alura_imersao_3_5.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 5,
  "metadata": {
    "colab": {
      "name": "alura_imersao_3_5.ipynb",
      "provenance": [],
      "collapsed_sections": [
        "820ae465",
        "4237a565",
        "d68fdfa6",
        "fcac57d9",
        "f2f3929b",
        "718fd57c"
      ],
      "toc_visible": true,
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.8.5"
    },
    "latex_envs": {
      "LaTeX_envs_menu_present": true,
      "autoclose": false,
      "autocomplete": true,
      "bibliofile": "biblio.bib",
      "cite_by": "apalike",
      "current_citInitial": 1,
      "eqLabelWithNumbers": true,
      "eqNumInitial": 1,
      "hotkeys": {
        "equation": "Ctrl-E",
        "itemize": "Ctrl-I"
      },
      "labels_anchors": false,
      "latex_user_defs": false,
      "report_style_numbering": false,
      "user_envs_cfg": false
    },
    "toc": {
      "base_numbering": 1,
      "nav_menu": {},
      "number_sections": true,
      "sideBar": true,
      "skip_h1_title": false,
      "title_cell": "Table of Contents",
      "title_sidebar": "Contents",
      "toc_cell": false,
      "toc_position": {
        "height": "calc(100% - 180px)",
        "left": "10px",
        "top": "150px",
        "width": "288px"
      },
      "toc_section_display": true,
      "toc_window_display": true
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/emerson-diego/ImersaoDadosAlura-3/blob/main/alura_imersao_3_5.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ei_vaWtJcpXq"
      },
      "source": [
        "# Aula 5"
      ],
      "id": "ei_vaWtJcpXq"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HqRkAujDc0wa"
      },
      "source": [
        "Olá Cientista!\n",
        "\n",
        "\n",
        "Seja bem vindo e bem vinda à aula 05. Parabéns por ter chegado até aqui!\n",
        "\n",
        "Na última aula nós analisamos separadamente, através de algumas técnicas, os efeitos em cada gene do grupo \"com_tratamento\" usados.\n",
        "Além disso utilizamos o boxplot para nos auxiliar na análise de forma visual tirando algumas conclusões muito interessantes.\n",
        "\n",
        "Você conseguiu fazer todos os desafios? Então vamos mergulhar juntos na aula 05!\n",
        "\n",
        "Nesta aula, vamos entender e aplicar o Machine Learning. Primeiramente, vamos entender o que o ML significa. Existem muitas definições para o termo, mas aqui, vamos trazer a definição clássica de um renomado e importante cientista da computação, o Arthur Samuel.\n",
        "\n",
        "***“Machine learning is the field of study that gives computers the ability to learn without being explicitly programmed” – (definição clássica de Arthur Samuel, cientista da computação, 1959)***\n",
        "\n",
        "*Tradução livre: \"Machine Learning é um campo de estudo que dá aos computadores a habilidade de aprender sem terem sido programados para tal.\"*\n",
        "\n",
        "Podemos interpretar a definição de Samuel, da seguinte forma: para que um computador reconheça determinada imagem, não precisamos passar um conjunto de regras, mostrar vários exemplos é suficiente para que o computador classifique suas imagens. Sendo assim, temos dois fatores determinantes neste processo: um conjunto de dados suficientemente grande e um poder computacional elevado. Por isso, até os anos 2000, era bem difícil encontrarmos problemas significantes resolvidos através do Machine Learning. Mas, a partir de 2009, empresas como a Netflix e Amazon, começaram a resolver problemas reais com o processamento dos dados através no ML, isso porque tinham uma infinidade de dados disponíveis e alto poder computacional.\n",
        "\n",
        "E quais problemas podemos resolver com esse tipo de algoritmo? Existem diversos casos em que podemos aplicá-los mas, vamos subdividir em dois grandes grupos:\n",
        "\n",
        "    - Problemas em que somos muito bons em resolver mas o Machine Learning, leva essa produção à um nível industrial, como é o caso da classificação de imagens\n",
        "    - Problemas em que não somos tão bons em resolver e o Machine Learning pode tomar uma melhor decisão em relação à nossa (imparcialidade, pode ser um fator), como é o caso da concessão de crédito.\n",
        "\n",
        "Mas como o Machine Learning vai ser aplicado no nosso problema?\n",
        "Bom, utilizando a nossa base de dados ```dados_combinados```, o primeiro problema que pensamos em resolver é: **dado os experimentos, queremos saber se qualquer mecanismo de ação foi ativado ou não ativado.** Ou seja, este caso é um problema de classificação binária.\n",
        "\n",
        "Mas, também podemos pensar se **dado os experimentos, queremos saber se um determinado mecanismo de ação foi ativado, recebendo como resposta, o composto relacionado**. E aqui, temos um problema de classificação multiclasse, já que cada composto químico é considerado uma classe diferente. "
      ],
      "id": "HqRkAujDc0wa"
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 253
        },
        "id": "0Yze9X0GdqnT",
        "outputId": "41823888-1c91-4259-91df-55f3da9e3e62"
      },
      "source": [
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "sns.set() #pega as configurações padrões do seaborn\n",
        "\n",
        "url_dados = 'https://github.com/alura-cursos/imersaodados3/blob/main/dados/dados_experimentos.zip?raw=true'\n",
        "\n",
        "dados = pd.read_csv(url_dados, compression = 'zip')\n",
        "\n",
        "dados.columns = dados.columns.str.replace('-','')\n",
        "mapa = {'droga': 'composto'}\n",
        "dados.rename(columns=mapa, inplace=True)\n",
        "dados.head()"
      ],
      "id": "0Yze9X0GdqnT",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>tratamento</th>\n",
              "      <th>tempo</th>\n",
              "      <th>dose</th>\n",
              "      <th>composto</th>\n",
              "      <th>g0</th>\n",
              "      <th>g1</th>\n",
              "      <th>g2</th>\n",
              "      <th>g3</th>\n",
              "      <th>g4</th>\n",
              "      <th>g5</th>\n",
              "      <th>g6</th>\n",
              "      <th>g7</th>\n",
              "      <th>g8</th>\n",
              "      <th>g9</th>\n",
              "      <th>g10</th>\n",
              "      <th>g11</th>\n",
              "      <th>g12</th>\n",
              "      <th>g13</th>\n",
              "      <th>g14</th>\n",
              "      <th>g15</th>\n",
              "      <th>g16</th>\n",
              "      <th>g17</th>\n",
              "      <th>g18</th>\n",
              "      <th>g19</th>\n",
              "      <th>g20</th>\n",
              "      <th>g21</th>\n",
              "      <th>g22</th>\n",
              "      <th>g23</th>\n",
              "      <th>g24</th>\n",
              "      <th>g25</th>\n",
              "      <th>g26</th>\n",
              "      <th>g27</th>\n",
              "      <th>g28</th>\n",
              "      <th>g29</th>\n",
              "      <th>g30</th>\n",
              "      <th>g31</th>\n",
              "      <th>g32</th>\n",
              "      <th>g33</th>\n",
              "      <th>g34</th>\n",
              "      <th>...</th>\n",
              "      <th>c60</th>\n",
              "      <th>c61</th>\n",
              "      <th>c62</th>\n",
              "      <th>c63</th>\n",
              "      <th>c64</th>\n",
              "      <th>c65</th>\n",
              "      <th>c66</th>\n",
              "      <th>c67</th>\n",
              "      <th>c68</th>\n",
              "      <th>c69</th>\n",
              "      <th>c70</th>\n",
              "      <th>c71</th>\n",
              "      <th>c72</th>\n",
              "      <th>c73</th>\n",
              "      <th>c74</th>\n",
              "      <th>c75</th>\n",
              "      <th>c76</th>\n",
              "      <th>c77</th>\n",
              "      <th>c78</th>\n",
              "      <th>c79</th>\n",
              "      <th>c80</th>\n",
              "      <th>c81</th>\n",
              "      <th>c82</th>\n",
              "      <th>c83</th>\n",
              "      <th>c84</th>\n",
              "      <th>c85</th>\n",
              "      <th>c86</th>\n",
              "      <th>c87</th>\n",
              "      <th>c88</th>\n",
              "      <th>c89</th>\n",
              "      <th>c90</th>\n",
              "      <th>c91</th>\n",
              "      <th>c92</th>\n",
              "      <th>c93</th>\n",
              "      <th>c94</th>\n",
              "      <th>c95</th>\n",
              "      <th>c96</th>\n",
              "      <th>c97</th>\n",
              "      <th>c98</th>\n",
              "      <th>c99</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>id_000644bb2</td>\n",
              "      <td>com_droga</td>\n",
              "      <td>24</td>\n",
              "      <td>D1</td>\n",
              "      <td>b68db1d53</td>\n",
              "      <td>1.0620</td>\n",
              "      <td>0.5577</td>\n",
              "      <td>-0.2479</td>\n",
              "      <td>-0.6208</td>\n",
              "      <td>-0.1944</td>\n",
              "      <td>-1.0120</td>\n",
              "      <td>-1.0220</td>\n",
              "      <td>-0.0326</td>\n",
              "      <td>0.5548</td>\n",
              "      <td>-0.0921</td>\n",
              "      <td>1.1830</td>\n",
              "      <td>0.1530</td>\n",
              "      <td>0.5574</td>\n",
              "      <td>-0.4015</td>\n",
              "      <td>0.1789</td>\n",
              "      <td>-0.6528</td>\n",
              "      <td>-0.7969</td>\n",
              "      <td>0.6342</td>\n",
              "      <td>0.1778</td>\n",
              "      <td>-0.3694</td>\n",
              "      <td>-0.5688</td>\n",
              "      <td>-1.1360</td>\n",
              "      <td>-1.1880</td>\n",
              "      <td>0.6940</td>\n",
              "      <td>0.4393</td>\n",
              "      <td>0.2664</td>\n",
              "      <td>0.1907</td>\n",
              "      <td>0.1628</td>\n",
              "      <td>-0.2853</td>\n",
              "      <td>0.5819</td>\n",
              "      <td>0.2934</td>\n",
              "      <td>-0.5584</td>\n",
              "      <td>-0.0916</td>\n",
              "      <td>-0.3010</td>\n",
              "      <td>-0.1537</td>\n",
              "      <td>...</td>\n",
              "      <td>0.4805</td>\n",
              "      <td>0.4965</td>\n",
              "      <td>0.3680</td>\n",
              "      <td>0.8427</td>\n",
              "      <td>0.1042</td>\n",
              "      <td>0.1403</td>\n",
              "      <td>0.1758</td>\n",
              "      <td>1.2570</td>\n",
              "      <td>-0.5979</td>\n",
              "      <td>1.2250</td>\n",
              "      <td>-0.0553</td>\n",
              "      <td>0.7351</td>\n",
              "      <td>0.5810</td>\n",
              "      <td>0.9590</td>\n",
              "      <td>0.2427</td>\n",
              "      <td>0.0495</td>\n",
              "      <td>0.4141</td>\n",
              "      <td>0.8432</td>\n",
              "      <td>0.6162</td>\n",
              "      <td>-0.7318</td>\n",
              "      <td>1.2120</td>\n",
              "      <td>0.6362</td>\n",
              "      <td>-0.4427</td>\n",
              "      <td>0.1288</td>\n",
              "      <td>1.4840</td>\n",
              "      <td>0.1799</td>\n",
              "      <td>0.5367</td>\n",
              "      <td>-0.1111</td>\n",
              "      <td>-1.0120</td>\n",
              "      <td>0.6685</td>\n",
              "      <td>0.2862</td>\n",
              "      <td>0.2584</td>\n",
              "      <td>0.8076</td>\n",
              "      <td>0.5523</td>\n",
              "      <td>-0.1912</td>\n",
              "      <td>0.6584</td>\n",
              "      <td>-0.3981</td>\n",
              "      <td>0.2139</td>\n",
              "      <td>0.3801</td>\n",
              "      <td>0.4176</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>id_000779bfc</td>\n",
              "      <td>com_droga</td>\n",
              "      <td>72</td>\n",
              "      <td>D1</td>\n",
              "      <td>df89a8e5a</td>\n",
              "      <td>0.0743</td>\n",
              "      <td>0.4087</td>\n",
              "      <td>0.2991</td>\n",
              "      <td>0.0604</td>\n",
              "      <td>1.0190</td>\n",
              "      <td>0.5207</td>\n",
              "      <td>0.2341</td>\n",
              "      <td>0.3372</td>\n",
              "      <td>-0.4047</td>\n",
              "      <td>0.8507</td>\n",
              "      <td>-1.1520</td>\n",
              "      <td>-0.4201</td>\n",
              "      <td>-0.0958</td>\n",
              "      <td>0.4590</td>\n",
              "      <td>0.0803</td>\n",
              "      <td>0.2250</td>\n",
              "      <td>0.5293</td>\n",
              "      <td>0.2839</td>\n",
              "      <td>-0.3494</td>\n",
              "      <td>0.2883</td>\n",
              "      <td>0.9449</td>\n",
              "      <td>-0.1646</td>\n",
              "      <td>-0.2657</td>\n",
              "      <td>-0.3372</td>\n",
              "      <td>0.3135</td>\n",
              "      <td>-0.4316</td>\n",
              "      <td>0.4773</td>\n",
              "      <td>0.2075</td>\n",
              "      <td>-0.4216</td>\n",
              "      <td>-0.1161</td>\n",
              "      <td>-0.0499</td>\n",
              "      <td>-0.2627</td>\n",
              "      <td>0.9959</td>\n",
              "      <td>-0.2483</td>\n",
              "      <td>0.2655</td>\n",
              "      <td>...</td>\n",
              "      <td>0.4083</td>\n",
              "      <td>0.0319</td>\n",
              "      <td>0.3905</td>\n",
              "      <td>0.7099</td>\n",
              "      <td>0.2912</td>\n",
              "      <td>0.4151</td>\n",
              "      <td>-0.2840</td>\n",
              "      <td>-0.3104</td>\n",
              "      <td>-0.6373</td>\n",
              "      <td>0.2887</td>\n",
              "      <td>-0.0765</td>\n",
              "      <td>0.2539</td>\n",
              "      <td>0.4443</td>\n",
              "      <td>0.5932</td>\n",
              "      <td>0.2031</td>\n",
              "      <td>0.7639</td>\n",
              "      <td>0.5499</td>\n",
              "      <td>-0.3322</td>\n",
              "      <td>-0.0977</td>\n",
              "      <td>0.4329</td>\n",
              "      <td>-0.2782</td>\n",
              "      <td>0.7827</td>\n",
              "      <td>0.5934</td>\n",
              "      <td>0.3402</td>\n",
              "      <td>0.1499</td>\n",
              "      <td>0.4420</td>\n",
              "      <td>0.9366</td>\n",
              "      <td>0.8193</td>\n",
              "      <td>-0.4236</td>\n",
              "      <td>0.3192</td>\n",
              "      <td>-0.4265</td>\n",
              "      <td>0.7543</td>\n",
              "      <td>0.4708</td>\n",
              "      <td>0.0230</td>\n",
              "      <td>0.2957</td>\n",
              "      <td>0.4899</td>\n",
              "      <td>0.1522</td>\n",
              "      <td>0.1241</td>\n",
              "      <td>0.6077</td>\n",
              "      <td>0.7371</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>id_000a6266a</td>\n",
              "      <td>com_droga</td>\n",
              "      <td>48</td>\n",
              "      <td>D1</td>\n",
              "      <td>18bb41b2c</td>\n",
              "      <td>0.6280</td>\n",
              "      <td>0.5817</td>\n",
              "      <td>1.5540</td>\n",
              "      <td>-0.0764</td>\n",
              "      <td>-0.0323</td>\n",
              "      <td>1.2390</td>\n",
              "      <td>0.1715</td>\n",
              "      <td>0.2155</td>\n",
              "      <td>0.0065</td>\n",
              "      <td>1.2300</td>\n",
              "      <td>-0.4797</td>\n",
              "      <td>-0.5631</td>\n",
              "      <td>-0.0366</td>\n",
              "      <td>-1.8300</td>\n",
              "      <td>0.6057</td>\n",
              "      <td>-0.3278</td>\n",
              "      <td>0.6042</td>\n",
              "      <td>-0.3075</td>\n",
              "      <td>-0.1147</td>\n",
              "      <td>-0.0570</td>\n",
              "      <td>-0.0799</td>\n",
              "      <td>-0.8181</td>\n",
              "      <td>-1.5320</td>\n",
              "      <td>0.2307</td>\n",
              "      <td>0.4901</td>\n",
              "      <td>0.4780</td>\n",
              "      <td>-1.3970</td>\n",
              "      <td>4.6240</td>\n",
              "      <td>-0.0437</td>\n",
              "      <td>1.2870</td>\n",
              "      <td>-1.8530</td>\n",
              "      <td>0.6069</td>\n",
              "      <td>0.4290</td>\n",
              "      <td>0.1783</td>\n",
              "      <td>0.0018</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.5477</td>\n",
              "      <td>-0.7576</td>\n",
              "      <td>-0.0444</td>\n",
              "      <td>0.1894</td>\n",
              "      <td>-0.0014</td>\n",
              "      <td>-2.3640</td>\n",
              "      <td>-0.4682</td>\n",
              "      <td>0.1210</td>\n",
              "      <td>-0.5177</td>\n",
              "      <td>-0.0604</td>\n",
              "      <td>0.1682</td>\n",
              "      <td>-0.4436</td>\n",
              "      <td>0.4963</td>\n",
              "      <td>0.1363</td>\n",
              "      <td>0.3335</td>\n",
              "      <td>0.9760</td>\n",
              "      <td>-0.0427</td>\n",
              "      <td>-0.1235</td>\n",
              "      <td>0.0959</td>\n",
              "      <td>0.0690</td>\n",
              "      <td>-0.9416</td>\n",
              "      <td>-0.7548</td>\n",
              "      <td>-0.1109</td>\n",
              "      <td>-0.6272</td>\n",
              "      <td>0.3019</td>\n",
              "      <td>0.1172</td>\n",
              "      <td>0.1093</td>\n",
              "      <td>-0.3113</td>\n",
              "      <td>0.3019</td>\n",
              "      <td>-0.0873</td>\n",
              "      <td>-0.7250</td>\n",
              "      <td>-0.6297</td>\n",
              "      <td>0.6103</td>\n",
              "      <td>0.0223</td>\n",
              "      <td>-1.3240</td>\n",
              "      <td>-0.3174</td>\n",
              "      <td>-0.6417</td>\n",
              "      <td>-0.2187</td>\n",
              "      <td>-1.4080</td>\n",
              "      <td>0.6931</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>id_0015fd391</td>\n",
              "      <td>com_droga</td>\n",
              "      <td>48</td>\n",
              "      <td>D1</td>\n",
              "      <td>8c7f86626</td>\n",
              "      <td>-0.5138</td>\n",
              "      <td>-0.2491</td>\n",
              "      <td>-0.2656</td>\n",
              "      <td>0.5288</td>\n",
              "      <td>4.0620</td>\n",
              "      <td>-0.8095</td>\n",
              "      <td>-1.9590</td>\n",
              "      <td>0.1792</td>\n",
              "      <td>-0.1321</td>\n",
              "      <td>-1.0600</td>\n",
              "      <td>-0.8269</td>\n",
              "      <td>-0.3584</td>\n",
              "      <td>-0.8511</td>\n",
              "      <td>-0.5844</td>\n",
              "      <td>-2.5690</td>\n",
              "      <td>0.8183</td>\n",
              "      <td>-0.0532</td>\n",
              "      <td>-0.8554</td>\n",
              "      <td>0.1160</td>\n",
              "      <td>-2.3520</td>\n",
              "      <td>2.1200</td>\n",
              "      <td>-1.1580</td>\n",
              "      <td>-0.7191</td>\n",
              "      <td>-0.8004</td>\n",
              "      <td>-1.4670</td>\n",
              "      <td>-0.0107</td>\n",
              "      <td>-0.8995</td>\n",
              "      <td>0.2406</td>\n",
              "      <td>-0.2479</td>\n",
              "      <td>-1.0890</td>\n",
              "      <td>-0.7575</td>\n",
              "      <td>0.0881</td>\n",
              "      <td>-2.7370</td>\n",
              "      <td>0.8745</td>\n",
              "      <td>0.5787</td>\n",
              "      <td>...</td>\n",
              "      <td>-2.1220</td>\n",
              "      <td>-0.3752</td>\n",
              "      <td>-2.3820</td>\n",
              "      <td>-3.7350</td>\n",
              "      <td>-2.9740</td>\n",
              "      <td>-1.4930</td>\n",
              "      <td>-1.6600</td>\n",
              "      <td>-3.1660</td>\n",
              "      <td>0.2816</td>\n",
              "      <td>-0.2990</td>\n",
              "      <td>-1.1870</td>\n",
              "      <td>-0.5044</td>\n",
              "      <td>-1.7750</td>\n",
              "      <td>-1.6120</td>\n",
              "      <td>-0.9215</td>\n",
              "      <td>-1.0810</td>\n",
              "      <td>-3.0520</td>\n",
              "      <td>-3.4470</td>\n",
              "      <td>-2.7740</td>\n",
              "      <td>-1.8460</td>\n",
              "      <td>-0.5568</td>\n",
              "      <td>-3.3960</td>\n",
              "      <td>-2.9510</td>\n",
              "      <td>-1.1550</td>\n",
              "      <td>-3.2620</td>\n",
              "      <td>-1.5390</td>\n",
              "      <td>-2.4600</td>\n",
              "      <td>-0.9417</td>\n",
              "      <td>-1.5550</td>\n",
              "      <td>0.2431</td>\n",
              "      <td>-2.0990</td>\n",
              "      <td>-0.6441</td>\n",
              "      <td>-5.6300</td>\n",
              "      <td>-1.3780</td>\n",
              "      <td>-0.8632</td>\n",
              "      <td>-1.2880</td>\n",
              "      <td>-1.6210</td>\n",
              "      <td>-0.8784</td>\n",
              "      <td>-0.3876</td>\n",
              "      <td>-0.8154</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>id_001626bd3</td>\n",
              "      <td>com_droga</td>\n",
              "      <td>72</td>\n",
              "      <td>D2</td>\n",
              "      <td>7cbed3131</td>\n",
              "      <td>-0.3254</td>\n",
              "      <td>-0.4009</td>\n",
              "      <td>0.9700</td>\n",
              "      <td>0.6919</td>\n",
              "      <td>1.4180</td>\n",
              "      <td>-0.8244</td>\n",
              "      <td>-0.2800</td>\n",
              "      <td>-0.1498</td>\n",
              "      <td>-0.8789</td>\n",
              "      <td>0.8630</td>\n",
              "      <td>-0.2219</td>\n",
              "      <td>-0.5121</td>\n",
              "      <td>-0.9577</td>\n",
              "      <td>1.1750</td>\n",
              "      <td>0.2042</td>\n",
              "      <td>0.1970</td>\n",
              "      <td>0.1244</td>\n",
              "      <td>-1.7090</td>\n",
              "      <td>-0.3543</td>\n",
              "      <td>-0.5160</td>\n",
              "      <td>-0.3330</td>\n",
              "      <td>-0.2685</td>\n",
              "      <td>0.7649</td>\n",
              "      <td>0.2057</td>\n",
              "      <td>1.3720</td>\n",
              "      <td>0.6835</td>\n",
              "      <td>0.8056</td>\n",
              "      <td>-0.3754</td>\n",
              "      <td>-1.2090</td>\n",
              "      <td>0.2965</td>\n",
              "      <td>-0.0712</td>\n",
              "      <td>0.6389</td>\n",
              "      <td>0.6674</td>\n",
              "      <td>-0.0783</td>\n",
              "      <td>1.1740</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.2274</td>\n",
              "      <td>0.3215</td>\n",
              "      <td>0.1535</td>\n",
              "      <td>-0.4640</td>\n",
              "      <td>-0.5943</td>\n",
              "      <td>0.3973</td>\n",
              "      <td>0.1500</td>\n",
              "      <td>0.5178</td>\n",
              "      <td>0.5159</td>\n",
              "      <td>0.6091</td>\n",
              "      <td>0.1813</td>\n",
              "      <td>-0.4249</td>\n",
              "      <td>0.7832</td>\n",
              "      <td>0.6529</td>\n",
              "      <td>0.5648</td>\n",
              "      <td>0.4817</td>\n",
              "      <td>0.0587</td>\n",
              "      <td>0.5303</td>\n",
              "      <td>0.6376</td>\n",
              "      <td>-0.3966</td>\n",
              "      <td>-1.4950</td>\n",
              "      <td>-0.9625</td>\n",
              "      <td>-0.0541</td>\n",
              "      <td>0.6273</td>\n",
              "      <td>0.4563</td>\n",
              "      <td>0.0698</td>\n",
              "      <td>0.8134</td>\n",
              "      <td>0.1924</td>\n",
              "      <td>0.6054</td>\n",
              "      <td>-0.1824</td>\n",
              "      <td>0.0042</td>\n",
              "      <td>0.0048</td>\n",
              "      <td>0.6670</td>\n",
              "      <td>1.0690</td>\n",
              "      <td>0.5523</td>\n",
              "      <td>-0.3031</td>\n",
              "      <td>0.1094</td>\n",
              "      <td>0.2885</td>\n",
              "      <td>-0.3786</td>\n",
              "      <td>0.7125</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 877 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "             id tratamento  tempo dose  ...     c96     c97     c98     c99\n",
              "0  id_000644bb2  com_droga     24   D1  ... -0.3981  0.2139  0.3801  0.4176\n",
              "1  id_000779bfc  com_droga     72   D1  ...  0.1522  0.1241  0.6077  0.7371\n",
              "2  id_000a6266a  com_droga     48   D1  ... -0.6417 -0.2187 -1.4080  0.6931\n",
              "3  id_0015fd391  com_droga     48   D1  ... -1.6210 -0.8784 -0.3876 -0.8154\n",
              "4  id_001626bd3  com_droga     72   D2  ...  0.1094  0.2885 -0.3786  0.7125\n",
              "\n",
              "[5 rows x 877 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 270
        },
        "id": "yjkZtbsidzAO",
        "outputId": "e475c738-6d0d-4446-9bf0-7b4fafc976c5"
      },
      "source": [
        "dados_resultados = pd.read_csv('https://github.com/alura-cursos/imersaodados3/blob/main/dados/dados_resultados.csv?raw=true')\n",
        "\n",
        "dados_resultados.head()"
      ],
      "id": "yjkZtbsidzAO",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>5-alpha_reductase_inhibitor</th>\n",
              "      <th>11-beta-hsd1_inhibitor</th>\n",
              "      <th>acat_inhibitor</th>\n",
              "      <th>acetylcholine_receptor_agonist</th>\n",
              "      <th>acetylcholine_receptor_antagonist</th>\n",
              "      <th>acetylcholinesterase_inhibitor</th>\n",
              "      <th>adenosine_receptor_agonist</th>\n",
              "      <th>adenosine_receptor_antagonist</th>\n",
              "      <th>adenylyl_cyclase_activator</th>\n",
              "      <th>adrenergic_receptor_agonist</th>\n",
              "      <th>adrenergic_receptor_antagonist</th>\n",
              "      <th>akt_inhibitor</th>\n",
              "      <th>aldehyde_dehydrogenase_inhibitor</th>\n",
              "      <th>alk_inhibitor</th>\n",
              "      <th>ampk_activator</th>\n",
              "      <th>analgesic</th>\n",
              "      <th>androgen_receptor_agonist</th>\n",
              "      <th>androgen_receptor_antagonist</th>\n",
              "      <th>anesthetic_-_local</th>\n",
              "      <th>angiogenesis_inhibitor</th>\n",
              "      <th>angiotensin_receptor_antagonist</th>\n",
              "      <th>anti-inflammatory</th>\n",
              "      <th>antiarrhythmic</th>\n",
              "      <th>antibiotic</th>\n",
              "      <th>anticonvulsant</th>\n",
              "      <th>antifungal</th>\n",
              "      <th>antihistamine</th>\n",
              "      <th>antimalarial</th>\n",
              "      <th>antioxidant</th>\n",
              "      <th>antiprotozoal</th>\n",
              "      <th>antiviral</th>\n",
              "      <th>apoptosis_stimulant</th>\n",
              "      <th>aromatase_inhibitor</th>\n",
              "      <th>atm_kinase_inhibitor</th>\n",
              "      <th>atp-sensitive_potassium_channel_antagonist</th>\n",
              "      <th>atp_synthase_inhibitor</th>\n",
              "      <th>atpase_inhibitor</th>\n",
              "      <th>atr_kinase_inhibitor</th>\n",
              "      <th>aurora_kinase_inhibitor</th>\n",
              "      <th>...</th>\n",
              "      <th>protein_synthesis_inhibitor</th>\n",
              "      <th>protein_tyrosine_kinase_inhibitor</th>\n",
              "      <th>radiopaque_medium</th>\n",
              "      <th>raf_inhibitor</th>\n",
              "      <th>ras_gtpase_inhibitor</th>\n",
              "      <th>retinoid_receptor_agonist</th>\n",
              "      <th>retinoid_receptor_antagonist</th>\n",
              "      <th>rho_associated_kinase_inhibitor</th>\n",
              "      <th>ribonucleoside_reductase_inhibitor</th>\n",
              "      <th>rna_polymerase_inhibitor</th>\n",
              "      <th>serotonin_receptor_agonist</th>\n",
              "      <th>serotonin_receptor_antagonist</th>\n",
              "      <th>serotonin_reuptake_inhibitor</th>\n",
              "      <th>sigma_receptor_agonist</th>\n",
              "      <th>sigma_receptor_antagonist</th>\n",
              "      <th>smoothened_receptor_antagonist</th>\n",
              "      <th>sodium_channel_inhibitor</th>\n",
              "      <th>sphingosine_receptor_agonist</th>\n",
              "      <th>src_inhibitor</th>\n",
              "      <th>steroid</th>\n",
              "      <th>syk_inhibitor</th>\n",
              "      <th>tachykinin_antagonist</th>\n",
              "      <th>tgf-beta_receptor_inhibitor</th>\n",
              "      <th>thrombin_inhibitor</th>\n",
              "      <th>thymidylate_synthase_inhibitor</th>\n",
              "      <th>tlr_agonist</th>\n",
              "      <th>tlr_antagonist</th>\n",
              "      <th>tnf_inhibitor</th>\n",
              "      <th>topoisomerase_inhibitor</th>\n",
              "      <th>transient_receptor_potential_channel_antagonist</th>\n",
              "      <th>tropomyosin_receptor_kinase_inhibitor</th>\n",
              "      <th>trpv_agonist</th>\n",
              "      <th>trpv_antagonist</th>\n",
              "      <th>tubulin_inhibitor</th>\n",
              "      <th>tyrosine_kinase_inhibitor</th>\n",
              "      <th>ubiquitin_specific_protease_inhibitor</th>\n",
              "      <th>vegfr_inhibitor</th>\n",
              "      <th>vitamin_b</th>\n",
              "      <th>vitamin_d_receptor_agonist</th>\n",
              "      <th>wnt_inhibitor</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>id_000644bb2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>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>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>id_000779bfc</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>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>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>id_000a6266a</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>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>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>id_0015fd391</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>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>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>id_001626bd3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>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>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 207 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "             id  ...  wnt_inhibitor\n",
              "0  id_000644bb2  ...              0\n",
              "1  id_000779bfc  ...              0\n",
              "2  id_000a6266a  ...              0\n",
              "3  id_0015fd391  ...              0\n",
              "4  id_001626bd3  ...              0\n",
              "\n",
              "[5 rows x 207 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 253
        },
        "id": "mU-NNTAQd5iv",
        "outputId": "b720f4b2-d1a3-4287-ffde-b5e36a26c467"
      },
      "source": [
        "dados_resultados['n_moa'] = dados_resultados.drop('id', axis=1).sum(axis=1) # número mecanismos de ação\n",
        "dados_resultados['ativo_moa'] = (dados_resultados['n_moa'] != 0)\n",
        "\n",
        "dados_combinados = pd.merge(dados, dados_resultados[['id','n_moa', 'ativo_moa']], on='id')\n",
        "dados_combinados.head()"
      ],
      "id": "mU-NNTAQd5iv",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>tratamento</th>\n",
              "      <th>tempo</th>\n",
              "      <th>dose</th>\n",
              "      <th>composto</th>\n",
              "      <th>g0</th>\n",
              "      <th>g1</th>\n",
              "      <th>g2</th>\n",
              "      <th>g3</th>\n",
              "      <th>g4</th>\n",
              "      <th>g5</th>\n",
              "      <th>g6</th>\n",
              "      <th>g7</th>\n",
              "      <th>g8</th>\n",
              "      <th>g9</th>\n",
              "      <th>g10</th>\n",
              "      <th>g11</th>\n",
              "      <th>g12</th>\n",
              "      <th>g13</th>\n",
              "      <th>g14</th>\n",
              "      <th>g15</th>\n",
              "      <th>g16</th>\n",
              "      <th>g17</th>\n",
              "      <th>g18</th>\n",
              "      <th>g19</th>\n",
              "      <th>g20</th>\n",
              "      <th>g21</th>\n",
              "      <th>g22</th>\n",
              "      <th>g23</th>\n",
              "      <th>g24</th>\n",
              "      <th>g25</th>\n",
              "      <th>g26</th>\n",
              "      <th>g27</th>\n",
              "      <th>g28</th>\n",
              "      <th>g29</th>\n",
              "      <th>g30</th>\n",
              "      <th>g31</th>\n",
              "      <th>g32</th>\n",
              "      <th>g33</th>\n",
              "      <th>g34</th>\n",
              "      <th>...</th>\n",
              "      <th>c62</th>\n",
              "      <th>c63</th>\n",
              "      <th>c64</th>\n",
              "      <th>c65</th>\n",
              "      <th>c66</th>\n",
              "      <th>c67</th>\n",
              "      <th>c68</th>\n",
              "      <th>c69</th>\n",
              "      <th>c70</th>\n",
              "      <th>c71</th>\n",
              "      <th>c72</th>\n",
              "      <th>c73</th>\n",
              "      <th>c74</th>\n",
              "      <th>c75</th>\n",
              "      <th>c76</th>\n",
              "      <th>c77</th>\n",
              "      <th>c78</th>\n",
              "      <th>c79</th>\n",
              "      <th>c80</th>\n",
              "      <th>c81</th>\n",
              "      <th>c82</th>\n",
              "      <th>c83</th>\n",
              "      <th>c84</th>\n",
              "      <th>c85</th>\n",
              "      <th>c86</th>\n",
              "      <th>c87</th>\n",
              "      <th>c88</th>\n",
              "      <th>c89</th>\n",
              "      <th>c90</th>\n",
              "      <th>c91</th>\n",
              "      <th>c92</th>\n",
              "      <th>c93</th>\n",
              "      <th>c94</th>\n",
              "      <th>c95</th>\n",
              "      <th>c96</th>\n",
              "      <th>c97</th>\n",
              "      <th>c98</th>\n",
              "      <th>c99</th>\n",
              "      <th>n_moa</th>\n",
              "      <th>ativo_moa</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>id_000644bb2</td>\n",
              "      <td>com_droga</td>\n",
              "      <td>24</td>\n",
              "      <td>D1</td>\n",
              "      <td>b68db1d53</td>\n",
              "      <td>1.0620</td>\n",
              "      <td>0.5577</td>\n",
              "      <td>-0.2479</td>\n",
              "      <td>-0.6208</td>\n",
              "      <td>-0.1944</td>\n",
              "      <td>-1.0120</td>\n",
              "      <td>-1.0220</td>\n",
              "      <td>-0.0326</td>\n",
              "      <td>0.5548</td>\n",
              "      <td>-0.0921</td>\n",
              "      <td>1.1830</td>\n",
              "      <td>0.1530</td>\n",
              "      <td>0.5574</td>\n",
              "      <td>-0.4015</td>\n",
              "      <td>0.1789</td>\n",
              "      <td>-0.6528</td>\n",
              "      <td>-0.7969</td>\n",
              "      <td>0.6342</td>\n",
              "      <td>0.1778</td>\n",
              "      <td>-0.3694</td>\n",
              "      <td>-0.5688</td>\n",
              "      <td>-1.1360</td>\n",
              "      <td>-1.1880</td>\n",
              "      <td>0.6940</td>\n",
              "      <td>0.4393</td>\n",
              "      <td>0.2664</td>\n",
              "      <td>0.1907</td>\n",
              "      <td>0.1628</td>\n",
              "      <td>-0.2853</td>\n",
              "      <td>0.5819</td>\n",
              "      <td>0.2934</td>\n",
              "      <td>-0.5584</td>\n",
              "      <td>-0.0916</td>\n",
              "      <td>-0.3010</td>\n",
              "      <td>-0.1537</td>\n",
              "      <td>...</td>\n",
              "      <td>0.3680</td>\n",
              "      <td>0.8427</td>\n",
              "      <td>0.1042</td>\n",
              "      <td>0.1403</td>\n",
              "      <td>0.1758</td>\n",
              "      <td>1.2570</td>\n",
              "      <td>-0.5979</td>\n",
              "      <td>1.2250</td>\n",
              "      <td>-0.0553</td>\n",
              "      <td>0.7351</td>\n",
              "      <td>0.5810</td>\n",
              "      <td>0.9590</td>\n",
              "      <td>0.2427</td>\n",
              "      <td>0.0495</td>\n",
              "      <td>0.4141</td>\n",
              "      <td>0.8432</td>\n",
              "      <td>0.6162</td>\n",
              "      <td>-0.7318</td>\n",
              "      <td>1.2120</td>\n",
              "      <td>0.6362</td>\n",
              "      <td>-0.4427</td>\n",
              "      <td>0.1288</td>\n",
              "      <td>1.4840</td>\n",
              "      <td>0.1799</td>\n",
              "      <td>0.5367</td>\n",
              "      <td>-0.1111</td>\n",
              "      <td>-1.0120</td>\n",
              "      <td>0.6685</td>\n",
              "      <td>0.2862</td>\n",
              "      <td>0.2584</td>\n",
              "      <td>0.8076</td>\n",
              "      <td>0.5523</td>\n",
              "      <td>-0.1912</td>\n",
              "      <td>0.6584</td>\n",
              "      <td>-0.3981</td>\n",
              "      <td>0.2139</td>\n",
              "      <td>0.3801</td>\n",
              "      <td>0.4176</td>\n",
              "      <td>1</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>id_000779bfc</td>\n",
              "      <td>com_droga</td>\n",
              "      <td>72</td>\n",
              "      <td>D1</td>\n",
              "      <td>df89a8e5a</td>\n",
              "      <td>0.0743</td>\n",
              "      <td>0.4087</td>\n",
              "      <td>0.2991</td>\n",
              "      <td>0.0604</td>\n",
              "      <td>1.0190</td>\n",
              "      <td>0.5207</td>\n",
              "      <td>0.2341</td>\n",
              "      <td>0.3372</td>\n",
              "      <td>-0.4047</td>\n",
              "      <td>0.8507</td>\n",
              "      <td>-1.1520</td>\n",
              "      <td>-0.4201</td>\n",
              "      <td>-0.0958</td>\n",
              "      <td>0.4590</td>\n",
              "      <td>0.0803</td>\n",
              "      <td>0.2250</td>\n",
              "      <td>0.5293</td>\n",
              "      <td>0.2839</td>\n",
              "      <td>-0.3494</td>\n",
              "      <td>0.2883</td>\n",
              "      <td>0.9449</td>\n",
              "      <td>-0.1646</td>\n",
              "      <td>-0.2657</td>\n",
              "      <td>-0.3372</td>\n",
              "      <td>0.3135</td>\n",
              "      <td>-0.4316</td>\n",
              "      <td>0.4773</td>\n",
              "      <td>0.2075</td>\n",
              "      <td>-0.4216</td>\n",
              "      <td>-0.1161</td>\n",
              "      <td>-0.0499</td>\n",
              "      <td>-0.2627</td>\n",
              "      <td>0.9959</td>\n",
              "      <td>-0.2483</td>\n",
              "      <td>0.2655</td>\n",
              "      <td>...</td>\n",
              "      <td>0.3905</td>\n",
              "      <td>0.7099</td>\n",
              "      <td>0.2912</td>\n",
              "      <td>0.4151</td>\n",
              "      <td>-0.2840</td>\n",
              "      <td>-0.3104</td>\n",
              "      <td>-0.6373</td>\n",
              "      <td>0.2887</td>\n",
              "      <td>-0.0765</td>\n",
              "      <td>0.2539</td>\n",
              "      <td>0.4443</td>\n",
              "      <td>0.5932</td>\n",
              "      <td>0.2031</td>\n",
              "      <td>0.7639</td>\n",
              "      <td>0.5499</td>\n",
              "      <td>-0.3322</td>\n",
              "      <td>-0.0977</td>\n",
              "      <td>0.4329</td>\n",
              "      <td>-0.2782</td>\n",
              "      <td>0.7827</td>\n",
              "      <td>0.5934</td>\n",
              "      <td>0.3402</td>\n",
              "      <td>0.1499</td>\n",
              "      <td>0.4420</td>\n",
              "      <td>0.9366</td>\n",
              "      <td>0.8193</td>\n",
              "      <td>-0.4236</td>\n",
              "      <td>0.3192</td>\n",
              "      <td>-0.4265</td>\n",
              "      <td>0.7543</td>\n",
              "      <td>0.4708</td>\n",
              "      <td>0.0230</td>\n",
              "      <td>0.2957</td>\n",
              "      <td>0.4899</td>\n",
              "      <td>0.1522</td>\n",
              "      <td>0.1241</td>\n",
              "      <td>0.6077</td>\n",
              "      <td>0.7371</td>\n",
              "      <td>0</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>id_000a6266a</td>\n",
              "      <td>com_droga</td>\n",
              "      <td>48</td>\n",
              "      <td>D1</td>\n",
              "      <td>18bb41b2c</td>\n",
              "      <td>0.6280</td>\n",
              "      <td>0.5817</td>\n",
              "      <td>1.5540</td>\n",
              "      <td>-0.0764</td>\n",
              "      <td>-0.0323</td>\n",
              "      <td>1.2390</td>\n",
              "      <td>0.1715</td>\n",
              "      <td>0.2155</td>\n",
              "      <td>0.0065</td>\n",
              "      <td>1.2300</td>\n",
              "      <td>-0.4797</td>\n",
              "      <td>-0.5631</td>\n",
              "      <td>-0.0366</td>\n",
              "      <td>-1.8300</td>\n",
              "      <td>0.6057</td>\n",
              "      <td>-0.3278</td>\n",
              "      <td>0.6042</td>\n",
              "      <td>-0.3075</td>\n",
              "      <td>-0.1147</td>\n",
              "      <td>-0.0570</td>\n",
              "      <td>-0.0799</td>\n",
              "      <td>-0.8181</td>\n",
              "      <td>-1.5320</td>\n",
              "      <td>0.2307</td>\n",
              "      <td>0.4901</td>\n",
              "      <td>0.4780</td>\n",
              "      <td>-1.3970</td>\n",
              "      <td>4.6240</td>\n",
              "      <td>-0.0437</td>\n",
              "      <td>1.2870</td>\n",
              "      <td>-1.8530</td>\n",
              "      <td>0.6069</td>\n",
              "      <td>0.4290</td>\n",
              "      <td>0.1783</td>\n",
              "      <td>0.0018</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.0444</td>\n",
              "      <td>0.1894</td>\n",
              "      <td>-0.0014</td>\n",
              "      <td>-2.3640</td>\n",
              "      <td>-0.4682</td>\n",
              "      <td>0.1210</td>\n",
              "      <td>-0.5177</td>\n",
              "      <td>-0.0604</td>\n",
              "      <td>0.1682</td>\n",
              "      <td>-0.4436</td>\n",
              "      <td>0.4963</td>\n",
              "      <td>0.1363</td>\n",
              "      <td>0.3335</td>\n",
              "      <td>0.9760</td>\n",
              "      <td>-0.0427</td>\n",
              "      <td>-0.1235</td>\n",
              "      <td>0.0959</td>\n",
              "      <td>0.0690</td>\n",
              "      <td>-0.9416</td>\n",
              "      <td>-0.7548</td>\n",
              "      <td>-0.1109</td>\n",
              "      <td>-0.6272</td>\n",
              "      <td>0.3019</td>\n",
              "      <td>0.1172</td>\n",
              "      <td>0.1093</td>\n",
              "      <td>-0.3113</td>\n",
              "      <td>0.3019</td>\n",
              "      <td>-0.0873</td>\n",
              "      <td>-0.7250</td>\n",
              "      <td>-0.6297</td>\n",
              "      <td>0.6103</td>\n",
              "      <td>0.0223</td>\n",
              "      <td>-1.3240</td>\n",
              "      <td>-0.3174</td>\n",
              "      <td>-0.6417</td>\n",
              "      <td>-0.2187</td>\n",
              "      <td>-1.4080</td>\n",
              "      <td>0.6931</td>\n",
              "      <td>3</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>id_0015fd391</td>\n",
              "      <td>com_droga</td>\n",
              "      <td>48</td>\n",
              "      <td>D1</td>\n",
              "      <td>8c7f86626</td>\n",
              "      <td>-0.5138</td>\n",
              "      <td>-0.2491</td>\n",
              "      <td>-0.2656</td>\n",
              "      <td>0.5288</td>\n",
              "      <td>4.0620</td>\n",
              "      <td>-0.8095</td>\n",
              "      <td>-1.9590</td>\n",
              "      <td>0.1792</td>\n",
              "      <td>-0.1321</td>\n",
              "      <td>-1.0600</td>\n",
              "      <td>-0.8269</td>\n",
              "      <td>-0.3584</td>\n",
              "      <td>-0.8511</td>\n",
              "      <td>-0.5844</td>\n",
              "      <td>-2.5690</td>\n",
              "      <td>0.8183</td>\n",
              "      <td>-0.0532</td>\n",
              "      <td>-0.8554</td>\n",
              "      <td>0.1160</td>\n",
              "      <td>-2.3520</td>\n",
              "      <td>2.1200</td>\n",
              "      <td>-1.1580</td>\n",
              "      <td>-0.7191</td>\n",
              "      <td>-0.8004</td>\n",
              "      <td>-1.4670</td>\n",
              "      <td>-0.0107</td>\n",
              "      <td>-0.8995</td>\n",
              "      <td>0.2406</td>\n",
              "      <td>-0.2479</td>\n",
              "      <td>-1.0890</td>\n",
              "      <td>-0.7575</td>\n",
              "      <td>0.0881</td>\n",
              "      <td>-2.7370</td>\n",
              "      <td>0.8745</td>\n",
              "      <td>0.5787</td>\n",
              "      <td>...</td>\n",
              "      <td>-2.3820</td>\n",
              "      <td>-3.7350</td>\n",
              "      <td>-2.9740</td>\n",
              "      <td>-1.4930</td>\n",
              "      <td>-1.6600</td>\n",
              "      <td>-3.1660</td>\n",
              "      <td>0.2816</td>\n",
              "      <td>-0.2990</td>\n",
              "      <td>-1.1870</td>\n",
              "      <td>-0.5044</td>\n",
              "      <td>-1.7750</td>\n",
              "      <td>-1.6120</td>\n",
              "      <td>-0.9215</td>\n",
              "      <td>-1.0810</td>\n",
              "      <td>-3.0520</td>\n",
              "      <td>-3.4470</td>\n",
              "      <td>-2.7740</td>\n",
              "      <td>-1.8460</td>\n",
              "      <td>-0.5568</td>\n",
              "      <td>-3.3960</td>\n",
              "      <td>-2.9510</td>\n",
              "      <td>-1.1550</td>\n",
              "      <td>-3.2620</td>\n",
              "      <td>-1.5390</td>\n",
              "      <td>-2.4600</td>\n",
              "      <td>-0.9417</td>\n",
              "      <td>-1.5550</td>\n",
              "      <td>0.2431</td>\n",
              "      <td>-2.0990</td>\n",
              "      <td>-0.6441</td>\n",
              "      <td>-5.6300</td>\n",
              "      <td>-1.3780</td>\n",
              "      <td>-0.8632</td>\n",
              "      <td>-1.2880</td>\n",
              "      <td>-1.6210</td>\n",
              "      <td>-0.8784</td>\n",
              "      <td>-0.3876</td>\n",
              "      <td>-0.8154</td>\n",
              "      <td>0</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>id_001626bd3</td>\n",
              "      <td>com_droga</td>\n",
              "      <td>72</td>\n",
              "      <td>D2</td>\n",
              "      <td>7cbed3131</td>\n",
              "      <td>-0.3254</td>\n",
              "      <td>-0.4009</td>\n",
              "      <td>0.9700</td>\n",
              "      <td>0.6919</td>\n",
              "      <td>1.4180</td>\n",
              "      <td>-0.8244</td>\n",
              "      <td>-0.2800</td>\n",
              "      <td>-0.1498</td>\n",
              "      <td>-0.8789</td>\n",
              "      <td>0.8630</td>\n",
              "      <td>-0.2219</td>\n",
              "      <td>-0.5121</td>\n",
              "      <td>-0.9577</td>\n",
              "      <td>1.1750</td>\n",
              "      <td>0.2042</td>\n",
              "      <td>0.1970</td>\n",
              "      <td>0.1244</td>\n",
              "      <td>-1.7090</td>\n",
              "      <td>-0.3543</td>\n",
              "      <td>-0.5160</td>\n",
              "      <td>-0.3330</td>\n",
              "      <td>-0.2685</td>\n",
              "      <td>0.7649</td>\n",
              "      <td>0.2057</td>\n",
              "      <td>1.3720</td>\n",
              "      <td>0.6835</td>\n",
              "      <td>0.8056</td>\n",
              "      <td>-0.3754</td>\n",
              "      <td>-1.2090</td>\n",
              "      <td>0.2965</td>\n",
              "      <td>-0.0712</td>\n",
              "      <td>0.6389</td>\n",
              "      <td>0.6674</td>\n",
              "      <td>-0.0783</td>\n",
              "      <td>1.1740</td>\n",
              "      <td>...</td>\n",
              "      <td>0.1535</td>\n",
              "      <td>-0.4640</td>\n",
              "      <td>-0.5943</td>\n",
              "      <td>0.3973</td>\n",
              "      <td>0.1500</td>\n",
              "      <td>0.5178</td>\n",
              "      <td>0.5159</td>\n",
              "      <td>0.6091</td>\n",
              "      <td>0.1813</td>\n",
              "      <td>-0.4249</td>\n",
              "      <td>0.7832</td>\n",
              "      <td>0.6529</td>\n",
              "      <td>0.5648</td>\n",
              "      <td>0.4817</td>\n",
              "      <td>0.0587</td>\n",
              "      <td>0.5303</td>\n",
              "      <td>0.6376</td>\n",
              "      <td>-0.3966</td>\n",
              "      <td>-1.4950</td>\n",
              "      <td>-0.9625</td>\n",
              "      <td>-0.0541</td>\n",
              "      <td>0.6273</td>\n",
              "      <td>0.4563</td>\n",
              "      <td>0.0698</td>\n",
              "      <td>0.8134</td>\n",
              "      <td>0.1924</td>\n",
              "      <td>0.6054</td>\n",
              "      <td>-0.1824</td>\n",
              "      <td>0.0042</td>\n",
              "      <td>0.0048</td>\n",
              "      <td>0.6670</td>\n",
              "      <td>1.0690</td>\n",
              "      <td>0.5523</td>\n",
              "      <td>-0.3031</td>\n",
              "      <td>0.1094</td>\n",
              "      <td>0.2885</td>\n",
              "      <td>-0.3786</td>\n",
              "      <td>0.7125</td>\n",
              "      <td>1</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 879 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "             id tratamento  tempo dose  ...     c98     c99  n_moa  ativo_moa\n",
              "0  id_000644bb2  com_droga     24   D1  ...  0.3801  0.4176      1       True\n",
              "1  id_000779bfc  com_droga     72   D1  ...  0.6077  0.7371      0      False\n",
              "2  id_000a6266a  com_droga     48   D1  ... -1.4080  0.6931      3       True\n",
              "3  id_0015fd391  com_droga     48   D1  ... -0.3876 -0.8154      0      False\n",
              "4  id_001626bd3  com_droga     72   D2  ... -0.3786  0.7125      1       True\n",
              "\n",
              "[5 rows x 879 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 253
        },
        "id": "1f612041",
        "outputId": "9257dc26-07db-46e2-8d7d-0d4dd89c37fb"
      },
      "source": [
        "dados_combinados.head()"
      ],
      "id": "1f612041",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>tratamento</th>\n",
              "      <th>tempo</th>\n",
              "      <th>dose</th>\n",
              "      <th>composto</th>\n",
              "      <th>g0</th>\n",
              "      <th>g1</th>\n",
              "      <th>g2</th>\n",
              "      <th>g3</th>\n",
              "      <th>g4</th>\n",
              "      <th>g5</th>\n",
              "      <th>g6</th>\n",
              "      <th>g7</th>\n",
              "      <th>g8</th>\n",
              "      <th>g9</th>\n",
              "      <th>g10</th>\n",
              "      <th>g11</th>\n",
              "      <th>g12</th>\n",
              "      <th>g13</th>\n",
              "      <th>g14</th>\n",
              "      <th>g15</th>\n",
              "      <th>g16</th>\n",
              "      <th>g17</th>\n",
              "      <th>g18</th>\n",
              "      <th>g19</th>\n",
              "      <th>g20</th>\n",
              "      <th>g21</th>\n",
              "      <th>g22</th>\n",
              "      <th>g23</th>\n",
              "      <th>g24</th>\n",
              "      <th>g25</th>\n",
              "      <th>g26</th>\n",
              "      <th>g27</th>\n",
              "      <th>g28</th>\n",
              "      <th>g29</th>\n",
              "      <th>g30</th>\n",
              "      <th>g31</th>\n",
              "      <th>g32</th>\n",
              "      <th>g33</th>\n",
              "      <th>g34</th>\n",
              "      <th>...</th>\n",
              "      <th>c62</th>\n",
              "      <th>c63</th>\n",
              "      <th>c64</th>\n",
              "      <th>c65</th>\n",
              "      <th>c66</th>\n",
              "      <th>c67</th>\n",
              "      <th>c68</th>\n",
              "      <th>c69</th>\n",
              "      <th>c70</th>\n",
              "      <th>c71</th>\n",
              "      <th>c72</th>\n",
              "      <th>c73</th>\n",
              "      <th>c74</th>\n",
              "      <th>c75</th>\n",
              "      <th>c76</th>\n",
              "      <th>c77</th>\n",
              "      <th>c78</th>\n",
              "      <th>c79</th>\n",
              "      <th>c80</th>\n",
              "      <th>c81</th>\n",
              "      <th>c82</th>\n",
              "      <th>c83</th>\n",
              "      <th>c84</th>\n",
              "      <th>c85</th>\n",
              "      <th>c86</th>\n",
              "      <th>c87</th>\n",
              "      <th>c88</th>\n",
              "      <th>c89</th>\n",
              "      <th>c90</th>\n",
              "      <th>c91</th>\n",
              "      <th>c92</th>\n",
              "      <th>c93</th>\n",
              "      <th>c94</th>\n",
              "      <th>c95</th>\n",
              "      <th>c96</th>\n",
              "      <th>c97</th>\n",
              "      <th>c98</th>\n",
              "      <th>c99</th>\n",
              "      <th>n_moa</th>\n",
              "      <th>ativo_moa</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>id_000644bb2</td>\n",
              "      <td>com_droga</td>\n",
              "      <td>24</td>\n",
              "      <td>D1</td>\n",
              "      <td>b68db1d53</td>\n",
              "      <td>1.0620</td>\n",
              "      <td>0.5577</td>\n",
              "      <td>-0.2479</td>\n",
              "      <td>-0.6208</td>\n",
              "      <td>-0.1944</td>\n",
              "      <td>-1.0120</td>\n",
              "      <td>-1.0220</td>\n",
              "      <td>-0.0326</td>\n",
              "      <td>0.5548</td>\n",
              "      <td>-0.0921</td>\n",
              "      <td>1.1830</td>\n",
              "      <td>0.1530</td>\n",
              "      <td>0.5574</td>\n",
              "      <td>-0.4015</td>\n",
              "      <td>0.1789</td>\n",
              "      <td>-0.6528</td>\n",
              "      <td>-0.7969</td>\n",
              "      <td>0.6342</td>\n",
              "      <td>0.1778</td>\n",
              "      <td>-0.3694</td>\n",
              "      <td>-0.5688</td>\n",
              "      <td>-1.1360</td>\n",
              "      <td>-1.1880</td>\n",
              "      <td>0.6940</td>\n",
              "      <td>0.4393</td>\n",
              "      <td>0.2664</td>\n",
              "      <td>0.1907</td>\n",
              "      <td>0.1628</td>\n",
              "      <td>-0.2853</td>\n",
              "      <td>0.5819</td>\n",
              "      <td>0.2934</td>\n",
              "      <td>-0.5584</td>\n",
              "      <td>-0.0916</td>\n",
              "      <td>-0.3010</td>\n",
              "      <td>-0.1537</td>\n",
              "      <td>...</td>\n",
              "      <td>0.3680</td>\n",
              "      <td>0.8427</td>\n",
              "      <td>0.1042</td>\n",
              "      <td>0.1403</td>\n",
              "      <td>0.1758</td>\n",
              "      <td>1.2570</td>\n",
              "      <td>-0.5979</td>\n",
              "      <td>1.2250</td>\n",
              "      <td>-0.0553</td>\n",
              "      <td>0.7351</td>\n",
              "      <td>0.5810</td>\n",
              "      <td>0.9590</td>\n",
              "      <td>0.2427</td>\n",
              "      <td>0.0495</td>\n",
              "      <td>0.4141</td>\n",
              "      <td>0.8432</td>\n",
              "      <td>0.6162</td>\n",
              "      <td>-0.7318</td>\n",
              "      <td>1.2120</td>\n",
              "      <td>0.6362</td>\n",
              "      <td>-0.4427</td>\n",
              "      <td>0.1288</td>\n",
              "      <td>1.4840</td>\n",
              "      <td>0.1799</td>\n",
              "      <td>0.5367</td>\n",
              "      <td>-0.1111</td>\n",
              "      <td>-1.0120</td>\n",
              "      <td>0.6685</td>\n",
              "      <td>0.2862</td>\n",
              "      <td>0.2584</td>\n",
              "      <td>0.8076</td>\n",
              "      <td>0.5523</td>\n",
              "      <td>-0.1912</td>\n",
              "      <td>0.6584</td>\n",
              "      <td>-0.3981</td>\n",
              "      <td>0.2139</td>\n",
              "      <td>0.3801</td>\n",
              "      <td>0.4176</td>\n",
              "      <td>1</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>id_000779bfc</td>\n",
              "      <td>com_droga</td>\n",
              "      <td>72</td>\n",
              "      <td>D1</td>\n",
              "      <td>df89a8e5a</td>\n",
              "      <td>0.0743</td>\n",
              "      <td>0.4087</td>\n",
              "      <td>0.2991</td>\n",
              "      <td>0.0604</td>\n",
              "      <td>1.0190</td>\n",
              "      <td>0.5207</td>\n",
              "      <td>0.2341</td>\n",
              "      <td>0.3372</td>\n",
              "      <td>-0.4047</td>\n",
              "      <td>0.8507</td>\n",
              "      <td>-1.1520</td>\n",
              "      <td>-0.4201</td>\n",
              "      <td>-0.0958</td>\n",
              "      <td>0.4590</td>\n",
              "      <td>0.0803</td>\n",
              "      <td>0.2250</td>\n",
              "      <td>0.5293</td>\n",
              "      <td>0.2839</td>\n",
              "      <td>-0.3494</td>\n",
              "      <td>0.2883</td>\n",
              "      <td>0.9449</td>\n",
              "      <td>-0.1646</td>\n",
              "      <td>-0.2657</td>\n",
              "      <td>-0.3372</td>\n",
              "      <td>0.3135</td>\n",
              "      <td>-0.4316</td>\n",
              "      <td>0.4773</td>\n",
              "      <td>0.2075</td>\n",
              "      <td>-0.4216</td>\n",
              "      <td>-0.1161</td>\n",
              "      <td>-0.0499</td>\n",
              "      <td>-0.2627</td>\n",
              "      <td>0.9959</td>\n",
              "      <td>-0.2483</td>\n",
              "      <td>0.2655</td>\n",
              "      <td>...</td>\n",
              "      <td>0.3905</td>\n",
              "      <td>0.7099</td>\n",
              "      <td>0.2912</td>\n",
              "      <td>0.4151</td>\n",
              "      <td>-0.2840</td>\n",
              "      <td>-0.3104</td>\n",
              "      <td>-0.6373</td>\n",
              "      <td>0.2887</td>\n",
              "      <td>-0.0765</td>\n",
              "      <td>0.2539</td>\n",
              "      <td>0.4443</td>\n",
              "      <td>0.5932</td>\n",
              "      <td>0.2031</td>\n",
              "      <td>0.7639</td>\n",
              "      <td>0.5499</td>\n",
              "      <td>-0.3322</td>\n",
              "      <td>-0.0977</td>\n",
              "      <td>0.4329</td>\n",
              "      <td>-0.2782</td>\n",
              "      <td>0.7827</td>\n",
              "      <td>0.5934</td>\n",
              "      <td>0.3402</td>\n",
              "      <td>0.1499</td>\n",
              "      <td>0.4420</td>\n",
              "      <td>0.9366</td>\n",
              "      <td>0.8193</td>\n",
              "      <td>-0.4236</td>\n",
              "      <td>0.3192</td>\n",
              "      <td>-0.4265</td>\n",
              "      <td>0.7543</td>\n",
              "      <td>0.4708</td>\n",
              "      <td>0.0230</td>\n",
              "      <td>0.2957</td>\n",
              "      <td>0.4899</td>\n",
              "      <td>0.1522</td>\n",
              "      <td>0.1241</td>\n",
              "      <td>0.6077</td>\n",
              "      <td>0.7371</td>\n",
              "      <td>0</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>id_000a6266a</td>\n",
              "      <td>com_droga</td>\n",
              "      <td>48</td>\n",
              "      <td>D1</td>\n",
              "      <td>18bb41b2c</td>\n",
              "      <td>0.6280</td>\n",
              "      <td>0.5817</td>\n",
              "      <td>1.5540</td>\n",
              "      <td>-0.0764</td>\n",
              "      <td>-0.0323</td>\n",
              "      <td>1.2390</td>\n",
              "      <td>0.1715</td>\n",
              "      <td>0.2155</td>\n",
              "      <td>0.0065</td>\n",
              "      <td>1.2300</td>\n",
              "      <td>-0.4797</td>\n",
              "      <td>-0.5631</td>\n",
              "      <td>-0.0366</td>\n",
              "      <td>-1.8300</td>\n",
              "      <td>0.6057</td>\n",
              "      <td>-0.3278</td>\n",
              "      <td>0.6042</td>\n",
              "      <td>-0.3075</td>\n",
              "      <td>-0.1147</td>\n",
              "      <td>-0.0570</td>\n",
              "      <td>-0.0799</td>\n",
              "      <td>-0.8181</td>\n",
              "      <td>-1.5320</td>\n",
              "      <td>0.2307</td>\n",
              "      <td>0.4901</td>\n",
              "      <td>0.4780</td>\n",
              "      <td>-1.3970</td>\n",
              "      <td>4.6240</td>\n",
              "      <td>-0.0437</td>\n",
              "      <td>1.2870</td>\n",
              "      <td>-1.8530</td>\n",
              "      <td>0.6069</td>\n",
              "      <td>0.4290</td>\n",
              "      <td>0.1783</td>\n",
              "      <td>0.0018</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.0444</td>\n",
              "      <td>0.1894</td>\n",
              "      <td>-0.0014</td>\n",
              "      <td>-2.3640</td>\n",
              "      <td>-0.4682</td>\n",
              "      <td>0.1210</td>\n",
              "      <td>-0.5177</td>\n",
              "      <td>-0.0604</td>\n",
              "      <td>0.1682</td>\n",
              "      <td>-0.4436</td>\n",
              "      <td>0.4963</td>\n",
              "      <td>0.1363</td>\n",
              "      <td>0.3335</td>\n",
              "      <td>0.9760</td>\n",
              "      <td>-0.0427</td>\n",
              "      <td>-0.1235</td>\n",
              "      <td>0.0959</td>\n",
              "      <td>0.0690</td>\n",
              "      <td>-0.9416</td>\n",
              "      <td>-0.7548</td>\n",
              "      <td>-0.1109</td>\n",
              "      <td>-0.6272</td>\n",
              "      <td>0.3019</td>\n",
              "      <td>0.1172</td>\n",
              "      <td>0.1093</td>\n",
              "      <td>-0.3113</td>\n",
              "      <td>0.3019</td>\n",
              "      <td>-0.0873</td>\n",
              "      <td>-0.7250</td>\n",
              "      <td>-0.6297</td>\n",
              "      <td>0.6103</td>\n",
              "      <td>0.0223</td>\n",
              "      <td>-1.3240</td>\n",
              "      <td>-0.3174</td>\n",
              "      <td>-0.6417</td>\n",
              "      <td>-0.2187</td>\n",
              "      <td>-1.4080</td>\n",
              "      <td>0.6931</td>\n",
              "      <td>3</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>id_0015fd391</td>\n",
              "      <td>com_droga</td>\n",
              "      <td>48</td>\n",
              "      <td>D1</td>\n",
              "      <td>8c7f86626</td>\n",
              "      <td>-0.5138</td>\n",
              "      <td>-0.2491</td>\n",
              "      <td>-0.2656</td>\n",
              "      <td>0.5288</td>\n",
              "      <td>4.0620</td>\n",
              "      <td>-0.8095</td>\n",
              "      <td>-1.9590</td>\n",
              "      <td>0.1792</td>\n",
              "      <td>-0.1321</td>\n",
              "      <td>-1.0600</td>\n",
              "      <td>-0.8269</td>\n",
              "      <td>-0.3584</td>\n",
              "      <td>-0.8511</td>\n",
              "      <td>-0.5844</td>\n",
              "      <td>-2.5690</td>\n",
              "      <td>0.8183</td>\n",
              "      <td>-0.0532</td>\n",
              "      <td>-0.8554</td>\n",
              "      <td>0.1160</td>\n",
              "      <td>-2.3520</td>\n",
              "      <td>2.1200</td>\n",
              "      <td>-1.1580</td>\n",
              "      <td>-0.7191</td>\n",
              "      <td>-0.8004</td>\n",
              "      <td>-1.4670</td>\n",
              "      <td>-0.0107</td>\n",
              "      <td>-0.8995</td>\n",
              "      <td>0.2406</td>\n",
              "      <td>-0.2479</td>\n",
              "      <td>-1.0890</td>\n",
              "      <td>-0.7575</td>\n",
              "      <td>0.0881</td>\n",
              "      <td>-2.7370</td>\n",
              "      <td>0.8745</td>\n",
              "      <td>0.5787</td>\n",
              "      <td>...</td>\n",
              "      <td>-2.3820</td>\n",
              "      <td>-3.7350</td>\n",
              "      <td>-2.9740</td>\n",
              "      <td>-1.4930</td>\n",
              "      <td>-1.6600</td>\n",
              "      <td>-3.1660</td>\n",
              "      <td>0.2816</td>\n",
              "      <td>-0.2990</td>\n",
              "      <td>-1.1870</td>\n",
              "      <td>-0.5044</td>\n",
              "      <td>-1.7750</td>\n",
              "      <td>-1.6120</td>\n",
              "      <td>-0.9215</td>\n",
              "      <td>-1.0810</td>\n",
              "      <td>-3.0520</td>\n",
              "      <td>-3.4470</td>\n",
              "      <td>-2.7740</td>\n",
              "      <td>-1.8460</td>\n",
              "      <td>-0.5568</td>\n",
              "      <td>-3.3960</td>\n",
              "      <td>-2.9510</td>\n",
              "      <td>-1.1550</td>\n",
              "      <td>-3.2620</td>\n",
              "      <td>-1.5390</td>\n",
              "      <td>-2.4600</td>\n",
              "      <td>-0.9417</td>\n",
              "      <td>-1.5550</td>\n",
              "      <td>0.2431</td>\n",
              "      <td>-2.0990</td>\n",
              "      <td>-0.6441</td>\n",
              "      <td>-5.6300</td>\n",
              "      <td>-1.3780</td>\n",
              "      <td>-0.8632</td>\n",
              "      <td>-1.2880</td>\n",
              "      <td>-1.6210</td>\n",
              "      <td>-0.8784</td>\n",
              "      <td>-0.3876</td>\n",
              "      <td>-0.8154</td>\n",
              "      <td>0</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>id_001626bd3</td>\n",
              "      <td>com_droga</td>\n",
              "      <td>72</td>\n",
              "      <td>D2</td>\n",
              "      <td>7cbed3131</td>\n",
              "      <td>-0.3254</td>\n",
              "      <td>-0.4009</td>\n",
              "      <td>0.9700</td>\n",
              "      <td>0.6919</td>\n",
              "      <td>1.4180</td>\n",
              "      <td>-0.8244</td>\n",
              "      <td>-0.2800</td>\n",
              "      <td>-0.1498</td>\n",
              "      <td>-0.8789</td>\n",
              "      <td>0.8630</td>\n",
              "      <td>-0.2219</td>\n",
              "      <td>-0.5121</td>\n",
              "      <td>-0.9577</td>\n",
              "      <td>1.1750</td>\n",
              "      <td>0.2042</td>\n",
              "      <td>0.1970</td>\n",
              "      <td>0.1244</td>\n",
              "      <td>-1.7090</td>\n",
              "      <td>-0.3543</td>\n",
              "      <td>-0.5160</td>\n",
              "      <td>-0.3330</td>\n",
              "      <td>-0.2685</td>\n",
              "      <td>0.7649</td>\n",
              "      <td>0.2057</td>\n",
              "      <td>1.3720</td>\n",
              "      <td>0.6835</td>\n",
              "      <td>0.8056</td>\n",
              "      <td>-0.3754</td>\n",
              "      <td>-1.2090</td>\n",
              "      <td>0.2965</td>\n",
              "      <td>-0.0712</td>\n",
              "      <td>0.6389</td>\n",
              "      <td>0.6674</td>\n",
              "      <td>-0.0783</td>\n",
              "      <td>1.1740</td>\n",
              "      <td>...</td>\n",
              "      <td>0.1535</td>\n",
              "      <td>-0.4640</td>\n",
              "      <td>-0.5943</td>\n",
              "      <td>0.3973</td>\n",
              "      <td>0.1500</td>\n",
              "      <td>0.5178</td>\n",
              "      <td>0.5159</td>\n",
              "      <td>0.6091</td>\n",
              "      <td>0.1813</td>\n",
              "      <td>-0.4249</td>\n",
              "      <td>0.7832</td>\n",
              "      <td>0.6529</td>\n",
              "      <td>0.5648</td>\n",
              "      <td>0.4817</td>\n",
              "      <td>0.0587</td>\n",
              "      <td>0.5303</td>\n",
              "      <td>0.6376</td>\n",
              "      <td>-0.3966</td>\n",
              "      <td>-1.4950</td>\n",
              "      <td>-0.9625</td>\n",
              "      <td>-0.0541</td>\n",
              "      <td>0.6273</td>\n",
              "      <td>0.4563</td>\n",
              "      <td>0.0698</td>\n",
              "      <td>0.8134</td>\n",
              "      <td>0.1924</td>\n",
              "      <td>0.6054</td>\n",
              "      <td>-0.1824</td>\n",
              "      <td>0.0042</td>\n",
              "      <td>0.0048</td>\n",
              "      <td>0.6670</td>\n",
              "      <td>1.0690</td>\n",
              "      <td>0.5523</td>\n",
              "      <td>-0.3031</td>\n",
              "      <td>0.1094</td>\n",
              "      <td>0.2885</td>\n",
              "      <td>-0.3786</td>\n",
              "      <td>0.7125</td>\n",
              "      <td>1</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 879 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "             id tratamento  tempo dose  ...     c98     c99  n_moa  ativo_moa\n",
              "0  id_000644bb2  com_droga     24   D1  ...  0.3801  0.4176      1       True\n",
              "1  id_000779bfc  com_droga     72   D1  ...  0.6077  0.7371      0      False\n",
              "2  id_000a6266a  com_droga     48   D1  ... -1.4080  0.6931      3       True\n",
              "3  id_0015fd391  com_droga     48   D1  ... -0.3876 -0.8154      0      False\n",
              "4  id_001626bd3  com_droga     72   D2  ... -0.3786  0.7125      1       True\n",
              "\n",
              "[5 rows x 879 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "c15186ae"
      },
      "source": [
        "Mas, na nossa base ```dados_resultados``` nós encontramos o ID do composto e quais mecanismos de ação foram acionados por determinado experimento. Aqui, como um composto pode acionar mais de um mecanismo, nós teremos uma classificação multilabel (quando uma única instância pode ter mais de uma classe associada) e, através de um algoritmo de Machine Learning nós também podemos resolver este problema: **não somente ter como retorno se um mecanismo de ação foi ativado mas qual ou quais são estes mecanismos**.\n",
        "\n",
        "É importante lembrar que nossas bases de dados estão separadas com um propósito. A segunda ```dados_resultados``` é como se fosse a resposta da primeira e foi construída por cientistas que analisaram o efeito de um determinado composto nos mais de 700 genes. Esse conhecimento, virou uma base de dados com o passar dos anos e é fruto de análise científica.\n",
        "\n",
        "E o papel de um modelo de ML neste caso é, a partir de todo esforço humano empenhado por vários anos para entender e tabular qual efeito de um composto quimíco em uma determinada expressão gênica, ser treinado e aprender, ou seja, ser capaz de produzir resultados compatíveis com aqueles feitos pelos(as) próprios(as) cientistas.\n",
        "\n",
        "Aqui na imersão, vamos começar resolvendo o problema menos complexo, a classificação binária: **dado os experimentos, queremos saber se QUALQUER mecanismo de ação foi ativado ou não**. Basicamente, queremos que o modelo faça predições compatível com a última coluna da nossa base de dados ```dados_combinados```, a ```ativo_moa```."
      ],
      "id": "c15186ae"
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 270
        },
        "id": "dfc9393e",
        "outputId": "3031eb85-e990-4304-deae-c279d7226bab"
      },
      "source": [
        "dados_resultados.head()"
      ],
      "id": "dfc9393e",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>5-alpha_reductase_inhibitor</th>\n",
              "      <th>11-beta-hsd1_inhibitor</th>\n",
              "      <th>acat_inhibitor</th>\n",
              "      <th>acetylcholine_receptor_agonist</th>\n",
              "      <th>acetylcholine_receptor_antagonist</th>\n",
              "      <th>acetylcholinesterase_inhibitor</th>\n",
              "      <th>adenosine_receptor_agonist</th>\n",
              "      <th>adenosine_receptor_antagonist</th>\n",
              "      <th>adenylyl_cyclase_activator</th>\n",
              "      <th>adrenergic_receptor_agonist</th>\n",
              "      <th>adrenergic_receptor_antagonist</th>\n",
              "      <th>akt_inhibitor</th>\n",
              "      <th>aldehyde_dehydrogenase_inhibitor</th>\n",
              "      <th>alk_inhibitor</th>\n",
              "      <th>ampk_activator</th>\n",
              "      <th>analgesic</th>\n",
              "      <th>androgen_receptor_agonist</th>\n",
              "      <th>androgen_receptor_antagonist</th>\n",
              "      <th>anesthetic_-_local</th>\n",
              "      <th>angiogenesis_inhibitor</th>\n",
              "      <th>angiotensin_receptor_antagonist</th>\n",
              "      <th>anti-inflammatory</th>\n",
              "      <th>antiarrhythmic</th>\n",
              "      <th>antibiotic</th>\n",
              "      <th>anticonvulsant</th>\n",
              "      <th>antifungal</th>\n",
              "      <th>antihistamine</th>\n",
              "      <th>antimalarial</th>\n",
              "      <th>antioxidant</th>\n",
              "      <th>antiprotozoal</th>\n",
              "      <th>antiviral</th>\n",
              "      <th>apoptosis_stimulant</th>\n",
              "      <th>aromatase_inhibitor</th>\n",
              "      <th>atm_kinase_inhibitor</th>\n",
              "      <th>atp-sensitive_potassium_channel_antagonist</th>\n",
              "      <th>atp_synthase_inhibitor</th>\n",
              "      <th>atpase_inhibitor</th>\n",
              "      <th>atr_kinase_inhibitor</th>\n",
              "      <th>aurora_kinase_inhibitor</th>\n",
              "      <th>...</th>\n",
              "      <th>radiopaque_medium</th>\n",
              "      <th>raf_inhibitor</th>\n",
              "      <th>ras_gtpase_inhibitor</th>\n",
              "      <th>retinoid_receptor_agonist</th>\n",
              "      <th>retinoid_receptor_antagonist</th>\n",
              "      <th>rho_associated_kinase_inhibitor</th>\n",
              "      <th>ribonucleoside_reductase_inhibitor</th>\n",
              "      <th>rna_polymerase_inhibitor</th>\n",
              "      <th>serotonin_receptor_agonist</th>\n",
              "      <th>serotonin_receptor_antagonist</th>\n",
              "      <th>serotonin_reuptake_inhibitor</th>\n",
              "      <th>sigma_receptor_agonist</th>\n",
              "      <th>sigma_receptor_antagonist</th>\n",
              "      <th>smoothened_receptor_antagonist</th>\n",
              "      <th>sodium_channel_inhibitor</th>\n",
              "      <th>sphingosine_receptor_agonist</th>\n",
              "      <th>src_inhibitor</th>\n",
              "      <th>steroid</th>\n",
              "      <th>syk_inhibitor</th>\n",
              "      <th>tachykinin_antagonist</th>\n",
              "      <th>tgf-beta_receptor_inhibitor</th>\n",
              "      <th>thrombin_inhibitor</th>\n",
              "      <th>thymidylate_synthase_inhibitor</th>\n",
              "      <th>tlr_agonist</th>\n",
              "      <th>tlr_antagonist</th>\n",
              "      <th>tnf_inhibitor</th>\n",
              "      <th>topoisomerase_inhibitor</th>\n",
              "      <th>transient_receptor_potential_channel_antagonist</th>\n",
              "      <th>tropomyosin_receptor_kinase_inhibitor</th>\n",
              "      <th>trpv_agonist</th>\n",
              "      <th>trpv_antagonist</th>\n",
              "      <th>tubulin_inhibitor</th>\n",
              "      <th>tyrosine_kinase_inhibitor</th>\n",
              "      <th>ubiquitin_specific_protease_inhibitor</th>\n",
              "      <th>vegfr_inhibitor</th>\n",
              "      <th>vitamin_b</th>\n",
              "      <th>vitamin_d_receptor_agonist</th>\n",
              "      <th>wnt_inhibitor</th>\n",
              "      <th>n_moa</th>\n",
              "      <th>ativo_moa</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>id_000644bb2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>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>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>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>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>id_000779bfc</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>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>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>id_000a6266a</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>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>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>id_0015fd391</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>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>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>id_001626bd3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>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>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>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>True</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 209 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "             id  5-alpha_reductase_inhibitor  ...  n_moa  ativo_moa\n",
              "0  id_000644bb2                            0  ...      1       True\n",
              "1  id_000779bfc                            0  ...      0      False\n",
              "2  id_000a6266a                            0  ...      3       True\n",
              "3  id_0015fd391                            0  ...      0      False\n",
              "4  id_001626bd3                            0  ...      1       True\n",
              "\n",
              "[5 rows x 209 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "98c17b80"
      },
      "source": [
        "As etapas lógicas do nosso processo é:\n",
        "\n",
        "    Dado um composto -> temos a nossa assinatura celular (```g e c```) -> analisamos o(s) mecanismo(s) de ação ativados (MoA) \n",
        "\n",
        "Mas, a nossa pergunta a ser respondida é, **\"Dado um composto e uma assinatura celular, houve algum MoA ativado?\"** Então, as etapas lógicas passam a ser:\n",
        "\n",
        "    Composto -> assinatura celular -> MoA ativado?\n",
        "\n",
        "E como nosso modelo vai aprender a partir da nosa base de dados?\n",
        "\n",
        "    Composto 1 -> Assinatura A -> MoA = 1\n",
        "    Composto 2 -> Assinatura B -> MoA = 0\n",
        "    Composto 3 -> Assinatura C -> MoA = 1\n",
        "    ...\n",
        "    Composto N -> Assinatura N -> MoA = 0\n",
        "\n",
        "Ou seja, toda a nossa base de dados ```dados_combinados``` será a fonte de aprendizado do nosso modelo. Onde, cada experimento (linha) representa um exemplo (assinatura) já que temos a reunião de todas as informações necessárias para criar a nossa classificação binária e, terá como variável resposta, também chamada de target, a coluna ```ativo_moa```, uma variável binária (0, 1).\n",
        "\n",
        "E, por fim, o nosso modelo deverá ser capaz de resolver o seguinte problema:\n",
        "  \n",
        "    Composto candidato -> Assinatura X -> MoA = 1 OU MoA = 0?\n",
        "\n",
        "Existem diversas técnicas de ML que são capazes de solucionar o nosso problema mas aqui, vamos usar a **Regressão Logística** e, essa técnica já está implementada em uma biblioteca bastante usada e bem importante para ML no Python, o [Scikit-Learn](https://scikit-learn.org/stable/).\n",
        "\n",
        "Sugerimos que você leia essa documentação e se familiarize com esta biblioteca, ela será muito importante na sua trajetória como Cientista de Dados. E, clicando em \"Classification\" na página inicial, encontramos, no primeiro capítulo, chamado \"Linear Models\", o nosso subtítulo de interesse: [**\"1.1.11 Logistic Regression\"**](https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression).\n",
        "\n",
        "\n"
      ],
      "id": "98c17b80"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ac08d8b7"
      },
      "source": [
        "Quando implementamos um modelo de classificação, precisamos separar a nossa base de dados em base de treino e base de teste. A base de treino, vai ser onde o nosso modelo vai aprender e, a base de teste serve para a verificação do aprendizado do modelo, ou seja, poderemos entender se, de fato, as previsões do nosso modelo estão de acordo com o esperado. \n",
        "\n",
        "Não podemos treinar e testar o modelo com o mesmo conjunto de dados porque, seria muito mais fácil dele prever corretamente. A base de dados de teste, funciona como um novo conjunto onde poderemos medir o quanto o nosso modelo está acertando, dado que já temos a variável resposta real.\n",
        "\n",
        "Para fazer esta divisão, usamos uma função do Scikit-Learn chamada ```train_teste_split``` (como já aprendemos, para usar uma biblioteca ou função, precisamos, primeiramente, fazer a importação padrão e, neste caso, usaremos ```from sklearn.model_selection import train_test_split```).\n",
        "\n",
        "Para fazer a divisão da base de dados, é necessário definir qual será o nosso ```x``` e o nosso ```y```. Um modelo matemático, define um ```y``` em função de um ```x```, ou seja, ```f(x) = y```. Neste projeto, o nosso ```y``` será o target, ```ativo_moa``` e, ele será definido a partir de uma assinatura (conjunto composto por ```g``` + ```c``` ou , expressão gênica + viabilidade celular).\n",
        "\n",
        "Assim, teremos: \n",
        "\n",
        "    x = dados_combinados.select_dtypes('float64')  ->  toda a base de dados que tem os valores com o formato, ```float64```, no nosso caso, todas as variáveis ```g``` e ```c```\n",
        "\n",
        "    y = dados_combinados['ativo_moa']  ->  apenas a coluna target, ou seja, ```ativo_moa```\n",
        "\n",
        "Para o ```train_test_split``` definiremos:\n",
        "    \n",
        "    x_treino, x_teste, y_treino, y_teste\n",
        "\n",
        "E a função receberá como parâmetro:\n",
        "\n",
        "    train_test_split(x, y, test_size = 0.2)\n",
        "\n",
        "Onde, \n",
        "\n",
        "    x -> base de dados definida como x, no nosso caso, todas as variáveis ```g``` e ```c```\n",
        "\n",
        "    y -> variável resposta, no nosso caso, ```ativo_moa```\n",
        "\n",
        "    teste_size = 0.2 -> tamanho que definimos para a base de teste\n",
        "\n",
        "A proporção entre treino e teste, varia de acordo com o volume de dados que temos. Mas, usualmente, encontramos proporções 30/70, 25/75 ou 20/80. Aqui, definimos como 0,2 ou 20%, isso significa que a base de treino será composta pelos 80% restante.\n",
        "\n"
      ],
      "id": "ac08d8b7"
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3f9a08e2"
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "x = dados_combinados.select_dtypes('float64')\n",
        "y = dados_combinados['ativo_moa']\n",
        "\n",
        "x_treino, x_teste, y_treino, y_teste = train_test_split(x, y, test_size = 0.2)"
      ],
      "id": "3f9a08e2",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9f6f0dbb"
      },
      "source": [
        "Agora, podemos fazer a nossa primeira regressão logística.\n",
        "\n",
        "O primeiro passo, é definir o nosso ```x```, ```y``` e o ```x_treino, x_teste, y_treino, y_teste``` do ```train_test_split```.\n",
        "\n",
        "Após isso, definimos um nome para o nosso modelo (```modelo_rlogistica```) e declaramos a função ```LogisticRegression()```. Acrescentamos um parâmetro ```max_iter = 1000``` no modelo para que ele seja capaz de convergir para um resultado e assim, não recebemos nenhum warning no resultado.\n",
        "\n",
        "Em seguida, ajustamos o modelo com o ```.fit(x_treino, y_treino)``` (lembrando que os argumentos desta função são ```x``` e ```y``` e, usamos ```x_treino``` e ```y_treino``` porque será através dessas bases que o algoritmo aprenderá).\n",
        "\n",
        "E, por fim, usamos a função ```.score(x_teste, y_teste)``` para verificar como o nosso modelo ajustado está se saindo nos dados de teste. O ```score()```calcula a acurácia, ou seja, quantas predições o nosso modelo acertou na base de teste.\n",
        "\n",
        "Como resultado, temos o valor da acurácia: ~0,627. Então, concluímos que o nosso modelo está acertando ~62% das previsões (caso tivéssemos 100 experimentos, o modelo acertaria 62 casos e erraria 38 casos).\n",
        "\n",
        "Mas, como podemos estabelecer se este é um bom valor de acurácia ou não?"
      ],
      "id": "9f6f0dbb"
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "44c0f902",
        "outputId": "46fa7e86-8905-4a6c-877b-d9dcba97c848"
      },
      "source": [
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "x = dados_combinados.select_dtypes('float64')\n",
        "y = dados_combinados['ativo_moa'] \n",
        "x_treino, x_teste, y_treino, y_teste = train_test_split(x, y, test_size = 0.2, stratify=y, random_state=376)\n",
        "\n",
        "modelo_rlogistica = LogisticRegression(max_iter=1000)\n",
        "modelo_rlogistica.fit(x_treino, y_treino)\n",
        "modelo_rlogistica.score(x_teste, y_teste)"
      ],
      "id": "44c0f902",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.6267058576527399"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "db7e4ee0"
      },
      "source": [
        "Para estabelecer se temos um bom valor de acurácia, precisamos de uma base comparativa, quer dizer, preciso dos resultados de um outro modelo para entender se estamos acertando mais ou menos casos.\n",
        "\n",
        "Para isso, o Scikit-Learn, já tem implementado alguns algoritmos que fazer modelos menos complexos e que podem ser usados como base comparativa. Neste caso, vamos usar o ```DummyClassifier```.\n",
        "\n",
        "O processo para ajustar este modelo é muito parecido com aquele que usamos para a regressão logística: dividimos a base em treino e teste, definimos um nome para esse modelo e o instanciamos, o ajustamos com os dados de treino e, ao final, verificamos a acurácia com o conjunto teste.\n",
        "\n",
        "Ao instanciarmos o ```DummyClassifier```, precisamos definir a estratégia que ele usará para fazer o ajuste do modelo. No nosso caso, definimos a estratégia será os dados mais frequentes, isso quer dizer que: dado o valor mais frequente da nossa variável resposta (```ativo_moa```), o modelo Dummy vai chutar que todos os eventos da base de dados assumem aquele valor no target. Assim, temos: ```DummyClassifier('most_frequent')```.\n",
        "\n",
        "Nesse momento, para calcular a acurácia, vamos usar outra estratégia, a função ```accuracy_score``` também do Scikit-Learn. Ela receberá como parâmetros a base teste da variável resposta (```y_teste```) e as previsões do modelo que, até o momento ainda não calculamos.\n",
        "Para calcular as previsões, usamos o ```.predict()``` da mesma biblioteca que, receberá como parâmetro a base ```x_teste```.\n",
        "Por fim, o cálculo da acurácia será: \n",
        "\n",
        "    previsao_dummy = modelo_dummy.predict(x_teste)\n",
        "    accuracy_score(y_teste, previsao_dummy)\n",
        "\n",
        "A acurácia do ```DummyClassifier```foi de ~60,7%, isso significa que nosso modelo ```LogisticRegression``` teve um desempenho um pouco melhor."
      ],
      "id": "db7e4ee0"
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4bfe002b",
        "outputId": "ad69ad0b-ef75-4c01-96e5-3a1c54f2f273"
      },
      "source": [
        "from sklearn.dummy import DummyClassifier\n",
        "from sklearn.metrics import accuracy_score\n",
        "\n",
        "modelo_dummy = DummyClassifier('most_frequent')\n",
        "modelo_dummy.fit(x_treino, y_treino)\n",
        "previsao_dummy = modelo_dummy.predict(x_teste)\n",
        "accuracy_score(y_teste, previsao_dummy)"
      ],
      "id": "4bfe002b",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.6067604450976275"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "c8a2ac97"
      },
      "source": [
        "Podemos entender a acurácia do ```DummyClassifier``` se fizermos um ```value_counts``` normalizado e então verificamos que a proporção entre a classe 0 e a classe 1 é 60/30, ou seja, o valor mais frequente (estratégia usada no ```DummyClassifier```) representa 60% da base de dados.\n",
        "Isso quer dizer que, caso o modelo chutasse a mesma classe para todos os eventos da base de teste, teríamos um acerto de 60% dos casos. "
      ],
      "id": "c8a2ac97"
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "95a9918f",
        "outputId": "55ee9f87-1201-4cfd-e4da-a8089f1d7eb6"
      },
      "source": [
        "dados_combinados['ativo_moa'].value_counts(normalize=True)"
      ],
      "id": "95a9918f",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True     0.60666\n",
              "False    0.39334\n",
              "Name: ativo_moa, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "37173d3e"
      },
      "source": [
        "Entretanto, toda vez que rodamos tanto a nossa regressão logística, quanto o nosso dummy classifier, recebemos como retorno um valor diferente de acurácia, apesar de em muitos casos, serem próximos.\n",
        "\n",
        "O ```train_test_split``` a base de dados em subconjuntos aleatórios de treino e teste. Isso significa que toda vez que você executá-lo, você obterá um resultado diferente.\n",
        "\n",
        "Por outro lado, se você declarar o parâmetro ```random_state = n```, poderá garantir que a saída de todas as execuções será igual, ou seja, sua divisão será sempre a mesma. Não importa qual número você escolherá para o ```random_state```, o importante é que toda vez que você usá-lo, sempre terá a mesma saída. \n",
        "\n",
        "Além do ```random_state```, existe mais um parâmetro bem importante que deve ser levado em consideração na divisão da nossa base de dados, o ```stratify```. \n",
        "\n",
        "Se repararmos na divisão da variável ```ativo_moa```, entre ```true``` e ```false```, percebemos que segue um balanceamento 60/40. E, é do nosso interesse que as bases de treino e teste sigam, além de uma divisão reprodutível (garantida com ```random_state```), a proporção original do conjunto de dados. Assim, atribuimos ```stratify = coluna```, onde ```coluna = nome da coluna que o stratify deve levar em consideração a proporção de classes```. No nosso caso, usamos ```stratify = y```, pois ```y``` é a nossa variável resposta e, é dela que o stratify deve considerar a proporção: 60/40.\n",
        "\n",
        "Esses parâmetros são úteis, porque garantimos que os resultados são reproduzíveis e compatíveis, independente do modelo (qual técnica, por exemplo, regressão logística, dummy classifier, árvore de decisão e etc) que vamos treinar e testar a partir desta divisão.\n",
        "\n",
        "Ou seja, ao final, nosso ```train_teste_split``` ficará:\n",
        "\n",
        "    x_treino, x_teste, y_treino, y_teste = train_test_split(x, y, test_size = 0.2, stratify=y, random_state=376)\n"
      ],
      "id": "37173d3e"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3386dd9f"
      },
      "source": [
        "Nesse momento, temos interesse a explorar outras técnicas de modelagem para comparar os resultados obtidos e ver qual o melhor algoritmo que conseguimos produzir. \n",
        "\n",
        "Sendo assim, vamos testar uma nova técnica: a árvore de decisão.\n",
        "\n",
        "Novamente, a sequência lógica para ajustar esse novo modelo é muito parecida com que estávamos vendo até o momento: definimos as bases ```x``` e ```y```, fazemos o ```train_test_split```, declaramos o novo modelo, ajustamos e verificamos a acurácia. \n",
        "\n",
        "Aqui, vamos usar o ```DecisionTreeClassifier```, também da biblioteca Scikit-Learn. O único parâmetro que vamos declarar para esta função será o ```max_depth = 3``` mas, vamos explorar seu significado a seguir. \n",
        "\n",
        "Como resultado, temos uma acurácia (calculada a partir da base de teste) de ~0,61% e, é um pouco menos do que a acurácia obtida com a regressão logística."
      ],
      "id": "3386dd9f"
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5d2178d2",
        "outputId": "019ea378-2483-43d7-d7e7-407a5503694c"
      },
      "source": [
        "from sklearn.tree import DecisionTreeClassifier\n",
        "\n",
        "\n",
        "x = dados_combinados.select_dtypes('float64')\n",
        "y = dados_combinados['ativo_moa'] \n",
        "x_treino, x_teste, y_treino, y_teste = train_test_split(x, y, test_size = 0.2, stratify=y, random_state=376)\n",
        "\n",
        "modelo_arvore = DecisionTreeClassifier(max_depth = 3)\n",
        "modelo_arvore.fit(x_treino, y_treino)\n",
        "modelo_arvore.score(x_teste, y_teste)"
      ],
      "id": "5d2178d2",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.6105395758975436"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8da4bba4"
      },
      "source": [
        "Agora, vamos entender melhor como funciona uma árvore de decisão e o que significa o parâmetro ```max_depth = 3```. \n",
        "Plotamos uma imagem que representa o nosso modelo e, nesse momento, é importante que você não se apegue ao código que usamos para isso. O que realmente é relevante é entender o algoritmo!\n",
        "\n",
        "A figura é dividida em 4 níveis e, o primeiro nível (estamos considerando a orientação de cima para baixo), é composto por apenas um quadro que é chamado de **nó raíz** ou somente **raíz** e ele tem como regras, os melhores atributos que podem dividir a nossa base de dados em dois conjuntos distintos (lembrando que nosso objetivo é separar a nossa base de dados em **mecanismo de ação ativado ou não ativado**).\n",
        "\n",
        "Entre o primeiro e o segundo nível (e nos demais níveis), temos duas setas: uma para a direita e outra para a esquerda. Essas setas são chamadas de **ramos** e subdividem o nível acima em dois outros conjuntos que separam novamente os dados em **mecanismo de ação ativado ou não ativado**, a partir de uma decisão.\n",
        "\n",
        "O último nível da árvore (mais abaixo), apresenta n quadros que apresentam características que devem ser levadas em conta na hora da classificação e se dividem em ```class = ativado``` e ```class = não ativado```. Esse nível não apresenta mais ramificações e, esses quadros são chamados de **folhas**.\n",
        "\n",
        "Se reparmos, a nossa árvore tem 3 níveis de decisão a partir da raíz e é exatamente isso que o parâmetro ```max_depth```representa: a profundidade da árvore."
      ],
      "id": "8da4bba4"
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 575
        },
        "id": "75df25b1",
        "outputId": "b5ec977a-1cc4-4c3c-d474-330b2b542902"
      },
      "source": [
        "from sklearn import tree\n",
        "\n",
        "fig, ax = plt.subplots(figsize=(15, 10), facecolor='k')\n",
        "tree.plot_tree(modelo_arvore,\n",
        "               ax=ax,\n",
        "               fontsize=10,\n",
        "               rounded=True,\n",
        "               filled=True,\n",
        "               feature_names=x_treino.columns,\n",
        "               class_names=['Não Ativado', 'Ativado'])\n",
        "\n",
        "plt.show()"
      ],
      "id": "75df25b1",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAA1MAAAIuCAYAAABac1I3AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nOzdZ3gV1drG8f9O7z0QQgu99w4iSACRpqAigkhCkyKIdFGqiEgHUalSReSAiCidIJ3QO9J7CQmEkF529vsBjeYNICTAJsn9+2JmZs3MPZxzJdezn9lrGXx9fU2IiIiIiIjIE7EwdwAREREREZHMSMWUiIiIiIhIOqiYEhERERERSQcVUyIiIiIiIumgYkpERERERCQdVEyJiIiIiIikg4opERERERGRdFAxJSIiIiIikg4qpkRERERERNJBxZSIiIiIiEg6qJgSERERERFJBxVTIiIiIiIi6aBiSkREREREJB1UTImIiIiIiKSDiikREREREZF0UDElIiIiIiKSDiqmRERERERE0kHFlIiIiIiISDqomBIREREREUkHFVMiIiIiIiLpoGJKREREREQkHVRMiYiIiIiIpIOKKRERERERkXRQMSUiIiIiIpIOKqZERERERETSQcWUiIiIiIhIOqiYEhERERERSQcVUyIiIiIiIumgYkpERERERCQdVEyJiIiIiIikg4opERERERGRdFAxJSIiIiIikg4qpkRERERERNJBxZSIiIiIiEg6qJgSERERERFJBxVTIiIiIiIi6aBiSkREREREJB1UTImIiIiIiKSDiikREREREZF0UDElIiIiIiKSDiqmRERERERE0kHFlIiIiIiISDpYmTuAiIg8uUqVKlGzZk28vL0Bg7njSAbExcZy7txZVqxYQWJiornjiIjIEzD4+vqazB1CREQeX+PGjZk4eQohsQaMBn0mltlZYMLZIoETh/fTIaC9CioRkUxExZSISCZStGhRfv3td46FWxCZoF/fWYUBKOFmZO2KpQwbOsTccURE5DHpO1MiIplIuXLlCItOVCGVxZiAG3HWVK9Rw9xRRETkCej9EBGRTMTJyYlkg+Vzu99vC77h6K4/MFgYcHb14L2+I3H1zEFsdCQLxn7KndCbJBuT8H/zfao3fAOAXk0q4utXGAB371x8MHzKc8sLEH3vLnO+6Mel08ep1qA5rbp/8sBx3385gFtXLwIQGxWJvZMzg75ZmnL8zq0bfPFBSxq37Yr/W+0BCFqxkF1rV2AwGMjlV4T3+ozA2sb2qeRONJrwdnJ+KtcSEZHnQ8WUiIg8lP+b7Wn6fg8A/li5mDWLZ9K652dsXfUTPvkK8sGIqUTevcOozm9Q+ZUmWFlbY21jm6ooeRLRkRE4OrtmKLOVjS1N2vXgxqWzXL909qHjOnwyNuXnn2dNwN7BKdXxFTMnULJyrZTtu2EhbFn5I5/O+BkbWzu+H92f/VvWUr3B6xnKKyIimZeKKRERYe3imewN+h0nV3fcvH3IV7gE/m+1x97xnwIjIS4Ww18zBxoMBuJiozGZTMTHxeLg7IqFZfo6ZrHRUezfspZd61ZQrEI1mgf0ytCz2NrZU6h0BUJvXH6s8SaTiYNb19NzzMyUfYd3BuHp44uNnX2qsclGI4kJ8VhaWZEQH4erh3eGsoqISOamYkpEJJu7dOoYh3ZsZNC3SzEmJTG2Z2vyFS6RcnzVvK/Zs+k37B2d6DlmFgAvN2vNzBEf8VnbBsTFRhP4yVdYWNz/Gm5SQgJje7XB0sKS+q0CKVez3gPve+7YQXau+5kLJw5RrlZ92vcfTY48+QE4fXgvP88cl+YcG1s7+kxc8FSf/9yxAzi7e5Ij9/17x8fGsPF/8/hw9HQ2LZ+fMs7NKyf+b77P0PcbYWNjR/GK1SlRqeZTzSIiIpmLiikRkWzu/IlDlKleF2sbW6xtbCldrU6q480CetIsoCfrf5rD1lVLaNKuOyf37yR3wWL0HDOLsBtXmDa4K4VKVcTe0YkR81fj5pWTsBtX+XpQZ3z9iuDtmzfVNZd99xV7gn6jVfdPaNt7eJquVtFyVdL9quCT2v/HWirVaZSyvXrRdF5p0RZbe4dU42Ii73Fk9x8Mn/s7Dk7OzBndn71Bv1OlXpPnklNERF48KqZEROSxVH6lMdOHfkiTdt3ZvWElDVp1wGAw4O2bD0+f3IRcvYBfsTK4eeUEwCtXHgqXrczVc3+mKaZeafkedg6OrPlhBif276R6g9cpUrYyBsP91wifpDN1eEcQaxZPB6DNR8PIV7TUYz+T0ZjE4Z2b6D/1x5R9F08d5dD2DaycM5nY6EgMBgusbGxxcffAM2dunN08AChX05/zJw6pmBIRycZUTImIZHMFS5ZnydejaPhOR5KNRo7t2UqtRm8CcOvapZTX347u+oOceQoA4OGdi9OHgilcuiL3wm9z6+pFvHzyEBN5D2tbO6xtbIiKCOfCiUPUfysgzT09c+amafsPafxeN07u38mWX3/kp69H0ahNF6rUa/JEnalytepRrtaDXyX8L6cOBpMzTwHcvXOm7Pt4/NyUn1cv+g5bOwfqNG/NxT+PcvHPIyTExWJta8fpQ8HkK/L4hZuIiGQ9KqZERLK5/MVKU6Z6Hb7s/jbObp74+hXB7q+JJ36dO5VbVy9iMFjgkSMX7/T8FIBGbTqzaMJQRnd7C0wmXu/QGydXd86fOMSSr0dhMFhgMiXToFUHcuUv9NB7W1haUqpqbUpVrU3k3TvcunrpqTzTsPavERcTTVJSIkd3bqb7F9+RK38hFk8ewUuN30rpXu3fspZKdRv9x9Xu8ytehvIv1eernu9iaWlJnkLFqfnam08lr4iIZE4GX19frfwoIpJJBAYGEvjRIM5FPt21puJjY7C1dyAhLpbJAzrybq8h5P3XJBTy7DlZG/A1hVGrRjVzRxERkcekzpSIiPDj1JHcvHyexIQEqtVvpkJKRETkMaiYEhERAgaOMXcEERGRTMfC3AFEREREREQyI3WmREQkQ35f8C2FylSkeIXqDx1zdPcf3Lh8noatOmToXpfPnGDRxKEkxsdTqspLvNl1QMp06v/fpVPHmNinPQGDxlChdoM0062HXLlIwKAxlKtZj0n9AomPjQYg8m44+YuVosvQyRnKKiIiWZ+KKRERyZAm73f/zzFlqtelTPW6Gb7XT9O+4N1eQ/ErXobvhn7IiX07KFXlpTTjko1GVs6dQvGK/xR4/55uPToygpEdmlGiYg0g9XTos0f1fSpZRUQk61MxJSIij2Xt4pnsDfodJ1d33Lx9yFe4BP5vtWfhhCGUrvoyFWo3YFj716havxnHgrdiNCbRYfA4fPIWYPeGlVw+c4JW3T9J9/0j7oQSFxNNgRJlAajq35SjuzY/sJja8uuPlK/lz6XTxx94rUPbNlCyci1s7OxT7Y+NjuL04T20/XhEunOKiEj2oWJKRET+06VTxzi0YyODvl2KMSmJsT1bk+8hM/45ubgzcNoStv72E0HLF9Cm97CHXvf/v3r3NxtbO/pMXJBqX0TYLdy8/llc180rJ3dv30pz7t2wEI7s3EzPr2Zx6fSD771/6zrqtWiXZv+RXZspVq4a9n+tsyUiIvIoKqZEROQ/nT9xiDLV62JtY4u1jS2lq9V56NhyteoBkK9wSQ7vCHrkdf/96t3TsnzGOJp3+AgLiwfPsRRxJ5QbF85SolKNNMf2b1lLzVdbPNU8IiKSdamYEhGRp8rK2gYACwsLko1Jjxz7JJ0pV68c3A0LSdm+GxaCm2eONOdePnOCeWMGAhB17y4n9m7HwtKScjXvF3kHt66nbM1XsLSyTnVeVEQ4l04do/OQiY/xlCIiIiqmRETkMRQsWZ4lX4+i4TsdSTYaObZnK7UavZnh6z5JZ8rVwxs7B0cunDyCX/Ey7Nn0G3WatU4zbsS81Sk///19rr8LKYD9f6ylWWCvNOcd2r6R0lVrY21jm44nERGR7EjFlIiI/Kf8xUpTpnodvuz+Ns5unvj6FcHODN8reqfH4JSp0UtUqUXJvyaf2P77/wB4qcnbjzz/dsg1wsNuUrhMpTTH9m9ZS4MMTt0uIiLZi8HX19dk7hAiIvJ4AgMDCfxoEOciLZ/7veNjY7C1dyAhLpbJAzrybq8h5H3IJBTy5JysDfiawqhVo5q5o4iIyGNSZ0pERB7Lj1NHcvPyeRITEqhWv5kKKRERyfZUTImIyGMJGDjG3BFEREReKCqmRETkmZoyoCMtOvUhX9FSz+2eq+Z9zZ5NvxETdY8JK3al7L8Tcp0fJg0nKiIcB2cX3u8/Gnfv+2tXBW/4lXVLZgHwauvOVGvQPCX/vTthWNven5iixxfTcXbz4OzR/SyfMY7rF84QMGgMFWo3eG7PJyIiLwYVUyIikuWUrlaHl5u3ZmTH5qn2r5g9kar+TanWoDmnDu1h1bypvN//C6IjI1izeAb9py7GgIGxvd6lTPW6ODi7ANB+wOg0xaB7Dh/e6zuSTctTT+EuIiLZh4opEZFsJj4ulu9H9+du2C1MyUZefbcLleq8ypofZnAseAuJCfEUKFGO1r2GYDAYmDKgI3kKFefc8YMkxMXSru/nrF/6PTcunqHiy6/StP2H3A65xref9SBf4RJcOfcnufIVol2/z7Gxs09175P7d7J60XSSEhPwypWH9/qMxNbegZXfT+HY7i1YWFpSvGINWnTuk6FnLFCi7AP337x8npZd+gH3p2WfPfLjlFzFK1TH0dkVgOIVqnNi/w4q133toffwzJkbAIPBkKGsIiKSeamYEhHJZk7u24GrpzfdRk4DIDY6EoCXm7fmtbYfALBg3KccC95Kmep1ALCysmbA1MX88csPzBz5MQO+XoyDkysjOzTllRbvAXDr6kXa9h5GwVIV+GHiMLb9thT/t9qn3DcqIpx1S2bz4ZczsLWzZ8PSuQT9vJCXm73DkZ1BfDbrFwwGAzFR99JkfpLFfR8ld8GiHN6xibpvtOXwziDiYqOJvneXiLBbuHn7pIxz88pJRNitlO1Fk4ZhYWFB+Vr1efXdziqgREQEUDElIpLt+PoVYcWsCaycM5lS1V6mcOmKAJw5vJeNy+aREB9HTGQEPvkLpRRTf/83l18RcuUviKuHNwCeufIQHnoTeydn3L19KFiqAgCV6zVhy6+L8eefYurin0e5efk8k/re32dMTMKvRFnsHJ2wsrFl8aThlKr2MqWrvpwm85Ms7vsoLTr1Yem3Ywje8CuFylTEzTMHBguLR57TfsBo3LxyEhcTzZxRfdmz6Teq1W+W4SwiIpL5qZgSEclmcuTJz4BpSzixdzu/z/+GouWrUv/tAJZ+M5r+Uxfj7u3D6kXfkZQQn3KOlbUNABYWhpSf4f4rbslG498bqe5jIPW2yWSiWIXqBA5KOytgv8mLOH0omEPbN7J11RJ6jZmV6vjT6ky5euag85CJwP11sw5v34SDkwuuXjk4e2Rfyri7YSEULlsZuN+lArBzcKTSK69x6fQxFVMiIgKomBIRyXYibt/CwdmVKvWaYO/ozM51P5P4V+Hk6OJGfGwMh7ZvpPxL9Z/ouuG3bnDh5GEKlCjH/j/WpHSp/uZXvAz/+/ZLQq9fxts3H/FxsUSE3cLV05uE+DhKVa1NwVLlGR7YNM21n1Zn6v4sfq5YWFiw/qc5VG/4BgAlKtVk1byviYm8/4rhyQO7aBbYC6MxidioSJxc3TEmJXI8eBvFKmhRXRERuU/FlIhINnP94ll+mT0Jg4UBS0sr3vnwUxycXKjZqCVfdn0LZw/PdE1jniOPH1tX/cQPk4bjk68gtZu8neq4s5sHbfuMZN6YQSQlJgLQtH0PbB0cmDmiN0kJCZgw0bJz3ww/4y9zJrF/8xoS4+MY8l5DajRqQeP3unHmyD5WzZsKBgOFS1fi7e6fAODo7Eqjd7sw7qO2ALzWpguOzq7Ex8Xy7WfdMSYlkZxspFiFatRs1BKAS6eOMfvzPsRE3eNY8FZWL/qOT2f8nOHsIiKSeRh8fX1N5g4hIiKPJzAwkMCPBnEu0tLcUVK5HXKNGcN6MXj6cnNHybScrA34msKoVUOdLxGRzOLR37oVERERERGRB1IxJSIiGeaZM7e6UiIiku2omBIREREREUkHFVMiIiIiIiLpoNn8RESymb4tajBhxa7nes/bIdf4oktLcuTJz6BvlhJy9SJzvxzwz/Eb12jcrhuvtHgvZd+m5Qv4ZfZEvlyyGSdXd2Ii7/HDpGGE3biKlY0NbT8ega9f4ZTxyUYj43q1wdUrB11HfP3IPGeP7mf5jHFcv3CGgEFjqFC7Qcqxbz/rzsU/j1CwVIVU11k4YQhnj+7H3tEJgPf6jCRPoeKcObKXmSM+xtPHF4ByNf15re0Hj3zGX2ZPZE/Q7/i3fB//t/5Z2FhERDIXFVMiIvJceOXKk7JWVM48fik/JxuNfNauIeVq1ksZGx56kz8P7MI9R66Ufet/mk3uQsXoPHQSN69c4H/ffEnPMTNTjv+xcjE58xUgLib6P7O45/Dhvb4j2bQ87YK//m+2JyE+jh1rlqU59kbHj1MVXn8rVLpCmgLuUc/4Rqc+2NjZ/2dOERF5sek1PxGRTGzl91PYumpJyvbqRd+xadl84mNj+HpQF776sDWju73FkV2b05x75shepg/rmbK99Nsv2b1hJQCXz5xgSv+OjO35Lt982o2IO6HP7BlOHQrGK1cePHL6puz7ecZ4Xu/YG8O/xt24fJ6i5aoC4JO3AHdCrnMv/DYA4aEhHN+zjRqvtnyse3rmzE3uAkUxGAxpjhWrUA07B4f0P9ADPOgZRUQk81NnSkQkE6v48qv8PGMcLzdrDcDBrRvo/sW3WNnY0GnIROwdnYiKCGfCx+9TpnrdBxYP/58xKZFl342h89DJOLt5sH/LOn6bN422fUakGrc36Hc2LZ+f5nzvXPno+Nn4x36GA1vWUanOaynbR3ZtxtXLmzwFi6Ual7tgUQ7v2ETh0hW5eOood27d4G5YCC7unvw8Yxyvd+xNfOx/d6Uy4rf501i7eCZFy1eleeBHWNvYAHDh5BG+7N4KV09vWnT6mFz5C6c67/8/o4iIZA0qpkREMrG8hYsTGXGHiNu3iIwIx97ZGXdvH4xJiaya/zXnjh7AYGG4fzz8Ni4eXv95zZCrl7hx8RzffNoVgOTkZFzd055XpV4TqtRrkqH8SYmJHA3eQrPAXgAkxMWy/qc59PjiuzRjG7zdgeUzxjKmRyt8/YqQp1AxLCwsOBa8FSc3d/IVKcmZI3szlOdRmgf2wsXdi6TERJZMHcnG/83ltbYfkKdQCUbOX4OtvQPH92xj1siPGTpn1UOfUUREsg4VUyIimVyFlxpwcPtG7oWHUfHlVwHYu3k1URHhDPh6MZZW1gxr/xqJifGpzrOwsMKUnJyynZSQcP8Hkwmf/IXoOynt94n+7Wl0pk7s207eQsVxcfcEIOzGVW7fvMaY7q0AuBt2i7E936Xf5EW4eHjxXp+Rf0U0MTygMZ4+eTiwdT3Hdm/hxN7tJCYmEBcTzfyxg2k/YPRjZXhcrh7eAFjb2FC94esp37f6e0IKgFJVa7P0m9FERYTj5Or+wGcUEZGsQ8WUiEgmV7FOQ36cMpKoe3f5aOwcAOKio3B29cDSyprTh/dy59aNNOd55MzFzcvnSUxIIDEhjlOHgilYqjw58vgRFRHOhZOHKVCiHMakRG5du5Tm1bWn0Zna/8daKtVtlLLtW6AIXy755/tdw9q/Rv+pi+/P5hd1Dxtbe6ysrdm59mcKlamEvaMTzQN70fyvrs+ZI3vZtHxBSiH169yp5C9amnK16pFREXdCcfXwxmQycWTn5pR/j3t3wnB298RgMHDx1FFMJhOOLm4PfUYREck6VEyJiGRyufIXJi42BjfPHCndk8qvNGbG8I8Y3e0t8hUpSc68BdKc5+7tQ4WXG/JltzfxzJmbPIWKA2BlbU3HT8exbPpYYqOjSDYmUfeNtmmKqYyKj4vlz4O7ad3rs8caH3LlAgsnDMGAAZ/8hWjbe/h/nnP94hnKVK+TZv+lU8eY/XkfYqLucSx4K6sXfcenM34GYFK/QG5duUh8XAxD3mtIm4+HU6JSTeaPHUxURDiYTOQuWIzWPe/nPrh9I9t/X4qFpRU2NrYEDBqT8t20J31GERHJXAy+vr4mc4cQEZHHExgYSOBHgzgXaWnuKE/kdsg1ZgzrxeDpy5/rfb/5tNsDv3/1Ili96Dts7RxS1plysjbgawqjVo1qZk4mIiKPS1Oji4jIM2dhYUlsdBRjerR6rvd9UQupX2ZPZG/Qaq01JSKSyamYEhGRh/p73aqMcvf24fOF61IWsU2vmSN7M6F3u1T7dm9YScTtWynbiyeP4Malcxm6z9/XXfrtlxm+zoO80akPw75fRe2mz7e4FBGRp0vFlIiIZAoxUfe4cuYksTFRhN24mrI/eMOvRNz+Z1HhNr2HkSt/IXNEFBGRbEYTUIiICADBG1cRtHwBGAzkLlCE9/t/ker4jjXL2blmOUlJSXj75uX9fqOwsbPn4Lb1rPlhBgYLS+wdneg97ntuXDrLoonDMCYlYko20fGz8eTInT9D+Q7vCKJ0tZdxdvdk/5a1vNq6Ewe3beDymRPMHzsYa1tb+kxcwHdDetCiUx8unzlB2I0rvNGpD3C/03T5zAladf+EmSN7czc0hMSEeOq+3oZajd+6P2b9L6xf+j32js7kLlgUK+v7i/LeDrnGD5OGEx1xFydXd9r2GYFHjlwZeh4REcn8VEyJiAg3Lp1l3ZJZ9JkwHydXd6IjI9KMKV/Ln1qvvQnAb/OnsWvdL9R5/V3WLJ5J91Hf4uaVk5ioewBs/30ZdV9vQ5V6TUhKTCQ52Zjmet9/OYBbVy+m2f9Ki3ZUq98szf79W9bQqM0HuLh5MvuLvrzauhMVajdg66oltOjUh3xFS6UaX66WPxP7tE8ppg5sXc+r73QCoO3HI3B0diUhPo7xH7Wl/Ev1SUpKZPWi6fSfuhh7R2emDuqUMsPhsm+/opp/M6o1aM6udb+wbPpXdBk6+Qn+hUVEJCtSMSUiIpw+tJcKLzVIWWjW0dk1zZjrF8/y24JviI2OJD42hhKVagJQsGR5Fk0cSoXaDSlfyx+AAiXKsm7JbO6G3aJcrXoP7Ep1+GTsY+e7F36b0GuXKVSqAgaDAUtLK65fPIuv38Ona3d288DLJzcXTh4hR+58hFy5QMFS5QHYsnIxh3feX88qPDSEW9cucy88jMJlK+Ps5gFAxZdf5da1SwBc+PMInYZMAKCqfxNWfq9CSkREVEyJiMhjWjRxKJ2HTiJPwWLs3rCSM0f2AdC652dc/PMox/dsY2zPdxnw9Y9UfqUx+YuV4fjebUwf+iHv9BxCsfJVU13vSTpTB7euJyYqkuEBjQGIi4lm/x9r8A3o+cjMFes04uC29eTM40e5mvUwGAycObKXUweD6TtxPjZ29kwZ0JGkxPgM/MuIiEh2pWJKREQoWr4Ksz7vQ72W7XB0cSM6MiJNdyo+NgZXD2+MSYns27waV88cAIRev4Jf8TL4FS/DiX3bCQ+9SWx0FF658lD39TaE37rJ9Qun0xRTT9KZ2r9lDd1HfUOBEuUACLt5jW8++YBmAT2xs3ckLjbmgeeVq1mP9UtmczWHD807fARAbHQUDk4u2NjZc/PKBS7+eRQAv+JlWD5jHNH37mLn4MjBbRvIXbAoAAVKlGP/lnVU9W/K3s2rKVSqwmNnFxGRrEvFlIiIkCt/YV59pxNTBnTEYGFJnkLFaNf381RjmrTrzvje7+Hk6o5fsTLExUYD8MucSYRevwwmE0XLVyV3wWJs+N9c9m76DUsrK1zcvWjYumO6s90OucadWzfwK142ZZ+XT27sHJ24+OdRqjVozk9fj0qZgOLfHJxdyJmvADcvn8evWBkASlSqxfbVyxjVpQU58uTHr/j9/a4e3jRu+wET+ryPvaMzeQoWS7nO290GsmjSMDYtm58yAYWIiIjB19fXZO4QIiLyeAIDAwn8aBDnIi3NHUWeMidrA76mMGrVqGbuKCIi8pi0zpSIiIiIiEg6qJgSEclEEhMTMZj0QkFWZGFx/39fERHJPFRMiYhkIhcuXMDV1oClwdxJ5GnzsIVz586aO4aIiDwBfWdKRCQTMRgMTJg4iToNXuNyrDUJRv0Kz+wsDfcLKTeieL1ZU65cuWLuSCIi8phUTImIZDIGg4EBAwfhX78+7u7uDx3n6OCIvYM9UVHRxMXFPseEAmBlaYWzizMmk4nIyEiMRuMDx8XHJ3Du3FmGfDqYy5cvP+eUIiKSESqmRESymPLlyzN+/HiuXr3KJ598wo0bN8wdKduysLCgY8eO9OrVi2nTpjF79uyHFlUiIpL5qJgSEcki7Ozs6N+/Py1btmT48OGsXLnS3JHkL/ny5WPcuHE4OTnRr18/Tp48ae5IIiLyFFg6OzsPN3cIERHJmJo1a7Jw4UIiIyMJDAzk0KFD5o4k/xIREcGyZcswmUxMmTIFBwcH9u/fry6ViEgmp86UiEgm5uzszGeffUa9evUYPHgwGzZsMHck+Q8+Pj6MHj0aPz8/+vXrx4EDB8wdSURE0kmdKRGRTKpBgwYsWLCA8+fP06lTJ706lklERUWxcuVKwsPDmThxIj4+PuzZs4ekpCRzRxMRkSekzpSISCbj4eHByJEjKV++PAMGDGDnzp3mjiTp5O7uzogRI6hcuTIDBgxg+/bt5o4kIiJPQJ0pEZFM5I033uD7779n7969dO3alQsXLpg7kmRAXFwca9as4cKFC4wbN46iRYsSHBxMfHy8uaOJiMhjsDB3ABER+W++vr7Mnz+fHj16EBAQwKhRo4iLizN3LHlKgoKCqFevHomJiQQFBdGwYUNzRxIRkceg1/xERF5gBoOBtm3bMmDAAL7//nu++eYbEhMTzR1LnqFq1aoxfvx4jh07xpAhQwgLCzN3JBEReQh1pkREXlAFChRg6dKlvPPOO7z11ltMnjxZhVQ2EBwcTIMGDbhy5QobN26kZcuW5o4kIiIPoc6UiMgLxtLSkk6dOvHhhx8ydepU5syZQ3JysrljiRmULVuWCe2mwtcAACAASURBVBMmcOPGDQYNGsT169fNHUlERP5FE1CIiLxASpQowdy5c8mdOzcBAQFs3rwZk0mfeWVXISEh/Pjjj+TJk4eJEycSExPDkSNHzB1LRET+os6UiMgLwMbGhl69etGuXTu+/PJLlixZYu5I8oIpUqQI48ePJykpif79+3P+/HlzRxIRyfbUmRIRMbOKFSuyYMECrKysCAgIIDg42NyR5AV0584dli5diqOjI1OmTMFgMHDgwAF1LkVEzEidKRERM7G3t2fgwIE0b96cYcOGsWrVKnNHkkwib968jB07Fjc3N/r27cuJEyfMHUlEJFtSZ0pExAxeeuklFi1aRHh4OIGBgRw+fNjckSQTuXfvHsuXLycxMZEpU6bg4uLCvn37MBqN5o4mIpKtqDMlIvIcubi4MGTIEOrUqcOgQYMICgoydyTJ5HLkyMEXX3xBkSJF6NevH/v27TN3JBGRbEOdKRGR56Rhw4YsWLCAM2fO0KlTJ06dOmXuSJIFREdHs2rVKkJDQ5k4cSK5c+dmz549WpNMROQ5UGdKROQZ8/Ly4vPPP6d06dL069dPE0zIM+Pu7s7QoUOpXr06AwcOZOvWreaOJCKSpakzJSLyDL355pvMmTOHnTt30r17dy5dumTuSJKFxcXFsW7dOs6ePcvYsWMpWbIku3fvJj4+3tzRRESyJAtzBxARyYp8fX1ZsGABXbt2TVk7Ki4uztyxJJvYsmUL/v7+xMTEsGnTJl577TVzRxIRyZL0mp+IyFNkMBh4//336du3L7Nnz+bbb78lKSnJ3LEkG6tSpQrjx4/n1KlTfPrpp4SGhpo7kohIlqHOlIjIU1KwYEGWLVtGy5YtadmyJVOnTlUhJWa3d+9eGjZsyPnz59m4cSNvv/22uSOJiGQZ6kyJiGSQpaUlH3zwAd26dWPSpEnMmzeP5ORkc8cSSaN06dJMmDCBsLAwBg4cyNWrV80dSUQkU9MEFCIiGVCqVCnmzZtHjhw5CAwMZMuWLZhM+oxKXky3bt1iyZIl+Pj4MGnSJOLi4rRgtIhIBqgzJSKSDra2tvTu3Zs2bdrwxRdfsHTpUnNHEnkihQoVYvz48QD069ePc+fOmTmRiEjmo86UiMgTqly5MgsXLgQgICCAPXv2mDmRyJMLDw9n6dKl2NnZMXXqVKysrDhw4IBeURUReQLqTImIPCYHBwcGDRpEkyZNGDJkCKtXrzZ3JJGnInfu3Hz11Vd4e3vTp08fjh8/bu5IIiKZgjpTIiKP4eWXX2bhwoWEhobSoUMHjh49au5IIk9NZGQkP//8M/Hx8UyZMgU3Nzf27dun2ShFRP6DOlMiIo/g5ubG0KFDqVmzJgMHDmTLli3mjiTyTHl7ezNq1ChKlChB37592bt3r7kjiYi8sNSZEhF5iMaNGzNv3jxOnDhBly5dOHPmjLkjiTxzMTEx/Pbbb9y8eZMJEyaQL18+goODSUxMNHc0EZEXjhbtFRH5f7y9vZk5cyYDBw6ka9euDBkyhOjoaHPHEnmu1qxZg7+/P/b29gQFBVG3bl1zRxIReeGoMyUi8i+tWrVi9uzZbNu2jR49enD58mVzRxIxm/j4eNavX8+pU6f46quvKFOmDMHBwcTFxZk7mojIC0GdKRERIE+ePPzwww907NiRNm3aMGbMGOLj480dS+SFsG3bNvz9/YmIiGDTpk00bdrU3JFERF4ImoBCRLI1g8FAQEAAffr0YcaMGUyfPl0zmIk8QuXKlRk/fjxnz55l8ODB3Lp1y9yRRETMRp0pEcm2ChcuzIoVK2jevDlvvPEG06ZNUyEl8h/27dtHw4YNOXXqFBs2bOCdd94xdyQREbNRZ0pEsh0rKyu6detGly5dGD9+PAsWLMBk0q9CkSdVsmRJxo8fT0REBAMGDODKlSvmjiQi8lxpAgoRyVZKly7N/PnzcXd3JyAggG3btpk7kkimFRoaypIlS/Dy8mLy5MkkJCRw+PBhfTghItmGOlMiki3Y2dnRu3dvWrduzahRo1i2bJm5I4lkKQULFmTcuHFYW1vTt29frcsmItmCOlMikuVVrVqVhQsXkpSUREBAAPv27TN3JJEsJzw8nP/9739YW1szZcoUbG1t2b9/P8nJyeaOJiLyzKgzJSJZlqOjI4MHD+bVV19lyJAhrFmzxtyRRLIFX19fxowZQ65cuejbty9HjhwxdyQRkWdCnSkRyZJeeeUVFi5cyLVr1+jYsSPHjh0zdySRbCMyMpIVK1YQFRXFlClT8PT0ZO/evZotU0SyHHWmRCRLcXd3Z/jw4VStWpUBAwZoggkRM/P09OTzzz+nbNmy9OvXj927d5s7kojIU6POlIhkGU2bNmXevHkcOXKELl26cO7cOXNHEsn2YmNj+f3337l69SoTJkygQIECBAcHk5CQYO5oIiIZpkV7RSTTy5kzJ7Nnz6Zv37507tyZYcOGERMTY+5YIvIv69evp169elhYWBAUFIS/v7+5I4mIZJg6UyKSqbVu3ZpZs2YRFBTEhx9+yNWrV80dSUQeIj4+no0bN3LixAlGjx5N+fLl2b17N3FxceaOJiKSLupMiUimlC9fPpYsWcL7779P69atGTdunF4bEskkduzYQf369QkLCyMoKIjmzZubO5KISLpoAgoRyVQsLCzo0KEDH330Ed988w2zZs3CaDSaO5aIpFOFChWYMGECFy9eZPDgwdy8edPckUREHps6UyKSaRQpUoRffvmFRo0a8frrrzN9+nQVUiKZ3MGDB2nUqBHHjh1j/fr1tGnTxtyRREQemzpTIvLCs7a2pkePHnTo0IFx48axaNEiTCb96hLJaooXL8748eOJiYmhf//+XLp0ydyRREQeSRNQiMgLrVy5csyfPx9nZ2cCAgLYsWOHuSOJyDMSFhbGkiVLcHd3Z/LkyRiNRg4ePKgPT0TkhaXOlIi8kOzs7Ojbty9vv/02I0aMYMWKFeaOJCLPkZ+fH+PGjcPOzo5+/fpx6tQpc0cSEUlDnSkReeFUr16dRYsWERMTQ2BgIAcOHDB3JBF5zu7evcuyZcswGAxMmTIFOzs79u/fT3JysrmjiYikUGdKRF4YTk5OfPrppzRo0IBPPvmEDRs2mDuSiLwAcuXKxZdffkmePHno168fhw4dMnckERFAnSkReUH4+/uzcOFCLl26RKdOnTh58qS5I4nICyIqKopffvmFu3fvMnnyZHLmzElwcDBJSUnmjiYi2Zw6UyJiVh4eHowYMYKKFSsyYMAATTAhIo/k7u7OyJEjqVixIv3792fnzp3mjiQi2Zg6UyJiNs2bN2fu3Lns37+frl27cv78eXNHEpEXXFxcHGvWrOHy5cuMHz+ewoULExwcTEJCgrmjiUg2pEV7ReS58/HxYe7cufTu3ZsOHTowcuRIYmNjzR1LRDKRjRs3Uq9ePUwmE0FBQTRo0MDckUQkG9JrfiLyXLVp04ZBgwYxb948pk2bpk+TRSTDatSowbhx4zh8+DBDhgzhzp075o4kItmEOlMi8lz4+fmxdOlS2rRpQ6tWrZg4caIKKRF5Knbt2kX9+vW5fv06mzZtokWLFuaOJCLZhDpTIvJMWVhY0LlzZz788EOmTZvGrFmztE6MiDwzZcuWZcKECVy/fp1PPvmE69evmzuSiGRhmoBCRJ6ZYsWKMW/ePPLly0dgYCCbNm3CZNLnNyLy7ISEhLBkyRLy5s3LhAkTiIqK4ujRo+aOJSJZlDpTIvLUWVtb07NnTwICAvjqq69YvHixiigRee6KFi3KhAkTiIuLY8CAAVy4cMHckUQki1FnSkSeqvLly7Nw4ULs7e0JCAjQGjAiYja3b9/mp59+wsXFhSlTpmAymTh48KA+3BGRp0adKRF5Kuzt7enfvz8tWrRg2LBh/Prrr+aOJCKSIl++fIwdOxYXFxf69evHiRMnzB1JRLIAdaZEJMNq1arFwoULuXfvHgEBARw+fNjckUREUomIiGDZsmUkJSUxZcoUnJ2d2bdvH0aj0dzRRCQT09ToIvJEKlWqRPPmzQFwcXFh7NixTJ48mWHDhtGzZ0/Cw8PNnFBE5OF++uknGjZsSPHixVm3bh2VKlUCwMvLi549e5o5nYhkNupMichjs7Ky4ocffmDz5s0ULFiQhQsXcu7cOTp16sSff/5p7ngiIo8lOjqaX3/9ldu3bzNp0iR8fX3ZuXMnY8eO5eTJk1y5csXcEUUkk9B3pkTksXXu3JkGDRoQFhZG2bJl6d+/P7t27TJ3LBGRdHN3d2f48OFUqVKF5cuX07hxYxo2bKjX/0TksagzJSKPxcvLi9mzZ+Pp6cmJEyeYNWsWERERhISEmDuaiEi62dvbExUVxdmzZwkMDMTe3h5LS0v27t1r7mgikgmoMyUij2Xx4sXUqVOHhIQErl27xo0bN9iwYQMzZ840dzQRkXQrUaIEgwYNIleuXPj6+uLm5obRaCR//vzmjiYimYCKKRF5LK6urtjY2BAaGmruKCIiz4y9vT2enp5cvXrV3FFEJBOwMncAkX8rVaoULVq+iW9ePwwGg7njyF+Sk41cuXien5b8qGJKRLIEHx8f2rV7n8JFimBlZW3uOJIBiYnxnPrzT+bOncvdu3fNHUeyGXWm5IVRsWJF5i/6kXWXTdyMTiZZ/898YVgaIK+LBS/lTOLtFq9z7tw5c0cSEUk3X19fVq5ahcnOnbhkC/29yeQsDOBgaSTy1lVavPG6Cip5rlRMyQvB0tKSw8eO8/XBJPbfSDB3HHmIen42NPONpnaNauaOIiKSbr+vWYtr7iKExOoNiKwkt0Myx/dto2NgoLmjSDaiRXvlhZA7d27ijQYVUi+4Py4lkDuXD3Z2duaOIiKSbsWKFiEsToVUVnMnwZLSpcqYO4ZkM/rOlLwQHBwciIlPeibXvrZ0GAm37y/AaIyLwtLOiQLdvgcg7uY5bq4aT3J8NAaDgfxdZmJhbcu9Y5u4vXUhpuRknIrWIEfDbs8k27/dO76ZsM1zSQi7RP7OM7DPXTzNmPiwy1z/3/CU7cTw63i90gGPGq0I3fw9Eft/w9LRDQBv/844Fa1BQvgNLkxrh41XPgDs85TEp1m/dGVMNkFcQiL29vbExcWl6xoiIuZma2OD0ZT4XO+5Z8t6Vsz/jhuXzzPsm8UUKFYq5diqxbPZumYFFhYWvPfhIMpUqQXAkT3b+eGbr0hOTqZO45Y0fbfjM8954fQJZo/9jIT4eMpVq03bHgPTfId59U9z2bVpNQBGYxLXL19g2vIt2NjZMbp3IEmJCRiNRqq8XJ+WAT0AmD56EBdOHcfSyoqCxcsQ8PGQp/5dNaPJhL29PuyT50vFlGR5uVuNSPk5ZO00LO2cADAZk7jx8+fkavkZdj6FMcZEYLC0whgTwa313+H3wWysHN24/vMXRJ/fj2PBSo91P2PMPSwdXJ44p22OAuRuPYqbq8Y/fIxXvpRC0JRs5OyEN3Eu8XLKcfcab+NZ690051l75E45T0REnr88foXpNWIi8yZ9nmr/tYvnCN68ltFzVnD39i2+6t+FsfNXAbBg6mgGjJ2Jh3dOhnd/lwo16pLbr9Bj3S/qXgROLq5PnHP+5FEE9hlGoRJlmfBJd47s2U65arVTjWn8TiCN37n/Kt3BnX+wbvlCnFxcMZlMDJowGzt7B5KSEvnio/aUrfoShUuWo4Z/Ez745EsAvvtiIFtW/4x/83eeOJ/Ii0bFlGQZYX/M596R9Vg6umHlkgM736KpCguTyUTk8c3kC5gMQPS5vdjmLISdT2EALB3u/9FJCL+OjUcerP7q8DgWqkzkiS2PLKZMxiSizuwm4sBvGOOiyd/h6yfOb+vt90TjY87vx8bdF2s3nye+l4iIPBsrF85g58bfcHbzwMM7J35FS9K4VQC++Qs+cPyBnZup9kojrG1s8M6Vh5y583H+z2MA5Mydjxy+eQCo9kojDuzc/MhiKjY6it1Ba9iyZgWlKlbj7U4fPVH2u7dDiYuJonDJcgDUatiMAzs2pymm/m335jVUr/caAAaDATt7BwCMSUkYk5JSulr/vkbB4mUID9WC75I1qJiSLCH22kkiT27Br9v3mJKNXJzeETvfoqnHXDqMlZMHNp55Af569c/AlQV9SYq5i0tpfzxfaoONRx4Sbl8hIfwG1i7eRJ7chsn44FcQE25f5e6B34k88Qf2eUvjUbM1Dn7lATDGx3D5+w8feJ7vm0OxzeGXoWe+dywIlzL+qfaF71nBvcPrsPMtTo5Xe2Bp7wxAYvgNLnzXEQtbB7z9O+GQv1yG7i0iImmd//MY+7Zt5PNZyzAmJTGs6zv4FS35yHPCw25RqETZlG0Pr5yEh90vNDy8c/6z3zsn504efeA1Th89wJbVP3Pm+CEq165P109G45PXD4CTB/ew+Ltxac6xsbVjyNcL02Rx//c9vXISHnbrodnj42I5uncH7XoOTtmXbDQyrFtrQq5dxv/11qmeDSApKZGdG1bRtsfAh15XJDNRMSVZQuzlozgVewkLa1sAnIrVSjPm3tFNOJf+p/gwJRuJvXzkr+9J2XF5/sfY+RbDsWAlcjbtc/+7SQYL7POWIjH8etrrnfiD6/8bgWftdvh1nYOlrUOq45a2Ds/s1TpTUiJRp3bgXb9Lyj73Km/gVac9YCAsaA631n1DrjcGYeXsSeE+/8PSwZW466e4+uNgCvRYgKWd4zPJJiKSXZ05fpAKNetiY2MLNraUr17nmd9z0bQx7Niwivd7DaZjvxFYWFqmOl6iQlU+n/m/Z3LvQ7u2UKRU+VSvE1pYWvL5zP8RHXWPqUM/5uqFM+QpUCTl+IIpX1CsbCWKlX28V+dFXnQqpiRbMBmTiDy5Fb8PZqXss3bJgX3+cimv8zkVqU7c9dM4FqyEc7FaOP9VkN3d9ysGC8s013QsWIWcjXoScWgNsVeP4VahMU7Fa6cUdM+yMxV1dje2uYpg5eSRsu/fP7tWasrVxYMAsLCyASsbAOx8i2HtkZuE21ceOMGFiIg8X+5eObgTejNl+05YCO5e97tDd/71Ktyd0BDcvXKkOf/Vt9ph5+DILwumc2TvDl5+9Q2Kl6+S8nrdk3Sm3L1ypHr97n6WtPf82+7Na1Ne8fv/HJ1cKFG+Ckf27kgpplYs+I7Iu+EEjBj60GuKZDYqpiRLsM9XhpurxuNZuy0kG4k6vRO3Ss1Sjkef34+NVz6sXf/5o+BYuCq3dywmOSEOg6UVMZcO4VGjFQBJUeFYObljjI0kfO8v5H57RJp7Wto54l6tJe7VWhJ34zR39/9G6MaZOJd6hRwNuz3TztS9o5twKVM/1b6kyDCsnL0AiDq5DdscBe7vj76Lpb0zBgtLEu5cJ/H2VWzcfZ9JLhGR7KxIqQrMmzSSpm06kWw0cmj3Vuo2ffOR51SoWZfpXwyi0Vvvc/f2LUKuXaJg8dKYMBFy7RKhN67i7pWT4M1r6frpmDTne/vk5q0OPWnZvjtH9+1kwy8/Mm/y57zeris16zd5os6Um6c3dg5OnD1xmEIlyrJj/SoatGjzwLExUZGcOrKPrp+MTtl37+4dLK2scHRyISE+juP7d9GkdQcA/vh9Ocf27mTg+FlYWGhlHsk6VExJlmCfuwROxWpx4btArBw9sM1REEtbp5Tj946lLT4s7Z3xqPEOF2d2AYMBpyLVcSpaA4CQNVOJDzkLgFedAGy88j7y/na5iuLTtA/JifHEXDiQrmeIPLmVkNVTMEbf5eoPA7HzKUze9yeQeC+Mm79+Rd737n+ymJwQS/S5fWmmN7+1fjrxN8+AwYC1m0/K8ZhLhwgL+h6DpRUYDORs1jddsw2KiMijFSxemgo16/JZp7dwdfcgb8HCODje/1u0b/smFn39JZER4Uwc3IN8hYvT/6vp5PErTNW6DfmkwxtYWlrSrufglFf12vUczLiB3UhONvLya2+Qx6/wQ+9tYWlJuWq1KVetNvfCb3Pz6qV0PUP7jz5l1l9To5et+hJlq74EQNCqpQDUa3b/Q8f924MoXakmtvb/vOJ+93YYs8Z+RrLRiMmUTNU6r1K+xv1XHedPHoVnzlx83rMdAJVe8ueN97umK6PIi8Tg6+trMncIkeLFizPrh+V8/Ed8uq+RHB+Dha0DyQlxXJ7bE59m/bDzLfYUUwrAgmYu1KpWhfDwcHNHERFJlytXrrDn+rNZZyouNgY7ewfi42IZ/XEggR8P/c9JKOTpsLGE/LYxlC1T2txRJBtRZ0qyjJurxhMfehFTUgKu5RqpkBIRkedu7sQRXL90nsSEeGo1bK5CSiSLUzElWYbvW/pCq4iImFe3T78ydwQReY5UTEm2Fxo0B4f85XAsVPmhYyL/3E5C6EU8a7+XoXvFXT/FjRWjSU5KwKlIdXK81itlxqW/xYde4sYvY4i/cRov/04pCw/Hh12+P137XxLDr+P1Sgc8arQidNNsok5tB4MFlo5u5HpjMNYuXhnKKiIiz8fPc7+hWNlKlKpU/aFjDuzczPVL52n6bscM3evC6RPM/us7UeWq1aZtj4Fp/g797fyfx/i8Zzu6f/YVVeo0BOCnGRM5FLwNkymZ0pVq0LbHQOJiYxjdOyDlvDuhIdSs30RrSUm2oGJKsj3vev/9h8m5+EtQ/KUM3+vmbxPwaT4AuzwlubpoANFng3EqkvqPp6W9Czkb9yLq5PZU+2298qXMDmhKNnJ2wps4l3gZAI9a7+Lt3wmAO7uXcXvLvDQTVIiIyIupZWCP/xxTseYrVKz5SobvNX/yKAL7DKNQibJM+KQ7R/Zsp1y12mnGJRuNLJ01idKVa6TsO3P8EKePH+KLWcsAGPVRe/48vI8S5aukmjFwaNd3qFTbP801RbIiFVOSbYT9MZ97R9Zj6eiGlUsO7HyL4lnrXa6vGI1T0Zq4lKrL2UmtcC3XiKjTO8CYhG+rkdh65+fuwTXEXf8TnyYfp/v+SZFhJMfHYJ+3FACu5V8l6uS2NMWUlZM7Vk7uRJ3e9dBrxZzfj427L9ZuPgCpFuA1JcQBD/6UUUREzGflwhns3Pgbzm4eeHjnxK9oSRq3CmDWV59RvvrLVKnTkL5tGlGrYTMO7dqC0ZhEj6ET8M1XgG1rV3Lh9HHe7zU43fe/ezuUuJgoCpcsB0Cths04sGPzA4upDb8spnLtBlw4dSxlnwEDiQnxJCUlYjKZMBqTcHX3THXezSsXibx7h2JltCivZA8qpiRbiL12ksiTW/Dr9j2mZCMXp3fEzrfoA8daOrhSoOscwves4M7OJeR6/eGvKURfOMCttdPS7LewtiV/p+9S7Uu8F4aVi3fKtpWLN4mRYel6nnvHgnApk/pTv9CNs4g4vBYLOyfyBUxJ13VFROTZOP/nMfZt28jns5ZhTEpiWNd3Hjo5hbOrOyNnLGXTyiWsWTqPjv3SrnX4tydZlDc87Bbu3jlTtj28chIedivNuXdCQ9i/PYhBE+YwZ9w/xVThUuUoUb4KH73tjwkT9V9vjW/+gqnO3b15LVXrvvrQVwdFshoVU5ItxF4+ilOxl7CwtgXAqVith451Lnn/1Tk732JEntz6yOs6Fqj4zBbmfRhTUiJRp3bgXb9Lqv3e9TvjXb8zt7cuIjz4Z7zrdXiuuURE5OHOHD9IhZp1sbGxBRtbylev89CxlV66/2GZX9GS7Nu+6ZHXfZJFeR/X4m/H0qpz7zSL64Zcu8yNyxeY9NMGAMb278KpI/spVvafLlTw5rV0+ddCviJZnYopkf/HYGn91w8WmJKNjxz7JJ0paxcvku6Fpmwn3QvF2vnJJ4mIOrsb21xFsHLyeOBxl7INuPLDABVTIiKZlLWNDQAWFpYkGx/9d+hJOlPuXjkIDw1J2b4TFoK7V4405144fZzvRt1/KyMyIpzDe7ZhYWlFyLVLFCpRFru/FuotW/Ulzp44nFJMXT53CqPRSAFNBy/ZiIopyRbs85Xh5qrxeNZuC8lGok7vxK1Sswxf90k6U1bOXljYOhB75Th2eUoScWgd7tVaPvE97x3dhEuZ+qn2Jdy+go1nXuD+zIO2Xvme+LoiIvLsFClVgXmTRtK0TSeSjUYO7d5K3aZvZvi6T9KZcvP0xs7BibP/x959B1RV/nEcf98BXPYQmYID9yr3LrflSk1NM1PLRE3FgXunaeaeCLjNWWra0DTNmStnjpyoIMpQZM977+8PiuKH5UA4jO/rn7jnPOecz7kh537vc87zXLmAV4WqHNv7HS06vp+l3dwNezJ+/ut5rhoNm3Lylz0c/HEbbfUfYzQauXbxN1p2+nuU2xMHdlO36VvZPich8hMppkShYO5eAatyDQjy64PW0gEzp1JozKxyPYdzm+E8+HYmxtRkLMvUwfLPwSeiTu8EwL7WO6TFPuJOQD8MyfGgUhN14htKfroOjc4SQ0oi8bd+yzJSX/g+f1IeBaNSqdDauuDSbkSun5sQQoh/V6p8ZarVb8yEvp2xtXfAo1RpLCxz/zrUy2c8gX8OjV61dkOq1k4fqfbAd1sBaNqu679uW+uNFlw5d4rxfd9FhYoqtRpQrX7jjPWnDv3E8BnLcjS/EHmNys3Nzah0CCHKly9P4IZtDDuYnGPHMCQnoDazwJCSxL3Vg3Fp54vOrVyOHa+gWtfOhgZ1ahEVFaV0FCGEeCnBwcGcCk3N9eMmJSagM7cgOSmRGcP60GfYpH8dhEK8OFMNFDdLoGqVykpHEYWI9EyJQuPhd3NIjriDMS0F29fekkJKCCFErlo9byqhd2+TmpJMg5btpZASogCQYkoUGm6dJykdQQghRCE2YPwspSMIIV4x9bObCCGEEEIIIYT4f9IzJcQLuLt6CE4tB2LuXj7XjvnXZLz6pDjKjf8py/qYKwcJ3TKJ4v0CMnIlPbzFw+/mYEiOR6VSUbxfAGoTM4LX+5IW+wijQY9F8ao4txmGSq3JtXMRQgjx4mYO/4hu3iMoiuQM7gAAIABJREFUWa5Srh3zm5WLOLbvO+JjYwj44WTG8g3LvuSP86cBSE5KIvbJY/x2HQOgd4vX8ShZBgAHJxeGTV8MwOWzJ9jiPw+j0YiZuQWfjJqGs7snj8IeEDBrAgnxsRj0erp+MpTX6jTKtXMU4lWQYkqIPM6qXH3s63Tk1qIeWdbpkxOIOvENumJ/33dv1KfxYPs0XDtNQOdSGn1CNCpN+j91ty5T0egsMRqN3N8ykdjLB7Gp0izXzkUIIUT+8Hq9N2neoTujPmybaXmPgaMyft63YyN3b/6R8drU1Oypw7SvXfA5Q6ctxK14Kfbv3MyurwL4ZPR0dm4IoHbjljRr/x7379xi3rhPmbtxT5bthcjLpJgS+ZohJZH7WyeTFhOB0WjA8c0PsancjMiDa4i79iuGtGTMPSrj0s4XlUrF3dVD0LmUIfHeRQwpibh2Gs+jI1+RHHYbm8pNKdrsE1KiHhDy1Uh0rmVJenAdM6eSuHYcj9pUl+nY8TdPEfHLaoz6FEzt3XHtMAa1mQXh+5YTd+0YKrUGS69aOLX6NFvnaO7x799ERh5YQZGGPXh0bNPfuW6dxszZC51LaQA0FrYZ6zQ6yz/fOD1GfRqoshVNCCEKpeTEBJZOG8njiDAMBj3vfOBNnSZv8e265Zw/cYiU5CRKV3qdPsMmoVKpmDn8IzxLl+f672dJTkqk3+jP+X7TSkKCblC7cSs6fzSYiIf3mTtmACXKVuTOjau4F/ei35jPMdOZZzr277/9yo41y0hLTcHJzYO+o6ahM7dga+ACzv16ELVGQ+Wa9eje3/fp4Z9T6YqvPbPNiQO76dhr4DPbqVSQmBAHQEJ8HHZFiqYvR0VSfDwAif9YLkR+IsWUyNfibp5Ea+2IxwdfAqBPSv9jbV+7E46NewMQum06cdd/xbpcAwBUGi0lvAN5fPxr7m8aRwnvQNTmNtxe2B2Heunza6RE3sPlndFYeFbhwbdfEHV6B0UadM84blr8EyIPr8Oz1zzUpuY8OrKBx8e3Yl+7I3FXj1By8FeoVCr0ibFZMscHnSV8z5Isy9UmZhTv6/fc554Ueo3U6HCsytbLVEylPAoGVASvG0FawhNsKjejSMO/J2UMXjeCxPtXsSpTF+uKjZ/7eEIIIdJdPH0MuyJFGT5jKQAJcel/65t36E6HD/sD4D9zHOePH8qYh0mrNWGq32b2bvuKhZN8mOq3GUtrW0b2bM1b7/YE4EHwHT7ynUrZytVYMXsS+3dtoXXX3hnHjY2OYtdXAYyeHYCZuQU/bFrFnq/X0fydbpw5up8v1uxCpVIRHxeTJfPVc6fY6Dc7y3JTMx0TF69/4fcgMiyUiIf3qVitdsay1JQUJg/ohkajoU23j6nRsCkAH42Ywtyxn2JqZoa5hRWTlnwFQMdeA5g92pt9324kOSmRUbMDXziHEEqTYkrkazonL8J/Wkb4Xj+sytXHonj6N2nxd87y+OgmjKlJ6BNjMXMqAX8WU1bl0ycoNHMuhWnRkmitHQEwsXclNToctc4Kra0TFp5VALCp2oKok9vgH8VUUsgVUiLucndleq+TUZ+KebFKqM0sUWlNebhzFlZl62FVtn6WzJYlq1NywKpsnbfRYCDsp6W4dhj7lHV6Eu9d/PM5KR331g5D51YOy1I1APD4cC6G1GRCt00jIegsll61spVFCCEKG4+SZdi8fC5bAubzet03KFc1/e/r1fOn+HHLalKSk4iLicG9hFdGMfXXf4uVKoN7ca+MXpiirsV4FPEQCytrHJxcKFu5GgD1m7dh3/aN8I9i6taVi4Tevc10n14ApKWmUrria5hbWWFiasbKOZN5ve4bvF73zSyZK1Sr/dRb8F7WyQN7qPVGC9Sav5+7nbtxDw5FnQkPDWGWb1+KlSqDs5sHP237ihEzl+JVoSo/blnNRr/ZfOw7lRMHdtOw5Tu83bUXNy9fIGDmOD5fuR21WsZHE/mHFFMiXzN19KCk9wribpwgYv8KLEvVwKFBd8K+n08J7wBMbJ2J+GUVhrSUjG1UGpM/f1Cj0pr8vTOVGqNB/9eL/ztS5tdGjFiUqol7l8lZMhXv50/C7TPEXjlE1KntePZemGn9q+iZMqQkkBIexL01PgDo4x5zf9NY3LvPxMTGCfPir6G1tAPAqkxdkkKvZxRTfx3LunxDYv84KsWUEEK8IBePEkxdvoULJ4+wbfUSKlarQ+tufVi38HOm+G2miJMLO9YuIzXl74noTUxMAVCp1Gj//BlArVZj0Kdfe1T/f+1R/d+1x2ikUo26DJzwZZZMk5du5Mq5k5w+vI+fv93EmLkrM61/1T1TJw7u4cMh4zItcyjqDICTWzHKv1aTezeuYm5hyb1b1/CqUBWAOo3fYs6YAQAc2r0D3y/Sr3ulK71GamoycdFR2NgXeeE8QihFiimRr6XGRKIxt8b2tZZodFY8Ofs9xj8LJ42FHYbkBGKvHMK6YtZv6f5LWnQYicGXMPeoTMzvP2P+Zy/VX8yLVSLsh/mkPArBtEgxDCmJpMVEorUugiE1Gauy9TD3rMKtBd2y7PtV9ExpdFaUGf1dxut/jjJo6uDOo2MbMaQkodJoSbh7Hod6XTEkJ2BISUBr7YhRn0bc9eNYFK+arRxCCFEYRUWGY2ljS4MWbbG0subQj9szCidrWzuSEhM4fXgftd5o8UL7fRT+gJuXL1C60msc3/9jRi/VX7wqVmXd4hmE3b+Hs7snyYkJREWGY+foREpSIq/VaUSZSq/j+0HrLPt+lT1TofeCSIiNyfRcVXxsDKZmOkxMTYmNjuLG5fO0fq8PltY2JMbH8TD4Di4eJbh05jhuxUsCUMTJhStnT9LorXf+nMw4BWs7h1eSUYjcIsWUyNeSw28TsXdZei+TWotz2+FozK2xq9GWoKW90Fo5YO724sOYmzp6EnVqBw++/QLToiWwr9Uh03qtpR2uHcYS+s1nGPXpxZtj076ozcwJ2TQuvaAzGnF+K3uDTwCE7/Uj5vefMaYmcXPuu9hWb0PRJh/9a3uNuTUO9d7jTkA/UKmwKlMXq7L1SIt7TMjGcRj1KRiNRixKVMOu5jvZzieEEIVNSNANNvvPQ61Wo9Fq6eUzAUsrG95s8y7jPu6ErYMjJctVfuH9unqU4Oedm1kxZxLuxUvRtH3XTOtt7Bz4ZNQ0/D4fTWpK+rXn3Y8GobOwZMFEnz8LOiPvD8je4BMAW/zncfzAj6QkJzH0vea82bpTxmATJ3/ZTZ0mb6H6R89Z6L3brJn/GSqVGqPRQJtuH+FewguAPiMms3jqcFQqNZbWNnzs+xkA3fv7smreVH7ath6VSkXfUdMy7VOI/EDl5uZmVDqEEOXLlydwwzaGHUx+duMclhL1gJCNYyj16Vqlo+RJ69rZ0KBOLaKiopSOIoQQLyU4OJhToalKx8gk4uF95o8fxIyVO5SOkm+ZaqC4WQJVq7x4ISvEy5In/IQQQgghhBDiJUgxJcT/MbV3lV4pIYQQuaqoi7v0SgmRD0kxJYQQQgghhBAvQYopIYQQQgghhHgJMpqfKDCufd6KcuN/ytVjpkQ9IGhJT0wdPTOGO3/w7RfEXf8VjaX9U28XfHRsMxF7l1F61C60lnYYjUbCdy8i7sYJ1CZmuHYYi86t3H8eN+LnQKIv7EGfFJfpnKNO7+TJqe2g1qA2Ncel3UjMnEoQf+s04fv8QZ8KGhOcWg7ImHcqeL0vabGPMBr0WBSvinObYajUGsJ/Wkb0xb041H+PIv+YsFgIIQT0a1OHgB9O5uoxIx7eZ2yfDrh6lGBawNc8CA5i2bRRGevDH4TQqfdAWr3bk22rl3D22C+o1Wqs/xwF0N7RiavnT7Nwkg9FXdwBqNGwGR0+7J+xD4Nez+SB3bEv4sTwGVnnRPynPy7+xsalXxJ8+wYDJ8yi1pstM9Yd/WknuzYEAtC+xyc0bPUOyUmJLP3Ml/DQYFRqDdXqvUnXT4YCcOC7rezfuRm1WoOZuQV9hk3KGA0Q4FHYA8Z+1IEOvQbQumtvUpKTmDa4J/fv3mLh1v1Y29pn/w0W4iVIMSVENpk4uGeaN8r29bewr92R0B0zsrRNjQ4j4dZptLbOGcvib5wg5VEIpYZsJCnkCg+/n0eJfv7/eUyrcvWxr9ORW4t6ZFpuU6U59rXShzuP/eMo4T8twaPnHDQWthR7/wtMbBxJDrtN8HpfSvtuB8Cty1Q0OkuMRiP3t0wk9vJBbKo0w6nVQFSmupd+X4QQQrx6Tm7FMuaLcvUomfGzQa9n6HvNqdGwGQCtu/bm3T6DANi7fQM71/vTe9hEAMpWrv6vhdLe7Rtw8yxJYnz8M7MUcXKl76jp7P56TablcTHRfLt+OVOWbUalUjF5wHtUq98ErYkJb3fpRYVqtUlLTWWWb18unDzCa3UaUa9pa5q2Sx8K/uyvv7Bp+Wx8v1iesc+NfrOpWrthxmtTMx3TAr5mxPtvPc/bJkSOkdv8RJ4Uvm85USe3Z7yO+GUVj45twpCcwL01Qwla/jFBS3sR+8eRLNvGB50jeMPojNcPf5jPk3O7AUgKvcbdVYMJWt6X4HUjSIuNfOXZLUq8jtrc5qnrwvcsoWjLAZlmtY/74yi2r7dCpVJh7lEJQ1LcM3OZe1RCa+2YZblGZ5nxszE1CUg/js61LCY26e1NnUpiSEvG8Nfkxn9tY9Bj1Kf9tYkQQhQaWwMX8PO3mzNe71i7jB+3riEpMYFZvn2Z5N2V8X07cfbYL1m2vXr+NPPGDcp4vW7RDI7s2QlA0PUrzBjWh0n932P26P48eRSRY+dw+dxJirp54OjsBoC5pVXGuuSkxOf62/444iEXTh7mzdadnuuYRV3c8fQqi1qV+ePk778do1L1eljZ2GJpbUOl6vW4ePooZjpzKlSrDYDWxITiZSoQFRn29Lz/CHzm6AGKurpn6qkSIq+QnimRJ9lUbkrY7sXY10n/gx57+Rc8es5BpTXFvdvnaHSWpMU/4e6KAViVa/hck/wZ9WmE/bgA9+4z0VraEXNpPxH7V+DaYUymdtEX9/L42OYs25s6uOP+3rSXPqfYP46gtXZE51I60/LU2Ei0Nk4Zr7U2RUmNiXxqsfQ8ok5u5/HxrRj1qXj2XpA1x5VD6FzLotaaZiwLXjeCxPtXsSpTF+uKjV/quEIIkV/VadyKDcu+pHmHbgCcOrgX31nLMTE1ZcjUBZhbWhEbHcVngz6gWv3Gz3XNSUtL5avFM/GZthAbOwdO/rKHb1Ytpu/IzzK1+/XnH9i9dU2W7Z3cPBg8Zd5zn8PJX/ZQt+nbmZZ9s3IRx/Z9h7mlFWPmrsxYfvPKBSZ80hm7IkXp1n8ExUqkX5c2LP2Srv2Gk5Tw7F6p/xIVGY6Dk0vGa4eizkRFhmdqEx8Xw/kTh2jZ6YOMZT9/u5k936xDn5bK6DkrAEhKTOCHzasYNTvgqe+TEEqTYkrkSTrXsujjo0iNiUSf8ASNzhoTW2eM+jQi9geQePcCqNSkxUSgj3uM1rrIM/eZEnmP5PAggtcNT19g0KN5yna2VVtiW7VlluXZYUhJ4tHhr/D4cO4r3e/T2NfphH2dTkRf3EfkoXW4dRqfsS45PIiIfcuz5PD4cC6G1GRCt00jIegsll61cjynEELkFcXLVCDmyWOiIsOJjY7CwtqGIk4upKWl8vXKRVy7eAa1Wk1UZDjRUY+wc3j2l10Pg+8Qcucms0d5A2Aw6LF1KJqlXf3mbajfvE228qelpnLu14N0+dgn0/LOHw+h88dD+G7jCn7+dhOden9KiTIVmLfpJ3TmFlw4eYRFk4by5brvOX/8EDb2DpQsW5Gr509nK8+z6PVp+E0fTYuO7+PkVixjefMO3WjeoRvH9//Arq8C6Dfmc3asXUarzj3RmVvkaCYhXpYUUyLPsq7UhNgrB9HHPca6clMAoi/uQx//hBLeK1BptNyc3zXjdrW/qNQaMBoyXhv/XG/EiGnRkpT4xO8/j5sTPVMpUfdJffKAIL+PAEiLieCOf19KfOKPibUjaTF/f2OXFhORcUtedthUbkbY939/q5kaHU7I5vG4dhqPqYN7lvZqEzOsyzck9o+jUkwJIQqd2m+05PThfUQ/jqRO41YAHP/5R2Kjo5i6fDNarQkj3n+L1JTkTNtpNBqM/7jm/LXeaDTiXtyLSUu++s/jvoqeqYunjlK8TAVsHZ7+xWL9Zm2YO24gnXp/mul2utfqNGLdws+JjY7i+uXznPv1IBdPHiU1JZnEhHiWzxhL/3EznyvDP9k7OvHH+d8yXj+OCKP86zUzXq+e9xkuxYrT6t2eT92+TpO3WbvwcwBuX/2d3w7/zNaA+STExaJSqzAxNaNFBxkYSeQNUkyJPMumclMe7vqStIRoivdZBIAhKQ6NpT0qjZb4oLOkPXmYZTsTO2eSI+5iSEvBmJpMwu0zmHtWxayIJ/qEJyQGX8LcozJGfRopj4IxcyqZafuc6JnSOXtRZtSujNc353elRL8AtJZ2WJVvSNTJ7VhXbkZSyBXUOsuMW/xuL/6AUoP/+0L8TymPgjEt4gFA3I3jmBZJ/8ZPnxhLyIbRODX3xsKzSkZ7Q3IChpQEtNaOGPVpxF0/jkXxqq/ilIUQIl+p3aQVq+dOJTb6CePmpw8qlBgfi42dA1qtCVfPnSIyLDTLdkWc3Qi9e5vUlBRSUpK4cu4kZStXx9WjJLHRUdy8fIHSlV4jLS2VhyF3M26p+8ur6Jk6cWB3llv8HobcxaVYcSB9QAdXj/Rr3ZPHkdjaF0GlUnHrj98xGA1Y2djRta8PXfum92xdPX+a3VvXZhRSW1cspFT5ytT8c3CLZ6lSswHfrFxEfGwMAJfO/EqXP/f9zarFJMbH8tGIKf+a98KJwzi7ewIwfuHfo+LuWLsMM3MLKaREniLFlMizzJxKYkhOwMS6aEZxYVO1BSEbxxK0tBc6t/KYOnpm2c7E1hmbSk0IWtoLE3tXzFzLAKDSmuDe9TPCdi/EkBSP0aDHoW6XLMVUdt3/eioJd86hT4jm5tx3cWzcB7sabf+1vWWZusRdP87thd1Rm5jh0mEsAGnxT8BofOo24Xv9iPn9Z4ypSdyc+y621dtQtMlHRJ3cTvztM6g0WjQ6a1w7jgMg6tR2Uh7fJ/LQWiIPpV+YPHrOBYyEbByHUZ+C0WjEokQ17Gq+80rfDyGEyA+KlShNUmI89o5O2BVJvx2vXvM2zB8/mPF9O1GibCVcPbNeL4o4uVD7zZaM79sJRxd3ipeuAKQPsDBo8ly+WvIFifFx6PV6Wr3bI0sxlV3JiQlcOnM8Y6S+v3y9YgEPgu+gUqlxdHal19D09acP7+PArq1oNBpMzcwYOOHLZz4DFnL7BtXrNc6y/PYfl1g0eSjxcTGcO36I7Wv9mLlqB1Y2trzzgTdTBqYXPe/07I+VjS2PIx7y3YZAXD1LMrn/ewA0e6cbjdu8y8/fbuLy2ZNotVosrGz4ZPT0V/DuCJHzVG5ubk//tCZELipfvjyBG7Yx7GDysxvnISlRDwjZOOap80llV9y1X0mJCsWhbudXvu/nFfHLKtSm5pnmmVrXzoYGdWoRFRWlWC4hhMiO4OBgToWmKh3jhUU8vM/88YOYsXJHrh539uj+jJy1/NkNFTDi/beY4rcJa1t7TDVQ3CyBqlUqKx1LFCIyNLoQ2aBSqzEkxWU8C/UqWZWrr2ghFf7TMmIu7kNtYq5YBiGEEH9TqzUkxscxsV+XXD1uXiykUpKTmNivC2lpaahU8nFWKEdu8xMF2tN6Vl4lE1tnSo/Y9tztQzaNIy3uMSU++fvC9OTcbiy9amUMOvFg5ywc6r2HmVOJbGV7cm43SaF/4NJm2Ett79RqIE6tBmYrgxBCFHZ/PefTumvvbO+riJML8zfvy9Y+Fk70IToqkklLNmQsO7JnJ5Vr1sPeMX2ajpVzJvNW5w+zPa/TkT07Cbp+mQ+HjMvWfp7mr0l7hVCalPJC5BJ9YixJodcwJMWR8vjvh5ijz+/ONEmv6zujs11ICSGEEP8vPi6GOzeukBAfR3hoSMbyo3t3ZppQ+GPfqTJBrhDPSXqmRIERfX4Pj3/dDKgwc/bC7d0JmdY/+e07npz5DqM+FRMHd9w6TUBtqiPm8i9EHlyDSqVGrbOk+EdLSA4P4sG3MzHq08BowP29aRmj5L2s2KuHsSrXAK2lPTGX9uP4Rk9iLh8kKfQaD7ZNQ2ViRvG+fgR/NRKnlgNJCr1G6uP7Gb1D/+xpCtk0jtTocIxpKTjU7YxdzfZ/tvmRR0e+QqOzwsy5NCqtCZD+bNfDnV+gT4hGY2GHa4exmNg5Z+t8hBBCZHZ07y52b12LSqXCo1RZvMfOyLT+4A/fcPCHbaSlpuLs7kG/MTMw05lz6tBevl3nh1qtwdzSivEL1hBy5yYrvpyEPi0Vg8HA4CnzMka7e1lnjuzn9XpvYmNfhJO/7KZdj084fWgvQdcus3zGGEzNdExcvJ65YwfSzXsEQdcvEx4aTDfvEUDmnqaFE314FPGQ1JQUWnbqQZO26belH97zLd9vXImFlTWeXmXRmqRPEB/x8D4rZ08iNvoJNnb29B05jSLOrtk6HyHyAimmRIGQHB5E5OF1FP94GVpLO/QJMVnaWFd8A7ua7QCI2B/Ik7M/4FD3XR4dXINHzzmY2BRFnxgLQNTpndjX7Yxt1ZYY01IzzSHyl/tbJ5PyKDjLcod6XbF9/a0sy2N+349j415oLR24v2UCjm/0xKZSY6JObcep5UDM3cv/X943ubtiQEYxFXvpAEXeSJ+Tw/WdMWgsbDCkJnMnoB/WFd/EqE8l8pdVlPBegcbMkntrfDJGMgz7cSG2r7+F7etv8+TsD4TtXkix7pkv8kIIIV5eyJ2b7PoqkImL12Fta09cTHSWNjUbNqdxm/Si45tVizm8ewctOr7PzvXL8f1iOQ5FnYmPS79+/fLd17Ts1IP6zduQlpqKwaDPsr+l00byMPhOluWtOvekYcv2WZafOLCbd3p6Y2tfhMVTh9OuxyfUerMlP+/cTDfvEZQsVylz3kbNmTa4Z0YxdfLgHtr3+ASAj0d+hpWNLSnJSUwZ2J1ajZqTlpbKjrXLmOq3GQtLa2aO+JjipdOvbV8tnknDlu1p2OodDu/ewVdLvsBn2sIXeIeFyJukmBIFQvzts9hUbILW0g4AjYVNljbJ4UFE7F+BISkWQ0oilqVrA2DuWYUHO2ZiU6kJ1hXfSF/mUYlHh9eTFh2BdcU3ntor5d516nPnS4t7TMrjEMw9q6YPQavRkhx2GzPnUv+6jdbSDhN7VxKDL2NapBgpkXcx/3OOqMcnvyHu6pH0fUeHk/IohLS4x1iUqJbxHlhXbppR7CWFXKZYt/RhZm1fa0XEvrz3MLEQQuRnV8+dovabLbC2tQfAysY2S5uQOzfZtmoxCfGxJCUmUKVmAwDKVKrGii8nUrtxS2o2bA5A6YpV2bVhBVERYdRo1OypvVKfTpz93PmiHz8i7P49ylapjkqlQqPREhJ0g2Ily/zrNjZ2DhR1LcbNKxdwcS/Og+AgylSuBsC+HRs4c/QAkD4p78P7d4l+/Ijyr9XExs4BgDqNW/Ew5C4AN69cZPDU+QDUb9GWLQHznzu7EHmZFFOi0HiwYwbu3WegcynNk3O7SbhzDgCXdr4khlwh7vpxgvw/oaR3ILZVW2BerCJx148T/NUoXNr5YlmqRqb9vUjPVMylXzAkxnJrQfq8GobkeGJ+30/R/yimAGwqNyPm8i+YOXpiVeENVCoV8UHnSLh9huJ9/VCb6ri7egiGtJTsvDVCCCFyQeCXE/D5bCGeXuU4smcnf1w4DUDvYRO5dfUiF04cYfKAbkz120y9Zm0oVaEqF04cZt64T+k9bCIVq9XJtL8X6Zk6degn4uNi8O2RPrlvYkIcJw7spvPH/15MAdRt8hanDu7F1bMkNRo0Q6VScfX8aS6fPcnExesx05kzc/hHpKbIdUgUTlJMiQLBslR1QjaPx6F+VzQWtugTYrL0ThlSEtFaFcGoTyPm4j60f46el/L4PubFKmJerCLxN06SGh2OPikeE3s3HOp2Ji06jOSwW1mKqRfpmYq99DMePWdj7pE+90VKVCjBa4dTtPknqE0tMKQkPHU76wpv8OjwepJtnSnawjv9PJLjUOusUZvqSI64S1LIFQDMi1UgbPci9AnRqM0sib18EDOX9AeIzT0qEXNpP7avtSL64j7MPas+d3YhhBDPVqFabRZNGsZbnT/EytaOuJjoLL1TSQkJ2Dk4kpaWyvH9P2SMnhcWGoxXhap4VajKxdNHeRTxkIT4OJxci9GyUw8ehT8g+Pb1LMXUi/RMnTiwG9+ZfpSu9BoAEQ9CmDWyH50/HoLO3IKkhPinblejYTN2bQikyM0/6NpvKACJ8XFYWlljpjMn9F4Qt65cBMCrQhU2LJ1FXPQTdJaWnD60Fw+vcgCUrvQaJ3/ZQ4MW7Tj+84+UrVL9ubMLkZdJMSUKBDOnkji+0ZO7q4egUqkxcy2DW8fMQ7E6NvmYu4HeaCzt0LlXzChgwvcuI/VRCEbAsmR1zFxK8/joBqIv7EWl0aK1cqBIo54vnS0l6gGpT8LQFfv7XnRTezfUOksSQ65gW+0twr6bmzEAxT9pzK0xLVqclIg7mBerCIBl6To8Ob2L24s/wNTRE92fy7XWjjg26cOdFQPSB6Bw+fvbRufWQ3nw7UweH9uUMQCFEEKIV6dYidK079GXGcP7oFZrKF66PJ+Mnp6pTac+nzJ1UA+sbR3wqlAlo4DZ4j+PsPt3MRqNVKxWB0+vcvyweRXH9n2PVqvF1qEI7d7/5KWzRTy8T2TYA7wq/v1FWlHXYlhYWnHr6kUatnqHNQumZQxA8U+W1ja4eZYi9N4tvMqn32pepVYDDny3lTF93sG1WImM/doVKUqHXgP4bHDP9AEoSpeMmo2EAAAgAElEQVTL2E/PwWNZ8eVEftyyJmMACiEKApWbm5tR6RBClC9fnsAN2xh2MFnpKOIZ1rWzoUGdWkRFRSkdRQghXkpwcDCnQlOVjiFeMVMNFDdLoGqVykpHEYWIzDMlhBBCCCGEEC9BiimRJyQnJ2NmIr+OeZ0KMDPRkpSUpHQUIYR4aalpaWhUSqcQr5papSJZBsIQuUw+vYo8ISQkBFtzU8oWMVE6ivgPtdzMeBwVTWJiotJRhBDipQXduYu9TukU4lWzMzVw4/p1pWOIQkaemRJ5RtOmTVm4dDmbr+l5EKfHYJRfzbxCo1JR3FZLRy8VvT7oztmzZ5WOJIQQL61MmTLs+HYnMZiTkApytcnfVICV1og2+Qnt27UlNDRU6UiiEJFiSuQpDRo0oHvP3ri5F0uf3DYH2dvb4+BQhNtBtzEaDDl6rJyg0Wgp5VWKBw8eEBcbm6PHMhgMhNy7y8oAPy5evJijxxJCiNxQunRpBg/xoWSpUmi1ygxurFarKVXKi8jICJ48eaJIhuxydHTE2tqaO3fuYFToS9CUlBRu3rjBvLlzpJASuU6KKVEoValShQ0bNtCxY0du3bqldJyXVqNGDVauXEn79u25d++e0nGEEEK8AH9/f548ecLo0aOVjvLSVCoVq1ev5u7du0yePFnpOELkOnlmShQ6dnZ2+Pv7M3bs2HxdSAGcOXOGxYsX4+/vj5mZmdJxhBBCPKe+ffvi6enJpEmTlI6SLUajER8fH1q0aEG7du2UjiNErpOeKVGoqFQq1qxZQ1BQEFOmTFE6zivj5+dHTExMvv52UwghCouaNWuyYsUK2rVrR3BwsNJxXonKlSuzcePGfH/HhxAvSnqmRKEyaNAgbG1tmT59+rMb5yO+vr7UrVuXLl26KB1FCCHEfyhSpAh+fn6MGDGiwBRSAJcuXeKLL74gICAAc3NzpeMIkWukZ0oUGg0bNmTRokW0bt2ahw8fKh3nlStbtizffPMN7733HlevXlU6jhBCiP+jVqvZuHEj586dY9asWUrHyRHz589Ho9EwZMgQpaMIkSukZ0oUCi4uLixatIghQ4YUyEIK4Pr160yePJnAwECsra2VjiOEEOL/+Pr6olKpmD17ttJRcszYsWOpUKECH374odJRhMgVUkyJAk+r1eLn58eaNWs4evSo0nFy1I4dOzh8+DDz5s1TOooQQoh/aNasGV26dGHgwIEY8uF0HM8rKSmJfv36MWLECF5//XWl4wiR46SYEgXe+PHjiYmJYfHixUpHyRVTpkzB1dUVb29vpaMIIYQAihUrxty5cxkwYACPHj1SOk6OCwoKYvTo0Sxfvhx7e3ul4wiRo6SYEgVamzZteOutt/Dx8VFsMsHclpKSgre3N/3796dOnTpKxxFCiELNzMyMgIAAli5dym+//aZ0nFyzZ88evv/+exYtWoRKpVI6jhA5RoopUWB5eXkxc+ZMvL298+3M8i/r/v37DBs2jKVLl1K0aFGl4wghRKE1depUgoODCQwMVDpKrps5cyYWFhb4+PgoHUWIHCPFlCiQzM3NCQgIYNasWVy8eFHpOIo4ePAgmzZtYtmyZWg0GqXjCCFEodO5c2fq16/PiBEjlI6iCL1ez4ABA/jggw944403lI4jRI6QodFFgbRo0SIMBgNDhw5VOoqi1Go169ev59KlS8ycOVPpOEIIUWiUL1+erVu30qVLF65du6Z0HEXVq1ePZcuW0aZNG0JDQ5WOI8QrJT1TosDp2bMnFSpUYMyYMUpHUZzBYGDQoEF07NiRFi1aKB1HCCEKBWtrawIDA5kyZUqhL6QAjh8/TmBgIP7+/piYmCgdR4hXSnqmRIFStWpV1q9fT8eOHbl9+7bScfKM6tWrs3r1atq3b8/du3eVjiOEEAVaQEAAjx49YuzYsUpHyVNWrVpFSEgIkyZNUjqKEK+M9EyJAsPe3p6AgADGjBkjhdT/OXv2LAsWLCAgIACdTqd0HCGEKLD69euHu7s7kydPVjpKnjN06FCaNWtG+/btlY4ixCsjPVOiQFCpVKxdu5abN2/y2WefKR0nz1q6dCkJCQmMHDlS6ShCCFHg1KpVi8DAQNq2bUtISIjScfKkSpUqsWnTJjp16sTNmzeVjiNEtknPlCgQhgwZgpWVFTNmzFA6Sp42cuRIatWqRdeuXZWOIoQQBYqjoyN+fn4MHz5cCqn/cPnyZWbMmEFgYCAWFhZKxxEi26RnSuR7jRo1YsGCBbRu3ZqwsDCl4+R5ZcqUYdu2bXTv3p3Lly8rHUcIIfI9jUbDpk2bOH36NLNnz1Y6Tr4wd+5czMzMGDRokNJRhMgW6ZkS+ZqrqysLFy5k8ODBUkg9pxs3bjBx4kT8/f2xsbFROo4QQuR7I0eORK/XM3fuXKWj5Bvjx4+nbNmy9OrVS+koQmSLFFMi3zIxMWH58uWsWrWKX3/9Vek4+crOnTs5ePAg8+fPVzqKEELka82bN6dTp058+umnGAwGpePkG0lJSfTr14/hw4dTrVo1peMI8dKkmBL51oQJE4iKimLp0qVKR8mXpk6dipOTE/3791c6ihBC5Euenp7MmTOHAQMG8PjxY6Xj5Dt37txh5MiRLF++HHt7e6XjCPFSpJgS+VK7du1o0aIFPj4+GI3y2N/LSE1NxdvbG29vb+rWrat0HCGEyFfMzMwICAhg8eLFnDlzRuk4+dbevXvZuXMnS5YsQa2Wj6Ui/5HfWpHveHl58fnnn9OvXz+io6OVjpOvhYaG4uPjw5IlS3ByclI6jhBC5BvTpk0jKCiIlStXKh0l35s1axampqYMHTpU6ShCvDAppkS+YmFhQWBgIDNnzuTSpUtKxykQDh8+zIYNG1i2bBkajUbpOEIIked17dqV2rVr4+vrq3SUAkGv1zNw4EDef/99GjdurHQcIV6IDI0u8pXFixeTmprK8OHDlY5SoKhUKtavX8/Vq1f5/PPPlY4jhBB5VsWKFdm8eTOdO3fm+vXrSscpUOrUqcPy5ctp06YNoaGhSscR4rlIz5TIN3r16kX58uUZN26c0lEKHKPRyODBg2nfvj2tWrVSOo4QQuRJ1tbWBAQEMGnSJCmkcsDJkyfx9/fH398fU1NTpeMI8VykZ0rkC6+//jpr167lnXfe4c6dO0rHKbDkfRZCiH+3YsUKwsLCGD9+vNJRCrQVK1bw8OFDJkyYoHQUIZ5JeqZEnmdvb8/y5csZPXq0fMDPYefPn2fevHkEBASg0+mUjiOEEHmGt7c3zs7OTJ06VekoBd7w4cNp3LgxHTp0UDqKEM8kPVMiT1OpVKxbt45r164xffp0peMUGkuWLCE5OZkRI0YoHUUIIRT317M8bdu25f79+0rHKRT+ejbt3Xff5caNG0rHEeJfSc+UyNN8fHywsLBg5syZSkcpVEaNGkX16tXp1q2b0lGEEEJRRYsWZenSpQwbNkwKqVx05coVpk+fTmBgIJaWlkrHEeJfSc+UyLPeeOMN5s2bR+vWrQkPD1c6TqFTunRptm/fTvfu3bl8+bLScYQQItdpNBo2b97MiRMnmDt3rtJxCqXZs2djaWnJwIEDlY4ixFNJz5TIk9zc3Fi4cCGDBw+WQkohN2/eZMKECQQEBGBra6t0HCGEyHWjRo0iJSWF+fPnKx2l0Jo4cSKlSpXio48+UjqKEE8lxZTIc0xMTPD39ycwMJDjx48rHadQ27VrFz///DMLFixApVIpHUcIIXJNixYt6NixI4MGDcJgMCgdp9BKSkqiX79++Pj4UKNGDaXjCJGFFFMiz5k4cSIREREsW7ZM6SgCmD59Og4ODnKLhRCi0ChevDhz5syhf//+REVFKR2n0Lt37x6+vr74+fnh4OCgdBwhMpFiSuQp7du3p2nTpgwdOlTpKOJPqamp9O/fn48//pj69esrHUcIIXKUTqcjMDCQ+fPnc/bsWaXjiD/t27eP7du3s3TpUtRq+fgq8g75bRR5RpkyZZg+fTre3t7ExMQoHUf8w4MHD/Dx8WHx4sU4OzsrHUcIIXLM9OnTuXHjBmvWrFE6ivg/s2fPRqPRMHz4cKWjCJFBiimRJ1hYWBAQEMDnn38uI8flUUeOHGH9+vX4+fmh1WqVjiOEEK/ce++9R40aNRg5cqTSUcRT6PV6Bg4cSLdu3WjSpInScYQAZGh0kUcsXbqUxMREfH19lY4i/oNKpWLt2rXcuHGDadOmKR1HCCFemUqVKrFp0yY6derEzZs3lY4j/kPt2rUJCAigTZs2MveXUJz0TAnF9e7dm9KlSzNhwgSlo4hnMBqN+Pj40KZNG95++22l4wghxCthY2ODv78/EyZMkEIqHzh16hR+fn4EBARgamqqdBxRyEnPlFBU9erVWb16Ne3bt+fu3btKxxHP6bXXXmPdunV06NCBoKAgpeMIIUS2rFy5ktDQUCZOnKh0FPECAgICiIyMZNy4cUpHEYWY9EwJxdjb2+Pn58fIkSOlkMpnLly4wNy5cwkICECn0ykdRwghXtqAAQNwcnLis88+UzqKeEEjRoygUaNGdOrUSekoohCTnimhCLVazfr167l8+TIzZsxQOo54SYsWLcJgMMhQ9kKIfKlu3br4+fnRpk0bQkNDlY4jXkKFChXYsmULXbp04dq1a0rHEYWQ9EwJRQwdOhRTU1NmzZqldBSRDaNHj6ZKlSr06NFD6ShCCPFCnJycWLJkCT4+PlJI5WNXr17ls88+IzAwECsrK6XjiEJIeqZErmvcuDFz5szh7bffJiIiQuk4Ipu8vLzYsWMHPXr04Pfff1c6jhBCPJNGo2HLli0cO3aM+fPnKx1HvAKzZs3Czs4Ob29vpaOIQkZ6pkSucnNzY/78+Xz66adSSBUQt27dYuzYsfj7+2NnZ6d0HCGEeKYxY8aQlJTEggULlI4iXpFJkybh6elJ3759lY4iChkppkSuMTU1xd/fH39/f06ePKl0HPEK/fDDD+zdu5eFCxeiUqmUjiOEEP+qVatWtG/fnsGDB2M0ys05BUVycjL9+vVj0KBB1KxZU+k4ohCRYkrkmkmTJhEWFsby5cuVjiJywPTp07G1tWXQoEFKRxFCiKcqUaIEX375Jd7e3kRFRSkdR7xiwcHBjBgxAj8/P4oUKaJ0HFFISDElckWHDh1o3Lgxw4YNUzqKyCFpaWn079+fPn360LBhQ6XjCCFEJjqdjsDAQObNm8f58+eVjiNyyP79+/nmm29YunQparV8zBU5TwagEDmubNmyfPPNN3Tr1o0rV64oHUfksIYNG7Jo0SJat27Nw4cPlY4jhBAAzJs3DxMTEwYPHqx0FJHD1Go1mzZt4syZM3z55ZdKxxEFnJTsIkdZWloSGBjI9OnTpZAqJI4ePcqaNWvw8/NDq9UqHUcIIejevTuvv/46o0aNUjqKyAUGg4GBAwfSpUsXmjVrpnQcUcBJz5TIUX5+fsTGxsoFrJBRqVSsWbOG27dvM3XqVKXjCCEKscqVK7Nx40Y6duzIrVu3lI4jclHNmjVZsWIF7dq1Izg4WOk4ooCSnimRYz766CNKlizJxIkTlY4icpnRaMTHx4dWrVrRtm1bpeMIIQopW1tb/P39GT9+vBRShdBvv/3GkiVLCAgIwMzMTOk4ooCSnimRI2rUqMGqVato164d9+7dUzqOUEiVKlXYsGGDfCMshMh1KpWKVatWce/ePSZPnqx0HKGg5cuXEx0dzejRo5WOIgog6ZkSr5yDgwN+fn74+vpKIVXI/f7778yaNYuAgADMzc2VjiOEKEQGDhyIg4MD06dPVzqKUJivry/16tWjc+fOSkcRBZD0TIlXSq1Ws2HDBi5cuMAXX3yhdByRRyxYsAC1Ws2QIUOUjiKEKATq16/PkiVLaNOmDQ8ePFA6jsgDypUrx9dff03Xrl35448/lI4jChDpmRKv1PDhw9FoNMyePVvpKCIPGTNmDBUqVKBnz55KRxFCFHDOzs4sXrwYHx8fKaREhmvXrjFlyhQCAwOxtrZWOo4oQKSYEq9MkyZN6NatGwMHDkSv1ysdR+QhSUlJeHt74+vrS9WqVZWOI4QooLRaLX5+fqxfv54jR44oHUfkMdu3b+fo0aPMnTtX6SiiAJFiSrwS7u7uzJ8/n4EDBxIZGal0HJEH3b59mzFjxhAQEIC9vb3ScYQQBdDYsWOJi4tj4cKFSkcRedTkyZNxd3enX79+SkcRBYQUUyLbTE1NCQgIwM/Pj1OnTikdR+Rhu3fv5scff2ThwoWoVCql4wghCpC3336bNm3a4OPjg9Eoj4OLp0tJScHb25uBAwdSq1YtpeOIAkCKKZFtU6dO5f79+/j7+ysdReQDM2bMwMrKSgajEEK8MqVKlWLWrFl4e3sTFRWldByRx4WEhDB8+HD8/PxwdHRUOo7I56SYEtnSqVMnGjZsyPDhw5WOIvKJtLQ0BgwYwIcffkijRo2UjiOEyOd0Oh3+/v7Mnj2bCxcuKB1H5BMHDhxgy5YtLFu2DI1Go3QckY/J0OjipckwoyI7/hq6uG3btoSGhiodRwiRT8nUC+JlyXQu4lWQninxUqysrAgMDOSzzz6TQkq8lF9//ZWVK1eyfPlyTExMlI4jhMiHevToQZUqVRg9erTSUUQ+ZDAY+PTTT+nUqRMtWrRQOo7Ip6RnSrwUf39/njx5IhcwkS0qlYpVq1Zx7949Jk+erHQcIUQ+UqVKFTZs2EDHjh25deuW0nFEPlajRg1WrVpFu3btuHfvntJxRD4jPVPiuWg0mozR1/r27YunpyeTJk1SOJXI74xGI0OHDqV58+a0b98eSP9dU6vlT5MQIqu/erHt7Ozw9/dn7NixUkiJbDtz5gwLFy4kICAAMzMzALljQjw36ZkSz2XUqFGEhYVx+fJlVqxYQbt27QgODlY6liggKlWqxKZNm+jYsSPvvvsuYWFhrF27VulYQog8xNLSkgMHDlC3bl3WrFlDUFAQU6ZMUTqWKED8/PyIjY1lwoQJHD9+nJo1a8ow++KZ5Otf8Vxq1KhBVFQUfn5+jBgxQgop8UpdvnyZmTNnEhgYyMOHD6lRo4bSkYQQeUylSpUIDw9n0KBB2NraMn36dKUjiQLG19eX2rVr06FDB/R6PZ6enkpHEvmAFFPiuVSuXJlevXqxY8cO3N3dadeundKRRAHywQcfkJKSwoULF2jevDmVK1dWOpIQIo+pUqUKkZGR9OnThzlz5jBx4kTMzc2VjiUKCCcnJ4YPH86UKVOYMGECd+7coUqVKkrHEvmAFFPimTw8PDAzM8PJyYl3332Xpk2bygh+4pW6fPky3bp1o06dOlSsWBEvLy/5kCSEyKR27do0bNiQu3fvsmjRIkJDQ0lMTFQ6liggoqKiSExMZPHixdy6dYvXXntN7pIQz0WKKfFMLVu2RKfTcfPmTT788EN69+7NjRs3lI4lCpBz587RpUsXhg4dyt27d9FqtdSvX1/pWEKIPKRRo0akpaWxa9cu6tevj7+/v9KRRAGSmprKnDlzqF+/PgcOHECr1dK6dWulY4l8QAagEM9kZ2dHyZIlOXfunNJRRCFRt25dzp07R3JystJRhBB5RL169Th79qz8XRC5wtramjJlynD27Fmlo4g8ToopIYQQQgghhHgJWqUD5CZnZ2eGjhhJqdJlMTE1VTqO+IeY6CecPHaE5X7LZBjSAqBSpUoMGjSY4iWKy5xR2WQ0GgkNDWXtmjUcPnxY6TjiJXl796dxkybY2dkpHUU8B4PRyP2QYFYEBnLq1Cml44j/U7NmTT7q2w/3Yh5yjcljHj58wDdbNvHTTz8pHSXXFJqeKWdnZ7Z9+x3nYq04H64nWa90IvFPNmYq3iml5vzhPYwZOUIKqnysatWqbN36NUZTC1IN8v/xVdCoVJgYkhn06UD27t2rdBzxgmbO/IK2HToRazRFb1A6jXheZloVtuoUPv6oD0ePHlU6jvhTnTp1WL12PRej1EQlGpCrTN6hAqxM1VR3NDJx/Bi2b9umdKRcUWiKqcDV64h2qcH6S0lKRxH/wlyrYtab5swcN4w9e/YoHUe8pN/OnEFnW5Qk+cLildKqwVqTRqWKFUlKkr9j+UWzZs1YuGQZocmmyHcL+Y+FiQoX01TKlSsrX/LlEZev/sHhB2rux8pFJq+y16lp76Wldq2aREVFKR0nxxWavtGy5cpxJCRN6RjiPySmGTkbbqRMmTJKRxEvydTUlKKOjlJI5YA0AyQnp+Dh4aF0FPECypQpQ5JBI4VUPpWQagSVCicnJ6WjCMDBwQGtiYkUUnlcVJKByNhEvLy8lI6SKwrNM1NmpqYkpeXc1ezxgZXEXzsKKjUaSzucOoxFa+1I1LFNxP3+MwBGg57UyLuUGLkTjblNjmX5f8a0FMK+nUFy6HU0FjY4d56MiZ1rlnb6pFgids0mJTwIVODUfjQ6j8pE7vUj4fqvqDRaTBzcKPrOGDQ6a4z6VCK+n0Ny6DVQqXF8azDmJaplK2uyUY1OJ/ML5VcmJiYYDAZA89zbbN/8FTu2bkKjUVO34Zv09/HNWBf2IJReXdrRu9+ndPvwI+7dCWLq2OEZ6x/cD6FP/8F0ef/DV3kaz7Tnu29Zv3I5AD0/7s9b7Tr8a9st61fjt2A23/58DDt7e44e3M8qv8Wo1Co0Gi2DRoyharX0uUya1qpMydLpXyY4u7gxY/7STPvSGwzodLocOiuRE3Q6HSqNFnLhZqR1S+fy268H0WpNcHH34NOx07C0tuHw3u/ZtWlNRru7t67z5cqtlCxTPscz/cVoNLJq4RecO3EEUzMdg8ZNp1S5ilnaTRrchyePIjE1MwNg4jx/bO2L8NO3W/lpxybUag06cwu8R07Go6RXrpxbappe/t3lETqdjpTUnCmkjmxcyO2zR9BoTbB1LkaLfpPQWVoDcGrnai4f2oVarebND30pUbUej0Pv8OPicRnbx4SHUrdzP6q//X6O5PvLvoBphAVdBaMROxdPWvafjKnOIlObu7+f5NjmJejTUtFoTWj0/hA8KtUC4NrxvZzeuRqDQU/Jao1o1H0wACFXz3Loq3lE3rtJ60GfU6ZOs2zlTNEbC82/m0JTTOU0uwbdcGj6MQBPTn5D1KG1FG07AvsG3bFv0B2A+GvHeHLi6+cupIxGA4bkBDQ6q2xlizn3AxqdNcWHbCT20n4e/eyPS+cpWdpF7lmMRenauHT9DKM+FUNq+q1EFl41KdL8E1RqLY/2LefJkQ0UadGfmDPfA+AxYA1p8VE82DCKYp/4o1IVmg5PkU3nTp/k6KEDrNy8A1NTU6IeP8q0fun8L6lTv1HGa88SJVm5aQcAer2ezm83plGT5/+DHxP9BBvb7A0AEBP9hLWBy/BfvxWVSkW/D7rQ4M0mWNvYZmkb/vABv534FWeXv7+8qF67Lg3ebIpKpeLWjWtMGT2c9dt/AMDUzCzj/IR4UVVr1aOHtw8arZb1fvPY/tUKeg4Yzhst2/JGy7bAn8XGOJ8XKjbiYqOxss76+/0izp04woOQuyze9AM3rlwkYO50vgjY+NS2QyZ9QenylTIta9SiNa06dAXg9NFfWLtkNhPmLs/2uQnxF8/KdWjw3qeoNVqObFrM6V1raNR9MI9CbnP9xD56ztpCfFQE22d+Sq+523BwK8EHM9N/hw0GPSsGtaZ0zSbPfbykuGh0Vi/+7+qND4ZhZpH+ufDQV/O5sHcrtdr3ztTG3NqO9r7zsLIvSmTwTXbMGsInS34kMfYJRzctovv09VjY2PPT8incu3QKz8q1sXZ0oaX3ZM7+8NULZyrspJh6QY8PrSXu931oLOzQ2hbFzLUcdvW7oTazzGhjTHn68wxxl/ZjXfnZH/xSnzwk9vxu4n7fR5FWg7EsWy9bmeOvHcPhzd4AWFV8k8gfF2I0GlGpVBlt9ElxJN29gNM7YwFQaUzQaEwAsPCqldHOrFhF4q8eAiAl4g7mJaoDoLW0R6OzIjn0Gjr3CtnKKwqedYF+7Nv9HbZ2Dji5uFC2fEW6ffgRO7/ZzPu9+2L65+ia9g5FMrY58svPuLq5ozN/ek/l2VMn/sfeWcZHdT0N+FmJ7cY9IUFCSCABgltwlwKlULxIcYoXp0hxt+Lu7hR3CAGCFQkQHILFfbNZfT8sbMh/gwRoeVvu8yW599ic/e3Ze+bM3BlyeeXG3SPXe8fOyMjg9LHD7Nu1HWdXV34bP/Wz5nLx3FlKlS1vVMpKlS1PWGgINeo2MKk7b+YUuvX9lREDehnvyWSZvxXK9PQs61BA4GPYumoRZw7vw9beASdXd3z8A2jcqgPFymQmuvYLDOLcSdNgJSFHDxBco94Hx0hKiOPkwb2cPLCbuj+0Mioyn8rFkBNUrdsIkUiEX2AQitQUEmJjcHB2+aj2MnnmoWKGMt3wpvv/8LFzE/i2ubBzGXfOHsDKxgFrJzfc8hWkZIOfyFO0nLGOh29h7oUdA+DB5VP4lauF1MwcO9dc2Ll58+pBOJ4FihrrR968iJ2rF7Yupl4/b6NRZXD/4nFuntiNtaMLdXuOy7H8bxQpvV6PRpUB2TxDXPP6G/938sqPRpWBRq0iKfo59u7eyGwdAMgdWIb7F4+Tu3AZ7Fw8DQ2EZ1KOEZSpHKB8fpu026fx6r4ctFqeLemMhUfmFzbu2FJSrh9CbGFNrvazs7TVqZUo7ofhXL9ftn3rtWrS7oSQfHUf2rQEbILqkqvTAiQyw4btbXfBt7HKUxTnen3fK7cmORapncHfWySWIraUo0tPMvYNoEl8iURmT8zuyWRE3cfCwx/nur0Rm2fdyKb8tR/rwOoAWLjnJ+3uWayL1ECTFEPGi7tokqJBUKYE3uJO+A1OHT/Mso070Wo0dGnTFL+CBveeyKePuXH1Msvnz8HcwoIe/QZRMLAICkUaG1cvZ/qCZWxeuzLbfo8f3k/1Ou/OTn//7h327drGhbNnKFOhEj37D0l5eIsAACAASURBVMavkGHc/3UXfJvZS1ZjY/Nu63FMdBQubpkPTBdXd2Kio0zqhZw8houLK75+pqfkZ44fZcm8WSQmxDF5ziLjfZVKRde2PyKRSGjdoTOVqtV8pxwC3yb3b9/kwqmjTF+5Da1Ww6BOzbN1lzu+byfB1euY3A89fpAhk+Zm27dOp+PaxVCO7d3Bs8cPqFirPr9NX4iTqzuAiUvdG9xz5Wbg+JnvlTsuJtrYD4CjixtxsdHZKlMLJv2GWCyhbJWaNGvfzXjgcGDHRv7cvAaNRs2Y2ctzNDcBAYBXD8K5F3acNhM3oNNq2DDiJ9zymf5Gh5/ag1+5WgCkJcTg7lvYWGbj6EpafEyW+hHnD+NfwXS9vSHmyV1untzN42uh5C1anspt+uH6etz/dRd8m2a/LTa6Gr7N4cW/8/ivUBxz5aNym+z3lW+4H3Yc17z+SM3MsXf3JuHFU5JiXmDj6MqDyyfRatTvbS/wYQRlKgcoI28i9w9GLLUAKcj8KmQpd6rRBacaXUg4s46ksB04VvvZWKaICMUyd+F3uvg9W9IVvU6La+OhWHqZPhjfdhf8O9DrtGS8vIdzvb5YegUQe2AuiSEbjK6LAAmn1yISS7AuYviBsSleH1XMU54t6YbU3g1L70BEQr4Hgf/hxrWrVKxSHQsLC7CwoELlTDcIrVZLcnISC1Zv4k74DcYMHcDGPYdZtXg+P7Zul8WK8zZqtYqzp07QpVf/bMu3rFvF0vmz6dF3ID36DTZavt7wtrvg34EyPZ31K5Ywbf6ybMsrVa9Jpeo1uXblEssXzmXmwhUAbP7zKC6ubrx4Fkn/7h3x8fUjl3fuv01OgX8fd25cpXTFaq/fKbKgVIUqJnW2r1mCRCKh0mv3tzfcDb+OhaUluX2yD/IzdXhfHt69RY8hv1OsTLCJ1fRtl7q/i76jJuPk4ka6Io1pv/Xn1KG9VK3bCIB6P7Si3g+tOHNkH9vWLKH3iAnGdh+am4AAwIu718hfsgpSc8P6yVeikkmdsF0rEEukFAz+OCunVqPm4eXTBLf4JdvyK/vXc3bLAiq16kOl1n2RmmV9Hr3tLvix1O42Gp1Oy8nV07h7/jCBVRplWy/u2QNCNv1Bk6HzALCU21L95yHs/2M4IpEYzwJFSIx+nqOxBUwRlKm/AeuitXi5fkgWZSo1/BjW73Hxc2k4iOTLe4neOR55wUrYFKuHuUteY3lOLFNxx5aiuHceAO/uy5HaOqNJikZq64pep0GnTENsldVPV2rrgtTWxajIyQOqkHg2c3En/3WAtHuheLabZXzAisRSnOtmui89W94TMych0pjAx+Pi6k7larUQiUQUKlwUsUhMUmICt29e59SxwyyaO4PUlBTEYhHmFhb80KINABfOnsGvYACOTs7Z9lurfkM0Gg17dmzh6qUw6jVqQpkKlZBKDT95ObFM3bpxjRkTxwDwc/feuLi68dflzCSeMdGvKFayTJY+XjyL5OWL53Rq1eR1nSi6tmnKwjWbcXrrJD6oRClePn9GYkIC9g4OuLi6AeDp5U2xkmW4F3FbUKYEcsSJ/bu4HHqK0bOXmShDZ48dILjGu625bbr15eje7SyfPYmgUuWpVv97fAtlnsjnxDJ1YMdGju015JgZPm0BTi6uxEW/MpbHx0Th5GwaIc/JxbAGrGRyKtWsz/3bN4zK1BuCa9Rj6YzxOZqbgMDHEH5qLw+vhtB0+ALj+pE7uJASl+l9kBIfjdwx83f88V+huOYtiNzOyaQ/gILB9dBqNdw4voPIW5cIrNKQvEEVEEsMz6NPsUwBiMUS/MrV5vKfa7NVplLiotg7azB1uv+OvZuX8b5Picr4lKgMwI3jOxCJPz5glED2CMpUDrD0LkzMnzOwr9QGdFoUd89hW7IhAKq4Z5g7Gb6sijshmDtnboC0ylTSH1/Dtclv7+7bKwBLrwB0KgWpN08QvWcq6PW4NOiPhYdfjixTbyxkb5D7BZNy7RCW3oVJvXUKq3zFTR6yUmsnpHYuqGKfYu6cm/RHVzBzzmuYz/0LJJ7dSK4OcxGbZUZm0amVoNcjNrdC8eAiIrEkiwIoIABQJKg4MyaMoXXHrmi1Gs6dOcl3TX4EoGLV6ly9FEbx0mWJfPIYtUaNnb0DfyzPfAF25eJ5WFnJjIoUwLFD+6lR990bJwdHJ1p36EzrDp25duUS+3dt54/pk2jSvDXN23bIkWUqoEhQlrrJSYksnT+blOQkAC6eDzWxkPkU8GPX0cwkny2+q8nitVuxd3DgWeQTcnnlRiQScff2LdQqFXb29qQkJ2FhaYW5uTmJCQncvHaFVu1/RkDgbQoWKc7iaWNp0rYzWq2Wy6GnqdmoGQBXL4Swe8NKfv9jJRb/ExVVp9Nx7sRhxs1f9c6+vfP50rHPENr2GEDY6WNsWDqXxLhY2v0ykGJlKuTIMvXGivSGUsHVOLBjA8E16nHv1nVk1tYmLn5ajYa01BRs7R3QaNRcDj1NkVKG91heRj7BwzsPAFfOncbdK/MZ+zFzExAA8PQL4tiKSZRu1AGdTsujqyEUqW6Ixvr4WiiX/1xLs5GLMbPI3OvkL1mZA/NHUqJ+G9ISYkh89RT3/JkBUiLOHcK/Qu13jimzc6R0w/aUbtieZ7evEH5qDyfXzKBY7eaUqN8mR5YpvV5PUtQz7N290ev1PLxyGgfPPCb1lGkp7J7en4otf8HTPyhLmSIpHpmdI8q0ZK4d2UaDPpM+amyBdyMoUznAMlch5P7BPFv4MxJrB8zdfIyBJ+KPLUYVG4lIJEJq74ZLg1+N7dLunEGWv7TJ+0fZITaXYVuiAbYlGqCKefxF5LYpUZ/onRN4Mrc1Eisb3JqNBkCTEkvMnql4tDG8kO9cry/RO8aj16qROnji2ngoADH756DXqnix1jAnS68AXL77FW1aAi/XDQKRCKmNC65NRnwReQX+WxQMLEKFKtXo1PJ7HByd8PH1w9racNJWv/EPTPn9Nzo0b4SZ1IxhYyZ+MCBDerqCyxdC+XX4mI8aP6hEKYJKlCItNZXb4dc/dzrY2tnTrnN3uv1keCG/fZcexmAUU8eOpFGzFhQMKPzO9qePHeHwvt1IpFIsLCwZNWkGIpGIJ48eMmPCGMRiMTqdjtYdupDXx/ez5RX4b+FbqDClKlbl1w5NsXN0Inf+AsbgDMtnTUStVjFuQFcACgQWpdvAUQDcunYZJ1d33Dw/7D1gZmZGcI26BNeoS8yrFyQnfX7SzRLlK3Hl/Gl6tayPhaUlPYdlWpYGdmzG9JXbUKtVjP+1GxqNBp1OR9FS5ajZsClgsHRdv3QeqVSK3MY2i4tfTuYm8G3jnj8QnxKVWTesNTI7R5y982NuZVg/J1ZPQ6tWsWOSwV3Pw7cINToNw8krP35la7J2cHPEEgnVOgxG/Nqao1am8/RmGDU6ZW9Z+l+8CpXAq1AJMhSpRD0Mz/kE9HoOLRqDKj0N0OOcuwDVOxr2ag8unyL60W3KN+vOtcNbSIyK5PyOZZzfYXA3/2HoPGR2jpxcO4PYJ/cAKPtDZxw8DMrYqwfh/DlrMEpFMo+uhnBu+2LaTd2Scxm/QUSenp7fRCrB85euMvqiiFepn5efQKdSIDaXoVMrebGyDy4NB2Lh4feFpBRoVVhO3PEVTJv2eRHXBL4OcrmcmzdvEqfK6jagUKQhk8lRpqfTp0s7Bo743RgMQuDjsdCk0rplC27cuPG1RRH4SPr168dP3fsRk/7lHrXpCgVWMhkZynRG9upA90Gjsw1CIfBl8LZUUb9ubZ48efK1Rfnm8fT05NCxk2y+++l7OZVSgbmlDHWGkq3julKz03BjMAiBL0etXBpG9OtBSEjIhyv/yxEsUzkkZu90VDFP0GtU2ATVERQpAYGPYMb4MTx+dB9Vhoq63zUWFCkBgc9g8bQxPHv8EJUqg6p1GwuKlIBADji2bCJxzx+iVasoVKmBoEgJfDaCMpVD3JqO+toiCAj86xg5cdrXFkFA4D9Dv9GC5V5A4FOp12v8hysJCOQAIY71P0j8ieUoHl56b520iLMkhKz/7LEyXkQQubADT+a2JvaAIUnv/6KKfcKz5T14ML4miaGbTMr1Oi2RizvxcsPQz5ZHQOBTWbHwDy5dCH1vnbOnjrN+5dLPHividjgdmzemdeM6zJ06Idt184Y74TeoXqYIJ48eMt5bPHcGHZo3okPzRhw/fMB4/3LYObq0bkqnVk3o9XNbnkUK7kIC/yybls3j+qVz761zMeQEO9dlH84/JzyICGdA+yb0almf5bMnvXcd3b99k+ZVi3HuRGaC4bULZ9K/XRP6t2vC2WMHjfdnjx1Cn9YN6d+uCfMnjUQj5McR+Ac5t20RT29eeG+dB5dPcXHPqs8eK+rRbdYOacnKAU04uXr6e9fQqwfhzPmpHPcuGJIMJ8e8ZP2Itqwb1po1g5tz/eh2Y12tRs3RZRNY9WtTVg9sxr2w458tq4BgmfpHcazW6YN15P7ByP2DP3usmH0zcWk4CItcAbzcMBjF/QvIC5TLUkdsZYtz3T6k3cnenzXpwjbMnfOgy1B8tjwCAp/Kzz16f7BOcJXqBFep/tljzZo0loEjxxJQuChD+nQjLPQMZYMrm9TTarUsnjuT0uUyc82dO3OKu3dusWzDDtRqFf26dqBshUrIra2ZNWksE2bOI0++/OzaspG1yxYz7PeJny2vgMDH0rJzrw/WKV2xGqUrVvtgvQ+xdMZ4ug8eQ4GAokwY1IOrF0IoUc40n49Wq2XdolkElS5vvHc59DSP7t5m+oqtqNUqRvf5meLlKiKTW1O5VgP6jpwMwOzfh3Bs7w7qNGnx2fIKCHwM5Zt1/2Cd/CWrkL+kae63nHJ8xWRqdh6Bu29hdk3ty+NroeQrZro31Om0hGyaR54iZY335A7OtBizAqmZOSqlgrVDWuJTsjLWDi6E7VqBzNaRDjO2o9fpUKYlf7asAoIy9bcQf2o1qTeOIJHZI7VzwcLDH/sKLYneNQmZX3msA6ryZHYLbILqkHY3FHQa3H78HXPnPCT/dYCMFxG41H9/Ruv3oUmJQ5ehwNLLELrTpmgdFHdCTJQpqdwBqdzBmJMqSx/J0Sjunce+0k8knROiuQj8/axZupAjB/ZiZ++Iq7s7fgUDaNnuZyaNHk75SlWoWrMOLb6rSd3vGhN6+iQajYYxU2aRJ58PB/bsJOJ2OP2GvDv9wIeIi4khLTWVwCKGMLJ1GjQm5OSxbJWpHZvXU7lGLe6E3zTee/LoPkHFSyGVSpFKpeQv4EdY6Bmq1a6HSCQiLTUVgLTUFJxdXEz6FBD4EmxdtYgzh/dha++Ak6s7Pv4BNG7VgXkTRlCyQhXKV6tNjx/rULVuIy6dPYVWq+bXsTPIlceHE/t38SAinM79Pz0ya0JsDIq0VPwCDeuoat1GXDxzPFtl6sD2DZStUpMHtzPX0bPHDygUVBKJVIpEKiVPfj/+uhBChep1KVE+cy36FipMXEyUSZ8CAp/LhZ3LuHP2AFY2Dlg7ueGWryAlG/zEoUVj8CleiQJla7C8byMCKjXg4dUz6DQaGvSdjKNnXsJP7SX60W2qdRj8yeOnJcSiSk/Do0ARAApVasCDy6eyVab+OrSZAqWr8erhLeM9idTM+L9WrQK9zngdfmoP7adtA0AkFmNlY//JcgpkIihTXxjl89uk3T6NV/floNXybElnLDz8s60rkdnh3W0ZSRd3khi6GddG71586Y+uEHtovsl9kZkFXp0WZLmnSYlBapu5WZPauqBJic3RPGIPzsOpZnd0KsEqJfD3cyf8BqeOH2bZxp1oNRq6tGmKX8HsX6q3s3dg6Ybt7Nqykc1rVzJ41Lh39nv14gXmzZxsct/S0or5K7Pm9YiJicLFzc147eLmRkx0tEnbmOgoQk4cZdbiVdwJz1Te8hcoyOqlC2jetgNKpZKrl8LIky8/AINGjmVo3+6YW1gil8tZsMrUrVZA4HO5f/smF04dZfrKbWi1GgZ1av7O4BQ2dvZMW7GFgzs3sWfjanoM/f2d/d68EsaqP0zf0zK3tGTiwnVZ7sXFRhsT7wI4urgRF2O6juJiogg7fYwxc1ew4C1lKo+vP1tXLqRRy/ZkKJXcvBKGV978WdpqNGpOH/qTjn2HvFNmAYFP4dWDcO6FHafNxA3otBo2jPgJt3cEqLCysafNhHVcO7KVy/vWUavLuw/zIsMvcWrdTJP7ZhaWtBizIsu91IRorB0zE1rbOLqSFh9j0jY1PpoHl07SbMQiXi0Zm6UsJe4Vu6f1JzEqkkqt+mDt4IIyLQWA0G2LeHb7MvauXlTtMOidyYYFPh5BmfrCKCNvIvcPRiy1ACnI/Cq8s668kOGUzcLDn7TbZ97br1W+Enh3X/5FZX0XaXdDkcjtsfD0J/3x1X9kTIFvmxvXrlKxSnUsLCzAwoIKld/talSpei0A/AoFcPrEkff2W7x02Y9OzvuxzJs+ia59fkUszvrKaenywdy5dYNffm6NvYMjgUWCEEsMIeK3rl/D5DmLCCgSxKY1y5k/c8p7lUABgU/hzo2rlK5YDXMLC8CCUhXe7W5UtkpNAHz8Arhw6uh7+y1cogzTV277kqKycu4U2vbob7KOipWpwIM7NxnR4yds7R3wKxxkUmfpjAkEFCtJQFDJLyqTgMCLu9fIX7IKUnPDGspXwtSi+gbf0obnlGu+Qty/eOK9/XoHlvroxLwfy6m1M6nYsjcisWn4Axsnd9pO3khqQgx7Zw7Et2wNxGIJqfHReBYoSpW2/bmyfz1n1s+hbs+x2fQukBMEZeorInpjihWL0evenzMhJ5YpqY0LmuTMUwxNcgxSG+ePlkv59CZpEaEo7l1Ar1Ghy0gjasd43H74dBcqAYEvhZmZOQBiiQSt9v3rJieWKRcXN2KiMt2GYqKicHF1/d+mRNwOZ+wwQwLrpMQELpw9jUQioVK1mvzUqTs/dTL41Y8bPgjv3HlITIjnwd0IAl67D1arVY/BvbvmYMYCAl+enKyjnFimnJxds7jfxcdE4eRiuo4eRtxi1hiDN0ZKUgJXzocgkUgoU7kGTdt1pWk7wxqZ/ftgPL3zGNttWbmQ5MR4ug2a/ZEzFRD4e5C8WUNiMboP7OFyYpmydnAlNT7TmpsSH43c0dQ1POrRbfbPM7jkKlMSeXwtFJFEgm+pqm/15YKTd35e3PkL3zLVkVpYGpXAAmVrcPPk7o+brMB7EZSpL4yld2Fi/pyBfaU2oNOiuHsO25INP7vfnFimpDZOiC1kKJ+FY5ErgJTrh7Ar0/Sjx3Kq2RWnmoYHWfrjqySGbhYUKYG/lSJBxZkxYQytO3ZFq9Vw7sxJvmvy42f3mxPLlJOLC3Jra8JvXCOgcFEO7dvNDy3amNTbtDfTGvbmfa5K1Wqi1WpJTUnBzt6eB/cieHA/gmHlJgGQmppC5JPHeOfJy6UL54zufwICX5KCRYqzeNpYmrTtjFar5XLoaWo2avbZ/ebEMuXg7IJMbs3d8GsUCCjKyYN7qN+0tUm9BVsyo/S9eZ+rTOUaaLVaFKkp2NjZ8/h+BE8e3COotMHD4+je7fwVdpbRs5eZWKsEBL4Enn5BHFsxidKNOqDTaXl0NYQi1b//7H5zYpmSOzhjbiXn5b0buPsW5vaZfRSrYxpo5efZmYrQm/e5fEtVJSUuCisbO6TmlijTknkRcY0SdVsjEonwKV6JZ7cv4x1Ymqc3L+KUy+ez5yYgKFNfHMtchZD7B/Ns4c9IrB0wd/NBbCH/x+VwbtCf6F2T0WsykPmWReZriPSSdMmw+OxKNUaTGsezJd3QZaQhEolJPL+N3L+s/iryCnzbFAwsQoUq1ejU8nscHJ3w8fXD2trmH5ej39CRTB4zHJUygzLBlYzBJ3ZvM7zj1LhZy3e21Wg09OncFgCZ3JoR46YglRp+Ygf9NpZRg/oiFouxtrVlyCghz4nAl8e3UGFKVazKrx2aYufoRO78BZDJrf9xOToP+I35E39DlaGkeLmKFH8dfOLQLkMwozrfN39nW61Gw8hf2gNgJbemz8hJSF6voyUzxuHi5sGI7oZ1VrZyDX7s2OPvnIrAN4Z7/kB8SlRm3bDWyOwccfbOj7nVP7+GqnccwuHFv6NRZZA3qAJ5gwwHCm/CnBet+e4D8vgXjzmzfjaIRKDXU7JBG5xz+wJQsWVvDi0czam1M7GytadW19F//2S+AUSenp7vDl7/H+L8pauMvijiVer7TbFfAp1Kgdhchk6t5MXKPrg0HIiFh9/fPu5/gVaF5cQdX8G0aUJSyn8jcrmcmzdvEqeS5LitQpGGTCZHmZ5Ony7tGDjid/wKZf/y/LeKhSaV1i1bcOPGja8tisBH0q9fP37q3o+Y9H/mUZuuUGAlk5GhTGdkrw50HzT6nUEoBD4Ob0sV9evW5skTIT/c18bT05NDx06y+e7ft5dTKRWYW8pQZyjZOq4rNTsNx/UdQSgE3k2tXBpG9OtBSEj26Xf+SwiWqb+BmL3TUcU8Qa9RYRNUR1CkBAQ+ghnjx/D40X1UGSrqftdYUKQEBD6BxdPG8OzxQ1SqDKrWbSwoUgICOeTYsonEPX+IVq2iUKUGgiIl8EEEZepvwK3pqK8tgoDAv46RE6d9bREEBP719BstWPUFBD6Her0EN2yBnCG8wSkgICAgICAgICAgIPAJCJap/0c8X9UXp9o9sPT850zKcceWknL9ELr0VHyGZ0ZXSrq0m+SLO0EkQWxuhUvDgZi75EWv1RC9dyqql3fR67TYFK2DQyXDy8CJ57aQfHUfIMLCLR8ujYca8m0JCPw/oG/X9vToN4iCAYX/kfEUaWn0fh2QAgyh1mvVb0jvgcM4sGcni+ZMx/l16PUmzdvwXZPPj7omIPApjOrdkXa/DMS3YOA/NuaGJXM5dWgPaSnJrDscZlJ+/uQRpo8cwOSlm/AtGIharWbJtN95EBGOSCSmY9+hFC5eGoAHEeGvA15kULxcJX7uOxSRSMTj+xEsmT4WZboCF/dc9B01+asE5BD49tg6vhuVW/fFzeefcbNVZyjZN3coSVHPEInF+JSoRMWWvY3ld88f4fz2pSACl9x+1Os13iRce8LLJ9TrNQHfUlWJDL/I6Q1z0GnUuOYrRK0uvyGWCCrDuxA+mW8cuX8F7Mr8wNM/soaAtilSE7tSjQFIizhL7KH5eLadRuqtE6BR491jFTq1ksj57bEuUgORWEpS2Ha8e65BbGbBq62jSb15HNti9b7GtAQEvjoyuTxLWPaubZpR+XXCYYBqtevRb4iQckDg26RUcBXq/dCK3q0bmJSlK9LYt20dBQKKGu8d3WsIzT5z9U6SEuKYMLAHk5duQiwWs3TGeLoPHkOBgKJMGNSDqxdCKFGuEgunjKZdz18JLF6aY/t2snvjSlp17m0ynoDAf4GS9dviHVgKrUbN9ok9efTXWfIVCybh1VMu7llF8zHLsJTbokiKB7KGa1emJrFywA/kKVIOvU7HoUVjaDp8AQ4eeTi3bRG3zuyjcNXGX3N6/68RlKn3oFOlE7VtDJrkGPQ6HY6V22FduDrxp1ahuBuKTq3C0jsQl+8GIhKJeL6qLxbuBVA+vY5OnY7r98NJDFmPKvoh8sDqOFXvjDrxJS/XDcbC04+Ml3cxd8mHa5PhiM0ss4yteHCR+JMr0GvUmDl64tp4KGJzGXFHF5MWcRaRWIJV/tI41+75WXO09Mr+JPLt8Og6VTqI3lyJ0KnT0es06NUZiCRSxBZy9OoM9Dotek0GeokEvTojR4mCBb490tMV/D5kADHRUWh1Wtp17kH12vVYvWQBoWdOkpGhpHDR4vw6YgwikYi+XdtTwL8Q169eRpmezvCxk1i/cikP79+lWu16dO7Zl5cvnjO4V1f8CwVw985t8ub3Zfjvk7C0ssoy9sVzZ1m5eB5qlQpPL2+GjJmATCZn8dyZhJ4+jkQipVS5CvTsP/iLzDXyyWMSEuIpWrzkF+lP4L+NMl3BzFEDiYuJQqfT0ax9N4Jr1GXryoVcCj2FKiMD/8JBdBs0GpFIxKjeHclXoCC3r18hQ5lO7xET2bFuGU8f3iO4eh1adelD9MvnjB/YHR//AB7dvY13Xl96/zYBC8usa+OvsFC2rJiPWqXGLZcXvwwbj5VMxrpFs7gUchKxREJQmQq0/2XgZ83RLzDonWWbls3j+9ad2L1xpfHes8cPKFzCkOLDzsEJmbUtD+6E4+zqjiIt1dhf1bqNuHjmOCXKVeJl5BMCipUCIKhUecb/2k1Qpr5R1Mp09v0xjNT4aPQ6LWW+74R/+dqc37GUR1fPoFFl4FGgKDU6DUckErF1fDdc8/jzPOIv1Bnp1Ok+hot7VhEX+QC/crWo0LwHSTEv2DWlD675ChL9OAKnXD7U6fE7ZhZZ93NPrp/n3PYlaDUq7F29qNVtFOaWMkI2/cHDy2cQSyTkLlKWym36ffL8zCws8Q40fNclUjNc8/obE//ePL6LoFo/Yim3BUBm52jS/l7YMfIGlcfMwhJFcgISqRkOHoZk2bkLl+XinlWCMvUeBGXqPSjuhyGxdsKj9RQAtMpUAOzK/IBjlQ4ARO0cj+JuKHL/YABEEileXZeQeH4brzaNwKvrUiRWNjyZ2xr7coYkpOq4p7g0GoxV7iJE755M8sVd2FfIzF+jVSSScHoNnj/NRGxuRULIBhLPbcGudBPS7pzB+5e1iEQitMoUE5nTH10h9tB8k/siMwu8Oi3I0fyTwnaSeH4Leq0az3aGbPPWAVVRRJzl8Ywf0KszcK7zCxIrW7AC+/IteTKrOSIzc2T5SyPLXzpH4wl8W4SFhuDk4srkuYsASE0xfJ+btGhN+66GQ4IJI4dw7sxJKlQ2ZGw3MzNjybqtbNuwlhEDerFk3TZs7Oxo3bgOP7Y25KaJfPKIwaPGUaRYCab8PoJdWzfSst3Pn786ZQAAIABJREFUxnETExJYu3wRMxYux8pKxoZVy9i6bjXfN29NyMmjrNm+D5FIREpKsonMVy9eYN7MySb3LS2tmL/y3QkZjx/aT7VadRGJjKcSnD52mOtXLuGVJy+9BgzB1d0jpx+hwH+Uvy6cxcHZleHTDL/ZaamGtVG3aWtjXqW544ZxOfQUpYKrAiA1M2Pqss3s27qOKcP6MHXZZqxt7filZT2+a94OgBdPH9NzyFgKFi3O/EkjObhzM41bdTCOm5yYwPY1ixk1aymWVjJ2rl/On5tXU/eHVoSdPs6c9XsQiUSkZbM2bl4JY9UfpsEvzC0tmbhw3UfP/WHELWKjX1GyQuUsylReX38unj1BxZr1iI1+xcO7t4iLfoVILMbJxc1Yz9HFjbgYwybSK19+Lp45TpnKNTh34hCx0a8+Wg6B/xaPr5/D2t6Z7wcZ9jIZCsN+rljt5pT7oQsABxeM4tHVM/iUMOQXFEvNaD1+DVcPbmTvzIG0Hr8WC2tbVvVvQvF6rQCDa1ytLiPx9A/i8JKxXD+6lZINfjKOm56SSNiuFTQdNh8zSysu7l3Nlf3rCar1Iw8unaTdtG2IRCKUaab7uf91w3uDmYUlLcaseOdclWkpPLxyhuJ1DfvKhFdPAdg8phN6nY5yTbsY81a9IeLcEUrUMyTXtrKxR6fVEvXwFm4+AdwLO0ZKXNRHfMrfLoIy9R7M3XyIO7yAuCOLkPmVxyqP4eQr/dFVEkM3olcr0aanYO6Sz6hMyV7/NXfzwdw1L1IbJwDMHDzQJEcjtrRGauuKVe4iAFgXrU3She1ZlCnls1uoYp7wfEUvAPRaNZZegYgt5Yik5sTsmYLMrwJyv/ImMlvlK4F39+VfZP52ZZpgV6YJKTeOkHBmDW7fD0f5/DaIxeQdsAOtMoUXK3tj5VMKsaUNaREh5Om7CbGlNa+2jibl+mFsitb+IrII/Pfw8fVjwaypLJ47g/KVqlC0uOFU7eqlMDatXo5SqSQlOYm8Pr5GZerNXx/fAuTN74uTiwsAnrm8iI56ibWNLa5u7hQpVgKAWvUasn3TuizK1K0b13j88AG9fja4tmrUagKLFkNubY25uQVTx/5G+UpVKV+pionMxUuXzeK697EcP7yf4eOmGK8rVK5GjboNMDc3Z8/2zUwaPZxZi1e+pweBb4ncPgVYPX86axfOpGSFKgQEGSya4VfC2LVhJaqMdFKTk/HOl9+oTJUOrmZs650vPw7OhrXh5ulFbPQr5NY2OLu6U7BocQAq1/6O/dvWZ1Gm7oZf59njh/zW06B8adRq/AoHIZNbY2ZuzoLJoyhZoQolK5iujcIlyjB95bbPmrdOp2PVvGn0Gm4aTa16/SY8e/yQIV1a4uzugX/hIMTi98fQ+mXoWJbPmcy21YspVbEaUjOzz5JP4N+Ls3d+Tq+fzZmNf+BTvCK5ChrWQeSty1z6cw2aDCXKtGScvHyMylT+13+dvX1x8vJB7mDwtrFzzUVKXBQWchtsnNzw9DfsDQsF1+Pqoc1ZlKmX924Q9/whm3/vBIBOo8GjQBEsZNZIzCw4snQcPsUrkq94JROZ33bD+1h0Wg0H5o2gWJ0W2Ll6AaDXakmMiqTZb4tJjY9i67iutJ28CUu5DQBpCbHERd4nT1HDnlIkElGv9wROrZuFVq0iT5FyiD6w1r51BGXqPZg7eePVbSmKe+eJP7Ecq3wlsA9uRez+WXh1WYLUzpX4kyvRa1TGNiKp4cdaJBIhkphndiYSo9e9TjL31ul0Npeg1yPLXxK3pqaZqb06L0Lx6Appt06SFLaDXO1nZyn/kpapN1gXrkHsvlkApN44iix/GUQSKVK5A5behcl4cQcQYWbvgURub2hTqBLKyJuCMiXwTrzz5GXp+m2cP3ua5QvmUqJ0OVq278TsyeNYvHYLru4erFw8D5Uqw9jGzNywpkRiMWZmmetLJBaj1RrWl8hkfZksMEqVq8CoidNNZFq4ZjNXws5x6thhdm7eYKLgfIpl6v7dO2i1WvwLZbrU2tnbG/9v8H0zFs+ZkW1bgW8Tz9x5mbp8C1fPnWbT0j8oUrIsjVv/zNKZ45mydDPObu5sXrEAlSrz2SN9a21I314bosy1YfrsMV0bRUuVp/8YUwvT5CUbuXH5POdOHuHgjo2MmZP10O5LWKbSFWlEPrrP6D6Gw4/E+FimDO3NkMl/4FswkI59hhjrDu/RFg/vvFjb2BIXk3lqHh8ThZOLIbBLrjw+jJq5BDBY5a6cO/1Rcgj893DwyEObCWt59NdZQrcuxDuwNKW+a8eJlVNoNX41Nk7unNu+BI06c01J3ijfIjES6Vv7ObEocz/Hh9dU7iJlqd9rgolMLceuIjL8IvfCjvHX4a00G7EwS/mnWKaOLp+Ig3tuo5UJwNrRFXffQCRSKXauuXDwyE3iq6e45zc8k+5eOEL+UlWRSDNVAs8CRWk+ailgcFN8Y90SyB5BmXoPmpRYxFY22BStjdjSmuQr+4yKk1hmh06lIPXWKawDTE/p3ttvUhTKyJtYehcm9cZRLL2LZim39Aokdv9s1PHPMHP0QqdKR5MSi9TGCZ06A3mBclh6F+bp3FYmfX8py5Qq7hnmToZTDcXdc5g5Gv6X2rmR/vgKNkF10KnSUT67hV25H9GrM1A+v4VOrUQktUDx6AqWHv6fLYfAf5fYmGhsbO2oXb8R1ta27Nu1zag42dk7oFCkcerYYarUyJlCHvXqJeHX/yKwaDGOHtxntFK9IaBIELMnj+NZ5BO8vPOQnq4gNjoaJxcXMpRKylWsQuGgErRubDrup1imjh3cT4069bPci4uJMVrVQk+dIHc+nxz1KfDfJj42GmsbOyrXaYjM2pZjf25H/Xpt2Njbk65QcP7kEcpVrfWBnrISG/WSiJt/4V+4GCFH9lOwaNa1USCgKMtmTuDls6d4eOVGma4gPiYaB2dXVBnplChfGf8ixfmlhWlgoS9hmZJb27DyzzPG67ejDGYo09Hr9Vhaybh2MRSJRIJ3vvwAyOTW3A2/RoGAopw8uIf6TQ0byaSEOOwcnNDpdGxbs4RajZt/lnwC/15SE2KwlNtSqGJ9LGQ23Dy526g4WdnYo1IquB92DN8yNXLUb0rcK17cu45ngaLcCT2Ep1/WdwHdfYtwYtVUEl9FYu/ujVqZTmpCNHIHFzQZSvIVC8bTL4iV/b836TunlqnQLQtRKVKp1TlrYKP8paoQce4wgVUakZ6SSMLLp9i55jKWR4QeJrjFL1naKJLikdk5olGruPTnaso0/hmBdyMoU+9BFfWQuCMLQSRGJJHg3GAAEksbbEp8R+TCDkitHbHMlXOFwcwpN0kXdxG9ZwrmLnmxLZ31pT6J3B6X74cRtX0seo0aAMfqnRGby3i1afhrhU6PU+1fsuk9Z8QdWUjKjWPo1Uoez2yGbYkGOFbtSHLYDhSPLiMSSxFbWeP6/TAA7Mp8T/TuyTxd0B70emyK1cPCzfBAsy5UhWeLu4BYgoWHL7YlG362fAL/XR7eu8uiOdMRiUVIpWb0HzYKGxtbGjRpRsfmjXF0dv6kMObeefKxc8sGpvz+G3l98tO4Wcss5fYOjgwdM5Fxwwehfn2y36lnH2QyOSN+7YUqIwO9Xk/P/kOy6z7HnDx6kMlzFmW5t33TWkJPn0AikWJja8fQMRO/yFgC/w2ePrjHmgUzEIvFSKRSuvw6ErmNLTUbNmVAuybYOzp/Uhhzz9x5ObhjEwsmj8Irb37qfJ9VubBzcOSX4eOZ/ftg49po1aU3VjI5U4b3QZWRAejp0GvQZ89x7YKZnDm6jwylkq4/1KDGd01p8fO7AyolJcQz/tfuiMQiHJ1d6fPbJGNZ5wG/vQ6NrqR4uYoUL2dwmQo5eoCDOzYBULZKDarXN92wCnwbxEbeJ2TDXBCJkEilVO84FEu5DYWrfc/aIS2R2Tl9UhhzB488XD+ylSNLxuGUKx9Fa2ZNcSGzdaB2t9EcmD8Crdqwnyv/Y3fMLeXsmfkrWrUKvV7/WcEnAFLiogjbvQIHz7ysH2FIyVGsdnMKV/uePEXL8+TGBdYMao5ILKZS675Y2Ri8I5JiXpASH4VXoawHK5f3reXh1RDQ6yhSoynegcI78O9D5Onpqf/aQvwTnL90ldEXRbxK1X648t+IOvElLzcMI3fPVV9Vjv+vtCosJ+74CqZNM3UXEfj/j1wu5+bNm8SpJF9l/JcvnjOsXw9WbdnzVcb/u7HQpNK6ZQtu3LjxtUUR+Ej69evHT937EZP+dR+10S+fM2lIL2atyfk7f9863pYq6tetzZMnT762KN88np6eHDp2ks13v+5eDgyKyJ7p/flpyuavLcr/S2rl0jCiXw9CQkK+tih/O8IbZQICAgICAgICAgICAp+AoEz9w5jZewhWKQGBvwkPz1z/WauUgMDn4OqRS7BKCQh8QexcPAWrlAAgKFMCAgICAgICAgICAgKfhBCAIgc8nFgXn+EH/9Ex1YkviZzfDjOn3MYofdG7J5N29xwSuUMWK5c2PZmobWPQJL5Cau+OW7PfkVjZkP74Kq82jUBqb0gKKi9UyZh0+F0khe0g8fw2NAnPyTtoNxKZ4WXFlOtHSDy7AdAjMpfh0mAAFu6+ACSe20Ly1X2ACAu3fLg0HopYakH07ilkvIwAvR4zJ29cvx+K2FxG4rktJJ3fisw/GJf6n/fypcB/h7oVS3Iw5PI/OubLF89p3+w7vPPkZfnGnWRkZNC3SzvUKhVarYYqNWrTsXtvACaNHs61KxeRW1sDMHTMRAr4F/rgGGmpqbT/sSEVq9ag3xBDtKWI2+FMHj2cjAwl5YIr03vQcEQiEQtnT+Pw/j20aNsxS44sgW+TtrXLsO5w2D86ZvTL5/Rr2xjP3HmNUfrSUpJZOGUMTx/dQyQS0XPoWPwLFzO22bNpNWvmT2fF3tPY2juQmpLEgkmjePU8EnMLC3oOHUtunwIA9PixDlYyGWKxBLFEwtRl7z/hT0tNYe64YcRGvUSr1dKoZXuqN2hiLFekpdLvp8aUqVSdzv1HALBhyVxOHdpDWkpytp/f+ZNHmD5yAJOXbsK3YCC3rl1mybSxiMRiwYr3H2b+z5X5ZcU/GyY/KeYFawY1x8EjtzFC35UDG7h5YhcikQgnb19qdx2F1NyCQ4vG8PzOVcyt5ADU7jYa17z+KNOSObJkHElRz5CYmVOr60icvX3fO+5fh7dw9eBGkqKe0W3REWPgiQxFKgcXjCQlLgqdVkPJBm0JrNKI6McRHF85BVV6KmKxhNKNO+Jf3hDZNin6OfvnjUCZmoRr3oLU7TkWidSMKwc2cPXABnxKVKZah8F/46f4/xdBmfoXIHXIlSXcuU2xetiV+YGonVkjgCWGrMcqX0kcKrYhIWQ9iSHrcarVHQDL3EXxaG2aH+ddWHoXxtOvPC9WZVVyzBw88OwwF4mVDWn3zhPz53S8Oi9CkxxDUth2vHuuQWxmwauto0m9eRzbYvVwrtsLsYXhRyH20DySwnbiULEN9uWbI7ayIeNFxKd+NAICXwxPL29j2HNzc3NmLlqBTCZHo1bTu1NbygRXJrCIIext974DqVqzTo76X7FwLkGvExO/YdaksQwcOZaAwkUZ0qcbYaFnKBtcmR79BmFpZfVlJiYg8Im45fLOEu58xdwpFCsbzMDxM1Gr1aiU6cay2KhXXAsLxdnNw3hvx5pl5C1QkMET5/D8yUOWzpzImDnLjOVj5qzA1t7ho2Q5uGMTXnl9GDZlHkkJ8fRt05BKtb/D7HUuoE3L5hmTG7+hVHAV6v3Qit6tG5j0l65IY9+2dRQIyExNEhBUkuHTFjBpSK+PkklAICfYu+UyKlKp8dH8dWgz7aZuRmpuyb65w16HLzdEQa7Uqg8FymYN035x90pccvvRsP804l885sSqKTQdvtBknLfx9AsiX/GKbBvfPcv9a0e24pjLh8YDZ6FITmD1wGYUDK6HmYUldXqMwcE9N6kJMWz47SfyFC2PpdyGkE3zKFGvNf7la3Ns+SRuntxNUM1mlKjXGguZDdGPbn/BT+vfxTfr5hd3dDFJYZknT/EnV5IYugmdSsGLNf2JXNyZyIUdSLtjGoUk/fFVXm4YaryO2T+b5L8OAJDxIoLnq/oQuaQLL9YNRJMS98Vlt8oThNjKxuR+WsRZbILqAmATVJe0iE+PoGLh4YeZvYfJfUvvwkhej23pFYgmOcZYptdp0Wsy0Os06NUZSG0M2cLfKFJ6vR69OoP/TXIn8N9l8dyZ7NySmSdj5eJ5bFqzAoUijQHdO9KldVM6Nm9MyMljJm2vXgpjaN8exuvZU8ZzYI9hzUbcDqdvl3Z0bdOMQb90IS4mxqT95yASiZDJDN9bjUaDRqP5rG9txO1w4uPjKFWugvFeXEwMaampBBYJQiQSUadB9p+DwH+LdYtmcWDHRuP15hUL2L1xFekKBWP6dmbQz80Z0L4JYWeOm7S9efUiEwdnpsRYNmsCJ/bvAuBBRDijenVgcKfmjBvQjYTYL7sm0lJTuH3tMjW++wEAMzMz5Da2xvJVf0zlp54DsiQtffb4AYVLlAEMCXRjXj0nMT72k8YXiUQoFQr0ej3KdAXWtnZIJIaooQ8iwkmMjyOodIUsbfwCg3Bwdsm2v03L5vF9607GROAC/05CNv3BtcNbjNfnti/h8r61qJQKtk/swfoRbVk7pCUPLp0yaRt56zK7p/U3Xp9YNZXwU3sBiHp0m63jurJhxE/smNybtIRP+96+D51Wg0aVYfibocTaIfvv6hvinj/CO9BwIOfomZfkmJekJb1/j+ma1x87F89sy9TKNPR6PWqlAktrW8RiCQ4eeXBwzw2AtYMLMltH0lMS0Ov1RIZfpECZ6gAUqtwg28/0W+WbtUxZB1Yj9tA87MoY3ARSw0/g2XY6Iqk57i3GI7aQo1Uk8mxZT2T+wdlktTZFr9UQc2AOHi0nIpHbk3rzOPHHl+LaeGiWeinXj5AYusmkvZljLtybj/3kOWlTE5DaOAEgsXZEm5pgLFM+Cydy0c9IbJxwrtUTc9d8nzzOG1Ku7kPmWxYAqa0L9uVb8mRWc0Rm5sjyl0aWPzMvQfTuSSjuXcDMJQ9OdT4/P5bAv4Pqtesyb8ZkmjQ3JNE8eeQg0+YtxdzcgnHT/0BubU1iQgI9O7QkuEr1j1pnGrWauVMnMGHmPOwdHDl++ADLFsxmyOisGeaP7N/LprWmWeJzeedh7NTZHxxHq9XStW0znkc+pUnz1gQUyUzGuHzBHNYsXUiJMuXo2nsA5u/ZkOl0OhbMmsqIcVO4fOGc8X5MTBQubm7Gaxc3N2Kioz8ol8C/mwrV67Jq7hTq/WBIun7u+CF+m7EYc3NzBk+cjUxuTXJiAsO7t6F0xWoftyY0apbPnsSQiXOxc3Dk7LGDbFg6l1+GjctS7/ThP9mzcZVJe/dcuRk4fuZ7x4h++RxbewfmT/yNxw/ukt8vgI59h2BpJSPszHEcXVzJ65s172IeX38unDpKQFBJ7t26QUzUS+JiorB3dEYkEjFuQDdEIqjV+EdqNfrxvePXa9qKyUN70+X76ijT0+g/ZjpisRidTsfqedPpO3IS1y+d/+BnBfAw4hax0a8oWaEyuzeu/Kg2Av8/8StXm1NrZxBU25Az7d75ozQZOhepmTnf9ZuGhcya9JRENo3uiE/Jyh+1nrQaDSdXT6PhgBnIbB2IOHeYs1sXULvrqCz17pw9wKU/15q0t3fz5rt+U947hrWjKyUbtGV5n4ZIzS3IXaQseYqWM5aHbl3AhZ3L8A4sTXDLXkjNzHHJXYD7F0+Qq2BxXj0IJzn2Fanx0cjtnD7mo8pCsdrN2TPjV5b2qoc6XUH93hMRibPaV149CEerUWPv6oUyNQkLuQ1iiUFtsHF0JS1BeF694ZtVpiw8/NCmJaBJiUWblojEygapnSt6rYa4Y0tRPrkGIjHalBi0afFIrT/8ZVXFPUUV/YgXa3813NBrkWTTzqZoLWyK5ixzfU4RiURGA5CFhx95+m1GbC4j7d55Xm0eQe7eH59VOzvSH10h+eo+cnWcB4A2PYW0iBDy9N2E2NKaV1tHk3L9MDZFDb62ro2HoddpiT0wx+D+V7z+Z40v8O+gQMEAEuLjiY2JJjEhHhtbO1zdPdCo1SydP5vrVy4hEouIjYkmPi4Wp3ecIr/N0yePefTgHr/27ASATqvLtl2t+g2pVf/TE0dLJBKWb9xJSkoyI3/tw8P79/DxLUDXXv1xdHZGrVYzY/xoNq5aRvuu7042umvrRsoFV8bVzf2TZRH47+DjV4ikxHjiY6NJTkxAbmOLs5s7Go2aDUvmcOuvy4jFYuJjokmMj8PByfmDfb54+pjIh/cZN6ArADqdFnsn0zVRufZ3VK793SfJrdVqeXj3Nj/3HYZfYFFWzJnMzvXL+aFtZ3asXcbImYtN2jRp24mVcyYzsGMzcvsUIF+BgojFBmvSuPmrcXJxIykhjrH9u5Irdz4CipUy6eMNf104S15ff8bMWc6r55GMG9CVQkElOHVwDyXKVcLJ9ePWl06nY9W8afQaPv6TPgeB/1+45vVHkZxAakIM6ckJWMhtsHFyR6vRELplAc/vXAWRiNT4GBRJccjtP7yeEl4+Ji7yITsmGQ5+9Tpdtu0KBtejYHC9T5JbmZbMg8un6Th7NxYyG/bNHcrtkP0Uqlifii16IbN3QqtRc2z5RC7tXU25H7pQqmF7Tq2dwbphrXH29sU1rx8i0ac5mD25fh6XPH40HbGQpKhn7JjcC0//YljIDO8CpyXEcmjhKGp3G2OiZAmY8s0qUwDWAVVJvXUSbWo88sBqAKTcOIJWkYhX16WIJFKezG6BXqPK2lAsAb3OeGks1+sxd82LV6f3+7D+XZYpibUDmpQ4pDZOaFLikMgNvuhv3OwA5AXKEbtvFlpFojGoRE7JiHpA9N5peLSZikRmB0D6w0uY2XsgkRv6tC5UCWXkTaMyBSASS7AuXIPEsxsFZeobomrNOpw6eoj4uFiq1TK4oR458CdJCfEsWbcVqZkZLb6riUqVdZ1JJBL0b60zVUaG4R+9nrw+vixYtZH38bmWqTfY2NhSvFQZwkLP4ONbACcXwybV3Nycuo2asHnt+0+2b13/i+tXL7Nr60bSFQo0GjVWVjKatfqJmKgoY72YqChcXF0/Wi6Bfy/lq9bm3MkjJMbFUqGGYU2cObyP5MQEpi7fjFRqRo8f66BWZWRp979rQp1hWDN6vR7vfPmZuGj9e8f9HMuUk4sbTi5u+AUa3jEqV7UWu9YZFJvol88Z2LEZAHExUQzu1JxJSzbi4OTML6+VFr1eT8/mdXHz9DL2B2Dn4ESZyjW4d/vme5WpE/t38X3bTohEIjy8cuPqkYvnTx4REX6NO9eucGjXZpTpCjRqNZZWMtp2759tP+mKNCIf3Wd0H0Ngl8T4WKYM7c2QyX/gWzDwvZ+BwP9P/MrW4N6FYyiS4vArZziovnP2AIrkBFqNX4tEKmV530Zo1FmfMeL/WU/Gcj04evnQ8nfT58fbfI5l6unNMOxcPJHZGvZpvqWr8fLedQpVrI/cwaC4Sc3MCajckCv71gFgIbOmdrfRBhH1elb0a4yda673jvMuwk/vpXTD9ohEIuzdvbF18STh5RPc8weSoUhl1/R+VPixJx4FigBgaW1HRloKOq0GsURKSnw0cgfhefWGb1uZCqxO9N5p6BRJeHaYC4BOmYZEZo9IIiX90RU0Sa9M2pnZuaOKeYJeo0KnySD90WUscxfB3Dk32rQklJE3sfQujF6rQR0XaeJS93dZpmR+waRcO4hDxTakXDuI3D8YAE1qHBK5o8Hn/Plt0OsQW/0fe3cdUPW9/3H8eYJuECQtEAO7cAp2oKKC9ObSu3C7P+d2V6677+7qruddIyXY3dgd2BiIgiDSeTjn/P5gOhkGSnzOgc/jr+345XxegF/P9/3JmiLowi9P4RLyImrbW48IAGgKL5Id9wptQ1/C1Mnr6utqu7ZUnD+MTlOBQm1G2ek9mLt1Qa/XU51/HhNHT/R6PaXHNmPSpl2jf++S4Ro5LoiP336NwoJ8PvvuF6BmZzt7R0fUJibs3bmdi1kX6nydq5s7Z06lU1VVRWVlBXt2bqNnn354dehAQf5l0g7sw69XH6o1Gs5lnKGjd+daX9+QkamC/Muo1GpsbGyprKhg1/YtxNz/D6BmrZOTszN6vZ7U9Wuutnvk0AGS4//gxTdrb/Ty8jsfXf3vZQuTOXYkjUdnPQ2AlbU1aQf3071HL1YsWcC0qHvuKK9kXIaODuLrD1+nuKCAN7+oKcbLSkuws3dErTbh0J4d5GbXvSec27qTeeYUmqoqqiorOLhnO1179cW9XUeKCvI5dmgfXXr0obpaQ9a5s3h1rL3TV0NGphyc2uDk4sr5jNN4tOvIwd3b8ezgTXtvX+Yu+mvtxMyI8Xzw/Txs7R0oLS7C1NwCExMTVi9Kolvv/lhaWVNRXrP2ycLSioryMvbv3ELEAzUL5Jcl1cyamBB2d63227R14+Du7XTv3Z+Cy5e4kHGGtu6ezH71r4fWdUtTSD+WdsNCCsDK2ob/Ld509f9f/b8Hue+JZ2QhZcR8B49l9Q/vUF5cSMTLNSOkVeUlWNo5olKrOZe2i+JLWXW+zraNK3nnT1OtqaK6qpJzaTtx9+2Ng3t7yovzuXDiAO6de6GtrqYg+yxOnt61vr4hI1M2Tq5knTyIprICtakZ59J20rZjza6wpfmXsHJog16vJ333epy8OgFQUVqMiZk5KrUJh9al4Nm179WRpKR3ZzL+sTewdqxfgWPj5EpG2k48uvaltDCP/Kyz2Ll4oK3WsPjTZ+kWMLHWBhgKhQKv7gM4sWMtXe4ax5GNS/DuP+yOvveWqFUXU6YuHdFXlaGybXN1rZFNrzFkxc7h3Ncw5zAfAAAgAElEQVQPYObe5boP/mo7F6z9RnDu6wdQ27th5lrzMKVQmeAa+QaXln2OrrIUvU6LvX94o6xPutbFpDcoP7MPbVkhZz4Jx3HEg9j2m4RDwN1cTHyd4r1LUNu50jbidQBKD2+gcNcCFEoVCrUZbcNfQ6FQoNfr0Fw+j9LCtk4bBdsTKdg8D23JZc59/RCWnQfjMuU58jf8jK68kNwl/6n5npUqPB/5DnPP7lh3G07mtw+DUoWZmw+2/ScDenJS3qv5eejBzNUb50lPN+rPQzJsHb07U1ZaShvntldHdcZMCObFpx7nwcipdOnuR7sOnep8nYurGyPHBvFg5BTc3D2vbj9uYmLKGx9+yhcfvUtpSQlabTXhMffVKaYaIu9SLu+9NgedVodOr2PkmCCGDBsBwNsvP0dB/mX06PHx7crTL9b0FF7MzsLMzOy22pn9wiu8//qLVFVUMmhoIP5D5YdTa+DV0YeKslIcnV2ubpAQOHYS77/wT56+PxTvLn54tK/7udGmrStDRo7nqftDcXHzoEPnrkDNZhD/eusT5n72HmUlJWi1WiZFTK9TTDXUjNlz+OzNF6jWaGjr7skTL7510+szz57iy3deRqFQ4NnRm8dfeAOAwvw8PnyxZqdYrVZL4NiJ9PUPAOB8xmm69uxb573CH3iUL999mafvD0Wvh+mPzb7lToC/fvUJm1YvobKigkemjWZ0cBhRD914Sq5knJw8vakqL8PawfnqqE7XoRNY8PHT/Pp8NG07dcPBvUOdr7NxcsXXfwy/PR+NrbM7zu19AVCpTZg06302/PJvKstL0Gmr6RsUU6eYagg3nx50HjSaP16ajlKlwrl9F3qMqlnDv+yrVygvygf0OLf3ZdRDcwC4fOE0K795A4UCHD06MfaRV4CaaYgF2ZmYW9d9ltu7fB67F/9KaWEev70QQ4c+Qxn78Mv4h85g5Tdv8Ovz0YCegOh/YmFjz5HUpZw/upfy4kIOb1wM/LU1e0DMP1n6xUtsSfgal/Zd8BsxtdF+HsZO4e7urhcdojls27WX13YqyC7Rio5yWzQFWWT9MafWeVKNpTLnFMV7l9JmvLhtYIv2LaPywrGr50zF9LAib+1cPvroQ2GZpDtnZWXFoUOHyKtSiY5yW7IunGfO7Jn8FL/wjt/j608/YtykKXh37nLri+vhf99+iYWFZa1zpsyqS7g7OoqDBw82ShtS05s9ezb3Pjab3HLj+qjNyTrPe8//s9nPW3r3uSd49p1Pr2553tyu9317mVcxMWgcZ8+eFZJJ+ou7uzsr1qwn7rhxPcsV5l5g4cdPce8HNz9P7U5cOneStA2LGD79xiOyTS1twyJyTh+pdc7UWI9qXpo9k9TUO99Z2ljIVWUGTqFQoass4dw3Mxr9vc1cOgktpAq2xlOQ+jtKM0thGSQJQKVUUlpSwoyY0FtffAMzZz/baIXU159+xKqli7CwkPeGJIZSqaKstPjqWqjm8uKH/xVWSB3ev5v3X/gnNnZ3tp5Ykm5EqVRSWVbCb3PuvvXFt6mNl4/QQmrPsj/Yteinq4cMt0atepqfMVDbudDhqcRbX2iE7O+KxP6uSNExJAkXVzcSltY910eUmbOfZebsZ0XHkFqxNm1d+TZptegYzap77/588nPzjsRJrYONkyv/+GKJ6BhNot+Eu+k3ofGLRGMiR6Ya4MpBv4Yia95LZP4ws9ZrRfuWUV3812FzOQs/pCr3TIPbKtq3jNyl9d8RTZKu58ohvobipaf/ycz7o2u9tmxhMpdy/zpP48M3X+HMqZMNbmvZwmQ+/UBuzyw13JWDfw3FB3NmMefR2puprFuawuVLf91HX7//GudOpze4rXVLU/jhP+/c+kJJuokrh/0aioWfPMO8Vx+s9VrahkWU5P91GPeq798mL/NUg9tK27CIdT/JpRUNIYupFkJbUUxl1jF0lSVo8v/aBap43/JaxZTLlOcwde4gIKEkGbbi4iKOHz1MaUkJFzLPXX19+eKUWsXUc6++RYdOjbuwX5JaitLiIk4dP0xZaTEXL/x1H61btoDLl/56EJz5wht4dWy8Bf2S1FJUlBaTc/oIleUlFOZkXn398KbFlF5TTI19+GWcPOtu3iQ1PznNr56K9y+nYEscKBSYtu1E29CXa/150e5FFO1ZhF5bjYmjBy6hL6E0MackbR2XN/yMQqlEaWaFx4NfUJVzmpwF76PXVoNeR9vItzB18mxQvtIjG7HyHYLKypGSQ2twCLyXksPrqbxwjJz5b6NQm+Ex4yuyfn8Op3EzqbxwDM3lC7QZVzOSde1GEFnzXqK6KAd9dRX2/mHY9p9Sc83epTVrnMytMW3rjUJtCtRskpGz4AN0ZYWorOxxnvoCJnZtG/T9SC3PisULiPv1fygUCjp19uWlt2qfw7F4fgKLkhOo1mjw8GrHi2++j7mFBetXLeen779CpVRhZW3N5z/8yun0E3zwxstoNFXodXre/OhTPNt1aFC+TWtXMSRwBA5OTqxduZTpDz3K+tUrOHb4EO+8/BymZmZ89b9Ynpv1KDNnP8uxw4e4kHnu6nS8K9uez37+ZV56+p/kXsymqqqSsJh7mTwt8s9r5vP7/77H2sYW785dMDGtuYeyLpznwzdeprAgH3sHB55/7R3aurk36PuRWqb1yxeyMPYnFAoF7b19mfXKe7X+fNXCRFYvSqRao8HVsx2zXn4XM3MLtqxbQcL/vkGpVGJpbc1bX/7MudMn+e97r1Ct0aDT6Xj27f/g5tW+Qfm2bVzDgCEjsHN0InX1csLue5it61Zy6lgan7/5AqZmZrzzzW+888xM7nviGdKPpnHx/Dnue6LmsPsr25v/46mX+GDOLPJysqmqqmJSxD2MnRIBwNolyST/9iNWNja09+6CiWnNGqucrPN89f6rFBXkY2vvyBMvvoVzW7cGfT9Sy3N405Kas5sUCtp4+RD0eO3zPQ+uTebQumS01dXYt/Vk/Mw3MTEz5/j21Wyf/z0KpQozC2siXv2OvMx0Vn77JtpqDej1TJr9AQ6uDTv+JX3nOjr1C8TS1oljW1cxaOqDnNi+hpxTR1j+1SuoTMyIfmMuyR8+ybC7n+TiqSMU5mQSePeTQO3NIBZ+8gwleRep1lTSNyianqOm/XnNQnYu/BkzS2uc23VGZVLzWVSYe4FV371FRXEBFrb2jH3kNWzbyAPnb0UWU/VQlXOa/I2/4jHjv6gs7dGWF9W5xqrbsD+3Aoe8tT9QvGcJdv5h5G/8GffpH6G2dUZbUQxA4a4F2PmHY9NrLHqtBr1OV+f9shNfR3PpXJ3X7e+KwKZ3UJ3XSw6twWHY/aisHbkY/woOgfdi3X0EhTuScRo3E3P3rrWut+42nMwfH79aTJWkrcUh8F4AXKY+j8rCFp2mkszvH8Wq23D02mry1/8Pz0e+R2luxYWfZ2P655bwl5Z9hk3vIGz7BFG0dwmXln2OW7ScdiH95XT6CX798Ru+nPsH9g4OFBUW1LkmcNRYgqfVPCz98NVnLF2QxLTo6fz8/dd89OX3OLu0pbi45t5bmBRHWPR0xk6cjEZThU5b9x5644WnyTh7us7rkfc8wPjgulu6rlmxlPsfnomDYxteffZJpj/0KCPGjCc5/g9mzn6Wrt171Lp++OhxPP5AzNViat2qZUx/6FEAnn/tbWzt7KmsqODR+yIZNmoc1RoN//vmS777PREra2ueevRBfP7c7v3zD99hfPBUgiaHsHRBEp9/9C7vfPLl7fyIpVbg3OmTJP38He98/Su29g4UFxXWuWbw8DGMnVKzaUTs95+zZvF8JobfQ+JP3/Dyv7/BybktpX/eRytT4pkYfg/DxgWj0WjQ6erukPbJa89wIeNMndeDo+5jRNCUOq9vXr2U8Acew97BiY9feYqw+x7mrpHjWDY/9rrnOQ0ePoaXZk6/WkxtXrucsPseAeDxOW9hY2tHZWUFLzwcw+DhY6nWaIif+xUf/BCHpbUNr896iI6+NZ9vP376HiOCpjBiwlTWLElm7qfv8fx7n9/GT1hq6fIy09mRMpeo13/EwsaeipK695DPwJH0/HOb8i3xX5O2fgF9xkexff4PhD7/BdaOLlSU1jzPHVgzn75B0XQdOgFttQb9de6hJZ/PIT+r7k6Q/SbeQ/fASXVeP7Z1Bf6h/8DSzpHFnz7PoKkP0tl/NPtWxTPs7idp26l77byDRhH3+kNXi6nj21YxKKRmF9hxj7yCubUd1VUVxL5yPz4DR6GrrmZb0nfEvP0rZpbWJL79GC4dajZPWv/zR3QPnET3YcGkrV/I+l8+ZsrTH9/Oj7hVksVUPZSf3oNV9xGoLGt2+FFd51ymqpzTXF73A7qKEnRV5Vh6DwLA3KsnOQvew7r7SKy6DfvzNT/yN/1GdVEuVt2GXXdUyjX89Xrnqy65jCbvPObteqFQKECppjLnFGYuNx7+VVnZY+LgRkVmGiaOnmguZWDuVXPSdeH2JEqP1hxqqC3KQXM5E23JZcw79EFlVfMzsPIbiSavZvi54txhXCNr1n7Y9BpP3qpv651dah327tzOiDHjsXeoORfG9jq7ZZ1OP8GPX31GSXEx5eVlDLyr5tDpHn368v7rLzJyTBCBo2oOu/br2Yff5n5Lbs5Fho0ac91Rqdfe/6Te+S7nXSIz4yw9+/RHoVCgVqs5dfIEnXxufHaVvYMj7h5epB3cj6dXezLOnKZnn34AJM37jdR1awDIzc4m89xZLl/Kpc+AQdg7OAIwcmwQ5zJqPmDTDuzjrY8+A2DcxCl889m/651daj0O7t7OXSPHXT1fycbWrs41GadOEPvDl5SVFFFRXk7vQUMA6NKzL/9992XuGjmewcPHAODbozdJv3xPXu5FBg8bc91RqaffqP+DVMHlS2RlZtCtVz8UCgUqlQkZp07QrtON7yM7B0dc3D05nrYfN8/2XMg4c/WcqaWJv7NjY819lJeTTVbmWQryLtG970Ds/ryPhowOIuvcGQCOp+3n2XdqzkAcPj6Y376u/78BUutwLm0Xnf1HY2FT8xlkbl33HsrLTGdLwjdUlhajqSynfc/BALj79mblt2/Q2X8MPgNHAuDm05MdC+ZSfDkHn4EjrzsqNWnWe3Veu5HSwjzys8/h3qUPCoUCpVrNpXMnaeN146nllrYO2Dl7kHXiIPauXuRnncXdtzcAe1fEkb5rPQDFeRcpyD5HaWEent36Y2lb8++I7+CxFGRnAJB14iDBs2sOmu8aMJFNsbIzoj5kMdVIcha8h2vUO5i5+lC0bxkVZ/YB4Bz8LyoyD1N2YiuZ3z2C5yPfYdNzLOYe3Sk9sZXsP56jTfAzWHbsV+v9bmdkqjRtHbqKYjI+q1k4r6sspeTgGsxG33wurXWPUZSkrcO0TTusugaiUCgoP7OX8lO78ZjxFUoTc87/9CT66qqG/GgkqV7ef/1F3v73F/j4dmXZwmT27d4JwL9efJ3DB/ezLXUjj0wP57vfEhkzIZhuPXqxLXUDz896jH+9+Dr9Bg2u9X63MzK1ftVySoqKiJ5cU6yVlZawdsUSOvnMvmnmUeMnsH7Vctp16EjgyDEoFAr27trB7u3b+O///sDcwoInH7mfqsrKhvxoJKne/vveKzz37md08OnCuqUppO2tuY8efeZVjqcdYM/WjTz3jyg+/CGOwLGT6Ny9F7u3buSd5x7n0WdepWd//1rvdzsjU1vWrqC0uIjHI2s+o8pLS0hdvYy7H7n5gdpDR09gy9oVeLTvyKDAUSgUCg7t3cnBXdt495vfMDO34NX/e5CqKvlZJDW9ld++yeSnPsK5vS9pGxaReWQ3AKNnzCHr5CHO7Evlj5fv4+63f6Hr0CBcfXpwem8qCz6czegZc/DyG1jr/W5nZOrEttVUlhYxd3bNZ1RVeSnHtq68aTEF0OWucRzfvhpH9/Z4DxiBQqHg3OHdnDu0g6jX52JiZk7C249SrZH3UFOQxVQ9WHTsR3bcy9jfFYnK0g5teVGd0SldZTkqGyf02mpKDq5CbVNzqr3m8nnMPbtj7tmdspPbqS7KQVdZitrBHXv/cKoLc6i6mF6nmLqdkaniQ2twu+dDzL1qpiFp8rO48OvTOI1+GKWZBfrKsut+nVXXYeRv+o3K7BM4jXms5vuoKEVpYY3SxJyqS2epzDwMgJlHdy4t/wJtWSFKMytKD6/HtG3NzW3u5UfJoTXY9B5P8cFVWLTvWe/sUuvQd6A/rzwzi4h7HsDO3p6iwoI6o1NlpaU4tXGmWqNh9fLFtHGuWXd3/lwG3Xv2pnvP3mzfsomci1mUlhTj7ulFWMy9XMzOIv3k8TrF1O2MTK1ZsZQPv/wOv159AMg6n8m/Zs7gH0/MxtLSivLS0ut+XeDIMfz243eccHXj0VlPA1BaUoyNrS3mFhacPX2Kwwf3A9C9R2++/Pg9CgsKsLKyYv3qFXj/OT2pR+++rF25jHGTprBq2WJ69e1f7+xS69Gzvz8fvjibyVH3YWNnT3FRYZ3RqfKyUhyc2lBdrWHTqiU4tnEBIPv8OXz9euHr14u921O5lJNNWWkxbd09mRR+D5cuZnE2/XidYup2RqZS1yzjpY+/pkuPmvvo4oVM3nzqYe5+ZBYWllZUlF3/PvIfNor5v3zH6RNHmf5YzXk5ZSXFWNnYYmZuwfmzpzhx+AAAnbv3Yu7nH1BcWICFlRVb162kg48vAF169GHz6uUMD5rMppVL6Nar33Xbk1ovL78BLPrPc/SbcPfVaX5/H52qKi/Fyr4N2upqjm1ZjpVDzfNcwcVM3Hx64ObTgzP7t1Ccd5Gq8hLsXDzoGxRNcV42uRkn6hRTtzMydWzrCkKe/xz3zr0AKMw5z/z3nmBo5OOYmltSVX795znvgSPYsWAuuWdcCYj5v5rvo6wEMysbTMzMuXzhDNknDwHg5t2DDb/8m/LiAkwtrDmxYw3O7Wo6PNx9e3F860q6BU7k6OZleHTtW+/srZkspurB1KUj9oH3cv6nJ1EolZi5dsYlZE6taxxHPsT5Hx5DZWmPmUc39FXlAOSt+hrN5Uz0erDs2A/Ttj4UbP6D4gMrUSjVqKwdcQicfsfZNAVZVBdexMzzr3noJg5uKM2sqMg8jE3vCeQu+eTqBhTXUlnYYNqmPVW5ZzD3qFm7YekziMLdC8j4772YOLXDzLNmbq7axgmH4Q9w/sfHazagcP2rl6TNhCfJWfA+BVvmXd2AQpKu1dG7M9MfepQnH7kPlVKFT5duzHnj3VrXzJg5i5n3R2Pv4Ei3Hj0pK6350Pjms4/JzDiLHj39Bw7Gx7crf/z0A6uWLkSlVuPo5Mz0hx6542xZF85zMesC3Xv2vvqam4cnVtbWHD64n6DJIXzy3htXN6C4lo2tHe06duLs6XS69aj58Bs0JJCFSXHcFxaMV/sOV9/XydmZBx59gicejMHaxhYf37/WMc567kU+eP1l5v0y9+oGFJL0d14dfQi772Fe/b8HUSqVdOzclX++VPvvSvSMfzLn0XuwtXegc7eelP9ZwPzy1b/JzjyLXl9TlHXw6ULK7z+yYcVi1Go19o5tmHbvw3ecLSfrPJeys/D1++s+auvuiaWVDcfTDjBywlS++/itqxtQXMvaxg6P9p3IPJNO5+41nXF9/QNYuSCeJ6dPwd2rA52719xfDm2ciXxwJi8+Nh0rGxs6+Px1UPaM2XP473uvsCD2f1c3oJCkazl5ejNo6oMkvv0oCqUK5/a+jH/s9VrX3BXxGPNeexALG3tcfXpQVV5zD2364zMKss8Berz8BuLc3pddi37mSOpSlCo1VvZODJr6YN1G66kw9wJFl7Jx8/mrQ9rOxQNTS2uyTh6i+7DJrP3fe1c3oLiWuZUtju4dyTt/ClfvmufB9r3v4sCaJH5+NgIHt/a4+tR0uFs5tGFw2MPEvT6jZgOK9r5X32fEfc+y8rs32b3k16sbUEi3pnB3d9eLDtEctu3ay2s7FWSX1F0cKBmOmB5W5K2dy0cfyTMPjJGVlRWHDh0ir0olOkqLZFZdwt3RURw8eFB0FKmeZs+ezb2PzSa3vFV81LZIXuZVTAwax9mzdadqSc3L3d2dFWvWE3dcPssZurEe1bw0eyapqamiozQ5ec6UJEmSJEmSJEnSHWg1xVRZeQVWJgrRMaRbsFBqKS0tER1DukOVlZUola3mn5Vmp1apKCu7/px5yTCVlZWh12pEx5AawNRETekN1k1KzausrAyLP88VkwybmUrZau6bVvPUc+jgAcZ1kEvEDJm9uRJ/VxWHDx8WHUW6Q9XV1ZzLzMRMWffcJ6lhTFWgUio4d67uLp+S4UpLS8NKrUfdaj5tWxZrE6isrODSpUuio0hAQUEBRcVFeNvLqeSGrK2VCgdrM9LT00VHaRatZs2Ura0tsQnzyTf3YP8lPVXaVvFtGwcF2JgqmdBewfzf5/JvuV7KqHXs2JEFCxdiYmmLDiXyTmsYBaBEj1JbwfR77mHnzp2iI0m36cknn+TRmU9QihlaHfKeMAIKBZgo9FhSQUR4OIcOHRIdSfpTt27diE9M4nSZKQWVoNfLO8pwKLA2VdDNXsfjjz7M+vXrRQdqFq2mmIKaguqBBx7Au0t3TEzNRMepY+jQIRw6dIjCwqImef8uXbpQXa0hPf1Uk7x/QxTm57F9SyopKSmio0iNoF27dtxzzz108vZGrWq+EeFu3btRVlbG2TNNs1Dc2cWFdu282L1rd5O8//Xo9DrOnjlDcnKy3HjCiE2ZMoWAwGE4tXFCgeFOOXd1dcXV1ZV9+/Y1yfubmppy15AhpG7ahFZruJsI6HQ6zpw+RUJCAseOHRMdR/obHx8fIiOjaN+xk8FNLTc3N8fffzCbUjeha6K/4/6DB3P82HHy8y83yfvfKT01x5ksWbyIXbt2iY7TbFpVMWXI/P39ee+99xg1alSTtdG9e3d++uknBg8ejE4np2FJLYulpSU7d+5k5MiR5OTkNEkbJiYm7Ny5k9DQUE6frnsgsCQZu/j4eH755RcWL17cZG388MMPrFmzhtjY2FtfLElGZvbs2Tg7O/PSSy81WRszZsygd+/ezJo1q8nakOrPsMr5ViwmJoZ58+Y1aRuHDx8mLy+PgICAJm1HkkQIDg5mx44dTVZIAWg0GubPn09UVFSTtSFJorRv356uXbuycuXKJm0nLi6O6OjoJm1DkkRQKBRERUU1eUfB/PnzGTNmDLa2tk3ajlQ/spgyANbW1owbN46kpKQmb2vevHnyQ0xqkaKjo5u8QwIgNjaW8PBwVCq5AFpqWSIjI0lOTqaqqqpJ21m7di2enp74+Pjc+mJJMiJDhw6luLi4ydfY5efns3HjRkJCQpq0Hal+ZDFlAKZOnUpqaip5eXlN3lZKSgojRozAwcGhyduSpObi7e1Nx44dWbNmTZO3deLECS5cuMCIESOavC1Jai5KpZLIyMhmmXqn1WpJSEiQHXtSixMdHd1s01djY2PlLAkDIYspAxAdHU1cXFyztFVYWMiaNWtkb4bUokRGRpKUlER1dXWztCdHeKWWZtiwYeTk5HD06NFmaS8+Pp6wsDDUanlkidQy2NnZMWrUqGbbSGvTpk04OzvTrVu3ZmlPujFZTAnm6+uLm5tbs24fGRcXR0xMTLO1J0lNSaVSERER0ayL2RcuXMjQoUNxcnJqtjYlqSk1x7rda506dYpTp04xevToZmtTkppSSEgI69evJz8/v1na0+l0xMfHy449AyCLKcGio6NJSEho1i1iN2/ejI2NDT179my2NiWpqYwaNYqMjIxmPRywpKSEFStWEB4e3mxtSlJTcXBwIDAwsNmPppAde1JL0pxT/K6Ij48nNDQUU1PTZm1Xqk0WUwKZmJgwbdq0Zpvid4Ver5e7KUktRnNtPPF3sbGx8h6SWoSwsDBWr15NcXFxs7a7aNEiBg4cSNu2bZu1XUlqbH5+fjg6OpKamtqs7WZkZHDkyBHGjx/frO1KtcliSqAxY8Zw4sQJzpw50+xtx8fHM3XqVMzNzZu9bUlqLM7OzgwePJhFixY1e9s7duxArVbTr1+/Zm9bkhpTc2zlfD3l5eUsWbJEjvBKRi8qKoq4uDj0+uY/unXevHlyIwrBZDElUExMTLOPSl1x4cIF9u/fT1BQkJD2JakxhIWFsXz5ckpLS4W0LzeikIxd7969sbS0ZNu2bULal/eQZOzMzMwIDQ0lPj5eSPvLli2jT58+uLu7C2lfksWUMK6urvTr169JT5m/lXnz5sn56pJRi4mJEdKjfkViYiKTJk3CwsJCWAZJaogrnXoietQB9uzZQ3V1NYMGDRLSviQ11Pjx4zl06BCZmZlC2q+oqGDhwoVERkYKaV+SxZQwERERLFmyhIqKCmEZVqxYQbdu3fDy8hKWQZLu1IABAwDYtWuXsAwXL15k586dTJ48WVgGSbpT5ubmBAcHk5CQIDSH7NiTjJmodbvXunLmlEKhEJqjtZLFlAAKhULYHPVrVVVVkZycLOfaSkbJED7AQB6cKBmvSZMmsWfPHrKysoTmSExMZPz48VhbWwvNIUm3y8PDg549e7J8+XKhOQ4ePEhxcTFDhgwRmqO1ksWUAP7+/lRWVrJv3z7RUYiLiyMyMhKlUv5VkIyHpaUlEyZMIDExUXQU1qxZQ6dOnejUqZPoKJJ0W5rzwPibycvLY/PmzUyZMkV0FEm6LZGRkSxYsIDKykrRUeT6Q4HkE7QAzX044s0cPnyYS5cuERgYKDqKJNXb5MmT2b59O7m5uaKjUF1dTVJSkhydkoxK+/bt8fX1ZeXKlaKjAHKqn2R8DGWW0RXJycmMHj0aOzs70VFaHVlMNTMbGxvGjh1LUlKS6ChXyd4MydgYyhS/K+bNm0d4eDgqlUp0FEmql6ioKJKTk9FoNKKjALB+/Xrc3Nzw9fUVHUWS6iUgIIDCwkLS0tJER8C1B5AAACAASURBVAEgPz+fDRs2EBISIjpKqyOLqWY2depUUlNTuXz5sugoV6WkpDB8+HAcHBxER5GkW/L29qZDhw6sXbtWdJSrTp48SWZmJqNGjRIdRZJuSalUEhkZaTA96gBarZbExETZsScZjejoaIO6h0AeJi+KLKaaWVRUlEH1qAMUFRWxevVqpk2bJjqKJN1SVFQUiYmJVFdXi45Sizw4UTIWw4cPJzs7m2PHjomOUktcXBzTpk3DxMREdBRJuil7e3tGjhxJSkqK6Ci1pKam4uTkhJ+fn+gorYospppRly5dcHNzY/369aKj1BEXFyd7MySDp1arCQ8PN7gOCYCFCxcyZMgQ2rRpIzqKJN2UIa3bvdbp06c5efIkY8aMER1Fkm4qJCSEdevWUVBQIDpKLTqdjri4ONmx18xkMdWMoqOjSUhIQKfTiY5Sx5YtW7CysqJXr16io0jSDY0cOZKMjAzS09NFR6mjtLSU5cuXExYWJjqKJN2Qo6MjAQEBLFiwQHSU65Ide5IxMLR1u9eKj48nNDQUMzMz0VFaDVlMNRMTExOmTZtmsDefXq8nPj5efohJBi0mJsbg5qhfKzY2Vu5IJhm0sLAwVq1aRXFxsego17V48WL69++Pq6ur6CiSdF1+fn7Y29uTmpoqOsp1nTt3jrS0NMaPHy86Sqshi6lmMnbsWI4fP87Zs2dFR7mh+Ph4pkyZgrm5uegoklSHs7Mz/v7+LFq0SHSUG9q5cydKpZL+/fuLjiJJ12WIi+avVV5ezpIlSwgPDxcdRZKu68r5bHq9XnSUG5JreJuXLKaaiSEPCV9x4cIF9u7dy4QJE0RHkaQ6wsPDWbZsGWVlZaKj3JQ8akAyVH369MHMzIxt27aJjnJT8h6SDJWZmRkhISHEx8eLjnJTy5cvp1evXnh4eIiO0irIYqoZuLm50a9fP5YsWSI6yi3FxcXJaUqSQTLURfN/l5iYyMSJE7G0tBQdRZJqiYmJIS4uTnSMW9q7dy+VlZUMHjxYdBRJqiUoKIiDBw9y/vx50VFuqqKiggULFhAZGSk6Sqsgi6lmEBERweLFi6moqBAd5ZZWrlxJ165dadeunegoknTVgAED0Ov17Nq1S3SUW8rJyWHHjh0EBweLjiJJV5mbmzNp0iQSEhJER6kXuRGFZIiMYZbRFVem+ikUCtFRWjxZTDUxhUJBVFSUQc9Rv1ZVVRXJyclyrq1kUAx944m/kxtRSIYmODiYPXv2kJ2dLTpKvSQmJjJu3DhsbGxER5EkADw9PenRowcrVqwQHaVeDh06RGFhIQEBAaKjtHiymGpigwcPpry8nP3794uOUm+xsbFERkaiVMq/HpJ4VlZWBAUFkZSUJDpKva1du5YOHTrg7e0tOookAYZ5YPzNXL58mdTUVKZMmSI6iiQBEBkZSUpKCpWVlaKj1JvciKJ5yKflJmYs6zyudfToUXJychg2bJjoKJLE5MmT2bZtG7m5uaKj1Ft1dTWJiYnyQ0wyCB06dMDX15dVq1aJjnJb5EYUkqFQKpVG1yEBkJyczKhRo7CzsxMdpUWTxVQTsrGxYcyYMUbVo37FvHnz5DQlySAY0xz1a82bN4/w8HDUarXoKFIrFxUVxfz589FoNKKj3JYNGzbg5uZGly5dREeRWrmhQ4eSn59PWlqa6Ci3paCggPXr1xMSEiI6Sosmi6kmFBISwqZNm8jPzxcd5bYtWLCAwMBAHBwcREeRWjEfHx/atWvH2rVrRUe5benp6WRkZDBy5EjRUaRWTKVSERERYZQdElqtloSEBDk6JQlnbOt2ryXX8DY9WUw1IWMcEr6iqKiI1atXExYWJjqK1IpFRUWRmJiIVqsVHeWOyGlKkmjDhw8nKyuLY8eOiY5yR+Li4pg2bRomJiaio0itlL29PSNGjCAlJUV0lDuSmpqKg4MDfn5+oqO0WLKYaiJdu3albdu2bNiwQXSUOyYXLkoiqdVqwsPDjeJcnBtZtGgRgwcPxtnZWXQUqZUy1mmyV5w5c4bjx48zduxY0VGkVio0NJS1a9dSWFgoOsod0ev18qiBJiaLqSYSExNDfHw8Op1OdJQ7tnXrViwtLendu7foKFIrNHr0aE6fPk16erroKHestLSU5cuXyxFeSQgnJycCAgJYsGCB6CgNIh8EJZGMcSOxv4uPjyckJAQzMzPRUVokWUw1AVNTU0JDQ426Rx1kb4YklrH3qF8h56tLooSFhbFy5UpKSkpER2mQxYsX069fP1xdXUVHkVqZHj16YGNjw+bNm0VHaZDMzEwOHTrE+PHjRUdpkWQx1QTGjRvH0aNHycjIEB2lwRISEpg8eTLm5uaio0itiIuLC4MGDWLx4sWiozTYrl27ABgwYIDgJFJrEx0dbbSL5q9VUVHBkiVLiIiIEB1FamViYmKIi4tDr9eLjtJgcg1v05HFVBMw5o0n/i4rK4s9e/YwceJE0VGkViQ8PJylS5dSVlYmOkqjkB9iUnPr27cvpqambN++XXSURhEbG0tUVBQKhUJ0FKmVMDc3Z+rUqcTHx4uO0iiWL19Oz5498fDwEB2lxZHFVCNzd3enb9++LF26VHSURhMXFyenKUnNqqVM8bsiMTGRCRMmYGlpKTqK1Eq0tHto3759VFRU4O/vLzqK1EoEBQWxf/9+Lly4IDpKo6isrCQlJYXIyEjRUVocWUw1soiICBYtWkRFRYXoKI1m5cqV+Pr60r59e9FRpFZg4MCB6HQ6du/eLTpKo8nNzWX79u1MnjxZdBSpFbCwsCA4OJjExETRURqV7NiTmlN0dLTRr33/uyu7NMsR3sYli6lGpFAoiIqKahFz1K+l0WhITk6W26RLzcKYD0e8GbkRhdRcgoOD2bVrF9nZ2aKjNKqkpCTGjh2LjY2N6ChSC+fl5YWfnx/Lly8XHaVRpaWlUVBQQEBAgOgoLYosphrRXXfdRWlpKQcOHBAdpdHFxsYSGRmJUin/ykhNx8rKiqCgIJKSkkRHaXTr1q2jXbt2eHt7i44itXAtad3utS5fvkxqaipTp04VHUVq4SIjI0lOTqaqqkp0lEYn1/A2Pvlk3Iha2hz1ax07dozs7GyGDx8uOorUgk2ZMoUtW7Zw6dIl0VEaXXV1NYmJifJDTGpSHTt2xMfHh9WrV4uO0iRiY2PlPSQ1KaVS2WI7JABSUlIYOXIk9vb2oqO0GLKYaiS2traMGTOG+fPni47SZObNmyenKUlNqiUcjngzcXFxhIeHo1arRUeRWqioqCiSkpLQaDSiozSJDRs24OrqSteuXUVHkVqowMBALl26xOHDh0VHaRIFBQWsW7eOkJAQ0VFaDFlMNZKQkBA2btxIfn6+6ChNZsGCBQQEBODo6Cg6itQCde7cGQ8PD9atWyc6SpNJT0/nzJkzjBo1SnQUqQVSqVRERES06A4JnU5HfHy8HJ2SmkxL3Hji72TneOOSxVQjaSmHI95McXExq1atIiwsTHQUqQWKjo4mISEBrVYrOkqTkvPVpaYycuRIzp8/z4kTJ0RHaVJxcXGEhoZiYmIiOorUwjg4ODB8+HCSk5NFR2lSqamp2NnZ4efnJzpKiyCLqUbQvXt32rRpw6ZNm0RHaXLyQVBqCmq1mrCwsBZzOOLNLFq0CH9/f1xcXERHkVqYlrzO41pnz57l+PHjjBs3TnQUqYUJDQ1lzZo1FBUViY7SpPR6vTxqoBHJYqoRREVFER8fj06nEx2lyW3duhUzMzP69OkjOorUgowZM4b09HROnTolOkqTKysrY9myZXKEV2pUbdq0YejQoSxcuFB0lGZx5bwcSWpMLX3d7rXi4+OZOnUqZmZmoqMYPVlMNZCpqSmhoaGtokf9iri4ODk6JTWq1jBH/VpyvrrU2MLCwlixYgUlJSWiozSLJUuW0K9fP9zc3ERHkVqInj17YmVlxZYtW0RHaRbnz5/n4MGDBAUFiY5i9GQx1UDjx4/nyJEjZGRkiI7SbBISEggODsbc3Fx0FKkFaNu2LQMHDmTRokWiozSbXbt2odfrGTBggOgoUgvRGtbtXquiooLFixcTEREhOorUQsTExBAfH49erxcdpdnIpRuNQxZTDdRa5qhfKzs7mz179hAcHCw6itQChIeHs2TJEsrLy0VHaVaxsbFydEpqFP369UOtVrNjxw7RUZpVbGwsUVFRKBQK0VEkI2dubs6UKVNa1SwjgBUrVtCjRw88PT1FRzFqsphqAHd3d/r06cOyZctER2l2cr661Fha8mHXN5OUlERQUBBWVlaio0hGrrXeQ/v376e8vJzBgweLjiIZuQkTJrBv3z4uXLggOkqzqqysJCUlhcjISNFRjJosphogMjKSBQsWUFFRITpKs1u1ahW+vr506NBBdBTJiA0aNAiNRsOePXtER2l2ubm5bNu2jcmTJ4uOIhkxCwsLJk2aREJCgugoQsj1h1JjaK0dEvBX57hSKUuCOyV/cndIoVC0yil+V2g0GubPny9Hp6QGiYmJaVUbT/ydnK8uNdTkyZPZuXMnOTk5oqMIkZSUxJgxY7CxsREdRTJS7dq1o1u3bqxcuVJ0FCHS0tK4fPkyQ4cOFR3FaMli6g4NHTqU4uJiDh48KDqKMPPmzSMiIgKVSiU6imSErK2tGT9+PImJiaKjCLN27Vq8vLzw8fERHUUyUq1t44m/y8/PZ9OmTYSEhIiOIhmpqKgokpOTqaqqEh1FGDnC2zCymLpDrXlU6opjx46RlZXF8OHDRUeRjNCUKVPYvHkzeXl5oqMIo9VqSUxMlKNT0h3p1KkTHTt2ZM2aNaKjCBUbGyvvIemOKJVKIiMjW/3zXEpKCiNGjMDBwUF0FKMki6k7YGdnx+jRo0lOThYdRTg5TUm6U63pcMSbiY+PJywsDLVaLTqKZGSioqJISkqiurpadBShNm7ciIuLC926dRMdRTIyw4YNIycnhyNHjoiOIlRhYSFr166VI7x3SBZTdyAkJIQNGzaQn58vOopwCxcuJCAgAEdHR9FRJCPi6+uLm5sb69evFx1FuPT0dE6fPs3o0aNFR5GMiEqlIjw8XHZIADqdjvj4eNmxJ9221nZg/M3IqX53ThZTd6C1z1G/VnFxMStXriQ8PFx0FMmIREdHk5iYiFarFR3FIMgRXul2jRo1iszMTE6ePCk6ikGIi4sjNDQUU1NT0VEkI+Hg4MCwYcNISUkRHcUgbN68GRsbG3r06CE6itGRxdRt8vPzw8nJidTUVNFRDIacry7dDhMTE6ZNmyZ7A6+xePFiBg0ahIuLi+gokpGQ63Zry8jI4OjRo4wbN050FMlITJs2jdWrV1NUVCQ6ikHQ6/XExcXJ0ak7IIup2xQVFUVcXBw6nU50FIOxfft2TE1N6du3r+gokhEYM2YMJ0+e5PTp06KjGIyysjKWLl0qR3ilemnTpg1Dhgxh4cKFoqMYFHmYvHQ7WvPZUjcSHx/P1KlTMTc3Fx3FqMhi6jaYmZkRGhpKfHy86CgGR05TkupLzlG/PnkPSfUVFhbG8uXLKS0tFR3FoCxdupS+ffvi7u4uOopk4Hr16oWVlRVbt24VHcWgXLhwgf379xMUFCQ6ilGRxdRtGD9+PGlpaZw7d050FIOTmJhIcHAwFhYWoqNIBszV1ZX+/fuzePFi0VEMzu7du9HpdAwcOFB0FMnAxcTEyHW711FRUcGiRYuIiIgQHUUycFcOjNfr9aKjGJy4uDjZsXebZDF1G+TGEzeWnZ3Nrl27CA4OFh1FMmAREREsWbKE8vJy0VEMUmxsrJyvLt3UgAEDUCqV7Ny5U3QUgxQbG0tUVBQKhUJ0FMlAmZubM3nyZDnL6AaWL1+On58fXl5eoqMYDVlM1ZOHhwe9evVixYoVoqMYLDlfXboVuWj+5pKSkggKCsLKykp0FMlAyXvo5g4cOEBpaSl33XWX6CiSgZo4cSJ79+4lKytLdBSDVFVVRXJyMpGRkaKjGA1ZTNVTZGQkKSkpVFRUiI5isFavXo2Pjw8dO3YUHUUyQIMHD6ayspK9e/eKjmKwLl26xJYtW5gyZYroKJIBsrS0ZOLEiSQmJoqOYtDk+kPpZuSB8bd2pXNcqZRlQn3In1I9KBQK2RtYDxqNhvnz58vRKem65MYT9SPnq0s3EhwczI4dO8jJyREdxaDNnz+fMWPGYGtrKzqKZGDat2+Pr68vq1atEh3FoB0+fJi8vDwCAgJERzEKspiqh4CAAAoLCzl06JDoKAZv3rx5hIeHo1KpREeRDIiNjQ3jxo2TPer1sHbtWjw9PfHx8REdRTIwcuOJ+snPz2fjxo2EhISIjiIZmKioKJKTk6mqqhIdxeDNmzdPruGtJ1lM1YMclaq/48ePk5WVxYgRI0RHkQzIlClTSE1N5fLly6KjGDytVktCQoIcnZJq8fb2pkOHDqxdu1Z0FKNwZSMKSbpCqVQSGRkpn+fqKTk5meHDh+Pg4CA6isGTxdQt2NnZMWrUKJKTk0VHMRqxsbHyQVCqRR6OeHvi4+MJCwtDrVaLjiIZiKioKBITE6murhYdxShs2rQJZ2dnunXrJjqKZCCGDx9OdnY2R48eFR3FKBQVFbFmzRpCQ0NFRzF4spi6hZCQENavX09BQYHoKEZj4cKFDB06FCcnJ9FRJAPQpUsX3Nzc2LBhg+goRuPUqVOcOnWKMWPGiI4iGQC1Wk14eLjskLgNOp2O+Ph42bEnXSXX7d4+OdWvfmQxdQtyjvrtKykpYcWKFYSHh4uOIhmA6OhoEhIS0Gq1oqMYFbkRhXTFyJEjycjIID09XXQUoxIfH09oaCimpqaio0iCOTo6EhgYSEpKiugoRmXLli1YWVnRs2dP0VEMmiymrqNv377MmTMHPz8/HBwcSE1NFR3J6FyZ6mdiYsLvv/8uOo7UzExNTfntt98wMTFh2rRpsjfwDixatIiBAwfStm1bPv74Y3mAYiv02Wef4e7uLjv17lBGRgZHjhxh/Pjx3HvvvfLIgVbowQcfZMKECYSFhbFq1SqKi4tFRzIqer2e+Ph4YmJi8PPz47XXXhMdySDJYuo6TE1NGThw4NUh4YEDB+Lu7i46ltEYOnQoZ86cQa1WM2rUKHx9fUVHkppZdXU1AQEBBAUFcfz4cXQ6Hf379xcdy2j4+Pjg7e3NkiVLCA8PZ8iQIfK8j1bIzc2Nvn374u/vz9q1axk3bpzoSEbD2tqasWPHXj0vp0+fPlhaWoqOJTUzOzs7evTocXXd7qRJkzAxMREdy2gEBwczf/58pkyZQq9eveTyjRuQn87XkZubS9u2bQkJCaG0tJRvv/0Wc3Nz0bGMho+PDykpKaxYsYLIyEh5JkorpNPpyMvLY/r06WzevJmUlBTc3NxExzIaNjY2/P7776SnpxMdHY2Li4u8j1qhnJwcJk6cyJo1a5g7dy5Dhw4VHcloqFQqXn31Vbp06UKfPn1o164dubm5omNJzSw3N5du3bphZmZGSEgITzzxhOhIRmXy5Mm8++67HDhwgICAAPk5dAOymLqOixcv4ubmRkFBAQ8++CDh4eGcOnVKdCyj8fPPP/Pll18SHh7OiBEjyMvLEx1JEiA/P59+/frx0EMPMWfOHBYvXiw6ktHYu3cv999/P4899hjW1tbo9XrKy8tFx5KaWU5ODiNGjMDf35/NmzfLKTa3obCwkJCQEIYNG0ZOTg6dO3fm4sWLomNJzSwnJ4devXpRVVVFu3btiIiIQKPRiI5lNB5//HEuXryIl5cXQ4YMkcXUDchi6jpKS0tRq9WYm5sTEhIiF/3egT/++IMXXngBExMTLCwsRMeRBDAxMUGtVvPII4+wYsUK0XGMzr59+wgPD8fU1FRuh91KmZiYYG9vz/fff8/7778vOo7RycvLIyIigrKyMpydneWDYCtUUFCAm5sbZ8+e5f7776e0tFR0JKOi1Wr517/+xYoVK3BxcZGfRTegsrGxeV10CEPk6OjIrFmz5D++DXDy5ElOnz5NWloaR44cER1HamZqtZoffviBLVu2iI5itPLz81m5ciUajUZuhNMKlZWVcfLkSb799lvRUYyWRqMhMTERV1dXuZNbK1RYWIiDgwOzZs2ShUADbNy4ETMzM5YtW8bly5dFxzE4Cnd3d73oEJIkSZIkSZIkScZGfbtfYGFhIXeVEkCn093RmgkrK6smSCNdUVFRUa/zk8zMzFCrb/t2k5pQWVkZev2d9SXJ3+edqaysvK3eYflzNnyWlpYoFAqqq6uprKwUHUe6icrKSszMzETHkK6h1WqpqKi4+v8mJibyXDQDU15ejk6nu+k19f6UeuChGTz73POYm5mi1cnBrOamUiqoqKzis0//w3fffH3Ta9VqNf/+9HMmTZwAwB0+L0r1oFYpOXbiJA/cew/Z2dl1/nz48OF88em/sXNwQlctF70aDIUCFAq2bNrIjEcfr/VhdjPDhw/nP59/iYOdLdXam//jKtWlVik5dTaDh+6bztmzZ2943fDhw/nyyy+xtbW95YeYJIZCqUSlUtV6Ta+veTjUy9+Z4VGqMFEr0er0crqbAVEplZSVV/DT3B+JDA/Fpa0bOq38/RgSpUpF2sED3HPfA+Tn51/3mnpN83vgoRk8/tRzvLSpjMyiW/fCS03D3UbFOwGW/O+bz/j266+ue41CoeC7H+fi1cOfhOM6yqtlJdWUVAoI8FTTy6aUKZMm1Np6NzAwkB+//YrK5R9Qde4gIH8XhkRhYo7ZiMc5UWZJ5N3Tqaqquun1gYGBfP39j/yUVs2JPI38bd4BlQKGepow0q2aqcETyczMrHNNYGAgc+f+iJmuErSyA8IQ6c2sqVKak5aroUrWTQZNpYBubUw5eVlD6rkKquXvy+D4OKiZ7GNC2Z6FaC4clj3ghkapRuUzlDxzDyZNnkphYWHdS+rzPs8+97wspAzAhWItczaV8fTT/7rhNV26dGGA/xD+OKqVhVQz0Ophw7lqsrTWhIaG1vqz55+ehWbjt1SdO4AspAyPXlNBxepP6ezZhoEDB97y+tnPPE/SCS3HZSF1x7R62HhOQ1qRGTExMde9Zs4Lz2OORhZSBkuB0sySAzmykDIGjhZKCip0rD8rCylD5WsPFUc3ojmfJgspQ6SrRnt8A46qSoKCgq57yS2n+VlYWGBuZkpmUVGj57uewvVzMWvXC/NOA254TfnxzWhyz2A79J4GtVWVdYzLC99HX12Juc9g7Mf9HwqFotY1er2egpVfUHFyGwoTcxwnv4Cpmy8ABWu+pfzkVgDsAu7D0m8UAJcXfUhV1jFAj9rRE8cpL6A0bZyT17NLtKBQYGtrS9F1ficeHh5cLKpqsn80jy7+DiefPjh3HXTjjAc2UZx9ms7j7mtQWwUZR9n761toNZW09RtCj/Cn6vx+sg5s5Oji71AolCiUKnqEz8bJuzcAC/9vKLbu3gBYOLTF/7GPANj902sUZBxFqVJj374bvWNeQKlq2LqMnHIVXu061HrN3d0DzaHmP5/sx12X6e1mzgCPG/+d23y2lDP5Gu7pY9+gto7lVvLehhyqtHr8vSyZdZdTnd/R2YIq3t+Qy4lLlfxjoCPRvWrarKzWMWtxFhqtHq1Oz/BOVjzU3xGADzbkcuxSJXrAy86EF4Y7Y2nSBGs19Tq0eWfx8PC45aXu7u4sP974HUqnln2PvXcfHH1vXNDlHtpE6cUzdBh9b4PaKjp3lCOx76DTVOLU7S46h86u8/vKPbSJU8u+R6FQoFCq6BzyJPadau6pfd8+TdHZNOw69aL3Pz664xznS/R4dfS+7p+5ubmjFzzN5Ytvf6R/394MGXTjz6G1G1NJP32Gh++f3qC20o4c48U336WisophQwbz4r9m1fmdXHHw8BHunvE4H7/9GuNHjwDg3198zYbN2wCYOeM+JowdDcDWHbv5+Iuv0On0WFla8M6rc2jv5dmgrAAolWi0OrSCn/mSf/ovvj3749d/8A2v2btlPRfOpjMpZkaD2jpz/DA/fvgKmqpKeg4K4O4nnr/h7+j00UO8M+s+Hnv5AwYMGwtA/Hf/4cD2jej1err3G3z16z94egYFl3MxNTMH4F/vf42tg1ODsv6dmUrBxdLmv592zf8W1y598fS78bPCmb0bKTh/mj7B9zeordwzR1j/w5toqyrx6jWEIff864a/n5xTh1nw9gxGz3ybTgNr7pWlH88iJ/0Qrr69CXrqP1ev1ev17Ez6mtM716BQqug+KoweY6MalPV67Mz0aAvrLhNoat+tPECfji4M6ux6w2s2Hs7kTE4h943wa1BbRzMv81bCViqrtdzVxZ2nJ/ev8zvamJbJt6sOoFTUTH+cPbkffTq4APDF0r1sOXoBnV7PoM6uPD25P2VV1Tz2zaqrX59TWE5Q3w48Nbl/g7LeiEn5pRs+K9zyCVKpVDbrGim7EQ/d8hoL36FY+Db8JPj8Zf/BYdIzmHp059K856lI34GFj3+tayrSt1N9ORPXx3+n6vxh8pf9h7YPfU35ia1UZR/H9eEf0FdryPl1NuY+/ijNrLAf9wRKs5qNH/JX/ZeSnckNLvyuVa3V1ZmrfoVSqWzSD7muwY/c8hrXXoG49gpscFsH4j6k991zcOjgx/avnybn8Dba+t1V6xrnLgNw7RmIQqGg8PxJds99iVGvxAGgMjFjxJxf6ryv58Dx9Lv/dQD2/PQaZ7cspGPgtAZl1er1qP62UF6hVEA9NqdobDMGON7ymqHtrRjavuFtfbL5Es8GOtPdxYznlmezPbOcwV61izhbMxWzhjiReqas1uumKgX/meSGpYmSap2efy68gL9nBX5tzfnnXU5YmdYUT19uzSM5rajBhd8N6bT12lSn5t5q/Jur04SHb3mNc49AnHs0/J46lvgxXSOfx7a9H/u/f4bLR7fh1K32PeXQuT+D/AJQKBSUXDjJoV9eYfALsQC0G3k3Ok0F57cuaFAOrR5UN+jAXtQ4YQAAIABJREFUUBjABkf/9+itH75HDQtg1LCABrf15gf/5s0Xn6NXj+48Ovs5Nm3dzrAhdQsErVbLJ198wxD/vwq8DalbOXzsBPN/+5EqjYYHHnuSwLsGY21txZsf/JsvP34X744diE1M5tu5v/Duay82OC8oDKLzPPSBJ255Td8hI+g7ZESD2/r1s7d54OnX6NStJ/958QkO7txMr0F1f/c6rZaEHz7Fb8Bf99TJtH2cTNvHm98lAvw/e2cZGNXRNeBnfbMb2XiICwkQIcGdAAUKLRQtRVpaKLSlgrQUihQvUFqo4C7FpRSKewiuIbiGIAkx4pv1/X4sJE0XaXDeL8+f7M69O3InM3fOnDPnMK7vR1w8dYzyUZbNk08GjSOg3NMtVB+JgJci+FZt++lj7/GvVB8q1X/qsvYt/JH6Hw3GLSicLZP6cvP0QXwr1ra6z2QycmTVZLzDi6/zIt96H4NWy/k9fxZLv7RvA/l3U+gwbhUCoZCCnOfjElyAAMwvXm34SdOKj72nfqg39UOffhNmwl9HGNSuBmE+zvSbv4eDl5KpXc6z2D1Vy7pTL7Q5AoGAy8mZDF26nxXftCA+MY34xDQW97X4Afh0xnZOXEulSpA7f/R5q/D3H07eTIMwn6eu68MQmM2IH7L2fmlukrJjF6E+vR2hwgGxvRuSMiHY1+pIxvpx2ATXQlGhAUmT30NZsRkFlw+A0YBzuxFIXPzIP7UZXfJFHJv1feLyjbkZmLT5yLwtk5gi4k0KLu6zEqYKLu5HEfEmAoEAmXcYJk0extwM9OmJyHwjEQjFCKRipG5BaK4eQRHasFCQMpvNmPVay2H314yLm+dx6+hWZLYqbBzdcfApR9nGXTj5x2jcw+vgWakR24e1wafGW6Sc3ofJaKDqxz9g5+HPjUMbybpxnood+j9x+ZrsdAyafJwCwgHwrt6cO/ExVsKUWFa0cDdqC4DHP2v3sKJJVuVXAU3m6xlLbOGJTLZfyUMlF+JqK6aci4yOFVWM25NKLV8FDQJteW/ZDd4MseVAohqDyczIxu74qaRsvpTLxTQtfeu4PHH5GWoDap2JMHfLruqbwXbsu55vJUw52ohwtBFx6EZxYUogEKCQWPrLYDJjMJkLh8p9QcpsNqM1mhDw4AnsdSJh23xSjm9FYuuITOWGvXc5fBt25tyyMbiE1sEtsiEHRrfDo1pz0s/ux2w0EP7hGJTufiQf2UjOzQuUa/dwE9/Hoc1Jx6jNx8HfMqY8qjYj7XSslTBVbEzpNPxzTDmFVCXzyoknrsOrxvS5C/l78zacHFV4uLsRWj6E7u93YvDIsUTXrc2bbzSgcasOtHq7GXti92MwGPhl3CgC/f1Yu2EzZ89fYOi3/Z64/LT0dPLy1URGWN5Drd56k50xsQ8UppasXEOTRtGcOXehMO1KwnWqVopELBYjFosJKRtI7MHDNG/SyCIM51vGXG5ePq6uTz7WXybrF8/k4I6N2Dk44uTmgX9wKM06fMjcCd8TWbM+Ves34dsuzanTtCVxB2MwGgx8PuxnyvgGsG/rOq5fOsv7Xz25EJmVkUaBOp+gUMvCs3aTlpzcv+uBwtSOv5ZRpV5jrl88W5QoEKDXaTEY9JjNZgxGwzPXPr1MTqyby+WDm5HbOWLr5I6Lf3kim7/Pntkj8Y2qS2C1N1j6TStC6r5NYlwsJqOBJp+PQ+Xpz8XYDaRdP0/dD7594vLVWenoCvJxLxsBQHCdt7h+IuaBwtTZ7SsJqNKItIRzxdK9QquTdP641f3ndq2h0WejCzd4bOwfv1H5KjJv52m2nLyOSinHXaWgvJcTXepXYNTKg9St4EWjCF9aj1/H21UCiD1/G6PRxA9d6uLv5sCGY9e4cDuD/q0ebwr/MNJzCsjXGgj3tcxBb1UOYO/ZW1bClEImKfys0RVpUwWAzmBEbzSBGQxGM0528mK/vZGWQ2aelqgA1yeu59PwUoQpbdIFCi7E4PHJHMxGIylzeiK5Zzr3b4QKBzx6zCb32F/kHlqBU4sBD81Xc/0kWdunWKULJHLcP5paLM2Ym4bIruihi+1dUeem/funGHPTENsX3Seyd8WYm4bULYjs2IXY1exgOXuReBKxa9FWf8b68WiuHkbi4oeqyecPfxivIJmJ50iO20ODQYswGw3E/PgRDj7lHnivVKki+ruFJOxdw9WdS4nq8vCXVvql45xZ85tVukgqo943s4ulabLSkKvcCr/bqNzQZFn3D0DyqT2cXz8dbW4mNT6bWJhuMuiI+bEbQpGIsk0+oExkdLHfmYwGbh3ZQnj7J18MvSzOp2nYm5DP3LZeGE3QY+0tyrk82OWtg0zEnLberD2XzYr4bAbUf/hkcyKpgKkHM6zSZWIB01oVV2+n5RtxVRZNIa5KEen5JdPEGU1mPll7m9s5elqH2hPqVjRBjotJ5fDNAvxUEr6o+XovPnJunCctfg/V+i/EbDRwdFJ37L0fPKYkSgeqfzOfW/v/5MaepVR4b9BD8828fJzL6363ShdK5VTtXTzQqzY7DZlD0ZiSq1zR5jx4TKXFx3B10wx0uZlE9vz5vzTxteP0ufNs2xXD2iXzMBiMtOv6MaHlH/wecnRwYM0fc1m2ei3zFy9n9NCBD8338LETjP/F+j1kI5exdG5xT6wpqem4uxWNR3c3V1JT061+m5Kaxo49sSyY/htDz40vTC8fHMS0OQv4qMt7aDQajhw/SVCgPwCjhgzgs74DkMtlKJUKls+d8cjn8SqScOEMx2N3MmrWKgwGAyN7dcQ/OPSB99raqxgxYwW71q1gy6qFdPtmxEPzPR93hOXTrf+vpTI5Q34vbs2QmZ6Ko4t74XcnV3cy06034DLTUzixfxcDfp7D/IvDC9PLhkZSPqoa/To0BrOZRq074ukXWHh93k/DEIpEVKn3Bi27fPJQ87RXkdRr50g4tot2o5ZgMhr4c0RXXPzLP/Beua0D7Ub+wdmdqzm1ZTHR3Yc+NN+k88c4uPQXq3SxTE6roXOLpeVnpmLrVDSvKR3dUD9ggzQ/M5XrJ/bQYuB0Yuaes7r+IHJSb3H18Haun9iDjZ0jtbt8g4OH73/67avCuZsZ7D5zkz/6vIXBZOLD3zdT3uvBQqGDQsai3s1ZffASS/ZeYEj7Gg+8D+D41RR+3WAtgMolYmZ/3rRYWlqOGlcHm8Lvbg4K0nLU//4pAHvO3GT61lNk5mmY+JFlzRbh50qVQHda/LAWsxna1w4hwM2h2O+2n0qkcUXflzZ+Xoowpbt5GpuQugjEMgRikIdY7yDcx6acxbRFWiaEggt7H5mv3L8SHj3nPvKeZ4U8qBq65AukLvgCoUKFzCsMgaDIPMX5ne8wm4xkbv0d9dnd2EY1fyH1ehbcvRaPR8V6iCQykMhwD3+4KUuZKMs/u8q3PMmn9jwyX5eQKg80u3taykQ2oExkAzKunOTCxlnU/moyAI1H/YmNyo389Nsc+P1L7D2DULoWqavjV/yEc9konMtGPfM6PW/O3NFSx1+BTGz5n6vt+/B4YvUDLNfKuciITXjwBHafyp42zG33DM5V/EdEQgFz23mTqzUydHsK1+7qCHSyxNgYFO2G0WTmtwMZ7Lqaz1vl7F5YvZ412QnxuIQXjSmXsIebKbtFNADAzrscafExj8zXMbgK1fsvfJZVBcC1YjSuFaPJvBrHtc2zqdTLehPkdefkqdM0iq6LTCZDJoOGdR/eJ00aWkyRQsuXY/vuR7+HalStzNol855pXcdNmsw3X35mZY5ap2Z1Tp+7QOePP8fJUUVkRBiie/csWraSGb9OIDI8lLl/LOPHX6c8Ugh8Fbl8No5KtRsgkcqQSGVE1Xy4SViVepbzL34hFTi+b+cj860QVZ2RM1c+07oum/YT7/boa9VHKbdvkJyYwMTl2wCYOOBTLp0+QUhEZT4ZPBZHF3cK1PlMG/k1B7ZvoE7Tls+0Xs+TlMun8KscjVgqA2T4RT18rRBQtSEArv7luX589yPz9axQlXajlzzLqnJgySSqv/tlicyIjQY9YomMtiMWkXBsNzHzRvPO4NmP/+ErRHxiGvVDvZFJRMgQUbfCw88HNwi3mMiV93Jiz9mbj8z33yZ2z4oG4T40CPfh5LVUZm6PZ0qPN7iZnsv11BzWD2oNQO+5u4hLKENUQJEQvT0+kREdHi5LPG9e+WiIArFF7ScQCMH06F3vkmimRHYWDdN9DDnFNVX/vM+Qk8b9PX/jP+6zr/sB9nUtB8Iz1o5G7FzcVlMgFKEIbUTuwWWvlTBVEoRiy8JXIBBifsz5oJJopuQqVzRZRbtLBVmpyFWPVt86l62EOj0JbV6WxTzxnmZL6eKFS3Blsm9dKhSmLm6aiy4vi8ge4x7fyNcciciyUyMUCB575qckmilXpYi0fxxsTss34qJ8MnM8O5mISp42HLmlLhSmwCJsvRGkZNmp7NdamCoJhXOeUIj5MXNeSTRTMgdXtNlFY0qTlYbM/tFjyjEoivMZSejyspDaPqcza68BUqmlT0RC4WODdJdEM+Xu5kJKatF7KCU1DTc3a3O8s+cv8M3QkQBkZmWz98AhRCIRjRvU47PuXfmsu8XZz7dDR+Hn68PdzCwuXr5KZLhFi9O8SSM+6fPkZtevA2KJZd4QCkWYHtNHJdFMObq4kZmeUvj9bloKji5u//4p1y+dZcYPFmE1LzuT+COxCEUiUm7dIDA0ArmNxXw2onodrp47RUhE5UKNl41CSY1Gb5Fw8fRrJUyVBNH9tYJQ+Nj+KYlmSunoRt7donktPzMVhaN1/6RfP8/O6RZtmCYvixvxBxAKRfhXafDQeigd3fCvarnuX6UBe+aOemS9X3ek9zZnRUIBxscctCuJZsrVXkFadkHh99RsNa72j3bKVinQjaTVeWTla4g5e5NwX+dCM8BaIZ6cvpFeKExdTsrEaDJT3vvlmWG+FGFK6hNB5qaJ2NfpjNlkRHP5IMpKLZ4635JopkR2zghlSrS3ziL1CkV9eiu21aydENiE1Cbv2FoUYY3Q3T6HUK5EZOeM2WTEpMlDpHBAl3IVXepVnAIHWWyiM28jcfLGbDajubwficvrpRZ2CqxI/LIfCW7aFbPJSMqZ/fjVafXU+ZZEMyV3cEEsV3I34QyO/mHcOrKZgOh3re7LS7uJ0sUbgUBA1s2LmAw6pEoHdOocRBI5IokUbV4Wd6/FU7axxetW4oH1pJ4/RO2vJr8Sh92fhHAPGRNj0+kSqcJohoM31LSs8PTCRkk0U84KMQqpkLMpGkLdZGy9nEu7MIfH//AeWQVGREKLIKU1mDh2q4DOkQ6YzWZu5xjwdpBgNpvZn6jGVyV5fIavMA4BFbmwagJ+b3yA2WQk/dwBvGq+89T5lkQzJbN3QSRTkn39DPZ+Ydw5tgXvuu2s7lOn3cLGxQuBQEDuLcuYkij/e7++LlSKjGDEuJ/55MMuGIxG9uw7wLttnn4hWxLNlKuLC7ZKBadOn6VieCjrNm2lSwfr99D2dUValPvnuRo3qIfRaCQ3Nw+VyoGLl69y8cpVxtWwmFrn5uVzPfEm/n4+HDx8lCD/Z+Bx5gUTHBbFwl9H83anjzEajZw6tJfot9s/db4l0UypnF2xUSi5ei6ewAoRHNj+N41bW7v1n7B4c+Hn++e5KtdpxJHdW4jZ9CfGTgbMZjMX44/TpG0XjEYD6rxc7BwcMRj0nDq0l9DKDzerehVxD44kduE4ot7+ELPJyI24fZRv0ObxP3wMJdFMKVQuSG2UpFw5jVtQOJf3byKscQer+zr9XOQ05/55rkcJUgD+laNJOn8ce1cvki+cQPWamfgBVPRzZfzaI3RtEIbRZGL/hSRaVy/71PmWRDPlYm+DUibmzI10wnyc2XQigQ61rU2qb6bn4u1si0Ag4MLtu+gNJhwUMtxVStYdvUJXo8VJx8mEVN6rU2Qmv+1UIk0jX+789lKEKZlneWyCa3Nn1scIlY5I3AIRymxfeD0cm/Ul4+/xmPU6bMpWRx5kmcjyjlsGnW2VVsjL1kRz5TDJU7sglMhwannPTMJkIHVRbwCEMgXOrYYgEIoxm00Wd+vafMyYkbqVxfGt1+tMjqNfKO4R9dgz9gNk9k7YewYhsXnx/VOxw7ecXDwGo16LW2hN3EItB+Wvx1o87vjXa0ty3B5uHd6MQCRGJJFRpfsYy8HrO9c5tezHe7v7Jso2+QC7MgEAxC+fgI2TB7ETLZ4Jy0RFU67507nOfdFUcJVTx09B9zW3cbQREegkKXTa8CLpV8eF8TGpaA0W1+g1fCx20evOWdz2twq1J0Nt4NO/bpOvMyEUCFh9JpuF7X3IUBsYG5OGyWxxNNEg0JbafkpMZjPjYlLJvxfEJshJxtd1X8/D8/ex962AS1hdjvz8IVJbR2w9AhHJX/yYKtf+G84v+wGjXotz+ZqFziduH1gLgFftNqTF7+HOMcuYEkpkhHcdVWiHfnxyL9SpNzBq1ewf2Zry7w3CufzrtQC8T0RoBRrWq0Przt1wdnIipGwgdrYvvk++H/A1g0eNQ6vVUq92jULnE8vXWN5DHds9fCPLYDDw/qdfAmCrVPLjqKGI73kVHTX4W/p8NxShQIi9vR1jvv/uObfk2RNQPpyoWg0Y1rM99o7OeAcEY6N88X30fu8hzPvpe3RaLRHV6xBxz/nE7r8tAlnDltaL9/tUrd+E83FHGNazPSAgolptomo1QFugZtJ3vTAaDJhMRkIr1yT6LevNjVcZt8BQ/KLqseb7ztg4OOHoXRbpS1gr1O06gD1zRmG45xrd557ziXO71gAQ2ujRz3X92J5kJSei1xSwpF8L6ncfgk9ELaLe/pBdM4dxetsyJDIb6ncb8tzb8qwJ9XGmXgUv3v9tE062coLcVSjlL35z8tvW1Ri96hBavZFa5cpQ657ziT8PXQagbc1gdp+5yeYTCYhFAmQSEaM710EgENAowofjV1Po8usmBAKoGVKGev/wMLjzdCKTPmrwwtv0TwSenp6P1OUplUpOxp+h1Spr05+nwaRTI5QqMOk1pC7qjdNb/QvjN5XyaFa3UVG3ZjUyMzOtrjVp0oS+Iyex5NLTlWHQqhHLFBh0Gvb/0ovIzt+heogTilKgjo+M7KN/Mfi7IgcpJ48eRLBxBMbs5OdSplpvQiERojGY6P13Ev3ruRLyECcUpTwcSaPejF2yg+XLlz/yvsPH45h5XkS6+slc2N4fU0adhhNTPqd8h4HYPcQJxf861b1kON7axxef9rC6FhcXh5Nc8Fiz7mdBvlqNUqGgQKOh6ydfMXJwf0LL///sk/+MUIxeruL4nRcTVFlToEZuo0CrKWD81935qN8w/IIrvJCy/xfwtheRlGvkwE3tc8lfr1EjkSswaDWsH/cJ9T8a/FAnFKU8mM7lxdieXYMhLeG55K/W6lHIJGh0Bj6buYPv2lZ/qBOKUh6OvFx9Zm07xYSfrGMsvrQzU5kbJ6JPv47ZoENZsVmpIPWKcWrpeHLvXMdk0OJT/a1SQeoV5OfYNBIz9eiMZt4MsS0VpF5xLq6cQH5KAiaDDo+qzf/fClKvEiPG/syVhOvodDpavd2sVJB6BVk4aRRJN66h12mp0+SdUkHqFWPvgrFk3U7AoNcRUvftUkHqFWT8n0dISM1GZzDxVuWAUkHqOfDShCnnNt+/rKJL+Q9U6fa/fdDyf4Fhjdwff1MprwxhH4x42VUo5V/8NGbYy65CKY/h0yHjH39TKS+NNz4b87KrUMpjGNXp4Z5KS3k2vJ6n70sppZRSSimllFJKKaWUUl4yr7xr9EeRuqgPqsa9kHq+OLVy1u45qOO3YtLk4j1wS2G6JvEUWdunoE+5inPbYSgqNCj6zc4ZFFw+BGYT8sCqqJp+hdmgJWPNCAyZt0EgwiakFqpGn76wdrwM9v/6OWFtvkLl92LMNAw6DcfmDkGdfguBQIR7RF1CW1kCKBv1Ok7+MYqsGxeQKh2o2n0MCucyAGTfvkL8sh8xaPJBIKD+gHmW+ED/j+izIYleNZwp7/pi2q0xmBi+I4WkHANCAdT2U/BpdUug3pQ8A2P3pJKnM2Eyw6fVnKjp+2i3qv8fODH1S8q+8wX2Pi/e7Cl+7gAKMpKoMWAxAGcWfY869QYAhoI8xDa2zyX+1avIh5/15tvenxMe+mLeQwUaDf0GDePmrSSEQiEN69Xm6y8/AyDpTgqDR44lJzcPk8lIvy8+JbpOrcLfJt1JoeV7Xfmi50d0f78TySkpDBoxlvS7dxEgoEOblnzQ0dpr6uvIj19/TIdPvyagXNgLK3PNvMkc2P436twcpm84VJi+dfUi9m5ai0gkwk7lSLf+I3FxtxzA379tPX8vsYQGadmlJ3WaWrx8GvR6Fk8ex8VTRxEIhbTt9hVV6zdm2bSfuHDqKAA6TQE5WZlMXbfvhbXxefH3uM+o2bE3rgEPDsr8rDFoNWyfOoic1FsIhUJ8o+pRo4PFmcvF2A0cXvk7ynvhWMIav0v56NakJ15i36Lx6AvyEQhFVGrZjaAaTQCImTuatOvnwQwOHr406DEMifx/8z3Va+YOer9diQrezi+szOlbT7H5RAK5BTp2jypy+LLh2DWmbD6Jq73F+VX7WiG0uuet8E5WPmPXHCYlS41AAJM+aoCn0/NxkPJaC1MvA5vgWthVbUPytC7F0sUObji1/I7cQyuKpWtvnkF78wwen1hctqcu/AptYhxSrwrY1XwPuX8lzEY9qYu/puDKYWzKvp6esV5Vyr7RGZeQKpgMeg5M/oqUswdxD6vFjYN/I7Gxo/GI1dw+tp1z66ZStfsYTEYDJxaOoHLX4Th4B6PLy0YoKh0mL4L3Kqqo7GmD3mim38ZkDt1UU9NHwaKTmTQMtKV1qD3XM3UM3HKHFb6vn4va/xVS4/cgkhZfJIR3HV34+fK6yYjlDw8iXcrT061LR2pUrYxOr6f75/3Ye+AQ9WvXZOa8RTR7oyEd27fmyrXrfNZvANHrioSpCb9OoV6toneMWCRiQJ/PCS1fjvx8Ne279qBW9WqUDfR/Ca16/YmqGc0brToy6MPiLvZ9y5Zn2LSlyOQ27F6/klWzfqHX9z+Rl5PNukUzGDZtGQKBgJG9OhJVqwFKO3s2LJ2NvcqJcQv/xmQykZ+bDUCnz78tzHfH2qXcuHLhhbbxf4nI5l3wrFAVo0HPxgmfcyP+AL73PAEGVm9C3Q++LXa/WCajYc8ROHj4kp+Zxp8juuIdXhOZ0o5anfsVejI8uOwXzu5YRVSLD194m/5XqVfBi3drhfDuz39bXWtc0Zf+rapZpY9ccZCPGoVRI7gMaq0e4T2vtM+DZ7pKNOkKyPhzJMacNMxmIw51u6IIa0T23oUUXD6A2aBF5h2O41vfIBAISF3UB4lHMNqb8Zh1GpxaDSZ3/xL0qddQhDbEoWEPDFnJpC0bgNSjHLo7l5C4+uPUajBCibxY2ZqrR8neOx+zUY/Y0ROnlgMRShVk7ZpJwaUDCIQii1ao8edP1UaZ94N3ucQqi1aDf3eWQIDZoMNsNABmzCYDIlsnhBI5cv9KlltEEqQeIRhz0niRGLQFHJs3FE1WKmaTiZBm3fCq0piLm+eScno/Rr0Wp4AIKnYaiEAgYP+vn+PgE0LGlVMYdQVU6jqMy9sWkZt0Fc/KjanQ8lPUGckcmtYXB5/yZN+8iF2ZQCp1HYZYWry/Us8f5uLGOZgMOhSuXlR6fyhimYJz66Zx53QsAqEIt/LVCWvb+4nbJ5bKcQmpAoBQLMHBp1xhIOA78bGUe8viDr1MpYacXjURs9lM2oUj2HuVxcE7GACp7asRX6dAb2LEzhTS8o2YzGa6VnKkUZAtC05kciBRjc5oIsxdTv+6LggEAvpsSCLYWUr8HQ0FBjODG7iyJC6La3d1NAq0pUc1J5Jz9QzYfIcQFxmXMrQEOEoZ3MAVubi49e/RW2rmHc9EbzTjaS/hu2hXFBIhM49ksD9RjUgooJqXDZ/XfPJdKrlYSGVPy86SRCQgxEVaGBBYAKjvuUnP05lwVjxZYODnjVFbwJlF36PNssx//k0+wr1SYxK2ziP93H5Mei0O/hGUe3cAAoGAE1O/xM4rmKxrpzDqNIR2/p7EnYvIS76GW9QbBL31CQV3kzk162vsvMuRe+sSSo8AQjt/j+hf4ynj4mEStszFZNBj4+JFhY6DEcsUXNkwnfSz+xAIRTiVq07wO18+VRsNWjU3Y1ZQ/t0BnFlkfe7VbDaTemoXlXpZBxJ+UagLCvh68HBSUtIwmkz0+rgrzZu8wbQ5C9gTewCNVkuliuGMGNQfgUDAh5/1pkJIMMfj4inQFDBuxBBmL1jMpavXaN64EX169eR2UjKf9PmW0PIhnL94ibKBAYwbMQQbefF+2H/oCFNmzUen1+Hj5cUPw75DqVAwacoMdsXuRywSUbtGNQb0+eKJ22cjl1OjamUApBIJoeWDiwL/CiAvPx+AvLw83FyKxuSOPbF4eZYpVmdXFxdcXSxhB5RKBYEBfqSmpT0XYUpboGb66AHcTU/BbDLSsssnVG/YjPV/zCDu4F70Og1BoVF82O97BAIBP379Mb5ly3PpzAl0mgJ6DBjDxuVzuZVwherRb9K2+5ek37nNpEGf4x8cSuKV83j6BdFj4BhkcptiZZ85doB1C6ej1+tw8/Sh+7ejkNsoWDX7V+IOxiASiQirWov3Pv3mqdoYFFrxgekVoqoXfg6sEMHBnRsL6xVWpSa29pb3TFiVmpw+up+ajZoTu+Uvxs77CwChUIidg6NVvod3b6H1h72eqs4PQ68tYMfUweRnpmA2maj8zscE1WjC8XVzuBEXi0Gnxb1sRep9NAiBQMDf4z7D2S+EO5fiMGg1NOg5griNC7h76ypB1RtTrV0vctOS2DSxD67+5UlPvIijVyANe45ALCs+jm6dOcSxtbMwGvTYu3oVann6rLdBAAAgAElEQVQOr5xCYlwsQqEI7/Aa1OzY54nbJ5bJ8axQFQCRWIKLX3ny/xH890GoPIpiGSkdXbGxd0STm4lMaVcoSJnNZgw6reXF9YIo0BkYsmQfqdlqTGYz3RqF0yTSj7k7TrPvwm20eiMRfi5816Y6AoGAXjN3UM7TkbjraRToDAzvUIuFe85y9U4WjSv68dmbkSTdzaPf/D2U83Lk4u1MAt0dGN6hFnJpcbHh8KVkZu+IR2cw4e1sy9D2NVHIJEzdHEfs+VuIhEJqBHvQ++3KT9XGcN+ShUdJSMnGaDJTI9iyNr8f8Pd58UyFKc3VI4hsnXHtaDkwatLkAWBbrQ0O9S0SesZfP6C5fBCbEIv0LxBJ8Ph4FrlHVpO+cggeH89CaGNH8tTO2NawmBsYMm7i1GIAMp8I7v79I3nH/sK+VsfCco3qLHL2/YFrl4kIpTbkHFhK7qFV2FZtTcGFfXj0WoRAIMCkybWu8/WTZG23jlYvkMhx/2jqUz8TmXcYcv8okn61BGK0rdoGiUvx4GImTS4Flw9gV/3FxphIPXcIuYMLNXtNBEBfYOmvgPrtC+MunVg4kpQz+/CIqAeAUCQheuB8ru1ewZGZA4keOB+Jwp6dI9sT1NDSJ3kpN4jsPBjnoEhOLh7D9b1rKNu4SJOnzcvi0pYF1Prqd8QyGy5v/4Oru5YRUK89yadiaPT9cgQCAXq1dX+lXzrOmTW/WaWLpDLqfTP7oW3Vq3NJOb2PwAYW9bAmOw0bR/d7bRIjtrFFl59NXuoNBAg4OKUvurxMPKs0IbjJ+yV+ts+aI7fUOCvE/NjMMjHk3RMu2oba81Fly0t2zO5UDtxQU8fPohUQCwXMauPN6jPZDNmWwuw2XtjJRHRecYN3Iywv7xvZegbUdyXCQ874mFT+OpdDx4qqwnKzNEYWncxi0ltlsJEIWRqXxcrT2bQJtSf2upo/3rUETM7VWruxPpFUwNSD1iEVZGIB01p5PbStuVojB26oaR9uqWO3Ko58symZP89lU6A3M+mtMk/yCJ87GRcOIbV3IbLnz4DF3A3Au257At7sDsDZJaPIOLcflzBLnBqBSEK1r+dxc+9K4ucNpNrX85Ao7Dn4Qwd8o98DQJ16g/LvDUIVUJHzy8dye/+f+DbsXFiuLi+L69sXUumz3xDJbEjcuZibe5bjXbcd6adjqPGdZddbX2A9njIvH+fyOmvBRyiVU7X3TKv0hM2z8Y3uiPBfwtx9sq6dQmrriMLVpySP7pmy7+Bh3FxcmPHLBABy8yz90Pndtnze4yMABg4fw559B2hYz3IwWyIRs2rRbP5Yvoov+w9m9aLZONjb82bbTnzYyTJnJCTeYPTQgVSOjGDI6PEsW72W7u8XBW/NzMpixrxFzJ06CYWNDXMWLmHh0pV0bt+GHXti2bhqMQKBgJxc6344fOwE43+xfg/ZyGUsnTv9oW3Nyc1lT+yBQtO8L3t2o8dX37Bk1Z8UFBQwd8ovgMUF/NxFS5kzZSLzFz84BMDtpGTOX7xMxbDnY2Z1+ugBVM6u9B1raac6z/IcGrXqxDsfWMwUZ48fzKlDMUTVagCAWCJm+LRlbP9zCb8P78vwactQ2jnwXde3adrOMi/fuXmdbt+MIDi8EvN+Gsbu9Stp1qFII5CbncmGJbPpP2EmMhsFm5bPY9vqRTR6pyMn9u9i7Px1CAQC1Hk5VnU+H3eE5dN/tkqXyuQM+f2/BaD/N7Fb1hJRzfJ/l5WeipOrR+E1R1d3stJTC+uydsFULpw6hpunD12+GoSDY5FwnJ6SRPqd28UEtWfJzfiDKFUuNP/a8j+kU1vGUdgb71KllSWkwa6Zw7kRtw+/Spb1gUgsoe2IRZzetpxtv/en7YhFyJT2LB/Qlog3LWMl+04i0R8PxSM4kj1zR3N212oimxe9YzW5WZxYP4+3B0xFIrMhbuNC4rcuJeyN9lw/sYcO41YhEAjQ5luPo6Tzxzi49BerdLFMTquhcx/aVm1+LolxsYQ3KVpXJhzbxZ2LJ3Hw8KVWp37YOhd3+JR67SwmgwF7t6J4R3vmjOJm/AFUngHU6tj30Q/4GXLwYhIu9jZM6tYAgDyNDoD2tUP4uHEEACNWHGDf+duF8ZnEYiELvmrGin0XGLBoLwu+aoa9Qkq7CevpVNdispyYlsPgdjWI9HdlzKpDrDl0mS71i8zKs/I1zN91hsk93sBGKmbRnnMsi71A+1ohxJy9yYpvWljWBwU6qzofv5rCrxuOW6XLJWJmf960RO3ffeYmJxPS8HWxo2+LyrirlNxIz8HORsLAP/aSfDefasEefN4sEpHw+biKeKbClMQtkKwd08jaOROb4FrIfC27NNrrJ8k9uByzXoNJk4vE1b9QmLr/V+IaiMTVH5GdZbIQqzwx5qQilNsisndD5mP5h1CENyHv6Br4hzClu30Offp1Uhdadl3NRgMyrzCEciUCsZTMDROQB9fCJrjI3OE+cv9KePR8+CB7WvR3b6FPv4Fnn1UApC3pj/ZGfOGzMZsMZKwdjV21togdPZ9bPR6EvWcQZ9f+zrm/puIeXgfnslEApF86wZUdizHqNOjVOdiVCSgUptwjLItAO88g7MoEIHew7BYonL0oyEpBYmOHjaM7zkGRAHhXa0ZCzCqgSJjKTDhD3p0E9k2ynBEzGfU4BYQjtlEikkiJW/ID7uF18Qi39kDjElKFBoNK9hIzGQ0cXzCMgAbvonR5+CIewGw0cvfaKep9Ow+RVM7B379C5VsO13LWKuQXSaCjlGmH7jLjcAa1fBVElrHsvJ5MLmDZqWw0BhO5WhMBjlLq3JPV7wtVgY5S/B2lOCssw72MnYTUfAO2UiFuShERHpaFcdOydqw5m03Hf2yunkvRkJip48v1SQDoTWbC3OQopUKkIgE/7k2jtq+SWg84w1TZ04a57byt0h+FwWRm1K5U2oU54Glv2UnacSWP5iF2vFdRxZkUDT/sSWVBe+/nqrJ/EmzLBHFl/RSu/D0Nl7DaqAIt4ynzynFu7F5aOJ6UHgGFwpRLuOWvskwQSo8AZPaW8SR39kSTlYLYxg6Zyh1VgKVT3Ku8ya3YVcWEqZzEs6hTrnN8smVBajIacPALRyRXIhTLuLBiHM6htXEJtR5PjsFV/vPZptzblyjIuE1w6z4U3H1w7LTUk9txr9zkP+X3vAgJCmLCb9OYOHk60XVrU7WSZS46cvwEc/9YhkajITsnl7KB/oXCVMP6ln4IDgqkbGBAobbG27MMyamp2Nva4uHuRuVIy3uoZbMmLF6xppgwder0Oa4mJNKlh0XrpDfoiQoPw9ZWiUwqZeiYH2lQtxbRdWtb1blG1cqsXTKvRO00GAz0HzqK999rh4+X5d2xcetOWrdoTrcuHYmLP8PAEWNYv2whU2fPp2und1EqHnyGI1+tps933zPo66+wtX0+JpregWVZMXMiq2b/QmTNaEIiLDvVF+KOsnnlfHQaDfm52Xj6BRUKU/f/egeUxcsvCJWz5QyLaxlv7qbdQWFrh5OrB8HhFiuPWo3fZsfaZcWEqavn40lKvMbYvh8BlrNIQaEVsbG1RSKVMf/n4UTWrE9kzWirOleIqs7ImSuf2TM4uGMD1y+eY+CkR/e10WgkMy2FsmFRdOz1LVtXL2LlzIn0/G5s4T1Hdm+har3GCEXPR1Pv5BPEoeW/cXjlZHwj61KmnOUZJ50/zqnNf2DQadDm5eDkFVgoTPlVqm/5rXcQjl6BKFSWcWTn6kleRioyhS1KJ3c8gi1jMrhWM85sX1FMmEq5eprMpATWj+lx71kYcA8KR2pji0giJWbeGPwi6+IbVdeqzp4VqtJu9JIStdNkNLBrxlDCG7+HvZvXvXbUpWzNpogkUs7t/pM9c0bQYmDRpoY6K53ds4bToMdwBP9YnDfoMQyTyciBxT9z9ch2ytVraVXe86Csh4rfN55kyuaT1C3vRVSAG2ARWBbvPY9WZyCnQEegu0OhMFWvguVvkIeKAHcHXO6dOfJysiUlW42tXIK7g4JIf8uYa1bJn5UHLhUTps7cyCAhNYdPpm8DQG80EeHrglIuQSoW8cPqw9Sp4EXd8tZr2ypB7vzR562nbnu9Cl40jfJDKhax9vBlRq08xNRP3sBoMhOXkMai3s1xVykYunQ/G48n8E61oKcu80E8W2HK2Qf3HrPRXDlE9p65yPwrY1+7I5lbfsW9+0zEDm5kx8zHbCiSUgWie6o3gaDo873vRUEbrU3nimEGeUBVnNtau7l17z4dzfUTFJyPIe/oWtw+KL5r8bw1UwUX9yH1CkV474yBPKgG2ltnC4WpzI0TETt5Y1fjxR/6tXX3JXrgAlLOHuTChpm4lKtK2cbvE7/yJ6IHzMfG0Z0LG+dg1Bf1l0gsBUAgECISF/WXQCDAbHxIf1lhxrV89Qe6X6/Xfy7pl46RdHIX1/eupnbv4n3zJJqpU8vGo3T1KdScAcgdXCnITMHG0Q2T0YChIA+p0gEblRtOQVHIbC3aGbewWmTfvPjShSkflZTZbbw4dFPN3GOZVPbS0KmiA7/sz2BWay/cbMXMP34XnbEooKxEZOkHgQCkwqI+EQrg/m2CxwgkZqCKtw3DH+CGfUZrL07cLmBPQh5/ns3m1xbFJ8wn0Uz9HJuGt4OkUHMGsOliLj81t2ijwt3l6IxmsjUmHG1eLXM/hZsv1b6eT8b5g1zbNBvH4Cr4NurCxTUTqdZvLnJHd65tmYvpH+NJeG8MCQQChPfGluW7EPP9+c+qi/6dYMYxpBrhH4y0qlPVfrO5e+kYafF7uLVvDZU/n1zsekk0U9nXz5Jz8wIHRrfDbDKiy8vkxNQvqfyFZYyajAZS42Oo9nXJhIJnjb+fD6sXzWHvgUP8PmMONatV4eMPOjF6wi+sXDiLMu7uTJk1D622qB+kEks/CIXCws/3vxsNln7491j593czZmrXqMrPY4Zb1WnFgpkcPHqcbbtiWLryT+ZPLz6HPYlmavi4n/Hz8aZrp6LD2GvWb2TW75aAklEVw9FpdWRmZRN/5jzbdsUwccoMcnPzEAgFyKRSunRoh95goO/A72nxZhOaNLQWKJ4VHt7+DJ++nNNHYvlz/hRCK1Wn+XvdWPz7DwybtgwnNw/+Wjgdva6oX8SSoveNWPLP940Qo/HB/WI9PMyEVqnJZ0N+tKrT91OWcP7kYY7t3c7OdcsZ8POcYtefpWbq7PFDbFg6h4ET5yKRWtqlcnHj4j1nEgCZaSmUi6yGrb0KqVxO5bpvAFCtflNiN68tlt+R3Vt4v/fgEtWhJKg8/Gg7chE34w9w7M8ZeIZWI7L5B+z/YwJthi/E1tmdY2tnYdAXBQMWFc5n/14fCDGb7pltP2YcYQbvsBq80cva5XqbYQu4fe4o147t4uzOlcUEHHgyzdTeBeOwd/cp1JwByG2LrDPKR7fi8MqieVNXkMfmX/pRrV0v3MtGWOUnFIoIqtGEU5v+eGHClK+rPQt7N+PAxSRmbounapA770eH8tO6Yyz48k3cVUpmb49Hqy+yIJHeM+cXCASFn+9/N5pM9z4XL8d6aJmpHuzB6Ae4Xp/35Zscu3KHXadvsvrAJaZ+8kax689KM+WgLHKS9U61IKZsigPAzUFBiKcjXs4W88voMG/O3EiH10GYMuamI7SxQxnRFKHclvyTGwsFJ6HCAZNOjfpCDIryJZuwjTkpFgHEOwz12R1IfYr/A0u9Qsnc8iv6u7eQOHlj0hVgzE1HZOeMWa/FpmxNZN7hJE/tbJX389ZMie3dyDu5AXOdzmAG7Y1T2FVvD0D27jmYtPk4t/j2Mbk8HzRZaUiU9vhUb4bExpYbB/8uXOhJlQ4YtGqS43ZTJqphifItyLzD3WuncQqM4PaxbTjd01Ldx9E/nNMrJ5KXdhNbVx8M2gI0WWnIVS4YdRrcw2rjFFiRHcOtzR5Lqpk6//dMDAX5RHUu/tLxiKjLzcObcAqMIPnkblxCqiAQCHANrcGVHYsx6DQIRWIyrpwsJoS9LNLzDdjJhDQNtsNWKmTjxVx0RjMADnIhar2JmIR8ogNKtquckmfgTIqGcHc5O67mUdGjuPlWmJucX/encytbj7eDhAK9ifR8A85KMVqDmZq+CsI95HRafsMq75JqpuYcvUu+zsSA+q7F0t1sxRxPKqB5iB3XM3XojGZU8lcvqoM2Ow2xwh6Pqm8itrEl6fDfmO7NfxKlCoNWTVr8blwrlmw8aTNTyL5+Bgf/cFJObEMVWPxchr1fGBfXTEKddguFqzdGbQHa7DSkDi6YdFpcQmujCqjIgR+sN2xKopnyrtMG7zptACi4m0z8nG8LBSmAzEvHULr5IVe5lah9z5rUtHQc7O14p3lT7G1tWb1uA9p7C3RHBxX5ajXbdsXQtFHJ3kPJd1KIiz9DVMVwNm7dQeWo4u+hyPAwRk/4hcSbt/Dz8UZdUEBqajqurs5oNFqi69SicmQETVtbzycl1Uz9Nn02eXl5jB4yoFh6GQ93Dh09QZsWzbmacB2tToeTo4rFs4v6acqseSgUNnTp0A6z2cz3o38kMMCPj7q8V6LnUVIy01OxtXegVuMW2NjaEbtpLXqdZSFu66BCU6DmeOx2qtQrmWYzIzWZK+dOUTY0kkO7Nhdqqe4TWKEii38fR8rtG7h7+aItUJOZkYrK2Q2dpoCKNepRNiyKgR+8bZX3s9JMJV4+z6JfR/P1uGnY/8NUL7xqbf6c9zv5uRazvrPHD9Lu4z4IBAKiakZz8dRRKlSqwbmTh/H0K1oEJt9IID8vl6DQSKuynhX5mWnIlPYE126OVGHLhZh1hRurcjsH9Bo1Ccd2EVC1UYnyzcu4Q8qVeNzLVuTKoa14BEcVu+4WFM6+PyaQnXITB3cf9NoC8jNTUapcMeg0+EbWwSM4kmXftrbKu6SaqaNrpqNT5xHdbUixdHVWeqFWLfHkXhzLBABgNOjZ9vsAQmq/RWC1IuHAbDaTk3oLB3cfzGYziSdjUZXx/8/1eFrSctTY28hoXikAO7mU9Uevoru3CeSglKHW6tl95iYNw0tmfn0nS83pxDQi/FzZGpdIRf/i7+ZwXxd+XneMm+m5+LjYUaAzkJatxsXeBo3eSO3yXlT0d6XthPVWeT8rzVR6TkGhVi323G383ewBqODtRG6Bjsw8DY62co5dTaGC9/MLVvxMhSld6jWyd86wiLNCMU7N+yGU22Eb9TZ3ZnZDZOuEtEzJ3ceKnX3IO7aWu3//iMTVH9sqrYpdFylVOLX8joy1o8GoB8ChwccIpQrSVw2xCHRm81M7nwCLm3P1mR2Y9VqSfmuPMuptHKK7oU26QMaqoZg0eWguHyQ7ZgFlPluATYVoNNdPcmdmdxAIkAdVxyakNoacVHL2L0bs7EvKnJ6A5TyVbaUWT13H/0pO0lXO/jUFgUCIUCSm4nvfIlHY4Ve7FbvHvo/M3gmVb8ndLtu6+5IQu4a4JT9g5xGAf722xa7L7ByJen8oJ+YPx3hvsVmhxaeI5QqOzBpgmbDN5qdyPgFQkJnK5a0LsHX3I+bHjwAIiG6PX+138K3dkhOLRrJjRHukSnuqdLN4I5Mq7Als1InYCZb+cgurhfsDzA1fNNfu6ph+5C5CQCQU8HVdF+xkIlqUs+Oj1bdwUogo9wRuzH0dJPx1Locf96bhr5LQKrS4EwmVjYhB0W6M2pWK3mQR3npUdUQhFTJ4Wwo6oxmz2cwXT+F8AiA1z8AfcVn4qiT0/PM2AG3C7GlR3p4vajrzU2waq05nIwAGRbs+VqP2MshLvsaVv6ciEAgQiMSUa98fiY0dnjXf4fBP7yOzc8buCdyYK9x8ubVvDeeXj0Xp7o9X7TbFrkttHanQaQhnFw/HZLDMf4HNeyKSK4if+51FoDObCW711TNp58NIiduBe+XGz7WM/8KlK9f4efI0BAIhErGYYQO/xt7OjvatWtCq04e4ODs9kRvzAD9flq5ey9Ax4wkK8Kdju+KLOSdHFWOHDeLboaPQ3Vt09v6sB0qlDV/2H4xWp8NsNjOw75M7nwC4k5LKzPl/EOjvS7sPLKZQXd5tS/vWLRjQ5wuGj53AoqUrQSBg7LBBjxwrJ06dZv3mrYSUDaRNF8u5vr6f9yzmTv1ZcTvhMitn/YJAKEQkEvNBnyEobO2p/1Y7vu/RDgcnF/yfwI25h48/u9YtZ/7Pw/H0DaRhyw7FrturnPh4wChm/vAdhnv90qbbl8htlEwe1gf9vX7p2Kv/U7dx5axfOLxrEzqthm86NqFe87a0/rAXK2f9grZAzbTRlo1TZzcPeo/+HVt7B1p2+YTRX1g2elu+/2mhM4r2PfsyZ/wQlk37CTuVI937F1lyHN69heoN3nyu8+DdW1c4vGLyPa25mLpdByJT2lE+uhWrhnRC4eD8RG7MHTz8OLtzNTFzx6DyDCC0UfFNUxt7Rxr0GMauGUMx6i3zWdV2nyGVK9n6W3+Meh1mzNTq9HRnkvLupnDy7/moyvjz5/APgCIX6Ge2ryDx5F4EIhEypQMNelisnq4d2UHypZNo87K5tG8DANE9huPsU5Y9s0ei0+SD2YyzTzB1Pxz4VPUrCVfvZDF5UxxCAYhFQga0roadjZRW1YLo8ssmnOzkT+TG3M/VntUHLzNm9WEC3BxoVzO42HVHWznfv1uTYcv3ozNYtFmfNq2IQiZhwKIYtAYTmM30eUrnEwCTN51kW9x1NHoDLceu5Z1qQfRsUpGVBy4Se+42IqEAe4WU79+tCYBIKOSrtyvx5ZxdYDZTzsuJVs9JKwUg8PT0ND/qBqVSycn4M7RaZW2u8yIwZCWTtmIQZT5d8FLKfxVZ3UZF3ZrVyMzMtLrWpEkT+o6cxJJLL6FigDojmcMz+tNwSMnsll936vjIyD76F4O/K9opPnn0IIKNIzBmP/h8ycsmOVfPoK13WND+5TkLeBWQNOrN2CU7WL78wQfz73P4eBwzz4tIV5seed+z5L4G6H48p/8VqnvJcLy1jy8+7WF1LS4uDif5P828Xz63k5Lp9fV3rF/+/yN21iMRitHLVRy/o3/ZNSH9zm1+G/oVo+f8+bKr8sribS8iKdfIgZvax9/8nMlNS2LLr1/z7g+Pnmv/v9G5vBjbs2swpCW87KoUknQ3j/4LY1jaz1pj+/8Vebn6zNp2igk//WR17dWzlSmllFJKKaWUUkoppZRSSnkNeOWFKbGqTKlW6jVC4Vzm/51W6nWljJ3k/71W6lXHxqnM/5xW6nXEy7NMqVbqFcTFw6tUK/UaYefqWaqVek3wdLIt1UqVgFdemCqllFJKKaWUUkoppZRSSnkVKRWmSimllFJKKaWUUkoppZRSnoBn6s3vYdz6sRneA7e8iKIKMWQlc2fGh4idfQpdnydNfs8S70koBKEIj49nAaC7c5nMzZMsXv+EIhyb9UPm9WivW2lLv0V7+xwynwhcO44vTM89+id5R1ZjyEzC8+u/ECks8QryT28n9+AyMJsRyBQ4Nu+H1L1s4e/MJiMpcz9FZOdSmF/G2jForh7G8e1vUFRo8CwfzyPZ+HUj3p6064WVBxbHFbvGdMTWza+Y63OzyUjMhG7YOLhSo9dEAPb98hkGjRoAbW4mjv6hVP/EOobIPzk4tS+Z18/iHFixMB+Ak3+MJuPKScRySyyCSh8MxcE7BJ06h7jFP5CffhuRREpUlyHYewZh1GmInfgJuXcSaPrD34XxqF4Vms1PYEu3gBdaZnKunq6rbuHrIGFuO29S8wz8sCeVzAIjAqBlBXvahxfFjVpzJpu/zuUgFEBNXwW9ajzcy9Cj8pp+OINtl/N4r6IDHSu+Wv3wKGK+a0z0+B0vtMyCu8kcHt8ZhZtvoSv0m3tXknRoPZjNeNZ8B5/oR7vGTjsTy7XNsy3eCoUiglv3QRVocc185e+pZJw7gNlsximkGsFt+iIQCDgx9Utyb16g0heTsX8CT4ZPSpXoNzkes/WFlQcWJxUt3vsAf1/fQjfnQ0aPJ2bfAZwcHYuZCU6ZNY/V6zbgqLL83973oJeVlU3fQcM4fe4CbVo0Y+i3/Qp/c/b8RQaPGotGq6N+7ZoM/qb3I725XbueyJBR4zl38RJ9evUoDDCs1Wrp+ulX6HR6DEYjTd9owFefWDz5DR09nrPnL2LGjL+vDz8MG4RSoWDBkhWsXr8BsUhEREQE348a+9By/wu9WtRk+oZDT5VHSUm/c5sh3dvg4eNfzN25yWhk1OedULm40fcHiwv58ycPs2LmJIwGPX7BoXTrPwKRSIw6L5fZ4weTkXoHk9HAm+9+SL1mFq+OHzethHeAxdvZfW99j2LZtJ+4cC/GlE5TQE5WJlPX7QNg/7b1/L3EEjexZZee1Gn6DgCHdm1m49I5CAQCVM6u9Bw0FjsHR1bOnETcoRjEYgmunt58/O0oFLb2XDp9ggupVwh94+niWM77NJruM2OeKo+SkpuWxMrB76Hy8KXd6CVkJSeyc1pRiJOctCSqtvmkWHyo+M1LOLTiN7pO3obcruidkHrtHOvGfMwbvcYUc2n+II6snsblA5vQ5ucWa/OBpZNIPm+Ji2TQaSjIyeSj6busYltlJSfyRq8x+FdpwO1zRzm04ndMBj0u/uWJ7j4UoUjM1cPbObpmOqoyfjTrZx0X61nScNhKdo/q8PgbnyFJd/PoNGkjvq52he7Px6w6xP4Lt3G0lRczH9wZf4M5O05zPS2beV+8Weh18J95gMUN+8A21f9T+Uv2nmfyppNs+b4tKqWc66nZjFl9iIu3M/nszcjCwMMavYGe07aRkJrDhsGtUSnlj8n54bwQYeplIXL0tIoh5frBL4UCzn2yds7Evt5H2JStQcGVQ2TvnIFbV+vAsP/ErlZHbPVa8k4U958v84nAJrgWqX8Ud9spVpXB7YPfENrYUXDlMJkbJ+LevSjgXN6RNUhc/DBp8wvTnEoqMM4AACAASURBVNsMJWP9uBK1+XVG6eJtFUPq2u6V2Ln7Y9AUPZe6/WYUfj46exAeFes9Nu+yjbtg1GlI3PeX1bXQ1l/iWal4rIzLWxfi4B1C9U9+JPfOdU6v/JnavacgksppMGgR24e1scrn/zNe9uLCmFIiIXxR05kQFxlqnYmea29T1csGf0cpJ5IK2J+oZm47b6QiAZkFj/bY9qi8etVw/j/2zjMwqipa28+UJJPee0/oJPQqvSOKiBRBFFBARcUCCIqAgNJRqiIdRIoCItJCDx1CQkIJNY2QXifJzGT6fD8OJMQJkBAs9373/ZWzz245Z/Y+e613FWTS/16I9P8qrN18ywQpRWYSGRf+pMWnaxFJpFxZPQHXBu2wcX98XjDn2s1p1bA9IpEIRUYC13+eRpsvtlGUfI2i5Gu0+lxYuzHLxyJPjMW5VjOafbiCyz989I/8f/8F+Pv6VsgX1f+l3gwb1J8vZpgLH8OHDioTcB7C0sqSce+N4m5iMglJSRXuzZr/HbOmTKJRWAPe+3QSp89fpOMLbR47F0cHB6ZM/JhjkWcqjmFpyfofl2BrY4NOr+fNMR/SsW1rGoc35IvPxmFnJ+Sqm794BVt3/M6YEW9Sv25tdmxag7VMRsSxUw/ydv3PM2zx8PEzyxt1ZPcWvANCKFUpADAajaxdMI3PF67Gyy+I3Rt/4OzhP+n44msc//NXfAJD+OTb5RTLC/jq7X607fYSUgsLLC2tqpWTaugH5fklj+7eSmrCLQAUxUXs+fknpv+4DZFIxMyxQ2jStjMyGxu2/Tifb9ftFgSo1Ys59sd2Xh0xlgbN2zBg9MdIJFJ2rFnM/m3rGDTmM+qEN8NFVA/5E2M2/3fh4OFbljfKyTuw7G+j0cCWT18iqHnnsrqK/GzS4i9g5+pVoQ+j0UDUjuX4hbWu0piBTToQ1n0w2ydXDNn+whvjy/6+fuRX8lKFkMmP5rZSK4r4dfIA/MLaYDIaiVw7k5cm/YCTVyDRv6/izpn91OvUj9DWPbB2cOFqxP9ef1hfV7sKeaReah7CwBfqMOu38xXqhXg5Mu+tDsz7PeqpfVQF2XIlUXcz8XKyKStzsLFifN8WnLyRVqGuzELK5k/68Oq8PdUaozJUezeUH19FSXR5Ju6ikxsoPr8do1ZFzi/jyVo7hqxVb1N6+4xZW3VKLLnbvyi7LoxYgvLKQQC0mbfJ+fkTsta+S+7WzzGU/HOh2EUiEaYHQoxRrURi7/bUNrLg5ogsrc3KLb1qI3XyNiu38g9DbC1I2Fa+DTCU5Jbd0xfnUJpwAdsmz9/Z78aeH0k+ubPs+tb+tSQc3YJeo+Lcso84OW8EJ2YPI/PqKbO2eXcuc3HlhLLrq78tIvXCfgDkqbc4u2QsJ+eP5PyKT1EX5T33uZcW5pAdf5aAF16p9L6uVEnenRi8Gj09+aZ73ZZIraqe0LYkKwW3Os0BsPcKQlWQhbq4oMrtnwdWReWzO76o7HpDTAHbr8pR6Yx8tj+D0b+nMXLnfc6kKM3axmaU8kVEVtn1krN5HLxTAsDtXA0f781gzO40Jh7IJF+lf67zdrWRUsdNyHllYykm0NmCXKUwxp4bxbzRxBFLiSAEOVtLnrmv/wIS9q0k7cyusuukiHWkntiKXqMiduXHRH33NhcXvEXu9dNmbQsTLnNlbfmB6vau78iMEtZX8f1bXF7xIZe+f4e4VZ+hKX6+60uVnYJDQEMkljLEEilOoU3IvfZkzbPUyqaMCTFo1cADQVYkwqjXYtTrMep1mAx6LO2fX3LE71f8xNYd5UEGVqxez/pftqFUqXj7g08Z8NYo+g0dwbGT5s84KiaWsZ+V53v5duFidu8TvjnxN28z/L1xDBw+mjHjJpCb9/z3sBbNmuDo4FDl+jbW1jRv0ggrK8sK5bl5eSiUKhqHN0QkEtGvT69K/99H4eriTHiD+kilFdeYSCTC1kY4aOj1evR6vZAbEsoEKZPJhFqjQfTgHbdu0QxrmaC1rV07BJOx/HS+Y80Sju0pDyrwx6aVRPy2CXWpioWfj2HG+68zbfQAYs+eMJvjrbhLLPmqXNj+ZfkczhwSDjYpd24wb/w7zBw7hO8mv488P9esfU1RkJvN1Yun6dinXDmmKJYjlVrg5RcEQMPmbYk5fQwAESLUKhUmkwlNqQpbe0fEkifvYVXBxRMRtO76IgDXo8/RsHkb7BwcsbV3oGHzNly7dBaTyYTJBBp1qfB+VAqcXIUkqmEtXkAiEXTjIfUbUZib8/ixfltB/NEdZdfRu1dz5eAv6NQq9s3/gF1fv8WOqUNJuWy+H2TcjCFicTlbembzQm6fFvIt5abcZO/c9/j96+EcWDQOlfz5r6eyedy4hIOHH/Zu5Wet89sW03rwOMr2pQeIP/Ibwc27Ym3vXKW+PWuFlyXsfRwSLx6mVuueZuXJ0cfxD2+L1EqGWlGEWGKBk1cgAL4NW5EcY74GqoMfDsax83x53ps1R66y5dRNVBodH605xvBlBxm2eD+n4tPM2sYkZjNhY2TZ9aI9l9gXLShsbqUVMHbVUUYsP8gn646TV1xao3lWhqYhHjhYW5qVB3s4Euhe9T3yaViy7zIfvdiUR38HLnYyGvi7IhX/fcrXajNTNg26Ij+8AvsWwuajuhmJ+9CFiKSWuA36BrGVLQaVnJwNHyCr065KSeVMBj2Fh5bhNmg2ElsnVPHHKYpci0vfiknPlNeOUHLBPBKM1NkXt4GzzMrNISJ36+eACLtmfbFr1hcAp54fkbv1c+RHVwImPEaseGIvNYUibj+y0HK6Un54BU7d3sOoUT33sXybdeP6riUEdxoIQEbsMdp+uASx1JKWY+ZjYW2LRiHn9KLReIV3qNL7Mhr0XNvxHa3eXYCVvTPpMUe5ufcnmr45tUK9tEuHSDhqHtnP1t2PlqOfbiZyfdcSGrz6UZlJ31+RdfUkbnVbYGFddSGpMtzcu4rbB9fjXrcF9V/5AImFJY6+tci8EolrrSYUpsRTWpCFWp6DzOHvy6D9V3QJsWPF+Xz6NxTM2k4kKVn0ojeWEhHf9vDC1lKMXG3ggz3ptAu0qdK70xtNLD2Xx5yeXjhZSzieqGDNpQK+6ORRod6RhBK2Xykya+/raMGs7p5V/h8yS3TczdPQwEM4iKUV6biapWbtpUIsJSLGtnGhvnvVqPW/9vVfgGeTbtz9Yyl+7QUtZs6V4zR593vEUkvC356LVGaLViEnZum7uD1gdZ4Go0HPnd2LafTOPCztnMmOPUrSgdXUHzKlQr2smEOknthq1t7azY/wkbOfOIatdwiJB1ejUxYhtrAi/+Z5HPyfnsg29+pJEg/8hLakkMZjFgHgGBSGU61mnJ3xCiZM+LUfgK1n0FP7qip69+jKvO+X88YgIfl3xLETrFm2CCtLS5YvmI2dnS2FcjlD3hlL145Ve8Y6vZ7Zi5awYtFcXJydOHjkGEtWrmX2tC8q1NsbcZj1m82/OYH+viyZ902N/q+tO3bz54FDNKxfj0mffIijg/1j62bn5OHp4V527enhTk7Osx9WDQYDA4ePITUtnTcGvkrjsPLkq1NmzeX0uQuEBgcxqZIEw8ciTzFsVHly3Vade7Ft5UK69RsCwKWTh5kwbyUWlpZ8NGMx1rZ2lBQVMnvcWzR5oXPV9im9ji0r5jFu1hIcnFyIOhHB7+uX887nFb/z54/tJ+I38yiLHj7+fPj1d2blf8W2HxcwaMxnqFXlCil7R2eMBgPJt+MJrtuQ6FNHKMgRFFNdXx3C8mkfM/717qhVSt6fugCxWNBJ67RaZn4wFIlYQp+h79CsXddKx/wr8rIzyMtKp34T4Uwgz8vBxb2cXXF290Sel4NUasHwT75i+piBWMms8fQN4M1xU8z6OxPxB60693rseKGte3B+6/c07C6Y/iVdOkafCcuQWFjS8+MFWFrboS6R88c37xDYtGPV9iy9nnO/LKLnx4uwdnAm8eIRonatpPOoaRXq3T0XwdWDm83aO3j60+OjeWblj0PCxSOEtikXZlIun8TW2R3XgDoV6ikLc0i5HMnLk1dyct2NKvf/JJTkZVKcm4FPgxZm9xIvHia8l5CAWWbvhMloIDf5Bu7BDUiOPo6iILtGY3dvFMCSfZcZ2Fb4P49dS2XpO12wlEqY/1ZHbGUWyJVqRv94mA4NfKu21gxGvvszmgXDO+JsJ+PIlXv8dOgKUwdVZL0jYpPZcuqmWXs/V3vmvvl066BnQUaBguFLD2Irs+C9no1oEuzxxPqn4tNwd7Cmtk/VBOfniWoLU5ZetTEoCzGU5GFQyRHL7JE6emAy6Ck6sQZN6lUQiTCU5GFUFiCxe3rWZV1+KrqcZHK3PmBBjEbElbSzDe+BbXiP6k65DB4jliN1cMegLCR3y0SkrgHIAhujiNmDU48PsanfCdWNExTsW4DHm98/8zhPgjolFmXcATxGLAeg9O45xLbOWHrXRZ0S+9zHc/Svi6akELU8F41CjoWNPdbOnhgNem7uXUl+QhwikRh1US6akgJkDk9/X4rse5RkJnF+xSeA4NckczTX5Pi17IVfy8dv6k9C1rUzWNk74xRQj7w7lyutkx5zhIC2lbNWVUX9V8Zi5eCKUa/jyrZ5JBzdTN0XR1Grx3Cu71xM5NzhOPiE4uhXB5H4nzVrqeNmRaHaQJ5Sj1xtwN5KjIedFL3RxJpLBVzJUiMGcpUGCkoNuNo8fTmnynUkF2qZcEBIJGwwmSpt16OWPT1qPf5wVxWodEamH81mXFs3bC3FZeMVq42s7OfDrVwNM47msH2I/1M3/cr6+i/A3q8OWkUhmqJctAo5Ftb2yB6sr8T9PyFPuoJIJBLulxRgVYX1pcpJRZmZRNxPgqmwyWjEspJ2Xs174dX82daXrWcQgV2GEbfqM8SWMux9a4Po6c/VvVEn3Bt1ojAxjqSDa2g6dimq3DRU2Sm88LVgsRD306fIk+JwCmnyTHP7KxrUrUNBYSE5uXkUFMpxtLfH29MTnV7PkpWriY69gkgkJic3l7z8Atzdnv6MU+6lcjcpmVEfCaY7RqOh0nZ9e/ekb29zDXRNMWTAq4wdNQKRSMSyn9axYOkPZoLc3wmJRMLuLespLinh40lTuZuYRO3QEADmTP8Sg8HA7EVLOXjkOK/1LTez+fPgYRKSUrC0sACEpL2BtetTIi+gMC+HkqJCbO0dcPHwQq/XsWv9Mu5cvYxILKYwL4fiwnwcXZ5u+ZF1P4X0lAS+m/w+IPg1Obmat2vb7SXadns2i464CydxcHIhqE4DbsVdKisXiUS8N3U+21cuRK/T0rD5C2XsU3z0OfxD6/H5orXkZNznu8nvUSe8Gda2dizcehBnN09yMtJY+PkY/IJr4+Hz9NQTUSciaNGh+1MZLr1ex4m9vzHjp19x9/Zjy4q57N+2jr5vvltWZ++WNYglEto84Zm4BdaltLgQZWEu6pJCrGzssXP1xKjXE7VzJVm3YxGJRCgLcyktyn8qSwMgz7pHQVoSBxYKLKPRZMSmkjNB7Rd6U/uF3k/t70kw6HXciz1Fq4EfAKDXqIndt5GXJi43q3tuy/e0GvTRc/12J148TEiLrojFFd+XSp5HQVoi/mFtAeF31G3st5zfthiDTodfWGvEVdhjn4S6vi4UKNTkFquQKzTYW1vi6WSL3mBk5aE4YpNzEYsgt6iUAoUaV3tz66m/4l5uMYnZcj5eJ/jJG40mXB3M2/VuGkzvpv+cP7abgzV7vngVR1srbqUVMGnzKbZ99hK2MotK66u1ejZGxrNsVJd/bI6P4pl8pmzqd0Z18yQGRQE2DYSJq64fwaAqwnPUakQSKRnLXxcCOjwCkVgCpnLzgEfvW7gH4fn2j08ct6bMlNRB0OxJbJ2xrtsebcZNZIGNUV49hFPPcQBY1+9MwT7z7MbPA9rsRAr2LcR96HwkNgLboLl/HfWds2QkXMCk12LSqMj/41tcX536lN6qDp+mXcmIO4GmOB/fZt0BgTXSKuR0mrwRsUTKken9Mer+8r4kEkyPvK+y+yYT9l4hdJi45onj1oSZKki6Sta102THn8Oo06JXK4nZNIPmI2YAoFHIKUy5QcsxVddmVYaHQqDEwpKANi+TeEyYr4W1LU3fEt6ByWTi6NevYePqW6OxngWdg22JTFZSUGqgS4gQJONIggK52sCa/r5IxSJe35aK1lDRKF4iFmGkvOzhfRMQ5GzJyn5P/l9qykzpjSamH8mme6gdHYPLmUN3Wykdg20RiUTU95AhFkGR2ojTE8z9HtfXfwUejbuQcyUSbUk+Hk0E5+bsmEPolHJajl+PWCLl3DcDMFa2HxqNZddl900mbL2CafHJ6ieOWxNmCsCnTV982gjsfOL+n7ByerLW71E4hzbhZn4GWoWc3GsncQhsiNRKMB1zrdeGopT45yZMAfTq1oVDxyPJyy+gdw9B478v4ggFhXJ2/LwWC6mU7v0Go9VWfMYSiQSjqfwZazTCfZPJRK3gYLatX8mT8HcxU26u5Qz3oFdfZuz4JwtSnh5uZOeUm7ll5+Ti4fH0Q+7T4GBvT6vmTTl9/mKZMAXCc+vToyvrNm8rE6bORUWzesPP/LLO/HfZomMPok8fobggn5adBeHzwrEDlMgLmb5yG1KpBZ8PexGdVlOhnfgv3xjdw/dnAt/AUL5abs5iPIqaMFMJ1+OIOx/J1agz6LQa1Colq+d+ybtfzqVWg8Z8uWQjIJjdZaXdA+BMxB76DH0HkUiEp28Abl6+ZN5PJqReOM5ung/G9qNe4xakJtyqsjD15sflDJOTmwe3r5QLd4W52dRt3JL7CbfL/jeAlp16cWB7uX/emUN7uHrhFBMXrn6qciqkZTeSo4+jKsontLWgoL57PgJ1SSGvzfgZsVTK1gn9MPzlTPDX92XQPXifJhPOvsG8Om09T8LzYKbuXz2HW2A9bBwF5UdxTholuRnsnDYMENioXV+/Rf/pG8hLucmxlcJ3XK2Qk3r1HGKxpIKvVXWRePEI7d6aZF4edZSgZp0RS8uP1Z61GvHKFOGclHb9AkVZqc887kN0Cw/g+LX7FJSU0r2RYEIYEZtCoVLDpnG9kUrEvDpvDxpdRX9kiUSE8ZF3p9EJ+6IJCPF0ZO0HT1bM/dPMlKVUguUDE+V6fi74utiRmldcFqDir0grUJBZoODNJYIZd26xihHLIlj/Ua8qCZU1xbMJUw27ULB/EUZVER5vCYEajBolEhsnRBIp6pRYDEXmdKbE0QtdXoogNOg1qJMvY+UfjoWrP0aVHE1aPFZ+DTEZ9OgL7mPhXlEKrgkzZdSWgsmE2MoGo7YUdXI0Dh2GC/Oyc0VzLw5ZUFM0KZeRughO2Jr0myiid+Paz5xKry70Rdnk75yGa78pWLiWb7BOXd/FqaugWVKnxFJy4dfnKkgB+DTvzpWtc9Eqimj3qSCw6ksVWNk5I5ZIybsTQ2lBllk7GxcvSrKSMei0GHUa8u5E4xLaGDvPQDSKQgqSruESEo7RoEeRk4qDd0iF9jVhphr0+4AG/QTNU96dyyQe21ImSAFkxh7HM6wdEgursrLClHiST+2k2fCvqzyOuigPmaMbJpOJrKsnsfcJBUCnKhH8SaQWpJ77E9daTWpsTvgs6Bpix8LTuRSpjSzrK9iHK7VGnGQSpGIRlzNKyVKY+xB52Um5V6hDazCh0RuJSS8l3EtGgKMFRWoD17PVhHnK0BtN3JfrCHapaMtcE2bKZDIx/2Qugc4WvP6XKHvtA22JzSilmY819+VadEYTjjIxuUo9cyJzWPyST5X7+q/Ao2k3bv02H52yiGYfCibCerUSywfrq/BuDOpC8/Ulc/ZCmZ2CUa/FoNNQeDcGp+BG2HgEoFPIKUq5jmNQGEaDHlVuKnZeFddXTZgpAG1JIZb2zqgLs8i9dpLmD4S3tNOCj6Vfh4EV6qty07B2E0xHStJuY9RrsbB1RObsScaFvRgNwu9QnhSHf8fnGz2qd/eufD1nAYXyIn5eJURJUygUuDg7YyGVcjH6MhmZ5s/Yx9uTxOR7aLVa1BoNFy7F0KxJI4ICAyiQy4m7ep0mjcLQ6fWk3LtP7dCK35y/i5nKzcvD3U0Qho5GnjYb969wd3PDztaGK9fiaRTWgD0HDjFssGD2uOU3wWdv2OABT+qiDAWFcqRSCQ729qjVGs5djGb08DcwmUykpqUT6O+HyWTi+OmzBAcFAHDj9h1mzl3EqqULcXJ0eMBJlaNV515s/H4WiqJCJn8vHKhLlQocnFyQSi24GRdFfnaG2VzcPH3IuJeETqtFp1VzM/YitcOa4uUfRElRIQk3rlCrQWP0eh3ZaffwDapVoX1NmKmBoz9h4GjBuuJW3CUidmzi3S+FgE/Fhfk4OLui02o5+OsGXn5jNAAuHl7cuHyROuHNKCrMJ+t+Cu7efihLirG0kmFhaUlJUSF34+Po/fpIAHauXUpwvTCatzePIpeZmoxSUUJog8ZlZWEtXuD39ctQlhQDEB9zngGjPkGn1ZBxL4lieQEOTi7Ex5zHO0D43VyLOsvBXzcy+ft1WMmefnAMad2d0xvmoC6R0/fLVQBoSxVY2zsjlkrJuBmNIj/TrJ2dqzeF6cKZQK/VkHHjEl61m+DoHYi6RE52wlU8azXCqNcjz76Hi29ohfbPg5lKuHCYWo+Y+Ln412L48vLInVsn9OO1GZuQ2TsxdFF5YIHINTMJaNK+TJD69YtBvD6v3HesKpBnpKBRluBZK9zsXuKFw7Qa9EGFstLiAqwdXDDotMTt/5mmfd+u1niVoXvjAObuikKu0rDyXUE5rlRrcbaVIZWIiUnMJktu7kft7WRLcnYxWr0Bjc5AdGIWjYPcCXSzR67UcO1eLuGB7ugNRlLzignxrPi9/aeZqUKFGgcbSyRiMen5CtLyS/BxERTKM389x8AX6tDQv1yhVMvLiYPTyvfAV+ftYeO4XjWK0FcdPJMwZeEejEmjQmLvhsRekBJtwrqT9+sUsla9jYV3XaSuAeaDOXpg06ALWaveRuLkjaWXEEZUJLHAdcBM5IeWY9QoMRkN2LcaaCZM1QRGZSF5OwT7XZPRgG1YN6xDheguLi9NpPDwCjAaEEktcXlJMDc0FGcjklpV2l/2pnHo81MxaUvJWDoQ55cnYR3aipKoXZSc34ZBUUDW6lFY12qNy8uTKD69CUNpMYURD8JgPhKa/e+Gg3cIerUKmZN7GRPj27IXUT99zonZw3AKqI+dZ6BZO2tnT3yadSNyzjBsXH1w8BPsdMVSC1qOnsO1HYvRqxWYDAZCurxuJkz9nUiPOUrtnm9VKCstzK4gXD2KM4vfR5F9D71GxeGpr9DkjSl4NGjD5U0z0JQUAuDgV5vGQwSNU0lWCrGbvwGRCHvvYJoMq7lA/SwIdrFEpTPiZispM8frXsuOLw9lMXLnfeq6WxHgZE57e9hJ6Rxiy8id9/G2t6C2myAsWUhEzOzuybJz+Si1RgxGEwPDHc2EqZrgWraGwwkKQlwsGbVLcIQd09KFNgE29Klrz/xTuYzceR+pWMSUTh6IRCLyVQYklWhTn9TXfwV2XiEY1CqsHNywchDWl2eznlxdN4mLC97C3r8eNh7m60vm7IlHk65cXPAWMhdvwdQOYX2FjfyWO7uXYChVYjLq8ev4upkwVVNc2zgFnaoYsVhKndcmYPEgQI4y5x6OwY3M6udejSQr+iAiiRSxhRVhw2chEonwaNyFwruXiVo4HEQiXOu1xq1h++c619qhwShVKjw93MuEkJd79+CD8V/Sb+gIGtavR0iQ+TfH29OT3t268MrQEfj5eFO/rvCMLS0sWDJvFnMWLUWhUKI3GBg+dNBThZrqYuLUmUTFxCKXF9Hl5QF8NOZtBvR7mUXLf+LWnbuIRCJ8vb2Y8eXEsjbd+w1GoVSi0+k5dvIMa5Z9R62QIKZNGs+UWXPRaDR0eKF1WSS/5HupNG1kfrjLzctn8Mh3USiViEViNm/fyd7tP5Obl8+XM+dgNBowGk307t6Fzh1ewGg0MmXmHBRKJSYT1K0dyteThW/homUrUZWW8tmXXxMYGMg38xbxaPwq36BaqFVKnN08yoIitOnWh2VTP2ba6AEE1W1QdvB/FC4eXrTs1JNpYwbg7uVDQC3Bb09qYcEH0xex9Yf5qJQKjAY9PV5700yY+rsQ8dsmrlw8hdFopEvfwdRvKpwV+r75LusXTmPa6AGAiUFjPsXe0ZmE+Dg2Lf4GkViMyWikz5C38Q0UBIm05Ls0eaFzpeNcPBFBq869KjBJdg6O9B32Lt98+MaDMd/DzkGwZHnlrfeYP/4dJBIprp7ejPpcYEe3rJiLTqctM4sMrR/O8E+n8Ti4+IaiVauwcfYoM+Or3bY3EUvGs2PqUNyD6uPkHWTWzs7Vk5BW3dgxdSj2bj64BtYFQCK1oPuH8zi3ZRHaUuFMENZzqJkwVVPoNKWkx1+k48gva9SPukQOVB7i8MKvy0i8cBi9Vs2Wz16mbsdXaNFfUHgnXDxMaOseZsxfSW4GioJsvOs2q1B+5cAvpF45g8lkpEGXAfg2aFmjeQOEeDqh0uhwd7DG7YE5Xq+mQUzcdJJhi/dTz8+10oAOnk62dGsUwBuL9+PjYkedB35FFlIJc4Z14Ps/o1GodRiMJl5vX9dMmKoppm07y+WkbORKDX3n7GZMj0a80jKUyOv3+e7PaORKDeM3nqSOtxNLR3UlNjmHNUeuIZWIEIlETHq1JY42wvkuIUuOu0PVzwD5JaWMXB6BUqNDLBKx/cwtto9/+bEmg88CkY+PzxODZtra2hJ79Tr9dvxz0fWeB/TyTHJ//RLv9zY+cx/yoyuxCe+Jpefz3RCqg/w/52Jdu22FPFM7+zvRvk1LCgsLzer36NGDT2d+z5Y7Zrf+01DlZ3Lxp4l0+crcLLCqiN+9HL9WL+Lo+/d/rZVLxQAAIABJREFUcI9M70/HSRvK8ky187ei6NIfTPminP6PvXQe0f4ZGIrMNXz/m5BZouPLQ1lsHPh0k5bH4ff4IjztpLQLrB77tyGmAGsLcY3yTFl0/Zg5W46yfbu5OdejuBgTx6qbEvJUxifW+y+itCCTq2s/p/Wkp4fivbL2c8JHzkEsfT4fmss/fEStVz6skGeqla8Vzmln+PC90Wb14+LicJGJwPjksPn/NaRnZDJ2/BcV8kn9Exj72WSWLvj2gR/TPwCxFJ3MiZisv/JT/23kZaWzdOo4vln7+9MrP0d8N/l9Jsz/6ekV/ya4iFTITdacS9M+vfJ/CCW5GUQsGc+g2U/el58F9+JOU5KbQViPJ+fW+zuRcTOGqxG/0PuzxbxRT4pd/C70ucn/2nyeJzIKFEzcdLJCPqnnBaVax+xdF5gz7PmZFlaVxZLV7cjqw1dYsNDcFei/48n9vCGSYFIryVoz6pm7cOo+9t8VpHZ/i+beFUTS58ca/FchEovRlSqInDv8mfto2H/c3y5IGbRqIucOx2TQVylSzv8PkIhEKLTGMuboWfBaQ8dqC1IrL+ZzJEGBTPq/dxt7XhCJxOjVSqIWjXhq3cajFz5XQao0PwOR+H91SkMAxBIxCoWC/sPe+UfHXbl4/j8nSP0PhlgsQaVU8PV7/2wC039TkLpz7TLnj+3/18avCURiCdpSBbse+EI9TwQ26fCvClKJF49wZvMCLG2eX0jw/xIkYhEKtY63lh547n3byiyemyCl1ul5a+kBDEZjjc9z/+gp5GFOqn8CUkcPfD7ZYZa091Hk/fYV2RvGVihTXjmIoaQ85GzBvgXoclNqPB/llYMURiypVhvX/lPxGbcd69ov1Hj8quBhDqp/A9bOnvT8dk+FpL1RqydzelFFzXXqhf2o5eWO2HFb5lCSWXNtTuqF/Vz9bdFT6z1M2ttz9l4sbR1rPC6U54/6t3HwTgmd1ySRmF/uJD5y530ySwQN9OWMUsbuSeerw1kcSVCU1fGwk7LzjcCypL2Pg95o4pXNKayKqshyb46tyLB+sCe9SvMd29qVra8H8GqDyj9IcyNziExSVHrv78TDXFP/FVxd/wXXN02l3fTdZUl7M6P2oykqX0c3f52LMqvm6ygzaj+3d5U7/jf7cAXtpv9eZsL4d+Fh7ql/E96enox7fzR3EhK5fTexrPyVISNIzxDY6YvRlxn6zljGff4V+yKOVHsMvV5Pu559+X5FxQP6qg0VnfrfGFXxu/asmDJzDoeORT6XvqqDh7mqnidcPLz4btvhaiXWfYjl0z/l24/erFB25tAeCvPK8zlt+G4G6fcS/9q02jhzaA+/LH966pCqoE54M7q9OqQsj9jzxMP8VH8X7Fw9Gfb9vrKEuE/DoaUT+WNWRUXG7dP7UBaW73Mn139LYXrSX5tWG7dP7+PM5mcPVBbaugeD5/xK1/dmVqvdw7xS/zb2RSfR9sut3M0s/3a/sXg/GQXC9zYtX4GHozU+znYcikupdv96g5He3+zih4NxFco3noivcD3mx8PVn/wjeJi0t2UtL2ISaxa2/v9bla5RXYI28w5GtRJ9YbljrPJKRAVhyuXlSVi4B/0LM/z/GzpVCfLUW+hKFSjzyg/X9y/sr5AguMmwKdh7/3NOkf/b4W4rYXNc5YJdMx9rVvbzZXZPL3rUsqt239Hppfg7WhCZpKwQEWrLX8b78SmRBv8PVYeutISStNvo1UpK88vXUealgxUSAdd//Utsvf5vHT0PeHq4s2rDz5Xea92iGdvWr2T5wtm83Lv6wZTORUUTFODPoWORFdbQ6o0VD7Vb1z05SuH/oepQKYpJuXuDUpWCnIxy9v3soT8rJBJ+e8KMMj+p/8M/C42yhLx7t9CWKijOKd/n7pzZh+oR5Wund6bi7PvP+Xb/b4aHg42ZcPMQzUM9WftBL+YP70ivJkHV7jsqIQt/N3uOX0utsM9t+st4az54/sGBnhV/m+2F8uohSi78Coiw8AjB9dWvKtxXXN6HInYvGPRIXXxx6TcFsYUM1Y1Iik5vRCSSILayxWPEMnS5yRTsnY/JoAOTCdeBs7BwebIW/GlQ3TqNrM4LSGydUcUfx6H9m6huRqLNvE3+H7MRWVjiMfJH8rZNwqn7WLSZt9EXZuDUXdD4Ka8cRJt5G+fen5L321foi3Mx6bXYtxpQlgxYEXeQknNbEMvssPAIRfTAdEYvz6Rg7wKMpUWIbZxw6TsZqWPVE6E+D9y/eICEY1sRiUQ4+NSi2YiKEfDund3DvbN7MBp02Lr50XTE10gtZWRcPsbtg+sRicVIZXa0/2wlxZlJxP3yLUa9HpPJSMvRc7HzeHb/GYDMK5F4hbfHyt6F9Jgj1Ok1kozY48hTbxGzaQYSCys6TFjNhR/H07D/OOSpN1HmpdOwvxDiPvXCfuSpN2k0eCJRqydTWpiNUacluPNggtq/KtQ5v4+7h3/GwsYOB9/aZaZNqvxM4rbMRqOQY2XnRJM3p2Lj4vXYuVYHEXdK+PVaESIgxMWSqV0qhqPee6uYvTdL0BtN+DpI+aqLBzKpmBNJCjZdLkQsEmFrKWZ5Xx+SC7TMO5WL3mDCCHzT3RM/x5qZ+7QNsOVqVimpci0BThXNS787k8vtXA0avYlOIba801wI7xyTXsqPF/MxGE3Uc7difHt3LCXmmtBjCQoGNHRkz81i4nM0hHnKWBWVj8ZgYtSuNIKcLZnW1YPeG5KJeDuYmcey6VnbnrYPgk3MjcyhbYANdd2tmH0iF7Ve8F36tJ0bYZ4yTCYTS8/lE51eioetFOkj0darOsfqIvPSQVIjtyFChK1PKA2HTa9wP/38n2Rc2INJr8fazZcGw6YjsZSRE3ec5MPrEYkkSKxtaf7Rjyiykri5bQ4mgw6TyUT4yNnYuNdsHeVePYlbg3ZY2ruQHXuUoO4jyLlygpL7t7jxy0zEFlY0/3g1V9ZMoNYrH1Jy/xaleenUekXIF5MZtZ/i+7eoO2ACV9d/gUaeg1Gnwa/jYHzb9gMgI2o/945tRiqzw86nVtk6Ki3I5Ob2OeiURVjaOVF/yBRkzjVfR3v2R7Bhy3ZEiKhTO5T5MytGP93xx1527N6LTqcjwN+XeTOnYi2TEXH0BD+u3YhELMbOzpbNq1dwNzGZqd/MRasT9q4l874hKKBmz7xz+xeIjr1C8r1UggMrBsWYOe87rt+4hVqjoWe3zox7V9Ckn4+KYeGyHzEY9IQ1qM/Xk8djaWlu3n3g0DHefH0gv/6+h7hr12naKJzvV/yERqOh/7B3qBUSxMJvptO8Uy9iTh5iwlczeOXFXnRqL+S+mTJzDp3av0BY/bpM/no2pepSAKZ+/ilNG4VjMpmYvWgJ5y5G4+XpgYVF+RHh4Rz9/P2Ys+B7npcu9uzhvRzasQlEIvxDajPmi4qszMn9uzi5fxd6vQ5PH39GfzEbK5k1l04e5s/NPyESS7CxteOLxRtIT0lg/cLp6PV6TEYjH379HZ5+5sFfqoOY08do0qYTDs6uREVG8PIbo4k+dYSUO/GsnvslllYyvlr2M4u//JDB740n5U48uRlpDH5PyGF25tAeUu7E8+a4KSyf/ikFuVnotBq69x9G55eFiJmnI/7gwLZ12NjZ4x9SF6mlsIbystJZv+hrFEVy7J2ceWfiLFw9vWv0/zwL7pzdz9WDW0AELn61zRiVm5F/cOvkbgx6HQ4e/nR9dyZSKxlJUUeJ2bMWkViMpbUdr0xZTUF6IifXfoNRL+xzPT6ah6OXefCY6iA55gSBTTpg7eBC4sXDNO37NkmXjpGbcpPjq6YjtbCi37R1HPzuU9oM+Zjc5JsU56TTZsjHgMA05abcpP1bn3No6USUBTkYdBrCeg6hfuf+D+rsJW7fRixt7HH1r43YQlifJbkZnFz/DeqSImT2TnQePR0715rvcwdikthy+hYioJa3EzNer2ih9EdUAnuiEtDpjfi52jPj9bbILKUcu5rKumPXEItE2Mks+On9HiRly/l2xwV0BiNGE8x9sz0BbjUzMWxX35e45Bzu5RabBbyYvzuKm2kFaHQGuob7M6aHENDoUkIWy/fHYjAaqe/nyqT+LcvCnz+KI3EpvN6uLr9fuMu11DwaBbrzw8E4NDoDby09QLCnI7OGtKPL9N84MWswU7ee4cVmwbSrJyhiZ/12nvb1fann68LM385TqhUizU7s14JGge6YTCa++zOaqLtZeDjZYCEp38uqOse/4m8RpnS5yRSf2YzHyBVIbJwwlBab1bGu1wG7Zi8DUHRiLcq4A9i3fI3i05twH7oQqYM7RnUJAIqYP7FrOQDb8B7CQcNo7gCe9/tM9PnmMfztWw/GtpF5+GBV/DEcO4xAbOtM/s7pOLR/E5v6nVFc2i34SvnU+8t8O5Gz8YMyYUp14wQO7QTa37nvZCTWDhh1GrLXv4d1vY5g0FN8agOeo1YjltmSs/kzLL0Ef57CQ8uwbdQL28a9UcQdQH5oGW6Dn54X5nmhODOJOxEbaT9hNVZ2TmiV5vmEvBt3JrCdcFi6uXcVqef2EtJ5ELcPrqfNh4uxdvJApxLez73Tuwnp/Dp+LXsJG2QljuPR66eiyDZ/P6Fdh+Dfuo9ZeVr0Eeq++A5W9i5cWvsldXqNxKdpV5JP7qRh/3E4BdavUN+7SRdOfzemTJjKuHyU2r1GAgJ7ZWnriEGr5tTCUfg06YLRoOP2gbV0nLQBC2s7zi77EMcH0Qqv7fgOv1YvEtDmJVLP7+X6zsW0end+NZ5w5Ugu0LI5Vs4P/XxwkkkoVps/p45BtvStJ2xMay8VsP9WCQPCHNl0uZCFL3rjbiulRCO023OzmIFhDvSoZY/OYKqQQ+IhZhzL5r7c3FF8ULgjveuYhz4Xi2BIIyd+iZMzpXNFQW9MCxccZBIMRhPjD2SSmK/Bz9GCuSdzWNzHG38nS2afyGHPjWIGhVc0gdTojcRklDKxgxsKrZGjCQrCPGW818qV3fHFlZoIdgmx40SSgrYBNugMJmIyShnf3g0T8F0fL6ykYtKKdMw6ns3q/n6cTlGRWqRj00A/CksNjNiZRp869mj0xirNsbpQZCWRcnQTzcf9hKWdEzql+T7n0agTvg8SSyceWE3Gxb34dxhE8uENNHl3MVZO7uhKhXWUce4P/DsOwqv5w3Vkvs9d/3kaqhzzdeTfaQjeLV80K8+OPUJwz7extHPh2savCOo+Ao/GXUg7s8ssKASAe6POxCx9r0yYyo47TlB3wY+x/utTsLB1wKDVEL1kFB6NOmM06EiOWEfL8euQyuyI/XEcdg9M+u78vhjvli/i3bIPGRf3cWf3Ehq9U7O8cHcTk/lp/c9sXfcjzk5OyIvMn3mPzh0Z9Kqg0Fq6cg279uznzdcHsHLdRtYsW4SnhzvFJcIz//X3Pbw5ZCB9e/dEq9NhNJg/8/FTvib53n2z8pFvDKbfS+ZhnsViEaPeGsrqDZuZO6OiEvGTsWNwcnTAYDDwzoefcftuIkEBfnw1aw7rf1hCUKA/X3w9m+27/mD40Iq+PRqNhvOXopnx5URKFAr2HzpG00bhjP/ofbbs2M3uLeY5fl7s3pWIo8fp1L4tWp2OC5dimD55AiZMrFvxHVZWVqSk3ufzqbPY8fMajkaeIvneffb++jP5BYX0fX04r/V9CY1GUz7H4GCel/FsekoC+7asZsqyn7F3dEZRbP4tat6+G51eEkIe/75+BacP7qZ7/zf4c/Mqxs9bibObJyqF8DuI3LuD7q8No223l9DrdBgr+Rat/ObzsrxRj6LngLdo17OvWfnFExG88tZ7ODi78MOMCbz8xmhadOzBsT+2M/i98QTXbVhxvh26M+fj4WXCVFTkobLQ6m9PnImdgyNajZpvPnyDFh26o9fr2LNpJdNXbsPG1p4FE0cTUEuIkrdlxTza9XyFdj1f4fTB3Wz9YT7jZlXPXaCmKEhPJPbP9fSbug6ZvRNqhfk7Cm7RmfqdBSXlpV0ruXVqD2E9Xifmz3X0mbgMW2cPNEphzd08/jthPYZQ+4XeGB5zXjj64xSKMs3fUXjvN6jTzjyoQeLFwzR7ZRTWji4cWfEFTfu+TUjLbsQf3UGbIR/jHtzgL/Ptyp5vR5UJU0lRR8rClncaNQ2ZnSN6rZrdM0cS3KILRr2e6N2reW3Gz1ja2LFv3tiySIZnf1lEnXYvUaf9y9w69Sdnf1lEr0+e7jLwJCRly9lwIp41Y3vgZCujSKUxq9OloT+vthLOlD8dusKflxIZ3K4u649dY8k7XfBwtKGkVAg68vuFBAa3q0vvpsHo9AYMlZwVvtp6htRc8/10aPt69GluzuaJRfBmx/psOhHP9MFtK9x7v1djHG2sMBiNfLTmOHczCwlwc+CbHRdYMborAe4OzPz1HL9fuMuQ9hXP2hqdgUsJ2Ux+rRWKUh2H4+7RKNCdD19sws7zd9j8ifl5sXujQI5dTaVdPV90egPRidlM6t8STLBsVFesLCSk5hUzfds5No7rTWR8GvdyS9g2/iUKFGqGfr+fvi1C0egMVZpjZfhbhCl1SizW9TsjsREibEmszSVgXW4yeZHrMKkVGLWlyEKFkJGW/mEU7J2HTf0uWNcTnMws/RpSfOYXDCW5WNfrWCkr5fZa1XMLGRQF6AvSsPQPF5zOJFK0OUlYejye/pXYOiFx8kGTFo/UxQ9dXiqW/kI4WkXULkpvnxH6Ls5FX5CGQVmAVWATJLbCM7Bp0AV9gfBB1qbF4zZQCGlqG96TomP/rINq3u0YfJp2LYtEV5nvT3FmIrf2rUanKsGgLcW93oMw8iGNiN38Lb7NuuHduDMAzsFh3D20iVJ5Dt6NO1fKSrV459sqz09dXIAy9z4uoY0RiUSIJVKKMxJx8Hm8CYWVvTO2br4UJF/Hzt0fRfY9XEIEbUhS5A6yrp4EhPDpitz7aIrzca3dFCt7ITyob7PuKB4cUguTr5clA/Zr9SI3/vihynN/Ei5nlNI5xBYnmaDlcJCZazuSC7WsvVSIQmugVGeilZ8Q+jTcU8bck7l0CbGlY5AQqKGhp4xfYgvJVRjoGGxbKSs1o1v1Gc/utez4JU5OZnFFIexEkpK9t4oxmCBfpSdFrsMEeNtb4P+Axepdx57dN4rMBJXzqSqaeltjJRXTKdiWn2MLGdfWFYn48exQa39rlp/PQ2swEXVfRWMvob1Ca2TJ2TwS8jVIRCLuFwnzvJJVSrdQWyRiEW62Upr6CJF57hfpqjTH6qLwbgwejbtg+WAdWdia73OKzCSSDq5GX6rAoC3FpW4rAByDw7mx/Vs8GnfDo1EnABwCw7h3dBMaeS7ujTpVykqFDa96olhtSQGluWk4BgvrSCSRoshMwu4JKQws7ZyRufpQlHIdG3d/VI+ESb9/egd5104BoJbnoMq9j7akAOdaTbG0E9aRR5NuqHKFdVR87zrhbwssg1eL3iTue3JS9qrgYvRlenXrgrOT8MydHM2f+d2kZJauXEuJogSVqpR2bYRn3rRROFNmzaV39y706NwRgCbhDVm1YTPZ2bl079KxUlbq+znV82sAeKlXd1Zt2ExaesXcShFHT7Djjz8xGAzk5uWTmJyCyWTC18eboEBh7H4v9Wbbzt/NhKnIM+dp1bwpMpkVPbt04qd1m/hy/DgkksdrTTu80Jo53y9Dq9Vy+vxFmjdtjExmRYlCwbcLl3Drzl3EYgn3UoVvU3TsFfr07IZEIsHD3Y3WLZoCkHzvfoU5SqVSoOZRLW/GRtGiU0/sHYXfz8MQ4I8iPSWB3zesQKUoQaNWEdZC0NDXDmvCugXTadmpZ1kOp9AGjdm3dQ2Fudk0b9+tUlZq7LSq+7oUFeaTnZ5K7bCmiEQiJFIpacl38Qt+vA+gg5ML7t6+JN64iqdvAFn3k6kdJjzHo7u3cvnscQAKcrLJTk+lqDCPuo1b4OAkMP0tO/ck+4Gwl3jjKh/O+B6Atj1eZseaf1aQAsi4EU1wy27I7IU1J7Mzf0cFaUlE/74SjUqBXq3CL0wI2+9VqxGRa2cR0rI7wS06A+BZK5zYvRtQFmYT3LxLpaxU9w+q7jOmKsqnKOs+XnWalJ0XCtIScfF7/HnB2sEZe3dfshOu4ejljzwzBc/aQq6v60d+JeVyJACKgmyKsu5TWpSPT71mWDsIv9PQ1j2QP0jAm514jZ7jFgBQ54U+XPxteZXn/jhEJ2TTNTygLLrcw5DgjyIxW86qw1dRlGpRafW0qSMwlo2C3PlmxwW6NQqgS0NhvYYHurHxeDw5RaV0DvOrlJWa/Ub101n0bBLExhPxZb5SD3Hsaip/RCVgMJrILyklJacITODjbEvAAxarT/MQdp6/YyaonL2VTrNQD2QWUrqE+bP++HU+69sMifjxTHjbuj4s3huDVm/gwu1MmgS7I7OQolBrWbQnmrsZhYjFIlLzBIE+LjmHno0DkYjFuDvY0DxUOCel5hZXaY6V4V8LsVTw5zzcBn+LpWctlFcOor4nOJq59JmAJv0G6rsXyF73Lp6jVmMb1h0rn/qUJlwgb9tknPtMQBZcMZ5/dZgp1c1IjGoFmSuGAGDSqFDFH3+iMAVg07ArqpuRWLgGYF2vAyKRCHVKLOrkGDze/gGxhYycnz/BpP+fFYK0MsT98i0tx8zH0a82qRf2k3/3MgCNh06mMCWe7OtnOblgJJ0mbcSvZS+cgxqSHX+OiyvH02jIZNzrtqjQX3WYqYzLx9CpSjj6tZCUUq9Wkh59BIdXnmyP7tu8OxmXj2HnGYhXo06IRCLy7lwm7/Yl2k9Yg9RSxtklH2DU/Xffz9zIXGb39KSWqxUH75QQlyGY4Uzo4M6NHDXnU1W8uzud1f196VHLjgYeVpxPVTEpIpOJ7d1p5lsxYWN1mSkAqVjE4HBHtl4p92XKLNbx6zU5q171xd5KwtzIHLSGJ2ZVqIBjiQquZWt4fduDg7bayOWMUlr6PT5XhJVUTBNvay6lqTiepKBbqOCnteOaHBdrCesH+GE0Qc/1/91wsje3zyb87bnY+9YmM2o/hYmxANQbNImie/Hk3zjHpe/foeX49Xg174lDYAPyb5znypqJ1B00CZfazSv0Vx1mKjvuGLrSEs5/K5gS6dVKsmOPYOf93hPn7Nm0OzlXjmPjEYh7eEdEIhGFCZcpvBtN809WIbGUcfmHjzD+R/e5KTPnsHzhHOrVqcXufQe5FCM88xlfTuTK9RucOnuegSPGsHPTGl7u3YNGYQ04efY87382iRlfTKRNy4rPvLrMFAjCxshhr7P25/JAJGnpGWzYsp3fNq7G0cGeKTPnoNFW/RkeOHyUy1eu0b2fIGTJi4q5GH2ZF1o/PneNlZUVrZo15cyFKCKOnODFnl0B2LT1N1xdnNm9ZQNGo5GmHarvv/VPYd3CaXw0cwkBoXU5c2gPt69EAzD802kk3rzK1YunmfnBUL7+cRttuvUhpH44Vy6eYvFXHzHi06llOaIeojrM1KXIQ6hKipn0pvCNUqsUXDwR8URhCqBV595cOnkIL/9gmrXrikgk4lbcJW5cvsBXy37GSmbN/PGj0GnNGYf/iTi5diY9P16Ia0Adbp/eR+atGAA6jPySnMTrpF45y+9fj+C1mZuo1bY3HqFhpF45y8HFn9FhxBdm+Zeqw0wlRR1Foypm20SBGdOplSRcOESrgR+YtX8Uoa17kHTpKE7eQQQ174xIJCLjZgzpNy7x6tT1SK1k7J37Pob/6Hnhmx0XWPBWR2r7OLMvOonLSUIAhcn9W3E9NY9ztzIYsTyCTeN606tJEA39XTl7K4PxGyL5on8rWtSqaIpYXWYKQCoR80aH+mw+eaOsLKNAwdbTN1n/YW8cbCyZ9dt5NLqqK14Ox93jyr1cXp0nJFwuUmmITsymde3Hm7daWUhoFuLBhTuZHL16j+6NBSXKttO3cLGTsfmTPhhNJjpN+7XK86gu/hZhShbUlLwd07BvPQiJjSOG0mIzdsqkVSGxc8Vk0KO8fhSJvZA4Tl+QjpVvA6x8G1CaeBFDcQ4mjRKJsw/2rQZgKM5Gl5NoJkxVh5lSxR/DfegCrPwEel5fmEnOlgk4dRmNyMoGo7a00nY2dTuQdeYXdI53cewqHEhMGiVia3vEFjJ0effQpAs/KiufBsgPrcCgKkJsZYvqZmRZmHVLvzBU8cexbdQT1fUjWAWYJ8j8O+FWtzmXVn9BaNehWNo5olUWmbFTerUKmaMrRoOetEuHsHYSEjEqc9NwDmqIc1BDcm5coFSejU6twMbNl5DOgyktyKI4I8FMmKoOM5Uec5g2HyzGJURg/pR5GZxf8TH1X3kfqcwGvUZVaTuvxp24c2gT1ml3aNBP2Eh1agUWNvZILWWUZKVQmCI4MDoHNeT6ziVoFUVIrW3JiD2Ow4Ow6s7B4aTHHMG/1YukXTqES2jjSserLpr5WDP1SDaDwx1xfGDm91d2qlRnxNVGit5o4kiCAncb4X56sY4GHjIaeMi4eL+UHKUepdaIj72UgWGO5Cj0JBZozYWpZ2CmAF6sY8/2q3JUOkFgUuqMyKRibC3FFKj0XEwrpYmPNf6OFmSV6Egr0uHnaMHhuyU08a44B6XWyNUsNTveCCzzUzpwu4RjiQpa+tkgEYvQG01IK2Gpuobase9WMbfzNHzZyaOsP3dbKWKRiIg7JTyU6Rp7WfPnzWJ617ZHrjYQm6Gme6hdleb4LHCu3ZxrG6YQ0GkIFraO6JTFZuyUQSMk8zUa9GRdPoyVo7COVHlpOAY2xDGwIfm3LqCWZ6NXK7B29cW/4yDU8myUGQlmwlR1mKns2KM0efd7HIPCACjNzyD2p08I7fMeEisbDOrK15F7eEdSjm5C5nSHWi8L60ivViK1tkdiKUOZfY/ie8I6cghswJ0/lqBTFiGR2ZJz5Th2PsI6cgwKIzv2KN4tepMdcwjH4Jrdb+C+AAAO/UlEQVSvo9YtmvHxpK8Y+cZgnJwckRcVm7FTSlUp7m6u6PR69kUcwdNd+LakpqXTOKwBjcMacPr/tXfn8VFW9x7Hv7NlJpnsk2TInpAMIQQiWwAFvdZKUUEFFFEERClC1bpUq3J7uViV1rpwFbV4fVVvVURK0aKBsLoVZRMioIgsQZCQDbJg9v3+MTiQJgg8IKb6ef/5ZOaZ88rMPHO+zznnd9ZuUGFJiSqrqxUfG6MJY69VYVGxdu3JaxemjIxMSdLIEZfrpdfeUE2N9/9cVV2jAIdDQYFOHS4t05p1G5TVr4+SE+N1sLBI+w/kKzE+TtnLViirb+8256qqqtbmLdv0XvYi31qqt7JztHTlu7pgYJZsVosam5pks7b/Sb986CVa9PYSbd/xpWbNnO47n9sdKbPZrMVLl6u52TvVqn+f87TwrXc0cvhlKisv18bNn2r4sKFt25iYpKamJp2NNVPpfQbouZn3aNg1ExQYEqqqb460G52qq6lRaHiEmpoatf7dHIVFeK8DJQUHlJKeqZT0TH228WOVHSpSTXWVIqPjNHTUjSorKdKBvbvbhanTGZna8P5y3fPYn5Xaw/vZPVSYryfvn6prbvm1HAEBqqut7vB5fYf8XEvm/0WuqC917ZS7JUm11VVyBgXL7vBX4ddfKW/HNklS1+699Mbzj6vqSIUcTqc2fbhK8SneKeepGedp4/vLdcHQK7X+3Rx5evU55bafLTE9+mvVnPuVedk4OQK90/z+dXSqoa5GAaERamlq0p71y+U82l/4piRfUSk9FZXSUwc+W6uq0hI11FQrKDJWPYeOVVVpkcoO7GkXpk5nZCpvw0pdce8zcqd6+1HfHDqopY/foQHX3iabI0CNJ7jOJfe7WJ9m/58CXbs08DrvtOaG2irZA4JktTtUUbBPJXmfS5KiUnpq7fzZqquqkJ8jUHs/Wa3weO975E7N1J4NK9Vt8BXavW65orv17vD1Tkf/VLceeG2Nxg3prhCnXUdq6tuNTtXUN8kV7K+m5hat2LJPkUc38M0vrVTPhAj1TIjQul0FKj5So6q6RsWGB2rs4DQVV1RrT1FFuzBlZGRKkob3S9a8f36h6nrvuqTqukY5bFYFOmwqrazVul2F6tvVrYTIYBWWV+vA4UrFRwRpWe5X6pvcdilBdV2jtu4r0dvTR/rWKS3ZlKdVW/ZroCdaVrNJTc0tslraX3suPS9R72zM046DZZoxZpDvfJEhATKbTcrZtFfNLd7OQu/kKC3esFtX9EtWeVW9cvOKNax30im18US+lzBli0xW8ODxKnntLplMFtm6pMp1Vdsdq0Munqzil38lizNUfjHpamnwfuAr3n1BTWX5alWrHEn9ZHOnqnLtfFV/tkomi0UWZ7iCBo/v6GVPSVNFoZqPFMsv9tgcWmtYtMwOp+oPfiFn5mUqz5ntK0BxPLN/kGwRCWo8vF/2WO9aA0fKAFXlvqPCuRNlc8XLfvS8liCXgi+apJK/3u4tQOE+tv9R2LA7VZb9J1WuX+ArQHEuBUd3lWfYTfr4mdtkMpsVEtdNfSa03S09bcStWvPkL+UXGKqwxAxfgNm++DlVl3jv0kak9VdwrEd7Vr2m/I3LZbJYZQ8Ol2fYyfeyOZGa0kLVlhUpLLmn75gzIkY2h1Pl+7YrfuBwbV3wuK8AxfH8AoIV5E5UZdE+hSV5g3JU+iDt/+gfeu+R6xXoTvQdd4REKO2KyVrz1BTZAgIVEtvNd55e1/1GW+bN0p7Vr/sKUJwNyeF+mtAnVHctKZDZZJLH5afp/7Iu6Zb+4Zq2+KBC/S1Kj7Sr9ugdnbkbSpV/dDpb3xh/pYb7af7WI1q5u1JWs0nhARaN7332diy3WUwanRGiZ9d5y5inuuzyuPw0YWG+ogIt6un2XtjtVrMe/I8ozXy32Ffc4ar0tp3bNfuq1TfGv03BhyGJAXphY6kamlt1Zfcg3fxmvrq57JpxSdv/R1acv2Z9UKLBiQGyHX3+yB4hmrG6WCt2V2lAnL/8rd7jFyYFKLegVjctyldUoFUZp9FGIwK7dFXSpROV+/ztMpksCozzqMcNbT8ryZdN0aZnpsjmDFVIYg/f9ygv+3nVHMqX1KowT38Fxni0/715Kt7k/R75BbmU9HPje67VlhWqrqxIwYnH1nP4u2JkdQTqyP7tis66XDsXPeErQHE8W0CwnO4kVRftU3Ci93rm6j5QB9cu1vrHxikgKsF3XntwhJKHTdamOVNldQS2KYHuGfUb7VgwS1+/P99XgOJMeVKSNfXmCZo47U5ZzGalp3n0h5ltz3vn1Mm6/uapCg8LVa+MHr4w8+ScP2v/gXy1tkqDsvqquydVf3n1db2Ts1JWq1WRrnDdOmnCGbfxW342m8aPvUZ/fGqOJKl7t1Slp3k0fMx4dXFHqU+m9xpnt9s1a8Z03TN9pq8AxdjRV7c51+oP/qmB/fu2KUpxyUVD9NSzc9XQ0KAxI6/UqHGTlJ7WTU880rYIygWDsvTAQ4/qkouG+Pajuv7akbr7wRl6Z+kKDTl/gPz9vR2xSy++SBs25erKsRMV3cWt83pltGvjsQIUZy42KVUjbpyiP917i0xmixJTu2vy/W1vGIyadLse/fV4BYWEqWv3Xqqr9b6fC1+creL8ryW1Kr3PQMWnpClnwctat3qJLFabQsJcGn5D+02hT9XhooMqLS5QSvqxm52R0XHydwYqb8c2Df7F1Xr16Ud9BSiO5wwKVnRCsgr271XX7t6bgj2zBuv9JX/X724ZqS5xSb7zhroiddXEaZp15wRvAYqUY1OKxt3xoF5+4r+1fOErvgIU51p4bIr6XHmzsv84TSazWREJabp4Stub11mjp2rxwzfLERSmqK4ZvgCz/m9zdKT4gNTaqtgeWXIleLR16avavTZHZotV/iEu9R4xyXDbKg8VqPJwkaJSevmOBUfGyi8gUCV5n6vbkBFa88pjvgIUx7M7gxUWk6Tygq8U1dX7OY/vdb52vP+WFk6/TiFdEhSV4v2OBoRGqN/IKXr7kcneAhQJx/oLg8ffpw9feljbls3zFaA4U13doZr0swz96sXVMptM6hYT1m5d0q2/yNTk51cozGlXj/gI1dR7+wfP5nyq/MOVapXUP8UtT3SoXvvwCy3L3SerxSRXkL9u+llGB69qjM1q0ZgL0vQ/2d7RSE9MmLrFhGns7CVyhwQoM9F7M8tus+i/xgzS717/yFfcYdSgtiO8H2w/oH4p7jYFHy7sEafnlm1RQ1Ozrh6YqhufzlFabJgevn5wm+cO9ETr939bpwt7xMl29Pmjz/do+ryPtCz3Kw1Ki5a/nzfyXJwRp815Rbph9lK5Q53qeRptPBFTTEzMd87VcTqd+nTb57r676Xf9TCcQ4tGhWrIoCyVl5e3+9vQoUN19+9n6/VdP0DDfsIGx9t15JPF+s8H7/cd+/STdTItfUjNRwp/wJbhZGyX3Kk/vL5aCxZ89x54GzZv0f/usOhwzZmvFfmpGxBrV1j+R7p9avvO7pYtWxTuMEkdLExHJ2C2qtERqs1F7acPo/OJC7aooLJZaw/8OKYU/hiN625V4PY31XSo805Zh+RIu0gvrtyqx59oP7r9k91nCgAAAADOxEnDVGNjo2wWs76j6BbOIZMkh59VdXV1Hf69rq5OficviY+zzM/snRN/vIb6Bpls7avwoJOxOk74fTpefX297GdhbypIDotJtTUdrzdpaKiX90qHzqlVZhPvz7+L1lZx3erkmlokk+XM9ojE96/ZZFXtCfoKJw1TDQ0NKig+pMHxjrPeMJy+QXF2lZZXqLa24yIZeXl5ig/zV0QAg47nitUsdQtp1s6dX7Y5vnPXLlk9F/5ArcKpMDvD5YjP0O7du0/62F27dqpPFHcqzpTZJPUMb9XOLz7v8O87v9ypZtMPVmgWJ9PSLKvZpEA/Ouj/DmoaW5UcapWdS1enVVJnkjXu3BYiw2my+qnVlXTCvsJJ10xJUmZmpua9sVCL8lqVV96oDvY1xPfMYpKSw2y6zmPWpPE3KDc394SPHTdunB6Y8ZBWfd2qI3UtOvUC1jhdATaTBka1atfmj3THbdN8VbEkye12K/sfbyqwOFfNB7ZITcxZ7zxMMjvDZMkapxf+Ol9Pzzn53iBut1tvvZ2t3fXB2lnWogaW9Jy2AJtJA91S+d6tmjxpoho6KA3udru1JDtbkaFBMqvZe2sdnYvFJtmd+qqiWXVNrfzGdHJup1mSSRvy61TL+9WpWEwmRTvNGtBFqi/YKRV98aPYXufHwySTn79aEvpr2Qfrde9v71drB79JpxSmJCkjI0O/nHa74hISv3OTQHw/WlpalP/1fr384lxt27btpI8fMWKErho9Rm63sdLYODWVlZXatGGdnp3zTJsg9S2326377rlLaZ4UXyUtdA6HSkuVs2K13lhw6ntPuN1u3X3vfUr1pMl2XGU1nJrq6mptzd2kp2c/1WGQ+pbb7dZv77tPad3T2lSwQ+fhdDoVGuaS1WaTSSa1tLaoob5BJYdK1NhIcYrOxuVyyepnV11tnUrLyn7o5uColuZm7du3V6+8/JJGj7xavTLSfdU10TlUVFTow4/Xae7cFzoMUtJphCkAAAAAwDEsrAEAAAAAAwhTAAAAAGAAYQoAAAAADCBMAQAAAIABhCkAAAAAMIAwBQAAAAAGEKYAAAAAwADCFAAAAAAYQJgCAAAAAAMIUwAAAABgAGEKAAAAAAwgTAEAAACAAYQpAAAAADCAMAUAAAAABhCmAAAAAMAAwhQAAAAAGECYAgAAAAADCFMAAAAAYABhCgAAAAAMIEwBAAAAgAGEKQAAAAAwgDAFAAAAAAYQpgAAAADAAMIUAAAAABhAmAIAAAAAAwhTAAAAAGAAYQoAAAAADCBMAQAAAIABhCkAAAAAMIAwBQAAAAAGEKYAAAAAwADCFAAAAAAYQJgCAAAAAAMIUwAAAABgAGEKAAAAAAwgTAEAAACAAYQpAAAAADCAMAUAAAAABhCmAAAAAMAAwhQAAAAAGECYAgAAAAADCFMAAAAAYABhCgAAAAAMIEwBAAAAgAGEKQAAAAAwgDAFAAAAAAYQpgAAAADAAMIUAAAAABjw/4VfMeuS8xkQAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 1080x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "39978034"
      },
      "source": [
        "Como vimos, o desempenho do nosso novo modelo com apenas 3 camadas de decisão não foi tão satisfatório assim. Por isso, vamos variar o ```max_depth``` dentro de um intervalo de valores (range) e comparar os resultados obtidos. \n",
        "\n",
        "O processo para obter os valores que queremos, é sempre o mesmo. Por isso, vamos criar um loop ou um laço de repetição (um loop, executa um bloco de código em repetição enquanto uma condição é atendida). \n",
        "\n",
        "Então, após fazermos a separação da base de dados em ```x``` e ```y``` e dividí-las no ```train_test_split```, vamos criar um ```for``` para que nosso modelo seja treinado e testado em repetição dentro de um range. Para isso, faremos:\n",
        "\n",
        "    teste=[]  ->  criando lista para armazenar os resultados da acurácia nos dados de teste \n",
        "    treino=[]  ->  criando lista para armazenar os resultados da acurácia nos dados de treino \n",
        "    for i in range(1,15):  ->  estabelecendo condição que deve ser atendida (i) e range de valor (range(1, 15))\n",
        "        modelo_arvore = DecisionTreeClassifier(max_depth = i)  ->  instanciando modelo que será treinado e definindo max_depth = i, ou seja, estamos variando a profundidade da nossa árvore\n",
        "        modelo_arvore.fit(x_treino, y_treino)  ->  ajustando o modelo a partir das bases de treino\n",
        "        teste.append(modelo_arvore.score(x_teste, y_teste))  ->  armazenando os resultados de acurácia de teste dos modelos produzidos na lista 'teste'\n",
        "        treino.append(modelo_arvore.score(x_treino, y_treino))  ->  armazenando os resultados de acurácia de treino dos modelos produzidos na lista 'treino'"
      ],
      "id": "39978034"
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "8b9eb8d8"
      },
      "source": [
        "from sklearn.tree import DecisionTreeClassifier\n",
        "\n",
        "\n",
        "x = dados_combinados.select_dtypes('float64')\n",
        "y = dados_combinados['ativo_moa'] \n",
        "x_treino, x_teste, y_treino, y_teste = train_test_split(x, y, test_size = 0.2, stratify=y, random_state=376)\n",
        "\n",
        "teste=[]\n",
        "treino=[]\n",
        "for i in range(1,15):\n",
        "    modelo_arvore = DecisionTreeClassifier(max_depth = i)\n",
        "    modelo_arvore.fit(x_treino, y_treino)\n",
        "    teste.append(modelo_arvore.score(x_teste, y_teste))\n",
        "    treino.append(modelo_arvore.score(x_treino, y_treino))"
      ],
      "id": "8b9eb8d8",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "16e232d2"
      },
      "source": [
        "Quando chamamos as saídas do nosso ```for``` (```teste``` e ```treino```), podemos observar um comportamento um pouco estranho: enquanto nos dados de teste, o valor da nossa acurácia vai caindo, nos dados de treino, esse valor vai aumentando."
      ],
      "id": "16e232d2"
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "2f89496e",
        "outputId": "ef1a4a83-6789-4a92-e30e-4546f7c75796"
      },
      "source": [
        "teste"
      ],
      "id": "2f89496e",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.6067604450976275,\n",
              " 0.6067604450976275,\n",
              " 0.6105395758975436,\n",
              " 0.6208272097417594,\n",
              " 0.6206172580306529,\n",
              " 0.6187276926306949,\n",
              " 0.6216670165861852,\n",
              " 0.6197774511862272,\n",
              " 0.6162082720974176,\n",
              " 0.6143187066974596,\n",
              " 0.6101196724753307,\n",
              " 0.6006718454755406,\n",
              " 0.60130170060886,\n",
              " 0.5937434390090279]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 0
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "7371ca7e",
        "outputId": "7f63f987-c18d-4db4-8044-d0b6ad4be9d3"
      },
      "source": [
        "treino"
      ],
      "id": "7371ca7e",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.6066348223190384,\n",
              " 0.6066348223190384,\n",
              " 0.6211222508004829,\n",
              " 0.6360820954280615,\n",
              " 0.6412261823526324,\n",
              " 0.6489423127394888,\n",
              " 0.6606477350270327,\n",
              " 0.6707259461445594,\n",
              " 0.6895176106241142,\n",
              " 0.706892026665267,\n",
              " 0.7293055482651829,\n",
              " 0.752926355571886,\n",
              " 0.7770720697076269,\n",
              " 0.7989606844785051]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 0
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "77e9507a"
      },
      "source": [
        "Para investigar o que aconteceu, vamos plotar um gráfico para entender o comportamento combinado dessas listas.\n",
        "\n",
        "Usaremos o ```lineplot``` do Seaborn para construir essa visualização. \n",
        "Como parâmetros, teremos: \n",
        "\n",
        "    x = range(1, 15)   ->  intervalo que usamos no for\n",
        "    y = teste e y = treino  ->  listas criamos no for e será usada no eixo y do gráfico\n",
        "    label = 'teste' e label = 'teste'  ->  etiqueta para identificar as duas linhas que estarão no gráfico \n",
        "\n",
        "Observando o gráfico produzido, podemos perceber que, inicialmente, os valores de treino e teste são muito próximos mas, conforme vamos aumentando o range, o valor da acurácia para os dados de treino aumentam muito enquanto, esse mesmo valor para os dados de teste, vai caindo (as linhas vão se afastando).\n",
        "\n",
        "Isso significa que, conforme aumentamos a profundidade da nossa árvore de decisão, a classificação para os dados de treino ficam muito boas pois, ela consegue captar muito bem as características desses dados. Mas, em contrapartida, o modelo fica tão bom os dados de treino que, quando apresentamos um novo conjunto de dados (teste), ele não consegue generalizar tão bem e assim, temos uma acurácia em queda.\n",
        "\n",
        "Esse problema de um modelo com performance muito boa nos dados de treino mas performance em queda nos dados de teste, é muito conhecido na ciência de dados e chamamos de **overfitting**.\n",
        "\n",
        "Então, conseguimos perceber que, para melhorar o nosso modelo, não é suficiente somente aumentar a profundidade da árvore de decisão e assim, precisamos traçar uma outra etratégia para melhorar a performance do nosso modelo.\n",
        "\n",
        "Nesse momento, vamos testar uma nova técnica de modelagem: o Random Forest. \n",
        "O que este modelo faz é criar várias árvores de decisões na qual ele considera amostras aleatórias do nosso conjunto total.  "
      ],
      "id": "77e9507a"
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "7abbe63b",
        "outputId": "a09411c4-5304-41a7-f89d-acd45f771089"
      },
      "source": [
        "sns.lineplot(x=range(1,15), y = teste, label='teste')\n",
        "sns.lineplot(x=range(1,15), y = treino, label='treino')"
      ],
      "id": "7abbe63b",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fe0384783d0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 0
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7f35ed36"
      },
      "source": [
        "A função que treina um modelo de Random Forest (```RandomForestClassifier()```) não aceita que tenhamos variáveis em formato de string. \n",
        "Por isso, precisamos tratar as variáveis que são strings e transformá-las em números (ou no formato float64).\n",
        "\n",
        "Para fazer essa transformação, vamos usar uma função do Pandas, chamada ```get_dummies``` e nela, declaramos a coluna que queremos que seja transformada. Por exemplo, aqui, usamos:\n",
        "\n",
        "    pd.get_dummies(dados_combinados['tratamento'])\n",
        "\n",
        "Isso porque, nossa intenção é transformar a coluna ```tratamento``` da base ```dados_combinados``` em dados numéricos.\n",
        "\n",
        "Para fazer a transformação desejada, o ```get_dummies``` pega todas as categorias de uma variável e as transforma em novas colunas binárias. \n",
        "Por exemplo:\n",
        "\n",
        "A variável ```tratamento``` apresenta duas categorias ```com_controle``` e ```sem_controle```. Quando aplicamos o ```get_dummies```, ele cria duas novas colunas binárias e análogas as categorias. \n",
        "Se temos um evento que apresentava na coluna original a marcação ```com_droga```, agora ela apresentará valor 0 na coluna ```com_controle``` e valor 1 na coluna ```com_droga``` e vice-versa.\n"
      ],
      "id": "7f35ed36"
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "b7e17036",
        "outputId": "eca9e011-0733-477c-96bb-ca467e88e517"
      },
      "source": [
        "pd.get_dummies(dados_combinados['tratamento'])"
      ],
      "id": "b7e17036",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>com_controle</th>\n",
              "      <th>com_droga</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23809</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23810</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23811</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23812</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23813</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>23814 rows × 2 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "       com_controle  com_droga\n",
              "0                 0          1\n",
              "1                 0          1\n",
              "2                 0          1\n",
              "3                 0          1\n",
              "4                 0          1\n",
              "...             ...        ...\n",
              "23809             0          1\n",
              "23810             0          1\n",
              "23811             1          0\n",
              "23812             0          1\n",
              "23813             0          1\n",
              "\n",
              "[23814 rows x 2 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 0
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "75a77532"
      },
      "source": [
        "Agora que entendemos como o ```get_dummies``` funciona, podemos transformar todas as nossas variáveis de interesse: ```tratamento```, ```dose``` e ```tempo```.\n",
        "\n",
        "Para isso, declaramos a base de dados completa e, no parâmetro ```columns```, listamos as variáveis que devem ser transformadas. Assim teremos:\n",
        "\n",
        "    pd.get_dummies(dados_combinados, columns=['tratamento', 'dose', 'tempo'])"
      ],
      "id": "75a77532"
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "ca86fba1",
        "outputId": "524a6278-c4a4-4698-88dc-3a082f071abb"
      },
      "source": [
        "pd.get_dummies(dados_combinados, columns=['tratamento', 'dose', 'tempo'])"
      ],
      "id": "ca86fba1",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>composto</th>\n",
              "      <th>g0</th>\n",
              "      <th>g1</th>\n",
              "      <th>g2</th>\n",
              "      <th>g3</th>\n",
              "      <th>g4</th>\n",
              "      <th>g5</th>\n",
              "      <th>g6</th>\n",
              "      <th>g7</th>\n",
              "      <th>g8</th>\n",
              "      <th>g9</th>\n",
              "      <th>g10</th>\n",
              "      <th>g11</th>\n",
              "      <th>g12</th>\n",
              "      <th>g13</th>\n",
              "      <th>g14</th>\n",
              "      <th>g15</th>\n",
              "      <th>g16</th>\n",
              "      <th>g17</th>\n",
              "      <th>g18</th>\n",
              "      <th>g19</th>\n",
              "      <th>g20</th>\n",
              "      <th>g21</th>\n",
              "      <th>g22</th>\n",
              "      <th>g23</th>\n",
              "      <th>g24</th>\n",
              "      <th>g25</th>\n",
              "      <th>g26</th>\n",
              "      <th>g27</th>\n",
              "      <th>g28</th>\n",
              "      <th>g29</th>\n",
              "      <th>g30</th>\n",
              "      <th>g31</th>\n",
              "      <th>g32</th>\n",
              "      <th>g33</th>\n",
              "      <th>g34</th>\n",
              "      <th>g35</th>\n",
              "      <th>g36</th>\n",
              "      <th>g37</th>\n",
              "      <th>...</th>\n",
              "      <th>c69</th>\n",
              "      <th>c70</th>\n",
              "      <th>c71</th>\n",
              "      <th>c72</th>\n",
              "      <th>c73</th>\n",
              "      <th>c74</th>\n",
              "      <th>c75</th>\n",
              "      <th>c76</th>\n",
              "      <th>c77</th>\n",
              "      <th>c78</th>\n",
              "      <th>c79</th>\n",
              "      <th>c80</th>\n",
              "      <th>c81</th>\n",
              "      <th>c82</th>\n",
              "      <th>c83</th>\n",
              "      <th>c84</th>\n",
              "      <th>c85</th>\n",
              "      <th>c86</th>\n",
              "      <th>c87</th>\n",
              "      <th>c88</th>\n",
              "      <th>c89</th>\n",
              "      <th>c90</th>\n",
              "      <th>c91</th>\n",
              "      <th>c92</th>\n",
              "      <th>c93</th>\n",
              "      <th>c94</th>\n",
              "      <th>c95</th>\n",
              "      <th>c96</th>\n",
              "      <th>c97</th>\n",
              "      <th>c98</th>\n",
              "      <th>c99</th>\n",
              "      <th>n_moa</th>\n",
              "      <th>ativo_moa</th>\n",
              "      <th>tratamento_com_controle</th>\n",
              "      <th>tratamento_com_droga</th>\n",
              "      <th>dose_D1</th>\n",
              "      <th>dose_D2</th>\n",
              "      <th>tempo_24</th>\n",
              "      <th>tempo_48</th>\n",
              "      <th>tempo_72</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>id_000644bb2</td>\n",
              "      <td>b68db1d53</td>\n",
              "      <td>1.0620</td>\n",
              "      <td>0.5577</td>\n",
              "      <td>-0.2479</td>\n",
              "      <td>-0.6208</td>\n",
              "      <td>-0.1944</td>\n",
              "      <td>-1.0120</td>\n",
              "      <td>-1.0220</td>\n",
              "      <td>-0.0326</td>\n",
              "      <td>0.5548</td>\n",
              "      <td>-0.0921</td>\n",
              "      <td>1.1830</td>\n",
              "      <td>0.1530</td>\n",
              "      <td>0.5574</td>\n",
              "      <td>-0.4015</td>\n",
              "      <td>0.1789</td>\n",
              "      <td>-0.6528</td>\n",
              "      <td>-0.7969</td>\n",
              "      <td>0.6342</td>\n",
              "      <td>0.1778</td>\n",
              "      <td>-0.3694</td>\n",
              "      <td>-0.5688</td>\n",
              "      <td>-1.1360</td>\n",
              "      <td>-1.1880</td>\n",
              "      <td>0.6940</td>\n",
              "      <td>0.4393</td>\n",
              "      <td>0.2664</td>\n",
              "      <td>0.1907</td>\n",
              "      <td>0.1628</td>\n",
              "      <td>-0.2853</td>\n",
              "      <td>0.5819</td>\n",
              "      <td>0.2934</td>\n",
              "      <td>-0.5584</td>\n",
              "      <td>-0.0916</td>\n",
              "      <td>-0.3010</td>\n",
              "      <td>-0.1537</td>\n",
              "      <td>0.2198</td>\n",
              "      <td>0.2965</td>\n",
              "      <td>-0.5055</td>\n",
              "      <td>...</td>\n",
              "      <td>1.2250</td>\n",
              "      <td>-0.0553</td>\n",
              "      <td>0.7351</td>\n",
              "      <td>0.5810</td>\n",
              "      <td>0.9590</td>\n",
              "      <td>0.2427</td>\n",
              "      <td>0.0495</td>\n",
              "      <td>0.4141</td>\n",
              "      <td>0.8432</td>\n",
              "      <td>0.6162</td>\n",
              "      <td>-0.7318</td>\n",
              "      <td>1.2120</td>\n",
              "      <td>0.6362</td>\n",
              "      <td>-0.4427</td>\n",
              "      <td>0.1288</td>\n",
              "      <td>1.4840</td>\n",
              "      <td>0.1799</td>\n",
              "      <td>0.5367</td>\n",
              "      <td>-0.1111</td>\n",
              "      <td>-1.0120</td>\n",
              "      <td>0.6685</td>\n",
              "      <td>0.2862</td>\n",
              "      <td>0.2584</td>\n",
              "      <td>0.8076</td>\n",
              "      <td>0.5523</td>\n",
              "      <td>-0.1912</td>\n",
              "      <td>0.6584</td>\n",
              "      <td>-0.3981</td>\n",
              "      <td>0.2139</td>\n",
              "      <td>0.3801</td>\n",
              "      <td>0.4176</td>\n",
              "      <td>1</td>\n",
              "      <td>True</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>id_000779bfc</td>\n",
              "      <td>df89a8e5a</td>\n",
              "      <td>0.0743</td>\n",
              "      <td>0.4087</td>\n",
              "      <td>0.2991</td>\n",
              "      <td>0.0604</td>\n",
              "      <td>1.0190</td>\n",
              "      <td>0.5207</td>\n",
              "      <td>0.2341</td>\n",
              "      <td>0.3372</td>\n",
              "      <td>-0.4047</td>\n",
              "      <td>0.8507</td>\n",
              "      <td>-1.1520</td>\n",
              "      <td>-0.4201</td>\n",
              "      <td>-0.0958</td>\n",
              "      <td>0.4590</td>\n",
              "      <td>0.0803</td>\n",
              "      <td>0.2250</td>\n",
              "      <td>0.5293</td>\n",
              "      <td>0.2839</td>\n",
              "      <td>-0.3494</td>\n",
              "      <td>0.2883</td>\n",
              "      <td>0.9449</td>\n",
              "      <td>-0.1646</td>\n",
              "      <td>-0.2657</td>\n",
              "      <td>-0.3372</td>\n",
              "      <td>0.3135</td>\n",
              "      <td>-0.4316</td>\n",
              "      <td>0.4773</td>\n",
              "      <td>0.2075</td>\n",
              "      <td>-0.4216</td>\n",
              "      <td>-0.1161</td>\n",
              "      <td>-0.0499</td>\n",
              "      <td>-0.2627</td>\n",
              "      <td>0.9959</td>\n",
              "      <td>-0.2483</td>\n",
              "      <td>0.2655</td>\n",
              "      <td>-0.2102</td>\n",
              "      <td>0.1656</td>\n",
              "      <td>0.5300</td>\n",
              "      <td>...</td>\n",
              "      <td>0.2887</td>\n",
              "      <td>-0.0765</td>\n",
              "      <td>0.2539</td>\n",
              "      <td>0.4443</td>\n",
              "      <td>0.5932</td>\n",
              "      <td>0.2031</td>\n",
              "      <td>0.7639</td>\n",
              "      <td>0.5499</td>\n",
              "      <td>-0.3322</td>\n",
              "      <td>-0.0977</td>\n",
              "      <td>0.4329</td>\n",
              "      <td>-0.2782</td>\n",
              "      <td>0.7827</td>\n",
              "      <td>0.5934</td>\n",
              "      <td>0.3402</td>\n",
              "      <td>0.1499</td>\n",
              "      <td>0.4420</td>\n",
              "      <td>0.9366</td>\n",
              "      <td>0.8193</td>\n",
              "      <td>-0.4236</td>\n",
              "      <td>0.3192</td>\n",
              "      <td>-0.4265</td>\n",
              "      <td>0.7543</td>\n",
              "      <td>0.4708</td>\n",
              "      <td>0.0230</td>\n",
              "      <td>0.2957</td>\n",
              "      <td>0.4899</td>\n",
              "      <td>0.1522</td>\n",
              "      <td>0.1241</td>\n",
              "      <td>0.6077</td>\n",
              "      <td>0.7371</td>\n",
              "      <td>0</td>\n",
              "      <td>False</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>id_000a6266a</td>\n",
              "      <td>18bb41b2c</td>\n",
              "      <td>0.6280</td>\n",
              "      <td>0.5817</td>\n",
              "      <td>1.5540</td>\n",
              "      <td>-0.0764</td>\n",
              "      <td>-0.0323</td>\n",
              "      <td>1.2390</td>\n",
              "      <td>0.1715</td>\n",
              "      <td>0.2155</td>\n",
              "      <td>0.0065</td>\n",
              "      <td>1.2300</td>\n",
              "      <td>-0.4797</td>\n",
              "      <td>-0.5631</td>\n",
              "      <td>-0.0366</td>\n",
              "      <td>-1.8300</td>\n",
              "      <td>0.6057</td>\n",
              "      <td>-0.3278</td>\n",
              "      <td>0.6042</td>\n",
              "      <td>-0.3075</td>\n",
              "      <td>-0.1147</td>\n",
              "      <td>-0.0570</td>\n",
              "      <td>-0.0799</td>\n",
              "      <td>-0.8181</td>\n",
              "      <td>-1.5320</td>\n",
              "      <td>0.2307</td>\n",
              "      <td>0.4901</td>\n",
              "      <td>0.4780</td>\n",
              "      <td>-1.3970</td>\n",
              "      <td>4.6240</td>\n",
              "      <td>-0.0437</td>\n",
              "      <td>1.2870</td>\n",
              "      <td>-1.8530</td>\n",
              "      <td>0.6069</td>\n",
              "      <td>0.4290</td>\n",
              "      <td>0.1783</td>\n",
              "      <td>0.0018</td>\n",
              "      <td>-1.1800</td>\n",
              "      <td>0.1256</td>\n",
              "      <td>-0.1219</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.0604</td>\n",
              "      <td>0.1682</td>\n",
              "      <td>-0.4436</td>\n",
              "      <td>0.4963</td>\n",
              "      <td>0.1363</td>\n",
              "      <td>0.3335</td>\n",
              "      <td>0.9760</td>\n",
              "      <td>-0.0427</td>\n",
              "      <td>-0.1235</td>\n",
              "      <td>0.0959</td>\n",
              "      <td>0.0690</td>\n",
              "      <td>-0.9416</td>\n",
              "      <td>-0.7548</td>\n",
              "      <td>-0.1109</td>\n",
              "      <td>-0.6272</td>\n",
              "      <td>0.3019</td>\n",
              "      <td>0.1172</td>\n",
              "      <td>0.1093</td>\n",
              "      <td>-0.3113</td>\n",
              "      <td>0.3019</td>\n",
              "      <td>-0.0873</td>\n",
              "      <td>-0.7250</td>\n",
              "      <td>-0.6297</td>\n",
              "      <td>0.6103</td>\n",
              "      <td>0.0223</td>\n",
              "      <td>-1.3240</td>\n",
              "      <td>-0.3174</td>\n",
              "      <td>-0.6417</td>\n",
              "      <td>-0.2187</td>\n",
              "      <td>-1.4080</td>\n",
              "      <td>0.6931</td>\n",
              "      <td>3</td>\n",
              "      <td>True</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>id_0015fd391</td>\n",
              "      <td>8c7f86626</td>\n",
              "      <td>-0.5138</td>\n",
              "      <td>-0.2491</td>\n",
              "      <td>-0.2656</td>\n",
              "      <td>0.5288</td>\n",
              "      <td>4.0620</td>\n",
              "      <td>-0.8095</td>\n",
              "      <td>-1.9590</td>\n",
              "      <td>0.1792</td>\n",
              "      <td>-0.1321</td>\n",
              "      <td>-1.0600</td>\n",
              "      <td>-0.8269</td>\n",
              "      <td>-0.3584</td>\n",
              "      <td>-0.8511</td>\n",
              "      <td>-0.5844</td>\n",
              "      <td>-2.5690</td>\n",
              "      <td>0.8183</td>\n",
              "      <td>-0.0532</td>\n",
              "      <td>-0.8554</td>\n",
              "      <td>0.1160</td>\n",
              "      <td>-2.3520</td>\n",
              "      <td>2.1200</td>\n",
              "      <td>-1.1580</td>\n",
              "      <td>-0.7191</td>\n",
              "      <td>-0.8004</td>\n",
              "      <td>-1.4670</td>\n",
              "      <td>-0.0107</td>\n",
              "      <td>-0.8995</td>\n",
              "      <td>0.2406</td>\n",
              "      <td>-0.2479</td>\n",
              "      <td>-1.0890</td>\n",
              "      <td>-0.7575</td>\n",
              "      <td>0.0881</td>\n",
              "      <td>-2.7370</td>\n",
              "      <td>0.8745</td>\n",
              "      <td>0.5787</td>\n",
              "      <td>-1.6740</td>\n",
              "      <td>-1.6720</td>\n",
              "      <td>-1.2690</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.2990</td>\n",
              "      <td>-1.1870</td>\n",
              "      <td>-0.5044</td>\n",
              "      <td>-1.7750</td>\n",
              "      <td>-1.6120</td>\n",
              "      <td>-0.9215</td>\n",
              "      <td>-1.0810</td>\n",
              "      <td>-3.0520</td>\n",
              "      <td>-3.4470</td>\n",
              "      <td>-2.7740</td>\n",
              "      <td>-1.8460</td>\n",
              "      <td>-0.5568</td>\n",
              "      <td>-3.3960</td>\n",
              "      <td>-2.9510</td>\n",
              "      <td>-1.1550</td>\n",
              "      <td>-3.2620</td>\n",
              "      <td>-1.5390</td>\n",
              "      <td>-2.4600</td>\n",
              "      <td>-0.9417</td>\n",
              "      <td>-1.5550</td>\n",
              "      <td>0.2431</td>\n",
              "      <td>-2.0990</td>\n",
              "      <td>-0.6441</td>\n",
              "      <td>-5.6300</td>\n",
              "      <td>-1.3780</td>\n",
              "      <td>-0.8632</td>\n",
              "      <td>-1.2880</td>\n",
              "      <td>-1.6210</td>\n",
              "      <td>-0.8784</td>\n",
              "      <td>-0.3876</td>\n",
              "      <td>-0.8154</td>\n",
              "      <td>0</td>\n",
              "      <td>False</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>id_001626bd3</td>\n",
              "      <td>7cbed3131</td>\n",
              "      <td>-0.3254</td>\n",
              "      <td>-0.4009</td>\n",
              "      <td>0.9700</td>\n",
              "      <td>0.6919</td>\n",
              "      <td>1.4180</td>\n",
              "      <td>-0.8244</td>\n",
              "      <td>-0.2800</td>\n",
              "      <td>-0.1498</td>\n",
              "      <td>-0.8789</td>\n",
              "      <td>0.8630</td>\n",
              "      <td>-0.2219</td>\n",
              "      <td>-0.5121</td>\n",
              "      <td>-0.9577</td>\n",
              "      <td>1.1750</td>\n",
              "      <td>0.2042</td>\n",
              "      <td>0.1970</td>\n",
              "      <td>0.1244</td>\n",
              "      <td>-1.7090</td>\n",
              "      <td>-0.3543</td>\n",
              "      <td>-0.5160</td>\n",
              "      <td>-0.3330</td>\n",
              "      <td>-0.2685</td>\n",
              "      <td>0.7649</td>\n",
              "      <td>0.2057</td>\n",
              "      <td>1.3720</td>\n",
              "      <td>0.6835</td>\n",
              "      <td>0.8056</td>\n",
              "      <td>-0.3754</td>\n",
              "      <td>-1.2090</td>\n",
              "      <td>0.2965</td>\n",
              "      <td>-0.0712</td>\n",
              "      <td>0.6389</td>\n",
              "      <td>0.6674</td>\n",
              "      <td>-0.0783</td>\n",
              "      <td>1.1740</td>\n",
              "      <td>-0.7110</td>\n",
              "      <td>-1.4470</td>\n",
              "      <td>1.0620</td>\n",
              "      <td>...</td>\n",
              "      <td>0.6091</td>\n",
              "      <td>0.1813</td>\n",
              "      <td>-0.4249</td>\n",
              "      <td>0.7832</td>\n",
              "      <td>0.6529</td>\n",
              "      <td>0.5648</td>\n",
              "      <td>0.4817</td>\n",
              "      <td>0.0587</td>\n",
              "      <td>0.5303</td>\n",
              "      <td>0.6376</td>\n",
              "      <td>-0.3966</td>\n",
              "      <td>-1.4950</td>\n",
              "      <td>-0.9625</td>\n",
              "      <td>-0.0541</td>\n",
              "      <td>0.6273</td>\n",
              "      <td>0.4563</td>\n",
              "      <td>0.0698</td>\n",
              "      <td>0.8134</td>\n",
              "      <td>0.1924</td>\n",
              "      <td>0.6054</td>\n",
              "      <td>-0.1824</td>\n",
              "      <td>0.0042</td>\n",
              "      <td>0.0048</td>\n",
              "      <td>0.6670</td>\n",
              "      <td>1.0690</td>\n",
              "      <td>0.5523</td>\n",
              "      <td>-0.3031</td>\n",
              "      <td>0.1094</td>\n",
              "      <td>0.2885</td>\n",
              "      <td>-0.3786</td>\n",
              "      <td>0.7125</td>\n",
              "      <td>1</td>\n",
              "      <td>True</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23809</th>\n",
              "      <td>id_fffb1ceed</td>\n",
              "      <td>df1d0a5a1</td>\n",
              "      <td>0.1394</td>\n",
              "      <td>-0.0636</td>\n",
              "      <td>-0.1112</td>\n",
              "      <td>-0.5080</td>\n",
              "      <td>-0.4713</td>\n",
              "      <td>0.7201</td>\n",
              "      <td>0.5773</td>\n",
              "      <td>0.3055</td>\n",
              "      <td>-0.4726</td>\n",
              "      <td>0.1269</td>\n",
              "      <td>0.2531</td>\n",
              "      <td>0.1730</td>\n",
              "      <td>-0.4532</td>\n",
              "      <td>-1.0790</td>\n",
              "      <td>0.2474</td>\n",
              "      <td>-0.4550</td>\n",
              "      <td>0.3588</td>\n",
              "      <td>0.1600</td>\n",
              "      <td>-0.7362</td>\n",
              "      <td>-0.1103</td>\n",
              "      <td>0.8550</td>\n",
              "      <td>-0.4139</td>\n",
              "      <td>0.5541</td>\n",
              "      <td>0.2310</td>\n",
              "      <td>-0.5573</td>\n",
              "      <td>-0.4397</td>\n",
              "      <td>-0.9260</td>\n",
              "      <td>-0.2424</td>\n",
              "      <td>-0.6686</td>\n",
              "      <td>0.2326</td>\n",
              "      <td>0.6456</td>\n",
              "      <td>0.0136</td>\n",
              "      <td>-0.5141</td>\n",
              "      <td>-0.6320</td>\n",
              "      <td>0.7166</td>\n",
              "      <td>-0.1736</td>\n",
              "      <td>0.3686</td>\n",
              "      <td>-0.1565</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.6158</td>\n",
              "      <td>-0.2977</td>\n",
              "      <td>0.0992</td>\n",
              "      <td>0.6838</td>\n",
              "      <td>0.5259</td>\n",
              "      <td>0.7882</td>\n",
              "      <td>0.3119</td>\n",
              "      <td>-0.7697</td>\n",
              "      <td>0.2203</td>\n",
              "      <td>-1.0710</td>\n",
              "      <td>0.5979</td>\n",
              "      <td>0.0848</td>\n",
              "      <td>-0.2555</td>\n",
              "      <td>0.6293</td>\n",
              "      <td>1.1660</td>\n",
              "      <td>0.3329</td>\n",
              "      <td>0.2754</td>\n",
              "      <td>0.4108</td>\n",
              "      <td>-0.1252</td>\n",
              "      <td>-0.2340</td>\n",
              "      <td>0.2267</td>\n",
              "      <td>0.1969</td>\n",
              "      <td>0.0262</td>\n",
              "      <td>-0.8121</td>\n",
              "      <td>0.3434</td>\n",
              "      <td>0.5372</td>\n",
              "      <td>-0.3246</td>\n",
              "      <td>0.0631</td>\n",
              "      <td>0.9171</td>\n",
              "      <td>0.5258</td>\n",
              "      <td>0.4680</td>\n",
              "      <td>1</td>\n",
              "      <td>True</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23810</th>\n",
              "      <td>id_fffb70c0c</td>\n",
              "      <td>ecf3b6b74</td>\n",
              "      <td>-1.3260</td>\n",
              "      <td>0.3478</td>\n",
              "      <td>-0.3743</td>\n",
              "      <td>0.9905</td>\n",
              "      <td>-0.7178</td>\n",
              "      <td>0.6621</td>\n",
              "      <td>-0.2252</td>\n",
              "      <td>-0.5565</td>\n",
              "      <td>0.5112</td>\n",
              "      <td>0.6727</td>\n",
              "      <td>-0.1851</td>\n",
              "      <td>2.8650</td>\n",
              "      <td>-0.2140</td>\n",
              "      <td>-0.6153</td>\n",
              "      <td>0.8362</td>\n",
              "      <td>0.5584</td>\n",
              "      <td>-0.2589</td>\n",
              "      <td>0.1292</td>\n",
              "      <td>0.0148</td>\n",
              "      <td>0.0949</td>\n",
              "      <td>-0.2182</td>\n",
              "      <td>-0.9235</td>\n",
              "      <td>0.0749</td>\n",
              "      <td>-1.5910</td>\n",
              "      <td>-0.8359</td>\n",
              "      <td>-0.9217</td>\n",
              "      <td>0.3013</td>\n",
              "      <td>0.1716</td>\n",
              "      <td>0.0880</td>\n",
              "      <td>0.1842</td>\n",
              "      <td>0.1835</td>\n",
              "      <td>0.5436</td>\n",
              "      <td>-0.0533</td>\n",
              "      <td>-0.0491</td>\n",
              "      <td>0.9543</td>\n",
              "      <td>0.4626</td>\n",
              "      <td>0.0819</td>\n",
              "      <td>0.1586</td>\n",
              "      <td>...</td>\n",
              "      <td>1.0570</td>\n",
              "      <td>-0.3355</td>\n",
              "      <td>-0.2555</td>\n",
              "      <td>0.8219</td>\n",
              "      <td>0.6995</td>\n",
              "      <td>-0.2942</td>\n",
              "      <td>0.2408</td>\n",
              "      <td>-0.7781</td>\n",
              "      <td>-0.0929</td>\n",
              "      <td>-0.0329</td>\n",
              "      <td>0.0781</td>\n",
              "      <td>-1.4440</td>\n",
              "      <td>-0.0921</td>\n",
              "      <td>0.3188</td>\n",
              "      <td>-1.1080</td>\n",
              "      <td>0.4895</td>\n",
              "      <td>-0.2144</td>\n",
              "      <td>1.0960</td>\n",
              "      <td>0.3335</td>\n",
              "      <td>0.4444</td>\n",
              "      <td>-1.1130</td>\n",
              "      <td>0.4286</td>\n",
              "      <td>0.4426</td>\n",
              "      <td>0.0423</td>\n",
              "      <td>-0.3195</td>\n",
              "      <td>-0.8086</td>\n",
              "      <td>-0.9798</td>\n",
              "      <td>-0.2084</td>\n",
              "      <td>-0.1224</td>\n",
              "      <td>-0.2715</td>\n",
              "      <td>0.3689</td>\n",
              "      <td>1</td>\n",
              "      <td>True</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23811</th>\n",
              "      <td>id_fffc1c3f4</td>\n",
              "      <td>cacb2b860</td>\n",
              "      <td>0.3942</td>\n",
              "      <td>0.3756</td>\n",
              "      <td>0.3109</td>\n",
              "      <td>-0.7389</td>\n",
              "      <td>0.5505</td>\n",
              "      <td>-0.0159</td>\n",
              "      <td>-0.2541</td>\n",
              "      <td>0.1745</td>\n",
              "      <td>-0.0340</td>\n",
              "      <td>0.4865</td>\n",
              "      <td>-0.1854</td>\n",
              "      <td>0.0716</td>\n",
              "      <td>0.1729</td>\n",
              "      <td>-0.0434</td>\n",
              "      <td>0.1542</td>\n",
              "      <td>-0.2192</td>\n",
              "      <td>-0.0302</td>\n",
              "      <td>-0.4218</td>\n",
              "      <td>0.4057</td>\n",
              "      <td>-0.5372</td>\n",
              "      <td>0.1521</td>\n",
              "      <td>-0.2651</td>\n",
              "      <td>0.2310</td>\n",
              "      <td>-0.8101</td>\n",
              "      <td>0.4943</td>\n",
              "      <td>0.6905</td>\n",
              "      <td>-0.3720</td>\n",
              "      <td>-1.4110</td>\n",
              "      <td>0.4516</td>\n",
              "      <td>1.2300</td>\n",
              "      <td>-0.1949</td>\n",
              "      <td>-1.3280</td>\n",
              "      <td>-0.4276</td>\n",
              "      <td>-0.0040</td>\n",
              "      <td>-0.3086</td>\n",
              "      <td>-0.2355</td>\n",
              "      <td>-0.5222</td>\n",
              "      <td>-1.1470</td>\n",
              "      <td>...</td>\n",
              "      <td>1.4690</td>\n",
              "      <td>-0.6142</td>\n",
              "      <td>0.6068</td>\n",
              "      <td>0.3434</td>\n",
              "      <td>0.9880</td>\n",
              "      <td>-0.0468</td>\n",
              "      <td>-0.1882</td>\n",
              "      <td>-0.0087</td>\n",
              "      <td>-0.0356</td>\n",
              "      <td>0.5718</td>\n",
              "      <td>0.4971</td>\n",
              "      <td>0.1057</td>\n",
              "      <td>0.2111</td>\n",
              "      <td>0.6992</td>\n",
              "      <td>0.0708</td>\n",
              "      <td>0.6169</td>\n",
              "      <td>0.2248</td>\n",
              "      <td>0.5994</td>\n",
              "      <td>0.2689</td>\n",
              "      <td>0.0305</td>\n",
              "      <td>1.2320</td>\n",
              "      <td>0.5409</td>\n",
              "      <td>0.3755</td>\n",
              "      <td>0.7343</td>\n",
              "      <td>0.2807</td>\n",
              "      <td>0.4116</td>\n",
              "      <td>0.6422</td>\n",
              "      <td>0.2256</td>\n",
              "      <td>0.7592</td>\n",
              "      <td>0.6656</td>\n",
              "      <td>0.3808</td>\n",
              "      <td>0</td>\n",
              "      <td>False</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23812</th>\n",
              "      <td>id_fffcb9e7c</td>\n",
              "      <td>8b87a7a83</td>\n",
              "      <td>0.6660</td>\n",
              "      <td>0.2324</td>\n",
              "      <td>0.4392</td>\n",
              "      <td>0.2044</td>\n",
              "      <td>0.8531</td>\n",
              "      <td>-0.0343</td>\n",
              "      <td>0.0323</td>\n",
              "      <td>0.0463</td>\n",
              "      <td>0.4299</td>\n",
              "      <td>-0.7985</td>\n",
              "      <td>0.5742</td>\n",
              "      <td>0.1421</td>\n",
              "      <td>2.2700</td>\n",
              "      <td>0.2046</td>\n",
              "      <td>0.5363</td>\n",
              "      <td>-1.7330</td>\n",
              "      <td>0.1450</td>\n",
              "      <td>0.6097</td>\n",
              "      <td>0.2024</td>\n",
              "      <td>0.9865</td>\n",
              "      <td>-0.7805</td>\n",
              "      <td>0.9608</td>\n",
              "      <td>0.3440</td>\n",
              "      <td>2.7650</td>\n",
              "      <td>0.4925</td>\n",
              "      <td>0.6698</td>\n",
              "      <td>0.2374</td>\n",
              "      <td>-0.3372</td>\n",
              "      <td>0.8771</td>\n",
              "      <td>-2.6560</td>\n",
              "      <td>-0.2000</td>\n",
              "      <td>-0.2043</td>\n",
              "      <td>0.6797</td>\n",
              "      <td>-0.0248</td>\n",
              "      <td>-0.0927</td>\n",
              "      <td>1.8480</td>\n",
              "      <td>-2.7180</td>\n",
              "      <td>-5.3160</td>\n",
              "      <td>...</td>\n",
              "      <td>0.4524</td>\n",
              "      <td>0.4135</td>\n",
              "      <td>0.1356</td>\n",
              "      <td>-0.5801</td>\n",
              "      <td>0.0411</td>\n",
              "      <td>1.0240</td>\n",
              "      <td>1.0340</td>\n",
              "      <td>-0.0270</td>\n",
              "      <td>-0.4194</td>\n",
              "      <td>0.7403</td>\n",
              "      <td>-0.6793</td>\n",
              "      <td>-0.1423</td>\n",
              "      <td>0.7307</td>\n",
              "      <td>0.7946</td>\n",
              "      <td>-0.0650</td>\n",
              "      <td>0.9038</td>\n",
              "      <td>0.2324</td>\n",
              "      <td>0.9676</td>\n",
              "      <td>1.0940</td>\n",
              "      <td>0.4290</td>\n",
              "      <td>0.5187</td>\n",
              "      <td>-0.1105</td>\n",
              "      <td>0.4258</td>\n",
              "      <td>-0.2012</td>\n",
              "      <td>0.1506</td>\n",
              "      <td>1.5230</td>\n",
              "      <td>0.7101</td>\n",
              "      <td>0.1732</td>\n",
              "      <td>0.7015</td>\n",
              "      <td>-0.6290</td>\n",
              "      <td>0.0740</td>\n",
              "      <td>1</td>\n",
              "      <td>True</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23813</th>\n",
              "      <td>id_ffffdd77b</td>\n",
              "      <td>972f41291</td>\n",
              "      <td>-0.8598</td>\n",
              "      <td>1.0240</td>\n",
              "      <td>-0.1361</td>\n",
              "      <td>0.7952</td>\n",
              "      <td>-0.3611</td>\n",
              "      <td>-3.6750</td>\n",
              "      <td>-1.2420</td>\n",
              "      <td>0.9146</td>\n",
              "      <td>3.0790</td>\n",
              "      <td>1.2460</td>\n",
              "      <td>1.9460</td>\n",
              "      <td>1.4370</td>\n",
              "      <td>2.9780</td>\n",
              "      <td>2.2370</td>\n",
              "      <td>-0.6818</td>\n",
              "      <td>0.6870</td>\n",
              "      <td>-1.1060</td>\n",
              "      <td>0.0182</td>\n",
              "      <td>-0.9247</td>\n",
              "      <td>-0.0738</td>\n",
              "      <td>-0.1919</td>\n",
              "      <td>-0.7722</td>\n",
              "      <td>-1.4050</td>\n",
              "      <td>-1.0050</td>\n",
              "      <td>-1.1170</td>\n",
              "      <td>-0.5293</td>\n",
              "      <td>-1.1720</td>\n",
              "      <td>-0.2885</td>\n",
              "      <td>0.1599</td>\n",
              "      <td>-0.4250</td>\n",
              "      <td>0.3591</td>\n",
              "      <td>-0.1420</td>\n",
              "      <td>-0.9530</td>\n",
              "      <td>-0.2005</td>\n",
              "      <td>-1.8340</td>\n",
              "      <td>-0.5768</td>\n",
              "      <td>0.5145</td>\n",
              "      <td>-0.9913</td>\n",
              "      <td>...</td>\n",
              "      <td>-1.1690</td>\n",
              "      <td>-4.1460</td>\n",
              "      <td>-1.2670</td>\n",
              "      <td>-1.1300</td>\n",
              "      <td>-2.4390</td>\n",
              "      <td>0.1591</td>\n",
              "      <td>-2.2490</td>\n",
              "      <td>-2.5860</td>\n",
              "      <td>-1.9520</td>\n",
              "      <td>-2.1810</td>\n",
              "      <td>-4.6690</td>\n",
              "      <td>-3.9450</td>\n",
              "      <td>-2.9560</td>\n",
              "      <td>-2.7930</td>\n",
              "      <td>-2.1560</td>\n",
              "      <td>-2.4100</td>\n",
              "      <td>-1.8190</td>\n",
              "      <td>-3.3480</td>\n",
              "      <td>-0.1414</td>\n",
              "      <td>-2.6430</td>\n",
              "      <td>-2.5810</td>\n",
              "      <td>-3.3890</td>\n",
              "      <td>-1.7450</td>\n",
              "      <td>-6.6300</td>\n",
              "      <td>-4.0950</td>\n",
              "      <td>-7.3860</td>\n",
              "      <td>-1.4160</td>\n",
              "      <td>-3.5770</td>\n",
              "      <td>-0.4775</td>\n",
              "      <td>-2.1500</td>\n",
              "      <td>-4.2520</td>\n",
              "      <td>0</td>\n",
              "      <td>False</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>23814 rows × 883 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                 id   composto      g0  ...  tempo_24  tempo_48  tempo_72\n",
              "0      id_000644bb2  b68db1d53  1.0620  ...         1         0         0\n",
              "1      id_000779bfc  df89a8e5a  0.0743  ...         0         0         1\n",
              "2      id_000a6266a  18bb41b2c  0.6280  ...         0         1         0\n",
              "3      id_0015fd391  8c7f86626 -0.5138  ...         0         1         0\n",
              "4      id_001626bd3  7cbed3131 -0.3254  ...         0         0         1\n",
              "...             ...        ...     ...  ...       ...       ...       ...\n",
              "23809  id_fffb1ceed  df1d0a5a1  0.1394  ...         1         0         0\n",
              "23810  id_fffb70c0c  ecf3b6b74 -1.3260  ...         1         0         0\n",
              "23811  id_fffc1c3f4  cacb2b860  0.3942  ...         0         1         0\n",
              "23812  id_fffcb9e7c  8b87a7a83  0.6660  ...         1         0         0\n",
              "23813  id_ffffdd77b  972f41291 -0.8598  ...         0         0         1\n",
              "\n",
              "[23814 rows x 883 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 0
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cb6081ff"
      },
      "source": [
        "Aqui, estamos estabelecendo qual será a base de dados usada para modelar o novo algoritmo. Agora, não mais consideraremos apenas os dados genéticos, também levaremos em conta as demais variáveis, exceto as seguintes variáveis:\n",
        "\n",
        "    'id'  ->  não será relevante nesse momento pois é somente um número de identificação\n",
        "    'n_moa'  ->  não será relevante nesse momento pois é a quantidade de MoAs ativados\n",
        "    'ativo_moa'  ->  retiramos da base pois é a nossa variável resposta\n",
        "    'composto'  ->  não será relevante nesse momento pois identifica o composto usado e foge do que estamos buscando fazer\n",
        "\n",
        "Para retirar essas variáveis da nossa base de dados, usamos a função ```drop```, declaramos as variáveis a serem retiradas em forma de uma lista e identificamos que são colunas através do parâmetro ```axis = 1```."
      ],
      "id": "cb6081ff"
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "28ef528c",
        "outputId": "90ac7c3a-4ff6-4182-f391-2a64fc0a06b6"
      },
      "source": [
        "dados_combinados.drop(['id', 'n_moa', 'ativo_moa', 'composto'], axis=1)"
      ],
      "id": "28ef528c",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>tratamento</th>\n",
              "      <th>tempo</th>\n",
              "      <th>dose</th>\n",
              "      <th>g0</th>\n",
              "      <th>g1</th>\n",
              "      <th>g2</th>\n",
              "      <th>g3</th>\n",
              "      <th>g4</th>\n",
              "      <th>g5</th>\n",
              "      <th>g6</th>\n",
              "      <th>g7</th>\n",
              "      <th>g8</th>\n",
              "      <th>g9</th>\n",
              "      <th>g10</th>\n",
              "      <th>g11</th>\n",
              "      <th>g12</th>\n",
              "      <th>g13</th>\n",
              "      <th>g14</th>\n",
              "      <th>g15</th>\n",
              "      <th>g16</th>\n",
              "      <th>g17</th>\n",
              "      <th>g18</th>\n",
              "      <th>g19</th>\n",
              "      <th>g20</th>\n",
              "      <th>g21</th>\n",
              "      <th>g22</th>\n",
              "      <th>g23</th>\n",
              "      <th>g24</th>\n",
              "      <th>g25</th>\n",
              "      <th>g26</th>\n",
              "      <th>g27</th>\n",
              "      <th>g28</th>\n",
              "      <th>g29</th>\n",
              "      <th>g30</th>\n",
              "      <th>g31</th>\n",
              "      <th>g32</th>\n",
              "      <th>g33</th>\n",
              "      <th>g34</th>\n",
              "      <th>g35</th>\n",
              "      <th>g36</th>\n",
              "      <th>...</th>\n",
              "      <th>c60</th>\n",
              "      <th>c61</th>\n",
              "      <th>c62</th>\n",
              "      <th>c63</th>\n",
              "      <th>c64</th>\n",
              "      <th>c65</th>\n",
              "      <th>c66</th>\n",
              "      <th>c67</th>\n",
              "      <th>c68</th>\n",
              "      <th>c69</th>\n",
              "      <th>c70</th>\n",
              "      <th>c71</th>\n",
              "      <th>c72</th>\n",
              "      <th>c73</th>\n",
              "      <th>c74</th>\n",
              "      <th>c75</th>\n",
              "      <th>c76</th>\n",
              "      <th>c77</th>\n",
              "      <th>c78</th>\n",
              "      <th>c79</th>\n",
              "      <th>c80</th>\n",
              "      <th>c81</th>\n",
              "      <th>c82</th>\n",
              "      <th>c83</th>\n",
              "      <th>c84</th>\n",
              "      <th>c85</th>\n",
              "      <th>c86</th>\n",
              "      <th>c87</th>\n",
              "      <th>c88</th>\n",
              "      <th>c89</th>\n",
              "      <th>c90</th>\n",
              "      <th>c91</th>\n",
              "      <th>c92</th>\n",
              "      <th>c93</th>\n",
              "      <th>c94</th>\n",
              "      <th>c95</th>\n",
              "      <th>c96</th>\n",
              "      <th>c97</th>\n",
              "      <th>c98</th>\n",
              "      <th>c99</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>com_droga</td>\n",
              "      <td>24</td>\n",
              "      <td>D1</td>\n",
              "      <td>1.0620</td>\n",
              "      <td>0.5577</td>\n",
              "      <td>-0.2479</td>\n",
              "      <td>-0.6208</td>\n",
              "      <td>-0.1944</td>\n",
              "      <td>-1.0120</td>\n",
              "      <td>-1.0220</td>\n",
              "      <td>-0.0326</td>\n",
              "      <td>0.5548</td>\n",
              "      <td>-0.0921</td>\n",
              "      <td>1.1830</td>\n",
              "      <td>0.1530</td>\n",
              "      <td>0.5574</td>\n",
              "      <td>-0.4015</td>\n",
              "      <td>0.1789</td>\n",
              "      <td>-0.6528</td>\n",
              "      <td>-0.7969</td>\n",
              "      <td>0.6342</td>\n",
              "      <td>0.1778</td>\n",
              "      <td>-0.3694</td>\n",
              "      <td>-0.5688</td>\n",
              "      <td>-1.1360</td>\n",
              "      <td>-1.1880</td>\n",
              "      <td>0.6940</td>\n",
              "      <td>0.4393</td>\n",
              "      <td>0.2664</td>\n",
              "      <td>0.1907</td>\n",
              "      <td>0.1628</td>\n",
              "      <td>-0.2853</td>\n",
              "      <td>0.5819</td>\n",
              "      <td>0.2934</td>\n",
              "      <td>-0.5584</td>\n",
              "      <td>-0.0916</td>\n",
              "      <td>-0.3010</td>\n",
              "      <td>-0.1537</td>\n",
              "      <td>0.2198</td>\n",
              "      <td>0.2965</td>\n",
              "      <td>...</td>\n",
              "      <td>0.4805</td>\n",
              "      <td>0.4965</td>\n",
              "      <td>0.3680</td>\n",
              "      <td>0.8427</td>\n",
              "      <td>0.1042</td>\n",
              "      <td>0.1403</td>\n",
              "      <td>0.1758</td>\n",
              "      <td>1.2570</td>\n",
              "      <td>-0.5979</td>\n",
              "      <td>1.2250</td>\n",
              "      <td>-0.0553</td>\n",
              "      <td>0.7351</td>\n",
              "      <td>0.5810</td>\n",
              "      <td>0.9590</td>\n",
              "      <td>0.2427</td>\n",
              "      <td>0.0495</td>\n",
              "      <td>0.4141</td>\n",
              "      <td>0.8432</td>\n",
              "      <td>0.6162</td>\n",
              "      <td>-0.7318</td>\n",
              "      <td>1.2120</td>\n",
              "      <td>0.6362</td>\n",
              "      <td>-0.4427</td>\n",
              "      <td>0.1288</td>\n",
              "      <td>1.4840</td>\n",
              "      <td>0.1799</td>\n",
              "      <td>0.5367</td>\n",
              "      <td>-0.1111</td>\n",
              "      <td>-1.0120</td>\n",
              "      <td>0.6685</td>\n",
              "      <td>0.2862</td>\n",
              "      <td>0.2584</td>\n",
              "      <td>0.8076</td>\n",
              "      <td>0.5523</td>\n",
              "      <td>-0.1912</td>\n",
              "      <td>0.6584</td>\n",
              "      <td>-0.3981</td>\n",
              "      <td>0.2139</td>\n",
              "      <td>0.3801</td>\n",
              "      <td>0.4176</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>com_droga</td>\n",
              "      <td>72</td>\n",
              "      <td>D1</td>\n",
              "      <td>0.0743</td>\n",
              "      <td>0.4087</td>\n",
              "      <td>0.2991</td>\n",
              "      <td>0.0604</td>\n",
              "      <td>1.0190</td>\n",
              "      <td>0.5207</td>\n",
              "      <td>0.2341</td>\n",
              "      <td>0.3372</td>\n",
              "      <td>-0.4047</td>\n",
              "      <td>0.8507</td>\n",
              "      <td>-1.1520</td>\n",
              "      <td>-0.4201</td>\n",
              "      <td>-0.0958</td>\n",
              "      <td>0.4590</td>\n",
              "      <td>0.0803</td>\n",
              "      <td>0.2250</td>\n",
              "      <td>0.5293</td>\n",
              "      <td>0.2839</td>\n",
              "      <td>-0.3494</td>\n",
              "      <td>0.2883</td>\n",
              "      <td>0.9449</td>\n",
              "      <td>-0.1646</td>\n",
              "      <td>-0.2657</td>\n",
              "      <td>-0.3372</td>\n",
              "      <td>0.3135</td>\n",
              "      <td>-0.4316</td>\n",
              "      <td>0.4773</td>\n",
              "      <td>0.2075</td>\n",
              "      <td>-0.4216</td>\n",
              "      <td>-0.1161</td>\n",
              "      <td>-0.0499</td>\n",
              "      <td>-0.2627</td>\n",
              "      <td>0.9959</td>\n",
              "      <td>-0.2483</td>\n",
              "      <td>0.2655</td>\n",
              "      <td>-0.2102</td>\n",
              "      <td>0.1656</td>\n",
              "      <td>...</td>\n",
              "      <td>0.4083</td>\n",
              "      <td>0.0319</td>\n",
              "      <td>0.3905</td>\n",
              "      <td>0.7099</td>\n",
              "      <td>0.2912</td>\n",
              "      <td>0.4151</td>\n",
              "      <td>-0.2840</td>\n",
              "      <td>-0.3104</td>\n",
              "      <td>-0.6373</td>\n",
              "      <td>0.2887</td>\n",
              "      <td>-0.0765</td>\n",
              "      <td>0.2539</td>\n",
              "      <td>0.4443</td>\n",
              "      <td>0.5932</td>\n",
              "      <td>0.2031</td>\n",
              "      <td>0.7639</td>\n",
              "      <td>0.5499</td>\n",
              "      <td>-0.3322</td>\n",
              "      <td>-0.0977</td>\n",
              "      <td>0.4329</td>\n",
              "      <td>-0.2782</td>\n",
              "      <td>0.7827</td>\n",
              "      <td>0.5934</td>\n",
              "      <td>0.3402</td>\n",
              "      <td>0.1499</td>\n",
              "      <td>0.4420</td>\n",
              "      <td>0.9366</td>\n",
              "      <td>0.8193</td>\n",
              "      <td>-0.4236</td>\n",
              "      <td>0.3192</td>\n",
              "      <td>-0.4265</td>\n",
              "      <td>0.7543</td>\n",
              "      <td>0.4708</td>\n",
              "      <td>0.0230</td>\n",
              "      <td>0.2957</td>\n",
              "      <td>0.4899</td>\n",
              "      <td>0.1522</td>\n",
              "      <td>0.1241</td>\n",
              "      <td>0.6077</td>\n",
              "      <td>0.7371</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>com_droga</td>\n",
              "      <td>48</td>\n",
              "      <td>D1</td>\n",
              "      <td>0.6280</td>\n",
              "      <td>0.5817</td>\n",
              "      <td>1.5540</td>\n",
              "      <td>-0.0764</td>\n",
              "      <td>-0.0323</td>\n",
              "      <td>1.2390</td>\n",
              "      <td>0.1715</td>\n",
              "      <td>0.2155</td>\n",
              "      <td>0.0065</td>\n",
              "      <td>1.2300</td>\n",
              "      <td>-0.4797</td>\n",
              "      <td>-0.5631</td>\n",
              "      <td>-0.0366</td>\n",
              "      <td>-1.8300</td>\n",
              "      <td>0.6057</td>\n",
              "      <td>-0.3278</td>\n",
              "      <td>0.6042</td>\n",
              "      <td>-0.3075</td>\n",
              "      <td>-0.1147</td>\n",
              "      <td>-0.0570</td>\n",
              "      <td>-0.0799</td>\n",
              "      <td>-0.8181</td>\n",
              "      <td>-1.5320</td>\n",
              "      <td>0.2307</td>\n",
              "      <td>0.4901</td>\n",
              "      <td>0.4780</td>\n",
              "      <td>-1.3970</td>\n",
              "      <td>4.6240</td>\n",
              "      <td>-0.0437</td>\n",
              "      <td>1.2870</td>\n",
              "      <td>-1.8530</td>\n",
              "      <td>0.6069</td>\n",
              "      <td>0.4290</td>\n",
              "      <td>0.1783</td>\n",
              "      <td>0.0018</td>\n",
              "      <td>-1.1800</td>\n",
              "      <td>0.1256</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.5477</td>\n",
              "      <td>-0.7576</td>\n",
              "      <td>-0.0444</td>\n",
              "      <td>0.1894</td>\n",
              "      <td>-0.0014</td>\n",
              "      <td>-2.3640</td>\n",
              "      <td>-0.4682</td>\n",
              "      <td>0.1210</td>\n",
              "      <td>-0.5177</td>\n",
              "      <td>-0.0604</td>\n",
              "      <td>0.1682</td>\n",
              "      <td>-0.4436</td>\n",
              "      <td>0.4963</td>\n",
              "      <td>0.1363</td>\n",
              "      <td>0.3335</td>\n",
              "      <td>0.9760</td>\n",
              "      <td>-0.0427</td>\n",
              "      <td>-0.1235</td>\n",
              "      <td>0.0959</td>\n",
              "      <td>0.0690</td>\n",
              "      <td>-0.9416</td>\n",
              "      <td>-0.7548</td>\n",
              "      <td>-0.1109</td>\n",
              "      <td>-0.6272</td>\n",
              "      <td>0.3019</td>\n",
              "      <td>0.1172</td>\n",
              "      <td>0.1093</td>\n",
              "      <td>-0.3113</td>\n",
              "      <td>0.3019</td>\n",
              "      <td>-0.0873</td>\n",
              "      <td>-0.7250</td>\n",
              "      <td>-0.6297</td>\n",
              "      <td>0.6103</td>\n",
              "      <td>0.0223</td>\n",
              "      <td>-1.3240</td>\n",
              "      <td>-0.3174</td>\n",
              "      <td>-0.6417</td>\n",
              "      <td>-0.2187</td>\n",
              "      <td>-1.4080</td>\n",
              "      <td>0.6931</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>com_droga</td>\n",
              "      <td>48</td>\n",
              "      <td>D1</td>\n",
              "      <td>-0.5138</td>\n",
              "      <td>-0.2491</td>\n",
              "      <td>-0.2656</td>\n",
              "      <td>0.5288</td>\n",
              "      <td>4.0620</td>\n",
              "      <td>-0.8095</td>\n",
              "      <td>-1.9590</td>\n",
              "      <td>0.1792</td>\n",
              "      <td>-0.1321</td>\n",
              "      <td>-1.0600</td>\n",
              "      <td>-0.8269</td>\n",
              "      <td>-0.3584</td>\n",
              "      <td>-0.8511</td>\n",
              "      <td>-0.5844</td>\n",
              "      <td>-2.5690</td>\n",
              "      <td>0.8183</td>\n",
              "      <td>-0.0532</td>\n",
              "      <td>-0.8554</td>\n",
              "      <td>0.1160</td>\n",
              "      <td>-2.3520</td>\n",
              "      <td>2.1200</td>\n",
              "      <td>-1.1580</td>\n",
              "      <td>-0.7191</td>\n",
              "      <td>-0.8004</td>\n",
              "      <td>-1.4670</td>\n",
              "      <td>-0.0107</td>\n",
              "      <td>-0.8995</td>\n",
              "      <td>0.2406</td>\n",
              "      <td>-0.2479</td>\n",
              "      <td>-1.0890</td>\n",
              "      <td>-0.7575</td>\n",
              "      <td>0.0881</td>\n",
              "      <td>-2.7370</td>\n",
              "      <td>0.8745</td>\n",
              "      <td>0.5787</td>\n",
              "      <td>-1.6740</td>\n",
              "      <td>-1.6720</td>\n",
              "      <td>...</td>\n",
              "      <td>-2.1220</td>\n",
              "      <td>-0.3752</td>\n",
              "      <td>-2.3820</td>\n",
              "      <td>-3.7350</td>\n",
              "      <td>-2.9740</td>\n",
              "      <td>-1.4930</td>\n",
              "      <td>-1.6600</td>\n",
              "      <td>-3.1660</td>\n",
              "      <td>0.2816</td>\n",
              "      <td>-0.2990</td>\n",
              "      <td>-1.1870</td>\n",
              "      <td>-0.5044</td>\n",
              "      <td>-1.7750</td>\n",
              "      <td>-1.6120</td>\n",
              "      <td>-0.9215</td>\n",
              "      <td>-1.0810</td>\n",
              "      <td>-3.0520</td>\n",
              "      <td>-3.4470</td>\n",
              "      <td>-2.7740</td>\n",
              "      <td>-1.8460</td>\n",
              "      <td>-0.5568</td>\n",
              "      <td>-3.3960</td>\n",
              "      <td>-2.9510</td>\n",
              "      <td>-1.1550</td>\n",
              "      <td>-3.2620</td>\n",
              "      <td>-1.5390</td>\n",
              "      <td>-2.4600</td>\n",
              "      <td>-0.9417</td>\n",
              "      <td>-1.5550</td>\n",
              "      <td>0.2431</td>\n",
              "      <td>-2.0990</td>\n",
              "      <td>-0.6441</td>\n",
              "      <td>-5.6300</td>\n",
              "      <td>-1.3780</td>\n",
              "      <td>-0.8632</td>\n",
              "      <td>-1.2880</td>\n",
              "      <td>-1.6210</td>\n",
              "      <td>-0.8784</td>\n",
              "      <td>-0.3876</td>\n",
              "      <td>-0.8154</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>com_droga</td>\n",
              "      <td>72</td>\n",
              "      <td>D2</td>\n",
              "      <td>-0.3254</td>\n",
              "      <td>-0.4009</td>\n",
              "      <td>0.9700</td>\n",
              "      <td>0.6919</td>\n",
              "      <td>1.4180</td>\n",
              "      <td>-0.8244</td>\n",
              "      <td>-0.2800</td>\n",
              "      <td>-0.1498</td>\n",
              "      <td>-0.8789</td>\n",
              "      <td>0.8630</td>\n",
              "      <td>-0.2219</td>\n",
              "      <td>-0.5121</td>\n",
              "      <td>-0.9577</td>\n",
              "      <td>1.1750</td>\n",
              "      <td>0.2042</td>\n",
              "      <td>0.1970</td>\n",
              "      <td>0.1244</td>\n",
              "      <td>-1.7090</td>\n",
              "      <td>-0.3543</td>\n",
              "      <td>-0.5160</td>\n",
              "      <td>-0.3330</td>\n",
              "      <td>-0.2685</td>\n",
              "      <td>0.7649</td>\n",
              "      <td>0.2057</td>\n",
              "      <td>1.3720</td>\n",
              "      <td>0.6835</td>\n",
              "      <td>0.8056</td>\n",
              "      <td>-0.3754</td>\n",
              "      <td>-1.2090</td>\n",
              "      <td>0.2965</td>\n",
              "      <td>-0.0712</td>\n",
              "      <td>0.6389</td>\n",
              "      <td>0.6674</td>\n",
              "      <td>-0.0783</td>\n",
              "      <td>1.1740</td>\n",
              "      <td>-0.7110</td>\n",
              "      <td>-1.4470</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.2274</td>\n",
              "      <td>0.3215</td>\n",
              "      <td>0.1535</td>\n",
              "      <td>-0.4640</td>\n",
              "      <td>-0.5943</td>\n",
              "      <td>0.3973</td>\n",
              "      <td>0.1500</td>\n",
              "      <td>0.5178</td>\n",
              "      <td>0.5159</td>\n",
              "      <td>0.6091</td>\n",
              "      <td>0.1813</td>\n",
              "      <td>-0.4249</td>\n",
              "      <td>0.7832</td>\n",
              "      <td>0.6529</td>\n",
              "      <td>0.5648</td>\n",
              "      <td>0.4817</td>\n",
              "      <td>0.0587</td>\n",
              "      <td>0.5303</td>\n",
              "      <td>0.6376</td>\n",
              "      <td>-0.3966</td>\n",
              "      <td>-1.4950</td>\n",
              "      <td>-0.9625</td>\n",
              "      <td>-0.0541</td>\n",
              "      <td>0.6273</td>\n",
              "      <td>0.4563</td>\n",
              "      <td>0.0698</td>\n",
              "      <td>0.8134</td>\n",
              "      <td>0.1924</td>\n",
              "      <td>0.6054</td>\n",
              "      <td>-0.1824</td>\n",
              "      <td>0.0042</td>\n",
              "      <td>0.0048</td>\n",
              "      <td>0.6670</td>\n",
              "      <td>1.0690</td>\n",
              "      <td>0.5523</td>\n",
              "      <td>-0.3031</td>\n",
              "      <td>0.1094</td>\n",
              "      <td>0.2885</td>\n",
              "      <td>-0.3786</td>\n",
              "      <td>0.7125</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23809</th>\n",
              "      <td>com_droga</td>\n",
              "      <td>24</td>\n",
              "      <td>D2</td>\n",
              "      <td>0.1394</td>\n",
              "      <td>-0.0636</td>\n",
              "      <td>-0.1112</td>\n",
              "      <td>-0.5080</td>\n",
              "      <td>-0.4713</td>\n",
              "      <td>0.7201</td>\n",
              "      <td>0.5773</td>\n",
              "      <td>0.3055</td>\n",
              "      <td>-0.4726</td>\n",
              "      <td>0.1269</td>\n",
              "      <td>0.2531</td>\n",
              "      <td>0.1730</td>\n",
              "      <td>-0.4532</td>\n",
              "      <td>-1.0790</td>\n",
              "      <td>0.2474</td>\n",
              "      <td>-0.4550</td>\n",
              "      <td>0.3588</td>\n",
              "      <td>0.1600</td>\n",
              "      <td>-0.7362</td>\n",
              "      <td>-0.1103</td>\n",
              "      <td>0.8550</td>\n",
              "      <td>-0.4139</td>\n",
              "      <td>0.5541</td>\n",
              "      <td>0.2310</td>\n",
              "      <td>-0.5573</td>\n",
              "      <td>-0.4397</td>\n",
              "      <td>-0.9260</td>\n",
              "      <td>-0.2424</td>\n",
              "      <td>-0.6686</td>\n",
              "      <td>0.2326</td>\n",
              "      <td>0.6456</td>\n",
              "      <td>0.0136</td>\n",
              "      <td>-0.5141</td>\n",
              "      <td>-0.6320</td>\n",
              "      <td>0.7166</td>\n",
              "      <td>-0.1736</td>\n",
              "      <td>0.3686</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0807</td>\n",
              "      <td>0.4024</td>\n",
              "      <td>-0.0895</td>\n",
              "      <td>0.4420</td>\n",
              "      <td>0.9641</td>\n",
              "      <td>-0.1846</td>\n",
              "      <td>-0.0014</td>\n",
              "      <td>0.3154</td>\n",
              "      <td>-0.2071</td>\n",
              "      <td>-0.6158</td>\n",
              "      <td>-0.2977</td>\n",
              "      <td>0.0992</td>\n",
              "      <td>0.6838</td>\n",
              "      <td>0.5259</td>\n",
              "      <td>0.7882</td>\n",
              "      <td>0.3119</td>\n",
              "      <td>-0.7697</td>\n",
              "      <td>0.2203</td>\n",
              "      <td>-1.0710</td>\n",
              "      <td>0.5979</td>\n",
              "      <td>0.0848</td>\n",
              "      <td>-0.2555</td>\n",
              "      <td>0.6293</td>\n",
              "      <td>1.1660</td>\n",
              "      <td>0.3329</td>\n",
              "      <td>0.2754</td>\n",
              "      <td>0.4108</td>\n",
              "      <td>-0.1252</td>\n",
              "      <td>-0.2340</td>\n",
              "      <td>0.2267</td>\n",
              "      <td>0.1969</td>\n",
              "      <td>0.0262</td>\n",
              "      <td>-0.8121</td>\n",
              "      <td>0.3434</td>\n",
              "      <td>0.5372</td>\n",
              "      <td>-0.3246</td>\n",
              "      <td>0.0631</td>\n",
              "      <td>0.9171</td>\n",
              "      <td>0.5258</td>\n",
              "      <td>0.4680</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23810</th>\n",
              "      <td>com_droga</td>\n",
              "      <td>24</td>\n",
              "      <td>D2</td>\n",
              "      <td>-1.3260</td>\n",
              "      <td>0.3478</td>\n",
              "      <td>-0.3743</td>\n",
              "      <td>0.9905</td>\n",
              "      <td>-0.7178</td>\n",
              "      <td>0.6621</td>\n",
              "      <td>-0.2252</td>\n",
              "      <td>-0.5565</td>\n",
              "      <td>0.5112</td>\n",
              "      <td>0.6727</td>\n",
              "      <td>-0.1851</td>\n",
              "      <td>2.8650</td>\n",
              "      <td>-0.2140</td>\n",
              "      <td>-0.6153</td>\n",
              "      <td>0.8362</td>\n",
              "      <td>0.5584</td>\n",
              "      <td>-0.2589</td>\n",
              "      <td>0.1292</td>\n",
              "      <td>0.0148</td>\n",
              "      <td>0.0949</td>\n",
              "      <td>-0.2182</td>\n",
              "      <td>-0.9235</td>\n",
              "      <td>0.0749</td>\n",
              "      <td>-1.5910</td>\n",
              "      <td>-0.8359</td>\n",
              "      <td>-0.9217</td>\n",
              "      <td>0.3013</td>\n",
              "      <td>0.1716</td>\n",
              "      <td>0.0880</td>\n",
              "      <td>0.1842</td>\n",
              "      <td>0.1835</td>\n",
              "      <td>0.5436</td>\n",
              "      <td>-0.0533</td>\n",
              "      <td>-0.0491</td>\n",
              "      <td>0.9543</td>\n",
              "      <td>0.4626</td>\n",
              "      <td>0.0819</td>\n",
              "      <td>...</td>\n",
              "      <td>0.1410</td>\n",
              "      <td>1.2640</td>\n",
              "      <td>-0.8663</td>\n",
              "      <td>0.8129</td>\n",
              "      <td>-0.1514</td>\n",
              "      <td>-0.4652</td>\n",
              "      <td>-0.7390</td>\n",
              "      <td>-1.3270</td>\n",
              "      <td>0.9925</td>\n",
              "      <td>1.0570</td>\n",
              "      <td>-0.3355</td>\n",
              "      <td>-0.2555</td>\n",
              "      <td>0.8219</td>\n",
              "      <td>0.6995</td>\n",
              "      <td>-0.2942</td>\n",
              "      <td>0.2408</td>\n",
              "      <td>-0.7781</td>\n",
              "      <td>-0.0929</td>\n",
              "      <td>-0.0329</td>\n",
              "      <td>0.0781</td>\n",
              "      <td>-1.4440</td>\n",
              "      <td>-0.0921</td>\n",
              "      <td>0.3188</td>\n",
              "      <td>-1.1080</td>\n",
              "      <td>0.4895</td>\n",
              "      <td>-0.2144</td>\n",
              "      <td>1.0960</td>\n",
              "      <td>0.3335</td>\n",
              "      <td>0.4444</td>\n",
              "      <td>-1.1130</td>\n",
              "      <td>0.4286</td>\n",
              "      <td>0.4426</td>\n",
              "      <td>0.0423</td>\n",
              "      <td>-0.3195</td>\n",
              "      <td>-0.8086</td>\n",
              "      <td>-0.9798</td>\n",
              "      <td>-0.2084</td>\n",
              "      <td>-0.1224</td>\n",
              "      <td>-0.2715</td>\n",
              "      <td>0.3689</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23811</th>\n",
              "      <td>com_controle</td>\n",
              "      <td>48</td>\n",
              "      <td>D2</td>\n",
              "      <td>0.3942</td>\n",
              "      <td>0.3756</td>\n",
              "      <td>0.3109</td>\n",
              "      <td>-0.7389</td>\n",
              "      <td>0.5505</td>\n",
              "      <td>-0.0159</td>\n",
              "      <td>-0.2541</td>\n",
              "      <td>0.1745</td>\n",
              "      <td>-0.0340</td>\n",
              "      <td>0.4865</td>\n",
              "      <td>-0.1854</td>\n",
              "      <td>0.0716</td>\n",
              "      <td>0.1729</td>\n",
              "      <td>-0.0434</td>\n",
              "      <td>0.1542</td>\n",
              "      <td>-0.2192</td>\n",
              "      <td>-0.0302</td>\n",
              "      <td>-0.4218</td>\n",
              "      <td>0.4057</td>\n",
              "      <td>-0.5372</td>\n",
              "      <td>0.1521</td>\n",
              "      <td>-0.2651</td>\n",
              "      <td>0.2310</td>\n",
              "      <td>-0.8101</td>\n",
              "      <td>0.4943</td>\n",
              "      <td>0.6905</td>\n",
              "      <td>-0.3720</td>\n",
              "      <td>-1.4110</td>\n",
              "      <td>0.4516</td>\n",
              "      <td>1.2300</td>\n",
              "      <td>-0.1949</td>\n",
              "      <td>-1.3280</td>\n",
              "      <td>-0.4276</td>\n",
              "      <td>-0.0040</td>\n",
              "      <td>-0.3086</td>\n",
              "      <td>-0.2355</td>\n",
              "      <td>-0.5222</td>\n",
              "      <td>...</td>\n",
              "      <td>0.6845</td>\n",
              "      <td>0.7127</td>\n",
              "      <td>0.7294</td>\n",
              "      <td>0.4718</td>\n",
              "      <td>-0.2020</td>\n",
              "      <td>0.2783</td>\n",
              "      <td>0.4934</td>\n",
              "      <td>0.4144</td>\n",
              "      <td>0.5449</td>\n",
              "      <td>1.4690</td>\n",
              "      <td>-0.6142</td>\n",
              "      <td>0.6068</td>\n",
              "      <td>0.3434</td>\n",
              "      <td>0.9880</td>\n",
              "      <td>-0.0468</td>\n",
              "      <td>-0.1882</td>\n",
              "      <td>-0.0087</td>\n",
              "      <td>-0.0356</td>\n",
              "      <td>0.5718</td>\n",
              "      <td>0.4971</td>\n",
              "      <td>0.1057</td>\n",
              "      <td>0.2111</td>\n",
              "      <td>0.6992</td>\n",
              "      <td>0.0708</td>\n",
              "      <td>0.6169</td>\n",
              "      <td>0.2248</td>\n",
              "      <td>0.5994</td>\n",
              "      <td>0.2689</td>\n",
              "      <td>0.0305</td>\n",
              "      <td>1.2320</td>\n",
              "      <td>0.5409</td>\n",
              "      <td>0.3755</td>\n",
              "      <td>0.7343</td>\n",
              "      <td>0.2807</td>\n",
              "      <td>0.4116</td>\n",
              "      <td>0.6422</td>\n",
              "      <td>0.2256</td>\n",
              "      <td>0.7592</td>\n",
              "      <td>0.6656</td>\n",
              "      <td>0.3808</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23812</th>\n",
              "      <td>com_droga</td>\n",
              "      <td>24</td>\n",
              "      <td>D1</td>\n",
              "      <td>0.6660</td>\n",
              "      <td>0.2324</td>\n",
              "      <td>0.4392</td>\n",
              "      <td>0.2044</td>\n",
              "      <td>0.8531</td>\n",
              "      <td>-0.0343</td>\n",
              "      <td>0.0323</td>\n",
              "      <td>0.0463</td>\n",
              "      <td>0.4299</td>\n",
              "      <td>-0.7985</td>\n",
              "      <td>0.5742</td>\n",
              "      <td>0.1421</td>\n",
              "      <td>2.2700</td>\n",
              "      <td>0.2046</td>\n",
              "      <td>0.5363</td>\n",
              "      <td>-1.7330</td>\n",
              "      <td>0.1450</td>\n",
              "      <td>0.6097</td>\n",
              "      <td>0.2024</td>\n",
              "      <td>0.9865</td>\n",
              "      <td>-0.7805</td>\n",
              "      <td>0.9608</td>\n",
              "      <td>0.3440</td>\n",
              "      <td>2.7650</td>\n",
              "      <td>0.4925</td>\n",
              "      <td>0.6698</td>\n",
              "      <td>0.2374</td>\n",
              "      <td>-0.3372</td>\n",
              "      <td>0.8771</td>\n",
              "      <td>-2.6560</td>\n",
              "      <td>-0.2000</td>\n",
              "      <td>-0.2043</td>\n",
              "      <td>0.6797</td>\n",
              "      <td>-0.0248</td>\n",
              "      <td>-0.0927</td>\n",
              "      <td>1.8480</td>\n",
              "      <td>-2.7180</td>\n",
              "      <td>...</td>\n",
              "      <td>0.3360</td>\n",
              "      <td>-0.6136</td>\n",
              "      <td>0.5011</td>\n",
              "      <td>0.9261</td>\n",
              "      <td>0.4419</td>\n",
              "      <td>0.0295</td>\n",
              "      <td>0.4220</td>\n",
              "      <td>0.4677</td>\n",
              "      <td>-0.1184</td>\n",
              "      <td>0.4524</td>\n",
              "      <td>0.4135</td>\n",
              "      <td>0.1356</td>\n",
              "      <td>-0.5801</td>\n",
              "      <td>0.0411</td>\n",
              "      <td>1.0240</td>\n",
              "      <td>1.0340</td>\n",
              "      <td>-0.0270</td>\n",
              "      <td>-0.4194</td>\n",
              "      <td>0.7403</td>\n",
              "      <td>-0.6793</td>\n",
              "      <td>-0.1423</td>\n",
              "      <td>0.7307</td>\n",
              "      <td>0.7946</td>\n",
              "      <td>-0.0650</td>\n",
              "      <td>0.9038</td>\n",
              "      <td>0.2324</td>\n",
              "      <td>0.9676</td>\n",
              "      <td>1.0940</td>\n",
              "      <td>0.4290</td>\n",
              "      <td>0.5187</td>\n",
              "      <td>-0.1105</td>\n",
              "      <td>0.4258</td>\n",
              "      <td>-0.2012</td>\n",
              "      <td>0.1506</td>\n",
              "      <td>1.5230</td>\n",
              "      <td>0.7101</td>\n",
              "      <td>0.1732</td>\n",
              "      <td>0.7015</td>\n",
              "      <td>-0.6290</td>\n",
              "      <td>0.0740</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23813</th>\n",
              "      <td>com_droga</td>\n",
              "      <td>72</td>\n",
              "      <td>D1</td>\n",
              "      <td>-0.8598</td>\n",
              "      <td>1.0240</td>\n",
              "      <td>-0.1361</td>\n",
              "      <td>0.7952</td>\n",
              "      <td>-0.3611</td>\n",
              "      <td>-3.6750</td>\n",
              "      <td>-1.2420</td>\n",
              "      <td>0.9146</td>\n",
              "      <td>3.0790</td>\n",
              "      <td>1.2460</td>\n",
              "      <td>1.9460</td>\n",
              "      <td>1.4370</td>\n",
              "      <td>2.9780</td>\n",
              "      <td>2.2370</td>\n",
              "      <td>-0.6818</td>\n",
              "      <td>0.6870</td>\n",
              "      <td>-1.1060</td>\n",
              "      <td>0.0182</td>\n",
              "      <td>-0.9247</td>\n",
              "      <td>-0.0738</td>\n",
              "      <td>-0.1919</td>\n",
              "      <td>-0.7722</td>\n",
              "      <td>-1.4050</td>\n",
              "      <td>-1.0050</td>\n",
              "      <td>-1.1170</td>\n",
              "      <td>-0.5293</td>\n",
              "      <td>-1.1720</td>\n",
              "      <td>-0.2885</td>\n",
              "      <td>0.1599</td>\n",
              "      <td>-0.4250</td>\n",
              "      <td>0.3591</td>\n",
              "      <td>-0.1420</td>\n",
              "      <td>-0.9530</td>\n",
              "      <td>-0.2005</td>\n",
              "      <td>-1.8340</td>\n",
              "      <td>-0.5768</td>\n",
              "      <td>0.5145</td>\n",
              "      <td>...</td>\n",
              "      <td>-6.9170</td>\n",
              "      <td>-1.8640</td>\n",
              "      <td>-2.5090</td>\n",
              "      <td>-4.7130</td>\n",
              "      <td>-1.7250</td>\n",
              "      <td>-3.8650</td>\n",
              "      <td>-3.0800</td>\n",
              "      <td>-4.1530</td>\n",
              "      <td>-1.2030</td>\n",
              "      <td>-1.1690</td>\n",
              "      <td>-4.1460</td>\n",
              "      <td>-1.2670</td>\n",
              "      <td>-1.1300</td>\n",
              "      <td>-2.4390</td>\n",
              "      <td>0.1591</td>\n",
              "      <td>-2.2490</td>\n",
              "      <td>-2.5860</td>\n",
              "      <td>-1.9520</td>\n",
              "      <td>-2.1810</td>\n",
              "      <td>-4.6690</td>\n",
              "      <td>-3.9450</td>\n",
              "      <td>-2.9560</td>\n",
              "      <td>-2.7930</td>\n",
              "      <td>-2.1560</td>\n",
              "      <td>-2.4100</td>\n",
              "      <td>-1.8190</td>\n",
              "      <td>-3.3480</td>\n",
              "      <td>-0.1414</td>\n",
              "      <td>-2.6430</td>\n",
              "      <td>-2.5810</td>\n",
              "      <td>-3.3890</td>\n",
              "      <td>-1.7450</td>\n",
              "      <td>-6.6300</td>\n",
              "      <td>-4.0950</td>\n",
              "      <td>-7.3860</td>\n",
              "      <td>-1.4160</td>\n",
              "      <td>-3.5770</td>\n",
              "      <td>-0.4775</td>\n",
              "      <td>-2.1500</td>\n",
              "      <td>-4.2520</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>23814 rows × 875 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "         tratamento  tempo dose      g0  ...     c96     c97     c98     c99\n",
              "0         com_droga     24   D1  1.0620  ... -0.3981  0.2139  0.3801  0.4176\n",
              "1         com_droga     72   D1  0.0743  ...  0.1522  0.1241  0.6077  0.7371\n",
              "2         com_droga     48   D1  0.6280  ... -0.6417 -0.2187 -1.4080  0.6931\n",
              "3         com_droga     48   D1 -0.5138  ... -1.6210 -0.8784 -0.3876 -0.8154\n",
              "4         com_droga     72   D2 -0.3254  ...  0.1094  0.2885 -0.3786  0.7125\n",
              "...             ...    ...  ...     ...  ...     ...     ...     ...     ...\n",
              "23809     com_droga     24   D2  0.1394  ...  0.0631  0.9171  0.5258  0.4680\n",
              "23810     com_droga     24   D2 -1.3260  ... -0.2084 -0.1224 -0.2715  0.3689\n",
              "23811  com_controle     48   D2  0.3942  ...  0.2256  0.7592  0.6656  0.3808\n",
              "23812     com_droga     24   D1  0.6660  ...  0.1732  0.7015 -0.6290  0.0740\n",
              "23813     com_droga     72   D1 -0.8598  ... -3.5770 -0.4775 -2.1500 -4.2520\n",
              "\n",
              "[23814 rows x 875 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 0
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "c621bf7f"
      },
      "source": [
        "Enfim, podemos partir para o ajuste do nosso modelo de Random Forest.\n",
        "\n",
        "Novamente, vamos seguir as mesmas etapas lógicas anteriores. Entretando, precisamos modificar o a nossa base ```x```. \n",
        "Então faremos: \n",
        "\n",
        "    x = dados_combinados.drop(['id', 'n_moa', 'ativo_moa', 'composto'], axis=1)  ->  base de dados de deve ser considerada com a exclusão das colunas que determinamos anteriormente\n",
        "    x = pd.get_dummies(x, columns=['tratamento', 'dose', 'tempo'])  ->  a partir do x definido acima, fazemos a transformação com o get_dummies nas colunas com o formato de string\n",
        "\n",
        "Por fim, temos como retorno uma acurácia de ~68,0% e, ela é, aproximadamente, 7% melhor que a acurácia que conseguimos com o ```DummyClassifier```. Aqui, não podemos comparar com a regressão logística porque estamos considerando um outro conjunto de dados. \n",
        "\n",
        "É muito importante ressaltar que, apesar de termos melhorado a acurácia do nosso modelo, pensando na vida real, talvez a nossa melhora ainda não seja suficiente. Ou seja, desenvolvemos um modelo com um algoritmo de Machine Learning mas, ele ainda não é aplicável à vida real (isso quer dizer que, ainda não nos traz ganhos suficientes para sua implantação).\n",
        "\n",
        "Por outro lado, durante todo o nosso projeto, desenvolvemos modelos simples e não nos aprofundamos em todos os pontos que podem ser modificados (variáveis, parâmetros de modelos, por exemplo) a fim de otimizar a nossa generalização. \n",
        "Deixamos em destaque também a sua trajetória até aqui, certamente ela deve ser seu foco (e é também o nosso)! O resultado é apenas uma consequência disso tudo e sabemos que, para melhorá-lo você precisa persistir no estudo dessa área. \n",
        "\n",
        "A Imersão de Dados 3 foi o seu primeiro passo na ciência de dados e aqui, deixamos o convite para que você continue estudando, explorando e aprendendo todos os processos e áreas desse mundo gigantesco de possibilidades!\n",
        "\n",
        "Nos vemos em breve, cientistas!"
      ],
      "id": "c621bf7f"
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "8a59ca7f",
        "outputId": "b4afff13-cf76-4ece-8a87-4d82d405157e"
      },
      "source": [
        "from sklearn.ensemble import RandomForestClassifier\n",
        "\n",
        "\n",
        "x = dados_combinados.drop(['id', 'n_moa', 'ativo_moa', 'composto'], axis=1)\n",
        "x = pd.get_dummies(x, columns=['tratamento', 'dose', 'tempo'])\n",
        "y = dados_combinados['ativo_moa'] \n",
        "x_treino, x_teste, y_treino, y_teste = train_test_split(x, y, test_size = 0.2, stratify=y, random_state=376)\n",
        "\n",
        "modelo_randomforest = RandomForestClassifier()\n",
        "modelo_randomforest.fit(x_treino, y_treino)\n",
        "modelo_randomforest.score(x_teste, y_teste)"
      ],
      "id": "8a59ca7f",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.6768843166071804"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 0
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2c3ccaad"
      },
      "source": [
        "# Desafios Aula 5"
      ],
      "id": "2c3ccaad"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "820ae465"
      },
      "source": [
        "## Desafio 01: Tente outros modelos ou mude os parâmetros dos modelos que testamos em aula."
      ],
      "id": "820ae465"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "a9cabb11"
      },
      "source": [
        "Com 10000 iterações o resultado não é muito diferente do resultado da aula com 1000 iterações. "
      ],
      "id": "a9cabb11"
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "f3a6b5bf",
        "outputId": "835d1e7e-baf9-4d15-9fdd-53af69f525f8"
      },
      "source": [
        "modelo_rlogistica = LogisticRegression()\n",
        "modelo_rlogistica.fit(x_treino, y_treino)\n",
        "modelo_rlogistica.score(x_teste, y_teste)"
      ],
      "id": "f3a6b5bf",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.6653369724963258"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 0
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "c287c0ad",
        "outputId": "51eaff52-3605-4f70-cd6f-bb6878426eef"
      },
      "source": [
        "modelo_rlogistica = LogisticRegression(solver='newton-cg')\n",
        "modelo_rlogistica.fit(x_treino, y_treino)\n",
        "modelo_rlogistica.score(x_teste, y_teste)"
      ],
      "id": "c287c0ad",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.6640772622296872"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 0
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "35c700e6"
      },
      "source": [
        "Outra opção de algoritmo é o dos K vizinhos mais próximos"
      ],
      "id": "35c700e6"
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "87920678"
      },
      "source": [
        "from sklearn.neighbors import KNeighborsClassifier"
      ],
      "id": "87920678",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "9004c61d"
      },
      "source": [
        "n_neighbors = 3"
      ],
      "id": "9004c61d",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "c7d965ce",
        "outputId": "174535c8-157b-4741-ab4b-71ed108a4d81"
      },
      "source": [
        "clf = KNeighborsClassifier(n_neighbors)\n",
        "clf.fit(x_treino, y_treino)\n",
        "clf.score(x_teste, y_teste)"
      ],
      "id": "c7d965ce",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.6155784169640982"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 0
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4237a565"
      },
      "source": [
        "## Desafio 02: Estude a documentação do Sklearn."
      ],
      "id": "4237a565"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5c9f2a95"
      },
      "source": [
        "https://scikit-learn.org/stable/index.html"
      ],
      "id": "5c9f2a95"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "d68fdfa6"
      },
      "source": [
        "## Desafio 03: Pesquisar o fluxograma 'Escolhendo o melhor modelo' choosing the right estimator"
      ],
      "id": "d68fdfa6"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ef38f079"
      },
      "source": [
        "https://scikit-learn.org/stable/tutorial/machine_learning_map/index.html"
      ],
      "id": "ef38f079"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fcac57d9"
      },
      "source": [
        "## Desafio 04: Teste outros problemas (perguntas). 0, 1 ou +"
      ],
      "id": "fcac57d9"
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "de310528",
        "outputId": "123f3520-f9cb-40e1-ceb4-74f56b10ad58"
      },
      "source": [
        "x = dados_combinados.loc[:,'g-0':'c-99']"
      ],
      "id": "de310528",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "KeyError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   2897\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2898\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2899\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;31mKeyError\u001b[0m: 'g-0'",
            "\nThe above exception was the direct cause of the following exception:\n",
            "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-57-3d1aeaf13eb6>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdados_combinados\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'g-0'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m'c-99'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m    871\u001b[0m                     \u001b[0;31m# AttributeError for IntervalTree get_value\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    872\u001b[0m                     \u001b[0;32mpass\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 873\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_tuple\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    874\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    875\u001b[0m             \u001b[0;31m# we by definition only have the 0th axis\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m_getitem_tuple\u001b[0;34m(self, tup)\u001b[0m\n\u001b[1;32m   1053\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_multi_take\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtup\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1054\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1055\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_tuple_same_dim\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtup\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1056\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1057\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_get_label\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m_getitem_tuple_same_dim\u001b[0;34m(self, tup)\u001b[0m\n\u001b[1;32m    748\u001b[0m                 \u001b[0;32mcontinue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    749\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 750\u001b[0;31m             \u001b[0mretval\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mretval\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_axis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    751\u001b[0m             \u001b[0;31m# We should never have retval.ndim < self.ndim, as that should\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    752\u001b[0m             \u001b[0;31m#  be handled by the _getitem_lowerdim call above.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m_getitem_axis\u001b[0;34m(self, key, axis)\u001b[0m\n\u001b[1;32m   1086\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mslice\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1087\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_validate_key\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1088\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_slice_axis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1089\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0mcom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_bool_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1090\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getbool_axis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m_get_slice_axis\u001b[0;34m(self, slice_obj, axis)\u001b[0m\n\u001b[1;32m   1121\u001b[0m         \u001b[0mlabels\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_axis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1122\u001b[0m         indexer = labels.slice_indexer(\n\u001b[0;32m-> 1123\u001b[0;31m             \u001b[0mslice_obj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstart\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mslice_obj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstop\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mslice_obj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstep\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkind\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"loc\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1124\u001b[0m         )\n\u001b[1;32m   1125\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mslice_indexer\u001b[0;34m(self, start, end, step, kind)\u001b[0m\n\u001b[1;32m   4967\u001b[0m         \u001b[0mslice\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4968\u001b[0m         \"\"\"\n\u001b[0;32m-> 4969\u001b[0;31m         \u001b[0mstart_slice\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mend_slice\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mslice_locs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstart\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mend\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstep\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mstep\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkind\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mkind\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   4970\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4971\u001b[0m         \u001b[0;31m# return a slice\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mslice_locs\u001b[0;34m(self, start, end, step, kind)\u001b[0m\n\u001b[1;32m   5170\u001b[0m         \u001b[0mstart_slice\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5171\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mstart\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 5172\u001b[0;31m             \u001b[0mstart_slice\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_slice_bound\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstart\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"left\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkind\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   5173\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mstart_slice\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5174\u001b[0m             \u001b[0mstart_slice\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_slice_bound\u001b[0;34m(self, label, side, kind)\u001b[0m\n\u001b[1;32m   5090\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5091\u001b[0m                 \u001b[0;31m# raise the original KeyError\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 5092\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   5093\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5094\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mslc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndarray\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_slice_bound\u001b[0;34m(self, label, side, kind)\u001b[0m\n\u001b[1;32m   5084\u001b[0m         \u001b[0;31m# we need to look up the label\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5085\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 5086\u001b[0;31m             \u001b[0mslc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabel\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   5087\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5088\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   2898\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2899\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2900\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2901\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2902\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mtolerance\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyError\u001b[0m: 'g-0'"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bace0af9"
      },
      "source": [
        "Neste exemplo estamos fazendo a classificação da coluna tempo. Vamos fazer um map para para que os valores 24,48,72 que são muito mais altos que os valores nas labels não tornem o modelo tendencioso."
      ],
      "id": "bace0af9"
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "75df7013"
      },
      "source": [
        "y=dados_combinados['tempo'].map({24:0,48:1,72:2})"
      ],
      "id": "75df7013",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "b78651cd"
      },
      "source": [
        "y"
      ],
      "id": "b78651cd",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "4e4370a5"
      },
      "source": [
        "x_treino, x_teste, y_treino, y_teste = train_test_split(x, y, test_size = 0.2, stratify=y, random_state=376)"
      ],
      "id": "4e4370a5",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "5b75e583"
      },
      "source": [
        "modelo_rlogistica2 = LogisticRegression(solver='newton-cg')\n",
        "modelo_rlogistica2.fit(x_treino, y_treino)\n",
        "modelo_rlogistica2.score(x_teste, y_teste)"
      ],
      "id": "5b75e583",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "9f349a6f"
      },
      "source": [
        "modelo_dummy = DummyClassifier('most_frequent')\n",
        "modelo_dummy.fit(x_treino, y_treino)\n",
        "previsao_dummy = modelo_dummy.predict(x_teste)\n",
        "accuracy_score(y_teste, previsao_dummy)"
      ],
      "id": "9f349a6f",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "f2f3929b"
      },
      "source": [
        "## Desafio 05: Escolha a droga mais utilizada e tente criar um modelo para prever está label."
      ],
      "id": "f2f3929b"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fbfaf0d1"
      },
      "source": [
        "A droga mais utilizada é a mais presente na base de dados. Desta forma, a base de dados é altamente desbalanceada. O algoritmo de machine learning pode simplesmente \"achar\" que é melhor classificar o resultado como sendo a droga mais frequente. Por isso, decidimos criar um algoritmo que diferencia as drogas 8b87a7a83 e 5628cb3ee que possuem um número de elementos semelhante."
      ],
      "id": "fbfaf0d1"
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "c0834197"
      },
      "source": [
        "dados_combinados['composto'].value_counts()"
      ],
      "id": "c0834197",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "251e66e4"
      },
      "source": [
        "dt_temporario = dados_combinados.query('composto in [\"8b87a7a83\", \"5628cb3ee\"]')"
      ],
      "id": "251e66e4",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4f66375c"
      },
      "source": [
        "Aqui modificamos o x. Se colocamos mais colunas de g e c o algoritmo consegue 100% de taxa de acerto. A diferença da ativação entre as duas drogas é muito grande e fácil do algoritmo identificar."
      ],
      "id": "4f66375c"
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "65b9a175"
      },
      "source": [
        "x = dt_temporario.loc[:,'g-0':'g-10'] #"
      ],
      "id": "65b9a175",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "86f63575"
      },
      "source": [
        "y = dt_temporario['composto'].map({'8b87a7a83': 0, '5628cb3ee': 1})"
      ],
      "id": "86f63575",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "d2b0fc63"
      },
      "source": [
        "sns.countplot(y=y)"
      ],
      "id": "d2b0fc63",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "a8266438"
      },
      "source": [
        "x_treino, x_teste, y_treino, y_teste = train_test_split(x, y, test_size = 0.2, stratify=y,random_state=376) "
      ],
      "id": "a8266438",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "4a82208a"
      },
      "source": [
        "modelo_rlogistica2 = LogisticRegression()\n",
        "modelo_rlogistica2.fit(x_treino, y_treino)\n",
        "modelo_rlogistica2.score(x_teste, y_teste)"
      ],
      "id": "4a82208a",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "f45d621e"
      },
      "source": [
        "modelo_dummy = DummyClassifier('most_frequent')\n",
        "modelo_dummy.fit(x_treino, y_treino)\n",
        "previsao_dummy = modelo_dummy.predict(x_teste)\n",
        "accuracy_score(y_teste, previsao_dummy)"
      ],
      "id": "f45d621e",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "718fd57c"
      },
      "source": [
        "## Desafio 06: Resumo do que você aprendeu com os dados"
      ],
      "id": "718fd57c"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "e389bc5f"
      },
      "source": [
        "O estudo sobre a base de dados é livre. Tente extrair o máximo de informações da base de dados com o que aprendeu nesta aula e compartilhe no Discord e nas redes sociais como forma de construção do seu portfólio em Data Science."
      ],
      "id": "e389bc5f"
    }
  ]
}