TP02_Abelhas.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Nkanga-Pedro/ExplorandoDados/blob/main/TP02_Abelhas.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "5b2df81f",
      "metadata": {
        "id": "5b2df81f"
      },
      "outputs": [],
      "source": [
        "# importar os pacotes necessários\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import scikitplot as skplt\n",
        "import pandas as pd"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "22b307dc",
      "metadata": {
        "id": "22b307dc",
        "outputId": "0cdedf4f-ad05-439f-eede-5bd161b725e8"
      },
      "outputs": [
        {
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>6.000000000000000000e+00</th>\n",
              "      <th>2.300000000000000000e+01</th>\n",
              "      <th>3.600000000000000000e+01</th>\n",
              "      <th>1.400000000000000000e+01</th>\n",
              "      <th>1.700000000000000000e+01</th>\n",
              "      <th>5.000000000000000000e+01</th>\n",
              "      <th>0.000000000000000000e+00</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>6.0</td>\n",
              "      <td>23.0</td>\n",
              "      <td>36.0</td>\n",
              "      <td>14.0</td>\n",
              "      <td>17.0</td>\n",
              "      <td>51.0</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>6.0</td>\n",
              "      <td>23.0</td>\n",
              "      <td>35.0</td>\n",
              "      <td>13.0</td>\n",
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              "    <tr>\n",
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              "      <td>5.0</td>\n",
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              "      <td>35.0</td>\n",
              "      <td>13.0</td>\n",
              "      <td>17.0</td>\n",
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              "      <td>54.0</td>\n",
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              "      <th>4</th>\n",
              "      <td>6.0</td>\n",
              "      <td>23.0</td>\n",
              "      <td>34.0</td>\n",
              "      <td>14.0</td>\n",
              "      <td>17.0</td>\n",
              "      <td>56.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   6.000000000000000000e+00  2.300000000000000000e+01  \\\n",
              "0                       6.0                      23.0   \n",
              "1                       6.0                      23.0   \n",
              "2                       5.0                      22.0   \n",
              "3                       5.0                      23.0   \n",
              "4                       6.0                      23.0   \n",
              "\n",
              "   3.600000000000000000e+01  1.400000000000000000e+01  \\\n",
              "0                      36.0                      14.0   \n",
              "1                      35.0                      13.0   \n",
              "2                      35.0                      13.0   \n",
              "3                      35.0                      13.0   \n",
              "4                      34.0                      14.0   \n",
              "\n",
              "   1.700000000000000000e+01  5.000000000000000000e+01  \\\n",
              "0                      17.0                      51.0   \n",
              "1                      17.0                      52.0   \n",
              "2                      17.0                      53.0   \n",
              "3                      17.0                      54.0   \n",
              "4                      17.0                      56.0   \n",
              "\n",
              "   0.000000000000000000e+00  \n",
              "0                       1.0  \n",
              "1                       0.0  \n",
              "2                       0.0  \n",
              "3                       0.0  \n",
              "4                       0.0  "
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "dados=pd.read_csv('dataset_bees.csv')\n",
        "df=pd.DataFrame(dados)\n",
        "display(df.head())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "7e60df36",
      "metadata": {
        "id": "7e60df36"
      },
      "outputs": [],
      "source": [
        "df = df.rename(columns={'6.000000000000000000e+00': 'temp_max_mes_quente',\n",
        "                             '2.300000000000000000e+01': 'temp_min_mes_frio',\n",
        "                             '3.600000000000000000e+01': 'zona_chuva',\n",
        "                             '1.400000000000000000e+01': 'temp_media_anual',\n",
        "                             '1.700000000000000000e+01': 'latitude',\n",
        "                             '5.000000000000000000e+01': 'longetude',\n",
        "                             '0.000000000000000000e+00': 'res_espece'})"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "2bd92139",
      "metadata": {
        "id": "2bd92139",
        "outputId": "bf531397-1421-4b57-da22-b1bfc48bf04a"
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      "outputs": [
        {
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>temp_max_mes_quente</th>\n",
              "      <th>temp_min_mes_frio</th>\n",
              "      <th>zona_chuva</th>\n",
              "      <th>temp_media_anual</th>\n",
              "      <th>latitude</th>\n",
              "      <th>longetude</th>\n",
              "      <th>res_espece</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>6.0</td>\n",
              "      <td>23.0</td>\n",
              "      <td>35.0</td>\n",
              "      <td>13.0</td>\n",
              "      <td>17.0</td>\n",
              "      <td>52.0</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>5.0</td>\n",
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              "      <td>35.0</td>\n",
              "      <td>13.0</td>\n",
              "      <td>17.0</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>5.0</td>\n",
              "      <td>23.0</td>\n",
              "      <td>35.0</td>\n",
              "      <td>13.0</td>\n",
              "      <td>17.0</td>\n",
              "      <td>54.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>6.0</td>\n",
              "      <td>23.0</td>\n",
              "      <td>34.0</td>\n",
              "      <td>14.0</td>\n",
              "      <td>17.0</td>\n",
              "      <td>56.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   temp_max_mes_quente  temp_min_mes_frio  zona_chuva  temp_media_anual  \\\n",
              "0                  6.0               23.0        36.0              14.0   \n",
              "1                  6.0               23.0        35.0              13.0   \n",
              "2                  5.0               22.0        35.0              13.0   \n",
              "3                  5.0               23.0        35.0              13.0   \n",
              "4                  6.0               23.0        34.0              14.0   \n",
              "\n",
              "   latitude  longetude  res_espece  \n",
              "0      17.0       51.0         1.0  \n",
              "1      17.0       52.0         0.0  \n",
              "2      17.0       53.0         0.0  \n",
              "3      17.0       54.0         0.0  \n",
              "4      17.0       56.0         0.0  "
            ]
          },
          "execution_count": 4,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "1415d68b",
      "metadata": {
        "id": "1415d68b",
        "outputId": "bd37b8ff-a1d5-4f54-8015-ccf66f25ab42"
      },
      "outputs": [
        {
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>temp_max_mes_quente</th>\n",
              "      <th>temp_min_mes_frio</th>\n",
              "      <th>zona_chuva</th>\n",
              "      <th>temp_media_anual</th>\n",
              "      <th>latitude</th>\n",
              "      <th>longetude</th>\n",
              "      <th>res_espece</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>9435.000000</td>\n",
              "      <td>9435.000000</td>\n",
              "      <td>9435.000000</td>\n",
              "      <td>9435.000000</td>\n",
              "      <td>9437.000000</td>\n",
              "      <td>9437.000000</td>\n",
              "      <td>9437.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>1.580286</td>\n",
              "      <td>28.060519</td>\n",
              "      <td>38.677477</td>\n",
              "      <td>12.766826</td>\n",
              "      <td>56.217548</td>\n",
              "      <td>98.471442</td>\n",
              "      <td>0.013564</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>2.790964</td>\n",
              "      <td>3.897244</td>\n",
              "      <td>14.777344</td>\n",
              "      <td>2.866861</td>\n",
              "      <td>23.236455</td>\n",
              "      <td>36.468249</td>\n",
              "      <td>0.115677</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>-11.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>11.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>17.000000</td>\n",
              "      <td>32.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>25.000000</td>\n",
              "      <td>27.000000</td>\n",
              "      <td>11.000000</td>\n",
              "      <td>36.000000</td>\n",
              "      <td>68.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>28.000000</td>\n",
              "      <td>37.000000</td>\n",
              "      <td>13.000000</td>\n",
              "      <td>54.000000</td>\n",
              "      <td>96.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>4.000000</td>\n",
              "      <td>31.000000</td>\n",
              "      <td>51.000000</td>\n",
              "      <td>15.000000</td>\n",
              "      <td>76.000000</td>\n",
              "      <td>126.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>8.000000</td>\n",
              "      <td>36.000000</td>\n",
              "      <td>78.000000</td>\n",
              "      <td>18.000000</td>\n",
              "      <td>109.000000</td>\n",
              "      <td>185.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       temp_max_mes_quente  temp_min_mes_frio   zona_chuva  temp_media_anual  \\\n",
              "count          9435.000000        9435.000000  9435.000000       9435.000000   \n",
              "mean              1.580286          28.060519    38.677477         12.766826   \n",
              "std               2.790964           3.897244    14.777344          2.866861   \n",
              "min             -11.000000          10.000000    11.000000         -1.000000   \n",
              "25%               0.000000          25.000000    27.000000         11.000000   \n",
              "50%               1.000000          28.000000    37.000000         13.000000   \n",
              "75%               4.000000          31.000000    51.000000         15.000000   \n",
              "max               8.000000          36.000000    78.000000         18.000000   \n",
              "\n",
              "          latitude    longetude   res_espece  \n",
              "count  9437.000000  9437.000000  9437.000000  \n",
              "mean     56.217548    98.471442     0.013564  \n",
              "std      23.236455    36.468249     0.115677  \n",
              "min      17.000000    32.000000     0.000000  \n",
              "25%      36.000000    68.000000     0.000000  \n",
              "50%      54.000000    96.000000     0.000000  \n",
              "75%      76.000000   126.000000     0.000000  \n",
              "max     109.000000   185.000000     1.000000  "
            ]
          },
          "execution_count": 5,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.describe()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "c0dd1a25",
      "metadata": {
        "id": "c0dd1a25",
        "outputId": "68ad42d4-7550-40af-becf-01d2fa3ff35b"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 9437 entries, 0 to 9436\n",
            "Data columns (total 7 columns):\n",
            " #   Column               Non-Null Count  Dtype  \n",
            "---  ------               --------------  -----  \n",
            " 0   temp_max_mes_quente  9435 non-null   float64\n",
            " 1   temp_min_mes_frio    9435 non-null   float64\n",
            " 2   zona_chuva           9435 non-null   float64\n",
            " 3   temp_media_anual     9435 non-null   float64\n",
            " 4   latitude             9437 non-null   float64\n",
            " 5   longetude            9437 non-null   float64\n",
            " 6   res_espece           9437 non-null   float64\n",
            "dtypes: float64(7)\n",
            "memory usage: 516.2 KB\n"
          ]
        }
      ],
      "source": [
        "# Com esses dados consigo notar que temos valores null comparando com valor 9437, deparamos nas colunas tem alguns dados com valor null\n",
        "df.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "2d69dd9d",
      "metadata": {
        "id": "2d69dd9d",
        "outputId": "53d0392c-a0fd-431b-bc3b-923fd3d609b9"
      },
      "outputs": [
        {
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>temp_max_mes_quente</th>\n",
              "      <th>temp_min_mes_frio</th>\n",
              "      <th>zona_chuva</th>\n",
              "      <th>temp_media_anual</th>\n",
              "      <th>latitude</th>\n",
              "      <th>longetude</th>\n",
              "      <th>res_espece</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>6.0</td>\n",
              "      <td>23.0</td>\n",
              "      <td>36.0</td>\n",
              "      <td>14.0</td>\n",
              "      <td>17.0</td>\n",
              "      <td>51.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>6.0</td>\n",
              "      <td>23.0</td>\n",
              "      <td>35.0</td>\n",
              "      <td>13.0</td>\n",
              "      <td>17.0</td>\n",
              "      <td>52.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>5.0</td>\n",
              "      <td>22.0</td>\n",
              "      <td>35.0</td>\n",
              "      <td>13.0</td>\n",
              "      <td>17.0</td>\n",
              "      <td>53.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>5.0</td>\n",
              "      <td>23.0</td>\n",
              "      <td>35.0</td>\n",
              "      <td>13.0</td>\n",
              "      <td>17.0</td>\n",
              "      <td>54.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>6.0</td>\n",
              "      <td>23.0</td>\n",
              "      <td>34.0</td>\n",
              "      <td>14.0</td>\n",
              "      <td>17.0</td>\n",
              "      <td>56.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   temp_max_mes_quente  temp_min_mes_frio  zona_chuva  temp_media_anual  \\\n",
              "0                  6.0               23.0        36.0              14.0   \n",
              "1                  6.0               23.0        35.0              13.0   \n",
              "2                  5.0               22.0        35.0              13.0   \n",
              "3                  5.0               23.0        35.0              13.0   \n",
              "4                  6.0               23.0        34.0              14.0   \n",
              "\n",
              "   latitude  longetude  res_espece  \n",
              "0      17.0       51.0         1.0  \n",
              "1      17.0       52.0         0.0  \n",
              "2      17.0       53.0         0.0  \n",
              "3      17.0       54.0         0.0  \n",
              "4      17.0       56.0         0.0  "
            ]
          },
          "execution_count": 7,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "b97cce51",
      "metadata": {
        "id": "b97cce51",
        "outputId": "264a5579-2510-433f-a05c-8f6d8dcb676f"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1600x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Apresentar o grafico antes da normalização\n",
        "plt.figure(figsize=(16,6))\n",
        "ax=sns.boxplot(data=df)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "0dabd438",
      "metadata": {
        "id": "0dabd438"
      },
      "outputs": [],
      "source": [
        "from sklearn.preprocessing import MinMaxScaler\n",
        "# Normalizar o dataset e criar um objecto para normalizar od dados\n",
        "obj_norm=MinMaxScaler().fit(df)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "06e1458e",
      "metadata": {
        "id": "06e1458e"
      },
      "outputs": [],
      "source": [
        "# Aplicar a normalização\n",
        "df_norm=obj_norm.transform(df)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "aa52b1fe",
      "metadata": {
        "id": "aa52b1fe",
        "outputId": "7b83f50c-d3e2-452d-9c0f-31a87713e9d7"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([[0.89473684, 0.5       , 0.37313433, ..., 0.        , 0.12418301,\n",
              "        1.        ],\n",
              "       [0.89473684, 0.5       , 0.35820896, ..., 0.        , 0.13071895,\n",
              "        0.        ],\n",
              "       [0.84210526, 0.46153846, 0.35820896, ..., 0.        , 0.1372549 ,\n",
              "        0.        ],\n",
              "       ...,\n",
              "       [0.84210526, 0.69230769, 0.97014925, ..., 0.98913043, 0.30718954,\n",
              "        0.        ],\n",
              "       [1.        , 0.65384615, 0.97014925, ..., 0.98913043, 0.31372549,\n",
              "        0.        ],\n",
              "       [0.94736842, 0.65384615, 1.        , ..., 1.        , 0.30718954,\n",
              "        0.        ]])"
            ]
          },
          "execution_count": 11,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_norm"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "ea19237a",
      "metadata": {
        "id": "ea19237a",
        "outputId": "8c204630-2060-42fc-d6e0-ca83c7508b1e"
      },
      "outputs": [
        {
          "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>temp_max_mes_quente</th>\n",
              "      <th>temp_min_mes_frio</th>\n",
              "      <th>zona_chuva</th>\n",
              "      <th>temp_media_anual</th>\n",
              "      <th>latitude</th>\n",
              "      <th>longetude</th>\n",
              "      <th>res_espece</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.894737</td>\n",
              "      <td>0.500000</td>\n",
              "      <td>0.373134</td>\n",
              "      <td>0.789474</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.124183</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.894737</td>\n",
              "      <td>0.500000</td>\n",
              "      <td>0.358209</td>\n",
              "      <td>0.736842</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.130719</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.842105</td>\n",
              "      <td>0.461538</td>\n",
              "      <td>0.358209</td>\n",
              "      <td>0.736842</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.137255</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.842105</td>\n",
              "      <td>0.500000</td>\n",
              "      <td>0.358209</td>\n",
              "      <td>0.736842</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.143791</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.894737</td>\n",
              "      <td>0.500000</td>\n",
              "      <td>0.343284</td>\n",
              "      <td>0.789474</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.156863</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   temp_max_mes_quente  temp_min_mes_frio  zona_chuva  temp_media_anual  \\\n",
              "0             0.894737           0.500000    0.373134          0.789474   \n",
              "1             0.894737           0.500000    0.358209          0.736842   \n",
              "2             0.842105           0.461538    0.358209          0.736842   \n",
              "3             0.842105           0.500000    0.358209          0.736842   \n",
              "4             0.894737           0.500000    0.343284          0.789474   \n",
              "\n",
              "   latitude  longetude  res_espece  \n",
              "0       0.0   0.124183         1.0  \n",
              "1       0.0   0.130719         0.0  \n",
              "2       0.0   0.137255         0.0  \n",
              "3       0.0   0.143791         0.0  \n",
              "4       0.0   0.156863         0.0  "
            ]
          },
          "execution_count": 12,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_norm=pd.DataFrame(df_norm)\n",
        "df_norm.columns=['temp_max_mes_quente','temp_min_mes_frio','zona_chuva','temp_media_anual','latitude','longetude','res_espece']\n",
        "df_norm.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "45523d6a",
      "metadata": {
        "id": "45523d6a",
        "outputId": "cf6f7862-6e65-49e8-972b-66ea4f356655"
      },
      "outputs": [
        {
          "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>temp_max_mes_quente</th>\n",
              "      <th>temp_min_mes_frio</th>\n",
              "      <th>zona_chuva</th>\n",
              "      <th>temp_media_anual</th>\n",
              "      <th>latitude</th>\n",
              "      <th>longetude</th>\n",
              "      <th>res_espece</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>9435.000000</td>\n",
              "      <td>9435.000000</td>\n",
              "      <td>9435.000000</td>\n",
              "      <td>9435.000000</td>\n",
              "      <td>9437.000000</td>\n",
              "      <td>9437.000000</td>\n",
              "      <td>9437.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>0.662120</td>\n",
              "      <td>0.694635</td>\n",
              "      <td>0.413097</td>\n",
              "      <td>0.724570</td>\n",
              "      <td>0.426278</td>\n",
              "      <td>0.434454</td>\n",
              "      <td>0.013564</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.146893</td>\n",
              "      <td>0.149894</td>\n",
              "      <td>0.220557</td>\n",
              "      <td>0.150887</td>\n",
              "      <td>0.252570</td>\n",
              "      <td>0.238355</td>\n",
              "      <td>0.115677</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>0.578947</td>\n",
              "      <td>0.576923</td>\n",
              "      <td>0.238806</td>\n",
              "      <td>0.631579</td>\n",
              "      <td>0.206522</td>\n",
              "      <td>0.235294</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>0.631579</td>\n",
              "      <td>0.692308</td>\n",
              "      <td>0.388060</td>\n",
              "      <td>0.736842</td>\n",
              "      <td>0.402174</td>\n",
              "      <td>0.418301</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>0.789474</td>\n",
              "      <td>0.807692</td>\n",
              "      <td>0.597015</td>\n",
              "      <td>0.842105</td>\n",
              "      <td>0.641304</td>\n",
              "      <td>0.614379</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       temp_max_mes_quente  temp_min_mes_frio   zona_chuva  temp_media_anual  \\\n",
              "count          9435.000000        9435.000000  9435.000000       9435.000000   \n",
              "mean              0.662120           0.694635     0.413097          0.724570   \n",
              "std               0.146893           0.149894     0.220557          0.150887   \n",
              "min               0.000000           0.000000     0.000000          0.000000   \n",
              "25%               0.578947           0.576923     0.238806          0.631579   \n",
              "50%               0.631579           0.692308     0.388060          0.736842   \n",
              "75%               0.789474           0.807692     0.597015          0.842105   \n",
              "max               1.000000           1.000000     1.000000          1.000000   \n",
              "\n",
              "          latitude    longetude   res_espece  \n",
              "count  9437.000000  9437.000000  9437.000000  \n",
              "mean      0.426278     0.434454     0.013564  \n",
              "std       0.252570     0.238355     0.115677  \n",
              "min       0.000000     0.000000     0.000000  \n",
              "25%       0.206522     0.235294     0.000000  \n",
              "50%       0.402174     0.418301     0.000000  \n",
              "75%       0.641304     0.614379     0.000000  \n",
              "max       1.000000     1.000000     1.000000  "
            ]
          },
          "execution_count": 13,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_norm.describe()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "10c68a91",
      "metadata": {
        "id": "10c68a91",
        "outputId": "8043cfe7-f5de-49e0-d9c5-07fb3c652f43"
      },
      "outputs": [
        {
          "data": {
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\n",
            "text/plain": [
              "<Figure size 1600x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.figure(figsize=(16,6))\n",
        "ax=sns.boxplot(data=df_norm)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "af1e5560",
      "metadata": {
        "id": "af1e5560"
      },
      "outputs": [],
      "source": [
        "# Eliminando features que não precisa da previsão\n",
        "df_norm=df_norm.drop('latitude',axis=1)\n",
        "df_norm=df_norm.drop('longetude',axis=1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "16f06e4f",
      "metadata": {
        "id": "16f06e4f",
        "outputId": "77036a6b-f0f7-40f2-c20b-1283554b2663"
      },
      "outputs": [
        {
          "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>temp_max_mes_quente</th>\n",
              "      <th>temp_min_mes_frio</th>\n",
              "      <th>zona_chuva</th>\n",
              "      <th>temp_media_anual</th>\n",
              "      <th>res_espece</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.894737</td>\n",
              "      <td>0.500000</td>\n",
              "      <td>0.373134</td>\n",
              "      <td>0.789474</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.894737</td>\n",
              "      <td>0.500000</td>\n",
              "      <td>0.358209</td>\n",
              "      <td>0.736842</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.842105</td>\n",
              "      <td>0.461538</td>\n",
              "      <td>0.358209</td>\n",
              "      <td>0.736842</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.842105</td>\n",
              "      <td>0.500000</td>\n",
              "      <td>0.358209</td>\n",
              "      <td>0.736842</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.894737</td>\n",
              "      <td>0.500000</td>\n",
              "      <td>0.343284</td>\n",
              "      <td>0.789474</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   temp_max_mes_quente  temp_min_mes_frio  zona_chuva  temp_media_anual  \\\n",
              "0             0.894737           0.500000    0.373134          0.789474   \n",
              "1             0.894737           0.500000    0.358209          0.736842   \n",
              "2             0.842105           0.461538    0.358209          0.736842   \n",
              "3             0.842105           0.500000    0.358209          0.736842   \n",
              "4             0.894737           0.500000    0.343284          0.789474   \n",
              "\n",
              "   res_espece  \n",
              "0         1.0  \n",
              "1         0.0  \n",
              "2         0.0  \n",
              "3         0.0  \n",
              "4         0.0  "
            ]
          },
          "execution_count": 16,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_norm.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "c4ee7d42",
      "metadata": {
        "id": "c4ee7d42",
        "outputId": "f13a1eb1-718f-4254-cfba-21b7ae85134c"
      },
      "outputs": [
        {
          "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>temp_max_mes_quente</th>\n",
              "      <th>temp_min_mes_frio</th>\n",
              "      <th>zona_chuva</th>\n",
              "      <th>temp_media_anual</th>\n",
              "      <th>latitude</th>\n",
              "      <th>longetude</th>\n",
              "      <th>res_espece</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>9435.000000</td>\n",
              "      <td>9435.000000</td>\n",
              "      <td>9435.000000</td>\n",
              "      <td>9435.000000</td>\n",
              "      <td>9437.000000</td>\n",
              "      <td>9437.000000</td>\n",
              "      <td>9437.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>1.580286</td>\n",
              "      <td>28.060519</td>\n",
              "      <td>38.677477</td>\n",
              "      <td>12.766826</td>\n",
              "      <td>56.217548</td>\n",
              "      <td>98.471442</td>\n",
              "      <td>0.013564</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>2.790964</td>\n",
              "      <td>3.897244</td>\n",
              "      <td>14.777344</td>\n",
              "      <td>2.866861</td>\n",
              "      <td>23.236455</td>\n",
              "      <td>36.468249</td>\n",
              "      <td>0.115677</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>-11.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>11.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>17.000000</td>\n",
              "      <td>32.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>25.000000</td>\n",
              "      <td>27.000000</td>\n",
              "      <td>11.000000</td>\n",
              "      <td>36.000000</td>\n",
              "      <td>68.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>28.000000</td>\n",
              "      <td>37.000000</td>\n",
              "      <td>13.000000</td>\n",
              "      <td>54.000000</td>\n",
              "      <td>96.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>4.000000</td>\n",
              "      <td>31.000000</td>\n",
              "      <td>51.000000</td>\n",
              "      <td>15.000000</td>\n",
              "      <td>76.000000</td>\n",
              "      <td>126.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>8.000000</td>\n",
              "      <td>36.000000</td>\n",
              "      <td>78.000000</td>\n",
              "      <td>18.000000</td>\n",
              "      <td>109.000000</td>\n",
              "      <td>185.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       temp_max_mes_quente  temp_min_mes_frio   zona_chuva  temp_media_anual  \\\n",
              "count          9435.000000        9435.000000  9435.000000       9435.000000   \n",
              "mean              1.580286          28.060519    38.677477         12.766826   \n",
              "std               2.790964           3.897244    14.777344          2.866861   \n",
              "min             -11.000000          10.000000    11.000000         -1.000000   \n",
              "25%               0.000000          25.000000    27.000000         11.000000   \n",
              "50%               1.000000          28.000000    37.000000         13.000000   \n",
              "75%               4.000000          31.000000    51.000000         15.000000   \n",
              "max               8.000000          36.000000    78.000000         18.000000   \n",
              "\n",
              "          latitude    longetude   res_espece  \n",
              "count  9437.000000  9437.000000  9437.000000  \n",
              "mean     56.217548    98.471442     0.013564  \n",
              "std      23.236455    36.468249     0.115677  \n",
              "min      17.000000    32.000000     0.000000  \n",
              "25%      36.000000    68.000000     0.000000  \n",
              "50%      54.000000    96.000000     0.000000  \n",
              "75%      76.000000   126.000000     0.000000  \n",
              "max     109.000000   185.000000     1.000000  "
            ]
          },
          "execution_count": 17,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.describe()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "40176bfd",
      "metadata": {
        "id": "40176bfd",
        "outputId": "3cc9b088-cb94-442b-99af-3818c285147b"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "temp_max_mes_quente    2\n",
              "temp_min_mes_frio      2\n",
              "zona_chuva             2\n",
              "temp_media_anual       2\n",
              "res_espece             0\n",
              "dtype: int64"
            ]
          },
          "execution_count": 18,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Verificando valor null\n",
        "df_norm.isnull().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "226ea55f",
      "metadata": {
        "id": "226ea55f"
      },
      "outputs": [],
      "source": [
        "# Eliminando os valores null na respectiva linhas\n",
        "df_norm=df_norm.dropna(axis=0)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "31e85167",
      "metadata": {
        "id": "31e85167",
        "outputId": "aa60cc32-b8d7-4434-866b-e2c22b00dd36"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 9435 entries, 0 to 9436\n",
            "Data columns (total 5 columns):\n",
            " #   Column               Non-Null Count  Dtype  \n",
            "---  ------               --------------  -----  \n",
            " 0   temp_max_mes_quente  9435 non-null   float64\n",
            " 1   temp_min_mes_frio    9435 non-null   float64\n",
            " 2   zona_chuva           9435 non-null   float64\n",
            " 3   temp_media_anual     9435 non-null   float64\n",
            " 4   res_espece           9435 non-null   float64\n",
            "dtypes: float64(5)\n",
            "memory usage: 442.3 KB\n"
          ]
        }
      ],
      "source": [
        "df_norm.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "accf7410",
      "metadata": {
        "id": "accf7410",
        "outputId": "85ebaba1-4829-4b83-d4cc-5b1a3d1be37a"
      },
      "outputs": [
        {
          "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>temp_max_mes_quente</th>\n",
              "      <th>temp_min_mes_frio</th>\n",
              "      <th>zona_chuva</th>\n",
              "      <th>temp_media_anual</th>\n",
              "      <th>res_espece</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.894737</td>\n",
              "      <td>0.500000</td>\n",
              "      <td>0.373134</td>\n",
              "      <td>0.789474</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.894737</td>\n",
              "      <td>0.500000</td>\n",
              "      <td>0.358209</td>\n",
              "      <td>0.736842</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.842105</td>\n",
              "      <td>0.461538</td>\n",
              "      <td>0.358209</td>\n",
              "      <td>0.736842</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.842105</td>\n",
              "      <td>0.500000</td>\n",
              "      <td>0.358209</td>\n",
              "      <td>0.736842</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.894737</td>\n",
              "      <td>0.500000</td>\n",
              "      <td>0.343284</td>\n",
              "      <td>0.789474</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9432</th>\n",
              "      <td>0.947368</td>\n",
              "      <td>0.653846</td>\n",
              "      <td>0.970149</td>\n",
              "      <td>0.947368</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9433</th>\n",
              "      <td>0.947368</td>\n",
              "      <td>0.653846</td>\n",
              "      <td>0.970149</td>\n",
              "      <td>0.894737</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9434</th>\n",
              "      <td>0.842105</td>\n",
              "      <td>0.692308</td>\n",
              "      <td>0.970149</td>\n",
              "      <td>0.842105</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9435</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.653846</td>\n",
              "      <td>0.970149</td>\n",
              "      <td>0.947368</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9436</th>\n",
              "      <td>0.947368</td>\n",
              "      <td>0.653846</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.894737</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>9435 rows × 5 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "      temp_max_mes_quente  temp_min_mes_frio  zona_chuva  temp_media_anual  \\\n",
              "0                0.894737           0.500000    0.373134          0.789474   \n",
              "1                0.894737           0.500000    0.358209          0.736842   \n",
              "2                0.842105           0.461538    0.358209          0.736842   \n",
              "3                0.842105           0.500000    0.358209          0.736842   \n",
              "4                0.894737           0.500000    0.343284          0.789474   \n",
              "...                   ...                ...         ...               ...   \n",
              "9432             0.947368           0.653846    0.970149          0.947368   \n",
              "9433             0.947368           0.653846    0.970149          0.894737   \n",
              "9434             0.842105           0.692308    0.970149          0.842105   \n",
              "9435             1.000000           0.653846    0.970149          0.947368   \n",
              "9436             0.947368           0.653846    1.000000          0.894737   \n",
              "\n",
              "      res_espece  \n",
              "0            1.0  \n",
              "1            0.0  \n",
              "2            0.0  \n",
              "3            0.0  \n",
              "4            0.0  \n",
              "...          ...  \n",
              "9432         0.0  \n",
              "9433         0.0  \n",
              "9434         0.0  \n",
              "9435         0.0  \n",
              "9436         0.0  \n",
              "\n",
              "[9435 rows x 5 columns]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "display(df_norm)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "197a3d2c",
      "metadata": {
        "id": "197a3d2c",
        "outputId": "28a456ba-a0ad-4745-8c25-12b3cc95c17d"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(9435, 5)"
            ]
          },
          "execution_count": 22,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_norm.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "99addfd7",
      "metadata": {
        "id": "99addfd7",
        "outputId": "e889992e-5462-41e8-bcf5-0a5f19955ff5"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[(0.0, 9309), (1.0, 126)]\n"
          ]
        }
      ],
      "source": [
        "# Contar a quantidade de presença e ausente\n",
        "from collections import Counter\n",
        "print(sorted(Counter(df_norm['res_espece']).items()))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "12dc4bcf",
      "metadata": {
        "id": "12dc4bcf",
        "outputId": "8d3e4b84-cd14-4756-89e6-cffa58635dab"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "9435"
            ]
          },
          "execution_count": 24,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Quantidade de dados - dataset principal\n",
        "df_norm['res_espece'].count()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "2f82036a",
      "metadata": {
        "id": "2f82036a"
      },
      "outputs": [],
      "source": [
        "#X=df_norm.drop('res_espece',axis=1)\n",
        "#y=df_norm.res_espece"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "fe548f18",
      "metadata": {
        "id": "fe548f18"
      },
      "outputs": [],
      "source": [
        "# suprimir os warnings\n",
        "from warnings import simplefilter\n",
        "simplefilter(action='ignore', category=FutureWarning)\n",
        "\n",
        "!pip install -q scikit-plot"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "af59ae74",
      "metadata": {
        "id": "af59ae74"
      },
      "outputs": [],
      "source": [
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.metrics import classification_report\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import roc_auc_score, roc_curve, accuracy_score\n",
        "from imblearn.under_sampling import RandomUnderSampler\n",
        "\n",
        "# configurar o estilo dos gráficos com o Seaborn\n",
        "sns.set_style('dark')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "d52607cb",
      "metadata": {
        "scrolled": true,
        "id": "d52607cb",
        "outputId": "7d13f1a6-3561-4bff-e420-753d43602c33"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "0.0    9309\n",
            "1.0     126\n",
            "Name: res_espece, dtype: int64\n",
            "\n",
            " presença de abelha representam 1.3355% do dataset.\n",
            "\n"
          ]
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# ver o balanceamento das classes\n",
        "print(df_norm.res_espece.value_counts())\n",
        "print(\"\\n presença de abelha representam {:.4f}% do dataset.\\n\".format((df_norm[df_norm.res_espece == 1].shape[0] / df_norm.shape[0]) * 100))\n",
        "\n",
        "# plotar gráfico de barras para as Classes\n",
        "sns.countplot('res_espece', data=df_norm);"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "89f5c52b",
      "metadata": {
        "id": "89f5c52b"
      },
      "outputs": [],
      "source": [
        "# separar variáveis entre X e y\n",
        "X = df_norm.drop('res_espece', axis=1)\n",
        "y = df_norm['res_espece']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "d4239a4d",
      "metadata": {
        "id": "d4239a4d"
      },
      "outputs": [],
      "source": [
        "# dividir o dataset entre treino e teste\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, stratify=y, shuffle=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "06b2dc35",
      "metadata": {
        "id": "06b2dc35",
        "outputId": "d826b0eb-632d-4ae6-8e07-0f45b42e3da9"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "C:\\Users\\Gebruiker\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1334: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "C:\\Users\\Gebruiker\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1334: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "C:\\Users\\Gebruiker\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1334: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Relatório de Classificação:\n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "         0.0     0.9864    1.0000    0.9932      2327\n",
            "         1.0     0.0000    0.0000    0.0000        32\n",
            "\n",
            "    accuracy                         0.9864      2359\n",
            "   macro avg     0.4932    0.5000    0.4966      2359\n",
            "weighted avg     0.9731    0.9864    0.9797      2359\n",
            "\n",
            "Acurácia: 0.9864\n",
            "\n",
            "AUC: 0.5000\n",
            "\n"
          ]
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# testando o modelo com essa descrepancia de dados\n",
        "\n",
        "# instanciar e treinar um modelo de Regressão Logística\n",
        "model = LogisticRegression()\n",
        "model.fit(X_train, y_train)\n",
        "\n",
        "# fazer as previsões em cima dos dados de teste\n",
        "y_pred = model.predict(X_test)\n",
        "y_proba = model.predict_proba(X_test)\n",
        "\n",
        "# plotar a matrix de confusão\n",
        "skplt.metrics.plot_confusion_matrix(y_test, y_pred, normalize=True)\n",
        "\n",
        "# imprimir relatório de classificação\n",
        "print(\"Relatório de Classificação:\\n\", classification_report(y_test, y_pred, digits=4))\n",
        "\n",
        "# imprimir a acurácia do modelo\n",
        "print(\"Acurácia: {:.4f}\\n\".format(accuracy_score(y_test, y_pred)))\n",
        "\n",
        "# imprimir a área sob a curva\n",
        "print(\"AUC: {:.4f}\\n\".format(roc_auc_score(y_test, y_pred)))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "c97c9b5d",
      "metadata": {
        "id": "c97c9b5d"
      },
      "outputs": [],
      "source": [
        "# usar técnica under-sampling para desbalancear os dados\n",
        "rus = RandomUnderSampler()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "ed6c6ba4",
      "metadata": {
        "id": "ed6c6ba4"
      },
      "outputs": [],
      "source": [
        "X_res, y_res = rus.fit_resample(X_train, y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "c82a0d93",
      "metadata": {
        "id": "c82a0d93",
        "outputId": "4b8709a6-6b09-4e48-eecc-12c34a6f5d1f"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "0.0    94\n",
            "1.0    94\n",
            "Name: res_espece, dtype: int64\n"
          ]
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# ver o balanceamento das classes\n",
        "print(pd.Series(y_res).value_counts())\n",
        "\n",
        "# plotar a nova distribuição de classes\n",
        "sns.countplot(y_res);"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "b58d06ed",
      "metadata": {
        "id": "b58d06ed",
        "outputId": "176e3cc8-35ec-45c6-cf04-f6d3dc7a7c7d"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression()</pre></div></div></div></div></div>"
            ],
            "text/plain": [
              "LogisticRegression()"
            ]
          },
          "execution_count": 35,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# instanciar e treinar um modelo de Regressão Logística\n",
        "model_res = LogisticRegression()\n",
        "model_res.fit(X_res, y_res)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "2bce2991",
      "metadata": {
        "id": "2bce2991"
      },
      "outputs": [],
      "source": [
        "# fazer as previsões em cima dos dados de teste\n",
        "y_pred_res = model_res.predict(X_test)\n",
        "y_proba_res = model_res.predict_proba(X_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "05cc7e27",
      "metadata": {
        "id": "05cc7e27",
        "outputId": "87957816-e9b3-4fdf-a76c-d2695b9e8483"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<AxesSubplot:title={'center':'Normalized Confusion Matrix'}, xlabel='Predicted label', ylabel='True label'>"
            ]
          },
          "execution_count": 37,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# plotar a matrix de confusão\n",
        "skplt.metrics.plot_confusion_matrix(y_test, y_pred_res, normalize=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "95176126",
      "metadata": {
        "id": "95176126",
        "outputId": "89fa5142-cd2a-4de2-f0df-c1d7fb4c73b8"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Relatório de Classificação:\n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "         0.0     0.9933    0.6326    0.7729      2327\n",
            "         1.0     0.0251    0.6875    0.0484        32\n",
            "\n",
            "    accuracy                         0.6333      2359\n",
            "   macro avg     0.5092    0.6600    0.4107      2359\n",
            "weighted avg     0.9801    0.6333    0.7631      2359\n",
            "\n"
          ]
        }
      ],
      "source": [
        "# imprimir relatório de classificação\n",
        "print(\"Relatório de Classificação:\\n\", classification_report(y_test, y_pred_res, digits=4))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "ae100daf",
      "metadata": {
        "id": "ae100daf",
        "outputId": "28e8e4d4-2779-4054-d85c-435f4fe4a6d1"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "from sklearn import metrics\n",
        "import math as m\n",
        "#y_pred_res\n",
        "#roc_auc_score(y_test, y_pred_res)\n",
        "fpr, tpr, _ = metrics.roc_curve(y_test,y_pred_res)\n",
        "auc = metrics.roc_auc_score(y_test, y_pred_res)\n",
        "plt.plot(fpr,tpr,label=\"Dado com predição em Logistica, AUC= \"+str(m.floor(auc*100))+'%',color=\"darkorange\")\n",
        "plt.legend(loc=4)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "f23cd3f1",
      "metadata": {
        "id": "f23cd3f1",
        "outputId": "df198438-f19a-4294-9d77-817c1de36420"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier()</pre></div></div></div></div></div>"
            ],
            "text/plain": [
              "KNeighborsClassifier()"
            ]
          },
          "execution_count": 40,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "# instanciar e treinar um modelo KNN\n",
        "knn = KNeighborsClassifier()\n",
        "knn.fit(X_res, y_res)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "977bc339",
      "metadata": {
        "id": "977bc339"
      },
      "outputs": [],
      "source": [
        "# fazer as previsões em cima dos dados de teste\n",
        "y_pred_resknn = knn.predict(X_test)\n",
        "y_proba_resknn = knn.predict_proba(X_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "49fc5d32",
      "metadata": {
        "id": "49fc5d32",
        "outputId": "edfeb685-0684-4add-c4ce-c179cd283fc7"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<AxesSubplot:title={'center':'Normalized Confusion Matrix'}, xlabel='Predicted label', ylabel='True label'>"
            ]
          },
          "execution_count": 42,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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Yq1Yt1qxZw4cffki1atXo0KEDY8eONZ3wJiAggCVLlvD2228THh5O/fr1mTFjBs8///x1X0fHjh355JNPWLZsGUuWLOHMmTNUq1aN5s2b89FHH5ktDuzQoQPLly9n3rx5jB07FicnJwICApg1axY+Pj4lefuK8PDwYN68ecyZM4eRI0dSr149wsPD+eyzz0xzmjZtygcffMB7773H2LFjycvLo0WLFixbtsx0aOSVDAYDixYtYt68eaxcuZJz585Rv359IiMji5xEyBruvfdeqlatSp06dWjcuHGxcyz93Dz22GPs2rWLkSNHMnr0aItO77xmzRr27dvH3LlzTYnUE088webNm5k8eTJt27a16lkhRazJYNTVO0RERCo9rSEQERERJQQiIiKihEBERERQQiAiIiIoIRARERGUEIiIiAhKCERERAQlBCIiIpw8c7GiQ6hwlebERHc+Oo3U9KyKDkOkTMRvmVLRIYiUmaou5fPb9a6HXyElLbNU+6jq7kL8v6ZZKaLyVWlOXZyansUlJQQiIvI3UtKzuZSeXbqdGGy38F5pEgIREZFrMgCXLwNeqn3YKCUEIiIiUPDrvrS/8G24QmC7kYuIiIjVqEIgIiICBe2CUrcMbLdnoIRAREQEKqRlcPbsWSZNmkRMTAz29vaEhIQwYcIEHByKfj3HxMTw1ltvER8fT9WqVRk4cCDDhw8HID8/H39/f4xGI4YrkpK9e/fi5uZmUSxKCERERCpIREQEXl5e7Nmzh+TkZEaMGMGKFSsYNmyY2byjR4/y3HPP8dprr9G7d2/++9//8uSTT3LHHXfQrVs34uPjycnJ4eDBgzg5Od1QLFpDICIiAn+1DEq7WSghIYGYmBjGjx+Pq6sr3t7ehIWFERUVVWTumjVrCA4Opk+fPhgMBpo2bcratWvx9/cHIDY2Fl9f3xtOBkAJgYiIyGV2f7UNbnQrwddqXFwcnp6eeHl5mcYaN25MUlISKSkpZnMPHTpE/fr1GTt2LIGBgXTv3p2YmBhq1aoFFCQEWVlZ9O3bl/bt2zNo0CAOHjxY0lcvIiIi1pSammq2ZWcXPeFRWloarq6uZmOFt9PT083GL168yMqVKwkJCWHv3r1MnTqVWbNmsW3bNgBcXFxo1aoVCxcuZOfOnXTt2pWhQ4eSmJhoccxKCERERMCqLYOgoCD8/f1N26JFi4o8nZubGxkZGWZjhbfd3d3Nxp2cnAgODub+++/HwcGBtm3b0qtXL7Zu3QrAxIkTmTFjBl5eXri4uDB06FDq1q3Lrl27LH75WlQoIiICVj3KYPfu3WbDxfX2fXx8uHDhAsnJydSsWRMoWDxYu3ZtPDw8zOY2bty4SJUhLy+PwssRzZ07l4cffphmzZqZ7s/OzsbZ2dni0FUhEBERsbIqVaqYbcUlBA0bNsTf358ZM2aQmppKYmIiCxcuJDQ0tMjcAQMG8M033/D5559jNBr5/vvv2bx5M7169QLgyJEjTJ8+nTNnzpCdnc2CBQtITU3lwQcftDhmJQQiIiJQ7kcZAMybN4/c3FyCg4N5/PHH6dSpE2FhYQD4+fmxadMmADp06MDChQtZuXIl/v7+vPjii0yYMIHg4GAA3njjDRo0aECvXr0IDAwkJiaG5cuX4+npafnLryyXP7696yRd7VBuWad2zqzoEETKTHld/vj2h6aV+nvCw82Z01+9YqWIypfWEIiIiEClP3WxWgYiIiKiCoGIiAhQ6S9/rIRAREQELrcMSpsQqGUgIiIiNkwVAhEREQA7Q8FW2n3YKCUEIiIiUOnXENhu5CIiImI1qhCIiIhApT8PgRICERERUMugogMQERGRiqcKgYiICKhlUNEBiIiI3BQqectACYGIiAhU+gqB7aYyIiIiYjWqEIiIiIBaBhUdgIiIyE1BLQMRERGp7FQhEBERAcAKLQMb/p2thEBERATUMqjoAERERKTiqUIgIiIClysEpT3KwHYrBEoIREREoNIfdmi7kYuIiIjVqEIgIiIClX5RoRICERERqPQtAyUEIiIiUOkrBLabyoiIiIjVqEIgIiICahlUdAAiIiI3BbUMREREpLJThUBERAQwGAwYSvkLv7SPr0hKCERERFBCoJaBiIiIqEIgIiICgOHyVtp92CglBCIiIqhloJaBiIiIqEIgIiICqhAoIRAREUEJgRICERERlBBoDYGIiIioQiAiIgLosMOKDkBERORmoJaBiIiIVHqqEIiIiFB49ePSVgisFEwFUEIgIiICGLBCy8CGFxGoZSAiIiKqEIiIiIAWFapCICIiAn8ddljarQTOnj1LWFgYAQEBBAYGMn36dHJzc4udGxMTQ79+/fDz86Nz584sWrTI7P4lS5YQFBREmzZtGDJkCMeOHStRLEoIREREKkhERARubm7s2bOH9evXs3//flasWFFk3tGjR3nuuecYOHAgBw8eZNGiRSxbtoxt27YBsHHjRlatWsXSpUuJjo6mefPmjB49GqPRaHEsSghEREQALrcMSrOV5DCDhIQEYmJiGD9+PK6urnh7exMWFkZUVFSRuWvWrCE4OJg+ffpgMBho2rQpa9euxd/fH4CPP/6YgQMH4uPjg7OzM+PGjSMpKYno6GiL41FCICIiAqVOBq5cg5Cammq2ZWdnF3m+uLg4PD098fLyMo01btyYpKQkUlJSzOYeOnSI+vXrM3bsWAIDA+nevTsxMTHUqlULgPj4eJo0aWKa7+joSMOGDTl8+LDFr1+LCkVERLDuosKgoCDS0tJM4+Hh4YwaNcpsblpaGq6urmZjhbfT09OpWrWqafzixYusXLmSuXPn8uabb/Ljjz8yfPhwqlWrRrdu3Yrdl4uLC+np6RbHroRARETEynbv3m1228nJqcgcNzc3MjIyzMYKb7u7uxd5fHBwMPfffz8Abdu2pVevXmzdupVu3brh6upKZmam2WMyMzOL7Oda1DIQEREBqx5lUKVKFbOtuITAx8eHCxcukJycbBo7evQotWvXxsPDw2xu48aNi7Qd8vLyTIsGfXx8iIuLM92Xk5PD8ePHzdoI16OEQEREBOuuIbBEw4YN8ff3Z8aMGaSmppKYmMjChQsJDQ0tMnfAgAF88803fP755xiNRr7//ns2b95Mr169AOjbty+rV6/m8OHDZGVlMWfOHGrWrElAQIDF8SghEBERqSDz5s0jNzeX4OBgHn/8cTp16kRYWBgAfn5+bNq0CYAOHTqwcOFCVq5cib+/Py+++CITJkwgODgYgNDQUJ566ilGjhxJ+/bt+fXXX1m0aBGOjo4Wx2IwluQgRRt2e9dJXErPqugwRMrEqZ0zKzoEkTJT1aV8frveNeozUjOLPymQpaq4OBA/v7d1AipnWlQoIiKCTl2sloGIiIioQiAiIgKqECghEBERgRu6OFGx+7BRahmIiIiIKgQiIiKgloESAhEREZQQKCEQERFBCYHWEIiIiIgqBCIiIkClP8pACYGIiAhqGahlICIiIqoQyPXVqu7OexP70umexuTm5bN220Emzv+SvLz8InM7+t3JjPAe3N3IiwuXMlj06X5mr9wBFGTOp7+ZisFg4Mprat3RYyrpmTnl9npErmYAXBwNONiBEcjJg6zc4q/75uxgwNG+4DH5xoJ5uVf8X8HDuegvxEtZleIacjavslcIKiQhOHv2LJMmTSImJgZ7e3tCQkKYMGECDg5Fw9m1axezZ88mMTGROnXq8I9//IMuXbpUQNSV16ppg0k6c5E7H52G120erH/rKUYP6MTcqF1m85rcUYvP3n6GMW9tJGrLAVrcVZutC4ZzNDGZjTtiubvR7Tg62FOr6yRycvMq6NWIFOXqaCCfgi9ugwHcHA0Y7SH7qo+poz042kFathGjERzsCh6bevm23eXvAiUAtsmAFRICG15EUCEtg4iICNzc3NizZw/r169n//79rFixosi848ePM2rUKMaMGcMPP/zAqFGjiIiI4NSpU+UfdCV1Z/3b6OzfmJcXbCEjK4fjSeeYufwbng+9t8jc50PvZfPuX4jacgCAn+P/pMuz77HvP/8DwP9ub36OP6lkQG4qBgM42BvIyin4Ejde/tXv5FD0D3tOHqYv/8LHFjyo4H/s7QqqBiK2qNwTgoSEBGJiYhg/fjyurq54e3sTFhZGVFRUkbkbN24kICCABx54AAcHB3r06EHbtm1Zt25deYddaTVr5MXZi2mcTE4xjf32v1M0qFOdalVczOYGNPMm4eQ5/jl1IInbXuPHteMIuqcxp86lAuDfrD4uzo58u2wUv299la/ff572Le8o19cjcjV7A+QbjVz5PZ5vBLtr/FK0tytoDbg4GMjK/eux9pcf4+5koIqzATcnA/a2+4Ox0ilsGZR2s1XlnhDExcXh6emJl5eXaaxx48YkJSWRkpJiNjc+Pp4mTZqYjd11110cPny4XGIVqOLuTHpGttlYembB7Squzmbj1au6EdavIx9tO0jDR14nfOYG3hj9CH26tAQgMyuX73/5nccn/JMmvWfw5Z5f2fTOMO6oU718XoxIMYr78228xn0AefkFbYH0HCPODgVrDwofl5cP6dlGUrOM5OYZcXMyYMPfEZWLwUqbjSr3hCAtLQ1XV1ezscLb6enp153r4uJSZJ6UnfSMbFxdnMzG3C7fvpSeZTaelZ3LF3t+Ydu+w+Tl5bP3p/+xZutB+ga3AmDivC8YMWM9SWdSyMzK5Z01u0k8dZ7u991dPi9GpBjFVfgN17jvSnn5BW0Ex8tlgKxcI5lXVAyy8wqqDQ46nktsQLl/TN3c3MjIyDAbK7zt7u5uNu7q6kpmZqbZWGZmZpF5UnZ+OXaKmp7u3F6jimns7kZe/HHqAilp5v9tDv/vFM6O5gtD7e3sTCW0yc8/TOsmdc3ud3Z0ICNLRxhIxSlsD1z5w87uchvhas4OBpyLWVtQONXZwWBaWFjIhn8wVjpqGZQzHx8fLly4QHJysmns6NGj1K5dGw8PD7O5TZo0IS4uzmwsPj4eHx+fcolV4GhiMnt/+h9vRYRQxc2ZO+pUZ+LTwazY/H2RuUs2fkfPzs0Z0M0PgPvaNGLAw36s2XYQgGZ31mZ2ZAheNarg5GjPi888gIe7C5t2/Vyur0nkSvlGyM034uJY8IfcYCj4Ys8pZu1rXr4RJ3tM6wIc7AqOPMjJK8gI7AwFhy8WfiU42QMGit2X3HyUEJSzhg0b4u/vz4wZM0hNTSUxMZGFCxcSGhpaZG5ISAgxMTFs2bKF3NxctmzZQkxMDL169SrvsCu1gS+twsHejt82TGT30lF8/d1/eWPZvwE4s/11BjxckADsOnCU0PErCH+8I6e+mcriVx7nxflf8OWeXwEYPu1jjp04S/TqSE78azJB99zJI6MXcz4l42+fW6Q8ZGQXfKFXcTbg7mQgN/+v8xB4OP+1RiA3HzJzC5IHD+eCakF6jpHL+QAZOUby88Hd2XD5cQbSs3XYga0wGKyz2SqD0VhMXayMJScnM3XqVKKjo7Gzs6N379688MIL2Nvb4+fnx5QpUwgJCQFgz549zJ49m99//5169eoxfvx4OnfuXOLnvL3rpCI9b5FbxamdMys6BJEyU9WlfH67tnn5a1Kzcku1jyrODvw0/UErRVS+KuTERDVr1mTevHnF3vfjjz+a3e7UqROdOnUqj7BERKQSK/iFX9ozFVopmAqgUxeLiIgAWKPkb8MJgQ6GEREREVUIREREQBc3UkIgIiKCdY4SsOF8QC0DERERUYVAREQEADs7A3ZXn2ryBvZhq5QQiIiIoJaBWgYiIiKiCoGIiAjoKAMlBCIiIqhloIRAREQEVQi0hkBERERUIRAREQFVCJQQiIiIoDUEahmIiIiIKgQiIiIABqzQMrDh6x8rIRAREUEtA7UMRERERBUCERER0FEGSghERERQy0AtAxEREVGFQEREBNQyUEIgIiKCWgZKCERERFCFQGsIRERERBUCERERAKzQMijpiQrPnj3LpEmTiImJwd7enpCQECZMmICDQ9Gv52HDhhEdHW1237vvvktQUBD5+fn4+/tjNBrNqhR79+7Fzc3NoliUEIiIiFAxLYOIiAi8vLzYs2cPycnJjBgxghUrVjBs2LAic3/++WeWLl1Ku3btitwXHx9PTk4OBw8exMnJ6YZiV8tARESkAiQkJBATE8P48eNxdXXF29ubsLAwoqKiisxNTEzk4sWLNGvWrNh9xcbG4uvre8PJACghEBERAf46yqC0G0BqaqrZlp2dXeT54uLi8PT0xMvLyzTWuHFjkpKSSElJMZsbGxuLu7s7kZGRtG/fnkcffZT169eb3Z+VlUXfvn1p3749gwYN4uDBgyV6/WoZiIiIYN2WQVBQEGlpaabx8PBwRo0aZTY3LS0NV1dXs7HC2+np6VStWtU0np2dTZs2bYiMjMTHx4fo6GhGjRqFu7s73bt3x8XFhVatWjFmzBiqVatGVFQUQ4cOZdOmTXh7e1sUuxICERERK9u9e7fZ7eJK+W5ubmRkZJiNFd52d3c3G+/duze9e/c23e7YsSO9e/dm69atdO/enYkTJ5rNHzp0KBs2bGDXrl0MHjzYopjVMhAREcG6LYMqVaqYbcUlBD4+Ply4cIHk5GTT2NGjR6lduzYeHh5mc9evX8/WrVvNxrKzs3F2dgZg7ty5/Prrr397vyWUEIiIiPBXy6C0m6UaNmyIv78/M2bMIDU1lcTERBYuXEhoaGiRuampqbz++uv8+uuv5Ofns3PnTr744gv69+8PwJEjR5g+fTpnzpwhOzubBQsWkJqayoMPPmhxPEoIREREKsi8efPIzc0lODiYxx9/nE6dOhEWFgaAn58fmzZtAuDJJ59k8ODBhIeH4+fnx+zZs5k1axYBAQEAvPHGGzRo0IBevXoRGBhITEwMy5cvx9PT0+JYDEaj0Wj1V3gTur3rJC6lZ1V0GCJl4tTOmRUdgkiZqepSPr9duy34jvTsvFLtw83Jnm3h7a0UUfnSokIRERF0cSMlBCIiIujiRlpDICIiIqoQiIiIgFoGSghERERQy0AtAxEREVGFQEREBMCAFVoGVomkYighEBERAewMBuxKmRGU9vEVSS0DERERUYVAREQEdJSBEgIRERF0lIESAhEREcDOULCVdh+2SmsIRERERBUCERERAAxWKPnbcIVACYGIiAhaVKiWgYiIiKhCICIiAmC4/E9p92GrlBCIiIigowzUMhARERFVCEREREAnJlJCICIigo4yUMtAREREVCEQEREBXf7YooRgwYIF150THh5e6mBEREQqSmVvGViUEERHR1/zflteRCEiIgJaVGhRQrBq1aqyjkNEREQqUIkXFR49epRp06YRHh7O+fPnWb16dVnEJSIiUq4KWwal3WxViRKCvXv30q9fP86fP8++ffvIzMzkvffeY/HixWUVn4iISLkoXFRY2s1WlSghePvtt5k7dy5z5szB3t6eOnXqsHjxYtatW1dW8YmIiEg5KNFhhwkJCQQFBQF/LZxo2bIlFy9etH5kIiIi5chweSvtPmxViSoEdevW5eDBg2ZjsbGx1KlTx6pBiYiIlLfCowxKu9mqElUIhg8fzogRI3jiiSfIyclhyZIlrFq1irFjx5ZVfCIiIlIOSpQQPPLII1SpUoWoqCjq1q3Ld999x8svv8zDDz9cVvGJiIiUi8p++eMSn7q4c+fOdO7cuSxiERERqTCV/cREJVpDkJuby/vvv0+3bt3w8/OjZ8+eREVFlVVsIiIiUk5KVCF45513+Oqrrxg2bBh16tTh999/Z9myZaSlpfHcc8+VVYwiIiLlwoZ/4JdaiRKCL774glWrVuHt7W0aa9++Pc8++6wSAhERsWmVvWVQ4jUEtWrVMrtdt25dUlNTrRaQiIhIRajsiwpLtIZg0KBBvPrqq6YEIDMzk1mzZvHEE0+USXAiIiJSPiyqEDRt2hSDwYDRaAQKWgceHh6kpaWRm5tL9erViYyMLNNARUREypJaBhZYuXJlWcchIiJSoSr7qYstSgjatWt3zfvPnTtnlWBERESkYpRoUeGhQ4d48803OXXqFPn5+QDk5ORw7tw5fv755zIJUEREpDxY4/LFlebyx1OnTqVWrVp07NiRRo0aMXjwYOzt7Rk3blxZxSciIlIuDAbrbLaqRAlBXFwcb7zxBoMGDSIvL4+nn36auXPnsnnz5rKKT0RERMpBiVoGVatWxcXFBW9vb+Li4gBo06YNJ06cKJPgREREyktlP8qgRBWCO++8k48++ghnZ2fc3Nz47bffOHr0qE2/ASIiIqCWQYkqBGPGjGHEiBHcd999DB06lMcffxx7e3udmEhERMTGlSghuOeee9i9ezeOjo7079+fu+++m0uXLnHfffeVVXwiIiLloiKOMjh79iyTJk0iJiYGe3t7QkJCmDBhAg4ORb+ehw0bRnR0tNl97777LkFBQQAsWbKEVatWkZKSQsuWLZkyZQp33nmnxbFYlBAkJSUVO16zZk1q1qxJUlISdevWtfhJRUREbjbWKPmX9PERERF4eXmxZ88ekpOTGTFiBCtWrGDYsGFF5v78888sXbq02HMDbdy4kVWrVrF06VIaNGjA3LlzGT16NJs3b7a4rW9RQtC1a1fTDo1Go9nOC2//9ttvFj2hiIjIzai8FxUmJCQQExPD7t27cXV1xdvbm7CwMN56660iCUFiYiIXL16kWbNmxe7r448/ZuDAgfj4+AAwbtw4Pv74Y6Kjo2nfvr1F8ViUEHzzzTcW7UxEREQochVgJycnnJyczMbi4uLw9PTEy8vLNNa4cWOSkpJISUmhatWqpvHY2Fjc3d2JjIwkNjaWmjVr8tRTTxEaGgpAfHw8zz77rGm+o6MjDRs25PDhw9ZNCOrVq2fRzm5mv3/1ekWHIFJmqrcNr+gQRMqEh7sLp7+dXS7PZUcJD737m30ABAUFkZaWZhoPDw9n1KhRZnPT0tJwdXU1Gyu8nZ6ebpYQZGdn06ZNGyIjI/Hx8SE6OppRo0bh7u5O9+7di92Xi4sL6enpFsdeokWFIiIityprtgx2795tNn51dQDAzc2NjIwMs7HC2+7u7mbjvXv3pnfv3qbbHTt2pHfv3mzdupXu3bvj6upKZmam2WMyMzOL7OdaSpsMiYiIyFWqVKlithWXEPj4+HDhwgWSk5NNY0ePHqV27dp4eHiYzV2/fj1bt241G8vOzsbZ2dm0r8ITBkLBdYaOHz9OkyZNLI5ZCYGIiAgFRwjYlXIrSYGhYcOG+Pv7M2PGDFJTU0lMTGThwoWmdQFXSk1N5fXXX+fXX38lPz+fnTt38sUXX9C/f38A+vbty+rVqzl8+DBZWVnMmTOHmjVrEhAQYHE8JW4ZZGdns2vXLk6cOEH//v1JSEigadOmJd2NiIjITaXwS720+yiJefPmMXXqVIKDg7Gzs6N3796EhYUB4Ofnx5QpUwgJCeHJJ58kPT2d8PBwzp49i7e3N7NmzTJ94YeGhnLp0iVGjhzJuXPnaNmyJYsWLcLR0dHiWAxGo9Fo6eTff/+dZ555hpycHFJSUtiwYQOPPvooCxYsoEuXLiV7F8pZZm5FRyBSdrSoUG5V5bmo8MUtR8jKzS/VPpwd7Hijh+Vl+ptJiVoG06dP57HHHmPnzp04ODjQqFEjpk2bxrx588oqPhERkXJRuKiwtJutKlFC8NNPPzFs2DCzF92rVy8SExPLJDgREZHyUtr1A9ZoOVSkEiUEHh4eZqshAc6cOUO1atWsGpSIiIiUrxIlBD179iQ8PJy9e/eSn5/PoUOHeOGFF3jkkUfKKj4REZFyocsfl0BYWBiZmZmEh4eTkZHBkCFDCA0NJTxcC5pERMS2VcTVDm8mJUoIHB0dmTBhAhMmTODcuXNUr17dphdQiIiIFLLmqYttUYkSgs8+++xv77vylIoiIiJiW0qUEFx9eOHFixfJyMjA399fCYGIiNg0a6wBsOWieYkSgu3bt5vdNhqNLFmyhAsXLlgzJhERkXJnhxXWEGC7GUGp2h0Gg4GhQ4fy+eefWyseERERqQClvvzx//73Py0sFBERm6eWQQkMGTLE7Ms/JyeH//73v4SEhFg9MBERkfJUERc3upmUKCEIDAw0u21nZ8dTTz3FAw88YNWgREREpHyVKCE4f/48kZGRVKlSpaziERERqRAGQ+lPLGTLLYMSLSrcvHkzrq6uZRWLiIhIhdGpi0ugb9++TJkyhccee4xatWqZrSeoW7eu1YMTERGR8lGihGD58uUAfPzxx6ZkwGg0YjAY+O2336wfnYiISDnRokILHDhwAH9/f7755puyjkdERKRCGDCU+rRCpd9DxbEoIXj22Wc5ePAg9erVK+t4REREKkRlrxBYtKjQaDSWdRwiIiJSgSyqEOhMhCIicqur7BUCixKCjIwMgoODrzlH6wtERMSWGQwGK5y62HYzAosSAkdHR8LDw8s6FhEREakgFiUEDg4O9OnTp6xjERERqTBqGVhAiwpFRORWV9mvdmjRUQa6mqGIiMitzaIKwZQpU8o6DhERkQplZzBYoWVguyWCEp26WERE5FZV2dcQlOhqhyIiInJrUoVAREQEwBqXL7bhCoESAhEREcAOQ6nL5nY2nBEoIRAREUGHHWoNgYiIiKhCICIiAjrKQAmBiIgIOg+BWgYiIiKiCoGIiAhoUaESAhERES4fdljaloENH3aoloGIiIioQiAiIgJqGSghEBERoaBkXvozFdouW45dRERErEQVAhEREcBgMFihZWC7PQMlBCIiIhRcqLASX+xQCYGIiAjoTIVaQyAiIiKqEIiIiBSy3d/3pacKgYiICH+dh6C0W0mcPXuWsLAwAgICCAwMZPr06eTm5l7zMUeOHKF169ZER0ebxvLz8/Hz86NNmzb4+fmZtvT0dItjUYVARESkgkRERODl5cWePXtITk5mxIgRrFixgmHDhhU7PyMjg3HjxpGZmWk2Hh8fT05ODgcPHsTJyemGYlGFQEREhMLDDku/WSohIYGYmBjGjx+Pq6sr3t7ehIWFERUV9bePmTJlCg888ECR8djYWHx9fW84GQAlBCIiIsBfZyos7WapuLg4PD098fLyMo01btyYpKQkUlJSisz/7LPPSEhIIDw8vMh9sbGxZGVl0bdvX9q3b8+gQYM4ePBgCaJRQiAiImJ1qampZlt2dnaROWlpabi6upqNFd6+uvd/9OhR5s6dy5w5c7C3ty+yLxcXF1q1asXChQvZuXMnXbt2ZejQoSQmJlocs9YQiIiIYN0zFQYFBZGWlmYaDw8PZ9SoUWZz3dzcyMjIMBsrvO3u7m4ay8rKIjIykpdeeom6desW+7wTJ040uz106FA2bNjArl27GDx4sEWxKyEQERHBumcq3L17t9l4cb19Hx8fLly4QHJyMjVr1gQKKgG1a9fGw8PDNC82Npbjx4/z8ssv8/LLL5vGn3/+eXr16sXkyZOZO3cuDz/8MM2aNTPdn52djbOzs8WxKyEQERGxsipVqlx3TsOGDfH392fGjBlMnTqV8+fPs3DhQkJDQ83mBQQEcOjQIbMxX19fPvjgAwIDA4GCQxF/+OEH3nnnHapVq8bixYtJTU3lwQcftDhmrSEQERGh/I8yAJg3bx65ubkEBwfz+OOP06lTJ8LCwgDw8/Nj06ZNFu3njTfeoEGDBvTq1YvAwEBiYmJYvnw5np6elr9+o9FoLFH0Nirz2ud5ELFp1dsWXXUscivwcHfh9Lezy+W5NsX+SW5+6b4SHewMhLSsbaWIypdaBiIiIujyx2oZiIiIiCoEIiIiYN2jDGyREgIRERFu7OJExe3DVqllICIiIqoQiIiIANhhKPWvZDsbbhooIRAREUEtA7UMRERERBUCERERAAMGKxxlYLslAiUEIiIiqGWgloGIiIioQiAiIgIF5f7S/kpWy0BERMTGVfaWgRICERERlBBoDYGIiIioQiAiIgI67FAJgYiICGBnAGMpv8/tbDcfUMtAREREVCEQEREB1DJQQiAiIoKOMlDLQERERFQhEBERATBQ+pK/DRcIlBCIiIiAjjJQy0BERERUIRDLONr9lfnmGSE3v/h5DnZgf3mekYJ5+cai8+wN4GgPmbllEq5IidSqXoX3Jj1BpwAfcnPzWbvleybO3UheXtEPekf/u5gxpjd3N67DhZR0Fn2yh9nLvjLd/2y/jowZHIxXzaocP3GWV+dvYuuen8vz5cgNss5RBrarQisE586d48EHHyQ6Ovpv5+zatYuePXvSpk0bunfvzo4dO8oxQoGCZAAgKw+y8woSA/tiPvX2hoL7svMK5ubmFzz26qkGChIHkZvFqlnPkJqexZ0PvUynIW/RJdCX0YO6FJnXpKEXn80fwaJP9lDrvnH0Gf0+YwZ3pc8DbQAY1DOQl57rwVMvraDWfeN4a9m/+Gj2UOrUqlbOr0huROFRBqXdbFWF/Vk+cOAA/fv35/fff//bOcePH2fUqFGMGTOGH374gVGjRhEREcGpU6fKMdLKzQDY20HO5R9Khb/6i/tCzzMWJAPGKx5b+JgrOdoXzBW5GdzpXZPObZvw8rufk5GZw/ETZ5m5ZBvPD+hcZO7z/YPYvOMQUZsLfsT8HJdEl6feZt+PRwGIGBLM1IVf8MMvCQB8vO0A9z85h5TUjPJ7QXLDDFbabFWFJAQbN27khRdeIDIy8rrzAgICeOCBB3BwcKBHjx60bduWdevWlVOkYjCA8aovb6Px2lmwnQGc7QuShqtbCw52BS2E4toIIhWhWeM6nL2QxskzF01jvx37kwZ1alCtiqvZ3IDmd5CQdI5/vvEUidtn8uOnrxAU4MOps5dwdXGkWePa5OXn8/XSCP7YMYsdK8bi7upMWkZ2eb8skRKrkISgY8eOfP311/To0eOa8+Lj42nSpInZ2F133cXhw4fLMjwppXxjQcsg53IloXDtgd3llsLfrT8QqQhV3FxIz8gyG0vPzL58n7PZePVqboQ90ZmPvvyehg++RPj0j3gjsjd9HmhDdQ837OzsiPi/YEZPX0ujB19i3dYf+GxBGA3q1Ci31yM3zg4DdoZSbjZcI6iQhKBWrVo4OFx/PWNaWhquruYZuouLC+np6WUVmlhRvrGgNWBvKCijOdpBTl5FRyViLj0jC1cXJ7Mxt8u3L6Vnmo1nZefyxc5DbPv2F/Ly8tl78Chrvvyevg/eQ1ZOwQrZeau389uxP8nJzeODdbv5/eQ5unVsXj4vRkpFLYObmKurK5mZ5v+HzMzMxN3dvYIiqnyKaw8U10aAgmpAcWsLjPxVJXCyL2gnFC5UdLa37eN2xfb9cvQkNatX4fYaHqaxu++szR9/nicl1fzvz+Fjf+LsZP5jxt7eDoMBzl5I49TZFJwdHc3vtzNgsOWVZlJp3NQJQZMmTYiLizMbi4+Px8fHp4IiqnyMFPzSL/wCLzxCoLhFgflXVAPgr6MR8vIL5mfl/bUVLlLMytN6AqlYR38/w96D8bw1vi9V3Jy5o+5tTHy2Gys+219k7pL139Lz/tYM6NEWgPvuacyA7gGs+fJ7AD5c/y0vPteNVk3qYW9vR9gTnal7uyebd/ynXF+T3KBKXiK4qROCkJAQYmJi2LJlC7m5uWzZsoWYmBh69epV0aFVKtmXy/zO9gW/8POvOA/Blb/wC8cd7f9aVJiTX/QoA5GbzcDxS3Gwt+O3L6awe9ULfL3vN95YshWAM3vnMKB7AAC7vj9CaOQiwgfez6k9b7F48mBenLuRL3fFAjB90Vbm/vPfrJr1DH/ufouBj7Sjz6j3SbpiwaLcvAxW+sdWGYzG4oq/5cfX15eVK1cSGBgIgJ+fH1OmTCEkJASAPXv2MHv2bH7//Xfq1avH+PHj6dy56OFA16MT4MitrHrb8IoOQaRMeLi7cPrb2eXyXD8cu1jqQ6LtDRBwp22ed6LCE4LyooRAbmVKCORWVa4Jwf8ulrqFaWeAgEa2mRDo1MUiIiJYZwmA7TYMbvI1BCIiIlI+VCEQEREB6/y8t+ESgRICERERdLVDJQQiIiJcvlqhFfZhq7SGQERERFQhEBERAR1loIRAREQEKv2iQrUMRERERBUCERER0FEGSghERETQUQZqGYiIiIgqBCIiIqCjDFQhEBERgb8ygtJuJXD27FnCwsIICAggMDCQ6dOnk5t77cvzHjlyhNatWxMdHW02vmTJEoKCgmjTpg1Dhgzh2LFjJYpFCYGIiEgFiYiIwM3NjT179rB+/Xr279/PihUr/nZ+RkYG48aNIzMz02x848aNrFq1iqVLlxIdHU3z5s0ZPXo0RqPl13NWQiAiIkLhUQal/8dSCQkJxMTEMH78eFxdXfH29iYsLIyoqKi/fcyUKVN44IEHiox//PHHDBw4EB8fH5ydnRk3bhxJSUlFqgjXooRARESEy0cZWGEDSE1NNduys7OLPF9cXByenp54eXmZxho3bkxSUhIpKSlF5n/22WckJCQQHh5e5L74+HiaNGliuu3o6EjDhg05fPiwxa9fiwpFRESw7qLCoKAg0tLSTOPh4eGMGjXKbG5aWhqurq5mY4W309PTqVq1qmn86NGjzJ07l48++gh7e/siz1vcvlxcXEhPT7c4diUEIiIiVrZ7926z205OTkXmuLm5kZGRYTZWeNvd3d00lpWVRWRkJC+99BJ169Yt9vlcXV2LrCvIzMw028/1qGUgIiICVj3KoEqVKmZbcQmBj48PFy5cIDk52TR29OhRateujYeHh2ksNjaW48eP8/LLLxMQEEBAQAAAzz//PJMnTzbtKy4uzvSYnJwcjh8/btZGuB5VCERERCj/Uxc3bNgQf39/ZsyYwdSpUzl//jwLFy4kNDTUbF5AQACHDh0yG/P19eWDDz4gMDAQgL59+zJ//nyCgoJo1KgRc+fOpWbNmqbkwRKqEIiIiFSQefPmkZubS3BwMI8//jidOnUiLCwMAD8/PzZt2mTRfkJDQ3nqqacYOXIk7du359dff2XRokU4OjpaHIvBWJKDFG1Y5rXP8yBi06q3LbrqWORW4OHuwulvZ5fLcx0+mUZ+Kb8R7QzQtI7lffubiVoGIiIi6NTFahmIiIiIKgQiIiKAdX7e23CJQAmBiIgI5X+Uwc1GLQMRERFRhUBERAQuX4vACvuwVUoIRERE0FEGSghERESg0i8q1BoCERERUYVAREQEdJSBEgIREREAKywqtOWMQC0DERERUYVAREQEKv2aQiUEIiIiQKXPCNQyEBEREVUIREREACscY2DTBQIlBCIiImCd0w7b8qmL1TIQERERVQhERESg0q8pVEIgIiICVPqMQAmBiIgIWlSoNQQiIiKiCoGIiAhc/nVfyp/4tlwhUEIgIiJCpV9CoJaBiIiIqEIgIiICWOnERKXfRYVRQiAiIgLY9td56allICIiIqoQiIiIgFoGSghERETQUQZqGYiIiIgqBCIiIqCWgRICERERdC0DJQQiIiJg29/mVqA1BCIiIqIKgYiICOgoAyUEIiIiaFGhWgYiIiKiCoGIiAjoKAMlBCIiImDb3+ZWoJaBiIiIqEIgIiICOspACYGIiAg6ykAtAxEREVGFQEREpIA1jjOwXUoIREREsE7LwJapZSAiIiJKCEREREQtAxEREaBiWgZnz55l0qRJxMTEYG9vT0hICBMmTMDBwfzrOT8/n/fee4/169eTkpJC/fr1GTFiBD169DDd7+/vj9FoxHDFC9m7dy9ubm4WxaKEQEREhIpZUhgREYGXlxd79uwhOTmZESNGsGLFCoYNG2Y2Lyoqis8++4xVq1bRoEEDduzYQVhYGC1atKBBgwbEx8eTk5PDwYMHcXJyuqFY1DIQERGpAAkJCcTExDB+/HhcXV3x9vYmLCyMqKioInMHDRrE5s2badCgAdnZ2Zw7dw5XV1dcXFwAiI2NxdfX94aTAVCFQEREBLBuyyA1NdXstpOTU5Ev67i4ODw9PfHy8jKNNW7cmKSkJFJSUqhatapp3M7ODjc3N7799lueffZZjEYjL774IrfffjtQkBBkZWXRt29fTpw4QePGjRk3bhz33HOPxTGrQiAiIkLBWQatsQEEBQXh7+9v2hYtWlTk+dLS0nB1dTUbK7ydnp5ebIzt2rUjNjaW5cuX884777BlyxYAXFxcaNWqFQsXLmTnzp107dqVoUOHkpiYaPHrV4VARETEynbv3m12u7hSvpubGxkZGWZjhbfd3d2L3W/hfjp06ECvXr3YvHkzPXr0YOLEiWbzhg4dyoYNG9i1axeDBw+2KGZVCERERMCqJYIqVaqYbcUlBD4+Ply4cIHk5GTT2NGjR6lduzYeHh5mc2fOnMnMmTPNxrKzs/H09ARg7ty5/Prrr0Xud3Z2tvjlKyEQERGh4CgDa/xjqYYNG+Lv78+MGTNITU0lMTGRhQsXEhoaWmRuQEAAa9eu5fvvvyc/P5/t27ezZcsW+vXrB8CRI0eYPn06Z86cITs7mwULFpCamsqDDz5o+es3Go1Gi2fbsMzcio5ApOxUbxte0SGIlAkPdxdOfzu7XJ4rNcs6X4dVnC1PCpKTk5k6dSrR0dHY2dnRu3dvXnjhBezt7fHz82PKlCmEhIQAsH79epYsWUJycjINGzYkIiKCTp06AXDhwgVmzZrFrl27yMjIoGXLlrz00ks0bdrU4liUEIjcApQQyK2qPBOCtGzrfB26O9nmRRG0qFBERARKUOy/NSkhEBERgUqfEWhRoYiIiKhCICIiAhVzLYObiRICERERKuZqhzcTJQQitwAPd5eKDkGkTFRxs/zEOlI6leawQxEREfl7WlQoIiIiSghERERECYGIiIighEBERERQQiAiIiIoIRARERGUEIiIiAhKCERERAQlBCIiIoISArkBZ8+eJSwsjICAAAIDA5k+fTq5ubnFzt21axc9e/akTZs2dO/enR07dpRztCI37ty5czz44INER0f/7Rx9xuVWoYRASiwiIgI3Nzf27NnD+vXr2b9/PytWrCgy7/jx44waNYoxY8bwww8/MGrUKCIiIjh16lT5By1SQgcOHKB///78/vvvfztHn3G5lSghkBJJSEggJiaG8ePH4+rqire3N2FhYURFRRWZu3HjRgICAnjggQdwcHCgR48etG3blnXr1lVA5CKW27hxIy+88AKRkZHXnafPuNwqlBBIicTFxeHp6YmXl5dprHHjxiQlJZGSkmI2Nz4+niZNmpiN3XXXXRw+fLhcYhW5UR07duTrr7+mR48e15ynz7jcSpQQSImkpaXh6upqNlZ4Oz09/bpzXVxciswTudnUqlULB4frXx1en3G5lSghkBJxc3MjIyPDbKzwtru7u9m4q6srmZmZZmOZmZlF5onYKn3G5VaihEBKxMfHhwsXLpCcnGwaO3r0KLVr18bDw8NsbpMmTYiLizMbi4+Px8fHp1xiFSlr+ozLrUQJgZRIw4YN8ff3Z8aMGaSmppKYmMjChQsJDQ0tMjckJISYmBi2bNlCbm4uW7ZsISYmhl69elVA5CLWp8+43EqUEEiJzZs3j9zcXIKDg3n88cfp1KkTYWFhAPj5+bFp0yagYLHhe++9x6JFi2jbti0LFy5k/vz5NGrUqCLDFykVfcblVmUwGo3Gig5CREREKpYqBCIiIqKEQERERJQQiIiICEoIREREBCUEIiIighICERERQQmBiE05fvx4RYcgIrcoJQQiV+jatSstW7bEz88PPz8/2rRpQ8eOHZk1axb5+flWe54hQ4Ywf/58AF599VVeffXV6z5m+/btDB069Iafc8OGDXTt2rXE911t/vz5DBky5Ibj8PX1JTo6+oYfLyJl4/qX8xKpZKZMmcJjjz1muv3f//6Xp556CldXV0aPHm3155s6dapF8y5cuIDOIyYiZUUJgch1+Pr60rZtW3799Veg4Nd9vXr1iI6Oxmg08sUXX3Du3DlmzJjBjz/+iJubGyEhIYwcORInJycAPvnkEz744APOnTvHQw89ZHbFyIkTJwIwc+ZMAP75z3+yevVqkpOTadSoEePHj8fOzo7XXnuNnJwc/Pz82LZtG9WrV+f9999n06ZNXLp0idatW/PKK69wxx13AAUXnZo8eTI///wz9evXJzAw0OLXvH79etasWcOJEyfIzs6mXbt2vPHGG9SoUQMouNT1xIkT2bFjBzVq1GD48OH07t0bgOzs7GvGJSI3J7UMRK4hJyeH6OhovvvuO+677z7T+L59+1i7di2bNm3Czs6Op556Ch8fH3bv3s2aNWvYt2+fqSWwf/9+pk6dyrRp0/j+++9p3bo1sbGxxT7fhg0bWLhwIW+++SYHDhzgiSeeYMSIEfj6+jJlyhTq1q3Ljz/+iJeXF3PnzmXnzp2sWLGCPXv20Lp1a5555hmysrLIyclh+PDh+Pj48N133/H222/z73//26LXfOjQIaZNm8bkyZOJjo5m69atHD9+nJUrV5rm/Pzzz7Ro0YJvv/2WV155hVdeeYUffvgB4JpxicjNSwmByFWmTJlCQEAAAQEBdOjQgddff52nn36awYMHm+YEBQXh5eVF1apV2blzJ9nZ2YwdOxZnZ2fq1KnDmDFjiIqKAmDTpk089NBDdOjQAQcHBwYOHEizZs2Kfe6NGzfSv39//Pz8sLOzo1+/fixbtgwXFxezeUajkbVr1zJ27Fi8vb1xdnZm5MiR5OTksHPnTn788UdOnjzJP/7xD5ydnfHx8eHpp5+26PU3adKEL774glatWnHx4kVOnz5NjRo1OHXqlGnO3XffzeDBg3F0dOS+++7j4Ycf5vPPP79uXCJy81LLQOQqr732mtkaguLcfvvtpn8/ceIE586do23btqYxo9FITk4OZ8+e5dSpUzRv3tzs8d7e3sXu98yZM9StW9ds7J577iky79y5c6SnpzNmzBjs7P7K63Nyckxl/urVq5slEg0aNLjmaypkZ2fHypUr2bx5M25ubvj6+pKammq2fqF+/fpmj6lTpw5Hjhy5blwicvNSQiByAwwGg+nfa9euTYMGDdi2bZtpLDU1lbNnz1KjRg1q165NYmKi2eP//PNPfHx8iuy3Tp06nDx50mxs7ty5hISEmI1Vr14dZ2dnli1bRps2bUzjx44dw8vLi99++41z586RlpaGu7u76TktsWLFCvbu3cvmzZupWbMmAM8//7zZnNOnT5vdTkxMpF69eteNS0RuXmoZiJRSly5dSEtL48MPPyQ7O5uUlBQmTJhAZGQkBoOBvn378u9//5sdO3aQm5vLxo0b+c9//lPsvh577DHWrVvHoUOHyM/P59NPPyUqKsr0RZuRkUFubi52dnaEhoYyZ84c/vzzT/Lz89m4cSOPPvooCQkJ+Pn50ahRI6ZNm0ZGRgYJCQksW7bMoteTmpqKg4MDjo6O5Obm8vnnn7Nnzx5ycnJMcw4dOsSnn35KTk4OO3bsYPv27fTr1++6cYnIzUsVApFSqlKlCitWrGDmzJl8+OGH5OfnExgYyPvvvw+Av78/b775JjNnziQyMpL27dubLVC8Us+ePUlJSWH8+PGcOXOGu+66iyVLllCjRg3atm3LbbfdRtu2bVm7di0TJkxg/vz5DBw4kAsXLuDt7c28efNM6xMWL17Mq6++yr333kvNmjUJDg7mq6++uu7reeaZZzhy5AhdunTB2dmZZs2aMXDgQL777jvTnHvvvZdvvvmGadOmUb9+fd59913T814vLhG5ORmMOrBZRESk0lPLQERERJQQiIiIiBICERERQQmBiIiIoIRAREREUEIgIiIiKCEQERERlBCIiIgISghEREQEJQQiIiKCEgIRERFBCYGIiIgA/w/rhWoyCqkGRAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# plotar a matrix de confusão\n",
        "skplt.metrics.plot_confusion_matrix(y_test, y_pred_resknn, normalize=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "838e21e2",
      "metadata": {
        "id": "838e21e2",
        "outputId": "f7ed7e78-eb3b-4ecb-9d38-0889905c19d5"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Relatório de Classificação:\n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "         0.0     0.9928    0.6528    0.7877      2327\n",
            "         1.0     0.0253    0.6562    0.0488        32\n",
            "\n",
            "    accuracy                         0.6528      2359\n",
            "   macro avg     0.5091    0.6545    0.4182      2359\n",
            "weighted avg     0.9797    0.6528    0.7776      2359\n",
            "\n"
          ]
        }
      ],
      "source": [
        "# imprimir relatório de classificação\n",
        "print(\"Relatório de Classificação:\\n\", classification_report(y_test, y_pred_resknn, digits=4))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "d3163b8c",
      "metadata": {
        "id": "d3163b8c",
        "outputId": "6fb41932-4d90-4951-b174-841a490e0d88"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "from sklearn import metrics\n",
        "import math as m\n",
        "fpr, tpr, _ = metrics.roc_curve(y_test,y_pred_resknn)\n",
        "auc = metrics.roc_auc_score(y_test, y_pred_resknn)\n",
        "plt.plot(fpr,tpr,label=\"Dado com predição em KNN, AUC= \"+str(m.floor(auc*100))+'%',color=\"navy\")\n",
        "plt.legend(loc=4)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "ea2b0183",
      "metadata": {
        "id": "ea2b0183",
        "outputId": "450e2ba5-659d-48d0-f417-6737bed11ee4"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1100x500 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# prepare plots\n",
        "fig, auc = plt.subplots(1, figsize=(11, 5))\n",
        "\n",
        "fpr, tpr, _ = metrics.roc_curve(y_test,y_pred_resknn)\n",
        "auc = metrics.roc_auc_score(y_test, y_pred_resknn)\n",
        "plt.plot(fpr,tpr,label=\"Dado com predição em KNN, AUC= \"+str(m.floor(auc*100))+'%',color=\"navy\")\n",
        "\n",
        "fpr, tpr, _ = metrics.roc_curve(y_test,y_pred_res)\n",
        "auc = metrics.roc_auc_score(y_test, y_pred_res)\n",
        "plt.plot(fpr,tpr,label=\"Dado com predição em Logistica, AUC= \"+str(m.floor(auc*100))+'%',color=\"darkorange\")\n",
        "\n",
        "plt.legend()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "cdf8fdf7",
      "metadata": {
        "id": "cdf8fdf7"
      },
      "outputs": [],
      "source": [
        "from sklearn.linear_model import LinearRegression\n",
        "\n",
        "reg=LinearRegression().fit(X_res, y_res)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "9f4a6777",
      "metadata": {
        "id": "9f4a6777",
        "outputId": "0e9c2434-ce1e-4f0a-d0dc-35ba849d8b5f"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
            ],
            "text/plain": [
              "LinearRegression()"
            ]
          },
          "execution_count": 46,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "reg"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "a78726d7",
      "metadata": {
        "id": "a78726d7"
      },
      "outputs": [],
      "source": [
        "# fazer as previsões em cima dos dados de teste\n",
        "y_pred_resreg = reg.predict(X_test)\n",
        "#y_proba_resreg = reg.predict_proba(X_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "47acabe8",
      "metadata": {
        "id": "47acabe8",
        "outputId": "5cecac08-84e8-4d56-d921-86d796da3879"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "AUC: Para Regressão Linear 0.6631\n",
            "\n"
          ]
        }
      ],
      "source": [
        "print(\"AUC: Para Regressão Linear {:.4f}\\n\".format(roc_auc_score(y_test, y_pred_resreg)))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "d53cebfa",
      "metadata": {
        "id": "d53cebfa",
        "outputId": "5e6a40a7-f7fb-4baf-b839-6acb08c49734"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "from sklearn import metrics\n",
        "import math as m\n",
        "fpr, tpr, _ = metrics.roc_curve(y_test,y_pred_resreg)\n",
        "auc = metrics.roc_auc_score(y_test, y_pred_resreg)\n",
        "plt.plot(fpr,tpr,label=\"Dado com predição em Regressão Linear, AUC= \"+str(m.floor(auc*100))+'%')\n",
        "plt.legend(loc=4)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "6acb043b",
      "metadata": {
        "id": "6acb043b",
        "outputId": "55c2c926-f164-4e82-f2a7-f9da6d78d030"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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3Vcfg5o9rnRaUqd0AtebJPtThkQWFlStX5q+//rJ47Pz589SsWfNRDYlKpcLeXvfI+n9U9u/fg62tHTVq1OKvv04xY8YUvv12kXm/3LJlS7l69TIA7u7u7N9vuWQuNjbafEOdu2cu3OL58PDr+WZd3N3dAYiOjjLvmbt06SK7dm3nuecak5ycRHp6mjkwTEtLIzk5CWdnFxRFAR7uMOmbwRxARkYGyclJuLt7mKtL3t63q6s777//ocWSwvDw6zg4OJKeng5AREQ4ZcuWAyAm5lbft3NzcycpKYmsrCxzYHjsWBjnzp2lZMmSrFmzkm+//cG8j2/69MlcuHAeyA2ew8OvW/QXEZH/a+vo6ISVlTXffDPHvBcU4OrVy7i4uN3jK/TfKlWqQt269QH46qtJvPtuV6ZMmcBnn43Jt/1bb3Xn6NEjfPHFSLTaO//Vv9d2t9uz5w/0+hyWLl1lkRX8/vv5LF/+izkodHNzJzo6bxAdHn7NHADWrFkbR0dHfv99R55g7Pz5c/To8Rbz5y+iTp26bNv2xz3ND6BcuXIoioLBoDcv3zUabwbdCnv2/EFCQjydOr1q/pmcnJx89wpev36NAwf2sWDBj/z55+94e/tgb1+MqlWrcfHi+XuekxBCCCEKiUmPOv4kiX/9Qc7l/TjnHMfdJgbzXd2NvEh8ui0n4soTZ1UbqzKNKePfCu9yXhTM3VzR8chC2g4dOhAaGsqWLVswGAxs2bKF0NBQOnbs+KiGfOLk5OTw2287mD9/Lr169cbOzp709DRUKrX5RvTUqZOsWrUMvV4PwIsvtuHixfNs2LAWg8FAaGgwe/b8Ye7zlVc6sX59EEeOHMJoNBIWdpgNG9bSrl2HPOO7urrh6+vPvHkzSU1NJT09jW+/nUV4+HWqVq1O2bLlmTJlImlpaaSlpTF16kS8vX2oVatOgVz/8uW/cP36NbKyspg9+xtKly5DzZq1823boUNnfvrpB65cuQxASMhBund/nePHw/Dw8MDPL4DZs6eTkpJCfHwcixYtzLef6tVrUqpUGebMmUFWVhYJCfHMmTODxMQE0tLSUKtzX3tFUQgOPsC2bZvNQWq7dh3Zt+9Pfv99F0ajkbNn/+aXX5bk+9qq1WpeeaUD8+fPJiYmGpPJxNatm+je/Q2uX79z1d4H5eLiypdfTmDHjq2sXPlrvm1UKhWff/4liYkJ/PXXyTv2da/tbrdmzQpefPFlPDw8cXNzN/8JDHyDU6dOcPLkcQBat27LkiU/cvr0KQCys7PYsWMb+/btMe891el09O8/lEWLFhIUtIqMjAyMRiPHjx/j88+H88ILLe74e/JffH0D8PLyZuLEsWRkZJCYmMh3382jadMXsLOzR1EUZs/+hsOHQ1EUhVOnTrB69XJz9dHbzZgxhX79BqLVavHxKc21a1dISkri1KkTeHv/d2EgIYQQQjx+qsxY1Bc3krJ5MOmLmlD8J0+ct75AxatjqK7eibtNDCYTnIh0Z825piyJ6c9apyBiX7tEzVEHeWH4Ahq9+T+8y3kV9qU8EgWaKaxXrx5ffvklHTp0oEKFCsydO5epU6fy2Wef4e3tzezZsylXrlxBDvnEmTp1ItOn5xbKsLa2pnTpsgwd+ql5n5qvrz+dOwfSt+8HGI0mvLy8CAx8kwUL5pCQEI+3tw+TJ89g9uzpzJw5lSpVqvH88y+Y+2/RohUZGWlMnz6F6OhIXF3d6N37E9q0aZfvfL74Yhxz5kyna9dAjEYDjRs/T//+g9FqtUyePJ05c2bw1ltd0OtzaNjQj+nT591z9uhu6tSpy8iRg4mOjqZu3XpMmTLrjnvP3njjbUBh+PBBxMfH4erqysCBw2jSpBkAY8aMZ9q0SQQGtsfe3p62bdubA4/b3byuWbOm0apVE4oXd6Bt2/b07NkLk8nEiRPH6N79dTQaDaVLl+X1199mzZqV6PV6atSoybhxX7No0XdMnDgWR0dHOnV6la5d38l3zn36DGDRooX06fMBycnJeHl5M27c11SufO/LMu9H/foN+eCDj5k7dybly1fMt42joxOjR4/nk08+/M++8msXFRVF9+6vMXXqLOrUqWd+/Pz5cxw/fpQBA/Lu4axcuSpVq1Zn2bKl1KpVh//9ryc6nY4JE74kJiYGlQoqVKjE+PFTLL5seOmlNjg5ObFs2c8sWrSA7Owc3N3deeWVDrz5Zrf7fWmA3Pd+zpyFzJ79DW+91Zns7ByaNHme/v1z592sWXP69RvEtGmTiI2NoWRJZ3r2/JDWrdta9PPnn7/j5FTCnKWtUqUqHTp04a23ulCyZElGjrxzYSAhhBBCPAYmA9rEvzBeP0j6uT3YpxzBRZu7UskZzBFQYoYNh8JLcc1QHaO7P+51W1DntUo8/wSuPHxYKuXmOsAiKi4ulX/PUK/PIT4+EmdnT/M+IfFkCQzMDcTatm1fKOPr9Xp69XqH1q3bPnCQ8SyaP38OzZo1p1q1GoU9FfEMks9+IYQoOq4nZdL5h0PYW2n4o1/hVuFWZcWjiw1Ff3U/+iv7KZHzFzZqy9omJhOcjnElLLIs8dZ1sCrTmAq+AdSu44FOV/TPpH5QKhW4uBS/a7tHtqdQiKJMp9MxfPjn9OrVg8DANwss+/k0y8jIICkp8Y7n+AkhhBBCPHImI5qk02hjQ8i5fABtTAhOyjXLNmpIyrQm5KoPfyVWIN2hAU7Vnqdex6q0rlTSogigyCV3wuKZ1LdvL86d+4d33nlPAsJ7ZGdnx4gRowp7GkIIIYR4hqiy4tHFHUITE4r+yn7sUo5hrcrM0+50tAsHL5fick5VjO7+lK7rj3+7UtT3cSiEWT955G5YFIrVqzcW6vhz5uRfiEYIIYQQQhQSkxFN8t/oYkNRR4eghB+kWM4lyzYqSMmyIuSqDyHXShGtqY112UbU8atKk/e86FDSNv++xX+SoFAIIYQQQjyVridl8k9MWmFP46kUn57z0H2oshPRxR1CGxuKOioEbexhdEp6nnZ/xzhz8EopjkSUId2hAW7VG+LfujTd6ns+kcfRFUUSFAohhBBCiKeO3mii+9Iw0rKNhT2Vp5rmXvfnKSY0yf+giw3NDQIjg7FOP5enWWqWFaHXvDl4xYdTCRVR3H2p0bAaAT29aV3T9akuClOYJCgUQgghhBBPnSy9yRwQ1vFyQGqLPBqtquR/jLsqJyk383cjCNTEHEZrTMnT7mxsSQ5eKcXBKz5czq6KY4WG+PmXonl3b3pVKolKJW/c4yBBoRBCCCGEeKrNf702Wk3+ZyGLAqCY0CSfMweAuthQNMl/o8LyXLn0HB2hV3OzgAculyLBujZV6lYl4AVvPg7wxsvr7kcniEdDgkIhhBBCCCHEPVPlpKCNy80C6mJD0cYdRp2TlKfd+bgS5izg4etlULvXwte/NP5vexPo50WJElIUpqiQoFAIIYQQQgiRP0VBk3LenAHUxYaiSTqdJwuYkaPl0LVbWcCTceUoU60yAQHevPS6N8PreWBnJ0VhiioJCoUQD+zatauUKlW6sKchhBBCiAKi0qeijTty21LQQ6hzEvO0uxjvZM4CHrxcigh9WRr4libgOR/6DfCiZk03tFpZsvukkKDwMQoMbE9CQjwaTW7VJEVR8PEpzauvvk779p0euN/x48cA8NlnYx5+kk+JsLDDfPLJR+zbd5ioqCi6d3+Nn39ehYeHx3/+3NGjR5g8eTyJiQlYW1tTvnxFpk6dZX7PiooffljA0aNHHsl5i0uWLOL48WNMmzbrP9udPfs3vXr14I8/ggGYMmUCAEOHflrgc/q3ixcv8M47b9K4cVMmTfrG4rktWzayaNHCfM/CDAxsT8+evWjbtj0AJpOJ9euD2LJlA9euXUWj0VCxYmX+97+eNGjg+8DzO3r0CPPmzeLy5YsUL+5A586BdO/+rvn5tWtXs3z5UhIS4vH09OLDD/vSuHFTAFatWs7ixd+h0Wjp2fMDOnUKBMBgMNCr1zuMGTOe0qXLPvDchBBCCDNFQZN6wRz85WYB/0KlmCyaZeq1HL7mlRsA3ggErUv4EBDgTcCr3nQN8KZChRJSFOYJ9nQFhYoChozHN57WDu7zl3/IkJHmG1K9Xs+BA3uZMOFLkpOT6NatxyOYpPDw8GDnzr331LZevQYsWxb0iGdUtP3vfz3vqV1aWhoGg8H8348jGLwpKGglbdu2Z+fO7Vy9eoXSpcvcdx+KovDZZ8MID7/G4MEjqVGjJiaTie3btzBs2AC+/HICTZo0u+9+r1y5zNCh/Rk8eARt2rTjwoXz9O//ET4+pWjevBVbt27ixx+/Y9KkaVSrVoNdu7bz+efDWLVqA3Z2dsyZM51Fi35BURTee68brVu3w9bWlpUrf8Xfv5EEhEIIIR6cPg1dfJhlFjA7Pk+zywmOt7KAV0pxItKdipU9CAjw5uUO3nwZ4I2npxSFeZo8PUGhouC07SV0sSGPbUi9awBJbbbfd2B4k06no1mzFiQnJzNjxlQ6dw7E3r4YJ08e57vvvuXKlcukpqZQrlwFBg4cRs2atQDYt+9P5s+fS1RUBPXrNwTA0dHJ3O/GjetYseJXYmKi8fDwoFu3Hrz00sv5ziExMZHZs7/hwIF9qNVqfH39GDx4JA4ODkRFRTJv3izCwg6jVqtp0MCXPn0G4OLiQljYYSZM+JIuXV5j+fJfyM7OokuX16lRoyZz5swkPj4OPz9/Ro8ej5WVVZ5xAwPb37ip30ZcXCyVK1dlyJCRlCtXnrCww4wbN5ratesSHLyfbt160LXrO6xevYKgoJUkJiZQvnxFPvlkMFWrVgMgLi6OKVPGc/RoGI6OTrRq9ZJ5rMjICF57rQOrVm3A09OL8PDrzJw5jePHw9DprGjRohX9+g1Cp9Oxb98eli5dzPXr18jMzKBatRoMH/65eYnknj1/sHjx91y/fg1nZ2c6dw4kMPBN1Oq8yyP0ej0//fQD27dvJS0tlRo1ajJgwFB8fEoB0KRJQ4YO/ZRly5YSGxtNgwZ+9OzZi5kzp3D+/DnKlCnHmDHjze3vx3+9dwCHDoUwb95MwsPDqVChArVq1eX06VPMmbPQIguZkZHO11+P4/DhUDQaLRUrVuKTTwaj0+kYMqQ/AC++2JTp0+eyfn1uMH0zY71y5TLWrFlBQkICpUqVpk+f/jRo4Et6ehpz5szg6NEjxMXFUqxYcbp0ee2+gtHt27cyZ85CcnJyWLHilwcKSHfv/o2QkAP8+muQRQa5fftOpKamcOnSJZo0acaUKRPYsWNrvn3k92VDUNBKmjZ9gZdffgWAihUr8e23i7C3twdg2bKfef/9j6hevSYAL77YhtKly2JnZ2/+PVIUBUXJ3auhUqmIiYlm27bNfPfdT/d9nUIIIZ5RioI67dKtYjCxh9AmnkKlWJ7bmGXQcuSap0UWMC7Tibp1c4PAgR974+vrhZOTTSFdiHgcnrKFvk9myrpRo6bk5GRz8uQJsrOzGD58EM2atWDt2i1s3vwb3t4+zJs3E4CrVy/z+efD+d//3mXbtj9o374TISEHzX1t2bKROXOmM3DgULZu/Z3+/Ycwbdok/vxzd75jjxo1nLS0NFasWMuqVetJTU1j2rRJGAwGBgzog1qtZvnytfzyy2oURWH48IHm7FBUVCTx8XEEBW1mzJgJ/Pzzj6xZs5KFCxfz00/LCAs7wm+/7bjjdW/YsJaxYyeyadNOypYtZ9F3TEw0ZcuWY9OmXXTp8hpBQatYvnwpX331NZs27aJt2/YMGNCbhITcb7dGjx6JRqNl3botzJmzgODg/fmOaTAYGDy4H87OLqxdu5Wff17ByZPHWbRoITEx0XzxxQi6devBpk07CQrajKIoLF78HZC7JPWLL0bQtes7bNnyG2PGTGD58l9YtWpZvmMtXDiPAwf2MnPmPNat20qNGrUYOLAv2dnZ5jY7d25j4cIfWbFiPSdOHGPkyMGMGPEFGzbsQKfTsWTJoju+fndyt/cuMjKC4cMH0alTIFu2/MZHH/Vjw4b8s6PLli0lPT2doKDNrF69EWdnF+bPn423tw9Tp868cQ17qVmztsXPbdmykcWLv2fUqLFs3/4HnTsHMnz4QFJSkvn22zlERETw3XdL2LlzLwMGDGHhwnlcv37tnq5vy5YNlC9fgSpVqvLqq2+wbdtmEhPz7nW4m/37/6RWrTr5Lil+++3/0b17DyA3A7pz5958/+Tn9Om/8PDwZPToT2nXriVduwZy9OgRnJ1dyMrK4tKli6jVavr0+YC2bVvy0Uc9ycrKxM7ODhsbGwYOHMawYQMYMWIQI0aMwsbGhlmzvqFXrz5YW8s/yEIIIe7AkIEuej+2p6bj8PubOK+qiPPaujjs64XtP9+jSziOSjFyLcmRFcdqMGB9a/xnvY/jZyNo89PHbEnvRZVXPuDbJe9x/nwfNm9+k1GjmvLii+UlIHwGPD2ZQpUqN2tXxJeP5sfJyQmAlJRktFodCxb8iI9PKXJysomMjMDBwZEzZ04DsGvXDqpVq27O/DVt+gKNGjU197V58wY6duxi3g/VoIEvHTt2Yf36IJo1a24xblRUJMeOhfHrr2vMmcbPPhtNcnIyx48fJTIynB9+WIK9fTEAhg37lJdfbsHff58x99G9e0+0Wi1+fgEAdOoUiIODAw4ODpQvX4GoqMg7Xvdbb3WjUqUqAPTrN4jWrZtx4sQx8/Pt2nVAq9Wi1WpZu3YV3bu/S8WKlQB45ZWObNq0nu3bt9K8eUuOHz/KsmVB2NnZY2dnT8+evRg5ckieMU+ePE5kZASLFw/GxsYGOzs7JkyYislkokSJkvz880q8vX3IyEgnJiYaR0cnYmNjza9t06Yv0LLliwBUqVKVbt16sHr1ct54o6vFOIqisG7dasaNm4yXlzcAPXq8z4YNazl4cB8vvNASgFdffR0HB0cAypevQOXKVSlTpqz5vbv99bhXd3vvjhwJpVKlynTs2AWAOnXq0aFDZ06f/itPX1ZW1pw/f46tWzfj5xfAyJFf5JsV/betWzfRsWMXc7DYvn0nypYth7W1Ne+91wuNRoO9vT0xMdFYWVkDEBcXe9esqKIoBAWt5oMPPgKgZs1aVKhQibVrV9GzZ697f5GAxMQkSpZ0vq+fuRepqSmsXr2CL7+cwKhRYzl16gTDhg3EwcGBmjVroygKy5cvZezYSZQqVZoNG4IYMuQTlixZgaenF506vUqnTq+a+wsNDcZoNFC7dl0+/3w4169fo1atOvTvPxit9un5CBdCPHqpWQb2Xownx2C6e+OnQNbTfJ2Kgjrtym1HQhxCm3ASlWKwaJZj1HLkugcHL9/KAoYnO+LiYkdAgDcdenkzIcCb6tVdpSjMM+7puqNQqUBnX9izuG+JiQkAlChRAo1GQ1jYYYYM+YTMzEzKlSuPRqNFubHhNzY2Fjc3y8yGt7cPyclJACQkxJuDkJs8Pb3Zt29PnnHj4uIA8PDwND/m7OyCs7MLO3eew9HRyRxUANjZ2ePg4EhUVIT5ZtrRMTeguVmIpXjxW+vLVSoVJtOdP5B9fG5VrbSxscHR0Yn4+DicnXOXOLq4uJqfj4yMYO7cGcyfP9v8mMFgoGrVasTGxgDg7n7rdfH29sl3zPj4OJycnLCxufWNl6enF5AbcOzcuY3164NQqVSUL1+B9PR087UlJiaYg9jbfza/wDcpKZHMzExGjRqBWn3riwO9Xk9k5K32ty/7VavV9/X63UlCQvx/vne5y4o9LX7Gy8s736CwW7d3sLa2YvPm9UyfnhvgfvRRX5o1a/Gfc4iPj7N4PwBq1aoDYF6++88/f+Pl5UWVKtUB7ulag4MPcP36VaZOncQ333wNQFZWFhER4XTt+g7W1tZYWVlhNBrz/Xmj0Whezuzi4nLHLy0yMtJRqzXY2Ngwdeokdu3alm+7bdv+yPOYTqejadNmNGrUBIC6devTunVbfv99F/Xq5S73fuONrpQvXwGAV199g7Vr13Dw4H66dHnNoq+cnBzmzZvFpEnT+PnnH3FwcODHH39h8OBP2LRpnbkIjRBC3Iu5+y6x5vidv6x9WmlUFMiX+IXKkIk2/tiNIDAEXWwo6qyYPM2i0hzZe8HbvBQ07LonOUYtZcs6EhDgw5B3vQgI8KF8eScpCiMsPF1B4RNq//492NraUaNGLf766xQzZkzh228XmffLLVu2lKtXLwPg7u7O/v2Wy9ZiY29lW3L3zIVbPB8eft0caN3O3d0dgOjoKPOeuUuXLrJr13aee64xyclJpKenmYOLtLQ0kpOTcHZ2sdjv9KBuBnMAGRkZJCcn4e7uYV5Cenvfrq7uvP/+h7Rq1driuhwcHElPTwcgIiKcsmXLARATk/eDEsDNzZ2kpCSysrLMgeGxY2GcO3eWkiVLsmbNSr799gdzxmr69MlcuHAeyA2ew8OvW/QXEZH/a+vo6ISVlTXffDPHvBcUcpf/uri43eMr9GA8Pb3+873z8PDM8zsUFRWVb1/nz5+jcePnef31t0lLS2Pt2lV88cVINm/+7T/n4ObmTnS0ZZ8LF87jpZdeZtSoETRu/DzTps1Gq9WSnJzExo1r7+nagoJW0qFDZ3r0eN/8mMFgoGfPbmzbtpmOHbvg5uZOYmIC2dlZFsstMzIySEiINwfEjRs35csvPycmJho3N3eLcX74YQEHD+7nl19WM2TICIYMGXFP8wMoW7Y8OTk5Fo+ZTEYURcHJyYkSJUre8fl/+/XXJbRs+SIeHp5cvHiB559/AZVKRZUqVblw4cI9z0kIIQASMvQAVHSxx9PBupBn8/gElC2JVv0EBUCKgjr92m3FYELRJpzIkwXUmzSciPJi73kvcxbwWpIjKpWK6tVdCWjlzXsB3vj7e+PhUewOgwmRS4LCQpSTk8PevX8wf/5cevXqjZ2dPenpaahUaqytcz+sT506yapVy8yZjxdfbMPPP//Ihg1radu2PWFhh9mz5w9zsPTKK52YNOkrAgIaUbdufY4fP8qGDWsZNGhYnvFdXd3w9fVn3ryZfPrpGNRqFd9+Ows7O3uqVq1O2bLlmTJlIkOGjARg6tSJeHv7UKtWnQda1vhvy5f/QsOGfri4uDJ79jeULl2GmjVrc+xYWJ62HTp05qeffqBSpSqUKVOWkJCDjBw5mLFjJ9KkSTP8/AKYPXs6o0ePQ6/PYdGi/I9qqF69JqVKlWHOnBn07TuAjIx05syZgZ9fAFZWVqjVua+9oiiEhBxk27bNlCuXm9Fp164jffq8z++/76JZs+ZcuHCOX35ZQocOnfOMo1areeWVDsyfP5svvvgKFxdXtm/fwqRJX/Hddz9RuXLVh3799Ho9MTHRFo/Z2Njc9b3z9vZh8eLv2bp1Ey+99DJ//32GDRvWmjNXt9u0aR1nz/7NhAlTcXIqgb19MWxt7dDpdOYvItLS0ihWzPIfm7ZtOzBr1lSef/4FqlSpxtatmwgKWslrr71FWloa1tbWaDQaEhMTmTlzKoBFJdP8hIdfJzj4AIsWLc0TxLVp05YVK36hQ4fOVK9eE09PL6ZN+5qPP/6EEiVKEBcXy/z5cyhdugzVqtUA4Pnnm1OvXkOGDh3AkCEjqFGjFllZmWzatJ6goFV8+eXEB/rSo1OnVxk0qC/bt2/hpZde5vjxo+zYsY3Ro78yP7948ffUrl2HcuUqsHbtamJjY3n++Rcs+omMjGDPnt0sWLAYgFKlSvHXXydp164Dp0//RaNGje97bkIIARBY15NX63gV9jTETcYstPHHzVlAbWwomsy8X9bGZzmy76IPey94mbOAWQYdVlYa6tXzoMs73vj7e+Hr64Wjo+wBFPdHgsLHbOrUiUyfPhkAa2trSpcuy9Chn5r3qfn6+tO5cyB9+36A0WjCy8uLwMA3WbBgDgkJ8Xh7+zB58gxmz57OzJlTqVKlmsXNZIsWrcjISGP69ClER0fi6upG796f0KZNu3zn88UX45gzZzpduwZiNBpo3Ph5816lyZOnM2fODN56qwt6fQ4NG/oxffq8AtvHVKdOXUaOHEx0dDR169ZjypRZd9yv9sYbbwMKw4cPIj4+DldXVwYOHGY+MmDMmPFMmzaJwMD22Nvb07Zte06fPpWnn5vXNWvWNFq1akLx4g60bZt7dp3JZOLEiWN07/46Go2G0qXL8vrrb7NmzUr0ej01atRk3LivWbToOyZOHIujoyOdOr1K167v5DvnPn0GsGjRQvr0+YDk5GS8vLwZN+7rAgkIAf766yRduli+ry1bvsiXX078z/fOzc2dceMmM3fuTL755msqVaqCv/9zJCXlLdby4Yd9+eabr+ne/XWys7MpU6YckyZNw9ramgoVKlK7dl06dWrDV19Nsvi5l15qQ2pqCmPHjiI+Pp6yZcsxdeosSpQowaefjmbWrGksX/4LxYsXp1Wrl6hcuQoXLpzHzy+AJUsWsWPHNpYuXWnRZ1DQSipUqJRnCS9Ahw5dWL16Bfv376FJk2ZMnz6PBQvm0KPHm6Snp2Nvb4+vbwAzZswzLwdWqVRMmjSNX375icmTxxMTE41Go6Vy5SpMnTrrgc8pbNDAl0mTvuGHHxYwbdrXODk50adPf/Pv6rvvfoCdnT1ffDGSuLhYypQpx9SpM3F1tcwgz5gxhT59BqDT6QDo1u1dRo8eSbt2LWnY0M9i36EQQognhzr9+r+ygMdRmfQWbYyKhn8Sfdj9tyf7LuYuB72S6ASoKF7cCl9fLwa97U1AgDd163pgYyO39OLhqJT81iwVIXFxqfx7hnp9DvHxkTg7e6LT5T3uQBR9/z5E/HHT6/X06vUOrVu35c03uxXKHApLdHQUyclJFsHp7NnTSUiIZ/TocYU4s1yKojBwYB9mzJhX2FMRRYx89gvxcIZtOM3uc3GMaFVRMoWPizEbbcLx246FCEWTEZGnWbLBiUPhpdl5yo0Dl0tx5Lonmfrczzk3N3sCAnKzgAEBPlSv7oJGI0VhxL1RqcDF5e5nSsrXCuKZpNPpGD78c3r16kFg4JvPVBXH5OQkevd+nzlzvqNq1WqcP3+OHTu28skngwp7agDs2bObV17pWNjTEEIIIe6bOiPCnAHUxYaijT+GyvSvfeRouJxemr0XvNlx0o2DV3y4lFCCm0erlSvnRKdA7xuBoDflyklRGPHoPTt3wkLcpm/fXpw79w/vvPPeMxUQAlSuXJX+/YcwZsynxMfHUbKkM9279+DFF9sU9tQA7lrZVAghhCgSjDloE0/kBn8xuUGgJuN6nmbpiiMn4yqw8y93dp125/B1LzJycrOAarWKGjVc+eDV3ADQ398Ld3cpCiMev2frblgUGatXbyzU8efMyb8QzbOifftOtG/fqbCnIYQQQjwx1BlRllnAhGOojFkWbRTURBrKceh6aTYeceaPs55ciC/JzSygtbWGevU9CAjIzQQ2bOiFwzNUCVYUXRIUCiGEEEIIcTuTHm3CyRtBYAi62ENo0q/maZalcuJceiX2nPdiXUgJDl7yJD3nVpDn4GBNq1ZeBAR44+fnTb167lhby+23KHrkt1IIIYQQQjzTVJkxFsVgdPFHURkzLdooqElQl+dEXHl2nHQjKNiJs7HO3MwCAri729Mq4OZ+QB+qVXOWojDiiSBBoRBCCCEKhKIo7D4XR3hy1t0bi8fuamJGYU+haDAZ0CaesswCpl3O00yvceSasTqh10qx4VBJNh4pQVq25VLPChVKmKuC+vt7U7asoxSFEU8kCQqFEEIIUSDOxaYzfOOZwp6GuAtr7bOVuVJlxf0rCxiGymAZICuoSLOuxLn0Svx5zouVex0IOWePotx6rdRqFbVru5qrgvr7e+PmZv+4L0eIR0KCQiGEEEIUiJQsAwDFrDU8X8G5kGcj8uNkq+OFii6FPY1Hx2RAk3TaIgjUpl7M20znQIy2Nidiy7H9pBu//m5HVKLGoo2NjYb69T3NAaCvryfFi0tRGPF0kqBQCCGEEAXKrZg1X75ctbCnIZ4Bqqx4dHGhaGMP5QaCcUdQGdLztMsuVoVrhmqEXCvNutASbNivIsfy+EAcHa3x8/PC39+bgAAf6tRxk6Iw4pkhv+lCCCGEEKLoMxnRJJ+xzAKmnM/bTOdAWvF6nE2txB9nPVm5txiHTmTmaefpWcxcFTQgwJtq1VxQq2U/oHg2SVD4GAUGtichIR6NJnd5gqIo+PiU5tVXX3+oM+PGjx8DwGefjXn4ST4lwsIO88knH7Fv32GioqLo3v01fv55FR4eHv/5c0ePHmHy5PEkJiZgbW1N+fIVmTp1lvk9Kyq2bNnIxIljsbGxMT9mMplwcirBiy+24cMP+zxRG90TExMZMWIQly9fRKezwsXFla++mkSpUqXvu68tWzayaNHCO56F2a3b6/zvf+/y0ksvP+y0C5zBYOC11zpgMBhYvXoj1ta3lilFRkbw2msdWLVqA56eXhY/l99nQGhoMCtX/srff59Gr9fj6elNp06v0qnTqw88v6ioSGbOnMrRo0dQqzU0atSEQYOGY2dnB+T+/Zk3bxaXL1+keHEHOncOpHv3d83PTZr0FUlJiTRv3ophwz5Drc7dq/P11+OpWbMW7dp1eOC5CSGePqrsBHSxh24UhDmENv4Ian1qnnYGh8rEW9fheGw5th13Zc0fKq5eS7utRW5AWLFiCXNV0IAAb0qXdnii/q0U4lF6qoJCRVHIMpge23g2WvV9f5gMGTKStm3bA6DX6zlwYC8TJnxJcnIS3br1eASzFB4eHuzcufee2tar14Bly4Ie8YwKhru7h0XgYzQaOXhwP6NGDcfHx4dXXulUeJO7TyVKlGDBgh8fy1hLl658LOM8iD///B0XF1eMRiPbt2+hQ4fOD9TPihW/8OOP3zNo0HDGj5+MTmfF8eNH+fLLz4mIuE7v3v3vu0+9Xs/AgX3w9fVn3bptZGZm8umng5k/fzaDBg3nypXLDB3an8GDR9CmTTsuXDhP//4f4eNTiubNWzFv3kxee+1NWrduR8+e3QgNDSYgoBGnT5/i6tXLDBv26QNdqxDiKaGY0CT9fasYTFwo2uSzeZqZtMXQOzfkuqk6wVdLsS7Eid8PJJOQcLPibe7SUY1GRa1abub9gP7+3ri62j3GCxLiyfLUBIWKovD+8uOciEh5bGPW8XLguzfrPPC3TDqdjmbNWpCcnMyMGVPp3DkQe/tinDx5nO+++5YrVy6TmppCuXIVGDhwGDVr1gJg374/mT9/LlFREdSv3xAAR0cnc78bN65jxYpfiYmJxsPDg27detwxK5KYmMjs2d9w4MA+1Go1vr5+DB48EgcHB6KiIpk3bxZhYYdRq9U0aOBLnz4DcHFxISzsMBMmfEmXLq+xfPkvZGdn0aXL69SoUZM5c2YSHx+Hn58/o0ePx8rKKs+4gYHtadu2PTt3biMuLpbKlasyZMhIypUrT1jYYcaNG03t2nUJDt5Pt2496Nr1HVavXkFQ0EoSExMoX74in3wymKpVqwEQFxfHlCnjOXo0DEdHJ1q1esk81r8zLOHh15k5cxrHj4eh01nRokUr+vUbhE6nY9++PSxdupjr16+RmZlBtWo1GD78c3PGas+eP1i8+HuuX7+Gs7MznTsHEhj4pjnjcTu9Xs9PP/3A9u1bSUtLpUaNmgwYMBQfn1IANGnSkKFDP2XZsqXExkbToIEfPXv2YubMKZw/f44yZcoxZsx4c/u70Wg0NGnyPOXLV+Ts2X/Mj+/atZ2ff/6RqKhIvL1L8dFHffHzCwAgOzuL2bOn89tvO7G1teXll19hx46tjBz5BfXrN6RJk4YEBr7Bzp3bqFGjNpMnT+fQoRAWLpzLtWtXcXFxs8i6Xbx4gWnTJnHhwnns7e2pV68BgwYNw87OnmPHwpg9ezrh4ddwdHSiUaOm9OnTH61Wy+XLl5g3bybnz58jKSkJLy8vPv74Exo3bgrAhQvn+fbbWfz11ylsbGxo3Ph5PvqoL8WKFbun1+Z2gYHt6dmzF23btqdv317UrFmbkyePc/bs37i5udOz54e0bPkiAAkJ8cyZM4PDh0NRqVQ0afI8ffr0x84ut9rcpk3rCQpaRVRUJHq9nnr16jNy5GhKlCjBDz8s4NSpE6SmphIefp2JE6dSt279/5zbmjUradnyRezs7Fmx4hfat+90358vcXGxzJs3iy++GGe+Dsj9wuPTT0ezY8dWDAYDv/++kylTJuTbx9Sps6hTp57FY/v37yEnJ4f+/Yeg0WiwsbHhq68mk5mZW8EvKGglTZu+wMsvvwJAxYqV+PbbRdjb575WGk3uPzeKogAKGo0ak8nEjBlTGT78c/m2XohnjCon6dY+wNhQtHFHUOvz3sMZHCqSVaIhZ9Mq88dZTzbs03HoSBQZGQYgG4gGcovCNGjgad4P2LChJ8WK5b3/EELk76mqSfyk3lI0atSUnJxsTp48QXZ2FsOHD6JZsxasXbuFzZt/w9vbh3nzZgJw9eplPv98OP/737ts2/YH7dt3IiTkoLmvLVs2MmfOdAYOHMrWrb/Tv/8Qpk2bxJ9/7s537FGjhpOWlsaKFWtZtWo9qalpTJs2CYPBwIABfVCr1SxfvpZfflmNoigMHz4QgyG3ulxUVCTx8XEEBW1mzJgJ/Pzzj6xZs5KFCxfz00/LCAs7wm+/7bjjdW/YsJaxYyeyadNOypYtZ9F3TEw0ZcuWY9OmXXTp8hpBQatYvnwpX331NZs27aJt2/YMGNCbhIR4AEaPHolGo2Xdui3MmbOA4OD9+Y5pMBgYPLgfzs4urF27lZ9/XsHJk8dZtGghMTHRfPHFCLp168GmTTsJCtqMoigsXvwdkLsk9YsvRtC16zts2fIbY8ZMYPnyX1i1alm+Yy1cOI8DB/Yyc+Y81q3bSo0atRg4sC/Z2dnmNjt3bmPhwh9ZsWI9J04cY+TIwYwY8QUbNuxAp9OxZMmiO75+/6bX6/n9912cP3+W555rDMDBg/uYOnUiAwcOY8uW33nvvQ/57LNhXLx4AYBZs77hzJnTLF78K0uX5gY3kZERFv2Gh19nzZrNjBo1lnPnzjJixCC6devB5s2/MXz4Z8yaNc38O/jNN1/TsKEfW7f+zg8/LOXcuX/YsGEtAF999QWBgW+wbdsfzJgxj927d7Fv358AfP75MMqXr8jKlevZvv0P/PwCmDZtEgDJyUn06/chZcuWZ926LXz//RKuXbvCuHFf3PNr8182bFhL//6D2bLld5o1a8GUKePJzs7GZDIxYsRgVCoVy5cHsWTJcmJjY/n66/EAnD59ihkzpjBkyAi2bPmNX35ZxbVrV1m9erm57yNHDvHxx/1Ys2YTNWvW/s95nDt3lrNn/6Zt2w689NLLJCYmcuDAvvu+nuDg/Wg0Wpo1a57nOT+/AD7//Eu0Wi0vvfQyO3fuzffPvwPC3Ov9i0qVKvP99/Pp0qUdXbq0Y9myJbi5uZmf9/DwZPToT2nXriVduwZy9OgRnJ1zqxz27v0JGzas5e23X6Vx46Y0bOjP2rWrqVWrDhUqVLzv6xRCPEFuZAFtzi2h2IG+lFjvh8vy0jj99ir2J77GKnI3an0KitaeHI/nia/Yn98cZjL00i/4zx9Cibeq0+BdNYMnRvPn3utkZBhwcrKmdevyfPFFUzZvfpPz5/uydu3rjBjRmBdeKCMBoRD36anJFKpUKr57s06RXz6aHycnJwBSUpLRanUsWPAjPj6lyMnJJjIyAgcHR86cOQ3Arl07qFatujkz07TpCzRq1NTc1+bNG+jYsQsNGvgC0KCBLx07dmH9+qA8N4lRUZEcOxbGr7+uMWcaP/tsNMnJyRw/fpTIyHB++GEJ9va52Zhhwz7l5Zdb8Pfft86g6t69J1qt1px56tQpEAcHBxwcHChfvgJRUZF3vO633upGpUpVAOjXbxCtWzfjxIlj5ufbteuAVqtFq9Wydu0qund/l4oVKwHwyisd2bRpPdu3b6V585YcP36UZcuCsLOzx87Onp49ezFy5JA8Y548eZzIyAgWLx6MjY0NdnZ2TJgwFZPJRIkSJfn555V4e/uQkZFOTEw0jo5OxMbGml/bpk1fMGdfqlSpSrduPVi9ejlvvNHVYhxFUVi3bjXjxk3Gy8sbgB493mfDhrUcPLiPF15oCcCrr76Og4MjAOXLV6By5aqUKVPW/N7d/nr8W3R0FG3avICiKOj1ekwmE76+/owfP4XnnmsC5GaeOnUKNGeoGjduSuPGTVm/fg39+g1i+/YtjB8/BXf33L2WgwcPZ9eu7RbjvPhiG2xsbLCxsWHBgrk0adKMZs1aAFCrVh3at+/MmjUr8fd/Disra4KDD1CmTDkaNvTlxx9/NWdRra2t+f33nTg4OFKvXn2Cgjabn5s8eQYuLq6YTCaioiIpXtyB2NgYAPbu/ROdTsvHH/dDo9FgbW3DgAFD6d79deLj48xBx4Nq3rwllSvnVkl8+eVXWLJkEYmJiSQkxPHPP2eYMWOeec9c374DePvtVxk0aBgVKlTk559X4unpRUpKCnFxsTg5lTDPG8DLy9v8d/Fu1qxZQZs27XBwcACgfftOLF++1JwtvVeJiUk4ODig1Rbsx3tqagoHD+6natXqLFsWRFxcLJ9/Poy5c2cyaNBwUlNTWL16BV9+OYFRo8Zy6tQJhg0biIODA82bt6J27bosWbLitnkmsG7dahYs+JFvv51NSMhB3N3dGTJkJK6ubgU6dyHE46XKSUYbd9gyC5iTlKedoXh5DK5+xGhrE3y1FFtDbTgYEsU//8QDiTf+5PLyKmaxH7BKFWcpCiNEAXpqgkLIDQxtdUWrIMi9SExMAHL3VWk0GsLCDjNkyCdkZmZSrlx5NBotipIb7MbGxuLmZlksxdvbh+TkJCB3udvNIOQmT09v9u3bk2fcuLg4ADw8PM2POTu74Ozsws6d53B0dDIHhAB2dvY4ODgSFRVByZK55085OuYGNDcLsRQvXtzcXqVSYTLdOUj38blVRMTGxgZHRyeLm3wXF1fz85GREcydO4P582ebHzMYDFStWs18E34zsLn5muQnPj4OJycniwItN4t2KIrCzp3bWL8+CJVKRfnyFUhPTzdfW2JigjmIvf1n8wt8k5ISyczMZNSoERb/aOn1eiIjb7W/fdmvWq2+r9fv9j2Fly9fYvTokeh0VgQENDK3iYqK5OjRI6xbt9r8mNFopEEDX5KTk8jKysLT89b7b2dnbzEnsHwfoqIiCAs7TJs2L9zWn8n8eo8dO5FFixawcOFcxowJp1atOgwePILy5Sswc+a3LFq0kGnTJhEfH4e/fyOGDBmBm5u7OQOZkBBPmTLlcHJyurHMMPd1d3f3tCj24+WV+55FRkY+dFB483cZMAdSimIiMjISk8lEly5tLdpbWVkRERFOxYqVWbVqGTt2bMPW1o6KFSuSkZFunve/X7v/kpKSzM6d21CrNezevQvI/f1OT0/n77/PULVqNfMybKPRmOfnjUYjtra2N8Z0ISUlGb1ej06ns2hnMplITk6mRIkS7NixjW++mZTvfL7+egZ16tS1eEyn0+Hs7EKPHu8DuX/HunfvyTfffM2gQcPR6XQ0bdqMRo1yv5CoW7c+rVu35fffd9G8eas8Y8ydO5N33+3FsWNHCQ7ez/ff/8yyZT8zZ850vvxy4j29bkKIIkAxoUk5f6MYTO4fTdIZVCiWzbR26J3ro3fx5XJOdXb/487u7RmEhIQTHp4KXLBoX7lySfNewIAAb0qVkqIwQjxKT1VQ+KTav38PtrZ21KhRi7/+yl2S9u23i8z75ZYtW8rVq5cBcHd3Z/9+y6IpsbHRWFnlVinM3TMXbvF8ePj1fG+c3d3dgdyM0809c5cuXWTXru0891xjkpOTSE9PMweGaWlpJCcn4ezsYr7xfZgP6NszKhkZGSQnJ+Hu7mFeQnp7366u7rz//oe0atXa4rocHBxJT8/dVB4REU7ZsuUAiIm51fft3NzcSUrKDYZuBobHjoVx7txZSpYsyZo1K/n22x/M+/imT5/MhQu55a49PDwJD79u0V9ERP6vraOjE1ZW1nzzzRzzXlDIXf7r4lLwWZCyZcsxZcpMevbsxsSJYxk1aiwArq5utG7dju7de5jbRkVFYW1tjaOjI9bW1kRFRVG6dFkAMjMzzV8w5MfV1Z2XX36FoUNvFQXJ/XJBwWQycfbs3/Ts+SGffDKY6OgoZs+ezoQJXzJ37ndcvnyRwYNHoNVquXr1Cl9/PY5Zs75hwIAhfPHFCMaPn0KTJs8D8Mcfv5mXPHt4eBIdHYnRaDQHhjffBxeXR3cAs5ubG9bW1mze/Jt53JycHCIjI/D29mH58qWEhoawZMlyc2A5bNjABxpr06b1+PiUYsqUmRaPT5w4luXLlzJmzHgcHZ1uvF+RefaZXr9+jaZNmwHg7/8cJpOJvXv/pEULy2DswIG9fPbZMFauXM9LL7XhpZfa3PMcy5Ytz+7dv2EymcwZXpPJCDdu/MqWLU/Ovw79MpmMFkHyTcePHyUhIYEWLVqxdOliypWrgE6no2rV6uzYse2e5ySKhoSMHNafjCJLbyQiJfvuPyCeaKqcFLRxR26cDZhbFTS/LKCxWFn0rn5klWzI6cSK7DpenIPboggJCScxMRy4da+i0aioXdvNnAX08/PCxUWKwgjxOD1VewqfNDk5Ofz22w7mz59Lr169sbOzJz09DZVKbS5Ff+rUSVatWoZerwdyl/JdvHieDRvWYjAYCA0NZs+eP8x9vvJKJ9avD+LIkUMYjUbCwg6zYcPafEu9u7q64evrz7x5M0lNTSU9PY1vv51FePh1qlatTtmy5ZkyZSJpaWmkpaUxdepEvL19qFWrToFc//Llv3D9+jWysrKYPfsbSpcuc8d9Vx06dOann37gypXLAISEHKR799c5fjwMDw8P/PwCmD17OikpKcTHx7Fo0cJ8+6levSalSpVhzpwZZGVlmQuJJCYmkJaWhlqd+9orikJw8AG2bdtsDlLbtevIvn1/8vvvuzAajZw9+ze//LIk39dWrVbzyisdmD9/NjEx0ZhMJrZu3UT37m9w/frVAnn9/s3NzZ3PPhvN9u1b2Lp1k/l1W716OWfO/AXA33+f5v33u7Fr1/Ybc+zIDz8sIC4ulqysLGbN+ibfTNRNr7zSkZ07txMaGozJZOLatav07fsBy5b9jFqtZsaMKXz33Tyys7NxciqBtbUVjo5OqFQqxoz5jOXLl2IwGHB2dkar1eLk5ERGRrpFpuvSpYv8+OP3QG5mNXcprIpvv51NdnYW8fFxzJw5jQYNfC2y3LczmUzExERb/ElMTMy37Z1Uq1YDH5/SzJkzg4yMDLKzc1+f/v0/xmg0kp6efmN5sw6DwcD27VsICTlg/rt6r0wmE2vXrqZduw64ublb/OnU6VV2795FVFQUWq2Wli1fYv78Oea/BxkZ6axc+SsXL56nRYvcZc0lSzrz3nsfMXXqRHbu3EZ2djYGg4EDB/YxadI4XnvtLYus+r1q0eJFTCYjs2ZNMwfHP//8I61b52ZSO3V6lb17/2D79i0oisKxY2Hs2LGNNm0sM60Gg4HZs3P3PQOUKlWa8+fPkZWVxcmTx/HxyT/LL4qunw9dZ96+yywKuca2M7lfyD2Jq3ZEPhQFTco5rC/8QrHgAZTY2Ajn5aVw2tUR+2PjsQ7fiTonCUVjS45bIzJqDCA6YAkbfXYz/Nx8XpzeGq+2OTTucoLRX+5n27YLJCZmYWurpWnTUgweHMCqVa9y7lwftm/vytixzWjbtqIEhEIUAskUPmZTp05k+vTJQO4eq9KlyzJ06KfmfWq+vv507hxI374fYDSa8PLyIjDwTRYsmENCQjze3j5MnjyD2bOnM3PmVKpUqcbzz79g7r9Fi1ZkZKQxffoUoqMjcXV1o3fvT2jTpl2+8/nii3HMmTOdrl0DMRoNNG78PP37D0ar1TJ58nTmzJnBW291Qa/PoWFDP6ZPn1dge5Xq1KnLyJGDiY6Opm7dekyZMivfKp4Ab7zxNqAwfPgg4uPjcHV1ZeDAYTRpkpsdGTNmPNOmTSIwsD329va0bdue06dP5enn5nXNmjWNVq2aULy4A23b5lajNJlMnDhxjO7dX0ej0VC6dFlef/1t1qxZiV6vp0aNmowb9zWLFn3HxIljcXR0pFOnV+na9Z1859ynzwAWLVpInz4fkJycjJeXN+PGfW3ev/YoPPdcE1599XWmT59C7dp1ad68FZmZmUyY8CXR0dE4ODjw+utvExj4BgAffdSP6dMn07VrIHZ29nTs2AW1Wp1n2eFNNWrUZMyY8SxYMJdRo4ZjY2NLq1at+eijvgB89dXXfPPNZDp2bIOimKhTpz7Dhn2KlZUVkyZ9w5w5M/j55x9RqzU891xjPvqoH8WKFaN37/6MHfs5WVlZuLq606FDZ+bNm8mFC+epWrUa06fPZc6cGXTu3A6VCpo0aUafPnc+ViEmJpouXSx/56tXr8nChYvv+bW8+bsyd+4M3nyzMzk52VSrVoPp0+dibW3NW29148KF8wQGtsfKyorKlavSpcvrHDkSmm9/x48fZciQT/Kcl3ngwF5iY2N48cW8FYIbN34eJ6cSrFr1K/36DWLw4OEsWvQdQ4f2JzExEa1WS7Vq1Zk1a77Fkunu3Xvg4eFBUNAqpk+fgsFgwMfHhw8++JiOHbvc82twOycnJ7799gdmzfrGvKT2pZde5sMPc9/7Bg18mTTpG374YQHTpn2Nk5MTffr0N/8dvWn16uU891xj8+qEpk1fYP/+vXTq1AYfn9J88cVXDzQ/UXhi03Kzg/V8HKnsao9KpaJN1XtbOi2KGH0aurgjtx0LcQh1dkKeZsZiZdC7+KJ39SNOV5u9/5Tg4IEYQkKuc+LEJYzGixbtS5SwsVgKWru2Gzr54kCIIkWl5Le2pwiJi0vl3zPU63OIj4/E2dkTnU6qSz2Jbj8WoDDo9Xp69XqH1q3b8uab3QplDkXBsWNhVKhQybyXMSMjnZdeasayZUEPdHC8+G+jR3/KgAFDKVGiRGFP5Ykkn/1F04CgU+y/lMCo1pXpUPP+s9CikCgK6tSLt4rBxB5Cm3QKlWK5l11RW2Nwrofe1Y8cVz+u5FRl31EDISHhBAeHc+5c3qDRx6e4RRBYubIUhRGisKhU4OJS/K7tJFMonkk6nY7hwz+nV68eBAa+WeCVGp8Uy5cvpVix4gwdOhJQ8f338ylduowEhI9AZGQE7u7uEhCKp05qdu4S+2LWz+bn6BNDn44uPuxWQZi4Q6iz4vI0M9qXQu/qi8HVj2xnP07HenEwNJaQ9dcJDg4nIuJ8np+pUsXZHAAGBHjj4+PwOK5ICFGA5BNcPJP69u3FuXP/8M477z2zASHA4MEj+Oabr+ncuR0mk5HatevmKXYiCoanpxe9e995yasQT6qbQWFxa1kOWGQoCuq0y5ZZwMSTqBTLPeOK2gqDc130rn7oXf3JcGrA0bNqgoPDCQkJJzQ0hKQky+JBWq2aOnXczJlAPz9vnJ1tH+fVCSEeAVk+KoQQ4okgn/1FU7sFwcSk5bCkWz2qud99iZJ4BAwZ6OKPWhwLoc6KzdPMaOeN3tUPg2vufsAkq2ocPppAcPB1QkLCCQuLIjPTYPEzdnZaGjTwMmcB69f3xN4+/33nQoiiR5aPCiGEEOKRu5UplFuKx0JRUKdfvVUMJjYUbcJJVIplMKeodRhK1rkRBPqhd/UjOqMkISHhhOwKJzj4HKdO7cdotPzm3dnZFj8/LwICfPD396JWLSkKI8Sz4In+BFeUOx/sLYQQ4ukin/lFj8GkkKnPfV9kT+EjYshEG3/sxj7A3EBQkxmdp5nR1tMc/Old/dCXrM2V69mEhEQQsuw6wcE7OX8+79E8pUo5WOwHrFSppBwSL8Qz6In8BNdqdahUapKT4ylWzAmNRisfYEII8ZRSFAWj0UBqahIqlRqtVpauFRVp2beyU8WsJJv00BQFdfr13Oxf3M0s4AlUJsvzTxWVFkPJ2hZZQIOtD2f+jjfvBwwOXkJUVHqeIapVc8bfPzcLGBDgg7e3LPkVQjyhQaFKpcLZ2YPk5ASSk/NWzhJCCPH0sbKywcFBshhFyc2g0FanRqvJ/5xZ8R+MWWjjj99WECYUTWZk3ma27rnBn8uNANC5LtlGHcePxxC88TohIUcIDd1IcnJ+RWHczVlAPz8vSpSQojBCiLyeyKAQcrOFJUu6YTIZMZlkSZEQQjzN1Go1arXmmQ8IFUVhxdEIriZmFvZUAEjOzM1gydLRe6NOD7coBqNNOI7KlGPRRlFpMJSsjeHG4fB6Vz9MxcqQlq4nNDSCkI3hBAdv4OjRKLKyLKuJ2tnp8PXNLQrj7+9F/fqe2NlJZl0IcXdP9Ke4SqVCo9GikRUrQgghngEX4jKYtvtCYU8jD9di1oU9haLHmIM24Ti62BC0sYfQxYaiyQjP08xk42oO/gyufuid64HWjpiY9NyiMCGXCA7ex6lTsZhMlkVhXFxs8fO7tR+wZk03tFrJ2Aoh7t8THRQKIYQQz5JMfW5mqLi1ltfreRXybHKpVdCikmthT6PQqTMiLbOA8cdQmSyXcyoqDYYSNc1HQuRmAcuhAJcvJxOyO5zg4L0EB4dz8WJSnjFKl3a8sRfQm4AAHypWLPHMZ8+FEAVDgkIhhBDiCeNgo+WjxmULexrPLmMO2sSTN7KAoehiD6FJv5anmcna+V9ZwPqgs8doNHH6dByhu8MJDt5McHA40dGWRWFUKqha1cWcBfT398bLS4rCCCEeDQkKhRBCCCH+gyoz2pwBzM0CHkVlzLJoo6jUGJ1q3AgCfTG4+mEsXgFUKrKzDRw9Gk3Iir8IDr7OoUORpKRYZhF1OjV163qYq4L6+Xnh5GTzOC9TCPEMk6BQCCGEEOImkx5t4im0MSE3zgY8hCbtSt5mViUsj4RwqY+iy83kpaRkc/hwBMHB+wkODufo0Siysy2Lwtjb3yoKExDgTb16HtjaSlEYIUThkKBQCCGEEM8sVWbsrQxgXCi6uDBURsvqrgoqjE7VLZaCGh0q5q7xBKKj0wnZGk5w8CGCg8M5fToun6IwduZloAEB3tSo4SpFYYQQRYYEhUIIIYR4NpgMaBP/QhsbYg4ENWmX8zazcjIvAdW7+GFwaYBi5QDkHgty6VISIVv+Ijg4nODgcC5dSsrTR5kyjuYsYECAD+XLO0lRGCFEkSVBoRBCCCGeSqqseIuD4XXxYagMlgVdcrOAVXODvxuZQKNjJVDlZvFuFoUJDj5PcHA4ISERxMTkLQpTvbqrRSbQw6PYY7tOIYR4WBIUCiGEEOLJZzKiSTp9IwjMrQqqTb2Yt5nOEYNrw1tLQV0aolg5mp/PyjJwLCTCnAU8dCiC1FTLA+atrDTUretuzgT6+nrh6ChFYYQQTy4JCoUQQgjxxFFlxaOLO2Q+EkIbdwS1IS1PO4NjFYuCMEbHKuYsIEBychaH9lw0B4HHjkWTk2NZFKZYMSv8/LzMmcB69TywsZFbKCHE00M+0YQQQojH5MClBLadiUG5e9N8JWXoC3Q+TwyTEU3y35ZZwJTzeZvpHDC4NLDMAlqXsGgTHZ1mDgBzi8LEovzrDXF1tbPYD1i9ugsajRSFEUI8vSQoFEIIIR6T6X9c4HJC5t0b3oXjU350gSo78V9ZwMOo9al52hkcKpkzgLlZwKqg1pifVxSFixcSCQ6+bg4Cr1xJztNPuXJOFvsBy5WTojBCiGfLfQeF8fHxjBo1itDQUDQaDR06dGD48OFotXm7+umnn/jpp59ISkrC29ubvn370rp16wKZuBBCCPGkyTGYAHizvjcexa0fqA+VChqXK1mQ0ypciglN8j+3isHEhqBNPpunmUlbzJwFzK0K2hDFxtmijcFg4vSpaHMAGBISTmxshkUblQpq1HA1ZwH9/b1wd5eiMEKIZ9t9B4UDBgzA3d2dvXv3EhcXx8cff8zixYt5//33Ldr9+eefLFiwgKVLl1K+fHm2b9/OgAED2LlzJz4+PgV2AUIIIcSTpk1VV2p4OhT2NAqFKicJbezh284GPIJanzd7ZyhewTIL6FTdIgsIkJmp5+jRKHMQePhwJGlplkVhrK011KvnYc4C+vp64eDwYAG5EEI8re4rKLxy5QqhoaHs2bMHW1tbSpUqRe/evZkyZUqeoPDixYsoimL+o9Fo0Ol0+WYUhRBCCPEUUkxoks9ZZAE1yf+g+teuSkVrh965AXo3fwyuvuhd/PJkAQGSkrI4dCjitqIwUej1Jos2xYvfLArjg7+/N3XruktRGCGEuIv7+pQ8d+4cTk5OuLu7mx+rUKECERERpKSk4OBw61vPdu3aERQURNu2bdFoNKhUKqZMmYKHh0fBzV4IIYQQRYYqJwVt3O1ZwMOoc5LytDMWL5ebAbxxNqChRA1Q570liYxMtSgK8/ffcXmKwri725v3A/r7e0tRGCGEeAD3FRSmp6dja2tr8djN/87IyLAICvV6PVWrVmX8+PFUrVqVjRs38tlnn1GhQgWqVKlSAFMXQgghRKFRFDQp529kAG9kAZPO5M0CamzRu9TH4Op/IxD0RbF1zac7hQsXEi2CwKtX8y4rLV/e6bb9gN6ULesoRWGEEOIh3VdQaGdnR2amZdW0m/9tb29v8fhXX31F/fr1qV27NgCvvvoqmzZtYu3atYwYMeJh5iyEEEKIx0ylT0Ubd+S2paCHUOck5mlnLFYWvauvuSCMoURNUOetlmowmDh1KsYcAIaGhhMXZ3mPoVarqFnT1ZwJ9PPzxt3dPk9fQgghHs59BYWVKlUiKSmJuLg4XFxcALhw4QIeHh4UL17com1ERAQ1a9a0HEyrRad7ustoCyGEEE88RUGTesEc/OVmAU+jUiz37ykaGwzO9dDfzAK6+qLYuufbZUbGv4vCRJCebnnuorW1hvr1PcxZQF9fT4o/YJVWIYQQ9+6+gsKyZcvSoEEDJkyYwNixY0lMTGTevHkEBgbmaduiRQuWLl1K8+bNqVatGjt27CAkJIRBgwYV2OSFEEIIUQD0aejiwyyzgNnxeZoZ7Uujd/U1VwU1lKgFGqt8u0xMzCQ09FZRmBMnovMUhXF0tMbPz8u8H7BuXXesraUojBBCPG73/ck7a9Ysxo4dS8uWLVGr1XTq1InevXsDUK9ePb788ks6dOhA37590Wg09OvXj+TkZMqUKcPcuXOpVq1agV+EEEIIIe6RoqBOu3SrGExMKNqkv1ApRstmamsMznXNWUCDqx8muzsXi4uISL3tfMDrnDmTN6j08LA3ZwEDArypVs0FtVr2AwohRGFTKcq/63gVLXFxqXkqjQkhhBBF0e/n4th4KuqOzx+6mkS2wcTit+s+vnMKDRno4sJuFYSJC0WdFZenmdHO50bwd2M/YMk6d8wCKorCuXMJFofEX7uWkqddxYolCAjI3QsYEOBNmTJSFEYIIR4nlQpcXIrftZ2s0RBCCCEKyLy9l7iSmHnXdk52j2h/vaKgTrty25EQh9AmnMgnC2iFoWSd3H2Abv43soBed+xWrzdy6lTsjSDwOqGhEcTH5y0KU6uWm8XxEK6udo/kMoUQQhQsCQqFEEKIAqI35S5tede/FD6Otvm2KVXCFu87PHffDJno4o/edixEKOqsmDzNjHZe5n2AeldfDCXrgubOBVwyMvQcORJpzgQeORJJRoZlURgbGw0NGniaA0BfXy+KFcs/syiEEKJok6BQCCGEKGBNyztTy6uAl4cqCur0a7cVgwm9kQU0WDZT6zCUrH1jKWjufkCTvc9/dp2QcLMozHVCQsI5fjwGgyFvUZibAWBAgDd16rhjZaUp2GsUQghRKCQoFEIIIYoiYxba+GO3loLGhqLJzLtf0WjrcVsW0A+Dc13Q2Pxn19evp1jsB/znn7xFYby8ilnsB6xaVYrCCCHE00qCQiGEEKIIUKdf/1cW8Dgqk+WSTUWlxVCy1r+ygKVyKwncgaIonD2bYN4PGBISzvXrqXnaVapU0rwfMCDAm1KlHKQojBBCPCMkKBRCCCEeN2M22oRjNw6Gv5EFzIjI08xk43YrA+jqh965Lmj/u3iLXm/kxIkYcxYwNDSchIQsizYajYratd3MWUB/f29cXKQojBBCPKskKBRCCCEeMXVGhEUxGG38MVSmHIs2ikqDoUStG0dC3MgCFivzn1lAgPT0m0VhcrOAuUVhLPcZ2tpqzUVhAgK8adDAU4rCCCGEMJOgUAghhChgmqQz2CYdRnsjE6jJuJ6njcnGBb3L7VnAeqCzv2vf8fGZhITcOiT+xIkYjEbLA31LlLDB398bPz8vAgK8qV1bisIIIYS4MwkKhRBCiIegzogyZwHVGb5ACYof7Esx3T/mNopKjcGppvlgeL2bP6Zi5e6aBQS4di2F4ODr5uWgZ88m5Gnj7V3cnAUMCPCmcmVnKQojhBDinklQKIQQQtwrkx5twonbCsIcQpN+1fy0yvg9AIrOgWyfNreqgjrXB12xu3dvUvjnn3hzFjAkJILw8LxFYapUcb5xPIQXAQE+lCpVwMdfCCGEeKZIUCiEEOKpdCUhg+l/XCQt23D3xndi0qPSp6LWp4Hhxv8qN8/v88v9o1KhaOxQdMWIwhmA5JZBpHg73rX7nBwjJ05E31YUJoLERMuiMFqtmtq13cyZQD8/b5ydbR/8moQQQoh/kaBQCCHEU2nHP7Hsv5R3qeX90wJON/7cgR64EcupAJfi1vk2S0vL4fDhW0VhwsKiyMy0DFrt7LQ0aOBlrgraoIEn9va6h78MIYQQ4g4kKBRCCPFUMppyi680KleCjrU88zyvyklBk3IObcp51Mnn0KZeRGXKtmijoMJk74PBoSJGh0oYHSphsvP8z72APo42eDrkHh4fF5dxW1GYcE6ezFsUpmRJG/PREAEB3tSq5YZOJ0VhhBBCPD4SFAohhHiq+Tja0qKCE5qk0+hiQtDF5RaF0aResmyoA5OVEwaXhrfOBnRpgGJ192WgkHtI/NWrKQTvvsr8kNzCMOfPJ+ZpV6qUw439gLlBYKVKJaUojBBCiEIlQaEQQoinjiorHk3KecAKq2sbcVk+GZUhPU87g2PVW0dCuPpjdKwEKvU9jWEyKfz9d5w5CxgcHE5kZFqedlWrOt9WGdQHb+/iD3t5QgghRIGSoFAIIcSTzWREk3wmNwt4oyqoNvUCNuldgbfQpEegUqdj0jlYZgFdG6JYOd3zMDk5Ro4dizbvBwwNjSA52XK5qVarpk4dd3NVUD8/L0qWlKIwQgghijYJCoUQQjxRVNkJ6GIPmY+E0MYfQa3Pe2yDydoZMiDH43kSWvbE6FjlnrOAkFsU5tChCHMWMCwskqwso0UbOzsdDRt6mvcD1q/viZ2dFIURQgjxZJGgUAghRNGlmNAk/Y0u9rYsYMq5PM1M2mI3Dob3zV0K6tKQzMMpEHwVo1M1jE4V7zpUbGyGOQsYHBzOqVOxmEyWRWGcnW0t9gPWrOkqRWGEEEI88SQoFEIIUWSoshPRxh1GF5tbDEYbdwS1PiVPO4NDxVsHw7v6YXSsBup/B2d5f+4mRVG4ciXZYj/ghQt5i8KULu1gsR+wYsUSqP6j8qgQQgjxJJKgUAghROFQTGiSz6KLDbmxFDQUbfI/eZtp7dG7NLwtC+iLYuN8X0MZjSbOnIkn5EZV0JCQcKKiLAvPqFRQtaqL+XzAgABvvLykKIwQQoinnwSFQgghHgtVTnLeLGBOUp52huLlLbOATtVBfX//XGVnGwgPz91nuH3HBRZ8uJ2UFMuiMDpdblGYm1lAPz8vnJxsHvj6hBBCiCeVBIVCCCEKnmJCk3IeXcyNLGBcKJqkv1FhuUdP0dqhd27ACn1HlkRVxqixhWQNJAPnAXKAY3cdzmRSyMzUk5FhIDNTT2amAbWdFo2djmvXUkhJycbeXoevr5c5E1ivnocUhRFCCCEAlaIoyt2bFZ64uFSK9gyFEEKoclLQxh1BFxdqrgqaXxbQWKzsbUdC+GEoURPUWt5YfJiL8RmPZG7+VlZ88Hw5atRwRau99+qjQgghxJNOpQIXl7tvhZBMoRBCiPujKGhSz6ONCTUvBdUknc6bBdTYonepj8Hl5lJQXxRbt3y7zDaYABjaogLlnO3+PRxRUWmcORPHmdNxnDkTl+8h8e7u9lSt5kL16i5Uq+aCl1dx7Kw0VPcojlqKwwghhBB3JEGhEEKI/6ZPQxd3xHwkhC7uEOrshDzNjMXKoHfxvZUFLFkL1Pe2PDPHmBsU1vFypKKLHadPx5mrggYHhxMTk7coTLVqLub9gP7+Xnh6SlEYIYQQ4kFIUCiEEOIWRUGdetGcAczNAv6FSjFZNlNbY3Cpb14Kqnf1Q7F1f6Ahs7IMpGcZABg5/DeO771GamqORRsrKw1167qbq4L6+Xnh6ChFYYQQQoiCIEGhEEI8y/Tp6OLDzEdC6GJDUWfH52lmtC9160gIVz8MJWqDxuqBhkxJyebQoQhzFvDo0Sjc+9RGbaUheP81DKk5FCtmha+vJwEBPgQEeFO3rju2tlIURgghhHgUJCgUQohnhaKgTrt8I/gLQRt7CG3iKVSK0bKZ2gqDc130rv7mpaAmO88HHjY6Os0cAIaEhPPXX7F5CoipbhSAGTY4gBaNSlG9uhSFEUIIIR4XCQqFEOJpZchAF3/UMguYFZunmdHO+0bwd2M/YMk6oLF+oCEVReHSpSRzEBgcfJ3Ll5PztCtb1tGcBfT18+Lt9acA6NmjDg42khEUQgghHicJCoUQ4mmgKKjTr+YGfzEhaOMOoU04iUoxWDZT6zCUrGOZBbT3fuBhjUYTf/0Va5EJjI21PFpCpYIaNVzN+wEDArxxdy9mfj4j51am0koj2UEhhBDicZOgUAghnkSGTLTxx3KDwBtnA2oyo/M0M9p6mvcB6l39MDjXAc2DF2jJyjJw9GiUOQt46FAkaWl5i8LUq+dhDgB9fb1wcLhz5jHHcKuIjU6CQiGEEOKxk6BQCCGKOkVBnX79xpEQIbn/m3gSlUlv2UylxVCyNno3f/PZgCZ7n9xU3QNKTs4iNPRWUZjjx6PJybHcg1i8uBV+fl7mTGDduh7Y2Nz7Py/ZN46j0KpVaNRynqAQQgjxuKkU5d/b/YuWuLjUPAUJhBDiWWF1fSvFQoaiSb+a5zmjrXtuFtDlZhawLmhtH2q8qKg05mw/x5/RKWTnGNEbTHnaaNQqrKw05j9anZqHCeUMJoWEDD32Vhr+6Nf4IXoSQgghxO1UKnBxufs5vpIpFEKIokhRsD0zF/vDn6FCuZEFrGXeB5ibBSz9UFlARVG4cCHxtqIw4Vy9moxnj+pYuduBrfaO/0joAb2ikJ5tgOwHnoKF8s72BdOREEIIIe6LBIVCCFHUmPQUCxmC7bkfAcis1IO0hhNB93BBk8Fwe1GY64SERBAXZ1kURq1WYWOvwwS84u5Aq/reODs/nkPiy0lQKIQQQhQKCQqFEKIIUWUn4vDnO1hF/YGCivSG48ms1ueBMoKZmXrCwqLMWcDDhyNIT7fch2htraF+fQ/zfkBfXy+6LT9GREo2r7YsT01Ph4K6NCGEEEIUURIUCiFEEaFOuYDj76+jTTmHorUnpekickq9fM8/n5R0syjMdXNRGL3eck+gg4M1fn5eBAR44+/vTd267lhbW/5TkG3M3cgtx0MIIYQQzwYJCoUQogjQRe/HYffbqHMSMdp5k9xiJcaStf7zZyIiUm87H/A6Z87E52nj4WFPQIAP/v5e+Pv7UK2aM5q7BHs3j4iw0kpQKIQQQjwLJCgUQohCZn3hF4of/ASVSY/euT4pzZdjsvOwaKMoCufPJ5qzgCEh4Vy9mpKnrwoVSpizgAEB3pQp44jqPpee5tw4IsJagkIhhBDimSBBoRBCFBbFhP3Rr7A7NQ2A7DKdSGk8H7R2GAwmTp6MMReFCQ2NID4+0+LH1WoVtWq5ERDgbT4n0M3t4Yq1KIpyK1Moy0eFEEKIZ4IEhUIIURgMGTjs64X11Q0AJFUdzO7sdwiecfxGUZhIMjIsi8LY2GioX9/TnAn09fWiWDGrgp2WSeHm0bASFAohhBDPBgkKhRDiMVNnRGK/83Wsk4+jV7R8FdyNiSMcMRiCLNo5Olrj7+99ozCMD3XquOUpClPQsm87rF72FAohhBDPBgkKhRDiMQgPTyU4+DoRx/bS22scNvZJxKbZ0XnxG+y/XAYw4elZzGI/YNWqLqjVD344/YPQG28LCjWPd2whhBBCFA4JCoUQ4hG6fDmJDz7YzPHj0XSo8Te/dl2DvZWe09Eu9PutN+Wb1uHNwblBYOnSDvddFOZeGUwKfVad4Ex06n+2M91YO2qlUT2yuQghhBCiaJGgUAghHpHQ0Ah69FhPXFwGQ5sfZFLbnahVCuE6fzTdlrB8qOdjm8v1pEzCriffc/saHsUf4WyEEEIIUZRIUCiEEI/A2rV/88kn2zEZslnz4e90qXQAgMzK72HlN4WS6sf78ZuWbQDArZgVC96oc9f2ng42j3pKQgghhCgiJCgUQogCpCgK06eHMGnSAUrYZrB7+EbqlDyDolKT3nAimVU/gkJYlnkzKHS01eHjZPvYxxdCCCFE0SVBoRBCFJDsbAODBu1k1aozVHKJY++gINytIjBpi5H6/CJyfNoU2txSs40AFHvE1UuFEEII8eSRuwMhhCgACQmZvPvuBg4eDKdlpcts7LUGW1UqRvtSJLdYibFEjUKd381MYXEJCoUQQgjxL3J3IIQQD+nChUTefnstly4l0fv5E8zusAE1BvQuDUluvhzF1q2wp2gOCotZawp5JkIIIYQoaiQoFEKIh3DgwDXefXcjyUkZfPvmXj5quBuArLKvktpoHmiLxv49yRQKIYQQ4k7k7kAIIR7QihWnGTRoB1aqTH7rv4UXfI4DkF57BBl1RhZKQZk7ubmn0F6CQiGEEEL8i9wdCCHEfTKZFCZPPsA334Tg7ZjMnoFBlC92BUVtTWqjuWSXf72wp5hHqmQKhRBCCHEHcncghBD3ISvLwCefbGfdun9o4BPOb/3W4KhJwGTjQvILyzC4+Rf2FC0YTApw+/JR2VMohBBCCEsSFAohxD2Kjc3gnXfWc/hwJK/VPcMv3dahIxuDUzWSW6zEVKxMYU/RwncHrvDdwSsotz0mR1IIIYQQ4t/k7kAIIe7BP//E07XrOq5eTWJM22BGt9gOQI5XK1KeX4xi5VDIM8xr/6WEfwWEGqp7FC+0+QghhBCiaJKgUAgh7uLPP6/w3nubyEpPZ9V72wmsdgiAjKofkt5wIqiL9kfpuLZVCShbAludBiuturCnI4QQQogipmjfyQghRCFbuvQkw4b9hqNVKn8MWUc913MoKg1pvl+TVbVXYU/vnthZaXC01RX2NIQQQghRRElQKIQQ+TCZFMaN28ucOYep4hrL7k9W42kbjUnnQMrzi9F7tyrsKQohhBBCFAgJCoUQ4l8yMvT06bOVzZvP06LiRTb2CsJOnYaxWBmSW6zE6FStsKcohBBCCFFg7ntzSXx8PL1796Zhw4b4+/szfvx4DAZDvm1DQ0N57bXXqFevHs2aNWPBggUPPWEhhHiUoqPT6NRpJZs3n+fjxmHs/OgX7NRp6F39SWy7WwJCIYQQQjx17jsoHDBgAHZ2duzdu5fVq1dz8OBBFi9enKfdhQsX6NWrF2+//TZhYWEsWLCARYsWsW3btoKYtxBCFLjTp2Np02YZJ45HMve1XczrvAE1RrLKv0HSSxtRbFwKe4pCCCGEEAXuvpaPXrlyhdDQUPbs2YOtrS2lSpWid+/eTJkyhffff9+i7a+//krLli3p3LkzAFWrVmX58uUUK1as4GYvhBAF5LffLvHBB5tBn8rOPhtoUfYvANLrfk5GraGgUhXyDIUQQgghHo37yhSeO3cOJycn3N3dzY9VqFCBiIgIUlJSLNqeOHECHx8fBg0ahL+/Py+//DKhoaG4uroWzMyFEKKA/PDDMbp2XUcJbQxHh/1Mi7J/oWhsSHl+MRm1h0lAKIQQQoin2n1lCtPT07G1tbV47OZ/Z2Rk4OBw6/Dm5ORklixZwvTp05k8eTJHjx7lww8/xNHRkTZt2hTA1IUQ4uEYjSZGj/6ThQuP4lvqOjt6r8JJl4zJxo3kFssxuDR8sH5NCqlZ+e+1fpwMJuXujYQQQgjxzLuvoNDOzo7MzEyLx27+t729vcXjVlZWtGzZkhdeeAEAX19fOnbsyNatWyUoFEIUurS0HD76aAs7dlzktTqn+KXbBnSqHAxONUhusRJTsVIP1K/BpPD2T0e4lJBRwDMWQgghhHg07isorFSpEklJScTFxeHikltw4cKFC3h4eFC8eHGLthUqVCAnJ8fiMaPRiKLIN9dCiMIVEZFKt27rOHUqhtFt9jGm1W8AZHu3JvX5RSi64nfp4c7i03OKVEDoWsyKau6yl1sIIYQQd3ZfQWHZsmVp0KABEyZMYOzYsSQmJjJv3jwCAwPztH3zzTd5//33Wb9+PR06dODw4cNs3LiRqVOnFtjkhRDifp04EU23butIiE1mRY8tvF4zDICMan1IbzAO1JqH6j/HYALAVqfmj36NH3q+D0sFqGRPpBBCCCH+w30fSTFr1iwMBgMtW7bk9ddfp2nTpvTu3RuAevXqsWHDBgCee+455s2bx5IlS2jQoAEjR45k+PDhtGzZsmCvQAgh7tHWrefp0GEFhtQYDgz8lddrhqGoNKT6Tyfdd+JDB4QA2cbcoNBaq0GtUhX6HwkIhRBCCHE3KqWIr+eMi0ulaM9QCFHUKYrCggVhjB79J1XdYtjVexVe9rGYdI6kNFuC3qt5gY11JjqV/y09ilsxKzZ/GFBg/QohhBBC3C+VClxc7r4t5r6WjwohxJPGYDAxcuTv/PTTCV6qfJ617wVhp8nAWKwsyS1XY3SsXKDj3Vw+aq2974UYQgghhBCFQoJCIcRTKzU1m/ff38Tu3Vfo3SiU2Z23oVaZyHFrRMoLv6DYOBf4mNk3gkIrCQqFEEII8YSQoFAI8VS6di2Fbt3WcvafGOYG7qR3QDAAWRXeJjVgJmisH8m4OTf2FFppJCgUQgghxJNBgkIhxFMnLCyS7t3Xk5WSwPaP1tGy/N8ApNUbQ2bNgbkL7B+RHGPuJmgJCoUQQgjxpJCgUAjxVNm48Sx9+mzF3TaO/UNWUqlEJIrGlpQmC8kp0/GRj58jy0eFEEII8YSRoFAI8VRQFIXZsw8xbtw+AspcY0uvlZSwTsVo60FK8+UYXOo/cN/ZBhMxqdn31DYqJQuQQjNCCCGEeHJIUCiEeOLl5BgZPvw3fvnlFG/WPcmStzegU+vRl6hNSosVmOy9H7hvvdHEaz8eIjLl3oLCm3SyfFQIIYQQTwgJCoUQT7SkpCzee28je/deZfRLfzLmpT8AyPZpS0rT70FX7KH6j0vPMQeE9lb3dri9TqOmVWWXhxpXCCGEEOJxkaBQCPHEunQpia5d13Ltcgwr/reR12ufACCjRn/S640B9b0Fcf8lNcsAQEk7Hds/fu6h+xNCCCGEKGokKBRCPJFCQsLp0WMDmuxY9n+yigZeV1BUWtICZpBV6X8FNk5aTm5QWMxaPi6FEEII8XSSuxwhxBNnzZoz9O+/g0olI9jRbwVexRIwWTmR8sJS9B7PF+hYqVlGAIpLUCiEEEKIp5Tc5QghnhiKojBtWjCTJx/k5apnWd0jCDttFobiFUhpuRKjQ6UCHzP9RqZQgkIhhBBCPK3kLkcI8UTIzjYwcOBOVq8+Tb8mIczouAO1ykSOe1NSXvgZxbrkIxn35p7CYtYPvz9RCCGEEKIokqBQCFHkxcdn0qPHBg4fusrcLtvo3egQAJkVu5PmPx00Vo9sbNlTKIQQQoinndzlCCGKtAsXEnnrrSASo6LY3iuIlhXPoaAivf5YMmt8AirVIx3/5p5CCQqFEEII8bSSuxwhRJF14MA1evTYQAl1JIcGraBSyWgUrR0pTb4np/Qrj2UOadmyp1AIIYQQTze5yxFCFEnLl//F4ME78fO+xMb3V1HCJg2jrScpLVZgcK77yMePSM4iKVNPZEoWIHsKhRBCCPH0kqBQCFGkmEwKX399gOnTQ+hW/ziL3tyITm1AX7IuKS2WY7LzeuRzOHQ1kd6rTlo8JstHhRBCCPG0krscIUSRkZmpp3//Haxff4av2uzm81Z7Acgu3YGUxgtAZ/9Y5nElIRMAa60aJ1sdLvZW+JUp8VjGFkIIIYR43CQoFEIUCbGxGfzvf+s5feIKK7uvJ7D2XwBk1BxEer0vQKV+7HNqVK4kkztUf+zjCiGEEEI8ThIUCiEK3T//xNO161pyEsPZ23cFDbyvo6h1pAbMIrti18KenhBCCCHEU02CQiFEofrjjyu8995GyhW7ypZBy/EunoTJqgQpzX9F7964sKcnhBBCCPHUk6BQCFFofv75BMOG/cbLVf5mxf+CsNNmY3CoRHKLlZgcKhT29IQQQgghngkSFAohHjuTSWHs2D3Mm3eYgc8fZGr7nahVCjkeL5DS7CcUaynqIoQQQgjxuEhQKIR4rNLT9fTuvYWd288y/9UtfPjcEQAyK71Lmv9UUOsKeYZCCCGEEM8WCQqFEI9NVFQa3buv58rZy+z4cBXNK1xEQUV6w/FkVusDKlVhT1EIIYQQ4pkjQaEQ4rE4dSqWbt3WYpt9hdABy6nkHIuitSel6SJySr1c2NMTQgghhHhmSVAohHjkdu26yAcfbKa++znW915JCZsMjHY+JLdYgbFkrcc6F73RxImIFAwm5Y5tLidkPMYZCSGEEEIULgkKhRCP1A8/HOWzz/6ge/0wvnt9Ezq1Eb1zfVKaL8dk5/HY5/P1rvOsPxV1T23VsppVCCGEEM8ACQqFEI+E0Wjiiy/+5PvvjzC+ze+MbLkPgKwynUltPB+0toUyr4iULADci1tT3PrOH4E6jYpOtR5/0CqEEEII8bhJUCiEKHBpaTl8+OFm9v/5D6u6r+XV2mcASK81lIy6n4FKXcgzhH5Ny9G6mlthT0MIIYQQotBJUCiEKFAREal07bqOhGsX2NNnOQ28I1DUVqQ2mkN2+TcLe3pCCCGEEOJfJCgUQhSY48ej6dZtHV7acxwesAIvh2RM1s4kN1+GwS2gsKcnhBBCCCHyIUGhEKJAbN16no8/3sKL5U7ya7e12OlyMDhWIbnFSkzFyxX29IQQQgghxB0U/sYeIcQTTVEUvv32CD16rKe3358E9ViJnS6HHM/mJL28UwJCIYQQQogiTjKFQogHptcbGTlyN8t+CeO7wM28538UgMzK75HmNwXU8hEjhBBCCFHUyR2bEOKBpKRk8/77mzgecobtH6ykecXLKCo1aQ0nkVX1Q1DJIX9CCCGEEE8CCQqFEPft6tVkunVbhzHuH0L6L6OSSzwmXXFSmy4ix6d1YU9PCCGEEELcBwkKhRD35ciRSLp3X09Nx78I6r+SEraZGO1Lk9xiBcYSNe6rL0VROBaeQkJGziOabV6PcywhhBBCiCeBBIVCiHu2YcNZ+vbdStfaoXwbuBmd2oTexZfk5stQbO//IPiQK4n0W3PqEcz07jRqWd4qhBBCCAESFAoh7oGiKMyadYiJE/Ywqd0uhr5wAICssq+S2mgeaG0fqN/YtNysnYONlgrOdgU237txtrfGv0yJxzaeEEIIIURRJkGhEOI/5eQYGTp0FxvWhBH0ThAda/4DQHqdkWTUHlEgBWVqeTowo0vNh+5HCCGEEELcPwkKhRB3lJiYSc+eG7l86i/29llGPe8oFLU1qY3nkV3utcKenhBCCCGEKAASFAoh8nXpUhJvv70Wp+xTHOq/HE+HVEw2riQ3/xWDq39hT08IIYQQQhQQdWFPQAhR9AQHh/Pyy79S134fe/ssxtMhFYNTdRLb/i4BoRBCCCHEU0aCQiGEhTVrzhAYuIpedXey+n+rsNXpyfZ+kaQ2OzAVK1PY0xNCCCGEEAVMlo8KIYDcCqNTpwYz85u9LAjcSA/f4wBkVP2I9IYTQC0fF0IIIYQQTyO5yxNCkJ1tYMCAHfyx9Qg7P1zB8+Wvoqg0pPlOJqvqB4U9PSGEEEII8QhJUCjEMy4+PpMePTaQdPEYwZ8so6JLAiadAynPL0bv3aqwpyeEEEIIIR4xCQqFeIadP5/A22+vpYIujG39V+Fkk4WxWFmSW6zE6FT1oftPztSz92I8BqOS7/PHI1IeegwhhBBCCPFwJCgU4hm1b99V3n13I29U28/cLlvQqk3oXQNIbv4rio1LgYwx48+LbPor+q7tdBpVgYwnhBBCCCHunwSFQjyDli07xdAhO5j08jYGPh8MQFb5N0h9bg5orAtsnJjUbACquhXDtZhVvm10GjVdG/oU2JhCCCGEEOL+SFAoxDPEZFKYOHE/P8zfy5ruq3ml+jkA0ut+TkatoaAq2IxdjtEEwLsBpWlRqWCyj0IIIYQQomBJUCjEMyIzU0+/fts5uieU/X1+pbZXDIrGhtTG88ku2+WRjJltyA0KrWR5qBBCCCFEkSVBoRDPgJiYdN55Zz2a2MOE9l+OR/E0TDZuJLdYjsGl4SMb92am0EqjfmRjCCGEEEKIhyN3akI85f7+O46XX15GecMu/vh4MR7F0zCUqEli292PNCAE0N+oOmqtlY8aIYQQQoiiSu7UhHiK7d59mXbtlvFOlY2s7L4aW52BbJ82JLXZjqlYqUc+vnn5qASFQgghhBBFliwfFeIp9dNPJxj9+TYWdFlPtwYnAcio1of0BuNArXksc8i5ERTqZPmoEEIIIUSRJUGhEE8Zo9HE2LF7Wf3zn+x4fwWNy11DUWlI859GVuWej3UuN/cUWktQKIQQQghRZElQKMRTJD1dT+/eW7gSdpCQT36lXMkkTDpHUpotQe/V/LHPx1xoRpaPCiGEEEIUWfcdFMbHxzNq1ChCQ0PRaDR06NCB4cOHo9XeuauzZ8/y2muvsXDhQvz9/R9qwkKI/EVFpdG9+3rcM/dzoN8qHG2yMRYvR3KLVRgdKz9QnycjUjgWnvzAczIXmpFMoRBCCCFEkXXfQeGAAQNwd3dn7969xMXF8fHHH7N48WLef//9fNtnZmYyePBgsrKyHnqyQoj8nToVS7dua+lU9ndmdd2KRq2Q49aIlBd+QbFxfqA+jSaFfmtOkp5jfKi5aVRgo5OgUAghhBCiqLqvoPDKlSuEhoayZ88ebG1tKVWqFL1792bKlCl3DAq//PJLWrVqxdmzZwtkwkIISzt3XuTjjzYwruUm+jUJBSCrwtukBswEjfUD92tSFHNA+GIVV3QPeAB9Ax8nbHSPp7CNEEIIIYS4f/cVFJ47dw4nJyfc3d3Nj1WoUIGIiAhSUlJwcHCwaL9u3TquXLnC+PHjmTdvXsHMWAhh9v33R5k8bhvL317Fy1XPA5BWbwyZNQeC6sGCuPyMbFWJ4jayBVkIIYQQ4ml0X3d56enp2NraWjx2878zMjIsgsILFy4wffp0li1bhkYjWQIhCpLBYGLUqD/4be1u9vb+lZoesZg0tqQ2+Y6cMh0Ke3pCCCGEEOIJcl9BoZ2dHZmZmRaP3fxve3t782PZ2dkMHDiQTz/9FC8vrwKYphDiprS0HHr12kzauT2EfLIMt2IZGG09SGmxAoNzvcKenhBCCCGEeMLcV/WHSpUqkZSURFxcnPmxCxcu4OHhQfHixc2PnTx5ksuXL/PZZ5/RsGFDGjZsCMBHH33EmDFjCmbmQjyDwsNTeeWV5bjGr2f3Rz/hViwDfck6JLXdLQGhEEIIIYR4IPeVKSxbtiwNGjRgwoQJjB07lsTERObNm0dgYKBFu4YNG3LixAmLx6pUqcL8+fPlSAohHtCxY1F067aOj+tuZsxLfwKQXaodKU2+A12xQp6dEEIIIYR4Ut13nfhZs2ZhMBho2bIlr7/+Ok2bNqV3794A1KtXjw0bNhT4JIV41m3Zcp43Xv2FGS8uMQeEGTUGkPLCLxIQCiGEEEKIh6JSFEUp7En8l7i4VIr2DIV4dBRFYd68I8z/Zgtr31nOc2Wvo6i0pAXMIKvS/x7p2HqjiUYz9gHwe59GUn1UCCGEEOIJo1KBi0vxu7aTuzwhiii93siIEb9zdOcuQj75lTIlkjFZOZHywi/oPZoW9vSEEEIIIcRTQoJCIYqglJRs3ntvE3bROznQdzXFbXIwFK9ASsuVGB0qPZIxDUYTQSciiU/PAcAoGXohhBBCiGeCBIVCFDFXrybTtetaXnLdwvSe29GoFXLcm5Lyws8o1iUf2bj7LyUy5fcLeR7XqFXoNKpHNq4QQgghhChcEhQKUYQcPhxBzx5rGdVkDb0bHQYgs2J30vyng8bqkY6dnKkHwMvBmqYVnM2P1/F2xEaneaRjCyGEEEKIwiNBoRBFxPr1//D50LUseW05L1W5iIKK9AZfkVm9X+4u4Ucs22gCoJpHcYa0qPjIxxNCCCGEEEWDBIVCFDJFUZg5M5Rl8zfy+we/Ut09DpPGjtSm35NT+pXHNo8cQ25QqNPc90k1QgghhBDiCSZBoRCFKCfHyJAhu7gWvIXQ/stxsc/EaOtFSosVGJzrPN653MgUWktQKIQQQgjxTJGgUIhCkpiYybvvbqRC9iZ++2gj1lojeud6pDRfjsnO87HP52am0EorQaEQQgghxLNE7v6EKAQXLybSru2vvOzwE0veWoe11kh26Q4ktd5aKAEh3MoUWkmmUAghhBDimSKZQiEes+Dg63z03mpmtFnOa3VOA5BRczDp9UaBqvACsmxzplCOnxBCCCGEeJZIUCjEY7Rq1Wm+/iKIdd2W8v/27jw8qvJg//g9mWwTSAgQSECWIARQtsQE2aQqoOwgGqkVW6wCrXlBlsL7K1orYEHUuoECSkWwYKQgqyAUiyxaJUFFQUFJgIAiSxJClskyy/n9odDmJUJGkjmZzPdzXfljnnnOnDvX9Zhw+5yc07X5SbktQSrsOU+lrUeZHY2dQgAAAD9FKQS8wDAMPf30R3ovda0+eChVzSPz5Qqur4Jb35QjupfZ8SRJZS5DkhTC3xQCAAD4FUohUAnf5hVr9b7vL+6mecLlcmv37uNynzuqgQ9Ytdh6r9yl9VTWaICMA/WkAxnVkNhz+0/mS2KnEAAAwN9QCoFKWJZ2Quv2n/r5HxAZLEW2U6qjneSQVCLpqyJJRVWUsOpE2oLMjgAAAAAvohQClZBf4pQk9WxVX9dFh1fqmNzcYq1be1A9mxxQYrPvJUmOBvEqu6afZLFWW9arUd8WpFviosyOAQAAAC+iFAKVcOGy0T5xURre6cqPjNi9+7gmP7JJS+74u/rGHZUhi4qS5qj4unGShbt7AgAAoOagFAKV4MmD3VNTD2jBnJX65+jlatc4Ry5rHRXevERlzQZWd0wAAADAY5RCoBIu7BSGXOYmLG63oTlzPtC+d1bpw5SValinWM6wa5Tfd5Vc9Tt6KyoAAADgEUohUAmlV9gpLC52aPz4LWp46h/aNm6jggPdcjRM1Pk+b8mwRXszKgAAAOAR7j0PVMLlHux+5kyR7hyxUj1dC/T6L9crONCtkpZ3Kq//ZgohAAAAajx2CoFKcPzEg90PHszW2PtX6slfLNOdnQ5Jkoo6TZM9/lHJwv9zAQAAQM1HKQQq4cLlo0H/tVO4ffsx/XnKCr058g0lNvtebkuwCnu9pNJr7zErJgAAAOAxSiFQCf/37qNLl36uf8xfru0Pvqlr6hXIGdxQBX1S5Wzc3cyYAAAAgMcohcCPHC63Fn2YpTOFpZe8V1D6w8PrAy3SY4/t0KkPVmjHQ2tUJ9ghR0Q75ff9h9zhrbwdGQAAALhqFsMwDLNDXE52doFqdkLUFv8+mquJaw785PvWAItaf5Gr+KJlmjvoPQUESKVN+qjg5mUygut5MSkAAABwZRaLFBUVfsV57BQCPzpnd0iSYhvYdEenJuXey88v1apX0nVPowV64NZ9kqTidmNU2PVpKYD/jAAAAOC7+Ncs8KMLl4i2iaqrUUnNLo7v339GE6av0ssDluiW1lkyFKDCrnNVct3vzYoKAAAAVBlKIfCjwh9LYXio9eLYP/95RM9MX6p1976huEa5clnrqvCWpSq75nazYgIAAABVilII/OjCTmHd4EAZhqHFiz/T+8te1/tjV6p+WIkctuYq6LdKrvrXm5wUAAAAqDqUQuBHF3YKw4Ksmj59uywHlujdMZsUZHWrrGFX5fdJlWFrbHJKAAAAoGoFXHkK4B8KS12SpLVvHdB1Z57W30ZuVJDVrZLYZJ0fsIlCCAAAgFqJnULgR9n5JZKk/pEbNDXpI0lSUZdHZO/8/364ny8AAABQC1EKAUn79p3SlwdPSQ3q6tbmh+WyBKvopkUqbZVsdjQAAACgWnH5KPzepk2HNSvlOTVpWCBJCgsKVP6AzRRCAAAA+AV2ClFrlTndem5Hpk4XlFb4vmFIWVl5KjiVpfhfRusjZ0NJkqXPAjkbtfFmVAAAAMA0lELUWp9+m6e3P//+yhMbN9dOZ3NJUlCARVGNW1ZzMgAAAKDmoBSi1rKX/XA30Rb1bRrdtfl/xosd+vuyT9Uj4n31ij0hSSptcovKYu9U68YRiggNMiUvAAAAYAZKIWqtUpdbkhQTHqJhnWIkSVlZ5zV+0mo923uRel97XG5ZVdTtGZW0G2NmVAAAAMA0lELUWmXOH0phcOAP91NKTz+pv0x5VStGvK7WUefkDKirwj5/l6NpXzNjAgAAAKaiFKLWKnUakqSQwACtW/e11r3wkjbf95YibaUqtbVQ0W2r5Ypsb3JKAAAAwFyUQtRajh8vH834JldBu17W+tGbFWg1VNKwmwr7psoIjTI5IQAAAGA+SiFqLXupU5JUN+8TLbprkySpOHakCnu9LFlDzIwGAAAA1Bg8vB61Um5usVat+lySlBjznSSpKP4xFfZeTCEEAAAA/gulELXOkSPnNGbky7ql6ceSpCCLS+d/sUz2ztMki8XkdAAAAEDNQilErfLxx9/qTw8+pVV3PKt6dR0/DLa5S2WxI8wNBgAAANRQ/E0hao0Vb32p5XvT1WJ4rMa7/qxjzhaSpMDwa0xOBgAAANRclEL4PMMw9NRTH+r8sbeUHTdS2a7y7zetF2pOMAAAAMAHUArh00pKnJo2eaOGhvxV1yQF6b3zI9Us1K4J/W6QAgIUERKohGb1zI4JAAAA1FiUQvis7Gy7Jo1brsfjX1DP2G+1vSxJklQ3orH6tGtscjoAAADAN3CjGfikb77J0cP3PqdFv/iLesZ+q7KACBUn/NnsWAAAAIDPYacQPmfXruNaMfuvejv5TUWElqk4pKWKB7wtZ26UpANmxwMAAAB8CqUQPmXFiv06umaOVv1qs6wBhooadFdxv1QZoQ2l3Fyz4wEAAAA+h1IIn+B2G3py9k61+fYJvTg8XZJUFPsr2XvNl6zBJqcDAAAAfBelEDWe3e7Q/05co/sbPaWBvTIkSYUJM1TccbJksZicDgAAAPBtlELUaKdPF+mPD/1Nc7vPU8eYs3IoRPabX1NZy2FmRwMAAABqBUohaqyvvjqr56c9ryWDl6hxXbuKrY1VMmCVnA0TzI4GAAAA1BqUQtRI27cf1dYXZ+utO99WaJBLzzvG6x3nMBkbXZL2VniMvczl3ZAAAABALUApRI3z+uv7VLL9MS25a6ckqTBmoJZ8M1gFpWWSyq54fNN6odWcEAAAAKg9KIWoMVwut2bP3KZeBTN0z21fSpIK2j+skq6z5P76I0nSzIHt1KjuT99tNMBiUccmEV7JCwAAANQGlELUCIWFZXp0UqoebjlX3RO+k8uwqqjHCyptO7rcvE5NItS8vs2klAAAAEDtE+DpATk5OUpJSVFSUpK6deum2bNny+l0Vjg3NTVV/fv3V0JCgvr3768VK1ZcdWDUPt9/X6Cp9z+nJzv9Sd1bfqcSS4QK+m+4pBACAAAAqHoe7xROmjRJ0dHR2r17t7Kzs/XQQw9p6dKlGjNmTLl57733np577jktXrxYXbp00b59+zRu3DhFRUWpf//+VfYNwLft339GSx6bo6WDlis8tExFwS1VOmitXBFtzI4GAAAA+AWPdgqzsrKUlpamadOmyWazqXnz5kpJSalwB/D06dMaO3as4uPjZbFYlJCQoG7duik9Pb3KwsO3bd2SoXdnT9SyO15XeGiZ8iN7qnjETgohAAAA4EUe7RQePnxYkZGRio6OvjjWunVrnTx5Uvn5+YqI+M8NPkaNGlXu2JycHKWnp2v69OlXGRm+zjAM/e3VdDXY90c9M+iHx0ucbzFKZb1flKw/fRMZAAAAAFXPo1JYVFQkm638TT4uvLbb7eVK4X87e/asfve736ljx44aMmTIz4yK2sDpdOsvj72j4QF/1u09jshtWFSQMEtlnR6WLBaz4wEAAAB+x6PLR8PCwlRcXFxu7MLrOnXqVHjMvn37lJycrFatWmnhwoUKDOSGp/6qoKBU08a9qvENJun2dkdUZoQq/9YVKus8kUIIAAAAmMSjUhgXF6e8vDxlZ2dfHMvMzFRMTIzCw8Mvmb969Wrdf//9Gj16tJ599lkFB3NpoL86cSJfjz/4F73Q9XFdF52tooDGKhqyTY4W7BwDAAAAZvJo2y42NlaJiYmaM2eOZs2apXPnzmnBggVKTk6+ZO7WrVs1Y8YMLVy4UL17966ywPA9n312ShuefEyvDVil4EC3zts6yjn4bbnDmlwy9+XdR7X5q9OXjBeVubwRFQAAAPA7FsMwDE8OyM7O1qxZs7Rnzx4FBATojjvu0NSpU2W1WpWQkKCZM2dq2LBhGjp0qDIyMhQaGlru+KFDh2rWrFkenK9AniVETbJxwyFlr5+m/715pyQpr9EgOW5bIgWGVTi/z0v/VkFpxc+9rBti1aZx3RUWbK22vAAAAEBtYbFIUVGXXtF5yTxPS6G3UQp9k2EYeuWlD3Rd1lQldz4oScprN1mOGx+XLD991fKtL32owlKXnh52vZpEhJR7r2m9UEWEBlVrbgAAAKC2qGwp5K4vqHIOh0tzHlmt0ZGPq2vnk3IagSrsMV+OtqOufPCPrm0YppYNKt5NBAAAAFB1KIWoUnl5JXpy8suamfC8mkfmy27UU9mAlXJE9zQ7GgAAAIAKUApRZY4dy9NrjzyhebcuVd0Qh85bW8k5bK3c4deaHQ0AAADAT6AUokqk7flOexZO1YLbNykgQMoN7yX34FQZwZFmRwMAAABwGZRCXLV1aw4o4P2HNavPXklSTrNfy33LC1IAN4UBAAAAajpKIX42wzC08IX3dFPuVPXtelQuw6K8LrPl7vI/P9zqCAAAAECNRynEz1Ja6tTT09/Q/zR9Qu3iclTitqm4z1K5Www0OxoAAAAAD1AK4bHc3GK9OO0ZPZH0shrWKVa+0VjO4evkqt/R7GgAAAAAPEQphEcyM89pzaw/6oWbVyo40K2c4I4yhq+VYYv26HN2Z+boia3fqMTpKjde7HBXZVwAAAAAV0ApRKX9+8MsHfv7eD3dd6ck6Uz9wbIMXCIF2jz+rN1HcnSu2FHhew3CgtQ4POSqsgIAAACoHEohKuXttz5Rk89SNLHnQUnSmdaTZen5uGQJuKrPvTfxGt0d37TcWFSdYIUGWa/qcwEAAABUDqUQl+V2G1r01w0a6pymGzqcksMdqPPdX5Kl/b1V8vkRoYFqFun5TiMAAACAqkEpxE8qKXHqhekLNeXaubqmXoEKXPXkGPgPGTE9zI4GAAAAoIpQClGhs2ftev2RGZrV7TXVCXYoR62k5PVyh8eaHQ0AAABAFaIU4hJfH8rW7ucf1jO935Eknbb1knX4WzKC65mcDAAAAEBVoxSinF07MmRfP1bTe30iSToV8xtZ+70gI4ClAgAAANRG/EsfF63++wfqkPmQfpGQJZfborOdZsuaON7sWAAAAACqEaUQcrsNvTo3Vb8Kna42156T3WlTUZ9lssYOMDsaAAAAgGpGKfRzdrtDr/7pGU1t+4Lqh5Uo1xUt4451UoMOVXoewzDkMv7z2m389FwAAAAA3kMp9GOnTxdqzcxpmtE1VUFWt04FdFLg3etk2BpV6Xny7A7dt/xTnS4ordLPBQAAAHD1KIV+6qsvT+nQq2P15+47JUnfhg9RyLAlMqyhVX6ub84WVlgIg6wWdWwSUeXnAwAAAFB5lEI/tHPblwrb8Vv9LvGQJOnbFlMUcvPjksVSreeNbWDT3+6Jv/g6JDBAoUHWaj0nAAAAgMujFPqZf7y2Vb3OTlB8u1MqdQUqt+tLCul4r1fObQ2wqJ4tyCvnAgAAAFA5lEI/4XK5tWTO3/Tb+jPUpGmhzjvqqXTgKgU27W52NAAAAAAmohT6gcLCMi1/fIamXr9ItiCnTjlbKXDkBlnCW5odDQAAAIDJKIW13Mnv8rXj6Yf0WOJGSdKJwF6y3bNSRjA3eAEAAABAKazV9u87rpzU32hi4qeSpGMNfqM6g16UEcDNXQAAAAD8gFJYS23flK5mnz6guztkyekO0HftnlCdHhPMjgUAAACghqEU1jKGYejtV9fqdvtktW55ToUOm/JvXqawNgPMjgYAAACgBqIU1iJOp1vLZz+nsTFPKbJhqc6URksj1ikoqkOVnqeozCmH06j0/MIyV5WeHwAAAEDVoRTWEgUFpdowa4omX79cgVZDx12dZLtvnWRrVKXneffgac1492u5K98JAQAAANRglMJa4MTxczrw0mhN6rRDkpQZMlgRyUsla0iVn+uL7/J/ViG0SOp9bcMqzwMAAADg6lAKfdzn6YdlvDNKv+l4SJKUETNF9W57XLJYqvW8Y7q30Nienj3nMKCaMwEAAADwHKXQh21ft1MdvnlQHVufUYkzSKcSXlK9hF955dwWCyUPAAAAqA0ohT7IMAytXbBMd2i6omOKlFNaTyX9V6lOi+5mRwMAAADgYyiFPqaszKW1T87UmGbzFRrk0omSWAX/8h0F12thdjQAAAAAPohS6EPyzhVr9zNjNb79BknSYXdPRf52tRRU1+RkAAAAAHwVpdBHHMs8pVNL79ED7T+VJB2s8xtFjXhRCrCanAwAAACAL6MU+oDPPvxckTtHaWjccTlcAcps9YSibplgdiwAAAAAtQClsIbbvuoddTuZothmecovtSmn1zI1vH6A2bEAAAAA1BKUwhrKMAy9M3++kkNnKaJ+mb6zR8u4Y73qxlxfbecsLHXqnN1x2TkFpc5qOz8AAAAA76MU1kClpU5tnTtF97dcJmuAoW+KOypy9EZZbA2r7ZzZhaW6c0m6ih3uajsHAAAAgJqHUljD5Jwt0P759+nBNu9Lkg5okKLHvCFZg6v1vMfzilXscMsiKSz48jevqRNsVY/YBtWaBwAAAIB3UAprkKOHjql41Ujd3eaQ3IZFB+pPUpOhMySLxWsZWtS3afUDXb12PgAAAADmohTWEJ/t+EjNP/m1bmx5RnZHsLKun6cmPe41OxYAAACAWo5SWAPseDNVN+dNUeNGRTpjj1RB35WKiuthdiwAAAAAfoBSaCK329C2eX/R3RHPKaSuS5mFsQq9Z6MiGrQ0OxoAAAAAP0EpNEmxvVQfP/c73ddijSRpf3EPNR7ztgKC65qcDAAAAIA/oRSaIPtUjk4sTtbIlp9Ikj4JuE8txr0kWQJMTgYAAADA31AKvezI/kMKfjdZt7c8LocrQPtjZqrFwIlmxwIAAADgpyiFXvTZP/+p675+UM1jzutcSZhOJr6m5omDzY4FAAAAwI9RCr3kgzcWqV/pnxRer0xZBdFyDl2nxi06yOFyKyO7SIZhbr5jucXmBgAAAABgCkphNXO73Nr94lSNaPCarCGGDpzvoPqjNygsopEkacraL/Vx1jmTU/6HxWJ2AgAAAADeRCmsRkUFdn01/14lN9suSUorHqDYlOWyWIMvzjmWa5ckNQgLUpDV3BvNBFikEZ2bmJoBAAAAgHdRCqvJmRPfqeDNERrU7JDcbos+Dp2guF8/8ZNbcc+N6KgOMeFeTgkAAADA31EKq0HGJ3sVtesedW9yRoVlwTrY6nnF9f212bEAAAAA4BKUwir2+aZVSjg+QQ0b2vV9YaRyeqcqtlMvs2MBAAAAQIUohVVoz5IndbueVnAdlw6ei1XILzcqOqal2bEAAAAA4CdRCquAy+nUp/PGakjU25KkPee6q/nYtxUUxt8IAgAAAKjZKIVXqSgvTycW36lB0XslSTtK7tX1E16WJcBqcjIAAAAAuDKPn4GQk5OjlJQUJSUlqVu3bpo9e7acTmeFc3fu3KmhQ4cqPj5eAwcO1Pvvv3/VgWuSM0cPq2jJTeodvVdlTqt2hc1Qh3GLKIQAAAAAfIbHpXDSpEkKCwvT7t27tXr1an300UdaunTpJfOOHTumCRMmaOLEidq7d68mTJigSZMm6fTp01WR23RHPtqu+pv7qEPUceXYw/RZ3Bu6LnmK2bEAAAAAwCMeXT6alZWltLQ07dq1SzabTc2bN1dKSoqeeeYZjRkzptzctWvXKikpSf369ZMkDRo0SGvWrNHKlSv18MMPV913YILtq5apQfYyHQmJ1Qe59VXW9XFFNrtWZ7POefxZpU53NSQEAAAAgMrxqBQePnxYkZGRio6OvjjWunVrnTx5Uvn5+YqIiLg4npGRobZt25Y7vk2bNjp06NBVRjbX0cyjevR4czk14z+DHxdJ2n9VnxtQ8TPtAQAAAKBaeVQKi4qKZLPZyo1deG2328uVwormhoaGym63/9ysNUKTa5oqUVv1raWJQhs0lcXi8RW4l2jZwKa4RnWrIB0AAAAAeMajUhgWFqbi4uJyYxde16lTp9y4zWZTSUlJubGSkpJL5vma0NAQvfSH35sdAwAAAACqhEfbXHFxccrLy1N2dvbFsczMTMXExCg8vPwz+dq2bavDhw+XG8vIyFBcXNxVxAUAAAAAVCWPSmFsbKwSExM1Z84cFRYW6sSJE1qwYIGSk5MvmTts2DClpaVp8+bNcjqd2rx5s9LS0jR8+PAqCw8AAAAAuDoe/0HcvHnz5HQ61bdvX40cOVK9e/dWSkqKJCkhIUEbNmyQ9MMNaF5++WW98sor6tq1qxYsWKD58+erVatWVfsdAAAAAAB+NothGIbZIS4nO7tANTshAAAAANQ8FosUFRV+xXlXf+tMAAAAAIDPohQCAAAAgB+jFAIAAACAH6MUAgAAAIAfoxQCAAAAgB+jFAIAAACAH6MUAgAAAIAfoxQCAAAAgB+jFAIAAACAH6MUAgAAAIAfCzQ7wJVYLGYnAAAAAADfU9kuZTEMw6jeKAAAAACAmorLRwEAAADAj1EKAQAAAMCPUQoBAAAAwI9RCgEAAADAj1EKAQAAAMCPUQoBAAAAwI9RCgEAAADAj1EKAQAAAMCPUQoBAAAAwI9RCgEAAADAj1EKf0JOTo5SUlKUlJSkbt26afbs2XI6nRXO3blzp4YOHar4+HgNHDhQ77//vpfTwpd5stZSU1PVv39/JSQkqH///lqxYoWX08KXebLWLvjmm2/UpUsX7dmzx0spURt4stbS0tJ09913KyEhQTfffLNeeeUVL6eFL/NkrS1btkx9+vTRDTfcoKFDh2rr1q1eTovaIDc3V7fddttlfy/6ZDcwUKH77rvP+MMf/mDY7Xbj+PHjxuDBg43FixdfMu/o0aNGp06djG3bthkOh8PYtGmT0blzZ+PUqVMmpIYvquxa27Ztm5GUlGR89tlnhtvtNj799FMjKSnJ2LJliwmp4Ysqu9YusNvtxpAhQ4y2bdsaH3/8sReTwtdVdq1lZGQYXbp0MdasWWO43W7j4MGDxo033mi8++67JqSGL6rsWtuxY4fRo0cPIzMz0zAMw9iyZYvRvn1748SJE96ODB+2d+9eo1+/fpf9veir3YCdwgpkZWUpLS1N06ZNk81mU/PmzZWSklLhrszatWuVlJSkfv36KTAwUIMGDVLXrl21cuVKE5LD13iy1k6fPq2xY8cqPj5eFotFCQkJ6tatm9LT001IDl/jyVq7YObMmerXr58XU6I28GStvfnmm+rbt69GjBghi8Wi9u3b66233lJiYqIJyeFrPFlrR44ckWEYF7+sVquCgoIUGBhoQnL4orVr12rq1KmaPHnyFef5YjegFFbg8OHDioyMVHR09MWx1q1b6+TJk8rPzy83NyMjQ23bti031qZNGx06dMgrWeHbPFlro0aN0rhx4y6+zsnJUXp6ujp27Oi1vPBdnqw1SVq3bp2ysrI0fvx4b8ZELeDJWvviiy/UrFkzTZkyRd26ddPAgQOVlpamRo0aeTs2fJAna23w4MGKiorSoEGD1KFDB02cOFFz585VTEyMt2PDR910003atm2bBg0adNl5vtoNKIUVKCoqks1mKzd24bXdbr/i3NDQ0EvmARXxZK39t7Nnz2rs2LHq2LGjhgwZUq0ZUTt4stYyMzP1/PPP69lnn5XVavVaRtQOnqy18+fP64033tCwYcP04YcfatasWXrqqae0ZcsWr+WF7/JkrTkcDrVv316rVq3Svn37NGvWLD366KP6+uuvvZYXvq1Ro0aV2ln21W5AKaxAWFiYiouLy41deF2nTp1y4zabTSUlJeXGSkpKLpkHVMSTtXbBvn37lJycrFatWmnhwoVc+oJKqexaKy0t1eTJk/XII4+oadOmXs2I2sGTn2vBwcHq27evbrnlFgUGBqpr164aPny43n33Xa/lhe/yZK098cQTiouLU+fOnRUcHKy77rpL8fHxWrt2rdfywj/4ajegFFYgLi5OeXl5ys7OvjiWmZmpmJgYhYeHl5vbtm1bHT58uNxYRkaG4uLivJIVvs2TtSZJq1ev1v3336/Ro0fr2WefVXBwsDfjwodVdq3t379fx44d06OPPqqkpCQlJSVJkn7/+99rxowZ3o4NH+TJz7XWrVurrKys3JjL5ZJhGF7JCt/myVo7efLkJWstMDBQQUFBXskK/+Gr3YBSWIHY2FglJiZqzpw5Kiws1IkTJ7RgwQIlJydfMnfYsGFKS0vT5s2b5XQ6tXnzZqWlpWn48OEmJIev8WStbd26VTNmzND8+fP1wAMPmJAWvqyyay0pKUlffPGF9u7de/FLkhYtWkQpRKV48nPtnnvu0b/+9S+tX79ehmEoPT1dGzdu5HcoKsWTtdanTx8tX75cX375pdxut7Zs2aI9e/Zc8e/DAE/5bDcw9d6nNdjZs2eNCRMmGDfeeKPRvXt3Y+7cuYbT6TQMwzDi4+ON9evXX5y7a9cuY9iwYUZ8fLwxePBgY8eOHWbFhg+q7FobMmSI0b59eyM+Pr7c12OPPWZmfPgQT36u/TceSQFPebLWduzYYdx5551GQkKC0bdvXyM1NdWs2PBBlV1rDofDmDdvnnHrrbcaN9xwgzFixAhj165dZkaHD/u/vxdrQzewGAbXaAAAAACAv+LyUQAAAADwY5RCAAAAAPBjlEIAAAAA8GOUQgAAAADwY5RCAAAAAPBjlEIAAAAA8GOUQgAAAADwY5RCAAAAAPBjlEIAAAAA8GOUQgAAAADwY5RCAAAAAPBj/x8rJB651TXEXwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 1100x500 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# prepare plots\n",
        "fig, auc = plt.subplots(1, figsize=(11, 5))\n",
        "\n",
        "fpr, tpr, _ = metrics.roc_curve(y_test,y_pred_resknn)\n",
        "auc = metrics.roc_auc_score(y_test, y_pred_resknn)\n",
        "plt.plot(fpr,tpr,label=\"Dado com predição em KNN, AUC= \"+str(m.floor(auc*100))+'%',color=\"navy\")\n",
        "\n",
        "fpr, tpr, _ = metrics.roc_curve(y_test,y_pred_res)\n",
        "auc = metrics.roc_auc_score(y_test, y_pred_res)\n",
        "plt.plot(fpr,tpr,label=\"Dado com predição em Logistica, AUC= \"+str(m.floor(auc*100))+'%',color=\"darkorange\")\n",
        "\n",
        "fpr, tpr, _ = metrics.roc_curve(y_test,y_pred_resreg)\n",
        "auc = metrics.roc_auc_score(y_test, y_pred_resreg)\n",
        "plt.plot(fpr,tpr,label=\"Dado com predição em Regressão Linear, AUC= \"+str(m.floor(auc*100))+'%')\n",
        "\n",
        "plt.legend()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "a694e7aa",
      "metadata": {
        "id": "a694e7aa",
        "outputId": "1b3a1cfb-6fbc-4dd3-bddb-543117814808"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "from sklearn.tree import DecisionTreeClassifier\n",
        "import math as m\n",
        "clf = DecisionTreeClassifier()\n",
        "clf.fit(X_train, y_train)\n",
        "y_pred = clf.predict(X_test)\n",
        "\n",
        "\n",
        "y_pred_probability = clf.predict_proba(X_test)[::,1]\n",
        "fpr, tpr, _ = metrics.roc_curve(y_test,  y_pred_probability)\n",
        "auc = metrics.roc_auc_score(y_test, y_pred_probability)\n",
        "plt.plot(fpr,tpr,label=\"Dado com predição em Arvore de Decisão, AUC=\"+str(m.floor(auc*100))+'%')\n",
        "plt.legend(loc=4)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "beda5910",
      "metadata": {
        "id": "beda5910",
        "outputId": "019399fd-9c69-4ebb-807d-21f23c4b62ff"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1100x500 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# prepare plots\n",
        "fig, auc = plt.subplots(1, figsize=(11, 5))\n",
        "\n",
        "fpr, tpr, _ = metrics.roc_curve(y_test,y_pred_resknn)\n",
        "auc = metrics.roc_auc_score(y_test, y_pred_resknn)\n",
        "plt.plot(fpr,tpr,label=\"Dado com predição em KNN, AUC= \"+str(m.floor(auc*100))+'%',color=\"navy\")\n",
        "\n",
        "fpr, tpr, _ = metrics.roc_curve(y_test,y_pred_res)\n",
        "auc = metrics.roc_auc_score(y_test, y_pred_res)\n",
        "plt.plot(fpr,tpr,label=\"Dado com predição em Logistica, AUC= \"+str(m.floor(auc*100))+'%',color=\"darkorange\")\n",
        "\n",
        "fpr, tpr, _ = metrics.roc_curve(y_test,y_pred_resreg)\n",
        "auc = metrics.roc_auc_score(y_test, y_pred_resreg)\n",
        "plt.plot(fpr,tpr,label=\"Dado com predição em Regressão Linear, AUC= \"+str(m.floor(auc*100))+'%')\n",
        "\n",
        "fpr, tpr, _ = metrics.roc_curve(y_test,  y_pred_probability)\n",
        "auc = metrics.roc_auc_score(y_test, y_pred_probability)\n",
        "plt.plot(fpr,tpr,label=\"Dado com predição em Arvore de Decisão, AUC=\"+str(m.floor(auc*100))+'%')\n",
        "\n",
        "plt.legend()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "c2aadc8d",
      "metadata": {
        "id": "c2aadc8d",
        "outputId": "5219e26a-6947-468a-b4fb-fcbebc548f5c"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "from sklearn.ensemble import RandomForestClassifier\n",
        "import math as m\n",
        "clf2 = RandomForestClassifier()\n",
        "clf2.fit(X_train, y_train)\n",
        "y_pred = clf2.predict(X_test)\n",
        "\n",
        "\n",
        "y_pred_probability = clf2.predict_proba(X_test)[::,1]\n",
        "fpr, tpr, _ = metrics.roc_curve(y_test,  y_pred_probability)\n",
        "auc = metrics.roc_auc_score(y_test, y_pred_probability)\n",
        "plt.plot(fpr,tpr,label=\"Dado com predição em Arvore Aleitoria, auc=\"+str(m.floor(auc*100))+'%')\n",
        "plt.legend(loc=4)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "17e01e85",
      "metadata": {
        "id": "17e01e85",
        "outputId": "c3a2a310-8168-4454-f095-c8d2611682ac"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1100x500 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# prepare plots\n",
        "fig, auc = plt.subplots(1, figsize=(11, 5))\n",
        "\n",
        "fpr, tpr, _ = metrics.roc_curve(y_test,y_pred_resknn)\n",
        "auc = metrics.roc_auc_score(y_test, y_pred_resknn)\n",
        "plt.plot(fpr,tpr,label=\"Dado com predição em KNN, AUC= \"+str(m.floor(auc*100))+'%',color=\"navy\")\n",
        "\n",
        "fpr, tpr, _ = metrics.roc_curve(y_test,y_pred_res)\n",
        "auc = metrics.roc_auc_score(y_test, y_pred_res)\n",
        "plt.plot(fpr,tpr,label=\"Dado com predição em Logistica, AUC= \"+str(m.floor(auc*100))+'%',color=\"darkorange\")\n",
        "\n",
        "fpr, tpr, _ = metrics.roc_curve(y_test,y_pred_resreg)\n",
        "auc = metrics.roc_auc_score(y_test, y_pred_resreg)\n",
        "plt.plot(fpr,tpr,label=\"Dado com predição em Regressão Linear, AUC= \"+str(m.floor(auc*100))+'%')\n",
        "\n",
        "fpr, tpr, _ = metrics.roc_curve(y_test,  y_pred_probability)\n",
        "auc = metrics.roc_auc_score(y_test, y_pred_probability)\n",
        "plt.plot(fpr,tpr,label=\"Dado com predição em Arvore de Decisão, AUC=\"+str(m.floor(auc*100))+'%')\n",
        "\n",
        "fpr, tpr, _ = metrics.roc_curve(y_test,  y_pred_probability)\n",
        "auc = metrics.roc_auc_score(y_test, y_pred_probability)\n",
        "plt.plot(fpr,tpr,label=\"Dado com predição em Arvore Aleitoria, auc=\"+str(m.floor(auc*100))+'%')\n",
        "\n",
        "plt.legend()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "3913e761",
      "metadata": {
        "id": "3913e761"
      },
      "outputs": [],
      "source": [
        "# Eliminando os valores null na respectiva linhas para igual com dados de baixo\n",
        "df=df.dropna(axis=0)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "42a58c48",
      "metadata": {
        "id": "42a58c48"
      },
      "outputs": [],
      "source": [
        "# Eliminando features que não facilita na localização\n",
        "df=df.drop('temp_max_mes_quente',axis=1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "60b1941b",
      "metadata": {
        "id": "60b1941b"
      },
      "outputs": [],
      "source": [
        "df=df.drop('temp_min_mes_frio',axis=1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "203eaab6",
      "metadata": {
        "id": "203eaab6"
      },
      "outputs": [],
      "source": [
        "df=df.drop('zona_chuva',axis=1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "3389b929",
      "metadata": {
        "id": "3389b929"
      },
      "outputs": [],
      "source": [
        "df=df.drop('temp_media_anual',axis=1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "aa62394f",
      "metadata": {
        "id": "aa62394f"
      },
      "outputs": [],
      "source": [
        "df=df.drop('res_espece',axis=1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "bf4fa401",
      "metadata": {
        "id": "bf4fa401",
        "outputId": "6db40e00-d200-4ec6-ac7a-3acb30f6ae20"
      },
      "outputs": [
        {
          "data": {
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              "<style scoped>\n",
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              "\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>latitude</th>\n",
              "      <th>longetude</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>17.0</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
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              "      <td>17.0</td>\n",
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              "      <th>3</th>\n",
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              "      <th>4</th>\n",
              "      <td>17.0</td>\n",
              "      <td>56.0</td>\n",
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              "      <th>9432</th>\n",
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              "    <tr>\n",
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              "      <td>108.0</td>\n",
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              "    <tr>\n",
              "      <th>9434</th>\n",
              "      <td>108.0</td>\n",
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              "    <tr>\n",
              "      <th>9435</th>\n",
              "      <td>108.0</td>\n",
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              "    <tr>\n",
              "      <th>9436</th>\n",
              "      <td>109.0</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "<p>9435 rows × 2 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "      latitude  longetude\n",
              "0         17.0       51.0\n",
              "1         17.0       52.0\n",
              "2         17.0       53.0\n",
              "3         17.0       54.0\n",
              "4         17.0       56.0\n",
              "...        ...        ...\n",
              "9432     108.0       77.0\n",
              "9433     108.0       78.0\n",
              "9434     108.0       79.0\n",
              "9435     108.0       80.0\n",
              "9436     109.0       79.0\n",
              "\n",
              "[9435 rows x 2 columns]"
            ]
          },
          "execution_count": 61,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "459f4f59",
      "metadata": {
        "id": "459f4f59",
        "outputId": "84a9a1e0-882e-4e3e-d863-2cce5d5d20cd"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(9435, 2)"
            ]
          },
          "execution_count": 62,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "5f8a362f",
      "metadata": {
        "id": "5f8a362f",
        "outputId": "137b9ed2-0a41-42b3-ac92-adf39f9febfc"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(9435, 2)"
            ]
          },
          "execution_count": 63,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "0961023a",
      "metadata": {
        "id": "0961023a",
        "outputId": "458e89c5-3b7f-4ec1-9207-77c3695bacbd"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
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              "\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>temp_max_mes_quente</th>\n",
              "      <th>temp_min_mes_frio</th>\n",
              "      <th>zona_chuva</th>\n",
              "      <th>temp_media_anual</th>\n",
              "      <th>res_espece</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.894737</td>\n",
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              "      <td>0.789474</td>\n",
              "      <td>1.0</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.894737</td>\n",
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              "      <td>0.736842</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
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              "      <td>0.736842</td>\n",
              "      <td>0.0</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.842105</td>\n",
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              "      <td>0.358209</td>\n",
              "      <td>0.736842</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.894737</td>\n",
              "      <td>0.500000</td>\n",
              "      <td>0.343284</td>\n",
              "      <td>0.789474</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9432</th>\n",
              "      <td>0.947368</td>\n",
              "      <td>0.653846</td>\n",
              "      <td>0.970149</td>\n",
              "      <td>0.947368</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9433</th>\n",
              "      <td>0.947368</td>\n",
              "      <td>0.653846</td>\n",
              "      <td>0.970149</td>\n",
              "      <td>0.894737</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9434</th>\n",
              "      <td>0.842105</td>\n",
              "      <td>0.692308</td>\n",
              "      <td>0.970149</td>\n",
              "      <td>0.842105</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9435</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.653846</td>\n",
              "      <td>0.970149</td>\n",
              "      <td>0.947368</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9436</th>\n",
              "      <td>0.947368</td>\n",
              "      <td>0.653846</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.894737</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>9435 rows × 5 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "      temp_max_mes_quente  temp_min_mes_frio  zona_chuva  temp_media_anual  \\\n",
              "0                0.894737           0.500000    0.373134          0.789474   \n",
              "1                0.894737           0.500000    0.358209          0.736842   \n",
              "2                0.842105           0.461538    0.358209          0.736842   \n",
              "3                0.842105           0.500000    0.358209          0.736842   \n",
              "4                0.894737           0.500000    0.343284          0.789474   \n",
              "...                   ...                ...         ...               ...   \n",
              "9432             0.947368           0.653846    0.970149          0.947368   \n",
              "9433             0.947368           0.653846    0.970149          0.894737   \n",
              "9434             0.842105           0.692308    0.970149          0.842105   \n",
              "9435             1.000000           0.653846    0.970149          0.947368   \n",
              "9436             0.947368           0.653846    1.000000          0.894737   \n",
              "\n",
              "      res_espece  \n",
              "0            1.0  \n",
              "1            0.0  \n",
              "2            0.0  \n",
              "3            0.0  \n",
              "4            0.0  \n",
              "...          ...  \n",
              "9432         0.0  \n",
              "9433         0.0  \n",
              "9434         0.0  \n",
              "9435         0.0  \n",
              "9436         0.0  \n",
              "\n",
              "[9435 rows x 5 columns]"
            ]
          },
          "execution_count": 64,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_norm"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "9a501856",
      "metadata": {
        "id": "9a501856",
        "outputId": "320803b5-0b5d-405c-dd28-2f78caba180e"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(9435, 5)"
            ]
          },
          "execution_count": 65,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_norm.shape"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "3ee7b45d",
      "metadata": {
        "id": "3ee7b45d"
      },
      "source": [
        "# Juntar os dois datasets para colocar no Map"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "d0971dcc",
      "metadata": {
        "id": "d0971dcc"
      },
      "outputs": [],
      "source": [
        "#df_mapa=df_norm.merge(df)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "428e1fc8",
      "metadata": {
        "scrolled": true,
        "id": "428e1fc8",
        "outputId": "09289146-22ff-4849-c4a1-35a238cd81c9"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<AxesSubplot:title={'center':'Mapa de abelhas'}, xlabel='longetude', ylabel='latitude'>"
            ]
          },
          "execution_count": 67,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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uueceHHnkkbb2OWi/UbuxkWMqiIiIiJxICKChwZljKgDA+Ek1ECrw7U/uKqjfKfA4Dht4pYKIiIiIiLLCTgUREREREWVl0M7+REREREQ0IHkePJ20TQfjlQoiIiIiIsoKOxVERERERJQVdiqIiIiIiCgrHFNBRERERGQHx1T0wysVRERERESUFXYqiIiIiIgoK+xUEBERERFRVtipICIiIiKirHCgNhERERGRHRyo3Q+vVBARERERUVbYqSAiIiIioqywU0FERERERFlhp4KIiIiIiLLCgdpERERERHZwoHY/vFJBRERERERZ4ZUKIooytf2Qru2AdEEJTYUwK4udEhEREZUAdiqICBIm9KrnIL2fAFIAkDDwGtSOc6EGjil2ekRERORwvP2JiGD6lkJ6PoksCNlzr6gJo/JFmOqhouZGREREzsdOBRHB8K1MskbA9K4paC5EREROJ0RxfpyMnQoiAkRX8lkllM6CpkJERESlh50KIoLQR/aMpUgkI+uIiIiIUuBAbaIyJ5VmmK5dENIDhI4EXPsh1WYIox4IDwPc2yDCIyBdOwCJvisWUgDSCykB07UDIjwWwumTZBMREVFRsFNBVKYkTOiVL0P6VsU8KCIDsa2WJRJIQOmGWfVvmACgD4Gr9UoIszaveRMREVHp4e1PRGXK9C2B9K5KeDSx5xCznO7bQdUm6NX/gOzf+yAiIhpcRJF+HIydCqIyZfiW9X8w8QUp1QtUv1gJ6ToIqe3PMjMiIiIqN+xUEJUrpTM/n2oorXnYKREREZUydiqIypUxxGKcRPaEMSz3OyUiIqKSxoHaRGVAPbwWSvtOGHVTYdSMg3TthhKaCNPfGB8YO7uT1XLKWAGER0GqTYDphZD+XB4CERFR6SjGGAeHj6lgp4KohInO/ah86Xq49n8IAAhOPBItV1wB6XNFAnqvVGTyQpQ2VgLu3dDdfwekAqX7BKidp3OaWSIiImKngqhkSYmq56+G2rgaAKDX1aL5M1cCSsxdjXbO923FmjD9H0CYlVC759rYkIiIiMoRx1QQlSj1wDJoh1ZCSAMA0H3c7EiHQknx3zqr2Z8SliVg+D7MLFkiIiIqa+xUEJUotXVr3LJeV1fYBAQAtZ3fW0FERES8/YmoVBm1k+KWtaYmBAuZgARgVnNMBRERDT4cqN0Pr1QQlaJwFyAl9IajIYUKAPAt/wgwzchPMim+UNt2rACU4HSY6iFerSAiIhrk2KkgKiVSwrviV6j700TUPLUASvsGhIcNBQBoLa2oe/RxiEDM9QqJzL+rwnasAtP/IfT6PyBc9zuY2u7Mj4OIiIjKCm9/IiohnrV/gH/x/4suN199BcLjx0Jtaoba0gq9YQikz9e3Qd5mfwKAmCsiajP02sfgavoChFljY0dERERUDnilgqhUSAnv8l9ELyaEhw9DeMJ4QFFgNAxBaNIEmLU12c3oNOBYCcCA4f0oRRARERGVK16pICoVejfUjj19iz23PTmHhNQOFTsJIiKi/ONA7X54pYKoVGhemJ7a6KLa2lq8XCwpEAZvfSIiIhqM2KkgKhFS6UbgmM9D9nxU4dq5G+rBQ4BhJASm2kma5axiJZTQREiEUgQSERFROWKngsjhTPUAwrUPI9xwD9rOdaF71mwAkaugdX99AmpzS/KN8zr7U+JjCvTaJxBu+A30ypfYuSAiIhpEOKaCyMGk0ga99lFAhCMPqCraLjsfnaefCNfuPTB9Xhi1tcl3IJB5R8GuxHs7lZ4rJsKA6V0FqbTD1XZlnhonIiIiJ2GngsjBDO/KSIdCxPcMjCF1MIbUWW+Uatamga6zHSshPVtgqgehGMOs8yQiIipVHKjdD29/InIw6drXr0NRSqS2v9gpEBERUQGwU0HkYMKsBGTp/jcVZmWxUyAiIqICKN2zFaIyJyEhumcAwkwXaGenmW9rJ7bftgIwqoDwaBsbERERUanimAoih5HQYVS8B9O7AlCCgOkBRDCyMvF+SqsT/d7HLGNlZIVpAorS93gm+00XG/e4AJQu6EN/CegN0DpPgxKakmSnREREVOrYqSByGL3qOUjPxr6xFCIYf8IeexKfOLtT4kl/TKzn47WoeulV6GNGQ28Ygq4TjoNZUQmoSr/Y6D4S9xf7eMpYs+9vtRF6zT+gtV4KJTQ17fETERE5Hgdq98Pbn4gcxFQPQXo3xA/OtpppKdlMTElilfYO1P7jWWidXfCu3wilswtmVVVfh8Jqv6n2bTdWAnrFW5B5m9+WiIiIiomdCiIHka7defleCdfefRCyb8fhMaMit0AVigCgNQGCX4hHRERUjtipIHIQYfrycnnT9Pni2+kO5L6RdKQKSN5xSUREVI7YqSByEBGaGBmYLW30LDK4shEePQp6bQ2kiOzXt3oNoKoDzHIAOUkBETgKAnlsk4iIqFBEkX4cjB8bEjmA6doOw/8+pGsvYLriB2BbDY6OXU7822pbIdBy1eWof/ivQCAA7dBhVL34KtrPO6sv0Gq/ydjJSQCQGqRnPUKeTRDhIyCVLkBrBEw/hN4AqTUCShcQboCQvkgdACB8BIQShNQOAqYfamA2lK557JwQERE5jJBSDsqRk42N7RicR05OY7q3QK9+EpETe2ndQUg2+1PsYxnEimAQ3jXroDY1wxhSj+6ZRwEed9b77Rcbuy6TWCD9tr2TYYWmwNV2GYiIqHwJATQ0VBU7jaTMe6qBUHthG3VXQbm9rbBt2sArFURFJCGhV7weWYhOIZsQlG459rE0sdLjQffcOdbJZLHfvMVazCglPRthanuh6EdY7JiIiIiKgWMqiIpJdAPaYcffJ+koUkC6dhQ7CyIiIorBKxVExSQ162+pphQkIN3FToKIiAYzfvldP7xSQVREAm6I0CR7sz2VGztjmyQAKPxmbiIiIofhlQqiItM6zka49gCgtKP/6OUeiYOYkw1wtopFhtvmKzZd/kgXKxGdVUEogFGFcO2fAX0ohFkZ+cJAYUIJTobaNQ9CVoKIiIgKi50KoiITZg1czTfB9KyFdO2DqR4GXHvjZ0iKvUUq3Ym81fpsTvozic0kp2T5J/7u+Vtpb0fVi6/CqKyAMXIEwg0N0MeMApQWQBGA0h7ZVc9+Td8ymJ5P4Gr+HISsABERERUOb38icgAhPVADx0LtPAXQer6jIXaGpMRZkBJnT0qMtdo2k98Djc0kJzv5A6h79Al4P9mAyiXLUfnmO9BH98z2pIj++wEis2cpnTD8S0BERESFxU4FkYOY7u2OH4hVCKKzC679ByB6bnsKTpyQ4YYSpntjHjMjIiJC/AdqhfxxMHYqiJxE8r8kAEBJqINpACKDV1MJ8K5OIiKiwuMZDJGDKKFJgFSLnUbRSZ8XwfHjIHs6Et6NmwHDyGBLATU4Pb/JERERUT/sVBAVQ7gLnlX3oeof56HqH+fCvf5u6L4Xodc8Aej18YOfe3+bsm8mpN4fU8bHxP62+slpbI5ziPst0XbBOZAeD6QQULq6UfXCK311SNaOPhRK11wQERHlFW9/6of3CRAVWrgL1f+8AOrBlQAkwiOGo3NeC6S2MnLbT+xMSKaE5+O1cO/eg9D4cZCKAteOnYDbjfCoI2D6fdBHj0Lc9EqZzP6UyE6sYcC3dBnU9g6Exo2B6fFCHzu65/akhFc8q30l27dpwvPRGrgOHkRo3FhIrweNX/o8/CtWQdu7H8IwoO7bD2PEiOT7EjogTHvffUFERERZY6eCqMA86x6CenAlRM+Zb/v5Z0NqWt84gpiZkHwfrUb1M89DAKhYsjxuP6bHg4Pf+EpPrIjftlfirEuZ/E4TU/X6m/AvXgIhJeTiD9F4x63WOVjtN1FMjP+DZah6+d+Rhz5YisOfvx5mdTU6FpyeettYajMM31JoXaemaJSIiIhyjbc/ERWYe+tz6P0o3fR6EB43tv/A5B6eTzYkHaAcmjAecBX+cwHPuvXRWZn0hgYYdbVJ87fD+8n66N+m14vw2DH29yskTM/69HFERESUU7xSQVRo0ua9OcniM5kNKR9i88llDolfmkdERORQVnf85r/RArdnE69UEBVYeMJF6H1lUAJBuHbuAkzTMjY4dUrS/bi3bgd0PQ8ZphacPi06K5N26BCUltak+dsRmD41+rfSHYBr1277+5UCSnBq+jgiIiLKKSGl3Y9Nc6epqQlXX301fvSjH2HevHkAgFWrVuFHP/oRNm/ejLq6Otxyyy248soro9s8/fTTuO+++3Do0CFMmDAB3//+9zFnzhzbbTc2ttv+wJgoJ8KdqP7HuVAb1wCQCB8xAk03XA9oauR2H1NGvjVaAjB01P/5r5ETbPQfT915wnFov+Dc+G0QE5A4+Drxd6rYxHUCgJRQOrtQ/7uHoLa1QUiJwKQJaPnMVZH1iQPNUw36TogRoRDq//gwtIOHACkRHjUSTTdcB6hq5vuVXiB8BBSzEiI8BlLbD6m2AcYQKHotpHsnpNAhQmMAYUC69kJIN0RoLKR2EFJtBYw6KHoDpHsHpAhDhEYBEJDu3RDS1RPbBKkeBowaKPrwnv0GoXQNg2/1erh3vg2puBE46kIEp9RDaochjGoIfQSkaxek0g0RHgkhPTDdOyGkAhEaB6m2QWqHIMxKiPARkK49kEonhD4MwqyAdO3seQ6NBZQuSO0AhFkB104NvjXPQ+ncD33ITHSdsABm9UFIYUIExwBKCNK1D8L0xddFHwLF6K2LASV0JJTADAi4kvyjZUZCQrp2wPR+DClCPceqwXTvhpAqlOA0iNBkCKd/7EZERSME0NBQVew0kpL3VwOh9sI26q6CuKWtsG3aULROxfLly/Htb38bO3fuxMMPP4x58+ahtbUV55xzDu644w5cffXVWLp0KW677TY89NBDmDVrFj788EPccsst+N3vfodZs2bh0UcfxQMPPIA33ngDPp/PVvvsVFCxSBgwvI/Au/ppeNethwiHIQF0zz0W4bGjIYUCY1gDotc5w2H4V6yE9+NPAMNAaOxYCD0M9+49MP1+dJx6EsJHjuuLz+REPtlykljl4EH4NmxCaPRomH4vpKrC//E6uDduBjQNXScch8CM6YBQrPebSacFkY6Ff+nyyFgSSHTPmY3u2bMiHQtb+xWAkBaxSQ7Ycr/2YkUwgPqHHoG2/wAAgdCY0Wi+/mpAc/VdJxcyfr9p80+M7e2B9T3sW7EK1c89DwgVUphovepyBKdNGdh+AQh9GLTWRRDS0//YM6RXvAHT/2GSGkZyEIEZ0NovYseCiCyxU2HB4Z2KooypePrpp3H33Xfjm9/8Jr761a9GH3/llVdQW1uLRYsWAQDmz5+Piy++GI8++ihmzZqFJ598EhdeeCGOO+44AMANN9yAxx9/HC+88AIuv/zyYhwKkW2mdw3Myn3oOvlEdJ18Iur+8hjcW7fB/cLLML1eHPzGHZHA3nMttwtdJ56ArhNPyLyRTGZcyjBW27cfDQ/+MfpQ+5mnofPUk9Bx5mnAmacNrJ0k66THjc5T5qPzlPlZ7lcmiU3xSUKWsRXvfwDtwMGec3iJtkvOBzQtcgUpLqeE/abMPzFWxsWKri5Uv/BSzyoDgZkzIh2KLPYrtUMwfB9C60rxb5uCqe2LdCis2onJQXrXQvZcsSAiotJXlDEVp5xyCl599VVccMEFcY9v2rQJU6bE30M+adIkrF8fmc1l8+bNKdcTlQLTsy76twgE4d6yNTqbUnDKpMiJaLEGYVvwrl0PGTMLU2DmUTmZ7anceFevjf47GkMbYDQ05L1O3g2bIQwj2mcIzJie/fgWIWF61w54c9OzHpAZHLcUnKmLiEqXKNKPgxXlSsXQoUMtH+/s7Ox3G5PX60VXV1dG64lKgjD6/jaNuNcIqakFTycdYcQPBpcaJ42zElsnqRao02XosV97GPm3yUmH1EgfkpNts2mHiIicxFEfN/p8PgQCgbjHAoEAKioqMlpPVAqU0ET0ngZKvx/hEcOjsyl5tmyzP+VsngUnToCI+fTbs3EzYGQ/21O5CU6Z3Dcr1sFGKB2def+3DE04Mm7Zs3lL9juVAkpo0oA3V8JHAiKT54eECB+ZPoyIiEqCozoVU6ZMwaZNm+Ie27x5MyZPjtxzO3ny5JTriUqB0n0sYNb0DGIF2s9dCAgBKRSorW3wv99zP3rvCalE/FjX2OXYGKD/mFhpsS7Vb4vHQhOORGDSxOjqynffhwgE4m+zSddO0vwLGAubsZZ1SVJvCXScehKk1wspBIRpouqlVyPreuuUh2M16uvQNe/4nocEfCtWQW08nMW/jQCkBilC0CvegKFth+H7AHrlizB8H8LUtkOveB165csw3eshY640SNEFw/chDPcGQK/r325C/kq7gsqX/wDvsp9DdOwFERGVNkd1Ks4++2w0NjbioYceQjgcxgcffIDnnnsuOgj7iiuuwHPPPYcPPvgA4XAYDz30EA4fPoyzzz67yJkT2SA9QHhYdHai0PjxOHzjZxGcNhlGZQW8qz+Ga+u2ntiebXon0TFNuDdthmfDBijtHRCx3xFhdYIqYrZFwvrE5WSxQqDlmivQfs5C6A0NgGGi8q13IQLBSKxuwLN2HVzbtvfk1BJz8i2h7dkD75qPobS2QWltA8KhSCPJTnR7c4i7ryfDWJEidkB1kXBt2w7Puk+gtLVH8tf1+Px7mDU1aPzi59F93BwYVVVw7dkH76o1yU/qY3MYyLH2rGs/9yy0XnIRwkeMgHS5UPPEP1Dx3gdQmlugtLUDwUDqusTtVwJKGNKzHqbvQxh1j8GoeBOmdw2Mijeg1z0G07cUpncV9Jp/Qq99BBIhmNo+hOsfgFHxJqT3Y0Btjj+e2GPVDfjfW4whv/05vGsfgW/JXah95Bi4dv4bRERUuor6PRUAMHXq1OiUsgCwZs0a3HXXXdi4cSPq6+tx66234rLLLovGP/PMM7j//vtx4MABTJo0Cd/73vdwzDHH2G6XU8pSsRiej2FU/yt1UOyJY4zqp5+Db/XHEFJCqioOfv12SK+3qAOn6x/8I1z7D0BICaOmGoe+cms0H9+S5ah+4eXooRz+j2sRHj82Pt8kx2rJTmwOVP77TVS8tzhSbwCNt38JRl1tYeo9gLp4PtmAusefij7ccunFCMycAeRrjIcUEN0nQHo2AEpr6hmzAEBKDHnwj5FZsmJegCUEpKcGLTdsBDRvfnIlopLi+CllH6gGwgWeUtZVBfElTimb1IYNG+KWjz76aDz22GNJ4y+55BJccskl+U6LKG9Mz9rIbSZ2piwFAMOE7+N1fTNFTRgP6ffnJ8kMqU3NcO/bH10OzJget963anX0b6OiAuEJ4/vvxE4nocAzX/g+Wh2ttz5yOIwh9YVrfAB18a3+OHL7lZSQihKZqSufg8aFhPSsAtRA+lgA6uEmuPYf6L8bSIhgC1y73kD4yPNznSURERWAo25/IhoUlGD6T3StmCaEEXMPu2fgX06WKyIYjFs2PR7EXgJUAsG+u2A87gJmlhsiFIr+bTqg3ulEbkmL1F+qat+XBua1UT19TG9oMJR6fci5n8AREVFq7FQQFZgIjUfvIG1bXBpCR4yMzjDk3rkr++8kyJI+tAGmt+92Ffe2HXEnssGJE6L5qr33+JfQfYehI8dH83ft3Q+Ew0XOKLXQhPHRKWWVcBja3v35fY5IAYTHAjKzi976sKFJO2cSAvoRJ+UyOyIiKiB2KogKRIpuGN7lkAgB0h3fschkJiAAHWed0bMvAbWtHf4Pl0VO0hNnJUq239iYXMRqGtoX9uQEAff2HXBv2Ro9ke08aR6kxxO9Jafq1dcjJ72xg8uTHXe65XT524lNcqwdZ5wCqAqkEFBCIVS++U5PzADrnerY7MQmWe46bg7MqspoR6jqtTci69PNBpVpTnGPR0ZfC7MKIjQmTWzPj0tDx8LTex4Scau651yC0PBtMN1bIPvtgIiInK7oA7WLhQO1qZBM11boNf8AoAOIGU8ROwNP7DIASB8ggoDpAdTu6DrXjh2ofOMduHfvgelyITB7FrpOOA5GTTVEdzdkRUXfDq32m4xVrNUMSRax3rXrUPHO+9AONUKvrUXrpRdDH30EAAG1qQmVr78F74ZNkIpA+4Iz0D13DqBafLqdmEOq/O3GJh5XhrHavv2ofOMteLZuh+lyof38cxA4+ihAWHwmkyqncAiuXXugDx8G6fMC3QGgsiLnx6q0t0fqvW49hGmi45T56DzpRMDlymq/yWsYs6L3z2AQrj17oY8cDul2A6EQ4Pf3PV8+XouKdxdDO9QIo7oKXfOOj0yNK1RAmBDhkdBar4aQHLRNNFg5fqD2g0UaqP1F594myk4FUZ5JEUB4yL0AwvZm84EKte1TMGqeto4xTAz91W+hdHZCSInQqCPQ9IUbcpJz0pzyMUtTqcUOYFulvQNDf/XbyLgYAF1zZqHtkotyl5MTYnuoTc1ouOcBQEauRXSeeDzazz0r9Td9J7YjBZTAbGgd59prnIjKBjsVFhzeqeDtT0R5Zro3wlaHAuiJNWD4P0g6/sK9bTvUjo7o7ETds2cBMQO5cy5fszSVWuwAtvV+vC56kg0A3XNmpx/r4IRjHUBdfKvWxG3adexs++0ICdO7BhL85nYiolLBTgVRvindGNhZqwBEd9KZopSu7rhl6fel/jSYikbp6or7tzEr/EX9bpF8Et3dcccq/f6BPS+FjsjtgkREVArK812NyEFE+IiBTSErJIQ+KumVivDoI+KWXbv3sFPhUOHRoyBirky4du7K71WlIsrJsUoA+hAIlN40xEREgxU7FSXIVA/B8C6H4VkDU3TBdG+B4V0G070FpuiC4VkDw7scpnooZ21KhGF41kX2q+1LWCdhart6clgPEwGY7vWRZW03TKUNhvcjGN6VMJUWmNrenvzX9cRuiMS6dsIUHTC8q2B4V0AqLTnLP18kQnF1kUorDO9KGN6PYCrtMLVdMLX9QLih/2xP/XcW87cA9HpAr0O/maJ6Yo36OnTPmhndzLdyNZT2jvgTOKtZgpK2mUWs1fpk+3FCbIHrEpw8EaGRI6KzMlW+90Hk9qfEW6DKoC6Bo6ZBbxjSd6zvvB9ZYedYBaCER8PwroIUHSAichxRpB8H40DtEiJhQK96HtK7rm9gY5rfIjADWvuFEFn0H03XDujV/4h8aVvvfoNHQmu7DBAG9Jq/Q7p2p84lVrq8YyjdJ0DtPBPCgf+TTNf2nrqE7B1rr9jlZOsS95u4H8NA5ZvvwL9kOZRgEKGRI9Byxadh1tdZX7VIlUNiHHIYa3UcmeSULt9kdUkXa3UMea6L6A6g6pXX4Fv9MYRhoHv6VLRddF5ktq5SqksGsaKzE9Uvvwbvx+sgTBNds2ag/fxzI7NeZXqsUQJq50Ko3XMtGiWicuX4gdr/V6SB2jc7d6A2OxUlxPAthlHxVuYnHj3LaucZULtPHFCbfTMX6fG38EgBpftYSCUA6VmXsA7WJx2WDaSPVdsuhhqcYS/xPEtal5QbIad1iYuVJkQoBOn2RF6Jk22b6cmz3dh02zohNlfHmm1dTAMirEe+YdzOjEjp9uvEGhoGhKH3//b3AdRQa14ERR+TSdZEVAbYqbDg8E4Fb38qIYZvRf833nTLvdsNkOlZj8jMRQknzkLC9H3Uv0ORJIek0sVKATOL/PPF9HwCy7qkksu6JMYqCqTXCygpOhRW+81VbLptnRDrlLqoKqTXk7pDMZD9FiLWbl00tX+Hwu5+AUAKGL5VaYKIiKiYLL59ihxL6bS/jRjgdj2k0o5I39NiakdRgIGmQvbk4Cwp60JEuSUk4MDXASIaxATsfUiTqzYdjFcqSojQhyLZTEBJSQGhDxtwm4o+HBAWJ84SgFER+bbnfJICIjwyv20MQNK6EFHuSQGhjyh2FkRElAI7FSVE7To58omdndlZhIQIjYXp3gwJHaZ6AIbnE5jaXpjQYbq2wfB8Aqm0QoquyOxN7k2QCEOqjTBFGDBq+ndmBKCEJkMJTuqfqJ2xKunyByD04TBd22DCgKntieSvHrTRSHYS62Kqh2AKHTCq7XXyclUXu7F2ni+52I/Vciqsi/3YbLbN5fHkaj8p9ysAaFC7j00RRERExcbbn0qIEpoCte0iGBVvAmrvNItpbsGRgFnxIUx8GHlzjhtQrUQ/bTckAMSs7xeLfn+bvo/6llOdW6eatSYtCbPy7cgRxuQLACI0DlrbpRDSa2eHtkQGx7/T124Gdcn4WAdal1Qz8CSLzVQ+BvyyLqlzGMh+nVCXbPK3tV8JpfNECLPGRnJERFRovFJRYtTgTLiaboXr8BeB0BFIeRbQ736/xFgzITbVR4up9muD3UGicfHxnSfp2gm96vkBJpKe6V4Po/KthNucnFiXMortjbf6O5ftlFpsb7zV37lsx6GxZsU7ke97ISIix2KnogQJKJFB0u699mZeKtZsOHlrR0K6N0Eq+ZlezfAvs7ztK+WynVgn/ls5LbZQ7ZRabKHacUysgOldmSKIiKjARJF+HIydihIl1dZip+AMAnmbHUoqLf07bURUeEJCqi3FzoKIiFJgp6JECb2+2Ck4gxQQRm1edi2MBvuzbRFR7kkBoTcUOwsiIkqBnYoSJcw6iODk/ie9uZpdplizydhqR0CEJkBqTZHZqkQQpmsnTG0/JCSk0grTtSMysxUkTG0/TNdOSIQgEeqJ3dcT29YT2xKJVQ9AhI6Erdm20uabYrlQswaVQizrkn55UNUlcs1f6CMg1aYUgUREVEyc/amEqR1nQ6/d1zcTVLI3apHwmNWH7/mOTXwsk9hUs8v0TlLl2QLdswUw1UgHoHdQtekClHBfrIxdViI77f3yPlMDhN7XTuK2dvN3QF2iDAOQEtB6/qubJqAqme+3zJ4vOYtNlpNpRmrucvUtK0pfbCgEeNyRv3UdMCXg7o01AEUtz7qkiw0Fgd5v3jbMnm+GFzGBEkb18zAAiNBoaO2XQJhVFg0REVGxsFNRwozKl+O/LVsg/o3f6s092d08dmMTTzDyEZtu8KaMCVYSvt1bhBP2E7ucMAVvbIfCcluLtpPmVIDYtHWJ/OlZvxG1Tz4NqSjQRwxD21lnQh8z2t5+y+r5ksNYi8dcO3ej7pG/QRgmwiOHo/Pk+QhOnRyJDeuo/9Nf4Nq3H8aQepg+H7SDB4GwDn3kcHSdMBeBo2eUZV1SxoZ11P/xYbj2H4AxpB7BCePRfsG58f+3E7aXrj0I1zwOV/ONEE4ftUhE5asYA6cd/pLH259KlFSaIT1b+g8kTndSkKtYkeG6bGLt5uSEWAfVperV1wHDgBIOQ+nqhj5ubORT8yz3O6BYB9UlX7GVr78JEdYhDANaUzOCUyZH6+1d+wlce/dBSAmt8TDcu3ZDCYagmCa0Q40IHDU9/b9NidYlVax37Tq49u2P1iU08cjIlQoh+sX2LUtAa4R07UjRABERFRo7FSVKaoeLnQI5nNrUHD0f0xuGFDWXwUA71AjR8wm7UVvbd5sZAK3xcP9OQw+zuhpwDc6Lxol10YcOjatbUhIcX0FE5DDsVJSofM14ROXDqKmO3q2iNrcUM5VBwairg+z5hF1pbYuMp4iuq41bjqW0t0fGYQxCiXVRm5qS1imO4GsgEZHTsFNRooTRABEai8E9+1OJx+a5Ll3zT4heqXAdPATX9p39T14HYV3yFds5/4TolQq1sxPetZ9ET5ADM6dD+nzRTkcsJRiCb+Wq/ifTZVKXVLGBmUdB+rzRulR8sLT/FZ1++xWAUQOYlZDQUzRCRESFxE5FCVPbLgJMf/IAidRv7LmMTbZtsllrCpGT02MzeSyLdrpOmIuOk06E7DlJq33yH9D2HUixXxl/Yps4WHYAOQwoNpPHctFOjmODM6aj/awzIVUVAFD93Itwb9kW2YXHg6brPwOjujp+tz0n09Uv/RueDZtS55DJY1nkX4zY3rqY1ZGZnDybt6LqhVfSX7lRWqHX/xHhIXfD8H0AmXEiREQ5Ior042BCylRnDuWrsbE95TlTKQhXPQvp+SR+sLZE8idd4rpixOazndhlJxyrQ+qidHTAtWsPpMeD0Lix8fes98Rqe/eh/pHHISERHjMaXcfORmjyxPhPjcusLvmKFd3dcO/YBalpkXrHjpcwJVw7d0Hp7kb4iJGQHg/cO3ZCKkoktnd62XQ55DH/osTG1mXUEdFORqb7VTsWQO0+IUljRFSKhAAaGpw7dbT8YzUQbi9so64qiM+3FbZNGwbn6MAyIJVWSM86R83kklFsPtuxMztOvnIYaGwe2zErKxGcPjXlttUvvgrR3Q1FSmD3XoSuvrx4M0Wl4oR/qzSx0udDcNoU620VgfD4sXEPBadOzqydVEqgLinXWdTFzn4N/2Io3XMhePGdiKho+ApcoqTW6PjLYFQ6tAMHo+MB9KFDks5URORISjegdBU7CyIiRzl8+DBuvfVWzJ07F/PmzcNdd90FXbcei/bnP/8ZCxYswLHHHouLL74YL7/8su32eOZQqozKYmdAZcSsqozela62FfhyLlG2pAqYnmJnQUTkKHfeeSf8fj/eeecd/P3vf8fixYvx0EMP9Yt766238OCDD+L3v/89VqxYgS9/+cu48847sXv3blvtsVNRooQxDCI8Ao6Y/SndtvmKdfoMN+m2zVfsAPLvOmFu9CGtqRmu7TsiX0KW5X4z4uC6FDQ23bb5ii31ukgBEZgBiAymoiUiypV8DsZO9ZOhHTt2YMmSJfjmN78Jn8+HMWPG4NZbb8Wjjz7aL3br1q2QUkZ/VFWFy+WCptkbJcFORYkSENDaLgWM+sgDyd6UC3Fikm67fMUm2y7XyqouPQ8kzFLQdfxx6Jp7bDSi9qlnoDU22tx3hrGOrEsOlXVdrJ8/udl3kti0x6pC+lYj3PArhGsehantt58bEVGZ2bRpE2prazF8+PDoYxMnTsTevXvR1hY/2PvCCy9EQ0MDLrjgAsyYMQNf+cpX8NOf/hQjRoyw1SYHapcwYdbA1XwTpGsHTPUQTP8HgNLZF2D3TTzVQOd0sclmerETm9huuth0baWKS2wr032WQl0SH++J9a34CBUvvQpj9CjoQxvQcepJkD5/ZDYoRaD9wvPQNf8EuLfvgHS5oMdMf5pR/nZic1GXbGpotX0ypfZ8yUddwmHU/PNfcG3eivCUiQiPGIGu+T2zLWUy/ibxtSiXdVH67g+Wrt3Qax6Fq/lGCLM2fV5ERCWoo6MjbtntdsPtdsc91tnZCZ/PF/dY73JXVxeqY97jw+Ewpk2bhrvuugvTpk3Dc889h+9+97uYOHEipk5NMtGLBV6pKHECAkp4PATckQ5F4ptxqhOcTE9+MolNtt5OrN12sznWcq5LYpwAlLZ2VD/3IrRQGJ6t2yF0A7KiIn56WQEYQ+rRfdwcBGbNBHze/OWXi7pkU8Nyfr7koS6+lavhXfsJtGAQvjXroI/s+fRKUfrFJm0zXbup8sv0WIUEhA7DtzRFY0REpe20007DcccdF/158MEH+8X4/X50d3fHPda7XFFREff4D3/4Q0yePBmzZs2C2+3G5ZdfjtmzZ+Ppp5+2lRevVJQJqe1FpI/I+4qpP9e+/dHZnQAgPGpk5Ivuer6ojSgV9569gBDR257Co49w7gxhQkK69hQ7CyIqd+k+LMlXmwDefvvtuIcTr1IAwOTJk9HS0oLGxkY0NDQAALZs2YIRI0agqir++z/27t2LmTNnxj2maRpcLhfscOi7Atkm/cjvjeJUykx//DevK13dSSKJ+jP9/kinoodw8vNHCsD0p48jIipRlZWVcT9WnYrx48fjuOOOw49//GN0dHRg165duO+++3DFFVf0i12wYAEeeeQRrF27FqZp4qWXXsKHH36ICy64wFZe7FSUCTUwE4B05uwyTo8tVDtFjA2PPgL6kHrInhND36o19j9pLpFjzXtsodpxUGz37KMjV7Z6+JevBMw0OypW/kJCCRyTsAk/cCGiwefuu++GrutYuHAhrrrqKpx66qm49dZbAQBz5szBs88+CwD48pe/jEWLFuH222/H8ccfj//7v//Dvffei+nTp9tqT0g5kGk8Sl9jY/uAJjBxMsO7HEblq30P9H6wGHucsYMeEx9PFpsoMdZq24HGpsvJTuxAjrWM66IdPIS6h/8KtbMTUlHQPXsW2i46r29fveMrSqkudmIH0/PFTmyG+fuWrUD1C5EvQ5KKgtYrP43gtKk9t0T1BAkBGEbktrpC1qW3w6MogO4B1CAgvRD6UEjtMCC6AGMotM6ToYSmWeyIiJxGCKChoSp9YJHIh6qBcIG/18lVBXFDW/q4ImGnokxI6AjX/RlQDyV/U+59TFisSxVr9bed2MS4xHbtxPYup4uN3Z/d2GRtJsZa/W0nNvFezGxq2LucLjYchnfdBqiHD8O3cjUAieCM6dDr69F97DGRk7LYKxgDqUu2NSxGXRLzd+JzywF1Udra4F23HiIcRnD8OOijRwGKgHvzVlQ//yLCo0ZBbxiC7mNmwqyp6Xsu5aEuIhBE9bPPQz3UiNCUSdCHD4tMMGCa8e0mbKe2XQA1OAtE5GzsVFhweKeCA7XLhOnZAGiH4h9MPAmJfSxxXarYVH/bjU21nMvY2MfyFZsup3zFZlMXlwuBY2bC+9EaaO2RF0Ptg6VovuaK/h2K2O1LoYZOf76UQV3Mmuq+qWR7Hw4EUPfYkxC6Aa25FYGjpqGzrs7WfjOOjfm74u334P1kA4SUcDU2ovGOWyNXTZLNSNXTgTEq3oISnAnBu3+JKAtCwPr1Kq+NFrg9m/iqWiakaxcg+c9JmXHv2AkZc/IVGj/WubP5kKO59u2H0I3ocmjcmMgtUHnm3rY9OqOZWVEBo64WsYPJLQkAaiegtuQ7PSKiQYdnEeVCumF90zFRf9LjiVsWwVCRMqFSZ1o+l/L/cZr0eqITD4iwnn7geCzTkz6GiIhsYaeiTCiBGYDIY6fCzq7zFetEJVqX7lkzIWJm8/F9tDpudp+slWhdHKVE6qKPHAG9vq5vZrHVH8d/qWKu9Rxr9zGzolcqlGAQno2b+l8hSayLFBChcRCyAkRElFscqF1GdN97MCvfST8w00q+YhPXJ26br9jEnOzEJraRj9h0OeUrNmZdxTvvo+r1NyEVBVLT0LzoKoTHjR30dclrbGI+2cTGLlvFF7Au2r79qH/4rxCBAKAo6DxhLjrOOwvRmaGyrouMdHqF0neLk5SoefpZ+NashVQUGFVVaL7hWhi1tTE7SaQBphuAgAiPBkQQ0nUAMH1QA8dA6Z4LARVSBGD4P4DpWQcIHSI4CVrXSRBmLQDA8KyD6VsKqTZDGA1Qu+ZBCU1O0iYRDYTTB2rjz8UZqI3POnegNjsVZULChF7zV0jX7uQnIL2PiSS/BxqbzcmWnf1a/YZFbLL87cSWY10sYrWDB+Fdsw4iFELwyHEITZkc/ylzIepSqFgbdXHE86XE6iK6u+Fb9THUw4dh1NWja/bRgM+XVV3UxsOo+teLMOrrYYwYhtDo0dBHDu9ZLwBTwrVzF7zrNwIC6J42FfqY0YAiBvT/U4SmQG27CHrdXwC1EdGrv1IA0gNX8w0wPZ/AqHwr8piQ0d9q+zlQA8eCiHLD8Z2Kh4vUqfgPdiocp9w6FaZ7A/Sap4udBhFRbpgSQ399L5S2NggA4RHDcfhLN+a9WaXzBJj+Jf0vdkgBEZgB6f0EEBYD0U03XIdvh4Ar7zkSDQbsVFhweKeCYyrKhOneBs7+RETlQm1uhtrToQCA4MQjczvux4oUMD1brNcJCenebN2hAAAlBOnal7/ciMhZRJF+HIxnoeVC8itHiKh8SC3+NU3oOgrxjiqkmqKdNK+zklcpiGjwYqeiTCjB6YDI86d4REQFYtZUI3TEyOisUt5165F8dHeuSCjdc2A5k54UEIGjAdPbPw0pAKMaQh+R5/yIiJyLnYoyoeijILrmRhZkkp/EdamWs4lN1W6+crB7rMWoSzFiWZfC19Bp+ZdwXdo+dSGkxw0pBJT2TlS99O+edTJHxyojt1T13lZl1MPwLwH0aotYL6RnA2BWABAW+3UhXPcHmOYT8L33DVT/bR6q/r4QnjW/A4wwiIjKHe+ZKRMSElJt6XuDS5ztRFosw2J9LmITf6fKyWq/hY4tVF2ccKx2YotdFxTwWO3Esi4Fq4s+fBgab/sifCtWwXXgACAE1MNNMOrrs6qLCHSj8rU3Id0e6KNGQq+u6pk16nDfzFH9Xte6Aa3b+jUOElAPQ21pQd2fH4LS3Q0hJSQEtAPL4Nr5KjoueAwQ/ByPiMoXOxVlQrq3Ap7N8Q+KJH/HLif+zmdssm2dEJvJtqxL4etiJ5Z1Sb2vQsRmsq3NWLOqEp2nnwxLA6xL7eNPwb1jF4SUMP1+HPz67ZEVikidY5r9Vr7xNpRAd/RL+URPz8S9/SW4dryM8PjzUyRMRCVFIPVrUL7adDB+bFImTPcWcPYnIqI0QmF4tu+MnvgHJ4wHVDUnu/Zs2Ahhyn6PS6HBte2lnLRBRORUPAslIqLBQ8TfGZXTLywSKT5G5K1PRFTm+CpXJpTgFHD2JyKiNFwuBCdOiM4q5dmyFdD1nOw6cNT06H5jCakjNOGinLRBRORU7FSUCREeBxGYEVno/eBNJvwkrksVm24bJ8Tm+xjzvX/WhXVhXQa4f9n3ewB1aT/3LEiPJzKrVCCIqhdfjcT03rqUUd4y4TfQccapMKsqIUVkNIXsuToRnHwl9DELQERUzjhQu4zI2DEVMnlcRqKzmhRYtu3mMmcn1ZB1yc++WBdrDq2LeugQfB+tRnjUKJhVldBrayCrKpHx6MWefRlDG9B4yxfgX7IM7l27oHZ2wrVjF8JjR6feXgJKezt8Hy6D3jAE5pA6qIeaoDa3oHvuxZD+MWj+jz/Av3Q5XDvfgXRXITTlKoQmX5761igiKj0cqN2PkDKXN5SWjsbG9pzeSltspmsH9Nq/FTsNIqK8UJtb0PCb+6LvqZ3zjkf7eWcV9mQ9HMaw/70bIhiMe2+XEJD+4Wj57DpA4Wd1RLkgBNDQUFXsNJJ7tBoItxe2TVcVsKitsG3awNufyoTp3gjO/kRE5cqzfmNcByIwY1rBc3Bv3wkloUMBRKaOVbr2Qz24ouA5ERE5Bc9CywUHaRNROTMTXuOUwr99icQc+q03CpQJEZHzsFNRJpTQRHYsiKhsBSdPjH63BAB4NmxCoe9hDY0bC6n1v71JAjA9tdCHH1vQfIiInISdijIhQhMhghP6z1JiNYsJkvweSKzVttnGJsspm9hkOeWrLvmoYVnURbIu5fp8sRM7gLoYw4ai8/jjIg8LAf/S5VCbmuOvYOS5LtLrQfvZC6I5RH5H3kY7zroOeu0rMDxrIJGbKWqJyMFEkX4cjCPKyoSAAIyhgNga/2YoLP6WCRvnOjbx90D2K3Mcayf/fMXaqUuy2JKri4Rr63Zozc0IjRsHKAJGdRWgaYAUqY/Var/FqItIWMfni3VdUsWmyz/D2Pbzz0F41BHwL18JpaMDFYuXoP3M0yArKqz3m4e6dJ0wF3p9HfwfLIV2uAn6sAZ0zZ+H0PgaAOsA71qY4aXQWq+FkF4QEQ0WnP2pTJjaXuh1Dxc7jYjYEw4a1PwfLEHVS/+OPh0Of+56hMeMKsr98EQFIwWU7hOgdZ5Z7EyISpbjZ3/6a5Fmf7qWsz9RnpmeT+CY2Z/YoaAe3lUfR/82KisQHjeGHQoqf0LC9HycPo6IqIzw3b1ciHCxMyDqRwmH++6ScbmKmgtRQfE1mYgGGXYqyoQSGs/Zn8hxgpMmRge0qi0tUFpaUVb3HRJZkQIidGSxsyCifOJA7X7YqSgTIjQZInxE3+BXIPUgRKvH08XKJLGJAyOtljONTZZTst928s9XLOuSNJfOk+bB9PkgFQEhgepXXous652xZ5DWxTI2l8+XVLHZ5p+v2LKpiwBMgYr3VsC35MdQmjeCiGgw4EDtMqL73odZ8XbfA8lmrbGSbWzs8kBjE9stVCzrkjynHNRFaW1F5Vvvwrt+I6QQ6DjjFHQfOxtQLSafG0R1ydvzpVg1zGddMomVEq5t2wFNhT58OBAKQVZWAD1TvsbvV8K1fQcAQB85EgiHIf0+QFHj9227LhKubTtQ8+IrUA+3AFJCSANdJ/0IgTl3JNmYiKw4fqD234o0UPszzh2ozU5FmTDVg9Dr/5i/BiQyv+yWr1gnckJdnFhDJxwr65J9bKHkIP/ax/4Oz4ZNEFLCdLtx6Bt3RMbxiP7B1U8/B9/qjyGkhFRVHPz67ZBeb9aTCFS+9iYq3l0c9yV9vVqvehfG0FlZ7Z9oMGGnwoLDOxW8/alMmN61iLv1Kdfs7DpfsU7khLo4sYZOOFbWJfvYQskyfxEIRjsUABCcOhnS7bbsUEA34Fuzti924pGQfn9OZiXzrVxl2aGQQoN7w2NZ75+IyMnYqSgXIgBnni0QEeWXCIXiTuZNrxfJLkULQ4eI+RZu6cvdF9SJYCjpOiXYkrN2iIiciJ2KMiHCo8HZn4hoMDIrK2HUVEeHPLh37rK+SgFAut0ID22Ixrp27s5ZHuGxY6KzncU3qiM8cn7O2iEiciJ2KkqchAHTvR5SaQOMKtia/SnZTCiJsan+zmesVY52Ygc6kwvr0n9/2dalEDVkXaxjB0NdFIH2hWdGx0u7DhyEZ90nfbOMxcYKgY6FZ0QeEgJacwu8Kz6Kv7IxwLp0nHEqIERcx0IKFWbtZISmXAEiKiO5nCbWzo+DsVNRwqTahHD9A9Br/gmz4l1AbQcgY9480TcrSe+PiFkHZBYLi1gkiU18o03cp1XbSBIrsowVFrHpjrWQdYmNtWo7WQ0LUhcJbe8+KG09A8IC3ZGTriSx6r4DUFpaemIDgDQzq6FhQNuzByIQiCx3d8XURUI9eBDq4cORx0KhyEliseoC9M8/2+dLsthSer4AjqhL4OgZaL7qMujDhgIAqp97Ca5tO/o6CzH7DE6dgpZrr4I+fBgAoOrl1+DetKUvNhiAtms3YBiRxwKBjOoSHjMKTZ9dhPCY0ZFQTUX37JlovuaXgOYDEVE54+xPJUpCIlz3B0A9DAgZuyLznuxginViTk6ITbJt3f/9Ce59+yGkhF5TjcY7bokMZLW4taP24b/Cs30HhClhVPhx6M7bAE2zjI0TDGLoL+6BEg4DUiIwdTJaP3NldHXVP/8F/6o1kdl8XBoO3vllwGdjhp58PV8G83OrVOpimIDS0zuxGavt3oOG3/850pQA2hecga6TT4x/3mWSg2n2/B+IBLqavwBhDEmzERH1cvzsT48Vafanazj7E+WY1A4AWmN8hwLI/I15sMU6MScnxFpsqx5qhGfvvujA18CsmZFXd4tOgtLaBu/W7RBmT+yMowBVTd+hAOD9ZAOUngG2AkDg2GP6blcJheHvmfITAEKTJgIVNmfoydfzZTA/t0qlLmpPB3gAsf6VqyB7nmdCAt1z5/R/3mWyXyV2vwKGd00GGxERlS52KkqV0lnsDKhMqR3xzy2zwo9kl/WUzq6MYy3biel8GJWV0ZM3JRiMm6HHrKjIeL9E2VA6uqKdWykA6cvRbUt8zSaiMsdORYkS+jDk9XspaNAKDx8a/aQWAFx790WuPlgwhtTDdLkyiu3XzqiRcdOAunfvidyKgkjnxKiqjN6+7tq7N6OrH0TZCo8aGX2uCQlo+w7ED/geEAkRHpl9ckTkHKJIPw7GTkWJEmYVlMCs/gMvZcLficupYlMZaGwu95XLYy12Xaxi81EXO7E9dZF+P7pOOC66yrtuPdTDTdET/tjtpMeNrpPmRWM9mzZDO3DAetadBKHx4xAaPSo6U47/g6WAoUe2VRR0nH5qdAysa+9+uLdsTX9yl8e6ZL2fdLGJSuT5kvV+0sUmynNduo+dDen1Rp+XlW++HbmClm52qNi/45YFIH2QkDBdOyDTJkVEVJrYqShhasc5ULrnA2bPJ8Uys0+ILfWevfVK9b5nJzZxfboBjnZirfLKtVAQSktr33I4nLr9bGqYj7rYrWGP9nMWovP0U2B6PBC6gdpHHoN6qBH9ZtIB0HH6qehYeAZMrxfClKh75InIp7u9seEQlOaWvp2HemooBJoXXY3uWTMhFQVacwtqH38KoueWqu65c9B60fkwKysBADVPPA3X9p3JT+5ij1XKyGxUYb2nzVB8cKq6GAaU5qZoO0pbG5T2tuh+YejJC5f475pKGT1fICWU1ta+L3/rGXwfVYy6SAmluRnQjchyKBSXk+johOjsuSXJNAEzEmdWVuDw569HaPw4AIB3wyZU//NfUHtnNzPNyKxQvQwDSlNz375Dwf5JKd0wq16FXvs3hOv+EJkCnIiozHD2pzIgoUOqB6HX/QWAzM3sLHZi021rJzZXOeUg1rNuA2qffAqQkdtxWi++AKEpk+zPApOL2GLVxTCgdHXB9PsAVUsdaxpQOrtg+ryA5gIE4N60BXV/fRxCAqbfj9bzzkZw5vR+NRThEEQgCLOyov+gWNOE0tkF6XFDutzp8zclhvzfH6AdPAQoCkKjRqH5s9cmHWweux+lpRUNv30QQtchPR5IlwalZ4yJWVGB5is/DX3M6PhbvJz4/8hObA7yr33kcXi2bAUUgXBDA5puuiEyADrZwPp8vxbpBhrufRBqSyugqQgeOR4t114VXV3x5ruofOsdABJGZQWabrgeZm1tJOeYdkSgGxXvfYCKdxcDAjAqKtB83TUwhjYAqgqluQUN9/5f9PnSefxx6Fx4evzzrF/+AkIfDq3lsxBOv5eBqIgcP/vT40Wa/elq534ooRU7AcqegAbTs7V3IVVg6uWBxqbb1k5sMfJPElvx3mIAAgISEAKhqZP7n5SWe100FWZ1Bi/qAoDaP7Zi8YeIfpxsGAjOmGY5k450uyHdbut9KwrMqsrk7SZw7dwJ1/6DkQXTQHD6lMinyKlmjurZj3/FRxCGEbl1NRgEgsFoiPS4ofd8ep0uh6TrCvV8sRObZf5q42F4N2+JLBgSoUkTUnco8pBDIs+mzdB6r46FdQSOmha5uqCqgGmi4v0PouN5jIYGmEPqLduVPh98K1dHmpSArK6GMWJ4NMS/PP75EpgzK4P8JaRrP6R2AEIfkf5giIhKBDsVZUIq7bB3jwGlo7a2RU88zMrK5J9yU1JqS2tMDSsyHsSdVZut8Z8cGdXVGU9Fq7Ym/wTIqKnOKq9ypbbF18ysqUaxLwOrbe1xFwjMmproc0+EwlBCoWisWZ3i39WUUDv7Zm1KfA4kHrtRXZX564TSDoCdCqKSxtOCOBxTUSaEMRRAtjOUUKzwiOHRwZpqcwugp7iXniyFR46IziSltrb13XOfR/rwoXHL2sFDGZ/khocPQ7KOuXaoMQezAJUfvaEh+v8EALQDh+x9n0gehIcPjXuv1w4ciE40ID3uuJnFtIMHk+9IEdCH1PfFHoiPDfd8I3dfO4cyfo4IvSGjOCKiUsFORZlQAjMB6UPKaWbTzcCSr9hCtZPj2M5TTgRkZK4WJRiEf8ny/ieng7AudmK75s+L1lDoOvwfLElfwyxz0kcMR3DC+L5ZpZavhAiHU5/s9eyne/YsSI837iS5l9rRCe+qNf33U4x6F6qdDGLN6qrIYPuemnnXrIXS2Rk/W1iec0gUHjcWoZEj+p4DS5b3DMaOfMt1x6knRzsdrn0H4Nq2Pem/a8dpfbFaUzM86zdEYyPPF0/fTFHvvp/BTFECIjAFwqxLfyBERCWEnYoyIaQXavtCxF2Lk8j8xMQqNtm2dmPTbTuQ2ExyyjI2PH4cWi77FGTP9zBU/fsNeJd/lPxT70FSFzux4dFHoOWaKyK3xACofPMd+JatTH6Cn6O6tFx1GQJHTYMUAmp7B+r+8lh0wHWq/Uq/D02fuw7hmPvmTbcLZs94j5rnX4Lnkw32ngPJZPN/zmHPl7aLzo9MxaooUEIh1D30aN9sSbnIwW5dhEDzdVcjOHkSJCKdgbq/Ph654gig+/hj0XbmaTA9kX/XusefgmvHLst/18AxR6Pt/HNger0AgJp/PAv35m2RznKFH003XAe954qFd/1GVL74SvzsUIlMH9SOhalrQERUgjj7U5mQohPhIQ8A0AGR4oQnVzOuxC7nKzaf+WcQ69q2HfWPPA7Rc4LQtvB0dJ10YuqZfwZBXQYUa0pojY2QQsAYMgRQUs2Og5zVRenshNLeDqOuFtLjtZW/2twMEQpDHzoEAKAdOgzpdsGoqytODUvg+SK6u6G2tsKorob0+x1RF6WjA64dO1H7939CAjAbhqBz7hx0zzse0HVojU0w/b7I2IpU7eg6tMbDML3eyBiNVM+XlK8RAiI8Bq7Wa5McMBEBcP7sT09UA3qBZ3/SqoCrOPsT5ZnpXY2UHQogtzOuiAzXZRNrN6ccx1a++W70E3XT70PXyfMtZy5KulymdRlQrCKgDxsKS3msi1lZERkgno7Ffo36+NtT9BHDYKlQNSyB54v0+6D7fSmC8pBDmrqYVZXwbN0OKAoU04Q4fBiBWTMjZywuF/SRw2EpcV+aBn1E8tjE50vynCSkeydM9RAUI8n/CSKiEsTbn8qEVFuQ+p2Z7NION/VNO2ljBiEichb1cBNEzwcE0u2OXEUpNrWl2BkQEeUUz5LKhDDqkPlNy5QJvWFI3+xPrW2p75MmIscyGoZEZyEToRBEV1eRM+p9zSYiKh/sVJQJJXA0IDUUbPanxMGR+Yi1m1OOY7tOmhe9UqF0d8OXycw/hahLCdXQctkJsYlYF2tlUpeuE45DdBYyCVS8/yHyPQtZ8lgBERoFKcKQCEPCgKnth1QbIfnBEBGVMA7ULiOG+2MY1S8AoufEt/f4YgcxJi6nWlcusVls61u6AlWvvgYlFIbUNLRc9ikEp09F9AuunHasua5LDmpYcrGZbMu65LEuMjIdrdYz2Nk0AaH0rQ/rgEuLWSfi/z/G7lfXAS0S6/lkA6qfewFqVzekANrOPRvd8+Zm/3/Zdl2UvtdoU+vJP9yTbz209oug6EeAaLBz/EDtJ4s0UPtK5w7UZqeiTEjRjXD9g4AIJB9akeqNMJ+xhWonT7EiFIJr915IVUV49ChAVZJvayeHxBORMq6hrVjWJf1ymdZFPXgI9Q89AhEKIzx6FLqPnYXA0T2Dqg0TtY89CfeWrTCGDUVo9Ci0n39OZF3veKfe/UqJmr//E55162EMbUD4iJFou+g8QEq49uyDkCZCo0cBPdNFO6cuAoAGV9PNEKaDT6aICoCdCgsO71Tw9qcyYXo/Tt2hAPqvK1SsE3OyESvdboQmjEd43Jj4DkW2OYgM12XbTqnFsi7pl8u0LlWvvwWlOwBF1+HasxeB6dPQeyXBs2kzvJu2QDElXPsPIjyq59P82AkUevbj3r4DvrWfQJESroOHIjM89cz2FB4/FqEjx8d3KIpwrNaxEoAOw/tRiiAiImdip6JMSLUR/OckolKm7T8YHcdk1lTHnfhrBw9Bxny/iT5iePz3QcTu52Bj9C4kANCHDUsa6zwSUmssdhJERLbxLLRMCLMaiHsbJSIqLUZdbXTGNaWjM27GNaO2BsLse41Tm1uSzshm1FTHXRBQWpLHOo+AMKqLnQQRkW3sVJQJJXA0ACV1vyJxXTFiC9VOqcUWqp1Siy1UO6UWW6h2ChzbNW9u34xrgQC8q9dGZ1wLTJ8Kw++Ldjr8S5cnvfoQnDwRRnVVX+yyFZHbpFINpHNQXdTA7BRBRETOxE5FmRBmNdT2cxH3TyqR+YmJndje+IHE2t0uH/mXQ13sxLIu1rGsi3VsEesSnDYF7WedGf1OieoXX4F76/bISpcLzdd9BmZlJQDAs20Hql54Of4KRG8bqoqm666BUVMDAHDv2YfqZ56PzCqVGJvD/C1j+3VkZPJYCKgdCyCMITaSIyJyBs7+VCakCCJc9ztA6RjYDDF2YtMmk8PYgeaYq2MdbHWBjdiBtpnqWNJhXQYWW2J1EV2d8GzdDqkoCE2cAOn19MUaJtzbtkPp7EL4iBEwhjZYH3Nv7I6dUNrboY8YDn34sPj1ea6Ld+kKVL38bxjDh0IfNhTtC86IfJu3qiTfj/RGZn+SDvjWb6IicvzsT38v0uxPVzh39iet2AlQbpiedfEdCqBws8kkymVsPo6HdcnvftLFJmJdrA3iusiKCgSOngFLqoLQpAnW66xiJ4xPvj6PdVE6OlDzwssQUkLdsw/6sKGQlRWIfi9G0v0EYXpXQe2enzxvIiIH4u1PZUJq+8F/TiIiZ9AOHIqODwEAfeTI6PiQ1CRM1/78JUZElCc8Cy0XZgXs3SRMRET5YlZWxC0rHR3xVymSUiDMivRhREQOw05FmVADR0f+SNWvSDFeMK1Sj2VdrGNZF+tY1sU6lnWxjrXYVh8+DKGRI6IzUPlWrYkM2k43mE+YUAJHQfJDIiIqMexUlAlh1kFtPweRG5ItApK9PyWLTbcPmfCTykBic5VnJvuwE5vJPlgX61jWxTqWdbEfaxVjN3YgbZtmT6fAYgOLh1qvvAxGbQ0AQGlrR+1Tz0Rmq7LqWPTu1nRDr3sE4SG/geF/FxKZ3DJFRAUnivTjYByoXSYkwjD8SwFI69lZks1aMtDYxCe21WPZxibmlNipyTQWMeusYq32l01dEvNyal1i11nFWu3PyXVJbCubuqSKtYphXaxjsn19SfWak+zxHNdFPdSI2sefgnS7ER47GoEpkxA+cnzy/cYcq1Ffi8YvfwmeTVugNjXBqKuNrOi9DcqqLghFfikBGP53YSqtcHVcaHFARETOwk5FmTA9nwBaU/yDVm+sVm/iTo1NtWwnNpc5OSE2V3VJF2snp1KLZV2sY1mXfn/X/PNf0A43RWZxam9H+zkLkX4GpxiqguC0yRaNZ5aT9K6B7DoZwqy13gcRkUPw9qcyIV17AMl/TiKinDElXHv2RmdxCo86IvLN3IUkANO1p7BtEhENAM9Cy4X0If0NxURElDEBSI87uqh0B4qThukrSrtERHY4slOxdu1aLFq0CHPnzsUpp5yCH/3oRwiFIveZrlq1CldeeSXmzJmDBQsW4Mknnyxyts6gBGYAwmanwk54qcVmwwn5O7EudjjhWFmX7GMLxQnHajkOS6B7zjHRGZxcO3dBaWnN8PsmckAKwKyACI8rTHtElDlRpB8Hc9yYCtM08cUvfhE333wz/vKXv+DgwYO44YYbUFdXh+uuuw4333wz7rjjDlx99dVYunQpbrvtNkydOhWzZs0qduo5ISFhej+C4VsKqG2AXgdhVkG69gHQIcIToHaeAsUYGon1fAzD/yGgtgCGH1C6+nYmojvtW04264lVbOyy02J716eKTYZ1sZZtDZPlVCqxybAu1gZJXTrOPB2uPXvh3rUHEAK1T/wDzdd/BtLriQmMkbYuEtGpZVW1f7tx2ylQWz8NATXZzoiIHMNxnYrW1lYcOnQIpmlC9tzHqigKfD4fXnnlFdTW1mLRokUAgPnz5+Piiy/Go48+WjadCqPiLZj+D/pmFNEOQYpD0fXSvRG6eytczZ+F6dkIo+LtvlhFj3+PS3yDRMxyihlL4h5LdYKaKhbIbNvEN1ORIjabnEqpLlaxxarLQGPt5ICY9fmsi91YFDkWMetZl/zXJclrkfS40XTD9XBv2Qr3jl2RzkQwCHi99vKXEv73FsO7Zi1CUybBqKtF96yZgKICasJNA9H9GpCeTYA+BkRETue425/q6upwww034H/+539w9NFH4/TTT8f48eNxww03YNOmTZgyZUpc/KRJk7B+/foiZZtbUumA6fswstD7htTvxFcC0BH2vw3D/16a2CSPDSRW2IzNdNvE2Hzln6/YfNTFKjZf+ecrlnXJPpZ1sY7NV11SxaoKQlMmoePsM9F56kmQtTW28/ds2Ijq196E++AhVL67GFLTIoO+rToUMb9N31JIpR1ERE7nuE6FaZrwer34/ve/j48++gj/+te/sGXLFtx9993o7OyEzxc/YM3r9aKrqyvJ3kqL1HYjo3ERQgLubYAw8p8UERFlzb1tO2TMzFGhiRP6bn9KRUiYrl15zIyIKDcc16l49dVX8fLLL+Paa6+F2+3G5MmTcdttt+Fvf/sbfD4fAoH42TcCgQAqKiqKlG2uuTIPlTZiiYioqKTLHbcseiYfyYTg6z2R84gi/TiY4zoV+/bti8701EvTNLhcLkyZMgWbNm2KW7d582ZMnpzki4VKjAiNA0xv+oGPUkAEjgGMSkA68BmWycDNwYh1sWanLoOphqyLtRKtS+DooyBiZo3yrVqTfhYpCcD0QISOzG9yREQ54LhOxSmnnIJDhw7hgQcegGEY2LVrF+6//35cfPHFOPvss9HY2IiHHnoI4XAYH3zwAZ577jlcfvnlxU47JwQ0aG2fAqD0DOxD35ti3N8eSO/HgFTj10XXi/g3U6v9pFtOXJf448T9Zrot6+KsGhYrNh/HaieWdSn+sRawhvrw4Wg/8/TIoqLA//4SuHbvSdOOAEwfwvX/h1DV0whX/guh+gcQqvs/hCufR7j6SYTq70W49i8wPGsh4xIgIiosIXunWHKQ999/H7/+9a+xdetWVFVV4VOf+hRuu+02uN1urFmzBnfddRc2btyI+vp63Hrrrbjssstst9HY2A7nHTlg+D6AUflm5A1FIP5NKna592+L3yI0FjD9kO7NgNBTxvZrp/fCR7p2nBJroy6W7bAu2dfFqk3WhXVhXSzXaXv3wbf6Y4hgEMFxYxE8egagqVnWRQBCQuk6EVrnGSAqB0IADQ1VxU4juaerAb3AkyhoVcClbYVt0wZHdioKwYmdCim6ER7yW2Q9ANt0QQSnQHrXwfYX4hERUWmSgKvpFgizptiZEGWNnQoLDu9UOO72p8FMunZk36EAACUcmducHQoiosFDAKZ7e7GzIBocRJF+HIydCkfJ5bem8p+WiGjQkXztJ6Li4KuPg0Rmf3LH31trlwRg+qEEZsCRM0MREVF+SBVKaFKxsyCiQYqdCgcRcEPtOD+y0NuxiP1t9dMvVgBGJUzXHkB60sTa2e8AYlNtaye2WPkXsi52YoudP+vCujgh1mpbO7GZtGNKQMoCH6tM+J1BrNmTo14PveYJ6BX/hlRaQERUSFqxE6AEItR/9pHev1PNohLdXgLaQetYq/hk+8pFbKbr08UmO1Y7dbHal53YVPnZjbVaP5Aasi4Dq0smsVbrWRfr9eVWFynhXrsenh07EBo/DqbPh/DoIwBNAxQlP3URgNLegYp33oNeWwtjaAOM6ioYw4dZ7kt0daLinfdhVlZCHz4s8nvkcEA9BKkAUtsP07cKWssiKPqIFEkQEeUOOxUOIkUQRuWrkTeP3juXEu9gEhZ/p/udaj/J2im12Gzrkkms3ZycEMu6DDyWdcnst93YfOSUw1jvmnWofeoZAEDF0hVovvJSQFUjHYpU+82mLqaJIb97CEpbGwSA8MjhOPzFG61jpcSQPz0C9XAThJTQh9Sj8fYvRdb13nsgJCB1GBX/htJ6XYoDJyLKHd7+5CCR2Z/01G9+RESUN571GyFF5EVYAghOmxLpVOSRduAQ1J4OBQAEpk4BDOuZANXmFmiNhyF6bn0KTp1s/c3cQkK6d0OKQJ6yJiKKxysVjmLnPiEiIioPCa/9IsUnS077giWiwUqg8B8CO/xDZ16pcBARHgfI/H4iRkREyQWnTI5eBRAAPBs3J71qkCv68GEwqiqjXQvvho1Jr44Y9XXQ6+uisZ6Nm/tuzYolBUR4FIT05iVnIqJE7FQUgYSE6d4AvepZhKufhuFdBQkdQnqhdpzVG9T3O3EWkUxmFLGKzWRbO7GWs5HYiDVl/o7VTqzT6pLPY7UTW0p1KWQNC1oXmd2xlm1d8hcbmDkdwfHjoqur/v0GRCjUd4tRPuqiKGi78HxACEhFgWvvfniXreyJSXgOCIG2i84HFAVSCGiNh+F/74P+saZE5avvouLlG6Bt+xsM3zsIVz8FvfIlGJ410CtfRrj6KRj+dyGVDhARZUtIOTivpTY2thflKrKECb3qGUjvBkS+RyKShNBHQGu5FtK9FXr1PxO2EoDREAlVWwERilsFieTLSLEum9jY5UxjdR2+lR9Bqhr0USMhhYAxbCjirucVKv98xQ6kLnZj85l/vmILUZdSrGGS/EVzC3xrPoZ+xBEwqipg+nyQNdWDvi4Fi9V1+Fd+BO+adYCuI3D0DHQdfyyguTJrBynWpYjV9u1HxQdLoB04CL2uFh1nngZj2LD+geEQql98Bb6P1gBSQq+tRevVl0EfMQKQEp6Nm1D1ymtQm1uhNwxB0+cWQXp9PbdViZ6B3L2JCEC6IzNFGRZtERWJEEBDQ1Wx00jumWpAby9sm1oVcElbYdu0gZ2KAjPd66HX/LP/CimgdM2H6VuG6LSyMetgVkMJjYfpXR15QyhBtX95DJ6t2yCkhOn14uA37ohc4k91/zDRIKO0tmHor++N3oLTNfdYtF14Lv+fUNSQB/4A7cBBCClh1FTj0FduA5TI88P/4TJUvfhK9C3k8GcXITxujPUtUr2kgNBHwtXyH/lPnihD7FRYcHinggO1C8zwfhLpJPTrGEiY3jWAEuq/kZCA2grTu65kOxQiGIRny9boG11wysTIvO9EFMe7fgNiP8runjm9eMmQ46jNLXDtPxBdDkyfiugVBwDej9dF15k+H8JHjku/UyEhXXshlXYI08EncUROwoHa/XBMRcEZsLz2LXrXpdu2RJlm/MWXPE/RSFSydANx7xyaxqsU1Cdh0Hi/11Jd7/tKC9XuW3wJv8cQUdGxU1FgSmiC9QopIIKTAWk1iwcA0wsRmtBzH2zpkT4fwiOGR+d/92zdDk6NSNRfaNKE6K1PAODZtMX6ewhoUDLq62FUV0c/mvJs2Rp3a1NoyqTo66zS0Qn10KH0zx8JwKgFzJq85ExEgwM7FXkiEYbhXQW98iXo/rdhunbC8L8HU9sLmJXxnQMpAOkCRBgiPKp3B32/BaB2LIDaeToArW/bVDOK9P7uf5dVZrGZzFhiM7b93LMis5sIBWprm/WMJXb2m3hcmdTDqh07sXmoS85jYTPWqh27Ncw6/wE+B8qwLvrwYeiac0x0tX/JMiitbfEnhsV8buWiLnZiS+X/XKHqogi0nX925CEh4Np/EN6VqyP/h6RE57y5kU6HEBAAql/6d2T7frNX9fzR87AID4FR+SpM9xbIfkkREaXHgdp5IJV2hGsfBZQWRPptsZ8S9cy4kTgLSWTDSGzi7CDSBa3lOgizBuGavwCuw/HbxsZa7jcHsYlSzaKSIlbbuw+V774P167dMCoq0H7OQoQnjO9LIFVO6XJIlb/D62I7NjYnO7FOq0t3N7wbN0fm6a+sgJQmUJ1ilqN0+43NyU5sMeqSKlZK+JavhH/ZCiidnQhOPBIdZy2AWVmZfr/pcrIT67S6ZJJ/If7POaAurh07UfHuYrj274deU4P2886CPno0AEDp7ETFe4vhXbceEgJdJ52AruPmAKoGBALwbtwEfWgDjKoqCNOAWVODyIdVAhAmRHAStLZLIcDbVKl4HD9Q+9kiDdT+lHMHarNTkQfhqqchPRvjB1VLDHyAjRSAUQ8ldCRM33LnD9a2c6zZ1KXUsC5xRGcnhv3inuh4m+5ZM9F62adSbzQI6hLF54s11sVaBscqugMY9ou7o+MuAkdNQ8tVl1nuS+04B2rg2HxkSpQRx3cqnitSp+Ji53YqePtTjkmE+3cogOze2IQEtMOlM52snWMdLG/4AOuSwPvJhrgB/N3HzEx/7/cgqEsUny/WWBdrGRyrZ8MmiJiB3N2zZiT9P2d61+QuNyIaFNipyDWh5+/EX+j52S9REYhgCLGzGkmPJ/Vc+kSUFSUYjL+z1uOF5cxiApAiWLC8iKg88B0816QX0Iekvld3IEw3RHgMSnX2J6JEofHj4mY5cm/dzlmOiPIoNH5s3AUN97btsLwPWAoooSMLlRYRlQl2KnJMQEDrPDOyEDfDE/rP5mG1LkmsCI8BjBr0fITUf/tM9ptNbLL8EmNT/Z1uX7mMRYFiM62LVWyqdgZBXfRRIxGYPjW6yr9kGURXN2AkmeUocblM65JRLOtif3kw16XnMX34MHQfPSO62r9sBZSOjoT/cwKQHqjdJ4CIUhBF+nEwDtTOE9O1DUbFu5DaPkSmiw0h+ipvNeNH7HKs6GwgPX+km8klcdvE2GSzi9jNyQmxiceVLDZdO1b7G2hOTogtpboYBireXQz/8hVQOrsQmDIJbRecC1lVld1+rWIzrUsmseX0fLETm6u62Ikth7okU4y6mCYq3vsA/mXLoXREZhZru+BimLU1AGRk5qeu0yCM+gwOgCh/HD9Q+19FGqh9kXMHarNTkWcSOsJDfguIYOZjLSQK0xu1006pxWbDCfmzLs6NLVQ7pRZbqHZKLbZQ7WQTKwVEaCJcbVdkuAOi/GOnwoLDOxW8/SnPpHsroAQy71AAhbu8ZaedUovNhhPyZ12cG1uodkottlDtlFpsodrJJlZISPdmSNFlYydERPHYqcgzqXQXOwUiIqLUBCJX1ImIBkgrdgLlToSPKHYKREREqZk+wKwudhZEpUOgcHcKxLbpYLxSkWeKMRQiMBX9ZoJKlDhzRzJWs3yk2k+6faWKTdaOnfzzFWsVN9C62K2hk+tilSvrwrqwLqxLmli18xQIqBYbEhFlhp2KAlC6TgBkzEWh3tk9el/YJVL3PmNj7fSMY2PTdTDiBu2lySl2dhI7scgwttB1sRPr9LokxrIu1rGsi3VsJnXJ1WvRYKiLlFAbGyNTJQNAMATImOlb81kXM+Y9R28AzMqebVXAGNb3nmRWQ20/H2rguDQHSESUGm9/yjMJE0bN0+j3bdgiyd/JZHvJK/HNN1UbdvKxm3u6eCfWxU5brEvqGNbFOqaYdbFbE6e+FmUSX+C61P3xL3Dv2QNICX3IEBy+5SZYfoN17/Y5rYsB0T0LWsfZEHBBQgIiCEgXBFRImIAIAdIDkfU/KhERr1TknXRvA9QOZDX7U6rX+3zFptvWCbGsi3Us62Idy7pkH8saWsdabKsdPATPrt0QpoSQQHD61EiHIlmnIsP9Zr6thPSuja4QEBDSi95bnASUnmV2KIgoN9ipyDOpFHgOYyIiKjqlvSNu2aiuQkG+HCmWMMAZnYioUNipyDOhDy92CkREVGD6sKGQMVclXPsOAEoB33IlALMCkL7CtUlEjnL48GHceuutmDt3LubNm4e77roLuq5bxi5ZsgRXXnkl5syZg9NPPx0PPvig7fbYqcgzRR8JERqHuNmfgNT3zqaaDSRxeaDrss0hlWxinVaXbGqYiHWxxrrYj81m21weT672UwqvRTaPx6yqRPecY6IdC++atVDa2wHTTLldymU7sQJQO0+C4Ns8UX6IIv3YcOedd8Lv9+Odd97B3//+dyxevBgPPfRQv7gtW7bg5ptvxrXXXosVK1bgwQcfxB//+Ee89NJLttrjq00BqB0LAOlKHhA7y0g6dq+e29nvQGPTbWcnNpuc7MjHiQnrkjyOdbGOG+h+nVCXbPLP137tPrcybScYhAjEfJGpYWTURtsF56Jr3vGQmgYlHEb9n/8K9XBT8pxSscpXqn3rzKq+9aYXascCKIFjM9w5EZWbHTt2YMmSJfjmN78Jn8+HMWPG4NZbb8Wjjz7aL/avf/0rFi5ciEsvvRRCCEybNg2PPfYYjjvO3qxwnP0pzyQk9Op/AiKcPMiJAw9zFVuodkottlDtlFpsYrwTcnJCbGK8E3JyQmxifB7a8S5biep/vQihCOg1NWi98lLow4dltl9NRfu5Z6FjwWlQ2jtgVlZAuj05zNeA2nIZFH0shPRCim5ABACzGvzOCaLBbdOmTaitrcXw4X234U+cOBF79+5FW1sbqqv7vuxy9erVOOmkk/C1r30N7733Hurr63HDDTfg6quvttUmOxV5Jl07AK25/4pizKJSrFgn5uSEWCfm5LTYQrVTarGFaqfUYvPQTtVb70TuOjAl4PFAP2Jk+m0T1km3G8aQ+rTp2s5XKpCerRDhKZGHpA8cQ0FU/jo64ieCcLvdcLvdcY91dnbC54t/Pehd7urqiutUtLa24uGHH8avfvUr/OxnP8PKlSvxxS9+ETU1NTjvvPMyzou3P+WZVFuKnQIREQ2EKaG2d0TP7Y262mJm058w+R5DNAiddtppOO6446I/VoOq/X4/uru74x7rXa6oqIh73O12Y+HChTjjjDOgaRqOP/54XHLJJXjxxRdt5cUrFXkm9IZip0BERAOhCOj1dVCbmiEAaIcai51RPCn4HkNULAMYOJ2TNgG8/fbbcQ8nXqUAgMmTJ6OlpQWNjY1oaIi8TmzZsgUjRoxAVVVVXOzEiRMRCoXiHjMMA9LmNNi8UpFnQh8FER6BnM7+VGqxxZgFphRiWZf0y6yL9TLrYr2ch7p0njw/et6gNR6Ge/OW+BmcBrjf7GMFAAUqB2MTDTqVlZVxP1adivHjx+O4447Dj3/8Y3R0dGDXrl247777cMUVV/SLveaaa/Daa6/hmWeegZQSS5cuxXPPPYdLLrnEVl7sVOSZgIDWeiVEaHzfg1ZvHMnePHIdm2rbfMVm8pjV46yL9eOsi/XjrIv146yL9eMZHmv3sceg/awzYboiM/jV/v2fcG3bgegX2eWyLno4YcpZmTwWgNJ5CoSRwVgNIhqU7r77bui6joULF+Kqq67CqaeeiltvvRUAMGfOHDz77LMAgPnz5+O+++7Dww8/jOOOOw7f+c538K1vfQsLFy601Z6Qdq9tlInGxvaCf7mpVFpguvbCqPoXADP5ZTOJ+HWJy06IjV3OV2w+889XLOuSfpl1sV5mXQYWW6AaimAI2oGDML0eGA0NgCKSb2sn/x4Vb7yFirffh/T7EB42DK1XXALp8yX/wjwJAApczTdCGEOSNE5UuoQAGhqq0gcWy4vVgN5e2Da1KuD8tsK2aQOvVBSQMGshtX0ApPNnUUkXKzJcl02s3ZycEMu6pF9mXayXWZeBxRaohtLjRnjsaBjDhsZ3KKy2tbFfAFA6OlH59vtQpITa2QWzphrS70/9DdwCACQM74oUjRMRFQ4HaheYVA8DYlBeHCIiIgtqcwtEzKVzo2FI5DYoNc13TQgJqTls8DjRYCGQ+gOFfLXpYLxSUWDCrEW/QdtERDRoGbU1ccMl1OaW1FcpeknBMRVE5BjsVBSY0j0n8keqixWZDvIrlViZ4bpC5uSEWNYl/TLrYr3Mulgvl2hdzKpKBI6aBikiHzh5P14LEQj0n2XKYj/R9xQioiJjp6LAFGMolM7TEXcNSyL1m1Isp8Ym29bqcSfmX4zYTB7LRTulFpvJY7lop9RiM3ksF+2UWmwmj+WinTzHtn3qQgQnTQAAKMEQ6v/yNyidXSn2K6B0ngrFGJZhY0RE+cUxFQUmlVaYFe8h7t3Bzt1QApm/Ydndbzaxyba3MyAz030mi3ViXTKNdWJOToh1Yk5OiHViTk6IdWJOGcZKrwct114NtbER2qFGGLW1MGO/9bbffiXMisWQwaMit9USERUZOxUFZvhWAtALN9uJnf0WIjaf7bAu5RdbqHZKLbZQ7ZRabKHayWOsMbQBxtAMviVbAJA6DN9KaJ1npo8notziQO1+ePtTgUn1IGd/IiKi7AkJqR0odhZERADYqSg4YVaBsz8REVHWpIAwHPzlYEQ0qLBTUWBKYDbS3vxfIjOWZLxtvmJZF+tY1sU6lnWxjmVdrGNLoS5C9rynEBEVHzsVBaboI6F2nRr/ZpGvN8l87ttO/vmKTbZdrrEu2e+bdUkfy7pYx7Iu1rFSgdp+LhR9VK4zIiIaEA7ULjApOmH4lsU/2DtzUaZ3RWUaa/fNKtVA53SxyXKyE5vYbr7rkmlsqdUl8fFyrUs2NbTaPhnWxZrduqRqM1X7TqiLndeiPNdF6ZoNoY+CEpoIIf0ZNEJEecGB2v3wSkWBmb6VgAiknhEk3bp8xaaSLjbZejuxdttlXdLHlnNdsqkh62Itn3VJ126q/Ipdl3SxhaqLBEz3TqjBo9mhICLHYaeiwExtLzj7ExER2SYAaE2QCBc7EyKiftipKDAh/eDsT0RENCDSBUAtdhZERP2wU1FgSmBW+isV+ZqxJJftlFNsodoptdhCtVNqsYVqp9RiC9VOqcXmsh0poASOhuBbNxE5EF+ZCkwJj4XacXrkzUKK/rORyBR/J4tFBusS92cn1urvdMsDjU13rAOtS6pY1qV062InlnVhXezE5q0uEpASMMzUsVZ/G0Ogdp4OIiIn4uxPRaB2z4cSnA7TswFSbYTpXRNZ0XtXVO8biNWMI7HrYh8bSGyyNhNjrf62EyuQfNtk2yWL7V0ut7pkEpssf6fVJdsa2q1LqudW73I+61Ko5xbrkvzvQtdFwv6xShOVr78Nz5p1CE2dBL2+Ht3HzQYUJfKTmL9Vm2ojpLYHIjwBREROwysVRSLMWqjd8yC1RgAi+YwgVjOHJJtNxG5ssjYTY63+thObKie7seVaFzuxTq9LtjVM5PS62IllXaxjS60uA6ih55MNqHznfbhaWlDx4TJIvw8QIr5DEbu/JK+VRsVbICIHEEX6cTB2KopIwoB07QNngyIiKm/u7TshYzoQoSPHA6rNAdcCkK4D4OxPRORE7FQUlQJIzuJBRFTupNeD2EEXIhiMjK2wvSMVnP2JiJyInYoiEhAQgZkoiSlm7bz35SvWiVgXa6xL9lgXayVal+6jZ0CYfQn5Plptv1MhBUTgKHD2JyJyIr4yFZnWeQZgNEQWpNI3ADD2Bwl/Jy4PNDZxu1ztN1fbJsayLqyLndhC1cVObC5rONBty70uA31u2anLAGpoDB2KtnPOiiwqCvwfLIV7247IesPomw0qVRtmDbTOM0FE5ESc/anIhPTB1XwDTM9GSNcuSPUQpHtX5E1EoO/NpPdiRuIyYpbtxib+HmisTIhN3FamiE38bSfWzrHaiS3XuljlkCzW7rHaic3lsTqhLqm2TVyXTV3sxLIu+a3LAF+LuuafgODkCfCt/hhqaxtqHvs7wuPHIjRxAvT6OoSmTAKkGRm8bdWO0gqpNkHofhCRAyT+Px/keKXCAQRUqMHpUDtPhnTt6X0w/jdilq0eG2isyEGssFiXaayd/Vr9topNlr+d2HKsSy5qmI+6FCo2MVfWhXUpQl2MoQ3oWHgGpKJAMXR4N21B1auvITz6CEDKvtmgkrRjVL4BIiInYqfCQaRrFyDM9IFERFTSPJu2RMdY6MOHQfr9gEjsjSQQEtK1BxJ6ATIkIrKHnQpH4d1oRESDgXTFvN6HbXQSpIL+l0KIiIqPnQoHEaFxgOmJvx+ZiIjKTuDoGZA9Vya0Q41QGxsBM82VaikgglMgOKUsUfGJIv04GDsVDiLggtZ+IQABSNE3sBDo+zvxJ3FdNrHIUWyqdvOVQynUpRixrEvha+i0/FkXR9al8+T5kdueAEAI1DzzPISuA6aMjzVlz2MSMFVI7SDCtQ/B8H0IiRCIiJxCSDmQb98pfY2N7QP63qFCkGoTDO8qSLUFUtsHKG2RFQJ9bzS9fyf+BpLHJq6z2jZfsbnIP1+xuT5W1iU3dYGNWNaFdYGNWKfUJazD9/E6uLdshdQ0dM+aifCR4wAA7s3b4PtoFUJHjoNZWYnQ2NGQXm/fzFAQEPoIaC3XQsCVpBBEpUsIoKGhqthpJPfvasBoL2ybahVwVlth27SBnQoHMzyfwKh+pthpEBFRAbl278WQ3z8UXW5feAY6Tz6xb2aoXhJQO86DGphd0PyICoGdCgsO71Tw9icHk+7NKIlv2yYiopzxbNwEqfS99gemT+nfoehhujcWKi0iopQ43ZCjsUNBRDToCBF/65SZ6rI6PxskKgqBwp+mOfy0kK9GDqYEpwDC4fdoERFRTgWmT4WIuT/Xu2699cxQoud9gojIAdipcDARmgzR+4YROxtU73tNJr/tbGP14+TYfB9jvvfPurAurEsZ1UX2/GS/f33EcHTOOz6yKAQqFi+B2ni43/5FaCyU4AwQETkBb39yMAEBre3TMD3rYHrWAUoAUj0EiHDfG1Ay6danbzz7fRSj3Vzm7KQasi752RfrYo11sZa4rZTQtu+Ad8tWhMaOhenzQm8YAni8SPvt2GnyaD/3LISOHAffytVQurvgXbseXfOPh/R4IwECkJKfCxKRcwx49qd169Zh9+7dOOOMM9De3o4hQ4bkOre8KoXZnxKFK5+H9H7MW6KIiBzAvXkr6h95LLrcetF56D52dtJB1fmgtl0INXh0wdojKhTHz/70WpFmf1pYRrM/HT58GNdccw2uuuoqfOtb38KuXbtw1llnYeXKlfnIj2JIz3p2KIiIHMK7bj1kTAciMGN6QTsUkIDp2VC49oiojyjSj4PZfvX78Y9/jClTpmDp0qXQNA0TJ07EzTffjJ/97Gf5yI9isUNBROQciZe7RTFuR7IYwE1EVAS2XwE/+OADfOc734HP54PouWf0pptuwubNm3OeHMUTwYng91YQETlDcPJEiJhZmTwbNgJGYU/yldDkgrZHRJSM7U6Fy+VCIBAAAPQOx+js7ERFRUVuM6N+tK7TAOnu61hkOrNI7N+5jrXaNtvYZDllE5ssp3zVJR81ZF2yO1bWJb91sRNbJnUJTp2C4JHjo6sr33wHIhjsm/4133UxK2G6dkCveANSPQwiomKy3alYsGABvvnNb2L79u0QQuDw4cP4r//6L5x++un5yI9imX7A9AGQ8W+kQPJlkfB3rmOlRWzi71SxVvtFHmKT5Z+4n3zG2qmLVSySxFrlkGmsnfzzFev0uvTex8q6xC8n1iVVLFCedVEUNF97FdrPPQv6iOGAEPB/uBQI64Wpi9IB6dkA07cE4brfw/CsAxFRsdie/amzsxPf+c538Morr0R2IAROP/10/PznP0dVlYNH6Scoxdmf9MpXYHpXOn9sReybMBER5Z8EAA2uw1+GkN5iZ0OUNcfP/vR6kWZ/WuDc2Z9sf09FRUUF7r77bjQ1NWH37t0YMWIEhg0blo/cKIHpWev8DgXADgURUaEJANBhurdA5RfiEVERZNypWLp0qeXjO3bswI4dOwAAxx9/fG6yImtCL3YGRETkVBKRL0clovwrxhSvDv/QNuNOxfXXXw8A0RmfAKCmpgbt7e0wTRO1tbVYvHhx7jOkKBEaB+neVhpXK4iIqOCU0Lhip0BEg1TGnYr169cDAP7whz9g48aN+N73voeqqip0dXXhpz/9KWpqavKWJEWoXadBd+8ApBnfsegdw5A4liFx8F+2sbG/Y2OtBhEmLmcamyynZLnk61jtxLIu1rGsi3VsIeqSKpZ1sY4ttbokxgJQAnMgzDoQERWD7YHaJ510El5//XV4vX0DwYLBIE477TR8+OGHOU8wX0pxoLZECOHaRwDXwf6zg1hJ90aby9jY5YHGJrZbqFjWJXlOrEvp1aVYNcxnXUr5uVWwugiorVdADU9MsgOi0uL4gdpvFGmg9pnOHahte0pZ0zRx+HD8fNi7d++Gqqo5S4qsGRXvAdqhyILI4KdX7PJAYxPXpVseSGy6ba1yzkVsuu0Gmm+qbe3E2s0/VSzrUv51sRNbKnVx2nPLkXWRMKqfgUQIRETFYHv2p0suuQQ33ngjbrrpJowcORK7du3C73//e1xzzTX5yI9imN7VKNp4CpE+JO+xTuSEujixhk44VtYl+9hCccKxlkNdRAimZwvU4PR8ZUREvRI7/IVq08Fsdyq++c1vwu/34/7778eBAwcwcuRIXHXVVfjCF76Qj/wolggWOwMiInIqCUAEip0FEQ1StsdUlItSHFMRrvkrpGtX8a5WEBGRo2lNN0IxhhY7DaKsOX5MxZtFGlNxhnPHVNi+UvHb3/426bovf/nLWSVDqaldp0Kv+SsgBXIy+1PsulSxqf7OZ6xVjnZie5dZl+LXxU4s65I+fzuxrIt1bDnWRR8B6d4MU++ECI+DcPq9EkRUVmx3KhJneGppacGWLVtw3nnn5SwpsibCRwDhIwD3nsgDiW8yiW9aiW9GVrEyg1jErEsXKy1iE+Ostk/MP7H9gcQ6vS6JudqpIRJiY9exLvmpS6pY1sU6lnWxjrXMX0LdfwDS54NZWwMEAoDbDUDJvIbafhjaAUBIiPAR0FqvgpBeEBEVgu1OxV/+8pd+jz3zzDMlNZ1sqTL9iwHX3r4HEt/4Yh9PtZzP2N6cRJq42NhM2s821iq+2HWxE8u6FL8udmJZF+vYcq1LDv7P1f7pUXh27oKQEnplJRrvvBUQYgA1jDQotX3QK16Dq+PCFBsQ0YAlfohQqDYdzPaUslYuueQSvPbaa7nYFaVgeFei33gKO284iQZTrBNzckKsE3NyQmyuOlbpti21WNYl+1iLbdXmFnh37IToGegXnDkdUJRIp2LA7UhI71pIhG0kS0Q0cDnpVCxZsgR+vz8Xu6JUlO5iZ0BERDmmdHbGLZuVFcjJTCLCBAS/t4KICsP27U8LFiyAiPn0JBwOo7GxEbfccktOE6P+hD4CUtvX/2oFERGVLL2hAVJTIXQDAODasw/I9gtlJQCzEpD8wI+ICsN2p+L222+PW1YUBRMnTsTMmTNzlhRZU7tOhl79ZPLZQRKXrWYaSRWb6kr7QGNzua9cHqud2EIcSy73Zed4clWXfNaQdWFdMj2WXO6rgHWRXg86589DxTvvQwDwbNgE9eAhGA1DIrdBDfBYROhImJ51UEJHQrBzQUR5ZrtT0dTUhBtvvLHf47/+9a9x55135iInSkKEjoQIjYf0bM/DzpH5m7Gd2MT1uYy1yivX8lWXfNYw2Xa5lJi/nVjWxTqWdbGOHSR16TjzdEjNhYrFH0IJBFD/yONo/swV0EcMR3TAts26SN8aGL41MKQCtWMB1MDcPBwE0SAlBGyNe8pVmw6WUaeiqakJW7ZsAQDcc889OOaYYxD7nXnt7e3485//zE5Fnpm+pZDu7ZlvYPe5N9AT+HTb2YlNtl2xYrPZN+tiHcu6WMfmsi5Oq2Gh8ijluigCnaedjM5TToTS2QXT5wM0LTc5CxNG1b8hjKFQwuNsJkhElJmMOhVutxt33HEHmpubAQDXXXddv/VXX3117rOjOIZvef8HE99kUr3p5Cs23bZ2YouRP+tS2Nh029qJdfqxFqsudmKdXpfB9txSVZjVGXyLsN26SAHDu4KdCqJcEcjPFcx0bTpYRp2KyspKLF68GABw3nnn4aWXXsprUpSE0uH4JxQRETmQkIDaWuwsiKiM2Z5Slh2K4hF6Q98gQCIiokxJBUIfXuwsiKiMZTxQ++KLL8Zzzz3Xb0rZWPwCvPxSuufDqH4m/sF0M5Y4IdaJOTkh1ok5OSHWiTk5LdaJOTkh1ok5OSUWgNCHwdT2QegjIHjZm4hyLONOxc033wyg/5SyVDhqcDrQHoRR8VbMF+EJJL18IWNC0rGKTfYmNZDYTHKyE5tJTk6IZV1YFzuxmf6fY12sY1mXCMOACIUhfd7IJqEgpNsDo+rVyHq9Aa62yyCM+gwaJiLKjK0rFUDqKWUp/9TAbCiBoyHVw9ArXwZce5MH2/kgyio22fZW/Zh8xGabfzFiWZfMYlkX1sVOLOuScV20fftR/8eHAV2H2dCAzvknoHvOMfGx6mGEa/4GV9OXIJDll+wRDVacUrYfTilbgiJvAirg3mO1MtWGuYsVGa7LJtZuTk6IZV2yj2Vd0i+zLtbLrAsq3lkMYZgQEhDNzeieeVT/ExEhAbUd0r0ZIjQ1RSNERJnjlLIlSqotxU6BiIgcRjt8GMI0AQCm3w+43daBUkCqzQXMjKjMCNi72pirNh2MU8qWKGHUFTsFIiJyGL1hCLRDjRCmCaWzCyIYgvRYdCyE5JgKIsqpnE0p29TUlHUyvVpaWvCf//mfmDdvHo4//njceuutOHjwIABg1apVuPLKKzFnzhwsWLAATz75ZM7aLSXCqIcITgBkQrc1yZhty3XZxMoM12UTazcnJ8QWoi6lWEPWJf2yE2ITsS7WHFyXrhOPB0wzMobbMOBfsgyQCcFSAEYVpOmHFIEUCRERZc52p2L16tW47rrrcPbZZ2PhwoVYuHAhTjvtNJx22mk5S+r2229HV1cXXn31VbzxxhtQVRXf//730draiptvvhmf/vSnsXTpUtx11134yU9+gtWrV+es7VKitX8KInRk8gCJzN/Q7MZm8li2sVY5DSTWattSroud2GzrYie22MdqJ5Z1sX6cdbF+vITqEh4zGq2XXQLpjcz8VPnG2/CuXNW/Y6G2w6h7BOEh90CveB0SZoZJE1GEKNKPc2U8+1Ov//7v/8aYMWMwefJk7Nq1CyeffDIefvhhfP3rX89JQh9//DFWrVqF999/H5WVlQCAH/7whzh06BBeeeUV1NbWYtGiRQCA+fPn4+KLL8ajjz6KWbNm5aT9UiKkF662qyCVZpjaARhVLwAiFBNgtVGyneUxVmYQl89Yq20zeSzfsYknBIWuodW22cSyLqyLnVjWJW91CcyagcD0qXDv3gMpgPCoUQmDtWMOThgwfUsAqUHryt2Hg0Q0+Ni+UrFp0yb85Cc/waJFi2AYBj73uc/hV7/6FZ577rmcJLR69WpMmjQJTzzxBM4++2yccsop+J//+R8MHToUmzZtwpQpU+LiJ02ahPXr1+ek7VIlzDpAbY90KBw0C0nB2ym1WJHhukLm5IRY1iX9Mutivcy69HFpCB05DuHx4wCXljpWAKZ/KSTCKRokojiiSD8OZrtTUV1dDa/XizFjxmDTpk0AgNmzZ2PPHovpTQegtbUVGzZswPbt2/H000/jn//8Jw4cOIBvfetb6OzshM/ni4v3er3o6urKSdulTKqNGMA/JxERESDCkQ+niIgGyPZZ6IQJE/C3v/0NHo8Hfr8fn3zyCbZs2QKRoy/kcPdMf/fd734XlZWVaGhowJ133om33noLUkoEAvGDygKBACoqKnLSdikTZjUyv5GXiIgohlQAk++lRDRwtjsVX/nKV/DrX/8aO3fuxI033oirrroKl19+OS699NKcJDRp0iSYpolwuO8yrNkz5/b06dOjV0d6bd68GZMnT85J26VKIgwRGg9AceaMJak4IScnxBaqnVKLLVQ7pRZbqHZKLbZQ7ZRabNptBURwGiBCkPxwiigzvP2pHyFl4pQQ1vbu3Rv9OxAIwOVyQVVVNDY2YsOGDTjppJMwatSorBMKh8O48MILMW3aNPzkJz9BMBjEV7/6VVRVVeGHP/whzjnnHNx2221YtGgRli9fjltvvRX33XcfTjzxRFvtNDa295sMo9RIGDAq3oLpWwEIPfJJE8y+J13v8cUui7gdDCzWatt8xeYj/3KoSynVMNv8WZfCxmaaf7nVxTQBRUkem2n+BauLRNzg637LdvarRd5DAEAfCq3jbCjhsSAqJiGAhoaqYqeR3OI6wCjwLYNqFTC/ubBt2pBxp2LatGlJb3GSUkIIgU8++SQnSR04cAA//elPsXTpUgSDQSxYsADf/e53UV1djTVr1uCuu+7Cxo0bUV9fj1tvvRWXXXaZ7TbKoVMRrnwB0rsGEDEHUog343RyGZuP42Fdkq+DjdiBtpnqWNJhXQYWy7qkXF/xxtvwv7sYxuhRCA8bivYFpwMed+pORhHr4lm3HjX//BeMuhqERwxHx6mnwKyvjc83lVR1kQKAgNZ8AxRjWGb7I8oD53cq6ovUqcjd98LlWsadikwGYufiSkWhlHqnQirtCNffm/mbKxER9aMdOIiG+38fXe447WR0nHFq5ifohRYOY9jPfwMlFJk+PDhpApqvuya3bUgBEZwBV/tFud0vkQ3sVFhweKci4++pKKUOw2AgtYPsUBARZUnbtz9uOXzEiPjbiBxGbW6JdigAIDxyBGAYgKrmrhEhIbXczOhIVLYECn8e5tyXJgCcg7R0cZYOIqKsmT1fstpL6eiMjK9wKLPCj9iL7EpHZ+6vqkgBYTr4E2IiciR2KkqU0IcD+tCe+19jpLqlK3Gdndu/Sj2WdbGOZV2sY1kX69gyrEvoyPEwqqsge65O+Fd8lP5T/yLWRVZUIDhtSjRf77pPIEIhex2hdDkICSUwCxLO7VwRkfOwU1GiBAS01ksB6em/0uoNI9mbSLLYdPuQCT+pDCQ2V3lmsg87sZnsY0B16QmOnhhI69hc5tn7oJT9T0gcU5cMY9O1bTc21T7sxLIu2bWXr7rExqkKmj9zFUy/HwCg7TuA6udfivyfsBp454C6tF50PvThkUHUIhRG7d/+DhEO999uoHUxPTCq/4Vwwy+hV74CKYJpdkA0CAlRnB8Hy3igdrkp9YHaAKBXvAbTtzTzWUis1g80NvF5nY/YxJwGGmu1LdKsL2BdtL37UPPEP2BWV0MfNRLdM6ZDP2Jk3y0N+aiLbqDqxZfhWb8JoamToQ9rQNfxx0VesDKZ8caJzxc7sdnUMF07dmJZl9zmn01sWIdn4yaorW3Qhw5FaOKRgCKs8y9WXWJjTQn3tu3QDhyEWVWJwNQpgNuV2bHaqqGA0EdCa7kewuk3dFNZcfxA7Q+HFGeg9rzDhW3ThowHapOzSNEJ07es/5uBSPJ3uvVOjLVzbNlsayenXMdKidon/gG1pRWipRVKIID2c89Kvo90yxnG+lavgX/5RxAAtBUfoenaq/p3KDLJv1Rjs6lhvnJyQuxgrotLQ3DGdIsgi22dUBdFIDTxyEjnJ9U+ss5BQrr2Qrq2QYQnWAQTEUXw9qcSJV37Eff9FFSSlM4uaC2tfR+WjhltfctFjrl27YnrQITHjXHuFJpEVFxSgenaXewsiMjheBZRqkxfsTOgHDDdbsiYk3mluxuFuGdS+rxxy6I7kPc2iahUSQjJ9xwiSo2dihIl9JGAXod+sz/ZYecD8VKLzUYh83e7EJgxPTqTi2fT5sgJfi5ncrHQPetoiJg2/Cs+yu00mk54DjjxQp4TjpV1yT62UJxwrBIABJRAklvDiAYrDtTuh52KEiUgoLV9GpCeyIu+VHp+x/z0il1OXJ8qNtm2TohNt22qn2z2m4djbTv3LOgNQyLLhomaJ/8B6EbkNqg81UUfPgxt554VWRQC/sVL4N66zTI24xrmuC4FjbVzrKwL65LPuiSuzzh/CUgZ+SK8XB4rAJhehIc8gHDtQzDdG0BEZIWzP5U4KYIwPZ9Aqk0w3esBtS1mEHDP79iOrexZlhaPp4qNXZdJLJDZtjIh1k5OTohNdizJYgHrbXUDng2b4Nq7F6a/At2zZkBWVuY9f7WpCd61kXnug+PHIzxhfN+MN1nsN25drp4vVrFOeA7k87kFsC6JsYhZn8u6lOprESR8K1fBv/hDBCdNhFFXi+6jZ0Zmgur9vo2c1UUAQkJtPx9q4BgQ5ZPjZ39a2lCc2Z+ObyxsmzawU1EmDM8nMKqfKXYaRERUQK7dezHk9w9Fl9sXnoHOk0/M78QLpg+uw1+GQJovCSTKAjsVFhzeqeDtT2VCunYAkv+cRESDiXvb9uiYLAAITpqQ/5nclG5I1bknNkRUHDwLLRfSVewMiIiowKTbhdh7mkQwmNtJF5IQ0p33NoiotLBTUSaU4FGAyP8bSUbK6LaynGJdrNmpy2CqIetijXWJEzhqOmIHRfhWr00/Q0w2dZECIjwcwqzLYidEVI7YqSgTij4SSuf8yEKqWTxkhsuJ6xJ/nLjfTLdlXeKXTRMwzMxi81HDYsXm41jtxLIuxT/WUqphkpzMykq0XXR+5GFFgfejNfCs35jjY5WR1wkpASkgRQCh+vuhV7wKqRT4nnIip+CUsv1oxU6AckPChHTt63sTSJwZJfYNLNmyROqZUGSRY63yHeix2oktx7pAwrtiFTzrNyA0cQLMqkoEJ08ENC1yP7bdGmaSUynUJRfPl3RtJq5nXaxzYl2sc7LIv/vY2QiNGQXfR2ugdHVBtHcAoXBkBiirY82wLq6du1Hx1rsIjx0No7YGofHjYFZVAlorAMD0rYDpXQ9X82chzGoQ0eDGTkWZkO4tkO7t8Q8Ki7/T/bazbanFWm07SOvi+WQjap97AQDg27QFLZdeHJl+sneA50BqWMD8CxKb7fOFdWFdClgXY9hQdJyzAJYGcKxKewfq//QXCCnh3bIVXcceg8CsmYj7pFRIAF0w/B9A6zjHum2icpb4/2eQ4+1PZcJ0bwVnf6JMeTZtgYyZISY4ZXLfnPZENOi5t26HiJl3PThpIiznYRcSpntTATMjIqfiWWi5YIeCbJBqwvPFNKwDiWhwSniNEKlmlJL8QIKI2KkoG0poKhwz+xM5XvCoaXEnCb6P1xVkGkoiKg3BSRMgNS06xMK7br31919IEZl9kGjQEUX6cS52KsqECI+B6J4dWZBJfpDkd75iU21rJ7ZY+ReyLnZiJSK3IUgJmHJAOYXGj0PXsbMji0Kg4q13oTa39Oy3lOuS45wKEpvi3zH6uIzfNvaxAT4HSj7Wals7scXO3+F1kR4vWi88L7JKUeD5ZAM869b3xMq+WGMI1O4TQETEgdplRIpA34JA35tF79+y/zb9YqM7y0FspuvTxVq1GRtrdazJDDQ2VX52Y63WZxobCqLi3cWAoiI8aiRMvw/66FE9ASI+NtWxCoG2i85HcMokeNeshQiF4N60Bd3Hzo7MGFNqdUnXpt1Yq/W5fL6YBnzLVkJtaUFo3FhIrwfhMaN7PgkW0Pbtg2/ZCoTHjIFZVYnQ8GFAZUWkkUAAFe+8B+n1Qh8xHGZFBfRRI2H5HMjkWJ1Ul8H6fMnla7TV+gHWJTB7FvThw+BftgJqWxu03XsRGjsGsqIiJi4MgFc5aRAqxoUDZ1+ogJDSauRV+WtsbLccc1aqTNd26LWPFTsNyrPaRx+HZ/NWCClhejw4+I2vAC5+NlBqKt58B5VvvQshJSSAw7d8AfrQIYCiQG1qxtC774/Gds4/Ae3nLIzOulP/+z/DtWcvhJQwKitw6Gu3W9+WQlQIUkDpPgFa55nFzoTKjBBAQ0NVsdNIbvkwwCzw97QoVcBxBwvbpg18JyoTpmcjOFi7zP3/7b15nBx1nf//+lR190zPlclkcpAQjtyQAAkMCeGUM4uIIHK4HAruurDhEHYFdpfvuurKiru6roAHuyrIT0AFuQQUVBAQAglHQkADOchNrrkyVx9V9f790T093T3VR00fVd39ej4enelP1etTn3e9Uv2pfnfV51OGmUgoACAy4xAmFBVK/Xt/Sfw/mq3jYEyemEgM6t5fD0matjN02NzEezU4iMD2HYm64dmz4PWHIZEqRwmsunVuR0FI+eGQilHwWyghlUTy5TV+mSQ8BIgnqKLL/oSQMcOkokrQwrM4+1O149MRnjkj8St2YNNmwDDcjYmMifBhcxP/j3pPL/S9+xKzb4XnzEp5PkD9ug8SyaQ0NCAy7YBE3br1G1FV93GSykMUtPA8t6MghHgAJhVVgooeChWaO3q2D7uZPZDhr1NtKWYuyRZ/qbQl9SW+Mnn2noza3NvtX3Y6JOCHKAUtFELzc3+IaZJnAKoIX1zU2sXvRFsEXwaWLobZNj6RHLQ8/dtYUmFZMCe0of+EpTGpUgi+8RZ8e/bG/o8F6Dt7GaDrEE2D3tePphdejm3X7hioMF8Kir+Kj5cxa8viSwCWvhdG89Ow/JuBxCqBFdgAo/nXiLY8CrP+LQgiIKRqUMqdl4fhQO0qQmDBql8Lq+5diBYCtD4g04xQdmVkWZetXCqt05i8oB0uR6NoeOttWHV1MA6YAkvTYE2aWBRf9J4eNLy2CoHNW2A1NKD/pOMRPfRgJO6F8bIvXtE6jb8EWhUKoWHVm6hb9wGgaRjsOBqhI+bHxlaIoO79D2IzRPX3I3LwQeg/5QRIQ2zWHb2zE40rVsK/fQfMpib0n3ICjOnT7dutMF9Glb2gdRq/F7Rl90UDlAVtcCn0gZNhNv0WVnANIEkVzAnw91wOJcEMO0XICJ4fqP32ZHcGai/aXd42HcCkokoxGp+HFVwFqCreSQ/T9qP74N/xUWyGnuYm7L3pOs7QQwipCbT9y2C1PDt6hShoQx3wDZxe/qBIxcGkwgaPJxX8llOlWHV/ZkLhEtr+PgS27xyZoWfeHM9fsiSEkKIgClbwbdjORqgkdm4ihFQlTCqqFcUBvG6hTDOlLLqOqr4sRgghCRRik4Zk6PN4biKkamFSUaWoyKz4vayk3Jit42CMb02cUgMbP+StT4SQ2kBZ0MKz7a+Ui4qdmwipBjhQexT8plOl+AZOACQwOrHIZ2aR9LJXtXCozdSOE20+8SuFvrPPApSCKAX/3n2of+OtuEbGvl0n++pEa9eOUw9r5XipRl+celgMX5xo6Uvp4y+WVgAYbbC0fsBsSdMqADoAC0bj8zD9m2EGX4fR9BuYwdcgagCEkOLS2dmJ5cuXo6OjA0uWLMHtt98OI8dU9B988AGOOuoovP76647bY1JRpShrPPzdV0GFjgDMplgHb8X/u+1OYgojs3oMl5O16X/LpVVZtMiihY0207460eYZf3j2LHRddTnCs2fCbGpE8M3V8G/ZFhNUgi9OtLV0vDjR5oq/nB5m8yWbNt99daK1+8y55YsTLX2x16bPHKV3AfXvAdr+NK0AyoDUvQ8ruApm689hNr4Aq34tzMYXEZ3wQ1i+7SCkYlAuvRxw4403oqGhAS+//DIeeeQRrFixAvfdd19G/dDQEP7xH/8RoVAooyYbvjHVIhWBslrh7/84BILo+B8Bmow+ILOVcx28mbSC/LebS5srDifxl0qbIaboQdPRc2nSNJ/0xf49fbF/T1/s3xfTFycx5KpLX5LeW1m06eviZTFgtDwBf9ffQ/H3TkIKZsuWLVi5ciVeeuklBINBTJ8+HcuXL8d//dd/4W//9m9t63z1q1/FGWecgQ8++GBMbfKTWwOIvhfwdaJss0E5yaQdZt0VDX2xh77YQ1/soS/2VLovSgC9D+Lf4XYkhFQF69evR2trKyZPnpxYNnPmTOzcuRP79+8fpX/88cexZcsWXHfddWNuk1cqagHFp5gSQgipAHi+IiQn/f39KeVAIIBAIJCybGBgAMFg6oMmh8uDg4NoaWlJLN+4cSO+853v4KGHHoKu62OOi0lFDaCMSYDlB7So26EQQggh9ogOFZ3qdhSEeJ6TTz4ZAwMjkxtcd911uP7661M0DQ0NGBoaSlk2XG5sbEwsC4fDuOmmm/Av//IvmDq1sM8fk4oaQCEAffAkmE3Pj9xLm35PbfJAu+RlymZdNm2293Z10wf/FUubHn+ptJniy0dLX3Jr6Yu9lr7Ya+lLbm36eg/5og0eByWpv6wS4lncmOI13t5LL72Usjj9KgUAzJ49Gz09Pdi3bx/a29sBxK5ITJkyBc3NI08qX7t2LTZv3ozbbrsNt912W2L5Nddcg/POOw9f+cpX8g9PpDafyrVvX19NPY9M9C5EW38KaOGRzj2ZXCdlL2olrZxJm6sdu+2NNSYvaOmLvTZfX/LRVpMvTrTF8sWJthp8yUSt+2I1AFoIMMdBH1oCLXQUVErWQWoZpYD29ubcQrd45wDA6itvm1ozcORHecsvvfRSTJkyBV/72tfQ3d2Nv//7v8eyZctGXdVIZ+7cubj//vuxZMkSZ+E5UpOKxWh+Eol7VRVGOvjk97nKXtOqLNpsLzut1/fViZa+OPfFibbafHGiLZYv1eZhLl+y7bddfS/vqxNtLl+0Qfi6r0Kg+2rooYVgQkEqikyf8VK/HHDnnXfCMAycfvrpuPjii3HSSSdh+fLlAIBFixbhySefHNu+Z4BXKmoA0fch2vYjt8MghBBCRhAFbWgxfAOnuh0J8SCev1Kx1qUrFUfkf6Wi3PBKRQ0gaii3iBBCCCk3PD8RUjVwoHYNoMyJgOiAMt0OhRBCCIkjUMY0t4MgZIyM4X6korTpXXilosoRvRNW3Z+hIofarEx7n347WPp6u/duae102bTZ9tWJNltMXvDFLlb6Ql/oC33xnC8KsFqhhQ63CYAQUonwSkWVIhCYTc/CCq4e6cgFmZNchVQd8tAqh1onMaTXsyNZ6yR5T9ZmS1Qyab3sS7qWvthr6Yu9Nh9fhrdXqIfV4othQO/sgjmxHdA0IBwC6uuzVEhrZ5hq9GVfF8xJMV+0rk4g0AKr2Q+IBhWeC9/A6VDwZ9kIIR6GFypGwaSiSrHq34JVvzpWSD6xZEOl/S22Nh+9E22+mlz1c/nipC36kl1DX+w1bvri1JNyeFghvmidXWi/+x4oEUDXMbB0MfpP/1juipnaqxZf9nWi/Xv/m/BFdA1aJAoBMHDKHQgv+Dsofv0gpOrg7U9Vihl8a/TC9JPBWH9p9Io2W91SaXPV9YKWvthr6UvhWnqYom14a03s+VcAlGli6OiFubdbg75okWhCEFzzYyYUhFQp/GRXK1q/5y+TEUJIJaP39aVcXTCbm1D2J+x6kHRfhlEQaAO7yh8QIaWAtz+NglcqqhRlTI4NhCOEEFISolMmp5T9u3YDluVSNN4h5svorEKUDmPiUeUPiBBSFphUVCn64PEAJP+ZOdJxos1V14m2kJiczEJSjO3YlbNBX+yhL861hdQt5v4UazuV0BfZ7M/QoiNhBesh8asTTS+9Ehusnfxk1Zr05ShYwWDCl8QqsTB43CWwAhshMGD5dsGs+wss30cQR0ER4gWUSy/vwidqVzFWYB2MpucBfX9sgWiASvoVLX12EKSVk4/dsWrt6jrRliImL2jt6tIX+7r0xb4ufbGvW2Zf9L37MO6p3yCwZRsAYODYY9B35mlAwF+2GPKq64Yvv/4NAltjvpjjWrD/rNMRnn9YXKsANXISVtGp8PVeACVNIASA95+o/d40d56oPX9Hedt0AJOKKkcggNaLaNNvgMDWlE581MkitWL2k5ATbbnaqTStF2PymtaLMXlB68WYvKB1MSatvx8qGoXZ2gpoKqvWcx56whcFZUyDv+fyDBsntYbnk4o/u5RUHO7dpIIDtascBQWBAuq22K3MVrF42nK1U2laL8bkNW252qk0bbnaqTRtudqx0VrNGX5h94IvLvZF+fsiEP92WPpeaObELI0QQrwKx1TUADJ8+xMhhBDiZfRetyMghIwRXqmoAZQxftT9q4QQQojXUMYEt0MgJD+UQtmnkPb4lNW8UlEDKGmCCs/HqClms+UY6esK0Uqe68oZkxe09CV3mb7Yl+mLfZm+2JcrwhcFRKZD9D6ICkMQheXflpgZSrQ+WP4tEK0ny0YJIW7CKxU1gt53KgzfttilZbtEd7iDz2dA3li06cvy0dq1UyptMfeVvhSuTV9GX+yX0Rf7ZfTFfllZfRGoSBRSF4iVTRPQ9OzxB7bBCDwIiBZboczYcssHKCOhVeFD4es7F0oabBomhLgFk4oawWz+Q2xq2UxXzuyWZ9Omn1xKoc01ILCYWvpCX5xo6UtpfXGirQQPa8wXNTCItv+7D3pvL8xJ7QjNm4uBU0+2316CpA2rtAcIJiUUACCBzTDGPcqZogjxGLz9qQYQrQ9S92eMGlOR60SZb7lUWqcxeUFLX3KX6Yt9mb6MTUsPc5fL7EvDG2/B19sLTQT+3XsRPnze6CeNF7Sv8ZmifLuyVCKElBsmFTWA6N3ZO2xCCCGkSPj2daaUjQltsSeNFxnRO3OLCCkVyqWXh2FSUQMoszX7gDlCCCGkSJjjW1PKek8vSvG0WWW25tQQQsoHk4oqR2ABKgoVOQSc/cljWvqSu0xf7Mv0xb5MX+zLZfZl8OiFgKYlpI0rXseoqTALikEBxkRAAhCYEAhE74JoPbH3iMDS90LUUJaNElIgw1PKlvvlYThQu4ox6/4Cs/EPgN4fWyAaMvbc2WbxyEeb78wi2bTFjqkStOnLvBCTF7Tpy7wQkxe06cu8EJMXtOnLvBCTF7Tpy8oUk9U6Dt1/fRHGPfok9MFBBN9cDbN1HAZOWJrfbVA5Y1CAby+Mth8Dlh+ADmiheOMNgAoDygREQYXnwtf/V1BSn8cOEUIKgUlFlWL5P4TZ8kTaycXKJHd2n56dNlN9J4Px8t1mJm22X7rKFcNYtV6MyQtaL8bkBa0XY/KC1osxeUHrQkyRWTOw9x+uR2DLVqhoFJHpBzobV5E1jqRzmRYFEE2qNzhSVwmk7n0YWj98vZdBef2GdEIqHCYVVYrZ8CpGPUXbyYmmVNpStqPyXFfKGMaqLVc7laYtVzuVpi1XO5WmLVc7laYtVzvp63w6IjMPdb5dJ319zu0KJLAd4tsJZUzLUpEQUigcU1GliG8PRk0hSwghhNQaEj8nEkJKCq9UVCtWE2L3lbodCCGEEOIiClBWs9tRkGrDjYHTHh+ozSsVVYo+dMzohaWa8SNX3VJpvTjjSjboS351S6WlL/Za+mKvpS/22orzRQFmExA9ABKvKCoMUeH4pgSiBiEw4mUjXk7Whhw0SEjtwisVVYoWWgQrsAkS2JD/1QqRpCw4x1QhyatzzSpSKm2mesVmrHHQF3stfbHX0hd7LX2x19IXe61dPW0ARvtdgNkISADwdceWG+MBLQxog4BogNkG6N2AMgEzCEgQ8HUBAFR0GvT+06EZUwveNVIlKJT/bhBvX6hgUlGtiG8HJLAxL63W24vxDz4MFQ4jMuMQhGceivD8w7NsHKMH1WU6ATjRwqE2V1vZdOlt5bvNfE/Mw1o3fElfXq2+FOKhXf1M0Bd7nPqSrc1s7XvBFyd9EX2xpxBfcmmzfuZkZJk2AKiBkVW+7qS4LEDfl6QdApKecyG+nTBaH4S/+yooc4KDAAmpHXj7U5ViNrwae5Ots42Xm597Hr69e+Hr6UVw7XuIzJo5eoO5vihlWu9Ea0ce8Sfel0o71viyrS+lL+m6avWlEA/piz2l9CVXu9nic9uXXFr6Yq+tpj5aCQATRvD1HA0SUrvwSkWVIv4dyHf2p8C27VBWTGu0tUHq6koZGiGEEFJ5KIH4t7sdBfEMuX4VKFWb3oVXKqoVqyHvS8hWY0NCqg0OliwkQgghpGIRBWU1uh0FIZ6FSUWVoocW5hbFM4nBYxaN1OvrR2DjJsA0bbV5UWnacrVTadpytVNp2nK1U2nacrVTadpytVNp2nK1U0ytEmiho9KqSMr79DKpYpRLLw+jRKQmj/p9+/pQzXsuMGGM+yUksCW1o1QYGdg2vNyy0PLrp9Gwei1EKZjNzej+3F/DnDDBvpMdPqjTB94VQ5seayFau30tptYupmQtfbHXFtMXJ1r6Yq+lL/Za+mKv9bovTrROfbHqARUCpA7KmATRu2KDv83xgNUI+HcBsIDoFECZgG8PIAFooSOgD54EJby12AlKAe3tHn6+yIaDAauvvG1qzcCsLeVt0wEcU1GliL4P4t820mEnViC1w1UAdA37zzsXg4uPRd2GjRBdhygttW76e8B+u5neZ+vM7d5n0g6Xc2nT99WJNlOb6Vq79060yqE2U/xe86VQD536kuuLQql9KdexRV8yvy+3L4La8sVJX1TNvqhQfFk4dn4dXqd3j0xTCwD+nSMxqTCs4JsQ/w74eq6A4g0i1UP6sVSuNj0Mk4oqxWx8BYCMPgBV2t+k5cbUKTCmTrHfoMrwPtf6TO2XWpu8rFTaXDGVSut1X9zykL7k1w59yV2uNA9zxeRES1/ya8eRViD+jyCBjVCR2TZBEFIdMGWuUsS/FfnO/kQIIYSQEiIaLP9Wt6MgpKTwSkW1IgEAIbejIIQQQgiEYyqqDgX7y1+lbtO78EpFlaKHjgSkiAefk4sepdJ6EfpiD30pHPpiD32xh585ezzji0ALH+60EiEVBa9UVBGCKKyGlTDr3wFUGJA6xGaqiAsUMneEKrGRzNpcAxvttOnr0+s61abHnG9MTrTpbeTS0pfa9sUtD5PL6e9r2ZdCjq30ONz2ML2NXNpa+cylt5FL6wFftP7Tocw2uz0glYy3LxyUHSYVVYLAgjHuF6lP0pb4y24Qmdj8xRi1uU4SkkWb/teJNlMMdvE70VarL05O4E731Ym2mPvqBV+y1U1fV4gvTrT0pbS+FNoXpZfpizNtqXxJb6eInyMJbIKEOqBG7RQh1QOTiipBAushge2pC+36LpXj71i0KsP7fLVOtpuPNlMdJ9pq9KUYHpbKl0rR5qpDX/Kr60TrtX2lL4VrM9Vxoi1V/PmUx7BdqfsQ4t8MFT0UhFQrTCqqBCvwISAaoCy3QyGEEEJIMqLBCnwIjUlF9aAS/5S5Te/CgdrVgjA/JIQQQryJ8DxNqh4mFVWCFj6MVykIIYQQL6Ikdp4mpIph2lwlaMY0qMEOSMMbqQPOklHIPFAwvVyINlu7pYohW5uFassRP33xrjYdL8TkBW06XojJC9p06KE9XvQlW7uFxmCMh9HyGGC0Q7PGQfzbISoKLTID+tBiKKspSyCEVAZMKqoEgUD0HvtObfh9rs5RiqRN/5stJrvtlltbLl+8sK9OtG77gjLuqxMtfaEvTrS56jrRVlNf5EVfSnnu0rtjZb0LVlJdS++EVf8e/N2fg7JaQCoIVSNtOoBJRZUggU1A3YbUhSrD++Ry+t9SajPV9YI2n7r0pfy+ONHSl+zbKoc2n7r0pby+eLkvyqeuF7SZ6hZFKwAGYTa8Al//2SCkkuGYiirBCmwEhP+dhBBCSEWhBFbdB25HQUjB8EoFIYQQQoirZLvsRjyJUij7/5vy9nHCn7arBC08B5z9iRBCCKkwREELz3M7CkIKhlcqqgQVPRgqNB9S/17qQLMUEewHo9lpc9XxgrbU+1ipHtIX+uKluOkLffHS9r2gTa8jAVj6XkRbHoUWmQ7xdUH0LiizDdrQImjmpAwNEOItmFRUEZI8piJTJ5cv2TrKUlJou8WM2Use0pfSbIu+2ENf7KEv9lSrL+VqV4UB/zYIALPug0TiIbIVVv1q+PafDy0yt3ixElIimFRUCeLfCgTXxgoqi1Dl+DtWba46lRqLE22p46Yv7my/VFr6kn8d+kJfKiHuoscigABG82/g75wJxa9sxOPwCK0SrMAHgGjguApCCCGkSlAAVAji3w4VPcTtaEgyHKg9Cg7UrhaYTBBCCCFVihv3dBHiDCYVVYIWmcnEghBCCKk2LD9UdJrbUZB0lEsvD8Pbn6oEFZkJFZ4Re7I2EDvwhn/YSH6fXE7/OxYtbOoWqs0UfyHaYuyr2x7SF/riZV/c9tBNX5xo6Utl+eK2hwpQxgEwm58FotOgRIcEtgLih4ocDPF1AXonYI6DHjoKymoFIW7BpKJKUFCAORFQm2IdkV1HO/xe0ioXW5v+dyzblSJrncRfKq0TXzJp6Yu9tpS+ZPpSQV8yr8vWjl38TrRe9MULnzn6Urg28Vegb9+BwK7diBx8EKDrMJuagIAfEIUUyuCL+LdBIEDde3F9PIbg6rguVrYaXodv/wXQIrNAiBswqagSLN9OSMPrsUJan5dSzrauWFpxoC1VDF5pJ31dOsXS0pfCY6AvzrXlaqfSfHHLw3ToizNt/NxV/857aH30ycTi7osvgNk6DqMGyZYtXslcVsOBAxCB0fwU/J3XgTNFETfgmIoqwar7CyAe+e/MdfIihBBCvEb83FW/9j1IPIEQnw/heXMAXXcxsDxRALQQJLDZ7UhIjcJUtlpQUbcjIIQQQioeFYkCEvv1X3QN0Dzyg13eGG4HUBtwStlRVNonhWRAixwCzv5ECCGEFEZk5qGJL29aOALfR7sAq0LOr6KgotPdjoLUKEwqKhxRIZj1b8H0bwWMcamDyLIN/rJbnksrGbTpA93syvlqM8WU6a+T+EulpS/ZY6Evmf+Wypds2kLjL5WWvmSPJZeWvthrx+DL4LFHwxzXkrgFqvm552Oa4cTCw76o6IEwG16DGVgH0/8hjMYXYDS8CNO3HWbdOzAafw8zuBKiBkAKRLn08jBKRNIP0Zpg374+VPqeW75dMMY9BKgwYvlh2i8pwwefJJUz7XOh2uTyWLXp7ZZLS18yx0RfKs8XtzwspS+VfGzRl8zteNgXbaAfjS++gvr3/gIlgv7jl2DwuGMBnz/ztkvhYa74bespJAZzS/y7QUKrxd/o8PV+Glr00DwadgelgPb2ZrfDyMz2mYD0l7dN1QQcuLG8bTqASUWFIhBE2+4BtF6MmhmiNA3mnyGXSutFvOCLFz30wr7Sl8K15cIL+0pfCtd6EfpijwCQuvhMURkSJZdhUmGDx5MK3v5UoYhvJ6D3lCehAJx1nqXSehEv+OJFD72wr/SlcG258MK+0pfCtV6EvtijAGhhSMC7X1C9j3Lp5V2YVFQqWsjtCAghhBBSwYgWdjsEUkVwStkKRUUPiN0ryRmfCCGEEDIGtOiBbodQubhx0cDbFyq8e6XCNE1cccUV+Kd/+qfEsjVr1uCiiy7CokWLcNppp+Hhhx92MUJ3UdIAbWhxrDB8B1T6nVCSYZndumzabO9LqbWL0YmWvnjHl3J4SF/stfTFXktf7LX0pXBtIR4ma5xo7d7n0KrQ4VDmBBBSLDybVNx999144403EuXe3l783d/9Hc4//3ysWrUKt99+O77xjW/gnXfecTFKd9FCRwBW3UjmOjy7gySVk5cJUrXIUwsbLTJo0zvD9G3atY0MWlWg1uu+JGvt2s7kYSX6kklbSb4A9IW+eNMXO21y+3Ztl9MXO60XfMl1bCXr7OJEBm0hHqb7YndsFcMX8UEbOhqkAJRLLw/jyaRixYoVeO6553DWWWcllj333HNobW3FZZddBp/Ph6VLl+Lcc8/FAw884GKk7iEQGC2PASoyemX6QZftYCy2Nv0kkW2bmdoohdZuudu+wEaXSUtf3PfFiZa+2C+vVl/y3adMy0v9mbPTsI/OfWxVsy/KhDnuUQjMDI0S4hzPJRWdnZ247bbb8O1vfxvBYDCxfP369ZgzZ06KdtasWVi3bl25Q/QE4tsN+PZh1OxPmTolO2pJ68WYvKD1Ykxe0GarW8se0pfCtV6MyQtaL8bkBW3J2hFAG4QENjmoREh2PDVQ27Is3Hzzzbjqqqswb968lHUDAwMpSQYA1NfXY3BwsJwhegeNT8MkhBBCyNgRfpcogEyXi0rdpnfx1JWKe+65B4FAAFdcccWodcFgEKFQ6jSqoVAIjY2N5QrPUyhjEiDePrgIIYQQ4l1U9AC3QyBVhKeuVDzxxBPYs2cPOjo6ACCRRPz+97/HLbfcgldeeSVFv2HDBsyePbvscXoBZTVDCx0Jq35NauJqN/BM2ayz02bLUcaqLea2csWPEmnLsS/F3JaT/SmWL6X0kL7Ql3z3pZjboi+Fx1gJvmTSFnNbnjx3KcCYBPHthgUNymqAFfgQgIIWmQElwSyVCQB3Lhp4/LdkTyUVv/3tb1PKw9PJ3nHHHeju7sZ//dd/4b777sNll12GN998E7/+9a/x/e9/341QPYHefxYgDbDq3wC0KCA6MNZBVwr5d0hpWtXXB+g+SEMQMM3YCj3pIlipTgB2cRWbAnwpmjZ9vRNfnHqYL+nxO9HSF3stfbHX0hd7LX2x17rlSyHnrnwZjt+JNmNMAvh3w/Q/M2q9KRr0/tOhh44pOGRSW3gqqcjG+PHj8ZOf/AS333477rzzTrS1teH//b//h+OOO87t0FxDQYdv4BTIwAkQfQ+M8f9fusDpBh1rm555Do2r3gQUYLa0oPNvr4wlF5m2W6okoZTJx1i3nasefSlcm6meW9pCtl1MX7zmYbnioC/2Wvpir6Uv9lplwWz+HZQ5EVr0IAcbIbWOEpF8896qYt++PlTTnpsNf4LZ8ApGzQZVQlQojEn/+R0oK/ZU76H5h6H3ok+VrX1CCCGElABRUOF58Ped51oISgHt7c2utZ+TXXMA6S9vm6oJmPJBedt0gKcGapOxI1ofSnO9NTPa4GAioQAAq6UZSCoTQgghpAJRAui9bkdBKgwmFVWCMicCKO8XerO5GVZdIFH27dkLaDykCCGEkIpGFJQx2e0oSIXBb4BVgOidgNkASD2yTjObfmdUtjul8tH6fRhYuiSxKrDpQ/h27wbMtOSm0HaqVevFmLyg9WJMXtOWq51K05arnWrTejEmL2i9GFPZtBr0IQ7UJs5gUlHBiBpCdNxDiLb9H8xxTwJaCCk9hSB7x5GyMRttprpJ2oGTT8TAccdClIISoPX+n8O38yMkBqxYFlTy80UMI/t2c8XkJH4vaIuxr/SlcG0+MXlBm8dnDgAQiQDR6Eg5+bbDWvaFx8vYtMWMyQvaavXFMKDC4ZGymWW2xwJ90QYXQ5nteQZGSIyKmf2JjMZoeQLi35pZUOgsEbmm5QPQ8MqraHj9DSgRiFLoufRCGFOmAEpB37MHE/7vp0A0Cqt9AgaWdGCo4+i8tluU+N3Qpnfa+e6rE61X9rUQLX0Z+2duxetoeu4PgM8PY0Ibei6+ANa4cbbaosTkBS2Pl/L54iQu+pJ/TAVqAxs2YvxDDwMiMNrb0XfGxxCZNdPZdh3sq9WwAlZkDjSDD8fLSHmHsbrXpgN4paJCEb0TEtg8eranbAdc+rpCtApQkQiaX/wTtPhVifDc2TCmTQN0HQDQ9KfXoEwDGgC9qxuhIxbEpnMoVUxe0GabgrBYWqcxVZqWvmQuGwaaXngJmgBaNAppbIDVNj71uTDljMkLWh4vucv00L5cQb40Pf8SYAmUJdCGhhCZPWv0GMai+qJgBt/MskFCRsMrFRWK6D1uhwCtfwDKGLn8ao5vjd2GEe/o9M4uKCuWcEh9PaS+zo0wCakatMEhaJGR256MtrbYrYbpyTohpKrwdXVDxX/AM1vHlf4zrwTQu0rbRqWjFMp+6cDjfT2vVFQoyhzvdggwm5ogvpG8VO/qTvnlxGifANFiHwAVGoIaCo3aBiEkf6yGBliBpBnXOjs9f5IhhBSOMaENEv+s6909KPmDtkQBxoTStkGqDiYVFYoy26DCMzBqtqds/Uy+g7by0QqAgB+Dxx6d6Ojq1m+A3t2dGDw2uKQDkJhUWYLG11aO7giLGZMXtOmDRt3WpkNf7KkUX3w6Bpd0JFYFPtwMfe++0QM2a82XUmvToS/20Bd7iqAdOH5J4kqF3j+A+nf/PPq5UEX2UA9x9qfsKJde3oVJRQWj9/0VYDVmFlhW6pcNy8rc6Qjy7/iSlvedfiqGFh0Vm/3JEoy//6HYFQsAxtQD0HPhpyDBIACg8aVXEHzj7cy/sNi1n0cMttpRs2JIZq3d9saqzWdZObXZPLSrW4jW7X11oq1gX/o/djIGjz0mMeNa289+Dn1vZ8HbTZQr1JeSaumL/fJC+uhs0JdRhOcfhv3LzoDl9wMAWn79DALrN2Y/n47ZQwVtcCk0Y0ruwAhJQomU+hqaN9m3r6/kVw9LTbT5UUjdeqQM1hYACvBv247xP/sFRATGtKkYOO5YRGbPTB3YFdfakr4uh1br74dv9x5YzU0wJk1K1Rom/Nu3QwkQOXAa4Pel1HXSTj7apt89j4ZXX4c1vhXRKZPQe+45QF0g84P5ShBDXtrkshNtKWPygpa+5C4LoA30w7drD6ymxthnTlOZ63oh/lJpebzkLtOXsWk9GJMKh+HfsRPi8yM6bWrqJA3FjAEafN1XQTMnZhCVHqWA9vZm19rPyZ55gPSXt03VBExaV942HcCB2hWKaD2Q+g9Gr4h3Es3PPQ8VicRmZtqzd3RCkaS1JX1dDq3V3IRIc5P9ep+O6CEHF6WdXFq9qxtNr7wGANC6uhGeMyt7QlGCGPLWqjzXlTMmL2jpS+6yAqymJkRmZfjMeTH+Uml5vOQu05exaT0Yk9TVITLj0PzqFhKDCKzgm9D6/yqLsLYRpaHsN/woLet/ndvw9qcKRXydWdf79uwdmSliQlv2L9VVhG/vvpSyMWli5svDhBBCCBmNEohvj9tRkAqjNr5pViNmS/bVreMSt0lq+/eXPh6PYLaOSynrvfvB2XEIIYQQB4gCzFa3o/A4yqWXd2FSUaFo5kSo6DRkmv1pcElH4tDz9fTGBnSZRZwpYqzaErdjTJ6EyIHTIPErM8G318QGqKfPklHCGMakLVc7laYtVzuVpi1XO5WmLVc7laYtVzuVpi1XO5WmBQAl0IeOziEiJBUmFRWMb//5UMYk23VDi45C/wlLE9O9tj72JPw7dqapJPVtIZ1vvlqn9fLViyRuc+q5+ILYbU8A9P19GP/zR6AikTRtCWIY1o9FW8o2SrGv9KVw7bDevgBIlh8BasoXB1r6Yq+tZl8K8ZC+pOqT3msDx0EzDnSwAULA2Z8qHYFAfDshvj0wG14CtKGRq2MCaH19CGzeivp330Pggw0wp05BdPIk9J96MqzGppGZIwSpV9VylbMHVTxt8voMMfl27sL4B38B0XVED5qOwYVHIHrIwfDv/Ai+3XvR9PwfoSIRRGbOQGTaVAyedHys/vBtUdn2tYJ9cawd/jzkqx1rm9n2JRdV6kvdX97HuMd/DXPcOBhTJkPr64Nvy1YYhxyM6JTJ6D/1ZEDX85/BLNv6CvIl577kgr6MTUtfnGtLtT+l2I5dGWnr4Ie/6/NQ1vgMotLj9dmfrL1HwI3Zn7SJa8vbpgN4paLCUVDQjGmxQnJCEVsJq6UZ4VkzULdhEzQA/p27oAwTVktL6lR06Z1LrnL2oIqnVRneJ5VbnvoNtIFB+Hr3I7BlK6IzDgV0HdHpBwJiQRscgmaYqH9/PYwDpwIiSBlnkW1fK9gXx1rlUDvWNtOpdV8MA+MefwpaOAL/nr0IvvMu6j7cAt0S1G3aDHN8ayyZcDKDWbb1leJLJmr9eMkEfbGnXH10qfanFNuxK49aZ8AMvplFRMhoOKVslSC+XYjliKPHDvj27oVKGlMQPWBK7OFwul6+AEuIf9fuxExX0cmTUhIG/85dsS9j8f2PTj2gZmbCIpWB3tMLLRzOuD42F311fFYJIRWCEog//ZZpkoJKz5TL1aZ34berasFqRKYbKK2m1Lns9f5+zx+YTrAaGhLv9f6B1HVNjUi+z03rH0BV3PdGqgarIZgY+2SH3teXfaIBQggpNqIAy7u3HhFvwqSiStBDR8Te2HxfNie0ITL9wMQXl/q17yF5YHNelHJAW4HawY5FiVW+j3bBt3tP7EoMgKGFR6ZUbXjjrdzb9fC+Fl2brS59KVybqy4AaWhAaN6cUYnFsDT45urcV9eq0JeK0tIXey19sddWgi9KoA8tdLDBWkRz6eVdvB0dyRtljYfedxYAZdtZ9Hz6fJhtsQFXWv8AWn/5KGAYscQi+VdQy8qccEja++RXNsaizdZ2mnbgxOMROmxubJlSGPeLX0Hriw2eMse3ovdTnwR0HQIguPJN1K+OD3LKNgtUlvZyaj3iS17abNtwoqUvqeT7OYqX93/ibESnTI4V05KLuvUb0fjHl0e2V8m+OG2vVMdLPl/c6At9yaStJl8kTRN/6QMnQ4semqESIfZw9qcqQRBFdPy9gN41covf8P4Nly0Ldes3Qd+7Fw1vvAUVCiE8by4kWA//1m1Q4fgMSQcdiPD8w5AyoFmQut30uzUyrS9Emx5/Dq3vo10IbNmKuvUb4d+8BZHZM2G0t2NwyTEQTUf9++tjs0AdcjCMKZNHt5WpneQYSrWvTrQOfcmqTV+XXM4VkxNtOXxxoi3Ewwx6bX8fWn/5KFQ4jMiMQxE5ZDrCh83L/3MkgsCmzfDt3gOruQnhQw9B3cYPoQ0MIDr1AEQPnp5622KF+DImbQ0cLyXXet0Xu/X0pby+RA+AHjkMKnoAYDVBAhsBKGiRWVDWOLiN52d/2rcQrsz+1L66vG06gAO1qwSr7i+Aryt1YXqHqGkIz52FOtOAr6cXANCw+p0Uia+nB/2nnhSvn7SB5G2lbzfb+kK02co264ypU2C1NKP5d3+AsgT176/HYDAIq7kZUApDxyy0CaaE8ZdK69CXMWudxFRp2hL40vzs7+HfuRPKktjn6LRT4uvz/BwphcjMQxGZeWhiUeioBTaN59iWx3ypCi19sdcWqy8qZkxe0FaKL4GPoPWfA2W2x5aFOmwqkUwIFOyNLiXlbs8ZvP2pShD/DkDy++8MbNueeOJ0OsaECZD6+mKGVlb8Oz+CsiRRjh50IAe5krIQ2LI1cewZ7e2QuoDLERFCSHYs/w63QyBVBK9UVAsSxMj1zuxYwSAy3fulDQ0VMajyYwVTE6JK3x9SOVjBILT+gdhvV6GQ2+EQQkhOlFW5PyK6jlIo+2/zHp+5k1cqqgQtNB9Q+SUVoSPmA7BPQfT9ffBv3pKYPSlBfpt2XRudNg3G+NaRma7WvOt8jv8K2dcxacuFF/a1zL4MHb0w8d7X3QP/1m0V+zkqO17YV/pSuLZceGFfveiho5gUYNVDRWaWLBxSe3CgdgUjEFh178JseB3QewArAGiDI4LhhFbiD8VTSHQ6wdXvoOXJp0ekcTNE02COa0HX5y6D1TrOvpNKbDdDuRza4fU2Wt+u3Wi7/0GooSFA0zDYcTT6zj5zRGjXTrY2ShG/C77kpBCt131x6mG2upkwTbQ+8hjq130A0TQYrePQ/bnLYI1r8e6+lsOXUsbkBW2uuk607Isyt1NtvgzPEDf8o1epfDHj5/7hW54lSWDVA1oUiE6CQgDi2wlAQUWnQrQQ4NsLWA3Qh46GNrQYqsy/Q3t9oLbZeTQgA7mFxUQ1Qp9gMzW+R2BSUcGYDa/CbHwJiRkdhv8OIwCgoEILAG0AUrcpRav19iL47p+hhkKITp8Gq64OdZs+hOg6QofPgzlxYvaTQnpb6R1cPnXTO8dR8WdpJ4tWhcOoX/tn6F3dMCe0YWjB4cDwPe4FbNd2X4vti522SL6UTZu8rpZ8sQT+rdtQt2ETxKdjaP5hsNrbx+4hUB2+FPvYAorvS4n6orJpkbSevozt/IO0crF9sSw0vvgy6t5fj8jc2TDGtyJ05AJAqdRn0RToiwqF0PzUb6D19SM6cwaMiRNiM9FZVqydMfiiwofD3/dJlBOvJxVG5zFwI6nwTXizvG06gElFhSIqhOiEuwBl5hICkYMB/05Ai5QnOEIIIYSkEHx7DVqeeDrx3b3r0osQmTkT0It7BaDl179B8O3VUJZANA17/+F6WI0NKPR+fF/X56GZk4oUZW6YVNjg8aSCA7UrFPHtzJ1QAIASILAVUJwBiRBCCHGLwIdbYt+U479oRg49pOgJBQAENm4amYluQhuspsbCNyoKEtgCDJUvqfA8yoUnXCtvD4X2dnQkC/78pcLckRBCCHET8ftTrhaoqFGadgL+kTuYotFibRUQTpNNssOkokJR0WmA2RS7vSkboqBCRwBGW25tsajg28pKCn2xx4kvteQhfbGHvhQOfbGnxL4MHTEfKum5ScE1a0vyHKWho45MjI/w9fTCt31nYe0IAGjQwrOLEV7VIFCuvLwMk4oKRUGDr+88AHosWRA10iEOlwFA6iF16wGYAOKa4ReQuZy+Lv2VTevWdvOt60RLX0rvoVvaUuyrEy19cX9fK8nDXDGlv8q1r7Xqyxi2Gz1oOgaWLokVNQ2NL70C3+49RfdlcHEHIoccnGhn3K+fiT07R8Rh/MPfJRT0vo9DSQMIyQYHalc4ovXBql8L0buhzPGAMRni/xCi9UMCGwEVTZ0ZIttfIPvMFl7Uwqauk32lL4X7YtcmfaEv9KV6fclXS19stf5t21D/7l+golGEZxyK8OHzYmMriumLaaHu/fWo27ARomsIzT8c0YOmA5rKL34rCBU9FMpqgR46EspsQ7nx+kDtaNdiuDFQ29+2srxtOoBJRZUSbXoaUv8u8n0gHiGEEEIIAEAAfeBU6ENLXAvB+0nFcS4lFa/lLe/s7MS//uu/YuXKldB1HZ/85Cdx6623wucbPdb2oYcewn333Yc9e/Zg0qRJ+OxnP4vLLrvMUXi8/alKkboNTCgIIYQQMiaswHq3QyAFcuONN6KhoQEvv/wyHnnkEaxYsQL33XffKN3vf/97/Pd//ze++c1v4q233sIdd9yB//mf/8Gzzz7rqD0mFdWK8L+WEEIIIWOFM0dmR8Uup5TzlbhHLTdbtmzBypUrcfPNNyMYDGL69OlYvnw5HnjggVHa3bt34wtf+AIWLlwIpRQWLVqEJUuWYNWqVY4c4RFTpWjhw2EF3+DVCkIIIYQ4RgvPczsEkoH+/v6UciAQQCCQOuXv+vXr0draismTJyeWzZw5Ezt37sT+/fvR0tKSWJ5+m1NnZydWrVqFf/7nf3YUF5OKCsfy7YQZfBPQOyFmGzSzDeLfDlEhQOoAhEbECoAIEg/fUSp1sFcyuQYv5qPNVteJtpgxeUFb6OBFt+MvlZa+0BcnWvpCX5xo7erQF3uhEqjowdBCR2TYGHGbk08+GQMDI+M5rrvuOlx//fUpmoGBAQSDwZRlw+XBwcGUpCKZvXv34uqrr8aCBQvwiU98wlFcTCoqGLPuzzCbnwSgAcoCfLtgZTihaPv70PDyKzDb2mC2T4AxrgXWpImJ9aM6l1wdUz7afNfn0mbq/NJPmNm0dttyos0Wn1Ot3fqxeEhfxuZLPlq79fTFfj19sV8/Fi19Kb0vTrSl8qVYfXQxfElsSwOMaVAShBaeDS18OBT0LAERgQtP1I6399JLL6UsTb9KAQANDQ0YGhpKWTZcbmy0f8r66tWr8cUvfhEdHR34xje+YTugOxtMKioUgQGzKT6ARsUfaqNg/9e0MOF/fwKtfwAKQGTaVHR94Up7rR2ZtuslrcpDa1fXidZpTF7Q0pexa+lLfn+daksRkxe0hfqS6bPrxX11oqUv2bWe8SX2WDX//guyVCReoampKadm9uzZ6Onpwb59+9De3g4A2LhxI6ZMmYLm5tGzaj3yyCP4+te/jhtuuAGf//znxxQXR/NWKOLfDmjh7B1HHP9Hu6DHEwoACM+bDZhmSeMjhBBCSIWgBBLYBlFhtyOpIJRLr/w45JBDcMwxx+A//uM/0N/fj23btuH73/8+LrzwwlHaZ599Fl/5yldw1113jTmhAJhU1Cj5H5SEEEIIqRWc3D9GvM6dd94JwzBw+umn4+KLL8ZJJ52E5cuXAwAWLVqEJ598EgBw9913wzRN3HDDDVi0aFHi9eUvf9lRe3z4XYUiiCI64S5ARXLnCKaFif99J7SBQSgAkenT0PU3nytHmIQQQgjxOqKgotPg773c7UgSeP3hd+HukwGU+eF3aETd+Jdyy1yCVyoqFAU/9P6zYgWJZxWC1MFbw+91DfvPORtQCqJp8G/bgfq334lrJFWbaTvpf+1exdTaxZQSr+ShzXO7+dR1onXFlxJoK8mXcnpIX8a+r0609KVwLX0pzr7Wgi/QoQ+cAUIKgUlFBaOHF8DXczlUeC5gTATM1pGrFpIkFCB82Fx0/u3nEJp/GIzJk1D3/gfQ9+wDoEZpAdhvx259qbRpMamBATS8/CoC6z6Avms3VHdPRq2T7eYVkxNtmX0pqbaSfHGipS+5Y6Qv3vTFiZa+ZI+pVNpK9UV8UDJ6BiFCnMDbn6oEy7cdxvifuR1GaYhEMelb34WKRKAAhGfPRPdll7gdFSGEEFIdiIKKHAr//ovdjiSB929/OgXu3P70YpnbzB9eqagSrLq/AFKd/511mz6EFk8oACB0+DzOXkUIIYQUCyWQwCYIIm5HQioYPqeiWhh+VkU1kpZAiM8X+wmDEEIIIcVBIfZdooru4iglbj78zqt4OzqSN1rk0KpNLCKHHATRRg7Vug2bAI2HLiGEEFIUREFFp0BJvduRkAqG38yqBBWZBRU5KPYLQ2LgFbIMyrL5mz64yyNaaWxE/yknxhYphfp3/wzf9p2AZY3S5txHu3acaD3kS9a4nWjt2nHqIX0pffxe0Gaqy88cfSnVZw4OtfRlDDHFHqqmD5wGQgqBtz9VCQoaVPQgSGDrSOeRPttDttkf7LTZOiFVJm183cBJJ8Bsa0PD66ug9/Sg6aU/oW/ZGTDb2nLvWz776kTrIV9s/+a7b+nlQj2kL858KaeHdpp8tMhSLuZnzi1fxuohspTpS/E/c/QlP19yadPjt1oAmFDGgdAHj4NmHADiABVLxsrcaJnbcwaTiirB8u2C1finWCH9mLObFi+fcq5jN5NWkP92c2mHy0ohdMThCB1x+NhiylWuVF/yiSFbO6XUZouJvuTW0hf798X0xUkMuerSl9zriqnNFlMt++JoX4fg77weCpxKlhQHJhVVglX3Z0A0eGJchZNE2om20qEv9tAXe+iLPfTFHvpiD32xRwFQUVh1G6GHD3M7mgpFofyjCLx9kHJMRbWgOA0cIYQQQhzA7w6kiPBKRZWgogcBwdVuh0EIIYSQCkGLTnc7hIpFYpd7ytyqt69UMKmoMAQC8W+B5d8KJXVQ4ZkQ/0ew9E7AbAG0PkDJsDiG3T2j6feO5tImr8umzfberm76ILJiadPjL5U2U3z5aOlLbi19sdfSF3stfcmtTV9PX0p/7kqPySO+qNBRUGYbCCkWTCoqCEEUxrhfQQKbEXt6tgU0vhBfGy/HhDHSO5fhZZJheXpdlabJ1mk50aa3PVx2qs0Wf3ocubT0hb440VaTL060xfIlXZsthmrwJbnsli/p2uT4s9UtRFuJvnipL0pfXkxfRIMWzjHxCSEOYVJRQZgNr0L8W2KFUQOyswzQVhnKkmVdJWjTdZmWZcLt+EulpS/22my+jMVDL+9rqbT0xV7r5DPnxfhLpWVfZK/1gi8QmC2PQeu8DopfBceG4hO10/F2dCQFK7gGiVubikEhnZjXtYXghfjpi3e15Wqn0rTlaqfStOVqp9K0heCF+CveFwG0ECSwsWThkNqD6WkloUJuR0AIIYSQKkG0IbdDqFg4UHs0vFJRQSjjAEC8fUARQgghpDJQ0Wluh0CqCCYVFYCoIZh170BFpwAQ+0FZdmWxWe9Em6kdu22WW2unG6svTj30si92sdIX+kJf6Eul+5JPfLnWe8UXJ9qS+aKgwnOgmROzbJAQZ/D2J49j1q2F2fxbAGZswVjv4xSbZelacahVDrVIe58thvR6diRrC/XFidbLvqRr6Yu9lr7Ya/PxZXh7pe6L6It9TPTFPqZK8qWYfdFYfREftIElDhono+BA7VF4O7oax9L3wmx+GoDpvPNJJ5+6KulvLv1YtPnEkbw9JzGPFacdOn2x19IXe20l+FLIF5uxUKq+KJ9t05fMWvpir61WX5QBc9xjkOEfLAkpAkwqPIxV/w4ALXvnMaqjKJI2V10vaOmLvZa+2GvpS+FaemivpS/2Wvpir/WELwLo/ZDAhw4qkVSUSy/vwqTCy2j9yH2/BSGEEEKIc0TrdzsEUkVwTIWHUcYkoG6d22EQQgghpApRxmS3Q6hYBBxTkY63o6txtNBRgNRh1DSyTmaKyLTOrjxWba66TrSFxFQKXwrxMB36Yg99ca4tpG4x96dY26mEvqgUxwR9KXw76dAXe0ZpFVTkYGjGAVkqEeIMJhUeRkkD9IGTUhc6vRsqX70UoM1Vz4m2kJicUIoTDX3JrKMv9rqxbtcLvhQSf6m26/TYKpUvTqAvhenpS2Zd1iTDD73/NIeNE5IdJhUeRrRumE1/QErP4GScjhe0dnWdaN2On76UVzust3tfzHYqTTust3tfzHYqTTust3tfzHYqTTusHwteiJ++lF6rojBaHoc4zmpIAqXceXkYJhUexgyuASA5OoYcZS9ovRiTF7RejMlr2nK1U2nacrVTadpytVNtWi/G5AWtF2MqmlYAXzfEvzlLJUKcwYHaXkbrdTsCQgghhFQpovcCUbejqEw4UHs03o6uxlHmBLdDIIQQQkiVogx+zyDFg0mFh9FCRwHwYdTsT8mUalaMYmrdmNWjErT0JXeZvtiX6Yt9mb7Yl+mLfbmmfVFQ0SlQxoFZKhHiDCYVHkZZzdD7Tk1dmG1GB7sOJt9Op5TafJbZLS90X51o6Qt9caKlL/TFiZa+0Bcn2nL4IgHofR+HGvPIdYLEaPhyv7wLkwoPI9p+mM3PI6UnyHZM2Q3ayncgl1NtPssK1Ra6r0609IW+ONHSl9L6Um0e0hf64kRbDl9UBGbzU+DsT6SYcKC2hzHr1wAwM3caQP4dTLG1Ks91hWidxuQFLX3JXaYv9mX6MjYtPcxdpi/25Zr2RSD+PRD/NqjoQVkqkkyI4kDtdLwdXa2jd7odASGEEEKqFNG73A6BVBFMKryM1ep2BIQQQgipUpTZ6nYIpIpgUuERBCZE3wfR+mNlFYKKTAegMg/EilXMXvaCVvJcV86YvKClL7nL9MW+TF/sy/TFvkxf7Ms17YsCjFbAaoTAyFKRZEa59PIuHFPhAcz6t2A2vgRoofiCRkAbBJRg1HSywx1DPsdVObWSoa5dJ5dJW+yYKkGbvswLMXlBm77MCzF5QZu+zAsxeUGbvswLMXlBm77MCzF5QZu+LJ9zl5fiL5ZW74HR9mPAqoM+eDy0ocXgbFCkEJhUuIxZtxZm83OpC7WBkY5ASWrnppD914hknPQNhWoz1XcykCzfbWbSetGXfLVejMkLWi/G5AWtF2PygtaLMXlB68WYiqUvlS/p55Sq80VGNFoYZtMLgPigh45xGFAto1D+G368nfTx9ieXMRtfGf1lOFfHp/Jcl44XtKVsh75Un7Zc7VSatlztVJq2XO1UmrZc7RRL66SvLySGQupWmy8CmA2vglPMkkLglQoXERiA3uN2GIQQQgipZRQAfQBQQ4A0uB1NRSBKofxXDnilgmREB6w6t4MghBBCSK0jPkD4nYSMHSYVLqKgoA0dk3kwdqay17S56pZKS1/stfTFXktf7LX0xV5LX+y19MVeW+m+iIIWWgAozgRFxg5vf3IZffAEiN4FqV+X3+wc+c7gMRb9WLW56pVKm6lesaEvGbYdPyspNdKQSGrZLo6q92WMcdAXey19sdfSF3ttpflSrnN6zn3VYQVXwwquhopMh95/JjRzkoPAahE+UTsdJhUuo6DD33c+rMG9EP92mHXvAf7t2Svl26k4/dUi24CwXNpMMTnRprebS5urrWy69Lby3WYl+GIzAK+Yvmg9PRj/4MNQkSgiMw6GMkwE1m+AObEd0UmTMLB0Cazx4+LJhU37xfKlEA/t6mei0o4Xr/qSrc1s7XvBFyd9EX2xpxBfcmnd/sylt1vqc9dYtpnLF23kCoX4t8No/Rn83X8DZY1z0CipdZhUeATNnAgRP8zmZ0evzNYxZCsXonUSQ7b1TrRO26UvubUl8KX52efh27cPyhL43upJrNa3bodVXw9rwvjs28nWbrk8rObjhb4UrrWjHH2Rkzazra82X7zuodN2ve6LEgBRmME34Rs4LceGahcO1B6Nt6+j1BiW/yO3QyAkJ4Ft26Es+5/MotOmAqZZ5ogIIYQUFSWQXHdNEJIGkwoPoayg2yEQkhOrsSHjVXhtcBCJ254IIYRUJqIAq9HtKEiFwaTCQ6joQYDZjFGzQWUj/dud03suqS1fO5WmzVB36JiFGSX17/4ZsCwkBnEXOyYvaMvVTqVpy9VOpWnL1U6lacvVTqVpy9VOLq0SaKEjHWywFtFcenkXb0dXYyho8O3/NCD1sQ+8xP97JOk1jF0527rkZU61du9zlceqzbWvTrTIU0tfHPkyeMzRCB25wFaqDYUw7pHHY7dAiZTeFyfaWjpenGjpC31xovW6L255WG2+WEGYLb9CZMKdMBpehoC3tZLccKC2x9CMKfB3LodVtw7Qe6GMdsCYDKlbD9E7YdWviQmTL2ZIvCw2y7Npk9dl09q9d6JVyFw3U71M2uGy0331ui/5aDPFX2ZfVGgIamAQFmK/SoSnH4j9550D35698O3ZC6ulBTAtwOfLvd1M74t1bA2XS+lLuY4t+pL5fbl9EdSWL076IvpSmb6ka9VQfP8HYTW8CvHthX//BSAjyKiDqRyUuz1nMKnwIAp+6OEjUhcOLUZ03EOIfauT9Aqpf9OXF6rN9t6pNlu5mNrkZZXgixOty760PPsH1G36MHYe8+noufQiSDAIs30CwofPy7yNfGKuYF+Kqs323qmWvuTXTiV7mCsmJ1r6kl87NeWLQOo+gOXbBc2YYiMmJAaTigpC/NsxKqEgpMwEPtwMFR8zYbS3Q4KcYIAQQqoaURD/VoBJxQiKD79Lx9vRkVTE73YEhMCqrx+5Wh4OuxoLIYSQciCA1LkdBPE4TCoqCC10BBzNDFVMnFwgKZXWi9SgL0MLj0xcGvd198C3fWdsxqdkatCXokNf7KEv9vAzZw99scfxvurQwnNLFAypFphUVBD64IlQw5ceRRvpFCTpBcQSj+TkI5M2vR7SyqO2OwZtIXXTtXYxlyL+qvdFRmZnchJD/DW4uAPhGTNiqzUN4554CtrgEBKzPVWsL2XQFvPYGmvdovuS3r8kl+36IpU7nrHuWzZtMX0p17HlBV+caOlL9fpiNSDa9kNEW34By78VBIj9uubGy7twTEUFoaQOvp4rIIH1sPxbIb59kMCW+EokPvwqejCUMQEqOh2ABvFvgehdkLoPU7Xpf4fXpTSah1bStOl1JYs2/a8TbaYY7OJ3oq1SX3zbtqPl2T8gMn0azLbxCM+cCau1BdC00dvNtq9+H3ouuwSBjZtQt2EjRPdB7e8DGhvH5ksh+1qM48Xu/yaT1s6XbHXT1xXiixNtuXwRDVpkHmCNgwrPAPy7IXoXlNkKFZ0KK7AJ0EJQkekAfBD/ZojeDanbVNm+FNoXpZer5XjxQh9dzOOdvoy81/oABUhgM4zAZvj2XwAtMhuEJKNEnDylqnrYt6/P0fO5vIaoMKIT7gKUYbPSB3/n9VDx+x8FRkyr8f73WkUNhTDp29+FMmJzjYcOn4eeizk9ICkQUVDhufD3nZ+fHCaiE+5GYrpKQkjlIQCsVvi7roYq4QdZKaC9vblk2y+U7qErAQyVudUgxgfvK3Ob+cPbnyoU8e2wTygAQBkQ384k7S4mFDWOf9v2REIBAOGZh8ZugSKkEJRAApvylotvL6AxoSCkolEA9B5A2+92JMRjMKmoWHLcuSbJ63mXW83jTz0GlJEhISXEMfn3L0rYFxFSNfDzTNJgUlGhqOiBgNWYeh8oAIgCzCYoY9qI1pgMmK1wbeYo4jqRg6bDbGhIHC717/0F0HVXYyJVgChoofn5680JgDGBfREhlYwoqMiBUNLodiTuopQ7Lw/DpKJCUdDg2/8JAHrsBC2In6g1+Po+AZX0X6ugbLTxlZnKwMjyjFqb93avbNpsZSfabG0Wqi1H/KX2RdPR+6lPAroOURr8W3cg+NqquFZqyJdsx3eW8qh1AliCUTNolX1fFUZmU8rz81lwDGqkLXMC9METkC8KCr6+cwDx2cSbT19U7n0dg9b2eCmS1mv7Wm2+ZGu3Kn3J9ZlL+qwn3itA6uDr/ysQkg4Halc4ovXADK6Jz7jSBn3oKCirNYN2P8z61RBfZ2x2FmNy7AmZKgREp0LBHytDh4ocDNF7Ib69UGYLVHQqJLAVovVDfNsALZR5xgy7WSWQVvaSNp/4S6Ut877q3d0Ivrkavu5uRNvaMLikA9LU5E1f4ECbpy8qehAgfoh/M6BMx9vVu7rR+MKLMCZOhDmhLfZ34oTYynL7YjVAGdOgrGaoyHSIfwdE3x8bb6UNOPIlZb0EoKIHQVmNUNGDIL69EL0bMNqgme0Q/xZARaGiB0MLzYeC84dyitYX74v2QZnjoKJTIIFtgBoCjAOgpC7eF6l4X9QH8e2BMpvjfdF2iNYH8W2PjdFIx0Ofubxi8oK2GPtKX7zti9UEZUxNOqdvB9QgYEyGshris0kqqMhBEG0Q4t8FZTZBRQ+E+LdD9H5o0SnQQkdBSQNKjdcHaneF/gZuDNRuq/9xmdvMHyYVxBFmw59gNrwCKJpHKgxB/KR6ACSwwfkxbBiY9O07oYZCUADChxyE7isvL0Wk+SEK+sAp0IeOSywy69+C2fRc7ItEAdvVho6Gb+DMwmMsIWZwBczGl9gXEZIvAuj9Z0MPHeV2JHnBpMIObycVvP2JOMIKfMCTOKlMFAC9HxLYOKZj2L/jI2jxhAIAwnNmuTyDlsCq+yBliRVYX/hm1ejtehGrbj0yX9oihNhRlD6CxFEuvbwLkwriEMXzOKlwxtgpa6n1lCcudabvi2azrBjb9SKVECMhXkKBX/tIKeHRRRyhheeBJ3NSkYgCzHFQ4TkYy+xD0alTYTY2JnLqur984PoMWrHPY1I5MgcFZ/2iRm3Xi2jhuW6HQEhloQRaeI7bUZAqxpNJxbp163DVVVdh8eLFOOGEE3DLLbegq6sLALBmzRpcdNFFWLRoEU477TQ8/PDDLkdbG1j+DxFtfgJmYBNgBeMDOtNeyPC3VFq7Ok60bm+fvpRRG7ts7Ov7K/gGTgGkYSSxyHf7mob9534c0FRsBq3tO1D/xuq4VsruizIOgDa0EMlooQXxwegFxGK2Qh8cGafhVbShRVDGAfwclVtb6rhLvX0vaMeyj2Opk/ZSkUOghQ8DKRIqdi4o58vrU8p6bqB2KBTCGWecgYsvvhjXXHMNBgYGcOutt0LTNHzzm9/EWWedhRtuuAGXXHIJVq1ahWuvvRb33XcfjjzySEftcKB2/hgNL8NqfAUQFbsXPZNvuWa6yEdrV8eJtpixeCnuSvdFdChzEiB1UJHpgDYA8e8GzJbYLGS+jwC9H4hOgbKCsZmAgLg2BPHvAsxGaNEDYPl3A/p+IDoJmjTB8m8DYEFFpwMqGnuavAShRafB8u0F9B4oYxL0oWOgzHYAgKgBmMG3YjMZST20yIGwfJ2ArwtitEEz2yD+7YAKx54JAxUrSwD+7QE0vPl76F3rEG0Pov/UebDa2ka8ED+UORECAXwfZfdHfFDmxBy+DCT5shUAoIXnQAsdCbuZlwQmrPq1sOrWJfkSiW1LGqBFp8Ly7QH0XsCYCM1siXmoTGiRGdBCC6GkPsN/tLcQRGHVvwOr7gOIigD6bkBZ1fs5KlXc9GVEE50IhQAQnQxlNcb7IiuWrKswxP8RYKV/jiZBM5thBbYBMGN9hjKS+qKpsf5F7waMdiizFeLfhtgsagcCkHj/UgcVnQ7Rh/ui8dDMCRD/DsRmaZwGBS2u9cc+2/FZGsVshWa0Q/w7ATWUNKPjNgAatPBhsR8dUDnPJ/L8QO3wFyAIlbVNhXq01f1fWdt0guceh7hz507MmzcP1157LXRdRyAQwCWXXIJbbrkFzz33HFpbW3HZZZcBAJYuXYpzzz0XDzzwgOOkguSH6F2xhAJAYnBrtkRZZfjrRGtXx4m2mLE40ZY67or2xQKsIPz7L86ygSSyTKihp/Xh2U6RmdYpaYRv8CQAJ2XWZoqhBRg89TxYvp0wxt9vIzAg5jjAn5RQZPxrAlYD/PsvyrIXyTHlfiaEgg49tBB6aGFGTfq+6uWewKRIKPihh46BHjoG0dafQZTk/kxU9OeoAK3b2y+Vtthx+/bC17UcymqJlbP1RellB1oMLc0STLo2f2nZJyOqaRSyH1SlatO7eO72pxkzZuBHP/oR9KR7lZ999lnMnz8f69evx5w5qfcDzpo1C+vWrSt3mDWDFXgffPotKRglkMAmCKJuR1I0rLr3AbHpQpUAde8Dek/u/l8JJLARAqMUIdYMoobiV5Z4+ZkUDmdIImRseC6pSEZE8J3vfAcvvPACbrvtNgwMDCAYDKZo6uvrMTg46FKENQBP0qRYKCDz/QeViJVlnYP9rDpf3CDb/wUhTlCA4vFEyFjwbFLR39+PG264Ab/+9a/xs5/9DHPnzkUwGEQolHrvQygUQmNjo0tRVj8qPJOJBSkcUVCR6bF7lasELTLT/suHKCAyAzCbcucKEnt67VieSk1GUNIIFZ3Eq6qkcJTEPtuE5EJp7rw8jCej27p1Kz796U+jv78fjzzyCObOjU0dOGfOHKxfn3pZcsOGDZg9e7YbYdYEmjkZauioWCF9VgnYlDPNQOFEa1e3UG2mmOy2U25tsffVc74oADr0gdNQTajowfHpaZG6r+KHb+Bj8PWfObIMyOLLqWWLuZrRB84AoGXxG/bHbbm1dnUzbacYWjf3taJ8iR032uCxUGbSxAuEkLzx3OxPvb29OP/883Hcccfh9ttvh6aN5D3d3d0466yzcO211+Kyyy7Dm2++ieXLl+P73/8+jjvO2RSInP0pfwQCq24trOAaiOoHlBGbkWbYv2yzaqSvT9dmK5dKCwBSB1hBKKmDik6F6Pshvs6Yxtcz9nbEB1hNAHzQotMhagDi2wNYTVDGJMC3B6L1A8YUKKmP3QcOA9DivmaiVL5AAWZr7J0xDUoULP92KAnEfemF+LoAoxWa1QbxfQRRIajoVCjo8ZmXfNAiB0L0foi+F7Ba4rOQ7IKoQajodPiGliRmXqomBBas+tWw6tdCVAha9GDog0ugrPEAAMu3HWbDytgxYNaOL25h6bvjfu+ILdB7UgVe7IuK1U46xdIqAMb42BVroz0+y1Gs31LR6VAwYfl3Qkk9VPQAiN4F8fUARhs0qxXi2wFREajoNCgFWL4dsf7FmAbReuL9y3ho5niIfydEhVP6FyV+qOg0iN4H0fcB1jhoxoT452gIiB4ADf54X6RDi06DqMF4v9sMzZgYmzFODST1u9sAKKjoNECFIL7dUFYTtNAiaOHDoTw+GLZW8PrsT52Rv4cbsz9NCPygrG06wXNJxb333os77rgDwWAQKm0+3rfffhtr167F7bffjg8++ABtbW1Yvnw5LrjgAsftMKkYG2b9mzCbfpd9AKrA6xMUAKKgDR0N38CZiUWWvhdG248L3q6KzIF//6fyrhJteQQS2OjObWaiAKsJ/q6/h/LmhUtCHCNqCNEJdwMwvd8XVQIC+LuugbJa3Y6E1BBMKkbDpMKjMKkYG9HWn8bmu6+GE7VVj0DnjYmi0fASrIYVhX+5FwX/vn+EymPGZlFhRCd8x3U/fd1XQDOmuRsEIUXCrFsLs+Vpt8OoHkRBHzgF+pD3H4pIqgfvJxXXupRUfK+sbTqBP00SR4iKuv4FuGik326kDBRl55QAysxT7JFfUlX1TPVKCFQ0++0+xDHCPoIQkgMmFcQRWmQGqmKGFVFQkUNSFmnRgwufSlAUVHQylNTlqQ8CRru7X4DEB2Uc4GIAhBSX2GfZ7SiqCCXQ0vpLQghJh0kFcYQ+tDj2RTg5scg1+0a2WUjsZurIpLX7a6dFLu3wrDsnpSxWkRlQkYMz71um7abNHuJkNh8FBV//aQAUUmatKYsvw/GenH8SREgFoMwJ0IYWOuuL7P7aaVFmbab47daNtd/NoVXhWVDGgSCEjCBKufLyMrlv+iYkCWU1wd/9ORgNr0Lq3kfs58AooBn2JzyV9B42ZdiUM2ntTszZtms1ASoCmK1QVmt8LEgEKnII9METoJmTUvcNCr7eC2E1vA6z/h1AhYHoZCj4If4dgOjxGZH6Ab0TsJqhzDaIby+gBqGiB0IfPB6aw5OvFp0BX++lMIOvxtqxglDG5NhsVFofYLRDSWNsHSwgMg1KMyC+XYDUxbR6N6DtB8w2KKs5vq/x2xW0sK0vymyDNrQEevgwR/ESUgno/cugjIkwg2/GZlcTH6APFq9/SamjAVIPwASiU2MTKPl3AhKAik6B6PsBvTveF42LfXZVJD4bkR7X6lDGVIjWB+hdgNUCZY6PzWKkhoDolJG+CBpUZCqgDUJ8++Kzy7XHtNogYEyKz3IUmwVrZJajPYDVAGVOhOidMV+MiVDSMNK/RKdBqWi8fwlCH1oIbWgxZ0QihOSEA7VJQURbfgkJfJg6uNnJ7E8lninK3/W3NTtlp9H4B1jBN0YPPBfA3127vpDawwqshzHuV9lFhfRFogCzLfa54pdvQoqC1wdq74ve4MpA7Xb/nWVt0wm8/YmMGVFDkMCm0V9anZxTS3n+FQWz/r0SNuBtrPp3MsxkpWDW/bns8RDiFmb92tTbGu0opC9SAvg6IfqeAjZCCCGVDZMKMnZU2OODIRWgyvsrgqdQkUwrAK2GfSG1hwqV5zkw/FwRQmoYJhVk7FgtgNno7sxF2VAWVLR2Bxeq6DT7X2dr3BdSe6jo9NxXKgpFfFDG5NK2QQjxEMqll3dhUkHGjIIGfeCU2DFuN7gx/X2u9blmRnGkVYDRDi081zb2WkAfPDn2JmU2KwUYE2vaF1J76ENHI+usdcjyPpM2bWC3PngclNQXJ2BCCKlAmFSQgtDDR0Lffx5gTogtsOqA6NTYXwAwxwPRA2Inc1HQd/dC7+yMrYtEACvpIXHDycnwKzkpHz6BD29X6kdrh6dFFR+00FHw91yW11OtqxUtehB8vZ8ZeQZFwpdLoaC7GxwhZURJI/zdn4UKz4nP1KQAY0pSv+WHik4FrGC83BLrx0SL9S/GZMCcGFsnPihjKmA1JrR631nQBk8o+34RQtxDlObKy8vU7jcuUjT08GHQw4dBYCE2MauCxL/tq3jeKhA0PH896tc9ACUmLJ8Pe2+8FtKQdghmurI3nHCoKPTui2COf9hGE4bWfzr0oQ7OwBJHix4MreezKf83hNQiymqFv+9TkL70vil7v5VJO1xW/G2OEEIA8EoFKSIKWuJkGzvtjhxeygij/v2fQ0nsykRk5qGQpkZAc3AIKgAQWI2vxn5BtMGqX80vzjYk/98QUsuM6puy9VtZtMNlQkitwjEV6bBHJGVBRfuhrJHZiKymxrFuCaINwXawhgKgDYxxu4QQQgghZKwwqSBlQerbYDZOTaQC/h0fjW1DyoIWPSBDIyp2XzQhhBBCCCkrTCpIeVAahhb/S2JohH/XbgTWbwQsK3u9UTM6TYA2cKr9TC4A9MHjixw4IYQQQkgqAs2Vl5fxdnSkqogc/lkMnHoXpGEKAKD1kScR2NA3Mj7CqgOM1pEKRgsgDbH3oqDCc+HvuRSaNMHfczlU9OARrTkBvt6LoBl8/gIhhBBCSLlRIuLVR5eVlH37+lCbe+4BLBNqaC8k0Az4GyGIxp5EazVCQYOoEAALShpis69oA4D4oYanjE0ipjUBaeBAZEIIIaRKUApob292O4yM7DFvhiBU1jYV6jFJ/6+ytukETilLyo+mQxqnJIoKfsDyj5STHiCloACrKeOm+LApQgghhBD34e1PhBBCCCGEkILglQpCCCGEEEIcIFAuDJz29m3evFJBCCGEEEIIKQheqSCEEEIIIcQRbjzhmlcqCCGEEEIIIVUMr1QQQgghhBDiBKWh/L/Ne/tagLejI4QQQgghhHgeJhWEEEIIIYSQguDtT4QQQgghhDggNqUsB2onwysVhBBCCCGEkILglQpCCCGEEEKcwIHao/B2dIQQQgghhBDPw6SCEEIIIYQQUhC8/YkQQgghhBAHcKD2aHilghBCCCGEEFIQvFJBCCGEEEKIIzhQOx1vR0cIIYQQQgjxPEwqCCGEEEIIIQXBpIIQQgghhBAHiFKuvJzQ2dmJ5cuXo6OjA0uWLMHtt98OwzBstS+++CLOPfdcLFy4EGeffTZeeOEFx54wqSCEEEIIIaTKuPHGG9HQ0ICXX34ZjzzyCFasWIH77rtvlG7z5s24/vrr8cUvfhFvvPEGrr/+etx4443YvXu3o/aYVBBCCCGEEOIIzaVXfmzZsgUrV67EzTffjGAwiOnTp2P58uV44IEHRmkfe+wxdHR04IwzzoDP58PHP/5xHHvssfjFL37hyJGanf3J4RUkQgghhBBSJrz+PU2hzrU2+/v7U5YHAgEEAoGUZevXr0draysmT56cWDZz5kzs3LkT+/fvR0tLS2L5hg0bMGfOnJT6s2bNwrp16xzFV7NJxYQJzW6HQAghhBBCKpADcKsr7Q4MDGDp0qWIRCKJZddddx2uv/76UbpgMJiybLg8ODiYklTYaevr6zE4OOgotppNKgghhBBCCKkk/H4/VqxYkbIs/SoFADQ0NGBoaChl2XC5sbExZXkwGEQoFEpZFgqFRulywaSCEEIIIYSQCsDuVic7Zs+ejZ6eHuzbtw/t7e0AgI0bN2LKlClobk69W2fOnDl47733UpZt2LABCxYscBQbB2oTQgghhBBSRRxyyCE45phj8B//8R/o7+/Htm3b8P3vfx8XXnjhKO0nP/lJrFy5Es888wwMw8AzzzyDlStX4rzzznPUphIRKdYOEEIIIYQQQtxn3759+NrXvobXX38dmqbh/PPPx5e+9CXouo5Fixbhq1/9Kj75yU8CAF5++WV861vfwtatWzFt2jTcfPPNOOWUUxy1x6SCEEIIIYQQUhC8/YkQQgghhBBSEEwqCCGEEEIIIQXBpIIQQgghhBBSEEwqCCGEEEIIIQXBpKJKME0TV1xxBf7pn/4psWzNmjW46KKLsGjRIpx22ml4+OGHXYzQXXp6enDLLbdgyZIlOPbYY7F8+XLs2bMHAH0a5r333sNll12Gjo4OnHjiifj617+eeGInPQK6urpw5pln4vXXX08sy+XLY489hjPPPBMLFy7EBRdcgLfffrvcYZcdO5+effZZnHfeeTj66KNx2mmn4e6774ZlWYn19CmVPXv24Pjjj8ejjz6asrzWfLLzaN26dfjc5z6HRYsW4fjjj8c3vvENGIaRWF9rHgH2Pj399NM4++yzcfTRR2PZsmV46KGHUurUok+kDAipCv7nf/5H5s2bJ7feequIiPT09MjixYvlZz/7mUSjUXn11Vdl0aJFsmbNGpcjdYfLL79crr32Wunt7ZW+vj657rrr5O/+7u/oUxzTNOWEE06Qn/70p2Kapnz00UeybNkyufvuu+mRiLzxxhtyxhlnyJw5c+S1114Tkdyfsddee00WLVokb7zxhkQiEbn33ntlyZIlMjg46OaulBQ7n9auXStHHnmkPP/882KapmzYsEFOPfVU+fGPfywi9GnYp2FM05QrrrhC5s2bJ7/61a8Sy2vNJzuPOjs7ZcmSJfLDH/5QIpGIbNu2Tc466yz50Y9+JCK155GIvU/vv/++HHXUUfL222+LiMibb74p8+fPl1WrVolIbfpEygOvVFQBK1aswHPPPYezzjorsey5555Da2srLrvsMvh8PixduhTnnnsuHnjgARcjdYd3330Xa9aswR133IGWlhY0NTXh3//93/GlL32JPsXp7e3F3r17YVkWJD7LtKZpCAaDNe/RY489hi996Uu46aabUpbn8uXhhx/GOeecg2OOOQZ+vx9XXnklxo8fj2eeecaN3Sg5mXzasWMHPvOZz+DUU0+FpmmYOXMmzjzzTKxatQoAfUrne9/7HqZMmYIDDjggZXkt+ZTJo8cffxyHHHIIrr76avj9fhx44IH4yU9+grPPPhtAbXkEZPZp8+bNMAwj0Z8rpaDreuIpzLXmEykfTCoqnM7OTtx222349re/jWAwmFi+fv16zJkzJ0U7a9YsrFu3rtwhus4777yDWbNm4Ze//CXOPPNMnHjiifjmN7+JiRMn0qc448ePx5VXXolvfvObOOKII3DKKafgkEMOwZVXXlnzHp144on43e9+h49//OMpy3P5smHDhpryLZNPy5Ytwz//8z8nyqFQCH/84x8xf/58APQpmddeew1PP/00/u3f/m3UulryKZNH77zzDubMmYMvf/nLOOGEE3DGGWfgySefxJQpUwDUlkdAZp9OPPFELFy4EH/913+N+fPn4zOf+Qy++MUv4sgjjwRQez6R8sGkooKxLAs333wzrrrqKsybNy9l3cDAQEqSAQD19fUYHBwsZ4ieoLe3F++//z42b96Mxx57DI8//jh2796NW2+9lT7FsSwL9fX1+Nd//VesXr0aTz31FDZu3Ig777yz5j2aOHEifD7fqOW5fKk13zL5lEx/fz+uvfZa1NfX48orrwRAn4bp7OzEv/zLv+Bb3/oWGhsbR62vJZ8yedTb24tHH30URx55JP74xz/i7rvvxi9+8Qvce++9AGrLIyCzT5FIBAceeCDuvfderFmzBvfccw/uuusu/OlPfwJQez6R8sGkooK55557EAgEcMUVV4xaFwwGEQqFUpaFQiHbk1W1M3zJ97bbbkNTUxPa29tx44034sUXX4SI0CcAv/vd7/Dss8/i0ksvRSAQwOzZs3HttdfioYce4rGUgVy+0LdUNm3ahM985jMwDAP3338/mpqaANAnABAR3HLLLbjiiiuwYMECWw19ivXlRxxxBC688EL4/X7MmzcPl19+OX7zm98AoEfD3HXXXQgEAjj++OPh9/vxsY99DOeccw5+8YtfAKBPpHQwqahgnnjiCaxcuRIdHR3o6OjAU089haeeegodHR2YM2cO1q9fn6LfsGEDZs+e7VK07jFr1ixYloVoNJpYNjzzzGGHHUafAHz00UeJmZ6G8fl88Pv9PJYykMuX2bNn07c4L774Ii666CKcdNJJ+PGPf4xx48Yl1tGn2Odv5cqV+N73vpfoz3fu3ImvfvWruPrqqwHQJwCYOXPmqH4qeRwYPYqxc+fOlPMdMNKfA/SJlBBXh4mTonLrrbcmZn/q6uqSjo4OuffeeyUSiciKFStk0aJFsmLFCpejLD+RSETOPPNMuf7666W/v186Ozvls5/9rFx77bX0Kc769etlwYIF8oMf/EAMw5CtW7fKJz7xCbnjjjvoURLJM6zk8mV4NqgVK1YkZlg59thjpbu728U9KA/JPr399tsyf/58efjhh2219Ok123WnnnpqyuxPtepTskcbNmyQBQsWyP/+7/+KYRiybt06Oemkk+SnP/2piNSuRyKpPv3yl7+UI488Ul566SWxLEtef/11WbRokTz//PMiUts+kdLCpKKKSE4qRETeeecdueSSS2TRokVy+umnp5ygao1du3bJjTfeKCeccIJ0dHTILbfcIr29vSJCn4Z55ZVX5KKLLpJjjjlGPvaxj8l///d/SzgcFhF6NEz6l8Bcvjz++OOybNkyWbhwoVx44YWyevXqcofsCsk+XX311TJ37lxZuHBhyutv/uZvEnr6NJr0pEKkNn1K92j16tVy6aWXSkdHh5x44onyve99TyzLSqyvRY9ERvt0//33y1lnnSWLFi2Sc845R5544okUfa36REqLEolfNySEEEIIIYSQMcAxFYQQQgghhJCCYFJBCCGEEEIIKQgmFYQQQgghhJCCYFJBCCGEEEIIKQgmFYQQQgghhJCCYFJBCCGEEEIIKQgmFYQQQgghhJCCYFJBCCFViGma2LZtm9thEEIIqRGYVBBCSBHZvn075s6di+3bt7sax0033YTHH398zPWvuOIK3HXXXcULiBBCSFXDpIIQQqqQ7u5ut0MghBBSQzCpIISQErFjxw7ceOONWLp0KU444QT84z/+I/bs2QMAeP3113HaaafhBz/4AU466SQsXrwY119/Pfr7+xP177//fpx66qlYsmQJbrrpJlx//fWJqweRSATf/e53cfrpp2Px4sX4whe+gC1btgAAbrvtNrzxxhu45557cM0119hePbnrrrtwxRVXJMoPP/wwTj/9dCxatAi33norhoaGEutEBPfffz+WLVuGjo4OXHrppXj33XdL6h0hhJDKgkkFIYSUAMMw8PnPfx66ruO5557Db37zGwDANddcA8MwAMSSjt27d+N3v/sdHn74Ybz99tt48MEHAQBPP/007r77bnz729/Gn/70J3R0dOC5555LbP873/kO/vjHP+K+++7Dyy+/jKOOOgqf//znEQ6Hcfvtt6OjowNXX301fvjDH+aMdcWKFfja176Gr3/961i1ahWOOuoorF27NrH+wQcfxL333ovvfve7WLFiBS644AJcddVV2LdvXzEtI4QQUsEwqSCEkBLwxhtvYNu2bfjqV7+K5uZmtLS04Ktf/SrWrVuX8iv/tddei/r6ehx88MFYsmQJPvzwQwDAI488gksuuQRHH300/H4/LrvsMhxxxBEAYlcOfv7zn+Mf/uEfMH36dNTV1eHaa69FNBrFH//4R8exPvnkkzjrrLOwdOlS+Hw+XHrppTj88MMT6x944AFcffXVmDdvHvx+Py688ELMnDkTTz75ZGEmEUIIqRp8bgdACCHVSGdnJ8aPH4+mpqbEsqamJrS2tmLHjh1ob28HAEycODGx3u/3Q0QAAB999BGWLVuWss3p06cDALq6ujA4OIgvfvGL0LSR34ai0Sh27NjhONbdu3dj/vz5tm0BsSsq3/zmN/Gtb30rscwwDCxYsMBxW4QQQqoTJhWEEFICFi9ejO9+97vo7+9PJBZ9fX3o7u7GxIkTE8lDJqZNm4adO3emLNu5cydmzJiB8ePHo66uDj/5yU+wcOHCxPpNmzZh8uTJo7al6zqAWNIxTPJA7ilTpoyafnbXrl2YPXt2Yv0NN9yAc845J7F+69ataG1tzboPhBBCagfe/kQIISWgra0Ns2bNwr/927+hr68PfX19+MpXvoKDDjoIRx99dM76F198MX75y1/inXfegWEY+NWvfoXVq1cDADRNw4UXXohvf/vb2LVrFyzLwmOPPYZPfOITicHagUAAfX19AIAJEyZg3LhxePrppyEieO+99/Db3/420danP/1p/P73v8cLL7wAwzDw2GOPYc2aNSmx/OAHP8DGjRsBAC+//DLOOeccrFq1qlh2EUIIqXB4pYIQQkqAruu45557cMcdd2DZsmWIRCI4/vjjce+998Lny931Llu2DFu3bsXy5csRiURw8sknY8GCBfD7/QCAW2+9FXfddRcuvfRS9PT0YPr06bjzzjsTYyHOP/98fOUrX8G7776LBx98EP/+7/+OO++8Ez/+8Y+xYMECXHzxxXjzzTcBAMcccwz+8z//E3fccQduuukmHHfccTjhhBMSsVx55ZUQESxfvhx79uzB5MmT8eUvfxmnn356CZwjhBBSiSjJdQ2eEEJI2Vm3bh2am5sxbdq0xLILLrgAn/nMZ3DxxRe7GBkhhBAyGt7+RAghHuS1117DNddcg71790JE8Mwzz2DDhg1YunSp26ERQggho+DtT4QQ4kEuv/xy7NixA5/61KcwMDCAGTNm4Ac/+EHKrEyEEEKIV+DtT4QQQgghhJCC4O1PhBBCCCGEkIJgUkEIIYQQQggpCCYVhBBCCCGEkIJgUkEIIYQQQggpCCYVhBBCCCGEkIJgUkEIIYQQQggpCCYVhBBCCCGEkIJgUkEIIYQQQggpCCYVhBBCCCGEkIL4/wFJ+ID/fD8KkgAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 1000x800 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "df.plot.scatter(y='latitude',x='longetude',c=df_norm['res_espece'],cmap='Wistia',figsize=(10,8), title='Mapa de abelhas')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "f70cbd6c",
      "metadata": {
        "id": "f70cbd6c"
      },
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3 (ipykernel)",
      "language": "python",
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