logreg_model.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/samuel-haddad/MBAUSP/blob/main/logreg_model.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yE-lSQyyPyLt"
      },
      "source": [
        "# **Modelo Logit**\n",
        "\n",
        "#### MBA Data Science & Analytics - USP & ESALQ\n",
        "Trabalho de Conclusão de Curso - 02/2022 <br>\n",
        "**Título:** Um estudo comparativo entre modelos Multinível, Logístico e XGBoost na detecção de fraudes ideológicas no setor financeiro. <br>\n",
        "**Autor:** Samuel Haddad Simões Machado\n",
        "<br>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AtZlgNbMQvbZ"
      },
      "source": [
        "## Bibliotecas"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ryLGoDUFOkpA"
      },
      "outputs": [],
      "source": [
        "# pacotes básicos\n",
        "import pandas as pd \n",
        "import numpy as np \n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# preprocessamento\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.model_selection import cross_val_score\n",
        "\n",
        "# modelagem\n",
        "from sklearn import metrics\n",
        "from sklearn.metrics import f1_score\n",
        "from sklearn.metrics import roc_auc_score\n",
        "from sklearn.metrics import log_loss\n",
        "from sklearn.metrics import confusion_matrix\n",
        "from sklearn.metrics import roc_curve\n",
        "from sklearn.metrics import precision_recall_curve\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "import warnings\n",
        "\n",
        "# bibliotecas google & auth\n",
        "from pydrive.auth import GoogleAuth\n",
        "from pydrive.drive import GoogleDrive\n",
        "from google.colab import auth\n",
        "from oauth2client.client import GoogleCredentials\n",
        "\n",
        "# autenticar e criar o cliente com PyDrive\n",
        "auth.authenticate_user()\n",
        "gauth = GoogleAuth()\n",
        "gauth.credentials = GoogleCredentials.get_application_default()\n",
        "drive = GoogleDrive(gauth)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZaShgLcqRwJT"
      },
      "source": [
        "## Dataset"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# link do arquivo no Drive\n",
        "link = 'https://drive.google.com/file/d/1agdy7Mrlw7339_VB6k3MXA_OunitNyQ8/view?usp=sharing'\n",
        " \n",
        "# extraindo o ID do link\n",
        "id = link.split(\"/\")[-2]\n",
        " \n",
        "downloaded = drive.CreateFile({'id':id})\n",
        "downloaded.GetContentFile('base_tcc_logreg20220927.csv') \n",
        " \n",
        "# criando o dataframe \n",
        "df = pd.read_csv('base_tcc_logreg20220927.csv', sep=';')\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 236
        },
        "id": "mURUu7fitk77",
        "outputId": "fda596f2-8020-43a4-d3d9-0904345df6e8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   target      var2  var3  var4  var5  var6  var7  var8  var9  var10  ...  \\\n",
              "0       0  1.610250     0     0     0     0     0     0     0      1  ...   \n",
              "1       1  0.160699     0     0     0     0     0     0     0      0  ...   \n",
              "2       0 -1.703008     1     0     0     0     0     0     0      0  ...   \n",
              "3       0  0.574857     0     0     0     0     0     0     0      0  ...   \n",
              "4       1  0.574857     0     0     0     0     0     0     0      0  ...   \n",
              "\n",
              "   var112  var113  var114  var115  var116  var117  var118  var119  var120   id  \n",
              "0       0       0       1       0       1       0       0       1       0   26  \n",
              "1       0       1       0       0       1       0       0       0       0  128  \n",
              "2       0       1       0       0       1       0       1       0       0  147  \n",
              "3       1       1       0       0       1       0       0       0       0  174  \n",
              "4       0       1       0       0       1       0       1       0       0  186  \n",
              "\n",
              "[5 rows x 121 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-56d4b667-c7e7-40d3-b216-b4e5f3b9aa96\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>target</th>\n",
              "      <th>var2</th>\n",
              "      <th>var3</th>\n",
              "      <th>var4</th>\n",
              "      <th>var5</th>\n",
              "      <th>var6</th>\n",
              "      <th>var7</th>\n",
              "      <th>var8</th>\n",
              "      <th>var9</th>\n",
              "      <th>var10</th>\n",
              "      <th>...</th>\n",
              "      <th>var112</th>\n",
              "      <th>var113</th>\n",
              "      <th>var114</th>\n",
              "      <th>var115</th>\n",
              "      <th>var116</th>\n",
              "      <th>var117</th>\n",
              "      <th>var118</th>\n",
              "      <th>var119</th>\n",
              "      <th>var120</th>\n",
              "      <th>id</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>1.610250</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>26</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>0.160699</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>128</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0</td>\n",
              "      <td>-1.703008</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>147</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0</td>\n",
              "      <td>0.574857</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>174</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "      <td>0.574857</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>186</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 121 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-56d4b667-c7e7-40d3-b216-b4e5f3b9aa96')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-56d4b667-c7e7-40d3-b216-b4e5f3b9aa96 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-56d4b667-c7e7-40d3-b216-b4e5f3b9aa96');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1G_vEgRhRsZ0"
      },
      "source": [
        "## Treino, teste e validação"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6aGayTSBSkyL",
        "outputId": "6f67a77b-2770-4677-b6b4-6491f24e9a48"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "+ ------------------------- RESUMO ------------------------- +\n",
            "X_train: (15860, 119) | y_train: 15860         \n",
            "X_test:  (5287, 119) | y_test: 5287         \n",
            "X_val:   (5287, 119) | y_val: 5287\n",
            "Train dist: 35.5% | Test dist: 35.26% | Val dist: 34.82%\n",
            "+ ---------------------------------------------------------- +\n"
          ]
        }
      ],
      "source": [
        "# remover a coluna de id\n",
        "df_silver = df.drop(['id'], axis=1)\n",
        "\n",
        "# extrair as variáveis explicativas e resposta\n",
        "X, y = df_silver.drop(['target'], axis=1), df_silver['target']\n",
        "\n",
        "# separar a base de treino\n",
        "X_train, X_rem, y_train, y_rem = train_test_split(X, y, train_size=0.6, random_state=42)\n",
        " \n",
        "# separar as bases de teste e validação\n",
        "X_val, X_test, y_val, y_test = train_test_split(X_rem, y_rem, test_size=0.5, random_state=42)\n",
        "\n",
        "# distribuição da variável resposta nas bases\n",
        "y_train_dist = y_train.sum()/len(y_train)\n",
        "y_test_dist = y_test.sum()/len(y_test)\n",
        "y_val_dist = y_val.sum()/len(y_val)\n",
        "\n",
        "# exibir a volumetria das bases\n",
        "print('+', 25*'-','RESUMO', 25*'-','+')\n",
        "print(f'X_train: {X_train.shape} | y_train: {len(y_train)} \\\n",
        "        \\nX_test:  {X_test.shape} | y_test: {len(y_test)} \\\n",
        "        \\nX_val:   {X_val.shape} | y_val: {len(y_val)}'\n",
        "        )\n",
        "print(f'Train dist: {round(y_train_dist*100, 2)}% | Test dist: {round(y_test_dist*100, 2)}% | Val dist: {round(y_val_dist*100, 2)}%')\n",
        "print('+', 58*'-', '+')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "K8E8X_OIT-Bt"
      },
      "source": [
        "## Modelagem"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "UOr0YDjlmLAW",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 166
        },
        "outputId": "5000c78d-5ae1-423d-e48b-0befc257eae6"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "+ ------------------ RESULTADO GRID SEARCH ------------------ +\n",
            "{'C': 0.1, 'penalty': 'l2', 'solver': 'lbfgs'}\n",
            "+ ----------------------------------------------------------- +\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     amostra  acuracia  precisao  recall  auc_roc  f1_score  \\\n",
              "0      teste     76.30     71.41   54.67    80.25     61.93   \n",
              "1  validacao     76.96     72.46   54.59    81.09     62.27   \n",
              "\n",
              "   logloss(normalizada)  \n",
              "0              0.502163  \n",
              "1              0.492416  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-74d0a7a2-ae9c-4b08-b7e9-32a127f427cb\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>amostra</th>\n",
              "      <th>acuracia</th>\n",
              "      <th>precisao</th>\n",
              "      <th>recall</th>\n",
              "      <th>auc_roc</th>\n",
              "      <th>f1_score</th>\n",
              "      <th>logloss(normalizada)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>teste</td>\n",
              "      <td>76.30</td>\n",
              "      <td>71.41</td>\n",
              "      <td>54.67</td>\n",
              "      <td>80.25</td>\n",
              "      <td>61.93</td>\n",
              "      <td>0.502163</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>validacao</td>\n",
              "      <td>76.96</td>\n",
              "      <td>72.46</td>\n",
              "      <td>54.59</td>\n",
              "      <td>81.09</td>\n",
              "      <td>62.27</td>\n",
              "      <td>0.492416</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-74d0a7a2-ae9c-4b08-b7e9-32a127f427cb')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-74d0a7a2-ae9c-4b08-b7e9-32a127f427cb button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-74d0a7a2-ae9c-4b08-b7e9-32a127f427cb');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ],
      "source": [
        "# construir o modelo com configuração padrão\n",
        "model = LogisticRegression(max_iter=100000)\n",
        "\n",
        "# treinar o modelo\n",
        "model.fit(X_train, y_train)\n",
        "\n",
        "# acurácia de treino\n",
        "acuracy_train = model.score(X_train, y_train)\n",
        "\n",
        "\n",
        "#---------- TESTE ----------#\n",
        "\n",
        "# previsão - teste\n",
        "y_pred_test = model.predict(X_test)\n",
        "\n",
        "# extrair as métricas - test\n",
        "acuracy_test = (metrics.accuracy_score(y_test, y_pred_test)*100).round(2)\n",
        "precision_test = (metrics.precision_score(y_test, y_pred_test)*100).round(2)\n",
        "recall_test = (metrics.recall_score(y_test, y_pred_test)*100).round(2)\n",
        "aucroc_test = (roc_auc_score(y_test, model.predict_proba(X_test)[:,1])*100).round(2)\n",
        "f1_score_test = (f1_score(y_test, y_pred_test)*100).round(2)\n",
        "logloss_test = (metrics.log_loss(y_test, model.predict_proba(X_test), normalize=True))\n",
        "\n",
        "#---------- TUNNING ----------#\n",
        "\n",
        "warnings.filterwarnings('ignore')\n",
        "\n",
        "# grid de parâmetros\n",
        "parameters = {'penalty' : ['l1','l2']\n",
        "              ,'C' : np.logspace(-3,3,7)\n",
        "              ,'solver' : ['newton-cg', 'lbfgs', 'liblinear']\n",
        "              }\n",
        "\n",
        "\n",
        "# grid search\n",
        "logreg = LogisticRegression()\n",
        "clf = GridSearchCV(logreg\n",
        "                   ,param_grid = parameters\n",
        "                   ,scoring = 'roc_auc'\n",
        "                   ,cv=10\n",
        "                   )\n",
        "\n",
        "clf.fit(X_train, y_train)\n",
        "\n",
        "#---------- VALIDAÇÃO ----------#\n",
        "\n",
        "# construir o modelo com parâmetros otimizados\n",
        "model = LogisticRegression(**clf.best_estimator_.get_params())\n",
        "\n",
        "# treinar o modelo\n",
        "model.fit(X_train, y_train)\n",
        "\n",
        "# previsão - validação\n",
        "y_pred = model.predict(X_val)\n",
        "\n",
        "# extrair as probabilidades\n",
        "yhat = model.predict_proba(X_val)[:, 1]\n",
        "\n",
        "# extrair as métricas - validação\n",
        "acuracy_val = (metrics.accuracy_score(y_val, y_pred)*100).round(2)\n",
        "precision_val = (metrics.precision_score(y_val, y_pred)*100).round(2)\n",
        "recall_val = (metrics.recall_score(y_val, y_pred)*100).round(2)\n",
        "aucroc_val = (roc_auc_score(y_val, yhat)*100).round(2)\n",
        "f1_score_val = (f1_score(y_val, y_pred)*100).round(2)\n",
        "logloss_val = (metrics.log_loss(y_val, yhat, normalize=True))\n",
        "\n",
        "# criar o dataframe comparativo\n",
        "columns= ['amostra', 'acuracia', 'precisao', 'recall', 'auc_roc', 'f1_score', 'logloss(normalizada)']\n",
        "data = [('teste', acuracy_test, precision_test, recall_test, aucroc_test, f1_score_test, logloss_test)\n",
        "        ,('validacao', acuracy_val, precision_val, recall_val, aucroc_val, f1_score_val, logloss_val)\n",
        "        ]\n",
        "df_metrics = pd.DataFrame(data=data, columns=columns)\n",
        "\n",
        "# visualizar resultados do grid search\n",
        "print('+', 18*'-','RESULTADO GRID SEARCH', 18*'-','+')\n",
        "print(clf.best_params_)\n",
        "print('+', 59*'-', '+')\n",
        "\n",
        "# visualizar tabela comparativa\n",
        "df_metrics"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Co_6J6ocwWgJ"
      },
      "source": [
        "## Gráficos"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "lq1EKK4u2LMg",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 554
        },
        "outputId": "098f6ae3-4c7a-4949-d2a7-870133c86a80"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1800x576 with 4 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAABaAAAAIZCAYAAABQwpj7AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nOzdd3xUZfbH8c+ZFAIECFWp0hVBEMWCDRWQFVT4uSh2cC2Lva2uroLg6lpWVwW7ghSxsJZFURQsWLCiKCKIVOnSS0gh5fz+uDdhCAkkkDABvm9f82Luvc99nnMnyEzOPPc85u6IiIiIiIiIiIiIiJS2SKwDEBEREREREREREZF9kxLQIiIiIiIiIiIiIlImlIAWERERERERERERkTKhBLSIiIiIiIiIiIiIlAkloEVERERERERERESkTCgBLSIiIiIiIiIiIiJlIj7WAYiIiIiI7OvMLB74G5AETHT3L2MckoiIiIjIHqEZ0CIiIiIiZW8AcD9wAjC1NDs2s35m5uFjcmn2LSIiIiKyu5SAFhEREZF8ZnaamY0ys9/MbJOZZZjZYjP7zsweNbPOBdoPikp+LtxJ342j2uY9bi6kXU0zSy/QblAx448+p18JLr3MmFlH4E7gO6CXu28p4fm9wtd5kJmdXBYxioiIiIiUFZXgEBERERHMrDbwEnBaIYcbhI8OwI1mVtHdM0pp6KvN7DF3z43adyVBqYq9npklE7yuvwGnu3vqLnTTC+gbtT25wPH3gBPD5xt2oX8RERERkTKjBLSIiIjIfs7MkoAJwJFRu18H3gKWA5WB1sAZBCUkSlMz4HTg3TCWeOCqUh4jZsKEc7MyHmMlsLIsxxARERER2VUqwSEiIiIiN7Bt8vlydz/H3V9290/cfby7P+juJwLtgaxSGndj+Od1Ufv+D2hY4HiZMbOmZva0mc0Ny42kmtlPZnaPmaUU0r6tmb1vZpvNbK2ZvWJmDcxsYVTpj5Oj2k8urCSImdU3s2fNbL6ZZYYlRxab2SQzGxy2OdnMnG1nP99dsN7zzmpAm1lPMxtvZivMbIuZrTazKWbWN6rNaWY21sx+NbM1ZpZlZhvM7Bszu9nMEnb/1RYRERGR/ZFmQIuIiIhIv6jnn7r7sKIauvuPpTjuCOB64DQza+Huc8JtgJ+A9UCnUhxvG2bWCRgPJEftrgC0DR8XmdmJ7r40bN8G+ByoGratBJwHdASqlGDcBOBTtp8ZnVfq5ETg7pJeTyHjGDCcbX++ADWB44BVwMhw36nAOQXaVQWODh8nEZQCEREREREpESWgRURERPZjZlYJOCRq1/sFjrcnKMERbbG7/14Kw48mmN1bDbjWzEawtcTHE8BFpTBGocKyIy+zNfn8LfBAuH0/UB9oAjwH9AjbPM7W5PNy4HZgM3APcFAJhm/H1uTzdIJkcypQDzgC6BIem0aQjP4HQZkSgBcJksqw83rPV7Bt8vl14FVgC0FS+cCoY58BS4FFwCbAgbrAYKA50NPMjnL374p5jSIiIiIigBLQIiIiIvu76gW2VxfYHk1Q/znagwTJ192VSjAL+gaCRGm9cP9aYAxlmIAmWGwxb7wtQC93Xw5gZmsJZkYDnG5mdYBcglnCea5x97fC9r8CM0owdnTieBXBAoVz3D0LGJV3wN03AF+YWXR950Xu/kUxx7ki6vlb7h49w/mdAm0nE5RXGQC0IJjRbQXaHAsoAS0iIiIiJaIa0CIiIiL7t/UFtmvu4fGfJJhtWxXoHe4b5u7pZTxu9KzveXnJ51B0gteAgwlmAUebkvfE3X9h+9dxR+YCH4bPOwO/AOlmNtvMRpjZsSXoa0cOjXr+ZlGNwlId7wH3EtQCr8r2yWfY/ssKEREREZGdUgJaREREZD/m7puB2VG7uhY43sbdjaBmcVmMP4dty37kAk+VxVi7yXeyXfyO3B04E+gPvEXw+ucCLQlKknxmZh12tf9d0JGttbZzCGZBdyYo/zEpqp1+dxARERGREtOHSBEREREZEfW8s5mdt4fHHxr1/B13X7gHxvw16nkzM4uuh3x81HMnSBDPZdukc/4s5XBxwpTiDmxm5u4Z7v6su5/t7ocQ1NkeEjZJYOtscAiS03lK8vl9ZtTz/yssjvBpo6jdP7r7ve7+MfB1gWMiIiIiIiWmGtAiIiIi8jhwHsHieABjzOx0gjrBq4FaFC8RWd3MHihk/zJ3H1LI/jzvA3cCiQQzgkvLOWZ2SCH7RwETgWUEdaATgbfM7EG2LkKYZ4K7rwQws48JZgYDPGlm1YB0goX6SuIAM5sCvAH8TLCgYSUgetZzUtTzNVHPu5vZF0Aa8Lu7L97BOC9E9Xm2mb0KvAZkEZTaaEBQJ3p+1DltzexqYAFwJUH5ERERERGRXaYEtIiIiMh+zt3TzawbQXKyE8Es20vCR2G2FLG/KvD3Qvb/xNbZvYWN78C/ih1w8XUPHwV97e4zzewCgsUGkwlmNBdMfi8A/hq1fQPwFcECfQ0JFmgEWEywcGKNEsTWFLi1iGPZwMtR25OAv4XPjwQ+CJ8PIKjbXJTnCWZzXxxu9wkfecaFf04FvgSOI5h9/WS4fzPBooNH7WAMEREREZEdUgkOEREREcHd/wBOAXoCY4GFQAbBbNlVBInXx4AuwN2xibJ0ufunwOHAswSzgLcQzGj+mSCxe4S7L4lq/wtwAsHs6TRgA/BfgiRv9OfqzTsZej1B8ngisCgcM5tgRvabwInu/m3UuBOBm4F5BDWai3t9ue5+CUE5j/eAleE4awkSzv/La0fwcx8B/BHG/wlwMtuW8RARERERKTELJpyIiIiIiMiuMLPDgOnhZi5Qy93XxTAkEREREZFyQyU4RERERESKwcySgMnAE8CPwCaCutkPRjUbr+SziIiIiMhWmgEtIiIiIlIMYQI6fQdN5gAnu/uyPRSSiIiIiEi5pxrQIiIiIiLFkwUMJZj9vJagnvJ6gvrYfwfaK/ksIiIiIrItzYAWERERERERERERkTKhGdAiIiIiIiIiIiIiUiaUgBYRERERERERERGRMqEEtIiIiIiIiIiIiIiUCSWgRURERERERERERKRMKAEtIiIiIiIiIiIiImVCCWgRERERERERERERKRNKQIuIiIiIiIiIiIhImVACWkRERERERERERETKhBLQIiIiIiIiIiIiIlImlIAWERERERERERERkTKhBLSIiIiIiIiIiIiIlAkloEVERERERERERESkTCgBLSIiIiIiIiIiIiJlQgloEREREREREREpFjO70MwmFrPtJDP7wswamdmbZR1bWTGzxmbmZhYfbk82s8v35JhlPJabWfNdOG+HMZrZIDN7afcjlL2dEtAiIiIiIqHwF8p1ZlahkP2XF9h3spktido2M7vezGaY2WYzW2Jm/zWzw3YwVoaZpZrZajN708zqFmhzqJm9bWYbzGyTmX1iZscVaJMY/oI3Jxx3oZkNN7PGu/t6iIjI3iV8D0gP31v+MLMRZpZcmmO4+xh3P60YsdQAlgB3A28AL5bG+OH7b254jZvMbLaZXVoafe9JZvZMeA2pZrbFzLKitifEOj6R0qQEtIiIiIgIwSwe4ETAgbN2oYvHgRuA64EaQEvgf0CPHZxzrbsnA82BZODhqHiaAVOAn4EmQD3gLWCimXWM6uP1MN4LgGpAO+B7oPMuXIOIiOz9zgzfW44AOgB3FWywJ2bVuvtad7/U3T9y96Pc/Z1S7H5ZeI1VgZuA583s4FLsv8y5e393Tw6v41/Aa3nb7n56SfvbEz9TkV2lBLSIiIiISOAS4GtgBNC3JCeaWQvgGuB8d//Y3TPdPS2cJfbAzs539/UEyerDo3YPAr5y9zvDX+I3ufsQYDTwYDhuF6Ar0NPdv3P3bHff4O5PuvuwklyDiIjsW9x9KTABaAP5ZRauMbM5wJxw3xlm9qOZrTezL82sbd75ZtYwvDtnlZmtMbMnwv39zOyL8LmZ2aNmttLMNprZz2aWN14PM5sW7l9sZoOi4zOzs8zsl3DsyWbWaheu0d39PWAt0DbsN2Jmt5vZvDDuseFs7LxxTwivdX0YV7/ixFtcZvaemT0Stf2qmQ3flb5CF5rZovBuqTuj+h1kZq+b2UtmthHoZ2bVzGyYmS03s6Vmdq+ZxYXtm5vZp+FdVavN7LUC43QJ76Zab2ZPmpmF50XM7C4z+z38OY8ys2pFXHuTcIxNZjYJqLUb1y37ECWgRUREREQClwBjwkc3MzugBOd2Bpa4+7e7MrCZ1QTOBuZG7e4K/LeQ5mOB482sItAF+NbdF+/KuCIisu8ys4ZAd2Ba1O5ewDHAoWbWHhgO/BWoCTwLvG1mFcKk5Xjgd6AxUB94tZBhTgNOIrjrpxpwLrAmPLaZ4L01heBuoKvMrFcYW0vgFeBGoDbwHvCOmSWW8BojZnYWQaIz7z30uvA6OxHcPbQOeDJsfxBBUn5oOO7hwI87i7eE/gJcbGanmtmFwNEEd0jtqhOAgwk+awwskKjvSXAnVArB55cRQDbBnVXtCX4+eSXE/glMBKoDDQheg2hnAEcRJPLPBbqF+/uFj1OApgR3bD1RRKwvE9yFVSscr0Rf6Mu+SwloEREREdnvmdkJwEHAWHf/HphHUNKiuGoCy3dh6CFmtgFYTfDL2nVRx2oV0edygs/xNXZjXBER2Xf9z8zWA18AnxKUd8hzf3hXTTpwJfCsu3/j7jnuPhLIBI4lSJrWA251983unuHuXxQyVhZQBTgEMHef5e7LAdx9srv/7O657j6dIOHcKTyvD/Cuu09y9yyCElQVgeO2H6JQ9cJrTCcoT3Wzu+cl2vsDd7r7EnfPJLijqHdYouIC4EN3f8Xds9x9jbv/WIx4i83dVwBXASMJynNd4u6bStpPlMHunu7uPwE/EZTayvOVu//P3XMJypF0B24Mf2YrgUeB88K2WQSfdeoV8fN8wN3Xu/si4BO23pV1IfAfd5/v7qnAHcB5BUt+mFkjggT2gPBOsM+A0iy7InsxJaBFRERERIIZOhPdfXW4/TLbztrJBhIKnJNA8MscBLO96lJy17t7NYLZRnkzkvKsLqLPukAuwYyuXR1XRET2Xb3cPcXdD3L3q8Nkc57oO2YOAm4JSy6sDxO6DQkSzw2B3909e0cDufvHBLNhnwRWmtlzZlYVwMyOsWDx3FXhl6392VqSoR7B7Oq8fnLD2OoX8xqXuXsKQdJ1CHBqget6K+qaZgE5wAHhdc0rrMOdxFtS7wBxwOwiEvclsSLqeRrBDOQ8BX+eCcDyqGt/FqgTHr8NMODbsPTJX4o5zjY/q/B5PMHrGa0esM7dNxdoK6IEtIiIiIjs38JSFucCncxshZmtIFjQqJ2Z5c0yWkRwC3K0Jmz9xeojoIGZddiVGNz9Z+BeIL/mIvAhcE4hzc8lmPGUFrY52swaFNJORESkII96vhi4L0xW5z0qufsr4bFGBWe5Ftqh+xB3PxI4lKAUx63hoZeBt4GG4ZetzxAkQAGWESRMgaCWNEFyeGmJLiaY4fx34LCochmLgdMLXFdSWBN7MdCsiO52FG9J3UeQ+K5rZufvYh/FUfDnmQnUirruqu7eGoKZ2e5+hbvXIyi78pSZNS/GGNv8rIBGBF/M/1Gg3XKguplVLtBWRAloEREREdnv9SKYGXUowe2mhwOtgM8JakECvAZcamZHW6AlQZL6VQB3nwM8BbxiZiebWaKZJZnZeWZ2ezHjGEkwm+iscHswcJyZ3WdmNcysipldF8b093DcD4FJBDO9jjSz+LBd/0JmNomIiER7Hugfzvw1M6tswUJ8VYBvCRKKD4T7k8zs+IIdmNlR4fkJBDWUMwju0oGgNMdad88ws6PZtrTVWKCHmXUOz72FIHn6ZdjvCDMbUZyLcPctwCPAwHDXM8B9Yb1nzKy2mfUMj40hWGzv3PA9s6aZ5ZWa2FG8xWZmJwGXErxf9wWGmllxZ3bvsrD0yUTgETOrGtbHbmZmncK4zon6wnodQfI6t4juor0C3BQuMJhMUNLltYKz4939d2AqMDj8HHQCcGbpXJ3s7ZSAlr2OmV1oZhNLqa/JZnb5zlvuGWZ2gJl9Fq4Y+8jOz9hhXy+Y2UwLVi7+qLRiFBER2Qf1BV5090Xh7KAVYf3GJwhWno939w+A24EXgQ0EiyWNBJ6L6ud6tt6GvJ7gFt//o5j1D8NfoB8HBoTbcwgWHmoHLCRIBPwZ6ObuU6JO7R3G81oY2wygA8HsaBERkUK5+1TgCoL3rnUEi/j1C4/lECQPmxPcBbSEoG5zQVUJEtnrCO4KWgP8Ozx2NXCPmW0iSA6PjRp7NnARwUJ4q8OxzgzfCyGYDR39XrczwwlmbJ9J8F76NjAxHPtrgoUXCesbdydIeK8lWIAw726nIuMtrrD8yCjgWndf6u6fA8OAF6PucCpLlwCJwEyCn8nrbC3VdRTwjZmlErw+N7j7/GL0ORwYDXwGLCD4kuG6ItpeQPBarwXuJngtRDB333krkZ0ws4UE9X7qRdVOxMymEcwiauLuC3fSR2OCf8wSdlZnqrSY2WTgJXd/YU+MtzNmNoBgpdo/+27+z2lm/yOYOfUkMNLdny2FEEVERERERETKjJklEiy21zZcoFBE9nI7reUjUgILgPMJvsHEzA4DKpXmAOEMpD2SnI6Rg4CZu5t8BnD3vPpXxV1FWERERERERCSmwlnQrWIdh4iUHpXgkNI0mq11EiG4nXWb2y3CelLTzGyjmS02s0FRhz8L/1xvZqlm1tHM+pnZFDN71MzWAIPCfV+E/R1nZqvNrGG43c7M1pnZIYUFaGZdzexXM9tgZk9QYEEBM/uLmc0K+/ggr2ZUEX2dYGZfWrC67GIz6xfur2ZmoyxYOfd3M7vLzCLhsX5m9oWZPRyOscDMTg+PjQhfs9vC6+8S1r26N2rMk81sSdT2381saViyY7aZdQ73H21mX4WxLTezJ8JvkfPOO87Mvgtfh+/MTElqEREREREREREpdUpAS2n6GqhqZq3MLA44D3ipQJvNBEnqFKAHcJVtXan2pPDPFHdPdvevwu1jgPkEi/LcF92Zu38JPAuMtGAF+5eAAe7+a8HgzKwW8CZwF1CLoC7j8VHHewL/AM4GahMsPPRKYRcaJqYnEMz2rk1QZuTH8PBQoBrQFOgUXu+lUacfA8wOY3gIGGZm5u79CBZEeCi8/h3WbTSzg4FrgaPcvQrQjaA+JAQLKd0UjtER6ExQzwozqwG8CwwBagL/Ad41s5o7Gk9ERERERERERKSklICW0pY3C7orMAtYGn3Q3Se7+8/unuvu0wkSvJ120ucydx/q7tnunl7I8UEECd9vw/GeLKKf7sAv7v56WEfqMWBF1PH+wP3uPiss8/Ev4PAiZkFfAHzo7q+4e5a7r3H3H6MS73e4+6aw7vUjwMVR5/7u7s+HizqMJFgQ4ICdvAaFyQEqAIeaWYK7L3T3eQDu/r27fx2+ZgsJkvR5r3MPYI67jw6PvwL8ilanFRERERERERGRUqYEtJS20QTJ2X4UstqpmR1jZp+E5Sk2ECR9a+2kz8U7Ohgmk0cAbYBHdlA/uV50X2G76L4PAh4Py1asJ1i11YD6hfTVkGAGdUG1gASC1X/z/F6gj/ykt7unhU+Ti4i5SO4+F7iRIAG/0sxeNbN6AGbW0szGm9kKM9tIkEzPe53rFYivsBhFRERERERERER2mxLQUqrc/XeCxQi7E5S7KOhl4G2gobtXA55hax3mohLHO1yQz8zqA3cDLwKPmFmFIpouJ0gc551n0dsEyei/untK1KNiWOajoMVAs0L2rwayCJLZeRpRYCZ4CWxm24UcD4w+6O4vu/sJ4XgOPBgeeppgVnMLd69KUFok73VeViC+3Y1RRERERERERESkUEpAS1m4DDjV3TcXcqwKsNbdM8zsaILZ0nlWAbkEtZOLJUwijwCGheMuB/5ZRPN3gdZmdraZxQPXs21C9xngDjNrHfZdzczOKaKvMUAXMzvXzOLNrKaZHR6W1RgL3GdmVcLyHTezfS3s4voR6G5mNczsQIIZz4TxHWxmp4YJ9wwgneD1g+B13gikhgsyXhXV53tASzO7IIy9D3AoMH4XYxQRERERERERESmUEtBS6tx9nrtPLeLw1cA9ZrYJGEiQrM07L41gkcEpYRmMY4sx3PVAHYKFB51gsb9LzezEQuJaDZwDPACsAVoAU6KOv0Uwg/jVsGzFDOD0Iq5xEcEs71sISnX8CLQLD19HMHN5PvAFwazv4cW4lsKMBn4iWFxwIvBa1LEK4bWsJijrUQe4Izz2N4Lk/ibg+ejz3H0NcEYY+xrgNuCM8PUREREREREREREpNVZ0uVwRERERERERERERkV2nGdAiIiIiIiIiIiIiUiaUgBYREREREREpJ8xsuJmtNLMZRRw3MxtiZnPNbLqZHbGnYxQRESkJJaBFREREREREyo8RwJ92cPx0gvVsWgBXAk/vgZhERER2mRLQIiIiIiIiIuWEu39GsNB5UXoCozzwNZBiZnX3THQiIiIlFx/rAIqSu6KlVkeUcqNbvXaxDkEk36Tc/1pp91lW/+ZGDvyt1GOV8qdWrVreuHHjWIchIiL7qO+//361u9eOdRzlSH1gcdT2knDf8oINzexKglnSVK5c+chDDjlktwZ2d5bMXkb65sz8fdVqVaFOw1pYRB/7RETKmxUZK8jIyaBx5cYAbMreRHpORv7xSnEVSY5PLpWxdvR+XW4T0CIiIrJ3aNy4MVOnTo11GCIiso8ys99jHcPeyt2fA54D6NChg5fW+/WWjC2Mf2YST988AtbAf2e+QCQuwi9TZtPxzA6lMoaIiOy+B2b9mxzP4c5Dby/zsXb0fq0SHCIiQm4Z/SciIiIipW4p0DBqu0G4b49JTErk7Bt7MCn3vzw4cQAptauxaOYSBvZ8kMG9HyYjLXPnnYiISJlzHMrBDSpKQIuIiIiIiIjsPd4GLrHAscAGd9+u/MaeckSXtgDUb1mPpEoV+OLNbzgz+SKevmlErEISEZEoVg4y0EpAi4gIOZ5bJg8RERERKRkzewX4CjjYzJaY2WVm1t/M+odN3gPmA3OB54GrYxTqNqrXqcZb60Zw03NBmG8+/i5zf1wQ46hERPZvJ9U+gZNqnxjrMFQDWkREREpfVlYWS5YsISMjY+eNRXZRUlISDRo0ICEhIdahiIiUGnc/fyfHHbhmD4VTIvEJ8XS/vDPtT21D1pZsahyYwujB/+WXr2ZzZJe21GlUi07nHhfrMEVE9hvH1yof/+YqAS0iIuTisQ5B9jFLliyhSpUqNG7cGLPY3/Il+x53Z82aNSxZsoQmTZrEOhwREYlSt+kBQLBY4dJ5y5n20c98P/EnACpWqcjRp7ePZXgiIvuNjVkbAaiaUDWmcagEh4iIxGQRQjNLMrNvzewnM/vFzAaH+5uY2TdmNtfMXjOzxHB/hXB7bni8cVRfd4T7Z5tZtzJ8qaSYMjIyqFmzppLPUmbMjJo1a2qWvYhIOZaYlMjto67n7Y2jeGnBUyQmJXBnj3+xJWNLrEMTEdkvPDH3aZ6c+0ysw1ACWkREYiYTONXd2wGHA38KF9J5EHjU3ZsD64DLwvaXAevC/Y+G7TCzQ4HzgNbAn4CnzCxuj16JFErJZylr+jsmIrJ3qFCxAgccVJsbn/krL/zyKIlJicz7aWGswxIR2adl5mSSnZsd6zAAJaBFRATIcS+Tx454IDXcTAgfDpwKvB7uHwn0Cp/3DLcJj3e2IPvUE3jV3TPdfQHBgjxHl9ZrIyIiIiKlo+slnTioVQMy0zPp3/5W7v6/h2IdkojIPuv2n+9k3ub5ZHtOrENRAlpERMqOmV1pZlOjHlcWOB5nZj8CK4FJwDxgvbvnfU27BKgfPq8PLAYIj28AakbvL+Qc2Y+ZGbfcckv+9sMPP8ygQYOKff4ff/zBGWecQbt27Tj00EPp3r07AJMnT+aMM87Yrv3bb7/NAw88AMCgQYN4+OGHAejXrx+vv/76du1FRET2V1mZ2dRtegBfjvuO91/8JNbhiIjsk06tcwoAVROqxDgSLUIoIiKU3SKE7v4c8NwOjucAh5tZCvAWcEiZBCL7pQoVKvDmm29yxx13UKtWrRKfP3DgQLp27coNN9wAwPTp03fY/qyzzuKss87apVhFRET2J8kplXlk8mAuaNSfRy57ipysbHpc2TXWYYmI7FPOrNeDo2scRaW4irEORTOgRUQEcvAyeRSXu68HPgE6AilmlvcFaQNgafh8KdAQIDxeDVgTvb+Qc6ScyF1z0XYPTxsDgHt6EcffDI7nrt3uWHHEx8dz5ZVX8uijj253bOHChZx66qm0bduWzp07s2jRou3aLF++nAYNGuRvt23bdrs23333He3bt2fevHmMGDGCa6+9tlixiYiI7O9qN6jJzc/3p1KViqxavAZ3Jz01PdZhiYjsM0YuHM1P63+iSjmYAa0EtIiIxISZ1Q5nPmNmFYGuwCyCRHTvsFlfYFz4/O1wm/D4x+7u4f7zzKyCmTUBWgDf7pmrkPLummuuYcyYMWzYsGGb/ddddx19+/Zl+vTpXHjhhVx//fWFnnvZZZdxyimncN9997Fs2bJtjn/55Zf079+fcePG0axZszK9DhERkX3R6Zd1ZtyGUfT753nM+vo3ru94J+tXbdj5iSIiUqS3l42n77eX8fOGGSzYvDDW4QAqwSEiIpRdCY6dqAuMNLM4gi9Ex7r7eDObCbxqZvcC04BhYfthwGgzmwusBc4DcPdfzGwsMBPIBq4JS3tIORKp+VKRx8wqYjs6Hqmxw+M7UrVqVS655BKGDBlCxYpbbz376quvePPNYIb1xRdfzG233bbdud26dWP+/Pm8//77TJgwgfbt2zNjxgwAZs2axZVXXsnEiROpV6/eLsUmIiIiWy2csZiFvyymf/tbOb7X0fS4sitN2x4U67BERPY67yx7F4BVmatpkdw8xtEENANaRERiwt2nu3t7d2/r7m3c/Z5w/3x3P9rdm7v7Oe6eGe7PCLebh8fnR/V1n7s3c/eD3X1CrK5Jyqcbb7yRYcOGsXnz5hKfW6NGDS644AJGjx7NUUcdxWeffQZA3RryUU0AACAASURBVLp1SUpKYtq0aaUdroiIyH6p+xVd+PONPVizbB1vP/UB1xz1dzZvKPl7t4jI/m7Aof+IdQjbUQJaRETIcS+Th0h5UKNGDc4991yGDRuWv++4447j1VdfBWDMmDGceOKJ25338ccfk5aWBsCmTZuYN28ejRo1AiAlJYV3332XO+64g8mTJ5f9RYiIiOwH+v+nHx9kv8aI34bQ+cKTqFytMhlpmbEOS0Rkr9KoUkM6VD8SgJxycnOwEtAiIkJuGT2kfDGz4Wa20sxmFHHczGyImc01s+lmdsSejrGs3HLLLaxevTp/e+jQobz44ou0bduW0aNH8/jjj293zvfff0+HDh1o27YtHTt25PLLL+eoo47KP37AAQcwfvx4rrnmGr755ps9ch0iIiL7ukgkQv3mdfnb8KuZ+dVs/tH9PtWFFhEpgV83ziY3/I08MZIY42gC5uV0hlruipblMzDZL3Wr1y7WIYjkm5T7XyvtPpctrVcm/+bWq7+s1GOVXWdmJwGpwCh3b1PI8e7AdUB34BjgcXc/Zmf9dujQwadOnbrNvlmzZtGqVatSiVtkR/R3TWTPmbB8CmN/n8TG7FQiGGYR4oiQlptBgsXn/5KbEImnSeW69DmoG62qNtntcc3se3fvsNsd7ecKe78u7z4d+yX3nvcoNQ5MYfSCp0iskBDrkEREyr0xv7/C56un8MyRT+zRcXf0fq1FCEVEhJzYLEIoe5i7f2ZmjXfQpCdBctqBr80sxczquvvyPRKgiIjE1ITlU/hy1U9UTUhmWfoq1mSuZ13WJsCJECGbom/jzfYc0nO3lkpYvWUd09bP5oF215dKElr2T53OPY4Px3zG1+98zyXNruGxL+7lwMZ1Yh2WiEi55jjlbSaYEtAiIiKSpz6wOGp7SbhvuwS0mV0JXAnk10UWEZG9x6yNC3hk1mhWZK4hziIkWgJpuRlFts/dQfK5KNmew8/r5ygBLbvln+Nu59Ern+G9Fz7i7l4P8eyPD8c6JBGRvUD5SkErAS0iIuRoArSUkLs/BzwHwS29MQ5HRER2YtbGBby+6EPWZm0kPTuTxekr8o9lew7ZpbZIkZP3S2+8xXFYSotS6lf2Zzc9158jT2tH+86HsWldKumpGdRpWCvWYYmIlEvumgEtIiIi5ddSoGHUdoNwn4iIlGPRtZkTLJ74SBwAmTlbSM/dgpdBqa2g5nMCcURIz80kweKoEJcInktCJJGmVRrQu2EXzX6WUnNS744AvPHoeJ65ZSQXDehN38F9YhyViEj5E7zrl68UtBLQIiISro8rwtvAtWb2KsEihBtU/1lEJHZmbVzAk7+9xh+Zazm4SmO25GxhYdpyKsdXJCWhCr+nLWdL7pZt0ssZbGEXqmVsp1JcEhUjFfJrQCdFEqkQl0hyQiV61j+Z0+sen9/WN48AT8eSr9r9gUV2on3nwwB46Z+vc8gxLTim+xExjkhEpOw8+tsQkuMrc0XTy4p9ztkNetKj7ullGFXJKQEtIiLklLNvR6VsmNkrwMlALTNbAtwNJAC4+zPAe0B3YC6QBlwam0hFRPZfw+ePY/yyz8nM3bLN/mnrf81/vjknnZWZa0ttzDjiSElIZkNWKrk47VJacm/bq4t1rqe9jm/6F1Q4DTwXs0ipxSVSmKZtD+KOl67n/ouGcNcZ9/PW2hEkp1SOdVgiIjuUmp2KYVSOL/6/V7mey4/rfwIoUQI6OT6Z5HKW8dWnAxERkf2Eu5/v7nXdPcHdG7j7MHd/Jkw+44Fr3L2Zux/m7lNjHfPuSE5OjnUIherduzfz58+PdRhFevTRR2ndujVt2rTh/PPPJyMjWJTsiSeeoHnz5pgZq1evLvL8kSNH0qJFC1q0aMHIkSPz97/22mu0bduW1q1b8/e//z1//9ChQ2nTpg3du3dny5Yg4fbFF19w00035bdZtWoVf/rTn0r7UkVibtbGBVw79QF6fX4zF311Fxd9eSdvLPlou+RzaaseX4VKcUk0qVyfhw+/ibdPepRRHf/JuJMe5Z2THit+8jljAr7xLkg8EUv5j5LPssecesGJ/PnGHrQ4ogkrFq5kw+qNbMko2/9vRER2xzU/3MDVP1zPiow/itU+MyeT66YFn4dPrXMyECSk8479sG4aG7M2FXruT+t/5oMVk3Y/6FJUzvLhIiISC7laQk7KgVkbF/Dz+jkcltKiXNYMzc7OJj5+9z46/fLLL+Tk5NC0adNin5OTk0NcXNxujVtcS5cuZciQIcycOZOKFSty7rnn8uqrr9KvXz+OP/54zjjjDE4++eQiz1+7di2DBw9m6tSpmBlHHnkkZ511Frm5udx66618//331K5dm759+/LRRx/RuXNnxowZw/Tp0/nXv/7FBx98wBlnnME///lPXnnllfx+a9euTd26dZkyZQrHH398keOLlGfD54/j/eVfkpGTSU4hxa/WZW0slXEqRBKpFFcB2LYGtAEVIhXoUe8E/tK0526P45mT8fV/g4QjsOpPYJa4232KlET///QDYMmc5bx45yt8+NJnDBh7M8f0ODK2gYmIAMvSlzFjwy+0r344tSvUzt8/cMZgrm1+FW1TDivyXHdnecYKDkw6gLmpqbRPOZxfNszk8TlP0C6lLcfX7MjQOU9xXK1jC50Z/cO6aUxbP41uB3Ytk2vbFUpAi4iISJl6bt4bzE/d8VqGm7PTWbB5WZgkMZpUrkfl+IpFtm+aXJ8rm/25xLH8+OOP9O/fn7S0NJo1a8bw4cOpXr063333HZdddhmRSISuXbsyYcIEZsyYwYgRI3jzzTdJTU0lJyeH9957j+uuu44ZM2aQlZXFoEGD6NmzJ2lpafTr148ZM2Zw8MEHs2zZMp588kk6dOiwzfhjxoyhZ8+tiZ+rrrqK7777jvT0dHr37s3gwYMBaNy4MX369GHSpEncdttt1KhRg7vvvpvMzEyaNWvGiy++SHJyMvfccw/vvPMO6enpHHfccTz77LOY7V5JnezsbNLT00lISCAtLY169eoB0L59+52e+8EHH9C1a1dq1KgBQNeuXXn//fdp3rw5LVq0oHbt4MN3ly5deOONN+jcuTPuTlZWFmlpaSQkJPDSSy9x+umn5/eRp1evXowZM0YJaNlrzNq4gBfnjWNO6iKyPGe3FwJsWPFAqsZX2q4GdHZuNrk4yfGV6NvkjG1qM5epnDWQcAhW/VnMiv73WqSs1WlYk+oHppC1JZu7znyAC+/6M/3uOS/WYYnIfu695e/z+eopjFn0Kk0qN+aACnU4pubRfLf2ezZkbchvl52bzSerPmV+6nwub/oX4iyOL9d8xXPzh3FMjaO4qPUFNK50EAs2L6R5cjO+Xfsd3679DoAvVn9Jo0qNtks0B585yleZTSWgRURENaAl5jZnp+cnZxxnc3b6DhPQu+qSSy5h6NChdOrUiYEDBzJ48GAee+wxLr30Up5//nk6duzI7bffvs05P/zwA9OnT6dGjRr84x//4NRTT2X48OGsX7+eo48+mi5duvD0009TvXp1Zs6cyYwZMzj88MMLHX/KlCmcf/75+dv33XcfNWrUICcnh86dOzN9+nTatm0LQM2aNfnhhx9YvXo1Z599Nh9++CGVK1fmwQcf5D//+Q8DBw7k2muvZeDAgQBcfPHFjB8/njPPPHObMceMGcO///3v7WJp3rw5r7/++jb76tevz9/+9jcaNWpExYoVOe200zjttNOK/fouXbqUhg0b5m83aNCApUuX8qc//YnZs2ezcOFCGjRowP/+97/8chvXXnstxx57LK1bt+b444+nZ8+efPDBB9v13aFDB+66665ixyISS/+eNYrJq3a/ipFh1K5QnXMbdd1zieWdcM/ErAJW6c9QsSdm+pVSYisxKZG+g/vQ/YouXNCoP2PufYNIJMIlg86NdWgish9bs2UNKQnV6HbgaeR4DmfW6wHA2fV75U8YWZK2lJELR/Nb6hwANmRt5OaWN/DRH58AMHXdD1x00IWYGU2Tm3DrwTfz5tL/8fay8XSucworMv7g1UVjOaVOJxIjwZ1IqzNXMy91Xrn7DV+fFkRERAloKVPFmak8a+MC/jH9CbJzc4iPxHFrq76lXoZjw4YNrF+/nk6dOgHQt29fzjnnHNavX8+mTZvo2LEjABdccAHjx4/PPy96Ru/EiRN5++23efjhhwHIyMhg0aJFfPHFF9xwww0AtGnTJj+JXNDy5cvzZwEDjB07lueee47s7GyWL1/OzJkz88/t06cPAF9//TUzZ87Mn/m7ZcuW/Fg/+eQTHnroIdLS0li7di2tW7feLgF94YUXcuGFFxbrNVq3bh3jxo1jwYIFpKSkcM455/DSSy9x0UUXFev8olSvXp2nn36aPn36EIlEOO6445g3bx4QJM4vvvhiAO655x6uv/56JkyYwKhRo2jYsCGPPPIIkUiEOnXqsGzZst2KQ2R35c1qXpi2nAhGjueQmZtNBIhE4jioUl3+yFjNhuzNJe67QiSRgyrVZcHmpcRZXKmVyihNnvUbvu5yqHY/VuF4JZ+lXKndoCZjV7zA3065mxN7HxvrcERkP/XbpjnUTTqQdVs2cHCVlnSvu+06JnnJ589Wfc6wBSMASIokcVb9Mxi7+HUum/rX/LbPHfkU8ZH4bc49u34vmic3o2nlJszeNIdfNs7k5w0zOLL6EQAMmDGItJx0UhJSyvhKS0afGERERCTmWlVtwr/aXlsua0BXrrx1pWp354033uDggw/epb4qVqyYv6jfggULePjhh/nuu++oXr06/fr1yz8WPa6707Vr121qIkOQ/L766quZOnUqDRs2ZNCgQducn6ckM6A//PBDmjRpkp8kP/vss/nyyy+LnYCuX78+kydPzt9esmRJfs3oM888Mz85/txzz21X13rZsmV8++23DBw4kE6dOvHxxx9z77338tFHH9G1a1cyMjKoWFG3+UvsTFg+hSfmvFbosRyA3Bx+S/29RH2Wdl3msuTZv+PrLgUM4hrutL1ILFSvU41hvzwGwKsPvEXDQ+pzfK+jYxyViOwvcjyH//z2OEfXOIp/HXYPW3awqPBxNTsyc+OvfLXma/6vQU/+dOBpfLPmW35PW8S5DXuTnZu9TfI5j5nRLiWYsNIu5TDOrNeDNlVbk5qVyj9mDCAtJx2A9Vnry+Yid5ES0CIiQq5rBrTEXquqTco08VytWjWqV6/O559/zoknnsjo0aPp1KkTKSkpVKlShW+++YZjjjmGV199tcg+unXrxtChQxk6dChmxrRp02jfvj3HH388Y8eO5ZRTTmHmzJn8/PPPhZ7fqlUr5s6dS+PGjdm4cSOVK1emWrVq/PHHH0yYMKHQBf6OPfZYrrnmGubOnUvz5s3ZvHkzS5cupU6dOgDUqlWL1NRUXn/9dXr37r3d+SWZAd2oUSO+/vpr0tLSqFixIh999NF2dax3pFu3bvzjH/9g3bp1QDBj/P777wdg5cqV1KlTh3Xr1vHUU08xduzYbc4dMGAA99xzDwDp6emYGZFIhLS0NAB+++032rRpU+xYRErDrI0LeH3Rh8zetJB1Raw0X1ztUw6hbUrzcvclW3F4zgp8bT/wLKzmy1h8o1iHJLJDubm5jLnvDcyM//7xAhUqVoh1SCKyj1uTuZYPVkwkPSed1lVbEbEISXFJRbaPj8Tz16aXc3SNo2hbLfiMe2er21mcvoTmyc2KNWZCJIHeDc4G4LPVX7AhakHjo2sctRtXU/qUgBYREZF9UlpaGg0aNMjfvvnmmxk5cmT+IoRNmzblxRdfBGDYsGFcccUVRCIROnXqRLVq1Qrtc8CAAdx44420bduW3NxcmjRpwvjx47n66qvp27cvhx56KIcccgitW7cutI8ePXowefJkunTpQrt27Wjfvj2HHHIIDRs2LHJxvdq1azNixAjOP/98MjMzAbj33ntp2bIlV1xxBW3atOHAAw/kqKN2/0PmMcccQ+/evTniiCOIj4+nffv2XHnllQAMGTKEhx56iBUrVtC2bVu6d+/OCy+8wNSpU3nmmWd44YUXqFGjBgMGDMiPZeDAgfnlS2644QZ++umn/P0tW7bMH3fatGkAHHFEcOvgBRdcwGGHHUbDhg257bbbgKDcSI8ePXb7GkUKmrB8CuOWfEpq9mayc3PAjJNqBXXc310xZbf7r5NYgxePHbTb/cSK527A1/YF34DVGIXFN491SCI7FYlEuH309Qw6+9+cUfkiXl70DLUb1Ix1WCKyj1m/ZQOfrJpM1zqdGblwFD9tCCahtKp6SLHONzOOqL517ZgKcRWKnXwu6KXfX85/flPL66mbdOAu9VNWzH33VmMuK7krWpbPwGS/1K1eu1iHIJJvUu5/S3268g+LGpXJv7lHNFqkqdX7gQ4dOvjUqdsutjVr1ixatWoVo4hKLjU1leTkZAAeeOABli9fzuOPP17s83NycsjKyiIpKYl58+bRpUsXZs+eTWJi4jbt0tPTOeWUU5gyZcp2JShkx0466STGjRtH9erVt9m/t/1dk9jIm8k8f/NSUrM2k5GbRQQjOyiesUsiREiKJGxXAxogIRJHo8p16XzA0XvdbOeC3HPwTfdjSd2wxNjMpjKz7929+LdjSKEKe7/e171w+0u89tA4GrSsy4u/Dol1OCKyF/px3U/M2PgL7VMOZ9amXzmx1glk5W5hyuqveG/F+wBUjKtIr3pn8vHKyfRueHZMZh/f9tMd/JG5EoBbWt5I25TD9ngMO3q/1gxoEREhh0isQxCJqXfffZf777+f7OxsDjroIEaMGFGi89PS0jjllFPIysrC3Xnqqae2Sz5DUAN68ODBLF26lEaNdAt7ca1atYqbb755u+SzyM4Mnz+Od5d9TkYhNRhzd6PfvX1Wc3F4bhr4RizuQKzqXbEOR2SXXP7ARUwa9SmdLzwp1qGIyF7qgz8mMnPjryREEnhv+fu8s+zd7dpsyd3CH5kreajd/TGIMHBW/TN5fv4wAB757TFGHj0sZrEURgloERER2e/16dOHPn367PL5VapUobizyrp167bL4+yvateuTa9evWIdhuwlhs8fx1erp7MpazObctJKrd9KkQokxCXQ5YBjyv2CgbvLPRNffxXkLIZaEzBT/VzZe728+BnSNqaTkZbJk9cN4/IHL6JaraqxDktE9hIzN/5KjcQa9Gl4Dl3qnMonKz/lneXvEmdxPH74I2zJ3UJqdip1kurENM4Tah1H8+RmZOVmkZWbFdNYCqMEtIiIaBFCKRPujpn+bknZKa+l5GTPGT5/HB+u+IZczyXe4tick84Wz97l/qKTzHUr1mLi8q+pUaEqvRt22etLaRSXexa+/kbY8hVW9QEln2WvFxcXR5XqyUwa/Snvv/gJObm53PbitbEOS0T2AuOXvQdAnAWl82pWqEnvhmdzZr0e5JJLxbiK+fvLgwOTDoh1CEVSAlpERERKXVJSEmvWrKFmzZpKQkuZcHfWrFlDUlLRq4vLvuXfs0bx/bpZHFm9FWfUP5GB058iLTezxP1EwrJTEYxccsnFqRafzIA2V2yXZD69buGLg+6r3HPxDXdA5kdYlbuwSmfHOiSRUtP14k58+NJnTBr5Kaf/pTOHnaj1A0Rke1NWf8nKzFUkRhL5dm1wh2P/Zlds06ZCnL6cLSkloEVEhByUIJTS1aBBA5YsWcKqVatiHYrsw5KSkmjQoEGsw5AyMGvjAn5eP4fDUlqwcPMynp3zOlnhgoGTV01l8qqSL6QWb3GcUKs9t7a6pLTD3XdsfgEy3saSb8Iq63WSfU+fW3vyw6Tp3PGnexm3cZQWBBaRbazfsp7n5m+tndyx5rHc3frO/BnQsuuUgBYREXJcixBK6UpISKBJk/3jdnURKT2zNi5g8M/PsSlnc6n0VyGSSL2Ktbmmxbn7TQmN3VLpPCxSBSqeF+tIRMrEEV3acuFdf+abd39g09pUUmpXi3VIIlKOVEuoxpn1erAifQULNi/ktAO6KPlcSpSAFhERERGRmBk+fxxvL5mcP8N5V1WKVKBCXCIJkXiaJjfYr+o27y5Pfw+STsUiVaHS+bEOR6RMXXjXn2l0SH1SalfjnWcmcmb/02IdkoiUA6MXjiHLs/lLk76xDmWfpAS0iIiQi2ZAi4jInvPvWaP4bNX35LJrC0meXLsDszcu5I/MtVSOr0jfJmfsd/WaS4tvHo1v+ieWcwsk/zXW4YiUuYTEBE694ESWzFnOkKufB3fOvKpbrMMSkRj7cs3XpOWkKQFdRpSAFhERERGRUjNr4wKe/O01lqSvJIKRFFeBynFJrNqyjggRsj2HHHKL3d+fG3Tm4xXfsiF7M4mRBHrUO4G/NO1Zhlew//D0t/BN/4QKnaHyX2IdjsgelVK7KgCj7/mvEtAi+6EV6StYkr6U9tUPZ3P2ZtJy0uhY89hYh7XPUgJaRES0CKGIiOyyWRsX8OK8cczbvITs3ByyC5TSyMzOYkN2arhV/DIb1eKTGdDmClpVbaKEcxnwjA/wDXdAYkcs5THMEmIdksgelZxSmV7Xns7/nphA35bX8dgX91K9jmpCi+wPsnOz+fvPdwJQMa4idxxyKwBNKzeOYVT7NiWgRURERESkRGZtXMDP6+cwYdkUVm5ZV2r9VotP5rja7eh8wNGq31yG3DPwjfdCQjss5SnMKsQ6JJGY6HfveSyavZQfJk3n/WEfc/4d/xfrkERkDxixcFT+81ZVDmbe5gUANEtuFquQ9nlKQIuICDmuGtAiIlI8/b+9j8UZf5Raf9Xjq3BWg04cltJCSec9xCwJaoyESC0sUjnW4YjETOWqlXjwgwEsn/8HdZsewMrFq6lZrzpxcXGxDk1ESsGXq7+mXcphVIyryOK0JQyd+yTnN+pDjcQaADx75JMkxSWRmp2Key5NK+tzSFlRAlpERMhVCQ4RESlg+PxxfLryew5IqkliJIG5qYvJyM4gqwRlNCJEqJJQiarxyazMXEscRoW4RLJzc4hEInQ54BiV19iDPOsXyPwUKl+FxTeNdTgi5Ubdpgfw+ZvfcE/vhwF4+oeHaH64ElEie4stuVnBZ5VNczmo8kF8s/Y73ls+gaXpy2hXrS0n1T6RoXOfBOCl31/m0cMf5uwGvfLPT45PpvMBp8Yq/P2CEtAiIiIiIrKN4fPH8caSjwBYvWV9sc+LJ47ESDwJcQlKLpcznj0XX/sXsEpYpQvAUmIdkki50q7TobQ6tgWzvp7DVUfcxgsz/sNBhzaMdVgishOZOZlc88P1VIqvxIasjVzU6ALeXPo/0nLSAOh2YFcWpv2e3/6mljfEKtT9mhLQIiJCDirBISIiQW3ngdOfJi03o0Tn1UpM4fZDL1UJjXLKsxfja/uBxWM1RmARJZ9FCqpaswpDvvwX30/6idu73cvHL3/BpfeeH+uwRKQIGTkZTF71GSkJ1cjybDZkbQSgQaX6XNfiaualzidiEVpWaUHraodyZEp7EuMS88tvyJ6lBLSIiIiIyH7q37NG8dWa6cRh5HgumZ5VovPbpxzCvW2vLqPopDR4zgp8XV/wLViNl7D4g2Idkki5dmTXdhzasSUtO2gxMpHyKjMnk79+fw0AVeKrMPLoYUxb9yO/bprNwVVaErEIh1Zttc05B1Y8MBahSkgJaJH/Z+++w6Oo2jAO/85uOiQkgdC7VJUqYAEURUU/CyoqTVBQOhak2AARK90GIl1sCKJiV1ARRFGKSjEISARBOgkhPdk93x+LESVAkGQn5bmvay93Zs7MPKsxyb45+x4R0SKEIiLFQGxiHOsTtrAuYStbk/4gw5OR64KzGzdhASFEBoaT6kmjXEhpetS8XjOeC4PMDeA9gomeiQms43QakUKh/7M9qFy3IqnJaYSEBWOM1ksRcZrXelmy90u2Jv1G4tHZzgD9zuoNQJOoxjSJauxUPDkFFaBFRASvWnCIiBRZ42Ln8vX+NVjsf77GmMb3qNhcyFhrMcZgQi6HoBYYV4TTkUQKjbrNa+HJ8vBcv+lsWLGJ6esm4A5wOx1LpFgbvuFRdqX+CcDNlW8iKiiS9pWup3xIOYeTSW6oAC0iIiIiUkTd9u0jxGcd+U/nhrvDuKnKZTSIrK3icyFjbSo2vh+EdcGEXKnis8h/kJmRxSczfYuxjuowjsfeHYbLpUkbIk75q/jcNKoJTaOacF3FaxxOJKdDBWgREcFj9bFCEZGiYvi6KaxL2IwH72mdF2wC8WKpHFaOAbVvVdG5kLI2Axs/EDK+g9BbnI4jUmiFhAXzSfqbjGw/hpUfrOGm0j0YvegBGl58ttPRRIql6ypeQ7ngsrSOaeV0FPkPVIAWERERESkCYhPjGL1+Gome5FOOdRsXpQMjSfKkUC6ktArORYS1WdiEwZCxHBPxJCZUs8NEzkRAYABPfPgQX77xDS8NmkNm+ukt1CoieWNX6p9cEtOamOAYp6PIf6QCtIiI4FEPaBGRQmnWtkV8+OdysrwePHhydU6TyHo80bB/PicTf7PWi00cDumfYcIfwoRp9rNIXnC5XFx+28VcftvFAKQmpxFaIsThVCLFQ6onlS/2fsWCnQvpWrUTV5a/wulI8h+pAC0iInitCtAiIoVJbGIcE2JfZXf6gVyfo8JzUWfARGBK3o0p0cPpMCJF0tov1vPSoNm8+P3TBIcGOx1HpEjbk7aXB9Y9nL1dNqSsg2nkTKkALSIiIiJSiHyyewUvbnkrV2PduCgdHMmtVa/g6got8zmZOMV6EzCuSAh/yOkoIkXars1/8vuGP5g9fB59J9zudByRIu1g+sHs59ObvUSQK8jBNHKmVIAWERG14BARKeD+Wlgw0BVAmjfjlONLuEO5qsJF9KzZ3g/pxEk2eQY2eQ6Uno9xV3Q6jkiRdm3fK5k35j0WTvqQ+L0JXNevHee2rOd0LJEi56t9S1m+fwXjG40hJriM03EkD6gALSIiu9xoawAAIABJREFUIiJSAMUmxvH2jiX8cGgDXiwAnpMUn+uUrMaFZRrQILK2FhQsJmzKPOyRsRDyP3CVczqOSJFnjOHJjx7m/otH8OUb33AkPpmnPnr41CeKyGn5ZPdnpHhSVHwuQlSAFhERPNY4HUFERPAtKvjdgXVkeT3syziUq3MqhMQwuN5tKjoXMzb1A2zioxDcBlNqHMa4nY4kUixUP6cK7xycw+64vZSrFsPhA4kciU+mcu0KTkcTKRKSMpPYm77P6RiSx1SAFhERERFx0Kxti1iy53tSslLJxHNa57aJacbQ+t3zKZkUVDZ9JfbwMAhqgYl8HmMCnY4kUuxUqOH71MG2ddsZdvloXtnyAhXPKu9wKpHCbVtSHEv3LwOgalgVh9NIXlIBWkRE8KoHtIiIIwatncDmpO2ndY5abQiB50JYN0zJezAmxOk0IsVaVqbvD4e3176bhftnEVE63OFEIoVPuicdj/Xw2C9PcH3Fa2ldpiVdq3V2OpbkIRWgRUQEj1UBWkTE34avm3JaxecQVzDXVGylhQWLMZsZC+6qGFdJTIT6zooUBM3bNebWIdczf/z7vDx0LkNnDXA6kkihsvXIVh6PfTp7+6ySNWkc2cjBRJIfVIAWEREREfGz4eum8GPCppOOceMm0BWgorMAvuKzPdTN1/M5crzTcUTkGL3GduPwgSMsfvVr7n2pN0HBaosjkupJZWfKLowx1Cp5FgC7Uv+kTFBpgt3BrDz4PS/9No1QdyjlgsuxN30voe5QGpVq6HByyQ8qQIuICF60CKGISH6LTYzj6Y2zOJh5+IRjogLDqRtRnZurXK72GpLNZm3DxvcEE4YJH+R0HBHJwWVdWxNZNgJPlgdUgJZi5LuD3zNvx1tUCq1EqzItaRHdjN1pexi+4dHsMaPOGcGojY9nb/eueRfTts0AfIXqqee9yMH0Q0QHRWGM3psWRSpAi4iIiIjkk9jEON7esYR1CZtJ8aafdGyV0PJMba62CvJP1rMLe+gOwGKi52DclZyOJCI5aNq2AU3bNgDg9jp388J3T6kftBQLX+79ioTMw7iMm5e3TcdlDLPj5gIQ5g7j4phWpHpS/3HOTwk/A9A0sjF31/a1rSkdHO3f4OJXKkCLiIh6QIuI5LFZ2xbxye4VpHjScjU+KiBcxWc5jrUWmzAUbDIm+lVMQE2nI4nIKWxYsYk/t+7hthr9mbXpOcpUVFFNiq5UTyplQ2KoE16bmyrfwMe7P6V5dDO2J+8gIjCCK8q1JcAVwP70/VxU+kJurdKBqKAoUj2pNI5sxIWlz8dl9F60ONB/ZRERwYMrXx4iIsXRrG2LWLjzi1wXn+uUrMZrFz2Zz6mkMDLGYEo9jYmaiQk82+k4IpIL57asx0Ov30tqUhqP3zqRzIxMpyOJ5Bmv9ZKSlQJAhtf3td2qTEuuKn8lbuPmuorX4DZuOla9hasrtCPA5Zv3GhMcQ5+z7iIqKAqAUHcoLctcqOJzMaIZ0CIiIiIieSQ2MY53dn55ynHBJpDSwZFcWKahFhiU41hvEqS+A2HdMAHVgGpORxKR03BZ51akJKbyXL9pPNPtBUa8db/TkUTO2N60vWw+soUZcbMpFRhBuieDNG8aj549nPBAtZuRk1MBWkRE8Fot9CAicqYGrZ3A5qTtJzxuMAS7grimYisVneWErE3DJvSHjFWYoPMg8BynI4nIf3Btnyv4dtEPNLz4bKy1WlhNCrWNh3/hmwPf0r16V2ITf2XFwW8BCHOHUj6knMPppDBQAVpERERE5AzdtHww6Tbnj1mHuYIZ3bA/9SNq+DmVFDbWZmIT7oWM7zGlxmFUfBYp1B57bxiJB5PY+mMc0RWiKF0hyulIIid0/09DOS+qKS2im/P53iX0O6s3md5MErMSGfvrBADuqN6N3mfdyY2VryfUFUqIOyS7zYbIyeirRERE1K9ZROQ0xCbG8faOJayNjyXTegCLPcl4FZ8lN6z1YA8Pg/SvMBGjMaHXOx1JRM5QYFAgJSPDmPnw6wAMmz3Q4UQi/5ThzaDX6n7cXas/BzMO8fneJXy+dwkAXap25JfEWKZtmwn4+jgHu4Ozn4ucDhWgRUQEr1UBWkQkN07VZuNYFUJiGFzvNhWfJXeyfoG0zzHhwzBhnZxOIyJ5JCgkiF1b9vDLt7+SnpKhftBSYKR6Uum7xvdHkSV7v2TaeVNIzEpkwR8L2Z22h8jASEoElCAqMIoSAWHcV+cehxNLYaYCtIiIiIjIScQmxjFy3UukeNNyfU6bmGYMrd89H1NJUWMCG0CZj48uOigiRYUxhscXPUCHmJ4sW/Advw7ZSt3mtZyOJcXYqkOrScw8wvIDK7L33VtnIMHuYGLcMfSv1Td7f+PIRjzbpJETMaWIUQFaRETwoEVRRET+bda2RXyw62sybNZpnafis5wOmzQF3FUxodeq+CxSREWUDueltWPp13QYUwbN4dnlj2tRQvGbQxnxxGfEs3T/MlYc+BaP9RAdFM2NldqzJj6Cfmf1JsQd4nRMKeJUgBYRERER+ZdxsXNZun91rsaWDYomMiic6OAIbq5yuVpuSK7Z5NnYpGch9GZM6LVOxxGRfFSrcQ2e+/ZJKtUqj9frBcDtdjucSoqD+X8soFxIOaz14rEeAG6ufBMty1zIxTGtHE4nxYUK0CIioh7QIiLHiE2My1XxuU7JakxqOtgPiaQosikLsEeehuB2mIjRTscRET84+4I6AHzz7vfMe+Zdxi55lLDwUIdTSVH365HNuHDT+6w7qRdRj9JB0dSPqOd0LClmVIAWEREREcFXeH564ywOZh4+4Zhwdxg3VbmMBpG1NdNZ/jOb+jE2cTgEtcZETsAYvS0TKU62rNnGr6t+Y9bDbzDwhTudjiNF1M6UXTyyYSQAZYJLA9CqzEVORpJiTL/piIiIekCLSLEVmxjH2zuW8HPCZlK96SccVyqgJCPO7aWis+QJm7UFAs/DRL2IMUFOxxERP+vyyE288dQ7LJr8KVfc3oa6zc5yOpIUMdZavtq3NHu7dUxL58KIoAK0iIigFhwiUjzlts+zAd646Kn8DyRFnrWZGBOIK/xerM1Q8VmkmAoODWbo7AGM6zGZlwe/wsSv1YZH8s5vSdt4Y8db9D+rD/Ui6tIs6jwteimOUwFaRERERIqV2MQ4HvjpeTx4cjX+pspt8zmRFAc242fs4fsh8kVMYH0Vn0WKuStvb8OqT3+k1Y3nOx1FipA/UnYy+pcnAUjKOkLz6GYOJxLxUQFaRETwaAa0iBQTg9ZOYHPS9lOOc+EiPDCMy8udT8+a7f2QTIoym/krNv4ucIWDK9rpOCJSQNw/vS9BoUFkZmQSGBTodBwpxKy1zIybzfIDKwC4IPp8qpWo5nAqkb+pAC0iIiIixUKnFQ9yxJNy0jE1SlRiQO1b1etZ8ozN+h0b3wNMMCZqDsZdzulIIlJAhJYM5eevNzLk0lG06XgRj7w5yOlIUshkebMY9+tEMr2Z9DnrLpYfWMFV5a+kY5VbnI4m8g8qQIuICF4tQigiRdygtRNOWnwOd4cxr+UzfkwkxYH17MEeugNsFqb0G5iAqk5HkkLCGHMV8BzgBmZYa5/51/GqwCtA5NExD1prP/Z7UDljNRv6ZqkufetbOg67gVpN9AdQyZ1dqX/y8PoR2dtlg8vySouZDiYSOTF95lpERPBYV748REScFJsYx+MbptN39VMnbLthMHSo3FbFZ8kfrlIQ1BgTPQsTUMvpNFJIGGPcwGTgauBsoLMx5ux/DRsOzLfWNgE6AVP8m1LySnhUSUYvegCAYVeMxuPJ3foEUrxZa1m8Z0n29oxmU7XQoBRomgEtIiIiIkXOuNi5LN2/+qRjmkTW44mG/f2USIoT600EDMYVjol81uk4Uvi0ALZaa7cBGGPmAe2BX44ZY4GIo89LAX/6NaHkqQuva0bDS85m3de/ELd+B7Uaaxa05MxrvRxIP8iIDaNoX+k67qjenUtiWuMymvwjBZsK0CIigtfqr+UiUjTEJsYxct1LpHjTTjquTslqKj5LvrDeZGx8L99G9JsYFQXk9FUC/jhmeydw/r/GjAI+N8bcDZQALs/pQsaY3kBvgKpV1QKmIBvz+QiWvLacWo1rsHnNbyxb8B23DmtPRHS409GkgNiTuocH1w/HYgH49chm7q7VX8VnKRT0VSoiIo4wxlQxxnxljPnFGLPRGHPv0f2jjDG7jDE/HX3875hzHjLGbDXG/GqMaXfM/quO7ttqjHnQidcjIs6KTYyj84qHGPLTpFMWn9vENGNS08F+SibFibXp2IQBkPkzpkRPFZ8lP3UG5lhrKwP/A141OXzBWWunWWubWWubxcTE+D2k5F5AYABX9bgUgO2/7OStsYuYev8rDqeSgsTtcmcXnxtHNuK+2ncT4NK8Uikc9JUqIiJ4nPl7ZBYw2Fq71hgTDqwxxiw+emyStXb8sYOP9j7sBJwDVASWGGPqHD08GbgC3wyhVcaY9621x35MVUSKqOHrpvBjwqZTjqtTshoXlmlAg8ja1I/QR5sl71mbiU24DzK+xZR6BhPS7tQnieRsF1DlmO3KR/cd607gKgBr7XfGmBCgDLDPLwklX11+28V89eY3LJ77NRu//ZWJX4+mdIUop2OJA6y17EzdxeK9S7ilcgctMiiFlgrQIiLiSAsOa+1uYPfR50eMMbH4PnJ6Iu2BedbadCDOGLMVX49EOHWfRBEpYmIT43jk58mk24xTjq0SWl4zniXf2SPjIf0LTPgITOhNTseRwm0VUNsYUwNf4bkT0OVfY3YAbYE5xpj6QAiw368pJd8YY3jo9XuZ8cBrfDzjCz6fs5TOD93odCzxM6/10mNVr+ztIFcQt1X797cCkcJBBWgREck3x/YdPGqatXZaDuOqA02A74GWwEBjTHdgNb5Z0vH4itMrjzltJ38XrE/VJ1FEipDcznp24eLimKYMrd/dD6mkuDMlukNAdUxYZ6ejSCFnrc0yxgwEPgPcwCxr7UZjzGhgtbX2fWAwMN0YMwjfgoR3WGutc6klr4VHlWTQtL7EVClDg9b1nI4jDlgdvyb7efuK13FT5RscTCNyZlSAFhERvPnUguNosfm4gvOxjDElgYXAfdbaRGPMS8Dj+N5MPQ5MAHrmS0ARKVRmbVvEop1LycJz0nGlAkoy4txearUh+c5aC+lLILgtxl0JVHyWPGKt/Rj4+F/7Rh7z/Bd8f7SXIu62ETcD8GSXZ+n6SAeqn1PlFGdIUfBb0jYSMg5zacwldKnWmSBXoNORRM6ICtAFRPxh6Hm/7/mBQ+ByQXSkb/utqRCUB99rut8LKanw9tFS0IZNMPYlmPvcmV9bip5PM9/i9/U7srcfvXEse7fn/Km+9xNf5fqIbmd0v6GzBtDgkrNJOZyC1+vlhYEziV25+YyuKQWfMSYQX/H5dWvtOwDW2r3HHJ8OfHh082T9EE/VJ1FECrkeK0exL+PQSccEmUCeajRQhWfxn+Sp2KRJmFJjIFQfjxeR/JGanMbSeStY8c73LNg7gxKlSjgdSfKItZbFe5cQExxDo8iGzPn9Vb7evyz7+JzmMzDG/+0SRfKaCtAFRFQpePdoL/kXZ0NYKPTs9PfxrCwIyIP/WofiYdlKuPiCM7+WFG0ZqRn0bTrUr/ecPuxVli9cyXlXNOS+qb3p03iIX+9fnHkc6AFtfL9JzQRirbUTj9lf4Wh/aIAbgQ1Hn78PvGGMmYhvEcLawA+A4dR9EkWkEOu04kGOeFJOOqZJZD2eaNjfT4lEwCbPxSZNgpDrIaS903FEpAgLLRHCdX2v5IOpn/PxjC+5ZfB1TkeSPPLB7o9YuPNdANqWvYyNhzdmH6sbXkfFZykyVIAuwB56GoKDIHYLNDkXSpb4Z2H6ujtg6tNQqQK8/zm8thAys6BhfRg5CNzu46/ZsxO8/NrxBWiPByZOgx9+hIxM6HIjdLwevF54/Fn4/kcoH+Mrgnf4H7Rrk9+vXgqakBIhjH5vGCWjShAQGMDsEW/y3fur/zEmunwkj8wbRImIMFwBLp7vP50N32zivCsa0n1URwKDA9j9217G9ZxCWnLaCe+1blksFWuVB6DDoGtp1+NSAD6Z+QXvPvcxIWHBDH/rfspUjsbldvH6Ewv5ev63+ffiJb+0BLoB640xPx3d9zDQ2RjTGF8Ljt+BPgBHex/Ox7e4YBYwwFrrAcipT6I/X4iI5I/cLDRYp2Q1LTAofmdT38EeeQKCL8eUegZj8qeVlYjIX/pOuoMPpn7OtKFzubRzS8pUjHY6kuSBr/ctB+Cysm3oWq0T3at3xWu9uPRzRYoYFaALuD374Y3JvmLyi7NzHvPb7/DJl/D6ZAgMgMcmwgeL4Yarjh/b+BxYshy+Xwslwv7ev/AjX4F7wTTIyIAuA6Flc9j4K+zaAx++Agfj4druvgK0FH1BoUFMXTsOgN1x+3j81gmMumkcKUdSiSgdzvPfPXVcAfqyLq1Y8/nPvPHUO7hcLoLDgogoHU6XRzrwwBWjSUtJp+Ow9tx8/7W89vjbJ7z3hdedR9z6HdRuWpN2d1zKPRc8DMbwwsqnWPf1L1SoWY6Duw8x/LqnAQiLCDvhtSR3vA7MgLbWfoNv9vK/fZzDvr/OeRJ4Mof9x/VJlOMZY64CnsNXqJ9hrX3mX8erAq8AkUfHPHj0362I332yewUvbnnrhMdLuEO5qsJF9KypmafiX9Z7CJs4GoJaYiKfxRi9pRKR/BcUHEivMbfx6mML+H3DHypAF3LWWvam7+NAxgHOKlGT26v/3dJSxWcpivTbUgF3VZucZzIfa+Va2LgZbu3j205Lh9JRJx7ftztMfRUG9/l734rV8Otv8PnXvu0jSbB9J6xZ78vgckFMaWjR5ExejRQm/27B4Q5w0/OpLjRoXR/rtZSpFE1UuUji9yZkj/l11W8Mntkfd6Cbb99bxW8//07DS86m2tmVefabJwAICArglxP0du41thtdH+lAwv5EJt71Ek3aNmDFez+QlpIOwDfvfk+D1vVZ9elP9Bnfnbue6crKD9ew4ZtN+fhvonjwWv2SU9QZY9zAZOAKYCewyhjz/tFFjP4yHJhvrX3JGHM2vqJ+db+HlWIrNjGO9Qlb+O7AejYnbT/huDYxzRhav7sfk4n8zbiiIeoVCKiNMUFOxxGRYuTWoe25Zcj1GGPY8/s+ylWLUYuGQshayx2r7qJKaGUeP3cU5YLLOh1JJN+pAF3AhYb8/dzt9rXE+EvG0U+jWuub7Xx/79xd84Km8NwM+PmYkoO1MPxeaNXin2O/XvnfckvR07Zra0qViaB/swfwZHl4ddtkgkL+uTrm+uWxDL5kJC2uacrQ2QN4e9IHJMUns3bxOp7qeurVLv/qAf2XJm0b5Dhu15bd9D/vAVr8rwk9Hu/Mj1+uP+mMahEBoAWw1Vq7DcAYMw9oj6+lyV8sEHH0eSngT78mlGIrNjGO2b8tYuORbaccq+KzOMVmrIasHZiwmzBBjZyOIyLFlDGG7bE7uefCh+k74XauvrOt05HkNO1O8y1380fqTqqGVTnFaJGiQVPeCpFK5eGXLb7nGzfDzqNLdF1wHny21NciAyAh0dc242T6doeZb/693ao5zFvk6yENEPcHpKRC03Ph82W+wveBQ7Dqp5yvJ0VfiVJhJOw/jCfLQ6M251C++vF/pS1btQzxew/zyYwv+GTmF9RuWpPYlVs4p2U9Kp7l6+kcEhZMpdoVcnXP9ctjuah9c4JDgwgJC6blDS1YvzyW0hWiSEtJ54vXlzN//CJqNamRp6+1OPJg8uUhBUol4I9jtnce3XesUcBtxpid+GY/332iixljehtjVhtjVu/fvz+vs0oxMnzdFIb8NOmUxecQVxDjGw9S8VkcYTM3YON7Y5OnYU/Sk1xExB+q1K1IcGgQE3tNZd8O/R5WmKR6Unlo/QgAnm083uE0Iv6jGdCFyJWXwKLP4NrboVF9qF7Zt79Wdbj3LrhriK9QHBAAI+7zFaxP5JILICry7+2br/UVrTvc5Zv+Fl0KXnzSd8+Va333LB8D9ev4ekVL8fPF68t5/P0HmfbzBDav/o0dsTuPG9OozTncMuR6PJkeUpPSGHv7ixw+kMi4HpN5+I17CQz2zZieM2Ieu7bsPuU9t/4Yx+evLOWF7329nj+Z+QW//fQ7za5sRK+x3bBeS1ZmFs/3n563L1ak+OoMzLHWTjDGXAi8aow511rr/fdAa+00YBpAs2bNrJ9zShHR6/vR/Jl+4JTjygZFM/uCUfkfSCQHNmsr9lBPMKUw0XPUdkNEHOdyubj7xbt4/NaJdK3en5ELBtO6wwVOx5KTOJJ5hBRPCqvj1wJwYekLiAo6Se9UkSLGWFsw3zN699QpmMGKoeQU34KF8YehY194/UVfP+jipF1FfcxSCo7F3gV5PrW475pu+fI9d+p5r2oadAFxtKA8ylrb7uj2QwDW2qePGbMRuMpa+8fR7W3ABdbafSe7drNmzezq1atPNkTkH2ZtW8S7O7/Ey8m/9YS4grmmYistNCiOsVk7sIe6ABYT/QYmoJrTkYolY8waa20zp3MUdvp5XfQsGP8+04a9SpW6FZm+fiLugFMsICWOOJRxiEE/+dZX6lH9dpI9yVxZ7nICXYGnOFOkcDnZz2vNgJZT6veQb1HCzEzo1734FZ9FigMtQlgsrAJqG2NqALuATkCXf43ZAbQF5hhj6gMhgD7XKXlqXOxclu4/eQGkSWQ9nmjY30+JRE4ifRnYDEz0ayo+i0iBc8uQ67nk1guJKh+JO8DNu89/zPnXNM1ufyjO8VovR7KOEOoOzS4+A4QFhNKm7MUOJhNxhgrQckpzT712nIiIFHDW2ixjzEDgM8ANzLLWbjTGjAZWW2vfBwYD040xg/B1ZLrDFtSPSkmhk5uFBuuUrMakpoP9mErk5EyJ2yDkaoxbMzBEpGAqWzUGgOTDybw8ZC6L5y7l+e+eIiBQ5R4nzY57hWUHvqFpZGMAwgPCebHpsw6nEnGOviOJiAheLRhYLFhrP8a3uOCx+0Ye8/wXoKW/c0nRF5sYx9CfJp204cbA2h25uoK+/MR51nsEm3APpuR9mKBGKj6LSKFQolQJOj1wA68/uZBHrnmKMZ+PPPVJkueSspIYsPbe7O0mUY3pfdZdhLhCHEwl4jwVoAuZ9HTodg9kZEKWB9pdAnf3hJ27YfBjkJAIZ9eBMY9A0NF2Qp98CZPnAAbqnQXjj/k5lJTsW2CwbSvfwoUiZyIwOJCJX48mMDgAd4Cb5QtXMnfUfJpcdi69xnbD5XKRmpTGuB6T+fO3PXQYdC1X39kWT5aHw/sTGX/nFPbtOPViVCIiUrhM2PTaCYvPJdyhPNagL/Ujavg1k0hOrE3FxveGzJ/BxjsdR0TktNw+uiNLXlvG2iXr+XbRKi5q39zpSMXKsv3fEOz6e6Hae2vfTdOoxg4mEik4VIAuZIKCYPYk36KAmVlw20BofT68Mh+63wLXtIVRE2DhR9D5Bvh9J0x/HV6fDKXC4eC/fo9+fiY0a+jMa5GiJzM9k6FtHyMtOQ13gJtJyx9n1Sc/cs+UXjx6w1h2bNrFdf2upOsjHRjXczJbf4xjQPMHSE/N4Nq+V9JrTDee7DzJ6ZdRLHmsZkCLSN77q+3G7rScW4lXCS3P1OYP+zmVSM6szcDGD4TMHzGlJmKC2zgdSUTktBhjGPflo3Q/ayAfvvy5CtB+9Pr2eXy+dzEjzn6YSY3HE+wKokRACadjiRQYWnWqkDHGV3wGyMryFaGNgZU/+mZDA7RvB19843u+4APofKOv+AxQOurva238FQ7EQ0v9TJI8lJacBkBAoJuAQDfWWqyFsIhQAEqUCuPg7kMA/Lx0I+mpGQDErtxMTOVoZ0ILXuvKl4eIFF9/td04Uc/nDpXbqvgsBYa1WdiEwZCxHBPxBCb0f05HEhH5TyrUKMfsX5/n8Q8eZMOKTWz89lenIxV53xz4ls/3LgagSmhlooOiVHwW+Zd8mwFtjKkHtAcqHd21C3jfWhubX/csLjweuLk37Njlm+VctSJElISAo/81y5eFvUe7GGzf6ftnlwHg8cLAO3wzpr1eGDMFxj4C361x5GVIEeVyuZiyegwVa5Xn/SmfsumHrUzs9RJPfvQw6akZpCSmcs+Fxxccrr6zLT98+qMDiUVEJD88vmH6CdtuNImsR8+a7f2aR+TkvIAHE/4wJuxmp8OIiJyRyrUrAPDm0+/ww8c/8lrcFMpVi3E4VdH12Z7PAehWrSvB7mCH04gUTPkyPc0Y8wAwDzDAD0cfBnjTGPPgSc7rbYxZbYxZPe3Vw/kRrUhwu+HdmfDVAlgfC9t2nHhslsdXhH7lOZgwEkaOg8Qj8OZ7cPH5vmK1SF7yer30bTqUzlX6ULd5LaqfU4UO913LI9c8RZeqfflszlf0nXj7P85p27U1dc6ryYJx7zuUWrzW5MtDRIqncbFzOZyVlOOxskHRPNGwv58TieTMWov1JmNMECbyRUyJO5yOJCKSZ87/33kADG7zKF6v1+E0RZO1loalGtC5akcuL3eZ03FECqz8mgF9J3COtTbz2J3GmInARuCZnE6y1k4DpgF499Q52ULpAkSEQ4sm8NNGSEzyteQICIA9+6BcGd+Y8jHQsD4EBkDlClC9iq8g/dNGWLMO3lwEKamQmQlhoTC4j7OvSYqO5MMp/Lx0I82vbkLNRtXY9MNWAJa+9S1Pf/JI9rgmbRvQ5eGbGNzmUTIzspyKKyIieWDWtkV8sOtrMuzx389dGG6sfJlmPkuBYpOehfQvIPoNjCvC6TgiInnqun5XsiN2J4smf8rrTyyk28hbnI5UaFnrK1HtTd/HA+sexoWLlmUuJNWTSqeqtxITrBnmIieTXw06vUDFHPZXOHpM/qNDCb4ZzABp6fDdaqhZDc5vDJ9R2FR4AAAgAElEQVR97du/6DO4rKXvedtW8MNPvufxCfD7H1C5IowbAV8ugC/egmH9fH2jVXyWM1WqTAQlSvmalAeFBNH08obsiN1JiVJhVDr6MbDzrvDtAzircXXum9qbke3HkLA/0bHcAl5MvjxEpHiYtW0RNyy/n4U7v8ix+AwwtvF9Kj5LgWKTpkPySxDYGEy403FERPKcMYZOD91ItbMr8/PSjXiyPE5HKlS81ktc8u+M2TSO3mv6s3jvFyRnJfuO4WX5gRWsjl9LbKL6bIucSn7NgL4P+MIYswX44+i+qkAtYGA+3bNY2H8QHnrK18/Za+GqNnDpRVCrOgx+DJ6fCfVrwc3X+Ma3agErVsG13cHlgiH9IKqUk69AirLoCpEMmzMQl9uFcRmWLfiO7z9ay6TeL/Po20Pwer0kxScz/s4pAPQe243QkiGMmD8YgH07DjDyhjFOvgQRETlNs7YtYuHOL046pk7JatSPqOGnRCKnZlPexCaNg5BrMBGPYYz+aCoiRVOZitFMXvUMAUEBuFwu9m7fr37QufTybzNYeej77O1UTyo1S9Rg2nlTSMg8DFhSslKpUbK6YxlFCgvz18cI8vzCxriAFvxzEcJV1tpc/clNLTikIGlXsZHTEUSyLfYuyPN3yZ1X9s6X77lvXjBN7+iLgWbNmtnVq1c7HUMccs2ye056vGxQNLMvGOWfMCK5YNM+wybcA8GXYiJfwJhApyPJKRhj1lhrmzmdo7DTz2uZ98y7zB7+Jg+9fi8X33IhLld+fSi+cFu85wt+T/mdi2Na81TsGFqXacktVTpQKlCz+URO5mQ/r/NrBjTWWi+wMr+uLyIiecdr9cuniJy+m5YPPuGxEu5QrqpwkdpuSMET2BhCO2IiHlHxWUSKlXNb1cPrtTzZ+Vme6zed+XumExik74PHSspM4rUdbwBwW7UuvNJipsOJRIoGVRxERERE5LQNXzeF9H+uNw1AsAlkfONBzG85RsVnKVBs5q9Ym4Vxl8NVajTGBDsdSUTEr85tVZ/nVjzBOS3rkpSQzJzh85yOVKB4rZcBP94LwJA6gwh1hzqcSKToyLcZ0HLm2naEEqHgdvseb0+DQaN8CwkCJCZBREl4N4c/yM19GxZ8CNbCLdfC7UcXu31xtm9/dKRv+75ecMkFsHY9PDYRAgNh/EioXtm32OGgUTB9nK9/tAhA5ToVGT5vUPZ2+ZpleeXRt3j3uY+z913WpRUdh92AMYaUI6k8338629ZtJzA4kIlfjyYwOAB3gJvlC1cyd9R8AB589R5qNKjK9x+tYdYjbwLQ5ZGb+H3DH3y7aJV/X2Qx5LXqlCEipxabGMfbO5ZwKDORzUe25zjmyUYD1e9ZChybsRYb3wPCbsOED3U6joiIY86+sC6Tlj3OA1c+TtMrGjodp0D59chmAELdoTSIPNfhNCJFiwrQBdwrz0JU5N/bk0b9/XzMZChZ4vhzNm/zFZnnT4XAAOg1DNpcCNUq+47ffgv07PTPc2a/BS+PgV174K1F8MAAmPoq9LlNxWf5p52b/6RvU98bN5fLxZs7X2bFuz/8Y8yeuH0MbvMoSQnJNL+qMfe93Id7LnyYzPRMhrZ9jLTkNNwBbiYtf5xVn/xIWko6GWkZ9Gk8hGc+G0FYRBghYUHUa1GbN558x4mXKSIi/xKbGMeQnyaddEyTyHoqPkuBYzN/wcb3AldZTNgdTscREXGcMYaxi0cC8Ne6YFqMFepH1ON/5a/i+krXOh1FpMhRabGQshY+/Qquufz4Y9u2Q8P6EBoCAQHQvBEsXnby6wUEQGo6pKX7nu/YBbv3QYsm+ZNfioYmbc9l92972LfjwD/2//LdZpISkgGIXbmFmMqls4+lJacBEBDoJiDQjbUWT6aHoJAgjDEEBLrxerzcPrpj9uxoyX9eTL48RKToeGbjrJMeDzZBPNGwv5/SiOSOzfoNG98TTElM9ByMO8bpSCIiBcaBPw8xuM2j/PDxWqejOGr1oTXc/sOdbD2ylY5Vb1HrDZF8oAJ0AWaAO4dAh14w//1/Hlu9DkpH+1pl/FvtGrBmHcQfhtQ0WLYS9uz7+/jr70L7HvDIM3D4iG9f767w4FMw7TXoeiM8OwPuvSvfXpoUEW06teSreStOOuaqOy9j1ac/Zm+7XC6mrh3Hgr0zWbtkHZt+2MqOTbs4fCCRl9aMZeWHq6lUqzzG5WLrj3H5/RLkKK81+fIQkaJh0NoJHMg8fNIxTzYa4Kc0IrljrQcbPwAwR4vPlZyOJCJSoIRHleDwgUSGX/cMsd9vcTqOI7zWy/Rtvr6mJQPDHU4jUnSpBUcB9vqLUC4GDsbDnYOhRjXfbGaAj5bANW1zPu+s6nBXF7hriG8WdL1a4HL7jnVqD/26gzHw/EwYOxmefBDq14a3XvKNWfUzxJT2zbIeNMrXxmNYfygTnd+vWAqTgMAALryuGTMfeuOEYxq1OYere17Gfa1HZO/zer30bTqUEqXCGPXOUKqfU4XfN/7BS4PmZI8ZvegBnus7jS4P30TNhtVYs2Qdn8z4Ij9fjoiInMDwdVPYnHR8v2c3boLdgZQLKc2A2req9YYUOMa4odTTYEIwAfr6FBH5t+DQYO54vDOjbx7PPRc+TNfhHbhjdKdTn1jIJWQcJtgdRIgrhB6regFwW9UulA8p53AykaJLM6ALsHJHPyFYOgoubw3rY33bWVmwZDlcfemJz735Glg4HV57AUqF/z1Tuky0b0FDl8u3OOG6Tf88z1qYOtdXpJ4yB4b0hZuvhdcW5vnLk0Ku+dWN2bo2joR9Oc+Iq9GgKvdP78vIG8Zy5FDScceTD6fw89KNNLuq8T/2X3h9M7as3UZIyRAqnFWOJzpN4uIOFxAcGpQvr0N8NANaRHIya9sifkzYlOOxMY3vYUHLsbx43gMqPkuBYr0J2NQPADBBTTCB9R1OJCJScLW+6XzGfzmKOs3O4r0XPmHlh2ucjpSvVhz4jnt/up8H1j3MlqStAIS4Qrii/Alm+IlIntAM6AIqJdVXDC4R5nu+YhX0v9137Ls1UKMqlC974vMPxvsK13/uhcXLYd4U3/59B6Hs0Xa8i5f72nUca9FncPEFEBnha9/hcoHL+PpDixzr0k6t+GreNzkei6lShkcXDmVM9xfYtWV39v5SZSLIyswi+XAKQSFBNL28IW+NfS/7uDvAzU33XsPwa5+mUu0KHF0PA5fbRUBQAOmpGfn6mkRExFd0XrLne1KyUsnEk+OYNjHNVHSWAsl6k7Dxd0HmJgg6D+Ou6HQkEZECr1Gbc3j2m8f5fcMf1G5a0+k4+SY+I55p22YAcGOl9pQLKcsF0efT5yz1HxXJbypAF1AH4+Hu4b7nWR649nJofb5v++Mvj2+/se8ADB8L08b6tu8dAQmJvgUFR9wHEUdbGY1/CTZt9bXgqFQeRg35+xqpafDuJzBjgm/7jluhzzAIDITxIxDJFhIWzHlXNOTZvtOy913b5woAPnx5Md1G3kxE6ZLcM9n3cSZPlocBLR4kukIkw+YMxOV2YVyGZQu+4/uP/l7w4voB7Vg892vSUzPYtm47IaFBTPt5Aj98spbkwyn+fZHFjGYriwj4ej3n1G7jWMEmiKH1u/spkUjuWZuGTegHmRsxkS+q+CwichoCgwKp2bAa70/5jJQjqXR64AanI+WJLG8Wq+LX0DzqPIb+/BAAHavcwqVl2wDQr1ZvB9OJFB/G/jXFsIDx7qlTMINJsdSuYiOnI4hkW+xdkOfV4muW3ZMv33M/uvh5VbaLgWbNmtnVq1c7HUPOUI+Vo9iXceiU48Y3HqTZz1LgWJuBTRgI6V9jSo3HhF7ndCTJQ8aYNdbaZk7nKOz081pOxVpLh5ieHDmUxIwNE6l2dhWnI/1ne9L2Mm7TBA5kHATghSbPMmLDo7SIbk7Xap0dTidSNJ3s57V6QIuIiIgUc8PXTTll8blMUKSKz1Jwpa+A9KWYiMdUfBYR+Y+MMQye0Q+Au869H48n51ZchcED6x7OLj43jmxEeEBJHjtnpIrPIg5RCw4REcGLJiqLFGcnWmgw0AQQFhDC5eXOp2fN9n5OJZJ7JuRSKP0BJrCu01FERAq1lje04Koel/Lp7K/47v3VtLrxfKcj/SclA0qSlJXEKy1mZu+LDIp0MJFI8aYCtIiIiEgx1mPlqBz3RwWE89pFT/o3jMhpsNZik57FBLfCBDVX8VlEJI90G3Urn87+im0/by90BejkrGQ+2fMZTzd4giBXoNNxROQoFaBFRESLEIoUUzctH0y6zTxuvwuXis9S4NmkFyD5JSyZmKDmTscRESkyylYpw9v7ZlKqTARH4pMoGVkCYwr++wVrLf3X3gNAmDuM/1W4yuFEIvIX9YAWERERKYauX3ZfjsVngLGN7/VzGpHTY5NnQfKLENoBU3KI03FERIqcUmUisNYye/g8xvWc7HScXJkZNzv7+dXl2zmYRET+TTOgRUREM6BFipk7Vj6KB2+Ox+qUrKaFBqVAsynzsUeegZCrMRFPYIzm1IiI5AdjDCs/XM3+Pw7S7IpGXNaltdORTig5K5lvDnxLREAE4xs9UyhmbIsUJypAi4iICtAixUiPlaPYnxGf47E6JasxqelgPycSyT1rLTbjBwi6GFNqHMa4nY4kIlKkPfvNEwxu8yhP3/Y8GMNlnVs5Hek41loAulbtzNml6hPsDnY4kYj8m6YLiIiIiBQTnVY8yL6MQzkeG1i7o4rPUqBZ68UYgyk1BhM1GWOCnI4kIlLkla1Shsk/PAPAa6MXZBd7C5JlB75h3h8LaFvuUiqFVnQ6jojkQDOgRUREM6BFioHbvn2EI56UHI8NrN2Rqyu09HMikdyz6SuxR56CqGkYd3lAM59FRPwlonQ4A57vyf4/DpKZkUVQcKDTkbJtOLyRWXFzKBNUGoPe04gUVCpAi4iIiBRxg9ZOID7rSI7HVHyWgs5m/IxN6AfuCmD0sWoRESfcMPBqwNfu4o9fd1GlbiXHsmR5swhwBfDyb9P59uBKAHrWuEN9n0UKMLXgEBERrDX58hAR5w1fN4XNSdtzPNYmppmKz1Kg2cxN2Pi7wBWNiZqNcUU5HUlEpFh7a8x79Kx/H7u27vb7va213P7Dndy5ug+rD60lNvFXAIbUGcQ5pc72ex4RyT3NgBYREbz6uJpIkTQudi4/JmzK8VidktUYWr+7nxOJ5J7N2o6N7wkmGBM1B+Mu53QkEZFir975tQG4o849LPYu8Ou971h1V/bzuOQ4xjV6mgAToJnPIoWAZkCLiIiIFEGf7F7B0v2rczxWJbS8FhyUgs+UhIC6mOg5mIAqTqcRERGg8aXn0rZrawCe7fOy3xYl9Fpv9vPpzV7iliodCHQFqvgsUkhoBrSIiGgRQpEiJjYxjhe3vJXjsSqh5Zna/GE/JxLJPeuNB1MS4y6NiZ7tdBwREfmXwTP78fX8b/lo+hKuvqstdZvXytf7zdsxnwxvBr1r3knjyEYEuYLy9X4ikvdUgBYREREpYob+9GyO+4NNoIrPUqBZ72HsodshoCYmMuevYxERcVZgUCBv/Tmd3XH7qHNezXy7T1JmEgN+vDd7u1u1rprxLFJIqQWHiIhoEUKRIiI2MY7rlw3CcvzHYV0Y3mk9wYFUIrljvcnY+N6Q9Rsm9Gan44iIyElElA6nbrOz+H3DDj6b81WeXz/Nk8bc7a9lb48+Z6SKzyKFmGZAi4iIWnCIFAGxiXEM+WnSCY9/cPFzfkwjcnqsTccm9IfMdZjI5zDBrZyOJCIiufBMtxf4Y9MuLrn1IkLCgs/4emmeNPqsGYDBcF5UE9qVu4LOVTuq+CxSyGkGtIiIiEgRMHzd5BMeG1i7ox+TiJw+e/gRyPgOU+ppTMiVTscREZFcuuuZrmRmZDGwxYNkZWad0bW81svIDY8BYLH0qH47Xap1UvFZpAhQAVpERNSCQ6SQu2HZINK8GTke61C5LVdXaOnnRCKnx5S4AxPxBCb0BqejiIjIaWjWrjHGGLb/spNrwrqyPXbnf77WlK1T2Zu+jxBXMK+0mEnJwJJ5mFREnKQCtIiIiEghFZsYx7XL7iUTz3HH3LgZ33gQPWu2dyCZyKlZa7HpKwAwgediwm51OJGIiJwuYwwfp71Bg4vr02/iHUSXj/zP12oc1ZjSQdFMajw+DxOKSEGgHtAiIqIe0CKF0LjYuSzdv/qEx8c0vof6ETX8mEgk96y12KRxkDwDomZjgjVLX0SksAoIDGDi0tEApKem07FiLxpcXJ97pvQiIjr8hOdlebN46bdprI5fQ6AJZOp5L9KqzEX+ii0ifqQCtIiIiEghM2vbopMWnwfW7qjisxRsyVN9xeewLhCkYoOISFGRlJDCoT0JfD3/O9KS03ns3WG4A9zHjTuceZh7frw/ezvYHUyASyUqkaJKLThERARr8+chIvnjoz+/OeGxgbU7quezFGg2eS42aRKEtMeEj9TiUiIiRUjpClEs9i7gur5X8v1Ha7kqqBO/rv4t+7i1luSsFDz27/ZhQ+oOYnLT55yIKyJ+oj8viYgIXvTmX6QwSfOmH7evhDuUxxr01cxnKdBsVhz2yFMQfAWm1NMYo/kwIiJF0T1TeuEOdPPLd5sJDg0CIDkrhf5r7wZgbMOneaXFTCcjiogfqQAtIiIiUkgMXzeFHxM2HbffhWF+yzEOJBI5PSagBkRNh6DzMUZvRUREirIBz/XEWktWZhYvvzmbFTWXY4whzB1G2eAYp+OJiB/ptz4REcFqEUKRAu9ExWeA8IASfk4jcnps+jLAjQluiQlu7XQcERHxk/jMBD6b9SVv9/+Y0HaBXD6pJffUG+B0LBHxM33mTURERKSAm7Vt0QmLzwCXlz/fj2lETo/NWIWNH4hNeg6rBQJERIqNr/cvZ9BPQzj35ro0vK4+qZ9lEvFGaadjiYgDVIAWERG81uTLQ0TOXI+Vo1i484sTHm8T04yeNdv7MZFI7tnM9dj43uCuiImaqgUHRUSKiYSMw8yKmwNAQFAA4997jODQIF5/YiGpSanOhhMRv1MBWkREsDZ/HiJyZnqsfJR9GYdyPBbmCmZ840EMrd/dz6lEcsdmbsEeuhNMJCZ6DsYV7XQkERHxg8TMI9z70/0AdK3amfoR9TDG0H3UrQB8Ne9bJ+OJiAPUA1pERESkABoXO5d9GfEnPD66YX/qR9TwYyKR02NT3wET6Cs+u8s7HUdERPLZxsO/4DIu6obXAaBOeG2uKNc2+/jNg6/jguuaUaVuRQ7sOkhk2VIEBKosJVIc6P90ERHRIoQiBUxsYhxL96/O8ViIK4gnGg5Q8VkKPBM+FErcruKziEgxcDD9IGN/ncAd1btTP6Iec5rPOK7tksvlomq9SvyycjNzRsyjXLUYBs/o51BiEfEnteAQERERKWCG/fRsjvvbxDRjYavxKj5LgWW9h/DG98Zm/YExLhWfRUSKiff+/ACAUoGlAE7a879a/Uq43C4+nfUlH8848ToXIlJ0qAAtIiJYa/LlISKnr8fKUXg5vol6h8pt1e9ZCjTrPYI91BPSvwPvHqfjiIiIH3itlzlxc1m2fzkATSIbnfKcEqVKMOqdodQ7vzZT75/DhhWb8jumiDhMBWgREcFrTb48ROT0xCbG5bjoYCBuetZs70AikdyxNhUb3xuytmCiXsQENXc6koiI5CNrLTO2zWJj4i+sO7yeIFcQExuNO+nM52OFhAXzyJv34fV4GXHdM3i93nxOLCJOUg9oERERkQJi+LrJOe7vU/tmPycRyT1rM7DxAyDzR0zkJEzwJU5HEhGRfPbOrvdYfmAF4QHhTGw87j9do3z1stw9+S6iy0ficml+pEhRpv/DRUQEa/PnISK5d8s3Q0nzZhy3PyognKsrtHQgkUgu2TSwRzART2JCrnY6jYiI5LOtR7by/p8fAtCu/BVndK12d1xK86uacPhAItMfeI3UpNS8iCgiBYwK0CIiIiIOG7R2Aine9OP2G+C1i570fyCRXLDWi7UZGFcEJvpNTFgHpyOJiIgfzN3+OgAj6j9EZFBknlxz/thFzB+3iOsjurPn9315ck0RKThUgBYRES1CKOKwzUnbc9w/oHZHPycRyR1rLfbIE9j4Pr4itFFnPxGR4qJ2yVqcG3EOtcJr5dk1e43txoDnegLQreYAfvv59zy7tog4T78pioiIisUiDpq1bVGO+ztUbqvWG1Jg2aRJkPIahN0JBDodR0RE8lmWN4s7V/ehXnhdhta9nwBX3peTbrj7aiJKl+Tp255n3pj3eOSN+/L8HiLiDBWgRURERBz0zs4vjtsX4S5Bz5rtHUgjcmo2aRokT4XQjpjwYRijP2KKiBRlqw6t5sWtLwGQ4c3Il+LzXy7r0pqE/YlUql0h3+4hIv6nArSIiKD1AkWcMWjthBz//xvZoLffs4jkhk2Zj00aDyHXYSJGqfgsIlLEeayH17a/AUDl0EqMPPuRfL/nTfdeA0BGeiZJ8UlEl4/K93uKSP5SAVpERETEAZ1WPMgRT8px+w1QP6KG/wOJ5EZgIwi9GRPxGMa4nU4jIiL5xFrLkn1fcmHp87m6fDuig6NpEd3cb/f3eDzcfcFD/LllD8Pn38/5/2vqt3uLSN7TIoQiIqJFCEX87IZlg3IsPgPcVLmtn9OInJrN2oa1FhNYF1eppzBGfZ9FRIoqj/Vw5+o+vLb9Db7Y+xVXVWjn1+IzgNvtpteYbqSlpDO+5xTSUtL9en8RyVsqQIuIyP/Zu+8wKcvrjePfM7OFhV1gQTqCoKCgAiJWjL2X2DC22LDEXtFg773FhrEAxsQaYyHWnxo1EaNiARFXRUFEQPqyC9tnzu+PHcziLn13nyn357rea+Y9MwM3hiwzZ573PCLSjE6fcBPVxBp8rG9+T81+lqTjlePxBQdB2ROho4iISBNbWrOU4RNOI+a171X27BTui/Ehew/k0ifOo3jeEi7a9epgOURk/akBLSIitUOgm+JYBTPb0MzeMbOvzGyKmZ2XqLczszfNbGritjBRNzO718y+M7MvzGxwnV/rhMTzp5rZCY30X0Wk0RWVTGdm+c8NPrZrhyHcPfiiZk4ksmpe9RlefCZk9Ya8A0PHERGRJraoavEv98du8witsloGTAO7HTWU3gN6MmfaXMqXVQTNIiLrTjOgRUQk1LiMGuAid//MzAqAT83sTeBE4G13v8XMRgIjgT8C+wF9Esd2wIPAdmbWDrgaGEJt2/tTMxvn7ovr/Y4igd359d8arJ/d50j26zK0mdOIrJpXf4UvPhUinbDCsVikbehIIiLSRKrj1Vw95TqGFG7NI0P+TE4kOUYtmRk3vXY5E16fSF6rFqHjiMg60gpoEREJwt3nuPtnifulQBHQDTgY+EviaX8BDkncPxh43Gt9CLQ1sy7APsCb7r4o0XR+E9i3Gf8oImtkzLSXmFMxv1591w5D1HyWpOPxstrms+Vj7R7DohuEjiSSUcxsXzP7JnHl18iVPOd3da4ke7K5M0r6WFS1mDM+PZtZ5bN5f8H4pGk+L9e+SyH7nrQbJYtKGTfqjdBxRGQdaAW0iIjgqxmXsa7M7DTgtDqlh9394QaetxGwFfAR0Mnd5yQe+hnolLjfDZhZ52U/JWorq4skjaKS6Tz/07/q1XMsi4v7HR8gkciqWaQltL4asvpg0a6h44hkFDOLAg8Ae1H7vmZC4uqur+o8pw9wKTDU3RebWccwaSXVvThrHC/MegmAni17cs3mVwROtHLP3/0KT9z4D3748kfOHXVq6Dgisha0AlpERJqMuz/s7kPqHA01n/OBfwDnu3vJr16/BtOkRZJbUcl0Rky8G2/gr/JB3XYJkEhk5Tw2D698HwBrsTeW1StwIpGMtC3wnbtPc/cq4GlqrwSr61TggeUjx9x9XjNnlBQ3tfQ7Plr4MTXxGgBGbHoB121xFRFL3jbRkSMPoUP39vzrqfcpX1oeOo6IrIXk/ckiIiLNxt2a5FgdM8umtvn8hLs/nyjPTYzWIHG7/APVLGDDOi/vnqitrC6SFG75amyD9SgRhvf+dT9BJByPL8YXn4QXX4DHS0PHEclka3J1V1+gr5mNN7MPzazB8WNmdpqZfWJmn8yfX38MlGSmpTVLuaHoZkZ9/xB7dtqdB7a6hy3bbBE61mrltWrBFc9cwLIlZTxwbsPvr0QkOakBLSIi4NY0xyqYmQGjgSJ3v6vOQ+OAExL3TwBeqlM/3mptDyxJjOp4A9jbzArNrBDYO1FLe2a2VtuSa55kGAuqihusH9J9t2ZOIrJyHl+KLz4FamZgbe/FIgWhI4nIqmVRuzHzrsDRwCNmVm+n0LpXo3Xo0KGZI0qyemz64wDs0mFn2ua0JT87P3CiNdd/h03Z7eihvPHYO0x4Y2LoOCKyhjQDWkREQhkKHAdMNrPl7x4vA24BnjWzk4EZwO8Sj70K7A98B5QBJwG4+yIzux6YkHjede6+qHn+CGGY2Y7Ao0A+0MPMBgJ/cPczV/EazZMM4KQPr2mwfnj3PbT6WZKGewW++HSoLsLa3o/l7hA6kkimW5Oru34CPnL3amC6mX1LbUN6AiKr8NniiUxY/Cl98jdheK8TVv+CJHTRo2dw6q3H0aF7+9BRRGQNqQEtIiJNtgnhqn9Pfx9Y2TLpPRp4vgNnreTXGgOMabx0Se9uYB9qV4Xj7pPMbOfVvOaXeZIAZrZ8nuRXdZ6jeZKNaMy0l5hXVf+7kPbZbdR8luRS/gJUT8Da3Im12D10GhGpbSL3MbNe1DaejwKO+dVzXqR25fNYM9uA2pEc05o1paScylglT//4LF3zunLxpheGjrPOcvNy6dA9F3dn/swFdOyh1f0iyU4jOERERFKQu8/8VSm2mpc02jxJ0EzJNTFu1nsN1i/dfHgzJxFZjbyjsHbPYnkHhk4iIoC71wBnUztSrAh41u9yD/EAACAASURBVN2nmNl1ZvbbxNPeABaa2VfAO8DF7r4wTGJJdnPKf+ax6Y8TtSi3DbyJG7a4htxobuhY6+32kx7g2I3OZMzlTxKrWd1bYREJSSugRUQEAqyAlvUyMzGGwxMbOZ5H7QfU9VV3nmR34N9mtqW71xti7O4PAw8DDBkyRH+DGlDtNfVqu3YYQr/WvQKkEVmRexxfeheWdxiW1RtyBoaOJCJ1uPur1I4fq1u7qs59By5MHCINcnfenvcOf53xBACbtt6UHdpvR9SigZM1juE3Hs2U8V/z1M0vUPTht9z21tXUbjMjIslGK6BFRAR3a5JDmszp1I4j6UbtpbmDgJXOf05Y03mS49y92t2nA8vnScpaGjPtpQbrF/c7vpmTiNTn7njpLbDsYah4K3QckbRjZqVmVtLAUWpmJaHzSeZ4dPrYX5rP2xRuzTaFWwdO1Lg26NaeR768G4CJ70zh5YfeDJxIRFZGK6BFRERSz6bufmzdgpkNBcav4jWaJ9mMXpz1br1aFumx2khSny+9D8oeg5bHQ6tTQ8cRSTvuXhA6gwhAy2hLAO4edAftcgoDp2kaObnZvFj8F0bsdg2Ddts8dBwRWQk1oEVERCM4Us99wOA1qP3C3WvMbPk8ySgwZvk8SeATdx+XeGzvxDzJGJonuU5emzOemNefQ3hw912bP4zIr/iy0bDsfsg7HCu4TJcqizQBM2u3qsfdvf4OtSKN6MkZT1OQXcCxPY/i2J5HhY7T5Fq1bsmDn94GQOnipeS0yCY3L/VnXIukEzWgRUREUoSZ7QDsCHQws7ozH1vD6pfXap5k03ttznjun/pMvXqu5TC898EBEon8j3sNXvE25O6Ltb4BM03jE2kin1L79X5D3/A40Lt540gmWFazjCd/fIb3F/zvgrgDuuxHJIN+1s+dMZ/f9zqT3gN78uCntxGJZM6fXSTZqQEtIiKa15w6coB8av/9rnt5bwkwLEgi+cUFn93Jt0tnNPjYjQPPauY0Iityd8yyoN1oIIqlyQZUIsnI3bXbrDS7zxdPXKH5fOuWN2ZU8xmgY48NGLBLf7547yueuOEfHHfVEaEjiUiCGtAiIiIpwt3fA94zs8fcveFOpwRx0ofXMK+q4Suqd+0whH6t1YuQcLziHbxsLLR9AItoNK1IczKzQmo39G2xvObu/w6XSNLN+/PHU+3V7NJhZ8pjFWzaui89Wm64+hemITPj9revZt/so3j8mmfZaIse/Oaw7ULHEhEgs74OExGRhnkTHdJUyszsdjN71cz+tfwIHSpTjZn20kqbz4VZBVzc7/hmTiTyP175IV58DsSX0vA0ABFpKmZ2CvBvavdYuDZxe03ITJJeJhZP4pHpY5i8ZAoRi7BX5z0ytvm8XCQS4Z4PbgTgH3f/k1is/r4cItL8tAJaRERQUyLlPAE8AxwInA6cAMwPmihDFZVM54WfGu79d8xpx9jtr2neQCJ1eNUkvPgMyOqJtRuNRfJDRxLJNOcB2wAfuvtuZrYZcFPgTJIGymrKOOOzc34537XDzgHTJJ9+2/XhsW/vJS+/BdFolGlfzKBn/+5EszR+SiQUrYAWERFJPe3dfTRQ7e7vuftwYPfQoTJNUcl0Rky8m3gDy/03zOus5rME5dVf44tPgUh7rHAsFikMHUkkE1W4ewWAmeW6+9fApoEzSQpzdypjlUxe8uUvtav6X86AtlsGTJWcum3ShXadC/nmk+/5w6AR/GHQCGI1Wg0tEopWQIuIiMZlpJ7qxO0cMzsAmA20C5gnI13/5SMN1ltHW/HnbS5r5jQiv2I5kNULa3MXFu0YOo1IpvrJzNoCLwJvmtliQHs4yDr5dPFn3Dv1Abrndeeq/pcxeshDRC2Kma5kXJU+g3ux65E78u4zHzBu1Bsceu7+oSOJZCQ1oEVERFLPDWbWBrgIuA9oDZwfNlJmGTPtJZbULG3wsau2PK2Z04j8j8eXgLXGsnpDu2fUmBAJyN0PTdy9xszeAdoArweMJCnqo4UfM+r7hwAYXDiInEiOfr6voUgkwsVjz+LHolk8cslfKSst59jLDw8dSyTjaASHiIhoE8IU4+4vu/sSd//S3Xdz962BhnfBkybx1tyP6tUMuGPQBfRr3av5A4kAHluALzwCX3oHgJoTIoGZ2fZmVgDg7u8B7wJbBQ0lKSfmsV+az+f3OYfDux+qn+9rKadFDlc+eyHVVTV8/vZk4vF46EgiGUcroEVEBFxvYlOBmUWB3wHdgNfd/UszOxC4DMhDH2qbTUl1/dXPh3XfQ81nCcbjS/DFJ0F8LparkfAiSeJBYHCd86UN1ERW6seymXTL68opvU7CLMJWhYNCR0pZ3ft25dWKJ5k/cyGLfi5m2qQZbLuf3jqLNBc1oEVERFLHaGBD4GPgXjObDQwBRrr7i0GTZZCTPrym3gL/lpFchvc+OEgeEY8vwxefCjXTsMKHsZytQ0cSkVrm7r/8k+HucTPTZ3BZrZjHuHbKjcwom8Fvux7I4d0PXf2LZLWyc7LpunFnnrjxHzx25dPc8sYVbL3XwNCxRDKC/vETERFc4zJSxRBgQOIDbAvgZ2Bjd18YOFfGKCqZzryq+tNOBrTtGyCNCLg7XnwuVE/G2t6D5Q4NHUlE/meamZ1L7apngDOBaQHzSAqoildx+qdnE/MYAAd20aZ5jW3DTbsC8OjIJxi85wCNNBFpBpoBLSIikjqq3D0O4O4VwDQ1n5vXLV+NbbA+rMeezZxEpJaZYS2PxdrcgrXYO3QcEVnR6cCOwCzgJ2A7QDvVyiqNnf44MY/Rr2AzRg95iNxobuhIaWfnYTtw4vVH8d3n03nnqfdDxxHJCFoBLSIi2jAwdWxmZl8k7huwceLcAHf3AeGiZYZFVUvq1frm99TsZ2l27rHaVc85g7AWmvkskozcfR5wVOgcklq2b78tcyvmcvFmFxK1aOg4aeuw8/Zn/AsfcfPv76VFfgt2/O02oSOJpDU1oEVERJsQpo5+oQNksjHTXiL+q29rsoly9+CLAiWSTOXueMk1UP53aP8Slr1p6Egi0gAz60vt+I1O7r6FmQ0AfuvuNwSOJkmmJl7DHz49iyM3PIK9O+/Jlm22IGK6YL0p5eXncd24kcz6dg4DdunPv556n79c/QwPT7qD3DytOhdpbPqJJiIikiLcfcaqjtD50tntRY/zj5/erlcvzG0TII1kMnfHS2+D8meg1SlqPoskt0eAS4FqAHf/Aq2Ill9ZVLWIkz/5AzVew4uzXgJQ87mZbNC1HQN33Rwz4+uPpjL7u5+5dtiduDbIEWl0+qkmIiKYN80hkg5emzOed+d/0uBjlbGqZk4jGW/Zg1A2Gloeg+Vr9b1Ikmvp7h//qlYTJIkkrQsmXgxAp9xO3D/4nsBpMteZfzqJzbbrw4TXPmfUeQ3v+SEi604NaBEREZGVeG3OeO6f+sxKH9+z83bNmEYynVdNwJf+CVocjBVchZnGJ4kkuQVmtjGJ3TbMbBgwJ2wkSSblsXLaZrdhk/xNuG3gTVr5HNhtb14JwIv3v8bz97wSOI1IetEMaBER0SaEKcjM8oAe7v5N6Czpqqhk+kqbzxGMQ7vvzvDeBzdzKslo2UOwNndDi30wNSlEUsFZwMPUbiI8C5gOHBs2kiSLmWU/MbdiLncOvC10FEnIy8/juXmj+fztyezyux2B2tFX+sJXZP3pnauIiEiKMbODgInA64nzQWY2Lmyq9DN2WsP/SQuiLfnnzveo+SzNxivewqu/xcywvAMw0xoSkVTg7tPcfU+gA7AZsAuwU9hUkgwWVC7gii+v5r7vRhG1KFkR/VxPFm02aM2uRw7FzHjx/te46ViNRRFpDGpAi4gIuDXNIU3lGmBboBjA3ScCvUIGSjdFJdOZUvJ9vXrHnHY8PfSWAIkkU3nlv/Hi8/Cld4WOIiJryMxam9mlZna/me0FlAEnAN8BvwubTkJzd0ZMGgnA/l321eraJPbUzS/w/j8+pHxZRegoIilPDWgREakdwdEUhzSVandf8qua/os3oudmvtVgfez21zRvEMloXjUBX3w2ZPXB2tweOo6IrLm/ApsCk4FTgXeAI4BD3V2Xz2S4f817F8cZ1HYgR254ROg4sgpn3XMSNdUxTh80Ane91RZZH2pAi4iIpJ4pZnYMEDWzPmZ2H/BB6FDpZEpx/dXPG+Z1DpBEMpVXT8YXnwbRrli7MVikIHQkEVlzvd39RHd/CDga6A/sk7hiSTKYu/OfBePZss0WnNfn7NBxZDV2HrYD/XfclNnfz+XtJ/4TOo5ISlMDWkREtAI69ZwDbA5UAk8CS4DzgyZKM0tjZfVqf97msgBJJFP5stEQKcTaPYZF2oWOIyJrp3r5HXePAT+5u67hz3ATF09i3OyXGdH3fE7rfQoRbSabEu5691p69u/O1x9NBdBKaJF1pJ94IiIiqWczd7/c3bdJHFfog23jGTPtpXrfn+RadpAskrmsza1YuyewqFbei6SggWZWkjhKgQHL75tZSehw0vymLZ3O3VPvJS/agvzsfFpn66qWVBHNinLVcyM4697hzJ0xn5M3v4BpX8wIHUsk5WirVRER0Wrl1HOnmXUGngOecfcvQwdKJ2/N/aherUU0N0ASyTQe+xkvvRlrfS0WaQvRLqEjicg6cPdo6AySHNydCyddwqKqRQD0b90/cCJZFz026wbA0uJlLJy1iDO2voTnF4yhVZtWgZOJpI7VroC2Wr83s6sS5z3MbNumjyYiIs3GrWkOaRLuvhuwGzAfeMjMJpvZFYFjpY3S6vrjN/bsvF2AJJJJPL4IX3QiVP4bYrNDxxERkUYwackXvzSfj+lxFN1bdgucSNbHxgM34oTrjiQei3P/uWNCxxFJKWsygmMUsAO1mycAlAIPNFkiERERWS13/9nd7wVOByYCVwWOlBaKSqYTJ16vPrz3wQHSSKbweAm+aDjEZmGFD2PZWiEnIpLK5lfOZ9R3D7FF6805uOtBPDLkQfbpvFfoWNIIDj13f7b8TT/eeWo882YuCB1HJGWsSQN6O3c/C6gAcPfFQE6TphIRkWZl3jSHNA0z62dm15jZZOA+4AOge+BYaeG5mW/Vq2WhK6ml6Xi8DF98GtRMxQofwHK2CR1JRETWw+zy2YyYNJKPFn3M7Io5HNb9EHIiaqGkCzPjj4+fQ8ceGzDvRzWgRdbUmsyArjazKIkJoWbWARpYGiQiIiLNZQzwDLCPu+ta/Ub0RfHUerWDu+/a/EEkc3gxxBdibe/EcncOnUZERNaDu/PwtNrRDCdtdDw9Wm4YOJE0hU49OzD2m3uIRqMUz1/ChNcmstfxu4SOJZLU1qQBfS/wAtDRzG4EhgGaMykikk60WjmluPsOoTOko9uLHqcsVrFCLZuoxm9Ik3CvAaJYtCts8ApmWh0nku7M7C2gGnjA3V8OnUca34kTTgFgv877sGtHNSTTWTQaJVYT45TNL2DJglK2O2AwrdsXhI4lkrRWO4LD3Z8ALgFuBuYAh7j735s6mIiIiKzIzJ5N3E42sy/qHJPN7IvQ+VJZUcl03p3/Sb16y6y8AGkk3bnH8SWX4iWX4+5qPotkjuOpXczVM3QQaVw/lc2iPFb+y/mw7ocFTCPNJZoV5dInzgPg6A3/wPQvfwycSCR5rXYFtJn1AMqAf9atubv+nyUiItK8zkvcHhg0RRq6dNK9Ddb37LxdMyeRdOfueOn1UPESln8eZhY6kog0k8TYrNnAp6GzSON5e+47PD7jb+zTaS9u3OI6uuR1JmraPyJTbL3XQH575j6MG/UG1w27g7FfN/yeUiTTrckIjleovTjbgBZAL+AbYPMmzCUiIs1IGwamBnefk7h7prv/se5jZnYr8Mf6r5LVueKLUVR7rF69b35Pjd+QRudL74KyJ6DlydDqzNBxRKQJmdlQ4BpqVzxnUfuZ2t29d8hc0ng+XjSBx2f8DYDdO+5K57zOgRNJCOfcfwplpeUsmrM4dBSRpLXaBrS7b1n33MwGA03+bnmfw49v6t9CZI1VHpgbOoKISF17Ub/ZvF8DNVkDXyypv/FgruVw9+CLAqSRdOZLH4FlD0HekVjBJVr9LJL+RgMXULviuf43nZLS/vTtfXxePBGAuwbeTvvcdoETSUiXPHY2VRVVVJZX8uojb7PHsb/RTGiROtZkBfQK3P0zM9P1qCIi6cTVBEkFZnYGtV8C9/7VzOcCYHyYVKkv1sDq5xsHnhUgiaS97P6QNwxrfY2azyKZYYm7vxY6hDQNT+zifdfA29R8FsyM3Lxc3nl6PKPOH0teQR77nrRb6FgiSWNNZkBfWOc0Agymdm6ViIiINK8ngdeo3Rh4ZJ16qbsvChMptY2Z9lK9mmH0a90rQBpJVx6bhUW7YblDsdyhoeOISPN5x8xuB54HKpcX3f2zcJFkfX1T+i198/tw9iZnAJAdyQ6cSJLJzsO258kbN+SBc0az1e5b0Klnh9CRRJJCZA2eU1DnyKV2JrQGIoqIpBNvokMam7v7D8BZQGmdAzPT0pt18PqcD+rVcvVBUhqRV/wfPn9vvOKt0FFEpPltBwwBbgLuTBx3BE0k62xq6XeM/OJybiq6lQ8W/pfsSLaaz1JPNCvKWfcOp6KsklO3vJB5P84PHUkkKaxyBbSZRYECdx/RTHlERCQENYtTxZPAgdTOkly+QfByDmhTo7VQVDKdZbHyevUDuv4mQBpJR145Hi++ALI3h5wdQscRkWbm7rr+Pg3EPMZfZzzJO/Pe/aW2VdtB4QJJ0hu02xbc8a9ruGSv63jmtpc45/5TQkcSCW6lDWgzy3L3msTOvSIiIhKYux+YuNV8iEZw59d/q1fLsSyG99aFXrL+vOpTvPhMyOqNFT6CRVqFjiQizczM2gBXAzsnSu8B17n7knCpZG3FPM7SmqUAHNz1IA7tdrDm+MtqDdx1c0Z/9Sc69tggdBSRpLCqERwfJ24nmtk4MzvOzA5bfjRHOBERaR7mTXNI0zCzoWbWKnH/92Z2l5n1CJ0r1cytWFivdlC3XQIkkXTjsfn44tMg0hkrHItF2oSOJCJhjKF2VNbvEkcJMDZoIlkr8yvnkxPJ5vTep/KXbUdzWPdD1HyWNda9Txc8Hmf6lz+GjiIS3JrMgG4BLAR2p/ay34MStyIiIhLGg0CZmQ0ELgK+B/4aNlJqKSqZTpz4CjXDtPpZGoVFO2AFF2HtxmJRrXwSyWAbu/vV7j4tcVyLxmWlhKXVSznh45MZMWkkz8z8O1mRVU4vFVmpZ28bx2kDLmLqZ9NCRxEJalUN6I5mdiHwJTA5cTslcftlM2QTEZHmok0IU02Nuzu1mwLf7+4PULtZsKyh52bW3xAuamvyvbzIynnNT3j1FACs5TFYtGvgRCISWLmZ7bT8JDHesv7mA5JUviiezFmfnwdAt7yuHN7t0MCJJJUdfPa+FBS24uztLuXj1z4PHUckmFV90ooC+YmjoM795YeIiKQLNaBTTamZXQocB7xiZhFA27CvhSnF39erbdmmT4Akki48NhdffAJefA7u1aHjiEhyOAN4wMx+MLMZwP3A6YEzyWrMr1wAQMfcjty05fVa/SzrpXX7Ak659Tg6dG/Plb+9hZ+mzgkdSSSIVf0knePu1zVbEhEREVlTRwLHAMPd/efE/OfbA2dKKaWxsnq1GwacGSCJpAOPL8YXD4f4QqzwL5jp+yARAXefCAw0s9aJ85LAkWQV5lfOJxaPsUen3di1485ELRo6kqSJ/U/Zgx0O2prjNz6b1x59m1Nv/X3oSCLNblUNaE3WFxHJENowMLUkms5PANuY2YHAx+7+eOhcqeKo8SPr1QqiLQMkkXTg8aX4opOhZgZW+CiWMzB0JBEJzMx+7+5/S4y0rFsHwN3vChJMVqoqXsWISSNpl9OOuwfdruazNLrCTm25+fXL2XTbTUJHEQliVSM49mi2FCIiIrLGzOx3wMfAEcDvgI/MbFjYVKnhii9GNbj6efM2GwdII+nAlz0KNV9jbe/DcrcPHUdEkkOrxG3BSg5JIgsqF3DqJ2cAsFcntUGk6WyxUz+yc7KZM20u0yfPCB1HpFmtdAW0uy9qziAiIhKQ66KXFHM5sI27zwMwsw7AW8BzQVOlgInF3zRYH9Zjz2ZOIunC8s+E3J2wnCGho4hIknD3hxK314bOIqtWFa/m+q9uBmC3Druwf5d9AyeSdFe+rILjNzkbgKueG8FvDtsucCKR5qHt3kVERJsQpp7I8uZzwkL0b/pqFZVMxxv4i3l2nyPp17pXgESSqtxjxEvvxuOLMMtR81lEGmRmt5lZazPLNrO3zWy+mWn4axJZVLmQslgZI/pewIm9jg8dRzJAXqsW3PTa5QBcN+wO3nl6fOBEIs1DH1ZFRERSz+tm9oaZnWhmJwKvAK8GzpT0bpkypl4tx7LYr8vQAGkkVbnH8ZIrYNmDUPF26Dgiktz2Tmw8eCDwA7AJcHHQRALAzxVzeX3OG3Rq0YlrN7+KLdtuETqSZJBt9hnEkz/+mRatcrnrlAeJxWKhI4k0uVVtQigiIhlCmxCmFne/2MwOA3ZKlB529xdCZkoFC6qX1Ksd1G2XAEkkVbk7XnoTlP8DWp2FtTwidCQRSW7LP28fAPzd3Zcs34hQwvlyyRRu/6Z2H8gdNtiernldAieSTNShe3vOuvdkPnjpY105KhlBDWgREZEUYWZ9gDuAjYHJwAh3nxU2VeqKEmF474NDx5AU4kvvhbLHoeUJWP65oeOISPJ72cy+BsqBMxJ7NlQEzpTRymrKfmk+n7XJ6bTJbhM4kWSyvU/Yhc223YRoVpSShaW0bq89SiV9aQSHiIhoBnTqGAO8DBwOfArcFzZO6rjii1H1aod03y1AEklVHl8KFa9C3jCs4FK0ilFEVsfdRwI7AkPcvRpYBuibz0Dcncu/vAqAIzc8gm3bbRM4kWS6SCTCRptvyLO3v8Swjicz5YOGN8sWSQdqQIuIiKSOAnd/xN2/cfc7gI1CB0oVXyyZWq+Wn5UXIImkKovkY+2fxlpfj5neQovIypnZ7onbw4BdgYMT9/eltiEtgWxa0Jcd2+/A/l32DR1F5Be7Hb0T2blZjDp/bOgoIk1GIzhEREQzoFNHCzPbCli+9DKv7rm7fxYsWZLLtSzKfMUNXrZs2ydQGkklXv4yXvku1uZmLFIYOo6IpIZdgH8BBzXwmAPPN28cmVU+m5xINqdvfFroKCL1dOjenmOvGMbYK57ilYff5IDT9godSaTRqQEtIiIal5E65gB31Tn/uc65A7s3e6IUUf2r5nPLaB79WvcKlEZShVf8C19yCWQPBmJAduhIIpIC3P3qxO1JobMIzK+cz2WTr6R1VmvuGHgLudHc0JFE6jn03P0Ye8VTPDryCYYeui1tO2g+uaQXNaBFRERShLtraPE6GDPtJaq9ZoVaZawqUBpJFV75X7z4XMjuhxX+GbMWoSOJSIoxs5uA29y9OHFeCFzk7leETZY5KmOVjJg0EoCz+5yh5rMkrbz8PO754EZeuv81WrZuGTqOSKPTADsREdEmhJLW3pr7Ub1ax1yNUpCV86qJePEZkLURVvgoFskPHUlEUtN+y5vPAO6+GNg/YJ6MMrdiLqd9eiYAO22wI5sW9A2cSGTV+m/fl7PuHU5ObjYfvfIpsVhs9S8SSRFqQIuIiEhaK6sur1e7qN9xAZJI6qiG6EZY4RjNfRaR9RE1s1+W3JpZHqAluM3kx7KZABzW7RBO7X1y4DQia6Z1uwK+fL+IKw66hb9e+/fQcUQajRrQIiKCedMcIqFd8cUoqllx9Ujf/J6a/ywN8vgyACxnG6z981i0Y+BEIpLingDeNrOTzexk4E3gL4Ezpb2lNUu5peh2Ni3oy2PbPMrB3RraC1IkeW2xUz+6btyJfz/3YegoIo1GDWgREZEUY7V+b2ZXJc57mNm2oXMlo8+Lv65XW1pTFiCJJDuPzcYXHICXPQWAmd4mi8j6cfdbgRuAfonjene/LWyq9FYRq+CaKTdQVPo1lfFKzCx0JJF1ss2+WzHz61lMeP3z0FFEGoXeWYuIiKSeUcAOwNGJ81LggXBxktOYaS81WN9hgwHNnESSnccW4ItOBC+F7IGh44hIeikCXnf3EcB/zKwgdKB0VR4r59zPL2R+5Xz26rQHHXI7hI4kss6GXVS7cv9fT70fOIlI41ADWkREtAlh6tnO3c8CKuCXTY1ywkZKPq/M/k+D9eG9D27mJJLMPF6MLz4J4nOxwkew7P6hI4lImjCzU4HngIcSpW7Ai+ESpa/S6lJu/foOKuOV7LTBUI7tcfTqXySSxDpv1JFNt9mY/jtsGjqKSKPICh1ARERE1lq1mUVJtPnNrAMQDxsp+VTEq+rVtmq7WYAkkqzcq/HFp0LNNKzwYSxncOhIIpJezgK2BT4CcPepZqbh8k2gIl5JhAgX9DmXQYW6kkXSw30f3kxVRRXffPI9/x03gWOvOJzsnOzQsUTWiVZAi4iINiFMPfcCLwAdzexG4H3gprCRkktD4zciGDcMODNAGklWZtlYi99ibe/BcoeGjiMi6afS3X/5NtTMstA1Yk2iQ+4GXNn/MjWfJa2YGbl5ubz/jw954oZ/cGDLY/lyfP39TURSgRrQIiKiERwpxt2fAC4BbgbmAIe4+9/Dpkour8yuPy9v23ZbBEgiyci9Gq+eCoC1Og5rsWfgRCKSpt4zs8uAPDPbC/g78M/AmdLK7PLZ3Fh0C7PL52jDQUlbJ914NMdecTjRrCgX/OZKHrvy6dCRRNaaGtAiIiIpxsx6AGXUfogdByxL1CShIl5Zrzash5qMAu4xfMkf8UVH4LG5oeOISHr7IzAfmAz8AXgVuCJoojQyt2Iel06+kqml39Eqq2XoOCJNJhKJcOJ1R/HAJ7eSnZvNf57/EHet9pHUoga0iIgEWwFtZmPM8YvLeAAAIABJREFUbJ6ZfVmndo2ZzTKziYlj/zqPXWpm35nZN2a2T536vonad2Y2cj3+S6SKV4CXE7dvA9OA14ImSiINjd8wjH6tewVII8nE3fGSq6HiZazVmVi0U+hIIpKmEns1FLn7I+5+hLsPS9xX16gRVMWreeC7BwG4qv/ltMluEziRSNPrtUUPXil7gtFT/kRVRRVP3fwCsVgsdCyRNaIGtIiIhPQYsG8D9bvdfVDieBXAzPoDRwGbJ14zysyiiQ94DwD7Af2BoxPPTVvuvqW7D0jc9qF2g6P/hs6VLN6a+1G92kYtuwRIIsnE3fHSW6H8WWj1Byz/tNCRRCSNuXsM+EZXKDW+ilgFp35yOjPKfuSCPufSO19fMEvmWD5q5sN/fsqYy5/kk9cnBk4ksmayQgcQEZHwQm0Y6O7/NrON1vDpBwNPu3slMN3MvqO28QrwnbtPAzCzpxPP/aqR4yYtd//MzLYLnSNZlFaX1aud1ffIAEkkqVS+DmVjoOWxWP6FodOISGYoBKaY2cfAsuVFd/9tuEipryJWSU4kh0O6HqRNByVjDdildr3NK4+8xXYHbB04jcjqqQEtIiLJ6GwzOx74BLjI3RcD3YAP6zznp0QNYOav6mndjDWzut2zCDAYmB0oTlIZM+0l4sRXqEWJavyGQO5eWOsbIe9wbVQlIs3lytAB0klVvIqHp41mr057MGrwvWRHskNHEgmmsFNbDjlnP166/3WWLCihzQatQ0cSWSWN4BARkSabAW1mp5nZJ3WONbnm/UFgY2AQMAe4s7H+mGmkoM6RS+0s6INX96I1nZVtZoebmZvZkEZL3Exen/NBvVqPlprzm8m84g08NhezLKzlEZjp7a+INC0za2Fm5wNHAJsB4939veVH4Hgp69FpY5i4eCJts9uo+SwC7H/qnrg7j1zyt9BRRFZLK6BFRKTJRnC4+8PAw2v5mrnL75vZI9RutgcwC9iwzlO7J2qsop52EjOvC9x9xDq87gFgL2pXiU8ws3Hu/tWvnlcAnAfUH6Sc5IpKprMsVl6vrvEbmcsrXsOLL4C8YVibG0LHEZHM8RegGvgP/9uj4rygiVLcjGU/8tGiCQB0aqEvlkWgdlPC3Y4eSuv2+aGjiKyWGtAiIpJUzKyLu89JnB4KfJm4Pw540szuAroCfYCPAQP6mFkvahvPRwHHNG/q5mFmWe5eY2ZD1+Hl27Jms7KvB24FLl6vsAHc8tXYBusav5GZvPI9vHgEZG+FFVwWOo6IZJb+7r4lgJmNpvb9iqyj4qpirppyLQC3bHlj4DQiyeXSv9V+t1VWWs7s739mk0F63yvJSdcgiohIk43gWB0zewr4L7Cpmf1kZicDt5nZZDP7AtgNuADA3acAz1LbMH0dOMvdY+5eA5wNvAEUAc8mnpuOln+AnWhm48zsODM7bPmxmtd2o/6s7G51n2Bmg4EN3f2V1QWpO15l/vz5a/NnaDILqorr1TbM6xwgiYTmVR/ji8+GrL5Y4cNYpGXoSCKSWaqX30m8T5H18M682qkl52xyJl3077rICswMM+PW4+/j3O0v49VH3godSaRBWgEtIiLBuPvRDZRHr+L5NwL1lr64+6vAq40YLdm1ABYCu1Pb6rfE7fPr+gta7WDcu4AT1+T5dcerDBkypImGuKy5opLpDdb/vI1WvmYad8dL74Jod6zdaCxSEDqSiGSegWZWkrhvQF7i3AB399XuFmZm+wL3AFHgUXe/ZSXPOxx4DtjG3T9plPRJZv8u+7J1u8H0aLnh6p8skqHOHXUqf9zrOu7+w0PMmjqHU279vTZdlqSiBrSIiKzRamVJCh3N7EJqx5Isbzwvt7r/FVc1QxtqNzTcAng38Wa1MzDOzH6bCh9on5tZf7VHmyzNw8tEZgaFD4JXYpF2oeOISAZy9+j6vD4T9m1YE9OX/sBXJV+xR6fd1XwWWY32XQq5451rOKHPOTx7xzgK2uVz1MhDQ8cS+YVGcIiICOZNc0ijiwL5iaOgzv3lx6pMIDEr28xyqJ2VPW75g+6+xN03cPeN3H0j4EMgJZrPAFOKv69X27PzdgGSSCheM4P4kqtwr8IihVhUl2mLSMr6Zd8Gd68Clu/b8GvL922oaM5wzWFZTRl/nfEEL81+efVPFhEA2nZow7NzHqFzr46071b7Jby7PpRJctAKaBERkdQxx92vW5cXJjYvXD4rOwqMcfcpZnYd8Im7j1v1r5DcymOVK5xHiDC8d0Of1SUdeexnfNGJ4Mug1cmQ1TN0JBGR9dHQvg0rfKtad98GM1vpxsFmdhpwGkCPHj2aIGrjW1C5gBGTRuI452xyJi2iLUJHEkkZuXm5/PX7BwCY9d0crvztrVz25HnanFCC0wpoEREJtgmhrLX1GuTm7q+6e1933zgxTxt3v6qh5rO775oqq5+LSqZTQ2yFWre8joHSSHPz2MJE87kYazcGU/NZRNJcnX0bLlrdc939YXcf4u5DOnTo0PTh1tPs8tncXHQbjnN+n3MY0m7r0JFEUtbiuUuY+fUsLtrlar7491erf4FIE1IDWkREJHXsETpAMnpg6rP1ajGPNfBMSTceL8EXD4fYbKzwYSx7i9CRREQaw9rs2/ADsD21+zYMabaETSRiURznin6XslXhoNBxRFLaFkM3497/3kRZaTkX7Xo1s76bEzqSZDA1oEVERCugU4S7LwqdIRn9VDa3Xm2HDQYESCLNLvYTxOdhhQ9gOduETiMi0ljSet+GX4t7nNHTxrKgcgGdW3Ti9oG30Kdgk9CxRNJCv+36cN+HNwFw/zmjNRNagtEMaBER0YaBktLiv3ojbaD5z2nOPY5ZBMvuDxu8jUVaho4kItJo0n3fhrrcnbE/PM6/F7xPr/xe7N5xV6IWDR1LJK1stm0fbvjnSCLRCPF4nGhU/x+T5qcGtIiIiKSsopLpxH41/9l0gVdac6/Bi8+D7C2w/DPUfBaRtOTurwKv/qp21Uqeu2tzZGpsS2uW8ui0sXxePJEDu+zPbh12CR1JJG1td0DtPPWy0nJ+mDKT/tv3DZxIMo0+oYmIiEZwSMpqaP5zTkTfr6cr9zi+ZCRUvgnWKnQcERFZRzGPcdZn5/F58UR22mBHDu9+KGbrtdeyiKyB2096gNtOuJ/pk2eEjiIZRg1oERERSVk/LJtdr3ZA198ESCJNzd3xkuugYhyWfz7W6vjQkUREZB3EPQ7AThsMZUTfCzi198lETK0JkeYweM8BzJo6h9MGjuDVR98OHUcyiH7Ki4gI5k1ziDQ1b2CpveY/pydfeieUPwmtToFWZ4SOIyIi62By8ZecNOFUKmKVnNp7OFu23SJ0JJGMctDpe/PsnEfYaPMNuf/sR1lWUhY6kmQINaBFREQkZWWx4iYq2WhTlXRl0Z6QdwyWf7Eu0xYRSUGLqhYxevpjAFTHq8OGEclghZ3acuh5B1BdVcOU8d+EjiMZQkMSRURE85olZXVsUcjsigW/nHfO6xAwjTQFj83Hoh2wlkdgHBE6joiIrIPyWDkXTrwEx7m830ja5rQJHUkko+138u5svdcAOvXUe2dpHloBLSIi2oRQUlbPll1WON+2/eaBkkhT8PIX8AV74lWTQkcREZF1FPMYD3//KI5zRPfD6VvQJ3QkkYxnZnTq2YGShaV8MG4CsVgsdCRJc2pAi4iISEoqKpnOh4smr1CbVT4vUBppbF7xf/iSSyF7EGRvFjqOiIiso6p4FcXVS/h9j2M4sOv+oeOISB3vPfsBVx9yG+88NT50FElzakCLiAjWRIdIU5pcPLXeQvtFlSVBskjj8sr38eILIHsA1nYUZrmhI4mIyFoqqS6hrKaMvGgel/cbyV6d9wgdSUR+Zf/T9qTrxp249fj7+O7z6aHjSBpTA1pERERS0pZt61/Cu3eX7QMkkcbkNd/hi8+ErI2xwkewSKvQkUREZC0trFzI7d/cxV3f3kPc42RFtP2USDKKRqMcd/XviESM84ZezvTJM0JHkjSlBrSIiGgGtKSkH5bNXuF81w5D2K/L0EBppNFEe2P5p2GFY7GINqkSEUklMY/xn/nvc0PRzfxcMZd9O+9NxNR2EElme/5+Z0YX3YM7vP/8x6HjSJrS15AiIoKpWSwp6C/TX17hfFLxt4GSSGPwmu/B8rBoV8g/O3QcERFZS+Wxcu759n6KSr+mR8sNOXeTs+mVv1HoWCKyBrr36cLDk+6gW58uq3+yyDpQA1pERERSTlHJdEprlq1QW1K9NFAaWV9eMxNfdAJEO0G75zDTFHkRkVRREaugRbQFRSXfkBPJ5rw+5zCo7QCtfBZJMd37dgVg8n+KmP/TQnY/eqfAiSSdqAEtIiIalyEp57mZb9Wr5Wi+ZEry2Fx88YnglVjrm9R8FhFJEXGP89SPz/J/c9/kyn6XMrhwEIMLB4WOJSLr6fFrnmHSu1/Ro183NhnUK3QcSRP6SlJERERSzqLKknq1A7r+JkASWR8eX4QvPgnii7DCR7HsTUNHEhGRNfBt6VTu/OZP/N/cN+nZsge50RahI4lIIznzTycRiUa465QHWTxvSeg4kibUgBYREW1CKCmna16HFc43zOvM8N4HB0oj68pLb4eamVjbP2M5A0PHERGR1Yh7nOdmPs+NRbfwTem3HNvjKK7b4mo2bNk9dDQRaSS9tuzJJX85m2lf/MjpW13MD1Nmho4kaUDXqoqIiDYhlJTz/oLPVzhfWFUcKImsDyu4FPIOw3K2CR1FRETWQMQidG7RiQO67M/+nfchPzs/dCQRaQK7H70THXtswG0n3M/cH+ax0eYbho4kKU4NaBEREUkpY6a9RI3HVqiVxSoCpZG15V4Fyx6BVidjkdag5rOISErZqcPQ0BFEpBlsMXQzxhT9iazs2tZhTXXNL/dF1pZGcIiIiEZwSEr574Iv6tXaZ7cJkETWlnsNXjwCX3oPVI4PHUdEREREVmF5w/mv1/6d/XKP5tVH3w6cSFKVGtAiIiKSUnbYYEC92qWbDw+QRNaGexwvuRIqX8cKLsVa7BE6koiIiIisgb5DepOX34K7T/szL973Wug4koLUgBYREcyb5hBpCsN7H0w08RamTVY+dwy6gH6tewVOJavi7njpTVD+D2h1NtbqpNCRRERERGQNbXfA1vxt+ii23nsgo84fy7vP6Eo2WTtqQIuIiEhKueKLUcSIA7CkZikvz/pP4ESyWvF5UP5PaHkiln9O6DQiIiIispZaty/g2hcuZoudNuP5e14hHo+HjiQpRNPDRURE85olZRSVTOfz4q9XqI1fMJGLOT5QIlkTFu0EG7wEkU6YWeg4IiIiIrIOcvNyufbFS2hZkEckEuHDlz9l2/23IhLR+lZZNf0NERERjeCQlPHczLfq1aKmtzPJysueJl76J9wdi3ZW81lEREQkxRUU5hPNilK6eClXH3obl+57A99P+iF0LEly+sQmIiIiKWNRZUm92gFdfxMgiayOl7+Ml1wNNVOAWOg4IiIiItKIWrVpydBDt+WztyZz+lYXc+aQS5g7Y37oWJKk1IAWEZHaERxNcYg0sq55HVY43zCvM8N7HxwojayMV7yNL7kYsrfB2t6Hmaa+iYiIiKSTSCTCVc9exEMT7+B3I37L1M+m8/YT2ptFGqZPAyIiIpIySqqXrnDeIbdtoCSyMl75AV58HmRvjhX+GbMWoSOJiIiISBPpPaAnvW87jg37dWfnYduHjiNJSiugRUREK6AlZbTOzl/luSSB+CLI2gQrfBSL6H8fERERkUyw70m70bIgjzf/+h6V5ZWh40iSUQNaRES0CaGkjEnF367yXMJxr/2gYXkHYu2fwyJanS4iIiKSSeZMn8vdp/6ZF+97PXQUSTJqQIuIiEjKWFJduspzCcNrpuPz98Yr/gWgmc8iIiIiGahjjw3YZr+teHTk37jz5FGUL6sIHUmShBrQIiKiERySMuxXb11+fS7Nz2Oz8EUnApWQ1TN0HBEREREJJBqNcvnTF7DVHlvy+th3OKrraXz76fehY0kS0Kc2ERERSRkb53df5bk0L4/Nr20++1KscAyWtXHoSCIiIiISUE5uNje/fjnXjxtJ6/b5bDxoo9CRJAno+kgREcFcy5UlNbTKylvhPP9X59J8PL4UXzwc4vOwwrFYdv/QkUREREQkCUSjUbY/cGuG7DOQaDQaOo4kATWgRURE4zIkZbjHVzhvnZ0fKIlgrSB3ZyxnRyxncOg0IiIiIpJksrKzePWRt1iyoJSjLz00dBwJSCM4REREJCUUlUxn4pJvV6jNrpgfKE3mcq/EY7MwMyIFF2O5Q0NHEhEREZEk9cG4Cbx436uULNLm4ZlMDWgREcG8aQ6RxvTczLfq1dpltw6QJHO5V+PF5+ILj8Ljy0LHEREREZEkd/gFB7Lo52JuOuae0FEkIDWgRUREJCVMKa6/g/awHnsGSJKZ3GP4kkug8h0s/0ws0ip0JBERERFJclvtviUHnbEPn/7fJCa8MTF0HAlEDWgREamdAd0Uh0gjqvKaFc6zLYt+rXsFSpNZ3B0vuRoqXsHyL8ZaHh06koiIiIikiJNvPoaCwlaMf/6j0FEkEG1CKCIiGpchKaFNVivmVVX9cl6o8RvNp+xvUP4stDodyz81dBoRERERSSGtWrfklFuPY6fDtg0dRQJRA1pERERSwuLqFTcuWVKzNFCSDJR3GGYRyDsmdBIRERERSUH7n7IHAAvnLMYM2nUuDJxImpNGcIiIiEZwSNIrKplO9a9GcNTEY4HSZA6veAOPL8MirbCWx2JmoSOJiIiISIoqX1rOCZuczTWH3R46ijQzNaBFREQk6U0untpAVd9yNCUvew4vPgdf9mjoKCIiIiKSBvLy8zj6ssMo+nAqk96bEjqONCM1oEVEBPOmOUQay4xlP9erDWjbN0CSzODlr+IlV0DOb7D8M0LHEREREZE0ccjZ+9KiVS6jzh8bOoo0IzWgRUREJKkVlUzn3fmfrFAzjBsGnBkoUXrzynfxJSMgeyus8H7MckJHEhEREZE00apNK/Y6bhemTZrBa6PfDh1Hmok2IUwCr//7Mn6YNu+X86tH/p25Py9p8Lnj3ryE3+5123r9fhdffhCDt+nF8Uc8QHV1jNZt8nhg9MkcN+z+9fp1Jf20LmjB3dcfCUC7wlbE43GKl5QD8IcRf6WmJr7ev8c9Nx5J+8J8qqprKK+o5pZ7X2PmrMXr/evKWtJqZUliz818q4Gq/tI2BfcqvOR6yNoUK3wYs7zQkUREREQkzRx75TCWLCxlwC79Q0eRZqIGdBKoqqzh9BObd75iPObsc8BAXn7xs2b9fSW1lJRWcPL5fwHgpKN3pLy8mqdfnPDL49GIEYuvfxPo+rte5pvv5nLQPgM488RdufTGF9b715S1o3EZksymLf2pXq1Pfs8ASdKfWQ4UjoVIPhYpCB1HRERERNJQ+y6FXPnMhQCUL6sgNy+HSERDGtKZGtBJqEVeNtfd8jvyC1qQlRVl7MPv8t/3v13hOe3a53P5dYfRqlUOkWiEe+94jS8nzWTrbXtz/Mk7k50dZc6sxdx+0z+pKK+u93s8/+zHHH7kdrz6z8/rPXbEMduzy+79yc6OMv7f3/D46H8DcOyJO7HHPluyZHEZ8+eV8O03c3juqQ+b5j+CJK1Lz9uPqqoa+vTuyOSi2ZSVV67QmH7svhMZef3z/DyvhL127c+wAweTlRWl6Ns53PXnN4mvomE96cufOOKgrQE448Rd2G7r/2fvvsOjqtY2Dv/e9ISSRkQgKEVUFEQ0ohQPKEqxYQHLsYCi6BGs2LvY5dhQPxEFKXY5KliwgGBBFFABKUpXQGpIAiE9s74/EjEhEELIzJ4kz31d+2J2mb2f2QysyZs1azUHB+PfncVX3/1OYnwdHrjtTGKiIwkNNZ5+6UsWLF4XkNctIt7JzM8qs+2ZY4Z6kKTmcvlLcTlTsLrXY2EHeR1HRERERGqB3+cs54XrRtOoZUPueuNGr+OIH6kAHQQiIsMYOfZKANb/lc5D9/6PB+58j6ysPOrHRjNi1OVlCtAnn3okP/24gjfHzyQkxIiMCqd+bDT/7t+F2294g5ycfC64uCN9LzyB11/7tsw1N23MYOGCNZza8yhmzfzn3Md2aEGT5ASGXDkGMxj2xAW0bXcQubn5nNjtcK7uP4qwsFBeGnMlS39f798bI0ErqUE9rr39TXw+x+UXddrtMQcnJ3Byl8O49vY3KSz0cdM1p3Bq1yP4fPqeZ7rt1KElK//YQteOh3JIiwO44oZxxNaPZtRTlzJ/0VpO6dqa2T+vZsJ7PxS97yP1X1iVceoCLcEry5dbaj0E8yhJzeQK/sClXQ4YxFwMoQ28jiQiIiIitcAhxzQnoVE809+ayVn/6UmbLq29jiR+oupNENh1CI7Q0BCuuOYk2rY7COccDZLqEZ9Qh7StO3Ye8/uS9Qy96wxCw0L5/tvfWbFsI0d1OoSDmzXg2ZH9AQgLC2Xxwj33Dn17wkwefPx8fvx+2c5txx7XnGM7tNhZEI+KjqBJ0wSiYyL4/tul5OcVkp9XWKpoLbXPjJm/l9uTGeDYdgdzWMsDGfXUpQBERoSRnlG2FyPAvTefQW5eARs2ZfDcqGmc3yeFad8swedzpKVnMW/hGg5vdSC/LdvA7df3IiwshG9/WM7yVZt2ez4RqTnuWfB/u9mqAnRVcYXrcVsHgMvHEt/EVHwWERERkQAJDQ3lplFXM2/6Qsbc8xbDp91PaGio17HED1SADkLde7YhNi6Ga68YTWGhjwkThxARUfqv6tf5fzJ08Hg6dGzFrXefycS3fyRzew4/z1nFow9UbPzcdWvTWLFsI127/zPou5nx9oTv+WRS6bGhzzm/w/6/MKkxsnP+GdalsNCHhfxTDCr5Xv1s+kJGjS/bA39Xf48BvTfzF63lujvfomNKS+68oTfvTppbbo9qqTiNAS3BatG2FWW2HRxzoAdJah5XmFpcfM7AEsZjYYd4HUlEREREapm4pFgGPnoxzw95lava3MzIX4YTERXhdSypYhrhOwjVqRNJeloWhYU+2h1zMAc2iitzzAENY0nbuoMpH/3ClI/m0eqwA1myaB1Htk2mcZN4AKKiwmnSNKHca705/jv6XnTCzvW5s1fS8/R2REWHA5DYoB5xcTEsWrCGEzq3IjwilKjocE7o1KoKX7FUZ+s3bePQFgcAcGiLA2h0QCwAPy34k26dDiMuNgaAenWjaJhUv0LnXLB4LSd3OZyQECO2fjTtjkxmydINNEyqT1p6Fh9/sYBPvlzAoS0P8M+Lqo2cnxaR/VQnJKrMtsGHXuBBkhqoYDH4UrH4l7HwNl6nEREREZFa6qxrezL4uSu4/OGLyMspO4+ZVH/qAR2Epn2xkIeevIBR4wex9Lf1/Ll6S5lj2h1zMP3+fQKFBT6ys/N48qHJZKRnMfyRj7jrwXMIDy/6ysLYV2awbs3WPV7rj1VbWL50A4ccWtSb7KfZKzno4ERGvHw5ANnZeTw+bBJLf1vPrO+WMWrcINLSdrBq5SZ2ZObu8bxSe3z9/VJ6nXQk4164nMVL17P2rzQA/liTyquvf8tTD/YjJMQoKCjkmZensnHztr2e85tZyzjysMaMea4/OBg59mu2pu+g18lHcuE5x1FQ6CM7O59Hn/3U3y9PRDyW4/JKrceERtG6fnOP0tQMzjnMDIs8EZK+wkIq9stBERERERF/Ofu63gCkbUzn87HT6dG/G2Yaeq+mMBekE0+d2vnh4AxWi0VFh5OTnU9kZBhP/99lPPPEpyxfusHrWAGRmxjpdQSRnb6ZfGuVt8KdLnjKL//nfv/OUH1iqAVSUlLc3Llzq/y8S7at4pZ5z5Ta1jT6QEYed1eVX6u2cC4XlzYYi+6DRZ/pdRwRkQoxs5+ccyle56ju/NVei4hUpbce+4Axd7/JkOcHcta1PVWErkbKa6/VA1oq7KbbTufg5g0IjwjjyykLak3xWUREvDFxzdQy2wpdoQdJagbnCnDpN0HeNxB9mtdxRERERETKOGtwT9558kNeuG40cUn16Xp+J68jSRVQAVoq7LEHP/Q6goj4i75zIkFoZebaMts6NjjKgyTVn3M+XMYdkDsVq3cvFn2u15FERERERMqoUz+G0Yuf5cojb+LhC5/hqG5HEl8815RUX5qEUEREMOefRWR/5PsKSq1HWjhXtOjjUZrqyzmH2zYMciZjdW/C6lzqdSQRERERkT1KbBTPve8NBeDPxWU7pUj1ox7Q1dy5F3Sg95ntcc6xesVmhj86metu7s2hhzfCDNau2crwRyaTk100i+i/Tm7NZVf8CwesXLZRvZplv91+fS86pbQgLSOLAdeNBaBe3SgeuO1MGh0Qy/pNGdz/xGQydxRNWnn9VSdzQkoLcnMLeOzZT1m6chMA0z8Yyso/iibc3LR5G3c+8oEnr0dEgldMWJTXEaqvkPpQ5yqoc43XSURERERE9qpd1yMY+ctwWrZrxsY/NvPb7OV07dfR61hSSSpAV2OJDepxdt8OXHnxSPLyCrhn2LmcdMqRjBzxBVlZeQBcfd0p9DnvON55/XuaJMdz0aWdufE/48jcnkNcXIzHr0Bqgs+mLeSDj3/mrpv+GU/04r7H8/P8P3jjf7O5+LwOXNL3eEaO+4YTjm1OcuN4/n31qxxxWCNu/s+pXHPrGwDk5hUw8MZxXr0MCdIJaaV2qxtWh7T87aXWZd84XzoWEofVuxnnnCZxEREREZFqITQslJbtmlFYWMjQbvez8Y/NbEu9ijOv6eF1NKkEDcFRzYWGhhAZGUZIqBEZFU7qlsydxWeAyMjwnYWl3me1Z/L7c8ncngNAenqWJ5mlZpm/aC3bMnNKbevS4RA++2oRAJ99tYgux7cq2n58Kz6fXrR98e/rqVsnisR4FZREZPda1k0ud13K53ZMwG3phSv4E0BW7VLRAAAgAElEQVTFZxERERGpdkJDQ3nl16dIbBzPiGtf4edpv3odSSpBBehqLHXLdia+NYs33r+edybdyI4dufw0eyUAt9x1Ju9+dCNND07kw4lzAEhumkiTpgk8+1J/RowaQMrxLbyMLzVYfFwMqWk7AEhN20F8cW/7Bol12bT5n96Mm1O30yCxLgAREWGMeupSXhp+MV2OPyTwoWs5jQEtwWjFjjXlrsueuez3cdsfgvD2ENrI6zgiIiIiIpUWXTeaB96/laiYSB48dzi52bleR5J9FPACtJldXs6+QWY218zmrt0wJ5CxqqW69aLoeOJhXNrvBS7s8xxRUeF079EGgP8++hEX9nmOP1dvoVv3I4Gi3tJNkhMYOmQCj97/ITfdfgZ16kZ6+RJEdjp/4MsMGjqBYf/9mOuuPJnGB8Z5HUlEPLY9r/Q3dTLz9c2dinA5n+My7oKIjljcs5iFex1JRERERGS/HN6hFfe/fyuhYSH8uWSd13FkH3nRA/rBPe1wzo1yzqU451KSDzwukJmqpWNSmrPhr3Qy0rMoLPTx3de/cUTbf76e7PM5ZkxdTJduhwOwZfM2fvhuGYWFPjasT2fdmlSaJCd4FV9qsLT0rJ1DayTG1yGteLiXLamZHJBUb+dxSYn12JKaWbRva9Gf6zdmMG/hGlq1OCDAqWs556dFZD8U4vM6QrXj8n7Gpd8M4e2wuP/DTL9oFhEREZGaoX33Nry74VVaHaNv9Fc3filAm9mCPSy/Ag39cc3aaNPGDFq3aUJkZNFcku1TmvPnH1to3CR+5zEdu7RizR9bAJj5ze8cdczBANSPjaZJ00TW/5Ue+OBS482cvZxeJxf1vO918pF8N3s5AN/NXk7Pk4q2H3FYI3Zk5ZKatoO6dSIJDwsFILZeNG1bN2H1mlRvwtdSGoJDglGhr6DUuiYhrIDw1hBzMRb/Chai+yUiIiIiNUdoaChh4WFsWZfKuPvfwedTh5XqIsxP520I9ATSdtluwPd+umat89viv/h2+hL+77UrKSz0sWLpRj6d9AvDR1xCTJ1IMFi5fBMjhn8KwNwfV3Jshxa8+vrV+HyOV16cyvZt2R6/Cqnu7rvlDNq3aUps/WgmjrmG196ayRv/+5EHbzuL0089ig2btnH/k5MB+GHuSjoe24K3Xr6K3Nx8HhsxBYBmTRO55doe+JwjxIw3/vcjf6gALVKrjVk5iSxf6bHdOiQe6VGa4Ofyl0JoIyykHlb/Lq/jiIiIiIj4zf+e+YSJT3/EivmrGfbh7V7HkQrwVwH6Y6Cuc27erjvMbIafrlkrjR/9DeNHf1Nq243/GbfH419+fiovPz/V37GkFhn23493u/2me9/d7fZnXi77/lv4218MuH5sVcaSfeXUXVmCy6wtC8psqxsW7UGS4OcKluO2XgIRKVj8/3kdR0RERETErwY+9m+Wz1vFrMlz+X3uCg5Lael1JNkLvwzB4Zwb6Jz7bg/7/u2Pa4qISPVjZmPMbJOZLSyxLcHMvjSzZcV/xhdvNzMbYWbLi4d1OqbEc/oXH7/MzPp78VqkajWMSiy1bhht41p5lCZ4uYI1uK0DwMKxeur9ISIiIiI1X1h4GHe9cQPhkeHc1fsR5nxepv+rBBkvJiEUEZEg4+EY0GOBXrtsuwOY5pxrBUwrXgfoDbQqXgYBL0FRwRq4Hzge6ADc/3fRWqov22W9Vd2DaF2/uSdZgpUr3IBL6w8uF4sfg4Ud7HUkEREREZGAiG8Yx/M/PEpS00T+Wr7B6ziyFypAi4gIOD8te7usc98AW3fZ3Af4eyyhccDZJbaPd0V+AOLMrBFFcw586Zzb6pxLA76kbFFbADPrZWa/F/civ2M3+282s8XFPcynmZlnFc1OSe1KrfdodIJHSYKXy7gbfGlY/KtY+GFexxERERERCaiW7Zrx4pzH6TNYP/4FO3+NAS37aeidZ3B851akp+1g0KWjALj0in9x2llHk5GeBcCYl6cze9aKMs8994IO9D6zPc45Vq/YzPBHJ5OfV0if81I45/wONElO4LzTnmJbRtEEhF26HU7/K7uyfVs299/xHtu3ZdOoSTxXXN2NR+77IHAvWoLa7df3olNKC9Iyshhw3VgAWjZLYui1PYiJCmf9pgweeuoTsrLzyjy3wzHNuP7K7oSEGp98sYA3/jcbgHNPb0/fs44luVE8Z178Ahnbi96TXTseyhUXd2bb9hzufvQDtm3PofGBcQy69EQeGP5RwF6zeKahc2598eMNFE1sC9AEWFPiuLXF2/a0XUows1DgReBUiu7RHDOb7JxbXOKwX4AU51yWmf0HeBK4IPBpYWH6ijLrvRt19iJK0LLYh6DwLyyi3d4PFhERERGpgUJDQ8nans2cz+bRst3BJB/a2OtIshvqAR2kvvh0AXfd/FaZ7f97ZzbXDHiVawa8utvic2KDepzdtwODrxjNoEtHERJinHTKkQAsXLCG2294gw3r00s95+zzjmPIwNF88uHPnNyj6NjLr+rG2FEzqv6FSbX12bSF3PrAxFLbbruuJy+P+5oB14/l2x+WcdG5x5V5XkiIcdPVp3LrgxO5bPAYuv+rNQc3LRrb9dcl67j53ndZvzGj1HPOPaM9g26ewOTP53PKv1oDcOUlXXj19W/99OrEX0NwmNkgM5tbYhm0L7mccxXsSy0V0AFY7pxb6ZzLA96mqFf5Ts656c65rOLVH4DkAGfcaVbqgnLXayvnsnE7xuKcDwttjEWkeB1JRERERMRTOTtyGD7gBZ77zygKCwu9jiO7oQJ0kPp1/p9s35ZdqeeGhoYQGRlGSKgRGRVO6pZMAFYs28jGDRlljvc5R3hEGJFR4RQW+GjTrilbt2aybm3afr0GqVnmL1rLtsycUtuaNk5g/qK1AMyd9wddOx5a5nmtWzVi3fo01m/MoKDAx7Rvf6PL8YcAsGzlJjZs2lbmOc45wsNDiYoMo6DQx1FHNGFr2g7W7vLLEwl+zrlRzrmUEsuoCjxtY/HQGhT/ual4+zqgaYnjkou37Wm7lLavPcUHAlP2tLPkLxc2b95cRRH/4fMVlrteGzmXh0sbgtv+OOTP9zqOiIiIiEhQSDgwnv4PXsC86Yv4eOSXXseR3VABuprpc14KL4+7iqF3nkHdelFl9qdu2c7Et2bxxvvX886kG9mxI5efZq8s95xvT5jJE89ezAmdW/HVl4u4ZMCJvPGaeprK3q3+c8vOYnK3zodxQIP6ZY5pkFiXTVu271zfvGU7SYl1yz3v6xN/5JmHzqfTcS2Z9s0S+l/QiXHvzKra8FKaz/lnqZzJQP/ix/2BSSW2X2ZFTgAyiofq+BzoYWbxxZMP9ijeJpVkZpcAKcDwPR1T8pcLSUlJVZ6hEF+pdd8ejqstnCvApQ+FvG+x+g9hEe29jiQiIiIiEjT6Dj2Ttie2Ztx9bzNv+kKKvkwrwUIF6Grkow9+ov/5L3LNgFfYmprJ1UNOKXNM3XpRdDzxMC7t9wIX9nmOqKhwuvdoU+55f56zisEDR3Pf7e/S6cRD+XHWcpIPSuTeh8/jpttPJzJSQ4XL7j0+4jPOOa09rzx9KTHREeQXVE0Pxbnz/uCqmydw58Mf0OX4Vvzw00qaNoln2O1ncevgHkRG6D1Z5TyahNDM3gJmAYeZ2VozGwg8DpxqZsuAU4rXAT4FVgLLgVeAawGcc1uBh4A5xcuw4m1SWoV6ipvZKcDdwFnOudwAZStlybZV+HZ5A8WERnoRJSg458NtuwdyP8fq3YXF9PM6koiIiIhIUDEzrn3ucnw+x8ih4/jzN30pNpioAF2NpKftwOdzOAefTv6Fw44oO7D6MSnN2fBXOhnpWRQW+vju6984om3FhvCMjAyjx2ntmPy/uVw28F8Mf3gyCxes4eQebav6pUgN8ee6rQy9/z2uunkCU79Zwl8byg6RsSU1kwMa1Nu5ntSgHptTMyt0/siIMHp3P5L3P/mFKy7qzKPPTuHXJes4tdsRVfYaxFvOuYucc42cc+HOuWTn3GjnXKpzrrtzrpVz7pS/i8muyGDnXEvnXFvn3NwS5xnjnDukeHnNu1cU1OYArcysuZlFABdS1Kt8JzNrD7xMUfF5027OERC/pi8rs+3I2JYeJAkSBcsh+xOs7nVYnQFepxERERERCUqHHN2ct9a+zPBp93Nw62T1gg4iKkBXIwklhi3o3PUwVq8sO+bmpo0ZtG7TZGev5fYpzfnzjy0VOn+/f3fkw/dmU1joIyIyHOcczueIilJvU9m9uNgYAMzgsvM7MumzeWWO+W3ZepIbx9OoYSxhYSF0P/FwZv64vELnv+jcDkz86GcKC31ERobhnMPnc0SpV36V89ckhBI8nHMFwBCKhidZArzrnFtkZsPM7Kziw4YDdYH3zGyemU3ew+n8qm1cq1LrIYTQ96Cy3/qpLSz8UKzBx1BniNdRRERERESCWnSdKOrF12XLulSuO+FO8vPyvY4kgKo4QequB87hqPYHERsXw5sfXM/40d/Qrv3BtGzVEOccGzdk8OyTnwKQ2KAuN99xBnff8ja/Lf6Lb6cv4f9eu5LCQh8rlm7k00m/AHB23+M4/+KOJCTUZdT4QcyetZynH/9k5zkOP6IxrxeP/Txp4hxeGD2QHdtzuP/O97y5CRJU7rvlDNq3aUps/WgmjrmG196aSXRUOOecVjQO6TezlvHp1IUAJCbU4fYhvbht2P8o9DmefXkq/32gLyEhIXw69VdWr0kF4LwzjuGiczuQEF+H10YM4IefVvLkC5/vPEfrVgcy9u3vAfjfxz8z6ulLydyRy12PfODBHRCp/pxzn1I0lEnJbfeVeBwUVd6P15Weh6Bd3KG0rt/cozTecTtGg8ViMX2xsIO9jiMiIiIiUm2smLea3+es4MdPfqbLOcd7HafWs2Dtjn5q54eDM5jUSrmJtXfsUQk+30y+1ar6nN16PeGX/3NnfHZ7lWeV4JOSkuLmzp279wMr6MxvbsRXYtrBEEL46F/PVtn5qwOX9TZu230QdToW+zRm+qckIrWXmf3knEvxOkd1V9XttYhIMCvIL+C8BlfQ9l+tefCD2wgNC/U6Uo1XXnutIThEREQkqJQsPu9uvaZz2R/htt0Pkd2w2CdVfBYRERER2Udh4WF0PqcDP37yM/1bXcemNRUbnlb8QwVoERHRGNASVKJDSn/rJCak9nwLxeVMw2XcBhEdsLgRmIV7HUlEREREpFq68eWrufLxS6iXUJfVC9d4HadWUwFaRETA+WkRqYQWdZqUWu+Q2NajJB4oWA7hbbC4lzCL8jqNiIh4xMx6mdnvZrbczO7Yzf6bzWyxmS0ws2lmpskCRER2EREZzgW39eGln56kQ+/25OXkeR2p1lIBWkRERILGkm2rWLR9Zaltf+Vs9ihN4DhXNDu31b0aS3gDC6nrcSIREfGKmYUCLwK9gSOAi8zsiF0O+wVIcc4dBUwEngxsShGR6iV7Rw6D2t3CuuXrvY5SK6kALSIimHN+WUT21cQ1U8tsSwiv70GSwHH5i3FbeuLyFwBgFuFxIhER8VgHYLlzbqVzLg94G+hT8gDn3HTnXFbx6g9AcoAziohUK5v+3MK6Zeu5s9cjLP1phddxah0VoEVERCRo/JL2e5ltfQ86xYMkgeEKVuLSrgBXACGJXscREZHg0AQoOVjp2uJtezIQmLK7HWY2yMzmmtnczZtr/jeKRET25ODWyVz77OVsXrOFGzrdzcibx5K+OcPrWLWGCtAiIgI+Py0i+yjXV3Zcttb1m3uQxP9c4Trc1gEAWMJYLLS82oKIiEhZZnYJkAIM391+59wo51yKcy4lKSkpsOFERILMOdefxpt/juSki7rw4QufaWLCAArzOoCIiHhPw2VIsAgnlHwKS63XRK4wtaj47LKwhAlYWAuvI4mISPBYBzQtsZ5cvK0UMzsFuBvo6pzLDVA2EZFqLb5hHLeNHcIVj/6bBo0TvI5Ta6gHtIiIiASNA6OTyl2vMULqQXg7LP4VLLy112lERCS4zAFamVlzK5oY4EJgcskDzKw98DJwlnNukwcZRUSqtQaNE8jLyePFG8awfN4qr+PUeCpAi4gIOD8tIvuoT3LXcterO+fLxPnSMYsgJO6/WER7ryOJiEiQcc4VAEOAz4ElwLvOuUVmNszMzio+bDhQF3jPzOaZ2eQ9nE5ERPYga3s2n4yaytNXjaSwsHDvT5BKUwFaREREgkbvRp0JtzDCLJRuSSn0btTZ60hVxrkcXPp/cFv7U1RbEBER2T3n3KfOuUOdcy2dc48Ub7vPOTe5+PEpzrmGzrmji5ezyj+jiIjsKi4pliseuYhlP63k8sNuwOfTREb+ogK0iIiAc/5ZRPbRlPUzyXcFFLhCZmyey5T1M72OVCWcy8elXw95s7E6V2KmaThERERERLx23k1ncNJFnVm/ciMPX/iM13FqLBWgRUQEc/5ZRPbVpHUzyl2vjpwrxGXcCrkzsPoPYtFneh1JREREREQAM2PIiIHE1I+ma9+OAOoJ7QcqQIuIiEjQyC7IK7WeW5jvUZKq4zKfh5xPsXq3YTEXeh1HRERERERKqJ9Yj0np4+l6fid++PgnhvV7ihXzV3sdq0bR9z9FRETDZUjQqBMWyZYSNeg6oVHehakiFnMRhCRidS71OoqIiIiIiJQj+bDGzP1sHtmZOTz+2T2YmdeRagT1gBYREZGgEUpoqfXwkHCPkuw/lzMN5wqw0IYqPouIiIiIVAPJrRpx1ZOX8vOXC3j19te9jlNjqAAtIiKYzz+LyL7qktS+1HqPRid4lGT/uB2v4dL/A9nveR1FRERERET2wZn/6UHLo5vx8agvWb9qo9dxagQVoEVERCRobMxNBSAyJJzzkrvTu1FnjxPtO5f1Hm77YxDZE6L7eR1HRERERET2QUhICPe9N5TQ0BDmfjbP6zg1gsaAFhERjQEtQWHMykl8vmEWALm+fD5cN52ODY6idf3mHierOJf9KW7bPRBxIhb3FGb6qCUiIiIiUt00bnkgY357jrikWK+j1AjqAS0iIuD8tIjsg1lbFpRaL3Q+fk1f5lGafed823Db7oPwY7H4FzCL8DqSiIiIiIhUUlxSLBlbtjH5/z4nLzff6zjVmgrQIiIiEhQaRiWWWjeMtnGtPEqz7yykPpYwFosfhVm013FERERERGQ//T5nBc8PeZX3n/3E6yjVmgrQIiKCOeeXRWRfbMlLK7WeFBlXLYbfcHnzcVlvAWDhbbCQuh4nEhERERGRqnDsqUdxYLMkRt/5Bgu/W+J1nGpLBWgREREJDs5KrUaGRHoUpOJc/u+4tCtxO17F+bK8jiMiIiIiIlUoNCyUp74eRmLjeIb1e4pfvvrV60jVkgrQIiJSNAmhPxaRfdCyTpPS63WTPUpSMa5gNS7tcrBILH4sFhLjdSQREREREaliBzRtwEOT78DM+HLC117HqZY0NbuIiIDP6wAikFawvdT6tvxMj5LsnStcj9s6AFwhljgeC2vqdSQREREREfGTVse0YPTiZ/EV6ofnylAPaBEREQkKxye0LbXeKamdR0kqIPd7cNuxhDFY2CFepxERERERET+rG1eH+on1mPj0RwzucAdb/trqdaRqQwVoERHRJIQSFJrWOWDn4xBCaFansYdpymcx52FJX2LhR3odRUREREREAiixcQLLflrJ45eM8DpKtaECtIiIiASFGZt+2vnYh49pG2d7mKYs59uBb+tAXN4cACwkweNEIiIiIiISaCdd2Jl+Q89kwdeLWb9qo9dxqgUVoEVERJMQSlAo85YJoreQc7m49Gshbyb49FU7EREREZHarNfAkzGDe898nMz0HV7HCXoqQIuIiArQEhS6NPhnzOcwC6X7gR08TPMP5/Jx6TdC3iws9jEsqqfXkURERERExENND2vC5Y/8m8QmCWRs2eZ1nKAX5nUAEREREYCW9ZIB6Jh4FOc17U7r+s09TgTO+XAZd0LuNKzefVj0OV5HEhERERGRIHDh7WdzwW19MDOcc5iZ15GClnpAi4gI+Py0iOwDX3Gv+XXZm1i94y+P0/ytECjE6t6M1bnE6zAiIiIiIhJEzIypr3/D84NfxelbwHukHtAiIiISFL7aOBeAP7M28MKydwDo3aizJ1mcc+CysJA6EPsUZvqdvYiIiIiIlJWxeRsfjfyC9C3buPedm9UTejf005SIiGDO+WUR2Refb/i+1PqkdTO8CQKwYyQu9Vycb6uKzyIiIiIiskfn3HAaXc49nm8n/sBlhwzh5VvGqzf0LvQTlYiIiAQF364f0pw3PQfcjvG4zGcgvC1YnCcZRERERESkeggJCeGet29i0JOXYmbM/Xwe2Zk5XscKKipAi4gIOOefRWQfdGzQttR6n+SuAc/gst/HbX8YIk/BYh9X72cREREREdmr0LBQ+t1yFmOXjuDZ7x4ipl40a5etZ9St48neoWK0fqoSEREVoCUoJEXGAxAbXpfzkrsHfPxnlzMdl3EXRHTG4p7FTFNliIiIiIhIxYWEhFAntg4Ac6b8wntPfcRlLYfw9XuzPE7mLRWgRURExHNLtq1i3KqPAMjIz2Tyuq9Zsm1VYEOEHwXRfbG4FzGLCOy1RURERESkRjnn+tN4cup9+Ap9jLn7TXw+n9eRPKMCtIiIqAe0eO7X9GUU8s8HsgJXwK/pywJybZe/FOfysdBEQmIfxkJiAnJdERERERGp2dqf3Jar/3sZfy3fwGv3vO11HM+oAC0iIiKeyyzILrUeYiG0jWvl9+u6/F9xWy/AbX/c79cSEREREZHa5+R/d+GEM4+lUYuGXkfxjAY3FBERqL3fBJIgMXvrwlLriRGxtK7f3K/XdAXLcVsHgsVhda7y67VERERERKR2CgsP46FJd3gdw1PqAS0iIphzfllEKiozP6vUer6vwK/XcwV/4rYOAAvHEsZioQf69XoiIiIiIlK7/bViA2Pve5vFs373OkrAqQAtIiIinqsbVqfc9arknA+Xfh24PCz+NSzsYL9dS0REREREBCAkNIRPXv6Sxy5+ji3rUr2OE1AqQIuIiCYhFM/1Se5a7npVMgvBYh/BEl7Fwg/123VERERERET+dmCzA7hlzLVsWL2ZJwe8iKtFPzOrAC0iIiKe692oMwfHNCI6NJIhrS6gd6POVX4N59uOy/4AAAtvg4UfVeXXEBERERER2ZPjTz+WAQ9dyC/TfuXrd7/3Ok7AqAAtIiLgc/5ZRPZBUmQ8ydEN/VN8dtm4tEG4jLtxBaur/PwiIiIiIiIVcdGd53BgsyRmf/aL11ECJszrACIiEgRq0Vd/JHhtzN1Kam46U9bPrNIitHN5uLQhkP8LFvcMFtasys4tIiIiIiKyL0JCQnjm24dIbJzgdZSAUQFaREREPDdl/UzWZG0A4IVl7wBUSRHauQJc+lDI+xar/ygW1Xu/zykiIiIiIrI/GjRJxDnHkh+XERoWwqHHtvQ6kl9pCA4REdEkhOK57zfPL3e90vJmQ+4XWL27sJi+VXNOERERERGR/bTxj81c3/Eunrjsea+j+J0K0CIiIuK5Tkntyl2vLIvshCVOxuoMqJLziYiIiIiIVIUDmx3A+becxZ9L1vHH4jVex/ErFaBFREQ9oMVzvRt1pnFUA+qGRjOk1QX7PfyGy3wRlzsTAAs/rCoiioiIiIiIVKk+1/Umqk4kT1z2PAX5BV7H8RsVoEVERCQoxEfUp2W9plVQfH4Fl/kcLvfLKkomIiIiIiJS9Q5o2oBBwy9j2c+rePH6MV7H8RtNQigiIuBTb2XxXiE+wrH9OofLeguXORyiTsfq3VtFyURERERERPzjzGt60PSwxkTXi/Y6it+oAC0iIuB8XicQYUtOOrm+fKasn1mpXtAuexJu2wMQeRIW+yRmoVUfUkREREREpIodfVIbADb9uZnExgmEhtWsn2VUgBYRERHPTVk/ky156QC8sOwdgH0uQru8ORBxPBb3HGbhVZ5RRESkpsnPz2ft2rXk5OR4HaVaioqKIjk5mfBwfe4Qkf03b/pCnr5qJG26HM6trw3GbP++HRpMVIAWERFNGCiem7RuRpn1ihagnfNhFoLVHwbkYhZV9QFFRERqoLVr11KvXj2aNWtWowodgeCcIzU1lbVr19K8eXOv44hIDdD2xNY0b3sQX47/ms5nd6Dz2R28jlRlNAmhiIiIeC4zP6vc9T1xeT/jUs/CFawpKkJbzR03TUREpKrl5OSQmJio4nMlmBmJiYnqPS4iVSY0LJT73htKwoFxPHT+02z8Y7PXkaqMCtAiIlI0CaE/FpEKCg8p/dXVumF19vocl78Yl3YVuFxQr2cREZFKUfG58nTvRKSqhYaFcu1zV1BYUMiEB9/D56sZ8zVpCA4REdEQHOKpJdtWsSl3a6ltLesml/scV7ASl3YFWF0sYSwWmuTPiCIiIiIiIgHRtV9H6iXcx470HYSE1Iy+wzXjVYiIiEi19Wv6sjLbtuVn7vF4V7gOt3UAYMXF5yb+CyciIiJ+FRoaytFHH02bNm3o168fWVkVG4arPPfddx9Tp07d4/6RI0cyfvz4/b6OiIi/HNO9LSeedwI/fPwTj13yHFnbs72OtF9UgBYRkaIe0P5YRCqgbVwrQij9FdZOSe32/ASrA2GtsPjXsDBN+iMiIlKdRUdHM2/ePBYuXEhERAQjR44stb+goGCfzzls2DBOOeWUPe6/5ppruOyyy/b5vCIigbZl3Va+evM7bu56H4WFhV7HqTQNwSEiIiKeal2/OQfFNGJ11l/UCY3i8hZ96N2oc5njnC8DLBoLicMSRnuQVEREpGZ7bMmTZbZ1SEihe8OTyS3M5emlz5XZ36VBJ05M6sL2/O28sPylUvvubH3bPl3/xBNPZMGCBcyYMYN7772X+Ph4fvvtN5YsWcIdd9zBjBkzyM3NZfDgwVx99dUAPPHEE5qaz/IAABq5SURBVLz++uuEhITQu3dvHn/8cQYMGMAZZ5xB3759ueOOO5g8eTJhYWH06NGD//73vzzwwAPUrVuXW265hXnz5nHNNdeQlZVFy5YtGTNmDPHx8XTr1o3jjz+e6dOnk56ezujRoznxxBP36fWIiOyv0wedQvb2bEbdNoF5Xy3k2FPL6agTxNQDWkRE1ANaPDV8yXhWZ/0FwI7CHBamryhzjPNl4tKuwKVfj9N7S0REpMYpKChgypQptG3bFoCff/6Z5557jqVLlzJ69GhiY2OZM2cOc+bM4ZVXXmHVqlVMmTKFSZMm8eOPPzJ//nxuu610wTs1NZUPPviARYsWsWDBAu65554y173ssst44oknWLBgAW3btuXBBx8slWn27Nk8++yzpbaLiASKmXHmtT0JCw9lxOBXmfHOTK8jVYp6QIuICNSQmXWlevopbUm5687l4NKugfwlWNxgzTgvIiLiJ+X1WI4MjSx3f73wevvc4xkgOzubo48+GijqAT1w4EC+//57OnToQPPmRUNtffHFFyxYsICJEycCkJGRwbJly5g6dSqXX345MTExACQkJJQ6d2xsLFFRUQwcOJAzzjiDM844o9T+jIwM0tPT6dq1KwD9+/enX79+O/efe+65ABx77LGsXr16n1+biEhViIqJZOBjF7Ps55W0aNfM6ziVogK0iIiIeOrY+NbM2Dy31PrfnMvDpV8P+XOw2KewqJO9iCgiIiJ+8vcY0LuqU6fOzsfOOZ5//nl69uxZ6pjPP/+83HOHhYUxe/Zspk2bxsSJE3nhhRf46quvKpwtMjISKJoosTJjUYuIVJW+N5+58/GmNVtY8PViTrnkXx4m2jcagkNERDQEh3jq1taX0TymMQD/anAMt7b+Z1Igt+0hyJ2B1X8Qiz5jT6cQERGRGqxnz5689NJL5OfnA7B06VJ27NjBqaeeymuvvUZWVhYAW7duLfW8zMxMMjIyOO2003jmmWeYP39+qf2xsbHEx8fz7bffAjBhwoSdvaFFRILVDZ3u5onLnmfD6k1eR6kw9YAWERERz3VOOppVf/zFLa0vLbXdYi6B8MOxmAs9SiYiIiJeu/LKK1m9ejXHHHMMzjmSkpL48MMP6dWrF/PmzSMlJYWIiAhOO+00Hn300Z3P2759O3369CEnJwfnHE8//XSZc48bN27nJIQtWrTgtddeC+RLExHZZw+8fytDu93P/934GsM+vN3rOBViwTqRz6mdHw7OYFIr5SZGeh1BZKdvJt9a5QPg9m74H7/8nztl40sarLcWSElJcXPnzt37geV4bumbfLHhB4a3u4HW9VtA3kyI6KzxnkVEBDP7yTmX4nWO6m537fWSJUto3br1Hp4hFaF7KCJeGP/Au0wY9h5nXH0qN7w0yOs4QPnttYbgEBEREU8t2baKqRtmA3DXghdZsvFJXNoVkDvN42QiIiIiIiLB56K7zqH7xScy7Y1vyc3O9TrOXqkALSIi4HP+WSSomFkvM/vdzJab2R272R9pZu8U7//RzJoFIte0jbPx4QMg3xXw1cZZEH0uRGrCQRERERERkV2FR4Rzwe1n89SMB4mMDv5v7asALSIiOOfzy1IRZrbazH41s3lmNrd4W4KZfWlmy4r/jC/ebmY2orhAusDMjvHjbalRzCwUeBHoDRwBXGRmR+xy2EAgzTl3CPAM8ERAwrldVkIOwuo/jJk+poiIiIiIiOxO8zYH0eqYFqz5fR0PX/g0sz7av2ER/Uk/2YmISDA4yTl3dInxou4ApjnnWgHTitehqHjaqngZBLwU8KTVVwdguXNupXMuD3gb6LPLMX2AccWPJwLdLQCDMMeERRU/KqpEx8R0w0zzJIuIiIiIiOxNQX4hc6bMY+TQcfh8FesIFmgqQIuISDAOwVGyEDoOOLvE9vGuyA9AnJk12p8L1SJNgDUl1tcWb9vtMc65AiADSNzdycxskJnNNbO5mzdv3q9gqzLX/X1WwFi1Y8N+nU9ERERERKS2aN7mIC6+5zz+Wr6B6W/N9DrObqkALSIiflOySFm87G56Xgd8YWY/ldjf0Dm3vvjxBqBh8eOKFFElAJxzo5xzKc65lKSkpP06V6ekduWui4iIiIiIyJ6dcU0PAGa8E5wFaH2/VUREwPlnwkDn3Chg1F4O6+KcW2dmBwBfmtlvu5zDmZlmNNx/64CmJdaTi7ft7pi1VjQGRiyQ6u9gvRt1BuD7zfPplNRu57qIiIjUfKGhobRt25aCggKaN2/OhAkTiIuLq7LzN2vWjLlz59KgQQPq1q1LZmZmlZ1bRCRYxNSLpseAbiz4ejG52blBNzGhCtAiIgIejhPlnFtX/OcmM/uAorGKN5pZI+fc+uIhNjYVH16RIqrs3hyglZk1p+ieXQj8e5djJgP9gVlAX+Ar5/z024ld9G7UWYVnERGRWig6Opp58+YB0L9/f1588UXuvvtuj1OJiFQ/Vz52MVnbs4Ou+AwqQIuIiIfMrA4Q4pzbXvy4BzCMfwqhjxf/Oan4KZOBIWb2NnA8kFFiqA4ph3OuwMyGAJ8DocAY59wiMxsGzHXOTQZGAxPMbDmwlaIitYiIiNQSQ0+6v8y2rv06cda1PcnJyuXu0x8ts79H/270HHASGVu2MazfU6X2PTX9wX26fseOHVmwYAEAK1asYPDgwWzevJmYmBheeeUVDj/8cDZu3Mg111zDypUrAXjppZfo1KkTZ599NmvWrCEnJ4cbbriBQYN2N/KbiEjNFd8wjviGcaRvzmDVr3/S/uS2XkfaSQVoERHx2xAcFdAQ+MDMoKhNetM595mZzQHeNbOBwB/A+cXHfwqcBiwHsoDLAx+5+nLOfUrRPSy57b4Sj3OAfoHOJSIiIlJYWMi0adMYOHAgAIMGDWLkyJG0atWKH3/8kWuvvZavvvqK66+/nq5du/LBBx9QWFi4c0iNMWPGkJCQQHZ2NscddxznnXceiYm7nUtZRKRGe/GG15gz5RfeXf8KEVERXscBVIAWEREPOedWAmVmnHPOpQLdd7PdAYMDEE1ERESk1imvx3JUTGS5+2Mb1N/nHs8A2dnZHH300axbt47WrVtz6qmnkpmZyffff0+/fv/8Xjw3NxeAr776ivHjxwNF40fHxsYCMGLECD744AMA1qxZw7Jly1SAFpFaqWu/jsx4eyafjZnOWdf29DoOACFeBxAREe85n88vi4iIiIhIef4eA/qPP/7AOceLL76Iz+cjLi6OefPm7VyWLFmyx3PMmDGDqVOnMmvWLObPn0/79u3JyckJ4KsQEQkeHc9M4cDmB/DFuOkEaEqfvVIBWkREiobg8MciIiIiIlIBMTExjBgxgqeeeoqYmBiaN2/Oe++9B4Bzjvnz5wPQvXt3XnrpJaBo2I6MjAwyMjKIj48nJiaG3377jR9++MGz1yEi4rXQsFDOveF0fp+zgk9GTfU6DqACtIiIiIiIiIgEgfbt23PUUUfx1ltv8cYbbzB69GjatWvHkUceyaRJRXNSP/fcc0yfPp22bdty7LHHsnjxYnr16kVBQQGtW7fmjjvu4IQTTvD4lYiIeKvPkF50Pvs4srZleR0F0BjQIiIC4FNvZREREREJvL8nEfzbRx99tPPxZ599Vub4hg0b7ixGlzRlypTdnn/16tV7vJaISE0VEhLCA+/fBkBm+g7WLd/AYSktvcvj2ZVFRERERERERERExG/eePh/THjwXU8zqAAtIiLgfP5ZRERERERERMQzMfWi+fGTn5nxzkzPMqgALSIiIiIiIlJLOU0cXWm6dyJSHVx01zkc3uEQRgx+lW2p2z3JoAK0iIjgfM4vi4iIiIgEr6ioKFJTU1VIrQTnHKmpqURFRXkdRUSkXGHhYfznmQFs35rJm4++700GT64qIiLBRcNliIiIiNQ6ycnJrF27ls2bN3sdpVqKiooiOTnZ6xgiInt1RMfDOOvaniya+Rs7MnZQJ7ZOQK+vArSIiIiIiIhILRQeHk7z5s29jiEiIgEweMQVhIR4MxiGhuAQERENwSEiIiISRMysl5n9bmbLzeyO3eyPNLN3ivf/aGbNAp9SRESqk7+Lz8t+Xsk3E2fh8wXum9DqAS0iIiIiIiISJMwsFHgROBVYC8wxs8nOucUlDhsIpDnnDjGzC4EngAsCn1ZERKqTDas3cWevh8nYsp1DU1py7g2nc/K/u2Bmfr2uekCLiEjRGND+WERERERkX3UAljvnVjrn8oC3gT67HNMHGFf8eCLQ3fxdPRARkWrvwGYH8M76V+hwWntW/fon82csIj+vwO/XNc12W7OZ2SDn3Civc4iA3o8iNZWZbQb+qIJTNQC2VMF5agPdq4rRfao43auK072qmKq8Twc755Kq6FxBz8z6Ar2cc1cWr18KHO+cG1LimIXFx6wtXl9RfMyWXc41CBhUvHoY8HuJ3XovV47uW+Xp3lWO7lvl6L5Vzv7ctz221xqCo+YbBKjgJ8FC70eRGqiqigJmNtc5l1IV56rpdK8qRvep4nSvKk73qmJ0n4JDceeP3X7+1t9R5ei+VZ7uXeXovlWO7lvl+Ou+aQgOERERERERkeCxDmhaYj25eNtujzGzMCAWSA1IOhERkX2kArSIiIiIiIhI8JgDtDKz5mYWAVwITN7lmMlA/+LHfYGvnMbXFBGRIKUhOGo+DXcgwUTvRxEpj/6PqDjdq4rRfao43auK072qGN2nSnLOFZjZEOBzIBQY45xbZGbDgLnOucnAaGCCmS0HtlJUpN5X+juqHN23ytO9qxzdt8rRfascv9w3TUIoIiIiIiIiIiIiIn6hIThERERERERERERExC9UgBYRERERERERERERv1ABugYzs15m9ruZLTezO7zOI7WXmY0xs01mttDrLCLivb21T2YWaWbvFO//0cyaBT6l9ypwn242s8VmtsDMppnZwV7kDAYV/cxjZueZmTOzlEDmCyYVuVdmdn7xe2uRmb0Z6IzBoAL//g4ys+lm9kvxv8HTvMjptb19xrMiI4rv4wIzOybQGUXtbmWpHa4ctcmVpza6ctRmV06g23AVoGsoMwsFXgR6A0cAF5nZEd6mklpsLNDL6xAi4r0Ktk8DgTTn3CHAM8ATgU3pvQrep1+AFOfcUcBE4MnApgwOFf3MY2b1gBuAHwObMHhU5F6ZWSvgTqCzc+5I4MaAB/VYBd9T9wDvOufaUzT52/8FNmXQGEv5n/F6A62Kl0HASwHIJCWo3a0ctcOVoza58tRGV47a7P0ylgC24SpA11wdgOXOuZXOuTzgbaCPx5mklnLOfUPR7NwiIhVpn/oA44ofTwS6m5kFMGMw2Ot9cs5Nd85lFa/+ACQHOGOwqOhnnocoKqrkBDJckKnIvboKeNE5lwbgnNsU4IzBoCL3yQH1ix/HAn8FMF/QqMBnvD7AeFfkByDOzBoFJp0UU7tbOWqHK0dtcuWpja4ctdmVFOg2XAXomqsJsKbE+tribSIiIl6qSPu08xjnXAGQASQGJF3w2Nd2fCAwxa+Jgtde71XxVwabOuc+CWSwIFSR99WhwKFmNtPMfjCz2vgNporcpweAS8xsLfApcF1golU7+pnEe2p3K0ftcOWoTa48tdGVozbbf6q0DQ/b7zgiIiIi4hkzuwRIAbp6nSUYmVkI8DQwwOMo1UUYRV+17EZRb75vzKytcy7d01TB5yJgrHPuKTPrCEwwszbOOZ/XwUQksNQOV5za5P2mNrpy1GYHAfWArrnWAU1LrCcXbxMREfFSRdqnnceYWRhFX5VLDUi64FGhdtzMTgHuBs5yzuUGKFuw2du9qge0AWaY2WrgBGByLZ30qCLvq7XAZOdcvnNuFbCUoh92a5OK3KeBwLsAzrlZQBTQICDpqhf9TOI9tbuVo3a4ctQmV57a6MpRm+0/VdqGqwBdc80BWplZczOLoGig9ckeZxIREalI+zQZ6F/8uC/wlXPOBTBjMNjrfTKz9sDLFP3QW5vHACz3XjnnMpxzDZxzzZxzzSgap/Ms59xcb+J6qiL//j6kqGcVZtaAoq/7rgxkyCBQkfv0J9AdwMxaU/TD7OaApqweJgOXWZETgAzn3HqvQ9UyancrR+1w5ahNrjy10ZWjNtt/qrQN1xAcNZRzrsDMhgCfA6HAGOfcIo9jSS1lZm9R1FA2KB536X7n3GhvU4mIF/bUPpnZMGCuc24yMJqir8Ytp2hijAu9S+yNCt6n4UBd4L3iuaL+dM6d5Vloj1TwXgkVvlefAz3MbDFQCNzqnKtVPSEreJ+GAq+Y2U0UTW40oDYW7Hb3GQ8IB3DOjaRorM3TgOVAFnC5N0lrL7W7laN2uHLUJlee2ujKUZtdeYFuw033XERERERERERERET8QUNwiIiIiIiIiIiIiIhfqAAtIiIiIiIiIiIiIn6hArSIiIiIiIiIiIiI+IUK0CIiIiIiIiIiIiLiFypAi4iIiIiIiIiIiIhfqAAtNYqZFZrZPDNbaGbvmVnMfpxrrJn1LX78qpkdUc6x3cysUyWusdrMGlQ2o4iISG1Wot3/e2lWzrGZVXC9sWa2qvhaP5tZx0qcY+dnCjO7a5d93+9vxuLzlPw89JGZxe3l+KPN7LSquLaIiEggmFliifZ/g5mtK36cbmaL/XC9B8zsln18zm4/e5SsNYjUFipAS02T7Zw72jnXBsgDrim508zCKnNS59yVzrnyGrFuwD4XoEVERGS//N3u/72sDsA1b3XOHQ3cAby8r0/e5TPFXbvsq6rPEiU/D20FBu/l+KMBFaBFRKTacM6l/t3+AyOBZ4ofHw349vb8ytYGRKRyVICWmuxb/r+9+4/1uqrjOP58QWKIxbJRI5ZmUykzl0JlGbcMQh2ZEhUjt2a2LDehsjndYrUs6QfVjOVqQYwmqJQpaXd1Lypw8QeBgoFQ8Ee0tqxg036giGSv/jjnq1+/fr/33hq34eX1+Od+P5/POefz+X63u3M+78857w+cVGcnr5d0B7BD0khJCyVtkrRV0qcAVHxP0k5JdwGvajQkaa2kyfXzeXXW028k3V1nW30a+Fx94jpF0jhJP6vn2CTp7Fr3lZJ6JW2XtATQ//cniYiIGL4kHVv75s2Stkm6sE2Z8ZL6mmYIT6n7p0t6oNb9qaRjBzhdH3BSrXtlbesRSZ+t+8ZI6q7jhUckza7710qaLOnrwOh6HSvqsX317y2SZjRd8zJJH+o0hhnAA8CE2s7b6nfcIul+SRMljQKuBWbXa5ldr32ppI217At+x4iIiMPYSEmL6313r6TR8GwffL2kB4HPSJokaZ2khyT1SBpfy82TtKP2tbc0tXtqbeP3kuY1drYbBzTrL9YQcaTIE58YlurTzPOBX9VdZwKn2d4t6TLg77bfKulo4D5JvcAZwETgVODVwA5gaUu744DFQFdt6zjbj0n6AbDP9rdquZsoT2DvlXQ80AO8EfgScK/ta+uN5SeG9IeIiIgY3kZLerh+3g18GJhp+x8qKa42SLrDtpvqfBTosX2dpJHAMbXsfGCa7SckXQ1cSQnMdnIBsE3SJODjwNspD5Z/LWkd8HrgUdszACSNba5s+xpJV9TZWq1WAh8BumuAeCpwOWXc8IIxjO3d7S6wfr+pwI/qrt8BU2z/S9I0YIHtWZK+CEy2fUWttwC4x/alKuk7Nkq6y/YT/fweERERh4uTgTm2PynpJ8AsYHk9Nsr2ZElHAeuAC23vrQ+KrwMupaxyOtH2AT0/jdUbgHOAlwE7JX0fOJ024wDbW5rqzWSAWEPEcJcAdAw3zTei6yk3XO8ENjbdnE0HTtdzOZfGUjqoLuBm288Aj0q6p037ZwF9jbZsP9bhOqZRno42tl9eZ1J1AR+sdbslPf4/fs+IiIioqSYaG/VmcoGkLsry2wmUG72/NNXZBCytZVfZfljSuyk3hffVvnsUZeZwOwslzQf2UgLCU4HbG8FZSbcBUygPwb8t6RvAL2yv/y++1y+B79Yg83mUscd+SZ3GMK0B6MZ4aALwW2B1U/kfSzoZMHBUh/NPBz6g53JdvhQ4vrYVERFxuNttuxEXeAh4XdOxlfXvROA0YHXt+0cCf67HtgIrJK0CVjXV7bZ9ADggaQ9ljPEu2o8DmgPQg4k1RAxrCUDHcLO/dSZR7UyaZ+wImGu7p6Xcocx9OAI4y/ZTba4lIiIihsbFwDhgku2Dkv5ACZ4+y3ZfDVDPAJZJ+g7wOLDa9pxBnOMq27c2NiRNbVfI9i5JZ1JyK39V0t22+5tR3Vz3KUlrgXOB2UBj+W/bMUwb+22/ReVlzD2UHNCLgK8Aa2zPVEkhtrZDfQGzbO8czPVGREQcZg40fX4GGN203YgNCNhuu90LhWdQgsYXAF+Q9OYO7SamFjFIyQEdR6Ie4PI68wlJp0gaQ8nlOLvmVxxPWVrTagPQJenEWve4uv+flGU4Db3A3MaGpEZQvI+y9BdJ5wOvOGTfKiIiIsYCe2rw+RzghNYCkk4A/mp7MbCEkqZrA3C2pEZO5zGSThnkOdcDF0k6po4nZgLrJb0GeNL2cmBhPU+rg43xSBsrKUt6G7OpofMYpi3bTwLzgM/X9GRjgT/Vw5c0FW0dx/QAc1WfnEs6o9M5IiIiXqR2AuMkvQPKKipJb5I0Anit7TXA1ZS+s7/3QrQdB7SUGUysIWJYy9OaOBItoSzB2VxvrPYCFwG3A++l5GP6I22W3tbcUJcBt9WOaQ/wPuBO4FaVl/TMpdzs3SBpK+X/rI/yosIvAzdL2g7cX88TERERh8YK4E5J24AHKTmPW70HuErSQWAf8LHav19C6aOPruXmA7sGOqHtzZKWARvrriW2t0g6l5Ku49/AQUoO51Y/BLZK2mz74pZjvcCNwM9tP91om/ZjmP6ub0sdj8wBvklJwTEf6G4qtga4pqbt+BplpvT19dpGUFJ8vH+AnyIiIuJFw/bTNaXVovqehpdQ+r5dwPK6T8Ai23/rtJq50zigpdiAsYaI4U7PfydLRERERERERERERMShkRQcERERERERERERETEkEoCOiIiIiIiIiIiIiCGRAHREREREREREREREDIkEoCMiIiIiIiIiIiJiSCQAHRERERERERERERFDIgHoiIiIiIiIiIiIiBgSCUBHRERERERERERExJD4D9OiszNLhjONAAAAAElFTkSuQmCC\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "fig, axes = plt.subplots(1, 3, figsize=(25, 8), gridspec_kw=({'hspace':0.5}))\n",
        "fig.suptitle('GLM Logística', fontsize=18, weight='semibold', y=0.98)\n",
        "pad = 15\n",
        "\n",
        "#---------- MATRIZ DE CONFUSÃO ----------#\n",
        "cnf_matrix = confusion_matrix(y_val, y_pred)\n",
        "plt.subplot(1, 3, 1)\n",
        "\n",
        "labels = [0, 1]\n",
        "tick_marks = np.arange(len(labels))\n",
        "plt.xticks(tick_marks, labels)\n",
        "plt.yticks(tick_marks, labels)\n",
        "\n",
        "group_names = ['True Neg','False Pos','False Neg','True Pos']\n",
        "group_counts = ['{0:0.0f}'.format(value) for value in\n",
        "                cnf_matrix.flatten()]\n",
        "group_percentages = ['{0:.2%}'.format(value) for value in\n",
        "                     cnf_matrix.flatten()/np.sum(cnf_matrix)]\n",
        "labels = [f'{v1}\\n{v2}\\n{v3}' for v1, v2, v3 in\n",
        "          zip(group_names,group_counts,group_percentages)]\n",
        "labels = np.asarray(labels).reshape(2,2)\n",
        "\n",
        "# criar o heatmap\n",
        "ax = sns.heatmap(data=pd.DataFrame(cnf_matrix), annot=labels, cmap=\"viridis\", fmt='')\n",
        "ax.xaxis.set_label_position(\"bottom\")\n",
        "ax.set_title('Matrix de confusão')\n",
        "plt.ylabel('True')\n",
        "plt.xlabel('Predicted');\n",
        "\n",
        "#---------- AUC ROC ----------#\n",
        "fpr, tpr, thresholds = roc_curve(y_val, yhat)\n",
        "\n",
        "# plot da curva roc\n",
        "plt.subplot(1, 3, 2)\n",
        "plt.plot([0,1], [0,1], linestyle='--', label='No Skill', color='#fde725')\n",
        "plt.plot(fpr, tpr,  color='#4ac16d', marker='.', label='Logreg (area = {}%)'.format(aucroc_val))\n",
        "\n",
        "# axis labels\n",
        "plt.title('AUC ROC', pad=pad)\n",
        "plt.xlabel('False Positive Rate')\n",
        "plt.ylabel('True Positive Rate')\n",
        "plt.legend()\n",
        "\n",
        "#---------- PRECISÃO, RECALL X THRESHOLD ----------#\n",
        "precision, recall, thresholds = precision_recall_curve(y_val, yhat) \n",
        "\n",
        "plt.subplot(1, 3, 3)\n",
        "plt.title('Precisão, Recall x Threshold', fontsize=12, pad=pad)\n",
        "plt.plot(thresholds, precision[: -1], \"b--\", label=\"Precision\", color='#5ec962')\n",
        "plt.plot(thresholds, recall[: -1], \"r--\", label=\"Recall\", color='#440154')\n",
        "plt.ylabel(\"Precision, Recall\")\n",
        "plt.xlabel(\"Threshold\")\n",
        "plt.legend(loc=\"lower left\")\n",
        "plt.ylim([0,1]);"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "collapsed_sections": [],
      "provenance": [],
      "toc_visible": true,
      "authorship_tag": "ABX9TyN+P3dnX9D1xpyva+NQpMXS",
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    },
    "gpuClass": "standard"
  },
  "nbformat": 4,
  "nbformat_minor": 0
}