Bioremediation_potential_predicting.ipynb

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  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyN6bz3d4KoLn10LfVXEFeBi",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/liberentaizp/Bioremediation-potential/blob/main/Bioremediation_potential_predicting.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Data loading**"
      ],
      "metadata": {
        "id": "W6VfrdjWr2oU"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "frvzu6Coqgf-",
        "outputId": "f91e61da-12d1-403b-d56c-049cb631a5dc"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Importing necessary packages\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from scipy import stats\n",
        "import numpy as np\n",
        "from sklearn.decomposition import PCA\n",
        "#!pip install scikit-bio\n",
        "from skbio.stats.distance import permanova, DistanceMatrix\n",
        "from skbio.stats.ordination import pcoa\n",
        "from scipy.spatial.distance import pdist, squareform\n",
        "from itertools import combinations\n",
        "from sklearn.model_selection import train_test_split\n",
        "import xgboost as xgb\n",
        "from sklearn import metrics\n",
        "from sklearn.metrics import mean_absolute_error\n",
        "import sklearn\n",
        "from sklearn.metrics import mean_squared_error\n",
        "from sklearn.datasets import make_classification\n",
        "from sklearn.model_selection import StratifiedKFold\n",
        "from sklearn.metrics import accuracy_score\n",
        "import shap\n",
        "# Set display options to show all rows and columns\n",
        "pd.set_option('display.max_rows', None)\n",
        "pd.set_option('display.max_columns', None)\n",
        "pd.set_option('display.width', None)\n",
        "pd.set_option('display.colheader_justify', 'left')"
      ],
      "metadata": {
        "id": "-VT82CE_r8VJ"
      },
      "execution_count": 85,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "First we **load the data** and make sure we have uploaded it okay by seeing the first 5 rows. The data can be loaded directly from a drive directory."
      ],
      "metadata": {
        "id": "jHEMvL_9sPNS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data = '/content/drive/MyDrive/CIIMAR/Libe/BLAST 60 genomes/gene_counts_and_metadata.xlsx'\n",
        "df = pd.read_excel(data, header=0, index_col=0, engine='openpyxl')\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 310
        },
        "id": "Jb55hIQZsMwl",
        "outputId": "41eb0419-b54a-4086-e534-c594fb0e1b0d"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            PobA  alkB  amoA  arsC  arsR1  bcsA  bcsB  BenR  bnzA  bphA1  bphA2  bphA3  bphA4  \\\n",
              "strain                                                                                          \n",
              "12917       0     1     1     0     0      1     1     1     0     0      0      0      0       \n",
              "991_21      0     0     1     0     0      0     1     1     0     0      0      0      0       \n",
              "ABACB       0     0     1     0     0      1     0     0     0     0      0      0      0       \n",
              "AHSWHJXPP1  0     1     1     0     0      0     0     1     1     0      0      0      0       \n",
              "B001        0     1     1     0     1      0     0     1     0     0      0      0      0       \n",
              "\n",
              "            bphB  bphC  bphD  chR  copA  copA1  dmpK  dmpL  dmpM  dmpN  dmpO  dmpP  intl1  katA  \\\n",
              "strain                                                                                            \n",
              "12917       0     0     0     0    1     1      0     0     0     0     0     0     1      1      \n",
              "991_21      0     0     0     0    0     1      0     0     0     0     0     0     4      1      \n",
              "ABACB       0     0     0     0    0     1      0     0     0     0     0     0     0      0      \n",
              "AHSWHJXPP1  0     0     0     0    0     1      0     0     0     0     0     0     2      1      \n",
              "B001        0     0     0     0    0     2      0     0     0     1     0     0     0      1      \n",
              "\n",
              "            merA  merB  merR  merT  mrdH  mreA  nahA1  nahA2  nahA3  nahA4  nahB  nahC  nahD  \\\n",
              "strain                                                                                         \n",
              "12917       2     1     2     2     0     0     0      0      0      0      0     0     0      \n",
              "991_21      1     0     1     0     0     0     0      0      0      0      0     0     0      \n",
              "ABACB       0     0     0     0     0     0     0      0      0      0      0     0     0      \n",
              "AHSWHJXPP1  0     0     0     0     0     0     0      0      0      0      0     0     0      \n",
              "B001        0     0     0     0     0     0     0      0      0      0      0     0     0      \n",
              "\n",
              "            nahE  nahF  nirS  norB  oxyR  pcaG  pcaH  pcaR  rubA  sodA  srpA  srpB  srpC  srpR  \\\n",
              "strain                                                                                           \n",
              "12917       0     0     1     0     1     1     1     1     2     1     0     0     0     0      \n",
              "991_21      0     0     0     0     0     1     1     1     0     1     0     0     0     0      \n",
              "ABACB       0     0     0     0     1     1     1     1     0     1     0     0     0     0      \n",
              "AHSWHJXPP1  0     0     1     0     1     1     1     1     0     1     0     0     0     0      \n",
              "B001        0     0     1     0     1     1     1     1     1     1     0     0     0     0      \n",
              "\n",
              "            srpS  tnpA  todA  todB  todC1  todC2  todD  todE  todF  todT  tolA  tolB  tolQ  tolR  \\\n",
              "strain                                                                                             \n",
              "12917       0     3     0     0     0      0      0     0     0     0     1     1     1     1      \n",
              "991_21      0     6     0     0     0      0      0     0     0     0     1     1     2     1      \n",
              "ABACB       0     0     0     0     0      0      0     0     0     0     1     1     1     1      \n",
              "AHSWHJXPP1  0     1     1     1     1      1      1     1     1     1     1     1     1     1      \n",
              "B001        0     0     0     0     0      0      0     0     0     0     1     1     1     1      \n",
              "\n",
              "            ttgA  ttgB  ttgC  ttgD  ttgE  ttgF  ttgG  ttgH  ttgI  xylA  xylB  xylC  xylM  xylN  \\\n",
              "strain                                                                                           \n",
              "12917       1     2     1     0     0     0     0     0     0     0     0     0     0     0      \n",
              "991_21      1     2     1     0     1     0     0     0     0     0     0     0     0     0      \n",
              "ABACB       1     2     1     0     2     0     0     0     0     0     0     0     0     0      \n",
              "AHSWHJXPP1  1     1     1     1     2     0     0     0     0     0     0     0     0     0      \n",
              "B001        1     2     1     0     0     0     0     0     0     0     0     0     0     0      \n",
              "\n",
              "            xylS  xylU  xylW Isolation matrix Specific isolation matrix More specific Host    \\\n",
              "strain                                                                                         \n",
              "12917       0     0     0        Clinical                 Rectal              No       Human   \n",
              "991_21      0     0     0        Clinical            Respiratory         Trachea       Human   \n",
              "ABACB       0     0     0     Rhizosphere      Agricultural soil            Kiwi       Plant   \n",
              "AHSWHJXPP1  0     0     0        Clinical                  Urine          Lesion       Human   \n",
              "B001        0     0     0            Soil                    Sea            Sand          No   \n",
              "\n",
              "           Pollution Localization  ST Cluster  Bioremediation potential  \n",
              "strain                                                                   \n",
              "12917       No             France  15          35                        \n",
              "991_21      No             Brazil   7          31                        \n",
              "ABACB       No         New zeland  19          18                        \n",
              "AHSWHJXPP1  No              China   5          33                        \n",
              "B001        No        South Korea  12          23                        "
            ],
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              "      <th>xylW</th>\n",
              "      <th>Isolation matrix</th>\n",
              "      <th>Specific isolation matrix</th>\n",
              "      <th>More specific</th>\n",
              "      <th>Host</th>\n",
              "      <th>Pollution</th>\n",
              "      <th>Localization</th>\n",
              "      <th>ST Cluster</th>\n",
              "      <th>Bioremediation potential</th>\n",
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              "      <td>Rectal</td>\n",
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              "      <td>No</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Clinical</td>\n",
              "      <td>Respiratory</td>\n",
              "      <td>Trachea</td>\n",
              "      <td>Human</td>\n",
              "      <td>No</td>\n",
              "      <td>Brazil</td>\n",
              "      <td>7</td>\n",
              "      <td>31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ABACB</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Rhizosphere</td>\n",
              "      <td>Agricultural soil</td>\n",
              "      <td>Kiwi</td>\n",
              "      <td>Plant</td>\n",
              "      <td>No</td>\n",
              "      <td>New zeland</td>\n",
              "      <td>19</td>\n",
              "      <td>18</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>AHSWHJXPP1</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>1</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Clinical</td>\n",
              "      <td>Urine</td>\n",
              "      <td>Lesion</td>\n",
              "      <td>Human</td>\n",
              "      <td>No</td>\n",
              "      <td>China</td>\n",
              "      <td>5</td>\n",
              "      <td>33</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>B001</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Soil</td>\n",
              "      <td>Sea</td>\n",
              "      <td>Sand</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>South Korea</td>\n",
              "      <td>12</td>\n",
              "      <td>23</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-2a234a4c-a849-4380-b434-6bae1cb9dccd')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-2a234a4c-a849-4380-b434-6bae1cb9dccd button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-2a234a4c-a849-4380-b434-6bae1cb9dccd');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-4f540b58-c2fa-42d9-8032-3ba818ad3920\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-4f540b58-c2fa-42d9-8032-3ba818ad3920')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-4f540b58-c2fa-42d9-8032-3ba818ad3920 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df"
            }
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Data cleaning and exploration**"
      ],
      "metadata": {
        "id": "4jgTJ70mxSPM"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## *Check missing values*\n",
        "\n"
      ],
      "metadata": {
        "id": "BawIYQPox3Fe"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "First of all we want to check for **missing values** that could hinder our analysis."
      ],
      "metadata": {
        "id": "iOHWaKWezpnX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "missing_values = df.isnull().sum()\n",
        "if missing_values.any():\n",
        "  print(\"Missing values found:\")\n",
        "  print(missing_values)\n",
        "else:\n",
        "  print(\"No missing values found.\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "V-v9EnGDsbTw",
        "outputId": "9d321f90-9dd3-4876-dde1-79ed4418d472"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "No missing values found.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Depending on the results of the missing values we will take different approaches. If we don't have missing values we can continue with the analysis.\n",
        "\n",
        "If not, we should consider:\n",
        "\n",
        "*   **Removing** rows or columns with missing values if they could be more misinforming than informing\n",
        "\n",
        "```\n",
        "df.dropna(inplace=True)  # Removes any rows with missing values\n",
        "\n",
        "```\n",
        "\n",
        "*   **Fill** missing values with specific value (mean, median...)\n",
        "\n",
        "```\n",
        "df.fillna(df.mean(), inplace=True)  # Replaces missing values with the column mean\n",
        "```\n",
        "\n",
        "*   **Forward or backward** filling, where we fill the missing values with the previous or posterior value in the column\n",
        "\n",
        "```\n",
        "df.fillna(method='ffill', inplace=True)  # Forward fill\n",
        "df.fillna(method='bfill', inplace=True)  # Backward fill\n",
        "\n",
        "```\n",
        "\n",
        "*   Using **interpolation** to estimate missing values, estimating the missing data by creating a smooth curve or line through known points\n",
        "\n",
        "```\n",
        "df.interpolate(method='linear', inplace=True)  # Linear interpolation\n",
        "\n",
        "```"
      ],
      "metadata": {
        "id": "ZBjOooqKyIIP"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## *Gene count subset*\n"
      ],
      "metadata": {
        "id": "bFIsyRysyNMk"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "For posterior analyses like clustering, we will need to only use the **gene count values**, so we will create a new dataset that will remove the categorical and bioremediation potential data called ***df_genecount***. For this we will have to write all the features that we want to drop from the df dataset.\n"
      ],
      "metadata": {
        "id": "hzV4JxuHzlhw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_genecount=df.drop(['Isolation matrix','Specific isolation matrix','More specific','Host','Pollution','Localization','ST Cluster','Bioremediation potential'], axis=1)\n",
        "df_genecount.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 258
        },
        "id": "gTbXLc0fzL1Q",
        "outputId": "9366f80f-ca0c-470f-d35d-89fa0458c335"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            PobA  alkB  amoA  arsC  arsR1  bcsA  bcsB  BenR  bnzA  bphA1  bphA2  bphA3  bphA4  \\\n",
              "strain                                                                                          \n",
              "12917       0     1     1     0     0      1     1     1     0     0      0      0      0       \n",
              "991_21      0     0     1     0     0      0     1     1     0     0      0      0      0       \n",
              "ABACB       0     0     1     0     0      1     0     0     0     0      0      0      0       \n",
              "AHSWHJXPP1  0     1     1     0     0      0     0     1     1     0      0      0      0       \n",
              "B001        0     1     1     0     1      0     0     1     0     0      0      0      0       \n",
              "\n",
              "            bphB  bphC  bphD  chR  copA  copA1  dmpK  dmpL  dmpM  dmpN  dmpO  dmpP  intl1  katA  \\\n",
              "strain                                                                                            \n",
              "12917       0     0     0     0    1     1      0     0     0     0     0     0     1      1      \n",
              "991_21      0     0     0     0    0     1      0     0     0     0     0     0     4      1      \n",
              "ABACB       0     0     0     0    0     1      0     0     0     0     0     0     0      0      \n",
              "AHSWHJXPP1  0     0     0     0    0     1      0     0     0     0     0     0     2      1      \n",
              "B001        0     0     0     0    0     2      0     0     0     1     0     0     0      1      \n",
              "\n",
              "            merA  merB  merR  merT  mrdH  mreA  nahA1  nahA2  nahA3  nahA4  nahB  nahC  nahD  \\\n",
              "strain                                                                                         \n",
              "12917       2     1     2     2     0     0     0      0      0      0      0     0     0      \n",
              "991_21      1     0     1     0     0     0     0      0      0      0      0     0     0      \n",
              "ABACB       0     0     0     0     0     0     0      0      0      0      0     0     0      \n",
              "AHSWHJXPP1  0     0     0     0     0     0     0      0      0      0      0     0     0      \n",
              "B001        0     0     0     0     0     0     0      0      0      0      0     0     0      \n",
              "\n",
              "            nahE  nahF  nirS  norB  oxyR  pcaG  pcaH  pcaR  rubA  sodA  srpA  srpB  srpC  srpR  \\\n",
              "strain                                                                                           \n",
              "12917       0     0     1     0     1     1     1     1     2     1     0     0     0     0      \n",
              "991_21      0     0     0     0     0     1     1     1     0     1     0     0     0     0      \n",
              "ABACB       0     0     0     0     1     1     1     1     0     1     0     0     0     0      \n",
              "AHSWHJXPP1  0     0     1     0     1     1     1     1     0     1     0     0     0     0      \n",
              "B001        0     0     1     0     1     1     1     1     1     1     0     0     0     0      \n",
              "\n",
              "            srpS  tnpA  todA  todB  todC1  todC2  todD  todE  todF  todT  tolA  tolB  tolQ  tolR  \\\n",
              "strain                                                                                             \n",
              "12917       0     3     0     0     0      0      0     0     0     0     1     1     1     1      \n",
              "991_21      0     6     0     0     0      0      0     0     0     0     1     1     2     1      \n",
              "ABACB       0     0     0     0     0      0      0     0     0     0     1     1     1     1      \n",
              "AHSWHJXPP1  0     1     1     1     1      1      1     1     1     1     1     1     1     1      \n",
              "B001        0     0     0     0     0      0      0     0     0     0     1     1     1     1      \n",
              "\n",
              "            ttgA  ttgB  ttgC  ttgD  ttgE  ttgF  ttgG  ttgH  ttgI  xylA  xylB  xylC  xylM  xylN  \\\n",
              "strain                                                                                           \n",
              "12917       1     2     1     0     0     0     0     0     0     0     0     0     0     0      \n",
              "991_21      1     2     1     0     1     0     0     0     0     0     0     0     0     0      \n",
              "ABACB       1     2     1     0     2     0     0     0     0     0     0     0     0     0      \n",
              "AHSWHJXPP1  1     1     1     1     2     0     0     0     0     0     0     0     0     0      \n",
              "B001        1     2     1     0     0     0     0     0     0     0     0     0     0     0      \n",
              "\n",
              "            xylS  xylU  xylW  \n",
              "strain                        \n",
              "12917       0     0     0     \n",
              "991_21      0     0     0     \n",
              "ABACB       0     0     0     \n",
              "AHSWHJXPP1  0     0     0     \n",
              "B001        0     0     0     "
            ],
            "text/html": [
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              "  <thead>\n",
              "    <tr style=\"text-align: left;\">\n",
              "      <th></th>\n",
              "      <th>PobA</th>\n",
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              "      <th>dmpM</th>\n",
              "      <th>dmpN</th>\n",
              "      <th>dmpO</th>\n",
              "      <th>dmpP</th>\n",
              "      <th>intl1</th>\n",
              "      <th>katA</th>\n",
              "      <th>merA</th>\n",
              "      <th>merB</th>\n",
              "      <th>merR</th>\n",
              "      <th>merT</th>\n",
              "      <th>mrdH</th>\n",
              "      <th>mreA</th>\n",
              "      <th>nahA1</th>\n",
              "      <th>nahA2</th>\n",
              "      <th>nahA3</th>\n",
              "      <th>nahA4</th>\n",
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              "      <th>nahC</th>\n",
              "      <th>nahD</th>\n",
              "      <th>nahE</th>\n",
              "      <th>nahF</th>\n",
              "      <th>nirS</th>\n",
              "      <th>norB</th>\n",
              "      <th>oxyR</th>\n",
              "      <th>pcaG</th>\n",
              "      <th>pcaH</th>\n",
              "      <th>pcaR</th>\n",
              "      <th>rubA</th>\n",
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              "      <th>srpA</th>\n",
              "      <th>srpB</th>\n",
              "      <th>srpC</th>\n",
              "      <th>srpR</th>\n",
              "      <th>srpS</th>\n",
              "      <th>tnpA</th>\n",
              "      <th>todA</th>\n",
              "      <th>todB</th>\n",
              "      <th>todC1</th>\n",
              "      <th>todC2</th>\n",
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              "      <th>todE</th>\n",
              "      <th>todF</th>\n",
              "      <th>todT</th>\n",
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              "      <th>tolB</th>\n",
              "      <th>tolQ</th>\n",
              "      <th>tolR</th>\n",
              "      <th>ttgA</th>\n",
              "      <th>ttgB</th>\n",
              "      <th>ttgC</th>\n",
              "      <th>ttgD</th>\n",
              "      <th>ttgE</th>\n",
              "      <th>ttgF</th>\n",
              "      <th>ttgG</th>\n",
              "      <th>ttgH</th>\n",
              "      <th>ttgI</th>\n",
              "      <th>xylA</th>\n",
              "      <th>xylB</th>\n",
              "      <th>xylC</th>\n",
              "      <th>xylM</th>\n",
              "      <th>xylN</th>\n",
              "      <th>xylS</th>\n",
              "      <th>xylU</th>\n",
              "      <th>xylW</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>strain</th>\n",
              "      <th></th>\n",
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              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ABACB</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>1</td>\n",
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              "      <td>2</td>\n",
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              "      <td>0</td>\n",
              "      <td>2</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>AHSWHJXPP1</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>B001</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-79539a72-cfe0-4d37-80f2-b434a72e53b6')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-79539a72-cfe0-4d37-80f2-b434a72e53b6 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-79539a72-cfe0-4d37-80f2-b434a72e53b6');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-f9d2a037-fda3-4d5a-9b2f-df62869f1563\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-f9d2a037-fda3-4d5a-9b2f-df62869f1563')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-f9d2a037-fda3-4d5a-9b2f-df62869f1563 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df_genecount"
            }
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## *Standarization (scaling) of data*\n"
      ],
      "metadata": {
        "id": "6BTg-2pKzRrs"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Some of the downstream analysis methods will need **standarized data**, as they are sensitive to scale of features (like PCA).\n",
        "\n",
        "Because of this, we will create another data frame called ***df_numeric_scaled*** where all the numeric features are standarized."
      ],
      "metadata": {
        "id": "_Ccn4JnuzuNE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "scaler = StandardScaler()\n",
        "df_genecount_scaled = scaler.fit_transform(df_genecount)"
      ],
      "metadata": {
        "id": "lflPSTDwzNu0"
      },
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "When we scale our data using any scaled from sklearn (as StandardScaled), the output is a NumPy array rather than a pandas DataFrame."
      ],
      "metadata": {
        "id": "gmY1DyR9z8WL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(type(df_genecount_scaled))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WRYHUq0nz7w5",
        "outputId": "4e77b001-7069-4b2f-cd20-7a3e242ab471"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'numpy.ndarray'>\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "However, as we want to visualize the scaled data using different plots, we should convert it back to a DataFrame for better manipulation."
      ],
      "metadata": {
        "id": "cA5bWLE10BTH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_genecount_scaled_df = pd.DataFrame(df_genecount_scaled, columns=df_genecount.columns)\n",
        "print(type(df_genecount_scaled_df))\n",
        "df_genecount_scaled_df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 244
        },
        "id": "T3GRnU920AEZ",
        "outputId": "5d0c37ec-a78b-441e-cf00-4e5ab4a1e971"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   PobA    alkB      amoA  arsC      arsR1     bcsA      bcsB      BenR      bnzA      bphA1     \\\n",
              "0 -0.3669  0.505291  0.0  -0.231455 -0.505291  2.213594  1.712698  0.222277 -0.187317 -0.187317   \n",
              "1 -0.3669 -1.979057  0.0  -0.231455 -0.505291 -0.451754  1.712698  0.222277 -0.187317 -0.187317   \n",
              "2 -0.3669 -1.979057  0.0  -0.231455 -0.505291  2.213594 -0.583874 -2.400593 -0.187317 -0.187317   \n",
              "3 -0.3669  0.505291  0.0  -0.231455 -0.505291 -0.451754 -0.583874  0.222277  5.338539 -0.187317   \n",
              "4 -0.3669  0.505291  0.0  -0.231455  1.979057 -0.451754 -0.583874  0.222277 -0.187317 -0.187317   \n",
              "\n",
              "   bphA2     bphA3     bphA4     bphB      bphC      bphD      chR     copA      copA1     \\\n",
              "0 -0.187317 -0.187317 -0.187317 -0.187317 -0.187317 -0.187317 -0.3669  0.658808 -0.131306   \n",
              "1 -0.187317 -0.187317 -0.187317 -0.187317 -0.187317 -0.187317 -0.3669 -0.895979 -0.131306   \n",
              "2 -0.187317 -0.187317 -0.187317 -0.187317 -0.187317 -0.187317 -0.3669 -0.895979 -0.131306   \n",
              "3 -0.187317 -0.187317 -0.187317 -0.187317 -0.187317 -0.187317 -0.3669 -0.895979 -0.131306   \n",
              "4 -0.187317 -0.187317 -0.187317 -0.187317 -0.187317 -0.187317 -0.3669 -0.895979  7.615773   \n",
              "\n",
              "   dmpK      dmpL     dmpM      dmpN      dmpO     dmpP     intl1     katA      merA      \\\n",
              "0 -0.231455 -0.26968 -0.231455 -0.396059 -0.26968 -0.26968  0.648919  0.269680  2.389144   \n",
              "1 -0.231455 -0.26968 -0.231455 -0.396059 -0.26968 -0.26968  3.594015  0.269680  0.889575   \n",
              "2 -0.231455 -0.26968 -0.231455 -0.396059 -0.26968 -0.26968 -0.332779 -3.708099 -0.609994   \n",
              "3 -0.231455 -0.26968 -0.231455 -0.396059 -0.26968 -0.26968  1.630618  0.269680 -0.609994   \n",
              "4 -0.231455 -0.26968 -0.231455  2.524876 -0.26968 -0.26968 -0.332779  0.269680 -0.609994   \n",
              "\n",
              "   merB      merR      merT      mrdH      mreA      nahA1     nahA2     nahA3     nahA4     \\\n",
              "0  4.320494  2.562050  2.932777 -0.177394 -0.177394 -0.187317 -0.187317 -0.187317 -0.187317   \n",
              "1 -0.231455  0.987457 -0.493636 -0.177394 -0.177394 -0.187317 -0.187317 -0.187317 -0.187317   \n",
              "2 -0.231455 -0.587137 -0.493636 -0.177394 -0.177394 -0.187317 -0.187317 -0.187317 -0.187317   \n",
              "3 -0.231455 -0.587137 -0.493636 -0.177394 -0.177394 -0.187317 -0.187317 -0.187317 -0.187317   \n",
              "4 -0.231455 -0.587137 -0.493636 -0.177394 -0.177394 -0.187317 -0.187317 -0.187317 -0.187317   \n",
              "\n",
              "   nahB      nahC      nahD      nahE      nahF      nirS      norB      oxyR      pcaG      \\\n",
              "0 -0.187317 -0.187317 -0.187317 -0.187317 -0.187317  0.287348 -0.131306  0.743392  0.231455   \n",
              "1 -0.187317 -0.187317 -0.187317 -0.187317 -0.187317 -2.538240 -0.131306 -1.345185  0.231455   \n",
              "2 -0.187317 -0.187317 -0.187317 -0.187317 -0.187317 -2.538240 -0.131306  0.743392  0.231455   \n",
              "3 -0.187317 -0.187317 -0.187317 -0.187317 -0.187317  0.287348 -0.131306  0.743392  0.231455   \n",
              "4 -0.187317 -0.187317 -0.187317 -0.187317 -0.187317  0.287348 -0.131306  0.743392  0.231455   \n",
              "\n",
              "   pcaH      pcaR  rubA      sodA      srpA     srpB      srpC      srpR     srpS      tnpA      \\\n",
              "0  0.131306  0.0   2.680215  0.505291 -0.30429 -0.421332 -0.336463 -0.30429 -0.336463  0.903268   \n",
              "1  0.131306  0.0  -0.720488  0.505291 -0.30429 -0.421332 -0.336463 -0.30429 -0.336463  2.383623   \n",
              "2  0.131306  0.0  -0.720488  0.505291 -0.30429 -0.421332 -0.336463 -0.30429 -0.336463 -0.577088   \n",
              "3  0.131306  0.0  -0.720488  0.505291 -0.30429 -0.421332 -0.336463 -0.30429 -0.336463 -0.083636   \n",
              "4  0.131306  0.0   0.979864  0.505291 -0.30429 -0.421332 -0.336463 -0.30429 -0.336463 -0.577088   \n",
              "\n",
              "   todA      todB      todC1     todC2     todD      todE      todF      todT      tolA  tolB  \\\n",
              "0 -0.187317 -0.187317 -0.187317 -0.187317 -0.187317 -0.187317 -0.187317 -0.187317  0.0   0.0    \n",
              "1 -0.187317 -0.187317 -0.187317 -0.187317 -0.187317 -0.187317 -0.187317 -0.187317  0.0   0.0    \n",
              "2 -0.187317 -0.187317 -0.187317 -0.187317 -0.187317 -0.187317 -0.187317 -0.187317  0.0   0.0    \n",
              "3  5.338539  5.338539  5.338539  5.338539  5.338539  5.338539  5.338539  5.338539  0.0   0.0    \n",
              "4 -0.187317 -0.187317 -0.187317 -0.187317 -0.187317 -0.187317 -0.187317 -0.187317  0.0   0.0    \n",
              "\n",
              "   tolQ      tolR  ttgA  ttgB      ttgC      ttgD      ttgE      ttgF     ttgG     ttgH      \\\n",
              "0 -0.803530  0.0   0.0   0.954427 -0.131306 -0.187317 -0.495663 -0.26968 -0.30429 -0.421332   \n",
              "1  1.092801  0.0   0.0   0.954427 -0.131306 -0.187317  1.332093 -0.26968 -0.30429 -0.421332   \n",
              "2 -0.803530  0.0   0.0   0.954427 -0.131306 -0.187317  3.159849 -0.26968 -0.30429 -0.421332   \n",
              "3 -0.803530  0.0   0.0  -0.922613 -0.131306  5.338539  3.159849 -0.26968 -0.30429 -0.421332   \n",
              "4 -0.803530  0.0   0.0   0.954427 -0.131306 -0.187317 -0.495663 -0.26968 -0.30429 -0.421332   \n",
              "\n",
              "   ttgI      xylA      xylB      xylC      xylM      xylN      xylS      xylU      xylW      \n",
              "0 -0.336463 -0.131306 -0.131306 -0.131306 -0.131306 -0.131306 -0.177394 -0.187317 -0.131306  \n",
              "1 -0.336463 -0.131306 -0.131306 -0.131306 -0.131306 -0.131306 -0.177394 -0.187317 -0.131306  \n",
              "2 -0.336463 -0.131306 -0.131306 -0.131306 -0.131306 -0.131306 -0.177394 -0.187317 -0.131306  \n",
              "3 -0.336463 -0.131306 -0.131306 -0.131306 -0.131306 -0.131306 -0.177394 -0.187317 -0.131306  \n",
              "4 -0.336463 -0.131306 -0.131306 -0.131306 -0.131306 -0.131306 -0.177394 -0.187317 -0.131306  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-506d778e-7a00-42e3-8582-6f8e7910e44b\" class=\"colab-df-container\">\n",
              "    <div>\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: left;\">\n",
              "      <th></th>\n",
              "      <th>PobA</th>\n",
              "      <th>alkB</th>\n",
              "      <th>amoA</th>\n",
              "      <th>arsC</th>\n",
              "      <th>arsR1</th>\n",
              "      <th>bcsA</th>\n",
              "      <th>bcsB</th>\n",
              "      <th>BenR</th>\n",
              "      <th>bnzA</th>\n",
              "      <th>bphA1</th>\n",
              "      <th>bphA2</th>\n",
              "      <th>bphA3</th>\n",
              "      <th>bphA4</th>\n",
              "      <th>bphB</th>\n",
              "      <th>bphC</th>\n",
              "      <th>bphD</th>\n",
              "      <th>chR</th>\n",
              "      <th>copA</th>\n",
              "      <th>copA1</th>\n",
              "      <th>dmpK</th>\n",
              "      <th>dmpL</th>\n",
              "      <th>dmpM</th>\n",
              "      <th>dmpN</th>\n",
              "      <th>dmpO</th>\n",
              "      <th>dmpP</th>\n",
              "      <th>intl1</th>\n",
              "      <th>katA</th>\n",
              "      <th>merA</th>\n",
              "      <th>merB</th>\n",
              "      <th>merR</th>\n",
              "      <th>merT</th>\n",
              "      <th>mrdH</th>\n",
              "      <th>mreA</th>\n",
              "      <th>nahA1</th>\n",
              "      <th>nahA2</th>\n",
              "      <th>nahA3</th>\n",
              "      <th>nahA4</th>\n",
              "      <th>nahB</th>\n",
              "      <th>nahC</th>\n",
              "      <th>nahD</th>\n",
              "      <th>nahE</th>\n",
              "      <th>nahF</th>\n",
              "      <th>nirS</th>\n",
              "      <th>norB</th>\n",
              "      <th>oxyR</th>\n",
              "      <th>pcaG</th>\n",
              "      <th>pcaH</th>\n",
              "      <th>pcaR</th>\n",
              "      <th>rubA</th>\n",
              "      <th>sodA</th>\n",
              "      <th>srpA</th>\n",
              "      <th>srpB</th>\n",
              "      <th>srpC</th>\n",
              "      <th>srpR</th>\n",
              "      <th>srpS</th>\n",
              "      <th>tnpA</th>\n",
              "      <th>todA</th>\n",
              "      <th>todB</th>\n",
              "      <th>todC1</th>\n",
              "      <th>todC2</th>\n",
              "      <th>todD</th>\n",
              "      <th>todE</th>\n",
              "      <th>todF</th>\n",
              "      <th>todT</th>\n",
              "      <th>tolA</th>\n",
              "      <th>tolB</th>\n",
              "      <th>tolQ</th>\n",
              "      <th>tolR</th>\n",
              "      <th>ttgA</th>\n",
              "      <th>ttgB</th>\n",
              "      <th>ttgC</th>\n",
              "      <th>ttgD</th>\n",
              "      <th>ttgE</th>\n",
              "      <th>ttgF</th>\n",
              "      <th>ttgG</th>\n",
              "      <th>ttgH</th>\n",
              "      <th>ttgI</th>\n",
              "      <th>xylA</th>\n",
              "      <th>xylB</th>\n",
              "      <th>xylC</th>\n",
              "      <th>xylM</th>\n",
              "      <th>xylN</th>\n",
              "      <th>xylS</th>\n",
              "      <th>xylU</th>\n",
              "      <th>xylW</th>\n",
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              "  <tbody>\n",
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              "      <th>0</th>\n",
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              "      <td>0.0</td>\n",
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              "      <td>-0.187317</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>1.092801</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.954427</td>\n",
              "      <td>-0.131306</td>\n",
              "      <td>-0.187317</td>\n",
              "      <td>1.332093</td>\n",
              "      <td>-0.26968</td>\n",
              "      <td>-0.30429</td>\n",
              "      <td>-0.421332</td>\n",
              "      <td>-0.336463</td>\n",
              "      <td>-0.131306</td>\n",
              "      <td>-0.131306</td>\n",
              "      <td>-0.131306</td>\n",
              "      <td>-0.131306</td>\n",
              "      <td>-0.131306</td>\n",
              "      <td>-0.177394</td>\n",
              "      <td>-0.187317</td>\n",
              "      <td>-0.131306</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>-0.3669</td>\n",
              "      <td>-1.979057</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-0.231455</td>\n",
              "      <td>-0.505291</td>\n",
              "      <td>2.213594</td>\n",
              "      <td>-0.583874</td>\n",
              "      <td>-2.400593</td>\n",
              "      <td>-0.187317</td>\n",
              "      <td>-0.187317</td>\n",
              "      <td>-0.187317</td>\n",
              "      <td>-0.187317</td>\n",
              "      <td>-0.187317</td>\n",
              "      <td>-0.187317</td>\n",
              "      <td>-0.187317</td>\n",
              "      <td>-0.187317</td>\n",
              "      <td>-0.3669</td>\n",
              "      <td>-0.895979</td>\n",
              "      <td>-0.131306</td>\n",
              "      <td>-0.231455</td>\n",
              "      <td>-0.26968</td>\n",
              "      <td>-0.231455</td>\n",
              "      <td>-0.396059</td>\n",
              "      <td>-0.26968</td>\n",
              "      <td>-0.26968</td>\n",
              "      <td>-0.332779</td>\n",
              "      <td>-3.708099</td>\n",
              "      <td>-0.609994</td>\n",
              "      <td>-0.231455</td>\n",
              "      <td>-0.587137</td>\n",
              "      <td>-0.493636</td>\n",
              "      <td>-0.177394</td>\n",
              "      <td>-0.177394</td>\n",
              "      <td>-0.187317</td>\n",
              "      <td>-0.187317</td>\n",
              "      <td>-0.187317</td>\n",
              "      <td>-0.187317</td>\n",
              "      <td>-0.187317</td>\n",
              "      <td>-0.187317</td>\n",
              "      <td>-0.187317</td>\n",
              "      <td>-0.187317</td>\n",
              "      <td>-0.187317</td>\n",
              "      <td>-2.538240</td>\n",
              "      <td>-0.131306</td>\n",
              "      <td>0.743392</td>\n",
              "      <td>0.231455</td>\n",
              "      <td>0.131306</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-0.720488</td>\n",
              "      <td>0.505291</td>\n",
              "      <td>-0.30429</td>\n",
              "      <td>-0.421332</td>\n",
              "      <td>-0.336463</td>\n",
              "      <td>-0.30429</td>\n",
              "      <td>-0.336463</td>\n",
              "      <td>-0.577088</td>\n",
              "      <td>-0.187317</td>\n",
              "      <td>-0.187317</td>\n",
              "      <td>-0.187317</td>\n",
              "      <td>-0.187317</td>\n",
              "      <td>-0.187317</td>\n",
              "      <td>-0.187317</td>\n",
              "      <td>-0.187317</td>\n",
              "      <td>-0.187317</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-0.803530</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.954427</td>\n",
              "      <td>-0.131306</td>\n",
              "      <td>-0.187317</td>\n",
              "      <td>3.159849</td>\n",
              "      <td>-0.26968</td>\n",
              "      <td>-0.30429</td>\n",
              "      <td>-0.421332</td>\n",
              "      <td>-0.336463</td>\n",
              "      <td>-0.131306</td>\n",
              "      <td>-0.131306</td>\n",
              "      <td>-0.131306</td>\n",
              "      <td>-0.131306</td>\n",
              "      <td>-0.131306</td>\n",
              "      <td>-0.177394</td>\n",
              "      <td>-0.187317</td>\n",
              "      <td>-0.131306</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>-0.3669</td>\n",
              "      <td>0.505291</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-0.231455</td>\n",
              "      <td>-0.505291</td>\n",
              "      <td>-0.451754</td>\n",
              "      <td>-0.583874</td>\n",
              "      <td>0.222277</td>\n",
              "      <td>5.338539</td>\n",
              "      <td>-0.187317</td>\n",
              "      <td>-0.187317</td>\n",
              "      <td>-0.187317</td>\n",
              "      <td>-0.187317</td>\n",
              "      <td>-0.187317</td>\n",
              "      <td>-0.187317</td>\n",
              "      <td>-0.187317</td>\n",
              "      <td>-0.3669</td>\n",
              "      <td>-0.895979</td>\n",
              "      <td>-0.131306</td>\n",
              "      <td>-0.231455</td>\n",
              "      <td>-0.26968</td>\n",
              "      <td>-0.231455</td>\n",
              "      <td>-0.396059</td>\n",
              "      <td>-0.26968</td>\n",
              "      <td>-0.26968</td>\n",
              "      <td>1.630618</td>\n",
              "      <td>0.269680</td>\n",
              "      <td>-0.609994</td>\n",
              "      <td>-0.231455</td>\n",
              "      <td>-0.587137</td>\n",
              "      <td>-0.493636</td>\n",
              "      <td>-0.177394</td>\n",
              "      <td>-0.177394</td>\n",
              "      <td>-0.187317</td>\n",
              "      <td>-0.187317</td>\n",
              "      <td>-0.187317</td>\n",
              "      <td>-0.187317</td>\n",
              "      <td>-0.187317</td>\n",
              "      <td>-0.187317</td>\n",
              "      <td>-0.187317</td>\n",
              "      <td>-0.187317</td>\n",
              "      <td>-0.187317</td>\n",
              "      <td>0.287348</td>\n",
              "      <td>-0.131306</td>\n",
              "      <td>0.743392</td>\n",
              "      <td>0.231455</td>\n",
              "      <td>0.131306</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-0.720488</td>\n",
              "      <td>0.505291</td>\n",
              "      <td>-0.30429</td>\n",
              "      <td>-0.421332</td>\n",
              "      <td>-0.336463</td>\n",
              "      <td>-0.30429</td>\n",
              "      <td>-0.336463</td>\n",
              "      <td>-0.083636</td>\n",
              "      <td>5.338539</td>\n",
              "      <td>5.338539</td>\n",
              "      <td>5.338539</td>\n",
              "      <td>5.338539</td>\n",
              "      <td>5.338539</td>\n",
              "      <td>5.338539</td>\n",
              "      <td>5.338539</td>\n",
              "      <td>5.338539</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-0.803530</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-0.922613</td>\n",
              "      <td>-0.131306</td>\n",
              "      <td>5.338539</td>\n",
              "      <td>3.159849</td>\n",
              "      <td>-0.26968</td>\n",
              "      <td>-0.30429</td>\n",
              "      <td>-0.421332</td>\n",
              "      <td>-0.336463</td>\n",
              "      <td>-0.131306</td>\n",
              "      <td>-0.131306</td>\n",
              "      <td>-0.131306</td>\n",
              "      <td>-0.131306</td>\n",
              "      <td>-0.131306</td>\n",
              "      <td>-0.177394</td>\n",
              "      <td>-0.187317</td>\n",
              "      <td>-0.131306</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>-0.3669</td>\n",
              "      <td>0.505291</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-0.231455</td>\n",
              "      <td>1.979057</td>\n",
              "      <td>-0.451754</td>\n",
              "      <td>-0.583874</td>\n",
              "      <td>0.222277</td>\n",
              "      <td>-0.187317</td>\n",
              "      <td>-0.187317</td>\n",
              "      <td>-0.187317</td>\n",
              "      <td>-0.187317</td>\n",
              "      <td>-0.187317</td>\n",
              "      <td>-0.187317</td>\n",
              "      <td>-0.187317</td>\n",
              "      <td>-0.187317</td>\n",
              "      <td>-0.3669</td>\n",
              "      <td>-0.895979</td>\n",
              "      <td>7.615773</td>\n",
              "      <td>-0.231455</td>\n",
              "      <td>-0.26968</td>\n",
              "      <td>-0.231455</td>\n",
              "      <td>2.524876</td>\n",
              "      <td>-0.26968</td>\n",
              "      <td>-0.26968</td>\n",
              "      <td>-0.332779</td>\n",
              "      <td>0.269680</td>\n",
              "      <td>-0.609994</td>\n",
              "      <td>-0.231455</td>\n",
              "      <td>-0.587137</td>\n",
              "      <td>-0.493636</td>\n",
              "      <td>-0.177394</td>\n",
              "      <td>-0.177394</td>\n",
              "      <td>-0.187317</td>\n",
              "      <td>-0.187317</td>\n",
              "      <td>-0.187317</td>\n",
              "      <td>-0.187317</td>\n",
              "      <td>-0.187317</td>\n",
              "      <td>-0.187317</td>\n",
              "      <td>-0.187317</td>\n",
              "      <td>-0.187317</td>\n",
              "      <td>-0.187317</td>\n",
              "      <td>0.287348</td>\n",
              "      <td>-0.131306</td>\n",
              "      <td>0.743392</td>\n",
              "      <td>0.231455</td>\n",
              "      <td>0.131306</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.979864</td>\n",
              "      <td>0.505291</td>\n",
              "      <td>-0.30429</td>\n",
              "      <td>-0.421332</td>\n",
              "      <td>-0.336463</td>\n",
              "      <td>-0.30429</td>\n",
              "      <td>-0.336463</td>\n",
              "      <td>-0.577088</td>\n",
              "      <td>-0.187317</td>\n",
              "      <td>-0.187317</td>\n",
              "      <td>-0.187317</td>\n",
              "      <td>-0.187317</td>\n",
              "      <td>-0.187317</td>\n",
              "      <td>-0.187317</td>\n",
              "      <td>-0.187317</td>\n",
              "      <td>-0.187317</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-0.803530</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.954427</td>\n",
              "      <td>-0.131306</td>\n",
              "      <td>-0.187317</td>\n",
              "      <td>-0.495663</td>\n",
              "      <td>-0.26968</td>\n",
              "      <td>-0.30429</td>\n",
              "      <td>-0.421332</td>\n",
              "      <td>-0.336463</td>\n",
              "      <td>-0.131306</td>\n",
              "      <td>-0.131306</td>\n",
              "      <td>-0.131306</td>\n",
              "      <td>-0.131306</td>\n",
              "      <td>-0.131306</td>\n",
              "      <td>-0.177394</td>\n",
              "      <td>-0.187317</td>\n",
              "      <td>-0.131306</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-506d778e-7a00-42e3-8582-6f8e7910e44b')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-506d778e-7a00-42e3-8582-6f8e7910e44b button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-506d778e-7a00-42e3-8582-6f8e7910e44b');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-cc45d393-0c20-4cc2-867f-a6edada0a7c6\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-cc45d393-0c20-4cc2-867f-a6edada0a7c6')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-cc45d393-0c20-4cc2-867f-a6edada0a7c6 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df_genecount_scaled_df"
            }
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## *Correlation analysis*"
      ],
      "metadata": {
        "id": "mOV7483n1qdk"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "The next step is to assess relationships between features. Are they correlated? Does one feature influence another?\n",
        "\n",
        "A **correlation analysis** reveals linear relationships, helping us identify redundant features (those that convey similar information). When features are highly correlated, we can choose to keep both, remove one, or combine them into a new, more informative feature. This simplifies the data, avoids overfitting and improves model performance.\n"
      ],
      "metadata": {
        "id": "ja_vEFv-1uD9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "correlation_matrix = df_genecount.corr(method='pearson')\n",
        "plt.figure(figsize=(24, 20))\n",
        "sns.heatmap(correlation_matrix, vmin=None, vmax=None, annot=False, cmap='coolwarm', fmt=\".2f\", linewidths=0.5)\n",
        "plt.title('Correlation Matrix of Features')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 788
        },
        "id": "mymiv3fv18tM",
        "outputId": "620f9e63-34b1-4af0-c15b-b7c8784c69ae"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2400x2000 with 2 Axes>"
            ],
            "image/png": 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DJC6TiPWy9KD87e4gDwV2XZK3zWxbXYlZW+VtuwFgfAeV7P1av8bA9gvy9wG/1lRi/i55cUa2VeJr4FTZuVj0mCw7jkWPybL3aUDifk3EvpFz+d/JZfcleduGNtVVsvdpQOJ+bdkhedtMn2YKYceYW/6Wl0vH2gpcufdBdi5VSuYSkouo86NHDx/KilGseHEcuy4/l0YVTHDn4XNZMUoVLyR7eYDEZRKRy4HQONm5NK9sZFDbF87F8HMxJCK2dYZm7B/fZc0/p7eZsH2siO+dqO3U8sPyYgxsCmHbb7n7JBH7IyBxP/Bx9gBZMXKOWyE7hqg4hpbLkwf3ZOfiWKIkDho7y4rhEXtX2HphjGUtg7nnHWOMMcYYY4wxxhhjjDHGGGP/6/4Vxbvg4GAoFAp8+vQpu1NhjDHGGGOMMcYYY4wxxhhjLNP8K4p3KQUGBsLW1jbN5xQKBfbs2ZOl+TDGGGOMMcYYY4wxxhhjjDEmwr+yeMcYY4wxxhhjjDHGGGOMMcbYf1GWFO/evn0Le3t7zJo1Sz3t77//homJCfz8/KBUKhESEqIxj6+vL4oUKYKEBPk3f2eMMcYYY4wxxhhjjDHGGPs3oIQEfuj5+K/JkuKdnZ0d/P39MWXKFISEhODLly/w9PSEj48PevbsCXd3dwQEBGjMExAQgB49ekCp5M6BjDHGGGOMMcYYY4wxxhhj7H9DllXGmjVrht69e6NLly7o168fLC0tMXv2bABAr169sHnzZvz48QMAEBoairCwMHh5eQnP48ePH/j8+bPGI+l9GWOMMcYYY4wxxhhjjDHGGMtOWdqtbf78+YiLi8P27duxceNGmJqaAgBat24NlUqF3bt3AwACAwPh5uYGR0dH4TnMnj0bOXLk0HgkFREZY4wxxhhjjDHGGGOMMcYYy05ZWrx7+PAhXr58iYSEBDx58kQ93cTEBN26dUNAQABiYmKwadMmeHt7pxvHxsYG0dHRqe6H9+nTJwBAjhw50p133LhxiIyM1HiMGzdO1nIxxhhjjDHGGGOMMcYYY4wxJoJRVr1RTEwMunbtig4dOsDZ2Rm9evVCWFgY8ubNCyBx6MyyZctixYoViIuLQ9u2bdON5ezsjLi4OFy7dg2VK1dWTw8NDQUAlCxZMt15TU1N1T3+NFHGFowxxhhjjDHGGGOMMcYYY4wxQbKseDdhwgRERkZiyZIlsLKywqFDh+Dt7Y0DBw4AAFxcXFCzZk2MGTMG3t7eMDc3TzdWmTJl0KhRI3h7e2PBggUoVqwY7t69i6FDh6JDhw4oWLBgVi0WY4wxxhhjjDHGGGOMMcaYMJTAnY3+12XJsJnBwcHw9fXFhg0bYGNjA6VSiQ0bNuDMmTNYuXKl+nU9e/ZETEyM1iEzk2zduhX169dH3759UaZMGQwePBitWrXC2rVrM3NRGGOMMcYYY4wxxhhjjDHGGMs0WdLzztXVFbGxsRrTHB0dERkZqTHtxYsXKFeuHKpVq5ZqfiLNSrOtrS0WL16MxYsXZ07SjDHGGGOMMcYYY4wxxhhjjGWxLOl5p0tUVBRu3ryJZcuWYdCgQdmdDmOMMcYYY4wxxhhjjDHGGGPZwiCKdz4+PqhSpQpcXV0lDZnJGGOMMcYYY4wxxhhjjDHG2H9RlgybqUtgYCACAwOzOw3GGGOMMcYYY4wxxhhjjDHGspWCUt5MjjHGGGOMMcYYY4wxxhhjjGWLem3OZncK/zqnd/+U3SkIZRA97wzByiPy5u/fBNgXEi87j5ZVVbJjAMAfx+XH6O0O3Hv4VFaMksUdsOyQ/PqwTzMFfPfJizO0pQLbzifIzqV9LSXWnpAXo1dDyI4hKg7nkvm5iNi+HL4aKzuXppWM4X9SXgzvBuK2L6uPyYvRt5G4XJYflhdjYFP5ywMkLpOI9bLqqPxc+jUGtl+Qt838taYSC/fK3wcMb6WQ/Vn3dgeCb36TnYtrWXMsPShvmQZ5KBDaUP4BZeUTZ2XHqXzi7H9yu8u5GH4uIrYvcvdpQOJ+7dj1GFkxGlUwEXaMuemsvO1L558UWH9KdiroVh+Yv0veMo1sqxR2fiR3v9avsfx9PZC4vxeRi6j9tIhcAoLk5+LlZljbF87F8HMxJL/vkLetG9POIO5Go2HmFnnb3gkdVcL2sSK+d5fuRMrOpXqpHAZ1niXi/FNULgeNnWXF8Ii9KzuGqDiGlsuXSwdl52Jd3QMfZw+QFSPnuBXC1gtjLGsZ3lEGY4wxxhhjjDHGGGOMMcYYY/+jDLJ4FxwcDIVCgU+fPmV3KowxxhhjjDHGGGOMMcYYY4xlGYMs3iUXFRUFY2NjbNmyRWN6x44doVAo8OTJE43pjo6OmDhxYhZmyBhjjDHGGGOMMcYYY4wxxpgYBl+8s7KyQtWqVREcHKwxPTg4GIULF9aY/vjxY0RERKBBgwZZmyRjjDHGGGOMMcYYY4wxxhhjAmRK8e7t27ewt7fHrFmz1NP+/vtvmJiYwM/PD0qlEiEhIRrz+Pr6okiRIkhISH2zYDc3N40iXXh4OL5//47+/ftrTA8ODoapqSlq1aolfJkYY4wxxhhjjDHGGGOMMcYyGyUQP/R8/NdkSvHOzs4O/v7+mDJlCkJCQvDlyxd4enrCx8cHPXv2hLu7OwICAjTmCQgIQI8ePaBUpk7Jzc0Nd+/exatXrwAAQUFB+Omnn9CgQQON4l1QUBBq1aoFMzOzzFgsxhhjjDHGGGOMMcYYY4wxxjJVpg2b2axZM/Tu3RtdunRBv379YGlpidmzZwMAevXqhc2bN+PHjx8AgNDQUISFhcHLyyvNWHXq1IGJiYm6UBccHIz69eujSpUqePfuHR4/fgwAOHXqFNzc3LTm9ePHD3z+/FnjkZQHY4wxxhhjjDHGGGOMMcYYY9kpU+95N3/+fMTFxWH79u3YuHEjTE1NAQCtW7eGSqXC7t27AQCBgYFwc3ODo6NjmnEsLCxQrVo1dfHu1KlTcHV1hZGREWrXro3g4GA8evQIT58+1Vm8mz17NnLkyKHxSCoqMsYYY4wxxhhjjDHGGGOMMZadMrV49/DhQ7x8+RIJCQl48uSJerqJiQm6deuGgIAAxMTEYNOmTfD29tYay83NDUFBQbh16xa+ffuGypUrAwDq16+PoKAgBAUFwcLCAjVq1NAaZ9y4cYiMjNR4jBs3TvayMsYYY4wxxhhjjDHGGGOMMSaXUWYFjomJQdeuXdGhQwc4OzujV69eCAsLQ968eQEkDp1ZtmxZrFixAnFxcWjbtq3WeG5ubpgxYwY2bdqEn376CSqVCgBQr149rFmzBkSkHl5TG1NTU3UPQMYYY4wxxhhjjDHGGGOMMcYMSab1vJswYQIiIyOxZMkSjBkzBiVLltToXefi4oKaNWtizJgx6NSpE8zNzbXGq127NkxNTbF06VLUr19fPb169ep48+YN9u7dq3PITMYYY4wxxhhjjDHGGGOMMUNGCQn80PPxX5Mpxbvg4GD4+vpiw4YNsLGxgVKpxIYNG3DmzBmsXLlS/bqePXsiJiZG55CZAGBmZoaaNWviy5cvcHV1VU83NTVVT+fiHWOMMcYYY4wxxhhjjDHGGPs3y5RhM11dXREbG6sxzdHREZGRkRrTXrx4gXLlyqFatWqp5ieiVHGDg4PTfL+goCB5CTPGGGOMMcYYY4wxxhhjjDFmADJt2ExtoqKicPPmTSxbtgyDBg3KjhQYY4wxxhhjjDHGGGOMMcYYMzjZUrzz8fFBlSpV4OrqKmnITMYYY4wxxhhjjDHGGGOMMcb+F2TKsJm6BAYGIjAwMDvemjHGGGOMMcYYY4wxxhhjjDHDRUyr79+/0+TJk+n79+/ZHodz4Vw4F86Fc+FcDCEXUXE4F86Fc+FcOBfOhXPhXDgXzoVz4Vw4F86Fc/l358IYyxxcvNMhMjKSAFBkZGS2x+FcOBfOhXPhXDgXQ8hFVBzOhXPhXDgXzoVz4Vw4F86Fc+FcOBfOhXPhXP7duTDGMke23POOMcYYY4wxxhhjjDHGGGOMMZYaF+8YY4wxxhhjjDHGGGOMMcYYMxBcvGOMMcYYY4wxxhhjjDHGGGPMQHDxTgdTU1NMnjwZpqam2R6Hc+FcOBfOhXPhXAwhF1FxOBfOhXPhXDgXzoVz4Vw4F86Fc+FcOBfOhXP5d+fCGMscCiKi7E6CMcYYY4wxxhhjjDHGGGOMMcY97xhjjDHGGGOMMcYYY4wxxhgzGFy8Y4wxxhhjjDHGGGOMMcYYY8xAcPGOMcYYY4wxxhhjjDHGGGOMMQPBxTvGGGOMMcYYY4wxxhhjjDHGDAQX7xhjjDHGGGOMMcYYY4wxxhgzEFy8Y4wxliFxcXE6X3P79u0syIQxxhhjDIiPj8/uFJgOHz58yO4UmADr16/Hjx8/Mv19+PuSNl4vjDHG2P8GLt5loUePHuHWrVtISEiQFefz589YuXIlqlatqvO1Hz9+xNKlS/H58+dUz0VGRqb7HGMs6+3YsUNInJs3b8qaPy4uDk+fPtX5ui5dumh9/vbt22jQoIGsXO7cuYOSJUvKipGdbty4gR07dmDHjh24ceOG3vP/+PED0dHRmZCZYfq3NbqePn1aUhGbMfbv9+rVK/j4+GR3Gv9JRITDhw+jXbt2GY5x7949jB49GoUKFcpwDidPnsTBgwfx8ePHDOchWmhoKJo3b57daQAAEhIScODAgQzPf+zYMbRv3x4FCxYUmBXTl9y2iCReXl6IjIwUEist/H1JG68X+UT9Bv7XhIeHY+TIkVnyXt++fcO+ffvw5cuXVM99/vwZ+/bty5KLBzLbqVOncOjQoSw97pg8eTJOnz6NmJiYLHtPxpg8XLzT06dPn7Bs2TKtr4mNjcXkyZPRokULzJw5E/Hx8ejUqROcnJxQvnx5lC1bFk+ePNH7vYOCguDp6Yn8+fNj+vTpqFGjhs55li1bhtOnT8PGxibVczly5MCZM2dSTuqaAAB3VElEQVSwdOlSvXNJ7uHDh7Ib6PUpSKZHVHFUNLmFlP8SIpIdIyYmBlFRUZJfL7KATUQICQnBjh07sHPnToSGhuq1THFxcbh58ybu3bunMX3v3r2oUKGCzmKYNl++fMGaNWtQvXp1VKhQIcNxAODWrVsoWrSoztedP38e/fr1S/O58PBwNGjQALVr15aVy48fP/Dw4UOdrytWrJikhzanT5+W9JDi0qVLKFeuHCpVqoT27dujffv2qFSpEsqXL4/Lly/rnP/t27do2rQprKysYGNjg5o1a+LBgweS3ju7yNnWGVqjq9TGUjc3t3/Flc8iGsazS8qC7sWLF3H69GnExsbKiuvl5YWXL1/KipERnz9/TvM4JT4+/l93MdXJkycztXidHQ0at27dwrJly7BmzRp8+vQJAPDu3TsMGzYMxYoVQ1BQUJblklVevHghO0ZGG/QeP36MiRMnwsHBAW3atMH379/1mv/r168ICAhA3bp1Ubp0aZw+fRrDhw/XOd+nT5/QvXt3lCtXDr1798bnz59Rt25duLu7o0WLFnBxcdF5wc39+/fRqVOndI8vO3fujEePHklajqNHj2LkyJEYP368ep47d+6gdevWqFatms5zm3fv3iEiIkJj2q1bt+Dl5YX27dtj06ZNkvJIz4MHDzB+/HgUKlQIbdq00WveiIgITJ48GY6Ojvj111+hVCqxfv36DOURHR0Nf39/LF++HPfv389QjJTxpBzXxcbGYvTo0ShRogSqV68Of39/jef/+ecfqFQqSe958uRJ+Pj4oHnz5mjRogUGDx4s+dgSAAYMGKBxHrR582aNi7w+ffqEZs2aaY1hbGyMN2/eqP8eNWpUho5lRJzXpWQI35e4uDjMmzcPlStXhpWVFXLlyoWaNWti9erVQpZ5165dKF++vF7zZOd6uXLlCtzc3NLd1rm5ueH69euS31/Ob0DE9x8Q8xtISEhAWFiY+u9Vq1ZhyZIl6sfy5csltUvlzJkTuXLl0vnQZvjw4ZIeGREdHQ0/Pz/Url0bZcqUwZEjR3TOExQUhAULFuDcuXMAgNWrV8PBwQF2dnbo3bs3vn37pjPGmjVrsHjxYlhbW6d6zsbGBkuWLMHatWu1xlCpVJIeuqxfv17SQ5vff/8dEydOVP9NRGjSpAnc3NzQvHlzuLi44NatWzpz2bdvX5qPU6dO4dWrVzrnT1oeV1dX2NraomHDhpgxYwbOnTvHF6QyZsiISXL8+HHq1KkTmZmZUa5cubS+dvjw4WRnZ0e9evWiYsWKUcuWLcnZ2Zm2bNlC27Zto3LlylHnzp0lve/z589pxowZVLx4ccqdOzcplUrasmULJSQkSJq/QoUKdPz4ca3LVbFiRUmx0nPt2jVSKpUZmvfkyZPUtWtXsrCwoPz589OAAQN0zhMTE0OTJk2i5s2b04wZMyguLo46duxISqWSlEolubi40OPHjzOUT5KdO3dSuXLlMjz/58+fafXq1VStWrUMr5skT58+JS8vL0mv/fr1K505c4Zu3bqV6rlv377RunXrdMa4ffs2+fv7U3h4OBERhYeHU79+/cjLy4tOnDihX/IpGBsb0+3btyW/3t/fn3x8fOjPP/8kIqKxY8eSiYkJKZVKcnd3p3fv3umMMW3aNGrXrl26z//66680Y8YMnXFOnjxJRYsWJaVSSQqFghQKBSmVSipevDidOnVK5/xhYWFUpEgR9fe0TZs29Pr1a6pXrx7lypWLxowZQ8+ePdMZJ6VTp05Rt27dyNLSkpycnGjMmDF06dIlveMkJ/U3ffv2bcqTJw+NGzdOY3p4eDjZ29tTq1atKC4uLktyUSgU5OjoSOPHjydfX990H7pipPdI+txUKpXOXG7dukVWVlZUrVo12rRpE129epWuXr1KGzdupKpVq5K1tXWav9HkvLy8yN7enmbNmkULFy4kZ2dncnV11fne+tJn+5IWOdu66Oho8vf3p59++olUKhXVqFGD5s6dq3O+jx8/Urdu3ahs2bLUq1cvioyMpDp16qg/q3z58tH169d1xjly5AiNGDGCxo0bRw8fPiSixO9uq1atSKlUUtOmTXXGUCgU9M8//+heWC1sbW0pZ86cOh8Z8ejRI/rtt9+oUKFCZGpqSh4eHrJyjYqKkrS9i4mJoVGjRlHx4sWpWrVq5Ofnp/H869evdX5fXr58SXXq1CGVSkX16tWjDx8+kIeHh/pzLlmyJL18+VJnLtevX0/zYWxsTLt371b/rcvnz58pJCSEvnz5QkREV65cIU9PT2rXrp16H6XLrl27yMnJiaKjo1M9FxUVRSVLlqR9+/bpjBMVFUUTJ06kMmXKkKWlJVlZWVG5cuVo6tSpacZOi4uLC71//179d//+/ent27fqv//55x8yNzfXGkOpVGp8/2vUqEHPnz+X9P7JzZkzh3777Tf13wkJCdS4cWON3/TNmzclxUp+XPD06VOaOHEijRw5kk6fPq1z3r1795KxsbH6fYsXL04nT56kPHnyUOPGjenw4cOScvj48SOtWLFC/Xfnzp2pTZs26ke7du3o48ePOuOk991N+cioV69ekY+Pj87POT1RUVG0du1aqlWrFikUCipTpoyk+b5//05//vknubm5kbGxMSmVSlq4cCFFRkZKfu/z589Tz549ycbGhsqWLUsqlUrSZ5ykZ8+e5OTkRDNmzKAaNWpQrVq1qGbNmnThwgW6dOkSubq6UvPmzbXG6N27N40aNSrd50ePHk39+vXTmcvatWtJoVCoz/Ps7Oxow4YNZGtrS3379pV03NyxY0caPny4+u9//vmHcubMSWXKlKGWLVuSsbExrV+/Xmec5L5+/Urr1q2junXrklKppPr169PKlSvp9evXOuf98eMHbd68mRo2bEhmZmbUvHlzUqlUdOPGDcnvHxERQfXq1SMrKytyd3eniIgIKlmypPr3aWFhIWl/pI3UY8zJkydTvnz5aN68eTRhwgTKkSMH9enTR/3869evSaFQ6IzTt29fUigUlCtXLqpZsybVqFGDcuXKRUqlknx8fCTlnHK7a21trT6GScpF1zKlPHZJGUMqhUJBb9680Xu+lAzp+/L161eqU6cOKZVKatSoEQ0ZMoSGDBlCjRo1IqVSSR4eHhQfH08PHjyggICAdOOsWrWKfvnlF+rUqRNduHCBiIhOnDhBFStWJAsLC0nbBkNZL506daJp06al+/zMmTOpS5cukvKR+xsQ8f0nEvMb2LhxI9WtW1f9t5WVFRUqVIgcHR3J0dGRrKysaO3atTrjBAYGqh8BAQFkZmZGc+fO1ZgeGBioNYarq6vOh5ubm17Ld/bsWfLy8iJLS0tSKpU0YsQIdfuQNmvWrCGVSkUlSpQgU1NTmjVrFllaWlK/fv1owIABZGNjQ2PGjNEZp1q1alqPiffv30/VqlXTGiOpfWDy5Mm0Z8+edB+62NrapvvImTOnun1Km0qVKtGWLVvUf2/bto3Mzc3p7Nmz9P79e/Lw8KBff/1VZy662is6d+4s6Vzg8ePH5O/vT926daMiRYqQQqEgKysraty4Mc2ZM4cuXryoMwZjLOtw8U6Lp0+f0tSpU8nR0VG9ITx8+DDFxMRonc/BwYEOHjxIRER3794lhUJBhw4dUj8fHBxMBQsW1Bpjx44d1LRpU7K0tKR27drRnj176MePH2RkZKSz0Tc5KysrioiISPf5iIgIsra21hpj8eLFWh+jR4/Wq9FWbkFSVHFUxEF1StlZSLl79656x6tUKqlevXoaDZtSDmYPHz5MJiYmlCtXLjIzM6PDhw+TnZ0dubu7U4MGDUilUkkq4A0bNizNh1KppG7duqn/1mbGjBlkbm5O7u7ulCtXLurXrx/Z29vTnDlzaO7cuVSoUCFJn5GIAvb9+/fJwsKC3NzcaM+ePXTnzh0KDw+nnTt3Uv369cnS0lLnAX+zZs2oYcOGtH//furcuTMpFAoqVaoUzZs3j75+/apzOZJ79eoVzZ49m0qUKEF58+YlHx8fvbYNlSpV0vooVaqU5N/0pUuXyNramubNm0dE/1+4a9GiBcXGxuq1XGmR+v3ftm0bNWnShMzMzKhNmza0f/9+io+P1+u9Pn36lObj5cuXNGbMGDI3N5fUOPnrr79SmzZt0tymJSQkUOvWrXUenBcqVIiOHDmi/vvevXukUqno+/fvei2TLhm9+ELOts4QGl1FNJYSiWm4EnHSnpyIhvH0ZGVDp6enJ9WuXZv27dtHHTp0oNq1a1PdunXp+fPnFBERQXXq1KGBAwfqzCVpn5jeSW7Sv9qcOnWKrK2t1Q1OR48eJWtraypVqhSVKVOGlEolrVmzRmcuP//8M/3xxx/pPu/n50eNGjXSGuPHjx9UpUoVMjU1pdatW9PYsWNpzJgx1LJlSzIxMaGaNWvqPE4l0t1wJeUzShnDysoqQw3AIho0bty4ob5AxtnZma5evUr58uUjKysrsrGxIZVKRbt379Yao1q1ajR06FD68uULLVq0iBQKBZUtW1bv47i5c+dqHIdaWVnRL7/8Qj169KAePXqQs7MzTZ48WWeclN/dpItI9PnufvjwgTp27Ei5c+em/Pnz0+LFiyk+Pp4mTpxI5ubmVKNGDY11L0VGG/RCQkKof//+ZGtrS1WrVqXFixfT69ev9Tp+mT9/PpUuXZoKFixII0eOpGvXrhER6X1+VKBAAQoODiaixPMShUJBQUFB6ucvXrxI+fLl0xqjZMmSWr8bISEhVLJkSZ25lCtXTn3hyo4dO0ihUFCtWrX0uqDL0dFRvTxERPPmzaPixYurj8PmzZtHNWrUkBTr0qVL1KdPH7KxsaFKlSrR/PnzSaVSSV6/Pj4+lDt3bqpZsyYtW7ZMXVDX9zP69ddfqWbNmvTnn39Sy5YtqVSpUuTh4UGvX7+mN2/e0C+//KJ3Y3RKUvdpJUqUoP3796v/vn//PpUoUYJ69OhBCQkJks6xdu3aRSYmJhQQEKBxfBgfH09+fn5kYmJCe/fu1ZmLru1uRop3Gd12KxQKKleunM7zCm0M7fsyadIkcnBwSPPCiGvXrpGDgwMNHjyYChYsSEuWLEkzxuzZs8nY2JiqVKlClpaWZGFhQTNnziR7e3uaPXs2ffjwQWcehrReihUrpvVCkRs3blDRokV15iLiNyDi+y8ljhTu7u4a+8+UMVauXJmhCy8z+nsU4Z9//qHff/+dnJ2dyd7enoYNG0aXL1/W63tXpkwZ9W/j8OHDZGRkpHEes23bNipevLjOOLa2tjrbMW1tbbXGuHz5MvXr149sbW2pUqVKtHTpUkm/P6levnxJffv2JWNjY2rcuLHW19ra2mqcX/bo0YM8PT3Vf58/f54KFSqU4Vw+ffpEJ06coFKlSqW6sFqKR48ekZ+fH3l6eqqPmxljhoOLdynExMTQtm3bqFGjRmRubk5t2rSh7du367XDMjIy0rjy2MzMjO7du6f+++XLlzo3hiqVisaPH0+fP39OFVufA7YcOXLQ+fPn033+/PnzlCNHDq0xFAoFFShQQH0VUcpHgQIFJB0kiSpIiiiOijioTiK3kLJ3716tj0WLFklav61btyYPDw96+/Yt3b9/nzw8PKho0aLqgx4pB7O1atWiCRMmEBHR5s2bKWfOnDR+/Hj182PHjqWff/5ZZy4KhYIqVqyY6oovhUJB1apVk3T1V4kSJWjTpk1ElHjgpVQqaceOHernDx06RA4ODjpzEVHAHjhwIDVo0CDN5xISEqhBgwY6rxa0s7Ojq1evElHiwZVCodD7KmgioubNm5ONjQ116tSJDhw4oO7Zps93ztTUlLp3705TpkxJ89G3b1+9CjonTpwgc3Nzmjx5MhUoUIA8PDzox48fei9bWvQtLiVdHFCiRAkqUKAAjRkzRmP7q4/4+Hj6448/qFChQuTg4ED+/v6SCoJ58uShy5cvp/v8pUuXKE+ePFpjKJVKevXqlcY0CwsLvXsVi9q+EMnf1hlSo6uIxlKixG1ds2bNNHrVpPXQR0ZP2kU0jOuSlQ2d+fPnVx+/vH//nhQKhcaFGCdOnKBixYrpzKVChQrk4eFB4eHh9OTJE3ry5Ak9fvyYjIyM6K+//lJP06Zu3brk7e1Nz58/p2nTppGtra3GifH06dOpQoUKOnPJnz8/3b9/P93n79+/T/nz59caw9fXl/Lly0d37txJ9Vx4eDjly5cv3QbF5AypAVhEg0aTJk2oefPmdPbsWerbty8VLFiQvL29KT4+nuLj42nAgAE6ixc2NjbqzycuLo5UKhX99ddfei9P9erVNeZLuV527doladSLpO9m0nfW0tKSTp06pTFd13e3T58+5ODgQCNGjKCyZcuqexV7eHhoPT9ISUSDnkqloqFDh6b67uobY/z48al69eu7nVOpVBoXuJmbm9ODBw/Uf7969Urn99/MzEzr+n/y5ImkXo3J9+0JCQlkbGxMZ8+e1TmftlyaNm2q0Svw7t27OkeOIUrcNxYpUoTGjRun0ds1I5+R3HPYfPnyqa/8T9oH/P333+rnr127Rrlz59YaQ1ePdhsbG0n7NHNz81THX8+fP6eSJUtSly5d6MWLFzrjtGjRgsaOHZvu86NHj6aWLVvqzMWQtt0KhYJGjhyZ7vlE0kMbQ/q+ECUW5ZOfb6a0bds2UigU5O3trTVGUrHi9OnTpFAoyMPDg6KioqQujkGtF1NTU3r06FG6zz969IjMzMx05iLiN2BIxbtChQpp7DdSxrh9+3aGRs7IzuKdmZkZde3alY4cOaJxzqvP987c3Fxjf5Ry5KWIiAgyMTHRGcfKyopCQkLSfT4kJISsrKwk5fTt2zfasGEDNWjQgCwsLKhDhw507NgxSfOm5fPnzzRhwgSysrKiGjVq0MmTJ3XOk/JzdXZ2ppUrV6r/joiIkPQ70uXw4cPk7Oys1zxPnjyhwMBAdS88c3Nz2RfHMMbE4uJdCnZ2dlS3bl1avXq1RgFHnx2WiIOKPn36UI4cOah27dq0cuVKdS76HrC5urpq7ZY+evRonVcEOTo60tatW9N9/urVq5IOkkQdhIoojoo4qCYSU0jR1isg+dXWuuTNm1djGI2EhATq168fOTg40MOHDyV975I3XMXHx5ORkRGFhoaqnw8LC9PZKE6UWBwtWrRoql56+qwXExMTevr0qcbfyRt8nj9/TsbGxjrjiChglylTRuuwDfv27dPZIyut7UJGikoqlYqGDRuWal591m2VKlU0hvRKSepvOrndu3eTkZERNWvWTFKvjyS6hgy0trbO8NCzwcHB5OrqSkqlUu+r7Hbu3EnOzs6UK1cumjdvnl493kxNTTW+uyk9ffqUTE1NtcZQKpWpenRZW1trPXlOi6jti4htnSE1uopoLCVKXL8dOnRQ96hJ76GPjJ60i2gYN6SGTjMzM43fkaWlpUbhKyIiQlLD+I8fP2jIkCFUunRpjf2ZPuslR44c6p5FP378IKVSqS4+EyUW3aQ0IJiZmWntoXT79m2dJ+716tWjZcuWpfv8kiVLqF69ejpzEXGsmnI7lZFtVFrvnZEGjdy5c6t7Bnz58oUUCoVGo094eLiki9VENGjnyZNH47tbpUoVjQsDHj58SJaWlnrHzUg+hQsXVh+LPX78mBQKRYauyBbRoNeoUSOytrZWj2SS1PNCnxizZs0iJycnKly4MI0ePZrCwsL0jkEk5vufL18+raNRHD9+XNIxs4jvXd68eTW2Sblz59YoQNy7d0/Sd87ExIQ8PT3p2LFjGj1j9Fm/mzZtInd3d7K0tKT27dvT/v37KS4uLkOfUfIhOlOOciHlM7KwsKARI0ak6sme9Jg6daqkfVrRokXTHMXjxYsXVLJkSfr55591xilYsKDWYcguXLig8+JTInHFu759+6pHQjExMSFvb+9UI6bom0tGGNL3hUjaMby+xy8mJiZaixBpMaT1UqhQIa3DRh86dEhSjyERvwGRxTu5vwFTU1ON8483b95o7B/v378vqUiVkpziXXqjHw0fPpzGjx9P/v7+GkOmp+Ts7Ky+DUXy49WsbgslShyKfc6cOek+P2vWLMk9ypN79OgRubm5kVKp1Lou0hITE0MLFiyg3LlzU8mSJWn79u2S561QoYJ6qN2IiAhSKBQa6/TcuXOS9gG6JF3spU1ERAStW7eOevTooR7itVGjRjRz5kw6c+aMsIuwGWPiGGX3PfcMTVxcHBQKBRQKheQbT6fl6NGjyJEjB4DEm9meOHECN2/eBJB4I11dVq9eDV9fX2zbtg3+/v4YOnQoGjduDCKSdOPbJD4+PujYsSMKFSqE/v37q5cpPj4eK1aswKJFi3TexLxKlSq4cuUK2rdvn+bzCoVC0s2be/bsieXLlyM4OBienp7o0KEDcubMKXlZksTHx8PY2Fj9t5GRkcZnpVQqdebz9OlTNGjQAABQt25dGBsbY+rUqbC0tNQrl8OHD2Pw4MHo378/nJyc9Jo3Sf78+bFixQq0atUqzeevXbuGKlWq6Izz7ds3GBn9/09aoVBg5cqV8PHxQf369SXfrF6hUABIXI9mZmbq7zEAWFtbIzIyUmeMsWPHomHDhujatStatGiB2bNna3xmUsTGxsLU1FT9t4mJSarPPT4+XmecSpUqYc+ePahZs2aaz+/evRuVKlXSGuPp06coV65cus+XLVsWERERWmMoFAp8+fIFZmZmICIoFAp8+/Yt1c2/bWxstMY5e/Ys/Pz8UKVKFbi4uMDT0xMdO3bUOk9KderUwd27d9N93traGvXq1dMZJ2fOnOrvS5IzZ84gX758GtO03QDc19dX5/vo6/v379ixYwf8/f1x8eJF/Prrr7CwsJA076lTpzBmzBiEhYVhyJAhGDNmjMZvQIoiRYrg0qVLKFy4cJrPX7x4EUWKFNEag4hQsmRJjfUbFRWFSpUqQalUqqfpurm6qO2LiG3d9OnTERAQgA0bNqBTp07w9PRE2bJl9Y6TkJCgsc1XqVQa6ynldzIt3759U38nFAoFTE1NkT9/fr1zAYAlS5Ygb968GZpXpIYNG8LPzw9v3ryBp6cnGjduLGldJPfjxw/0798/3e1dREQEpk6dqjOOvb09Hj58CEdHR/W0ggULIigoCG5ubujRo4fOGHnz5sWrV6/UvyMfHx/kypVL/fzHjx8l7bNNTEzg6+uLw4cPo2XLlhgwYADGjBmjc77kPn/+rH5vExMTWFhYwNraWv28tbU1vn79qjOOo6MjQkJCUKpUqTSfDwkJ0bltuH37NlxdXdN93s3NDdOmTdOZS9Kxbspp+iAiNGzYUH3s8fXrV7Ro0QImJiYarwsNDdUap3jx4jh9+jSKFSuGp0+f4t69exr7oOfPnyN37txaY3z48AH29vYAACsrK1haWmocY+bMmRNfvnzRuUzajt+TtGzZUmuM6OhoREZGqr+7ISEhqZ7X51hejpcvX8LFxQVA4vfPzMwMXbt21TtOkSJFcPbsWTg4OKBIkSLpfoe1OXr0KJ49e4aAgAD0798f3759Q4cOHQBI/+6NGzcO48aNw6lTp+Dv748aNWqgRIkSICJ8/PhRr3zWrl0LKysrAInnf4GBgciTJw8ASPqu1KtXD0uXLlWfT6S0ZMkS1K1bV3YuSQYPHpzu/DVr1sSSJUvwxx9/YNeuXfjy5YtGXvfu3Uv3mCS5R48eITAwUP35dOrUCV26dNFr29CpUyd06tQJjx8/RmBgIAYOHIivX78iISEBt2/fRunSpSXH0nffnlLFihVRuHBhdO/ePc3nr1+/Lmmf1qBBA2zatAkNGzbUmF6gQAGcPHlS6zY5ybt371CoUKF0ny9UqBDev3+vMw4ATJo0SX0MExMTg5kzZ6q3W1L2RfXq1dM4D6hduzYePXqk8Rop6zsjn0lKhvR9ARLPw968eZPu7+X169caxyJp+fHjB8zMzNR/m5iY6JwnJUNaL+7u7pg5cyaaNGmS6jkiwsyZM+Hu7q4zjqjfgNzvPyDmN5AvXz7cvXsXxYsXBwDY2dlpPB8eHq4+LskqV69eRWhoKOLj4+Hs7AwgcfuvUqlQqlQprFixAiNGjMDZs2fT/A7duXMH586dg5+fH6pVq4aSJUuqjxmkfnfSavOIiopSt3mkbPtIj7e3N4YPH44yZcqgefPmGs/t378fM2fOxMKFCyXFAhKPJQMDAxEYGIivX79i1KhROttdkhAR1q9fj0mTJiEuLg6zZs1Cz5499WovHjhwIHx8fHDmzBlcuHABtWrV0vgMTp48qbNNSopHjx6hQIECWl/j6OgIBwcH9O/fH/3790eVKlVktX0zxjIfF+9SePnyJXbu3Ak/Pz8MGTIETZs2RdeuXfU+0El5ktC3b1+Nv6XEMzc3R/fu3dG9e3fcv38fAQEBCAkJQZ06deDh4YF27dqhbdu2WmP88ssvGD16NAYPHowJEyagWLFiABI36lFRURg1ahTatWunNca0adO0HgiVLl0ajx8/1rk8ogqSgPziqIiDakBMISWpOJpe47rU4mipUqUQEhKibqRJsmzZMgC6G5uAxB35/fv31Qeh58+fh4ODg/r5p0+fSm7grlatGq5cuYKBAweiatWq2Lhxo96/o9u3b+P169cAEg+a7ty5g6ioKACJJwBSiChgR0VFaS3+WFhY6DxZSCrGJP87+QFa0sGtroJkzZo1UbNmTfj6+mLr1q3w9/fH8OHDkZCQgL/++guFCxfWaFROy+LFi7U+X7x4cQQFBWl9DSCm8JZeg0pGXLx4EX5+fti2bRuKFSsGb29v7Ny5U/JFAs2aNcPx48fh7e2NPXv2ZPiEq2PHjhg+fDicnZ1TFafCwsIwcuRIdOvWTWuMgICADL13SqK2LyK2dYbU6KorRhJtjaWAmIYrUUQ0jBtSQ2fFihVx/vx5VK9eHQAwZ84cjefPnj2L8uXL64yTpGnTpggJCYGXlxcOHz4seT4gdaErrcKXFG3btsWECRPw888/p7rI4fXr1/jtt990FlY+ffqktZCVO3duSRfZpCy8ffv2TaPwFhcXpzPG5MmTNf5Obzuji6gGDbnFSEDa8buu/XSxYsUQGhqa7sUJISEhKFq0qN65ZQQRaVzYpVKpYG5urnccEQ16AFC4cGFMmjQJkyZNwl9//YWAgAAYGRmhVatWaNeuHdq1a4fKlSvrjFO/fn3Ur18fy5Ytw6ZNm+Dv74/69eujevXqaNeuHYYPH651fgcHB/zxxx/qv+3t7bFhw4ZUr9Fm3LhxqFWrFtq1a4fRo0erG0rv3LmDuXPn4ujRo/j77791LouUXBQKhdb90fTp09GwYUP8+eefiIuLw/jx4zWOfbZs2YL69evrzKVgwYKYMGECJkyYgJMnT8Lf3x916tRR7yN79eqlcSyrTdGiRTF16lRMmTIFx44dg5+fH7p27YqhQ4eibdu2WLJkic4YchvpPTw8tJ4X5sqVS+fxGABMnDgRd+7cSfO5ggUL4tSpU/jrr7+0xoiJidF6IaORkRFiYmJ05iKl6KDrArzg4GCd7yOFlONHqQzh+wIkXgAza9Ys7Ny5M83n58yZAzc3N51xJk6cqJHLjBkzUl0MKKXwYAjr5bfffkOVKlVQo0YNjBgxQmNbt2DBAty7dw+BgYE644j4DYj4/gNifgMNGzbEzJkz0axZs1TPERFmz56d6jg4LSn3Vyk/oyRSvi+tWrVCrly5EBAQoC5MRUZGolevXvjpp5/Qu3dvdO7cGcOGDcPRo0fTjFGnTh3UqVMHS5YswebNmxEQEID4+HgMGDAAnTt3RuvWrVMVKpOT2uahS58+fXD69Gm0bNkSpUqV0vje3bt3D+3bt0efPn20xoiJicHu3bvh5+eHM2fOoGnTpvD19UXTpk31KlaVL18ejx49wqBBgzB06FBYWFggOjo61eu0FQN79+4NlUqF/fv3o169eqmOoV++fAlvb2/JOaXl2rVrGDlyJDw8PLS+rn379jh16hR+//13nDt3DvXr14ebmxsqVapkUOe2jLH/pyCRR13/MQ8fPkRAQADWrVuHFy9eoFOnTujRowcaNGiQbVcmJCQk4ODBg/Dz88Phw4fx48cPSfNdunQJGzduxIMHD9Q71M6dO6sbxbJDUkFy3bp1iIqKklyQTN7rJD26GleUSiX69OmjPpBdvnw5unbtmqGDJCDxKuqkQsqlS5cQHx+PhQsXwtvbW2ch5cyZM4iOjk7zarak2CEhITpPumfPno0zZ87g0KFDaT4/YMAArFq1SmuhdNWqVShcuHC6O/zx48fjzZs3WLt2rdZcUtqyZQuGDh2Kt2/fIiwsTNLVgkqlMt3CQtJ0KY1oADBhwgTMnj0b1tbWaRawUzYKp5XLyZMn0y3wvnv3Dj///LPWXE6dOqUzTwCSGldSunv3Lvz8/LBhwwZ8+vQJP//8M/bt26d3nORu3ryZoV5RcoWEhCA8PBwA4OLigqpVq0qar0yZMnjz5g06d+4Mb29vVKhQQe/3ViqVMDIygqWlpdYDV1293b5//46GDRvi4sWL+Pnnn+Hi4gIiQnh4OI4fP47q1avj5MmTGhcQZBZR25fkr8/oti6lL1++qBtdr1y5IrnR1dHRUdKJhbaLSqTEUCgUqRoEUlIqlXj9+rWsnncpl1fu/ihJUsP47t27UbhwYUkN47NmzUJsbGyqE8okz549w6RJk3QWlyMiInDnzh00btw4zedfvnyJv/76S1bx/tKlS7CwsMjQdmrJkiUICgrC0qVLtV4FnkSpVKJs2bLqIsiNGzdQqlQpjULXrVu3dO6Pvnz5glq1auHp06fo2rWrRkPExo0bUbhwYVy4cEHrb0mlUuH169fpNpz8888/KFCggM5cpBRhgdQFuszi7++P/fv3w97eHpMnT9a4eGLAgAH4+eef0aZNm3TnVyqVaNq0qbrH/v79+9GgQQN178wfP37gyJEjko4Z5Jo4cSLWrVuHy5cvp1mkrV69Orp164YZM2boFdfa2ho3btzQq/Cn67ubRFfvyOSioqLUDXoXLlxA/fr1JTXopefjx4/4888/4e/vjxs3bmT4MwoLC4Ofnx82bdqEN2/eZCiGvg4cOABvb+9UvUVy586NtWvXSrpwTpR3797h3LlzsLe3R40aNTSeO3jwIEqXLp2honFkZCQ2btwIf39/dVH6xo0bGcrxw4cPWL9+PQICAnD9+nWtr3V1dZW0r5dysZkhSHnumdLXr1/xxx9/ZMk2SpSIiAg4ODhkWmNvdnxfbt++jRo1aqBMmTIYPnw4SpUqpT6GX7RoEW7fvo0LFy6gTJkysnJRKBQ4efKkznzTkh3rJSQkBD169MDt27fV8YgIpUuXRkBAAKpVq6bzPf5rv4GHDx+icuXKKFWqFEaOHKkuWN29exfz58/H3bt3ceXKFZQoUUJrHCnFYKnfl4IFC+Kvv/5K1dZy69YtNGrUCC9evEBoaCgaNWok+UJoILEXYVJbw4cPHxAbG5vua0W3eWzbtg2bNm3C/fv3Ndox0xsVLLncuXPD2toa3bt3h6enZ7rna7p64CVvf0zr96RPu5RcaY18BCSeo8fFxeHnn3/Gtm3bJPUqvHPnDoKCghAcHIxTp07h+/fv+Omnn1C/fn24urpK+l0zxrIGF+8kSEhIwJEjR9QNC9bW1pJ2dp8/f053o/ngwQOdO3Jd3rx5YxBDdX3//h3Lli3DyJEjMzR/RguScmTmQXVmFFL+C54/f44rV67A3d1d0lBnuoahTKJriLEkcgrYIguJmSk+Ph779++Hv79/hr5zX758webNm7F27VpcuXJF7+XJaOENSPx+dOrUCefOnYOtrS2AxB4mtWvXxpYtW3Q2sCuVSlhaWsLIyCjDhbd169ZJylVKwSEmJgaLFi3C5s2bce/ePQBAyZIl0bFjRwwbNkxjSFgp5KzbzCJyW5cdja4inDp1CrVr10aTJk2watWqDA0pKvKkPS2iGsZFSkhIwKFDh1INg5PVMaTGEVnoioyMxLhx47B161Z1j1NbW1t07NgRM2fO1NlTOGUxJiWphURDExcXh02bNqFx48apCl5SeHl5SXqdlF7N0dHReg+jntyXL19Qo0YNPH/+HJ6enhoNen/++ScKFiyIS5cu6bzgIeUV0BkpvGV2kTY8PBxr167Fn3/+qbNBT4rQ0FBJPe+0iY2N1Xuodjm+ffuGI0eOaBxfNmrUSPJw3f8m165dg7+/v6TePv8lV65cwciRI7F3795U5/aRkZFo3bo1fH19tV44ZijFSF0XSCWn7aIhUXEM0YULF9CzZ0+Eh4drFKpKlSqFtWvXonbt2tmcYfa5evWqxrauYsWKkuc1lN9Acul9jxUKBczMzFCiRAl1b7a0XLp0CT169MCdO3dSfVcCAgJSXUiR2aysrHDgwIFUI1wEBwejRYsW+PLlCx49eoSKFStKHr4yudjYWOzfv1/nxfaGQlTRTW5BUp91ravoll57hY2NDZydnfUaUjel27dvY9OmTVi6dKm6GMgYMwxcvNPT27dvsWHDBkkHrHXr1sVff/2VqnfF3bt30bBhQzx//lzSez58+BC+vr7qhtvSpUtj6NCh6h5EUhw5cgRWVlb46aefACRe2f/HH3+gdOnSWL58uc7Gordv3+LixYswMTFBw4YNoVKpEBsbixUrVmD27NmIi4vT6+qd9BhKQVIEuYUUIPGA4siRI/Dz88OOHTsynIuIOIaUS1YTXUhMSEjAgwcP8ObNm1Q9IaUM9ZHSs2fPAEDS/UzScvr0afj5+WHnzp0oUKAA2rZti19++UXy1VZyC28A0KRJE3z69Anr1q1T90a5e/cuvLy8YGNjgyNHjmidX2ThzZCIWLeZLWlbFxAQgL1792p9bWxsrNZCl9RGV11xpBARI4mdnR3+/vtv2XEym9yGcbkFswcPHsDf3x+BgYF4+/Zthhr6RcQQGSejiAjv3r0DEcHOzk5yzwVD6zEHJPb2GzlyJE6cOIE3b96kutBFaiHRwsIC4eHhkvelmcXKygrt27eHt7e3+rhZXx8/fsS4ceOwbds29bB9tra2aN++PWbNmiVpqHZD/KzTo6tBT5+eWlKHwz1x4gQWLVqkcWHL0KFDJd17KTPiiCAiF5HL8+bNG/UQdc7OzjrP0aQWdBQKBRYsWKB3PvoSUXQDgM6dO8PFxQUTJ05M8/lZs2bh9u3b+PPPP4XlnhYRBbOUFwyFhoYiLi4u1f2xqlSpovWiIRFxDO37ktLVq1dx//59AICTk5OQ+1HpS25x6b9K1Hpxc3PTeo+4u3fvQqFQpHuPuCSG8F0BgC5duuD8+fNYsGCB+jz+8uXLGDlyJGrXro0NGzZgy5YtmD9/fqr78YooMIkqUomKk5kjH+kj6UJwbbLrYvB//vkHwcHBCA4ORlBQEO7duwdTU1PUrFnzX9OznbH/BVy80yF5b4fSpUujSpUqkudt2rQpFAoF9u3bp75COjw8HA0aNED79u113ncKSLyHTcuWLVGxYkXUqVMHAHDu3Dlcv34d+/fvx88//ywpl3LlyuH3339Hs2bNEBYWhqpVq2LEiBEICgpSXxmUnrNnz6J58+b4/PkzFAoFqlatioCAALRu3RpGRkYYPHgwunfvLvkeGuvWrUOePHnUQzOOHj0aa9asQenSpbF582bJjTai4oiwfv16VK1aNdVB3ffv37Ft2zZJ91NI7vHjxxqNiu7u7jhw4IDeeYmIYwi5fPr0CZcuXUqz2KXPuhUVR44LFy6gc+fOiIiISNXAqc8BW1xcHKZOnYolS5ao7wNoZWWFQYMGYfLkyToLIK9fv0ZgYCD8/Pzw+fNntG/fHqtWrcL169f1vmJLbuENSLzH599//53qZOfKlSuoW7eu5HtViCSqt5uc/YicdavPVZG7du2S9Dpvb28sXrw4VY+R6OhoDBo0CP7+/jpjiCp0iYgjKpek3pS6huDNKmfOnMHq1avx8OFD7NixAwULFsSGDRtQtGjRDBUj5BS6vn37hu3bt2Pt2rU4d+4c6tati44dO6JNmzaSe1iJiCEyzn+JiMJb06ZN8fTpU/j4+CB//vypGiik3gvP1dUVQ4cORevWrSXnn1xsbCzMzc1x7do1WcM+79mzB4GBgTh06BAcHR3h7e2Nbt26oUCBAnrHIiK8ffsWAPQq0hqy+Ph47N69W2O/1qpVq3R7gwKaIxjoWgdSvnMrVqzAkCFD0K5dO9SqVQtA4vHVjh07sGjRIgwcOFDSsoiKI6JgJiIXUcvz5csXDBgwAFu2bFF/HiqVCh06dMDy5ctTDeucRFRhSFSvLlFFt+LFi2P37t3pFpbDwsLQqlUrnUNtyyVq/SZZuHAhgoODsW7dOvWFvB8/foSXlxfq1q2LESNGSMoro3GkjDwA6B59wJB6AYrORVRxSUSxy5AKiaLWi6+vL86cOaPzHnHfvn1L9x5xoohYv1FRURg2bBjWr1+v7jVlZGSE7t27Y+HChbCyssK1a9cAIFWvSREFJlFFKkMqdokoJEotIgLaC4miiprbtm1TF+zu3r0LY2NjVKtWDW5ubnBzc0Pt2rX1HiWIMZa5uHiXDhG9Hb59+wZ3d3cUKlQIW7Zswa1bt9CwYUN06dJF8oFjpUqV0Lhx41QNgmPHjsWxY8ck36PCysoKN2/ehKOjI6ZMmYKbN29ix44dCA0NRbNmzfD69et053V1dUWBAgUwfvx4rFu3DgsWLICTkxNmzpyJdu3aSXr/5JydnbFy5Uo0aNAA58+fR8OGDeHr64sDBw7AyMhIciNyyjju7u5YtGiRXnHi4+MRGBiobrRKWdCROkxZ0pB9gYGB+OWXX9TTpd57Bki8H8uOHTvg5+eHs2fPIj4+HvPnz0fPnj0ljVktMo4h5bJ//3506dIFUVFRsLGx0TiQUygUOu8/JjqO3AJgxYoVUbJkSUydOjXNRs70GkRS6t+/P3bt2oVp06apG2jOnz+PKVOmoHXr1li5cmW687Zo0QKnT5+Gh4cHunTpgiZNmkClUsHY2DhDxTsRhbeSJUvizz//TDWM6aVLl9C5c2c8ePBAr5yycwhPkXHkrFupw8gB0oaSAxIb7169epXq6vt3797B3t5e0vAaogpdIuKIymXQoEFYv349nJycUKVKlVRD7qW3z8+MBqedO3fC09MTXbp0wYYNG3D79m0UK1YMy5Ytw6FDh9K9L2pKcgtdly9fxtq1a7FlyxYUL14cXbp0wZgxY3Djxg3J2xgRMUTGEdXDTFQcEUQU3qytrXHmzBm9hs9Ky7Zt2zBu3DgMGzYszd+RlB5ZxYoVw+7duzN039OUkkbbCAwMRHh4OBo3bgxvb2+0bNlSa6HKUO3YsQPbtm3D06dPERMTo/Gc1POJW7duoWXLlnj9+rVGg6mdnR0OHDiQ7n2gko9gcPXqVYwcORKjRo3SOH5ZsGAB5s6dK6l4W6hQIYwdOxY+Pj4a05cvX45Zs2bhxYsXkpZHRBxRBTMRuYhaLx06dMDVq1exdOlSjc9oyJAhqFixIrZs2aIzhpzCkKgilaiim5mZGcLDw9O9Z+Djx49RunRpfPv2Lc3nM2NfL6LwVrBgQRw7dizV7/bmzZto1KgRXr58KSkXUXEyypCKgKJySSKquCSi2GVIhURR60XEPeIMrRcgkFjES9quFStWDFZWVlpfD4gpMIkqUomII6rQZUiFRFG5mJiYoGrVqupiXZ06dSR3xGCMZRNiaWrcuDHVqFGD7ty5o552584dqlWrFjVu3FhynI8fP1KFChWoXbt2lDdvXho5cqReeZiamtK9e/dSTb979y6ZmppKjpMzZ066desWERHVqVOHVq9eTUREjx8/JnNzc63z5sqVSz3v169fSalU0p49eyS/d0rm5uYUERFBRESjR48mT09PIiK6efMm5cmTJ0vjDBw4kCwtLal9+/Y0ZMgQGjp0qMZDKoVCQQsWLCBzc3OaPHmyevrr169JoVBonTckJIT69+9Ptra2VLVqVVq8eDG9fv2ajIyM1OtdChFxDCmXJE5OTjRkyBCKjo7Wa77MiLNv3z6ytrYmhUJBOXLkIFtbW/UjZ86ckmJYWFjQ/fv3M5xDEhsbGzp06FCq6QcPHiQbGxut86pUKho2bFiqbUtGPh+ixHV78eLFVNMvXrxIxYsXlxRjz549VL16dbp8+bJ62uXLl6lmzZq0e/duybk8e/aMfvrpJ1IoFJQzZ07KmTMnKRQKqlOnDj179kxSDFHbfxFxRKxbESIjI+nTp0+kUCjowYMHFBkZqX58+PCB1q1bR/nz55cUy8fHh2xsbKhKlSrUp08fGjZsmMZDKhFxROXi6uqa7sPNzU3yfDY2NmRhYUGVKlWiSpUqkaWlJdnY2GiNkVLFihVp3bp1RERkZWVFDx8+JCKi0NBQypcvn875L126RH369CEbGxuqVKkSzZ8/n1QqlV7bhnLlylGRIkVo3LhxdPPmTfV0fbYxImKIjENE1KRJEypdujStWLGCdu/eTXv27NF4ZGWc169fU9euXSl//vykUqlIqVRqPKSysrKiq1evSn59WlxcXCg0NFRWDKLEY6mUD6VSqf5XirVr11KzZs3o/fv3svNJbsmSJWRqakoKhYLs7Oxo4sSJko4ntm/fTr/++ivVqFFD/btOeuhDbpzFixeTlZUV+fj4kImJCfXt25fc3d0pR44cNH78eMl51KxZk1q0aEEfPnxQT/vw4QO1bNmSatWqJSlGtWrV6ODBg6mmHzx4kCpXriwphqWlZZrHUvfu3SNLS0tJMUTFKViwIC1dujTV9GXLllGBAgWyNBdR68XCwoLOnDmTavrp06fJwsJCUowCBQpobHOThIWFST5eICJasGBBmt+5Vq1a0fz587XOa2pqSo8ePUr3+UePHpGZmZnOHAoVKkSHDx9O9/lDhw5RoUKF0n0+M/b1ItavlZUVBQUFpZp+8uRJsrKykpyLqDhJnj17JvmYXSRtx3JSj+tEK1CgQJrHKjdv3lRvX65cuUK5c+fWGmfRokXUtm1bioyMVE/79OkTtWvXjnx9fSk6OppatWpFjRo1ytQYRKT+DVhaWlLlypWpcuXKZGVlRTly5KAaNWqoz6u1HaOJWi+WlpZpfneDgoLU392HDx+StbV1pi4PkZj16+XlRZ8/f041PSoqiry8vLS+/39N0rGjtoeU48vg4GDJDykcHR1p6tSp6nZMfYjKJSoqSu/3ZoxlLy7epcPMzCzNhoiQkBCtxa7kDZpJjzt37lDhwoWpf//+GtOlKFSoEG3bti3V9K1bt1LhwoUlL0/z5s2pcePGNG3aNDI2Nqbnz58TEdHRo0fJyclJ67wKhYL++ecf9d9WVlb04MEDye+dkp2dnXrdVqxYkdavX09ERA8ePNDrxFJEnNy5c6fZgKCvpHV0/vx5sre3p19++YW+fv1Kr1+/1nlAoFKpaOjQoRoN/ET6NyyKiGNIuSSxsLBQNz7LISKOiAKgm5ub1gYAqezs7Oj27duppt++fVtn8fr8+fPUq1cvsra2purVq9PSpUvp7du3GS7eiSi82drakomJCSmVSjIxMdH4f1IRLumhjYiCWUa3/5kRR1RR083NjT5+/JhqemRkpKRGCF0nQCqVimbMmCEpF1ENIiLiGErjDJG8BsrkzM3N6fHjx0SkWbx7+PChzot+RBW6TExMyNPTk44dO0YJCQkZiiMihsg4RGIKXaLiiCokiii8HT16lBo1aqT+3mXUkydPtD6kqFixIllZWZGpqSmVLFlSVsHs9evX9Pvvv5OLiwtZWFhQly5d6OTJk7R+/XoqU6YM/fzzz1rnF1UwExHH2dmZNm3aRESa24WJEyfSwIEDJediZmaWbsFAShEkKUZ6xy9SY3Tq1Inmzp2bavq8efOoQ4cOkmKIiiOqYCYiF1HrpXDhwnTjxo1U069fv04FCxaUFENUQUdOkUpu0S1Jjx496KeffkrzuYSEBKpTpw716NFDZxwicft6EevX09OTHB0daefOneqC2Y4dO6ho0aLUrVs3ybmIiBMfH09Tp04lGxsb9bFljhw5aNq0aRQfHy85l+SyqwiYlozmIqK4RCSm2GVIhURR66Vz585UtGhR2rVrl/oz2rVrFxUrVoy6du1KRESbN2+mKlWqZOryEIlZv0qlUqPdLsnbt29JpVJpff/k5BSYRMaQE0d00U2URYsWUYUKFUilUpG7uztt3ryZvn//nmXvn1Z7dXoPxpjh4OJdOjLa2yG9Bs6MXkE8depUsrW1pTlz5tDp06fp9OnTNHv2bLK1taVp06ZJXp6IiAhq3rw5lS9fntauXauePnToUBo0aJDWeRUKBQUFBdH169fp+vXrZGlpSQcPHlT/nfSQqnPnzlS5cmXq2bMnWVhY0Lt374iIaO/evVSmTJksjZM/f366e/eu5PdMT/IDpYiICKpQoQJVrFiRLly4oPOzbtSoEVlbW1Pnzp3p8OHD6sZFfRsWRcQxpFyStGnThrZu3arXPJkVJ6MFwOS/k127dlHp0qUpICCAQkJCMvw7mjp1KnXq1EnjYO/79+/UpUsXmjJliqQYUVFR5OfnR3Xq1CFjY2NSKpXk6+ub5hV72ogovAUGBkp+aCOiYCaqt5uIOKKKmikvwkjyzz//kJGRkc48goODKSgoiBQKBe3atUvjhOfvv/+mFy9eSFqe/7r79+/TkSNH6OvXr0REGsUiXUT1UihatCj99ddfRKTZSL9u3TpycXHROq+oQtfz589pxowZVLx4cSpQoACNGDGCQkNDydjYWHIcETFExiES18NMRBxRhcSMFt6SriRPeiRtm6ysrPTaNok2ZcoUrQ8pdu7cSc2bNydjY2OqUKECLV26NNXFDw8ePCBjY2OtcUQVzETEMTc3VxdA7ezs6Nq1a0SUWGDKlSuX5FzKly9PJ06cSDX9xIkTVLZsWUkxKlWqRJ6envTjxw/1tB8/fpCnp6fkAuv06dMpR44c1KxZM5o+fTpNnz6dPDw8yNbWlqZPn06LFy9WPzI7jqiCmYhcRK2X1atXk7u7O7169Uo97dWrV9SoUSNatWqVpOURVRiSU6QSVXR78OAB5ciRg6pXr05bt26la9eu0bVr12jLli1UrVo1ypEjh+RRNUTt60Ws3+joaOrfvz+Zmpqq2yxMTEyof//+evXMEBFn7NixZGdnRytWrFCfEy1fvpzs7Oz0utjBkIqAInIRUVwiElPsMqRCoqj18uXLF+rVq5f6GCbpu9u7d2/68uULERFdvXpV6/GWIfQCFDlCCpGYApOoIlV2F7uSE1WQJEr8TgwaNIjy5MlDOXPmpIEDB9KVK1cyPRdRPRIZY1mLi3fpyGhvB9FXeCQkJNDChQupYMGC6gJgwYIFydfXV3KjYGxsLK1bt07jBEwfyYuOcoczIkocSnTgwIHUsmVLjashJ02aJLnnhqg48+fPpwEDBujVwJqWlA3j0dHR1Lp1a7K2tpa0bp4+fUpTp04lR0dHypcvHw0ePJiMjIzSvDI5s+MYQi579+5VP9auXUsODg40efJk2rFjh8Zze/fuzZI4STJaANT2G8ro7yjp+5UnTx5q2LAhNWzYkPLkyUM2NjbUpk0bjYcUd+7coVGjRpG9vT2ZmZlRixYtJOciqvAmgiEN4Skijtx1m9QAkvIijOvXr1NoaCjNmjWLihQpInmZnjx5kuHGj5TkFLpEx5Eb4927d9SgQQP17zipcd3Ly4uGDx8uKYaoXgqzZs2i0qVL04ULF8ja2prOnDlDf/75J9nZ2dGSJUu0ziuy0JXkxIkT1KVLFzI3NyeFQkGjRo3S+6IZETFExBHVw0xEHDkFQBGFN1Hb/b1791JMTIz6/9oeWcXGxob69OlDly5dSvc1X79+1VkMFFUwExGnaNGi6u9LlSpV1AWYo0eP6iywJm8EPHjwIJUpU4a2b9+ubjDdvn07lStXTvJIFhcvXqS8efOSnZ2d+vjFzs6O8ubNm+b+Oy2Ojo6SHkWLFs30OKIKZiJyEbVeknqwGhsbU/Hixal48eJkbGxMVlZWknuziioMySlSiSy6Xb58mcqUKaPR8KlQKKhMmTJatxUpidrXi1q/RIkX8yUdG8oZTk1OnPz586e5nd+zZ49ew88aUhFQRC4iiktEYopdhlRIFLVeksdL+oyS5pfKEHoBihwhJTm5BSZRMeTGEVV0y4xCYkxMDPn6+qq35RUqVCA/Pz+d56IZzcVQeyQyxrTj4l0y6TVmpNXbITt8/vxZ7x4xSZKf+OtL1zBG+gxnZGhat25NOXLkoKJFi1Lz5s0zVPQgSrzSO62hFCdNmkSurq565XTs2DHq1KkTmZmZkZOTE40bNy5DBzgi4mRXLukVudIqemkjIo6IAqDU35A+v6MePXpIfugjLi6Odu/erVfxTqR//vmHwsLCMtwj0ZCG8BQVR46UjUwpHxYWFuTn56d33OjoaAoPD8/Q5ySi0CUqjqhcPD09qXHjxvTs2TONnjFHjhyh0qVLS44hopdCQkICzZgxgywtLdWfs5mZGf3222+SYxCJK5gl+fTpEy1fvpyqVKlCCoWCypUrly0x9I0jqoeZ6J5qcgqAhnTBRfKLn+Ts71MKCQmhDRs20IYNG/Qucsq9x24SOQUz0XF69uypLjYuW7aMzM3Nyd3dnWxtbcnb21vrvCkbBZN/Jin/lioqKopWr16tvr/omjVr/rX3YBFVMDMkunqw6tObVW5hSG6RSlTRLUloaCht27aNtm7dmqHez6L29UlEFd5EDTGZ0TimpqZpHmPcuXNH8nC6RIZVBBSVC5G84lLS/HKLXYZUSEyek5z1IuIecYbQCzD5CCk7d+5Mc4QUOYWrjBaYRMfIaBzRRTcRBcmYmBjaunUrNWnShFQqFdWpU4f8/f1p2rRplC9fPurUqVOW5cIYM3wKIiIwAMC6deskv7Z79+5pTr9x44bkGOXLl5f8WrlcXV0xdOhQtG7dOsveMz0BAQGwsrLCr7/+qjF9+/bt+Pr1a7rrNjPieHl56XyP7PLx40f8+eef8Pf3x40bNxAfH59tcQwpl6ymVColvU6hUEhantmzZyNfvnzw9vbWmO7v74+3b99izJgxGcpTrmfPngEAChcunOEYb968wZs3b5CQkKAxXcq27sqVK+jevTvCw8ORcrckdd0CQM6cOfH161fExcXByMgIANT/t7S01Hjthw8f0owhYl8gMg6Q8XUbEREBIkKxYsVw6dIl2NnZqZ8zMTFB3rx5oVKpJOf59u1beHl54fDhw2k+L+Vz6tatG968eYO1a9fCxcUF169fR7FixXD06FEMHz4ct27dkpSLiDiicrG3t8fRo0dRoUIFWFtbq+M8evQI5cuXR1RUlM4YX79+xciRI+Hv74/Y2FgAgJGREXr27Il58+al+v6mJT4+HufOnUP58uVhYWGBBw8eICoqCqVLl4aVlZWkZUkpMjISGzduhL+/P0JDQ1G2bFm9jnXScu3aNfj7+2PJkiXZGkNKHEPaHuTMmRMKhUL9d3R0NOLi4mBhYQFjY2ON16a3fcts379/R0xMjMY0GxubLHv/N2/eoGPHjggODoatrS0A4NOnT3Bzc8OWLVs0toHpCQwMRI8ePVJNj4uLw8SJEzF79mxJufTq1QuFCxfG5MmTsXz5cowaNQp16tRBSEgI2rZtCz8/vyyLk5CQgISEBPV+ccuWLfj777/h5OSEvn37wsTEJN15T506JSlPAKhfv77k12ZUbGwsSpUqhQMHDsDFxSXb44ggIhdDWp6Unj9/DgAoVKhQhmNER0fj4cOHAIDixYtL2icmd/XqVTx48ABEhJIlS6JixYoZzkUOEfv6lDK6fhMSEjBjxgwsWLBAfZxibW2NESNGYMKECZLPgUTEqVGjBmrUqJFqXzxo0CBcvnwZFy5ckJSLmZkZbty4gZIlS2pMv3v3LipWrIhv375JilOgQAGsWrUKLVu21Ji+d+9eDBgwAC9evMiSXLy9vbF48WJYW1trTI+OjsagQYPg7+8vYWn+X1RUFB49egQAKFasWIaODeXGiIqKwrBhw7B+/XrExcUBSPwNdO/eHQsXLoSVlRWuXbsGAOn+TkWtF5VKhVevXiFv3rwa09+9ewd7e3t1fpm9PCnjZXT9KpVKvHjxAvnz59eY/v79e+TNm1fvtpfY2Fjs3r0bAQEB+Ouvv1CzZk307NkTz58/x/Lly9GgQQNs2rQp02OIihMaGorAwEBs3rwZ8fHx6Ny5M7y9vVG5cmW91kvynFasWIExY8YgNjYW5cqVw+DBg+Hl5aVxvJ4yh4CAAGzevBlKpRLdunVDr169UKpUKfVrbt68iWrVqkneXmU0l6JFi8LLyws9evSAg4ODfgvPGMta2Vo6/A/SNTyevlenvnv3jgYMGEAuLi6UO3fuDPfW2Lp1KxUrVoyWLl1Kf//9d4Z6Sxw+fJjOnDmj/nvZsmVUoUIF6tSpk8ZNt3VxcnKikydPppoeHBxMJUuWzPI4oty7d49Wr15N06dPp6lTp6of+tybMD2irp4REceQcvk3KlKkCJ07dy7V9AsXLpCjo6Pe8d68eUNnzpyhM2fO0Js3b/SaNzY2ln777TeNIWFsbGxowoQJ6mHMpAgJCaEyZcqkue2Tuq0rX748tWnThi5cuECPHz/OcI9EQ+pRIoKIdStS586dqU6dOnT58mWytLSkY8eO0YYNG8jZ2ZkOHDggKUa+fPnUw74l76X28OFDsrS0lJyLiDiicrGysqJ79+6linP58mVJw9rFxcXRqVOn6MOHD7Kvojc1NaVHjx7pPZ8UV69e1XmfXJZ5Mnv79u3btwzdrD4qKooGDhxIdnZ2aQ7XpI/jx4/TuHHjqGfPnuTl5aV+6OodlqR9+/ZUtWpVjSG6b926RVWrVqWOHTtKimFtbU3t2rXTOLa9c+cOVa5cWa9hhuPj4yk2Nlb99+bNm2nQoEG0ZMkSjfu9ZVUcQ/LgwQPy8fFRD5s5ePBgevDggeT5CxQooPdw7pkRJyYmhooVK2YQuYiKkdyPHz/o2bNnFBERofGQwpDuPSZHUu9QKQ99yN3XG8qwjqLiBAcHk6WlJbm4uJC3tzd5e3uTi4sLWVlZ0enTpyXnUr169TSPU3x8fKhGjRqS44joCSgiF6VSmeY9q9++fUsqlUpSDCIxPcxExEhOTq85uetF9D3iiAyjF6BCoUizPeDJkydkYWEhOZcrV66Qj48P5c6dm+zs7GjEiBEUHh6u8ZqwsDCtvwURMUTGSU5uL0A5veaUSiU1atSItm3blm57S1RUlOTRk+TkYkj3E2SMacc977SIj4/Hnj17EB4eDgAoU6YMWrZsqbWXQkREhOT4RYoU0fmaZs2a4cGDB+jZsyfy5cuX6qoJqb3UtF3xJrVXS7ly5fD777+jWbNmCAsLQ7Vq1TB8+HAEBQWhVKlSknupmZmZ4c6dO3B0dNSY/uTJE7i4uEi+wkRUnLi4OAQHB+Phw4fo3LkzrK2t8fLlS9jY2Ei+0umPP/5A//79kSdPHtjb22t8TgqFAqGhoZLifPr0CZcuXUqzh023bt0kxRAVx5ByOXHiBBYtWqT+Lbq4uGDo0KFwd3eXnIfIOHKYmZkhPDwcRYsW1Zj+6NEjlC5dGt+/f5cUJ+nKwvXr16vXq0qlQrdu3bB06VJYWFjojNG/f3/s2rUL06ZNQ61atQAA58+fx5QpU9C6dWusXLlSUi4VKlRA8eLFMWbMmDS3U1K2ddbW1rh69SpKlCgh6T2zgpyehKLiiFi3Se7fv4+goKA0c5k0aZKkGPnz58fevXtRvXp12NjYICQkBCVLlsS+ffswd+5cnD17VmcMa2trhIaGwsnJSaOXWkhICBo3boz3799LykVEHFG5NGvWDFWqVMH06dNhbW2NGzduoEiRIujYsSMSEhKwY8cOnTHS2zboq2rVqvj999/RsGFDWXFE+OeffzBy5EicOHECb968SdWrVsqxh4gYIuMkEdXDLLt7qkVHR2PMmDHYtm1bmt93Ketl4MCBCAoKwvTp0+Hp6Ynly5fjxYsXWL16NebMmYMuXbpIymXq1KmYNm0aqlativz586fa3u3evVtnjBw5cuD48eOoVq2axvRLly6hUaNG+PTpk84YDx8+RNeuXfHs2TMEBATg3r17GD16NFq3bo0VK1YgR44ckpbHkIga9eL06dNan69Xr57OGEePHkXLli1RsWJF1KlTBwBw7tw5XL9+Hfv378fPP/+sM8asWbNw7949rF27Vt2bMCNExClYsCCOHz8uu7ebiFxErZd79+6hZ8+e+PvvvzWmE5Hk88Zx48bBz88PU6dOVX/OZ8+exZQpU9C7d2/MnDlTUi4Z7dU1fPhwSfEBYOHChek+5+bmpvF3aGgo4uLi4OzsDCBxXalUKlSpUgUnT56U/J6AvB6JItaviN5louI8ffoURkZGWL58Oe7cuQMg8TxtwIABiIuLk9wr5NSpU/Dw8ICDg4PGuc2zZ89w6NAh1K1bV1IcET0B5eTy+fNnEBFy5syJ+/fva/Qaj4+Px/79+zF27Fi8fPlS0vKI6GEmIgYgr9ecqPWiVCrT7Y0EJLbfTJ06FRMmTMjU5UlOzvpN2t4tXrwYvXv31mgHiI+Px8WLF6FSqXDu3DnJubi7u6NXr15o3bp1qtEdgMTl8/HxSbcNUEQMkXEA+b33RPSa8/f3TzUCU5LVq1ejb9++WpdBZC7JY4nskcgYywTZVzc0bPfv3ycnJyeysLBQ35DbwsKCnJ2d9boylCjxit/Dhw9r3Btr3759kua1srJS9wqQQ8R9tiwtLdX3Vpk8eTL98ssvRJR4NUy+fPkk51K4cOF0x38vWLBglsZ58uQJlSpViiwsLEilUql7SwwePJj69u0rORcHBweaM2eO5NenZd++fWRtbU0KhYJy5MhBtra26oc+vSxFxDGkXJYvX05GRkbUsWNHWrx4MS1evJg6depExsbGtGzZMsm5iIpz/Phx8vDwoGLFilGxYsXIw8OD/vrrL8nzlyhRgjZs2JBq+vr16/W6H0qfPn2oWLFidOjQIfWVggcPHqTixYtTv379JMWwsbGhQ4cOpZp+8OBBsrGxkZyLlZUV3b9/X/Lr09KqVSvasWOHrBjJybl3nqjebiLiiFi3RERr1qwhlUpF+fLlowoVKlDFihXVj0qVKkmOY21trd4PODg40NmzZ4mI6NGjR2Rubi4pRtOmTdX3X7OysqJHjx5RfHw8/frrr+r9SlbFEZVLWFgY5c2bl5o0aUImJibUrl07cnFxoXz58kk+ZqhSpQodP35c8num5/Dhw1SxYkXav38/vXz5MkM9qV6/fk1du3al/Pnzk0qlynBPqiZNmlDp0qVpxYoVtHv3btqzZ4/GI6tiiIojqoeZyJ5qRBnvMUdE6tEdduzYQebm5uTv70/Tp0+nQoUK0Z9//ikpRuHChSkoKIiIErcRSdus9evXU9OmTSXnYm9vT+vXr5f8+rRYWVmleT+Y0NBQsra2lhwnPj6eBg0aREqlkoyNjWnTpk165+Lv70/btm1LNX3btm169Y4UEUfUaBXpjSaiz3e3YsWKNGbMmFTTx4wZI3l/1Lp1a7K2tqb8+fNTo0aNMnzPahFxZs6cSd27d9foHZkRInIRtV5q165N9erVo0OHDtHVq1fp2rVrGg8psvveY66urhoPGxsbjfN6S0tLsrGxITc3N8m5LFiwgFq0aKHRK/fDhw/UqlUrmj9/vqQYonokili/ou4zJyJOej2p3r17p9d+MSIigl68eEHjx4+ntm3bUtu2bWnChAl63/NLRE9AObmk3LamfKhUKpoxY4bOHET0MBPdS01OrzlR60XkPeIMoRdg0nZOoVBQ7dq1NbZ9jRo1oj59+qhHCJFC2/3Qk+67mxUxRMUR1XtPRK85ExMTGjlypMb8b9++pebNm5Otra2k5RGVS0qi7kvIGBOPi3fpaNq0KTVp0oTev3+vnvbu3Ttq0qQJNWvWTFKMhw8fUvny5VMNpanPCW7VqlXp/PnzGVqGtMgpJObMmZNu3bpFRER16tSh1atXExHR48ePJTfaEhGNHj2aihQpQidPnqS4uDiKi4ujEydOUJEiRWjEiBFZGqdVq1bUtWtX+vHjh8ZQZ0FBQVSiRAnJuVhbW6vnzSgnJycaMmQIRUdHZ3scQ8qlYMGCtHTp0lTTly1bptfJv4g4IgqAv//+O+XOnZv8/f3VxXM/Pz/KnTs3zZo1S/Ly5M6dW91YmtzJkycpT548kmLY2dmlObzS7du3JccgElN4e/v2LTVr1oymTJlCO3bs0NhGpdU4kR5DGsJTRBxRRU0RFxgQJe6Tjhw5QkRELVq0IE9PT3r+/DmNHj1acvFZRKFLVBxRuRARffz4kWbMmEG//vorNW3alCZMmEAvX76UPL+IohsRpduors9vQFTBLL1Cij5ExBAVR0ShS1QcUQVAEYU3S0tLdSNXwYIF6eLFi0SUWNTXZ/jZXLly6f27S6lly5ZUr149evHihXra8+fPqX79+tS6dWvJcfbt20d2dnZUp04dsrOzo4YNG2rElMKQhok3NTVVX3iR3OPHj/VqpP/06ZPG4+3bt3Ts2DGqUaOG5IsPTE1N02xEvHv3LpmamkqK0aNHD60PqUTEEVUwE5GLqPViYWGRqmFTX6IKQyKKVCKKbkSJw5LevHkz1fSwsDDJxQtRQ1UayrCOouIoFIo0CyD6DvdnSEVAObmIKi6JKHYZUiFRZNEtadnSOlaX8hmJKmqKWr9EifsAfc4Z0iOiwCSqSCUijqhCl4hC4rlz56h48eJUoUIFunXrFh04cIDy5ctH9erV06udQVRxlEje0JuMsazBxbt0WFhY0I0bN1JNv3btmuSGiObNm1OrVq3o7du3ZGVlRbdu3aIzZ85Q9erVJV+xdenSJWrQoAEFBwfTu3fvMtyYJ6KQ2Lx5c2rcuDFNmzaNjI2N6fnz50REdPToUXJycpKcy48fP6h9+/akUCjI2NiYjI2NSalUkpeXl1737hARJ1euXHTnzh0i0rxPkb4FSW9vb1q5cqXk16fFwsJCdgFQVBxDysXS0jLNnkf37t3Tq1FQRBwRBcCEhAQaPXo0mZmZqX9/FhYWNHXqVEnzJzE3N0+z8Hbz5k3JJ7lTp06lTp06aYxt/v37d+rSpQtNmTJFci4iCm/79u2jHDlyyLpHKJGYgpmo3m4i4ogqaoq4wICIaMOGDRQQEEBEiYXSPHnykFKpJFNTU9qyZYvkOHILXSLjiMrl27dvdPHiRdq/f3+GPicRRTci0mjISOshhaiCmYuLC4WGhmZ7DFFxRPUwExFHVCFRROGtXLly6u9Ww4YN1RdQLV68WK8RFUaPHi37PsFPnz6lihUrkrGxsbqHvJGREVWqVEnyvbL69OlDpqamNG/ePEpISKBXr15R06ZNKVeuXLR161bJuYgqmImII2rUi/QEBwdT5cqVJb22UKFCafYk3Lp1KxUuXFhSjK9fv2rcJ+zx48e0aNEi9cUlUomII6pgJiIXUeulatWqGvc5zwhDuveYiKIbUeK+Mb0L56ysrCTFENUjUcT6FXWfOTlxku4XqFQqqW/fvhr3EBw8eDDVqFGDateuLTkXQyoCishFTnGJSEyxy5AKicljyVkvyeNk9B5xhtgLUBQRBSZRRSoRcUQVukQVJL98+UJdunQhU1NTMjY2pjlz5ujdu01ELplxP0HGWObI+GD4/3Gmpqb48uVLqulRUVEwMTGRFOP8+fM4efIk8uTJA6VSCZVKhZ9++gmzZ8/G4MGDcfXqVZ0xbG1t8fnzZzRo0EBjOulxzwEAGDJkCIoWLYoTJ06gaNGiuHjxIj58+IARI0Zg/vz5kmIsX74cAwcOxI4dO7By5UoULFgQAHD48GE0adJEUgwAMDExwdatWzFjxgxcvXoV5ubmKF++vF73bhIVJyEhIc11+Pz581TjlmtTokQJTJw4ERcuXEC5cuVSjcU9ePBgnTEaN26MkJAQFCtWTPL7ZlYcQ8qlZcuW2L17N0aNGqUxfe/evWjevHmWxvn06VOa3/VGjRphzJgxkmIoFAr8/vvvmDhxIsLDw2Fubg4nJyeYmppKmj9JrVq1MHnyZKxfvx5mZmYAgG/fvmHq1Knq+yrocvXqVZw4cQKFChVChQoVAADXr19HTEwMGjZsiLZt26pfu2vXrnTjnD9/HufOncPhw4dTPSd1OzVo0CB07doVEydORL58+STln5ZHjx5h586dsu6d17BhQ1y/fl32/fdExBGxbgHg119/xbFjx9CvX78M5wIk3nega9euAIAqVaogIiICd+7cgYODA37//Xd06NBBUhwzMzP8/PPPqFChgvr+e5cvXwaAVPdMyew4ImIcOXIEnp6e+PDhQ6p7qUn9nIKCgiS9ly7169fH9+/fcePGjTTvbyhF4cKFUy1HRvj6+mLs2LFYvXp1qvvTZmUMUXE+fPig3p/Z2Njgw4cPAICffvoJ/fv3z9I4+/fvx/r16+Hq6govLy/UrVsXJUqUQJEiRbBx40bJ95krVqwYHj9+DAcHB5QqVQrbtm1D9erVsX//ftja2kqK4eXlhevXr6N+/foYO3YsWrRogWXLliE2NlbrvaRS+v79O9asWYPjx4+jfPnyqY6lpMQqXLgwQkNDceLECY372+pzb9tz587h4sWL6v2ivb09Dh06hOXLl8Pb2xvt27eXFCdv3ry4ceNGqu/b9evXkTt3bsn5iIjTqVMnDB48GNbW1ur70p06dQpDhgxBx44dJeeSnnz58uHu3buSXtu7d2/06dMHjx49Qu3atQEkrvM5c+ZgxIgRkmK0atUKbdu2Rb9+/fDp0yfUrFkTxsbGePfuHRYuXCj5dyQizooVK5CQkABLS0sAiffe3rNnD1xcXNC4cWNJeYjKRdR6+f333zF69GjMmjUrzfMaKfflnDt3Ljw8PHD8+PE07/clVYUKFbBs2bJU9x5btmyZ+jeqy+fPn/H27dtU09++fZvm+X562rRpAy8vLyxYsADVq1cHAFy8eBGjRo3SOGbW5sOHDxr3JEpSqlQp9b5AChHrt2jRorh3757Gfebatm2rvs+cVHLiJLWHEBHCwsI02llMTExQoUIFjBw5UmcOSff8UigUmDRpUpr3/KpYsaLkZUrvGCgqKkp93pVVuaR1/0opeQCJx4RJatWqhfz582s8//79exQuXFjrcaqIGEDicS4RoUGDBtixY4fG/svExARFihTR63snZ70k/4wmTpyYoc9I1PKIWr8i1a5dG9euXUO/fv1QuXJlJCQkYPr06Rg9erTWewWKjiEqTv/+/REeHo5Zs2ap92fv3r2Dl5cXzp49K/k+c0FBQejWrRv++usvbNq0CY8fP0bPnj3h7OyMa9euSV6me/fuISQkBIUKFcLLly9x9+5dfP36VX0ckVW5VKtWDe7u7li5cmW69xMsWrSokONExpg8XLxLR/PmzdGnTx/4+flpHJz369dPcmNefHy8ugCUJ08evHz5Es7OzihSpIjkE9wuXbrA2NgYmzZtQr58+fTa0SUnt5AYFxeH4OBg/PHHH7C3t9d4btGiRXrn4+fnh0WLFuH+/fsAACcnJwwdOhS9evXK0jiNGjWCr68v1qxZAyDxAC4qKgqTJ09Gs2bNJOexZs0aWFlZ4dSpUzh16pTGcwqFIt3i3b59+9T/9/DwwKhRo3D79u00T5S1fe9ExDGkXJIrXbo0Zs6cieDgYPXJ6YULF3Du3DmMGDFC42ReW5FURBxRhUQAsLKyQrVq1fSaJzlfX180adIkVeHN1NQUx44dkxTD1tYWv/zyi8a0woUL652LiMLb+/fvMWzYMFmFO0BMwWzt2rXo3r07bt68ibJly2boeysqjqiipogLDIDEkx9bW1s0bdoUAGBhYYHKlStj+PDh2Lx5M+bNm6czhohCl6g4onIZNGgQ2rdvj0mTJmX4cxJRdAMSl6lbt2549+5dquekLpOcQlfOnDk1jlWio6NRvHhxWFhYpPrepddQKSKGyDhJRBS6RMURVUgUUXgbNmyY+v/u7u64c+cOrly5ghIlSqB8+fKSc7lx44a6sezmzZsaz+lz/Hvy5EmcPHlS/Tu6evUqNm3aBADw9/fXOf+VK1fw7ds3LFiwQF0ALFOmDLy9vfUqAooqmImIM336dDx58gQNGzZUN3jGx8eje/fumDVrluRcbty4ofE3EeHVq1eYM2eO5MboiRMnwtraGgsWLMC4ceMAAAULFsTUqVMl74tCQ0PV5x87duxAvnz5cPXqVezcuROTJk2S/BsQEUdUwUxELqLWS9L3vGHDhhrT9bl4VFRhSESRSkTRDQBWrVqFkSNHonPnzoiNjQWQWEDo2bOnpOMfQEwxEhCzfosWLYpXr15h5syZGtP1LRbIiZN04ZKXlxcWL14sqTCcFkMqAorIRURxKSU5xS4RMUQUqUStFxGfUWYU3UR8RqKIKDCJiCEijqiim4hC4pw5czB58mT06dMH8+bNw4MHD+Dp6Yny5cvjzz//lHwRtohc/vjjD3h7e6f53OrVq9G3b19YWloiICBAUjzGWOZRkIhLq/+DPn36hB49emD//v3qnWhcXBxatmyJwMBA5MiRQ2eMunXrYsSIEWjdujU6d+6Mjx8/4rfffsOaNWtw5cqVVA0TabGwsMDVq1fh7Owsa3ly5syJ0NBQFC1aFMWLF8fatWvh5uaGhw8foly5cvj69aukXMLDw/XuIZfSpEmTsHDhQgwaNEjjJGzZsmUYNmwYpk2blmVxnj9/jsaNG4OIcP/+fVStWhX3799H7ty5cebMGeTNm1fv5Uv6SUnZaSqVSkkxdZ0oi4hjSLkkV7RoUcnxHj16lKlxZsyYgfnz56NOnTppFgCTn3BKbYCS4+vXr9i4caP6pN3FxQVdunSBubl5pr93ctbW1rh27RqKFy+e4Rjdu3dH3bp19S7gp/Tu3Tt0794d1atXz3DBbP/+/fD09MTnz59TPadPQUdEHBHrFtD+/df120nu4MGD6NKlCw4cOICffvoJQGLhaufOnTh58mSaV5On5OTkhEaNGskqdImKIyoXGxsbXL16VdbnJKLoBmR8mdIqdMXFxeld6Fq3bp3k9+zevXumxRAZJ8miRYugUqkwePBgHD9+HC1atAARqQtdQ4YMkfReIuKUL18eS5cuRf369eHu7o6KFSti/vz5WLJkCebOnYvnz59LyiWliIiIDBXeTpw4gRMnTqRZeJZSMBNl6tSpmDZtGqpWrYr8+fOnOhbbvXu3zhghISFo3LgxzM3N1Q39ly9fxrdv33Ds2DFUrlxZUi4xMTHw9PTE9u3bUxXMVq1aJXkkD1FxAOD+/fuyRr1QKpVQKBSpLnaoWbMm/P39Je0Dvn37BiKChYUFvnz5gsePH+PEiRMoXbq05J5qFhYW6l7f7du3R5kyZTB58mQ8e/YMzs7Oks5rRMXJkycPTp06hTJlymDt2rVYunSpRsEsqQCcFbmIWi/r1q1D4cKFoVKpNKYnJCTg6dOnkraXKpUKr169SnUu9f79e+TNm1fyPu3p06cwMjLSKFK5uLioi1QODg46Y3z9+hUjR46Ev79/mkU3fRuSo6Oj8fDhQwBA8eLF9Zr/1KlT8PDwgIODQ5rFyLp160qKI2L9KpVKvH79OlWMiIgIlC5dGtHR0ZJyERVHBLlFQDc3NwCJn1OtWrVSFXUcHR0xcuRIODk5ZWouovJIKnYtXrwYvXv3TrPYpVKpcO7cuUyNkZxSqcQ///wDOzs7jelSvi8iPx9A/vcFkLc8gPj1K0J6BabPnz9LLjCJiCEyTlRUFPr164cdO3ZkuBcgkHiRTOfOnREXF4eXL1+iY8eOWLp0qeT9QP78+eHv76++EBYAYmNjMX78eCxZsgQ/fvzIslxMTU0xePDgdHskfvz4UXIujLHMxcW7FBISEjBv3jzs27cPMTExcHBwQPfu3aFQKODi4qJXb46jR48iOjoabdu2xYMHD9C8eXPcu3cPuXPnxtatW1MNhZmWevXqYdKkSXpd6ZsWEYVEV1dXDB06FK1bt5aVi52dHZYsWYJOnTppTN+8eTMGDRqUZsNlZsaJi4vDli1bcOPGDURFRaFy5coZKoCI6k3I0qdPYTQz4ogqJIowe/Zs5MuXL9XVUv7+/nj79q3kYTyBxGGDknoDOzs7pzr50EVE4W3mzJnw9fWFh4eHrJ5hIgpmjo6OaN68uezebiLiiCpqirRp0yb4+Pjgr7/+gp+fH/bu3YugoCCULFlS0vwiCl2i4ojKxdvbG3Xq1EHPnj0zHCO7C4miC13/CzJa6BIRR1QhEZBfeBNRMBMlf/78mDt3Ljw9PTMcI2kI0j/++EPjAr5evXrh0aNHOH36tF7x5BbMRMWRe5waGxuLxo0bY9WqVeqhvpVKJezs7PTqFdCoUSONnmqlSpXSu6da+fLl0atXL7Rp0wZly5bFkSNHUKtWLVy5cgUeHh54/fq1pFxExBFVMBORi6j1YkiFIVFFQEBe0S2lpAskChUqpNd8IoqRgLz1K6pYYIhFB1FEFHUMIQ8RxS5DKiQmMYTPR9TyiC5IiiCiwCSqSCUqjtxCFyCmkPju3TvkyZMnzedOnTql0aMzs3P5+++/0a1bN1hZWaXqkbh+/XrZnTYYY+Jw8S6F6dOnY8qUKXB3d4e5uTmOHj2KTp06Cbtq+MOHD6mubtdm+/btmDJlCkaNGpVmg7bURh4RhcRt27Zh3LhxGDZsGKpUqZJqRyc1F1tbW1y+fDnVAci9e/dQvXp1fPr0KcvivH//Xj0++bNnz/DHH3/g27dvaNmypeQrHwFxvQlZ2gxlmNXkRBUSM8rR0RGbNm1S3y8mycWLF9GxY0c8fvxYZ4zo6GgMGjQI69evVzfYqlQqdOvWDUuXLtU4CdFGROFNVM8wEQUzUb3dRMSRs26HDx+O6dOnw9LSUn2CmRaFQoEFCxboldeKFSswfPhw2NnZISgoSK8LW0QUukTFEZXL169f8euvv8LOzi7DvwFDKiRmhu/fvyMmJkZjmr6NLiJiyI0jqoeZ6J5qGS0kiii8iSiYiZI7d25cunRJ1u/I3NwcV69eTdWL7Pbt26hatarkYgxgOMcvoo5T7ezscP78eVlDU4voqbZjxw507twZ8fHxaNiwoXq48NmzZ+P06dNp3ic2s+KIKpiJyEXUepHTkyQzeumI7NWV0aIbkHiR74wZM7BgwQJERUUBSDzWGzFiBCZMmCBp9BG5xUgR61dUscAQiw4sbSKKXYZQSDQkhtgLUBQRBSZRRSoRcUT13hPZa04uUbmI6pHIGMtcXLxLwcnJCSNHjlTftPT48ePw8PDAt2/fJA8HKJK299RnGK206FtIFJXLoEGDYGxsnOpeKiNHjsS3b9+wfPnyTI8TFhaGFi1a4NmzZ3BycsKWLVvQpEkTREdHQ6lUIjo6Gjt27JDcy1BUL8ATJ05g0aJF6oYLFxcXDB06VO+elyLiGEouhjTMKmA4PSzNzMwQHh6equj16NEjlC5dGt+/f9cZo2/fvjh+/DiWLVuGOnXqAADOnj2LwYMH4+eff8bKlSsl5SKq8CaCIQ3hKSKOnHXr5uaG3bt3w9bWVn2CmV6ckydPpvt8eoW/7du3o3LlyhrrWso9skQUukTFEZWLn58f+vXrBzMzM+TOnVtjvyr1N2BIhcTk5BS6oqOjMWbMGGzbtg3v379P9byU4wYRMUTFEdXDTFQcEQVAEYU3EQUzUcaMGQMrKytMnDgxwzHy5cuHDRs2oFGjRhrTjx49im7duuGff/6RFMeQjl9EHacOGzYMpqammDNnjqTXp0VUT7XXr1/j1atXqFChgvoc5dKlS7CxsZE0fKeoOKIKZqKWSU4MQyoMiSwCiii6AcC4cePg5+eHqVOnahw3T5kyBb179051z7e0yC1GGsqwjpkRh/1v+K99X/5ry/NfJKrQJaogKYKoXET0SGSMZT4u3qVgamqKBw8eoHDhwuppZmZmePDgQYau0JMrIiJC6/NZ2ZVZTi7JG3/j4uIQGBgIBwcH1KxZE0Bib6GnT5+qe/xkdpymTZvCyMgIY8eOxYYNG3DgwAE0btwYf/zxB4DEwuCVK1dw4cIFrcucREQvwBUrVmDIkCFo166dxv3UduzYgUWLFmHgwIGSchERx5ByMaRhVg2ph6WTkxMmT56Mrl27akzfsGEDJk+eLKlYkCdPHuzYsQOurq4a04OCgtC+fXu8fftWZMqpaOsNlpw+PcMMaQhPUXGym7bCX3K6ioBJRBS6RMURlYu9vT0GDx6MsWPHZvhCH0MqJIoqmA0cOBBBQUGYPn06PD09sXz5crx48QKrV6/GnDlz0KVLlyyJISqOqB5mIuKIKgCKKLyJKJiJMmTIEKxfvx7ly5dH+fLlU/2OpFxgMHjwYOzevRvz589X924/d+4cRo0ahV9++QW+vr6ScjGk4xdRo14k9dZ3cnJKcxQOKetXVE81QyKqkJjdDKkwJDIXEUU3AChQoABWrVqV6v7Je/fuxYABA/DixYt05xXdI5ELBowxJo0hFd0MiageiYyxzMfFuxRUKhVev36tMUyItbU1bty4Ifl+V5nh9u3bePr0qcbV7wqFAi1atPhX5CKq8VdUnDx58uDkyZMoX748oqKiYGNjg8uXL6NKlSoAgDt37qBmzZp6NWbI7U1YqFAhjB07Fj4+PhrTly9fjlmzZmk9IRQdx5ByMaRhVkU1xIkwd+5czJ07F/PmzVMPe3vixAmMHj0aI0aMwLhx43TGsLCwwJUrV+Di4qIx/datW6hevbqkYZF00VZ4S/l7Dg0NRVxcHJydnQEkfjYqlQpVqlSRVBQCDGsIz4zGyYyipiERUegSFUdULrly5cLly5dlFUAMqZAoqmDm4OCA9evXw9XVFTY2NggNDUWJEiWwYcMGbN68GYcOHcqSGKLiiOphJiKOqEKiiMKbiIKZKHJ6GSeJiYnBqFGjsGrVKsTFxQEAjI2N0b9/f8yZM0d9vzddDOn4RdSoFyLWr8ieaixzGFJhSEQucopuyZmZmeHGjRup7vF79+5dVKxYEd++fUt33v/akIGMMcb+3QxpGFDGmHZcvEtBqVSiadOmGifm+/fvR4MGDTSuLt21a1eW5PPo0SO0adMGYWFhUCgUqe6zJWfYzH9zLnKlHLLE2toa169fR7FixQAA//zzDwoUKKB1mUT1AkxiZWWFa9eupbqPyP3791GpUiX1MC9ZEceQcjGEYVaTiGqIE4GIMHbsWCxZskRdSDczM8OYMWMwadIkSTEaNmyI3LlzY/369TAzMwMAfPv2Dd27d8eHDx9w/PjxdOcVXXhbuHAhgoODsW7dOuTMmRMA8PHjR3h5eaFu3boYMWKEpGUypCE8MyozipqGREShS1QcUbkMGzYMdnZ2GD9+fIZjGFIhUVTBzMrKCrdv34aDgwMKFSqEXbt2oXr16nj8+DHKlSsneX8kN4aoOKJ6mImII6qQKKLwJqKgY4i+fv2Khw8fAgCKFy8u+T6wSQzp+CWpx1zhwoXTPE5N/rlnRbH1v9JTjf07yCm6JVejRg3UqFEDS5Ys0Zg+aNAgXL58WdKILYZUGGWMMfa/i3skMvbvYZTdCRia7t27p5qWcli6rDRkyBAULVoUJ06cQNGiRXHx4kV8+PABI0aMwPz58/9ncxEh5RBT+t6U9erVqxp/J/XaS2royZMnD/LkyYNbt25JiteyZUvs3r0bo0aN0pi+d+9eNG/eXHJeIuJkdy7JC6MKhQJr167FsWPH0mxw0kZUnCSenp5YuXJlqoatNWvWSO6JIopCocDvv/+OiRMnIjw8HObm5nBycpLcIwAAfH190aRJExQqVAgVKlQAAFy/fh2mpqbqK+HTExQUpP7/woULYW1tnW7hTYoFCxbg2LFj6vkBIGfOnJgxYwYaNWokuXj3+PFjSa9LSVRvNxFxRK9bQ9O9e3ds3bpVVqFLVBxRucTHx2Pu3Lk4evRohgsgMTEx6NChg+z764pYpg8fPqgvZrGxscGHDx8AAD/99BP69+8vOU6xYsXw+PFjODg4oFSpUti2bRuqV6+O/fv3w9bWNstiiIrz/ft3rFmzBsePH5fVw0xEnF69emHTpk2yC4k3btxAxYoVAQA3b97UeE7qcVHybdZ/iYWFBcqVKycrhp+fX7rHHcn3F7o+c7lxbt68icqVKwNIfZya/HPX91g4o+zt7WFvb68xrXr16lny3ux/T4UKFbBs2bJURbdly5apj3+lmDt3Ljw8PHD8+HGN4fOfPXsm+aKWgIAA6YkzxhhjmSS9wh0ALtwxZmC4552BSz68Y44cOXDp0iU4Ozvj5MmTGDFiRKoC0v9KLnKl7GGZsnfljx8/cOTIkSztTThjxgzMnz8fderU0bg/3Llz5zBixAiNKzS1Df0nIk5252JIw6yK7mFpaL5+/YqNGzfizp07AAAXFxd06dIF5ubmkmMULFgQx44dQ5kyZTSm37x5E40aNcLLly91xrC2tsb+/fvTvP9ey5Yt8eXLl3TnNaQhPEX3mhOxbg3N4MGDsX79elSoUEFWAUREHFG5iOh5JKL3HiBmmcqXL4+lS5eifv36cHd3R8WKFTF//nwsWbIEc+fOxfPnzyXlsmjRIqhUKgwePBjHjx9HixYtQESIjY3FwoULMWTIkCyJISqOqB5mIuIY0lCVLG2GdCzD2P+yU6dOwcPDAw4ODmkW3aReDPX06VMYGRlh+fLlGsfNAwYMQFxcHBwcHDJtGRhjjDHG2P8mLt4ZuJw5cyI0NBRFixZF8eLFsXbtWri5ueHhw4coV64cvn79+j+Zi1xeXl6SXpeVV0dKvaeirqH/RMQxpFyy23+50Wz27NnIly8fvL29Nab7+/vj7du3GDNmjKQ4cgpvSbp164YzZ85gwYIF6qvvL168iFGjRqFu3bpYt25duvMa6hCeIuKIWLeGxpAKIIY03J8hFRJFFcxSioiIwJUrV1CiRAmUL18+22KIjJNdDOm7yxhjhkxU0U2lUuHVq1fq2y4kef/+PfLmzfuvuoUEY4wxxhj7d+DinYFLauBt3bo1OnfujI8fP+K3337DmjVrcOXKlVRDHP2v5PJfl/J+gtkZx5ByYWI5Ojpi06ZNqF27tsb0ixcvomPHjpKHoJRTeEvy9etXjBw5Ev7+/oiNjQUAGBkZoWfPnpg3b57GPUe1EVEwE9XbTUQcEeuW/TsYcjFGTqHrxIkTOHHiBN68eYOEhASN5/z9/bMshsg4jDHG/j1EFd1S3jM9SUREBEqXLo3o6GhhOTPGGGOMMQbwPe8M3m+//aY+EZg2bRqaN2+OunXrInfu3Ni6dev/bC7/VX5+fli0aBHu378PAHBycsLQoUPRq1evLI9jSLmwzPH69Wvkz58/1XQ7Ozu8evVKcpxVq1Zh5MiR6Ny5c5qFNyksLCywYsUKzJs3T30/nuLFi0su2iURce+8z58/4+3bt6mmv337Vq+ebiLiiFi37N/B0O4bJqLQNXXqVEybNg1Vq1ZF/vz5M3TxhogYIuMwxhj7d0nvWuWoqCiYmZnpnD9peHaFQoFJkybBwsJC/Vx8fDwuXryovncoY4wxxhhjInHxzsA1btxY/f8SJUrgzp07+PDhA3LmzJnlDU+GlMt/0aRJk7Bw4UIMGjRI434Mw4YNw9OnTzFt2rQsi2NIubDMU7hwYZw7dy7V8Kbnzp1DgQIFJMcRVXgDAEtLS1lD2IkomLVp0wZeXl5p9nZr27at5FxExBG5bhmTSlSha9WqVQgMDISnp2eGcxERQ2Qcxhhj/w6iim5J93UnIoSFhcHExET9nImJCSpUqICRI0eKTZ4xxhhjjDHwsJmMGQw7OzssWbIEnTp10pi+efNmDBo0CO/evcuyOIaUC8s8c+fOxdy5czFv3jw0aNAAQGJvm9GjR2PEiBEYN25cNmeoP0MawlNUHMayWv78+TF37lzZha7cuXPj0qVLKF68eLbGEBmHMcbYv0PScNSnTp1CrVq1UhXdHB0dMXLkSDg5OUmK5+XlhcWLF8PGxiZT8mWMMcYYYywlLt4xZiBsbW1x+fLlVCeQ9+7dQ/Xq1fHp06csi2NIubDMQ0QYO3YslixZgpiYGACAmZkZxowZg0mTJmVzdhkjsmAWHR0tpLebqDiMZRVRha4xY8bAysoKEydOzNYYIuMwxhj7d+GiG2OMMcYY+7fi4h1jBmLQoEEwNjbGwoULNaaPHDkS3759w/Lly7MsjiHlwjJfVFQUwsPDYW5uDicnJ5iammZ3SrJxwYyxjBNV6BoyZAjWr1+P8uXLo3z58jA2NtZ4PuW+IbNiiIzDGGOMMcYYY4wxlhX4nneMZaOkezEAifdjWLt2LY4dO4aaNWsCSBzu7+nTp+jWrVumxzGkXFjWsrKyQrVq1bI7DaHk3juPsf9l379/x5o1a3D8+HFZha4bN26o7yd08+ZNjeek3kdPRAyRcRhjjDHGGGOMMcayAve8YywbJd2LQReFQoGTJ09mahxDyoUxxlj20bYd5203Y4wxxhhjjDHGWObj4h1jjDHGGGOMMcYYY4wxxhhjBkKZ3QkwxhhjjDHGGGOMMcYYY4wxxhJx8Y4xxhhjjDHGGGOMMcYYY4wxA8HFO8YYY4wxxhhjjDHGGGOMMcYMBBfvGGOMMcYYY4wxxhhjjDHGGDMQXLxjjDHGGGOMMcYYY4wxxhhjzEBw8Y4xxhhjjDHGGGOMMcYYY4wxA8HFO8YYY4wxxhhjjDHGGGOMMcYMxP8BD2f9+gAgjY8AAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We see that the genes that are part of a same gene cluster (for example xylA, xylB, xylC...) are closely correlated, and that the presence of one of them implies the presence of others. This is biologically evident.\n",
        "We could join the information of genes from a same cluster and form a new feature for each cluster. This would reduce the nosie, however, we will mantain all the genes as not all the clusters always contain all the present genes.\n",
        "\n",
        "We can also see that some genes do not give any correlation information, as amoA, tolA, tolB, tolR and ttgA. This happens because the genes are present in all the genomes, so they do not give us any information. We will remove them from the dataset."
      ],
      "metadata": {
        "id": "zaZ3O1Yc2LA7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print('Number of genes counted before cleaning: ',str(len(df_genecount.columns)))\n",
        "df_genecount.drop(['amoA', 'tolA', 'tolB', 'tolR', 'ttgA'], axis=1, inplace=True)\n",
        "df_genecount_scaled_df.drop(['amoA', 'tolA', 'tolB', 'tolR', 'ttgA'], axis=1, inplace=True)\n",
        "print('Number of genes counted after cleaning: ', str(len(df_genecount.columns)))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hS8T-oth390Z",
        "outputId": "241494ac-b784-4686-dc76-c7f034fddc49"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of genes counted before cleaning:  85\n",
            "Number of genes counted after cleaning:  80\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Unsupervised learning**"
      ],
      "metadata": {
        "id": "PHCyY9BJ3py3"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## *PCA*"
      ],
      "metadata": {
        "id": "qy1SVbMS7OnC"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Unsupervised learning consists on giving unlabeled data to a model and seeing how it organizes the data depending on the hidden patterns that it discovers.\n",
        "In our case we will perform a **Principal Component Analysis (PCA)** in our numerical data (df_genecount).\n",
        "\n",
        "In order to perform the PCA we will need to use our standarized data (df_genecount_scaled_df)."
      ],
      "metadata": {
        "id": "hlq6S4Vx3tqY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#We run PCA on scaled gene counts\n",
        "pca = PCA(n_components=2)\n",
        "df_genecount_pca = pca.fit_transform(df_genecount_scaled_df)"
      ],
      "metadata": {
        "id": "ZeJYMdIk2FFp"
      },
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#We Plot the PCA results\n",
        "plt.figure(figsize=(10, 8))\n",
        "plt.scatter(df_genecount_pca[:, 0], df_genecount_pca[:, 1], cmap='viridis')\n",
        "#We will now add the data labels (we can later replace the data labels with any feature)\n",
        "for i, name in enumerate(df['Isolation matrix']):  # assuming `df_numeric` has row indices as names\n",
        "    plt.text(df_genecount_pca[i, 0], df_genecount_pca[i, 1], name, fontsize=9, ha='right')\n",
        "\n",
        "plt.xlabel('Principal Component 1')\n",
        "plt.ylabel('Principal Component 2')\n",
        "plt.grid(True)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 745
        },
        "id": "4rFv6Y9A5i0T",
        "outputId": "3f9b24f4-0bdb-462e-a1c5-079c04822830"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-41-ef774e8a50b9>:3: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored\n",
            "  plt.scatter(df_genecount_pca[:, 0], df_genecount_pca[:, 1], cmap='viridis')\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x800 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Now we want to know the explained variance ratio (weights) in percentages\n",
        "explained_variance_ratio = pca.explained_variance_ratio_ * 100\n",
        "print(\"Explained variance ratio:\", explained_variance_ratio)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bWGSpsyw5orP",
        "outputId": "77770241-f170-419a-9ffb-e796ef3eaecb"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Explained variance ratio: [20.60958905 13.28137516]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#We will now determine the loadings for each feature in percentages\n",
        "loadings_percent = pca.components_ * 100\n",
        "feature_names = df_genecount_scaled_df.columns\n",
        "loadings_df = pd.DataFrame(loadings_percent.T, index = feature_names, columns=['Component 1', 'Component 2'])\n",
        "print(\"\\nPrincipal components (loadings) with feature names:\")\n",
        "print(loadings_df)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LB826WlB6hND",
        "outputId": "00737ab0-5b09-49ce-f354-7f634ce68c00"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Principal components (loadings) with feature names:\n",
            "       Component 1  Component 2\n",
            "PobA   -2.258526    -2.151272  \n",
            "alkB    1.599544     2.602691  \n",
            "arsC    3.113196    -6.079783  \n",
            "arsR1   7.618194    -0.253456  \n",
            "bcsA   -3.141297    -2.981396  \n",
            "bcsB   -3.727311    -5.955186  \n",
            "BenR    5.002463     1.623480  \n",
            "bnzA   -4.535066    28.283743  \n",
            "bphA1  21.144727     3.752705  \n",
            "bphA2  21.144727     3.752705  \n",
            "bphA3  21.144727     3.752705  \n",
            "bphA4  21.144727     3.752705  \n",
            "bphB   21.144727     3.752705  \n",
            "bphC   21.144727     3.752705  \n",
            "bphD   21.144727     3.752705  \n",
            "chR     5.915257    -0.663906  \n",
            "copA    6.444969    -5.847527  \n",
            "copA1  -0.523646    -0.445225  \n",
            "dmpK   22.192567     3.676290  \n",
            "dmpL   22.561616     2.982043  \n",
            "dmpM   22.192567     3.676290  \n",
            "dmpN   15.881378     1.122154  \n",
            "dmpO   22.561616     2.982043  \n",
            "dmpP   22.561616     2.982043  \n",
            "intl1  -1.903380     3.478744  \n",
            "katA    2.489698     1.379548  \n",
            "merA    7.869445    -4.261253  \n",
            "merB   -0.659658    -2.356562  \n",
            "merR    2.682592    -6.415739  \n",
            "merT    3.728415    -4.416517  \n",
            "mrdH   -1.124079    -0.304324  \n",
            "mreA   -1.124079    -0.304324  \n",
            "nahA1  -1.940161   -11.499161  \n",
            "nahA2  -1.940161   -11.499161  \n",
            "nahA3  -1.940161   -11.499161  \n",
            "nahA4  -1.940161   -11.499161  \n",
            "nahB   -1.940161   -11.499161  \n",
            "nahC   -1.940161   -11.499161  \n",
            "nahD   -1.940161   -11.499161  \n",
            "nahE   -1.940161   -11.499161  \n",
            "nahF   -1.940161   -11.499161  \n",
            "nirS   -0.367097     1.352256  \n",
            "norB   -0.260815    -0.486342  \n",
            "oxyR   -9.028492     4.433049  \n",
            "pcaG    2.036002     1.358752  \n",
            "pcaH    1.015905     0.719370  \n",
            "pcaR    9.278105    -0.938083  \n",
            "rubA    1.246310    -3.670250  \n",
            "sodA    2.611222     2.483370  \n",
            "srpA   21.794830     2.797328  \n",
            "srpB   13.670260     1.037131  \n",
            "srpC   20.746874     2.522264  \n",
            "srpR   21.794830     2.797328  \n",
            "srpS   20.746874     2.522264  \n",
            "tnpA   11.687757     0.083085  \n",
            "todA   -4.535066    28.283743  \n",
            "todB   -4.535066    28.283743  \n",
            "todC1  -4.535066    28.283743  \n",
            "todC2  -4.535066    28.283743  \n",
            "todD   -4.535066    28.283743  \n",
            "todE   -4.535066    28.283743  \n",
            "todF   -4.535066    28.283743  \n",
            "todT   -4.535066    28.283743  \n",
            "tolQ    4.695063    -5.319398  \n",
            "ttgB    0.499926    -6.447150  \n",
            "ttgC   -0.760781    -0.929228  \n",
            "ttgD   -4.535066    28.283743  \n",
            "ttgE   -4.834372    12.449053  \n",
            "ttgF   -3.151904    10.419433  \n",
            "ttgG   21.794830     2.797328  \n",
            "ttgH   13.670260     1.037131  \n",
            "ttgI   20.746874     2.522264  \n",
            "xylA   -0.801951    -0.301227  \n",
            "xylB   -0.801951    -0.301227  \n",
            "xylC   -0.801951    -0.301227  \n",
            "xylM   -0.801951    -0.301227  \n",
            "xylN   -0.801951    -0.301227  \n",
            "xylS   -0.924481    -0.647894  \n",
            "xylU   10.035766     1.818047  \n",
            "xylW   -0.801951    -0.301227  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We can see that the PCA grouped almost all the strains together and some strains are spread across the graph. We have labeled the points that are spread across the graph depending on their source environment.\n",
        "We can see that the strains that are grouped together mainly belong to strains that come from Rhizospheres, water and clinical samples.\n",
        "\n",
        "In order to see if this has a relationship with the bioremediation potential we can also label the data with each corresponding BP."
      ],
      "metadata": {
        "id": "NIvtt4wp8jlu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#We Plot the PCA results\n",
        "plt.figure(figsize=(10, 8))\n",
        "plt.scatter(df_genecount_pca[:, 0], df_genecount_pca[:, 1], cmap='viridis')\n",
        "#We will now add the data labels (we can later replace the data labels with any feature)\n",
        "for i, name in enumerate(df['Bioremediation potential']):  # assuming `df_numeric` has row indices as names\n",
        "    plt.text(df_genecount_pca[i, 0], df_genecount_pca[i, 1], name, fontsize=9, ha='right')\n",
        "\n",
        "plt.xlabel('Principal Component 1')\n",
        "plt.ylabel('Principal Component 2')\n",
        "plt.grid(True)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 745
        },
        "id": "9NZBf3LU8qTe",
        "outputId": "253c2baf-632c-42b9-d775-5301074aef2a"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-44-c6903a915af7>:3: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored\n",
            "  plt.scatter(df_genecount_pca[:, 0], df_genecount_pca[:, 1], cmap='viridis')\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x800 with 1 Axes>"
            ],
            "image/png": 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ITAAAAABggsAEAAAAACYITAAAAABggsAEAAAAACYITAAAAABggsAEAAAAACYITAAAAABggsAEAAAAACYITAAAAABggsAEAAAAACYITAAAAABggsAEAAAAACYITAAAAABggsAEAAAAACYITAAAAABggsAEAAAAACYITAAAAABggsAEAAAAACYITAAAAABggsAEAAAAACYITAAAAABggsAEAAAAACYITAAAAABggsAEAAAAACYITAAAAABggsAEAAAAACYITAAAAABggsAEAAAAACYITAAAAABggsAEAAAAACYITAAAAABggsAEAAAAACYITAAAAABggsAEAAAAACYITAAAAABggsAEAAAAACYITAAAAABggsAEAAAAACaKHZgcDofp+cTExH9cEAAAAACUF0UOTOnp6Ro0aJD8/f1VrVo1PfXUU7Lb7c72kydPqk6dOqVSJAAAAAC4gkdRL5w8ebK2b9+ud999V6mpqZoxY4a2bdumzz77TF5eXpIkwzBKrVAAAAAAKGtFHmFaunSp3njjDd1666265557tGXLFp08eVJ9+/ZVbm6uJMlisZRaoQAAAABQ1oocmE6ePKmYmBjncVhYmFatWqWMjAzdcMMNys7OLpUCAQAAAMBVihyYatWqpd27dxc4FxgYqO+++05nz57VzTffXOLFAQAAAIArFTkw9ejRQ4sWLSp0PiAgQCtWrJCPj0+JFgYAAAAArlbkRR+mTZumY8eOXbAtMDBQK1eu1LZt20qsMAAAAABwtSIHpipVqqhKlSqm7YGBgercuXOJFAUAAAAA5UGxN64FAAAAgMsFgQkAAAAATBCYAAAAAMAEgQkAAAAATBQ7MLm7uys5ObnQ+ZSUFLm7u5dIUQAAAABQHhQ7MBmGccHzubm58vLy+scFAQAAAEB5UeRlxV9++WVJksVi0VtvvaWAgABnm91u1w8//KDGjRuXfIUAAAAA4CJFDkwvvviipPwRpvnz5xeYfufl5aXatWtr/vz5JV8hAAAAALhIkQNTQkKCJKlr16767LPPLrqJLQAAAABUBkUOTOesXbu2NOoAAAAAgHKn2IHJbrdr8eLFWr16tZKTk+VwOAq0r1mzpsSKAwAAAABXKnZgeuihh7R48WL16dNHzZo1k8ViKY26AAAAAMDlih2YPvroI33yySe64YYbSqMeAAAAACg3ir0Pk5eXl+rXr18atQAAAABAuVLswPTwww/rpZdeMt3AFgAAAAAqi2JPyduwYYPWrl2rb7/9VldccYU8PT0LtH/22WclVhwAAAAAuFKxA1NISIhuvvnm0qgFAAAAAMqVYgemRYsWlUYdAAAAAFDuFPsZJkmy2WxatWqV3njjDWVkZEiSjh07pszMzBItDgAAAABcqdgjTIcOHVKvXr2UmJio3Nxcde/eXYGBgZo1a5Zyc3M1f/780qgTAAAAAMpcsUeYHnroIbVp00ZnzpyRr6+v8/zNN9+s1atXl2hxAAAAAOBKxR5hWr9+vX788Ud5eXkVOF+7dm0dPXq0xAoDAAAAAFcr9giTw+GQ3W4vdP7IkSMKDAwskaIAAAAAoDwodmDq0aOH5s6d6zy2WCzKzMzUlClTdMMNN5RkbQAAAADgUsWekvfCCy+oZ8+eatq0qXJycjR06FDt3btXYWFh+vDDD0ujRgAAAABwiWIHpho1amj79u366KOP9MsvvygzM1N33323hg0bVmARCAAAAACo6IodmCTJw8NDw4cPL+laAAAAAKBc+VuBae/evVq7dq2Sk5PlcDgKtD311FMlUhgAAAAAuFqxA9OCBQs0ZswYhYWFKTIyUhaLxdlmsVgITAAAAAAqjWIHphkzZuiZZ57RY489Vhr1AAAAAEC5Uexlxc+cOaOBAweWRi0AAAAAUK4UOzANHDhQ3333XWnUAgAAAADlSrGn5NWvX1+TJ0/Wpk2b1Lx5c3l6ehZoHzduXIkVBwAAAACuVOzA9OabbyogIEDff/+9vv/++wJtFouFwAQAAACg0ih2YEpISCiNOgAAAACg3Cn2M0znMwxDhmGUVC0AAAAAUK78rcC0ZMkSNW/eXL6+vvL19VWLFi307rvvlnRtAAAAAOBSxZ6SN2fOHE2ePFkPPvigOnbsKEnasGGDRo8erVOnTmnChAklXiQAAAAAuEKxA9Mrr7yiefPm6Y477nCe69evn6644gpNnTqVwAQAAACg0ij2lLykpCRdc801hc5fc801SkpKKpGiAAAAAKA8KHZgql+/vj755JNC5z/++GM1aNCgRIoCAAAAgPKg2FPypk2bpsGDB+uHH35wPsO0ceNGrV69+oJBCgAAAAAqqmKPMN1yyy2Ki4tTWFiYli5dqqVLlyosLEybN2/WzTffXBo1AgAAAIBLFHuESZKuuuoqvffeeyVdCwAAAACUK38rMNntdn3++efavXu3JKlp06bq37+/PDz+1u0AAAAAoFwqdsL59ddf1a9fPx0/flyNGjWSJM2aNUvh4eFatmyZmjVrVuJFAgAAAIArFPsZpnvuuUdXXHGFjhw5om3btmnbtm06fPiwWrRooXvvvbc0agQAAAAAlyj2CFN8fLy2bNmiKlWqOM9VqVJFzzzzjK6++uoSLQ4AAAAAXKnYI0wNGzbUiRMnCp1PTk5W/fr1S6QoAAAAACgPih2Ynn32WY0bN06ffvqpjhw5oiNHjujTTz/V+PHjNWvWLKWnpztfAAAAAFCRFXtK3o033ihJGjRokCwWiyTJMAxJUt++fZ3HFotFdru9pOoEAAAAgDJX7MC0du3a0qgDAAAAAMqdYgemzp07l0YdAAAAAFDu/K2dZnNycvTLL78oOTlZDoejQFu/fv1KpDAAAAAAcLViB6bly5frjjvu0KlTpwq1leZzS1OnTtW0adMKnGvUqJH27NlTKt8PAAAAAIq9St7YsWM1cOBAJSUlyeFwFHiV9iIPV1xxhZKSkpyvDRs2lOr3AwAAAHB5K/YI04kTJzRx4kRVq1atNOq5KA8PD0VGRpb59wUAAABweSr2CNOtt96qdevWlUIpl7Z3715FR0erbt26GjZsmBITE11SBwAAAIDLQ7FHmF599VUNHDhQ69evV/PmzeXp6Vmgfdy4cSVW3PnatWunxYsXq1GjRkpKStK0adN07bXXaufOnQoMDLzge3Jzc5Wbm+s8PreZrtVqdb7OHQOljf6GskJfQ1mhr6Gs0NdQGoranyzGuV1ni2jhwoUaPXq0fHx8VLVqVefmtVL+og8HDhwoXqV/U2pqqmJiYjRnzhzdfffdF7zmQgtFSNIHH3wgPz+/0i4RAAAAQDmVnZ2toUOHKi0tTUFBQabXFTswRUZGaty4cZo0aZLc3Io9o69EXX311erWrZueffbZC7ZfaISpZs2aOnXqlIKCgmS1WrVy5Up179690EgZUNLobygr9DWUFfoaygp9DaUhPT1dYWFhlwxMxZ6Sl5eXp8GDB7s8LGVmZmr//v26/fbbTa/x9vaWt7d3ofOenp4Fftn+egyUJvobygp9DWWFvoayQl9DSSpqXyp26hkxYoQ+/vjjYhf0Tz3yyCP6/vvvdfDgQf3444+6+eab5e7uriFDhpR5LQAAAAAuD8UeYbLb7Zo9e7ZWrFihFi1aFEpmc+bMKbHiznfkyBENGTJEKSkpCg8PV6dOnbRp0yaFh4eXyvcDAAAAgGIHph07dqhVq1aSpJ07dxZoO38BiJL20Ucfldq9AQAAAOBCih2Y1q5dWxp1AAAAAEC5849Wbjhy5IiOHDlSUrUAAAAAQLlS7MDkcDg0ffp0BQcHKyYmRjExMQoJCdHTTz8th8NRGjUCAAAAgEsUe0re//3f/2nhwoWaOXOmOnbsKEnasGGDpk6dqpycHD3zzDMlXiQAAAAAuEKxA9M777yjt956S/369XOea9GihapXr67777+fwAQAAACg0ij2lLzTp0+rcePGhc43btxYp0+fLpGiAAAAAKA8KHZgatmypV599dVC51999VW1bNmyRIoCAAAAgPKg2FPyZs+erT59+mjVqlXq0KGDJCk2NlaHDx/WN998U+IFAgAAAICrFHuEqXPnzvr999918803KzU1VampqRowYIB+++03XXvttaVRIwAAAAC4RLFHmCQpOjqaxR0AAAAAVHpFHmHau3evhgwZovT09EJtaWlpGjp0qA4cOFCixQEAAACAKxU5MD333HOqWbOmgoKCCrUFBwerZs2aeu6550q0OAAAAABwpSIHpu+//14DBw40bR80aJDWrFlTIkUBAAAAQHlQ5MCUmJioiIgI0/awsDAdPny4RIoCAAAAgPKgyIEpODhY+/fvN23ft2/fBafrAQAAAEBFVeTAdN111+mVV14xbX/55ZdZVhwAAABApVLkwPT444/r22+/1a233qrNmzcrLS1NaWlpiouL0y233KIVK1bo8ccfL81aAQAAAKBMFXkfplatWunTTz/VXXfdpc8//7xAW9WqVfXJJ5+odevWJV4gAAAAALhKsTauvfHGG3Xo0CEtX75c+/btk2EYatiwoXr06CE/P7/SqhEAAAAAXKJYgUmSfH19dfPNN5dGLQAAAABQrhT5GSYAAAAAuNwQmAAAAADABIEJAAAAAEwQmAAAAADARJEWfUhPTy/yDYOCgv52MQAAAABQnhQpMIWEhMhisVz0GsMwZLFYZLfbS6QwAAAAAHC1IgWmtWvXlnYdAAAAAFDuFCkwde7cubTrAAAAAIByp9gb156TnZ2txMRE5eXlFTjfokWLf1wUAAAAAJQHxQ5MJ0+e1J133qlvv/32gu08wwQAAACgsij2suLjx49Xamqq4uLi5Ovrq+XLl+udd95RgwYN9OWXX5ZGjQAAAADgEsUeYVqzZo2++OILtWnTRm5uboqJiVH37t0VFBSkZ599Vn369CmNOgEAAACgzBV7hCkrK0sRERGSpCpVqujkyZOSpObNm2vbtm0lWx0AAAAAuFCxA1OjRo3022+/SZJatmypN954Q0ePHtX8+fMVFRVV4gUCAAAAgKsUe0reQw89pKSkJEnSlClT1KtXL73//vvy8vLS4sWLS7o+AAAAAHCZYgem4cOHO7++6qqrdOjQIe3Zs0e1atVSWFhYiRYHAAAAAK70t/dhkiTDMOTr66vWrVuXVD0AAAAAUG4U+xkmSVq4cKGaNWsmHx8f+fj4qFmzZnrrrbdKujYAAAAAcKlijzA99dRTmjNnjsaOHasOHTpIkmJjYzVhwgQlJiZq+vTpJV4kAAAAALhCsQPTvHnztGDBAg0ZMsR5rl+/fmrRooXGjh1LYAIAAABQaRR7Sp7ValWbNm0Knb/qqqtks9lKpCgAAAAAKA+KHZhuv/12zZs3r9D5N998U8OGDSuRogAAAACgPPhbq+QtXLhQ3333ndq3by9JiouLU2Jiou644w5NnDjRed2cOXNKpkoAAAAAcIFiB6adO3c6lxHfv3+/JCksLExhYWHauXOn8zqLxVJCJQIAAACAaxQ7MK1du7Y06gAAAACAcudv7cMEAAAAAJeDIo0wDRgwQIsXL1ZQUJAGDBhw0Ws/++yzEikMAAAAAFytSIEpODjY+UxScHBwqRYEAAAAAOVFkQLTokWLLvg1AAAAAFRmxX6GKSEhQXv37i10fu/evTp48GBJ1AQAAAAA5UKxA9PIkSP1448/FjofFxenkSNHlkRNAAAAAFAuFDsw/fzzz+rYsWOh8+3bt1d8fHxJ1AQAAACgAjh79qzq16+vkJAQ57ldu3bp+uuvV5UqVRQZGal7771X2dnZrivyHyp2YLJYLMrIyCh0Pi0tTXa7vUSKAgAAAFD+PfXUU4qJiSlwbujQoWrUqJFOnDihHTt2aPv27Xr66addVOE/V+zAdN111+nZZ58tEI7sdrueffZZderUqUSLAwAAAFA+bd26VcuXL9djjz1W4PyBAwc0fPhweXl5KTw8XP369dOOHTtcVOU/V6RV8s43a9YsXXfddWrUqJGuvfZaSdL69euVnp6uNWvWlHiBAAAAAMoXm82mUaNG6bXXXpPD4SjQ9sgjj2jJkiVq1aqV0tLS9Pnnn+vuu+9R7P4UJWfkKCLQR23rhMrdzeKi6oun2CNMTZs21S+//KJBgwYpOTlZGRkZuuOOO7Rnzx41a9asNGoEAAAAUI4899xzatWqla677rpCbb1799aGDRsUGBioqKgoeQWHa3FKHQ1ZsEkPfRSvIQs2qdOsNVq+M8kFlRdfsUeYJCk6Olr/+c9/SroWAAAAAOXcvn37NH/+fP3888+F2s6cOaNu3bpp+vTpGjNmjL74ab/uvPd+Ge8/o/D+f07dO56WozHvbdO84a3Vq1lUWZZfbH8rMKWmpmrz5s1KTk4uNAR3xx13lEhhAAAAAMqfDRs26MSJE2rYsKEkyWq1KiMjQ2FhYfr666919uxZjRs3Tg5DmvPDMQVc2UvJ/51a4B6GJIukact2qXvTyHI9Pa/YgWnZsmUaNmyYMjMzFRQUJIvlzw9nsVgITAAAAEAlNmjQIHXr1s15HBsbq3vuuUfx8fEKCgpSQECAXn/9dbXodquOJp9W5vYV8qpWt9B9DElJaTnanHBaHepVLcNPUDzFfobp4Ycf1l133aXMzEylpqbqzJkzztfp06dLo0YAAAAALjRy5Eh5eXkpICBAERERaty4sQ4fPqwaNWooPDxcFotFNWrUkKenp/z8/PTQQw+pV5uGOjr/bjlys1S1z0TTeydn5JThJym+Yo8wHT16VOPGjZOfn19p1AMAAACgHLr//vs1d+7cQue7dOmi1NRUSfn7MjVq1EiZmZn6dut+DVmw6ZL3jQj0KeFKS1axR5h69uypLVu2lEYtAAAAACqov+7L1LZOqKKCfWT2dJJFUlRw/hLj5VmxR5j69Omjf//739q1a5eaN28uT0/PAu39+vUrseIAAAAAlA9LlizRkiVLFBUVpbvuuksTJkyQm1v++MuF9mVyd7NoSt+mGvPeNlmU/8zSOedC1JS+Tcv1gg/S3whMo0aNkiRNnz69UJvFYpHdbv/nVQEAAAAoN8aNG6fnnntOoaGh+umnnzRo0CC5ublpwoQJkgruy7Ru3TpJkt1hKNjXS3d1rK3P44/qdJbVeb/IYB9N6du03C8pLv2NwPTXZcQBAAAAVG6tW7d2ft2+fXtNmjRJS5Ys0YQJEy64L5PNYajTrDVKSvtzQYdQfy/ddGW0ujeNVNs6oeV+ZOmcYj/DBAAAAODydm4qnlRwX6awsDD16dtPWRkZ2vz0AOUe+8153ZmsPC3aeFBpZ/MqTFiSijjC9PLLL+vee++Vj4+PXn755YteO27cuBIpDAAAAED58Mknn6hXr14KDAzU1q1bNXPmTD3wwAOSCu7LZHcY6j5pgfb97wVFjXxZ7v7BzntUpM1qz1ekwPTiiy9q2LBh8vHx0Ysvvmh6ncViITABAAAAlcyrr76qe++9VzabTdWrV9f999+vhx9+WJLk5+fn3HIodn+K0uUrWSzyCAordJ+Kslnt+YoUmBISEi74NQAAAIDK74cffijSdckZOfKp1UK1xn98yesqimI9w2S1WlWvXj3t3r27tOoBAAAAUEEVdRPa8r5Z7fmKFZg8PT2Vk1Nx0iAAAACAslNZNqs9X7FXyXvggQc0a9Ys2Wy20qgHAAAAQAV1brNaSYVCU0XarPZ8xd6H6aefftLq1av13XffqXnz5vL39y/Q/tlnn5VYcQAAAAAqll7NojRveGtNW7arwD5MFWmz2vMVOzCFhITolltuKY1aAAAAAFQCvZpFqXvTSG1OOK3kjBxFBPpUqM1qz1fswLRo0aLSqAMAAABAJeLuZqkwS4dfTJGfYXI4HJo1a5Y6duyoq6++WpMmTdLZs2dLszYAAAAAcKkiB6ZnnnlGTzzxhAICAlS9enW99NJLzt19AQAAAKAyKnJgWrJkiV5//XWtWLFCS5cu1bJly/T+++/L4XCUZn0AAAAA4DJFDkyJiYm64YYbnMfdunWTxWLRsWPHSqUwAAAAAHC1Igcmm80mH5+CO/J6enrKarWWeFEAAAAAUB4UeZU8wzA0cuRIeXt7O8/l5ORo9OjRBfZiYh8mAAAAAJVFkQPTiBEjCp0bPnx4iRYDAAAAAOVJkQMT+y8BAAAAuNwU+RkmAAAAALjcEJgAAAAAwASBCQAAAABMEJgAAAAAwASBCQAAXNLZs2dVv359hYSESJKSk5N1xx136O6771bVqlXVqlUrffnll64tEgBKAYEJAABc0lNPPaWYmBjncWZmpq688krNmjVLJ0+e1PTp0zVkyBDt2rXLhVUCQMkjMAEAgIvaunWrli9frscee8x5rm7dupo4caLCwsLk5uamvn37qlGjRtq0aZMLKwWAklfkfZgAAMDlx2azadSoUXrttdfkcDhMr0tOTtbu3bvVokWLMqwOAEofI0wAAMDUc889p1atWum6664zvSYvL0+33XabBg0apFatr1Ls/hR9EX9UsftTZHcYZVgtAJQ8RpgAAMAF7du3T/Pnz9fPP/9seo3VatXgwYPl5+enW8dPV6dZa5SUluNsjwr20ZS+TdWrWVRZlAwAJY4RJgAAcEEbNmzQiRMn1LBhQ4WFhal///5KT09XWFiY4uLilJeXp+eee05Wq1X3Tn9V4z7eWSAsSdLxtByNeW+blu9MctGnAIB/hhEmAABwQYMGDVK3bt2cx7GxsbrnnnsUHx+v8PBwDRw4UDk5Ofr4k/+q9+s/6UKT7wxJFknTlu1S96aRcnezlFX5AFAiCEwAAOCC/Pz85Ofn5zwODw+XxWJRjRo19P3332vZsmXy8vJSdHSUcqz5C0IEdxik4A6DCtzHkJSUlqPNCafVoV7VsvwIAPCPEZgAAECRdOnSRampqZKkzp07Ky8vT998841Us5Ue+mTHJd+fnJFzyWsAoLzhGSYAAPCPhAV4F+m6iECfUq4EAEoegQkAAPwjV8VUUVSwj8yeTrIof7W8tnVCy7IsACgRBCYAAPCPuLtZNKVvU0kqFJrOHU/p25QFHwBUSAQmAADwj/VqFqV5w1srMrjgtLvIYB/NG96afZgAVFgs+gAAAEpEr2ZR6t40UpsTTis5I0cRgfnT8BhZAlCRMcIEAH8xduxY1axZU0FBQapevbrGjx+vvLw8SdLkyZPVvHlzeXh4aPz48a4tFCiH3N0s6lCvqvpfWV0d6lUlLAGo8AhMKFdyc3M1atQo1alTR4GBgWrcuLHefvttZ3t6erqGDh2qoKAgVatWTU8//bQLq0Vldf/992vPnj1KT0/X9u3btX37ds2ePVuSVL9+fc2ePVv9+vVzcZUAAKAsMCUPLpebm6sHH3xQq1at0qlTp+Tt7a1HHnlEjz32mOLi4tSrVy+9++672rp1q6xWq2rUqKHExEQlJyerW7duiomJ0R133OHqj4FKpEmTJs6vDcOQm5ub9u7dK0kaMWKEJOnjjz92SW0AAKBsMcIEl7PZbIqKitKqVauUnp6ur776SrNmzdLKlSvVvn17BQUF6fDhw9qzZ4/sdrsyMjL05ZdfqmHDhho7dqwWLlzo6o+ASmjmzJkKCAhQRESEtm/frrFjx7q6JAAA4AIEJricv7+/pk+frnr16slisah9+/bq2rWrNmzYoNOnT+vw4cMaNWqUTpw4IavVqgkTJmjhwoXKzc3V2rVrtWHDhkLT95KTkzVs2DDVqFFDQUFBatWqlb788ksXf1JUJJMmTVJmZqZ27dql0aNHKzIy0vRau8NQ7P4UfRF/VLH7U2R3GGVYKQAAKE0EJpQ7OTk52rx5s5o3b67hw4dLkiZMmKDMzEz5+/urdevW+uWXX2Sz2RQdHS1JWvFzgu56YrbGT5iob5evUGZmplq1aqVNmzYpNTVV06dP15AhQ7Rr1y5XfjRUQE2aNFHLli01cuTIC7Yv35mkTrPWaMiCTXroo3gNWbBJnWat0fKdSWVbKAAAKBU8w4RyxTAM3XPPPWrQoIFWr16tAwcOyN/fX4Zh6MUXX1RWVpb69++vs2fP6j+vLJQtpr0sXh9r6FtxktxlDWugPn37qU69+tq/51fnffv27atGjRpp06ZNatq0qes+ICokq9XqfIbpfAdTsjTmvW3663jS8bQcjXlvG3vPAABQCTDChHLDMAzdf//9+u2331S3bl1t3rxZb7zxhrKzs5WTk6NGjRrJ09NTd06YLDcvX82c+oQ+/fRTeYTF5L/flqfcxF/kERKpw2fOFvgv/MnJyfrll1/0+OOPX3Cp6K1bt6pTp04KCgpS3bp1tWTJEpf8DOB6mZmZWrRokVJTU2UYhnbs2KEZM2aoZ8+ekvLDU05Ojmw2uzbtOymHLU+G3VbgHucC1LRlu5ieBwBABUdgQol79dVX1aZNG3l7e+umm24q0LZr1y5df/31qlKliiIjI3XvvfcqOztbhmHogQceUFxcnFq0aKEtW7Zo5cqVuvrqq+Xp6al9+/bp2Wef1XW9+uvN+a/JMzxGXlENlL1nvQJa9pBhGDrxyVOyuHsq8OqbJf35L6t5eXm67bbbdOONN2rvvv1a8XOCZr63XOs3bdHMWbOUmpqqG264QcOHD9eZM2f04YcfauzYsdqwYYMLfnrlz8WWek9MTFRAQECBl4eHR4VecttiseiDDz5QvXr1FBgYqP79+6tPnz6aO3euJGnUqFHy9fXV+++/pxObvlDiCwOUsvyVQvcxJCWl5Whzwumy/QAAAKBEVbgpea+99pqee+45HT9+XC1bttQrr7yitm3burosnCc6OlpPPvmkVq1apSNHjhRoGzp0qK655hp9++23SktL04033qinn35a6enp2rhxo9555x21atVKkhQWFiZJ8vDw0M0336z47b9o868HZDt9VOf/93y34CilfPuyco/tUeTw52U9eVBS/r+sbvztuJ5/7D75+fnp7ikvqeercUpKy5E9O02njmco8fP1UtU68vb21ujRoyVJ7dq104ABA/TWW2+pU6dOpf7zKu/OX8Wwbt26iouLU+/evVWjRg316NFDmZmZzmvz8vIUHR2t2267zYUV/zP+/v5auXKlafvixYu1ePFifRF/VA99FH/J+yVn5JRgdQAAoKxVqMD08ccfa+LEiZo/f77atWunuXPnqmfPnvrtt98UERHh6vLwhwEDBkiS4uPjCwWmAwcO6PXXX5eXl5fCw8PVr18/rV69WmvXrpW3t7c6deokiyV/V3jDyJ/KlJKSovvuu081a9ZQdlaWJMmnfltZU47IfuaYTn7wqNx8gxR09U3yjqzvDEyG3aoxdw2Xt5tdw/7vNY37eKdSN/1XaT9+LMOaIzffIHl2vlOvrdkrR17BKVUOh0M7duxQ7P4UJWfkKCLQR23rhF6WO9afW8XwnPNXMezRo0eBa5cuXSqHw+HsA5VZRKBPiV4HAADKpwoVmObMmaNRo0bpzjvvlCTNnz9fX3/9td5++21NmjTJxdWhKB555BEtWbJErVq1Ulpamj7//HPdffc9embBfwsFEw8PD9ntdkVFRSk7O1shVSOUnZUli2+Q8o79VuC5EcfZdNkyzkiS0jb9V7bTR5X4wi35je6eSv56qzwCqyq4/UAFtx8o66nDyty1Tm7+VeQRUk3H0jP18iuvaMzo0dq8ebP++7/P5PAO0pAFm5zfIyrYR1P6Nr3sH+I/t4rh0KFDC7UtXLhQw4YNk6eXd6UPm23rhCoq2EfH03IKLfogSRZJkcH5nx0AAFRcFeYZpry8PG3dulXdunVznnNzc1O3bt0UGxvrwspQHL1793bumxQVFSWv4HAtTqmjPqP/T4N6d9E1jSIV3qxTgQUbBg0aJD8/P6WePilJMs6my7DbZeRmFbh39q+rdWjWjbKdPpp/wnBIyn8m5diC+5Q451alxX4iSfIMqymviDpK+eZFufkGKWzAZC1Y9K4iIyN137iJ8mjcVYZ3QIH7n1v57HJeLvr8VQz/Oop06NAhrVq1Sk269L8sltl2d7NoSt/8FRf/GgXPHU/p27TSBUUAAC43FWaE6dSpU7Lb7apWrVqB89WqVdOePXsu+J7c3Fzl5uY6j9PT0yXlr3J17nXuGCXPbrfL4XA4f75nzpxRt27dNGXKFN133336+ucE3TN6rIwPZiiwybXy7ThIWQe3y5pxSuM/3CrHH1PyvvnmG0VHR+vIkSPKybNJDpuM3D+fm7F4+sgjKFzW9JNy9/KVxcNLhi1X0bc+qRNfzVVox0EKatb1vMry75srq2xnjsnb3ZB3TBPNmPipel4RqZ5zf9Chd6bJP6aZvN0Ljh1YJD379a/q0qBqsf9FuKL3N8MwNHbsWO3Zs0fLly+X3W6X3W53tr/11luq2/gKvbwtf8TF2/3P957JPKvxH27Vi4OvVLcm1QrfvIK6vlGYXh/aUjO/3aPj6X8+qxQZ5KNJvRvr+kZhLvnnXdH7GioO+hrKCn0NpaGo/anCBKa/49lnn9W0adMKnf/uu+/k5+fnPL7YA974+/bu3asTJ07om2++kSTt27dP2dnZqlu3rlatWiVvSZOGdtfTTz+tec8+Ikn68MP9Skg4qSfa2nXzH4HJ4XDIYrEoJ+fCD88b1hxZMk6oalCgMjIyFOLvqeS0VB1+J/+eqd+9LtuPS5Samqrw8HANGDBADRo00PNLPlaoj0Un592h7OxsjVoQpK5du6pZWJi2H9uhF/79okJD7Rf4jllasfzbv/1zqYj9zTAMvfHGG/r99981ffp0bdy4sUC7w+HQG2+8oVtuuUW92l7oZ5YvL2Grvkko7WrL3sTGfz2TVS4+a0Xsa2XhzTffVFxcnLKzs+Xr66trrrlGI0aMkKenpw4fPqw333xTBw4ckKenp66++mrdc8898vb2dnXZ5Rp9DWWFvoaSlJ2dXaTrKkxgCgsLk7u7u06cOFHg/IkTJxQZGXnB9zz++OOaOHGi8zg9PV01a9ZUjx49FBQUJKvVqpUrV6p79+7y9PQs1fovJzabTTabTZs2bVJmZqb+9a9/yc3NTdddd52eeeYZHThwQGt+3KIVK1fJlnlaFjd33bdgjYJbdtepo27KSXHo9ifmOBd9yMvLU2RkpBISEmSz2dS1z83auGGD8tJOSha3/Kl3IdWVkpwgubnrVFqm87ybb6ByzmbIUq2hPAOq60TSXr322muyeHjJ4u4hw26XR1CYfCOjdTblkD755BPn57jrrrvl5huoKlf3U9VOg3Xq+/eU+fsm5Z06rBtvG6nP3plfrJ9LRe5v48aN09GjR7Vx40ZVrVq1UPt3332njMxMrfTrojWb3S9whz+9PeJqnuspZRW5r5WFOnXqqFatWvL399epU6c0ZMgQ/frrr3riiSfUpk0bXXPNNYqNjVVaWppuuukmbdmyRc8884yryy6X6GsoK/Q1lIZzs88upcIEJi8vL1111VVavXq1c28fh8Oh1atX68EHH7zge7y9vS/4XwU9PT0L/LL99Rj/zDPPPFNgZC8oKEidO3fWunXrtGzZMj3yyCPasnWb7BZP+dRprYDm3XTy25dl+IfJbkhnjx+Qe0i48ifAGcrIyNCuXbsUEBCg1NRUrf36c9WsFaOUOr2VHZ8/0pOXckQWTx8ZhiGHNVcW3yAZeWdlGJJ7cKTyUo6oxgP5m9Ge+N90ObLSZE05LPcqUara80FlfvG05s6dq0cefUy51a5QWO/xsiYf0IlPJis1foUUVE2W4GiFdL5TGb+skJ/33+8zFa2/HTp0SPPnz5e3t7fq16/vPD98+HDNn58fGt955x11uL6PDngGyGY+wCRJOpVtq1CfvyKraH2trLRo0cL5tYeHh9zd3Z0jSgkJCZo3b578/f3l7++v/v37KzY2lp/jJdDXUFboayhJRe1LFSYwSdLEiRM1YsQItWnTRm3bttXcuXOVlZXlXDUP5cPUqVM1derUC7Z17NhRsbGxit2fUmAFuqxd65R7ZJcMh11G1mnZsv7c7NMwDKWkpEjK/5cbm82mw4mHpMRDf97YbpVht0ruXpLDLjdvP/nUvUrZv22UPe245OauzF++k3/TLrIe35//FJPhkO3kQfnELVRyVobuuecexcTEKOSGUUr18JJ39cbyqt5E1uP7ZTt9TCHXDpNFkuPAj4oMunym58TExDhH+8x88sknhf6ZmmGZbZQHM2fO1IwZM5SVlaWqVatq1qxZki68kueoUaNcXC0AwJUqzCp5kjR48GA9//zzeuqpp3TllVcqPj5ey5cvL7QQBMq3V199VWMH91Ti8zcr+bMZMmx5yjv2uzzDa8t6Jn8lNYu3nyxuHurZq5fef/99595MHTp0UIu2HfNv5O4huXnIo2otWbzyn0kL7jREbj6Bsp85pqydq2VY85978opupNNrFurogtGyn02TIzNFHlWi5ebtJ1+P/JX0vLy8dDotXad2xSrlu9eV+MIA5SZsk2TIt34bHX7pNh1dNFZX1ghx1mN3GIrdn6Iv4o8qdn+K7I6LB4vK7Nwy22ZLYViUvzQ70/FQHkyaNEmZmZnatWuXRo8e7Zza/deVPGvWrKkRI+/k9xwALmMVKjBJ0oMPPqhDhw4pNzdXcXFxateunatLQjFFR0frySef1I0Dh0mGoZRvX5Z7lSj51mujnIPxkqTg9oMVU7+BVixfrmHDhjlHONavX69fNv+x4IA9f8U8W0qiDLtVIdffJ4unjxw5GZIskodX/rNMkmxpyXLzCZA9J1Nu3gHyrn2l3H2D5DCkY0nH1aZNG322eb9U5xod/e5N+dZvJ98GHeRZrZ4CruyttB8/kW9kPVX195KXh5sMw9DynUmXxfLZRcUy26iImjRpopYtW2rkyJHOlTxHjRql7OxsnT59WmlWN1W/uge/5wBwGatwgQkV34ABA3TTTTepVcNa8jj5m87ujVXekV91eM6tzuXCU79fJA+HVUFBQX9594X/Zdvi7qH0Hz9U6uo3/zhjSA67zi0h7sg4JXv6SXmF1ZK7f4jyjuxWzqHtUl62rDa7ps37QP+3dJfcA6vK4umrtB/ele3MMUUO+Y/sGaeUd/x3+TTtqpSsPH3/+0l9sDlRo9/bpqS0giv3Xe57NfVqFqV5w1srMrjgtLvIYB/NG976st/0F+WT1WrV3r17tX//fp09e1bjxo2Tl5eX4o7maHdwW53ZE1fg+sv99xwALjcEJpSZV199VW3atJG3t7duuukmffPNN7Jb87Ro4QJFREbL4vZnd/T19VViYqKyCi33eG4qzB/BycNL7lWiZeSdlV/TLqo5/hPJzV2yuOcHpvOevXHzDZQ9O03+TTvLu+5VcvMLliRlZ2Xqzle/1fG9vyj1p6Uy8s7KnpOhiMFPS5LO7ouTe0BYgSpyrY4LfsZz323asl2X7bSdXs2itOGxf+nDUe310m1X6sNR7bXhsX8RllAuZGZmatGiRUpNTZVhGNqxY4dmzJihnj17qnHjxgoICNDrr7+u3Dyrnvp0izK2r5BXtboF7sHvOQBcXghMKDPnpuKNGjVKv/zyixITE9W5c2fdcfvtql2zuurXq6fAwEDnnkt5eXmy22z5b3b3kF+z6+VTp7UsPoFyC/xjaWtbnuxnjkmSMuO/lZuXr7yrN5WMP5Zq8/hzcQZHdprsqUlK/f4d5fz+oxzZqfkNhkNJ74zXqW/myuKwSxaLIm57Rm7efjr11RwZdpv8GnbIX5DCcOQvY244ZNjyZNhthT6nISkpLUebE04XartcuLtZ1KFeVfW/sro61Cv+Jr9AabFYLPrggw9U74+/N/3791efPn00d+5cBQQEaNmyZfrwww8VFhamrbOGyZGbpap9Jha6D7/nAHD5qFCr5KFiGzBggCRp3bp1SkhIkLu7u7777jsFBAQ491rKyspSlSpVdP311+uzzz6T3Z4ffKoNnaUzK+fJI7yuDOsvcvcLkUOSxctXbr5BsqclS3abDj13k+SwSbJI7p6SLffPAizuirz9eRkOu06890jB4hwOybDLyM2SZFHSG/c4m7wi6yuo/a068f5jsp08JNvJ/NX5MrZ9Jf9m1yusz4QLft7kjAtvtAvAdfz9/S+68WXHjh21YcMGfRF/VA99FH/J+/F7DgCVH4EJZS4kJET9+/d3rnS4dOlSTZ06VWvXrtXhw4cVEhKiTz75RNWq11LyscOSpBPvPizfBu2Vved7yW6T7fRhyWKRe1CE/OpdrfS4T+XbqJPO7v/pj8BkSPa8P76jRbJYZPH00pnVC2RLPf+5A4s8QiJlz82SI/esajz0kdx9AiRJqRveV3rcZ7KlJevIK8Pzly23uMnNJ0BRd70qj4CLr/bG8tlAxVXU319+zwGg8iMwocw5HA7Z7XbZbDY5HA7l5OTIbrfr1KlTkqQDBw6oXbt2zrB0ztm9f9njxzBkO3VI6SmH5d/s+vxz548o/Xlh/v/lnZX9bLqi75mvwy8NdradC1Bu/qFy9wmQYbPq9Mp5OnvwZ8likZu3nwKuvEEWNzdlbv9OEYOm6+y+OKX/tFT2jFNy8w1WaLd75degvaT8p6siWT4bqNDOLZN/PC1HF3pKid9zALh8EJhQ5n744Qd9//33+uqrryTlL/AQExOjiIgIRUZG6uTJk9q8ebMkyT04Uh4h1WTPTpXdmicjNUkeYTGSm5scmWfk7huoqJEv5S8fbjiUc/BnufkEyZZ6TIYtT/pjvySLp4+MvBy5+4ecF5bOZ5EjJ12Jc26VYRjyrFpD1W77jzxCIpV37Dcl/3eK/Fv0kNzddfbAFqX/9IXC+z0qz4i6cmSnymEtGNRYPhuo2M4tkz/mvW2ySAVCE8vkA8DlhUUfUOa6dOmi/v37yzAMWa1WnT17VsOHD1dkZKTCw8PzV8tzy8/y9rTjyj20XbaTh2SkJkluHvIKj5EtOUGO7FRZUw4r8YUBOjLvTiW+NET2zDOy52ZIkvybXa/q97/jHF2SDOUe3SOLl68zSDlZLJJh5G90a7fKkXVGGVuXSQ6bvKs3lleNpspJ2Cpb6nGdXvGavKMayqtaPVksFrn7V5FnSP6mlyF+niyfDVQSLJMPAJAYYUIZstlszte5qXjPPPOMZsyYUehar1otFdLhVqVt+FB5pw7JyMtxrnx3dm+c9Md/8/WKaqjQ3uN14t2Jkj1PFi8fWWSRw2ZV1m8blfXrWkmSxctPbr6BMuxWOTL/WNXKzT2/zc1DHlWi5eYforzj+1S1+33yqd1KJ7+YqbS4/yn62oHKTfxFHlWiFTHoaZ147xFl74tT4ktD5ObhJd+6V6nKv+6Rm7efXhvSWh0bhBX6PAAqpl7NotS9aaQ2J5xWckaOIgLzp+ExsgQAlw9GmFBmZsyYIV9fXz3zzDNatmyZfH19tX79ehmGoZYtWxa4Ni9xu5I/nqy85ANy968indujyWGTYcuVxdNbgW1uUl7yQR1fPFaGNVdy95JhzZVXtbrybdBenqHV5eYTKM/wGFUf87bcPH1k8fCSV2QDSRbVemSpQq67Q/5NOyu0+33KPbxTFhnya3iNpPzlh22njyln5auSLU91+o93hjZ33yB5hlZX1J0vy5Z2QqdXL1Cov6eSM3MVuz+FvVmASoRl8gHg8kZgQpmZOnWqDMMo8Fq3bp0kKT4+XoZhaOnPRxTccYh8G7RXzGNfqcaD78m3flvJzV0h/7pH1YbOlMXbX15RDWXPTFFAi+7yrFpTNca9r1rjP5KbT4B8G3RQTsI2WVMOy80vUBGDZ+jM2rdlPXVI9tTjsqUel8XLR5k/fy3DblVeymFlbl8hi4eXPEJr6PDLQ3XklWHKS06Qw5qjYwd/k2EYmnP/zXLz9JUkBbTsKVvKYbn7BSuo/UCd3b9Zp7OsmvBxvIYs2KROs9Zo+c6ki/w0AAAAUBEwJQ/lyl+X6HXz8pF/42uVseVLBbXpL4vFIp+YFnLz8lXWno35q+K5e+rovLtkOOyS3aozqxfIkCHZ8mRLOaKjrw5X/hQ+i9z8QxRwRVd5RTdS2sYPZT15UBYPL1k8vGTkZatqz/vlFVFXeacSdeqrF2Q7fVQhXe5U2lfPqc+VNTXvgT66+b1HdHb/Fjlys5T44kBZ3Dxk2HLzp/vlZOn0mgU6krhTN0zNVp26dfXirP+oX79+Lvl5AgAA4J9hhAnlhs1mU4soPwV4WiTDIcOWJ8NulWfVGnL7Y0TIkXdWecd+ky31hLwj6ynmsa8U88jnqjXxU3nXuEIeodVl5GVLDocsnj7OV8CVPeXX8BpZLBalb/tap76YJcNuU8i1t6vmxE/lU6OpvKrVk1dEXUlSxrav5TibLjffAHkEhCov56y+jj+s/m3qqke/W+U4my6Ll59qjFkkz/AYWbz9lRb3PzmsOfKKqKfI219QrfEfy/Pq2zRkyBDt2rXLxT9dAAAA/B2MMKHcmDFjhqZNm+Y8TnxhgLxrNlPk0JkKv+UpnVm7SKdXL5AkeVarp9Bu9zmvtaaeUO6heHmE1lCNhz7SVfWjdTDlrFLPWp3XnPr6Rfk16iTv6IY68/07qj5qviTJMBzK3LFKwe0HSpJsacnK/PlryeIue/pJJb33qCRDtw8fquPx3+t0iyGy/LhZhjVHRxeMlm/tK2U9c0zpsf9Vetz/5OblK3vGKVXpeqdyolvJxy9APXr00PHjx9W7d2/dcMMNZfDTBAAAQElghAnlxvnPOH2745ja/2eVIofOlCR5V2+ikBr1VLN+E9Uc94Gq3TrFuZS3JJ36YqYsHl6KHD5b7j4Bij+SrtSzVjnyzsq2a6Xcc1NlOOyyZ6cqdeNH8ql9pfO9OQk/y56dJsNwyJGTKfegcEXd9ao8Q6MV0LKnYh7+n/yv6KqzZ8/qjZU7dPToMdmz0+TX8BoZ1hxl7VonWXMVMXCaak34r6LufEV5JxOUFvc/2bNSlZZ6Rv/+97914403lvFPFAAAAP8UI0wol85fyvdE+lktef4pHbQe1Xc/fq++b/6s42k5zo0kranHlXd8r+TmrqPz7nLew/+KLqrS9R4lrVkix7KXCtzfK7K+8+vMX1bKr2EHnf19k9LWvy/DbpW7X7D8GnVUcKehkqTQ7mOUsuJVPTLgGtncPBXY+kaFdByS//1PHVbmrnXyOC/AWSwWWVOO6OSXs9Wj7wA9OHacVnz/o9zt2dqccFrt60ew0hYAAEAFQGBCuXVuKd8HHnhA+3du1Zo1a1S1aqim9G2qMe9t+2MnJsl2+qgsnt6q8cASuXn7O99/euV8HXtrtAxrjtwDQuXXqJOqdL1TFndPHf9gkg49f5Msbn/+CkSPekMegVUvWIubt5/C+z2qyX2a6Omvdxdo8wyrKa+IOjrx4eOyZ52RYc2RxSdQXmF2+foH6L7/m6lOs9Zo175Tui7GT3e985NCA3w1pW9TNr4EAAAo5whMKNcOHTqk119/Xd7e3oqJiXGe73zDAJ1uNUJJaTn5I0SNOhYIS5IU2KqPQjqPlJuXj+zZac6NaEOuuU2SVKXznQq6un+R6rBIigz20e0dauutDQkFRrgk5e8P5bCp1sRPlXciQSe/eFayWDRx1nyN/++v+uuuTMfTcjT6vW26q2NtdW8ayUaYAAAA5RSBCeVaTEyMDOPCm8DaHYY2J5xW8m0fae+JTL26dl+Bds+wmgWOLRaLbGeO6a+x5NxI1b8ah2vNnpPO4/PbJWlK36by8nDTo9fHaNTUV+XXsIMs3v6ynjqktB8/lm+d1jLsNqVufF+eXt6qHxmkb3alFApLOu/+b288qLc3HlRUsA8jTgAAAOUQgQkV1rkpe5IUuz+lUGCSpLRN/1Xajx/LsObIzTdI4/89RT+m+yhJUlrsx0r78UP5VKmmMQ+O0/Mj+2j5ziRNW7ZLSWk5zntE/iXM9LgiSrXObNOON9+W3fbn8051e4xUK78ULdq7SZ6entqZfFCKz1/oIbjDoIt+luNpORrz3jbNG96a0AQAAFCOEJhQKbStE6qoYJ9CU+WC2w9UcPuBsp46LMv+9RrX92rNrlFDC+s/p4DIGNUMr6KMhHgNuW2wOjSqoegrO+vRno10OitPoQHeigzyKTRdzt/fX9tif3COcB06kaId61do4qhOCq0Sogk3XqXBgwerRpNW+r3BUBl2m2Q4dGbFy3I4HHLY8mQ43GVx//PXz1D+SNa0ZbvUvWkk0/MAAADKCQITKgV3N0uhxSDOsUjyCqupoU066+677tSqVat078Bef17QuJd63DJM9017RQE3eDtPn5smZxZezo1wtYj00cIpX6rh7KnKzc1VRESEbrnlFt0wYpzufO8XpSx/RVk7V0uSvtkp6Ztv5N/seoX1mVDgfoakpLQcbU447Rw5AwAAgGsRmFBp9GoWpXnDW5tOqTv9S5re27u30PuW70zSd7tOyma1K+C880WdJufv76+VK1cWOm93GIoK/l2WPhMU1meCvN0NzW5r16Ob3ZVrNx9BSs7IMW0DAABA2WLjWlQqvZpFacNj/9LCoc3Uz/c3vTm4idY/2lXVjVOaMWOGevbsqdTUVH3zzTfKzs5WntWmiS++p4z4b+XXqGOBe50bpZq2bJfsjgsvPHEx50a9JBVaaOJiIgJ9iv29AAAAUDoITKh03N0sal8vTLs3fKsh3a5WSHCQ+vfvrz59+mju3LmyWq2aNm2aIiMjVaVKFe1b9rpC/3W3/Bt3KnSv86fJ/R3nRr0igy8dgizKnwbYtk7o3/peAAAAKHlMyUOlZDZNTpL8/PwUFxcnSfoi/qge+ij+kvf7J9PkejWLUvemkdq0L1mndm+64DXnL13Ogg8AAADlByNMuKwVdfrbP50m5+5mcY4czR18paL+MuIUGezDkuIAAADlECNMuKyZLUd+jkX5YaYkp8l1a1JNPZpVz990NyNHEYGFly4HAABA+UBgwmXtUsuRS6UzTe78TXcBAABQfjElD5c9s4UZmCYHAAAARpgA/bkwA9PkAAAAcD4CE/AHpskBAADgr5iSBwAAAAAmCEwAAAAAYILABAAAAAAmCEwAAAAAYILABAAAAAAmCEwAAAAAYILABAAAAAAmCEwAAAAAYILABAAAAAAmCEwAAAAAYILABAAAAAAmCEwAAAAAYILABAAAAAAmCEwAAAAAYILABAAAAAAmCEwAAAAAYILABAAAAAAmCEwAAAAAYILABAAAAAAmCEwAAAAAYILABAAAAAAmCEwAAAAAYILABAAAAAAmCEwAAAAAYILABAAAAAAmCEwAAAAAYILABAAAAAAmCEwAAAAAYILABAAAAAAmCEwAAAAAYILABAAAAAAmCEwAAAAAYILABAAAAAAmCEwAAAAAYILABAAAAAAmCEwAAAAAYILABAAAAAAmCEwAAAAAYILABAAAAAAmCEwAAAAAYILABAAAAAAmCEwAAAAAYILABAAAAAAmCEwAAAAAYILABAAAAAAmCEwAAAAAYILABAAAAAAmCEwAAAAAYILABAAAAAAmCEwAAAAAYILABAAAAAAmCEwAAAAAYILABAAAAAAmCEwAAAAAYILABAAAAAAmCEwAAAAAYILABAAAAAAmCEwAAAAAYILABAAAAAAmCEwAAAAAYILABAAAAAAmCEwAAAAAYILABAAAAAAmCEwAAAAAYILABAAAAAAmCEwAAAAAYILABAAAAAAmCEwAAAAAYILABAAAAAAmCEwAAAAAYILABAAAAAAmCEwAAAAAYILABAAAAAAmCEwAAAAAYILABAAAAAAmCEwAAAAAYILABAAAAAAmCEwAAAAAYILABAAAAAAmKkxgql27tiwWS4HXzJkzXV0WAAAAgErMw9UFFMf06dM1atQo53FgYKALqwEAAABQ2VWowBQYGKjIyEhXlwEAAADgMlFhpuRJ0syZM1W1alW1atVKzz33nGw2m6tLAgAAAFCJVZgRpnHjxql169YKDQ3Vjz/+qMcff1xJSUmaM2eO6Xtyc3OVm5vrPE5PT5ckWa1W5+vcMVDa6G8oK/Q1lBX6GsoKfQ2loaj9yWIYhlHKtZiaNGmSZs2addFrdu/ercaNGxc6//bbb+u+++5TZmamvL29L/jeqVOnatq0aYXOf/DBB/Lz8/t7RQMAAACo8LKzszV06FClpaUpKCjI9DqXBqaTJ08qJSXlotfUrVtXXl5ehc7/+uuvatasmfbs2aNGjRpd8L0XGmGqWbOmTp06paCgIFmtVq1cuVLdu3eXp6fnP/swwCXQ31BW6GsoK/Q1lBX6GkpDenq6wsLCLhmYXDolLzw8XOHh4X/rvfHx8XJzc1NERITpNd7e3hccffL09Czwy/bXY6A00d9QVuhrKCv0NZQV+hpKUlH7UoV4hik2NlZxcXHq2rWrAgMDFRsbqwkTJmj48OGqUqWKq8sDAAAAUElViMDk7e2tjz76SFOnTlVubq7q1KmjCRMmaOLEia4uDQAAAEAlViECU+vWrbVp0yZXlwEAAADgMlOh9mECAAAAgLJEYAIAAAAAEwQmAAAAADBBYAIAAAAAEwQmAAAAADBBYAIAAAAAEwQmAAAAADBBYAIAAAAAEwQmAAAAADBBYAIAAAAAEwQmAAAAADBBYAIAAAAAEwQmAAAAADBBYAIAAAAAEwQmFMnYsWNVs2ZNBQUFqXr16ho/frzy8vIkSZMnT1bz5s3l4eGh8ePHu7ZQAAAAoAQRmFAk999/v/bs2aP09HRt375d27dv1+zZsyVJ9evX1+zZs9WvXz8XVwkAAACULA9XF4CKoUmTJs6vDcOQm5ub9u7dK0kaMWKEJOnjjz92SW0AAABAaWGECUU2c+ZMBQQEKCIiQtu3b9fYsWNdXRIAAABQqghMKLJJkyYpMzNTu3bt0ujRoxUZGWl6rd1hKHZ/ir6IP6rY/SmyO4wyrBQAAAAoGUzJQ7E1adJELVu21MiRI7Vq1apC7ct3Jmnasl1KSstxnosK9tGUvk3Vq1lUWZYKAAAA/COMMOFvsVqtzmeYzncwJUtj3ttWICxJ0vG0HI15b5uW70wqqxIBAACAf4zAhEvKzMzUokWLlJqaKsMwtGPHDs2YMUM9e/aUlB+ecnJyZLPZtWnfSTlseTLstgL3ODchb9qyXUzPAwAAQIVBYMIlWSwWffDBB6pXr54CAwPVv39/9enTR3PnzpUkjRo1Sr6+vnr//fd0YtMXSnxhgFKWv1LoPoakpLQcbU44XbYfAAAAAPibeIYJl+Tv76+VK1eati9evFiLFy/WF/FH9dBH8Ze8X3JGziWvAQAAAMoDRphQYiICfUr0OgAAAMDVCEwoMW3rhCoq2EcWk3aL8lfLa1sntCzLAgAAAP42AhNKjLubRVP6NpWkQqHp3PGUvk3l7mYWqQAAAIDyhcCEEtWrWZTmDW+tyOCC0+4ig300b3hr9mECAABAhcKiDyhxvZpFqXvTSG1OOK3kjBxFBOZPw2NkCQAAABUNgQmlwt3Nog71qrq6DAAAAOAfYUoeyrWxY8eqZs2aCgoKUvXq1TV+/Hjl5eVJkrp06SJvb28FBAQ4X8eOHXNxxQAAAKhMCEwo1+6//37t2bNH6enp2r59u7Zv367Zs2c722fNmqXMzEznKzo62oXVAgAAoLJhSh7KtSZNmji/NgxDbm5u2rt3rwsrAgAAwOWEESaUezNnzlRAQIAiIiK0fft2jR071tk2Y8YMhYaGqlWrVlqyZIkLqwQAAEBlxAgTyr1JkyZp0qRJ2r17t95//31FRkZKkp599lk1bdpUfn5+WrNmjQYNGiR//wBFX9mZ1fkAAABQIghMqDCaNGmili1bauTIkVq1apU6dOjgbOvZs6d63DJM9017RQE3eDvPRwX7aErfpuz/BAAAgL+FKXmoUKxW6wWfYVq+M0nf7Tqps1Z7gfPH03I05r1tWr4zqaxKBAAAQCVCYEK5lZmZqUWLFik1NVWGYWjHjh2aMWOGevbsqdTUVH3zzTfKzs5WntWmiS++p4z4b+XXqGOBexh//O+0ZbtkdxiFvwkAAABwEQQmlFsWi0UffPCB6tWrp8DAQPXv3199+vTR3LlzZbVaNW3aNEVGRqpKlSrat+x1hf7rbvk37lToPoakpLQcbU44XfYfAgAAABUazzCh3PL399fKlSsv2Obn56e4uDhJ0hfxR/XQR/GXvF9yRk5JlgcAAIDLACNMqPAiAn1K9DoAAADgHAITKry2dUIVFewjs8XDLcpfLa9tndCyLAsAAACVAIEJFZ67m0VT+jaVpEKh6dzxlL5N2Y8JAAAAxUZgQqXQq1mU5g1vrcjggtPuIoN9NG94a/ZhAgAAwN/Cog+oNHo1i1L3ppHanHBayRk5igjMn4bHyBIAAAD+LgITKhV3N4s61Kvq6jIAAABQSTAlDwAAAABMEJgAAAAAwASBCQAAAABMEJgAAAAAwASBCQAAAABMEJgAAAAAwASBCQAAAABMEJgAAAAAwASBCQAAAABMEJgAAAAAwASBCQAAAABMEJgAAAAAwASBCQAAAABMEJgAAAAAwASBCQAAAABMEJgAAAAAwASBCQAAAABMEJgAAAAAwASBCQAAAABMEJgAAAAAwISHqwsoS4ZhSJLS09MlSVarVdnZ2UpPT5enp6crS8NlgP6GskJfQ1mhr6Gs0NdQGs5lgnMZwcxlFZgyMjIkSTVr1nRxJQAAAADKg4yMDAUHB5u2W4xLRapKxOFw6NixYwoMDJTFYlF6erpq1qypw4cPKygoyNXloZKjv6Gs0NdQVuhrKCv0NZQGwzCUkZGh6OhoubmZP6l0WY0wubm5qUaNGoXOBwUF8cuHMkN/Q1mhr6Gs0NdQVuhrKGkXG1k6h0UfAAAAAMAEgQkAAAAATFzWgcnb21tTpkyRt7e3q0vBZYD+hrJCX0NZoa+hrNDX4EqX1aIPAAAAAFAcl/UIEwAAAABcDIEJAAAAAEwQmAAAAADABIEJAAAAAEwQmM5Tu3ZtWSyWAq+ZM2e6uixUAq+99ppq164tHx8ftWvXTps3b3Z1SaiEpk6dWuhvWOPGjV1dFiqBH374QX379lV0dLQsFouWLl1aoN0wDD311FOKioqSr6+vunXrpr1797qmWFRol+prI0eOLPR3rlevXq4pFpcNAtNfTJ8+XUlJSc7X2LFjXV0SKriPP/5YEydO1JQpU7Rt2za1bNlSPXv2VHJysqtLQyV0xRVXFPgbtmHDBleXhEogKytLLVu21GuvvXbB9tmzZ+vll1/W/PnzFRcXJ39/f/Xs2VM5OTllXCkqukv1NUnq1atXgb9zH374YRlWiMuRh6sLKG8CAwMVGRnp6jJQicyZM0ejRo3SnXfeKUmaP3++vv76a7399tuaNGmSi6tDZePh4cHfMJS43r17q3fv3hdsMwxDc+fO1ZNPPqn+/ftLkpYsWaJq1app6dKluu2228qyVFRwF+tr53h7e/N3DmWKEaa/mDlzpqpWrapWrVrpueeek81mc3VJqMDy8vK0detWdevWzXnOzc1N3bp1U2xsrAsrQ2W1d+9eRUdHq27duho2bJgSExNdXRIquYSEBB0/frzA37ng4GC1a9eOv3MoFevWrVNERIQaNWqkMWPGKCUlxdUloZJjhOk848aNU+vWrRUaGqoff/xRjz/+uJKSkjRnzhxXl4YK6tSpU7Lb7apWrVqB89WqVdOePXtcVBUqq3bt2mnx4sVq1KiRkpKSNG3aNF177bXauXOnAgMDXV0eKqnjx49L0gX/zp1rA0pKr169NGDAANWpU0f79+/XE088od69eys2Nlbu7u6uLg+VVKUPTJMmTdKsWbMues3u3bvVuHFjTZw40XmuRYsW8vLy0n333adnn31W3t7epV0qAPwj509jadGihdq1a6eYmBh98sknuvvuu11YGQCUjPOneDZv3lwtWrRQvXr1tG7dOl1//fUurAyVWaUPTA8//LBGjhx50Wvq1q17wfPt2rWTzWbTwYMH1ahRo1KoDpVdWFiY3N3ddeLEiQLnT5w4wfxrlLqQkBA1bNhQ+/btc3UpqMTO/S07ceKEoqKinOdPnDihK6+80kVV4XJRt25dhYWFad++fQQmlJpKH5jCw8MVHh7+t94bHx8vNzc3RURElHBVuFx4eXnpqquu0urVq3XTTTdJkhwOh1avXq0HH3zQtcWh0svMzNT+/ft1++23u7oUVGJ16tRRZGSkVq9e7QxI6enpiouL05gxY1xbHCq9I0eOKCUlpUBYB0papQ9MRRUbG6u4uDh17dpVgYGBio2N1YQJEzR8+HBVqVLF1eWhAps4caJGjBihNm3aqG3btpo7d66ysrKcq+YBJeWRRx5R3759FRMTo2PHjmnKlClyd3fXkCFDXF0aKrjMzMwCI5UJCQmKj49XaGioatWqpfHjx2vGjBlq0KCB6tSpo8mTJys6Otr5H4qAorpYXwsNDdW0adN0yy23KDIyUvv379ejjz6q+vXrq2fPni6sGpWeAcMwDGPr1q1Gu3btjODgYMPHx8do0qSJ8Z///MfIyclxdWmoBF555RWjVq1ahpeXl9G2bVtj06ZNri4JldDgwYONqKgow8vLy6hevboxePBgY9++fa4uC5XA2rVrDUmFXiNGjDAMwzAcDocxefJko1q1aoa3t7dx/fXXG7/99ptri0aFdLG+lp2dbfTo0cMIDw83PD09jZiYGGPUqFHG8ePHXV02KjmLYRiGq8IaAAAAAJRn7MMEAAAAACYITAAAAABggsAEAAAAACYITAAAAABggsAEAAAAACYITAAAAABggsAEAAAAACYITABwGapdu7bmzp1bYvcbOXKkbrrpphK7nyStW7dOFotFqampJXpfAACKg8AEABXYyJEjZbFYZLFY5OXlpfr162v69Omy2WwXfd9PP/2ke++9t8TqeOmll7R48eISu19x/Pzzzxo4cKCqVasmHx8fNWjQQKNGjdLvv//uknrKq6KG5DfffFNdunRRUFAQgRUARGACgAqvV69eSkpK0t69e/Xwww9r6tSpeu655y54bV5eniQpPDxcfn5+JVZDcHCwQkJCSux+RfXVV1+pffv2ys3N1fvvv6/du3frvffeU3BwsCZPnlzm9VQG2dnZ6tWrl5544glXlwIA5QKBCQAqOG9vb0VGRiomJkZjxoxRt27d9OWXX0r6c6rcM888o+joaDVq1EhS4dEGi8Wit956SzfffLP8/PzUoEED5z3O+fXXX3XjjTcqKChIgYGBuvbaa7V///4C3+ecLl266MEHH9SDDz6o4OBghYWFafLkyTIMw3nNu+++qzZt2igwMFCRkZEaOnSokpOTi/y5s7Ozdeedd+qGG27Ql19+qW7duqlOnTpq166dnn/+eb3xxhvOa7///nu1bdtW3t7eioqK0qRJkwqMwnXp0kVjx47V+PHjVaVKFVWrVk0LFixQVlaW7rzzTgUGBqp+/fr69ttvne85N2Xw66+/VosWLeTj46P27dtr586dBer83//+pyuuuELe3t6qXbu2XnjhhQLttWvX1n/+8x/dddddCgwMVK1atfTmm28WuObw4cMaNGiQQkJCFBoaqv79++vgwYPO9nM//+eff15RUVGqWrWqHnjgAVmtVufnO3TokCZMmOAckTQzfvx4TZo0Se3bty/yPwsAqMwITABQyfj6+jpHkiRp9erV+u2337Ry5Up99dVXpu+bNm2aBg0apF9++UU33HCDhg0bptOnT0uSjh49quuuu07e3t5as2aNtm7dqrvuuuuiU//eeecdeXh4aPPmzXrppZc0Z84cvfXWW852q9Wqp59+Wtu3b9fSpUt18OBBjRw5ssifc8WKFTp16pQeffTRC7afG/E6evSobrjhBl199dXavn275s2bp4ULF2rGjBmF6g0LC9PmzZs1duxYjRkzRgMHDtQ111yjbdu2qUePHrr99tuVnZ1d4H3//ve/9cILL+inn35SeHi4+vbt6wwqW7du1aBBg3Tbbbdpx44dmjp1qiZPnlxo+uILL7ygNm3a6Oeff9b999+vMWPG6LfffnP+nHr27KnAwECtX79eGzduVEBAgHr16lXgn/PatWu1f/9+rV27Vu+8844WL17s/D6fffaZatSooenTpyspKUlJSUlF/jkDwGXPAABUWCNGjDD69+9vGIZhOBwOY+XKlYa3t7fxyCOPONurVatm5ObmFnhfTEyM8eKLLzqPJRlPPvmk8zgzM9OQZHz77beGYRjG448/btSpU8fIy8u7ZB2GYRidO3c2mjRpYjgcDue5xx57zGjSpInpZ/npp58MSUZGRoZhGIaxdu1aQ5Jx5syZC14/a9YsQ5Jx+vRp03sahmE88cQTRqNGjQrU8tprrxkBAQGG3W531tupUydnu81mM/z9/Y3bb7/deS4pKcmQZMTGxhao76OPPnJek5KSYvj6+hoff/yxYRiGMXToUKN79+4F6vn3v/9tNG3a1HkcExNjDB8+3HnscDiMiIgIY968eYZhGMa7775bqP7c3FzD19fXWLFihWEY+T//mJgYw2azOa8ZOHCgMXjw4ALf5/x/5pdyqZ8/AFwuGGECgAruq6++UkBAgHx8fNS7d28NHjxYU6dOdbY3b95cXl5el7xPixYtnF/7+/srKCjIOUUuPj5e1157rTw9PYtcV/v27QtM/erQoYP27t0ru90uKX/0pW/fvqpVq5YCAwPVuXNnSVJiYmKR7m+cN73vYnbv3q0OHToUqKVjx47KzMzUkSNHnOfO//zu7u6qWrWqmjdv7jxXrVo1SSo0bbBDhw7Or0NDQ9WoUSPt3r3b+b07duxY4PqOHTsW+Dn89XtbLBZFRkY6v8/27du1b98+BQYGKiAgQAEBAQoNDVVOTo5zSqQkXXHFFXJ3d3ceR0VFFWuKIwDgwjxcXQAA4J/p2rWr5s2bJy8vL0VHR8vDo+Cfdn9//yLd569hyGKxyOFwSMqf5leSsrKy1LNnT/Xs2VPvv/++wsPDlZiYqJ49exaYZnYxDRs2lCTt2bOnQGj5uy70+c8/dy5wnfuZlKSL/ewzMzN11VVX6f333y/0vvDw8CLdAwDw9zHCBAAVnL+/v+rXr69atWoVCkslpUWLFlq/fr3z2ZyiiIuLK3C8adMmNWjQQO7u7tqzZ49SUlI0c+ZMXXvttWrcuHGxR0N69OihsLAwzZ49+4Lt55bDbtKkiWJjYwuMSG3cuFGBgYGqUaNGsb7nhWzatMn59ZkzZ/T777+rSZMmzu+9cePGAtdv3LhRDRs2LDAadDGtW7fW3r17FRERofr16xd4BQcHF7lOLy+vAqNaAICiITABAC7pwQcfVHp6um677TZt2bJFe/fu1bvvvutcmOBCEhMTNXHiRP3222/68MMP9corr+ihhx6SJNWqVUteXl565ZVXdODAAX355Zd6+umni1WTv7+/3nrrLX399dfq16+fVq1apYMHD2rLli169NFHNXr0aEnS/fffr8OHD2vs2LHas2ePvvjiC02ZMkUTJ06Um9s//3+D06dP1+rVq7Vz506NHDlSYWFhzhUDH374Ya1evVpPP/20fv/9d73zzjt69dVX9cgjjxT5/sOGDVNYWJj69++v9evXKyEhQevWrdO4ceMKTCm8lNq1a+uHH37Q0aNHderUKdPrjh8/rvj4eO3bt0+StGPHDsXHxzsXAAGAyw2BCQBwSVWrVtWaNWuUmZmpzp0766qrrtKCBQsu+kzTHXfcobNnz6pt27Z64IEH9NBDDzk3yw0PD9fixYv13//+V02bNtXMmTP1/PPPF7uu/v3768cff5Snp6eGDh2qxo0ba8iQIUpLS3Ougle9enV988032rx5s1q2bKnRo0fr7rvv1pNPPvn3fhh/MXPmTD300EO66qqrdPz4cS1btsz5zFjr1q31ySef6KOPPlKzZs301FNPafr06cVaDdDPz08//PCDatWqpQEDBqhJkya6++67lZOTo6CgoCLfZ/r06Tp48KDq1atXYCrfX82fP1+tWrXSqFGjJEnXXXedWrVqVWiZeQC4XFiMoj41CwBAEXXp0kVXXnllgb2eKpt169apa9euOnPmjEs27QUAlA1GmAAAAADABIEJAAAAAEwwJQ8AAAAATDDCBAAAAAAmCEwAAAAAYILABAAAAAAmCEwAAAAAYILABAAAAAAmCEwAAAAAYILABAAAAAAmCEwAAAAAYILABAAAAAAm/h/gjsueRdrfLgAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We can see that the strains positioned farest from the big group are the ones that have a higher bioremediation potential. This makes sense as the BP is the sum of the gene counts, and these gene counts are the only input that the model has received.\n",
        "Seeing this PCA we could hypothesize that the source environment of the strains is an important factor that determinies the bioremeidation potential of Pseudomonas putida.\n",
        "\n",
        "However, we will also label the data points depending on the pollution of the sites, as soil samples are the ones with more polluted samples."
      ],
      "metadata": {
        "id": "x34F9uXJ85nu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#We Plot the PCA results\n",
        "plt.figure(figsize=(10, 8))\n",
        "plt.scatter(df_genecount_pca[:, 0], df_genecount_pca[:, 1], cmap='viridis')\n",
        "#We will now add the data labels (we can later replace the data labels with any feature)\n",
        "for i, name in enumerate(df['Pollution']):  # assuming `df_numeric` has row indices as names\n",
        "    plt.text(df_genecount_pca[i, 0], df_genecount_pca[i, 1], name, fontsize=9, ha='right')\n",
        "\n",
        "plt.xlabel('Principal Component 1')\n",
        "plt.ylabel('Principal Component 2')\n",
        "plt.grid(True)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 745
        },
        "id": "FRVPdsUx9Ivm",
        "outputId": "8aa1ed79-d189-4f3b-872b-3fb13df77eb8"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-45-b9d97d24ec31>:3: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored\n",
            "  plt.scatter(df_genecount_pca[:, 0], df_genecount_pca[:, 1], cmap='viridis')\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x800 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "When we label the strains based on the poollution present in their sampling site, we see that some of the far away points seem to be distant from the group because of the pollution present.\n",
        "\n",
        "For example, these two strains that we see were collected in a byphenil polluted soil, contain a whole operon of biphenyl degrading genes, that surely increased their bioremediation potential.\n",
        "\n",
        "However, not all of the far away points represent strains collected from polluted sites, and not all the strains that we see grouped together were collected from non-polluted sites.\n",
        "\n",
        "So we cannot assure that the pollution of the collection site is crucial to determine bioremediation potential, even if we know that it has an important effect on it.\n"
      ],
      "metadata": {
        "id": "j3H_ehU_9Qbk"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## *PERMANOVA*"
      ],
      "metadata": {
        "id": "HXWDi75m7KJK"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "To see if our hypothesies are statistically fundamented, we performed a **PERMANOVA** analysis. We will use this statistical test to determine if grouping the PCA by source environment is significant.\n",
        "\n",
        "This is, to see if the grouping of the strains by source environment when taking into account their gene counts has statistical relevance.\n",
        "\n"
      ],
      "metadata": {
        "id": "KN4TJ3CS9VWI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Determine the groups for PERMANOVA analysis\n",
        "groups = df['Isolation matrix'].tolist()"
      ],
      "metadata": {
        "id": "usQPBlDA-nOk"
      },
      "execution_count": 49,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Calculate a distance matrix from the PCA data (Euclidean distance)\n",
        "distance_matrix = squareform(pdist(df_genecount_pca, metric = 'euclidean'))"
      ],
      "metadata": {
        "id": "VURMw9hF-yEF"
      },
      "execution_count": 50,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Convert distance matrix into skbio DistanceMatrix format\n",
        "dist_matrix = DistanceMatrix(distance_matrix, ids=[f\"Sample_{i}\" for i in range(len(df_genecount_pca))])"
      ],
      "metadata": {
        "id": "1A8qG1ZH-9Tv"
      },
      "execution_count": 51,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Run PERMANOVA\n",
        "permanova_result = permanova(distance_matrix = dist_matrix, grouping = groups)\n",
        "\n",
        "#Display PERMANOVA results\n",
        "print('\\nPERMANOVA Results:')\n",
        "print(permanova_result)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "g061m90v_Ld0",
        "outputId": "35f6151f-9372-4b07-a805-2622a226b63f"
      },
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "PERMANOVA Results:\n",
            "method name               PERMANOVA\n",
            "test statistic name        pseudo-F\n",
            "sample size                      59\n",
            "number of groups                  4\n",
            "test statistic             2.267631\n",
            "p-value                       0.033\n",
            "number of permutations          999\n",
            "Name: PERMANOVA results, dtype: object\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Seeing the results we can see that the test supports that grouping the strains by their source environment is significative.\n",
        "\n",
        "However, we already saw that the strains collected from soil show a different pattern than the rest, so we performed a **Pairwise PERMANOVA**.\n",
        "In this statistical test, all the source environment groups will be tested against each other."
      ],
      "metadata": {
        "id": "qpk9rEr4_gH2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#We will define a functoin to perform pairwise PERMANOVA\n",
        "def pairwise_permanova(distance_matrix, groups, group_labels):\n",
        "    \"\"\"\n",
        "    Perform pairwise PERMANOVA for all combinations of groups.\n",
        "\n",
        "    Parameters:\n",
        "    - distance_matrix: A skbio DistanceMatrix object.\n",
        "    - groups: List of group labels for each sample.\n",
        "    - group_labels: Unique group names.\n",
        "\n",
        "    Returns:\n",
        "    - results: A DataFrame summarizing pairwise PERMANOVA results.\n",
        "    \"\"\"\n",
        "    results = []\n",
        "    for group1, group2 in combinations(group_labels, 2):\n",
        "        # Subset data for the two groups\n",
        "        indices = [i for i, g in enumerate(groups) if g in [group1, group2]]\n",
        "        sub_matrix = distance_matrix.filter([distance_matrix.ids[i] for i in indices])\n",
        "        sub_groups = [groups[i] for i in indices]\n",
        "\n",
        "        # Perform PERMANOVA\n",
        "        result = permanova(distance_matrix=sub_matrix, grouping=sub_groups)\n",
        "        print('for groups:', group1, group2)\n",
        "        print(result)\n",
        "    return pd.DataFrame(results)"
      ],
      "metadata": {
        "id": "I2By4M5K_xGK"
      },
      "execution_count": 55,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#We prepare the data for pairwise analysis\n",
        "group_labels = list(set(groups))\n",
        "print('\\nPairwise PERMANOVA Results: ')\n",
        "pairwise_results = pairwise_permanova(dist_matrix, groups, group_labels)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KcERfOI6__DV",
        "outputId": "bcd97fb4-3a64-4f0d-f8e5-1dfb28da3749"
      },
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Pairwise PERMANOVA Results: \n",
            "for groups: Clinical Water\n",
            "method name               PERMANOVA\n",
            "test statistic name        pseudo-F\n",
            "sample size                      29\n",
            "number of groups                  2\n",
            "test statistic             0.307819\n",
            "p-value                       0.524\n",
            "number of permutations          999\n",
            "Name: PERMANOVA results, dtype: object\n",
            "for groups: Clinical Rhizosphere\n",
            "method name               PERMANOVA\n",
            "test statistic name        pseudo-F\n",
            "sample size                      29\n",
            "number of groups                  2\n",
            "test statistic             0.776559\n",
            "p-value                        0.78\n",
            "number of permutations          999\n",
            "Name: PERMANOVA results, dtype: object\n",
            "for groups: Clinical Soil\n",
            "method name               PERMANOVA\n",
            "test statistic name        pseudo-F\n",
            "sample size                      29\n",
            "number of groups                  2\n",
            "test statistic             1.855167\n",
            "p-value                       0.171\n",
            "number of permutations          999\n",
            "Name: PERMANOVA results, dtype: object\n",
            "for groups: Water Rhizosphere\n",
            "method name               PERMANOVA\n",
            "test statistic name        pseudo-F\n",
            "sample size                      30\n",
            "number of groups                  2\n",
            "test statistic             0.302691\n",
            "p-value                       0.901\n",
            "number of permutations          999\n",
            "Name: PERMANOVA results, dtype: object\n",
            "for groups: Water Soil\n",
            "method name               PERMANOVA\n",
            "test statistic name        pseudo-F\n",
            "sample size                      30\n",
            "number of groups                  2\n",
            "test statistic             3.440213\n",
            "p-value                       0.026\n",
            "number of permutations          999\n",
            "Name: PERMANOVA results, dtype: object\n",
            "for groups: Rhizosphere Soil\n",
            "method name               PERMANOVA\n",
            "test statistic name        pseudo-F\n",
            "sample size                      30\n",
            "number of groups                  2\n",
            "test statistic             4.354877\n",
            "p-value                       0.021\n",
            "number of permutations          999\n",
            "Name: PERMANOVA results, dtype: object\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We find that only some group comparisons involving soil strains showed significant differences. However, not all other groupings demonstrated statistical significance.\n",
        "\n",
        "This is why we thought that a further analysis was needed, and we chose Machine Learning approach.\n"
      ],
      "metadata": {
        "id": "IvAbnnpvAT5H"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Supervised learning**"
      ],
      "metadata": {
        "id": "YDvabL4UAePN"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## *XGBoost model*"
      ],
      "metadata": {
        "id": "i9_Cu4o5Ak4M"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        " To see which of the variables is more important in the determination of bioremediation potential, we tried to predict the bioremediation potential of some strains.\n",
        "\n",
        " For this we used the **XGBoost** model.\n",
        " The hyperparameters used were the following:\n",
        " - *n_estimators:*\n",
        " - *early_stopping rounds:*\n",
        " - *learning_rate:*\n",
        "\n",
        "We set a high value of n_estimators, and to avoid overfitting, we also set early_stopping rounds that sill avoid unnecessary iterations. Also, we set a lower learning_rate than usual, in order to make sure we don't have any overfitting.\n",
        "\n",
        "The model will have a lot of iterations to go through, but if the last 5 rounds do not have any improvement (early stopping), it will stop iterating.\n",
        "Besides, the predictions from each component model will add less information each (lower learning rate).\n",
        "\n",
        "We will perform cross validation in the model, as our dataset is quite small and could be prone to overfitting."
      ],
      "metadata": {
        "id": "Sax2XPHAAxkj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "target = 'Bioremediation potential'\n",
        "#Now we identify and separate numerical and categorical columns, as the model can't handle categorical data.\n",
        "categorical_columns = [\"Isolation matrix\", \"Specific isolation matrix\",\n",
        "                   \"More specific\", \"Host\", \"Pollution\", \"Localization\", \"ST Cluster\"]\n",
        "numeric_columns = df.select_dtypes(include=np.number).columns.drop([target, \"ST Cluster\"])"
      ],
      "metadata": {
        "id": "VfZS0WYYAHo6"
      },
      "execution_count": 58,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "We will encode the categorical data using One-Hot Encoding, as our categorical data is not hierarchical."
      ],
      "metadata": {
        "id": "6hWXB4kHC3Y5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_encoded = pd.get_dummies(df, columns=categorical_columns, dtype = 'int')\n",
        "df_encoded.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 292
        },
        "id": "_Dg5xx7YC16t",
        "outputId": "87cb6326-1f71-4fb7-88a9-bb71fad17779"
      },
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            PobA  alkB  amoA  arsC  arsR1  bcsA  bcsB  BenR  bnzA  bphA1  bphA2  bphA3  bphA4  \\\n",
              "strain                                                                                          \n",
              "12917       0     1     1     0     0      1     1     1     0     0      0      0      0       \n",
              "991_21      0     0     1     0     0      0     1     1     0     0      0      0      0       \n",
              "ABACB       0     0     1     0     0      1     0     0     0     0      0      0      0       \n",
              "AHSWHJXPP1  0     1     1     0     0      0     0     1     1     0      0      0      0       \n",
              "B001        0     1     1     0     1      0     0     1     0     0      0      0      0       \n",
              "\n",
              "            bphB  bphC  bphD  chR  copA  copA1  dmpK  dmpL  dmpM  dmpN  dmpO  dmpP  intl1  katA  \\\n",
              "strain                                                                                            \n",
              "12917       0     0     0     0    1     1      0     0     0     0     0     0     1      1      \n",
              "991_21      0     0     0     0    0     1      0     0     0     0     0     0     4      1      \n",
              "ABACB       0     0     0     0    0     1      0     0     0     0     0     0     0      0      \n",
              "AHSWHJXPP1  0     0     0     0    0     1      0     0     0     0     0     0     2      1      \n",
              "B001        0     0     0     0    0     2      0     0     0     1     0     0     0      1      \n",
              "\n",
              "            merA  merB  merR  merT  mrdH  mreA  nahA1  nahA2  nahA3  nahA4  nahB  nahC  nahD  \\\n",
              "strain                                                                                         \n",
              "12917       2     1     2     2     0     0     0      0      0      0      0     0     0      \n",
              "991_21      1     0     1     0     0     0     0      0      0      0      0     0     0      \n",
              "ABACB       0     0     0     0     0     0     0      0      0      0      0     0     0      \n",
              "AHSWHJXPP1  0     0     0     0     0     0     0      0      0      0      0     0     0      \n",
              "B001        0     0     0     0     0     0     0      0      0      0      0     0     0      \n",
              "\n",
              "            nahE  nahF  nirS  norB  oxyR  pcaG  pcaH  pcaR  rubA  sodA  srpA  srpB  srpC  srpR  \\\n",
              "strain                                                                                           \n",
              "12917       0     0     1     0     1     1     1     1     2     1     0     0     0     0      \n",
              "991_21      0     0     0     0     0     1     1     1     0     1     0     0     0     0      \n",
              "ABACB       0     0     0     0     1     1     1     1     0     1     0     0     0     0      \n",
              "AHSWHJXPP1  0     0     1     0     1     1     1     1     0     1     0     0     0     0      \n",
              "B001        0     0     1     0     1     1     1     1     1     1     0     0     0     0      \n",
              "\n",
              "            srpS  tnpA  todA  todB  todC1  todC2  todD  todE  todF  todT  tolA  tolB  tolQ  tolR  \\\n",
              "strain                                                                                             \n",
              "12917       0     3     0     0     0      0      0     0     0     0     1     1     1     1      \n",
              "991_21      0     6     0     0     0      0      0     0     0     0     1     1     2     1      \n",
              "ABACB       0     0     0     0     0      0      0     0     0     0     1     1     1     1      \n",
              "AHSWHJXPP1  0     1     1     1     1      1      1     1     1     1     1     1     1     1      \n",
              "B001        0     0     0     0     0      0      0     0     0     0     1     1     1     1      \n",
              "\n",
              "            ttgA  ttgB  ttgC  ttgD  ttgE  ttgF  ttgG  ttgH  ttgI  xylA  xylB  xylC  xylM  xylN  \\\n",
              "strain                                                                                           \n",
              "12917       1     2     1     0     0     0     0     0     0     0     0     0     0     0      \n",
              "991_21      1     2     1     0     1     0     0     0     0     0     0     0     0     0      \n",
              "ABACB       1     2     1     0     2     0     0     0     0     0     0     0     0     0      \n",
              "AHSWHJXPP1  1     1     1     1     2     0     0     0     0     0     0     0     0     0      \n",
              "B001        1     2     1     0     0     0     0     0     0     0     0     0     0     0      \n",
              "\n",
              "            xylS  xylU  xylW  Bioremediation potential  Isolation matrix_Clinical  \\\n",
              "strain                                                                              \n",
              "12917       0     0     0     35                        1                           \n",
              "991_21      0     0     0     31                        1                           \n",
              "ABACB       0     0     0     18                        0                           \n",
              "AHSWHJXPP1  0     0     0     33                        1                           \n",
              "B001        0     0     0     23                        0                           \n",
              "\n",
              "            Isolation matrix_Rhizosphere  Isolation matrix_Soil  Isolation matrix_Water  \\\n",
              "strain                                                                                    \n",
              "12917       0                             0                      0                        \n",
              "991_21      0                             0                      0                        \n",
              "ABACB       1                             0                      0                        \n",
              "AHSWHJXPP1  0                             0                      0                        \n",
              "B001        0                             1                      0                        \n",
              "\n",
              "            Specific isolation matrix_Agricultural soil  Specific isolation matrix_Basin  \\\n",
              "strain                                                                                     \n",
              "12917       0                                            0                                 \n",
              "991_21      0                                            0                                 \n",
              "ABACB       1                                            0                                 \n",
              "AHSWHJXPP1  0                                            0                                 \n",
              "B001        0                                            0                                 \n",
              "\n",
              "            Specific isolation matrix_Blood  Specific isolation matrix_Branch  \\\n",
              "strain                                                                          \n",
              "12917       0                                0                                  \n",
              "991_21      0                                0                                  \n",
              "ABACB       0                                0                                  \n",
              "AHSWHJXPP1  0                                0                                  \n",
              "B001        0                                0                                  \n",
              "\n",
              "            Specific isolation matrix_Farmland  Specific isolation matrix_Fluid  \\\n",
              "strain                                                                            \n",
              "12917       0                                   0                                 \n",
              "991_21      0                                   0                                 \n",
              "ABACB       0                                   0                                 \n",
              "AHSWHJXPP1  0                                   0                                 \n",
              "B001        0                                   0                                 \n",
              "\n",
              "            Specific isolation matrix_Industrial  Specific isolation matrix_Lake  \\\n",
              "strain                                                                             \n",
              "12917       0                                     0                                \n",
              "991_21      0                                     0                                \n",
              "ABACB       0                                     0                                \n",
              "AHSWHJXPP1  0                                     0                                \n",
              "B001        0                                     0                                \n",
              "\n",
              "            Specific isolation matrix_Rectal  Specific isolation matrix_Respiratory  \\\n",
              "strain                                                                                \n",
              "12917       1                                 0                                       \n",
              "991_21      0                                 1                                       \n",
              "ABACB       0                                 0                                       \n",
              "AHSWHJXPP1  0                                 0                                       \n",
              "B001        0                                 0                                       \n",
              "\n",
              "            Specific isolation matrix_River  Specific isolation matrix_Root  \\\n",
              "strain                                                                        \n",
              "12917       0                                0                                \n",
              "991_21      0                                0                                \n",
              "ABACB       0                                0                                \n",
              "AHSWHJXPP1  0                                0                                \n",
              "B001        0                                0                                \n",
              "\n",
              "            Specific isolation matrix_Sea  Specific isolation matrix_Seed  \\\n",
              "strain                                                                      \n",
              "12917       0                              0                                \n",
              "991_21      0                              0                                \n",
              "ABACB       0                              0                                \n",
              "AHSWHJXPP1  0                              0                                \n",
              "B001        1                              0                                \n",
              "\n",
              "            Specific isolation matrix_Skin  Specific isolation matrix_Soil  \\\n",
              "strain                                                                       \n",
              "12917       0                               0                                \n",
              "991_21      0                               0                                \n",
              "ABACB       0                               0                                \n",
              "AHSWHJXPP1  0                               0                                \n",
              "B001        0                               0                                \n",
              "\n",
              "            Specific isolation matrix_Urine  Specific isolation matrix_Wastewater  \\\n",
              "strain                                                                              \n",
              "12917       0                                0                                      \n",
              "991_21      0                                0                                      \n",
              "ABACB       0                                0                                      \n",
              "AHSWHJXPP1  1                                0                                      \n",
              "B001        0                                0                                      \n",
              "\n",
              "            More specific_Agricultural soil  More specific_Avocado  More specific_Basin  \\\n",
              "strain                                                                                    \n",
              "12917       0                                0                      0                     \n",
              "991_21      0                                0                      0                     \n",
              "ABACB       0                                0                      0                     \n",
              "AHSWHJXPP1  0                                0                      0                     \n",
              "B001        0                                0                      0                     \n",
              "\n",
              "            More specific_Blood  More specific_Coal waste  More specific_Deep sea  \\\n",
              "strain                                                                              \n",
              "12917       0                    0                         0                        \n",
              "991_21      0                    0                         0                        \n",
              "ABACB       0                    0                         0                        \n",
              "AHSWHJXPP1  0                    0                         0                        \n",
              "B001        0                    0                         0                        \n",
              "\n",
              "            More specific_Electronic wastes  More specific_Fluid  More specific_Ginseng  \\\n",
              "strain                                                                                    \n",
              "12917       0                                0                    0                       \n",
              "991_21      0                                0                    0                       \n",
              "ABACB       0                                0                    0                       \n",
              "AHSWHJXPP1  0                                0                    0                       \n",
              "B001        0                                0                    0                       \n",
              "\n",
              "            More specific_Grass  More specific_House  More specific_Industrial  \\\n",
              "strain                                                                           \n",
              "12917       0                    0                    0                          \n",
              "991_21      0                    0                    0                          \n",
              "ABACB       0                    0                    0                          \n",
              "AHSWHJXPP1  0                    0                    0                          \n",
              "B001        0                    0                    0                          \n",
              "\n",
              "            More specific_Infection  More specific_Kiwi  More specific_Lake  More specific_Legume  \\\n",
              "strain                                                                                              \n",
              "12917       0                        0                   0                   0                      \n",
              "991_21      0                        0                   0                   0                      \n",
              "ABACB       0                        1                   0                   0                      \n",
              "AHSWHJXPP1  0                        0                   0                   0                      \n",
              "B001        0                        0                   0                   0                      \n",
              "\n",
              "            More specific_Lesion  More specific_Lung  More specific_No  \\\n",
              "strain                                                                   \n",
              "12917       0                     0                   1                  \n",
              "991_21      0                     0                   0                  \n",
              "ABACB       0                     0                   0                  \n",
              "AHSWHJXPP1  1                     0                   0                  \n",
              "B001        0                     0                   0                  \n",
              "\n",
              "            More specific_Oceanic epipelagic zone  More specific_Ornamental  \\\n",
              "strain                                                                        \n",
              "12917       0                                      0                          \n",
              "991_21      0                                      0                          \n",
              "ABACB       0                                      0                          \n",
              "AHSWHJXPP1  0                                      0                          \n",
              "B001        0                                      0                          \n",
              "\n",
              "            More specific_Papernill  More specific_Pistachio  More specific_Potato  \\\n",
              "strain                                                                               \n",
              "12917       0                        0                        0                      \n",
              "991_21      0                        0                        0                      \n",
              "ABACB       0                        0                        0                      \n",
              "AHSWHJXPP1  0                        0                        0                      \n",
              "B001        0                        0                        0                      \n",
              "\n",
              "            More specific_Pulp mill sludge  More specific_Ranch  More specific_Rectal  \\\n",
              "strain                                                                                  \n",
              "12917       0                               0                    0                      \n",
              "991_21      0                               0                    0                      \n",
              "ABACB       0                               0                    0                      \n",
              "AHSWHJXPP1  0                               0                    0                      \n",
              "B001        0                               0                    0                      \n",
              "\n",
              "            More specific_River  More specific_Sand  More specific_Sediment  \\\n",
              "strain                                                                        \n",
              "12917       0                    0                   0                        \n",
              "991_21      0                    0                   0                        \n",
              "ABACB       0                    0                   0                        \n",
              "AHSWHJXPP1  0                    0                   0                        \n",
              "B001        0                    1                   0                        \n",
              "\n",
              "            More specific_Septic joint  More specific_Sewage sludge  More specific_Soil  \\\n",
              "strain                                                                                    \n",
              "12917       0                           0                            0                    \n",
              "991_21      0                           0                            0                    \n",
              "ABACB       0                           0                            0                    \n",
              "AHSWHJXPP1  0                           0                            0                    \n",
              "B001        0                           0                            0                    \n",
              "\n",
              "            More specific_Tobacco-rice  More specific_Tomato  More specific_Trachea  \\\n",
              "strain                                                                                \n",
              "12917       0                           0                     0                       \n",
              "991_21      0                           0                     1                       \n",
              "ABACB       0                           0                     0                       \n",
              "AHSWHJXPP1  0                           0                     0                       \n",
              "B001        0                           0                     0                       \n",
              "\n",
              "            More specific_Vineyard  More specific_Walnuts  More specific_Wastewater  \\\n",
              "strain                                                                                \n",
              "12917       0                       0                      0                          \n",
              "991_21      0                       0                      0                          \n",
              "ABACB       0                       0                      0                          \n",
              "AHSWHJXPP1  0                       0                      0                          \n",
              "B001        0                       0                      0                          \n",
              "\n",
              "            More specific_Wheat  More specific_Willow  Host_Human  Host_No  Host_Plant  \\\n",
              "strain                                                                                   \n",
              "12917       0                    0                     1           0        0            \n",
              "991_21      0                    0                     1           0        0            \n",
              "ABACB       0                    0                     0           0        1            \n",
              "AHSWHJXPP1  0                    0                     1           0        0            \n",
              "B001        0                    0                     0           1        0            \n",
              "\n",
              "            Pollution_BPA  Pollution_Biphenyl  Pollution_Cadmium  Pollution_Diphenylamine  \\\n",
              "strain                                                                                      \n",
              "12917       0              0                   0                  0                         \n",
              "991_21      0              0                   0                  0                         \n",
              "ABACB       0              0                   0                  0                         \n",
              "AHSWHJXPP1  0              0                   0                  0                         \n",
              "B001        0              0                   0                  0                         \n",
              "\n",
              "            Pollution_Estrogen  Pollution_Heavy metal  Pollution_Industrial  Pollution_Lignine  \\\n",
              "strain                                                                                           \n",
              "12917       0                   0                      0                     0                   \n",
              "991_21      0                   0                      0                     0                   \n",
              "ABACB       0                   0                      0                     0                   \n",
              "AHSWHJXPP1  0                   0                      0                     0                   \n",
              "B001        0                   0                      0                     0                   \n",
              "\n",
              "            Pollution_Mercury  Pollution_Methyl parathion  Pollution_No  Pollution_Oil  \\\n",
              "strain                                                                                   \n",
              "12917       0                  0                           1             0               \n",
              "991_21      0                  0                           1             0               \n",
              "ABACB       0                  0                           1             0               \n",
              "AHSWHJXPP1  0                  0                           1             0               \n",
              "B001        0                  0                           1             0               \n",
              "\n",
              "            Pollution_PAH  Pollution_PCB  Pollution_Quinclorac  Pollution_Waste  \\\n",
              "strain                                                                            \n",
              "12917       0              0              0                     0                 \n",
              "991_21      0              0              0                     0                 \n",
              "ABACB       0              0              0                     0                 \n",
              "AHSWHJXPP1  0              0              0                     0                 \n",
              "B001        0              0              0                     0                 \n",
              "\n",
              "            Localization_Belgium  Localization_Brazil  Localization_Canada  Localization_Chile  \\\n",
              "strain                                                                                           \n",
              "12917       0                     0                    0                    0                    \n",
              "991_21      0                     1                    0                    0                    \n",
              "ABACB       0                     0                    0                    0                    \n",
              "AHSWHJXPP1  0                     0                    0                    0                    \n",
              "B001        0                     0                    0                    0                    \n",
              "\n",
              "            Localization_China  Localization_Czech Republic  Localization_Finland  \\\n",
              "strain                                                                              \n",
              "12917       0                   0                            0                      \n",
              "991_21      0                   0                            0                      \n",
              "ABACB       0                   0                            0                      \n",
              "AHSWHJXPP1  1                   0                            0                      \n",
              "B001        0                   0                            0                      \n",
              "\n",
              "            Localization_France  Localization_Greece  Localization_India  Localization_Japan  \\\n",
              "strain                                                                                         \n",
              "12917       1                    0                    0                   0                    \n",
              "991_21      0                    0                    0                   0                    \n",
              "ABACB       0                    0                    0                   0                    \n",
              "AHSWHJXPP1  0                    0                    0                   0                    \n",
              "B001        0                    0                    0                   0                    \n",
              "\n",
              "            Localization_Mozambique  Localization_Netherlands  Localization_New zeland  \\\n",
              "strain                                                                                   \n",
              "12917       0                        0                         0                         \n",
              "991_21      0                        0                         0                         \n",
              "ABACB       0                        0                         1                         \n",
              "AHSWHJXPP1  0                        0                         0                         \n",
              "B001        0                        0                         0                         \n",
              "\n",
              "            Localization_Pacific ocean  Localization_Poland  Localization_Russia  \\\n",
              "strain                                                                             \n",
              "12917       0                           0                    0                     \n",
              "991_21      0                           0                    0                     \n",
              "ABACB       0                           0                    0                     \n",
              "AHSWHJXPP1  0                           0                    0                     \n",
              "B001        0                           0                    0                     \n",
              "\n",
              "            Localization_South Korea  Localization_Spain  Localization_Thailand  Localization_USA  \\\n",
              "strain                                                                                              \n",
              "12917       0                         0                   0                      0                  \n",
              "991_21      0                         0                   0                      0                  \n",
              "ABACB       0                         0                   0                      0                  \n",
              "AHSWHJXPP1  0                         0                   0                      0                  \n",
              "B001        1                         0                   0                      0                  \n",
              "\n",
              "            Localization_USa  Localization_Ukraine  ST Cluster_1  ST Cluster_2  ST Cluster_3  \\\n",
              "strain                                                                                         \n",
              "12917       0                 0                     0             0             0              \n",
              "991_21      0                 0                     0             0             0              \n",
              "ABACB       0                 0                     0             0             0              \n",
              "AHSWHJXPP1  0                 0                     0             0             0              \n",
              "B001        0                 0                     0             0             0              \n",
              "\n",
              "            ST Cluster_4  ST Cluster_5  ST Cluster_6  ST Cluster_7  ST Cluster_8  ST Cluster_9  \\\n",
              "strain                                                                                           \n",
              "12917       0             0             0             0             0             0              \n",
              "991_21      0             0             0             1             0             0              \n",
              "ABACB       0             0             0             0             0             0              \n",
              "AHSWHJXPP1  0             1             0             0             0             0              \n",
              "B001        0             0             0             0             0             0              \n",
              "\n",
              "            ST Cluster_10  ST Cluster_11  ST Cluster_12  ST Cluster_13  ST Cluster_14  \\\n",
              "strain                                                                                  \n",
              "12917       0              0              0              0              0               \n",
              "991_21      0              0              0              0              0               \n",
              "ABACB       0              0              0              0              0               \n",
              "AHSWHJXPP1  0              0              0              0              0               \n",
              "B001        0              0              1              0              0               \n",
              "\n",
              "            ST Cluster_15  ST Cluster_16  ST Cluster_17  ST Cluster_18  ST Cluster_19  \n",
              "strain                                                                                 \n",
              "12917       1              0              0              0              0              \n",
              "991_21      0              0              0              0              0              \n",
              "ABACB       0              0              0              0              1              \n",
              "AHSWHJXPP1  0              0              0              0              0              \n",
              "B001        0              0              0              0              0              "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-378e5d9c-d84e-44fa-b53d-3fa93c56f144\" class=\"colab-df-container\">\n",
              "    <div>\n",
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              "  <thead>\n",
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              "      <th></th>\n",
              "      <th>PobA</th>\n",
              "      <th>alkB</th>\n",
              "      <th>amoA</th>\n",
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              "      <th>arsR1</th>\n",
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              "      <th>bphA2</th>\n",
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              "      <th>bphB</th>\n",
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              "      <th>bphD</th>\n",
              "      <th>chR</th>\n",
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              "      <th>copA1</th>\n",
              "      <th>dmpK</th>\n",
              "      <th>dmpL</th>\n",
              "      <th>dmpM</th>\n",
              "      <th>dmpN</th>\n",
              "      <th>dmpO</th>\n",
              "      <th>dmpP</th>\n",
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              "      <th>merB</th>\n",
              "      <th>merR</th>\n",
              "      <th>merT</th>\n",
              "      <th>mrdH</th>\n",
              "      <th>mreA</th>\n",
              "      <th>nahA1</th>\n",
              "      <th>nahA2</th>\n",
              "      <th>nahA3</th>\n",
              "      <th>nahA4</th>\n",
              "      <th>nahB</th>\n",
              "      <th>nahC</th>\n",
              "      <th>nahD</th>\n",
              "      <th>nahE</th>\n",
              "      <th>nahF</th>\n",
              "      <th>nirS</th>\n",
              "      <th>norB</th>\n",
              "      <th>oxyR</th>\n",
              "      <th>pcaG</th>\n",
              "      <th>pcaH</th>\n",
              "      <th>pcaR</th>\n",
              "      <th>rubA</th>\n",
              "      <th>sodA</th>\n",
              "      <th>srpA</th>\n",
              "      <th>srpB</th>\n",
              "      <th>srpC</th>\n",
              "      <th>srpR</th>\n",
              "      <th>srpS</th>\n",
              "      <th>tnpA</th>\n",
              "      <th>todA</th>\n",
              "      <th>todB</th>\n",
              "      <th>todC1</th>\n",
              "      <th>todC2</th>\n",
              "      <th>todD</th>\n",
              "      <th>todE</th>\n",
              "      <th>todF</th>\n",
              "      <th>todT</th>\n",
              "      <th>tolA</th>\n",
              "      <th>tolB</th>\n",
              "      <th>tolQ</th>\n",
              "      <th>tolR</th>\n",
              "      <th>ttgA</th>\n",
              "      <th>ttgB</th>\n",
              "      <th>ttgC</th>\n",
              "      <th>ttgD</th>\n",
              "      <th>ttgE</th>\n",
              "      <th>ttgF</th>\n",
              "      <th>ttgG</th>\n",
              "      <th>ttgH</th>\n",
              "      <th>ttgI</th>\n",
              "      <th>xylA</th>\n",
              "      <th>xylB</th>\n",
              "      <th>xylC</th>\n",
              "      <th>xylM</th>\n",
              "      <th>xylN</th>\n",
              "      <th>xylS</th>\n",
              "      <th>xylU</th>\n",
              "      <th>xylW</th>\n",
              "      <th>Bioremediation potential</th>\n",
              "      <th>Isolation matrix_Clinical</th>\n",
              "      <th>Isolation matrix_Rhizosphere</th>\n",
              "      <th>Isolation matrix_Soil</th>\n",
              "      <th>Isolation matrix_Water</th>\n",
              "      <th>Specific isolation matrix_Agricultural soil</th>\n",
              "      <th>Specific isolation matrix_Basin</th>\n",
              "      <th>Specific isolation matrix_Blood</th>\n",
              "      <th>Specific isolation matrix_Branch</th>\n",
              "      <th>Specific isolation matrix_Farmland</th>\n",
              "      <th>Specific isolation matrix_Fluid</th>\n",
              "      <th>Specific isolation matrix_Industrial</th>\n",
              "      <th>Specific isolation matrix_Lake</th>\n",
              "      <th>Specific isolation matrix_Rectal</th>\n",
              "      <th>Specific isolation matrix_Respiratory</th>\n",
              "      <th>Specific isolation matrix_River</th>\n",
              "      <th>Specific isolation matrix_Root</th>\n",
              "      <th>Specific isolation matrix_Sea</th>\n",
              "      <th>Specific isolation matrix_Seed</th>\n",
              "      <th>Specific isolation matrix_Skin</th>\n",
              "      <th>Specific isolation matrix_Soil</th>\n",
              "      <th>Specific isolation matrix_Urine</th>\n",
              "      <th>Specific isolation matrix_Wastewater</th>\n",
              "      <th>More specific_Agricultural soil</th>\n",
              "      <th>More specific_Avocado</th>\n",
              "      <th>More specific_Basin</th>\n",
              "      <th>More specific_Blood</th>\n",
              "      <th>More specific_Coal waste</th>\n",
              "      <th>More specific_Deep sea</th>\n",
              "      <th>More specific_Electronic wastes</th>\n",
              "      <th>More specific_Fluid</th>\n",
              "      <th>More specific_Ginseng</th>\n",
              "      <th>More specific_Grass</th>\n",
              "      <th>More specific_House</th>\n",
              "      <th>More specific_Industrial</th>\n",
              "      <th>More specific_Infection</th>\n",
              "      <th>More specific_Kiwi</th>\n",
              "      <th>More specific_Lake</th>\n",
              "      <th>More specific_Legume</th>\n",
              "      <th>More specific_Lesion</th>\n",
              "      <th>More specific_Lung</th>\n",
              "      <th>More specific_No</th>\n",
              "      <th>More specific_Oceanic epipelagic zone</th>\n",
              "      <th>More specific_Ornamental</th>\n",
              "      <th>More specific_Papernill</th>\n",
              "      <th>More specific_Pistachio</th>\n",
              "      <th>More specific_Potato</th>\n",
              "      <th>More specific_Pulp mill sludge</th>\n",
              "      <th>More specific_Ranch</th>\n",
              "      <th>More specific_Rectal</th>\n",
              "      <th>More specific_River</th>\n",
              "      <th>More specific_Sand</th>\n",
              "      <th>More specific_Sediment</th>\n",
              "      <th>More specific_Septic joint</th>\n",
              "      <th>More specific_Sewage sludge</th>\n",
              "      <th>More specific_Soil</th>\n",
              "      <th>More specific_Tobacco-rice</th>\n",
              "      <th>More specific_Tomato</th>\n",
              "      <th>More specific_Trachea</th>\n",
              "      <th>More specific_Vineyard</th>\n",
              "      <th>More specific_Walnuts</th>\n",
              "      <th>More specific_Wastewater</th>\n",
              "      <th>More specific_Wheat</th>\n",
              "      <th>More specific_Willow</th>\n",
              "      <th>Host_Human</th>\n",
              "      <th>Host_No</th>\n",
              "      <th>Host_Plant</th>\n",
              "      <th>Pollution_BPA</th>\n",
              "      <th>Pollution_Biphenyl</th>\n",
              "      <th>Pollution_Cadmium</th>\n",
              "      <th>Pollution_Diphenylamine</th>\n",
              "      <th>Pollution_Estrogen</th>\n",
              "      <th>Pollution_Heavy metal</th>\n",
              "      <th>Pollution_Industrial</th>\n",
              "      <th>Pollution_Lignine</th>\n",
              "      <th>Pollution_Mercury</th>\n",
              "      <th>Pollution_Methyl parathion</th>\n",
              "      <th>Pollution_No</th>\n",
              "      <th>Pollution_Oil</th>\n",
              "      <th>Pollution_PAH</th>\n",
              "      <th>Pollution_PCB</th>\n",
              "      <th>Pollution_Quinclorac</th>\n",
              "      <th>Pollution_Waste</th>\n",
              "      <th>Localization_Belgium</th>\n",
              "      <th>Localization_Brazil</th>\n",
              "      <th>Localization_Canada</th>\n",
              "      <th>Localization_Chile</th>\n",
              "      <th>Localization_China</th>\n",
              "      <th>Localization_Czech Republic</th>\n",
              "      <th>Localization_Finland</th>\n",
              "      <th>Localization_France</th>\n",
              "      <th>Localization_Greece</th>\n",
              "      <th>Localization_India</th>\n",
              "      <th>Localization_Japan</th>\n",
              "      <th>Localization_Mozambique</th>\n",
              "      <th>Localization_Netherlands</th>\n",
              "      <th>Localization_New zeland</th>\n",
              "      <th>Localization_Pacific ocean</th>\n",
              "      <th>Localization_Poland</th>\n",
              "      <th>Localization_Russia</th>\n",
              "      <th>Localization_South Korea</th>\n",
              "      <th>Localization_Spain</th>\n",
              "      <th>Localization_Thailand</th>\n",
              "      <th>Localization_USA</th>\n",
              "      <th>Localization_USa</th>\n",
              "      <th>Localization_Ukraine</th>\n",
              "      <th>ST Cluster_1</th>\n",
              "      <th>ST Cluster_2</th>\n",
              "      <th>ST Cluster_3</th>\n",
              "      <th>ST Cluster_4</th>\n",
              "      <th>ST Cluster_5</th>\n",
              "      <th>ST Cluster_6</th>\n",
              "      <th>ST Cluster_7</th>\n",
              "      <th>ST Cluster_8</th>\n",
              "      <th>ST Cluster_9</th>\n",
              "      <th>ST Cluster_10</th>\n",
              "      <th>ST Cluster_11</th>\n",
              "      <th>ST Cluster_12</th>\n",
              "      <th>ST Cluster_13</th>\n",
              "      <th>ST Cluster_14</th>\n",
              "      <th>ST Cluster_15</th>\n",
              "      <th>ST Cluster_16</th>\n",
              "      <th>ST Cluster_17</th>\n",
              "      <th>ST Cluster_18</th>\n",
              "      <th>ST Cluster_19</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>strain</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
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              "      <th></th>\n",
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              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
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              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
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              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
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              "      <th></th>\n",
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              "      <th></th>\n",
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              "      <th></th>\n",
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              "      <th></th>\n",
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              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>12917</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>35</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>991_21</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>31</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ABACB</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>18</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>AHSWHJXPP1</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>33</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>B001</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>23</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-378e5d9c-d84e-44fa-b53d-3fa93c56f144')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-378e5d9c-d84e-44fa-b53d-3fa93c56f144 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-378e5d9c-d84e-44fa-b53d-3fa93c56f144');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-8cbf4187-8d7a-445c-a764-d044d0886b5a\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-8cbf4187-8d7a-445c-a764-d044d0886b5a')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-8cbf4187-8d7a-445c-a764-d044d0886b5a button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df_encoded"
            }
          },
          "metadata": {},
          "execution_count": 67
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#We will now select the features\n",
        "X = df_encoded.drop(columns=[target])\n",
        "y = df_encoded[target]"
      ],
      "metadata": {
        "id": "mRPTEm53DKAv"
      },
      "execution_count": 63,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#We will now divide the features and the target into test and traininig sets\n",
        "X_train_xgb, X_test_xgb, y_train_xgb, y_test_xgb = train_test_split(X, y, test_size=0.3, random_state=42)"
      ],
      "metadata": {
        "id": "QbO5mAf5DN7S"
      },
      "execution_count": 65,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#we will now define and run the model\n",
        "xgb_model = xgb.XGBRegressor(n_estimators = 1000, learning_rate = 0.85, eval_metric=mean_absolute_error, early_stopping_rounds=5)\n",
        "xgb_model.fit(X_train_xgb, y_train_xgb, eval_set=[(X_test_xgb, y_test_xgb)], verbose=False)\n",
        "y_pred_xgb = xgb_model.predict(X_test_xgb)\n",
        "from sklearn.metrics import mean_absolute_error\n",
        "print(\"Mean Absolute Error : \" + str(mean_absolute_error(y_pred_xgb, y_test_xgb)))\n",
        "print(\"Mean Squared Error : \" + str(mean_squared_error(y_pred_xgb, y_test_xgb)))\n",
        "print(\"Mean Absolute Percentage Error : \" + str(metrics.mean_absolute_percentage_error(y_pred_xgb, y_test_xgb)))\n",
        "print(\"R2 Score : \" + str(metrics.r2_score(y_pred_xgb, y_test_xgb)))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1S_Ra2NVOigt",
        "outputId": "a7f7395d-d430-45b3-949a-bd018b83838f"
      },
      "execution_count": 98,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean Absolute Error : 2.8893725872039795\n",
            "Mean Squared Error : 12.58305549621582\n",
            "Mean Absolute Percentage Error : 0.117146797478199\n",
            "R2 Score : 0.8021976947784424\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        "The comparison of these results with the results of an XGBoost model with cross validation could be interesting, and it is being revised at the moment."
      ],
      "metadata": {
        "id": "jZaHd0HhOujc"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## *SHAP analysis*"
      ],
      "metadata": {
        "id": "HbuZY3duO7Pf"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "After we have the model prepared, we can see which are the most important features in the dataset that better explain the bioremediation potential of the strains.\n",
        "\n",
        "For this, we will perform a **SHAP analysis**, that will explain the importance of each feature and how the values of each variable affect the bioremediation potential."
      ],
      "metadata": {
        "id": "5M6FuaZ_PA45"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "explainer = shap.TreeExplainer(xgb_model)\n",
        "shap_values = explainer.shap_values(X)\n",
        "shap.summary_plot(shap_values, X)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 957
        },
        "id": "z4tQDzmoHrKC",
        "outputId": "8afa1a89-5050-4281-a50f-2d732aba04cf"
      },
      "execution_count": 90,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x950 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We can see in the graph that the most important variables are not the source environments of the strains but different genes. merR, tnpA, bcsB... we can see that the presence of these genes is indicative of a higher bioremediation potential.\n",
        "\n",
        "This results demonstrate that the genomic component of P putida is relevant in the determination of bioremediation potential, and that these markers can be useful in the search of potentially useful strains for bioremediation.\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "M_mNbI5NPdGN"
      }
    }
  ]
}