India choropleth map.ipynb

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  "metadata": {
    "colab": {
      "name": "Covid 19 dashboard",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "YEq7iHFlWjvH",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 124
        },
        "outputId": "b130ca5d-f8ae-46ca-9224-84e67270f860"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3aietf%3awg%3aoauth%3a2.0%3aoob&response_type=code&scope=email%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdocs.test%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive.photos.readonly%20https%3a%2f%2fwww.googleapis.com%2fauth%2fpeopleapi.readonly\n",
            "\n",
            "Enter your authorization code:\n",
            "··········\n",
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EpJJt2T0WpEL",
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 260
        }
      },
      "source": [
        "pip install geopandas"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: geopandas in /usr/local/lib/python3.6/dist-packages (0.7.0)\n",
            "Requirement already satisfied: pandas>=0.23.0 in /usr/local/lib/python3.6/dist-packages (from geopandas) (1.0.3)\n",
            "Requirement already satisfied: fiona in /usr/local/lib/python3.6/dist-packages (from geopandas) (1.8.13.post1)\n",
            "Requirement already satisfied: shapely in /usr/local/lib/python3.6/dist-packages (from geopandas) (1.7.0)\n",
            "Requirement already satisfied: pyproj>=2.2.0 in /usr/local/lib/python3.6/dist-packages (from geopandas) (2.6.1.post1)\n",
            "Requirement already satisfied: numpy>=1.13.3 in /usr/local/lib/python3.6/dist-packages (from pandas>=0.23.0->geopandas) (1.18.4)\n",
            "Requirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.6/dist-packages (from pandas>=0.23.0->geopandas) (2018.9)\n",
            "Requirement already satisfied: python-dateutil>=2.6.1 in /usr/local/lib/python3.6/dist-packages (from pandas>=0.23.0->geopandas) (2.8.1)\n",
            "Requirement already satisfied: click-plugins>=1.0 in /usr/local/lib/python3.6/dist-packages (from fiona->geopandas) (1.1.1)\n",
            "Requirement already satisfied: cligj>=0.5 in /usr/local/lib/python3.6/dist-packages (from fiona->geopandas) (0.5.0)\n",
            "Requirement already satisfied: six>=1.7 in /usr/local/lib/python3.6/dist-packages (from fiona->geopandas) (1.12.0)\n",
            "Requirement already satisfied: click<8,>=4.0 in /usr/local/lib/python3.6/dist-packages (from fiona->geopandas) (7.1.2)\n",
            "Requirement already satisfied: attrs>=17 in /usr/local/lib/python3.6/dist-packages (from fiona->geopandas) (19.3.0)\n",
            "Requirement already satisfied: munch in /usr/local/lib/python3.6/dist-packages (from fiona->geopandas) (2.5.0)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UUif9CFxWs4X",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import geopandas as gpd"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kSnAkNZhXLJq",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import plotly.graph_objects as go\n",
        "import pandas as pd\n",
        "import requests\n",
        "import matplotlib.pyplot as plt\n",
        "import geopandas as gpd\n",
        "import numpy as np\n",
        "from shapely.geometry import Point, Polygon\n",
        "from datetime import datetime\n",
        "from plotly.subplots import make_subplots\n",
        "%matplotlib inline"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yiJScdGYXSfz",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "raw= requests.get(\"http://covid19-india-adhikansh.herokuapp.com/states\")\n",
        "raw_json = raw.json()\n",
        "df = pd.DataFrame(raw_json[\"state\"])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ipw0zR2PXadx",
        "colab_type": "code",
        "outputId": "db0912b4-2a44-4797-ea2a-03baa9cf1e6a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 202
        }
      },
      "source": [
        "df.head()"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th></th>\n",
              "      <th>_id</th>\n",
              "      <th>name</th>\n",
              "      <th>confirmed</th>\n",
              "      <th>cured</th>\n",
              "      <th>death</th>\n",
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              "      <th>0</th>\n",
              "      <td>5ed0a190956123b324f035c8</td>\n",
              "      <td>Andaman and Nicobar Islands</td>\n",
              "      <td>33</td>\n",
              "      <td>33</td>\n",
              "      <td>0</td>\n",
              "      <td>66</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>5ed0a190956123b324f035c9</td>\n",
              "      <td>Andhra Pradesh</td>\n",
              "      <td>3251</td>\n",
              "      <td>2125</td>\n",
              "      <td>59</td>\n",
              "      <td>5435</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>5ed0a190956123b324f035ca</td>\n",
              "      <td>Arunachal Pradesh</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>5ed0a190956123b324f035cb</td>\n",
              "      <td>Assam</td>\n",
              "      <td>856</td>\n",
              "      <td>104</td>\n",
              "      <td>4</td>\n",
              "      <td>964</td>\n",
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              "      <th>4</th>\n",
              "      <td>5ed0a191956123b324f035cc</td>\n",
              "      <td>Bihar</td>\n",
              "      <td>3296</td>\n",
              "      <td>1211</td>\n",
              "      <td>15</td>\n",
              "      <td>4522</td>\n",
              "    </tr>\n",
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            "text/plain": [
              "                        _id                         name  ...  death  total\n",
              "0  5ed0a190956123b324f035c8  Andaman and Nicobar Islands  ...      0     66\n",
              "1  5ed0a190956123b324f035c9               Andhra Pradesh  ...     59   5435\n",
              "2  5ed0a190956123b324f035ca            Arunachal Pradesh  ...      0      4\n",
              "3  5ed0a190956123b324f035cb                        Assam  ...      4    964\n",
              "4  5ed0a191956123b324f035cc                        Bihar  ...     15   4522\n",
              "\n",
              "[5 rows x 6 columns]"
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          "metadata": {
            "tags": []
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          "execution_count": 10
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      "source": [
        "df.set_index(\"_id\")"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
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              "  <thead>\n",
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              "      <th></th>\n",
              "      <th>name</th>\n",
              "      <th>confirmed</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>5ed0a190956123b324f035c8</th>\n",
              "      <td>Andaman and Nicobar Islands</td>\n",
              "      <td>33</td>\n",
              "      <td>33</td>\n",
              "      <td>0</td>\n",
              "      <td>66</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5ed0a190956123b324f035c9</th>\n",
              "      <td>Andhra Pradesh</td>\n",
              "      <td>3251</td>\n",
              "      <td>2125</td>\n",
              "      <td>59</td>\n",
              "      <td>5435</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5ed0a190956123b324f035ca</th>\n",
              "      <td>Arunachal Pradesh</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5ed0a190956123b324f035cb</th>\n",
              "      <td>Assam</td>\n",
              "      <td>856</td>\n",
              "      <td>104</td>\n",
              "      <td>4</td>\n",
              "      <td>964</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5ed0a191956123b324f035cc</th>\n",
              "      <td>Bihar</td>\n",
              "      <td>3296</td>\n",
              "      <td>1211</td>\n",
              "      <td>15</td>\n",
              "      <td>4522</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5ed0a191956123b324f035cd</th>\n",
              "      <td>Chandigarh</td>\n",
              "      <td>288</td>\n",
              "      <td>189</td>\n",
              "      <td>4</td>\n",
              "      <td>481</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5ed0a191956123b324f035ce</th>\n",
              "      <td>Chhattisgarh</td>\n",
              "      <td>399</td>\n",
              "      <td>83</td>\n",
              "      <td>0</td>\n",
              "      <td>482</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5ed0a192956123b324f035cf</th>\n",
              "      <td>Dadar Nagar Haveli</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5ed0a192956123b324f035d0</th>\n",
              "      <td>Delhi</td>\n",
              "      <td>16281</td>\n",
              "      <td>7495</td>\n",
              "      <td>316</td>\n",
              "      <td>24092</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5ed0a192956123b324f035d1</th>\n",
              "      <td>Goa</td>\n",
              "      <td>69</td>\n",
              "      <td>38</td>\n",
              "      <td>0</td>\n",
              "      <td>107</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5ed0a192956123b324f035d2</th>\n",
              "      <td>Gujarat</td>\n",
              "      <td>15562</td>\n",
              "      <td>8003</td>\n",
              "      <td>960</td>\n",
              "      <td>24525</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5ed0a193956123b324f035d3</th>\n",
              "      <td>Haryana</td>\n",
              "      <td>1504</td>\n",
              "      <td>881</td>\n",
              "      <td>19</td>\n",
              "      <td>2404</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5ed0a193956123b324f035d4</th>\n",
              "      <td>Himachal Pradesh</td>\n",
              "      <td>276</td>\n",
              "      <td>70</td>\n",
              "      <td>5</td>\n",
              "      <td>351</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5ed0a193956123b324f035d5</th>\n",
              "      <td>Jammu and Kashmir</td>\n",
              "      <td>2036</td>\n",
              "      <td>859</td>\n",
              "      <td>27</td>\n",
              "      <td>2922</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5ed0a194956123b324f035d6</th>\n",
              "      <td>Jharkhand</td>\n",
              "      <td>469</td>\n",
              "      <td>212</td>\n",
              "      <td>4</td>\n",
              "      <td>685</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5ed0a194956123b324f035d7</th>\n",
              "      <td>Karnataka</td>\n",
              "      <td>2533</td>\n",
              "      <td>834</td>\n",
              "      <td>47</td>\n",
              "      <td>3414</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5ed0a194956123b324f035d8</th>\n",
              "      <td>Kerala</td>\n",
              "      <td>1088</td>\n",
              "      <td>555</td>\n",
              "      <td>7</td>\n",
              "      <td>1650</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5ed0a194956123b324f035d9</th>\n",
              "      <td>Ladakh</td>\n",
              "      <td>73</td>\n",
              "      <td>43</td>\n",
              "      <td>0</td>\n",
              "      <td>116</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5ed0a195956123b324f035da</th>\n",
              "      <td>Madhya Pradesh</td>\n",
              "      <td>7453</td>\n",
              "      <td>4050</td>\n",
              "      <td>321</td>\n",
              "      <td>11824</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5ed0a195956123b324f035db</th>\n",
              "      <td>Maharashtra</td>\n",
              "      <td>59546</td>\n",
              "      <td>18616</td>\n",
              "      <td>1982</td>\n",
              "      <td>80144</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5ed0a195956123b324f035dc</th>\n",
              "      <td>Manipur</td>\n",
              "      <td>55</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>60</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5ed0a196956123b324f035dd</th>\n",
              "      <td>Meghalaya</td>\n",
              "      <td>21</td>\n",
              "      <td>12</td>\n",
              "      <td>1</td>\n",
              "      <td>34</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5ed0a196956123b324f035de</th>\n",
              "      <td>Mizoram</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5ed0a196956123b324f035df</th>\n",
              "      <td>Nagaland</td>\n",
              "      <td>18</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>18</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5ed0a196956123b324f035e0</th>\n",
              "      <td>Odisha</td>\n",
              "      <td>1660</td>\n",
              "      <td>887</td>\n",
              "      <td>7</td>\n",
              "      <td>2554</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5ed0a197956123b324f035e1</th>\n",
              "      <td>Puducherry</td>\n",
              "      <td>51</td>\n",
              "      <td>14</td>\n",
              "      <td>0</td>\n",
              "      <td>65</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5ed0a197956123b324f035e2</th>\n",
              "      <td>Punjab</td>\n",
              "      <td>2158</td>\n",
              "      <td>1946</td>\n",
              "      <td>40</td>\n",
              "      <td>4144</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5ed0a197956123b324f035e3</th>\n",
              "      <td>Rajasthan</td>\n",
              "      <td>8067</td>\n",
              "      <td>4817</td>\n",
              "      <td>180</td>\n",
              "      <td>13064</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5ed0a198956123b324f035e4</th>\n",
              "      <td>Sikkim</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5ed0a198956123b324f035e5</th>\n",
              "      <td>Tamil Nadu</td>\n",
              "      <td>19372</td>\n",
              "      <td>10548</td>\n",
              "      <td>145</td>\n",
              "      <td>30065</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5ed0a198956123b324f035e6</th>\n",
              "      <td>Telengana</td>\n",
              "      <td>2256</td>\n",
              "      <td>1345</td>\n",
              "      <td>67</td>\n",
              "      <td>3668</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5ed0a198956123b324f035e7</th>\n",
              "      <td>Tripura</td>\n",
              "      <td>242</td>\n",
              "      <td>167</td>\n",
              "      <td>0</td>\n",
              "      <td>409</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5ed0a199956123b324f035e8</th>\n",
              "      <td>Uttarakhand</td>\n",
              "      <td>500</td>\n",
              "      <td>79</td>\n",
              "      <td>4</td>\n",
              "      <td>583</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5ed0a199956123b324f035e9</th>\n",
              "      <td>Uttar Pradesh</td>\n",
              "      <td>7170</td>\n",
              "      <td>4215</td>\n",
              "      <td>197</td>\n",
              "      <td>11582</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5ed0a199956123b324f035ea</th>\n",
              "      <td>West Bengal</td>\n",
              "      <td>4536</td>\n",
              "      <td>1668</td>\n",
              "      <td>295</td>\n",
              "      <td>6499</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5ed0a287e7179a6b63654969</th>\n",
              "      <td>Cases being reassigned to states</td>\n",
              "      <td>4673</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>4673</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                                      name  ...  total\n",
              "_id                                                         ...       \n",
              "5ed0a190956123b324f035c8       Andaman and Nicobar Islands  ...     66\n",
              "5ed0a190956123b324f035c9                    Andhra Pradesh  ...   5435\n",
              "5ed0a190956123b324f035ca                 Arunachal Pradesh  ...      4\n",
              "5ed0a190956123b324f035cb                             Assam  ...    964\n",
              "5ed0a191956123b324f035cc                             Bihar  ...   4522\n",
              "5ed0a191956123b324f035cd                        Chandigarh  ...    481\n",
              "5ed0a191956123b324f035ce                      Chhattisgarh  ...    482\n",
              "5ed0a192956123b324f035cf                Dadar Nagar Haveli  ...      2\n",
              "5ed0a192956123b324f035d0                             Delhi  ...  24092\n",
              "5ed0a192956123b324f035d1                               Goa  ...    107\n",
              "5ed0a192956123b324f035d2                           Gujarat  ...  24525\n",
              "5ed0a193956123b324f035d3                           Haryana  ...   2404\n",
              "5ed0a193956123b324f035d4                  Himachal Pradesh  ...    351\n",
              "5ed0a193956123b324f035d5                 Jammu and Kashmir  ...   2922\n",
              "5ed0a194956123b324f035d6                         Jharkhand  ...    685\n",
              "5ed0a194956123b324f035d7                         Karnataka  ...   3414\n",
              "5ed0a194956123b324f035d8                            Kerala  ...   1650\n",
              "5ed0a194956123b324f035d9                            Ladakh  ...    116\n",
              "5ed0a195956123b324f035da                    Madhya Pradesh  ...  11824\n",
              "5ed0a195956123b324f035db                       Maharashtra  ...  80144\n",
              "5ed0a195956123b324f035dc                           Manipur  ...     60\n",
              "5ed0a196956123b324f035dd                         Meghalaya  ...     34\n",
              "5ed0a196956123b324f035de                           Mizoram  ...      2\n",
              "5ed0a196956123b324f035df                          Nagaland  ...     18\n",
              "5ed0a196956123b324f035e0                            Odisha  ...   2554\n",
              "5ed0a197956123b324f035e1                        Puducherry  ...     65\n",
              "5ed0a197956123b324f035e2                            Punjab  ...   4144\n",
              "5ed0a197956123b324f035e3                         Rajasthan  ...  13064\n",
              "5ed0a198956123b324f035e4                            Sikkim  ...      1\n",
              "5ed0a198956123b324f035e5                        Tamil Nadu  ...  30065\n",
              "5ed0a198956123b324f035e6                         Telengana  ...   3668\n",
              "5ed0a198956123b324f035e7                           Tripura  ...    409\n",
              "5ed0a199956123b324f035e8                       Uttarakhand  ...    583\n",
              "5ed0a199956123b324f035e9                     Uttar Pradesh  ...  11582\n",
              "5ed0a199956123b324f035ea                       West Bengal  ...   6499\n",
              "5ed0a287e7179a6b63654969  Cases being reassigned to states  ...   4673\n",
              "\n",
              "[36 rows x 5 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RagHP3uKXgKq",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df=df[[\"name\",\"confirmed\",\"death\",\"cured\"]]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "j8DjO9PEXjwS",
        "colab_type": "code",
        "outputId": "08d9167d-feed-481d-d5e2-e80f73c37b9f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "df"
      ],
      "execution_count": 27,
      "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>name</th>\n",
              "      <th>confirmed</th>\n",
              "      <th>death</th>\n",
              "      <th>cured</th>\n",
              "      <th>text</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Andaman and Nicobar Islands</td>\n",
              "      <td>33</td>\n",
              "      <td>0</td>\n",
              "      <td>33</td>\n",
              "      <td>Andaman and Nicobar Islands&lt;br&gt;Confirmed: 33&lt;b...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Andhra Pradesh</td>\n",
              "      <td>3251</td>\n",
              "      <td>59</td>\n",
              "      <td>2125</td>\n",
              "      <td>Andhra Pradesh&lt;br&gt;Confirmed: 3251&lt;br&gt;Deaths: 5...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Arunachal Pradesh</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>Arunachal Pradesh&lt;br&gt;Confirmed: 3&lt;br&gt;Deaths: 0...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Assam</td>\n",
              "      <td>856</td>\n",
              "      <td>4</td>\n",
              "      <td>104</td>\n",
              "      <td>Assam&lt;br&gt;Confirmed: 856&lt;br&gt;Deaths: 4&lt;br&gt;Recove...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Bihar</td>\n",
              "      <td>3296</td>\n",
              "      <td>15</td>\n",
              "      <td>1211</td>\n",
              "      <td>Bihar&lt;br&gt;Confirmed: 3296&lt;br&gt;Deaths: 15&lt;br&gt;Reco...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Chandigarh</td>\n",
              "      <td>288</td>\n",
              "      <td>4</td>\n",
              "      <td>189</td>\n",
              "      <td>Chandigarh&lt;br&gt;Confirmed: 288&lt;br&gt;Deaths: 4&lt;br&gt;R...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Chhattisgarh</td>\n",
              "      <td>399</td>\n",
              "      <td>0</td>\n",
              "      <td>83</td>\n",
              "      <td>Chhattisgarh&lt;br&gt;Confirmed: 399&lt;br&gt;Deaths: 0&lt;br...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Dadar Nagar Haveli</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Dadar Nagar Haveli&lt;br&gt;Confirmed: 2&lt;br&gt;Deaths: ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Delhi</td>\n",
              "      <td>16281</td>\n",
              "      <td>316</td>\n",
              "      <td>7495</td>\n",
              "      <td>Delhi&lt;br&gt;Confirmed: 16281&lt;br&gt;Deaths: 316&lt;br&gt;Re...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Goa</td>\n",
              "      <td>69</td>\n",
              "      <td>0</td>\n",
              "      <td>38</td>\n",
              "      <td>Goa&lt;br&gt;Confirmed: 69&lt;br&gt;Deaths: 0&lt;br&gt;Recovered...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>Gujarat</td>\n",
              "      <td>15562</td>\n",
              "      <td>960</td>\n",
              "      <td>8003</td>\n",
              "      <td>Gujarat&lt;br&gt;Confirmed: 15562&lt;br&gt;Deaths: 960&lt;br&gt;...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>Haryana</td>\n",
              "      <td>1504</td>\n",
              "      <td>19</td>\n",
              "      <td>881</td>\n",
              "      <td>Haryana&lt;br&gt;Confirmed: 1504&lt;br&gt;Deaths: 19&lt;br&gt;Re...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>Himachal Pradesh</td>\n",
              "      <td>276</td>\n",
              "      <td>5</td>\n",
              "      <td>70</td>\n",
              "      <td>Himachal Pradesh&lt;br&gt;Confirmed: 276&lt;br&gt;Deaths: ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>Jammu and Kashmir</td>\n",
              "      <td>2036</td>\n",
              "      <td>27</td>\n",
              "      <td>859</td>\n",
              "      <td>Jammu and Kashmir&lt;br&gt;Confirmed: 2036&lt;br&gt;Deaths...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>Jharkhand</td>\n",
              "      <td>469</td>\n",
              "      <td>4</td>\n",
              "      <td>212</td>\n",
              "      <td>Jharkhand&lt;br&gt;Confirmed: 469&lt;br&gt;Deaths: 4&lt;br&gt;Re...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>Karnataka</td>\n",
              "      <td>2533</td>\n",
              "      <td>47</td>\n",
              "      <td>834</td>\n",
              "      <td>Karnataka&lt;br&gt;Confirmed: 2533&lt;br&gt;Deaths: 47&lt;br&gt;...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>Kerala</td>\n",
              "      <td>1088</td>\n",
              "      <td>7</td>\n",
              "      <td>555</td>\n",
              "      <td>Kerala&lt;br&gt;Confirmed: 1088&lt;br&gt;Deaths: 7&lt;br&gt;Reco...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>Ladakh</td>\n",
              "      <td>73</td>\n",
              "      <td>0</td>\n",
              "      <td>43</td>\n",
              "      <td>Ladakh&lt;br&gt;Confirmed: 73&lt;br&gt;Deaths: 0&lt;br&gt;Recove...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>Madhya Pradesh</td>\n",
              "      <td>7453</td>\n",
              "      <td>321</td>\n",
              "      <td>4050</td>\n",
              "      <td>Madhya Pradesh&lt;br&gt;Confirmed: 7453&lt;br&gt;Deaths: 3...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>Maharashtra</td>\n",
              "      <td>59546</td>\n",
              "      <td>1982</td>\n",
              "      <td>18616</td>\n",
              "      <td>Maharashtra&lt;br&gt;Confirmed: 59546&lt;br&gt;Deaths: 198...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>Manipur</td>\n",
              "      <td>55</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>Manipur&lt;br&gt;Confirmed: 55&lt;br&gt;Deaths: 0&lt;br&gt;Recov...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>Meghalaya</td>\n",
              "      <td>21</td>\n",
              "      <td>1</td>\n",
              "      <td>12</td>\n",
              "      <td>Meghalaya&lt;br&gt;Confirmed: 21&lt;br&gt;Deaths: 1&lt;br&gt;Rec...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>Mizoram</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>Mizoram&lt;br&gt;Confirmed: 1&lt;br&gt;Deaths: 0&lt;br&gt;Recove...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>Nagaland</td>\n",
              "      <td>18</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Nagaland&lt;br&gt;Confirmed: 18&lt;br&gt;Deaths: 0&lt;br&gt;Reco...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>Odisha</td>\n",
              "      <td>1660</td>\n",
              "      <td>7</td>\n",
              "      <td>887</td>\n",
              "      <td>Odisha&lt;br&gt;Confirmed: 1660&lt;br&gt;Deaths: 7&lt;br&gt;Reco...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>Puducherry</td>\n",
              "      <td>51</td>\n",
              "      <td>0</td>\n",
              "      <td>14</td>\n",
              "      <td>Puducherry&lt;br&gt;Confirmed: 51&lt;br&gt;Deaths: 0&lt;br&gt;Re...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>Punjab</td>\n",
              "      <td>2158</td>\n",
              "      <td>40</td>\n",
              "      <td>1946</td>\n",
              "      <td>Punjab&lt;br&gt;Confirmed: 2158&lt;br&gt;Deaths: 40&lt;br&gt;Rec...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>Rajasthan</td>\n",
              "      <td>8067</td>\n",
              "      <td>180</td>\n",
              "      <td>4817</td>\n",
              "      <td>Rajasthan&lt;br&gt;Confirmed: 8067&lt;br&gt;Deaths: 180&lt;br...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>Sikkim</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Sikkim&lt;br&gt;Confirmed: 1&lt;br&gt;Deaths: 0&lt;br&gt;Recover...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>Tamil Nadu</td>\n",
              "      <td>19372</td>\n",
              "      <td>145</td>\n",
              "      <td>10548</td>\n",
              "      <td>Tamil Nadu&lt;br&gt;Confirmed: 19372&lt;br&gt;Deaths: 145&lt;...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>Telengana</td>\n",
              "      <td>2256</td>\n",
              "      <td>67</td>\n",
              "      <td>1345</td>\n",
              "      <td>Telengana&lt;br&gt;Confirmed: 2256&lt;br&gt;Deaths: 67&lt;br&gt;...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31</th>\n",
              "      <td>Tripura</td>\n",
              "      <td>242</td>\n",
              "      <td>0</td>\n",
              "      <td>167</td>\n",
              "      <td>Tripura&lt;br&gt;Confirmed: 242&lt;br&gt;Deaths: 0&lt;br&gt;Reco...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>Uttarakhand</td>\n",
              "      <td>500</td>\n",
              "      <td>4</td>\n",
              "      <td>79</td>\n",
              "      <td>Uttarakhand&lt;br&gt;Confirmed: 500&lt;br&gt;Deaths: 4&lt;br&gt;...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>Uttar Pradesh</td>\n",
              "      <td>7170</td>\n",
              "      <td>197</td>\n",
              "      <td>4215</td>\n",
              "      <td>Uttar Pradesh&lt;br&gt;Confirmed: 7170&lt;br&gt;Deaths: 19...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34</th>\n",
              "      <td>West Bengal</td>\n",
              "      <td>4536</td>\n",
              "      <td>295</td>\n",
              "      <td>1668</td>\n",
              "      <td>West Bengal&lt;br&gt;Confirmed: 4536&lt;br&gt;Deaths: 295&lt;...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35</th>\n",
              "      <td>Cases being reassigned to states</td>\n",
              "      <td>4673</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Cases being reassigned to states&lt;br&gt;Confirmed:...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                name  ...                                               text\n",
              "0        Andaman and Nicobar Islands  ...  Andaman and Nicobar Islands<br>Confirmed: 33<b...\n",
              "1                     Andhra Pradesh  ...  Andhra Pradesh<br>Confirmed: 3251<br>Deaths: 5...\n",
              "2                  Arunachal Pradesh  ...  Arunachal Pradesh<br>Confirmed: 3<br>Deaths: 0...\n",
              "3                              Assam  ...  Assam<br>Confirmed: 856<br>Deaths: 4<br>Recove...\n",
              "4                              Bihar  ...  Bihar<br>Confirmed: 3296<br>Deaths: 15<br>Reco...\n",
              "5                         Chandigarh  ...  Chandigarh<br>Confirmed: 288<br>Deaths: 4<br>R...\n",
              "6                       Chhattisgarh  ...  Chhattisgarh<br>Confirmed: 399<br>Deaths: 0<br...\n",
              "7                 Dadar Nagar Haveli  ...  Dadar Nagar Haveli<br>Confirmed: 2<br>Deaths: ...\n",
              "8                              Delhi  ...  Delhi<br>Confirmed: 16281<br>Deaths: 316<br>Re...\n",
              "9                                Goa  ...  Goa<br>Confirmed: 69<br>Deaths: 0<br>Recovered...\n",
              "10                           Gujarat  ...  Gujarat<br>Confirmed: 15562<br>Deaths: 960<br>...\n",
              "11                           Haryana  ...  Haryana<br>Confirmed: 1504<br>Deaths: 19<br>Re...\n",
              "12                  Himachal Pradesh  ...  Himachal Pradesh<br>Confirmed: 276<br>Deaths: ...\n",
              "13                 Jammu and Kashmir  ...  Jammu and Kashmir<br>Confirmed: 2036<br>Deaths...\n",
              "14                         Jharkhand  ...  Jharkhand<br>Confirmed: 469<br>Deaths: 4<br>Re...\n",
              "15                         Karnataka  ...  Karnataka<br>Confirmed: 2533<br>Deaths: 47<br>...\n",
              "16                            Kerala  ...  Kerala<br>Confirmed: 1088<br>Deaths: 7<br>Reco...\n",
              "17                            Ladakh  ...  Ladakh<br>Confirmed: 73<br>Deaths: 0<br>Recove...\n",
              "18                    Madhya Pradesh  ...  Madhya Pradesh<br>Confirmed: 7453<br>Deaths: 3...\n",
              "19                       Maharashtra  ...  Maharashtra<br>Confirmed: 59546<br>Deaths: 198...\n",
              "20                           Manipur  ...  Manipur<br>Confirmed: 55<br>Deaths: 0<br>Recov...\n",
              "21                         Meghalaya  ...  Meghalaya<br>Confirmed: 21<br>Deaths: 1<br>Rec...\n",
              "22                           Mizoram  ...  Mizoram<br>Confirmed: 1<br>Deaths: 0<br>Recove...\n",
              "23                          Nagaland  ...  Nagaland<br>Confirmed: 18<br>Deaths: 0<br>Reco...\n",
              "24                            Odisha  ...  Odisha<br>Confirmed: 1660<br>Deaths: 7<br>Reco...\n",
              "25                        Puducherry  ...  Puducherry<br>Confirmed: 51<br>Deaths: 0<br>Re...\n",
              "26                            Punjab  ...  Punjab<br>Confirmed: 2158<br>Deaths: 40<br>Rec...\n",
              "27                         Rajasthan  ...  Rajasthan<br>Confirmed: 8067<br>Deaths: 180<br...\n",
              "28                            Sikkim  ...  Sikkim<br>Confirmed: 1<br>Deaths: 0<br>Recover...\n",
              "29                        Tamil Nadu  ...  Tamil Nadu<br>Confirmed: 19372<br>Deaths: 145<...\n",
              "30                         Telengana  ...  Telengana<br>Confirmed: 2256<br>Deaths: 67<br>...\n",
              "31                           Tripura  ...  Tripura<br>Confirmed: 242<br>Deaths: 0<br>Reco...\n",
              "32                       Uttarakhand  ...  Uttarakhand<br>Confirmed: 500<br>Deaths: 4<br>...\n",
              "33                     Uttar Pradesh  ...  Uttar Pradesh<br>Confirmed: 7170<br>Deaths: 19...\n",
              "34                       West Bengal  ...  West Bengal<br>Confirmed: 4536<br>Deaths: 295<...\n",
              "35  Cases being reassigned to states  ...  Cases being reassigned to states<br>Confirmed:...\n",
              "\n",
              "[36 rows x 5 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jwvnGfngXlmR",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "total_confirmed = df[\"confirmed\"].sum()\n",
        "total_recovered = df[\"cured\"].sum()\n",
        "total_deaths = df[\"death\"].sum()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lakMaeHeXnwt",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df_top10 = df.nlargest(5, \"confirmed\")\n",
        "top10_countries_1 = df_top10[\"name\"].tolist()\n",
        "top10_confirmed = df_top10[\"confirmed\"].tolist()\n",
        "\n",
        "df_top10 = df.nlargest(5, \"death\")\n",
        "top10_countries_2 = df_top10[\"name\"].tolist()\n",
        "top10_recovered = df_top10[\"death\"].tolist()\n",
        "\n",
        "df_top10 = df.nlargest(5, \"cured\")\n",
        "top10_countries_3 = df_top10[\"name\"].tolist()\n",
        "top10_deaths = df_top10[\"cured\"].tolist()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kJ3xKIIiXqnk",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GQMLeBMheSMa",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "message = df[\"name\"]+ \"<br>\"\n",
        "message += \"Confirmed: \" + df[\"confirmed\"].astype(str) + \"<br>\"\n",
        "message += \"Deaths: \" + df[\"death\"].astype(str) + \"<br>\"\n",
        "message += \"Recovered: \" + df[\"cured\"].astype(str) + \"<br>\"\n",
        "df[\"text\"] = message"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NKRtWT8Lbv1l",
        "colab_type": "code",
        "outputId": "505c743d-8d77-4544-a571-c6c4703a6b55",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "fp = \"Indian_States.shp\"\n",
        "map_df = gpd.read_file(fp)\n",
        "# check data type so we can see that this is not a normal dataframe, but a GEOdataframe\n",
        "map_df"
      ],
      "execution_count": 28,
      "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>st_nm</th>\n",
              "      <th>geometry</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Andaman &amp; Nicobar Island</td>\n",
              "      <td>MULTIPOLYGON (((93.71976 7.20707, 93.71909 7.2...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Arunanchal Pradesh</td>\n",
              "      <td>POLYGON ((96.16261 29.38078, 96.16860 29.37432...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Assam</td>\n",
              "      <td>MULTIPOLYGON (((89.74323 26.30362, 89.74290 26...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Bihar</td>\n",
              "      <td>MULTIPOLYGON (((84.50720 24.26323, 84.50355 24...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Chandigarh</td>\n",
              "      <td>POLYGON ((76.84147 30.75996, 76.83599 30.73623...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Chhattisgarh</td>\n",
              "      <td>POLYGON ((83.33532 24.09885, 83.35346 24.09627...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Dadara &amp; Nagar Havelli</td>\n",
              "      <td>POLYGON ((73.20657 20.12216, 73.20797 20.10650...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Daman &amp; Diu</td>\n",
              "      <td>MULTIPOLYGON (((72.89335 20.44539, 72.89281 20...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Goa</td>\n",
              "      <td>MULTIPOLYGON (((74.11918 14.75344, 74.11350 14...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Gujarat</td>\n",
              "      <td>MULTIPOLYGON (((71.70375 20.99958, 71.70375 20...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>Haryana</td>\n",
              "      <td>POLYGON ((76.85065 30.87512, 76.86594 30.86691...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>Himachal Pradesh</td>\n",
              "      <td>POLYGON ((76.79634 33.25490, 76.80351 33.25275...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>Jammu &amp; Kashmir</td>\n",
              "      <td>POLYGON ((74.73451 37.02068, 74.73647 37.01937...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>Jharkhand</td>\n",
              "      <td>POLYGON ((87.60582 25.31512, 87.61279 25.31184...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>Karnataka</td>\n",
              "      <td>MULTIPOLYGON (((74.69694 13.32782, 74.69562 13...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>Kerala</td>\n",
              "      <td>POLYGON ((74.99575 12.79227, 75.00006 12.78777...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>Lakshadweep</td>\n",
              "      <td>MULTIPOLYGON (((74.10131 11.20431, 74.09908 11...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>Madhya Pradesh</td>\n",
              "      <td>MULTIPOLYGON (((74.17932 22.39059, 74.17776 22...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>Maharashtra</td>\n",
              "      <td>MULTIPOLYGON (((73.46270 16.03710, 73.46178 16...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>Manipur</td>\n",
              "      <td>POLYGON ((94.57602 25.64221, 94.57487 25.63858...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>Meghalaya</td>\n",
              "      <td>POLYGON ((91.85632 26.10353, 91.86717 26.09906...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>Mizoram</td>\n",
              "      <td>POLYGON ((92.80022 24.41630, 92.80310 24.41603...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>Nagaland</td>\n",
              "      <td>POLYGON ((95.21458 26.93095, 95.21719 26.92818...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>NCT of Delhi</td>\n",
              "      <td>POLYGON ((77.09361 28.86940, 77.10973 28.86722...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>Puducherry</td>\n",
              "      <td>MULTIPOLYGON (((79.76528 10.99681, 79.76963 10...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>Punjab</td>\n",
              "      <td>POLYGON ((75.88469 32.48841, 75.90303 32.47169...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>Rajasthan</td>\n",
              "      <td>POLYGON ((73.89849 29.97896, 73.90021 29.97140...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>Sikkim</td>\n",
              "      <td>POLYGON ((88.65381 28.09883, 88.66265 28.08956...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>Tamil Nadu</td>\n",
              "      <td>MULTIPOLYGON (((78.19188 8.72730, 78.18996 8.7...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>Telangana</td>\n",
              "      <td>POLYGON ((81.05614 17.79097, 81.05834 17.75464...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>Tripura</td>\n",
              "      <td>POLYGON ((92.22108 24.50131, 92.22771 24.49862...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31</th>\n",
              "      <td>Uttar Pradesh</td>\n",
              "      <td>MULTIPOLYGON (((80.44802 24.99631, 80.44080 24...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>Uttarakhand</td>\n",
              "      <td>POLYGON ((79.21047 31.34846, 79.21386 31.34680...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>West Bengal</td>\n",
              "      <td>MULTIPOLYGON (((88.01861 21.57278, 88.01889 21...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34</th>\n",
              "      <td>Odisha</td>\n",
              "      <td>MULTIPOLYGON (((86.38937 19.96351, 86.38840 19...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35</th>\n",
              "      <td>Andhra Pradesh</td>\n",
              "      <td>MULTIPOLYGON (((81.10380 17.82269, 81.10610 17...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                       st_nm                                           geometry\n",
              "0   Andaman & Nicobar Island  MULTIPOLYGON (((93.71976 7.20707, 93.71909 7.2...\n",
              "1         Arunanchal Pradesh  POLYGON ((96.16261 29.38078, 96.16860 29.37432...\n",
              "2                      Assam  MULTIPOLYGON (((89.74323 26.30362, 89.74290 26...\n",
              "3                      Bihar  MULTIPOLYGON (((84.50720 24.26323, 84.50355 24...\n",
              "4                 Chandigarh  POLYGON ((76.84147 30.75996, 76.83599 30.73623...\n",
              "5               Chhattisgarh  POLYGON ((83.33532 24.09885, 83.35346 24.09627...\n",
              "6     Dadara & Nagar Havelli  POLYGON ((73.20657 20.12216, 73.20797 20.10650...\n",
              "7                Daman & Diu  MULTIPOLYGON (((72.89335 20.44539, 72.89281 20...\n",
              "8                        Goa  MULTIPOLYGON (((74.11918 14.75344, 74.11350 14...\n",
              "9                    Gujarat  MULTIPOLYGON (((71.70375 20.99958, 71.70375 20...\n",
              "10                   Haryana  POLYGON ((76.85065 30.87512, 76.86594 30.86691...\n",
              "11          Himachal Pradesh  POLYGON ((76.79634 33.25490, 76.80351 33.25275...\n",
              "12           Jammu & Kashmir  POLYGON ((74.73451 37.02068, 74.73647 37.01937...\n",
              "13                 Jharkhand  POLYGON ((87.60582 25.31512, 87.61279 25.31184...\n",
              "14                 Karnataka  MULTIPOLYGON (((74.69694 13.32782, 74.69562 13...\n",
              "15                    Kerala  POLYGON ((74.99575 12.79227, 75.00006 12.78777...\n",
              "16               Lakshadweep  MULTIPOLYGON (((74.10131 11.20431, 74.09908 11...\n",
              "17            Madhya Pradesh  MULTIPOLYGON (((74.17932 22.39059, 74.17776 22...\n",
              "18               Maharashtra  MULTIPOLYGON (((73.46270 16.03710, 73.46178 16...\n",
              "19                   Manipur  POLYGON ((94.57602 25.64221, 94.57487 25.63858...\n",
              "20                 Meghalaya  POLYGON ((91.85632 26.10353, 91.86717 26.09906...\n",
              "21                   Mizoram  POLYGON ((92.80022 24.41630, 92.80310 24.41603...\n",
              "22                  Nagaland  POLYGON ((95.21458 26.93095, 95.21719 26.92818...\n",
              "23              NCT of Delhi  POLYGON ((77.09361 28.86940, 77.10973 28.86722...\n",
              "24                Puducherry  MULTIPOLYGON (((79.76528 10.99681, 79.76963 10...\n",
              "25                    Punjab  POLYGON ((75.88469 32.48841, 75.90303 32.47169...\n",
              "26                 Rajasthan  POLYGON ((73.89849 29.97896, 73.90021 29.97140...\n",
              "27                    Sikkim  POLYGON ((88.65381 28.09883, 88.66265 28.08956...\n",
              "28                Tamil Nadu  MULTIPOLYGON (((78.19188 8.72730, 78.18996 8.7...\n",
              "29                 Telangana  POLYGON ((81.05614 17.79097, 81.05834 17.75464...\n",
              "30                   Tripura  POLYGON ((92.22108 24.50131, 92.22771 24.49862...\n",
              "31             Uttar Pradesh  MULTIPOLYGON (((80.44802 24.99631, 80.44080 24...\n",
              "32               Uttarakhand  POLYGON ((79.21047 31.34846, 79.21386 31.34680...\n",
              "33               West Bengal  MULTIPOLYGON (((88.01861 21.57278, 88.01889 21...\n",
              "34                    Odisha  MULTIPOLYGON (((86.38937 19.96351, 86.38840 19...\n",
              "35            Andhra Pradesh  MULTIPOLYGON (((81.10380 17.82269, 81.10610 17..."
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "up12wxebY1EQ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "map_df[\"st_nm\"].replace({\"Dadara & Nagar Havelli\": \"Dadar Nagar Haveli\", \"Andaman & Nicobar Island\": \"Andaman and Nicobar Islands\", \"Arunanchal Pradesh\": \"Arunachal Pradesh\",\"Jammu & Kashmir\":\"Jammu and Kashmir\",\"NCT of Delhi\":\"Delhi\",\"Telangana\":\"Telengana\"}, inplace=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mqtvb_C0fgAU",
        "colab_type": "code",
        "outputId": "546104cb-7f97-4093-f2e9-4e3a78d79c45",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        }
      },
      "source": [
        "map_df.plot()"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f0ec2156710>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 31
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6BY-hZzCXUYB",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "c54f9998-2430-4373-e02f-f3dcd9842443"
      },
      "source": [
        "\n",
        "# join the geodataframe with the dataframe\n",
        "merged = map_df.set_index('st_nm').join(df.set_index('name'))\n",
        "#deleting states which are not present in either of the dataframes\n",
        "merged.drop(['Lakshadweep', 'Daman & Diu'])"
      ],
      "execution_count": 35,
      "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>geometry</th>\n",
              "      <th>confirmed</th>\n",
              "      <th>death</th>\n",
              "      <th>cured</th>\n",
              "      <th>text</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>st_nm</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Andaman and Nicobar Islands</th>\n",
              "      <td>MULTIPOLYGON (((93.71976 7.20707, 93.71909 7.2...</td>\n",
              "      <td>33.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>33.0</td>\n",
              "      <td>Andaman and Nicobar Islands&lt;br&gt;Confirmed: 33&lt;b...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Arunachal Pradesh</th>\n",
              "      <td>POLYGON ((96.16261 29.38078, 96.16860 29.37432...</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Arunachal Pradesh&lt;br&gt;Confirmed: 3&lt;br&gt;Deaths: 0...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Assam</th>\n",
              "      <td>MULTIPOLYGON (((89.74323 26.30362, 89.74290 26...</td>\n",
              "      <td>856.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>104.0</td>\n",
              "      <td>Assam&lt;br&gt;Confirmed: 856&lt;br&gt;Deaths: 4&lt;br&gt;Recove...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Bihar</th>\n",
              "      <td>MULTIPOLYGON (((84.50720 24.26323, 84.50355 24...</td>\n",
              "      <td>3296.0</td>\n",
              "      <td>15.0</td>\n",
              "      <td>1211.0</td>\n",
              "      <td>Bihar&lt;br&gt;Confirmed: 3296&lt;br&gt;Deaths: 15&lt;br&gt;Reco...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Chandigarh</th>\n",
              "      <td>POLYGON ((76.84147 30.75996, 76.83599 30.73623...</td>\n",
              "      <td>288.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>189.0</td>\n",
              "      <td>Chandigarh&lt;br&gt;Confirmed: 288&lt;br&gt;Deaths: 4&lt;br&gt;R...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Chhattisgarh</th>\n",
              "      <td>POLYGON ((83.33532 24.09885, 83.35346 24.09627...</td>\n",
              "      <td>399.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>83.0</td>\n",
              "      <td>Chhattisgarh&lt;br&gt;Confirmed: 399&lt;br&gt;Deaths: 0&lt;br...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Dadar Nagar Haveli</th>\n",
              "      <td>POLYGON ((73.20657 20.12216, 73.20797 20.10650...</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Dadar Nagar Haveli&lt;br&gt;Confirmed: 2&lt;br&gt;Deaths: ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Goa</th>\n",
              "      <td>MULTIPOLYGON (((74.11918 14.75344, 74.11350 14...</td>\n",
              "      <td>69.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>38.0</td>\n",
              "      <td>Goa&lt;br&gt;Confirmed: 69&lt;br&gt;Deaths: 0&lt;br&gt;Recovered...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Gujarat</th>\n",
              "      <td>MULTIPOLYGON (((71.70375 20.99958, 71.70375 20...</td>\n",
              "      <td>15562.0</td>\n",
              "      <td>960.0</td>\n",
              "      <td>8003.0</td>\n",
              "      <td>Gujarat&lt;br&gt;Confirmed: 15562&lt;br&gt;Deaths: 960&lt;br&gt;...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Haryana</th>\n",
              "      <td>POLYGON ((76.85065 30.87512, 76.86594 30.86691...</td>\n",
              "      <td>1504.0</td>\n",
              "      <td>19.0</td>\n",
              "      <td>881.0</td>\n",
              "      <td>Haryana&lt;br&gt;Confirmed: 1504&lt;br&gt;Deaths: 19&lt;br&gt;Re...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Himachal Pradesh</th>\n",
              "      <td>POLYGON ((76.79634 33.25490, 76.80351 33.25275...</td>\n",
              "      <td>276.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>70.0</td>\n",
              "      <td>Himachal Pradesh&lt;br&gt;Confirmed: 276&lt;br&gt;Deaths: ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Jammu and Kashmir</th>\n",
              "      <td>POLYGON ((74.73451 37.02068, 74.73647 37.01937...</td>\n",
              "      <td>2036.0</td>\n",
              "      <td>27.0</td>\n",
              "      <td>859.0</td>\n",
              "      <td>Jammu and Kashmir&lt;br&gt;Confirmed: 2036&lt;br&gt;Deaths...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Jharkhand</th>\n",
              "      <td>POLYGON ((87.60582 25.31512, 87.61279 25.31184...</td>\n",
              "      <td>469.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>212.0</td>\n",
              "      <td>Jharkhand&lt;br&gt;Confirmed: 469&lt;br&gt;Deaths: 4&lt;br&gt;Re...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Karnataka</th>\n",
              "      <td>MULTIPOLYGON (((74.69694 13.32782, 74.69562 13...</td>\n",
              "      <td>2533.0</td>\n",
              "      <td>47.0</td>\n",
              "      <td>834.0</td>\n",
              "      <td>Karnataka&lt;br&gt;Confirmed: 2533&lt;br&gt;Deaths: 47&lt;br&gt;...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Kerala</th>\n",
              "      <td>POLYGON ((74.99575 12.79227, 75.00006 12.78777...</td>\n",
              "      <td>1088.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>555.0</td>\n",
              "      <td>Kerala&lt;br&gt;Confirmed: 1088&lt;br&gt;Deaths: 7&lt;br&gt;Reco...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Madhya Pradesh</th>\n",
              "      <td>MULTIPOLYGON (((74.17932 22.39059, 74.17776 22...</td>\n",
              "      <td>7453.0</td>\n",
              "      <td>321.0</td>\n",
              "      <td>4050.0</td>\n",
              "      <td>Madhya Pradesh&lt;br&gt;Confirmed: 7453&lt;br&gt;Deaths: 3...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Maharashtra</th>\n",
              "      <td>MULTIPOLYGON (((73.46270 16.03710, 73.46178 16...</td>\n",
              "      <td>59546.0</td>\n",
              "      <td>1982.0</td>\n",
              "      <td>18616.0</td>\n",
              "      <td>Maharashtra&lt;br&gt;Confirmed: 59546&lt;br&gt;Deaths: 198...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Manipur</th>\n",
              "      <td>POLYGON ((94.57602 25.64221, 94.57487 25.63858...</td>\n",
              "      <td>55.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>Manipur&lt;br&gt;Confirmed: 55&lt;br&gt;Deaths: 0&lt;br&gt;Recov...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Meghalaya</th>\n",
              "      <td>POLYGON ((91.85632 26.10353, 91.86717 26.09906...</td>\n",
              "      <td>21.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>12.0</td>\n",
              "      <td>Meghalaya&lt;br&gt;Confirmed: 21&lt;br&gt;Deaths: 1&lt;br&gt;Rec...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Mizoram</th>\n",
              "      <td>POLYGON ((92.80022 24.41630, 92.80310 24.41603...</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Mizoram&lt;br&gt;Confirmed: 1&lt;br&gt;Deaths: 0&lt;br&gt;Recove...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Nagaland</th>\n",
              "      <td>POLYGON ((95.21458 26.93095, 95.21719 26.92818...</td>\n",
              "      <td>18.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Nagaland&lt;br&gt;Confirmed: 18&lt;br&gt;Deaths: 0&lt;br&gt;Reco...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Delhi</th>\n",
              "      <td>POLYGON ((77.09361 28.86940, 77.10973 28.86722...</td>\n",
              "      <td>16281.0</td>\n",
              "      <td>316.0</td>\n",
              "      <td>7495.0</td>\n",
              "      <td>Delhi&lt;br&gt;Confirmed: 16281&lt;br&gt;Deaths: 316&lt;br&gt;Re...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Puducherry</th>\n",
              "      <td>MULTIPOLYGON (((79.76528 10.99681, 79.76963 10...</td>\n",
              "      <td>51.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>14.0</td>\n",
              "      <td>Puducherry&lt;br&gt;Confirmed: 51&lt;br&gt;Deaths: 0&lt;br&gt;Re...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Punjab</th>\n",
              "      <td>POLYGON ((75.88469 32.48841, 75.90303 32.47169...</td>\n",
              "      <td>2158.0</td>\n",
              "      <td>40.0</td>\n",
              "      <td>1946.0</td>\n",
              "      <td>Punjab&lt;br&gt;Confirmed: 2158&lt;br&gt;Deaths: 40&lt;br&gt;Rec...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Rajasthan</th>\n",
              "      <td>POLYGON ((73.89849 29.97896, 73.90021 29.97140...</td>\n",
              "      <td>8067.0</td>\n",
              "      <td>180.0</td>\n",
              "      <td>4817.0</td>\n",
              "      <td>Rajasthan&lt;br&gt;Confirmed: 8067&lt;br&gt;Deaths: 180&lt;br...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sikkim</th>\n",
              "      <td>POLYGON ((88.65381 28.09883, 88.66265 28.08956...</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Sikkim&lt;br&gt;Confirmed: 1&lt;br&gt;Deaths: 0&lt;br&gt;Recover...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Tamil Nadu</th>\n",
              "      <td>MULTIPOLYGON (((78.19188 8.72730, 78.18996 8.7...</td>\n",
              "      <td>19372.0</td>\n",
              "      <td>145.0</td>\n",
              "      <td>10548.0</td>\n",
              "      <td>Tamil Nadu&lt;br&gt;Confirmed: 19372&lt;br&gt;Deaths: 145&lt;...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Telengana</th>\n",
              "      <td>POLYGON ((81.05614 17.79097, 81.05834 17.75464...</td>\n",
              "      <td>2256.0</td>\n",
              "      <td>67.0</td>\n",
              "      <td>1345.0</td>\n",
              "      <td>Telengana&lt;br&gt;Confirmed: 2256&lt;br&gt;Deaths: 67&lt;br&gt;...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Tripura</th>\n",
              "      <td>POLYGON ((92.22108 24.50131, 92.22771 24.49862...</td>\n",
              "      <td>242.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>167.0</td>\n",
              "      <td>Tripura&lt;br&gt;Confirmed: 242&lt;br&gt;Deaths: 0&lt;br&gt;Reco...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Uttar Pradesh</th>\n",
              "      <td>MULTIPOLYGON (((80.44802 24.99631, 80.44080 24...</td>\n",
              "      <td>7170.0</td>\n",
              "      <td>197.0</td>\n",
              "      <td>4215.0</td>\n",
              "      <td>Uttar Pradesh&lt;br&gt;Confirmed: 7170&lt;br&gt;Deaths: 19...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Uttarakhand</th>\n",
              "      <td>POLYGON ((79.21047 31.34846, 79.21386 31.34680...</td>\n",
              "      <td>500.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>79.0</td>\n",
              "      <td>Uttarakhand&lt;br&gt;Confirmed: 500&lt;br&gt;Deaths: 4&lt;br&gt;...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>West Bengal</th>\n",
              "      <td>MULTIPOLYGON (((88.01861 21.57278, 88.01889 21...</td>\n",
              "      <td>4536.0</td>\n",
              "      <td>295.0</td>\n",
              "      <td>1668.0</td>\n",
              "      <td>West Bengal&lt;br&gt;Confirmed: 4536&lt;br&gt;Deaths: 295&lt;...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Odisha</th>\n",
              "      <td>MULTIPOLYGON (((86.38937 19.96351, 86.38840 19...</td>\n",
              "      <td>1660.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>887.0</td>\n",
              "      <td>Odisha&lt;br&gt;Confirmed: 1660&lt;br&gt;Deaths: 7&lt;br&gt;Reco...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Andhra Pradesh</th>\n",
              "      <td>MULTIPOLYGON (((81.10380 17.82269, 81.10610 17...</td>\n",
              "      <td>3251.0</td>\n",
              "      <td>59.0</td>\n",
              "      <td>2125.0</td>\n",
              "      <td>Andhra Pradesh&lt;br&gt;Confirmed: 3251&lt;br&gt;Deaths: 5...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                                                      geometry  ...                                               text\n",
              "st_nm                                                                           ...                                                   \n",
              "Andaman and Nicobar Islands  MULTIPOLYGON (((93.71976 7.20707, 93.71909 7.2...  ...  Andaman and Nicobar Islands<br>Confirmed: 33<b...\n",
              "Arunachal Pradesh            POLYGON ((96.16261 29.38078, 96.16860 29.37432...  ...  Arunachal Pradesh<br>Confirmed: 3<br>Deaths: 0...\n",
              "Assam                        MULTIPOLYGON (((89.74323 26.30362, 89.74290 26...  ...  Assam<br>Confirmed: 856<br>Deaths: 4<br>Recove...\n",
              "Bihar                        MULTIPOLYGON (((84.50720 24.26323, 84.50355 24...  ...  Bihar<br>Confirmed: 3296<br>Deaths: 15<br>Reco...\n",
              "Chandigarh                   POLYGON ((76.84147 30.75996, 76.83599 30.73623...  ...  Chandigarh<br>Confirmed: 288<br>Deaths: 4<br>R...\n",
              "Chhattisgarh                 POLYGON ((83.33532 24.09885, 83.35346 24.09627...  ...  Chhattisgarh<br>Confirmed: 399<br>Deaths: 0<br...\n",
              "Dadar Nagar Haveli           POLYGON ((73.20657 20.12216, 73.20797 20.10650...  ...  Dadar Nagar Haveli<br>Confirmed: 2<br>Deaths: ...\n",
              "Goa                          MULTIPOLYGON (((74.11918 14.75344, 74.11350 14...  ...  Goa<br>Confirmed: 69<br>Deaths: 0<br>Recovered...\n",
              "Gujarat                      MULTIPOLYGON (((71.70375 20.99958, 71.70375 20...  ...  Gujarat<br>Confirmed: 15562<br>Deaths: 960<br>...\n",
              "Haryana                      POLYGON ((76.85065 30.87512, 76.86594 30.86691...  ...  Haryana<br>Confirmed: 1504<br>Deaths: 19<br>Re...\n",
              "Himachal Pradesh             POLYGON ((76.79634 33.25490, 76.80351 33.25275...  ...  Himachal Pradesh<br>Confirmed: 276<br>Deaths: ...\n",
              "Jammu and Kashmir            POLYGON ((74.73451 37.02068, 74.73647 37.01937...  ...  Jammu and Kashmir<br>Confirmed: 2036<br>Deaths...\n",
              "Jharkhand                    POLYGON ((87.60582 25.31512, 87.61279 25.31184...  ...  Jharkhand<br>Confirmed: 469<br>Deaths: 4<br>Re...\n",
              "Karnataka                    MULTIPOLYGON (((74.69694 13.32782, 74.69562 13...  ...  Karnataka<br>Confirmed: 2533<br>Deaths: 47<br>...\n",
              "Kerala                       POLYGON ((74.99575 12.79227, 75.00006 12.78777...  ...  Kerala<br>Confirmed: 1088<br>Deaths: 7<br>Reco...\n",
              "Madhya Pradesh               MULTIPOLYGON (((74.17932 22.39059, 74.17776 22...  ...  Madhya Pradesh<br>Confirmed: 7453<br>Deaths: 3...\n",
              "Maharashtra                  MULTIPOLYGON (((73.46270 16.03710, 73.46178 16...  ...  Maharashtra<br>Confirmed: 59546<br>Deaths: 198...\n",
              "Manipur                      POLYGON ((94.57602 25.64221, 94.57487 25.63858...  ...  Manipur<br>Confirmed: 55<br>Deaths: 0<br>Recov...\n",
              "Meghalaya                    POLYGON ((91.85632 26.10353, 91.86717 26.09906...  ...  Meghalaya<br>Confirmed: 21<br>Deaths: 1<br>Rec...\n",
              "Mizoram                      POLYGON ((92.80022 24.41630, 92.80310 24.41603...  ...  Mizoram<br>Confirmed: 1<br>Deaths: 0<br>Recove...\n",
              "Nagaland                     POLYGON ((95.21458 26.93095, 95.21719 26.92818...  ...  Nagaland<br>Confirmed: 18<br>Deaths: 0<br>Reco...\n",
              "Delhi                        POLYGON ((77.09361 28.86940, 77.10973 28.86722...  ...  Delhi<br>Confirmed: 16281<br>Deaths: 316<br>Re...\n",
              "Puducherry                   MULTIPOLYGON (((79.76528 10.99681, 79.76963 10...  ...  Puducherry<br>Confirmed: 51<br>Deaths: 0<br>Re...\n",
              "Punjab                       POLYGON ((75.88469 32.48841, 75.90303 32.47169...  ...  Punjab<br>Confirmed: 2158<br>Deaths: 40<br>Rec...\n",
              "Rajasthan                    POLYGON ((73.89849 29.97896, 73.90021 29.97140...  ...  Rajasthan<br>Confirmed: 8067<br>Deaths: 180<br...\n",
              "Sikkim                       POLYGON ((88.65381 28.09883, 88.66265 28.08956...  ...  Sikkim<br>Confirmed: 1<br>Deaths: 0<br>Recover...\n",
              "Tamil Nadu                   MULTIPOLYGON (((78.19188 8.72730, 78.18996 8.7...  ...  Tamil Nadu<br>Confirmed: 19372<br>Deaths: 145<...\n",
              "Telengana                    POLYGON ((81.05614 17.79097, 81.05834 17.75464...  ...  Telengana<br>Confirmed: 2256<br>Deaths: 67<br>...\n",
              "Tripura                      POLYGON ((92.22108 24.50131, 92.22771 24.49862...  ...  Tripura<br>Confirmed: 242<br>Deaths: 0<br>Reco...\n",
              "Uttar Pradesh                MULTIPOLYGON (((80.44802 24.99631, 80.44080 24...  ...  Uttar Pradesh<br>Confirmed: 7170<br>Deaths: 19...\n",
              "Uttarakhand                  POLYGON ((79.21047 31.34846, 79.21386 31.34680...  ...  Uttarakhand<br>Confirmed: 500<br>Deaths: 4<br>...\n",
              "West Bengal                  MULTIPOLYGON (((88.01861 21.57278, 88.01889 21...  ...  West Bengal<br>Confirmed: 4536<br>Deaths: 295<...\n",
              "Odisha                       MULTIPOLYGON (((86.38937 19.96351, 86.38840 19...  ...  Odisha<br>Confirmed: 1660<br>Deaths: 7<br>Reco...\n",
              "Andhra Pradesh               MULTIPOLYGON (((81.10380 17.82269, 81.10610 17...  ...  Andhra Pradesh<br>Confirmed: 3251<br>Deaths: 5...\n",
              "\n",
              "[34 rows x 5 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EifYQxTJb2Db",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 756
        },
        "outputId": "e8a80c90-bd72-4d63-fa0f-bc9a2ecc9282"
      },
      "source": [
        "variable = 'confirmed'\n",
        "# create figure and axes for Matplotlib\n",
        "fig, ax = plt.subplots(1,figsize=(18, 13))\n",
        "ax.axis('off')\n",
        "merged.plot(column=variable, cmap='bone_r', linewidth=0.5, ax=ax, edgecolor='0.6', legend='True')\n",
        "\n",
        "\n"
      ],
      "execution_count": 147,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f0eb961d4a8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 147
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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YDPqysrPNjqSkFYqi0KWLF7cQEX198qQ2Nz+fGIahO+0DE+vOnTnTREQdaucAAACA2IaRGYgKURSZpOTkbfMWLHjQYDSqHQdi3N4vv9z58EMPPah2DgAAgLGm1eqUhIRUtWMQEVF3d0vcjczE19wTiFscx5UXTJkyC0UG7kWWZQoGg4Nq5wAAAIDYhzIDUaF8MwqIYUC4p8uNjf0+r/ef1c4BAAAAsQ9lBqLix+vWnWi5evVSwO+nQCBw7wfAhHW1qenE888/v0/tHAAAANHBEMPExiUexefZwRCXPB6P9OUXX2wgIo/NZnvEYrUaikpKUrXYgwauCwQCFAwGG9TOAQAAAPEBZQai5pnVq2fe+FoUxT9mOS6xva3tPYPB4CyvrMxXFIXsCQlx+8kAjNyh/ftP9PX2/kztHAAAANHEMJgsNVwoM6CK6zu7txHRnFdffTV9wOV6OhAI/N7iBx/McSQlxcdW9TCqFEUhv99/VhAEnPwPAAAAQ4IaCKpbt25dezAY3JaRmWkxGI0oMhMUwzCk1enMaucAAACA+IGRGYgJPM+XyLKsGLF084SlKArJkUhY7RwAAADRhin2w4eRGYgJiqKszp8yJVHtHKCe7mvXQsFA4HO1cwAAAED8QJkB1YmiyBBRSmJiIoZlJrAL58+fHxwcfFPtHAAAABA/MM0MVCWKolmv16/KKyio1vM4XWaiutbVFRx0uV4TBCGidhYAAIDowzSz4UKZgagTRZElojStVpuf6HD8U2V1dUmK04nNZiYov99PdUeObBsYGPhXtbMAAABAfEGZgah54403plis1r91pqY6k5KTcwxGI59fUGDjOE7taKCiro4OT39f3y8FQZDVzgIAAADxBWUGokIURU1aevrG+YsWlaO8wM20Op2eYRgsyQwAABMSwzBYzWwEUGYgKhISEz+sqa0tQZGZ2BRFoSuXL7uCgUDQaDTarl271jE4MNChKEqT2tkAAAAg/qDMwJgTRZE1mc1TeZ7Hf28TWDAQoCOHDl3t6e7+kc/nu8CybLYsy8cFQQiqnQ0AAEBNDIMFhocLby5h1ImiyBGRjjcY8iKRSC4RNaenp2t1Op3a0UBFp06e7G9rbf3BunXr9l2/qk3VQAAAABD3UGZg1DmSkj5NSk4u5nmeT3E6U640NjbnT52arnYuUFdHR4dPVpROtXMAAADA+IEyA6POaDLl1MyYkX3j+xSnc5KaeSD6FEWhnu7uIMuyepvNRhqtlpYuX56x54sv3iGiarXzAQAAxBIsADB8KDMwqkRRTCurqLCpnQPU4/f5aPvWrU0sy/5DJBLp1+l0yzUajU6R5WAoHH5Z7XwAAAAwfqDMwKjS6XQ5NpvNonYOUI/BaCSLxRLyer11Lzz//EEiekvtTAAAADA+YekEGFU6vf7vExMTsWfIBFdTWzuFIZqrdg4AAIB4cGOvGbUv8QhlBkaVx+1+/tjRo/WRSETtKKAig8FAGo1mqto5AAAAYHxDmYFRJQjCld7e3i+PHTnSo3YWUI9OpyNbQsJCURSxHjcAAACMGZwzA6Mu4Pf/UTAYXEhESWpngeiLRCLU2dER8Xm9/YIghNTOAwAAENvid4pXLMDIDIw6QRC8six3qJ0D1HHx/Pmu/fv2/airs3Oh2lkAAABgfMPIDIyJcDgsqZ0B1NF97dpgOBx+UxAERe0sAAAAsY5hsM/MSGBkBsaEFA53YxGA8auvt1f2+3zfuU6SJNqza9dpt9v9RygyAAAAEA0YmYEx4Q8EPunv63s2KTkZHzXEuVAoRGdPnWrp6+u7muhw5KRnZGQcPXz4NxzHXdVqtckGgyGJ4zi9RqtN83o8Tc+sXv2x2pkBAABgYkCZgTHh83oPdF+71pOUnJysdhYYmSMHD15qbmqaJwhCpyiKzvoLFyolSTogCMLgzfdbv2HDVJ7nM9XKCQAAEK8YBpOlhgtlBsaEIAjNH3388emCqVMXaTT4zyyeSZGIQkRhIiJBELqIaNvt7vfSiy9eJKKLUYwGAAAAExxqIIyZ/r6+H/xm797TaueAkamZMWOKzWb7B7VzAAAAANwKZQbGTCgU6iAinAge58xmMyU6HDWiKFrUzgIAADAeMTHyTzxCmYExwzBMUWZ29iS1c8DIVVRVFRuNxh2iKKaqnQUAAADgBpQZGDMsy7par17tVDsHjJzRZKLyqqoyIsJmqAAAABAzUGZgzKxbt67d6/F8LUnYP3M88LjdHq1WWyuKol3tLAAAAOMHc2PnTPUvcQhlBsaMKIqT0zMzF2I1s/imKN+c9lQwdaozJzf3fZvd/tcqRwIAAAAgIizNDGMr1NHeHi4qKSGe59XOAsO0a8eOq3q9/posy0o4HO6RwuE8tTMBAAAAEKHMwBgSBKHtjTfeeOjc6dNbq6ZPT1c7DwwPy7KBq83NMwVBiKidBQAAYDxi4nSKVyzANDMYU6FQyNbX16dXOwcM39Siokk6nW6+2jkAAAAAboWRGRhTBqOx0mqzoTTHMa/H443IcpvaOQAAAMYrjMwMH95kwpiSI5HfmzJ1KjZbjGMcx7EMEf43BAAAAGIYpolhmNMMw5xkGKbu+nWJDMPsZBjm0vV/J1y/nmEY5p8ZhmlgGOYUwzBVNz3P2uv3v8QwzNqbrq++/vwN1x9716aHMgNj5uWXX542tagozZ6QgBHAOJaTm2s3Wyz/RxRFfGwEAAAAREQLFUWpUBSl5vr3PyeiXYqiFBDRruvfExE9REQF1y8CEf070Tflh4j+lIhqiWgGEf3pjQJ0/T6/c9Pjlt8tCMoMjBlFUVrPnzuHN8BxjuM4qqyqKk1MTNzz63feWYFSAwAAMHq+2eKFjYnLCDxBRK9f//p1InrypuvfUL5xiIjsDMOkEdEyItqpKEqfoij9RLSTiJZfv82qKMoh5Zu9Id646bluC5+Yw5gRBKHvvffee8vj8fyu2WxWOw6MQHpmpjEtI2Ne69WrVc1NTZe2bd/uZ1hWQ4qi83q95+VIpJdhGJ3H4/kgGAzuEARBVjszAAAAjAmFiHYwDKMQ0X8oiiISkVNRlI7rt3cSkfP61xlE1HLTY1uvX3e361tvc/0doczAmPL5fP96+MCB1QuXLHGw7MQaCFQU5Tsn9AX8fvr8k09knufp4ccfj7sfBsMwlDVpkjlr0qTKm6+PRCIVkUiEGIahUydOrL5w/nwNETWqFBMAAACGL+nGeTDXidfLys3mKIrSxjBMChHtZBjmws03KoqiXC86UYEyA2PqpZdeOvfqq6/+xdXm5r/MmTzZqHaesRQMBOjkiRNenVZLHo9HCQWDFAgG9fMXLtT29vaGLjc0yFVVVbq2jo4gERnUzjtaOI4jjuOIiMjn87USUbO6iQAAAOIJE0urmfXcdB7MbSmK0nb939cYhvmAvjnnpYthmDRFUTquTxW7dv3ubUSUddPDM69f10ZEC265fs/16zNvc/87QpmBMWcymx9JcTrHdZFpuXo1eOHcOWnhggUmlmWJ53nSaDTU1dWlnDpxwpuRkWGaPWsWSZJEl69cYSRJIo1mfP36XW5o6HW5XH8mCIKkdhYAAAAYfQzDmIiIVRTFff3rB4noz4noYyJaS0R/c/3fH11/yMdE9BOGYd6lb072H7heeLYT0V/ddNL/g0T03xVF6WMYZpBhmJlEdJiIfkhE/3K3TOPr3RTEJI7jkuovXGjLzctLt9psMfPRw2hpqK/39vf1cY89+qjp1ql0TqeTcTqdppuvy8nJYT7/+OPw/EWLtDa7PapZx9LlxsYTzz7zzBa1cwAAAMCYcRLRB9dHkjRE9GtFUbYxDHOUiDYyDLOOvpmhsfr6/T8nooeJqIGIfET0EhHR9dLyKyI6ev1+f64oSt/1r3+PiDbQN7NYtl6/3BHKDIy5/r6+2f19ffrmpqbXFy1d+rjFMj62LPF5veTz+ai5qUl57NFH+aE+rrSkRJ+ZkUFHjx3zznrgge8VoHgUiUSIfnuyHwAAANyHGJpmdleKolwmovLbXN9LRItvc71CRP/lDs/1GhG9dpvr64iodKiZUGZgzAmC4CcivyiKa36zd+/2hISEbEmSrpnM5pT0jIwUZ2rqkItALHC73XRo/36f0WhU7Dabbv68efe9VJvdbqesjAzdrh07vI6kJMZmtzMJCQkGi8VCWp1uSM8x4HKRxWqlWChDWz/91BMKhT5TOwcAAABMLMw3hQkgOjZu3CgwDONftWrVm6IoOq02208zMjP/S3FJiY03xNY58bIsk6u/n0xmM+n1eiIi6u/rk44dPRpatHChcTSWm5ZlmQYGBqipqSnY3d3NsBwXmTVnjuFun9D0dHdHWpqbA52dnRqGiGbPm6exWq3ciMOMgCzLdLyurrunu3tHb0/PD7E0MwAAwNDwvEmZNKlY7RhERFRfX3fsXgsAxBqUGVCVKIp6i9X6I47j1qSlp2enpqWlpzid2lg4Of54XZ03Eg5rfT5fyJ6YSAzDML09PcriRYvMPD82g0kbNmxQUlJT/dNnzOBNZjNLRBQIBEin01E4HKZLFy96rnV2srW1tcbk5GQKhUL00UcfBecsWKCzxcD5SP19fdKh/fv/78qVK39f7SwAAADxAGVmZFBmIGaIomjV6/WzjSbTn1fV1FQ7U1PZu41QyLJMiqJ8uyzwaDt54oTHYjTqysrKdPX19WGLxaJNS0sbk2PdrKuri77cvVtKT08PZU2apD1x7FiEiEir1cpTCgo0hYWF35mHtmnTpmBNba02xem8688rWhovXeq5eP78L1etWvXvamcBAACIdSgzI4MyAzFHFEWT0Wh8gmGYXy1/9NFcvV7/7QaUN96sX2ls7D954sTl/IKCnLLycsdY5FAUhRobGvxtLS3hh5Yvt47FMe7m/Pnz0vnz56WlS5fyd1s0IRAI0MGDB/2hcDgyd8GCkc99GwXH6+pamq5cefQHL7xwSu0sAAAAsYznTUrOpCGf7z6mLtYfQZkBGC3rN2zIsNvtv5ZleXJEko7wBkNJfkHBlGAo5L147hwTDoebHnvyydKhnjA/XI0NDd5IKMRWVFTE1kk9tzh48KBn0O1m5i5YYLrTfaRwmOovXvSazWYmOydnzPb+kWWZjh050trb27vh6aee+sVYHQcAACDeocyMjPrLIAHcwUsvvti24skn5/f29OSsXr16ZWdHx/TjdXV/fKKu7km32700LT2dH+siQ0SU4nSa+vv7Y/6E9lmzZpn9fv8d55n5fT7asXVrJCUpydjc1HTH16MoCvn9/hFlYVmWikpLM4iocERPBAAAAHAX6p9lDXAPN1bGEgTBQ0R/T0QkiiI/ODgYDgYCpB+jk/Fv8LjdIYqBc1GGgmVZxe/3k8FgIFmWqauzU2q6fNmvKArn83qVmpoaU0JCApnN5u+9oEgkQi3NzZ6Ojo5ev8+nW7R06bBPEAr4/bR/374DvT09L4zsFQEAAIx3DMXL+4xYhDIDcYnn+akcx/HR2GPFbLFoA4FAcMwPNAoWLVxo2r17t3/uwoWGbZ9+KttsNrmgoECXmZmpv7ECmyRJ1NbWpq+sqSFFUaittdXj6u/3BgIBaqivfzYSiZxyJCW9EwwE0oZTFCORCB3cv//rnu7uhwRBiIufGwAAAMQnlBmISwzDFKQ4naljPc3M6/HQ3l27Qk8++eSdz8CPIWazmfLy8tj9+/a5sydNYmbPmvWdBQHq6up817q75Tnz5hm+PnGiua+3t3nA5foPv99/wGw2z1i3bt0eIqLNW7a0RyKRIR3zSmPjwPlz544kp6TkV1ZVTd69a9fxAZdrpSAI7tF/hQAAAAC/hTIDccnv93/QUF/fNqWwMH+s9nwhItqxbZuUmZFBuiicmzNaiouL9cXFxfrb3dbe0UFzFywwnzpxwt3e3r75uTVr/uimm5tu+jrA3mPJa0VR6HhdXXP3tWufrFq58vc3b9nyP7/au3fFwMDAc2vXrr0y8lcCAAAw/jEMUSxsrRCvsJoZxK33Nm78hd1uF+wJCZq+3t6ALMuByXl5Bc7UVM49OBj6+sSJqxmZmclTCgtt97sXzYljx3w93d1KekYG19neLj300EPmWNjIc6Q++PBDP8dx8ozp002nT5++1tTUNCChfNsAACAASURBVEMQhOZb7/faa6/ll5SV/aZ02jTnrbeFQiE6Xld3ye/393k9nl89s3r1Z9FJDwAAMP4YDGYlJ6dM7RhERHThwqG4W80MZQbimiiKiUSkJyIvEfn1ev0qPc//QKPRtPb19v5Eo9WuyMzK+o+8/HyjMzVVM5RPPro6O6m5qclTPX26mWVZGnC5pCOHDoWefOKJMVvKOFpk+ZtFzFiWpatXrw7U1dXtDwaDf/bcc88dvfW+W95//3Vnaur8iqqqSTf/3AJ+P+3+4ov3V65c+XT0kgMAAIxPBoNZmTx5mtoxiIjo/PmDcVdm4v+jZpjQBEHou+WqX1+/3PDuW2+9VdHR3l5it9unlVdWZienpHzvedpaW+X6Cxd8RBSxWK1sVU2NhWVZam5q8jZeuqSkp6WNi2XMb14wITs726YoyoOHDx8+QUTfKzNPP/XU2vfee+8hj8fzD9MqKqb29fb2tbe2XvW43Zb+/v7/Ec3cAAAAALeDkRmYMF599dVkg9H477zBMF+r0ehtNpv22rVrzKScHC7g94fLKioMN08lq7940TPoctH8efPMd3nauKUoCr3//vsH+/r6FgiCELrT/URRNNkTEl5jGOb8qpUr/yyKEQEAAMY9jMyMDMoMTDhvv/323sqamgesVisXDAZDu7/4Qvf0M8+QVqslIqJwOExNly8HBl0ued68eXE/texOFEWhTz75ZNMTTzyxWu0sAAAAE5XBYFZyc8vVjkFEROfOHYi7MoNpZjDheL3eFWdOnTrw8GOPTSUi3WNPPklarZaCgQAd3L/fw7EsZWRk6ObNmze2u3GqrLGxMeh2u4s/+eSTD/1+/0cul2uDIAj4dAMAAADiBsoMTCiiKGptdvuxyurqpBvXmczfzCLr6emJGA0G48KFC8fF+TH3kp+fr8/LyythGKbk7NmzDx45cqSViHaqnQsAAABgqFBmYEKx2e1/XVZenpSWnv6982BS09K4+gsXQj6fT2c0jtvZZd9xY5Uyt9vtj0QiXSrHAQAAmIAYYpgJ8TnqmECZgXFPFMUSo9H4WCgU+sP0zEx/9qRJ3ysyiqLQhbNnvRazmYxGY/zskDlKpk2bljgwMPDG5s2b/3LlypWb1M4DAAAAMBRYAADGjTfeeCNfz/P/bjAYTK7+/r06vT5Tp9OVZU+alOP3+6XSsjKHVnf7niJJEh06cMCn1Wpln9dL02tqzE7n9/aLHNcURaF9+/Zdbm5uXrF27dpTaucBAACYCAwGi5KXV6F2DCIiOnv2N3G3AADKDIwL69evz9bqdAeWLFuWYTQaqbenRzGZzcxwpou5Bwfli+fP+zUcx9bW1n5nuebxLhKJ0J49exp7enr+bXBw8B+xIAAAAMDYMhgsSn5+pdoxiIjozJmv4q7MYIIexDVRFJPefPPNn2q02k8XLV2aYTabiWVZSk5JGVaRISKyWK1sTW2tKSUtjd29Z4/3888/d8uyPMrJYxPHcbRo0aI8q9X6n9XOAgAAAHAvE+cjZxhXRFHkbTbbm0nJybOKiouTM7KydDfvbj8a0jMy9OkZGfrOjo7g/gMHvHPnzDGN6gFi1MDAQMTv92/HqAwAAMDYY5jfLsgD9w9lBuLOa6+9lmtPSNg384EH0hITE8d8dDE1LU3fUF8fDgQCxPPjeusZIiJqbGy81tvb+z/VzgEAAABwL5hmBnHHYrFsWbp8eUY0iswN4XCY/+STTyLROp5awuEwtba2dhBRr9pZAAAAAO4FIzMQV0RRzJqUk+PUarVRPa7Vag1WVVaO+2lmoVCIgsFgk9o5AAAAJhZMMxsujMxA3Hj717/+k8l5eUdKpk1Ljvaxy8rLTceOH/dE+7jRZjKZyGKxVBMRp3YWAAAAgHtBmYG4EfD733APDg7YbLaojyjq9HrS8zzX3t4e7UNHXUVFRYrD4Tj4yiuv5KmdBQAAAOBuUGYgbqxbt67D5/P9bUd7e1iN41dWVRmOnzjhU+PY0ZSWlmZ4+OGHa1JTU9999dVXJ9bOoQAAAFHHEMOwMXGJR/GZGiYsn9f71vG6ut3tra1RLxVGk4lS09LYurq6YLSPrQaj0ehgWXb8L98GAAAAcQtlBuKKIAhBKRz+wzOnT/epcfzC4mK+p7c37PV61Th8VJw/f/7ajh073r9y5cq8l156qVntPAAAAAB3gjIDcYdh2bWSJGn8vujP+GIYhqqmTzfv27dv3C4G0NDQ4AkGgzvWrVvXeuttoihaRFHEkisAAACjiGGYmLjEI5QZiDtej+fnAy7Xaw2XLg2ocXyTyUScVst0d3d/5/qDhw5JkiSpEWlULV++PLe6uvpvtmzZ8me33pacnLw/IyPjN5s3b35ehWgAAAAA34EyA3HHZDL9P0UlJX+YkZWl2r4vxSUl/NGjR/03vt+5c6f/6tWr7Lbt20NqZRotWq2WjEaj2e/3LxFFUXfj+vfff/8XVVVVOcsfemh2bm7u/3pv48bFauYEAAAYL9QekcHIDECUvPrqq8uyc3L+sLyyUp+YmKjapq8ejyes1WqVG993dnbqK6urFZZlwz4Vpr+NttTUVM3ixYtnW63WPxFFkd+yZcs/paWnv5iZlWUhIppWXp6RYLf/67vvvjtT7awAAAAwcaHMQFzR6fV/WlhcbFP704Os7Gxez/ORrVu3Dn762WeesooKX0ZmJlczY4bp7bffphMnToT7+/sp1qad9fb20pkzZ5TOzs573tdmszEGg2GlVqt9pGb69J/OmDEj98ZtDMPQosWLCw0Gw3+IomgY09AAAAAAd6DaJ9sAw2EymViDITbeO1fPmGG59TreYKDljzyinDtzhurq6shitYZXr1qlZVn1PjeQJIm8Xi8dOXrUHwwEuLJp07jDhw9HNBpNZNmyZQaNRkODg4Ok0+mI53+7ErPBYKC5c+eWfPnll/+UkpLyvfbIMAzxPG/V6/V5RHQmmq8JAABgPFH7Q9p4hjIDcUWv1+vVznA3HMdRosPBzJk/X0tE9MGmTZotW7aEV61apY1mDlmW6erVq3TlyhVfIBBgeJ5XCgoKtNnZ2VoiorS0NG5gYED+4MMPQ0aDIWyxWhmv18tEJIlNS0vjrr+WcEVFhSEzM9N+6tQpX3V1tfHWP7Zz587N+XL37peJaFY0Xx8AAAAAEcoMxBFRFNmcyZMTFUUhv89HOp2ONNqodoT7ZrZYgo8+8khUNp50uVz01Vdf+YiIvF6vrmzaNKm4uFiXnJLyvd9zjUZDDoeDfeKJJ3REpNNoNCTLMrndbgoEAkREdOTIkXBpaSnV1taa9+3bF2hsbAzk5+d/57XoeZ7MZnOqKIrJgiB033ocAAAAgLHEKIpy73sBxIh33nnntcl5ec+c/vprjdFo3FdQWFhZXFLiUDvXnfxm376gzWKRZ86cOaZz42RZpo8++si/eMkSg9lsJlmWaaRT23p7e6VDBw8GWZZlOI5TJEnSPfzII99rj8FgkA4dPHjZ5XKd7enpeVoQhPCIDgwAADCBmExWpbAwNtbTOX585zFFUWrUznE/MDIDccXtdv+nc2fPPsBy3Mscx02xWiwxPb3JnpAgpyYnj2mRkSSJtmzZEp49ezaZzWYiohEXGSIih8OhWbZ8uSYQCBDHsmQwGm97P71eT/MXLMhtb29PPXjgwM+I6K9GfHAAAACAIUCZgbhiMBqfKJs2bZKe53+VnJzM8zGyGMCdcAyjyLI8psfQaDTEcZySkZk56j8MjUbzbUG6l/T0dGN6evp/3rt370vd3d0nV65cuerGbaIockSUIwhC42hnBAAAgIkLZQbiwssvv1zG8/xGW0ICl5mdrb951a1Ylpufbzx6+LD39JkzVFVZqc/MzBzV37mGhobQqVOn5HA4HBMLI8yaPTuLiOjgwYOO9evXP8/zfKder//VpEmTOHtCQs7WrVvrQ+FwoKuzc6UgCANq5wUAAFAfQwyD3VKGC+fMQMzbsGFDRSgUOrFi5co7TnWKdZFIhPbs2hVIcjgoJSVFyc/PH5VRlK+++so3ODiodaamMlVVVTHz4YSiKHS5sdEjRSLhvLy8BI3mt9EGBgZo544dWwYGBlYJgoA/QAAAMKGZTDalqCg2Zs0fO7Y97s6ZQZmBmCaKYrme5w88+vjjRn2cjMbcSTgcpp3btgV5vV7S6/VksViUWbNmDW0O1x00NDSE9+/fr3no4YflpKQkbrSyjrWOjg7fmTNnznjc7s/6+vr+QhCEsZ2LBwAAEKNMJptSXDxb7RhERFRXty3uykzMfJILcKvXX3+9OC09fXtJWZkm3osMEZFWq6WS0tLIlcuX5Vlz5lj27d7t7ejoiNjtdm64G4GeO3cuUlNTw9pstrgpMkREaWlpxrS0tBk9PT2VRw4ffurd994TBgcGLhJRRBAEj9r5AAAAID5ggh7EpA0bNuTJirJ75uzZzhSnU6d2npHy+/20f98+X92RI8ZAIGAIBAK0YPFi0569e5WzZ88GhvOcXV1ditlsjhQVF3PaGN9v506SkpK0Dz38cLnVal2fnJx8wOl07hFFMVntXAAAABAfUGYg5oiiyGq02rVp6emaeD1H5lanTp4MWi0W3axZsyJEFLFaraTRaOjRxx7TNDY2clu3bnXfb6lpamoKcBwX97/DDMPQ0qVLix997LHimbNmlSQkJBwXRRGjxgAAMGEwMfJPPIr7N0Iw/lgslp9WVlf/bPacOYlqZxktNTNm6K+2tFBPT09EkWWmvbWViIh0ej09+fTT2kVLl1q6e3uVY8eODXmKVW1trWFgYID6+vrGLHe0sCxLLMtSKBSSNBrNSUEQJLUzAQAAQOxDmYGYIYpi5saNGzcVFhf/YlJODj8aGz/GCo7j6OHHHtOkpKVxWp2O27dvnxwKhb5zn1mzZxuudXff14ocS5YsMezYvn3cnDzf398fDIVC76idAwAAAOLD+Hm3CHHPYDB8OWf+/JUFU6eOmxGZmzEMQ6lpadySZcu4OfPm0a6dO4M33+71eomh+xvj3bt3b0iri/tTir6VkZHhkCKR+ThvBgAAJhSGiY1LHMK8dFDdm2++OUOSpCeSkpMTrTab2nGiwuf1Kjqd7jt/Nfbu3h2YOmUKH4lEiOOGtjjZ3Llzdbt37/YePXKEps+YYRqTsFFks9moqKhoxYDL9eSHH3542eVy/SQUCjUJgtCrdjYAAACIPSgzoJr169dnaXW63zcajf9pSlERl56eHvdvxocqweFg6y9eDN983YPLl/ONDQ3yF198EZg+fTqfmHjvAare3t4Iy7KK1WaLz49TbsEwDJWXlycTEUUikZRLly7tbG1p6dq0adN7Pp/vDSJSwuGw9OMf/7hF5agAAAAQA1BmQE3VPq/3Z0889RQxcTq0eT8kSaJwKEQarZa6u7qCk3JyvrN5jkajoamFhawkSdyRo0d9HMtGZFnmpk2bpj1x4gRTWVmpWK1WbSgUotOnTwdcLpeclZWlLFu+3Dyezi+6geM4KiwsTCgsLEzwer1/evDAgTUsx3EWs1m3adOm9/v7+/9UEIQBtXMCAACMDDMh3geNFZQZUA1vMPDJKSmtDMNkqp1lrHncbnnv7t0Sx3GRiCRxCQ4H29/XFyqbNu17J7yUlJZqS0pLtUREbrebdm7bJnk8Ho3P5wuyHBfo7enhLRaLdumDD7IJCQkT4q+fyWSiJUuXTrnxfWFR0R8cPXJkyaZNm/5u1apVG1SMBgAAACoafx/nQlxYv359qU6n+9cFixeP+yJDRDQ4OCgHAgHNo48/bnjiqad0gy6XbLFYQvd6nMVioadWrdK8sHYtPfbkk/ra2lreZLGEi2truV27doW8Xm804scci8VCixYvLikoKPi7t9566wG18wAAAIA6GEW5r5VgAUbNexs3/rx6+vT/NzUtbdyeK6MoCvV0d8sH9++Xi0pK5KKiohEtPdbR0aGcOnvWP6W83BgKhejK6dMes9GozJgxw2Iyjdsf4x3JskyHDx++3H3t2h6v17vZ5/PtxB41AAAQT8xmu1JaOk/tGEREdPjwJ8cURalRO8f9QJkB1YiiyKQ4nQeWLFs2U+0sY+Hrkyf9Vxob9WaTSVqweLGO5/l7P+guvF4v7d6zx19UXW1gb1rtLBgIUPP58x4NwyhpqanstPJy00SbexsOh6mrqytwtbm5zePxtHh9vn9e+fTTH6idCwAA4F5QZkYG08xANYIgKMFg8HQ4dM/ZVnFjcGCAbnxA0N3VpXc6nZLVbldGWmSIiPbs3u2ZUlHxnSJDRKTneZpSWWnOmTbN0tDYqJGkiTcwodVqqb29vb+8oiJv3vz5C1iG+e9qZwIAABgqhmFi4hKPsAAAqEIURcZosfw86PO9GA6HKR43flQUhfr6+mSHw8FKkkQet5u2ffYZLVyyRHamprJLli1jJUnSfbF9e2Akx3H199PevXv96bm5zN1+TlcvXvRWVlQwWq12JIeLSy0tLcFL9fWbOjs6SiRJqvf5fBvUzgQAAABjD2UGou6VV15JsSQk/DprypTZLfX1ik6vVzvSsHi9Xtr+2WdsWnp62GK1Uv2FC9pEhyN08fx5JSk5WS+Fw3Rw/35v2bRpI2oX+/fvD04uLjZYEhJue7skSTTY10dGrVbOzcuzjORY8SgUClFrS0u3oihvrFix4pjaeQAAACB6UGYgakRRzOKNxkfsycm/n1dWVtzd1uZLS0sLazSa+BuWISKz2UyFxcWBUCjEpKWm6isqK4nned3xujrvzm3bFJ1OF549Z47FbDbf93PLskxNly/Thfp6N6fXa4yWO3eUS8ePexPtdnb2Aw9MuCLjdruVPXv2HPR5vX+wdu1aFBkAAIhL8TrFKxagzMCYEUVRqzcan9Xz/FNavX7SpMLCTJvDkWwwm0kKh8nb36/MfvDBuF2Cy+/zUdOVK7rVzz77nXPPqmpqTFXffDnsE2UOHTwYiHAclzdtmuVuI1ehQIAYRdHOnTcvLgvhSMiyTAcPHvz6WlfXUkEQfGrnAQAAgOhDmYFRJYoio9XpHuBNpj9IzsgoTcnMzDfb7ZpbP3GIhMPk83q1e3bv9pCiKAzLMnqdjuV5Xms0mbRGo5EMPE8Go5GMNy05rCjKt59eSJJEzU1NQb/PJ7W3tXElZWVai8XCWW22qLxWv99PmVlZYSIalXlyZ8+coZLSUvK43dTT26uUzpypvdcnNZe+/jowb86cka8uEEf6+vooHA7TkSNHjoRDoV+gyAAAAExcKDMwqix2+7vO7OzliampVpa982J5vMlExbW1OlmWdQwRyYpCEUmiiCSROxiM9HV3h6RwWJbCYUX6ZrUzRZFlxTMwYMrIyAhMKSw0Ha+r86WlpxuzsrP1pWVldObUqdD+ffvYiqoq75TCQjPRN4XDYDCMyWs9f/ZsQKMZvV+hktJSkiSJdu/e7ZtSUWEcypBzJBxmLHeZgjbenDl9uv3cuXP/oihKm8fjeV8QhIm5aygAAIwj8buSWCxAmYFRI4qi3pmdXZ2Unm4dyv05jYa+s8jwb6dTcUR02wbScOpUgGEYzZlTp3ypqanasrKyb2+rqKrSVVRV0a6dO4nluGAoGJTPnj7NP/Too4x5DN7wJzoc1HL1quJyuchut4/Kc16qrydndrZWP4QC5h0cJKPBoOjicCW44ero6Ghbs2bN36idAwAAAGID9pmBUaPj+UcSnc6ssXr+gM+nkKJEzGazvGDhQuO08vLbrhK2cPFi87XOTrLb7bxOp4tEIhE6efy4P+D3D/lYoVCI/H4/ybJ829tlWSabzcZJ4TAXCYeH94Ju4+rVq15bUtKQVj9jOY5C4TBzp4zjiaIodPTo0XaP1/vvamcBAACA2IGRGRg1eoPhOWtCwpgNEzRfuOBdtmSJWVYUkiSJ7jTFi2VZmrdggZ6IqOXq1eCuHTs4vV6vt1gsgbyCgm/PL7na1CR/ffKkMmPmTNlmt2t1Oh0NuFxUf/Gi1+/zKUTEMAzDzlu40HBj+FdRFPpqzx6/z+tlUlJTlUefeMJwt+l096Ors5PCkQg71KWqDSYT2VNSmHNnz/pLy8qGNJcuEolQ3dGj/vyCAoPdbifulg04Y00wGKQvvvjinCLLAbfb/csXXnjhM7UzAQAAjDaGwfjCcKHMwKgQRZFLSk+fyozSG/vbYRiG3bZ9u5flOIYhUh555JF7roT2wNy5pt7uboooCnP08GEmr6CAiIj6+/qkixcuSA898gh/4De/Cft8vohWq1U4jtOEw2HmoevPffrrrwNfbN8emJSTowSDQbmzo4PNyMzUTFuyZNR3pjxw8GCgZMaM+zrBJz03V9d86pSv9KbpdnciSRLt/vJLryk52Vh38qRfCgSkpKQkqq2tjdmTbnbt2nW5p7v78R/96EeNamcBAACA2IMyA6NCo9PNTXQ6c8fyGFMqK41E3yxH3HHpkmeoj3MkJ9Nnn3wSmF5byxMRXevqCh09fFhZumwZz/M8LVqy5NbVwL4tKmXl5XxJWRm1t7WRRqulyqqqUXktt5OYmEj93d3BlIyMIa+OJssy9fX3a3t6eqihocF38cIFY2ZWVmTx4sXfDrn4fD7at3evNxgKUVZhoclss1FSWpqBiKjp3LlAR0eHlJaWFpN/C1iG8UmSdFntHAAAAGMLCwAMF8a0YFTwBsOP7ElJxmgcq6e93VdcVDTknSgHBweJYRguITGRFEWhA199pcnNy2ONxqHFZVmWMrOyKDU1ddiZh8LV30/2pKT7WuaZZVmaUlmp3X/woJeMRkPtgw9SZ0fHt+fRtLa2BrZt3+5PLSgwFdXWmsy3LFvtyMjgO9rbg6P3KkZXZVVVbnJy8t+rnQMAAABiU0x+GgvxRRRFxpGWVs5G6fwLv8cjJzudQ76/1Wole0JC8OjhwyG9Xs9Nzs1ly6ZNi6klwNpaWsjqcChDPV/mZiarlYqmT/92yl0gEGBDoRBdvHjRd/nKFSqdNeuOrc1is9HF+nolPT09mJqWNir75Ywmm81mjEQio7NUHAAAAIw7GJmBEdPx/NLElJTJ0TqeoihMd3f3fS3hNXPWLHNrS4vB1d8fMVssMbf8l9FoJJ/bPeLnkWWZjCaTtH3bNrnp6lW2aMaMew4/FVRXm4+dPBk+cOCAJxAIjDjDaOnt7Q1u27bt8MDAwP9QOwsAAMBYYRgmZi7xCCMzMCKiKOrsycn/KzE1NWonkecUF5uOHjniz1ixYsgny7MsS48/+STL8/w9Fw0YKVmWqf7iRSosKhryYwbcblkzCvvFsCxL5XPnasLBIOn1en4oK62xLEsFlZVmr9tNO3bs8CQmJjKlpaW83W5XbamzUChEJ44f73b1969bt25dh1o5AAAAILahzMCIGC2WX00qLJwWrTbf097u7Wpp0VSUl9/3lCiev/U8/7HBsux9FRkiopycHPbrkydJCodJox3ZQmkajeaOy1bfjcliocIZM8xSKET7Dx70aDlONhqNTCQS4WfPnq3VjjDXUPX39UmfffaZxDDMunXr1p2NykEBAOKYKIqMw+H4Q5ZlU1esWPFHaucBiCaUGRi2l195JT01O/tZg8kUtemKiqKQ0WAITc7N/U6ZaW1pUTo7O0NV1dX60dr3JdpsdjvjHRyM2BwOVTd/0eh0VFBZaSYiGujtpb6WloBGo4lOkyGis2fPdkmStC4SieyM1jEBAOKNKIocEVmISHI4HOtra2sfIiLasmWLobe39yeCICjqJoT7Ea9TvGIBygwMm8lq/beMvLzsaB4zOSPDFJEk98kTJ/zNzc0Kp9HIZrOZ9YdCSmJqqv6zzz/3Tc7JYUpLS+9rv5ZY4B4cJHNSkkxEqu9k6fd66fjeveR0OkNLli7lo/lHduasWRk9PT2/ePrpp7dH7aAAAKNEFMUCIrosCEJkLJ7/rbfeKo9EIvbk5OSfZ2ZmTgsGg4Gqqqrcm1bo/OGhQ4cuExFWgoQJAWUGhuX1N9+ckz558pyRTokajuTMTMvFurpgel6ebE1IMIeCQdIbDMQwDCUkJ2vOHT4cKC0tjXqukcrLz1euNDdHHE5n9H+oNzmya1eEYxi2qrpaKi8vj/qqbxqNhrKzs/M3bdr0g1WrVr0Z7eMDAAyXKIq61NTUrRqNxvXOO+/83po1a46M4nMzWq12QVZW1lspKSn8lClTEm83fTozM9NsNBofI5QZmCBQZuC+iaLI2hyOv0/JynKocXyO46i4tvbbaWb8LfvFWBITQx99/HFEjkS4ZcuW8RqOo0OHDgWmlZfzhw8d8g663bopBQVyalqa1ul0xtKcNF1iSoqkdoipFRXchWPHlKSkJK6/v58SEhKiniHF6Uy4cOHCYNQPDAAwAk6n88uFCxdONplM7JEjR7a88847P16zZs2IRpnXr1+fqNFo5jgcjlVTp059vLi42Hqv6dS5ubkV77333rMDAwPv2+32n7Esu6yvr2+RIAiq/38M3B6mmQ0fygzcN6PF8o9ZU6ZUxOovXlZBgZWIKODz0Y4dO/xavZ6sDgd74PBhb2pODh+or4+0dHRw/f39IafTGZ1VAYbA7/OFGY5T/YdqczioetEi5tT586HB3l52/vz5lJqaGrXRoq6uLv/ZM2dCfr//WLSOCQAwHK+//nqp3W7/VSQS6SAiS1lZ2VSLxcISEdXW1mYS0Stvvvnmwz/4wQ9OD+f533333XKe5/9t+vTpVenp6fxQN3suLCy0DQ4O/pXL5fqT6urq8pMnTzb09fXpiQhlBsYdlBm4L2+9884vswoKXjTbbDG16eTt8EYjFdfWfnvujDMrS09EVDR9Otfd3h6Q3O6Y2m9Gp9frFKKYyKTRaCivtFQvhUJ06NCh8KJFi0ij0ZDX6w0nJyePabHx+XxMV1fXESLqH8vjAAAMlyiKWo7jyouKirbW1tYmKYpCDMMQd9Pm0QzD0OTJkzMbGhr+adu2bTQ4OLjDEGm9BwAAIABJREFU5XId5nn+6UAg8HNBELz3Og7Lsg8tWLBgdkpKyn1nrK6untzd3a20tbX19ff3vzGU44F6GCaWJorEF5QZGLJXX3utOjM//6cJKSlWtbOMVGdzs2Z6TU1M/eUYdLlCZqfzvpecHksanY6kSETe+cUXAVdfH6/V6TQpKSne/Px8Jj8/f2gfEQ6R1+ulo0ePXmaIeKPR+PYzzzyD/+MFgJizcePGNXl5eX9ls9n05eXlSTcXmFslJyfT008/vchgMJDL5VrU09Pj+/LLL40lJSVPfPzxx1d7enp+KUnSJSIiQRBabn7s5s2bf6Qoyo+Gs9T+wMCA8uWXX+51u93/GggEjhFR830/CUCcQJmBITNaLH/uzM5W5TyZ0WZLTAwOuFwmyo7qYmx3xbBsxDMw4LUmJJjVznKzaQ88oCciutbWRtaEBEbH86ZLX3/tzs/PH9XjfPXVV8faWlsXEJFBEITuUX1yAIBR8MEHH/y8rKzsvxUVFSUP5f4sy5LB8M0EAbvdTna73Xj9b2dWMBjM6u7u3uFyufrq6+t9oijOI6KWG0sqsyz78FNPPVVwP9sN+Hw+2rt3b2c4HD7Z3d39lCAI/ptvF0XRxvP8Cz/84Q//z5CfFCDGoczAPYmiyOn0+qeckyZVxeseLrdKmzzZ1HLpUrh/zx5teWUl2Ww2tSPR7AceMH3++edue1KSYjSbVT935lYpGRnffu31enVnz55V8vPzGb1+5INJiqKQ1+PxCYLgISLPiJ8QAGCUvf766wUlJSU/GWqRuRe9Xk+ZmZlcZmZmcmJi4v/P3n3HR3VlCeI/94XKOaiqVKWcUCZK5GRswMbGCRxwwqarp6d7e7pn57ezM7/9zExPz+z09MTu6d7eeTY2BowJxjhhbGMDNsEEISSCUI4oVUmqnF69sH8gGIwJQkgqhfv9p03p1Xun1FWld+4991y2tbW1MhqNXvnkk086eJ6ncnNzZ9/r31yZTAYqlYqvq6v74bVEZvfu3SspinqeJEl5WlpamdfrrQUAnMyMKwg3ALgPOJnBbuv111/PUmg0f26y2eaZ7PYcrdE4rkqg7gctkYA9K4sKejzs4SNH+MfXrh0X+9KsWLFC/emnn0ZLFywYN40JbiUtP1/S2NzMNTY2xhcvXizR6XSUKIow3GS3vq7OE4/HL41wmBiGYSNGqVT+rLCw0H73I+9dcnKyJDk52QgARgAoHe55CIKAsrIye39//68YhtlgNpuZ4uLitdOmTTMjhODkyZMdbW1tPx65yDEs8XAyg33P29u3r5UpFH+ampeXb7bbzYnYS2YsSGUyFESIIxEaN7skcxwHEql03MRzO1qjEWmNRloQBPrY8eMBURBILh6XPPzII9St9j24k1AoBJcvX965YcOGPx6lcDEMw+7Lrl271trt9pXXSsbGswsXLrhjsdgXOp3u/8ybN+9Fq9V6fSAyKyvL1NnZ+TnDMAVOp5NNZJwYNlJwMoNd99aWLUVylep3abm5M/UWi3oqTHnGYzGUn5+f8M5sVzo6IMligUAgIPgGBmSiIACaACV9BEFA3uzZagCAlsuXhb6+PsHhcBButztG0zSp0+koAACfzwdtbW3hjIwMuVqt/s4bq7Gxsae/v/8XiYgfwzDsThiGIZOSkrbPmjXr4aysrAnR/CYjI8M4MDDwV7Nnz04xGAzfuc8zm83ywsJC4zfffJMNADUJChG7CUJ4n5n7gZMZDN544w2rQqP5nS0jY6E1Lc0yWdbFDIU+KUl++dKlcHZOTkI+CzWXLvFXrlzh+8IREbi4KMY5QqVWC4ggxnWZ2a0YkpKIs2fPRqqrqzlOEMhwIEArVaoQSZIUJwi82eFQ7N61C8rLy8P5BQWKax2AeJ4nACCdYZiw0+kMJPZVYBiGXcUwjMZkMn26aNGicqPROGHul4xGI/HQQw9l3O7naWlpuqSkpF8CwFNjGBaGjZoJ8+HERt6WrVsXSeXyn1vT0mYmZ2amTdZysjuRyuUgimJCyroOHzkS6fb6SFKjldCWqw0IREGAcHurOFFmZm6kNRpBazRer8EQOA4IilJe+3c4eHVd/6lTpxSdnZ38ylWrSACAjo4ObWZh4ae+vj73lq1bN7zy0kt4s0wMwxJOJpMtys/PL5lIicxQyOVymDNnzkMffPDBrx5//PH/meh4MOx+TaoPKDZ023bs2GhNS/u1LS3NNNFumkcaSdMoGAiASq0e0+tSJEkQKjVN3lCDjUgSOFrCN168yGUVFckm8iwZcdPeCAqVChatWQMcy8Lliop4IBAgZTIZIIQ4e0aGITk93dBeX79v9/vvN7PR6N+98PzzXyYodAzDMNBoNGt1Ot3E/RK+g+TkZJVEIlmQ6Diw/4LLzIYPJzNT0LYdO54wWq2/TM7IMCU6lvHAZLdLOzo6IL+gYEyvq1arKTHk4gDgO1NiEkcK1T/QD57Dh/niuXNJuVJ5mzNMTJREAtmlpbKvv/46AAiBIy9PDXD1izwtLy+FjcVSLldU6N/ZufMXG5599v1rz3vjjTcsBEkWcvH4EafTKSTuFWAYNpExDGO+cS8rhmHkCKE8URRrTSbT3wMA6/V6/zU7O3uJzWabVF/A4XAYzp8/3+Lz+fp8Pt+fJToeDBsJKEEVNliCMAyjMCUnV2eXlIzsjocTWCQUgoH29siy5cvHrE0Nx3Gw5729MdKWLCUkt+4/wHd3hgtLS+VKjWZKDtfUnjtX5+vv/yEbjfYCQIPOZKpV63T27ra233Ict9v5gx9UJjpGDMPGP4ZhKJqmF2s0mqc4jlut0WhU8Xi8GiHkpShKo9Vqc4xGo7mrq6t71qxZ2TzPC+fPn++YO3du+kjsozWefPHFFz3t7e2LN23a1JDoWLD/otEYxTlzHkl0GAAAcOjQtrOiKM5OdBz3As/MTDESmewhs90+fra9HwdkCgUEQiH48uDB8NJlyxQUNbofi1AoBB9+8kkMpBK4XSIDACAgku7v7Y0qNZrx3wt0FOSVluZdaW7eQ1IU1dPeflAik4XT8vLkOpPpv3e1tjrf3rZt48svvvhhouPEMGx8U6vV/1RcXPyj3NxcKT24NhQhtOLm4/Ly8nIG/5NcsmRJ+hiGOOoEQYC+vj6OZdkOQRC8iY4Hw0YSTmamGKlcvlFjMCS8FfF4ghCCvFmz5N2trdHW5mbIzs0d1et9sn9/lLRYpaRUhgAARFG8Za2snADOnpk5JRMZAABEEJCSnW0GACAI4sHW2loKAEBnMlEavV5/ubLyn3fs2vU/gj7fE06n05XYaDEMG48YhiHT09NXFxUVTa4plnvg8/n4kydP9vf29l6IRqMrnU4nn+iYMGwk4WRmCmEYRm5JTS3Ei8y+DyEElpQUWU11dUwEoLKys8nRWny/dMkS2cGvvuIEuSImsDGKD4ellE4Xpk1JCjQ4KyQKAsQAoLe9PZackTFl/whfY01N1VtSUq4nfQRJQuGcOdmxSCS7trLy4Ja3395CkGTrSy+8sC/BoWIYNo4oFIoNBQUFt21TPBUcO3bM7fV6H49Go6edTideWzAuIdwA4D7gNTNTyNbt23+YM336/1FqNJOyO8tIEEURetraQu7OTnrBvHmUzW4fld9VMBAAmVwO1xKmcCgEH33xRViS7FDwkQgnul2cQiHnpDRNIppGGQUFstEuf5uoRFEEd1dXtL2hIcRGo2/yHLcdAC47nc54omPDMCwxGIYhTCbTv+Tl5b1QUFBgmqo3iqIoQl1dXX9NTc32J5988mfXHt+xY8cDEolkHs/zXp/P9wc8W5NYGo1JLCsbH2tmvvpqK14zg41fUrn8SYVajROZO0AIgS09XanW64VvvvlGXLxkCdiSk0f8Oje3gVap1WDSaLg+V08IkRRp0Gn5lStXqgAAGhsahEtnzkRySkokcqWSHPFgJjiEECTZ7TKd0Sjram39k4DX+0fe/n402LEomuj4MAwbewqF4kGpVPpCfn7+lE1kOI6Djz76qINl2Z8+++yzHzAMozWZTL9GCCXPnDlzQUpKij4UCnHV1dUb9+3bt+uJJ574daJjxrDhwDMzU8TWd955zJKS8n9t6em2RMcyEfAcBwGPR/D290f8fX2SkuJiPnfaNNloX7e9vR0QANgdDrixzM3V2wuV1dXhvBkzFKMdw0QniiIM9Pbyrs7Oi2wsVs9zXGM0HP7P1159tS3RsWEYNnb27t275dFHH31ZcodGK5OZIAjQ2toaq6+vrw0Gg500TYdXrlz5NE3TQJLfHRc7ffp0R0tLyz+TJJlEkmS+2+1+wel0RhiGUUql0kWxWOyY0+kMJuilTHoajUksL1+T6DAAAODLL9/GMzPY+PP2tm2LjVbr73AiM3QkRYHObCZ0ZrNSFEW4dPo0eLzeePncufTdnz18qam3bjQXjcWAZVn8eR0ChBAYrVbSaLWWAkApG4tB3blzKwFgVqJjwzBs9DAMowCANIPB8DIARCQSSelUTWQAAAiCgMzMTGlmZmZpR0dHrsfj8clktx6Tmz17dkpubu5vCIIAQRDg6NGjR955553X7Xb7fysqKppWV1fXtG3btrUvvvgibumMjTt4ZmYSYxgGKdTqv9InJb3myM5OmapT7SNB4Hloq6sLmrRactbs2WPeYayvrw8OHzrEz162DJeZDUPzpUtt3W1t8zdt2tSV6FgwDBtZDMNQAGDR6XS7SkpKCm02m5aiKCSVSgGvNRweURShr68PDAYDkCQJHMfBkSNHagKBQA/Lsrt8Pt82p9MZSXSckwWembk/OJmZpBiG0al1ut2peXmL1Hr9qJdHTQWxaBQaq6qiK1askKlvWvMyVCzLQl1tbdxkMlG25OQhZ5eRSAQOffVVNM7zdOn8+TihuUc8x0FNRUV1wOtd89qrr15JdDwYho0MhmEsDofjc4qiTCqVKj5//vz0RMc0mcViMfD7/WJFRcWlQCDwm/Xr17+R6JgmA43GJM6d+2iiwwAAgIMHt+BkBks8hmEItU73ed7MmSuoKTzFPhq6WloEb09P/PEnn7zeLrm1tZVramyMFRcXK5Msljs+f9/777MmhwNdaWigyubORVlZWUO+9rYtW0ChVvMzFy/Gycww8BwH57/99nScZX8CAPmxSGSn0+lkEx0XhmH35s033zTpdLqPo9Eoa7FYDAsXLiySSqd8B/sxFYvFYM+ePT5RFJ958cUXP090PBMdTmbuD55/nYSUWu2/pxcULMGJzMgRRRHa6uqilCiKfr9fHolEQC6/Wm0m8Dy4XC75lwcPimqtNsSyLJ2elkbOmj37+uert7dXOF9dHZEoFJDkcCjNyclQd/Fi2O1yobnz5g2pbE2uVMYzCgp4AMDJzDAQBAFBn69MrdfvzszPT2u4cOFxhmGedjqdQqJjwyaXN99806RUKpc+88wz7yU6lsmEYRhkNBr/Oicn57ny8vLceDwOSqUy0WFNSceOHWsMh8PlTqdzINGxTAYIAd5n5j7gZGYSkiuVS5UazaguVJ9qmi5ciIksS2rNZvGlV165/rggCJBst1NcPA4pOTmx5IwM1YUTJ7i21lZx1uyrAxvHjh8PeDweKjUvTylXqQAAAJEkJGdlKSJu95Bqjt1uN3AcR+lNJhrganIVDgZBOcxyt6kIEQQsWrMGACAdAMBktS7uCIVmA8DpRMaFTS67du2aqVar/52m6aLt27dbOI5rY1n2mNPp9CY6tomOpunSgoKCn+Xn52sBAKby4v5EEgQBwuFwCABCiY4FwwBwMjMqGIbRSOXyNSRFZcWi0Y9e27ixegyvbUnJyTGP1fWmiszCQunpgwfB5/OJ5XPnAgDA/k8+iXk9Hqlar4/m5uVJ+7xeEQCAIEki6PUSly5ejBQWFcl9Xi/kzJghv7HVMgCA+8qV4Ozp01VDub5arQaVRhMDABnA1REcnMjcH0dWlhERxAfv7tlzNODx/NzpdOLmANiwMQyjs1gsbxMEkfXEE08UkiQJvb29vxNFEaqrq2u3bNny1CuvvFKT6DgnMoVCoWxra+vMycnR4oX9iSOKIuh0OrXP57vAMMxcPDuDJRr+NhhBW7ZuLZarVNvtWVkGndlsk8pkpMfl+tk7O3d+HPL7NzqdzlFfoERSVIlKp8PJzAiLhMM8AJBPr1+PAACi0SiEw2EyZ/p0Tq3Tybrb2kLp+flKAICCsjIiFolAXVUV2dzaGkAEgQSOA+KmUcRYOCzq9fohXV8mk4HZaBQ6GhvDKdnZeK+ZEUBSFKTm5NhsaWnrO5ub5+/cs+dkPB4/HgkGf4tLz7Ch2Lx5s1Wj0fyA4zghJyfnpXnz5uXeuIeH1WoFAICkpKRpR44c+YJhmNlOp7MnkTFPZM8888zxt95665GKioqjc+fOdSQ6nkQRBAEikQiQJAm3a7U8mkiShEWLFmUaDIbg+fPn/xoA/mTMg5h0ECCE9zQfLpzMjJAtW7cu1Oj1m7NKSnJvrHu0pKYa5CrVurbaWpphmBdH6yaJYRizXKVyqnS6p5QaDV5TMcI8vb28XKEQJRIJBXC1vIGkKE5nMskAABxZWdcLtxFCIJFKQWM08vasLDV5mxFEhNCQ3wscx0EwEJD5AwEhJTv7Pl8NdiNaIoH0adMcoiA8HfB6H226dOmhwbU0YYCrdfoAkAYAHgAIOZ1OLqEBYwnz7rvv/lgul2/ged4MAJGioiJzfn6+NRaLiWaz+bYF7yRJQnJysqa5uTkLAHAycx+USuVsg8EwpQZ0jh8/HvX7/SxFUSJBEIgkSUIqk4HA8xCPx6G1rU35yMMPI7N5bMcxXS5X23PPPYcTGSzhcDJznxiGkap0up32zMxFSSkpxlst4NIYDIrkzMwnG6qqfgcA347ANUm4Wm5ESeXyx2QKxSv2rKw8s91ulymm1Hf8mPH19yNaIrmefBAEAQghMRIMcnKV6nufI4IkITUv744L+8PBoEoQBLi5/OxmVzo6OJfLRUU5TsyZPn3YrwG7M0QQoDEYpAVz5qxuqK4+/s7OnbtCfv8/KtTqPzdaLH+GEIp6+vr8DMMsczqdvYmOFxt7Uqn04UceeWQeSZI3f27vunI3NTVVlZSU9B8Mw5ThhPjeMQyj12q1r6Wnp/80NzfXkOh4xsqBAwfCNpsNlZWXa251f9Hb28v39/fHzGbzmEzRuN3uaGVlZTXP8w4AsDAMo3c6nZ6xuDaG3Q5OZu4DwzAylVb7QU5p6Uqp/M4NqSQymYyi6Tv37f2v8xIESc6RK5Uv0hJJJiWRWAiS1CKECBBFwZqWRhIkSSEApEtKsqq0WhJ3wRhdeTNn0hdOnOD3798fikWjlMFoZHNycuSunh7BMYyZElEUQaZQRAiCuO2aGa/HA8eOHw8F/X65NTU1mjt9ugzXiY8+qUwGReXl0/0DA9Pa6usflSuVaenTphkBAOyZmfamixe/eXf37i4uHv+HFzds+CLR8WJjJxaL/Wb//v2qGTNmzEhLS7unRWtKpRLZbDZrb2+vFgD6RynEhGIYRqJSqY6pVCqfKIoRn8/305deeqn1bs/R6/V/qlarH4jFYpfWrl37sxt+hkiSXAAADWlpaUfKy8un6XS60X4ZY87lckH1+fNBjuMEgeeRwWCQeDyeGE3TUFBYSNvt9lveYAQCAfjyyy/R0089NSaJTGtrq7+ysnJnX1/fH8nl8tUSiSQbAHxjce2pAN/HDR/eZ2aYGIZBKp3uwFASGYCre1xcrqj4POj1rr7d2pnX33gjVaFW/y+5QjFXn5SUqTWblfjmdXzhOA4IhKCvuzvU295OaY1GMTUv757/kPg9HuhpbGTXPPaY5NixY0Gfz4cWLlyo/OTDD0W5QsHRNC0EAgHJzCVLkDQBNdHYnYmiCF2trT3uzs4PA17vH+M1NlPH9u3b/2zevHm/zMrKuqcPpiiK8N5771V4PJ6ysVg/mSj79u17Ry6X5y9atGjGt99+ezEQCJzheV4nCMLW9evXf3DjsVu2bEkxm83vzZs3b7bBYCBOnz7dcfHixQdpml4jl8t/TBBEW35+/pwrV670LFmyJGsydS9raWmBurq6IEJIMJlMZHZOjlKpVIIgCDDQ3w96g+H62qtbaWxoCDY2NhIrVqxQjPa6GY7joKqqqqulpWXrunXr/mJULzZFabVmcf78xxMdBgAAfPbZG3fdZwYhRAJABQB0iqK4BiGUAQA7AcAIAGcB4EVRFFmEkBQAtgLALLg6iPOMKIqtg+f4CwB4DQB4APipKIqfDz6+CgB+A1e3oXhDFMVf3S1mfKc8TCRNl1lSUuYOJZEBuLrY2JGdvbilpuaPAOAP1x5nGAZJFYpn5QqF05GVlWdJTbVRNO6qPF5dSy6THA5lkmP460/9fX2hufPnKzmOg+7OTplcrRaPHj8e0yclEQarlQQAQezoCEllsiF1O8PGFkII7BkZVq3RuLH18uWZ23fs+I8Xnn9+W6LjwkYfRVG8Uqm855W6PM8DSZLnJ3MiAwDwxBNPbNi8efPSrq6uT1esWFHE83wRQgg+/fTTHIZhPlar1TlKpfL3oigOpKenlyxcuDD32vfqzJkzUwDgiMPhSLLZbIQoihkEQUB+fv7Qdxcep1wuFzQ2Nka8Xm+cpCgwmUzkosWLVTdv9kkQBJjusvbF7Xazra2tsGbNmlGtKxdFEVwul3j27NmBvr6+J1966aVTo3k9bEL5EwC4DACawX//IwD8myiKOxFC/xeuJil/GPxfjyiK2QihZwePewYhVAAAzwJAIQAkA8CXCKHcwXP9HgAeBIArAHAGIfSRKIp37ASJk5lhUqhU/9Nos2nv5Tk6k0luTU39q9379j3Jc5xXEATWYLXmJzkc07RGoxxPMU4dQb9fMtDXFztXWRk32Gx0JBgUc6ZPl93wHpC21tRQoUAgrlSrcXY7Tqk0GklRefmcnvb2/9j2zjsZkVDo7wBAAgBSiVS6hpJIno/HYiVxll3ldDovJTpe7P4wDCPJysr6qdVqvecpAo/Hw3EcNynLy26mUCgeNpvNcgC4Prswf/783FOnTl2kKAotW7Ys71ZVBxRFQVlZmfXavyfD30SWZaGurk7o6OiIz5w1S2Ycgb/1x44eJVavXj3qA101NTV9VVVVfxOPx+teeeUVnMiMsonyfkcIOQDgEQD4ewD4U3Q18OUA8PzgIW8DwN/A1WRm7eB/AwC8BwC/Gzx+LQDsFEUxBgAtCKFGACgbPK5RFMXmwWvtHDwWJzMjjWEYvTU9ffrdFm7fii093WpLT7cCXB31mChvXmxkkSSJKisraVoqRcXFxbe8MSqYO5fsbm2NZhcX42RmnLOmpmoVavVfdrW2riNJUkNLJKTebLapdToiHAzCxVOnXgaA/5HoOLH7o1Kp5prN5mFtOe/1eoVwOPzVSMc03uzevXtdenr6azevbTEYDJLVq1dPS1BYY0oQBPj6668jPp8PKIoSc3NzqQdWrJDeqWzsXhiNRqGxsVGYPn36qPXyraqq6mpqamrZsGHD70frGtiE9e9w9e/ZtXWDRgDwiqJ4rbHJFQCwD/63HQA6AABEUeQQQr7B4+0AcPKGc974nI6bHi+/W0A4mRkGpUbzy+SMjPT7PQ9OZKaurOJiiovHASF02xHe3vb2sEqrxY3nJwiNXi/V6PVFNz+u1GhAIpM9AjiZmdAYhpGmpKT8S3Fx8T33v+V5Hs6ePXucZdlJ3zBCKpX+cXl5+aTtNnbixInY5cuXpWvXrgWTyQQcx0EoFAKtVgsulwvOnTsXjHMcys/PpxwOh3Sw8+WIxpCZmUmdO3eOmz59+qgsIqqsrOxuamr67+vWrds5GufHbmfc3BOaEEIVN/ybEUWRAQBACK0BAJcoimcRQksTEt0t4GTmHr3+xhs2k832gOSmOlcMuxcESYLkLqN0tERC9/X0gDU1dYyiwkYDQgiUGo2cYRiZ0+mMJjoebHj0ev3WBQsWzBrOjemRI0faYrHYTyf7ehkAgFgsNhCNRkExSbcJCIVCkJOTE6+qrubDoZAgkUrFgN9PS6XSuNlshvK5c1Uq1ehWgNkdDuJSTQ1UV1fHi4uL6eFUidyJ2+2OeDyeD0f0pNhE0neHBgALAOAxhNDDcHWLEA1cXayvQwhRg7MzDgDoHDy+EwBSAOAKQogCgGvdHK89fs2Nz7nd47eFR33vwfZ33/1RksNxPD0/f0pMlWOJpU9KonmOg0gohDtlTXDRcJgHAJphGC3DMBKGYUa93v2dHTv+cse77zpH+zpTAcMwhFKpLNNoNMMaOuV5PhaLxRpGOq7xyOv1bjpw4EBtLBaDeDye6HBGXFlZmdTtdgtLly6VPfzII4oVK1YoV61eLXnwoYeUc8rKlKOdyABcHSB58MEHJX19fWJDw8i/rWbNmpWi1+vxTDL2PaIo/oUoig5RFNPh6gL+Q6IobgCAwwDw9OBhLwPAtWT4o8F/w+DPD4lX2yh/BADPIoSkg53QcgDgNACcAYAchFDGYOXKs4PH3hGemRmirdu3r7amp//SmppqTHQs2NQgVyohLS+Prjt3LlpYViajJ1Fb0qnGkZmZFDAaL4MoioIgiJFQSNy6bdv/jkYiH8HVHeElAKAHANeNbZ637djxFk3TckEQIgAQFEWRjbPsp2w0euhOo/y79+z5u+y8PGdPV1do565dRr/P9y9Op5Md9Rc6SVEUtSQ7O9s23OfLZDISAKbE79/pdHo2b9689MMPP9yDEJKnp6fbaZpGHo/HL5PJlGazWdff3++12Wzq1NRUzd3POH4cOHAgKJfL0YMPPfSddVPyIXY1HUkEQYBWpwOO43i42sJ2xLjdbh/P81+P5Dmxu5vgSw/+HAB2IoT+DgDOAcDmwcc3A8C2wQX+A3A1OQFRFC8hhHbD1YX9HAD8WBRFHgAAIfQTAPgcrr6v3xRF8a7Nc/A+M0PwxubNZp3JdCRn+vSCCf4ydnesAAAgAElEQVRmwyaglpqauM5oFJPsdpzNTBKiKEIoEACPy9Ubi0YjJEmSFE1L+rq7TwR9vmecTmccAODd3btrpi9cmC+KIgg8D6IogsftDvR2dFyKhEL/FA2H96k1mp/J5PJXZDIZisViNElRYb3BkJxfVGQFAGiorR3o7OgIB/3+B19++eXaxL7yiYlhGEVqauq5VatW5d796O+rra31V1ZWbnj++ec/GenYxjOGYWi4mqQTAOAFAJqiqCKO43qsVuvHjz76aOFE+JsqCAIcPHgwmJ6RgbKysobVAGI0vLl5MyiVSi4UClELFiwQCwsLR+SX2dPTEzp27NjbAwMDP5kKpZHjgVZrFhcufCrRYQAAwKef/udd95kZb3Aycxdvb9++SKlW/yGruLiQxBtYYgkQDYeh+eLFsEavF9Lz8lRohOujsfEj5PdzjRcuVPk9nnkkRSnUev2x4vLy4puPE0URPC5XuKej43IkGPxKLpevWvzAAyW3q52PRaNw/OuvawVBOMhzXD0Xj3dEo9HjANCPb1bubseOHS8uX758q9VqvfvBtyCKIhw4cKAxGAx+6PV6/z/8OwfYs2fPbx5++OGfKpXjJje4rQMHDgRLS0ulSRbLuOos6Xa7wTMwwKWkplJfHjwYffTRR2X3stF2IBAAQRAgGo3yZrOZvPH7o7q6OlRZWblu48aNB0Yjduy7cDJzf/Dd+W0wDIPkKtWfWxyOn9kyMiwTYfQIm5xkCgUUlJUpBlwu7vy330bkKhWyOByE1mjEMzWTjFKjoRzZ2Tnt9fXvEwRhySku/l4iA3C1HMFgsSgMFsssd1dXcUtNjVhdWRkpnTlTfquERiqTwfKVK6exLDstGolALBrle7u7XWws5t//2WeecCh0wdPf/0N8k31rsVjsm+bm5k6r1Wq/+9HfhxCC1atXZ9fU1Lx2/vx5GQD8ZIRDnHDi8fjJ/v7+P1IqleP2e6y3txdOnT4dzJ82jRhviQwAgNlsBrPZTNVevswFg0GJIAx9eWVjY2OkoqLiI4IgKnieRxKJpEin05UtX758GkIICgsLlW1tbX/LMMznN5a+YqMDIQQI4YHK4cLJzC0wDKNU63TvObKzF2tNpsnZkgWbcAxJSZTOZKIunDjBm2w2fNM5SRktFq0hKenRoQ6gqHQ62pKaGu3r6yNZlgWZTHbbYyUSCUgkEgCtljRbLDYAsAEA9HZ3l1SfPXsGAF4fidcw2ahUqhfz8/OHlchcI4oiZGZm6hoaGrJHKq6JLBKJHDt8+PDFGTNmOEpKSpISHc/Nurq6xAsXLoQfeugh1b3Mdow0nufhxv1pBEGA/v5+aGlpiUpoWoxEo4Lb5SLXrVsnk9ywrlIURejo6GDdbndfMBgMxeNxIR6P9wiCEBMEITkUCv2b3+9/68YBjJ07d845evTojsWLF2dTFAULFiwo+frrrw8yDPOQ0+nkx/aVY9jQ4WTmJgzDqNV6/We506fPp3H7ZWycIQgCUnNzidbaWs6QNO7+/mMj5F5mguUKBUrNyZH7+vqil86fD80qK7vnuh2LzabQ6HT/jWGYLdfW62D/RRTFJ9Rq9d0PvI1AIAAHDhw4hRA66/F4/noEQ5uwVCrVBrvdnpuRkTH67b/uUUtLC9fY2BhbumyZcqQ2uhyOsxUVwba2NonJbObMZjPq7Ozk4ywLRqORcjgcsu7u7jBFkvDkk09eH8EYGBiAmpqaRq/X2zIwMPAHAKiLRqONABC/28zrs88+e+a99977h5MnT/793LlzrUajUbJo0aJFx44d2wEAz4zyy8WwYcPJzA3eevvtfJ3ZvDOruLgEd47CxiutyYTcnZ1cR2Mjm5KdPf4LzrFRhxCCkvnzZc01NeErHR28IyXlnu/AFAqFHq52VcPJzM0QSn9v71722WeeueMfBkEQ4Pjx4xF3X5+YkZ6O5AqFLCM9HV2+fNkTiUR++PLLL1ePVcjjHcuyrRaLRbyfJHE09Pb2Ql19fWz58uXKkd6/5V74vF5w9/WR69evl3R2dtIcx6HCBx+EG2NKTU29XjkSj8fh2LFj9X19fTs8Hs+/OJ3O4HCuy/N8kcViud611Ww20zk5OYvee++9l59++um37+tFYXeElzMMH05mbiBTKP4lb8aMErzAGhvPGs6di0ikUkiy23Eig30HG42Kcrl8WF9gXo+HAAC8qedNNr/5ZkZmTk4UEYSssrKSsFqtxJUrV+I2m00qCAKkpaVdP7a6ujre7/HQ5UuWUB0tLaGI3x+reO89CiHUAAjhJPEGWq32l9nZ2eMqk+np6YGq6urI0qVLE5rIiKIIbrcbZFIpAQBgt9vvepd74cIFV0tLy9pXX331vjoW+ny+v6ioqOg6f/78LJVKVTx79uz8oqIim9/v/8fdu3f71q9f/8H9nB/DRgNOZga9/vrrWSk5OTNxIoONV00XLrAhvx80er2YU1KC13Jh38FGoxD0eqUVJ0/GH1i1SnIvdf6CIADHcT5cF/99CoXiIalcLk3NyFB0trdHLtbWCmaLRdLW1RWORaNExdmzgkwm4wFAqtLphDkLF8oQQpCamakEAHCkpwMXj5e1NTcf3Llr17lnn3lmTYJf0rigVCr58TYSrVKpQOB5MVFrZFiWhYozZ8IdHR0yu90enz9//pBq3bu6uiJNTU3/fr+JDACA0+mMAcA/A1xdP+z3+/+gUqkKQqGQFa6218awcQcnM4MUGs2fJ6WkWBIdB4bdjtpgkHhcLlGuVJKiKMJAby+rT0qSJHIEERs/aKkU8mbOpC6dPi1eaW+PpWdmDnnRn6unhwv6/fsZhpHgzTW/67lnn/3PPXv3Pq3T61c40tLkKenp1350vbtVPB6HcDAIWr3+e8+nKAooioK8wsLkSDg8MDZRj39er7e5ra3NkpaWphvqc3p6ekAmk4EgCECSJGi12hGLx+fzwd69eyElNXXMM6y2tjYgEILq6urIvHnzFIsXLwYAGNLn1+VyxSoqKk57PJ5fjXRcTqczBAAvAVxNbAb/jY2S8ZbcTyR4n5lB73/yyaXskpKCRMeBYXcS8HqFrpaWaCwcJrl4nGSjUWrGokWgGsE/6tjEFotG4fKZM7GHHnlkyMmMKAjg6u2NtjY1tXgGBgiEEIiieDIUDP4ANwQAYBhGJ5PL/3VacfGz9tRU+XBvOipPnTrb19v7CzYW24/b3QLs3bv3/ZUrVz6hUt29B4AgCLBv375YSkpKnCAI4DgOvD6fAABowfz56vtZe1NZWRnx+f3C7NmzlXK5fNjnuVcsy8LhQ4eCXq9Xodfr+SVLltBD2XcnHA7DN998cxkA+oLB4KcDAwP/7HQ6udGPGBstOl2SuHjx+kSHAQAAH3/8e7zPzETEMIzanpWlSXQcGHY3ap2OSLLbFd2trSFaIhEtKSmCQqW67aLkAZcrGg2FRJlSKdXo9QRFj7utErARhhCCcCh0Tx1MEEGAxWaTWWy2fFEUQRQE8Hg8Wa1NTfP2ffDBFbfL9ZTT6ZyyJSZOp9PLMMxrdZcuFdgcjvLhdriKhEIpjrS0d3u6unYyDPP/A4B7Kic1wWDwr+rr6xfNnDnTdO0xQRCAZVlobW3lm5qawhRFCbwgIAIhNGv2bMrhcKhuOgd88cUX0bKyMmlKSgq6dg6PxwONjY2xAY8nLgqCMJigI7lcTlgsFmVvb28oGo2KCCFebzAQCxcuVI/myLggCNDY0CCYk5IIrVYLzc3NfO3ly+ySJUsUH330EbFq1SpiqLPs33zzzbn29vZFeKZkcsEzM8OHZ2YAgGGYgqzi4hNmux0Pb2PjEsey0FpbG+LjcV4ildKZhYVygiTh/IkTrCAIAi2R8DqTiezv6aFNVqsABBEPeDxUf0+PJG/mTHB3dcV9/f1U+QMPICKBrUax0RdnWag4ckSYXV5OWGw2uN/6/8729lB1ZeXJSDi80el0doxQmBPSW2+9tWH2ggXbjWbzsJ4vCALwPA+XqqpqSZJUuXt6mjiOe/mVl19uG+FQv+fNN980KBSK9SJA03PPPntwtK93s3379v0lRVELgsHg5ueee+59AACGYdKysrKqMjMzdW1tbaFgMAgcx1FGkymuVqmIgsJCxVBu8OPxOFyuqQn19vbyAAA0TRM6nQ6S7XalwWBAN34GBvr7OY/XK1gsFolUKgV6jAZ4PjtwIGC32yVut1tgWVbMyclBmZmZcpqmoaOjI9rQ0CAsX778tmshRVGElpaWiM1mk+/fv3/f008//eSYBI6NCZ0uSVyyZHx0v/7oo9/hmZmJSCKTlcpVKpzIYONS0OeDxvPnubzp05Vq3dXy8oDXK7bU1LAKtTqeUVioAgDobm2NmZKTBZ7nyc76ekqh0cQLyspArddDfVUVlVlQEG+6dCkWZ1mUmpsrU2k0+PM/CdESCaTl5kYunj8vP3fmjKjWaiMESYqLli4dVh2OPTVVKYji8tPHj7fvePfdc2qNxhsJh9vXr1v3yq2OZxgGyRWKP6Uo6oloJPK7eDy+Z7I0FhAE4fOGmppLxiVLCofzfIIggCAImD5nzjQAgEg47Dj5zTdvAcDya8e8tWVLJk3TP6RoWvB7vb9wOp3D7jC3devWNIVC8fMYy1YaDIYfzV+wYK7b7R7Y+/777/f39f3U6XRGhnvuoWIYhtBqtX88LT//JwUFBbamxsaFe/fuXev3+3dqtNo/MxqN0mgsFi0uLlbKFQqgaRoQQve0yRtN01BSWjqk7o4Go5EyGI13P3AEHTt6NDht2jQiOzv7e69LEAQ4deoUmZ+ff8dzVFVV9Vy6dOmvFQrFQz6f7yejFiyGTUB4ZgYAduzata2wvPwF3MkMG48GXC4x7PNF+nt6pFKFgo2Fw5RKqxVS8vKkktts7MqxLBAUBRzLgrurK+K+coUkSBIy8vMlSp0OLp85E1aq1Xx2cfF3SitEUQSe4wCXo00OHY2NIY3BQLXV1aEVK1cOe/MsURDA7/eDQqkEkiTh64MHv3j6qadWXvs5wzAIACiSohaqNZpfp2dlTUtNS1P19/XF6mpqLva5XMucTmdgRF5Ugu3es+f9ecuWPTFSmylWnT7dFvD5zooApEQikWn0+kJrcrKNi8cjjXV1gVAg8FuZXD6fZdn3IqHQO/eSGH6yf/+3y5YtmxsMBECj1V6fhQgGAnDy5Mnzbrf7F5FI5KzT6bzjzBDDMKTeYPhPhJAPAAgQRW8kEvn4xRdfrNy1e/eP5HL5j8Kh0P/y+/2nSJKcZjAaXwZRFHme98nl8gUlpaUlNpvt+saO0WgUvF4v6HQ6kMlkd7jyxNfc3AzNTU3RVatW3fKFiqIIu3fv5tetW0fePAvFcRwcPnz4kiAI8Ugk0vb4448/PiZBY2NOp7OIS5c+m+gwAADgww9/O+FmZnAyAwBb33nnJzmlpf+u1Ghw/Q027kRCIbhw/DhI5fJ46aJF95RlnPv6a04ik7H2rCypISnpO+/v3itXov3d3XGKosCWlqbs6+6OBP1+go1EaLXBwBqSksDicOAW0BOcKIpw8dSpUElpqdJsGZmGjccOH26IhMPbAoHAUYqiogqF4m81Ot00qVQqK5o+3XzjjX5nR0fs5NGjS5xO56kRuXiCvfPuuz+es2DB71QjtNmjKIogCAIghODmm1me5yEUCIBaq4Xerq5QfU2NPxaNvvDySy8dutM5GYaRqVSqtVar9R8XL1mSdqtjRFGErq6ueH9/v7e7u7s9zrIdEolESRAEkCQpQwjRJEUpCITksVgsMmPmzHyFQkEjhIDjOGhtbXW7Xa4rycnJqdk5OcbW1lZfwO+PGIxGo9lspgmEgBcEkEqlU3ItgCAIcOirr8KCIJDLly+X3ilpa2lpYc+cOUOsX7/++my5KIpQXV3dd+7cueUbN268MCZBYwmDk5n7g8tMACAaCr3eculSmUKjKU9OT8+VDaGbCIaNFblSCUarNaY1mYY0dSiKIoiiCBzLAkXTXPG8ebdMSCwOh8zicMi4eBy6WlriZodDmaZWA0EQEAkGqc7m5hACiMqVSplKqwU8czlxIYSouvr6WJ/LBfnFxfdUwnMrcxctyonFYn8bCgQEv8/nS83I0Eskt574CQYCAQDou99rjhcCz0cFfuSq5hBCcLtZHpIkQTNYWmq125WXqqoEiUTy+nvvv+8LBYNuiURyQhSElRqNxg0AQNO0CRGEUFBQYEtPT0+1WK23HfxACIHdbqftdru5pKTEDACzhhqzRCKBgoICMxQUXF88lJGRoQWA75RrT6X5XUEQoK2tDc6cPs3TNM3L5XJ21qxZEpvNdtcZUZ7nxYyMjO88duHChd66urq/w4kMht0dTmbg+iZRLzEMowp4PD+XKRRrzXZ7nj4pSTUVR5Sw8SerpOSuN6ACz4Pf4xH6urqi7NUuPUhnMt31DUzRNKTm5n7nvkOuUkF2SYmyra4u4unrYz1uN21LSwum5eaOqx27sbtDCEFReblUFAS4cOpUpNflCswpL1crh9AO93au7Z2iVCqJJKv1+5ur3IBlWQ4Agjc+xjCMgSCIIoVSWRwJh3e/9tpr7mEHM8aUavUGdYJaoT/wyCNqAFADAPS73axcoXjowtmz4VWrV+MZ1ARyu93i6VOnogaDgdLr9ejBBx+UUBQ15LLO5ORk6ZkzZ0JpaWlUe3t7V19fX0ckEnn3mWee+d1oxo2NHwjhbmb3A5eZ3QLDMEgqlz+q0ul+nVFYmHdzN6BrvzP8xsPGizjLQldzMysIAp1ktyP1LTbvGy6O42Cgt1cY6O6OFpaV4ZumCYCNRqHy6FGRoijBkJQUNdvtSoQQH/L74/29vaRRpyOKZ84ck7LaSCQC506frggFg58GAoFtCCCm0Wo/yC8uLlIolZLmhoZGd2/vWY7j9sZZ9ohUJltDkqQj4Pf/3ul0jqtNJt/eurXInpr6cUFpafqNj9dUV/dm5eVZpAlY//HNF1/EH3vsMfp2M2PY6Orp6RGPHzsWX7VqlWQ4m3g2NTV5z58/f7S/v/+/qVSqNX6/f5fT6Zw0M5nY0Oj1FnHp0ucSHQYAAHzwwW9wmdlk4HQ6RQD46I033jgZi0T+WSKTZZEkqRQEIcRzXIBjWTdCSKHS6WY5srPTyPtsfYph96uhqiqc5HAokhyOET83RVFA0TQRCYcn90rdScTV1RXOzskhphUVySLhsPLypUvRYCAAObm5suSkJNCPYTcnuVwO85csmd3d2TntzLff/qk1OdlTWFKScm1maGZZWXafy5Xq9XgedbtcrvzCwnRBFPm25uYf7N6zpyLOsr/dsGHDkaFeb+euXX9JkCS5/umnfznSr4UgiN9m5+enC4JwfX2Ld2CAb21s9JotFqPZah3zPwYkRQEelBxbHMdBNBoFlUoFVefORR599FGFQnHv4zyCIEBtbW39E0888djgQ78f2UgxbGrAd+F3sGnTJhcAvHS7n7/+xhvJoigeTc/PzxzDsDDsus7mZn9gYAAUKhWMRiJzjcZggFgkQgz09rIGiwUPAY9D3v5+PhIMciG/n49Ho8LMBx5QAAAoVSqYXV6e8ETUZrerVq5ZA1KZ7Hv1baakJIkpKUmSnZeXPvgQaTAaU3ieT7nS1rbsk08/rQ+HQpvXr1vH3O78W7ZsKZfJ5eusyclPez2empGOn2EYWq3Vzj1fURHv6eykF65YATq9HjpaW9sEQVh0sarq0ySbLTm/uDh5qJsfjgSJVBqVSqVTaXlKwu3/5JOoTCaDRYsXyzwej2y4HdkqKircgUDg1yMcHjZh4Wqf4cLJzH34waZNXXs//rgfAHAyg40ZURShrbY2GA4EQKZUQkFZmWa0r0lRFJQ98ACc++Ybco7ZjJsBjEMtly5xKpUKZpSVDWuUeCzcaxkWSZKQlpmpS8vMLKu9eNG69/331/T39TmdTmfPjcft2rPnf9scjo2FJSVWuUIBp0+cGPYaHIZhMgCgUyaX/000EvmV0+n0D/7IqFQqowRCGloiAbVGAxzHwZW2Nr1CqfyLaDj80862tvRwMPjraUVFdpKiQKFUjvrdCc9x4/rDGIvFIBgIgNFkSnQoI+KrL7/0z5o1S1JRUYE++fhj/tFHH/1eF7qh8Pv9QkdHx7d+v//9UQgTw6YUnMzcJ4Ig8O8QGzN93d0hd2enaElJkWUWFo7pe4+gKCBpmjt96BDKnzVL1Oj1uJX5OCKRyXg2HhePfPEFseLhhyfdGoppRUWpGTk5qU11dRXv7d3bzPG8qFKpZBzHxTOysnKzcnOvd9Yymc0pO3ft+iO/z/cmAEgAIOp0OjmGYaQEQczRGQw/EgTB4/d6/2nTpk1tDMMolCrVP4SCwT+oNZqv4/G4kOxwmK60tS0FgPkAADRNFyVZrVRWbi556LPPuKrTp7mZc+fKkh0OOj07+6eegYHXWhsbT3gHBpacOX78H2Ry+fTyRYtyby5D7nO5Ime//ZZdsGyZVqW5v3GIwbbO4zqZkUqlIL3NflgTTeXZs5GsrCxJenq6jGXZSFpaGjnc11ZfX+8RRfHkYFk7huF12PcBNwC4T+9/8smp7JKSskTHgU1OoihCnGWh9swZlpbJYiqNhkibNi1hvcM5joOqb74RZi5eTNCT7GZ5MhBFEUJ+v1hbWcnLZLK4xWqlc/LzqcmW2PA8D/F4/I4bLnZ3dgbrL1/2ymQyyuvxNAYDgaeMJtO+3IKC2UlWq0QQBLhUXd3i6um5QFFUXvGMGVktjY3evMJCnU6vpwAAWpubBxpra2sEQfg3U1LSr6fPnp1FEAR0d3bCuTNnxKWrVqFrbZVZloVzp0719btcr27atOnjbdu3T4/H40ceeOQRLX3DJrRff/FFHReP/1oUxZdUarUxNTMzPTklZVit5c5++y3f09VFbty4cThPx+7Roa++Ci1cuFA5UjOfn3/+ecvAwMDDzz33XO2InBCbsPR6i7hs2YZEhwEAAPv2/RtuADDliCKuVcZGxUBvb7irpQUIkhR0ZjNKz89PeFvkeDQKXDxOeN3uuNlux+/9cQYhBCqtFs1cvJjieZ7qam3l21taIDsvL9GhjSiSJG+7N8s1NrtdZbPbVQAA/X195pbGxjM6vV6W7HBcz+xmzJmTwcZiGZLB0fUkq/U7tVDpmZkGrU43u6G29t9mlpWlXnvcbL46CdTW1BTPzM2l/T4fV/ntt+2hYNBO0XQOAMCLL7xQ9fbWrV8d+eyzh5etWiWjBhMam91urK+p2SyRSsv73W6vCHDK5nDc06isKIrQcPky233liuTpdeuG/Dxs+C5euMC1t7cr7/a+uxfFxcUZX331VREA4GQGw+4DTmbuA8Mwyam5ucmJjgObfOIsCz1tbWLJ/PnjagdXNhoFhJCIxnKFM3bPCJIEgiTBkJREDnR2RgEg4Q0AEsloMpFGkyn1Vj+T3KVMSG8wyMrmz7/+XI7j4NSJEzGr3Y7Ss7MlbU1NvU319efCweBrTqez69pxmzdvnsHz/JMAAKeOHq222u0mpVptVGk00tnz5/MVJ06comn6H/pdLl0wEAD1PZSc7X/vPZBIJNTMWbM4jUaD/46PspbmZq6+vp545plnRrRkThRFkWVZ34idEJvAECACl5kNF/4SvD/9bCwWTXQQ2OQQDYchHAzykUAg6vd4hJzS0oTPxFzDsSzUVVVFfH198mkzZ3Immw3PyoxTvoEBTiqTUc01NXGP202XL1w4pROZkdTZ3g71tbWR9OxsMjklRQIA0NPVVffC88+vvsXhr0uk0v44yz4eDAQU7c3Nf2BZ1k9S1J8YTaZ/dKSlka6ennVqjebXAs//HADu+pniOA6ikQgAADy2di2h0WjwoMIo83q9UFNTwz/99NPUSI/heL1eP8/zE2bDWAwbr3Aycx8oiWS6UqPRJToObGITBAFi4TA0XbgQMVitUrlKJU3JyRlXn81IKAS+vj55el5ezJycPDlW805Sl8+eJWVSadxssaCFixfDzZv+YvdO4DhorK9nO9rbUdmiRXKSJKGtqcnb09nZz7LssVs957XXXpu9+c03mwiC2MnF47Y4yz6GEOre+MorlQzD7AeAKACQbCwmaW9pebZYr7/lzNGNTn79ddA7MKACANDcZ/MAbGhOnzoVWbp0qXw0JqMVCoVUqVQuBYCqET85hk0h+K/cMDEMg9Q63a+MNtu9b/mLYYPYaBQaqqtDMqUS5c6YoZBdXVg67kZbu9vaogq1mk7JycGJzDinVKvjOTk5ktSMjESHMil88ckncZ7nkcVuFxYsXy7zeTzR6oqKqlAg8MeCIFQ7nU7hds8VBWEhJZGs4qPRNwHgqx/84AdRAACn0xkYPCTOMIwYi0bDN27EeSs9nZ1hn8dDkiR5hOf5pd3d3WGbzTY+e3BPEm63G1QqFWi1o/NnfmBgwBcKhfaMysmxiQXhbmb3Y9zdNE0USo3m71OnTZuLlw5gwxENh6GzqSnUeP58MKe0VJlTUnItkRl3BEGAoNdLZhYW4jf7BJA3Y4bk/LlzfHtrK5foWCYygeOgu7MTAn4/PW/pUqqwtFSGEIL6mprGgM83f9OmTefulMgAAGzatKmbjUa3yOTynzidzluWJDudzljA7//x8a++evtSVdWV23UYjUWjlCiK8tdee20ZABDHjh3rZln2/l/oKGNZFg5+8UX4swMHgm63+46/r/GmpqaGV6lUo9YKkKZpkiCIJQzD4IFlDLsP+AM0TDKl8kG1Todr0bF7xnMcNF24EEqfNk2ZkpOT6HDuiiAIMNpsQldLS1xvMk2uHr+TkEQqhekLF5JXmptDsWhUyJk2Df9/do/63W44e+oUS1IUWTxzZlyuUFz/ruc4TgsAWgDwDuVcg/uI/P5Ox2x47rlDAHBo2/btb0qk0iez8vK0PM+DKAjXGxTQEgkNAMAwjNXpdPa8+dZbH9bV1f24uLh43M6Wut1u+PbEiRhDf6UAACAASURBVNicOXNomqbp5pYW1mw2S/z+q/uQulwu4UpHB+vz+QiZTBbr6upSz1+wQMjLyxsXAyfBQICdP2+efLTOn5eXZ4pEIq83NjYCAOwYretg2GSHk5lhEgUhnOgYsImp7ty5aEZBgVI1SqULoyHo8UDxvHn4pniCkMhkkJGfr6yvqopGIxHIKyyEybbXzGg6f+5caMEDDygH1xtd78UbDoUgGg5HASA4GteNhMOvNdfXv9nvcv0+FotJEELS7GnTrNFIJOLq6XEBwAKn0zkAAACiSJw5fVqanp4uqtXqu9anNNTXs42NjXGbzSbt6elhy8rLFQaDYcRfw/79+8PhUEg6d+5csr29Pbxs2TKFXq+HeDwOZ8+eFd7fu5c1mUxxpVIp6nQ6srS0VG4ymaCqqiquUqmCeXl5w9pzZ6TFYjFQqVSiXD5quQxQFAV9fX2NoVDo3VG7CDYhIMBlZvcDJzPDJPB8KNExYBNPLBIBEEVhIiUydZWVcaPNhr9oJxiEEKTm5tLuzk72xNGjrMFgoAAApaalSXWjcBM7mZAkecvGCS0NDVfi8fhqp9M5KiV8g7M4xwCglGEYBACaqtOn8+UKxSs8xz1JSyQOABgAAHj11Vd//tZbb9k+/uijx597/nnpjZ/P+ro6trm5mSdJkpBIpVw0EhF0Oh165JFHVHV1ddGlS5cqDh0+HMrMzJQYDAZaLpdDY0ODoNPrBZqiKDYeB61WCzRN31OjgZMnT0ZsVqsgkUj4YChExGIx8VorY5qmYdWqVTKWZUEul1/PrAVBgP3790fsdju5IIGd98LhMJytqIgvWryYBrg6gz6azTNYloWvv/66oa+v78nB/98xDBsmnMwMU5xlK0KBwINKtRr/DrE7Cni9vCAI4HG5+MDAgJA/e/b4XBxzCwO9vfFIKETklJTgVswTkFypJFNzc0kAkAy4XDGCJKlLNTUhrVpNFJWWjt6Q8wRWf/myEI/Hv3dTHfD7wd3be2zjK680jUUcgze4PgA4CQAnt7z9tpqiqO+MKMhksn+NseyiaDSaLJfLgeM4OFdZGfZ4PNTy5cvlHo9H0Gq1UqVSeX0wIj8/XwYA4Pf5FAP9/ZETx4/TWVlZQbvdrjp86BCRm5vLkiQJRw4flmi1Wn71ww+TQ9nxnuM4qLl0Sf7yyy/DmTNnYr29vXGdTodunNkgSRJunukIBoPg9XolSRZLwtZ4nT51KtjT20tHIxEJN5jEkBQFAwMDo1LuduXKldC33357OBgM/njjxo3to3ENbOLBA4bDh2/Ehynk9/+i7fLlB6fNno2bAGC3JAgCtNfVhYM+H9KZTBRJknzx/Pmj0uJzNAiCAG21tZBTWkrSuERpwjMkJUkBAHRGo7L50qXQ4S+/DAd9PtnqtWsJ3L75v6SmpxNNDQ18zfnz0ey8PNm1NSs8z4PA872JiuuVl1/ecPNjCCFLWVmZ/FqC8MG+ffHCwkI0b7AkVKFQ3PbLZuHChXx6eroiOzubS05OVomiCKmpqSCXyyUIIVi4cCG4XC7+8KFDvFKpFMvKy6UEQcDAwABYrdbrnddYloWB/n748ssvRQBAJEnCwoULpV6vF7RareRuN2hqtRocDgfn8/kS1hygv78fPfH449LPP/883NnZKfP7fNDZ2RlbtGjRqAzi1NbWtq9bt+7R0Tg3hk1FE+OuahxyOp18OBB4rfnixTaB5xMdDjbOiKIIzRcvhnRms6xo7lx5am4unZaXN6ESmcojR3ie5ymNXp/ocLARlllYqCwsK1MoNRruyJdfhn3eIa1lnxJkcjmsXLOGbKmvl7U0NFz/clep1QAAxQAADMPQr7/++uzBUrCEIUny1YyMDD0AwL7334+ZTCahqKhoSDNu6enpFABAcnIyBXB1VFihUFwfHUYIgcVikWg0/4+99w6O68rvfH/n5tsR6G4AjW6gkQORwQAwU2IeUcEaaTQaSaMZyTPt530ue9d+9ar2vdr3XF6/51BrP+/W2t5tecbjGY2k0chKw5GG0kjMCSBBgCQSARA5o9HocPvevuHc9weDmYnQSOT9VKGKBLrPOX37ht/3d37Bpl+9epX96MMP9YazZ2Nf/fa3IMsyYIzh4K9+FW9saIiNjo7Gt2zZor7++us3Q7NSUlJm5WmWJAmi0aj65JNPLstO4dEjR8SZmRmTqqogyzLR092dwBjLTz31FO9yuRZF5Xu9Xu+PfvSjDYsxtoHB44jhjlsAb77xRtuPfvzj7V2a9nlxTU2ZsUVocINwMKiwJhPtSE9fHerlDoa6u0WaYdgUl0tCCBnhSI8oFfX1jJxIMMe++kp75oUXyIe/4/GAIAjIyctTJ0ZHyeLyckAIAUVR4HC5qt55770OV0aGCSFki4bD3wGAz5drnbqud8iyDAghsNls6u7du83JnuOJJ55gn3zySYBrOcqWzz77LMZxnOX06dPx9evXU1lZWQvatp2cnMTZ2dm3rfthPXeSgaqq8MWhQ6rb7UavvfYaQgjBrl27OJZlF73RbGFhoa2tre2PAOC1RZ3IYFVh2JDzxxAzC+R333xz4J//5V/+ZHps7F+d92hgdr1Hh85wHFqpfUQMkgvWNJgaGdEtdvuqvL7CwSCEp6bo2m3bCEPIrE50XYexgQFptL+frairQwzHga7rcOnMGTE6M8Ov3b4dWI6D4MQEHuntVVRFWbHlfZeL6vXrqebz5+Vzp05B1fr1DMuyUFNX59J13YUQunY8m5p+/O4vfvFZNBz+X+/XR2aRsYZCITh18qS2c+fORfkO7zSwKIoiD/7qV/GcnBx9oUIGAMBqtRLt7e1xh9NpkiQJ+np7hZmZGUaWZfLJnTtRRkbGolh4n37yiVxUVETW1tbezI8ym5OuBe8CYwxnzpzpBYCTiz6ZgcFjwqo0tlYaSiJxaKin588nR0b2m6zWfKxpqqZpMR1jNSGK/fFoNMDy/B9WbNq0b7WEGRnMn1g4rMmSBO6qqlX5ZVtTU8Fks2kDXV1yTnGxocBXIcGxMVkTRbqktBR1NDXFi2tqTFOjoypNUXplTY10+exZSlNVUpFlIjs3V921d68hZu6AoihYX1/PjI2MwJHf/AbXbNigZXg89K1hWFXr1rmnp6Zeb25oyHnrrbf+4Ic//GHHUq5xZmbmv/R0d/+O0+m0K4pCwRI80/fs2cNjjIEkk7ORhzHWJyYmWJZlRY/HgzZv3mw2m83Q1dWVOHniBHr2ueeYZO+UnD1zJuFwOFBtbW1SdyNnZmbg5MmTyoEDB+6ZayPLMhw7dqxjcnLyle985zsXkjm3gcHjjCFmksD1qjN/AQB/EQgEUgAg7vf7b2vN/Pa7727SFGUfwRo2w6OOzeEgMcbKahWuBEFAXmkp233pUnS512Iwd+REAkKTkzjd5SLzCguB5Xl2uK9PSEgSvbauzmS1WqGwpARUWQYgCKAoavHd0asYt8cDNrsdJkZHyQyP566/O1wuasOWLTsuNTUdAoCcpVyb3+/v++ijj07t/8Y3vvXlF1/EZVmWCwoKFrVaB0IoaUIGAMDhcKADBw6QDofjtl3g4uJiNjU1VTv89dfSxk2bOOu1nKUFMTo6ig9//TVkZGSgvXv3Ju04YYxhfHwcnzhxQo1Go/cd98KFCwMDAwM73nzzzYlkzW3wiIAA0Oo0GVYEhphJMn6//56ZtAihNJI2qts+LvBmswIAy9YzYTZgjGGsvx/bXS5ClWUIT0+LQjisIYR0mmFQVmGhobxXEbquw9jgoBAaG9Nr1q2zpFwv3ODxekmP13uXYKGMCnWzZiYUQpXr1t033Mlqt1Mms5l49733/vA7L7/835ZybRjjiKZpkJ6eTp85c4bMy8tb9HyTZIIQgvs170xLSyPLysq0w19/Le3es4c7dvSotP8b35jXfXVwYADOnj2rf/vb3yZZln3gyY/xtcJqiqLAkSNHlC1bttAWy717ecqyDF988YVIkiRRWFiIOI4TAeCu8NzJycnE8PBwoyFkDAySjyFmlghN0+w6xgCr6CFjMH9olkVCNArmJHgTF4PpiQmtv6NDzyoo0KdHR+NY0wiKYVBuaSlnsdlu3hd6Ozrio319pvyyMsHt8xke/BWKqqrQef583Ov1ktW7d69oEb1akGUZzp05E+c5jmRZlowLgm6xWu/rkaresCGr/eLFP/75u++qQjT6jzcaIQYCAQKu7dgM37ljnwwkSfrdC01NMbfbTdXX19OPWhJxTk4OgzGOnzl9Oh6Px+flEezv74cjhw/DK6+8QrL3iY7AGENDQ0N8ZGSE5DhO1nUd6boOLpeLO3ToUKKqqopua2vTysrKcFFREQtwTew0NjYm3G43tX79evro0aNKenr6XUKmo6NjvLW19WIikfj7+azfwMDgwRhiZonAmnZEEsWXV6pxa5BcXJmZXH9nZ6xs/fp7u/OWmeDoqO4rKkLpXi8FALcZCMO9vaLJYuFjkYgcHB2lrCkpCYphjF2aFUx7Y6NUXVvLu9LTHy1LdpmQRBF+8+mnUFNXx06MjWlxQaDOnTwJT73wwn3fgxCCsurqHEQQfyZEo//hvfffvxQNh/+Z5bi/ZVk2XdO0jwDg+8lcZyAQQBaLBbZu3Wp51ETMrVgsFkqSJLWuvn5e3sDs7GxwuVyiIAgUz/P3FESCIEB3dzf3xBNP6FlZWbfd72RZps6fPy/V1NTQTU1N2sDAQFQURQYhpNTW1lrcbjcAAKSmpuLe3t642+02kSQJkiRBf39/rLOz88ILL7zwjfms3eBxAQE8wtfwYmOImSWCoul8xsiXeWwwWa0URZIoFg6DxW6f8/sxxjAzNQUpLlfSQ0baz52La4pC3FltLRoOa6GJCVmIRNBQTw+WJYlhOE6TEwk1IYpJXcOD0HUddF1fVaEyy4WmaXDq88+hsLSUMITMwsEYw4mvv5YEQaDqtm3DzrQ0JjMriyyvqYGENLtiZWsqK50A4BRFsXB6cnJvRmammaJpaDh+fEey12symarz8vJmEEIpyR57JTEzM4Pj8TidkKR5JesQBAG79+zhf/vll3GfzyfV1tbe5VW0Wq1QVlYmJhKJux7UDMPApk2bOACAnJwcEgA4VVWBoqjbXltVVcX29/frhw4diuq6LiYSiX+dmZn5jaqqv5nPug0MDGaHIWaWCl13ga4v9yoMlpDcNWvMnRcuCOUbNpiJOVTjUWQZOpuahJS0NLK3rY1c98QTSUu2ioZCwLKsXrhhA3fDk6upKvRcvixPjY3RmqryBeXleGp0lLDY7UpWQQHd19mpX21tpdw+X1ITf+9E13XoaW2VhHAYVFVFxTU1hNVuNxLN7kFkelrraW2FhCSRKQ6HWl5RYSTAJIG+nh4gaZratmcPRd+S40hRFFD3yZm4HzzPg/eW0MxoJGIPBAJmv98vJGu9FovlP9XV1T3SQgYAoKioiIvFYuLZs2fpq1evCvUbN5ptNtucxmBZFg48/bTp0KFDQllZGdwZbtbc3BwNh8No7dq1s7pZ36/CWk5ODtI0Tb506dLb3/zmN/9kTos0eKx5lHdXFxvD9blExGOxP+lqaTmjyEkPmTZYoVAMA77iYrrt3Dmx++JFUb7u2b2RXHor0VAIpHgcAAA6m5qEvPJykycvj2NYNqknTM/ly4k0r/e2uPrWxkY1OD5Ob9i5E9mdTmV0YEA3Wa2JnLIyeqCrS5MEgQKAa9Wv7kFCkmbttb4TTdNgemJC72tvj19pbpbkREKv2LSJK6ioYKdGRpR5DfoIgzGGvs5OpeXUKbJ+0yZy+86dsGv/fspI5p8f42Nj0H7p0s0T25OdDfFYDNOLUKylcu1aymQ2//WPfvSjtGSNyXGcazEdDCuJ2tpa/vXXX4fa2lrmq9/+Vg4Gg/MaB2saKMrtt5ZTp06JU1NTxJNPPpmUsOCurq7O5557zhAyBgZLhLEzs0T4/f6ZQCCwf6i7+1xeWVnhcq/HYGmwpqYyazZsgHg0Cr1tbVFN04BmGNA0TadomkQIEbGZGWS22zUCIV0UBD0WDlt5sxkwxpAQRVN4agrsLldS1pPicsHU6Kie4nLBWH+/NNDVxSKCgJpt2xBBUVBeX0/LsgzB0VG1o6EBSIoi3D6fGpme1hmOu8vCi87MqFdaWrSEJDFrt21DnMkEuq7P2sM01N0tJRIJzuV2m/Tr6wMAICkKEEJ3q77HFCkeh6Zjx4DlednlcunPv/yyEYa3ACRJgsaTJxUdY0zTNBw5dEhSVJXcc+AA7XC55HOnTinVGzaY7yVqbjgj5nr80zMzreW6/kZfd/fO9z/4oE3TtH/+zre/fXAhnyMUCn14+fLlraFQaJDnebaqqsrNPOLC1u1203v37sXHjh2LfeOpp+YkPjDGIIoic+t3NzU1pU9NTaFnn302KQVOhoaGxJmZmUAyxjIwMJgdhphZQvx+f/id99//PBwMvmF3OldkYrhB8kEIgdlmg6KamtvitPs7OuJ2p5PJKiwk6XsYIARBAMNxKplEN7GvpIS9dOqUJEsSTI2NgSUlRdEUBY/29ydySkrMANfiw+0OB8tbLLKcSFDRmRlVise5c4cPazVbt962VjmRUOPRKAcAcP7oUZ2iKJATCbTlqafua+wJkYg2PTEB0xMTmqooqGrLlrteS1IUzExNEWMDA0qa10s/Lt7nOxEiETzY06NGQyFiy44dVFpGxqNtqS4RsWgUxHic2rJ1q+T1enkAgLNnz4rHv/5a8Xi99EBfH4qGw7rD5bpLlYeCQWBZFixzDHMCAMjwePj0zMzSprNn+ZlgcAoAFiRmotFo4Ny5cx2yLJ+mado1MjLyNw6Ho2Lt2rWFJtOj2+9WURQky/Kc7ouqqsJnv/61mJ2dTX388cd469atkJ2dTZw/fz6+d+/epFVq7O3tHQmHwz9N1ngGjwcIjDCzhWCImSXmlZde+sOfv/cekV1U9HpKWppR2uwxJqe09KHWRnFtLX3lwgWpYuNGLhmeeIIgwFtQQLU3NSnVmzdzAACqokDHhQuxW19nslohLSsLD1y5gjI9HnJ6bEyJx2LUxVOntLXbt5Po+loc6ekcSVGgqSpUb92KWk6cgM379z/Qa9127hzwFgvklZUx5vsYhCzPQ/nGjabuixflhCQJOcXFDzQ21OthI9QcdZ+u66CpKhAkueJ2OmLhsN554YJWWVPDeLZsgbnkXRnczakjR+KCINBms1mORiLsvn37qJTU1JtldOvr63lFUeDgwYMJQRDoewkZAABn2r2jxLra2wVJFGfS3e4MhmW1idHRoKqqqisjw+FKT7cMDwxMmy0WC8OyKBwKnfvua6/93kI/k9/vFwHg0PX/RgDg+R//+MfOiYmJ/8zzfC7P877MzMwsl8tlN5vNwHH3rtqdSCTuyiG5ga7rEIvFYHJyUjSbzbwkSQld15GiKGJhYaF9PgaYpmk3d3Dn6qiQJAmuXr0qx2KxOQn7M6dPixs3bqQ8Hg+dkZGBh4aG9IaGBs3r9RL3Oy7zQRCEUNIGMzAwmBXG03EZePXll//g5++9F9V1/fdT09PnXurK4LGBYVmQJYnGmpY0Y9uVmUlNDg/HW06eJKq3bCEpmgaEEGBVvc1gzszJ4SaGhqRIMKiUrF1rvdraimVJQo2HD+OaLVsITdMgODYmOdLTsZxIsJ1NTXpFXR1FUhQI0aja39mpFlRUcCzHga7rALoOiCDAZLUmiqurTbMxzhmW1Wampqic4mIAuGZYxaNR3WyzIU1VQQcAHWNoOXlS1QHQ+h07bgqtO+nv7JSmRkeZ7KIiKd3rNQEAnD9yRMcYa4osUxt27oSVVHGwtbFR37VvH20yG+19kgHGWH/xxRdphBA9PT0NtntUGSRJEkxms7xh27Y5nwjjIyODM9PTu8eGh/+YYRgyGon8KQBIE6Ojb5IU9QwCOK8oSh5BENWKLP9lEj7SPXnzzTeDAPDvAAACgQDT1dVVZLPZtlMUtTkjI2Ozz+fLDgaDwVAoNIgxFhVFQTMzM47MzEx7JBKJ0DQt2u12l6IokqqqYVEUx0VRPBsOh79mGMYty/I4xli1Wq25ly9ffpPn+RSWZdMrKyt9NE2TV65cGZmenjbv3bvX3tvbG7ly5UqcYZiBRCIR1jQtpqqqpOu6A2M8SpIkl5WVtWndunU59xNFt4atnj17Nh4MBnHpmjWW50pKZn1MMMaQSCSwx+OhAQDy8/OJ/Px8mJycBKfTeVdfmIVQU1NTBgCXPvjgg4ORSOS/vPnmm1PJHN/AwOBuDDGzTLz68sv/8YNPPtmWmp6+ZbnXYrAy0HUdQhMTkiSKWFMUPTM31yxEIoqu68RcdxwehBCJgKoogDXtpvXgSEsjwqEQpN7hda7ctIkjCIIDAEjPzsaTQ0MJ1mQiL505w8ZjMQQAHMOyGBEERgRBXTx9GtKzspR4NIqdmZlMy8mTKm82i4AQYIxRqtOJ4tEoq2IMD3OrzkxNgRiLUVWbNtFyIgFY0wARBDQdO4bWPfEEtDU26qqq6gRBYFVRSEd6uoII4p5uXlVRIDg2huKxGDE2OIg6L1wAt8+nSfE4WbN9O3Xh6FGIhcOaIz19xcSzsRyncTy/sraLViFxQYDuzk4tNTWVuGEU36/jvCRJwHAcmk9kJ80wit/vHwaAOxO//+H6z5JzvUln6/WffwwEAuzVq1f3SJLU7Pf7h259bSAQ4ABA9fv9aiAQIP1+v/aQ4RsA4P3r702dnJz8YwCQI5HIz8xm88uffPLJ3kQi8ZNoNHrsBz/4Qd/9Bvnggw+e7ujo+MfU1FT7U089Zb1V1IiiCIcPH8ahUEhPSUlJWCwWYu++fXOOn4tEIsAwzF3Xdtp9dtkWgtvtNu3fv788EomUHzt27DuBQKDS7/dHkj6RwSOHEWY2fwwxs4wosjw2l2Rpg/mjqSqQKyxMR9d1GLhyJarKMrI5HPT44CBQFKV78/NNBElCx/nzcZPFArXbtiUt+F2IRmHwyhWpbP160627EHank7ly8aJoTU3lby05eutukCM9nXKkp1NYVUGIRoEzm4GkKCAIgui5dAmPDw6COy9PjQaDWlpWFuHJzSW8+fkEANwMpwxNTOg2hyPBMMwDv4zQxAT0d3aq9pQU4vzRo1iMxQgAAI7nNQAgm0+c0C12uy6Hw0R6VpYsCQKkpqXd90LSdR10ALTxWggc39fZqc5MTGic2Yx5s5kGAOhtb0ckRclmm425X9nVpUKRZdAxxist9G210dLUJMQjEcrn84E3K+uhXyrHcRCLRKC/pyeenZdnmu3x13UdMMbxBS94kfH7/Qm4T56O3++Xbvn3w4TMne8NAcB/uuVXf3n956G8+OKLBwHg4Pvvv//CZ5999nsY44x169ZVZGZmEk1NTfH0jAxTRUUF9lzfTZ0rsVgMjh09Ku3fvz95sWSzwGazQWFhoXVkZMQN10IADQwMFomVZd09ZqiKci4hii9wj3Ci5kogODam9La1kWarVQAAiM3MWEvWr8e21NRlsRSv93URNVVVXW434ywsZKcnJqCsru623gVVmzcn/cTgeR5ohlEYlr3twW6yWim7wyHJkvTQfhoERYE1NfW237E8jyia1mmKkqu3br3vulPT01FqevpDjYqulhbdYrcTY4ODBGcy3TSs0n0+yCoogHAohBRRRJl5ecCbTNyV5mYlPSvrnu50XddhbGBAzMjOJm4Yp7klJRSUlNw82OV1dUCQJHG1tRXrAFJaZiamKAqZbDbOmpKCllpUTE9MyCzLrphdotXIyNAQ5mkadjz11KxDxgiCgGeeftrS1d2tnjt1Kr5u0ybTbHI6FFmGhCga39cCeOmll/4VAP41EAhYT548+R9Zlv2OLMue33n+eYB5tpGQJAk+/eQTvb6+nliOggjRaDQOAPOrIW1gYDBrDLffMiIJwi9CExOTy72ORwFFlmGsv/8uz6giyzDY1UXklJSo5fX1VoqmWbPdHl8uIXNjTZIgkOV1ddYMn4+laBrSvd77NmFLFqqqQkdTUyzN673nRPFYDExzbAx4g6zCQrJuzx6UXVSUFIuhZts2xFksSmF1te7MzMQkSYIrM1NOcbkIAAB7aiq4PB5wpKdDb3u7xJvNiiLLMDE0JOm6Dqqqgn69hK4QjcLowACVmZt7X6PW7nKBNTUVqrZs4aq3bOE4i8WkI8RcPH0aSULSehzOmnSPh5EVRV3yiR8hdF0nrDbbvK7zosJCqrqykm9tbo7rdzQ71nUdIuHwzRLNmqZBc2PjUCwafXXhqzbw+/3Rb33rW/+HoiiNFZWVC9rtwhiDpmmopKRkyasATk1NqQMDAx/4/X5DzBg8HASACLQiflYjxs7MMvLDH/6w9xcfftjt9vnS7pe0bDA7JoaGhODoKB0cHZXLN268+eBKiCI409PVNK+XVWQZgmNjDMOyy3reszwPCCEIjo2pTrd7SdYSDgb1wa4usbCykjdbrff0IPMmE93b3i7lrVmzpOEY94LhOMgvK7shPuica8m+9zRIEEIa6Lp2/sgRTNI01dvejhVFIXizWc4tKWFuVE+bC470dAAAUlNVTYhGNZPVuqTGECII4M1mXZZleNT7hiwWWNOAXEAMr8/nQ3FRpNovXoyWVVdbAQCEWAwaT55sx5r2D2lu9x+XlJfnnTpy5AJBEB/6/f4ryVv940cgEOAQQuUmk0l69dVXW4PB4Ctnz549zTBMpc/nm1dljp6enrsaZC4VgiBokiR1LcvkBgaPGYaYWWbi0egPBq5c+TyntNS33GtZzVjsdkqKx1UdY9xy4gSZ5vFIJEWxE0NDujMzkxjo7JRHensZmmF0XddRz6VLMU1V6eLa2iUvX4UQguLaWubymTOa3emccznhuTLY1RVLiCJU1tdbiAeEzOSVlbFXWlqEtoaGuK+kxGS5R7Wnlcia9etvlvu6lmZyzTEQj8WYzvPnlfU7IV5y1QAAIABJREFUd9IUw8zrIMuJhAJ3eOaXCkWWSSEaBcbpXJb5VzuKooCUSMz7/aqqgigIekKSbgoik9kMCUlyvvH97//3n/zLv7wSDYcpjPGFV15++c+TsujHGJ7nv5dfUPD/KbI8+f777zcCwJ8xNP312NiYx+fzeeYzZkdHh/r000+TcK2Nx5IhSRJcvHixXRCE/7GU8xqsZpCRP70ADDGzzLzxve+1vf3uuz8SIpH/02yzGS7YeWJ3Otnhnh6hrK7OkojHQYrHzcGxsXjl5s0mgiAgIYow0tsLBEli1mRSlUQC0rOz55TkmgzisZg6cvWqpOs65kwmEnR90evuJkRRt6akwIOEDMA1kVVSU2NOiCJ0NjcL5fX1q64m8K25LSaLBWp37FiQUswtLeXaGhtjwfFxiaQoNS0zk8rIyuLk60Yyyye1quttFJSXM81NTcKTe/asuu/hVmRJgoH+fi2/oICUVfW+vU6SycULF6REPE5s3Lhx3s6Kd999F5eUl6Oaurqb23qXmppkhFALAICcSGyZTiSMe3aSIAhCZ2g6UV9f74tEIln9fX1PZPt8zvv1v3kQoVAI2tvbdZZhSI/Hs+QWYkNDQ+fo6Og35lpIwcDAYH4YYmYFEI9G/5/hq1efL66pqVnutaxWrjQ1adGZGfPlM2dEiqIUTdPI/IoK8w3jtvvSJbGgooK2OhxEQpJ0u8PxwLijG3HyyfaUYE3TZUlCFRs32m7E3C82hVVV1rbGxpjL7QZmFobk9TA4hDFecY0kl4OyDRssN76ryaEh7fyxY7IYizEMy0Lttm2zOqbzgbdYQJZlSojFwDzPXKaVQOOZM3F3ejrdcOpUIiUlBQeDQbTlySfNcUGAzra2RGV1NUslOZRuZGCAeu655yh+HknfGGP49JNPNI7jwJefz9y4B4SCQXWory+BMf4uAIDf79cBYP5bPwa38eqrrwY++uij369du7bGZrMRlVVV89qSnJychF99+ilwHAfPPfdcspc5K0KhUI/f7x9blskNDB5DDDGzAvD7/drP3nnnQzEWq+YtFmOfcY7EwmFtZmqKrNi0CZutVh4A+NazZ2VZksBsvVYVGGsaJCRJp2IxzZKSwt1aEhtjfC2+/non+4HOzpgkioA1jcguKiLsTmfSrFWL3U7bnU65rbExCgBIx5gor69f9DI7JouFEaJRzHDcrNQJQZKKIWT+jRvHIsPnIzN8PvJyQwOOTE0Rnc3NWuXGjYtWxapswwb29MmTcafTCbXr16/Ksoc0SWrrrq2dBgDo6uoSTh4+HNN1nRLjcfbM8ePx4vJyLt3tTtoJR9E0no+QAQCQZRmmp6dJt9erYIzJG9XM2i9ebMYYb71e3thgEbDZ7Qs+B3q6u/V169bhdevWLXl1OV3XYXx8HDRNG1/quQ1WP0aY2fwxxMwKQYzF/uZqa+urhVVVJfcLXZElCcLBYIIgCIKzWGiTxQIIIdAxBkkUgeV5iIZCuiUlBQnhsDR89aqiaRpVXlfHP8oXSWxmRjFZrXrn+fNE7Y4dMHjlSgIhpKWmpd10965Zt46fHBnRE6KoTY6MxLCqYpKmdYQQoSQSCBAi4Jq4wPkVFRaTxQIYY+hqbo7Zk5yz4C0ouBk2ND0+LracPCldb06Z1HluJauoiLnS1BRLcbksDzsXdF0HTVWNe8MDqKirI3ouXVKkeJxUVRUoioL+zs5oPBajM7KzGbvTSYiCoI/19ws0w+g5JSXWh496NyzHQUV9veniqVNycGoKnC5Xsj/KonL8q6/EwsLC207soqIic15eHpAkCQghUFXVdOjQoVi6252U7SdVlsFut8twn4IRD4PjOPj+G2/AJ598osciEbCnpkLn5cvDiiz/N0PILB4ff/zx3xYXF2csdJy+vj584MCBZSmTfeTIkdH+/v4ZWZb/9+WY38DgccUwWFYAgX/6Jy9hMv2DRNPO9osXYyxFYVtKCpnm9Zr7OztjBEFQmqZpgigCWK1mXdMA+vokhiCAJElFUVVQZJnJzMqihwcHdZ5hQNU0IHw+Kx4eFga7uoT0rCxekWWdZhiS5TjQMF70UsBLhc3h4Eb7+5XswkL1wtGjJM2yUL1ly21uWYphIDM3FwEA5cnLu6fRpMjyjSaQAHDNKBLj8UWNiXdkZPA0wyi9ra3xgsrKRfO8UxQFuq4jVVGAfkhIz8zUlMaZzZSqqtDb2ipmFxXxRi+kuzHb7Sg8Pa13NDXJZevXM0I0Srg8Hm56fFwf7O6OszyvZxcVWbpbWsSEKMZcHg/lmEWPnTshCAIcGRla28WLyradOx+YAyTLMrRfuiSvqaxkVkgVNLxmzZq7cn5uvfeQJAmqoszZ+MQYA2AMxPWxfvPpp7i0okKPx2J6aWnpgpKZCIIAnueJyYkJyZaSwk1PTUWFWOzrhYxp8FDyC4uKFixmOI7DmqYBACy5oFEUZfj73//+hqWe18DgcefRsGZXOQTH/T2fk/Msuh7OoAHApCDAxIULApGSwmFFIUibjaBv9co6nRzGGIAgOAIAqGhUGRsaUhiPh1VVFQizmSQIAojsbPN0PA7Tzc2Cpmm0Jkkk1jTI8PkSef9W+nbVomMMfe3tsicvj7weAgQwz4fYnUY+QZIQj0SYyeHhhMvjYRdrd8uSkkIPdXcvuseX4XmkyvJDxQxvNpPRUEhtO3s24XS7yauXL0fL6urmtbPwKOP2+aj0rCy4ePKkeunMmXhCFLmStWvB6XYjALip/srq63lVlqHlxAl9/ZNPzqt6nTcvj286evShfWeaGxuFHJ+PO/rll/KeAweWTc2cPX48piNEKLJMaZoGD2o8iRACk8mknz9zRhofHWUcaWnY4XRC8Zo1tz2fJFGEL3/9a337rl2Iomk4efiwEo1G6edffhk6W1vFgvx8Eqmq1tvdzVdXVs5r3RhjaL18WS8qLkbDQ0NUsd0OCCHYsGVL6dnjx/8ZAPbOa2CDh4IQSk/G7nQ8Hid5nl/SGFld16G1tTUYDof/ZinnNXh0QADwCAfQLDqGmFkZ8HDHTZwymwHMZjPA/S3zW2/8pNVKk9Z725uUyQTg85kJjIHo65NpmiblREK/NW9ktaLIMmBNUzNzcpJuuFE0DRv374cLR4+SNMPglLS0BT8gdV2Hsf5+ARAi7U4nZbJYqOvJ5Yta/zcejUI8EiHZWeywcCYT1GzZwhLXw4CGKUrp6+iI55aWGtszd0AQBNRs2/ZAp4AsSdDa0KDSLKvJiQQ7HzETnp6WrCkpGtxxz1ZlGSbGxyESieDQ1JTodrtxSUkJKUmSeqW9XRUFAQ0PDgIAIEmSiP3PPQcP64SOMQZZlmdVdSwWi4GqKDA1Pq6EQiFZliRQVZUlCYLbunUrJUqS9iAhc4Pde/aYQqEQmLdsgc6ODmhta8PFa9bc/PvkxAQ0NzQoe/ftI650dookRaHde/awY2Nj0pEvviBD09P889/8JjgcDqheQB0VWZbhfFMTtHV0qOmZmWp+cTEHcO1ekFdUVPf2O++8+dorr/x43hMY3EUgEEAAwGdmZo4udKzx8XGQJIm4s9HpYtPY2DjY19f3Ry+99NJHSzqxgYEBABhiZmWgaY26qu5Fi9xvhCAIgNxcBssyCFNTSmdTk+DIyACn223ua2+XfCUl3MO89pqmQUIUMUII82bzsp8/XS0tKmc2s8M9PVpmXh6Z7LwTgiAAaxqBCEKFecbg34qqKDA5PExRDIMkQUjklJRQbQ0Nsq+4ePFq/ALAzOSkmOJyodkeH/LWMCCKooOjo0RuaeliLe+Rx2yxKCW1tfxchcxgd3dECId1wJhxOZ3QdPp0GBACjDGoqgosyxJej4fK9ni4mspK8w0BUlFRwX704YdyOBxmKioqFJ/PRyKCgObz54WN27bdt9RzLBaDc6dOiVjTmNLKStKTlXXftXV1dIi9XV1McXGx6ExNZYoLC80sy8LVq1el4uJijiRJSJnlLilCCBwOBwAAFBQWEgODgxJcfz5hjKGrtVV86sAB3mw2Q0ZGxs0wUYfDQZWVlYGqqkmpvMdxHJAEAd7sbFRUVnabmvNkZ9vjgvDnv/jlL2vCodC/9/v9S1OO8BGHIIgtXq/3l7FYbNbH89cHD0q5eXlkeXn5bRfUucbGxEsvvcRalrD6nyAIMDo62mAIGQOD5WPZjVEDAEDIpysKaKoKBMMAmoUnc74QBAHAccBkZXEJSYJYJBIPjo7KCYuFa2tqirIMgymapgvvkb8xPjAgjPT10UBRqs1mwwUVFcteL7asro6aHh9XgqOjMH78uLZ2x46k7NBEQyFoO3dOr96yBfFWa4K3WJIiNmiGAZphUFF1NXOlpUVua2yU3D4fnZqWtqjx3Z78fP7CsWNyZk4O8Oa5tS0Z6ukhCYIAKR4HI3dm7nAmE2Bd1+ezIzMzOUm98Pzzcz7oJEnC1m3bkNPpBJq+NvGvPv1Uzs7Lu+95fPnChZgYjxP79+83jY+NqV9//TXUb90KNwSNLMuQkCToam+XpHhc92Zl4Re/9S2SIIjb7gNr1qxZUPW/5uZmRdd1enJ8XE/LyEDDg4OQlZVFmB9w3iYz/y8rO1vTrm2X3nVNFpaWZjrT0n7v8oULJe+8997BWCTy36+XaDaYJxjjsZycHGZwcJCLCwKYHnJ/isfjYLFY1KHBQeVOMYMQwvPpS7MQvvrqq5GZmZk/WNJJDR49jDizBWGImUUgEAiQQBA1BMN8D1HUel1RfvaD733vH+/3eixJfyQNDn6ua1oeZbP9HpeV5VuKdZIcB5GJCR00TacyMwGsVqsKAMrwcPReIWihyUmCzMpicDxOEAShLMUaHwZCCJxuNx2ZnhbTUlKScicIB4PQ1tAAxbW1Ss/ly7IjPR0xSXxA6roOiCCgor5+ScVgSW0tc6WlRcjKzyecbjcPcC1MjyDJB+Y0rNuxgxzs6sJj/f2J3DVrVn2e1VIjRCIAuj4vkU2QJHn+/Pn4unXr5ixo3G73bYbezl27mF++/z7YU1Ig9XqFPowxHD50KGGxWFRZlqlnnnmGAwDIyc2lnn3uObjQ0iK6XC6+4dSpmBCLmbCu6yaeh23btzMOh2NRBPjmzZvpYDAIzRcuxIVYjOzr6cE7Ftj8dC709fVR23bvvu/fU51OZtvu3XunJiZ2tDY3bw4EAq/7/f4VcT9cLQQCgUIAGPX7/UJ6evpfY4zRxo0bTcdPnIjv27fvged684ULYlVVlfncuXPqiePHZVmWkShJWMcYwuEwyy9iI9s7mZmZUXRdH//e975n9JQxMFhGDDGTJAJvveVDNP00IohqKiVlO2mxeCir1SZPTY2qicTF+73vrZ/85GmC474Puj5I2e3fYdLSFlzNZS5QPt9dbjDNauU7zp+P0wyjYYwBEQRkFxZaY+Ewx6SmAuI4Kjwxkejv7BSCo6Omio0b0WI1DpwNsZkZTVUUXFBRkZRO6RRNg93pVFLT05nU9PTFyMXRsLb0jaFNViuU1dWZW06ckG0OB1A0DW2NjXFEEJDp80Ga13tPIwIhBHIiIWYXFa3qTvTLhaooICcSeHJkRErzeOZ0oaxZt45tPnFCWbdu3YLXYTab4elnntHOnD6N7Q6HUl5dbfrtr3+t1tbWYo/Xe9d363Q6AWFMXGpqim3fvt1MURTq7OwUa2pqFrXUO0IIXC4X7Nq929TU1BTLyMiA1NTUJbFQVVUFu90uWazWh35PrvR0dt2mTd9qv3ix8rNDh1RVVXEsEvlFLBr9n36/f2Yp1rsaeffdd7+NEPp5aWlp3+HDh4WqqqrsvLy8VIwxKLL8wPdOTk5CMBgk0tLS0N69e+kjR47IZWVlFMMwiGVZOHTokAIAS5b8Pzw8nIhGo3+3VPMZGBjcm0dGzLz14x/vAIL4A12WvwcABBBEhv8HP+iZyxiBQIADAHk2sdCBQMCCKGovoul9iKYrmPT0Aspmy0AUdVuYmK4oYV1RGu7xfprguLeYtLTnKJstRdf1myVGlxvKZqM0m426YW5jRYG2piaR8np54npODfZ4zBNXr2opTqfGcNyyLnxqbAzbHI6kGdq8xQKqoqijvb2KOzfXdC/DTdd16GxqSmQXFZFmm21On5/heVWWJLhfP6HFRJYk4E0mTNE0dDY3x9J9PtqVmcleaWoSsa6LGVlZdy1qcmQknhBFPdld2h8X7E4nFFVXM9NjY0KaxzPr92FNg5aTJ+Wa6uqkhTG5XC7yqQMHyK+++go+eu898Hg8UPSAfK1du3ezAHBzN662tnbJTlqEEKxbt27Jdi9lWYYTx48rBaWl9GzFmsVqJTds2VJ+4//RcLim6cyZV9/++c81kiSvUDSN5ETi71595ZVTi7bwVUQgELDn5ub+ndvtntm8eXPBrXlOXV1dYklJyX13fk+fOhW/evWq6cUXX2QBroVS7tq167abEkmSgDFOSv7UwxgaGlJaWlreee2113666JMZPBYgwggzmy8rw3pOAohh/iubkVElDQ5+SrDsnwBBWAKBQBVimP2Iot7QVfVjXZY/RgzzMsEwLyCCYHSMI4ggUnVNmwGC4KnU1GwsST2BQOB3/H5//EHzETz/j6zb/RrBcQ/McSFY1gEARQDQdsefsnSMn6FSUlIQQrCST2GCpoHJzb3NiMFjY7GC8nI+NS1t2c8hIRzGkiBEpkZGSLvTiXzFxQtK7CAIAtZs2MC3njkjOzIzgb3HrtNYf78oSxItCYJuttlAiseBoum7yu7GwmF1tK9PVRIJsqyujgYA8OblWbtaWoSKjRuXfKeDM5nAkpoKzSdPStbUVJTu9bIAAKXr1/MXT56UaIaRLDYbpygKmK1WkCUJhnp6UNWWLcueH7WaiUxPKyabbU5hWQRJAs2y2kJ7ptw1LkHAnj176E8/+QTXrl277NfvSmBkZATOnDmTSPd4IMPjmXdIm9VuRxu2bq1kOQ50Xa8hSRIaT5woAYDqJC53VREIBDi/3y8BAPj9/vAXX3xx9Yknnth862t0XYf29nZUVFxMtra2AkVRwDAM5OXlAQBAMBiE6elp9Oqrr943PyoYDILValUJgliSkMRQKBSOxWJ/vRRzGRgYPJhH5kFG0HSU4DgEAH/KZGTkIIKABMZHKIejlLLZrFgUv6FGo39G2Wxu8gE16LGi5CeGho4E3nrraf8Pfzhxr9cEAgEH7XRuIWeRSE3Z7Wm6qv7qRz//eYeOcRQQIhBBWKjU1AI6JeWh3dhXImo0qvMsyzoyMpaly/KdlNfXswDAypIEHefOJXzFxQseMxoKKZqmkZqq6gB3a01NVXWXx0MOX72qTgwNJQiCwBhjFWPMEySpefPzCYbjiLaGBqpq82aqr6NDAAAaAIBmWfDk5ZGXz54VyzZs4JfCi3grWQUFXFZBwV2/r9i0ietsaorJkiRpqsoUV1fDYHe3klNWtvRbSI8YU6OjSvmGDXMS2aqiAGC8aMnlzzz7LLEa7z+LwW8+/xzWbtxIZmZlLfiZeGcCO80w0kLHXI385Cc/aZBlebfJZDr3ySefDAiCMBKLxd4sLS1Nv9frq6qqiKmpKa2jo4PYsWOH1tLSoubl5XGyLMOZ06fl8vJy9KBCD2azGWZmZpbkmaSqKnR1dTX4/f45RX8YGDwI4348fx4ZMQMADCJJMBUX56PrxiGfl3ezEy9pMpGkyfTQGA+CpoHz+TYkhocP/9Pbb1+Ea4YshhsZ8bqOSau1jE5Ly5vNogiGQWxmZj4A5M/vY6089HhcclxrDLhkSbmzgaJpIEgSq4oyr8aEtzLa15eo3LLFcr+H58zUFFRt3ow8OTm0ijHc6LY+MzUFJpuN6jx/XkAEweSVlRGIIBDWNGLgypW42+czMRwHJpuNk0VRWaqQiNlAEASsWb/eAgDQfelSrPvyZbqwqoq12O3LvbRVC8YYouEwkBSlPazs+Z1Epqdln8+3aMaZ8eAEiEQicObMGTEnJ4fuamvTMx9Qinq+aJpGBgIB9DhVPQsEAiglJSWbYZi/tFqtHQcOHHimubl55OLFi//B5/Nl3/l6hBDk5+cz+fn5MDw8rOTm5tLDw8OJK1euQMPZs/qePXsIr9f7wAtodHQUu25tLL2IdHV1RQRB+IclmczAwOChPDpiBiEWAAAlwTBEJAmcz1cGAGULHuwRhM7I4Ad7e0VJFAWX223mLRbAGMPglStKhs9HL1f5XoIkwVdSwnScOydVbNq0oIoEkiiy4/39SobPR99LGNEMowEAEBR1W/OZlOsP04LKSjPWNLghBAqrqngpHocrzc0iw7JaQpLovLIyNpklZZNJYWWlEVaWBKZGRmCouxtn5efPSZRomgaj/f3qswcOGLWwF4nGhgbFmZZGDw8P8/XbtuE0r1eGJDtoQsGgGBcEEwBwACAmc+yVQiAQyAIANwBc8vv9ieu/zistLaWcTufvdnZ2hgEAampqPKFQ6N9nZGQ8sCIix3FYlmWoqqoy/fa3v1V37dpFeb3eh94oGxoa9J07dzL3chDpug6CIECy+s8MDQ21fPe73/11UgYzMDBYMCvTkpoPCBmhMEsIk5vLB2MxmDh3Tlu7YweZiMdhYniY0HU9nrtmzbIZYDaHg+QtlsTM1JSW4nLN26tdu307feHYMRyPxeSi6uqbeiU0OSlNDA6qiiw/8Nox3fHQ5Ewm4EwmMFksPMUwK2Y3xmBxSc/KgpmpKXFydJSiGEZO83geuj2j6zp0nD8varJMfv7ZZ/Gi4mK9pKTEqCSXRDDGcPHiRdqWkqLUb9tGO9PSCLgmOJKKruugyLLg9/sfSSEDAOByuX5UUVGxbWJiYurgwYO9iqJMOp3ODbm5uQ6r1Up4PJ60G6/duXPnQ6MjZmZmaIZhgGEY9Pzzz8/aRtm9ezd56dIlIRgM0haLBZtMJjWRSADP83pxcbH1008/FUpKSkbq6+uL6AXs3CuKApIkPTq2k8EKARm75QvgkbggA4EAoh0Oo9TSEkNZLIBoWpkYHFTHhoYwm5fHh8bH9SxVTWoTu7mSVVho6rl0SUhxuR5qAKqqCpdOnUq4MjPBW1DAdjY1ibmlpXx4ehqcGRmat6CAAQCQRFEf6uqK67qOS9autc5XjCxnCWuD5aG4psbc1dIiUDQ9KyETHBvDqTYb7HzySRYA4NChQ9HhoaFoXX29dSk7mz8qnD17Nj4yOgqqopD19fWsx+OBluZm4Hgeb9+zZ1FDZR0uF48Qyr81Cf5RQxCEjy0Wy+6ioqJsALgrhGwezCscz+l0whNPPGGenp6Gzs5Oied5wmw26xcuXLBMTk7KBEH8bx0dHb9hWfb0hg0b3ADXRK0kScBxHCA0O2NyampKn5mZMarTGRisIFaFmHnrxz+u01WVAQALAAQBIAYAN7w9OgBYYIkqmBjcDvJ6ueHpaZXKymIJggBF00hVlpdVzIQmJxPmlJRZnQ+dTU2xwqoqfqy/X+04dy5mSUnhWxsaNIQQVG7eTJPXP4ckCCpJUUSyetkYPF443W6mp7VVL6qsBPv1hpX3YqinR1AFgdy1c+fNneZ9+/ZZGxsb1XA4jC0Wi7GlN0d82dmm3r4+nOXzJdo6OsTzTU1oemqK2/X000tyLBVFsXA8/3cA8L8sxXxLxU9/+tM/tVqtuxFCRW63O2nHkmVZDQDmvavucDhg0y1hxhUVFTA9Pc189dVX1ldffbXvww8/bKypqXmmt7c3PDMzw3R1dR3keV7WNM1TVlZWVV5eft8LVNd1aGlpuSSK4v813/UZGBgknxUtZgKBAAKA7Yiifk5arRmaIFCg69eqS+k6kGazQtpsJMGyQHKc8ZBfBgiKAsbl+rfziGGYhCjqnMm0oP3SyPQ0aJoGWNPA6XbP+n3RUEgJjo3hNevXPzTsEGMMNMOA2WYjCyorSbjeT8PmcGCz3U6Qt5TcZnmeEmOxB3d0M1jViLEYsDwPxANKrc+HeDSqY4wRgZA20tMTlyUJpXm9d52fciIBM5OT6Fvf/OZd23dr1qyhTp8+HfN6vcbWzBzJ9HjAZrVq9tRUtqisbEmfE5qmgdVmm5yemvr9pZx3sXn77bdfTk1N/YOnnnrKiTFOangMxg9t8zYnGIaBjIwMyMjI+P4vf/nLRlEU//BXv/oVp6rqKUVR2gVBeP/VV1/VAQDef//9/ymK4jeDweCwqqrDKSkpNR6Px56bm2tGCEFXV1c0HA5/+LDWDQYGcwYZRVkWwooVM4FAIAUI4lPAeJuuqqDFYgC6DnCjTC5BqJog0JTdrpEmE4kTCSBXaDL144IqCGBjmITFbl9w/pI1NRUA5n5xTwwNSSW1tbMOA9MUhbja2hrPKSkx3Th/hq9elc1Wq+ryei1jfX2CpqqgKgoU19QYuzKPKJ1NTRJNEFjWNLJ03bqbCcrXcx6AYR+Ys3xfBru64lo8jrxeL7vlxRc5iqLgq6+/jl2ZnIw5MzNZSRAUmmWpaCikKKKItm3efM98M8u1Iht6e1tbfE1ZmVEU4A4uXLgger1e3mazAXc9lDMcDgPLsjA+Pg6jIyN0ps+nwhJ1h49GIiBLEu64fHkgLgh/9ahVMuN5/o93797tJAgiqfl/iqIAxjjp3xFCCHbu3Fn28ccf/+7rr7/+XQDYe6/XzczM/EFTU9OfA8CQ3+/XA4FARm9v7xOdnZ1/vnbtWu/Q0FBXOBz+f5O9PgMDg4WxYq1/RFH/pKvqNgAAxLKankiQgJAOCJ0EgHWAMQ8EoSZGRqjEyAjQTqdO8rwha5cRfXpa8NXWmpMhKufroVBVFc22LPP15pimaCgEl06f1qq3biURQqBjTEVCISREImLumjVmluOAmmNZXYPVRUIUqfUbN0JXd7fWffGiXFBZyciSBAOdnXFZFAmaphUVY7q4tpbTMYbQxEQ8FomQvuJi9s7zTdd1ECIRkEUxMT0+Tu3bvZtx3hJatmvnTsv09DSMjo7qzoLibJQlAAAgAElEQVQCOhKJQE1xMWOz2R64xvr6euvhw4elwcHBaF5+vqmwsJB83D15F5qaFFlRtNbWVr63r0/meF7VNQ3v2rXLcuTIEWkmFOJUVQWvz6d4srOXJBRZ13U4c/ToOEGS/1WV5Z+88cYbo0sx71KCEGpGCG14+Cvnxpdffpmw2+06LIJtMpucGL/frwDA4C3/HweAXwQCgV9GIpFmgiAGrr/GwMBgBbFixQwglE7abFiLRAhdUcjrvxMBoS2gaQhRVJeuaf8DUdR/Ztxujk5JMcLMlhuEIDI9jR0ZGbeFaC0lFE0TwbExxel2z9pwsaamAkEQantjo0yzLIqFw9z6XbuolVo22SD5lNfXU+cuXIh+87nnrB0dHUr7+fMxhmHQk1u3mu3Xymtz/f392uW2thhCCOw2G0Goqtp36ZKqYowRQjpCCDDGiOF5amJwkK9duxa99MILzL3OI4fDAQ6HAwEAuGcZRpmSkgLPP/88p6oq99VXX8kYY7mkpOSxruLY3tFB5BYWajUbNqhen48BAEYURTh89Gg8Eolw+cXFQmZ2tpnj+SXLqYwLAsYYQ0KS/vJR25G5AcuylUySHTwYYwgGg/Rrr722KM9yhBAwDJM7n/f6/X4cCARq/X6/luRlGRgAwLWQo8fdObUQVqy1pivKNk257gDBGBDLgp5IXAuvQEjQMf4LRJJ/ayopMRknwMoApaaaBvv75cnhYa107VpTsnMPZkN2YaGp5/Ll+FzEDABAzbZtLADAYHe3SrNsgqKo+cUVGaxKSIoCkmHIC83NidqaGra0tPSu8ycnJ4fMycl5aM7K2NgYpGzfDhzHLcp2HkVRsG/fPubgwYNCSUnJYkyxasjOylLtqam0Kz395s2G53lYu3HjjVC8JQ8NpSiKYDnu0Bvf//4jJ2TeeeedaqvV+lccx2XN97kbj8dBURTdbrcjAABRFCEcDicyMjJYl8slEARhTeqib2EhDipDyBgYrFxWrJgBghgAjH0AAMAwGmW1yoos/yno+nYgCETy/N9zOTm8IWRWDpTZjMBsZmVBgMtnz8plGzYwsw35ShbT4+NSalravCfNLiykYCVfFwaLhjs3lxvo6pJqa2oWNs4cClYsBE3THrubn6qqcKWzU+sfGBBTU1PpyWAQF1dXL8828H2QRBE0VT2+3OtYDBiG+V5hYeHWoqKieYvEo0ePjimKMvTss8+ul2UZDh8+fCkej/empKRsj0ajV+Px+FrTIjVettvtOe+88075K6+80rooExgYLABEPHa39KSxckOzdP0UXBcqprw8Upma4v0//OFfI4q6Apr2FJ+bawiZFQplNoOCsS4JAnS1tMSCo6NLVvlFkWWdIEnjxDCYE5PDw/HB9nbpG/v3r6bkejQ1NXVb6SdVVUGMPzqFllRVhYH+fhgaGoJDhw4Jn//mNwkZIa2qrs7CmM1QU1e34sLsNE3DmqaVLfc6FgNBEH57/PhxPhqNznsMgiB6ZVn+q+PHj/cePHiwPRgMPhMKhV4YGhqKkCR5LInLvYvc3NxMgiA2LuYcBgYGS8+K9EAHAgETALx84/9id7cIBLE1EAjoQJJgqahYxtUZzAaC57WetjYBeTyWvu7uBGexgNm6aNEDAAAgSxJEp6d1Kj1dgRV6bhusPBRZhonBQeKlF15YVR1NN23axJ89c0bauGkTl5qaCsNDQ4mWlhZNSiTo559/nl6uvLW5EAmHwWQ23zP8JxwOwxdffCFneL06y7JaYXm52XLLPcTr863IUFB7SgqBNe3f/fRnPyt7/bvf3b/c60kmBEHQTzzxhGK32+d97M1mc8bAwMBnoVDoIAAgv98vXv9Tzttvv/2XY2Njan5+/qLcvzVNI+LxeN9ijG1gYLB8rMidmes13J8AgI8AAHSM9/p/8IMmAADA+HqrGYOVDJ2RYSJ9PjNBUUBRlMzxi+dAlSUJwtPTWvfFi2JhdbXJW1Cw4ry1BiuXaCgkI10nAQDGx8fh/Q8+EI+fOCGpqgoY46T3vUgW6enpsH37du5iS4v4xaFDsYaGBlRbW2tyOZ2yKIoPH2CZ6eruxr/9+mv1s88/F3/5wQfy1OTkbX//8ssvpXWbNzMl5eVsbmGhybLIzpBkQVIU7Pud32ELS0s3/PRnP3txudeTTERR/NXly5fPLOT8stlsVgBw+f1+6RYhAwAA8Xj8/x4YGOhe6DrvhyiKcQCY/7aSgcEigtDK+FmNrFjvtd/vPwoAR+/6g64DliQgF9E4NkguGGNKliTgLcnt96cqCkwODcnB8XGVM5mI4poanuFWlXPdYAWQmp7OTI2MCL/45S81xmTSitetM4eDQfyvH38sUjStYVUlnnvmGVOyqzclA7vdDrt27brtZtjc3AyLlXOQTLKzsoimpia8+ckn+a62Nikai4ErLQ0ArlW2IgiCNCf5nrFUIIQgp6DAIYni3737i1+slUTxqCLLx1d7s0W/349/+tOf/lNHR8e6tWvXzuvLcTgcKSaTaRsA/Pwe4yd+/etfL5rYGBsbG1NVddHEkoGBwfKwYsXMnQTeeqv+xr/Fnh4j1GwVQQBAeHpaIimKk0RR4Uwmer5NCG8w2NUVn5maIjKys7mKujqGMMooG8wThBBkFxfzqixr1tRU7v9n787DozrvPNF/37PWrh0tgISEhNiEwSwGjI2DwXbi3fGebjsGdHom3dN3Zm53J7m3k5v49qQ7033TM0k66S7A8ZI4tvEOmMXY7ItAIFYJCS1oX0tVpdrP9t4/EARjFgkkVUl6P8+j55FOnTr1K6l0zvm9y+8FgPTsbC49O9sKAF2trbGKEyeidy1alPCZ8tGjR6NTCwv5oVzIcCgdPnw41N3TQwlACMfRgmnTeACCxWajxysqYvv27ZMee+wxsmfPHg2EJOabGCBCCKaXlEx0JSd/7/jhwz8EUAygJt5x3a6XXnrp92+//faywsLCv7jZ2khXM00TLS0trRaL5brDK0KhUIRSOixlavv6+hoURekd8gMzDBNXo+cOkNKfXvmj5vPFxOTkhBwzzXwVdTq5Nq9XaqmtNTlZ1qlhmIWzZpGktLRbaupuqKwMSrLMlyxZYknUmzZmdLHa7Rzs9mt+mCZMnCgf373b6OrujixdvNjqdDqRiL00AOD3+81pxcUJ2y3j8XhI0cyZsjMp6SvrOOXm51tz8/NxeM8effuOHZzT5SJ3LV6c+JN+BiApJSVJtlh2xaLRunjHMlSCweAPjh8/vuq+++4rGOhzWlpaIuXl5WWGYex++umn377efpTSj48fPz5z/vz56UMT7Z/EYrGeoT4mwwwJwtaZuR2j6U7wLwA8CeBHABBraZGDZ87ENyJmQITUVFlKSyNSYSEn5eVZxSlT5AvV1Vo0HEbl0aNRQ9cHfKxIKKRHgkFMLiqyskSGGSnzli/no6rK7ysri37w0UdqX19fvEO6ppiq0rNnzoTLy8sTcjjT1MJC+dSxY/z11vtYvHy5cN+DD3LzlywRxsqFPeD3m4SQD8bSOiWKovhaW1tVj8czoAllqqri8OHDO7u7u+9/+umnf3qjfZ977rl/ra+v79QHcV0YiN7eXj0Sicwa0oMyDJMQRs3doKIojQA+ATCpfxMFgOCZM6AJOkGX+apLyQfHcaBpafbqEyciUY4TzpSVRft6ewdU2KGyrIyfWlIyOgfSM6MWIQQzFy2Siu64wzJl5kxp286dkfb29niH9TWrVq60T5w40dbW1gbDSLx75872dmP67NnjpoKLGouhoqzMiEYiH8Q7lqFGKd3Z2toaG8B+2LNnT21fX1+poigDulgbhnEkPMQlxquqqjqDweATQ3pQhmESwqhJZgBAURSqKMp/AqADGBvNduOUYLeDz821SpmZApk40VJz+rR26uDBaDgQ0G70vLzp0yM1FRXq2SNHgueOHw91t7aqIxUzMzoFvF6o0eiQHc+VmgpnSgq5nbU2hoskScjLy4MkikailWbu9XgQjET0CdnZo+q6czu8Hk8EhJwF0Nm/5MCYoev6HwKBQEzTNGzevFm9XvLR29sLj8fzr2vWrOkc6LEjkcjb3d3dQ/dPC8ButwsAbnh9YZj4SYAyZqO4nNnovKgQUnLpW+vUqSBsuNGoxgkCLFOnisjMtPS0t9+wpS89J8dWsnSpNGXGDHt2Xp7t/MmTUktd3ZBe9JixxZmSgqGscheLRKCFQsa0adOG7JhDTRCEhOv9iMZiSJswYVyNC8/MybHmFxam2R2OQwBCGz/4YPNbv//979/6wx/+4o033ph00wMksFdeeeVwIBBoqquri3R1df3u008/9V6rd12WZYiiOHkwx3Y4HK9Go9EhTTzS0tKSXC7X0qE8JsMwiWFUZgFKaek5cNzFk9I4ujCOdYbfrwqiKNadORM8f+JExLzBMBlqmmbjuXNRq92uJqWmJuZsbGZMCvr9etaECQlbGz4YDCIRK4ElJSWhqa5O8Hm9QzsZIsFNLymZvGzlyrsefOIJ3L1ixcNFM2Y8EwmF/l1V1VE/XFaSpFBHR0enYRjfi8Vie86dO/e1BWgcDgdsNtugkojU1FTHrFmzhnRhIY7jpGg0GhrKYzLMUCKEJMTXaJRwF7yBUtauPQTgJ5HaWujBIOs6HgOEjAypq6dHD2gaCYqitaOpKQwApmFcnk8TDgS08ydOBJtrasIz77rLOm/5csmZkjJqP8fM6BKLRGDouh4IhW46VyBeuru74XQ4+KGeQH277HY7HnvsMel0ebl+ZN++yJF9+wKH9+6NjIdFkEVRhCiKoJSiqaGhA4BcWlp6Lt5x3a5AINCelZWVRgiZaxjGf6mpqbnmappOpzPH7XYP+DwdjUaH/P+rpaUlxPN86lAfl2GY+Bs9pZmvQVGUn7rd7ldjLS3v6w7HCmnChGQuAUqmUkqhBwImb7NxbP2TgeM4Dlx2th24uB5BT0sLb7HZ9PaGBhWEUIvNRiOhEDf9zjsdbHFMZqQEfD6tp61N1TWN8KYJcBx99Fvfssc7ruvJz8+H3+/H1q1bw0uWLBHS09Pjf1LsZ7FY8NRTT1mAi//vZ06fjhzavTvscDrpnAULEvZ3OlQO7d5dH41EnlEUZUzM9evr6/u5z+dbkZKS8p+ffvrp0o0bN540DOMbV87X0nUdmqZNlGX5FQAbBnLccDjsj0ajsAzheb6jo6M2Eon8ccgOyDBMwhj1d9qKolAA33a73XN0n++kmJbml7KykuLVVWbqOiLnz4MaBmctLARYMnNLOI6DnpoqN1VX63PvvdfGcdylVcHjHRozzlw4e9b45oMP2gkhGOwigfEyd+5cqyAItK+vz+B5HikpKfEO6bIr/4dnl5RYZ5eUYMeOHaGWCxdiObm58qVz92gd7nAjoiT5X3rqqePxjmOovPTSS0fefffdtRzHxQAgGo3ub2hoWFpYWHh5DbgzZ84EGxsbN2qa9ruBHjcUCv2yq6vr3tzcXPFW4jpy5Ehbe3t7bVJSUuby5cuLW1paYuFweKOiKInVXckw/QgAwo29c95IGTN32oqinAJA1m3Y8Ljm8XxsycvTBKfzlk6EA0ENA6GqKsiTJhlU0zgxPZ0QQqB1dWnUMEQiSSHeYhnzLY3DSXA4oPb26pFgEHaXS2CJDBMPss2mO53OUZdIFxYWkk2bNpFIJELn3XmnkZaaSlRNQ3p6Om+zJVZhrQceeMC+ZcsWtaWxMaJrGiGEEI7nicVi0SkAUZLEnMmTSVpGxqi9ZoWCQfT29GTGO46h9txzz10uOx2JRNynT5/+65ycHNnj8aj19fVdHo9nnaZp/zDQsswAIMtykcPhuOXrdzAYNDo7O5+IxWJ3bN68+ceapp0NBAI/v9XjMQyT2EbtheF6Stes+cTtdvOx1tZfqoLwhJydncXb7UNeozRUVQUAiLW3h2EYTrWrSxVcrqgZjZrguLA1Ly9nqF9zPCIX1xASWK8MEw+hvj5TjUb50fj5s1gsyMrKUouLi60XLlzgKhobo5MmTbJWVVWFHnzwwYRraHn44Ye/MhwuEAggGAhIe/ft04KBgJgzadKoblW3OxxwulzchtdeW7Zm9er98Y5nOCiK0vL2228/89FHH/1v0zR/Eo1GtyiKMuhJ95Ik3X87Q8yKioqympub73322Wc/AbD7lg/EMMyoQMby5Eu32y0TQfg3IkmPWvPyJpBbXHeBUgqYJmJtbR4jEqEgxATghWn+R+krr/zrFa9XDI77TExNzZCzsoa0Est4ZaoqaHt7VNd1Lrew0MicPDlhq0gxY0c0FEJvV5fmaW2lT3/729L1VqwfbUzTxJYtW6IPP/LIqJh0du7cObWmtpZbtGyZMBaGnamxGPbt3PlZJBx+9lZu8seL9evX51ut1s33339/QVZW1qA/q8ePH/dUV1f/8oUXXnh1OOJjmKE2IWsSffo7fx3vMAAAv/3F949RShfEO47BGBtX6OtQFCUGYK3b7S4K19a+x9ts+bzL5eKtVgIARBRvOC6b6jpC5y4WnCGCcIgaxmrlBhVoiCA8JWZkZElpaYk1hmMU4yQJyMuzENOEt7s7kDl5UMsVMMyghYNBXDh9OjanpES+f+nSUdcjcz1+vx9ffPllrKSk5Po1zxNMb2+vOSE7mxJCxsS1SpJlTCksvKutqeltAI/HO55EtXbt2ga3231HWVnZmccff7x4sM/v6OioZ4kMw4wfY+ICcTOKopwHMM/tds/Wg8HnCMelU8O4l3CcXUxNTeIdjmRqmuD6S2dy4sWhuqHz59sAzFYUxXuz13C73Q4APxNTWeXH4WC2twdTJk0a/U2zTOKj1ExNTdWLi4vlm+88epSXl0fvnDePz83LS7ghZtfj6e3V5y9ZMurXY7nSxNzctPaWlrGRIQ8jRVH0TZs29Q32eR6Ph3q93rPDERPDMIlpXCQzlyiKcgbAmSu3rfvd755Xu7v/CRz3JgH+nBrGFAAAz2ug9PEBJjI8gADvchmEkCGfnzPemaoKmyzTzMmT2dA9ZtjxgsD5A4Exkzibpont27cHnS4Xyc3LGxXDyy6x22w4d/ZscNYdd4yZhOZMRcVpX2/vd+Idx2gQi8U+7+jomJuVlTXgYgCCIBBRFBuHMy6GGQ5jYShtvIyrZOZaSl955R0A7/T/+GOgv5fFMP5VUZTyAR5GBwBrbi5LZIaB2dUVKpg/nyUyzIhoqq6OrFi+fMwMFfX7/UhJTeUWLVo06t7TihUrHEeOHIl8sWWLkZKWFp15xx12i3V0T5tzJSen9/l8dwH4PN6xJLre3t5Xy8rKFj7++OOrBvocv98fi8VirGeGYcaRcZ/MXIuiKEEApQPZ99KqxrZp04Y1pvHGVFWYmgaO5yHxvCnJY2rED5OATNNET2ur7u3psXZ2diI5OTneId0y0zQRjUbR29uLEydOBKdPnz5qm/wWLVpkXbBgAfx+v33//v2h/OJiPiMzc1T1MF0pfcKEtLampgVgycxNKYoS++CDDyqi0eiqgVY3czgcsiRJBcMcGsMMMcJ6Zm4DG7c7CO716190u91Jbrf7yk9cO9A/UZ0ZEqZpwmhp0dIFIZyk6+HMyZPZ55QZdjUVFWGXJHGr7r8fRUVF8Q7nlum6jm3btoWOHz8e7vF4QitXrXIUTJ06aubJXAvHcUhJScHixYvtXo9nwOuVxJthfL3WgqerK6TrOhsGNUChUOhMT0/PgP/mqampcDgc33W73aOuJ5JhmFvDemYGyO123wmeLwXwBxBS7Xa7nwMwEcAE+6xZcY5u9KCUghoGuKtK3eqhEDhZBjgOal2dPn3ePOpKTWUXI2bYqdEodE0zY+GwMP/OO0d94rx127bg/PnzpaysrIRuYdF1HXt2744sXLjQ6kpKGtBzUlJS4OnsRLnX26fpujBr7lzbQJ870gzDwNYPP8SDTzyBaCQCp8uFnq4uta25WY5GIqfiHd9oYRhGazgcjgAYcEJusVgEADYA4WELjGGYhDEukxm3250EwAdC/hzATqW0tOOG+69f/yqAH6G/lU1wuQo5Wd4LnpcEl4t1DQ6SGY2Cc/xpPq8eCsFsb6eSzabFolEyc8EC4khKSugbMWb0MXQdbQ0NQcJx3MSCAptpmjhz6JBm6Dqx2+3qqvvvH/XJ86lTpyKxaNRaXV2tTpgwIWHLSseiUezcuTPiTEoS9u7bF3zkkUcGNMFfEAQ88cQTNgDwer34ctcubdn999/ySvHD6dLvfvvHHwMAlj/4oHH80CFJtlq59AkTfgHggTiGN2pomnaop6ena9q0afkD2Z9SCo/HE1UUpWe4Y2OYocRuJW/dmE5m3G63DELKADhBqaQoymQAIDz/9xT4XzCMj/vnx1z5nLsBVIDjfgfgpzDNCQAkABDT0yFnZQEAD8A1om9mjCCEQHB8/b5F13XK8Tw/a+FCvrOlJepIShrTn01mZETDYZwrL9fypk8XG8+d05JcLkiyzJ0rLze83d38t771LX7ixIkcxsi5MDMz01JfXx+RZFnYsmVLODs7GwsWLBh0kmaa5uWb8c7OTtTV1UXa29v5O+64gyssLLzt39WBgwejJXPnWjOzstDR3q5v2bw5QjhOJ4DtwYce4geShO3fvz8siGLC/t0IIXjkmWcQDoXw5Wef4eyJE9QwTUzKy6OhQGDGa6+9lrd69Wo23OwmFEWJ7NmzZ8ALjBJCkJmZ6XC73SkDqUbKMMzol7AXgtvldrtTCc9XUcOY0L/ph+716zfCNJ8Fx9UTnq8uXbMm2L/vIgBl4LiXALwJIAzTtAF4FgDA84aclUWF5GSWNw8DwW4HV1jIRZqatBP79/MgRA74/cHiuXMdIpuLxNyiUCCA5qqqyEMPPGCtrKqKffvJJ2WLxSK2t7fj+IkTwcdeftkhSVJidl3coszMTDK1sFBQNU1fsWqVbdfOnde9CTx//rxeV1cX03UdHMeZkiRxAs+jLxAQAEDXNC4SjfJp6elGyZw5stVmi2mqOiQVG/Pz84VTFRXRu5cvt2RlZ1uzsrNBKUXNuXORTz/9VFi0aBGXk5Nzw9eSZZkKsqwG/H7emZQ05JUkD+/ZE9U0jSy7/375dnrfbXY77lm5Ut+3c6fgcLm0uupqLn3ChFTTNAsBsGRmADRN8w1m/wULFkwJBALlbrf7TkVR/MMVF8MwiWHMJjNEFD+wTpkywQiFAprPRzlJ+r91n88BQIJp/pGa5l4Ac/qHnD0KADDN34GQH4KQH8o5OWHebrfpXm9UmjBh1FbOuZ5oa6tqhsOwFRUlRrZgmjCtVnAWi0Z1nQt6vfbmmhqjYPZsVu6auSVqNGpOKyy0pqWl4Z5lyy6Xw8vOzsbD2dljZt2SS9ra2tDT02NW19SQhx97zEYIQcHUqXTTp5+GZVmmK1etsnMcB9M0sWf37lAkGhXvWb7cLkoSCCEIh8OglMJqtYLjuIvz2ygF199N4kpKsh7Yuzc4Y+bM2/7d5efnC36/X6irrdVL5swRgIst6sUzZlgLi4rwxeefhx12+w3nwzzwwAP2aDSKbdu3h5fcd9+QDxG86957LVvefx9+nw/JKSm3dayklBRhcn4+bWlsFKlpoq252QagamgiHfvC4XAPpXTAQ7rtdjuKi4uz29rapgE4OrzRMcwQIAA41l5+q8ZkMuN2u5OJJM3mZBmcLDvF1FSo3d06CPmNUloaAxBzb9hQ7d6w4dfg+YWgdLatqAjh6moelP6jJS9PFRwOCQDGYiIDAILLRaNeb8LUOzY6OsITMjKk7s5OXQ0GxTu/8Q1qsVpZIsPcMpvDwbXW1YVKSkpGdSWvm6moqIg0t7SYqWlpyMzKklc99JB46aZvalGRo6CwEN7eXm3nzp0BwnHgeZ6kpqXxS2fN+kpDhs321XyAkK+XCu3t7bWqqgppCHpMz1VVGTOv0VjBCwKWr1hh27dnT9gqy4LP7zcBkMcee0wWriocIkkSOJ7nrhwWN1Quvfc+rzeanJIy6OtANBLBkf37zXtXreIAQLZYwPM8tdjtaiwa5UDITABtQxr0GBUMBj/p6el5IiMjY8DPsdvtVovFsggsmWGYMW9MJjMAXLzN9pUrG+H5GOH5TLfbfRc4bh0RxalyVpaNCAL4/kXY7DNmABwHQkhi9FYMI6ppPACE6+u7rVOmZESbmnQhKSmq9/WFLBMnZhJBAKUUamdnn5iSYjdVFYLTOWzJBWeafO60acKkwkLBNAyIksSaKJjbEotEYkNx053Iyo4cCQWDQax66KHrJmyEEKSmpYnLV6y4rYny1DTBcxzp7e1F1sW5g7dFlmXd4/EIuDgH8erHcP+qVbbmpqbYvIwMi8/rje3YsSMWDofJwoULhby8PA4AWpqbkZacLJYfPBhZtGzZkK+mKVssPsMwTACWw3v39k7IzrYWFBUN7HUIQZ/PxwGAqqrobG0lrqQkaKoay8jKQmdb2xu4WBGTuYlQKFQZCASiGRkZA04qbTYbZFlOyOIQDMMMrTGZzCiK0uRevx7UNEH6W+vE1FQ7EcVHtN7eB6UJExz8NVaRJvz46QiItbUJAEBV9fNQZeWLAAQjHP4YprklXF//c95q5XW/fyIAl9bTAxBCHcNYglonhGurrzcnTp3K8ePo78AMPdM00VBZGYgGg/zTTz455nplmpub9VOnT0d4njdTUlL4pffcMyJD5np6eszCoqIhSWT6+vpgsVqxdNmy6/6zE0KQm5cnA4DNZpNzJk6EGoth6+bNRk93t+lwOrn9+/Yha+JEOiEri952UNcwc84c82R5uYPjuFBPZ2fq/CVLBvzcnZs24Y6FC82as2eNmsrKK2+qXXans8eZlGRdt359QenatfVDH/nYQik92dHR0VFQUDBlIPv7fD7s2rWriuO4hmEOjWGGBAFYZdzbMKYmv17lgUhd3VcmDQpOp2zNy7tmIjNeUEoRPHPmTz/r+nsAAgAA0/wzIpkzi9YAACAASURBVAi/p6qaYwSDfySS1OmYPRvWwkLYZ84c1v8yfsIEob2lRVOj0eF8GWYc0GIxaKGQ+Oy3v20b6dLEra2tw3r8aDSKs5WVsXvvu8+5fMWKpDnz5o3Y3J+68+cjs2fNGpJfqMvlgsNuN89XVw/qH16SZTzyxBN8XzBo1NXXx4pnzzYWLF0qTCksHJay2hPz8lJL7rwzVFNZaQIXyyzv2b69KRQMXjd5ioTDqD5zhhKOw9kTJ8yaykrR7nDolx7nOA4Bv1/WdZ1LTkl5YzjiHmsURYn5/f7IzfZrbGwMfvLJJwfOnDnTZhhG1TPPPPPJSMTHMOMJIcRCCDlCCDlJCDlLCPlp//Z8QkgZIaSWEPLupVFOhBC5/+fa/senXHGsH/ZvryaEPHjF9of6t9USQn5ws5jGbDKjrF17jOr6dyMNDaw04xW+kvlz3G8VRflEURQXCAnZZ86Effp03j5rlmDJy/sbW0FBJgDwFsuwtxgIViuBzcYH/X795nszzNdR04Suaejr7dXtDkdcVol3Op3Devy9e/eG5sydaxHEkR09EwqFoGsakS1fHeVjmiaOHTtmvv/++9r27dsDBw8eDJnmwH71oXDYyJk0adBzUXieR05Ojm4ahtba2GgO9PVu1eT8/JRVjz7qfPjpp7Hy0UdBOK7ubEXFdXtTrDYbzldVEUmSovesXCkUzZjhByEaIQSiJOkAEItGnZRS0evxLPvd668/OaxvYAzYuHHjt7u7u10AcOjQoQsVFRVd1/q7q6pKurq6/qampmaF3+//3ogHyjC34dI8xXh/DUAMwApK6R0A5gJ4iBCyGMDPAfwrpbQQgBfAmv791wDw9m//1/79QC7OG3wewCwADwH4DSGEJ4TwAP4NwDcBzATwQv++1zUmh5ldUrpmzSfrXnvt7yNNTa9ac3PT4h1PIghWVl78hpB5ytq1JwDA7XZbeKfTSzjOfvEhAt4Wh/UDRVFoqKw0rQ4HrPYxNzqIGWYNVVWBsN8vBQMB+aknn4zLuc3lGr7lp1paWmCYppiSmjqi4zA1TcMnH3yA5JQU6dNPPgnfOX++LSsrC4IgYPu2bcG8/HzpvvvvF5xOp7O9rU3dvn17mFKKlJQUURJFff6CBdb9+/eHqs+ds3/zW98yc3JyONM0oaoqb7/F//MpBQXWKQUFaG1uVg/t2hWTZFkVBIHkFhTY0iZMGLJGOk3TsP3jj3H3ihVGSloab7FYkDNp0vyG8+d7b/S8lLQ0GotGQ4319Xp3R4cFF0eRQFNVAbhY5CAUCNg4nn/hle9+96Ohines0nVdLykpSVFVFadPn55isVhWtLW1/cvSpUvnpqSkcAAQi8Vw9uxZj8vluue5557753jHzDBjFaWUAri0RqPY/0UBrADwYv/2NwD8BMBvATze/z0AvA/g1+Ri1vQ4gHcopTEADYSQWgCL+verpZTWAwAh5J3+fSuvF9OY7Zm5pHT16t+Y4fC/x9rbB1WnfqzR/H6qdndH0N+apZSWnrj8ICFLBJdr4GVihglnsYBSCukarb/hQCBOUTGjgRqLQQ2F+CV33SULomjKcsIU6hsyJ0+eDKqqCl0f2c5LQRDwrccew7cefVRYcs89lurqau2dP/6R7tu3T8vNz+enFhVJTqeTA4DsnBzpGytX2pYuW2bLyskRwtEo3bFjR0DVNDkzM1Nz2O0cABw4cCA8b/78207KJk6eLD382GPyygcecN61ZImjub4+UlNZOeAFFm9GFEXc//DDOPDll7yqqjh17FhjW0vLr+wuV931nhMJh+H1eAghRO3p7DSoaQZDgYDE8/zlVk/DMAAAPMexnugBeOGFFz7p6empJ4TA4XBseemll3a1trYu2bFjx8bGxsaQrutoaGjo8/v9z7BEhmGGX38PygkAXQA+B1AHwEcpvXROa8GfCpxMBNAMAP2P+wGkXbn9qudcb/t1jflkBgBKV6/+e93v/32svT14873HHkopdK+Xqp2dVgAgovg/r3ycCMJq3uGI+92f3tJiTi0pMa8sABDq68O7//EfkRP79oHNp2Gux9vVFSmZNcuam5uLl//8z7mrywyPBdFYTFj10EPS1eWJhxshBMnJyQAAl8vFLVu+XPzWY48Rn89HpxYWfm0CIiEEVpsNORMnkkWLF9uWr1jh1FRVy83NFcORCL7YuTMqWyxkQmbmkJWaI4TAZrPhvvvvt/s9nsvzWYZiCJrVZkP6hAnh3du2eZvq6/Oi4XBZWkbGjKv3271tG/V0d+OLLVtgdzg00zS7DV23m6Zp8IIAh8vltzocYaB/si/HwWq3/4vb7R4X1+Hb1dfXp+/du/ecJElvAoCiKKrf7//zU6dOnf3www+bT58+beLiDRLDjErxHl52xTCzdEJI+RVfytWxUkoNSulcAJNwsTdl+gj/ur5i3JxES1ev/i+az7dL7ekZdwkN1XUYweDlvzXVtPfc69aVXPqZcNwUThShB4NmrLNTjUeMpmkiKgjawc8/56LhMKLhMLa+807wf//DPwR/8YtfWN98883g5x9+GI2Gw/EIj0lgkWAQrXV14rRp08Z0KRhREIxggvRQ2u12PPDNb0oDnUt319KlVvC82tbeHpYtFm7O3LnDUoUlFAoB/TGZpoldn31m7vv889gQnDcak1NTqd3p/LXVbv/ltJkzcy49cKG2lm7euBHBQIAc2r0bACDJch8FUkLBoBCJRNIMXcf0khLnkuXLbWkZGY0p6ekmARDw+yehfwgac2OxWOxvL1y4cN/TTz/93qVtiqJofX19J3Rd/yWAV3Rdr4hjiAwzVvRQShdc8eW+3o6UUh+AXQCWAEgmhFxqbZsE4FJFnFYAkwGg//EkAJ4rt1/1nOttv64xPWfmasqaNY+te+2116lhPClnZg7f4PYEw4kibEVF0LzemObxVIPSs7g4nvE54GLPDTVNRBsbdTElRQUwYotzmKqKtpMnQ9WnT9MD+/fLra2tXOXZs8FgMIizZ88K1dXVFgA4cOCAo7KyEjzPhx967rmx1+zO3DJOEJCSkhLDGD+f3X333bbPtm41H3vySW60rZ9jtVpRPH26hGE+t3S0t8eSUlNtZ0+ciKiRCFmxapXg8XhI/fnz6sw77ril1+7z+cxwOHzA0PWFuqZ92+5wUEIIDMMAz/M4U1HxtWTE6/GkiaIYIxz3HVEQ/kA4Tjt++DBxOJ0BNRZzBAMBzmqzdRuG8ZvvvvyycfvvfOx78cUXd15reygU+ksAUBSFDdljmBFACMkAoFFKfYQQK4BVuDipfxeApwG8A+BlAJeqCX7a//Oh/se/pJRSQsinAN4mhPwCQA6AIgBHcLGBp4gQko+LSczz+NNcnGsa0xf/ayldvfq76157rcuMRtdYcnNTx0tdb06WIWdlyUYgoK59+eUo+hMZAKC6/utIXV0H4XmBd7keGu5YTF2HHgqh/ezZYE1lJf34o4+cPt+fpjS9+eab1yw329fXB14U41KliklcgiAgFAqN+cWJJEkiSUlJUUmSxm9t+ZvILyiQOw4cCPp9PnHFqlWypX8eXnNz8y2PUT1fVXUuHAz+FQBDkuUKjudhmmbO1g8/vLyPKEnagqVLxVPHjvVR03SYpuk1DCOTAH+dlJpqdnd0iBzPU1GS5N6eniQAiITDGQD+x22/6XGOJTHM2DDgSmKJIBvAG/1VxzgA71FKNxNCKgG8Qwj5BwAVADb0778BwFv9E/x7cTE5AaX0LCHkPVyc2K8D+EtKqQEAhJC/ArAdFxdVfo1ebIS/rnGXzABA6erVf7duw4bPwjU1G22FhenjYbFMIxRCpKEBnCx/rRSqsnbtu263+x0xM7NbcDgGXSp1MEzThO/UKWzatCm8d+9eR3QQ82Cys7PNjKyssf/HYgaFEAJJFMd06/Ynn3yi+vx+ceGiRcOyOORYwXEc7r5qEdGU1FT4PB7p7IkTesG0aYJ1gPOpKKVorK/3hAKBf1MUJfaHt99+rnj27IJzZ8701VZVdWRmZyfFYjEOgKapqlh+4AAFIQFd0yyU0jReEFRCiNnd0cFJskwJIXo4GOQEQbiyiANrnGEYZlShlJ4CMO8a2+vxp2pkV26PAnjmOsf6H7hGow6l9DMAnw00pnEzZ+ZqpWvW7Ka6vix8/rzHVOMyTWRE8XY7xPR0asZis91u99fTf573yxkZw17RjOM4aKapHTt2zDaYRKb/uXS415RIFB0tLdrBHTvChz7/PHR8377wtvfeC9dXVrIWyCtQStHR2BhpqatTZ8+aNaxJeDwFg0FwPE9WPfggyZ86lQ2xHCRCCB598kkuLSXFLNu71zz45ZfRc6dP33ASDaUUxw4dCtRVV2/WNK0WAGx2+3/LnjTJ5nA6I+0tLWZne7vV19srq7GYJRqN8pQQU1PVFEqpxPO8LknSXtlq/ZgXBKprGuE4Tgv09Um6rkOS5UtJ6ahpimUYhklU47Jn5hKltLTa7XYXR+rqyix5eVN4m21Mt/pLmZlE6+kBgKcAfHBpu3vdusc4i2XE3nvy1KnkpZdfDv/2N78ZVELj8/n4tsZGYXJBgZGRkzNm/lZBnw+HvvgiFAgETLvdLmfm5NCO5mb+rbfesjU1NUGSJHi9XjzzzDORp154wSieOzfuleeAi8MFv/z0U23WggUkOzd3xM8l3u5uDZGIJHOcOnXq1NE1iWQQOjo6TFdSkpqSmjqyK2WOIYIgYPrMmdL0mTNBKbW89/bbKJwxA9eqDKdpGs5WVJz39vb+ggB10Ujk8O//8IeVUwoLCwkh0FTVzwvCZNliUQVBIIauixzHQdd1w+ZwvK5r2vcMXefVWCxdtliWchwHThAAQjjJYumhpukydF0SRBGCKB4CsHDkfyMMwyQUAhCOtW3cqnGdzACAoiget9s9I9rUdFzOySkSXK6EuFEcDoQQSJmZEbWr66tj7in1mqoqh86fbyU830l1PZkaRjZvsahmLOawT58+pImDnJIizFm+XJi2c6d+6tSpAX8G+/r68I8/+5n49xxnFsyYoU7Mz5ccSUlDGdqIMk0TezZvDhzct49s2rTJEQqFkJqaipI5c8J79+yRL65L9Sd//OMfrRkZGaGi2bNlboTL816t7cIFPRIMCjt37DD27Nql5+fnw+5w0L6+Pi41NRWP/dmf2cVhnqTu7erS7l6wwJaWljZm55B4PB4cPHiQe/Lpp9kqskOEEILsnJwIgK98bgxdx5mKisZAX9++Pp/v79auXdt+6bE/vvPOi5Pz89MikQiCgcAkSmnavQ88gINffhkxKQ1oqpoiSpIRDgY3AfjexZchx8Kh0Or+70EptVhttlpXaqrU2dYmCYTQtPT0wtffeGPld19++ZqT2xmGYZibG/fJDHCxvCOAErfbTcFxmn3GDHEUTcQaFDEtzap5PD9wr1/foqxduxsAFEXZ53a751HDOEN5voPwfAonihJ4PipPnnyTIw5Ob01NpKmqCn6fz6ypqRn0DVpfXx9+9KMfcdnZ2dKiRYuCc+fNo9m5ubzT6ZSrTp+OWq1Wc9mDDzrtw7gS++3qamkxCMfxe7dtC61ft87a0tJy+f+wt7cXe3bvvuZQIkopDh8+LDzU1GRMKii45QRTjUZRX1WlFs6aJQkDTDia6+rUcCBA07Oy5MqKisihfftodXW1XltbK/f29n5liNfMmTPNqcXF2ty7777ck6DrOjhcrDx2LZqqorW+3jx28GDEbreTwtmzUThr1g2HVPEcR0Z6AcmRpKoqDhw8GIrFYvZ9e/bo31i5kp2vh0jGhAlc9ZkzutVuDwb9/h5d13si4XBjwO///urVqxuv3t8wjF8e2r17tiCKLp7nf2Wa5q99vb3gOE41DKMOwEJN00SO4yaKkmTEolGe47jXDMNYL8nyflVVuwFkGYbxpa+3dxHH84sBkD6fD4Iopoz4L4BhmIRCgNFUACDhsIvjFRRFIes2bFgeqa39yFpYmDIWP1ix9vYeapp/DdPchSvGayuKcta9YYMiZ2X9WkxJkahhgPD8kF9kk3JzrVxNTdjtdl9OZAghSEpKgiAIIIQgHA4jFAohNzeXtra2kkurZV9+D7EYLly4gAsXLjg2btyIjIwMpKeno6amxu5wOHDPrl2hkjlzUDJ/Pj9z/nwLxyXO1LCPXn89cvrUKdPr9ZLDhw/bg8GBLXskyzJM00Rra6vc3damDiaZ0VUVkVAI5Xv2GKFQyCCEkOamJgGEaNPnzr3p0KUvPvjA2Lp1q+bxeHhJkiKUUu7QoUPX7cFsbm7mmhsa1Dl33SWGQiEc2L49VFdbC9MwzJJ583DfI484TdNEX28vHMnJCPl82LVlS+jTTz6xHDt2zG6aJtauXRta3NkZvGvFimtWtgMATVVp0ijumbuZ8vLy2B3z5lkdTickSWLn6tvU5/dj95dfhqbPnEkb6usPRMLhL6PR6BZKaZWiKDecjBeNROas/c53FgGA2+2mhJB/bG9u/q+maZ7RVPVeUZJ+oKnqb8FxvbFoFHaH48PvvPjiwfUbNtxtmiZAaZYky7VWq/WJcDh8n2kYsmS1rgr09a1XFCUyMr8BhmGYsYldIK9SumbNHveGDd+O1Nf/0VpQkDnWEhozFutU1q79EteaeErpNrW7uyPW2poLAI7Zs4f89XmLBamZmWZaWhqKiopiTz31lBzTtIgtOdnkRZGYmoaQ1yuYpglXcjI5sG+f9v7GjddtoaeUoqurC11dXQAAn8+HTZs22bds2YIFCxbE/vK//bfQncuWJcQQHV9PD21uasLGjRsHFc+CBQsiz73wAjieN9tbW2lPR4dd1zQI4sCmUBz+8svw9s8+IydPnLA0NjbypmnCbrdj2owZYdM0dU9npzlv6VL79YbsmaaJI0eO2L1e74BeLxAI4KMPPzSbmpqCTY2NpLy8XO7q6hIAYObMmdHO1tZAckqK5YsvvjBKSkroyRMnaFlZmf3K8tzr16+3BwKBIAUCi1escF7rdZLS04Wamhpzzpw5iZOtDiGvz6fNnDNHluUxO/J1WBm6job6el9He/t5wzACpmmaaiw28/jRo52rV68eVAl60zQvzzEkhNxBKa3t6ug4buj64f5E6Gdut1s2DQOSJP2PUDD4/wDA2jVrDrjdblFRFPqHt9++VzeM/8LzvPCKopwDcG5o3zHDMMz4xJKZa1DWrNnl3rDh+fD585/aCgqcJM7zE4aKqaqgmnbdVVSVtWub3evW/Q2R5bftRUXD9qazS0ocf/fDH8ZcWVmcnJRExeRka7CpSXXk5EimriM5FEKguzsa9vvN1paWWypF238DLt9TXq7OXboUidA7Y7HZiM1mG/T7aWhosFit1tjyRx8ddFLW0tBgtDQ2ki2bN1s1Tbu8PRQK4fSJE+J777xjnquqspeWlkaTU1MNSRT55NRUztfbK3R1dQUFQRBEQRCdTie8Xu+Ae4OOHj1qO3r06Ne2V1ZWWiorKy395WnFrZ9dv/Liu+++60hNSwvMWbQINsfXO2gEURxbLQ1XIYRwLJG5NZ6eHvPY0aP7fV7vf/3ud79726vCK4oSuvR9aWnpqf5v379qnxiu0UikKAoFgO+8+OJeAHtvNxaGYcamMdZ2PqLGxl36MFDWrNntXrfuzdC5c385HD0U8RC5cMFPNe0/3XAnSt+isdiwfi54iwWTliy5fJemB4PoqKkh3rKyqM/vR1dnJ/V6veLJkyeFwZZvvtq7775rESUpOKOkhBTfcYddlCQIkhSX5Obzjz4KbN++fdC/W4/HQ7Z99hkpvuMOOJOTcfV8oEvlqi+9J9M0ceHcOa3i8OHo7l27+D179tiuLiYAAOvXr7/8N/inf/onC8dx4HkeU6ZMMXt6ejiv1ztsE48GOtfl/Y0bnbNmzw4vf/TRr/XO9XZ2xormzbvuMLQxwOzp7tbTMzLYeXoQ2tvaIocPHvwsFAy+0D8fkmEYhhnD2EXyBpTS0r9a99prbbH29r+Vs7OT4x3P7Yg0NHTDNP9aUZSG6+3jdrtfByDbZ8wYkZhM00R3ebm5Y8eO6LHycmt3d/eQl55taWkR/+kf/1HMz8/XFixYECooKJDtDocmCAKhgKFrmulwuYjD4ZC9vb1acmoq7e3poRwhIBwHjuOIz+s1BUGg+YWF4qyFC62apsHudA44IVKjUezatCnw6ccfk3Pnzt1S5a2dO3fKqx54gOqaBldKitbT1WW4nE4pMy9PO3/mDGlra9PzCwp4EGI2NjSYe3bvlk6ePOm8VhJzPaZpwjRNnD9/Pv7dWP0mT56sWq1W+eM33tAFQYje//jjDqvDAUPXQTUNWVlZ8Q5xyHV1dZnt7e3mA6tWObZv3x5a8cAD7Dw9AJqm4WhZ2blej8cdCgb/16UeEYZhGGZsYxfJmyhdvfpn6157rZiT5W+LqakJMfdisGIdHX4zFvtN6erV71xvH7fbTUCICEpB+JFbwuXEiRPhbVu3DnvrekNDg9jQ0HApWRI4jgOlFJRSWCwW5OTkoLm5WbTZbAgEAl/p7bj0fW5uLv3GN74RBCCsevhheseSJTdNTAI+Hz58443wunXrnH6//5bjj0ajePWnPyWqquLuu++mu3btspaUlBjZ2dl08+bNFkqpbLfbYRgGbrc3K5FUVFRIr/7kJ2hra4Msyw5N04x5S5aQ3s5O7cH77x+TvTKHDh+O5RcUCF/u2hWIxmJjdjHQoVZ28GBVc1PTN9asWdMZ71gYhmEGjY0zu2UsmRmA0tWrX1732mtTiSQtEhyOUbVwnRmLQfN4dKW09Cc32k9RFOp2u1+0z5o1/DH1JweBxsZYQ0NDXHoBLsUAXEwU6uvrAQBXJxxX7tfU1ETeeOMNBwBMmjw5PGvhwmsuuncl2WKBaZr0dhKZSzweDwBg27ZtMgCUl5fzuGKtjFAodO0njmKUUjQ2XqyUq2kafvqTn/AlJSX6448/zlsefjjO0Q09XdcR6OuTsrKz+aLi4lF1romnrs7OiNfr/Q1LZBiGYcYflswMENX1e2LNzedIfn4Rb7GMmvQ51t4eAaW/Guj+RjAIwXnN4lFDpq2sLHrm1Cm9qrJSKC8vv+FaIolq/759/F333mvmz5hxw2RMkCSkpaezm9IhQilFSkqKOnfuXOpyucbc+Wv7jh3heQsWiDa7feS6R0c5TdNw4vjx436f79/iHQvDMAwz8sbczcBw6e+5mBdtaKi25OZO4u2jY8QZb7PxRjD4EwA/HcDuz0cbG9+xz5o1ZIs3mboOXJx7cvFnTUMsEhHeevPNUT18prq6Wmqoro7kz5jxtWQs6PPhZFlZyNPdbVotFmnL5s03XMOCuTmLxYKnnnoqXFhYaD788MPSwoULB7baZ4IyTRPRaBQ221c/Pqqqkin5+WIiVN8bLc6ePt3c0939PJsjwzDMqEXYopm3gyUzg6AoStjtds+KNDR4bMXFAjfAdT7iQevt1WJtbSI4biuAbw/waY2c3R4N19SI8sSJ5lAMqes9edIUBYHzhUIhiyjKDfX1WmVlJcUo/uxJkoS/+/73SXpW1jVvno7u2RP+55//3N7e3g5K6VeGqjGDZ7PZ8Oqrr6pPPvmkLT8/P97hDMpnW7cGKaVUEAQzFApJFovFiITDot3pVPt8PmtycnJs5cqVdo7j0NLSArvdbrJEZnD8Pl/d2rVrW+IdB8MwDBMfo/aGMl4URelzu9054dras5ZJk9IFpzNhUmlqmoi1tvqMYFClhjEBhMxW1q49O4hD1JqhkAWEAJQOyTCX1HnzOG9FBdXCYUtaVhb/u9deu+1yy/Gmqio+37Ej9O1nn+UvnDtnirJsTszPFzRVxf5t28IH9u2jzc3N8Q5zzFi8eHFs4cKF4mhLZEzTRDAQkB598kkJuDgf5oo5VjIAdHV2Gtt37AgahkFTUlK4BXfdNTq6fBMIz/PJbrebsJ4ZhmGY8YklM7dAUZRut9udG21sjFhyc1XB5Yr7kJdQTY1Gdb0blL6slJbuvMXDeMBx1QDORpuaHrcXF/O3u2Aox3FImz+fpAG8r6kp8n/+zd8gHApJ//zP/zyq5wRs3brV3tHRoQUCAa6oqCiSlZ0dMXSd27Ztm72rqyve4Y0pLS0tAsdxJoBR1WVx+vRpvaCw8PIN9rWKRUzIzJQnZGaylTFvw8TJk6d2dHQsB7A73rEwDMPcGgLCJUzb+KjDkplbpChK1O12c7HW1l1qV9cS69Sp0kiPd9QDAVXt6tLMSEQCYALIVxRFvdXj9bdsTgeA1955pwo8P32IQgUAJOfmWl05Oaj58svwpEmT5JaWllGd0FRUVIgAUFtby1rTh9H8+fPVrKysuDcYDFZHR0ds1pw5LFEZZnlTpjhra2r+ye12L2G9MwzDMOMPS2ZuQ/+F8z73+vWvRxoaHrFOmZJGhnm8uxGNQuvt1XSfzwQhO2EYBwD8y1CvdE0No4aqarHq8QSlrCznUL0vThAwbeVK299kZ0crDh0y3njjjVF3k8qMrJqaGi4YDMY7jEGpqKiIZubkkLT0dHaOHWYcx6F4xozZwUDgEQCb4h0PwzDMrWAFAG7dqBq2kaiUtWu/a0aj94eqqqLR1lZ9MKuuD4YZjSJSWwvd6/3PME2nsmbNI4qi/ONQJzIAYKrq/wyfP0+ILA95lQOO45Ccl2fRDeOWe5GY8ePkyZOy2+2Ofv/73499/vnnCf2ZMU0TWz77LBSNxfRpxcWjsuz4aDQ5N9fudLl+7Ha72d0AwzDMOMOSmSGirF17EpTadJ/vn0Nnz8LUhia/oLqOUE2NETxzBpELF+oBTFFKSzcMRwJzpdJXXjnAWa3/LrhcluHobRJdLswsKWE3HsxN6bqO3/72t/Z/+Zd/kWtqahIumYlGo3jtd7/DF198EYhEInC6XJg7f76DtbKNHEII5t55Z4krKen38Y6FYRiGGVlsCMQQ6h929n+5zzCjFwAAIABJREFU3e7fRWpr3+Os1klydnY6Jw9+2Lze16erXV1eahjHqK5vBPBm6erV+pAHfQNmJPL3Wm/vo3Jm5sSBPkcPBiE4HAPaNyUnR5w9e7Z25syZxK1xzSQUVU24XAbt7e1YtHhxNBIOY9v27eq9993HemRGkGmaCIdCSM/IkCVJGl0l7xiGYQAQsGFmt4MlM8NAUZTzAOa5160rjNTX/w4cV8JZLBbLpEky4S/OeTc1DUQQvvLhNWMx6MGgqvf2XjB1fQcM428VRYlbHWNFUTwb3n67GcCAk5mBJjIAQE2TRi4WL2CYm3I6nUhPT0+4s70sy/AFAubsOXOcs+fMiXc440qvx4OtmzeblFKO53kqiGKe2+3mRFF8Q9O0lxVFYYs8MQzDjHEsmRlGSmlpLYB73G63YGja34dra5/nLJZ0KT09OdLQwAMAEcUGqmn5nMVy2lRVAYT8fzCMNxRFGdFemOuhur7f1LTFg1kgNNTZqQKAPTPzmomKaZroPHMmQINBmyRJGgDWM8PcVF9fH1paWhLu5jQ1NRVnzp7lps+YEe9Qxp3yI0d6lyxbZrHb7daG+vqYrmmu5qam75umqQJglc0YhmHGAZbMjID+xOQnAH7idrvTo5HI/wIhywA8WPrKK9Xxje7GzFjsV7rP9+dSRkbmgPZXVbSeOgW7zcZrwWA0eepUy9X7dB45Ynz5xRfcZ599xgMY1eWZmZGTnZ0NTdOsfr8fSUlJ8Q7nMovFAlVVEy7JGg8e+OY3UwHA6/VqoWDQaG9rcwD4IYBsVqaZYZhRg7BhZreDJTMjTFGUHgB/Fu84BkopLW3a8M479QAGlMx0VVRoG997j09OTjZEUaTzFywIFi9eLDpyci5PHJJdLqOiooKtzcIM2Lx58yL//b//d3rXXXfZdD0hOi2/wjAMmKYJbphLszPXZrVYREEQql0u14+ef/75j+MdD8MwDDNyWDLD3BTV9SYASway74T588Vng8HIO2+/jXPnzln37NmDH/34x5EcTYuZmkbazp/Xy48eJZ2dncMcNTOWtLe3iw0NDfpHH30UnD9/vvX73/8+nyiJg9/vhyiKXKLEM960tbSET508uaunu/sFRVEC8Y6HYRiGGVksmWFuinDcgAsAcIKAqcuXW58zDOOnP/0pAODXv/qVNS8vL9bb2yu1t7dL2hCVrWbGj46ODuHHP/6xAAAFBQUhjuMSpmevo6NDt1qthPXMjKxYLIZjR47U9fT0/EOf3/8GG1bGMMxoxoaZ3TqWzDA3tG7Dhlze5RpwMgMAfRcuqO+9997lz5bX64XX6x18fWqGuQZZlhNqnFlxcbHQ29tLz5w6pZXccYfILkjDLxqJ4JMPP4xxPL/x5Zdeej3e8TAMwzDxw5oRmRsjxNC93nxqGAPa3dR1nC0r06qqqthnixkWSUlJCVf9buHCheKZU6fEnp4eVghgBGiaBkEQPo5Foz9wu92sUY5hmFGPcInxNRqN0rCZkeB2u3mq6yo4rt6IRG7aGm6aJnynTuHDDz9kvTDMsMjOzkZubm7CdX0IgoDMrKyI1WJh59QRUF9XF4mp6lJBFDsADKylhWEYhhmTWIsWc01ut9vG2Wx7eJstBwDHW60CAFBKYUYi0L3eqDxx4tfKLhuEaKZpJlzLOTM2dHR0oLKyMt5hXBOl1HQ4nfEOY1wonjHDyvP8pHOVlad1TSNga8owDMOMWyyZYa6N5++WMjLmCE6nBABGMKhSSkm0sVFMy8iIeQMBKVRVRe0zZlxuJec4Dq7cXHHy5MlqV1fXNRfMZJhbQQjBlClT9DVr1sSmTJmScD0zACCJIufp6THS0tPZ2knDLBaLmR6P55wky//HSy+9xIb2MQwzyhGAzbe8ZSyZYa6Jk6Tv8Dbb5YRE6+yERRTpgw8+iNTUVHnT1q1h1WKRcNVnKOL1xlRVZUNtmCGRm5urfvOb31SLioqQkZFBvvOd79gTtWKYz+/nrTYbS2SGUTQaxbGjR1t6e3re9Pv9/6+iKNF4x8QwDMPEF0tmmGsyY7Glut+v+1pbta6mJsHn82nTpk/n8/Ly8Nrvfx/jUlJEs6/P1CVJ5RwOqaOiIiQRYgWloqqq8Q6fGSOcTiddvnw5ef755xOmFPP1yLKsW61W1iM5DCilqK6q6m6or9/m83r/ds2aNWyhKoZhGAYAS2aY6+AslvaYqub2trdLP/vZz3gA4g9+8INwOBwG1XVB6+oihfn5Zu2FCxAzMrB7+3ZSVlbGmaYJr9cb7/CZMSIzM9OcMGGCfPr0aZqfn08cDke8Q7omj8cDi9VKWFnm4XHi+PGmxoaGNS+++OLOeMfCMAwz5AhbZ+Z2sGSG+Rq32y23tbfP7u7qIu++++7lYTOUUuH1118377vvPr6goACGYXDHjh0zag4ejO7cudMWi8XiGTYzBh06dMj67//+76HW1lZp8eLFxve+9z1p6tSpIzrO7MSJE7GpU6fKzhtM7rdarbDIMquqNQy8vb16W0vLWyyRYRiGYa6FJTPM13R3d//ZW2++mVpfX/+V7T//+c+lZ599Nnj06FHZ4XBoU6ZMIadOnqRvvfWWLU6hMmNcJBLB+++/bweA1tZWfsWKFbGpU6daR+r1y8vLYzFNM3ft3h26a9Eiu8/nM4uLi7+WTNlsNoSCQUHTNIgiK+Y3VLq7uvSjZWX7vF7vT+IdC8MwDJOYWDLDfE1qamrZypUr/W63O+nqx957771L43xEp9OJcDg8wtEx45XFYqHJyckjlinU1NSYkWiUzF+0yBqNRrF10yY6JT9fq/z4Y2N6cbGYnp4uJiUlQZIuTpMpLi62HNizJwhCYHc4UDxjhj0SDhNJlpGcnMyGEAxSKBhE2cGDvT6f7zFFUW66zhXDMMxoRcCGmd0OlswwX+F2u4nX610yf/78pGXLlvn379//tYTmkkAgMJKhMeOUKIoQRRFWq5XGYjEDI3Teqq2rCy9bvtxBCIHVasVTzz5LAMimaeLUiRORrp4eze/z0ccefdQOAIWFhSgsLHQAQDgcxr79+0Mpycmyx+OhhcXFZNLkyex8O0CUUuzZtcsXDAbPKooSjHc8DMMwTOJiF1fmKzwezw/Ly8t/uGrVKrWkpAT79++Pd0jMEFqwcKHOcxwtKysbFWOhJEnCq6++qouiiHA4rC5btmzEhjRqqspdq6WM4zjMvfNOKwCcq6oK1dbW6oWFhUI0GkVnZyeSkpKQnJyMBx94wA4AZWVlmiiKiVlPOsHs2LrVb7PbW8OhULWu67tWr179q3jHxDAMMxJYz8ytY8kM8xV2u70+FApF3n33XSktLY19PsaYv/qrv9JEQeDKysqCv/zlLxOzNNgVNE3Dtm3b1KKiIvHFF18ULg3pGglLliyx7fnii0BKaiqZNWeOQ5blr+0zrbjY/sWOHSHDMPi6+vpIdk4OqayqIn1+P5+Zmandd999NsMwzP+fvfuOj+q8EsZ/bps+o94rEioIRJeFBBLNNOO4xcYYGxsDvknsXTv5vXHJu07yepM42WRTvLv5ZVd+k41jr+04doIbNpgqTBNIIJCEukB1VEbT7525c8v7B+CATREqc2ek8/185mNr5s69R8PM6J77POc8PMcRAIAJzZcoigJejwc6L1wYGB4e7nK73c0D/f1PsCzrVTs2hBBC4QFPVtFVXC7XMzabLaa/v186ePBgyK/tgW5N54ULYklpKb1y1SpFp9d7D+zfr58/f76/u7tb29TURLa0tKgd4lW0Wi3YbDbtQw89RFVXV3uzsrKotLS0oCxMGR8fD3feeae5q6sLztXXe+fMm2f88pUzkiShYNYs6nx7u698+XIDRVGQX1AAHMfB7p07icrKSo/NZqMysrLCYiRsokmSBD3d3V5rX59VEIRBv8/n4jiu0ulw/Ill2S6140MIIRR+MJlBV9HpdI+xLNv0q1/9yg8AeAI2yfzgBz8wv/jii75vPPmkuXjRIlh5++189vTpekVRlMGBAW9Pd7fU1NSk0Wo04p///GdTXV0d+HzqLbJuMplg8+bNJADA7373O2NmZqY/WMnMZWlpadDU1KTs3b3bk5qWRuQXFFyV5KekpupSUlOveo6GYUAURdJksUBRSUnQuq+FIkEQoLuz09Xb09Pq9Xia3R7Pf/l4/jDLsgG1Y0MIoVBBkDjNbLQwmUFXsVgsXXV1df1NTU0JaseCJkZScrJkNJmAYRgoXbLk8ok2kZiUZCycMwdKy8pAr9dD+fLl/IG9e6Xnn39eteloQ0ND8NxzzxEkSYKiKPDGG2+IVqs18NBDD5mioqKCFsftt99uOnXqlGAwGpWRbE8zDNy3YQNJkmTIT+WbKBzHwZnTp5vtNlul0+n8/0VRPM2y7IheP4QQQmikMJlBV2FZlv/lL395Ii8vb21TUxO+PyaRmJgY+MnLL3MLi4qIG62FYrFYAACgYOZMfXNjozMlJQVcLpeq3etkWQYAgB07dhh3794NfX19vh/96Ee6YMZgdzjE5PT0ETcgIMmpWSLjcbuh/uzZZpvNtnfYZvsey7JOtWNCCCE0eeHJKvoKu90+0NPTg++NSSYxMVHOzMxU8mbMGHEtVEpqKv3m2297erq7mU0PPfTVCngVcBwHbrfbDwBBTWbi4+IMbqdTslgsQZ3mFi6sfX2+5sbGRrfL9bHdbv8FJjEIITRCBIHdzMYAT1jRV6Snp79cXl6+fufOnTjVbBKpr68nm5uboWTJkhGPGhQVFxsBAPw+nzclJUXb09Nz1eOvvvpqgNFofH/4/e/Np06d+sroTUxMDBQVFclLly7lMjMzlQ8++IB56623xpyE1NbWUtu2bfPk5ubCM888Y9JoNBM+EtLb28vNnjcvaK2hw4VtaEg4W1vb4HI6X3G5XH9iWVZWOyaEEEJTByYz6Frienp6gleQgILCbDZDhMWiH83VH5fLpX355Zd9vb294ttvv22qra0FAACD0civWrvWUjBzpq+ttVXe89ln1Ouvv6792c9+xr/yyiv6n/3Lv3BFxcUGi8Vi0un1EBcfzzc0NMi1tbVjyjwOHjxoOnjwICQkJAQaGxs98+fPJ5944gmDTjdxgzUMw5CKgiUfl4miCKdras5b+/peddjt/8KyrKR2TAghhKYeTGbQtZxctWpV05kzZwrx5G3ycLvdAAThBQDzrT53zR130Iqi0DzHQXFJCVdz8qTUcO4cVTBzJqPX62HGzJm6vBkzoLikRFm+YoWQX1AAaRkZnvz8fDIhMfGL/SwsLtb/669+5Tl75ozy4j/9k5njuDH9Tv39/cxrr73GHDp0SPB6ve4XXnjhln+3kbLZbOTBffsCJUuWEPEJCVP6u3NocNBfc/LkUfvw8GOPP/54p9rxIIRQuMNpZqM3pf8go2tjWVZ86aWXOg0GQ6HXi2vXTQYRERFw9913+wiC0MqyDBR162UfBEGAwWiEouJiw7SsLCH1yBE5+oqFVUmShLj4eOLu++7TAADk5ed/ZR8URcGi0lLTnLlzwWAweL75jW+MudtXUlISFBYWBiZ6Qc37779fY7fboeb0aV6r09ERERETerxQpCgKtDQ1DbY0Nb1ut9u/i93JEEIIqQ2TGXRNFovlPxMTE9e3tbWpHQoao7S0NPjpz34mzp0/n8jIzByXM/7YuDjN+rvvHvXz9QYDzFuwgFq4cKFy8uTJEV2OIkkS1q1b59u1a5dOFMUv7v/GN74hPvXUU8ZgtGqOiooCi9kMB/fuDaz72teYG3WFm4xqTpxo6+nufnrjxo071Y4FIYQmCwIAcGBm9KZm71B0UzRNt86ePbtP7TjQrYmLi4P4+Pir7ktISIDCOXOkjMzMkOhGdhlFksBx3Ii+viMiIuCb3/wm99RTTzE5OTlXFZi/++67yjvvvCMEqxVyVGSkXgGgaHrqXAsS/H44duRI60B//79iIoMQQiiUTJ2/xuiW/OM//mOjw+HYv2fPnk1qri+CRu43r7zCTcvKUhRZJt7fsQP++7//2wAA0NDQAN2dnWJWdnZIJTODg4NKe3v7iLZ9+umnvU899ZQxMjISHnzwQZ9Wq4WPPvqIYBhG2rBhg7x169agLU6ZlZUF1TU1oiSKGnoKjMx0tLc7mhsbD9mHh7du3bp1SO14EEIIoSthMoOuq7+/PxETmfBAkiQYDQZDTm6ukJaerklOSfGmpKT4T548yTgcDjIpOTmkRmEFQYDW5mbF5/PdcDuz2QyrV6/mVq5cSV4ecfrBD36g4zgOUlNTvenp6VRZWVlQ2yWLogixsbEBmmEmtkhHZbIsw4ljx9r7rdbnN27c+K7a8SCE0GRGkDjPbLQwmUHXJcsyLg4YJlJTUyE7J4dLTUszAADkFxQYdXq98MhjjykXOjq8ySkpIbU+irW3V3jnnXduunjnww8/7P3+979vTEpKuup+g8EADz/88IgX/xxPJEmCoiiT+q+OLMtw5NChln6r9d7NmzfXqx0PQgghdD0hdbUWhZaEhAQlWHUIaGzKysqkmJgY7eXWjgRBwLSsLE1KaipVWlZmNJpMIXXybTSZqLvvvpu/0TbTpk3zp6SkEHFxccEKa0RomoYLFy4Y1Gpb3tzUNCHruRw5dIg/394uAABUnzhx3mq13oeJDEIIoVCHZ6rougwGg/Loo48Oqh0HujGDwQAlpaVyckpKWIyktTY3e/7y9tvcCy+8oL/Rdps2bZK+973vGUKy0F5RoKOtzR/swx49coQ7VV1N9fb2jts+W1ta5B1//WvA5/dTdWfPKrahIamvt/c/Ht28uW7cDoIQQuj6LrczC4VbGMJkBl3Xs88+uwIA+tWOA12fwWCA559/PpCYmBg2q9N3dXbKL730kvnK9spfZjAYQK/XQ6iODG7cuBHa29qC8q3f2twsvPPmm/InH38sdJ0/r1+ybBk01NWNbbXRS5wOB3S0tYkel4uhaBqycnLg88rKIZfT+e/jsX+EEEJoooXmmQIKCRUVFaSiKEHrEoVuXV5eHsydN4+8fc0aSm8IqbKY64qKjqbe+9vfuGeeeea6K7Lee++93OOPPx6yv5BerwdFUQITlUDKsgyna2oCTocDZEXRCIJAOoaHNWUrVxIxcXEQGRWlHK6s5BsbGgKCINzSvgVBgNOnTkmHKyvdDQ0NnM/vJ/MKCgJzFyzQ1FRVaXmOe5pl2VvbKUIIIaSSEJy/gUIFz/Mz29rapt4y52GktrYWHA6Hcr6jg8uePj1kT/6vNHf+fKPf54Ojhw9ft1VeYWGh8uWi/1ByqXucwvM8GCYgidz9ySd+mmEYm80mDFitmqKSEikmLo4ymc0AAJA7Y4bxg3ffBRkgMDAw4C5ftsw80n3TNA11tbVU/syZxKy5cw2KLANBkjBsswUsERE/2/jgg++M+y+EEELoBgggwnSKVyjAkRl0XT6f74np06cH1I4DXZ8sy/D4li30iWPHdD3d3eExzwwANFrtVxb3vIwgCKBpmpBl+ZqPhwJZlkGWZaPT4ZiQINPS0zXW3l4yOS2NmLtgAZ+clvZFIgNw8fXLyc/3DVittNfrpW9ldIbjOIhPTPTPmjvXBABAkCQMDQ7yp6qq3nc5nS+N/2+DEEIITRwcmUHXFRUV9SubzfaA2nGgG5NlGX73n/8pAQCsWruWjomNDfnLOwRBAEmS10y+FEWB1157jYqNjfU8+uijITnN8W87dvim5+RAVFSUbiL2P2PmTILneaGtqYlasXat/lq1Q7Pnz9fFxscLHreb6Whrg7wZM266308++ki2DQ2RCxctogAAAoIArU1NXE9X1zsOu30ry7JhkxAjhNBkgiMzo4cjM+i6WJY9P3PmzN0Wi0XtUNBN1FRXM2+88Qa1+5NPCJfLpXY4N3Xi2DGupaXlut/cZ8+e1b733ntEX19fMMMaMb1eL+bm5+t0+hs2ZBsTnudFQRDgeiNUBEFASlqaJj0zk25tafHVnz2riKIIN2qs4Ha7iZXr1gUys7Ppy/toa262Oez27ZjIIIQQCkc4MoNuaNq0af/f8uXLb3///feT1Y4FXd+//8d/uMuXLmUiIiN1RqMqa0neEp1eL1kiIohVq1ZJn3322VUtpWmahtLSUm9xcbGsn8BkYbR6e3tBkiRmIvbtdruht6dHOt/eLuTNnMnMu+026matqfUGAywsKdEOWK2BzysrRQWAlEVRXrpiheHYkSOC1+MRFUUhREki4uLjiajoaO3l5w4ODAQIknyWZdkJWbsGIYQQmmiYzKCb8Wi1WqybCWHx8fGQn59PZE2fPiFTniZC4Zw55pzcXLjn3nsDOz/6iP/e9773xXSy1atX85s2baI3bdqkvdE+gs1ms8GBgweFiMhIKF++fNxjs9lsULlvnxiflCSWLF2q12g0I35uRGQkEREZqcnJz9cAALS1tHD79+7lU9PTmdnz5xsAABiN5qppDD1dXc6Gs2f/xHPcu+P9uyCEELo1OM1s9DCZQTeTNTAwgPPMQtQjmzf7li1dqiu8VMwdTnR6PWRNn87c8/WvS9ExMZxtaAgAgEzPyJA0Gg3pdrvBbB5xk64J4/F4YP+BA57omBgiLT2dLpwzZ9wTGVmWoXL/fnH+okVUQmLimL+Xs3NyDNk5Odd9vL+vz3/u7NnXHnzggWfGeiyEEEJITZjMoJvpiI2N9QBAlNqBoK9at26dsmbdOtDqwmZQ5itS09J0995/P8iyDJzXK8fFx+s8bjecOHHCu2LFCqMoitDb2wvx8fGgC/LvueP9930cxzGlS5boxiPJuJbTNTX8wMAAFC1aRMYmJEz4d7JtcBAO7dunBYBvT/SxEEIIoYmGDQDQzfizsrLaoqIwlwlFVqtVpijq5huGOL1eD0ajEeLi40kAAJPZDLzPB+++955v7759bpvdzn+6axc/ODgY1Liypk1TKIoCs9k8IUnGQH8/1J89q1+4aJE+NiFhwqfV+Xgezp4+XQUAkVjwjxBCoYEgAAiSCIlbOMKRGXRDLMsqFRUV9z766KOnX3nllQy140EADzzwgL+8vJxqamoSkpKSaOomBeLhanFZmZGkKKAoSgcAMC0rC2qqqz0amlaWLFkSlPlnbo+HJElSIUeYMNrtdrDbbGLW9Okj+kdpaWryJaemUrIsUzDBF5faW1o8NVVVJpIk72NZ1jmRx0IIIYSCBUdm0EjozWYz1s2EiHXr1mlWrFolf/f553Vfu+ce7WQtGmQ0Grhy1InRaKC4pMSkMxjoqqoq/0Qfv6qqipdlWVp35530SKa3CYIARw8f9p2qqSEb6utH1DSjZMkSndvlUhiGmdDvYrfLBTVVVSaNVvvk9u3beybyWAghhFAwYTKDbsrtdi98//331a/EnkI2b97sf/bZZ6X58+dftcjIr3/9ayUQCBBp6emamNjYKfn5zS8o0NsdDrGurm7Cuuw1NjYKLo9HXnDbbYaRTOOTZRn2793rKSopYeYuXEjW1dZ+ZWTGYbdDb2/vVVO7Duzbx+XNnClPZM2TIAhwpqamQavT5W557LHfTdiBEEIIjRpBECFxC0dT8mQI3RqdTnc2Pj7ernYcU4XFYoGUlBRl1uzZgccee4xISUkBAACz2QwGg4FYu369GK5fOOOBIAgoLSsznjlzhurq6hr3uo+WlhZxcGgoULpkiXGkr3PlgQO+/FmztGaLhUpOTQVBEAhBEK7aZv/evWJLU5P/wx07/F6vFwAAYmJidPW1tRNWKyMIAhw7dOjkQH9/+WOPPtoyUcdBCCGE1DI5J9ujccUwzIWlS5eKe/fuVTuUKaGsrMy//q67mNlz51Iejwfcbrf44osv0gkJCZCemclFx8QY1I5RbSRJwqw5cwIej2fcEgG32w19fX1QV18vrl67dsSJDACAXqejAoJAAFxMtkrKy4UPd+yg19xxB2kyXeyardVqA7ctXqzvPH+e27dnj58kCMVgNCrTc3MDADAu0zgFvx/6rVZ/Smqq1uVywdlTp2oGrNbVLMvixQiEEAphU/ga5ZjhyAy6qUtdj76xfv16q9qxTHY6nQ4WL15MzZo9mwIA+OhvfxN6enrIuLg4kWEYydrby7jdbrXDDAkZmZna1rY2z1j3I8synDx5UtixY4csAwSKiou1t9JUwcfz0NrSwvh9vi+GYlLS0jSSJBEkSULNyZOBj95/35+dm6sQBAEZ06YZVqxZo12+Zo2upLxcn1tQMG71aCRJwoX2dumvb78Ne3buhMH+fhYTGYQQQpMZjsygERFFcX5bW1vYLcwYbh577DFfckoKkCRJAwCsu+suTV5BgRwbGyszDEPSNC3t/+wz6q777pvyFyJomoa4+Hji9OnTvrlz546q6GRwcBCOHDnCTc/Npe994AGSoqhbfl0DoghR0dF8RFTUVcU1GoaRjx896k9JT2dWzp59VaOGiWqnTTMM3FZaamisr+8csFr/4rDbT03IgRBCCKEQgckMGhGGYaK1Wq1G7Tgms9mzZyu3FRcT9z7wwBdTpyIiImDeggVkbl6eRqvTgc/no+tqa3kA0KsYakgQRRGGBgcJmqKod955J7Bs2TImPj7+lvZx9uxZb0lZmdFsHn1/ixPHjon5s2ZpY+PirkqEFi9fTjWdO0ekZ2Yyo975LWhpbPS6nM5+x/DwPqfD8ePt27dfCMZxEUIIjVX4Ft+HAkxm0IjEx8e//LWvfS2rrq7uTkmS1A5nUpg9e7Zw7733gtfrJXfv3k1v27ZNvvOee65ZA2K8VHcxYLXKOp0u/FfJHAeyJAHP8xRJkhJN07Rer4fa2lofQRAwe/bsEY3UuD0e0OvHlhdyHCdKkqTAlxJMs8UCC4uLJzzp9Ho8UH/mTOuwzfaGx+X6LcuyQxN9TIQQQihUYDKDRoRl2f4f/vCHAZqmAZOZ8fHII49QD2/ZQtltNsjNzeUK58yhb5aoZGZlkXt37cJ/AADQaLWwctUqLUmS0N1MTLXyAAAgAElEQVTd7f388GFOFEV9fHy8cPNnX8QwjAzK2BqiFc6dq/v84EFIywjemrKKokB/Xx/f1tx8lvN6P3U6HL9jWRZr2hBCCE05mMygEaMo6pPs7OzVDQ0NRrVjmQw+//xzeVp2trh63TrtgxkZI+pQ1tXZKQIAjkVfcnl9lmlZWcZpWVlw7MgRrnDWrBG9P2VZBgUu1pmMhG1oCAYGBuTY2Fgy7tJ0No7jgPN6IScvzwsAQflcOOx2OHH0aJcYCDzr9XjeudSgAyGEUDjDaWajhskMGrHY2NiDzzzzjPGnP/2pdP78eZzqNAYEQcCcOXOIWbNn31Jr4XN1dVJOXh6hKArOr72G6Tk5hjNnzgiLFi26aX1XTU2NMD0nZ0TT0WpOnhTsdjswDENa+/pEr9crL1u+XLd/716/y+nULl62bMI/D36fD/r7+lytTU2fetzup7Zt3YrTyRBCCE15mMygEXvyySebX3rppabz58/nqR1LuKMoChISE/3JKSm39Blce+ed2qqjR/2N9fXS1+69FxPKL4mMioKGs2cFALhpMuP3+5Ukk+m628myDG0tLVJHRwefkJQEpUuXmi4nkC6nUzxVU+MFAOa+hx4CgiBG1U3tVvR0dfFnT59+7fEtW56e6GMhhBBC4QKTGXRLJEn6ICYm5rs2mw2HBcZAp9MBwzCjeg1zcnO17a2tIMsykOSU79B8FZqmQRxhTZdteFias2DBNR+rOnbMzfM8JCYnM0uWLzd9+XW2RETQRaWlQfn+DAQC4Ha5hM6OjhMBQXg+GMdECCEURAQAQeJp1WhhMoNuSWpq6v959tlnt7344ovRoiiqHU5Y2bJlC9fS0sLMKiwUE+LjiTlz597yyIooirBvzx5l9bp1IkmSQWn5G25MZjMhCAJoNNcedOE4Dvbu28dFRkUpV6730t3VFWhva5MCgYCYkJQEsxcsGH2/5nFUuXdvvyxJv3I6HL9kWRabPyCEEEJXwGQG3RKWZbmf/OQn//z000//y29+8xutLMtqhxQ2LBYL+cgjjxB33HWXPjIq6pafL8syHDl0KJCTm0vExsVhInMd0TExZF9fHyQlJcHBykqPEAgoBp2OLCsrM9I0DTabDfR6PRUVHa2VZRmsVqvs4ziytbU1MD03l46MjjYZjKHR46Kxrs4FivL6gxs2/FztWBBCCKFQhMkMumVxcXH/V1GUHxEEcUvF61OZwWCAo0eP0rl5ef7IqKhRfe6OHznCUSSpmTNvHn5ur0OWZUhNTdUf2LuXI0iSmF9cbIiIjCSHbTbpQGWlBwAgEAhQAEA6nU7fwQMHxKjoaIJhGLJ48WLD5e5oocLldLrtw8P/qnYcCCGEJg4BgE19xgBPitAtY1nW+/zzz+8kSfJBXHPm2ubMmcNv2LBBMzA4KP3tr38ls7Ky4AmWJdfeeeeoL/lnZGVpqw4fJrxeL5guLaKJ/u5kVZXX7XYrgt9P+gWBWbJ8OWO2WAAAIDomhoqOiQmrF63+zJnz9uHh77Is2692LAghhFCowmQGjUpUVFRlTk7OfQ0NDTjd6Uvi4+PhoYceouITEoSIyEhl+/btFEVRdNmyZSRNj+4jd7q62jPQ388UzpkjmUymEa1JM9X4fD5YvGyZSRRF8Pt8YAzjhK+jtXWwv7f3nYc2bnxP7VgQQghNPByZGT1MZtCo8Dw/c3BwEN8/XxIfH688/vjj0rKVK5kZM2dqAACGh4bg/PnzQkRk5KhbKbtdLmXt+vU4re8GOK8XxEAAaIYBOkwTmeGhIVkURV9bc/OfNjzwAHYuQwghhG4CT0bRqMTExLxaUlKy6YMPPohUO5ZQEBkZCdnZ2f61a9fCE9/6ljYqOvqLx6JjYyE6Nvam657ciCRJ2IP5Bj56/31BkiR9e2srnzFtmj7Ual9GQpZl2L97NxiMxv/wejwvqB0PQgghFA4wmUGjotPpmtPT0+0AgMkMAKxatcr7ne9+1zgtOxsMhvGfBSZJEnFw3z5BVhQ5NjYWCufMCb+z9Qm0dPlyjW1oCDQ6nf744cPeJcuXG8NlDR5ZlmFoYACsfX1dEZGRf3HY7d9jWVZROy6EEEJBQhA4zWwMMJlBo+UzmUxnY2JiMnEBTYBjx44ZThw/Ls0sLBz1VLIbWblmjeF8e7sUGRWlOXv6NP/hjh1ifkFBICc3Vz8Rxws3ZosFLhf7816v7siBA5zRbFZEUYSikpLQ6LP8JYqiQGdHh73rwoXW4aGhGlEUf7p9+/YLaseFEEIIhZPwuHSJQg7LsvK0adPY4uLiSdNpqXD2bH7Lli3caIr0s7Ky/CRBTOjV9MysLMpoNILeYNBwXi/d092Nn99ryM7JoVasWmWYOXOmUZEkuv7MGb/aMX2Zj+ehtrq691xd3XN33XnnbVu2bPkmJjIIIYTQrcORGTRqoihG5+TkTJrpTmtWr5bW3HGHjuM4wW63i42NjZoFCxYIXV1dVHV19Q2L73melzmel2ACP1M1J07wA1arJjI6Wr5vwwaKYRhsCHADJrMZlixdqj1cWemu3LMnYI6IgMzsbG1UdLRqHfh4nodzZ8+22G22/fbh4e+zLDugViwIIYRCB04zGz1MZtCoDQ0NzbNYLDxN05GiKKodzpisWLGCz87OhoW33UanpKZCb3c3cfzoUSI9M5NsbWkhqqurgSAIMJlMYDQa5cjIyMDw8LC2rKzMV1VVpauurjaUl5f7Du7b51m6YsW4t9Kq3LfP5/f7dYlJSf75RUWTJoEMhsXl5WYAAJ7j4NjRo9ziZcuCnswMWK18W0tLo9fjqXQMD/+AZVlXsGNACCGEJiNMZtCo/eAHP3jzl7/8ZW5paenzlZWVYX2C3dDQQDudTiBJElJSUyElNZVZcNttQJIkteO99zzf/va3xcjISCk1LY10u1za9MxMeWhgAErKynS7du7kCQBNe3s7PWyzTcgqooFAQC6cM0dOSEwM69dZDUcPH3b5fD5FDATouUVFQR3NcjocUn1tbb3b5XrF7XL9kWVZOZjHRwghhCY7TGbQqFVUVNBms/mHdrvdq3YsY2W1WpmOjo7AscOH/YsWL9YCAFzuhnXP179uuufrX79qe1EU6c8++cSfkZmpZZ98Ui/LMrQ0NUFOXt64f6YEQQCP261LTknBGplR8Pv9sHjZsohgHjMQCEDd6dPtQwMDrzsdjpdZlhWCeXyEEELhhcC/8KOGyQwaNZZlxd/85jdLoqKiPgSAkOwYdSteffVVQ2RkpPtyMnMjNE3Duq997YvtSJKEvBkzxj0mzuuFzz79VFqybNm473uyEQQBfDwPlogIGBwYgJ7ubmHYZvMbTKag/omwDw/7T584Uemw2x/dtm2bNZjHRgghhKYaTGbQmBgMhqMrVqxwV1ZWRqkdy1jl5+f70tLTQ6oCr662lktLT6fi4uKw2P8mTtfU+N1uN0EQhI+kKDo7J0czo7DQTARxvZmuCxecTQ0NbzmGh5/CKWUIIYRGggBsADAWmMygMWFZVn755Zd3pKWlPd3V1aV2OGPC8zxhsVhC6tskIIpgMpulQCAADKNaE66QYBschJrqai4+IYGZWVjIXNlC+8KFCzA4OEiUlJVpTGazRo34WhobBzpaW//Pxgcf/J0ax0cIIYSmIpyhh8ZMp9NV3HHHHTa14xirjo4ObdXx47TTblc7lC8sLi83GIxGza6PPxY4jlM7nKDr6uwM2Gw2pb2tTayrqxMWlZcbdAYD8fEHH4jtra3g8/kAAKD7wgXuttJSjclsViXOhrNne9pbWr6DiQxCCCEUXDgyg8aM47j7UlJSwn7YgKZpyM7O1lIhNgKSmZVFR0VFQf3Zs56i4uJxb/scKgRBgN2ffBJYXF7OREVdnLVYe+qUQtG0X5YkonzlSq1Wq4XUjAx6aGCAlwCU0zU1Pp7nFbfLpTea1Hlp6k6f7urt7v5fD23c+BdVAkAIIRTeLs4zUzuKsIXJDBozrVZrbmxsDK0M4CZ0Oh0wDAOxsbFSR0cH9fDDD3t4nlfOnz+vMRgMIVefwmi14PV4KLXjmAiiKELNyZP80NAQMWvuXKqhvt7rcbth1Zo1RgCAuQsWaPUGA2h1F7tSMwwDRaWlegCAjGnTGAAAMRCAK6edBUtfT4+ns6PjFxzH7Qn6wRFCCCGEyQwau4iIiJeLi4uf7enp8e3atUvVdVCMRiMUFBSIUVFRAVEUKUmSxIMHDxouP75hwwZPYmKilJWdTVMUReh0Orq7q0tKSEwErVZLDg0OUqIogkajStnFdTU1NPhy8/ImXTIjyzLs3b2bT83IgML583UURUFyaipj7e0N1NfVBYwmk9jU0OAzWyxau82mxCclSdNzc430l0bPvvxzMCiKAufb262yolQCgA1w2i5CCCEUdJjMoPEQsFqtrt27d1vUDsRoNIpbtmyh5y1cSAQEAU5VVwscx3EnTpwwAACkpqbKP/zxj79Yc0SWLzacIkkytLKXLxkcGFAKZ88O6RhH4/ChQ545CxfqomNirvouik9IYM7V1XFr1q0zAADUnjolLLztNm1TY6PS3dkpZGZnq/5a8DwPdpvtyKObN9dWVFQQFRUVBMuyitpxIYQQCj/YzWz0MJlB44ELBAJuAFA9mSkuLuY1Wi0zs7BQBwAwd8ECE80w7vXr18s9PT181vTpV21PBrFt71jQNC1ptCE3+23MBEEA2+CgLzom5qqCF5KiwGQ2E5X793OiKFIrVq3SAgA4HA4+b9YsvTrRXk2n00F0TMyyN996643LdwEAr2ZMCCGE0FQTHmdyKKSxLKtwHNeiRs3Cl6WmpiobNm26aqrb5scfN9//4IPk8y++aNy8ZYvqCddokCRJ+C917ppMVq5aZerr7qaHBgakLz9WVFKiZzQaKiktDT756COfKIpAUZTS09kZEi8ESZKwqKwsvXTp0ocv3RUScSGEEEJTifpnn2hSIElyUFHUmWGTnJwszZs3zz9r1iwlNjZWL4riV4rB0zIyVIltPAiCAG63Wy+r9PqOF1mWrz0SRhBiVHT0NWutFixapAUAiIyKkqqOHXMzDENEx8aq3mxCkiQ4d/ZsYEZhIRMQBDE2Pv7j++65J7z/gRBCCKmEwGlmY4DJDBoXJElyFEWBKIpBO2ZsbCxs3LjRW1hYCCvXrjXGxcUF7djB1NPVBSRBABmmX3QupxO6Ojvl3t5eP0kQSmRUFFU4Z45Wo9HAiePHPYnJyUDdZFQvJjaWiomNVWcRmWvo7eoSGuvrNa3NzYoYCNA0TYdvtowQQgiFMUxm0LhITU397dNPP/3AL37xC8PNt751DMPA9OnTA+fOnfviqvzmzZuF7d/8pjE1LW0iDhkyJEkSAqJIh9tl/5amJqGzs9Ov0WjIzOxsfXZent7lcil+nw+OHTnizZg2TUsxDOTOmBF2a+ekZWZqUjMywOlwEC6Hg6+uqvrKNDmEEEJopAgyPC9YhgJMZtC40Gq1Q9OnTzd8//vf53/0ox+Ne4F2VlYWfOtb32KcTqfwy1/+UkOSpJyWlsaYLWFZAnNL4uLiND4+POrKrX19cn1dnddgMBBRsbHkkuXLzVcOnUdGRREAADzHgX142D+zsDDsEpnLiIujZXLVkSN6ANigdjwIIYTQVITJDBoXfr8/TqvVgt1uH7f3FEEQkJaWBvPmzeNWr1mj3HnXXcauzk7S5XLJ0dHRSsnixaTRaByvw4Ws40eP+qOjo0maplWvFfkyp9MJJ6uq3IxGI5MEATTDkKVLl5op6sZL4mRmZ4f1P9xgf7/PZDZre3t6eAAwAkDw5lcihBBCKiEIIg0A/gQACQCgAECFoiivEAQRDQB/BoBMADgPABsURbETF69ovgIAdwAABwBbFEWpubSvxwDgxUu7/rGiKK9dun8BAPwRAPQAsBMAnlFuUJiNyQwaF8PDw+VvvfWW0tTUNC4n3NOmTVNeeOEFQpIk2efzaR7ZsoUGADBZLHQgEJBmzZlDhdrClhPBYbeDx+3W3PvAA8TNEoRgk2UZqo4e9ZaUl5snY9vo63HY7YGDe/boEpOTz9sGB90arfa/tjz2WKfacSGEEApTRFitMyMCwP9SFKWGIAgzAFQTBPEZAGwBgL2KovyMIIgXAOAFAHgeANYBQM6lWzEA/A4Aii8lPz8EgIVwMSmqJgjiA0VR7Je2eQIAjsPFZGYtAHxyvYAwmUHjIikp6e0FCxY809TUlDoe+1u+fLkEAPSqdevI+ISEL1pg0TQN84uKQuusfpzZbTY4UVXlk0SR1Gg0yh133aUNtUQGAEAURdDp9TCVEhmP260c//xzG0VRNmtv7xKWZR1qx4QQQggFi6IofQDQd+n/3QRBnAOAFAC4GwCWXdrsNQA4ABeTmbsB4E+XRlaOEQQRSRBE0qVtP1MUZRgA4FJCtJYgiAMAYFEU5dil+/8EAPcAJjNoorEs293c3HxKq9WmpqSkQF9fH/BjqPP4wx/+QB8/ftzvcrmAfeqpqXO2DAAmsxkIAGXt+vWaUExiLuvr64OYuLjQDXCc2IaGxEGrVXA6nR1up/Mjt8v1zyzLcmrHhRBCCE2AWIIgTl7xc4WiKBXX2pAgiEwAmAcXR1ASLiU6AABWuDgNDeBiotN1xdO6L913o/u7r3H/dWEyg8ZNQkLCO//2b//2tYGBAfHnP//5mN9bra2tWovFAp3nz0N6ZuY4RBgeGI0G5syfr63ct8+t0WrJlLQ02mG3i6AoSl5BgUmvH/f+CqPiGB4OxCcnX3N9mFDFcRwosgzDQ0NcWmamAQCgpqrKP6+oSPvlIX6nw6F0dnR09vb0uCVRrOG83idYlhVUCRwhhNCkRUBITTMbUhRl4c02IgjCBADvAcC3FUVxXRm/oigKQRBBa8KKyQwaNxERETsBAH7961/Tbrd7zPuLj48HWZYhKTl5zPsKN/EJCWR8QoJZlmVob2uT0jMztSRJwpHPP/cYjUZmUWmp6qNVGZmZTGtrqycmNjbkO5LJsgztzc2B2poagaJpnxgIRPZ0dX0eCARcg/398/0+35BWp9NSFJXudDhkjVbb6nI43nA5nf/FsqxT7fgRQgihUEEQBAMXE5n/URTlr5fu7icIIklRlL5L08gGLt3fAwBXrqGReum+Hvj7tLTL9x+4dH/qNba/Lkxm0LhhWXb4ueeee9fr9d4/HvuzWq1AkiS8+ac/+VeuWUOlpqVNufcrSZIwPSeHAgAYHBgASRRJr8ejdlgAAGCJiABBCI+BiqGBAfl0dTUDAAxBEN8AgLfuWLtWvnKbiooKmtFo3tFoNOcGrNYfsSzrUydahBBCKDRd6k72ewA4pyjKr6546AMAeAwAfnbpv+9fcf8/EATxNlxsAOC8lPDsAoCXCYKIurTdagD4nqIowwRBuAiCWAQXp689CgD/fqOYptzJIZpYKSkpv8nPz19fW1s75rlQoijCb3/7W6m/v1/b398vxMbG8g9t3qzXGyZkXc6QV3XkiLRo8WJtXHx80OtUerq7oamx0U2SpKwoCkHTNKnRaOio6Gjy5s9W37m6OhdFUa9u27btuettw7KsCAD3BTEshBBCCAAAQmeW2U0tBoDNAHCWIIjTl+7733AxiXmHIIhtAHAB/r7+2k642Ja5FS62Zn4cAOBS0vIjADhxabt/vtwMAACehL+3Zv4EblD8D4DJDBpner3+ZFFRUWdtbW3eWPelKAqcOXOGAgD4yU9+olm5cmVg9ty50sLi4klfdH4tCoASExsb9M/s/r17XfGJiXTx4sVm5lI7bK/HAz6fT4qJjQ35fwuO40Dw+yu2bdv2vNqxIIQQQuFMUZTP4WKZz7WsvMb2CgA8dZ19/QEA/nCN+08CwKyRxoTJDBpXLMv6XS4XlJaW8kcurow+bmbPni3NLyoKuYUjgyUuLs5HkmTQ6lNO19Twfb29hEarJabn5RmuLO4zmkxgNJlCPpEBAOjt6rK5XK5X1Y4DIYQQuq4wGpoJNZjMoHGn0+n2Jicnbx6v/c2ZM0dYvXp1ICIyUkeSYTGraUJYrVaDoijj0vGkt7cXhgYHxdiYGDoxORmam5r4nu5uqXjRIpPJbAYAAIIkGXNEhBIQBGnMB1SRw24fVGS5Te04EEIIITT+MJlB487v9zd7PJ5xucQQGRkJTz75pGZ4eDhQumSJDABhMRowEUwmk48giFEXDPVbreB2u+FCR4c7Nj6ejE9KMjrsdq7t0CHJHBEBxUuWmI8cPMjNmTvXoGEYGOzv9y9ZvtxIUlTYjoZxHAcuh+M4y7JBaxGJEEIIoeDBZAaNO4qi/pqXl3ffp59+Wj7WfTkcDvjxj38MGRkZxPKVX5mKOaUoigKyLMNoR6dqT53isvPy6EVlZV/UvsTExhqyc3K+2GbJsmWG9pYWbyAQgJKlS41kCC/aeTOnTpzgHcPDvmGb7dtqx4IQQghdF0EAQeI0s9HCZAaNu29/+9tdL730khAfHw8DAwM3f8JNdHV1wcaNG5UZs2aF7QjBSHjcbqg7c8br43lFURTQ6HQQYbFozBaLhtFqQavTKTzPg9FoHNX+NRqNmJaRccORHZphILegYHQHCDHT8/L0uz78MDRWGEUIIYTQhMBkBk0Ii8Xy6Le+9a2fnz9/fsnrr7+eKcvyzZ90DQRBwMaNG/l7779/0iYysizD5wcOuPVGI5E3c6bJaDKBoigg+P3gdrlEnuN8nMMhUzRNnTh+3CVJEpGYmEjlzZhh8Pl8YDJd7AnQUF8fSE1NZSwREVB35gyfkJioi4uPJ5wOB5yoqvLk5OWF7zDLNVxskHLtVZOHBgd9Z6qrKymKenTbtm2OYMeGEEIIoeDAZAZNiO985zt9ALD5F7/4xYI77rhj90cffRQ9mv08uHGjf9myZfq09PRxjjB01Jw44ZmWk0MnpaRcNYqgNxhAbzDQcPXnVAcA0Nbc7Dl29KhHq9UC5/XKBEFAXEKCpqmpyc9xnCJLkqa3p4dfsHChrur48cDCkhJDRGRk2HZP8Pt8sH/37gBFUZzZYmkUBKE/EAi4KIpKKZw3ryQmNlZ3eVuvx6OcPnHirw67/RGslUEIIRQOxqO5z1SFyQyaUM8++2y1x+M5tGfPnrt9vr8vqG40GsHr9V73efn5+fL9998v79y5U7u4tFQymc2TalThSm6Xi6RoWklKSRnxc7Jzc03Zubk33MY2OBhobGzkF5aUGCMiI8capmqGBgaElsbGFjEQOOhxu//3gxs2OC8/VlFRQdRWV+9Ly8hYkJicbLYPD/v7eno6HHb7VkxkEEIIockvbK/UovARFxf3rW3btl1e1RUWLFjg/8lPfgIM89WZYwaDATZs2MCvWLFCkmWZWLZsmVA4d644mVsyFxQWGjivd9x/wZi4OKaotDRsExmO4+BIZWXtyWPHnurp6pr76KOPPsWyrPPKbViWVWRZdsbExZl2ffghOB0Oqev8+XwAsKgUNkIIIYSCCEdm0ITTaDTO3NzciO985zs+hmHE1NRUctGiRb7f/OY3yrvvvqvs37/fAAAQGxsLzz77rLCkvJxub28XBb9fWbt+veHyuieTlcvpFHmep8ZrDZnJgOM4OHbo0L7hoaE7WZblb7St1+1e5Bge9pAkqbP29LSazOYXPW73ULBiRQghhMYK//6PHiYzaMKxLMu99dZb96xbt+7NnJycLzKToqIisFqt/P79+wEA4KGHHhIzpk1ThoeHiQVFRVRKaqpGtaCDKCcvj+7u7PSfq6tzFRQWTvkRBVEUoerzz48ODw2tZ1nWd7PtA4HAvDOnTmVs3779WDDiQwghhFDowGQGBYXb7f74+PHjvMPhoIuKir4odI+MjJQLCgqUoqIi77Rp07Qmo1GKsFjg5LFjdOK99wIVxuucjNTBffs8Wr2e6O/r0xcUFqodjuoa6+q6HXb7ppEkMgAALMv2AUDfBIeFEEIITQgCcGRmLDCZQUHBsqzy+uuvn8zJyVl35f3/8A//YJw1a5a/tLTUdKnFMAMAMDg4yImiaJgKyQxBENJtpaURasehNs7rBY/HE7APDzds3br1vNrxIIQQQij0Td6qahRyRFF8rqur66paBo1GA6tXr9ZeXivlspycHG1LYyMX1ABVIIqi2iGEjOrjx32Ve/YwbpfruNqxIIQQQig8YDKDgubxxx+vb2hoaK+oqICbLaLZ0dHhT54CNTMf/vWvsslimbQLgt6KshUrdAAAnNf7R5VDQQghhIKKIIiQuIUjTGZQUPn9/uf0en3T4cOHu1taWnwcd+3Bl9zcXMNAf/+kH7YoXrKE9HEcrocCAILfDzGxsbtZlm1XOxaEEEIIhQdMZlBQEQSRxfN8XldXF7V//36d3++/5nZxcXEw2N9PTvZpWMnJyeD3+ZTrvQ5ThaIocPLYsXMOh2OT2rEghBBCKHxgMoOCavPmzX+MjIz8BkEQPQ888IAcFRV1ze2io6MhJjqa6WhrG1FHq3AWl5BANZ875/e43WqHopq2lpYhh93Obtu61aZ2LAghhFBQEQQQZGjcwhEmMyjoNmzYUOF2u5c1NjZeuN42oiiCLMuKbWjoxsU1k8Cs2bP1FEEQRw4eDHjc7sk9FHUd/b299Y88/PDnaseBEEIIofCCyQxSBcuyXqfTef56j3/yySd+UVFg9rx5hiCGpZrZ8+ZpYuPilJNHj065z6Tg9wPHcdVqx4EQQgih8DPlTpxQ6PB4PEdcLtc1H1u5cqXWNjjoD9fOGqOhKIqUkJw86dtRX0kMBGDXRx+JiqIkqR0LQgghpBaCCI1bOMJkBqnGbre/+fbbb1/zMYPBAARBKDdr4TyZJCQmkgNW65Rq00wzDGRMm+ZyO52/VjsWhBBCCIUfTGaQagwGwxOlpaXXHInw+XxAMwxhNBqDHdaEGhwcvO4aOy6nk0hITJw62dslPMd1PfHEEyfUjgMhhBBC4QeTGaQagiDcFy5c6JQYTNEAACAASURBVOF5/iuPaTQaAIBJtf6Kx+OB6uPHpcr9+70d7e3Clx8fGhry582cqVcjNjVJkjSodgwIIYSQqtSeXxbG88xotQNAU9emTZt+8Oqrr+44fPjwx8uWLUuk6b+/Ha39/XDlz5MBSRBgslh8xYsXG8+eOsX19fYKbqeTBoKgvW43VThvHjmVaoS+QBBTamodQgghhMbP5DpbRGHniSeeqPn973+/ateuXQfWr18fM2SzgY/nlQMHDsjlK1ZMqk5mnRcuCH6fjzp++LAvJz+fiYmNZQAuLhjp9/tBp9NNrjl1IxQRGZlbUVGRyLKsVe1YEEIIITVMyYuZ4wSTGaQ6SZIaJEkSD1ZW+trb2rQGg0EpLi2VIqOiKLVjG0/5BQWa/IICaG1qAsfwMHc5mSEIAnQ6ndrhqSY+ISG+vbl5IQB8pHYsCCGEEAovWDODVMeyrGy1WtdYrVZ+9rx5fqfTSba2tHylpmSy6Ghv98fExU252pjriY6JoQwm011qx4EQQgih8IMjMygkaHU6hee4juqqqvkLi4uFxORkk9oxTRRJkhST2YzjyZcoigKC3z9f7TgQQgghNVysvcfTgtHCZAaFBL/PBxRFpaamp3tT09ONk3Xa1a6dO3mT2UxQ1KSaQTc2BAGMRjPlWlIjhBBCaOxwmhkKCSzLniEI4uG+np73d338sWvPp5+2BwIBtcMadwxNS4vKynR4BebvaJqGlNTU1D+9/nqB2rEghBBCKLxgMoNCxtatW/ds27btYZ7j8jwez3PtLS0OtWMaTz6fDwwm06RaO2e8ZGRlJfn9/pNqx4EQQgipgSCJkLiFI0xmUMhhWdbq4/nPOi9cEGVZBlmWYXhoKOyTAGtvLyQkJWnVjiMUGYxGmDFrlr2ioiJG7VgQQgghFD4wmUGhirMNDcW+8z//A+/8z/+AbWjIr3ZAYyUGApLTbg/7pGyixCcmJhtNph+pHQdCCCGEwgcmMygksSwrGk2mZ/NnzvRnTZ/uyMnPD/uOANPz8qhhmy3gcbvVDiXknD550nlg924ARVmudiwIIYRQcBFAEKFxC0eYzKCQJUnSJ63Nze3tra2RDrtd7XDGxYLbbjOdPHqUb21qEhrr6ry2oaHJ1+VgFHLy8yPu27gRTGZzq9qxIIQQQih8YDKDQtajmzfXi4HAAgCAPbt2TYrpWREREVC+YoVeDAQIH8/Tne3tmMwAgNFkAoIkIRAITKqmDwghhNBIqD0igyMzCE0QlmV5o9FYede994bnJ+waNBoNeNxu/8DAADVr3jyD2vGEClEUQZblIbXjQAghhFD4wGQGhTxFUV71eDxqhzGu/D6fsnr9epphmC/u8/E89Pf1yYqiAM9xIEsSAABwHAeKMikGpm7I7/OBLMt9aseBEEIIofBBqx0AQjfjFwT6dHU1X7JkiV5vmBwDGXGJidShffu8t5WWGrW6i70NztTUeGmKYprPnfN5PR793KIihqFppfrYMdFoNsPCkhJGq528nZ27OzutHrf7z2rHgRBCCAUTQUDYTvEKBTgyg0Letq1b/zjQ369//733oKe7O+xbNAMAFMycafDxPO1yOmFocFDx+/3g9XqJ20pLNctvv91SUlbGNNfXe2uqqkSaYUS9Theo3LNHUDvuiSJLEvR1dw9t3779gtqxIIQQQih8YDKDwkJEZOSvSsvLJVEUJ817Nm/GDLqns9Nv7e4OHKus9BoMBunyYzExMbBi9WpjZFSU4ON53ex58ww6g2FSJHLXIisK+AWhV+04EEIIIRReJs2JIZrcnA7H9/p6epoyMjOZm28d+mprarzn6utln98vDA0NiSvXrDEuLi83f3m7+QsXGhUA+fjRo06Xw2EQRVGNcCecoihAU1SD2nEghBBCarg41Uz9WzjCZAaFBZZlBb/f3692HOPF6/GAVqcTFxYXm2VZvu7Xx9/+8heIjo2VFi9bFrH2rrsomp6cZW4URQFJktFqx4EQQgih8ILJDAobnNfL+3w+tcMYF6Xl5caUlBTt0UOH/JIoUlarFXiOu2obURTBEhERyM7NJQAunvBPViRJglanS1Y7DoQQQgiFl8l5mRdNChUVFQRN04u3bt36OQCAx+227fjLX2Dj5s1qhzYuLpw/L8ycM0drtliICx0dfG1NDZWUlASSJCk93d2k3+ejDUYjqdfrZbVjDQZJFKepHQNCCCGkCjJM53iFAByZQSGLZVlFFMXayz+Lovh8bFxchyxPjnP7pStW6E6fOCHRNA0zZ8/WL1qyhFYAIDUzU7ts1Spm5bp1hNfjIY8dOgT9fX38SPfL8yPeNKSkZmQkvvn22y+oHQdCCCGEwgcmMyiksSzrvuL/+3w8/+DBvXvrJ8MiknqDAUrLyqhjhw7xkiSB0WQiI6OjFUtEBOj0ejCZzaA3GESNVispsnzdUVRJFOHgnj2+g3v28Ofb2gKfvv8+hGPCl5WTY2Ro+mG140AIIYSCjSCIkLiFI0xmUFjZtGnTCa/H88u+np5J0dYrKiaGEASBlmUZxEAAjlZWav/29tvQ1tzsE/x+SJ82TRD8ftpksVy3YIYgSeC8Xrp0yRJ914ULYDKbRWtvr7xn507py3U4oW7a9Okp//3HP85TOw6EEEIIhQdMZlDY8Xg8b1Tu3z8p6r32f/YZP3fhQplhGGhvaRF0er00b8ECqa25mdz76aeBvu5uiqQoORAIXPP5oijC8cOHffGJiYLeYIAZs2YxHo+HHrRaxeycHOXz/fv94TSKlZGVFRUREfGS2nEghBBCKDxMihNCNLWwLBv46OOP9wHACrVjGauAKBIEQVCiKEJ2bq6mt7ubyysoMOQVFFCyLANJkszRzz/3V+7ZQ969YQMAACiyDBc6OnhFUciWpibImj6dzJ8xQwcAEB8fD+vvvhsMBoMGAKDzwgU/AGjV+w1vDUVRoNPr89WOAyGEEAqaMJ7iFQowmUFhye/3DyiKEvYf/oSEBPnE0aMgy7IcERnJ6w2GLx4jyYsDpyVLlmj379njdTocxqOHDsk0Tctul0tHEATExcX582fM0F75HMOlfXg9HgAAOtxeI0ajCb+CH4QQQgipApMZFJYEv//on994Y2N2To5UtGhR2C7AMm/hQsO8hQsv/2i83nY+n4/at2sXRMfEBBaXlWlphoFLC2jqrvecmpMnfTdakDNUCYIwaRZHRQgFR0VFhQEAUlmWbVY7FoRQcGHNDApLXo57FwCgraWFamxoCK8q91FYXF6u+9o998DK1au1Or3+ciJzQwRBKKAocGjvXj6s2jWHU5EPQihUeAGg6bXXXvuu2oEgNBpqdzEL525mODKDwtL2bdt6KyoqIDYujk/LyDDc/BnhzWKx3PJzSsvK9AAAvT09ysHPPpOn5+UFKIqSFUUBnV5PJqemhmQtjd5oTKuoqLCwLOtSOxaEUGj74x//+CtBEP4JAEiNRvOPkiS9rXZMCKHgwpEZFLbiEhLeu33tWr3ReN3ZWVMaSZJAkiRERkYSOp0uoGEYrdFg0JuMRn1jfX1I1KVYe3t9XefPi71d3b7L9yWnpGRRjOZNNeNCCIW+iooKQlGUs/TF9alkQRBeYRgmbKcdI4RGB0dmUNgSAwGbJElAUfi360ZMZjPcvnbtF6MwdrsdYuPjQyKZOX3ypNbjdhORUdFET3cXnzV9un7AauVFBQrUjg0hFPISCIKYHQgEvr1gwQLe5XK5WlpaPGoHhdCtIgCADNMpXqEAR2ZQ2PK43aa/vPkmnG9vF9SOJZzYBgchJjZW9SlmA1ZrgJfBL8UlgU1SqHa7V3/g8LFAW1s7rWj18f/16v99Vu0YEUKhqaKiggCAGIIgtj/yyCMwf/58fUtLSwIACBUVFcxEHvv1118/VlFRkTKRx0AIjRwmMyhsKQDHAABMZjMOzdwCg8kEPp5XPQGsrqoiBZ1BByQNoDcBaDQgmiIYX0QsQ3BuhlDkn6sdI0IoNNE0fb9Wq/1cq9VShita2lMU1UlR1ISuVUVR1C8AwDqRx0BTj9qF/9gAACEVyJIUk52T49dotfg+vgX9fX2CLMsTeuXyZoZttoAkiiRcXNvzK+SYRA05bB0OclgIoTAhiuJfRVF8Z926dYrX64X6+nofAOgURSG3b99+diKPvWnTpvcmcv8IoVuDJ4EobFEUdaKtpUVbtGiR2qGElazp0zWffPAB2G02f0p6upwxbZre7/dLZotlTCNc1ceP+619VqAYJsDQFFFSVmY0GAwgyzKQJAntLS3CsM0mDg0OMgGFEDiD5fqdGygKFJ3BXVFRwbAsGxhLXAihSckYERHhiI+Pj+zp6RFOnz6tAwCQZTm2oqKCYllWUjtAhFBwYDKDwtbjjz/+8e9//3uXoiiWcB0aVUNERARs3LwZBEHQnjp5kv9s505Jo9Eoq9avH9N+ZxQWaq19fYJTazKB4IOTR4/6eZ4nvB6PJr+gQG5ubKQDxghS1kfQAHDzkSEFRADANWcQQtdCOZ3OSFmWISUlRTN//ny5pqaGpGm6YuvWrZjIoLCDDQBGD2tmUFgzmUw/9Xq9aocRljQaDRSXlurLV6ygIqKixLHuj6Fp4L1eDQh+AI0O+gKK1qExaRSjxd9w9izJ0DRBSNKIkxNCEttZlh1zXAihSemqjowLFy4kS0tLvTRNV6kVEEJIHZjMoLDmdrvfON/e3qd2HOGs4cyZgI/nRzVK6+N5EEURhoeGArs/3ilS5kgONJcapdEaAJIEUW/SSnEp4NFbCMlgGlmtjiQByBKelCCErokkye0mk0kkyb+fxlit1m6fz/ffKoaFEFIBTjNDYW379u3dn+za1SfLctKVf9TQyHjcbnA6ncSyVatu+buA83ph14cfgkarlRVFobxa4/9r776j7ajue4F/95459famgkDCFMsYMEgUU0VvtmMDNoiOUNlK4mU7xVlJ1staTpyX9/Kc95IYx3E4RjJgmhFGgEAgQELFSICoQpUroS7denqdMzP7/aErIgmVW47unHPP96M1S7pzZvb9CriH85vdJAKhw7cjJSAPP9n/cEQh2wO7OHegmYioOhiGkU6n04e+3xSUUmWxhxbRQAix76DBYTFDFW/n9u2TTzv9dD167Fi+FQzQ5k8/tVvb2rQ/sK83RWuNWDQKAei1a9Zk06lUYPSYMchms8Wamlrnq2efVRsIBuG6LlYuX54v1rcEihDQAhK+/hcrx+Q4RQFw4j8RHeThhx/+TGv9Pa31f1166aX2rl27zPfee8+9+eabZaFQ4DL9RFWIxQxVvEAg8HPDNH/kdY5Ks27Nmlw2lxOTLrggCACFfB5b2tutTz/9VMAXKBaCtTUChs60twunaZSJTBQdr7+eHzV6tO33+41swYKurRHYt3lxSQmnuFEptbPU7RJRZXNdd5JSKvHoo4/2nn766S0vvviiG41GZSQSQX19/VKv8xHR8GMxQxXPsqxfJeLx+1rb2pq8zlJJtm3d6jv3/POlz7dvGsvyxYvtqAM/apoA0/QBgA7XCSdc9/k9SdcfTEbTgGMD4YbjE0xrwHW3H5/GiahSRSKRUcFgcFkkErkSQMv69evtaDRqCiE0AJFOp//d44hEgyQgSv9csGqwmKGKp7Vuj0WjPQBYzPTDW8uXW1prYRiG2drW9vm7p2maAhCAeZS3BSn3HUe7ZqiKlgvHXnD8vgERVSLTNH/a1NR0Wmdn52yfzxevq6sLSykLPp9vfaFQmDRr1qzNXmckouHHGdNU8ZRSbqFQiHudw0u2baO7qwuvvvxy0bYPv5qx67r4YPVqN9rb60smkzIei4lYNKoBwHEc5AqWVdJ5L4MkbCsqXPd9r3MQUXmpqalJn3DCCXBd90d+v79x8eLFfillwHGc3ejP3lVEZUwKURZHJWLPDI0I6XT6g6JlXeDze/9hfLi9s+rt3Nbt2w3XHyzKTLLmuWeecU2f34EQ7je/9c1AIBgEACxauLAQ8Ptx6ZVX+usbGozenh4nGAwaANC5d6+VhTBRDivCOU4OQKfXMYiovAghbtq4caMG0NzS0pK54IILatra2vDKK6+cm0wmuYoZUZUqg08uREOXTiYf3Nze3mPbNjZ/+ilWLF1aNSthxaK9PidcZ+rahhqnsbVoh+qcgobIhesDCxa8lOvq6ECxWERDQ0NhyrXXBuobGgQAtLS2GjW1tQCAjj17bMcMlMeTTe06ALiDNxEdJJ1OI5/P+xoaGvT1119fc9ppp6GhoQGjRo3yAWjzOh8ReYM9MzQiPPDAA+uffPLJzp6urvCECRPMro6OquiisSwL8URSoGXMvgcTgZBPBwCNfZP2C7VNoTdWvZOVhYKcdO7XvvDPZOf27UUhhNy5c5cPDa3DG/5I9nVzB8GlmYnoALZtTwHQc/LJJxew7z0CACCEMOvr6y8G8Lxn4YiGSFToEK9ywJ4ZGjGsYvGuceNOyHx54pf9o0aNcp6fN89dvmRJIZvJeB2t5NLpNNLpNFYsXWq5gZBzxN22DAO6piHsE/C1b9zoTyWTB/V4vPOHP/jeXrHCyBv+MumV0RDF4jqlVMrrKERUXpRSvYFA4Meffvqp3r179+fDys4///wWy7J+7GU2IvIOixkaMabdf/+aHdt3rAGA62+8wWgbNarY0FCvF738Mnp7e72OVzIvL3gpveC1N9IvvfCC7oonpa5tOGYvlBUMI2kGxVvLlxd2bNuWB4B4LAZ/MGg7jW1AuPb4B++PYqEIx/6t1zGIqDw5jvNGLpcLvfzyy59/fikUCvD5fE94mYuIvMNhZjSipFKpB3fu3Hnx+PHjw9ded20AAJqamjOrli8PnX3uuXLDurWFs84513/iSSdVbH9uNp833IbWEGobgX7+DLuhWgMAEq4//N4n63Mb1m9I2bZtFgI1PgSCx7p92Ih8dptwnfle5yCi8hOJRAwAH11//fWFhoaGwP7zHR0dbiqV6vAwGtGQCHCY2VCwZ4ZGlEwms2Bze/umA89N/MrEmquuvgrr1651tKvNjevWWV7lGyrLsuBIOfjJ8VKiGKoNxf01delwQ8gNhsvngYbWEI69RSlVsf9+iOi4CtTU1Hxy8sknB5qa/ntbsQkTJsjGxsZLPMxFRB5iMUMjilJKJxPJl+Pxg7edaW1rk7d+91ajrr7OHnvCCb6OPXuwa+dOj1IOnt/vhyhaATiH30umolkFC3bxV17HIKLyFAqFLq2tra059Lxt2zAM40QvMhGR91jM0IgTi8X+deVbb+043GtXX3NNoKujw122ZAnWfPBBRa6WJbQ2ZCo+4novZDaZFa671uscRFSe7r333teTyeTni5XE43GdTCYxb948pNPphV5mIxoqrzfLrORNM1nM0IijlIrZRXt+MpHUh75mGAZu/MZNZmNjY+Hiyy4rjxW8Bugb3/qWhGFWZCF2RI4D2MV2pdRnXkchovJVV1fXDQAdHR145plnxNNPPw0hhC4Wi3u8zkZE3mAxQyNSb2/vX69atfJDrb9Qz0BKiS+dckpx7ZqP0x5EG7L2TZts1x8IHPvKCqFdyGTvauG6V3odhYjKm23bIQAYM2YMRo0alQMArbVwXXepp8GIhkIIiDI5KhGLGRqRlFKFdCr9r3t2784d7vVzzj2n1rIsw3Xdw71ctjr37sWmrTss+IPlM3F/iEQy9imK1q1KqazXWYiovOXzef/u3buxZs0aa+LEiX4ACAQCP1RKOZFIRMydO/c8rzMS0fBiMUMjVjKZfHLDho0bjvR6fX2Db8WSJflNGzaU/fyTdCqF5+fPz7654q2cU98YPuImmRVG5DIxUbT+bvasWbu8zkJE5a9QKDS+/vrr+uOPPzZ37txZ6Dv3IACYpnm1bdu1kUikZbDtP/HEEzOffPLJ10qVl4iOPxYzNGIppXQ6nXotnT78aLIpV0wxb7jpxuDe3bvdbLa8OwV27tjhZqXfdBpbQ/tWpB8BtAuRTW2cPWP6PK+jEFH5+/Wvf/0NrXWjbdvihhtukGeffXbQ7/dDSpkCANu23wSw1DTNhwb7PTKZzFPpdPrekoUm6gcBLgAwFCxmaESLRWP/sn7d+u1Hel0IgQsuvMD31rJl+eHMNVBjxo6VQceyQulYOpxNpEOZeMqXiiZF9giVWgUQ6cQ2OPadXucgosqgtX7ZdV1DCAGtNRYsWCCFEC9JKecAgM/nu8QwjKKU8h8H+z2UUhmlVGfpUhPR8TZixt0THY5SKvrCCy987LruBCkPX7u3trYakyada694c0kuGArrCy66KDzMMY+pqbkZt956S+2h519btCjTbRUc+AOGF7kGzSoUhFWYM3vWrCMWmkRE+0UikRPPPPPMjhNPPHH0hx9+mN++fbuUUu4uFArfNgzjqjlz5mQDgUCgrq4u09PT8zcA+KCEqEqwZ4ZGvGQq9X8/27IlcbRrTho/PnD9DTeEtOPoBc8957zz1lu5Tz766LCLB5STU089NSisfMHrHAOiNWQ68YGwi//L6yhEVBmCweAD48aNGzVhwgThuq750UcfBVzXvVgppV3X/ScAoWw2Kzs7O+scx/kzr/MSDZQQ5XFUIhYzNOLde889K7Z+tnV9Lnfs2uSKq66sue76692zv3Z2oLurS9i2PQwJB2fP3r3u6g/XFHRNfdn1JH2ukHdgFTSKVhHaBewijGinBdv6oVKqspaSIyLPSClHhUIhCQDRaNTn8/l+p5TqAgCt9d2O4yAYDG4CcAuHiRFVFxYzVBXS6fSjL724YNGqt1am88coapqam3wtLS1y8nmTfW//4Q9f6PUoWhZyZbBgwKpVb2ec+qayXtnMiHcbRqxLGNFOn4x12yKftYXWydmzZr3ndTYiqgyRSKTO5/Od1draikwmA9d14bruG/tfDwaDi6ZMmQK/3/+fSqnnvcxKRMOPc2aoKtx+++0PRSKRyKZNm3o3btyIe++/D6Z59P/8x4wZY+zcsTO/fMmSYiAQELV1daHurq6clMI0TZ97yZQpoWGKDwB4b/Xqgl0swrZt3d3T6xYgZTkXMl9QtExpF6Gl8S9eRyGiymGa5s8dx7nSMAxs2LChCMDn9/tT+1/P5/PXfvDBB++ZpvlnkUjkP9jrS5VIjJSVSj3AnhmqGkoprbU+IRgMpvuzWaYQAhd+/cKaa669pnbyeZNrRo8epa++5uqaSy+9LGCa5rD+z/KD997Pt2/5zNgSSwe2pwvBbE1jWNc21AxnhgHT+qAvTZ/PbW5ujsOxf+1RIiKqML/97W+/1dbWdvv+Jfabm5t9fS/9/f5rlFLbc7nc5EKhMFpKeb4HMYnIQyxmqKoopfKGYdy26JVX9xxp/5lDmaaJ+vp6nHTSSUYgEIDpM9Hd1RVcuXx5+pOPPhqW8WY7d+7UbkOLCZ8fMH2VMUvPPngvUqdYlD3RaKNSKuZRIiKqMH6//08uvvjimsbGRmf/uYaGBtvv93cdeJ3jON2O48wJh8MNw5+SiLzEYoaqzt133/1qT0/PaSuWLX/Xsqxj33CIYDCI791+m3H1NVfXdu7dKxcvWpQd6ByaVCqFzzZvxsKXX869+PwLuWQyCQCIxWL46MMP7fm//31hxbLluUQigWw2i6LrOjAqbFSoLwCnse2gFRSkRlnv50NE5cW27SeXLVvWe/vttxuZTAZvvPEGJk+ebNq2vfSQS10hxFVSypu8yEk0VF5vllnJm2YKfchQEKJqMXfu3JZQKLTmyxO/3BoIBOXEr0wccLWgtUYul8Mbr7+RG3fSib69u/foa264wXes+15asCCTdIQP0C4cx/Q7lnYcVziBkKX9wTBMH0Qhl5HFAuA4phsIuTpcO6xzdEpGa4hcJidS8ZA0jJ/MmjH9p15HIqLKMX/+/E9vueWW07XW0Fpj8+bNueXLl39r5syZSw699rHHHrv6vvvu+8J5onL25TPP1P/5u995HQMAcN3ZZ7+vta6o4ZrsmaGqNX369N5sNjtp7Sdr/WvWrBnU4wghBMLhMC6//DJ/S3MzEvG4r1Ao4P3Vq618/vCdEJ179yJdKGodrvXrcF1Q1zWaVqDGdnwBV9c2hOEPAFJCh2pqnPrmGqepLVCxhQwACAEdrg3B749rx37S6zhEVFnS6XT9Sy+9lBdCYNmyZfbSpUtDruuuPNy1LGSoEgns+zxRDkclYjFDVW3GjBldrutec9ppp8aH0k5Tc7Nx8sknm41NTdnn5z9vbdrTJRctei11uGvffvfdtFPbWHvgOR2qCen6Jv9QMpQ7LY3PlFKbvc5BRJXFtu0bc7lccteuXU57e/v+HnTOjSEiAFyamQgPPPDAkpdeemmN1vqqwTyVSMTjCIXDWLlyVa47npB2XZOEz2/mklHxysJXskJKV7sObNsWNTU1KGRzIQTqjsPfpLwJ1+3wOgMRladIJFIDIKuU0oecrzNN04jFYqMWLlwIAAiHw4l77rmHG2MSEQAWM0QAgEwm82w0Gr2ipaVlQL2Vr73+RmZXV48p7KLpBsNaN7SG9q805tQ11Ua1C0AAhg8wBJKWA+Hz21W5nLzWX9iAlIgoEok0h8Phz4LB4AoAf3Tga1LKa2zbnh8IBNzx48enrrjiioannnpqj0dRiY6PCh7iVQ44zIwIQCKR+M3GDRvbB3qfFMJ0Q7UBp2WMoWvqwwctmSwEIA1ASkDIfV8bJnR9swlRhT96QoyNRCJ8tyaiQzlSyqTrul/osjYMYxmADyzLKvb29hY2btzoAnh1+CMSUbmqwk9URF+klMrF4/H1XZ2dA9oMs62lxS8TvVrkMjbs4vGKNyK4oZqztDRu9DoHEZUXpVTirrvuGh+Px68+9LUHHnggBuDChoaGeVLK0MqVKzsymcyDHsQkojLFYoaoT1dn5/RlS5f9cO0nn/R7bseZZ58l7ph6mzhj3Gh3rF9kjFiXBS53fniBUC1M8y+9jkFE5UkpddiHSUopx7IsZ+zYgOlAigAAGUlJREFUsbV+v3+BUmrbMEcjOu683l+mkveZYTFD1EcpFb/rrrt+uW3rtqX5XK5f95imiXA4jEsuudj/zW/cVDPl0otlIBVLi2w6UxVFjV3EgP6edvECDjUjooGYM2fODwzD+KPOzk5LStnmdR4iKi8sZogO0d3d/efr1q3bO5h7Tz3lFPPeu6bWnja6xTQSvSN/wrvpw0HzhBz76Ne7bp0W4objG4qIRhLHcR4LBAIrLct6rlgsvvrII4982etMRFQ+uJoZ0SGUUh0LFixo11qPHezqIsFgULhmFf54GUf5OwsBt7HNlYkea/gCEVGlU0ol0LfK2Zw5c9ZqrV/zOBJRyXE1s8FjzwzRYaTT6Ud6e3qcwdw77/fzk+u27bB0TcO+TTC1hkgncnAH1VzlsfJHHnrm8xkwzG8ObyAiGim01rmZM2du9zoHEZUPFjNEh5FKpZ7ZsGHjLj3AeS+u6yKdiNcHtCsCmUQimIkng+lY+oT6GlNmUvnjFLe8+IMHDz07kDQAIScMbyAiGgkefvjhCYFA4G6vcxCVmsC+nplyOCpRFY6DITo2pVTm0Ucf3dPb2zuhtbW13/dJKfHA9AcAoObA84888pjr1tRX5rtEqQlRc+yLiIgO5rpuZy6XO+WRRx75+rRp097xOg8RlQf2zBAdQaFQ+Pa2rdt2lqKtu+66Q/pyaV0VK5wdGx+iENGAKaXyADZYlvWu11mIqHzwQwXRESilel555ZUtAE4aalvRWEy7dtEnY92u2zyq2h8isKIjokFRSvH9g0YkybEbg1btH6qIjiqbza6zrKEtvuW6Lha9sSTnNLS4bk1ddb5duS5kMlqQqVhB2MWveB2HiMpLJBKpO9Y1jz322FcikUhwOPIQ0eEJIeYKIbqEEGsPONcshHhdCNHe93tT33khhHhQCLFZCLFGCDH5gHvu77u+XQhx/wHnzxNCfNJ3z4OiHxN5WMwQHUUul1uRSCSG1MbKlW/ni4EaA4GQRCBUlcWM7O3QIpcJXH7eJKOlqWntse8gomoSCARuO9rrkUikxTRNfmYh8t4jAG485NzfAFistT4dwOK+rwHgJgCn9x0KwK+AfcUPgJ8A+DqACwH8ZH8B1HfNrAPuO/R7fQGHmREdRTabXbp3z56Otra2MYNtw3Vdx7Ry2oY24Q8YkEYpI1YE7Q84Ip81XceRgB75m4kS0YDcf//9cw89F4lEpGmaV9u2vSwYDF6VTqcXKKX4/kEjkkBlPOvUWi8XQpx8yOnvALiy78+PAlgK4K/7zj+m9y0N+7YQolEIMbbv2te11lEAEEK8DuBGIcRSAPVa67f7zj8G4GYArxwtE4sZoqNQSnUuXrKkE8Cgi5kpUy6rucS2sebjNcWtO3bmkllLOzUNdUdcvngE0g0tphMI6ZUrV8pQKNR07DuIqNo1NjbOHzt27HV79ux5d+rUqVd6nYeIjmi01npv3587AIzu+/M4AAcupLSr79zRzu86zPmjYpct0TEU8vmuobZhmiYmnzfZ991bvlPbFAz4ZDqRq7qVzYJh4dY2FAtWsdnrKERUviKRiPnMM8/8wznnnHPZ5ZdfHqqvrx8diUT8XuciqhKtQoj3DjjUQG7u64UZ1g84LGaIjiGVTs3fu2dPrlTt3Xzzt4MntTYJOMVSNVkxdLjO50i5zuscRFS+amtrn/zSl740c+LEic0AcM4553y5vr5+pte5iI4XIQRkmRwAerTW5x9wRPrxV+jsGz6Gvt/3PwTejYNXhD2x79zRzp94mPNHxWKG6BgS8cR/bdq0aUsp2+zo6gYMXymbrAxVNLSOiAYnEAikJ0+efML+r+vr66VhGMccakJEnnkRwP4Vye4H8MIB5+/rW9XsIgCJvuFoiwBcL4Ro6pv4fz2ARX2vJYUQF/WtYnbfAW0dEYsZomNQSulEPLEwlUqVpL1UMglba1G1H+yFCHsdgYjKl+u60Wg0au//OpPJ2K7rjvcyE9HxJoQoi6MfOZ8CsArARCHELiHEDAD/DOA6IUQ7gGv7vgaAhQA+A7AZwK8B/CkA9E38/0cAq/uOn+5fDKDvmof77tmCY0z+B7gAAFG/RKPRf1r7ydrvXnzJxacOpZ0d27fjzRVv5WH6igACJYpXWYTgPhFEdESxWOx/vPfee1fcdNNN59u2jZUrV36QzWZ/5HUuIgK01nce4aVrDnOtBvD9I7QzF8AXVjHUWr8H4KyBZGLPDFE/KKWS3d3db+7ZvafvZ3NwmltaoDWEU9t4zA3iRjC+7xDR0Uifz1cLAFprFIvFcLFYjHkdiojKEz9UEPVTPBb7yaJXXxVbP9s66Jn7tbW1MEyj+mb+H0RU4WQhIuqvcDh8e09PT2DJkiUbOjo6AOAtpVSVLf9I1cbr4WX9HWZWjljMEPXTjBkz9gCAaZpD+jDuQrilSVSZNMBihoiO6J577nn0jjvuOGXz5s2XL1++/N54PP7nXmciovLFOTNEA2AYxok9Pd3vjJ8wflAr62SzWbjV/hCBc2aIqB+UUr0AHvc6BxGVt+r+UEU0QDNmzNi9d2/HR47jDOp+0zSBolWdE/8/J7j5HRER0QG83l/mgH1mKg6LGaIBSiYSv+zYu9cazL1+vx9+n+/w99pF7UtG02a8Ow93cMWSpxwbsC1Au5DRroTs2RsTid44rLyFQs7G/oUTBIKRSKQy3zGJiIiorLCYIRqgfD7/+tatWz8b7P1FywqIbLrw+QnXgZHozbUZbmbqrd+pHTd6lAPbto/SRHnRGjLeE5Xxnrky3jtTRrt+Baf4LrROyHy20Yh1+414j2l07QKKFqB1Ew7e4ZeIiIhoUDhnhmiAlFL2iy++uAPAVwZz/9TbvmvOf3GBncumHe0PGEY6kZ905lcC506aJAHgqquurPntE0/ZTvMoQJT58wbtQsZ71qBYvHP2rJnr+87O2f9yXw9MixbiIkBcKRO9u+HYSwHs9iIuERFRuRFAxa4kVg5YzBANQi6ff6Wzo+PK0WPGDHj+Rygcxq03fye4Yf0Gd9uOHfmrvnlTsLGp8fPXTdNEa1Oj6CzkbQTDZf0zKvK5NGz7ZwcUMgfpW061B8BLfQcRERFRyZT1ByWicpWIxx/csH7DzNFjxpw5mPuDwSAmTZ4kJ02e9IWVvVzXRbZg5RCsqx160uNIuxDZ1GvCdZ70OgoREVHFquA9XspBmY9hISpPSik3m8u1a13afdxs28bjTz5VyBRtA+X+xuY4gNaruZkdEREReYXFDNEg5XO5NxOJREnblFLCZ/ocN1Rb/ssXGwYgxM2RSITvI0REROQJfgghGqREIvHbTRs3bStlm1JK3Pa9W8NmJlEsZbvHxb7NdlwAPq+jEBERVTIpyuOoRCxmiAZJKRXr7OhYVbSOQ91h2+VdILguZCq2VtjFq5RShWPfQERERFR6LGaIhiBfKDzb1d2VL2Wbi5e8mbFrG8p3HorWkMnoBmEVrmEhQ0RERF5iMUM0BJl0+oUd27bvLGWbnV3dBvzBsl1pUOQycRStv1BK9XqdhYiIaCQQZfKrErGYIRoCpZSTy+c7Stmm3zQlHLs8e2ZcByKf+Ui4zhKvoxARERGxmCEaokI+31XK9qQUgHbLbwEA7UImY+2iaN2mlLK8jkNERERUtkNZiCpFsVhMuK4LKYf+bMB1XeQKlkaoIVCCaKWjNWQiugmF3HVKqR6v4xAREY0UAoAs973lyhh7ZoiGSGvdZVml6ajo7OyE4wvY5bZhpsile2DlvztbqZLODyIiIiIaCvbMEA1RPp//MJVKIRgMDrmtN5ctz7mh+poSxCodrSEKufbZs2at8zoKERHRSCTK7CFmJWHPDNEQZbPZZXv37C3JIgBtra3an4xaIh3PGd17XNnpfUeIyKb2wrH/yuscRERERIdizwzR0HUlk8kYgDFDbei6a68Ju66LvXv2oKm5GfOefa7o2kUfTA/30LSLO2bPmPGWdwGIiIiIDo89M0RDpJTSlmWVbFK8lBLjTjwR4XAY3/zGjT4j3uOUqu0Bc10Ix+Z+MkRERMeREKIsjkrEYoaoBLLZ7E7XdUve7uuL38y4oZp8yRvuL60Brfd69v2JiIiIjoLFDFEJ5LLZl3u6u0vag7J71y5ktRC6xsMFAQwDOlz7nYfmzJ3lWQYiIiKiI2AxQ1QC6XT6uY0bN24uZZsff7zG0T7/0JdIGyIdqm3VoZp/eGjO3GleZyEiIhpphBCQZXJUIi4AQFQCSqn8vGfnPZdMJv+2vr5+yO3t2LHT7uiNCTS1lcUDB11TPxZC/uyhub+5DK7zBlx3FwANwzgD0vghXHfZ7BnTf+B1TiIiIqouLGaISiQWjf1sw/oNd3z9oq9/aahttbe3O25tQ8CzzTO1BmzLEfncHrhOVjjOOggRgjQuAnAdtLY1ZFQIOU4bZki4hSZvghJRtYlEIrV+v//uadOmPeR1FiLyXlk89SUaCZRS8Z6e7jW2bQ+5ra7ubj3syzFrDRQLEKn4HhnvWSwTUSWzqa/KfPYqUSzMmj3tvm/ALv4/OPYPofVMrZ0nhF08SRRyF8F1r3no1w+vjEQiHq4hTUQj3Zw5c77W1NS0qq2t7f88/fTT13udh6hUvF7FjKuZEREAIJlI/kP7p58OeSnjglWUkMP046k14NiQse4eGev+FXLpRbCtH8N1oJRKayHmaSG/AwBw3ZUQ8t3ZSr35J0r9u1LKmT1r1iah9QkQWK2F+OXwhCaiauT3+0+fMmXKWTfeeGODlPJPvM5DRN4TWmuvMxCNKAsWLFhy/Y03XCUHWYwsXbo8s6WzR+uautoSR/tvVj4ncpmNwnE6oF0ftHaEY8/UQFEADoC0UmrAS0I/FIlco4WY/sezZt19HFITUZV7+OGHT25ubv69aZrBTCbz93feeec8rzMRDdVZ55yjn331Va9jAADOOOGE97XW53udYyA4Z4aoxGKx2N9s2rRp4RlnnNEy0HsT8Ti27t4jdENLaZdj1i4gJKA1RDq+XRTy/yoc+xdaiPuh9WIIUa+U2jXUbzNbqcWRSGRpCRITEX3BzJkztwE4z+scRFQ+2DNDdBwsXLhw6XU3XH/FQO5JJZN4dv7zjlPfouHzl/RBg4x1F4WV92nTtw6uM3X2zJnrStk+ERERDQ57ZoaGPTNEx0E6nV6YTCavGMgyzaFwGD6fz3IMI1TqPG6oJiMd5y1hF29WSg19hQIiIiIqGVmZc+/LAhcAIDoOksnk09u3be8ayD2maeKySy4Oyd7O0naXug5kJrkArvMLFjJEREQ0krCYIToOZs6cuWPbtm0ftbe39wzkvvHjx2PC+JPyIpPKliqLyGW6Ydu/BPTqUrVJREREVA5YzBAdJ7fecssNa9d88tOtW7cm+nuPlBJXXDElZNiF0nU4O3aPhj4XEFbJ2iQiIqKSEABEmfyqRCxmiI6j22+//RdrPvr42Wg06vTnetu28eyzz1l2qK5km08K13WEkHWzZ81Ml6pNIiIionLAYoboOItGo99/e+Wq1cVi8ZjXzn/u+WIGUsMfKN3iHI59EqC/WbL2iIiIiMoEixmi40wpVYjFYretemvl5mMthf7VM7/qQshjVz395dgA8Duh9U0la5OIiIhKSghRFkclYjFDNAymTZu2q6u76y/XfrK242jXnXnmVwM+xxLQbkm+ryjkemEXf6aUypekQSIiIqIywmKGaJjcecedL27ZvPnfdu/anTnadddccXmNkU4c9Zr+0tIIQMqTStEWEdFIMmfOnNZIJGJ4nYOIhobFDNEwisVi//LB+++/mskcuVYZN24c6oN+AbdfawYcldAa0Jq9MkREhxBC/IeU8q+8zkEEISDL5KhELGaIhpFSSvf09Nz/hxUr1jjO4YuVx594qpDKZAVECX48HTsptN489IaIiEYWx3F+5bru//Y6BxENDYsZomGmlMr09PR8960Vf9hy6IIAruvCdmxpN7SGMNQnJI4NYeXXA+j3PjdERJXqqaeemvvkk0/2e7ETrTWH4FLZ8HriPxcAIKIBmXb/tM2dnZ33rvn44737z7mui+XLV1hOsNYdciEDQKbiH4mi9W2lVOlWRyMiKlOpVOpv0+n0u/29Xkr5jt/v/+dIJFKZn+CICACLGSLP3HXXXau2bN7y4tpP1kYBoJDPY+uWLX4dqgkMqWHXhcimo7CLW5RSuZKEJSIqc0qpTgCFxx9//AeRSCR0rOtnzpzZ7rru40qpo6+ZT0RlrXQb8xHRgE2dOvWPf/fM7xbFYtEHt27bMVqbfgNikJNltAaKli1TsTdg238xW83aUOK4RERlrbm5+a/9fv8PstnsUwCO+TBn+vTp64YhFtFRCaBiJ9+XA/bMEHls6u1T5+/YvuN7NsRnbmPrwH8mtQasvC2S0S0y2ft9YRe/xUKGiKqRlLLN7/fHARSfffbZe+fMmXOC15mI6PhiMUNUBqZNm/aOKBYukPHuBbDyhYHcK1KxrTLRe4fMZ782e8aMiFJq6Gs6ExFVoJ6enr9Lp9OFurq6ra7r3hEOh7976DXPPffcoueff/6/vMhHRKXHYoaoTCilUsIufkcmY/8kUrHdcN3DX6g1UCxo5LNZEe/5TBStf5s9c+bvlVLZ4U1MRFRelFI90Wj0DAB/pbX+uzvvvPMXB74+b968x6WUlyeTyVM9ikh0WF6vYlbJq5mJQ5eGJSLvRSKRVu3zL9Y+f4MO102A0Te9zcqnZSqxDK7zBFxnkwA+4WplRET98/jjj28YP358844dO37s8/l8U6dOnet1JqKvnXuufmnxYq9jAAAmtLa+r7U+3+scA8EFAIjKkFKqB8A5kUikWRfyfwrDuFhLY5Swi3XCLt6llEp6nZGIqNJorf9x48aNTxiGEclms0EALGaIKhyLGaIyppSKAvifXucgIqpUkUgkGA6HbxZC7M3lcq8CgOM4wUAg8KrX2Yj2q9QhXuWAc2aIiIhoxGpsbHzwvPPO+41hGL8CUPT7/XsAIBAIrPc4GhGVAIsZIiIiGrEcxzlvw4YN5qRJkyaMHj16kWVZZ9TX17+nteajcCoLQgCyTI5KxGFmRERENGKlUqmbpJTvrVu3bmcmkzkLQEsymbwQgOF1NiIaOhYzRERENGIppboikcjJALSU8jQA25RSGoDtbTIiKgUWM0RERDSiKaX2b9zV7mkQosMSEKjQMV5lgHNmiIiIiIioIrGYISIiIiKiisRhZkREREREHuI+M4PHnhkiIiIiIqpILGaIiIiIiKgisZghIiKiqvKb3/zm+15nIDqQFKIsjkrEOTNERERUVR544IFfAsDcuXN/ZNv2HABFpVTB41hENAjsmSEiIqKqNH369J8Hg8HHg8Hgf86ZM2ec13moOgnsWwCgHI5KxGKGiIiIqtLTTz+9uKmp6dKWlpabDMM4zes8RDRwLGaIiIio6kQikbrW1tZTrr322lbHcfZalrXK60xENHCcM0NERETVyMnlcuGFCxe+HY/Hb1NKWQ8//PDJrutuV0ppr8NRFangIV7lgD0zREREVHWUUtlcLvdQKpX6ZynlWZFI5FHXdbcGg8Ebvc5GRP3HnhkiIiKqSvF4/B+bmppWZTKZr55++unGxIkT8e677/4cwCteZyOi/mExQ0RERFUpFArdNm7cuFMvueSS0P5ztbW1MhKJCA41o+FUqXu8lAMOMyMiIqKq5LruZXV1deEDzwUCgYDf7+cyzUQVgsUMERERVSXbtl8E4B547sILLzzR7/f/2qNIRDRALGaIiIioWnWsXr06vmnTpt79JwKBANLp9I2RSGSil8Gouni9WWYlb5rJOTNERERUlWbMmPERgLGPP/74mlNOOaXF5/PBdV2EQqHeXC7X7XU+Ijo29swQERFRVRNCvLZs2bIEAOzatcuSUj6slIp6nYuqgwAgRXkclYjFDBEREVW1u++++8dbt26tBYBkMpm1LOsNrzMRUf+wmCEiIqKqFw6Hf7lw4cLsBx98IH0+n+N1HiLqH86ZISIioqpXKBR+19vbe2s+nz9TKZX0Og9VF4EKHeNVBljMEBERUdWbPn36SgAneZ2DiAaGw8yIiIiIiKgisWeGiIiIiMhDlbrHSzlgzwwREREREVUkFjNERERERFSROMyMiIiIiMgjQghIDjMbNPbMEBERERFRRWLPDBERERGRh7gAwOCxZ4aIiIiIiCoSixkiIiIiIqpIHGZGREREROQhDjMbPPbMEBERERFRRWIxQ0REREREFYnDzIiIiIiIPCIA7jMzBOyZISIiIiKiisRihoiIiIiIKhKHmREREREReYirmQ0ee2aIiIiIiKgisWeGiIiIiMgrApDsmBk09swQEREREVFFYjFDREREREQVicPMiIiIiIg8IyDAcWaDxZ4ZIiIiIiKqSCxmiIiIiIioInGYGRERERGRRwS4z8xQsGeGiIiIiIgqEosZIiIiIiKqSBxmRkRERETkIclhZoPGnhkiIiIiIqpILGaIiIiIiKgicZgZEREREZGHuJrZ4LFnhoiIiIiIKpLQWnudgYiIiIioKgkhXgXQ6nWOPj1a6xu9DjEQLGaIiIiIiKgicZgZERERERFVJBYzRERERERUkVjMEBERERFRRWIxQ0REREREFYnFDBERERERVaT/D2fhQG1zPweWAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 1296x936 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vZMC-flvfHEn",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OB_nDdxQZEpW",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}