Cancer.ipynb

{
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  "metadata": {
    "colab": {
      "name": "Cancer.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "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/riskydav/Belajar-Python/blob/main/Cancer.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RPOBtvpUb1Kt",
        "outputId": "2fa9a328-4921-4292-f5ab-15bff9abb852"
      },
      "source": [
        "!git clone https://github.com/Kemi41/Dataset.git"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cloning into 'Dataset'...\n",
            "remote: Enumerating objects: 37, done.\u001b[K\n",
            "remote: Counting objects: 100% (37/37), done.\u001b[K\n",
            "remote: Compressing objects: 100% (37/37), done.\u001b[K\n",
            "remote: Total 37 (delta 9), reused 0 (delta 0), pack-reused 0\u001b[K\n",
            "Unpacking objects: 100% (37/37), done.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DNI7wR73cByz"
      },
      "source": [
        "data = '/content/Dataset/Breast Cancer.csv'"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-wSb58eycE73"
      },
      "source": [
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "from sklearn.preprocessing import LabelEncoder, OneHotEncoder\n",
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hpdEs6WucHjr"
      },
      "source": [
        "df =pd.read_csv(data)"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Hn24DP5-cL-W",
        "outputId": "400924df-3527-4d1c-876c-986890f88d32"
      },
      "source": [
        "df.info()"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 569 entries, 0 to 568\n",
            "Data columns (total 33 columns):\n",
            " #   Column                   Non-Null Count  Dtype  \n",
            "---  ------                   --------------  -----  \n",
            " 0   id                       569 non-null    int64  \n",
            " 1   diagnosis                569 non-null    object \n",
            " 2   radius_mean              569 non-null    float64\n",
            " 3   texture_mean             569 non-null    float64\n",
            " 4   perimeter_mean           569 non-null    float64\n",
            " 5   area_mean                569 non-null    float64\n",
            " 6   smoothness_mean          569 non-null    float64\n",
            " 7   compactness_mean         569 non-null    float64\n",
            " 8   concavity_mean           569 non-null    float64\n",
            " 9   concave points_mean      569 non-null    float64\n",
            " 10  symmetry_mean            569 non-null    float64\n",
            " 11  fractal_dimension_mean   569 non-null    float64\n",
            " 12  radius_se                569 non-null    float64\n",
            " 13  texture_se               569 non-null    float64\n",
            " 14  perimeter_se             569 non-null    float64\n",
            " 15  area_se                  569 non-null    float64\n",
            " 16  smoothness_se            569 non-null    float64\n",
            " 17  compactness_se           569 non-null    float64\n",
            " 18  concavity_se             569 non-null    float64\n",
            " 19  concave points_se        569 non-null    float64\n",
            " 20  symmetry_se              569 non-null    float64\n",
            " 21  fractal_dimension_se     569 non-null    float64\n",
            " 22  radius_worst             569 non-null    float64\n",
            " 23  texture_worst            569 non-null    float64\n",
            " 24  perimeter_worst          569 non-null    float64\n",
            " 25  area_worst               569 non-null    float64\n",
            " 26  smoothness_worst         569 non-null    float64\n",
            " 27  compactness_worst        569 non-null    float64\n",
            " 28  concavity_worst          569 non-null    float64\n",
            " 29  concave points_worst     569 non-null    float64\n",
            " 30  symmetry_worst           569 non-null    float64\n",
            " 31  fractal_dimension_worst  569 non-null    float64\n",
            " 32  Unnamed: 32              0 non-null      float64\n",
            "dtypes: float64(31), int64(1), object(1)\n",
            "memory usage: 146.8+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PpGEhDsucmbh"
      },
      "source": [
        "df = df.iloc[:,1:-1]"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-twOgCgovTWg",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 454
        },
        "outputId": "20efeef5-efa3-445f-db10-85ad30a36986"
      },
      "source": [
        "df"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>diagnosis</th>\n",
              "      <th>radius_mean</th>\n",
              "      <th>texture_mean</th>\n",
              "      <th>perimeter_mean</th>\n",
              "      <th>area_mean</th>\n",
              "      <th>smoothness_mean</th>\n",
              "      <th>compactness_mean</th>\n",
              "      <th>concavity_mean</th>\n",
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              "      <th>symmetry_mean</th>\n",
              "      <th>fractal_dimension_mean</th>\n",
              "      <th>radius_se</th>\n",
              "      <th>texture_se</th>\n",
              "      <th>perimeter_se</th>\n",
              "      <th>area_se</th>\n",
              "      <th>smoothness_se</th>\n",
              "      <th>compactness_se</th>\n",
              "      <th>concavity_se</th>\n",
              "      <th>concave points_se</th>\n",
              "      <th>symmetry_se</th>\n",
              "      <th>fractal_dimension_se</th>\n",
              "      <th>radius_worst</th>\n",
              "      <th>texture_worst</th>\n",
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              "      <th>smoothness_worst</th>\n",
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              "      <th>symmetry_worst</th>\n",
              "      <th>fractal_dimension_worst</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>M</td>\n",
              "      <td>17.99</td>\n",
              "      <td>10.38</td>\n",
              "      <td>122.80</td>\n",
              "      <td>1001.0</td>\n",
              "      <td>0.11840</td>\n",
              "      <td>0.27760</td>\n",
              "      <td>0.30010</td>\n",
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              "      <td>1.0950</td>\n",
              "      <td>0.9053</td>\n",
              "      <td>8.589</td>\n",
              "      <td>153.40</td>\n",
              "      <td>0.006399</td>\n",
              "      <td>0.04904</td>\n",
              "      <td>0.05373</td>\n",
              "      <td>0.01587</td>\n",
              "      <td>0.03003</td>\n",
              "      <td>0.006193</td>\n",
              "      <td>25.380</td>\n",
              "      <td>17.33</td>\n",
              "      <td>184.60</td>\n",
              "      <td>2019.0</td>\n",
              "      <td>0.16220</td>\n",
              "      <td>0.66560</td>\n",
              "      <td>0.7119</td>\n",
              "      <td>0.2654</td>\n",
              "      <td>0.4601</td>\n",
              "      <td>0.11890</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>M</td>\n",
              "      <td>20.57</td>\n",
              "      <td>17.77</td>\n",
              "      <td>132.90</td>\n",
              "      <td>1326.0</td>\n",
              "      <td>0.08474</td>\n",
              "      <td>0.07864</td>\n",
              "      <td>0.08690</td>\n",
              "      <td>0.07017</td>\n",
              "      <td>0.1812</td>\n",
              "      <td>0.05667</td>\n",
              "      <td>0.5435</td>\n",
              "      <td>0.7339</td>\n",
              "      <td>3.398</td>\n",
              "      <td>74.08</td>\n",
              "      <td>0.005225</td>\n",
              "      <td>0.01308</td>\n",
              "      <td>0.01860</td>\n",
              "      <td>0.01340</td>\n",
              "      <td>0.01389</td>\n",
              "      <td>0.003532</td>\n",
              "      <td>24.990</td>\n",
              "      <td>23.41</td>\n",
              "      <td>158.80</td>\n",
              "      <td>1956.0</td>\n",
              "      <td>0.12380</td>\n",
              "      <td>0.18660</td>\n",
              "      <td>0.2416</td>\n",
              "      <td>0.1860</td>\n",
              "      <td>0.2750</td>\n",
              "      <td>0.08902</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>M</td>\n",
              "      <td>19.69</td>\n",
              "      <td>21.25</td>\n",
              "      <td>130.00</td>\n",
              "      <td>1203.0</td>\n",
              "      <td>0.10960</td>\n",
              "      <td>0.15990</td>\n",
              "      <td>0.19740</td>\n",
              "      <td>0.12790</td>\n",
              "      <td>0.2069</td>\n",
              "      <td>0.05999</td>\n",
              "      <td>0.7456</td>\n",
              "      <td>0.7869</td>\n",
              "      <td>4.585</td>\n",
              "      <td>94.03</td>\n",
              "      <td>0.006150</td>\n",
              "      <td>0.04006</td>\n",
              "      <td>0.03832</td>\n",
              "      <td>0.02058</td>\n",
              "      <td>0.02250</td>\n",
              "      <td>0.004571</td>\n",
              "      <td>23.570</td>\n",
              "      <td>25.53</td>\n",
              "      <td>152.50</td>\n",
              "      <td>1709.0</td>\n",
              "      <td>0.14440</td>\n",
              "      <td>0.42450</td>\n",
              "      <td>0.4504</td>\n",
              "      <td>0.2430</td>\n",
              "      <td>0.3613</td>\n",
              "      <td>0.08758</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>M</td>\n",
              "      <td>11.42</td>\n",
              "      <td>20.38</td>\n",
              "      <td>77.58</td>\n",
              "      <td>386.1</td>\n",
              "      <td>0.14250</td>\n",
              "      <td>0.28390</td>\n",
              "      <td>0.24140</td>\n",
              "      <td>0.10520</td>\n",
              "      <td>0.2597</td>\n",
              "      <td>0.09744</td>\n",
              "      <td>0.4956</td>\n",
              "      <td>1.1560</td>\n",
              "      <td>3.445</td>\n",
              "      <td>27.23</td>\n",
              "      <td>0.009110</td>\n",
              "      <td>0.07458</td>\n",
              "      <td>0.05661</td>\n",
              "      <td>0.01867</td>\n",
              "      <td>0.05963</td>\n",
              "      <td>0.009208</td>\n",
              "      <td>14.910</td>\n",
              "      <td>26.50</td>\n",
              "      <td>98.87</td>\n",
              "      <td>567.7</td>\n",
              "      <td>0.20980</td>\n",
              "      <td>0.86630</td>\n",
              "      <td>0.6869</td>\n",
              "      <td>0.2575</td>\n",
              "      <td>0.6638</td>\n",
              "      <td>0.17300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>M</td>\n",
              "      <td>20.29</td>\n",
              "      <td>14.34</td>\n",
              "      <td>135.10</td>\n",
              "      <td>1297.0</td>\n",
              "      <td>0.10030</td>\n",
              "      <td>0.13280</td>\n",
              "      <td>0.19800</td>\n",
              "      <td>0.10430</td>\n",
              "      <td>0.1809</td>\n",
              "      <td>0.05883</td>\n",
              "      <td>0.7572</td>\n",
              "      <td>0.7813</td>\n",
              "      <td>5.438</td>\n",
              "      <td>94.44</td>\n",
              "      <td>0.011490</td>\n",
              "      <td>0.02461</td>\n",
              "      <td>0.05688</td>\n",
              "      <td>0.01885</td>\n",
              "      <td>0.01756</td>\n",
              "      <td>0.005115</td>\n",
              "      <td>22.540</td>\n",
              "      <td>16.67</td>\n",
              "      <td>152.20</td>\n",
              "      <td>1575.0</td>\n",
              "      <td>0.13740</td>\n",
              "      <td>0.20500</td>\n",
              "      <td>0.4000</td>\n",
              "      <td>0.1625</td>\n",
              "      <td>0.2364</td>\n",
              "      <td>0.07678</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>564</th>\n",
              "      <td>M</td>\n",
              "      <td>21.56</td>\n",
              "      <td>22.39</td>\n",
              "      <td>142.00</td>\n",
              "      <td>1479.0</td>\n",
              "      <td>0.11100</td>\n",
              "      <td>0.11590</td>\n",
              "      <td>0.24390</td>\n",
              "      <td>0.13890</td>\n",
              "      <td>0.1726</td>\n",
              "      <td>0.05623</td>\n",
              "      <td>1.1760</td>\n",
              "      <td>1.2560</td>\n",
              "      <td>7.673</td>\n",
              "      <td>158.70</td>\n",
              "      <td>0.010300</td>\n",
              "      <td>0.02891</td>\n",
              "      <td>0.05198</td>\n",
              "      <td>0.02454</td>\n",
              "      <td>0.01114</td>\n",
              "      <td>0.004239</td>\n",
              "      <td>25.450</td>\n",
              "      <td>26.40</td>\n",
              "      <td>166.10</td>\n",
              "      <td>2027.0</td>\n",
              "      <td>0.14100</td>\n",
              "      <td>0.21130</td>\n",
              "      <td>0.4107</td>\n",
              "      <td>0.2216</td>\n",
              "      <td>0.2060</td>\n",
              "      <td>0.07115</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>565</th>\n",
              "      <td>M</td>\n",
              "      <td>20.13</td>\n",
              "      <td>28.25</td>\n",
              "      <td>131.20</td>\n",
              "      <td>1261.0</td>\n",
              "      <td>0.09780</td>\n",
              "      <td>0.10340</td>\n",
              "      <td>0.14400</td>\n",
              "      <td>0.09791</td>\n",
              "      <td>0.1752</td>\n",
              "      <td>0.05533</td>\n",
              "      <td>0.7655</td>\n",
              "      <td>2.4630</td>\n",
              "      <td>5.203</td>\n",
              "      <td>99.04</td>\n",
              "      <td>0.005769</td>\n",
              "      <td>0.02423</td>\n",
              "      <td>0.03950</td>\n",
              "      <td>0.01678</td>\n",
              "      <td>0.01898</td>\n",
              "      <td>0.002498</td>\n",
              "      <td>23.690</td>\n",
              "      <td>38.25</td>\n",
              "      <td>155.00</td>\n",
              "      <td>1731.0</td>\n",
              "      <td>0.11660</td>\n",
              "      <td>0.19220</td>\n",
              "      <td>0.3215</td>\n",
              "      <td>0.1628</td>\n",
              "      <td>0.2572</td>\n",
              "      <td>0.06637</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>566</th>\n",
              "      <td>M</td>\n",
              "      <td>16.60</td>\n",
              "      <td>28.08</td>\n",
              "      <td>108.30</td>\n",
              "      <td>858.1</td>\n",
              "      <td>0.08455</td>\n",
              "      <td>0.10230</td>\n",
              "      <td>0.09251</td>\n",
              "      <td>0.05302</td>\n",
              "      <td>0.1590</td>\n",
              "      <td>0.05648</td>\n",
              "      <td>0.4564</td>\n",
              "      <td>1.0750</td>\n",
              "      <td>3.425</td>\n",
              "      <td>48.55</td>\n",
              "      <td>0.005903</td>\n",
              "      <td>0.03731</td>\n",
              "      <td>0.04730</td>\n",
              "      <td>0.01557</td>\n",
              "      <td>0.01318</td>\n",
              "      <td>0.003892</td>\n",
              "      <td>18.980</td>\n",
              "      <td>34.12</td>\n",
              "      <td>126.70</td>\n",
              "      <td>1124.0</td>\n",
              "      <td>0.11390</td>\n",
              "      <td>0.30940</td>\n",
              "      <td>0.3403</td>\n",
              "      <td>0.1418</td>\n",
              "      <td>0.2218</td>\n",
              "      <td>0.07820</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>567</th>\n",
              "      <td>M</td>\n",
              "      <td>20.60</td>\n",
              "      <td>29.33</td>\n",
              "      <td>140.10</td>\n",
              "      <td>1265.0</td>\n",
              "      <td>0.11780</td>\n",
              "      <td>0.27700</td>\n",
              "      <td>0.35140</td>\n",
              "      <td>0.15200</td>\n",
              "      <td>0.2397</td>\n",
              "      <td>0.07016</td>\n",
              "      <td>0.7260</td>\n",
              "      <td>1.5950</td>\n",
              "      <td>5.772</td>\n",
              "      <td>86.22</td>\n",
              "      <td>0.006522</td>\n",
              "      <td>0.06158</td>\n",
              "      <td>0.07117</td>\n",
              "      <td>0.01664</td>\n",
              "      <td>0.02324</td>\n",
              "      <td>0.006185</td>\n",
              "      <td>25.740</td>\n",
              "      <td>39.42</td>\n",
              "      <td>184.60</td>\n",
              "      <td>1821.0</td>\n",
              "      <td>0.16500</td>\n",
              "      <td>0.86810</td>\n",
              "      <td>0.9387</td>\n",
              "      <td>0.2650</td>\n",
              "      <td>0.4087</td>\n",
              "      <td>0.12400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>568</th>\n",
              "      <td>B</td>\n",
              "      <td>7.76</td>\n",
              "      <td>24.54</td>\n",
              "      <td>47.92</td>\n",
              "      <td>181.0</td>\n",
              "      <td>0.05263</td>\n",
              "      <td>0.04362</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.1587</td>\n",
              "      <td>0.05884</td>\n",
              "      <td>0.3857</td>\n",
              "      <td>1.4280</td>\n",
              "      <td>2.548</td>\n",
              "      <td>19.15</td>\n",
              "      <td>0.007189</td>\n",
              "      <td>0.00466</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.02676</td>\n",
              "      <td>0.002783</td>\n",
              "      <td>9.456</td>\n",
              "      <td>30.37</td>\n",
              "      <td>59.16</td>\n",
              "      <td>268.6</td>\n",
              "      <td>0.08996</td>\n",
              "      <td>0.06444</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.2871</td>\n",
              "      <td>0.07039</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>569 rows × 31 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "    diagnosis  radius_mean  ...  symmetry_worst  fractal_dimension_worst\n",
              "0           M        17.99  ...          0.4601                  0.11890\n",
              "1           M        20.57  ...          0.2750                  0.08902\n",
              "2           M        19.69  ...          0.3613                  0.08758\n",
              "3           M        11.42  ...          0.6638                  0.17300\n",
              "4           M        20.29  ...          0.2364                  0.07678\n",
              "..        ...          ...  ...             ...                      ...\n",
              "564         M        21.56  ...          0.2060                  0.07115\n",
              "565         M        20.13  ...          0.2572                  0.06637\n",
              "566         M        16.60  ...          0.2218                  0.07820\n",
              "567         M        20.60  ...          0.4087                  0.12400\n",
              "568         B         7.76  ...          0.2871                  0.07039\n",
              "\n",
              "[569 rows x 31 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 240
        },
        "id": "MFYDSpAnc1p9",
        "outputId": "eaed79b1-38eb-4d7f-b5ac-2a40ef760d57"
      },
      "source": [
        "df.tail()"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>diagnosis</th>\n",
              "      <th>radius_mean</th>\n",
              "      <th>texture_mean</th>\n",
              "      <th>perimeter_mean</th>\n",
              "      <th>area_mean</th>\n",
              "      <th>smoothness_mean</th>\n",
              "      <th>compactness_mean</th>\n",
              "      <th>concavity_mean</th>\n",
              "      <th>concave points_mean</th>\n",
              "      <th>symmetry_mean</th>\n",
              "      <th>fractal_dimension_mean</th>\n",
              "      <th>radius_se</th>\n",
              "      <th>texture_se</th>\n",
              "      <th>perimeter_se</th>\n",
              "      <th>area_se</th>\n",
              "      <th>smoothness_se</th>\n",
              "      <th>compactness_se</th>\n",
              "      <th>concavity_se</th>\n",
              "      <th>concave points_se</th>\n",
              "      <th>symmetry_se</th>\n",
              "      <th>fractal_dimension_se</th>\n",
              "      <th>radius_worst</th>\n",
              "      <th>texture_worst</th>\n",
              "      <th>perimeter_worst</th>\n",
              "      <th>area_worst</th>\n",
              "      <th>smoothness_worst</th>\n",
              "      <th>compactness_worst</th>\n",
              "      <th>concavity_worst</th>\n",
              "      <th>concave points_worst</th>\n",
              "      <th>symmetry_worst</th>\n",
              "      <th>fractal_dimension_worst</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>564</th>\n",
              "      <td>M</td>\n",
              "      <td>21.56</td>\n",
              "      <td>22.39</td>\n",
              "      <td>142.00</td>\n",
              "      <td>1479.0</td>\n",
              "      <td>0.11100</td>\n",
              "      <td>0.11590</td>\n",
              "      <td>0.24390</td>\n",
              "      <td>0.13890</td>\n",
              "      <td>0.1726</td>\n",
              "      <td>0.05623</td>\n",
              "      <td>1.1760</td>\n",
              "      <td>1.256</td>\n",
              "      <td>7.673</td>\n",
              "      <td>158.70</td>\n",
              "      <td>0.010300</td>\n",
              "      <td>0.02891</td>\n",
              "      <td>0.05198</td>\n",
              "      <td>0.02454</td>\n",
              "      <td>0.01114</td>\n",
              "      <td>0.004239</td>\n",
              "      <td>25.450</td>\n",
              "      <td>26.40</td>\n",
              "      <td>166.10</td>\n",
              "      <td>2027.0</td>\n",
              "      <td>0.14100</td>\n",
              "      <td>0.21130</td>\n",
              "      <td>0.4107</td>\n",
              "      <td>0.2216</td>\n",
              "      <td>0.2060</td>\n",
              "      <td>0.07115</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>565</th>\n",
              "      <td>M</td>\n",
              "      <td>20.13</td>\n",
              "      <td>28.25</td>\n",
              "      <td>131.20</td>\n",
              "      <td>1261.0</td>\n",
              "      <td>0.09780</td>\n",
              "      <td>0.10340</td>\n",
              "      <td>0.14400</td>\n",
              "      <td>0.09791</td>\n",
              "      <td>0.1752</td>\n",
              "      <td>0.05533</td>\n",
              "      <td>0.7655</td>\n",
              "      <td>2.463</td>\n",
              "      <td>5.203</td>\n",
              "      <td>99.04</td>\n",
              "      <td>0.005769</td>\n",
              "      <td>0.02423</td>\n",
              "      <td>0.03950</td>\n",
              "      <td>0.01678</td>\n",
              "      <td>0.01898</td>\n",
              "      <td>0.002498</td>\n",
              "      <td>23.690</td>\n",
              "      <td>38.25</td>\n",
              "      <td>155.00</td>\n",
              "      <td>1731.0</td>\n",
              "      <td>0.11660</td>\n",
              "      <td>0.19220</td>\n",
              "      <td>0.3215</td>\n",
              "      <td>0.1628</td>\n",
              "      <td>0.2572</td>\n",
              "      <td>0.06637</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>566</th>\n",
              "      <td>M</td>\n",
              "      <td>16.60</td>\n",
              "      <td>28.08</td>\n",
              "      <td>108.30</td>\n",
              "      <td>858.1</td>\n",
              "      <td>0.08455</td>\n",
              "      <td>0.10230</td>\n",
              "      <td>0.09251</td>\n",
              "      <td>0.05302</td>\n",
              "      <td>0.1590</td>\n",
              "      <td>0.05648</td>\n",
              "      <td>0.4564</td>\n",
              "      <td>1.075</td>\n",
              "      <td>3.425</td>\n",
              "      <td>48.55</td>\n",
              "      <td>0.005903</td>\n",
              "      <td>0.03731</td>\n",
              "      <td>0.04730</td>\n",
              "      <td>0.01557</td>\n",
              "      <td>0.01318</td>\n",
              "      <td>0.003892</td>\n",
              "      <td>18.980</td>\n",
              "      <td>34.12</td>\n",
              "      <td>126.70</td>\n",
              "      <td>1124.0</td>\n",
              "      <td>0.11390</td>\n",
              "      <td>0.30940</td>\n",
              "      <td>0.3403</td>\n",
              "      <td>0.1418</td>\n",
              "      <td>0.2218</td>\n",
              "      <td>0.07820</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>567</th>\n",
              "      <td>M</td>\n",
              "      <td>20.60</td>\n",
              "      <td>29.33</td>\n",
              "      <td>140.10</td>\n",
              "      <td>1265.0</td>\n",
              "      <td>0.11780</td>\n",
              "      <td>0.27700</td>\n",
              "      <td>0.35140</td>\n",
              "      <td>0.15200</td>\n",
              "      <td>0.2397</td>\n",
              "      <td>0.07016</td>\n",
              "      <td>0.7260</td>\n",
              "      <td>1.595</td>\n",
              "      <td>5.772</td>\n",
              "      <td>86.22</td>\n",
              "      <td>0.006522</td>\n",
              "      <td>0.06158</td>\n",
              "      <td>0.07117</td>\n",
              "      <td>0.01664</td>\n",
              "      <td>0.02324</td>\n",
              "      <td>0.006185</td>\n",
              "      <td>25.740</td>\n",
              "      <td>39.42</td>\n",
              "      <td>184.60</td>\n",
              "      <td>1821.0</td>\n",
              "      <td>0.16500</td>\n",
              "      <td>0.86810</td>\n",
              "      <td>0.9387</td>\n",
              "      <td>0.2650</td>\n",
              "      <td>0.4087</td>\n",
              "      <td>0.12400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>568</th>\n",
              "      <td>B</td>\n",
              "      <td>7.76</td>\n",
              "      <td>24.54</td>\n",
              "      <td>47.92</td>\n",
              "      <td>181.0</td>\n",
              "      <td>0.05263</td>\n",
              "      <td>0.04362</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.1587</td>\n",
              "      <td>0.05884</td>\n",
              "      <td>0.3857</td>\n",
              "      <td>1.428</td>\n",
              "      <td>2.548</td>\n",
              "      <td>19.15</td>\n",
              "      <td>0.007189</td>\n",
              "      <td>0.00466</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.02676</td>\n",
              "      <td>0.002783</td>\n",
              "      <td>9.456</td>\n",
              "      <td>30.37</td>\n",
              "      <td>59.16</td>\n",
              "      <td>268.6</td>\n",
              "      <td>0.08996</td>\n",
              "      <td>0.06444</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.2871</td>\n",
              "      <td>0.07039</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    diagnosis  radius_mean  ...  symmetry_worst  fractal_dimension_worst\n",
              "564         M        21.56  ...          0.2060                  0.07115\n",
              "565         M        20.13  ...          0.2572                  0.06637\n",
              "566         M        16.60  ...          0.2218                  0.07820\n",
              "567         M        20.60  ...          0.4087                  0.12400\n",
              "568         B         7.76  ...          0.2871                  0.07039\n",
              "\n",
              "[5 rows x 31 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FwSdf84Uc7WJ"
      },
      "source": [
        "label_encoder = LabelEncoder()\n",
        "df.iloc[:,0] = label_encoder.fit_transform(df.iloc[:,0]).astype('float64')"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "mUJDHI0Grvgk",
        "outputId": "ccda2456-3d8e-4292-b29d-c63a967fc257"
      },
      "source": [
        " jum = [df['diagnosis'].loc[df.diagnosis == 1].count(),\n",
        "        df['diagnosis'].loc[df.diagnosis == 0].count()]\n",
        "sns.barplot( x=['M','B'], \n",
        "                y=jum)"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fbe15751a10>"
            ]
          },
          "metadata": {},
          "execution_count": 10
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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99Mxw70xVHQZuWe0+pHnOSbK/jQPsrKrjq9lQ7/z6AUnqkGvuktQhw12SOmS4S1KHDHdJ6pDhLkkdMtwlqUOGuyR16P8ATxmC8uFo23gAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 240
        },
        "id": "pUBt2PgKdQt9",
        "outputId": "4c3c1007-8b0f-47cb-d9c9-9d119b0a3fb5"
      },
      "source": [
        "df.tail()"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>diagnosis</th>\n",
              "      <th>radius_mean</th>\n",
              "      <th>texture_mean</th>\n",
              "      <th>perimeter_mean</th>\n",
              "      <th>area_mean</th>\n",
              "      <th>smoothness_mean</th>\n",
              "      <th>compactness_mean</th>\n",
              "      <th>concavity_mean</th>\n",
              "      <th>concave points_mean</th>\n",
              "      <th>symmetry_mean</th>\n",
              "      <th>fractal_dimension_mean</th>\n",
              "      <th>radius_se</th>\n",
              "      <th>texture_se</th>\n",
              "      <th>perimeter_se</th>\n",
              "      <th>area_se</th>\n",
              "      <th>smoothness_se</th>\n",
              "      <th>compactness_se</th>\n",
              "      <th>concavity_se</th>\n",
              "      <th>concave points_se</th>\n",
              "      <th>symmetry_se</th>\n",
              "      <th>fractal_dimension_se</th>\n",
              "      <th>radius_worst</th>\n",
              "      <th>texture_worst</th>\n",
              "      <th>perimeter_worst</th>\n",
              "      <th>area_worst</th>\n",
              "      <th>smoothness_worst</th>\n",
              "      <th>compactness_worst</th>\n",
              "      <th>concavity_worst</th>\n",
              "      <th>concave points_worst</th>\n",
              "      <th>symmetry_worst</th>\n",
              "      <th>fractal_dimension_worst</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>564</th>\n",
              "      <td>1.0</td>\n",
              "      <td>21.56</td>\n",
              "      <td>22.39</td>\n",
              "      <td>142.00</td>\n",
              "      <td>1479.0</td>\n",
              "      <td>0.11100</td>\n",
              "      <td>0.11590</td>\n",
              "      <td>0.24390</td>\n",
              "      <td>0.13890</td>\n",
              "      <td>0.1726</td>\n",
              "      <td>0.05623</td>\n",
              "      <td>1.1760</td>\n",
              "      <td>1.256</td>\n",
              "      <td>7.673</td>\n",
              "      <td>158.70</td>\n",
              "      <td>0.010300</td>\n",
              "      <td>0.02891</td>\n",
              "      <td>0.05198</td>\n",
              "      <td>0.02454</td>\n",
              "      <td>0.01114</td>\n",
              "      <td>0.004239</td>\n",
              "      <td>25.450</td>\n",
              "      <td>26.40</td>\n",
              "      <td>166.10</td>\n",
              "      <td>2027.0</td>\n",
              "      <td>0.14100</td>\n",
              "      <td>0.21130</td>\n",
              "      <td>0.4107</td>\n",
              "      <td>0.2216</td>\n",
              "      <td>0.2060</td>\n",
              "      <td>0.07115</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>565</th>\n",
              "      <td>1.0</td>\n",
              "      <td>20.13</td>\n",
              "      <td>28.25</td>\n",
              "      <td>131.20</td>\n",
              "      <td>1261.0</td>\n",
              "      <td>0.09780</td>\n",
              "      <td>0.10340</td>\n",
              "      <td>0.14400</td>\n",
              "      <td>0.09791</td>\n",
              "      <td>0.1752</td>\n",
              "      <td>0.05533</td>\n",
              "      <td>0.7655</td>\n",
              "      <td>2.463</td>\n",
              "      <td>5.203</td>\n",
              "      <td>99.04</td>\n",
              "      <td>0.005769</td>\n",
              "      <td>0.02423</td>\n",
              "      <td>0.03950</td>\n",
              "      <td>0.01678</td>\n",
              "      <td>0.01898</td>\n",
              "      <td>0.002498</td>\n",
              "      <td>23.690</td>\n",
              "      <td>38.25</td>\n",
              "      <td>155.00</td>\n",
              "      <td>1731.0</td>\n",
              "      <td>0.11660</td>\n",
              "      <td>0.19220</td>\n",
              "      <td>0.3215</td>\n",
              "      <td>0.1628</td>\n",
              "      <td>0.2572</td>\n",
              "      <td>0.06637</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>566</th>\n",
              "      <td>1.0</td>\n",
              "      <td>16.60</td>\n",
              "      <td>28.08</td>\n",
              "      <td>108.30</td>\n",
              "      <td>858.1</td>\n",
              "      <td>0.08455</td>\n",
              "      <td>0.10230</td>\n",
              "      <td>0.09251</td>\n",
              "      <td>0.05302</td>\n",
              "      <td>0.1590</td>\n",
              "      <td>0.05648</td>\n",
              "      <td>0.4564</td>\n",
              "      <td>1.075</td>\n",
              "      <td>3.425</td>\n",
              "      <td>48.55</td>\n",
              "      <td>0.005903</td>\n",
              "      <td>0.03731</td>\n",
              "      <td>0.04730</td>\n",
              "      <td>0.01557</td>\n",
              "      <td>0.01318</td>\n",
              "      <td>0.003892</td>\n",
              "      <td>18.980</td>\n",
              "      <td>34.12</td>\n",
              "      <td>126.70</td>\n",
              "      <td>1124.0</td>\n",
              "      <td>0.11390</td>\n",
              "      <td>0.30940</td>\n",
              "      <td>0.3403</td>\n",
              "      <td>0.1418</td>\n",
              "      <td>0.2218</td>\n",
              "      <td>0.07820</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>567</th>\n",
              "      <td>1.0</td>\n",
              "      <td>20.60</td>\n",
              "      <td>29.33</td>\n",
              "      <td>140.10</td>\n",
              "      <td>1265.0</td>\n",
              "      <td>0.11780</td>\n",
              "      <td>0.27700</td>\n",
              "      <td>0.35140</td>\n",
              "      <td>0.15200</td>\n",
              "      <td>0.2397</td>\n",
              "      <td>0.07016</td>\n",
              "      <td>0.7260</td>\n",
              "      <td>1.595</td>\n",
              "      <td>5.772</td>\n",
              "      <td>86.22</td>\n",
              "      <td>0.006522</td>\n",
              "      <td>0.06158</td>\n",
              "      <td>0.07117</td>\n",
              "      <td>0.01664</td>\n",
              "      <td>0.02324</td>\n",
              "      <td>0.006185</td>\n",
              "      <td>25.740</td>\n",
              "      <td>39.42</td>\n",
              "      <td>184.60</td>\n",
              "      <td>1821.0</td>\n",
              "      <td>0.16500</td>\n",
              "      <td>0.86810</td>\n",
              "      <td>0.9387</td>\n",
              "      <td>0.2650</td>\n",
              "      <td>0.4087</td>\n",
              "      <td>0.12400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>568</th>\n",
              "      <td>0.0</td>\n",
              "      <td>7.76</td>\n",
              "      <td>24.54</td>\n",
              "      <td>47.92</td>\n",
              "      <td>181.0</td>\n",
              "      <td>0.05263</td>\n",
              "      <td>0.04362</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.1587</td>\n",
              "      <td>0.05884</td>\n",
              "      <td>0.3857</td>\n",
              "      <td>1.428</td>\n",
              "      <td>2.548</td>\n",
              "      <td>19.15</td>\n",
              "      <td>0.007189</td>\n",
              "      <td>0.00466</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.02676</td>\n",
              "      <td>0.002783</td>\n",
              "      <td>9.456</td>\n",
              "      <td>30.37</td>\n",
              "      <td>59.16</td>\n",
              "      <td>268.6</td>\n",
              "      <td>0.08996</td>\n",
              "      <td>0.06444</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.2871</td>\n",
              "      <td>0.07039</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     diagnosis  radius_mean  ...  symmetry_worst  fractal_dimension_worst\n",
              "564        1.0        21.56  ...          0.2060                  0.07115\n",
              "565        1.0        20.13  ...          0.2572                  0.06637\n",
              "566        1.0        16.60  ...          0.2218                  0.07820\n",
              "567        1.0        20.60  ...          0.4087                  0.12400\n",
              "568        0.0         7.76  ...          0.2871                  0.07039\n",
              "\n",
              "[5 rows x 31 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sE4jnOAwdj_3"
      },
      "source": [
        "df_corr = df.corr()"
      ],
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 844
        },
        "id": "3qMIGqxVd0e-",
        "outputId": "24ff3ebf-1abc-4dab-842a-14d9aae02b7f"
      },
      "source": [
        "plt.figure(figsize=(12,8), dpi = 100)\n",
        "sns.heatmap(df_corr)"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fbe14bef790>"
            ]
          },
          "metadata": {},
          "execution_count": 13
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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P1/SVNEfSVwtxjpf0nqQ1m4nZX9Kkqvr3lBSF8wGSnpH0I0mvA7Ny+sqSrpL0vqQpku6XtFkr77ES82BJb+Z7ulRSQ273u7ntJ1dd12ydknpL+nN+PtMkPSFp56oYYyWdJOkaSVNz/T9u1Q/HzMzMzMysLUVj/Y4Oxp0Tbe8o4DHgSmCtfEwF7gdGAFsB/YA1gZsBIuJBYBAwRNJKkjYHzgB+FBHjm4g5biHatC7wXWAvoE9OuwVYA9gN2BJ4GrhP0qqtjNk7X9sP2Bf4IXAX8FlSR8oJwJmStilc01KdPYC/AjsBmwP3AMMk9ayq+1jgyVzmUuAPkjZoZbvNzMzMzMysnfGaE20sIiZLmgPMiIh3AST9ChgRESdVykk6GBgnaf2IeBn4FfB14ApgE+C6iPhLUzFzjNY2qwtwQES8n6/bHvgSsEZEVDaVP07SnsD3chta0gk4OCKmAi9IegDYAPhGRDQCoyWdAOwAPN6aOiNiJDCyUMcpkr4DfAu4uJD+14i4NN/LOcDPcz2jW/tAzMzMzMzMrP1w58SSsRmwg6RpNfJ6Ay9HxBxJ+wHPAm+QXrjbyhuVjolCe3oAE6o6OFbI7WmNsbljomI8MD93TBTT1mhtnZJ6AAOA3UmjQ5bL+dUjJ56tfBMRIendQj2fIKkr0LWY9vyGO9O1kwcOmZmZmZnZYvBWom3GnRNLRg9gGGmqQ7V3Ct9/JX9dNR/TW4jbCFQPn+hco1x1nB653r41yk6qkVbL3KrzaCKt0gPQmjoHkkaPHAe8AswEbiWN/Gip7uZ6Gk4ETismXPbBaxy1xrrNXGJmZmZmZmZLijsnyjEHaCicP01a82FsRMyrdYGk3sAFwCHA94HrJO1cGIlQHRPgfeBTkrpHRKUDog8texr4NDAvIsa2onxbaE2d2wGDI+IO+GgkRa82qPts4PxiwmGrf35yG8Q1MzMzM7NlWQdcmLJePK69HGOBbfKOGqsDl5BGQtwoaeu8K8Wukq7NO1w0AP8HDI+Ia4GDgC+SFn6sGVNSJ+BxYAbwmxzzB0D/VrTvXtICm0Ml7ZJjfkXSWZK2apMnsGh1jgH2ktQn7+LxR9rgMxoRsyNiSvHwlA4zMzMzM7P2w29o5RgIzAdeII1u6EIaFdAA/A0YRdqdYxJpasbJwOeAQwEi4h3gx6TdLjZrImbPiPgQ+F/gGznmvqQ1G5oVEZGv+QdwLfAycFNuw/jFufHFrPMYYCLwKGkazHDSiAszMzMzMzPrwDytowR5941ta2Tt1cQlp+ejGON2Cos4NhUzIoYCQ6uSryzkD6BGh0VezPLIfCyUWjEjon+Ncn0Xps483WPHquRLqsr0qnFda6aymJmZmZmZta3G+fVuQYfhkRNmZmZmZmZmVlceOWGfIOl50nSLWg6NiBuWZHvMzMzMzMzaJS+I2WbcOWG1fIPaW5JCSWtSmJmZmZmZ2bLLnRP2CRHxRr3bYGZmZmZm1u41euREW3HnhC2T1j52s5YLLaKe3zystNgAT33xuFLjb/nswFLjM39uqeF33+KMUuPvsss7pcbv/OWNSo2vDTcvNf6Y/e8vNf7VE18pNX7f06vX5G1bu58/odT4d524UqnxtcoqpcY/+pi7S43/7c4blxq/R6NKjb/1yh+UGv8vU6LU+HMbupcaf5PHziw1/sNblvvvy/ZPldv+mDm11Pj/2OnSUuNv+vm3S43f4wvlLsXXpd9XS43/yrFjSo0/arly//9mBl4Q08zMzMzMzMzqzCMnzMzMzMzMzBaFF8RsMx45YWZmZmZmZmZ15ZETJZM0GFg5Ivasd1vMzMzMzMysDXlBzDbjzonyHQWUu0IWIOlB4JmIOLrsuszMzMzMzMzakjsnSiKpAYiImFzvtiwMSV0iYk6922FmZmZmZtbueeREm/GaE5mkByVdnI/Jkj6QdIYk5fyukgZK+o+k6ZIel9S3cH1/SZMkfUvSC8BsoKekwZKGVtVzkaRBkiZKGi/pEEndJV0raaqkVyTtVtW+TSTdLWlavmaIpNVz3mDga8BRkiIfvVq6ruq+B0n6ABjeimcVkg6VdKekGZJelLStpHVzvOmSHpXUu+q6b0t6WtIsSa9JOk3ScoX8YySNytePk3SppB41nvGuuc5pku6RtFYrfsRmZmZmZmbWTrlzYkEHAvOAL5GmYxwD/CjnXQxsC+wDfBG4BbhH0nqF67sBJ+RrNgbea6aeD3I9FwF/yPEeBbYA/gYMkdQNQNLKwP3ACGAroB+wJnBzjncU8BhwJbBWPsa14rpie+YA2wGHtfSQslOA64E+wEvAH4HLgbNzXSI9M/I9fDWXvxDYCDgU6A+cXIjZCBxJenYHAjsC51bV2w04Dtgf+G+gJzCwlW02MzMzMzOzdsjTOhY0Dvh5RAQwWtKmwM8lDQcOAnpGxNu57EBJ/XL6STmtM/CTiBhZCZgHXlQbGRFn5vyzgV8CH0TElTntdOBwUifIv4AjgBERUakHSQeTOiDWj4iXJc0BZkTEu4UyLV6Xk8dExPEL+ayujYibc8xzSJ0jZ0TE8Jx2IXBtofxpwG8j4rp8/pqkU0idD78GiIhBhfJjJf0KuAz4SSG9M3BYRLya67kYOLW5hkrqCnQtpk28/Od07eyPv5mZmZmZLbqI+fVuQofhkRML+lfumKh4DFgP2BRoAF7OUwmmSZpGmkpRnLowB3i2FfV8VCbSp3kCMKqQPz5/XSN/3QzYoarul3LeAlMnqrT2uqda0eYm76HQ3up7WF7SioW2nFrVliuBtQojRHaWdF+eOjMVGAKsVsnPZlQ6JrJ3+Pg5NeVEYHLxOO+ux1t/p2ZmZmZmZlYq/+q4dXoA84Et89eiaYXvZ1Z1bjRlbtV5FNMiIvKIi0rnUQ9gGGnKSLV3mqmntddNb6G9tRTvIZpJK97DacDtNWLNymtk3Ema4nIy8CGwPXA10AWYUaOOSj0t7YZyNnB+MeEXu2+zVC1UamZmZmZm7ZAXxGwz7pxY0DZV518GxpDWbGgA1oiIfy7xVsHTwHeBsRExr4kyc0htXNjrlpSngQ0i4pVamZK2JHVkHBsRjTlt77aoOCJmkxYo/cjMaxd2FouZmZmZmZmVxdM6FtRT0vmSNpC0L/Az4MK8NsMNwPWS9pK0jqQvSTpR0u5LoF2XAKsCN0raWlLvvGPFtUpblgKMBbaR1EvS6pI6tfK6JeV04IC8Q8fGkjaUtI+kM3P+K6T1JH4m6fOS9qf1i3OamZmZmZktedFYv6ODcefEgq4HVgD+TXqxvxC4IucdlPN/B4wGhgJbA2+W3ai8COd2pJERfyOt7TAImETa4QLSjhXzgReA9/l48c6Wrlsi8kKZewC7AE+QFvr8OfBGzh9J2h3lBOA5YD/SWhFmZmZmZmbWwXlax4LmRsTRpJ0yFhARc0lrJpxW68KIGAwMrpHev+q8b40yvWqkqep8DLBXUw3Pozu2rZHe0nWfaE9LarRtLFXrPkTEgzXShgPDm4l7AXBBVfKQQv5gqp5xRAytrsfMzMzMzMyWLu6cMDMzMzMzM1sUXhCzzXhahy1A0n7F7T6rjufr3T4zMzMzMzPreDxyIluU6Q0d1F+Ax5vIq97G08zMzMzMbNnVARemrBd3TtgCImIqMLXe7TAzMzMzM7NlhzsnbJkUM2aUF3x+uQNM5jeWPBur5PbT0LnU8DNKfjxz35tXavzO02eWGp/ZJX72geklL087Z365z18rfqrU+F1U8j+73bqVG3/lVUsN32255UuNP62h3L8gyv7d2cyZXUqN36By//6fW/YTKvnfr3llz4Yuuf3qUu6frxmKUuNPnVBu+5f/YHqp8TtP/LDU+JMbSg3P5PAAaiufOyfMzMzMzMzMFoUXxGwzXhDTzMzMzMzMzOrKIyfMzMzMzMzMFoUXxGwzHjlhZmZmZmZmZnXlkRMdjKTOEV6xxszMzMzMrHRec6LNeOREOyepn6SHJU2SNEHSnZJ657xekkLS9yU9JGkWsF/O+5GkFyXNkvSSpJ9UxT1H0suSZkh6TdIZklq1jYKkAZKekXSwpDclTZN0qaQGScdLelfSe5JOrrpuZUlXSXpf0hRJ90varJDfW9KfJY3PMZ+QtHNVjLGSTpJ0jaSpuf4fL+LjNTMzMzMzs3bAnRPtX3fgfGArYCfSTmV3SCr+7H4LXAhsCAyXtB9wOnByTjsJOEPSgYVrpgL9gY2Ao4BDgJ8vRLt6A7sB/YB9gR8CdwGfBb4GnACcKWmbwjW3AGvk67YEngbuk1TZm64H8Nd8n5sD9wDDJPWsqvtY4Mlc5lLgD5I2WIi2m5mZmZmZWTviaR3tXETcVjyXdDDwPqlTYVpOHhQRtxfK/Bo4tpD2uqSNgEOB63LcMwthx0oaCOwDnNvKpnUCDo6IqcALkh4ANgC+ERGNwGhJJwA7AI9L2h74ErBGRMzOMY6TtCfwPeCKiBgJjCzUcYqk7wDfAi4upP81Ii7N93oOqVNlB2B0K9tuZmZmZma2+Dyto824c6Kdk7QeaRTENsDqfDzapSfwQv7+yUL57qRRDVdLurIQajlgcqHc94Ejc9keOX/KQjRtbO6YqBgPzM8dE8W0NfL3m+V6JkgqxlkhtwFJPYABwO7AWrlNK+R7LXq28k1EhKR3C/V8gqSuQNdi2ocXHEbXzg3N36GZmZmZmZktEZ7W0f4NA1YlTbvYJh8AXQplphe+75G/HgL0KRybAF8GkLQtcANpCsUepOkRZ1XFbEn1opvRRFrlM9YDeKeqTX1Ioy3Oy2UGAt8hTUP5as4fVaNdzdVTy4mkjpmPjoF/e6qZ4mZmZmZmZq0QjfU7FpKkn+Y1/GZJelzSl1oof7Sk0ZJmShon6QJJyy/ys2qBR060Y5JWI728HxIR/8xp2zd3TUSMl/Q28PmIuKGJYl8B3oiIswp1fa6Nmt2Up4FPA/MiYmwTZbYDBkfEHblNPYBebVD32aR1Oz5y3C5bTm6irJmZmZmZWYeSR86fDxwGPA4cTVqvcIOIeK9G+R+Q1jY8GHgUWB8YTPrF8DFltNGdE+3bRGAC8GNJ75CmN/y2FdedBvxe0mTSopJdSQtqrhIR5wNjgJ6S9gGeIE2j+E4J7S+6F3gMGCrpeOBlYO1c9x0R8WRu116ShpE+9GfQBqN78hoXs4tpMy45YnHDmpmZmZnZsm7pWXPiGODKiLgWQNJhpHexg6n9jvkV4JGI+GM+HyvpRj4eyd/mPK2jHcvrN+xD2tniOeAC4BetuO4q4EfAQaRpEQ+RduZ4Pef/Jce6GHiG9ME7o81vYME2BfAN4B/AtaTOiZuAz5HWpoD0B2YiqWduGDCcNOLCzMzMzMzMCiR1lbRi1dG1RrkupHfKeytp+V3zXmDbJsI/CmxZmfoh6fOk97m/tvV9VHjkRDsXEfeSduYoUhPfF6/7I/DHWnk5/3jg+KrkQa1s0wDSwpXFtP41yvWtOp9KWoTzyCbijgV2rEq+pKpMrxrX9WmhyWZmZmZmZh3NiaRR80W/pupdjbSxQgMf/1K4YjzwhVqBI+KPklYHHlba0WA54LKI+M3iNrop7pwwMzMzMzMzWxSLsDBlG/rE2npUTWdfVJL6kjYq+AlpjYp1gQslnRIRpYy6d+eEfYKk50nTLWo5tJmFNs3MzMzMzGwJqLW2XhM+AOYDa1alrwm828Q1ZwBD8pIBAKMkdQeukHRWnhbSptw5YbV8A+jcRF71UCAzMzMzM7Nl01KwIGZEzJH0FLATMBRAUqd8fnETl3UDqm9ufv5ac2mBxeXOCfuEiHij3m0wMzMzMzOzNnM+cJ2kJ4F/k7YS7U7arABJ1wP/iYgTc/lhwDGSRvDxtI4zgGERMb86eFtQ2kTBbNkyoue3S/vgPxKfKis0AA91mlpq/N3nldv+GSXvEXTIiNNLjT/nslNLjT/7qXGlxm/oUXKfdMk/304rNDWoq208cceKpca/c4VyH9DuM8v9N31ip3I/P5/VzFLjz28s9/mPp0up8b/QfXKp8R+ftUqp8T8zb16p8d9drtzP5yOd22Qad5N2nl3u52d6p1J+0fmR71++Ranxpwz8S6nxZ00p9/PTY625pcZ/f0z3UuOv3nt6qfEBVr/7oXI/pOoHCGUAACAASURBVCWZeftv6vZCvcJeJy3UM5N0BGn3x0+Tdm08MiIez3kPAmMrGx1IWg44Gdgf+AzwPqnD4uSImNRGt7AAj5wwMzMzMzMz6+Ai4mKamMZRY6fFeaSdP35dfsuSkn/HZWZmZmZmZmbWPI+cMDMzMzMzM1sUS8GCmEsLj5yoE0mDJQ2tdzvMzMzMzMzM6s0jJ0omqRfwOrB5RDxT39aYmZmZmZlZm/HIiTbjkRNmZmZmZmZmVlcdrnNC0vckjZI0U9IESfdK6l6ZRiHpJEnjJU2SdKqk5SSdJ+lDSW9JOqgq3qaS7i/Eu0JSj0J+pxznLUmzJT0jqV8hxOv56whJkbdoKcY/TtI7OfYlkjoX8sbm9l4jaaqkNyX9uOr6/yfp5nw/H0r6cx6tUcnvK+nfkqbnMo9I+lzO20zSAzn2FElPSdqqFc+4f461h6TRkmZIulVSN0kH5nZPlPR7SQ2F67pKGijpP7k9j0vqW8hfTdKNOX9G/jnuW1X3gznuufl+35U0oKU2m5mZmZmZtbmI+h0dTIfqnJC0FnAjcA2wIdAXuB2o7P+6I7A28N/AMaRtUe4EJgLbAJcBl0v6bI7XHRie87cG/gfYmQW3XzkKOBY4DvhiLv8XSevl/C/lrzsDawF7Fa7dAeidvx4I9M9H0bHAk8DmwKXAHyRtkNvXOdc3FfgqsB0wDbhHUpe8N+1Q4KHctm2BK4DKJ/kG4K18b1sCvwVauwlzN+BIYB+gH+lZ3wF8Ix/7A4cC3ytcc3Fuwz65Pbfktlae1fLAU8DuwCa5rUMkfYkFHQhMJ/3MjgdOlfT1VrbbzMzMzMzM2pmOtubEWqR7uj0i3shpowAkAXwIHBkRjcBoSccD3SLiN7nM2cAvge2Bm4AfkF6YD4iI6bnMEcAwSSdExHhSp8Q5EXFTru8ESTsARwM/Bd7P6RMi4t2q9k4EjoiI+cBLku4CdgKuLJT5a0Rcmus+B/g5qTNjNPB9UgfTjyJS11ke+TGJ1FnwJLAScGdEvJrjvViI3RM4LyJeyudjmnu4VToDh1fiSrqV1CGxZkRMA16Q9EBu658k9QQOAnpGxNs5xsA8yuQg4KSI+A8wsFDHRZJ2BfYG/l1IfzYiKvvtjsk/k52Av9dqqKSuQNdi2r8+uztdPh7UYWZmZmZmZnXUoUZOACOB+4BRkm6RdIikVQr5z+eOiYrx5M4LgNxJMAFYIydtCIysdExkj5Ce2waSViSNxHikqh2P5Gtb8nyus+KdQt0VzxbaF8C7hTKbAesCUyVNkzSN1AGzPNA7Ij4EBgPDJQ2TdFQeXVJxPnBVnvryS0m9W9HmihmFDg9Iz3Js7pgoplXauinQALxcaWtu79dIo0eQ1CDplDyd48OcvyupE6XmM8lqPbeiE4HJxeOaKQvTD2NmZmZmZlZDY2P9jg6mQ3VO5Bf9rwO7AS8APyONkFgnF6meshBNpC2p59Kaupsr04M0DaJP1bE+8EeAiDiINJXiUdJIi5clfTnnDQA2Bu4iTXl5QdJ3FqPtLbV1Pmn6SLGtG5KmxgD8In9/DmnERR/StJUurai7uZ/Z2aQRJB8dB6+4XjPFzczMzMzMbEnqaNM6KqMLHgEekXQ68AbQ2hfuai8C/SV1L4ye2A5oBEZHxBRJb+e0hwrXbcfH0xDm5K9lzCF4mtTh8F5ETGmqUESMAEYAZ0t6jDRd5V8572XgZeACSTeSpljcUUJbR5CewRoR8c8mymwH/Dki/g/SYqOkjpYXFqfiiJgNzF6gMT2/vTghzczMzMzMOuQIhnrpUCMnJG2Td7fYKq9xsBfwXyy4zsLCuAGYBVwnaZO8lsRFwJC83gTAeaR1Jr4vaQNJvyX9xv/CnP8eMBPoJ2lNSSstYluaat8HwJ8lfVXSOnl3jt9L+mw+P1vStpI+J2kXYD3gRUkrSLo4l/+cpO1IC2Mu6rNqVu4EuQG4XtJeuW1fknSipN1zsTHA1yV9RdKGwOXAmmW0x8zMzMzMzNqPDtU5AUwh7cTxV9JogDOBYyPi7kUJFhEzSGserAo8AdxKWtPiiEKx35PWbvgdaf2KfsC3ImJMjjGPtKvFocDbwJ8XpS3NtO+/gTdJu5K8CFxNWnNiCjAD+AJwG+l5XAFcQnrpnw+sBlyf824G7gZOa6v21XBQru93pAU9h5I6RN7M+WeSRoMMBx4kra8xtMT2mJmZmZmZWTvQoaZ1RMSLpM6BWnn9a6T1rZHWq+p8FGk9hqbqbCRtSfrrZspcBVzVivYc3VxbclqfqvN3SVtr1jKFpqe0zAH2barNzYmIwaSFNotpA4ABVWn9q87nkjo/anaA5AU892yh7r410pq9xszMzMzMrBThaR1tpaONnDAzMzMzMzOzpUyHGjlhbUPS3cBXm8j+TUT8Zkm2x8zMzMzMrF3ygphtxp0TVsuPgBWayPtwSTbEzMzMzMzMOj53TtgnRMR/6t0GMzMzMzOzdi+i3i3oMNw5YcukdfecX1rsnq+OKy02wJpPf7bU+Lvs8k6p8ee+N6/U+HMuO7XU+F0OO73U+A2vjyg1fkx+v9T48+68q9T4MXVWqfG/2Gd8y4UWw62jy90decudPyg1/vTXSg3PkLfXLjX+UQPKjT/30WdKjd+w1qqlxh95Vbn/wd5yq3L/fWnoUe5Saj0eWqvU+F//3sRS4895Y3qp8efeUe7fnyuddUip8VecMqHU+GX7r0tvKzX+Cl9br9T4ZuAFMc3MzMzMzMyszjxywszMzMzMzGxReEHMNuORE2ZmZmZmZmZWVx450U5ICuA7ETG03m0xMzMzMzOzVvDIiTbjkRNmZmZmZmZmVldLXeeEpE6Sjpf0iqTZkt6UdHLO21TS/ZJmSpog6QpJPQrXDpY0VNJJksZLmiTpVEnLSTpP0oeS3pJ0UOGaXpJC0j6SHpU0S9Jzkr5WKNMg6WpJr+e6R0s6qkbbD5b0fG73O5Iuzuljc5E7cl1jc/oASc9I2l/SWEmTJd0k6VNVz+PEQt0jJX2vkL+KpBskvZ/zx1TuT1IXSRfntsyS9IakE1v5cwhJh0q6U9IMSS9K2lbSupIelDQ9P6/eVdd9W9LTub7XJJ0mablC/jGSRuXrx0m6tOpn2D//3HbNdU6TdI+kcpfQNjMzMzMzqxaN9Ts6mKWucwI4G/glcAawEfADYLyk7sBwYCKwNfA/wM7AxVXX7wisDfw3cAzwa+DOfN02wGXA5ZKq92s8D/gdsDnwGDBM0mo5rxPwVq5zI+B04DeS9q5cLOlw4BLgCmBT4FvAKzl76/z1IGCtwjlAb2BPYI98fC3ff8WJwAHAYcDGwAXA/xU6TyrPaTdgQ+BwoLLX3JG5HXsDGwD7AWNpvVOA64E+wEvAH4HLST+jrQBReP6SvprLX5jbdCjQHzi5ELMxt2tj4EDSz+vcqnq7AccB+5N+jj2BgQvRbjMzMzMzM2tHlqo1J/KIgaOAIyLiupz8KvCwpEOA5YEDImJ6Ln8EqRPhhIiobL78IXBkRDQCoyUdD3SLiN/kayqdH9sDNxWqvzgibstlDgf6AT8Ezo2IucBphbKvS9qW9NJ/c077FfC7iLiwUO4JgIh4XxLApIh4t+q2OwH9I2JqrnsIsBNwsqSuwEnAzhHxWC7/mqTtSS/+D5Fe3EdExJM5f2whdk9gDPBwRATwBgvn2oi4ObfrHFKnzRkRMTynXQhcWyh/GvDbws/uNUmnkDoffp2fxaBC+bGSfkXqMPpJIb0zcFhEvJrruRg4dSHbbmZmZmZmZu3EUtU5QfrNf1fgvibyRlY6JrJHSC/3GwCVzonnc8dExXjgucpJRMyXNAFYoyr+Y4Uy8yQ9mesEQNJPgYNJL/wrAF2AZ3LeGqTRGrXa3ZKxlY6J7J1C29YljSL4e+7cqOgCjMjf/wG4TdIWwN+AoRHxaM4bDPyd1ElzD3BnRPxtIdr2bOH7yvMdVZW2vKQVI2IKsBmwXWUaTtaQy3SLiBmSdiaNBvkCsCLpM/pRfr5mRqVjIis+k0/InThdi2nv/6QfXRsaWn2jZmZmZmZm1aIx6t2EDmNpm9Yxsw1izK06jybSWv1sJO1DmlZwNbALaZrDtaROAli8djfXtspaDLvnOivHRsD3ACLibuBzpOkeawP3SRqY854G1iFNz1gBuFnSrYvYtmgmrdje06rauimwHjBLUi/SFJtnge8CWwI/zddWnmV1HZV6RNNOBCYXj989+Wozxc3MzMzMzGxJWtpGTowhvejvBFxVlfci0F9S98Loie1IaxiMboO6vwz8AyAv4LglH6+nsB3waERcWilcXAgyIqbmRS53Ah5oIv5c0iiChfECMBvoGREPNVUoIt4HrgOuk/RP0voZx+W8KcCfgD/ljol7JK0aER8uZFta42lgg4h4pVampC1JHRnHVka3FNftWAxnA+cXE47dqvfkNohrZmZmZmbLMm8l2maWqs6JiJiV1zY4V9Ic0rSN/yItnngDad2C6yQNyOkXAUMK600sjp9KGkPqBPk5sApwTc4bAxwgaVfgddJCjVvn7ysGAJdJeg+4G/gUsF1EXJTzxwI7SXoEmB0RE1tqUO70GAhcIKkT8DCwEqmzZEpEXCfpdOAp4HnS1IY98j0g6RjSlIgRpE6c/wHeBSYt3KNptdOBOyW9Cdya69wM2CQifkVaILQz8DNJw/J9HLa4lUbEbFInzkemHrnH4oY1MzMzMzOzNrK0TeuAtPvE70gvui+Sfuu/Rl6PYFdgVdJCk7eS1ng4oo3q/WU+RpIWy/xWRFR2vbgcuD235XFgNeDS4sV5EcijSQs7Pk+avrBeocixwNeBcXy8XkRrnEJ6JieSnsc9pGkelY6ROaSRA8+SRn7MB/bJeVOB44EnSc+sF/CNqjU52kxeKHMP0tSXJ4B/kTp63sj5I0k7qJxAWgdkv3xfZmZmZmZm7Y+3Em0zS9XICYD84nxWPqrzRpG2nmzq2v410vrWSOtV4/IXI2KbJuLOJm0DelBV1olV5S4ndWTUijEMGFaVNoA04qKYNggYVDgP0tacxV1AiuXPBM5sIu9K4MpaeS2JCFWdj6Vq3YeIeLBG2nDSlq9Nxb2AtD5G0ZBC/mDSQp7Fa4ZW12NmZmZmZmZLj6Vx5ISZmZmZmZmZdSBL3cgJK5+k/WhihAfwRkRsvCTbY2ZmZmZm1i55K9E2486JFtSarrAM+Atp7YxaqrfxNDMzMzMzM1ss7pywT4iIqaTFMs3MzMzMzKwp3kq0zbhzwpZJDZ//TGmxV/jMnNJiA6z25PRS43f+8kblxp8+s9T4sx4ZU2r8htcXZjOdRYi/zualxm8c/3rLhRbD7NFTSo3/6sjVSo2/yUENpcbvNrrc+J03XLvU+CutXe7fP9NuKfc/eI3vtsXO4k1bbsPPlRq/7KHDExtmlRp/5gflfv5X3al3qfFXfnByqfGX6/OFUuM3/L8Wd6lfLB/e/kap8Vd7/YVS42uDLUqNz6wZpYaf+OrypcZXp3L/fwXQ7bjSq7B2zp0TZmZmZmZmZovCIyfajHfrMDMzMzMzM7O6cueEmZmZmZmZmdWVOyc6IEn9JU2qdzvMzMzMzMw6tIj6HR2MOyc6pj8B61dOJA2Q9Ewd22NmZmZmZmbWJC+I2QFFxEyg3C0RzMzMzMzMlnVeELPNeOREDZI6STpe0iuSZkt6U9LJOW9TSfdLmilpgqQrJPUoXDtY0lBJx0l6J5e5RFLnQpmuks6RNC7Hf0XSD3Neg6SrJb2e6xgt6ajCtbtImiVp5ao2Xyjp/vz9R9M6JPUHTgM2kxT56C/pGkl3VsXoLOm9SltaeEYPSrpI0iBJEyWNl3SIpO6SrpU0Nd/XblXXbSLpbknT8jVDJK1eyO8n6WFJk/Kzu1NS70J+r3wPe0l6QNIMSSMlbdtSm83MzMzMzKx9cudEbWcDvwTOADYCfgCMl9QdGA5MBLYG/gfYGbi46vodgN7564FA/3xUXA/sCxwJbAgcCkzLeZ2At3LsjYDTgd9I2jvn3wdMAr5bCSapAfg+cEONe/kT8DvgeWCtfPwJuAroJ2mtQtk9gG45vzUOBD4AvgRcBPwBuAV4FNgC+BswRFK33M6VgfuBEcBWQD9gTeDmQszuwPk5fyegEbhDUvVn9SxgINAHeBm4UZJHApmZmZmZmS2F/DJXRdKngKOAIyLiupz8KvCwpEOA5YEDImJ6Ln8EMEzSCRExPpefmK+fD7wk6S7Si/aVktYH9ga+HhH35vKvVeqPiLmkkQ4Vr+dRAXsDN0fEfEk3kTpMrs5ldgJWBm6rvp+ImClpGjAvIt4tZD0qaTSwP3BuTjsIuCUiplXHacLIiDgzP4dKh84HEXFlTjsdOBz4IvAv4AhgREScVAkg6WBgnKT1I+LliFjgHnL++6SOmucKWQMj4q5c5jRS58u6wEvVjZTUFehaTPvwvB/SdbmGVt6mmZmZmZlZDY0db2HKevHIiU/akPQie18TeSMrHRPZI6TnuEEh7fncMVHxDrBG/r4PMB94qKkGSPqppKckvZ87Fn4M9CwUuQHoK2ntfL4fcFdELOwOHVeROiSQtCawG3DNQlz/bOWbfL8TgFGF/EpnTeXeNwN2yFM6puV7q3Qm9M7tWE/SjZJekzQFGJvzi/e/QN2k51usp9qJwOTiMfDeEa24PTMzMzMzM1sS3DnxSW2xkOTcqvPg42fdbHxJ+5CmK1wN7ELqzLgW6PJRsIgnSKM59pG0AvAdak/paMn1wOfzyIz/BV6PiH8uxPW17vOjtIiP9rep3HsPYBjpnorHesA/cplhwKrAIcA2+YDC/deou7qeamcDKxWP43bevJnbMjMzMzMza4VorN/Rwbhz4pPGkDoQdqqR9yJpYcnuhbTtSOsijG5l/FGk5/61JvK3Ax6NiEsjYkREvEIeVVDlBtKIiW/m+u9qps45wCfmMETEBGAoafREf1InSJmeBjYGxkbEK1XHdEmrkUagnBkR90XEi8Aqi1tpRMyOiCnFw1M6zMzMzMzM2g93TlSJiFnAOcC5kg6Q1FvSl/MOFjcAs4Dr8q4TO5AWghxSWG+ipfhjgeuAayTtKWkdSX0LC16OAbaStKuk9SWdQVp8s9oNpEUnTwZujYjZzVQ7FlhHUh9Jq+c1GCquIi1suWFuV5kuIY2KuFHS1vnZ7pp392ggrdUxAfixpHUl7UhaHNPMzMzMzKz9aYz6HR2MOydqO4O0w8XppNESfwLWiIgZwK6kF+wngFtJa1McsZDxD8/XXkpac+FK0i4VAJcDt+c6HwdWy+UWkEdU/Ju02GRLUzpuA+4BHiAtLrlvIe9e0poNwyPi7YW8j4WS429HGsXxN9IokkGk3UcaI6IR2AfYkrT45QXAL8psk5mZmZmZmdWfd+uoIb8kn5WP6rxRwI7NXNu/RtrRVeezgGPyUV12NmmaxUFVWSfWKLtNdVpOHwwMror5vSaa3J00deLqJvJrioi+NdJ61UhT1fkYYK9m4t5L2pmjSIX8scXznDapOs3MzMzMzMyWHu6cWEZJ6gSsDhxLGrnwl/q2yMzMzMzMbOkSjR1vYcp6cefEsqsn8DrwFtA/IuZVMiT1BF5o5tqNIuLNkttnZmZmZmZmywh3Tiyjak2PKHibtMVnU0pdm8LMzMzMzGyp0AEXpqwXd07YJ+RRFK/Uux1mZmZmZma2bHDnhC2TtPZnygu+0qrlxQaCf5YaXxtuXmp8Zs8oNXzDyNdLjR+T3y81fuP4ctvfac11So0/d2q5m0D9u6FbqfE3XaNHqfEn8UGp8bXKauXG77ZCqfHHM6nU+DPu/0+p8WdOLPe/Vav2Lffz+Z+G7i0XWgxPvfHpUuPvskm5/37N56FS47P+F0sNrykflht/6Bulxm8c91ap8TuVvG5Apz59S40/a1bnUuO/NbJLqfEhbYe4VAqvOdFWvJWomZmZmZmZmdWVOyfMzMzMzMzMrK48rWMpJKkXaaeNzSPimfq2xszMzMzMbBnlBTHbjEdOLJ3GAWsBz7X2AkkDJLkjw8zMzMzMzNodj5xYCkXEfODderfDzMzMzMxsmVbyYqrLkrqNnJDUSdLxkl6RNFvSm5JOLuRvKul+STMlTZB0haQehfzBkoZKOk7SO7nMJZI6F8p0lXSOpHG5jlck/TDnNUi6WtLruY7Rko4qXLuLpFmSVq5q94WS7i+cby/pnznGOEm/l9TkcteVEQySDs3lZ0i6WdJKVc/mVElv5XY/I6lfIb+XpJDUJ5/3zec7SXoyx3xU0gY5vz9wGrBZLheS+isZkJ/9bElvS/p9K39+YyX9StL1kqZJekPStyT9l6Q/57RnJW1VdV2zz0vS/vkepkp6V9IfJa1RyG/2Xs3MzMzMzGzpU89pHWcDvwTOADYCfgCMB8gvq8OBicDWwP8AOwMXV8XYAeidvx4I9M9HxfXAvsCRwIbAocC0nNcJeCvH3gg4HfiNpL1z/n3AJOC7lWCSGoDvAzfk897APcBtwBdz3vY12lltXWBv4JtAP2Bz4NJC/lHAscBxOe5w4C+S1msh7ln5uq2AecA1Of1PwO+A50nTQdbKad8Ffp6fy3rAnsCoFuoo+jnwSG7/XcAQ0jP/P2AL4FXgekmCVj+vzsApwGa5Pb2AwQtxr2ZmZmZmZktGY9Tv6GDqMq1D0qdIL+BHRMR1OflV4OH8/Q+A5YEDImJ6vuYIYJikEyJifC43MceYD7wk6S5gJ+BKSeuTOgC+HhH35vKvVdoQEXNJowkqXpe0bb7m5oiYL+mm3Jarc5mdgJVJL9cAJwI3RMSgfD5G0pHAQ5IOj4hZTTyCyr39J9/bz4C7JB0bEe+SOiXOiYibcvkTJO0AHA38tImYACdHxEM55m9zzOUjYqakacC8HJ9cpidpesi9+Xm8Cfy7mfjV/hoRl+dYpwOHA09ExC057RzgMWDNXE+Lzyv+P3t3HiZHVbZ//HsnJIEQdgRB3xBEQNZEFlmVIKvLq4AL8KISFFwQ2UWjyI4RiWwGEAHZfoiAIgoKKCiILLJDIIGEJRAgBAJk3zPP749zGiud7pmeydR0Mrk/11XXTNc59dSpmp5J6umzRBSTDC/m8ocl9YuI6YWyetda756bmZmZmZnZEqpZPSc2AfqQeifUK3+ykpjI7iO1t9h9/5mcmKiYAFSGAAwCFgD31GuEpO9KelTSW/nh/ZtA/0KVa4HBktbNrw8C/hIRk/PrgcCQPIRheo5xR27n+vXOC7xSSUxkD1SuTdLKwLr5eovuI92X1jxV+H5C/rpWrYrZjcAKpCTApZL2ldSehFXxfJWE0cga+yptaPN+Sdpa0i15qMk0/vvzK/5cqs/d6rUqDe9ZubjNmTe/0Ws0MzMzMzOzkjUrOTGrk+LMq3od/PeaWj2HpAOA4aReEXuSkhlXAL3fCxbxMKlHxwGSVgD2JQ/pyPoBl+RjK9tA0hCJFzp0RYuneD8q/Xzq/owjYjwp2XM46X5dBPxLhXk7Gj1fRET1vhptaPV+FYbzTCUlgrYl3XMo/FwaOE+1ocCU4nb2zfe2cWlmZmZmZmZtiJbmbd1Ms1brGEt6GN4NuKxG+WjSJ+wrFnpP7AS0AM81eI6RpIfVXYA7a5TvBNwfEe/N9ZDnRKh2LelB+dV8/r8Uyh4DNo2I5xtsU0V/SetGxOv59fY59nMRMVXS67l9xV4fO9G+IRfV5gI9q3dGxCzgFtKQmQuBZ4EtSNfW2Vq9X5K2ANYAfpgTJ1RPqNlBw4Bziju+v8/Hp3RCXDMzMzMzM+sETek5kecFOAv4uaSvSdpA0vbKK2mQEgKzgaskbZ7nW/glcE1hvom2zjEOuAr4jaR9JK2fV3qoTHg5FthG0l6SNpJ0OumT+mrXkiZ3/DHw+4iYUyg7C9hR0ghJgyRtKOnzktqaELNybQMlfRy4gDTPRWU+iLNJ80zsL2njPKfCIOD8Rq69jnHA+rmda+ahDkMkfSPf4w8BXyEljV5ejPO0pq379QopifI9SR+S9DnS5JiLJSLmRMTU4tanl1fRNTMzMzOzxeQJMTtNM1frOJ20gsRppJ4S15PnDIiImcBewOrAw8DvSfNTHNHOc3wnH3sRqUfApUBl2cpLgJvyef9D+sT+ouoA+VP+h0irS1xbVfYUqWfGRsC9wOP5el6ndc/nc/8V+Btp/oTDC+UXkD7p/wWpB8jewOciYmzbl1zXH0grZfwTeIu0islk4DDSfBZPkVZE+d+IeHsxzlNXW/crIt4irbbyJWAUaTWX48toi5mZmZmZmS05mvbxcUS0kJaDPLNO+Ujgk60cP6TGvqOrXs8Gjs1bdd05wCF5Kxpao+52rbTjYdKcFe0SERcDF9cpawFOzVut8nGACq/vLr7O+56oqjMH+GKNcDe3r+XvxRtQY191GxZqZ97X6v2KiOuA66p2t+tazczMzMzMbOnivu1mZmZmZmZmHRAt3W9iymZxcsIWkefBuK1eeUT068LmmJmZmZmZWTfn5EQXi4hTgFOa3Iy2PEKagNPMzMzMzMzq6YYTUzaLkxO2iLy8aHuXRzUzMzMzMzPrEEU402PLngfX3a+0N/6/l+tbVmgAXu8xv9T4Gy0oN2c5o+SpSw/ddUKp8XusVu7Pd85zU0uNP29auYs0rXnz5aXGf+dL1XMYd67Ro9YqNf59y/cuNf4n5sxpu9JieKXH8qXGn17yGmJ7rtHQauQdNmlSuaMeV1qx3J9vv9Vmlxr/1fGrlhr/yR7l/n0et1y548o3m1vuL8DUnqWG59Ml/36tsOq8UuPPnlru/3+ipdz/AK11yAalxp90zeIsGtiY/o/ctVROcD/9+/s27YG639l/XCrvWT3NXErUzMzMzMzMzMzJCTMzMzMzMzNrLs85YWZmZmZmZtYR4aVEO4t7TpiZmZmZmZlZUzk50Y1IGifp6Ga3w8zMzMzMbJnQEs3b2knSKggV+AAAIABJREFUd/Mz42xJ/5H0sTbqryrpQkkTJM2RNEbSpzt8r9rgYR3LGEk9gYhw/yMzMzMzM7NlgaT9gXOAbwP/AY4G7pC0cUS8WaN+b+DvwJvAF4HXgPWAyWW10T0nAElflDRS0ixJb0u6U9IukuZJen9V3fMk3Zu/HyJpsqTPSnpO0kxJv5fUV9LBOSv1rqQLclKgEmOcpBMlXS1puqSXJX1O0vsk/Snve0rSNlXn3lnSvbmd43PcFXPZ3aQ3y7mSQlJUtfFzkkYBc4Cd27q2Nu5XR6+7j6Thkl6TNCNn6wYXyteQdF0un5l/JgdWnfvuHPfnkt6R9IakU9pqs5mZmZmZWWeLlmja1k7HApdGxBURMYqUpJgJfL1O/a8DqwP7RMR9ETEuIu6JiCc7fLPasMwnJyStA1wH/AbYBBgM3AQ8CrwIfLVQtxdwUK5b0Rc4EjgA2Dsf/0fg03n7KvAtUrap6BjgPuCjwF+Aa4Crgf8HbAW8AFwtSfncGwC3A38AtgT2B3YGRuR4+wGvAicB6+St2MYfAIcCmwGPNHhtrenIdY8AdsjHbAncCNwuacNcvjzpvn8G2Bz4NXBNje5GBwMzgO2AE4CTJO3RYLvNzMzMzMyWevnD35Wrtj416vUGtgburOzLPenvJD2f1fI54AHgQkkTJT0t6UfFD5872zKfnCA9xC8H3JSzQSMj4qKImA5cDhxSqPu/pAfoGwr7egHfiYjHI+JfwO9JSYNvRMSoiLgV+Cewa9V5/xoRl0TEWOA0YGXg4Yi4MSLGAGeRkiVr5/pDgWsj4ryIGBsR95OSA1+TtHxEvAMsAKZFxBsR8UZVGw+PiPsj4rmImNngtbWmXdctqX8+35ci4t6IeCEihgP/rrQjIl6LiOER8UREvBgRvyQlZL5cde6nIuLUfB+uJiVbdqvX0Fq/tHNjQYOXaWZmZmZmtkQaCkyp2obWqLcm0BOYWLV/IvD+RasD8CHSB809SR8+nw4cB5y42K2uw8kJeBK4Cxgp6UZJh0laLZddCXxY0vb59RDghoiYUTh+ZkS8UHg9ERiXkxvFfWtVnfepqnKAkTX2VY4bCAzJQz6mS5oO3EH6Ga7fxjXOrTofNHZtrWnvdW9BemOPqbqGXYANIM2HIekneTjHO7l8L6B/1bmrr2UCi97fokV+aa+ePqbByzQzMzMzM6ujuRNiDgNWqdqGddKV9SDNN/HNiHg0Iq4HziQNBynFMj8hZkQsyEMCdgT2BL4HnClpu4h4SdItwCGSXgI+RRq+UDSvOmSdfdWJoPfqRETk0Rvzqo6hcFw/4BLgghqX8Urtq3vPrIhYaFBSRLzZwLW1pr3X3Y/Us2Pr/LWoktD4PnAUaXKWkaShG+cBvRs4d2uJtmGkyV/e87V+G01ppb6ZmZmZmdkSLSLmkOYUbMsk0jPY2lX71wbeWLQ6kD4AnhexUJfz0cD7JfWOiLntbW9blvnkBKTkAGn+h/sknQa8DOxLeqC9jDQnxavACxFxX5Oa+RiwaUQ830qduaTeCY3qymt7nNS2tSKi3qSbOwF/ioj/ByCpB7ARMGpxTlzrl/bBdfdbnJBmZmZmZmbQsuQvghgRcyU9ShoKfzO896y1G/+dw7DafcD/SepRWOlxI2BCGYkJ8LAOJG2XJ/bYJs+LsB/wPlJWCNLQiamksTVXNKmZkOag2FHSCEmDJG0o6fOSim+mccAnJH1A0poNxOyya8vzaFxLmuRzP0nrS/qYpKGSPpOrjQX2kLSjpE1IPUWqs3tmZmZmZmbWPucAh+XVFTcBLgZWJD8HKq0kWRwScjFptY7zJW2Un9l+BFxYVgOX+eQE6eH8E8BfgTHAGcBxEXEbvDeL6ZWkT/2vblIbiYinSPMzbATcS+qJcBrweqHaScAA0kofbzUQs6uv7ZB8nl8Az5Gydtvy32EpZ5B6iNwB3E3qYnRzF7TLzMzMzMys/Zo750TD8pwRx5OeIZ8ABgF7R0RlrsP+FFZ8jIjxpPn/tiXN+XcBcD7ws8W9ZfUs88M6ImI0aSnM1nyAtLrGhKpjryQ93Bf3nQKcUrVvSNXrATXaoarX44DqfQ+T5sWoKSIeJE2c2Wobq9S8ttYsxnXPA07OW6247wD7tHHuwTX2tXqMmZmZmZnZsi4iRlBnGEed56wHgO0XrV2OZT450RpJq5BWmfg/0jqv3UZ3vjYzMzMzMzNbujg50bo/AR8DfhURf292YzpZ3WuTdBvw8TrH/TQiflp248zMzMzMzJZ47RxeYfU5OdGKWl1buos2ru1QYIU6Ze90fmvMzMzMzMxsWaa0iqbZsuXA9fYp9Y1/44SHywzP4LU3LzX+w++2tmLt4pu7YH6p8d88eJNS48e8BW1XWgyjb1up1PgP9exbavwvf3h8qfFXv7HchZMO2fr4UuNvG+Xe/2d7lLK613/jz3+31PiXrNSeFbHbb+3ty/37M/Xpcv9f9cbrK5caXyq3/Zse+75S43/w5LtLjb/jahuVGn/kjHL/fs5ZMK/U+M/stFap8ee8W+5c/q+/skqp8R9Sv1Ljb71gZqnxt/zFFqXGX+HAU9V2rSXP1G/t1bQH6pUvuWOpvGf1eLUOs07mxETrnJhonRMTrXNionVOTLTOiYnWOTHROicmWufEROucmDBrm4d1mJmZmZmZmXWE55zoNO45YWZmZmZmZmZN1a7khJJfS3pHUkgaVFbDOkLSlZJuXozj75Z0XuH1OElHd07ryiFpwJL4szAzMzMzMzNrVHuHdewNDAEGAy8Ckzq5PUBKEgBPRESzEwPbAjOa3Ia2jAfWoaSfhZmZmZmZmdXhYR2dpr3JiQ2ACRFxf61CSb0jotzZuLpQRLzV7Da0JSIWAG80ux1mZmZmZmZmHdXwsA5JVwK/BPrnYQTj8jCIEZLOkzQJuCPXPVbSSEkzJI2XdJG08BS1knbKx8+U9K6kOyStls+zC3BUPk/koQs9JV0u6SVJsyQ9J+mojl64pBUlXS1puqQJko6rUWehYR25Ld+SdGtu92hJO0j6cL6WGZLul7RBVZzPS3pM0mxJL0o6WdJyVXEPlfTHHHespM8VyleTdK2kt/K1j5V0SC5bZFiHpF0kPSRpTr62n1Wd725JF0j6eR6i84akU9px78q6D62+byQNkTRZ0l75nNMl3S5pnUbbbmZmZmZm1lmiJZq2dTftmXPiKOAk4FXSMIJt8/6DgbnATsC3874W4Ehgs1z+SeDnlUD5QfouYBSwA7AzcAvQM5/nAeDSfJ51SEMXeuRzfwnYFDgN+KmkL7fjGorOJiVBPg/sSRqqslUDx/0EuBoYBDwL/Ba4BBgGbAMIGFG41o/n+ufndn+LNDTmx1VxTwZuALYE/gpcK2n1XHZ6PvZTwCbAd6gzjEPSB/LxDwMDc91vACdWVT2YNGRlO+AE4CRJezRw/RVl3IdW3zdZX+B44KvAJ4D+wPB2tNvMzMzMzMyWMA0P64iIKZKmAQsi4g0ASQBjI+KEqrrnFV6Ok3Qi8Cvg8LzvBOCRiDi8UO+ZyjeS5gIzK+fJFpAe4CtekrQD8GXSQ33D8qfx3wC+EhF35X0Hk5IfbbkiIm7Ix5xFSqScHhGVXiPnA1cU6p8M/CwirsqvX5T0E9JD96mFeldGxHU5xo9ID+kfA24nPYA/HhGP5LrjWmnf4aRkzhEREcCzktYFzpJ0WkS05HpPRUTl/GMlHQHsBvy9gXtQyn1o4H0D0Av4dkS8kM8zgpQ0MzMzMzMzs6VUe+ecqOXR6h2SdgeGAh8BVs7nWV5S34iYSfq0/cb2nkjSd4Gvkx7WVwB6A090oM0b5GP/U9kREe9Ieq6BY58qfD8xfx1ZtW95SStHxFRS74WdJBV7CPRk4fuxUNyImCFpKrBW3nUx8AdJWwF/A26uN+8HqWfFAzkxUXEf0A/4IPBKjesAmFA4XyM6/T408L6BlLR6oT3tltQH6FPc96X/+Sw91bOhCzUzMzMzM6upGw6vaJZ2LSVax0KrWUgaANxKenj9ArA18N1c3Dt/ndXek0g6gNR9/3LSMIxBpE/me7d2XAnmFb6PVvZV7m0/Uq+BQYVtC2BDYHaduJU4PQAi4jZgPeBcYF3gLkmLO5Sh7vk6cPxi34cG3zf12q022joUmFLcRk0Z28YhZmZmZmZm1lU6o+dEta1JD6THVYYQ1JgX4inSEIKTqW0u6VP1op2A+yPiosqO6gkX2+EF0kPuduSeBJJWAzYC7ulgzHoeAzaOiOcXJ0heOeQq4CpJ95LmzDi+RtXRwBckqdB7YidgGo0NWylLq/dBUiPvm44aBpxT3LHpKhtO6aTYZmZmZma2rGppu4o1pozkxPOkeQG+J+kWFp4os2IYMFLSRaQ5BeYCuwI3RsQk0pwK2+VP06cD7wBjga9J2gt4iTQh4rb5+3aJiOmSLgfOlvQ28CZwJuW8tU4DbpX0CvD7fI6BwOYRUT1JZU2STiMNn3mGNDzhs6QkRC0XAUcDv8zzMWxMmtPhnMJ8E83Q1n1o5H3TIRExB5hT3Hfgevt0RmgzMzMzMzPrBJ0xrGMhEfEkcCzwA+Bp4CBSt/pinTGkoRkDgYdIkyl+HpifqwwnTYA5CniLNMfEJcBNwPWkuSLWID2Id9T3gXtJq4TcCfybGvNnLK48QeRnSdf7MPAgcAzwcjvCzCUldJ4C/kW6NwfUOd9rwKdJk2k+SUr+XA6c0bEr6Bxt3YdG3jdmZmZmZmZLEi8l2nna1XMir6ZwXuH14Dr1ziXNj1B0TVWde0ifjtc6fgxpidFqh+St6L0H2IgYUrvlNc8xndT74quF3WdX1RlQ9VpVr8dRNd9BRNxdY98dwB2ttGWRORMiYtXC92dQJ7lQpw33kJIT9c43uMa+hrsSlHgfWn3fRMSVwJVVx9xcfR4zMzMzMzNbunR6zwkzMzMzMzMzs/YoY86JppPUnzQkpJ5NI+KVVsqXWZIOIg2hqeXliNisK9tjZmZmZma2xOqGwyuapVsmJ4DXSUtVtlZutf2ZNKdHLdXLeJqZmZmZmZkttm6ZnIiI+aTVH6ydImIaadlRMzMzMzMza42XEu00inA3FFv2TP3GHqW98Zf7yP+UFRqAu4fPLDX+4B+sVGp8rVxu/Ad+/Gqp8bccNLHU+Ctss1ap8bXWGqXGf+inb5ca/7LlF5Qa/4pHh5ca/7qBJ5Ua/4D//KDU+MydVWr4iwdfUGr8T5Sce58zv2ep8Tfattzfr7se+mCp8eeXPH3153+yeqnxrz3z3VLjH3RiuX+f1bdvqfF/+ZPxpcYfNHt+25UWwwf7lfv3YZ2tZpQaf/jD65Yafwrl/vsLMGLc9UvlJPeT99+1aQ/Uq17/z6XyntXTLXtOmJmZmZmZmZWtOy7p2SxercPMzMzMzMzMmsrJCTMzMzMzMzNrKicnrCZJQyRNLrw+RdITzWyTmZmZmZnZEqWliVs34+SENWo4sFuzG2FmZmZmZmbdjyfE7OYk9Y6IuYsbJyKmA9M7oUlmZmZmZmbdgifE7DzuOdHNSLpb0ghJ50maBNwh6VhJIyXNkDRe0kWS+lUdN0TSK5JmSvojsEZV+ULDOvJ5zquqc7OkKwuvD5c0VtJsSRMl/b7Ba/hibu8sSW9LulPSioXyQyWNznGflXR4u26SmZmZmZmZLVHcc6J7Ohi4GNgpv/4UcCTwEvAh4CLg58DhAJK2Ay4HhgI3A3sDpy5OAyRtA1wAfBW4H1gd+HgDx60DXAecAPwRWCkfp1x+EHAacATwOPBR4FJJMyLiqsVps5mZmZmZWbt0w7kfmsXJie5pbEScUHj9XOH7cZJOBH5FTk4ARwG3R8TP8+sxknYkJSk6qj8wA7g1IqYBL5OSCW1Zh/S+vCkiXs77RhbKTwWOi4ib8uuXJG0KfAuomZyQ1AfoU9z35sGfpE9PdxwyMzMzMzNbEvjprHt6tPhC0u6S7pL0mqRpwDXAGpL65iqbAP+pivHAYrbh76SExIuSrpF0UOF8rXkSuAsYKelGSYdJWi1fx4rABsDlkqZXNuDEvL+eocCU4nbOky91/MrMzMzMzMysUzk50T3NqHwjaQBwK/AU8AVga+C7ubj3YpyjhTzUoqBX5ZvcW2Ir4EBgAmkoxpOSVm0taEQsAPYgDUUZBXwPeE7S+kBlnozDgEGFbXNg+1bCDgNWKW7HDly/7Ss0MzMzMzNrRbQ0b+tunJzo/rYm/ZyPi4gHI2IMsG5VndHAdlX7WnvYB3iLNAQDAEk9SUmC90TE/Ii4Mw8x2RIYAHyyrQZHcl9EnEyaU2IusG9ETAReBz4UEc9XbXW7QkTEnIiYWtw8pMPMzMzMzGzJ4Tknur/nST0avifpFtIkmd+uqnMBcJ+k44E/AXvR9nwT/wDOkfQZ4AXgWOC9XhGSPkuafPNfwLvAp0lJkucWDfVfeXLO3YC/AW+SkibvIyVQAE4GLpA0BbidNJfENsBqEXFOG202MzMzMzPrPN2wB0Oz+OPjbi4iniQlDn4APA0cRJqDoVjnQdJQiaNIcz7sCZzRRujfkCagvBq4B3gR+GehfDKwHymJMZqUEDkwIp5pI+5U4BPAX4ExuR3HRcRtua2XAYcCh5AmyrwHGEJaicTMzMzMzMyWQu450c1ExOAa+84Fzq3afU1Vnd+QEg5FvyiUnwKcUng9j7Tax+HUEBH/BhZpS1siYjRt9NqIiN8Cv21vbDMzMzMzM1syOTlhZmZmZmZm1gHdcWLKZnFywrqUpP6kVTjq2TQiXumq9piZmZmZmVnzOTlhXe110vKfrZWbmZmZmZkt+dxzotMoIprdBrMu93/r7VvaG//V+VPLCg3AJX37lBr/8FnzSo3fW+XmRDfruUqp8WeX/C9QX3qWGn8y5f58149y35/9QqXGX21BqeE58MnTSo1/w5YnlRp/bK9y/89w4ArvlBr/iSmrlxp/as9y35/9Wsq9/x9bfVKp8ce+tVqp8U9f7s1S41+1Sq9S4x8yZX6p8Xur3H9feqrcefbnRrn3p4/K/flOWTCr1PgLSv7/yfuXW7nU+AC3vHJruX9ESzJpr12a9kC95h33LJX3rB73nDAzMzMzMzPrAM850Xm8lKiZmZmZmZmZNZWTE2ZmZmZmZmbWVB7WYWZmZmZmZtYBHtbRedxzYikk6W5J5y3pMc3MzMzMzMwa4Z4T1qkk9Y6Iuc1uh5mZmZmZWdncc6LzuOfEUkbSlcAuwFGSIm8DJG0u6TZJ0yVNlHSNpDXzMYMlzZX08UKcEyS9KWntVmIOkTS56vz7SIrC61MkPSHpUEkvAbPz/lUlXSbpLUlTJf1D0sAGr3GgpH9KmpaPfVTSNoXynSXdK2mWpPGSLpC0YodvqpmZmZmZmTWVkxNLn6OAB4BLgXXyNg34B/A4sA2wN7A2cANARNwNnAdcI2kVSR8FTgcOjYiJdWKOb0ebPgx8AdgPGJT33QisBXwK2Bp4DLhLUiOLzF8LvApsm4/9GTAPQNIGwO3AH4Atgf2BnYER7WivmZmZmZnZ4gs1b+tmPKxjKRMRUyTNBWZGxBsAkk4EHo+IH1XqSfo6MF7SRhExBjgR2AP4NbA5cFVE/LlezByj0Wb1Br4WEW/l43YGPgasFRFzcp3jJe0DfDG3oTX9gbMj4tn8emyhbChwbURU5scYK+lI4B5J34mI2Y022szMzMzMzJYM7jnRPQwEds1DOqZLmg5UHuw3AMjzQBxE6uGwPHBMJ57/5UpiotCefsDbVW1av9KeNpwDXCbpTkk/zL0lirGHVMW9g/ReXr9WMEl9JK1c3BbEgo5cp5mZmZmZmZXAPSe6h37ALcAPapRNKHy/Y/66et5mtBG3BajuPtGrRr3qOP3yeQfXqDu5xr6FRMQpkn4LfIY0LORUSQdExB9z7EuAC2oc+kqdkEOBk4s7Rk0ZwxarbtJWU8zMzMzMzOryhJidx8mJpdNcoGfh9WOkHhHjImJ+rQNy74NzgcNI8zRcJWn3iPd+napjArwFrCRpxYioJCAG0bbHgPcD8yNiXAP1F5GHoowBzpV0HXAI8Mcce9OIeL4d4YaRemO8Z9NVNprSkXaZmZmZmZlZ5/OwjqXTOGC7vKLGmsCFpJ4Q10naVtIGkvaSdIWknpJ6Av8PuCMiriA96G8JHFcvpqQewH+AmcBPc8z/A4Y00L47SRNs3ixpzxxzR0lnFlfdqEXSCpJG5BVG1pO0E2lizNG5ylnAjrnOIEkbSvq8pLoTYkbEnIiYWtx6qjoPY2ZmZmZm1j7RoqZt3Y2TE0un4cACYBSpd0NvYCdSz4e/ASNJq3NMJg3N+DGwHvAtgIiYAHwTOKOwvGd1zP4R8Q7wFeDTOeaBwCltNS4iIh/zL+AKUg+I3+U2TGzj8AXAGsDV+bgbgNvIwzIi4inSsqcbAfeSVig5DXi9rXaZmZmZmZnZksnDOpZCecjDDjWK9qtzyGl5K8a4CejTVsyIuBm4uWr3pYXyU6iRsIiIacCReWtYnrjzwDbqPAzs2Z64ZmZmZmZmnc1zTnQe95wwMzMzMzMzs6ZycsK6nKRnikuBVm0HNbt9ZmZmZmZm1rU8rMOa4dPUXpIU2p6TwszMzMzMbIkQ0f0mpmwWJyesy0XEy81ug5mZmZmZmS05nJywZdLFg6eWFrvXpv1Liw3wr3NmlRr/L0NXKTU+ffuWGv6Bn5S7cMvWu08qNX6vTdYtNb5WW6PU+I+e/kap8X+7fLmjEc+//yelxr9hy5NKjf/lp05ru9JiaHmn3N+vEbv/qtT4+675Zqnxp05dvtT46+82o9T4/7pjrVLjz+lR7u/v37+/Yanxrxte7v2/4yfl/n3WcuUuo37e6eX+fu0+v9z7v8ZqJcffqtz7f+7dHyg1/ix51sd6PCFm5/GcE2ZmZmZmZmbWVO45YWZmZmZmZtYB0eI5JzqLe06YmZmZmZmZWVM5ObGUk3SlpJub3Q4zMzMzMzOzjvKwjqXfUUDpfYkk3Q08ERFHl30uMzMzMzOzpUFEs1vQfTg5sZSS1BOIiJjS7La0h6TeETG32e0wMzMzMzOzJYeHdXQRSXdLGpG3KZImSTpdknJ5H0nDJb0maYak/0gaXDh+iKTJkj4naRQwB+hfPawjn+eXks6T9K6kiZIOk7SipCskTZP0vKRPVbVvc0m3SZqej7lG0pq57EpgF+AoSZG3AW0dV3Xd50maBNzRxn2SpFMkvSJpjqTXJV1QKG/1PpmZmZmZmXWVaFHTtu7GyYmudTAwH/gYaTjGscChuWwEsANwALAlcCNwu6Tiot19gR/kYzYD6i0ofTAwKZ/nl8DFOd79wFbA34BrJPUFkLQq8A/gcWAbYG9gbeCGHO8o4AHgUmCdvI1v4Lhie+YCOwHfbuMefQE4BvgWsCGwDzCyUN7IfTIzMzMzM7OliId1dK3xwDEREcBzkrYAjpF0B3AI0D8iXs91h0vaO+//Ud7XCzg8Ip6sBMwdL6o9GRFn5PJhwA+BSRFxad53GvAd0sP9g8ARwOMRUTkPkr5OSkBsFBFjJM0FZkbEG4U6bR6Xd4+NiBMavEf9gTeAOyNiHvAK8FCO3b/B+7QQSX2APsV9Ew8aTJ+ezs2ZmZmZmZktCfx01rUezImJigdIvQO2AHoCY/LwiOmSppOGUmxQqD8XeKqB87xXJyIWAG+zcO+DifnrWvnrQGDXqnM/m8uK56/W6HGPNtDmihuBFYAXJV0qaV9JlSRao/ep2lBgSnE7Z+S4djTJzMzMzMxsUR7W0Xncc2LJ0A9YAGydvxZNL3w/qyq5Uc+8qtdR3BcRkXtcVJJT/YBbSENGqk1o5TyNHjejjfb+t6ER4yVtDOwO7AFcBHxf0i40fp+qDQPOKe44dosBS9VEomZmZmZmZt2ZkxNda7uq19sDY0lzNvQE1oqIe7u8VfAYaa6HcRExv06duaQ2tve4douIWaSkxy2SLiT1xtiCDt6niJhDmkD0PVMO3q2zmmtmZmZmZssoLyXaeTyso2v1l3SOpI0lHQh8Dzg/z81wLXC1pP0krS/pY5KGSvpMF7TrQmB14DpJ20raQNJeeXWPSkJiHLCdpAGS1pTUo8Hj2iWvSvKNvArIh4CvALOAl5eA+2RmZmZmZmYlcM+JrnU1aT6Fh0jDEs4Hfp3LDgFOBH4BfIC02saDwK1lNyoiXpe0E3AWaSWPPsDLwO1AS642HLgKGJWvYf2IGNfAce01mTSB5zmkXhIjgf+NiLdzedPuk5mZmZmZWVF3nPuhWZyc6FrzIuJo0koZC8krU5yct0VExJXAlTX2D6l6PbhGnQE19qnq9Vhgv3oNz70Wdqixv63jFmlPayLiZuDmVspbvU9mZmZmZma29PGwDjMzMzMzMzNrKvecsC4l6SDgkjrFL0fEZl3ZHjMzMzMzs46q6pBui8HJiS7S3uEN3difgf/UKateAtXMzMzMzMyWAU5OWJeKiGnAtKa3Y355a/5ozTVKiw0wR6+XGl+rrVZqfFZdvdTw7/Z4s9T4M14sNTyrrDuj1Pjqu0Kp8V/psXyp8Z+dP7HU+MydVWr4sb3KXW+s5Z1y/z70WH3dUuNP6LGg1PjPv1nu3595lPvp2UqPzmm70mJ4uk+HFtpqWAvlvv8/s+aapcaHkv8+r7RyqfFZtdz/n7RQ7t/nd+f3KTX+ijPnlho/Zpf7Gdx0dXQu+sa8488Q64pyb32nkvRd4PvA+4Enge9FxEMNHHcAcB3wp4jYp6z2ec4JMzMzMzMzs25M0v6kFRFPBbYiJSfukLRWG8cNIK3ceG/JTXRywszMzMzMzKwjWkJN29rpWODSiLgiIkYB3wZmAl+vd4CknsC1pJUSS+4/7OSEmZmZmZmZ2VJHUh9JK1dti4yBktQb2Bq4s7IvIlry6x1aOcVJwJsRcXlnt70WJyfMzMzMzMzMlj5DgSlV29Aa9dYEesIik8NMJM0/sQgCKgA5AAAgAElEQVRJOwPfAA7rrMa2xRNimpmZmZmZmXVAk5cSHUaaR6JosWdPlrQScA1wWERMWtx4jXJywtpFUq+I8HS9ZmZmZmZmTRQRc2gsGTEJWACsXbV/beCNGvU3AAYAt0jvJV96AEiaD2wcES90oMmt8rCOZZykvSX9W9JkSW9LulXSBrlsgKSQtL+keyTNBg7KZYdKGi1ptqRnJR1eFfcsSWMkzZT0oqTTJfVqsE0DJf1T0jRJUyU9KmmbQvnOku6VNEvSeEkXSFqxE2+LmZmZmZlZm6JFTdsabmPEXOBRYLfKPkk98usHahzyLLAFMKiw/Rn4Z/5+fEfvV2vcc8JWJHUFegroB5wG/FHSoEKdnwHHAY8DsyUdlOsdkfd9FLhU0oyIuCofMw0YArxOemNfmvf9vIE2XZvjfoeU4RsEaXHlnDi5HTiRNLPs+4AReTuk3VdvZmZmZmbW/Z0DXCXpEeAh4GjSs+AVAJKuBl6LiKERMRt4uniwpMkAEbHQ/s7k5MQyLiL+UHwt6evAW8CmwPS8+7yIuKlQ51TguMK+lyRtCnwLuCrHPaMQdpyk4cABNJac6A+cHRHP5tdjC2VDgWsj4rxKmaQjgXskfSf/IpmZmZmZmZUuotktaExEXC/pfaQPmd8PPAHsHRGVSTL7Ay3Nah84ObHMk7Qh6Q26HWkW18pQn/7AqPz9I4X6K5LGIF0u6dJCqOVIs8NW6u0PHJnr9svlUxts1jnAZZK+Slre5sbCmKaBwJa598Z7p8vtXh8YXeMa+wALLanzxv670KenRzWZmZmZmdmyISIqPc5rlQ1u49ghJTRpIX46s1uA1UlLxGyXN4DehTozCt/3y18PY+ExSJsD2wNI2oE0NOOvwGdJwz7OrIpZV0ScAmwG/AX4JDBK0r6F819Sde6BwIZAvUlZFlli59xnXm6kKWZmZmZmZtYF3HNiGSZpDWBj0hIx9+Z9O7d2TERMlPQ68KGIuLZOtR2BlyPizMK51mtP2yJiDDAGOFfSdaT5JP4IPAZsGhHPtyPcIkvsHLPZelPq1DUzMzMzM2tIeyamtNY5ObFsexd4G/impAmkoRw/a+C4k4ELJE0hTU7ZB9gGWC0iziHNEdFf0gHAw8BngH3rBSuStAJwNvB74CXgg8C2QGVujLOAByWNAC4j9erYFNgjIo6oFbPWEjuTD/pkI80xMzMzMzOzLuDkxDIsIlpyAuEC0mysz5Hmibi7jeMukzQT+D4pkTADGAmcl8v/LOlc0nimPqThGacDpzTQrAXAGsDVpHV3JwE3kRIiRMRTknYhDRO5lzTfxAvA9Q1etpmZmZmZWadoCfec6CxOTizjIuJOUs+DItX5vnjcb4HfthL3BOCEqt3n1apbddxc4MA26jwM7NlWLDMzMzMzM1s6ODlhZmZmZmZm1gHhnhOdxqt1WJeT9Iyk6XW2g9qOYGZmZmZmZt2Je05YM3wa6FWnbGJXNsTMzMzMzMyaz8kJ63IR8XKz22BmZmZmZra4Iprdgu5D4btpy6C9/udTpb3xn57+SlmhAdh9lY+UGv9f054vNX7f5ZYvNf7lPT9Qavy7e/UtNf50tZQafyLzSo2/zfw+pcbfdaVJpca/bcaapcbfu+/bpca/deYapcaf0GNBqfGHPXJmqfEf2/L4UuNPWlDu+3+1HnNLjb/2GtNLjd9zuXL/vm0/7tVS42+50nqlxh81o9z29+1Z9r+/A0qNP6z3zFLjl23agjltV1oMLZT7TLdrr/eXGh/gjHG/XSonb3hqwP827YF6y3G3LJX3rB73nDAzMzMzMzPrAC8l2nk8IaaZmZmZmZmZNZWTE2ZmZmZmZmbWVB7W0U1JuhJYNSL2aXZbzMzMzMzMuqPwsI5O454TSzlJAySFpEHNbouZmZmZmZlZR7jnhJmZmZmZmVkHePHLzuOeE+0k6YuSRkqaJeltSXdKWlHSlZJulvQjSRMlTZZ0kqTlJJ0t6R1Jr0o6pCreFpL+UYj3a0n9CuU9cpxXJc2R9ISkvQshXspfH889KO6uin+8pAk59oWSehXKxuX2/kbSNEmvSPpm1fH/I+mGfD3vSPqTpAGF8sGSHpI0I9e5T9J6uWygpH/m2FMlPSppmwbu8XqSbpH0bo77jKRPF8o3l3SbpOn5Xl8jqdz1/8zMzMzMzKw0Tk60g6R1gOuA3wCbAIOBm4DKQKNPAusCnwCOBU4FbgXeBbYDfgVcIumDOd6KwB25fFvgS8DuwIjCaY8CjgOOB7bM9f8sacNc/rH8dXdgHWC/wrG7AhvkrwcDQ/JWdBzwCPBR4CLgYkkb5/b1yuebBnwc2AmYDtwuqbek5YCbgXty23YAfg3vLbR8LfBqvratgZ8B8xa9s4u4EOhDuo9bAD/I50XSqsA/gMeBbYC9gbWBGxqIa2ZmZmZm1mlaQk3buhsP62ifdUj37KaIeDnvGwkgCeAd4MiIaAGek3QC0DcifprrDAN+COwM/A74P2B54GsRMSPXOQK4RdIPImIiKSlxVkT8Lp/vB5J2BY4Gvgu8lfe/HRFvVLX3XeCIiFgAPCvpL8BuwKWFOn+NiIvyuc8CjiElM54D9iclsA6NSB2Wcs+PyaTEzCPAKsCtEfFCjje6ELs/cHZEPJtfj23t5lYd94eIGJlfv1goOwJ4PCJ+VNkh6evAeEkbRcSY6mCS+pCSHe/Z/QN70kPOzZmZmZmZmS0J/HTWPk8CdwEjJd0o6TBJqxXKn8mJiYqJ5OQFQE4SvA2slXdtAjxZSUxk95F+LhtLWpnUE+O+qnbcl49tyzP5nBUTCueueKrQvgDeKNQZCHwYmJaHUEwnJWCWBzaIiHeAK4E78jCMo3LvkopzgMvy0JcfStqggTYDXACcmIeInCppy0LZQGDXSntymyrJj3rxhwJTituLU1+oU9XMzMzMzMy6mpMT7ZAf9PcAPgWMAr5H6iGxfq5SPWQh6uzrqvveyLlbq9MPeBQYVLVtBPwWICIOIQ3nuJ/U02KMpO1z2SnAZsBfSENeRknat61GR8RlwIeAa0jDOh6R9L1Cm26p0aYNgX/VCTmM1MPjve1DKzeaJzEzMzMzM6stQk3buhsnJ9opkvsi4mTSPA1zgTYfuOsYDQzMc09U7AS0AM9FxFTg9byPqjqj8vdz89eeHWxDax4jPfS/GRHPV21TKpUi4vGIGBYROwJPk4arVMrGRMS5EbEnaX6OQ6pPUktEjI+IX0XEfsAvgMMKbdoMGFejTTPqxJoTEVOLm4d0mJmZmZmZLTn8hNYOkrbLq1tsI6k/afLJ97HwPAvtcS0wG7gqr0CxK/BL4Jo83wTA2aR5JvaXtLGkn5F6Cpyfy98EZgF7S1pb0iodbEu99k0C/iTp45LWz6tzXCDpg/n1MEk75BU29iQlM0ZLWkHSiFx/PUk7kSbGbPNeSTpP0l45/lakOTAqx10IrA5cJ2lbSRvkuldIKiNBY2ZmZmZmVpMnxOw8nhCzfaaSVpA4GlgZeBk4LiJuk7R/e4NFxExJe5ESDQ8DM4E/kFb6qLiANBThF6S5IEYBn4uIsTnGfElHAicBpwH3kiarXGy5fZ8AziL1elgJeI0078ZUYAXgI6SVQNYgzWlxIXAJ6b21BnA1aTWNSTnGyQ2cumeO88F8nttJE3USEa/nRMdZwN9IE12+nOu01IxmZmZmZmZmSzQnJ9ohIkaTlq6sVTakxr7BNfYNqHo9kjQfQ71ztpCWJD21lTqXAZc10J6jW2tL3jeo6vUbpORDLVOpP6RlLnBgvTa3JiK+10b5WBZeMtXMzMzMzKzLRbMb0I14WIeZmZmZmZmZNZWTE9blJN1WXAq0avtRs9tnZmZmZmZmXcvDOqwZDiXNV1HLO13ZEDMzMzMzs47qjhNTNosiPErGlj13rb1/aW/88cv1Kis0APNK/vu3xvxy5xWd3rPcDlsbLJhdavythn2o1Pgtb0xsu9JimPmPcaXGn/5m71Ljr75Vuf9mvXjXim1XWgxj5vUrNf62a75Vavzn31y91Pir9JzbdqXFsNVTw0uNP//+m0qNz7uTSg3/8plPlRp/veF1p9jqFL/7zhOlxn+r5I/0Np5T7r+/k5Yr99/fvi3l/n3+/DF9So0fU6eXGn/G/eX++/7Y0+uUGn+nL00tNT7ASiP+ulQ+5d+/zhea9kC944Q/LJX3rB73nDAzMzMzMzPrgHDPiU7jOSfMzMzMzMzMrKncc8LMzMzMzMysA8odkLVscc8JMzMzMzMzM2sqJyeWAZJC0j7NboeZmZmZmZlZLR7WYWZmZmZmZtYBgSfE7CzuOVEgqYekEyQ9L2mOpFck/TiXbSHpH5JmSXpb0q8l9Ssce6WkmyX9SNJESZMlnSRpOUlnS3pH0quSDikcMyD3ajhA0v2SZkt6WtIuhTo9JV0u6aV87uckHVWj7V+X9Exu9wRJI/L+cbnKH/O5xuX9p0h6QtJXJY2TNEXS7yStVHU/hhbO/aSkLxbKV5N0raS3cvnYyvVJ6i1pRG7LbEkvSxrawM9AuW2v5Gt5XdIFhfI+koZLek3SDEn/kTS47Z+umZmZmZmZLancc2Jhw4DDgGOAfwPrAB+RtCJwB/AAsC2wFnAZMAIYUjj+k8CrwCeAnYDLgR2BfwHbAfsDl0j6e0S8WjjubOBoYBRwLHCLpPUj4m1SAulV4EvA2zneryVNiIgbACR9BzgH+CFwG7BKPj+5vW8ChwC3AwsK590A2Af4LLAacEOO8eNcPhT4CvBtYGy+rv8n6a2IuAc4HdgU+BQwCfgwsEI+9kjgc8CXgVeA/8lbW75Auv8HAM8A7wcGFspH5HMeALwO7AvcLmmLiBjbQHwzMzMzM7NO0RLNbkH34eRElnsMHAUcERFX5d0vAP+WdBiwPPC1iJiR6x9BSiL8ICIm5vrvAEdGRAvwnKQTgL4R8dN8zDDSw//OwO8Kpx8REX/Idb4D7A18A/h5RMwDTi7UfUnSDqSH/hvyvhOBX0TE+YV6DwNExFuSACZHxBtVl90DGBIR0/K5rwF2A34sqQ/wI2D3iHgg139R0s7At4B7gP7A4xHxSC4fV4jdn5TQ+HdEBPAyjekPvAHcma/9FeCh3L7+pCRL/4h4PdcfLmnvvP9HDZ7DzMzMzMzMliAe1vFfmwB9gLvqlD1ZSUxk95Hu38aFfc/kxETFRGBk5UVELCD1flirKv4DhTrzgUfyOQGQ9F1Jj+bhE9OBb5Ie4pG0FrBunXa3ZVwlMZFNKLTtw0Bf4O+Splc24GukHhcAFwMH5OEhP5e0YyHWlcAgUpLmAkl7NtimG0m9L16UdKmkfSVVkmhbAD2BMVVt2qXQpkXkoSArF7e5saBedTMzMzMzM+ti7jnxX7M6Ica8qtdRZ1/DSSFJBwDDgeNISYxpwPdJw0Rg8drdWtsq82l8Bnitqt4cgIi4TdJ6wKeBPYC7JF0YEcdHxGOS1icN+dgduEHSnRHxRVoREeMlbZyP2QO4CPh+noejH2lYytYsPDwFYHorYYeycO8TrpsxmoP7bd5aU8zMzMzMzFrV4gkxO417TvzXWNKD/m41ykYDA/PcExU7AS3Ac51w7u0r3+ReAlvnc1bOc39EXBQRj0fE8xR6CeSeD+PqtLtiHqnHQXuMIiUh+kfE81Xb+ML534qIqyLiK6R5M75ZKJsaEddHxGGk+Ta+IGn1tk4cEbMi4paIOBIYDOxA6jXxeL6OtWq0qXrIStEw0jwc720HrrhJK9XNzMzMzMysK7nnRBYRsyWdBfxc0lzSsI33AZsB1wKnAldJOiXv/yVwTWG+icXxXUljSQmJY0iTU/4ml40FviZpL+Al4KukSS5fKhx/CvArSW+SJsRcCdgpIn6Zy8cBu0m6D5gTEe+21aCImCZpOHCupB6kCUIrE21OjYirJJ0GPEqauLIPaWLN0QCSjiUNE3mclMT5EmkuicmtnVfSEFIC4j/ATNKEnLOAlyPibUnXAldLOi7Hfh8pMfNURPylzrXMIff2qLhr7f3bugVmZmZmZmat8lKincfJiYWdDswHTiPN4zAB+FVEzMzJgfNJE03OBP5AWlmjM/wwb4OA54HPRcSkXHYJ8FHgetKwi+tIQx0+VTk4JwqWJyU2hpNWzvh9If5xpNU8DiMN0RjQYLt+ArxFGhbxIVJi4THgp7l8LqlXwgBSAuFe0ioakIafnABsSBqC8TDw6ao5OWqZTLoX55CSFCOB/80rl0Ca+PJE4BfAB/K1Pgjc2uA1mZmZmZmZ2RLGyYmC/OB8Zt6qy0aSlgqtd+yQGvsG19g3oMbhoyNiuxr7K5/6H5K3oqFV9S4hJTJqxbgFuKVq3ymkHhfFfecB5xVeBykhU1wFpFj/DOCMOmWXApfWKmtNRNwM3NxKeWX1kpPr1TEzMzMzM+sKbX3yao3znBNmZmZmZmZm1lROTliXknRQcRnQqu2ZZrfPzMzMzMzMup6HdTRRRIyDZW4GlT+TJruspXppUzMzMzMzsyWWJ8TsPE5OWJfKS59Oa3Y7zMzMzMzMbMnh5IQtk6apZ2mx55WcPH23vKYD0K+l3Asoe9KgifQuNf68+58oNf5ym6xXavxZ75b7Z3/SpH6lxl/u6amlxp8zv9xfsKk9y/39mjp1+VLjzyv506FJC/qUGn/+/TeVGn+5HfcrNf78u64pNf6bU1csNX7/N14rNf7ckv/9napy/wWb2aPcC5hfanSYVPLft+m3v1Bq/BU/We6/v7OnlPvv72u9yo3/7oNzSo0PsFLpZyiHJ8TsPJ5zwszMzMzMzMyayj0nzMzMzMzMzDrAPSc6j3tOmJmZmZmZmVlTOTlh7SJpiKTJzW6HmZmZmZmZdR9OTlh7XQ9sVHkh6RRJ5c4QaGZmZmZmtgQK1LStu/GcE9YuETELmNXsdpiZmZmZmVn34Z4TXUxSD0knSHpe0hxJr0j6cS7bQtI/JM2S9LakX0vqVzj2Skk3Szpe0oRc50JJvQp1+kg6S9L4HP95Sd/IZT0lXS7ppXyO5yQdVTh2T0mzJa1a1ebzJf0jf//esA5JQ4CTgYGSIm9DJP1G0q1VMXpJerPSljbu0RcljSzchzslrVgoP1TS6NzWZyUd3q4fgpmZmZmZWSdoUfO27sY9J7reMOAw4Bjg38A6wEfyw/cdwAPAtsBawGXACGBI4fhdgQn564dJwyyeAC7N5VcDOwBHAk8C6wNr5rIewKvAl4C3gR2BX0uaEBE3AHcBk4EvAJdDSmgA+wM/rnEt1wObA3sDu+d9U4AxwL8krRMRE/L+zwJ98zF1/X/27jTMjqpc+/j/TpuEIcwKgr4hiIDMYRIxKEFEkIMeAQcQxeABBwZBQRBFCJPIIAIiDoAEOIgyyRFQUMAEDIiMIUIIQZIQCImBzPPQz/thrQ2Vnb27dzpd2Z3m/nHV1V2rVj21qnbTUE+vQdLGwE3AKcAfSEsefwRSvyVJhwNnA8cBTwE7AVdJmhMR17UV28zMzMzMzLomJydWIklrAScAxxVepP8N/F3S0cBqwBERMSfXPw64U9KpETE515+Wz18CPC/pbmAf0gv6lsDngX0j4r5c/6XK9SNiEamnQ8VYSXvkc26OiCWSfgd8kZycyLHXBW6rvp+ImCdpNrA4IiYVDj0saTTwZeDCXHYkcEtEzG7nMW1M+rm8PSLG57KRheNnASdFxO2Fe9gG+DpQMzkhqTfQu1h284afp6da2mmKmZmZmZlZfa3dcO6HZvGwjpVra9JL8v11jo2oJCay4aTPaKtC2bM5MVHxGqmXBUB/YAkwrF4DJB0r6QlJU3Ji4WtA30KVG4GBkjbJ+4cDd0fE8q7QcTUpIYGkjYBPAr9p4LwRpOczUtItko6WtF6OsyawOXCNpNmVDTg9l9dzGqlHx5vbbXOeW87bMTMzMzMzs7I4ObFydcZEkouq9oO3Psc240s6FLiY1CviE6RkxrVArzeDRTxG6s1xqKTVgYNICYvldT3wvtwz40vA2Ih4qL2TcuJlX1Iy4zngeGC0pM2AyvwbR+e2V7btgA+1EfZ8YJ3idsia23TglszMzMzMzKwMTk6sXGNICYR9ahwbRZpYcs1C2QCgFRjdYPyRpM90rzrHBwAPR8SVEfFURLxI7R4HN5J6THwqX//uNq65EFhmfEREvAHcQeo9MYiUBGlIJMMj4kzSnBILgYPy0JaJwPsi4sWqbWwb8RZExMzi5iEdZmZmZma2oqKJW3fjOSdWooiYL+kC4EJJC0nDNt4FbEtKCJwFXCdpcC7/GXBDYb6J9uKPk3Qd8BtJlQkxNwU2zBNejgGOkLQfMJY0J8Ru+fuiG4HBpEkwb42IBW1cdhywmaT+pMk2ZxXqXw3cRUpeNDRZpaTdScmbvwD/AXYnPYtRucqZwOWSZgD3kIbJ7AqsFxGXNHINMzMzMzMz61rcc2LlOwf4CWnFiVGk1Ss2jIi5wH7A+sBjwK2kuReOW87438znXgk8T1rFo9Ib41fA7fmajwIb5HpLyT0q/gnsQPtDOm4jJQn+BkwBDiscu480J8a9ETGxwfbPBD4K/Im06se5pAkw/5zbdjVwFKlHxkjS/BqDWDbBYmZmZmZmVqrWJm7djXtOrGQR0Qqcl7fqYyOBj7Vx7qAaZSdW7c8HvpO36roLSC/1R1YdOq1G3d3rtGEIMKQq5mfrNHlNYD3eWvmjXRExirQ0aVt1fgv8ttGYZmZmZmZm1rU5OWGdTlIP4J3AScB04I/NbZGZmZmZmVnna5WXEu0sTk5YGfqShlm8AgyKiMWVA5L6klbhqGebiHi55PaZmZmZmZlZF+LkhHW6iBgH1EshTiQt/1lPo3NTmJmZmZmZWTfh5IStVLkXxYvNbsd2679RWuz583uWFhvg/sXrlhp/t3VfLzX+vHm9So3f0qPc6YFaNl6/1Pi0lrsw1PoD+5Qaf+4f21rcZ8VNmrh2qfG33K283w0AYx9bvdT4m+0zp9T4az1R7uc7+Y1yfz6ZVu7vt8X331Bq/Hfs8+VS48OppUZvfeGlUuN/sORp3l9ijVLj79RneqnxZ83pXWr8UbFm+5VWwKK55S4Dv/j5V0uNv96e5f78vPGHUsMz7pWS//+H1PV6VdQdl/RsFq/WYWZmZmZmZmZN5Z4TZmZmZmZmZh3QHZf0bBb3nDAzMzMzMzOzpnJywpYhqZ+kkNTWxJVmZmZmZmZmncLDOqyWCcDGQMMzh0kaDHwmIpzQMDMzMzOzt4XWemsU2nJzcsKWERFLgEnNboeZmZmZmZm9PXTbYR2Sekg6RdKLkhZIelnSDwrHt5f0gKR5kt6Q9GtJfQrHh0i6Q9LJkl7LdX4uqWehTm9JF0iakK/xoqT/ycdaJF0jaWy+xmhJJxTO/YSk+ZKWWhdS0mWSHijs7ynpoRxjgqTLJdVdq0nSYElPS/p6rj9X0s2S1ql6NmdIeiW3+2lJ+xeOLzWsQ9LAvL+PpMdzzIclbZWPDwLOBHbM9ULSICWD87NfIGmipMsb/PyOkTQmP6PJkm6tav9phWc7QtJnG4lrZmZmZmbWWVpR07buptsmJ4Dzge8B5wDbAF8EJgPkl/t7gWnAbsDngI8DV1TF2BvYPH/9CjAobxXXA4cB3wK2Br4OzM7HegCv5NjbAGcDP5L0+Xz8fmA6cEglmKQW4AvAjXl/c+Ae4DZgh3xszxrtrPZ+4PPAp4D9gZ2AKwvHTwBOAk7Oce8F/ihpi3binpfP2xVYDPwml/8e+AnwLGk4yMa57BDg2/m5bAF8BhjZzjWQtCtwOXAGsFW+hwcLVU4DjgC+AWwL/BT4X0l7tRfbzMzMzMzMup5uOaxD0lqkF/DjIuK6XPxv4O/5+y8CqwFHRMScfM5xwJ2STo2IybnetBxjCfC8pLuBfYCrJG1JSgDsGxH35fovVdoQEYtIvQkqxkraI59zc0QskfS73JZrcp19gHVJyQhIL+E3RsSleX+MpG8BwyR9MyLm13kElXt7Nd/b8cDdkk6KiEmkpMQFEfG7XP9USXsDJwLH1okJ8IOIGJZj/jjHXC0i5kmaDSzO8cl1+pKGh9yXn8fLwD/biF/RF5gD3BURs4DxwFM5Zm/g+8DHI+KRXP8lSXuSkiDDGohvZmZmZma2wqLZDehGumvPia2B3qTeCfWOj6gkJrLhpOexVaHs2ZyYqHgN2DB/3x9YQhsvw5KOlfSEpCn55f1rpBfvihuBgZI2yfuHA3dHxPS8vyMwSNLsykbq5dAD2KzedYGXK4mJ7JHKvUlaG9gk32/RcNJzacszhe9fy183rFUxuwVYnZQ8uErSQZIaSYj9lZSQeEnSDZIOl7RGPvZ+YA3gr1XP5QhSL5dl5OE3axe3Ba1ekdjMzMzMzKyr6K7JiXmdFGdR1X7w1jNr8xqSDgUuJvWK+AQpmXEt0OvNYBGPkXp0HCppdeAg8pCOrA/wq3xuZduRNETi3x26oxVTfB6VJGHdn6GImEBK9hxDel5XAg8W5+2oc94sYGfSkJnXSENiRuT5OSrzgvwXSz+XbYB6806cBswobr9646U6Vc3MzMzMzGxl65bDOoAxpJfhfYCraxwfReqRsGah98QAoBUY3eA1RpJezPcC7qtxfADwcES8OddDnkOi2o2kHhOv5OvfXTj2JLBNRLzYYJsq+kraJCIm5v0P5dijI2KmpIm5fcVeHwNobMhFPQuBlurCiJgH3EkaMvNz4Hlge9K91RURi0nP9T5JZ5Hm5/gYqVfFAqBvZYhJA84HLikWfH2D981o8FwzMzMzM7OavJRo5+mWyYmImC/pAuBCSQtJQxbeBWwbEdeQEgJnAddJGpyP/Qy4oTDfRHvXGCfpOuA3eR6IEcCmwIYRcTMpQXKEpP2AscCXSZNvjq0KdSMwGPgBcGtELCgcuwD4h6QrSEmWOaQeAvtGxHFtNG9+vreTgbVJk0veXJgP4iLgLEn/Bp4GjilQU+oAACAASURBVCT1Pji8kXuvYxywWV7h4xVgFqnnQwvwKDAX+BIpaTS+rUCSDgTeR5oEcxpwACkRNDoiZkm6GPippB6keUTWISVXZhbmGHlTfqbF58qL2+zX4Rs1MzMzMzOzztVdh3VAWqXjJ6QhAaNIq0dsCBARc4H9gPWBx4BbSfNTtPXCX8s387lXknoEXAVUlvn8FXB7vu6jwAYsvWIGuS0vknos7MDSQzqIiGdIPTO2BB4iTQp5NjCRtr2Yr/0n4C+kuSKOKRy/nNST4CekHiD7A5+OiDHt33Jdt5FWFvkbMIWUmJgOHE1KDj1DWhHlUxHxRjuxpgMHAw+QPrtvAIdFxLP5+A9Jn+9p+fg9pGEe1YkfMzMzMzOz0rQ2cVteeU7EcZLmS3pU0gfbqHu0pIckTcvbfW3V7wzdsucEQES0kpa+PK/O8ZGkYQL1zh9Uo+zEqv35wHfyVl13AalHwpFVh06rUXf3NtrxGGnOiuUSEb8AflHnWCup58hZdY6Pg7cWzo2IocX9XPZ0VZ0F1J7z4Y7lazlExN+BgW0cD+CyvJmZmZmZmVkbJH2B9Afqb5D+eH4icK+krSLiPzVOGQjcBDxM6pl/KvAXSdtWLb7QabpzzwkzMzMzMzOz0kQTt+X0HeCqiLg2Ip4jJSnmAl+teV8Rh0fElRHxdEQ8DxxFyh/ss/yXboyTE7bSSfpIcRnQ6q3Z7TMzMzMzM+suJPUCdqGwkEPuTX8fsEeDYdYAegJTO72BWbcd1vF2FRGDSRNsdmWPkybgNDMzMzMzsw6Q1BvoXVW8oGqRBYB3khYqqF78YTLwgQYvdwFp7sNaK1V2CicnbKXLy4su7/KoZmZmZmZmXUqTlxI9DTizquwsOvmP1ZK+BxwKDMzzLpZCaW5Bs7eXL256UGk/+BMWzywrNADv67luqfFfWTyr1PgtKnc02WGt7yw1/mol/86c1lLuf+FebenI3M6N++ZGk9qvtAImvbp2qfHHLlmz/UorYOcNXi81/nOvr19q/H/1bik1/ufXmlJq/EULy23/f2aW+/NTtj3+dUGp8UftekKp8f9n4YxS46/Tsnqp8Re0Li41fmtHRqgvh77vKPf384ZardT4Czu09kHjJpf3PgfAeb3K/fm5dtE6pcYHuHDcTc19ze+ga977paa9UB/16o2r0UDPiTysYy7w2Yi4o1B+HbBuRPx3vWtIOhk4Hfh4RDzeaY2vwXNOmJmZmZmZmXVAM5cSjYgFETGzaqse0kFELASeoDCZpaTK5JaP1Ls3SacAPwT2LzsxAR7WYWZmZmZmZtbdXQJcJ+lx4J+kpUTXBK4FkHQ98GpEnJb3TwXOBr4IjJP07hxndkSUsoiBkxNmZmZmZmZmHVDugKDOExG/l/QuUsLh3cDTpB4RlUky+7L07XwT6AXcWhWq0+e0qHBywhomaRxwaURc2uy2mJmZmZmZWeMi4grgijrHBlbt91sJTVqK55ywTiWpJY9fMjMzMzMzM2uIXyJXAkmflTRS0jxJb0i6T9JekhYVxu5U6l4q6aH8/SBJ0yUdKGm0pLmSbpW0hqSvSBonaZqkyyW1FGKMk3S6pOslzZY0XtKnJb1L0v/lsmck7Vp17T0lPZTbOSHHXTMfGwpsCvxUUkiKqjZ+WtJzwAJgz/burZ3ntamkO/O9zZH0rKQDCse3k/TnfB+TJd0gqdwlGszMzMzMzKqEmrd1N05OlEzSxsBNwG+ArYGBwO2k2VJfAr5cqNsTODzXrVgD+BZpXdn98/l/AA7I25eBrwOfrbr0t4HhwE7A3cANwPXA/wI7A/8GrpekfO3NgXuA24AdgC8Ae/JWt5+DgVeAM4CN81Zs46nAUcC2wOMN3ls9PyctifNRYPsce3aOsy7wAPAUsGt+JhsBNzcQ18zMzMzMzLogzzlRvo1Jz/n2iBify0YCSLoGOBK4KJd/CliNpV+0ewLfjIh/53NuJb30b5RnSX1O0t+AvYHfF877U0T8Kp9zNmlCk8ci4pZcdgFp2ZiNgEnAacCNhfkkxkj6FjBM0jcjYqqkJcCsiJhUdY89gWMiYkSloMF7q6cvcFtEjMz7LxWOHQc8FRHfL1zrq8AESVtGxAsNxDczMzMzM1thq8qEmKsC95wo3wjgfmCkpFskHS1pvXxsCPB+SR/K+4OAmyNiTuH8uZXERDYZGFe1fMtkYMOq6z5TdRxyUqSqrHLejsCgPFRitqTZwL2kn5HN2rnHhVXXg8burZ7LgdMlDZd0lqQdCsd2BPauaufz+djmtYJJ6i1p7eK2JJY00AwzMzMzMzNbGZycKFlELAH2BT4JPAccD4yWtFlE/Ae4EzhS0ka5TvWwh0XVIeuUVX+Wb9aJiKgRq1JWOa8P8Cugf2HbEdiCNASkLfMK16hcs5F7qykirgbeRxqKsj3wuKTjC+28s6qd/XM7H6wT8jRgRnF7boY7WJiZmZmZmXUVHtaxEuQX9+HA8DzEYjxwEHAJcDVpTopXgH9HxPAmNfNJYJuIeLGNOguBljaOV+vwvUXEBOCXwC8lnQ8cDfwst/MQUu+RxQ2GO5/0rN+0zTpbzmi0LWZmZmZmZrV4WEfncc+JkknaXdL3Je0qqS9pYsl3AaNylXuBmcDpwLVNaibABcCHJV0hqb+kLST9t6TiOrjjgI9Kek+Dq2N06N7yqh77SdpM0s6k+TQqz+vnwPrATZJ2k7R5rnttccWSoohYEBEzi1tL7apmZmZmZmbWBE5OlG8madWJPwEvAOcCJ0XEnwEiopU0P0MLaTWNpoiIZ4C9gC2Bh0irYZwNTCxUOwPoRxrmMaWBmB29txZSEmIUaQWRF4BjcsyJwIBc5y+keTQuBabjxKWZmZmZma1E0cStu/GwjpJFxCjScpdteQ9pdY3Xqs4dQnq5L5YNBgZXlQ2q2u9Xox2q2h8HVJc9BnyiXiMj4h+keSjabGOVmvfWlog4vp3jY0g9UMzMzMzMzKwbcHKiiSStQ5rw8YvAp5vcnE7Vne/NzMzMzMwMoFXt17HGeFhHc/0faWjCLyPir81uTCere2+S/lxcCrRq+35zmmtmZmZmZmbN4p4TTRQRA5vdhrK0c29HAavXOTa181tjZmZmZmZmXZmTE7bSRcSrzW6DmZmZmZnZivKM/J3HyQl7W3qvVist9pyWRaXFBlin5H9tF7WsWW78kn+Fv2fh4lLj77Jrw3O7dsi818td5vaJ8e8uNf4rE9YtNf4OJ29QavwxF88tN/6U9UqNv6BHuaM1W0ueG7zlHeX+ftjkR/uWGr/vpHJz760vvFRq/FG7nlBq/K0fv6zU+O/dudz2r6mepcYve7D1ApaUGn9Aa59S4++zWrmda5csKfcDeHJBuf99/H3Jv59/+KlppcY3AycnzMzMzMzMzDrEPSc6jyfENDMzMzMzM7Omcs8JMzMzMzMzsw4od0DN20uX6jmh5NeSpkoKSf2b3aYiSUMk3bEC5w+VdGlhf5ykEzundeWQ1K8rfhZmZmZmZmbWfXS1nhP7A4OAgcBLwOtlXETSUODpiGh2YmA3YE6T29CeCcDGlPRZmJmZmZmZmXW15MTmwGsR8XCtg5J6RcTCldym0kTElGa3oT0RsQSY1Ox2mJmZmZmZdTWtanYLuo8uM6xD0hDgZ0DfPIxgXB4GcYWkSyW9Dtyb635H0khJcyRNkHSlpD5V8Qbk8+dKmibpXknr5evsBZyQrxN56EKLpGskjZU0T9JoSR1ek0rSmpKulzRb0muSTqpRZ6lhHbktX5d0V273KEl7SHp/vpc5kh6WtHlVnP+W9KSk+ZJeknSmpHdUxT1K0h9y3DGSPl04vp6kGyVNyfc+RtKR+dgywzok7SXpn5IW5Hv7cdX1hkq6XNKFeYjOJEmDG3xukjRY0ss5/kRJlxeO95Z0saRX8/N4VNLARmKbmZmZmZlZ19RlkhPACcAZwCukYQS75fKvAAuBAcA3clkr8C1g23z8Y8CFlUD5Rfp+4DlgD2BP4E6gJV/nEeCqfJ2NSUMXeuRrfw7YBjgb+JGkz3fwfi4iJUH+G/gEaajKzg2c90PgeqA/8DzwW+BXwPnAroCAKwr3+pFc/7Lc7q+Thsb8oCrumcDNwA7An4AbJa2fj52Tz/0ksDXwTeoM45D0nnz+Y8COue7/AKdXVf0KacjK7sApwBmSGllg/hDg2/k+tgA+A4wsHL+C9Jkemu/lFuAeSVs0ENvMzMzMzKzTtDZx6266zLCOiJghaRawJCImAUgCGBMRp1TVvbSwO07S6cAvgWNy2SnA4xFxTKHes5VvJC0E5lauky0hvcBXjJW0B/B50kt9w3Ivjv8BvhQR9+eyr5CSH+25NiJuzudcQEqknBMRlV4jlwHXFuqfCfw4Iq7L+y9J+iEpWXNWod6QiLgpx/g+KbnzQeAeoC/wVEQ8nuuOa6N9x5CSOcdFRADPS9oEuEDS2RFR+ffkmYioXH+MpOOAfYC/tnP/fUnDSO6LiEXAy8A/c7v7AkcCfSNiYq5/saT9c/n324ltZmZmZmZmXVBX6jlRzxPVBZI+Lun+3LV/FnADsIGkNXKVSs+J5SLpWElP5OENs4GvkV6Wl9fmQC/g0UpBREwFRjdw7jOF7yfnryOrylaTtHbe35HUK2F2ZSP3Cik8j6XiRsQcYCawYS76BXCopKfzUIwPt9G+rYFHcmKiYjjQB3hvnfsAeK1wvbbcAqxOSrJcJemgwpCR7Um9X16out+9SM+8pjwUZO3itjiWNNAUMzMzMzOz+qKJW3ezKiQnllrNQlI/4C7Sy+8hwC7Asflwr/x13vJeRNKhwMXANaRhGP1JPRR6tXVeCRYVvo82yiqfXR9S74n+hW170pCI+XXiVuL0AIiIPwObAj8FNgHul3TxCt1FG9drS0RMALYi9dCYB1wJPCipJ+lel5A+8+L9bk0arlPPacCM4vbojOeW517MzMzMzMysRF1mWMdy2IX0kntSZQhBjXkhniENITiT2haS/gJfNAB4OCKurBRUTzy5HP5NejnfnTQsAUnrAVsCwzoYs54nga0i4sUVCZJXDrkOuE7SQ6Q5M06uUXUUcIgkFXpPDABm0diwlUbaMo80R8idkn5Omntje+Ap0ue2YUQ8tBwhzwcuKRbsvs42MzqjrWZmZmZmZrbiVsXkxItAT+B4SXey9ESZFecDIyVdSZqLYiGwN3BLRLxOmlNh99wLYzYwFRgDHCFpP2As8GXSpJxjl7eBETFb0jXARZLeAP4DnEc585acDdwl6WXg1nyNHYHtIqJ6ksqaJJ1NGj7zLNAbOJCUhKjlSuBE4GeSriD1cjgLuKQw30SHSRpESkA8CswFvkTqQTE+It6QdCNwvdLqJ08B7yIlop6JiLtrxYyIBcCCYtkp/Q5b0aaamZmZmdnbXGu3HGDRHKvCsI6lRMQI4DvAqcC/gMNJ3faLdV4gDc3YkTSZ4iOkVTMW5yoXk4YHPAdMIc0r8SvgduD3pBfjDUgv4h31XeAhUg+A+4C/U2P+jBWVJ8o8kHS/jwH/IK12MX45wiwkJXSeAR4kPZtD61zvVeAA0mSaI0jJn2uAczt2B8uYDhxNmsfiGeDjwKci4o18/EjS6iQ/Ic3hcQcpifRyJ13fzMzMzMzMVrIu1XMir8JxaWF/YJ16PyXNj1B0Q1WdYaReFbXOf4G0HGW1I/NW9GbiIyIG1W55zWvMJvW++HKh+KKqOv2q9lW1P460dGixbGiNsnuBe9toi2qUrVv4/lzqJBfqtGEYKTlR73oDa5R9pl79qnp3kBIO9Y4vIg3XqTdkx8zMzMzMbKXojkt6Nssq13PCzMzMzMzMzLqXLtVzYlUhqS9pSEg920SEhxnUIOlw0hCaWsZHxLYrsz1mZmZmZmYd5RknOo+TEx0zkbSEZVvHrbY/kub0qKV6+VEzMzMzMzN7G3ByogMiYjFp1RBbThExi7TsqJmZmZmZmRng5IS9TZ114dalxW758EGlxQb41x4NrRDbYds90lkLr9SxpNwOMr/78CWlxm/pU+5UPevvs3mp8T+x3U6lxr/+qw+XGv+AM4eWGv+VC/cvNf6+5z5bavy/fneLUuP/1zvfWWr8zY57stT453/z6VLjL1xm+unO9cGSZwr72uIZpcZ/784nlBr/5icvKzX+s7ucWGr8bR/7SanxWbK4/Tor4Pe7nFdq/PXfM7fU+Ktv0avU+Jt/cptS4999bLn/ffnxXeu2X2kFnXdF6ZcohSfE7DyeENPMzMzMzMzMmso9J8zMzMzMzMw6oLXkXnNvJ+45sQqTNEjS9ML+YEnl9lk1MzMzMzMz62ROTnQvFwP7NLsRK4ukkPSZZrfDzMzMzMzMVoyHdXQBknpFxMIVjRMRs4HZndCkpuusZ2JmZmZmZlaWVqLZTeg23HOiCSQNlXSFpEslvQ7cK+k7kkZKmiNpgqQrJfWpOm+QpJclzZX0B2CDquNLDevI17m0qs4dkoYU9o+RNEbSfEmTJd3aQPsPlDRdUkve7597Mfy4UOdqSf9b2D9E0rOSFkgaJ+mkqpjjJP1Q0vWSZgK/ltQrP6fXcvvGSzqtUj+f+od87XGYmZmZmZnZKsnJieb5CrAQGAB8g7QKzbeAbfOxjwEXVipL2h24BrgC6A/8DVihNSUl7QpcDpwBbAXsDzzYwKkPAWsBlTUJ9wJeBwYW6uwFDM3X2QW4GfgdsD0wGDhH0qCquCcDI3Lcc0jP49PA53P7DgfG5bq75a9HAhsX9s3MzMzMzFaKaOLW3XhYR/OMiYhTCvujC9+Pk3Q68EvgmFx2AnBPRFQSFi9I+jApodBRfYE5wF0RMQsYDzzV3kkRMSP30BgIPJ6//hQ4M/f2WAd4PzAsn/Id4P6IOKfQ9m2A7wJDCqEfiIg3F/mW1BcYA/w9IiK3r9KGKZIApkfEpOW7bTMzMzMzM+tK3HOieZ4o7kj6uKT7Jb0qaRZwA7CBpDVyla2BR6tiPLKCbfgr6YX/JUk3SDq8cL32DAMGKmUIPgLcDowC9iT1mpgYEWMKbR9edf5wYIvK0JDs8ao6Q0i9REZLulzSJxps21Ik9Za0dnFbsGhxR0KZmZmZmZm9qbWJW3fj5ETzzKl8I6kfcBfwDHAIsAtwbD7cawWu0QpUr7zbs/JN7i2xM3AY8BpwNjBC0roNxB5KSkTsCCyKiOdz2UBScmJYvRPbMKe4ExFPApsBPwRWB25uZE6MGk4DZhS3i+54qANhzMzMzMzMrAxOTnQNu5A+i5Mi4h8R8QKwSVWdUcDuVWUfaifuFNJ8DADkXgrbFStExOKIuC8PMdkB6Eea76I9lXknvs1biYihpOTEwPx9se0Dqs4fALwQEUvaukhEzIyI30fE0cAXgEMkrZ8PLwJa6p/9pvNJQ03e3L77mY80cJqZmZmZmZmtDJ5zomt4kdSj4XhJd/LWJJlFlwPDJZ0M/B+wH+3PN/EAcImk/wL+TZr74c1eEZIOBN5HmgRzGnAAKUkyetlQS4uIaZKeIU1SeVwufpA08WVPlu458RPgMUk/BH4P7JHPOYY2SPoOqUfHU6ReIJ8DJgHTc5VxwD6ShgMLImJanbYuABYUy+bdfHZ7t2hmZmZmZtYmLyXaedxzoguIiBGkxMGpwL9IL/ynVdX5B3A0aWLMEcAngHPbCf0b4DrgelKy4CXSKh8V04GDSUmMUaSEyGER8WyDTR9G6rkwNLdxKvAcMCki3kxw5OEZnwcOzfd3NnBGRAxpJ/4s4BTSXBSPkXp1HBARlSFWJwH7AhNoYCJPMzMzMzMz65rcc6IJImJgjbKfkla8KLqhqs5vSAmHop8Ujg8mLdNZ2V9E6p1Qs4dCRPydpZf/XC4RcSJwYlVZ/zp1bwNuayNWvxplVwFXtXHOncCdDTbXzMzMzMysU7nfROdxzwkzMzMzMzMzayr3nLBlSOpLGp5RzzYR8fLKao+ZmZmZmVlX1B2X9GwWJyeslolAzeEZheNmZmZmZmZmncLJCVtGRCwmrSBiZmZmZmZmVjonJ+xtaepPh5YW+/njG13spGP2/GubK7CusL/vck6p8ReXPNXN8NWXlBq/z7CNS42/7tAZpcZfstQqv51v3Ootpcb/8Hpblhr/xvNqrkjcaa5bp2ep8W+6eE6p8aHc+DustWmp8aeUPNPWTJXbufcl1ig1/jot80uNv6bK/fl/dpcT26+0ArZ94tJS44/a9YRS4y9cXO7vZ9Gn1PivjV271Pg9xpU7rWHrX/5RavwlrFVq/K0XeqrCeryUaOfxT5mZmZmZmZmZNZV7TpiZmZmZmZl1gPtNdB73nDAzMzMzMzOzpnLPCTMzMzMzM7MO8FKincc9J0oiaaikTp05qYyYqzI/DzMzMzMzs+7ByYm3IUm9mt2G9qwKbTQzMzMzM7PO4eRECSQNAfYCTpAUeesnaTtJf5Y0W9JkSTdIemc+Z6CkhZI+UohziqT/SNqojZiDJE2vuv5nJEVhf7CkpyUdJWksMD+XryvpaklTJM2U9ICkHRu4v3UkLZG0a97vIWmqpH8U6nxJ0oTC/vY5/jxJb0j6taQ+heNDJN0h6QeSJgKjc/kxksZImp+f2a1tPeOGPiAzMzMzM7NOEE38p7txcqIcJwCPAFcBG+dtFvAA8BSwK7A/sBFwM0BEDAUuBW7IL/87AecAR0XE5DoxJ9C49wOHAAcD/XPZLcCGwCeBXYAngfslrd9WoIiYATwNDMxF25Mmqt2pkHDYCxgGIGlN4F5gGrAb8Dng48AVVaH3AbYC9gUOzMmPy4Ezcvn+wIO57oo+DzMzMzMzM+siPCFmCSJihqSFwNyImAQg6XTgqYj4fqWepK8CEyRtGREvAKeTXsx/DWwHXBcRf6wXM8dotFm9gCMiYko+b0/gg8CGEbEg1zlZ0meAz+Y2tGUoKTlxcf76V+ADwJ7APbnswlz3i8Bq+fpz8vWPA+6UdGpOvgDMISVjFuY6B+eyuyJiFjCelNyp+zzMzMzMzMxWFk+I2Xncc2Ll2RHYOw/pmC1pNvB8PrY5QH4pP5zUw2E14NudeP3xlcREoT19gDeq2rRZpT3tGAbsKamF1EtiaN4GStqE1FNjaK67NTCikpjIhpN+/rYqlI2sJCayv5ISEi/lITCHS1qjobstkNRb0trFbUGrf42YmZmZmZl1Fe45sfL0Ae4ETq1x7LXC9x/OX9fP25xlqy+lFajuPtGzRr3qOH3ydQfWqDu9Rlm1B4G1gJ2BjwLfByYB3wNGABMjYkwDceq2MSJmSdo5t/ETwNnAYEm7RUQjbaw4DTizWHDFq+M56f9ttpzNMzMzMzMze0trN5z7oVncc6I8C4GWwv6TwLbAuIh4sWqrDHXYHPgpcDTwKHCdpB5txASYAqyV53Wo6E/7ngTeDSyu0Z7X2zs5JweeAY4DFkXE86SExU7AgeT5JrJRwI5VbRxASqyMbuc6iyPivog4BdgB6Ad8LB+u9TxqOR9Yp7gd955NGzjNzMzMzMzMVgYnJ8ozDtg9r6jxTuDnpJ4QN0naTdLmkvaTdK2kljw84n+BeyPiWuBI0sv4SfVi5sTFo8Bc4Ec55heBQQ207z7ShJJ3SPpEjvlhSedVVuFowFDSMJRhABExlZSI+AJLJyduJK0Qcl1esWRv4GfADYX5JpYh6UBJ35LUX9KmwBGkn9lKQmMcyz6PZUTEgoiYWdx69/CPvpmZmZmZWVfhN7TyXAwsAZ4j9W7oReot0AL8BRhJWp1jOqkHwQ+ATYGvA0TEa8DXgHMLy3tWx+ybEwJfAg7IMQ8DBrfXuIiIfM6DwLXAC8DvchvqJgyqDMv3M7RQNrS6LCLmAvuRkjOPAbcC95N6XbRlOml1kQdISY9vAIdFxLP5+DLPo8F2m5mZmZmZrbBo4tbdeM6JkuTVN/aocejgOqecnbdijNuB3u3FjIg7gDuqiq8qHB9MjYRFXgHjW3lbbvm6qio7ETixRt2RvDUco1asQTXK/k7tOTEqx+s9YzMzMzMzM1uFODlhZmZmZmZm1gGeELPzeFiH1STp2eISo1Xb4c1un5mZmZmZmXUf7jlh9RxA7SVJofE5KczMzMzMzMza5eSE1RQR45vdBjMzMzMzs66stdkN6EacnLC3pfVPqjs35wr7yO6fKi02wLMDzig1/p5PnFtqfJYsKjX8jA/9pNT4+352Wqnx39H/A6XGZ8sdSg0/+dD7So3/2wUTSo1/83nvLzX+fue8Vmr8e3+4QanxtdbapcY/8/inS41/bK/1S40/t4far7QCduozvdT4D89ZXGr8sgcTb/tYub//R+16Qqnxt378slLjx8J5pcYfu8uPSo2/6Qdnlhq/Z7/1So3f8rG9S43/6qBHSo0/crWWUuObgZMTZmZmZmZmZh0SnhCz03hCTDMzMzMzMzNrKvecWAkkDQHWjYjPNLstZmZmZmZm1jk850Tncc+JleMEYFDZF5E0VNKlZV+nK5A0UFJIWrfZbTEzMzMzM7MV454TJZLUAkREzGh2W5aHpF4RsbDJbegZEeXOnGhmZmZmZmZdgntOFOSeB1fkbYak1yWdI0n5eG9JF0t6VdIcSY9KGlg4f5Ck6ZI+Lek5YAHQV9IQSXdUXednki6VNE3SZElHS1pT0rWSZkl6UdInq9q3naQ/S5qdz7lB0jvzsSHAXsAJuUdBSOrX3nlV932ppNeBe9t5ThdLuquwf2K+3v6FshclHZW/7yHpDEmvSFog6emquv3y+V+QNEzSfOBwSZtKujM/ozmSnpV0QL6vv+XTp+Vzh7Tz8ZqZmZmZmXWqaOI/3Y2TE8v6CrAY+CBpOMZ3gKPysSuAPYBDgR2AW4B7JG1ROH8N4NR8zrbAf9q4zuv5Oj8DfpHjPQzsDPwFuEHSGgB5+MIDwFPArsD+wEbAzTneCcAjwFXAxnmb0MB5xfYsBAYA32jnGQ0D9sw9QyAlRV4HAhUokgAAIABJREFUBua2vgfYHBhaaNtJwMmk53Yv8Meq5wbwY+AyYOtc5+dAb+CjwPak5zobmAAcks/ZKt9ruet7mZmZmZmZWWk8rGNZE4BvR0QAoyVtD3xb0r3AkUDfiJiY616cewAcCXw/l/UEjomIEZWAueNFtRERcW4+fj7wPeD1iLgql50NfJP0Mv8P4DjgqYioXAdJXyUlILaMiBckLQTmRsSkQp12z8vFYyLilAaf0UPAWsBOkp4gJQ8uAioTfg4EXo2IF/P+ycAFEfG7vH+qpL2BE4FjC3EvjYjbC+3sC9wWESNz0UuFY1Pzt/+JiHIXfjczMzMzM6thVZoQU9KxwHeBdwMjgOMj4p9t1P8ccA7QDxgDnBoRfyqrfe45sax/5MRExSPAFqS/3LcAL+ThEbMlzSb1Gti8UH8h8EwD13mzTkQsAd4ARhaOT85fN8xfdwT2rrr28/lY8frVGj3viQbaXGnvdNIP80DSc1kI/JqUrOhDeibDACStDWwCDK8KM5zUQ6Lo8ar9y4HTJQ2XdJakHRptY1EejrN2cVuwaHFHQpmZmZmZma1yJH0BuAQ4i9RTfwRwr6QN69T/MHATcA2wE3AHcIek7cpqo5MTjesDLAF2AfoXtq1ZekjBvKrkRj3Vkz1GsawQo/IZ9QHurLp2f1Li5MF22t3IeXMaaHPRUFJyYi9gWERMBUYBe1JITiynpdoQEVcD7wNuICVBHpd0fAfingbMKG4X/aGtR2ZmZmZmZta+1oimbcvpO8BVEXFtRDxHGso/F/hqnfonAPdExEURMSoifgg8SerRXwonJ5a1e9X+h0hdWJ4i9ZzYMCJerNomLROl8z1JmsNiXI3rV17qF+Y2Lu95HTGMlIjYh7fmlhgKHAZsWSmLiJnARNJcFkUDgOfau0hETIiIX0bEwcBPgKPzocpqItX3W8v5wDrF7bsHfbSB08zMzMzMzFZtknqR/sh+X6UsIlrz/h51TtujWD+7t436K8zJiWX1lXSJpK0kHQYcD1yW52a4Ebhe0sGSNpP0QUmnSfqvldCunwPrAzdJ2k3S5pL2y6t7VF7QxwG759Uv3impR4PndcSDpHknDmTp5MThwGuFuSwgzUdxal6NYytJPyb13risrQvk1UP2y896Z2BvUu8MgPGk3iYHSnpXHk5SU0QsiIiZxa13T0+3YmZmZmZmq65aw9cl9a5R9Z2kP+pOriqfTJp/opZ3L2f9FebkxLKuB1YH/kl6sb+MNJ8CpIkvryf9BX80adzNbsDLZTcqT8I5gPRD9RfS/BSXAtN5ax6Wi0lDT54DpvDW5J3tndeR9kzLsaZERGUOiwdJP1PVQzouJ41v+kk+Z3/g0xExpp3LtJA+g1HAPcALwDH5+q8CZ5JW+JhMWknFzMzMzMxspYkmbtQYvp7LVkn+8/GyFkXEiaSVMpYSEYtIL8Rn1joxIoYAQ2qUD6raH1ijTr8aZaraHwMcXK/hubfCMt1sGjhvmfY0IiL6V+1PpUbCK3cZOitvteKMA5ZZ0iQi2pxfIiLOIc0ea2ZmZmZm9nZzPumPwEULatR7nfRH7I2qyjcC6k1RMGk5668w95wwMzMzMzMz64BWomlbreHrEbFMciIiFpJWZ9ynUpanANiHtDplLY8U62f7tlF/hTk5YcuQdHhx6dGq7dlmt8/MzMzMzMyWyyXA0ZK+Imlr4BfAmsC1AJKul3R+of5lwP6STpL0AUmDgV0pcTi9h3UUdHR4Qzf0R+DROseql0A1MzMzMzN7W4rK7A9dXET8XtK7gLNJk1o+DewfEZVJL/tSmJMwIh6W9EXgXOBHpBUsPxMR/yqrjU5O2DIiYhYwq9ntMDMzMzMzs84REVdQp+dDnXkRbwFuKblZb3Jywt6eZkwvLXTMn1NabIDFS8odjRXzys1Lqddqpcaf02OZuVU71cLx5X6+Lf9vWqnxNXNqqfFnrsgCxQ1YsKTczltaY41S4/daoRWc26d3lPwBrLtBqeHXaCn398Pr7yj39+fiUqPDrDm1VofrPK3MLzX+ApaUGp8l5X4CCxeX++9XLJxXanz1Wr3U+GX/93fW2HKf/9rvKO//DQFa/jOx1PhTSv79P1Ul//trhpMTZmZmZmZmZh3S2n4Va5AnxDQzMzMzMzOzpnLPCTMzMzMzM7MOaF1FJsRcFbjnhJmZmZmZmZk1lZMTtsqSNFTSpc1uh5mZmZmZma0YD+vohiT1jIhyp7QvmaReEbGw2e0wMzMzMzOrJzyso9O458QqQNL+kv4uabqkNyTdJWnzfKyfpJD0BUnDJM0HDs/HjpI0StJ8Sc9LOqYq7gWSXpA0V9JLks6R1LOB9qwjaYmkXfN+D0lTJf2jUOdLkiYU9reX9ICkefkefi2pT+H4EEl3SPqBpInA6Fx+jKQx+R4mS7q1Uh/YCzgh339I6tfBR2xmZmZmZmZN5J4Tq4Y1gUuAZ4A+wNnAHyT1L9T5MXAS8BQwX9Lhud5xuWwn4CpJcyLiunzOLGAQMBHYHrgql13YVmMiYoakp4GBwOP53AB2ktQnImaTEgfDACStCdwLPALsBmwIXA1cka9fsQ8wE9g3n7crcDnwZeBhYH3gI7nuCcCWwL+AM3LZlLbabWZmZmZm1pm8lGjncXJiFRARtxX3JX2V9CK+DTA7F18aEbcX6pwFnFQoGytpG+DrwHU57rmFsOMkXQwcSjvJiWwoKTlxcf76V+ADwJ7APbmsEueLwGrAERExJ7fvOOBOSadGxORcbw5wVGU4h6SDc9ldETELGE9KtFQSJAuBuRExqYH2mpmZmZmZWRflYR2rAElbSLopD72YCYzLh/oWqj1eqL8msDlwjaTZlQ04PZdX6n1B0nBJk/Lxc6titmUYsKekFlIviaF5GyhpE+D9eR9ga2BEJTGRDSf9/G1VKBtZNc/EX0kJiZck3SDpcElrNNi+N0nqLWnt4rZg0eLlDWNmZmZmZraUiGja1t04ObFquJM0pOFoYPe8AfQq1Cm++Ffmcjga6F/YtgM+BCBpD+BG4E/AgaRhH+dVxWzLg8BawM7ARykkJ0jJiokRMabBWLXugdxbYmfgMOA10jCVEZLWXc64pwEzittFf/rncoYwMzMzMzOzsjg50cVJ2oDUu+DciLg/IkYB67V1Th4mMRF4X0S8WLWNzdU+DIyPiPMi4vGcSNi00XZFxHTSHBjHAYsi4nlSwmInUrJjWKH6KGDH3KOjYgBpiNbodq6zOCLui4hTgB2AfsDH8uGFQEsDzT0fWKe4ffeADzZwmpmZmZmZma0MnnOi65sGvAF8TdJrpGEXP27gvDOByyXNIM0B0RvYFVgvIi4BxgB9JR0KPAb8F3DQcrZtKHA8cCtAREyVNAr4AnBsod6NwFnAdZIGA+8CfgbcUJhvYhmSDgTeR0p6TAMOICXUKgmNccDueZWO2cDUiFhmTpqIWAAsKJbNu+bk5blPMzMzMzOzZbR6KdFO454TXVx+2T4U2IW0MsVPge82cN7VwFHAkcBIUk+GQcDYfPyPOdYVwNOknhTnLGfzhpF6LgwtlA2tLouIucB+pKEpj5GSGfeTel20ZTpwMPAAqffFN4DDIuLZfPxiYAnwHGmC0EbnyzAzMzMzM7MuxD0nVgERcR9pZY4i1fm+eN5vgd+2EfcU4JSq4kuXo113VF87Ik4ETqxRdyRvDceoFWtQjbK/k+awqHfOC8AejbbXzMzMzMysM3kp0c7jnhNmZmZmZmZm1lTuOWE1SXqW+hNkfj0iblyZ7TEzMzMzM+tqwnNOdBonJ6yeA4CedY7VncTSzMzMzMzMbHk5OWE1RcT4ZrfBzMzMzMzM3h4U4W4o9vYzdsd9S/vB/9/pG5YVGoAZKnfanY1bW0qNP1fl/s45+ee7lBp/0R/+XGr8mf9aUmp8lTzT0KJ55f78rPWehaXGv+Zf/6/U+Pe1vl5q/L17vLPU+GUvl/bRBQvar7QCXunRu9T4r7fUnJ+606xT8qxrf37H7FLjD2jtU2r8dcv99Vl79vFO1Kvk/yef06PcOzh8xNmlxp936tdLjb94arn/femxRrn/fVz8ern/Aqy2w/qlxgfoc9Efyv7XrBQH9D2gaS/Uf3r5T6vkM6vHE2KamZmZmZmZWVN5WIeZmZmZmZlZB3gkQudxz4kmkjRE0h3NboeZmZmZmZlZMzk5sRJI6icpJPVvdlu6Eyd3zMzMzMysmVqbuHU3Tk5YlySpV7PbYGZmZmZmZitHt0xOSPqspJGS5kl6Q9J9ktas/KVd0vclTZY0XdIZkt4h6SJJUyW9IunIqnjbS3qgEO/XkvoUjvfIcV6RtEDS05L2L4QYm78+lXtQDK2Kf7Kk13Lsn0vqWTg2Lrf3N5JmSXpZ0teqzv9/km7O9zNV0v9J6lc4PlDSPyXNyXWGS9o0H9tR0t9y7JmSnpC0azvPV5KmSPpsoexpSa8V9vfMz2KNvN83t2t2vs7NkjYq1B+cYxwlaSwwv53PcjDwFeC/8zMNSQPbareZmZmZmZl1Td0uOSFpY+Am4DfA1sBA4HbeWgHqY8AmwEeB7wBnAXcB04DdgV8Cv5L03hxvTeDefHw34HPAx4ErCpc9ATgJOBnYIdf/o6Qt8vEP5q8fBzYGDi6cuzewef76FWBQ3opOAh4HdgKuBH4haavcvp75erOAjwADgNnAPZJ6SXoHcAcwLLdtD+DX8OZ6cDcCr+R72wX4MbBo2Sf7lkizvjxIerZIWo/0rFeX9IFcbS/gsYiYK6kH8H/A+rl8X+B9wO+rQr8fOCQ/n/7tfJYXAzcD95Ce6cbAw22128zMzMzMrDNFE//pbrrjah0bk+7r9ogYn8tGAkgCmAp8KyJagdGSTgHWiIgf5TrnA98D9gR+B3wRWA04IiLm5DrHAXdKOjUiJpOSEhdExO/y9U6VtDdwInAsMCWXvxERk6raOw04LiKWAM9LuhvYB7iqUOdPEXFlvvYFwLdJyYzRwBdISaajctKA3PNjOull/nFgHeCuiPh3jjeqELsvcFFEPJ/3x7T1cAuGApUFpz8KPAVMytd8Pn8dlo/vA2wPbBYRE3IbjwCelbRbRDyW6/UiPecpuc7O1Pks8/F5QO8az9TMzMzMzMxWId2u5wQwArgfGCnpFklH57/sVzybExMVkym88OYkwRvAhrloa2BEJTGRDSc9u60krU3qiTG8qh3D87nteTZfs+K1wrUrnim0L0hJgEqdHUk9DmblIROzSQmY1YDNI2IqMAS4V9Kdkk7IPRIqLgGuzsMlvidp8wbaDCnxsI2kd5F6QwzN28Dcm+PDeR/Sc5hQSUzk+3iOlEApPqPxlcRE1t5n2RBJvSWtXdwWtHbHKWTMzMzMzGxlaiWatnU33S45kV/09wU+CTwHHE/qIbFZrlI9ZCHqlK2sZ9PItduq0wd4AuhftW0J/BYgIo4kDed4mNTT4gVJH8rHBgPbAneThrw8J+mgBto9kpQE2YulkxN7kYaI9GT5h1kUE0CNfJaNOg2YUdx+8Z+xbZ9hZmZmZmZmK023S05A6l0QEcMj4kzSPA0LgUZeuGsZBeyY556oGEBavWV0RMwEJuYyquo8l79f+P/Zu/P4y8f6/+OP5wzGljWiMkgIWVIp2UZKKklRkWxFJYriR1IMKvsu2df0jZAILWjGWtkNBjPMMBgMM4zZzczr98d1HfOe43yW+ZxzzZn5zPPu9r7NeW+v93Vd53w+Ptd1riX/27eHaejMg8CawKsRMbxue7N2UUQ8FBHHRcRngMdIw1Vq556OiNMiYlvSnA571z+kXu7BcSfwVVLjxl2kHh79SMM97q/0NhkKrCJpldr9ktYFlmFWGXX4nE7ey2l0r0yPIw1teWfbb8U5bd8wMzMzMzObXUS0bettel3jhKRP5dUtPiGpP2lyxRWYfZ6FOXElaeWIyyR9NM8lcRZwRZ5vAuAk0jwT35K0tqTjSb0XzsjnXwUmA9tJep+kpXuYlo7S9xrwV0lbSFo9r85xpqQP5v3jJG0qaVVJ25IaM4ZKWkzS2fn6VSVtRur10N2yGgTsCjwcERPycJk7gN2YNd8EwK2knhZXStpY0ibA5cDgiLi/o+DdeC9HAhvkMn+vKqucVEXE1IgYX9369el1H30zMzMzM7P5Vm+soY0nTdB4M/A08Gvg4Ii4pSfBImIS8AXSShP3AdeQ5kE4oHLZmaS5G04hVcK3A3aIiGE5xnTgJ6QeBS+RVq5oiZy+LYHnSb0ehgIXkeacGA9MAj4CXEsqj/OB3wHnATOA5UkNBU+TVr+4BTiqm48fTOq5MKhybFD9sdzL4qukyT/vIDVWPEsaYtKZrt7LC0iTgt5PmnS0vveKmZmZmZmZzQd63WodETGU1DjQ6NxeDY4NaHBstbr9IaT5GDp65kzSkqRHd3LNhcCF3UjPQZ2lJR/bqG7/ZdIypI2Mp+MhLdNIPR96JCIeZtYSrbVjpwOnN7j2eVIDRUexBgID6451+F7m82OAbeckzWZmZmZmZq3SGyembJfe2HPCzMzMzMzMzOYjbpywhiTdUluatMH2i3anz8zMzMzMrN2ijf/1Nr1uWIe1zD7AYh2cGzs3E2JmZmZmZma9mxsnrKGIeLHdaTAzMzMzM7MFgxsnbIH03i0arjraEt8e9Fqx2ADDxixbNP76H3qpaPy3Xl+0aPzxJ99QNP7Sv9m3aPzlRzxRNP7MUS8UjT/hH88VjT91XNnRiBtNmV40/s0Ll43/uekTi8YfN71f0fjH9ZtUNP41P16qaPwJf3+maPy3J/UtGv+B0SsWjb/NomU7Xi73gbKfn9Ejyn5+Vt1kfNH4b40o+/mZfNgPisZf7ITzisaf8dyjRePHi8OLxl/ojjuLxu87YKui8ednM6P3Da9oF885YWZmZmZmZmZt5Z4TZmZmZmZmZj3gfhOt454TZmZmZmZmZtZW7jlhZmZmZmZm1gMz3XeiZdxzYh4iKSTt2O50zC8kDchltky702JmZmZmZmY958YJm2dJKrekhpmZmZmZmc0z5svGCUl9JB0qabikqZKel3REPre+pNslTZb0uqTzJS1ZufdSSddL+oWkVyS9IelISQtJOknSWEkvSNq7cs9q+Rv6XSTdI2mKpMckbVW5pq+kiySNyM9+StKBDdL+XUmP53SPlnR2Pj4yX/KX/KyR+fhASQ9L2l3SSElvSvqTpPfUlcfhlWc/ImnnyvllJV0paUw+P6yWP0mLSDo7p2WKpOckHd6N9+BkSX+r7B+U071d5dhwSftU0nhkLtupOU/Va2tl/C1JgyVNAXaTtKqkGyWNkzQxl92XJK0G/DvfPi7fe2lX6TYzMzMzM2uVmUTbtt5mfp1z4jhgX+CnwF3AysBHJC0B/AO4F/gksCJwIXA2sFfl/s8CLwBbApsBFwGfAe4APgV8CzhP0r8i4oXKfScBBwFPAD8DbpS0ekS8TmroeQH4BvB6jne+pNERcTWApP2AU4GfA7cAS+fnk9P7KrA38HdgRuW5awA7AtsDywJX5xhH5POHA98BfggMy/n6g6QxETEYOBZYF/gi8BrwYWCxfO9PgB2AbwLPA6vkrSuDgX0k9Y2IGcBWOfYA4O+SPpDTPShffyBwMPAD4CHgu8ANktaLiGGVuMfn6x4CpgAXAIvkPE3M+ZgAjAJ2Aq4F1gbGA5O7kW4zMzMzMzObx8x3jRO5x8CBwAERcVk+/Axwl6R9gUWBPSJiYr7+AFIjwmER8Uq+fizwk4iYCTwl6VBg8Yj4bb7nOFLlf3PgT5XHnx0R1+Zr9gO2A74HnBgRbwNHVa4dIWlTUqX/6nzsl8ApEXFG5br7ACJijCSANyLi5bps9wH2ioi38rOvALYBjpDUD/gF8LmIuDdf/6ykzUkNAYOB/sBDEXF/Pj+yErs/qUHjrogI4Dm6507gPcDHJD1Aajw4idSIAqmR4sWIGJ73DwFOiIhaeR4maWtSY8/+lbinR8R1tR1J/YFrI2JILW+Vc2Pzy1cj4o2OEprLqF/12Jgfbku/vn27mVUzMzMzM7N3S1Uoa4X5cVjHOqSK5m0dnHuk1jCR3U3K59qVY4/nhomaV4Ba5ZfcE+B1Us+Lqnsr10wH7s/PBEDS/pIeyMMnJgDfJ1X+kbQi8P4O0t2VkbWGiWx0JW0fBhYH/iVpQm0D9iD1XAD4PbBLHkpxoqTPVGJdCmxEaqQ5U9K23UlQbgx4hNQIsT4wDTif1FixJKknxWAASUuR8n53XZi7qZRfdn/d/pnALyXdLeloSRt0J311DgferG6nPPBs53eYmZmZmZnZXDM/Nk60ouv+23X70cGxbpePpF2Ak0lDRLYlVfgvIQ1JgObS3VnaavNpfDk/s7atC+wMEBG3AKsCp5EbSCSdnM89CKwO/Io01ONqSdd0M12DSI0TWwGDI2IsMJTU4+Sdxok5VG1YIiIuBD4EXEFqBLlf0o/nMOZxpCE072wHf/xDPUiamZmZmZnZLJ5zonXmx8aJYaSK/jYNzg0FNsxzT9RsBswEnmrBsz9deyFpIeDj+Zm159wTEedExEN5OEOt5wK558PIDtJd8zYwp2MNngCmAv0jYnjdNqry/DERcVlEfIc0lOL7lXPjI+KqiNiXNN/GTpKW68azB5MaIrZh1twSg4BdgbVqxyJiPPASs+bXqNksp79TETEqIs6NiK8Dp5DmG4HUWwO6KLOImJrz+M7mIR1mZmZmZmbzjvluzomImCLpBOBESdNIQwNWANYDrgSOBi6TNDAfPwu4ojLfRDP2lzSM1CDxU9LklBfnc8OAPSR9ARgB7E6a5HJE5f6BwLmSXiVNiPkeYLOIOCufHwlsI+luYGpEjOsqQRHxVu4FcZqkPqQJQmsTbY6PiMskHQM8ADxOGhKzfc4Dkn5GGibyEKkR5xvAy0CHczhU3JHzsD1pjg5IDRLXAKMj4unKtScBR0t6BniYNPHnRsBunT1A0umksnqaVN5bM6tB6DlSL5LtJd0MTI6ICd1It5mZmZmZmc1D5rvGiexYYDpwDGmYwmjg3IiYlBsHziBNNDmJtJrDz1r03J/nbSNgOLBDRLyWz50HfAy4ilRh/j/gHNIKGQDkhoJFSQ0bJ5NWt6gOoTiYtJrHvsCLwGrdTNevgDGkuRU+RGpYeBD4bT4/jTS0YTVSr5M7gV3yubeAQ4E1SSuE3Ad8qW5OjoYiYpykIcD7IuLJfPgOUo+c+iEdZ5IaTU4hzZfxBKn8htG5vsDvgA+SVuT4O6n8iIgXJR1FWuHjEuByZl+VxczMzMzMrJjohcMr2mW+bJzIFeff5K3+3BDSUqEd3btXg2MDGhxbrcHtQyPiUx3EnUrqDbB33anD6647j9SQ0SjGjcCNdccGknpcVI+dDpxe2Q9Sg0x1FZDq9b8Gft3BuQtIy3X2SERsVLc/lgbDhfJ7dnTeGsUZCajB8U7nl4iIY0mNVWZmZmZmZjafmi8bJ8zMzMzMzMzazUuJts78OCGmzQWSdqsuTVq3Pd7u9JmZmZmZmVnv4Z4T3dDRkINe7gbgvx2cq1/a1MzMzMzMbIHTG5f0bBc3TlhDeenTt9qdDjMzMzMzM+v93DhhC6Q+Ky1XLPZya79QLDbAImO6XEilKUt+pOxor0Vfm1g0/oQXFy4af6nxrxeNr7U3Lhq/z8yyn58p418sGn/Mq0sWjf/BJcu2yfabVvbzufyyZX++lpg0rWj80v3yYnzZ1aaX+OyqReNPf7Lsz9e00WV/P8yYUfb/L4utuUjR+H1Glv12dOHVli0af6mFurNKfM9NHzu9aPwZzz1aNH7fVTcoGn/6xLLl//ZzbxaN32foY0XjA/Dl8o+weZsbJ8zMzMzMzMx6wBNito4nxDQzMzMzMzOztnLjRC8laS9JZfuPmZmZmZmZLcBmEm3behs3TvReVwFr1XYkDZT0cBvTU4SkkLRju9NhZmZmZmZmPec5J3qpiJgMTG53OpohaZGIKDz7mpmZmZmZmbWbe050QFIfSYdKGi5pqqTnJR2Rz60v6XZJkyW9Lul8SUtW7r1U0vWSDpE0Ol/zO0kLV67pJ+kESaNy/OGSvpfP9ZV0kaQR+RlPSTqwcu+2kqZIWqYuzWdIuj2/fmdYh6S9gKOADXNPg8jnL5b0t7oYC0t6tZaWTspne0lvSOqb9zfKcY+vXHOhpD9U9neS9HjO70hJB9fFHCnpV5IulzQeOF/SIpLOzuU4RdJzkg6vXZ9v/Ut+9kjMzMzMzMzmkmjjf72Ne0507DhgX+CnwF3AysBHJC0B/AO4F/gksCJwIXA2sFfl/q2B0fnfD5OGWTwMXJDPXw5sCvwEeARYHXhvPtcHeAH4BvA68BlSRX10RFwN3Aa8AewEXASpQQP4FnBEg7xcBXwU2A74XD72JvA0cIeklSNidD6+PbB4vqczdwLvAT4G3A9sBbwGDKhcsxVwQk7fx4GrgYE59meAcyS9HhGXVu45BDgGODrv/wTYAfgm8DywSt4glf+rwN7A34EZXaTZzMzMzMzM5kFunGhA0nuAA4EDIuKyfPgZ4C5J+wKLAntExMR8/QHAjZIOi4hX8vXj8v0zgCcl3QRsA1wgaS1SZfvzEXFrvv7Z2vMj4m1ST4eaEZI2zfdcHREzJP0J+Da5cSLHXga4tj4/ETFZ0gRgekS8XDl1j6SngN2BE/OxvYE/R0Sni8FHxJt5DosBpMaJAcBpwFG5F8nSpEaZwfmWnwG3RcSxef9pSesC/w+4tBL69og4pbYjqT8wDLgr0jo9z1XSMEYSwBt1+TIzMzMzMytuppcSbRkP62hsHaAfqYdCo3OP1BomsrtJZbl25djjuWGiZjSplwXARqRv+QfTAUn7S3pA0pjcsPB9oH/lkiuBAZLen/d3A26KiDldoeNCUoMEkt4HfBG4uJv3Ds5pELAFcB0wFNic1GvipYgYlq9dh1ROVXcDa9aGhmTugfd3AAAgAElEQVT3111zKam8npJ0pqRtu5m2d+QhNEtVt6nT3cnCzMzMzMxsXuHGicZaMZHk23X7wazy7jS+pF2Ak0m9IrYlVc4vARZ5J1jEfaTeHLtIWgz4GqnBYk5dDnwo98z4DjAiIu7s5r2DSA0RGwJvR8ST+dgAUuNEh40vnag2+hARD5KGvPwKWAy4WtI1cxjzcNIwlne2k+94vAdJMzMzMzMzm8VzTrSOGycaG0ZqQNimwbmhpIkll6gc2wyYCTzVzfhDSGW/VQfnNwPuiYhzIuKhiBgOrNHguitJPSa+kp9/UyfPnAb0rT8YEa8D15N6T+xFagTprtq8Ez9lVkPEIFLjxID8umYoKV9VmwFP1/UweZeIGB8RV0XEvqR5NXaStFw+/TYN8lXnONIwk3e2Q7Zcr4tbzMzMzMzMbG5x40QDETGFNJHjiZL2kLSGpE/nFSyuBKYAl0n6qKStgbOAKyrzTXQVfyRwGXCxpB0lrS5pgKRv5kuGAZ+Q9AVJa0k6ljT5Y70rgY1Jk2BeExFTO3nsSGD1vKrGeyX1q5y7ENiTNPTiskY3d5CPccCjpAaSQfnwHTlNazF7z4lTgG3yahxrSdoTOIDUQ6RDkn4maVdJH8lzdXwDeJk0IWgtX9tIWknSsh2kc2pu4Hhn67dQV+0ZZmZmZmZmNre4caJjx5Iq1MeQvvW/ClgxIiYBXwCWA+4DriHNTXHAHMbfL997DvAkaRWPWm+M80jzN1wF/BdYPl83m9yj4n/ABnQ9pONa0ooW/wbGALtWzt1KmhPjHxHx0hzmYzCp58KgnKaxwBPAyxHxTk+SPDzjm8AuwGOkcj2ybqWORt4CDiXNRXEfsBrwpYiYmc8fDHweGAU8NIdpNzMzMzMz67GZEW3behuv1tGBXPn9Td7qzw0BPtvJvXs1OHZQ3f4U0goWP2tw7VTSMIu9604d3uDaT3WQhkuprIKRY+7cQZKXAJZl1sof3ZbzVZ+3jTq49loarCZSOb9ag2MXMGv51Ub33Ajc2M3kmpmZmZmZ2TzIjRMLMEl9gPeSeh+8AdzQ3hSZmZmZmZnNP3rjxJTt4saJBVt/YATwArBXREyvnZDUnzQ8oyPrRsTzhdNnZmZmZmZmCwA3TizA8sSc6uD0S6QlTDsyp3NTmJmZmZmZ9Sq9ce6HdnHjhDWUe1EMb3c6zMzMzMzMrPdz44QtkPqs99FisRdd5YPFYgPEXSOKxl9kuy2Kxl943Nii8bnx4bLxS5syqWj4PhsNKBo/Zt5fNP7/tGTR+Ltu/ELR+G/eXTQ8y29cdpnkmPJ20fhv3VV2EbGJ90wsGn/Km2X/rFp288WLxn8lphSN/+DUZYrGX+OL6xaNP/Of/ykav+9nty4b/9WynV6n/vN/RePHi2W/M5s+8Y2uL2rCQutuWTT+jAmXFI0/efCwovEBFj+k+CNsHufGCTMzMzMzM7Me8ISYrVP2KwozMzMzMzMzsy6454SZmZmZmZlZD3hCzNZxz4n5lKTVJIWkzlbUMDMzMzMzM+s2SctJulLSeElvSLpI6njir3z9WZKekjRZ0vOSzpS09Jw8140T869RwMrAY929QdJASfP5bIGzuIHGzMzMzMzaKdr4X0FXAusBnwe2B7YEzu/k+vfn7RDgo8BewHbARXPyUA/rmE9FxAzg5XanoyRJC0dE2anhzczMzMzMDABJ65AaFj4ZEffnYz8GbpZ0SES8a+mfiHgM2Kly6BlJRwB/kLRQREzvzrPb2nNCUh9Jh0oaLmlq7v5xROX8+pJuz11DXpd0frU7iaRLJV0v6RBJo/M1v5O0cOWafpJOkDQqP2O4pO/lc31zF5UR+RlPSTqwcu+2kqZImm3tK0lnSLq9sr+5pDtzjFG5C8sSneR7oKSHJf0gXz9J0tXVbi+5bI6U9EJO98OStqucn63XgKQBeX8bSffnmPdIWjuf3ws4CtgwXxeS9lIyMJf9VEkvSTqzG+/dAZIeq+zvmGP+sHLsVkm/ruzvJ+kZSdNyWe9eFzPyNTdImggcIWnZ3KVoTC7fYZL2zrfU1tR8KN87qKt0m5mZmZmZ9Qa5rrtU3davybCbAm/UGiayW4GZwKfmIM7SwPjuNkxA+4d1HAf8HDgWWBf4NvAKQK7c/wMYB3wS+AbwOeDsuhhbA2vkf/ckdSHZq3L+cmBX4CfAOsAPgAn5XB/ghRx7XeAY4LeSvpnP3wa8QaUVSFJf4Fukri5IWgP4O3AtsEE+t3mDdNb7MPBN4CuklqmPAedUzh8IHEzqGrNBLosbJK3ZRdzf5Ps+AUwHLs7HrwJOAR4nDQdZOR/bCfhpLpc1gR2BIV08A2AwsK6kFfL+VsBrwABIvR5IH+xBef9rwBk5DR8FzgMukVS/qPdA4C/A+jnttc/GF0nv3375OQCb5H8/l/Pz9W6k28zMzMzMrCUiZrZtAw4H3qzbDm8ySysBr86ex5gOjM3nuiTpvcCv6HwoyLu0bViHpPeQKuAHRMRl+fAzwF359beBRYE9ImJivucA4EZJh0XEK/m6cTnGDOBJSTcB2wAXSFqL1ADw+Yi4NV//bC0NecjAUZVkjZC0ab7n6oiYIelPOS218TLbAMuQGiMgvflXRsTpeX+YpJ8AgyXtFxFTOiiCWt5ezHn7MXCTpIMj4mVSo8QJEfGnfP1huSJ/ELB/BzEBjoiIwTnm8TnmohExWdIEYHqOT76mP2l4yK25PJ4H/tdJ/JrHSB/QrYBrSI0Sp5DeU0gNBwsD9+T9Q4BLI6LWAHOqpE/n4/+uxP1jRFxSl76HKi13IyvXjsn/vl7NU73cejhbC+K4q46l38Ie1WRmZmZmZvOt44BT645NbXRhrhse1kW8dZpNkKSlgJuAJ0hfPHdbO3tOrEOqMN7WyflHag0T2d2kNK9dOfZ4bpioGQ2smF9vBMwgfcvfkKT9JT2Qhw1MAL4P9K9cciUwQNL78/5uwE0R8Ube3xDYS9KE2kbq5dAHWL2j5wLP1xomsntrectv6PtzfqvupusPzKOV16Pzvys2ujD7M7AY8KykCyR9TVKXtfaICOAOUtksQ+rdcA7QT9JHSI0W90XEpHzLOt3Mz/11+78HdsnDWk6U9Jmu0tbAu1oUT7p2UA/CmJmZmZmZzTKTaNsWEVMjYnzd1rBxgvRF8jpdbM+Svrierf6Y64fL0cWch7kDwt+Bt4Cvzen8ge1snJjcojj1GQ5m5avTZ0jaBTiZ1CtiW1JjxiXAIu8Ei7iP1KNjF0mLAV8jD+nIliQNUdiosm1IGiLxTI9y1JxqedSmcO3wfY6IUaTGnh+Ryusc4A5V5u3oxCBSj4ktSL0bxpMbLEiNEx02CnWi2hhFRNwCrAqcRmqwuU3SyXMY8zjSmKd3tv+304AeJM3MzMzMzGz+ExFjIuLJLrZppC/Nl5H08crtnyXVKf/bUfz8Bfs/gWnADp2MIOhQOxsnhpEqw9t0cH4oafLG6sSSm5Em4niqm88YQsrjVh2c3wy4JyLOiYiHImI4af6KeleSekx8JT//psq5B4F1I2J4g21aJ2nrX+mNAfDpHPupXMl/KaevPr1PdBKzK9OAvvUHI2JyRNwYET8hNSxsSprzoSuDST0mvkGeWyL/+7mc1kGVa4fSw/zkH6TLIuI7pGEt36/kBxrkqe7+d7UoekiHmZmZmZk1KyLathXKz1BS74cLJG0iaTPSfIp/qq3UIekDkp6UtEnerzVMLAF8D1hK0kp567SuVtW2GlpETJF0AnCipGmkLv4rAOtFxEWkBoGjgcskDcznzgKuqMw30dUzRkq6DLg4zwPxCOlb+BUj4mpSA8kekr5AWvlhd9LkmyPqQl1JGi9zBHBNXVeZE4D/SDobuJD0zf+6pHkuDugkeVNy3g4BlgLOJM1zUesqcxJwtKRngIeBvUm9MnbrTt47MBJYXWmFjxdI3W12JVXu/wtMAr5DajR6rhvxHiXN+fFt0vq3kBokTib12qgO4zgJuFrSQ6TZXr9CmsDyc509QNIxwAOkiTz75ecMzadfzWndTtILwJSIeLMb6TYzMzMzM7PGdiM1SNxG+gL9WtICEzULk3rfL573N2bWSh7D62KtzuzzBnao3V8fH0taUeIYUpf90cC5ABExKTcanAHcR6o4Xwv8bA6fsR/wW9JwheVJEz7+Np87j7RKxlWkyvT/5eu+WA0QEcMl/Y80yeNBdecelbQVaZWMOwGRhnNc1UW6hgPXATeTxu/8jTS0ouZM0hCEU0hjfp4gdY8Z1s18N3ItqUHg36RJPfcmrUbyc9JEKn1JvU2+EhGvdxUsIkLSncCXmTWR6aPAeFIPkImVa69XWqb1ENJ7OgLYOyIGdfGYaaRhGauRGiLuBHbJMafnRqcjSZ+hO8mrhZiZmZmZmdmci4ixpC+gOzo/klTvre0Pqu73VFsbJyKtf/KbvDU6P4Q0vqWj+/dqcKy+8WAKqUHjXY0auQfE3nmretfyKxHR4ZqueV6KbTs638l9vydN+Njo3ExSz5GjOzg/ki4+EBHxcN01U4GdG4S7fs5SPtszdqzbn0lqbGl0bYf5zeff9YGOiF8Dv+7kngtJPVbMzMzMzMzmqpmUGV6xIGrnnBNmZmZmZmZmZm0f1mHzKElbALd0dD4ilpyLyTEzMzMzM5vnlJqYckHkxok2iIiBpAk252X3kybgNDMzMzMzMyvKjRPWUERM5t0zrZqZmZmZmZm1nNwNxRZEz66/bbEP/o1vrlgqNACv9yn7M/uB6U1PtNupN7u90nHP7NBvbNH4K6w9seuLmjDumUWLxp8yZeGi8T904AeLxh9ywqtF49+wSNny/+e0F4rG//LCqxSNP0Ezi8a/9+1urRTeY7+aXvb384sLl/3O5/XCvz+/skjZ359XTVu2aPyPTisanhllw7PkzLI/X2MWKvsB2v6jo4rGX3T9ZYrGf/u5sqvRz5hQ9u+3pa+4pGj8ift/r2h8gGWu+nfZP0ILWXmZddtWoR79xhPzZZl1xBNimpmZmZmZmVlbeViHmZmZmZmZWQ+ElxJtGfecMDMzMzMzM7O2cuNELyNppKSD2p2OuUHSpZKub3c6zMzMzMxswRQRbdt6GzdOLIAk9ZU0T7/3khZpdxrMzMzMzMxs7pinK6hzk6SdJQ2RNFnS65JulbSVpLclrVR37emS7syv95L0hqTtJT0laZKkayQtLmnP3JNhnKQzJfWtxBgp6ZeSLpc0QdJzknaQtIKkv+Zjj0r6RN2zN5d0Z07nqBx3iXxuELAqcJqkkBR1adxB0hPAVGDzrvLWSVlJ0hhJO1eOPSxpdF06p0paPO/3r+RrvKSrJb2vcv3AHGMfSSOAKZ28L0tIGgjsCXy1lldJAzp/l83MzMzMzGxe5MYJQNLKwP8BFwPrAAOA64AHgGeB3SvXLgzslq+tWRz4CbALsF2+/y/Al/K2O/ADYGdm91PgbuBjwE3AFcDlwB+AjYFngMslKT97DeDvwLXABsC3gM2Bs3O8rwMvAEcCK+etmsbDgH2A9YD7u5m3d4nUh+iOnE8kLZvLbTFJH8mXbQXcFxGTci+NvwLL5eOfBz4EXFUX+sPATjkfG3Xyvgg4Gbg6l0ctr/d0lm4zMzMzM7NWmkm0bettvFpHsjKpLK6LiOfysSEAki4C9gZOyse/AixKqhjXLAzsFxHP5HuuIVX63xcRE4AnJP0b2JrZK+Q3R8R5+Z5jgP1IFfo/52MnAPcC7wNeBg4HroyI0/P9wyT9BBgsab+IGCtpBvBWRLxcl8eFgR9FxCO1A93MW0cGkRpcALYEHsppHAA8mf8dnM9vA6wPrB4Ro/Kz9wAel/TJiLgvX7cIsEdEjMnXbEwH70s+Pxno1yCvZmZmZmZmNh9xz4nkEeA2YIikP0vaN/cGALgU+LCkT+f9vYCrI2Ji5f5JtYaJ7BVgZG6YqB5bse65j9adh0rlu3Ksdt+GwF55aMQESROAf5Dex9W7yOO0uudB9/LWkcHAupJWIPWGGJS3AbkHxmfyPqReD6NqDRMAEfEE8EY+V/NcrWEi6+x96TZJ/SQtVd2mzpw5p2HMzMzMzMxm4wkxW8eNE0BEzCANNfgi8ATwY+ApSatHxKvAjcDeeY6EL/LuYQ9v14fs4Fh9eb9zTcz6dFXvqx2r3bckcB6wUWXbEFiTNASkM5Oj7hPczbx1ZAgwltQwUW2c2Ar4JKmnxpwOs5itUaSz92UO4x4OvFndzh0zYg5DmJmZmZmZWSke1pHlivvdwN15iMVzwNeAU4ELSXMfvAA8ExF3tymZDwLrRsTwTq6ZBvTt5Hy9HuUtIiJPnPlV0hwWdwGTgH6k4R73V3pgDAVWkbRKZVjHusAypEaHTp9Dx+9Ld/N6XL7+HT9cYfU3u5NPMzMzMzOzjszshT0Y2sU9JwBJn5L0C0mfkNSfNCHjCqRKNaShE+OBXwKXtCmZACcAn5F0tqSNJK0p6auSzq5cMxLYUtIHJL23GzGbydsgYFfg4YiYEBEzSRNl7sas+SYAbiX1tLhS0saSNiFN/Dk4Iu7vKHg33peRwAaS1pb03jyc5F0iYmpEjK9u/fr4o29mZmZmZjavcA0tGU+a1PFm4Gng18DBEXELQK50X0r6lv7yNqWRiHiUNGxiLeBO0iSUxwAvVS47EliNNMxjDF1oMm+D832DKscG1R/LvR++CowjNV7cSlop5FtdxO/0fQEuAJ4irTwyBthsDtNvZmZmZmZm8wAP6wAiYihpCdDOfIC0usbounsvJVXuq8cGAgPrju1Vt79ag3Sobn8kadnM6rH7gG07SmRE/Ic0D0WnaazTMG9diYiHG6TvdOD0Btc+T2qg6CjWQN5dZp2+L3nyzA7LwszMzMzMrKTeODFlu7hxoguSliYtg/ltYIc2J6elenPezMzMzMzMbP7hxomu/RXYBDg3Iv7V7sS0WId5k3QLsEUH9/02In5bOnFmZmZmZmbzspm450SruHGiCxExoN1pKKWLvO0DLNbBubGtT42ZmZmZmZktqNw4YQ1FxIvtToOZmZmZmdm8zHNOtI4bJ2yB9PJr7ykW+8F+k4vFBvhwLFo0/pCF3i4a/80oG/+7a0wsGn+xrdYsGl99hhWN/8IjixSN/9oVZdO/wSmbF41/xeFPFo2/0kJLFY0/WTOLxh9L2Z/frRdeqWj8zb72RtH44/4ztWj8kS8sVzT+JVq6aPxffWVc0fjH/22ZovHXmVZ2kbshi/YtGn+sZhSNv/MGZT+ffQdsVTR+n6GPFY0/eXDZ/z9O3P97ReMv8buLisY3Ay8lamZmZmZmZmZt5p4TZmZmZmZmZj0w08M6WsY9J8zMzMzMzMysrXrUOKHkfEljJYWkjVqdsGZIulTS9U3cP0jS6ZX9kZIOak3qypC02rz4XpiZmZmZmfVW0cb/epueDuvYDtgLGAA8C7zWovTMRtIg4OGIaHfDwCeBsrPsNW8UsDKF3ot5kaQBwL+BZSOi7CxnZmZmZmZmVkxPGyfWAEZHxD2NTkpaJCKm9TxZ85aIGNPuNHQlImYAL7c7Ha0kaeGIwks7mJmZmZmZ9ZDnnGidOR7WIelS4Cygfx5GMDIPgzhb0umSXgP+ka/9maQhkiZKGiXpHElL1sXbLN8/SdI4Sf+QtGx+zlbAgfk5kYcu9JV0kaQRkiZLekrSgT0tAElLSLpc0gRJoyUd3OCa2YZ15LT8QNLfcrqHStpU0odzXiZKukfSGnVxvirpQUlTJD0r6ShJC9XF3UfSX3LcYZJ2qJxfVtKVksbkvA+TtHc+965hHZK2kvQ/SVNz3o6ve94gSWdKOjEP0XlZ0sBultvJkv5W2T8oP3+7yrHhkvbJr/tIOlLSCzk9D9ddW0v/tyQNljQF2E3SqpJuzJ+NiZIel/QlSauRek0AjMv3XtqdtJuZmZmZmdm8pSdzThwIHAm8QBpG8Ml8fE9gGrAZ8MN8bCbwE2C9fP6zwIm1QLkifRvwBLApsDlwI9A3P+de4IL8nJVJQxf65Gd/A1gXOAb4raRv9iAvACeRGkG+CmxLGqqycTfu+xVwObAR8CTwR+A84DjgE4CAsyt53SJff0ZO9w9IQ2OOqIt7FHA1sAFwM3ClpNrC0cfme78IrAPsRwfDOCR9IN9/H7BhvvZ7wC/rLt2TNGTlU8ChwJGSPt+N/A8GNpdUW5R7q5yWAZXnrwEMyucPBA4GDsl5+wdwg6Q16+IeTyqjdfI1vwP6AVsC6wOHARNIn4Wd8j1rkz4fPW6kMjMzMzMzs/aZ42EdEfGmpLeAGRHxMoAkgGERcWjdtadXdkdK+iVwLvCjfOxQ4P6I+FHlusdrLyRNAybVnpPNIFXga0ZI2hT4JqlS3225F8f3gO9ExG352J6kxo+uXBIRV+d7TiA1pBwbEbVeI2cAl1SuPwo4PiIuy/vPSvoVqbHm6Mp1l0bE/+UYvyA17mwC/B3oDzwUEffna0d2kr4fkSrwB0REAE9Kej9wgqRjImJmvu7RiKg9f5ikA4BtgH91kf87gfcAH5P0AKnx4CRgx3x+APBiRAzP+4cAJ0TEn/L+YZK2Bg4C9q/EPT0irqvtSOoPXBsRQ/KhZyvnxuaXr3Y254SkfqQGjnf8+31fZZF32lXMzMzMzMzmXHhYR8u0cinRB+oPSPqcpNskvZgbNK4Alpe0eL6k1nNijkjaX9IDeXjDBOD7pIr7nFoDWAT4b+1ARIwFnurGvY9WXr+S/x1Sd2xRSUvl/Q1JvRIm1DZyr5BKecwWNyImAuOBFfOh3wO75CERJ0r6TCfpWwe4N2b/abkbWBL4YAf5ABhdeV6HcmPAI6RGiPVJvWbOJzVWLEnqSTEYIJfB+/Pzq+7O6ay6v27/TOCXku6WdLSkDbpKWwOHA29WtysmdOctNjMzMzMzs7mhlY0Ts61mkecE+Bup8rsT8HFmfUO+SP538pw+RNIuwMnARaRhGBuReigs0tl9BVQnaoxOjtXKeElS74mNKtv6wJrAlA7i1uL0AYiIW4BVgdNIlf3bJJ3cVC46eV43DCI1TmwFDM4NO0NJw3PeaZyYQ7N9jiLiQuBDpIat9YH7Jf14DmMeByxd3XZfcu0eJM3MzMzMzGwWLyXaOq1snKj38Rz/4Ij4T0Q8TapQVz1KGkLQkWmk+SeqNgPuiYhzIuKhPGxgjXff2i3PkCrnn6odkLQssFYP43XmQWDtiBjeYJvZ5d1ZRIyJiMsi4jukIRHf7+DSocCmymNuss2At+jesJXuGExqiNiGWXNLDAJ2JZXhoJzm8cBL+flVm5HmG+lURIyKiHMj4uvAKcC++VRtRZhOx2dExNSIGF/dPKTDzMzMzMxs3tHTpUS7YziwMPBjSTcy+0SZNccBQySdQ5qLYhqwNfDniHiNNKfCp3IvjAnAWGAYsIekLwAjgN1Jk3KOmNMERsQESRcBJ0l6HXgV+A1pIs9WOwb4m6TngWvyMzYEPhoR9ZNUNiTpGNLwmcdJcyhsT2qEaOQcUuPFWZLOJk0aeTRw6pw0hnThDtK8E9sDP8/HBpHyNzo3SNWcBBwt6RngYWBvUu+R3Tp7gKTTgVuAp4FlSZ+PWp6fI/X02F7SzcDkiJjQfLbMzMzMzMxsbirWcyIiHgF+Rlpd4TFSJfTwumueJg3N2BD4H2lSya8C0/MlJ5MmwHwCGEOaV+I84DrgKtJcEcuTKuI99f9IkzveCNwK3EWD+TOalSfK3J6U3/uA/wA/JVWwu2saqUHnUVLDwAxglw6e9yLwJdJkmo+QGn8uAn7dsxw0fMY40jwbYyLiyXz4DtLnqn5Ix5nAqaSeD0OA7YAdImJYF4/pS1qxYyhpUtCnyROq5jweRVrh4xUqq6OYmZmZmZmVFhFt23qbHvWcyKtwnF7ZH9DBdaeR5keouqLumsG8u7t/7dzTpCVG6+2dt6p3Gj4iYq/GKW/4jAmk3he7Vw6fVHfNanX7qtsfSVo6tHpsUINj/yAtj9lRWtTg2DKV17+mg8aFDtIwmNQ40dHzBjQ4tmODSzsUERvV7Y+lQaNX7q1xNLOvTFI9P5K69Ofjnc4vERHHkpZYNTMzMzMzs/lUyWEdZmZmZmZmZr1Wb+zB0C4lJ8RsO0n9q0t3Nth6svzoAkHSbp2U2+PtTp+ZmZmZmZn1Hr2958RLpEkXOztvjd1AmtOjkfrlR83MzMzMzBY47jfROr26cSIippNWDbE5FBFvkZYdNTMzMzMzMyurnbOLevM2P2ykZVsHAv0c3/Ed3/Ed3/Ed3/Ed3/Edv73xvfXOTRHuiGLWGUlLAW8CS0fEeMd3fMd3fMd3fMd3fMd3fMdvX3zrnXr1hJhmZmZmZmZmNu9z44SZmZmZmZmZtZUbJ8zMzMzMzMysrdw4Yda1qcDR+V/Hd3zHd3zHd3zHd3zHd3zHb29864U8IaaZmZmZmZmZtZV7TpiZmZmZmZlZW7lxwszMzMzMzMzayo0TZmZmZmZmZtZWbpwwMzMzMzMzs7Zy44SZmb2LpEUkfVBS/+rW7nSZmZn1RpJul7RMg+NLSbq9HWkym9vcOGFmZu+QtKakO4HJwHPAiLyNzP/aXJAbh9aWtFC709JTpfMg6cOSviBpsbyvEs+Z30jaQtIfJN0r6QP52O6SNm/xc1z+dXIj7rvKQcl80bhbOg+Stmz0O0HSQpK2bEH8iyW9p8HxJSRd3IL4z0pavsHxZSQ922T4AcAiDY4vCmzRZGygbPmXLntbMHgpUbMGJO0JvBYRN+X9E4HvA08Au0bEc+1MX1ckrQlsDaxIXSNkRBzTlkTNodJ5kLQNsE0H8b/bZOz3ASdX4s/2h15E9G0mfn5GH+DDNE7/HU3EvRuYDhwPjAZm+59ERDzS09h1zylS/nOj7EuStDhwFrBnPrRWRDwr6SzgxYg4vn2p657SecgVg6uAz5I+n2vm+BcD4yLi4Gbi52esAewNrAEcGBGvSmFzCEIAACAASURBVPoi8HxEPN6C+LsDPwRWBzaNiOckHQSMiIi/NhF3J+AK4Epgd2DdXDYHAF+KiC+1IO1Fy38ulP2zwCcj4vW648sAD0bEh5qIPQNYOSJerTu+PPBqi373X0wql7fqji8BnNWC/38VzUMb478XeDkimmoslTQTWKlB/PeRPqP9ehBzg/zyYdLP1djK6b7AdsAPImK1HiV69mcVK//SZW8LBn9IzBr7BbAfgKRNgf2BnwLbA6cBX2/2AQUrl/sCvwdeA15m9splAK2o2BetAJbOg6SjgCOB+2lQAW+BS4H+wLEl4kv6NPBHYFXqyj4/q5ny3wj4eEQ82USMThUu/0spWPY1pSqXwHHAhqRv0P5eOX4rMJDUaNS0whXA0nk4jdSA1h8YWjl+FXAq0GzleCvgFuBuYEvgCOBVUp6+B+zcZPz9SL/DTs+xaz+vbwAHAc18fn4J/DAiLpe0S+X43flcKxQr/9Jln61G49+R/YAPNBlbNP6dsyQwpcnYNXsCPwfeqju+GLAH0FTjBOXz0FH85YGJPQ4qLZVjC3iPpGpa+wJfIn2Wehp/h8ruFyS9WRd/G1IPw554mFQmATQavjEZ+HEPY9drefmXLntbsLhxwqyxVYDh+fWOwLURcX7+VnlQs8ELVy5/CRwRESc0EaMrl1K2Alg6Dz8E9oqIKwrF3xzYIiIeLhT/XFLF/su0vvyfAN7bwniNlCz/0mVfunK5I/CtiPiPpOr7+jipIaFpc6ECWDoP2wJfiIgX6nqfDyP9Tm3W8cAvI+JUSdUK4O3AAS2I/2Ng34i4XtLPK8fvJzX6NmNtoFHj9pvAu8ay91DJ8i9W9iUrl5JOzS8DOFbSpLrYnyJVQHtsLlS+i+ZB0nWV+JdKmloXfwPgnp7GJ/3+rVXwn25wPoCjmoh/fSXOZXXn3iZ9dnraMLc66X19FtgEGFM5N43Uo2FGD2MDxcu/dNnbAsSNE2aNTSC1Ij9P+kOs9j/tKaRvJ5pVsnK5LPDnFsZrpHQFsHQeFqG5P4K6Mop3Nzq10prAzhExvMsr59xhwImSfgEMIf3R9Y6IGN+CZ5Qs/9JlD2UrlyvQuIKxBK37PVG68l06D0sAkxocXw6Y2uD4nFof+HaD46/Smoa71YGHGhyfSspbM14m9cgbWXd8c1LFpxVKln/Jsi9ZufxY/lekPEyrnJsGPELzvxtKVwBL56HWGCRSr4/JdfH/A1zQRPytc+zbgZ2YfWjENOC5iHipp8Ejog+ApBGkYUGvNZHW+ti1ocIl5wIsWf5Fy94WLG6cMGvsX8CFkh4C1gJuzsfXo+fd9qpKVi7/TGpQObdA7JrSFcDSebiQ9AfwsYXiHwQcL+kHETGyQPz/kiogJT4/t+Z/b6s7XusK2oo5G0qWf+myh7KVy1qj5Vl5v1aZ3we4t8nYNaUr36XzcCep+/qvavHzMLlDgX+3IP4bwMq8ewLYjwEvtiD+CNLwqfq5i7Zj9mESPXEBcIak75LK/f15aOLJtO7nrWT5Fyv7wpXLrXPsS0jDpFrRiFuvdOW7aB4iYu8cfyRwckT0eAhHB/EH5/irk4anFRnSFxGr1x+TtExEvNFs7JLznZUs/7lV9rZgcOOEWWP7A78mDe/YqTJx1seB/2tB/JKVy+GkLpmfpvE332e24BmlK4Cl87Ao8H1JnwMebRD/Z03GvwpYHHgmd42tj79ck/HPAk6RtBKNy+fRJmJv3UzCuqlk+ZcueyhbufwFcIukdUn/jz4wv/4MsFWTsWtKV75L5+FQ4DZJnyD1wjmR1HC8HLBZC+L/CThB0jdIFfw+kjYjVfAvb0H8U4HfSVqUVNncRNKuwOGkBpxmHE/69vU20s/BHaRGs5Mj4qzObpwDJcu/dNkXrVzWKoCVuEuRJjh8stl5fOZi5btYHrITqXy5IWlV4GvAExHxzxbEX4f0t9tdOf7+wL6kCv7+ETGumeCSDgNGRsRVef/PwE6SRpMmnW1m0uj6+c4OIP291bL5zihb/kXL3hYMXq3DrA0kfY3U+HESLa5c5m+FOhLNzEReecY40h++C5G697a0Alg6D5I6+3YvIuKzTcbfs7PzEVHfpXhO489sFJbcu6EVM8KXVLL8S5d9fsY+pIkdDwYuIlUo1yBXLiPiT03GX4M04d2GpEnoHgROiIghzcStxD+ZNH78G6Tu4RsD7yNV/i6PiKNb8IzSeVia9Id7Nf7vImJ0C2IvAvwO2IvUU2h6/vePpLlSmhr7nZ+xG+kzVJuD4yXgqIi4qNnYOf4ipAbwJUmVjgmtiFuJX6T851LZN6xckoZYNlW5lHQ1cEdEnK20xOojpAk4BewSEde2IP3bARMiolTlu2geJP0TuC4izlVaIeUpUs+P9wI/i4jfNxl/CHBYRNwsaX1ST65TSA3vT9Y3vvQg/ghgt4i4R9LngauBbwHfBPpHxLZNxJ4EfCQinpd0Amnliz0krQcMiogVmkl7fkax8i9d9rZgcOOEWaa0lNNjETFTs5Z1aqjJb6Z7Q+WyeAXQOpa/6ehQM10/K89YnDTp6Wxrrjf72e8tSlcuS5obFcDeQNIqpCEwSwIPRcSwAs9YHFgy6pbea2H82rfeT0VEs7165pqSZV+4cvkyabLQRyR9Gzia1ICzJ/D9iPhYpwG694zSle+ieZD0GrBVRDyeG3p/TOq1tRNwTESs02T8CcBHI2KkpIH59c6SNgZujoiVmow/mbQ88ihJZwCLRsQPJK0F/Dcilm0i9quksn8oDys+NSKuyI29j0TEks2kPT+jWPmXLntbMHhYh9ksDwMrkcZd15Z1qs6r8E7jAc2Pu39Xt9L5iRsfui933a6v4Dc1lrcVjQ8dkbQCcAnwxQ4umacbzqpKlH0lzpXAla2uXOY/4t6u9TCQ9FXSkp9PAAMjYlpn93dHjrGvpGMoUAEsnYfS3xzXRMQoYJSkvsD6kpZtRez8bbQiYlJETJK0gtIytE13q27wrfd95JUAJM0X39xDubLPViLNmwSpu/zVEfFPpbH4/20y9tLMmgtiO9JKX5Mk3UTqKdkKq5PKGlKF8saI+EWtAtiC+KXzsDizlkHdlvQt/kxJ/6E1q+1My88A+ByzhgONBZZqQfxxpKELo0jlU1uiVzT//8fS851B2fIvXfa2AHDjhNksqzNr+aaijQclK5cAkj4I7EDjb76bnU+h/llFKoCl85DHS3+zg/hNjeuUtARwQo6/fINLWlLBVxrH3yj9NzQR9nTSkoOfIi2b+zVSl/9f0vOZ7N+lVPnPjbIvWbkEziPNGzBE0odIc2hcRxqCsThp/HFLFKwAls7DSaRVZcjfHJ/KrG+OTyU1hPSYpNOBIRFxUS6bwaT5MiZJ2j4iBjUTn7TU7HVArVv1/8jdqiU12619S+A3+fXXSPNPLEP61vuXQNONExQs/7lQ9lC2cjkK2FTS2Bx7l3x8WdJqX61QugJYOg/DgR0l/QX4AmkuBYAVgVY0Ht8NnKq09PsmpF4xkCr7L7Qg/nXAHyUNI/0/5pZ8/GM0P4/Y/qSJa/tTZr4zKFv+pcveFgQR4c2btzZtwLqk//nvUN2ajLkNMJFZc1k8RPpj7A3g9halewngbFIvkxn1WwviF80D6Y+tacCNpMnibiSNu3wDuKQF8X9H+mZrJ9KcHHuT/gAeRepO3Gz8D5HGAc/MZT6z8rqp8ieNu94kvx5P6r5K/mze1aLPT7HyL132+Rn/BH6YXy8DvJLjTwb2azL2m8Aa+fVhwD/y682AUS1K/+nA9/LrvqTJy2aSllAe0IL4RfOQ07lafj0QuCa/3hh4uQXxXwA+kV/vSBqysxap0nB3C+K/BqyXX++Tf5b7kBpvhjYZezKwSn59OXB8ft2f1NuhFZ+fYuVfuuxz3LNJ30L/K78XS+bjuwAPNhn7R6T/Z40j9cDsk4//GPh3i9J/I/B30mop04AP5OPbAk+3IH7RPAA753TPAP5ZOX44cEsL4vfPZfRI7fdcPn4acGYL4i9Maqg/A/hY5fhPSXMO9TTuQsCRwAdb8TlpR/mXLntvC8bW9gR48zYvbqRvmb5c2T+RVHG6B1i1BfFLVi7/BxydX7+Vn7Uk6du6pipOlWeUrnwXzQNphYj96+ILOL/23CbjP0+u5JEq+B/Or3cnjbtsNv6NwPWkCazeIs2QvTmpS/IWTcYez6yKx3PAZvn16sCkFn1+ipV/6bLPsUpWLscDa+bX/yIt6Vf7o29yi9JfuvJdNA+kb4jXza/vIo2DhzRpX9OfUdK3wx/Mr88HTs+vVwfGtyD+JNLcBpDmOzgqv16l2fSTJjj9JqkB+VXgs/n4hqQlClvx+SlW/qXLPscqUrmsxPk4qdfKkpVjX679Lm1B/OIVwLmQh5VIPQ36VI5tQpoMspm4C5GWuV2pFens4LNzMbB6ofjvNPyV3EqUf+my97bgbG1PgDdv8+JG+ha39kfdpvmPye8DN5DG5zUbv2Tl8i1mfWs5jlmVqA1JM5S3onxKV76L5oHUK2O1/Pp1YP38eh1gdAviT2BW5eMFZvVEWJ0WfHtJqhxvkF+/CaydX3+WNHdAM7HvI03IRf68Xw58gDRU4pkWfX6KlX/pss+xSlYubwcuyz9L0yo/W1u18Oe3dOW7aB7y57LkN8fP5Vh98++6L+fj6wHjWhD/UeAn+fPyJrBpPv5xmu95MDe+uS9W/nOh7ItVLnPsZ4B1Wh278oy5UfkuloccfzpposRSZTSJFnyJ1En8N0t8fnLsvwJ7Fkx70fIvXfbeFoytD2bWyCrMGju4I6nb6vmkbm9btCD+psCREfEauddEpMnFDgfObDL2RGaN4R/NrNUEIDWGtMJywLP59fi8D+lbtC1bEL90HsYB78mvXwQ+ml8vw6yxvM14llnzljxJ+iYT4CukHjjN6susCa1eA96fXz8HrN1k7DOAlfPro0kTYz5Pqkz9osnYNSXLv3TZw6wxu6uQxuzW5ploxZjdg0jd488GfhMRtd9DO5N6brXCK8C6eUz/dqTeDZDKvhUrdZTOwwGkP7B3JvWkejEf/yKp0tysS0iNTo+RJkC+NR//FOkz1axjgJNJQwv+GxH35uPbkoaw9VhEnAN8GvgusHlE1FaGepZZcys0q2T5Fy37iHib1OOv5XLsRUvErjxjOnAu0K9Q/KJ5yPGfp+zEyv8j9Qoo5XrS34Ul3AIcL+lkSbtK2qG6NRt8LpR/6bK3BYAnxDRrbAJpoqPnSX8wnpqPTwEWa0H8RpXLp2hN5fI/pF4YQ0kzPZ+SJy37ej7XCrUK4PPMqgD+j9ZVAEvn4Q7g86Q5Lf4MnCHps/nYbS2Ifwmpl8dg0sSAN0o6gPStRSsmJH0sxx9B6m1zqKRppN49z3Z2Y1ci4g+V1w/kZUs/AjyfG9NaoWT5ly57SJXLP5K6Ud/W4srlo6QVNOr9PyoNB5J2BW6IiIk9eEytAjiaMhXAonmIiOdJqyzUH/9pdV/Sz4FzI2KOfidFxEBJj5Eaqf8cEVPzqRmkz1RTIuIaSXeRGgEfqZy6DfhLbSdPCvxSpYGhu/EfAB6oO3ZTdV/SeGCjiJjj3xcly7902We1yuVpXV3YA78DDpO0T25IKKFWASw1sXbpPPwG+K2k3SP+P3vnHW5XUb3/zyJ0EDB8KVJC772DgESqiAgC0qSDCBqR9gOkdylCIAhSAwRBKSKCQXpHRSnSQgkQegklSAv9/f2xZueee7JvOzNzk5vM+zz7uefOOeedvefssmbNWu/Se11+uuc4B7cZ5sGvg3b3F8WXwx4JHGlma3bAH7PAdE74W/esEmmcCjnHP/fYF0wGMEkTeh8KCiY6mNnl+ITsEWA7PIT73eC5PlHS0p0SdM1/L3CapOvM7ApcBft4fHK5Ugx/UMefUdJjoXLBabja+UhgfyWoFGJm++HaGEPMbH08TcUIE0BJZ0byZz0GM+uP1yZ/3cymAA5q4D9e6UrWVf3Nh4dsP5fi4WxmGwEzSLrWzBYG/oZrBrwLbCPpjgR9TI07oJ5PbaD25vinHvsG3jkJk8tq8mhmq+JpEU+H/1uaXHaz/5Ynl+H7W9E2AXw1tO0MvC/pr+n2tNN9iDqGiYD/ceD78qonOfiz7b+ZfQgsl2tsQh8597/lsTezqvLQ7SSeXIYKCOvhCxyP13BHVYIKfWwN/AZ3riSfAOY+hlAmc2HcXniphn/FSP66+60IpeAlRU3wzWxUJ29L0oIx/LmRc/xzj33B5IHinCgoqEEo73Y8brz/XtJNof0Y4HNJJ3T2/W7wZ59c9iZyTQAnBZjZtJJSlZDrrJ/+eE521E3dzKYHzsJFYcGrdbxgZmcBr0lKtXqZHb019p3039cnl7kn31mPofBPGO7e6COGO+fk0swu7ux9SVFlbkMfuSffWY/BzI7qgv+YSP75uuDPWsp9YkfO8S9jX5ACJa2joKAGIQx1UE17pzf1HvDf3PD6OWDxVJNLGOdc2QrXajhV0ntmtiLwVkN+cBKECeBLJA4xzX0MZrYQXmVkIbyawGgz2xhPX3gykrsfrs+wFzCHmVUT/ONwQcCLYvc/9LMwvv/3hPGxBLS/wdMiBtI+f/w2vGxgEudErvHvrbHv7u70Yl85MD++ulZQMMlA0gJdf6pl7mjnQzeQbf8h/zHEOh+6wd9rE+DqmZvCbmvgXAc4EBeIBq+Mdqqke1Pw5xz/4nwoSIEiiFlQ0AHMbBYzO8DMLgzbfmY2c+I+FjazjcxsulS5f2a2LF5O7mD8ATdLeGsLfOKZoo9+ZnaEmb0GfBTSMDCz48xs9wT8WY8hPPwfx3Pst8DLlIJPylM8uA8DdsHTFT5vaH8CLz0ZBTOb1cxux8foRtoELC8ys9Mi6TcHBskFWhsNridpL0zaMjKPf9axLygomHRgARl4ZzOztcI2W0puSS91tqXqJ+cxBP6VzGyHsCUVUTSzhczsLDO7LWxDgkM8Ff9OIbJsLDDWzB4zsx0T8O6ALwR8goujDwl93G5m28fyN/WVZfxzj33BpI/inCgoqIGZrYyX09oPr0TRHxcoej6s3sfy55xcng5cImkRXMCzwo2kqaQB+SeAuY/hJOBwSRvQfv/vwJXuY7ETsKeky2lf/eBRXMskFoPxcoEDcCOmwpV49YUYzAaMrmmfgfbOihjkHP/cY19QMClgss7pzTi5nMHMhuJis/eE7XUzuyikzCVBzglg7mMws9nN7A68bHU1AX/IzG5P4QQxT5sdAayKl+19DHeEP2lmGyTg3x/4PW6PbB22m4BzzfW4YnAYcJCkbSQNCds2wCF46d5o5Bz/3GNfMHmgOCcKCuoxGK/lPr+kLYIA1AK4NsQZifhzTS5XAc6raX8NmDOSu0LuCWDuY1iGBlX8BowmTanSuWkrRduIKUgTJr8hcHAlZNiAkUCnOZ/dwIPAJg3/V5OYPYB/jv/xlpBz/HOPfUFBbyGnA6Gvpxy1jMyTy9OBdfDKVbOEbbPQFrvwAPTKBDD3MZyFl5JeSlJ/Sf3xctIzEV9KHdz5PVjSapL2D9tquO12cgL+X+IldA+WdH3YDgJ+jpfcjsGCuMB4M64nXTpPzvHPPfYFkwGK5kRBQT1WBn6qhioFkr40s1PwyVssNgQ2kvRqU0RpisnlZ/hDphmLAm9HclfIPQHMfQzv49EqzcJoK+AOkFiMANZmfB2OrYgsNRkwA+2dWhX642MXg0OBv5vZkvgz4lfh9bdx4zQFco5/7rHvCXJOLl/CHZx9GbmP4V58ZbyvItqBEDRYlgFeUvsqOBuT5l7XGSbW8a8ml8Ma2q43sydxXZ2YEqNbAltJuquh7UYzG4uX7907grtCNQE8pLHRzE7CJ4C3RvLnPobvAetLeqpqkDTCzH4B3BLJDa7VsHVN+1Bg3wT83wL+UdP+D9qiYFvFK3illGb7av3wXgrkHP/cY18wGaBEThQU1OMDPKqhGfMCHybgzzm5vB6vwV05CWRmA3Cj5c+R3BWqCWAzUk0Acx/Dn4CTzctBCpjCvGb5b4FhnX6zezgW+J2ZHYzfZ7cwswvwkM1jE/Dfi0evVJC1leS8M4Y4aE0sjzsmHscdaaOBNSQ9FMPdgJzjn3vse4KWJpdB72YPM/uNuVAuZraimc1dfUbS0rmqaPQWWj2GMBbLNPy/mZldZ2YnmpfArfi/L+mNFPtqLtDbjJ8Bb7XAtWs3w+OXpIdCw2Z2hgXdn+CYuBt4GHjFzAZWn5N0n6SWnjVmdndIi5ius8+lHP8atDT2ATknl9NTv1+jw3spsARQJ+w7FD9nYpH7GKag3in5BWnmJW/jz7BmLE99ymJP8Rz1E/Bt8AWmGJwGDDGz35vZjmE7F488+G0kd4Wc45977AsmB0gqW9nK1rThoW2v4A+becO2bWg7IwH/jcBx4fWHeLjeFPiqxDWR3DPjKydjgC+Bl/G8/rvx8qUpxmczfPX7YLxG9oHABbhjZYME/FmPAZg67O8XwNeB+yvgMqBfojFaOxzDaNwRdR+wYSLupXHj8e9hzK/GHUZvAgul6CPnlnv8c459B/3NhAuJLtHUPm9PjwdYNuz3yDA+C4b244FhCfZ1KuB2YJFufHb7Vq43oF+4J/w7nJPvNW4JjuE/wJbh9YL46vwVYcxS3J8Pxks6V/9fFc7P1/DylbH8b+EO8IuAbyc+F18FVg6vNw/7vChwHHB/oj7OCOfo/8J1vHriY1gPOBG4EJ9wj9sS8T8BHFrTfjjweCT37eF8mbahbbrQdlui/X8F+HFN+9Z4taNY/qzHAPwVf5bP1dA2N3AX8JcE/EfitsPB4VmwNq7ZMAY4IgH/lrhdchOuA3FEeP0F8KME/D/Cn1nvhu0+YLMU507u8c899mWbPDaTJmtNpIKCWoTVt1PxcoRV+tMXeJ7qIWpxxamBf2ncAHgYWBePFFgKj5xYU9LzMfyhj7Xwic6MwMOSbovlbOJfG38QLVf1ARwrKUVYZtVH7mMYgE/0ZwQekRS76tFrMK8cM4j243+20q0Uzw7MTtNKiqTHUvCHPvrk+JvZVXj51t+F1eNH8bKbBmwrqeXoHjO7DT/XDzKzD/HJ8Atm9m3gCknzJ9j/t/FJcZbxNrNjcY2S03Cnygn4+GyO3yOi8prN7H/AipKeDxEy60raKETf/EnSvJH8o4CfSPpHyOG/CndUbw0MkLRhJP+UeD7/Lnh6xQvAxcClkt6M5P4UWFieMng+8Imkfc1sAeBRSXXpcq30MyXwQ2Bn/Biewx0Il0lqNaIBMzsKf648iAsytjNSJf2oVe6GPrbE9Z1uA+4PzWviTpGtJdXp4XSXe2ngZmAa/L4Afo/+FE/ljCpTHfo4EhfrPom2CJA18Qnh6ZKOi+TPegxmNi9tNk8VOTUv7jT6ocbXUuopv+EpBAcAc4Xm13GbbogSTHzMbCX8N6jKfT4FnCapt1MHe4yc498bY18w6aM4JwoKOkEIva0UsJ+XVJeK0Sp31sllwcQBM5uR8Sf4H0yg3ekSwei6FDe6mtMSJKlf7+9Va8g19mb2Jm6kP2pe3u0Y/DreGReKbbksW9PEu9E5MR/wjKRpE+z/YOAzNeWsp4KZPQ/sI2l4OIblw/Hsg6+yR5XEM7MPgJUkjTSzW4G/STozOLuekdRpukE3+McCi0p6xczOxFeQf2ZmiwIPSPpmDH9TX3MAO+DnzuL4CuxFwA2Svm6B7yXgp7jzexSurTDczJYC7ku57w19zg7siadO9cMjA4dIuqMFrjfwagWXpd3L8frJNrkMdsNPaBOHfgq4XFIS/Y1emnz3xjGs38ifevEh9PMNAEkp0nGzIzh27wT+KenTrj4f0U/28e9rY18w8aA4JwoKJkGY2SrAd6lf+d4/cV+5JoDZjiE8mLfqhH+LSP4FgN8BA4HGyaSRaIJvZtPiUSV1+399BO+jeBndk/Hw8+aVyx7lwHfQR7bx76Wxb5y8DgNel3RImByPkDRjBPdo3PHxSJNzYgM8rD0qKiD0cRauWTISeAhPzRqHBNfXx3iKy8thsrmJpIfNbEE8QmbmSP478BW/2/CJ/JKSnjOzdfDog/kj+V/HBQH/YWbP4GVvrzazxYD/pIo+aOhvNWA33EHxBvBNPAx6V7UXJewO19H4xPUNXB9gUUmfmdluuMjzGgl3HTNbFdgVT3v8ALgEDxHfHjhH0oE95HsXWDVF9ODkgL44ATSzaTNPvHcD7pTULLicin8Y7kC4W9ILiblvBdbAI3b/g6df3IWnZKVyDGUb/9xjXzB5oFTrKCiogZnNgOfJrUf95GnBBH3kmlweiodSP8P4k8sk3siuJoD46lkMf+5jOAMXVLuzhj8F/oCPxW45+M3se7hwZF3ZzdjxXxDP56+rxpIKOcc/69gHvAKsYWbv4crn24b2b+KhzzGoxGArwbUcgrZL45Fa4HoEjUgxXq/iwoIv446uDUN/qxAv+As++b4cTxM5oeFc3Yp6ocOe4lrgCjMbCcyKa7uAV5NJcl2EiIkd8Yn9gsB1wA8k3RaeP0fiEUw9qt4k6WgzewIP0766IQXxKzwNIMW+z96w74vgpQ+3A26uVu3N7BI8CqRHzglcZ2J7XCMjCzJPLl/GJ5N345O0pPyhj3ETwBxOiV44hvfN7N8VPx4lkLKqy6+BC8zstdDH3cBdCZ9pn4c+Lmro4y78fIpKlZO0QUiZWg34Dl4hax9gGjP7j6S1ovbckXP8c499wWSAEjlRUFADM/sj/lC4jPq81zMj+TudXMas7prZW8DBki5plaMbfdyPTwDPpH51/e5I/qzHECaVO0i6MRP/R3jY+TOZ+EfiJb+Ojcnv7oD7OjxvPNVEuK6PbOOfe+xDHz/Hz/2P8GoKK0r62sx+CWwh6bsR3DMD1+DljL+Bh2vPCfwT+L6kjzv5+kQB85KGH0g60cy2wR1GL+IVkMYrgZiw32mBryRFlSc1rxL0K3yCK02buwAAIABJREFUf0kV6m9m+wEfSrowkv8GYCPgWXwyPkzSe02fmR14U1KP1PPNbCfgSjXpIpnrKG2r9uUzW4KZfY47nYbi4zNeeWczmwn4a0+vhZBGsxPwWNja/ZYpIv/M7EJ84rcwLhiabHJpZjsE7oFN/NUkLVrnJdz/F6zhTuU4y3oM5lpSFf+38YXSBxv4Y0uhYl7ZaCBtE/xFcFvuLkk7xPI39FHxr4M7et+QNE8i/kXx6ML1cUfs/yTV2Yw95c06/r0x9gWTNopzoqCgBmb2Ph6KfH+XH26NP+fk8g3gOymMoE76yD35znoM5oJ3G0t6OhP/nfiKbvIc2sD/AbBCjtBnM/s/fMX237hAVvPkoOWonoY+so1/7rFv6GdlfPJ6q6SPQtsmwPsp7hvm4o7j9Ggy5WMvjGvq3CNprJlZinz1mn7WwEOVR0q6IQHfvLgT99Xw/6r4avsISefH8ueGmV0EXCjpn518xnDxzZ6WEv0K+Jak0U3tswKjY9Oawn6tBTyYeLW74u+sFLIkrZuwr9yTy28F3h/ggqpTpEgrC9y9MgHMeQyBf0o8oupnuMZFav7p8YoR2wV+k5Qkajxwr4U7EAYCK+L3oBjNoT0D1zq4IOm9uOPsLuCx1PfnnOOfc+wLJm0U50RBQQ3C5On7kp7KxJ9zcnkQXiJq39TcDX3knnxnPQYz2xkPx98tk4G9EHAuvmJcN8GPqnhhZkPxHNS6WvdRMLNN8Yihurz6qKiehj6yjX/usc+JsGI/FheQfCJjP7PiFSi+i0c9LSLXtRgKjJF0QK6+U8DM7gXOl3SZmc2Jp389iU/QzpJ0bCT/zsA7koaH/0/BBR9HANv11GFQw58tusHMvgbmaI5mMLPl8BD9/q1yB54p8NSlpXI6wHsDOSaXTbwDA/cKuKDkXZL2i+HuoK/kE8DcxxCiAgY2bNMA9wT+2MjUDRt4q/2uomPukTQmkv/EjNxfA2/jlY7OqRzfqZFr/HOPfcHkgeKcKCioQQhr3AzYWQkrdDTw55xcTgEMx1eBRjD+5CxK7DH0kXvynfUYzMs//gUvv/ZiDf+KkfyrA1fg5RPH0UIaUcZgOF6NGzGPM/7+t1yq0cxeBP4GHJc6qqehj2zjn3vsQx9DO3tf0m4R3C8AP5L0aJcfbr2PYbjWzR648ViJbm6ElyJcKkEfO+KlmBcA1pD0kpntC4yS9NdI7jF41Y9nzCuAbCNpzWAYn6tITSBzEcy9Jd0Roj5uwys7/AD4MsH9J3l0g5k9gp/ny+GOmi8b3u6H/w43Sdq65us97etJYHdJ/4rl6qKfeQAUWVqyhjfn5PIfDbx3Be6kk7JemHxnPQZzPYLpaIsIuJuEUQFNE/zzJb2fgreGfzBwraRnE3JvTlvKxRLAI7SN030p7NGc45977AsmD5TwmoKCehyAhzu/FSZrSSeveAnRq81sbRJPLoEh+ErHncC75BEEnA0fn4sb2sZNAIkUxCT/MVwKrIQ7V3KIJg7FjYrtMvFvh4sMfoobMc2CoTHnz6y4LkAWx0RAzvHPPfbgwpeNmAoXmZwF6HH5xCacAJxoZjuqSYcgITbEK4K86lH64zCSHgow1sHM9gaOxYVPq/KSAO/jYpZRzgl8vKuog/VxEVGAp3EhzljMS5vw5ebAnyWdb661c1cC/uo+2Yx5gP+1yHld+Ls8cDOuh1Lhc9wJmEpH5hDgVDPbO3WET3BMH44/g2cMbR/ik50T1EJ51Rocgk+gjiHx5BIvzfgxfi4+jZdoTL1afBNtE8DvZ5gA5j6Gt0Mfc4ZtDnyynGohaH98gn8Q8Cszqxw3dyX6rVfA0y4GAgeYa7Ak6UPSdYRr2Vx/aG3gx/iCwde0FyBvFTnHP/fYF0wGKJETBQU1MLOjOntf0jGR/LvjkQefMv7kWzErf8GQ27YKSc4BMxuBr6qcQoZyk7mPwbzU4UaS7svIv5wyKVSb2Zu4A+KkRMZ6I/elwL2KFP3roo9s45977Dvpdwrg98Dzkk6J4HkEF6GbChfbbC7zGesYra6vFSWNtPblSlfGKy7MGsk/AjhU0nVN/EvjRmqUqJuZPYA7Lofj2j2rS3o0RM1co0jNAGtfzvURPJrkshAx9qhaLBXbG9ENISXlSuUt1TgGL1M6Je74aJeaFZM6Yma/AXYHjgIq7Za1gKOBCyQd1ip3Qx/L0Ta5XBs/hiSTqKDJsQxtugHfaeC/U9IFEbte9bFv4P0O7qRLOgHspWOYhfZ6H0sC/w380b9xQz/LBP518cin0bH3h5o+lsMjq5JoNoQIqur8HAgshZcWvlfSj6J2tq2P7OPfG2NfMGmiOCcKCiYAMk8uX8IN6yxij6GP3JPvrMdgZk8DW8emn3TCfwOuYp+l4oV5tYtVlEez5DB8dXs46aN6qj6yjX/use+i78XwCULLq/e5HaOhjxuBhyQdEZwHy+KOkD/hxvVWkfxjgcVDKkejc2IRPHx4ukj+gXha0EzApVUaTQjXXzxB2sXl+MpiFYEzQNK7ZvZD4ERJS7fIW/22R+Gr3rXRDZI+b3XfG/qamvoy1S8n4N6FTiKSJF0awf06sJeahHfNbDM8B3/uVrk76TPp5LKB1/AIsUGpuRv6yDoBzH0MYSI+EE+j3S4Vf9jvFWjTzFgLr370uOI1RRq5BwbumfDqMncrQpPDzB7H0znGEDQgAmcuWyX5+Occ+4LJAyWto6BgwmBqfHUrqWMi4GjgGDPbNUV+Yge4A1/9y7U6fTR5j+EA4BQz20vSixn4bwAGB8OxboIfW/HiUlw5/cRInjrsgU+aqhWVRsSmjFTIOf65x74zLETkczWF86EbOAi4PURKTI1HQC0F9Md1QGIxCk8vaI6g+h4ecRUFSXeZV5WZqSnc/HzShCb/AjgeT+/YUtK7oX0l4I+tkla/bUgVzBLdEBxAQ/ESge3eIk3KHcpYpho/B+uc0k+H96LR1eQyknvFJt5v4Pehs2K5m/qpmwBOgYfsx3JnPQYz26KBf0ngPeA+/LmQgv8G/D42E/AoPsG/ANfNSJEC8x6ecvQovr8X4FENKbjPxZ0ROQWRs41/L4x9wWSAEjlRUFCDELZad3EIT8V4Dl+dvbjmM93hHwy8LSn55DKEDi+EG6Mvkl4vA/NyV4fjRnDyCWDuY2gKS/6khj9W0b4zp5MSrEwMAXbCH/6PMf7+7x/Dnxs5xz/32Ic+Tm9uwrUONsFX8gfF9pEbIZ95EA3lSoGzJb2RgHsP3MF4AHAR7vBaCPg1sIekPyXoY0rcuF4IuELSh2Y2F/CBMinc9wUEXYwvgZPw0pLNKXfRQquWsVxpSNl5QNI+Te1n4dFiq7fK3cA1hvaTy7tINLk0sy/xiJu7aROSbFVHpKM+6iaAVV8T/TGEtKnGqIDHU3EH/lPx/b439dgH/k0C9wddfG4e4PUci1DmFd+Wl/RCC9/NNv65x75g8kCJnCgoqMexuJDb34F/h7ZV8ZW/s/Hc4N+b2ZQt5l/2Aw4yV8dPPbm8ruuPROPc8PfImvdSrM7lPoZsZVYBJE3R9aeisAxuPIILMbbrPnPfQJxxRMbx74WxB1+xbESlUH4A7rBrGcG50lnIfArnygDgFUkn1L0XG/ov6cKQ2nE87oS6Angd+FUix8R8uCjgALwE3q3Ah8DB4f+9EvSxNvAzYEHgx5JeM69AMkotaKWEVKxFJb3TifMbiHaOLg+slDOtD3fG1WEaPD0lBgcBw81sfeCfoW0NPIrl+5HcFXYg3+Syf1e8gXs74HpJH3f12Ro8DZxHvglg1mOQNHt3Pmdmh+DVd3rkcJH0/7rJ/zguKPpKD/m7q4U1Ar8eW3lGdoWOrsEukXP8c499weSBEjlRUFADM/szcKukc5vafwZsKGlLM/slsKekZVrgv7OTtyVp3Z5ytrAPMcbRRIHcx9CqcdQD/qwP6MwrN+O0BFJzN/SRbfwnVuMo5NY3YircGbIzcJQSlB/OvPI9JbA9Lqz5lnnZ2xmb+4qBmV2HOyN2xwWFK02Lgbho4iKR/FsClwGXAzsCSwb+Qfg50+NJsrlQ5Z8kfZZZs+E/wH6tOFC6wV1FMwwGjqC9ZkY/XGBv/gQ5/XPhqTWLh6ancL2J12N4W9iPGAfsBONu6CP38yXrMfQCf9ZnWE7+Xnr+5jz/s+9/Qd9FcU4UFNTAzD7Cb8rPNbUvDPxX0ozmyu2PSZoh435MlGGBPeijGEed8+c2jvq0cdHX9z8lzGx7YBtJzc6LVri+BuaQ9HZT+3zAiNh7mpl9AiyhyKo9nfC/C3xb0jPWXnBzfnz/p4/kfwQvpzusiX8F4O+S5ow9hlwws3XxiJVDqU+563JFvBPuUeHlfMCrwFcNb1eCnkdKeqDVPiYmTAKTyz47+S78E467N/roa8/fgt5FSesoKKjHe8Cm+ApRIzYN7wHMgK/e5cREGRbYA8yPr/zmQu5j6I0xyomy/4lgZg8D60kaY20lIWuhBLouNfgXLvjYMhq0MgQcF5wIFfoBq+Hl5GLxbzzaI4tzAhf+q4vumIc09+TF8JzsZvwPmCWW3MxuA/4AXBvjLOgAt4W/tzd3S2TKnaQFYFzk3xZqL0baMsxsWeAJSV+H153tQ5aqBQUFBQUFEweKc6KgoB7H4ZoS36VNc2IVPOe1ymfegITq2x1gopmcFRRM5vgr8Fl43Ru6LuNgZtMB+wCvRVJV4faG65Y06gN8jovr/TayD4BzgNNC5NdDQLu0qwQTzFtw3ZI9K0ozmxE4BrgxkhvgTWBhPBKgEWuRxlH8JPAb4BwzG447Km6U9EXnX+sWvpuAo1NI+i6AebnSBYDnJX0ZQflfYE5gdHgt6p99SaqNFBRMAihh7wWTLIpzoqCgBpIuMLMRuJr9FqH5GWAdSf8InzltQu1fQcFEgMnKOFJDiU9lLPdZI5ZoeCm/T3Ahv5bRMKm8GBenTL1qX6ESvawrO5tignkAcHO4R0+LC24uArwDbBfJDV767kwz2w3f37nMbA3ccXNcLLmkX5nZfsD6uD7HMOArM7sGuFxSy07vmO92F8FZ9jtcBwVgUeCFUFHjNUkn9ZByAdpKYC6QZi8LCiY4cj4jy8JVwSSL4pwoKOgAku4H7p/Q+1FQMJGiGEd5sB/tjdqqEsgDqcLoJe0K4zR0FsJLBY41M1MaIaqsE0xJr5rZcsC2wLJ4WciL8In92ARdnISnjtyOVxu5B4+a+a2ksxLwE3SEbgFuMbO98JTBw3CRzx45byZAWsRJeAnagXjVlAq34SVke+ScaNQmyaVT0iImKwdsQXJEPyPNrB8e5fZS0/1/Y+Ij6QoKJkoU50RBQRcws2mBqRvbMq44FhSkRLRxXYwjR1flHxuhuFKQd+BlPsfrK0WZz8DTH7gaTwEQHnXwAnCRmY2RdEAMfzXBNLMl8XKfjfdPkUCLIqQR/CGWpwNuASeY2al4eseMuNDmR51/s+cwszlxJ8sOuKPl351/oxa9nRaxOS7O+i8zazxPn8SdXS0jVDV5R6Fco5mdgqfvjAC262XnRU4H7Es0iZX2QeQ+hnuBFM5GzGyWmqpPPwPeaoFrV+BKSZ908dEl8RLKPeE+A3hc0kXh2Xs38G3gEzP7gaS7AHJU46lBsvGvQUtjXzB5oDgnCgpqEMrfnQJsDcxa85HeynvNuXJTjKOuMVEaRz3ptsdfKMZRR9i34fWswOHAzcA/Q9sawEbEh/2PAr6FTzTHIZT5HEWae88Z+HUzAC/TWOFK4HQ8baJlmNmCwF9wp1bjRLm6n0Ufg5ktgjtXZsejHMZB0rGx/IHnc3xSnBRmNhOwJZ7SMRB3DF2OT/ifb4Gyt9MiZqPp/AyYgfhn1qHA3gAhlWYQfu39ABeo3qLjr7aG8HusCzwjqfF66PHkMvDNAmyFO2pOlfSema0IvCXpNQBJS7e4r1Ph0Sp7SRrZxcezPl8ijmFF4AtJj4f/NwN2xa+1o8N1RyslewPfwcCLkq4M/18FbGlmb+LVwx4N/Fe0wo9HBp1pZlcDF1Wpvs1osUrZVrQ5XTfFr+fF8ZLGJwBrtsDZDmZ2Nx5pdnVnkWYR478esB719+bdwt9Wx75gcoCkspWtbE0bcDb+oNwSz/XeFZ+MvAL8pBf340NgwRa+NwuwBy661j+0rQjMnWCfpsLDnRfpxme3B2aY0L9nzX6tCCzT8P9muMjhicDUCfgPxica1f9X4WX3XsPLZ8Xy7wpM343PzQv06yH3q8DK4fXmYZ8XxSfd9yca/7uBnYDpMv2+64Xf8kJgaOOWiP/PwKCa9kHAdZHcXwOz17TPB3ycaP/frM7DxnsMsCDwUQL+G8L19H+Bf0lcTPIBYO0E/D8FvgzH8V/gkYbt4QT8M4Tz/R/Ac7jzYNyWgH8sPukdXF1rfWnD01x+2XD+LBBenwXcFMn9CTAgvD4ZGBZeLwW8nWj/r6quX2A64FlcEPYLYMtI7mVxx83IwFddW8dXx5Jg/9+mG8/fCP5+wIF4FM+beIWycVsC/v9U4xzuOWNx3ZiRwBkJ+EfhpYbBhcvHABuG58EtCfinBH6EiyR/DjyNP/PnTMD9KTBPeH1+NR64k+KDRL/vGeEc/R+ur7N6wnPnKNzWeSA8A/7SuOU6Z8s2aW0TfAfKVraJcQNeBgaG1x8AC4fXO+Kq6qn7mwmfCC7R1N7K5LIYR13z93Xj6K1wXl5U9ZNw7Itx1HUfH1X3hKb2hWlxco9HLJwe9v3chv9PB87ES4mmcg59WF2/tHdOrAy8m4D/HWDZ8Pp/wGLh9brAIwn4XwIOTnXO1PD/EXcenIyv2v+qcUvAvwEwRcb9Xwh3FNwWtiHAQgn51wrnze/DvfMMXD/jI2ClSO7RwArh9SPAjg3HFO04C1yNzrntw31/ejxiI+r8DON9SnjdeG19G1/NT7H/g4GTMp4/x4bz/4Dw+x6OP7veAfZJwP+/6nzEJ/U3h9dr4iltsfxjgXnD6zOB88LrRYExicdqjjBOj+GOiuvxxY6Wru9wb9sQt4FeBjYJ7Uul3HfcwbIFbQ6WEbjNNUck7xvVNVu2srW6tQu3KSgoGIf+tJWM+yD8D3Af8J1YcjO7yswGhdfTAQ/iqzmPmdmW1eckvSLpqx7Snw5cImkRfKJZ4UYS7HvAH3Dhtlw4CtgfDzOfGT+ma/FV5aMT8C+Kr7gC/BgXBNwe2AWPlonFnHiUDXg48lWSbsFThVZJwD83rpT/f8BdZva0mR0c8tdj8RawZEjp+B5wa2ifHp84R0PSvsBceATI7MA9ZjbCzA40szki6fcCdpG0mqTNJf2ocYvd94B3cQO0GZuF91rBCmGrynyu0LAtjpf53KVF7mbci0euVJCZTQEcBNyZgL8fPjEDn9DMFV6/BCyWgP+buGZGLmwM/FjSwZLOkHRm4xZLLulWuSBmcpjZRvhEY1V8wvQYsBrwpJltkKIPeUrX8vgE53F8MjUaWEPSQ5H0twIXmtmF+H26Kg27FOOXdm0VM+OObvB73J/l+gHDcf2VGKwCnFfT/hr+XEiBKYG9zexBMzvPzE5v3BLw/wT4qbwi2ZfAHyXtgTstVk/Ab7SF+69P22/8Cv5Mi8UYfGEH/Pe9raHfpCm5kt7C7cJ/4vbJMsClwPNmNrAFyotxW/AJPEWq2vfV8AiNJJD0paRrJW0GzIMvzhwHvGJm15nZui1ST41HnBUUtIyiOVFQUI8X8JXil/EHwtb4Kv6mQLNuQCv4Dp4/CB4eaHgqxs74KsWfI7hXwXNNm5HaONrNzNYHHgI+bnxT0v6R/JVxNNzMjsaNo+fN7DHcOKorUdgTNBtHfwuvUxtHr+DG0eEN/UYbR3IxwL8AfwmT+R3wc+c4M7sJj6i4ocUJUGUcvUFm4wh3OF1rZrPjonfHASea2Y3AEEl3tEDdG8bRUfgEaiAeoQE+Pt/DUw56DPVemU9wJ8TtZrYyPl6n4JO//iTIacYN6+XwCKIHgIPM7HP8N36hsy92E1fjE+JzE3DVYQxtk9csMLOt8OdKs2AoklaMoD4JGCzpkKb+TsIjQW6t/VYPIdfGaOlc7wK/wKP85sWj2ypn30p4REsKvAKsYWbv4dfstqH9m7R36LeCz/BIyGYsSpsuSCyWBh5u4G1ECp2qOXGnE3g0zMzh9d9IUEoXX4w53MxuA9YhaIzgNlcKjYxrgSvMbCSuD/T30L4CnqYVjfDc3RF3sC+IR+n9QNJtZjYDcCTupJivJ7ySjjazJ/Dz/2pJn4W3vqKHVXC6AzNbFT+GbXEH4yX44sffzOwcSQf2kPJCPBopxXlSMJmiOCcKCupxMW5c340/EG4IkQ5T4Sv6sahduTGz4cCpkdzFOOoafd44qiDpLTO7D/8dFqVt5WaMme2qIGDZA75iHHUBSZeY2VPAPrQJ9D0FrCXpgY6/2S0c1JFjwsyWURCRi4GkJ8xsUVwj40O8GsW1wNmS3ojlxyeXM4TXR+LX7b14VMk2Cfifwx1xq+P3iXaiuJJinZdHAMea2c7qWpG/xzCzfXDn9CV4tM3FeNrCKrjeUQyWwJ0ezRhKe1HXaASnYp3oXcvlSuWiwYNq2o9qlbMGZ+ACpB/h0Tx3hfbv0PbcaRXXA0eaWfUbyMwG4I6hmEWHcagcmRnxKi7K+zLwPO4IfBg/Pz/r5Hvdxb74+G8OnCCpeiZuRRrH8n54lM28+P20qrLzLeCcWHIzuwEXP34WT0scJmmcM1PSx2Z2GvD/WuDeCa8E0jzOf6TNiRaFcN1WjpVFcI2g7fD0GoXPXIILr/b0+TstsGdYuHqM8e/NKezngkkcFs7DgoKCTmBm8+ErN8/FGF4NfM/iq+nD8dXFbSXdYWbLAbdLann1PoTDzoobqO/hGhRf4Z79e0JI/UQNM3sG2EnSA2Hi/TdJJ5nZNsBZkmaP5F8WN44GAKdLOia0nwXMGlI8YvinwvPT58VTbB4J7fsBH0q6MIY/cNWt3FzUtHKzraQerdx0ZByZ2dSBb1iCfa8zji6kvXG0Fi6uN2MPuc/EUxaqkPY+ZRwFRfndFUopNrQfCBwnaboJs2dxCOVLxyiB0WFmozp5W5IWjOR/BHcWGD7JaT6HYiIbMLOngWMk/dHMPsT1D14ws2NxAePxJuc94H4F2F/S1U3tWwO/lTQgZt8D10q4A3QJxq8IJElR0WGh2sWqjO/4kKTLYrgb+lgZvz/fWk1ezWwT4H1J90fwzgxcg+u3fAPXbpgTD/v/vqSPO/l6T/taGD9P75E01sws0fV1Eq4vdGJ45v4Bvw4GUBOVkwrmZdu/kjRRVxEzs4uACyX9s5PPGC7s2qPSt2b2FfAtSXXVmkbHXluB63Pc6TQUt0/GW7QKFWz+2lNHmJl1lhYoSa2mixRMRijOiYKCCQAz+zku1FSt3Kwo6Wsz+yWwRczKSDGOovrtK8ZR48rNhTSt3ITPzA68KalH2kLFOOoZwjnTHJbfckqGmR2E53ZfjEdp9QeG4RExP5P0l9b3tl0/0+KOy7qV7+tT9NFXEVLJOryPVc7MCP5PcPHjl8xsNLCBpEfNy6P+S1Jd+eruch+JrxyfRNsq9Jq48ODpkqIjiszsUfz6PRmPNGs3Vj2dkDVxb4o7jmfE9Z4auSWpf+0XJzKY2Zp49OWMeAWZ27r4Sk+4Z8VT776Lj88iwbk1FHcARpUCrulvDbxU8khJNyTgmxf/LV8N/6+KR7uNkHR+Av6dgXcqB6+ZnYKnlI0Atos5PwNfNge+mX2Ni1K+3dS+HHBn7PkfnCZrAQ+qkzKiBQUTEsU5UVAQEEJtz5f0aXjdIRKEDWdbuWngL8ZRx3x93TjKuXJTjKMuYGbT4zoNW+NRSu2QYOV4BeAyYBrcOfEAsJukN2N4G/i/hzs86iK0ole+CzqHmb2A6yk8YmYPAhdIOs/MNgT+FHONhetrX7yCQCVE+jqeLjgkkfP4Q7yiRtIUtcD9LC6QeKgypNSEPoZ29r6k3VrknQqvFLG8pCda4ehmP8Nwp+IeeDpZFXmzEe6AWipX3ylgZvfittZl5iLOzwBP4lF0Z0k6NpL/GWBveTTqGrhu0n64OPWXkrbolKBr/uQO/BCtJdxmexIXIq3QD085vUlSXcpWT/qZAtdVWUrSyBiubvQ1D0BlZxUUdBdFc6KgoA374Ss2n4bXHUHECzIi6UFc+6CxbXgHH+8Wmoyj+4FoJ0cHGIyHOg/AjaMKV+KVNZI6J8IkvMOJeAu4Ai+TWRlHt+IGwU/MbM5Y4wg4lKBjEYyjX9BmHA2mTaegVdxNm+bHODSu3IRJSLcdEw3GkXCxxFrjKGqvQ1fA7bgAY181jk7FHXN7406EX+A6GT8DUkT1PIeLSlaVY65M5ZgIOAsXlTxWrjbfp2BeSWYXYD3qIz+iomOC82AVtYkxVu2z4I7eqLQR4A7gh3ipzIuBweYCmSvj2h8tI1z3gwPnN0Lbh51/q8e4HZ9EJXdO4NfRkFyOiYBvNv0/Fa6jNAv+27QESV+Y2cskrghRgw2BjSS96r6ocRhJDwUYO4KZ7YhXPloAr8LykpntC4yS9NdI+qVxgXFwB+8TktYMzrlz8cixGMxL27m5Oa7pdb6Z3U+bvkgMjPrIqnnwMqmt4Lrwd3ngZjyqtsLneORotGZJiNCttLCSP3+D8+Nw3AacMbR9CJyG64tkqVJUMGmhOCcKCgIkLVD3OgdyrdwU46jb6OvG0cW4o2B0U/s3wnuthJUW46j72BTXRLnLvLrGvZKeM7OX8Eozl7dKHCKe/kCbXsyawFlm9n1gL0lj4nefOfAV1j7nmAg4E3dODKet5F5KzE/9PXQafAISiz0JDhVJZ5vZu8C3cTHFujKUPUZI61osvH66LnUqAnsAl5rZ0vj4N2tyxKQF3Yw7aVJUdamFakoKh/vG7/F0lRicgFcc2rE51S4hZgDqnDf9SSBYaWZ748/AM4DDaLsW3sejcmLL4Y16AAAgAElEQVSfv1PRtp/r4+c9eDWob0Vygz+7ZsUFPTfEF0zAF55a1uzJ6cBXm+7Vi7gzOrZqTGc4BDjVzPbOEOFzAl5m/hDaFsfWwkvAT4ufTwUFnaI4JwoKAqz79cGVIG0hy8pNQDGOukafNI4akHzlphhHPUJ/2iZPH4T/wevd/z6S+w585fsIufbJU0FH4w94JYEUk+NrgIHET8QmFLYFtpZ0Y0pSM/thw78bmVnjtdQPj9ToTIyzWwgOsq8b/v8T8KdYXoAQLXEOrr5fRZR8ZWZXAr+Q1OrKbiPWwJ1mG9e8J+Kc48Pxe8OS1FdiyaKHEpymp+PO41MiqAYBCwOvB2dlc5ntKDHVgHtx0d8jKtrgXDkI6Exzp7v4JV7K+zoza4wEexD4bQL+J4G9zKuTbUDbccyFV/SJxa14qedH8ApW1X1iKdzJ3iqyO/AlXQrjoiDrosJejuEPGAZMDzxqrv/ULr0yMnVzZ2CPpuv0MTN7Db8vFedEQZcozomCgjas0PT/ivg18kz4f1G86sVDsR1lXrkpxlHX6JPGUW+kXhTjqFt4AR/rl3GH1tZ4JM6muAMtBhtKuruxQdLzIaIilWE3CLjazNYmTynO3PicPCkF1eRDeDWKRnyBX7vRKWtmtivwkcavqPFjYPrqGmwRF+LPsk1oS4VbA482OY805QjPwp1lx2WIvrkg/D2y5r1Yx0dXWIh4u/i6rj8SjYPw+//KuBjvKfizpT/uNIrFAnjKUTM+o61EcAwOBv6Cl9q8VNKjof2HtEU0xuAXeDnjeXFtl+qZvhJekrMl9IYD31wUdygeSdXuLdKd//uRPtqsQn/8mdiMp2lz4hcUdIoiiFlQUAMz2x9fWdy5CqM2s2/iIfP3SjotU7+LAXdJann13sw6rQevSKX50MfSeN7xw8C6eOTBOONIUpSDxczGAouHVI7GUnuLAI8pspyimQ3EjaOZcONot9B+Yug3VjBrFtqMo99Luim0HwN8LumEFnmr3/YoPE2hduVG0uct7nqXxlEKsUQz24XOqyG0PDkzs0+BZSU929S+GPDf2HMncO2HV3UZYl7P/QZ8fKbCyziemaCPLJVwAvfuePrSp7gzrrkiQqymQlaY2QF4+dxBqcakiX8UrjnxTmruwP8sXnnlzqb2dXChwMUiuD/GU+7ua2pfGxfUi55chnvy8rH3+QmFmihJwyPmNsGfBy2Xcu0tmFflGkSD6DVwtqQ3EnCPAH4t6a9Nz99fArumWOAIujEzNaapmdn8wCdqEpqcnBBSP7/Eq+28wfiVcB6t+97EAjN7AHhA0j5N7Wfh99TVJ8yeFfQlFOdEQUENwirrhpKebGpfGrhF0lz134zu9/u4cTRbDv6UKMbRhIN5NZBcKzfFOOqcfyo8OmUvBbVzM5sPX5V7TtJjkfydVcJ5T9KBMfyhjzdxUd+T+opAmZk1C0Wui+tyPMn4kR+xgrNZERxoi0t6sal9fuCpGAda0BzaRNLjTe3LAjdKik4LMrNLcSf9hbFcXfQzbaZ7XHN039fA23hK1VBJX47/rYkHZjYAeKXOMWdmA2Kj28xsDzwN7gDgIlxjZCHg13hUWnQKkplNiS8ALQRcIelDM5sLLyH+Uadf7h7/2rhA8YLAjyW9Zq5jNarZcddNvveARSW9Y2Zj6Ny5HlNt52NgJUl10QdJYBnLhQcH63A8qrAxcmtevJT9va1yF0w+KGkdBQX1mAmocxDMhosORqGrlZtY/txoMI7GiwBIYRzhGg1nm9m0+NisambbEYyjSO4KBqxkZuOMIzz6IIlKfGrjqBGRYd9dYXn6sHGEhzwPDxEN4xlHEbzAONHZZZvaXqIHlVG6QFeVcKKdE3go+JV9xTER0KyV8JdcHZnZENzRNKSpfRCwsKR9I7sYjYudvtjUvhzxaWXHA6ebaw69CWBekehU4LhI7grPAr8xs7VInBYUnMaH4mLIc5jZosE5dxzwoqSLIva72r/vxnJ0BPNSzJ1NXFOE5Y/C7YW6++coIkP/JV0YohePx9PvrsDL0f4qkWNiPtzBOwAXmb0V+BBP95gG/+1j+LfEqyhdjqfnThPemhk/t1p5DuwX9rF6nWtldwT1JZ5Twjponwa3gVqGpLvNbFE8tWbx0HwtcI6k12O4CyYflMiJgoIamNcRXxtfOahyIFfDDbx7Je0cyZ9t5aY3jKPMk8uK6yf46s1Coel14KgUxmmNcVQZwGcC00hKaRztCCwZ+Afhqwc9No56ceXmP8B+sQ6ULvr4Gpiz5vyZC3g+QdrOXLQ3jp4ioXFkZoOBzySlKBvazP0mHpb/aFPU0IJ4StOMCfoYDLwt6cRYrkkRIXLuh5IeampfEbg+NvrAzE4GtgF2Be4Jzevg6VTXxETHmOvSLIzf1yon8QBcL6BddZxWI9BC2ktHiEoLMrMjcd2YI3H9iaXD+b8NsK+kNVrl7g2Y2WZNTVPhGiA7k+759TUwh5oqsITn2oiY1J0Q0bA9cLOkt8xsemDGlNGEZnYdPtHfHXfGVfe4gcAFkhaJ5H8EGCxpWNM9dAXg75LmjDyEbDCzdXGn0KHUO/4+iOCuogkH4zpbjREq/YDvAPNLatZfKyjoVZTIiYKCeuyFCy9egRsX4KHuF+EiTlHIuXIDNItttjOOEvXRUbWIGfE89taJ2xtHl+cwjnBxuAcZf6XyL7QJssXgcDzsf5iZNQrQ3R/eawW9tXJzMHCKmeU0jgTsYWZ1xlF0xEZwQuRUBZ8S2C1EZzzE+KKz+0dwZ62EE9APOMjMNgIeY/zfOGb/s8PM7gC2kPR+U/tMwHWS1o3sYlbqq958QJpVzSPwcqW3488VcOHZYfikJAbZBRmVt9T2TsCekm43s3Mb2h+lzdnYY5jZw8B6ksZYm7BwLWLSBlVf5voaM3sSd0i17JxoiLgUcJyZNd4n+uELKP9tlR9A0pdh3JcI/39ComjCBqwNfFvS59a+FPmLwNwJ+BejzenXiP/hFdGiYGa34YKw18Y8DzvAbeHv7c3dEi+IuV8D1164wHuFSrOqxwszIZLwCXnFm2U7+2xs2mPB5IHinCgoqEF4IP/czP4fbSv3z0v6uJOvTRQoxlG30OeMo8ZUDkmXtLZb3UIxjrrG0rjGCng1lnZdRHLnroQDsAxtavxLN73XF8IpB+KpKc2YFr+2Y/Ec8D3gd03tG9NWQrZlyAVrtzGzI3AH6Vjg8ZAeFMsdLXg8gTE39ZVYpqBtoaAV/JU2515vVNRoxr+A8yM5qhVtw6/hxhD8z3EHTopqVv8OfaVKVWvGFNQ/R+ahzQEfgzfx6KEXm9rXIsH1i+vc/AY4x7zi1x9wPZcvOv9at5Bt4apyKobI3S3UoLcVif8Cc+JpRv/FnyF1qSO5q+0UTCIozomCgk4QnBFJPL29tXLTCYpx1IY+bRxlXrkpxlEXyBz5lLtMYO79z4Ymx9OSQUuhQj/cofBagq5OB35nZrPhqXYA6+FpfrF6E+MgryjzbJcfnMhg7tHdCr9X1JUbjhEkHYE7mJrv/VtRX96yW2h02vS2A8fMpgP2IfLcrK5bM7sY139Ife+vcA5wmpnNQ31kWKxNdAt+He1ZUZrZjMAxtJXdjsEFwJlmtht+z5/LzNbAbZNo3RVJvzKv2LQ+HuU5DPjKzK4BLldTKegecrf83R70UZ1HUwML4AtvMSKwC+BpydXrgoIoFOdEQUHvYYKt3BTjaDz0aeOIjCs3xTia4PgAjxraG3eUzYgLip1N3MrxpIDK8STanAaNGAv8MrYTSUPNbBo8NaiKYHkR2FvSsFj+IPq4C+7wqJvcx6al5MYZuNjvncBbpI22ORa41MzmxsdlC/MywDsBP0jYTxbU6AEZLqL9CbBDij4k7Rr6ylVuuBK9rBM2TeHgPQC42bwq17R4+uwiwDvAdpHc4JWmpsCj/6bHoxg/A34r6awE/MjFhG8BbjGzvYBN8fvF7vRwfHo78i/Yg7/DU33Bo/9eMK9o9Zqkk3rC1xjxlSL6q6CgCGIWFExi6Mo4knR9wr6yGEdB8KsjKFZwMzg9bsbHZhFcf6Iyjr4Tq28RVhYPxauLTB+aK+PoiA6/2LM+pqBt5eZHeIpESys3E4NxFATLWjKOJiXkEps1L8W5i6QPbPyynO0QufKdDUHwz/Doo1Vpc0iBR26NlvRV3Xcj+pwNGKsE5Q0bOH+HOyeGU1+ud7+ar000COK8O0hK4cit418bF8RsLFN9rKRbIjg7FRFuhOIEhXdp6qcSu34gVaSYmfUHrqa+3PAYSQdE8s/X2fspJqBBW2pbvGpN9RtfLmlsLHdDH1PjEYwz4kKhya7hhj7mxI9jB7wyyL/Vw3LV1iAQbW2C5rWRf7G2T+jvTDwKb19cGHzZcP5sBhwdI4hpXub8HUnDw/+n4ItAI4DtivOioDsozomCgkkMxTjqdh+ThHEU+pmWtpWbZXpqwBTjaOKBdVzJJEqJP0Q77SPpw/C6Q1QrswV5YGbvADvlmtznhnm1jo2VsdxwaoT7QoVZcWHim2lfbngj4DhJgyP6qcpsj2dcW5oy21U1sdnxstpP0VaNYiPgdElLxfYR+lkSr/TSqO8iSTek4O+rCMK7W+ILAwNxZ+nluP3wfAt88wEvS1Iv2T4vAdtI+pe1r2ayMPCwpJkiuJ/BI8zuCNGit+PP+R8AX06sju+CiQvFOVFQ0EvoxZWbYhxNRki0clOMowkMaxOb/RWeFlQnNvuVpCjdiRDVMy9eSjSZI663YWYL4b/rEqFpBHBmK5ODDvi3ArZm/PtPtCaQmb0ODJRrTmRBwpSpOu6dcX2P3XKeQyHVrjnlJTqV0Mz+DNwp6XdN7YOA9SVtHsHdG2W2s5YbDjx/wXWlGh3VgmTlyBehY82SYyO5ZwAOoeO0qZZL3Qb+scAY4ErcIfFgDF9vw1zIvCrR23j+LIdHwc4cyb24pJfNSyZ/S9JOZrYUcJek2dIcRcGkjKI5UVDQe2gUUut05Sayn1HAt3BxwHEIxtEo0qglb4gbR69a+2oXI4FOJ7fdQVfGEQmOoY8bRx2t3GzTyuRsAuSMzkbT+RkwA/H56/PSpva/OXCNpPPN7H7grkjunOgtsVnDx2cp/HrtcwhO0OtxDYr7Q/OawJNmtqmkWyP59wFOAC4BNgMuxtPXVsG1P2JxGvArMxuUIg2uEeall88iUT55B7gK1wYYbWYvMn4p2padN2a2AJ7yNRDXIxj3FunU/jfCSyY34yZcryAGdRFnkKDMdgNylxs+E7cV1gt/VwvcpwEHxpKb2U+B3+NplG/S/p4vXHckBhcC6wCXUZM2lQA/BG6X604kR27HK57Gugl+n4C28dmDNnu0VXyE27cv43Zi5XT/FJgukrtgMkFxThQU9BLUUAoyrNwc2bRyM6RauQFaDiulGEddYhIwjt6ibeXm16lXbopx1PtQL4nNynVFRuJj1CedE/gEcrCkQxobzewk4GQgyjkB/BzYU9IfQ5rcKWFl8Vj8PhSLtXDH6MbmJZ6bJ/cx0T2/wbUaBuKT7Qq3AUcTP/kGuBRYCRfiTS2I+Qf8GbZbBu4K7+JOp9Oa2jcL7/UY1r7M9rGWocx2A3KXG14DWFfSOyHN7CtJ95nZr3GRzJbT7gIOBw6TdHLsjnaAjYFNJN3f5SdbQKzzszPkdrwGHAr8PUSmTok7SpcEvo3bLTG4FbjQvCLdorQJjC/F+NXLCgpqUZwTBQUTBslXbopx1CP0aeOIjCs3xTiasOglvYdDgFPNbG9JT/RCf6mxBJ5y0YyhpCn1OQD4R3g9FhcUBnc2/gsYFMn/Ph4ZlgOb05Yy1TixfxKP/kiBTfDIufsS8TViOWAlSc9k4K5wFH6PGAg8ENpWw1NVftoiZ29FPkH+csP9aCup/Q4wF/AMXt51sQT838Q1q3JhDPBeRv6caV+5Ha8EW2p5/DnwOO7EfxhYQ9LjkfS/AI7HIxi3lFQ5+1YC/hjJXTCZoDgnCgomDJKv3FCMo56gTxtHOVduKMbR5IBheBWZR83sc3wCPg4xmje9hLeB5Rk/8mN56tOFeoo38XvZS3gEzur4/XMBOo5M6zYyO6BypkxVeAUveZsD/8Gv3WzOCUmXmNlTeHntKkrlKWAtSQ90/M1OOXurzDaSnjCzRXEnWbtyw5LeSNDFE7iTaBTuvDko3Cf2xFMIY3E1fs8/NwFXHY7AF2h2llQX4RmFzGlfuR2vAIQoyFYdcZ3xvk+N81bSUan7Kph0UZwTBQUTBslXbopx1CP0aeMIsq7cFONo0key33EC4QLg/KBNU0U4rIlHo53e4be6jzvw6KRH8InH4HC9rYzf55LAvExp5Wx9RtLbnX2+m8iZMlXhAOAUM9tL0ouJOCvsAZxrZnPjz4HmlJfoUsaB5wHgJym4mnBQR89eM1smgfMVAEn/wyfIOXA87swCL+n6Nzxa8l1gmwT8zwHHmdnquHO6+TceEsl/AO4seCu1JkpAzrSv3I7XcTCz2anXxIq6xsxsFrzUczO3JF0Ww10weaBU6ygomEAws9XwlZsqr/8pYEirKzcNvLN1ZOSmNI5yIqQWzCDp2lDB4W94iP67eMjyHZH8vwb2B4aTwTgKKQUL4ausL9bwx6r9N67c7EnTyo2kwyK4XwH2l3R1U/vWwG8lDWiVu6avYhwV9Bih4si++CRkrtD8OnAqfg+NMmxCitoUVZULM9sWTzkaCZwn6fPOvt8N/hlw58FOtJ2fX+ERLb+McWia2VrA33Hthl2A84BxKVOSHmp9z8f1MQaPvJkS1x5qvr/FVJtaHbgCmL+RkiCImaJSRFN/0zK+c7dlx36opLG7QinjhvYD8TKlSXRvwn4vS/398/oUfTT11x8vEx49aTAvRdsRlEAw+mg6iRKSdEwk/yfAEpJeMrPRwAbyyimLAP+SNGsE95HAfngE43iOV0mxgumY2Uq4bswSjB8JFnWNmdmmuDj3jHh0VTs9rz4QlVcwEaA4JwoKJjEU46hbXH3dOHoaOCas3DSWAjsW6C+p5Zz4YhxNHgiip7viTq1fSRptZhvjJWWfnLB7132Y2TcAJH3Y1WcnFpjZebjw8SDadF3WwvV0bpW0dyT/QnjK1HL4dfAwcHIqx3RYLe7s/nZpR+91g3sE7qg/hRpBTKUpZTx94N8aF4Zth8j7z0G4oPLFuAO8P+50Wgb4maRorREz+17g/L+at5M7cAraw8xewFMGHzGzB4ELJJ1nZhsCf4p0zmV1vIY+HgWex9M0k15jZvYsrvN0aK6o0YJJH8U5UVAwgZFh5aYYR5M4Mq/cFONoEoeZrYOvrt8PfAc/l14ws0OAlSVtNUF3sJtoSot4WtI7Cbm/CexO+4o1F0uK1pIxs3eArSTd1dT+XeAqSbPF9tFXYWYf487W57r8cOt9nI1XSzkCFzn9BTA38DPgEEmXR/KvEHinwZ+/DwC7SXozhreBfyRwC3CspLdScE5KCM6DVdSmN1S1zwI8nGDx4ULgFUnHmNkv8Gfj/YS0L0m7x/A39JPF8RoWNFbIcY2F63cZSSnSbwsmU0zR9UcKCgpSw8ymN7PfhYnlx7iAYuPWMiSdgle7WBt4LGyfAcumcEwEnIXrNnxL0hRN22TvmDCzF8xsPAeBmc0SDKdYVIJ90CbYBwkE++QYLGkeYGZgZknzSDozhWMiYEE8N/sBSS9Keqlxi+SeG3eiFMdExzgJOFzSBrQXzr2DtnNpooWZzWBmQ/EyvfeE7Q0zuyisisfyfwfXu9kHF8/9Zng9KrwXi+lxp1wzRof3WoaZ3WZmu5jZTDE8XfRxt5ntZGY5SvPegUd85MSmwM8l/Rn4ErhX0vF4FaEUOhTP4XoZ8wMzAVemckwEzIFHsfVJx4SZ9TOz3c3sinC+3tG4JehiflxUuxnTAPMk4N+ToPch6Wy87O1TuD5HVNRThZDyuDywfHDCpsTt5LvGbsadNAUFLaMIYhYUTBiciq/c7E3Nyk0C/so42jL8X4yjBphZPzwfez3q01LWjexifvIaR9kF+4JxtFh4/bTSiPVVqIyjHKujlXFUVm46xjLA9jXto6mPhprYcDpecnZTxk+LOI34CcLZwFXA3pK+gnH3jHPCe8tE8v8TOMbMdpL0aeCfDhdKjhWtfBL4DXCOmQ3HtSdulPRF51/rER7BKz+dZWZXARdJ+lci7hvw+9ky1OsBpUgZ7E/b/eED2hy99wG/jyE2szXxMX8PT3tcEx+n7wN7SYpafAi4BhiIR5/1RZyJP3+H43ZKEqe3mf2w4d+NzOx/Df/3w5/3naV0dgvyEt5fN/z/J+BPsbwwLlriHGA7GvRozOxK4BdyIdRY7AFcamZLUy86G3ONDcfLVC9Jvuu3YBJHSesoKJgAMLOXgZ0k3WVmHwArSnrOzHYEtpP0/QjuRuNoB9w4Oh0P405iHIVVy/slXRTLNSFgZr+jzTh6g/HTCvZrkbcyjq4DdgbqjKMNJEWVQ7WMgn0dGUdAMuPIzP4P15z4N4mNIzPbHV/BuphiHNXCzF4Ftpb0jybNkh/hoqcLTeBd7BS50yLMbCywvKRnmtoXA/4bq9sTJt434c7KR0PzcniE24axmh/h/rA+7oD6EX79XgNcLunuGO6GPqbEHaQ7AxvjjsahwGUxTmsz+7qTt5OkDJrZY7jw6N1mdhv+mx5oLjR8UIgaa5X7M2AwcETlEAoaIH8A5o3hbuhjejxy8W3yVLvIinD97iTpxsS81blTCag24gtcnPoASX+L7GdX4CONLxr9Y2D6SM2VK/Gy8L+kzVG5Bu7Q+a+kbVvlbuhjU3xRrC66Kuoa643rt2DSR3FOFBRMAJjZR8CSkl4OE4UtJP3bzBYAHpc0YwR3MY66QF83jnKiGEeTPszst3jp4h8DzwIr4tFQw4BhihRszY2gubKSpKea2pcC/i1phvpvdpv/fuBUSdc1tW+OaxJEp76Ee+hPgMVD01O482BsLHdTP9PiESaH4bngyc//EGW1Z+ijH675MkSRVZVywcz2A76SNMTM1sejNQyYCq9UdGYE9zp1DqDgMDpMaQSFd8fLYH+KV7BqFv2N0lTIDTN7HRgo6dlM/KNwzYlkGjRN/M/i+l13NrWvA5wfs/gQNBs2knRfU/vawE2x97bA9SJeAe24vhr9WjBpozgnCgomADKv3BTjqAtMAsZRzpWbYhxN4jCzqfH0hF3wyeSXeJrn5cAuVSrDxAozux2/7zSnRVyKV6tZP5J/G7yaw1lAla6wOp5+dwjuSABaK3trXsr4LUlDm9p3A2aTdHKLu97cz5zAtngE3Yq44yappoiZrYpXfdkWT5G4BE9R3B44R9KBKfuLhZlNhUet7CVpZGibD1gJeK6V37ODfhbGK+HcI2msmZkSGdzmFbmGACeFFIM+BTM7ANcdGpRqTHoTZvYpsLikF5va5weeiomsClG1m6ipso6ZLYunZ6VYXPoQjwzLmhZkZtNW9+eCgp6gOCcKCiYAcq7cNPRRjKMOMAkYRzlXbopxNJnAzObF9RNmAB5RxgoJKRFypW9m/LSIT3HHWmxaRFf3tCoyqqVInOCc217SP5raV8NLES7QU84GjplwraHtcV2CF3Cn0+WprrcQKbEj7pRYBH9+XQjcXN1PzWwt3JnZZRRgcMqfL+nT8LpDpIjKM7O3gW9XzomUMBdCvgrXlBKwSEiZGgq8l8JZY2bv4c7vPqM5YWbNWkjr4qmnTzJ+5OUWkX0NwR1NQ5raBwELS9o3kv9l3Ha4vql9M+DsyMWlPfGIth0VdMKCk/FSvBLIea3v+bg+LsVFYC+M5arh7ocLy+6FR+MtGs7/44AX1UdTgQt6F8U5UVDQy8i9clOMo3pMYsZRzpWbYhxNBgjRT/vhk0twvZIzcvwmOZAzLSLcj7sFtVBdJly/S0ga1dS+IDBC0rQ95WzgGItXfLoSH48HW+XqpI/PcTHGocAlqhHLDU6Sv0r6bjf4RuElbN8NrztCkqg8MxsMfCYphfh0M/cwXGR5D/ycrPRcNsJFpJdK0Mdg4G1JJ8Zy9RbM7OLuflbSrpF9vQb8UNJDTe0rAtfHOtjN7GRgG9w5d09oXge/Hq6JsbHM7BFgYdzx+nJoHoDr0bRzpklascU+DsPLhQ8ncVqumR2J69AcCVwALB3O/22AfSWt0Sp3weSDUq2joKCXIemLsArd2PYSEFtCscJg/GEzgIbwY9xYPR1IEWZ7Kf5w7jPGEe3FKQFSlVWtw5a4WFwz/oGHhUc5J/CqCsviGhaNWA4Pd4/B3rhx9HJYIYI242g2M/tZ9cFWjSNc5+A3YXU1tWbJYbhxdBBuHFV4Ah/3yd45YWbHAvvjaQuNuiKDzWyApCMn2M51E/JSsRd0+cHWuFPdi/9/e3ceJmlVnn/8ezOAMKAScAxoBIGIoiIKAsZBdkdJgppIkGhEGAyLsij8XDAuYQwXq8giCKKguMAPFVQiggqKiBAiIggCjrIqwgBKhmUGAtz547zNFDXV00vVW29V9/25rr7oPlV96pmhp9+nnvec54zmTkqj4vY34rOBu7qc+03AxTWvaNsBuNr2w/BUMecfKIXRiwBsL6QUyMfUulKkm1UjE7A8MLdatXg15Tjv1ngO6mLuOZTVO7+XntZ2aD4w7qLXGGYAH6wKHtex9O/PbuKvRbcFhwlag6Wv91C2HfXiNKKPUU7kupiyJQ5K8+gzKYXxbnxr7Kd07d3AQ5SCytZtj5myKnaydgP2sn2xpFNaxq9lSSE5YplSnIhoxleAPenNsaHtkhx1MMWSo7OAE6rtEa13bo6n+yPNkhxNffsC/2r7rJax71S9cE6k3PUaaJKeRzk+tNNRwL1Y+l/n/KcBx1Wr6EaaRm5P6XPxqW4mtv2DLmMbj49Sjiw+RdJqlFN3HgOeI+kg210dx9kHLwd+UX2+Qdtj3S4nXgV4pMP46pQCb29X/ZAAACAASURBVC9sRDnOFcqfpdXAL4eWdAmlCfgDbePPAr7l7o/y/i3wRuAzbeM70oMjpl1Ow3qbpI9RbggsojQy77qo6T40I665APh8Oh8Rvhxl23LEmFKciGhGnXdukhyNYdiTI2q8c5PkaFpYAei03P9qhiAvkLQ7cCrlDfFSDXnprrhV+/zA0ZQC5snAitXYYuBI24d3OTeSdgZ2oax4WrH1sS5WO7XahLIlCGBn4G7KCT9vBeYBEypOSDp2vM/tReF7PFtNunAZpUD6sZGXU2lG/UHgR6N+1wTUHH8/bEPbz2VlJeB1PZj/WOAzkmbx9OLfwXS/avEpLg21a2mqPcR+Tfl/2F6o2ZklOWPEMg18EhIxRdV55ybJ0di2YYiTozrv3EwBSY7G9mXK6on2N3p7UZonDrpPUt4EH17T9oVa56+aRn6o6oOyIeXf73zbXRePq4aSh1FOzXgzcAalMfJmlBNaemEm8GD1+RxKL5onJV3J5Fbnvart600o+enN1dcbAE9QimeD7oPAxZJeTbnGHAW8jHJzYHaTgTWtbTvrS6teRiNmUAr6f+j2dWyfLukZlC1+I3nQbcC+ts/sdv6qr9HulGt6p5VV3d7cqJXKktqdKduuOsXfTc+tecCXJD2/mvcfJb2YkpP+fRfzxjSS4kREA2p+c5/kaBRTJTlqeZ2hvHOT5Ggg7ClpDkuOytyCcqf9zNY72YO4RYvy5vjsGvsq1D0/ALYfAv67x9O+h7Kt6axqBchRVUO6eZRrQC/8FniLpPOAN1D6HEH5t7xwopO1Xg8lHUQpfLzL9p+rsb+gFFku6zLuflhIKTjtS/lzrErZAnMSXazcqho67257YYfmzk/TbUPnGv2ScvPFLCnat1oE7N+LF6q2Fn22ukGwqPq31ivHU4oT36X0Mhr41aJtjgP2ptysuocexm/725J2omwNfJhyPf4FsFOftpzFFJDTOiKmGElrU/bz70u5q74q5eJwErCC7TuW8e3LmnfokyOVIwJHfumpw1MWAfvbPr2Hr9nz5GgK3Lk5nmUkRz3o1v46SnLU+vM/z/b3u5l3qpA03hVUHsSfJUlHUU4eOmIY56+TpEcoJ4HcLmkB8Hrb10p6EXCl7TV68Bo7A1+jFHQvtj2nGj8E2Mr2jl3M/QdgjtuOg1U5Pvb7tp83+cjrJ+kJYC3bC9rG1wAWeBJHz1bffwZwgO0Hxzr5os/9lcatapwqytbGzYHWU14eo/z9PNFEbBMh6T5gN9sXNB3LZFSnrf3LsMYfU19WTkRMPbdSkqPDWger5Oj3lIRyMv6HJW8iOzV7HAbr0ufkqNMxez0w7Hdu3knp+VFLcmT7MuD1dcw9FUyBbVmHAP8p6Y10Pu2l29Uedc9fp7spKyRupxxF+BpKM9iR331ds/0NST8F1qrmHnEx3Z+C9CxgVofxWcAzu5y7H0b7O16V0ldkUkYKDtWqs09QjhLt+tjcfmrZdrjcMp/YAzX3XXmMzn2NekbSipR/s7+z/fhYz5+g/6E3va+WSdKqLH3jZMIrq2L6SXEiYupJcjSKKZQc7QrsUuedjyRHMcAOoWwnGOlJ0N6wctDnr9MllONEr6Fshfh09bvo1ZTtBT1h+25KIaR17KoeTH0ecIakgymngEDZcnQ0PYy/11q2QhmYV61gGTGD8mf4ZS9eivLG+GWUE7iGkqT1Kf2XNqyGfg0cb/t3PZi77r4rnwIOlLSfe7z8XNJMyolJ76qGNgBukXQi8Icereb6d+ATkub2OoeTtC6lEfg2lB5eTz1E+bcx2ZtjMY2kOBExRSQ5mpghT45qu3OT5CiGwMHAXNtfHNL567QXVUHO9kmS7gdeC3yHcgLJoNsHOIaybWSkR8PjwBeADzQV1DiMNPUU5TSrx1oee4yywuSYbl+kajw6n3Lay1Bef6sjyL9DyUcur4ZnAzdI6kVvgrr7rmxJ6Ze0o6QbWHplVTfbWg+nbEfcBriwZfyHlOtmL66/5wD/DCyQdBtLx9/NzZOvUP4NzKXH/Sxi+kjPiYgpomUf+dbAFSydHN0GHGO764SmuiDvafvKMZ88gJaRHG1MDxo3SboJOLRKjh4ENm5Njmzv1+X8BwPrAXXcuTme8nfxPkpy9Ioq9jcD/267vbP+ZF5jZcod0tmUn8ueJUeSLqckR8fTuZ/FpZOdOwaDpLuB1/Xid1kT88fYJK1CKehCWb31cNvjfwXcVXfT0omq+kEcWOcKrarh4AcpDZavr+t16iLpGuAi2x9uGz+C0m+kq5WFdfddqbPnh6TbgbfZvrItd/hr4Be2nzXZuVte4xxKceUbdL5GTvo4cUkPAZvavnnMJ0eMIsWJiCkmydHYpkBydB4lufgT0NM7N0mOYtBVjRfXsn3AMM5fJ0l7AA/Z/nrb+D8BM21/qZnIekvSQuCVtmvfHjZoJP2ZcqLM8pQbD09bfWa7V6ey1ELSYmCj9uKfpA2A62yv1Pk7xz3/LcBbbV8j6efAabZPVTmd6OxB/vupcoeXV9fc1uvvxsBPbD+7B6/xMPAG2z/tdq4Oc/8IOMz2D3s9d0wf2dYRMcX0qVP3mZTk6FpJQ5ccUbZy7NJh/HTKioFu1d2U7gG6bzw3mlnAgg7jq9C7JZp/R03JEeVoxhewpF9ATD2bA9tJ+nt6XJzr0/x1OoRyEk67BcDngClRnKBHzT2HVC+uUU26F3glS29LeSWdrz0T1Ze+K9VJXC+uvry5R82vf065Pp5YfT1yzX03ZUVsL9zJJI78Had3A6eoHOV9PUv/7ryupteNKSTFiYiYjCRHy1ZrclRzASrJUQy6B6i3OWLd89dpbcqJTe1urx6LITcFVr+cBnxO0nrAz6qx2cCHgGNH/a7xq7XvSrXl6ERgN5Y0XH5C0pmUo8gfGfWbx/YR4HuSXkp5j3Zg9flrKVt2e+Fg4ChJ+9i+rUdzjphF2Y7VuvXFpOdTTEC2dUTEtCPp48D7Kc2llkqObH+yy/mXA5YbOeVC0q6U5GI+cKrtx5b1/RN4nZ7fuZG0JfA9SmOr3SnJ3FPJke2re/AafwfsD/Q8OZL0GkozvRe2DD+VHNlOcjTkqp4ly430IZD0QuAtwI22L2owtMZJuoPSi+Y7beNvBk6y/VfNRNZbrUvem46lCVVD5z0obwQPtL1A0o7AHbZvaDa6ZatO/Hof5U3y86rhuygnspzQ6z5KvSbpVGAHYD+W9KzaEjgB+IHtfbucf33gw5QeWKsCvwCOtP2rbuZtmb91W9AjLF3An/TKV0m/Bm4EjqLzls3bO31fRKsUJyJiUpIcNWe0OzeU7Tbd3rlJchQDTdL3gXNtnyJpNeAmys/Qc4CDbH+2R69Tx7LtWkk6Engb5XfzT6rhrSlb1r5h+/81FVsvTefihKStKQXky4GtKP2NbpH0YeDVtnduNMAJkPRMANsP9njevwD25OmncZ1h+089mPs+YGfbP24b3xY4x/asbl+jTtUJJqPmON2szKn6WWxsu5bTxGJ6SHEiIiYsydG45q0zOar1zk3dkhxFN6o3B1vbvkHSuymrcF4FvBWYZ3vDZU4w9vy1Fv/qJGlF4MvAP1GO4ITyZziTslKpJ6u2mjbNG2JeAXzd9rFtTRM3pxTthmJ1TFvx7ybb9/Vo3q0oWzgWUrYpAmwKrEY5jesno33vOOd/hNJ0+ca28ZcBV9lepYu5f0hZtXhunU3N6yLpfOCLtr/ZdCwxvFKciIgJS3I05rx1J0e13blJchSDrnpz8BLbd1Qnv9xg+1BJL6CscJjZ5fxDXfyDp04+2JjSrPhXU23F0DRfOfEQ5bSLW9uuvy+kXMe6Ou2ibn1Y+fcrSn+kfW0/UY3NAE4GXmt7oy7nvxi4H9jN9uJqbGVKs9nVbe/QxdzHU5p1Pxv4LuVafIHt/13mN07sNS4FvkDJ4RaN9fwJzr0X8FHKSq1fsfSqyO90+r6IVilORMSEJTkac/66k6M679wkOYqBJuk64POUE2uuB95o+wpJmwLftb1ml/MP9bLt6aAqRN018vt1OpH0e2AX2z9ru/7+A3CM7fUbDnGZ+tCzYRFlVc3NbeMvBn5pe+Uu598IuBB4BuUULiiFwEcpR5F3ta216lm1A/B24B8ouck3gK/avrSbuav5j6vmfgZwDvAF21d2O28195PLeDg9n2JcUpyIiAlLcjTm/HUnR7XduanmSnIUA0vl5JuvUTq/X2x7TjV+CLCV7R27nL+24l/dqiLo7sD2wHNZUnwFwPZ2DYS1TJLGfTKKB/sY176QdAywBWXrzm+ATYC/pBTXz7R9aIPhjanu4p+ky4GjbX+rbfwtwIdtv6ab+au5ZgLvAF5SDd1IuT72uti+ErAT8G+UG0I9uX5JWp5yoti7gB2B31IK+l+2fU8vXiNislKciIgJS3I05vy1Jkd137lpe60kRzFwJK0JrAVca/vJamxzYKHtm7qcu9biX50kfYZSnPgu8EeWbgj7/gbCWiZJZ4z9rML1HqM8FKq+IidR/j/PoPQWWR74KrD7oK8mqbv4J+ltlIbIJwIjRe/XAO+lNHp+6nU9iaOlqyLoPbZPbxufC8yyfeQkQ29/nTWBXYF/oeRYV/WisNLhdZ5LOX713yg/TxdQGoNf0uvXihiPFCciYsKSHI05f63JUfUatd+5SXIU01E/i3+9VhVed7N9QdOxRL2qrS0bAasA13hImgD3YeXfslbPQZdHS0u6DXi77Z+1jW8BnG173YnO2TLHsyiNfd8ObAPcQsmrvmr7d5OddxmvtznlZJ9dKT2yvgg8v3r9kz2O030kHQB8zvbi6vNR2T6h66BjyktxIiImLcnRqPPXnRzVducmyVFE/5Zt95qku4BtbP+m6ViiPpL2BN4PvKgamg8cZ/vzzUU1PpJeDlzE0sW/xcAbetCzYZ3xPncyjWIlLaacUHZr2/h6wK+76blVbQn9M/D/Kb9vfj7Gt0zmNZ4LvJNy3X0RcD6lh89Frt4UStoSuND2quOY71bKKW33V5+PxrbX6/oPEFNeihMRMSlJjpY5f93J0W3Ud+cmyVFMa/1atl0HSQcD6wH7eUgTvKqnyC7A2sCKrY/Z3qSRoAaIpHnAQZSVeVdUw39D6aH0adsfbyq28RrW4h+ApPnAoba/0jb+zmp80tcYSa+n9NEZ6wbHpEl6DPgdZRvlF23f2+E5zwK+bXvbuuKIGE2KExExYUmOmlXznZskRzGt1Vn8q5uk84BtgT8BN7D0aTUD3VCyWvl0GGUF1V7AGcD6wGbASbb/rbnoBoOke4EDbJ/VNv7PwIm2n9NMZIND0vMoTa47NYXtavWcpA8CHwQ+AIxsPdyespXzU7YP72b+uqkcdX617Yerr9ehNL6+0fZFjQYXQYoTETEJSY7GVnNyVNudm35IchSDrM7iX93Gai456A0lJd1E+R12lp5+EtQ8ypa7/RoOsXGSHgA2sz2/bXwDSl+g1ZqJbPxqvj7uDpwKPEbZvtn6Rqfr1XOSBBwBHMCSlT2LgSNtz+tm7mr+WlcOSfo+cK7tUyStBtxM+bt6DnCQ7c9OcL5jx/tc2wdNKNiYlpZvOoCIGEorAJ2W+1/NkPxeaTI5ohxZ2o3TgOMkrUCHOzddzt2PZdUfBc4FRpKjq6iSI0lJjqJpdwKzgfYtQrOBu/ofzvgNevFhHNYGRlasLAKeWX3+ZUpz4WlfnKD8XexLWb3Yai9Kf6CB1ofr4yeBecDhdawArLZLfUjSJ4ENKT+n820/2u3cbSuH3kzbyqFu569sQtmSC7AzcDfwKkqvqXnAhK6/1fe2z788pegBsAHlOPKrJxNsTD9D8SYiIgZOkqNlqzU5Ao4G1gBOZuk7N10tKU1yFFFv8a8fJM0CXlx9eXOnrVMD6m5gdeB24A7KKUfXAutSmghHsaekOSw5DWoLSmHnzNZi7YAWY+u+Ps6kbL+qbWsigO2HgP/u8bTvAfaqVg7tDhzVunKoR68xE3iw+nwOZRXFk5KuBMbdL2tE69ZLSQdVc7/L9p+rsb+g5BGXdRt4TA/Z1hEREybpRGA3yh3GpZIjWvY5D2JyJOlO4BRqSo4k3Q9sXsfpFm2vsyq9v3NT+7JqlaNcX2L7DknnADfYPrQ6/eVm2zO7mPsgyikjHZMj20Px5jKaU/ey7TpJWoXSC2g3lqwIe4Lye3l/2480Fdt4SPo8cGf1++C9lELs5cCrKW+i9mw0wAEg6UfjfKptb1drMJNQ9/VR0lHAn2wfUcf8daqujRvavl3SAuD1tq+V9CLgSttr9OA1rqM0oD4PuB54o+0rJG0KfNf2ml3M/Qc6HLdcNSH/vu3ndRF6TBMpTkTEhCU5GnP+JEfLfo0kRzHw6ij+1U3SqcAOlO0Pl1fDW1JWg/3A9r5NxTYekpYDlrP9ePX1rsBrKadBnWr7sSbji+7VfX2UNAP4T2Bl4Fcs3RR24G6YjJB0C/BW29dI+jlwmu1Tq1UyZ9vuevVEtW3za8AMSvPrOdX4IcBWtnfsYu4HgZ1s/7htfFvgO7af2fEbI1qkOBER006So9ElOYoYXpLuA3Ye5ef/HNuzGglsnCStTVk54bZxAS+wfUczkUWv1H19lPRRyraRm4F7WLoh5sDdMBnRr5VDktYE1gKuHVk9KmlzYKHtm7qY90zgdcDBlF5SUFbVHk1ZufiurgKPaSHFiYiYdpIcjS7JUcTwqlY+bWr7xrbxl1FOclilmcjGR9ITwFq2F7SNrwEssD2jmciiV+q+Pkr6M/B+21/sZp4mDPvKIZUj2o8B5lIapwM8DnwB+ICrE7oiliXFiYiYdpIcjS7JUcTwknQxpcnvbrYXV2MrA1+i9IzZocn4xiLpSeAv2xt4qhw3/OtBL67E2Oq+Pkq6G3id245ajf6pet+sX335u/brrqS/Au6qu2lpDKcUJyJi2klyNPUlOYrpqOqtchHwDMopFwAbUxp6vqG9F8ugaDlh4kDKaSmtjTtnUFY/PWF7dr9ji96q+/pYbQ9cy/YBdcxfJ0l7AA/Z/nrb+D8BM21/qZnIekvSQuCVtm9pOpYYPClORMS0k+RodEmOIoZbtXroHcBLqqEbga/aXtRcVMvW0mR5a+AKyjHPIx4DbgOOScF3+NV9fZR0HrAdZQXRDSy9bfMf63jdXpD0G2Bv2z9qG98a+JztF3f+zuHSehJY07HE4Fm+6QAiIhpwPLA/5ajAOmwObCfp7xmy5Ag4BNi7w/gC4HOU5eFTgZoOIKIO1XGhpzUdx0TY3hZA0hnAgbYXNhxS1Kfu6+MDwLldztGUtYFbO4zfXj0WMeWlOBER01GSo9ElOYoYUtVd6Xtsn942PheYZfvIZiIbt/fRITeVtDrweIoWU0Kt10fbe9Q1dx8sAF5BWSnUamPKSpCIKS/FiYiYjpIcjS7JUcTw2ht4e4fxG4CzgUEvTpwNnA+c3Da+C/Am4G/7HlH02nsoTZcfBpD0QuAtwI22L+rVi0iaBYxsg7i5vcnqgDoLOKHa9vCTamxrymrPsxuLKqKPUpyIiOkoydHokhxFDK81gT92GL+XcnTvoNsC6HSU84+Bw/obStTk25SbA6dIWg24krJ68TmSDrL92W4mr5ohnwjsBixXDT9RHTO9f7XtaVB9DHghcDHllCkof4YzgY80FFMd0vAwRrXc2E+JiJhyvg28E6AlOToY+JakfbudXNIqkk6nvEn4SfVxl6QvVM3qBtnHgP+iJEeLqo/vA5eQ5Chi0N0JdDrRYjZwV59jmYxn0PnG2QrAyn2OJeqxCXBZ9fnOlOO816EUE3rRB+pYSkF9J2C16uPN1dinejB/bWw/ZvttlGa27wD+EVjf9txBP8Z7gtLzKUaV4kRETEdJjkaR5ChiqJ0GHCdpD0nrVB9zgU8zHE0yrwL26jC+D3B1n2OJeswEHqw+nwOcWx3pfCXlOtyttwJ72v6e7YXVxwXAv1Ku9wPP9m9sf932f9q+vel4avBSSh+riKVkW0dETEcdkyNJvUyOdrb945axCyQtAs4Bul6dUTfbvwF+03QcNXopw3EnOWIijgbWoPRsWLEaWwwcafvwxqIav48CP5S0MWX1FsD2wGaU39Ux/H4LvKU68vMNlMIZwHOBXjQ8nUm54dBuQfXYwJI0A9id8jP/XNpuItveroGwlknSuPt3jTQbt31nfRHFsEtxIiKmoyRHo0hyFDG8bBv4kKRPAhtStmXNt/1os5GNj+3LJf0N8AFKE8xFwHWUO+HzGw0uemUe8DXKdfdi21dU43OAa3ow/xXAoZJ2s70YQNLKwCeqxwbZ8ZTr73eB6xmO7Yf/03QAMbWoXMciIqYPSTtTkqMZlORoTjV+CLCV7R27nP9iyskW7cnRl4DVbe/Qzfx1kvQZliRHf6QtObL9/gbCWiZJZ4z3uUN+kkpExNCTtCalQeu11ZYOJG0OLLR9U5dzbwRcSOlfcm01vDHwKDDH9g3dzF8nSfdR8oYLmo4loikpTkTEtJTkqLMkRxExCCStxJKtKQDY7sXKtpjiqsbT76D0TgK4Efiq7UXNRTU2SXcB21TbKiOmpRQnIiJqkOQoImJiqt+bR1G2dKzR/rjtGX0PKoZKtQLyHtunt43PBWbZPrKZyMYm6WBgPWA/D+kbtGpl6i7A2ixdXNykkaBiqKTnREREj7UkR6e1jc+VNNDJEeU0kQMlJTmKiH47GtiW0jT4y8B7gecDewMfbjCuGB57A2/vMH4DcDYwyNffLSk//ztKugH439YHR3omDSpJBwCHAV+knFB2BrA+paHtSc1FFsMkR4lGRPTe3kCnrSE3UI7EG2RbUlZ8/E7S+ZLObf1oOrixVMnRGZSGpK+iHE14P+Vu1PcaDC0ixrYT8B7b3wQeBy6z/R/ARyi/lyLGsialX1K7eylbOQfZA8B5wKXAfZRmk60fg+49wF629wceA46y/XrgBODZjUYWQyMrJyIiem8qJEfDaiQ5OkvS7pTk6BZJ84DVmw0tIsawOnBL9flClvyb/Snw2UYiimFzJzAbuLVtfDYDfnz0FGjYvDbws+rzRcAzq8+/DFwJ7NdEUDFcUpyIiOi9JEfNSXIUMbxuAdYF7qCsPtuFsvppJ0rhNGIspwHHSVoBuKQa257Sy+RTjUU1AZJmAS+uvrzZ9r1NxjMBd1MKirdT/g2/htIUfF1ADcYVQyTFiYiI3kty1JwkRxHD6wzKyUaXAkcA50vaD1gBOKjJwGJoHE1ppnoyS3oOLQaOtH14Y1GNg6RVgBOB3Viy9f4JSWcC+9t+pLHgxucS4E3ANZR/y5+uekC9Ghj4baExGHJaR0REj0kSJbE+gKWTo3mNBTYOoyVHwFAkR5I+D9xp+1BJ76UkqpdTJUe292w0wIgYN0nrAJsCv7V9XdPxxPCQtCqwIWUF3XzbjzYc0pgknQrsQFnhd3k1vCWlZ8MPbO/bVGzjIWk5YDnbj1df7wq8FpgPnGr7sSbji+GQ4kRERE2SHPVfkqOI4VStNLsQ2Mf2/Kbjieg3SfcBO9v+cdv4tsA5tmc1Etg4SVqbcnPAbeMCXmD7jmYii2GS4kRERDwlyVFENEXSvcBrU5yI6UjSI8Cmtm9sG38ZcJXtVZqJbHwkPQGsZXtB2/gawALbM5qJLIZJjhKNiIhWMynHcLZbUD026G4FOhVQVmfpBqURMVi+AmTrVUxXVwCHSlppZEDSysAnqscGnYBOd71XpWxtjRhTGmJGRESrkeRoN9uLIclRRPTN8sBcSTsAVwMPtz5oO00xYyo7ELgI+L2ka6uxjSnXrjc0FtUYJB1bfWrgk9UKkBEzgC2AX/Y9sBhKKU5ERESrJEcR0TeSXgFcb/tJ4OXAL6qHNmh7avYhx5Rm+3pJLwLeAbykGj4L+KrtRc1FNqZXVf8VsBHQ2tvpMcqJWcf0O6gYTuk5ERERTyNpJk9Pjm5kwJMjST+qPt2assKjPTm6DTgme9kjBkvrPnVJtwCb2b6/6bgiYmIknQEcaHth07HE8EpxIiIipowkRxHDRdL9wN/a/i9JTwJ/afvepuOK6DdJhwD32D69bXwuMMv2kc1ENj6Sng3MsP2ntvHVgcdzXY7xSEPMiIh4iqRDqkSofXyupA81EdMEvY8OWxYlrS7pWQ3EExHL9k3gUkm3UrZu/FzSLZ0+Go4zom57Azd1GL8B2KfPsUzG2cCuHcZ3qR6LGFNWTkRExFMk3Qa83fbP2sa3AM62vW4jgY2TpO8B59s+uW18H+BNtv+2mcgiYjSS3gj8NXAC8HHgwU7Ps318P+OK6CdJi4ENbd/aNr4e8GvbK3X+zsEg6U/A7A5Hob4EuNz2Gs1EFsMkDTEjIqLVmsAfO4zfC6zV51gmYwugU0f/HwOH9TeUiBgP2xcCSNoUON52x+JExBR3JzCbpY+9ng3c1f9wJuwZdH5vuQKwcp9jiSGVbR0REdFqJDlql+QoImple48UJmIaOw04TtIektapPuYCn64eG3RXAXt1GN+HcjRwxJiyciIiIlqNJEcrAJdUY9sDRwGfaiyq8RtJjvZvG09yFBERg+xoYA3gZGDFamwxcKTtwxuLavw+CvxQ0sbAxdXY9sBmwJzGooqhkp4TERHxFEkCjgAOYOnkaF5jgY2TpNnAD4H/pkNyZPuypmKLiIgYi6RVgQ2BRcB82482HNK4SXol8AHglZT4rwMOzzHeMV4pTkRExFKSHEVEREREP6U4ERERERERET0haSWWrL4EwPbChsKJIZKeExERMSUlOYqIiOgPSTMp/al2ofTOaDejvxHFMMppHRERMWVIminpM5IWAA8Df277iIiIiN47GtgO2Bd4FHg38AnKSV+7NRhXDJEUJyIiYipJchQREdF/OwHvsf1N4HHgMtv/AXwEeEejkcXQSHEiIiKmkiRHERER/bc6cEv1+cLqa4CfAls1ElEMnRQnIiJiKklyFBER0X+3AOtWkmBJyQAAAiNJREFUn99E6T0B5abBA41EFEMnxYmIiJhKkhxFRET03xnAxtXnRwDvlbQY+DRly2XEmHKUaERETBmS3g88YfsESTsA5wMCVgAOsn18owFGRERMA5LWATYFfmv7uqbjieGQ4kRERExZSY4iIiLqJWkF4EJgH9vzm44nhle2dURExJQgaQVJF0t60ciY7dttn5vCRERERD1s/y/wiqbjiOGX4kREREwJSY4iIiIa8xVgz6aDiOG2fNMBRERE9NBIcvThpgOJiIiYRpYH5lb9nq4GHm590PZBjUQVQyXFiYiImEqSHEVERPSBpFcA19t+Eng58IvqoQ3anpomhzEuaYgZERFDrTU5kvSjZTzVtrfrV1wRERFTmaQngLVsL5B0C7CZ7fubjiuGV1ZORETEsLsGWAtYAKxDkqOIiIh+eABYl3L9fSHpZxhdSnEiIiKGXZKjiIiI/vsmcKmkP1K2bvy8Wk2xFNvr9TWyGEopTkRExLBLchQREdFntveSdC7w18AJwGnAg81GFcMsPSciImLoSXojS5KjjzNKcmT7+H7GFRERMR1IOgM4wHaKEzFpKU5ERMSUkeQoIiIiYjilOBERERERERERjUrTsIiIiIiIiIhoVIoTEREREREREdGoFCciIiIiIiIiolEpTkREREREREREo1KciIiIiIiIiIhGpTgREREREREREY1KcSIiIiIiIiIiGpXiREREREREREQ06v8AMyOdJHNRTxEAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 1200x800 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_az2hPDIeRL2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "cccd6502-6c46-441b-9203-a13848c4d606"
      },
      "source": [
        "df_corr"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>diagnosis</th>\n",
              "      <th>radius_mean</th>\n",
              "      <th>texture_mean</th>\n",
              "      <th>perimeter_mean</th>\n",
              "      <th>area_mean</th>\n",
              "      <th>smoothness_mean</th>\n",
              "      <th>compactness_mean</th>\n",
              "      <th>concavity_mean</th>\n",
              "      <th>concave points_mean</th>\n",
              "      <th>symmetry_mean</th>\n",
              "      <th>fractal_dimension_mean</th>\n",
              "      <th>radius_se</th>\n",
              "      <th>texture_se</th>\n",
              "      <th>perimeter_se</th>\n",
              "      <th>area_se</th>\n",
              "      <th>smoothness_se</th>\n",
              "      <th>compactness_se</th>\n",
              "      <th>concavity_se</th>\n",
              "      <th>concave points_se</th>\n",
              "      <th>symmetry_se</th>\n",
              "      <th>fractal_dimension_se</th>\n",
              "      <th>radius_worst</th>\n",
              "      <th>texture_worst</th>\n",
              "      <th>perimeter_worst</th>\n",
              "      <th>area_worst</th>\n",
              "      <th>smoothness_worst</th>\n",
              "      <th>compactness_worst</th>\n",
              "      <th>concavity_worst</th>\n",
              "      <th>concave points_worst</th>\n",
              "      <th>symmetry_worst</th>\n",
              "      <th>fractal_dimension_worst</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>diagnosis</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.730029</td>\n",
              "      <td>0.415185</td>\n",
              "      <td>0.742636</td>\n",
              "      <td>0.708984</td>\n",
              "      <td>0.358560</td>\n",
              "      <td>0.596534</td>\n",
              "      <td>0.696360</td>\n",
              "      <td>0.776614</td>\n",
              "      <td>0.330499</td>\n",
              "      <td>-0.012838</td>\n",
              "      <td>0.567134</td>\n",
              "      <td>-0.008303</td>\n",
              "      <td>0.556141</td>\n",
              "      <td>0.548236</td>\n",
              "      <td>-0.067016</td>\n",
              "      <td>0.292999</td>\n",
              "      <td>0.253730</td>\n",
              "      <td>0.408042</td>\n",
              "      <td>-0.006522</td>\n",
              "      <td>0.077972</td>\n",
              "      <td>0.776454</td>\n",
              "      <td>0.456903</td>\n",
              "      <td>0.782914</td>\n",
              "      <td>0.733825</td>\n",
              "      <td>0.421465</td>\n",
              "      <td>0.590998</td>\n",
              "      <td>0.659610</td>\n",
              "      <td>0.793566</td>\n",
              "      <td>0.416294</td>\n",
              "      <td>0.323872</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>radius_mean</th>\n",
              "      <td>0.730029</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.323782</td>\n",
              "      <td>0.997855</td>\n",
              "      <td>0.987357</td>\n",
              "      <td>0.170581</td>\n",
              "      <td>0.506124</td>\n",
              "      <td>0.676764</td>\n",
              "      <td>0.822529</td>\n",
              "      <td>0.147741</td>\n",
              "      <td>-0.311631</td>\n",
              "      <td>0.679090</td>\n",
              "      <td>-0.097317</td>\n",
              "      <td>0.674172</td>\n",
              "      <td>0.735864</td>\n",
              "      <td>-0.222600</td>\n",
              "      <td>0.206000</td>\n",
              "      <td>0.194204</td>\n",
              "      <td>0.376169</td>\n",
              "      <td>-0.104321</td>\n",
              "      <td>-0.042641</td>\n",
              "      <td>0.969539</td>\n",
              "      <td>0.297008</td>\n",
              "      <td>0.965137</td>\n",
              "      <td>0.941082</td>\n",
              "      <td>0.119616</td>\n",
              "      <td>0.413463</td>\n",
              "      <td>0.526911</td>\n",
              "      <td>0.744214</td>\n",
              "      <td>0.163953</td>\n",
              "      <td>0.007066</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>texture_mean</th>\n",
              "      <td>0.415185</td>\n",
              "      <td>0.323782</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.329533</td>\n",
              "      <td>0.321086</td>\n",
              "      <td>-0.023389</td>\n",
              "      <td>0.236702</td>\n",
              "      <td>0.302418</td>\n",
              "      <td>0.293464</td>\n",
              "      <td>0.071401</td>\n",
              "      <td>-0.076437</td>\n",
              "      <td>0.275869</td>\n",
              "      <td>0.386358</td>\n",
              "      <td>0.281673</td>\n",
              "      <td>0.259845</td>\n",
              "      <td>0.006614</td>\n",
              "      <td>0.191975</td>\n",
              "      <td>0.143293</td>\n",
              "      <td>0.163851</td>\n",
              "      <td>0.009127</td>\n",
              "      <td>0.054458</td>\n",
              "      <td>0.352573</td>\n",
              "      <td>0.912045</td>\n",
              "      <td>0.358040</td>\n",
              "      <td>0.343546</td>\n",
              "      <td>0.077503</td>\n",
              "      <td>0.277830</td>\n",
              "      <td>0.301025</td>\n",
              "      <td>0.295316</td>\n",
              "      <td>0.105008</td>\n",
              "      <td>0.119205</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>perimeter_mean</th>\n",
              "      <td>0.742636</td>\n",
              "      <td>0.997855</td>\n",
              "      <td>0.329533</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.986507</td>\n",
              "      <td>0.207278</td>\n",
              "      <td>0.556936</td>\n",
              "      <td>0.716136</td>\n",
              "      <td>0.850977</td>\n",
              "      <td>0.183027</td>\n",
              "      <td>-0.261477</td>\n",
              "      <td>0.691765</td>\n",
              "      <td>-0.086761</td>\n",
              "      <td>0.693135</td>\n",
              "      <td>0.744983</td>\n",
              "      <td>-0.202694</td>\n",
              "      <td>0.250744</td>\n",
              "      <td>0.228082</td>\n",
              "      <td>0.407217</td>\n",
              "      <td>-0.081629</td>\n",
              "      <td>-0.005523</td>\n",
              "      <td>0.969476</td>\n",
              "      <td>0.303038</td>\n",
              "      <td>0.970387</td>\n",
              "      <td>0.941550</td>\n",
              "      <td>0.150549</td>\n",
              "      <td>0.455774</td>\n",
              "      <td>0.563879</td>\n",
              "      <td>0.771241</td>\n",
              "      <td>0.189115</td>\n",
              "      <td>0.051019</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>area_mean</th>\n",
              "      <td>0.708984</td>\n",
              "      <td>0.987357</td>\n",
              "      <td>0.321086</td>\n",
              "      <td>0.986507</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.177028</td>\n",
              "      <td>0.498502</td>\n",
              "      <td>0.685983</td>\n",
              "      <td>0.823269</td>\n",
              "      <td>0.151293</td>\n",
              "      <td>-0.283110</td>\n",
              "      <td>0.732562</td>\n",
              "      <td>-0.066280</td>\n",
              "      <td>0.726628</td>\n",
              "      <td>0.800086</td>\n",
              "      <td>-0.166777</td>\n",
              "      <td>0.212583</td>\n",
              "      <td>0.207660</td>\n",
              "      <td>0.372320</td>\n",
              "      <td>-0.072497</td>\n",
              "      <td>-0.019887</td>\n",
              "      <td>0.962746</td>\n",
              "      <td>0.287489</td>\n",
              "      <td>0.959120</td>\n",
              "      <td>0.959213</td>\n",
              "      <td>0.123523</td>\n",
              "      <td>0.390410</td>\n",
              "      <td>0.512606</td>\n",
              "      <td>0.722017</td>\n",
              "      <td>0.143570</td>\n",
              "      <td>0.003738</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>smoothness_mean</th>\n",
              "      <td>0.358560</td>\n",
              "      <td>0.170581</td>\n",
              "      <td>-0.023389</td>\n",
              "      <td>0.207278</td>\n",
              "      <td>0.177028</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.659123</td>\n",
              "      <td>0.521984</td>\n",
              "      <td>0.553695</td>\n",
              "      <td>0.557775</td>\n",
              "      <td>0.584792</td>\n",
              "      <td>0.301467</td>\n",
              "      <td>0.068406</td>\n",
              "      <td>0.296092</td>\n",
              "      <td>0.246552</td>\n",
              "      <td>0.332375</td>\n",
              "      <td>0.318943</td>\n",
              "      <td>0.248396</td>\n",
              "      <td>0.380676</td>\n",
              "      <td>0.200774</td>\n",
              "      <td>0.283607</td>\n",
              "      <td>0.213120</td>\n",
              "      <td>0.036072</td>\n",
              "      <td>0.238853</td>\n",
              "      <td>0.206718</td>\n",
              "      <td>0.805324</td>\n",
              "      <td>0.472468</td>\n",
              "      <td>0.434926</td>\n",
              "      <td>0.503053</td>\n",
              "      <td>0.394309</td>\n",
              "      <td>0.499316</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>compactness_mean</th>\n",
              "      <td>0.596534</td>\n",
              "      <td>0.506124</td>\n",
              "      <td>0.236702</td>\n",
              "      <td>0.556936</td>\n",
              "      <td>0.498502</td>\n",
              "      <td>0.659123</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.883121</td>\n",
              "      <td>0.831135</td>\n",
              "      <td>0.602641</td>\n",
              "      <td>0.565369</td>\n",
              "      <td>0.497473</td>\n",
              "      <td>0.046205</td>\n",
              "      <td>0.548905</td>\n",
              "      <td>0.455653</td>\n",
              "      <td>0.135299</td>\n",
              "      <td>0.738722</td>\n",
              "      <td>0.570517</td>\n",
              "      <td>0.642262</td>\n",
              "      <td>0.229977</td>\n",
              "      <td>0.507318</td>\n",
              "      <td>0.535315</td>\n",
              "      <td>0.248133</td>\n",
              "      <td>0.590210</td>\n",
              "      <td>0.509604</td>\n",
              "      <td>0.565541</td>\n",
              "      <td>0.865809</td>\n",
              "      <td>0.816275</td>\n",
              "      <td>0.815573</td>\n",
              "      <td>0.510223</td>\n",
              "      <td>0.687382</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>concavity_mean</th>\n",
              "      <td>0.696360</td>\n",
              "      <td>0.676764</td>\n",
              "      <td>0.302418</td>\n",
              "      <td>0.716136</td>\n",
              "      <td>0.685983</td>\n",
              "      <td>0.521984</td>\n",
              "      <td>0.883121</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.921391</td>\n",
              "      <td>0.500667</td>\n",
              "      <td>0.336783</td>\n",
              "      <td>0.631925</td>\n",
              "      <td>0.076218</td>\n",
              "      <td>0.660391</td>\n",
              "      <td>0.617427</td>\n",
              "      <td>0.098564</td>\n",
              "      <td>0.670279</td>\n",
              "      <td>0.691270</td>\n",
              "      <td>0.683260</td>\n",
              "      <td>0.178009</td>\n",
              "      <td>0.449301</td>\n",
              "      <td>0.688236</td>\n",
              "      <td>0.299879</td>\n",
              "      <td>0.729565</td>\n",
              "      <td>0.675987</td>\n",
              "      <td>0.448822</td>\n",
              "      <td>0.754968</td>\n",
              "      <td>0.884103</td>\n",
              "      <td>0.861323</td>\n",
              "      <td>0.409464</td>\n",
              "      <td>0.514930</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>concave points_mean</th>\n",
              "      <td>0.776614</td>\n",
              "      <td>0.822529</td>\n",
              "      <td>0.293464</td>\n",
              "      <td>0.850977</td>\n",
              "      <td>0.823269</td>\n",
              "      <td>0.553695</td>\n",
              "      <td>0.831135</td>\n",
              "      <td>0.921391</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.462497</td>\n",
              "      <td>0.166917</td>\n",
              "      <td>0.698050</td>\n",
              "      <td>0.021480</td>\n",
              "      <td>0.710650</td>\n",
              "      <td>0.690299</td>\n",
              "      <td>0.027653</td>\n",
              "      <td>0.490424</td>\n",
              "      <td>0.439167</td>\n",
              "      <td>0.615634</td>\n",
              "      <td>0.095351</td>\n",
              "      <td>0.257584</td>\n",
              "      <td>0.830318</td>\n",
              "      <td>0.292752</td>\n",
              "      <td>0.855923</td>\n",
              "      <td>0.809630</td>\n",
              "      <td>0.452753</td>\n",
              "      <td>0.667454</td>\n",
              "      <td>0.752399</td>\n",
              "      <td>0.910155</td>\n",
              "      <td>0.375744</td>\n",
              "      <td>0.368661</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>symmetry_mean</th>\n",
              "      <td>0.330499</td>\n",
              "      <td>0.147741</td>\n",
              "      <td>0.071401</td>\n",
              "      <td>0.183027</td>\n",
              "      <td>0.151293</td>\n",
              "      <td>0.557775</td>\n",
              "      <td>0.602641</td>\n",
              "      <td>0.500667</td>\n",
              "      <td>0.462497</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.479921</td>\n",
              "      <td>0.303379</td>\n",
              "      <td>0.128053</td>\n",
              "      <td>0.313893</td>\n",
              "      <td>0.223970</td>\n",
              "      <td>0.187321</td>\n",
              "      <td>0.421659</td>\n",
              "      <td>0.342627</td>\n",
              "      <td>0.393298</td>\n",
              "      <td>0.449137</td>\n",
              "      <td>0.331786</td>\n",
              "      <td>0.185728</td>\n",
              "      <td>0.090651</td>\n",
              "      <td>0.219169</td>\n",
              "      <td>0.177193</td>\n",
              "      <td>0.426675</td>\n",
              "      <td>0.473200</td>\n",
              "      <td>0.433721</td>\n",
              "      <td>0.430297</td>\n",
              "      <td>0.699826</td>\n",
              "      <td>0.438413</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>fractal_dimension_mean</th>\n",
              "      <td>-0.012838</td>\n",
              "      <td>-0.311631</td>\n",
              "      <td>-0.076437</td>\n",
              "      <td>-0.261477</td>\n",
              "      <td>-0.283110</td>\n",
              "      <td>0.584792</td>\n",
              "      <td>0.565369</td>\n",
              "      <td>0.336783</td>\n",
              "      <td>0.166917</td>\n",
              "      <td>0.479921</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000111</td>\n",
              "      <td>0.164174</td>\n",
              "      <td>0.039830</td>\n",
              "      <td>-0.090170</td>\n",
              "      <td>0.401964</td>\n",
              "      <td>0.559837</td>\n",
              "      <td>0.446630</td>\n",
              "      <td>0.341198</td>\n",
              "      <td>0.345007</td>\n",
              "      <td>0.688132</td>\n",
              "      <td>-0.253691</td>\n",
              "      <td>-0.051269</td>\n",
              "      <td>-0.205151</td>\n",
              "      <td>-0.231854</td>\n",
              "      <td>0.504942</td>\n",
              "      <td>0.458798</td>\n",
              "      <td>0.346234</td>\n",
              "      <td>0.175325</td>\n",
              "      <td>0.334019</td>\n",
              "      <td>0.767297</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>radius_se</th>\n",
              "      <td>0.567134</td>\n",
              "      <td>0.679090</td>\n",
              "      <td>0.275869</td>\n",
              "      <td>0.691765</td>\n",
              "      <td>0.732562</td>\n",
              "      <td>0.301467</td>\n",
              "      <td>0.497473</td>\n",
              "      <td>0.631925</td>\n",
              "      <td>0.698050</td>\n",
              "      <td>0.303379</td>\n",
              "      <td>0.000111</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.213247</td>\n",
              "      <td>0.972794</td>\n",
              "      <td>0.951830</td>\n",
              "      <td>0.164514</td>\n",
              "      <td>0.356065</td>\n",
              "      <td>0.332358</td>\n",
              "      <td>0.513346</td>\n",
              "      <td>0.240567</td>\n",
              "      <td>0.227754</td>\n",
              "      <td>0.715065</td>\n",
              "      <td>0.194799</td>\n",
              "      <td>0.719684</td>\n",
              "      <td>0.751548</td>\n",
              "      <td>0.141919</td>\n",
              "      <td>0.287103</td>\n",
              "      <td>0.380585</td>\n",
              "      <td>0.531062</td>\n",
              "      <td>0.094543</td>\n",
              "      <td>0.049559</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>texture_se</th>\n",
              "      <td>-0.008303</td>\n",
              "      <td>-0.097317</td>\n",
              "      <td>0.386358</td>\n",
              "      <td>-0.086761</td>\n",
              "      <td>-0.066280</td>\n",
              "      <td>0.068406</td>\n",
              "      <td>0.046205</td>\n",
              "      <td>0.076218</td>\n",
              "      <td>0.021480</td>\n",
              "      <td>0.128053</td>\n",
              "      <td>0.164174</td>\n",
              "      <td>0.213247</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.223171</td>\n",
              "      <td>0.111567</td>\n",
              "      <td>0.397243</td>\n",
              "      <td>0.231700</td>\n",
              "      <td>0.194998</td>\n",
              "      <td>0.230283</td>\n",
              "      <td>0.411621</td>\n",
              "      <td>0.279723</td>\n",
              "      <td>-0.111690</td>\n",
              "      <td>0.409003</td>\n",
              "      <td>-0.102242</td>\n",
              "      <td>-0.083195</td>\n",
              "      <td>-0.073658</td>\n",
              "      <td>-0.092439</td>\n",
              "      <td>-0.068956</td>\n",
              "      <td>-0.119638</td>\n",
              "      <td>-0.128215</td>\n",
              "      <td>-0.045655</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>perimeter_se</th>\n",
              "      <td>0.556141</td>\n",
              "      <td>0.674172</td>\n",
              "      <td>0.281673</td>\n",
              "      <td>0.693135</td>\n",
              "      <td>0.726628</td>\n",
              "      <td>0.296092</td>\n",
              "      <td>0.548905</td>\n",
              "      <td>0.660391</td>\n",
              "      <td>0.710650</td>\n",
              "      <td>0.313893</td>\n",
              "      <td>0.039830</td>\n",
              "      <td>0.972794</td>\n",
              "      <td>0.223171</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.937655</td>\n",
              "      <td>0.151075</td>\n",
              "      <td>0.416322</td>\n",
              "      <td>0.362482</td>\n",
              "      <td>0.556264</td>\n",
              "      <td>0.266487</td>\n",
              "      <td>0.244143</td>\n",
              "      <td>0.697201</td>\n",
              "      <td>0.200371</td>\n",
              "      <td>0.721031</td>\n",
              "      <td>0.730713</td>\n",
              "      <td>0.130054</td>\n",
              "      <td>0.341919</td>\n",
              "      <td>0.418899</td>\n",
              "      <td>0.554897</td>\n",
              "      <td>0.109930</td>\n",
              "      <td>0.085433</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>area_se</th>\n",
              "      <td>0.548236</td>\n",
              "      <td>0.735864</td>\n",
              "      <td>0.259845</td>\n",
              "      <td>0.744983</td>\n",
              "      <td>0.800086</td>\n",
              "      <td>0.246552</td>\n",
              "      <td>0.455653</td>\n",
              "      <td>0.617427</td>\n",
              "      <td>0.690299</td>\n",
              "      <td>0.223970</td>\n",
              "      <td>-0.090170</td>\n",
              "      <td>0.951830</td>\n",
              "      <td>0.111567</td>\n",
              "      <td>0.937655</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.075150</td>\n",
              "      <td>0.284840</td>\n",
              "      <td>0.270895</td>\n",
              "      <td>0.415730</td>\n",
              "      <td>0.134109</td>\n",
              "      <td>0.127071</td>\n",
              "      <td>0.757373</td>\n",
              "      <td>0.196497</td>\n",
              "      <td>0.761213</td>\n",
              "      <td>0.811408</td>\n",
              "      <td>0.125389</td>\n",
              "      <td>0.283257</td>\n",
              "      <td>0.385100</td>\n",
              "      <td>0.538166</td>\n",
              "      <td>0.074126</td>\n",
              "      <td>0.017539</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>smoothness_se</th>\n",
              "      <td>-0.067016</td>\n",
              "      <td>-0.222600</td>\n",
              "      <td>0.006614</td>\n",
              "      <td>-0.202694</td>\n",
              "      <td>-0.166777</td>\n",
              "      <td>0.332375</td>\n",
              "      <td>0.135299</td>\n",
              "      <td>0.098564</td>\n",
              "      <td>0.027653</td>\n",
              "      <td>0.187321</td>\n",
              "      <td>0.401964</td>\n",
              "      <td>0.164514</td>\n",
              "      <td>0.397243</td>\n",
              "      <td>0.151075</td>\n",
              "      <td>0.075150</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.336696</td>\n",
              "      <td>0.268685</td>\n",
              "      <td>0.328429</td>\n",
              "      <td>0.413506</td>\n",
              "      <td>0.427374</td>\n",
              "      <td>-0.230691</td>\n",
              "      <td>-0.074743</td>\n",
              "      <td>-0.217304</td>\n",
              "      <td>-0.182195</td>\n",
              "      <td>0.314457</td>\n",
              "      <td>-0.055558</td>\n",
              "      <td>-0.058298</td>\n",
              "      <td>-0.102007</td>\n",
              "      <td>-0.107342</td>\n",
              "      <td>0.101480</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>compactness_se</th>\n",
              "      <td>0.292999</td>\n",
              "      <td>0.206000</td>\n",
              "      <td>0.191975</td>\n",
              "      <td>0.250744</td>\n",
              "      <td>0.212583</td>\n",
              "      <td>0.318943</td>\n",
              "      <td>0.738722</td>\n",
              "      <td>0.670279</td>\n",
              "      <td>0.490424</td>\n",
              "      <td>0.421659</td>\n",
              "      <td>0.559837</td>\n",
              "      <td>0.356065</td>\n",
              "      <td>0.231700</td>\n",
              "      <td>0.416322</td>\n",
              "      <td>0.284840</td>\n",
              "      <td>0.336696</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.801268</td>\n",
              "      <td>0.744083</td>\n",
              "      <td>0.394713</td>\n",
              "      <td>0.803269</td>\n",
              "      <td>0.204607</td>\n",
              "      <td>0.143003</td>\n",
              "      <td>0.260516</td>\n",
              "      <td>0.199371</td>\n",
              "      <td>0.227394</td>\n",
              "      <td>0.678780</td>\n",
              "      <td>0.639147</td>\n",
              "      <td>0.483208</td>\n",
              "      <td>0.277878</td>\n",
              "      <td>0.590973</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>concavity_se</th>\n",
              "      <td>0.253730</td>\n",
              "      <td>0.194204</td>\n",
              "      <td>0.143293</td>\n",
              "      <td>0.228082</td>\n",
              "      <td>0.207660</td>\n",
              "      <td>0.248396</td>\n",
              "      <td>0.570517</td>\n",
              "      <td>0.691270</td>\n",
              "      <td>0.439167</td>\n",
              "      <td>0.342627</td>\n",
              "      <td>0.446630</td>\n",
              "      <td>0.332358</td>\n",
              "      <td>0.194998</td>\n",
              "      <td>0.362482</td>\n",
              "      <td>0.270895</td>\n",
              "      <td>0.268685</td>\n",
              "      <td>0.801268</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.771804</td>\n",
              "      <td>0.309429</td>\n",
              "      <td>0.727372</td>\n",
              "      <td>0.186904</td>\n",
              "      <td>0.100241</td>\n",
              "      <td>0.226680</td>\n",
              "      <td>0.188353</td>\n",
              "      <td>0.168481</td>\n",
              "      <td>0.484858</td>\n",
              "      <td>0.662564</td>\n",
              "      <td>0.440472</td>\n",
              "      <td>0.197788</td>\n",
              "      <td>0.439329</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>concave points_se</th>\n",
              "      <td>0.408042</td>\n",
              "      <td>0.376169</td>\n",
              "      <td>0.163851</td>\n",
              "      <td>0.407217</td>\n",
              "      <td>0.372320</td>\n",
              "      <td>0.380676</td>\n",
              "      <td>0.642262</td>\n",
              "      <td>0.683260</td>\n",
              "      <td>0.615634</td>\n",
              "      <td>0.393298</td>\n",
              "      <td>0.341198</td>\n",
              "      <td>0.513346</td>\n",
              "      <td>0.230283</td>\n",
              "      <td>0.556264</td>\n",
              "      <td>0.415730</td>\n",
              "      <td>0.328429</td>\n",
              "      <td>0.744083</td>\n",
              "      <td>0.771804</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.312780</td>\n",
              "      <td>0.611044</td>\n",
              "      <td>0.358127</td>\n",
              "      <td>0.086741</td>\n",
              "      <td>0.394999</td>\n",
              "      <td>0.342271</td>\n",
              "      <td>0.215351</td>\n",
              "      <td>0.452888</td>\n",
              "      <td>0.549592</td>\n",
              "      <td>0.602450</td>\n",
              "      <td>0.143116</td>\n",
              "      <td>0.310655</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>symmetry_se</th>\n",
              "      <td>-0.006522</td>\n",
              "      <td>-0.104321</td>\n",
              "      <td>0.009127</td>\n",
              "      <td>-0.081629</td>\n",
              "      <td>-0.072497</td>\n",
              "      <td>0.200774</td>\n",
              "      <td>0.229977</td>\n",
              "      <td>0.178009</td>\n",
              "      <td>0.095351</td>\n",
              "      <td>0.449137</td>\n",
              "      <td>0.345007</td>\n",
              "      <td>0.240567</td>\n",
              "      <td>0.411621</td>\n",
              "      <td>0.266487</td>\n",
              "      <td>0.134109</td>\n",
              "      <td>0.413506</td>\n",
              "      <td>0.394713</td>\n",
              "      <td>0.309429</td>\n",
              "      <td>0.312780</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.369078</td>\n",
              "      <td>-0.128121</td>\n",
              "      <td>-0.077473</td>\n",
              "      <td>-0.103753</td>\n",
              "      <td>-0.110343</td>\n",
              "      <td>-0.012662</td>\n",
              "      <td>0.060255</td>\n",
              "      <td>0.037119</td>\n",
              "      <td>-0.030413</td>\n",
              "      <td>0.389402</td>\n",
              "      <td>0.078079</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>fractal_dimension_se</th>\n",
              "      <td>0.077972</td>\n",
              "      <td>-0.042641</td>\n",
              "      <td>0.054458</td>\n",
              "      <td>-0.005523</td>\n",
              "      <td>-0.019887</td>\n",
              "      <td>0.283607</td>\n",
              "      <td>0.507318</td>\n",
              "      <td>0.449301</td>\n",
              "      <td>0.257584</td>\n",
              "      <td>0.331786</td>\n",
              "      <td>0.688132</td>\n",
              "      <td>0.227754</td>\n",
              "      <td>0.279723</td>\n",
              "      <td>0.244143</td>\n",
              "      <td>0.127071</td>\n",
              "      <td>0.427374</td>\n",
              "      <td>0.803269</td>\n",
              "      <td>0.727372</td>\n",
              "      <td>0.611044</td>\n",
              "      <td>0.369078</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.037488</td>\n",
              "      <td>-0.003195</td>\n",
              "      <td>-0.001000</td>\n",
              "      <td>-0.022736</td>\n",
              "      <td>0.170568</td>\n",
              "      <td>0.390159</td>\n",
              "      <td>0.379975</td>\n",
              "      <td>0.215204</td>\n",
              "      <td>0.111094</td>\n",
              "      <td>0.591328</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>radius_worst</th>\n",
              "      <td>0.776454</td>\n",
              "      <td>0.969539</td>\n",
              "      <td>0.352573</td>\n",
              "      <td>0.969476</td>\n",
              "      <td>0.962746</td>\n",
              "      <td>0.213120</td>\n",
              "      <td>0.535315</td>\n",
              "      <td>0.688236</td>\n",
              "      <td>0.830318</td>\n",
              "      <td>0.185728</td>\n",
              "      <td>-0.253691</td>\n",
              "      <td>0.715065</td>\n",
              "      <td>-0.111690</td>\n",
              "      <td>0.697201</td>\n",
              "      <td>0.757373</td>\n",
              "      <td>-0.230691</td>\n",
              "      <td>0.204607</td>\n",
              "      <td>0.186904</td>\n",
              "      <td>0.358127</td>\n",
              "      <td>-0.128121</td>\n",
              "      <td>-0.037488</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.359921</td>\n",
              "      <td>0.993708</td>\n",
              "      <td>0.984015</td>\n",
              "      <td>0.216574</td>\n",
              "      <td>0.475820</td>\n",
              "      <td>0.573975</td>\n",
              "      <td>0.787424</td>\n",
              "      <td>0.243529</td>\n",
              "      <td>0.093492</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>texture_worst</th>\n",
              "      <td>0.456903</td>\n",
              "      <td>0.297008</td>\n",
              "      <td>0.912045</td>\n",
              "      <td>0.303038</td>\n",
              "      <td>0.287489</td>\n",
              "      <td>0.036072</td>\n",
              "      <td>0.248133</td>\n",
              "      <td>0.299879</td>\n",
              "      <td>0.292752</td>\n",
              "      <td>0.090651</td>\n",
              "      <td>-0.051269</td>\n",
              "      <td>0.194799</td>\n",
              "      <td>0.409003</td>\n",
              "      <td>0.200371</td>\n",
              "      <td>0.196497</td>\n",
              "      <td>-0.074743</td>\n",
              "      <td>0.143003</td>\n",
              "      <td>0.100241</td>\n",
              "      <td>0.086741</td>\n",
              "      <td>-0.077473</td>\n",
              "      <td>-0.003195</td>\n",
              "      <td>0.359921</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.365098</td>\n",
              "      <td>0.345842</td>\n",
              "      <td>0.225429</td>\n",
              "      <td>0.360832</td>\n",
              "      <td>0.368366</td>\n",
              "      <td>0.359755</td>\n",
              "      <td>0.233027</td>\n",
              "      <td>0.219122</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>perimeter_worst</th>\n",
              "      <td>0.782914</td>\n",
              "      <td>0.965137</td>\n",
              "      <td>0.358040</td>\n",
              "      <td>0.970387</td>\n",
              "      <td>0.959120</td>\n",
              "      <td>0.238853</td>\n",
              "      <td>0.590210</td>\n",
              "      <td>0.729565</td>\n",
              "      <td>0.855923</td>\n",
              "      <td>0.219169</td>\n",
              "      <td>-0.205151</td>\n",
              "      <td>0.719684</td>\n",
              "      <td>-0.102242</td>\n",
              "      <td>0.721031</td>\n",
              "      <td>0.761213</td>\n",
              "      <td>-0.217304</td>\n",
              "      <td>0.260516</td>\n",
              "      <td>0.226680</td>\n",
              "      <td>0.394999</td>\n",
              "      <td>-0.103753</td>\n",
              "      <td>-0.001000</td>\n",
              "      <td>0.993708</td>\n",
              "      <td>0.365098</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.977578</td>\n",
              "      <td>0.236775</td>\n",
              "      <td>0.529408</td>\n",
              "      <td>0.618344</td>\n",
              "      <td>0.816322</td>\n",
              "      <td>0.269493</td>\n",
              "      <td>0.138957</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>area_worst</th>\n",
              "      <td>0.733825</td>\n",
              "      <td>0.941082</td>\n",
              "      <td>0.343546</td>\n",
              "      <td>0.941550</td>\n",
              "      <td>0.959213</td>\n",
              "      <td>0.206718</td>\n",
              "      <td>0.509604</td>\n",
              "      <td>0.675987</td>\n",
              "      <td>0.809630</td>\n",
              "      <td>0.177193</td>\n",
              "      <td>-0.231854</td>\n",
              "      <td>0.751548</td>\n",
              "      <td>-0.083195</td>\n",
              "      <td>0.730713</td>\n",
              "      <td>0.811408</td>\n",
              "      <td>-0.182195</td>\n",
              "      <td>0.199371</td>\n",
              "      <td>0.188353</td>\n",
              "      <td>0.342271</td>\n",
              "      <td>-0.110343</td>\n",
              "      <td>-0.022736</td>\n",
              "      <td>0.984015</td>\n",
              "      <td>0.345842</td>\n",
              "      <td>0.977578</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.209145</td>\n",
              "      <td>0.438296</td>\n",
              "      <td>0.543331</td>\n",
              "      <td>0.747419</td>\n",
              "      <td>0.209146</td>\n",
              "      <td>0.079647</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>smoothness_worst</th>\n",
              "      <td>0.421465</td>\n",
              "      <td>0.119616</td>\n",
              "      <td>0.077503</td>\n",
              "      <td>0.150549</td>\n",
              "      <td>0.123523</td>\n",
              "      <td>0.805324</td>\n",
              "      <td>0.565541</td>\n",
              "      <td>0.448822</td>\n",
              "      <td>0.452753</td>\n",
              "      <td>0.426675</td>\n",
              "      <td>0.504942</td>\n",
              "      <td>0.141919</td>\n",
              "      <td>-0.073658</td>\n",
              "      <td>0.130054</td>\n",
              "      <td>0.125389</td>\n",
              "      <td>0.314457</td>\n",
              "      <td>0.227394</td>\n",
              "      <td>0.168481</td>\n",
              "      <td>0.215351</td>\n",
              "      <td>-0.012662</td>\n",
              "      <td>0.170568</td>\n",
              "      <td>0.216574</td>\n",
              "      <td>0.225429</td>\n",
              "      <td>0.236775</td>\n",
              "      <td>0.209145</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.568187</td>\n",
              "      <td>0.518523</td>\n",
              "      <td>0.547691</td>\n",
              "      <td>0.493838</td>\n",
              "      <td>0.617624</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>compactness_worst</th>\n",
              "      <td>0.590998</td>\n",
              "      <td>0.413463</td>\n",
              "      <td>0.277830</td>\n",
              "      <td>0.455774</td>\n",
              "      <td>0.390410</td>\n",
              "      <td>0.472468</td>\n",
              "      <td>0.865809</td>\n",
              "      <td>0.754968</td>\n",
              "      <td>0.667454</td>\n",
              "      <td>0.473200</td>\n",
              "      <td>0.458798</td>\n",
              "      <td>0.287103</td>\n",
              "      <td>-0.092439</td>\n",
              "      <td>0.341919</td>\n",
              "      <td>0.283257</td>\n",
              "      <td>-0.055558</td>\n",
              "      <td>0.678780</td>\n",
              "      <td>0.484858</td>\n",
              "      <td>0.452888</td>\n",
              "      <td>0.060255</td>\n",
              "      <td>0.390159</td>\n",
              "      <td>0.475820</td>\n",
              "      <td>0.360832</td>\n",
              "      <td>0.529408</td>\n",
              "      <td>0.438296</td>\n",
              "      <td>0.568187</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.892261</td>\n",
              "      <td>0.801080</td>\n",
              "      <td>0.614441</td>\n",
              "      <td>0.810455</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>concavity_worst</th>\n",
              "      <td>0.659610</td>\n",
              "      <td>0.526911</td>\n",
              "      <td>0.301025</td>\n",
              "      <td>0.563879</td>\n",
              "      <td>0.512606</td>\n",
              "      <td>0.434926</td>\n",
              "      <td>0.816275</td>\n",
              "      <td>0.884103</td>\n",
              "      <td>0.752399</td>\n",
              "      <td>0.433721</td>\n",
              "      <td>0.346234</td>\n",
              "      <td>0.380585</td>\n",
              "      <td>-0.068956</td>\n",
              "      <td>0.418899</td>\n",
              "      <td>0.385100</td>\n",
              "      <td>-0.058298</td>\n",
              "      <td>0.639147</td>\n",
              "      <td>0.662564</td>\n",
              "      <td>0.549592</td>\n",
              "      <td>0.037119</td>\n",
              "      <td>0.379975</td>\n",
              "      <td>0.573975</td>\n",
              "      <td>0.368366</td>\n",
              "      <td>0.618344</td>\n",
              "      <td>0.543331</td>\n",
              "      <td>0.518523</td>\n",
              "      <td>0.892261</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.855434</td>\n",
              "      <td>0.532520</td>\n",
              "      <td>0.686511</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>concave points_worst</th>\n",
              "      <td>0.793566</td>\n",
              "      <td>0.744214</td>\n",
              "      <td>0.295316</td>\n",
              "      <td>0.771241</td>\n",
              "      <td>0.722017</td>\n",
              "      <td>0.503053</td>\n",
              "      <td>0.815573</td>\n",
              "      <td>0.861323</td>\n",
              "      <td>0.910155</td>\n",
              "      <td>0.430297</td>\n",
              "      <td>0.175325</td>\n",
              "      <td>0.531062</td>\n",
              "      <td>-0.119638</td>\n",
              "      <td>0.554897</td>\n",
              "      <td>0.538166</td>\n",
              "      <td>-0.102007</td>\n",
              "      <td>0.483208</td>\n",
              "      <td>0.440472</td>\n",
              "      <td>0.602450</td>\n",
              "      <td>-0.030413</td>\n",
              "      <td>0.215204</td>\n",
              "      <td>0.787424</td>\n",
              "      <td>0.359755</td>\n",
              "      <td>0.816322</td>\n",
              "      <td>0.747419</td>\n",
              "      <td>0.547691</td>\n",
              "      <td>0.801080</td>\n",
              "      <td>0.855434</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.502528</td>\n",
              "      <td>0.511114</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>symmetry_worst</th>\n",
              "      <td>0.416294</td>\n",
              "      <td>0.163953</td>\n",
              "      <td>0.105008</td>\n",
              "      <td>0.189115</td>\n",
              "      <td>0.143570</td>\n",
              "      <td>0.394309</td>\n",
              "      <td>0.510223</td>\n",
              "      <td>0.409464</td>\n",
              "      <td>0.375744</td>\n",
              "      <td>0.699826</td>\n",
              "      <td>0.334019</td>\n",
              "      <td>0.094543</td>\n",
              "      <td>-0.128215</td>\n",
              "      <td>0.109930</td>\n",
              "      <td>0.074126</td>\n",
              "      <td>-0.107342</td>\n",
              "      <td>0.277878</td>\n",
              "      <td>0.197788</td>\n",
              "      <td>0.143116</td>\n",
              "      <td>0.389402</td>\n",
              "      <td>0.111094</td>\n",
              "      <td>0.243529</td>\n",
              "      <td>0.233027</td>\n",
              "      <td>0.269493</td>\n",
              "      <td>0.209146</td>\n",
              "      <td>0.493838</td>\n",
              "      <td>0.614441</td>\n",
              "      <td>0.532520</td>\n",
              "      <td>0.502528</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.537848</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>fractal_dimension_worst</th>\n",
              "      <td>0.323872</td>\n",
              "      <td>0.007066</td>\n",
              "      <td>0.119205</td>\n",
              "      <td>0.051019</td>\n",
              "      <td>0.003738</td>\n",
              "      <td>0.499316</td>\n",
              "      <td>0.687382</td>\n",
              "      <td>0.514930</td>\n",
              "      <td>0.368661</td>\n",
              "      <td>0.438413</td>\n",
              "      <td>0.767297</td>\n",
              "      <td>0.049559</td>\n",
              "      <td>-0.045655</td>\n",
              "      <td>0.085433</td>\n",
              "      <td>0.017539</td>\n",
              "      <td>0.101480</td>\n",
              "      <td>0.590973</td>\n",
              "      <td>0.439329</td>\n",
              "      <td>0.310655</td>\n",
              "      <td>0.078079</td>\n",
              "      <td>0.591328</td>\n",
              "      <td>0.093492</td>\n",
              "      <td>0.219122</td>\n",
              "      <td>0.138957</td>\n",
              "      <td>0.079647</td>\n",
              "      <td>0.617624</td>\n",
              "      <td>0.810455</td>\n",
              "      <td>0.686511</td>\n",
              "      <td>0.511114</td>\n",
              "      <td>0.537848</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                         diagnosis  ...  fractal_dimension_worst\n",
              "diagnosis                 1.000000  ...                 0.323872\n",
              "radius_mean               0.730029  ...                 0.007066\n",
              "texture_mean              0.415185  ...                 0.119205\n",
              "perimeter_mean            0.742636  ...                 0.051019\n",
              "area_mean                 0.708984  ...                 0.003738\n",
              "smoothness_mean           0.358560  ...                 0.499316\n",
              "compactness_mean          0.596534  ...                 0.687382\n",
              "concavity_mean            0.696360  ...                 0.514930\n",
              "concave points_mean       0.776614  ...                 0.368661\n",
              "symmetry_mean             0.330499  ...                 0.438413\n",
              "fractal_dimension_mean   -0.012838  ...                 0.767297\n",
              "radius_se                 0.567134  ...                 0.049559\n",
              "texture_se               -0.008303  ...                -0.045655\n",
              "perimeter_se              0.556141  ...                 0.085433\n",
              "area_se                   0.548236  ...                 0.017539\n",
              "smoothness_se            -0.067016  ...                 0.101480\n",
              "compactness_se            0.292999  ...                 0.590973\n",
              "concavity_se              0.253730  ...                 0.439329\n",
              "concave points_se         0.408042  ...                 0.310655\n",
              "symmetry_se              -0.006522  ...                 0.078079\n",
              "fractal_dimension_se      0.077972  ...                 0.591328\n",
              "radius_worst              0.776454  ...                 0.093492\n",
              "texture_worst             0.456903  ...                 0.219122\n",
              "perimeter_worst           0.782914  ...                 0.138957\n",
              "area_worst                0.733825  ...                 0.079647\n",
              "smoothness_worst          0.421465  ...                 0.617624\n",
              "compactness_worst         0.590998  ...                 0.810455\n",
              "concavity_worst           0.659610  ...                 0.686511\n",
              "concave points_worst      0.793566  ...                 0.511114\n",
              "symmetry_worst            0.416294  ...                 0.537848\n",
              "fractal_dimension_worst   0.323872  ...                 1.000000\n",
              "\n",
              "[31 rows x 31 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dfD5PgmIo2Pj"
      },
      "source": [
        "Feature reduction Corelation -0.5< x >0.5 "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mW2Z-RVslz5M"
      },
      "source": [
        "X_red=df[['radius_mean','perimeter_mean','area_mean','compactness_mean', 'concavity_mean',\n",
        "          'concave points_mean','radius_se','perimeter_se','area_se','radius_worst',\n",
        "          'perimeter_worst','area_worst','compactness_worst','concavity_worst','concave points_worst']]\n",
        "y_red = df[['diagnosis']]"
      ],
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 454
        },
        "id": "0yunOI3Yods9",
        "outputId": "97355b8b-44d9-4c53-9b7a-11c57e06ad1f"
      },
      "source": [
        "X_red"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>radius_mean</th>\n",
              "      <th>perimeter_mean</th>\n",
              "      <th>area_mean</th>\n",
              "      <th>compactness_mean</th>\n",
              "      <th>concavity_mean</th>\n",
              "      <th>concave points_mean</th>\n",
              "      <th>radius_se</th>\n",
              "      <th>perimeter_se</th>\n",
              "      <th>area_se</th>\n",
              "      <th>radius_worst</th>\n",
              "      <th>perimeter_worst</th>\n",
              "      <th>area_worst</th>\n",
              "      <th>compactness_worst</th>\n",
              "      <th>concavity_worst</th>\n",
              "      <th>concave points_worst</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>17.99</td>\n",
              "      <td>122.80</td>\n",
              "      <td>1001.0</td>\n",
              "      <td>0.27760</td>\n",
              "      <td>0.30010</td>\n",
              "      <td>0.14710</td>\n",
              "      <td>1.0950</td>\n",
              "      <td>8.589</td>\n",
              "      <td>153.40</td>\n",
              "      <td>25.380</td>\n",
              "      <td>184.60</td>\n",
              "      <td>2019.0</td>\n",
              "      <td>0.66560</td>\n",
              "      <td>0.7119</td>\n",
              "      <td>0.2654</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>20.57</td>\n",
              "      <td>132.90</td>\n",
              "      <td>1326.0</td>\n",
              "      <td>0.07864</td>\n",
              "      <td>0.08690</td>\n",
              "      <td>0.07017</td>\n",
              "      <td>0.5435</td>\n",
              "      <td>3.398</td>\n",
              "      <td>74.08</td>\n",
              "      <td>24.990</td>\n",
              "      <td>158.80</td>\n",
              "      <td>1956.0</td>\n",
              "      <td>0.18660</td>\n",
              "      <td>0.2416</td>\n",
              "      <td>0.1860</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>19.69</td>\n",
              "      <td>130.00</td>\n",
              "      <td>1203.0</td>\n",
              "      <td>0.15990</td>\n",
              "      <td>0.19740</td>\n",
              "      <td>0.12790</td>\n",
              "      <td>0.7456</td>\n",
              "      <td>4.585</td>\n",
              "      <td>94.03</td>\n",
              "      <td>23.570</td>\n",
              "      <td>152.50</td>\n",
              "      <td>1709.0</td>\n",
              "      <td>0.42450</td>\n",
              "      <td>0.4504</td>\n",
              "      <td>0.2430</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>11.42</td>\n",
              "      <td>77.58</td>\n",
              "      <td>386.1</td>\n",
              "      <td>0.28390</td>\n",
              "      <td>0.24140</td>\n",
              "      <td>0.10520</td>\n",
              "      <td>0.4956</td>\n",
              "      <td>3.445</td>\n",
              "      <td>27.23</td>\n",
              "      <td>14.910</td>\n",
              "      <td>98.87</td>\n",
              "      <td>567.7</td>\n",
              "      <td>0.86630</td>\n",
              "      <td>0.6869</td>\n",
              "      <td>0.2575</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>20.29</td>\n",
              "      <td>135.10</td>\n",
              "      <td>1297.0</td>\n",
              "      <td>0.13280</td>\n",
              "      <td>0.19800</td>\n",
              "      <td>0.10430</td>\n",
              "      <td>0.7572</td>\n",
              "      <td>5.438</td>\n",
              "      <td>94.44</td>\n",
              "      <td>22.540</td>\n",
              "      <td>152.20</td>\n",
              "      <td>1575.0</td>\n",
              "      <td>0.20500</td>\n",
              "      <td>0.4000</td>\n",
              "      <td>0.1625</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>564</th>\n",
              "      <td>21.56</td>\n",
              "      <td>142.00</td>\n",
              "      <td>1479.0</td>\n",
              "      <td>0.11590</td>\n",
              "      <td>0.24390</td>\n",
              "      <td>0.13890</td>\n",
              "      <td>1.1760</td>\n",
              "      <td>7.673</td>\n",
              "      <td>158.70</td>\n",
              "      <td>25.450</td>\n",
              "      <td>166.10</td>\n",
              "      <td>2027.0</td>\n",
              "      <td>0.21130</td>\n",
              "      <td>0.4107</td>\n",
              "      <td>0.2216</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>565</th>\n",
              "      <td>20.13</td>\n",
              "      <td>131.20</td>\n",
              "      <td>1261.0</td>\n",
              "      <td>0.10340</td>\n",
              "      <td>0.14400</td>\n",
              "      <td>0.09791</td>\n",
              "      <td>0.7655</td>\n",
              "      <td>5.203</td>\n",
              "      <td>99.04</td>\n",
              "      <td>23.690</td>\n",
              "      <td>155.00</td>\n",
              "      <td>1731.0</td>\n",
              "      <td>0.19220</td>\n",
              "      <td>0.3215</td>\n",
              "      <td>0.1628</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>566</th>\n",
              "      <td>16.60</td>\n",
              "      <td>108.30</td>\n",
              "      <td>858.1</td>\n",
              "      <td>0.10230</td>\n",
              "      <td>0.09251</td>\n",
              "      <td>0.05302</td>\n",
              "      <td>0.4564</td>\n",
              "      <td>3.425</td>\n",
              "      <td>48.55</td>\n",
              "      <td>18.980</td>\n",
              "      <td>126.70</td>\n",
              "      <td>1124.0</td>\n",
              "      <td>0.30940</td>\n",
              "      <td>0.3403</td>\n",
              "      <td>0.1418</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>567</th>\n",
              "      <td>20.60</td>\n",
              "      <td>140.10</td>\n",
              "      <td>1265.0</td>\n",
              "      <td>0.27700</td>\n",
              "      <td>0.35140</td>\n",
              "      <td>0.15200</td>\n",
              "      <td>0.7260</td>\n",
              "      <td>5.772</td>\n",
              "      <td>86.22</td>\n",
              "      <td>25.740</td>\n",
              "      <td>184.60</td>\n",
              "      <td>1821.0</td>\n",
              "      <td>0.86810</td>\n",
              "      <td>0.9387</td>\n",
              "      <td>0.2650</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>568</th>\n",
              "      <td>7.76</td>\n",
              "      <td>47.92</td>\n",
              "      <td>181.0</td>\n",
              "      <td>0.04362</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.3857</td>\n",
              "      <td>2.548</td>\n",
              "      <td>19.15</td>\n",
              "      <td>9.456</td>\n",
              "      <td>59.16</td>\n",
              "      <td>268.6</td>\n",
              "      <td>0.06444</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.0000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>569 rows × 15 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     radius_mean  perimeter_mean  ...  concavity_worst  concave points_worst\n",
              "0          17.99          122.80  ...           0.7119                0.2654\n",
              "1          20.57          132.90  ...           0.2416                0.1860\n",
              "2          19.69          130.00  ...           0.4504                0.2430\n",
              "3          11.42           77.58  ...           0.6869                0.2575\n",
              "4          20.29          135.10  ...           0.4000                0.1625\n",
              "..           ...             ...  ...              ...                   ...\n",
              "564        21.56          142.00  ...           0.4107                0.2216\n",
              "565        20.13          131.20  ...           0.3215                0.1628\n",
              "566        16.60          108.30  ...           0.3403                0.1418\n",
              "567        20.60          140.10  ...           0.9387                0.2650\n",
              "568         7.76           47.92  ...           0.0000                0.0000\n",
              "\n",
              "[569 rows x 15 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "f3nnXgHwxdKW",
        "outputId": "26b50f44-cc49-44ce-c9ef-30a281130114"
      },
      "source": [
        "# Modelling\n",
        "# Karena multi-variabel, paling sederhana menggunakan KNeighborsClassifier\n",
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "\n",
        "X = df.drop(['diagnosis'], axis=1)\n",
        "y = df['diagnosis']\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y,test_size = 0.2, random_state=40)\n",
        "\n",
        "scaler = MinMaxScaler()\n",
        "X_train_scaled = scaler.fit_transform(X_train)\n",
        "X_test_scaled = scaler.fit_transform(X_test)\n",
        "\n",
        "n_neighbors = 45\n",
        "knn = KNeighborsClassifier(n_neighbors=n_neighbors)\n",
        "knn.fit(X_train_scaled, y_train)\n",
        "print('Accuracy of K-NN classifier on training set: {:.4f}'.format(knn.score(X_train_scaled, y_train)))\n",
        "print('Accuracy of K-NN classifier on test set: {:.4f}'.format(knn.score(X_test_scaled, y_test)))"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy of K-NN classifier on training set: 0.9473\n",
            "Accuracy of K-NN classifier on test set: 0.9912\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "53cuEv1HyCEO"
      },
      "source": [
        "**Model Evaluation**\n",
        "\n",
        "Akurasi cukup bagus: 0.993/1. , dan akurasi antara `data training` dengan `data testing` tidak berbeda jauh (tidak overfit maupun underfit).\n",
        "\n",
        "Maka, model yang dibuat sudah cukup baik memprediksikan apakah tumor kanker tsb. `malignant` atau `benign` dari 30 variabel."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lxoXI8CnWF1b"
      },
      "source": [
        "from sklearn.metrics import f1_score"
      ],
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bpOBySKe1ENX",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d89cb7b3-f181-4296-ee23-f5b53554f23d"
      },
      "source": [
        "y_pred=knn.predict(X_test_scaled)\n",
        "from sklearn.metrics import classification_report,confusion_matrix,accuracy_score,mean_squared_error,r2_score\n",
        "print(classification_report(y_test,y_pred))\n",
        "print(confusion_matrix(y_test,y_pred))\n",
        "print(\"Training Score: \",knn.score(X_train_scaled,y_train)*100)\n",
        "print(\"Test Score: \", knn.score(X_test_scaled,y_test)*100)"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "         0.0       0.99      1.00      0.99        75\n",
            "         1.0       1.00      0.97      0.99        39\n",
            "\n",
            "    accuracy                           0.99       114\n",
            "   macro avg       0.99      0.99      0.99       114\n",
            "weighted avg       0.99      0.99      0.99       114\n",
            "\n",
            "[[75  0]\n",
            " [ 1 38]]\n",
            "Training Score:  94.72527472527472\n",
            "Test Score:  99.12280701754386\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YFCHBf3ilFu-"
      },
      "source": [
        "Model KNN yang berhasil dibuat berhasil memprediksi dengan benar 38 kasus kanker payudara dari total 39 kasus yang ada."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-nkFXbDh7oKx"
      },
      "source": [
        "**Decision Tree**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "A-xx873omqxD",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "bd6ccfa2-3726-407f-d8bc-669813feb46b"
      },
      "source": [
        "from sklearn.tree import DecisionTreeClassifier\n",
        "dtree = DecisionTreeClassifier(max_depth=8)\n",
        "\n",
        "dtree.fit(X_train_scaled,y_train)\n",
        "y_pred=dtree.predict(X_test_scaled)\n",
        "from sklearn.metrics import classification_report,confusion_matrix,accuracy_score,mean_squared_error\n",
        "print(classification_report(y_test,y_pred))\n",
        "print(confusion_matrix(y_test,y_pred))\n",
        "print(\"Training Score: \",dtree.score(X_train_scaled,y_train)*100)\n",
        "print(\"Test Score :\",accuracy_score(y_test,y_pred)*100)"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "         0.0       0.96      0.89      0.92        75\n",
            "         1.0       0.82      0.92      0.87        39\n",
            "\n",
            "    accuracy                           0.90       114\n",
            "   macro avg       0.89      0.91      0.90       114\n",
            "weighted avg       0.91      0.90      0.90       114\n",
            "\n",
            "[[67  8]\n",
            " [ 3 36]]\n",
            "Training Score:  100.0\n",
            "Test Score : 90.35087719298247\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "sh9hihl_3xkL",
        "outputId": "2944a688-95e7-4ddb-a316-fd5d8f9c1028"
      },
      "source": [
        "from sklearn import tree\n",
        "\n",
        "plt.rcParams['figure.dpi'] = 85\n",
        "plt.subplots(figsize=(20,20))\n",
        "tree.plot_tree(dtree, fontsize=10)\n",
        "plt.show()"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1700x1700 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}