main.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "main.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true,
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/vitorsr/ccd/blob/master/main.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TqGQeZ0sCA-k",
        "colab_type": "text"
      },
      "source": [
        "# Importando dados"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hB420eiaCfAW",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pandas as pd\n",
        "import os"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "69CSdZsN-XVT",
        "colab_type": "code",
        "outputId": "0c248915-3b8c-49f0-a623-7aabac1b35d6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 479
        }
      },
      "source": [
        "!wget https://www.dropbox.com/s/7rriacb7c6vzf3m/ccd_2019.zip -O ccd_2019.zip\n",
        "!unzip ccd_2019.zip"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2019-10-25 23:43:08--  https://www.dropbox.com/s/7rriacb7c6vzf3m/ccd_2019.zip\n",
            "Resolving www.dropbox.com (www.dropbox.com)... 162.125.9.1, 2620:100:601f:1::a27d:901\n",
            "Connecting to www.dropbox.com (www.dropbox.com)|162.125.9.1|:443... connected.\n",
            "HTTP request sent, awaiting response... 301 Moved Permanently\n",
            "Location: /s/raw/7rriacb7c6vzf3m/ccd_2019.zip [following]\n",
            "--2019-10-25 23:43:08--  https://www.dropbox.com/s/raw/7rriacb7c6vzf3m/ccd_2019.zip\n",
            "Reusing existing connection to www.dropbox.com:443.\n",
            "HTTP request sent, awaiting response... 302 Found\n",
            "Location: https://ucb5672dcb58eb2db049874feb21.dl.dropboxusercontent.com/cd/0/inline/ArLNzK43V_IIqxWcfHIuV1jtBVfUzRbPQHuMyXctQqh1Gere93LfY_2fxJsSTbVRwnjdde5UIjJMws8eh8XnvxtV6KQ_QMvPX5zffiT7Q6SHoPBrZWQr0XMfXRXbgxj4cGg/file# [following]\n",
            "--2019-10-25 23:43:08--  https://ucb5672dcb58eb2db049874feb21.dl.dropboxusercontent.com/cd/0/inline/ArLNzK43V_IIqxWcfHIuV1jtBVfUzRbPQHuMyXctQqh1Gere93LfY_2fxJsSTbVRwnjdde5UIjJMws8eh8XnvxtV6KQ_QMvPX5zffiT7Q6SHoPBrZWQr0XMfXRXbgxj4cGg/file\n",
            "Resolving ucb5672dcb58eb2db049874feb21.dl.dropboxusercontent.com (ucb5672dcb58eb2db049874feb21.dl.dropboxusercontent.com)... 162.125.9.6, 2620:100:601f:6::a27d:906\n",
            "Connecting to ucb5672dcb58eb2db049874feb21.dl.dropboxusercontent.com (ucb5672dcb58eb2db049874feb21.dl.dropboxusercontent.com)|162.125.9.6|:443... connected.\n",
            "HTTP request sent, awaiting response... 302 FOUND\n",
            "Location: /cd/0/inline2/ArLR91vuBoe2aDFghD1OVkt5wz2Z-C5TPaVtmx_FwyKyxgIUy4LNJa8bVCspWAc4HlzIedjMJ5gRXlQJ27aT4e19WNz_l4kL-VE37FwGWcJfB_bMDlY53PFMj32I0YHYRh-QO1TYx_sOKAK7P7XXBu5bMcfU-KFtliyijYuonNWNpuCi20ok4rZ2tc94td7Uu01dVebFA2-tRjy0v7TBMch5_BFm9ky25EFck7A504o2HOdwF9hvdVsEsGeWof5MOktRAyFy_-Tqq7jpBr2eIYLcvLe65CiGs2ImgsDWjetvGYrgT5L4IrkMrsNo_pveQYVHMagI0WRu6WmwD8gxpX7oKlhIITxHNFxCHQhofXfTNQ/file [following]\n",
            "--2019-10-25 23:43:09--  https://ucb5672dcb58eb2db049874feb21.dl.dropboxusercontent.com/cd/0/inline2/ArLR91vuBoe2aDFghD1OVkt5wz2Z-C5TPaVtmx_FwyKyxgIUy4LNJa8bVCspWAc4HlzIedjMJ5gRXlQJ27aT4e19WNz_l4kL-VE37FwGWcJfB_bMDlY53PFMj32I0YHYRh-QO1TYx_sOKAK7P7XXBu5bMcfU-KFtliyijYuonNWNpuCi20ok4rZ2tc94td7Uu01dVebFA2-tRjy0v7TBMch5_BFm9ky25EFck7A504o2HOdwF9hvdVsEsGeWof5MOktRAyFy_-Tqq7jpBr2eIYLcvLe65CiGs2ImgsDWjetvGYrgT5L4IrkMrsNo_pveQYVHMagI0WRu6WmwD8gxpX7oKlhIITxHNFxCHQhofXfTNQ/file\n",
            "Reusing existing connection to ucb5672dcb58eb2db049874feb21.dl.dropboxusercontent.com:443.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 214105380 (204M) [application/zip]\n",
            "Saving to: ‘ccd_2019.zip’\n",
            "\n",
            "ccd_2019.zip        100%[===================>] 204.19M  41.9MB/s    in 4.9s    \n",
            "\n",
            "2019-10-25 23:43:14 (41.9 MB/s) - ‘ccd_2019.zip’ saved [214105380/214105380]\n",
            "\n",
            "Archive:  ccd_2019.zip\n",
            "replace bdmep_meta.csv? [y]es, [n]o, [A]ll, [N]one, [r]ename: N\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OfzV4keyqh68",
        "colab_type": "text"
      },
      "source": [
        "Import csvs from git"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1lK6A5mmB9C6",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df_m = pd.read_csv(\"bdmep_meta.csv\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FU_92u4FKZVQ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df = pd.read_csv(\"inmetr.csv\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OSJ9YEOGCyew",
        "colab_type": "code",
        "outputId": "e65a3173-2b8a-42e2-c086-edc0fe0cd995",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "df.head()"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>date</th>\n",
              "      <th>id</th>\n",
              "      <th>prec</th>\n",
              "      <th>tair</th>\n",
              "      <th>tw</th>\n",
              "      <th>tmax</th>\n",
              "      <th>tmin</th>\n",
              "      <th>urmax</th>\n",
              "      <th>patm</th>\n",
              "      <th>pnmm</th>\n",
              "      <th>wd</th>\n",
              "      <th>wsmax</th>\n",
              "      <th>n</th>\n",
              "      <th>cc</th>\n",
              "      <th>evap</th>\n",
              "      <th>ur</th>\n",
              "      <th>ws</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1970-05-04 00:00:00</td>\n",
              "      <td>83010</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>32.6</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",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>89.75</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1970-05-04 12:00:00</td>\n",
              "      <td>83010</td>\n",
              "      <td>NaN</td>\n",
              "      <td>25.3</td>\n",
              "      <td>24.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>23.7</td>\n",
              "      <td>90.0</td>\n",
              "      <td>1005.9</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>5.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1970-05-04 18:00:00</td>\n",
              "      <td>83010</td>\n",
              "      <td>NaN</td>\n",
              "      <td>29.2</td>\n",
              "      <td>27.1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>85.0</td>\n",
              "      <td>1004.2</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1970-05-05 00:00:00</td>\n",
              "      <td>83010</td>\n",
              "      <td>NaN</td>\n",
              "      <td>25.0</td>\n",
              "      <td>27.0</td>\n",
              "      <td>32.6</td>\n",
              "      <td>NaN</td>\n",
              "      <td>92.0</td>\n",
              "      <td>1007.5</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>8.0</td>\n",
              "      <td>2.4</td>\n",
              "      <td>88.25</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1970-05-05 12:00:00</td>\n",
              "      <td>83010</td>\n",
              "      <td>0.0</td>\n",
              "      <td>25.9</td>\n",
              "      <td>24.8</td>\n",
              "      <td>NaN</td>\n",
              "      <td>23.0</td>\n",
              "      <td>91.0</td>\n",
              "      <td>1006.4</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                  date     id  prec  tair    tw  ...   n   cc  evap     ur  ws\n",
              "0  1970-05-04 00:00:00  83010   NaN   NaN   NaN  ... NaN  NaN   NaN  89.75 NaN\n",
              "1  1970-05-04 12:00:00  83010   NaN  25.3  24.0  ... NaN  5.0   NaN    NaN NaN\n",
              "2  1970-05-04 18:00:00  83010   NaN  29.2  27.1  ... NaN  4.0   NaN    NaN NaN\n",
              "3  1970-05-05 00:00:00  83010   NaN  25.0  27.0  ... NaN  8.0   2.4  88.25 NaN\n",
              "4  1970-05-05 12:00:00  83010   0.0  25.9  24.8  ... NaN  4.0   NaN    NaN NaN\n",
              "\n",
              "[5 rows x 17 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9XL-kyfjK1gX",
        "colab_type": "code",
        "outputId": "6f3173e1-8447-4830-8e47-9978d28b6c0e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "df_m.head()"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>lon</th>\n",
              "      <th>lat</th>\n",
              "      <th>alt</th>\n",
              "      <th>name</th>\n",
              "      <th>state</th>\n",
              "      <th>uf</th>\n",
              "      <th>time_zone</th>\n",
              "      <th>offset_utc</th>\n",
              "      <th>time_zone.1</th>\n",
              "      <th>offset_utc.1</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>83010</td>\n",
              "      <td>-68.733333</td>\n",
              "      <td>-11.016667</td>\n",
              "      <td>260.00</td>\n",
              "      <td>Brasiléia</td>\n",
              "      <td>Acre</td>\n",
              "      <td>AC</td>\n",
              "      <td>America/Rio_Branco</td>\n",
              "      <td>-5</td>\n",
              "      <td>America/Rio_Branco</td>\n",
              "      <td>-5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>82704</td>\n",
              "      <td>-72.666667</td>\n",
              "      <td>-7.633333</td>\n",
              "      <td>170.00</td>\n",
              "      <td>Cruzeiro do Sul</td>\n",
              "      <td>Acre</td>\n",
              "      <td>AC</td>\n",
              "      <td>America/Rio_Branco</td>\n",
              "      <td>-5</td>\n",
              "      <td>America/Rio_Branco</td>\n",
              "      <td>-5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>82915</td>\n",
              "      <td>-67.800000</td>\n",
              "      <td>-9.966667</td>\n",
              "      <td>160.00</td>\n",
              "      <td>Rio Branco</td>\n",
              "      <td>Acre</td>\n",
              "      <td>AC</td>\n",
              "      <td>America/Rio_Branco</td>\n",
              "      <td>-5</td>\n",
              "      <td>America/Rio_Branco</td>\n",
              "      <td>-5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>82807</td>\n",
              "      <td>-70.766667</td>\n",
              "      <td>-8.166667</td>\n",
              "      <td>190.00</td>\n",
              "      <td>Tarauacá</td>\n",
              "      <td>Acre</td>\n",
              "      <td>AC</td>\n",
              "      <td>America/Rio_Branco</td>\n",
              "      <td>-5</td>\n",
              "      <td>America/Rio_Branco</td>\n",
              "      <td>-5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>83098</td>\n",
              "      <td>-36.166667</td>\n",
              "      <td>-10.150000</td>\n",
              "      <td>56.13</td>\n",
              "      <td>Coruripe</td>\n",
              "      <td>Alagoas</td>\n",
              "      <td>AL</td>\n",
              "      <td>America/Maceio</td>\n",
              "      <td>-3</td>\n",
              "      <td>America/Maceio</td>\n",
              "      <td>-3</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      id        lon        lat  ...  offset_utc         time_zone.1 offset_utc.1\n",
              "0  83010 -68.733333 -11.016667  ...          -5  America/Rio_Branco           -5\n",
              "1  82704 -72.666667  -7.633333  ...          -5  America/Rio_Branco           -5\n",
              "2  82915 -67.800000  -9.966667  ...          -5  America/Rio_Branco           -5\n",
              "3  82807 -70.766667  -8.166667  ...          -5  America/Rio_Branco           -5\n",
              "4  83098 -36.166667 -10.150000  ...          -3      America/Maceio           -3\n",
              "\n",
              "[5 rows x 11 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-N5Chu1-spS2",
        "colab_type": "text"
      },
      "source": [
        "### Dicionário de dados:"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CNa0QZa1szu-",
        "colab_type": "text"
      },
      "source": [
        "<br><b>variável | descrição | unidade </b></br>\n",
        "<br>date | data e hora da coleta             -\n",
        "<br> id | ID da estação de coleta             -\n",
        "<br>prec | precipitação |       mm\n",
        "<br>tair | temperatura do ar  | graus Celsius\n",
        "<br>tw | temperatura de bulbo úmido |  graus Celsius\n",
        "<br>tmax | temperatura máxima do ar  | graus Celsius\n",
        "<br>tmin | temperatura mínima do ar  | graus Celsius\n",
        "<br>urmax | umidade relativa máxima  |             %\n",
        "<br>patm | pressão atmosférica         |    hPa\n",
        "<br>pnmm | pressão atmosférica média ao nível do mar  |           hPa\n",
        "<br>wd | direção do vento        |   graus\n",
        "<br>wsmax  |  rajadas de vento    |         m/s\n",
        "<br>n |  horas de sol          |     h\n",
        "<br>cc |   cobertura de nuvens    |           -\n",
        "<br>evap  |   evaporação        |      mm\n",
        "<br>ur  |   umidade relativa          |     %\n",
        "<br>ws   |   velocidade do vento       |      m/s\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PHi9-XWjq2LY",
        "colab_type": "text"
      },
      "source": [
        "## Explorando"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ShEluODmsRYn",
        "colab_type": "text"
      },
      "source": [
        "> **Hipótese**: as queimadas que ocorreram na amazônia, entre 8 e 22 de Agosto de 2019 influenciaram as temperaturas na região e/ou no restante do país?"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XyXuVGsAHmvd",
        "colab_type": "text"
      },
      "source": [
        "Primeiramente, vamos fazer que a coluna date seja do tipo **datetime**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "la0H-B-Fshza",
        "colab_type": "code",
        "outputId": "4f7cac57-555e-4627-c37d-0e7166a1456b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "df.head()"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>date</th>\n",
              "      <th>id</th>\n",
              "      <th>prec</th>\n",
              "      <th>tair</th>\n",
              "      <th>tw</th>\n",
              "      <th>tmax</th>\n",
              "      <th>tmin</th>\n",
              "      <th>urmax</th>\n",
              "      <th>patm</th>\n",
              "      <th>pnmm</th>\n",
              "      <th>wd</th>\n",
              "      <th>wsmax</th>\n",
              "      <th>n</th>\n",
              "      <th>cc</th>\n",
              "      <th>evap</th>\n",
              "      <th>ur</th>\n",
              "      <th>ws</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1970-05-04 00:00:00</td>\n",
              "      <td>83010</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>32.6</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",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>89.75</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1970-05-04 12:00:00</td>\n",
              "      <td>83010</td>\n",
              "      <td>NaN</td>\n",
              "      <td>25.3</td>\n",
              "      <td>24.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>23.7</td>\n",
              "      <td>90.0</td>\n",
              "      <td>1005.9</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>5.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1970-05-04 18:00:00</td>\n",
              "      <td>83010</td>\n",
              "      <td>NaN</td>\n",
              "      <td>29.2</td>\n",
              "      <td>27.1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>85.0</td>\n",
              "      <td>1004.2</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1970-05-05 00:00:00</td>\n",
              "      <td>83010</td>\n",
              "      <td>NaN</td>\n",
              "      <td>25.0</td>\n",
              "      <td>27.0</td>\n",
              "      <td>32.6</td>\n",
              "      <td>NaN</td>\n",
              "      <td>92.0</td>\n",
              "      <td>1007.5</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>8.0</td>\n",
              "      <td>2.4</td>\n",
              "      <td>88.25</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1970-05-05 12:00:00</td>\n",
              "      <td>83010</td>\n",
              "      <td>0.0</td>\n",
              "      <td>25.9</td>\n",
              "      <td>24.8</td>\n",
              "      <td>NaN</td>\n",
              "      <td>23.0</td>\n",
              "      <td>91.0</td>\n",
              "      <td>1006.4</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                  date     id  prec  tair    tw  ...   n   cc  evap     ur  ws\n",
              "0  1970-05-04 00:00:00  83010   NaN   NaN   NaN  ... NaN  NaN   NaN  89.75 NaN\n",
              "1  1970-05-04 12:00:00  83010   NaN  25.3  24.0  ... NaN  5.0   NaN    NaN NaN\n",
              "2  1970-05-04 18:00:00  83010   NaN  29.2  27.1  ... NaN  4.0   NaN    NaN NaN\n",
              "3  1970-05-05 00:00:00  83010   NaN  25.0  27.0  ... NaN  8.0   2.4  88.25 NaN\n",
              "4  1970-05-05 12:00:00  83010   0.0  25.9  24.8  ... NaN  4.0   NaN    NaN NaN\n",
              "\n",
              "[5 rows x 17 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vZhijR7lH_FC",
        "colab_type": "code",
        "outputId": "ae1a7af1-6684-47f6-8dc7-88dcfb0cbe75",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 323
        }
      },
      "source": [
        "df.dtypes"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "date      object\n",
              "id         int64\n",
              "prec     float64\n",
              "tair     float64\n",
              "tw       float64\n",
              "tmax     float64\n",
              "tmin     float64\n",
              "urmax    float64\n",
              "patm     float64\n",
              "pnmm     float64\n",
              "wd       float64\n",
              "wsmax    float64\n",
              "n        float64\n",
              "cc       float64\n",
              "evap     float64\n",
              "ur       float64\n",
              "ws       float64\n",
              "dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yrsOkvZNIrtU",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df['date'] = pd.to_datetime(df['date'])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CPlyAIIbI1sa",
        "colab_type": "code",
        "outputId": "7abe758f-a4ac-4e40-ace8-0b06461ab125",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 323
        }
      },
      "source": [
        "df.dtypes"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "date     datetime64[ns]\n",
              "id                int64\n",
              "prec            float64\n",
              "tair            float64\n",
              "tw              float64\n",
              "tmax            float64\n",
              "tmin            float64\n",
              "urmax           float64\n",
              "patm            float64\n",
              "pnmm            float64\n",
              "wd              float64\n",
              "wsmax           float64\n",
              "n               float64\n",
              "cc              float64\n",
              "evap            float64\n",
              "ur              float64\n",
              "ws              float64\n",
              "dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "I_AlNUFXO5Z0",
        "colab_type": "text"
      },
      "source": [
        "> <b>Não</b> temos dados de <b>2019</b>!!! Vamos baixar da fonte"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VG3RlnHMqwpL",
        "colab_type": "code",
        "outputId": "6e43893d-b0f7-470e-eefa-486c5d6396d6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 343
        }
      },
      "source": [
        "!wget \"https://onedrive.live.com/download?cid=BCBADB0836C97273&resid=BCBADB0836C97273%21115485&authkey=AJlb41BWz0YiBd8\" -O 2019.zip\n",
        "!unzip 2019.zip"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2019-10-25 23:43:47--  https://onedrive.live.com/download?cid=BCBADB0836C97273&resid=BCBADB0836C97273%21115485&authkey=AJlb41BWz0YiBd8\n",
            "Resolving onedrive.live.com (onedrive.live.com)... 13.107.42.13\n",
            "Connecting to onedrive.live.com (onedrive.live.com)|13.107.42.13|:443... connected.\n",
            "HTTP request sent, awaiting response... 302 Found\n",
            "Location: https://c9quda.bn.files.1drv.com/y4mb07ei-ZvmiiDMz0a2DP1uW3XfvF8dAzIqmH13GFKlQ1-PwEQHvV0Cmibw3Sv4O0DCiI0ytNbUxCtctJoLiKznRzqJS9fxZvQVYyVh-v6l0uiyVWrATQeO9aJ5zVBNlda3larkxeMpcmY9pQhRWoEPJnmNqzmVbxFsGomKNZCvLyfCV3WE_d0MgQVwez1CwOzkORhUmg8nTXzlDuOpKUiNA/2019.zip?download&psid=1 [following]\n",
            "--2019-10-25 23:43:52--  https://c9quda.bn.files.1drv.com/y4mb07ei-ZvmiiDMz0a2DP1uW3XfvF8dAzIqmH13GFKlQ1-PwEQHvV0Cmibw3Sv4O0DCiI0ytNbUxCtctJoLiKznRzqJS9fxZvQVYyVh-v6l0uiyVWrATQeO9aJ5zVBNlda3larkxeMpcmY9pQhRWoEPJnmNqzmVbxFsGomKNZCvLyfCV3WE_d0MgQVwez1CwOzkORhUmg8nTXzlDuOpKUiNA/2019.zip?download&psid=1\n",
            "Resolving c9quda.bn.files.1drv.com (c9quda.bn.files.1drv.com)... 13.107.42.12\n",
            "Connecting to c9quda.bn.files.1drv.com (c9quda.bn.files.1drv.com)|13.107.42.12|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 1857551 (1.8M) [application/zip]\n",
            "Saving to: ‘2019.zip’\n",
            "\n",
            "2019.zip            100%[===================>]   1.77M  --.-KB/s    in 0.09s   \n",
            "\n",
            "2019-10-25 23:43:52 (19.3 MB/s) - ‘2019.zip’ saved [1857551/1857551]\n",
            "\n",
            "Archive:  2019.zip\n",
            "replace 2019/82024.csv? [y]es, [n]o, [A]ll, [N]one, [r]ename: N\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "K-J94GmjsGnT",
        "colab_type": "code",
        "outputId": "ce3bc639-670b-42a8-96d0-b37cd46f6ad3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "%ls "
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\u001b[0m\u001b[01;34m2019\u001b[0m/     bdmep_meta.csv  inmetr_201908.csv  \u001b[01;34m__MACOSX\u001b[0m/\n",
            "2019.zip  ccd_2019.zip    inmetr.csv         \u001b[01;34msample_data\u001b[0m/\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zjvGMa2KsMCY",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "ead5f4e7-10af-440c-d714-df3c6a5a864b"
      },
      "source": [
        "%cd 2019"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/2019\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "we-yMZrxsQD3",
        "colab_type": "code",
        "outputId": "3bca7737-d066-4467-b36b-384be3acb302",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 493
        }
      },
      "source": [
        "%ls"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "82024.csv  82445.csv  82777.csv  83049.csv  83338.csv  83565.csv  83767.csv\n",
            "82042.csv  82460.csv  82780.csv  83064.csv  83339.csv  83577.csv  83781.csv\n",
            "82098.csv  82476.csv  82784.csv  83076.csv  83358.csv  83579.csv  83789.csv\n",
            "82106.csv  82487.csv  82792.csv  83090.csv  83361.csv  83587.csv  83805.csv\n",
            "82113.csv  82493.csv  82795.csv  83096.csv  83368.csv  83589.csv  83807.csv\n",
            "82141.csv  82533.csv  82797.csv  83097.csv  83376.csv  83591.csv  83811.csv\n",
            "82145.csv  82562.csv  82798.csv  83114.csv  83377.csv  83592.csv  83813.csv\n",
            "82178.csv  82564.csv  82807.csv  83179.csv  83379.csv  83623.csv  83836.csv\n",
            "82181.csv  82571.csv  82861.csv  83182.csv  83384.csv  83630.csv  83842.csv\n",
            "82184.csv  82578.csv  82863.csv  83184.csv  83386.csv  83632.csv  83844.csv\n",
            "82188.csv  82586.csv  82882.csv  83186.csv  83393.csv  83639.csv  83851.csv\n",
            "82191.csv  82588.csv  82886.csv  83190.csv  83423.csv  83648.csv  83897.csv\n",
            "82198.csv  82590.csv  82890.csv  83192.csv  83428.csv  83669.csv  83907.csv\n",
            "82212.csv  82596.csv  82893.csv  83195.csv  83437.csv  83676.csv  83912.csv\n",
            "82246.csv  82598.csv  82900.csv  83221.csv  83441.csv  83683.csv  83914.csv\n",
            "82263.csv  82610.csv  82915.csv  83222.csv  83442.csv  83687.csv  83916.csv\n",
            "82280.csv  82659.csv  82970.csv  83228.csv  83464.csv  83692.csv  83919.csv\n",
            "82317.csv  82668.csv  82975.csv  83229.csv  83470.csv  83695.csv  83927.csv\n",
            "82326.csv  82676.csv  82976.csv  83235.csv  83479.csv  83696.csv  83936.csv\n",
            "82331.csv  82678.csv  82979.csv  83236.csv  83483.csv  83698.csv  83942.csv\n",
            "82336.csv  82683.csv  82983.csv  83242.csv  83488.csv  83714.csv  83948.csv\n",
            "82353.csv  82686.csv  82990.csv  83244.csv  83498.csv  83718.csv  83964.csv\n",
            "82361.csv  82689.csv  82992.csv  83249.csv  83522.csv  83726.csv  83967.csv\n",
            "82376.csv  82693.csv  82994.csv  83286.csv  83526.csv  83736.csv  83980.csv\n",
            "82382.csv  82723.csv  82996.csv  83288.csv  83536.csv  83737.csv  83985.csv\n",
            "82392.csv  82753.csv  83007.csv  83309.csv  83538.csv  83738.csv  83995.csv\n",
            "82397.csv  82765.csv  83013.csv  83332.csv  83550.csv  83744.csv  83997.csv\n",
            "82410.csv  82768.csv  83033.csv  83334.csv  83557.csv  83766.csv\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NyYfadsgtrH5",
        "colab_type": "code",
        "outputId": "1af4acc7-2492-4224-c46f-14aa53e1cd76",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "%cd .."
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-FbsLr8XsS5s",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "directory = '2019'\n",
        "files = []\n",
        "for filename in os.listdir(directory):\n",
        "    if filename.endswith(\".csv\"): \n",
        "        files.append(filename)\n",
        "        continue\n",
        "    else:\n",
        "        continue"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xiwQKcGltyoe",
        "colab_type": "code",
        "outputId": "e54b93d4-fa30-4053-ae59-a0d2255b3acc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "files"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['82562.csv',\n",
              " '83565.csv',\n",
              " '83339.csv',\n",
              " '83737.csv',\n",
              " '83309.csv',\n",
              " '83718.csv',\n",
              " '82768.csv',\n",
              " '82784.csv',\n",
              " '83980.csv',\n",
              " '83985.csv',\n",
              " '82397.csv',\n",
              " '83813.csv',\n",
              " '82571.csv',\n",
              " '83630.csv',\n",
              " '82263.csv',\n",
              " '83479.csv',\n",
              " '83033.csv',\n",
              " '83179.csv',\n",
              " '83587.csv',\n",
              " '83182.csv',\n",
              " '83228.csv',\n",
              " '83222.csv',\n",
              " '83186.csv',\n",
              " '82410.csv',\n",
              " '83114.csv',\n",
              " '83907.csv',\n",
              " '83384.csv',\n",
              " '83488.csv',\n",
              " '83498.csv',\n",
              " '83648.csv',\n",
              " '83897.csv',\n",
              " '82900.csv',\n",
              " '82979.csv',\n",
              " '83423.csv',\n",
              " '83995.csv',\n",
              " '82777.csv',\n",
              " '83236.csv',\n",
              " '83914.csv',\n",
              " '83464.csv',\n",
              " '83242.csv',\n",
              " '83696.csv',\n",
              " '83842.csv',\n",
              " '83358.csv',\n",
              " '82686.csv',\n",
              " '83097.csv',\n",
              " '83692.csv',\n",
              " '82668.csv',\n",
              " '83076.csv',\n",
              " '83789.csv',\n",
              " '82683.csv',\n",
              " '83437.csv',\n",
              " '83766.csv',\n",
              " '83235.csv',\n",
              " '82797.csv',\n",
              " '82113.csv',\n",
              " '83948.csv',\n",
              " '83332.csv',\n",
              " '83195.csv',\n",
              " '82353.csv',\n",
              " '82886.csv',\n",
              " '83836.csv',\n",
              " '82678.csv',\n",
              " '82807.csv',\n",
              " '83726.csv',\n",
              " '83393.csv',\n",
              " '82280.csv',\n",
              " '83589.csv',\n",
              " '82460.csv',\n",
              " '82861.csv',\n",
              " '83470.csv',\n",
              " '82976.csv',\n",
              " '83851.csv',\n",
              " '83683.csv',\n",
              " '82598.csv',\n",
              " '82610.csv',\n",
              " '83714.csv',\n",
              " '83007.csv',\n",
              " '82693.csv',\n",
              " '82596.csv',\n",
              " '83579.csv',\n",
              " '83379.csv',\n",
              " '83669.csv',\n",
              " '83919.csv',\n",
              " '83483.csv',\n",
              " '83695.csv',\n",
              " '82376.csv',\n",
              " '83441.csv',\n",
              " '83526.csv',\n",
              " '83577.csv',\n",
              " '83428.csv',\n",
              " '83229.csv',\n",
              " '83964.csv',\n",
              " '82246.csv',\n",
              " '82798.csv',\n",
              " '83744.csv',\n",
              " '82863.csv',\n",
              " '83538.csv',\n",
              " '82996.csv',\n",
              " '83361.csv',\n",
              " '83687.csv',\n",
              " '82445.csv',\n",
              " '82994.csv',\n",
              " '82915.csv',\n",
              " '83942.csv',\n",
              " '82753.csv',\n",
              " '83676.csv',\n",
              " '82792.csv',\n",
              " '83244.csv',\n",
              " '83221.csv',\n",
              " '83334.csv',\n",
              " '82361.csv',\n",
              " '83013.csv',\n",
              " '82780.csv',\n",
              " '83967.csv',\n",
              " '82212.csv',\n",
              " '82145.csv',\n",
              " '83090.csv',\n",
              " '82533.csv',\n",
              " '82181.csv',\n",
              " '83767.csv',\n",
              " '83522.csv',\n",
              " '82578.csv',\n",
              " '83591.csv',\n",
              " '83386.csv',\n",
              " '82392.csv',\n",
              " '83781.csv',\n",
              " '83736.csv',\n",
              " '82476.csv',\n",
              " '83096.csv',\n",
              " '83192.csv',\n",
              " '82990.csv',\n",
              " '82590.csv',\n",
              " '82106.csv',\n",
              " '83916.csv',\n",
              " '82795.csv',\n",
              " '82564.csv',\n",
              " '83064.csv',\n",
              " '83805.csv',\n",
              " '82487.csv',\n",
              " '82893.csv',\n",
              " '82882.csv',\n",
              " '83376.csv',\n",
              " '83338.csv',\n",
              " '82184.csv',\n",
              " '82188.csv',\n",
              " '83249.csv',\n",
              " '83912.csv',\n",
              " '83190.csv',\n",
              " '82326.csv',\n",
              " '83592.csv',\n",
              " '82317.csv',\n",
              " '83997.csv',\n",
              " '83639.csv',\n",
              " '82890.csv',\n",
              " '83844.csv',\n",
              " '82723.csv',\n",
              " '83550.csv',\n",
              " '82586.csv',\n",
              " '82493.csv',\n",
              " '83807.csv',\n",
              " '82689.csv',\n",
              " '82141.csv',\n",
              " '82382.csv',\n",
              " '83286.csv',\n",
              " '82178.csv',\n",
              " '82975.csv',\n",
              " '83738.csv',\n",
              " '82765.csv',\n",
              " '83442.csv',\n",
              " '83936.csv',\n",
              " '83536.csv',\n",
              " '82336.csv',\n",
              " '82970.csv',\n",
              " '82042.csv',\n",
              " '82024.csv',\n",
              " '83368.csv',\n",
              " '83184.csv',\n",
              " '83811.csv',\n",
              " '82198.csv',\n",
              " '82098.csv',\n",
              " '83698.csv',\n",
              " '83623.csv',\n",
              " '83927.csv',\n",
              " '82331.csv',\n",
              " '83288.csv',\n",
              " '83049.csv',\n",
              " '83377.csv',\n",
              " '83557.csv',\n",
              " '82659.csv',\n",
              " '82191.csv',\n",
              " '82676.csv',\n",
              " '82983.csv',\n",
              " '82992.csv',\n",
              " '82588.csv',\n",
              " '83632.csv']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rN3s4jMUt_zR",
        "colab_type": "code",
        "outputId": "aa83b17c-4c3d-4285-c773-b3d0f95272e8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "len(files)"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "195"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ey30P7pnuHOQ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df = pd.DataFrame()\n",
        "for file in files:\n",
        "    df_aux = pd.read_csv(directory+'/'+file, delimiter=',')\n",
        "    df = pd.concat([df_aux, df], sort=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5vd8SRYYu_TF",
        "colab_type": "code",
        "outputId": "7bf26989-94b5-44ff-8683-1cd12293cfdb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        }
      },
      "source": [
        "df"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>cc</th>\n",
              "      <th>date</th>\n",
              "      <th>evap</th>\n",
              "      <th>id</th>\n",
              "      <th>n</th>\n",
              "      <th>patm</th>\n",
              "      <th>pnmm</th>\n",
              "      <th>prec</th>\n",
              "      <th>request_status</th>\n",
              "      <th>tair</th>\n",
              "      <th>tmax</th>\n",
              "      <th>tmin</th>\n",
              "      <th>tw</th>\n",
              "      <th>ur</th>\n",
              "      <th>urmax</th>\n",
              "      <th>wd</th>\n",
              "      <th>ws</th>\n",
              "      <th>wsmax</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>-9999.00</td>\n",
              "      <td>2019-01-01T00:00:00Z</td>\n",
              "      <td>3.0</td>\n",
              "      <td>83632</td>\n",
              "      <td>-9999.0</td>\n",
              "      <td>-9999.0</td>\n",
              "      <td>-9999.0</td>\n",
              "      <td>-9999.0</td>\n",
              "      <td>Success: (200) OK</td>\n",
              "      <td>-9999.0</td>\n",
              "      <td>-9999.0</td>\n",
              "      <td>-9999.0</td>\n",
              "      <td>-9999.0</td>\n",
              "      <td>-9999.0</td>\n",
              "      <td>-9999</td>\n",
              "      <td>-9999</td>\n",
              "      <td>-9999.0</td>\n",
              "      <td>-9999.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>-9999.00</td>\n",
              "      <td>2019-01-01T12:00:00Z</td>\n",
              "      <td>-9999.0</td>\n",
              "      <td>83632</td>\n",
              "      <td>-9999.0</td>\n",
              "      <td>-9999.0</td>\n",
              "      <td>-9999.0</td>\n",
              "      <td>6.2</td>\n",
              "      <td>Success: (200) OK</td>\n",
              "      <td>-9999.0</td>\n",
              "      <td>-9999.0</td>\n",
              "      <td>-9999.0</td>\n",
              "      <td>-9999.0</td>\n",
              "      <td>-9999.0</td>\n",
              "      <td>-9999</td>\n",
              "      <td>-9999</td>\n",
              "      <td>-9999.0</td>\n",
              "      <td>-9999.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>-9999.00</td>\n",
              "      <td>2019-01-02T00:00:00Z</td>\n",
              "      <td>2.0</td>\n",
              "      <td>83632</td>\n",
              "      <td>-9999.0</td>\n",
              "      <td>-9999.0</td>\n",
              "      <td>-9999.0</td>\n",
              "      <td>-9999.0</td>\n",
              "      <td>Success: (200) OK</td>\n",
              "      <td>-9999.0</td>\n",
              "      <td>-9999.0</td>\n",
              "      <td>-9999.0</td>\n",
              "      <td>-9999.0</td>\n",
              "      <td>-9999.0</td>\n",
              "      <td>-9999</td>\n",
              "      <td>-9999</td>\n",
              "      <td>-9999.0</td>\n",
              "      <td>-9999.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>-9999.00</td>\n",
              "      <td>2019-01-02T12:00:00Z</td>\n",
              "      <td>-9999.0</td>\n",
              "      <td>83632</td>\n",
              "      <td>-9999.0</td>\n",
              "      <td>-9999.0</td>\n",
              "      <td>-9999.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Success: (200) OK</td>\n",
              "      <td>-9999.0</td>\n",
              "      <td>-9999.0</td>\n",
              "      <td>-9999.0</td>\n",
              "      <td>-9999.0</td>\n",
              "      <td>-9999.0</td>\n",
              "      <td>-9999</td>\n",
              "      <td>-9999</td>\n",
              "      <td>-9999.0</td>\n",
              "      <td>-9999.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>-9999.00</td>\n",
              "      <td>2019-01-03T00:00:00Z</td>\n",
              "      <td>3.5</td>\n",
              "      <td>83632</td>\n",
              "      <td>-9999.0</td>\n",
              "      <td>-9999.0</td>\n",
              "      <td>-9999.0</td>\n",
              "      <td>-9999.0</td>\n",
              "      <td>Success: (200) OK</td>\n",
              "      <td>-9999.0</td>\n",
              "      <td>-9999.0</td>\n",
              "      <td>-9999.0</td>\n",
              "      <td>-9999.0</td>\n",
              "      <td>-9999.0</td>\n",
              "      <td>-9999</td>\n",
              "      <td>-9999</td>\n",
              "      <td>-9999.0</td>\n",
              "      <td>-9999.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>503</th>\n",
              "      <td>2.50</td>\n",
              "      <td>2019-08-02T12:00:00Z</td>\n",
              "      <td>-9999.0</td>\n",
              "      <td>82562</td>\n",
              "      <td>-9999.0</td>\n",
              "      <td>1003.6</td>\n",
              "      <td>1014.3</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Success: (200) OK</td>\n",
              "      <td>28.3</td>\n",
              "      <td>-9999.0</td>\n",
              "      <td>23.0</td>\n",
              "      <td>-9999.0</td>\n",
              "      <td>-9999.0</td>\n",
              "      <td>77</td>\n",
              "      <td>36</td>\n",
              "      <td>-9999.0</td>\n",
              "      <td>-9999.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>504</th>\n",
              "      <td>3.75</td>\n",
              "      <td>2019-08-02T18:00:00Z</td>\n",
              "      <td>-9999.0</td>\n",
              "      <td>82562</td>\n",
              "      <td>-9999.0</td>\n",
              "      <td>1000.6</td>\n",
              "      <td>1011.3</td>\n",
              "      <td>-9999.0</td>\n",
              "      <td>Success: (200) OK</td>\n",
              "      <td>35.5</td>\n",
              "      <td>-9999.0</td>\n",
              "      <td>-9999.0</td>\n",
              "      <td>-9999.0</td>\n",
              "      <td>-9999.0</td>\n",
              "      <td>47</td>\n",
              "      <td>14</td>\n",
              "      <td>-9999.0</td>\n",
              "      <td>-9999.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>505</th>\n",
              "      <td>0.00</td>\n",
              "      <td>2019-08-03T00:00:00Z</td>\n",
              "      <td>-9999.0</td>\n",
              "      <td>82562</td>\n",
              "      <td>-9999.0</td>\n",
              "      <td>1002.9</td>\n",
              "      <td>1013.6</td>\n",
              "      <td>-9999.0</td>\n",
              "      <td>Success: (200) OK</td>\n",
              "      <td>30.3</td>\n",
              "      <td>-9999.0</td>\n",
              "      <td>-9999.0</td>\n",
              "      <td>-9999.0</td>\n",
              "      <td>-9999.0</td>\n",
              "      <td>66</td>\n",
              "      <td>9</td>\n",
              "      <td>-9999.0</td>\n",
              "      <td>-9999.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>506</th>\n",
              "      <td>-9999.00</td>\n",
              "      <td>2019-08-03T12:00:00Z</td>\n",
              "      <td>-9999.0</td>\n",
              "      <td>82562</td>\n",
              "      <td>-9999.0</td>\n",
              "      <td>-9999.0</td>\n",
              "      <td>-9999.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Success: (200) OK</td>\n",
              "      <td>-9999.0</td>\n",
              "      <td>-9999.0</td>\n",
              "      <td>-9999.0</td>\n",
              "      <td>-9999.0</td>\n",
              "      <td>-9999.0</td>\n",
              "      <td>-9999</td>\n",
              "      <td>-9999</td>\n",
              "      <td>-9999.0</td>\n",
              "      <td>-9999.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>507</th>\n",
              "      <td>-9999.00</td>\n",
              "      <td>2019-08-04T12:00:00Z</td>\n",
              "      <td>-9999.0</td>\n",
              "      <td>82562</td>\n",
              "      <td>-9999.0</td>\n",
              "      <td>-9999.0</td>\n",
              "      <td>-9999.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Success: (200) OK</td>\n",
              "      <td>-9999.0</td>\n",
              "      <td>-9999.0</td>\n",
              "      <td>-9999.0</td>\n",
              "      <td>-9999.0</td>\n",
              "      <td>-9999.0</td>\n",
              "      <td>-9999</td>\n",
              "      <td>-9999</td>\n",
              "      <td>-9999.0</td>\n",
              "      <td>-9999.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>117586 rows × 18 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "          cc                  date    evap     id  ...  urmax    wd      ws   wsmax\n",
              "0   -9999.00  2019-01-01T00:00:00Z     3.0  83632  ...  -9999 -9999 -9999.0 -9999.0\n",
              "1   -9999.00  2019-01-01T12:00:00Z -9999.0  83632  ...  -9999 -9999 -9999.0 -9999.0\n",
              "2   -9999.00  2019-01-02T00:00:00Z     2.0  83632  ...  -9999 -9999 -9999.0 -9999.0\n",
              "3   -9999.00  2019-01-02T12:00:00Z -9999.0  83632  ...  -9999 -9999 -9999.0 -9999.0\n",
              "4   -9999.00  2019-01-03T00:00:00Z     3.5  83632  ...  -9999 -9999 -9999.0 -9999.0\n",
              "..       ...                   ...     ...    ...  ...    ...   ...     ...     ...\n",
              "503     2.50  2019-08-02T12:00:00Z -9999.0  82562  ...     77    36 -9999.0 -9999.0\n",
              "504     3.75  2019-08-02T18:00:00Z -9999.0  82562  ...     47    14 -9999.0 -9999.0\n",
              "505     0.00  2019-08-03T00:00:00Z -9999.0  82562  ...     66     9 -9999.0 -9999.0\n",
              "506 -9999.00  2019-08-03T12:00:00Z -9999.0  82562  ...  -9999 -9999 -9999.0 -9999.0\n",
              "507 -9999.00  2019-08-04T12:00:00Z -9999.0  82562  ...  -9999 -9999 -9999.0 -9999.0\n",
              "\n",
              "[117586 rows x 18 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jXK1WGo9JUoi",
        "colab_type": "text"
      },
      "source": [
        "\n",
        "Vamos selecionar o período desejado para a análise: 01/Ago a 31/Ago"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gl8N4rvfxNpF",
        "colab_type": "code",
        "outputId": "e79f7415-9c4c-44c5-a98f-b3a7b096c4b4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 340
        }
      },
      "source": [
        "df.dtypes"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "cc                float64\n",
              "date               object\n",
              "evap              float64\n",
              "id                  int64\n",
              "n                 float64\n",
              "patm              float64\n",
              "pnmm              float64\n",
              "prec              float64\n",
              "request_status     object\n",
              "tair              float64\n",
              "tmax              float64\n",
              "tmin              float64\n",
              "tw                float64\n",
              "ur                float64\n",
              "urmax               int64\n",
              "wd                  int64\n",
              "ws                float64\n",
              "wsmax             float64\n",
              "dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QEHocujOxYz9",
        "colab_type": "code",
        "outputId": "88b17675-6ce3-4f56-8e5b-6ad3329fe4ec",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 359
        }
      },
      "source": [
        "df.head(10)"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>cc</th>\n",
              "      <th>date</th>\n",
              "      <th>evap</th>\n",
              "      <th>id</th>\n",
              "      <th>n</th>\n",
              "      <th>patm</th>\n",
              "      <th>pnmm</th>\n",
              "      <th>prec</th>\n",
              "      <th>request_status</th>\n",
              "      <th>tair</th>\n",
              "      <th>tmax</th>\n",
              "      <th>tmin</th>\n",
              "      <th>tw</th>\n",
              "      <th>ur</th>\n",
              "      <th>urmax</th>\n",
              "      <th>wd</th>\n",
              "      <th>ws</th>\n",
              "      <th>wsmax</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>-9999.0</td>\n",
              "      <td>2019-01-01T00:00:00Z</td>\n",
              "      <td>3.0</td>\n",
              "      <td>83632</td>\n",
              "      <td>-9999.0</td>\n",
              "      <td>-9999.0</td>\n",
              "      <td>-9999.0</td>\n",
              "      <td>-9999.0</td>\n",
              "      <td>Success: (200) OK</td>\n",
              "      <td>-9999.0</td>\n",
              "      <td>-9999.0</td>\n",
              "      <td>-9999.0</td>\n",
              "      <td>-9999.0</td>\n",
              "      <td>-9999.0</td>\n",
              "      <td>-9999</td>\n",
              "      <td>-9999</td>\n",
              "      <td>-9999.0</td>\n",
              "      <td>-9999.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>-9999.0</td>\n",
              "      <td>2019-01-01T12:00:00Z</td>\n",
              "      <td>-9999.0</td>\n",
              "      <td>83632</td>\n",
              "      <td>-9999.0</td>\n",
              "      <td>-9999.0</td>\n",
              "      <td>-9999.0</td>\n",
              "      <td>6.2</td>\n",
              "      <td>Success: (200) OK</td>\n",
              "      <td>-9999.0</td>\n",
              "      <td>-9999.0</td>\n",
              "      <td>-9999.0</td>\n",
              "      <td>-9999.0</td>\n",
              "      <td>-9999.0</td>\n",
              "      <td>-9999</td>\n",
              "      <td>-9999</td>\n",
              "      <td>-9999.0</td>\n",
              "      <td>-9999.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>-9999.0</td>\n",
              "      <td>2019-01-02T00:00:00Z</td>\n",
              "      <td>2.0</td>\n",
              "      <td>83632</td>\n",
              "      <td>-9999.0</td>\n",
              "      <td>-9999.0</td>\n",
              "      <td>-9999.0</td>\n",
              "      <td>-9999.0</td>\n",
              "      <td>Success: (200) OK</td>\n",
              "      <td>-9999.0</td>\n",
              "      <td>-9999.0</td>\n",
              "      <td>-9999.0</td>\n",
              "      <td>-9999.0</td>\n",
              "      <td>-9999.0</td>\n",
              "      <td>-9999</td>\n",
              "      <td>-9999</td>\n",
              "      <td>-9999.0</td>\n",
              "      <td>-9999.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>-9999.0</td>\n",
              "      <td>2019-01-02T12:00:00Z</td>\n",
              "      <td>-9999.0</td>\n",
              "      <td>83632</td>\n",
              "      <td>-9999.0</td>\n",
              "      <td>-9999.0</td>\n",
              "      <td>-9999.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Success: (200) OK</td>\n",
              "      <td>-9999.0</td>\n",
              "      <td>-9999.0</td>\n",
              "      <td>-9999.0</td>\n",
              "      <td>-9999.0</td>\n",
              "      <td>-9999.0</td>\n",
              "      <td>-9999</td>\n",
              "      <td>-9999</td>\n",
              "      <td>-9999.0</td>\n",
              "      <td>-9999.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>-9999.0</td>\n",
              "      <td>2019-01-03T00:00:00Z</td>\n",
              "      <td>3.5</td>\n",
              "      <td>83632</td>\n",
              "      <td>-9999.0</td>\n",
              "      <td>-9999.0</td>\n",
              "      <td>-9999.0</td>\n",
              "      <td>-9999.0</td>\n",
              "      <td>Success: (200) OK</td>\n",
              "      <td>-9999.0</td>\n",
              "      <td>-9999.0</td>\n",
              "      <td>-9999.0</td>\n",
              "      <td>-9999.0</td>\n",
              "      <td>-9999.0</td>\n",
              "      <td>-9999</td>\n",
              "      <td>-9999</td>\n",
              "      <td>-9999.0</td>\n",
              "      <td>-9999.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>-9999.0</td>\n",
              "      <td>2019-01-03T12:00:00Z</td>\n",
              "      <td>-9999.0</td>\n",
              "      <td>83632</td>\n",
              "      <td>-9999.0</td>\n",
              "      <td>-9999.0</td>\n",
              "      <td>-9999.0</td>\n",
              "      <td>9.4</td>\n",
              "      <td>Success: (200) OK</td>\n",
              "      <td>-9999.0</td>\n",
              "      <td>-9999.0</td>\n",
              "      <td>-9999.0</td>\n",
              "      <td>-9999.0</td>\n",
              "      <td>-9999.0</td>\n",
              "      <td>-9999</td>\n",
              "      <td>-9999</td>\n",
              "      <td>-9999.0</td>\n",
              "      <td>-9999.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>-9999.0</td>\n",
              "      <td>2019-01-04T00:00:00Z</td>\n",
              "      <td>2.7</td>\n",
              "      <td>83632</td>\n",
              "      <td>-9999.0</td>\n",
              "      <td>-9999.0</td>\n",
              "      <td>-9999.0</td>\n",
              "      <td>-9999.0</td>\n",
              "      <td>Success: (200) OK</td>\n",
              "      <td>-9999.0</td>\n",
              "      <td>-9999.0</td>\n",
              "      <td>-9999.0</td>\n",
              "      <td>-9999.0</td>\n",
              "      <td>-9999.0</td>\n",
              "      <td>-9999</td>\n",
              "      <td>-9999</td>\n",
              "      <td>-9999.0</td>\n",
              "      <td>-9999.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>-9999.0</td>\n",
              "      <td>2019-01-04T12:00:00Z</td>\n",
              "      <td>-9999.0</td>\n",
              "      <td>83632</td>\n",
              "      <td>-9999.0</td>\n",
              "      <td>-9999.0</td>\n",
              "      <td>-9999.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Success: (200) OK</td>\n",
              "      <td>-9999.0</td>\n",
              "      <td>-9999.0</td>\n",
              "      <td>-9999.0</td>\n",
              "      <td>-9999.0</td>\n",
              "      <td>-9999.0</td>\n",
              "      <td>-9999</td>\n",
              "      <td>-9999</td>\n",
              "      <td>-9999.0</td>\n",
              "      <td>-9999.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>-9999.0</td>\n",
              "      <td>2019-01-05T00:00:00Z</td>\n",
              "      <td>2.2</td>\n",
              "      <td>83632</td>\n",
              "      <td>-9999.0</td>\n",
              "      <td>-9999.0</td>\n",
              "      <td>-9999.0</td>\n",
              "      <td>-9999.0</td>\n",
              "      <td>Success: (200) OK</td>\n",
              "      <td>-9999.0</td>\n",
              "      <td>-9999.0</td>\n",
              "      <td>-9999.0</td>\n",
              "      <td>-9999.0</td>\n",
              "      <td>-9999.0</td>\n",
              "      <td>-9999</td>\n",
              "      <td>-9999</td>\n",
              "      <td>-9999.0</td>\n",
              "      <td>-9999.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>-9999.0</td>\n",
              "      <td>2019-01-05T12:00:00Z</td>\n",
              "      <td>-9999.0</td>\n",
              "      <td>83632</td>\n",
              "      <td>-9999.0</td>\n",
              "      <td>-9999.0</td>\n",
              "      <td>-9999.0</td>\n",
              "      <td>11.1</td>\n",
              "      <td>Success: (200) OK</td>\n",
              "      <td>-9999.0</td>\n",
              "      <td>-9999.0</td>\n",
              "      <td>-9999.0</td>\n",
              "      <td>-9999.0</td>\n",
              "      <td>-9999.0</td>\n",
              "      <td>-9999</td>\n",
              "      <td>-9999</td>\n",
              "      <td>-9999.0</td>\n",
              "      <td>-9999.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       cc                  date    evap     id  ...  urmax    wd      ws   wsmax\n",
              "0 -9999.0  2019-01-01T00:00:00Z     3.0  83632  ...  -9999 -9999 -9999.0 -9999.0\n",
              "1 -9999.0  2019-01-01T12:00:00Z -9999.0  83632  ...  -9999 -9999 -9999.0 -9999.0\n",
              "2 -9999.0  2019-01-02T00:00:00Z     2.0  83632  ...  -9999 -9999 -9999.0 -9999.0\n",
              "3 -9999.0  2019-01-02T12:00:00Z -9999.0  83632  ...  -9999 -9999 -9999.0 -9999.0\n",
              "4 -9999.0  2019-01-03T00:00:00Z     3.5  83632  ...  -9999 -9999 -9999.0 -9999.0\n",
              "5 -9999.0  2019-01-03T12:00:00Z -9999.0  83632  ...  -9999 -9999 -9999.0 -9999.0\n",
              "6 -9999.0  2019-01-04T00:00:00Z     2.7  83632  ...  -9999 -9999 -9999.0 -9999.0\n",
              "7 -9999.0  2019-01-04T12:00:00Z -9999.0  83632  ...  -9999 -9999 -9999.0 -9999.0\n",
              "8 -9999.0  2019-01-05T00:00:00Z     2.2  83632  ...  -9999 -9999 -9999.0 -9999.0\n",
              "9 -9999.0  2019-01-05T12:00:00Z -9999.0  83632  ...  -9999 -9999 -9999.0 -9999.0\n",
              "\n",
              "[10 rows x 18 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "h_lRCbNFzkLi",
        "colab_type": "code",
        "outputId": "a3d689d8-5ad2-44d2-f391-35b98c9c5815",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 340
        }
      },
      "source": [
        "df.dtypes"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "cc                float64\n",
              "date               object\n",
              "evap              float64\n",
              "id                  int64\n",
              "n                 float64\n",
              "patm              float64\n",
              "pnmm              float64\n",
              "prec              float64\n",
              "request_status     object\n",
              "tair              float64\n",
              "tmax              float64\n",
              "tmin              float64\n",
              "tw                float64\n",
              "ur                float64\n",
              "urmax               int64\n",
              "wd                  int64\n",
              "ws                float64\n",
              "wsmax             float64\n",
              "dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1gaH8SiPxMas",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df['date'] = pd.to_datetime(df['date'])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Gi3JD8dtLmJB",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "start_date = pd.to_datetime('01/08/2019 00:00:00', dayfirst=True, utc=True)\n",
        "end_date = pd.to_datetime('31/08/2019  00:00:00', dayfirst=True, utc=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6kPq6uKay480",
        "colab_type": "code",
        "outputId": "a6458e71-ee65-4ebb-f65b-d5121166dd1a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 221
        }
      },
      "source": [
        "df['date'][0][0]"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0   2019-01-01 00:00:00+00:00\n",
              "0   2019-01-01 00:00:00+00:00\n",
              "0   2019-01-01 00:00:00+00:00\n",
              "0   2019-01-01 00:00:00+00:00\n",
              "0   2019-01-01 00:00:00+00:00\n",
              "               ...           \n",
              "0   2019-01-01 00:00:00+00:00\n",
              "0   2019-01-02 12:00:00+00:00\n",
              "0   2019-01-01 00:00:00+00:00\n",
              "0   2019-01-01 00:00:00+00:00\n",
              "0   2019-01-01 00:00:00+00:00\n",
              "Name: date, Length: 195, dtype: datetime64[ns, UTC]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PyFM2AjIKO6F",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "mask = (df['date'] > start_date) & (df['date'] < end_date)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XVQRYNoJKyJf",
        "colab_type": "code",
        "outputId": "efcd96fc-5dab-49cd-eca7-3d5c43e10f3f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "df['date'].max()"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Timestamp('2019-08-31 18:00:00+0000', tz='UTC')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tpormI1CzcOe",
        "colab_type": "code",
        "outputId": "6f9b0036-c61d-4c30-afb1-b7d36bbddad0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "df['date'].min()"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Timestamp('2019-01-01 00:00:00+0000', tz='UTC')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1dim6ENCzo92",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df = df.loc[mask]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mBUUAfWwztph",
        "colab_type": "code",
        "outputId": "7549ad24-b4ea-4bd4-e19e-252655674f1e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "df['date'].min()"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Timestamp('2019-08-01 12:00:00+0000', tz='UTC')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jmjgmbUDz1w3",
        "colab_type": "code",
        "outputId": "f617f67d-ee6e-4103-ddef-ea0ac024ff53",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 221
        }
      },
      "source": [
        "df['date']"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "303   2019-08-01 12:00:00+00:00\n",
              "304   2019-08-02 00:00:00+00:00\n",
              "305   2019-08-02 12:00:00+00:00\n",
              "306   2019-08-03 00:00:00+00:00\n",
              "307   2019-08-03 12:00:00+00:00\n",
              "                 ...           \n",
              "503   2019-08-02 12:00:00+00:00\n",
              "504   2019-08-02 18:00:00+00:00\n",
              "505   2019-08-03 00:00:00+00:00\n",
              "506   2019-08-03 12:00:00+00:00\n",
              "507   2019-08-04 12:00:00+00:00\n",
              "Name: date, Length: 14103, dtype: datetime64[ns, UTC]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Y_Gzhg9H_TNN",
        "colab_type": "text"
      },
      "source": [
        "Podemos salvar o csv no git da forma comentada abaixo, porém iremos upar manualmente"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Qb-TiyEF1AwC",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "## !apt install git"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vcQXymmW1_4q",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "## !git clone https://github.com/vitorsr/ccd.git"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aEaUDXnt_VTO",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df.to_csv('inmetr_201908.csv')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eLIgbRhrRlZm",
        "colab_type": "text"
      },
      "source": [
        "### Sobre a nuvem preta de SP"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MgRJpOB0Rowy",
        "colab_type": "text"
      },
      "source": [
        ">Segundo o documento, a passagem da frente fria fez o vento de camadas mais altas da atmosfera (entre mil metros e 5 mil metros de altitude) mudar de direção. Com isso, a fumaça acabou “direcionada para o estado de São Paulo, mas também para a região sul de Mato Grosso, Mato Grosso do Sul e Paraná”. O texto destaca, ainda, que o satélite Terra/MODIS, operado pela Nasa, detectou que uma grande quantidade de fumaça vinda da Bolívia e de Rondônia se encaminhava ao sul do Brasil no dia 17 de agosto. A fumaça, antes concentrada do sul do país, teria ganhado Paraná e Mato Grosso do Sul e alcançado São Paulo no dia 19 de agosto – justamente quando a tarde ficou escura na capital paulistana.<\n",
        "\n",
        ">Segundo o documento, a passagem da frente fria fez o vento de camadas mais altas da atmosfera (entre mil metros e 5 mil metros de altitude) mudar de direção. Com isso, a fumaça acabou “direcionada para o estado de São Paulo, mas também para a região sul de Mato Grosso, Mato Grosso do Sul e Paraná”. O texto destaca, ainda, que o satélite Terra/MODIS, operado pela Nasa, detectou que uma grande quantidade de fumaça vinda da Bolívia e de Rondônia se encaminhava ao sul do Brasil no dia 17 de agosto. A fumaça, antes concentrada do sul do país, teria ganhado Paraná e Mato Grosso do Sul e alcançado São Paulo no dia 19 de agosto – justamente quando a tarde ficou escura na capital paulistana.\n",
        "\n",
        ">Para notar a tal frente fria, bastava olhar os termômetros. Segundo o Inmet (Instituto Nacional de Meteorologia), na tarde do domingo (18), a temperatura às 15h era de 28,4°C. No mesmo horário do dia seguinte (19), registrou-se 15,3°C – um declínio de 13°C. Isso deixou o dia encoberto e com garoa em diferentes cidades do estado de São Paulo já no começo da segunda-feira. \n",
        "\n",
        ">“A sensação de escuridão foi por conta da formação de uma nuvem bastante baixa e densa, provavelmente uma nuvem Nimbustratus ou Cumulonimbus, que se formou na região de São Paulo, muito próxima a superfície. É normal acontecer, se tem o avanço da frente fria, dependendo das condições de umidade, pode se formar essas nuvens”, explicou o meteorologista do CPTEC, Maicon Veber.\n",
        "\n",
        ">“As partículas [de fumaça] funcionam como núcleo de condensação da água. Assim, gotículas de chuva menores são formadas, mas em grande quantidade, e isso faz com que uma maior parte da radiação solar seja refletida de volta para o espaço, a ponto de escurecer o solo”, explicou. [fonte](https://www.oeco.org.br/noticias/pesquisadores-descrevem-fenomeno-que-escureceu-sp-e-sua-relacao-com-as-queimadas-na-amazonia/) "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iGkV20FcJJaW",
        "colab_type": "code",
        "outputId": "488ae3da-ad86-49cf-ef20-c7cbb1911584",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "df['id'].nunique()"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "180"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ITUWS3SNK_Zh",
        "colab_type": "text"
      },
      "source": [
        "# Baseline"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CkPGob3rTJ9x",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df_m = pd.read_csv(\"bdmep_meta.csv\")\n",
        "df = pd.read_csv(\"inmetr.csv\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1o1tl3jeLBJb",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "7f79cd2e-fb26-4cf9-95ef-aece93a45fb5"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>date</th>\n",
              "      <th>id</th>\n",
              "      <th>prec</th>\n",
              "      <th>tair</th>\n",
              "      <th>tw</th>\n",
              "      <th>tmax</th>\n",
              "      <th>tmin</th>\n",
              "      <th>urmax</th>\n",
              "      <th>patm</th>\n",
              "      <th>pnmm</th>\n",
              "      <th>wd</th>\n",
              "      <th>wsmax</th>\n",
              "      <th>n</th>\n",
              "      <th>cc</th>\n",
              "      <th>evap</th>\n",
              "      <th>ur</th>\n",
              "      <th>ws</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1970-05-04 00:00:00</td>\n",
              "      <td>83010</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>32.6</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",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>89.75</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1970-05-04 12:00:00</td>\n",
              "      <td>83010</td>\n",
              "      <td>NaN</td>\n",
              "      <td>25.3</td>\n",
              "      <td>24.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>23.7</td>\n",
              "      <td>90.0</td>\n",
              "      <td>1005.9</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>5.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1970-05-04 18:00:00</td>\n",
              "      <td>83010</td>\n",
              "      <td>NaN</td>\n",
              "      <td>29.2</td>\n",
              "      <td>27.1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>85.0</td>\n",
              "      <td>1004.2</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1970-05-05 00:00:00</td>\n",
              "      <td>83010</td>\n",
              "      <td>NaN</td>\n",
              "      <td>25.0</td>\n",
              "      <td>27.0</td>\n",
              "      <td>32.6</td>\n",
              "      <td>NaN</td>\n",
              "      <td>92.0</td>\n",
              "      <td>1007.5</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>8.0</td>\n",
              "      <td>2.4</td>\n",
              "      <td>88.25</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1970-05-05 12:00:00</td>\n",
              "      <td>83010</td>\n",
              "      <td>0.0</td>\n",
              "      <td>25.9</td>\n",
              "      <td>24.8</td>\n",
              "      <td>NaN</td>\n",
              "      <td>23.0</td>\n",
              "      <td>91.0</td>\n",
              "      <td>1006.4</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                  date     id  prec  tair    tw  ...   n   cc  evap     ur  ws\n",
              "0  1970-05-04 00:00:00  83010   NaN   NaN   NaN  ... NaN  NaN   NaN  89.75 NaN\n",
              "1  1970-05-04 12:00:00  83010   NaN  25.3  24.0  ... NaN  5.0   NaN    NaN NaN\n",
              "2  1970-05-04 18:00:00  83010   NaN  29.2  27.1  ... NaN  4.0   NaN    NaN NaN\n",
              "3  1970-05-05 00:00:00  83010   NaN  25.0  27.0  ... NaN  8.0   2.4  88.25 NaN\n",
              "4  1970-05-05 12:00:00  83010   0.0  25.9  24.8  ... NaN  4.0   NaN    NaN NaN\n",
              "\n",
              "[5 rows x 17 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zmabJDd6LIWJ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "49cb4b7c-2821-40fd-be17-384b6056a0a8"
      },
      "source": [
        "df_m.head()"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>lon</th>\n",
              "      <th>lat</th>\n",
              "      <th>alt</th>\n",
              "      <th>name</th>\n",
              "      <th>state</th>\n",
              "      <th>uf</th>\n",
              "      <th>time_zone</th>\n",
              "      <th>offset_utc</th>\n",
              "      <th>time_zone.1</th>\n",
              "      <th>offset_utc.1</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>83010</td>\n",
              "      <td>-68.733333</td>\n",
              "      <td>-11.016667</td>\n",
              "      <td>260.00</td>\n",
              "      <td>Brasiléia</td>\n",
              "      <td>Acre</td>\n",
              "      <td>AC</td>\n",
              "      <td>America/Rio_Branco</td>\n",
              "      <td>-5</td>\n",
              "      <td>America/Rio_Branco</td>\n",
              "      <td>-5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>82704</td>\n",
              "      <td>-72.666667</td>\n",
              "      <td>-7.633333</td>\n",
              "      <td>170.00</td>\n",
              "      <td>Cruzeiro do Sul</td>\n",
              "      <td>Acre</td>\n",
              "      <td>AC</td>\n",
              "      <td>America/Rio_Branco</td>\n",
              "      <td>-5</td>\n",
              "      <td>America/Rio_Branco</td>\n",
              "      <td>-5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>82915</td>\n",
              "      <td>-67.800000</td>\n",
              "      <td>-9.966667</td>\n",
              "      <td>160.00</td>\n",
              "      <td>Rio Branco</td>\n",
              "      <td>Acre</td>\n",
              "      <td>AC</td>\n",
              "      <td>America/Rio_Branco</td>\n",
              "      <td>-5</td>\n",
              "      <td>America/Rio_Branco</td>\n",
              "      <td>-5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>82807</td>\n",
              "      <td>-70.766667</td>\n",
              "      <td>-8.166667</td>\n",
              "      <td>190.00</td>\n",
              "      <td>Tarauacá</td>\n",
              "      <td>Acre</td>\n",
              "      <td>AC</td>\n",
              "      <td>America/Rio_Branco</td>\n",
              "      <td>-5</td>\n",
              "      <td>America/Rio_Branco</td>\n",
              "      <td>-5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>83098</td>\n",
              "      <td>-36.166667</td>\n",
              "      <td>-10.150000</td>\n",
              "      <td>56.13</td>\n",
              "      <td>Coruripe</td>\n",
              "      <td>Alagoas</td>\n",
              "      <td>AL</td>\n",
              "      <td>America/Maceio</td>\n",
              "      <td>-3</td>\n",
              "      <td>America/Maceio</td>\n",
              "      <td>-3</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      id        lon        lat  ...  offset_utc         time_zone.1 offset_utc.1\n",
              "0  83010 -68.733333 -11.016667  ...          -5  America/Rio_Branco           -5\n",
              "1  82704 -72.666667  -7.633333  ...          -5  America/Rio_Branco           -5\n",
              "2  82915 -67.800000  -9.966667  ...          -5  America/Rio_Branco           -5\n",
              "3  82807 -70.766667  -8.166667  ...          -5  America/Rio_Branco           -5\n",
              "4  83098 -36.166667 -10.150000  ...          -3      America/Maceio           -3\n",
              "\n",
              "[5 rows x 11 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nXLGk6vhQY2r",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df_m= df_m.drop(['lat','lon','alt','time_zone','offset_utc','time_zone.1','offset_utc.1','uf'],axis=1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4UHnHcG-LLYN",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "74433fe8-08f8-48f8-83ea-bcbf2be03475"
      },
      "source": [
        "df = pd.merge(df,df_m,on='id')\n",
        "df.head()"
      ],
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>date</th>\n",
              "      <th>id</th>\n",
              "      <th>prec</th>\n",
              "      <th>tair</th>\n",
              "      <th>tw</th>\n",
              "      <th>tmax</th>\n",
              "      <th>tmin</th>\n",
              "      <th>urmax</th>\n",
              "      <th>patm</th>\n",
              "      <th>pnmm</th>\n",
              "      <th>wd</th>\n",
              "      <th>wsmax</th>\n",
              "      <th>n</th>\n",
              "      <th>cc</th>\n",
              "      <th>evap</th>\n",
              "      <th>ur</th>\n",
              "      <th>ws</th>\n",
              "      <th>name</th>\n",
              "      <th>state</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1970-05-04 00:00:00</td>\n",
              "      <td>83010</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>32.6</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",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>89.75</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Brasiléia</td>\n",
              "      <td>Acre</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1970-05-04 12:00:00</td>\n",
              "      <td>83010</td>\n",
              "      <td>NaN</td>\n",
              "      <td>25.3</td>\n",
              "      <td>24.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>23.7</td>\n",
              "      <td>90.0</td>\n",
              "      <td>1005.9</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>5.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Brasiléia</td>\n",
              "      <td>Acre</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1970-05-04 18:00:00</td>\n",
              "      <td>83010</td>\n",
              "      <td>NaN</td>\n",
              "      <td>29.2</td>\n",
              "      <td>27.1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>85.0</td>\n",
              "      <td>1004.2</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Brasiléia</td>\n",
              "      <td>Acre</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1970-05-05 00:00:00</td>\n",
              "      <td>83010</td>\n",
              "      <td>NaN</td>\n",
              "      <td>25.0</td>\n",
              "      <td>27.0</td>\n",
              "      <td>32.6</td>\n",
              "      <td>NaN</td>\n",
              "      <td>92.0</td>\n",
              "      <td>1007.5</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>8.0</td>\n",
              "      <td>2.4</td>\n",
              "      <td>88.25</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Brasiléia</td>\n",
              "      <td>Acre</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1970-05-05 12:00:00</td>\n",
              "      <td>83010</td>\n",
              "      <td>0.0</td>\n",
              "      <td>25.9</td>\n",
              "      <td>24.8</td>\n",
              "      <td>NaN</td>\n",
              "      <td>23.0</td>\n",
              "      <td>91.0</td>\n",
              "      <td>1006.4</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Brasiléia</td>\n",
              "      <td>Acre</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                  date     id  prec  tair  ...     ur  ws       name  state\n",
              "0  1970-05-04 00:00:00  83010   NaN   NaN  ...  89.75 NaN  Brasiléia   Acre\n",
              "1  1970-05-04 12:00:00  83010   NaN  25.3  ...    NaN NaN  Brasiléia   Acre\n",
              "2  1970-05-04 18:00:00  83010   NaN  29.2  ...    NaN NaN  Brasiléia   Acre\n",
              "3  1970-05-05 00:00:00  83010   NaN  25.0  ...  88.25 NaN  Brasiléia   Acre\n",
              "4  1970-05-05 12:00:00  83010   0.0  25.9  ...    NaN NaN  Brasiléia   Acre\n",
              "\n",
              "[5 rows x 19 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aUmgmXoPLVSe",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IHKVYLH5LWcU",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ca-NPl4OJMMZ",
        "colab_type": "text"
      },
      "source": [
        "# Prophet\n",
        "## Quick Start\n",
        "\n",
        "Source: https://facebook.github.io/prophet/docs/quick_start.html"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "g1hwNGOEHs4d",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pandas as pd\n",
        "from fbprophet import Prophet"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fhIpiZ2tIcUJ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "92d9f8a5-5615-41cd-917d-b9b67ba73169"
      },
      "source": [
        "df = pd.read_csv(\"https://raw.githubusercontent.com/facebook/prophet/master/examples/example_wp_log_peyton_manning.csv\")\n",
        "df.head()"
      ],
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>ds</th>\n",
              "      <th>y</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2007-12-10</td>\n",
              "      <td>9.590761</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2007-12-11</td>\n",
              "      <td>8.519590</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2007-12-12</td>\n",
              "      <td>8.183677</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2007-12-13</td>\n",
              "      <td>8.072467</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2007-12-14</td>\n",
              "      <td>7.893572</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "           ds         y\n",
              "0  2007-12-10  9.590761\n",
              "1  2007-12-11  8.519590\n",
              "2  2007-12-12  8.183677\n",
              "3  2007-12-13  8.072467\n",
              "4  2007-12-14  7.893572"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "b89ThyctJFmZ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        },
        "outputId": "1eef087f-71df-4aab-a5c6-aad5ebc8f9a8"
      },
      "source": [
        "m = Prophet()\n",
        "m.fit(df)"
      ],
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "INFO:numexpr.utils:NumExpr defaulting to 2 threads.\n",
            "INFO:fbprophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<fbprophet.forecaster.Prophet at 0x7faf9a947dd8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WfZpFYriJWWx",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "c8ed2d9c-bbf5-4cf1-b880-ba56359ac343"
      },
      "source": [
        "future = m.make_future_dataframe(periods=365)\n",
        "future.tail()"
      ],
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>ds</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>3265</th>\n",
              "      <td>2017-01-15</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3266</th>\n",
              "      <td>2017-01-16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3267</th>\n",
              "      <td>2017-01-17</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3268</th>\n",
              "      <td>2017-01-18</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3269</th>\n",
              "      <td>2017-01-19</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "             ds\n",
              "3265 2017-01-15\n",
              "3266 2017-01-16\n",
              "3267 2017-01-17\n",
              "3268 2017-01-18\n",
              "3269 2017-01-19"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IE6S-F3iJXZI",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "9db0e8e7-8c19-46fe-8ec4-11f73a415282"
      },
      "source": [
        "forecast = m.predict(future)\n",
        "forecast[['ds', 'yhat', 'yhat_lower', 'yhat_upper']].tail()"
      ],
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>ds</th>\n",
              "      <th>yhat</th>\n",
              "      <th>yhat_lower</th>\n",
              "      <th>yhat_upper</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>3265</th>\n",
              "      <td>2017-01-15</td>\n",
              "      <td>8.199274</td>\n",
              "      <td>7.483214</td>\n",
              "      <td>8.882697</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3266</th>\n",
              "      <td>2017-01-16</td>\n",
              "      <td>8.524244</td>\n",
              "      <td>7.810651</td>\n",
              "      <td>9.213301</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3267</th>\n",
              "      <td>2017-01-17</td>\n",
              "      <td>8.311615</td>\n",
              "      <td>7.601762</td>\n",
              "      <td>9.000959</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3268</th>\n",
              "      <td>2017-01-18</td>\n",
              "      <td>8.144232</td>\n",
              "      <td>7.414405</td>\n",
              "      <td>8.857569</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3269</th>\n",
              "      <td>2017-01-19</td>\n",
              "      <td>8.156091</td>\n",
              "      <td>7.442962</td>\n",
              "      <td>8.847375</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "             ds      yhat  yhat_lower  yhat_upper\n",
              "3265 2017-01-15  8.199274    7.483214    8.882697\n",
              "3266 2017-01-16  8.524244    7.810651    9.213301\n",
              "3267 2017-01-17  8.311615    7.601762    9.000959\n",
              "3268 2017-01-18  8.144232    7.414405    8.857569\n",
              "3269 2017-01-19  8.156091    7.442962    8.847375"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UXzhagfOJZUL",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 441
        },
        "outputId": "ce2d61d2-fff4-479f-fa9d-4751c527f918"
      },
      "source": [
        "fig1 = m.plot(forecast)"
      ],
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAsgAAAGoCAYAAABbtxOxAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjEsIGh0\ndHA6Ly9tYXRwbG90bGliLm9yZy8QZhcZAAAgAElEQVR4nOzdeXxU5b348c+ZyQzgLliqiGKLC5A9\nmSwDiLEotFVpEVtcWrVo6WZ/tb3XXm3rvS5XsbZXsdjbXtxRRFRAqRVcwCiECdnYcWlV6sIOAiGQ\nOWfOeX5/nDmzJJMASSY5Q77vvizJJJlzzjNzznzP83yf76MppRRCCCGEEEIIADw9vQNCCCGEEEK4\niQTIQgghhBBCJJAAWQghhBBCiAQSIAshhBBCCJFAAmQhhBBCCCESZPX0DhyOU045hbPOOqundwPD\nMPD5fD29G64gbWGTdrBJO9ikHeKkLWzSDjZphzhpC5tb2mHTpk3s3Lmz1eMZESCfddZZ1NXV9fRu\nsHnzZgYNGtTTu+EK0hY2aQebtINN2iFO2sIm7WCTdoiTtrC5pR0CgUDKxyXFQgghhBBCiAQSIAsh\nhBBCCJFAAmQhhBBCCCESSIAshBBCCCFEAgmQhRBCCCGESJC2AHnKlCkMHDiQnJyc2GO33347eXl5\nFBQUMG7cODZv3pyuzQshhBBCCNEhaQuQr7/+ehYvXpz02C233MLatWtZvXo1l156KXfddVe6Ni+E\nEEIIIUSHpC1AHjNmDP3790967IQTToh93dTUhKZp6dq8EEIIIYQQHdLtC4X89re/ZdasWZx44om8\n9dZb3b15IYQQQggh2tXtAfI999zDPffcw7Rp03j44Ye58847U/7ezJkzmTlzJgBbt251Rb7yjh07\nenoXXEPawibtYJN2sEk7xElb2KQdbNIOcdIWNre3Q48tNX3NNdfwzW9+s80AeerUqUydOhWwlwF0\nw3KEgGv2ww2kLWzSDjZpB5u0Q5y0hU3awSbtECdtYXNzO3Rrmbd//OMfsa9ffvllhg0b1p2bF0II\nIYQQ4pDS1oN81VVXUVlZyc6dOxk8eDB33nknr776Ku+//z4ej4chQ4bw17/+NV2bF0IIIYQQokPS\nFiDPmTOn1WM33HBDujYnhBBCCCFEl5CV9IQQQohuFAqFmDZtGqFQqKd3RQjRhh6bpCeEEEL0NqFQ\niLFjx6LrOn6/nyVLlhAMBnt6t4QQLUgPshBCCNFNKisr0XUd0zTRdZ3Kysqe3iUhRAoSIAshhBDd\npKKiAr/fj9frxe/3U1FR0dO7JIRIQVIshBBCiG4SDAZZsmQJlZWVVFRUSHqFEC4lAbIQQgjRjYLB\noATGQricpFgIIYQQQgiRQAJkIYQQQgghEkiALIQQaSZ1b4UQIrNIDrIQQqSR1L0VQojMIz3IQgiR\nRlL3VgghMo8EyEIIkUZS91YIITKPpFgIIUQaSd1bIYTIPBIgCyFEmkndWyGEyCySYiGEEEIIIUQC\nCZCFEEIIIYRIIAGyEEIIIYQQCSRAFkIIIYQQIoEEyEIIIYQQQiSQAFkIIYQQQogEEiALIYQQQgiR\nQAJkIYQQQgghEkiALIQQQgghRAIJkIUQQgghhEggAbIQQgghhBAJJEAWQgghhBAigQTIQgghhBBC\nJJAAWQghhBBCiAQSIAshhBBCCJFAAmQhhBBCCCESSIAshBBCCCFEAgmQhRBCCCG6UCgUYtq0aYRC\noZ7eFdFBWT29A0IIIYQQR4tQKMTYsWPRdR2/38+SJUsIBoM9vVviCEkPshBCCCFEF6msrETXdUzT\nRNd1Kisre3qXRAdIgNxJMowihBBCCEdFRQV+vx+v14vf76eioqKnd0l0gKRYdIIMowghhBAiUTAY\nZMmSJVRWVlJRUSFxQYaSALkTUg2jyIkghBBC9G7BYFDigQyXthSLKVOmMHDgQHJycmKP3XLLLQwb\nNoy8vDwmTpzInj170rX5biHDKEIIIYQQR5+0BcjXX389ixcvTnrs4osvZv369axdu5Zzzz2XadOm\npWvz3cIZRrn77rslvUIIIYQQ4iiRthSLMWPGsGnTpqTHxo0bF/u6vLycF198MV2b7zYyjCKEEOJw\nhEIhyUsVIkP0WA7y448/zuTJk9v8+cyZM5k5cyYAW7duZfPmzd21a23asWNHT++Ca0hb2KQdbNIO\nNmmHOGkLm9MOdXV1TJ48GcMw8Pl8zJ07l0Ag0MN7133k/RAnbWFzezv0SIB8zz33kJWVxTXXXNPm\n70ydOpWpU6cCEAgEGDRoUHftXrvcsh9uIG1hk3awSTvYpB3ipC1sgwYNYsOGDRiGgWmaAGzYsIEJ\nEyb08J51L3k/xElb2NzcDt0eID/55JO88sorLFmyBE3TunvzQgghRLdzJnU7ZUFlUrcQ7tatAfLi\nxYu5//77efvttznmmGO6c9NCCCFEj5HauEJklrQFyFdddRWVlZXs3LmTwYMHc+eddzJt2jTC4TAX\nX3wxYE/U++tf/5quXRBCCCFcQyZ1C5E50hYgz5kzp9VjN9xwQ7o2J4QQQgghRJdIWx1kIYQQQggh\nMpEEyEIIIYQQQiSQAFkIIYQQQogEEiALIYQQ3SgUCjFt2jRCoVBP74oQog09tpKeEEII0duEQiHG\njh0bq4e8ZMkSqWwhhAtJD7IQQgjRTSorK9F1HdM00XWdysrKnt4lIUQKEiALIYQQ3cRZUc/r9cqK\nekK4mKRYCCGEEN1EVtQTIjNIgCyEED0gFApJkNRLyYp6QrifBMhCCNHNZKKWEEK4m+QgCyFEN3PT\nRC0pOSaEEK1JD7IQQnQzZ6KW04PcUxO1pCdbCCFSkx5kIYRIg/Z6Zp2JWnfffXePBqVu6skWQgg3\nkR5kIYToYofTM+uGiVpu6ckWQgi3kR5kIYToYpnSM+uWnmwhjjaS25/5pAdZCCG6WCb1zKazJ1tK\n2cVJW/Qektt/dJAAWYguIh+AwiGLQUBdXR1XXnmlBAlIwNTbpBpBktc780iALEQXkA9A0ZIbcox7\nUigUkiAhSgKm3iWTRpBE2yQHWYgukCk5pw7JjxPpFgwG8fv9eL3eXh8kOAGTtEXvILn9RwfpQRai\nC2RSj4H0dovuEAgEen2aiUNSbnqf3j6CdDSQAFmILpBJH4Ay3Cu6iwQJcdIWQmQWCZCF6CKZ8gGY\nSb3dRzuZ2CmEEO4kAbIQvUwm9XYfzSTVRQgh3EsCZCF6oUzp7T6aSaqLEEK4l1SxEEKIHiCVDXoP\nqRojROaRHmQhhOgBkurSOxxtC6ZI3rzoLSRAFkKIHiKpLke/o2nBFMmbF72JpFgIIYQQaXI0LZiS\naQsiCdEZ0oMshBBCpMnRtGCKlIgUvYkEyEKkgeTpCSEcR0sqjeTNi95EAmQhupjk6YkjJTdUIlMc\nLcG+EIciAbIQXUzq24ojITdUQgjhPjJJT4guJvVtxZGQiU9CCOE+0oMsRBeTPD1xJGTikxBCuI8E\nyEKkgeTpiUTt5RgHg0GmT5/OvHnzmDRpkrxvhBDCBSRAFkKINDpUjnEoFOLmm29G13WWLVtGbm6u\nBMkuJBMphehd0paDPGXKFAYOHEhOTk7ssRdeeIHs7Gw8Hg91dXXp2rQQQrjGoXKMJQfZ/ZybnNtv\nv52xY8cSCoV6epeEEGmWtgD5+uuvZ/HixUmP5eTkMH/+fMaMGZOuzQrRI0KhENOmTZMPTtHKoSZt\nyqRO95ObGCF6n7SlWIwZM4ZNmzYlPTZ8+PB0bU6IHiNlukR7DjVpUyZ1up9MpBSi95EcZCE6Seoe\ni0M51KRNmdTpbnITI0Tv49oAeebMmcycOROArVu3snnz5h7eI9ixY0dP74JrSFvYduzYQXZ2Nj6f\nDwCfz0d2drYr3q/dSd4PNmmHuKOtLYYMGcJ1110HcETn99HWDh0l7RAnbWFzezu4NkCeOnUqU6dO\nBSAQCDBo0KAe3iObW/bDDaQtbPn5+SxdurTX9y7J+8GWqh16awUEeU/YpB1s0g5x0hY2N7eDawNk\nt+utH3giNRkiF22RHHUhhMg8aaticdVVVxEMBnn//fcZPHgwjz32GAsWLGDw4MGEQiEuueQSxo8f\nn67Np5WU/BGiNankkZpUQMgM8v4VQiRKWw/ynDlzUj4+ceLEdG2y28ikLCGS1dXVceWVV0ovaQpS\nAcH9OtrLLyOJQhy9JMWiA+QDT4hkoVBIbhrbkMkVEHpLANiRTg9JnRHi6CYBcgdk8geeEOkQDAbl\nprEN+8MRzhhewG0Zdp3oTQFgy06PAQMGMG3atHav7zKSKMTRTQLkDpJJWULEBQIBuWlM4ZMvDrBj\nv45CMfikfj29O0ekNwWAiZ0eAwYM4Oabbz7kjYGMJApxdJMAWQjRJeSmsbVtjWE8moZS7f+eG1MZ\nelsA6Lx/p02bdlg3Bp0ZSXTj6y2ESCYBshBCpIkTF1vtBMhuTWXoralkR3Jj0JGbQre+3kKIZBIg\nCyFEmigFaO3/jptTGXrjqEC6bwzc/HoLIeIkQBZCiDSxohFyeykWvS2VIROk88YgE15vSQERQgJk\nIYRICz1ioRQoVDRQbs0JRKZPn86uXbskIDlKtQw43Zy6ks4UEAm8RSaRAFkIIdJg3ZZ9KIgGya1J\nLqp7dWUg19YiOm59rdOVAiKLCYlMk7alpnsTWaJUCNGS02uc+P+JZAlqd3JuXG6//XbGjh3b6et6\nqkV03MxJAfF6vV2aApJp7SCE9CB3kvQCCSFSsStXKJTSUlaxyIRc1N6oq3tQM20RnXSlgGRaOwgh\nAXInyYxkIURbnNTjVCnIbstFlfxQW1ffuGTiIjrpSAHJxHYQvZsEyJ0kvUBCiJZCoRCzFrxKfuko\nCgJlKBRfHNA5+Rh/0u+5JRdVRsLi0nHj4pbXuadJO4hMIgFyJ7mtF0h0HelREx3hBJvhsI7P7+NP\ns1/ivLxiPtzVRKBFgOwW6RoJy9RzKF2BXKa2hxC9kQTIXUDuio8+0qMmOsoJNi3LxDCgIbScc3KL\nDrncdE9Kx0iYVC1IJtcUITKLVLEQIgWpMCA6ygk2PV4vPp+PovLRdrm3nt6xdjgjYXfffXeXBW5S\ntSCZXFOEyCzSgyxECpJbLjoqr6iEPz/7EqHlyygoG0l2UQnNhunqHmTo+pEwqVqQTK4pQmQWCZCF\nSKGzueWSa9h7mUqRU1TCV3OKQAN7Lb3UlSyOZlK1IJnMVxEis0iALEQbOtqjJrmGvZuGFkup0FR0\nJT1lLxyilELTtJ7exSPW0Rs+mZ+RzGkPZ3EpCZSFcC8JkIXoYlIbWzgBsUezFwlxew5ye3rbDV+6\nR396W3sKkalkkp4QXSxdS7WKzOH0GoOdYKGUHSJnYppFb5pc1pFlpp3e4MNdkro3tacQmUx6kIXo\nYpJr2Ltpmt17bCnwqHhQnGq56UzQmyaXHenoT0d6gwcMGIDH40EpddS3pxCZTAJkIdJAci97N4XC\nUgrQOBix8KDZvco9vWMd0FU3fJalOGiYHNvHvR87R3oz0JGA+uabb8Y0TTweD9OnT5frhBAu5d4r\nlRBCZBhLKQzTiqVYKOBvz82i5s1XufCblzHqtl8m/X6mVDvp7A2fYVpsawyztbGZwBknd+Geda1g\nMMj06dOZN28ekyZNOuQxdzSgtiwLTdPYtWtXF+69yDSZcv73VhIgCyFEF/l8TzNZ1gEsBRaKV+Y+\nzV/v+jUADSsqGTrgWH7y4x8BvWuy1sZtjURM9+dgOz28uq6zbNkycnNz231NjrR3vTelq4j29abz\nP1PJJD3Rqx3pBJtDWbt5H/vDkS55LpF5IpadWqGiE/JeeX520s8XzJ8f+7qyspJwOIxpmoTD4aN6\nspZhWnab9PSOHEJHJtAFg0Fuu+22wwpugsEgL7/6GlP/7TcSENH1199MIpM13U96kEWv1dV38KFQ\niGdeWsQ3Lx7LJRdd0IV76g4yHHgYNDutYn1DLX9/cQ4fblyb9OOJl18e+3rAgAFYlgWAZVkMGDCg\nO/e0W8Uqerg8Qu6OHt7SsnJO+EoOZUPcm2rSHXp7D6qMJrifBMii1+rKesXOxT4c1nl0+u+ZMmUK\n1157bcZe8FsGw0f7h1lXBf/rV6/itUWLeOXF2UQMI1rezTbqom9ww40/jH2/a9cuPB4PlmXh8XiO\n6nxUpxXcHiCnSpnoyhvD3Qd0tjWGWVtfw9JnG3r1zWZvrxcv1Y7cTwJk0Wt15R18fPKNia6b/N//\n/R9PPfVURgaSqYLho/nDrKuC/1AoxC9+eC16OJwUGAN4vVmUjhmb9FhFRQV9+vTpFT1IGlq09J3L\nI2SSJyR29Y3hln1hqkMr+Pk1E4kYR+fN5uGSHlSpduR2koMsei3nDv7uu+/u9IeUc7F3lhFWSmVs\nXlmqYPhIFz8JR0ysDCn8u2TpW12SC1hZWYmh6/Hg+LgBaOP+H9rwCzB9/fjfe39HKLQi9vtOxYTi\n4mLGjx/fBUfiPk6O6dq6lUDmlbnr6jxRDagPVWFI7mmXXn+FSAfpQRa9Tssh0664MJeXlzPnpVd5\n4qmnWDRvDmYkkrG9Iql6do50OHD9lka+dJyfM08+ppv2uuPOyisly+cHknuy2htaj5gWWd7k/oWK\nigp8fj+GYeD1ejn5e//N9mNOh5yL4F+rCc//L555+mkuOH907Pl//vOfo+s6AIsWLeKtt946agKF\nuro6rrzySsK6js/nZ8bsBQwvCPT0bh2RruzlVEqhaVBYPgqf30fEIGOvEV1FelCFm0mALI5YKBRi\n4cKFTJgwIeMubunKpTUtxenD8/nVXX/kR1OuZ3XNiozNK+uKPExLKUyrG3a2C+QUlfDnZxew/b2G\npONt732yZvM+zup/DAOO9cceCwaD/OmRWTSsWcvQgjKe+lCxfW/0hyd+GZTiqSefYMoPricYDNo9\nzoYR+/ujMXVF13Us0ySCTkP1coZlUIDsvOenT5/Orl27OnQ+J14r/YOHAzCisIQHZi3gk3U1XDr+\noqPm9RbiaCMBcgf05tn8iYHDQw89lHFDY+nMpbUXh1CUlJXzjbGZXcWiM3mYKrrMsmFZhCMmfbK8\n3bXbHZZbXErpxHiaw6HeJ86CIC3lFBQyfOTX2N4YZl/d6+BxKlPYqTeRSCT2XBUVFXi9XiIRuyyg\nUqpHK1l8vvcguw8Y5J52QqefKxQKsXr1agA8Hg9ZPj+FZaNdP0nP0RU30i2vlTNmL6AgUMa6+hpW\nr1xO+ejzKQiUpukIhBCdJTnIR8i56N1+++2MHTu219VvzPTajUeaS3skLGUHTk8/8Rjjx49n5syZ\nXfbcPelIX/OGz/aiFDQ2R1i3ZV/37GQntQzcDvU+UcCuAwY794dbPQ5gKjjx5P7xH0Rz0xOfKxgM\ncumllyb9/apVqzp5JB23P2zSbJidfp5QKERFRQWLFy/GNE3QNH7xn/eggGf+8mBGXDO74jrX8jnq\nq6tYW1/Dzd+fyGMP3sdPr5rIymr3t4UQvZX0IB+ho3k2/+HI9JnH6Sqt4wRGf5s7iwdv/3cAXn/9\ndQCmTp3aJdvoKUf6mjsLZYDm6h5DZyTotBHFDC8oSfrZod4nSkGzYbLpiwOcclyf2OMa8WBbS/h9\nf99+jP3utfzqJzcmPdepp57a1YfVIQcNE9PqmoU8WqaOKMvig/Vreeiu32DoBs/87wOuH3nqiutc\ny+coKhtFXWg5hm5gWSYRQ2fZO+8w9oLzu/4AhBCdJgHyEcr0ALGznMAhU3OQoesnhoQjJl8cMLCU\n4p3XXkn62bx58zIqQE6VPnSkNxUKuzedhH/dJla3OjqB7OHZCygbklxJor33iVLEVstzbNzayAHd\nxNdPUbWyjnWb98Cx9mIQ/Y47nh//+PeUnp2cQlFYWNju991lw9Z9aF10Q1NRUYHP54tNPrQnQBIL\nDHVdZ6nLJyN2xY10y2slp52HpoHP78Mw7HYZM2ZMGva+83pzGqEQDgmQj1DLC2dZWXlP71K3CwaD\nDBkyhEGDBvX0rrjCln1hdjaFUQpGX3wp9csrYz+bNGlSz+3YEWov7/JIbiqcXGznazeK1a2OTiCr\nr67ixsuTA2Q9YrFuyz6Kzzip1d/bwbGGwl78of8xfpr0CArY0FDLnz7QYsEx2BPwlFKt2sMti4Uo\nZWeBdEWdYmcC4l/+8hcOqCwu/vZkABbNfy4hMHR/jn5X3EgnXitXfLyb3OJS/mfWfBqqqygdeT5l\n5e4LPo/2RYGEOFxpC5CnTJnCK6+8wsCBA1m/fj0Au3fvZvLkyWzatImzzjqL559/npNPzrzlNhMv\nnPWf7iF/0AmtSj6J3sMZVlcovv6d73OM38PSRX9jyjVXZlTvcVelDymV2IPszgg5cSQoy+enqHxU\n0s+VUoQjVsr9331Aj77e9rF+tKuJ/sf4sRRsXLuK//jJD+Cnzyf9jc/vxwL+ubOJ7FOPp6/PG9uP\nrKwsDMMgKyurR0akQqEQTy9YRMnI0ZyX3zVVJoLBIKefcSbb1THUrVzJ6pVV3PS7e3l//Rp8Xu3Q\nT3AUit1URW+UWi4m4xa9PY1QCEfaAuTrr7+em266iWuvvTb22H333cfYsWO59dZbue+++7jvvvv4\n/e9/n65dSAtn6Gnk6PM5Y0QhCuXaYWTRfZxgyUIx9LwR7PniC3Jzc3t6t45IZ9KH6urq2LBhAxdc\ncAGcNgwnm9Wtp4YzEvT0gkUUB0cxorCEDVsbyT71eAA+3XOQHfv1VvtvWoqPdjXFgh2wbwbqP92D\npRRr6mowdIOWvFl2AK1Q7GuOxAJkILa4jPNvd0pcIv3xGX4eemY+fbLKU/aaHykNWFNnT0ozdANv\ntJqJGTFZPH9ur+uZVApeevZJ7r/9FizT5Jk/9+Gc199wXQ5yb08jFMKRtgB5zJgxbNq0Kemxl19+\nOTYb+LrrrqOioiKjAmSn8L2u6/j8fv787AJyiqRMjyDWo7ixoZbf3fhdDF3nyRl/zKggoKN5l6FQ\niMmTJ2MYBn6/nwefXsB5+cXRINKtIbJ9vNppw9A0O/A9aERiPzto2L3HqXbf7v2zv7aUwoOG0uxl\nlPOKS/H5fYRb/1ms/F2iWbNmxdIvEkvAdZdYqkl00lhDdRVFJWVd9vz2qnF27rEyLPuGI2GVSTee\nG5al0E0r6SamK6ytr40FxwCGHubZ2c+4LkBO10RmITJNt+Ygb9u2jdNOOw2wZ29v27atzd+dOXNm\nrEzW1q1b2bx5c7fsY3vefPPN2NATuk7Vm68x+Iyz2KId6HXDhjt27OjpXeiUuro6QqEQwWCQQKDj\nw8o7duwg0qeZPU0GhmlS984bGHoYy7LQdZ2FCxcyZMiQLtzz9BoyZAjXXXcdwGGfcwsXLowGWfYx\nh5YuZtDgMzB8XnTTYnPfVOGiO+zZuQ+PpmEphdej8a6+jxP6ZrF7TzONzXZO8WbfwdjvW0rxxY79\nGJaFz+PBUgpN0/BooJsWZ545hDv/9Ci3vpe8HcsyafpiB3sifdmh9yXS6KOuro7HH3886SbC6/V2\n67UuOzsbn8+HArKyfJwzbDh7dm5nc1b8mJVSbNkXZtCJfVv9vaUUDfX1Kc+lHTt3cs7wEWT5fEQi\n4PF40QDTMvH5fGRnZ7viut7S9sYwuw7oDP/y8V1ynXCulSvfei0WHDsONjW5sg2GDBlCdnY2Cxcu\nZMeOHQQCgU63RaZ/ZnQlaQub29uhxybpaZrW7pDi1KlTY/mbgUDAFRPCLrroIh599NFYD3Lwa+M5\n8ZSBnHbaifizel8Oshtek44IhUKxkYCumIQS6XcS+t5m9IhF4fkX8dKsRzAM+7knTJiQse10uCZM\nmMD06dPtPFqfn9KK8RzX/0sc488iHLEYNKjnFr84lI/DfcjyeIhYiiyPRpMHvjLwBJr8B/EcMDAt\nxRbTQ/apx2Naih1NOieecgzNEZM+Xi+mUmiA16PRbJj4PB4Kx5wN71UnbWdv2GLFxn/h2VTHZePH\nkj32AjZs2GDfbEdZlsUdd9zB+eef3229dhMmTGDp0qU8Nf9Vykadz1dziujr8zJoUHxuiGkptlh7\nkh5zPPHS6/z06isxUpxLhqkoqxjHH2fNY31tiHMKy/BoGh80VHPVhPGxJbfdJvLFAYzGMP/613td\ndp0YNGgQpReOY85j/4sRre7hzcpiyg9/5MrrQ8tr5PTp07n55ps73RZuPNaeIm1hc3M7dGuA/OUv\nf5ktW7Zw2mmnsWXLFgYOHNidm++0QCAQG3rKKxnJKefkJdR8FZkiHZNQnEVCzs0P8Ivb76Fy8d+4\n4XtXHnXDk22VgZs7dy4rG9YwtKCUobnFmJbKiHMj3nlrpz94VPym3dl/haI5YrH3oMHOprA9cS+a\nT2z/vxadmGg/Ylqp19ie+aGGZ/q9PPnwH1m6ZEks17O5uTk6catnUg+CwSCRL59LP5+H/WEz5cTE\ntjJl6kLLY9VAUu27QjGioISCQBk7mnT6ZnkoKSunpAtynNPJUp27TiSeJ84IUnZhCTOeXchLz8/G\nsuDS71xJiUurILU89nnz5nX9NdNSrN68l6LB7n4viN6rWwPkCRMm8NRTT3Hrrbfy1FNP8a1vfas7\nN98lgsEgX8kuZNv+MAd1y7VlrETbunoSimbXscAC3ltTx0N32wsirKurJjc396gJktsr/xQIBCi7\ncDz/+uIAB3S7V9Sy40dXsxLyiROrb2gJP0vMG3a+d/KT7b+1X3s7LFY0622vRmdZJkY0wLjttttY\nsmQJs2bN4oknniASifTYpCjnWJwKC0veXsbKqmVUVFRQWFIWbQuVNOoXCoXY8vlnZGVlYUKKfVdJ\n1T6S/uvm4zsSTj3or+aXdug60fI8ee6555gwYQJKKXKKAnzp3Dz0iMUpx/pd2w4tr5GTJk1i2bJl\nnb5mOjcOJcHRnJVT5NoqN0JAGgPkq666isrKSnbu3MngwYO58847ufXWW/nud7/LY489xpAhQ3j+\n+ecP/UQutHlfM5ZSsR4jOcczSzomoTgf/OtrViQtiODGiUgdXQTgUD1qTrk757xwdxhki+2pAjR7\n9T/DdCbTqaQA2n4k+jjxo7OiNYSJ/u6kWfXtbtObUM7NKRl57bXX9vikKCsauK5rqOEX37uciBGf\njJxdmDwZObH6RVaWlx/ccB62M44AACAASURBVANTrr++1b5bKh54A7GecjdP3gT7fTE0t6hD14mW\n50koFLIDZOyJeu+8/TbDiss52YU1kB2prpG5ubmdeo8mTXL3+fnzHJnkLtwtbQHynDlzUj6+ZMmS\ndG2y29g9DE5w7PZBZJFKV6+mZ0V7FkcEgnizvCjDwuv1uq5EUmcWATicnvdY76CKB1xu5tSvtpTT\na6x4f0cjGlqs11Qp2NYYpjliopQd5NSFllM68nwspVhVvZyi8tEMzS1m49pVwHHtbnPsxeNatXlX\nvx+PRCgU4pl5f6dk5Pl8NbeIV+fPRQ/baR/oOnWhKkYUlMYWE4Hk6hemCf6Tvpy0/3rEojFs4iTZ\nRKJ3kM0RRcLK3K5lKbtE3fb3Gg47INy5P8zmfeFW58mg8/IB2LCqlluum4ShG3i8Xi6+/Ep+8aMb\nGH+hO1fTa/meTPy+IzfZoVAoPskd+32VXVhK/ad7OG/gcRzXR9YtE+4i78gOiGdWSnDc28VHEhSW\npTAT0k97oq7tobTs3Xp50RuH/QF3OD3vzvLLTrDp9p5CZ4jX2W+l7MDYq8V7whWKxrCBhsba+hp+\nGa3r+/TDfwA0zEiEWX4fP77tv/nLvb+Dn7U/MjZw4KnpP7DDlNgTPOvPD/DT39zNohefjb1umsdD\nUdmohFEBmxMEhttYaOXzvc1sa2zGe/yxADQbJgowrLbL5/U0J+gbURxky75mbv6+3Yt+uDeS+3UT\n3TSTzpPckiD9BpxGKBTiqRl/sEv6WRaWZbLo+aepXPhCRpWChI7fZAeDwdiNg8/nT3pfHTRMCZCF\n68g7soOcXEQ3r4gkul7LnpP3t+/npFOOBQWmsni/IYQZMXusru2htFxBLjsw8oj+vr2eTnupYvs/\nj2ZPwgG7Z+0UF3YbxkeBiHV12zc6Gl4v0V5wFTsmrwYN1cuT6vo6z2MYsOz1V4gYervb9Pn9TL76\nmrQe15FI7AnWw80sevFZIgn1oJVlxdJJVn++l3O+dCwn9PXFgkCn+sWIwpKk59W0hHSbaM+xpez3\nhBtT0xODPp/fz7hvT8Zw2kXXeeuttygIlNLP52XPQYNjfN5WlYsaalfy5tK3ML79jdh5sq0xzHPz\nFnDbT6fQ3BxGKSveOC6vB92Wjk5eTJzkfm5hOYOG5bfK8RfCTSRA7oDEST0KRcRSrSawiKNPqp4T\n1ac/YAcCYVMxcvQYXnxkOhEj1aSlnucENi8veoNzCsvID7TOAexojrIt2musaUQseG9NLS8+Ws33\nokGDWyilaPhsb6waRSxdSmlYxIM4ZxKeUhpoUFg2Gp/fh2HYNYsBTNOu6zv64ktYX1dNeyHy75+c\nT6mLck8rKirweL2Ypn1T94+Na9E8Gio6z1ApRUNoOdmFARSKJt3khL4+APyDh/P9n55H3ywPhtk6\nyrFUNK2bxNzj5IVW3CIx6EPXAYXP74/1IBeWjWLj1kaKzziJf+7cT/9j/Hx1wLGxv1/69jKu+fYl\n6CkWCFpTV0NY11HKQvN4+MrwPD75YCOWZeJz4TXiUDozydm5cdi0+wCf720mdm4J4UISIHeAczo7\nw7L/2NHEqSf04fQT+/Xofon0qqysJBy2FwEJh8NUVlZSPO5yANavqqWhuooxYy7gjkee5+PVK/nB\npEtcFRQ6gsEgvsHDktJBHIczfNpeAG0lBECralfynz/8DoZh8IQLVxV00iucXGOnp9R+PD7PIHGi\nXnZRCX94ch4NK6soCdp1fOurqygsG8nQ3GIGnnoqv/ug7W2eV9DxRWnSIRgMctl3r2bB7KdiQWzw\nwnFUV76Bsix8/j4UlI2K9aInSuwLtpRi70GDE/v5kn7HSuhMSHzMbclpLUdWxk2czDcnXcXO9+v5\n2oUXMjSniH99cSAW5Lc079U30A095eTcvOJSfD4/BjpZWT6u+bc70JTiozU1XD1hvKvOicPR2UnO\nLyxaytzZz2AqxbiJk7t05UYhupIEyB2QXLbIzkHVI+664IvUOtM7OmDAAKxojVvLshgwYEB0ZnpN\nbPLNnL88yH/OnMs1P76Z4NmnpOEIukZbw5qHGj49VACtFFgovGisq3VvRQ+lYP7sJ3nj1YVUfP0y\nvvnd78dSKpwyX855vq6+lnW1VZSMHM25eQGGFZRwbn4Ar0cjy6NxTl4AjwfCEYuhI/Lhg03tbNd9\n14lvXn4lf3/xOQzDwOfzMfnGm/jej/4fa2pWUDrSnnyYqtc3qUQe8I+d+wmcYS8mYhf1iFcIScxh\ndmMbOEHf0wsWUTJyNEOyC+mT5aHs2xeT5fWwvTGMAho+25sytC8sGxULglv2qg7PK+BPzyzgby/O\nYdu2bdy93sPZxynuv/HnBL7Sv7sOsUt1dFJpKBTimm9/I7ZYyt9ffJaH5yzkq+MqungPheg8CZA7\nILZ8gEou9yTcrTMVHAB27dqFx+PBsiw8Hg+7du1iCIq6UDwvNWLobKhdwUiXBIJtSbzBS3So4dPE\nALq5uZlZs2YRDAbtIXbTPidMS6FhMaIkiM/vc2W6ySOPPMJ9v/kVAPXLK7GU4hvfudZOudBUrALH\nhlW13PoD++bniRl+Hnx6Pl/NKQKgMRzhpL5ZWErhUXZQHTlEQuXGVXWUnXlxug/viOQUlfLHp+ax\nauUKzissZ1hBCX19HvIDpfi8Gk1hEzRYU1/Dy2tquOzrFxEMBpPyR530lEQqeoextbGZW155l51N\nOpeNGMhPRp7luhQLsIM+z6BheD2wtzmCpex/T+rni+dUk7oHOaeolL/MeYmVVcu4esLXW980KsVr\n8+dgDPgqFMI/92u8u7qO0V8Z141HeGQ6l2qVWmVlJRHDiH0fMQwaQsuZ6PIAOR1tIdxPAuTDFAqF\nWLhwIRMmTMA3eFj0YgkbG2p5a+GL9PN7uWnqFDl5XKyjk0scFRUV9OnTJyl43KugqHxULC/V4/Wy\nY8vnrG+ooeLsb6bxaDrHyZ/XTYsd+8N8KTqJ7lDDpxUVFXgTclYfeeQRCgsLOa+sgmM5JpaicDCi\nODcvwP1PzuPd+hDXTvymq86NefPnJX3/zuJX+Pp3rrWDXU2LTdxbU1OVdPOzqrqKs7ILeW91HfXV\nVZSNsnuVnbrJ5iEiv7U1K1DfcleADDCisIQh2UXopoq9hkrFVwjcuKqOf7t2Eroe5g/33sWvfvUr\n9qk+5JYEKS0vj6WiAGzd18yeZgOFwrQsFr23nZ1Ndo/h3zZu50fBs1zbqZCYI60UfLy7idNP7EeW\nxw7+W9bFjv0ditziEs7JK6afz85N33PQYM9Bux0aqpdjfGkoTP597G821K5AfdudAXJnOxPaUlFR\nQZbPF+tBzvL5KGxRAcVt0tUWwv0kQD4MiSfIQw89xIxnFzAsv4QNDbX8+7UTYyf7vDlP89Zbb8nJ\n41L2xdkPKYZBD0eq4HFR3XvkFpdy1yMv8NrCebyz9A2WzH+Wd155geIzlrr2vZC4lMe/vjgQC5Ch\n/eHT8/KLueba63ni0ZmAPUHtpptu4n9mzqL8a+NjyzBblkIDhheUECgtJ+iyoeTLL5/EG6+/Hvt+\n1MWXREeENDwJE/fySkbGbn6yfH4KykayvqGW3934HSK6wYszH+Tex18ku7CE/WGT31VuaXe72SXu\nez84udiGFV/Ew+kNdrKMV1dXoYftKgwRy+L+++9H83jw+/386r/uZefO3ZSOHE3ZkPF8tvdgLE0l\n1eQ9ty8U4uRbr2+oYUNdiK9f/DXKy4Px9JsW4X39p3tifxfPYYcPdzYBdkCdWzoSz9/fITHtf3hx\nkIOGScS0yPImV8ToaZ3tTGhLMBjk4TkLeX3+XMKmxbiJ3yW7RQUUt0lXWwj3kwD5MLQ8QepXVDEs\nv4TVK1ckDRfJyeNuwWCQPz+7gI31Ib5x0dgOvU6tg0d7yHVobhEb678gct3l8MAEdF1xxx13cMcd\nd7jy/eAsjOHk2h6uj3Y1MfqSy3n6yceJROxyYKZpsqa+hrKvjU+alBXLOXVhf+GNN97IhzubeGvR\nQgJf+wYXX/G9aHCvobR4T+GwggB/eGoeG2pDBIKjOTuvmEdnPEAk2qtsGHavcHZhCWs27+OzRqPd\n7Z6TF3BdekFivnV8Ge3EnGHIKxuJx+vBjMRDPGVZ6LrO/b+7BcuyeHKGD2vGDFZ/+BklwdEMHHQG\npopPana4eQEZZefYsKGhhtumXIGhGzw+44/MfflVTjk7L+V7OXHZ8XX1NayvDRGZ+A18g4dFnxMi\npmrVDufkFbOjKUzYtDj3S+0vLtPdKioq8Pn9EC1715XpUTmFJRSXlEV71937XnB0pmqHyGwSIB+G\nljOc7SEhRW5JMGm4qDefPLuadE7u58PjcXfJntziUvIDpZzUz5/0uGFaeDQNbzv7v37LPr58fJ9Y\nb2tiL5hhKbZ6TrK/8XhQlsWbb77JsmXLXDkk55Rig9YT9iKmxRcHjaReZbAXe7AU5BaV8ttp/8O9\nv/l3TNOkT58+5BeXxmuDE53spmmxOsI94VB5g5ddeS0XTbqGxnCEsKno57EXCfFE8yucD+/hBSUU\nBsrI8nhoMiJklwTJiuZWZ/l85JREexcP463v1AF2E4WdKlZdtYxhRUGKSsuiPZ0qtnDKiIISbr7j\nfh6649ex9BqPxwOaFl0ZDQxd56c//SlKweN+P//98KN8peTCVuUv3dx7DPYrvzZhyXhD13ntzaVc\nfXYeqcI5532ytr6GX117OYZu8OTDf2TG7AXkFZeilGJ1TRWWGUn6O4t4bWi3CQaDzPvbIl55bQmB\nkaMo7sJKE87Z1bLH3a2OpGqH5CofXSRAPgzOCbJw4UKGllSQU1RKxLI4ryDAfU/O551XXuQ4fxY/\n68U5yB/vbiLrlONalXlyGyd3vKW1m/fRz+flqwOOoW80hzDR6s/3ErEsGsORWODY8NneWI6macWf\nNafsAjasfBsr2sPmxlEFReuePbAX9dgaXVY5MUA+oEd4d9t+ohXQ+M73fsD4USVUVlaSVzISf/9T\nY5P+9hw0qP5kDxcOHRCtEd5dRxV3yGob2EPotaHlDM0vY3hBIL5wCHagf1CPkOXVYh/k9iIicF5+\ngP/6v7msqw2RWzqSr+QUJ9RMbp/bVhdUSrGuvoZbrp+EHtbRNI3SinFcNfUmikvtoMhpk8smf5/h\nI0ZQH6ripP4ns2vXbjaurmf5m4tiz+cEyxFDZ219LYMLx7BfN5O2+cKjM/BcNo5LvnaB626ond70\n3NLk1JqiaKeIM/LyxQGDF2qW8s/VK/nSeUUMLyyhoboqqWpLQ3UVucWloCAnMBLPq2+T2BKW5b56\n0IlKysr5fG8z9aEqhg44lgvOt8sadiYITExHiQXGGVAH+XCqdkiu8tFHAuQ2tLwIBINBhgwZwid6\nv1h+4gHdZFhBgEBpOWee3I+z+h/T07vdY9x8oU/U1n5aSnHQMNkQXQwgUcS08y5bTtBxSle1fHzC\njb/gg1UhTMNw7aiCPRmNVpUHPtvbHDvW5N+PDyU7nPOi7tMv2L19e3TRHIv73/qQd7fvJ+fLx9En\ny9MjAeGh8garQ6FYb1+Wz8d/zpxLTlEJWR77DsBSiuaIxbEeb6yHTyW0wTl5AYbmFuP1aOimic+j\nYR5GT2BbQ8pN4QiGpTipB24wV62sQg/bC1koBdVLF1G/fAl/mv0yBYGyWG62pRR5xaXkFNmLy4Qj\nFmvqV7LynaVEDB2PNwuPx4NpRuxVGosCPPjOx9REgyLHnIfv58WZ05n78qt8y2XVC5zM/GEF9gTT\njXUhSkeNJqeolHDEik3iXFtfw0+u+jYRwx5VfOiZ+RQmTNZ1gurEyhetzwOFYVquXWCqbmU1P7/G\nnmPj1DEHOhUEWs7ojNJi32fIR8chSa7y0UcC5BTauxN0TmdLKQzLQqmeCQDcwjAt3t++P2OWC7WU\nwoPGAd1k9wGd/sfYqRYq4eeJvjig89GuAylnryeWuPq3v22MPf7wh35++efnyfpkNZeMv8iVF8nY\nTH0tHvRu3NqIaal2Zurb7eNtNWQe//eAbqdnAOiWZfcgp/E42nKovMG33347PoSufGysC2FZ8NGa\nlRQFR5ETnTikIHqee1EaRCyLg4bJ9sZmBp3YD6XANBVmlmLAsf7WO9KCmaJH/YAe4Z+7mjBMK1ZH\nuLsoBfmlo+z84oHnwdZ/gBUhYhisWllFfqA0ur8KhZaUl6yUIruwhBnPvkTdiuUUlo/G79WoCy2n\nbNT5nHzq6dS88FGrbToVQaqWve26AFnTwLTstIfhBSUUl5bh93qSFsCxUNSHlmMYOpZporB7i6/7\n6S/5w5PzWF8XoiQ4mtxo2pEC5tf/E+uy25K29faHu7mysG/PHOhhqFr2TtJy25WVlQCdCgKdkZbE\nkogtb7wzVWdylQ8aZqz6iXAPCZBTaO9O0BliI3qSm5b7JxmkU7Nh0RwxD/2LLuEEeYZp8eHOJvqf\n6eejXU2x1IGWr2XEUkkX8IilCEdMGmprmLVgEUPPG07ZhePYvj++wPC+5ggfZp3Gr6eMpuTM7g14\nDsUwLdZu3he9qYsviBEKhXjmpUUUB0eRXVja6gNrZXWIeX9/g/yykeQVJy9PrRL+NSyLiN4MaNy9\naCN3XZJL/2N8dPdS7IfKGxwz5gJ8fh96/3NQV97P7j6f8N8/nkzEMJjzVx/THn+R04YV0GxY+Lzx\nygWGqbj3zX/w7vb9fOt0iz4fVzOssIz8kjJO6Hs4l9PWKRYbtzXGqj50NwVkFwaY/JsHeHb/WVD/\nMrz9GF5vVmwFvY2ra1lXs4Ki8lGUlgdjJQKd3r8RhXZ5M69Ho4/Xy/DCEvpkafzr080pt+nxesny\n+SkfPaY7D/WwOAs/HTQsfE5gDEnH7EWjODgan89PBJ0sn4+CslF2vnphCXmBUvpmeeMBtQUf9B1K\nyzXIH1n5CVfkD+qJwzwkpRSjzh8T6xFPDPg6M2HNeY9bShExLTweLWM+Pw+VWtKZFQY3bN1H0ekn\nuS7lqLeTADmFtu4Eq1fW8tqKOorLR3Nuvr26lGGpwxpaPVppWnz4fWt0tameGCY+XPbkNPtr52Xb\nfUBP6gVNpJGcL7c/HOHZV5bws6u/TThsfzg++PT8lNs61KIRPcEwLfuDnvhw//qGGn75/csJ6zo+\nn58ZsxeQU1TCm5XLqA0tZ8CAAdx8882Ewzo+v48/PfMSZcFy9ocjHNcnK6EKguKDNfXs3LIZ+g9m\nj6Hxl6XrmHG1HVR190hye3mDZcFy7ntiHrNX/pM6oO7jHRi6nWZgGHbawanD8okohRdv7C7AMBXv\nbt8PwMufe9BC7+F7ZDr/9cjznHJ2buod+fsf4JJbAPB5PYQjVlJpL6dtuvvdsj8ciY7+KHLGfB1e\nfQ8GfhXQwKPFqjLcNuUKIrp94/Dw7JfIKSpNqkThTDxUKnkI3Ui1ljlwxY//nYu/dmGXTvzqCu9v\n3x8Las2EyhROcOx0iijNXj56xuwFrFpZRW7JSHKKSnAq2ihlL6qyurqKgvJRDDh1MP4+fUAPt9pm\nxLK6/bw4HP/c2cTAc/N5YNYC1tZUcWXCkthLlizhmZcW8e1vXHzEo2POTZUC9OjiQj6Px5VtkOhw\n84s7usKgUt1//otDkwA5hVR3glVVK7j6qisxDIOn/H7uffwFvnxuPpalaNIzpwc1HZwPhcawgdlD\neZSHI1ZBIHolMmNDfPb3itRDfU51BucaXhdaTjg69BiJwOL5c2HQpFZ/48YAGexj2dBQy/raEMXB\nUTRUV9nHY5pE0GmoXo6l4Bffm0jEsCduWZaFZdnBY0P1copKS3lveyOBM06O31wAL7y1Er4U/4DY\nt2e36y7+4YjJ9kad4QUBTttswuewc9tmUBaax4PP5+O8ovLY71tWPB954+paIF6zVvU7AcPQmfvX\nByn+/i0pt5dTMor10a9P6utj5wGdJsMk97QT7OdXRPPB03XEqdlVSeyUmuSbfIVlmqxeWYWmkVTS\nrqG6iuyEYBBAt6L57M7/lF3R4ZknHoFzrmu13Yu+92OyTz2hW47xSDSG7dQgpxfZWS3Vfiw+edMT\nfSy7sIS8Yjs32Zn8q4B1DTXcct0VGNESab+bPhOPNjDlNi3LXZM2HU26/d7ILgyQV1ySNC8jGAyS\ndfowzmmjNJ1SinDESjnZOZaqgp2imOXxELEs16dYpDu/2FKweW8zpxznl1QLF3FXdXIXCQaD3Hbb\nbbGToPLtSjvnzDIxDJ1VK6vAGWZ02cz07hYffuzpPWlbY3OEVZ/vjQUETk9Gs2HG9j/WU5TAKVXm\nfPxDdOU8nx+P14vH42Xx/DmtNxgdQnQbTdNYW1fDr6+fxGMPTuMX35vI8Sf1jx1Pls9PYdloe+Uv\nw/5AsJfW9uLxevH5fBSW27PZ44Gx3T7r6mvY+KXkD40TTurfxgSlnrO9UWfb/mY+2tXEG/+yF3NA\nsy+FZ4/I478ffYGzc4uTehB10+KlObO4+4dXJD9ZtKTf2n7DeOIfEVr61TBFxcSrEx6x32epele7\nu4k0zR7ettOG7P3RPJ7o+8BHQdlI8ktHkuXzxR4rdCaeKXsZ7mf/7yHW1K20g0ecBTZq+cmVE6j8\n27yU2536wjpXlvZyesUNK35T7OynHRyrhB7QeK95YhANsHrliljurmHorG+oRUtZM8ZeetytnGN2\nRocA9IhF/ad72rzhbWyOsGn3QTZsbWznWZ1AObmDws2cUWWv19ulE6+VUjSFIygF2/Y3s72x9SiD\n6DnSg9yO+k/3kH3q8fT1ebngggp8Pj9GxMDn83HsCSfz8uMPk1MyirziEtffAaeLnYIQH1p0K920\nYqu8xYN5xYatjS0qU8S/NkwL3bRiPUfOR1xOUSkPz15AzYrlfPbRB7z2cutAQAEpFhFzhfqEclSG\nAfu+2MXDsxdQX72c4vLRDC8MYCoVy7H0+/3c8/s/8v4nW8gpCZJTVJLUK+z0Cq1eWQUkD5sfe8JJ\nrpyprhT8+pV3ac46Nunxs4blMjSvGD1isbvJoK/Pwwl9PWxYVcv0O36NGUkOgk85bTA70WBIQcrt\nnOSD7UmPaEQshc8bD5gsJx+8m1updmU1Cxe/yeeDSln4qb3tM84ZQfmP/42C8tFkF5ZgKsVtf3mO\nf65eGXvtLQXr6mv59fWT7Cogfh/3PzEv+jPFqpVVSQsopeLG0RXnOrBfj0AsDLYfX99QQ82K5eQE\ngi0mLsYrwtjPochPKBHn8/kYlh+gamfqba6rXcGEiy9I96EdMRW9pts3PXZOdp8s+0bK6UzYuq+Z\nmpXV1IeWx0ZaP9hhpx+1dQNkJXRCODcXyoXXh5Y6k1/cnu37dT7dcyB68+XyPJNeSALkdijs0l99\nfV6CwSD3/eUJ1q1fz4D+A3jo7t8QMQxefuxP3PHo8+Se9rWe3t0e4wSQbg6QnRxPZwELp5pAfCjV\nCfgU721rZNiXj+fdbfvRo4siOOWdwD7O3OJSzskrpqbydZa8urDl/Bt2btuCpc7q1mM8XInlqHw+\nH/llo8gtLmV4YQlZHjt3dERhgIdnL2DLu3WcW1jO0NwiSgz7hsFuKTtHdef+MM5HXl7pSFiZnG7U\nVm53T7MUsV5TAAouwbP7Uy647AosBRFl8fOX1nNC3yxmXpHH+toQlmnBhVOTnmfn6aVwdjltebc+\nxIALvxL7fvwjK1n643L6ZiWkaSSMYHSXUCjE5Zd+nXBYR139P9HcY/jkgMaZ513Gd/OGYipFxIJz\n8orILSrBE11IBWD1yuWxm6yIAa+/NJc1NSsoDo4iv3SUvYBS3qVtbl+PuOwNQUK+cezaYH+/tqGG\nn1/tlHTz8T9PL6CsPBhrCwvwKGiOploMKyjhgVkLWFdbRX7pKPoNOA1t+faU28wuCboyONQ0ze7h\njbbBx7ubOOXYPny4rp55r75BYdkovB742VUTMYx4Xq6zemBb9z+xHvnETgncNcLUlo7mF7cn1rlE\nvPSdcA8JkA/BOW2VUgzPKyRn1FheeORPRAwDS/NgoLG+NsR3vt6LA+RokOnGYVOHs6yyBXixL0zr\nG2pZW7uCgtJRZDu9osrpQYpfvJwPSisWQseD62G5BXzr93N54ZPk7S1f9BIbzvQy7rxLuvU4D0d2\nYQn3Pv4iG+pCnNy/P6uqq/B5PWQXBsAp5aUgu6iE70+4iI93HaAxHIn1pkV/jFKw6YsDrKuvpfqt\n18kZNbbVttz4vnAmBHk8WtInufW1H9vL/zaG+cn8dYBdkURhBzK+Pn70whRB34TbYM/WlNvaufVz\njE8+xH7X2dY31DJq5EiA6HK7Tg9y93FyKi3LBCv5pmb5pi+4eYzdaxj/AI9PxNQ0+2bIWU3Q4/Hy\nxoLnMCMmz/7FxwOzFvDQ7Jf5aajtXmQ3Tmx2cqidXTOiJQ8XzXsOIzrBztB1Xl/wPKVlwfhf2S9f\nrJpPX81LdmGAgkApplJ88PGnGM0HSbU0z7n5AVcGh4lpc57oe6CuppprL780OlnXzyWTJqNHS905\nebkXXTMs5fM1Gybvbmsk6/gBQPzmVOHOlQS7k5WQztO7W8J9JEBuR7z3S7F8xQqee24++edfREHZ\nKLw+P9Z1/4s6bgDZ+b0zB9m0VCwFwbmgunk2cmKKxdr62thCET6/j4eeeQmvBjUrllM2ajSBM8YD\n8aDAVIos4j3IYAfbXxyM8MInrQ/asizW1qyAq9wXIIO9EIICfjvlCgzd4Jn/fYAHn55PYaAs3rOu\n4L3t+4mY8drIsdc62o7zZz/F/bf/Gsu08D7yZ/j5iy22lDhY7R6WUimzQpVSfL6vudXvnpsX4M5H\nnue2VW084Umnpnz4zXmz0dZtilWxALjl+kn8Zc7LFJ0xjn/utCsnrKmroXbFcjyXf7Nb6mY7OZVh\nXU/Iro97b3UdI4pKkhZJiY2yKLu0272Pv8jamhVs+exTlsx/Npays3rlcq75yc0QCrW5fctl7wdI\nvvkDiJh2haJUexq79dEa5gAAIABJREFUeSbeLhtX17G+ZgWB4GgKoxU6nOcMH2gCT+tJbYmpSm7j\n7JuTW175VmVscrKuh3lv/VqyvFmYJJeBc0bqEjkBsTOCd9AwMUzL7rDQvHy+t5km3WToKckpT5mi\no6sLxjpuJMXClWSSXjucYOBvb77N18eNY9ZfH+LWH0xCKYXxs7lwnH03fG5+ICMmGnS1j3Y1RRfR\ncCYquneozB4yjE/ESc7DNVg0/zluumYijz04jZ9dPZHlVSvix0RCcBibkmb/LNLG8Xo8XnJLR7qu\nPex0EfvrVUltoLOquirei4Z9jM5kMue1TSzvtba+lvtvt/NylbKIGC0TTeLbdFkzoBR4U9QcdQL6\nRI/dexsz7/6PDgUySllYTbuTHtONCHWh5bH9WFtfy8+unsgjD0xj7NixhNoJLLvC5r3NFAZKWfD3\nxdz4y9sYMvTcVr+zvi4US52C+I2iM8lVKTgnt5gJP7iJMZdeEZvI54vWBD5Ur6DbzguI3sQpCG3a\nbY8cKLvSwviJk/H5/WiaRpbfz9hvfTcpLcZSinUNNdxx43d57s9/4NYpV7CuoSYxi5ljjk0d+CUu\nUe82zmvupFk4tZ+16MTUd9etBhQ//OEPY2XP4iOu9uqQDqccqPNzU8GU59dyy982xiY/7mtuPck1\nEzgl4G6//fYjOn+37GuOlkZ1ro/uOyd6OwmQD0EpRdWyt6MVLCwMw6C+uirpdyxlDyHtD2fmCd5R\nziIaOD0puC/X1KGRPNyfEwji88c/1DVImnn+3MLFsVqolorXuo5PLLF70Nuq655TNtrupXVZe8R6\nAhUMLy5PaoOCspH2/kb/c4b+oPVsdoBV1cvtvFzHSae12p7zHC5rBvtDyWxdnlGhWl0Ul556Ka+/\n+DT/dcMVrX6/XXujaRefbYCNb8Ue9vh8BILRSiBE83mN1iuWOXYfSH3j0VGb99lVBr50Th7X/Phm\njj3++Fa/M6I4aOfiRr933vMRM56GoFC8t7qWDXUhrv33Oxk/6Rou+tZkANY11La7D27sUFAK9jUb\nTF/2Mf/z9ke8v6ae52f+CaUU0595ick/+w/ueuxFvpJdGAumnXapD9kTE+2cbIPVK1cQHa4CoF+b\nAbJbr5nR8D6hYyCvuJQ/P7uAklEXxIJk0zQ588wzCQaDvLutMWl06d3tjUmVfOLXnviIq7PAkqW6\nv056V0lVAu5wfL73YMLKpW2vYCp6jqRYtMO5OJSPGmNXsEDH5/MxvLgc6uPv5PdX1/HWxlpKRo5m\nyrfH99wOd7OkRTQy4OROTBM4Nz/AA7Pms7ZmBXmlI/FoGovmz8Uw7MUynHJWsQl8zoUd+8N/VXUV\nw4vLOem0wSm3tb6mio35Axl/3je79RgPh/PhdG5egB/dejfL3/g7F35jAiOiyysn9hI6+ZWxldMS\nHi8oG4W/Tx90PYyGhjXl/9rYnvt6DNfV13KgcS8cc2LS46FNX/C/VZuSf7nPMQCHrMyQaPRX+nPm\nlg+Ym5WFZVloH1VjjbgQAE3zxm42lIL8slGx60vLElLhiMlHu5piS6J3BaWcHj072PWmCEzOzS+O\n7WPVpt3083kpH9Kf/XqEk/r6sJTivdV13DXVXn3Q4/WCUkTMCIvnzcbj8cBNL7Z+4ii35aWDfX1w\nKs9s3dvEPXfbx/bCzAd5YNZ8hhWXs75mBRowauTIeK86ihHFQbJ8PiIRyPL5yC8dGX1O+wnbKvMW\niU16dZfYiBv20L9zQ5NTVMoP/t+vWVMbwjAMsnz+2LE26fEOosRrrWFaNBtW0uhMY0JnkhW7xrqv\nHQ5HR5eYtq+tdqs41UKEu0iAfBiKS8t4ePYClr25mILzL+KM4QVQ3xD7ubNE7RMz/AxfmnqFnaOV\nk4eXOBTrRppG7IJvl9qyYoX+I5bC69F46JkF1KxYRunI88kpKo31iFsKNqyu5b26EKecMoAH7/pN\nrLzVL//4GNC6B86yLNbVrkBN/gapJuf0FHvY3L4ovzHvGR6/77eYpsmalcuxlMWka34QzxtWTo97\nPD3FyR21lGJEYQkPPTOf2hXLGTZiBLdsTLG9hPQbt9A0e6U81DmtfjZj+aY2/87r83G4Y0TFg09k\n5OiryBl+LmtWruDjPTo10Z+ZSlEfWs4PvnUxSsGIggAzZi+gLrSc8lHnx5e1j9Yn7sre1g1bG2ML\nkzhBjCdF153CHiF5qu4zFr1nV2B47nsno5QWOy/W14ZivaaWineFmpEIh1o6yY09hop4XroeMTEj\nJiqaV714wVxemz+XiGHw4iM+Hpy1gOEFxbG2GJpXzO/+Opf1dVUEguczrMCe9Lu6roblby4mPOhi\nUl0HXp71V7RxFeRcdnF3Huoh2deJ1j2+StmTd53OhdJRoxl4bh6QPCrgXC/AXpXvgG5CdOShsdlg\nytw1LX7XvVUcLEuxpbGZ00/sl/LnR1oCLhQKsXDhQs4urSCvuDS5M0K4igTI7VAKduzXsZQip6iU\nL59+Bv4TT2HPgeSepPjQWtevsNNZHZ08cKjnXLhwIXmjx3JWdlFyfq6Lz/LYhT7aU5QYCIJGTlGA\ns3OL6JPlifWyKeDvc2fx8N23RhfM8GBZFsqyiBiwcd0aOHZ0qo1x/Eknu/KGYf2qWl6d9xyvz3sW\nK5pmYEYi/OnOWzlveA5n5xVFe8u12EXbSZXYuKqODxpCFAftFJLswhLOzi2mcfc22Phxq20p4uk3\nbpJfOhLeTF16K5ULJ13HW0MmHfoXo1Q0CB2WH+Cr2UVU1zVQ8579syx/H4rKRrGvORJLXckuLOG8\n/AD9fPEEj0/2HGRXk87auhqWzG7o9DmslOKgEUn4vu2Z86+9v53yM0+OBccQP1+saK/pF0OCWDcv\nQJv9S9T2j0l8lT1eL+1l17o1xcKMvuEjWhbcMBPPYzeS5fNhWip2nTfCFq8teI5hBcWx3FGlFOfm\nF/OVnEKO75OFUop1DbXcOsVeUY/vDIbTR7Ta5vzHZvD3Rx9kqMs6VrTo6NkB3eS4Pt54qk10JGlE\nYQn5gTL83vg1IjHIi89Jib/PnIoYe1rkGr/8+Ay+duGFlJS55/gTNekmW/Y1c0IfH8f18aKluLM7\n3BJwiUtWZ/ke4lf/dQ87d+5iRGAk+cUl6dh90QkSIB/CASOCFi3i73yUtJyYFR9a67oVdrpC4sno\n8/tZ2sb68R1+zukPMf2ZBXw1pygaeHZvqaojEc8ltoPjiKlieV+Jw4GJJXc0pbG+oYYZd/1HLJC0\nlMLj9aI0jSyfj7OG58EnKTaoaTR+sdt1vQLVoRC3XDcJPRxudTOjLIuG6uXR1zOhRnT0fxsaavjt\nDVcQ0Q1m/dnHA7Pmk1Nk94AYbURDTk/Sjv06x/pTf7j0hOzCEk5qCLHnMNN7+192E6zdctjPr5tm\n7Fg1TSMnLw/eWwvArQ89yYiiEv6xc39S4GH/G2daitV1K7npGnvJb6fWbEfPYZXw3lYJj6XKo3+y\n9jMG7v+UxF5PJ/d4VV0Nq6urqDnW3g91zYNo7zyBqltg/6LHw3k//P/svXeYFFX6/v2p6jBDzoJI\nUCQNA0xOgIpZV0VMYMCwBsxr2DWva17TqiC6rllZI5KMqyhGYHKCGZKZnCbH7grn/aO6qqvTJGag\nf+/X+7qU6erqOhVOnfOcJ9z342xo5WSi7d3QhECxq/v0HMD5191mpWCtWPoeum7wov9vybucMHMW\n4xPMtCTYXe+hd4wTVTdYb4pzVqF6FYSuh1DpmRAClCh0rEhG0YZPYMlhhf913yrJrjBoN5CNbWYx\np2BPvYdmVbN6d7hnvviFp/jw1WcNZpdhJ3XthXUAZoHhpr11jBnYkz7dXB0+1hNPPEFTUxMAQnh4\n8t7b0XWBy+3iyTeXcPjx0zvprP9AZ+CPIr0WYOfENAuNNuyuZ847gVxPd73wHlfechcL3l4WVYOc\nvXhAaUfxQJuPqXgpylllDYYQfZ5CO0wbTtF0VN2XLCD8eYeqbmOo8F3J/5a+bxnHYEjx3nz/41x2\n053887XFDDtyXNi2ZIeTSelTCe+fO3j44YfvULxKiHEsSRIut5uE9Kkoul8tyyocEfDFsvfxejwW\n80dxzmorL1uNIBvokA3BgYpGD/We1gLvXY93P/2KR//5KOuK8ohxtz2vd0k7jGOAzT//hiQZ5qUs\nQTennwd51ISEkLx2s1An2IAw2FbaXwAUDqYxYzdoNBE+xQLgidXbA38vYFNJPndffi7vPPcEe3du\n8393WLx/x54D2dA9lBnDjmj1IAdLgM+8/EZGT05l9OQUjp4xy8oL0TSNktw1VgTqt8oGblpezluF\n21hfXMC7/5lPt159DXYP2RGR5VpyuaPOsfLTvgYaFb9Ra3LGv/7c05QV5Vm1CMGjvX3cNKMNu+qa\nbV5kY59gFUV9znyj+N3H7BKNsN7X/TjGSy+9xPLlywO2abpujacluWuiOgL7fxF/eJBbgDWZSICv\nInntztqQ/cZMTubYo6fikqNrvWEvHnC63BxzzDGddsxmjwckid59+9tSLIhaC9k8NyGEYczZztkU\nOlF1M1/WX5RSsTcwDD85OZ1RY+MozFnNzkbBvMJIhpNR/FS+q46kw/pEhedU1wVTph2N0+1CEW6k\nSadw3OE9GT9xMg011SSkTyE+KZWaZtUwbDH6/9rCPD784F2+WvauZV05HA4mZ0yxFkbBhoWJ0h21\n/G/jHq7OGnmgLjMEui7YUt3Ezk2l/PncGb5CTBc9b1nUZW3u2bEVmXTAGD4ctko4Rfd748x+qVuG\ncuALlJxhqB6qCu0qAArGvnoPv1c12dhIDGNF0/XIfbN/YAHqRW8Xc+KWxag+asAAr2g7+3e0DRNN\nikZNs8rLuYHhIF0IVi5+i5yvPsPVfyhy3yGImt04XS4SfTSOn6zfw4s5xu8Kf9/Hd8+ch6oouFwu\net/4FqMaf6Jp6CTKqkKv+k8XX8vsk4+OKsdKdZMRVjFZfNYX53PfVbNQfNf05JtLiEs0POdmpKlw\na3XA4s4aV4Vk1X9gi2AEYOAIXC4XyVlhUtWiBELQYspQW7BkyZKAzyNGjWbntq0oPoXGRF+x4x+I\nHvxhILcAe/jIDL2H87a8W7yDW44+AmeUGchm8cA333zDIeNTyMzc/0E4KyuLefPmcd3116PrOgse\nvpsho8YyPiHFCslHK1RdoAFCF4zs341mRfdVaRuWiqLpBhuDEEYOnoD+Aw8JOEavvv0sgRFOvx19\ndPh7KgSszV/DUdOmoOkCZziqgAOMLdVNDBw9mUueXsQrPxn9O37aEZw4bhBOWUKSJBRdoOjGc3Tq\nEmWledx6yTl4Pc2Wd0OSJE4++wImJKVZMrEbytYCfcO2+1reVuZmHjwDuVnV2Nfg4e3l/7Oo/DyH\nxuHxdF2b7thuVphakiUcttn103dfRz86i4SUdH/kJYwHWZIM1oBn/ruMyk1FHHfcsR02pJpVmyqe\nLb9caSfP2NcfLsLhdCDUwHoDSXa0682PJk9Zo1dl/e46/vVpATn7Ar/7asnbvPLIHcaHWz+CcRfg\nmH82l9/+IHFJaSiazrpdddb+TU2NqF7DwFS8XipELBXdJkIY4xhgzhVXEzc4tMj3YMJ0DK0tyCN7\n1ffU7t3p50sHSnLXMC4hLTD32PIe+8WU/HOmvz5FQqCooabmw698QEJK+oG7yHbAYGsSyPtZbH3O\nOeewYsUK6/P5l1/DqHETyF71AxNSs4hLSovi2bPrYah0Hvx50o4/DORWYAlDCDMsGNqFP92wh3Mm\nHcrYQ6JPBSgrK4v0jEzyt1RT61HpFeMMK5DQHlRUVCB8hWpmqH3UxGQkSY+6vEI7DA+xr4IeifLi\nfErz1jAhNYuk1HQ0XeCQCShOO/msWXy2+B00VcHhctFv4CC8Xq+RV6hGpv2SHQ4mp02JqlCykU8o\neOUn/7YmRaPBq7F9QzHr8rOZkJrF4fFJNHh1YhwOinONEL/dOHY4jRy88qJ8JiSlsb44nyfvuAGu\neSti2weTwsikrErKnIrL7UbxetBn/bNL2yxZ8x2bJw9mbEIKsgDJtnb+aOHLfP7S4zz71nLG+ejU\nvJrAKROywBQIJiSlkjnzRGJsaRodgT99yI+bPyzn18qmNh9DB5KnHUd+nwwYdLi1/ciJSSQfegf9\n+g9gV1U9H7Z6nOhZSgthKBnm/fANxB0b8N03y98N3R9BXXVVAFODH+0bWzUh9tsz2dnQBf4CQ48X\nSZKQZRlJMuptJqdNsVKDTONYF5JV2GzmGtsXQWaU0aPorP6tKqTN0ZOTo6hHhKItrp/WCuLnzp0L\nGJ7k+LSpnHnBZQgEIyYkBUhv/19Eg0dl4556UoaHd7IcLPxhILcAu6fFormJsK8pORqtEAh+2lfP\nob1jI9LVtBXTp0/H5XKjqgpOl4vxyZlsLC1gfX42xx83naQoLLQQQuDRdEzqsvLifG6/7ByLru1f\nbyxhRHwSMn7+T8M4SeOeFxexuSiHielT2Pf7ZsM4BhCBU5uU+z4iwxBKOO7UGcQlRZdQiMlbbYeO\nEUJ99LrzUTxeZFnmirse4eiZF6IjSEifYhiVihdZdpA47ThKV3/NZ4ve4svl7/PY60vIzs9Hm/Ns\ni20f7IWCLmBSSjoL3l7Gq/MfJ7eL2xPbyigrGMbYhBS6u500K/50BIFAURSKclYxZnIyYPAdO1yO\nkP5ihqbf/uQrdq4v5LhjO+5FDijS8xlBv1a2T4TE6Y6hX/9BMCIlYPtP9RJzzriM+CG92VXbzIfL\ny1s8jqaLVtX2DhQkyRD60NVQEr9fN/tKDWc/Zm1z9DmECamGalxpQS7bf6kAeSAAHmd35PjjEOu/\nQXK4WjV+TZGpykZvp3Jd7y+Kc1aheLwIoVtphmeefzEnnDmb0ZNT/BLkljFsXItDEqwtzCd/zSpS\nsqaSnJYBviJ3HcFr+Vv5bEMoe0yEDK2ogOUgA+qaVWKcMt1cgYtVe/F6S8W0c+fOZe7cuXxVtMm6\ndx5VRxMigEf6/xoUXRxUJ0okRFdOQJRBBP2nCT2ihRwtg3042FV6OqsPHn/6mZx5/sU8+eYShID7\nr5zFO889wTXnz+xyqdz2oqLBy646j4+5wthml1lWFYXi3DWBxSQ+A+LJv/+VHz5dTFxKFuMmp1Bd\nWYXlJQqSiT1z1oXW3wJ478X5rC3MixqvgOlJtUMIwQ+fLsbb3IwQOpqm8upj9/D10rd4/8X5CODZ\nt5Zx2U138o+XPmB0fCKaqhlpCs3NvPDPv7M1dgT07N9i20b4rOuurS0QAiYmp3HcqWd0aTvSa3Nx\n7dzA5PSpSEi4HRKb1xb4d5AdSJJsSDL7zkvR/cV6AeeMIWN83QVn8Y9//IPjOihFbfGA+1x56wrz\nuGnOzHYf56/zF3L0GeEVBe9f8WObC5m0KBsve/frByK0iFS/8QPjDxtFm3LZiwweM5ni/BwemDub\n3zcHLgb0k29m9vV3MH3m+a22u7OmGa+m80tFw/5dQCfCjLbItpRBIQSDhw4jPjkVUxzFzmxhepLL\nivK55eKzeHP+Y/z1krNZW5hvHRNgX334BZmqi+DhNCqwpaqRTXvqrejx7vpmfqlotL4v31VHg0cN\nUdP75ptv8Ko6u2qbwx7X7P7rivJ598X5bCwtCPG6/19DNF75HwZyCzArdc2JRdUiSwtHU8gwGOZa\nvzPmJHOl/PmyD/h0yfvoOqwv9AsGKIqX9z7+fP8b6kRsq2myVudmkVJK5lQcToN2zOFwMCktCx1j\n4ha+qu0bLpjBZ+8v5Oslb/Hg1eexYvFb7Nq+BYcZ6g7yIA89zF/U9PUXn/HfZx/nxovOIntN9CwY\n1hbmBXzeUJzH9x8FFqtpmsabT9zLWwue4LZLz0EguODqmxibkEJcSpahmubD5nXF5H3zRavtagd5\n4PdX3uNb5HQdLr38au598X3ifYVMTlmirMDWB2QHQugWZRYYefHBE6Tki2YU564xcqf3k43GvAe6\nEBT5Fojtxci4hBZnsspGL6/mbW31OJomLNGZgwldN4pzqysrI3oPJGcorVdpaSmluWsMdcUwFG4z\nL7+Bqae2zpt9x2cbWfFD7kGPsNhh8P6nkfzXF5CHjkeSZVxuN4kZ0ygvyue9F+ezoaQAVROsK8zn\nrReeMRwBAqtf+ZluVgWkKUZaJJsLxGhDo1ez8qfDSUE3KSo/7mtgUloWbrcbh8OB2+1mTFIm63bW\nsq0mcvrSusJ8bpozk0X/fpL7r5zFhtKCqLwHBwIS0RmB/8NAbgFmCASMh7e3USX79+qw++p69PF6\ngpHb82tlY6cNwOZKWdd1VMVLce5qS2ZVdjhw2WRWowVG4R38tK+ewm017KprtgZqIQSqqrLo1efZ\nWFKA4svTLc1b45cWdnVDld28+tg9fLH4HSRZJvXYUzjs8FEB7XhtcUIdrAXD999/d4CutGVIQFFO\nIJXS5tKCABo7kHDIDnTNTz9UlLMar6azo6aZ+gFHMn3G7IBjhAtNB0M7CO9HdnY2jz76KLk52ZZh\nKAQkZ07t0nYT0jMYn5hq5Gw6jCr+xHRbm5IMQlCSu8pisjC8x4aRULi1ms1766ls9CLAl+biwuFw\ndIgSrFnRUDRhTfQCMx+7/Xyutywp5oG/Xhfx+zcKtlK6I5TpJxiDesZExXj5e1UTP+5tIClzWkTn\nx+DLnwrZ9mN5KRNSs5CyzocJx4V8L4TAe8joNp3DE48+zNqCvNZ3PEAwjdl8dTD6+U9w3rW3cf/L\niwDBrZeczcJnH+fuy8/lo3ff5C9zzuLVZx7jL3POYl1RHn369keSJcOodrlIyDD6vRmZq6+JNH9G\np4tJkiTrHTXFTuzQhTG2DRmXwMqVK3nooYdYuXIl8Ulplv3QpIQuoASCopxV/iimqlCWn01Vo8Km\nPfUH5uKiDH+kWPw/BjNcKITBF/rgD7so310Xdt8INLAHHVVNCrXNij+8GoSNu+vYVx9azm8aF8Hh\nXJPmTfZN1uNTMhmTYMiszr72Nub9dxkTk6OrGtkML9/zv008+e3P3PLher7Nzkf1qYoJXSdn5f+4\n/8pzWV9srOInp0/B6fIZEHNfg+veQVNVQ1ZX0xgdn0hcUuB1btrjD5PKrhhrwXDU0UcfqEuNiEav\niqoLEjMCjcN9E86AUanW8zzhnDn8+c5HcLndSLKMJEn06tufZlXn1o/X8+S3vzDt9HOQZVsOnmg9\nNlpWlH9AJ0Az0nHvvffyp5NPYl1RnuXJ6ur+eceV57O5tABZkujpdgISiam2NmUnDqeTyelTfEa7\nKVgjLCaY2mZjcaYLwfjENB5/fQlzb72b/7y3vN05yOW76qho8AakD030yQW3F804US59IeL3OREc\nCABDesVYf2tCUNGgUL4r/Hh6IFC0rZrKRkMpNT4plWknnBp2v109Dw/Z1r1nb8ZMTkHLmB36A+DD\n8t08/s3PYb+7/6RAjmhN1SjMWd2+k+9SGBE0EylnX8HYyakB3mFVUfhhxScWL7rX62HFskU8+/Dd\n6JqhOHrNXQ8Tn5RGaUEeb70wjw1ri6mr2he2RUWLTg8iGGweb70wj3LbPTFhMXgIoyD+rrvu8kvF\n+9639bvqqPeoIb9LypyGy204lpxOF/EpmQiB9e7/X8HvlY38tK8hKp9/qwbyggULqKrq3JDk/Pnz\nmThxIvHx8cybN69Tj92Z8BfmGZNYZXNkoQMtalfAfhGCcF7kOo9KVZMSYBDbjYvjg3Ies7KyeP6d\n5cyZeyPPvb2MI+KTyf29ijx1MDOvuJH45LSo6+jhwjcjJqYG5NiBIRleVmCQtcclpjLvrQ854ZyL\nISaQnUR2OJiQkhVCSdMzxl/zOnnKccy54XaeXriMjE6g19tfrN9tDNITkkLlTOXTbmPWtbdx+38+\nYFfG5Yw+9ixm33K/Jav93MN3U17onxzGTE7hyrv/aaRaSFKb6vaLc1cf0H4RnBNYlONvv6s9FYrX\nw/rCHMCguJIlcDlsfc3X78zFt2IVj5qeKlsusu/dHZ+YyqXX39IhKqyAXFFrG2H7QlfilqOPsP7W\nfFSCdulrAFXTaTxAxUpmHrR5zwccMqTNv/3ozX+zubQw4vfvleyI+N3IfoFF0k6HTFJQVEPXBQ2e\ng1O0tbYwn5svPsv6fMenG0EIEtKn+g06l4uR4yYifItjoev8/tMmnwiR0Z9rqispK8rj+gvP4tVn\nHuXBm67yOx2CsLmsJCqTUE0ly9fnPcatl5xNaVCKWktDiX28CR5zBIKJyak8tXAp51z9V+598X3G\nJqT69u/US4h61Hu1qKWIbZXFYvfu3aSlpZGcnMzll1/OySefvF9cdWVlZbz88svk5eXhdrs55ZRT\nOP300xk9um3hqAMNw8Pj48Yl8jus6S18eZDh9x6HnqAuoDg/l8vOPcOqwL300ksDjItgGdSJyWkM\nOnQYAwcfym9Vjcz74VcA5qQMizrj2ETwADV6/AT+fNcjvPbo3VaKgcPlIi4li7KifDYW5ZCWNY05\ntz/MV++XWr+TJInpZ8xm8RZYW7M34JhlxQXQbQQAIrYn5131F3rFdC5RTGtUQpFg5v+FKwKRHQ6E\nBsWVULarjjs/20i/pgFomuHWURSF8sJscBupM1e8X8r8mbM4Ylwc6/LWsGq3FlZt247JaVMO6OsR\nLJKTkD7VqrJfF8YT1Jlwyg4mpmUZSnqShISE074Wk2RLie2I+GS8lnCInxHA2M8f2jXzkTXd8DD1\njm17eoTf1jY8g+vy1zBwwAAqKiqAjE644rbhm2XvgDvZOhfTo23HluomqhqVLqd7qmtW/ffFtwhp\nD/uldngKawtzwN3+xa/TEbgwv+zWvxMftFjZU+9hW00TqcP7tfv4+4vinNV4g/LTN5YWkJmZxeOv\nLyEvexWT0rJYl5/tD88B5cX5OJxO0Aw6uElpUwyaSMXr8zqD5vVCGLbCZ+64llEvvUrWEacciEts\nM/LW/GBxpysCeBREAAAgAElEQVQKFGWvYuoUfwqhaR8Ej23+FD4Qtn6l6YKS7TXWO37kxBT6HDER\nl0O2lAYDfhCFKN1Rw6j+PegV23lzm+ksiDa0eoUPP/wwDz30ECtWrOD111/nhhtuYNasWVxxxRUc\neeSR7W5ww4YNZGRk0L17dwCOOeYYli5dyu23397+sz8A0HWBkIVFeB4plUIV0bj+8Rf6mF4SO8zw\n4ppVPwQYxECAcTEpLQuvquP2zfLmcezSosZnY1s05hKFO6MTzr6IEUeO57tPl7Cv+1DOmp6FQOcf\nV85CVRTefeFpPNfZCthu/Qjny5dy+PiJfFUTOoht/f1XGG8YyHuqjNCx3on9oq1UQsFYu6PW6r/h\nno3X08wHLzyJlHImHHUZAFXdBlsjlsPhoHZkJvhEA5tVnbJddWROSELTBe/NfxWGHdXiORyIAvXg\nxcPKlStZ/OkXTEjJYvTkFExlr6Kc1UDXpVnc9fw7xPm8QbIUWpgkOZy4fHyymi4QumBjaQGbi3JI\nn3JUQMTB33+EjyoLNu+tb5fhJAQgGWkud/75HLw++i5JluHm5a3+vjPgeO48vug1BC41DOSL3ynh\ni6sycMiB1lKkVLDOxqa9ddZ9Mdtsj99HZF7AlgECKtrX7rhBPQjWDBpx5JiQhWu4grADhcSMKbhi\n3Nj5Jh6cO5tHXvuAySkZDBmXgNMhG7zxDgeaWYMgBKeecz49Bx7KuOQsxiWk4nJIuFxuFLw4Bo6g\nMmYghHGMq7puvJfnRJeBnDHlKIvm0uVykZQ5LeBZmW6ncM/KXPBKwlgkgz9yYi6EvZrOu8U78Gg6\nV2WM8HH0R9/8aYei6TR41c41kMMslqMBbbpCSZIYMmQIQ4YMwel0UlVVxbnnnsuJJ57IE0880a4G\nJ06cyD333ENFRQXdunXjs88+IzU1tUMnfyBgDlSNXq1FA3nl8vfpMS2Z6aPD57EdTNg9U7vrPOhC\ncGjvWH6paEAXRhGQ0+UGDMPrkksu4bRzZvP5l1+TmDGVIeMS2NfgZWifWOuY5qCwu84T0I5XFbii\nLLPdp+0UsM0c0MYmpLBWGsrKtTvp1zSA3uWf2hg5Qo8194k3qCjPhrCUpf6Zr7LJl0NK51H3BKcN\nBHv2I8FrK8KL1H912QG9BoVslySJ42fO5n87Ax+qWXS3Jr8EfeKJrZ7D3Zefy8QhX3DysV2Tjx1p\n8TBw9CQqGxWaFc2azBLSp0JO1+X5yTIseXUBaVnTSM/MCjG8jj7zfE7JmMT4xFSaFY2NJQX885rz\nURWFJS/P49m3ljMxOQ1zqtR0gewrFpJF+MVeaxAIi9rQHhY/UNAUbwCNWrOqs7fBG5aT/UDMk37J\nYyM6qEO7BZTyKtrv6bt4aD2OoNQuPeiZmuPFwTIY4hJTefL1xdyU539eqqpQmruGUZN8/NdCMHZy\nKjff/zjz7r8Doeu43G5OOHMWh45PpFnRfLndaTz39jJyV//AK2SgRMgacbpjAgtZowRJaRnMf2sp\nOat/ICljKhOTU2lWNQq3VpMyvK/vWYXvB/YaJhNWyqPviatC8NlGgxf6qowRPrGq6PYgiw6OQZFg\nKhVaaosHmw/UhlYN5Pnz57Nw4UIGDhzIlVdeyZNPPonL5ULXdcaMGdNuAzkuLo477riDk046iR49\nepCYmIjDERpzeemll3jppZcA2LVrFzt2RM7p6irUVVah6hDjkKmt87SYa/nl0nf59sWHGR7zflQZ\n/HvrvdTUNtOsaLhkCcXtoHof/Ih/BTti5EieeOE1dm4qJSsri5EjRxJT18yZsy9CF1C9dw+uphho\nMKzC6n21NNRU4nRI3PKhX5atqWofFQ0O1G4udsSE5388GKjc10BdUD5fXU0lsst4orurjKr7b3+u\n4NDeKThdTlQFnE4nwaydQw49lL5qHPwapiFJhoZq6NEXD04+L/2VE0b1Zpe7mVgbsfyG3XXEOh30\n6eaif/e2h8rj4+Nx+XL4XC4X8fHxLb4XHlVnS1UTiqajCXDK8MmmMEVUMT3gkgXQ99Cg6zFU8zKO\nOZHPg+qNvA11NFdrrIhJhIGtD2iqV+GLzz5h0jh/KpVX1XE5pE4ZED/66KOAxcNHH33EyJEj+e7b\n1axek01cYgrjJiXilGVGjBgBOeELqDqKITE6msPN3kaVh+eeh6aqvPtvF0+++CaTEpMCjB3vIWMp\n+v4r9IZa6geMZkFxE96Ln4flD6JUbmPN118wfORI3A6JOo9Kg1fD5ZCRGhyWnP0OZxNNikaMU0Zu\n5f5V76vDIcGY8XE4XS4UrzCM5AM5EYVZJNZU7qOn6maH26DCqmlS2FPvxaPq7HC2Xd2vI6jeW4cm\nBG6HhFfT8ag6anNj6z/cT5T88BXDR4zk8WOHcsc3xrvbXFdDzT6NHbGGs+HXykaaFQ1Nhx2urr0P\nwRBCUFtZzZBhwyHvN2u70+nkyHFx1FXsAeHPrMiafjIDnh/M+pJ84hLTGDR0OI1Ve1E0nfomB6rT\nwfCRIxl02DBeWfRLxHYvu/0hho8ccVDm+ZZQWd3EsBEjOW3IMGRZombfHty+FJkdjkZqKmpwyTIC\n2OZstN7FyspGaptVhDAM3l3UUed2GpSCe+tpqK5CkiQaG/0zTGP1XiqaXcS65KiaP4NRtbeOGE8M\non7/hW327t1LvdxInUdF0XR2xHr+3zKQKysrWbp0KSNHjgzYLssyn3zySYcaveKKK7jiiisAuPvu\nuxk2bFjIPqbiDEBqaipDhw7tUFv7g14NLppUnR4uB7GOJmRpG5HWTgIJVVUoLy9nxowZB/ZEW4BU\n20ydswHZa0ymvWKcaLZ8ao+q080lk3HsyWRc5ie216qaUGqbrRWtKksosW5G9u/Ob55YPKpGz/6H\nAH4DObbvALrHuOjV3cXQoQMO+LVGQpVUi2hUsJ/r/d/v4u8njGFon1hcMQ2AwUBRRyx//88iNhfn\nEJeSyT2lgcfasWMnlZvWgzuMt0OW4c3rYc4z0PsQVm5pYkbKaAYP6UcPWy7ydrUKSQKH28nQwb2s\n7a3lF8+YMYOvv/66zTnIFQ1eekgNaLrhhXQ7Jea/tTn8zsHGMX6/iLtXaD5oZUUFX+R+hBo7rcVz\nMOF0uzjp1NMD3uPCrdXEOhwM6uHmEBvDQUcwY8YM5s+fb3mQZ8yYwe+//86NVxj59C63iyffWEJC\naoaPdqlzDeTuPXry/NkTufPBf1LqCz2oipcfVq7g6BNP8RWEGfc+uzoGKXs9H/73JWJvWkS9ozv0\n7g4Z5+Fc+RyZx55E7wGDiHHK6I0qarNCjFOmZ6yLGKeMEDB0aD8KtlYxpEcsQ/u2rIz5ac4XFOeu\nZlxSJg+8sojvP/6A6op97N290/ZGHAAETXzd+/SnV7/uDB1qiMzs2FpFz/4SMZpg6NCuzb3d4q1C\nEzqxTgdNioZD0Ynp5gFqurTdXoOG0qPfIEb3BzCMwZiefejVfwBDhxpqfNvVKmIlw3A3701XQ9cF\nXk2nfFcd3fu6+LJ8d8D39/xnEfHJaaiawV4tSxJCCJxOmeSjTyRt+klouqDeqyFpOrKm43I5iHE7\n6BnrwqFoQGQDeVRiBr0G9LfuQbSg0d2Ap6YZPCpOWaK72xHwDv7Y5CbWaShg7tYlUob3ZXedh+56\nD1BUX92DxOAhvdAF7KptpveA7jSpKr36H0KjywO+Co6YPgNxxzrpFRtd82cwtipVDOrXjUN7x7a+\ncxvgdvSCRmNhPOTQgVHlQW/VQH7ggQcifhcXF9ehRvfs2cMhhxzCli1bWLp0KTk5OR06TldDliS8\nqk43p8yeOg/NLXC5SU4XLper3RylBwKm98rM7bPn3tmpn0wIX7jDX1lqdNi9DYaHw078bsdH5buZ\nnTg0+jKoJImyosDq46omhdfzt3Jp6jDytvq9qqqmsr4wm8kZUzgyPhlKiwJ+9+KCfyF+yoMbw4UD\nJWiugz2/QO9DqGhULN5ZO4T1Pz/aml+clZXVruI8f754+yHGH4O++QfK89eAK5DbelnuesQnr8DV\nbTOQH3h5EWkZmUHnZrAYbKvR9ttANnOO7YuHRx99FK/iL7ApyVtDQmoGHrXzUwsEgkE9Y3BsWxew\nvXLfHhKG9mHD7jqmD4ZvfXaH6N4PVVFQvR6QjGsfPOxwkmfMst4tuyy0KcVq0kqB//1tCd//sJob\nLzoLxbdI+PPtD7Lyow9QvYrBQnKAVOElWcbprcee2BKcWmAYE/7r60rYOW11AY0elV+3be/ydt9+\n+gFGj58QwCASrn7fzsF/ILCzrpldtUb63Zeb9/L86t8Cvh+TkGylfuyu81DR4CVuSE/rvC1GEHt+\nrjD1AUSIQFEwericxDrDVO8dZBgMSMY1mNN/QJ68rR7H7E9bqxv9tT/CqLlTNEFVk0K9V7XULAFu\n/3S91daikh38OW149M2fNnhUrVP7ZV2zgjdWt9kiAkebeJEODA5Ktug555zDhAkTOOOMM3j++efp\n27drK5Y7CkkyckjrvRq3fryhxX2POmM2T765pN0cpV0Nw1NsGrv+gdf6z6om9/f6sl117K33WnRT\ndsN6b4PHekHswhgAi0p3kr+1OuqK9CQMmrFguGSJ2z7ZQL3HlhvZ2GhTNgqlcdJPvQ1hys8GQ5aR\nHQ4rRLSvwRv2XoQrSAiXX7w/qPeo1Nj4r4XowIR76q3IsoO9O0MNB3FYPJx5T9ifDXB4Od+7huMG\n+xscMzk1YiFSuPPSdBFRpjUSgnlIp0+fjtvltvioJ6VNQSBQuiC5s7mpmbWFeVRXBDKbjB05DIcs\nUVqQx+pnb/N/IUk4nS7cMf6Fwd4dW/li8dvccvFZrCvKt1KgAIQu0KyFq+F9b8tVLPrkC1sVvkL2\nl5+imly2177TGZfeJsy+7nb+seANrrVRAKtBz8GyObpw+KhrVn33ziYCheCvH68ne2/Lv+0MKIqX\n8sKcAGe6rgcZlvjzVMOJTHQ2dtU2+8Z7o2/VeULbvOvTjXhVw3t884flPPTVjyx/9TlLItkUWNIF\nXP5+KZ9t2GPlz+sCSsKMv3Z0c8m+hWF0zR1gjJ+abijpmqfnp070z426rf/6nUjG2PtbZSM1TQql\nBXks/PczbFhbgi50fqv0p9B8WL6bat+YHY3QdMG6nbWdunjbVtNs0FxycAtTI+GgGMg//PAD69ev\np7S0lOOPP/5gnEKbIGFMTMEDeTiknXA6ExIPLK9oW2H3EoczioOT7pt9g7LBzSqCXnj/QOBRQwdS\nPdgtFBUQDIwPXbgE0y2ZMJWN1hStbVcrR44dx4XX3844n4CI8dIHDibNSvgVuElLZkqV7m8k4peK\nRoulRAhYV5TP688/0+7jCCH4auXX4b8cMibs5u69enPWFTfSZ5A/bUPXBb9XNQUUdbZU7FHnUdlW\n00Th1mq8HfT4pmdksnDpJ1xx85089voS4hKN2oCvNgdaQof27qD3erc/TaO6upJrL5jJrxvLjQ2S\nhMsdw2WXXQpAQfYqVK8/33DI8MO589/vEmMzkHUhLEO2xMcb7dXMha1pSIW+ly3BUMxz+1T4XGSe\ncJqleokzMIcwvQuo1Q7tFcO8MycwISWL7z9ezMv33WR9t6GsFHsPsBus4bCjppmNu+uoaep4gaXX\nZ8iZ98705v1S2fX5xyYmpmVhZw8PlmG3OzDW76ozxtQgqJoedntHsK2myceuYLQZblj8vaqJHbWB\nglKLX/gX9101i/Ul+Xh9EY6KBg+Nisa7JTvYVFLA4lcWUFaUR7feLfctTRhjY9G2rk1xaS8kH9Wi\nR7NHVP0KeuY9M59EdnY2rz/nl9623l2fF/36C8/i5acf5b6/XMnydxaGtHcA62Y7hACO9k6Ava+H\n44s+2Ohcktb/n6G8uIAfvv+O+LTWpZM7k86rM2FIZdonVAPWq24ZzP6z91OChRox9gpcJUzKSbTR\nQRdurUbVBY+VhY48+VsjFKyddCPyb4WscCW0uZ3MQTJXTD+awb1OZNOXm6HWGOjt96uuWWXz3np0\nIZCDwkhmisDHn3/F6KT0TolEmAPPuqJ8brn4LBSvAjcva9cxtN5D4LLn2/Ubr6qHBMnMyaWy0ctg\nXzqFLgRSGA5R/29AlgSK5qcYbA9KttcwamIyF42ZTLNqhPEaPCr/yf49YL9R/buzszZUTTISRg3o\nTq9vnqe02zgYbFBdepo9CK/HqMKWZdKmHsPlN91hPce0KdNwvfqGVfQZnzaVMZOTkLf4I1OSbAiv\nOBwOEtKnGt5uTUeWJDaWFPBzaS6ZU48mLjG1zd6WSSnpzH9rGSW5qzl8chqjJ6UwYvQ41uVn816Y\n68oL907sB5KG9aFsxRJef/weg1e7uz+3eOPaIk47JpDSDl9oOhx21PqK+ZoV+nRrv0y2CdNbhek4\n6PCR2okvn0PoOlt+3MgkW4qFqgfOHWZ9iD16F4zSHbUM6OHm8P7d9+uUfqts9Ke2+NrbtTU8q/ma\nLz+hqLEnOA4BQL/hPZTXrqY0bzUjJySRt72GB7/80dr/4WtmoyoKH7z0DNNOOw9GRk7JXLuzlrGD\nekSdgYQwuMeNFAvDWjbvU/H2atsiVVBamM9NF52F1+vltQVunl64hPGJqcjCYKApzF5lRXNUBb5d\n8RlkBKoq6mETbqID/ihk5zFOBNgjUehbizJCrujBTW9+zfVvr+K955/kgatmtbr/tl8PaLlLm9Cs\naD6VrqDVrrUKNLatLcxn4b/n8fryFUE5ZKZhHShgUFaUz+I3Xqa8OLzgQjSFyTo04E48kSOmtI+u\n79aTE+kV4wwRG9CFoFkxvD32vO5w5zV2cgpzrr2ZSZ0khWwM24KinFWWRGy7EcFL3Fq7wffBMkoC\ntgnru7DH2U9vhT0MKoQ5+YSi3UN8zW7Kv/s0sC1dR+gGt7DL7ebym+5gUrLfCEpMzeC+p/0LjQFD\nDjPajti4sLxUm0oKeOjq2Syc/zg3zZlJWVFeSFpUxKMIQ9jn4mtvZkR8EgBjE1KZefkNIfv2dHd+\nDuiu3Xt4/bF7DK5cIcDr99QeGZ8U8DyE7V8t6MH75bj3/5xML7z92F2OXT/CuhUA5Hz5aYBhoeuh\n/TIgahfmcLoQnZJLv8+WMmcupj/870th91328jx+9xnHADhjkEcmMCrBEJv5cV9DwP5+ukwFj9wy\n48G/1/zOdz9XRp2BJEmg6LqVLtGs6pYxp+mBz6Yoe5VV86AqXisKZM6fKVnTcLndlhJhyrGhnM9R\nLTiGfyzdVeth05566zuvGjmioemCwggLb8soFqAJHbWFOq+DgT8M5AjI3bwFfch4K9zeGpa9uoAN\nxfkhA/vBRNmuWh/vseHZ9foS7E2zRBeG+tFNc2by6jOPcu0FM3njwy+twVkToau60sI8/nrJ2bz9\n34X8vTR0do+2DIuOPo6f1hW07wemalqQxbP45QV8uvI7ttc0W+cTaaX8a2UDTYrWaSEs08BMzPBL\nxLYbp9zc7p801NXy5ZK32VTiL8yxp5oIISwObvNzMEx58P2xXUzvvY5hHAM0eEMXCWdOHEy3dpB3\n79u9C01Tob7SdsLGc5+QnM4DLy9iUnJaCP1aaoo9BUv4fibZthjMAIbK3mpAUN3kpaQwL8DYKM5d\nbRlPe+u9/BRkmOi6wKvq7KptxlTQFAg0zRAkWfLKAjaWhvbv6u/fZXRVacj2/cG+vXvQbXFjSfeP\npSPGxgft7RdRKNkeGmrf3/4AfnoyM+J3wCJ/kr9/ZZ14WsDCaOtvP4Mw8niNfW0eNRH6fpic3p0B\n+z3VhbGY1sIRwAPMfjRk0zEzZjFmckrY85HHTTOMQaeLVcPPbPVcttU0RV0OqrA9F10YrE+675mo\nuu4fz4UvncllGsBuEjOmWvOhLgSTU9J59q1lXHHzndz37CtMn3F+SHudlTbTFTDnE/MM622S8Ot2\n1lpzXDBakpG2L4o9mgibtnkw8UeKRQSMGHYYuTUV1gsezIcbDE0XFOeuoWTKlC6XSW0rhDCJyY3B\n1qv7fRLmi13iEw8wV70F2asYn5iKEHYFPn/3Lso2vJFi5MSDeGUtQ9cFzapmpYB0ZNAVU+a0a/9F\n77+Hu2EfqVnTAD912zvPPcEHL83jneWfcuIxhtqcEdIMPSnzeQHsrG3eLxod0xAQAuKT0nh64VLW\n5q3hxQOQ49bw0VO8+HMuZF0IWYayYG8fzZ2EkZpT2ei1zi+SF1UgoIUUjDZDgKoZSmlNYbxuh/bu\nxhOnxXHj8vI2Hc7pjjFENn54AzwNkDnbt91N8lEnUJ6fTf9ubhJSAyMBbqfdGIb7vtgcMKlIhjY1\nDofDmlxvWFaOIyYdp8uFqhr81wnpU6wIEEBVoxfoYR3n96om4/5iLyqCH9cW8th1F6AqCg6XC663\nqUQCS174F3K3nnDNW226D21Br/6DLCUyWZa55u+P8rxBO26JzWi6f2I0DcJg77hlLHaCaWgKg8g+\nr/SBMZCNZ586/WROOe9iNpb4FyiLX36WKYOd9O02haTD+gSE8cONXeW76jrNkPQvn4y/E9KnIn9Z\nQFgzJaZHyKZvlr/H4VINp866OCQSo576Vy44Pov4tCz+3sZ11/5eV2tUme2FJEF5UT5r89cQnzKF\nSSlp4Ft0qz6GDvPeTfCJohRkryJtyjSOnJSCx5faZV5WfHIaoycnU7V3D7VhjGEtSlM1TVjRZAL7\npi7C12pVNXrZUeuJ6PTxR6cPLGtLW/GHgRwBIw4bSswvzcy45m/sPGwKq/a1vL/DJ8kcTTlUZi6x\nv+gutFhvcsYUXG4XigJOl5vkzKnWb61oR/Aq2e3CK4U3XESYcOGBxq46Dztrm61UBqkjtDGu9hVu\nLX3paajezZKX5zHqzvcxA/fmwiNn1feceMxRtpSVCOpLvnPeXtPE4J4xyB3khDSp+sqK8ijJXU1i\nxlTmXHszLz6/pkPHayuk5Q8ifjEnf39PcDok692w1KQsz0L4a9TF/smu6kIg+wxsj6rzc1khn3/8\nIQwL9GZJUgQVtQWz4cb3QzZX7tll/KF6oewryJxNbI9eXHz7Q7zx5D/QFJX3//MUZ5x3IX+7fq41\nUTuDFNTCFYbZr9a8X5qQ+PsL7/FTSS5TjzqasQkp1sQc/BvA59kKZK3RhWBDYTZK9wGIuOnok0PD\nu0LoaJ7OFabo2acf9728iPL8NUxKn8LklHSefyXPd13G8ze9xaYhYSzKg86NzvH2mqw+ZrcTkcI5\nnQ1Jxh0byxmXXockwbr8NSAZtIe6EJTkriErK4vi7TWW0R7pmk3DuTg/l6Vr8/bLGLRPV0II4pNS\nOXzM+DazhAsheOPxexg1Ng7JGapnEJeSxdjJqRCGESjkWOzfAqitVJltRdG2aorzc3lgrpFLvczl\n4oFXFjFwyhSEgLWFeRTlrCZz6lHEJaYhEExMTmd8Yiouh0yjV/WldoF9GSIE7GlQuO3rspA2FT2w\nHiiaICzHWqgxqwuobPQiSQTkxVc3qTQpakTjN9IYFi34w0COAKcsoyFx5KmX8MHXrQ8XybNv5MhJ\nyVH3oAWCRlXDLftpdMzQs+ld/NebSyjNXUP/gQMozF6NpkNCarr1ogpLrNmQDn30tcV8sqqQcNwG\nZvHLwYS9GtYs9OryNnXAx7dbW10JskH0bobb+vUfwFNPPs7QCamMmZyM7Aun22E6BTsjlAywrjCf\nWy85G8Wr4HK7eHph+wr0OgLZ4fR7n+yuYRE8GdtYVIKO4VV1X1GdaFeCcLD3yE67tKm0gAfmnofq\n7A7X+A3kwzVD5lWWwqRYKBFo5mzXJTtkdKBX3/40VK9FVRSErqN7NZa+/QafLn6Xb775ht5HTAww\nwjcU5oBzZMBhxZEZMOkUtPIVFOeutlg3AEZPSmZSSjqDerh9lFq23wnDU9Ovuz/PU2BfCBv/xozL\nQvy5Fd7qjuSptwCBPx9dIvBxmh4nu1MhXEqBsd2ffrG33kuTojPukJ4dOyefo8Av+9v16D/oEG57\neRFHxCcjAZPTpkCBYTrJTrcVFZCkQEPKfHZ26D7j7KY5Z6Eo+2cM+tM4jLbWFeXx28Z1MOK4Nh5B\nQtd0yguykTPPC/n24Wtmc8nfHgBa10ww56aOIhxV5v4YyJouKMxZbaU3qSqsy8+mT6yLTxe/x4pl\n76KpGu++8DRPL1zKhKQ0ywwG3wLHjIIJf7/TBKz4pTZsupemRSeNhaYLGrxagMc8HPY1eEIKR83+\nu7O2mYE93LhsNCnCPgdE4cLgDwM5AlwOyWA/aME4dn7+FOopfwUgb5/OmwXbuP3Y0RH3t6OzQ0GR\nIISPHkrG5pnwe5UEEOerqP7bpef6BAXcLHh7GcPiEpGQAiYwXUBcYhrlu+pgW5j2OOj2sUXNY4Vu\nDkCbDpcT4ePb7d23P9t9YeRZ197GiKGH8OA9t6N4vThdLp58cwmJqRlhxwPz+eidkFpgL85TFOMz\nZOznUVuGmW8qyTIjx0/iN992k03FhOlZEQJE0ALmx30NhtcB2tyZgr1HX331FRw6DoFEk6Lx3ceL\nDZo1l9+ochQs5crLz0KSQosKWfu/yI0XfWz9edqZZ7FzWB9mxA9hx+q1yJKMhn+S83g8LFy4kBvu\neyIgJ7ksyDi2cMK1ODd/Q0L6FBoV/3GO6N+dXXWeAC+OCV0Ifq5oILW728phtE/Ikk+o4K3fI+Sg\nexrg83nG36JzJ+ghWhX3XTUb1auw+GUXj7222PpO81lldoPCntZlh/mdaUDVNnec6s1kh5CRrDzS\nrkaf/gOIS4ynWdHp5nYwMTkdCgyBrD9dco01Bgdw6VrjV9C9EMa77FW86PtpDNrTK0xPtq6qrf3M\nj7RzcOz5kfXdx1JaFMqXrqoKuSs/gymtG8i7t29DiBFtbzsIJlWmOQbsL1WmAJIzpiJlnItUtRPH\njnJ69enLLRefhdfjsd5BRTF4nk12GbPvlhfnU5C9iuSMaSSnZwTMSZHSytQoTbHYXtPEnnpPQEQj\nOMUCpFjziJ4AACAASURBVLDvknlPttc04ZCkAFEoawEYjRfNH0V6EeFsQ2hbDVrtbatpbtMiyJzM\n7733Xo4//niys7M7epqtwlyfBeT5CNPL6ttHQEnuGpuggJeinFW+4j7jN+sK83jz+WeMCnoEv/20\nKXx7UZJLZJ6DLqALhNNCMPCQQznx7At5euFSevbxU1ldev0t1FRWWp4Ng+N2jf/8bCOKEMH/dvxG\nlhbksWvHNhxOhyWUEZ/a9SI2lkdA16mr8hexfbJ+dwDfq53WJ/gyzT5k76OtIdh79M6Hn1vpRAGC\nNjZPcXxCIuMSU5GQQiesr14w/n06qLjotathS4n1sWfP3vxt+pGwYyOvPfEPNF0Lm1Q9om83NpS0\nrfDzwZcXEZ+UFnDe774438hbtRa5gYtd8x4Wb6+htlm1LYBF6D0IxqYf4OfcNp1be/CXUY10/zXb\nL0yiKKzN96f4qCbHs82L6S+e8/clTRfUeVQr8gUdm09VTfdRqvnHqGZVpyS/8689GE1NRjRCkiRi\nnY6ALjJw6Egr79ruzY2UUiIQRuGta/950wPGHAEJ6VPal9Y16HB6/XkepWp4iWh53FEcMj08C9Qx\nowJltL/4YCHrisIzI7UFJlXmQw89tN/pFWDck0kp6WhZFyH+9DeUK1+nrrrKqMHx3ThJknC6XEbN\ngPErELD8nTe45aIz+e+zj3PHn89hXWG+ldao6iKkgNdEMOVftMB6P4k0Zvv/Xbez1tpuXqZ9cRv4\nQ+MfRYvO6/7DQI4AVwQRiQDsCFXXa0sOcmerpkVC8MRjGLt2jwE+YxkSfLnIpjGVlDHNRxEnKC/O\n54aLDILzWy85m/LifI4YPS58mxzcxeDeeg9VjYYaUWlhHm+98Mx+DbptwooF7C7PZeWH74ewWEj4\nq5sdQYpuDYpGcVC1vjmQ7E8u+xfffM9Vs8/k4/f+C8CfzpvDUwuXMmZySoeP2WbYDNCKdausv59b\n/VuAd9PP42sYBdtrAnNfzb4ZXAwSCcFCK0mZfilwTRccffq50H8YXOGnsKrcs8tgHgG6ReBZdgTJ\n38pOP/+u0+0mPi0LWZJYX2gYgtgmTkmScLvdXHLJJfSKdbK5KKf1C8GgYSsrzmfxy89a21546p88\nePVsyovzbcaUsBlTxn5mv1lbaCh2lfn6vqcFA7kTyiDD4j8P3EHvfv2QnQ7rfuzd4fcymjLEdgPf\nb/D7z2lPvYdfKhoCFt8deT027W3g96pGyoryee+l+awvykfRdErzWlZ522/oGlO865GQkCVjTOjm\n8vcr3ee1txvHwvY8w6VYxCen8e93lzH31rv3yxi0B8x1IRifmMbE9Kkt/ygI3hbWXuopt7LSOzzs\nd8N/+Srgs65pFOesCrtvWxGsprk/0IVgXZBE9oSULJy+edLpdnPqrIt58OVFRnqFb1wrK87nyX/c\njqaqCF3H6/VSkuu7LmFSGIbvwNFGc2bCjMqaYkUm1u2spbpJ8S9cRaiAmLCN4/ar8/rS6ASCBm/k\nPOWDiT9SLCKgNQ/y5SKH1+srAru5aJtx2NmhoEgQGAVq963YjCzBAyePo1nVcTsk/yQkSei6YIKP\n6aA0bw3JmVOJT05lW3UzEnKQdxlK89ZwxDEzYdvukDb1g2oew+9VjUhIrC3M4y9zzjLSGrr1gKs7\nrzI/BGVfAqB6vXyx7H3mXHOvJULyl+VlvDorjRfeXU5B9iriU7M4cpJhqAoR/n4Zg0nHzZaPPv8K\nxcfHiQaDDh3GhMRUKhs7HpbuCKSfcxjcuIVd3Y2wqS4MaiDDw+DPExcY+WmH9elm/VYIIx+yNG8N\nyuknc8L0o1psy/Qeffvtt0yddjQxIyb4KJmMheHYhBRGnH8XWxz+IW/UmHFWakWs28mL507i6sXr\njHOXZWRJ5vSL5/KhrZ0zr7iJ+l9KqKvYR8/+A428WslQR1vqdqF4BELoCEnC6XCwYMECa7KeetTR\n8L9aWsOG4nwevHo23qGTYKYhUqRrGipQnLOaURNTArzIxv8lftxbj4CAvm/kni9l0JjIojdCaznv\nWNpSihjRdtEcE17Fw68by3yUfQJNVfnsg//CzecAoNo8pcZV+Pq9zUj007IF7duBl0PVdUoLDJpK\nxavw3n9c3P/yIsYnZ8E6/34nbVtK8SFHsdc9qP2NhIHro4c5/al/IllUkOCwLaR7up1+I8K3zSzW\nRRhiHsP7dqOHjwnGXDxMSk5nQmI6mYf3C26yTQhW9TTvb3xKJqW/tv0GN0odY9t5+6UFMPd167Ps\nY2+JFqwtzOemOWfBdf5C3TEJqTzyygeUFWQzISWLCUmpBjuUbaFXkrMa3bYglWWZxIyplqNKC7YU\nbdCFQa5cvquO+CG9wu90kGCev2wT82lWNH7aVx+wQNeDCtDtkZFt1U2ous5hfbqxdmeNdRs00QE+\n+gOAPzzIEeBytPy4evftFxIC/21TeciKMxxJdmeHglqCLgS/VDTy075GhBAWh6YQRg6gJgyCboRB\nU3PRNTcTn5TmS00wQpLjkzNxud1IsowkSai9BvPG2srw7en7lxqwvzBfVCP/1mfUt2IAdCYkDGW2\n6UcaRXqb9zag6gYH5qXX38KEpLSAATJs1KkdntNwSLbxcRoe6yxqmlXOXdh6Jfn+olf/gZYXXZJk\ndhd/Z323rsgfZvQPqACBqRSSJLG2KI+/XXoOrz3zGKefejIfffltq21nZWVx3c1/o8fh8QHtCAE5\nW6rYEhvoyZqUmGINypIEA3vGcOsxR3DhSJ1zr/kbD7y2hB69+gT8pqmhjuNmnEfRmm/4euk7PDB3\nNpvXFjI+MY1HXl1MfEoGkiyDz8NbUVFh/Tazje95WX423m4DEDPvDdguOxyMS870942gCFFNsxE5\nsSt2WbLVLbQndQ+8Rl76c8BH8e2rbTrvEGgqNRV70WweJWHjRNZ0KyhtnZ/dEN5e04yi6YHcxZYh\n2f6XQ0KiMHu1LS9foSw/m9iREwL2+8t9j7Hg8pPbffxIOGPWRUxOMSj/wk0re3dus/qq9T7oft9u\nvVelotEgGlU13Rq/je87Nkg0KRplu2oRGO/lG88/Q3lxProuGDTs8A4ds70QQiCtetP6fPKsS5iQ\nlNbCLw4sin1ziB1CCMYmpDJr7l8Yk5Dio3qzpcQIQULGFNwxxnzpcDiZe/c/ifcV8On42aXCwe10\nIIAmpR154AcIoek//j7rjwCFSb8g8De76wzVUjtZwIGqFWov/vAgR0AwJVMwamuqkSQZ8XMuHGkU\nPu3ZsYW/XHQzCYd9Yxm9kfTFs7KyutQwhtCOOqxPNyqbFKuzarpA1TRrujEo0UBI/qpbgHEJKdx0\n7yM8dd/t6LrO+9tciP7hvZEHm+ZOCBCSsOjoFMUIhbfGY91ZGDdxMjFOBzG2kL3XxsdsTvJmPm6A\nB8e2Co9EA9cWTExO57m3l5G35gdievVlbd4afg9lFOt8lP6PusIvQAhkWTa8Ic1+taWS3NVMnZIV\nOKCK8IOqybdt0uR9++23zDhxequnoJnpB1bIT9CoaMz7/teQfauavWwqLaAkdw0JGVMYn5BK2vB+\niOEpaFOS6eZyGPmYxbbf7NvDtx99YOXVKopg0QtPkXXiaTTVVpN2zAn8WFaCqoRGh9qa2jkhNQvH\n0iXYp0hJkjjmjFmM9okymJOs/f6Z99VQ7DL6vsvlYlL6FGb/N/LiSIw7ipO670LXjf67asUnWLS1\ntXuQ6/bQYhr/nl/gkFFhv5IAp9uF12PEZiUbPaSf89Vm8PvSwBASu+ubifUJuJjXZ/4thOF8CEvP\n1wICxgWXiwkpmdz+SWCqXGdI6NqxbesW/jPvX4xLzmRMQgoSEmVFfkfKkrcXkj5AkJaeCb6C1UYf\nNac9xcIuVW+cZ4S8zjbA9HgaTDdnWUw3afcvYtWejl9ru6A2Ixd+iDbtUsCIdB3s+cOOhIypuNxu\n7CL05jzZ6NUDPJ5W38RwNM377zK+++47xqdkMjkl3T/GhRvsbOgT69qv59pVMIv1dQSS7z21c9T7\ni0kFdl+wyX+lC+EbryR0BL9WNO73Iu9A4A8DOQKcrXiQf/vtN2SHA+3DR+DWj4yNAhTFX1EsfEbo\nwXz89tbPfrOAF8+bHOB5UoWwurPdk6PYvDs6UF1VaXAc67qP0yw8TPL/gwnh84Y/vXAZhTmriEud\nym0FB2ZF/tP6dZx4bmCOoaqbSkx+g0DRwt8n/2Kl4wOHpgsmpaTj0XRuveRsVK+CY/gEOOeRDh2v\nzVj5gvWnpZ7m9ecWx6dm+b1fwl+IZ2e4yM7O5v2PP6dPv/4B/NzpU1pOsbDDvH8I0BBctWht2P32\n7tjGP/5xPqpXYekrLh58eRGHxydjsuVKskRcQip3efL5+Ls1lHn6krf0VRyywOF0IFTjfSjN+Z7S\n7O8MmWmXi1vve5Ta6irmzDw1YBHcVqNrzOQUrr//KeZv9G9zx8SScvK5fuYPX/6B5VWVzLQcI1rx\n9MKlFOesJj5tCqMnpkBuXsT20FRGxU1i+swL6eF2MGz0OEp9iwL3f2/gkr89wIr6vWxxhEk5WHQX\nDJ8cwUCW6DtgEHPvfIgXHr7LkJu23QPN5y4OoKwjcCFpHMW/oHfIkuVlLtle005RJsHEZIOmsih3\nNXHJmYwJw9Gbn5vDpJTOkXsHKMhZTcGvRTjdLh5+5QOGTJtKUc5qpOWfI2beiw6U5q0mNS3DWhh7\nNYHDt5gw74/Fby3st7FjY4TxzkFJbiDTzardAQfvMjhfuxLV0xggSLJ3xzaECORSblY0K90g1tX5\nUugtYUJSKs++vYyrV/vdK8tfW0BixlRGTEjC7fAv3jyqhmy+jxjzT78jJ6HbomOmx/V/qwv44OcI\n3O9m5CAKbUaB7/x89IgOyeZUIzCaZYdVTyL85nNFoydgoSfMP6IMf6RYRICrFc/E10vfRgsjQS3L\nsuU12lnr6VTVo/Yi+EWrbFTwKrovrcLXOW0vpH1ialZshSMYCksut8sXPo5sIPfr7vJ7gQ4SzFV+\nfHIqF159E0fGh+ZPDu7WNZOAQ5JwSFKAdLEpP26FqITBKhAcKrYzEgiET4WpffextlmxjlOcs9rG\nIHCgfOgGnE6jkEW2SQuPTfDnXptX1eTLg9SFYRwfe9zxLHjiEeY/dDdX3/kQl/7lTp57exmJqW0z\nWPwS1cadDafuZOLXDev8qQheL+UF2VbRnnmsTaUF/F6azeDGHciL/45QPeiaxgkzzych4ygrnQKM\n9AFVUampruLP198aEiFqa4/bVFrIqHGBMsxzn1vCExtl1u+qg6B+Yk8lMS93QlIa519zE3GJqaxf\n20pqTVMNLz1yF18vfZsPXnkWCRgu1+NuruaiW+/jhHMu4srEUJaCwd0k3Ht/jmhPOdwxHDdjFnXV\nVVZOZmCKhT9yZRoWBXm5vPfSfMqK89EFbK9p9tHbBeYyml6ptqLBo6L5CozGJ6Zy7pU3MjYhNax5\n+dh1F7C5tXsWDh+HSjGDsVg0WTx+LDYKNZMzp+LaaQhFiCkXUTU8K4C1RRf+dyPgWObiKOh5dwRC\nmF5Sf3F2JHaFzoZeWxGybdfWXwOeh6YLaptVdCEo31VHk3LgUuUqGrwgYGJQysf7zz3J3y87m5VL\n3gb8Y5mq+99B8DtDzLGoSdHQhUH99moE4xiMInNd16POVpR8ojpeXaBqui0FyE87adHICt88FBDp\n8N8fEdR398cZ1NX4w4McAb/9+kv7fyRJ3Hz/42RmGgpJXk23DeqdG7ZrDUaej2BtYSCDw8a1RUzJ\nzMSr6UbIA3yhTd+/vlNUNb/XUxYS8Ump3PD3R5h3/x1GznIESD7duqomhf424YIDBWtgMV9WoEkJ\nPd9usbHQ1LmqYQ6nk/MuvAhJCq5S9w0U+AcR09MuMCZvswDH2N+4jh/3NnBo71iG9ml7EYwRgjVW\n9wkZUyyJYlmWCfahO5VGVFf3sMfpCGSHg1Fxk/jTuRcxfEwcy157nt+Vbli8BTajzkyDMOh9jHth\nsLt4ELqOIgS11VVccM3Nlkx1W2EffNcWRPacNjR7LINN6Dq9+/ZHlkD30b5tKi3gvqtmoXoVw9h3\nOEAyQvMnzpyFognKC7OtPEVZlnG6XKRkTt2vAf+Ra2Zz3bN+NUaAipjBwHZ+3NdA4tDe6JLAYRbL\nWH3d7PF+D/364nzuvuYSuPKNyA1qKrqm8cqjd4PAYJ0ANFXjbZeLUeMm8P2ni2FEoBCEyx3DP156\nn3cLthBOpPvcS69iQlIqLoeEJEuIIPtm+2+/ICYMsc6/rCiPey4/F1UxCuiee3s5iWnpSD5RncBi\nvvb5TjfsqUMX8OG7b/LFJx+SdeJpHHvWRWEXoIrXw3cffwDDQ4UvWkTEE5IsOrCULCMSkpCazpOv\nf8BNeUb/W9vQzXqGwpD4s4wuvwKlT50Tw7O1P3UKplc+LjHVKs6O7dmH52o0kLreU+t0uVAUEbBg\nGjL88IDr+XFvPQ1eLSAv+0Dh18qG8OH/W5aivXgZbz7xd0aNjWNMQqoRKRYSTt/4LgNe27kaUuo6\nPdxGpMBUUAyHh1f+xM1HHcGsxKFdc2H7AdP41SUjwuF0GNcrYV8MGDbE5r31loKtlXxhvb+2KBg+\nZ5EgKqv0/vAgh0F2djZPPBbeGwDAF/PDbj583AROm32J9dkYhMJT9XQ1tlYbSacWvYwP5UV5qLqO\nV9Wt8KYxmdp4OBGoAn5cW8jy155jY0k+qi6o9aVZtJRisaW6CY+m80tFQ5deX0vwr1gFe+o9XPRO\nccg+nuYIKmkdgCw7yDzuVP7z/iecddKxSEgBBrLl6fPda00PMGXYuKeeBo9q28//PNQW7nUw7GkL\nAsHYyanc9cJ7HH/2haQdfXzI/j0dnUsQPX3m+Tz+zmecdv6l/PbjBtas/B/bN/nTG8xXwO5pMQsW\ndQEDBgywJkxd1+nlK4TVhaHiZFLBBfOPW8f3HdQslikryuPOy8+NeL6u7n2QfLR0kixTV1OJhJkr\nLFGe7+fw1XWNo884j9nX3sY/X1tMXGIasuwLGUoSDqeTU8+7mCtvuYOinNWsKwo1zNu6PlZc3di8\nriRgm8ljLIQpR2tGSrAmH8tDg78YqDRvDUprROC+e6BrmuXptHOif//xYr79MFRyWxeC8QmpHDf9\nmNBjvn4tSxY8wsbSyNzPv5UZXlrhO1Zp7hpLtUxR/j/2zjpMqrL9459zJhZpEFAERUVige2ABVQU\nuykJW1+79TURW7E7AROUDltQEQR22Q6WJRWUkF5YNmfmnOf3x3NyZ2ZZMF6v3+XthbNz+jzzxB3f\n+3sHyV++1OGRszlinZ6qxoouJDftc2PvojhrMe88fi/fzZoSmUVGCL43PIQu+bUofJspP+dAVeTE\nZcXj5Yzhl/LUe7OIT0mz+kF8irtoT33vmjkPON/TnqMd3uRDEKf3r3dyGol9B/D202PR/6ZI0/1v\nTWX0zfdy48PPWdvaHXW069eQBsLBG0N/hphzdZhOrqhwdBy6rvNJ4VZGTs63FUeHMVMb1HhswRom\nfWesocV5CCGITT5w3lHx1op/nAcZjHcz2iRoeLndUQ9hrV/OtrD7tP1dHg8IObcVbalga8Wftyb/\nWfKvBzmCLFq0iFCgLup+z5rFRAr2hIJBqzM4CbIFsHL7fo5v29TlKfwrxXyOTR37wVZ7tHVLSKcu\npLO6OI91hdn0SOpHz8RUqoMaTX0eq7OvLsrlmZtGEwoGmePz8cwHs0joOwCvz0eggelqetFW9tUE\neeysyDzJf6WY8AKQXhYELP+1POKxNTVVwJ/kPVWge3wSqX374VGl97GpA2JhWcwgywQ79pgTyaod\n+11WuKngHIzYC6v8bipViz+fSbB1J7jkDNfxdbU10PTQyvVGkhPPHYEKrCnOZcY7L8uNQXscrSrK\n48gTB+L0dZqvKIRgwRKbJ1hRVfbtlb+d+ZuaVHDFWyvo2q4ZrQ+zOYkBftldbXByyusWZ2dKbuIo\n0rRtB3wxfoKBAKqq0rJ1WxQrrALx6f2Z4fcRCkrIyInnDScuOZ1mfi+qqlCam4UekuVXdU1j7+4d\nfDdvOqFQEJ/PT+zCQyz/e91HHN9NwDp726yS3wHZV2pDmuXZiPEYJcsVxYKVWF5lIYhL74/3o0k0\nSPCnqni8XhllMLhbrWfRdbIXfh0RolNVU8vaFflsy1rKdXEDmeB4Xsq3EFJkGymKVL7DxMBsmn02\n3hHxMLnYTUXONgBspbGxkTkzIWjh13YFRLwxfFv8C5Nqwo1nkAaasuQjxIlX2I+78nuatO9MTdNw\nuMmoo0P8VhdL5owH4eKn3a+582dOvWAssYmpqAaEJ6w4jYHrVIQsImGPC7dxbbMG2DC2/E17DwqL\nLYT0xlqsGUIyNoSCQf7sUuMNybBrb8OrKLz9Qa71XPUVftN4UqIkLZvwtYNN1owke2uC7KsJsqsq\ngC6gKhDihlml4QcqCj6fn1WeTgB8UbaN82KPYH1JIavzl5PUbwBH9Uxk9Y4qVqOivvU8cyb6eOHj\n2RzTOwkKCxp8DrPP/5NEMYHGIKMbjiI/rtiVkB5kc71TFVuJNtlozE+rEmFI5/lFsmLxZSmd+SfJ\nvx7kCDJo0KAGMciPTJxN6qCzJP7QIS3btEUgaXlAdgTTm1UX0qiMUHv9r5C6kGZh2L7d6h5oXWLj\nWV2cx9M3jGLqG8/x5A0jWV2UR1Cziw8gYGVuFsGki9CTzicUClKUk0mvROmRbNfhyAbvn71hZ0NO\n5r9M1u6sDPPCRMPUqcqf1PU/uhkFhdat21BZFzKu7YZY2N4eqDOS84TuWORx47F0I+ZsvoN2kJqy\nJLjPZdq7r7Lkq1mG4VbvGptXULP/wJy8ByPd4lNYU5zH3ZcNYee2rcbD2H3+ketGsqIgx16YwcLA\nryjI4fMZttfO6/XSrGVrpr77KivyzQXUfr9IXmQZjrW97/F9++NpE72v1gSCXHv/E5JxQ9eZ8Mw4\n1hbnGXy1Esc7/v1ZjLnlXq685zFW5WexpiTf4LGFtP4DUY1CIkIIMhfOJ1BXKzmLg3+sANAb6yL3\n251bN6NpAg2jMpywcbxOT5b5d4/4FP774qQG76V4vFxw2fWM/3AOPfokhu2vKA/HiwKoFTt49NqL\nmf3Oi3xw9+iw/V6vj/j0/rRsVY+nd+G7AMSl9nN5hXsnpfHQO9MZceN/Gf/+LHonp7rGCCBx+cbf\nAkHB5n1hhQmcIoRgd3Udmi445ezz7R2n38zm7udGPQ9A5M5xv6/Hy4CdP0Y8tkXrNmR/9yVsrqdQ\nvXQB+r6dLPpipryGYjtPXHOT4zdz5nBohjGwqyrAL7urLSUyqLsp8Q5GNu2tYfWOSnu+R2KRPV4f\nf3ap8Wiy9KtZqLiZXSJNc04DIZKUbqtg3c7KyDsPUjbsrmZnVZ11v9JtlWyJ4NXsd8YF3P/WVOv7\npwVbeXXGt4y9aigfvfYM9145jDWOqpm6rhEKBSlcntnoufyf5kE2WSxMQ85d7VQ+rHDORQ5HjeUM\nEbbib81XNJwn8r+WfxXkCJKRkcETjz8adb+O4IQ+iSj1QDPNW7VBF1JR218rlSUzjBV58AuqAyH2\n1fw5BRwCIZ3dVQFKf99v3DtcQkKnLC9LhjJ9TQh27U9Zfpbb+wR0ScxA9B8DJ1+N1+ujT2oGIR26\nxiXR9oiODT9HXa1r8jXlYBW9gxXngDQ9s9E8CzGV4UVODkXU8q3ous67z4xjZaGJ91asDGeA1cZ2\ngQGvEMK1yNuTiY3HNRMXdlcFKNnaOEXWnJxWFuZy26VDmPbm8/z0+QyJ9XEYBJ23ZMHsxyD452Kw\n16/IZ6XRt+yHshfckK5TYITNnVXghBAUZWeiGV5GRVFIP2kw7z37MJNfe5Y7LhtCcX62rVQ38AzO\nybht1ziCV7wd9VjFfxj5SxZaFa9CwSArc7Os4h9eVaF3UhqtWrflvWfGMuPN53n46qHMnzkZj6oQ\nl5zOWUNHWxqP0/Pq9Xr/kgJA2zb/atxLwigsTw5uRUkIGUEI6oIWx/Rs8JpCF8z94E3yFs3n59UR\nPGam1DhKyK76kbhdmXYSaJ074nbq0Et58JVJKMDbT491X2d9FgAfrgux5JfdDlgQdEtIITYlg+Kc\nTMswAnu/JkyOYHuBDmlyHt1V6X6GqroQv+yutsbSRWOu5O4nXySu30m06+2GN1jy/VvWnz6/O0Jx\nx+23c3xsn4inTXzyfjQtOlOOWRyk1WE+jmndlBZNvG7aP8Ud0jd/yVWFuXz81iuU5OdYbaALQVDT\nHFCsqLeNKLVGAR3bCBd07pnI2Hen4zkAvemfJT99MZM1hjFqSsgI2zuNXyGQyZVRRn0gpDdYJfJg\nRFGw1mpB9LUj+aTTw6qS5iz9yZpH6vwteajEQXXWKRav10eP5Ch9rp44HSb/FFEsxdgxx5pjEBwG\nnvzDrTjbfdvtFJLn/0OLBwL/KshRJa5XbNR9T15/MS1a2yVUTTErRpmWkrS6olvABZv3sWp7Jet2\n/XELWNcFv5ZXs2FPldU5I3kWduwPyHKZPh+ceTvirDto29tBv4Wgoi7EM2X2e419Zzrd41OoCWo8\n+PUa1u5sGF/sj2kS8Z2LtuyLqiQHNd0yKg5VTI8KDg9MtA6+rfiPlTS1RSCETjAYpHC5LFmrAFt/\nWWMd8dh1IynOz7HC3s5JwunxBqnwhHTdNUmaOOTaA2Rxm9d2Vj4MhYLovqaQeJ513ObVRaAF4KsX\nYHOk9KpDk6dvGGXAFByt7lCQvb4mMmwuZAKSTGKVC3ZCen+ruIk/pglt2nVw41GzliGETNwRAn4t\nr2HD7nByZ6eytauqYcNz49oylv/wjevk7IXfsGDWFNYXFzD3vdf5ZsZk3nnKoChDVoN79bH7KCvM\nQ1Xg7KGjiImJcc8DisJVV131l/Ccd+gsqxKa/aM2pFmKDsI0yIWhROks+nk3D369usFrmrJkwVeu\nq48i9AAAIABJREFUoh7tOtYLd354E+z6DYDe7fycds5FVtldj8c90rr06E33PgmU1jeYcNPdLd1Q\nbvX9YEiwxohumYbRivwcl+Fu9hf7XeX2Lftq2Vju7g/lNUHKawIu6NF5Iy+nycgn2KUfRn1RN+Ry\n7QWncOqwSzl75OU8/t4sa9+UMUm08sGHzz8SpfUiz2tmct5ZQ0bi9SiccHgzOrSIoUWMl5UFeXiN\nzEWTfUU3DIDnflzP18vyGfufEbz38nhuHH2RS0k2I3442qYupBE8gLJYG9TQHMl/AkFlQBaPOiEu\nhZgmMQ2e/6fIBzfKCMsXM119YWVeFmVFuRRvrWDZskwmvf4SJQU5UY0As2jKnyXOuVlGHyMf91bm\nr6zb5V4DnQq8emQ3976Rz/Lwu9PplpBKw+a9+RzRDYL/pdSGdFvZBUspxhqbTuicsD7tvibF3mN8\n/6e5yx3yLwY5iqxdE31RCQUCLJw7FfTwbuwMB0J4uIF6+1SDj/KPSuGWfa77m5Z3fXny+3XcOvB4\nHnh7Km+UVLMb6HB8DyusJxSFinoe7W4JyZQV51Gak8lmX/8DPovf7494b3Ph9kRIV920t4Y91QFS\njz60sqm2yPYuK8ylOCeTUNcISsrEqxGxg/7gfaQoioqigs/nJ6nfQGMbbFhdCqr0NoVCQQqyltLd\n8DqYlnb9rPwV+blkLfuJrgn96NuvnzVxCCTt0MY91fQ6soULvhHh7aWyaXAIC10gzrwdujpo0nQN\nRVVp0yyGPdkzoPNjf7whJl1LKBTkl1Ur3NsdXtX7XvuYPslpBDSdgKZb3vS6kE5sUhqvfzKX/Kyl\npGQMZH9diAVzp1t41GSDGcKsFmdWjnOKiW0zFcblC78Bjon8vFtXwfdu77Ku66wrLWJdaREerw+h\n6yiqYnm2reM0jQXzppOQmk5cShrPfjCb7+bNYMHcqWiaht/v5/LLL+evELXlEQQ1Hb/XgweZPS+A\n0oJcVuZmktRvAAkGJd7qojy+LtwOngYgUeVb4ItnABh05nnM+mgiwWAAr8/H0P/cSsGShezbtZ2O\nXbqybP5naHs2QbtjaHdUFxQFTjl/BCEdvCrMd1z24+fH0aVzJ5L7DWCyz2cxfXg8Hq7771je3mce\nKceBJgTVAY33inYTbNkRsXMDwSAULF9GXEq6zRtuGFdOpXlLRS37a0PGnOf2/NkKj51AlLtpH5Ek\nMbUvp52TxClCp00TP3Waxrm1v5G5sRxFgbL8cGW/vnQ85jh+d3wfcdO99OzVmz7JaS7vbF7Ocm67\ndAihKydA01bU1VSjI+fG2pCgcEsFhXhQHTSN+VnL6BqXgq7LCqgmrlMXwopa+r0qcR1bRn2+0m0V\nBiuI9PwJZNKnJbruZhP4rQSOiW/wnQ9KtJDsc8CCWZ/QvVc8IPNVVuZm8cCEe7n7kad55fGx1AUC\n+Hx+XvhoFklp4d7XPzOZrToQsr33htbXEOXdK4vW4m4o+bfq8XD68EuYXw+ZcUJ8CkIXzF5cwIH9\nktEhJf8rUcAyvmxnmtEHMfqhBZeS5aZVRX7a0Wn5aZIXOL3R/1T5V0GOIqXFxcBxUff/XFYcUet1\nek6dYPRIFqFppf4Zg6G+sm16kiLJmh376dLmWHarmwB4dP5aHj+rO/EdW6IICNY7b/bifL64f5Rc\nHO6Y26jnccII9teGWL+r6oCGwB9tB10ISgty+WbOdL6e9SlaSEPtdRKccad9UG0lanU5qGrD1cGc\nsm0tHNk94i5NC6EoKhdfeS1xyZIzU1UUuveK45vV8oW8Xh9J/aTndMGanazbVcXNA44lpAkjiUFS\nXN1xmaxo5TWSIlPSJR2QEAbtkCAq1VFlXYjfymsQwiySMoeffvqJnftrWdjMXUpXiWmGz+dn+HV3\nMGnqrMa3Q+VuaH54xF1q5S68Pp/s885ndHiQj+udaIfWcGBljXfsk5xGbGIqHlVh895aHnxnGusK\ns+k/8ET6JKfbXKNGfMYp+Zv2Gttlv/9y2kdMe+dduOSlyO+y5KOorAOAxXGuoOLxeAwPsi3rVpbw\n+dSP2Fu+hz6pGdz1xPOcO3wUuYsWMOyiCyN6j3OWL8c3836CI56Jet8DyYK1OymvCXD3yV3RUVB1\nwYr8HB64erhVDe3lyXOpqA3y6LUjCJ10DSScHfV6h+XP5PCWfi68+XnGXHE1g844h6xlSzisRWsm\nPjOOUDCI1+/j2vufoFdKX979OhPRfQCLP3qFJbs2IHQdr8/HNfc9jvrBQ+gV26FmP7qus6o4j5vv\ne5g3pn7O/DnTaX2Yj7OGjpTZ6jluo0MIweLleWzytIez70L95A6ZqNdvAOAuXGTxiiP7UkVt0IrW\n1S8eUj+C19AU07pNGxRgXXEBG0qyiU3O4LKUJEYnHYWiQJ/UDGZ5PQ2Ol4uuvJEWqV15zkg6uvDq\nWxCVsp858cdZS5YQDAYgJA2H2mqZSIgCFXW2Eu41k0R9fhL7DbA9cUJQkp9DaV4W6f1PJCU93TIE\nGhJhKNVmm6wqyqNg+TJ6p2ZwTGyirBxpXML76R2wZzOhW2Y1eM2DEofSqes6bz75ANwmsd5CUQgF\ngyz85gsCgQC6phEkQGF2Jgmp4Qqy+a5/Bv1b2Xa7XoGZVOZpQEGu2l8BMS1s6JoiqfwGDxlD+46d\nYUP4OauL85i7pXFB+3+SV9VKprPgXFK7NWGUAtlW++s0mvk9tvFq6T8OrzMCIRRXVOef6C035V8F\nOYqkJicyaVFkT4OiqAhj4XeWTgU3iwDYHaOhMfxnjAU7XGF/RoMzCGBi9ibXtqkFW+l+RnNivGrY\ns87epKIEQ4jGZjgrtpcUFKoCITQhwzM7KwO0beqLWBXpj85zJfm53H7pEAJ1dbb3tV72/ZnHNkO7\naAy/turhJAloWPLmwnn3Rd0thM7USW8x8LRz6H/cmQB0i+0NBp7zoXem0zsplYCm836ubPebBhxL\nQNONktSCguXL7NLKIUnPlZze17j+gdtmX22QKsMLIgR0jUvh9V+asHpHOHwn8eSzGH7bVRI206En\nkzfWO6DsR+h1SvhNSr+H1YvhyrfCdg29+ibiB5xG8xgP382dZnkMnQqypkuflWvSdITlTPyfx/je\nLT6FPklptGtm8mnLyVUXoAoJPfmtvJpj2jRFVuSzvRJLv/uKBhOOhMDr9zPksmv5dW0ZoJCzZKHr\nEFVV8fljuP7+x1m1soSFc6dZinNZcQFlxQUoqorX6+WsYaO58vLLGX319cR3OzbiLZf8tBhtyxrY\n9Su06xL92Q4geZv2OkKZkO/oO8GgZCTY+NtvknniAElXNdU1/P7bRrp064miQFxKOsfHJfPRWy/b\nEJeAYOpbL9Ch0zGI/M9hfTZi7+8Wk08oBPv3lXPtlZcwafxYdF3H5/PTOykNBYhPTqd/RgY9OjRn\n3oJF3HflMLhpBgAVe/dYEZ93nnkYhj4BwOlDx3De8FH0SU6TuFTdxts6sbe60R/Mwqem0vR7RS27\nqgLGNjt83hDjnUdRWFucxxPXjUALBfH4fDw6YSbH9klGQaFnYiqDLxzJ/E/vhjEvypNq98MMibH2\neH0c2y2W449pzYThcVa5aOlhU1xwgowTT8Tn81OnS8OrSdOm1nwZCtmD/aG3p7NxRQ4DTzyJHgmp\n1jVXF+fx+PUjCQWCfPLWi7z56TziUtLR9XBedaeYThldwMqCXB68ejghwyg/6qpnqWxqO4VuvuFG\nDu/SnYdXRLzUoUk9pdNpeCoeL16fj1POOp+S3OUEAjKSkZjePywya76L2TcOlsmjvjgjr6aR3RAx\nhhbSwC9sJ/JxqXjLvue4nn14//XxcN4D7usDi74I5xKP+jyH9BZ/jZRu208gpBPUdTyKgq6DUO32\nN+ddTTjZKmxFWBOC2qCOV1VsxdmI5mhCsGN/dMaw/7X8i0GOIsNPjUDmXbmb/lu+xueXWEmf38/5\no+qHUt3eYrM0bKQub1nAf4KGbHtIbKUj2nVLtu6P+Cw6hjIfQRvzxsTIQgkRJIyaRbgxR4qiWBPP\n1ooattUbEKu276e8+o8nKhYul2VTTeVYURRXWPO582IZ0B5++nIm61dEpneKJB6vD96+rMFjdF2n\nMNvAIBsJXqZ0S0gJCyUJhEXDpgssWITq8VhJkWabuRP5IosT7w4SaxhJOQY4+ujOlOVl8d3sKWxa\nV+beufh9WPJhlJsosGezFSJ1So+EVHokppLerx9nD3MwGjiMKrOSmQCmFmxh1JQC1hbnM2fS66w0\nsIbmu64pyuOz999gdVGelclvc2saxo+AHZV17DOp3RyL3IDTz22wwYaMvpyn35/N1Xc/zLV33Edx\nbpb7VVWVtAEn8/onc7lg9BXcMO5ZrrrvyTDmGqHrBAMBvpz2MZcOPY/VJdH71cCTTsbj9UBNZMO7\nsSKExOqaSmJsSj8LC+zz+UjoO8B+9QMZtYr0lpfmZkoKMkVCM3Zu3WK9q9B1CrN+4rs5n0r9Zq8N\nIlAUBa/XR1xqf04ffhmPTJzNyJvu5f63ptIjLtFiBOnTsSU+j0pO5hKCDuq9fbt3oSOYlfcLIUM5\nRlFo17EzvYwqZgKDVsrhiTKnVCdPsHPO2bKvxi7G4zDCGsLobiyvYfGXswgFAwghCAUCLP5ylq3T\nKQqnXDACf/kmyPpUbiv6CnZtNH4XnZV5mShA8yZe2jePke8PNr2bcbGTBw7g7anzaGEwfMQc1pSQ\nLti6r5ab59oaadf4ZIZcfQtxKWkOI1JQ6uDodmL0TcrISOKc14UQFOUsc1wjwEaHcgzS6OmRmBq1\nvQ5JFJXY5L4RScET0wfw9PuzuGD0FUyY/jnX3/0gr0yeS8/ENFcUwH4H45M/voY6vZnmFHPAKzrv\n2f5YvFe9wfLvvyYUCKdEXFOcF5FLPOqlG33kXy9m7osQgtJt+5mY/Su6MNiyHJFqs286Mce6EFTW\nheTfmI4e+7dct7uWOz4ri3DXf4b860GOIoc3C68Cp2xbyzlDz+ass85mTeFyThl0Mt0TUvn8zUzr\nmDDGChFhm+NY4xA2762hc+vwxJHGir1ImJ+C1SsiE9tvr4xusUXzPN/3xqesL8xiWoRzIk1OpqJi\nHxPuXTelsi5k1bGvDoRo6j+0bmmWTQ0GZfGOwReN5IiTR/CxURRRVRQLRygOgs7omB692bj6pwYn\nLUX1kGKEg1UFvB57AVhdlMfClXn0SbPD7pvLa+nWvpnVThIWMZclPy2me1JfuiekurOAjfOqAhp+\nr4rPE27bWp5ZGqbO+XLODMSSybINjkmE4Y/bO/PnQbPIOPCWh7enU3Jf1vtjwnh1n//vdYx7dwZd\nBp3ImUNG8cX0yZLVwelBNhUVAbNXSCXrsaw9KDPeZ9aEl3l58lxiE1NZUZDLkzeMJBQMMm+Sj2c/\nnE16334OPKnxvsb/1u2qtDwT5vazLr6MLxcuiRTpBOC84aM5oqVUYKT33r2oeb1e/nPHfcSlpFsl\nbiv3lYetXIrJPywEgUCAFQW5XDU6cnGS9L79OH/EGObU/sGse0Xl+aWbuBeFHgkp9EhI5an3ZrIy\nL4uUfgPomZjKSVVBfvx+Plq9hKFwUaWCmyZzC1bk53DX5UMJ1AXcY0RIruf6ik3fU87i/CtuoGdi\nKiEh6JGYSs9EGS1Ra/fKOzhO6TvgRHx+H+YM1KptO4SAfa2PB5MkQ1HpmWz/3ooi2Xd+LimgNC+L\nhL79ZX/AaTTZkQXjcW2aTSyTqsFE13W7qji2flM7PhUFYhPTeGTCdN74JseFNZaRBB9x6QNQVAVV\nV9AVmaiNYXg4m65lEx8jzj6Ve3K+gTqoq6kmoAk+W7mNfY5kZadnDgSaLhNbzURrizO63wBW5OdQ\nmL2M5H4DSD36zLD3K9yyz9VW8Wk277SCEsbt37lPGutK8hyt8OfI0V27U1G+hy0b3DG8XkmpxCZ2\nQgiMfp1CbUinOqAhIvAgm+aQEMoffkSnJ9Sp6EUVVUb+nFIjvJT8/BsMeTjs8MeKdYT3INb3CPfe\nWxMM437/O8SMOmzYXc2T38vf7IZ+XdCFitkKIV16P0K6jldVEQbOXSjS8aYKJ2GBXRlza+Wfw+D1\nV8m/HuSDkA4dO5G7eD6z3n2JFq3bEJ+SHjYund5imxolMujexiYLtu0/9CoyWVlZfPTWS5Tk51iL\nNQJKC6JXsYokQsD2iloe+nZN2L5jeyewodnxEc+rCoRIP8YObzk9xu7rR550hLVfYsEOVXobSual\nt97L5fc8xuEdO7uUfY8CfdIyDKxsvV+ueh9nHBFZcdmQt8RF4WXJLMdEKHRrAVRQXDzaT980hvde\nHs89Vw6ztt371So27Kl2hfT6JKdx4dW30C0+xV7Yhe0hEQh+21vNhj3h7A2mMmAn/kUXXddt5ady\nV/gBUbwxFXt2s6ogm2BdeF8NBUOU5cmiEPEpaVx63S3mzexjDBon19VbtEP0OMliARFCyEhAk1bo\nXr/k4M5eZmHfnN50czEzPWcgsdyfvvMqZYW5dOjUJWob/LKmzOoDlfv3IZ/K/AHdfcP0AvZJy8Af\nE4NiFNa47IbbuGD0Ffj8Mrri9/s577STad5AMaAefSInPPnVhn6xcKlqdTSPLNvJyqI8hICeCalc\nfO1t9E5Koyag0S0hBf+1E+Go6Gw8AKgq1z/0NLFJqQ5joQEDsl7fEAh6JMrqcGuL85j01P1MfOp+\n1q/IR0FGcJo5DN7ktL68NmWe9b1F67YIIWjespW1rV3HThzXJ9mVNLWmKI/H77yOGd9n8eh/Lqas\nMNfCQNpDXPaNX/dUu4wop4frQJRSg84fjs/vR1EUfH4/gy4cYfMWY/QFVWHHEoMjeUO+TMwadgkP\nvyuhVAqwtjifz95/g3XF+a55wSl5OcvZvUPSTW7ftJE1RXlhiWHuoiiS1kwX0D0hhUcmTOfK2+/n\nhY9mI4BbLhnCOy8+zc1jhvDD4iVh7+as6CoQHB+XzLh3p3PWjeM49bJbw47vnpBCWV5W2PY/Kgtm\nTmbrrz/bG7Ikp3BQ0y0KP73ePCGEYGdVHVV1tvHgdLiYn1lZWYwfP56srIN7bue8KcxIagNe6Zgm\nh0WeJ8+/P2KehlC9MOaFiNe66JjIcEMnDrk2qPHzrv9NdVrzMcbNX2tt04WRlCckdWmtwU8esOop\n4J6rjQuZOGa7fQ9u3vu75V8P8kHI9t+38NnnEn9ZsnwxbQ7zcu7IK6z9Tpo3U2wWC5ml7fSQWgpQ\nFM9qYyQrK4vBgwdTVxfgg9df5KWP5xCXkoYuBE1btYZGRnPNBWVG8e8Wds8p/5lRAkTOhhcC/nty\nV+78bCVbKmpd7+U8Ro/yrqb3z7QuD1aCmk7J1gqEoWTuqwvy6H8uJug9DOXEauglyyxruiyN++ik\nGUzL2UCJ8yLvXMb2S/4LR5zkvvjMsbApAghv+XT4zfbQCyFxxNcMOZPkzq1c8IZgKAS6Rqiesbyz\nKuD6/QWyNHZzvwfNObHgDClH7isK9oSk65LeK6o4i6Ts2Szpu658y4BOKLRodhhhZkpVOZR8K/+O\nELb3+ryWh1xR4LWXXkBB4eP33rGO+bmslISjBoSXz1YUPB4PiX37I4DEvgMQejqUb8X76R30Ts3A\nVH7qLxxgRydW5uVw65iLJAtDTBN6XzEuahNkLpzPiX1TWDB7CpPfec29Uwg0TaMgWzIomMk6PRPT\neOOTueRlLSV9wEASUvoS0HQuHDGa5cuWcOXQc+jSJbpSnp+znJceGwvn3B+279jAVtZ6O0U9N6K0\n7EBJfjZ9ktII6IImqsyHqDMWqRqtEWNJUajcW45HUVEVSMkYgM/vJxCoM1g8VFRVlYqo4SVySvbC\nb/l+9hS6do9l3DXDrfDyj59PZ/ybH3DkUR3pdWQL1znxKemQadAsKjYbgym+mCYS4qBLXgcQzCna\nRPCqiQAE376EouxMeiWlGXONrQCrilSkwDTQBdaHgJGT8xtsjh6JaTz+3ix+W5FLt8S+HNsnmbqQ\nLjHESE9aWW4W+tbV8NIF8j7Gw5flZ9HU7yGg6VYEZK7Px7hXJ3Jkx45hqIIlPy0GjpDXEIKy/CyU\nlKGuY15dsoEHT+tmzQW1IQPLCXRPSCWtbwaqCjMnvGZTOxJg7tffMfjkE2V7aTrbKuos5U/eTzKf\nzCvYQL4/GdqGt4WiKPRJ7w+NR6MdWAwubZfDIWsqvr5DmVH8O7UhnXsGdUUXQuJdhQkZVNhTHSCo\nCXp0aG61mWnk6kJYa2EgEMDv9/PDD42vZGmvPTbEoqFiV3I+jnBA0wZw0K2OiLi5cm0Od50xgpd+\n+sV1fVFvXPwZUMxDlfr3DglpRCjCdnwJIRP5/B573XIaMbqQzjS/x4+uS0rTaBUS/ynyrwf5YKRe\n9bWF33zhCh82j/Hi8dhJbgoKtQZORwC7q+tYtV0qTpLVwbZaDzVBbdGiRTLj16ACKspeKjFqhblM\nfjNKBn8EWberirkrfo8EDTugHN26iStDG9x2oa28RWPzsCftQ2kHyetpTqSC0pxMCaO48CF0QzkG\n6T1SVIUeCakccXS4IrNp/arwixvKserx0KZ2h+Ol3A3l98dw5mmnGrsU1pc5lWpFeh1bukvUmoue\nlXCkC26ft5LXl24kZHAEmxEJs+2itY9iMJjoApb8soc3l22M0lry2Y8+vpuNp92zGd4YRcuvngAE\n+3f+Dm9d4j7n/euhwnj/L5+x/wb6hH7j3hcm0NNIylJQaH2Yj6bUuZTpnEXfsr8uFBEH6vT0mdhT\n2hzFg+9Mo3t8iqUY2yFzt2dNCMHXc6YRDNTJUF/f0RQ3i1zUAWDj+jWsK8njm5mfhO80Eqpat2nD\nivwcprz9Cj+vKEBVjMgq0OYwPylHt0ZBIT4lnctvuuOAi3F25tKIJZuBAxbfiSbdE1IRQlj8tqan\nZncEIzeSeLw+EtL74/cqmP+dM2wU54+8jJPOHcYRnbsw+NwhnD38Urr3SUSJUIFy+XdfsSIvE81B\ngaYFg5QV5YVF2Do0j6E+OigkBKrjSK+i0KF5DEFN/tK6gBXeY+39/iYkGIlbLn5VET6HOI9pbLZ8\nn6Q0rrr5Tnonp7sWSKmMKfRJy5B5CYaoHg8/fjadaW8+zwNXD+fHz2daCY6hUJDVRXnhpaWBk046\n2Z5HFIXYlIywyFbe5n02ptN4/EU/72b0lAJ2mzzfQhqVPr8fj8eD1+cnqd8Ay3CoqA2xvbLWNjAR\nfD7tIx65Zhj5azZEbQdFkZGJlMriRrXbAWXbOvj0v/La9bD8upGs9/nK7S44njtKJI8NaTr5m/Za\nXmbzOHMt1DSNQKBxlSyFEBRs3uvyRJvwCq0BhTQQiJIA6z94mGRedjZ9u7hhbfWdAIpNLvK3S6R1\nJ2isT0FNNziRHZV4HUaGNfKEk7nInscPRd/4O+VfD/LBSL3FoU3bdq6Q2Mb1a1jXao/FPqALnZAm\n8KrOEI783GsQ2JsD/FBl0KBB+P1+6gIBvD4/CX0HgDCKRTSUsh1BZpVs49QTItN4RZO7enlIO7Zt\nWE930ryBTekVEWLhsDAPpS2sCkhIi69XqoRRBNq6kwc3/LyOFZ8toXnrNiz8NhsGXuHav2dbePIZ\nyAXwurHj6X/+Gbyb9SvLNpbjBL31G3w2Dz9wH2cPPtnatuy7L6H9WcYFVLr2jGf9GY+7rmuFiI0y\nnmYl8qKtFejI8uSa7rEWh/qqidODYl5PF4L9gQMUXFFUtv62gbRBZ5Cz0PAKB6qp2OGAbtS6fchn\nDBmFrms0b9GSX1aV0v7wWn4IQlKnltx/6hBq9u60ns8VJna4YQQqmi4i4KMVdE1j/twZFGZnktTX\n5tqetr0l8UgjqLQwl5KcTOLT+5OS1g8UYXlZpGLouG8UWj5TNqxdxX+vGEZsfFJ48ygKuq7zwiP3\nAwpaKISiKnQ85ji2bdqIrul8/OaL/PDDD6hH9TTe4MAz/aBBg/D5/Wj1xor6xXi4MjpLSkPy/GoP\nz3atplOrJui6TGbTBdw2r3EFYC64+lbiUtLxe1RqfivjxtHSA68oisUwsO23DVbBC4/PK+keHQP1\npDPP4+husXh8PsuDrHo8JKamh92veYyXY9o0dW2rC2mu6WPTvlo+/TGHiwZK3vD63qt+Z15IbdCA\n61jMJeYirIRBcMwe2Ji5RVGgRYzXgtVgfJreY1WBnompXH7P4yz+bBpt2h9Ju/YdmD97ipGwKdlU\nTGyv1+sjMbWvfT3nvawrG8+HiOyxMhV8IcPZ89fsBGQi4nGHN0UX0Ds5jdemzKUoexmJfQcQl5Lu\n8kCa7bEiP5cfPpvBvKkfobc8ArqE939T1hbnk5iSTkXmbDgj4cCNdwDp5a+geXIibdq159iecbz/\n7Dhp0AJaXQ34mxnPaipkiqGkOuBmwuTBtp0t5hFHxqbg9/stD3JjKlk6FWLhuM8BMchNWjSw8+Ck\ne2JaGGOGM3ro3Pa/EDPC6xSZNCs57A/ze+pBmuxnD+kOWI+QvMgIe0z/qyD/vxL3r/n9l3MZfvk1\n9GqlULZPsH7Nau5/80Ze+ngOiUedBsgOYmdT2x3Cia8zLapDkYyMDH744Qc+nPM16f0HcnyfZASS\ns9M7dVpYMtWBpHTbwWGA37jzEh567SP6GFnWppiTmWNLVMyR5RHEtkLDMMJRpDoQorzGKB6hY7qr\nGXjuMH7yenCqipNeexG9bJEs/hB/rrXdW76JEArs3lT/8gwedimnXDCCPklphASceFxblm0s5/QT\n+7JD/43U/idxyfW3EnuEe8Lcu3s3tDe+XD2B1munhr+35fqSHwGHQaPrOBYCw+AwQo5CCAtWYlIb\nKdiL4AFbTlGl0oesehgwvK4NyZi7H2Xz6mKLWkotD8G5UqkzJ3ePaicjmRhAj9djJf/kdDqLbZV1\ntGrinnYUVUX1ePhm1ifomsZknw8M7tXVOyoRQGFutovr99Up84hLTnMtaGcPG8mXsz4xkjBYiMLp\nAAAgAElEQVQPPJ6CwSBdTujBivwcV5lrzDYOBLA6lA6bf7GTikwP1eBLelKSn0NO5hKUIQ1DLDIy\nMpj31bdc9fmvdoLXtnXwSw57tm8Fb+eo5zYk367eyTV9j0YH6kIHV13MPFZRFBYtXkQwKPlnw44T\nEnZy6kVjaNexE3VV+/l17UoGnn4uQy+9kqqAxvUPPMXbT96PrmmoqmolttWXgpzl1t8V5XuY996X\nVHXoj7PnfrRe0KNFHu369w/rmku/nkf2Z1N45oNZUhHExqya9FLmuwkDYhE+H0UX1ejQUiFWjPaR\nbbS6KI8F86bz/bzp6CGNTetXc/4l11iQAV3X6Robz0nnj6AkO5M+qRn06d09LMIG8JMDYkGzthTk\n5RKTFg6zsfJYgHHfruEXIwfBfF+MeaFPchoJqekW1n/1jkqONhK/dQElBTncdfkQ6mrr5NlXv9tg\nO6zKX872DWtZX1YCZ9jbm+vVVKpNo5+YOwfShoZtPum0szg9/norsSstKZ4JLz9L7rLFiJAd8Sgt\nzKWPwSkvBFQFjMQvx89XH3+sC4hLSeP7779n8eLFDBo0qNHwCjtPyObnBZOW8q+XwUNHhc3ZOqY3\n1mFA/Q8U5FVGTlB9/cSkbbUrB9vjzSwAJVCspFgdI2HV6MdmtOsfrh//C7E4GOl0ZAfXd03TKVi+\njL77cqCuClq0I6h4KVouk4pMJSeg2fXii/NyGD9+PCV5ZglNwwr+A50/IyODS2+8gz7J6Vao+rje\nycTe+PJBX2tHZeNCs6YEAwFW5WeF1RRyLtNOftrIVrkbzH8wbbG1oo7fK2ot7+nn0z7ikf8MY2FO\nCSHVzUSiaRq6rqHrOsquDQComZ8wpmO1LL1dudvCFZpyzdjx9ExMM3hMofeRLZgwPJ5rLr6Ix96d\nyuALhuP3qmFe0VaHOzzxTVvRpP3R4W/t8HIB1ISciolwtZf5u5qJRpV1obBJy6TqO2Dz7foVgLbt\nO3D1fY+jOsKdqsfDCX0SueHh51yn/FxaRFn+cosWSttUSowIMqRPR8sP5jdi56UFOQwePJiJEyeG\nJR1t3F0dRiPYK30gqQMHo4VCDsXU3U7FOZkOrt8ghcuX2gqC0X/iktN5eco8Lr75Po465tgGm0Ax\nKNHOGTaKJUuWcNFFF+HxeBzXtO4e8XzTQ7UiP4cbR1/EhBfHM3jwYPLyGk6MHdC/Px2PdlT3UyT7\nweFHHtXgeQ2JjvT2zZ70uvSyb208jdwXH73NqsJcFGDQyYPweb0oihJG6agoCj6fj0HnD2foNbdy\n5V0PMf69GVww+gpURaGZz8P+fXus5tI0jaL8nLD7ZWVlMex8u3DJypylzHr7BcryMsOOLc3NsuAF\nThHI6pQluZlyfFjjyKaWctJ1mcpz9QFKtYPtKVZQLI+x+f5rivN44OrhfDtjCiFHKfcVuctc1/hl\n1QpiE1K56Opb6GEUvgGFFjFuBoKTTz7Zjko2bcXnMX0jOgbshF1hKceAtX6YSdnm3Gn+qw6GXNUm\nC7KWGhR7jZtgW7Rqw4SnHwxLUK4u+Ablgxujn7hiQcTNv2/dLBMdkTSYCanpXHPHffj9fqtgCsDd\nlw+ltCAXIaSnMqTrBpOH/dzW713vvTMyMnjggQcOAntsX0t+l/epDob4riA8Wf3PljM7QtyRLVHq\nuZCFgJqgu91N5fPvlKpAKOK6ffOclZbybDoUzJUn6MiErQ3Z+o1pyOpCUGccozSyL/6v5F8FOYrU\nz4I9raNKami9yw3g8agMPvUU0jL6o1Rsh449EJe+Snzf/o5FVg5iTZeVj24eM4Rx48Zx0+gLeOnh\neyjJz8XG6hy6uBRM4JPCLZTs/UOXbJR4vR76pGWEeUeEkJmtv+6plhXecC5i9RY8S3m2sUoHI+bg\nKyvM5dXH7kfrcyZc/FTYcYrHi+rx4PF6UbeugglXoRbMpVv37jw6cSapg86MyPVsen/MV7SodgzN\n0KSoc8rJZ57v+p526jlh1zXbQjc8wl+Wbbf2Pf3DetcioFsxN4k3/Xm3XZmwOhBiR6VMwglpB2i/\nL5+F0u9QVZVzh41i/16bukxRFE65aAzPfvo1Z4241HXaMzeNomXrNjbfrlbH2Hgv3ds3syb3pn4P\nCgoFy5dZWEBd02hTZzNlCODRBWtd1+6ZmEarw934bKesKcqlT1qGxRNtV1hz4NqMtuyZKBWTwzs0\nUF4Z6HRcd2556CkSUtLJyMggPT3daF536ymKwrEnuOEaJ59xjpUAVLB8mfS66hp1dXW89NJLDWbQ\nq2bc3pDDjziSRybOoE37yAk8jZF9e3bxzE2jmfrGczx675089cP6Rp+r795MUY7NgWzOWx6Ph9HX\n3Uraiadw+Q23c93dY3l58lx6Jkq2C1WR5aX9Xukp9nlU4tMHWP3D6/ORmJIeBj0xcaKmCF1H79AV\nvWs473zvlH4IBIFQvR7t8eDxeIhLs+dZt3LseD+HYlVf4Ygu8pn9XlUaC0bbrMjNJFAXwKlgKoqC\nL6aJ6+zyXTvt4iDYSreZXGZKRkYGber1+xnFWyM+kTR+3aIZIW5bwXNEnAxlZE91kJ2VdehCkNRP\n0mDWx/9Gk8p95WEVJMFgLyjf4uLDdkkwMiPTF7NnsKZYGpA+VZX4/eR0Xp0ylxgH00kwGKRouTQ6\n6kI2B2/9lcOhLluwjIN1NJkeUCe3NsBrSzayeEcDJ/5Jctd5ffGoqksRM42htTsrqQ6ELGiNELJS\n5L6av48azW7P8IYt3CoTLi1qWSOHN2gYNELYP4hM8pVXqQ3p1AY1vlq1g1fzGkgm/wfIvwpyFKkP\n8Pf+mkd8YpKL5umh8S9y7uCTiEtOo1MXgwKtRXt6JaaRk72cN15+nnUl+dbEnZ8lF1NN0wgGAnw1\n/WNuv/QiSgtz/5TwiTOcWBdqZNW7PyinD7tU0jxhZxEoyEHzy+4qdlbVWUqg6e1xSrWBl3V6Uxvi\nKo0kppJUkpOJhhdOvT7qcemDzmDwhSOlV6RyN7qmSSojBbr2SeKCy2+wT9A11hUXoGJ6lbAgBKqx\ncIL8u/7vFxfvxux16RmeMCaw8bjzSrcxOd/GQJdtrzQmFdsbZhoZ4F4IKus0dhuMGGVFueQviuzB\nAWCHzJTunZxOfEq6pL0zFU9/DCedN9xKtOsbWgc5s2DZJ2ihIJV7y3nqvZmMuukeHnxnGqoC895/\ng7XFeS4DYuBJJ+E3EoZUj4eK+TaTxeZf1rN+l5umTgAZ5wzD6/WBxwdn3eHaX5qbRff4VJ79YDaX\n33Yf49+fRe+kNJe3zGwbaSCIMM91fdm8YR1vPDmWFYaXc9CgQXgjGEcej4eHnnuN/z75EuknnsKD\n41/m+YlTLA/VeWcMxufzo6oquq6zZMkSBg8eHFVJlv3G/t6s9eH0TEylY4smEY9vjOzYtYegLqRH\n0xPT6POUD2/Et/NnkvoNQFHknKdpmgWnaN6iFS9/NJPbHnyUy2+6U5ZSN5S+1cX5TH1X0umZTd07\nMY2n3pvJ6Jvv5fGJM+iVkBRmOJs5E5bEnizpr9Twtu8Wn0ptUK8XWQFO6MeJ54+iZ2KqOzJleZPd\nESnAGksHbBPkWC/Jz2HyW6+wvkTStKlAct8BqPUyDIUQtGzlTrBq2749a4rzmfve66xdkW/NF2H3\nUhrnP9Mx5oF6B2/+eR0zJrxGWVFuBKPAUFY0ncpACCHsEvTX3fUgtz76fNT7dW2hMP2yZFq0drzX\ndknL1qZmG77S+VGLRqFrKNV7ydiXHb5LCFYac63P64CuoFBXWWEd5/F4iO/b38jLENa6YL6/xW/t\nGPeNobeMJk7ng7lWr9369yhu0ya8yrqSPPc4cTmMYMu+WlZtr3Q5U/5KsahiMdtTEOmW5jGVgRC5\nm/bijASHrLLUMqdmw+5qTEOmLiTNkbkl4QbW/4rKLpr8i0GOIoMGDYJZtufr+F4JICoYfMEIdAHD\nRl3CgP4ymUhRFDyKwFQRSvJzePDqEQSCAbxeH+PenU7v5DSS+w3A5/MT0GutTmhay3HJ4QktByNy\n8CjWhKHWR/3/RfL1x+/g96hcc/dDLvveHNwmvMJcuOqvUXLg45gEBat3VHLc4U1p2zS8WEskMSfI\nuPT++GLeiIq7FgKyf5xP+qAzZEUzTSbRtGzThseuHUEwIJOTuFOS7KuvD2fVjfeQmJpueIPkw1uK\nsnFds3SrU45o0YT+x7Yhc2O5vHeEqVsgeSO9qmB/XbinRghZGtdvXF8RRhKSI1QFENA09tWG+KW0\ngEeuvZjAsf3gnLuiNIKc6Y7p2gMFyR399PuzWDB3Ovt272TJl7PwexX6JKVxfsoJFL47llAoKKv7\npWXQKymN4/qksLIgV+KRg0HmTPJx34vvcvwxnWnm9zD63NM49ocfWLRoEXkr1zJ3qZ0Fv2n9avC5\nuXmdDCR0Hwi9TnXt75WagRCC2KQ0eiSkWm0n28ieyE2Dw+x3DUqglkBdHd/Mmc5/hp5JRkYGI8Zc\nzicfTnIddtOtt5OY2pceCakMGXMFMV7V9VuePfhkPp33Fc89/QS5Sxej67qFT44U5lXrZWsJpPJ1\nygmHs74om8WBg4dabPUcjjL0MdRZY1FVhQOkaNrPUrGdKx94irhkyWN8yimnWElOXp+f5H4DMB9W\nNZQ5VYFVxfk8dt3FhAJBJr/p59Upc4lPSbMKaZwQlyIL2dTtDfsdMjIy+PKb+Qz+/MDhLYGdLe+S\nk6+BI4xok2Iv6OaC7KSNM+cGBaVBDuSW1DL2vCQUZAnmWy4ZQjAQwOf38fCEGfROTCW9XwZ3PPos\nLz18jwU7ELpOm3bt8fn9hIJBvD4/J8TGMe7aEYQCcmy88PYHpJ8SXrgjKyuLvXv2QPMIHGvOdjDm\nxlC9F/j028WIHycye9IrvPDhbJLS+jqMaBuLbSWgGUpyfEo6u6sCvP5hZDhQ8xYtUBSo3FtuT+Cz\nx6EefjS3PzkeJX0Cawqy+KbZkZTXR+S9MgQUhZwPn4Xb57h2KYpqFaRp4vUY26AweymEbPjJGUNG\nGxR+zipttifSZRgby67TeXAwIpz/HGtUbXUlKM0PcPYfl49efRavz8cTk2baGx2QRJB5BXYk8S9/\nJDbtrWF3VZCkzq2sNo6ExzZ/k1d/2kDx79LAufOk40jp3AohBDVGIu2Li6VTZtblKQR1OxEy0hx9\nZMvGG/h/h/zrQY4iYYubovDsXdcxf9an/PjFTGuNU1BYWZDLbyttssjvPpshqdc0iVFbmZdlJBGk\n88Yncxky5ko8XlmswrSWBcKq2HWoYoWLhLC4W/96EXz20dusLrYn28qKvawqyrOsfqc1LIDy6iBb\n98kwnBNCYC9wB4e1MgdxbGIq9702OfqBegih62Qv/NZIponjynseY8PqUgv3KoSAb1+BmWPxen30\nSsmQWEQjnV0xXcgGFZiCQjO/l7ZN3fhCRYGBx9kLX/7mcFyo6Q2Qikf47yWA2pDmCh07FwbToKgJ\n6lQFQxTnLCPoOQyOS26gDeRE16xFCxRFoYnXgwIs/Gy6wWk7mYevGc7q4nx6JqYx9p3pDLv+bqMI\nQppFx7XKqEio6xrBujqWzf8CJ6LMxAKeM2wUXscs0+mEHmGPNK90O199t1CGcyNMxLanyMRY2x41\ns23M42qCGj+s20XR1oqw65iiznsCyrcghOCLmZ9a3t5ho8bg8do+A1VVOaJdW3p2aG5FDlo08dKr\nXkJmSlpfrr/zfmJiYvAYBUOiZdBbfckhiiIN2hbV2yOfExZcDxfRqRcZN40nNGL8AY+1zhGCyr3l\nVhlkM+H3/oce4Y1P5hKXkmbPc4qNHS3Ly3KVKC7KXmrQmClWpMXrSHSrLytXljbq+dYU5UloWgRM\ntd68nYUtDupOKjA7EO+cUyoDId5d/mvUe8U1raZk3vusLs5zwWaCwaAVYVIUOH/U5Zx2vp2AJoSg\nW694nv5gNtfdPZa3pn7G/n17CdYZ5wcCFOXnRGyHnxYvalQ7rC3OQxNQVy95Uo89BXHazQTr6pg/\nd4ZD0bNZGeQzmmPIbBeoaYDhyGN4dXunZRBjRkyDtVxx+eV0NyrcDf3Pbfj9kaMeMgIRwUwLVDmK\npkhH0rMP3k1ZcQGKebzQGXzhCCsSamy0fuvKgMbKbftdyrNw/vaN1JKtgiKZWY510zbKmjb765Vj\nwMKxr3Rg8Jt4VMugMcWpxEeSkKa7krz/iFQHNEK6TtGWfdb9IhmXzfxevKrqqsy7+OfdFmSlPtyp\nfXO/td5D+Jp3WUonV1Ghf4L8qyA3UvIXL7C5LYNBCrIlRkpawMsQdY7QgBAWXtLr9RGbkgGGlywu\nJZ1zho10JUbJc6Bs235XpaCDEscAByjfse3QrnMwYla+EILS3CzKd0nQ1v695Txy7cWUGOFrc8A4\n8XFbK2rMxzY+TRyZ28scaiCcZIZMzYlRE4KKNsdFPrjoK1j9k/VVC4VYt7KYD59/hH27d7qPLVtI\nXKc2koM3IcVK0AMnxEJ+elSFru2a0uqwcAXZaaRMyQ+nkNN1gS50hBDs3h6OPbSxZ8K1yJltKIRg\nw55qdlTW0b6pn94pGbLMaeygqG1mzk6zP3yHlQW5+DwKK3LcHLahYJDS3EwURZZ8veCqWwzsqc1S\n0SslA9XwAgkh+Ombz1lZkBt2u4TUdO4a94T1vWOXrhEfK/+o043nCzcSy/Kz0JFJVqZSHNRt/Lbk\n4TQovQS8nxPORuKUrs2ClvtC10IWnCq9Xz/ue+J5iVFXVWJiYjjllFM4zOcBJKTGp6phpdA7tz6M\ni88+lR9++IF77rmnwQIFiqLQpa3NldrUZ3jSUOgZgXKO9dmcvufHBt/HlGW+no06zhSv10ef9P4u\ntomMjAxu/++9xBn4YecapioKMUbOgY0H95Pcb6A1JsxFr4lPlkM/prWb7WDChAncdvNNjXq+J28Y\nyTczp/D4d+vC9ulCyKpyuqRDdCrDlgcVo0/oggVrd7K4geI5yxf/wNQ3nuPhay+mdds2+HwSIuTz\n+YhLy7CMiLLCPBZ+ZVcCRFGo2FtOXHI6l910J/Gp6bRq0wazEqHQdVq1ah3RAB40aFCj0vjH3zia\nkvwcSvPDxxctOyCEYMHcqZQW5AJyPNgKn60Umx5SgWD9rsrwaxmyr2I/c997HQV49oPZXHH7/Tz+\nwRxOHXoJqsMQSuzU8sAPb8oPbyOKv2VFbpZ0KhXmcv3I85nzyQf8tOBrhG6se4pCcU4mKwtzXeuZ\nE2JRvyKgPSdC8dYKyqsbTjQ3C4qMGzeOs848nRXGvCWQld6qgxrNmjdr/Lv9ATETYuPSbWrLJj4P\nMV6PW0GuZ+TUl7U7qyj5vfHJuQd6JtNQMNs2kvLt96h4VIWAY42O8cpt5rhzGiwnvyWNEfPF6g+J\n9s3/Wd5j+BdiEVWysrLwzBqLlnAudOtP9ryPJZjeyOZO6TcAgZzfWrdpAwG7Cs6xPeN4bugocjOX\nUta+L4XiSGId3tT8rGVWxr6maRRlZ9InKS3M4joYMWxpS8nc+ftmUA+NNqrR8vpwQDIfrMjNpKb7\n0eBvBaqHQF0d+VlLSTC4UM0J20wqsZ7bGPS6sPlLTdlVWcev5TUWlVl9KdwiJwSBVMA/K93OpOzf\nIj9r5qeEEbsLQSgUpPXh7fF4vJbXw+P1MuL6uzkhIdlSClXFLqQABlYRBb8n8gqnIJPWGpI3lv3K\nnuogo5I6sX3zJvC4mS7WFOdb9HlmGxmPbSmDOysD6EImSXWNTyGmSFDbkCPBsXAXZi+jR2IK8en9\nXRy2Xp8sHiGx17ZloCp24lGPxFQGXziS+bOmSONP0yjMXsaJA/u7bhfj9ZCQkAClMsJSHYgeJfH5\n/QQjuEi+b5HBuQGNGI8K6FYmvE81WSdMmjvBmqI8DqR1DDxvJBvXlMmQuNdreXs7tmjCg3fewnHd\nY9mxOp927dqxaNEihBCs3LafouXLOPXUQRx3lhsC4lEVmsV4ycjIoEuXLhx1VHSYhAL8J/1oerZv\nzoY9VQw49nCrV3XvHQ9lJe4TfnyXBVV74I7BkS538LJpBcx5lBOSMrjk7Wn0TEhFVRRiO7i94qoC\nuuFJtL4Dfo9CbEIqz3wwm7K8TNL7n0hcihzj1oKnSA/yYTEeOrRwL3qzZ89u9KMGgwEmPjsObpkZ\ncX9tSDMYInAYkorBoKMAMkITcGpaUUTTBegawTqdtaUreHvqPHKzlhKX1p+jYxMlZAQozl6G5sBE\nmxUgY4xkRQWo2FuOoqoIXUdVVWoqK4jrGK5MZmRkcPiXu9ldF7bL/WxGuXVNF+DvH/kYTaNw+TJJ\nfehSpBTHHCtQkFCNcd+ujXgdgJ/3Cza8+Txen4+XJs9hzPV3kJ+7nM8/eIP49AHSWAYy/DvYHtpI\nkTeKU8Ipxd9Inn5DESxcvkzyaZvi0IQnv/Ys097x8dLHcygUHencqgm9j2xpeVWdhpCuC0cOiFTn\nqgIabRpgoatfUKRw+VK6xqUQ0gRVhhFeV/X3YGHNnirZS2yHj+0KcbBACRtGWV9C+gF4mxspVUYh\nJztqJ+fsc98LZ6T5OH8zH+dvdm3zqiptm/rZXxciZBzjFCd0pL7R+DehQg9K/icK8ssvv8ykSZNQ\nFIW4uDg++OADmjQ59CSVv0IWLVqEvqkUfrMrognVw1kjLuGCEWMsxQ9g395yCNqz3P695fRMTOOo\nnolM+TifgvLtjE7qbHR4xcomDgQk32+r1m0sb4dZj/5QMMRC2NV/OhzVGf5qJ7Jp0WsaBcsWQfeR\ncoOighBGNSdboQsZaazOUJExF1ihc4FkhPitXHqYdSHYXxuiRZPwrqoLMxlLXnT7/gZWmghVj8zi\nB4MvvJjjYuOY8NQDNoeroRyYirCq2mFmC4Ns/ougkKmKws5fwz1f9WVB2RaGxnWkQ6ejw36vsvxM\nehtcv7oAVQiwlGTZWiYURVEkvMDr9UADSqjZDj6/n5R+A1BQ6JWUxvj3Z7Pw8xmEdDhjyMX0Skqj\nNmgXcDC9g6oCPlUhpAlOuWAEP34x01I0U/oNCLtdbIfmfPeTHT58d3kUAwa4+r4nmDj98zBAwa46\nhXU7Kul5ZHOEpuL1KFZyo9m3VEWWWC7Lz4qqRLiawfx0KOSqqtDE5yE+NR2tY0urbK2kf1PQtBAf\nvP4CnRY2voRtJPF7PcR1bEnPI5rjNftaveQ9+0FFGM3WH5LfikELkpTejx6JKVYIsVlMPW5qxb2A\nmcNMAVAgMTWdpLR0w7sufwePojiU6vDEVYD27duHb4wiqqJGxQ3v27MbIY5BM357sz+Y+GPz3uZi\nfyDEmaqqhudV8OWsTxk2egxX33IndSGd/bUah/k8KAq0atMWp7Z90RXX0yspDZ/HZrtI7z+QT97y\nEwwG8fl8JKf1xe8ND9YqisI7I1MY8XHD5a/VI7vRK6UfubuA+oEoRVbp9Pp8tGzTlslvv0JsSgaJ\nqekOiJsdgUKRhsWBRIb+oTQ3E10XPHKtxJzPnfgKp144kq694njv2YcJHpMC57uL3Hj9fs675Fq+\nrN5DqKmEmfWMTybj9HPplZiGjpAlzX0+i9FEUVXLySOhLTIyO0nIolvTLk2uN+bl/BfQwOtxQvUO\n+GpWomjAWVwLI0JlZHvW7N8Haiv7pNWLoefJB774wUhQ8s8Hg0FW5GSCKplc5JyuuLD0Tqq/SOLs\n839EVu3Yj+IYu6bB0VixMeOwvaKWr1e5qUB6tG/GhnIJr6w/3/19sNDGy98OsdiyZQuvvfYaeXl5\nlJaWomka06ZN+7sf44AyaNAgyY3rEIGgfcfOxKek0bapn6NbS6U+pd8AVN0O6/RKyzDC545zHbiw\n3klp3Dz2SSvz/a2nH2JloQzz/LK7mvW7qw4aj1xakMPkt1+hKC8HXRd/E7+gOWHVu5cq6ZF0xyCr\nb5WCu9y2pgvLK+r0gAhgzc79ETPQncVWnJNJRKmvZCgKZwy/lMcmzqBXUqqL7iwUCjHznZdYWywX\nLp9HwasqRiEMQ2k2buaJUHoXIOGolhQtnt/AA0nZtW0rq4ry6NAx3Ou46/ctrCnKNeADkg9UKss2\nHtlZqcijKAc0rFRV4YLRV/DqlLnEpaRbk1RsUho3P/Icg84fzqr85awqyrMw16ZhYCoAzWO8qIpM\nyHp84gwuu/U+HnxpouVJdEpuTjb3XTnsgO0AsK+8HKFH7vdm2Bjj0ySat8LGAoKaxnGJB1Zccxd+\ngx6y2RrqM9YouL1MwWCQkFFAIxhsXAnbaOI0rJyMFqoSZYGIVM72UGXOo5A9E6/fT6/UDBRkfwnU\ngzEd5vPYkRLjOVs18VkRBI+i0NTvsWAHpsEY41XxeRT8HjWqN2jnzp2Rd0SQs8b8B18UnOteExZl\njIXaoGZBrezF3eBkNTXDBqTzsTb0JxQKUbB8maXoN/Gp1txSsXePRZOmqCotWrSyWF9MYzkuOZ2X\nPp7DmJvvleMsMXpOQJc2h5F4VMNQheDI5+gcm8S8LZHeQUFVVM4ZfQ1vPPkgk14az/1XDaO0IMc1\n3zqTGBtLguDxeEjrP5DinEwLcx4KBlgwewoTnn6QYDBgQUn+j73zDq+iTN//552Zc5JAIHQQEESl\nhJIEUuAAQrDg2rDQrNixr2UV61qwr7uurn4VsYuNJvaOIgIBUkgIXVFR6SWQhJQzc2Z+f0w9OSWH\nJv6uy3svFzhn+pl553mf537uG4Cd5uS3e5/+XHLz3bx79UnOV+tWlPHWs/9idWkhSYpEZk4e73z4\nOZdecSWjL7yUPtkD3fNxqC3uRHfOK89a3gFe1QkzKeCYUVg/9daqetZsrYrZxxIIBJjzyefcMOke\n/u/tOaRnmXbtmu7m3VObN/hNKhO/bxPGM2PNPw2Dmqo9TjXy1921VNdpgMHqrdVU1KgunSzKKQU1\n27TDfKceCLw0lpBhNzwnHkus3lbN9up6bKOQhrD7ZurUkKVm4TmPQ6zOsT84LBxkTVXsSp4AACAA\nSURBVNOora1F0zRqamriliQPFwKBAFNnfkxO/t+QZBkhSfisEpFA0Kapn+bJZqNdVs5ATjrDDQK6\n98tGM3ReKPjF+cwJbKxne3dFBYZuZodUVaV0ySKnm7+qTmPllthNRg1RUFDArReP5rWnHuOBK8fx\n0btvMO/9Qz/psF8UkiTh8/txXkLCTLcuW7wAMB+66nrNGaDBvBa7a4POZ1WW8YWdGfV2XkerkHq5\nybaiQ9xXYANuqyQEx48aR3qWWWK25c7s0mj50u95+OrxrFteRGqSgkBY2p32+laAI0UPzAuXLmHR\npwmUkw2YOeVJdm6O5CjP/W4hD0wcz/LipSbfMmxCYDhBM5gvZkVq3PD41idf4daH/kPfAXkuZcIK\n1FaXFvPAVeN57anHuOPS0awrK3aNEjB1bxWbj22tk56Vy/iJfyfdkvRqOFeaN28eWn1t49cBSM8O\nIGKkQX5bv8550WMYThOIl49Ypxk8uqrxLETu8aeg+H1RG+rsYM/OMtk8VMXipCZqYRsLdmOnG1gK\n508lWlSp63Tt2Xu/9+dFl3atOHH0hdw/dSbdM7LDJj1epCYpZB/Zwmm4S0tR6NAs2Qns/YrsfGev\nL3CdFG097GgYPTqxyRLAF21P5vZn3oj6XVorMxNtAIZu3g8rSwp594WnWVlS6Iy3dkWtsYTBb7/+\n4vxdURTO+ttJHNOmqXtemM/YgEFD8fuTLFlEPxl5tpKROw4IAX0H5HHe1TfSzzJvigVJCCcLHw8N\ngwkvDAyr0dhjpLNkkZO4MLCbpszlE3JtFYLjzxxPRnYeAwaZ+taOiYlhoFv0EcfgYtNqeMtUzlm9\nbAlfznoLIQly1B9g23oruNYoLyxAEYJUv8KJw4fy8tQXePGFKTSzpfIkiXOvm8Tjr87mmH7ZzuHM\nfO4J7rhsDOUlS8OSBCHdsGyPw/m5VfUapRtjc3JbH9vPNddyrgtOoNmsuZs9bi/XMewIX4wtHRz8\ntGYlz5xtSoGu2lrN9XPKIxstiZ5BLt9cSTAUct6pBwI3qeVOqvYFu2pULnqn1KqVR65tVwGum7OC\nnTXhelN/wvj4j6dYdOrUiVtvvZUuXbqQkpLCyJEjGTlyZMRyU6dOZerUqQBs2bKFTZuiC6gfSnTr\n2pXr73uMteXj+bF8GU2T/RTP/wpRV0XrEYOptpp1UlWVtq1bwmZT0mtvxXZ+r9X45ke3KaR293b2\nBH34ZRndMOjeKx3F50NTzQG5e8909uzYhiQEsslQYJMcPbDYXFlHixSfM7B++OGHqMEguq6jqfD1\nrGmEmvWIuu7BxLBTz6Z6dwVt27Yh/9RRvLd6N0sBmrZEnH473Xv1ZveObeiGQU1VHapVTt4TTDKV\nNiSBGtIJ6QbV9RqSAL8ko/klJwul6ebLd5NU4/ANwbTA1HQDRRLUqiHqNQM1XiAWZWBZ9v1XdOna\nFU2S6NqlK3f+Zyrvvz6F5YWLMXQdTVUpWzCX3D49TTcn3aC+TkUTgqAsocqC0N5KNm/eHBHcfPjh\nh4RqE7DtFrB89VrKi1dDpwaB0ISnUZ+7gMXffkmLDp3M7uYkGZ8so4V0J6Pgk82Qq7Y2iK4GiTfv\n7XxkF6p2bkOWzIBMDRnUh8xyavH8ryyHMB1VhZL5X9HpyC7UqTqSX8Kf4nMS8XV1KiHZLMUFhcDY\nu4fKHT5Un8wm3T1vWZYTrv117tKFdkd0jMoMWr9iGcelH4kiSeakBDB8ChgQwrwPqhNocD2aHQw5\n4WS6denMrytLOOOkfLp27Ro2voi9dXTt2pV3332XgoICAoEAv+yq4ffVZQwdEohY3otEMqRVu/ZQ\nWxMkZJhcXU0yKSPRgiAhCU44fTSvHISC0G8bfuHiCRfR5aiu1O3eiSIJfMkKKBKbUiJfrJU7rd9x\nr4K/eTJyTZDqqjp0A/bUSARDBke2T2VvMMTmyjqTuxnSrcmUhFxfyaZN4Rng008/nauvvpYpEXuL\nhIZMx06dYcXvEd+t3q2zYeNmOqclIQn4ceVyHrvlSjRV5W2fn4f/7xV69c1kb3U96yqCvLA4vuuD\nQ2MRgtFjxtGt21Hs2LqFql01hHQdIaBeluja9SiuuuV2vp/7JdnDTqTrUUdRXbENTVFM9QdrzArp\nOmpIp6pGhtpKNm1KibrfVE2jvmIrEF/SsnZPjAZDIZAVhQGDh7G6ZAmaZjZf9uiVTuXObdQrMiFD\np17TCenmJDehkrlhcORR3dizYxtdux7F3U++yDefvs/CLz5CD4VQfD4uuP42tlXW8AnQ4fdFbPG4\n4b35v0do2749I3u0puzWqwhJpkFTj/R09lZsp0lyc6p3VVGNVR3zUBRHnjWeZskKu7a7I4Guh9CC\nsOibL+h0ZBdUXSeomcpPiiRQZQlNkahTJBSrSUwAm+RwzXWAilqVij11DjWnJhgylT1UjZABBSVl\nrP09BSTz3n381F58/Nb3jV+zA0DukOGkhao4Ks3PL3uCbK0OUrljG3WK+R6sU3VUXUcSsEmvpq6J\nz3kfVmw3n1PVeh/GihsSwZ4dVchSeK9QVc2+G5PUVGynvjbyPnuz4Adyj0gmGIU7Vbu3im1btxCs\nPLSTkX3BHx4gV1RU8MEHH/Dzzz/TokULxo4dy5tvvsmFF4Y7d02cOJGJEycCkJOTc1iyzDvYw1Zj\nL/7UluzZXcH7708nFAox67Wp9PnsC07KPw4wSxz+JhWAGSAnp7WhRZMQ4L5Ek9La0LRZEsmKGSAP\nGDaSyS/NoHTJIvrlBuifN4jUJBlFMh9wTTfo2LFllKOCTaEKfKlJdLQ6EUaNGsV/nnwSQ1URQrDh\nxzWQeayzvPi1DKVrBqpxcDk+BV99ghoMIskSfbIHcu/lF3DLjKWsqwL92ADZwwfRxKcQMnR2y7X4\nrYxz87QUQob5MAdDuilRU6sR0g1SkmRS/QpJiuQMYAJB+w5pYTy+jVoFwZBBsiKh1GtIaghf0l4g\nRua9Qak6KSmJAcNOIrVVWxRJwq/rDBpxMmktW3HHZWMsXq2PAcNOJK1NOzRdRwsZBGtU00FMNrv0\ndZ9CxyOOQGlgIDBq1CiefOppGp/PC7jgSYzk6LJCSkoT+g05gaS0NmxYtYy1JYsZOPg4emZmo+km\nr7uJX0YWgsLvFlK1twZibAsgtVVrmrc2ua8dmiexpbKekGFOUHKGn8R7r7+Apqr4fH6yh4+kacu2\nCC1EU59CWlO/k33SaoIokgQYyEJC98u0aNuOJj6Fjh3chq9QKOR0RTeG3VIqDBpHNFWzbn36k9yi\njRkgW1lLRRL4FAlV00lSZFYuWUy8Unr/ljq3nzYSTdfJGX4Sx598CkO6tY5Yzh5qRo0axahRpvV4\n0W8VZFw4LiqXNHL9+GPVT3VJ1CUFMaxnwCebExV/SAciFTiCqsapLbbyafX+u+2BmRH68K1XGHf1\nLRzVdwCKJNGmmRlgduwYeR18zeuprNNo3dRPixQfnYGFP+8CDJIVmWBIp+uRJse05jdz7KtTdeu3\nkVBqfVGvRafOnWBXYse8ORQ9sFSFzD3zt/LuhQOQhODHNavQVM1MEmgq61avInvYiSTJdfz387Ko\n2wiDJzs6ZMgQOnbsSFWdxk5RZVVpBMmKRGnREl548nHUYJCVZSX0yx5IVs5Aq3vfzDJrumWPrJu0\nk1CVEvOe+Oa77/lpeSEcFcnf90Jp1pJIAjKktWnPTc+/S5/+eXTPymVN8WL65QbIzB6ILJlSXGpI\nR1Z1QrqOphskazqwobELAqEQLdq0p07TyTruRLoPyudv4y9mVVEBmQOH0K3vAFRN5yLgy+RVvLLg\nA2ftyt0VPHzj5dz74izufO5d1i1bQnrvPuQOH0mKT+aII1o4jY8LFy5iy6aNgPm+87doTUqyj5oa\nFTBpG6LXMJRflhIYcTKprdoRDOkomo4RDKHI5m+TokgkKTJJimS+NwQ0aZnqOp9a2PRbBc1bN0eR\nBHWaDtbEWq/TWFNaxAsbW4blGJq3akPW0BOZ1aB/9kBxxsXXsmHtSvL/djqjzruYOi2E378drLdG\nauu2JCuyOfkPatRrBooEWrKPKkWmtzXO/qZWIITJt1ckKWbckAg2BHfhlyVq1JBDa6zx19P4/RKO\npLQ2KEo93hgIYErJdoJ5R0Zdx98klXbtO9DmT6Rm8YdTLL7++mu6detG27Zt8fl8nHPOOSxatKjx\nFQ8DBPBDWRGPXnMuX81609HG1NQgC+Z/5yxnluDcF/OcV55l58+rw7ZlGDYN1nCoBMdmmLa43TNy\n3GUMt6QSC7G+M0tPOqFhl8Pwy9zzWDiN/F9m7cupJwQ1aHLQQprGf++bxAsP3o5X1CGkhzeI2FSJ\nhvqVK5cV8t5L/2N1aaFbRsflz+mGQfnmSrZV1aPrBrtqgg6XCWt7qqbHd0/zcJB7Zw7g0y++JN0j\nXSYJM1DpMyCPh1+eyfnXTeKeKdNJz8xxG/ZsJQeEy7+MsctAIMD4S65s/CIKETegvenxqXTPyGZd\nWTH3XTGON55+nJsuOtt0XyRc8mhyOXG3BaAIySkHH9WqiRNoSkKQnpXDf994jytvvpNn35pDn/45\nIKwmRU/5WAiTZ2r/3fxc0LF5Cke1Cg9o8vPzIyUNY+Dxb9ezRY8ur7Q21JLKOs05Z92wysYWb7C0\neAkPTroh5rZP6tGGW0+1S7bCejknPmFskeKPToHYD7hyeS69QbY47g0hyQrbN/9O0vqFsHHVge6Y\n5Yvn88BV4/lhebGpBCJHUixstE1N4pg2TcMCDK9VtnfMS/EpDg8XYm8TYNiw4YiSDxM65Ae/jt/o\nalO0emcHUHwem+uBg90Gv4R43NY9KgS7drnZWuE87+a/SxYvtKp1ptznyqICR/Kxa8smHspJjKZL\nDwoKCjhl5Ins/v2n+AsCG/dEzwrukZqyeg+89/IzGAacfdn19MjICddNx+6BMXm2Xs36WJAVhZzB\nQwGLH28F/+mZOYy78u/0yfJqZAtOHHMBnY7uHrYNTVOZ//EsVhcXkJrWktWlhawqLXQoOQUFBVxz\nzTWccMLx/PaTa49uGPDFgqVc9LbrLWCceisPvDjDbFp2uLgNJTC9zbc2J7c6op8nnMfrutMZwMri\nSAdMWZiqPQcbF9x4F3c99xajzrsYRRYokkSK4tJtvO95b6+FbhiosfTiD7DS5O3zcGgs+7HNqjqV\n2WXRrch/3x3dijxRDes/En94BrlLly4sXryYmpoaUlJSmDt3Ljk5B//mOxhYXlLIzBeetAJBi8sm\nBIrPz7Dhw53lbO6djZnPPcHst16DS6c6n+2t1wim+EgxJKu5ylSr0DGQLOUG0y3NXD4e+6chUf+L\nr78xpYeswJus08K+l2WZ3bu2Q9f9ugwxIUkSIYs4pIdCfDz9DcRp7U03NHCaJJxBy/o/+1MDs7nw\n1otHowZVFJ+Ph1+eSc7AQWFdtOY5G2yqrGNnTZC9lnWqvV0ds7wU/31kWMcsc/O9jxAIBHhmxmes\nKCwgM28w6Vm5yJJAMgx698+le0Y29Zrh4Yia/6UosjM4SRaRNNYv9cOqcsg5Mf5FbHFE3K+79jZ5\naWHGHCqULllEj4wcQrrpxueTBEYCXcBenqT3T5tT2i87j6ycgfgVwc69qrWMq39sGJaShSyhhnRP\nUATNk5UIjWB7/USwrTp2vn3pb7upVUPceeKxVpe12QRSHQyRpEh8U7gC9aJnY67vfUaFMAPDfcGx\nbQ6eLqobWLiKD6l+icq68Dupbe1GdtVX89Xst03nx7MnH+COzeumBoOsLi6gt2URvy9Xwgz87LHQ\n/bxXu1Q2V9axoaLWuZdiYVAgwMktnubznb9B6+jZpESh6To+SeLYjGzunjKd1cUFZA0cTJ8BuVTX\na1TVa4lVzkLmve7z+RnuGdudZx+BX5E465STePl//0ZVg/h8PnICQ52Ar0WKj+3VQUJWFt3kaBPT\n2XDevHmoqgp1sTWJbTwyd33M72b8JiM99wTvvejjgZdmkJ7pJlzsP03zJVheUsQDHy+HLplx93f8\nWec67q7ehACiId/aVfo5/YIreeHBSc42ZFnmu4+mo6kahqEjhMScN6byzFvvo/2eyskjT6Kuri4i\nMJo7+01e//gbODF8wmu7aOqGaRSkhQz21Km0thxXXd6sJZFmRFdScQNq850b1HS27Q1yxyerqfBH\nNvnOfm0qPbMG8MTp6dz28erIDR4AhDCNpoIhnSRFItnnJhPsSQ32eOdJMnmhG2YM4U0+7S+cq2f1\neQg7pthHPDn/Z36I0TD45broNLQ/YXz8x2eQBw4cyJgxYxgwYAD9+vVD13WHSvFnQkFBAVeMHUVZ\nwXzr4TYD4zPPu5ivvvqKE4YfF7a892VhSuSEz1qvml2OZs1SvR2z3pmvmzltXNPQezMNGnJceBNF\nA9zxyH9o2SZxeaVE8PiII7jkjoeQFcXZr2EY6B7DlJBh8pjKiwuZ88qzfDHzTd57+RnKi5c6D/uy\nxQud5hJNU1leuCjsIbeX0w0z4HaDY3c2jQFTFm3gvRWN69qdce5F9MvOY+nixdx9+Vim/e9x7rxs\nDGvKitwGJM8LQbECKVtxIEkxP/fLAtnKjMaSpxl5+pn7eXVd2ImC9OxBTobMZ+kU2+dfHUzcXMak\nRXgywtZ/PllyX4KYL0W7MmI38wFOBt1nfed9UUYb4Gwd4YOB8i1VnP/mMi6dXuo0Z9nPVNNufWOu\n19NfxZiMIxyTAyEIa7j8o+EET3gNZ8wSvRd/8/+KYU2KQqEQKQfi7lW1HXaY5WpJkuidPThikpT4\nsQvn7zbsDLi3whILSxYX8NXHc6D+wLVmL3q7lJnLN2MYBt0zB3DWZdfTIzPH4kMbXPteAq59JR8i\nCmcTOOEUpkz/gIGDAtY52c+9ec/IQjDiuCE8984crrz5Tv47bQ4Z2XngGS9apPg85+82MEZDfn6+\n+Vyp+88ZtWGPn+WFBa6LmZMFNAhapg/fl6zAaCQ4Bhh+xhjnNxTWhNhpMLU/x50ECCE4eeyFTLzn\ncbr3yyJwwimMHH0BIS3kKF0YhtmQvmzxQubP/46gJ/HkxdK5n6GrkbzXWS89Y6pzAKquM/+nXVw1\nq5zV26rNccATPDrviCjn1vD9EcKkDu2qUaMqL0x7/kkemDiO9fPmwJ6Dq51qvmPcMcHbsOlV7tEx\npVA13VWscJbzTIT230kBtlXVexJT7kRiX1xtbcQKjuPhTxgfHx4ViwceeIA1a9awYsUKpk2bRlLS\nn4dzYmPevHnUB+tx8p2GwZFHHc3fzjmX44ZG8sW8SihCCIhSVnYoA9Yyi5YUccFby5jzXZGnY9Qu\nGZn/2l2rhkmc1VpC5l70G5DLfVNnMHjsFci+yGvZI70v+aPG7tsFiIMuLVPo2MzHkcf24m+jL+C4\nk07BZ3V2C0+zRUiH8pJCbplwNrNqj+GVvd2Z/n//4qaLzrbciwwyBg5GVmSTviDJ9MkJhD3o9qzY\nGSgM+zN3ANQNmPvjjkaPW/H5OOWc8UgC5n/3nccuV6W8cBGSsJQghKvRmKJInkDAfVkmKaYuanr7\nZjGl1Xr17rM/lzcMS3/djaobdM/I5p4p0xl/zW088dps0i09UWcQTSDImXZ+Fj5ZWEG+uUKype8q\nCUhWpLCyvx28CUzHN8cqxXoz2jQBRY6dhXSCgIOIWlUPM9UxdIMOnWOXR5IXv0mqXwk7jnjUgkMN\nLz0nbOIhJN6/xK2m+ZqmOZMiSZKp3ZtA02csvHg5aOazOeqiq+iZmR2WHU0U3uxhw9veCgkRAo5p\n05R2MbiEC76fb9qK1zeeOU0Es5dvZs12d1sGlmpOgi/2CQM68ODUd7j//15j3CknODzzJn6FPhbP\nU1iVIlkSBAKDufi6m63g2LwOfY9ohiJLdExL5tg2TZ1nqmvLJnRuEZ1HHQgEOOOMM/b/xBtACEHv\n7EGm7Bce223D5IYDHNUjsTGpl0UtM7dr07A8kzuLkiasm8iuGpw05kKefOcz7nv2NY4/cxyKz+eo\nHSEEsiyTHRjC8OGmSozw7sRC5fBro46p7z77L2688GxWLitE1Q1WbjWfh5921lBvK1k42eFw9z0v\nvGpS9vfxngAjpKLW1zP3vbcPWhTXKlkOG2vNyyhI8vQ4WLYB1kGYBi9qFL6DQzNJILEWD7/urvEo\nebiyq6FoF/EQ4M9IsfjLajoG8vPznQyhjZ9/WMP1551BQUEkT8n7hA0//1okJfLlELLLPwa89XUh\nT68zV3r3qwV8NuPN8AfXWufHHdXs9FhnrtpSFVFGqQpqtDy6Hws7n0HohkjXqTuuv5Jff1iT2Ikn\nAF03+GHlch65+lw+nTGNhd98yYhTRtGzbyYE3a7h1aWFLFu8ADWoQosOICtOFqF0ycKwAcxodSQh\nQ/DLD2vC5HbsDHGY3I09gTBcDnMiMKxmGyHM39e1yzU5iwKT8jF96v9YXVpsZZQtKTs7EyRcaavG\nOIZFixbs24WNgjdLNvJe+WYMA3pkZnPW5TfQKyvXneHrpuTS3+c0nilrlqSAEDT1KY47Yb8jmtPU\nrzjBmtcu2M6MC8kduO3ztjNrkp2NFbGzF4di4JtW9LvZYAM0Nj/oGxjhUmVwg7i4nPVDDDvIsO8r\nOwhZW+aaRrzxxL1cOmkyF91wO8efNf6AapBCuNq9tsarLdG1Txlk3CC5axS7Mvu7VL8clVMN0Lq1\n1RCYALUgUTy74Bc27jG5jYYBwX1wFvt1wwbnuU5tYJiS7HMl7cB8RtLbN0MgWFlSyOv/919WlBSS\npIRLtdn3md1kFQv/uPU2N4A8QOi6zncfzWJ1aaEpf2ZlGzXDpPJhQJfujdiRV+9Eev1a1loVNedc\n7PsUd/xLts7Zew+DW43q3T+Hy26fTLf0DCQ5/LoGAgHmzp3LVVddhc/nw/sE/6o1YeyVN0U5v5Ar\nieqh1LkBnS0TCvZb1DBMxaNtHhMpb5Y5ZBjUqTrrfoljT29t96c1Kzxr7z+6+GoZuu1r5s5+kzmv\nPEt5caEz9vo9ZST7fOxjDob0MM41WFlfz4kdyFDbMDFlX1N7cnWocTCcAA82/gqQYyAQCHD/409G\nvD00VY1qFLB1oytFNK/9yUhypLalprtB3Yc/eUsQgucfusMJ9JzA0Pq7pruOaU7zhWft6nqNitrY\nUiwbf/qBKZMnxfx+X7G3ppY1pUVhTXpffjCT1WUlGHXueZUXFjiugc6ZWq5PmXmmy17pkkWo/mZw\n8bMY+Vfw4iN3OWU0O/vhFUi3B4f333mDWy8dy7OP3c/tlyamrWoYBqVLFiAQDB4c4Ok353Dx3+/g\n8VdnkzEgj5XLirju/LN5/enHuPvyMaxbXowVTrmlVsyOaTfcio1BQ4+L+32imFO+hdnLN3voOTr1\nWoiQ5bgY1AxKNjaum+3QJaI+9W5zkQ1b/1XCDSrtwNLkIbumELFigINJsfDi87XbmfuDyWXbuKeO\nj1dvjbns8DPGOcfovuhFBKXhj4I38LB1cAVmJv7bj9wJrqYG+XlNOedOvJGTzhx3QPuUFcXV7rUM\nGNygL/Ht2NnvJEWmbYwMcWOb++yzz8y/HASKhY2dNSpTFm0w/2EFEd+ub7yqBDBv9jTuueQs7rvu\n4qjJD+89b2NFyVL+fuHZvPCfR7jhgrPD1vMrrtJKYzhok7Trp2PoOl/NfpNHrzmPlcsKHfthWyc4\nRAIv/C3rMHZtZGXx4vDGXJty5bkWtu66wJxs2feGTxb4FcHa0iJe+de9/LSyDD2kgWE4lthgvmOf\nf/55TjvttIibcMeWSMUOL73s5501TtXQa2rhDX51591hsLtORQvpZmXBW30y4MGPSli0I87v4LyL\njXCa06bV8Mw4+HFx1NXE/6K/l34vX8JHLz/F1Idu551nH+ea885iZUlhWELGPi/7PRjS3d/QwDzf\nWjVkZX0d1jWGYfDLrpqoxlqNwTXp8iaiiNoQeCjwJ4yP/wqQ46Fneh/SB4S7gyk+X4RRQEFBAR/P\nmBb2mRbFEcwb5IY9jkKgh3RKlywKe3DNZWHTnjrWbqt2yh4Nb6Wv1u7g7s/iZIhtrmxNgtpKFvql\nRb9lg8F602AjWuZDdTtUe+cE6NM/lydecw0zxl5zGw++OJM+A0z3on65AaQUa9Dp1Btd11m2ZCH2\n7F83DMcCFsx/z3n7Nf5zzy0ULZjHm1P+h1ofx2LaA0mWGTBoKNlHtkAIQUZ2HhOuvYl+2blIkqB0\nyQJHqUStr+e7j2ZawYBEz3apTvbUr0hOoBUPOXn7b0fcEO+v3Mqkj1ehY+rlBjWdOk2nU/PkhEtg\na0uLiKXd4LzowkroIkxWzV6u7xHNADOwky2jlBSfHNXwID8/H/8holDZz9Pkr9bxW4zOaICfV5U6\nL3Ews4DtUpPo2yG+g9mhgl2JEEI43F0hLBOWBs+2/Zuk988lucn+Nwoaus6J55zPI6/MIr1/TgO+\neeIY0DkNRZLCSsFeeCsQseBoSGsHZmoQDXbyoE4N8bwdMMfCW7fAk6OsFQ0Wff0ZI0aMiAiSu7dJ\npU+HZmGNmiVLvGoW4e6KKT7ZqdA0hvnzvzs4VuL+FLj8RUhJM7nISxc5GUHboazw1wr++cXaRjZk\nJjD65AQsVZomtEjxOzxk87mXHFdRhDuJtgNqnyzRxCezvHARan0Q7/tKlhXGnD7SqS4UFBTw8ccf\nRxzF3NnTIj4bd81tPPG6SS+b9Mlqh24QTjHwOCl6qBZVdRplmypZs63Ko4BhjiFr98QfP30+xVFH\nMQ2xLNRUWu+76OuPuepmspTISZquezLDuo6mBq13nggLyLyVVFtlw9Dd4161pSqcVmJNBnbsrad+\nP1033GuJReEzomoWHwr0aHvwGqEPFv4KkGOgoKCAi0afzpqSQoSQaN/pSAYNP4Ep0z8iEAgPfObN\nm+eoOdgQUmSwsLqsxOGGpXhfdkKg+H30tfi3EF6W1g3DEe72llfAdJR7e1nkvdTh3gAAIABJREFU\nbDv8YMzBSH/jRjqmJH6zr3r+lqif67rO6tIiBo0cFfll0G046ZmRjW7A0f1cq9Xscy6nZ2a2RZuA\nXlk5jJ14s3Wcplth7xzz+jqZciOcczzv84+inl9j6NG7n8MbBPNFbuqXmhmR7EFDTWMLzOv/7QfT\nWbmsEElYWWMreLTtpf/oEv1vu+vQdZPDbjsSyZJIuHO5sOB7JElQURPe1OeWTcObioQAnyLCFB9s\n/rUt/WRn2I9q1cTRNfUiEAgw88NP9/1kE8DuWjMrVFsfP9BaV7oU4c12YTZfJifgYHYo4OW6yx7K\niiyZ7mXyzLth5t0oPj8jRo11yq/t27TZ732GQhoC6G3JVXnL4vtyHwshyOjYnKNbRaNXmOdk83Fj\n4fLLL9/Xw08I3qRCoxm0mj2w9ceIj4PBSCvxtBQfyT457P7OHjQEn+W0qPiiuysmogro0E0OBtLa\nI3oOQVF89M4OOJlA1QqqZsaQ3Qo7nvYdePDlWfTKyqFZkkLrpn46pyU75X8hsMZLt6LkcmmFlUk2\n76nMvCFI3vtACE4dcx75xw11PjLfnSHYGU5xMM64M+LYzrzsenpm5kaEo9NKNvLFoqIwhQc7m2oH\nwuBWIr3KSPWaHq1dKAwX3XwvF94wiYdfnkV31WNaU1fl7iwK+uQMZtXT18bfOKaUY/agIUgCvIIr\n9riuG+54760eO+djhH+3v1SFhvKrTnCuHfoA+dMr8ugSg6t/OPFXgBwD8+bNI1hfj2HoGIbOjq2b\nGTvh8rAAy0Z+fr5ruWmhR5/ITuHHb7qM8pKlqCEjrDGxRdv23D1lBj088jx2uG2XWXTDoLJec4j4\n9kNfr+mND8R2Tb2uCn9VfEcpL0JadHWE2poaZr38LIu/jKJlqrvrrCktwsBgc6Wb4b39kzXs8ejZ\n1qk6/t7DAEht0Yq7prxLj8ycsFm+dxAAg/y/hTe2nHbRVfFPZLFpu33mueFmNPZls+kT/bLzGDXu\nAqdxxC4HGuBwWG0qAuxbafrgwbomuFkILcERMT07YJbzoxy323zjBkx2IOcNDDqlpTjf2a1+jV2H\nnLxBCR3fvuKjVdu4/YMyakPxD6B3/4FOlstuKArGse491OjToZk5yWiQwxUCevfP4b7JD3Humadx\n15Tplvygeb3P75UK65fs/44dCo0ZyMiSoGWyz6oIJA5ZElGbqOxGzoZ83IaYOHEif7/lHwf9Afpp\nVw3zf9qJrRIUFzH2LUlSQlbiWTkD+b+353DVP+7i+Xfej0ia2LuIpypQUFDAdddd1+i+9gXGiKvI\nn/wu3a0xtF4zDY7ASGgilNo8jfSsXLPSFDbOmbQJxYomvZNNe8wQApI8+uJ9B+Ry9d2PmkpHkkSS\nP4lTR58btr/8/HyTg/z96+akJd65YRDUQlEpW9N+DPHZjGlxG/UaUgcMzKZ3KTrnzMHeygrGT7yR\nPv1zyG4eNKsOXz8P374Yd73VxYvRoqhxeO89IQSjxp5PRk6eOZ56Ts1pssQc413+sScgtmIFg/Dz\n2x/YE4yQlYnWDYMNFTXc8enBlbaLBkW46ih/JvwVIMdAQ4MDQ9dZXrw06rgaCAToeky4tXOnrt0i\nltN0g+KChWiGgRp0g8Y9u0x3KvuBsBvTwPtwG/y4ozpsBgyWtWu8G6v4A9i8DjB5iFW7XZpFUwWG\n/f4h4rfy6OtW7YD3H4r8XAh0XUc3dCfj6sAjyr+iqADDgCtnhlsQmUoc5sP+auGvTCs2Z+XVUgoV\n9S4H2wmOCVeyOP3cCdw8+d/kDM3npsn/Jnv4ybHPH2DR2ww99RzOOv+SKJ33bsZDEnDa6HPxJyU7\nfLfsgKtY4i1LJ4KSwgMIZmKgbFMluq6HDYKJyvB0z8iOWRoHc9xu2zSJ3h2a0bKJyRsXuN3qQsAR\nzcOtg+1A+XBhW338fafU7qRPf1uxwVzWcdA6TEj2yY4tsdsYCWaJVdArK4ezL7+B7hnZrC0rYsbU\n//FjeTGFn8+CwtmNbT4qFJ+fE0aNc3nkQBO/TH1IbzSgTRTNkpWEqQVpzWMst37pAR3D84s2UKeF\nmLU8frY0afPKiM8kWea5556LGuw2RHr7VDKy87jkups597QToi4jEGFNVw3xxhtvoMVIQtySvv/P\n1Lc7ZNaWFvHeS/9jWdES1pUVcee0rxKS3mreolUEF9Z+wiUhaOqXPf0IwgqkXX622Z8By4uX8vR9\nt/HT6nIuvf1hLrrhdh59/lUyGlAWA4EA8+bNo3e/TPi1NO6xrVlWbPGqo413gikP38mqZYXYjFy3\n6d3NjNqBnx1ihgwj/jxt52/UVVciW+Nc9Z4Ks+F1+WeuPF+USsQjp/Skj2VeE3GksoLi8yPJMv6k\nZE4bc67bH+E5FisB7vQhgYeX7AT59rkZTqV1f7SQ7Wy7vY+9aoiaYIh3l22Kv+JBgt3v8mfDH24U\n8v8LAoEAD/7rKe6ZdBO6ruPzJ5GRnUeHZslRl2/eMrxUVlsbyYlUfEn0zB5EnRqievcuSDFtWo2j\nc5m5bAP9c92ZodOUhoFhCM8M0QgLjhrNIH/3MgBHHt2DTb/+xM4tv0ObdAD2Vlfz/cyXMQacCUf2\nC1/P4uaJnwsjciDSp/8GyeRk/eP+R1izopyff1jD8qIl6F7HuuwAe4NRuNie2f2WqnD+8NPrJNql\nFNJ68GAM4Sn7WOxMe2A4dfwETj93ArvrNB5/8W3w9YjYj430AQP5+8PPRGQ7hcAaE4TjFJWRnccz\nb82hqGABWQOHkJmTh2EYyJKgZ7tUyjdXhvFx42HxwvnAgEaW2jc8Me8nHvpbT45q1cQ5gMYC5KNb\nNeGW4UcDxHxh2wFaV6t0LgtXqcPOnhsRyx+uLHriuHdw6zDpPiFAkaWYCgt/FCRJIPRwVQCX8mBe\n6x+WF/PYdeehBVVkRTafrTZH7/O+ji17kwtemEFvm3ssBJowzJL5wT6xBHHc8OFI322IdBX/YREc\nE1ml2xe8Xvg7367fGfN7acHr1BeFV78GHX8K50+8nonjT01oH00s5RcgKvceSHiyEA3/u38SjH98\nv9Y1dJ2Hrh6PpqrMeME8Nu36BJ1Uk5qafReee8OhUhie5976VpYEa8qKKF+6iIy8wWTnDmTlskKu\nO28UatCkPil+P4++Ops+PbvHTDDdct/DXPniF3EbtR69/gIemPoOPTKiGIsJs+L35ZwZZOcNcih8\n9vsTwjPH3n8L0XB0s1CzG16/jg+Ao7odzSnjLqJPTgCf348arHcz2Utn0b5ZElsz3UbaHm1TCbUx\nVTymNBBr6ZM7hHGn9WPN6lUMHz6cjOw8WqT42FkTDHsebeqvafJiOGooum5gSAYYwowTnHN1s8g1\nwRA+ScSlOtmoVUOs37HXzSDrpjMtuEmFQ414jd6HE39lkONg9IUX89jrc7jg+tt5/p336Z3RP6am\n5a5t4QLiBd9/G7HMDY9NoUdGDl/PepO6Bpqmq3xdnZvdnhdW1WlhwbDbYeqWXqrqtLilMyEEit9P\n527HmJQJ7wYXTGtUYSAiOP7s31x56QTGXnEdD7w0g7PPv4TUZs2c4Nh7KN0zs6nXIgNkPLSRaMe+\norDAKilF8qHsQU833MFgaZzgGKDz0T0QwiwBN/U4vQlPENizbTP6HdGcFJ9MRnYeF11zE/0G5NI8\n2UfHNHNSlJqkhGVLGxs7Bg0ZFn+B/URD69R4AfLojA7cO7I7bZr6nXNtCK8epxd2QOlQAMLWcf/8\nswfJCOEEhmDLUB3mQyLcvrtbq6Yun9P6Yk1JgaPVramqydXcj17vCRdeQI/MbKsp0OSOJ8eg2vxR\nGDI4QEbe0IjPMwePOOBtxwuOAYyqHaCHl753bd+yz9fjQKsnEyZMiOkBoAUTazyOBl03HNfNkKpG\nL/M3AqWB06TXQtvlHsP68hLuumyMY7j00fRpvPzU46ZDoIWQqrKisMDaTvT9CUGjqibqNe9QVFiI\nGrUBTYBh8OWcd1wVpIZZVvt/hp1NNt+lMccCD/Xi89lvIQlBz6xc7p4ynRPOudDR/vcnJTF+bLjP\nwKplRUgCqndXRGz23EAvevTNZPQVN5CZnUdqkkLnFslWX4d7MLbahnnMuvNvzfCcj/0udUy0zHP8\neddefqlIzICmul6jTnP9FSotCqTBHxe0ij/pi+SvADkOhBBk5Qxk3MS/k5GdF/f3696jZ/gHUmRy\nvkuvvhiGwWdrtkGbrhHfe4M+wzBYu73Kk0n2UA8861TXa3F1Ni+56U6uuOMhlsz70vxgyUzYvBae\nOx9p5ZemCUEUSTr7/BsqVeQEhjFy7EWcdeEVpGfm8Myj9zNtyv+czLG3I3ttWRGzXo60//1xdTlv\nT3maVcsKqd6zO+L73jkBk4Zhl8poUFYyXB7WmtLGNZDzzxiDANJSFNLbu3xLJ0OCoFmyYskzeRtP\nBB2aJdGqidu57GT6IOzzaBiQOzDyw29fgmfPPaAOflv9174POqVFr2rI371EP32zZWpiBblRxqEO\nzZJonuwL+7xFis8qvQtPoOyiT4fmppoH+6aCEIGdvx7I2o1iZWGBk52wG9PisEz+MNj3EdafrZua\n95Lw/JeePdjR6lZ8Pnw+H6Im8oXbGHpkZgOmi6IiSShCkCRbezlML6VkRUYLRlbZBo08eMYZsSCi\ncE7XlS/j1otHR9e4j4E+HZo5RiL7g0AgwLfffku3HlG0iXdugLXf79d2dSEhMkYiyTKy4kPKPCXh\nde2JkyJJzvjiSQl4qmfmpHNl0SLXcCkY5Kn7J7F0wXdhiRjF56P/QJOqFut2KylYGCYRGgtpPXKi\nUwg8fSMlixeGNbCFNW8aHtqiYTD/p53UxtL59dwnP64sZ22ZKcXWMzObifc8xgvTP+T86ydxz5Tp\nHJ0e7uT50NXjWbe8mL65AXxvuDzzm3uG6N42NeI6+CQJf4P3sKt6BRPeLeOez9c6luENs+GaYVpv\nu+ft4So3kgQTePp8DNcYxCCSfpLV8dAp/xzupEU0/AleFX9eNPXL+GXJCUDjZQseuDRc0UHyR2YG\nvlq3ndXLitjSLXqWZM2yQuo13QmKQzoNZrxG2MMNUFmvUVMVu7khM28wP68pRw9ZWcfKrfDObYj6\nvZx64UTOu24SJ465KHJFITimT2aUEovBurIiAH5aUcKbUyMDYBuPXH0u7z7/ZMTnz95zI6899Rh3\nXDaGHZt/j/i+e0a2o/0c3mQRzi3DwMlMxEPPrBwUSbBzb/RMSsNTdDr8Y2zPXv7o1vshSyOEaaZS\nt/+uaF+s2caeWpe7+OPq6Bxyo/QT1pQUeM7FbazzIi3FR6fmyWEDVFqKj55tU61r4XHQs5Dik2nT\n1O80ODaG/B1zIz+sqzZ57ocQR2fmYbcV2ccZTW3jQFFQUMCjjz5KUVFRQsvbEzZwmyIdPjyAEPTM\nyua/0+Yw8ZY7eebtD5n88izOu/gKRu76ep+OzbXXDp/8Hc73kSQJ2h3R6bDsOxZHU42hcR8LSYp8\nwEoogUCAYSefHvmFHoJPntjv7YaOv4Zx19zG/S/NRD/+6oTWOVrazcRBXRxOsX2ZvJU2G/YjnzVw\nqDOJkyTJ6o/QkSSJ9MwBjDrvEia/PIvMHJM2c0yMMXP0aSchaY1nPDsf3YN1HjMdL+yJZGbeYKfS\najewmXCbzwwMNMPgxSVxDEKSXc1jXQ+xfOki7F9bCMjIzmPclX8HYMbz/w5bVVWDrCoqID0rl/v/\n8ywphlkROCo9w9NvYDVyGqaefUaD4NOmO9jvu/U7azAMqA3aGV73Oy3k+iPYQbKmG1TWqZT8Hr/5\nEcKDbW+fU0MS1qHUjv8zBqN/xmP606B9ahKylNgL9YflRYhCD89LjswufrJ6G/c/+3LMbcz7eBZ1\ndua0QUAMDUsq5sNTp4bCGu8a4raLz2H3juhByJfvvkqzFi3Rf4t0YfP7k2jVroNJy/j4X87nhQUL\nuO/Kcfywsow1JYsjZqeSR97ONBKJfBlpIcMpG0crGa8rK8bAtBQOWbwTb7Bsdu6a6/XObryhRiCs\nzEj4w27/0zvwt27qR7Gc4aIFft4yYyKYcVEDDvJByNiVbKzkujkrmFNuNiItL4kelCmKKR3obaIB\nkz/ZEGaTROT18Z5rw2auI5onk31kC3q2S6UxtND2mJw+G5vWwGuNSyAdFHhe7n5ZchwCDxYKCgo4\n4YQT+Oc//8n48eMTykI61xbhcFWFk9R1A+asnDwuvvYW+g3Ipd+APM654u9sWVMGryemfpBUt9vZ\nVpIiOzznWPf3H4k27TuE/fv4Y9uQ3TmNjs1j6GYfNOc96/pKEkf3ycLn91tNudHl2g41Oh+177zy\nRNAre1B0vm4MtP2tgGZJCoZh3hveWqU9UQbv/QP9snN5/NXZXHLjHdx4/+P4Lek7nz+Jm+99hFsf\n+jeZ2XlOH0DLGFW3QYHBnH3+JY0eo24YvFoW+T4TksSYq2/lgRdn0DMzx2lus/9sqP4EZvU1USg+\nP2ktWzP75Wf4YXmx84yuLS3ioavHs+jz98OWlySJvrkBk76XlUvesR0BSFEkZ90Uy6XRC+/bUDcM\nh2roXcLJ7xruCrZrojdjXhMMsW57ddymva1V9WyqrHfMVsxNu0m5hhJ4h8Lt7upAV7MJ8U+YQf6r\nSS8O7JdYis+1G46F8sICjBrXzUyPMffQfXG0/gzTKQcrAHStMw0EIkz+zQCT3ysEfp8fYlTsNVWl\nVZs2VmNB0Hk4DcMgWF/H1IfuQDcMlCbN0K5601nvpHGX8Pk7L5n/WLcApm2C0fdj/LIMTVVZtayQ\n/JGnkJyUTH19HUIIhg4dyvcbPdy5wHhEyYcRIbBQFJAkFMVH+45HsrUBy2Jl8SJ6D8hFC+kEQ4al\nvGDmAmyzFMPANMpIz4LystjXFNOiM01WIn4/k1cXzoNum5pEik9m5ZYq6zo1XMf8s2VKfHqFd3vh\nJ3/w5qQzyjYzacSx9M7KhuLI7PhdU96lV1YOBpg6wMKkY7RvFhmAJPtksjqlhR8q7suwQ/OkmA2q\nqUkKjfn49eibAV42xa7fEHWV+8Go3TesKSkgO2+Q8xunJslW1ubg7WPevHkEg0GLI2z+uzElBDvY\niLgnCXf8UyRBSDfv1SY+iTpNZ+CJp7J88e2NH9ivZaif/Zsf8t7g2Ixs/IqgTnU5l3b26nDBe+qy\ngA6rP2Rj8iAu7yrxYLSiyO7N0KH7QdixVUnw+5lw6/008cmsL11CTmBoQgoWBxu92zegaTwfpaK3\nH1hetAS9QyPW0gDzX4XqnTTt08WtZngzyNgTZbfk7owNQEZ2LkmKRMnihdz4z0eor95NZt4QevfP\nRdN1UnyyIw8XCwI45phj4Nf1jR7ur3K7iM8MBKMuvY4Uv0JVvUZqkmJR80TUKqyuGyTifyEkiUEj\nTmbYCSfx1OS7UIMqis/H/S/NoOXAQZQXLTITPcGtsPIbWPMd0vafOeX8K1hZWIAiCY7pl83w5B00\nqS9m81qDHYqgfMFcBh8/kmOPH06bptHfJYZFd/AaQV34dimzL8lxMr425bImqIfRM82gWIRlhqNh\nc2WdJ7g2P7Pfr9Em0cmHgJ92Yvc2bK6sIy0lUvHjcOOvADkOhFU+tmdN8RIu55xyEk9/5DrhocT6\nsWNvZNjpY5yHQrcypk5A7OFPCcx5ZL2mgwGaWg9EL11JssKxvTPMblYDOhzbm1cf/yeaakbUNndY\n2+sJcT56nE9/XBT+8tz+E0yZYDb9JSXRu7+Z0Zr98WeULV3kZF6GXXob9rzcGHQewzul8F2DY9IR\nyEJiwq0PsKZlK2iQAd9bVYlhGISEoF4LIUnC5Y/hSvVsraqPkJCLBoHJ8YrWkSuA3jF4hNF+b4Hg\nyBYptIsSZCa0jUOQteveux8Ul0R83jMzB4RAGIZHISG23mRDZQevcYhtZLG/qKqoAMNrdCEwdB3F\n5ydaHufkJltIrtjAB0lReNz7gPTswQ6twM6CHezMaX5+Pn6/n2AwiC+K02Y0OIFGg7K1qbChI3TD\noa/oljiq3Sg1csyFGAZ8Pvc5fg9cHXvStWsj+t49fPfxLI7NyLaCHFeR5LD3xHgOIKRpvPt/T6D4\nfAw781zofE7c5Q9sv+ZYcPmkyfTMzCHVLzNk8OAwQ5w/El1apPDEAMFtJdZ4W9t4SRw9BFHMqLx4\nLynAe1+ta3xbRXOQZYXht//DbcDzfC1JZpVj6a8Vzudeo5D15SXcdNHZqEEVn9/Px599wdH9BrBj\nbxA8gXW8yyuESWlsDHqccG966SYuzjkSQxJU1mskyVJ4Dw/2O9VA0wVGAulQWVE4f+L1rCxajGo3\nzGqwqrCAo/r0JyNvCIrPh6apSN88x/BR42mSGuDTt6ai6zozpvq5fNJkXn78XjRV5SvZnKCHNI33\nXn+BPl98yTHDjouxdzMOaKjZbuhuk7vXL8H800oleYLdxmBTKnTDVdABg9qgzp46N/FyXLdWTMjp\nzIotVVTVR2m+30+YUnvCs+8/D/6iWDSCFSVLmfb8U/y0ooTubRspJXtLgHL0AFkoseck3TOzwTCo\nCYZMvUePkgNENhzUWw9Oq9axHbayhoxg6mP/5ON3p/HprLcQAkacOT7+y+aHhVEzS7KicPw5F/Dw\nyzPp0TcTSQhy8gZx5513EggECAQCnDY+PPsxv75DxHawyndVeyqiZuU//a6AtaVF7gBmDQReDciQ\nbrCzpvFGt7wuZvnaJ4uo5yREdJkmm4fccI2+RzSjbWpi2WNzOw1O0A5mYmXuEiyd2zAMg1eWRufR\nmYEtrsXyftSwHBOAfV7ThSIJWrRqSdjVtK6LPVFriG/+cwttWxx4Q0j3jGwkAUmywCeJuFWg/UUg\nEGDu3Lk8+OCDTJ8+PaEspMd2JuzzJj7ZY9RiLSs89Atg3fJiqvdUcNXV13B897axd7JhmfNXl+fs\nlsej3d9/JMLOXAgr+FARRpSmqW0/HRAnNwxb1oIQVO02A741ZUW8M/Vpfl4ROcn8I7ByWSF3XzG2\n8QUtyOgH3aY7a+jx9LSUTsC8X5J94eGBfe+A2aA4oHMLBMKy3bYa9dQgC76f7/Q6eI2H4t1rshCN\nZpkBy/QkOj5atc3l4Vo9LHbjmU1HsJNMqh4v1PbsT1X5+oOZjnOiJMsmdS03QJIik56Vw71Tp3Pe\ndZN44OVZDD99DJ++OZWQpmHoOmowSMHXn7i25JqKqqrouo6qqsyfPz/mvnXdDB5rGkilGp6/eJvx\nwKyW2pQSZzED1u/YGxFo765VrWvkNul5aRr3fbmOxRvc8u5luZ1plqTw1Jl96RyjMXx/IVmurIex\noBUVfwXIcVBQUMD1F5zN1P88woRzTqe0JHpzAMD8+d+FUSxiBchG3rionwN8NustdMO0Bq3XdMvR\nxlovrMHAvOmrgxqKBC1btYpzFoJgfRDD0AlpGi8+fCdNmjXD709C2AOSMKXgAPgldgPEpbc/zJV3\nP0Z6Vq7z0jZwG5SmTp3KJzPeDFvPODaKi1reWPClsL4uifk/RfKn9XMmM++jWeFNA7rBypJC3p7y\nNCuWLUUHNqyL7/BzZBODW4aZ/D5FEhEDYodmSRHGFzbsbKu/QdrDqwiRCCICssbW3fmbqTSSIDbu\nqePztdsjPpdfv5p1ZcVmyR636XBfknB2abVXu1TaNaSK7AN+KC/hP/fdET4paORA1Ppavn3/nf3e\nJ0D3XWX8WF5sOgKaQtdh9+3BRCAQ4M477yQnJzHOZ7QMsn2vOKL5nkDZzuKvLSvigYnjmPHcEzx4\n1Xj27Iz87QGzTL9+CZIsk3/GmLBg25Yns40fDhfCSstWZUOSZEacMSZy4bnPmxSLA8WTo2Dnb0iS\nRLMWLflxeTF3Xz6W5554mEvGnLFPKhYHC8sWL3Q0gxOBYRgoMSuU+4e0Nu08VRbo3yktoufA25Ng\nNycK4dpuC6tK18p6HwkBK0oKefP5pyiPYbJlQ5YEJd992ehxPjHvp7jfG4b7X40a8iSU7Pem+YHq\naYZvbIOfzHwLgP+9NYfLb7qT+6aaDpd+6zntlZXL6CtuID0rh1XFBWFeAJIkcUyvvhj2pM8wUGQF\nSTI578OGDWu4OwcT3i1FN8zmPC+cXhzP8rbSkxpBlzApmhW1QaqD4bW6H3dUO4G0N7ZYu72aL9Zu\nZ+OecJUZk6YnSEvxRaXp7S8EZjPgnzGD/BfFIg7mzZuHqgadmXFBQQGjRo2KuuyI/Hx8z76KU5Bo\n2XGf9/dGXTrdy4pI75+Lji3nYt3CVulk1bJCygsLGDT0OHIHDsKvSGzc8DMQnSZQtuhbhCQwrEmo\noet8PG0qV9z5MD+vWcGendtp0bot+aPG0q57Bt9/uIrXYhyf6SIUrt9atGQxY0edQtDiN2tHJRAg\nHDsIvWYPRc2zYy7yzftv06RZM5KapJKRNwQh4IErx6KpKrLi49LbH+KVD76Ek/4ecxu9OrcNy4ik\nNmhO6xAjOG7ik+mUlhIzeE4UURuhfmzkBSwELJwGAxPLKO2qia7MEdq5ifkfzyIjJ9exL91XaoFd\nWj1QvPHGGwSDQcIzyNbkTI2U+gJA1/ll7QpoxCQxJmqrWP/G/Tzyjo9/vz7b4SEfXt8/F/ZP4f1N\nUv0Ke4OhsObRjmnJSELw++5aBKZsneYp9e7evhWUIyJ3YLl8nTHhanpm5jhVBC/H9OjWTWhxGHl/\nozOOQAeeW/gLCEtWTMTI2thv/DmT4ex793uf46+8nlmvTkHXdV7+173knz7WuZ7BYDAh/vjBRlqr\nVmHymI1Br9uLLgQk77/EnINP/43i93PcaWNcBRWiz19bpvjZGwyFuXEKBJk5edz4z4f5z32T0HWd\nO277B1kZGVQHNW644GzUYJDX/X6emPIKx2f1jNwwsHjxYua8PgXGPnJAp6MbBpJ1q4R0k78byc01\nm9+Kf4uUGI2GkKZRsnghE669mfSsHLZVBx0DJfBMdoG+OQHe8/tR1SBpjdwuAAAgAElEQVSSJHHd\nPY+yu2IXQpIwdB0hSZx0zrm0bNmK4SNPYeCg8Hut4WXfWlXP7Z+EJ4JCuu407OuGwaY9dVw2o4x7\nR3ane+umjooFRAbS4dfKqiIZblV2zdZK7vsiOi3HDmIbUnAOBmw66+HsiYiGvwLkODB94v1oBPH5\n/HEHzsGDBzPp0ad4eLk10A2IHkg3hpVFBaRnmST8WjVkCbabhPuVJYXccdkYtKDK28//hyffeI8B\nuQPZtul3aJ0edXu6HuK4E/7G/K8/cwZhXdf5ec0K5n00Ey2oIlmZgq49V7Ni0byIbQghUBQfvbMD\n/LC8iB+XLeHYXul0O7IzC7+f7zQoSZJkSv0kcqLJ8ekqeijEh689Bwjee+FJemRmO1kWTQ0yc8U2\nQnGC45M66EzI7owkzIGgW+smjeoW25AkccDBMUDntGR2ewJY6enR6CH739EHgjYdOkWVvouFXbXm\n9lo38bEzSrBsdqO7UkJ/huAQcN8qXz8HSanQJaPBAkZYJmbft481sTVYWVRAzsBBCE/jie9wO+lF\n2X3Pdqlsrqynsk5FYGbVJCFol+qniU9m+eY99M0NmK56Vtm1S7LK+mgkbl0HIUht1hyECAs6vZma\nP8opKxoUSZB/TCszQAaTShUKsbK4AOQGY619nD8XmTa/GYlr+3qR2izN5HDqutlcJUDx+9BU8PsP\nj4pFVYVpX5xQaLD9F/j4cbNCee7j4I/T9N0IumwrYfCJg+mT8w+69R1g3gsiNhXrmDaRfS72z7Kn\nYpdzXYPBIAsXzDcd2awEk6YGKStaChefG3Xb8+bNQ49hu70vWFdW7DQm27JudqO7zds1DNgbDPGv\neY03BIKpYJEdGOJSk0R4T0OSLFBDJs+6Z1YOD7w0g9IlC8kZNJSMnIEsL15qxhGaiqL4OP7M8XQ7\n6ig6dewYEbge370N75S69s7RlDZCVjOOPTqWbTYr1/PX76J766bWpMC+AvZ1CP9NK+tUpxHPrU5D\nRW1sUxk7MD4UI4a9zT9XePwXxSIuAoEA334zl6v/cRdvvf9Jo+XT3n0bvuT3Hb+tX8d7Lz9jSp0Z\nBmpId5rTSpd4RNlVlcKCBSiSoEPHzjG3pyg+JlxzA7c9+G9kRTFflpLE7p3b3UxUMMgXM6cx9aHb\nKZr3efgGhODksRdx95TpCAH3XzGW159+lPtvuJwVJYWMGJHvSPskJSUx6tyLD/gaAOCofRiEQhpr\nli0N+zrYskvMVTvu3cAF+QPw27JWHJqHujEoskRqslumvOT2B11TlvVLo67TtWdv5H0on9760SoA\nahpYm8uK4hik2PzVwxUTTpgwAcXna5DGsA6mttLMmEfBgQVvFu9R1x0Kkp2l6NkulSNjOGL+UbD5\nluHaslaW16pzN/HLhHSzgaVZsgII/LLkTBz0UIjv/3sLt/Rs8Fp58TLQNXw+kyvprWS4FIvDDyFA\nsX/j+hqH39m8RTTKmFneTc8eSPMWrfd7n2ktW6J4zFdGjBrLv16dzcRb7mLu3LmHRcViwKAh+JL8\nUPppmKRmVCz7CCo2wo5fYMHrB7TfPXt20ycnQI/MHId65DbfJXaH2LSLAYGh+Kz3gD3RGD58OD6f\nxdv1+R0t5GjIz89HPghNkg/ecjVry4ocHq3dvGYYsLKkkLeef5ryZYWsW76skS2ZyMn/G8+/+wH9\nLKOwFSVFzHnpGdaUFYXRTWwKlBCC9MwczrrsBtL757B6WSErixZx6aTJjL3mVh54aQZ9+rvXuyH6\nHdGcU3tFqnR4oXmy4rqTFXfpIw7v2qP65IVhGKzbXh2WXXYUS+L8BJIwXT6lRpbbV0jWZMPMIB+8\n7R4M/JVBbgSBQIABuXkoksTWLfE5cHICTQaNYeFn77EQgT8piQdemkGvTDObjDDolxtwsh2Kz0e/\n3MEmJygtDbZFz7bdNeVdMrLzSM/KBXDKYCULvkFWZAxVj1vWGHvZtVx8yz1U1Yd48h9XOE1Vmhpk\n8efv8cKUKcydO5d58+aRn59PZcujef/F6MHfPuGG6bDrd/hmKvy+AkPXENYDpPh9JKekUBNj1dPb\n1eBXzCBASAJCxmHLlHkbT04YfQEbf1jJFzOnwbcvmiYZx4VPKI7ocgylfLPP+6k1wvmC/YeeaL34\nBMLqUG7qVxKSNjrYCAQCnH/RJbzh/XDTKvfvemRHtCwrZA87kf2+k+yAUJLYU1FhUSvMk1ckYQah\nhxGZHZvz084aKmojuad2MJskS2HNNgJTTtIx/QFCmsqG8iXg93D9q3YghCB/1HjSM3MI4SqUSMJ8\nCZdvrkyoKerQwpywXJrXmWa7f2PndZNIzx7EumWLI1M3koTs8/Fj+TLUFv0h9vw4LiorKrhs0mSW\nzv2U4X87nczsPJr4ZAYFAgzs2vKAz2h/kJmTx6OvzOKz2dP5dv5i4uoDWJHVmZdcy2+px3AgbYV7\ndlcweeJY7ps6k6MzBji9CvtCxbKXzBiQx7NvzaF0yULOPvUkAoEAum7w/DvvU7hoAXlDjqN/emyJ\nvkAgwJARJxG7ZS0xqJe+wPQXnmLcxBvp1re/EwQuLy7ktktGowZVZr/oo89xJ0PG5Y1u75i+WWRk\nm4H98uKl3HihSRmZMdXHY6+YBiiKJDkNtN5m2rVlxdx12RhHGu7uKdPplZlj9kRIsYNMr1pQtPeW\nqhnUqTqS1Vdjj+lN/DIpPtkyGAunV3i1kO2AWDcMhCGszLoVUMeZOjvUCvf/HKS3S6Vvh2bMXB4Z\nI13QvyNvLdsU8TlA/w4pzsYlEd5c+GfA4R4h/79AkiInJHHV0L9+vzDQbOJT1SArCgswMEXAdQN6\nZeUw+cUZnH/dJB5+eRY9M7NZXryUgu9iOGtVbgNwGnK8ZTA9FOLEs87l5LEX4vP73YBCCGRZ4Zg+\nWdw8+d9cedt9gOCHsmKKv/sq6m7sBqVAIMCvq0oR819p/DyjWG1HoFVnGDMZrjebtQxAlmUuvm0y\nLdvEnmW/9q/7+LGsxMyIcHBnu/sKvyxxrFWalIQgf9RY83obOlQ3aFD89iU2rFsZFgAljNJPw/6Z\n1rqNJ0thhofJinTYOF7jz7/AGVN9s+8hu4VnQheFSnHZnQ9z9mXXIr16zf7tUEiO+UN2wLW5/bPY\nmQoh6JSWTLdWkWVrW96tYaAiCUH/QYORfW6FQVZ89B4QLoUnWUYNI84c68i62UYNSYqMX5Ho3ynN\nykofPtjv2ZE92jFkYA7nXHEDvbJy6Zc7OGLZlu2O4IQzxxPSQgdUh12yaAGv/OteSgu+57mH7+Hn\nFSWHxFlxXyAE9M7KJf+MMYw48zw6HR1P61nQtXs6qc2ac0TXAzQYEQJNVVn5/9g78zg5qqr9f++t\nqu7ZJ5N9n+yZLLP3TDJJCAHCIiprIKAIgojwAu4ru8giqCiKoIAggkIIu/IqanwjSyYkmZA9kLAI\nSEjIvs/0UvX749atqp7pniWZpDv8ePiEpKura73Luec85zlLGj1vqBCiS9XSgpGJikg9P73peo47\nWsmWSSm48LQTuODyb3DysdM7VP+ZeuyBJhwkY1W/adzw1Yv411N/VCoNwLJXXw7ItMXYtnVrh8eR\ny59nQm2DN38sdZMpdZGrlYsbMaX0q40GIjNCCN5YujDpnGubGpXH2ZQYQqZUkREiuVpd29RySDg2\nzfEEjqPKS2u3619f38yDi973PcngU0wcxWfesqeFPdF4UrU8nee08sNd/KgdWUDNUZdC0CvfH4MG\nFedw3Qlj0oZpz6kayG9nlbfZPq5vAd+f0p/muO3NVQfDqjsU+MRA7kZ0C69x6nkw/UII5TK+djIO\nsLsl7q0GR1dGOO2iKyirrMVxoKnx5bTcLfngV1jbtND7XNMwDcNVYZCGwejx5fQdMJhTv/BlDMNw\n6RcGF191Mzc//BdO/dwFXsNd09SYZFxJw+D8L5wP+CoWjY2NPPLbX+K0dFwylJ7paSFtYLoZs46D\n7di8ujuft/akf9bxeIw1TY1oHV/IDMUClGFy44ljuO3TZR1SPczVf6P+uJOTDKDOwng3WX3kqM/M\nSsoOzrRxOGlyA8UlSo7whzff1qp6WKtJYOFj7NmxnXGVEb58xdegZW+XzzekRw7nXv5drvvtHC88\nKgPtIRuQYxn0SlEkQLr0itYhRyFgYnUdt//+KU46+wucdPb5XHPv40yoquWksj5c2jCUK0Y7nPmV\nb3HdvXMYVxnx3nleyKBnXojyAUo6L9MedAioIuDToITAi3YF0XDiaRx36tmYIeugVrwrF/6bWEuL\nR1NbvmjBQel7dweG98xn7bLF3PiV2fzr6T+x6f13FR0O4O7PJ+8sBO+tX8uf7rqNv/0pfVXWzkFi\nWhYT6ho8xRop2uqht4eKgUWETemNL6lKb3f2cKPGTej0edvFiAj28Vfy0J238PqyJUTjNpWTlF6x\nNAzk6Cm8e8x32z/Gzk0cV7KPMZW13uLhxJnHBqouWh4vWQjlHNMGpH6ONZOn+dJwlsrhkQEibypP\nvRQqSVzjvzvbJjEPcAs22Y7D3974iLsXvOt99/zrH7k6yH6lPfWXw95onHe37/foFZp6YTtKFu93\n7ZXdBpdnrfrrOdUDOatCJQe3xG0EShkkFRxIuRh3gN3NMc8zbUrZPU7GbsQnFItuxLqVy9puXPkC\nlHdxZRw5jZxBoxhdEcGxHdaubOLtZa8yvGoSYysjrpyLQ8iQ1DZMQy5/PmVinDQMJrilmLU3Va9J\nHdvmnluuJhFPIKRQvEaX3L97x3alg2ka4CSIJlJk537nGhoaGrwyu9FolFAoRP9BQ0C2o816kHBs\nm1Vm+95nKY0k7mWqVfjhghRQlGMysCgHIQSrlzQqLxi0meiv+a0qkxo2DO7uYlXdKdOP4aXA5zGV\ntaANQkf5KlRCRmYgBFw+fSS/evk/TJkUYc6vf5x2X2PRXCZ85QkQUDqmDGNxc/th51YYHv+Q6h3v\nMXFSA6PLa5M8NaN6F6ScxLMJwuUaC9E2+1wKwfjqOsZU1mI7SupRCsEFkSHq+1F9iEaqyQ2ZnsGp\nwuaH/TY6hDY8NAcR0kd8Nr7/DqJ8GDffP5ffLt7Amwdywv2uDKebh2FZFtWTpqZ8zocTvfJDrFis\nKrLZdgIEjBxXzvpVy6B5d/LOws30dxwSifQJVZ1Bfo8SvvD9myirjBC3HY8HanahsViG9Djt6Zjt\nnV/PdGMjHVaDPesWVi1pZHxNHWWVEa75zRzWNDWyNHc86zoaUNxoH6h7LMm1iBw/g7v++DSLFrxE\nXlEJ/3jmcf75zON86oxzGFVRQzSuZCF0nyuvqfP2H1E1iRETawPFimBMnwIKwskmmECQF1Bb+vmL\n77S5NMuQtCSUjvNDi9smdNt2QLkDbSyrYluG0NsdL2HRcRxitt3honnz3hZK8pTzJmRITirry9wV\nHzJ1WAlCiLRSoFv2RskNtTU1bTdxHFS/twxBr04m0h8ufGIgdyNWN70KtJIuWzin6wYysN8q4u9P\nPMKwMWXc+JXZxF0e0/X3Pc6o8lricZt8y6Citp6px8Z4aVNbE3nGZ2cztkolFkqhQkTxWEwNrvG4\nb3Q4bnhIao/CFNeToLwJbyxbwpqmRi763o3s27WD+inTGDViOEIkl9mNRqMMHTGad17/oMv32yG+\n+RzceQbCTnTKyBNC+Pq/MnOeQyn9ULkAKuqm8HjIIhp1fG1MF2WV6l3t3rkN3loAY9qGmtPh5X/9\nHc6cBsCtJ6vysr4UkWaWZc5KMoTgqOG9qRhYzNLFrxLKDSTJtbJOjj3tHMoqI6xb3sR1X5pF4uIH\nwey8qsh//v0M7678O3+53+LG+x6n9Ohp4GZsZ1LWrDMQIr1Rq/mNUmhzxEHq0Lj7CNcvX8LyRa9Q\n3TCNCVV1Hgcdsi8BxnOmuf8LepKfvbCOUx9crHbcs5Wlc+9h5dy7uOXBJxk+rpw3123p+gnvOQ+A\nMy68jJLiHkSmHMXEmrpUlMrDjsr6qV5OiGEYfGb2edy1bk0KfWT/QjutfJEGe7ds4oHbf82QkWO9\nRD2lwd21h6G8p+klIcf2KSQ/ZLBxd8qvPaQao3tuWcu23qkVmjpE71LGlw/Fth2aFr3K6qZGxlQ3\n8NLK7R3Gznv16UtZVYREwmFwjxxPAakiUk80YfP1807z3s3zTzzKLx55mpHltf5Y78455bX1jKmM\nsG1flNVLF7N+2avUTzmK4cOHpfSqVg8qpqADbfKvPLGCm08uI2RYKZ9Z3FYSsZpeoYpsucOswKtK\nqxuP7ThEE+2xjxUcR3iqSAKlof672RVqMQ9MG15CfthoY9TvaoknGf3+8fDGLwT0ygsROgSlrA8G\n2XU1RziqJwUSZQ5ydY8QvDrv+Va6pzFWL17AG8uW8I9H7uH9NSoTd6dMLZl2dLBAAKqaWWujDFCe\nY8dBSsmXvnsjAE8/cBcrmxaz+rXF/OCiWTz265/wwO3XUVk/hYk1deiBWpfZ1dnLU4+ZiYx3gmJx\nIAjld8yh/f3/YNsJ1ixp9CZfM4PZscrQcVfJAsZV1/GT3z9JTcN02szK7seK+imYf7+jS+dx3ES3\nwt3vE3t/reelCBt+aelMehHDpsQyBGtfW8L3L5xF0/x/IA2DXv0GUD19ZtK+Mz57FgjBqiULSMS6\n3o8c8HiCawIUo2z0oqaC5oLmWAZFgUlUm8U6nK33021s3fImbvzKbJ78zc+44ctns275EgRaLi77\nEmBE4B58bXXhfefh3gshup9YNMq/nn2csR1VNG0HZdWTuPhb1/H5y75OeU2d9xwzDUP63r5EPMHG\n998DUiRpeQaYZOqJpx74CZc8Ba/OURzkpoUuHU14XPWuYETPPCYOSK/JXJhjdorS89ba1W22bfvj\n1V27mFYYXVnL68uWcO2Xz2Lu3T/hpn+9yQeyvcJaCqYV8hapwSsXwPJFytuvEY9FWfbqAi8SIkgu\nq73mtcXcd9P3+eElZ/HwL2/jm+efzusrU6toSCnI68BA/u/OZua/tZV4wkmbUBlN2J60nVa0UP/0\nvca6iIgNRBOJjpMzhZPk7AHIs0xvfBFCUDkwufpp3ZBiSnJDKZMNbcf1uLs5Uu1JzGUKnxjI3YiK\n6oAM3N7t6m9xgI9YCOqOPZnxkQaPO2WaFuuac7mucRsP3Hkbl3/+dJYveZUVH+5KeYixVUrFoE9B\niLK+BSqbP13mumskv/P6Sn54ydk8etftXHbuafztqTl+okEsxsrFC9TloTpEsMzuvHnzyEvsw9nf\nRX5AZ2EY8KlvtruL2LHBKwUq3KWplUFekxRCeWW0l0zA+Oo6Lvra9zBaVapSXk7BuKo6Zp6aWi80\nHfQd7t65i5sunc265U0YQhA2jYC4ewYXCq5Rt7ap0Qsl24kEWz/ayMpXk3PXVclbKK+bqvjYXbxo\nw0l4vL/qSVM9ozLXOjICZtqLOq5vAcUBj7cvh0SbiUoKlSegn208FmPlkkY3ZC6zUkJJ34GmfwnU\nQqo9H+Y/n5lDv93vcNnoA7uZISPGYLkeT58DnXnZu5WLFpCIqVwS207w2P13EY/H2zoE9m7HME0u\nvebHTIykqFLaSYSW/wWJrSKGkQaXPyu8YjpdgWnINlX3DgRvpDIaEwml+3yA+HB3M6uWNBJrUcl1\nTr/2EiB97Nu3jyfv/xVvrEjO7ZBCUO3ymTVMK0T1ZK2TrJ5dnmUiEKxauoivf+EM/jr3YS/BLxqN\nsqJpcdpzt6ZdpIQDewJFhYKwUfKwvlpFcoVBTxMa93vb9eZ26ELW7QNvQSVQ7UZfSGtlnGtnjsEy\n1bj1wxPHtL4F75yZzgNIhyNjxjhCkBQd8Aa2A3vxwjD46L/vcsKs87j+3jmsblpIOL+IB3cMgdwi\nCOURa9nDgsaFQE3qY6ASB/fHVJZobcNUQqEw0WgLjm0jpVQVflzj2LIsBML3WMeiOIAVsoi50nI1\nk31FAI2GhoYk/dAbf/JzOl84tQu45Pcd7+M4nHDOhYyrinjXmMks9da6omowgfLaOs646ErmtsqL\n0F6LkePLoYOQZBDjyivRwmmxWJS1TY1EJk1CunJCppQZ9ZTpLG+94ItFbZ9L2SrJtCVhkysNJlRH\nuPWBJ7lqcTOd9S18MTKYoRP/h7eW1VJe10CVq70qgAn9u6Hy2CGGJQVBTm4QZX0LWLZhJ4YQxNxA\npwwsvCbWNfCUZRGPq75aUT/FUyXIBi9pa2gPsuIdCxwcci2DaMJOkiMcMaGSt1cvByCRSPDcQ/fQ\n9OoCuPSR1AdOA2kYHH3KLCWF5SR73zL9fGonT+MBQ5LQiU5uRC9IlT0j/32sE6cTafgBoytq2dMc\nZ/mb79KYptp4e7joGz9g19bNVE+ayoiK2kBEInPPYkJlDU8sa93THVj3ygEf85vPruHi4jSR03aw\ne9dOHr3/dsyQRe3gFzjxGFUSekL/Qppr67jnsef48xOPIQV8+sxzGFsZUZX7bIEtHEKG6pNNrvJF\ncKHj2DbFxekrlHZEsQCteeykHCccB59WQYCH7Mq42Vr2zfGVLiBFtCKAhtIS+heGkRIMJFIkVCU+\nlxOvf5qKv66XXGP7FBAZXMyS/+4E4JiRvbzvjEx3wDT4xIPcjQiGkSbq9h/dB39IX/EtHRwH/vKH\ne/jjnbcwuiLChNrJ/Pu5x5Q8GHie6cEVk9Ie441lSwgZElOqhLvjj57OXX98mku+eRXfuulnfPac\n85HSwHGLEdRMPYa8wkIlBi4lphXi02eew88ffprzrvguP7pvLpH6ye16XBoaGvjR3b/v8v12FxzH\n4X8fuZd1y5u8Qd8yMpem5xsA6k/YFBjuyjuVTJP2Qry9dkWXzjNEq0IIgWPbFBSXeKt50w3bZdRA\ndkOPYyprueY3czjxrPMwTFOF13d/RA7JRrKWE0IIptvrO32e0uZ3GV8V4cyLr6SsKuItjrJ0/G2D\n3gVhKgcWJS2qNPLDyitlGhJPoUX4Htiyygg33P84Z132bW6+fy4TXDUIy6NYZBcEbl8QvidJClVy\nO3jv533zBkI5OUqBwDBoevEf2NGWrp1s7b+58Ps3U1ZV54WEwe9vA4syWzimqm4Sl15zq+oTUhIK\n53DUzJOS9jG2vssZX7oSIQRP3P8r/rP6Nb5zcm2aI7aPNatWcMbFX2VcdZ1H0UlJbzmMOHFaPacN\nST55WXX6+a2zWPaKqytf9enO/6hlnxeJeenf//Y2q6Igqrz2d276Gd+7+Q6qIpPcsd3wnqFWY6id\nPBUrlEwxkFKya2f6Mtc5Vsdmmfb+pqJFOA5KztUtEqJ5yN53WuFCbfGS9dp77VfPHI1lSEzhajjj\n/5HCr9TZ2hOsx30h/agNwI9OGsv0ET19wzrL1Cs0PvEgdyOMAJ3i/ivO4PZf/JKnmve0zUTuFAQM\nGs8L77fw4swqdm7dDOOOgTzX8hbK6Fv51vtA/5RHWL2kkckNrpqDFAwpyaUyUu8Nig/d/XMS8TiO\nY+MkoHHeX71jG4bBt66/hYraemIJm+ETaojbNqYUbnWv9FfeFaH5QwE7YbNq8QLGVysDIZRBD7I/\ngCij2DKkt/Ke0N/nUh49spcXslI/7Noz/PtzT8LZEc8S37NzOzmm9M/vDlSZ5KHqSaKsKkJuSDLv\nmTlK8znaTOKuc+CKJwBYv6KJqkg961Ys4eovqdLqRsUJJI7tWBN5bdNCxlXVJcktCQF98lNnWGcj\nNCUnVQuQQmAa0BwDaUiirsdRAAjB2MoIIybUUBA2vWOYhqRHboi+HejQHm7UDunB39/Y7EUXhINn\n+EsEXztqOO/v2M+Y8v7ceN/jvLnsVTZ+8F+en/MwyC54BN9fyaDXn+PYm/4V6FZ+2+hXGGZg8cGX\nlj8YmFJw8tlfYNjoMpa9uoDp04/m3fVrmf/C87B6Hkw4jsIePVm/Ygk3fmU2sRalJvTlq28Byrp8\nvpf+PJdPnXomlW4RDBkwfDIV7ZZScOlJ9Txz36vetimfOo03li0+qFFr2+ZN6h/HfqVzP9i4Dp7/\nCcJVOply1PSkr31qjvLgVg0qZuG72zEN/ex8F1JFrSqg8pcnH+N/n/gTdiKBaVleZCsVOuNN1R7p\nVHNtLJ4gbEpitoNta61jP0nP9yirbS1xm1vmrWftR+mpkbpdSHelLaRA2I47p+jnkroAk57XhPCT\npAcW5WBI6RnWYSOzEc50+MSD3I3Q73d8vwJMQ9JQX6f0hQ/oYAJm/5ho1anKOAb41DcC3xs4ts0r\nTek9jRPqGtqEPIIrtpoUq1vA4yPv3L7N/Y3AMkXAGyc8uZfWaGxs5OqLZ3fhRrsfZsiivH5qwCsl\nMlYgQ+hltvtX0PjplRfi6S9GePS8Gi5rKPV+IIVg5qlnd+k8zpZ31T+W/hkch4LiEk9w3vULIYUg\nnKHFgh4gtdG6arGSu3McBzuhNGl56gbEnO8pQX0EqwIJqonOaGsDhT1Kkr1hCPoX5jC0JO8Q3l33\nIpi01vY7dU+mFFhSkO/JufmLIFCTrCmFy7dVutBFOdmn4KE51YamguBHW6YN78msygGAYEJ1Hedd\n9nU+dcZsVZa5KyFzIfj0eRcnLTo0j3tkr/zsWDi41zauqo4zL76Sitp6dm7fpnJGXrgTfnEae3Zu\nZ03TQmItURzHJpGIc98tVx3Q6Zx4lPnPzfVk3QwhyHXlDzPFyE5lIP3hJ9dzyhcPsFiQi4mfuaDz\nO7/2F3js+7BzE0IIrrjmZiL1yVzvYHEUIbTyh46I+Pvo78tr6/n2j37KHQ+r6O2djzzDhMrqtJfQ\n2QqXO/bH2Jkise3Cx1fgOLCnJc6eaNyVdEuuqufxjx1I2A5rNrWfNySEMopx7zNZIcrlIrv997ZP\njyM/pHJfWuK2rw8tBJ+rHsg3po+gcmAR+jZ1nk420iw+MZC7GfecOZFfnjYRgPFVES6+6uYDi1l1\n9BvX8E4U9GrzVY4T5coxDmMrI5iGTErOGd+v0AuNlLvlQU8591JOJ4YAACAASURBVAKsUNhL4FMr\n5xC1DdO88KcphcerEkD/wtQel/nz5xNr6WL4sxtRd8xJXH/vXMZXR7x1fKb7nc7SFd5nnz4QDFMF\njYPxVXUM3PN250/SvAfuOAXWvQzAnp3b1ZmFbzxVDSqmdxqtykONoAEnpKBq0lSskEo+NSwLy7KQ\n7y/H2vIO42uVzGBtwzRvH7l+ASx8DP7ajrrHSw/x8E9vYN2KJUmcyiMReZaZ8tr1NsOdmEKGeqhG\nIOypJ2fL9cpkPgUtPbRqghAqAucrvvhGhxC+0VBeW89N98/lxDM/h7zzdFjTcVn2stKBnHDmed7E\njnvcsX0LKMkLYWa4kh4kF5jQz6S2YRoht9BEyAoxITKF8roGZOB67QMtPebyRoVrGBvuYkoI0sq1\nHWr4spQ+4rEo+/ekTkLvLJ6LjaTntx7vcL/Qo9+A/7sXbEX3cmybPTu2p/VeSyG8edBrt9KnDXnj\nvXtf5bX1fPGKb1BeU8fgHukpPZ3xqTkOXPnMamJ26qvT3OOE7XiFQNR2x/cm688dn07dG74n2ZCB\n+UsvCtz+NbxnHnefMZEHZleyP5Zwo1u4Y5JB7eAiZTi7q0LfI599+IRi0c0YVpLvVcIRAk446wu8\n9L9PewlUrTG1t8MrW9qZCUEl5Q2tSv5eqldn9hzU5qfTxgxkav3QlOR3rUeoJ6XKSD0Taur4zJnn\nsLjxZfKLe7Bnx3bqphxFRW296kBCNV+tBtEehWLGjBlYlnFokvQ6gVETqiirivj6sG7HyxwH2Q/l\nIvwJXwgIGQLbcbx9HMDAl/D6n6nDufb3f8ap/mzqgy973uPVCSlx3MnSCoWUioe+BpKLMWQOyqiT\nQEVtHVdecwsvvvBn6o87mdJRZTQtfIUJkQZGVdSSYxlU1tbzk4ee5NVXXiZcUMwjP7uBeDyGPWEm\nDK1oe/jFTxI3DNY0LaQqMilQcjXb2LcdY3yahEJ/0hVIHBL4/FHh3qmekA0pXDpPZgthtAfLlSHU\n1BFtWEjtsXLpXKbhq01U1NZjOw7/enYO9t9+AXu2Qv1ZKY//2cGCs4861u2AfiEH6KRawGGEukRV\nEl4IdZ93/OFpnpv7J7Z89BEv/eUJRk8oZ/KM42n8v7+rxOpQqOtj7d9/hRRw3CkqSmUZwuWgZnaA\nGFycy/Z9ATriHacAtH9VH6yBQeM7PPY2p2MKTeIjVcFQJw2blkWkYVqb6GNw+tOGrt625rUlLG18\nmcnTjqIyUu9RMVYvXUzTwpepnzKNcVV1FLbT9jrjQX5/R/sRNUWhUBeVsB3i2BRi4DjC4yYD7I92\nboHlOXXQc5ckZieQuHOrXtxoh5q7iLNtx+Mf62Q+xxG8vXUvIdPIGidWOmTXCHGEQ/Pn3tq6l+E9\nVUh33fIlrFu5FI5O/ZseffrBlo9SHc3/52UpsrUNEysUwigZAPtUaz+7cgA7muPMKu+flHDRem7U\njVEEjMjy2nom1tTTEk8gpfB4u1IIEi7XyjM4RfrZtqGhgV/98Rm+8kqrYfsfv4bjL0/7u+6A+GA1\nE05u8DqcL4sl2jXqD+k14RssegLS28KmQXMsgUANIo7tEDaVNyduq6SryshmlqWr+hQIM5954WVs\n36EUDo4/7WxGVtQmURoyPQCJ4B8Bry9r4lc3XUUsGmP54kZu+t1cTv/SlUihBvQcVxKmoqaeweOq\n2R9LMGLMOOb/+QnWLn2ID+RFMDi5NK2WQpxYp2SrOiVddIQhSd5NgHAElpQqP921hLWBqffL9Ltv\nH/4E6Xm8vbEGr8cYge1hU7IqUJFSvPIIo0cOZ12vSJujf7j47zz/9itUTprC8Ak1SZN8NkHLZjmO\noxYN7nZTCuY9O4e4W5Tin0+qfU3L4thTZ3PsKWfz1NZiXn0vfdJXa8i1/+LiH9zi5aLoeQAyayT3\nL8ppw4OVhsGIceWQjgHQsq/bzu/gcMypn/Pa48xTz6aqrr7N/BlM8AxiRdMivvGF04lFYzx89x3c\n8+gzVNTWs2rpYr563unEolEeuivEL//4NMMml6e9DrMTjfPfb2/r4F7cRD2UJrIpRVKhEG30R105\nuI4gAgtYEIRNyb5Ywh2D1D7SnW+Ci1C1d7Av+8fQnzVvOdv6JHxCseh26IEu7i7R5j83N0lUvM3+\n6Q/U7nnOvvBSfvPYc2yN+vuVxf/LRXVDKMgxPfmn1OcU3qA4tk+hPzh6Xk7V2EvyLMb0KQgYWJ1r\nyFMDkm8ak6rLEe+3o8zw1qL2D9oBhu5/j2tPGM/YSpWopuWtDCmYOKAwYxXUvMEgMMHr8LEp2xoD\nIVMpjgiUB+3KM2dSZ25Kd3TvX08/9FtAMOPUsxhXHXGVMvz3pZpD5kYg777dv5cvesXT147FYqxc\n3Jg0iAZDluuWL+G5B+9i8b9f4P+efZQP3k5daHjWpd/m2t/OYVxlJGsNoYNF9aBiVXjBfb9SKGWU\nYIRAeJxI/91nK5I9U8KbZIOZ8QLlkZKBvlQemYJ0tXcdx+HNFUtTHn/pi/N47Ne3cd2XZrFueZOi\nL5B9z0SPuWqh4C/ol736SptiOY4rjThoyFDGVUf4zoyRXTrXjFPP4fhZ5yUph6Qz+g43ggaJYZo4\ntsPvbr8u/Q/+8Ut4O72mcFdgmhZHf2YWX77mx3zl2tsYVx2hb0G4Tfnjklz1Obj4lgKaGl8O1AyI\n8sHqJVQPKubdlYs9DeRYtIXf/eI2lixZkv46umFVH6ROJFxD2XarhtgBikXM7nx8TXu2g+OKH71K\npggqOpM7huPbRR4dQ/rtvTvu91DhEwO5W+F6b9ynunbZEuY9OydtfPNTof+kP5Rsn4g0pryKoeOr\naY77XsQf/8+5rF/R5NIL3MbZTtsTAq/cpddwhW/MOY6uwuV3gI6OCbC8qa2xO+OsC/nhyelXzZo7\ne6BoaJjMhJqIN9EoLp+SuOsOEfuDgcDnb0GykSwC34PyopmuDJVEecs+O64v4r4vpjiw/yIS8Tjz\nnnyYG758NmuXLfGLkwTOl2kEFwLVk1SCqGEYWJ5mL14iiN5/zWuLue7LZ/P4r3/Csw/e7YY/nTbK\nMEVhk1MuvIKxbslcIQQh00AKQUGKMqdHKqRsNREJPNk36ZK8k9fGIuNGT2eg20W++66k0J4odfGW\nVD1IR4TGVUc47tTZ3o06iXjK49p7t4PjEItGefEvT3ilbLOhP7SGNz4gGN4rj/yQQW1DclEKb18p\nqZ081Y0kiDZFGNrDtE/Pch0I0usr2eBBhuRFvJIqs9t1MLF3Byx64qDPazlxrvrNY4yprE1aMPQv\nzGlT/nhk73x65oWS2pAQOmfC5YyHQhx37DFIKThx5rEqGV5KbNtm8Sv/Zvbs2TQ2Nqa8ls5UHewI\nwaIgoIzjuEvBU0l6Wt3CYfu+jkk62qHjOXvw56+UC3P8d6nblx+Bdv92f+hXb8y+PvmJgdzN8MIM\nwIpFC7DjaeLjm97kHz/7Ni1vpln95rWfKLFm5Qo+2JWcDBePx5QCQLBBCtJyqIIGmz84q/8P7ZHH\n4B455FqGyjgVvlejMNy+N3bxgrbG7msv/4u1TakHBAC5fUO7x+wIthtaRqgkCZ1NnOl5UHvDlKKA\nr8k7rl+BvxhxE64IPOPgO/nzH+7B2Z0qpNb25mLRKCsXN6qKZAEPUaYSbzREq3udWFPHXX98mku/\ndRV3/OEpJlTVEZS509f+2sJXiEdjbYX+//4reEO1s34FYe48bUKSh96QkGtJKgYUJVWj+7jAX/So\nZ2ZJkjw2IS9BD4b3zCdsZudQH1y8CQGje+cTMiWGTPYs+0mIfhs59pSzCYXCilrz5ivU9Uwe56Zs\nfAF2fJi0LceUWIbMeH9oDd12Qd13jikxDUllpJ6fPfw0kRknIg0V4zZMk2/deDsTa+q9343tU8CX\nRnTMJz0+9B6j3cIgeZbfRoLRpkxCn7/YcvwKsikWCADsPxD51LYYmi/41niDtU0LWb+iqY1DIxWG\n9cylamCx97lfQQ4VtfX88o9Pc8k3f8C8efO84lkNDQ3c/ejT1E092ssVicVizJ8/P+Wxu0PN4dlV\nG0nYAfqE45CwfRWLWMImmrCxHbj0yZUdHi9uO9785ekZezaGGxkkOVqpF+rK2RNI0nZbW9AplH2m\nscLHx7WSBfC8Hu7nivopmCGLeAzaDl2CeCzKvJ9+HXqWwhd+0aVzjRpfwYe7mgGwnvkh8U1vYZrB\nsqHqnAUhk76tlAuCnkV13b6TW4c5i3NNz/NqBjQKcyyDsX0L2LAhfWZx3ZRp8H/JSQR/f/z38J+l\n8I1nU/7mggsvpGSYwx1vHFhXCfJNTUN5jROOk3GPCOiwqfSMFIGbLOmumgW+rrS+WkPoKIBg20cb\n1cY3Xoax07zjDh9TxjtvNsJAP0nFsW0Ke5QQNqX3PHrmZYGMFb6nXGd5V0bqOe+zM1n03g6V7ewN\nsn7EorZhGr8PWUSjjpeECCgPcuOfYOw0RvXO86qjQTL/9uMI3Udt8P4OmwYxO4FICMKWxHR5yVII\neuVnx/tPBY//KpK9uoaQbagQwUW/AMZVq8IoqxY3Mqqynok1Ec5+2C8NPPO4mSyaex+JeAzDsphx\nyixAZGVIVwhFrTKE7Y0F+SGDnfsF5TX1fPuO3/HmyibeXr6I2slTqa2fTCxhAypZc/2KJva8vpBe\nC5eydfoVqU/SvJt///rbNIweRHXdJC+8nU0VBaUQXDfRYe3ShRR+6waa9+ykd8+e3HPXZcTOv8ff\nccNavwT1QV50bNsG7rj9a8RjMZ62LG64/3HGV0XaPXTQ6w4wpCSXuO1QWVtPRU09k4eVJO1/zFFH\nkbBh+eKFxGNRLMtixowZKY/dHWWXH1/+IRUDihjeKy9J0UJTLRIOJFw5185g274ovQtCxN0Sl56h\nKxSVwnacgAPICdgUvrMOR3i/DUaqfQ9y9uGwG8hvvPEGs2f7Orlvv/02N954I1//+tcP96UcEngV\nwFAyb5d8/0cs+MfztGHIufvYiQTs297l8yQc2OAayL+47WZe/vd8RldPoqy6zq2MB8JRWbats7V1\nWxzRK9/9rHbWE1Iqo1KItEyRNqiumwT/N7/1Edo9wJ6d29m/shFCUzp3klZwHJWuL4TyEoVMwf5Y\n+wVNDgf0+U3Dl1EKeor0ZK/5WLrteHrJAibWTeGt1cvg+duhtApyVIGRHdu3YTx/O4mEH6UQUrJn\nx3YvktGnIJQ1GsBCqPtSVZPUxJxjGb4XAkDgJWSBzuR/ipdefJGWvbt55qF7fEN52385IfQe506u\nRAqB7VKcfO+FTzX6OGFIjxwKwybvbd/n9VWlI4rLSZbehHQk3H7QHvCoYbJV6D/oadYUMicwZom2\n1K/RFbXccN9cXl+60EvSy+bH4dNK1FA5oCiHorDJkvdVAl5ZZYTp06Z6++vx4Y3lS7jxkrOIxWKI\nQRPSHR72bPOijDV1qkJdMEFPXcMhubVOY/XSxdx6qboX07K4+Ps38fI/nie+7UOINYPlqlFsehO5\nfxcYBtK0SE2w6RxaWlqIx1zucBzWLG5koltk6kCQqs8N75VHRW099zz6DKuWLODo+hrPw9wa3aUH\n/NiyDXyloZTe+SEcBxKOje1IX92nC/xj8OcoPV4n6ZajSmTvjSXQyh3JHmaBDCT3B/uzKQU980JZ\nWczpsBvIY8eOZdmyZQAkEgkGDRrE6aeffrgv45CgJM9id0vcG2TWLlvCvT++llhLFAaflCxHIySG\nqR5/wk4nU5Aed/74RqZdfDXFOSaTJk2i3+hyX3MwKKuS4rfao6k9i9WDilm+YRe2YycZcEm/ofML\n9ZS7ifZDvONqGxTXa3nnztEa2/bHPKk0FbLRfKjMT4lSqMxkrfNa1lcZuCL4R7TyJAvhhcnzC4vQ\nGe5BbN+6BYLGsRBYoRDl9VMAwYheeZRkifcYYFBxLh/tiXoevOpBfogyuDjTTUUPvBNq6ug7phLL\nEEw69kSefvButmzayDGnncvMM0/z93UUr00H7Fp7ID8uCJsGJbmC/+4Q2MJxK9CpdpYXMrLSQ5oO\nQkCuZRCN24Fwv188BJLfoRRgu/e6elkT13/5bOJRZUzdcL+vdXvUiJ6Aqto4sbYOUwpaEplfMKeD\nb+SnkGN0jQyEUiyJ27a3ABbA/OeeIOaqXDix5tQneG85vHAnpmkxPtLgRaekSH4mVoY1of/29GPe\nvcSjUe69+QceFzkJQgmMHXf658gZVcefD0JXVJghDNPAidkYhuGp4BzQsTr43bHTp3HhaSewYUN6\nSmF3pcys2ribbz67mptPHsvQHnlKOMDzHCtP8rrN7RcICSLIOxZAjmkQt1WUKpj/oAUChCvrpqvI\nJpcz9zWdpBAUhU0vHyqbkNHeMG/ePEaOHElpaWkmL6PbMKKXq4HsNoIVixZ4VY94/Cr47ypv36Je\nfbjxgaeYceo5ByRSmnDg7U3bGFDkrqgDPCDtxQ7WPg+iOMeiJMDL1EkBOgEmFdIdKxVeW/Jqm20z\njpmhOHSpjr1AydiVVbaVaeo0HJ83pi+zdRgsU/BX0eoatUdfCO29V9ccCtSjFyhPoEQwIdKgCrko\nt2DwyN6/rFCIE2adxy0PPMH4qkjGPUGp0L8wTNhNQhQE5H1IDp8b+M8maAgIBOOq6rj6lw9yyyPP\nM/PMz6vfC38ZJNCaudmZiNVdCJmS6kHFSdQcXZGqdQ5CtsOUaoLVbXaUmwTlVSiTvuqObheGlKxa\nvMCrtBiPx1i9uJE/fb6Ghz9Xxf9MGab29SgEwtUYz94HohcII3vlkxfytfS9UvGCpG3++BEYEza9\nyehty9oce6yxnXMuuJjr7p3D6IqIvxgRflJzj1yLygCvNiNwkt+PnUjgODZCyjbRIMdx6N1/EPv3\nHhwXOaegyC+c4fjcfgiOsJ1DMMGvNTrb9IwOnEldQcx2+O5fXgd8/nFLXN3sove2c/0L6zo8RvC6\ng+OKx0mWeKpJ2gjWRrJEkBuSPpUC34gO8pSzFRk1kB977DHOPffcTF5Ct0PLl4DiIHtVjxwb4v4y\nt7CkF+OqIhz1mVlYVtdXTkKaRHNKGFikwhLaCNNhDSnwCnu0xug+BQwpSa7kU5Rjet7WVB28dkgP\neueH6d0JPuOiBS8jHvue9/lsp4nLr/wat/z+6bY7x1tg8ZMqufAArToBnDSuT5sOlw2dz5RKRUFf\nR7J5K5JCTZZhBKSXfKNvbGWE6+97nJmzzkO88WLgAGqP4aPHctvvn+Kya2/zuHPZaAgI4Za1bfWe\nvffm3nGOZXj33ic/7HskhE6C9ScuTw1EG90o3r0MUFQ+rmitaEFgUaFxJNy+7zn2339h2GxVKj0Q\nYRCKRqVzPLT+dXldA5YhAoUWRHJbycZVYwD6XZbkhdp42gTB8UxQ4SZOCwHHnXo2Zkj9xrRCXDCj\nss2xK6Ycw2kXXUFZVZ13LvW3YHBxLoOKc+lfmPkQ96fPnO3di+Fq/UvDwAqFsEKBuUdKrFCICXUN\nDC+beFDn3L93D/F4TBXQsBOsWrIALSnYFRSGDU/xKRU6e7Tu4CC3hoPKVbBBcdcdh4/2ds7t/sdz\nq5MWZ0N65HltTwqB6Y7rUrjOCZF8rzmmgeVS67z5DZLmumxFxnza0WiU5557jltvvTXl9/feey/3\n3nsvABs3bmw3JHG4sHnz5g732bVlN827W9i9z2RoaSmXfuca7r7txuQEI5Qs1/7tmxlaWsp3brmT\nW97p4sWYFlubbXpZNju2bGL/7hZiCQfp8g/jQmKasGljrFMZ7LlAczTGhzubEQI2ir1twm0hINEM\nG3a3/yzKxk3A2vqOV+Gpsmw0zTs2M2zYMHjtP632lpimyaiycezf3vHzTYWHTx1GPLGP/TtUeHFX\ni0WzoYptGAI2mO1XHToYdKZNFAHvbNuJKVV2/gZDidvv3raLPS0J4raNKRRvetc+t4S4DdFEgpZ9\nMWJCUFpaygVXfJu6Fct4bNFi3u6jJjrTNPnGVTcwsHQY+3dsIe6+M2OfyaZEDvtzDo+CQ2eeA8Ce\nbTsJmRLbcdgQVu9r99ZdtMQTnmShCBns2q8mm74in93bdpFoSbDPrToI0BxLqMqD3sAtiCccbFOw\nKxomZttEDckGc99h5SF39jl0F3Zt3U3cVhJMzTHb9dSoBE3bUSWoD2X7bw+dGi+37kYKiCYcRgws\nYuNG1Td27I+xb/te9u+PYwjYEVfGW1nfAtZs3kPctiktHca1v7iPlU2LGVNRy9DSUpp3bCbhDrUJ\nR42HMTdL3pSCXS0mLaZkg9P50PLBojPPYfuWvUQTNrGEw4awr07UErfZvW03zftiCGBnNIyDw+Zw\nMzu37KY5Hmdo6TCu+fn9rHptMWMrI5QOK4UV7yUdvyi2nZadW4hLQTRhs6/ZwGpWY4MdLgJg5x7Y\n2X233QadeQ6jRg7nujt/x6ql6p32yLVY0bSIMZW13PS21GK+DBs7kS+efi9DS0uxdsfgrQ87OHJ6\nbN+8yYviSmkwpmw8+7ZvAWDjxniXDLj+7pS5YUPbAia7tu7G3B/Cag63+yz2bNvNhZW9eHD5Vm/b\noEKLhkF5PPH6gb2h/Ts2q3cvJc3RODtth9X/bb/YiEZ011YSjsMOuU9xmc1Cdm7ZTdxWShgtrtc4\n6srA7W9JEE0ksFGJgbucHOK2g+04NO+PYTsOcSmIGwbNu7azc4vFpngO0V3ZpzaUMQP5r3/9KzU1\nNfTr1y/l95dccgmXXHIJAJFIhIEDBx7Oy0uLjq5js7OT3dY+CsIWVsLm/Eu/Sn5BET+7/rskAv7D\n6iE9ye/ZBxmzqZp2LLyTWug+HZxepcQcwfD+PenRux+7zP1E4zZ5IcPTAM4PGfQf0INcq3OkJmtP\nC/tDKvFn0MAir1xkOqR7FtOPP4mfPPQkX1uk+LFDa6aTFzYJSQH8J2lfaRpcd+/jDJtQ7erVvtup\naw1ir1VAj6IwBWEDiaA41yRkSOK2gyFh4MCSjg9yEOhM23ynJYeQWwBg4EAlMfVeNBcjGqclbmMI\nQWGOQWlJHtv2xdgfS6jBZ3eLy6tVqD5qJsXjG/je868zTO5k+sWX88baNSTChYysqCVkSAwpKM4x\n6euGqg8XOvMc3moOk2OqdzNwoOKJvhvNIRq32RNN4DgOxbkWeZZB7/wQA4pzeD+WixlVg66XxBVN\nqHCo64YwhDIminJMivNCxG2bsCkZNOjQvvtUOJxjVU6PKOs378WUgn2xhNf3DakWWWP7FmSsSA50\n/Cw+iG9HCmW06fYAENrTwhb2EN0XJSQF/XvlM6p3HmHTYENiO9GEg2yJUzv9eMqnHEdLwiFsChIO\nxBMqO9/GId8yVSllVGQhLyQJmwYDBxQd4jtPRkfPobBnnE17Wti8pyXpOTTHEnyYyKM53KLUhQpz\ncBw1pn0Q306h7bBjf4yq6TOZMPU44rbj6vb6BrL45Zm8c+ps9r+7hon1UxgxoYaiHIuiXNMdjw5f\nH+noOey19lB91EwmTj2OaNxmSI9cJh97AntaEoh3FqNjcMPGV1E9fQSxhM2IHnDZmA+5p2O2QGq8\nr2TOhBDMPH02tdNPUAtNQzBgQO9u8+j27JMgZEhvzEr3LN6PbeecST2TDOS7zqgkajs88XpTyt90\nhKtf/Ig7T52oknhjCW5+YR3vbOvcwjm3pA8Jx6ZH7wIcHAYOLOG/8e1E4zaGu+AyApRO2azmtITj\nYNsOPUpyiSVU6RKjOcHeaJyQKckxDcKGoEefvvTtmUefgsxHMFojYwbyo48++rGjVwBYblUrB8et\nbw+nnnsBg0eN5ZYFm9kIfP/YkVQOKPa4lQeStepEVGLjQJeDrMJvfvY+gc+dRWHYpChHVc87WEys\nqYNFC93r8K9v5uje/HP9Fm8/ISRllbU0x+0D9vQJpKe1qMq06vv3Cw9kGsEMYA0hwJKCmPBDpz3z\nLFriNs1xN+EySBtx29O+d1cT+vW5vBuN8geXn2dZIW5+YC5VkUmumkE2pCe2hUxxbVIIt1JawpMu\n0wmGCVeRxZBAIjkBDxGQdBOqfeW5i0HBx6tASDqoBdBewGdNerxt4WTUOO4MdH9o7aXTPEaJn3io\nZSel2xmUxBQeh19nz0uhcjSCHHbc7YaQ9MzLvmdSmGMSt2227Gkb9hZC51f445r+Wwsiqj7leDSj\no/rCSx+p/QwhePHPj2PHE5j3WVxzzxyOPmpqWipAJuFxWF1DWAhY+9oSnnn8UWL9PwsyFNhX/f3P\nJx/h/luvhq8+1eXzfXW8yT2//jMJl6Zz7Clne9SBcDfr8+Z00lEFKWiOrWgLXcWHu1pUrQC38by3\nvWsluiWC/JBBkRuR1POZZQhiieT8AC9fyXGpb96FC3Isyd6YcCM6EJXZ2Ap9ZISDvHfvXv7xj39w\nxhlnZOL0hxRhl0dqO8kG0cTqOvoPGQYoQ1RzdVQG6IGfb0CRv+rypMIOMMEgxzK6xThW1xI8u/A4\nR1dMHcYpE/yoge3g8UuFgHPj6YuJpIOQjpeYZAU0mwUwrl/hwdxGt0FPZEGDXRl+geIgBPSo8Y0E\nQXIG8NqmhcRjfvEMx7aJx2OsWtzocr0k4/sXZpWChY/kSV7DTxD1RIK8vYMJpz7f1vUkB/YBn78n\nBJRlybs/1Eg2mJI5+NmO0pK8tMoJeoEfNmWSF69qULHrWPD39RagtHUWeIaku/AaVJycf5FNaN0v\n9LWHTcObMzRG9y7w+kOyVB584+QIs4faDNq1nn6DS4m7pY7jMSXzFgoULco2SPclCiFYtXQxl51z\nCn957CHiexW9oH8OnFHeHyEE61Y2cf8tV7sVNruOSVUTuf63j3HeFd/lunvnML66TklRSt/RdLjR\nmsML8MGO/d2SeG0H6h10+nrc/UvyQgws1g45f6zWfG0/p8bPFTHw+57fN3W/FIFxPzuREQM5Pz+f\nrVu3Ulyc4YzZQ4Bcy8CUko27W9ixP+Y3GikocT0Xe6NxkNMMLwAAIABJREFUP3FEgBSSu04/sESD\nJA+y+y/dADPV8FxJYg+u3ePJwSRsn2qi/6W9ipV1qbUh0+HzNYMoCFne4BGcSA9FssOBQgilST2m\nT763TYrka2wt2K+9Qfrd6mS18ZGGNoogpmlSWT8FhPIYdZZWc7iR6o3ohaIf+QgWrvF/pwdqvT1o\nAEGylz6bB93uhvD+Vs8iaDhlO/oUqMTfVF1VJf20rWmmNcItQ8kmWob0y9wKkpJ9dbZ8jiWTFl7Z\nitbvTI+lOZZ0y237xnBxruUWWfAsSm/cF8C4Ytj00A/44J31geNLJtQ1pDSsswU60U3gVtLUpabn\nXgMvPcT0nS/TrzAHIWDNkkZsu+MKgumwbs0q1jQ1Ul4/hbKqOgzhF5KRmexDrc5rKGHwgzqkjaIe\nPbniQyX51gkMKAynbCe1Q3r4445oq7Kix+mcQERPj/HJi3jBmD4FnUr+zwSys/7oEQxt9OSFDK/e\nuW4c50cGc0nDUMb3K/Sz7t3BXq/MuoJcYkmGkA5nBEu3ZgKWy7U9s2IAI3vltTFYWl9W0Ngpq+qa\n1NtJY/t4hqQ+thAqZBnU2c00gvfobXPbRVCaC2BIj1yG9MjzPMzam6K/H1NZy7GnzvZesBCCz3/h\nfCoi9Z6RlK3Q7SA44HqRjzTGredxQBXD0HJBiACdAP1ss186qLsR7O9SCArDJmP6FDC6d/dEgw41\n9HgRhPacFrg5Fakqfllu8R1LykD/F/6i0jUWLdk1mcpMor0FZK4l27Rt3wvn9yH979ebFpKIx5KO\n5TjJVLbiw5TE2xV4kpgCaiZP9UtN7/gQ2fQMhT16epS6qklTscIhxAGGYW/63tf4069+zPfOP41/\nPvkIhlQVDfV4kwloZ0AQhjz4xcwTyz8kZts8s3pTh/uW5Fr8z5RSrj1+tLom0fZpBCOfwXnHlNLT\nPs6x/EiF9we874UQFOaYGfPWd4RPDORDAOFO4poz5w1wpsExI3tjSJ/blB8yU3aIzqB/YWDVJfxj\nBCvcZMJjMqJXPrmm5Jyqgdx40lgvDJOOFyvwvUIH4vnT4Zqg96S1MZpppBrcagYXY0qBJdUkrhEy\nJcU5KoEmZBre8wuGi48+5SxCoTBSSqRhUFVV7XmYspnVpV9JMKyeHzIxhAgoUvgGUdA7IaXyBJpS\nEApUJUziIevzHKb7yQZM6F9Ij1wr6b0XhE2Ks5x/rNE7P0R5iqS54MKozXcITydZyuD4kdwGVMUv\neUREFVLptnsGhQjQh1o9DxHYpu9ZIBhf24BhJrcBx3FYs6TRO8/obqLUdSe8sVIIKiL1fOeHt1E6\nagxSqipwD//0Bv755CPMufdOQlLww/se5wtf/f4BnUtTM+xEgntv/gFvLF+s5k/ht7/DjeA45m87\n+FH9qZUbeey1zqmB3XXGBKYO70lRjoUAwoagOZ5c0Ew/mmBFWCEUJco0ktuqv6+/n/o+u/GJgXwI\noPm2OkmotfdQuPsIoRregTb+Uf17JX023OMYUierZQaGFKrcLcHOrnhduW5pYQ1NOQjqfXYF61c0\nJZ0nWydCgfBW094216A3Db9Qgse99VbjyYlt65Yv4dkH7kIKuOh7NyKkxLZtvv+db/HG8iVZH1oX\nQiWWVQQMonH9CinMMTENiSnbJsZo2bLCsFosIHytZH9xhEqqce8/m+g1hxo5luGVl872958KQghX\neSGwjWSvU+uFvh9xEBhCuIU1fHqZ/p0pk8eGVJ7obEFxjsnIXvlJ25L496jqmrVDenjfCwJl7AHc\ncUIKVUXwmnsfp+6Yk5CGoZJ5QyHK6xqyOsri513AyqZF/OyHV/HeW+uxbRvHsYlGW7j/1qv5wy9v\n4ztfPBNTKk3sA0KgKIdt26xYpHJgMhmJ61ugi0Ilo73rqR/aI/2XATy/9qMO93ni/FqkW/VVO+bb\nSzBMMpRRDjqtdy+97/TCR233I6OduuyM4eOf5p0BSDdknmP6nLHWnk0vHCzczM5OcoKC8KroqSNh\nGuqcplQ6sxlXMgh4f0wZrOyntjWUlvDlSUNVpxL+oPTlSUO579X30h7WO/zcq1nb/CkqauuTPLSl\nJblZ5z0LTmpBqIkgNS1AG3qaa7nmtcVead2n7rc45rNnqTKstk00GmX5q68wvjqS1YOOt4hpZcDq\nCTHHlG442R+QqwYV88o725BCKG1bITD1wIsqt+w4Ps/UkpLKLKLXHE5k8avvMvSk2t5aRxu/pnY+\nCLd8svt9nmUSs+1u8cAdauhEqCAsQ1VMbHx3e5rfoBLu3IRdPdfYbgnp0RW1fO/nv+PNlU2sbVrI\n+NoGyqoiXl/KRmgHhykFry18hVgsmrywcRy3wp5DLAZvNDUStx3Eww/jXPDrTp+npsRm2fb30Qxm\ny7KomTw1iaqSCQwtyWPjLl8Le0ppCQVhs13Hz0V1g4klbF77YNdBn39/LOFF+IRrxYZSJNIGjV8H\nFclatXGXu02pXgSNZ9tVksmxDEKGxHE/ZzM+MZAPAaRQclSeFIo0SNhKo9R2ksn/SnrowFarrXnL\nlpQBKSQoH1DUxjNzuGDKgJqEUCFxTQPR2wf3yCHfLbusQ6QCOGFsH0pyLW6f/1b6E8RbsD5az7ja\nG13utfBCY/lhM21mfLZBr9ClUM8sWNRF82vDpqIVrFrS6JfWdamFpmURj0MoFOKoo48GRFYPOlL4\nSTCtoRZIDjWDi9t4UJJoFE4g4iBAOoIEDpbUXtQsfgCHEH6S4pF//8U5FkN75LF+y17X89t2H738\nlkLgOL5cpq5WLKTANCBu+wvRI2VcCCJYMbG1la89zAglGWnrvhGIrCAE46vrqIpMIpqwfQWLw30j\nnYTn/ReSTx1/HL/71U9paW72jWQhkIaB4zhYlkX15Km0xG3Mu39KrP1DJ+EbJ9XwzuC5NP71CYQQ\nfHbWuZTX1nmOmpCRuUTn4Bh52ZRSV2c4/f4FYZML64bw2geru+X8SbkyQnmFW/fBoPHr4JBjGRTn\nWOxqjiuj2ZUj6Z0fYlBxDu/vaGbT7hZChprnWuI2/Yu6nnt1OHHkjRZHAIKSVH3yw5SW5HoDFWgv\nhz+ZSffzA2dXdslQHhiQeBMCQmYw2aVt2PJwIjdkJCkwWIZE5ybPGNmLXEsybXhP7/s8y1CrZNer\nXjmoiEc+V8UfP1+d8vjSsLjmN3MYXVHreqC1pzW7w6itofmCQihPqZ7AFdXA525bhqRqUqC0rqU0\nO29+YC7nXv5d/vzXF6ium5TER8xGlA8o8pRXggj2g5ThRQIJebrvkBxOPwJtn26FEDC0R16mL6Nb\nIKWgb2EYAQwqzmVoSbI0W3DxraMPlum3BU01CLaXmsHFFISPTJ9QR0mGAkUzydXUI3QfCXCzhYqu\n6OhMtird6IVO2BQ0TGlgzrP/y+mf+6JXdjoUCnPp1bdy/le/xy8feYaJNfWAS8OZ0zUu8tiqCJdf\n/xOuuP52ymvr0MsGgSrnnSm0jrBpesJvZ5UrZ1MKHEg9hXQQgT/+lmSUluQxuk++K7uovh/dp4CB\nxTke1VH/yjQkw92EfSHcRGuhkgGzGUfmaJHlkLoBoFZWwZCNCBgBOpNTfy7KMfnypKHcu7BjegGQ\nZGgIfK9tCkfDYYfmRILuEJK4rXQ9Bhbl8Idzq0nYfojFMqTXafQfxxFpO70wDMqqamiJJ5Dg8Z0E\nAvNghKUPM7x1k5N8n4YU1A7pwSvvbPVWsRU19fzovrksX/QKlfVTGVcdIWRIRpbXMnlMHz7Y1YwU\nIi2dIxvQ3qJNSkgk0rzvQLvAUT3LNNQ7txFIB8ggbzDTyA8pfnbfwuyrRnUwkEJVhWxtzIng36pR\nYElJwrbdthD0/uhF6JHbOPS4nu47HYES2H6RFP29+38jsIgYVJxD3yysXAZ6HlMJygANDQ0MKqvi\nuFPP4qUXX6S4pCe7d26jetI0ymvriNsOKxYvwI4n4IM1cPfn4fjLYXT7vOT1K5ood+l5/nJKIdNN\nxZKCQcU57I8lvAuSAvoVhBnZK5+1H7UulS46rHzbFQTbm7ZRWrudgpXvWi8mtPWTWp3GP2624xMD\n+RBACkFB2MR2PZn5IZM8yyTPMtiViKskPoTv8cCtECYEx47qTb5l8POX3unwPP1dD3JzPNGGY5fp\nRDURuAaB8u4lnIDXT+/XiqzvVcfC//zto0fw03+/nXT8maN7B7KddUgqu43DdGh38sN/PkIIKiP1\nbknpgHc54G3I9MB+oNCDcLowou4nuiKajaqUl3ActbDyngc4Wa922/0oyQtRm5WFYQ4O6dqzzrNw\nbWMchJfQ6gT6C2RYz7YbIUVqh4HvLVaeR8etPhn0AvpRFuk7aLKUi2VIRRdU0UClcpJnGeypqWPr\n3hg3XTabeDTGnNDPueuPz1BWFaGyfipmyCLWYuM074a//6pdA1k8/DV+vOtDrr93DrX1kwEChnLm\no3BSCu46fSItcZtYwvbzmIQglkL3WYjkwjkHis+OV0W89MLTm5c7eB6tqUt+hK/VdeL3yyNhwXrk\nuNqOEOiXXjO42OfGSUFZ3wLfqxoIMwQ1THV7qR5UxMX1Q9o9z2kT+5PjrrCjccUr80PPmW94fvZ1\nwHhzh2vp9pygMexRTtB/fE5y3dCSpGN/ddowzo8M8jqw1hLO0vG+Q+jBLxWCz0RnB69fsYQn7vsl\nry9vUmopYTPtgHSkoH9hmN754bRtV2/1aBaovhZyExg1VSVsGgwozG5e2yc4eJQPKKRXfsgbZ7QM\nmun+EeDx+TOpSNBdEELlnJT1TZZl8ytx+j5QX1fdd1B41VWV5ZO1NDRNJTFlwFgVwqPorWny8zBi\nsRhLF76CEIpjffP9c5l55nmYVoiOytM6m98hHo+xpmmhl+MRbCeZbi95rdSePMdSivk9x7UrDla5\nZ1BRDp+vGeSe0C/13tV5RTvHUo3lQdrFkTBff2IgHwJI0c6KCj9RKUloW+hyzGpgmzmmT7vnuHxK\nqffvWMIhZIqA4ZD5xpd0Dbpji2QdRN8o1k/G9/wE92l9L1v3RpFCC5AHaCpZsDDoKob1zKNnXijt\nAJTMPRSsXaaULB751e1cddEs1ixbnGRcH3lPQCE/bNLfrdqUCn6BEX9RJERbIfpBxTkfO5rB/8/Q\nXsTWMF1KFvhjiBBK0cF0Jc+0ipAeexsbG7n11ltpbOx6OftMQwq8MvJBjOmTz8DinCT6XtgwksdN\n4VIr8CM12YqhJbmUluR5BbQ09FwwMdLg5WFYlkVtw1TvvsZVR7j0utu4+rdzKG+v4NSODxFCYJoW\nE13JO03vCyYDZxKmITyqib6eVHNhaUku95xZrhZGB3nRig4amKsJRnZT98P2EHT6adQO6eFty3SU\nuzP4hGJxCOA3gFa8HdfqC8oXSQF2K8NRHaP9xtMS98MsNk4Sfxcn843P8yATuCfXfNOhPb1YcJy2\nhrO3vxAIx+GUCf14zq0AVBA2k8KG2ityBPS3NigIm8QSNpv3RNPuE/Smr1i0IEnJYuWiBVRFJnke\no0y/94NBewu76kHFCAHb9sV4a+teIOBNTjN5fIIjHx01Zz1xByMoAlWdywgY0CuXLuKKz51ONBol\nFAoxb948Ghq6VtY+kxCkTrq2DKmihyjOJyK5UJQXWTGkkkJzn092+o/dKrSW4UUFgp5uIZS284/u\nm8uby16latJUKiP1xBOB/B4cRldEOOuSr7P2klnEr3wi+QTvLkP89Q4iM07k0+dfyriqiIroIpIM\nukyPJX5SsoIUSsI14b5DjV55FoU5FrGEnVRi/UCgFbbU+dX/pBCKytTFY6l3J9raQGTeNukKPvEg\ndzNKci36uyHe5BVwMIQjkj5725M6RPpz/PDEMckbAglK2ghNVZnqcEKFyXzDLjgAFbkGrl4sBJ+B\nChf5Ny8FCCn4XPUgfj+7kkunDOWokT0941sGvM+ZHtQOFNrzkwpB/qQQUD15WpIHpWrSNK/NlJbk\nMq5f9lXG6grSepC9sHHQSy6S/n8gXo7ugvZOLlmyJDMX8DFF2DSSpA+DCC6MRGAckYGxB1T7eL1p\nIdFolEQiQTQaZf78+YftHroDNYOLKWqnLLRoNY7qeUY/G1Vsxx2Jj4BxMuhY0Z9BIKRgXHWECy7/\nepIkW17ISPrN2KoIx5w6u+1xt70P+3exfMF8t634c4cABhfn0q8wPdXrcCIYPdQLALNVATAHf3EY\nNuVBzYHRhJ00osrAvCRE53M7gipegtQaymq/A7/Ww4VPDORuRsiUDO6hJIkGF+fSt8BPnNGdMOgt\nHdkr3wtnSLdV6Qb/08+Ma3P8YT1zGdOnoI1n2jcT1XEzKfGm4SlUBEOhAU+fdAdxzZ9W21wOofY+\no0M9ils3Y0RvTJG8v+KnZT4sdjBId+1B77sUgoraOm5/8Em+cOX3uO3BJ6morfMWFJYhyQsduUGh\nkCHpnd8xPUK3IdHq86DiXIpzDv/9NzY2ctxxx3Httdcye/bsIzKEn60oH1CUNjvfcqUQRau2IEQy\nB9KUktNPPoFQKIRhGIRCIWbMmHF4bqCb0FFE0btn1yjxved+FM/rNxlcSHYG+lpH9sr3CqdoJ4Kq\nlwja+Nche8sQ3jvX7WHGZ2e1ObYDOI5NPB5jbVNjIAqlfp8XMhjcI5eawZkvNOS/O392b02jiCcc\nz4gzpeDJC9qhlnQAx8HzQnt2ivswS0vyGNIjt/0DuOhTEGJc30KEUMVDBhQlj+nBuTvbceTOpkcA\nWotgB72poBqIGgD2EjIlIprwVv4IwYxRvdse1Al60Nzj4lMa9OdMQ4AncK/levxJLJmobxpB77fj\n1XFX++JSRgLHFr4BLQXkh9QQMeQI1YDNswxyrdRdsbQkj3e27fM+C5SSRVlVRJWXDSwkjnQIIRjW\ns/13WBAy6ZEbYsf+aKC9q/8GZEh0fv78+Z53Un8+ksL3Ryr6FIQoyVPh5bWbtOyV49ENhIAeuRYj\neuUhBjcwb9485s+fz4wZMz5W70fPKw6+0WFK5ThoSdgepU0bg9nuudPR1dZVBf0ERP+z+js4p/oG\n9tiqOlieHNEx31yAbRiYpsX42gZv4SFILqyVDQoLQe9tawqmRnPc9ozog3USOS6VIhiBMITAFoLC\nLhTfEkKQHzYDkZzki5JCaXCP7pPP5k370hwlO/CJgXxYoRqxpavKBTqAT8hXS/t0g9hZlQMQQnge\ngHjCVh5UAWV9C9iwqzmJn5xJSKFCYrpfJRnwAoTLlTYDBrNONlD7uF6gwLNKUv1AGeFa8qnfEZqc\nFTIlE/oXpvyuT0GYd7erQSToJSp0ZQR75Frsau5K/agjGyFTMqp3Pq9vstnZHAdaJzIefsyYMYNQ\nKEQ0GsWyrCPOO3mkQqkbKENwbN8C1m/eixA+z9YzoNzG0dDQ8LEyjIPQUUjb8wwLwoZgH5DvKSKo\nflKcY2Uk0tIVtO7P2nAzpSThGnLBuQCSPegCWL9iCZM2vMCrA08E4MHZlbxffguvL21kfKSB0RUR\ntzCV+m02RF2DEO5Ne/fkjf+i1X7KbgguFA4EDv5zDtIrgouSLl0/ba8VINcyKc49MqrdZncv+ZhB\nG4ZhU6qSoIHtUiavElO1x6uOG0XlwOIk4zlmOxSETRxHrcpG9c4/1LfRKXgeXsAUqi57ac88WuI2\nyzfsVD4eobJmZWu5O9zkRcdPdNSrWu979zzZ7g3pDozvV8iHu1rYti/qDV6GFGDD+H4FLHpvR9Ym\n3RwqlPUrZNF724GgxyMzaGjwvZMTJkz42Bph2YyCsErKi9s68ddxeZMffxTnWAzpkcf7O/YlReY0\nZ1/zVvVYOap3flZ4SNtDygITQlWLbYk73jaAcX0L+c/2/UgRQ5uIb6xo4vqLZxGPRuF4oPxE3l79\nGlYg+ggkJXJmG7w5DuGp9UgBF0QG84P/fR3Aq7Ug3TDiwcyHl08d5p1PBoxx6wAPWjGwKKX0XDpn\nUDbiEwP5MEK4IYewKWmO262MQoHhVkLSRmBzPMGlDaX8pvFdAM+zKoSfhRxN2PTMs9jj0jOyaeDT\nvGFDKiM419KlUP1V8f9r795j4yrvvIF/n3POzNjjexLfbWJMQjKOb4mdi1kaJaQJ6eUNSoE2FASi\nRJHa6g20UqnUvq+g21ahLdVCEVop7dJSITXt7qqEEsELb0hCYF2lxCEsULItm0i5EZyLTZw4ntuz\nf5zr2GNix2dmzpn5fiSadMYeP+cXn5nfec7v+T3mibiwphQfnruMhEwYdU/6iuTxCbGAXidlHmaJ\nUXPrhUUVmRIOaigJxjE0as8UmLz0751t1gXmDD8Y3GDOTp46dSq3AylgpSEVsUQSSSlzflchm1RF\noLYshI9HxjAW18t89PdYaZRuCcdjORzoFKUbopnUqsZMr/OiuCSkGTXI9te+/1Y/EjHjztr//2dg\n/7P4jy9sxL4//R7xWAyBQACPbP9XfObmf5hxaUKmOH+HiwOqddd4/pwS/MuXu/DAHw7ji221jkml\na//3/dH6BZhfXZpSBgnorxtQxDV9vvphhvhq/H8EPiKg77Cn/x3Wrm/VxsIks/TWvDVx7Pxl3DJv\nDv759nbctqgWkdrSlK4GgN4Ducyo9/Eas9ZIVUTKohDnLLH5p7kC177FYybCdvcCs9bCWtAIpCzw\ny2e1ZSEsqiuzruqdHxCFcPzpOEt2Zof9WV5D7mmdXYKWWWHr3BjfJizfddSXp3RQEgCCqmMSxnp/\n9f4bRrrPM3MCyOxvPf5r7BILoH1pH9SA0fVDJqElohg+N6jPKEuJWDSKfS/+G8LB9K/lBfZOkPbg\nzM/FspCGHfcsQd/cKnvSSP+ua/pZ0tHKzdyxMaQpCKjubmHtN5xBziJnLZHzTWrurDA0VWD4Ssxa\ncGV+jSKAiuIA7uxq0GeYFT1BNFuuSABF1qysd85y/Tj02zPmLU+TvZDCntmwvs94g1OF3n9RgdAb\nmMNOCu0PP+F408/u8WWbEAJFARU9zZX4j6PnU59DYSUCpo76cpy5OIbBS2O4frY/F2iS+4zqIwjo\nd60KSVNlMS7HEhgajRkX0nbiY5Zned1kJVMtVWF88PEINEWBlLB2krW/z0wogUhXL7Y98+94decf\ncOHsIMqq5uCTC2fH/SD783gq3XOyzexnbbYyTAKAFFAFkDC+xjxmFTO7kzYaT6aUaZi7F8aTyZSW\niYWGCXIWOX/J0v2+qdYJbpcemAvVzDc6keZ7Q5pizUZ7iZkETzy5jJPayG7Nr7thTgkGR8ZwfGgU\nIU3BWFwvN4EUjsU3zuJ/ac+oF1B+aG3AgokzDIUkqCloripGc9XU2g9R/isO2JtmRGpLCy5BNpmf\nE86Fa4qQWFDj/T7pQVWxZsKd5pSGIAZH9NpyCbQ7ev0LGIvJAGuWJbK4F3Ep8cjmLyMei0FRVaia\nhmQiATUQwOoNd1rffbXuOblgFjZYJYXSLNNUEBOJlO3DNVUgoKpIJiUe/Mz1eHL/0Wn9rPOXo8Ys\ntB47VcDOQQoYE+QssmaGjYQ39TlnOYG+ntS5khQwfmGNF7Ielpi0EXcuOWd6x++mY97mC2nGimTj\nYIoDKspCGgQEgqq94CbpWKFrXlGrQiAhzHW3hWX8DMuCmlJf9z8mcks4qKGnuRIHjw9Z76eFJmBs\nCqIvdJbWhXQ4oFklfl4mhLD2EpjwnJk0pvln1YyORppIWp+n7/2lH/GYvvMoBPDZL30VdY3NiPSs\nwIKuXuM1vclZQma2QBVCr+29Ekuk5AGaoiAcUHApmsBnWmfj6TePIZ6c+qyR85M0pOqfzeaFphfL\nT7LF+2dLHnHeAklX9K4IvVwilpRWYuksuYDjDV9CWiu1J9tpKtfM2d2SoIbqErunpRD6jnlBVXHs\n3oOU5/UNQNTUshTYybXzdpr55lFInBdZfvjQI8qmG6sLd/Z4blUxqsIBHPl4xLqYDmoK2nzUPWAy\n1uRAmrd7RehdLuJJBaqiIJmU6Fh6E7RgAPEYoGn6rHFXzzJ9kbzjNb3IKik0jtX8PDUaWjgu/vRS\nImdZ5nQOaUF1CXqbK4wNufTdK1M2mhHe3lgmk/jJmkWaomAMqS1qnBSh964cUwXsGWX9ObPjg/V9\nUiCelBO2ZvaK0qCG5sowPjx7CddVFqPEkcQJCGvr03Qrb81jD2lAMumsXRZWjZQqgNKgal1oeDAE\nGVPIV/REU1Hm8T6/maQowl6ch/yqH7V3SJQTHjf/CxqTL5ejCUQW9+IHv/wDDv35DSxecTMiXb1W\niZq5IZVXywiEcac0ZFzo2dtiC2OyzE6EVePWsjmJ9H/Wzscj/++/pvRz/u/a+Y59GABNcUxKSeD6\nWSWe6xGdLYV51DnS3ViRsmBiPDMBNK8GVWt21HgeqbvrRBNJlHl09lAxWg8pQky42BfCbj4uhL4D\nnrNfol2aoXf9sN7qhXmhoMdJS9miOksH5gGFeMxEND3WYmjk3wzg+M/PuVXFmFtVrCe+5iyq8efC\nrl5suP9/Y2FXr70wXHj/zpuAPqlWFFBQX16EnuZKLG6ssCbOFIwr24R993VBdSme+XInPh+pmdJP\ncs48p36uCswuCX7qd+czJshZ9mnbQVulBYo9s2otdDO+ydnkPJaQCIe8fRvxahfnikDKDnjOFmYC\nqTv6mCexdfvH8fohzdtxcNPwf7+L3zz9T/jPgQO5HgoReZRzQsHcaMLv7BLFVJqqGBNL5rSq+blh\nJ8RWMikESoOaY0Y2O2OfDjMXKAooxny5THnO/HP83505Q0BVcG9P01V/lrVJWZrcxKOT61nj7Uuo\nPOS8Mhv/uH0SC6iK1DcGcXyZeWsIAC5HEwiqAmUeX5yVrn7JeWsoXS22MB83iq+Cxs6D0YTe1aI4\noGIsnrAS5urSIJoqCqOTQX9/P/7X527VtzUOBhHZvZs7txFRipCmIhzQcDEZx9yqYswK58cs4Pjt\npccTjq/RFIG4EIjDvjtrPZeysUiGB30NaktDCGkU5z1gAAAYoklEQVQK/vP0JynjM++uWp+RsP8u\nIa27zM5JpU/zzJc7ra+z2iM6vseLscmm/Lis9JmigJp2u0UzORbC3ADDeRrYJ79eSK9AQnh2gZ4p\nbcN3pF75Tnjeuliwj1xThRUT1fjPvAVVXhSA4sVpgAzYu3cvotEoEokEYtEo9u7dm+shEZHHqIpA\nW12ZVc6WL++PnQ3laKsrQ6Qm/YJDa5IJQElQtY7b/CTVFGeLTP25ag/2QFYUe5Y7dZ8Ae/T2Qn67\n/LAkpKXcZRYAvr9m3qQ/J2h0qzBLGq0Woo4Ju0Lm7enHPGReARaNW2FtJb9GWxVNUVJmkFNbFgmE\nNP2K0eulBZqiTEji9at4adRRpZ6CpUEVs8P6tqlJ2PvMC+g7KFmta4wTuKM+/X7v+WrVqlUIBoOI\nRqMIBoNYtWpVrodERB6lv9Pmj4CqYLLmJMKZLFp3amHNHJs7u8aTSSu5rAoHMNeDPZCB1LIQ513Y\n1NlhCU218wRz8shaxCeAroaKtK+vd5Yykm04SjqE/Xvj1QWM2cIEOcsmy+VqSkMoDqh4/8xFa4e4\ngObo+SidjbslVEXf5cbrM8jdjRNPzkhtKd45dRHAxBlkTVVw/ewwPh4Zs66c9Tc3qSfCwq7Pcpac\nFIq+vj7s3r0be/fuxapVq1heQUSTMt8nC4W5eM36uzDrsPUJGU0R+r6sKXcovcm6ozxunM79AKQx\nK27ttGd+jzC+0kwexmmpKsYjt95of/4KvROGKoR1kQEJT25Alk1MkLOsoiiQtvOEYuyvbs2uQm/4\nbu2Rbp3osG4TqYpAQPX2SZ5OOKhdtRND6glv/1dkbIriv6N2T19fHxNjIrqq9voyz99ldJMQgJD2\np4MiBFQFiCXtkgqryMLjHyJWZwkx/nFYs+JJKY18QL/7DOjdr8yvMxPl8cJBFcWaamzUla60Qlhr\ngAqZt6cf89D86lLUlU/cRhNw1OU6bxMJGLdQ9Mc0VS81UBUgbK7a9SkBoGiSN++UFbrG/yrC8SY3\nyZsHERHpCik5BjAhyRNGOaPV8s66A5l+AbmXGGn8hHEKoZeaWImtEFjcWAFNUaznzTiYm3/8Q0tV\nymtL2DPN9qeq/fMAQBVMDxkBD3EmfdbGIBDGyQC7+btxtef1Po5XowiB9vrytM9Vl4Ss2WWzfsw8\nkZ1vAEREROk2ntIUofdFBqwE0lrYlpthTku6hXKKMLcTh1VeoSgCHfVlmF0StGeDHfXFWz/Tiutn\n6Z2eigMK7uttcuxCa+Qeip0wV5cG0zYSKDRMkD3GvLoF7CQ5qOpL2cJBzbrCUxUFJT7eStU5Q5zO\n3FlhqEJBdWlQX40NkXLFqwiBquKgr2fQZ6q/vx/btm1Df39/rodCRJRTRQEV7fVlKXcWnZtKFQeU\nlPIKr9dnO2e8nTRFQUA18wD7GDR13MJ+2LPEigAajDvX/3jrjWipCtuzxo7ZdDMmIU0t2N3znPw9\nBZmHnLU/+i50Eqpxq0RV7CtKRfH3dqoCV7+91d2ozy6PxZNGbZnzihe4YU5JhkfpXf39/VizZo3V\nzWI3+yETUYELaSoqigIYGo0BMCeZjLJEI3M0E+h5c0o8fRfWvKN8w+zUcXY3VmDfh2ehTlJHbdcT\nm4vt9D83r7gON7VUoaGiWJ9YEnrdshBmC1U9Lm11ZShicgyAM8ie09NcaW16YRbYK0LvA2xvJ6n3\nP/Z6B4urudrFu7O1nXnFP9lVdaFx9kOOsh8yEREAfZ2PNYtqTCjpG4PY3SsEgKKA4vkWoUWaisri\nAAJq6me9mfgDExe6q0IgoClWWaLZ0q5IU9FZX27NKDs3SrF27wXsHQmJCbIX1ZUXIWQsxDNyZBQb\n5RTmCV+sqQip/v3nm+75N77muNBPX7Mfsqqq7IdMRORQU6pv/jF+wZ7zMS8v0DO115enTVb1vRSU\nlOOynxMIGWWZ+rHa26LoC/ec26SYd6ftzULI5t37CwVufnUJzl6K4viFK8ZCPcc6U6FfJfu5Rui6\nyqk3ZzdnjyUkpETaN4VCw37IRETpXVcVhoDAmZEr0O/C2tmwEEBTRbGv78A6O3E0lBdPeE5VBISx\nS0pqtyfjjqy5IE+YPZX1JnEdkyyaL1Q5SZCHhoawefNmvPvuuxBC4JlnnuEH/DghTUWJ0S/YeQvF\nXHnq5+QYAGrKpr69Z0hTMW9OCf5rcMQ6+du5wpb9kImIJlFepOHcZQUxkUAS9qyxACZtteoXdsEl\n0FCReizmXWYVjtlzx9qd1C4Xeg2yqug9lf2eV7gtJ9F48MEHsX79enzwwQc4fPgwIpFILobhG86a\n20KdPa0oDlg1ZHpNNk9kIiJKr6I4gHazA5K1fic/ygjsLh0THzfXKamKsLpaKOZssbD7IztrkPMl\nLm7L+gzy8PAwXn/9dfzmN78BAASDQQSDwWwPwzfMK0Dp/P8F+nvc3ViBeDLpkw6WRESUS+Nbgyak\nRHdjRa6HNWP2Z2C6+mRzx10FZSENl6IJx4YgZrtUO3E2d+b1QUl21mU9QT569Ciqq6tx//334/Dh\nw+jp6cGTTz6JkpLUll3bt2/H9u3bAQAfffQRTp06le2hTjA4OJjVn3dxLI5PLowiYawmuKIIJJL6\nL/UpbTSrYxkv27HwKsZBxzjoGAcbY6FjHHS5iIOUEsNnRyABmNUDZ3L82QnMPBafnLsIVQHiSYlT\noSv2E1KiNBbHx+cvI5pI4uJlDVdicUQTEqOxBKQEEqpA3KhBjiUkQiENw6N6E4BTgezGxuvnRtYT\n5Hg8joGBATz11FNYvnw5HnzwQTz22GP44Q9/mPJ1W7ZswZYtWwAAvb29aGhoyPZQ08rmOIZHYxjR\nLiGe1BenBVT9ClgRQEND1dVfIMO88m+Sa4yDjnHQMQ42xkLHOOiyHYdkUuJUYghJqW+4VVMWRENF\n8dW/MQtmEosT8QtQhUAsISfkArFEEufEEGKJJIoCKqKJJEbG4hDRBBJJiZCmGIv+BWKJJCpLglZH\njIaGyhke1fR5+dzIeiFnU1MTmpqasHz5cgDAHXfcgYGBgWwPwxcqigOI1JYCSK05un5W4W6QQURE\nNBXOvvm1ZSE0eiQ5niln/fB4ZjmJWX2hCrvHsV2HrP8ZMDYIKdJUdrBII+sJcl1dHZqbm3HkyBEA\nwO7du9HW1pbtYfhGOKilFOILCMwuYc02ERHRp9HbmOk1uTKPqmxnh+0+z+PZddd25wpzQxS7e4Uu\nHFBgbkbGDhYT5aTN21NPPYW7774b0WgUra2t+PWvf52LYfiGueo0HFRxw2zOHhMREU1FT3Ml/vvc\nJVQVB3I9FNdcPzuMeDKJ4SuxCc/Z3TrM/y+gGg8KyJQNQQLGhiL5c+ngrpwkyN3d3Xjrrbdy8aN9\nKaiqiCYSvMojIiKaptY8nFiqKytCSXBiCufcRMTs/Wx2tkhKWF0sVKWwu2JNBbMtH+hsKEdIU1Ea\nUnM9FCIiIsqxsiJtwiYhgJ4AF2kqzCILs+ZY0Z80ZpDN9m7OggsajwmyT3TUl+fNAgMiIiLKjPb6\n8tS1SwIIaoq1IUhAFdbGW/osM4ss0mGCTERERJRHnLvuKgIIB1Srk4VizSQX5s68U8UEmYiIiCiP\npCa+ets3BUBAMTvAOf+X0mGCTERERJRHzFZv5qI9RQhoqkA4qDke0x8vCnB9Uzo56WJBRERERJkh\nHH/qS/EkwgEVmiIQT9rJc8uscF61wHMTE2QiIiKiPGL2OrZ3ExRQFb1owEyOb5hdgoqiABSFhRbp\nsMSCiIiIKI8sqClFUUC1dhI0W74BdtJcFQ4yOf4UTJCJiIiI8khpSEOkphRNlcVWezfF6mrB7sdT\nwQSZiIiIKM8oisDscABNFcXG7LFddqGwv9tVMUEmIiIiykOaqqCuvMjoaqETABY3VuRyWL7ABJmI\niIgojwnYs8alIY21x1PALhZEREREeUwRAkkhUVkcwA1zSnI9HF/gDDIRERFRHutsKOe20tPEBJmI\niIgojwVUBQKAzPVAfIQJMhEREVGec7Z6o6tjDTIRERFRnmurK0OAGfKUcQaZyEf6+/uxbds29Pf3\n53ooRETkI8UBFZrKtG+qOINM5BP9/f1Ys2YNotEogsEgdu/ejb6+vlwPi4iIKO/wUoLIJ/bu3Yto\nNIpEIoFoNIq9e/fmekhERER5iQkykU+sWrUKwWAQqqoiGAxi1apVuR4SERFRXmKJBZFP9PX1Yffu\n3di7dy9WrVrF8goiIqIMYYJM5CN9fX1MjImIiDKMJRZEPsIuFkRERJnHGWQin2AXCyIiouzgDDKR\nT7CLBRERUXYwQSbyCbOLhaIoEEJg9uzZuR4SERFRXmKCTOQTfX19eOKJJ6CqKpLJJB566CHWIhMR\nEWUAE2QiHzl37hySySSSySTLLIiIiDKECTKRj3CzECIiosxjFwsiH+FmIURERJnHBJnIZ7hZCBER\nUWaxxIKIiIiIyIEJMhERERGRAxNkIiIiIiIHJshERERERA45WaTX0tKCsrIyqKoKTdPw1ltv5WIY\nREREREQT5KyLxZ49ezBnzpxc/XgiIiIiorRYYkFERERE5JCTBFkIgXXr1qGnpwfbt2/PxRCIiIiI\niNLKSYnFG2+8gcbGRnz88cdYu3YtFi5ciJUrV6Z8zfbt263k+aOPPsKpU6dyMdQUg4ODuR6CZzAW\nOsZBxzjoGAcbY6FjHHSMg42x0Hk9DjlJkBsbGwEANTU12LhxIw4cODAhQd6yZQu2bNkCAOjt7UVD\nQ0PWx5mOV8bhBYyFjnHQMQ46xsHGWOgYBx3jYGMsdF6OQ9ZLLC5duoSLFy9af3/llVfQ3t6e7WEQ\nEREREaWV9RnkM2fOYOPGjQCAeDyOr371q1i/fn22h0FERERElFbWE+TW1lYcPnw42z+WiIiIiGhK\nhJRS5noQVzNnzhy0tLTkehgYHBxEdXV1rofhCYyFjnHQMQ46xsHGWOgYBx3jYGMsdF6Jw7Fjx3D2\n7NkJj/siQfaK3t5e7vpnYCx0jIOOcdAxDjbGQsc46BgHG2Oh83ocuFEIEREREZEDE2QiIiIiIgf1\n0UcffTTXg/CTnp6eXA/BMxgLHeOgYxx0jIONsdAxDjrGwcZY6LwcB9YgExERERE5sMSCiIiIiMiB\nCTIRERERkUNBJ8jHjx/H6tWr0dbWhkWLFuHJJ58EAJw/fx5r167F/PnzsXbtWly4cAEAIKXE1q1b\nMW/ePHR2dmJgYMB6rYcffhiLFi1CJBLB1q1b4bfKFTdj8d3vfhft7e1ob2/H73//+5wcz7Wabhw+\n+OAD9PX1IRQK4fHHH095rZdffhkLFizAvHnz8Nhjj2X9WGbCzTh87WtfQ01NjS+3lHcrDpO9jp+4\nFYsrV65g2bJl6OrqwqJFi/DII4/k5HiulZvnBgAkEgksXrwYX/ziF7N6HDPlZhxaWlrQ0dGB7u5u\n9Pb2Zv1YZsrNWAwNDeGOO+7AwoULEYlE0N/fn/XjuVZuxeHIkSPo7u62/isvL8cTTzyR/QOSBezU\nqVPy4MGDUkopP/nkEzl//nz53nvvye985zty27ZtUkopt23bJh9++GEppZS7du2S69evl8lkUvb3\n98tly5ZJKaV888035U033STj8biMx+NyxYoVcs+ePTk5pmvlVixefPFF+dnPflbGYjE5MjIie3t7\n5fDwcG4O6hpMNw5nzpyRBw4ckN/73vfkz372M+t14vG4bG1tlR9++KEcGxuTnZ2d8r333sv+AV0j\nt+IgpZT79u2TBw8elIsWLcruQbjArThM9jp+4lYsksmkvHjxopRSymg0KpctWyb7+/uzfDTXzs1z\nQ0opf/7zn8u77rpLfuELX8jeQbjAzTjMnTtXDg4OZvcAXORmLO699175y1/+Ukop5djYmLxw4UIW\nj2Rm3D43pNQ/S2tra+WxY8eycxAOBT2DXF9fjyVLlgAAysrKEIlEcPLkSezcuRP33XcfAOC+++7D\n888/DwDYuXMn7r33XgghsGLFCgwNDeH06dMQQuDKlSuIRqMYGxtDLBZDbW1tzo7rWrgVi/fffx8r\nV66EpmkoKSlBZ2cnXn755Zwd13RNNw41NTVYunQpAoFAyuscOHAA8+bNQ2trK4LBIDZt2oSdO3dm\n92BmwK04AMDKlSsxa9as7A3eRW7FYbLX8RO3YiGEQGlpKQAgFoshFotBCJHFI5kZN8+NEydOYNeu\nXdi8eXP2DsAlbsbB79yKxfDwMF5//XU88MADAIBgMIjKysosHsnMZOJ3Yvfu3bjhhhswd+7czB/A\nOAWdIDsdO3YMhw4dwvLly3HmzBnU19cDAOrq6nDmzBkAwMmTJ9Hc3Gx9T1NTE06ePIm+vj6sXr0a\n9fX1qK+vx6233opIJJKT43DDTGLR1dWFl19+GZcvX8bZs2exZ88eHD9+PCfHMVNTicNkJouPH80k\nDvnErTg4X8evZhqLRCKB7u5u1NTUYO3atb6NxUzj8NBDD+GnP/0pFMXfH8UzjYMQAuvWrUNPTw+2\nb9+e6eFm1ExicfToUVRXV+P+++/H4sWLsXnzZly6dCkbw3adW++XO3bswF133ZWpYX4qf5+VLhkZ\nGcHtt9+OJ554AuXl5SnPCSGuOrvx97//HX/9619x4sQJnDx5Eq+99hr279+fySFnzExjsW7dOnz+\n85/HTTfdhLvuugt9fX1QVTWTQ86ImcYhXzAOOrfi8Gmv4xduxEJVVbz99ts4ceIEDhw4gHfffTdT\nw82YmcbhxRdfRE1Njaf7wE6FG78Pb7zxBgYGBvDSSy/h6aefxuuvv56p4WbUTGMRj8cxMDCAr3/9\n6zh06BBKSkp8t34FcO/9MhqN4oUXXsCdd96ZiWFeVcEnyLFYDLfffjvuvvtufOlLXwIA1NbW4vTp\n0wCA06dPo6amBgDQ2NiYMht64sQJNDY24o9//CNWrFiB0tJSlJaW4nOf+5yvCutNbsQCAL7//e/j\n7bffxquvvgopJW688cYsH8nMTCcOk/m0+PiFG3HIB27FId3r+I3bvxOVlZVYvXq1r8qwAHfi8Oab\nb+KFF15AS0sLNm3ahNdeew333HNPxsfuJrd+H8z3xpqaGmzcuBEHDhzI3KAzxI1YNDU1oampybqj\ncscdd6QsgPcDN98jXnrpJSxZsiRnJasFnSBLKfHAAw8gEong29/+tvX4hg0b8OyzzwIAnn32Wdx2\n223W47/97W8hpcSf//xnVFRUoL6+Htdddx327duHeDyOWCyGffv2+a7Ewq1YJBIJnDt3DgDwzjvv\n4J133sG6deuyf0DXaLpxmMzSpUvxt7/9DUePHkU0GsWOHTuwYcOGjI7dTW7Fwe/cisNkr+MnbsVi\ncHAQQ0NDAIDR0VG8+uqrWLhwYeYG7jK34rBt2zacOHECx44dw44dO3DLLbfgueeey+jY3eRWHC5d\nuoSLFy9af3/llVd81/HGrVjU1dWhubkZR44cAaDX37a1tWVu4C5z+3Pjd7/7Xc7KKwAUdheL/fv3\nSwCyo6NDdnV1ya6uLrlr1y559uxZecstt8h58+bJNWvWyHPnzkkp9dXX3/jGN2Rra6tsb2+Xf/nL\nX6SU+irLLVu2yIULF8pIJCK/9a1v5fKwrolbsRgdHZWRSERGIhG5fPlyeejQoVwe1rRNNw6nT5+W\njY2NsqysTFZUVMjGxkara8euXbvk/PnzZWtrq/zRj36Uy8OaNjfjsGnTJllXVyc1TZONjY3yV7/6\nVS4PbVrcisNkr+MnbsXi8OHDsru7W3Z0dMhFixbJH/zgBzk+sulx89ww7dmzx3ddLNyKw4cffig7\nOztlZ2enbGtr8917pZTu/k4cOnRI9vT0yI6ODnnbbbfJ8+fP5/LQpsXNOIyMjMhZs2bJoaGhnB0P\nt5omIiIiInIo6BILIiIiIqLxmCATERERETkwQSYiIiIicmCCTERERETkwASZiIiIiMiBCTIRUZ54\n9NFH8fjjj+d6GEREvscEmYiIiIjIgQkyEZGP/fjHP8aNN96Im2++2dqB6xe/+AXa2trQ2dmJTZs2\n5XiERET+o+V6AEREdG0OHjyIHTt24O2330Y8HseSJUvQ09ODxx57DEePHkUoFLK2dSYioqnjDDIR\nkU/t378fGzduRDgcRnl5OTZs2AAA6OzsxN13343nnnsOmsZ5ECKi6WKCTESUZ3bt2oVvfvObGBgY\nwNKlSxGPx3M9JCIiX2GCTETkUytXrsTzzz+P0dFRXLx4EX/605+QTCZx/PhxrF69Gj/5yU8wPDyM\nkZGRXA+ViMhXeO+NiMinlixZgq985Svo6upCTU0Nli5dCiEE7rnnHgwPD0NKia1bt6KysjLXQyUi\n8hUhpZS5HgQRERERkVewxIKIiIiIyIEJMhERERGRAxNkIiIiIiIHJshERERERA5MkImIiIiIHJgg\nExERERE5MEEmIiIiInL4H9yrqSY8ZFNgAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yXHiF6f6JcKy",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 657
        },
        "outputId": "bb3d0410-560c-4017-a37f-99d270e9183c"
      },
      "source": [
        "fig2 = m.plot_components(forecast)"
      ],
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAn4AAAKACAYAAAACSHUJAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\niCwEEz8iIiIiC/E/vJqnzClGwY8AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 648x648 with 3 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    }
  ]
}