1_Tratamiento_nulos.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/ea-analisisdatos/googlecolab/blob/main/1_Tratamiento_nulos.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sS70ZSOuUwQU"
      },
      "source": [
        "# Tratamiento de Valores Nulos"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "J8B1SFRNUwQZ"
      },
      "source": [
        "### Importar Librerías"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "WQFSi2kNUwQa"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xsAftEShUwQc"
      },
      "source": [
        "### Importar los datos"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "8cxjUWBWUwQd",
        "outputId": "d7a10ab6-d30b-4870-c575-ece0859f836c"
      },
      "outputs": [
        {
          "data": {
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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>CODIGO</th>\n",
              "      <th>FECHA</th>\n",
              "      <th>ANO</th>\n",
              "      <th>MES</th>\n",
              "      <th>DIA</th>\n",
              "      <th>DIA_SEMANA</th>\n",
              "      <th>HORA</th>\n",
              "      <th>ESTACION</th>\n",
              "      <th>SO2</th>\n",
              "      <th>PM10</th>\n",
              "      <th>...</th>\n",
              "      <th>ICA_PM10</th>\n",
              "      <th>ICA_O3</th>\n",
              "      <th>ICA_NO2</th>\n",
              "      <th>ICA</th>\n",
              "      <th>VEL_VIENTO</th>\n",
              "      <th>DIR_VIENTO</th>\n",
              "      <th>TEMPERATURA</th>\n",
              "      <th>HUMEDAD</th>\n",
              "      <th>PRECIPITACION</th>\n",
              "      <th>DIR_VIENTO_CARDINAL</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>28079008</td>\n",
              "      <td>2022-01-01 00:00:00</td>\n",
              "      <td>2022.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Sabado</td>\n",
              "      <td>0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>16.0</td>\n",
              "      <td>...</td>\n",
              "      <td>32.0</td>\n",
              "      <td>15.32</td>\n",
              "      <td>87.5</td>\n",
              "      <td>34.51</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>28079008</td>\n",
              "      <td>2022-01-01 01:00:00</td>\n",
              "      <td>2022.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Sabado</td>\n",
              "      <td>1</td>\n",
              "      <td>8.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>27.0</td>\n",
              "      <td>...</td>\n",
              "      <td>54.0</td>\n",
              "      <td>6.82</td>\n",
              "      <td>120.0</td>\n",
              "      <td>46.00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>28079008</td>\n",
              "      <td>2022-01-01 02:00:00</td>\n",
              "      <td>2022.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Sabado</td>\n",
              "      <td>2</td>\n",
              "      <td>8.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>27.0</td>\n",
              "      <td>...</td>\n",
              "      <td>54.0</td>\n",
              "      <td>5.88</td>\n",
              "      <td>117.5</td>\n",
              "      <td>44.94</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>28079008</td>\n",
              "      <td>2022-01-01 03:00:00</td>\n",
              "      <td>2022.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Sabado</td>\n",
              "      <td>3</td>\n",
              "      <td>8.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>17.0</td>\n",
              "      <td>...</td>\n",
              "      <td>34.0</td>\n",
              "      <td>11.85</td>\n",
              "      <td>95.0</td>\n",
              "      <td>35.81</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>28079008</td>\n",
              "      <td>2022-01-01 04:00:00</td>\n",
              "      <td>2022.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Sabado</td>\n",
              "      <td>4</td>\n",
              "      <td>8.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>...</td>\n",
              "      <td>10.0</td>\n",
              "      <td>20.55</td>\n",
              "      <td>65.0</td>\n",
              "      <td>24.49</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 27 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     CODIGO                FECHA     ANO  MES  DIA DIA_SEMANA  HORA  ESTACION  \\\n",
              "0  28079008  2022-01-01 00:00:00  2022.0  1.0  1.0     Sabado     0       8.0   \n",
              "1  28079008  2022-01-01 01:00:00  2022.0  1.0  1.0     Sabado     1       8.0   \n",
              "2  28079008  2022-01-01 02:00:00  2022.0  1.0  1.0     Sabado     2       8.0   \n",
              "3  28079008  2022-01-01 03:00:00  2022.0  1.0  1.0     Sabado     3       8.0   \n",
              "4  28079008  2022-01-01 04:00:00  2022.0  1.0  1.0     Sabado     4       8.0   \n",
              "\n",
              "   SO2  PM10  ...  ICA_PM10  ICA_O3  ICA_NO2    ICA  VEL_VIENTO  DIR_VIENTO  \\\n",
              "0  4.0  16.0  ...      32.0   15.32     87.5  34.51         NaN         NaN   \n",
              "1  4.0  27.0  ...      54.0    6.82    120.0  46.00         NaN         NaN   \n",
              "2  3.0  27.0  ...      54.0    5.88    117.5  44.94         NaN         NaN   \n",
              "3  3.0  17.0  ...      34.0   11.85     95.0  35.81         NaN         NaN   \n",
              "4  3.0   5.0  ...      10.0   20.55     65.0  24.49         NaN         NaN   \n",
              "\n",
              "   TEMPERATURA  HUMEDAD  PRECIPITACION  DIR_VIENTO_CARDINAL  \n",
              "0          NaN      NaN            NaN                  NaN  \n",
              "1          NaN      NaN            NaN                  NaN  \n",
              "2          NaN      NaN            NaN                  NaN  \n",
              "3          NaN      NaN            NaN                  NaN  \n",
              "4          NaN      NaN            NaN                  NaN  \n",
              "\n",
              "[5 rows x 27 columns]"
            ]
          },
          "execution_count": 106,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "ruta = '../data/calidad_aire_2022.csv'\n",
        "ruta_nuevoCSV = '../data/calidad_aire_2022_clean_nulos.csv'\n",
        "data = pd.read_csv(ruta, delimiter=',', engine='python', index_col=0)\n",
        "data.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qKjBvq2zUwQf"
      },
      "source": [
        "#### Cantidad de Registros Importados"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "wwIyKdJ5UwQf",
        "outputId": "a6a4dfa2-367a-4013-b284-9d06c7c93745"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(87072, 27)"
            ]
          },
          "execution_count": 107,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "data.shape"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1hE5Uv6sUwQg"
      },
      "source": [
        "#### Identificación de los tipos de columnas"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "nuIjP0OOUwQg",
        "outputId": "1ea623a6-242b-4662-f5d0-f158d2c47265"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 87072 entries, 0 to 87071\n",
            "Data columns (total 27 columns):\n",
            " #   Column               Non-Null Count  Dtype  \n",
            "---  ------               --------------  -----  \n",
            " 0   CODIGO               87072 non-null  int64  \n",
            " 1   FECHA                87072 non-null  object \n",
            " 2   ANO                  87072 non-null  float64\n",
            " 3   MES                  87072 non-null  float64\n",
            " 4   DIA                  87072 non-null  float64\n",
            " 5   DIA_SEMANA           87072 non-null  object \n",
            " 6   HORA                 87072 non-null  int64  \n",
            " 7   ESTACION             87072 non-null  float64\n",
            " 8   SO2                  25293 non-null  float64\n",
            " 9   PM10                 32016 non-null  float64\n",
            " 10  O3                   34320 non-null  float64\n",
            " 11  NO2                  43097 non-null  float64\n",
            " 12  PM2.5                16127 non-null  float64\n",
            " 13  NOM_TIPO             43536 non-null  object \n",
            " 14  LATITUD              43536 non-null  float64\n",
            " 15  LONGITUD             43536 non-null  float64\n",
            " 16  ICA_SO2              25293 non-null  float64\n",
            " 17  ICA_PM10             32016 non-null  float64\n",
            " 18  ICA_O3               34320 non-null  float64\n",
            " 19  ICA_NO2              43097 non-null  float64\n",
            " 20  ICA                  43318 non-null  float64\n",
            " 21  VEL_VIENTO           43536 non-null  float64\n",
            " 22  DIR_VIENTO           43536 non-null  float64\n",
            " 23  TEMPERATURA          43536 non-null  float64\n",
            " 24  HUMEDAD              43536 non-null  float64\n",
            " 25  PRECIPITACION        43536 non-null  float64\n",
            " 26  DIR_VIENTO_CARDINAL  43536 non-null  object \n",
            "dtypes: float64(21), int64(2), object(4)\n",
            "memory usage: 18.6+ MB\n"
          ]
        }
      ],
      "source": [
        "data.info()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "U6ELEcfNUwQh"
      },
      "source": [
        "### Estadística Descriptiva"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "HuGiINHNUwQh",
        "outputId": "39b8c851-8b54-4691-d02a-d1de24e747e0"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>CODIGO</th>\n",
              "      <th>ANO</th>\n",
              "      <th>MES</th>\n",
              "      <th>DIA</th>\n",
              "      <th>HORA</th>\n",
              "      <th>ESTACION</th>\n",
              "      <th>SO2</th>\n",
              "      <th>PM10</th>\n",
              "      <th>O3</th>\n",
              "      <th>NO2</th>\n",
              "      <th>...</th>\n",
              "      <th>ICA_SO2</th>\n",
              "      <th>ICA_PM10</th>\n",
              "      <th>ICA_O3</th>\n",
              "      <th>ICA_NO2</th>\n",
              "      <th>ICA</th>\n",
              "      <th>VEL_VIENTO</th>\n",
              "      <th>DIR_VIENTO</th>\n",
              "      <th>TEMPERATURA</th>\n",
              "      <th>HUMEDAD</th>\n",
              "      <th>PRECIPITACION</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>87072.0</td>\n",
              "      <td>87072.0</td>\n",
              "      <td>87072.0</td>\n",
              "      <td>87072.0</td>\n",
              "      <td>87072.0</td>\n",
              "      <td>87072.0</td>\n",
              "      <td>25293.0</td>\n",
              "      <td>32016.0</td>\n",
              "      <td>34320.0</td>\n",
              "      <td>43097.0</td>\n",
              "      <td>...</td>\n",
              "      <td>25293.0</td>\n",
              "      <td>32016.0</td>\n",
              "      <td>34320.0</td>\n",
              "      <td>43097.0</td>\n",
              "      <td>43318.0</td>\n",
              "      <td>43536.0</td>\n",
              "      <td>43536.0</td>\n",
              "      <td>43536.0</td>\n",
              "      <td>43536.0</td>\n",
              "      <td>43536.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>28079024.1</td>\n",
              "      <td>2022.0</td>\n",
              "      <td>6.5</td>\n",
              "      <td>15.7</td>\n",
              "      <td>11.5</td>\n",
              "      <td>24.1</td>\n",
              "      <td>3.9</td>\n",
              "      <td>20.6</td>\n",
              "      <td>53.2</td>\n",
              "      <td>27.9</td>\n",
              "      <td>...</td>\n",
              "      <td>3.1</td>\n",
              "      <td>41.2</td>\n",
              "      <td>44.3</td>\n",
              "      <td>69.8</td>\n",
              "      <td>44.3</td>\n",
              "      <td>0.6</td>\n",
              "      <td>104.6</td>\n",
              "      <td>17.1</td>\n",
              "      <td>57.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>10.5</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.5</td>\n",
              "      <td>8.8</td>\n",
              "      <td>6.9</td>\n",
              "      <td>10.5</td>\n",
              "      <td>2.6</td>\n",
              "      <td>25.2</td>\n",
              "      <td>31.4</td>\n",
              "      <td>20.8</td>\n",
              "      <td>...</td>\n",
              "      <td>2.1</td>\n",
              "      <td>50.3</td>\n",
              "      <td>26.2</td>\n",
              "      <td>52.1</td>\n",
              "      <td>21.5</td>\n",
              "      <td>0.7</td>\n",
              "      <td>99.8</td>\n",
              "      <td>9.2</td>\n",
              "      <td>20.8</td>\n",
              "      <td>0.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>28079008.0</td>\n",
              "      <td>2022.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.7</td>\n",
              "      <td>1.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.6</td>\n",
              "      <td>2.5</td>\n",
              "      <td>0.9</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-8.4</td>\n",
              "      <td>6.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>28079018.0</td>\n",
              "      <td>2022.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>5.8</td>\n",
              "      <td>18.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>29.4</td>\n",
              "      <td>12.0</td>\n",
              "      <td>...</td>\n",
              "      <td>1.6</td>\n",
              "      <td>18.0</td>\n",
              "      <td>24.5</td>\n",
              "      <td>30.0</td>\n",
              "      <td>31.6</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>9.9</td>\n",
              "      <td>41.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>28079024.0</td>\n",
              "      <td>2022.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>16.0</td>\n",
              "      <td>11.5</td>\n",
              "      <td>24.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>16.0</td>\n",
              "      <td>53.8</td>\n",
              "      <td>22.0</td>\n",
              "      <td>...</td>\n",
              "      <td>3.2</td>\n",
              "      <td>32.0</td>\n",
              "      <td>44.8</td>\n",
              "      <td>55.0</td>\n",
              "      <td>40.4</td>\n",
              "      <td>0.7</td>\n",
              "      <td>123.0</td>\n",
              "      <td>15.9</td>\n",
              "      <td>63.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>28079035.0</td>\n",
              "      <td>2022.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>23.0</td>\n",
              "      <td>17.2</td>\n",
              "      <td>35.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>25.0</td>\n",
              "      <td>74.1</td>\n",
              "      <td>38.0</td>\n",
              "      <td>...</td>\n",
              "      <td>4.0</td>\n",
              "      <td>50.0</td>\n",
              "      <td>61.7</td>\n",
              "      <td>95.0</td>\n",
              "      <td>52.8</td>\n",
              "      <td>0.7</td>\n",
              "      <td>195.0</td>\n",
              "      <td>24.2</td>\n",
              "      <td>68.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>28079036.0</td>\n",
              "      <td>2022.0</td>\n",
              "      <td>12.0</td>\n",
              "      <td>31.0</td>\n",
              "      <td>23.0</td>\n",
              "      <td>36.0</td>\n",
              "      <td>41.0</td>\n",
              "      <td>678.0</td>\n",
              "      <td>198.7</td>\n",
              "      <td>172.0</td>\n",
              "      <td>...</td>\n",
              "      <td>32.8</td>\n",
              "      <td>1356.0</td>\n",
              "      <td>165.6</td>\n",
              "      <td>430.0</td>\n",
              "      <td>468.6</td>\n",
              "      <td>7.3</td>\n",
              "      <td>359.0</td>\n",
              "      <td>42.2</td>\n",
              "      <td>100.0</td>\n",
              "      <td>12.1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>8 rows × 23 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "           CODIGO      ANO      MES      DIA     HORA  ESTACION      SO2  \\\n",
              "count     87072.0  87072.0  87072.0  87072.0  87072.0   87072.0  25293.0   \n",
              "mean   28079024.1   2022.0      6.5     15.7     11.5      24.1      3.9   \n",
              "std          10.5      0.0      3.5      8.8      6.9      10.5      2.6   \n",
              "min    28079008.0   2022.0      1.0      1.0      0.0       8.0      0.0   \n",
              "25%    28079018.0   2022.0      4.0      8.0      5.8      18.0      2.0   \n",
              "50%    28079024.0   2022.0      7.0     16.0     11.5      24.0      4.0   \n",
              "75%    28079035.0   2022.0     10.0     23.0     17.2      35.0      5.0   \n",
              "max    28079036.0   2022.0     12.0     31.0     23.0      36.0     41.0   \n",
              "\n",
              "          PM10       O3      NO2  ...  ICA_SO2  ICA_PM10   ICA_O3  ICA_NO2  \\\n",
              "count  32016.0  34320.0  43097.0  ...  25293.0   32016.0  34320.0  43097.0   \n",
              "mean      20.6     53.2     27.9  ...      3.1      41.2     44.3     69.8   \n",
              "std       25.2     31.4     20.8  ...      2.1      50.3     26.2     52.1   \n",
              "min        1.0      0.7      1.0  ...      0.0       2.0      0.6      2.5   \n",
              "25%        9.0     29.4     12.0  ...      1.6      18.0     24.5     30.0   \n",
              "50%       16.0     53.8     22.0  ...      3.2      32.0     44.8     55.0   \n",
              "75%       25.0     74.1     38.0  ...      4.0      50.0     61.7     95.0   \n",
              "max      678.0    198.7    172.0  ...     32.8    1356.0    165.6    430.0   \n",
              "\n",
              "           ICA  VEL_VIENTO  DIR_VIENTO  TEMPERATURA  HUMEDAD  PRECIPITACION  \n",
              "count  43318.0     43536.0     43536.0      43536.0  43536.0        43536.0  \n",
              "mean      44.3         0.6       104.6         17.1     57.0            0.0  \n",
              "std       21.5         0.7        99.8          9.2     20.8            0.2  \n",
              "min        0.9         0.0         0.0         -8.4      6.0            0.0  \n",
              "25%       31.6         0.0         0.0          9.9     41.0            0.0  \n",
              "50%       40.4         0.7       123.0         15.9     63.0            0.0  \n",
              "75%       52.8         0.7       195.0         24.2     68.0            0.0  \n",
              "max      468.6         7.3       359.0         42.2    100.0           12.1  \n",
              "\n",
              "[8 rows x 23 columns]"
            ]
          },
          "execution_count": 109,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "##estadísticas descriptivas\n",
        "data.describe().round(1)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "I-3IuBkoUwQi"
      },
      "source": [
        "### Tratamiento Nulos"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WDhPAUpBUwQi"
      },
      "source": [
        "####  1. Identifica valores nulos"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "w36aRGiPUwQi",
        "outputId": "2daedc9f-d937-4524-aff4-bcd698f93ec7"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "CODIGO                     0\n",
              "FECHA                      0\n",
              "ANO                        0\n",
              "MES                        0\n",
              "DIA                        0\n",
              "DIA_SEMANA                 0\n",
              "HORA                       0\n",
              "ESTACION                   0\n",
              "SO2                    61779\n",
              "PM10                   55056\n",
              "O3                     52752\n",
              "NO2                    43975\n",
              "PM2.5                  70945\n",
              "NOM_TIPO               43536\n",
              "LATITUD                43536\n",
              "LONGITUD               43536\n",
              "ICA_SO2                61779\n",
              "ICA_PM10               55056\n",
              "ICA_O3                 52752\n",
              "ICA_NO2                43975\n",
              "ICA                    43754\n",
              "VEL_VIENTO             43536\n",
              "DIR_VIENTO             43536\n",
              "TEMPERATURA            43536\n",
              "HUMEDAD                43536\n",
              "PRECIPITACION          43536\n",
              "DIR_VIENTO_CARDINAL    43536\n",
              "dtype: int64"
            ]
          },
          "execution_count": 110,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "data.isnull().sum()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "60k4O_a_UwQi"
      },
      "source": [
        "#####  1.1. Identifica si hay valores nulos en TODAS las columnas del dataframe \"data\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "CjsjjrShUwQj",
        "outputId": "24a40ca4-497c-490b-a2c1-0e33a5564b27"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "La columna SO2 tiene valores nulos.\n",
            "La columna PM10 tiene valores nulos.\n",
            "La columna O3 tiene valores nulos.\n",
            "La columna NO2 tiene valores nulos.\n",
            "La columna PM2.5 tiene valores nulos.\n",
            "La columna NOM_TIPO tiene valores nulos.\n",
            "La columna LATITUD tiene valores nulos.\n",
            "La columna LONGITUD tiene valores nulos.\n",
            "La columna ICA_SO2 tiene valores nulos.\n",
            "La columna ICA_PM10 tiene valores nulos.\n",
            "La columna ICA_O3 tiene valores nulos.\n",
            "La columna ICA_NO2 tiene valores nulos.\n",
            "La columna ICA tiene valores nulos.\n",
            "La columna VEL_VIENTO tiene valores nulos.\n",
            "La columna DIR_VIENTO tiene valores nulos.\n",
            "La columna TEMPERATURA tiene valores nulos.\n",
            "La columna HUMEDAD tiene valores nulos.\n",
            "La columna PRECIPITACION tiene valores nulos.\n",
            "La columna DIR_VIENTO_CARDINAL tiene valores nulos.\n"
          ]
        }
      ],
      "source": [
        "datos_seleccionados = data\n",
        "for columna in datos_seleccionados:\n",
        "    if datos_seleccionados[columna].isnull().any():\n",
        "        print(f\"La columna {columna} tiene valores nulos.\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "t82wQ5wZUwQj"
      },
      "source": [
        "#####  1.2. Visualización de los valores nulos en la gráfica"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "jKeJF_eiUwQj",
        "outputId": "04bfbffa-3719-4bbf-e865-14bc0aa6b15b"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "execution_count": 112,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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tW7YAAA4ePIiwsDAAQGRk5CufW6VKFZQoUQLh4eH466+/oi3PnDkzvLy8YDKZGD5IBhhAICIiIiIiIiIiIiIiIiKiBJMrVy4sWrQIJUuWxPXr11G0aFFcvnwZdnZ2rwwh5M+fH6VKlQIA7Nu3DwBgMpmirRfTNA6U+PgpEBERERERERERERERERFRgrGzs0P58uUxZswYFC9eHNevX8fHH3+MK1euwNbWNtYQgvG4r68vACBPnjwAGDZIzvjJEBERERERERERERERERFRgrK1tUW5cuUwduxYHUIoX748rl69GmsIQUQAAH/88QcAwNvbO1G3md6cEuNTIyIiIiIiIiIiIiIiIiIiimciAqUUACAiIgJ79+5F9+7dcezYMWTNmhV79uxBjhw5EBkZCVtbWwAvp1mwsbHBixcv8P777yNt2rTYu3cvHB0dk3JX6BVYAYGIiIiIiIiIiIiIiIiIiOLF0aNHsXz5cgwePBibNm3CP//8A6UUTCYTgJfTMcRUCcF8OgYjfAAAXbt2xfnz51G3bl3Y2dkl5a7Ra2AFBCIiIiIiIiIiIiIiIiIiemfDhw/H7NmzcfnyZQCAp6cnihQpgilTpiBfvnwWwYK4KiEYJk6ciEGDBqFEiRJYvnw5PDw8kmK36A0wgEBERERERERERERERERERO/km2++QXBwMDJmzIjy5cvj1KlTuHnzJp4+fYrSpUtjyZIlyJ49u8VzYgohHDlyBF5eXpgyZQoGDRoEd3d37Ny5E1mzZrUIMFDyxE+HiIiIiIiIiIiIiIiIiIjeWkBAAIKDg+Hv74+dO3dixYoV2LlzJwIDA5E9e3YcPXoUK1euBACYj4+PaTqGkiVLYsSIERgyZAgcHBywbds2ZM2aFZGRkQwfWAF+QkRERERERERERERERERE9Fa++eYbzJ07F61atcKIESNQsGBBREREwMfHBx07dkS1atUQERGBo0ePAgCUUhbPjymE0L9/f9jb2+PAgQPIkSMHIiMjYWtrmxS7R2+IAQQiIiIiIiIiIiIiIiIiInpjnTp1QnBwMEqXLo1+/frB19cXERERsLOzg4ggc+bM6Nq1KwDA0dEx2vNNJhOA/0II48ePR5YsWeDi4oIDBw4gZ86cDB9YGQYQiIiIiIiIiIiIiIiIiIjojVy8eBGHDh2CnZ0dbt26he3bt0NEYGdnp4MFJpMJjx8/RqZMmRAWFoYRI0Zg9OjR2LdvH27cuGExpYKdnR1Kly6NpUuX4sKFC8iePTvDB1ZIifkkG0RERERERERERERERERERK9h7969GDFiBDZv3oycOXPiu+++Q8eOHQFAV0KYMmUKOnfubPG8tGnTIl26dGjQoAHef/991KlTB3Z2dnByctLrMHxgnRhAICIiIiIiIiIiIiIiIiKi1yYiUEoBAPbt24ehQ4di69atyJkzJwIDA9GxY0copbBgwQI0a9YMSik0a9YMbm5uOH78OO7evYuzZ8/q1/P09ETt2rURFBSkX5esk11SbwAREREREREREREREREREVkPpZQOIZQtWxYDBgwAAGzduhWTJk2Ch4eHDh0UK1YMgwYNQs2aNQG8rGwQGhqKJUuW4NKlS5gzZw4yZMiA3r17M3yQArACAhERERERERERERERERERvbGYKiFs2bIFGTJkwN27d1G8eHFMmDAB5cuXBwCEh4fD1tYWNjY2+jUuX74Md3d3uLq6ctqFFIAVEIiIiIiIiIiIiIiIiIiI6LWYhwRiq4Swc+dO2NjY4KOPPtLhg4iICNjb20d7vZw5cwIATCYTwwcpgM2rVyEiIiIiIiIiIiIiIiIiotSoQ4cOmDRpEg4dOgQAOiRgFNo3QggAULZsWfTr1w8VK1aEUgqbNm3CtGnTYDKZYGdnB5PJFOv/x7wqAlkvVkAgIiIiIiIiIiIiIiIiIqJoAgMDERQUBADIkSMHPv30U3zzzTfIlSsX3NzcLKZgMP5evnx59O/fHyKCrVu3Yty4cTCZTOjYsSNsbGxgMpkYNkjBlBhxFCIiIiIiIiIiIiIiIiIiAoBU31H+4sULlCpVCqdOnYKTkxPCwsIQGRmJrFmzIm/evPj++++RJ08eeHt76+eYT8+wf/9+DBkyBFu3bkXOnDnRtWtXtG/fPql2hxJJ6v3GEBERERERERERERERERHFYOrUqVi9ejXCw8OTelOShIggTZo0GD58ONKlS4eiRYti48aNaNiwIdKkSYPt27ejevXqaNy4MYKCgvD48WNERETA1tYWERERAIAyZcpg4MCBqFKlCm7cuIG+ffvip59+StodowTHKRiIiIiIiIiIiIiIiIiIiP7fzJkz0alTJ3zwwQdwdHRE5cqVYW9vn9SblaiMaRXy5csHb29vHDhwAKGhoVi0aBEuX76MCRMmYPv27di1axd27dqFkJAQFC9eHL1794a7uzvs7F52Q5cpUwYDBgzAgwcPcPPmTVSrVi0pd4sSAadgICIiIiIiIiIiIiIiIiL6f8ePH0fXrl2xa9culC5dWo/iT20hBMOUKVPQuXNnVKxYESEhIfDy8gIAXLhwAfv27cO0adNw5swZPHv2DDly5MAXX3yBOnXqoGLFivo1Dh48iDx58sDT09NimgZKeRhAICIiIiIiIiIiIiIiIiLCy6kHlFI4ffo0AgMDsWPHjlQTQjD2Peq/z58/j9q1a+PGjRvYtm0bSpQoAZPJBBsbGwDApEmTMGDAADx58gTp06fHo0ePAAAdOnRAtmzZ0LNnT/2a5s+jlImfLhEREREREREREREREaU6Dx8+tPi3yWRKoi2h5MTogC9cuDAmTZqEihUr4rfffsOQIUOwdetWhIeHJ/EWJhzjO2D813gv8ubNi7Jly+LRo0cYMmQIHj16pEMEv/zyC8aMGYMnT55g+vTpWLVqFTp06AClFKZOnYoxY8bg3r17+v/B8EHKxwoIRERERERERERERERElKoMGDAAixYtwsiRI/Hee++hQIECelnUUeCUOqWmSggXLlzAkSNHsHXrVvTo0QP58+fXy4yKBX/++ScqV64MpRTWrFmDwoULY/ny5QgMDMTNmzcRHByMgIAA/bzNmzfj8OHDaNasGbJly8bvVSrCAAIRERERERERERERERGlGt9//z2GDBkCAHBwcEC2bNnQrFkzNGjQALly5YK9vb3uLGWnacp38eJFpE+fHl5eXvoxk8mkP/eUHkKYNGkSZs2ahTNnziBNmjQICAjAmDFj4OzsbLHegwcP0Lx5c6xZswaDBg1CmTJl0KJFC9y8eROzZ89Gy5YtAQDh4eH6fTHCC5GRkbC1tU30faOkwQACERERERERERERERERpRohISFo1qwZ7O3tUaJECezbtw8AkD9/fhQvXhz9+vWDt7c33Nzc9HMYREiZOnfujBkzZiB79uxo1qwZ/Pz84O/vj/DwcDg5OVmse/LkSXTp0iVFhRA6dOiAoKAgZM2aFQMHDkSxYsWQP39+ODk5xThVwvbt2/U+Ozs74+HDh5g1axZatWoF4L/AAaVuDCAQERERERERERERERFRqnH69Gn4+/vj/PnzWLBgAbJly4bBgwfjzJkzuHXrFjw9PVGqVCm0atUK1atX1x3R7FxNWcwrYZgrWrQosmfPjoCAAPj4+KBEiRJ62YkTJ9ClSxfs3LkTpUqVwqBBg6w2hNC5c2dMmTIFdevWxaBBg1C4cOE41xcRPHnyBM2bN8eqVasAAEuXLoW/vz8Afj/oPzwKiIiIiIiIiIiIiIiIKNUoXLgw6tevDwBYtGgRypcvj5CQEGzduhUNGjSAi4sL1q1bh7p166JRo0YYMWIEXrx4AWNML8f2pgy+vr5Inz49lFLImTMnevTogeLFi+POnTtYvXo16tati48++ggNGjTAwIED8ccff+C9997DvHnzUKFCBRw6dAiDBg3Cli1bEB4entS780ZmzpyJKVOm4Msvv8SoUaNQuHDhVx7XSimkS5cO5cqVAwBkzZoVVatWBcDwAVliBQQiIiIiIiIiIiIiIiJKFYyO0nPnzqFWrVp49OgRtmzZgkKFCgEAwsLC8Pvvv2PXrl3o2bMnwsLCALwcFV+9enU0btz4lSPFyXqsWLECAQEBePToEbp3746WLVsiffr0WLp0KX7//XcsXboUYWFheP78OdKlSwcfHx907twZ//zzDzZt2oRDhw7h448/xnfffYcaNWpYRSWE27dvo3bt2jh27Bj279+PYsWKvdbzjGlIIiIiUKlSJfz222+YNm0aAgICEBkZCVtb2wTecrIWDCAQERERERERERERERFRqvL8+XM0a9YMy5cvR+PGjTF//nyIiB7F/dtvv+Grr77CrVu34Ovri8uXLyMiIgLu7u6oXbs26tWrh08//TSJ94LeltGZDgC//PILmjdvjqdPnyIgIAAjR45ExowZAQC///477ty5g7lz5+LSpUvYs2cPAMDW1hYODg6IiIhAeHg4qlevjmXLlsHFxSXJ9ul17dy5E5UqVcK3336LadOmvVF4QEQQERGBAQMGYMyYMahQoQK2b9+ewFtM1oa1MIiIiIiIiIiIiIiIiCjFMZlMMT4uInB0dMSgQYOQMWNGHDp0CGfPntXhg/Xr16Nx48a4desWZs6ciXPnzmHOnDmoU6cO7t+/j1WrVsHPzy8xd4XigfnxICKIjIwEANSpUwfz5s2Di4sLZs+ejd69e+PatWsAgAIFCuDjjz/G7NmzsXPnTmzYsAETJkxAoUKF4OnpifDwcLi7u2PMmDHJPnxgjEk/dOgQgJdTKAB4o8oF4eHhsLe3xzfffIOsWbNi586d+Pnnn+N/Y8mqMYBAREREREREREREREREKU7UOemNDmilFEQEmTNnRsmSJXHx4kWsW7cOALB582Z06tQJf/31F8aPH4/WrVtDKYWmTZti+fLlCAkJweHDh5E7d+5E3x96N+bHg42NDWxtbXUIoXbt2jqEMGfOHAwdOhTXr1/X6yuloJTCp59+isDAQOzduxc7d+7EuHHjcODAAauYlsOo+PDkyRMAgJOTE4CXoYLXNXv2bJw5cwa5cuVC5cqVkTVrVlYCoWg4BQMRERERERERERERERGlGDt37sTp06exadMmfPLJJyhSpAiqVasG4GUIwbwjeunSpWjYsCEKFSqENm3aYNKkSfjrr78wYcIEBAYGAoDupOYc99bp+PHjOHHiBEJCQhAeHo5MmTLBx8cHAQEByJkzJ5ydnfW6K1euRLNmzfD06VO0bt0aAwcO1JUCjGkboh5D1mbo0KEYNGgQqlSpgvXr18POzu61nvfo0SOUK1cOXl5e2LRpE/bu3Yu8efPC29v7jaZxoJSPAQQiIiIiIiIiIiIiIiJKEUaNGoXx48fj7t27AF6O+s6cOTN69eqFzp07A/ivFL1SCqGhoahbty527twJJycnPHjwAOPGjUOXLl0ARA8skHWZMGECpkyZgr/++ivaMi8vLzRt2hQNGzZEyZIl9eNxhRBSgv3796NGjRpwdXXFzJkzdTgnNka44NKlSyhQoACaN2+OmTNn6uX8jlBUPBqIiIiIiIiIiIiIiIjI6nXr1g19+/aFo6MjunTpgsaNGyN79uy4efMm+vbti0WLFgH4r5w+ADg7O6NUqVJ48eIFHjx4gOHDhzN8kEL06NED3bp1AwBMmTIFa9aswZYtWzBq1CiUKlUKt2/fxtSpU9GlSxfs2LFDP898OoZZs2ZhyJAhFtMxWDtvb29kzpwZV69exZw5c3Dnzp1Y1zWZTLqyQb9+/aCUgr+/v14GRJ/qhIhHBBEREVktFnIiIiIiIiIiIiIA6NKlCyZMmIDatWtj/fr1GDduHBYsWIAJEyagSpUqCA0Nxbp16xAZGanblIz/9uzZE0WKFIGjoyPy5csHAAgLC2PHqhUbO3Ysxo0bh9q1a+OXX35B+/btUaNGDVSuXBk9e/bEnj170LlzZ7i7u2Pfvn3o06cP9u/fr58fNYQwYsQIXL16NQn3KP7kypULo0ePBvByCpJ+/frh/v37erkRLIiIiNDfgfHjx2PVqlWoUaMGPvroIwAMHlDseGQQERGR1TKS6sZFMRERERERERERpT49evTApEmT0LhxYwwfPhyFCxdGREQEAOCrr77C119/DQDYu3cvHj9+bDEFQ2RkJBwcHFC1alU8f/4cixcvBgA4ODgkzc7QOzt48CAmTJiAnDlzYuDAgXj//fcB/Bc4iYiIgL29PcaOHYvAwEBkyZIFR48exYwZM3Dz5k29nhFCcHd3x/Tp0zFhwgRERkYm2X7FB2PfatasiR9++AEAMGvWLHTu3Bm//fYbwsPDdbDAzs4OADBy5EgMHToUPj4++PHHH5EuXTq2x1KclHDoIBEREVmZwMBAeHt7o1atWihQoEBSbw4RERERERERESWRESNGoH///siWLRuCg4NRrVo1i6qZxgCWQoUKwcPDA3v27IGI6McNR44cQZkyZRAREYFFixahYcOGibofFH+Cg4PxzTffYPr06Wjbtm2M6xjTa0RGRmLQoEEYMWIEMmTIgCVLlqBSpUqIiIjQHfBLlixB9+7dsWHDBhQuXDgxdyVBPXz4EDNmzEDv3r0BAO+//z4KFiyIpk2bQkTw77//Ys6cOdi1axd8fX2xZcsW5MiRA5GRkXpaBqKYMIBAREREVqV169aYM2cOAKBIkSIoXrw4+vXrBw8PD7i7u+v1YrqRJCIiIiIiIiKilOPFixfo3r07li1bhsePH6Nhw4bo1asX8ubNC+BlJ7PJZMKDBw9QqVIlODk5IWfOnDCZTPj444+RI0cOfPbZZ1BKwc7ODr1798aYMWMQGBiIH374QXdAk/V4/vw5atWqhS1btmDHjh345JNPYm0nNEIIJpMJVatWxY4dO1C1alWsXbsW9vb2Fs978uQJ0qZNm9i7k+BMJhOWLl2Kzp074+nTp3j27JnFcnd3d1SvXh1jx46Ft7c3wwf0WnjmJCIiIqty/PhxAC/L4P355584efIktm3bhiJFiiAwMBClS5dGunTpoJRiCIGIiIiIiIgoBYp6v2/eicg5yVOXNGnSYPjw4XBzc8OMGTMQEhKCsLAwDBw4EH5+fhAR2NnZYceOHTh9+jQA4PDhwwCAFStWAAAqVKiAnDlzIjAwEM+fP4e3tzfatm3L8IGVsrOzw4sXL6CUgr29PYDYByrZ2NjoSge9e/fG4cOHcefOHf3Zm7cvuri4JOp+JBYbGxs0bNgQhQoVwsmTJ7FmzRo8efIETk5O8PPzw1dffYUCBQogXbp0DB/Qa2MFBCIiIrIKRiPC4sWL0aFDB+TIkQOzZs3C6NGjcfjwYVy+fBkAUKtWLXzyySfo3LkzTCYT7Ozs2ABBRERERERElMJcvXoV4eHhePbsGdKmTYucOXPqZWwHSPn+/vtv+Pj46H8/evQIo0ePRnBwMJ48eYK6deuif//+yJcvH9auXYuaNWvCyckJ7dq1Q968efHXX3/hr7/+wq+//goRQVhYGACgbNmymDNnDvz8/JJq1+gdiAhCQ0NRo0YN7N69G8HBwQgICHit5166dAnvvfceXrx4gcuXL8PHxydVnEdiCmdERkZCKWWx/xzoRW+C8S0iIiKyCsYFb8mSJZE+fXr873//w6VLl7B48WKcPXsW8+bNQ0hICH799Vf8+uuv+OWXX1C+fHkEBAQgd+7c+nXYCEFERERERERkvZYsWYJ169Zh2bJliIyMREREBNKlS4eaNWuiTp06qFKlCtKlS8f7/xSsW7duuHjxIgYNGoTixYsDANKnT49evXoBAIKDg7F8+XK4uLigRIkS+Oabb1CwYEEMHjwYdevWtXitkydP4tKlS5g/fz5u3LiBGTNmMHxgxYxKBaVKlcLu3buxdOlSVKtWDdmyZYvzeSKCjBkzws3NDeHh4XBycko1ne3m+2mEDMyrHBiPpZb3g+IHKyAQERGR1Zk1axbatm2L2rVrY8GCBXBycgLwcnqG06dP44cfftBl9Tw8PPDtt9+ibNmy+Oyzz/RrMLVLREREREREZF26du2KqVOnIjw8HHnz5kWmTJlw48YNXLp0CQCQJUsWVKxYEePGjYOnp2eqCSGkpjaOQ4cO4cMPPwQANGvWDJ07d9YhBAB4/PgxRo8ejZkzZ+LBgweIiIiAr68vZsyYgYoVKwIAwsLC4ODgYPG6IoInT54gXbp0ibcz9M5mz56NLFmyoFq1ahbTsCxduhRNmjSBq6srJk+ejEaNGsX6GsZzbt68iTx58qBixYpYt25dIu5F/ElN5wJK3lL+Ly8RERFZrdhykiVKlEDmzJmxZs0aHD9+XK9brFgxfP3112jfvj2cnZ0BvLzxHDFiBGrUqIGOHTtiypQpeh44IiIiIiIiIrIOHTt2xMSJE1GxYkUcOHAAZ86cwa5du3DgwAFs3LgRpUqVwoMHD7BgwQI0adIEd+7c0R2SKc2jR4/w5MkT3L9/H8B/I5gjIyOTcrMSRalSpTB9+nQ4ODhg3rx5mDBhAo4dO6aXp0uXDr169ULbtm2RKVMmAC+DKb6+vgBevkcxhQ+UUgwfWJnBgwejTZs2GD9+PHbv3m0ROKpfvz6qVKmCe/fuoUePHti9e7fFc402R/PnjBkzBs+fP0f16tUt1knOUvO5gJI3VkAgIiKiZMm4AYiMjLQo+2Xo1KkTpk6dijp16iA4OBju7u4AgBUrVqBz5864efMm5syZA09PT2zZsgU//vgjACBXrlw4fPgwPDw8EnV/iIiIiIiIiOjtTJ06FZ06dYK/vz9GjhyJPHnyICIiAnZ2drrd4MKFCwgKCsKiRYtw+/Zt1K1bF8HBwXBzc0vqzY83W7ZswdGjR7F8+XKEhYVBKYXixYujdOnSaNasmR6MkVKZdxYHBwejXbt2MJlMaNKkCbp06WJRCeHRo0cYPXo0Zs+ejUePHsHf3x99+/ZF/vz5k2rzKZ6tXr0aLVu2xP3791GtWjX06tULn3zyiT5G/vrrL9SrVw/Hjh2Dj48Ppk2bhnLlysXYJhgcHIyePXvC19cXa9asQebMmRN7d95IQp8LWEmB3hUDCERERJTstG/fHuHh4QgKCrJoTACg//6///0PtWvXho2NDXbs2IHs2bNj+fLlCAwMxM2bNzFz5ky0bt1av+auXbuwceNGdOjQAVmzZuWFNBEREREREVEyJyK4e/cuatWqhZMnT+LgwYMoXLhwrOtfv34dkyZNwqxZs2BjY4Phw4fjm2++AQCrbwMYMGAAJk+ejMePH+t57h8/fqyXlyxZEsHBwXj//feTcCsT3puEEMynY3j8+DH8/f0xcOBA+Pn5JdXmUzwwPwY2b96MJk2a4N9//40WQhAR7Nu3D4GBgTh+/Dg8PT1Rp04d1KxZE2XLlkV4eDgiIyMxevRozJs3D0op7N69O9mHVBL6XGD+/t6+fRteXl7xst2UujCAQERERMnK9u3bUaVKFdjY2KBLly4YOXJktBAC8DLJ3qBBA2zatAm9evXCRx99hHbt2uHmzZuYPXs2WrZsCRGByWSKFl6IraoCERERERERESUv+/btQ/ny5dGsWTP89NNPr7yn//PPP9GhQwds3rwZNWrUwJo1axJxaxPGt99+i5kzZ6JIkSK6A93Ozg4XLlzAwoULcfDgQVy7dg158uTBlClTULVqVd2BmBK9SwihQYMG6NWrFwoUKJBUm0/v6MWLF0iTJo3+96ZNm9C0aVMdQujZsyc++eQT3QZ49uxZdOzYEXv27NHPKViwIF68eIFHjx7hzp07KFq0KBYuXJjsj4uEPheYf7fmzZuHkJAQDBw4EB999FFC7RKlUAwgEBERUbJy69YthISEYMSIEbh37x4CAwMxevRoixCCUb1g//79qFq1KpycnGAymfDgwQMEBwcjICAAgOVFMxERERERERFZD+OeftasWWjbti26deuGH3744bWeu3fvXnz88ccAXnZOVq1aNSE3NUGNGDEC/fv3R6NGjTBo0CDkzZvXYvmdO3dw+PBh9OjRA7///jty586NhQsXonTp0il6AEZcIYQePXqgSJEiel0jhDB37lzcvHkTbdu2xeTJk2Fvb59Um09v4dSpUzh48CBWrFiBhg0bokWLFnpZXCEE4OXxMnz4cPzvf//Dxo0bdTihVKlS+Oyzz9CkSRNkyZIlifbs9ST0ucD8O/Xzzz+jV69eCAsLw6FDh+Dr65tg+0Upk11SbwARERGRuUyZMqFp06YAgKFDh2LSpEkAEGMIIU+ePChVqhR27doFAAgJCUGDBg0AMHxAREREREREZM2Me/rnz58DAJ49ewYACA8Pj7Pj2GQyoWzZsqhatSq2bNmCJ0+eJPzGJpBjx44hODgYvr6+GDBggO5wNMaVKqXg6emJzz//HD4+PmjatCnOnj2Ltm3bYt++fUibNm1Sbv476d27N/Lly4eWLVvGuNzGxka3/bRp0wYA0K5dOyxcuBDp06fH999/D09PT0RERCBdunTo1asXnj59inXr1uG7775j+MDKTJkyBVOmTMH58+fh7u4ONzc3VKlSBVmzZgUAfPrpp1iwYAGaNm2KzZs36+dVqFABNjY2sLGxwYABAwAAFy5cAPDyexS1Ez+5SuhzQdTwQZ8+ffDixQvs2rWL4QN6K2yVJyIiomQnY8aMaNq0KQYMGABXV1dMmjQJvXr1QkREhE4oK6WQKVMm+Pv7AwBy586NihUrAmD4gIiIiIiIiMhaTZ48GQcPHtT/dnFxAQBs2bIFDx8+fGXHsY2NDZRSejSzEVywRsePH8eVK1fQtWtX5M+f36KzUSllsW7RokXx448/IleuXDh16hSGDx8Ok8mUFJv9zhYtWoQxY8YgICAAS5YsiXU9I4QAAG3atMGUKVMAAEFBQVi6dCkAwM7ODiaTCenSpcPQoUOxb98+5M+fP+F3guJNz5490blzZ7x48QI//fQTDh06hMWLF8PHxwfAf53wRgghQ4YM2Lx5M8aMGYNdu3bpY8T4r5+fH/z8/KJ14idnCXkuiCl8EBoait27d1tUEiF6E2yZJyIioiQX9UYAeHUIITw8HADQpEkTlC9fHrdu3cLOnTsBWMeNAxERERERERFZat68OQIDA3Ho0CG8ePECAFC1alXkypULly9fxrx58xAWFhbnaxjLHz9+DABwcHBI2I1OAEb7yC+//AIAuqP1VUqUKIGaNWsCAP73v/9Z7eCMr776CvXr1wcANG7cGIsXL451XfMQwrfffovBgwcDAIYNG4Y///xTryMiSJs2LTw9PRN46yk+ff/99xg7dixq1qyJFStWoFmzZsidOzcA6I53pVSsIYTRo0frEIJxHEQVtQM/OUnoc0FM4YOnT59iz549eO+999518ykVs85fHyIiIkoRFixYgBMnTiA0NBTAf+UVIyIiAMQdQrC3t9cJ9hIlSuDp06cYO3Ysnj9/nmLnNyQiIiIiIiJKqQICAjB//nw0adIEtWrVQpo0aSAicHFxwfvvv4/w8HAsX74cx44di3Xggclk0oGDs2fPolChQqhcuXJi7ka8MPbPqN5gPto5Lq6urvD394ednR127NiB33//PWE3NJ4FBASgS5cucHZ2RnBwMJo0aQIRQZMmTV47hNCmTRu8//77ePHihZ6+A0jencwUsxUrVmDMmDEoV64chg8fjuLFi1ssN/9MXzeEYG3HQUKeC2KrfLB3716GD+idMYBARERESaJVq1Zo1qwZPvzwQ3z22Wfo168ftm7diqdPn8LOzk6vlzFjRjRs2BD9+/ePFkKwsbGBra0t+vTpg/z58+PIkSNYtWpV0u0UEREREREREb2xVq1aYe7cuWjevDmGDh2KHDly6I42d3d39O7dG7a2ttizZw+GDRuGo0eP6sqIkZGRAIDw8HDdmTZkyBD8/vvvqFSpEpycnJJmp97CkSNHAEAPrMiZMycA4NSpUwD+G7ARGxFBsWLFULhwYdja2lpVZ2u3bt0wd+5cnD17Fnfu3EG6dOkwZcoUNG7c+LVDCADg4eEBFxcXPHjwANevX0+szad4JCKIjIzE0qVL8fz5cwwePBiFChXSy2M7ruMKIYwbNw5btmyxmmlJEuNcYHxnfvrpJ/Tt2xehoaGsfEDxhgEEIiIiSnQ//fQTfvrpJwDAixcvsH//fowcORLVqlVDyZIl8c0332DZsmW4dOkSACBz5sxo06YN+vbtCzc3N0yYMEGHEAAgbdq0yJ8/Pzw9PVGxYsWk2i0iIiIiIiIiekPffPMNfvrpJ7Rp0wb9+vXTHW3G3OYiglKlSmH58uVQSmH9+vXo3bs3QkJC8OjRI91BZ29vDwAYN24cJkyYgPz586Nv375wdHS0iqkamzRpglKlSumORwDIlCkTAGDhwoV48OAB7Ozs4uxAVUohPDwcz58/R2RkpFXsNwB06dIFEyZMQMOGDfHjjz/qaRJcXV0xderU1wohGO+Lg4MDHj9+jAIFCqBkyZKJtg8Uf5RS+PPPP7FixQpUqlQJFStW1EGj13lu1BBC5syZsX79egQHB1tUxUiuEvNcsGzZMvTt2xePHz9m+IDiFQMIRERElOjy5s2LJk2awNHREd7e3mjRogWGDBmCihUr4q+//kJwcDAaNGiA8uXLo0aNGpg2bRquXr2KRo0aYdiwYfDy8sLUqVPRvXt3REREwMnJCd27d8exY8eQKVOm174pISIiIiIiIqKk065dOwQHByNbtmzo0qULfH19dWUDgzFyt1atWli6dCkAYPv27ejevTuqVq2KqVOn4ueff0ZQUBCqVq2KHj16IEOGDNiwYYNuI0julQACAgIQEhKCBg0aIEOGDPrxb7/9Frly5cK5c+fQrVs3PHnyxGK6AXPGYy9evMD9+/dRvnx5FChQINH24W116dIFkyZNQr169TBw4EDky5cPwH+l5mMKIYSEhFi8RlhYmB7NPXr0aJw6dQqffPIJXFxcEndnKN5cvHgRJpMJ3t7eAPBG060a0xUAL0MIM2bMQO7cufH999/D2dk53rc1PiX2ueD+/ftIly4d9u3bx/ABxSsGEIiIiCjRlSlTBoGBgahduzb++ecfnD59Gn5+fti2bRuOHTuGiRMn4quvvkJkZCQ2bNiAjh07okiRIggICMCRI0dQvnx5hIeHY9GiRWjbti3Cw8NRpkwZZMmSBSaT6Y1uSoiIiCjpWcvoPIo/cY3Y4vFARJQ6BAQEYMaMGXB0dMS1a9cwaNAghIaGwt7ePtaBBXXr1sWePXtQtmxZ2NnZ4fDhw+jUqRNatmyJDh064Pjx4/D398fu3buRI0cOREZGJvs2goCAAD39xKhRo5ArVy4AL38PM2XKhAYNGsDZ2RmrV6/GqFGj8PTpU9jY2Fi8R5GRkbozcujQobh16xaqVKmiXye56tWrFyZNmoTatWtj2LBhyJ8/f4xz3Lu6umLatGk6hNC0aVMEBwfj9u3bAF5WPQCAmTNnYsKECciZMyd69uwJR0fHxN8pihdPnz4FAD1N65sMNgoJCcH8+fP1v7/88kucOHEChQsXjt+NjGdJcS5o27Yt9u/fz/ABxTslyfnXh4iIiFIcEdE3kUePHsXYsWOxbNky5MmTB0OGDEGDBg0AvEyv37p1CwcOHMCOHTuwc+dOnDt3Tr+OnZ0dIiIiUKhQIezevRvu7u5Jsj9ERET05h4+fAiTyQQHBwfY2dkhTZo0Sb1JlASuXbuGP//8Ew8ePICLiwvKlCkDR0dH2NraWkWHERERvT2jo61+/frIly8fgoOD8c8//+Crr77CokWL4OjoGONvgdGmcP36dVy9ehXLly/H48ePoZRClixZ8MUXXyBv3rxIly6dVfyWtGnTBrNnz0aLFi0wcOBAPf2EubNnz+K7777D1q1b4enpifr162PEiBFIly5dtHV//PFHDBo0CHnz5sW6deuQMWPGRNiLt9OzZ0+MHTsWdnZ2GDp0KHr16vXK5zx8+BCdOnXCggULoJRCw4YN8eGHH8LX1xdLlizB0qVLkT59emzbtg2FChVKhL14dyaTSVdveJ3HU4u1a9eiZs2a8PLywq5du3RljLiYTCYopVC7dm0cOXIEu3btQq5cuWBjY2PRHpkcJcW5ILUfY5SwGEAgIiKiRBc1hDBu3DgsXboU+fPnR8+ePdGsWbNoz7lz5w7++OMPbNiwAbt27cLx48eRNm1aHD58GDly5Ej2NxJEREQErFmzBnv37sXPP/+MsLAwuLq6wtnZGe3atUOpUqVQqlQpAODvegq3Z88erFu3DlOmTEFoaKh+vEyZMvjggw8wcuRIODo6slGUiCiFMjramjdvjuHDh8PDwwNBQUEYPXo0bt++/coQwuuwhmuJ9u3bY/r06ahWrRqmTJkCX1/fWLf7yJEj6NmzJ/bv34+wsDAUKVIEvXv3hq+vLzJnzowHDx5g7NixWLp0KTw9PbFnzx5kz5492f6WGtMuZM6cGf/88w88PDwwduxY+Pv7I23atK98fmBgICZPnmzxmL29PT788EMEBwcjb968CbXpCeLixYu4fPkyrl69ihw5ciBfvnzImjVrUm9Wkrpz5w4qV66Mv/76CyNHjkTbtm11pYuYGMd6aGgo/Pz88N5772Hjxo2JuMVvLzWfCyjlYgCBiIiIEkXUC+e4Qgh9+vRBkyZNAADh4eGwt7eP9vwDBw4gb968yJAhAyIiInRJNkqe+vfvj7x588YYLiEiotShT58+mDx5MkJDQ+Hm5gZnZ2dERETg9u3bSJMmDXLlyoVu3bohICAAgHV0HNCb+/HHHzF+/HhcvXoVfn5+urTsli1bdAnZDz74AKtWrYK3tzePAyKiFKZRo0ZYsmQJWrZsif79++vfgdu3b2PRokUYNWpUvIUQkrPWrVtjzpw5AID33nsPwcHBKFq0aJwdrCdPnsT06dOxfv16XL16FQ4ODggLC0O6dOnw+PFjAECFChXw888/I1u2bMn2fTPCB40bN8agQYPQq1cvrFq1Cu7u7hg/fjz8/f3h4uIS43PNrwvWrFmDM2fO4MyZM8iYMSOqVKmCDz74AF5eXom5O+9kx44d2LhxI2bOnInQ0FCEh4fDwcEBHh4e6Nq1KypXroxixYol9WYmiRcvXqBFixZYsmQJ8ubNi3nz5umwclTmnetGOCU4OBgBAQHJ/loyNZ8LKIUTIiIiogRy/PhxefDgQazLTSaT/vuRI0ekUaNGYmtrK4UKFZIFCxboZZGRkfrvERERFq9hvoySp5UrV4pSStKkSSPLli1L6s0hIqIk8M0334hSSsqWLStbtmyRq1evyj///CPnzp2Tjh07Sr58+UQpJUopGT9+vH6e+bUCWb8+ffqInZ2d+Pn5ybJlyyyuE/ft2yffffed+Pj4iFJKihUrJpcvXxYRkfDw8KTaZIpH5t9n4++8lidKfbp06SL169eXS5cuiYjleeD27dsyYcIEyZQpkyilpHbt2vLs2TMRid4WYM1atWolSimpV6+eZM+eXf/ubdu27ZW/ef/++68cPXpUGjVqJBUqVBBXV1fx9fWVJk2ayPz58+X+/fsiknzfr8DAQFFKSf369eX06dP68S+++EKUUuLh4SE//fSTPHnyJNbXiLpvyXVfX2XSpEn68/f19ZVatWpJsWLFJH/+/KKUEhcXF6lSpYqsW7cuqTc1QcV0vW88duvWLcmbN68opeS9996TY8eORbueCAsL0/+eMWOGuLm5yccffyz//PNPwm/8O0rN5wJK+RhAICIiogTRtGlTUUpJjRo1ZPz48RIaGqovns0bGN4mhEDW5dq1a/Ltt9+KUkqcnJxkyZIlSb1JRESUiH744QdRSkmjRo3k/PnzIhK9IWz16tXi7++vQwhBQUFJsamUgL777jtRSslXX30lR48eFZH/jgPjOu/27dsyf/58yZEjhyilpHjx4vL06VOLdch63bt3Ty5evCiXLl2Su3fvJvXmEFEiW79+vf77nTt3Yl0vpYcQAgICRCklzZo1k8ePH8v9+/ctfve2b9/+RsG7e/fu6ffHkFx/MydNmiRKKfH395c//vhDRMSi8/jLL7987RBCTKwpuNqnTx+xtbUVPz8/WbFihTx8+FBERJ4+fSq3b9+WVq1aiaurq+54X7NmTRJvcfz75Zdf5OzZsxaPxTQAadu2bZI7d25RSknBggVl8eLFcvXq1WivN2rUKPH09BRPT099fCVnqflcQKkDAwhEREQU70JCQnQHgq2trSilpHz58tKzZ0+5du1atPXNL4jjCiFY080kWfr777+lffv2DCEQEaUyp0+flrx584q3t7fFKDeD+TXAwYMHpUGDBqKUEm9vb9m+fXtibioloG7duunRjmfOnIlz3adPn8qiRYska9asopSSFi1aWHROkHUaN26cfPzxx+Lm5iYeHh5SsmRJGThwoNy8eVN3MPBanyjl6tSpkyilZNiwYfqxuMIEKTWEYIx2btmypfz111/68StXrrxRx6P5+TIyMlJfTyX3zsbffvtNBgwYIOfOnbN43Hxf3zWEYA2MUOaXX36pQ5nGZ2p+fE+YMEF8fX1FKSVlypSRffv2Jcn2JgSjfShdunQybNgw2bhxo8Vy82M5PDxcNm7cqN+LTJkySYkSJWT8+PEya9YsGTVqlHzyySeilJJcuXLFeM+R3KT2cwGlDgwgEBERUbz7888/xcPDQ5RSUr16dcmXL5+kTZtWlFLi4+Mj3bp1k82bN1s8x7xh2Qgh2NnZyfvvvy+zZs1K7F2gBHDjxg2GEIiIUplly5aJUkq+//57EYm7xKqIyKZNm6RkyZJiZ2cnffr0ERE2oFm7rl27ilJKKlWqpEvhvuozvXfvngwdOlRcXFwkb968curUqcTYVEogRiUsV1dX+fDDDyVXrlw6rFytWjVZtGiRhIaGigi/70Qp0f3796Vr167i5OSkOxsNcX3n4wohWOPUPAcPHpQsWbJIixYtdIejefn4q1evvtPo5+QsMjJSX+89f/48xnVSSwjBuC5q2LChRSjT/HrYPIQwadIk8fb2FkdHR+nRo4c8f/7c6gN7f/zxh74OMB+41Lp1a1m7dq08evQoxudduHBBKlasKJ6enhbPN9oav/76az21S3KWms8FlLowgEBERETxymhAGDZsmCilZMSIEfLvv//Khg0bpEqVKuLo6KhvENq2bSvz5s2L8ebp8OHD8vXXX+sGa6P8LlmH2BqSGEIgIkpdjBGPwcHBIhL7CGfzxwcMGCBKKfHy8mKZdit39OhR3dlszNsr8vLzflXj+YkTJ8TLy0uUUjJlypTE2FxKAOPGjROllDRp0kR3tFy9elWWLl2qP19fX18ZMWKEPH78WERYCYEoJbp+/boMHjxYnJycxNnZ+a1DCPXq1dOBJWtiBCcOHDgQY1VIo8M5JXY8Ru1MjquCRUoPIXTv3l2UUlKhQgU99UBsx7/54126dNFBvhMnTiTKtiakhw8fSu3atUUpJR9//LFuP1RKiYuLi5QsWVJ+/fXXGKdRePr0qezevVtGjBgh7du3l65du8ro0aPlwoULehqL5Cw1nwso9WEAgYiIiBLEli1b9A3EgQMHROTlhfTq1at1uTnjT5kyZWTu3Ll6XmjD3r17pUOHDnLlypWk2AV6Czt37pSIiIhooxrMGxkYQiAiSj2aNm0qSin54YcfRCTuEYtGQ+uTJ08kX7584ujoaBXzt1LswsLC5Oeff5YPPvhAN6Du3r1bL39VR7PRUN+zZ8/XWp+SD5PJJP/++6+UL19e3NzcYpx64/Dhw1K9enWxt7cXHx8fGTZsGEPHRCnY9evXZdCgQW8dQjCm5mndunVibG68qV27tnzyySdy//59/VhMv2cpseOxf//+kjdvXlmxYkW0a7rYftPjCiFY82/E2bNndRtYhQoV5NChQ6+cgsi8nH6ZMmVEKSWDBw+O8znWYubMmaKUEj8/P7l+/bocPnxYevToIQULFtRBBD8/P5k4cWK0tkJrlZrPBZQ6MYBARERECaZdu3ailJJ+/frpi2TjBurkyZNSq1YtfQPm4OAgPj4+MnnyZPntt9/0axglyKx5nsfUomfPnqKUktKlS0v9+vVlw4YNcvLkyRjXvXbtmj4+GEIgIkq5evToIUopad++vX4srgbTiIgIiYiIkJIlS4pSSg4ePJgYm0kJKDw8XObPny/FixfXDah79uzRy2M6HozrxcGDB1tM4UHW5fz58+Lq6iofffSRfixq9YuzZ89KgwYNxMHBQXLmzCmzZs2ymJqNiKxTbL/17xJCGD58uBQtWtRivvTk7vLly7rNo379+vLvv//GuX5K6ng0KloZ9/z58uWTkSNHyoULF/S+vEkIIVOmTDJ9+nSrDiGsWbNGV//57LPPZPv27a+ceig8PFwiIyOlbdu2opSSgICARNra+HXgwAG5d++e/nd4eLhUqVJFXFxcZNWqVSLysjrAv//+K7169ZIqVaro46dIkSLy9ddfyx9//KFfwzh2rGXqptR8LqDUiwEEIiIiemdRL36NG4BVq1aJg4OD+Pn5ye3bt0Xkv5uENWvWiLe3tx7BUL16dVFKiZ2dnSil9LzPZB2mT58ebQ4+Ozs7SZMmjTRr1kxGjBghV65ckTt37ujn3L17V99EOzo6MoRARGTFzBuQzf++aNEiUUqJs7OzbNq06ZWvY1xTfPTRR6KUkv/973/xv7GUoKZOnSrz58+3OA7eNIRgHAfGdcL06dMTZ+MpXl28eFHc3d0ld+7cusxwTJ1NZ8+elZo1a4pSSsqXLy+///67iFhPpwIRWXrx4oXFv00mk8X3+W1DCHfv3tVTtVhDB5xxvjt8+LC4urqKUkr8/f3fuuNxx44dVrHfhp9//llsbGzEyclJj95XSknhwoWlefPmcv78eXnw4IHFc6JeOxi++uorUUpJ7ty5oz3HGpjv17p168TDw0OHEHbs2KGP+7hCukFBQaKUkl69eiX49sa3b7/9Vjw9PWXbtm0WwQEjpFKmTJkYp1YJDAy0aGPKlSuX1K5dW3bv3m1V07Sl9nMBpV4MIBAREdFbM+8wjqlCwbNnz3QHQu/evfXjK1asEB8fH1FKyfz58/XjU6dOlc8//1yyZ88uFy9eTNiNp3gTGRkpv/76q5QvX16UUpIjRw6pU6eOfPfdd+Ls7Cy2trZ6Lu/ixYvLmDFjZO3atfLixQu5cuWK9O/fX5fYW7RoUVLvDhERvYWHDx/KuXPn5PLly3Lz5k39+Pnz56VcuXJiZ2cnLVq0kHPnzsX6Gkbj67NnzyRr1qxStmzZBN9uil/jxo3T1ZCWL1/+ViEE83LDhQsXlvfffz/a/NGU/JlMJrl165YULVpUlFKyfPnyONc/duyY+Pr6ilJKAgMDE2cjiSje9erVSypWrCghISGyf/9+i2XmvwlXr16V77///o1DCFFfJ7kz9uXw4cPi4uLyTh2POXLkkH379iX4NseXEydOiJ+fnyilZPHixbJt2zapUKGCeHp6ilJKvL295auvvpI1a9bIs2fP9PPMP3/zTtaGDRvK8ePHE3MX4tXbhhDCw8PFZDLJt99+K0opGTduXKJu97sypmD98ssv5c8//xSR//bx9u3bkidPHkmTJo2sXLlSTCaT/sw3bdqkj59BgwbJ559/rr8LSilp3ry5PHnyJMn2602l5nMBpV4MIBAREdFbMW5+OnXqpB8zDyGYV0FwcXGRatWq6X8blQ/mzJljsa6I9Y1qoJfCw8Nl06ZNUq5cObG1tZWKFSvKmTNn5ObNm7JhwwZp0KCBlCpVyiK9XrJkSWnSpInMmDFDKlSoIEopyZAhg8yePTupd4eIiN5AUFCQfPrpp+Li4iJeXl5Srlw5mTdvnl4+ZMgQXQWhb9++cuHCBb3MvLNZ5GWDpDFtw4ABAyQiIsKqOhpSu9WrV0uBAgVEKSUfffSRLF269JUhhN27d1ssN3Tp0kXPdRx1NC1Zj4EDB+og6qFDh+Jcd8OGDfp68MyZM4m0hRRfYquEQ6mH0UZgVMOzs7OTGjVqSJcuXeT48eO6Eorh6tWrMnDgQHFychInJycZOnSoXpaSKqAY+3LkyBFJmzatKKWkbt26rxzBbbSvXL9+XT/PPORpDfr06SNKKalZs6aIiPzzzz9y4sQJ8ff3l5w5c+rjpU6dOjJq1CgJCwvT+22cR1LSNYD5uXH9+vWSIUMGUUpJ9erVYwwhmH8PihcvLoULF7aqkf9G+KBBgwbRQsjGvhnHSNu2bfWy9evXS65cuSwCF0+fPpWjR49K+/btJVu2bHL27NnE25F4kprPBZQ6MYBAREREb2zUqFEWHcnmo5SiVkI4f/68vnFo3bq1ZM2a1SJ8IBJ9LljjMbIuRgjBKK9Yvnx5OXHihIi8PC5evHghv/76q4wZM0YKFSqk5z5USomDg4M4OTnpNPfDhw95DBARWYEOHTroc7kxksv4M3nyZL1ekyZNdLWbdu3aya5du2J8vYkTJ4qbm5uUKFFCT99E1mXz5s1SqFChV4YQSpQoEWMIQURkxowZkiFDBilbtqz8/fffib0LFA+MRva///5bqlWrJkop+fzzz2MNFphMJnn+/LlUq1ZN7O3tLapjkHUwmUxy/fp1efTokR7Ryev51OP8+fM6VK6UEj8/P8mWLZsuN+7q6io+Pj7Sr18/mTNnjjx9+lTCwsLk6dOnMnjwYEmbNq2kSZNGhgwZol8zpiqL1iZqR/Lbdjz+/fffcuPGDYvHkjNjf8+ePSt+fn6SJUsWPb2OiMjz58/l2LFjMm7cOLG3t9fHzQcffCD9+/e3ys7l2MTV1hVXCME8lNm9e3dRSkm/fv2sJpDRrVs3UUpJo0aN4gwV7t27V3/+R44cka1bt0ru3LlFKSUTJ06M8Tn3799PoK1OOKn1XECpGwMIRERE9EbCwsL0PG1GOTSllHTp0kWvE/UieOrUqRYdEsHBwXpZShrZkBpFvYkyr4RghBAOHz4c7Xn37t2TCxcuyPDhw6VJkyb6xsvOzi5FNTYQEaVkrVq10g2mO3fulOvXr8usWbPkiy++0L/5S5cu1es3atRIh85y5MghgwYNknXr1snRo0dly5YtUq9ePXFycpLs2bPLlStXRITXCdbEvEF906ZNrxVCMCohFCtWTA4ePCgiInPnzhVvb2/JkiVLnFN2kHWIiIiQhQsXSp48ecTe3l4aNGgQ47We8V1v3bq1KKUspnqj5G/u3LnSqFEjcXd3l7x580rJkiVl+/btSb1ZlMh27NghdevW1WXlx48fLydOnJBOnTpJ5cqVLdoEChYsKJ9++qmsXLlSlixZIsOHDxdHR0fx9PS0CCFYk2HDhsmvv/4qR44cibYsMjJSt5O8bcdj1L9bg9DQUKldu7YumS9ieW138OBByZQpkyilJE+ePGJnZydKKfH09JS2bdvK5s2bk2jL39748eNl0aJFsU4rIfLqEEJYWJhePn36dMmYMaOUKVPGakKZPXv21CFT41ourmO3U6dOopSS2rVr68oYEyZM0MtjqpiWnPFcQPQSAwhERET0xq5evSqZMmWSUqVK6bl+YwohGDcFp06d0uV4jbkdTSYTOxWs2PPnz8VkMkloaGi0ZTGFEI4dOyYiLz/3mG6Uzp07J6tXr9ZzAhIRUfIWEBCgG5Ojnru3b98uZcqUERsbG+nQoYNFA2zv3r2lYMGCFp0QRsObvb29fPbZZ7pEc3JuWIur4TO5N4ompLcJIZhXQujbt69kzZpVXF1d5fTp00mxC2/k2rVrsnPnTpk/f74cPnw4VZbD3bNnj54+LTahoaEyfPhw8fb2FgcHB6lVq5bFtaH5PUG1atXE09NTTp06laDbTfGnY8eO+nzu4uIi7u7uopQSW1tbWbt2rYik7vNiSnfnzh2Lf+/cuVM+//xzPZrdvJrJtm3bZO7cufLxxx/rEc7GVA0lSpQQFxcXcXBwEBsbG6ub594ITzk7O4uTk5P4+/vLwIED5dy5czHO8f7bb7+9UcdjchZbu47xvT9x4oRkyJBB8uXLZxFAW7dunT4OgoODJTw8XH7++WepVauWno7H2toH2rdvr8+HtWrVkmHDhsnz5891NYPYpqoxDyF8+umnsm/fPhERmT9/vmTOnFmyZMkif/zxR+LuzFvq2rWrfg/y5MkjP//8s14W27ESEhJicW8wZcqUVz4nuUrN5wKiqBhAIIoDG5WIiKIzLv779+8vSilZtGiRnq81agjBvGRcy5YtRSkllStXlnv37iX6dlP8+O2332T27NlSo0YNqVq1qlSrVk0GDRokv/zyi8V6cYUQDMZvKX9TiYisi1H5wN/fX5dAjRow69Gjhz7/h4WFWZzr9+/fLxMmTJCKFStKoUKFpEyZMtKqVStZsWKFPHjwQESSd/jAcO3aNTl8+LCsXLlSzp07lyo7n2PyLpUQjKk8rKHzefTo0fL+++/r7XZ2dpYvvvgi1ulFUqLmzZuLUkpmzJghT58+jXEd4/N+8uSJDB06VI9s9PX1lfXr11uUkv7xxx/FwcFBatSo8cpQAyUPxqjV6tWry969e+X69euya9cu8ff31x2IrGSScrVt21YqV64cLYSwa9cuXQ2pZMmSsnLlSovlT548kX///VemTp0qgYGB4unpKW5ubvp86u3tLdevX0/EPXk3O3bssPgtyJ07t6RPn17/puXLl09GjBghy5Ytk6dPn+rz5ZEjRyRdunR65HdK6Xg07zA2mUxy+/Zt+fTTT0UppYMlGzdu1OED85HuhoULF8qlS5cSa5PjRWhoqGTJkkUHaxwdHfV3oG/fvnp6SkNkZKTFe2UeQvjss8+kd+/ekjlzZkmfPr1VhDJFRL777jtRSkmFChWkbNmyYmNjI3nz5pWgoCC9TmyBgipVqohSSurXr//KdZMrnguILDGAQBSHsLAwOX/+vOzYsUO2bNkie/futWhYs7YfQSKi+LR161Y9quH27dsyb968GEMIz58/FxGRM2fOSO7cuSV79uy6DBnPo9Zl7Nix+oY66h8HBwepU6eOnotO5PVCCEREZF02b94sLi4uopSSqlWr6gYy4x7J+O/MmTNFKSWVKlXS1XKi/u5HRETI8+fPoz2e3K8P9u/fL0OGDJGMGTOKk5OTLhVcokQJi1FeKdnmzZvln3/+iXV5bCGEcuXKyYYNGyzWCQ8Pl4ULF4qvr684OzvHOU9wctG2bVtRSomXl5fUqVNHKlSoIB4eHqKUkrJly1pNR8G7GD58uL4OTJcuXZwhBOM7/fTpUwkODpby5cvr51atWlVatWolVatWFXt7e8mRI4eugsKQavI2e/ZssbOzk6+++iraKOVLly5JqVKlxMnJSdasWZNEW0gJyQgjtm7dWm7duiUilt9Z8xBCqVKl5Ndff9XLzAcqiIicP39efvvtN2nbtq3Ur19f/vrrLxGxjjCiYfLkyfq8Vq9ePTl9+rS0bt1aPvroI4v75hIlSsiXX34pK1eulH/++UfOnTunRz/XrFkzxhHSydXOnTtl6tSpUqtWLRk/frxs2bJFL4t6Lbdw4UJRSkmRIkVk8uTJOnwwceJEvU5ERIRVfebmjGN/5MiR4ujoKMWLF5f169dLsWLFxMHBQZRS4uTkJH369Ilx8IbBPISglBJ3d3erCGWKiHTp0kWUUtKgQQMd0q1cubIOIUyfPl2va358GJ/5/PnzJU2aNFKuXDkdTrTG64DUeC4gig0DCESxWLhwodSuXVtsbGwsfhwqVqwovXr10iNzrPGHkIjoTcXWEdCmTRtxcnKS/fv3i4jITz/9FOt0DPfu3dOlGJs1a5Yo203xxxjJmjNnTpk+fbps375ddu/eLVOmTJFKlSrpVHfZsmXl0KFD+piJGkL4+OOPGUIgIrJid+7ckTFjxuiG4xo1augRauHh4fr+qH379mJvby8hISHRXiO2CjjJPXggIjJlyhTJmzevKKXEx8dHPvroIylUqJBuMFRKSffu3eX3339P6k1NMG3atBGllAwcOFBu374d63pRQwj58+fXjbFGo6rR6BwWFiaLFy+2itGOvXv31vthXg45ODhY8ufPL05OTjJr1iwRsY5j+m2sX79ez9ldp04dPdJv5syZr6yEEBYWJmfPnpXAwEBxd3cXe3t7HeKpUaOGVUzBQiLXr1+XChUqiIuLi8V0GubBonr16olSSnr37p2Um0oJwAgftGjRQocFDHGFEFavXq2XmU/ZaP6Y0fEYNaRgDaZMmaKvBSZNmqQfX7Vqlfz4449StGhRHei3tbXV1wyff/652NnZ6ftla6gYOWrUKPHy8tL7a2trK1mzZpWpU6fqdczPCY8fP5aqVauKo6OjnqZl/Pjxet2U8nt5+PBhcXZ2FqWU7NmzRx49eiQrVqyQhg0bWvQvNG7cWEJCQmIc6b5+/XpRSkn69OmtIpQpItKuXTtRSknDhg0tptnYsWPHa4UQRF5O9ZojRw5RSsmIESMSbdsTQmo6FxDFhQEEohj069dP7OzsxMbGRipVqiR16tSRL774Qt8YG4lNI+HNEAIRpVTbt2+PdkNkPk/rihUrRCkln3/+uZ7fef78+TGGEERE9u7dK0opyZcvH8uqWpFx48bpBub//e9/0Zb/9ddfMnfuXMmYMaMopaRMmTIWpVajhhAqVaokhw4dSsxdICKieHTv3j0ZM2aMbiT84osvLEa/GlWRatasaXVz98alX79+Ym9vL7ly5ZKFCxfKjRs3JCIiQh4/fizLly/Xc74aI0JT6ij4wMBAvZ+DBg167RDCr7/+qp83efLkGNdJ7lavXi2urq5StmxZOX/+vIiI7iwLCwuTDh06iFIvpxwTsa59e123bt2Sxo0bW5TSNkouv24IwXDq1CnZvXu3hISEyOnTp/X9AcMHyd+SJUtEKSU//PCDiFh+tsa94owZM0QpJYGBgUmxiZRAXid88LohBGs+R3bq1MlixL9h2rRp+rduwIABFsvu378vV69elTFjxkibNm0kffr0euCb0eno7OwcbUqL5KZr166ilJKsWbPKd999Jw0bNpTs2bOLUkrSpk0rS5YsifF5/fr10+/NyJEj9eMpJXxg6Nu3r+5QNvfLL79Inz59xNXVVZRS4urqKvnz55eFCxdGa2dZv3691Uxfc+HCBSldurTUrFlTBzPNP9OdO3e+MoRgnAtmzJghjo6OUrFixTgrbSUnqflcQPQqDCAQRWFcJFSvXl327dtnsezw4cPStWtXPWdhvnz59MiWlHaxRERkNKIXKFBAFi1aFGMjelhYmJQtW1bSpk2rqyCIxB5C+OuvvyQgIECuXr0qItbd4JBaHD16VHLmzCl58uSxuCmOOte3yMvRjUYIoXr16hbLjBBChQoVdGjFmJ6DiIiSt82bN0cLJMYUQnj+/LmsXr1aT9G0c+fOJNri+GeUlf3iiy/k8OHDMa4TGRkpw4YN09dAnTp10h2qKeGaZ/bs2bpjeeDAgW8VQhg7dqwo9XJu44iICKu6j37y5Ik0btxY7O3tZdeuXRbLjP343//+Jy4uLlKyZMmk2MREceHCBcmVK5dUqlTJYj5r4zvyqhCCSNztJynhu5Ia3Lx5U3Lnzi3Lli2LdZ3FixeLUkratm0rImw3SwlatmwZa/jAvGKBMfWSIa4QgjUeF0YloA4dOsR4TxsUFGTxG2kICwuzWO/333+X7du3S5MmTeSDDz4QDw8PuXLliogk3/fFCJzVqVNHTp48qR9fvny5VK5cWZRS8vXXX1tUuDD+++jRIylcuLA4OzvLypUrReS/EF9KsmbNGlFKiZubm0WlJMPkyZP1FF7G1Aw+Pj4yduxY2b59u1WF8Izj9ezZs3L58mWLZea/568TQhB52f6k1MupnaxhCoLUfC4geh0MIBCZWbJkiTg4OEjVqlV1iaOoc5nevXtXli5dKvny5dMhBKMcDm+UiSilaN++vSilxN7eXpePy5kzpwwdOlTOnz9vcb5buXJljNUOzEMI5slvo1KCNd1UpWazZs0SpZQEBQW91vpLly7VJRXHjBkjIpZzPK9du1Y+++yzFDsqlIgopalbt64+p0ctAxo1hFC8eHFRSknp0qVl06ZNej1rv08yGtvr1atnMbVCbPs1fPhwfQ00bdq0xNrMBGW8B8Zvu4jIgAEDXjuEYDSe7ty5U5RSUrRoUQkLC7OqY+Py5cuSN29eHbKMadsvXrwoadKkEXd3d3nw4EGKajQ2398tW7bImjVroj1uHkKYMWNGtBCC+brW9NmTJeM+LraKdsZnu3nzZlFKSUBAgF6Wkr4Tqc0333yjK/xcvHjRYpl5+KBdu3Yyffr0aJ1xUUMIxjnE2hgVIAICAuKs8mTe8fj999/rx82nqzIY3wtjut/k2lbSvXt3UUpJkyZNdJl9889+7ty5opSSXLlyRfsNNKbXMH4nGjVqlOjbn5iM6Yl69Oghz58/15/p+vXrxdfXV5RSMmHCBBkzZoyeqlQpJSVKlLCaUe89e/aU8uXLy969e/VjUc/xrxtCMD/mBw4cKMePH0+4DY8nqflcQPS6GEAgkpcn9+fPn+v56aKOZojq+fPn8ssvv0iePHlEKSXNmzfXHWpERClB165dxcbGRuzs7KROnTrSvXt3Pb+fj4+PNG3aVE6fPi0PHz6Uu3fvStasWSV9+vRy4MABi9eZP3++nr5m4MCBSbQ39DaMG52KFSuKUko2btwoIq9uLL5z546untG4cWP9uPG8iIiIOEfEERFR8tKkSRNdDjSuEIJRJc7NzU3mz5+vl1t7Z5Mx3UD9+vXl77//jnNd8301OuwzZMggp06dSujNTFDGvtSvXz/aXMSvG0IwrisuXrwoNjY24u/vn+DbHd8ePnwoP/74oyxfvjzG5REREXLnzh3JnDmzuLu7y/3792MsR26N4pqTPWpVrNhCCDGdC9iwbr3MP8/YzvO7d+8WpZS0atVKRCw/76CgoDirJ1Dy0rlzZ1FKyfvvvy+XLl0Skf/OB+bnhZo1a4pSSoYNG6bbSWOajsHBwUF8fX1l8+bNibgX7y6m6SfiOr/H1vFo/l0wmUxWEc4yKjzlyJFDl5uPjIyMtv358+eX8uXLi0jM+3Lw4EE95/3SpUsTZ+PjWdT9Mv+38dmuWrVK0qZNKx988IEe7b5+/XrJnTu3xRRGIi8rhsyfP1+++OILi6Brcnbw4EF9bNepU8eiLyWu9yeuEELUqgDJWWo+FxC9CQYQiP7fhQsXxMnJSUqVKiUir24oe/jwoYwcOVJcXFykUKFCcuHChcTYTCKiRDN48GB9gTx79mzZs2ePzJ49WwoVKqQ7GL788ks5duyYjBgxQmxsbGTq1KkiYnkODQ4OlixZsqSoOaBTk3LlyolSSvbs2fPaz/nll1/0nIZGAxUREVkX85GJRif8qyoh5MqVS5RSUqtWLT3dkjUHEI4dO6b3++OPP9aPv04D6cGDByV//vySJk0aWbt2bUJuZoIyDx+Yz0Vs3uH0qhCC+TFgHEvGNaO1Na6ahyhj2vbw8HDJly+fZMmSRR8n5vt//fr1hN/IeNavXz/p1q2bPHnyRERiDw3EFkKYOXOmPHz40GKZeUjVGljzeSyhxfUdNiqeGAEEw/Tp08XOzk58fHwYTLYCN27ckAYNGoizs7O4uLjI4MGD9eduPhjryy+/FKWUdO7cOdrc7ebHyZ49e6RcuXKSNWtWq5njXSTmDkdD1HBW1LDNqzoek7tnz55Ju3btxMvLS1xcXKRNmzYW7eAmk0nCw8Pl9u3bUrhwYfnwww+lUaNG0qBBA5k2bZqsXbtWwsPD9T4blRS6du0aY7AtJbh586YULlxYlFIyffp02bJliw4fTJw4Ua9n/vtibYMbg4KCJEuWLKKUkq+++uqtQwgzZ85MtG2OD6n5XED0phhAIPp/+/fv1z+Yr+vMmTOSKVOmFFVak4hSr99++003LBqGDBmiL5DHjx8vJpNJHj9+LD///LPUrl1bLzNKyGXJkiXGcnFGw1JKvblMyWrVqqU/f5G4P0PjpvL+/ftSoEABcXd3ZwCBiMgKGXM8r1q1Sj/WqVOnOEMI//77r8V0DDVq1NCNctbaeff06VOZN2+eeHt7i1JKqlWrZjGtUFxMJpPujOnVq5eIWN/7YHQQNGzYUM6fPx9tuXmDqXkIoX///tEaZEVehlLd3NykZMmScvPmzYTc9HhhfF4mk0n//VWf4dOnT8XX11cyZMggjx8/jtbw/PHHH8vu3bsTbqPjWbt27fSI1yFDhuh7hdjeh9hCCD///LM8efJEevToIUopyZQpk1XM7RwbawvOvIv//e9/snDhQmndurX4+/tLYGCgzJo1yyKQFFvnyZ49e3TVUJGXx820adPE3d1dsmXLxoC6FTl16pR8++234uTkJM7OztKnTx+LMJ7xe9epUye5deuWiPz3PTE/lxoOHjyow2rW8NtodDg2b948zg7H/v37y7Vr16I9P7aOR2vYd8P9+/elT58+kiFDBnFycpLmzZvr77BxDli6dKnez6h/KleuLK1bt5ZTp05J586dxdvbW0/jYC3u3r0r27Ztk5YtW0q1atXk66+/lm7dusmFCxd0m5d5VaCQkBCxtbWVwoUL6476CRMm6NeL6bthDcyP+enTp+u+kbcJITg6OkrGjBll7ty5Cb7d8YHnAqI3wwAC0f9bv369novyyZMnr91J1rdvX1FKyeDBgxN4C4mIEk6jRo0kT548cvDgQRGxbEQaOnSovkAeMWKERUPD8uXLpWPHjuLk5KRHx5H1M29o//bbb0UpJRUqVNDLX3VzdPv2bcmVK5ekT5+eFYKIiKyMecPa5cuXLZYZJZhfVQnBCCF88cUX+jWsrXHVEBoaKgsXLtRTUVWrVk0vi+2e0Xj8+++/F6WUTJ48OVG2NT716tVLlFKSL18+XWpZJPo1gPk1Y//+/fXx0axZM1myZIk8efJEHjx4IAMGDBAvLy/JkCGD1XQ4/Pvvv/Ls2TO5e/fua1W9CA8Pl/v370uOHDnE2dnZYmRvUFCQuLq6io+Pj9y4cSMhNzverFu3TpRSYmNjI46OjuLp6fnGIQSj4oWzs7NUr15dlFKSNWtWuXLlSpyvkVzs3r1bpk2bJvXr15dJkya9crrOlGb06NF66tGof0qXLi0DBgzQ68Z0PjRKdHfo0EFERKZNmyYeHh6SOXNm3XHDgHryFTVYcvr0aWnbtq04OTmJk5OT7jwzBibEFT4QkRhDR8n9HCAi0rx5c93WYXQoxhRGNEIYU6dOjfGax7zjsUePHomz8e8o6hSb9+/fl169eknGjBnF2dlZmjVrpgOKa9asEaWUODk5SdeuXSUoKEi6d+8u9erVEwcHB0mTJo3e//Lly8cYbEzOVqxYIZ9//rmePsL8T/78+aV///5y8eJFEfnv+Dh9+rSueqCUkqCgIP161nDsx8SYdsNcUFDQW4UQdu3aJSVLlpQMGTLo9y45S83nAqK3xQAC0f87duyYODg4iJeXlx6tGdfFgPEDMmbMGFFKSb9+/RJlO4mI4ltAQIC+iDZvEI0thDB69OhoHQ4nT56UH3/8Uf/bWm+mUrPvv//eYqSr8RkeO3ZM3NzcLEZwmi83Zzx28+ZN8fT0lEqVKiXwVhMRUXyKraSoeaPam4QQbGxspFy5cjGOAEquJk2aJDt27LB4LDQ0VBYsWPDaIQSjcbZhw4ailJIFCxYk9GbHK2PaBaMjYcCAAXE2DJtfMw4fPlxsbGx0A33BggUla9as+u9nzpxJjF14Jxs3bpS+fftK7ty5JU+ePOLl5SXVq1eXYcOGye3bt/X+xtSo/OLFCylQoIB4eHjo5Uanq7e3t/5eWUu53cqVK4udnZ00aNBAsmTJIh4eHm8cQjA/nszDB8n9Pejfv794eHhE62Ravnx5Um9aovjmm29EKSXZsmWT0aNHy4IFC2Tq1KnSs2dPcXNzE3t7e1FKSe3atfVzon6m+/bt01MwGCEc8/BBcj8GUrOBAwfK8uXL5cWLFxaPm4cQnJ2dJV++fLqUvtGWEFP4YNKkSVKwYEGrCaAZTp8+rb//lSpV0uc+Y8oBQ82aNaOFMGIyY8YM/XrmU9MkR507dxZfX1/Zvn27iPz3eZqHEJycnKR9+/YSHBwsSikpVKhQjOfIY8eOyfLly6VWrVrywQcfWMW1gLmhQ4eKm5ub2NnZSfv27WXy5Mkyb9486d+/vw7dpk+fXmrWrBlt3/r16ydKKSlVqpQ+ZqyxvWzDhg0yZswYqVGjhjRt2lS2bNkiDx480MvfNoSwd+9efV2QnKXmcwHRu2AAgVKd2H70Xrx4ISVKlNAlJs1/SGJiXIQPHz5clFIyduzYBNxqIqKEYXQ0tGzZMsZSueY3RuYhhB9++EHu378vItHPk2xIsj6zZs0SpZS4u7vLxo0b9eMRERHy+PFj6dChgzg4OEi2bNks5is0v9Ey/9y7du2qjxMR6x31SkSUmrRu3TrWkqIiluf51wkhjBs3TlxcXMTb21uXWU7uJk6cKEopKVasmOzbt89i2euGEIxrpxcvXkj+/PmlePHiVjXC1+gsbtSokbRp00aUUuLg4CA9evSIs1y6+fEREhIirVq1Ejc3N0mfPr2ULFlS+vTpI1evXk2MXXgnvXv3lvTp04tSSuzt7SV79uwWHdCffvqpzJo1y6LUsrnw8HDJnz+/5MqVS0JDQyUoKEjc3d2trtPVOI5//PFHUUpJ3bp1ZeDAgeLh4fFalRDMHzOqaWTLls1qwgdt27YVpZQULlxY5s6dK+PHj5e6deuKjY2N1K5d26LTJSUyOswaNWoUY0fhrl275JtvvhFXV1dRSsmXX36pl5l/9nv37tXBEw8PD8mUKZNVfQ9SK+O+38/PT9atWxdjCKFNmzbi7OysO1aNii9GtRjz42DatGni6ekpadOmtYrfgajWr1+vw0jVq1eXv//+22K5Mdq5c+fO0SpAxGTevHnJvjqUMYWOv7+/xTVh1BBChgwZdBgpb968OqwgItGOG5GX33tr62w1pqMqX7687Ny5M9rymzdvSqNGjcTNzU1sbW3l888/t5ii5tSpU+Ln5yeZMmWSI0eOiIj1BRCGDx8u6dOnFzs7O3095OjoKEOGDLEIqL7tdAzWIjWeC4jeFQMIlCpdu3ZN/vjjD/1v48bnxx9/lPTp04u3t7dMnDhRnj17JiLRfwTMLxSqVq0qmTNntri4ICKyBrGNchSxPM+Zl5yNGkIwv3nkBbP12rhxo1SrVk2UUpIhQwbZsGGDxfI9e/ZIqVKlRCkluXLlkqFDh1osN//sZ86cKRkzZpSiRYvK9evXE2X7iYjo3RjzvFerVk2fu2P6XX+TEMLdu3clKChIj4hM7o2tkZGRMn36dHn//fdFKSUlSpSQvXv3WqzzqhCC+T4a78/IkSMlIiLCKq6TjA6HevXq6QbRHj166BBCz5494wwhRP2M//nnH7l69Wq09ya5Mr4HH374oWzYsEGuXLki4eHhsn79el35UCklvr6+Mnz48GghhIiICHn06JHkyZNHz5Hu5uZmdeEDc1evXhUvLy8pVqyY7NmzRwc0Xnc6hk6dOlld5YM+ffrogSnm7TyHDh2SrFmzSqZMmaxmGo23sW7dOkmXLp2ULl1al0g3mUwW85qLiFy6dEkGDx6sK6UFBgZGe62jR4/qzsksWbJY7fcgtfn333+lSJEiulN57dq10TqTT506JW3bthVnZ2dxdHSUwYMHWwTwDObTbhi/K9b0+Rvn940bN+rATfXq1eXx48ci8l+H46umnzDal80l13CiEURs0KCBRdu5IWoIwcfHR48KN86NMX3G1nAdFJXxXtSpU0dOnjwpIv+dD0X+28+7d+9K165dxcPDQ5ycnKRjx45y9+5dERF5/vy5+Pv76055a7geMme8Bzly5JDhw4dL165d5dNPP9VVssaMGWOx/puEEKxJajwXEMUHBhAoVdm1a5f06dNHMmbMKBUrVpT9+/dbLP/jjz+kbNmyOuk+depUfUNtXFSYd8RNmjRJjwZI6Ql4IkpZzOd3jho+ML/4Nc55rxtCIOsUEREhO3bs0HPzxhRCWL9+vRQrVkyXVK5Ro4bs2LFDrl69Ko8ePZKbN29Kly5dxM3NTTw9Pa2uvCYRUWplTMVknP+Ne6TYGgnfJIQQtYE2uQsLC5P58+dLsWLF3iqEYJg2bZq4u7vLxx9/rEeFJndGp2tMHQ5vEkKI7bhJ7o3Oo0ePFqWUNG7cWC5cuCAi0TvVd+zYIe+9954opSR37twyefJki2tkk8kkT58+lQIFCoijo6O4urpa9YhvY/+N92bhwoUSGhoqvXv3FldX11eGEFatWiV2dnaSIUMGqwkfLFq0SFxdXaVixYr6e2B+b1ShQgXJlCmT3L9/P1onQnI/xl+XUf1g/fr1r1z38uXL8t1334mjo6PkzZtXjw423ourV6+KjY2NuLi4WM0xkJr98ssv+jfrwYMHOpD3qhCCMR1D37599ecbHh6ufwutLYQ1Z84ci3vZmDoea9SooQP8nTp10u9bTB2Os2bNkunTp8u///6biHvxdozO5vr160e7FojpHHf//n3p2bOneHl5ibOzs7Rs2VIHl6ydUfmgQYMG+rogJsZnfe/ePV1NLE+ePBaVtE6cOCE+Pj7i6upq0SGf3BnHQ7169eR///uffjw8PFwaNWqkrw0PHz5s8byUEkJIzecCovjCAAKlGuPGjZNs2bKJUkpKliwp/fv3l3PnzkW7Sd63b5/kypVLXzB06NBB7ty5E+31JkyYIJkyZZJs2bLFOR9mcmVtiUsiij/GfJ7NmzePNiezeQNbuXLlpGrVqjHOU2ceQhg3bhxDWFYqNDRUf74RERGyfft2nWaPKYSwbds2qV+/vr7ZcnV1lXTp0omPj48uwVm8eHGGD4iIrIQRSKxXr5588sknopQSDw8P2bx5c5zPiy2EMHbsWD3iy9oYDYVhYWHy888/v1EIoWrVqnrZrFmzxNvbW7Jly2Y1VfIePHggzZo1k5YtW8rvv/+uHze/LnyTEIK1OXfunOTNm1d8fHz0/sfWQP7bb7/pY6N8+fL6msdY32QySfny5fWUA9bU6WaI2lawd+9eSZMmjRQqVEju3LkjN27c0NUd4goh/PHHHzJgwABdcj25vwfXrl2TokWLWnSmmEwmvU8XL14Ud3d3KVmypAwZMkTee+89+eKLL2T8+PH6NaypYyUqk8kkjx49kmzZsknatGnl4sWLr9Vu9Ntvv4mfn58opWTYsGEWryfysuPNKFOd3I+B1MwIEphXsDMPIfj5+cUYQjh9+rRFCGHgwIFiMplk9uzZVhk+MDqdmzZtatGRHlPHo52dnbRq1Up3JhrfF/PvzfTp08Xe3l58fX3l0aNHibgnb868szlq+MD8eiBqsPL+/fvSu3dvyZgxozg5OUnz5s2t/hqhd+/eul9g7dq1+vHYzonG43fu3JH8+fPrwYoiL4+de/fuyfvvvy92dnZWUyXSeA8aNmxocW1kfI//+ecfKV26tA4hi1geJ1FDCLt37078nXgHqflcQBSfGECgVMFoLClUqJCsXLkyxnmozG8U9+zZI0WKFBEHBwd9wdG2bVv54YcfpE+fPlKpUiVdRvD06dOJuStvrV+/fjJ69GiraQQjooRh3FTmzJlTfvnlF/14ZGSkxc1CrVq1dEjBPFxg3mhgHkIICgpKnB2geHH06FGZOnWqfPLJJ7J8+XJdxSIyMlK2b98eZyWEq1evyrp166R8+fJSuHBh3Vn12WefyaRJk6LNg0dERMmTeTWka9euyaNHj6Rq1ar6vL5ly5Y4nx9bCGHatGlW2wn3LiGETz/9VObMmSNZsmQRNzc3q7lPPH78uIi8HLkXU7UG8885pYYQli1bJkopGTVqlIi8Oqy/Z88ecXJyEqWUdO3aVT8eGRkpoaGh0rNnT/nggw+sptx4r169ZMaMGdFGMJpvd/v27cXGxkZfF16+fPm1QgjGayT398DQqVMnGTRokIi83A9jX/7++29p2rSprn7h6+urO2Zjm37AGj18+FC8vLwkY8aMumLB6xg7dqwopaRMmTISHh7+yil8KHl5/vy5FC1aVM9dLvLf9/h1KiGcPn1a2rRpo0MINWrUsLrwgclkktDQUOnYsaN4enpK2rRpY+14XL9+vaRLl06UUlK5cmV97hOxPP8ZFSCyZMmify+T6/WR0dnapEmTaO3G5p9d/fr1pU2bNnL79m0RiT4dQ8aMGcXZ2VkCAgKsuhKCURXK1tZWAgMDX+uaznifli9fLk5OTlK8eHGLKkm//vqrnDlzJsG2OT4NGzZMlFLi6empQ8lRj92wsDD5/PPPRSllMUVn1E73TJkyiYODg1SsWNGiKkRyldrPBUTxjQEESvEGDhyo03bm5YJELEuKR70I+N///ifdu3eXvHnz6ptK44+Pj480atTIahpcunbtqre9ePHiEhAQIH/99Zfcv3/fYj3++BGlfMHBwZI2bVqdZN6/f3+0774xd1lgYKCeu8yc+Q1o7969LcqqUvI3ceJEyZMnj9jY2Iitra106NBB/vzzT/25viqEYBwvoaGhEhYWJn/99ZfVlJcmIjKXmq99mzVrJkopadGihcVUTNevX3/rEEKLFi1EKWX11wRvGkJYuHChDiEY1YFOnTqVFJv+xpo3by42NjaycOFCiw6lqN+NlBpCMBqHjepgc+bMee3nzpw5Uw9WuHLlisV7dvv2bR3uTO7z+rZp00bP4+zp6Sl9+/aVAwcO6OXGe7Rt2zaxt7eXjz/+WJ4/fy4iLysGRA0hPH36VESSf2djVOYdBcZ8zobbt29L+/btRSklX375pRw7dkxPwbBixQr93Z88eXJib3a8e/bsmeTNm1cyZMig28/iCuQYy3bs2CG2traSJ08efQyQdTDOXevWrZN06dJZhKeM7/rrhhDatm2rRwRbWwUYY58fPHggAwYMEE9PT3F0dIyz49F8HvioIfyYpp9Irr8HP/zwgyilxMvLSxYvXqwfj4yMtPjsvv76a33taN6ebB5C6N27t3h7e4tSStq3b2/R7m5tjOmHlFLy7bffvnZ44NChQ6KUEkdHR/n777+t4viPaufOnfpzLFu2rMUUFEZloBcvXkilSpXExsZGT79jMP/dmDlzptjZ2YmHh0e0CqzJUWo+FxAlBAYQKEVbtmyZODo6Srly5SwagczL6ImILFy4UHx9fS0SeyIiT58+levXr8vEiRNlwIABMmDAABk5cqScPXvWquY8L1WqlG4kcnFxEaWU5MiRQ7766ivZtm2bxQ0ip2YgSpnMG0VDQkL0qK26devKnj179DIjfNCpUycdPohp7jJzxvnQGm+sUhuj0yBXrlyyYMECuXLlisXnav5ZxxVCMP+sIyMjLcoOExEld/Pnz7fKKdTiy/nz5yVDhgzStGlT3RBmMpn0OfzGjRtvHUK4d+9etMeSo23btsUZnosthPDhhx9GGyUeGhoqixYt0iVYrWUaIpPJpEe8Zs2aVUJCQiQ0NDTW9VNqCEHkv074JUuWiEjcx69xbBw/flyXm44tcJLcr4vWr1+vO1eyZcsmhQsXFkdHR3FxcZG6devK0aNH9ffk+fPnUrlyZbGxsZFNmzbp1zAPIfj4+EivXr3iPI6s0datW8XHx0dq1apl8bhxnEyfPl2XLRdJ/p97XMLDw6V48eKilJKWLVvq+4RX7dOZM2fEzc0t2ohfsh4XLlyQAgUK6EpGBqOj7HVDCA0bNhQ/Pz+rqQAjItKkSRN577339PS7jx49kn79+r2y49G8BHv16tV1p/zUqVOtqgLE8OHDJU+ePOLg4CBffvmlHqVuvs1NmjQRpZS0bdtWT6tjzrxiRseOHSVPnjwWUzpZgyNHjkQrjT9q1Cj9O9muXbvXCiFcvXpV0qdPL++9955V/h4an+WBAwcke/bs+vrXuHcyjotFixaJjY2NNG/ePMYpBczbmebOnWsVFZlT+7mAKCEwgEApUmRkpDx+/FiXEN++fbvFcvObpxUrVliUzhs9erReZu03TsZ+rlixQjJmzCiFCxeWgwcPir+/v+TIkUPvs7+/v0yaNElExGIELBGlLOYNBCEhIeLs7KwrIRw9elSHDzp27Bhn+ODgwYPRKiNYc0NbajF48GBdEejYsWMWy2L6/F63EgIRkTXp1KmT2NnZyeTJk1P16JNz587FOQrpXUIIyf33oXv37mJvby9Dhw7VDYwxiRpCKFKkiL5uivrePX36VJYsWRJtzuTkyvz6rkqVKqKUEm9v77cOIfTp08eqSy23a9dOlFLSqFGjN2oDKFu2rNjb20cLpViLO3fuyNChQ8XW1lZcXV2lVatWsnnzZvnoo49EKSXp06eXMmXKyLJlyyQsLEx27doladOmlbZt21q8zrVr16R///6ilJIiRYpY1WANEctzVmznr1WrVum/G98DY93t27eLUkpKliwpz549s5q2lKCgIDl06JD+t7FfwcHB4uLiIrlz55Zly5bp5bHdL4iI7Nu3T5RS0qxZswTeakpIU6ZM0d9j8+pIbxJC+OOPP/Q86NZwnXX8+HFxcXGRXLlyWYRT36bj8bPPPpNRo0aJh4eHVXQ4mg8imDx5suTKlUtsbW2levXqsnv3br1e48aNRSklrVu31uGDuAapPHz4MMZKmsnZt99+K9mzZ5c9e/aIyWSy+MxeN4RgHO8bNmwQpZS0atUqUbY9vty4cUP/3fhc9+/fr0MIpUuX1t+BtWvX6irLO3bsiPU1reX3UCR1nwuIEhIDCJRinTx5Uuzs7KRmzZoiEvOP3tKlS8XX11fs7e2lU6dO+oLCmPtRJGX8OPz555/i6+srSikJCQkRk8mkp5gwSioppeSTTz6RgQMHyqVLlyyeb00XDERk6dSpUxISEqJLiZqf08wrIWTJkkVXPjAaDGK6qZw8ebLkz59fhg0bxnODFfnll1/E0dFRSpUqFa0iUFwYQiCilOS7774TpZTUqVPHqjtLE8ubhhCswdOnT3WHe/bs2WXYsGF6HuOYmIcQJk+eLJ6enuLp6SkbN24UEeu+TzI6jSIjI6VSpUrvFEJQSsmgQYOsLsBvfH5Lly6VNGnSSLFixeTkyZMWy2ISHh4uz58/l8KFC4tSSg4cOGBV10Tmn+Hdu3d1SFUpJTNmzBCRl50L9evX14+XLVtWWrVqJUWLFpV06dJFK7d85coVGTlypA7nWMP7YUyVEXU0r/m2Rz0OzN87o7Np//79VtfZNHz4cFFKSa1ataJNVXr8+HF9bH/55ZcWHZHm74d5dYRGjRrp9qao61HyZxzzf/75pxQvXlzSpUsnW7duFZH/Pss3CSGYv2Zy9/DhQ/0b2Lp1a4tlb9Lx6Obmps+X3t7eVtfhaDKZZNKkSZI7d26xtbWVGjVqyIkTJ/T0WgEBAXGGD44cOZIk2x0funTpIkop+eKLLyzaw83371UhBPN1a9SoIW5ubnraLmv4LvTu3VsqVKhgMVAlphDCxx9/LMHBwaKUkkKFCsnatWv1+tawn3HhuYAoYTCAQCnWwoULRSkl3333XYzLT5w4oacmMNJ6EydO1D8Sw4cPT8StTXhz5swRpZTUrFlTnjx5oh8/fPiwzJ49WwoVKqT3PWPGjDJo0CDdsGaw9osJotQmKChIihYtKg4ODtKjRw/daBB1Ooa0adOKUkr8/Pxk3bp1elnU6WqCgoLEw8ND3NzcYiy7R8mPyWSS8PBw3XBgXjL3dZ4rEj2EkDlzZvn1118TapOJiBKEET5o0KCB1YxSTw5SUghhzZo1elSe0bGaJUuW1w4hhIaGSp06dXQ1IfNl1sQ8JBAfIYT27dtLmjRprGLqiXPnzsU4XcLZs2fFw8MjWidyTJ2oxmOPHj0Sb29vqVChgtU3Kt++fVuGDh2q2wOM6ogiIqtXr5aePXvqUX3Gn0WLFkV7HeO9Se7vx/r166VPnz6SJ08e8fPzkwwZMkjNmjWlV69e8uDBg9f6XpsfGw0aNLCqzneTySQ//fSTFC9eXOzt7eWrr76S48ePW6xjTCtjnO/Wrl2r35eIiAiL+8pJkyaJi4uLVKtWzWJeeLJO7du31x2NxiAGQ0whhIIFC8qKFSusLoAmYlnFxNnZWfLmzSsnTpwQkf/29U06Ho3KMVeuXBGR5HsufPDggZw+fVqOHDli8Z01mUwyceJEyZUrl9jZ2ekBa+3bt48WLotaXj9XrlwyYMCARN2P+GB+fxDTFAFvEkIwX6dFixby4MGDBN32+LJjxw5JkyaN2NraSt26dV8ZQrC3txc/Pz+LgSnJ/XfvVVLruYAoMTCAQCnW5MmTRSklgYGBIiLRErmPHj2S7777TicSjR/LqVOn6guKBQsWJOo2x4fYbpZPnjwp2bNnF3t7e4v53g1z587VjQq2trb6PQgMDJQZM2bI8+fPE3rTiSgedevWTZycnMTd3V2mTJkSbWSL+Q3CggULdAihYcOGes4/c9OmTRN3d3cmeK3QuXPnJH369FKyZEmLOb7fREREhGzfvl1q1KghSinJkyePPHnyxCo7XlIbfkZEIoGBgXp+bvPwwevM806WIYQMGTLoUZHWxOhQMe7vbty4IXXr1n3tEIJx3bR9+3ZxcnKSGjVqJMp2x6e///5b1q5dK127drUIkhgNq+8SQrCGUss//fSTlCxZUtKmTSsbNmyI1lg+f/58i3tgc8b5wLyceLdu3UQpJcOGDbOaa+Ldu3fLjBkzpEWLFhIcHKzbQkRehhCGDRum34MRI0ZYPPfs2bMyYMAAKVasmFSuXDmxNz3e9OrVS9/3ODo6Ss6cOS3aQMqUKSMLFy6Ue/fuxfoa5p/36NGjRSklVatWtarO94iICFm6dKkUL15chwyihhBmzpypj4f3339funXrJnfv3pVnz55JRESEPH78WLp27Squrq6SPXt23UFp7R1RKVlc1zbG53bt2jXJkyePeHl5ybZt2yyWiViGEEqUKCFKKSlfvrw8e/YsAbc8Yd24cUM++eQTUUrJ5MmT9ePmgbPX6Xjctm2b/h4k19+FGTNmSOXKlUUpJXZ2dpIjRw6ZOnWq/PnnnyLyX6jIz89PXyOtXLlSPz8yMjJa+CBz5szi6uoqp0+fTuzdeSdG+KB+/frRwsnm+2gerokaQjCqJom8nL4mc+bMkj9/frl8+XLC70A8efHihYwcOVJy5sypQ2mxhRB8fHxEKSUFChSQ69evi0jyPdbfRmo6FxAlFgYQKMWaPXu2nocv6snePLkt8vKHxBglKiLSoUMHUUrJxIkTE3ej39GrRhwYF1dffvmlLrEuIrJu3TrJkyePHu3w66+/SseOHS1KBv3999+Jsg9E9O6+/fZbXWI6avDAXNRKCM7Ozvp55g2SU6dOFXd3d6uauywkJMRiH0SS/zYnlB07doidnZ3Uq1fvjZ/74MEDPY9vRESEbNq0Sfz9/WMcPUjWgY3ClNoYJeKbN28uv//+u8WyFy9eyMmTJ2XcuHHSr18/Wb58uUWDG0MI/7lx44ZUq1ZN3x+Yl+RO7ox7oFq1almMbvv777/F399flFLi4+MTZwjBOHfu2LFDj5SzJgsXLpQKFSroz69nz54Wc/2ahxCMjonMmTO/UQghOevVq5c4OTmJq6ur9OjRI8aw7b///iu9e/fW71GbNm3kypUrMc5hPnHiREmfPr0ULVo0zuBKcjJgwADx8vKyqGKQJ08eCQoK0uvcuXPHIoQwcuRIi9eIjIy06GS0ls/f0K5dO1FKyYcffigbN26Uq1evyvPnz2Xz5s0ycuRIyZgxo64KN3bsWLl7926crzd69Gjx8PCQHDlyWFXnu3l72JIlS3Qncq1atSx+A0VE5s2bJ9myZRMHBwdRSknOnDmlUKFCUqxYMcmUKZMopeSDDz7gKE8rMGTIEJkyZYpFh6mI5WdmMpnk8ePHekqNb7/9NsbXMs6L9+/fl2rVqlnMl26tgoKCRCklWbNmtbheNO947N+/v3h5eYmjo6M0adLE4prC/JoxuX4PevXqJUopSZMmjXh6euoKB15eXtKrVy/9PRZ5+TuXN29esbGxkS+++CLG6QTmzp0r3t7e4ubmZnXtA3GFD8w/P2MwnnkIwQieKaWkY8eO8vvvv8vPP/8s3t7ekjFjRquoCGV8juZTjY0ePVqyZcv2yhBCtmzZ9G9pTFUjrF1qOBcQJSYGEChF+Oeff/Tfzecuy5w5szg5Ocns2bNfedI3v1H86KOPpECBArrTxRr06dNHAgIC9I2A+f4a+3b69GnJkyeP5MyZU98grFu3TnLnzi1KKRk/frzFa+7YsUO6dOmSIi8oiFIqI5Fdp04dixS7SMwNYuY3UosXL9YhhHr16sn+/ftl2rRp4uHhYVXhAyOAoZSSrl27yuLFiy2Wp7YOJWNKolKlSsmzZ8/eqGF048aNsnz5cosSzU+fPk2oTaV4dOLECVm5cqVMmTJFVq9ebdEwmNq+A5R6rV+/Xv8eGPOaG44cOSJdunSR9OnT63VsbW2lUKFCFuXH+X35z99//y0ffPCBKKV0Z1tyZ97AbH5PY1zLvE4Iwfx307jG+Omnn0TEOo6P/v37i5OTkzg6OsqoUaN09YOo225cE75pCCG5a9OmjSil5LPPPpPDhw/Hue6FCxd0aMkoQd6lSxc5ePCg/Pbbb7J7926pV6+e2NvbS86cOXVnTXLvdG7durXuPG7fvr34+/tLlixZRCml20sM//77r0UIYfTo0XqZeRgjue9zVEaHUaNGjSxGK5o7e/as5M+fX4czgoODo1XSfPLkiRw/flxq1qwpjo6OUqhQIT3SNbnfI5l/Zm8SQti3b58MGjRIcuTIIS4uLvrYqFChggwZMkTu3LmjX4uSpwEDBujS6W5ubvLDDz/oqWgN5r8Ju3fvFqWUODs7y65du2J8zajhrJjCWtbA2O/79+9LhQoVJG3atHrEv/Gdia3jsVmzZjGW4U+OjOuhypUry759++TmzZty4cIFXfkoc+bM0dpNfvzxR8mdO7fY2tpK9erVLarpzpkzR7y9vcXV1dXqwgfG73z9+vWjhWfMz2M1atSQ8uXLW7SFGMxDCFWqVBEPDw+rey+uXLliMU3E64YQDhw4oKdjKF26dIoIIImknnMBUWJjAIGsXt++faV+/foWP/IRERFy+/Zt+eyzz0QpJZ9//nm0cnLmzC8ivv/+e7GxsZH+/ftbzRxmq1at0uWzunXrFmMIQeTlD+SXX34pSinp0aOHbNmyRYcPzKs9mN84WOtNBFFqdPHiRSlQoIB4eXnpi9+oFV9EXn6vr169Ko8fP442vYp5COGDDz4QJycnqwofXL16Vezt7UUpJTY2NmJnZ6fLioaEhFiM9hOxvsbTt3HkyBFxdXWVwoUL65FcrxPKi4iIkCpVqoiXl5f+/Mk6DB06VI9KM/7kzJlTOnToYNWdSERvwxjtpZSSNWvWiMjLzhSjwfWDDz6QZs2aSatWraRgwYJ6XWurhJZY/vnnH11uP7lfE3Tv3l2UUtKkSZMYR6MZ1wBRQwjff/+9vl4wvx+cOXOmuLq6StmyZS0C8MmZcfxXr1492qj/mO51jdHtKSWEMGTIEF2x4sKFCyISczDX/Fi+evWqTJs2TV9DGteUxt8dHBykatWqVlNat2XLlvp7YN5JsHTpUqlYsaIopaRcuXIWoaJ79+7FGkKwxmvn8+fPS/78+aONZhSJfq906dIlKVy4sK6maR4uMJlMsnXrVilfvrzY2NhIgwYNdKXI5HwcRA1cGNtq3m70qhCCiMjjx4/l0qVLcuzYMX1OjdopQ8nT5cuX5fvvv9fT6SmlxN3dXVq0aCH79+/X94jG5/js2TP5+uuvxcbGRsaNGyci1hG4e5VXTUFhdEx/+OGH0UL3UTsejVL0nTp1StbffxGRLl26iFIvp9uM+t19/vy5lC5dWpRSUrx4cQkPD7f4PpuHED7//HM5cOCAhISEWG34YODAgfp6b9GiRfpxo/3D0LRpUz0wxzyYGnUKHuNawcPDw2qmoFi/fr20bdtW7O3tJWvWrHLnzp03roRgHkIoW7ZstCoSyV1qPRcQJQUGEMiqbdq0STcCtG3bNtqFz5YtW/TFdaNGjeT06dPRpikw72CfNWuWeHt7S4kSJfRcRtbg7Nmz0qFDB3FzcxMHBwfp0qVLtBCC8eP622+/iYuLi3h4eOjOCfPKB7xxJLJe69atE6WUdOjQQURelpaOemE9atQoqVOnjmTKlEkKFCgggYGBcuDAAYt1Fi9eLO7u7rrsmLWM6hERCQ0NlWbNmumbhR9//FHvi729veTOnVvmzp0rR48etXheSj73XblyRTJnzixKKencubN+PLZ9Nh5/8eKF5M+fX8qVK5co20nxo1+/fqKUkqJFi8ro0aNl0KBBUq1aNXFyctIjQPfs2RNtRB9RSmP+m9W/f399TzB79mxp27atLi9u3qh48OBBvczPz09+++23pNj0eJHQv2vJ/XezZ8+eopSSfPny6c8xpuuYmEIIGTNmlKZNm8rvv/+ur6OGDRsmXl5ekjFjRqtpZJ00aZIopeSLL76wGJVlMpksPr85c+ZYlOGPLYSwePFiq6qCtH//fvH29pZs2bLFGcwVeTmqPeo186FDh2T48OHy2WefSalSpeSjjz6Sb7/9VtasWSP379+P8XWSG+N81qZNG10ZzTx4MmPGDH1ujHo/kJJCCMuWLROllIwaNUpEYt9+4/O8cOGCeHp6ilIvp+8xFxoaKr/++qusW7dOHj16ZPG85Khdu3bi4+MjjRs3lmnTpsnFixctptEwREREyOLFi2MNIcQ2MMXajoXUyPiMTCaTvHjxQmbMmCG1a9fWoX0fHx8pW7asbNmyxaIE/8yZM0UpJZkyZbL6Ec4LFy6UEydOWDwWU0WQW7duSZ48ecTR0VGWLVsWbT3j748fP5bvvvtOihQpIlevXk3ozX8nnTt3FqWUVKtW7f/Yu+vwKI7/D+A7MZJAiENCcAkOwfnhUNytuLuU0kKBQqFA8aIFihVri0O/FHenuBUrXooTgtN48v79kWene5dLuECSu829X8+TB3KW2b3d2dmZz3xGBh+o26veD+7YsQNubm4IDAyUwYYJBSEEBQXBw8NDl8EHQNyyMm5ubhBCoGXLljhy5Ei8eqx9+/by2mkq45fxPYZell0A4iYqqP1jzZs3R/fu3WW7Vt0PSQlCUJdzrlmzpi4mcdpyXUBkKQxAIN0bMWIEMmbMCBcXF/To0SNeA2j58uXyprlZs2bYtGmTyU73SZMmwdfXF76+vvGi4vXgxo0bGDBgANzc3BINQnj27Blq1aplMh0tbx6J9G3u3LkQQqBbt24Gj7958waHDx9G/fr1DWZEq+v/lStXDseOHTN4z5IlS1CgQAFdrue5ceNG2Zly/fp13L59G+PGjUPFihVlIEKWLFkwYsQIXLhwIV4WiLQwu0OlbsvMmTORLl06eHl5JVrva39XZ0rMmTPH4LPIem3evBmOjo6oVq2awWDT48ePsXHjRtnZUq5cOWzatCnesU+U1iQUhGA8mKZ18uRJOft12bJlqVTS5KOd5W6rbXv1+mVnZwcfHx+MGjUKz58/B2B6n6iPPX78GN26dZPrwLu4uKBEiRIyY1y+fPl0k1715MmTyJ07N7JmzYozZ84YPKfdB+vXr5cDjt9++6183FQQgqOjI3777bfU2YBksHTpUtjZ2WHixIkA4rbFuC3z448/om/fvsiVKxcaNmyIadOmmfyssLCwBGfAWSs1+0XevHllCnV1H2jLHhQUBCGEyaU5jIMQtMeIHqjXAHUJCnWpicTatOp7lixZAldXVxQpUkRmOUis/rBGX3/9dbx7P3d3d1StWhVTp07FoUOHEBUVZTA5Z/369ShevDiEEGjcuLHJTAikf+/evcOJEyfw6aefIn/+/PKaV7FiRUyfPh2hoaGIjY1FmzZtDO4H9dQnoFIH4F1cXDB69GiZDUulnaQWFRUlX9+rVy+Tn6e+/t27dzJ9vTXul9jYWNy6dUue+7Vq1cKdO3dM1llqVt3s2bPj5cuXJjMFzZ49GwULFoQQAh4eHrqZ7a/Sbstvv/0m74ubNGmCgwcPyufatWsHIQR69OghB5QTW9IUgMwgYu3UWf0VKlTA7t27Tb4mqUEIR44cQdGiRRPNOm0tbLUuILI0BiCQbhl3KLq6uiYYhLB48WLZ6MqVKxcaNWqEDRs2YNOmTVi4cCFq1qwp0xPrpVPJlOvXr2PAgAHIkCGDDEJQIxC1+2vBggUQQiBHjhwyvag13zgTkXnUta4zZ84s08ndvn0bI0aMkDeL7u7uGDx4MBYvXoyBAweiUKFCsLe3R79+/RAWFmYwu0XtaNXjUiwtWrSAk5OTHDyKiIhAREQEpkyZgubNm8trQr58+VCvXj2cPHkSjx8/NvgMPQ64J1TmM2fOoEqVKhBCoHDhwgZr/arXB+11YuHChfDy8kKFChV0k2aagAkTJsDe3h6bN28GED8L0oULF+RAWpkyZbBnzx5dHudESaGt29TBmG7duslrm6lz4LPPPoMQcUuW6Ym61v3ixYvlY7bWxlfXOK5fvz5atmyJ9OnTw9vbG0OGDMGLFy8AJD6IGBISgp9++kku5aemJP7iiy8MZoZaOzUoVRt0aGzDhg3Ily8f0qVLJ7d17Nix8nltEIIapHD9+vUUL3ty6du3L4QQGD9+PADIoLt///0X58+fR7NmzeINzgohMGjQIPkZ2jawWlfo4br577//onfv3sicOTPSp0+PXr16GcxgVlNN37t3D5kyZYKTk1OCM5xfvHiBSZMmyXsMdda/HqjfVefOnSGEwMqVKw0eT8zZs2fh7u4OIUS8QG09UI8BNTV0yZIlUaVKFXkuqz9lypTBgAEDsHXrVtnm37hxowzcbtKkSbwgJtIv43rszZs3uH37Nj7//HMZfKkGK/fr1w8DBw6UKdb16N27d/jmm2/QsGFDCCFgb28POzs7dO7cGdu2bZPtAq3Dhw/L/bBjxw6Tn6utQ6z1mqCWa//+/TLbRaNGjfDnn3/GywY0ceJECGF6+TFtm2ny5MkoW7as7vrN1awO2smI2iCE5s2b4/z58zL4oHv37okGH5w+fTpe35G1mzZtmqzT1eCRhNo1SQ1CMJVVx9rYcl1AZGkMQCBd0zYA3heEsHnzZhQrVkymGtL++Pr6okOHDrhz505qb0Kyu3LlCr788ku4ubnBzc0Nn3/+uQxCUP998+YNatSoAVdXV6xYsQIAo/SI0oL79++jXr16sLe3R/r06VG7dm05iy99+vSoUaOGQaR6TEwMFixYAHt7e+TIkUOmoU4LDecZM2bI1MvqWtVa6tp/6k+mTJnwySef4LfffpOBWXrxv//9Dzdu3JA31gndSK5btw758uWTAWijRo0CEL/+V9NM6zUjkC1S20NqQOW2bdsAGH636msuX74sj4NPPvlEF2sXE30s7fE9YcIEzJs3z+Tr1AFKdcbLmDFjUqV8yWH48OEG17WlS5fK52wlCGHQoEEQQqBVq1Z4+PAhnj17hi5dusDV1RXe3t4YOnRookEIxtfNa9eu4fLly4iMjNTVsjVhYWEoUaIEHBwccPr0aQDxt23r1q3ImTMnHBwccOTIEaxbt04eO9999518nXpOxMbG6ioAA/jvnKhevbr8/oKDgzFx4kQ5w9vV1RVdu3bFoEGD0Lt3b7kPEsqEoCcvXrzA119/DW9vb7i6uqJLly6yv0M9/ufPnw8hBIYOHQog4XuAkJAQ/PDDD/IY0Nu9glo3tGvXDu/evTP7fbVq1YKLi0u85Sn04sWLFxg2bBi8vb2RI0cOTJgwAZcvX8a+ffswdOhQFClSxCAAKUuWLOjatSsWLFiA8ePHo1ixYnBxcUHLli1x4sQJS28OpbDbt29j2bJlKFKkiAy+cXFxkceINoBdT9T6asmSJejevbs83t3c3BAUFISNGzfGW1qpT58+Bu1APbajtNlNDh48CDs7O5nZ5MKFC/K5zZs3QwiBatWqJbikgnb7TQ3UWrMhQ4agdOnSMhNWQkEI2bNnl5kP1OACU8EHixcvRtGiRfHNN9/oZqLOH3/8gaxZsyJPnjxmZ7VJLAhBD9kOTLHVuoDI0hiAQLphfJNrvGYV8P4ghGvXrmHbtm3o1KkTWrRogQ4dOmDYsGE4e/asTJejF6YuemoD8tGjRzKNWoYMGfDFF18YLMcQExMj10UtWbKkHLSyZgl9/7z4Exn6/fffUaNGDYNBiEqVKmHGjBkGGU+0ax5ny5YNzs7OuHjxoiWLnizUuiEsLAxlypSBnZ0dfv75Z8TGxsp6cMeOHXIAdvLkyejcubPMECGEQIsWLXQzu0tNo1eoUCE0adIEFy5cwLNnzwxeo70xXr16NUqVKiVnQdSsWRP9+vXDsmXLMGLECJklIW/evLqb2UD/zdr+4YcfTD6vnvcXL16El5cXhBDo2LFjahaRyGLeF2SjbVNWr14dvr6+uhl0evv2Lbp06SKzvanXs+XLl8vXfEybWQ+DjWrQSJs2bQyC527duoWuXbuaHYSgfVyvs5revXuHPHnywMvLCzdv3oz3fHR0NCZNmoQsWbJg//798nF1MFoIgdGjRxu8Xo+uXr2KTJkyyVnenTt3Rt68eWFvbw8HBweUL1/eYGZ3eHg4pk+fDiEEqlSpgrCwMF1971rqMfzixQsMHTrUIAhBzXSwcuVKCCFQuXJlnDp16r2faTxjVg/U/bBs2TIIIVCkSBHcvn0bQOIZ3mJiYhAaGoqSJUtCCIHDhw+nSnmTk/ExkD59emTOnBnjx4/H27dvAcTNCj5x4gQmTpyIevXqyUFndeDZxcUF9vb2clBOD+t7U9IZXwv//vtv7Nq1C1WrVoW/v7/MfqK3Gd8q4+07fvw4evXqhcKFC8uJGgULFsT06dNl/bB161Y5WS2h7DDWatmyZTJbUXR0tMkghEaNGiE4OFgGHxQpUiTBGd4qPfa9qv3eQgjUq1dPtv+MgxDUui9nzpwGS01p+82AuH2bJUsWpEuXThcTNdTr9tSpUyGEwC+//JKk95sKQnB1dUX16tXx559/Jnt5U5qt1QVE1oIBCKQrL168QHBwMG7evCln7Bl7XxCCnk2fPt1gxpb24qntCOjUqZMckEqfPr1cjkF7k/38+XOZYu3nn39OnQ34SPfu3cPly5dx5swZOTBmq4EIeu0Mo5SjPSYuX76MFStWYPLkyVi4cCHevXsXL9W0es7cv38fvr6+qFy5sm5m95k6/rWPqdumpott0aKFfG779u1ycGb69OkA4urPv/76C2PGjEFgYKBuBt5Pnz4NJycnCCHkzBQfHx/Ur18fa9asMUiFp+0w3L9/P7766iuDGU9qZ0S2bNnQrVs3/P333xbYIvpYS5Yskd9jQjPV1PNj3759cHFxgZ2d3Xs7nMh6sT3w4bT7TtuOnjJlCoQQaNiwoZwtpQcHDhyAvb09GjRoIJchSI4gBO17bt++naTZw6klODgYbdu2RYsWLWSHsHad+zt37iQ5CEHP3r59i4IFC8LLy0t2EBvXFW/fvpUdqdHR0fL5efPmQQgBT0/PeLPA9ET9XteuXSsD7tSfEiVKYMyYMXjw4AEAw4How4cPw97eHlmzZjVYB1uP1LIbByH0798fs2bNghACpUqVwt69ey1c0pT35MkTObu1fv368nFTwRTqsfPmzRtkz54dlStX1u3Au3oMvHz5EkOHDkXGjBnh4+ODCRMmxMv4FhkZievXr+OXX35BmzZtDIKz3d3dZTpysh2RkZHYu3cvRo4cKe8N9RSAZIp6Tvz7778IDg7G4MGDUblyZXmsFy5cGH369MHdu3dRvnx5CCHw7bffIiYmRhfXg8GDB0MIgQYNGsiMN+okNMAwCEHdvsKFC8vseUDaube4du2aDKBRAwzq1q1rMghh3bp18jUtW7bE4cOH4+2HZcuWwd/fHx4eHroZZ4iNjUVoaCgCAwPh6OiIS5cuJflYVs/5yMhITJ06Fa6urvD19cX9+/dTqtipIq3XBUTWhAEIpAvnz5/HvHnzkDdvXvj7+8tOgb59++Lo0aMyNaQqsSAEc9JtWiN1RkqBAgUMAgbU9RtV6ppVffr0wcGDBzF48GC4ubnFC0IIDw9H27ZtkSFDBqsfaFq/fj06dOhgMFimrtM1d+5cOVM5LXYgGnvy5An+/fdfAPo4bil1JXZMmBqgj42NletFjxs3Tjfn0IsXL3Dz5k1cuHABr1+/lo8bl//KlSvIkCEDhBDYu3cvdu/ejdy5cye4viEAOSNIL5o3by47Uz/99FPkzZtX1pF169bF6NGj8ebNm3gdp9HR0Th37hxmzJiBnj17Yvjw4Zg4cSJu3LhhlYNLZJ6XL1+idu3acHBwQO/evRNMlx0bG4uwsDC5PvbUqVNTuaRE1kN7fZw/fz4yZcoEf39/Xc5yad++PZydnfHHH39g5syZHx2EoH3tokWLUKlSJaxZs8aq2qDqWsbBwcGJLqFkS0EIsbGxqFChAoSIW8fYONud8fZqs0TdunULrq6u+PTTT1OtvCnt3Llz6NSpEwYMGIAxY8bg6dOnBktLAP8FIVy5cgUODg5o1qyZxcqbnIxnwXt5ecksWMWKFcOxY8fka63pvE5Oal/JqlWr4OHhES/7k/Z80AajqAN5Y8eO1XUdoZZdG4SQKVMmTJgwwWCZOuOB5cePH2PLli34+uuvZbCO3gefyXymjnm9pJs3h7a+CwkJwfLly1GpUiV4e3tDCIE8efLA19dXBq3poQ748ccfDTKYNG7c+L1BCF5eXpgzZ478jLR2jvfo0QNCCLRv317Ock8oE8KGDRvkcgxNmjTBoUOH5HNLly6Fv78/3N3ddRN8oAoNDUVAQAD8/PySnMUkLCzMoL8tIiICP/zwA27cuJHcxbSYtFgXEFkbBiCQ1VuyZImcqa+mhXZ1dTWYwTB69Oh4g0ZpLRPC5s2bUbFiRTg6OqJQoUJYtmxZvNe0b99epsdTI9SvXbuGzz77zCAIQR2IOnr0qNU3HEaNGiUDD4oXL46aNWuiVKlS8vtX17lXU46n5cbA1q1b0bBhQ5QoUQLBwcGWLg7plHpTGRsbi1mzZiFdunQoV65cvLT91mrp0qWoX78+PDw84OTkhCZNmmDRokXyeeO0yd9++60cjM+ZMyeEEJg5c2a815tKt2zN1I7zXbt2wc3NDbVq1cK7d+/w4MEDTJ48GXny5JHXyqCgIAwbNszs9f70SC/fW0qLjY3F3Llz4e7uDg8PD0ycODHRjoYVK1bImR56nd1nq/7++28cOHAAc+fOxYEDB2QHo61IznM+MjISz58/R58+feDh4YGsWbPi8uXLyfb5qUnNgvLpp5/ixYsX+Oabbz44CEH7muXLlyMgIABCCJnW1xoMHjwYOXLkwKpVq2T7JrFtSywIQa/XkbVr1+Ls2bPyd3U/zJ49Gy4uLihevDj27NmT6PZp91mXLl3g6uoqZ0Pqdb+o1PKbu/2dO3eGEAKzZ8+O95xeGQchZMmSBUII1KlTB0+ePAGQ9gadTHnw4AEGDx4sg5ObNGmCkJAQk4OqM2fOhLu7O4KCgtLEfbc2CGHYsGEmgxCMs+QZ/24LxwjZFuPrwoMHD3D8+HHUqVPHYDkrV1dXXWTEWrBgAYQQyJ49u1xuMqEghP3798sghKZNm+LKlStpKsBE3c7Lly8jU6ZMqFq1Kvbs2YNixYq9dzkGNQihWbNmOHbsmK6DD4C4AIRcuXLBx8cHV69eBWB+22bbtm0oWbKkbvoKP1RaqwuIrA0DEMiqjRw5EunSpYO3tzcWLVqECxcuAADOnj2L5cuXw8fHR6ZT6tu3b7w1u42DEPTYmahtGOzevRuVK1eGnZ0dChUqhMWLF8vn1MwH2uAD9SJ6/fp1DBgwAG5ubsiQIQO6d++ui4GGESNGyDXKtdGnEREROHjwIJo3by7X9SxWrFiaDkKYMmUKvLy84ODggHr16tlEqkxKWd988w28vb2RNWtWOUva2s+d4cOHQwgBe3t7ZM6cWd4IZMmSxSCoQGvbtm1yppcQAj/++KN8ztq315QtW7YY/B4cHIzixYtDCIEJEybIx69cuYK1a9eiePHiyJgxI4QQcHBwwKBBg7B27VqDz9B2Nlj7QMPt27exd+9e/Prrr/jtt9/w5s0bswYX0prY2Nh426udxdmnTx+5JMfkyZPlzDX1dWob4PLlyxBCoFu3bqlXePpoP/zwg+xAU4Mxy5QpY1Ntg4cPH+LEiRPYuXOnwRr2H2Lz5s0oU6YMhBCoVq2a1QfnqhIaDKpatSqyZs0qz/uvv/46yUEIxsEHasrZixcvJlPpP97t27eRNWtWCCFQrlw5rFy50iDIMiFpKRPC999/DyEEOnXqFO+7+fPPP5EtWzbZiX7p0iWT+0W7zfPmzYO7uztatGiR5jpYtdue0PIr06ZNg6OjI6pUqZLmtl8bhDBkyBD4+voiffr06NatG27evGnh0qWeq1evYuDAgTITQqlSpTB8+HAcPHgQZ8+exeHDh9GqVSs4OjoiZ86curlHMoc5QQhEtsj42vjvv//i9OnT+Oyzz1C8eHFd1QN169aFp6cn5s6di4oVKyYahHDgwAEZhNCoUSNcvHgxzd1Pv3r1CvXr14cQAr/88gt27dolgzPMCUIoW7Ys3N3ddRt8oGY9LFCgAIQQGDRokMFzCVH7h5YtWwYhhMGEn7QsLdUFRNaEAQhktb788kvZKDh58iSA+DNVT506hUaNGsHZ2Rlubm4YOnRovBSTahBCxowZ0aZNGxnxpxfG27Nr1y4ZhFCkSBGsWbMGHTp0kCk2tcEH2ovnjRs35HqwmTNnlrMdrNWKFSvg6OiIevXqyfXY1UaQ2lH0zz//YNq0aXJWc8WKFfHy5UsAaWsg6quvvoK9vT3Kli2LXbt2Wbo4pGP//PMPNm3ahKpVq8obKrURbe2zWtT6q3Llyti5cyfu3LmDRYsWoVixYnBwcEDp0qUTHBhp06aNvPlW6fGmQd0Hq1atAvDfNmzYsAFOTk6oU6eOyUGUL7/8Us72sre3hxACrVq1wtq1aw0Gpq3dDz/8YJARSe0s2bBhgy6/zw8xdOhQGYSS2EBSZGQk2rZtK1NrDh48WK4Drt1XI0eOhBAC8+bNS/Azybp89dVXEELA29sb7dq1Q61ateTSK7ly5cKpU6csXcQUN2/ePJQtW9agLujevbu8X0iqN2/eYNy4cZg5c6bVt48BGMx219LOfBdCYODAgfK5pGRCMBV84O7ublXBByEhIQDiZvCpmdFKly79wUEIw4cP1+WA89y5c5E3b164uLigc+fOsp5Xbdq0SX7vLVu2xLFjx2QGpcjISIN9NG/ePPj7+yN79uy6XH7kY40bNw6enp7Inj27vJ9Oa20L9ftWMyF4e3vD1dUVXbt2lYNTttAOuHPnDubNmwc/Pz+D64j64+TkhNq1a8v1ra39Hikp1O/3fcsxENkq43pfXf5UD/VAdHQ0vvvuOwgh0Lt3b6xZs0YGLJuzHEPjxo0TDFbUC1PBhocOHYK9vT369esHANixYwcCAwPfG4SgpuD39vbWzWTGgQMHYuHChfJ3dR/88MMPcHJyQsGCBbFjx454z2tpz4HKlSsjd+7cNnl90HNdQGRtGIBAVkkNPmjVqpXBhd7ULMdLly6hSZMmsLe3R758+fC///0PAAxm+KspuP39/fHo0aNU2oqPc/r0acyaNQv58+fHL7/8YvCcNghBjczs1atXvPR5xq5evYqhQ4fi2rVrKV7+DxUbG4s3b96gfv36sLOzM1iX0pSQkBDMmjVLpoUdMWKELrI7mGvixIkyLZpxx6+2QaTnmwRKHc+ePUO3bt3g4OAAT09P9O3bV9YZ1t6IHjRokLwmGEee//7773KGn/HMfvUc2bhxIzJkyICiRYvqNtWyGnzQqlWreHX4tWvXkD9/fggh5DVQtW/fPpk27ssvv8SYMWPg5OQkO1nLlCljcLNtrdRBVw8PDzRv3lxm/RFCoGrVqnJATm/fa1KMGjVKbrM6093U9qrnc0REBLp27QpHR0c4OzujTJkyWLduHZ48eYLo6GjMmDEDmTNnRv78+WUnO1m3SZMmyQ5CdUmVyMhIbNq0CZUqVYIQAqNGjQKQ9gbOVEOGDJFr21arVg21atWS50WrVq1kAEFCdYG6X4zTzEZHR+ui/dirVy+4ubmhcePG+OOPP0wusXLv3j1kyZIFuXLlksslREZGmhWEoIfgg+7du6NgwYKyDXPw4EEEBQV9cBCCu7s7hBAYPXq0Ls+bZcuWITAwEI6OjiaDENTZa0IIVKlSBePGjZPr+UZFRSEkJAT9+/dHhgwZkDlzZt10sieH+/fv49y5c6hVqxYcHBwQFBSkm8DcD2W8HIMahNCjRw+byoQAxC1lNHz4cHz66acoUqQIKlSogL59+2Lr1q149eoVgLR5HCSUCWHSpElJXh+cKK3SLkWip/vLly9fImfOnHKiycaNG1GoUCGzgxCqVq0qJ4DpidquMRYbG4sXL16gWrVqEELg+PHjAICdO3fK/pOEghB+/fVX5MyZUzf7Y/DgwXIZNuP9cezYMRl00bZtW5w5c0Y+pz2+te1gtS964MCBCAsLS/kNsFJ6rQuIrAkDEMjqDBs2TM5qNDcS/+zZs/Ji2qZNG/m49oZx/Pjx+Ouvv1Km0Mls5syZcsDIw8MDderUQXBwsEFjYNeuXahatSocHByQMWNGTJ8+XT6X2I2yHjpXL1++DAcHB9SuXRvA+zvRHz58KNNNV65cWWZB0Ls9e/bAw8MDuXPnxvnz5+XjsbGxabIzhFLeunXr8N133+HAgQO6ieBVB5vq1q0rO4VjY2MNBo/UQbkffvjB5Ge8fPlSLlPw1VdfpUq5k1NiwQeqcePGQQiB6tWry7Vqd+7cidy5c8fbNxcuXMCwYcNQuXJlg7rFWk2ePDneoCsQV0eqwWdff/21BUuY8n755RcIIWTnkLlBCFFRUZg4caKcIaxmQfL394cQAgEBAbrpVLF1+/fvR+bMmVGsWDE5wKjWg+Hh4Rg7dqwcgE2rxo8fDyEEGjZsaJDp4bfffpPH97Rp0977OefOncPUqVPx8OHDlCxusuvVq5dBJpsMGTKgYsWK+O233+LNTJo+fTqEEPjpp5/kY9HR0QZBCMYBznoIPlCD1OvUqSPvE4GPC0Jo2bIlsmTJIoM19EL7fS1dujTRIIR169bB2dlZfvd58uRB1apVUb58eXk9KF++vFUHqSe3kJAQGcDk7u6ODh06yMFXa28bfyzjTAhqJoAvv/wyzW+7yjizZmhoaLx+Ej0GJJlLmwlh2LBhcqbv7NmzOcCiEyn5PRkf+2n5XNA77Xej1t9jxowxaAP+9ttvKFq0aKJBCIcOHZJtBDULkF6MGTMG7dq1w8aNGw0e117PFixYYNAXFBYWZlYQgvEyz9ZK7S9q27Ztglmf58yZI7/jTp064eDBg/K5qKgog/21cOFC+Pr6okCBArIPjojoQzEAgazKihUrZNq7IUOG4N27d2a/d/PmzfJiqr2Q6u0mWh1sCwwMxKpVqxAcHCxTZQKGNxo7duxA1apVYWdnh6JFi2Lp0qXyOT3fJGzfvh1CCHTu3Nns95w+fVrO6lXTk+vd1KlTYW9vb7DelvGN5rZt2zBr1ix8+umnmDRpEo4ePZraxSQdSKiDwto7mLZu3Srr9fbt28ebpa3W70uXLoWXlxfWr1+Py5cv4/Lly/HqwNWrV8PNzQ0lS5bU1U2UNvuDqYEBdTtv3bqFggULws/PDw8ePMCRI0dkINusWbPk69XO1YiICF1Esh88eBD+/v4oXry4HASLiYmRx646szMoKAjPnz/X9bUvIefPn5dreC5YsAB9+/ZNUhBCTEwMLl++jPHjx6NUqVLw8/PD//3f/6Ffv34GA3hkvSIiItCvXz/Y2dnJJThU6nd/7949+Pj4IGfOnDLALC3ZvXs3fHx8UKZMGTlDW3vcT506VQYnREREmDwnYmNjERISIgfbevfurZvMaLGxsXKwNH369OjQoQNatGgh64KSJUti3LhxCAkJQWRkJC5duiTvJ7Tp9KOjo+XSK0IIrFu3Lt7f+vnnnxEQEICMGTNaVfCB2rnaunVrGSygrfM/NAjh7t27ujkOgIQzVrwvCOH48ePo3bu3DEzUZkWYOnWqzc18joqKwurVq9GuXTts2bIFb9++BaC/voMPpZ0F369fPxQpUkR3g07JQRuIoJ3laAuMj4ECBQroamk2W7VixQr8/fffAFLmWNVeV7Zt26aL66OtnLOqI0eOxJtcp90Hx44dk23DkJAQ/Pvvv1i/fv17gxCOHj2quyWL1bah+tO0aVP8+OOP8foE3r59izJlyiBHjhx49uwZgLgA7oSCENQ+Ez0cW++brKLdF+qkFSEEKlWqhClTphg8//btWwwaNAgZM2aEr6+v7iYq6OH7IrJFDEAgqxIdHY1WrVpBCAEfHx9Mnjz5vbOT1DQ4L1++ROnSpSGEiNc5qxfq7LWmTZvKWakJdaCqtMsxFCpUCMuWLZPP6XUgZs2aNbIBCOC96cHV/dGtWzcIITB//vwUL2NKi46ORo0aNSCEwObNmwHEzcwA4jrMHjx4gJ49exrMZrKzs4Orq2u8yF/SJ1vpAE1MdHQ0Bg0aBDc3N2TIkAF9+/Y1uCk2jvJPly6dPB8++eQTfPbZZzh58iQeP36Mx48fw93dHenSpdPNGnZqRqAKFSoYDCCZui5ERkbKZQnKlCmDnDlzxgs+0Ns1ITo6Wt5Qb9q0yeA5dR/8+eefcHNzQ0BAgG6+16TQpk3v2bMn/v33X7x58wZdu3Y1KwjB+LF///1XphU2TkFP1iskJATFihVDiRIl5Pem/W6jo6Px+PFj+Pr66nLm0vtERETITFfbt283eE6t13bu3AkhBPLnz//eTFizZs2SmQT0UG+o33V0dDTatm0rs5fs2LEDv//+O0aNGoX06dPL7f/888/x8uVL9O7dG+nTp8fWrVvl+9V/v/jiCzg5OclBDNW2bdvkUk3WFHygZj5o3bp1op3uHxqEoAfG98SmlhN5XxBCVFQU3r17h3PnzuHkyZM2tdxCQrTtbb0fI0mlbu+rV69kvcn7D9uiHgOvX7/mMaAD6rWwV69eMjA/Oest40xI3t7eqF69OqKioqyqfgwODsbJkydx9uzZNNfmfR81zX6xYsUwbNgw3Lt3T05Y035/X3zxBRwdHeV94rt377BhwwaTQQhRUVG66yeIjY3FtWvX5P1w1qxZ0bhxY5nNpWTJkpgxY4bsO4qJicHo0aNl5kRttkBtEEKFChV0lVE3seCDhOryadOmwdPTU2ZUK1asGCpUqGCQFatEiRK6yCBty3UBkZ4wAIGshtp5Eh0dLQdRvLy8MGXKFLNnZDRp0iTB2TzWbtOmTUifPj3KlStnsMa5OTOX00IQgnZ71E7kgIAAGZ2a2A2P+tzQoUMhhDBYjkKP1BkYDRs2hBACQ4cOlc+9fv0aS5YsQZUqVWTnecWKFVGzZk2ZYtvBwQGHDh2y4BZ8nLTSUfyhtm/fLmdhqZJzX+hlv2pnbw8ZMgTOzs5Inz49+vbtaxCJvXHjRnnjWbRoUbnUgoODg7yOBAYGYuTIkahTp45B/WrNtNH8rq6u2LFjBwDT359az1++fBmZM2eW79Muu6CXa4HW48ePUbZsWdSsWdMgE5BWREQE8uXLB29vb11ltkiKdevWIWfOnNi2bZt87O3btzLo7n1BCNrHtdkj9FIXUFwgym+//Ybly5ebfD4mJgbh4eEoVqwYnJycDGYw6vHcN3b79m1kzZoVTZs2TfA1wcHByJw5M/LkyYPnz5+bfI12X8yfP9+qBtjfRxs8oN4nZc2aFf/73/8AANeuXcOECRPkNdDDw0MOxFeuXDne+R4TE2MyGOnBgweoWbOmwXI3lqYG49WqVctk5gMg7QchDB48GEWKFEG7du3wv//9Dzdv3kzwtYsXL04wCCGhwDNr3y/WXr7UkFL7QPu51r6frb18KY3HgG1T2/4eHh5wd3dHr1695IBbcnxvppZh8vDwiBfIZmmzZs2SmeFcXFzg6emJ1atXJ1v2L2s+B7799lt575ctWzbY29sjICAAbdu2xaVLlwyWC/jf//4HIeKWZ1QH1ENDQw2CEJo3b24wyUGPjDNmnj9/Hl26dEGePHkghICnpyfGjRuHM2fOICIiAjly5ECVKlXk5C4grm20e/du3QVyJ5YpUxt8MGzYMBw/ftzg+W3btmHgwIHImDGj7DcTQqBixYoYP368Lpaps+W6gEhvGIBAVsVU55q3t/d7gxCio6MRHh6OMmXKQAiBXbt2pVaRP5o6GNClS5cklz2hIITixYtjwYIFKVHcZKftQFO3582bNyhUqBCEEBgyZIhB49AUNUPCwIEDZYpqPTJOL6+u+S2EQJ8+fTBv3jxUq1YNnp6eEEIgV65c2L59O0JCQgAAZ86cwaeffgohBAYMGABAf42mqVOnokOHDvj7779TdBDeWqn1QNGiRbF8+fJ4gyMfO5Ckfb963FgzbVpUbRBCnz598PLlS+zZswdCCOTLlw9LliyRrz1+/DhWr16NGjVqoHDhwhBCwNfXF7dv37bk5phNDT5o2rQp6tevL7M7bNiwIdH3vXjxAk2bNpUdCio9z2SaM2eOwfJCWjExMQgNDUWuXLng5OSEK1euJJg+V4/1h7bM2mNXffx9QQhcvzVt0WaDSuh4Ll++PNKnT4/g4GAAhue+3tMqf/PNN5g3b16Cz4eEhMDLywu+vr64d+9egnWB3jJ/aMuvpoPV3idlzpwZa9euNXjN999/j8aNG8u6oU2bNmZ9vsqa9pE600+9rt24cSPB16bVIAR1xp72J0OGDGjTpg2mTZuG69evx1u2cMmSJciTJ4/JIAS9bb/x/VFyl18P7YWU3gfGbHEfWPtxwGPAkK22aYODgzFo0CB4eHggQ4YMSQpCSOwYNxV84O7ubnWBmuoydE5OTsicOTMCAgLk72pfwMccu9r9cPfu3QQD4C1l06ZNcHNzgxACBQsWxKJFi1CtWjUIIeDm5oaWLVti/fr18vUtWrSAs7Mz/vjjD/mYGoRQsmRJCCHQrl07q2r3mSsiIkJ+X+oSvkIIDB8+HC9fvsTTp0/x7bffIjAwEHZ2djJAoUKFCib7jCMjI7Fv3z7dBGT0798fQgjUrl073pKK2vs/9X5hzJgxiIyMjNcv9PDhQ/z55584fPgwzp49G+/91srW6wIivWEAAlmMunSCSq3g1Q5Wc4MQ1Ivj8+fP4e/vjxo1aqRC6ZPX3bt34erqiuLFiyM2NjZJF3zjIITq1atDCIHy5cvLmU3WaujQoShcuDD27NkjH4uMjER4eDgGDRoEOzs7FC1aFOvXr09wDS5tw6BixYrIkSOHLmfBDh48GK1btzZoPD5+/FgGVahLLAghkDt3bvTp00dGuWqPlwULFkAIgZo1awKw/s4DLTWloBACWbJkQevWrbF9+3aD7dNDY/hD7d271+B7dnR0hLe3N77//vt4Ecsf8r1qz5UFCxagdOnS8ibDGiR0bqvXBG0QQoYMGeTAfNGiRRNcdicsLAwvXrzATz/9pJubSW0avVu3biEqKgotW7aUQQi//fZbou/fsGGDnP166tSpVCp18jPVsZhQx1mJEiXg5uYmv2Pte2/fvq27euPq1asICwsDkPg1D0g4CEG9ZgJx64Rqf9cLPV2/kpNx+9icQdPY2FhERUWhXLlysLe3x/379w2OlSVLlqBIkSIG7S1rpz0PjJnaFxEREciZMyeyZMki7xe0++DJkydp4phKKAhh/fr1BjN+oqKi8L///Q9jx46Vj+ltwEa9HpYsWRJOTk4QQqBjx46Jtl0SC0JYvXq17q4HUVFRaNeuHTJkyAAHBwcUK1YMNWrUQNasWWW97+Pjg1KlSmHSpEnYvHmzbDetXbsWhQoVgr29PTp37iyX99OTTp06QQiBgQMHxgvCTI7vUntOqFn3rA33AfeBrW8/AHz33XfYtGlTvCC0tHBdf59jx44Z/B4cHIwvvvgiSUEI2uPk0aNHBs+ZCj7ImDGj1QUfqPc7DRs2xJkzZxASEoIDBw7IgEsXF5ePytag3Q8LFy5E8eLFrXJp0127dsHDw0MGDwBx13s1G7DaFzh37lwsX74c6dKlQ9euXQ0+IywsDCtXrkTFihV1txTTmDFj5PccGRkZbyk2IQR69+4tz4WbN29i3bp1KFasGNzd3eVrBg0aZLFt+FiPHz+WS03kz59fTlRQ7wdV7du3hxACPXr0MJnVIaFsYtZer7IuINIfBiCQRb1+/Rr//vsvQkJCDGa5a9c6TSwIQduQVi9C6uwoa79oap06dQpCCDRp0iTJ79XOiAOALVu2oH79+gbrpFujGzduyKjbatWqyUET1fnz55EtWzb5/KZNm2RHtPq9awdUZsyYITsmtanH9EAdeG/Xrl28IJu///4bs2bNQq5cuVCpUiV06NAB586dk2nU1ONc3Rdq9G+XLl1SdRuSQ79+/SCEQMaMGZE3b16DmXs//PCDwaCMns5vc/3zzz8oV64cHBwc0LBhQ/Tq1Uvug0yZMqF9+/Y4efJkvI4hc/aFcceCem5Z27puz549w5MnT3D37l2T63KrQQjqWtdeXl4GM2KN1/HV23Hy2WefmUyjFxISYnYQQmhoKBo2bAgnJycsWrQIgD4GnUx9V+Z2qlaoUAGenp7xgs8WL16MevXqGcwOtnZffPEFcuXKhd9//93sSHvjIARtJqVvvvkG2bJlw8yZM1OoxMlPD8drSkuofQwkfK5ERESgXLlyEEIYZJdatmwZsmTJgvTp01tdnZ+QxM6DxOr1AgUKIHv27PEy/Pz0009o3749jh49miLlTW63bt3Cli1bMHz4cPTu3Rvz58+XSy1oae+T/Pz8sGHDhgSDNvQ28K62jVu3bo0nT57g559/lgGanTp1SnSJiISCEPLmzWswM9DaqdsRGhqKfv36wcXFBVmyZMGiRYtw/Phx7Ny5E126dJGplNWfYsWKoXv37ti6dSuGDRuGokWLwtnZGT179sTp06ctvFXmmz59utwmNQClQ4cOWLJkiVn14vtorzU///wz+vXrZ3X7h/uA+8DWtx8ARowYIe+BihcvjkmTJuGff/5JcIJKWtK1a1cUKlRIBiGo31dSghC01/+vv/4a7du3l4POegs+6Ny5c7zZ3v/88w9q1qwJIQQWL178QZ9vvB/UID9r6FM1DkAB4oIQvLy8ZP+nauPGjejRo4ccaPfw8ICLiwt8fHziTUwICwvTXb/pvHnz5Hap301CQQg9evQw6C9+8OABdu/ejUaNGqFhw4a6axcbu3jxIooVKyazYVy4cMHgeW3wgZpBJy3UlbZcFxDpGQMQyCJ2796N4cOHI1u2bAgICICHhwfKli2Lb7/9FteuXTOI2jMnE8LcuXORMWNG1KtXz+SglbXbtWsXhBCoUqUKgPhBBYlZvHgx1qxZY/CYcRpOa7Vr1y7UqVMHQsStT2schLB3717Z2Vi2bFlMmTJFDrxrzZkzB5kzZ0aOHDniNUKsnTq7q02bNgYDA8aNw9evXyfYYNQ2ktRzZdWqVSY/x5qtXbsWDg4O8PT0xJo1azB58mR4enrC0dERQsStRzZhwoR465ulpcEqdQC6Ro0aePfuHVavXo2OHTsiY8aMEELA398flStXxvbt2+PNXjDn+NCmVLx06VKKbktSbNmyBf3794eXl5ec5efv74/+/ftj165dBgNQMTExMvWks7MzBgwYkOhayHoQGxuLhw8fIlu2bGjevLnBMa5+r8+fP5dLrLwvCGHChAnyeEls+SJr8/TpUzx8+BDXr19HZGSkbAskdI6rHQfFixeHk5OTQWfZ8uXLkTlzZqRLl0432S8GDBggO01KlSqFzZs3f3AQwoEDBzBt2jSZklMP58iRI0cwe/ZsdOrUCYMHD8apU6d0055JLua2jxM6J6pUqQJ7e3vZVlq6dKlcw9ea6vzEfMh5EBsbi7CwMGTPnh0+Pj74+++/5XPLly9HpkyZ4ODggLt376Zw6T/ezJkz5YC5+mNvby+D01asWGGwP0wFISTlPsIaqcdA69atceXKFfn4/PnzPygI4dChQ8iRIwcyZMigu/sE9TqnBiGoWcLmzJkj64PIyEhs2bIFEydORIkSJeDj4yPbCm5ubjJdsxBxS7TpJY3ssWPH5IBrnTp15P+FEAgKCsKiRYvizXAzd0DBeNDVy8sLXl5eVrfuM/cB94Etb39sbKzMciqEgLOzs8G2d+nSBXfu3MHr168tXdQU0b17dznQpr3vV69v5gQhaI+FIUOGQIi4bJrPnz83+FvWvOxC586d5UD7ixcvAMSfaPDDDz9AiPhp9c2ZvGLNfSVqmnnjflIg7p5BDULQLr0YFhaGmzdvom/fvnKJ4kaNGum+zyw2NhaHDh1CxYoV5UQUtY2YWCYEU4G5ps4PPbp06ZJcbrRgwYIyQ4x6X6DNfGBqiUZt5jQ9sOW6gEjvGIBAqe67776TKaNcXV2RJ08eODg4yEZChQoVMGnSJIOGgnEQwqRJk+QFZ9myZfDz80OOHDl00cFuyrFjxyCEgLu7u+wYe9/AsdpYUlNt3bhxQzcNKO227d69W0YpmgpC2L17tzxenJycUKRIEYwbNw6LFi3CtGnTUK9ePdjb2yNLliy6Sx+mTbVuPKhuTPvdahtG2sd/+OEH2Nvbo1q1anL9Z71RA1ImTJgAADhx4gR++ukng4wInp6emDBhArZv327wXj3fVKllf/DgAQIDAxEQECBvqN68eYMLFy6gTZs2MsrZzs4OVapUwYQJE/Ds2TODZQpMfS5gvR0LY8aMQYYMGWSneokSJeDn5ye/77x586Jfv34GA5ExMTH46quv4OzsjPTp06NPnz66mdlrinoe37p1K9HBkfcFIWiXrciWLRvc3Nx0se77tm3b8PXXXyNnzpxy0LVSpUro27dvot+rut9KlCgBe3t7mX5QbRd4enrq6oaxWbNmEELIdbuLFSuWpCCEly9fyswpage1t7e3wQCetZowYYLsRFN/ChYsiKVLl+q6bk+KpLaPTe2XihUrImPGjHj69CmWL18OPz8/3XWcfMh5EBsbi/DwcOTIkQOenp4yU5BaF3h5eVnVdS8hffr0gRBxSyoMHToU06dPx9ChQ9G2bVt5HOTIkQNDhgwxmPmaloIQtm3bBi8vL4NgPOOlxj4kCOHo0aPyGqE3aqBBWFiYXPM3U6ZMmDt3brzA+5CQENy6dQtTpkxB69atZTCCel3QS1tJPafVzuZVq1bh77//xrRp01CkSBG5PT4+PpgyZYrB+tbvY6pt7Onp+VHpelMC9wH3ga1vv2rp0qVwdHREtWrV8PPPP6Ny5coy/XjWrFnRunVrbN261eTyrnqlBhV37drVIKhSZU4Qgnb291dffQUhBLJnzx4vGHPp0qXw8/ODh4eH1bWVzpw5I69h5cuXl48bZ79YsWIFXF1dsXz5cqxcuRInT55MdL+prLmvRO0rbNy4cYLX7oSCEIC4/oDnz59j+fLl7w1g1ouYmBgcP34clStXTlIQgnoPoZ3oqKeJWonRBiEUKVIEDRo0MCv4YOnSpRg3bpzMjmDtbLkuIEoLGIBAqWrYsGFyNvvmzZvx6NEjxMbG4ujRo5g7d64chPLy8sLAgQMT7Fzz9vbG1KlTMWvWLGTJkgXu7u666GAHgF9//TVeqquYmBiZNrdfv3549epVop+hXhyjoqJQoEABVKpUKcXKm1KSEoRw/PhxfPrpp8iZM6fB4IQQceueNm/eXDczXFWJBR98SMNYzQLh5+eH69evJ2tZU4Paubxy5Uo4OTmhcuXKBs8/evQIy5YtQ5s2bSCEgIODg1zPbtOmTWlm5sPbt2/lQEOvXr0MngsLC0NERAQaNmxocA6UL18en332GR48eBBvkF5lrY3or7/+GkIIVKpUCbt378bbt28BxC09smbNGhQuXFjOdmnevHm87RsyZIgMQujbt+97A3ms0YULFzB79myzZxs9f/480eUYoqOjER4ejokTJ+qiLvjuu++QIUMG2NnZwcfHB3nz5pVLbKiBeUuXLsWTJ08S/IyKFSvC398fDx48wKpVq3Qbrb569Wq4urqiWLFiqFWrFoQQKFGihFlBCOp1IzQ0FKVLl47XKWPNhg4dCiHi1rBcuHAhfvvtNzRu3Bh2dnYoW7as7mZnfIgPbR+r9by6BEOFChUghMCwYcNk+9iWzoMCBQogMDAQL1++xIoVK3RVF6jnQevWrU2Wd9u2bShVqhQcHR3h5uaGL7/8MsFgbT8/P/z222+6DEJ48+YN1qxZEy9YQNum+dAgBD1Tt18bhJA5c2b8+OOPBjNZjYPRr1y5gk2bNqFTp066aBMYW7JkCYSIy+ikDpq9ffsW06ZNQ9OmTWVbIXPmzGjVqhWOHj0aLyhDe+zooW1sjPuA+8DWt//8+fMyDfbZs2cRGRmJEydOoHHjxggICJDb/+mnn2Lq1KmIjo6W26jHa4AafNClS5d4A2emtsdUEIL6vujoaBl8EBAQIJerU+8b/vzzT5QpUwYuLi5W+f2HhoZi7dq1yJ49u+wrVNs22jbOyJEjDQKw3d3d4e3tjX79+mHevHl4/vx5vOujNZ8H2r7C9127tUEILVu2lI9rA1AAwz5GPYuJicGxY8c+OAghLTAOJNEGITg4OKB58+ay78RU8MHy5cvh4eEBNze3RPtYrImt1gVEaQUDECjVLFiwAEIING3aVM5UVyt+9aJ4+PBh1KlTB+nSpYO7uztGjhxpcDHRdq55enrCwcFBV2ll1Ybk7NmzDRoCUVFR+Pbbb+Hg4IB8+fLh559/jremn0p7cVRvJn744QcA+rvB0m7L+4IQgoODcfnyZYwYMQL9+/fHZ599hpEjR+Ls2bMml2WwZoMHD4YQAu3atYsXzaxtDHXr1g0rVqxI8Ht9+/YtgoOD0bFjR2TIkAE5c+bUXRYIY/fu3UPu3LnleQIY3jzt2bMHnp6esnEtRFx68YoVK2L58uW6C0Qx5fjx43BwcEBAQABOnTplcExs2bJFdrR89dVXKFy4sNwfQgiMGjUKUVFRBu+x1vUclyxZAjs7OzRu3FjeNBpH51+4cAG9e/eW29i5c2eDARdtEIKHhwc6duwoU8/pwaJFi1C8eHEIIdCsWTOzB1rNWY5BD50M6qBr6dKlsXnzZjx9+hSvXr3CpUuXMHHiRDmY6ubmhokTJya47Ej58uXh5uaGIUOG6GrA0djbt29RrFgx5M+fH6tXr0bdunXNGnzVXkvHjh0rO2P0sFbh5MmTIYRAkyZNDOqnhw8fokSJEhBC4Pz58/JxvbVzzJEc7WMg7jioWrUqHBwc4O7ubnPnQWxsLPLnz48sWbLgu+++01X2h/Xr1yNdunSoUqWKvIbFxsYiJibG4Jg/ffo0GjduDCcnJ/j5+eHHH39ETEyMPF6io6Plmq/29vbYtm2bRbYnqdRtVK9b5iwplZQgBL24efMmdu7cieHDh6NPnz6YMWMG1q1bFy/I9n1BCADiDb7pJUueSnsMNGjQAE5OTvj111/lY+qxsm3bNhl0pwbfVKxYETt27IjXZtDLWucq7gPuA1vffi31nqFZs2Zysk50dDROnTqFUaNGIUeOHHLAqXz58hg/fny8fhY9tCG7du0q73mNgw+093bGASbGQQj9+/fHP//8gxEjRsQLPtBeD8LCwjB58mSrzo4TFhaGdevWyT6QypUrG7QFV65cKY/92rVrIygoCEWLFjWYsFGyZEmULFkSR44cAWDd50FiE5WMr+XqMZ1QEIIe+gM+xMdmQtCTs2fPYuPGjfjuu++wceNGmeXN+Lu9ePEi8ufPDyEE8uXLJwN51fsJ1bJly+Dv7w9vb2+rzHiTGFurC4jSEgYgUKq4d+8eSpYsCTc3N5w5cybe89qbgXPnzqFBgwaws7NDkSJFsGfPHgD/RbVFR0ejQ4cOMhOCXgZctQ1JUzMSb926hUKFCkEIgTJlymDVqlUyU4La0NQOxC5cuBBeXl4oX7681a/xvWjRIkyYMAEPHjyI10GmbTglFoSghxtGc3zzzTcQQqBw4cIyTaK6bpX2hqJTp04QQqB///4mg1EePXpkkFq1bt26uhl8DwkJMfm4+h0vXboU9vb2aNWqlcGA7LZt25AnTx4IIbBkyRJcvXoVAwYMQMGCBSGEQLZs2fDw4cNU2YaPsXbt2gRv8tVUm61btzYILgKA3377Ta6BuXjxYgDAP//8g507d6JKlSoICAiIN4t++fLlCAgIsKoI3tjYWDx9+hSVKlVCunTpcOrUKZOvUd26dQv9+/eHm5sbfH19sXTpUgCGwQpqJoWAgACrrw9Vo0aNgouLC7y9vbFgwYIk3wAaByFs3LgxZQqaQhYuXAgh4takNDVAGBkZiYcPH6Jx48YQQiBDhgwyKEk76zs2Nlaul+7i4oKMGTPqYsDRmFr///rrrzIA6/Xr16hXr57ZM8C1wQd6yHywadMmZM6cGaVLl8aFCxcAxJ37apCRut75gwcP5ABcWkkhqkqO9rHxAIWaHUov7WOtDz0PYmJiEBoaKmfFZMiQQVd1wYABA+Do6IgDBw6YfF77HZ89exZVqlSBEAL16tWTAzHqPUJ0dDQaNmwIZ2dnXSzBAyBefZXYYLm5QQh6u29YsGABSpYsCUdHR4OOUvU6OWfOnHiDRu8LQtCy5v2RUNp0tdN80qRJECJuGRrjGZ3bt29HpkyZIIRAw4YNZUezs7MzsmXLhvnz5wOAycBca2sbq7gPbHMf2Pr2J0QblJ4zZ07kyJFDDsxr99nUqVPh7u4OIYSsR93c3DB58mSsWbPGEkVPMnUZtZ49eyYafNCgQQMMHDhQ1vkJLcegBvImFHxgrdcFU30lpgYegbh7CSEEihUrhnXr1gGIy6T05s0b/PLLL/j222+RI0cOeUwYp5u3tr4Sc4MPTGVFSItBCO9rDyY1CKF9+/a6yg42c+ZMuSSdEAIZM2ZE2bJlZZ+f8f65ePGizIRQsGBBeX+tUoMP9BKgbct1AVFawwAEShUHDhyAEAJffvklgPd3HJ84cUKm29emIVcbT9HR0ejevbsuOtiB96fQUvfH+fPn5fq/RYoUwbfffmuy83D8+PHw8fGBr6+vVUcrA8CgQYNkgy9r1qwICgrCzJkzsXnzZkRGRsZrNO3cuROffPIJhIhLy652sAP6H3B4+/YtatasCQcHB/j5+WHKlClysFTbkaDOXuvdu7e8WTR2+/Zt9O7dG+XKlcPChQsRHBycKtvwsUaNGoUuXbokOkv9zJkz8PX1hRAC+/btAwDs2LFDZkaYOXOmfG1oaCiePHmCIUOGWP25AEB2Fn/++ee4efNmgq9bvHgxhIhLtRkcHIzNmzcjS5YsEELIAXhjaiYQ9Zzavn073NzcrDKC9+TJk3LAAHj/uX3lyhVUr1493g21uq0xMTEYPXq0Lo4B4L9ZPI0aNcLJkycNnktKZ9CLFy/QqlUrWcdu3rw5uYuaIu7fv4/y5cvD09PTIAAloW1X14TPkCEDTp8+He+1aiCGp6enLgZd1aAzU65evYosWbIgY8aMuHfvHu7cuWMwA3zTpk0mgxAePXqE8uXLw9fXVxdto1evXsnvbf/+/fJx9Zx+8uQJAgMD4e/vjwkTJiAwMBA1atTA119/La+Lem8TAMnXPo6JiUFMTAzmz5+PEiVK2Ox5oHZC6ilA+dGjR/D29kb27NkREhJi1jVg3759st6fO3eufFx7TVSDPa195vuQIUMghECfPn0MZvYC5mVCWLhwIezt7SGEQMeOHQ0ypuiFug+8vLzw1VdfYcaMGRg5ciS6d+8uv2c3Nzd0797dYCBBG4SQKVMmzJ07N9EgBGumXUbMuB4MDg6W9wDLli2Tj2/YsEG2jVeuXCk/Z+jQociVKxeyZMkSbxDvl19+sdpBV+4D7gNb334t42tXWFgYmjRpAiFEvLpw165dct9MmDABq1evlq9V61Z11rC1UjMVZM2aVd7PqcEn2m1Vl9zo37+/XLpQfS0APHv2DF9++aWctJA1a1aTwQfWKrG+krCwMKxfv14ux6FOQilXrhx27NghX2fcdnj8+DEuXrwoU82r+2Hfvn0yeN0azgN1QkWbNm0SzZLarl075M6d22Rf0p49e+Dt7Q0h4pb00qv169fL4PPE7o2MgxB8fX1l4EZUVJQ8Fvbs2SPrA71MVlGz5mbMmBEtWrRAYGCgDDTLnz9/vOw2qkuXLqFIkSIQQqBAgQLyWFq6dKmugg9suS4gSosYgEApSq3wv/vuOwghMHnyZLPfu2bNGggh4OrqahC5p7cIzqSk0ALiZjapM7gcHByQPXt2DBs2DFOmTMGgQYNQrlw5CCGQJ08eqx9kOHjwoEydbm9vLxtM6k/58uXRpk0b7N692+BCf+zYMdSoUQNCCFSsWBG7d++Wz1lrpLa5Hj9+jDZt2sjB5bFjxxoEmahLjPTo0UNGtqrbbLztz549w+vXr3UzCLN582YIIZA+fXp88cUXiQYhqJ2x3bp1w6pVq2SnwqxZs+Rr1NnPehEdHY0uXbrAyckJHh4e+Pzzz+PtA+13Xa9ePbi4uKBr166yE0EbfKCdBW7q/fv27UOdOnWssjN+0aJFECJuGQlz7dixQ9YdpgYs9eKnn36CEAI1a9Y0qMON0+NpH9f+a+zFixdyYE4PKfcBYO/evfKGEkh429Tv9vXr1zIwrVq1avHSUY8dOxaFChXSzfYbd4Iaf+/ff/89hIjL9AIA165dM2vw9cKFC/E6ma1VZGQkfvrpJ0yaNAkADLIAhYSEoGfPnhBCIHv27ChatCiKFCki2xOVKlWSayDrVUq0j4G4wA51Rry1S4nzoFevXrpZfkR169YtuLm5oUCBAnjx4oXZ71MD2fr06WPwuPaaaO3tw6VLlxrcFwghUL16dfz666/x6jLjbUkoCKFp06a6Sik7YcIECBG3DI0aYKe1Y8cO1K9fHxkyZIAQcWm5jYMQPvvsMxmEMH/+fKsfaNPavn07Pv/8c2TJkgW1atWS2f9U6vc8ZcoUg8CrjRs3yraxWkdoXb58WQbmqvtLzR7o7e1tVR3M3AfcB7a+/UDc0lu7d+/GqFGj4l0L1e0/ffo0PD09UbhwYXkPvW3bNtlPMGPGDIP3LV++HP369bP6PjMAmDRpEnLkyIF06dKhefPmMkBbW983atQIQggMHDjQ5Lrt6n569uwZunfvjqCgINle1sP9sjl9JerAozr72cPDA9u3bweQeGCrStt2uH79Olq1amUVfSXTp0+XwYbq0orGy1MB/01U6tGjR4ID6Xv27EHmzJkNJnvoyZgxY2Q/oFoXmhOEULFiRQgRt7SjOrtde0zs379fF3UB8N8YQuPGjWXb8J9//sHMmTMRGBgIIQSGDRuW4DGvDUIoWLAgxo0bhyxZsugm+MCW6wKitIoBCJQqRo0aBSEEpk6dCgDx0sZpqReKJ0+eoEiRInB0dMSxY8dSpZzJzdzgg61btxo899dff6Fbt25ySQbtT65cudCrVy9dDDJERkZi8uTJyJ8/PxwdHfH5559j8uTJmDt3LvLnzy8bxo6OjnBxcUGXLl0wbdo0PHr0CCtXrpRp6KtXr24QhKA36mwE1ZMnT+SsZX9/f0yaNAmhoaHo3LlzgsEH2gaSXgdeIiIi8Pnnn8PV1RVubm4mG5Lqdp46dQpZs2ZF5syZZceKtlPB2jvVjanfY2RkJL788ks4OTkluA/UgWi1U1pNE/bzzz/L15iz/ZGRkQYzI6zJDz/8ACEEPvvsMwBINBWe9uahVq1aEEJgy5YtKV7GlPDixQtUrVoV6dKlw/Hjx+XjxsEHb9++xfXr13H//n2D7CYJfe8vX77URapt9bscOXIkhBAYP348gMSPZ/U9a9asgbe3N3LmzCnrQO37kjJwZ0mDBw+Gk5MTJk+ebBBIA/zXNrpw4QJ8fX0RGBgo13i9efOmQRp67eCr8TrxehEaGmqwzA4Q9z0OHDgQQghUqVIFly5dQnh4OKKiorBnzx4ZhDl8+HDdBaSaYqvt45Q4D1R6yQil1l/Xrl2Du7s7smXLZlY9rh4H6jI2JUqUkBkw9ObBgwfIkSOHDBzIli2bbPdky5YN8+bNi5clSLud2v+rwX2urq66WI4LiMv65u7ujpIlSxoMBBp/nxcvXkTv3r3h5uYGe3t7jBkzxqDOV4MQnJyc4ODggJ9++kkXx8PIkSNluuhKlSrhyy+/TLBj/OjRo/JeeNiwYbLD2TgwN7FAlSVLlkAIYVUBKtwH3Ae2vv1AXFrwSpUqyW0bNGiQyTbe06dPUa1aNRlwcfLkSZOTFLT3lYm1q6xBx44dMWHCBMTGxmLatGnIli0bHBwc0KhRI5w4cUK+Tg0+GDBggGwTmeonUoWEhMQLPrFmSekrUVOwq31EVatWlX2rSd1Wa0nH/+eff8rU+YGBgXJZNlNZUnv27Blv2U1jamCOdmkqPXjx4gX69esHDw8PODk5oXv37mYFIURHR2PHjh0oVKgQ0qVLhwULFgD4byBaT/fJ6kSstm3bymxu6rYHBwfLbCnNmjWT73lfEIIQcZki9RB8YOt1AVFaxQAESnamLn7qusSffPJJkqJv69evDyEEtm3blpxFTBVqw6F169aJBh+0b98eTk5O2LlzJ4D/GhevXr3C3bt3MW3aNIwePRpjx47FrFmzcPfuXasdVNRStyMqKgrff/89MmfODA8PDwwaNAivX7/G27dvcfv2bYwePRodO3ZEunTp5DquuXLlQqlSpVC/fn04OzvD1dUVJUuWlOn49USdodaqVSuDx7VBCAEBAShevLicyaamyTN1U7l8+XLUrl1bdwOw6jEfGRmJgQMHJtqQBOIGptRBBr0HH6jM3Qfq9j158kR2xg8cOBCAedG81sj4WFYHCooXL56kxn6HDh0ghIiXplkvzp8/L6+FQNyNjvHxPGnSJNSpUwfp06eHn58f6tatiw0bNsjn9fj9q9Syq8F5o0aNMvu9Dx8+RL58+SCEwNq1a+XjeqoPhg8fLus0Ozs7ZMyYEd27d8e+ffviDaKqx/qyZcvkfrt165bBDPAtW7YgLCzMEpvyQRLrKFXdvn0b6dOnR8WKFeMd65GRkVixYgUcHBxQqVIlXXcUqPtAzYBgS+1jWz8Pbt26ZXBsv337FgUKFIAQAmPHjjV7sOT48eNwcnJC/fr1U6qoKSomJgZv375Fly5d5PXg9evXmDJlCmrWrCmPEV9fXwwYMABnzpyJd/9jHLy3bNkyXWW/UAOQfv/9d5PPa4+Ty5cvo02bNrC3t0f58uXldqrHS1hYGDp37gxPT0+Ty/1ZG3Wt8/Lly+PAgQNm1X9qOmIHBwcIIfDTTz/J58xtC6hLk1gD7gPuA1vffiCuTeDo6AgvLy+MHz8eO3fuxKNHjxLcFytWrIAQAu7u7nLQSbs8o57uCzZu3Cgz4ABx9fmUKVOQPXt2ODg4oHnz5rh48aJciu59wQfaZe1UerpvTEp/kXEKdu19gR4CLlQzZszAoUOHAMRNQitRogSEEMibN69BAKY280FiE5W0jIO8rZ26PXfu3MHw4cPh5eUFOzs7s4MQXr9+jX79+sUbnNcT9R6pePHiuH37NgDDpSSAuNn66dKlQ9OmTeO9X7sUGxDXdlQn/empfWyLdQFRWscABEp2oaGhOH/+PI4cOSIbPceOHYOHhweyZs0q0+IkRu1MUdf81q7jowcbNmyQHWdjx46Vj8fGxiaYQksPs1eTShuEMG3aNPj7+8PFxQUDBw6Mt47TpUuXsG3bNrRs2VI2vNWABCHi0oqqA/N6oaaZV3/atm1r8LwahODk5AQhBEqVKoWzZ88C+G/fGQcf+Pr6wtPTE7du3Uq9DfkI2sayel6/ryGpNjhPnDgBLy8v5M6dW3731j6LQSuxG341G0RC+0CtJ9TBqW7duqV4eVPK7NmzsXTpUoOOpDt37iAgIABOTk5YsGDBe28O1JuIli1bQgiBdevWpWiZU4q63nv16tUNHn/37h0OHjwoO5i0dZ8QcWv/6XWbVdrzYd68eRBCoFGjRvj333/f21moPq8OVKlro+rJ/fv30ahRI9jb28PPzw81atRAqVKl4ObmJiP2d+zYITsbrly5Al9fXzRs2NDgc27duiWDs3LmzGlWm8paqOexceCAcSezdlae8XNXrlxBxowZkTNnTt3MclYtWbIE+/btMzgXbK19bOvnQffu3VG2bFkcOXJEHgfR0dEYMGAAhBCoXbv2eweP1XNCXdZKXcpGr9R1eT09PXHlyhW5X+bPny8zoanfc61atXD48OF4697qqW2oevXqFXLkyAEPDw/8/fffZgWYHjp0SC5Fo2ZNAf67RoaHh+tibeNvvvkGQgi0a9dOHu+m7ntU6n75/fff4e7ubjDgmNDyVcbU11jLYBz3AfeBrW8/8N9gW7169Qwyw5milvn58+dyWTYhBBYtWiRfo6fgAwA4cuSIbP+o9zZRUVGYOnUqsmfPDkdHRzl4OHjw4ESDD+bNm4esWbPi+++/T/0N+UAf01cCJD7wqIclJ9SMbyNGjEBoaCgAwyCEwMBAXLt2DR07djQr+ODUqVPvzYxgrWbPno0lS5bI7+3OnTsYNmxYkoMQDh8+jHTp0qFixYpyn+rF/fv30bBhQ9jb28PDw8Mgq4ua7TA2NhbXrl1D7ty50bRpU3z33XeYOHEiduzYgeDgYJPn1NWrV62+79jW6wIiW8AABEpW69atkwMorq6u2LJlC969e4e7d++iYMGCEEKgTZs2ia5Nqb0xCgwMRIkSJfDu3bvU2oRkERkZKSPahRCYN28eANPrd2lTaFnLzWByMg5CyJIlC9KnT48vv/wyXhACELcPwsPDsW7dOrl8Q+HChXWRLkorJiYGM2fOlBHMzs7OEEKgQ4cOBq97/PgxWrZsCScnJ2TKlAlTpkyRnYfaxtKyZcvg7+8PT09Pq1qv8UO9ryEZExODhw8fyrXcpkyZYsHSfrgbN27g+PHj+O2333D8+HGDSPSIiAgMGDAg0cb0oUOHZD2ycePGVC79x1PPgVKlSmHNmjXymH769CkaNmwoB1zUwBtTtDeZQUFBKFiwIJ4/f57iZU8uly9flrN6//rrL7lO9dixYxEeHo47d+5g6NChMu2iq6srRo8ejbVr12LOnDlypnPFihV1ufzK06dP5Zr06jXwt99+gxACLi4uMoX8+9Z2BICmTZtCCIFNmzalcKlTxvHjx9GhQwfY2dmhevXqmDRpEg4fPowGDRrA0dERDg4OKFq0KBYtWoQLFy6gdu3aEEJg9erVAP5rI9y+fRsVKlSAi4sL7ty5Y8lNMsuZM2fwyy+/oEWLFmjUqBEaNWqEKVOmxBs0Nh6A07aZ1Lrjxo0bSJ8+PerVq5c6hU8m6rIzderUwdGjRw2+S1tqHwO2ex7cuHFDZjVq0KCBQRCC9lrfo0ePeOt/q7T1ZLNmzeDs7Bwvg5oedejQAQ4ODvJ+SRUREYE///wTJUqUkIF5aoaUn376Ca9evdLtdgcHByNTpkzw9/dP0oDBxIkTIYSQmS/MySxjTTZv3gw3NzeULl1aDrom5f63Ro0aciZweHi4LjuWuQ+4D2x9+4G4vg17e3vUrVvXYF12c/bD119/DSEEChQoIB/T4z4AgC+//BJCCLRs2VK2AdUgBHV5iVy5cmHXrl0A/msra+v8+fPnw9PTE35+frq7V/zYvhLjgcfChQvrIihRzQbYsmXLeNlytUEIHh4eEEKgd+/eiWZJXbx4MXx9fTFixAhdbL+Wtr9o1apVHxSEoL7n1KlTsLOzQ/PmzVN3I5LJiRMn0LFjRzg6OsLDwwOTJ0+Wz6nf67JlywwmqqiTV7Jly4Zu3brhl19+wePHj3WXKdBW6wIiW8EABEo2I0eOhIuLC+zs7DBgwABMmTIFz549kx3Iv//+u7xA9unTRw5IAIaD1Kpx48bJaF/jlKzWasWKFTJ6Ozw8XM5oEkJg7ty58nUfkkLL2hk3AEyt0WoqCEGd5QbENRyNbzqfPn2qq8FGrdevXyMwMBAFChTAnDlz5GwFU0EI6nIM/v7+GDt2rMHsLjX4wN3dXTeBGE+fPsWuXbvQtm1bVKtWDc2bN0fv3r1x8eJF+X2a05D8+eefIYRA9uzZdbPtALB37158/fXXyJgxI1xdXSGEgLOzM4KCgrB06VK5nWo9kdg+UDsmunXrprvBpp07d6Jo0aIQQqBMmTIGN5XqrEch4pYouXjxoqwrTK3dNmXKFHn90EtKwT59+qBo0aI4ffo0YmJiEB0djcGDByNdunQQIi59uI+PD4QQcHNzQ40aNeIFYxw8eBC5c+dGxowZceHCBQttyYfZsmULmjdvjiJFisQbYFFnLxcqVEgGo5m6/mkfK1GiBAoVKoTXr1+nbMGTmbZT9Ny5c2jZsiXs7OxQtGhRuZzO0aNH8dlnn8mUuuXLl0dQUBAcHBzwxRdfxJu1dufOHV3Mcpk+fTqyZ88er6NEDUDp3Lnze1MBa4+B3r17G7Sp9BK4uXv3bgQFBcmZfkeOHJHbpQbkpOX2MWDb54Hq4MGDKF26tDwODh8+LLfpxx9/lMdBr169cO/ePfm9R0VFyf0XHR2NGTNmwMnJCY0aNdJdfWjK3LlzIYRAtmzZcP/+fYPnjh8/LuuQBg0aoFixYnI/BQYGYsyYMRYq9ccJCQlBjhw54OfnJ9t9idVn6nGi3k/nyZNHV+e/6osvvoCdnZ3BYJo5tJk/MmbMiHLlyuHFixdJ+gxrwX3AfWDL2x8bG4sHDx6gQoUKcHJykoHI5r4XAF6+fImCBQvCxcVFLsumtz40tbyHDx+Gn58fsmfPHi8b4pQpU5ArVy65HIO6xIJx5gNPT0/4+/vj77//BqCPYIzk7CsJCwvDb7/9Jif8PHnyxBKbZDY1+KBVq1bxgg9Uf/31l2zveHp6yiXXTGVJWbZsGbJmzYp06dLpcqKScX/RypUrzQpC0LaLVf3794cQ/y1No5d60fgeqU2bNnBwcIgXhKAdU+nUqROGDh2KunXryoks6o+Hhwfat2+vi+235bqAyJYwAIGShbrOfa1atXDkyBGD57Qz2r7//nt5UezcuTNOnTplMvX2ggUL4Ovri3z58ukm7f5nn30GIQTmzJkj16INCwuTjwsRt4atucEHO3fuxMmTJ3VxA6GmD+vevbvBOoQATA4oJhaEoKWHBlNC1O2dNWsWhBCYPXs2Tpw4IdPsGQchqMsxqEEIEydOxLNnz7Bq1Sr4+fnpKvhg/fr1qF27drw08kII5MiRA1999ZUcSH1fQ/LJkyeoVasWhBBYsWKFpTYpSb7//nsZeRsQEIA6deogX7588PPzgxBxy4l8+umncl0/U/tAmx1EvdEoVqyY7lLJAXHLDhQvXtzgplKt95cvXy6PjcaNG2PDhg0m1/KeN28eMmXKhNy5c8vOFWunBo40atTI4Dp27tw59OnTRwYhqINsc+bMka+LiYkxSKlcvnx5g/ScejBlyhR4enrC3t4eLVq0kLN01bpx48aNyJs3r5wVrqYG1F7ztP9XB10/++wz3UT0Hz58GD/++CN69uyJr7/+GhcuXEBUVBSuXbuGTz/9FPb29ihQoIDB8hqHDx/G1KlT4efnh/Tp08tjSEsv18YhQ4bIALI5c+Zgx44d2Lt3L6ZMmYJKlSrJzoHatWsbLLugpT0GfvzxR6RLlw7lypWLl4JdDw4dOoSSJUvKwedDhw7J83zy5Mlptn1s6+cBYFjWgwcPxjsOYmNjERERIZddEiJuTeiFCxcaBB5GRUXJdXFz5swpB+v1NvBiijqjd+nSpfKxHTt2yBmganaE69evY86cOfD390eWLFkMZs7qSXh4uOxw/+yzz8xep/bq1atwd3dH+fLlU7iEye/Ro0dwd3dHjhw58OzZsw86bu/cuYM8efJACIHRo0cnfyFTGPcB94Gtbz8Qdx1U6z4gadfzmJgYhIeHo2/fvhAibgkLPQsNDZVLSnTs2NGg3atdjsHBwQGNGzeW/QdAXLtYzXygp+CD5OgrMR54DA0NxdatW+WyttbaLlLvjTp06IDLly8n+tqrV6/KIITAwECcPn0agOksqe7u7u/9PGtmqr8ooSCErl27yuBb7ff8008/wd3dHWXLltXNfWJC90h//fWXDEJwd3fHggULsHPnTjl5Q3vPFBMTg7CwMPz666/45ptvkCNHDuTKlUsXE1dsuS4gsjUMQKCPps5aadSokewESugm4uXLlzJ1pBACJUuWROfOnbF//34cOnQIe/fuRfv27eHi4gI/Pz/ddCqpUazNmjWLF8VqHIQghOllF4yjWF1dXdGwYUOrH3BUZ6gLIeDo6Agh4tKCzpkzJ946pGoKpKQEIejdmTNn4OzsjDx58uDevXvYunUrMmTIYFYQQosWLeDr66ur4IPvvvsO7u7ucrbiokWLsHHjRkyYMAGFChWCEHEp5mvVqiVnPISHhye6HEO3bt0ghHjvusjWYMCAAbC3t0dgYCC2bt2K4OBgAMCzZ89w7do11KpVC87OzrC3t0fVqlVx4sQJAKaXpFC3NyIiAm3btjW5ZIk1014H9u/fL2+gy5QpgxUrVsjO9p9++knWIdmyZUPt2rWxcuVK/P7771i9ejVatGgBJycn+Pv76+bGWr0mtG7dWl4TtPsjODgYR44cwaJFi7B69WqEhobK542vCeHh4ShYsCAKFiwojydr99VXX8HOzg5lypSRs7uMvXjxAsOHD0emTJkghEDZsmUTHISeP38+/Pz8kC9fPt2kFp00aRJ8fX0Nrv2FCxfGmDFjEBoair/++gutW7eGvb09ChcujJ9//tng/Q8ePMDMmTPxzTffyHNFTwOuU6dOhRACzZs3N9kBcuPGDcybN09eD2vWrCm/W1MDcTNnzoSfnx98fX0TnC1krRIbfD58+DAA4N27dxg/fnyaax/b+nmgldBxULduXRw9ehRAXKfZjBkz5L6yt7dHkSJF0KxZM9SvXx/58+eHEAJBQUEyAEUPgw2JUde1VesMdWmB/fv3I1euXBBCxFsHF4jrmFfXw9Yb9ViYMmUKnJ2dUaZMGRw8eDDR96jbfeTIEd0Out24cQNOTk4oVapUku9vg4ODZTCOeu9Zp04dk0Gr1oz7gPvAlrdfrftGjx4NIQSmT59u8Lg51Lrw+PHjMkDxf//7X/IXNhVosyB4e3ujaNGiMpBA22+mDUJo1qwZTp8+jaVLl+oy+CA5+0qMBx5V1rofhg4dKgdV//rrLwB4b4p47XIMefPmlVkwgLg+Y71NVDKWWH+RqSAENXNk5cqVcfXqVTx+/BhRUVEYM2YMfHx84OPjI/ettXvfPdLVq1fRtm1bODo6wsXFRd4TaPtWoqKi4t03//PPPzIzjjWz5bqAyBYxAIE+ytWrV1GoUCH4+PjIiMz3iYyMxC+//AJ3d3c5+01NtSqEgJOTE6pXr66LwUbAMIVWQmUOCwuTUdpCCEyYMAFAwsEHAQEBcHd310XU4u3bt2XDqW7duvD29jYYTJwxY4bsWNVSb6a0QQh6WMM3MQndPI8YMQL29vZy9vKmTZveG4Tg4uIiU67p5Ybiq6++ghACVatWxaFDh+I9/+bNG/To0QO+vr6ws7NDjRo1cObMGQCJD8C/fftWF7Pe1UCJTz/9VA4OGWcAefv2Lb7++mv4+fnB3t7eIHArIiICAwcOlPvgyy+/lN+9emyZO0POWmjPid27d8sb6LJly2LFihUGqUSDgoIM6g/1J0OGDKhbt26CNxbWJrG0iokNoJlatgaIqz/UWUJ66GBUgwybNWsWLw2kcfr0x48f46uvvpKR766urhgxYgRWrFiB8+fPY+/evejYsSPSpUunq0FXNftFlixZ0L9/fwwcOBCenp4yuGzKlCkA4gLU2rZtKzsUfv31V4PPMZ4FpRenTp1C9uzZERgYaBBUEhsba7BNsbGx2Lhxo7wetmzZ0uBzXr9+jStXrqBFixZwdnZGvnz5dHMMAIbnuTmDz0BcVpi00j629fPAuL5TB9pVpo4D9fmdO3eiQYMGcgaQ+lOqVCkMGTJEDrynpY6127dvyzZA//79ZeYD4+ADvQagmHLs2DHZmd6hQweDdo52O7Vtgq5du0IIgTVr1sR7nbVSy3/p0iWkS5cOhQsXxtu3b5P0GevXr0fDhg3x6tUrnD59Gp988olussAA3AcA94Gtb7/W4MGDIYTAwoULAbx/EBb4r65bsWKFXLqrWbNm8PLy0kVwcmLX63v37slsd1OnTpWPa5fhUoMQHB0dUaJECaRPn153wQcp0Veil8FmtX/AyclJBiGbWmbNFG0QQr58+XD+/Hn8+uuvug8+UCXUX1SuXDmD/qK7d+9iwoQJMjjV29sbAQEBCAgIgBAC+fPn1819orn3SKdPn0b79u3lcdOlSxf5Gcb1ph7agypbrguIbBUDEOijrFy5EkIITJs2LcnvPXv2LBYtWoQGDRqgatWqqFatGrp3744tW7boZpZnYgNNxh2PoaGhBkEIP/74o8HrAMMUWnpoSKodgepN5PTp0xESEoKFCxeiatWqsvPcxcUFQ4YMwb59+wy2NzIyEt9//z38/f3h4eGBnj176mKg2dj+/fvj3fxrO0n37t0LR0dHlC1bVkajbt26NdHlGOrUqaOr1LLqudCiRQs56KhdfkW9sXr37h1GjhyJTJkywcnJCd27d5fpsbQNSS8vL3Tr1k03g849evSAEAJdu3aVqeSNbwLUY//ff/+V65x5enpi+vTpMqV8ZGQkBg4cKGd0jBo1ClFRUbq6oTAWGxsrv/8TJ07Aw8PDIAhBfe6vv/7Cjh070LlzZzRv3hwtWrTA559/jgMHDrx3jXhrkdg1wdyOIe3rFi1aBHd3d+TPn18XHYx79uyBp6cncuXKhfPnz8vHY2Nj46W/U4/pV69e4aeffkLFihUNBtrs7e3lNeSTTz7RzaCrti7UDr5fvHgRZcuWlbM61AHE06dPGwy+rly5Ur5HrykD58+fDyFEvCWZEvLzzz/LNR/nzJkjHz979izKlSsHFxcXtGzZUjeZku7fvy/XJ1UZB18YDz4fOXJEft+nT5/GokWL0LBhQ923j231PPj+++8xdepUXLhwAS9fvjR4TtthaHwcHD58WG7vy5cvcf/+fWzduhU7duzAvn37EBkZKa+ZehhsMJe6zRMnTpQzvYQQmDlzZrzXpDXapai6dOmCM2fOyO82MjLS4HtesGAB3NzcUK1aNV3UBeoSIaonT57IgIv58+eb1bZVj3d15uj+/fsBQA7cWvt5wH3AfWDr26+l1uPqpIWkZnJ59eoVChcujFatWgEA1q1bZ/UZAtVZu6qEvm81o0WhQoUM7iGNgxDy5csnBy7VwAs9fP8p2Vdi7duvtombN2+O6dOny+DSmjVrytckJQghW7Zs8PT01E2fsTkS6i8qV66cQSaEkJAQHD16FDVr1kSePHng5OSESpUqYcSIETLDsLVL6j3SyZMn0aFDBzg6OsLT09NkkJKe2HJdQGTLGIBAH0St2OvVqwch/luX2pwbKOPXREdHIywszOqXGjCmrt/VunXreDc+2gufNiLbeDkGNQgB0HcKra1bt8qIXu2g08KFC9GlSxe5vd7e3qhRowZ27dolBxIiIyMxa9YsODg4ICAgAE+ePLHQVnyYQYMGQYi4Na4XLVqEs2fPmnxd06ZNIYTAjh075GOJBSE8ffpUDsxbu8TOBS1tQ/Lzzz+HEHFrfe3du1e+JjIyUu7TbNmy6eJ46NOnD4SIW4ZGPd8TuhlQH3/z5g2aNWsGIQQKFChg0JEcGRmJbt26IWfOnLq5kVq7dq1BSkAtbX2oztxTI7yNl2PQ0lvQhbnXhAYNGsiOMy3j7R09ejT8/Pzg7e2tm0CkKVOmwM7OTs5oAuJv144dOzBr1iy0bNkS06dPlx1zoaGhGD9+PHr16oWCBQvik08+Qc+ePbF161bdBKCYOga0A8+nTp2SM9sXLVok32c8A1yvg6/R0dGIiYmRwST79+83CERLyOPHj9GxY0fY2dmhe/fuBs/t27cPW7du1UUqSSAunaS3tzdKlSqFefPmYefOnQBMZz85cOCAwXIMhw4dipcJJTw8PE20j23pPFDbBGpWl7Jly2LYsGHYsWNHvGAEIO4YVzuV1eNAb9c/Y4m1CRKza9cuGXwwePBg+bievn/V+/aB9jueOXOmPGZq1aqFKVOmIDw83OD1Y8aMQebMmXWzDE2PHj3QpEkTg/vCqKgodOzYEUIItGnT5r33OdqBt0KFCqF06dL4999/U7LYyYr7gPvA1rcfMF0Xbtu2DU5OTggMDMQff/zx3s9Q2w83btyAo6MjevTokSJlTW5qe6BFixbYs2dPvOVJtRNWgoODUbFiRaRPn172rarfvfYYGD16NCpWrKir4IOU7CsxDvCxNmq/VqtWreTEmhUrVsglCGvVqiVfa04QQqlSpSCEgK+vr26WplR9TH+RNggBiNtXwcHBuHr1KmJiYszKomINPvQeSc2E4OjoCHd3d5khAdBHHaCy5bqAyNYxAIE+StWqVWFnZyfTqCfFiRMn8OrVq3iP66HTTU2JnTNnTmzcuFE+HhMTY9AA6NixI8qVK2cwiGgchLBgwQIsX75ct8EHqq5du8LOzk6u5Qf812g4deoUqlatKme0enh4oFixYli+fLkcWFqyZIluZriq1OPAzs5OBhL4+/tj0KBBuHLlikG69MOHDyNdunRo3769wWdogxA6d+6cylvw8RI7F0xRH3/37h1Kly4NIQRq164N4L/Gc0REBIYPH46rV6+mbOGTwd9//y3P5U8//dSsWRjqPnj8+DFy5swJIQSWLl0K4L8bz8jISDlQYe03FepsnE6dOsVbNkZb9nbt2snUymvWrJEDLmXKlMGqVavkaxOaKW/NknJNUI+V58+fx/uce/fu4eeff8Ynn3wCIeLWgddLOrnIyEjUrl0bQgg56KoOnEZFReHhw4fo2bOnnOmu1p1ZsmSRx79KjWzXE3PrQnUfGaeZNx58Xb16dWoUO0WUK1cOjo6OCQbkmfLrr7/KDrX79+8nuHyBNfvmm28MsnioP9WrV0e3bt1w7NixeAOH+/btQ1BQkMEM+IS2Vw/7wdbPA22bwNnZGXny5DE4FnLlyoUePXpg5cqVuHXrlvxOT58+jeLFi8vO6CNHjsjnrL0NYCyxNoE5evbsCSEE+vTpg9DQUN10Kmt9yD5YtGiRwbFSqFAh1KxZE3Xq1EH+/PkhhEDBggV1EZB49+5duR1du3Y1GHxdu3atfG7kyJEJDrho6ww1097EiRN1E4zCfcB9YOvbDyRcF164cEEuvzZw4EC8e/cuwc/QbmunTp3g5OQkJy9Yc7voyZMnqFq1quzn8fHxQcmSJbFx48YEM3qpmSFKliwZ7z5Rm55czbKlh2WpbLmvpHv37hBCoG3btgb38+/evcPKlSuROXPmJAchXLp0CRUqVIi3zKG1S+7+IpVaB1hzXaD62HskbRCCh4eHQSYEPbDluoCIGIBAH0nNgDBv3jwA5jeCL1++jOLFixtcePXkyJEjcjC9Z8+eOHfuXLzXtG/fHkII9O7dO16aTOMgBBcXF90GH6iNvcWLF0MIgdy5c+PZs2cGzx0+fBjZs2eHEAKNGzfG//3f/8lt9/b2xuTJky1W/o9x5MgRODo6ws7ODnXr1sWoUaPkdvr4+KBx48Y4efIkXrx4gX///VcOMqiDc+r+2bp1K7y8vOTxoifmnAvG1Ibh9u3bkSFDBhQpUkQGa+ix0Xjy5Ek4OjrKIBJz9kFUVBTCw8PRoEED2Zmk0t6E6OFm6o8//pDnc+fOnQ062FRqfdi9e3cZkLV3714UK1bM4KbSeOkavUjKNaFXr14mM1v8+++/GDNmDBwcHODp6Ym+ffvqYtkFVVRUlDye+/fvLx9/+fIlli5darAsT5kyZVC7dm05qOLj4yPrRSCuHtBThwKQ+DGgHtdv375F0aJFYW9vj5MnTwIw3D518NXZ2RlZsmTB+vXrU3cjkkmtWrXMbhuq2//s2TPkyZMHmTJl0u0MhiNHjsDJyQkODg4IDAzEZ599hjx58sgZPOnTp4eXlxf69euHGTNmyAw/Fy9elB1stWvXTjQIwdrxPIgLsFbTgfbu3RsbNmzA+PHjUbVqVbi7u8vrZZYsWVCnTh3Mnj0bt27dwo4dO1ClShUIIdCgQQODIAQ9MadNYIp6fGzduhXp06dH3rx55cxgve2HD90Hu3fvRufOnWV6ZvUnKCgIX375pW6yYgFxwefatrE2IG3kyJFy20aPHo2QkBCDJdu0GWNmz56NjBkzonz58vL+Ui+4D7gPbH37jetCbZtgzpw58rmxY8caBB8brwUOAHPnzoWrqysaN26sm6xYERERuHjxIlq3bo2iRYtCiLgl5ooXL45x48bhwYMHBtsdHByMwoULw8PDQ94XafeB3voIVLbaV7J3717UqVNHBh9ryxoaGvrBQQh6DNRnf1Hy3COpQQjqhI5Zs2al7kZ8JFutC4iIAQj0gdTKXY3qbNiwoXwusYhstTG1f/9+ebOlV6dPnzZoRJ0+fVo+p0Zu9uzZM8HOorCwMJmG3sPDQxczWhITERGB8uXLQwiBadOmyce3b9+O3LlzGwxGPHv2DFOnTkXhwoWROXNmg7Wv9ObUqVOyITljxgwcP34cq1atQoUKFWRwySeffIJ9+/bJ2U1jx44FYNhI+t///ods2bKZ3UlpTYzPBXO34fLly7C3t4e9vT3++ecfXTcaT5069UH7YNiwYRBCYO7cuSlbwBRmTn3Yo0cPWR+q3/X+/fvlTeX//d//YdmyZbqZ1WPsQ/eB9rg/f/48fvzxRxw9elSu66onq1atQvr06WFnZ4euXbvil19+QY0aNWSAVc6cObFjxw4ZlHfgwAGZUm/EiBEA9H3z+L66cMWKFTIIJSwszOQxcPbsWTRo0AA+Pj4JzpCyVup2qEsvNWjQQD73vvP60aNHCAgIgI+PD/7++++ULGaK0nasfP/997h8+TL++usvDB8+HC1atIAQQj6fJUsW/N///R/Wr1+Pb775Ri7H0KRJE+zbt0+354KtnwdA3HGg7oNJkybh1atXiI6OxpUrV7BgwQK0atXKYJDZ3t4ejRo1QunSpWXq1UqVKsVbO1ovPrRdCMTdH5UrV05eM/UYmAokbR8YD0qEhITgyJEj2Lt3Lw4fPozo6GhdZoIwbhurGRNfvXplEIjfo0cPbNiwweC9YWFhGDhwINzc3JA1a1YZkKm3NiL3AfeBrW+/cV2ozZyqroUuhMCwYcMSzPo2e/ZsZM6cGQEBAWbNmrU27969w+PHjzFs2DCZAVKIuEwH3bt3x82bN2UWiH79+kGIuJniaYmt9pUktlzKxwQh6BH7i5LnHunMmTNo1KiRbpbkMmardQGRrWMAAn2Uo0ePyrU6x40bJx831SDQPtawYUP4+vrqftBde/FUbx60kZvqLL6EOpHDwsIwaNAg3aXQMqZ2Di5ZsgR2dnZo0qQJgLiI1Vy5ciUYnXnjxg1dRfEnRHscjB49GqGhoYiOjsa6devQqVMn+ZwaiJE+fXqTHeqJpR+0dh/SkHz06BE8PDyQP39+vH79WreDLaqk7IOoqChERkbKSF7tWtd69b76UHszqb0e7N+/X65n+Mknn+D169eW2oSPlpR9ABheFy9duqSrNV1Nef78OXr06CGj8h0cHCBEXNrxPn36yJtkbafK7NmzIYRAzZo1DdZC1SvtMdCpUyc5223Hjh0QQqBUqVI4fPhwvPcZB6K8b01ga3b06FE5iPrdd9/JxxNrG967dw+enp5ySR490x4Dw4cPN1iO6cSJE9i4cSMaNGggsyKp7YIMGTLI4IQ2bdrIJUz0iOeB4T4YOnSowbJzsbGxePLkCdavX4/PPvsMBQoUkN+9+uPt7a3rNnJS24XaQAO1A7ZixYq6DMZTfcw+ePr0qcFzer02Gu8DNfXy8+fPZUpmNWC7WrVq6NixIxo2bCjvmcqWLSvbTnoNRuE+4D7g9htuvzrrNTg4GAMGDJDP1ahRA19//TXOnTuH06dPY/fu3WjRogUcHBzg7++vuzXvTbl9+zZWr16N4sWLw9vbG0IIZMqUCR06dMDBgwdx5swZ2NvbI0OGDNi+fbuli5usbL2vxBRbC0Jgf1Hy3SM9fPgw1cqc3FgXENkeBiDQRwkPD0fPnj3h4OCA3LlzY+HChfI57c2R9v8zZ86Evb09OnXqJNcv0zPtxVNd67Vnz5549OgRAP12GH2Iq1evylTDAwYMkPtDG3wQExOj24jVxBh3NGs7THft2oUhQ4bA19cXQgjUr18/ze+DxBqSxplQ0lKEvzn7QP3u37x5g1y5cqFKlSq6TKVnSlLqQ+3/d+3ahUqVKuk+KA0wfx9o64DFixejVKlSmDJliu7rhqdPn2LGjBnIkSMHKlSogLZt2+L06dNybT51LlknrQAAYuZJREFU+9Vjftu2bbLTIa3QHgP9+vXDDz/8ACEEChcujC1btiT4vrTSXggLC0OPHj3e2zbUHusDBw40aC/ofV9oZ7h8/fXX8QYTw8LCZKfjqFGjkCNHDtlGcHd3TxOd7LZ+HgCG+2DEiBEy+4txPf/06VMcO3YMo0aNQqVKlRAYGCg7YfV8TTC3XaitF4YOHYq8efOidu3auHr1aiqVNOV8yD4YMmQIqlatqsul+Uwx7mzXrv+8dOlSVKlSRU5oUH/KlSuHUaNGySAcPQ66anEfcB9w+01vf3h4OCZOnAh7e3sZiJc+fXr5f2dnZ9SvX1+XmQ+0jNs29+/fx+HDh9GoUSMZiCCEQLt27eDj4wMnJyeMGTPGQqVNObbeV2KKLQchsL+I90isC4hsAwMQ6KPdvn0bxYsXhxACgYGBmD59eoKvnTdvHjJlyoQcOXLgzp07qVjKlKVd3y8wMBA7duyQz6WlRoI5pk6dCiH+SzE8Y8YM+ZyeO1HNYTzr0XjA4e7du5gzZ47sPNBzJ0JC3teQ1B4DjRo1QsaMGXHw4EEAaedcSWwfaNfz7Nq1K4QQmD59epo6N5JSH2p/1/vsf6337QPt9718+XL4+/sjXbp0ukyjl5B3794leFxrH2/bti3s7e2xdu1aAGmzHrCzs0OePHmwdetW+Xxa2c6E3L59W87wDwwMNFiaCTA8BhYtWgQvLy+ULFlS17M5jBkPPmtnsxt//48fP8bFixcxfPjwNFUP2Pp5AMQ/DkJCQuRzajvQeD+oM7vSQsfz+9qF2rbw6NGj5eDTjRs3UrmkKScp+2DMmDHytWq69bQgoRl/QNws6KtXr2LDhg3YtGmTXIJG3S9ppY3MfcB9wO033H5tXbhr1y58//33qFChAkqWLIkKFSqga9eu2L59u8F1M62JjY3Fvn37MHLkSLi6uiJdunRyH+XOnRvh4eFprq1k630lphgHIdSrVy/Nfe9a7C/iPRLAuoDIljAAgZLFlStXULhwYdjZ2UEIgbZt2+LEiRMIDg7Gy5cvcevWLXTr1g3Ozs7w8/NLEzO7jJ04cUI2onr27GlwQ2lLDh06BH9/fzkIr7KVhkJCs92MG5BpMfhAZU406/fffw87Ozt07NhRzoxOSxLbBzExMZg2bRoyZsyIevXq4cWLF5YraApJSn2YVm+uzNkHy5Ytg7+/Pzw8PNLMTEdTtPW/tu6bNWsWhBCoXr26rCvTklOnTsmlCD755JME17ZNq4zbhm3atMGxY8cQHByMd+/e4dmzZxg8eDA8PT3h6+ubJvdPYoPPptb1TIts/TwAEj8OtLT1Y1o6LsxpF6rBB15eXmlidpuxpOwDHx8fm9gH77tXTkvngIr7gPuA22+4/WfOnDF4PiwsDO/evUtTA40JMe4fO3HiBObPn48sWbIgT548uHv3roVKlvJsva/EFDUIISAgAEIINGvWzNJFSlHsL+I9EsC6gMhWMACBks3169fRtGlTmTrOy8sL3t7e8PPzg5OTE4QQqFSpEq5fv27poqaYpK71mVZ16NABQgi0atUKb968SdOD7aaYM+CQ1iXWubB48WL4+fkhf/78+Pvvvy1XyBRmPMtDvaFYsGABPD09ERgYKNe3TosBOqwPEz8Pli5dCn9/f7i7u6fp4IOEzJ07F5kyZULmzJnT1ExXYydPnoS9vT2EEOjatavNBSdev34dTZo0MWgb+vj4IHv27HBzc4MQAkFBQWki1XpCzB18Tsts/TwAeBwkdj389ttvIYSAp6dnmhx4V3EfJN42tJX7Re4D7gNuv+H2nzt3LsHX2kLfiXE/wPPnz2UbIS1kQkqIrfeVmBIaGoqlS5ciMDAw0fMirWB/Ee+RANYFRLaAAQiUrF68eIENGzagbNmyyJ8/P4QQyJQpExo3boylS5fiyZMnli5iirPlRpR6g/jHH3/EG2C2tYaCrXc0A4b7oFu3bvjzzz9lunlvb+80PeCkMq4PRowYAU9PT/j5+cnUumm5o8mW60NVYueBrQUfvHv3Dk+fPkWnTp2QIUMG5MyZM01mRDJm6+fBixcvsH79epQuXRr58uWDEAJubm6oXbs2pk+fnqaWXUgI2wQ8DwAeB9rt7969Oy5evChn/af1gXcV90H8ukBdC96WcB9wH3D72SYwRdtnZgv9Z7beV2JKaGioXIrLFrAu4D4AWBcQpXUMQKAU8fbtW4SGhuL27ds2EXRgzLhz6fTp05YuUqp6/vw5ypcvL9Np2SrtcTBq1Kg0mWL8fbT7oGrVqvD09LS5QddTp07Bzs5OpiEPCAiQKRVtoRFt6/UhwPMAAB48eIDu3bvD29sbQgjUqVMHt27dsnSxUg3PA+DNmzcIDQ3FjRs3cO/ePQC2MbNNxTYBzwPAcB98/vnnNpdOVLv9pUqVsqmBdxX3wX9tYzXNtC0EIxrjPuA+4PazTUDsKyHWBQD3AcC6gCgtYwACpajY2FibWePW2KlTp5AuXToIIdCvXz+Eh4dbukipau/evbJjzZYieI2dOnVKrm02btw4m4hkN3bq1CmD9Nu21rkC/HdDkTVrVpuM4LX1+hDgefD06VN07NgRlStXxvz582128NXWzwOAbUO2CXgenDlzBkII+Pr64tWrV5YuTqo7deoUXF1dIYSAt7e3TQ28q7gPgNOnT8tsic+fP7d0cSyC+4D7wNa3n20CAthXQqwLAO4DgHUBUVolAEAhohRx/PhxpWHDhsrhw4eVwoULW7o4qerZs2dKx44dlZkzZyoFCxa0dHEs6vjx40r9+vWVo0eP2txxoPrjjz+Uli1bKvv27VMKFSpk6eJYxIULFxQfHx8la9asSkxMjGJvb2/pIqUqW64PVbZ+Hrx9+1YBoKRPn97mjn8VzwNim4DngaIoyp9//ql4eXkp2bJlUwAoQghLFylV2fr1UFG4DxTF8DyIjY1V7OzsLF2kVMd9wH1g69vPNgEpCvtKiHWBonAfKArrAqK0iAEIRCksPDxccXZ2tnQxLCIiIkJJly6dpYthFWz5OFCFhYUpLi4uli6GxdlyI5rnAc8D4nlAPAYUhftAFR0drTg4OFi6GBbB6yH3gcqW28Yq7gPuA1vefrYJSGXL5wGxLlAU7gMV6wKitIMBCERERERERERERERERERERPTRbCu3VyoIDg5Wtm7dqnz77bdKvXr1FB8fH0UIoQghlC5duli6eERERERERERERERERERERCnCNvM9pqDMmTNbughERERERERERERERERERESpjhkQUlD27NmV2rVrW7oYREREREREREREREREREREKY4ZEJLZt99+q5QpU0YpU6aMkjlzZuXu3btKrly5LF0sIiIiIiIiIiIiIiIiIiKiFMUAhGQ2duxYSxeBiIiIiIiIiIiIiIiIiIgo1XEJBiIiIiIiIiIiIiIiIiIiIvpoDEAgIiIiIiIiIiIiIiIiIiKij8YABCIiIiIiIiIiIiIiIiIiIvpoDEAgIiIiIiIiIiIiIiIiIiKij8YABCIiIiIiIiIiIiIiIiIiIvpoDpYuACVNtWrVLF0Ei3F2dlZ27typKIqi1K1bVwkPD7dwiVKXrW+/onAfKAr3ga1vv6JwHygK94Gtb7+icB8oCveBonAf2Pr2Kwr3gaJwH9j69isK94GicB/Y+vYrCveBonAfaLefiIiIUs8///yjXL9+XVEURQFg8Jzx7wk9rv3d1HtiYmKUmJgYg+cBmHytqccBKEIIg+cT+5sJPZ/QZyflPQk9fv/+feXYsWOKj4+Psn79+njbpUcMQCAiIiIiIiIiIiIiIiIiIrP89ddfSr9+/SxdjDQlJCTE0kVINlyCgYiIiIiIiIiIiIiIiIiIzPL8+XNLF4GsGAMQiIiIiIiIiIiIiIiIiIjILOqyBkSmMACBiIiIiIiIiIiIiIiIiIiIPhoDEIiIiIiIiIiIiIiIiIiIiOijMQCBiIiIiIiIiIiIiIiIiIjMAsDSRSArxgAEIiIiIiIiIiIiIiIiIiIyy507dyxdBLJiDpYuQFpz9OhR5datW/L3kJAQ+f9bt24py5cvN3h9ly5dUqlkREREREREREREREREREQfx9vb29JFICvGAIRktnjxYuXnn382+dwff/yh/PHHHwaPMQCBiIiIiIiIiIiIiIiIiPQid+7cli4CWTEuwUBERERERERERERERERERGbx8vKydBHIijEAIZktX75cAWD2DxERERERERERERERERGRXnh4eChVq1a1dDHISjEAgYiIiIiIiIiIiIiIiIiIzHLp0iXl0KFDli4GWSkGIBARERERERERERERERERkVlcXFwsXQSyYgxAICIiIiIiIiIiIiIiIiIis3h5eVm6CGTFHCxdACIiIiIiIiIiIiIiIiIi0gd/f3/lwIEDli6G8u7dO+XkyZOKoigKAIN/VdrfE3suqa819TdNveZ9r71//76yceNGxcfHJ8H36g0DEIiIiIiIiIiIiIiIiIiIKFUlNOBvzkB+VFSU0rhx40RfqychISGWLkKyYQACERERERERERERERERERGZ5eHDh0qHDh0sXQyyUnaWLgAREREREREREREREREREelDWFiYpYtAVowZEIiIiIiIdGznzp2WLgIRERFZAbYJiIiIiCi1REREWLoIZMUYgEBEREREpGN169ZVwsPDLV2MVOfs7MyBFiIiIg22CYiIiIgotWTKlMnSRSArxgAEIiIiIiIiIiIiIiIiIiIyi6+vr3LgwAFLF0N59eqVsn//fkVRFAWAwb+qhB5P7DVJee3ixYs/pOhpGgMQiIiIiIiIiIiIiIiIiIhINyIjI5UWLVoosbGxli4KGbGzdAGIiIiIiIiIiIiIiIiIiIjM5ejoqJQpU8bSxSATmAGBiIiIiIiIiIiIiIiIiIjMFhERobx7907+ntDSB6ZonxNCJPrexD7niy++SPRvJqVMiZUxJT/rzp07yrhx45TMmTOb/XnWjgEIRERERERERERERERERERklrt37ypdu3a1dDHSlKdPn1q6CMmGSzAQERERERERERERERERERHRR2MGBCIiIiIiIiIiIiIiIiIiMkvOnDmVXbt2KREREQaPCyESfZ/2eeOlF4zfm9hnqc9FR0fLzAEJLXOg/qv9e8ZLPJhaTsHczzO1XERSPu/u3bvK7NmzuQQDERERERERERERERERERHZJicnJ8XJyclifz86Olpp37698uLFC4uVITlxCQYiIiIiIiIiIiIiIiIiIiILEEIozs7Oli4GmcAMCEREREREREREREREREREZLbTp08rp0+fNuu1ppZBSOr/TalQoYISFRUlX6t631IQ7yunua/RltPUa2JjY+UyDbGxsfEeA6A8efJEuXLliuLj4/NBZbZGDEAgIiIiIiIiIiIiIiIiIiKzXLx4URk6dKili5GmhISEWLoIyYZLMBARERERERERERERERERkVmyZs2q2NlxmJlMYwYEIiIiIiIiIiIiIiIiIiIyi5eXl7Jv3z5LF0O5ceOGsmTJEpPLIhj/bvyvsfe9LqHn3759q9y7d++jtiOtYQACERERERERERERERERERHpRmRkpNK7d29LF4NMYG4MIiIiIiIiIiIiIiIiIiLSDScnJ6Vly5aWLgaZwAwIRERERERERERERERERESkK/3791f69+9v0TJUr17don/fGjEAgYiIiIiIiIiIiIiIiIiIyMj69euVW7duKQAURVEUAAY/2bNnV+7du2fhUloXBiAQERERkW7t3LnT0kUgsjieB0REcVgfEhERERGljmfPnimtWrWydDHSlIIFC1q6CMmGAQhERERERERERERERERERGSW4OBgSxch1fXp00cRQiiKoihCiHg/6uN2dnbxXqMoimJnZ5fg++3s7JSgoKDU36gUwgAEIiIiIiIiIiIiIiIiIiIyS0BAgKWLkOpat25t6SLohp2lC0BERERERERERERERERERPpw+fJlSxeBrBgDEIiIiIiIiIiIiIiIiIiIyCwuLi6WLgJZMS7BQEREREREREREREREREREZilRooTyxRdfKPv377d0UZIFAPmjfezVq1fKo0ePLFgyfWIAAhERERERERERERERERERmcXOzk5p0qSJ0qRJkxT7G/fu3VM6d+6cYp9PKYdLMBARERERERERERERERERkdV48eKFpYtAH4gZEIiIiIiIiIiIiIiIiIiIyGoEBQUpv/zyi/LPP/8oQghFURRFCCH/r/3d1GPq/7XMfb3675MnT5QJEyYoGTJkSIEtTLsYgEBERERERERERERERERERFYlW7ZsSrZs2T74/Xfu3FEGDhyovHv37qPK8e7dO6V69eof9Rnv07ZtW6VXr14p+jdSCwMQiIiIiIhI13bu3GnpIhAREZEVYJuAiIiIKHVER0crS5YsUbZs2WLwOACD39VMAsaPJ0T7uqS+V/ta9b1hYWFmv9fSVq9ezQAEIiIiIiJLq1u3rhIeHm7pYqQ6Z2dndrBr2OJxoD0GbHH7FYXnARHFZ4v1IetCQ7Z4DCgKjwMiIiJKfRcuXFDWrFlj6WKQlbKzdAGIiIiIiIiIiIiIiIiIiEgfChcurJQuXdrSxSArxQwIRERERERERERERERERERkFhcXF2Xq1KkWLUNkZKRSp04di5aBTGMAAhERERERERERERERERERmSU0NFQZPHiwcu3aNUsXhawQl2AgIiIiIiIiIiIiIiIiIiKz/PXXXww+oAQxAwIREREREREREREREREREZklKChI6d69u7J79+4EXyOEMPl/c18HQLl79+7HFZQsggEIRERERKRbO3futHQRyArY+nFg69tPRKRifUg8BoiIiIhSh729vdKhQwelQ4cOKfY3jh07pnzzzTcp9vmUchiAQERERES6VbduXSU8PNzSxUh1zs7O7GDXsMXjQHsM2OL2KwrPAyKKzxbrQ9aFhmzxGFAUHgdERESUNgUGBipOTk5KZGSkpYtCScQABCIiIiIiIiIiIiIiIiIisho+Pj7Krl27Puozvv32W+XIkSPJVCIyFwMQiIiIiIiIiIiIiIiIiIjILG/fvlX69OmjPHr0yNJFIStkZ+kCEBERERERERERERERERGRPty8eZPBB5QgZkAgIiIiIiIiIiIiIiIiIiKzBAUFKYMGDVIOHTokHxNCJPh67XPGr0vofYm9zty/9b7Pe/36tXLx4sUEP4s+DAMQiIiIiIiIiIiIiIiIiIjILHZ2dkqjRo2URo0aWbooH2XXrl0MQEgBDEAgIiIiIiIiIiIiIiIiIiKbUrVqVeXixYsGmRy0zMm6YOrxpGRhePnypbnF1Q0GIBARERERERERERERERERka6sXLlSWbx4saWLQUbsLF0AIiIiIiIiIiIiIiIiIiIic0VGRjL4wEoxAwIREREREREREREREREREemGk5OTMm3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            "text/plain": [
              "<Figure size 2500x1000 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "import missingno as msno\n",
        "%matplotlib inline\n",
        "msno.matrix(data)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "I9xRhF3pUwQj"
      },
      "source": [
        "#####  1.3. Rellena las columnas que contienen valor nulo con la mediana y crea un nuevo DataFrame."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "tSeEBgfHUwQk"
      },
      "outputs": [],
      "source": [
        "# función que recibe una lista de columnas, calcula la mediana\n",
        "def nulos(dataframe, columns):\n",
        "    resultado = pd.DataFrame(columns=['Columna', 'Valor_mediana'])\n",
        "\n",
        "    for col in columns:\n",
        "        median_value = dataframe[col].median()\n",
        "        dataframe[col] = dataframe[col].fillna(median_value)\n",
        "\n",
        "        resultado = pd.concat([resultado, pd.DataFrame({\n",
        "            'Columna': [col],\n",
        "            'Valor_mediana': [median_value]\n",
        "        })], ignore_index=True)\n",
        "    df_cuartil = resultado\n",
        "    return resultado"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "n6qWg3AsUwQk"
      },
      "source": [
        "#####  1.4. Selecciona solamente aquellas columnas que se quiere calcular la mediana"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "CMl3cqURUwQk",
        "outputId": "b407cc6f-56d9-4e0a-8cef-5182ee6b16df"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "         Columna  Valor_mediana\n",
            "0            SO2           4.00\n",
            "1           PM10          16.00\n",
            "2             O3          53.75\n",
            "3            NO2          22.00\n",
            "4          PM2.5           8.00\n",
            "5     VEL_VIENTO           0.67\n",
            "6     DIR_VIENTO         123.00\n",
            "7    TEMPERATURA          15.90\n",
            "8        HUMEDAD          63.00\n",
            "9  PRECIPITACION           0.00\n"
          ]
        }
      ],
      "source": [
        "data_nulos = data\n",
        "numeric_columns = ['SO2', 'PM10', 'O3', 'NO2', 'PM2.5', 'VEL_VIENTO', 'DIR_VIENTO', 'TEMPERATURA', 'HUMEDAD', 'PRECIPITACION']  # Lista de columnas\n",
        "\n",
        "resultado = nulos(data_nulos, numeric_columns)\n",
        "print(resultado)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "yfgT4yJqUwQl",
        "outputId": "64ba2a89-96bb-4f05-db94-63dab7ab3118"
      },
      "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>CODIGO</th>\n",
              "      <th>FECHA</th>\n",
              "      <th>ANO</th>\n",
              "      <th>MES</th>\n",
              "      <th>DIA</th>\n",
              "      <th>DIA_SEMANA</th>\n",
              "      <th>HORA</th>\n",
              "      <th>ESTACION</th>\n",
              "      <th>SO2</th>\n",
              "      <th>PM10</th>\n",
              "      <th>...</th>\n",
              "      <th>ICA_PM10</th>\n",
              "      <th>ICA_O3</th>\n",
              "      <th>ICA_NO2</th>\n",
              "      <th>ICA</th>\n",
              "      <th>VEL_VIENTO</th>\n",
              "      <th>DIR_VIENTO</th>\n",
              "      <th>TEMPERATURA</th>\n",
              "      <th>HUMEDAD</th>\n",
              "      <th>PRECIPITACION</th>\n",
              "      <th>DIR_VIENTO_CARDINAL</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>28079008</td>\n",
              "      <td>2022-01-01 00:00:00</td>\n",
              "      <td>2022.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Sabado</td>\n",
              "      <td>0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>16.0</td>\n",
              "      <td>...</td>\n",
              "      <td>32.0</td>\n",
              "      <td>15.32</td>\n",
              "      <td>87.5</td>\n",
              "      <td>34.51</td>\n",
              "      <td>0.67</td>\n",
              "      <td>123.0</td>\n",
              "      <td>15.9</td>\n",
              "      <td>63.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>28079008</td>\n",
              "      <td>2022-01-01 01:00:00</td>\n",
              "      <td>2022.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Sabado</td>\n",
              "      <td>1</td>\n",
              "      <td>8.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>27.0</td>\n",
              "      <td>...</td>\n",
              "      <td>54.0</td>\n",
              "      <td>6.82</td>\n",
              "      <td>120.0</td>\n",
              "      <td>46.00</td>\n",
              "      <td>0.67</td>\n",
              "      <td>123.0</td>\n",
              "      <td>15.9</td>\n",
              "      <td>63.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>28079008</td>\n",
              "      <td>2022-01-01 02:00:00</td>\n",
              "      <td>2022.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Sabado</td>\n",
              "      <td>2</td>\n",
              "      <td>8.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>27.0</td>\n",
              "      <td>...</td>\n",
              "      <td>54.0</td>\n",
              "      <td>5.88</td>\n",
              "      <td>117.5</td>\n",
              "      <td>44.94</td>\n",
              "      <td>0.67</td>\n",
              "      <td>123.0</td>\n",
              "      <td>15.9</td>\n",
              "      <td>63.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>28079008</td>\n",
              "      <td>2022-01-01 03:00:00</td>\n",
              "      <td>2022.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Sabado</td>\n",
              "      <td>3</td>\n",
              "      <td>8.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>17.0</td>\n",
              "      <td>...</td>\n",
              "      <td>34.0</td>\n",
              "      <td>11.85</td>\n",
              "      <td>95.0</td>\n",
              "      <td>35.81</td>\n",
              "      <td>0.67</td>\n",
              "      <td>123.0</td>\n",
              "      <td>15.9</td>\n",
              "      <td>63.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>28079008</td>\n",
              "      <td>2022-01-01 04:00:00</td>\n",
              "      <td>2022.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Sabado</td>\n",
              "      <td>4</td>\n",
              "      <td>8.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>...</td>\n",
              "      <td>10.0</td>\n",
              "      <td>20.55</td>\n",
              "      <td>65.0</td>\n",
              "      <td>24.49</td>\n",
              "      <td>0.67</td>\n",
              "      <td>123.0</td>\n",
              "      <td>15.9</td>\n",
              "      <td>63.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 27 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     CODIGO                FECHA     ANO  MES  DIA DIA_SEMANA  HORA  ESTACION  \\\n",
              "0  28079008  2022-01-01 00:00:00  2022.0  1.0  1.0     Sabado     0       8.0   \n",
              "1  28079008  2022-01-01 01:00:00  2022.0  1.0  1.0     Sabado     1       8.0   \n",
              "2  28079008  2022-01-01 02:00:00  2022.0  1.0  1.0     Sabado     2       8.0   \n",
              "3  28079008  2022-01-01 03:00:00  2022.0  1.0  1.0     Sabado     3       8.0   \n",
              "4  28079008  2022-01-01 04:00:00  2022.0  1.0  1.0     Sabado     4       8.0   \n",
              "\n",
              "   SO2  PM10  ...  ICA_PM10  ICA_O3  ICA_NO2    ICA  VEL_VIENTO  DIR_VIENTO  \\\n",
              "0  4.0  16.0  ...      32.0   15.32     87.5  34.51        0.67       123.0   \n",
              "1  4.0  27.0  ...      54.0    6.82    120.0  46.00        0.67       123.0   \n",
              "2  3.0  27.0  ...      54.0    5.88    117.5  44.94        0.67       123.0   \n",
              "3  3.0  17.0  ...      34.0   11.85     95.0  35.81        0.67       123.0   \n",
              "4  3.0   5.0  ...      10.0   20.55     65.0  24.49        0.67       123.0   \n",
              "\n",
              "   TEMPERATURA  HUMEDAD  PRECIPITACION  DIR_VIENTO_CARDINAL  \n",
              "0         15.9     63.0            0.0                  NaN  \n",
              "1         15.9     63.0            0.0                  NaN  \n",
              "2         15.9     63.0            0.0                  NaN  \n",
              "3         15.9     63.0            0.0                  NaN  \n",
              "4         15.9     63.0            0.0                  NaN  \n",
              "\n",
              "[5 rows x 27 columns]"
            ]
          },
          "execution_count": 115,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "data_nulos.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BtYKUNUhUwQl"
      },
      "source": [
        "Verifica si todavia hay valores nulos para las columnas selecionadas"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Q64yoxnGUwQl",
        "outputId": "7c49f533-5c47-4c1e-df19-dc00cd1cf46c"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "CODIGO                     0\n",
              "FECHA                      0\n",
              "ANO                        0\n",
              "MES                        0\n",
              "DIA                        0\n",
              "DIA_SEMANA                 0\n",
              "HORA                       0\n",
              "ESTACION                   0\n",
              "SO2                        0\n",
              "PM10                       0\n",
              "O3                         0\n",
              "NO2                        0\n",
              "PM2.5                      0\n",
              "NOM_TIPO               43536\n",
              "LATITUD                43536\n",
              "LONGITUD               43536\n",
              "ICA_SO2                61779\n",
              "ICA_PM10               55056\n",
              "ICA_O3                 52752\n",
              "ICA_NO2                43975\n",
              "ICA                    43754\n",
              "VEL_VIENTO                 0\n",
              "DIR_VIENTO                 0\n",
              "TEMPERATURA                0\n",
              "HUMEDAD                    0\n",
              "PRECIPITACION              0\n",
              "DIR_VIENTO_CARDINAL    43536\n",
              "dtype: int64"
            ]
          },
          "execution_count": 116,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "data_nulos.isnull().sum()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AVsxo5nSUwQl"
      },
      "source": [
        "#####  1.4. Genera el CSV después del pocesamiento de los valores nulos"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "eWgMyHLEUwQm"
      },
      "outputs": [],
      "source": [
        "data_nulos.to_csv(ruta_nuevoCSV, index=False)"
      ]
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "nombre_kernel",
      "language": "python",
      "name": "nombre_kernel"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.10.10"
    },
    "orig_nbformat": 4,
    "colab": {
      "provenance": [],
      "include_colab_link": true
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}