 488 Profile 2/5                           0.003505  4.254592e-03   \n",
       "Cell Contact Area with Neighbors [%]                0.003719  1.010195e-03   \n",
       "Nucleus Alexa 555 SER Bright 0 px                   0.001052  1.452051e-03   \n",
       "Cell 555 Profile 5/5                                0.001156  1.155113e-04   \n",
       "Cell 555 Threshold Compactness 50%                  0.000847  2.862064e-04   \n",
       "Intensity Cytoplasm Alexa 568 Mean                  0.000355  1.123636e-02   \n",
       "Intensity Nucleus HOECHST 33342 Mean                0.140311  5.755259e-03   \n",
       "Cytoplasm Alexa 488 SER Bright 0 px                 0.000420  1.450861e-03   \n",
       "Nucleus 33342 Profile 5/5                           0.000090  1.449777e-03   \n",
       "Cytoplasm 488 Profile 4/5 SER-Edge                  0.001948  1.578526e-01   \n",
       "Cell 488 Threshold Compactness 60%                  0.001840  2.143602e-04   \n",
       "Cytoplasm 33342 Profile 3/5 SER-Spot                0.008877  4.582515e-04   \n",
       "Cytoplasm 33342 Profile 1/5                         0.000345  7.020700e-19   \n",
       "Nucleus 33342 Radial Relative Deviation SER-Saddle  0.000032  8.666750e-04   \n",
       "Cell 488 Threshold Compactness 60% SER-Edge         0.000627  4.407808e-04   \n",
       "Cell Mito Symmetry 03 SER-Saddle                    0.001365  1.047847e-03   \n",
       "Cell 568 Symmetry 03                                0.003079  1.554245e-04   \n",
       "Cell 488 Radial Relative Deviation SER-Hole         0.008299  2.498666e-06   \n",
       "Cell 555 Symmetry 03                                0.000180  6.042472e-06   \n",
       "Cytoplasm 488 Profile 3/5 SER-Bright                0.005545  1.718145e-03   \n",
       "Cell 488 Symmetry 03 SER-Hole                       0.000465  5.588665e-05   \n",
       "Nucleus 33342 Radial Mean SER-Hole                  0.000531  4.261162e-04   \n",
       "Cell 488 Symmetry 03 SER-Edge                       0.002292  4.079614e-03   \n",
       "Nucleus 33342 Threshold Compactness 30%             0.005312  1.185663e-04   \n",
       "Cell 488 Radial Mean Ratio SER-Dark                 0.001797  8.383902e-05   \n",
       "\n",
       "                                                            EZET      HYCQ  \\\n",
       "Intensity Nucleus Alexa 555 Mean                    6.345250e-05  0.009493   \n",
       "Cell 568 Radial Mean Ratio SER-Dark                 5.174633e-04  0.004376   \n",
       "Cytoplasm 568 Profile 4/5                           1.244406e-02  0.065218   \n",
       "Nucleus Alexa 555 SER Dark 0 px                     7.787227e-04  0.000877   \n",
       "Cytoplasm 33342 Profile 3/5 SER-Bright              1.536339e-02  0.014208   \n",
       "Cytoplasm 488 Profile 2/5                           2.677322e-02  0.004106   \n",
       "Cell Contact Area with Neighbors [%]                4.216135e-03  0.002823   \n",
       "Nucleus Alexa 555 SER Bright 0 px                   1.011731e-04  0.001120   \n",
       "Cell 555 Profile 5/5                                2.508944e-04  0.002474   \n",
       "Cell 555 Threshold Compactness 50%                  4.267643e-05  0.002892   \n",
       "Intensity Cytoplasm Alexa 568 Mean                  5.046302e-04  0.001921   \n",
       "Intensity Nucleus HOECHST 33342 Mean                1.398156e-03  0.006328   \n",
       "Cytoplasm Alexa 488 SER Bright 0 px                 2.103880e-16  0.000519   \n",
       "Nucleus 33342 Profile 5/5                           1.372205e-01  0.002508   \n",
       "Cytoplasm 488 Profile 4/5 SER-Edge                  8.028016e-04  0.000844   \n",
       "Cell 488 Threshold Compactness 60%                  8.379633e-04  0.002948   \n",
       "Cytoplasm 33342 Profile 3/5 SER-Spot                6.406879e-02  0.013784   \n",
       "Cytoplasm 33342 Profile 1/5                         5.283937e-04  0.009410   \n",
       "Nucleus 33342 Radial Relative Deviation SER-Saddle  3.221609e-05  0.001324   \n",
       "Cell 488 Threshold Compactness 60% SER-Edge         2.479943e-03  0.003889   \n",
       "Cell Mito Symmetry 03 SER-Saddle                    1.271068e-16  0.001582   \n",
       "Cell 568 Symmetry 03                                7.608382e-04  0.002294   \n",
       "Cell 488 Radial Relative Deviation SER-Hole         3.304580e-04  0.000010   \n",
       "Cell 555 Symmetry 03                                1.509324e-03  0.000364   \n",
       "Cytoplasm 488 Profile 3/5 SER-Bright                8.811762e-04  0.000012   \n",
       "Cell 488 Symmetry 03 SER-Hole                       1.125359e-16  0.000168   \n",
       "Nucleus 33342 Radial Mean SER-Hole                  1.731078e-03  0.001003   \n",
       "Cell 488 Symmetry 03 SER-Edge                       1.355940e-03  0.000843   \n",
       "Nucleus 33342 Threshold Compactness 30%             1.435315e-03  0.000633   \n",
       "Cell 488 Radial Mean Ratio SER-Dark                 1.696564e-02  0.025248   \n",
       "\n",
       "                                                    ...          WURS  \\\n",
       "Intensity Nucleus Alexa 555 Mean                    ...  8.900952e-03   \n",
       "Cell 568 Radial Mean Ratio SER-Dark                 ...  1.496420e-03   \n",
       "Cytoplasm 568 Profile 4/5                           ...  2.551329e-04   \n",
       "Nucleus Alexa 555 SER Dark 0 px                     ...  7.549316e-16   \n",
       "Cytoplasm 33342 Profile 3/5 SER-Bright              ...  9.624268e-03   \n",
       "Cytoplasm 488 Profile 2/5                           ...  2.356938e-04   \n",
       "Cell Contact Area with Neighbors [%]                ...  5.414299e-04   \n",
       "Nucleus Alexa 555 SER Bright 0 px                   ...  1.719757e-04   \n",
       "Cell 555 Profile 5/5                                ...  1.559529e-01   \n",
       "Cell 555 Threshold Compactness 50%                  ...  5.523068e-02   \n",
       "Intensity Cytoplasm Alexa 568 Mean                  ...  3.073107e-04   \n",
       "Intensity Nucleus HOECHST 33342 Mean                ...  8.380744e-05   \n",
       "Cytoplasm Alexa 488 SER Bright 0 px                 ...  7.583675e-04   \n",
       "Nucleus 33342 Profile 5/5                           ...  6.287657e-03   \n",
       "Cytoplasm 488 Profile 4/5 SER-Edge                  ...  1.224877e-03   \n",
       "Cell 488 Threshold Compactness 60%                  ...  3.729677e-04   \n",
       "Cytoplasm 33342 Profile 3/5 SER-Spot                ...  9.867377e-03   \n",
       "Cytoplasm 33342 Profile 1/5                         ...  1.312187e-03   \n",
       "Nucleus 33342 Radial Relative Deviation SER-Saddle  ...  6.047187e-03   \n",
       "Cell 488 Threshold Compactness 60% SER-Edge         ...  3.250006e-03   \n",
       "Cell Mito Symmetry 03 SER-Saddle                    ...  3.556850e-04   \n",
       "Cell 568 Symmetry 03                                ...  4.835994e-04   \n",
       "Cell 488 Radial Relative Deviation SER-Hole         ...  6.055466e-03   \n",
       "Cell 555 Symmetry 03                                ...  3.405900e-03   \n",
       "Cytoplasm 488 Profile 3/5 SER-Bright                ...  8.171641e-04   \n",
       "Cell 488 Symmetry 03 SER-Hole                       ...  1.057855e-03   \n",
       "Nucleus 33342 Radial Mean SER-Hole                  ...  3.018494e-04   \n",
       "Cell 488 Symmetry 03 SER-Edge                       ...  2.233095e-04   \n",
       "Nucleus 33342 Threshold Compactness 30%             ...  3.434321e-03   \n",
       "Cell 488 Radial Mean Ratio SER-Dark                 ...  5.856416e-02   \n",
       "\n",
       "                                                        CISP          IMAT  \\\n",
       "Intensity Nucleus Alexa 555 Mean                    0.000237  9.000704e-07   \n",
       "Cell 568 Radial Mean Ratio SER-Dark                 0.004878  5.288007e-07   \n",
       "Cytoplasm 568 Profile 4/5                           0.003337  6.831089e-04   \n",
       "Nucleus Alexa 555 SER Dark 0 px                     0.011314  1.326905e-06   \n",
       "Cytoplasm 33342 Profile 3/5 SER-Bright              0.000557  1.930664e-04   \n",
       "Cytoplasm 488 Profile 2/5                           0.000106  3.197068e-05   \n",
       "Cell Contact Area with Neighbors [%]                0.000165  1.051916e-05   \n",
       "Nucleus Alexa 555 SER Bright 0 px                   0.000276  1.018304e-06   \n",
       "Cell 555 Profile 5/5                                0.001225  5.865807e-06   \n",
       "Cell 555 Threshold Compactness 50%                  0.000293  1.436033e-02   \n",
       "Intensity Cytoplasm Alexa 568 Mean                  0.000191  1.018811e-06   \n",
       "Intensity Nucleus HOECHST 33342 Mean                0.001503  5.017040e-03   \n",
       "Cytoplasm Alexa 488 SER Bright 0 px                 0.000491  1.949338e-07   \n",
       "Nucleus 33342 Profile 5/5                           0.001170  2.053441e-04   \n",
       "Cytoplasm 488 Profile 4/5 SER-Edge                  0.001463  2.591288e-07   \n",
       "Cell 488 Threshold Compactness 60%                  0.014213  1.749323e-04   \n",
       "Cytoplasm 33342 Profile 3/5 SER-Spot                0.001149  2.948308e-05   \n",
       "Cytoplasm 33342 Profile 1/5                         0.000254  5.152939e-03   \n",
       "Nucleus 33342 Radial Relative Deviation SER-Saddle  0.003409  2.669001e-04   \n",
       "Cell 488 Threshold Compactness 60% SER-Edge         0.005844  4.744491e-04   \n",
       "Cell Mito Symmetry 03 SER-Saddle                    0.000560  7.235690e-04   \n",
       "Cell 568 Symmetry 03                                0.001734  3.876868e-04   \n",
       "Cell 488 Radial Relative Deviation SER-Hole         0.000715  4.787706e-04   \n",
       "Cell 555 Symmetry 03                                0.015209  4.729278e-03   \n",
       "Cytoplasm 488 Profile 3/5 SER-Bright                0.006633  5.089387e-04   \n",
       "Cell 488 Symmetry 03 SER-Hole                       0.001196  7.256780e-04   \n",
       "Nucleus 33342 Radial Mean SER-Hole                  0.002020  5.041212e-03   \n",
       "Cell 488 Symmetry 03 SER-Edge                       0.000819  2.718900e-04   \n",
       "Nucleus 33342 Threshold Compactness 30%             0.000184  1.556893e-06   \n",
       "Cell 488 Radial Mean Ratio SER-Dark                 0.016468  2.006600e-06   \n",
       "\n",
       "                                                        OLAN          SUNI  \\\n",
       "Intensity Nucleus Alexa 555 Mean                    0.003198  2.157955e-07   \n",
       "Cell 568 Radial Mean Ratio SER-Dark                 0.000053  1.292887e-01   \n",
       "Cytoplasm 568 Profile 4/5                           0.000376  2.165313e-04   \n",
       "Nucleus Alexa 555 SER Dark 0 px                     0.009321  2.652436e-07   \n",
       "Cytoplasm 33342 Profile 3/5 SER-Bright              0.001129  1.257379e-03   \n",
       "Cytoplasm 488 Profile 2/5                           0.215042  4.293040e-06   \n",
       "Cell Contact Area with Neighbors [%]                0.000038  3.573937e-04   \n",
       "Nucleus Alexa 555 SER Bright 0 px                   0.002499  5.865214e-02   \n",
       "Cell 555 Profile 5/5                                0.000511  6.021116e-05   \n",
       "Cell 555 Threshold Compactness 50%                  0.052913  3.797844e-04   \n",
       "Intensity Cytoplasm Alexa 568 Mean                  0.001028  3.624342e-05   \n",
       "Intensity Nucleus HOECHST 33342 Mean                0.000292  5.233858e-04   \n",
       "Cytoplasm Alexa 488 SER Bright 0 px                 0.000783  1.377133e-01   \n",
       "Nucleus 33342 Profile 5/5                           0.000019  1.978381e-06   \n",
       "Cytoplasm 488 Profile 4/5 SER-Edge                  0.011296  2.446881e-05   \n",
       "Cell 488 Threshold Compactness 60%                  0.000958  3.222321e-06   \n",
       "Cytoplasm 33342 Profile 3/5 SER-Spot                0.000089  1.099902e-02   \n",
       "Cytoplasm 33342 Profile 1/5                         0.011395  1.616052e-06   \n",
       "Nucleus 33342 Radial Relative Deviation SER-Saddle  0.012256  3.304411e-06   \n",
       "Cell 488 Threshold Compactness 60% SER-Edge         0.000288  6.344714e-02   \n",
       "Cell Mito Symmetry 03 SER-Saddle                    0.000165  2.160443e-05   \n",
       "Cell 568 Symmetry 03                                0.000319  3.027619e-07   \n",
       "Cell 488 Radial Relative Deviation SER-Hole         0.001038  3.159805e-05   \n",
       "Cell 555 Symmetry 03                                0.009392  5.610291e-05   \n",
       "Cytoplasm 488 Profile 3/5 SER-Bright                0.001252  3.536955e-05   \n",
       "Cell 488 Symmetry 03 SER-Hole                       0.004499  1.265610e-04   \n",
       "Nucleus 33342 Radial Mean SER-Hole                  0.000979  4.462663e-04   \n",
       "Cell 488 Symmetry 03 SER-Edge                       0.000254  6.153843e-06   \n",
       "Nucleus 33342 Threshold Compactness 30%             0.000656  1.575395e-06   \n",
       "Cell 488 Radial Mean Ratio SER-Dark                 0.000418  2.954205e-02   \n",
       "\n",
       "                                                        TEBU      ZIDO  \\\n",
       "Intensity Nucleus Alexa 555 Mean                    0.001470  0.001359   \n",
       "Cell 568 Radial Mean Ratio SER-Dark                 0.001247  0.000565   \n",
       "Cytoplasm 568 Profile 4/5                           0.006907  0.000346   \n",
       "Nucleus Alexa 555 SER Dark 0 px                     0.026293  0.005519   \n",
       "Cytoplasm 33342 Profile 3/5 SER-Bright              0.000005  0.000370   \n",
       "Cytoplasm 488 Profile 2/5                           0.038738  0.004044   \n",
       "Cell Contact Area with Neighbors [%]                0.000003  0.001785   \n",
       "Nucleus Alexa 555 SER Bright 0 px                   0.001887  0.000961   \n",
       "Cell 555 Profile 5/5                                0.000859  0.002877   \n",
       "Cell 555 Threshold Compactness 50%                  0.003159  0.001784   \n",
       "Intensity Cytoplasm Alexa 568 Mean                  0.004368  0.000852   \n",
       "Intensity Nucleus HOECHST 33342 Mean                0.000058  0.003803   \n",
       "Cytoplasm Alexa 488 SER Bright 0 px                 0.022504  0.002214   \n",
       "Nucleus 33342 Profile 5/5                           0.001409  0.002082   \n",
       "Cytoplasm 488 Profile 4/5 SER-Edge                  0.000002  0.003294   \n",
       "Cell 488 Threshold Compactness 60%                  0.000008  0.000779   \n",
       "Cytoplasm 33342 Profile 3/5 SER-Spot                0.000005  0.000746   \n",
       "Cytoplasm 33342 Profile 1/5                         0.000775  0.003128   \n",
       "Nucleus 33342 Radial Relative Deviation SER-Saddle  0.000545  0.002174   \n",
       "Cell 488 Threshold Compactness 60% SER-Edge         0.000028  0.002993   \n",
       "Cell Mito Symmetry 03 SER-Saddle                    0.000778  0.001662   \n",
       "Cell 568 Symmetry 03                                0.164620  0.001123   \n",
       "Cell 488 Radial Relative Deviation SER-Hole         0.000735  0.000160   \n",
       "Cell 555 Symmetry 03                                0.090950  0.002864   \n",
       "Cytoplasm 488 Profile 3/5 SER-Bright                0.000012  0.001795   \n",
       "Cell 488 Symmetry 03 SER-Hole                       0.000015  0.003742   \n",
       "Nucleus 33342 Radial Mean SER-Hole                  0.000004  0.000585   \n",
       "Cell 488 Symmetry 03 SER-Edge                       0.003813  0.001759   \n",
       "Nucleus 33342 Threshold Compactness 30%             0.000003  0.000175   \n",
       "Cell 488 Radial Mean Ratio SER-Dark                 0.000009  0.001549   \n",
       "\n",
       "                                                    sum_of_scaled_importances  \\\n",
       "Intensity Nucleus Alexa 555 Mean                                     0.608270   \n",
       "Cell 568 Radial Mean Ratio SER-Dark                                  0.388030   \n",
       "Cytoplasm 568 Profile 4/5                                            0.184558   \n",
       "Nucleus Alexa 555 SER Dark 0 px                                      0.297151   \n",
       "Cytoplasm 33342 Profile 3/5 SER-Bright                               0.212931   \n",
       "Cytoplasm 488 Profile 2/5                                            0.586511   \n",
       "Cell Contact Area with Neighbors [%]                                 0.274758   \n",
       "Nucleus Alexa 555 SER Bright 0 px                                    0.214902   \n",
       "Cell 555 Profile 5/5                                                 0.250130   \n",
       "Cell 555 Threshold Compactness 50%                                   0.226225   \n",
       "Intensity Cytoplasm Alexa 568 Mean                                   0.107764   \n",
       "Intensity Nucleus HOECHST 33342 Mean                                 0.396727   \n",
       "Cytoplasm Alexa 488 SER Bright 0 px                                  0.306371   \n",
       "Nucleus 33342 Profile 5/5                                            0.288135   \n",
       "Cytoplasm 488 Profile 4/5 SER-Edge                                   0.344450   \n",
       "Cell 488 Threshold Compactness 60%                                   0.083656   \n",
       "Cytoplasm 33342 Profile 3/5 SER-Spot                                 0.222980   \n",
       "Cytoplasm 33342 Profile 1/5                                          0.175986   \n",
       "Nucleus 33342 Radial Relative Deviation SER-Saddle                   0.103738   \n",
       "Cell 488 Threshold Compactness 60% SER-Edge                          0.269897   \n",
       "Cell Mito Symmetry 03 SER-Saddle                                     0.063839   \n",
       "Cell 568 Symmetry 03                                                 0.311335   \n",
       "Cell 488 Radial Relative Deviation SER-Hole                          0.093371   \n",
       "Cell 555 Symmetry 03                                                 0.163793   \n",
       "Cytoplasm 488 Profile 3/5 SER-Bright                                 0.072386   \n",
       "Cell 488 Symmetry 03 SER-Hole                                        0.093704   \n",
       "Nucleus 33342 Radial Mean SER-Hole                                   0.204540   \n",
       "Cell 488 Symmetry 03 SER-Edge                                        0.066122   \n",
       "Nucleus 33342 Threshold Compactness 30%                              0.108585   \n",
       "Cell 488 Radial Mean Ratio SER-Dark                                  0.302021   \n",
       "\n",
       "                                                    median_rank  \\\n",
       "Intensity Nucleus Alexa 555 Mean                           43.5   \n",
       "Cell 568 Radial Mean Ratio SER-Dark                        49.0   \n",
       "Cytoplasm 568 Profile 4/5                                  54.0   \n",
       "Nucleus Alexa 555 SER Dark 0 px                            56.0   \n",
       "Cytoplasm 33342 Profile 3/5 SER-Bright                     62.0   \n",
       "Cytoplasm 488 Profile 2/5                                  62.0   \n",
       "Cell Contact Area with Neighbors [%]                       71.0   \n",
       "Nucleus Alexa 555 SER Bright 0 px                          74.0   \n",
       "Cell 555 Profile 5/5                                       76.0   \n",
       "Cell 555 Threshold Compactness 50%                         81.0   \n",
       "Intensity Cytoplasm Alexa 568 Mean                         81.5   \n",
       "Intensity Nucleus HOECHST 33342 Mean                       84.5   \n",
       "Cytoplasm Alexa 488 SER Bright 0 px                        84.5   \n",
       "Nucleus 33342 Profile 5/5                                  86.5   \n",
       "Cytoplasm 488 Profile 4/5 SER-Edge                         86.5   \n",
       "Cell 488 Threshold Compactness 60%                         87.5   \n",
       "Cytoplasm 33342 Profile 3/5 SER-Spot                       90.0   \n",
       "Cytoplasm 33342 Profile 1/5                                90.5   \n",
       "Nucleus 33342 Radial Relative Deviation SER-Saddle         91.0   \n",
       "Cell 488 Threshold Compactness 60% SER-Edge                91.0   \n",
       "Cell Mito Symmetry 03 SER-Saddle                           91.5   \n",
       "Cell 568 Symmetry 03                                       92.5   \n",
       "Cell 488 Radial Relative Deviation SER-Hole                94.0   \n",
       "Cell 555 Symmetry 03                                       94.0   \n",
       "Cytoplasm 488 Profile 3/5 SER-Bright                       95.5   \n",
       "Cell 488 Symmetry 03 SER-Hole                              98.0   \n",
       "Nucleus 33342 Radial Mean SER-Hole                         98.5   \n",
       "Cell 488 Symmetry 03 SER-Edge                             100.5   \n",
       "Nucleus 33342 Threshold Compactness 30%                   101.0   \n",
       "Cell 488 Radial Mean Ratio SER-Dark                       102.5   \n",
       "\n",
       "                                                    top_20_frequency  \n",
       "Intensity Nucleus Alexa 555 Mean                                10.0  \n",
       "Cell 568 Radial Mean Ratio SER-Dark                              5.0  \n",
       "Cytoplasm 568 Profile 4/5                                        7.0  \n",
       "Nucleus Alexa 555 SER Dark 0 px                                  8.0  \n",
       "Cytoplasm 33342 Profile 3/5 SER-Bright                           7.0  \n",
       "Cytoplasm 488 Profile 2/5                                        7.0  \n",
       "Cell Contact Area with Neighbors [%]                             9.0  \n",
       "Nucleus Alexa 555 SER Bright 0 px                                5.0  \n",
       "Cell 555 Profile 5/5                                             4.0  \n",
       "Cell 555 Threshold Compactness 50%                               6.0  \n",
       "Intensity Cytoplasm Alexa 568 Mean                               4.0  \n",
       "Intensity Nucleus HOECHST 33342 Mean                             4.0  \n",
       "Cytoplasm Alexa 488 SER Bright 0 px                              6.0  \n",
       "Nucleus 33342 Profile 5/5                                        5.0  \n",
       "Cytoplasm 488 Profile 4/5 SER-Edge                               6.0  \n",
       "Cell 488 Threshold Compactness 60%                               4.0  \n",
       "Cytoplasm 33342 Profile 3/5 SER-Spot                             9.0  \n",
       "Cytoplasm 33342 Profile 1/5                                      4.0  \n",
       "Nucleus 33342 Radial Relative Deviation SER-Saddle               3.0  \n",
       "Cell 488 Threshold Compactness 60% SER-Edge                      2.0  \n",
       "Cell Mito Symmetry 03 SER-Saddle                                 1.0  \n",
       "Cell 568 Symmetry 03                                             2.0  \n",
       "Cell 488 Radial Relative Deviation SER-Hole                      4.0  \n",
       "Cell 555 Symmetry 03                                             5.0  \n",
       "Cytoplasm 488 Profile 3/5 SER-Bright                             2.0  \n",
       "Cell 488 Symmetry 03 SER-Hole                                    3.0  \n",
       "Nucleus 33342 Radial Mean SER-Hole                               5.0  \n",
       "Cell 488 Symmetry 03 SER-Edge                                    2.0  \n",
       "Nucleus 33342 Threshold Compactness 30%                          2.0  \n",
       "Cell 488 Radial Mean Ratio SER-Dark                              9.0  \n",
       "\n",
       "[30 rows x 33 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "#scale = StandardScaler()\n",
    "scale = MinMaxScaler()\n",
    "scale = None\n",
    "\n",
    "# feature importances from RF models\n",
    "if scale is not None:\n",
    "    scaled_importance = scale.fit_transform(importance_df.T)\n",
    "    scaled_importance = pd.DataFrame(scaled_importance, index=importance_df.T.index, columns=importance_df.T.columns)\n",
    "else:    \n",
    "    scaled_importance = importance_df.T.copy()\n",
    "    scaled_importance = pd.DataFrame(scaled_importance, index=importance_df.T.index, columns=importance_df.T.columns)\n",
    "scaled_importance_rank = scaled_importance.rank(axis=0, ascending=False).median(axis=1).sort_values(ascending=True)\n",
    "scaled_importance['sum_of_scaled_importances'] = scaled_importance.sum(axis=1)\n",
    "\n",
    "pd.DataFrame(scaled_importance_rank, columns=['median_rank'])\n",
    "scaled_importance['median_rank'] = scaled_importance_rank\n",
    "scaled_importance.sort_values(by=['median_rank'], ascending=True)\n",
    "scaled_importance['top_20_frequency'] = importance_sums_df['Top20Frequency']\n",
    "scaled_importance.fillna(0, inplace=True)\n",
    "scaled_importance_top30 = scaled_importance.sort_values(by=['median_rank'], ascending=True).head(30)\n",
    "scaled_importance_top30"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "plotlyServerURL": "https://plot.ly"
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       "data": [
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         "hovertemplate": "<b>%{hovertext}</b><br><br>median_rank=%{x}<br>sum_of_scaled_importances=%{y}<br>top_20_frequency=%{marker.color}<extra></extra>",
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          "Cell 33342 Radial Mean ",
          "Cell 33342 Radial Mean Ratio SER-Bright",
          "Cell 33342 Radial Mean Ratio SER-Dark",
          "Cell 33342 Radial Mean Ratio SER-Hole",
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          "Cell 33342 Radial Relative Deviation SER-Hole",
          "Cell 488 Axial Length Ratio ",
          "Cell 488 Axial Length Ratio SER-Bright",
          "Cell 488 Axial Length Ratio SER-Dark",
          "Cell 488 Radial Mean Ratio SER-Bright",
          "Cell 488 Radial Mean Ratio SER-Dark",
          "Cell 488 Radial Relative Deviation SER-Bright",
          "Cell 488 Radial Relative Deviation SER-Dark",
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          "Cell 488 Symmetry 03 ",
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          "Cell 488 Symmetry 13 SER-Spot",
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          "Cell 568 Threshold Compactness 50% ",
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        },
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         "text": "Sum of feature importances against median ranks for individual RF models"
        },
        "width": 900,
        "xaxis": {
         "anchor": "y",
         "domain": [
          0,
          1
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         "title": {
          "text": "Median rank"
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         "anchor": "x",
         "domain": [
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          "text": "Sum of scaled importance"
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      }
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# scatter plot of median rank vs sum of scaled importance\n",
    "import plotly.express as px\n",
    "fig = px.scatter(scaled_importance, x=\"median_rank\", y=\"sum_of_scaled_importances\", hover_name=scaled_importance.index, color=\"top_20_frequency\", size_max=10, width=900, height=800, title=\"Sum of feature importances against median ranks for individual RF models\", color_continuous_scale='viridis_r')\n",
    "fig.update_xaxes(title_text='Median rank')\n",
    "fig.update_yaxes(title_text='Sum of scaled importance')\n",
    "fig.update_coloraxes(colorbar_title=\"Top 20 frequency\", colorbar_len=0.7, colorbar_thickness=20 , colorbar_tickmode='array')\n",
    "fig.show()\n",
    "# a feature is more important if it has a low median rank and a high sum of scaled importance\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
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       "      <th></th>\n",
       "      <th>NCAP</th>\n",
       "      <th>ATOR</th>\n",
       "      <th>CERI</th>\n",
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       "      <th>Intensity Nucleus Alexa 555 Mean</th>\n",
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       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <th>Cell 555 Threshold Compactness 60%</th>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <th>Cytoplasm 488 Profile 3/5 SER-Hole</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
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       "      <td>False</td>\n",
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       "      <th>Intensity Nucleus HOECHST 33342 Mean</th>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <th>Nucleus 33342 Threshold Compactness 60% SER-Valley</th>\n",
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      "text/plain": [
       "                                                     NCAP   ATOR   CERI  \\\n",
       "Intensity Nucleus Alexa 555 Mean                    False  False  False   \n",
       "Cytoplasm 488 Profile 2/5                           False  False  False   \n",
       "Cell 555 Threshold Compactness 60%                  False  False  False   \n",
       "Cytoplasm 488 Profile 3/5 SER-Hole                  False  False  False   \n",
       "Intensity Nucleus HOECHST 33342 Mean                False  False  False   \n",
       "...                                                   ...    ...    ...   \n",
       "Nucleus Mito Profile 5/5 SER-Dark                    True   True   True   \n",
       "Nucleus 33342 Threshold Compactness 60% SER-Valley   True   True   True   \n",
       "Cytoplasm Alexa 647 SER Bright 0 px                  True   True   True   \n",
       "Nucleus 33342 Threshold Compactness 60% SER-Dark     True   True   True   \n",
       "Nucleus 568 Profile 5/5 SER-Dark                     True   True   True   \n",
       "\n",
       "                                                     CLOF   COLC   DAPT  \\\n",
       "Intensity Nucleus Alexa 555 Mean                    False  False  False   \n",
       "Cytoplasm 488 Profile 2/5                           False  False  False   \n",
       "Cell 555 Threshold Compactness 60%                  False  False  False   \n",
       "Cytoplasm 488 Profile 3/5 SER-Hole                  False  False  False   \n",
       "Intensity Nucleus HOECHST 33342 Mean                False  False  False   \n",
       "...                                                   ...    ...    ...   \n",
       "Nucleus Mito Profile 5/5 SER-Dark                    True   True   True   \n",
       "Nucleus 33342 Threshold Compactness 60% SER-Valley   True   True   True   \n",
       "Cytoplasm Alexa 647 SER Bright 0 px                  True   True   True   \n",
       "Nucleus 33342 Threshold Compactness 60% SER-Dark     True   True   True   \n",
       "Nucleus 568 Profile 5/5 SER-Dark                     True   True   True   \n",
       "\n",
       "                                                     DEXA   DOXO   EZET  \\\n",
       "Intensity Nucleus Alexa 555 Mean                    False  False  False   \n",
       "Cytoplasm 488 Profile 2/5                           False  False  False   \n",
       "Cell 555 Threshold Compactness 60%                  False  False  False   \n",
       "Cytoplasm 488 Profile 3/5 SER-Hole                  False  False  False   \n",
       "Intensity Nucleus HOECHST 33342 Mean                False  False  False   \n",
       "...                                                   ...    ...    ...   \n",
       "Nucleus Mito Profile 5/5 SER-Dark                    True   True   True   \n",
       "Nucleus 33342 Threshold Compactness 60% SER-Valley   True   True   True   \n",
       "Cytoplasm Alexa 647 SER Bright 0 px                  True   True   True   \n",
       "Nucleus 33342 Threshold Compactness 60% SER-Dark     True   True   True   \n",
       "Nucleus 568 Profile 5/5 SER-Dark                     True   True   True   \n",
       "\n",
       "                                                     HYCQ  ...   NELA   SIMV  \\\n",
       "Intensity Nucleus Alexa 555 Mean                    False  ...  False  False   \n",
       "Cytoplasm 488 Profile 2/5                           False  ...  False  False   \n",
       "Cell 555 Threshold Compactness 60%                  False  ...  False  False   \n",
       "Cytoplasm 488 Profile 3/5 SER-Hole                  False  ...  False  False   \n",
       "Intensity Nucleus HOECHST 33342 Mean                False  ...  False  False   \n",
       "...                                                   ...  ...    ...    ...   \n",
       "Nucleus Mito Profile 5/5 SER-Dark                    True  ...   True   True   \n",
       "Nucleus 33342 Threshold Compactness 60% SER-Valley   True  ...   True   True   \n",
       "Cytoplasm Alexa 647 SER Bright 0 px                  True  ...   True   True   \n",
       "Nucleus 33342 Threshold Compactness 60% SER-Dark     True  ...   True   True   \n",
       "Nucleus 568 Profile 5/5 SER-Dark                     True  ...   True   True   \n",
       "\n",
       "                                                     VORI   WURS   CISP  \\\n",
       "Intensity Nucleus Alexa 555 Mean                    False  False  False   \n",
       "Cytoplasm 488 Profile 2/5                           False  False  False   \n",
       "Cell 555 Threshold Compactness 60%                  False  False  False   \n",
       "Cytoplasm 488 Profile 3/5 SER-Hole                  False  False  False   \n",
       "Intensity Nucleus HOECHST 33342 Mean                False  False  False   \n",
       "...                                                   ...    ...    ...   \n",
       "Nucleus Mito Profile 5/5 SER-Dark                    True   True   True   \n",
       "Nucleus 33342 Threshold Compactness 60% SER-Valley   True   True   True   \n",
       "Cytoplasm Alexa 647 SER Bright 0 px                  True   True   True   \n",
       "Nucleus 33342 Threshold Compactness 60% SER-Dark     True   True   True   \n",
       "Nucleus 568 Profile 5/5 SER-Dark                     True   True   True   \n",
       "\n",
       "                                                     IMAT   OLAN   SUNI  \\\n",
       "Intensity Nucleus Alexa 555 Mean                    False  False  False   \n",
       "Cytoplasm 488 Profile 2/5                           False  False  False   \n",
       "Cell 555 Threshold Compactness 60%                  False  False  False   \n",
       "Cytoplasm 488 Profile 3/5 SER-Hole                  False  False  False   \n",
       "Intensity Nucleus HOECHST 33342 Mean                False  False  False   \n",
       "...                                                   ...    ...    ...   \n",
       "Nucleus Mito Profile 5/5 SER-Dark                    True   True   True   \n",
       "Nucleus 33342 Threshold Compactness 60% SER-Valley   True   True   True   \n",
       "Cytoplasm Alexa 647 SER Bright 0 px                  True   True   True   \n",
       "Nucleus 33342 Threshold Compactness 60% SER-Dark     True   True   True   \n",
       "Nucleus 568 Profile 5/5 SER-Dark                     True   True   True   \n",
       "\n",
       "                                                     TEBU   ZIDO  \n",
       "Intensity Nucleus Alexa 555 Mean                    False  False  \n",
       "Cytoplasm 488 Profile 2/5                           False  False  \n",
       "Cell 555 Threshold Compactness 60%                  False  False  \n",
       "Cytoplasm 488 Profile 3/5 SER-Hole                  False  False  \n",
       "Intensity Nucleus HOECHST 33342 Mean                False  False  \n",
       "...                                                   ...    ...  \n",
       "Nucleus Mito Profile 5/5 SER-Dark                    True   True  \n",
       "Nucleus 33342 Threshold Compactness 60% SER-Valley   True   True  \n",
       "Cytoplasm Alexa 647 SER Bright 0 px                  True   True  \n",
       "Nucleus 33342 Threshold Compactness 60% SER-Dark     True   True  \n",
       "Nucleus 568 Profile 5/5 SER-Dark                     True   True  \n",
       "\n",
       "[310 rows x 30 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaled_importance_clean = scaled_importance.copy().sort_values(by=['sum_of_scaled_importances'], ascending=False)\n",
    "\n",
    "mask = scaled_importance_clean.mask(scaled_importance_clean['sum_of_scaled_importances'] < scaled_importance_clean['sum_of_scaled_importances'].quantile(0.5), True)\n",
    "mask = mask.mask(mask['median_rank'] > mask['median_rank'].quantile(0.5), False).drop(columns=['median_rank', 'sum_of_scaled_importances', 'top_20_frequency'])\n",
    "mask"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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kyWLqMIQQwuhOnDhBeHg4efPmpVq1amTOnNnUIQlhNiQhaGKyy7ChmTNn8vTpU3r16vXO72s0Gi5fvmzcoIT4AOh0OllmL4QQZuDYsWPodDrs7OyoWbOmqcPB09OTBg0a0L59e1OHIv6B1NRUtFrp7iTEvzFo0CAiIyOxsrIiJSWF3LlzU6pUKZk3C/H/JCFoYjLMG2rZsqWpQzC6R48ecffuXezt7cmUKROFChUydUhmNUg+fvwYFxcXU4dhwJwm6fpYzOXzEh+OxMRErK2tTR2G+ECZ03XQnAwcOJCIiAiePn2Ko6Mj/fr1M+ncxsfHh0ePHkky8ANx/fp1EhISKF26NFZWVmY1HxPvJtfCtyUnJ2Npafo0w6BBg3j06BGbNm0yiCc5OZnY2FiyZctmwujEh+S/fJ6b/kz9yMkQb8jHx8fUIRjVpEmTePDgAVevXiVTpkw8e/aMgQMH0rVrV5PeqOsnoKa++K1cuZLbt28zYMAAihcvbrI49CZMmMCwYcNwdHQ0dShs376d27dvc/PmTTJnzkzv3r0pVKgQ9vb2pg5NfABGjRpFixYtqFOnjqlDASQ5+SFZvnw57dq1I3fu3CYfI9Izh8SJt7c3T58+5csvv+TOnTuMHTuWn3/+2WQJQX08u3fvVl5LSUnBwsLCJPGIPzd48GBu3LihJJO3bt0qY7oZmzNnDu7u7uTNm9fUoZiVlStXcvfuXbJnz06XLl1MVuhw7Ngxnj17xtatW5XXrl+/zsKFC3nw4AHOzs60a9eOpk2bmiQ+8WEIDg4mPDycyMhIGjVq9J88XsxjFvcRs0RjlK8PSUJCAhcuXODcuXOcPXuWs2fP8sMPP3D8+HEWLFhg6vAyjJeXF9evX2fs2LF89dVXrFq1in79+jF37lzWr18PpN3gGNOyZcuYOnUqw4cP59GjR2i1WqPHkF7u3Lm5e/cuYWFh3Lhxw2RxQFqy+uzZs2aRDBw0aBBffvklWq2WkiVL8vDhQ/r06UNISAjR0dGmDs8sHDlyhLi4OFOHYcCU51J6Pj4+XLlyxWySge7u7qxbt87UYYi/ITw8nJMnT+Lj48Pjx4/RarWkpqaaLJ5p06bh6ekJpLUUMeU5NnDgQGJiYggLCwOgUKFCVK5cWfm+sWPz9PQkLi5OiQfg4cOH+Pv78+rVK6PGYq7Wrl3L1atXTR0GkJa8ffz4MYGBgfj7+/P69WuTXxfNZcwyR48fP+bkyZN89913ACa9DpoTT09P9u7di42NDWFhYSxatMhkscTFxZGUlATA3bt3CQoKokOHDlhYWPDFF1+QJ08eTpw4QUJCgtFji42NNfrvFP+cj48PBw4cIFu2bGTNmpVMmTKZOiRVSIWgiX1YqTr1nTlzhsGDB/PixYt3fj9z5syMGDHCyFFlvBUrVhAdHW0wUc+WLRseHh5YW1uzYMECqlevTqVKlYwWk6enJ0+ePMHV1ZXbt2/j7e3Nxo0bTdKEXF/p0aZNG+zs7AgICCA1NZUuXbqYpFJw6NChREVFcejQIcC0FRYjR47k8ePHBscOwNy5cwkMDKRIkSJ88cUXZlW5Y2x9+/YlLi6O+vXrmzoUIG1SmilTJpNXL0HaTWd0dDR79+41dShA2mQrMTERb29vg9dNdfwGBARQu3ZtsmbNSq5cuUwej7nJnz8/Y8eOZdWqVXh7e7Ny5UpcXFxM9v7UqVOH0aNHM3bsWGbPnq0kBY19ro0ePZpLly7x9ddfK6+Fh4dz8OBBhgwZAmDUmHbt2sXx48cNKmMiIiJo3749AwYMkM1FgAEDBvDixQv69+9v6lCUSk79uJ4zZ05KlSpFzpw5TRLP6dOnqVmzpsnOpw+Bi4sLpUuX5tChQ3Ts2FHGB9KO45iYGA4cOABArVq1mD17NtHR0Tg4OBj9OLKwsOD169f07t2buLg4YmNj8fX1pV+/fgDs3r2bL7/8EisrK6PGNW/ePD755BOaN28uFdvvYQ7XnfHjxxMVFcWWLVv+9OeMGatacy25epmYFo1Rvj4UixcvJnv27CxdupQGDRrQqFEjVq1aRdeuXdFoNKxdu9bUIWaI8PBw2rZtC6A8vYK0i0rTpk0pUaIEt27dMlo83t7ePHv2jB07djB58mR69epFUlKSQWzGlL7So3HjxvTo0YOLFy+yefNmrl27ZtRYhg8fzoEDB2jRooVBfKZw/vx5Hj9+zKpVq4C0HijJyclA2g1ppUqVWLFiBYBRJ6djx45VqlpNbeDAgSQnJ7N582bA9BUOw4YNw9fXlxkzZvD48eO3ngobs6rAy8uLV69eGUxuHj9+zNatW5XjyJi8vLx48uSJQXJb//6Y4uYqJCSEefPmERAQQO/evdm0aRM///yzQTwfcxWI/t9eoUIFPD09yZEjB15eXiatFPzss89YvHgxR44cYfTo0YBpKgW1Wi2ZM2fm5MmTQNp51b17d9q0aUPz5s2NGgukfUatW7dm1KhR3Lp1i9evX9OtWzfc3d3p06eP0eMxN15eXrx48eKtGz1TjBdjx47l0qVLBtfBBw8e8Pvvv5MnTx6jx7Nnzx7mzJlDSEgIYPrKW3Okv9Z5e3tz9+5dTpw4YeKIDJni83rXOWVtbU3t2rXfmQxUK8YHDx5w584dABo1akSrVq1wcXGhZcuWTJs2jX79+im/OzIykvz58xt97Lp69Srbt28HMHky0N/fn8OHD5s0hjclJiaaPBn4+++/ExkZyfLlywHeuh8+d+4cw4cPB4x7T6jWsSoJQROzMNLXh+LatWv4+vrSsGFDPv/8cx48eICbmxsTJ06kffv2+Pv7mzrEfy0mJoYTJ04oT6TSP5nSaDS4uLiQnJxMeHi4UeIZOHAgL168MJiM5syZE3t7e5KSkow6sQgODmbr1q1cunSJq1evKpWizZs3Z8iQIVy5coUdO3Zw8+ZNo8Tj5eXFrVu36N69O8ePHycoKAjAZDe/jx8/5vXr19jb2ysNm9M3Se7RowcuLi7s3LmTkydP8vLlS9VjSkpKokCBAsyfP19JwpnKqFGjOHLkCCtXrgTg9evXJp1UREZGcvXqVfLly8ejR49wd3dnyJAhHDhwgIcPHwLGSzQFBgZy7Ngxg2vo/fv3adWqFUlJSUZv/j1o0CCePXtmcN25f/8+ffr04fLlyya5oalWrRr58uWjXLlydO/enb179zJ58mSGDBnCvn37ePbs2UdZBRITEwMYXvcqVKiAt7c3zs7OjBgxgujoaKO+N/o4NBoNNWvWxM/Pj2PHjjF27FjldWPGMXv2bGrUqIGfnx8bN26kQ4cOdO3alYEDBxr8nNoWLFjAtm3bKFiwID4+PpQvX56+ffvy6aef0rdvX6PHY458fHyIjo42SFw8efKEO3fuGH28ePDgAS9evCBv3rycO3cOSKvk7Nq1K61atTJJlXvVqlWpUqUKR48elaTgO+grdHQ6HTlz5qRYsWJcunQJMH4i7ty5c4SFhbFhwwYOHDhAYmIiYPzPa+7cuZw5c4YNGzYorz148IAFCxZw6tQpVqxYwZo1a9i3bx8nTpxQLeFz4cIFvL29ad68OdOmTUOr1dKrVy9mzZpFt27dlFVXGo2GwMBA1q1bR/fu3Y1WIai/7urbOejbFZjq3AoPD+fy5cusW7eOU6dOmSSGNw0bNozAwEBTh0FkZCTh4eFYW1uTmpr61jGiv0/Vn3Nqmz9/PoMGDWLQoEEcOXIkw//+j29ma2akQtBQamqqskyrcOHCBomfL774gsuXL5sqtH/tp59+IikpCTs7O4oVK8bjx48Nvp+amkpqaiqJiYkUKFCAGjVqqB5TaGgoJ06cMFgyEx4ezoQJE/jiiy9wdnY22gT5u+++Y+bMmUyaNIlx48bh7u5O27Ztady4Md7e3ty6dYvU1FSOHTvGqlWruHfvnqrxjBo1isjISHbt2sWIESPIly8fx44dUybIpkgKRkZGEhMToyQC9ZOI6OhoTpw4QVBQECdPnmTr1q08evTIKJ+dlZUVAwYMYOzYsUydOpVNmzap/jvfxdvbmwsXLlCjRg0GDhxIdHQ0NjY2pKSkmCQeAGdnZxo2bMi1a9dYvnw5s2bNokaNGgwdOhRvb2/Gjx/Pb7/9ZpRkStGiRalevbqSEHj16pVSMdStWzdVf/ebhg8fzsmTJ5VzCdJugrt06ULdunX55JNPlGPXWBPllJQUChYsSK1atUhMTKRbt24EBgZib2/PqVOnWL9+PS1atGDatGn88MMPRonJHMyZM4clS5bw4MEDwPC6V758efr164eVlRVHjx41SjwnT54kJibGoL+tRqOhVq1aLFq0iJMnT+Ln52eUWJYtW8aUKVMYPnw49+7dY9KkSVSuXJm5c+dSoUIFPDw8gLRjyxjJ0u+++45169axc+dOvvzyS/LkyYOvry+ffvopWq2WevXqAWkPcT7GxDbAiBEj+Pnnnw0eRERERNChQwfOnz9v9Hjy5MmDp6cnxYoVw8/Pj507d9KzZ086deqktFEw1jwjMTGRV69ekTt3bgYMGEDhwoU5fPiwWSQFzSEZuXfvXm7fvq2cOxqNhqxZs9K0aVM2bdrEvXv3jJpQHjFiBMuXL2fXrl2cOnWKoUOHMnDgQE6fPq3EZwz665urqysBAQFA2py0Y8eO5MmTh8qVK3Pt2jV2797NmDFjGD9+PE+ePMnwOPQrIEaPHs2kSZPYtGmTMi5ptVouXbpEly5dGDVqFJMnTyYgIIANGzZQokSJDI/lffTHTrFixXjx4oWS2DHFg+uEhATy589P//79KVasGMuXL1cq3E3Fy8uL+/fvM2DAAJPGAfDy5Uvlfiv9vEf/v9myZcPGxsYo1yYvLy/Onj1LqVKlSEpKYsWKFURERGTo7/g4ZwRmRKszzteHokCBAsqS0IIFCxIfH8/vv/8O/LFF/Ifo1q1bzJw5EysrK2xsbChfvjzr1q3jwoULys9otVq0Wi1btmzh7t27FClSRPW4ypYtS8OGDdm6dSvnzp3j2bNnuLu70759e3r27AkYbyJWunRpJk6cSMmSJalQoQJHjhxhzpw5NG/enFy5cnHkyBFev37NkydPOHXqFLa2tqrG06NHD3bs2AGAra0t3t7eFC1a1GCCbOykYJkyZUhJSWHv3r2kpKQoE3R7e3vu3r3L+fPnadeuHf7+/nTo0EHVPlH79+8nNDSUX3/9laSkJHr27MnYsWOZNm2a0ZOCw4cPJzIykkOHDjFq1ChSUlLw9fXl5cuXWFhYmCQpqD8u2rRpA8DNmzepUqUKDx8+pEiRIrRp04aff/6Z3r174+3trXo1bq1atfD19SU5OZl27drRuHFjevXqZZKKoSpVqhAfH8+2bduAtI0OunXrRqdOnZR4AA4fPszZs2dVjUV/DbawsMDGxoYCBQoQHBwMwMKFC4mLi2Pnzp34+fnRpk0bLl68SO7cuVWNyZyUKFGCY8eOsWXLlreSghqNhipVqqDT6Yyy6dPBgweZPn06u3bt4sWLFwYJCn1S0Nvbm59++kn1zZXOnTvHN998g5OTE6dPn1YqE6dNm0aXLl24ffs2X331Fa9evTLakrA8efKQJ08enj17xvHjx5WkoIeHB/Xq1aNPnz78/PPPWFlZmUWCxRTy5MnD8+fP+fbbb4G0ZGD37t1p37497dq1M2os+iVo5cqVo2PHjhQsWJDp06dToUIF5TqYnJxslOTtjBkz8PLyolOnTpw6dQoXFxcGDBjw1pzHmEnB4OBgg4SJKY/ZEydOMHfuXNq2bcvixYs5duyY8r1GjRpRqlQpfvvtNwCjzDd8fX0JDw9n+fLlhIaGsn79enbv3s3du3fx9/fn0aNHqsegZ2Fhga+vL1WrVuX7779nxowZtGrVij59+rBu3ToWLVrEsmXL2LVrFydPnmTnzp0Zviuzvjfyp59+Ss2aNWncuDGVKlUymAPnz5+fHDlyYGFhgZOTExs3bqRMmTIZGsffkZKSgqOjI97e3hw9elQZV41p8eLFNG3alJcvX+Lq6krHjh0pUqQIK1asMFlScPjw4YSHhysPa0zVskqvfPnyODk5sXDhQhISEtBqtQYP9+7evUvJkiVVH9/1x/bWrVvx9vamU6dOPH/+PMPHBdlUxMQkI2uoRYsWLFiwgNTUVNzd3SlbtiwzZszA3d2dVatWUaxYMVOH+D+xs7MzSGANGjSIGzdu0L9/fwYPHkzFihVJSkri9OnTrF+/nqCgIFxcXFSPq3z58vTu3ZuAgACWLVvGpUuX8PLyUhruGqNRvH6S5+joSJMmTdDpdKxdu5aCBQvSt29fqlSpYvDzL1++JCkpSbXdfh8+fMju3bvJkSMH8Ec1hZOTE56enqxatYrDhw+j0Wjo1q2bqu/PyZMnuXfvHlu3bqVevXqUL18eFxcX1qxZQ+bMmfn888/RaDRYWVkRFxeHjY0Nnp6eODg4qBYTpC0zv3XrlrIkeerUqdSvX5/OnTsDaTfFAF27dlU1Dkg7fkqXLs3ChQuBtCevgwYNYvny5crnZW9vb/TGzfrjIn/+/NjZ2bF7925SUlI4c+YMAQEB5MqVix49evD1119TunRpVZasxMfH8+uvv5KSkkLhwoWpUqUKo0aNYtGiRTx+/JhevXoBaZUh1tbWGf7736dLly6kpqYydepUHj16xN69ew0qYiCtennWrFl89dVXqsVx584dFi9ezIYNG5Tjo3fv3ly4cIEvvvgCa2trli5dSoECBYC0Cat+g5iPRdu2bZWNrnQ6HV26dCFPnjxotVqlbUHRokUz/AbvXRo3bmywcUfbtm3Jli0bOp0OnU6HVqslV65cJCYmYmNjo1oc3t7eJCYmsmfPHgBq1KjBkCFDuH37NoULF2bChAlMmjSJVatWkZCQQNOmTbGzs1MtHr3ChQvTr18/jhw5QkpKCjt27ECj0dCyZUt8fX1ZuXIlHh4erF+/nnLlyqkejzk5fvw4derUYdiwYWg0Gry9vRk3bhzr16+nQ4cOBg9G1J7zPH/+/K0laOXKlVOSXhEREZw6dYratWsbpZXDuXPncHJyokmTJly4cIFhw4axe/dupXrxzTmPMaqZXrx4wa5du8ibNy82NjbUrl3bZBucXL9+HTc3N3bs2MHx48c5cOAAO3bsoGzZsrRo0YIvvviCcuXKsWnTJpo0aaL6PGPHjh1ERUURHBysjNvJycmUKlWKlStX0qJFC/bv3696r9DXr18THx/P/fv3yZs3LwMGDMDS0pJt27ZRoEAB5fcnJSVhZWWFpaWlKnPTQYMGERUVxdatWxk6dCiQtgJCX9kVGxuLhYUF2bJlU/rBGfM4mjVrFtWqVaNYsWIUKlRIOT6KFi1KUlIS9+/fJ0+ePEbdmKtLly4cOXKEvn37sn79elxdXQHYsmWL0oe8Tp06RokF0j7Dr7/+mjZt2vDo0SNcXFyMvtHLm3LkyEG9evX49ttvWbZsGYMGDVLmFaGhoWzZsoXg4GBVr9Hpj209Z2dncuTIkeEJU8lHmZj0EDTUr18/OnfuzK+//grA5MmTuXLlirJkdNSoUSaO8H9jZWXFgwcPiI2NJSkpCWtra+bNm0eTJk1Yv3493bt3Z86cOVy4cIGQkBA++eQT1WK5desWkZGR7N+/n/v37+Pq6oqvry/ZsmUjV65cBgk4tQfMN3u8OTo60rRpU/r27UtoaKjSC07/swD29vaqJQO9vb25fv06QUFB9O/fX9l9zMLCAp1OpyQFS5QowY4dOwwu0hltyJAhypPebdu2cfnyZaytrVm1ahUWFhbMmTOHHj16MGbMGEaMGMHWrVtZs2YN+fPnVy0mSNvY5OnTp+zYsYNTp06RK1cudu7cCYCNjQ1dunRh7NixzJo1y6CfjBrc3d2JjIxUEtj6c6ty5cr4+PhgaWmJp6enySoFU1NTsbCwYODAgWzYsIHvvvsOf39/cuXKpQzmjRo1UuUzmzJlChcvXsTX15c+ffowYMAAVq1ahaurK8OGDaNgwYJ069aNmJgYrK2tVX9vAgMDuX79OqtWrSIpKYlu3boxefJkVq9eTYECBd5KBi5dupStW7dSqFAh1WJKTU0lPj4e+KOxt/48j4yMZNOmTRQuXJjU1FTlwYUxEjv62Exl+/bt3L17lz179pCcnEzz5s0ZOXIke/fuZfPmzcpSFUtLS0JDQzl48CB169ZVNSb9+z9ixAgqV67MoUOH2LFjBzExMQbVQ7///js5cuRQbezy9PQkPj6emjVrKq/FxsZSpEgRsmbNqpxH06ZNo0SJEoSFhRnluqP/95ctW5a8efMyZswYihUrxtatW9mzZ4+S3GnatClZs2ZVPR5z8vDhQxYuXMi6detISUlh6NCh9OvXj2nTplGmTBmjJgPHjx/P999/T4cOHdizZ49BG5yyZcvSqVMn8ufPz8qVK42yDF+f3N65cyf9+/end+/e5MiRQzlmnZyc8PDwoHjx4qrPefSuX79OQkICc+fOJS4ujrCwMKW/mUajMbg27t+/32ClTUbz8fHh/v37QNquwp06dWLRokUEBgZibW3Nli1baNGiBU5OTty8edMoy87v3btHsWLFsLa2VuYRlpaWJCUlUaxYMbp168Zvv/2m6nVn4cKFjB49WjmWx4wZw8WLF/H09KRTp05YWlqybNky4uLisLKyUuZCGW3r1q0cPXpUebgJaW2PunTpwo8//sjixYtp3LgxPXv2xNPTU9n8zljVpvpVbvPmzaNPnz6sWbNG6TdZpkwZqlSpwtKlS43axiE1NRUXFxc2bNhAfHw8vXv3VioFO3fuTJEiRVi1apXR2oAMGDCAly9fsn37di5cuICfn5+yMtBUdDodlpaWeHt7U716dU6ePEnTpk3x9vZm8ODBBAYGEhAQQPHixVWLITw8/J3Hto+PD40bN6ZgwYIZ+vukQtDEPqT+fsag1WqVnQIh7anp4cOHuXXrFkWKFFF1CWRGS1+RpH+KZ2NjozxNyJYtG1OnTuX+/fskJCTg4uKCVqtVtfpk3LhxREZGcunSJZYtW8ajR4/o3Lkzffr0Yfjw4fj5+eHv70+nTp2U6jO1DBo0iJs3b5IpUybOnj1L1apVgT+SggAbNmzA0tKSAQMGqFrxASh959zc3Jg8eTKzZ89m9+7dNG3aFCsrK+Wm08nJib59+2JlZUXt2rVViWXo0KE8evSI0NBQRowYwfz587G0tCQ1NRV7e3u2bNnCV199xYULF0hKSqJq1aoMHjxY9WSgt7c3r1+/NmjG3qhRI4Ofsba2pkuXLsTHx+Pv70/btm3JmjVrhh9Lvr6+xMbGGgyKVlZWynmnTwrqdynfvHmz0a8f6asES5QoQYsWLcifPz8pKSmqPv309PTk+fPnfPLJJ8yePZvnz5+zefNmtm7dSnx8PEOHDmXYsGGsWLECd3d31d8bLy8vIiIiiIqKYseOHTx48IApU6bQpUsXbGxsGD9+PIGBgfTq1YtNmzaxZMkSNmzYoNpyHn1FpJ2dncEkXD8J7Nu3L9988w2nTp2iSZMmBj+j9kOSCxcuUL58eaVHnrGrYby8vIiMjCQiIoKNGzfy8OFDPDw8aNasGRqNhqVLl/L06VMcHR2xtbUlNDSUdevWqZa41b8H+kSAVqtlxIgRLFiwgG+++Ybk5GTatGmDk5MTmzZtYu3atWzcuFGVcXTv3r0cP36cb775hkWLFgFpS06nTJlCr169lKpy/TVo4cKFPHnyRLVza8eOHZQvX55cuXIpx4mrqyt+fn6EhIQwceJEJk6cyK5du0hMTKR9+/ZMmDDBqBsIbdmyhUePHpE3b15q1qxJvnz5jPa79XLkyEHVqlX58ccf0Wq19O3blyFDhmBlZYW/vz/ffPMNDRs2NMpqCGdnZwoWLEjt2rXZtWsX165do2nTpri6utKyZUsqVKhAjhw5mD9/PqGhodSoUQM7OztVrgO+vr5ER0dTq1Yt5bXMmTNTsGBBg/PH2dkZDw8PgoKC+PTTTzM8jvQ8PDx4+vQpzs7OODs7Kw8Xw8LC0Ol01KlTR/mc9ElefcVwRtMv1dP33tRzdHTE0dFRWUoYFBTEDz/8gE6nU7WdhD6J9fPPPyt979LPI/R/fvXqFa9fv1atUlH/kFU/Z+7evTtLlizhzp07+Pj40KdPHxITEzl//jxxcXEMGTJEtfn7559/znfffcfu3bvJlCkTcXFxdO/enYYNG1K/fn2SkpK4evUqT5484bfffsPNzQ3AKMm3uXPn8ujRI3bv3s3vv//O999/ryyDz549OyNGjKBOnTokJiby8OFDZSWC2vT/9pw5cxIQEECvXr3o2bMnQUFBuLq6otFoWLdunerXH0hL6D98+JC9e/cCaa0LxowZw/r16+nbt69RVgWuX7+eLl26GLymn2/Y2toyYsQIrl+/zvHjx3n16hXFihVj5MiRqo9lLi4u5M2bVzm2ixYtSo8ePejYsaNSfZuRc0RJCJqYpAMN9ejRg8mTJ1O0aFHltSxZsuDq6srVq1eVKgVzN2zYMCIjI8mSJQu5c+fm6dOnREVF8fXXX1O5cmUsLS2VmwcnJyejLNfTT26WLFnC1KlTWblyJStWrGDv3r3ExsYydepU+vbtS2BgIOvXr8fKykq1kvGUlBQePXqEhYUFT548YejQodSoUYOqVavSsmVLcubMqSw/XbJkCRYWFvTt21eVWCDtJvjly5ds3ryZYcOGkT17dnLlyoW1tfVbu0Drn64NGzZMlQnXsWPHePbsGRs3bsTCwsLgyapWq+XcuXO8ePGCdu3aGbXn0ciRI7l586bBzYO+0rRs2bKcOXMGrVZL2bJliYuLw9PTk86dO5MtW7YMj8XLy4vo6Gh27typLBHRJ3nSv1dVqlShb9++BAcH8/z5c9VuzI8ePfqnySsXFxeaNm1KSEiIUk2gFn3CZtu2bQwdOpScOXOSM2dOvLy8sLGx4ciRIzRp0oSKFSvSt29fQkJCVH1v9J/V3r17GTx4MI6Ojty6dUvpxdK2bVsSExOZOXMmJ06c4Nq1a6omA+GPBzR2dnZERUXx/Plz7O3tlSRc1qxZqVatGufOnaNJkyaqxfGm8PBwpk2bRu/evWnevPlfLpHL6ISht7e3Uv07aNAgChQowO3bt4G0a3bTpk1xcHDg+PHjXLlyhcKFCxMcHKzqpF2j0SiVLvrehfqk4PLly5WdLatXr87ly5cJDAykVKlSqsRSpUoVGjVqRIcOHahcuTKvX7+mW7dudOzYUXmKr9PplGpkfa8qNYwdO5Zdu3ZRoUIFbGxs0Gg0REVFkTNnTiZOnMjSpUt5/vw5EyZMYOTIkRw5coSmTZsa9aGI/kFAvnz5CAgIoH///vj6+hrt98Mf48Lw4cPp2rUrO3bsQKfTKTsuJyYmMnLkSGbMmEHz5s1VjUWj0VCmTBnWrVtH165dGTp0KJcuXaJ9+/bY2NiwevVqmjRpgpubG15eXuTIkUO1B8Rz587l6NGjHDx4UElu66tPIiIiWLRoEU+fPiV//vw4OztTp04dhg8frkoset7e3jx//lypvk1NTaVYsWKMGjWKefPmKdWJderUYenSpYSEhLB161ZVHoTqx62wsDBljvH48WOOHz9Op06dSE1NxdLSkixZsjBw4EBevXpFamqqqtW3+mt95cqVOXPmDHfu3FEexOjHU41Gg62trdISIKPHiBkzZijzC4Bt27bRqFEjChcuzKhRowgODqZ27dr079+f2NhYbt++TWxsbIYnBMeNG0edOnVo0qQJkyZNYurUqaxbt46ff/4ZLy8vfHx8lJ/VV68b8wHbuXPn2LBhA/b29mzevJkWLVrg7u5O/fr1efToEUuXLmXu3Lk8ffpUGUvVvMdZtGgRWbNmJTw8nF9//RVHR0eyZcuGk5MTmzdvpk+fPnh5ebFixQrKlSvHgAEDcHJyUr09SuPGjZUCkMTERCpVqsSCBQsYMWIEgOpJwejoaJYvX86pU6ewt7c3+J6+Z6CVlRVlypQxar/J2NhYrK2tmThxonJsX7t2jZ49eyrHdkZXtEtC0MQsP86+zgbOnTunPPn68ccfOXv27Dsbgh87dozw8HBjh/ePxcfHU758eSwtLbl06RKPHz8mIiKCuLg45WZdq9WSJ08edDodlSpVYty4cVhYWKg2WG3YsIHo6Gg2bdqEVqtVBueBAwdibW3NihUraN++Pa6ursqkWa2LsP6GqX79+sTFxXHz5k2GDh3Kli1bmD59OkFBQdSvX5+GDRvSoEED7OzsqFatmiqxACxYsEC5wdWLiIjgq6++MljGqKe/AKv19PXBgwc4OjpibW1NYmKiMsnT6XQkJyfj5+dHkSJF+Pzzz5X/Ru2Jzr1793j48CF58uTh1atXwB/LMl6/fo2lpSVfffWVkjAtVKgQmzdvJnv27Bkei/6pdPpdIu/fv8/ixYsZNmyY8oRe/57UrFmTSpUqqbYJzYkTJ5gxYwbt2rVTlrWnp4+jYcOGbN++nbNnzyoToIyWnJzM7du36dGjB/DHslOdTkeOHDno1KkTO3bs4LfffqNUqVLUqlWLypUrq/bevPlZabVaKlWqBBhWNnTu3JnU1FRmz57N1q1bKV26tCrxTJkyhWvXrlGwYEEKFCjAgwcPlI2K4uPjlUore3t7atasyfz58xk2bBiZM2dWJZ436SfoX375JYCSFHxz4qfvVZeR57ynpyeJiYnKTbeFhYVSYerl5UV4eDhTpkyhZs2aVK5cGWtra6WHoBpWrlzJ/fv3mTlzptKyAf6YpFtYWODj48PVq1e5dOkSTk5OjB49WtUKndy5czNz5kzGjh3LsWPHqFu3Lj4+Pri7uwOGE3S1+4g1bNiQXbt2KXOJ/fv3M3v2bCpWrEizZs14+fIlJ0+epFWrVsyfP5+XL18aNRl4/vx58uTJozzAXbRoEV9++SW9e/c2WhwpKSlK8j8wMJC7d+9Sv359vv/+e3Q6Hf369WPo0KHExcUxc+ZMPvvsM1ViS9/jskGDBjg6OrJ79248PDzYv38/JUuWZO7cuYSFhfHNN98QGhrK119//dYNakaqW7cuhw4dIiQkhMTERB49ekSXLl2oWrUqnTp1IjY2lkePHnH+/HkiIyPfWgmQ0fTVilu2bFEScPqdaEuVKsW4ceOYNWsWu3btIiwsjG+//ZZNmzZRtmzZDI/F09OTV69evbUTdefOnfHy8gLerjBT85iOjY0lPj6enDlzAlCtWjVCQ0PZsWMHnTp1Il++fMr1Jjg4mOPHj9O7d28gYyvanz59yu3bt5VVXPrlyqmpqRQvXpxRo0bRu3dvzp07R5UqVRg6dCivXr3K8BY/iYmJWFpaMnz4cGxtbalXr56yqke/aaOevn+hseXLl4+iRYvy4MEDTp8+rVSy6zd+CgwM5LfffuPXX39l+/btqvbr+/7771mzZo1yzE6cOJHbt2+TM2dO8uTJg7OzM5UrVyYoKIiBAwfi7++vevLr4cOHwB/nkb7dT2pqKq6urkpSUKvV4u7urspDPp1Oh6OjI3v27KF3797ExcURHx9v0BZGf17t27dPeWCk5v3WmjVriIiIIDw8XIlvypQpzJo1i6dPn1KhQgXlZzM6BkkImphUCKb1K9q9e7eyLGjq1Klv/Yz+ZkDtJ7gZwc7OTtmhV8/Ly4unT5/Srl07ypQpw6NHj3jw4AG3bt2ia9euqi/huX79Op999plyMwV/3Lz079+fw4cPc+DAAVxdXalcuTLlypVTrWpRfxGrVq0affr0oUKFChQrVozY2FjKli1LxYoVuXjxImvXrqV58+ZMmTJFtclWYmIiWbNmpUKFCixcuJDhw4fz+vVrunbtSqdOnWjbtq0qv/fP/P777zx+/BhIq2RK3+vEysqKAgUKvLXbtprJwOvXr1OiRAkGDhzItm3b+OGHHzh69CgzZsygU6dO+Pj4kJycTGRkJAkJCfz8889UqVJFlV5rgwYN4tq1axw+fFh5LT4+no4dO+Lu7m6QDNC/J/on5mrQLzHv3LkzR44cITY2lsjISJydnQ2WOkJak/T8+fOrcgOj9/LlSy5duqTsbJz+PYC0Xdzz589PVFSU8rpa7837Pqu1a9diZ2fH/v37SUhIoGDBgtjb29O1a1datWqlWvLt5cuXZMuWjfLlyxMbG8vJkyeJjo5Wlj69evWKbNmyYWFhgYuLC1988QUHDhwwWjJQX5k4YMAA1q5dy65du9DpdLRo0cKgMm7RokVcvXqVRYsWZdh18dy5cxw/flzZKRcgLi6OKVOm0KRJE8qWLcuhQ4cYPXo0Bw4cUMYGtcYtffXNrVu3mDNnDmPGjFEqBS0sLJTKaa1WS8mSJVWrCIS0fpYRERE8efKERo0a0ahRIyWxfP/+feVcM9bGPJMmTWLo0KF8/vnnrFmzhgEDBtC0aVPu3btHpUqVWLZsGdeuXSM2NpZVq1ZRtWpV8uTJY9SNcHx9fUlMTDRoLVGsWDHKlCljlAqdKVOm0LhxY2rUqAGk7fB+7tw59uzZoyzHPXXqFBqNhj59+jB+/Hi8vLxUm2foxwH9mJA9e3Z+/PFH7ty5o7RvKVSoEJMnT+bp06dKaxC16HQ6atasybRp05g0aRI6nY4OHTrg7u6Oh4fHWz8fGxur6nVw0KBB/PTTT3z33XfKaxEREbRt25ahQ4fSpUsXihUrxvjx4xk7dizh4eFs2bJFlV7bv//+O8ePHzeo1oqLi8Pd3Z3OnTvTrVs35fXHjx+TM2dOVZP/s2bN4tq1a9y7d49MmTJhZ2dH9erVGTNmDJMnT+batWt89tlnaDQaHjx4wI4dO5RN+TJaTEwM165dI0+ePMAf1399UqdcuXLkzp1bmZ9aW1ur0u/b2tqa0aNHky1bNry9vVm+fDn169dn3LhxnD17lg0bNvD8+XOl3Y+esaoDdToduXLlwtvbm9mzZ2NlZaU8MG/Tpo1yLumrztq1a6fq2FGrVi0mTJjAl19+yatXr5g9ezbJyck8fPiQ3377jYiICO7fv0/OnDm5ceMGL168UPX6M2jQIH744Qdy5szJiRMncHNzM/icUlJScHV1ZdGiRUp7pnHjxmX4e6TffCx//vwEBgbSokULPD098ff3Nxgv/fz8WL16NeXLlyd//vyqHUfe3t7ExMTw6aefYm9vz88//wyAg4MD48ePZ8qUKaxbt46YmBiaNm0quwz/18iuLmlNltu2bYtOp6Nnz55MmjTpreo0rVZL1qxZVW3g+W/Fx8cTHh6Oo6MjWbJkwdbWVrlpsbW1xd7ensTERMqWLatqUiC9Fy9ekDlzZn755Rel353+Ypa+R9Wb5fxq39jodDoqVqxImzZtOH/+PKNHj+bXX39lw4YN5MqVi1evXnHx4kVcXFxUm6TrdDqsra3p2rUrdnZ2HDhwgLFjx/Ldd9/h4+ND//79AeM0Gp85cyZubm7Url2b8uXLc/r0ab7//ntq1aqFRqN5qxJHzU1n0uvTpw8FChRQKoM0Gg3nz59n8ODBtGrVSln+ZWlpqewymn65f0ZKSkoic+bMPH/+nKtXr1KqVCnCw8M5ffo0Hh4eeHp6Kj/7ww8/ULhwYZydnVWJBaBXr16UKVOGkSNHMmDAAADWrVvHtm3b6NChg8HvnjVrFmfOnCEwMFC1DXHgjx6KV65coW7dukp1mf6GNCEhgWzZsik7yqnl5cuXODg4vPVZff/99+TOnRtnZ2eWL1/O/fv30Wg0ZM2alT179qhSUQqwc+dOGjVqpFSeQNrxNGLECJ49e0bFihWpX78+t27d4vfff+f58+eUK1dO1c9KLyEhAVtbWyXxX6RIEfr168e6devYvXs3Op2Oli1botVq8fPzY926dWzdujXDrovR0dFUqVKFSZMmMXPmTDJlykTr1q05c+YM/fr1U85x/WeWkJCg6vgQEBBAkyZNlOvyV199xaxZswAMluJqtVrWrVtHamoqAwYMUOWpvY+PD9HR0VSvXh1ra2vlGpwzZ06KFStGqVKlqFevHlu2bKFo0aKq72Tu6+vL9evXlZ0669aty9KlSxk0aBBFixalW7duNGnShO3bt/PkyRPu3LljtE1w9JYsWcI333xD48aNldcePHjAokWLGDBggOoJ9tjYWF68eIGvry87d+7k8OHD3L17l3379iltWry8vFi7di0HDhzAysqK3r17q3Ku6/ul3rx5k/79+/PJJ5+g0WgoVKgQFy9e5ObNm4SFhZErVy7l2NHHqIbo6Gju3buHra0tefPmpXbt2sydO5f+/ftTuHBhunbtqvxs+gS32snkvHnzcvz4cQ4fPkyDBg2Ij4+nW7du9OrVS+ntlZqaStGiRVm4cCGWlpaqVQIXLVqU1atXM3jwYGxtbenYsSNnzpxhwIAByuYzkHadOnDgAIGBgaod056enkRHR+Pl5YWtrS27du3i66+/ZsqUKUyePBkHBwf27dvHhg0byJMnDyVKlCA4ODjD52CBgYHUq1cPGxsbEhMTuXnzJnny5FGS3Po5ckxMDHny5FG1F57+PMmcOTOenp6kpqbi4+PDihUr+PzzzylTpgx2dnYEBQVhZWXFF198oVos76Mfi4oWLYq1tTVDhw5l27Zt7N27F41GQ+vWrcmcObPyb1GrgnHWrFlcvHiRzZs30717d168eEFQUBDHjh2ja9euuLq6Grw/+g261Gjzk56VlRXVq1fnp59+YsmSJfj5+dGxY0eqV69ucOyWLVuW9evXY29vn6FzjsDAQCIiIjh58iRFixalVq1adO/enWrVqnHr1i28vLyUpKCfnx9BQUHs2LFD1R7t48aNUyqk9QYNGsSNGzd4/vw5VapUYfr06UyZMoXAwEBVjm1JCJqYJATTdo3VLwkdPHgwlStXpmTJkiaO6p+ZPXs29+/f57vvviNXrlxYWFgwdepUKleurPyMRqMx6s5JEydOVHryFSxYkAMHDlCvXj0yZcqkLGHRc3FxoWLFioBx+mzo//5ixYoRGhqKhYUFwcHBypKIzJkzG+ziqFYMqampZMmShTZt2qDT6QgLC8PGxkZJBhqj6uPRo0fs3LlTubjXqFGDlStXsmbNGiVRq78RDQ0N5bvvvjNIfqlFXzU1ZcoU5bUaNWpQoEABypcvz6NHj7hy5QqlS5dW/ZhZvnw5TZo0Yfbs2QD07NmT6dOnM3fuXPLnz2+wtDs4OJg5c+YYVKZltIEDBxIXF8fIkSOV1wYMGKBsOqDT6ejYsSPOzs74+fmxdetWNm7cqGqCSafTkSVLFjp27MjWrVuVhyfpk9lbtmzhyZMnqiVtASZMmED16tWZOnUqiYmJBp9VwYIFlSWESUlJJCQk8PDhQ7JkyaJaMrBv377Ex8e/Ve2rP16fPn1K8eLFcXV1VRKlxuo1NH78eKUpe/qHYEWLFqV3795s2LCBL7/8Ejs7O6W34rZt2zJsOY+vry/58uVj9OjRtG/fHkirrpo0aRKlSpXC19dXudm7cuUKmTNnVrWavX///rx69UppmK2/Lu/fv59nz54Bfyzh0SdHd+3aBWR89ce4ceOIiooymKCnZ2try5w5cxg9ejTNmjXj66+/VvVGWN8D+NChQ8prKSkpNGrUiGXLluHj48P8+fMNHlA8efJESR4ayxdffMHWrVv55ZdfgLSlYV26dKFDhw5Kwkmt82vChAnUqFGDRYsWMWbMGBo2bIi9vT2ffvopBQoUICUlBY1GQ5YsWejXrx9WVlaqLYX18fEhJiaGQoUKkSVLFm7evEnZsmWVGJo1a8bdu3cNkoFqmjp1Ko8ePeLHH38kR44cPH/+nDFjxtCmTRsqVKjAvXv3WLhwIT169KBIkSIG8x61roVbtmyhc+fOjB49GisrK4YPH87w4cM5c+YMHh4eyvJc/TVIp9OpvnEagJubG0uWLGHQoEGsWLGC4sWLGyQDQ0NDWbNmDWvWrFEtGejt7c2zZ88MdnWuWbMmV69e5fTp0wQFBdGnTx/c3NxITk5Wlltm9MNr/TW5V69evHr1iooVK/Lll1+SN29eihYtikajUX7ngQMH0Gq1qsxz9HPN9NXhmTNnVuZ+I0aMwN/fX9mkbMGCBUofRWMIDg6mfPny5MuXT/n3lypVitTUVJYuXcq8efOYPXs2Bw8eJCEhgS5duiiJdrXOrzZt2rB//368vb1ZuXIl3t7eHDhwQNk9uH379ri4uABp55hac7A3FStWjFu3blGxYkXmzJlDYGAgs2bNwtLSkubNm1OpUiUaNmyIra1thhfPDBw4kJiYGBo0aED79u2JiIhg1qxZXLlyhUyZMrFhwwZ69OjBkCFDKF68OCEhIYSGhqq6hPrx48c8fvyY+fPnA2kPta5cucLJkye5fPkyERERdO3alUmTJjFu3DgWLlyoyrEtCUETs5AeggYCAgIoU6bMB5UQ9PLyIiYmhuHDhzNkyBAePnzIli1b6NevH8uWLVMq8woWLEhMTIxRYvL29ubBgwdMnz4dSEvk7Nq1i71799KyZUuDpYwbN27k119/Vapn1Bic3jfh7dq1KytWrKBKlSpKMlCtGPT2799Pvnz5cHV1VSYyWbJkURIGK1euZPbs2YwZM0a1SZaefmnBJ598QmRkJJCWnPX396dfv37Mnj2bmJgY1q1bx+PHj9m/fz9r1qxRZTlIevqG2uknozExMWTPnp2cOXPSsWNHwsLCmDdvHkOHDlW14kzfPN/S0pKiRYsya9YsJkyYwKBBg2jXrh1xcXHKz4aGhrJixQrCwsJUqyB413vz/PlzsmXLRrFixShdujTffPMNNjY2PHz4kO3bt6vW6yg9/TlTp04dfvnlF+bOnUumTJmIiori8ePHnDp1ivXr1xMUFKRa5aS3tzf3799nxowZQNqDkvHjxzNo0CDatm1LfHy8wc/b29urujTF29ubuLg4Nm/ebPC6vmpSq9WSL18+5dzTX6eMkQx8+PAhP/74IwUKFGDSpEk4OTnx7NkzfvvtN4oXL06JEiXw8PBg7dq1TJs2TenFmFETU29vb548ecKyZcuAtKrwDh06YG1tzfTp0w025wkODiYgIEC13Xv18bx69crgs3rzujxr1izGjRvHihUrCAwMJCwsTNltMyPduHGDJ0+esGLFCuDtPlQ//PADv/76q3LzaWdnR3JycobHoaff7CV9T7NHjx7x1Vdf4e7uTsOGDZUeUK9fv2bIkCFkyZLFYExV28GDB5Xl24GBgbRt25bevXtz9+5dg95rao2nb157Zs2ahYODA1u2bFHapOiPaf2S3IEDB6pyro8ZM4anT5++dd3Rx6DRaGjUqBHdunXju+++U33nXi8vL549e8bUqVPJlCkTycnJBAUFMW7cOOLi4nBwcKBfv35Mnz6duLg4fHx8VN/x1Nvbm6ioKGXzuGHDhqHRaJg1axbOzs7vPF6MueO6m5sbq1atYuDAgQYPz0NDQ1myZImqG1/pN3DT756cmJiotEooWrQo2bJlIyAggE6dOpE5c2bl2pTR74+vry/x8fHKcZwlSxaaNGnC4sWLWbduHa1atQIgKiqKffv2KTu8Z3SF2Zo1a/jpp5/o1KkT9erVM2ihkTlzZjw8PIiJiWHPnj0kJyfj6OjIjBkzjLKTMKQ9RNuyZQtlypTBwsICT09PPvnkE3LlykXp0qVJTEwkIiKCsWPHMmbMGE6fPq08gFNT6dKlCQwMpFevXnh6erJq1SqKFStGyZIlOXLkiLKpm4uLi9HeKwB3d3eaNm1Krly5yJw5MzExMZQtW5Z27dopFbBhYWFs2LAhQ6snx4wZQ1RUlME4mpKSQp06dRgyZAiFChUiX758BAcH07VrV7799lt27Nihej/FmJgYbt++TUJCAj/++CPff/89AQEBODk5sXjxYqKjo/H29qZ9+/Z88sknqh3bkhA0MakQNFSwYEGuX7+u7Apl7ubOnfvWBgfFihWjevXqjB49mnHjxikVMdbW1oSHh5OcnKzqTeeIESOIjo5m9+7dymtdu3blzJkzBAcHc/HiRV69eqVspBEYGKgsOchoBw8epHHjxgZP9fT0N+UODg7cunVL1Qb1egEBAcybNw9bW1tatGhB4cKF6dmzJ8nJyWTJkoVWrVqxa9cufv31VyZOnKjqpEL/74e0Y+Onn36iadOmJCcnU6RIEUJCQti2bRthYWGcPn1aWQ5SpEgRVeLRGzhwIC9evHiroXaTJk1YvXo1gFK9uW7dOvz9/VmyZAlWVlYZfkx7eHjw7NkzqlWrZtAPb9q0adjY2PDVV18py6dDQkJYunSpqhN1X19fYmJi3npvevTooTzd01fnBAYGKpNpNeJ58eLFO3c0LFOmDAMGDGD//v2EhobSsWNHHB0dyZEjBxs3blSt35r+uqPfEEOfRHnXZwWo3uj7XUmUBw8e8OrVK4Mkko2NjbKhkNpVOully5aNokWLUqdOHRo3bsyvv/7KuHHj6N27NwUKFKBHjx7UqFEDb29vrK2t6d69e4ZVduqrzfQ7RULa0mWtVkurVq14/fo106ZNY/fu3djY2LBs2TJVd+/VJwfSJ9nv37/P77//Tt26dWnbti27du3iypUrfPHFFzx8+FDVJHtkZCT379/H2tpa2WUwvYsXL/L8+XNSUlJwcnJixowZqh07np6eb12P79+/T8eOHfHw8FAeWrm4uLBgwQImT56sVM0YK4Hyww8/MG/ePJo2bUqnTp0oXrw41atX58GDBwBKcketSrj3XXtGjx5NdHQ0+/bt45dfflEasquZYLp69SpRUVFKMvnNec3Ro0e5ffs25cqVo0qVKuzbt4/q1aurNvdZunQpz549e6vSdcqUKVhbW7NgwQKqV69O7dq1GT9+PAsXLlR9mbn++pP+fAcYOnQolpaWrFy5kv3799OgQQPVV2i8bxyFtHmOn5+fkkxxcHBQPRl45coVrl+/jqurK/fu3aNAgQLKe6BPbA8dOpTDhw9z5coVqlSp8lav4IwwcuRIvvnmG7766ivgj9UyrVu3JjExkZ07d+Lj40NqaiqRkZEkJSURFBSU4WNEYGAge/fuJWfOnMomSm5ubgZJwaxZs5IzZ05+//135VptzARXmzZt2LNnDzqdjgoVKjBt2jRcXV0pVqwYWq2W+Ph4jh49So8ePZgzZw5RUVGqLstNf69VrFgxNmzYQO/evfH29sbGxgZPT080Gg07d+5UWiaoOfcJCwujZs2aFChQQFnJ0qpVK44dO8bYsWP59ddf8ff3p0CBAjRp0oSnT5+i0Wgy9OHj9evXiYiIYMGCBcAfY4SFhQUNGjSgb9++bNy4kejoaPLmzcvmzZvR6XRKKyQ16PuzlyxZEkdHR3r06IGFhQUlSpRgwoQJnD59mjJlypCUlETt2rWVdhJqHduSEDQxSQga+uyzz1i8eDHHjx+nePHib/VT0Wg0BqX7ppScnEx4eLjShDn9xM/Kyoo+ffpw4cIFLl++DKT1fevQoYOqSa9Bgwbx9ddfK43OU1NTSU1NxdramiVLlrB8+XLOnDnD1atXiYmJoUCBAgQHB6tSZfHrr78yZMgQ+vXrp+wW9ebTXo1GQ968eTlx4gR79uxRfROPQoUK0bRpUzp37szhw4fZu3cve/fupXz58rRs2ZJKlSpRqFAhypUrx/fff09UVJRqVRbpL+oVK1ZUNhLRJ0/z5MnD4MGDuXPnDn5+fqrE8KalS5dy8uRJg4l6eHg4PXv2xMPDg1q1ainfq1mzJvb29uTMmVOVSbs+obN9+3bGjh1LREQEkHZMW1hYMGnSJOLj4/nqq6+YMWMGX331laoT9WnTpnHq1CnlqT2kvTfu7u506NCBypUrExwcDKQlBZ2dnSlfvjyFCxfO8FjmzJlDSkoK/fr1U5Z8wB9L8SpVqkSlSpWIiIjAx8cHJycnsmTJotrypnddd/QT8/Sf1b59+5QdCNU0ePBgZSm+3v3792nevDleXl6UKFFC2YE5e/bsPHv2zGjLhCHt/cmUKRNdunRhzJgx1K1bl3r16pE5c2YcHBwoVKgQixcvJjIyEm9vb8aOHZth55iXlxfPnz83SBCEh4czceJEJk6cSNGiRenYsSM7d+5kzJgxaLVatm3bplrf0qFDh3L//n0lmQNpn1XLli0ZMGAAdevWJUuWLOTNm5eKFSvy1VdfERYWptpO1JDWa+358+fY2NgYPMxKfwOq1WpJSkpSKnfUcP/+fU6dOkWLFi2U1yIiIujevTtdu3ZVdhPXarXExsbSuHFj6tSpY7SNcPSqV69O3759lc+wY8eO2Nvbs2TJEnr06IGPjw8LFy58q1dxRvizaw+kPbQ9e/YsXl5eLFu2TPVrz507d4iMjFSSTG/O986fP8+dO3cAaNKkiWrJQP317NatW3Tq1An4Y46qP449PDz49ttvefr0KTqdjrp161K1alVVE4LvelDz9OlTrl+/Ts2aNfH19WX//v2MHz+e169f07JlS9XOr/eNo+m5ublRqVIlFi9eDKT1o1Wzh3Pp0qXx8vJi9+7d+Pn5Kb0n04uKisLFxUWVh/iQ9lA4KiqKRo0a0adPH1atWkXp0qWVhH7Hjh2pWbMmjx8/Zu7cuUyYMAEXF5cMXyqcnJxMTEwMRYoUoX379gQHByvVivqkYPrETq5cuXjy5InRxvKNGzfi5uZG+fLlCQgIoHv37tSvX5/Zs2fz+PFjli5dSmpqKvnz52f9+vXUqVOHwoULq3ZPsW/fPmrUqEHOnDkN7rWKFy9OQEAAPXv2VMYGDw8PbGxsqF+/vqrJwA0bNjB37lzatWuHt7e3kmCrWrUqa9asISkpic2bN+Po6KgkC9XoG3/37l0ePnyovPdvPuSrXr0669atIzExEUC1c0svNDSUffv24eHhwWeffcbWrVvZtm0bRYsWJWfOnBQqVIjvv/8ejUZDaGgoz549U2X8TE/yUSam0Rnn60OxfPlykpOTOXfuHJs3b2b58uVvfZmLBw8e8O23375z4qfRaChTpgyxsbFKIqNFixaqVnf169dPqfo4duwYo0aNQqvVYmlpSXJyMjY2NgwbNowtW7ZQo0YNQkNDmTlzpirJQABXV1cWLlzI5s2bmTt3LoDBLsf6ATsuLo5hw4ZRqVIlVeKAP3aprlq1KmfPnuXq1auMGzeOXbt2ER0dzeHDh3F3d6d///5cvXqVhg0bsnDhQlUG7pkzZ9K2bVtGjBjBpk2b+O677yhUqBC3b98mOTnZYCBPv1Ql/Z/VUrFiRUqVKsW2bdsIDw/n6dOndO/enbZt2+Lj4wOgPFkMCgqibNmy5MqVK8Pj0DfT3r59O5C2YU/6nXH1yZzZs2eTM2dOQkJCWL9+vaql/aVLlyZbtmzs2LGDhIQEHj9+TPfu3enQoYPykEKj0bBv3z6+/vprWrdurUoyENKe+h46dIitW7fy6NEjg++lP070PVhcXFxUSxK877rzptmzZ+Po6MioUaN4/fq1KrEAfP311xw9epS2bdsqx0l4eDhdu3bFw8NDqeDUarUkJCTQoEEDfH19jXIDcePGDeXP+p30ypcvz+PHj5kyZQrPnj1j8eLFzJ8/nxUrVjBhwgQaNmyYYcnAJUuWcOzYMTZt2qS8dv/+fdzd3SlfvrxSgajfzXzOnDns27dPtfPq9OnTnDt3jho1aijLyfWfVb9+/Qx2PE1JSaFv374EBgaqkgzcsGGDsstp9erVsbe3Z+HChSQmJirjlv64vn//PlmzZlW9oj1v3rxs2rSJ48ePM3nyZCUZmP6ao4/93LlzvHz50qi7CSckJJCQkABAt27daNWqFWfOnGHr1q3ExsZSvHhxgoKCuHjxIh4eHhm+rPrvXnvKly9P1apVVb/2QNo8UKPR8OLFC4PX9dciJycn5TPq0KGDaktzL168SGpqKqdOnVISNW/uCpsjRw4SExNJTExUrn9q7ToPaeN6QkLCWxX2rVq14unTp8prpUqVom3btixYsOCtNhMZ6X3j6JtzLWdnZzZt2sSBAwdUSwb6+fnRoUMHABo3bkzr1q2JjY1l/fr1SlGB/nM7deoUmTNnVmVMnzx5Mvfu3SMsLIylS5dStmxZ+vfvz/Xr15WHIwD58+enSpUq5MuXj9KlS6vSN9DS0pLevXtz6dIlfvrpJxYtWkRKSgphYWEcOXIESBurNm/eTGhoKJ06dUKr1RplLL906RJ79+5l9uzZREREUKFCBQICAvD39+fIkSM0bdqUAwcO4ODggIuLC/Hx8apWBZ46dYrVq1ezZs0aoqOjlQdYeiVKlGD69Ok8e/ZMeSDRq1cv1XtyFilSBK1Wy44dO5g1a5ZSNV63bl3y5ctHrVq1cHR0VHqrqsXKyopMmTIZtBiCP6puIW1JvDE2koO0+wknJye2bt3K8ePH0Wq1dOrUiSpVqijJyuTkZAIDA1m+fDlTpkxRvcejJARNzNJIXx+Kq1ev/umXfmmXOXByciJ//vxKokJPp9ORkpLCs2fPKFOmjKqJLr3Nmzfz6tUrQkJCKFeuHCtWrODYsWOMGTMGQHkqrNFolB26rK2tVXnicPnyZQ4fPszBgwdxdHRk0aJFbN++nXnz5gF/7BQJaZMg/VLZQoUKZXgsevqBxt7ensGDB3Px4kUApk+fTpYsWdizZw+rVq0if/78PHjwAEtLS1UGbz8/PxwcHKhTpw6JiYls376dIUOGsGTJEn7++WcePHhg8LQu/QCp5mB57do1zpw5Q8GCBRk4cCC//fYbixcvplmzZnTr1k1JBkJaBcT06dNV2/Tll19+wdra2qCCKWfOnMoxo9PplIlxeHg4n3zyCd9//71qE/XAwEB0Oh0dOnTAx8eHLVu2sGDBAtq3b0/Xrl0Nbszv3LnDuHHjVNu049q1a5w+fZpatWoxYsQIduzYQVhYmFJdmr436NKlS7lz547BhCej/dV1502VKlViy5Ytqj7pbNSoEb169WLfvn3s2LGDa9eu0aNHDzp06GCwGU9oaCi//fYblSpVUu2hSHqHDx+mffv2nD59Gq1Wi4WFBQ4ODhQpUoTu3btz/vx5qlWrpjxBL126NJ07d1Y2h/m3pk2bhlarpVChQsoDmqioKLp27UrHjh0NdmDetm0bL1++pHXr1qo+xKpZsyaDBw/m+vXrrFy5kkuXLtGzZ086dOigLDOFtM/q3Llzf7q879/YvXs3c+fOZdu2bZw+fRpnZ2caNmzI6dOnWbFihdLmQx9LWFgYhQoVUi0huHv3bhYtWsS8efPImjUrq1ev5uDBgzRo0ICOHTu+tcHB6tWrKV++PPb29karcp0xYwYDBgxgyJAhBAYGAmlJwXbt2nHmzBnCw8O5d+8eJUqUYPXq1Tx8+PCtudK/8U+vPUuXLlXt2rNx40YliWRvb8/t27f59ttvgbeTS1FRUWTJkoXk5GTVHvLt3bsXDw8PIiMjKVWqFNeuXSMpKUn5vn5n2Li4OEqVKmWw8Yxax8/Jkyc5fvw43bp1U17TV9h37tyZ5s2bG8Q3ceJE9uzZo0q10N8ZR/WWLl3K7du3qVChgmoP+JYtW0a1atV4+vSpcm43btyYNm3a8PLlS9atW8dvv/2GRqPhzp07+Pv7M27cuAyfoyYmJtKgQQOlzZFOp2PJkiVUrFiRPn36cP36daMsxb127Zry52zZsjF79my+/PJLbt26xdSpU7G0tCQkJITu3bszY8YM/P39CQgIyLCx8u8oW7Ysw4YNIzU1lRkzZhAeHk61atUIDAxky5YtTJ06VRlvFyxYoNwPqaV27dr07NmT69ev4+/vb5AUTL8awsrKymhJL0ir5PT19WXUqFFcvHiR8ePH8/DhQyAtAXf69Gni4+NVqVK8ceMGZ86c4ddff8XR0ZHw8PC3rsv6c/2nn35SKqPVLL7Q32tlz56dvn37YmVlRVhYmBJXdHQ0/fv3p2LFivz888/s2LGD4OBgVVdE6H1IuaL/JO0HVL1nbL///jsvX77E0dFR9QbHf9cvv/zC06dPsbS0pG7duuTKlYsNGzZQrVo15SKr0+mwsLBg7969vHz5UvVd/vr160eZMmWUBEpSUhKVK1dm3bp19OvXjzFjxjBnzhyDgVyNniOQ1rT1wYMHPHv2jNjYWJKTk/nkk08YP348M2bMIDU1lTFjxmBhYcGiRYsIDAykevXq5MuXL0Pj0NN/XhYWFtSqVQtra2tKlCjBsmXLcHd359WrV/j7++Po6EidOnWoWbMmUVFRqmxIcfXqVQ4fPkzJkiVZtGgRAK9evSIpKYlHjx6xdu1aunbtyvr16426qc7o0aN58OABjx49ws7ODn9/f7y8vFi8eDF58+Y1WK4WEhLCjRs3VGvm37t3b5o1a8bSpUuBP/rW5MmTh6tXrxosBdmwYQOHDh0iZ86cqk1wrl27xsaNGzl9+jSrVq2iffv2aDQa5s2bR/HixZXdUMHwvVEjIaj/nB4+fEimTJlYu3YtQ4YMUZaTd+zYUTluFy9ezKpVq6hVq5Zqy0H+7nXnTWptaPLLL78QGRlJ9uzZGTx4MIsXL2bHjh3MmTPnraR2aGgoCxYsoGLFiqrccL5JX4ndtm1bJk6caJBQ79GjB+fPn6djx46cOnVKlT5r3t7ehIeHs23bNgoXLszy5cuJiori/PnzdOvWTamaBNi0aRPTpk1TdaODkJAQ7t+/z8uXLxkxYgSxsbEcPnyYjRs3Kp+V/lwPDQ3Fz88PV1dXVZKBkPbAwc7OjocPH7Jnzx4yZ86Mr68viYmJysOtkiVLYmlpya+//kpAQABr165VJRZ9n9JSpUrx+vVrbG1tKVSoEJs2bcLDw8Ogkin9Bgfr1q1TJZ530W9S0atXL77//ntOnjxJgwYNyJcvH126dCE1NZUlS5awY8cO2rRpQ6lSpdi7d2+GVbqa07Xn2rVrBAYG8v333+Pv70/16tXp0KEDkyZNwsbGhs8++ww7Ozu0Wi2bNm1i+/btlCtXTtXqUhcXFxwcHHj58iXFixcnLCyM2rVrU7x4caysrJSHatu2beP+/fuqL4+DtCpNDw8PBg0axPr166lSpYpS7Zr+2rxu3Tp++uknUlNTVRnX/9dxVK1E2IQJE9i+fTtLlixh/vz5+Pr64uXlhb+/P02aNAFg165dhIWFYWdnp8wxMrpX37Fjx8ibNy916tRRXktJScHS0pJFixYxbNgw+vTpw4YNG1RNvE2YMIGDBw/y2WefMWrUKLJnz06FChWoXbs2Bw8eZNSoUYwePZpLly5x+vRpChUqxMaNG1UtKEgvJCREuccZNWoUHTt2ZOvWrcycOZPx48dTpUoVAgMD6d+/PwkJCUoiXq37wDfH0ri4OI4cOYK/vz8DBgzAycmJ5ORktFotFy9exMLCwihJ3fQJt+TkZB4+fMj+/ftp2bIl48aNY9GiRRQpUoSzZ8+yevVqhgwZkqG/X3+eP378GFtbW1asWEGXLl2YPHkyNjY21K5dW2n9ERwczMaNG3F1dVW1Z+mb91rLli2jV69ebNiwgU2bNmFhYcGnn37KkiVLOHPmDN988w3z589/bzuDjCYJQRMz3p5ZH459+/Yxd+5cg6fJOXPmZPjw4bRu3dpkcQ0dOpRHjx4RHh6ORqPB3t6eTp06ERAQgI+PDz4+PpQpU4YXL17wzTffsHLlSoKDg1V9GqPfQl1f3ZGcnIyVlRWpqamUL19emSCPHz+emTNnqhYH/NEoevny5Tg4OBAbG8t3333H7NmzSUpKYuHChQwbNgw7OzuyZMlCYGAgmzdvVu1GJv3npX9PJk6cSIMGDfjiiy/Yv38/gYGBSqNbjUaDpaWlagmUggULMmrUKFatWoWvry/Lli1TkhEODg7MmzeP4cOH065dO7788kvVqszS8/X15cmTJ6xcuVIZuJ2cnMibNy82NjbKhNjb25tvv/2WpUuXUq1aNVWWEPr4+PD8+XNl9zV970tI2/ghPDyc+Ph4MmXKRGhoKKtWrWLt2rVs2LAhw2PRK1y4MJMnT2blypVKY/F27dphYWHBkiVL8Pf3Z+DAgWzfvl3V9+Z9n1Pr1q2xsrJi/vz5aLVaunfvruwIu2PHDtXOLXO67sC7r80jRozg9evXxMbGUrBgQV69ekWWLFmUzWdCQ0NVS+qkp98UYvz48dy/fx9AeV9q1qyJg4MD9vb2fP/990DGb2wybNgwXr58qVR91K1bl+TkZPz9/bG3tzdIBuoTTDt37lTtvdGPE+XKlePFixfcuHGDnj17YmdnR0JCAlZWVkRHR+Po6EhwcDDLli0jKChI1YRX7dq1adWqFb///jsRERGsWbMGT09PRo8eTcuWLTl06BCvX7+maNGiDBs2TLUHWPrel8HBwcrSzcjISBo3bkzdunVZtWoVvXr1wsLCgmLFiqm+wcG76HvA6XvJlitXjr59+3Lnzh1lE5hu3bqxc+dODhw4gJWVFR4eHhm2iZC5XXveHCNWr17NxIkTSUpKYsSIEXz++eekpqaSPXt2Tp8+zfr161VP3larVo1cuXKxatUqZs+ezc8//8zQoUPp27cvbm5uREdH891337F69WqCgoKMch3MmjWrUqHdq1cvbGxsGDVqlEHFoP6a/Mknn6iStDC3cdTT05PHjx+TL18+EhISqFy5MkuXLmXIkCEGSUGNRkNAQAD37t1TZY6xdu1aFi5ciIuLC7Vq1aJatWq0aNHCoCe6n58fw4YNo02bNqrNTxMSErC2tqZ06dJERUXRrVs3mjZtSqtWrejcuTMDBgygSZMmlCtXjrx58/LFF19keAx/Jv3YFRsby7Vr12jQoAFarZYtW7YYJAVXrlzJ8OHDlY2M1Ki8fddYqt+Y4vDhwyxfvhwvLy9y5cpFaGgoK1euxNXVVdWHoPpd09Ov5PHx8aFDhw6cPXuW7du307JlS0aOHIlWq6Vz5860bNkyQ2N433k+cOBAUlJSGDlyJFWrVlVev3z5suoP1d4XU8GCBbGysmLt2rWEhIQA8Omnn1K8eHF++uknoyUDQRKCJmcpFYIGjh49ysiRI6lRowbDhg0jZ86cREZGsmfPHsaOHUv27Nn57LPPjB6Xl5cXUVFRrF27lqSkJJKSkpg5cyb+/v707duXgwcPMnr0aGJjYylbtiypqamEhISotisjGG66oJeUlISlpaVyIS5fvjzr16+nY8eOWFtbM3nyZFViCQoKMtglMjU1laxZs9KwYUNsbGyUysGZM2cqT4K2b9+u2o3Mm59XcnIyM2fOZMSIEcyaNYuKFSty+vRp7O3tAVRvQpyUlISdnR1ubm4cOHCAL7/8Em9vb1auXAn80ex7wYIFjB8/3ihP8LZs2UJMTIxSZfFmmXyRIkVo1qwZBw8epH///jx48EC1G3P9sZx+E4jXr18rJfwODg7KrmObN29W/UZY/15YW1tTq1YtdDody5YtU5KCrVu3JikpidWrV/PDDz9w8+ZN1SYU7/uc9MdszZo1ad++Pbt27eLw4cPcvXuXTZs2qfbemNN1B959rk+dOlXZJEOr1RIWFoaFhYXycxs2bFC1Mbyet7e3wWeXN29eevfuTUpKCuPHj2fatGnUrl0bHx8fevbsSbly5TL09w8ZMoSDBw8a9OLLmjUrDRo0AGDVqlVMnz6diRMnsm3bNuW8Uuu9GTly5FsbCiQmJhIVFUXt2rV5+vQpFy5cICQkBCsrKwIDA1VPeOmb0ru5uZEtWzYaNWrEvHnz8Pf3p1evXlStWtUoCbcvv/ySqKgoQkJClGWt9+7do1evXhQuXJgff/wRS0tLAgMDadu2rbLTpzGOYz19ZY5+eROkjfUREREsX76cS5cuUaJECbZs2UKBAgX49NNPqV69eoYlA83p2vO+MUI/rk+bNo1q1apx69Yt7t69i6urKx4eHqqudtEvBbawsKBHjx6sW7eO+Ph4QkJCGDx4MOvXr2fGjBmUKVOGzJkzExwcrOoc9U12dnZ4eHiQJUsWFi1aZLDKQP+gJjAwUJUEpbmNo15eXjx9+pRdu3bRq1cvZbmyfgOT9EnBxo0bY2dnR7FixZRdUjOSg4MDlStXZuLEiaxdu5Zt27axatUqWrRoQc2aNalcuTKWlpYsXbpU2SBQDba2tri5ufHtt98SHBzMxYsX+frrr+nSpQtTp06lRo0aLF68GD8/P9Wqxd/nfWNXZGQkJUuWpFmzZhw9epQ5c+YwatQoatasydGjRxk7dqxR43n69Cn16tUjKSmJX375hUaNGvHpp59y+/Zt1ZP/o0eP5ssvv6RVq1YUK1aM1q1b4+TkhEajoU2bNpw9e5Z69eqxc+dO6tWrh4uLC8uWLcvQNg5/dl+TJUsWunfvTp48edDpdISHh1OrVi1GjhypapX0X91r5ciRg/r163Ps2DH8/f3RarWqtWT6M9JD0MQ0Rvr6UOgHvw0bNtCmTRvq1KlDu3btCAoKonHjxqxevdroMekrl7Zt20b27NlxcnIiT548rFixgurVqxMWFkZQUBBBQUEsWrSIuXPnsnz5clUnWj4+Pjx79uyt5szt2rXjxo0bSs8InU6Hq6sr27dvx93dXbV47t27R7169YC0Cbp+wmBpaUm1atUoWrQoUVFRNG7cGH9/fw4dOkTZsmVVieVdn1fu3LlZvnw5bm5uLFiwgLp162JnZ6fsHKfWBEffwFZ/Q7R27Vq+++47pk2bxrNnz5Q+WZaWlsqN6bx581TrVZPe/fv3leUh+qbiGo0GnU5HTEwMjRs3Jnfu3AwZMoSsWbOqNjn+s2P5+vXrABQoUABnZ2fmz5/PokWLVE0SREdHG/Tis7Kyok6dOgwdOpSYmBj69++v9BTs378/jx49IigoSLV43vc5ATx79owGDRpQpkwZxo4dy6tXr9iyZYuq55Y5XXfed66vWrWKSpUq4efnh6+vL59++ikLFixg9erVRquo0r9X6XthRkZGYmNjg5eXF25ubkyaNInTp09TpkwZatSokaFP7gcMGEBERATTp0/n0KFDBjeSWbJkoWHDhnh6evLDDz/QokULFixYoOp7c/XqVR4+fKgkzRMSErhw4QLt27enffv29OjRA2tra6pVq8b+/ftZt24dAQEBqsTzww8/EB0dDfxxbS5Xrhz79u0jIiKCmTNn8urVKzZv3syJEyeU/07N3kL37t2jZMmS2NjYkJKSQlxcHN7e3rRu3Zr169fTr18/Nm7cyIsXLwgLC+PQoUNGTQbeu3ePXLlyKUlb/WtdunShf//+rFmzht27d3Pr1i3lwU779u0zrGm9OV17/myMiIqKUsaI5s2bM2jQIBYuXEinTp1USwbqe3Lpe0RD2qZu9+7dY9OmTWTJkoX169ezceNGgoKCWL16NYsXL1Z1jvq+c0W/u/qAAQNwd3fn/PnzHDhwgKVLl6r6MMKcxtGePXsq4xakPRDWX4+0Wq2SFLx8+bJyDLu5uSn9ZTNaq1atePjwIRcvXmThwoVs2rSJV69esXPnTtzd3Rk6dChz584lNTWVBQsWZPj8VL/RBKT9O2vVqsXkyZP57LPPmDVrFuPGjcPf35+YmBjOnj2r9AA3lj8buzp16oS7uztRUVE0adKEp0+fsnTpUpKTk1Vbfvpn8bRr1w53d3dSUlIYM2YMc+fOpX///gQFBak6XoSHhyvX+tu3b/P48WOaNWtGUFAQly9fpkGDBuzcuZMffvgBZ2dnTpw4QdGiRTO8p+v7zvPU1FSeP39O69atyZ49O71792bSpEk0aNBA9ZYJf3Wv1axZM6WSPl++fEZb/v4mqRA0MS3mXyKYmprK8uXL2bZtGy9evKBy5cpMnjyZggULvvPnb9y4wfz587lw4QJarZaqVasyZsyYv3XSXb9+HV9f33d+r02bNgwePPhf/Vv+qfHjx3Pu3Dm+/vpr4I9KLn2fpxkzZtCmTRv27NlD165dVW3A/r6YAGX3wfbt2xv099BPeNSa2EBaAvDChQvUr18fMNxtWb+NfPbs2ZXeR5999plq1Xh/9/Pat28f9erV48yZM8pSwow2d+5csmbNSpcuXciePTurVq0iNDSUrVu3kjt3bnLkyMHKlSvx8fFh2bJlGVZF8XfodDp++ukn5bhIP3HRaDRkz56dwoULc/PmTTw9PVmzZo0qu1f+1bGsryCwsbHh119/JSIigo0bN6rWYHfYsGEcP36cGjVqoNPpKFOmDIUKFcLZ2ZlChQrRpUsXgoODGTlyJPPnz6dTp040a9ZMtSUYf/U5OTg4ULhwYa5du4anp2eGJ5XSM7frzl+d67NmzaJt27YcOnSIwYMHY2VlRePGjU16jW7cuDFjxoyhe/fu9OzZE61Wy8CBA9m8eTPLli1j9OjRGfL7Fy9ezMuXL5VKqqSkJAICAtBqtQwbNgyAzJkz07BhQ5KSkggLC1Nt91695ORk4uLiiImJ4datW5w6dYo1a9bQoEED2rdvz48//sjOnTvZvHkz2bNnp0qVKqokUDw9PTl+/DifffYZefLkYciQIVhaWpIzZ05GjRrFkSNH+OKLLxg1ahQTJ07k4MGDVKtWDTs7O1WryC9evEiOHDmAtGXjOp2O+fPnK59JSkoKJUuWpEiRIqrvNvimFStW8PjxYzp27AhA165dmT17NsuWLaNz585KDzh7e3tcXV0z/AbLnK49fzVGdO3aleDgYEaNGsX8+fNViSG9hQsX8tVXX9G5c2fq16+vLOV0dHRk0KBBHDx4kMjISJydnXFxcTHKErRJkyZRrVo1mjZt+s6HrVmyZMHDwwONRkO3bt2wtLQkLCxM1Yp/cxlHf/vtNwCDnd5tbW159eoVgDJ+ValShYkTJ7JkyRIePnyoSl9r/e+zsrKic+fOyqaNU6ZMwcHBgZUrV3Lnzh22b9/OoUOH6N69e4YnJVesWMHhw4fp27evsrFM165dWbx4MefPn6dmzZq0atWKihUrcv78eV68eKH6zrhv+quxS78UVt/nsUSJEqr2CP078WzZsoX27dsrfSjVpB8f2rZti7W1NX5+fgwePJgaNWqwc+dOgoKC6NixIzVq1OD777+nQoUK5MyZM8PPsT87z7VaLdmyZaNIkSIGfXjV9nfvte7evUuTJk2YPHmysjLK2CQhaGIfwqYiK1euZMuWLcyePRsXFxfmz59P//792bdv31tPQJ49e0bv3r2pWrUqISEhvH79mrlz59KvXz927dr1l08DHBwciImJeef3nj17pmrDz3dp3bo1p0+f5tChQ9SrV4+cOXMCf/R5srOzI1u2bDx79szoMR04cIBOnToRGRlJt27d3tp98PDhw8pSKDXpL2r6rez1T2P0f4a0CU/16tVVjQP+/ucVFxdHq1ataNGihWoTvzp16jBhwgTy5MnD7du32bVrF6GhocrErk6dOlhaWipLmRcuXKhKHO+i0WjInz8/t2/fJi4uTkn2pd/QwMHBQXlSpdYA9XeP5R9//JGsWbMSHBysam/FNm3asH//fl6/fk2mTJm4cOEC27ZtIz4+HgcHB6KiosicOTO//fYbNjY2zJw5k8yZM6sWz9/9nPQPZ9SMxdyuO3/nXM+aNavSt8/b21vVeN4V25vv1cCBA+nevTuAkjywsrLC1tY2Q8e2Ll26GDTpbt68ORYWFspyofRJwaZNm9KkSRPVN1dxcHDg/v37DBo0iOjoaPLmzWvQQ6xkyZKcPXsWKysr2rZtq1ocPXr04Pjx41hZWXHp0iV69OhB1apVadGiBaVLl2br1q1cu3aNMmXKMHPmTLJmzWqUCXrhwoW5cuWK8oAqc+bMBhVct27domjRokafA0FaT6NZs2Zx8OBBGjdujEajYdSoUTRv3hwfHx9SU1OVpfkREREUK1YsQ3+/OV17/skYYWtry/Tp01WLBdLeG3t7e4KDgzl06BB58+bFx8eHXLlyUa1aNVavXk1UVBTOzs6qt0bRq1u3LiNHjsTW1pbPP//cICmojyFTpkz069ePHDlyUKtWLVXHdXMaR8uUKUNQUBCQVtlla2uLk5MTr1+/Bv5YrRIYGMixY8cIDQ1VdXms/t9fqVIlvL29uXHjBs+fP8ff35/8+fNToEABKleuDKjzvjRu3Ji4uDjGjBnDjz/+SIsWLahatSrOzs6sX79eWTpZoEABChQoYNDb0Fj+7thlaWmp+rzn78Zz/vx5VSva09OPD4cPH6ZRo0b0798fDw8Ptm3bxsqVKzl37hzLli3j5s2b3LhxQ7W52N85z7Nnz26Uh8L/JKb091r63sGmIAlBE1Nn+4KMk5iYSEBAACNHjlQudH5+ftSpU4dvvvmGZs2aGfz84cOHiY+PZ86cOUryb/78+bi5ufHTTz/95br4mjVrsmzZMqpUqWLwlPn+/fusWLFC1Z0P36Vq1arMmzePUaNGER8fT4sWLXBwcFASXpaWlhQuXNgoSzzfjGn06NE8fvyY7du3061bN4M+USEhISxYsIDdu3erHo+lpSVdu3Zl4MCBVKhQgS5duhhMAIODg7l06RIjRowA1Gmuq/d3Py9nZ2fVy8Rr1arFvHnz8PHxISkpSdnARL/jl5WVFTVq1GDSpEkm2UW7Y8eO9OjRgzVr1uDr64uFhYUyQIWEhHDv3j3Kly8PqPeZ/Z1jOf1OmmpvtFKnTh02bNjAwIEDWbx4MXXr1iU+Pp7k5GRu377N77//TkxMDJcuXVJ2GFb7BuvvfE5qNq7WM7frzt891/XJCWPdDKeP7X3vlb6asWjRogwfPjzDq4P1VUD6SWfWrFmVSoG1a9ei1WqVhKGaN7/p5c2bl02bNnHkyBGKFClC/vz5DXZT37FjB1ZWVqSkpKgaR61atQgICMDX15clS5YQERGhbGzi6elJREQES5cuZcGCBapWTL6pefPmhISEsHbtWnx9fbG0tFSO19DQULZs2UJoaKgqldp/pUKFCkyePJkpU6aQmppKo0aNsLa2ZsWKFbRv354aNWoQHBzM8uXLCQgIyPCx1ZyuPf9kjOjVq5eqsQAULVqUokWL0rp1a3755RdCQkIYPXo09vb2DB8+nHLlyrF27Vrmzp1rtGRygwYNWLJkCYMGDWL+/PnUr19fmRPqj+m1a9dSsWJFunfvbpTrsrmMo+npb/6zZ8/OhQsXlMR6aGgo/v7+rF271mi98qpUqUKjRo344YcfWLNmDfnz51fGTDXHiKJFizJy5EgaNWrE0qVLWbVqFUePHmXo0KF07dqVrVu3KpXJgNGTgfD3xi5LS0ujJeD+Tjz6ewxjeHN8aNq0KampqbRv3561a9dSq1Ytli9fzv3798mSJUuGLxNO7++c5xndqzkjYlL7XuvvkISgiWmNdAH5X129epXY2Fhq1KihvJY1a1Y++eQTzp49+1ZCsGbNmqxYseKdJ/zz58//8vcNGzaMdu3a0bhxYypUqICTkxNPnjzhl19+IVu2bAwfPvzf/6P+oSpVqig3npA2cdfvHLxp0yYuXryo7HhnzJj0lZdVq1Z9K4GybNkyQkNDjdaLoHbt2vTt25epU6dy48YN3NzcSE5O5tKlS2zatImAgADV+p+8yZw+rypVqrB69WoGDRrEzZs3yZ07t1LJpN9F19hJbr1KlSoxfvx4pk+fTnh4OPXr1ydLliz8+uuvBAcHs2HDBtWWqaT3V8eysXfSrFmzptJ7bt68edSrVw87OztcXV1xdXU1SgzpmcvnBOZ33fkn57qxJ1rve69SU1MNbmrUnLSn37FYnxTUarXMmzcPKysrg+oqY9AnLwBevnwJpD103LRpk9JSQe1KRUhLCup3vZ81axadO3fm888/59ixYwDcvHlT9cTkm8qVK8fEiROZNm0ad+7coXbt2gDcvXuXvXv3EhAQkOGVd/9EmTJlmDJlClOmTAGgadOmvH79Gk9PT5o3b87hw4dV6/kI5nXtMbcxAsDZ2ZlGjRrRqFEjfvzxR86cOcPIkSPRaDTY2toa/XiuW7cuS5YsYfDgwcyfP5/PP/9cuR4tXryYVatW8eWXXxrtumxO46iePuFma2tLbGyskgw0xe7hkNZ38ocffsDJycmovxfSNgOaP38+Fy9eZPny5Zw9e5b8+fPz7bff8sUXX5AtWzajx5Te3xm7jPVwzRzjedf4oNVq8fLyYunSpbi5uRllAyNzPM/NMaZ3kYSgiZn7ri76tfZvHqzOzs5KI+P08uXLR758+QxeW716NTY2NlStWvUvf5+TkxO7du0iICCAs2fPcunSJbJly4a7uzu9e/dWkinG9uaNZ48ePQgLC2Px4sUEBgYaLdn1ZkwBAQGMGjWK4OBg3N3dCQsLM8lkwtramoEDB1K4cGFWrFjBt99+i6OjI0WKFCEkJMSgx48xmNPnVb58eRYsWMDo0aN5+fIlLVu2JHv27EbZSfivdOnShQIFCrB48WKWLFlClixZKFCgAMHBwQY7AKrNnI5lSGtsvXTpUuVmJn2FAxi32gzM53MC8/uszOlcf1ds6d+rZs2aKQlLU8iaNSuNGjXC0tKSSpUqmSyOhw8f0qpVK/LkyYOLiwt37twx+o6nn332GfPnz1eqmBo1akSNGjXw9vYmNjbWKInJN3Xu3JkCBQqwcuVKgoODcXBwoFy5cmzYsMGoy5ze582bvmbNmpGcnMyWLVvYuHGj6ue+OV17zG2MAJQKs2rVqlGtWjVat27NrVu3KFCggEn6Urm5uRkkBRs2bMjixYsJCAhgx44dBlVNxmBO42h6BQoUIG/evAQFBbF8+XICAwONPo5CWiXT5s2bWbx4MRMmTDD68evo6Iibmxtubm6sWbOGEydOKJWT5sIcxi5zjedd40N8fDyjR4/m2LFj2NraGuWYMsfz3BxjepMkBE3Mwkibiug3fHifI0eOvPP1+Ph4gLeWGtjY2Pytir+NGzeyadMmxo4dqzTM/is5cuRg5MiRf+tnjUl/4zlhwgS+++47fvnlF1V3Rfs7KlWqxJw5cxg/fjynTp1SYjLFZMLOzk6p7kxMTCRTpkxGLVt/kzl9XvolT+PGjSMhIYFOnTqZ/Imn3qeffkrlypVJSkrCwsICS0tLk/SpMqdjGf64mRk+fDjTpk2jcePGby17MiZz+ZzA/D4rczrX36R/r8aNG0d8fLzJz/1s2bLRunVrky5NyZEjB7NmzeLSpUsUL16cChUqmCRxq0/qDBs2jKSkJBo1aoSjo6NJk7a1atWiatWqaLVagwpPc6G/6ZsxYwbx8fG0adOG/v37G22jE3O69pjbGPHmQ0Z93zVT0r9HI0aMYPv27fz4449s2rTJZGOFOY2j+mMka9asHD16lPPnz6u+G+z76BPYFStW5N69e8THx5skiaxPag8YMICWLVtiaWmJg4OD0eN4H3MZu8w1njfHh44dO+Ll5WX0Y8mcznNzjik905eofOS0OuN8/a/0PS4SExMNXn/9+vWfnuA6nY7Fixczc+ZMPDw8/lEvlcuXLzNkyBBq166Nq6sr9erVY9y4cYSHh/9P/4aMVKVKFaZNm8Zvv/1GUFCQySY1b8Y0ffp0s4kpc+bMODg4YGNjY7JkoJ45fV5VqlRh6tSp7Nu3z2QxvI+trS329vZkypTJpAOUuR3Lbm5uzJkzh9mzZysPR0zJXD4nML/PypzO9TeZ27lvymQgpD1gbNCgAUOGDKFZs2YmvYFxc3Nj7ty5zJkz5615jqlYWloqyUBj9aX6J8qUKcOYMWO4c+cOefPmNfqux+Z07TG3McIcubm5sWDBAk6cOMGmTZtU3X3+7zCncRTSzqeSJUuyadMmkz3E0o8JvXr1YsKECSbb6VSr1SrXvFy5cplsVdj7mNPYZY7xgOH44OzsbLIHbOZ2noN5xqQnFYImpjFSheD7KgD/in6pcGRkpMGTxsjIyPeWJCclJTF27Fj27dvHqFGj6Nu379/+fadPn1aeNru5uZEjRw6ioqL49ttvOXToEJs3bzZ5eW21atU4fPiwSXcDepM5xmQuzOm9qVmzJmFhYSabbH0IzOnzgrQG6TVr1jRqP5YPhbl9VuYWT3py7psvczvH0ydsTZ28fZ/y5cuzatUqVRvE/xlzOtfN7fgxR25ubvz8889y/XuHXLlysXXrVpOdS+mZuqIUzPeaJ/4+U48P4p+ThKCJmXuJZqlSpciSJQs//PCDMlC8ePGCy5cv071793f+N6NGjeKbb75h4cKFb2068lf8/PyUjRjSX0gSEhLo168f8+bNY926df/7PyiDmMMk9E3mGJO5MKf3RibEf82cPi8w3k6sHyJz+6zMLZ705Nw3X3KO/3Omvtkzp3Ndjp+/Jte/9zP1uSRERpNj+sMiCUETM1YPwf+VtbU13bt3Z8GCBTg6OpI3b17mz59Prly5aNiwISkpKURHR2Nvb4+trS07d+5k//79jBo1imrVqvHkyRPl79L/zJ+5fv06S5YseetCYmtrS9++fRk2bJgq/04hhBBCCCGEEEKIj4W5F6j952mN9PVvDBo0iPbt2zNhwgS6dOmChYUF69evx9ramocPH1K7dm32798PoPRImjdvHrVr1zb40v/Mn8mdOzcRERHv/F50dLRJm30LIYQQQgghhBBC/BdIhaCJGauH4L9hYWHByJEj37nzb758+bh27Zry/wcEBPyr3zV69GhGjx6Nvb09zZo1Uxprnzp1isWLFyvbmQshhBBCCCGEEEKI/40kBE3MUmP+CUFjmjZtGomJiYwePZpx48bh4ODA8+fPSUpKQqfT4ePjo/ysRqPh8uXLJoxWCCGEEEIIIYQQ4sMjCUET+xAqBI2pbdu2pg5BCCGEEEIIIYQQ4j9NEoImJk0cDaWvAPyvqF+/Pk+fPjV1GApzisecYgGJ569IPO9nTrGAxPNXJJ73M6dYQOL5KxLP+5lTLCDx/BWJ5/3MKRaQeP6KxPN+5hQLSDwgCUGT08iS4bckJiZy69YtXr58+c7vV61a1cgRCSGEEEIIIYQQQvx3SELQxCwkIWjg9OnTDB8+nGfPngGg06W9PxqNBp1Oh0aj4cqVK6YMUQghhBBCCCGEEOKDJglBE9NKQtDArFmzcHBwYMqUKWTPnt3U4QghhBBCCCGEEEL850hC0MRkybChe/fu4efnx+eff27qUIQQQgghhBBCCCH+kyQhaGJSIWioZMmSynJhIYQQQgghhBBCCJHxJCFoYlqtJATTGzduHCNGjECr1eLq6oqdnd1bP5MnTx4TRCaEEEIIIYQQQgjx3yAJQROTCsG3JSYmMm7cuPd+XzYVEUIIIYQQQgghhPjfaXT6bVyFSVws3MIov6fc7b1G+T3/VuvWrXn+/DldunTBycnpnT/Tpk0bo8WTFHXLaL/rg5OSZOoIDOgSYk0dgiGthakjMKCxszd1CObNzI5nc5J8OMTUIRhKfG3qCAxYNu1r6hAMabSmjkCRGvPY1CEY0NhkMnUIhqxsTB2BAY0ZHTsACXNHmToEhWXFMqYOwcDtaRdNHYKBIiHdTR2CAYtCFUwdgiEzO9fNiS7+palDMGB244SZ3U+ID4dVziJ/6+ekQtDELLSppg7BrNy6dYslS5ZQr149U4cihBBCCCGEEEII8Z8kCUET00gPQQMFCxYkPj7e1GH8IcmMKlG0llwJuciN4KMkRMaYNBRb5+wUdf+cayFHiTdxLAB2ztlpf2oqqfHPITXF1OGA1gKtbVZS42PMJx5LO1JePYGUZFNHAxaWaC1tSY66C8lmcI5Z2mBVpAqp8S9AZ+LPS2OB1iazWR071i0HkRoXA6lmcOxoLdFmdjCr90ebycGsrj2a2JckP7wBSSYeS63ssCr1qVlddyyy5zGvY8cuu3nFY5XZrD6vTLM2ynX5XbQWlOyQwzzeG0h7f+yymlc81nakxD4zm3HLwlzmhFoLNFobUp4/Mo+VERZWWOYqZl7HjrkdyxKP+cdijvH8A+a1NuAjpNEY5+tDMXjwYPz8/Pjuu++IjTWzJaCmprXEdXhb7FyymzoS7FyyU2F4W+ycTR8LpCUELTLnAK2ZPOPQWmKRJad5xZM9NxoLK1NHAoDGwgrLXCXQmMkSGo2VDRaZHMxjWYbWwvyOHXsn84rH3N4fM7v2WBYsj8b67Q25jE1jbWd21x2zO3bMLR5z+7zkuvxuWkvzeW8g7f0xt3jsndFYmMfnpbEwo3Nda4lljvxoLK1NHQkAGktr8zt2JJ73M6d4zCkWML94/gEzuDJ93KRC0NDChQuJioqiX79+7/y+RqPh8uXLRo5KCCGEEEIIIYQQ4r9DEoImJj0EDTVr1szUIRgykwomAP7/qX3DLZ6QkmjiWNKeLDYxh1hAiUf38im616avLNXYZAbHAujiXqBLjDN1OGisM0EOSH3xBF38C1OHg8YuK+Qujda5iFkdP6mPfkcX/9ykoWjssoFDPlIj75o8FiWeHIWY28uf8N9umzoc8pcpzISv5qVtnGEOGyDoY9BozeOp8P/Hoy1c3vTn1v+fV9jam0c1sD4erSVYmMHcS18tZG7Hsrl9XqnJpl/a+P/vje7ZI3QJpt8AQWNrDzkKmToM82dpg8YclkmZ07VHf91JMYPzSh+H+GvJSebRYkdnBueTyHCSEDQxc5gDmhMfHx9ThyCEEEIIIYQQQgjxnyYJQRPTaGTJ8NmzZ/nkk0/InDkzZ8+e/cufr1q1qhGiEh8iTVYnNCnZTB2G8jRYk8URTUoWEweDEo+5VeSZG61LYdM/gbVMq8zR2udAZ2MOfeAyATA62Mf07w0o74+5VeShSzV9s3gA/aoDc6gmMNdKguTX6JISTB0FGisznf+Z27GclGjyz0vz/8eyJkc+NGY0hqb8/A26549NHAxosrlg9XlvU4fxNnO4DoJZXgtTfzmO7ul9U4eBJkdeKFyZNd1X8uC3O6YOhzxlCjHwq1mmDuNtllZgDjkDM+kxa0DO839NEoImprUwg5PbxNzd3dm6dSuurq64u7uj0WjQ6QzfF/1rGo2GK1eumChSIYQQQgghhBBCiA+fJARNTDYVgY0bN1K0aFHlz0IIIYQQQgghhBBCPZIQNDFJCEK1atXe+WdzoEuMN3UICrNdXiTeT5am/TlzW5pmTqxsADP43KzMc4nuy7FTSL5xw8TBgGXx4jiuWsepTqt4fvGOqcMhW7lC1PtmlnksL9IvLTK389zCCo3ODM759O+POcRjDjG8i00mNJYmvl0x0zYXFhUbShuQP6PVgNYMmrVr/38poRltkGPxWSezOnYGhHibVTziL5jDMl39El1zmO+A+c15/gFJCJqYOWx+JYQQQgghhBBCCCE+HpIQNDGtpRlktMV7aaxN39RfYY5PrczhaSeY73bdljZozCHrb47HDphd1ZlZxKOPRWuJxsIMhmitGcTwDvazp5hVNUHtrWZW3WAG1clKZbI5nFf6OMD8KvI0WvMYw8zpOqiPw9yYS+WHOVa1myOtJViYwXulH0fN4VzX/35zqPCCD3ojho9ZrWae/PzLJZPGULFCWc7+eMikMbyTuYyh/4B5zvQ/IrLLsBBCCCGEEEIIIYQwJkkImpipHxQJIYQQQgghhBBCiI+LJARNTJYMiw+auS3HMDdmtiQj5ex+dDGPTBwMaLLnwqrRAFOHYd4sLEBnBse1xf8vezC39gBmdm6Z3ftjDu0K9MuXzey9SX0agS7+hYmDAY1dVshRCJJMv7wbzKMn+zuZwzJd/RLdlBRISTZtLACa/x8bzO06aG7M7f0xh6WE/38dTD4ahi4qwrSxAJqc+bDqNMY8znNQzvXUR7+ji39u4mBAY5cNHPIRP3MqKbdNv5GaReHiWC1Yy/eH1pu+TYp+jvE6Dl2S6TcB1Vj9f5sxc7nu/ANmcLfxkTPbGZjx9OjR4x/9/MaNG1WKRAghhBBCCCGEEOK/TxKCJiZLhkGnM0yK/vzzz2g0GipUqICTkxMxMTH88ssv6HQ66tWrZ6IoxYdA9/Iputexpg4DjU1mcMgHllbmkfS3sEr7nyqNzeOplaWNqSMwf+ZQSaCPA0iNvGs+T8tzFDK7c0uqpT8c2pwFzOs6aG2HxsIMznVz3XzKHDaB0f9+c7vumFslubmR9+e9LBt1N32FF/xx3TGzOY82V1Gzen/sxk82q3hSzn6F7plpVxxpHHJh1ciDxI0rSI343aSxAGjzFcVq1BJiZ84k5XfTV3MC5Nh74m/9nBlcJT9ukhCE4OBg5c+BgYFER0ezfv16cuXKpbweHR3NgAEDyJcvnylCFEIIIYQQQgghhPjPkHSUiWksjfP1oVi3bh1DhgwxSAYCODo64unpSVhYmIkiE0IIIYQQQgghhPhv+IBSRf9NUiFoKCEhgeTkdzdsjo01/lLQ8HqeRv+d72Nduhj5tq40dRiG4l+gS4wzdRRorDOBA2jsc6DJZG/qcMx26ZVZNUl2LGDqMN5mDk2ttWaw7PQdtI550CU5mjoMNFa2aX8wh88KzPbzMiuJ8egSzaDht/X/f1ZxZjRuOULK7+fRvXpq6nDQZMmBVZV8kGwmm5xY/f/S3FQdpJr4PEv9/1jM7L1JvXcJXewzE0cDmswO5jmmmxtz2GBJbjxFBrCo2sz0S5j//17Luv9I08cCSjyZJ00yj7Yk/4BcFUxNa6SvD0SNGjXw8/Pj5s2bBq//9ttvLF68GDc3NxNFJoQQQgghhBBCCPHfIBWCJiYPagyNHz+ebt260bJlS/Lnz4+DgwNPnz4lIiKC4sWLM27cOKPGk//YKqP+vj9ljlVndlnRWNuaOgrzfG/MkDZ3cbN6imZudM8fo0t49X/s/XmUZWld5/t/nnPOPkMMmRGZlWRVEZU1UVIgiBYWokLjlUEW2GjTV5ALzoKoDVwWC4cfKkjb1kVw0dKotAKXK0034kDbousqYndfbaWspkXaZhCLoqCoKSvHGM/4/P44Z8fJqow4GScyI5/Pjv1+rRUrssuw4+M+ez/Pjr2/3+dJmiE054aVFm4VcG4bH5gtQO7m9GvfrO4/pF3UOvuqm3Tkfb8hNecVMoONhPJzpzHrcS7XhnNn9Yav86gmcN3syWEjj3wTj1pDIYS0WaTNc7ly7dcwp0/iNk8YVEtvVkq31xS7BpXbWSt1hC31//L3FU/emzqGwqGrlf3TV6eO4W31tE/V/+KSBvd9XnHtdOo4kqTsUTft6Od4IJhYqBncWBi56qqr9Ed/9Ef6/d//fX3iE5/QmTNn9OhHP1o/8iM/ou/4ju9QlmWpIwIAAAAAABQaDwRT8ywuSKrVaumlL32pXvrSl6aOAgAAAAAAsO/wQDCxUKFC8JFOnjyp97znPfqrv/orHT9+XO9+97v1Z3/2Z7r55pv1rGc9K3U8nMutrRG4COHgUYXZhbQh8rY9s1anwfEvKq6fTRxGCq0D0uJS6hjnc1gsXtr8vBbe/nPpWwld2wh7G8nb9iRp8+6va7JRxagrt//5/664fDxtGElh/oiyIzdq7c1vUv8LadvfqzfcpINvf7ffPY/ZJifaWPa4turDNtR49rhi+/JvSPhIoTE7nLcclk/YXHYjeCx54dCCv4XK136LTxuqpHj6vuRL2kijZW3c7sFmFxSaM6lTjJdyuPqx6e+/pmQwEpRcLVyer4L48pe/rBe84AX60Ic+pKNHj+rEiRPq9/u666679OpXv1r/5b/8l9QRAQAAAAAACo0KwcSoEHy4t7zlLTp8+LDe//73a2ZmRk94whMkSb/8y7+sdrutd73rXfqWb/mWtCExZlbFZPf23i2PWRUTJjA7dypHrvN445lXN7idy5WaVDWoVK4Mb+vu+YG3qP3pO5NGaTz+Rl3/H/+Ner//bzW4/+6kWSSpcuW1yn7oX3pU6UjjczlreBTJjKqTqzfeYlJ1Ntx0Zebn3pR+7Mk/q35XsZd+HAz5uFPNFKLBuFP1XN87zC16VA45btjjtlGYmTB3SKE/lzrG5vEJC1cppB4HJdvPy4rL/fsU+Ksstcpl+iqIv/7rv9aP/diP6cCBA+ft5PbiF79Yn/982rYRAAAAAACAoqNCMDUqBM9T3eaNVafTOe8hIQAAAAAAAKbDA8HEQq1A5XuXwdd//dfrN37jN/RN3/RNajSGJfYhBA0GA/2H//AfdMstt1zeQE4lv2yccWFuLcxueczaGu04fF6u7dSddZM2wtGY7HYum7UwP/ptL1XcWE4bpTkvSaq98EfSt3xKvu3m/b7U76XNIklheC7HtdMWi9erOWrXM9ioIt+kQrWGx4vp/FyOg+FXanmGekuhYjCHjVp0B1/5B8W1U4nDSGFmUTp8nUcrYf63hNuc7mb9rM+mIovSyv/vTer9Y/ouudpjbtLir787dYyHiQ99SXE97f2OJIXW/HDDFYe/JaZk+ldZiVAh+DCve93r9JKXvETPec5z9A3f8A0KIeg973mP7rzzTt1999369//+36eOCAAAAAAAUGg8EEzM4k2jka/6qq/S7/7u7+qd73ynbr/9dlWrVf3VX/2Vbr31Vr3lLW/RYx/72MsbyOkJv2vlkBOHt6/S5hvYwfEvKq6fTRxGCq0D0uKSen/+24on7kkdR+HwkrJ//jrFs8cV26up4yg0Zodv9XpdqddOGyaazgluC5CbXesKFY8xepQhHF5KvwC56+LjbtWl3XWPSpQYh99nFqTRhh4p5ZuKWGwC4zru2J3LHY+qs9E8Wrnm8V7VyU76Bvc70ubfWf1P/L+Kp+9PHEYKC1cqe9YPK/Z7ig6V26MMM696qeL6mcRhpNA6mDrCecLCVQpzi6ljSLXhnBWXT1j8bSNJuuKGHf0YDwRTo2X4PNdff71++Zd/OXUMAAAAAACAfYkHgqk5rLVxAYPBQO985zv1O7/zOzp79qye/OQn641vfKOuvfbaC/7vvfzlL9fXfu3X6lWvetW2P3fHHXdMlefWW2+d6ucBAAAAAAAwxgPB1AqwhuCv/dqv6YMf/KBuu+02HT16VG9961v18pe/XB/5yEdUr29dBr+xsaE3vOEN+su//Et97dd+7cT//7/ne77nvNbpOGpdyYUQFGNUCEGf+cxnLur/HlxCq6c9Wp3qM14LuY7a9ipHrrNqV6l964ut8oQDRxT6Bu0HeTtPLZNCnPyze54lG343O5ftuB0ftzxOGzFgshA8rrP8PsxteQAnbte5WwtzY0ahZvCnpeO5I3lsAhPPbTfP0mbJc0iqPvm5Vveng8983KaFWdd8jSpX3WR1fP7ni35Fq//zrqRRZp94vW756Fs97t2lzespzB9WmJlPHGY6BqN2uQXzB4KdTkfvfe979frXv17PeMYzJElvf/vb9fSnP10f/ehH9fznP/+8/53/8T/+h97whjeo2+3qwIEDF/wdv/Vbv3XJcwMAAAAAAGBrPBBMzeEt7ASf/exntbq6qqc+9amb/+3AgQN6/OMfrzvuuGPLB4J/8Rd/oWc/+9l6xSteoRe84AUX/B1PecpTLmlmXEa1uhQN3k7XTN8Go1gGveFC2yk5VAptxaDiTDqn6mz9rFF1sjzOHWnz/Bksn0i+oVFoHZCuShpha511j40PstHcWaunrxrKc0h217rDAu3jjafaJufOqBrGoeIszyGp/xe/p3jiK4nDSOHwo5W94DXqffg3NHjg7tRxVDl6rbIffLM0iNIg8ec1GJ07VHNOVL31eVYVeW6e+KHXpD8+psfG5f5UEpuKFIZ5heD99w/Lpa+66uF39Y961KN03333bfm/85rXvOaifucnP/lJ/c3f/I263e5m63CMUWtra/rEJz6hD33oQxf1/z8AAAAAAECZ8UAwtcv0QPCZz3zmxP/5xz72sS3/+/r68C3xI9cKbDQaOnPm0m9//oEPfEC/8Au/cN4agpJUqVT0tKc97ZL/zokc1mbJVQzeAj+S2RvGwb2fU1w7nTaLpDCzMKwmMFvfJz70JcX15cRhpNCalxaXtPIzb1L/Hz+fOo6qj7lJi7/67mF1VeoKvfz3m1WcqTmvkDXSZpHGb4RbBxTqzbRZpHEeh3MnzyGpcsUxqddOm6VmcL5spZYpyGA+rY3W76pUFaoG82i+Np7ZtR5mDyo0El/r+bns9lk5VJzlOSTpqmPS/GzaLJI0d1iSVPtnr0hfxSSN54lqVYqJz5+8M83s/tSu+rbXTT+HSlL0LhzCFlzuT6dgcPdabqFWvSxfu9VsDk/oTufhE2q73VardekXDP93/+7f6WlPe5puv/12/dAP/ZBe9KIX6ZOf/KR+5Vd+RY1GY0ctyAAAAAAAANiewWuukrtMFQXbVQBeSN4q/OCDD+rYsWOb//3BBx/UzTfffEmyneuee+7RT/3UT+ngwYN64hOfqH/zb/6Nms2mvu3bvk133XWXfuu3fkvf/u3ffsl/LwAAAAAAQFnwQDA18zUEb775Zs3Nzen222/ffCB49uxZffrTn9bLXvayS/77sizbrEq87rrrdPfdd6vb7SrLMt1yyy1673vfe8l/50QVo01fHNrRzFWufqxXe0ioeJxDo3MnXHFMwej4zP3Cm7w+LyeVmlQ1aAWrjG4T3Npn3Nqd3DiMPa5zVq+r2E0/7oRRk07vo/9B8fiXE6eRwpFrlH33T6vzrv9Lgy/fmTqOKtfcqOyn36nBA3cprl/6JWqmEVoHpcPX2S0NEM88oLhhsAxIc146cr2qj7nVa053myecxmWHLHkOya8dv5ZJ4fzlqy67apY6AUrAYFYruUrl8nztUr1e18te9jK97W1v08c+9jF99rOf1Wtf+1pdeeWVevazn61+v6/jx49rY+PSrPvwuMc9Tv/5P/9nSdK1116rwWCgT37yk5LGG5wAAAAAAABg9wwexZdc1eDtzAW8+tWvVq/X08/8zM9oY2NDt956q97znveoXq/rnnvu0TOf+UzddttteuELX3jRv+sHfuAH9C/+xb/QmTNndNttt+mZz3ymfuInfkLf9m3fpj/8wz/Uk5/85Evwf1HBGSy8u7norhu3t8FmG0PEs8cV26uJw0ihMTvcdMWt6szhjbnrpiJub8vjYPiV2ihDPHW/T6XOoWMX/sHLqPv/vF2Dr3whdQxVHn2Dsv/zlxU3VizGQTV6kqTac17qMQ6ONs6o/+Brkt9jSFLIht0ilatuSl91llec9buKvfQVcCGv+p87JDUu/Xre0wrZMMPgy59WXDuVOI0UZhaH9xgOc7o0nkcd7lFN70/tuiJQGG4bJhYRZ31q5i3DklStVvX6179er3/968/7ny0tLelzn/vctv+7f/7nfz7V73rWs56ld73rXbrzzmG7ypvf/Ga97nWv0wc/+EE98YlP1M/93M9NFx4AAAAAAAAPwwPBxMJFtPPuV9/yLd+ib/7mb5YkLS4u6p3vfKc6nY4WFhbSBgMAAAAAANgHeCCYWgEqBC+nTqejN7/5zfpf/+t/6cMf/rAk6ZOf/KRe8YpX6CUveYl+6qd+StUCtFnvKYeFdx1aMLbSTd9OLXl0Vm4lHDii0D+YOsa4/apalaLBNOQ4ppgtXm/HrL3o7G2/qd4/fD5xGKn2VTfpinc/JXWMh8m+77XpWz6lzXEnzByQsvQbCeUtsXZqmYIMrq2a4WL61UzBYamCfOmEelPB4e+IUbt55ZrHW13rFi260rhN12Gpi/z3d9YVO+tps0gKddMWZjP92z+ieOq+1DEUFq9S9txXpo7xMG4bJjos7TUtz7O+TKrVy/NVEO94xzv0x3/8x/rO7/zOzf/21V/91frJn/xJffjDH9Zv/uZvpgsHAAAAAACwDxiUZpQcLcMP80d/9Ef6yZ/8Sb34xS/e/G8HDx7U93zP96hSqeh973ufXvlKrzcTl51DZQyL7u6Mw2eV53DktuD3+lnFzlraKPUZaVEelQSSR4YCOPjWN3pVxrTXFLtpqz/yjQbsDAbDr9TyDGbj4Mbb/y8N7k5f7Vq59iZlv/Auq3M5nnlAcWMlaRZJCs254eZBzBOTmV1bFveE+f1grSmD2lKpNqqUdjg2eQ5J/Ts/obhyInEYKcwdVvb1S6p+w7d73WNgey73GFMw/SuxRILFcGzj1KlTWlraeoee66+/Xg888MBlTgQAAAAAALC/8EAwtRofwbluvPFG/cmf/MnmpiLn+uhHP6prr702QSoAAAAAAID9g6dRqTksBmzkB3/wB/W6171Op0+f1rOe9SwdPnxYJ0+e1J/92Z/pT//0T3Xbbbeljghnbouhuy1obbLQbchMd11pzimkXsh+tDg7m4oUTGfd5NoajTeNGYXULxzz1iK3xeKzhkdzxuhajyfvVdxYThxGCs15aXFJzde83uRcHrUSOmycMfqswoEjw01pUsvnifZa8mUuJCnUR+OO2z2PWx6HsTAfB/tdxV76FtQQhi3dD77yX6nz2X9MnEaq3/wYPfq3f13VG59Miy52p95SKND+DRIPBNPjj62Hef7zn6/l5WW9853v1J/+6Z9u/vfFxUX97M/+7MM2GwEAAAAAAMD0eCCYGpuKnOe7v/u79eIXv1h33XWXTp8+rQMHDuiGG25QhWNlp/+pP1c882DqGAoHH6XsGd8rDaLHQq6DUQWc24LfLgvdOmTYisPnFT03GrCobJB8X6K5VSc7fF5OC+nnOSTTa6uXNkueQ5JqDQWHEsrNDXJWFTuJNxWpj46N27ncOqBQb6bNIo0/K7dryzFP6jks//3VTCEaVE9Wh3PW4R99quKZ6xOHkcLBo5KkeOIeq8ptN3HlZPLq5OEmfH7Hpoh4IJhY0UpKL5cQgm644QadOXNGX/rSl7S6uqr5+fnUsQAAAAAAAArP9FV7iVQql+fL3Kc+9Sm98pWv1H/8j/9x87+9//3v1z/5J/9EL3rRi/T0pz9d73nPe9IFBAAAAAAA2CeoEEytAA/r9tpnPvMZvexlL9OhQ4f0whe+UNLwAeEv/uIv6jGPeYxe85rX6Atf+ILe/va369prr9WznvWsxInT+ofvfrvW/v4LSTPMPOEGffWf/LKqX/OtXovu1jIpGGxYMWqBsGsvclno1nWRZIfPq2I6LTscmzyH5NESK/m2xTrkGWVxaC2SzmkvMjt3whXHFJzmUTeNWYVq4nEx38TD7NzBBbh9Xg4tzHkWs/vl6pOeafX3RDi8xLg8QZg7pNCfSxtic1mJNcVu2mUlJClkrdQRds30L48ScfjjPLHf+I3f0OMe9zi9733vU6s1vJje//73S5Le+ta36uabb5YkPfTQQ3r/+99f+geCAAAAAAAAF4MHgqnxlk933HGHfuqnfmrzYaAk/eVf/qWuueaazYeBkvS0pz1NH/7wh1NEtPJVH3xt+rdopm+s1OtKvXbqFFI0WJB9K25vy4HdcttACNuyqCSQxvOW27mzetqqgjIun1Bsr6aOo9CYHVZ0djaSV38E0+s8nr5PcWMldQyF5pzn4v4Om3jkOaThpmGDxBt5VIZjXzz9gGLb4NxpDM+d9jt/UYMv/WPqOKoce4yyn/211DEwjcaMQs3gkZbr38Y7YHD0So6WYZ0+fVpXXnnl5v/7zjvv1KlTp86rBGy1Wup0DMq3AQAAAAAACowHgqnxQFALCwt66KGHNv/fH//4xxVC0Dd+4zc+7OfuvPNOHTp06HLHAwAAAAAA2Fd4IJgaawjqKU95in77t39bz3nOczQYDPR7v/d7ajQaevrTn775M51ORx/4wAd0yy23JEwKe/2uYi99FWkIo+varBXMYqMBaXOzgf5f/yfFU/clDiOFxauUPe/HPFqqnVqLpM32Ijtu1zqKoxI8XsZWRktL1OpSNLjWa6PF9OcPK8zMJw6jcfuVQzuYaStYmLti2K6bWq2ZOsHWNlZ87sEkq01FwtyC1Ej/uYVsmKHxw69S7BhsDFEfLl/l1lKtjWWr4+NkcPenFFdPpY6hMLvouXTCDvBAMDWHm9LEfvRHf1QvfvGLN1uE7733Xv34j/+45ueHN6S/93u/pw984AO666679Eu/9EspowIAAAAAABQeDwRTc1jsNrGbbrpJH/rQh/Te975XJ06c0Mtf/nK95CUv2fyf/+t//a9Vq9X0q7/6q3rc4x6XMKmH//miX9Hq/7wraYbZJ16vWz761qQZtuRQSSCdU9kwq+BQBWz69r76jS9Iv0GONP68HCooR9WTFpUEeQ7Jr2KxWpUGDte6wWeE6Thc53kOaVgd6HBtOVQpunPbpKKWScFgw5NqljrB1ho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      "text/plain": [
       "<Figure size 1300x1000 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "scaled_importance_clean.drop(columns=['median_rank', 'top_20_frequency', 'sum_of_scaled_importances'], inplace=True, errors='ignore')\n",
    "\n",
    "\n",
    "#clustermap of scaled feature importance\n",
    "sns.set(font_scale=1)\n",
    "\n",
    "cm = sns.clustermap(scaled_importance_clean, \n",
    "                    cmap= \"rocket_r\", figsize=(13, 10), \n",
    "                    annot=False, fmt='g', \n",
    "                    xticklabels=1, yticklabels=False, \n",
    "                    row_cluster=False, col_cluster=True,\n",
    "                    dendrogram_ratio=0.15,linewidths=0.00, \n",
    "                    metric='correlation', method='average',\n",
    "                    cbar_kws={\"label\": \"Scaled importance\", \"orientation\": \"vertical\"}, \n",
    "                    cbar_pos=(0.08, 0.75, 0.02, 0.2))\n",
    "\n",
    "cm.tick_params(axis='x',labelsize=10, rotation=45)\n",
    "cm.ax_heatmap.xaxis.tick_top()\n",
    "cm.ax_heatmap.vlines(range(1,len(scaled_importance_clean.columns)), 1,ymax=len(scaled_importance_clean.index), colors='white', linewidth=1)\n",
    "\n",
    "plt.savefig(\"E:\\OneDrive - National University of Ireland, Galway\\Figures\\FigS6.tif\", dpi=300, pil_kwargs=dict(compression='tiff_lzw'))\n",
    "# SCALED IMPORTANCES"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1300x1000 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "scaled_importance_top30.drop(columns=['median_rank', 'top_20_frequency', 'sum_of_scaled_importances'], inplace=True, errors='ignore')\n",
    "\n",
    "\n",
    "#clustermap of scaled feature importance\n",
    "sns.set(font_scale=1)\n",
    "\n",
    "cm = sns.clustermap(scaled_importance_top30, \n",
    "                    cmap= \"rocket_r\", figsize=(13, 10), \n",
    "                    annot=False, fmt='g', \n",
    "                    xticklabels=1, yticklabels=1, \n",
    "                    row_cluster=False, col_cluster=True,\n",
    "                    dendrogram_ratio=0.15,linewidths=0.00, \n",
    "                    metric='correlation', method='average',\n",
    "                    cbar_kws={\"label\": \"Scaled importance\", \"orientation\": \"vertical\"}, \n",
    "                    cbar_pos=(0.02, 0.75, 0.02, 0.2))\n",
    "\n",
    "cm.tick_params(axis='x',labelsize=10, rotation=45)\n",
    "cm.ax_heatmap.xaxis.tick_top()\n",
    "cm.ax_heatmap.vlines(range(1,len(scaled_importance_top30.columns)), 0,ymax=len(scaled_importance_top30.index), colors='white', linewidth=1)\n",
    "cm.ax_heatmap.hlines(range(1,len(scaled_importance_top30.index)), 0,xmax=len(scaled_importance_top30.columns), colors='white', linewidth=1)\n",
    "\n",
    "plt.savefig(\"E:\\OneDrive - National University of Ireland, Galway\\Figures\\Fig5_v2.tif\", dpi=300, pil_kwargs=dict(compression='tiff_lzw'))\n",
    "# SCALED IMPORTANCES"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.LineCollection at 0x20896b66cd0>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1300x1000 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "colors = ['#F1F7ED','#070707']\n",
    "cmap = LinearSegmentedColormap.from_list('Custom', colors, len(colors))\n",
    "cm2 = sns.clustermap(import_df_T, cmap= cmap, annot=False, row_cluster=False, dendrogram_ratio=0.15,figsize=(13, 10), metric='cityblock', vmin=0, vmax=1, cbar_kws={\"ticks\":[False,True], \"label\": \"Top 20 feature\", \"orientation\": \"vertical\", \"shrink\": 0.2, \"pad\": 0.05},linewidths=0.0, cbar_pos=(0.08, 0.75, 0.02, 0.2), xticklabels=1, yticklabels=0)\n",
    "cm2.tick_params(axis='x',labelsize=10, rotation=45)\n",
    "cm2.ax_heatmap.xaxis.tick_top()\n",
    "cm2.ax_heatmap.vlines(range(1,len(scaled_importance_clean.columns)), 1,ymax=len(scaled_importance_clean.index), colors='white', linewidth=1)\n",
    "\n",
    "# TOP 20 FEATURES PER MODEL"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "base",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}