DataSF_Project.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/janna-in-sf/DataSF-311-Cases/blob/main/DataSF_Project.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NwLJBr9K0D7h"
      },
      "source": [
        "# DataSF Project\n",
        "\n",
        "Data from Data SF on 311 requests/cases to city of San Francisco. \n",
        "\n",
        "*    See: https://data.sfgov.org/City-Infrastructure/311-Cases/vw6y-z8j6 \n",
        "*   Started: January 2023\n",
        "*   Status: Work in Progress\n",
        "*   Janna Voss\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Mr7QOJmq0Nvm"
      },
      "source": [
        "## Imports"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "iJg0RP3h0Azg"
      },
      "outputs": [],
      "source": [
        "from google.colab import drive\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import statistics\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "import math\n",
        "import time\n",
        "from datetime import datetime"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uK-yPc1G3ACZ"
      },
      "source": [
        "## Load Input File"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Mount Drive"
      ],
      "metadata": {
        "id": "qHiKqA7AtYq2"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zrfxWTXz6QYt",
        "outputId": "83d8c1b9-b848-4856-ed11-c40e1497e07a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Read CSV File"
      ],
      "metadata": {
        "id": "s9HeF8Trtx3c"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "id": "x-XSZ7Sd3HaD"
      },
      "outputs": [],
      "source": [
        "# set variables\n",
        "file_path = '/content/drive/MyDrive/colab_files/data/'\n",
        "file_name = '311_Cases.csv'\n",
        "absolute_file_name = file_path + file_name\n",
        "\n",
        "# hardcoded column names for cols to keep from input CSV file\n",
        "col_names = ['CaseID', 'Opened', 'Closed', 'Updated','Status', 'Status Notes',\n",
        "             'Responsible Agency', 'Category', 'Request Type', 'Request Details',\n",
        "             'Address', 'Street', 'Supervisor District', 'Neighborhood', \n",
        "             'Police District', 'Source']\n",
        "date_col_names = ['Opened','Closed','Updated']\n",
        "category_col_types = {'Status': 'category', 'Supervisor District':'category', \n",
        "             'Police District':'category','Source':'category'}\n",
        "col_index_name = 'CaseID'"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "t0fZ5ep07VCX",
        "outputId": "39119f2f-1117-41c1-b316-73a7b065f5af"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Loading file:  /content/drive/MyDrive/colab_files/data/311_Cases.csv \n",
            "\n",
            "Start Time = 21:12:57\n",
            "End Time   = 21:13:00\n",
            "Elapsed time: 2.9613149166107178 seconds\n",
            "\n",
            "Loaded file with (rows, cols) =  (2500, 15) \n",
            "\n",
            "Before converting col names . . . \n",
            " Index(['Opened', 'Closed', 'Updated', 'Status', 'Status Notes',\n",
            "       'Responsible Agency', 'Category', 'Request Type', 'Request Details',\n",
            "       'Address', 'Street', 'Supervisor District', 'Neighborhood',\n",
            "       'Police District', 'Source'],\n",
            "      dtype='object') \n",
            "\n",
            "After converting col names . . . \n",
            " Index(['opened', 'closed', 'updated', 'status', 'status_notes',\n",
            "       'responsible_agency', 'category', 'request_type', 'request_details',\n",
            "       'address', 'street', 'supervisor_district', 'neighborhood',\n",
            "       'police_district', 'source'],\n",
            "      dtype='object')\n"
          ]
        }
      ],
      "source": [
        "# Load the file\n",
        "print('Loading file: ', absolute_file_name,'\\n')\n",
        "\n",
        "rows_to_load = 2500\n",
        "#csv_date_format = '%m/%d/%Y %I:%M:%S %p'\n",
        "\n",
        "start = time.time()\n",
        "print(\"Start Time =\", datetime.now().strftime(\"%H:%M:%S\"))\n",
        "\n",
        "\n",
        "df = pd.read_csv(absolute_file_name, usecols=col_names, \n",
        "                 dtype=category_col_types, index_col= col_index_name, \n",
        "                 nrows=rows_to_load, parse_dates=date_col_names)\n",
        "\n",
        "#df[date_col_names] = df[date_col_names].apply(pd.to_datetime)\n",
        "\n",
        "end = time.time()\n",
        "print(\"End Time   =\", datetime.now().strftime(\"%H:%M:%S\"))\n",
        "print('Elapsed time:',end - start, 'seconds')\n",
        "print('\\nLoaded file with (rows, cols) = ',df.shape,'\\n')\n",
        "\n",
        "# rename columns that contain spaces and convert to lower case\n",
        "print('Before converting col names . . . \\n', df.columns, '\\n')\n",
        "df.columns = df.columns.str.replace(' ', '_').str.lower()\n",
        "print('After converting col names . . . \\n', df.columns)\n",
        "\n",
        "# also convert list of date col names\n",
        "#date_col_names = [x.lower() for x in date_col_names]\n",
        "#df.head(3)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# initial exploration\n",
        "print(df.info())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-qPy3cLxGNCf",
        "outputId": "919c9b4a-4dc6-4291-8567-bc6e871420d6"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 2500 entries, 6501676 to 6489588\n",
            "Data columns (total 15 columns):\n",
            " #   Column               Non-Null Count  Dtype         \n",
            "---  ------               --------------  -----         \n",
            " 0   opened               2500 non-null   datetime64[ns]\n",
            " 1   closed               2495 non-null   datetime64[ns]\n",
            " 2   updated              2500 non-null   datetime64[ns]\n",
            " 3   status               2500 non-null   category      \n",
            " 4   status_notes         2497 non-null   object        \n",
            " 5   responsible_agency   2500 non-null   object        \n",
            " 6   category             2500 non-null   object        \n",
            " 7   request_type         2500 non-null   object        \n",
            " 8   request_details      2440 non-null   object        \n",
            " 9   address              2500 non-null   object        \n",
            " 10  street               2372 non-null   object        \n",
            " 11  supervisor_district  2372 non-null   category      \n",
            " 12  neighborhood         2367 non-null   object        \n",
            " 13  police_district      2364 non-null   category      \n",
            " 14  source               2500 non-null   category      \n",
            "dtypes: category(4), datetime64[ns](3), object(8)\n",
            "memory usage: 245.1+ KB\n",
            "None\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 656
        },
        "id": "uqgrLf6A8xhi",
        "outputId": "c759dac9-8c37-453a-d04b-194fef3c15c7"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                     opened              closed             updated  status  \\\n",
              "CaseID                                                                        \n",
              "6501676 2016-11-06 10:10:45 2016-11-07 09:11:12 2016-11-07 09:11:12  Closed   \n",
              "6484030 2016-11-01 11:26:26 2016-11-01 16:59:33 2016-11-01 16:59:33  Closed   \n",
              "6505388 2016-11-07 11:08:09 2016-11-12 16:40:17 2016-11-12 16:40:17  Closed   \n",
              "6504738 2016-11-07 09:41:28 2016-11-07 11:11:06 2016-11-07 11:11:06  Closed   \n",
              "6507086 2016-11-07 15:40:03 2016-11-08 10:10:08 2016-11-08 10:10:08  Closed   \n",
              "\n",
              "                                              status_notes  \\\n",
              "CaseID                                                       \n",
              "6501676                              Case Resolved - 10-98   \n",
              "6484030                   Case Resolved - Pickup completed   \n",
              "6505388  Case Resolved - entire area is trashed out nee...   \n",
              "6504738  Case Resolved - If the bridge is closed DPW ca...   \n",
              "6507086  Case Resolved - meter shop has closed this req...   \n",
              "\n",
              "           responsible_agency                      category  \\\n",
              "CaseID                                                        \n",
              "6501676         DPW Ops Queue                   Encampments   \n",
              "6484030    Recology_Abandoned  Street and Sidewalk Cleaning   \n",
              "6505388         DPW Ops Queue  Street and Sidewalk Cleaning   \n",
              "6504738         DPW Ops Queue  Street and Sidewalk Cleaning   \n",
              "6507086  DPT Meter_Bike Queue              Damaged Property   \n",
              "\n",
              "                  request_type        request_details  \\\n",
              "CaseID                                                  \n",
              "6501676     Encampment Reports     Encampment Cleanup   \n",
              "6484030            Bulky Items               Mattress   \n",
              "6505388            Bulky Items            Electronics   \n",
              "6504738  Human or Animal Waste  Human or Animal Waste   \n",
              "6507086  Damaged Parking_Meter          Parking_Meter   \n",
              "\n",
              "                                                   address  \\\n",
              "CaseID                                                       \n",
              "6501676         2953 HARRISON ST, SAN FRANCISCO, CA, 94110   \n",
              "6484030    Intersection of ISADORA DUNCAN LN and TAYLOR ST   \n",
              "6505388  Intersection of ARELIOUS WALKER DR and REVERE AVE   \n",
              "6504738          1366 VERMONT ST, SAN FRANCISCO, CA, 94110   \n",
              "6507086          2664 MISSION ST, SAN FRANCISCO, CA, 94110   \n",
              "\n",
              "                     street supervisor_district    neighborhood  \\\n",
              "CaseID                                                            \n",
              "6501676         HARRISON ST                   9         Mission   \n",
              "6484030   ISADORA DUNCAN LN                   3  Lower Nob Hill   \n",
              "6505388  ARELIOUS WALKER DR                  10   Hunters Point   \n",
              "6504738          VERMONT ST                  10    Potrero Hill   \n",
              "6507086          MISSION ST                   9         Mission   \n",
              "\n",
              "        police_district          source  \n",
              "CaseID                                   \n",
              "6501676         MISSION  Mobile/Open311  \n",
              "6484030         CENTRAL           Phone  \n",
              "6505388         BAYVIEW  Mobile/Open311  \n",
              "6504738         MISSION  Mobile/Open311  \n",
              "6507086         MISSION           Phone  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-5289dd06-1901-4012-b628-03142352ad7c\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>opened</th>\n",
              "      <th>closed</th>\n",
              "      <th>updated</th>\n",
              "      <th>status</th>\n",
              "      <th>status_notes</th>\n",
              "      <th>responsible_agency</th>\n",
              "      <th>category</th>\n",
              "      <th>request_type</th>\n",
              "      <th>request_details</th>\n",
              "      <th>address</th>\n",
              "      <th>street</th>\n",
              "      <th>supervisor_district</th>\n",
              "      <th>neighborhood</th>\n",
              "      <th>police_district</th>\n",
              "      <th>source</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>CaseID</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></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>6501676</th>\n",
              "      <td>2016-11-06 10:10:45</td>\n",
              "      <td>2016-11-07 09:11:12</td>\n",
              "      <td>2016-11-07 09:11:12</td>\n",
              "      <td>Closed</td>\n",
              "      <td>Case Resolved - 10-98</td>\n",
              "      <td>DPW Ops Queue</td>\n",
              "      <td>Encampments</td>\n",
              "      <td>Encampment Reports</td>\n",
              "      <td>Encampment Cleanup</td>\n",
              "      <td>2953 HARRISON ST, SAN FRANCISCO, CA, 94110</td>\n",
              "      <td>HARRISON ST</td>\n",
              "      <td>9</td>\n",
              "      <td>Mission</td>\n",
              "      <td>MISSION</td>\n",
              "      <td>Mobile/Open311</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6484030</th>\n",
              "      <td>2016-11-01 11:26:26</td>\n",
              "      <td>2016-11-01 16:59:33</td>\n",
              "      <td>2016-11-01 16:59:33</td>\n",
              "      <td>Closed</td>\n",
              "      <td>Case Resolved - Pickup completed</td>\n",
              "      <td>Recology_Abandoned</td>\n",
              "      <td>Street and Sidewalk Cleaning</td>\n",
              "      <td>Bulky Items</td>\n",
              "      <td>Mattress</td>\n",
              "      <td>Intersection of ISADORA DUNCAN LN and TAYLOR ST</td>\n",
              "      <td>ISADORA DUNCAN LN</td>\n",
              "      <td>3</td>\n",
              "      <td>Lower Nob Hill</td>\n",
              "      <td>CENTRAL</td>\n",
              "      <td>Phone</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6505388</th>\n",
              "      <td>2016-11-07 11:08:09</td>\n",
              "      <td>2016-11-12 16:40:17</td>\n",
              "      <td>2016-11-12 16:40:17</td>\n",
              "      <td>Closed</td>\n",
              "      <td>Case Resolved - entire area is trashed out nee...</td>\n",
              "      <td>DPW Ops Queue</td>\n",
              "      <td>Street and Sidewalk Cleaning</td>\n",
              "      <td>Bulky Items</td>\n",
              "      <td>Electronics</td>\n",
              "      <td>Intersection of ARELIOUS WALKER DR and REVERE AVE</td>\n",
              "      <td>ARELIOUS WALKER DR</td>\n",
              "      <td>10</td>\n",
              "      <td>Hunters Point</td>\n",
              "      <td>BAYVIEW</td>\n",
              "      <td>Mobile/Open311</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6504738</th>\n",
              "      <td>2016-11-07 09:41:28</td>\n",
              "      <td>2016-11-07 11:11:06</td>\n",
              "      <td>2016-11-07 11:11:06</td>\n",
              "      <td>Closed</td>\n",
              "      <td>Case Resolved - If the bridge is closed DPW ca...</td>\n",
              "      <td>DPW Ops Queue</td>\n",
              "      <td>Street and Sidewalk Cleaning</td>\n",
              "      <td>Human or Animal Waste</td>\n",
              "      <td>Human or Animal Waste</td>\n",
              "      <td>1366 VERMONT ST, SAN FRANCISCO, CA, 94110</td>\n",
              "      <td>VERMONT ST</td>\n",
              "      <td>10</td>\n",
              "      <td>Potrero Hill</td>\n",
              "      <td>MISSION</td>\n",
              "      <td>Mobile/Open311</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6507086</th>\n",
              "      <td>2016-11-07 15:40:03</td>\n",
              "      <td>2016-11-08 10:10:08</td>\n",
              "      <td>2016-11-08 10:10:08</td>\n",
              "      <td>Closed</td>\n",
              "      <td>Case Resolved - meter shop has closed this req...</td>\n",
              "      <td>DPT Meter_Bike Queue</td>\n",
              "      <td>Damaged Property</td>\n",
              "      <td>Damaged Parking_Meter</td>\n",
              "      <td>Parking_Meter</td>\n",
              "      <td>2664 MISSION ST, SAN FRANCISCO, CA, 94110</td>\n",
              "      <td>MISSION ST</td>\n",
              "      <td>9</td>\n",
              "      <td>Mission</td>\n",
              "      <td>MISSION</td>\n",
              "      <td>Phone</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-5289dd06-1901-4012-b628-03142352ad7c')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-5289dd06-1901-4012-b628-03142352ad7c button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-5289dd06-1901-4012-b628-03142352ad7c');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ],
      "source": [
        "df.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Verify there are no duplicated CaseID values (index)"
      ],
      "metadata": {
        "id": "4Ozdqc7kwAb6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# check for duplicate case nbrs, which are the index\n",
        "assert df.index.duplicated().sum() == 0, 'Duplicated indexes - CaseID'"
      ],
      "metadata": {
        "id": "P9BTf7nboJum"
      },
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "10-rD3zpZmgg"
      },
      "source": [
        "### Sort by index (CaseId)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fP1kqAe_FOfG",
        "outputId": "1e52f19e-fe89-43ba-f32e-64636ed635b3"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Index is monotnoic: False\n",
            "                     opened              closed             updated\n",
            "CaseID                                                             \n",
            "6501676 2016-11-06 10:10:45 2016-11-07 09:11:12 2016-11-07 09:11:12\n",
            "6484030 2016-11-01 11:26:26 2016-11-01 16:59:33 2016-11-01 16:59:33\n",
            "6505388 2016-11-07 11:08:09 2016-11-12 16:40:17 2016-11-12 16:40:17\n",
            "\n",
            "*** After sorting index\n",
            "                    opened              closed             updated\n",
            "CaseID                                                            \n",
            "721686 2010-08-27 20:32:34 2010-09-02 15:02:39 2010-09-02 15:02:39\n",
            "730283 2010-09-10 15:40:46 2010-09-15 14:42:02 2010-09-15 14:42:02\n",
            "732258 2010-09-14 04:25:57 2010-09-16 14:02:37 2010-09-16 14:02:37\n"
          ]
        }
      ],
      "source": [
        "# if Index values are NOT monotonically increasing (nothing but continuously increasing data), then sort index\n",
        "print('Index is monotnoic:',df.index.is_monotonic_increasing)\n",
        "# print first 3 rows, first 3 cols (verify that dates increase)\n",
        "print(df.iloc[:3,:3])\n",
        "\n",
        "if (not df.index.is_monotonic_increasing):\n",
        "  df.sort_index(inplace=True)\n",
        "  print('\\n*** After sorting index')\n",
        "  print(df.iloc[:3,:3])\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "39giO_fS8wQh"
      },
      "source": [
        "## Exploratory Data Analysis"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Address Any Nulls"
      ],
      "metadata": {
        "id": "IVj54Xwl7gis"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# NOTE will remove\n",
        "\n",
        "print('keep cols', keep_cols)\n",
        "print('cols_below_threshold', cols_below_threshold)\n",
        "print(type(cols_below_threshold))\n",
        "\n",
        "# which cols contain nulls AND are equal to or less than threshold\n",
        "#cols_below_threshold = df.columns[(df.isna().sum() > 0) & (df.isna().sum() <= threshold) ]\n",
        "#print('cols_below_threshold', cols_below_threshold)\n",
        "\n",
        "#print(df.columns[df.isin(keep_cols)])\n",
        "\n",
        "a =  df.columns[~df.columns.isin([keep_cols]).any()]\n",
        "print(a)\n",
        "\n",
        "#print((df.isna().sum() > 0) & (~df.columns.isin(keep_cols).any()))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iFDYVGn7t3cQ",
        "outputId": "4208a2a0-834b-479a-df6a-641a600b2b85"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "keep cols ['closed', 'updated']\n",
            "cols_below_threshold Index(['closed', 'status_notes', 'request_details'], dtype='object')\n",
            "<class 'pandas.core.indexes.base.Index'>\n",
            "[['opened' 'closed' 'updated' 'status' 'status_notes'\n",
            "  'responsible_agency' 'category' 'request_type' 'request_details'\n",
            "  'address' 'street' 'supervisor_district' 'neighborhood'\n",
            "  'police_district' 'source']]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-32-aebe6772cabc>:11: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version.  Convert to a numpy array before indexing instead.\n",
            "  a =  df.columns[~df.columns.isin([keep_cols]).any()]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Drop row(s) containing nulls in 5% (or fewer) rows out of the total nbr of rows\n",
        "# Note: Don't drop rows with null in 'closed' - doing so will remove all open cases\n",
        "\n",
        "if df.isna().sum().any():\n",
        "  valid_null_cols = ['closed', 'updated']\n",
        "  threshold = df.shape[0] * 0.05\n",
        "\n",
        "  # which cols contain nulls & is total number of nulls less than or equal to threshold?\n",
        "  cols_below_threshold = df.columns[(df.isna().sum() > 0) & (df.isna().sum() <= threshold)]\n",
        "\n",
        "  # don't want to remove anuy rows with null in 'closed' or 'updated', so remove\n",
        "  cols_below_threshold = cols_below_threshold.drop(valid_null_cols, errors='ignore')\n",
        "\n",
        "  if len(cols_below_threshold) > 0 :\n",
        "    #print(cols_below_threshold)\n",
        "    print('Threshold(5% of fewer rows out of total nbr of rows):',\n",
        "          threshold, '\\n\\n*** Number of na, by column')\n",
        "    print(df.isna().sum(),'\\n')\n",
        "    print('Nbr of columns below threshold (excluding', valid_null_cols,'):',len(cols_below_threshold))\n",
        "    print('Names of columns below threshold:', cols_below_threshold)\n",
        "\n",
        "    #rows, cols before\n",
        "    nbr_rows_before = df.shape[0]\n",
        "    nbr_cols_before = df.shape[1]\n",
        "    # drop necessary cols\n",
        "    df.dropna(subset=cols_below_threshold, inplace=True)\n",
        "    print('\\n*** Number of rows dropped due to nulls:', nbr_rows_before-df.shape[0])\n",
        "\n",
        "    # validate \n",
        "    assert df.shape[1] == nbr_cols_before, 'ERROR: Logic to drop rows for nulls inadvertently dropped columns!'\n",
        "    for col in cols_below_threshold:\n",
        "      assert df[col].isna().sum() == 0, f'Did not remove rows with nulls in col {col}'\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_JIl4ey27fnG",
        "outputId": "a036c7cf-eafe-4a5a-cfef-b19872b60cc4"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Threshold(5% of fewer rows out of total nbr of rows): 125.0 \n",
            "\n",
            "*** Number of na, by column\n",
            "opened                   0\n",
            "closed                   5\n",
            "updated                  0\n",
            "status                   0\n",
            "status_notes             3\n",
            "responsible_agency       0\n",
            "category                 0\n",
            "request_type             0\n",
            "request_details         60\n",
            "address                  0\n",
            "street                 128\n",
            "supervisor_district    128\n",
            "neighborhood           133\n",
            "police_district        136\n",
            "source                   0\n",
            "dtype: int64 \n",
            "\n",
            "Nbr of columns below threshold (excluding ['closed', 'updated'] ): 2\n",
            "Names of columns below threshold: Index(['status_notes', 'request_details'], dtype='object')\n",
            "\n",
            "*** Number of rows dropped due to nulls: 63\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Address Duplicate Rows"
      ],
      "metadata": {
        "id": "cnVcTR2VTqZe"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Drop any duplicated rows, keeping the first instance\n",
        "if df.duplicated().any():\n",
        "  before_dropping_dups = df.shape[0]\n",
        "  print('Number of duplicated rows:', len(df[df.duplicated(keep=False)]))\n",
        "\n",
        "  df.drop_duplicates(keep='first', inplace=True)\n",
        "  print('Number of rows dropped due to dups (keeping 1st occurrence)',before_dropping_dups - df.shape[0])"
      ],
      "metadata": {
        "id": "2mrrqzHfTk_s"
      },
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### View Data & Summary Stats"
      ],
      "metadata": {
        "id": "1T2g0ht_dHtm"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 639
        },
        "id": "o7nOviJz8vkf",
        "outputId": "2afaa3f3-e604-42f9-a268-b0c797f79028"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                     opened              closed             updated  status  \\\n",
              "CaseID                                                                        \n",
              "721686  2010-08-27 20:32:34 2010-09-02 15:02:39 2010-09-02 15:02:39  Closed   \n",
              "730283  2010-09-10 15:40:46 2010-09-15 14:42:02 2010-09-15 14:42:02  Closed   \n",
              "732258  2010-09-14 04:25:57 2010-09-16 14:02:37 2010-09-16 14:02:37  Closed   \n",
              "2899186 2013-10-02 14:57:53 2015-04-15 04:05:25 2015-04-15 04:05:25  Closed   \n",
              "4328069 2014-12-23 10:43:18 2014-12-23 12:45:40 2014-12-23 12:45:40  Closed   \n",
              "\n",
              "                                              status_notes  \\\n",
              "CaseID                                                       \n",
              "721686                         Case Transferred - # 372527   \n",
              "730283                           Case Transferred - 373563   \n",
              "732258                           Case Transferred - 373691   \n",
              "2899186  Case Transferred - Per process - cases are clo...   \n",
              "4328069       Case Completed - resolved: Pickup completed.   \n",
              "\n",
              "                       responsible_agency                      category  \\\n",
              "CaseID                                                                    \n",
              "721686                    MUNI Work Queue                 MUNI Feedback   \n",
              "730283                    MUNI Work Queue                 MUNI Feedback   \n",
              "732258                    MUNI Work Queue                 MUNI Feedback   \n",
              "2899186  311 Service Request Queue - Hold              Damaged Property   \n",
              "4328069                Recology_Abandoned  Street and Sidewalk Cleaning   \n",
              "\n",
              "                                              request_type  \\\n",
              "CaseID                                                       \n",
              "721686         MUNI - Services_Service_Delivery_Facilities   \n",
              "730283         MUNI - Services_Service_Delivery_Facilities   \n",
              "732258   MUNI - Conduct_Discourteous_Insensitive_Inappr...   \n",
              "2899186                             Damaged Traffic_Signal   \n",
              "4328069                                        Bulky Items   \n",
              "\n",
              "                                 request_details  \\\n",
              "CaseID                                             \n",
              "721686             605_Vehicle_Maintenance_Noise   \n",
              "730283   608_Station_Stop_Appearance_Maintenance   \n",
              "732258             303_Fare_Transfer_POP_Dispute   \n",
              "2899186                           Traffic_Signal   \n",
              "4328069                             Refrigerator   \n",
              "\n",
              "                                         address  street supervisor_district  \\\n",
              "CaseID                                                                         \n",
              "721686    Not associated with a specific address     NaN                 NaN   \n",
              "730283    Not associated with a specific address     NaN                 NaN   \n",
              "732258    Not associated with a specific address     NaN                 NaN   \n",
              "2899186  Intersection of 3RD ST and INGERSON AVE  3RD ST                  10   \n",
              "4328069   Not associated with a specific address     NaN                 NaN   \n",
              "\n",
              "        neighborhood police_district             source  \n",
              "CaseID                                                   \n",
              "721686           NaN             NaN              Phone  \n",
              "730283           NaN             NaN              Phone  \n",
              "732258           NaN             NaN              Phone  \n",
              "2899186   Bret Harte         BAYVIEW              Phone  \n",
              "4328069          NaN             NaN  Integrated Agency  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-41edb1f1-bda7-4c38-8ed4-58c68df827ec\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>opened</th>\n",
              "      <th>closed</th>\n",
              "      <th>updated</th>\n",
              "      <th>status</th>\n",
              "      <th>status_notes</th>\n",
              "      <th>responsible_agency</th>\n",
              "      <th>category</th>\n",
              "      <th>request_type</th>\n",
              "      <th>request_details</th>\n",
              "      <th>address</th>\n",
              "      <th>street</th>\n",
              "      <th>supervisor_district</th>\n",
              "      <th>neighborhood</th>\n",
              "      <th>police_district</th>\n",
              "      <th>source</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>CaseID</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></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>721686</th>\n",
              "      <td>2010-08-27 20:32:34</td>\n",
              "      <td>2010-09-02 15:02:39</td>\n",
              "      <td>2010-09-02 15:02:39</td>\n",
              "      <td>Closed</td>\n",
              "      <td>Case Transferred - # 372527</td>\n",
              "      <td>MUNI Work Queue</td>\n",
              "      <td>MUNI Feedback</td>\n",
              "      <td>MUNI - Services_Service_Delivery_Facilities</td>\n",
              "      <td>605_Vehicle_Maintenance_Noise</td>\n",
              "      <td>Not associated with a specific address</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Phone</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>730283</th>\n",
              "      <td>2010-09-10 15:40:46</td>\n",
              "      <td>2010-09-15 14:42:02</td>\n",
              "      <td>2010-09-15 14:42:02</td>\n",
              "      <td>Closed</td>\n",
              "      <td>Case Transferred - 373563</td>\n",
              "      <td>MUNI Work Queue</td>\n",
              "      <td>MUNI Feedback</td>\n",
              "      <td>MUNI - Services_Service_Delivery_Facilities</td>\n",
              "      <td>608_Station_Stop_Appearance_Maintenance</td>\n",
              "      <td>Not associated with a specific address</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Phone</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>732258</th>\n",
              "      <td>2010-09-14 04:25:57</td>\n",
              "      <td>2010-09-16 14:02:37</td>\n",
              "      <td>2010-09-16 14:02:37</td>\n",
              "      <td>Closed</td>\n",
              "      <td>Case Transferred - 373691</td>\n",
              "      <td>MUNI Work Queue</td>\n",
              "      <td>MUNI Feedback</td>\n",
              "      <td>MUNI - Conduct_Discourteous_Insensitive_Inappr...</td>\n",
              "      <td>303_Fare_Transfer_POP_Dispute</td>\n",
              "      <td>Not associated with a specific address</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Phone</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2899186</th>\n",
              "      <td>2013-10-02 14:57:53</td>\n",
              "      <td>2015-04-15 04:05:25</td>\n",
              "      <td>2015-04-15 04:05:25</td>\n",
              "      <td>Closed</td>\n",
              "      <td>Case Transferred - Per process - cases are clo...</td>\n",
              "      <td>311 Service Request Queue - Hold</td>\n",
              "      <td>Damaged Property</td>\n",
              "      <td>Damaged Traffic_Signal</td>\n",
              "      <td>Traffic_Signal</td>\n",
              "      <td>Intersection of 3RD ST and INGERSON AVE</td>\n",
              "      <td>3RD ST</td>\n",
              "      <td>10</td>\n",
              "      <td>Bret Harte</td>\n",
              "      <td>BAYVIEW</td>\n",
              "      <td>Phone</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4328069</th>\n",
              "      <td>2014-12-23 10:43:18</td>\n",
              "      <td>2014-12-23 12:45:40</td>\n",
              "      <td>2014-12-23 12:45:40</td>\n",
              "      <td>Closed</td>\n",
              "      <td>Case Completed - resolved: Pickup completed.</td>\n",
              "      <td>Recology_Abandoned</td>\n",
              "      <td>Street and Sidewalk Cleaning</td>\n",
              "      <td>Bulky Items</td>\n",
              "      <td>Refrigerator</td>\n",
              "      <td>Not associated with a specific address</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Integrated Agency</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-41edb1f1-bda7-4c38-8ed4-58c68df827ec')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-41edb1f1-bda7-4c38-8ed4-58c68df827ec button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-41edb1f1-bda7-4c38-8ed4-58c68df827ec');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ],
      "source": [
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 605
        },
        "id": "IMllr0IB86Zq",
        "outputId": "f1db28c1-eb2c-4d21-b5f5-9c8492ff4fcd"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                      opened              closed             updated  status  \\\n",
              "CaseID                                                                         \n",
              "10401883 2019-01-24 16:26:00 2019-01-24 17:57:19 2019-01-24 17:57:19  Closed   \n",
              "10405876 2019-01-25 14:25:00 2019-01-25 20:24:48 2019-01-25 20:24:48  Closed   \n",
              "15560868 2022-07-12 05:59:00 2022-07-12 14:14:40 2022-07-12 14:14:40  Closed   \n",
              "15564300 2022-07-12 18:11:00 2022-07-14 19:34:27 2022-07-14 19:34:27  Closed   \n",
              "16205667 2022-12-20 15:08:00 2022-12-20 15:23:05 2022-12-20 15:23:05  Closed   \n",
              "\n",
              "                               status_notes  responsible_agency  \\\n",
              "CaseID                                                            \n",
              "10401883  Case Resolved - Pickup completed.  Recology_Abandoned   \n",
              "10405876  Case Resolved - Pickup completed.  Recology_Abandoned   \n",
              "15560868  Case Resolved - Pickup completed.  Recology_Abandoned   \n",
              "15564300                      Case Resolved       DPW Ops Queue   \n",
              "16205667                      Case Resolved       DPW Ops Queue   \n",
              "\n",
              "                              category  \\\n",
              "CaseID                                   \n",
              "10401883  Street and Sidewalk Cleaning   \n",
              "10405876  Street and Sidewalk Cleaning   \n",
              "15560868  Street and Sidewalk Cleaning   \n",
              "15564300  Street and Sidewalk Cleaning   \n",
              "16205667                      Graffiti   \n",
              "\n",
              "                                              request_type  \\\n",
              "CaseID                                                       \n",
              "10401883                                       Bulky Items   \n",
              "10405876                                       Bulky Items   \n",
              "15560868                                       Bulky Items   \n",
              "15564300                  Missed_Route_Mechanical_Sweeping   \n",
              "16205667  Graffiti on Other_enter_additional_details_below   \n",
              "\n",
              "                                           request_details  \\\n",
              "CaseID                                                       \n",
              "10401883                             Boxed or Bagged Items   \n",
              "10405876                                          Mattress   \n",
              "15560868                                         Furniture   \n",
              "15564300                  Missed_Route_Mechanical_Sweeping   \n",
              "16205667  Other_enter_additional_details_below - Offensive   \n",
              "\n",
              "                                               address         street  \\\n",
              "CaseID                                                                  \n",
              "10401883  2899 SAN BRUNO AVE, SAN FRANCISCO, CA, 94134  SAN BRUNO AVE   \n",
              "10405876        501 GROVE ST, SAN FRANCISCO, CA, 94102       GROVE ST   \n",
              "15560868      139 WEBSTER ST, SAN FRANCISCO, CA, 94117     WEBSTER ST   \n",
              "15564300      Intersection of LINCOLN WAY and LA PLAYA    LINCOLN WAY   \n",
              "16205667      600 CLAYTON ST, SAN FRANCISCO, CA, 94117     CLAYTON ST   \n",
              "\n",
              "         supervisor_district    neighborhood police_district          source  \n",
              "CaseID                                                                        \n",
              "10401883                   9         Portola         BAYVIEW           Phone  \n",
              "10405876                   5    Hayes Valley        NORTHERN           Phone  \n",
              "15560868                   5    Lower Haight        NORTHERN  Mobile/Open311  \n",
              "15564300                   4    Outer Sunset         TARAVAL           Phone  \n",
              "16205667                   5  Haight Ashbury            PARK  Mobile/Open311  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-034d29a8-0ee8-4b2c-ab3b-24f5a9968367\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>opened</th>\n",
              "      <th>closed</th>\n",
              "      <th>updated</th>\n",
              "      <th>status</th>\n",
              "      <th>status_notes</th>\n",
              "      <th>responsible_agency</th>\n",
              "      <th>category</th>\n",
              "      <th>request_type</th>\n",
              "      <th>request_details</th>\n",
              "      <th>address</th>\n",
              "      <th>street</th>\n",
              "      <th>supervisor_district</th>\n",
              "      <th>neighborhood</th>\n",
              "      <th>police_district</th>\n",
              "      <th>source</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>CaseID</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></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>10401883</th>\n",
              "      <td>2019-01-24 16:26:00</td>\n",
              "      <td>2019-01-24 17:57:19</td>\n",
              "      <td>2019-01-24 17:57:19</td>\n",
              "      <td>Closed</td>\n",
              "      <td>Case Resolved - Pickup completed.</td>\n",
              "      <td>Recology_Abandoned</td>\n",
              "      <td>Street and Sidewalk Cleaning</td>\n",
              "      <td>Bulky Items</td>\n",
              "      <td>Boxed or Bagged Items</td>\n",
              "      <td>2899 SAN BRUNO AVE, SAN FRANCISCO, CA, 94134</td>\n",
              "      <td>SAN BRUNO AVE</td>\n",
              "      <td>9</td>\n",
              "      <td>Portola</td>\n",
              "      <td>BAYVIEW</td>\n",
              "      <td>Phone</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10405876</th>\n",
              "      <td>2019-01-25 14:25:00</td>\n",
              "      <td>2019-01-25 20:24:48</td>\n",
              "      <td>2019-01-25 20:24:48</td>\n",
              "      <td>Closed</td>\n",
              "      <td>Case Resolved - Pickup completed.</td>\n",
              "      <td>Recology_Abandoned</td>\n",
              "      <td>Street and Sidewalk Cleaning</td>\n",
              "      <td>Bulky Items</td>\n",
              "      <td>Mattress</td>\n",
              "      <td>501 GROVE ST, SAN FRANCISCO, CA, 94102</td>\n",
              "      <td>GROVE ST</td>\n",
              "      <td>5</td>\n",
              "      <td>Hayes Valley</td>\n",
              "      <td>NORTHERN</td>\n",
              "      <td>Phone</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15560868</th>\n",
              "      <td>2022-07-12 05:59:00</td>\n",
              "      <td>2022-07-12 14:14:40</td>\n",
              "      <td>2022-07-12 14:14:40</td>\n",
              "      <td>Closed</td>\n",
              "      <td>Case Resolved - Pickup completed.</td>\n",
              "      <td>Recology_Abandoned</td>\n",
              "      <td>Street and Sidewalk Cleaning</td>\n",
              "      <td>Bulky Items</td>\n",
              "      <td>Furniture</td>\n",
              "      <td>139 WEBSTER ST, SAN FRANCISCO, CA, 94117</td>\n",
              "      <td>WEBSTER ST</td>\n",
              "      <td>5</td>\n",
              "      <td>Lower Haight</td>\n",
              "      <td>NORTHERN</td>\n",
              "      <td>Mobile/Open311</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15564300</th>\n",
              "      <td>2022-07-12 18:11:00</td>\n",
              "      <td>2022-07-14 19:34:27</td>\n",
              "      <td>2022-07-14 19:34:27</td>\n",
              "      <td>Closed</td>\n",
              "      <td>Case Resolved</td>\n",
              "      <td>DPW Ops Queue</td>\n",
              "      <td>Street and Sidewalk Cleaning</td>\n",
              "      <td>Missed_Route_Mechanical_Sweeping</td>\n",
              "      <td>Missed_Route_Mechanical_Sweeping</td>\n",
              "      <td>Intersection of LINCOLN WAY and LA PLAYA</td>\n",
              "      <td>LINCOLN WAY</td>\n",
              "      <td>4</td>\n",
              "      <td>Outer Sunset</td>\n",
              "      <td>TARAVAL</td>\n",
              "      <td>Phone</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16205667</th>\n",
              "      <td>2022-12-20 15:08:00</td>\n",
              "      <td>2022-12-20 15:23:05</td>\n",
              "      <td>2022-12-20 15:23:05</td>\n",
              "      <td>Closed</td>\n",
              "      <td>Case Resolved</td>\n",
              "      <td>DPW Ops Queue</td>\n",
              "      <td>Graffiti</td>\n",
              "      <td>Graffiti on Other_enter_additional_details_below</td>\n",
              "      <td>Other_enter_additional_details_below - Offensive</td>\n",
              "      <td>600 CLAYTON ST, SAN FRANCISCO, CA, 94117</td>\n",
              "      <td>CLAYTON ST</td>\n",
              "      <td>5</td>\n",
              "      <td>Haight Ashbury</td>\n",
              "      <td>PARK</td>\n",
              "      <td>Mobile/Open311</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-034d29a8-0ee8-4b2c-ab3b-24f5a9968367')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-034d29a8-0ee8-4b2c-ab3b-24f5a9968367 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-034d29a8-0ee8-4b2c-ab3b-24f5a9968367');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ],
      "source": [
        "df.tail()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 407
        },
        "id": "PDeuOo_uLAo6",
        "outputId": "4d280d99-8ef9-4e78-ebbb-5de2f21ad156"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                     opened              closed             updated  status  \\\n",
              "CaseID                                                                        \n",
              "6496304 2016-11-04 12:29:46 2016-11-09 11:47:50 2016-11-09 11:47:50  Closed   \n",
              "6494623 2016-11-04 03:17:39 2016-11-21 10:41:09 2016-11-21 10:41:09  Closed   \n",
              "6472005 2016-10-28 15:55:26 2016-10-31 11:28:08 2016-10-31 11:28:08  Closed   \n",
              "\n",
              "                                              status_notes  \\\n",
              "CaseID                                                       \n",
              "6496304                                   Case Transferred   \n",
              "6494623  Case Resolved - Multiagency - SFDPH has no aut...   \n",
              "6472005  Case Resolved - WHEN SUPERVISOR ARRIVED AT LOC...   \n",
              "\n",
              "                               responsible_agency               category  \\\n",
              "CaseID                                                                     \n",
              "6496304                           MUNI Work Queue          MUNI Feedback   \n",
              "6494623  DPH - Environmental Health - Noise Queue           Noise Report   \n",
              "6472005           SFMTA - Parking Enforcement - G  General Request - MTA   \n",
              "\n",
              "                                      request_type  \\\n",
              "CaseID                                               \n",
              "6496304  MUNI - Conduct_Inattentiveness_Negligence   \n",
              "6494623               garbage_recycling_collection   \n",
              "6472005                                  complaint   \n",
              "\n",
              "                                       request_details  \\\n",
              "CaseID                                                   \n",
              "6496304  212_Did_Not_Enforce_Rules_Contact_Authorities   \n",
              "6494623    Noise Report - garbage_recycling_collection   \n",
              "6472005                parking_enforcement - complaint   \n",
              "\n",
              "                                        address      street  \\\n",
              "CaseID                                                        \n",
              "6496304  Intersection of DOLORES ST and 16TH ST  DOLORES ST   \n",
              "6494623   948 CAPP ST, SAN FRANCISCO, CA, 94110     CAPP ST   \n",
              "6472005  1 MARKET PLZ, SAN FRANCISCO, CA, 94105  MARKET PLZ   \n",
              "\n",
              "        supervisor_district        neighborhood police_district  \\\n",
              "CaseID                                                            \n",
              "6496304                   8     Mission Dolores         MISSION   \n",
              "6494623                   9             Mission         MISSION   \n",
              "6472005                   6  Financial District         CENTRAL   \n",
              "\n",
              "                 source  \n",
              "CaseID                   \n",
              "6496304           Phone  \n",
              "6494623  Mobile/Open311  \n",
              "6472005           Phone  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-7d7fa6f3-180e-4b87-a2c9-f66f6c889fc0\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>opened</th>\n",
              "      <th>closed</th>\n",
              "      <th>updated</th>\n",
              "      <th>status</th>\n",
              "      <th>status_notes</th>\n",
              "      <th>responsible_agency</th>\n",
              "      <th>category</th>\n",
              "      <th>request_type</th>\n",
              "      <th>request_details</th>\n",
              "      <th>address</th>\n",
              "      <th>street</th>\n",
              "      <th>supervisor_district</th>\n",
              "      <th>neighborhood</th>\n",
              "      <th>police_district</th>\n",
              "      <th>source</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>CaseID</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></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>6496304</th>\n",
              "      <td>2016-11-04 12:29:46</td>\n",
              "      <td>2016-11-09 11:47:50</td>\n",
              "      <td>2016-11-09 11:47:50</td>\n",
              "      <td>Closed</td>\n",
              "      <td>Case Transferred</td>\n",
              "      <td>MUNI Work Queue</td>\n",
              "      <td>MUNI Feedback</td>\n",
              "      <td>MUNI - Conduct_Inattentiveness_Negligence</td>\n",
              "      <td>212_Did_Not_Enforce_Rules_Contact_Authorities</td>\n",
              "      <td>Intersection of DOLORES ST and 16TH ST</td>\n",
              "      <td>DOLORES ST</td>\n",
              "      <td>8</td>\n",
              "      <td>Mission Dolores</td>\n",
              "      <td>MISSION</td>\n",
              "      <td>Phone</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6494623</th>\n",
              "      <td>2016-11-04 03:17:39</td>\n",
              "      <td>2016-11-21 10:41:09</td>\n",
              "      <td>2016-11-21 10:41:09</td>\n",
              "      <td>Closed</td>\n",
              "      <td>Case Resolved - Multiagency - SFDPH has no aut...</td>\n",
              "      <td>DPH - Environmental Health - Noise Queue</td>\n",
              "      <td>Noise Report</td>\n",
              "      <td>garbage_recycling_collection</td>\n",
              "      <td>Noise Report - garbage_recycling_collection</td>\n",
              "      <td>948 CAPP ST, SAN FRANCISCO, CA, 94110</td>\n",
              "      <td>CAPP ST</td>\n",
              "      <td>9</td>\n",
              "      <td>Mission</td>\n",
              "      <td>MISSION</td>\n",
              "      <td>Mobile/Open311</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6472005</th>\n",
              "      <td>2016-10-28 15:55:26</td>\n",
              "      <td>2016-10-31 11:28:08</td>\n",
              "      <td>2016-10-31 11:28:08</td>\n",
              "      <td>Closed</td>\n",
              "      <td>Case Resolved - WHEN SUPERVISOR ARRIVED AT LOC...</td>\n",
              "      <td>SFMTA - Parking Enforcement - G</td>\n",
              "      <td>General Request - MTA</td>\n",
              "      <td>complaint</td>\n",
              "      <td>parking_enforcement - complaint</td>\n",
              "      <td>1 MARKET PLZ, SAN FRANCISCO, CA, 94105</td>\n",
              "      <td>MARKET PLZ</td>\n",
              "      <td>6</td>\n",
              "      <td>Financial District</td>\n",
              "      <td>CENTRAL</td>\n",
              "      <td>Phone</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-7d7fa6f3-180e-4b87-a2c9-f66f6c889fc0')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-7d7fa6f3-180e-4b87-a2c9-f66f6c889fc0 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-7d7fa6f3-180e-4b87-a2c9-f66f6c889fc0');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ],
      "source": [
        "# print random sample\n",
        "df.sample(n=3)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "l29P7Dhd9P2D",
        "outputId": "601262f7-1192-48cc-ce9d-489dd2659bd1"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 2437 entries, 721686 to 16205667\n",
            "Data columns (total 15 columns):\n",
            " #   Column               Non-Null Count  Dtype         \n",
            "---  ------               --------------  -----         \n",
            " 0   opened               2437 non-null   datetime64[ns]\n",
            " 1   closed               2435 non-null   datetime64[ns]\n",
            " 2   updated              2437 non-null   datetime64[ns]\n",
            " 3   status               2437 non-null   category      \n",
            " 4   status_notes         2437 non-null   object        \n",
            " 5   responsible_agency   2437 non-null   object        \n",
            " 6   category             2437 non-null   object        \n",
            " 7   request_type         2437 non-null   object        \n",
            " 8   request_details      2437 non-null   object        \n",
            " 9   address              2437 non-null   object        \n",
            " 10  street               2315 non-null   object        \n",
            " 11  supervisor_district  2315 non-null   category      \n",
            " 12  neighborhood         2310 non-null   object        \n",
            " 13  police_district      2307 non-null   category      \n",
            " 14  source               2437 non-null   category      \n",
            "dtypes: category(4), datetime64[ns](3), object(8)\n",
            "memory usage: 239.0+ KB\n"
          ]
        }
      ],
      "source": [
        "df.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0XEtlQok09RS",
        "outputId": "d8023007-4c1b-4ca8-b8b6-1954371fb97e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Nbr unique values per column, out of total of 2437 rows\n",
            "\n",
            "opened                 2435\n",
            "closed                 2380\n",
            "updated                2381\n",
            "status                    2\n",
            "status_notes            796\n",
            "responsible_agency       85\n",
            "category                 34\n",
            "request_type            142\n",
            "request_details         327\n",
            "address                2021\n",
            "street                  588\n",
            "supervisor_district      13\n",
            "neighborhood            110\n",
            "police_district          11\n",
            "source                    6\n",
            "dtype: int64\n"
          ]
        }
      ],
      "source": [
        "# nbr unique for each col\n",
        "print('Nbr unique values per column, out of total of',df.shape[0],'rows\\n')\n",
        "print(df.nunique(dropna=False))"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Consider Catagorical Features"
      ],
      "metadata": {
        "id": "AQU-rVCO3968"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# These are numbers, but not measures\n",
        "# Does the number indicate a supervisor or (more likely) an area of the city?\n",
        "# How does this district compare to 'Police District' or 'Neighborhood'?\n",
        "# There are 13 Supervisor Districts and 11 Police Districts, (118 Neighborhoods) -\n",
        "#      are the line designating these districts the same?\n",
        "df.supervisor_district.value_counts()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tkm6eURHfBwe",
        "outputId": "304224ff-89cb-4ff9-817e-53d909eb4dad"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "9     388\n",
              "6     368\n",
              "3     319\n",
              "8     235\n",
              "1     218\n",
              "10    208\n",
              "5     203\n",
              "11    112\n",
              "4      97\n",
              "2      89\n",
              "7      73\n",
              "0       5\n",
              "Name: supervisor_district, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.police_district.value_counts()\n",
        "# Why are these in ALLCAPS"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AbOB7663fKpN",
        "outputId": "edb1fdbb-a5ec-4114-8277-36b4ca7904d2"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "MISSION       503\n",
              "SOUTHERN      330\n",
              "CENTRAL       294\n",
              "RICHMOND      235\n",
              "NORTHERN      218\n",
              "INGLESIDE     212\n",
              "BAYVIEW       183\n",
              "TARAVAL       176\n",
              "PARK          119\n",
              "TENDERLOIN     37\n",
              "Name: police_district, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.source.value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DX-xB1J-qpRv",
        "outputId": "dcfae27c-65b7-4ec3-c451-b4c1b0e27c63"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Mobile/Open311       1267\n",
              "Phone                 772\n",
              "Web                   323\n",
              "Integrated Agency      66\n",
              "Twitter                 7\n",
              "Other Department        2\n",
              "Name: source, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 39,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sDssiQrQMVRF",
        "outputId": "90f07ee8-585a-4630-d615-d6d297490d72"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "CaseID\n",
            "6489102    Case Resolved - 10/31/16 inspected property po...\n",
            "6503618                      Case Resolved - WASTE NOT FOUND\n",
            "6497466                                        Case Resolved\n",
            "6499678                                        Case Resolved\n",
            "6501257    Case Resolved - Fixed news rack pedestal forwa...\n",
            "6498970    Case Resolved - FOR DRIVEWAY ENFORCEMENT CONTA...\n",
            "6502525                                         Area Cleared\n",
            "6473063                         Case Resolved - duplicate sr\n",
            "Name: status_notes, dtype: object\n"
          ]
        }
      ],
      "source": [
        "print(df['status_notes'].sample(n=8))"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.status_notes.value_counts()\n",
        "# in 'Case Resolved - 10-98' what does 10-98 mean?"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SDd9mq3oOGj3",
        "outputId": "c98245a1-b896-4618-c6bc-6d85e66e1152"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Case Resolved                                                                                                                                                                             394\n",
              "Case Resolved - Pickup completed.                                                                                                                                                         287\n",
              "Case Transferred                                                                                                                                                                          114\n",
              "Scheduled                                                                                                                                                                                  68\n",
              "Case Resolved - 10-98                                                                                                                                                                      66\n",
              "                                                                                                                                                                                         ... \n",
              "Case is a Duplicate - 11/02/16  DUPLICATE COMPLAINT                SR # 6485783                                                                                                             1\n",
              "Case is a Duplicate - Duplicate with case 6484856                                                                                                                                           1\n",
              "Case Resolved - 11/4/16: Spoke with Owner Mike, he Will inform his staff to no longer serve food to the parklet customers he has pagers on order for customers to use for future. GS -      1\n",
              "Case Resolved - Signs removed.                                                                                                                                                              1\n",
              "Case closed - Case resolved                                                                                                                                                                 1\n",
              "Name: status_notes, Length: 796, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print('number closed:',len(df[df.status=='Closed']))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dCcCd6oFDDtr",
        "outputId": "ce21dbee-7fa7-4834-ac0e-124295e0d961"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "number closed: 2435\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Should these been changed? Maybe add col for 'case_resolved'?\n",
        "# not all rows with status of 'closed' contain 'Case Resolved'\n",
        "print('number where status equals \"closed\":',' '*13, len(df[df.status=='Closed']))\n",
        "df_quick = df.query(\"status_notes.str.contains('Case Resolved').values\")\n",
        "print('number where status_notes contains \"Case Resolved\"', len(df_quick))\n",
        "df_quick.filter(['status', 'status_notes'])\n",
        "del df_quick"
      ],
      "metadata": {
        "id": "MXFJ_HA82E84",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9cff5d21-07e6-46da-8d7c-96520f62a573"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "number where status equals \"closed\":               2435\n",
            "number where status_notes contains \"Case Resolved\" 1844\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 43,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8u1TzGhpcTF_",
        "outputId": "ac139bdd-141f-48ce-b3af-d2723e4786a5"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Bulky Items                                         440\n",
              "General Cleaning                                    349\n",
              "Encampment Reports                                  165\n",
              "Human or Animal Waste                               111\n",
              "Graffiti on Building_commercial                      74\n",
              "Abandoned Vehicles                                   70\n",
              "request_for_service                                  56\n",
              "Hazardous Materials                                  48\n",
              "Graffiti on Other_enter_additional_details_below     45\n",
              "Individual Concerns                                  42\n",
              "Name: request_type, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ],
      "source": [
        "# Request type: most common 'General Cleaning' & 'Bulky Items'\n",
        "df['request_type'].value_counts().nlargest(n=10)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 44,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 359
        },
        "id": "KrGhpusqdmhU",
        "outputId": "3fe08100-8dbf-44ba-d20a-1431b65f0554"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                     opened closed             updated status status_notes  \\\n",
              "CaseID                                                                       \n",
              "6472767 2016-10-28 23:52:37    NaT 2016-10-28 23:52:37   Open     accepted   \n",
              "6497603 2016-11-04 17:00:27    NaT 2016-11-04 17:03:33   Open     accepted   \n",
              "\n",
              "                                 responsible_agency  \\\n",
              "CaseID                                                \n",
              "6472767                      PUC Streetlights Queue   \n",
              "6497603  DPW - Bureau of Street Use and Mapping - G   \n",
              "\n",
              "                               category  \\\n",
              "CaseID                                    \n",
              "6472767                    Streetlights   \n",
              "6497603  General Request - PUBLIC WORKS   \n",
              "\n",
              "                                        request_type  \\\n",
              "CaseID                                                 \n",
              "6472767  Streetlight - Other_Request_New_Streetlight   \n",
              "6497603                            customer_callback   \n",
              "\n",
              "                                     request_details  \\\n",
              "CaseID                                                 \n",
              "6472767  Other_Request_New_Streetlight on Metal Pole   \n",
              "6497603                      bsm - customer_callback   \n",
              "\n",
              "                                                  address     street  \\\n",
              "CaseID                                                                 \n",
              "6472767  Intersection of MARKET ST and SOUTH VAN NESS AVE  MARKET ST   \n",
              "6497603                Intersection of 4TH ST and KING ST     4TH ST   \n",
              "\n",
              "        supervisor_district     neighborhood police_district          source  \n",
              "CaseID                                                                        \n",
              "6472767                   6  South of Market        SOUTHERN  Mobile/Open311  \n",
              "6497603                   6      Mission Bay        SOUTHERN           Phone  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-8412650c-4733-456e-89c7-71cb0d2adfcf\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>opened</th>\n",
              "      <th>closed</th>\n",
              "      <th>updated</th>\n",
              "      <th>status</th>\n",
              "      <th>status_notes</th>\n",
              "      <th>responsible_agency</th>\n",
              "      <th>category</th>\n",
              "      <th>request_type</th>\n",
              "      <th>request_details</th>\n",
              "      <th>address</th>\n",
              "      <th>street</th>\n",
              "      <th>supervisor_district</th>\n",
              "      <th>neighborhood</th>\n",
              "      <th>police_district</th>\n",
              "      <th>source</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>CaseID</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></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>6472767</th>\n",
              "      <td>2016-10-28 23:52:37</td>\n",
              "      <td>NaT</td>\n",
              "      <td>2016-10-28 23:52:37</td>\n",
              "      <td>Open</td>\n",
              "      <td>accepted</td>\n",
              "      <td>PUC Streetlights Queue</td>\n",
              "      <td>Streetlights</td>\n",
              "      <td>Streetlight - Other_Request_New_Streetlight</td>\n",
              "      <td>Other_Request_New_Streetlight on Metal Pole</td>\n",
              "      <td>Intersection of MARKET ST and SOUTH VAN NESS AVE</td>\n",
              "      <td>MARKET ST</td>\n",
              "      <td>6</td>\n",
              "      <td>South of Market</td>\n",
              "      <td>SOUTHERN</td>\n",
              "      <td>Mobile/Open311</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6497603</th>\n",
              "      <td>2016-11-04 17:00:27</td>\n",
              "      <td>NaT</td>\n",
              "      <td>2016-11-04 17:03:33</td>\n",
              "      <td>Open</td>\n",
              "      <td>accepted</td>\n",
              "      <td>DPW - Bureau of Street Use and Mapping - G</td>\n",
              "      <td>General Request - PUBLIC WORKS</td>\n",
              "      <td>customer_callback</td>\n",
              "      <td>bsm - customer_callback</td>\n",
              "      <td>Intersection of 4TH ST and KING ST</td>\n",
              "      <td>4TH ST</td>\n",
              "      <td>6</td>\n",
              "      <td>Mission Bay</td>\n",
              "      <td>SOUTHERN</td>\n",
              "      <td>Phone</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8412650c-4733-456e-89c7-71cb0d2adfcf')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-8412650c-4733-456e-89c7-71cb0d2adfcf button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-8412650c-4733-456e-89c7-71cb0d2adfcf');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ],
      "source": [
        "#earliest requests that are still open\n",
        "df[df['status']=='Open'].nsmallest(n=5, columns='opened')\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nFyaFsL64Z3i"
      },
      "source": [
        "### Drop rows where 'Status Notes' contains 'Case is a Duplicate'"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 45,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Z9gWKc141wsf",
        "outputId": "bd8e9190-2025-403d-b487-501cd30d29f5"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Found   58 rows with \"Status_Notes\" containing \"Case is a Duplicate\"\n",
            "Dropped 58 rows containing \"Case is a Duplicate\"\n",
            "DataFrame now contains (rows,cols): (2379, 15)\n"
          ]
        }
      ],
      "source": [
        "# making the assumption that 'Case is a Duplicate' indicates at least\n",
        "#   2 calls for the same case and, therefore, will drop those rows\n",
        "\n",
        "# df with 'Status_Notes' contains 'Case is a Duplicate'\n",
        "df_quick = df.query(\"status_notes.str.contains('Case is a Duplicate').values\")\n",
        "\n",
        "#if values were found...\n",
        "if not df_quick.empty:  \n",
        "  print('Found  ', df_quick.shape[0],'rows with \"Status_Notes\" containing \"Case is a Duplicate\"')\n",
        "  before_drop = df.shape[0]\n",
        "\n",
        "  # drop those rows\n",
        "  df = df.drop(df_quick.index)\n",
        "\n",
        "  print('Dropped', before_drop - df.shape[0], 'rows containing \"Case is a Duplicate\"')\n",
        "  assert df.shape[0] == before_drop - df_quick.shape[0], 'Dropped unexpected number of rows'\n",
        "  del df_quick\n",
        "\n",
        "  print('DataFrame now contains (rows,cols):', df.shape)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LYsexx0RaSp0"
      },
      "source": [
        "###Feature Engineering"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 46,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "08tfn8sWW5t9",
        "outputId": "15bec0f1-c9bd-4e38-944e-2a39e61cec29"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(2, 13)"
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ],
      "source": [
        "# Create dataframe for open 311 cases\n",
        "df_open = df[df['status']=='Open'].copy()\n",
        "\n",
        "# don't need these cols\n",
        "df_open = df_open.drop(columns=['closed','status'])\n",
        "df_open.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 47,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "d28sunPpXCn_",
        "outputId": "b6871b9b-a255-42c2-fee3-bc29286db8a5"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(2377, 14)"
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ],
      "source": [
        "# Create dataframe for closed 311 cases. Drop status col\n",
        "df_closed = df[df['status']=='Closed'].copy()\n",
        "df_closed = df_closed.drop(columns='status')\n",
        "df_closed.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 48,
      "metadata": {
        "id": "45gJ1KL3VaoN"
      },
      "outputs": [],
      "source": [
        "#  For open df (cases still open): \n",
        "#   calculate how long open cases have been open (days_open)\n",
        "\n",
        "# for df_open (cases still open)\n",
        "df_open['days_open'] = (datetime.now() - df_open['opened']).astype(\"timedelta64[D]\").astype('int')\n",
        "\n",
        "# For closed df (cases that have been closed):\n",
        "#  calculate processing_days for closed cases\n",
        "#  break out year opened\n",
        "#  create col for month & year combined\n",
        "df_closed['opened_year_month'] = df_closed['opened'].dt.to_period('M')\n",
        "df_closed['opened_year'] = df_closed['opened'].dt.year\n",
        "df_closed['processing_days'] = (df_closed['closed'] - df_closed['opened']).astype(\"timedelta64[D]\").astype('int')\n",
        "\n",
        "# if processing_days is < 1, then closed date is earlier than opened date,\n",
        "#  (usually the same day, but the time is set to AM instead of PM) \n",
        "#  Set to 1 (1 day to close)\n",
        "df_closed.loc[df_closed['processing_days'] < 1, 'processing_days'] = 1\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# does processing_days look correct?\n",
        "df_closed.filter(['opened', 'closed', 'opened_year_month', 'opened_year', 'processing_days']).sample(10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 390
        },
        "id": "2t_O94L7FKYo",
        "outputId": "2155a3af-fbab-4f3b-d0c5-f54d54a51d70"
      },
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                     opened              closed opened_year_month  \\\n",
              "CaseID                                                              \n",
              "6498285 2016-11-04 23:44:12 2016-11-08 12:10:52           2016-11   \n",
              "6490038 2016-11-02 17:52:03 2016-11-06 23:10:22           2016-11   \n",
              "6498505 2016-11-05 07:00:45 2016-11-05 15:10:51           2016-11   \n",
              "6483416 2016-11-01 09:48:26 2016-11-09 10:49:15           2016-11   \n",
              "6477754 2016-10-31 07:39:53 2016-10-31 12:03:10           2016-10   \n",
              "6497244 2016-11-04 15:38:49 2016-11-08 11:21:41           2016-11   \n",
              "6483492 2016-11-01 09:59:06 2016-11-01 20:57:10           2016-11   \n",
              "6486997 2016-11-02 08:55:39 2016-11-02 10:33:03           2016-11   \n",
              "6485797 2016-11-01 18:10:29 2016-11-03 12:15:29           2016-11   \n",
              "6485611 2016-11-01 17:06:44 2016-11-02 11:08:03           2016-11   \n",
              "\n",
              "         opened_year  processing_days  \n",
              "CaseID                                 \n",
              "6498285         2016                3  \n",
              "6490038         2016                4  \n",
              "6498505         2016                1  \n",
              "6483416         2016                8  \n",
              "6477754         2016                1  \n",
              "6497244         2016                3  \n",
              "6483492         2016                1  \n",
              "6486997         2016                1  \n",
              "6485797         2016                1  \n",
              "6485611         2016                1  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-6d177eca-b402-4286-a522-121a7c0cf9cc\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>opened</th>\n",
              "      <th>closed</th>\n",
              "      <th>opened_year_month</th>\n",
              "      <th>opened_year</th>\n",
              "      <th>processing_days</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>CaseID</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>6498285</th>\n",
              "      <td>2016-11-04 23:44:12</td>\n",
              "      <td>2016-11-08 12:10:52</td>\n",
              "      <td>2016-11</td>\n",
              "      <td>2016</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6490038</th>\n",
              "      <td>2016-11-02 17:52:03</td>\n",
              "      <td>2016-11-06 23:10:22</td>\n",
              "      <td>2016-11</td>\n",
              "      <td>2016</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6498505</th>\n",
              "      <td>2016-11-05 07:00:45</td>\n",
              "      <td>2016-11-05 15:10:51</td>\n",
              "      <td>2016-11</td>\n",
              "      <td>2016</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6483416</th>\n",
              "      <td>2016-11-01 09:48:26</td>\n",
              "      <td>2016-11-09 10:49:15</td>\n",
              "      <td>2016-11</td>\n",
              "      <td>2016</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6477754</th>\n",
              "      <td>2016-10-31 07:39:53</td>\n",
              "      <td>2016-10-31 12:03:10</td>\n",
              "      <td>2016-10</td>\n",
              "      <td>2016</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6497244</th>\n",
              "      <td>2016-11-04 15:38:49</td>\n",
              "      <td>2016-11-08 11:21:41</td>\n",
              "      <td>2016-11</td>\n",
              "      <td>2016</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6483492</th>\n",
              "      <td>2016-11-01 09:59:06</td>\n",
              "      <td>2016-11-01 20:57:10</td>\n",
              "      <td>2016-11</td>\n",
              "      <td>2016</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6486997</th>\n",
              "      <td>2016-11-02 08:55:39</td>\n",
              "      <td>2016-11-02 10:33:03</td>\n",
              "      <td>2016-11</td>\n",
              "      <td>2016</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6485797</th>\n",
              "      <td>2016-11-01 18:10:29</td>\n",
              "      <td>2016-11-03 12:15:29</td>\n",
              "      <td>2016-11</td>\n",
              "      <td>2016</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6485611</th>\n",
              "      <td>2016-11-01 17:06:44</td>\n",
              "      <td>2016-11-02 11:08:03</td>\n",
              "      <td>2016-11</td>\n",
              "      <td>2016</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-6d177eca-b402-4286-a522-121a7c0cf9cc')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-6d177eca-b402-4286-a522-121a7c0cf9cc button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-6d177eca-b402-4286-a522-121a7c0cf9cc');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 51,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 639
        },
        "id": "sqLg92hPLSvq",
        "outputId": "82776c9b-2798-4d15-e139-6c460330f12b"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                     opened              closed             updated  \\\n",
              "CaseID                                                                \n",
              "721686  2010-08-27 20:32:34 2010-09-02 15:02:39 2010-09-02 15:02:39   \n",
              "730283  2010-09-10 15:40:46 2010-09-15 14:42:02 2010-09-15 14:42:02   \n",
              "732258  2010-09-14 04:25:57 2010-09-16 14:02:37 2010-09-16 14:02:37   \n",
              "2899186 2013-10-02 14:57:53 2015-04-15 04:05:25 2015-04-15 04:05:25   \n",
              "4328069 2014-12-23 10:43:18 2014-12-23 12:45:40 2014-12-23 12:45:40   \n",
              "\n",
              "                                              status_notes  \\\n",
              "CaseID                                                       \n",
              "721686                         Case Transferred - # 372527   \n",
              "730283                           Case Transferred - 373563   \n",
              "732258                           Case Transferred - 373691   \n",
              "2899186  Case Transferred - Per process - cases are clo...   \n",
              "4328069       Case Completed - resolved: Pickup completed.   \n",
              "\n",
              "                       responsible_agency                      category  \\\n",
              "CaseID                                                                    \n",
              "721686                    MUNI Work Queue                 MUNI Feedback   \n",
              "730283                    MUNI Work Queue                 MUNI Feedback   \n",
              "732258                    MUNI Work Queue                 MUNI Feedback   \n",
              "2899186  311 Service Request Queue - Hold              Damaged Property   \n",
              "4328069                Recology_Abandoned  Street and Sidewalk Cleaning   \n",
              "\n",
              "                                              request_type  \\\n",
              "CaseID                                                       \n",
              "721686         MUNI - Services_Service_Delivery_Facilities   \n",
              "730283         MUNI - Services_Service_Delivery_Facilities   \n",
              "732258   MUNI - Conduct_Discourteous_Insensitive_Inappr...   \n",
              "2899186                             Damaged Traffic_Signal   \n",
              "4328069                                        Bulky Items   \n",
              "\n",
              "                                 request_details  \\\n",
              "CaseID                                             \n",
              "721686             605_Vehicle_Maintenance_Noise   \n",
              "730283   608_Station_Stop_Appearance_Maintenance   \n",
              "732258             303_Fare_Transfer_POP_Dispute   \n",
              "2899186                           Traffic_Signal   \n",
              "4328069                             Refrigerator   \n",
              "\n",
              "                                         address  street supervisor_district  \\\n",
              "CaseID                                                                         \n",
              "721686    Not associated with a specific address     NaN                 NaN   \n",
              "730283    Not associated with a specific address     NaN                 NaN   \n",
              "732258    Not associated with a specific address     NaN                 NaN   \n",
              "2899186  Intersection of 3RD ST and INGERSON AVE  3RD ST                  10   \n",
              "4328069   Not associated with a specific address     NaN                 NaN   \n",
              "\n",
              "        neighborhood police_district             source opened_year_month  \\\n",
              "CaseID                                                                      \n",
              "721686           NaN             NaN              Phone           2010-08   \n",
              "730283           NaN             NaN              Phone           2010-09   \n",
              "732258           NaN             NaN              Phone           2010-09   \n",
              "2899186   Bret Harte         BAYVIEW              Phone           2013-10   \n",
              "4328069          NaN             NaN  Integrated Agency           2014-12   \n",
              "\n",
              "         opened_year  processing_days  \n",
              "CaseID                                 \n",
              "721686          2010                5  \n",
              "730283          2010                4  \n",
              "732258          2010                2  \n",
              "2899186         2013              559  \n",
              "4328069         2014                1  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-e729fe27-1abb-42f4-808d-813d867134ce\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>opened</th>\n",
              "      <th>closed</th>\n",
              "      <th>updated</th>\n",
              "      <th>status_notes</th>\n",
              "      <th>responsible_agency</th>\n",
              "      <th>category</th>\n",
              "      <th>request_type</th>\n",
              "      <th>request_details</th>\n",
              "      <th>address</th>\n",
              "      <th>street</th>\n",
              "      <th>supervisor_district</th>\n",
              "      <th>neighborhood</th>\n",
              "      <th>police_district</th>\n",
              "      <th>source</th>\n",
              "      <th>opened_year_month</th>\n",
              "      <th>opened_year</th>\n",
              "      <th>processing_days</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>CaseID</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></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>721686</th>\n",
              "      <td>2010-08-27 20:32:34</td>\n",
              "      <td>2010-09-02 15:02:39</td>\n",
              "      <td>2010-09-02 15:02:39</td>\n",
              "      <td>Case Transferred - # 372527</td>\n",
              "      <td>MUNI Work Queue</td>\n",
              "      <td>MUNI Feedback</td>\n",
              "      <td>MUNI - Services_Service_Delivery_Facilities</td>\n",
              "      <td>605_Vehicle_Maintenance_Noise</td>\n",
              "      <td>Not associated with a specific address</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Phone</td>\n",
              "      <td>2010-08</td>\n",
              "      <td>2010</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>730283</th>\n",
              "      <td>2010-09-10 15:40:46</td>\n",
              "      <td>2010-09-15 14:42:02</td>\n",
              "      <td>2010-09-15 14:42:02</td>\n",
              "      <td>Case Transferred - 373563</td>\n",
              "      <td>MUNI Work Queue</td>\n",
              "      <td>MUNI Feedback</td>\n",
              "      <td>MUNI - Services_Service_Delivery_Facilities</td>\n",
              "      <td>608_Station_Stop_Appearance_Maintenance</td>\n",
              "      <td>Not associated with a specific address</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Phone</td>\n",
              "      <td>2010-09</td>\n",
              "      <td>2010</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>732258</th>\n",
              "      <td>2010-09-14 04:25:57</td>\n",
              "      <td>2010-09-16 14:02:37</td>\n",
              "      <td>2010-09-16 14:02:37</td>\n",
              "      <td>Case Transferred - 373691</td>\n",
              "      <td>MUNI Work Queue</td>\n",
              "      <td>MUNI Feedback</td>\n",
              "      <td>MUNI - Conduct_Discourteous_Insensitive_Inappr...</td>\n",
              "      <td>303_Fare_Transfer_POP_Dispute</td>\n",
              "      <td>Not associated with a specific address</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Phone</td>\n",
              "      <td>2010-09</td>\n",
              "      <td>2010</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2899186</th>\n",
              "      <td>2013-10-02 14:57:53</td>\n",
              "      <td>2015-04-15 04:05:25</td>\n",
              "      <td>2015-04-15 04:05:25</td>\n",
              "      <td>Case Transferred - Per process - cases are clo...</td>\n",
              "      <td>311 Service Request Queue - Hold</td>\n",
              "      <td>Damaged Property</td>\n",
              "      <td>Damaged Traffic_Signal</td>\n",
              "      <td>Traffic_Signal</td>\n",
              "      <td>Intersection of 3RD ST and INGERSON AVE</td>\n",
              "      <td>3RD ST</td>\n",
              "      <td>10</td>\n",
              "      <td>Bret Harte</td>\n",
              "      <td>BAYVIEW</td>\n",
              "      <td>Phone</td>\n",
              "      <td>2013-10</td>\n",
              "      <td>2013</td>\n",
              "      <td>559</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4328069</th>\n",
              "      <td>2014-12-23 10:43:18</td>\n",
              "      <td>2014-12-23 12:45:40</td>\n",
              "      <td>2014-12-23 12:45:40</td>\n",
              "      <td>Case Completed - resolved: Pickup completed.</td>\n",
              "      <td>Recology_Abandoned</td>\n",
              "      <td>Street and Sidewalk Cleaning</td>\n",
              "      <td>Bulky Items</td>\n",
              "      <td>Refrigerator</td>\n",
              "      <td>Not associated with a specific address</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Integrated Agency</td>\n",
              "      <td>2014-12</td>\n",
              "      <td>2014</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e729fe27-1abb-42f4-808d-813d867134ce')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-e729fe27-1abb-42f4-808d-813d867134ce button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-e729fe27-1abb-42f4-808d-813d867134ce');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 51
        }
      ],
      "source": [
        "df_closed.head()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_closed.opened_year_month.value_counts().head(10)\n",
        "# Why were so many cases opened in 2016? (And this shows only the cases that were closed)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eyzhv4XOU0Z4",
        "outputId": "7d8aff1e-dcf2-43ca-97c4-b2624d1f4878"
      },
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2016-11    1682\n",
              "2016-10     664\n",
              "2014-12       9\n",
              "2019-01       3\n",
              "2022-07       2\n",
              "2016-09       2\n",
              "2010-09       2\n",
              "2018-04       2\n",
              "2010-08       1\n",
              "2018-02       1\n",
              "Freq: M, Name: opened_year_month, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 54,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qEmozJ36ISJ0",
        "outputId": "e47604cf-fb3f-467c-a66e-662aa59afb3a"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1     1458\n",
              "2      166\n",
              "3      116\n",
              "4       77\n",
              "6       75\n",
              "5       70\n",
              "7       61\n",
              "8       41\n",
              "10      35\n",
              "9       28\n",
              "11      17\n",
              "13      13\n",
              "44      11\n",
              "38      11\n",
              "40      10\n",
              "Name: processing_days, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 54
        }
      ],
      "source": [
        "df_closed.processing_days.value_counts().head(10)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 55,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "J_2egQJ4Nrlj",
        "outputId": "e45a5b4d-5062-4480-8b80-b9b7cc829985"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 2377 entries, 721686 to 16205667\n",
            "Data columns (total 17 columns):\n",
            " #   Column               Non-Null Count  Dtype         \n",
            "---  ------               --------------  -----         \n",
            " 0   opened               2377 non-null   datetime64[ns]\n",
            " 1   closed               2377 non-null   datetime64[ns]\n",
            " 2   updated              2377 non-null   datetime64[ns]\n",
            " 3   status_notes         2377 non-null   object        \n",
            " 4   responsible_agency   2377 non-null   object        \n",
            " 5   category             2377 non-null   object        \n",
            " 6   request_type         2377 non-null   object        \n",
            " 7   request_details      2377 non-null   object        \n",
            " 8   address              2377 non-null   object        \n",
            " 9   street               2259 non-null   object        \n",
            " 10  supervisor_district  2259 non-null   category      \n",
            " 11  neighborhood         2254 non-null   object        \n",
            " 12  police_district      2251 non-null   category      \n",
            " 13  source               2377 non-null   category      \n",
            " 14  opened_year_month    2377 non-null   period[M]     \n",
            " 15  opened_year          2377 non-null   int64         \n",
            " 16  processing_days      2377 non-null   int64         \n",
            "dtypes: category(3), datetime64[ns](3), int64(2), object(8), period[M](1)\n",
            "memory usage: 286.5+ KB\n"
          ]
        }
      ],
      "source": [
        "df_closed.info()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uIbACQ41cXYj"
      },
      "source": [
        "### Descriptive Analytics"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 56,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "2QapozM6agzg",
        "outputId": "596406a0-eb27-4f5c-e90e-9f71da083e1b"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                           opened              updated    days_open\n",
              "count                           2                    2     2.000000\n",
              "mean          2016-11-01 08:26:32  2016-11-01 08:28:05  2391.000000\n",
              "min           2016-10-28 23:52:37  2016-10-28 23:52:37  2388.000000\n",
              "25%    2016-10-30 16:09:34.500000  2016-10-30 16:10:21  2389.500000\n",
              "50%           2016-11-01 08:26:32  2016-11-01 08:28:05  2391.000000\n",
              "75%    2016-11-03 00:43:29.500000  2016-11-03 00:45:49  2392.500000\n",
              "max           2016-11-04 17:00:27  2016-11-04 17:03:33  2394.000000\n",
              "std                           NaN                  NaN     4.242641"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-5ff4da6f-4dd0-4cf6-8c94-88ccb2bfa565\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>opened</th>\n",
              "      <th>updated</th>\n",
              "      <th>days_open</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>2016-11-01 08:26:32</td>\n",
              "      <td>2016-11-01 08:28:05</td>\n",
              "      <td>2391.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>2016-10-28 23:52:37</td>\n",
              "      <td>2016-10-28 23:52:37</td>\n",
              "      <td>2388.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>2016-10-30 16:09:34.500000</td>\n",
              "      <td>2016-10-30 16:10:21</td>\n",
              "      <td>2389.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>2016-11-01 08:26:32</td>\n",
              "      <td>2016-11-01 08:28:05</td>\n",
              "      <td>2391.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>2016-11-03 00:43:29.500000</td>\n",
              "      <td>2016-11-03 00:45:49</td>\n",
              "      <td>2392.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>2016-11-04 17:00:27</td>\n",
              "      <td>2016-11-04 17:03:33</td>\n",
              "      <td>2394.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4.242641</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-5ff4da6f-4dd0-4cf6-8c94-88ccb2bfa565')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-5ff4da6f-4dd0-4cf6-8c94-88ccb2bfa565 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-5ff4da6f-4dd0-4cf6-8c94-88ccb2bfa565');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 56
        }
      ],
      "source": [
        "df_open.describe(datetime_is_numeric=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 57,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "j0WAWRiEJl0I",
        "outputId": "1e29f9a2-8989-415d-e760-e936d6acdba0"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                              opened                         closed  \\\n",
              "count                           2377                           2377   \n",
              "mean   2016-11-02 00:25:01.138830592  2016-11-08 20:53:46.114009088   \n",
              "min              2010-08-27 20:32:34            2010-09-02 15:02:39   \n",
              "25%              2016-10-31 14:10:13            2016-11-02 10:58:09   \n",
              "50%              2016-11-02 17:01:04            2016-11-05 09:24:17   \n",
              "75%              2016-11-05 09:57:13            2016-11-08 15:46:30   \n",
              "max              2022-12-20 15:08:00            2022-12-20 15:23:05   \n",
              "std                              NaN                            NaN   \n",
              "\n",
              "                             updated  opened_year  processing_days  \n",
              "count                           2377  2377.000000      2377.000000  \n",
              "mean   2016-11-08 20:53:46.114009088  2016.000000         6.978124  \n",
              "min              2010-09-02 15:02:39  2010.000000         1.000000  \n",
              "25%              2016-11-02 10:58:09  2016.000000         1.000000  \n",
              "50%              2016-11-05 09:24:17  2016.000000         1.000000  \n",
              "75%              2016-11-08 15:46:30  2016.000000         4.000000  \n",
              "max              2022-12-20 15:23:05  2022.000000       806.000000  \n",
              "std                              NaN     0.364687        38.200951  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-63073492-c6f2-40fa-8802-138f2a62701b\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>opened</th>\n",
              "      <th>closed</th>\n",
              "      <th>updated</th>\n",
              "      <th>opened_year</th>\n",
              "      <th>processing_days</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>2377</td>\n",
              "      <td>2377</td>\n",
              "      <td>2377</td>\n",
              "      <td>2377.000000</td>\n",
              "      <td>2377.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>2016-11-02 00:25:01.138830592</td>\n",
              "      <td>2016-11-08 20:53:46.114009088</td>\n",
              "      <td>2016-11-08 20:53:46.114009088</td>\n",
              "      <td>2016.000000</td>\n",
              "      <td>6.978124</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>2010-08-27 20:32:34</td>\n",
              "      <td>2010-09-02 15:02:39</td>\n",
              "      <td>2010-09-02 15:02:39</td>\n",
              "      <td>2010.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>2016-10-31 14:10:13</td>\n",
              "      <td>2016-11-02 10:58:09</td>\n",
              "      <td>2016-11-02 10:58:09</td>\n",
              "      <td>2016.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>2016-11-02 17:01:04</td>\n",
              "      <td>2016-11-05 09:24:17</td>\n",
              "      <td>2016-11-05 09:24:17</td>\n",
              "      <td>2016.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>2016-11-05 09:57:13</td>\n",
              "      <td>2016-11-08 15:46:30</td>\n",
              "      <td>2016-11-08 15:46:30</td>\n",
              "      <td>2016.000000</td>\n",
              "      <td>4.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>2022-12-20 15:08:00</td>\n",
              "      <td>2022-12-20 15:23:05</td>\n",
              "      <td>2022-12-20 15:23:05</td>\n",
              "      <td>2022.000000</td>\n",
              "      <td>806.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.364687</td>\n",
              "      <td>38.200951</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-63073492-c6f2-40fa-8802-138f2a62701b')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-63073492-c6f2-40fa-8802-138f2a62701b button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-63073492-c6f2-40fa-8802-138f2a62701b');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 57
        }
      ],
      "source": [
        "df_closed.describe(datetime_is_numeric=True)\n",
        "# processing_days: max is WAY higher than 75 percentile... outlier(s)?"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1thJ8goBVqRH"
      },
      "source": [
        "##Exploratory Visualization"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.set_style(\"whitegrid\", {'axes.grid' : False})"
      ],
      "metadata": {
        "id": "TAnFK5N6rNKJ"
      },
      "execution_count": 58,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Visualize source"
      ],
      "metadata": {
        "id": "lGuF-f0zq-GW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# for a small number of rows, these visualizations are misleadig"
      ],
      "metadata": {
        "id": "qa_ZSBBQUQ9K"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.source.value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "W-PWWX6lrQ12",
        "outputId": "bbb924bd-5d6c-412f-f224-d063af1cfa07"
      },
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Mobile/Open311       1237\n",
              "Phone                 755\n",
              "Web                   313\n",
              "Integrated Agency      65\n",
              "Twitter                 7\n",
              "Other Department        2\n",
              "Name: source, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.source.value_counts().plot(kind='bar')\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "bWm4XXjErETv",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 545
        },
        "outputId": "37db5798-c2aa-472d-c951-a039bb647f8b"
      },
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0YORa_GNYaAY"
      },
      "source": [
        "### Histograms for time open"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 63,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kW0uU3wFfXhQ",
        "outputId": "f1c18521-d0f2-4258-a976-15dce3a378a8"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Quick stats on \"days open\", use to set range on histogram\n",
            "min       2388.0\n",
            "max       2394.0\n",
            "mean      2391.0\n",
            "median    2391.0\n",
            "mode      2394.0\n",
            "Name: days_open, dtype: float64\n"
          ]
        }
      ],
      "source": [
        "print('Quick stats on \"days open\", use to set range on histogram')\n",
        "print(df_open.days_open.agg([min, max, np.mean, np.median, statistics.mode]))"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_open"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 359
        },
        "id": "md2Ucd83ETms",
        "outputId": "c7ec4866-5eff-4526-d83e-7d6d33cdf27c"
      },
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                     opened             updated status_notes  \\\n",
              "CaseID                                                         \n",
              "6472767 2016-10-28 23:52:37 2016-10-28 23:52:37     accepted   \n",
              "6497603 2016-11-04 17:00:27 2016-11-04 17:03:33     accepted   \n",
              "\n",
              "                                 responsible_agency  \\\n",
              "CaseID                                                \n",
              "6472767                      PUC Streetlights Queue   \n",
              "6497603  DPW - Bureau of Street Use and Mapping - G   \n",
              "\n",
              "                               category  \\\n",
              "CaseID                                    \n",
              "6472767                    Streetlights   \n",
              "6497603  General Request - PUBLIC WORKS   \n",
              "\n",
              "                                        request_type  \\\n",
              "CaseID                                                 \n",
              "6472767  Streetlight - Other_Request_New_Streetlight   \n",
              "6497603                            customer_callback   \n",
              "\n",
              "                                     request_details  \\\n",
              "CaseID                                                 \n",
              "6472767  Other_Request_New_Streetlight on Metal Pole   \n",
              "6497603                      bsm - customer_callback   \n",
              "\n",
              "                                                  address     street  \\\n",
              "CaseID                                                                 \n",
              "6472767  Intersection of MARKET ST and SOUTH VAN NESS AVE  MARKET ST   \n",
              "6497603                Intersection of 4TH ST and KING ST     4TH ST   \n",
              "\n",
              "        supervisor_district     neighborhood police_district          source  \\\n",
              "CaseID                                                                         \n",
              "6472767                   6  South of Market        SOUTHERN  Mobile/Open311   \n",
              "6497603                   6      Mission Bay        SOUTHERN           Phone   \n",
              "\n",
              "         days_open  \n",
              "CaseID              \n",
              "6472767       2394  \n",
              "6497603       2388  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-faa220dd-e500-40d9-b60e-4d9909089b72\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>opened</th>\n",
              "      <th>updated</th>\n",
              "      <th>status_notes</th>\n",
              "      <th>responsible_agency</th>\n",
              "      <th>category</th>\n",
              "      <th>request_type</th>\n",
              "      <th>request_details</th>\n",
              "      <th>address</th>\n",
              "      <th>street</th>\n",
              "      <th>supervisor_district</th>\n",
              "      <th>neighborhood</th>\n",
              "      <th>police_district</th>\n",
              "      <th>source</th>\n",
              "      <th>days_open</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>CaseID</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></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>6472767</th>\n",
              "      <td>2016-10-28 23:52:37</td>\n",
              "      <td>2016-10-28 23:52:37</td>\n",
              "      <td>accepted</td>\n",
              "      <td>PUC Streetlights Queue</td>\n",
              "      <td>Streetlights</td>\n",
              "      <td>Streetlight - Other_Request_New_Streetlight</td>\n",
              "      <td>Other_Request_New_Streetlight on Metal Pole</td>\n",
              "      <td>Intersection of MARKET ST and SOUTH VAN NESS AVE</td>\n",
              "      <td>MARKET ST</td>\n",
              "      <td>6</td>\n",
              "      <td>South of Market</td>\n",
              "      <td>SOUTHERN</td>\n",
              "      <td>Mobile/Open311</td>\n",
              "      <td>2394</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6497603</th>\n",
              "      <td>2016-11-04 17:00:27</td>\n",
              "      <td>2016-11-04 17:03:33</td>\n",
              "      <td>accepted</td>\n",
              "      <td>DPW - Bureau of Street Use and Mapping - G</td>\n",
              "      <td>General Request - PUBLIC WORKS</td>\n",
              "      <td>customer_callback</td>\n",
              "      <td>bsm - customer_callback</td>\n",
              "      <td>Intersection of 4TH ST and KING ST</td>\n",
              "      <td>4TH ST</td>\n",
              "      <td>6</td>\n",
              "      <td>Mission Bay</td>\n",
              "      <td>SOUTHERN</td>\n",
              "      <td>Phone</td>\n",
              "      <td>2388</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-faa220dd-e500-40d9-b60e-4d9909089b72')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-faa220dd-e500-40d9-b60e-4d9909089b72 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-faa220dd-e500-40d9-b60e-4d9909089b72');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 64
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_open.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uMGhem1tTJcE",
        "outputId": "5474503a-bc20-4776-af46-f020d8a8ffd4"
      },
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(2, 14)"
            ]
          },
          "metadata": {},
          "execution_count": 62
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 65,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "mHKYvKrzfV5W",
        "outputId": "cbac02b5-2d52-4aff-c1bb-7f73596e06bf"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# hist to visaulize nbr of days open cases have remained open\n",
        "\n",
        "# NOTE \n",
        "# Currently need to manually change the plot range to account \n",
        "# for range cases have been open, esp. in regard to the *mean*\n",
        "\n",
        "sns.set_style(\"whitegrid\", {'axes.grid' : False})\n",
        "plt.title('Number of days open 311-cases have remained open')\n",
        "plt.xlabel('Number of days')\n",
        "#df_open['days_open'].plot(kind='hist', range=[500,5100], figsize=(9,5))\n",
        "df_open['days_open'].plot(kind='hist')\n",
        "plt.show()\n",
        "# Note for 2500 rows, there are only 2 values for days_open"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Histograms processing time "
      ],
      "metadata": {
        "id": "D3Zd4guBe9sd"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "As9DlSZ0j3X6",
        "outputId": "808962f6-eab7-4fee-9eaa-f7c4227c207b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Quick stats on \"processing days\", use to set range on histogram\n",
            "min         1.00000\n",
            "max       806.00000\n",
            "mean        5.59043\n",
            "median      1.00000\n",
            "mode        1.00000\n",
            "Name: processing_days, dtype: float64\n"
          ]
        }
      ],
      "source": [
        "print('Quick stats on \"processing days\", use to set range on histogram')\n",
        "print(df_closed.processing_days.agg([min, max, np.mean, np.median, statistics.mode]))"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# hist to visualize processing time for cases that have been closed\n",
        "plt.title('Number of days required to close 311-cases')\n",
        "plt.xlabel('Number of days')\n",
        "df_closed['processing_days'].plot(kind='hist', color='lightgreen')\n",
        "plt.show()\n",
        "# frequency scale is too broad to show any detail"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "3Gielwh_jDEk",
        "outputId": "ee396d14-f6e2-4062-9448-fcf1672445d4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "id": "tlfKpBUnzX-X",
        "outputId": "36ecc3f6-abee-4388-e5e8-aacf3a957b8c"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 900x500 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# hist to visualize processing time for cases that have been closed\n",
        "plt.title('Number of days required to close 311-cases')\n",
        "plt.xlabel('Number of days')\n",
        "df_closed['processing_days'].plot(kind='hist', range=[0,50], color='DarkMagenta', figsize=(9,5))\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "Fw7KgTHJTDJW",
        "outputId": "b342b4a4-53cc-43f0-e50d-923a22771d9c"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# hist to visualize processing time for cases that have been closed\n",
        "plt.title('Number of days required to close 311-cases, focusing on cases that took more than 50 days')\n",
        "plt.xlabel('Number of days')\n",
        "#df_closed['processing_days'].plot(kind='hist', range=[100,900], color='LightBlue')\n",
        "df_closed['processing_days'].plot(kind='hist', range=[50,500], color='LightBlue')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "caTOJm1yTvUJ",
        "outputId": "1b63101b-0405-483a-b9a6-8bd4a481a346"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(23798, 18)\n",
            "(27, 18)\n"
          ]
        }
      ],
      "source": [
        "df_proc_less_100 = df_closed[df_closed['processing_days']<100]\n",
        "df_proc_more_100 = df_closed[df_closed['processing_days']>99]\n",
        "\n",
        "print(df_proc_less_100.shape)\n",
        "print(df_proc_more_100.shape)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 417
        },
        "id": "tyG6X9lIUv26",
        "outputId": "34e05c8c-e3cf-4cb2-d8b9-d1e76ada6d78"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x500 with 2 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAABOcAAAHyCAYAAACgQao+AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAACWDklEQVR4nOzdd3xO5//H8XemFWIkFLG5YwSJlUYTMVpb29AaNao1atWq1vxWaY0qarbULGrVqq20qogtRmy1EikSikSQSO7fH345dUuQkPRGXs/HIw8517nu63zOybnjyue+ruvYmM1mswAAAAAAAAD852ytHQAAAAAAAACQXpGcAwAAAAAAAKyE5BwAAAAAAABgJSTnAAAAAAAAACshOQcAAAAAAABYCck5AAAAAAAAwEpIzgEAAAAAAABWQnIOAAAAAAAAsBKScwAAAAAAAICVkJwDACAV9evXT+7u7goNDbV2KKli27Ztat68uSpXrix3d3d16dLF2iHhGVj7/ly2bJnc3d21bNkyqxw/Ne3atUvu7u6aOHGitUMBAAAvOHtrBwAAQFJCQ0NVq1YtSZKvr69mzJiRqM6BAwfUrFkzBQQEaOTIkf91iC+90NBQdenSRdmyZVPjxo3l5OSkokWLPvY1EydO1KRJk4xtW1tbZcmSRbly5ZK7u7v8/f1Vr149Zc6cOa3DT3U1a9bUxYsXjW1bW1tly5ZNJUuWVPPmzVWvXj0rRvdyqlmzpiTp999//0+Od/nyZc2bN0/btm1TSEiI7ty5o+zZs6t06dKqW7euGjZsKEdHx/8klrRy5swZzZw5U8HBwbp06ZKioqKUO3dulShRQm3btpWPj0+i1/z+++/avn27jhw5ouPHj+v27dvq1q2bPv744ySPceHCBf3yyy86cuSIjhw5oitXrih//vzP/HMMDg7W/PnztXfvXl25ckXx8fHKnTu3vLy89Pbbb+u11157pvYBALAWknMAgOfetm3btGPHjiT/aETa2bFjh+7evau+ffuqUaNGKXptnTp1VKJECUlSVFSULl68qN27d2vDhg2aMGGCRo0aJW9v77QIO03Z2dmpc+fOkqR79+7p/Pnz2rRpk3bu3KkLFy7oo48+snKEj9e7d2916NBBefLksXYoz53Vq1dr4MCBunPnjsqUKaM333xTWbNmVXh4uHbu3Kn+/fvrl19+0Y8//mjtUJ/JyZMntXHjRnl6esrLy0tOTk66fPmyfvvtN/3xxx/q2bOncY8nmDVrlnbv3i0nJyflzp1b58+ff+wx9u7dq0mTJsnOzk7FihVTRETEM8UcHx+vr7/+WrNnz5a9vb1effVV1axZU/b29goJCdGWLVu0cuVKde/eXV27dn2mYwEAYA0k5wAAz7X8+fPr77//1ujRo7VkyRLZ2NhYO6R04/Lly5Kk3Llzp/i1derUUYMGDSzKYmJiNHv2bH377bfq1KmTFixYoJIlS6ZKrP8VOzu7RKOF9u3bp1atWum7775TmzZtlClTJitF92S5c+d+qp/ny+7PP//Up59+qmzZsum7775LNALLbDZr06ZN+vnnn60UYeqpWbOm6tSpk+h36eXLlxUQEKDJkyerZcuWypYtm7GvR48ecnFxUaFChbR27Vr17t37sceoXLmyFi1apJIlSypjxowqW7bsM8U8btw4zZ49W6VKldKECRNUsGBBi/137tzRvHnzdP369Wc6DgAA1sKacwCA51qRIkX01ltvKTg4WOvWrUvWa2rWrGlMh3tY69at5e7ublE2ceJEubu7a9euXVq6dKkaNWqkcuXKqWbNmpozZ46k+3+cz5w5U3Xq1FHZsmVVu3ZtrVix4pExmM1mTZs2TbVr11bZsmVVs2ZNTZo0SbGxsUnW37Nnjzp16iRvb295eHiodu3a+vbbb3X79m2Leg+uc7V//359+OGHqlSpUqJzepSTJ0+qR48e8vHxkYeHh2rWrKlhw4bpn3/+MeqEhoZarKXVpk0bubu7G9foaTk6Oqpjx47q2rWroqOjNWbMGIv9wcHBGjp0qBo2bKiKFSuqXLlyatSokX744QeL6xYfH68aNWrI29tbMTExSR6rZcuWKl26tC5dumS85ueff9Y777yjKlWqqFy5cqpWrZo6der0TOckSRUrVlTRokV1584dnT59OtH+TZs26f3331flypVVtmxZNWzYUDNmzFBcXFyiunfu3NHo0aPl7+9v1F28eHGS65sl/Jz69euXZFzu7u5q3bq1RVlSa84l554ym81asmSJmjdvrgoVKqh8+fJq3LixlixZkuSxr1+/rs8//1xVq1ZV+fLl1aRJE23cuPHxF/IBCed28eJFXbx40bj/klrjbenSpXr33Xfl5eUlLy8vvfvuuyla0y4uLk5Dhw5VfHy8xo0bl+TUSBsbG73xxhsWU7YfJznvswTnzp1T//79VbNmTXl4eKhKlSp68803NWzYMJnNZou6UVFRmjBhgho0aKBy5cqpUqVKateunfbu3Zvs83V0dEzyQ448efKoQoUKio2NVVhYmMW+SpUqqXDhwsn+cKRAgQLy9PRUxowZkx3Xo5w/f17Tp09X9uzZNX369ESJOUnKmDGj2rdvr+7duxtlZ8+e1ahRoxQQECBvb2+VLVtWderU0ejRo3Xr1q1EbVy5ckVfffWVateubVzbevXq6fPPP1dkZKRF3ZiYGM2aNUsBAQHGCMT33ntPv/32W6J2IyMjNX78eNWvX19eXl6qUKGC3njjDfXt29dimjwAIH1j5BwA4LnXvXt3rVmzRuPGjdMbb7whBweHNDnOjz/+qN27d6tWrVry9vbWr7/+qmHDhilTpkw6evSofv31V1WvXl0ODg5au3at+vbtq/z586ty5cqJ2ho2bJiCgoJUt25dZc6cWZs3b9bEiRN18uRJTZgwwaLu/PnzNXToUGXLlk01atRQzpw5FRwcrClTpmjXrl2aM2dOonWugoKCNHXqVHl7e6tp06b6+++/n3h+e/fuVfv27RUbG6s6deoof/78OnDggObMmaM//vhDixYtUs6cOZUtWzZ169ZNu3fv1u7duxUQEKD8+fNLkvHvs/jggw80ffp0bdu2TZGRkcqaNaskafHixdq8ebMqV66satWq6c6dO9q9e7fGjBmjw4cPG0kZW1tbvfPOO5owYYI2bNiQaMrtmTNntHfvXlWvXl2vvPKKJGnMmDHGH/YNGzZUlixZdPnyZe3bt0+BgYGpNsXW3t6yazVmzBj98MMPypMnj9544w1lzZpVe/fu1ahRo3Tw4EGLeyE+Pl6dO3dWYGCgTCaTGjZsqOvXr2vEiBH/yRTgR91TZrNZffr00erVq1W4cGFj3bXt27dr4MCB+uuvv9S3b1+jndu3b6t169Y6efKkvLy8VLlyZf3999/q1atXstcES7gHE6aQvv/++8a+KlWqGN9/9dVXmjt3rvLkyaMmTZpIkn799Vf1799fR48e1aBBg554rF27dikkJEReXl5PnDqfnPXmkvs+k+6PVnv33Xd1+/Zt+fv7q379+rp9+7bOnTunBQsWqG/fvsY9df36dbVq1UqnTp1ShQoV1Lx5c0VFRem3337T+++/r/Hjx+v1119/YnyP8s8//+jgwYPKlCmT3Nzcnrqd1LZs2TLFxcWpefPmcnFxeWzdB38+Gzdu1NKlS+Xt7a0qVaooPj5eBw8e1LRp07Rnzx7NmzfP+L/k9u3batGihS5evKjXXntNr7/+umJjYxUaGqqVK1eqXbt2xu+pmJgYtWvXTrt371apUqX0zjvvKDY2Vlu2bFGXLl30v//9T61atZJ0/73Trl07HTx4UBUqVJCfn59sbW118eJF/f7773rrrbdS5XcqAOAlYAYA4DkUEhJiNplM5g8//NBsNpvNI0eONJtMJvPcuXONOkFBQWaTyWTu27evxWtr1KhhrlGjRpLttmrVymwymSzKJkyYYDaZTOYqVaqYL1y4YJSHhYWZy5QpY65YsaK5du3a5qtXrxr7Dhw4YDaZTOaPPvrIoq2+ffuaTSaT+dVXXzX//fffRvndu3fNLVu2NJtMJvP69euN8lOnTplLly5tfvPNN83Xrl2zaGvq1Klmk8lknjFjhlG2c+dOs8lkMptMJvOSJUuSvnhJiIuLM7/++utmk8lk/vPPPy32ff3112aTyWTu379/ktdl586dyT5OwmtWr1792Hrvvfee2WQymQMDA42yixcvmu/du2dRLz4+3ty/f3+zyWQy79271yi/dOmSuXTp0uZWrVolajvhXtm4caNRVqVKFbOvr685Ojo6Uf1//vknWedWo0YNs4eHR6LyvXv3mkuWLGmuUqWK+c6dO0b5tm3bjHv41q1bFuf0+eefJ7oXli5dajaZTOZ27dpZXIfjx4+by5QpYzaZTOYJEyYY5QnvkYfv/wQmkynR9Um4P0NCQoyyJ91TixYtMptMJnO/fv3MMTExRvndu3fNH330kdlkMpkPHz5slCfcA4MGDbJo588//zSOs3Tp0iRjftjj3su7d+82m0wmc7169cw3b940yq9fv26uXbu22WQymffs2fPEY0ycONFsMpnM3377bbJiSpBw3R78maT0fTZnzhyzyWQyz549O1H7D9+XvXv3NptMJvPixYstyiMiIsz+/v7mV1991eL+e5IzZ86YJ0yYYP7222/N/fv3N3t7e5tLly6dqP2HrV69OtF5P4mHh8cjf45PkvA7+8HfFclx6dIl8927dxOVJ/y8f/nlF6Pst99+M5tMJvOwYcMS1Y+KirJoZ+zYsWaTyWQeN26cOT4+3iiPjIw0N27c2FymTBnzpUuXzGbz/feuyWQyd+nSJVG7d+/eNUdFRaXonAAALy+mtQIAXgidOnUy1oNKakpSamjdurUKFChgbOfNm1cVK1ZUZGSkOnfubIx2kaTy5curQIECOnHiRJJttWnTxhi1Jd0f0dGzZ09J0vLly43yhQsX6t69e/rf//6nHDlyWLTRvn175cyZU6tXr07UfpkyZYyRQsmxf/9+XbhwQdWqVZOfn5/Fvq5duyp79uxavXr1I6eJpraEdc8enOaXL18+2dnZWdSzsbFRy5YtJd1/QEWCPHnyqEaNGtqzZ4/F4vSxsbH65Zdf5OrqqurVq1u05eDgkKh9ScqePXuy446Li9PEiRM1ceJEffvtt+rZs6fef/992draavDgwcqQIYNRd968eZKkL7/80uLptDY2NurTp49sbGy0Zs0aozxhmnSvXr0s4nR3d9dbb72V7Bif1qPuqXnz5ilz5swaPHiwxahVR0dH9erVS5ISnYeDg4PFFENJ8vPzS9WHuiS8j7p162aMapIkZ2dndevWTZKSNb01PDxckizer0/rad9nSU3/fPC+vHbtmtatW6dXX31V7777rkW9XLlyqV27drp27ZoCAwOTHeu5c+c0adIkff/991q6dKnu3r2r4cOHJ2rf2hIeJpHSh5jkyZMnyZGOCaPaHvx9kiCpn0OWLFmMduLj47VgwQIVLFhQ3bt3t5jm6+TkpK5duyo2NjbRFO6k2nV0dFSWLFlSdE4AgJcX01oBAC8EZ2dndejQQWPGjNHMmTMTLcqfGkqVKpWozNXVVZKSfHCBq6urDh06lGRblSpVSlTm5eUle3t7HT161Cg7ePCgJGnr1q1J/rFob2+vs2fPJir38PB4xFkkLeGYD04JTJAlSxZ5eHho27ZtOnv2bLLXr0ttMTEx+umnn7RmzRqdOXNG0dHRFmtuXblyxaJ+s2bNtHHjRv3888/q06ePJOn333/X1atX1alTJ4sppvXr19f8+fPVsGFD1a9fX97e3vLy8krxmlhxcXGJ1h2zt7dPckrhwYMHlTlzZi1dujTJtjJmzKgzZ84Y2ydOnFDmzJlVpkyZRHUrVar0yPXdUktS99Tt27d18uRJ5c6dW9OmTUu0/969e5JknEdUVJRCQ0NVvHhx473zoEqVKiV5nz+NY8eOSVKSU34Tyo4fP54qx0qulL7PatSoobFjx2ro0KHasWOH/Pz8VKVKFYsPCSTp8OHDiouLU0xMTKI196T7iTbp/s+hRo0ayYq1Ro0aOnHihGJiYnTx4kUtXrxYffv21eHDh5M1HTi13Lx5M8kn4D7r73iz2aylS5dq+fLlOnXqlCIjIxUfH2/sf/D3SeXKleXq6qoffvhBx48fV/Xq1VWlShUVK1bMIgF39uxZ3bhxQ7lz505y/cFr165J+vf9UKxYMbm7u2v16tW6dOmSXn/9dVWpUkWlSpWSrS1jJAAA/yI5BwB4YbRp00Y//fSTZs6cqffeey/V23dyckpUlpDgedS+hOTEw3LlypWozM7OTtmzZ7dYXPzGjRuSpClTpqQo1ietvfSwqKiox74uIZGSUC+tJfxh/OBoxO7du2vz5s0qXLiw6tevr1y5csne3l43b97UnDlzEo028vX1lZubm1asWKGePXvK3t5eP//8s2xsbPTOO+9Y1B04cKDc3Ny0bNkyff/99/r++++VIUMG1atXT3379rWI43EcHR11+PBhSdKtW7e0c+dODRgwQJ9++mmip8/euHFD9+7de+xDBKKjo43vIyMjHzl6K6n7KbUldW/cvHlTZrNZly9fTtZ5JNw/j7qeqXkeUVFRsrW1TfJYLi4usrGxSdb9nHDvJzyd+FljSjj+446VUM/NzU2LFi3SpEmTtGXLFuOhN0WLFlX37t1Vr149Sf/+nti/f7/279//yOM//ACZ5HB0dFSRIkXUt29f3blzR3PnzpWfn5/8/f1T3NbTuHnzZpL3VkJyzsXFRWfOnNHly5dVtGjRZLf71Vdfad68ecqbN69q1qwpV1dXYwTcpEmTLH6fZM2aVYsXL9aECRO0efNmbdmyRdL90dMdOnQwRu8mPA321KlTOnXq1COPnfBzsLe3148//qhJkyZpw4YNGjlypKT774+WLVuqc+fOSY7mBQCkPyTnAAAvjIwZM+rjjz/WwIEDNWnSpEdO9bOxsXnkU1EffupeWrl69WqiPyTj4uJ0/fp1iwRFQtJv3759SSYAHyW5T018+DgJU8QeljC1LyUxPK1bt27pyJEjsrOzM0aJHTp0SJs3b5avr69++OEHiz9YExbTf5iNjY2aNWumMWPGaPPmzfLw8ND27dvl4+OTaOSRvb292rVrp3bt2uny5cvas2ePli1bphUrVigiIkIzZsxI8XlkyZJFtWrV0rhx49S2bVv1799fy5YtM342CdcyuU+DzZo1a5JP85Tu308PSxh5k1SC+Gnu86TuqYRpd2XKlEnWFNGEc04YQfSwpM7jaTk5OSk+Pl7Xrl1LlPS7evWqzGZzsu7nChUqSLo/zbFHjx7PHJOUsveZyWTShAkTFBsbqyNHjujPP//U3Llz1atXL+XOnVsVK1Y06n/44YcWD99Iba+99prmz5+v3bt3/2fJOTc3t0cuDyDd//ns3r1bO3fuTPa06KtXr+qnn36Su7u7Fi1apEyZMhn7wsPDk0wG5suXTyNHjlR8fLxOnDihbdu2ae7cuRo6dKicnZ3VsGFD4+dQp06dRA/2eZQcOXLof//7nwYNGqQzZ85o586dmjt3riZOnCgHBwd99NFHyWoHAPByYzw1AOCFEhAQoBIlSujnn3+2WGvsQc7Ozrp27VqipEV0dPQjX5Pa9u7dm6gsKChI9+7dU+nSpY2ycuXKSfp3emtaSTjm7t27E+2Ljo5WcHCwMmbMqCJFiqRpHJI0a9Ys3b59W9WqVTPWCgsJCZEkVa9ePdFIkqSuZYLGjRvLwcFBP//8s5YuXar4+PgnrpmVJ08eNWzYUNOnT1ehQoUUGBioO3fuPPX5+Pj46PXXX9fRo0ct1gcsV66crl+/bkw5fBJ3d3dFR0fryJEjifYldQ2yZcsmKekRXw9OnX4WTk5OKlasmM6cOaObN28mq76bm5vOnz9vJKIe9LifZVJsbW0VFxeX5L6EaehJJT8T7vOkpqM/zNvbWwUKFFBQUJB27tz52LpPWpPxWd5nDg4O8vT0VPfu3TVw4ECZzWb98ccfkqSyZcvKxsZGQUFBTzyfZ5EwovXhpw5bU+PGjWVnZ6dFixY9MumbIOHnExISIrPZrKpVq1ok5qQn34O2trYqVaqUOnTooLFjx0q6P11euj9N1cnJScHBwY/8AOhRbGxsVKxYMbVs2VKzZs2yaBcAAJJzAIAXip2dnXr16qXY2NhHTrPz8PBQbGysVq1aZZSZzWaNHTvWYhphWpozZ44uXbpkbMfExGjcuHGS7icYE7z33nuyt7fXl19+qbCwsETt3Lx5M1USLRUqVFDBggX1559/Jlo0/vvvv9f169fVoEGDJBdQTy0xMTGaNm2aJk+erMyZM6t3797Gvnz58km6P4LwQadOndIPP/zwyDZdXFxUq1Ytbd26VQsWLFCOHDkSrf0WExOT5FTA6OhoRUdHy97e/pnXf/r4449lY2OjSZMmGcmk1q1bS5IGDBiQ5Ii48PBw/fXXX8Z2wkjQb7/91iIhdeLECf3yyy+JXu/k5KQiRYpo//79FknnqKgoI6mQGlq3bq3bt29r0KBBSb5/QkJCFBoaanEesbGxiUYWbdu2LcXrzTk7O+uff/7R3bt3E+1LeB9NnjzZYvpqZGSk8bvhwffao9jZ2enzzz+Xra2tevbs+cgYf//990QPuXhYSt9nwcHBSU69TRhhmPCAEVdXV9WrV09BQUGaPn26xVqMCQ4ePJisaa3BwcFJvv7ixYvGe61atWpPbOe/UqhQIbVv317//POP2rdvbyTyH3T37l3NmjXLWI8v4fdJUFCQxTpzly5dSvK9cerUqSRHOyaUJfwc7O3t1aJFC128eFFff/11kgm6kydPGj+/0NBQi/fGw+2m5e9bAMCL5fn5WAwAgGSqVauWKlasmCiRk6BVq1ZatmyZBg0apO3btytnzpzau3evIiMjVbJkyf9kkfjy5cvrrbfeUr169ZQpUyZt3rxZZ8+eVe3atVWnTh2jnslk0uDBg/XFF1+obt268vf3V4ECBXTr1i2FhoZq9+7dCggI0NChQ58pHltbW40YMULt27dXx44dVadOHeXPn19BQUHavXu3ChYsaDxUITVs2LDBWBQ9OjpaoaGh2rNnj/755x/lzZtX33zzjUwmk1G/XLlyKleunNatW6fw8HCVL19ef//9t37//Xf5+/trw4YNjzxW8+bNtX79ekVEROjDDz9M9AfvnTt31KJFCxUuXFgeHh7KmzevoqOj9ccffyg8PDzJ16RUyZIl9cYbb+jXX3/VypUrFRAQoGrVqqlLly767rvvVLt2bfn5+Slfvny6fv26zp8/r3379qlnz54qVqyYpPuJpNWrV2vr1q16++23Va1aNd24cUNr1qyRr6+vNm/enOi4H374of73v/+pWbNmqlu3ruLj47V161aVLVv2mc7nQc2bN9fBgwe1fPly7d+/X1WrVlXu3Ll19epVnTlzRgcPHtSYMWPk5uYm6f5Thjdu3KjFixfr1KlTqly5sv7++2+tX79e1atXN0aDJcerr76q4OBgtW/fXpUqVZKDg4MqV65sfLVu3Vpz585Vw4YNVbt2bZnNZv3666+6dOmSWrdurcqVKyfrONWqVdOoUaM0aNAgtW3bVh4eHvLy8lKWLFkUERGh3bt368KFC6patepj20np++yXX37RokWLVLlyZRUoUEBOTk46ffq0/vzzT2XPnl2NGzc26g4ePFhnz57VN998o19++UVeXl7KmjWrLl26pODgYJ07d07btm1LNFLsYV9//bXOnz+v8uXLK2/evLK1tdWFCxf0559/KjY2Vh9++KEqVqxo8ZpNmzZp06ZNkmQkmzZt2qSLFy9Kur9GXseOHY36165d06hRo4zte/fu6Z9//lG/fv2Mss8++yzZaz327NlTd+/e1ezZs1WvXj15e3vLZDLJ3t5eoaGhCgwM1PXr140nYufOnVt16tTRhg0b1KRJE7366qu6evWq/vjjD7366qu6cOGCRfvbt2/XN998owoVKqhw4cLKnj27QkJC9PvvvytDhgwWa5x2795dR48e1dy5c7VlyxZVqlRJuXLl0uXLl3Xy5EkdP35cixYtUq5cuXT8+HF169ZN5cqVU7FixeTq6qrLly9r06ZNsrW1Vdu2bZN1/gCAlx/JOQDAC6lPnz5q0aJFkvtMJpOmT5+usWPHasOGDcqcObP8/f3Vt29f44+3tDZw4ECtW7dOS5YsUVhYmHLnzq2PP/7Y4g/YBE2bNlXJkiU1e/Zs7dmzR5s3b5aTk5Py5cuntm3b6u23306VmCpVqqRFixZp8uTJ2r59u6KiopQ7d261adNGnTt3TvYfysmxYcMGbdiwQba2tsqcObNy5cqlKlWqqHr16kbC8kF2dnaaOnWqRo8era1bt+rw4cMqVKiQPvvsM1WrVu2xyblXX31V+fLlU1hYWKIHQUhSpkyZ1KdPH+3cuVN79+7V1atX5ezsrCJFiqh3795q0KBBqpxz165dtXHjRk2ePFmNGjWSvb29evToocqVK2vOnDnasWOHIiMjlT17drm5ualbt25q1KiR8XpbW1t99913mjRpklatWqU5c+aoYMGC6t+/vwoVKpRkcq5p06aKjY3VnDlztGTJErm6uiogIECdO3d+7DVLCRsbG40cOVLVqlXTzz//rD/++EPR0dHKmTOnChUqpL59+1qsBZY5c2bNnTtXY8eO1caNG3X06FEVL15c3377rSIjI1OUnOvSpYtu3rypzZs3a9++fYqLi1O3bt2MpNugQYNUqlQpLViwQIsXL5YkFS9eXN27d1eTJk1SdJ6NGjVSlSpVNHfuXG3fvl3Lly/XnTt3lD17dpUqVUqdO3e2+Hk9SkreZw0bNtTdu3cVFBSkQ4cOKSYmRq+88opatGihdu3aGSPAJCl79uxauHCh5s2bp7Vr12rVqlWKj4+Xi4uLSpYsqc6dOytHjhxPjK9ly5Zat26djhw5om3btik2NlY5c+ZUjRo11LRpU/n5+SV6zbFjx7R8+XKLsuPHjxsfdFSpUsXid1t0dHSi+g+XdevWLdm/c2xtbdW/f381bNhQCxYs0N69e7V3717Fx8fL1dVVvr6+atKkiUXydMSIEcqfP782bNigefPmGb9PO3TokOi94efnp4sXL2rv3r369ddfFR0drTx58qh+/fpq3769ihcvbtR1dHTUtGnTtGTJEq1YsUK//vqrYmJi5OLiomLFiql58+bGBw8eHh7q0KGDdu/erS1btujmzZtydXVV1apV1a5dO3l6eibr/AEALz8bc1Lj2gEAAF4QV65cUY0aNeTp6amffvrJ2uGkiV27dqlNmzbq1q2b8RRLAAAAvBxYcw4AALzQfvzxR927d++RIykBAACA5xnTWgEAwAsnMjJSCxYs0MWLF7VkyRIVL15c9erVs3ZYAAAAQIqRnAMAAC+cGzduaMyYMcqQIYMqVKigIUOGyM7OztphAQAAACnGmnMAAAAAAACAlbDmHAAAAAAAAGAlJOcAAAAAAAAAKyE5BwAAAAAAAFgJyTkAAAAAAADASkjOAQAAAAAAAFZCcg4AAAAAAACwEpJzAAAAAAAAgJWQnAMAAAAAAACshOQcgDS3bNkyubu7KzQ01NqhWKhZs6b69ev3xHru7u6aOHHifxDRi2nixIlyd3fXtWvXrB0KAABIA/TlkFpq1qypjz76yNphAM8de2sHAKS2CxcuaPr06dq+fbuuXLkiBwcHmUwm1atXT82aNVPGjBmtHeJT27hxoxYtWqTDhw/r1q1byp49uypWrKjmzZvLx8fH2uGliS1btujQoUP6+OOPrR1KqunXr582bNigoKAga4eSpF27dqlNmzbJqnvixIk0jubprF27Vr///rsOHTqk8+fPq0qVKpo7d26SdWNiYjR+/Hj98ssvunnzptzd3dWzZ0+99tprieru379f33zzjY4ePSonJyfVq1dPvXr1UpYsWR4bT2hoqGrVqmVs29vby8nJSUWKFFGVKlXUvHlz5cuX79lOGgBeEvTlXi4vY18uwd69ezV37lzt379f//zzjzJkyKCiRYvK399fzZs3l4uLi1XimjJliooXL67XX389zY/Vr18/LV++/In1AgICNHLkyDSP52kEBwfr22+/VVBQkMxms7y8vPTpp5+qVKlSFvViY2M1depULV++XJcvX1aePHnUpEkTdezYUfb2j0+t0BfEk5Ccw0vljz/+UI8ePeTo6Ki33npLJpNJsbGx2rdvn7755hudPn1aX375pbXDTDGz2awBAwZo2bJlKl26tD744AO5uLgoPDxcGzduVNu2bbVgwQJVqFDB2qGmui1btuinn36yaofu0KFDsrOzs9rx/2vFihXTqFGjLMrGjh2rzJkzq1OnTlaKKmUWLFig4OBglS1bVtevX39s3YRkaZs2bVS4cGEtX75cHTt21I8//qhKlSoZ9Y4dO6a2bduqWLFi6tevny5duqSZM2fq3Llzmj59erLiatiwoapVqyaz2awbN27o8OHD+vHHHzVnzhwNGzZMDRo0eJbTBoAXHn05+nJpIS36cuPHj9d3332nAgUKqHHjxnJzc1NMTIyCg4M1a9YsrVixQps2bUrVYybX1KlTVadOnf8kOdesWTOLxHJoaKgmTJigZs2aqWLFikZ5wYIF0zyWp3HkyBG99957yps3r7p166b4+HjNnz9frVq10s8//6yiRYsadT/99FOtX79eTZo0kYeHhw4ePKjx48fr77//TvbvJfqCeBSSc3hphISEqFevXsqXL59+/PFH5c6d29jXsmVLnT9/Xn/88Yf1AnwGM2fO1LJly/T++++rf//+srGxMfZ17txZK1aseOKnNXh6GTJksHYI/ykXFxe99dZbFmXTpk1Tjhw5EpU/r0aNGqU8efLI1tZWDRs2fGS9Q4cOac2aNfrss8/Url07SdLbb7+thg0bavTo0Vq4cKFRd+zYscqWLZvmzp0rJycnSZKbm5sGDRqkbdu2ydfX94lxlS5dOtE1vHjxoj788EP17dtXxYoVU8mSJZ/mlAHghUdfjr5cWkntvtzatWv13XffqV69eho1apQcHR0t9g8YMECzZ89+bBtms1l37959YUaC3r17Vw4ODrK1tVwZy8vLS15eXsb24cOHNWHCBHl6er4Q/cbx48crY8aMWrhwoXLkyCFJevPNN1WnTh19++23xnToQ4cOad26derSpYt69OghSWrRooVy5MihWbNmqWXLlsnqw9EXxKOw5hxeGtOnT1d0dLSGDRtm0ZlLUKhQIb3//vvG9tKlS9WmTRv5+PjIw8ND9evX1/z58xO97vDhw2rXrp28vb1Vrlw51axZU/3797eoEx8fr9mzZ6tBgwYqW7asqlatqs8//1w3btxIcVsPu3Pnjn744QcVLVpUffv2tejMJXj77bdVrlw5YzskJETdu3dXlSpVVL58eTVt2jRRZ3bXrl1yd3fX2rVrNWnSJPn5+cnLy0vdu3dXZGSkYmJiNGzYMPn4+MjLy0v9+/dXTEyMRRvu7u4aOnSoVq5cqTp16qhs2bJq3Lix9uzZ89hzSrBlyxa999578vT0lJeXlzp27KhTp04Z+/v166effvrJOFbCV4LkXnez2azvvvtO1apVU/ny5dW6dWuL4zzJw+uUJKyxdv78efXr10+VKlVSxYoV1b9/f92+fTvZ7T7JwYMH1a5dO1WsWFHly5dXq1attG/fPos6UVFRGjZsmGrWrCkPDw/5+Pjogw8+0JEjR4w6586d08cff6zXXntNZcuWVbVq1dSrVy9FRkamWqySFBkZ+cTrkdz3XcJ6JHv37tU777yjsmXLqlatWlqxYkWyYsmbN2+izmNS1q9fLzs7OzVr1swoy5Ahg9555x0FBQXp77//lnT/OgcGBurNN980EnOS9NZbbylz5sxat25dsuJKSv78+TVy5EjFxsZq2rRpRvn169f19ddfq1GjRvLy8lKFChXUvn17HT9+3Khz69YteXp66quvvkrU7qVLl1SqVClNnTpV0v2pGJMmTVLt2rVVtmxZeXt7q0WLFtq+fftTxw4AqYm+HH25F6UvN378eOXIkUPDhg1LlJiTpKxZsyYaKZjQt9m6dasaN26scuXKGR8C3rx5U8OGDZO/v788PDz0xhtv6IcfflB8fLxFGzNmzFDz5s2N+69x48Zav359onONjo7W8uXLjev94Lp8ly9fVv/+/VW1alV5eHioQYMGWrJkiUUbCffWmjVr9O2338rPz0/ly5dXVFTUE69Ncj2pj5ecftCDsa5du1bff/+9qlWrprJly+r999/X+fPnkxWHj4+PkZiTpNy5c6tKlSravHmzbt26JUlGH/zhkW3169eX2WymL4hnxsczeGls3rxZBQoUSPZ0gAULFqhEiRKqWbOm7O3ttXnzZg0ZMkRms1ktW7aUJF29elXt2rVTjhw51LFjR2XLlk2hoaHauHGjRVuff/65li9frsaNG6t169YKDQ3VTz/9pKNHj2rBggVycHBIdlsP27dvn65fv642bdokazh+RESEmjdvrtu3b6t169bKkSOHli9frs6dO2vChAl64403LOr/8MMPypgxozp27Kjz589r3rx5sre3l42NjW7evKlu3brp4MGDWrZsmfLnz69u3bpZvH7Pnj1au3atWrduLUdHRy1YsEDt27fXzz//LJPJ9Mg4V6xYoX79+snX11d9+vTR7du3tWDBAr333ntavny53Nzc1KxZM125ckXbt29PNM0yudddut+B+v777+Xv7y9/f38dOXJEH374oWJjY594PR+nZ8+ecnNzU+/evXX06FH9/PPPypkzpz799NNnaleSduzYoQ4dOsjDw0PdunWTjY2N8Yn7/PnzjQ784MGDtWHDBrVq1UrFihXT9evXtW/fPv31118qU6aMYmJi1K5dO8XExKhVq1ZycXHR5cuX9ccff+jmzZvKmjXrM8eaIDnXIznvuwTnz59Xjx499M477yggIEBLly5Vv379VKZMGZUoUSJVYj527JgKFy5skXCTZFzfY8eOKW/evDpx4oTu3bsnDw8Pi3qOjo4qVaqUjh079kxxeHl5qWDBggoMDDTKQkJCtGnTJtWtW1dubm6KiIjQokWL1KpVK61Zs0Z58uRRlixZ9Prrr2vdunXq37+/xe+I1atXy2w2q1GjRpKkSZMmaerUqXr33XdVrlw5RUVFKTg4WEeOHElyfT0A+K/Rl7uPvtzz3Zc7e/aszp07p3ffffeJa84m9dpPPvlEzZo1U9OmTVWkSBHdvn1brVq10uXLl9W8eXPlzZtXQUFBGjt2rMLDwzVw4EDj9XPmzFHNmjXVqFEjxcbGas2aNerRo4emTp2q6tWrS7o/e2DQoEEqV66cmjZtKunfKaURERFq2rSpbGxs1LJlS+XMmVN//vmnBg4cqKioKLVt29Yi3u+++04ODg5GXzLh5/GsktPHS04/6EHTpk2TjY2NPvzwQ0VFRWn69Onq06ePfv7558fGEhMTk+ToxYwZMyo2NlanTp2Sp6enkdh+eBRmpkyZJN1ft+5Z0BeEzMBLIDIy0mwymcydO3dO9mtu376dqOzDDz8016pVy9jeuHGj2WQymQ8dOvTIdvbs2WM2mUzmlStXWpT/+eefFuXJaSspP/74o9lkMpk3btyYrPrDhg0zm0wm8549e4yyqKgoc82aNc01atQwx8XFmc1ms3nnzp1mk8lkbtiwoTkmJsao27t3b7O7u7u5ffv2Fu02a9bMXKNGDYsyk8lkNplM5sOHDxtlFy9eNJctW9bctWtXo2zp0qVmk8lkDgkJMeKpVKmSedCgQRbthYeHmytWrGhRPmTIELPJZEp0nsm97levXjWXKVPG3LFjR3N8fLxRb+zYsWaTyWTu27dvUpcx0XlOmDDB2J4wYYLZZDKZ+/fvb1Gva9eu5ipVqjyxvb59+5o9PT0fuT8+Pt5cu3Zt84cffmgR8+3bt801a9Y0f/DBB0ZZxYoVzUOGDHlkW0ePHjWbTCbzunXrnhjX4zRo0MDcqlWrJPel5Hok531nNpvNNWrUSHQfX7161ezh4WEeOXJkqsXeoEEDc5s2bRKVnzp1ymwymcwLFiwwm81m87p16xLFk6B79+7m11577bExhISEmE0mk3n69OmPrNO5c2ezyWQyR0ZGms1ms/nu3bvG+/XBdjw8PMyTJk0yyrZu3Wo2mUzmLVu2WNRt1KiRxXm/+eab5o4dOz42TgCwFvpy/6Iv93z35TZt2mQ2mUzm2bNnW5THx8ebr169avEVGxtr7E/o2/z5558Wr5s8ebLZ09PTfPbsWYvy0aNHm0uVKmUOCwszyh6+52NiYswNGzZM1Jfx9PRM8roMGDDA/Nprr5mvXbtmUd6rVy9zxYoVjfYT7q1atWol+T57nEOHDplNJpN56dKlSe5Pbh8vuf2ghFjr1atnvnv3rlGe8L47ceLEY+Nt2LChuXbt2uZ79+5ZHLt69epmk8lkXr9+vdlsNps3bNhgNplM5hUrVli8fsGCBcb78HHoC+JJmNaKl0LCEOuUfHr14CckkZGRunbtmqpUqaKQkBBjul/CqKI//vjjkZ/MrV+/XlmzZtVrr72ma9euGV9lypRR5syZtWvXrmS3lRrntmXLFpUrV85iIfssWbKoWbNmunjxok6fPm1R/6233rL4FKxcuXIym81q0qSJRb1y5crp77//1r179yzKvby8LEYT5cuXT7Vq1dK2bdsUFxeXZIyBgYG6efOmGjRoYHHNbG1tVb58eeOaPU5yr3tgYKBiY2PVqlUri2kkD06LeVrNmze32K5UqZKuX7/+zEP+jx07pnPnzqlRo0b6559/jHOLjo6Wj4+P9uzZY0xzyJYtmw4ePKjLly8n2VbCiLBt27al6pTbpCTneiTnfZegePHiFvdxzpw5VaRIEYWEhKRazHfu3ElyOkrCp6J37tyx+PdRdRP2P4vMmTNLkjF9wtHR0ZiaGxcXp3/++UeZM2dWkSJFdPToUeN1VatWVe7cubVq1Sqj7OTJkzpx4oTefPNNoyxbtmw6deqUzp0798yxAkBqoy/3L/pyz3dfLmFfwv/bCSIjI+Xj42Px9fDIejc3N/n5+VmUrV+/XhUrVlS2bNksrkPVqlUVFxdnMcX4wXv+xo0bioyMVMWKFS36BY9iNpv166+/qmbNmjKbzRbH8vX1VWRkpMWyKNL96dZpsSZecvp4ye0HJWjcuLFFPy2h/Sf1G9977z2dO3dOAwcO1OnTp3Xy5En17dtX4eHhkv7tA/r7+yt//vwaNWqUfv31V128eFFr167Vt99+K3t7e/qCeGZMa8VLISEBkfCLLDn27duniRMn6sCBA4mSFpGRkcqaNauqVKmiOnXqaNKkSZo9e7aqVKmi119/XY0aNTJ++Z8/f974zzgpV69elaRktZUa5xYWFqby5csnKk940lBYWJjFFIWHH9md0PHMmzdvovL4+HhFRkZarMlQqFChRMcqXLiwbt++rWvXrsnV1TXR/oT/EB7VqXp4imFSknvdw8LCjJgelDNnTjk7Oz/xOI/z8LXLli2bpPudpeScw6MkXJ++ffs+sk5kZKScnZ3Vp08f9evXT9WrV1eZMmXk7++vt99+WwUKFJAkFShQQB988IFmzZqlVatWqVKlSqpZs6befPPNVJ3SKiXveiTnfZfg4XtQkpydnROtQ/MsMmbMmGj9Hen+oscJ+x/891F1U6PjGh0dLenfP97i4+M1Z84czZ8/X6GhoRZ/IGXPnt343tbWVo0aNdKCBQt0+/ZtZcqUSatWrVKGDBlUt25do1737t3VpUsX1alTRyaTSb6+vnrrrbdYdBjAc4G+3L/oy933vPblEv6fTvh/O0HmzJk1a9YsSfc/FJ0xY0ai17q5uSUqO3/+vE6cOPHI63Dt2jXj+82bN+v777/XsWPHLPokSa1jmFQ7N2/e1KJFi7Ro0aInHutR8aaG5PTxktsPSvCon+XNmzcfG0uLFi106dIlzZgxQ8uXL5ckeXh4qF27dpoyZYrx886QIYOmTp2qnj17GusJOjo66tNPP9WUKVMSJWufBn3B9I3kHF4KTk5Oyp07d7IXhr1w4YLatm2rokWLql+/fsqbN68cHBy0ZcsWzZ492xiVZGNjowkTJujAgQPavHmztm7dqgEDBmjWrFlatGiRsmTJovj4eOXKlUujR49O8lg5c+ZMdltJSeiInThxIk0eh/6oRfMfVW42m5/5mAltjBo1KskOX3LWY0nudU9LaXWNEl7/2WefqVSpUknWSegA1K9fX5UqVdLGjRu1fft2zZgxQ9OmTdPEiRPl7+8v6f5izAEBAfrtt9+0fft2ffXVV5o6daoWL16sV1555ZlifdCTrkdy33cJknMfPCtXV9ckRx0mfFqasCB5wn165cqVJOsmtXB5Sp06dUq5cuUy/hiYMmWKxo8fryZNmqhHjx5ydnaWra2thg8fnugee/vttzVjxgxt2rRJDRs21OrVq1W9enWLZGflypW1ceNG4z5YsmSJfvzxRw0ZMkTvvvvuM8cPAM+CvtzToy/39J7mGiX8PB++V+3t7VW1alVJ9xfiT0pSH+bFx8frtddeU/v27ZN8TUJScu/evercubMqV66swYMHy9XVVQ4ODlq6dKlWr179yHgfPI50/0mkAQEBSdZ58GEdj4o3NSTn/khJP0h6tvu9V69e+vDDD3Xq1CllzZpV7u7uGjt2rCTLpHCJEiW0evVqnT59Wjdu3FDx4sWVMWNGjRgxQpUrV37icZ6EvmD6RnIOL40aNWpo0aJFCgoKsnicd1J+//13xcTE6Pvvv7f4lOVRQ/A9PT3l6empXr16adWqVerTp4/Wrl2rd999VwULFtSOHTtUoUKFZP0H9ri2klKxYkU5OztrzZo16tSp0xP/M8uXL5/Onj2bqPzMmTPG/tSU1FOQzp07p0yZMj2yU5UwqitXrlxGJ+ZRHvVJYHKve8L5njt3zjiudP+TwdQcgZWaEuJ0cnJ64vWR7ieQWrZsqZYtW+rq1asKCAjQlClTjOSc9O8T0rp06aL9+/erRYsWWrBggXr16pVm5/GwlL7v/gslS5bUrl27FBUVZfEJ+cGDByXJSI6aTCbZ29srODhY9evXN+rFxMTo2LFjqlev3jPFERQUpAsXLlhMPdiwYYO8vb01fPhwi7o3b960GPGQEF/p0qW1atUqvfLKKwoLC9OgQYMSHSd79uxq0qSJmjRpolu3bqlVq1aaOHEiHTIAzwX6cvfRl7P0vPXlihYtqsKFC2vTpk0aMGDAM4+YKliwoKKjo594HTds2KAMGTJoxowZFiM1ly5dmqzj5MyZ00hGJ6d/aW0p6QelBmdnZ4uptoGBgXrllVeMZGwCGxsbiweTbdmyJVWuKX1BsOYcXhrt27dX5syZNWjQIEVERCTaf+HCBf3444+S/v205sFPHCIjIxP953bjxo1En0ok/LGeMJS8Xr16iouL03fffZfomPfu3TOGUienraRkypRJ7du3119//aXRo0cn+enPL7/8okOHDkm6vx7CoUOHFBQUZOyPjo7W4sWLlT9/fhUvXvyRx3oaQUFBFutT/P333/rtt9/02muvPbLz6efnJycnJ02dOjXJNVseHFKf8ASkh4ekJ/e6V61aVQ4ODpo3b57FtUu4F55HHh4eKliwoGbOnJnkFJiE6xMXF5donbZcuXIpd+7cxj0VFRWVaG0Zk8kkW1vbx953aSG577v/Ut26dRUXF2cxvSMmJkbLli1T+fLljWkXWbNmlY+Pj1auXGmxDs0vv/yi6OhoiykDKXXx4kX169fPeBpaAjs7u0Tv93Xr1j1yfcG33npL27dv148//qjs2bOrWrVqFvv/+ecfi+0sWbKoYMGC//l9AACPQl+OvlyC570v161bN/3zzz/63//+l+T5p2R0Yr169RQUFKStW7cm2nfz5k2jH2dnZycbGxuLqY2hoaH67bffEr0uc+bMia63nZ2d6tSpow0bNujkyZOJXvPwlFZrS2k/KDWtXbtWhw8f1vvvv//IEXnS/fXoxo8fL1dXVzVo0OCpj0dfEBIj5/ASKViwoEaPHq1evXqpfv36euutt2QymRQTE6OgoCCtX79ejRs3liS99tprcnBwUKdOndS8eXPdunVLP//8s3LlymVMZ5Ok5cuXa8GCBXr99ddVsGBB3bp1S4sXL5aTk5Pxy65KlSpq1qyZpk6dqmPHjhltnzt3TuvXr9fAgQNVt27dZLX1KO3bt9fp06c1c+ZM7dq1S3Xq1JGLi4siIiK0adMmHTp0SAsXLpQkdezYUWvWrFGHDh3UunVrOTs7a8WKFQoNDdXEiRMf+x/M0zCZTGrXrp1at24tR0dHLViwQJKMtRiS4uTkpC+++EKfffaZGjdurPr16ytnzpwKCwvTli1bVKFCBX3++eeSpDJlykiSvvrqK/n6+srOzk4NGjRI9nXPmTOnPvzwQ02dOlUfffSR/P39dfToUf35559p8qlbcsXGxibZGXV2dlbLli311VdfqUOHDmrYsKEaN26sPHny6PLly9q1a5ecnJw0ZcoU3bp1S/7+/qpTp45KliypzJkzKzAwUIcPH1a/fv0kSTt37tTQoUNVt25dFS5cWHFxcfrll1+MDtp/Kbnvu9SwZ88eYwHlhIdpJFzvypUrG1MPypcvr7p162rs2LG6evWqChUqpOXLl+vixYsaNmyYRZu9evVS8+bN1bp1azVt2lSXLl3SrFmz5Ovr+8T3cIKjR4/ql19+kdls1s2bN3X48GH9+uuvsrGx0ahRoyzW/KhevbomT56s/v37y8vLSydPntSqVassRg08qGHDhvrmm2+0ceNGtWjRwmJxcEnG+6ZMmTLKnj27Dh8+rA0bNqhVq1bJu6gAkMboy9GXe1H6co0aNdKpU6c0depUHTp0SPXr15ebm5tu376tU6dOafXq1cqSJUuy1sRr166dfv/9d3Xq1EkBAQEqU6aMbt++rZMnT2rDhg367bfflDNnTvn7+2vWrFlq3769GjZsqKtXr2r+/PkqWLCgTpw4YdFmmTJltGPHDs2aNUu5c+eWm5ubypcvr08++US7du1S06ZN9e6776p48eK6ceOGjhw5oh07dmj37t1pdclSLKX9oKe1Z88eTZ48Wa+99pqyZ8+ugwcPatmyZfLz81ObNm0s6vbo0UO5c+dW8eLFFRUVpaVLlyokJEQ//PBDstebpi+IRyE5h5dKrVq1tHLlSs2YMUO//fabFixYIEdHR7m7u6tfv35q2rSppPvD0SdMmKBx48bp66+/louLi1q0aKGcOXNqwIABRntVqlTR4cOHtXbtWkVERChr1qwqV66cRo8ebfFLcejQofLw8NDChQv17bffys7OTvnz59ebb76pChUqpKitpNja2mrUqFGqVauWFi9erJkzZyoqKko5cuRQ5cqV9emnnxrTP1xcXLRw4UJ98803mjdvnu7evSt3d3dNmTJF1atXT+Urfj/R4enpqcmTJyssLEzFixfXiBEjnriwaKNGjZQ7d2798MMPmjFjhmJiYpQnTx5VqlTJ6HhLUu3atdW6dWutWbNGK1eulNlsNj6ZSs51l6SePXvK0dFRCxcu1K5du1SuXDnNnDlTH330Uapfj+SKjY3V+PHjE5UXLFhQLVu2lLe3txYtWqTvvvtO8+bNU3R0tFxdXVWuXDk1a9ZM0v11QFq0aKHt27fr119/ldlsVsGCBTV48GC99957ku5PZ/X19dXmzZt1+fJlZcqUSe7u7po2bZo8PT3/y1NO9vsuNezcuVOTJk2yKEu43t26dbNYF2TUqFEaN26cVq5cqRs3bhjvl4fXDilTpoxmzZql0aNHa8SIEcqSJYveeecd9e7dO9lxrV69WqtXr5a9vb2cnJxUqFAhvf/++2revHmiaUqdOnXS7du3tWrVKq1du1alS5fW1KlTNWbMmCTbdnFx0WuvvaYtW7borbfeSrS/devW+v3337V9+3bFxMQoX7586tmzp8UntABgbfTl6Mu9KH253r17y9fXV/PmzdPSpUt1/fp1ZciQQYULF9aHH36o5s2bJ7ke38MyZcqkuXPnaurUqVq/fr1WrFghJycnFS5cWB9//LGxZpiPj4+GDRumadOmafjw4XJzc1OfPn108eLFRMm5fv366fPPP9e4ceN0584dBQQEqHz58nJxcdHPP/+syZMna+PGjVqwYIGyZ8+u4sWLq0+fPmlynZ5WSvtBTytPnjyys7PTjBkzdOvWLbm5ualnz55q27at7O0t0yUeHh5atmyZFi1apIwZM6pixYoaM2bMI9eITgp9QTyKjTk1VgQFkC65u7urZcuWxiejAKyra9euOnnypDZu3GjtUAAALwD6csDLhb7gi4s15wAAeAlcuXLlkZ+UAgAA4OVGX/DFxrRWAABeYCEhIdq/f7+WLFkie3t7Y9ozAAAAXn70BV8OjJwDAOAFtmfPHn322WcKDQ3VyJEjk7W+DQAAAF4O9AVfDqw5BwAAAAAAAFgJI+cAAAAAAAAAKyE5BwAAAAAAAFgJD4RIJfHx8bp3755sbW1lY2Nj7XAAAMALwmw2Kz4+Xvb29rK15XPT5xH9PAAA8DSS288jOZdK7t27p8OHD1s7DAAA8IIqW7asHB0drR0GkkA/DwAAPIsn9fNIzqWShAxo2bJlZWdnZ+VoAADAiyIuLk6HDx9m1NxzjH4eAAB4Gsnt55GcSyUJUxzs7OzotAEAgBRjuuTzi34eAAB4Fk/q5/ERLQAAAAAAAGAlJOcAAAAAAAAAKyE5BwAAAAAAAFgJyTkAAAAAAADASkjOAQAAAAAAAFZCcg4AAAAAAACwEpJzAAAAAAAAgJWQnAMAAAAAAACshOQcAAAAAAAAYCUk5wAAAAAAAAArITkHAAAAAAAAWAnJOQAAALzU4uLiNG7cONWsWVPlypXT66+/rsmTJ8tsNls7NAAAANlbOwAAAAAgLU2bNk0LFizQ119/reLFiys4OFj9+/dX1qxZ1aZNG2uHBwAA0jmScwAAAHipBQUFqVatWqpevbokyc3NTWvWrNGhQ4esGxgAAICY1goAAICXnJeXl3bu3KmzZ89Kko4fP659+/apWrVqVo4MAACAkXMvlLh4s+xsbawdxlN70eMHAAAvpo4dOyoqKkr16tWTnZ2d4uLi1KtXL7355pvWDg3AS+BF/zvnRY8feBmQnHuB2NnaqMfCIJ2+EmXtUFKseG4njW/uZe0wAABAOrRu3TqtWrVKY8aMUfHixXXs2DGNGDFCuXPnVkBAgLXDA/CC4+80AM+K5NwL5vSVKB0Ju2ntMAAAAF4Yo0aNUseOHdWgQQNJkru7u8LCwjR16lSScwBSBX+nAXgWrDkHAACAl9qdO3dkY2M5ZcvOzk5ms9lKEQEAAPyLkXMAAAB4qdWoUUNTpkxRvnz5jGmts2bNUpMmTawdGgAAAMk5AAAAvNwGDRqk8ePHa8iQIbp69apy586tZs2aqWvXrtYODQAAgOQcAAAAXm5OTk4aOHCgBg4caO1QAAAAEmHNOQAAAAAAAMBKSM4BAAAAAAAAVkJyDgAAAAAAALASknMAAAAAAACAlZCcAwAAAAAAAKyE5BwAAAAAAABgJSTnAAAAAAAAACshOQcAAAAAAABYCck5AAAAAAAAwEqsmpybOnWqmjRpIi8vL/n4+KhLly46c+aMRZ27d+9qyJAh8vb2lpeXlz7++GNFRERY1AkLC1PHjh1Vvnx5+fj46Ouvv9a9e/cs6uzatUsBAQHy8PDQG2+8oWXLliWK56efflLNmjVVtmxZvfvuuzp06FDqnzQAAAAAAADw/6yanNu9e7datmypxYsXa9asWbp3757atWun6Ohoo87w4cO1efNmjRs3TnPnztWVK1fUrVs3Y39cXJw++ugjxcbGauHChRo5cqSWL1+uCRMmGHVCQkL00UcfydvbW7/88ovef/99DRo0SFu3bjXqrF27ViNGjFDXrl21fPlylSxZUu3atdPVq1f/m4sBAAAAAACAdMeqybkZM2aocePGKlGihEqWLKmRI0cqLCxMR44ckSRFRkZq6dKl6tevn3x8fOTh4aHhw4crKChIBw4ckCRt27ZNp0+f1jfffKNSpUrJ399fPXr00E8//aSYmBhJ0sKFC+Xm5qZ+/fqpWLFiatWqlerUqaPZs2cbscyaNUtNmzZVkyZNVLx4cQ0ZMkQZM2bU0qVL/+vLAgAAAAAAgHTiuVpzLjIyUpLk7OwsSQoODlZsbKyqVq1q1ClWrJjy5ctnJOcOHDggk8kkFxcXo46vr6+ioqJ0+vRpo46Pj4/FsXx9fY02YmJidOTIEYvj2NraqmrVqgoKCkr18wQAAAAAAACk5yg5Fx8fr+HDh6tChQoymUySpIiICDk4OChbtmwWdXPlyqXw8HCjzoOJOUnG9pPqREVF6c6dO/rnn38UFxenXLlyJTrOw+vbAQAAAAAAAKnF3toBJBgyZIhOnTql+fPnWzsUAAAAAAAA4D/xXIycGzp0qP744w/9+OOPeuWVV4xyFxcXxcbG6ubNmxb1r169KldXV6POw6PbErafVMfJyUkZM2ZUjhw5ZGdnl+jhD1evXk004g4AAAAAAABILVZNzpnNZg0dOlQbN27Ujz/+qAIFCljs9/DwkIODg3bs2GGUnTlzRmFhYfL09JQkeXp66uTJkxaJtcDAQDk5Oal48eJGnZ07d1q0HRgYaLTh6OioMmXKWBwnPj5eO3bskJeXV2qeMgAAAAAAAGCwanJuyJAhWrlypcaMGaMsWbIoPDxc4eHhunPnjiQpa9asatKkiUaOHKmdO3cqODhYAwYMkJeXl5FY8/X1VfHixfXZZ5/p+PHj2rp1q8aNG6eWLVvK0dFRktS8eXOFhIRo1KhR+uuvv/TTTz9p3bp1atu2rRHLBx98oMWLF2v58uX666+/9MUXX+j27dtq3Ljxf31ZAAAAAAAAkE5Ydc25BQsWSJJat25tUT5ixAgjKTZgwADZ2tqqe/fuiomJka+vrwYPHmzUtbOz05QpU/TFF1+oWbNmypQpkwICAtS9e3ejToECBTR16lSNGDFCc+bM0SuvvKKvvvpKfn5+Rp369evr2rVrmjBhgsLDw1WqVClNnz6daa0AAAAAAABIM1ZNzp04ceKJdTJkyKDBgwdbJOQelj9/fk2bNu2x7Xh7e2vFihWPrdOqVSu1atXqiTEBAAAAAAAAqeG5eCAEAAAAAAAAkB6RnAMAAAAAAACshOQcAAAAAAAAYCUk5wAAAAAAAAArITkHAAAAAAAAWAnJOQAAAAAAAMBKSM4BAAAAAAAAVkJyDgAAAAAAALASknMAAAAAAACAlZCcAwAAAAAAAKyE5BwAAAAAAABgJSTnAAAAAAAAACshOQcAAAAAAABYCck5AAAAAAAAwEpIzgEAAAAAAABWYm/tAAAAAIC0VLNmTV28eDFR+XvvvafBgwdbISIAAIB/kZwDAADAS23JkiWKi4sztk+dOqUPPvhAdevWtWJUAAAA95GcAwAAwEstZ86cFts//PCDChYsqCpVqlgpIgAAgH+x5hwAAADSjZiYGK1cuVJNmjSRjY2NtcMBAAAgOQcAAID0Y9OmTYqMjFRAQIC1Q3mpxMWbrR3CM3nR4wcAvNiY1goAAIB0Y+nSpapWrZry5Mlj7VBeKna2NuqxMEinr0RZO5QUK57bSeObe1k7DABAOkZyDgAAAOnCxYsXFRgYqIkTJ1o7lJfS6StROhJ209phAADwwmFaKwAAANKFZcuWKVeuXKpevbq1QwEAADCQnAMAAMBLLz4+XsuWLdPbb78te3smjwAAgOcHyTkAAAC89AIDAxUWFqYmTZpYOxQAAAALfGwIAACAl56vr69OnDhh7TAAAAASYeQcAAAAAAAAYCUk5wAAAAAAAAArITkHAAAAAAAAWAnJOQAAAAAAAMBKSM4BAAAAAAAAVkJyDgAAAAAAALASqybn9uzZo06dOsnX11fu7u7atGmTxX53d/ckv6ZPn27UqVmzZqL9P/zwg0U7x48f13vvvaeyZcvK399f06ZNSxTLunXrVLduXZUtW1aNGjXSli1b0uakAQAAAAAAgP9nb82DR0dHy93dXU2aNFG3bt0S7d+2bZvF9p9//qmBAweqTp06FuXdu3dX06ZNje0sWbIY30dFRaldu3by8fHRkCFDdPLkSQ0YMEDZsmVTs2bNJEn79+/XJ598ot69e6tGjRpatWqVunbtqmXLlslkMqXmKQMAAAAAAAAGqybn/P395e/v/8j9rq6uFtu//fabvL29VaBAAYvyLFmyJKqbYOXKlYqNjdXw4cPl6OioEiVK6NixY5o1a5aRnJszZ478/PzUvn17SVLPnj0VGBioefPmaejQoc9yigAAAAAAAMAjvTBrzkVERGjLli165513Eu2bNm2avL299fbbb2v69Om6d++ese/AgQOqVKmSHB0djTJfX1+dPXtWN27cMOr4+PhYtOnr66sDBw6kzckAAAAAAAAAsvLIuZRYvny5smTJotq1a1uUt27dWqVLl5azs7OCgoI0duxYhYeHq3///pLuJ/Xc3NwsXuPi4mLsc3Z2VkREhFGWIFeuXIqIiEjDMwIAAAAAAEB698Ik55YuXapGjRopQ4YMFuUffPCB8X3JkiXl4OCgwYMH65NPPrEYLQcAAAAAAAA8b16Iaa179+7V2bNn9e677z6xbvny5XXv3j2FhoZKuj9K7uERcAnbCaPlkqpz9erVRKPpAAAAAAAAgNT0QiTnlixZojJlyqhkyZJPrHvs2DHZ2toqV65ckiRPT0/t3btXsbGxRp3AwEAVKVJEzs7ORp2dO3datBMYGChPT8/UOwkAAAAAAADgIVZNzt26dUvHjh3TsWPHJEmhoaE6duyYwsLCjDpRUVFav359kqPmgoKCNHv2bB0/flwhISFauXKlRowYoTfffNNIvDVq1EgODg4aOHCgTp06pbVr12rOnDkW02HbtGmjrVu3aubMmfrrr780ceJEBQcHq1WrVml8BQAAAAAAAJCeWXXNueDgYLVp08bYHjFihCQpICBAI0eOlCStWbNGZrNZDRs2TPR6R0dHrV27VpMmTVJMTIzc3NzUtm1bi8Rb1qxZNWPGDA0dOlSNGzdWjhw51KVLFzVr1syoU6FCBY0ePVrjxo3T2LFjVbhwYU2ePFkmkymtTh0AAAAAAACwbnLO29tbJ06ceGydZs2aWSTSHlSmTBktXrz4iccpWbKk5s+f/9g69erVU7169Z7YFgAAAAAAAJBaXog15wAAAAAAAICXEck5AAAAAAAAwEpIzgEAAAAAAABWQnIOAAAAAAAAsBKScwAAAAAAAICVkJwDAAAAAAAArITkHAAAAAAAAGAlJOcAAAAAAAAAKyE5BwAAAAAAAFgJyTkAAAAAAADASkjOAQAAAAAAAFZCcg4AAAAAAACwEpJzAAAAAAAAgJWQnAMAAAAAAACshOQcAAAAAAAAYCUk5wAAAAAAAAArITkHAAAAAAAAWAnJOQAAAAAAAMBKSM4BAADgpXf58mX16dNH3t7eKleunBo1aqTDhw9bOywAAADZWzsAAAAAIC3duHFDLVq0kLe3t6ZNm6YcOXLo/PnzcnZ2tnZoAAAAJOcAAADwcps2bZpeeeUVjRgxwigrUKCAFSMCAAD4F9NaAQAA8FL7/fff5eHhoe7du8vHx0dvv/22Fi9ebO2wAAAAJJGcAwAAwEsuJCRECxYsUOHChTVjxgy1aNFCX331lZYvX27t0AAAAJjWCgAAgJeb2WyWh4eHevfuLUkqXbq0Tp06pYULFyogIMDK0QEAgPSOkXMAAAB4qbm6uqpYsWIWZUWLFlVYWJiVIgIAAPgXyTkAAAC81CpUqKCzZ89alJ07d0758+e3UkQAAAD/IjkHAACAl9r777+vgwcPasqUKTp//rxWrVqlxYsX67333rN2aAAAAKw5BwAAgJdbuXLlNGnSJI0dO1aTJ0+Wm5ubBgwYoDfffNPaoQEAAJCcAwAAwMuvRo0aqlGjhrXDAAAASIRprQAAAAAAAICVkJwDAAAAAAAArITkHAAAAAAAAGAlJOcAAAAAAAAAK7Fqcm7Pnj3q1KmTfH195e7urk2bNlns79evn9zd3S2+2rVrZ1Hn+vXr+uSTT1ShQgVVqlRJAwYM0K1btyzqHD9+XO+9957Kli0rf39/TZs2LVEs69atU926dVW2bFk1atRIW7ZsSf0TBgAAAAAAAB5g1eRcdHS03N3dNXjw4EfW8fPz07Zt24yvsWPHWuzv06ePTp8+rVmzZmnKlCnau3evPv/8c2N/VFSU2rVrp3z58mnZsmX67LPPNGnSJC1atMios3//fn3yySd65513tGLFCtWqVUtdu3bVyZMnU/+kAQAAAAAAgP9nb82D+/v7y9/f/7F1HB0d5erqmuS+v/76S1u3btWSJUtUtmxZSdKgQYPUsWNHffbZZ8qTJ49Wrlyp2NhYDR8+XI6OjipRooSOHTumWbNmqVmzZpKkOXPmyM/PT+3bt5ck9ezZU4GBgZo3b56GDh2aimcMAAAAAAAA/Ou5X3Nu9+7d8vHxUZ06dTR48GD9888/xr6goCBly5bNSMxJUtWqVWVra6tDhw5Jkg4cOKBKlSrJ0dHRqOPr66uzZ8/qxo0bRh0fHx+L4/r6+urAgQNpeGYAAAAAAABI76w6cu5J/Pz89MYbb8jNzU0hISEaO3asOnTooEWLFsnOzk4RERHKmTOnxWvs7e3l7Oys8PBwSVJERITc3Nws6ri4uBj7nJ2dFRERYZQlyJUrlyIiItLw7AAAAAAAAJDePdfJuQYNGhjfJzwQ4vXXXzdG0wEAAAAAAAAvsud+WuuDChQooBw5cuj8+fOS7o+Au3btmkWde/fu6caNG8Y6dS4uLolGwCVsJ4yWS6rO1atXE42mAwAAAAAAAFLTC5Wcu3Tpkq5fv24k3ry8vHTz5k0FBwcbdXbu3Kn4+HiVK1dOkuTp6am9e/cqNjbWqBMYGKgiRYrI2dnZqLNz506LYwUGBsrT0zONzwgAAAAAAADpmVWTc7du3dKxY8d07NgxSVJoaKiOHTumsLAw3bp1S19//bUOHDig0NBQ7dixQ126dFGhQoXk5+cnSSpWrJj8/Pz0v//9T4cOHdK+ffv05ZdfqkGDBsqTJ48kqVGjRnJwcNDAgQN16tQprV27VnPmzNEHH3xgxNGmTRtt3bpVM2fO1F9//aWJEycqODhYrVq1+u8vCgAAAAAAANINq645FxwcrDZt2hjbI0aMkCQFBAToiy++0MmTJ7VixQpFRkYqd+7ceu2119SjRw+LJ6+OHj1aX375pd5//33Z2tqqdu3aGjRokLE/a9asmjFjhoYOHarGjRsrR44c6tKli5o1a2bUqVChgkaPHq1x48Zp7NixKly4sCZPniyTyfQfXAUAAAAAAACkV1ZNznl7e+vEiROP3D9jxowntpE9e3aNGTPmsXVKliyp+fPnP7ZOvXr1VK9evSceDwAAAAAAAEgtL9SacwAAAAAAAMDLhOQcAAAAAAAAYCUk5wAAAAAAAAArITkHAAAAAAAAWAnJOQAAAAAAAMBKSM4BAAAAAAAAVkJyDgAAAAAAALASknMAAAAAAACAlZCcAwAAAAAAAKyE5BwAAAAAAABgJSTnAAAAAAAAACshOQcAAAAAAABYCck5AAAAAAAAwEpIzgEAAAAAAABWQnIOAAAAAAAAsBKScwAAAAAAAICVkJwDAAAAAAAArITkHAAAAAAAAGAlKU7OHTlyRCdOnDC2N23apC5dumjs2LGKiYlJ1eAAAACQPtHnBAAA6UWKk3Off/65zp07J0kKCQlR7969lSlTJq1fv17ffPNNascHAACAdCg1+5wTJ06Uu7u7xVfdunXTIGoAAICUS3Fy7ty5cypVqpQkad26dapcubLGjBmjESNG6Ndff031AAEAAJD+pHafs0SJEtq2bZvxNX/+/NQOGQAA4KnYp/QFZrNZ8fHxkqQdO3aoevXqkqS8efPqn3/+SdXgAAAAkD6ldp/Tzs5Orq6uqRkiAABAqkjxyDkPDw99//33WrFihfbs2WN0lEJDQ+Xi4pLa8QEAACAdSu0+5/nz5+Xr66tatWrpk08+UVhYWCpHDAAA8HRSnJwbMGCAjh49qi+//FKdOnVSoUKFJEkbNmyQl5dXqgcIAACA9Cc1+5zlypXTiBEjNH36dH3xxRe6ePGiWrZsqaioqLQIHQAAIEVSPK21ZMmSWrVqVaLyzz77TLa2Kc71AQAAAImkZp/T39/fot3y5curRo0aWrdund59991njhUAAOBZPFU27ebNm/r55581ZswYXb9+XZJ0+vRpXbt2LTVjAwAAQDqWVn3ObNmyqXDhwrpw4UIqRAkAAPBsUjxy7vjx42rbtq2yZcumixcvqmnTpsqePbt+/fVX/f333xo1alRaxAkAAIB0JC37nLdu3VJISAgPiAAAAM+FFI+cGzlypBo3bqxff/1Vjo6ORrm/v7/27t2bqsEBAAAgfUrNPufXX3+t3bt3KzQ0VPv371e3bt1ka2urhg0bpnbYAAAAKZbikXOHDx/W0KFDE5XnyZNH4eHhqRIUAAAA0rfU7HNeunRJvXv31vXr15UzZ05VrFhRixcvVs6cOVMrXAAAgKeW4uSco6Njkk+2OnfuHB0cAAAApIrU7HN+++23qRUWAABAqkvxtNaaNWtq8uTJio2NNcrCwsI0evRo1a5dO1WDAwAAQPpEnxMAAKQXKU7O9evXT9HR0apataru3r2r1q1bq3bt2sqSJYt69eqVFjECAAAgnaHPCQAA0osUT2vNmjWrZs2apX379un48eOKjo5WmTJlVLVq1RQffM+ePZoxY4aCg4MVHh6uyZMn6/XXX5ckxcbGaty4cfrzzz8VEhIiJycnVa1aVZ988ony5MljtFGzZk1dvHjRot1PPvlEHTt2NLaPHz+uoUOH6vDhw8qZM6datWqlDh06WLxm3bp1Gj9+vC5evKjChQurT58+8vf3T/E5AQAA4NmlZp8TAADgeZbi5FyCihUrqmLFipKkmzdvPlUb0dHRcnd3V5MmTdStWzeLfXfu3NHRo0fVuXNnlSxZUjdv3tSwYcPUuXNnLVu2zKJu9+7d1bRpU2M7S5YsxvdRUVFq166dfHx8NGTIEJ08eVIDBgxQtmzZ1KxZM0nS/v379cknn6h3796qUaOGVq1apa5du2rZsmUymUxPdW4AAAB4dqnR5wQAAHiepXha6w8//KC1a9ca2z169JC3t7f8/Px0/PjxFLXl7++vXr166Y033ki0L+HT0vr166to0aLy9PTU//73Px05ckRhYWEWdbNkySJXV1fjK3PmzMa+lStXKjY2VsOHD1eJEiXUoEEDtW7dWrNmzTLqzJkzR35+fmrfvr2KFSumnj17qnTp0po3b16KzgcAAACpIzX7nAAAAM+zFCfnFi5cqFdeeUWStH37dgUGBmratGmqVq2aRo0aleoBPigqKko2NjbKli2bRfm0adPk7e2tt99+W9OnT9e9e/eMfQcOHFClSpXk6OholPn6+urs2bO6ceOGUcfHx8eiTV9fXx04cCDtTgYAAACPZM0+JwAAwH8pxdNaIyIilDdvXknS5s2bVa9ePfn6+ip//vwWU0tT2927dzV69Gg1aNBATk5ORnnr1q1VunRpOTs7KygoSGPHjlV4eLj69+9vxOvm5mbRlouLi7HP2dlZERERRlmCXLlyKSIiIs3OBwAAAI9mrT4nAADAfy3FI+eyZcumv//+W5K0detWY8SZ2WxWXFxc6kb3/2JjY9WjRw+ZzWYNGTLEYt8HH3wgb29vlSxZUi1atFDfvn01b948xcTEpEksAAAASHvW6HMCAABYQ4pHztWuXVt9+vRRoUKFdP36dVWrVk2SdOzYMRUqVCjVA4yNjVXPnj0VFhamH3/80WLUXFLKly+ve/fuKTQ0VEWLFpWLi0uiEXAJ2wmj5ZKqc/Xq1USj6QAAAPDf+K/7nAAAANaS4pFz/fv3V8uWLVWsWDHNmjXLeDJqeHi43nvvvVQNLiExd/78ec2ePVs5cuR44muOHTsmW1tb5cqVS5Lk6empvXv3KjY21qgTGBioIkWKyNnZ2aizc+dOi3YCAwPl6emZeicDAACAZPsv+5wAAADWlOKRcw4ODmrXrl2i8rZt26b44Ldu3dKFCxeM7dDQUB07dkzOzs5ydXVV9+7ddfToUU2dOlVxcXEKDw+XJDk7O8vR0VFBQUE6ePCgXn31VWXJkkVBQUEaMWKE3nzzTSPx1qhRI02ePFkDBw5Uhw4ddOrUKc2ZM8dYk06S2rRpo9atW2vmzJny9/fX2rVrFRwcrKFDh6b4nAAAAPDsUrPPCQAA8DxLcXIuwenTpxUWFmYxIk2SatWqlew2goOD1aZNG2N7xIgRkqSAgAB169ZNv//+uyTprbfesnjdnDlz5O3tLUdHR61du1aTJk1STEyM3Nzc1LZtW33wwQdG3axZs2rGjBkaOnSoGjdurBw5cqhLly5q1qyZUadChQoaPXq0xo0bp7Fjx6pw4cKaPHmyTCZT8i8IAAAAUl1q9DkBAACeZylOzoWEhKhr1646efKkbGxsZDabJUk2NjaS7k8rTS5vb2+dOHHikfsft0+SypQpo8WLFz/xOCVLltT8+fMfW6devXqqV6/eE9sCAABA2kvNPicAAMDzLMVrzg0bNkxubm4KDAxUxowZtWbNGs2bN08eHh6aO3duWsQIAACAdIY+JwAASC9SnJwLCgpS9+7dlTNnTtna2srGxkaVKlVS79699dVXX6VFjAAAAEhn6HMCAID0IsXJufj4eONpWTly5NCVK1ckSfnz59fZs2dTNzoAAACkS/Q5AQBAepHiNedKlCihEydOqECBAipfvrymT58uBwcHLV68WAUKFEiLGAEAAJDO0OcEAADpRYpHznXu3Fnx8fGSpO7duys0NFQtW7bUli1bNHDgwFQPEAAAAOkPfU4AAJBepHjknJ+fn/F9oUKFtH79el2/fl3Ozs7G07MAAACAZ0GfEwAApBfJHjkXFxen48eP686dO4n2ZcyYUSdOnDA+3QQAAACeBn1OAACQ3iQ7OffLL79owIABcnBwSLTP3t5eAwYM0KpVq1I1OAAAAKQv9DkBAEB6k+zk3JIlS9SuXTvZ2dkl2mdvb6/27dtr8eLFqRocAAAA0hf6nAAAIL1JdnLu7NmzKl++/CP3ly1bVn/99VeqBAUAAID0iT4nAABIb5KdnLt9+7aioqIeuf/WrVtJrg0CAAAAJBd9TgAAkN4kOzlXqFAhBQUFPXL/vn37VKhQoVQJCgAAAOkTfU4AAJDeJDs517BhQ40bN07Hjx9PtO/48eOaMGGCGjZsmKrBAQAAIH2hzwkAANIb++RWbNu2rf788081adJEPj4+Klq0qCTpzJkz2rFjhypUqKC2bdumVZwAAABIB+hzAgCA9CbZyTkHBwfNnDlTs2fP1urVq7V3716ZzWYVLlxYPXv21Pvvv5/kI+8BAACA5KLPCQAA0ptkJ+ek+52lDh06qEOHDmkVDwAAANI5+pwAACA9SfaacwAAAAAAAABSF8k5AAAAAAAAwEpIzgEAAAAAAABWkqzkXFRUVFrHAQAAgHSOPicAAEiPkpWcq1y5sq5evSpJatOmjW7evJmmQQEAACD9oc8JAADSo2Ql5zJnzqzr169Lknbv3q179+6lZUwAAABIh+hzAgCA9Mg+OZWqVq2qNm3aqGjRopKkrl27ysHBIcm6c+bMSb3oAAAAkG78F33OH374QWPGjFGbNm00cODAp44VAAAgtSQrOffNN99o+fLlunDhgvbs2aMSJUooY8aMaR0bAAAA0pG07nMeOnRICxculLu7e6q1CQAA8KySlZzLmDGjWrRoIUkKDg5Wnz59lC1btjQNDAAAAOlLWvY5b926pU8//VRfffWVvv/++1RpEwAAIDUka825B82dO9foJJnNZpnN5lQPCgAAAOlbavc5hw4dKn9/f1WtWjU1wgMAAEg1KU7OSdKKFSvUqFEjlStXTuXKlVOjRo20YsWKVA4NAAAA6Vlq9TnXrFmjo0eP6pNPPkn9IAEAAJ5Rsqa1PmjWrFkaP368WrZsqZ49e0qS9u3bpy+++ELXr19X27ZtUzlEAAAApDep1ef8+++/NWzYMM2cOVMZMmRIu4ABAACeUoqTc3PnztUXX3yht99+2yirVauWSpQooYkTJ5KcAwAAwDNLrT7nkSNHdPXqVTVu3Ngoi4uL0549e/TTTz/p8OHDsrOzS+XoAQAAki/Fybnw8HB5eXklKvfy8lJ4eHiqBAUAAID0LbX6nK+++qpWrVplUda/f38VLVpUHTp0IDEHAACsLsVrzhUqVEjr1q1LVL527VoVLlw4NWICAABAOpdafU4nJyeZTCaLr8yZMyt79uwymUypGDEAAMDTSfHIuY8//li9evXSnj17VKFCBUnS/v37tXPnTo0bNy614wMAAEA6RJ8TAACkFykeOVenTh0tXrxYOXLk0G+//abffvtNOXLk0M8//6w33ngjRW3t2bNHnTp1kq+vr9zd3bVp0yaL/WazWePHj5evr6/KlSuntm3b6ty5cxZ1rl+/rk8++UQVKlRQpUqVNGDAAN26dcuizvHjx/Xee++pbNmy8vf317Rp0xLFsm7dOtWtW1dly5ZVo0aNtGXLlhSdCwAAAFJPavY5HzZ37lwNHDgwlSIFAAB4NikeOSdJHh4eGj169DMfPDo6Wu7u7mrSpIm6deuWaP+0adM0d+5cjRw5Um5ubho/frzatWuntWvXGk/b6tOnj8LDwzVr1izFxsZqwIAB+vzzzzVmzBhJUlRUlNq1aycfHx8NGTJEJ0+e1IABA5QtWzY1a9ZM0v1PYT/55BP17t1bNWrU0KpVq9S1a1ctW7aM6Q4AAABWklp9TgAAgOdZikfOpSZ/f3/16tUryU8/zWaz5syZo86dO+v1119XyZIlNWrUKF25csUYYffXX39p69at+uqrr1S+fHlVqlRJgwYN0po1a3T58mVJ0sqVKxUbG6vhw4erRIkSatCggVq3bq1Zs2YZx5ozZ478/PzUvn17FStWTD179lTp0qU1b968/+ZCAAAAAAAAIF2yanLucUJDQxUeHq6qVasaZVmzZlX58uUVFBQkSQoKClK2bNlUtmxZo07VqlVla2urQ4cOSZIOHDigSpUqydHR0ajj6+urs2fP6saNG0YdHx8fi+P7+vrqwIEDaXV6AAAAAAAAwPObnAsPD5ck5cqVy6I8V65cioiIkCRFREQoZ86cFvvt7e3l7OxsvD4iIkIuLi4WdRK2H2zn4ToPHgcAAAAAAABIC89tcg4AAAAAAAB42aUoORcbG6vSpUvr5MmTaRWPwdXVVZJ09epVi/KrV68ao9xcXFx07do1i/337t3TjRs3jNe7uLgkGgGXsP1gOw/XefA4AAAA+O/8l31OAAAAa0tRcs7BwUF58+ZVfHx8WsVjcHNzk6urq3bs2GGURUVF6eDBg/Ly8pIkeXl56ebNmwoODjbq7Ny5U/Hx8SpXrpwkydPTU3v37lVsbKxRJzAwUEWKFJGzs7NRZ+fOnRbHDwwMlKenZ1qdHgAAAB7hv+xzAgAAWFuKp7V26tRJY8eO1fXr15/54Ldu3dKxY8d07NgxSfcfAnHs2DGFhYXJxsZGbdq00ffff6/ffvtNJ06c0GeffabcuXPr9ddflyQVK1ZMfn5++t///qdDhw5p3759+vLLL9WgQQPlyZNHktSoUSM5ODho4MCBOnXqlNauXas5c+bogw8+MOJo06aNtm7dqpkzZ+qvv/7SxIkTFRwcrFatWj3zOQIAACDlUrPPCQAA8DyzT+kLfvrpJ50/f15+fn7Kly+fMmfObLF/+fLlyW4rODhYbdq0MbZHjBghSQoICNDIkSPVoUMH3b59W59//rlu3rypihUravr06cqQIYPxmtGjR+vLL7/U+++/L1tbW9WuXVuDBg0y9mfNmlUzZszQ0KFD1bhxY+XIkUNdunRRs2bNjDoVKlTQ6NGjNW7cOI0dO1aFCxfW5MmTZTKZUnp5AAAAkApSs88JAADwPEtxci5h1Fpq8Pb21okTJx6538bGRj169FCPHj0eWSd79uwaM2bMY49TsmRJzZ8//7F16tWrp3r16j0+YAAAAPwnUrPPCQAA8DxLcXKuW7duaREHAAAAYKDPCQAA0osUJ+cSHD58WH/99ZckqXjx4vLw8Ei1oAAAAACJPicAAHj5pTg5d+nSJfXu3Vv79+9XtmzZJEk3b96Ul5eXvv32W73yyiupHiQAAADSF/qcAAAgvUjx01oHDhyoe/fuae3atdq9e7d2796ttWvXymw2a+DAgWkRIwAAANIZ+pwAACC9SPHIuT179mjhwoUqWrSoUVa0aFENGjRILVu2TNXgAAAAkD7R5wQAAOlFikfO5c2bV/fu3UtUHh8fr9y5c6dKUAAAAEjf6HMCAID0IsXJuU8//VRffvmlDh8+bJQdPnxYw4YNU9++fVM1OAAAAKRP9DkBAEB6kaxprZUrV5aNjY2xHR0draZNm8rOzk6SFBcXJzs7Ow0YMECvv/562kQKAACAlxp9TgAAkB4lKzk3YMCAtI4DAAAA6Rx9TgAAkB4lKzkXEBCQ1nEAAAAgnaPPCQAA0qMUrzkHAAAAAAAAIHUka+ScJJUsWdJiDZCk2NjY6OjRo88cFAAAANIn+pwAACC9SXZybtKkSY/cd+DAAc2dO1fx8fGpEhQAAADSJ/qcAAAgvUl2ci6pJ2KdOXNGY8aM0ebNm9WoUSN17949VYMDAABA+kKfEwAApDfJTs496PLly5o4caJWrFghX19frVixQiaTKbVjAwAAQDpGnxMAAKQHKUrORUZGasqUKZo3b55KlSql2bNnq1KlSmkVGwAAANIh+pwAACA9SXZybtq0aZo+fbpcXFw0ZsyYJKccAAAAAM+CPicAAEhvkp2cGzNmjDJmzKiCBQtqxYoVWrFiRZL1HreILwAAAPA49DkBAEB6k+zk3Ntvv/3Ex9oDAAAAz4I+JwAASG+SnZwbOXJkWsYBAAAA0OcEAADpjq21AwAAAAAAAADSK5JzAAAAAAAAgJWQnAMAAAAAAACshOQcAAAAAAAAYCUk5wAAAAAAAAArSfbTWgEAAIAX0fz587VgwQJdvHhRklSiRAl16dJF/v7+Vo4MAACA5BwAAABecq+88or69OmjQoUKyWw2a8WKFeratauWL1+uEiVKWDs8AACQzpGcAwAAwEutZs2aFtu9evXSggULdODAAZJzAADA6kjOAQAAIN2Ii4vT+vXrFR0dLS8vL2uHAwAAQHIOAAAAL78TJ06oefPmunv3rjJnzqzJkyerePHi1g7LEBdvlp2tjbXDAAAgxV70/8Oeh/hJzgEAAOClV6RIEa1YsUKRkZHasGGD+vbtq3nz5j03CTo7Wxv1WBik01eirB1KilV3d9WndUpaOwwAgJW8yP+HFc/tpPHNrT+SnuQcAAAAXnqOjo4qVKiQJMnDw0OHDx/WnDlzNHToUCtH9q/TV6J0JOymtcNIsWKuWawdAgDAyl7U/8OeF7bWDgAAAAD4r8XHxysmJsbaYQAAADz/ybmaNWvK3d090deQIUMkSa1bt0607/PPP7doIywsTB07dlT58uXl4+Ojr7/+Wvfu3bOos2vXLgUEBMjDw0NvvPGGli1b9p+dIwAAANLOmDFjtGfPHoWGhurEiRMaM2aMdu/erUaNGlk7NAAAgOd/WuuSJUsUFxdnbJ86dUoffPCB6tata5Q1bdpU3bt3N7YzZcpkfB8XF6ePPvpILi4uWrhwoa5cuaK+ffvKwcFBvXv3liSFhIToo48+UvPmzTV69Gjt2LFDgwYNkqurq/z8/P6DswQAAEBauXr1qvr27asrV64oa9ascnd314wZM/Taa69ZOzQAAIDnPzmXM2dOi+0ffvhBBQsWVJUqVYyyjBkzytXVNcnXb9u2TadPn9asWbPk4uKiUqVKqUePHho9erS6desmR0dHLVy4UG5uburXr58kqVixYtq3b59mz55Ncg4AAOAFN3z4cGuHAAAA8EjP/bTWB8XExGjlypVq0qSJbGz+fcztqlWr5O3trYYNG2rMmDG6ffu2se/AgQMymUxycXExynx9fRUVFaXTp08bdXx8fCyO5evrqwMHDqTtCQEAAAAAACBde+5Hzj1o06ZNioyMVEBAgFHWsGFD5cuXT7lz59aJEyc0evRonT17VpMmTZIkRUREWCTmJBnb4eHhj60TFRWlO3fuKGPGjGl5WgAAAAAAAEinXqjk3NKlS1WtWjXlyZPHKGvWrJnxvbu7u1xdXdW2bVtduHBBBQsWtEaYAAAAAAAAQLK8MNNaL168qMDAQL3zzjuPrVe+fHlJ0vnz5yXdHwEXERFhUSdhO2GdukfVcXJyYtQcAAAAAAAA0swLk5xbtmyZcuXKperVqz+23rFjxyT9m3jz9PTUyZMndfXqVaNOYGCgnJycVLx4caPOzp07LdoJDAyUp6dn6p0AAAAAAAAA8JAXIjkXHx+vZcuW6e2335a9/b8zcS9cuKDJkycrODhYoaGh+u2339S3b19VrlxZJUuWlHT/wQ7FixfXZ599puPHj2vr1q0aN26cWrZsKUdHR0lS8+bNFRISolGjRumvv/7STz/9pHXr1qlt27bWOF0AAAAAAACkEy/EmnOBgYEKCwtTkyZNLModHBy0Y8cOzZkzR9HR0cqbN69q166tLl26GHXs7Ow0ZcoUffHFF2rWrJkyZcqkgIAAde/e3ahToEABTZ06VSNGjNCcOXP0yiuv6KuvvpKfn99/do4AAAAAAABIf16I5Jyvr69OnDiRqDxv3ryaN2/eE1+fP39+TZs27bF1vL29tWLFiqcNEQAAAAAAAEixF2JaKwAAAAAAAPAyIjkHAAAAAAAAWAnJOQAAAAAAAMBKSM4BAAAAAAAAVkJyDgAAAAAAALASknMAAAAAAACAlZCcAwAAAAAAAKyE5BwAAAAAAABgJSTnAAAAAAAAACshOQcAAAAAAABYCck5AAAAAAAAwEpIzgEAAAAAAABWQnIOAAAAAAAAsBKScwAAAAAAAICVkJwDAAAAAAAArITkHAAAAAAAAGAlJOcAAAAAAAAAKyE5BwAAAAAAAFgJyTkAAAAAAADASkjOAQAAAAAAAFZCcg4AAAAAAACwEpJzAAAAAAAAgJWQnAMAAAAAAACshOQcAAAAAAAAYCUk5wAAAAAAAAArITkHAAAAAAAAWAnJOQAAAAAAAMBK7K0dAAAAAJCWpk6dql9//VVnzpxRxowZ5eXlpT59+qho0aLWDg0AAICRcwAAAHi57d69Wy1bttTixYs1a9Ys3bt3T+3atVN0dLS1QwMAAGDkHAAAAF5uM2bMsNgeOXKkfHx8dOTIEVWuXNlKUQEAANzHyDkAAACkK5GRkZIkZ2dnK0cCAABAcg4AAADpSHx8vIYPH64KFSrIZDJZOxwAkuLizdYOId1ydcrwUlz/l+EckL4919NaJ06cqEmTJlmUFSlSROvXr5ck3b17VyNHjtTatWsVExMjX19fDR48WC4uLkb9sLAwffHFF9q1a5cyZ86st99+W5988ons7f899V27dmnkyJE6deqU8ubNq86dO6tx48b/zUkCAADgPzNkyBCdOnVK8+fPt3YoAP6fna2NeiwM0ukrUdYOJcWqu7vq0zolrR3GU8uWyf6Fvv6SVDy3k8Y397J2GMAzea6Tc5JUokQJzZo1y9i2s7Mzvh8+fLi2bNmicePGKWvWrPryyy/VrVs3LVy4UJIUFxenjz76SC4uLlq4cKGuXLmivn37ysHBQb1795YkhYSE6KOPPlLz5s01evRo7dixQ4MGDZKrq6v8/Pz+25MFAABAmhk6dKj++OMPzZs3T6+88oq1wwHwgNNXonQk7Ka1w0ixYq5ZrB1CqnhRrz/wsnjuk3N2dnZydXVNVB4ZGamlS5dq9OjR8vHxkXQ/WVe/fn0dOHBAnp6e2rZtm06fPq1Zs2bJxcVFpUqVUo8ePTR69Gh169ZNjo6OWrhwodzc3NSvXz9JUrFixbRv3z7Nnj2b5BwAAMBLwGw268svv9TGjRs1d+5cFShQwNohAQAAGJ77NefOnz8vX19f1apVS5988onCwsIkScHBwYqNjVXVqlWNusWKFVO+fPl04MABSdKBAwdkMpksprn6+voqKipKp0+fNuokJPcerJPQBgAAAF5sQ4YM0cqVKzVmzBhlyZJF4eHhCg8P1507d6wdGgAAwPM9cq5cuXIaMWKEihQpovDwcE2ePFktW7bUqlWrFBERIQcHB2XLls3iNbly5VJ4eLgkKSIiwiIxJ8nYflKdqKgo3blzRxkzZkyr0wMAAMB/YMGCBZKk1q1bW5SPGDGCdYYBAIDVPdfJOX9/f+P7kiVLqnz58qpRo4bWrVtH0gwAAADJcuLECWuHAAAA8EjP/bTWB2XLlk2FCxfWhQsX5OLiotjYWN28ablo5dWrV4016lxcXBQREWGxP2H7SXWcnJxIAAIAAAAAACBNvVDJuVu3bikkJESurq7y8PCQg4ODduzYYew/c+aMwsLC5OnpKUny9PTUyZMndfXqVaNOYGCgnJycVLx4caPOzp07LY4TGBhotAEAAAAAAACklec6Off1119r9+7dCg0N1f79+9WtWzfZ2tqqYcOGypo1q5o0aaKRI0dq586dCg4O1oABA+Tl5WUk1nx9fVW8eHF99tlnOn78uLZu3apx48apZcuWcnR0lCQ1b95cISEhGjVqlP766y/99NNPWrdundq2bWu9EwcAAAAAAEC68FyvOXfp0iX17t1b169fV86cOVWxYkUtXrxYOXPmlCQNGDBAtra26t69u2JiYuTr66vBgwcbr7ezs9OUKVP0xRdfqFmzZsqUKZMCAgLUvXt3o06BAgU0depUjRgxQnPmzNErr7yir776Sn5+fv/5+QIAAAAAACB9ea6Tc99+++1j92fIkEGDBw+2SMg9LH/+/Jo2bdpj2/H29taKFSueJkQAAAAAAADgqT3X01oBAAAAAACAlxnJOQAAAAAAAMBKSM4BAAAAAAAAVkJyDgAAAAAAALASknMAAAAAAACAlZCcAwAAAAAAAKyE5BwAAAAAAABgJSTnAAAAAAAAACshOQcAAAAAAABYCck5AAAAAAAAwEpIzgEAAAAAAABWQnIOAAAAAAAAsBKScwAAAAAAAICVkJwDAAAAAAAArITkHAAAAAAAAGAlJOcAAAAAAAAAKyE5BwAAAAAAAFgJyTkAAAAAAADASkjOAQAAAAAAAFZCcg4AAAAAAACwEpJzAAAAAAAAgJWQnAMAAAAAAACshOQcAAAAAAAAYCUk5wAAAAAAAAArITkHAAAAAAAAWAnJOQAAAAAAAMBKSM4BAAAAAAAAVkJyDgAAAAAAALASknMAAAAAAACAlZCcAwAAAAAAAKyE5BwAAABeanv27FGnTp3k6+srd3d3bdq0ydohAQAAGEjOAQAA4KUWHR0td3d3DR482NqhAAAAJPJcJ+emTp2qJk2ayMvLSz4+PurSpYvOnDljUad169Zyd3e3+Pr8888t6oSFhaljx44qX768fHx89PXXX+vevXsWdXbt2qWAgAB5eHjojTfe0LJly9L8/AAAAJD2/P391atXL73xxhvWDgUAACARe2sH8Di7d+9Wy5YtVbZsWcXFxWns2LFq166d1qxZo8yZMxv1mjZtqu7duxvbmTJlMr6Pi4vTRx99JBcXFy1cuFBXrlxR37595eDgoN69e0uSQkJC9NFHH6l58+YaPXq0duzYoUGDBsnV1VV+fn7/3QkDAAAAAAAgXXmuk3MzZsyw2B45cqR8fHx05MgRVa5c2SjPmDGjXF1dk2xj27ZtOn36tGbNmiUXFxeVKlVKPXr00OjRo9WtWzc5Ojpq4cKFcnNzU79+/SRJxYoV0759+zR79myScwAAAAAAPKdcnTIoLt4sO1sba4fy1F70+PHsnuvk3MMiIyMlSc7Ozhblq1at0sqVK+Xq6qoaNWqoS5cuxui5AwcOyGQyycXFxajv6+urL774QqdPn1bp0qV14MAB+fj4WLTp6+ur4cOHp/EZAQAAAACAp5Utk73sbG3UY2GQTl+JsnY4KVbd3VWf1in5wsePZ/PCJOfi4+M1fPhwVahQQSaTyShv2LCh8uXLp9y5c+vEiRMaPXq0zp49q0mTJkmSIiIiLBJzkozt8PDwx9aJiorSnTt3lDFjxrQ8NQAAAAAA8AxOX4nSkbCb1g4jxYq5ZpH04sePZ/PCJOeGDBmiU6dOaf78+RblzZo1M753d3eXq6ur2rZtqwsXLqhgwYL/dZgAAAD4v/buPDqq8v7j+CcLmySAhlAW+SGEZoBM1pIGYmSHqiyGBFB/iIhWpKL8lMhij1oTg1E8UAgiRlkaFUWUSAFFC9VQEXKkNaymLI2yiEKIQEjYM8/vD2TqSJRtMjeZeb/OyTnM8zwz873Pc7n55jv33gEAAMAlq9Hf1npeRkaG8vPzlZubq+bNm//i2OjoaEnS7t27JZ07A+7QoUMuY84/Pn+fup8bExQUxFlzAAAAtVxFRYWKiopUVFQkSdq3b5+Kioq0f/9+iyMDAACo4cU5Y4wyMjK0atUq5ebmqnXr1hd9zvmk63zhLSYmRjt27FBpaalzzLp16xQUFKT27ds7xxQUFLi8zrp16xQTE+OmLQEAAIBVtm7dquTkZCUnJ0uSsrKylJycrOzsbGsDAwAAUA2/rDU9PV0rVqzQSy+9pIYNGzrvERccHKz69etrz549Wr58ubp3764mTZpo+/btysrKUnx8vDp0OHdDwqSkJLVv314TJ07UhAkTVFJSohkzZmj48OGqW7euJOmOO+7QwoULNXXqVKWmpqqgoEArV65UTk6OZdsOAAAA90hISND27dutDgMAAKBKNbo499Zbb0mSRowY4dKelZWllJQU1alTR+vXr9drr72m48ePq0WLFurXr58efPBB59iAgAC9/PLLevrpp3X77berQYMGGjx4sMaNG+cc07p1a+Xk5CgrK0uvvfaamjdvrszMTN10002e2VAAAAAAAAD4pBpdnLvYJ5wtWrTQG2+8cdHXadWqlV599dVfHJOQkKClS5deTngAAAAAAADAVanR95wDAAAAAAAAvBnFOQAAAAAAAMAiFOcAAAAAAAAAi1CcAwAAAAAAACxCcQ4AAAAAAACwCMU5AAAAAAAAwCIU5wAAAAAAAACLUJwDAAAAAAAALEJxDgAAAAAAALAIxTkAAAAAAADAIhTnAAAAAAAAAItQnAMAAAAAAAAsQnEOAAAAAAAAsAjFOQAAAAAAAMAiFOcAAAAAAAAAi1CcAwAAAAAAACxCcQ4eERpUT5UOY3UYV6W2xw8AAAAAAGqeQKsDgG9o1CBQAf5++r9Fhdp1sNzqcC5b+2ZBmnlHrNVhAAAAAAAAL0NxDh6162C5tu0vszoMAAAAAACAGoHLWgEAAAAAAACLUJwDAAAAAAAALEJxDgAAAAAAALAIxTkAAAAAAADAIhTnAAAAAAAAAItQnAMAAAAAAAAsQnEOAAAAAAAAsAjFOQAAAAAAAMAiFOcAAAAAAAAAi1CcAwAAAAAAACxCcQ7wEZUOY3UIV6W2xw8AAAAAQFUCrQ4AgGcE+Pvp/xYVatfBcqtDuWzxN1yrJwdEWB3GVal0GAX4+1kdBgAAAACghqE4B1yC0KB6XlFc2XWwXNv2l1kdxmULC21Yq4uL7ZsFaeYdsVaHAQAAAACogSjO/cTChQs1b948lZSUqEOHDnryyScVFRVldViwWKMGgbW6ONTDFqoJv+tgdRhXrbYWFwEANQN5HgAAqIkozv3IBx98oKysLKWnpys6Olq5ubm677779OGHHyokJMTq8FAD1NbiUFhoQ6tD8GnecOZlbY8fAMjzAABATUVx7kcWLFigYcOGKTU1VZKUnp6u/Px8LVmyRKNHj7Y4OgC1VW0/85LLcgF4A/I8AABQU1Gc+8Hp06e1bds2PfDAA842f39/JSYmqrCw0MLIAHiL2nrmJWf+AajtyPMAAEBNRnHuB4cPH1ZlZeUFlzWEhISouLj4os83xkiSKisrqyW+8zo2b6h6AdX6FtXihpAGqqysJH6LEL+1anv8UdcHS8ah2X//j/YfPWF1OJft182C9b8J/yOZ2luco7jo3c7nDudzCbgfeV71qu2/59qFNqz2tcXF1db9p7bv/7U9fqn2bwPxW6u6fwdcap7nZ8gEJUkHDhxQt27dtGjRIsXG/vfyralTp2rDhg165513fvH5p0+f1pYtW6o7TAAA4KUiIyNVt25dq8PwSuR5AADAShfL8zhz7gfXXnutAgICVFpa6tJeWlqqpk2bXvT5gYGBioyMlL+/v/z8OLsBAABcGmOMHA6HAgNJy6oLeR4AALDCpeZ5ZIE/qFu3riIiIrR+/Xr16dNHkuRwOLR+/XrdddddF32+v78/n3YDAADUQOR5AACgJqM49yOjRo3SpEmTZLfbFRUVpdzcXJ04cUIpKSlWhwYAAICrQJ4HAABqKopzP3Lrrbfq+++/V3Z2tkpKStSxY0fNnTv3ki53AAAAQM1FngcAAGoqvhACAAAAAAAAsIi/1QEAAAAAAAAAvoriHAAAAAAAAGARinMAAAAAAACARSjOAQAAAAAAABahOAcAAAAAAABYhOJcDbRw4UL16tVLkZGRGjp0qDZv3mx1SF4vJydHqampio2NVdeuXfXggw+quLjYZcypU6eUnp6uhIQExcbG6uGHH9ahQ4csiti3vPLKK7LZbJoyZYqzjfXwvAMHDuixxx5TQkKCoqKiNHDgQG3ZssXZb4zRzJkzlZSUpKioKN1zzz36+uuvrQvYi1VWVmrGjBnq1auXoqKi1KdPH82ePVs//gJ21qP6bNiwQWPGjFFSUpJsNptWr17t0n8pc3/kyBGlpaUpLi5OnTt31h//+EdVVFR4cCtwqVjvqrkrd9q/f79Gjx6t6Ohode3aVc8//7zOnj3ryU1xqzfffFMDBw5UXFyc4uLidPvtt2vNmjXOfl+ck5+60rzOG+dl1qxZstlsLj8333yzs99X50VyT97pbcfeXr16XbC/2Gw2paenS/Ld/cVdebHl+4tBjfL++++biIgI8+6775qdO3eaJ554wnTu3NkcOnTI6tC82r333muWLFliduzYYYqKisz9999vevToYSoqKpxjnnrqKdO9e3ezbt06s2XLFjNs2DBz++23Wxi1b9i0aZPp2bOnGThwoMnMzHS2sx6edeTIEdOzZ08zefJks2nTJrNnzx7z6aefmt27dzvH5OTkmN/85jdm1apVpqioyIwZM8b06tXLnDx50sLIvdOcOXPMb3/7W/PJJ5+YvXv3mpUrV5qYmBiTm5vrHMN6VJ/8/Hwzffp087e//c2Eh4ebVatWufRfytzfd999ZtCgQWbjxo1mw4YNpm/fvmb8+PGe3hRcAta7au7Inc6ePWsGDBhg7rnnHvPll1+a/Px8k5CQYKZNm2bFJrnF3//+d5Ofn2+++uorU1xcbKZPn24iIiLMjh07jDG+OSc/dqV5nbfOS3Z2tunfv785ePCg86e0tNTZ76vz4q6809uOvaWlpS77ymeffWbCw8NNQUGBMcZ39xd35cVW7y8U52qYIUOGmPT0dOfjyspKk5SUZHJyciyMyveUlpaa8PBw8/nnnxtjjCkrKzMRERFm5cqVzjG7du0y4eHhprCw0KIovV95ebnp16+f+eyzz8xdd93lTOJYD8974YUXzJ133vmz/Q6Hw9x4441m7ty5zraysjJjt9vNihUrPBGiTxk9erR5/PHHXdoeeughk5aWZoxhPTzpp8WaS5n788erzZs3O8esWbPG2Gw2891333kueFw21vvnXUnulJ+fbzp06GBKSkqcY958800TFxdnTp065dH4q1N8fLxZvHixz8/J1eR13jov2dnZZtCgQVX2+fK8uCPv9IVjb2ZmpunTp49xOBw+vb+4Iy+uCfsLl7XWIKdPn9a2bduUmJjobPP391diYqIKCwstjMz3HDt2TJLUuHFjSdLWrVt15swZl7UJCwtTy5YttXHjRitC9AkZGRnq3r27y7xLrIcVPv74Y9ntdo0bN05du3ZVcnKyFi9e7Ozft2+fSkpKXNYkODhY0dHRHL+qQWxsrAoKCvTVV19Jkv7973/rX//6l7p16yaJ9bDSpcx9YWGhGjVqpMjISOeYxMRE+fv7cyuLWob1/q8ryZ02btyo8PBwNW3a1DkmKSlJ5eXl2rVrl+eCryaVlZV6//33dfz4ccXGxvr8nFxNXufN87J7924lJSWpd+/eSktL0/79+yX59ry4I+/09mPv6dOntWzZMqWmpsrPz8+n9xd35MU1YX8J9Mi74JIcPnxYlZWVCgkJcWkPCQm54B4eqD4Oh0PPPvus4uLiFB4eLkk6dOiQ6tSpo0aNGrmMDQkJUUlJiRVher33339fX375pd59990L+lgPz9u7d6/eeustjRo1SmPGjNGWLVuUmZmpOnXqaPDgwc55r+r4xb0A3W/06NEqLy/XLbfcooCAAFVWVurRRx/VoEGDJIn1sNClzP2hQ4d03XXXufQHBgaqcePGHMNqGdb7nCvNnQ4dOuTyR6Ik5+PaPDfbt2/XHXfcoVOnTumaa67R7Nmz1b59exUVFfnsnFxtXuet8xIVFaWsrCy1bdtWJSUlmj17toYPH67ly5f79Ly4I+/09mPv6tWrdezYMQ0ePFiSb/8/ckdeXBP2F4pzwE+kp6dr586devPNN60OxWd9++23mjJliubPn6969epZHQ507iaqdrtd48ePlyR16tRJO3fu1KJFi5xJATxn5cqVWr58uaZNm+b8gy8rK0vNmjVjPQB4HLmTq7Zt22rp0qU6duyYPvroI02aNElvvPGG1WFZhrzu53Xv3t357w4dOig6Olo9e/bUypUrVb9+fQsjsxZ558UtWbJE3bp1069+9SurQ7Gct+TFXNZag1x77bUKCAhQaWmpS3tpaekFFW5Uj4yMDOXn5ys3N1fNmzd3tjdt2lRnzpxRWVmZy/jS0lKFhoZ6Okyvt23bNpWWliolJUWdOnVSp06d9Pnnn+v1119Xp06dWA8LhIaGKiwszKWtXbt2zksvzs87xy/PmDp1qkaPHq3+/fvLZrMpOTlZI0eOVE5OjiTWw0qXMvdNmzbV999/79J/9uxZHT16lGNYLcN6X13u1LRp0wvO5j3/uDbPTd26ddWmTRvZ7XalpaWpQ4cOeu2113x2TtyR13njvFSlUaNGuuGGG7Rnzx6fnhd35J3efOz95ptvtG7dOg0ZMsTZ5sv7izvy4pqwv1Ccq0Hq1q2riIgIrV+/3tnmcDi0fv16xcbGWhiZ9zPGKCMjQ6tWrVJubq5at27t0m+321WnTh2XtSkuLtb+/fsVExPj4Wi9X5cuXbR8+XItXbrU+WO32zVw4EDnv1kPz4qLi3Pex+G8r7/+Wq1atZIkXX/99QoNDXVZk/Lycm3atInjVzU4efKk/Pz8XNoCAgKcXxnPeljnUuY+NjZWZWVl2rp1q3NMQUGBHA6HoqKiPB4zrpwvr7c7cqeYmBjt2LHD5Q+mdevWKSgoSO3bt/fIdniCw+HQ6dOnfXZO3JHXeeO8VKWiokJ79+5VaGioT8+LO/JObz32SlJeXp5CQkLUo0cPZ5sv7y/uyItrwv7CZa01zKhRozRp0iTZ7XZFRUUpNzdXJ06cUEpKitWhebX09HStWLFCL730kho2bOi8rjw4OFj169dXcHCwUlNT9dxzz6lx48YKCgpSZmamYmNjKQZVg6CgIOc9a8675ppr1KRJE2c76+FZI0eO1J133qmXX35Zt9xyizZv3qzFixcrIyNDkuTn56e7775bc+bMUZs2bXT99ddr5syZatasmfr06WNx9N6nZ8+eevnll9WyZUvn6fsLFixQamqqJNajulVUVGjPnj3Ox/v27VNRUZEaN26sli1bXnTuw8LCdNNNN+nJJ59Uenq6zpw5o2eeeUb9+/fn8pQaiPWumjtyp6SkJLVv314TJ07UhAkTVFJSohkzZmj48OGqW7euhVt35aZNm6Zu3bqpRYsWqqio0IoVK/T5559r3rx5Pjsn7sjrvHFeJOn5559Xz5491bJlSx08eFCzZs2Sv7+/BgwY4LP7i+SevNNbj70Oh0N5eXlKTk5WYOB/yzm+vL+4Iy+uEfuLR74TFpfl9ddfNz169DARERFmyJAhZuPGjVaH5PXCw8Or/FmyZIlzzMmTJ83TTz9t4uPjTXR0tBk7dqw5ePCghVH7lrvuustkZmY6H7Menvfxxx+bAQMGGLvdbm6++Wbz9ttvu/Q7HA4zY8YMk5iYaOx2uxk5cqQpLi62KFrvduzYMZOZmWl69OhhIiMjTe/evc306dPNqVOnnGNYj+pTUFBQ5e+MSZMmGWMube4PHz5sxo8fb2JiYkxcXJyZPHmyKS8vt2JzcBGsd9XclTvt27fP/P73vzdRUVEmISHBPPfcc+bMmTOe3hy3efzxx03Pnj1NRESE6dKlixk5cqRZu3ats98X56QqV5LXeeO8PPLII+bGG280ERER5qabbjKPPPKI2b17t7PfV+fFGPfknd547P30009NeHh4lTmdr+4v7sqLrd5f/Iz54Vw/AAAAAAAAAB7FPecAAAAAAAAAi1CcAwAAAAAAACxCcQ4AAAAAAACwCMU5AAAAAAAAwCIU5wAAAAAAAACLUJwDAAAAAAAALEJxDgAAAAAAALAIxTkAPiUvL0+dO3f22PudOHFCDz/8sOLi4mSz2VRWVuax9wYAAPAl5HkAaiuKcwC8wuTJk2Wz2fTKK6+4tK9evVo2m82iqKT33ntP//znP7Vo0SKtXbtWwcHBF4zJy8uTzWaTzWZTx44dFR8fr6FDh+rFF1/UsWPHLIgaAACg5iDPA+DtKM4B8Br16tXTq6++qqNHj7r1dU+fPn3Fz927d6/CwsIUHh6u0NBQ+fn5VTkuKChIa9eu1Zo1a7Ro0SINGzZMf/3rX3XbbbfpwIEDV/z+AAAA3oA8D4A3ozgHwGskJiaqadOmysnJuejY1atXq1+/foqMjNR9992nb7/91tk3a9Ys3XbbbXrnnXfUq1cvRUVF/ezrfPTRR+rfv7/sdrt69eql+fPnO/tGjBih+fPna8OGDbLZbBoxYsTPvo6fn59CQ0PVrFkzhYWFaejQoVq0aJGOHz+uF154wTnuH//4h+6880517txZCQkJeuCBB7Rnzx5n/913362MjAyX1/7+++9lt9u1fv16SdLChQud256YmKhx48ZddL4AAACsRJ5Hngd4M4pzALyGv7+/xo8frzfeeEPffffdz447efKk5syZo+eff15vvfWWysrK9Oijj7qM2bNnjz766CO9+OKLWrp0aZWvs3XrVj3yyCO69dZbtXz5cj300EOaOXOm8vLyJJ1L/oYNG6bY2FitXbtWs2bNuqztCQkJ0cCBA/Xxxx+rsrJS0rl7m4waNUpLlizRX/7yF/n5+Wns2LFyOBySpKFDh2rFihUunwIvW7ZMzZo1U5cuXbRlyxZNmTJF48aN04cffqi5c+d69N4sAAAAV4I8jzwP8GYU5wB4lb59+6pjx47Kzs7+2TFnzpzRU089pdjYWNntdj333HMqLCzU5s2bXcZMnTpVnTp1UocOHap8nQULFqhr164aO3as2rZtq5SUFA0fPlzz5s2TJDVp0kT169dXnTp1FBoaqiZNmlz29rRr104VFRU6cuSIJOl3v/ud+vXrpzZt2qhjx4569tlntWPHDu3atUuS1K9fP0nnPjE+Ly8vTykpKfLz89O3336rBg0aqEePHmrVqpU6deqku++++7LjAgAA8DTyPPI8wFtRnAPgdR577DEtXbpU//nPf6rsDwwMVGRkpPNxWFiYGjVq5DK+ZcuWuu66637xfYqLixUXF+fSFhcXp927dzs/Ab1axhhJct7D5Ouvv9b48ePVu3dvxcXFqXfv3pLkvFyjXr16GjRokJYsWSJJ2rZtm3bu3KnBgwdLOndJSMuWLdWnTx9NmDBBy5Yt04kTJ9wSKwAAQHUjzyPPA7wRxTkAXic+Pl5JSUmaNm3aFb9GgwYN3BjRlSsuLlZQUJDz09gxY8bo6NGjyszM1DvvvKPFixdLOvcJ8HlDhw7VunXr9N133ykvL09dunRRq1atJJ27IfF7772n6dOnKzQ0VNnZ2brttttUVlbm8W0DAAC4XOR55HmAN6I4B8ArpaWl6ZNPPlFhYeEFfWfPntXWrVudj4uLi1VWVqawsLDLeo927drpiy++cGn74osvdMMNNyggIODKAv+R0tJSLV++XH369JG/v78OHz6sr776Sn/4wx/UtWtXhYWFVfmNZTabTXa7XYsXL9aKFSuUmprq0h8YGKjExERNnDhRy5Yt0zfffKOCgoKrjhcAAMATyPPI8wBvE2h1AABQHWw2mwYOHKjXX3/9gr46deromWee0RNPPKGAgAA988wziomJ+cVv66rKvffeqyFDhmj27Nm69dZbtXHjRi1cuFB/+tOfLjteY4xKSkpkjNGxY8dUWFionJwcBQcHKy0tTZLUuHFjNWnSRG+//bZCQ0O1f//+n/3UeOjQocrIyNA111yjvn37Ots/+eQT7d27V/Hx8WrUqJHWrFkjh8Ohtm3bXnbMAAAAViDPI88DvA3FOQBea9y4cfrggw8uaK9fv77uv/9+paWl6cCBA+rcubOmTJly2a8fERGhGTNmKDs7W3PmzFFoaKjGjRunlJSUy36t8vJyJSUlyc/PT0FBQWrbtq2Sk5M1cuRIBQUFSTr3LWV//vOflZmZqQEDBqht27Z64oknNGLEiAter3///nr22WfVv39/1atXz9keHBysVatW6cUXX9SpU6fUpk0bTZs2Tb/+9a8vO2YAAACrkOeR5wHexM+cvwslAMBr7Nu3T3379tW7776riIgIq8MBAACAm5DnAd6HM+cAwIucOXNGR44c0YwZMxQdHU3CBgAA4CXI8wDvxRdCAIAX+eKLL5SUlKQtW7YoPT3d6nAAAADgJuR5gPfislYAAAAAAADAIpw5BwAAAAAAAFiE4hwAAAAAAABgEYpzAAAAAAAAgEUozgEAAAAAAAAWoTgHAAAAAAAAWITiHAAAAAAAAGARinMAAAAAAACARSjOAQAAAAAAABahOAcAAAAAAABY5P8BuFc1cR7rjfIAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "fig,ax =  plt.subplots(1,2, figsize=(15,5))\n",
        "\n",
        "fig.suptitle('Number of Days Required to Close 311-Cases',fontsize=14)\n",
        "ax[0].set(xlabel='Nbr of Days')\n",
        "ax[1].set(xlabel='Nbr of Days')\n",
        "ax[0].set(ylabel='Nbr of Cases')\n",
        "ax[1].set(ylabel='Nbr of Cases')\n",
        "\n",
        "ax[0].set_title('Cases Completed in Less Than 100 Days')\n",
        "ax[1].set_title('Cases Completed in Greater Than 99 Days')\n",
        "\n",
        "ax[0].hist(df_proc_less_100.processing_days)\n",
        "ax[1].hist(df_proc_more_100.processing_days)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 67,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-RDXQhPea3xZ",
        "outputId": "4448a10f-1a8d-4cb9-a544-3745cf38ac01"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(2361, 17)\n",
            "(9, 17)\n",
            "(7, 17)\n"
          ]
        }
      ],
      "source": [
        "# break it into 3 blocks...\n",
        "df_proc_less_50 = df_closed[df_closed['processing_days']<50]\n",
        "df_proc_less_100 = df_closed[df_closed['processing_days'].between(50,100)]\n",
        "df_proc_more_100 = df_closed[df_closed['processing_days']>99]\n",
        "\n",
        "print(df_proc_less_50.shape)\n",
        "print(df_proc_less_100.shape)\n",
        "print(df_proc_more_100.shape)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 68,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "id": "-DvHwCSlbYOi",
        "outputId": "665dcd36-9f43-4871-e8fc-6b851f231ced"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x500 with 3 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "fig,ax =  plt.subplots(1,3, figsize=(15,5))\n",
        "fig.suptitle('Number of Days Required to Close 311-Cases',fontsize=14)\n",
        "for i in range(3):\n",
        "  ax[i].set(xlabel='Nbr of Days')\n",
        "  ax[i].set(ylabel='Nbr of Cases')\n",
        "ax[0].set_title('Cases Completed in Less Than 50 Days')\n",
        "ax[1].set_title('Cases Completed in 50 - 99 Days')\n",
        "ax[2].set_title('Cases Completed in More Than 99 Days')\n",
        "\n",
        "ax[0].hist(df_proc_less_50.processing_days)\n",
        "ax[1].hist(df_proc_less_100.processing_days)\n",
        "ax[2].hist(df_proc_more_100.processing_days)\n",
        "plt.show()\n",
        "\n",
        "# misleading because compares frequency scales that are completely diffefrent"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "e41hdBJwdT_w"
      },
      "outputs": [],
      "source": [
        "del df_proc_less_50, df_proc_less_100, df_proc_more_100"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ubav_8caF69R"
      },
      "source": [
        "### Plot, over time, nbr of days required to close cases"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 69,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wX0lLNaDPEmJ",
        "outputId": "d5b6c635-530d-444e-e325-9945815e5084"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Processing days range: 1 to 806\n"
          ]
        }
      ],
      "source": [
        "print('Processing days range:',df_closed.processing_days.min(),'to',df_closed.processing_days.max())"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Create df with aggregate stats for month & year (reset_index to make opened_month_year a col)\n",
        "grouped_df = df_closed.groupby('opened_year_month')['processing_days'].agg([np.mean, min, max]).reset_index()\n",
        "\n",
        "# chg col names: 'process_mean' refers to mean nbr of days required to close a 311-case\n",
        "grouped_df.rename(columns={'mean': 'processing_days_mean', 'min': 'processing_days_min', 'max':'processing_days_max'},inplace=True)\n",
        "grouped_df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "Pf5Z4IshbkTk",
        "outputId": "540fdebc-c0ba-47ef-aa0d-5966273b07b3"
      },
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  opened_year_month  processing_days_mean  processing_days_min  \\\n",
              "0           2010-08              5.000000                    5   \n",
              "1           2010-09              3.000000                    2   \n",
              "2           2013-10            559.000000                  559   \n",
              "3           2014-12              2.333333                    1   \n",
              "4           2015-04              1.000000                    1   \n",
              "\n",
              "   processing_days_max  \n",
              "0                    5  \n",
              "1                    4  \n",
              "2                  559  \n",
              "3                    7  \n",
              "4                    1  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-3dcbe30d-d11e-4b55-9c41-08dd1f498cd1\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>opened_year_month</th>\n",
              "      <th>processing_days_mean</th>\n",
              "      <th>processing_days_min</th>\n",
              "      <th>processing_days_max</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2010-08</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2010-09</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2013-10</td>\n",
              "      <td>559.000000</td>\n",
              "      <td>559</td>\n",
              "      <td>559</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2014-12</td>\n",
              "      <td>2.333333</td>\n",
              "      <td>1</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2015-04</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-3dcbe30d-d11e-4b55-9c41-08dd1f498cd1')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-3dcbe30d-d11e-4b55-9c41-08dd1f498cd1 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-3dcbe30d-d11e-4b55-9c41-08dd1f498cd1');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 71
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "grouped_df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UMoWtiFO7Ccg",
        "outputId": "f69a1161-6ed1-4962-c549-b93c9a037911"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 46 entries, 0 to 45\n",
            "Data columns (total 4 columns):\n",
            " #   Column             Non-Null Count  Dtype    \n",
            "---  ------             --------------  -----    \n",
            " 0   opened_year_month  46 non-null     period[M]\n",
            " 1   closing_days_mean  46 non-null     float64  \n",
            " 2   closing_days_min   46 non-null     int64    \n",
            " 3   closing_days_max   46 non-null     int64    \n",
            "dtypes: float64(1), int64(2), period[M](1)\n",
            "memory usage: 1.6 KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Visualize nbr of days it took to close cases, over time\n",
        "\n",
        "# ISSUE [TODO]\n",
        "\n",
        "# What was I trying to do here???\n",
        "\n",
        "# when more recs were read time frame expaned; now x-axis labels are too close together\n",
        "# could this be switched to the year, rather than the month?\n",
        "# to do that, would need to create new col for year opened, then groupby that col\n",
        "\n",
        "labels = grouped_df.opened_month_year.dt.strftime('%m:%Y').astype(str)\n",
        "x = grouped_df.closing_days_max.max()\n",
        "x_max = int(math.ceil(x / 100.0)) * 100\n",
        "\n",
        "fig, ax = plt.subplots(3,1, figsize=(18,12))\n",
        "for i in range(3):\n",
        "  ax[i].set(ylabel='Nbr of Days')\n",
        "  ax[i].set_xticklabels(labels, rotation='vertical')\n",
        "  ax[i].set(xlabel='Date Case Was Opened')\n",
        "  ax[i].set_ylim([0, x_max]) # makes y scale same on all 3\n",
        "\n",
        "ax[0].set_title('Minimum Number of Days Required to Close Cases, Over Time')\n",
        "ax[1].set_title('Maximum Number of Days Required to Close Cases, Over Time')\n",
        "ax[2].set_title('Mean Number of Days Required to Close Cases, Over Time')\n",
        "fig.tight_layout(pad=4.0)\n",
        "'''\n",
        "annotate start of pandemic?\n",
        "ax.annotate('pixel offset from axes fraction',\n",
        "            xy=(1, 0), xycoords='axes fraction',\n",
        "            xytext=(-20, 20), textcoords='offset pixels',\n",
        "            horizontalalignment='right',\n",
        "            verticalalignment='bottom')\n",
        "'''\n",
        "ax[0].plot(grouped_df.opened_month_year.dt.strftime('%m:%Y'), grouped_df.closing_days_min, color='g')\n",
        "ax[1].plot(grouped_df.opened_month_year.dt.strftime('%m:%Y'), grouped_df.closing_days_max, color='r')\n",
        "ax[2].plot(grouped_df.opened_month_year.dt.strftime('%m:%Y'), grouped_df.closing_days_mean, color='b')\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "qmFZ_eGFCVS1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 708
        },
        "outputId": "ad8edc77-86e3-4c40-e3bc-39fd5d0e81c9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-72-9a389c6ba49d>:18: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
            "  ax[i].set_xticklabels(labels, rotation='vertical')\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1800x1200 with 3 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAABqUAAARVCAYAAAA0Q8qTAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAAEAAElEQVR4nOzddXiV9f/H8edZwljQ3bGRY9TolEYkDFRCAUFilF9AREREQEmJ0agooIhKp4iI9ADp7q4NVgyW9++P/XZ0ArrBtnvxelzXLnbufN07sXHe5/35WAzDMBARERERERERERERERFJRjZmBxAREREREREREREREZH0T0UpERERERERERERERERSXYqSomIiIiIiIiIiIiIiEiyU1FKREREREREREREREREkp2KUiIiIiIiIiIiIiIiIpLsVJQSERERERERERERERGRZKeilIiIiIiIiIiIiIiIiCQ7FaVEREREREREREREREQk2akoJSIiIiIiIiIiIiIiIslORSkRERGRdMzDw4MZM2Y8076NGjVi2LBhSZxInqZRo0a8++67ZsdIsAULFvDCCy9QpkwZ2rRpY3acDG3YsGE0atQoRc61fPlyPDw8uHbtWoqc73lcu3YNDw8Pli9fbnYUScf0OBMRERFJHDuzA4iIiIjIv1u+fDkffPABAEuWLKFq1arx1huGQYMGDbh16xYNGjRg7ty5ZsRMEzw8PAB4//336datW7x1cT/nn376iQoVKpgRL83YsWMHEydO5KWXXqJfv35ky5btqdsOGzaMFStWWG87OTmRPXt2ypUrR6tWrWjSpAk2Nqnzs3KdO3fGz8/PetvR0ZEiRYrw8ssv06VLl1SbOzXYtm0bR44coV+/fsly/L1797Jo0SIOHjxIUFAQLi4uVKxYkfbt29O0adNkOWdyCA0NZeHChfzyyy9cvXqV6OhoChcuTP369enSpQt58uQxO2KSun//PvPmzeO3337jxo0bZM6cmQoVKtCpUycaNmxodjyrGTNm4Ovr+5/beXt789lnn6VAIhEREZH0Q0UpERERkTTC0dGRtWvXPlaU8vPz49atWzg4ODy2z5EjR7C1tX2m823cuBGLxfJM+6Z2X375JW+88QaZM2c2O0qatGfPHmxsbBg7duwTH3f/5ODgwJgxYwAIDw/n+vXrbN26lf79++Pt7c3s2bNxdnZO7tjPJG/evLz33ntA7Bvqa9eu5bPPPuP+/fsMGjTI5HSxPv30UwzDMDtGPNu2bWPJkiXJUpSaPn06M2fOpGjRonTo0IH8+fMTGBjItm3b6NevH5MmTaJ169ZJft6kdvXqVd5++21u3rxJ8+bN6dChA/b29pw+fZqffvqJX3/9lU2bNpkdM8lcuHCBt99+m3v37tG+fXsqVKhAcHAwa9asoVevXnTr1o3333/f7JgANGnShMKFC1tvh4WFMWrUKJo0aUKTJk2sy3PmzEmBAgU4cuQIdnZ6e0VEREQkIfRXk4iIiEgaUb9+fTZu3MiIESPivfm1du1aypUrR2Bg4GP7ODo6PvP5ElJsSIvKlCnDyZMnWbp0KV27djU7ToqKiooiJibmue/bgIAAMmXKlODj2NnZPTbE36BBg5g3bx6TJ09mxIgRTJ069bkyJRcXF5d42d944w1atGjBokWL6N+//zMXfZOSvb39f26TVPe92TZu3MjMmTNp1qwZkydPjnft77zzDtu3bycqKsrEhAkTFRWFj48PAQEBfPvtt4992GDQoEHMnz/fpHRJLzIykgEDBhAcHMySJUuoWLGidd3bb7/N4MGD+eqrr6hQoQItW7ZMsVxPe16ULl2a0qVLW2/fu3ePUaNG4eHh8cThSp/nd62IiIhIRqPxJkRERETSiFatWhEYGMjOnTutyyIiIti0adNTuwL+OafUjBkz8PDw4PLlywwbNoyqVatSpUoVPvjgAx4+fBhv33/OKRU3l8z+/fsZM2YMNWrUoGrVqowcOZKIiAiCg4MZOnQo1apVo1q1akyYMCFe98bevXvx8PBg79698c7zpPk4hg0bRqVKlbhx4wbvvvsulSpVom7duixZsgSA06dP06VLF7y8vGjYsCFr1qxJ8M+xcuXK1KhRgwULFvDo0aN/3bZz58507tz5seX/nMMn7hq+/PJLlixZwgsvvEDFihXp1q0bN2/exDAMZs6cSb169fD09KR3795PLCJC7NB4bdq0sb45+8svvzy2TXBwMGPHjqV+/fqUL1+eJk2aMG/ePGJiYp6YaeHChTRu3JgKFSpw/vz5p15vVFQUM2fOpHHjxpQvX55GjRoxZcoUIiIirNvE3VdhYWF4eHg811wqPXv2pE6dOmzcuJGLFy9al//666/WdeXLl6dx48bMnDmT6Oho6zbTp0+nXLly3Lt377HjfvTRR1StWpXw8HAAjh49Svfu3alevTqenp40atTIOiRmYjk6OlK+fHkePHhAQEBAvHWrVq2iffv2eHp64u3tzaBBg7h58+Zjx/jhhx9o3Lgxnp6evPLKK+zfv/+xx9rT5m560vPo3x6PT7rvz58/b+1Sq1ChAu3bt2fLli2P5Tx79ixdunTB09OTevXqMWvWrHiPsacZNmyY9bka9xiJGzoTYrtOPv/8c+vjt1mzZnz55ZcJ6vaaNm0aWbNmZdy4cU8sxtWtW/c/h4HbvXs3b775Jl5eXlStWpXevXs/9rwIDQ1l7NixNGrUiPLly1OzZk26du3K8ePH4213+PBhunfvTpUqVahYsSKdOnXiwIED/3kdv/zyC6dOnaJXr16PFaQAnJ2d43Xi7d+/n/79+9OgQQPKly9P/fr1GTdu3GOvYXfv3uWDDz6gXr16lC9fnjp16tC7d+/HHkfbtm2z/gwqVapEz549OXv27DMdKyF++eUXzpw5Q48ePeIVpABsbW0ZPXo0rq6u1t9X/v7+lC1b9olD6F24cAEPDw8WL15sXZZcr4kJkVy/wxJyTSIiIiJpkTqlRERERNKIAgUK4OXlxbp166hfvz4Af/zxByEhIbRs2ZJFixYl+FgDBw6kYMGCvPfee5w4cYIff/yR7NmzM2TIkP/cd8yYMeTMmZN+/fpx+PBhfvjhB1xcXDh48CD58uVj0KBB/PHHH3z55Ze4u7vTtm3bZ7re6OhoevToQdWqVRk8eDBr1qxh9OjRZM6cmS+++ILWrVvTtGlTli5dyvvvv4+XlxeFChVK0LH79etHx44d+f7775O0W2rNmjVERkbSuXNnAgMDWbBgAQMHDqRGjRrs3buXHj16cPnyZRYvXsz48eMfm4vk0qVLDBo0iNdff5127drx888/M2DAABYsWEDt2rUBePjwIZ06deL27du8/vrr5MuXj4MHDzJlyhTu3r3Lhx9+GO+Yy5cvJzw8nNdeew0HBwfc3Nyemn/EiBGsWLGCZs2a0bVrV44cOcLcuXM5f/48M2fOBGDChAksW7aMI0eOWIfkq1y58jP/zF566SV27NjBrl27KFasGAArVqzAycmJrl274uTkxJ49e5g+fTqhoaHW4b3atGnDzJkzWb9+PZ06dbIeL65Q27RpUxwdHQkICKB79+5ky5aNnj174urqyrVr19i8efMzZ75+/ToWiwVXV1frstmzZzNt2jRatGjBK6+8wr1791i8eDEdO3Zk5cqV1m1//PFHRo4cSaVKlXjrrbe4evUqvXv3xs3NjXz58j1zpid50n1/9uxZ3njjDfLkyUOPHj1wcnJiw4YN9O3blxkzZliHJrt79y5dunQhOjqanj17kjlzZpYtW5agjpAOHTpw584ddu7cyYQJE+KtMwyD3r17s3fvXl555RXKlCnD9u3bmTBhArdv32b48OFPPe6lS5e4cOECL7/88jMP97hr1y569OhBwYIF8fHx4dGjRyxevJg33niD5cuXU7BgQQA+/vhjNm3aRKdOnShRogSBgYEcOHCA8+fPU65cOSC2uNWjRw/Kly+Pj48PFouF5cuX89Zbb/Hdd9/h6en51BxxRcAndd08ycaNG3n06BFvvPEGWbNm5ciRIyxevJhbt24xffp063b9+vXj3LlzdOrUiQIFCnDv3j127tzJzZs3rde2cuVKhg0bRp06dRg8eDAPHz7k+++/580332TFihXW7RJyrIT67bffAJ76+8DFxYUXXniBFStWcPnyZYoUKUK1atXYsGEDPj4+8bZdv349tra2NG/eHEje18Tn8Ty/wxJ7TSIiIiJpiiEiIiIiqdrPP/9suLu7G0eOHDEWL15sVKpUyXj48KFhGIbRv39/o3PnzoZhGEbDhg2Nnj17xtvX3d3dmD59uvX29OnTDXd3d+ODDz6It13fvn0Nb2/veMsaNmxovP/++4/l6NatmxETE2Nd3qFDB8PDw8MYOXKkdVlUVJRRr149o1OnTtZle/bsMdzd3Y09e/bEO8/Vq1cNd3d34+eff7Yue//99w13d3djzpw51mVBQUGGp6en4eHhYaxbt866/Pz5849d59O4u7sbn3zyiWEYhtG5c2ejdu3a1p/l33/OcTp16hTvGv6er2HDho9dQ40aNYzg4GDr8smTJxvu7u7GSy+9ZERGRlqXv/fee0a5cuWM8PBw67KGDRsa7u7uxqZNm6zLQkJCjNq1axtt27a1Lps5c6bh5eVlXLx4MV6mSZMmGWXKlDFu3LgRL1PlypWNgICA//zZnDx50nB3dzc+/PDDeMs///xzw93d3di9e3e86/fy8vrPYyZk2xMnThju7u7GuHHjrMvi7pO/++ijj4yKFSvG+5l16NDBePXVV+Nt98svv8R7nG3evPmx+zWhOnXqZDRv3twICAgwAgICjPPnzxvjx4833N3d4z3Xrl27ZpQpU8aYPXt2vP1Pnz5tlC1b1ro8IiLCqFmzptGmTZt41/HDDz8Y7u7u8R5rcY/Hq1evxjvmk55HT3s8Pum+f+utt4wXX3wx3vljYmKMDh06GE2bNrUuGzt2rOHu7m4cPnzYuiwgIMCoUqXKE3P90yeffGK4u7s/tjzu/pg1a1a85f369TM8PDyMy5cvP/WYv/76q+Hu7m58/fXX/3ruOE96bWnTpo1Rs2ZN4/79+9ZlJ0+eNEqXLm0MHTrUuqxKlSrW14oniYmJMZo2bfrY6+HDhw+NRo0aGV27dv3XbG3btjWqVKmSoOuIO+4/zZ071/Dw8DCuX79uGEbsa6S7u7uxYMGCpx4nNDTUqFq1qjFixIh4y+/evWtUqVLFujwhx0qMNm3a/Of1fv3114a7u7uxZcsWwzAMY+nSpYa7u7tx+vTpeNu1bNnS6NKli/V2cr0m/l1AQMBTf88kx++whF6TiIiISFqk4ftERERE0pAWLVoQHh7O1q1bCQ0N5ffff3/q0H3/5vXXX493u2rVqgQGBhIaGvqf+77yyitYLBbrbU9PTwzD4JVXXrEus7W1pXz58ly9ejXR2f7u1VdftX7v6upKsWLFyJw5My1atLAuL168OK6urok+V79+/bh79y5Lly59rox/17x5c1xcXKy34zolXnrppXjzgHl6ehIZGcnt27fj7Z87d25rpwrEDuHVtm1bTpw4wd27d4HYjokqVarg6urKvXv3rF+1atUiOjqaffv2xTtm06ZNyZ49+39m37ZtG8BjnWPdunWLtz6pOTk5AfDgwQPrskyZMlm/Dw0N5d69e1StWpWHDx9y4cIF67o2bdpw+PBhrly5Yl22Zs0a8uXLh7e3N4D1/vj999+JjIxMdL4LFy5Qs2ZNatasSYsWLfjyyy9p1KhRvC63zZs3ExMTQ4sWLeLdJzlz5qRIkSLWofaOHTtGQEAAr7/+erw5bNq1axfvcZNU/nnfBwYGsmfPHlq0aGH9ud67d4/79+9Tp04dLl26ZH1Mbtu2DS8vr3jdPtmzZ3+m15u/++OPP7C1tX1sWMxu3bphGAZ//PHHU/eNe33KkiXLM537zp07nDx5knbt2pE1a1br8tKlS1OrVq14j3FXV1cOHz782HM0zsmTJ7l06RKtW7fm/v371p9lWFgYNWvWZN++ff86zFpoaGiiruPvz4mwsDDu3btHpUqVMAyDEydOWLext7fHz8+PoKCgJx5n165dBAcH06pVq3iPVRsbGypWrGh9rCbkWInx4MGD/7zeuPVx93OTJk2ws7Nj/fr11m3OnDnDuXPn4s07lVyviUnhWX+HJfaaRERERNISDd8nIiIikoZkz56dmjVrsnbtWh49ekR0dDTNmjVL9HHy588f73bc0GJBQUH/OSzWP/eNezP9n0OPubi4PNebmY6Ojo+9ceji4kLevHnjFcXilgcHByfq+NWqVaN69eosWLDgsSLds3rSz+DflgcFBcUbcrBIkSKPXVvRokWB2CHjcuXKxeXLlzl9+jQ1a9Z8YoZ/zrGU0GG2rl+/jo2NDYULF463PFeuXLi6unL9+vUEHSexwsLCgPiFhrNnzzJ16lT27NnzWKE0JCTE+n3Lli0ZN24cq1evxsfHh5CQELZu3crbb79t/Tl6e3vTrFkzfH19WbhwId7e3jRu3JjWrVvHKww9TYECBRgzZgwxMTFcuXKFOXPmcP/+/XjD2F26dAnDMGjatOkTjxFXkLxx4wYQez//nb29fYKHnkyMf973V65cwTAMpk2bxrRp0564T0BAAHny5OHGjRuPzf0DWIdYfFbXr18nd+7cj73OlChRwrr+aeL2+XsBMzHifv5PuoYSJUqwY8cOwsLCcHJyYvDgwQwbNowGDRpQrlw56tevT9u2ba3306VLlwCsw0k+SUhIyFOHhnN2dk5UIf3GjRtMnz6d33777bHX1bjniIODA4MHD2b8+PHUrl2bihUr0qBBA9q2bUuuXLni5X7rrbeemiuhx0qMLFmycP/+/X/dJu5+jXstyJ49OzVq1GDDhg0MHDgQiB26z87OLl7xPrleE5/X8/wOS+w1iYiIiKQlKkqJiIiIpDEvvvgiH330Ef7+/tSrVy/evDYJZWPz5IZ5wzCeed+nLY/zzzfh4jytm8DW1jZRyxOS/Z98fHzo3LkzS5cuTdTPMTo6OlHZnufn/U8xMTHUrl2bd95554nr44pYcf7eYZEQT7ufksuZM2cArMWw4OBgOnXqhLOzM/3796dw4cI4Ojpy/PhxJk2aFO/x4ubmRsOGDVmzZg0+Pj5s3LiRiIgIXnrppXjXM336dA4dOsTWrVvZvn07w4cP5+uvv+aHH374z+4NJycnatWqZb1duXJl2rdvzxdffMGIESOA2PvEYrEwf/78Jz4G4rrBEiOxz5cn+ed9H7dvt27dqFu37hP3+WdRMjUpXrw48NdjJjm1bNmSqlWrsnnzZnbu3MmXX37J/PnzmTFjBvXr17c+d4cOHUqZMmWeeIx/u9+LFy/OiRMnuHnz5n/OJRYdHU3Xrl0JCgrinXfeoXjx4jg5OXH79m2GDRsW7zHx9ttv06hRI3799Vd27NjBtGnTmDdvHt988w1ly5a15p4wYcITi0t/f/z+17ESo0SJEpw8eZIbN2489sGGOKdPnwagZMmS1mWtWrXigw8+4OTJk5QpU4YNGzZQo0aNeMWe5H5NfFbP8zsssdckIiIikpaoKCUiIiKSxjRp0oSPP/6YQ4cO8cUXX5gdJ8Hiij5/73SBf++MSG7e3t54e3uzYMEC+vTp89h6Nze3J3YzxHVcJLXLly9jGEa8gkRcZ0OBAgWA2KJBWFhYvEJJUihQoAAxMTFcvnzZ2rUC4O/vT3BwsPX8SW316tVYLBZq164NgJ+fH4GBgfj6+lKtWjXrdteuXXvi/m3atKFPnz4cOXKENWvWULZsWUqVKvXYdl5eXnh5eTFo0CDWrFnD4MGDWb9+fbzhtRKidOnSvPTSSyxdupRu3bqRP39+ChcujGEYFCxY8F87ieLejL98+XK8DojIyEiuXbtG6dKlrcuS4/kS1+Vjb2//n4+f/Pnzc/ny5ceWX7x4MUHnelpRrUCBAuzevZvQ0NB43VJxwzL+2+OsWLFiFCtWjC1btiRoOLh/ivv5P+kaLly4QLZs2eIVknLnzk3Hjh3p2LEjAQEBtGvXjjlz5lC/fn3rz9LZ2fmZnosNGzZk7dq1rF69mnffffdftz1z5gyXLl1i/PjxtG3b1rp8586dT9y+cOHCdOvWjW7dunHp0iXatm3LV199xaRJk6y5c+TIkaDc/3asxGjQoAFr165l5cqVT3ytDQ0NZcuWLRQvXjxeJ2Hjxo0ZOXKkdQi/S5cuPfbzSq7XRDOlx2sSERERiaM5pURERETSmCxZsjBq1Cj69etHo0aNzI6TYAUKFMDW1vaxuTC+//57kxLFiptbatmyZY+tK1SoEBcuXIg3VNKpU6f4888/kyXLnTt32Lx5s/V2aGgoK1eupEyZMtauhhYtWnDw4EG2b9/+2P7BwcFERUU907nr168PwDfffBNv+ddffx1vfVKaN28eO3bsoGXLltZP/sd1lf29ayAiIoLvvvvuiceoV68e2bJlY8GCBezbty9elxTEDpH4z460uM6WiIiIZ8r9zjvvEBUVZf3ZNG3aFFtbW3x9fR87l2EY1mHLypcvT/bs2Vm6dGm8c69YseKx4SfjOpb+/nyJjo5+4uM0oXLkyIG3tzc//PADd+7ceWz93x/n9evX59ChQxw5ciTe+jVr1iToXJkzZwZ47Lrq1atHdHQ0S5Ysibd84cKFWCwW6tWr96/H7d+/P4GBgYwYMeKJj/UdO3awdevWJ+6bO3duypQpw8qVK+PlOnPmDDt37rQ+xqOjox8rBubIkYPcuXNb77fy5ctTuHBhvvrqqycOJ/hfw6s1a9YMd3d35syZw8GDBx9bHxoaav3QwZOeE4Zh8O2338bb5+HDh4SHh8dbVrhwYbJkyWLNXbduXZydnZk7d+4T51iLy52QYyVGs2bNKFmyJPPnz+fo0aPx1sXExPDxxx8TFBSEj49PvHWurq7UqVOHDRs2sG7dOuzt7WncuHG8bZLrNdFM6fGaREREROKoU0pEREQkDWrXrp3ZERLNxcWF5s2bs3jxYiwWC4UKFeL3338nICDA1Fxx3VJ+fn6PrXvllVdYuHAh3bt355VXXiEgIIClS5dSsmTJZ57X5t8ULVqUDz/8kKNHj5IjRw5+/vlnAgIC+Oyzz6zbdO/end9++41evXrRrl07ypUrx8OHDzlz5gybNm1iy5Ytj81jkhClS5emXbt2/PDDDwQHB1OtWjWOHj3KihUraNy4MTVq1Hjm64qKimLVqlVAbCHo+vXr/Pbbb5w+fZrq1aszevRo67aVKlXCzc2NYcOG0blzZywWC6tWrXrqUIf29va0atWKxYsXY2trS6tWreKtX7FiBd9//z2NGzemcOHCPHjwgGXLluHs7PyfBZCnKVmyJPXr1+enn36iT58+FC5cmIEDBzJ58mSuX79O48aNyZIlC9euXePXX3/ltddeo3v37tjb2zNw4EBGjhzJW2+9RcuWLbl27RrLly9/bE6pUqVK4eXlxZQpUwgKCsLNzY3169c/95vRH3/8MW+++SatW7fmtddeo1ChQvj7+3Po0CFu3brF6tWrgdjC26pVq3jnnXfo0qULmTNnZtmyZeTPn986zNq/KVeuHABjxoyhTp061vumUaNGVK9enS+++ILr16/j4eHBzp072bJlC2+99dZ/Dh/YsmVLTp8+zZw5czhx4gQvvvgi+fPnJzAwkO3bt7N7924mT5781P2HDh1Kjx496NChA6+88gqPHj1i8eLFuLi4WAsiDx48oH79+jRr1ozSpUvj5OTErl27OHr0KMOGDQNiC0VjxoyhR48evPjii7Rv3548efJw+/Zt9u7di7OzM3PmzHlqDnt7e3x9fenatSudOnWiefPmVK5cGXt7e86ePcvatWtxdXVl0KBBFC9enMKFCzN+/Hhu376Ns7MzmzZteqzgd+nSJd5++22aN29OyZIlsbW15ddff8Xf39/6vHB2dmbUqFEMHTqU9u3b07JlS7Jnz86NGzfYtm0blStXZuTIkQk6FsDy5cv54IMP+Oyzz2jfvv1Tr9fBwYHp06fz1ltv8eabb9K+fXvKly9PSEgIa9eu5fjx43Tr1u2x52/cfT5kyBC+++476tSp89hQq8n1mmim9HhNIiIiInFUlBIRERGRFBPX3bB06VIcHBxo3rw5Q4cO5cUXXzQ1l4+PD126dHlseYkSJRg/fjzTp0/ns88+o2TJkkyYMIG1a9c+sYj1vIoWLcpHH33EhAkTuHjxIgULFuSLL76IN/9P5syZWbRoEXPnzmXjxo2sXLkSZ2dnihYtSr9+/XBxcXnm848ZM4aCBQuyYsUKfv31V3LmzMm77777WPdCYkVERDB06FBr/uzZs1O+fHn69u1LkyZN4s25lS1bNubMmcP48eOZOnUqrq6uvPTSS9SsWZPu3bs/8fht2rRh8eLF1KxZk9y5c8db5+3tzdGjR1m/fj3+/v64uLjg6ekZbyizZ9G9e3d+//13Fi9eTL9+/ejZsydFixZl4cKFzJw5E4C8efNSu3bteB2NHTp0IDo6mi+//JIJEybg7u7O7NmzmTZt2mPnmDRpEiNHjmTevHm4urryyiuvUL16dbp27frMuUuWLMnPP/+Mr68vK1asIDAwkOzZs1O2bFn69u1r3S537tx8++23jBkzhnnz5pE1a1Zef/11cufOzYcffvif52natCmdO3dm3bp1rF69GsMwaNWqFTY2NsyePZvp06ezfv16li9fToECBRg6dCjdunVL0DUMGjSIGjVqsGjRIr7//nuCgoJwdXWlYsWKzJo1ixdeeOGp+9aqVYsFCxYwffp0pk+fjp2dHdWqVWPIkCHWx0OmTJl444032LlzJ7/88guGYVC4cGFrQS9O9erV+eGHH5g1axaLFy8mLCyMXLly4enpSYcOHf7zOooUKcLKlStZuHAhmzdvZsuWLcTExFCkSBFeffVVOnfuDMQWsObMmcOYMWOYO3cujo6ONGnShI4dO9KmTRvr8fLmzUurVq3YvXs3q1evxtbWluLFizN16lSaNWtm3a5169bkzp2befPm8eWXXxIREUGePHmoWrWqtbCU0GOFhYUBPHF+qn8qUaIEq1evZt68efz2228sX76cTJkyUb58eWbPnv3Uzt9GjRqRKVMmHjx4QMuWLR9bn5yviWZJj9ckIiIiEsdiPMvsyiIiIiIiIqnEqVOnaNOmzWNz7qQlcQWIRYsWmZxEJOEGDBjA9evX+emnn8yOIiIiIiJphOaUEhERERGRNG3ZsmU4OTnRtGlTs6OIZBiGYeDn58fAgQPNjiIiIiIiaYiG7xMRERERkTTpt99+49y5cyxbtoyOHTvi5ORkdiSRDMNisbB7926zY4iIiIhIGqOilIiIiIiIpEljxozB39+fevXq0a9fP7PjiIiIiIiIyH/QnFIiIiIiIiIiIiIiIiKS7DSnlIiIiIiIiIiIiIiIiCQ7FaVEREREREREREREREQk2WlOKSAmJoaoqChsbGywWCxmxxEREREREREREREREUkzDMMgJiYGOzs7bGye3g+lohQQFRXF0aNHzY4hIiIiIiIiIiIiIiKSZlWoUAEHB4enrldRCqxVuwoVKmBra2tyGhERERERERERERERkbQjOjqao0eP/muXFKgoBWAdss/W1lZFKRERERERERERERERkWfwX1Mk/XvJSkRERERERERERERERCQJqCglIiIiIiIiIiIiIiIiyU5FKREREREREREREREREUl2KkqJiIiIiIiIiIiIiIhIslNRSkRERERERERERERERJKdilIiIiIiIiIiIiIiIiKS7FSUEhERERERERERERERkWSnopSIiIiIiIiIiIiIiIgkOxWlREREREREREREREREJNmpKCUiIiIiIiIiIiIiIiLJTkUpERERERERERERERERSXYqSomIiIiIiIiIiIiIiEiyU1FKREREREREREREREREkp2KUiIiIiIiIiIiIiIiIpLsVJQSERERERERERERERGRZKeilIiIiIiIiIiIiIiIiCQ7FaVEREREREREREREREQk2ZlalIqOjmbq1Kk0atQIT09PGjduzMyZMzEMw7qNYRhMmzaNOnXq4Onpydtvv82lS5fiHScwMJD//e9/VK5cmapVqzJ8+HAePHiQwlcjIiIiIiIiIiIiIiIiT2NqUWr+/Pl8//33jBw5kvXr1zN48GAWLFjAokWL4m2zaNEiRo0axbJly8icOTPdu3cnPDzcus3gwYM5d+4cX3/9NXPmzGH//v2MHDnSjEsSERERERGRVCrwUSBhkWFmxxARERERybBMLUodPHiQF154gQYNGlCwYEGaN29OnTp1OHLkCBDbJfXtt9/Su3dvGjduTOnSpZkwYQJ37tzh119/BeD8+fNs376dMWPGULFiRapWrcqIESNYt24dt2/fNvPyREREREREJJW4EXKDEtNLUPPLmkTHRJsdR0REREQkQzK1KFWpUiX27NnDxYsXATh16hQHDhygXr16AFy7do27d+9Sq1Yt6z4uLi5UrFiRgwcPArGFLVdXVypUqGDdplatWtjY2FiLWyIiIiIiIpKxzd0/l3sP73Hk9hHWnllrdhwRERERkQzJzsyT9+zZk9DQUFq0aIGtrS3R0dEMGjSIl156CYC7d+8CkCNHjnj75ciRA39/fwD8/f3Jnj17vPV2dna4ublZ9xcREREREZGMKyI6grkH5lpv++7zpU3pNiYmEhERERHJmEwtSm3YsIE1a9YwefJkSpYsycmTJ/nss8/InTs37dq1MzOaiIiIiIiIpBM/nfiJ2w9uk9MpJ/ce3uPXC79y8u5JyuQqY3Y0EREREZEMxdTh+yZMmEDPnj1p1aoVHh4etG3blrfeeou5c2M/wZYrVy4AAgIC4u0XEBBAzpw5AciZMyf37t2Ltz4qKoqgoCDr/iIiIiIiIpJxzfCbAUB/7/60dm8NwMx9M82MJCIiIiKSIZlalHr06BEWiyXeMltbWwzDAKBgwYLkypWL3bt3W9eHhoZy+PBhKlWqBMTOSxUcHMyxY8es2+zZs4eYmBg8PT1T4CpEREREREQktdp/Yz97ru3B3saenlV64uPtA8A3h78hODzY5HQiIiIiIhmLqUWphg0bMmfOHH7//XeuXbvG5s2b+frrr2ncuDEAFouFLl26MHv2bLZs2cLp06cZOnQouXPntm5TokQJ6taty0cffcSRI0c4cOAAn376Ka1atSJPnjxmXp6IiIiIiIiYzNfPF4DXyr1GHuc8vFDsBUrnLE1oRCjfHPrG5HQiIiIiIhmLxYhrSzJBaGgo06ZN49dffyUgIIDcuXPTqlUr+vbti4ODAwCGYTB9+nSWLVtGcHAwVapU4eOPP6ZYsWLW4wQGBvLpp5/y22+/YWNjQ9OmTRkxYgRZsmRJUI7o6GgOHTqEl5cXtra2yXKtIiIiIiIikrLuPrhLoS8KER4dzp7ue6hesDoAM/1m4rPBB/cc7pzsexIbi6mf1xQRERERSfMSWmcxtSiVWqgoJSIiIiIikv58tv0zhv82nKr5q+L3jp91+PiQ8BAKTClASEQIv3T6hSYlmpicVEREREQkbUtonUUfBxMREREREZF0Jyomitn7ZwPQz7tfvPmMXRxdeNvrbQBm+M0wI56IiIiISIakopSIiIiIiIikO6tPr+Zq8FVyOuXktXKvPba+b7W+AKw9s5aL9y+mdDwRERERkQxJRSkRERERERFJd3z9fAHoWbknmewyPbbeI6cHTUs0xcBg1r5ZKR1PRERERCRDUlFKRERERERE0pVjd46x9dJWbC229Kra66nb+VTzAeDLg18SFhmWUvFERERERDIsFaVEREREREQkXZnpNxOAtqXbUsit0FO3a1mqJUWzFuX+o/t8f/T7lIonIiIiIpJhqSglIiIiIiIi6Ubgo0C+PfItAD7ePv+6ra2NrXVuqRl+MzAMI9nziYiIiIhkZCpKiYiIiIiISLqx8NBCwiLDKJ+7PPWL1P/P7btV6kYmu0wcvn2YnVd3pkBCkb/EGDFmRxARERFJUXZmBxARERERERFJCjFGDDP3xQ7d51PNB4vF8p/7ZM+cnY4VOvLlwS/x9fOlTuE6yR1TBIDXf3qdn0/+TNGsRSmVvRTuOdz/+jdHKQq5FsLWxtbsmCIiIiJJSkUpERERERERSRc2ndvEuXvncHN0o5NnpwTv5+Ptw5cHv+Tnkz9zI+QG+V3yJ2NKEdh7bS8/HP8BgHP3znHu3jk2nNsQbxtHW0dKZi9JqRylcM8eW6hyz+GOew538mTJk6Ciq4iIiEhqo6KUiIiIiIiIpAsz/GYAsUPyZXHIkuD9vPJ6UadwHXZc2cHc/XP5pOEnyRVRBICJuyYC8Hr51+lVpRdnAs5wJuAMZ++d5UzAGc7fP094dDjH7x7n+N3jj+3v7OAcv7Pqbx1W2TNnT+nLEREREUkwi6GZXImOjubQoUN4eXlha6vWeBERERERkbTm3L1zlJpRCgsWzvQ7Q8nsJRO1/7Ljy+jwUwfyZMnDlUFXcLB1SKakktGdDTiLh68HBgbHeh+jXO5yj20THRPNlaAr1iLV3wtWlwIv/etcVDky57AWqP7eYVUye0mcHZyT89JEREQkA0tonUWdUiIiIiIiIpLmzfSLnUuqRakWiS5IAbQr3Y58zvm4GXqTn078xJsV3kzqiCIATNo1CQODF91ffGJBCsDWxpZi2YpRLFsxmpZoGm9dRHQEF+5f4GzA4wWr6yHXCXgYwO5ru9l9bfdjx83vkt/aVfX3Dqvi2YrjaOeYLNcrIiIi8ncqSomIiIiIiEiaFhoRyteHvgagn3e/ZzqGva09var24uPfP8bXz1dFKUkWt0Jv8c3hbwAYWmvoMx3DwdaB0jlLUzpn6cfWPYh4wLl7557YYeUf5s+NkBvcCLnBtsvb4u1nY7GhiFsRa3eVtdMqhztF3Ipga6NRZURERCRpqCglIiIiIiIiadqSI0sICg+iVPZSj3WVJEbPKj0Z88cYdl/bzYEbB6iSv0oSphSBGXtnEB4dTo2CNahTuE6SHz+LQxYq5q1IxbwVH1t3/+F9zt47+1eH1b0z1u9DIkK4GHiRi4EX+eX8L/H2s7exp0T2Eo/NX+Wew538LvmxWCxJfh0iIiKSfqkoJSIiIiIiImmWYRjM8JsBQN9qfbGx2DzzsfI65+XVcq/y3dHv8N3ny9dtvk6qmCKEhIcwa/8sILZLKqWLOdkyZ8O7gDfeBbzjLTcMgzsP7sTrqor7/mzAWcKjwznlf4pT/qceO6aTvROlspd6bP4q9xzu5MicQwUrEREReYzFMAzD7BBmS+gEXCIiIiIiIpK6/H7pdxp+05As9lm4/t513DK5Pdfxdl/dTa2vauFo68i1966R0ylnEiWVjO6L3V/w3i/v4Z7DnZN9Tz5XATWlxBgxXAu+Fluk+keH1YX7F4g2op+6b9ZMWeN1Vln/zVEKV0fXFLwKERERSQkJrbOoU0pERERERETSrLguqS4Vuzx3QQqgRsEaVMlXhQM3D7DgzwUMqzPsuY8pEhkdyZQ9UwAYUmtImihIQexcU4XdClPYrTCNizeOty4yOpJLgZee2GF1JegKgY8C8bvuh991v8eOmydLnscKVe453CmRrQSZ7TOn1OWJiIiICVSUEhERERERkTTpStAVVp5aCcQO3ZcULBYLPt4+dF3Vldn7ZzO41mDsbPRfZ3k+3x/7nmvB18jrnJdOnp3MjpMk7G3tKZUjdui+f3oY+ZDz988/scPq9oPb1q/tV7bH28+ChUJuhZ7YYVU0a1Hsbe1T6vJEREQkmegvaxEREREREUmT5uyfQ4wRQ6NijSiXu1ySHbdDuQ4M/mUwV4KusPbMWtqWbptkx5aMxzAMJuycAMCA6gPIZJfJ5ETJL7N9ZsrnLk/53OUfWxccHmwtVMV1WMX9G/gokCtBV7gSdIVfL/wabz87GzuKZS32xA6rgq4F00z3mYiISEanopSIiIiIiIikOY+iHjH/z/kA+FTzSdJjZ7bPTI/KPfh85+fM8JuhopQ8lw3nNnD87nFcHFzoVbWX2XFM5+roSpX8VaiSv0q85YZh4B/m/1ehKuCstbvqTMAZHkY95Oy9s5y9d/axY2a2iy2CVcxTEc88nlTMG/tv1kxZU+iqREREJKFUlBIREREREZE054djP+Af5k9ht8K09mid5MfvVbUXE3ZN4LeLv3Hi7gnK5iqb5OeQjCGuS+rdKu+qSPIvLBYLubLkIleWXNQqVCveuhgjhhshN57YYXX+3nkeRj1k34197LuxL95+hd0K/1Wo+v9/S2Yvia3N0ydfFxERkeSlopSIiIiIiIikKYZhMMNvBgC9q/ZOljmfimQtwkseL7Hy1Epm+s1kZquZSX4OSf/2XtvLtsvbsLexZ0CNAWbHSbNsLDYUdC1IQdeCNCzWMN66qJgozt87z5HbRzhy+wiHbx/myO0jXA66bB0KcM2ZNdbtneydKJ+7PJ65/+qoUleViIhIyrEYhmGYHcJs0dHRHDp0CC8vL2xt9WkZERERERGR1GzPtT3U/LImjraOXHvvGjmdcibLeX67+BsvfPsCWeyzcP2967hlckuW80j69fKyl1l+cjlve73N122+NjtOhhL4KJCjt49y+PZhDt86zJE7Rzh6+ygPox4+cfu4rqq/DwFYIlsJdVWJiIgkUELrLOqUEhERERERkTTF188XgDcqvJFsBSmAhkUbUjZXWU7cPcE3h7+hf/X+yXYuSX/OBJxhxckVAAyuOdjkNBlP1kxZqVukLnWL1LUui46J5vz98xy+ddjaUXX49mFrR9XTuqr+PgRghTwV1FUlIiLyHFSUEhERERERkTTjVugtlh1fBkA/737Jei6LxYJPNR/6rO/DzH0z8fH2wcZik6znlPRj8q7JGBi86P4i5XKXMzuOALY2trjncMc9hzuvlnvVujzwUeBfw//9f8Hq2J1jhEWG4XfdD7/rfvGOU8StSOzQf38bAlBdVSIiIgmjopSIiIiIiIikGfMPzCcyJpKaBWtSOV/lZD9f54qdGbZlGGcCzrD5/GaalWyW7OeUtO9W6C2+OfwNAENrDTU5jfyXrJmyUq9IPeoVqWddFh0Tzbl75+LNUxXXVXU56DKXgy6z+vRq6/Z/76qK66zyzOOpYT9FRET+QUUpERERERERSRMioyOZc2AOkPxdUnGcHZx5u+LbTPebju8+XxWlJEFm7J1BeHQ4NQvWpE7hOmbHkWdga2OLR04PPHJ6xOuquv/wPkfvHI2dp+r/C1WJ6aqqmKciJbKXUNeliIhkWBbDMAyzQ5gtoRNwiYiIiIiIiHmWHV9Gh586kCdLHq4MuoKDrUOKnPdMwBk8fD2wYOFc/3MUz1Y8Rc4raVNIeAiFpxYm8FEgKzqsoG3ptmZHkmQW11X1946qw7cOczX46hO3d7J3okLuCtZ5qirmrUiF3BXUVSUiImlaQuss6pQSERERERGRNGGG3wwA3q3ybooVpADcc7jTrEQzNp3fxKx9s5jUdFKKnVvSnvl/zifwUSDuOdx5yeMls+NICvh7V9Vr5V6zLr//8P5fc1X9f8Hq6J2jhEWGsff6XvZe3xvvOEWzFv2rUPX/QwCqq0pERNIbdUqhTikREREREZHU7tCtQ1SaWwk7GzsuD7xMfpf8KXr+tWfW0vr71mTNlJXr713Hyd4pRc8vaUNEdAQlppfgWvA15reezzuV3zE7kqQy0THRnL13NrZQdeswR+4cSVBXVVyRqmLe2H9dHV1TOLmIiMi/U6eUiIiIiIiIpBu+fr4AvFzm5RQvSAG0KNmC4tmKc+H+BZYcWUKPKj1SPIOkfkuPLeVa8DXyOuelk2cns+NIKmRrY0vpnKUpnbN0vK6qew/vcfT20XhDAMbNVfW0riproer/hwAsnq24uqpERCTVU1FKREREREREUrV7D++x5OgSAPp59zMlg62NLX2q9mHw5sH47vPlncrvYLFYTMkiqZNhGEzYOQGAgdUHkskuk8mJJC3Jnjk79YvWp37R+tZlcV1Vh28djjcE4NXgq1wKvMSlwEusOr3Kun0W+yxUyFMBz9x/dVSpq0pERFIbFaVEREREREQkVfvyzy95FPUIr7xe1CpUy7QcXSt15aOtH3Hk9hF2XNlB3SJ1Tcsiqc/6s+s5fvc4Lg4uvFv1XbPjSDrw966qDuU7WJf/vasqbgjAY3eO8SDyAXuu7WHPtT3xjlMsazFrR1XcEIDqqhIREbOoKCUiIiIiIiKpVnRMNLP2zwJiu6TM7E7Knjk7nTw7Mf/P+czwm6GilMQzYVdsl9S7Vd4la6as5oaRdO1JXVVRMVGcu3eOw7cOxxsC8FrwNS4GXuRi4MUndlX9fQjA8rnL45bJzYxLEhGRDMRiGIZhdgizJXQCLhEREREREUlZq0+vps3SNmTPnJ1rg66R2T6zqXkO3zqM11wvbC22XB54mQKuBUzNI6nDnmt7qPllText7Lkw4AIFXQuaHUkEiO2qOnL7SGyR6v8LVsfvHudR1KMnbu/m6EZ+l/zWrwIuBf763jX2+7zOeXGwdUjhKxERkdQuoXUWdUqJiIiIiIhIquXr5wvAO5XeMb0gBVAxb0XqFq7L9ivbmXtgLqMbjjY7kqQCE3dNBKCjZ0cVpCRVyZ45Ow2KNqBB0QbWZVExUZwNOBtvnqq4rqqg8CCCwoM46X/yX4+byymXtUiV3/mvgtXfi1m5suTSEIEiIvIYdUqhTikREREREZHU6JT/KcrMLIONxYbz/c9TNGtRsyMB8OPxH3ntp9fInSU3VwZewdHO0exIYqIzAWco7VsaA4PjfY5TNldZsyOJPJOQ8BBuhNzgesh1boTciP0++Do3Qv/2fcgNImMiE3Q8Oxs78jrnjddt9aTuKzdHN1OHZhURkaSRZjqlbt++zcSJE9m+fTsPHz6kSJEijBs3jgoVKgBgGAbTp0/nxx9/JDg4mMqVKzNq1CiKFi1qPUZgYCCffvopW7duxcbGhqZNm/Lhhx+SJUsWk65KREREREREntdMv5kAtHZvnWoKUgBtS7elgEsBrodc56cTP9HRs6PZkcREk3ZNwsCgtXtrFaQkTXNxdMHD0QOPnB5P3cYwDAIeBsQrUj1WyAq5zu3Q20TFRHEt+BrXgq/963kz22WOP0Sgc/zhAuO+nOydkvqSRUTEBM/dKRUaGsqePXsoVqwYJUqUSNS+QUFBtGvXjurVq/PGG2+QLVs2Ll++TOHChSlcuDAA8+bNY968eXz++ecULFiQadOmcebMGdavX4+jY+yn0d555x3u3r3L6NGjiYyMZPjw4VSoUIHJkycnKIc6pURERERERFKX4PBgCkwpQGhEKJs7b6Zx8cZmR4pnzB9j+GjrR9QoWIPd3XebHUdMciv0FkWmFiEiOoLtXbdTp3AdsyOJpApRMVHcDr39xILV37uw7j+6n+BjZs2U9YndVn+/ndc5L/a29sl4ZSIi8jQJrbMkuig1YMAAqlWrRqdOnXj06BFt2rTh+vXrGIbBlClTaNasWYKPNWnSJP7880++++67J643DIO6devStWtXunfvDkBISAi1atXi888/p1WrVpw/f56WLVvy008/Wbur/vjjD3r27Mm2bdvIkyfPf+ZQUUpERERERCR18fXzpd+GfpTOWZoTfU6kuqGdbofeptAXhYiMiWRfj31UzV/V7EhiguFbhvPZjs+oWbAmO7vtTHWPU5HU7mHkQ26G3vzPzquwyLAEHc+ChdxZcv/rcIH5XfKT0ymn5rsSEUliyTZ83/79++nduzcAmzdvxjAM9u3bx4oVK5g9e3aiilK//fYbderUoX///uzbt488efLw5ptv8tprrwFw7do17t69S61ataz7uLi4ULFiRQ4ePEirVq04ePAgrq6u1oIUQK1atbCxseHIkSM0adIksZcoIiIiIiIiJooxYvD18wXAp5pPqnyjP49zHl4r9xpLji7B18+XhW0Xmh1JUlhIeAiz9s0CYGjtoanycSqS2mW2z0zxbMUpnq34U7cxDIPg8OCnFqzivr8RciO2Q+vBbW4/uM3BWwefekw7GzvyOef7a4hA58eHCyzgUgBXR1c9t0VEkliii1IhISG4ubkBsH37dpo2bUrmzJlp0KABEydOTNSxrl69yvfff0/Xrl3p1asXR48eZcyYMdjb29OuXTvu3r0LQI4cOeLtlyNHDvz9/QHw9/cne/bs8S/Kzg43Nzfr/iIiIiIiIpJ2bLmwhdMBp3FxcKFLxS5mx3mqft79WHJ0CUuPLWVik4nkypLL7EiSgub/OZ+g8CA8cnjwksdLZscRSbcsFgtumdxwy+RGmVxlnrpdjBGDf5h/vOEBn1S8uvPgDlExUVwNvsrV4Kv/em4ne6fHu63+MXRgfpf8ZLbPnNSXLSKSbiW6KJUvXz4OHjyIm5sb27dvZ8qUKQAEBwfj4OCQqGMZhkH58uV57733AChbtixnz55l6dKltGvXLrHRREREREREJB2Y4TcDgLe93sbF0cXkNE/nXcCbqvmrsv/Gfhb8uYAP6n5gdiRJIRHREUzZHft+yJBaQzQMmEgqYGOxIXeW3OTOkhuvvF5P3S4yOpJbobf+s/Mq8FEgYZFhnLt3jnP3zv3rubNlymYtUBV2K8zgWoMpnbN0El+hiEj6kOiiVJcuXRgyZAhOTk7kz5+f6tWrA7Bv3z7c3d0TdaxcuXJRokSJeMuKFy/Opk2brOsBAgICyJ07t3WbgIAASpeOfWHPmTMn9+7di3eMqKgogoKCrPuLiIiIiIhI2nDx/kXWnlkLQN9qfU1O8+8sFgs+1Xx4e9XbzN4/myG1h2Bnk+j/Zksa9P3R77kecp28znnp5NnJ7Dgikgj2tvYUcitEIbdC/7pdWGRYvKEBrZ1XoX/dvh5ynUdRj7j/6D73H93n+N3jAPx580/29diHrY3mrhcR+adE/7XcsWNHPD09uXXrlnXuJoBChQoxcODARB2rcuXKXLx4Md6yS5cuUaBAAQAKFixIrly52L17N2XKxLbnhoaGcvjwYd544w0AKlWqRHBwMMeOHaN8+fIA7Nmzh5iYGDw9PRN7eSIiIiIiImKiWftmYWDQtERTPHJ6mB3nP3Uo34HBmwdzNfgqq0+vpn2Z9mZHkmQWY8QwcVfs9AUDqw/E0c7R5EQikhyc7J0omb0kJbOXfOo2hmEQFB5kLVhdC77GwE0DOXjrIAsPLaR75e4pmFhEJG1IdH/5nj17qFChAk2aNCFLlizW5Q0aNKBKlSqJOtZbb73F4cOHmTNnDpcvX2bNmjUsW7aMN998E4j91FmXLl2YPXs2W7Zs4fTp0wwdOpTcuXPTuHFjAEqUKEHdunX56KOPOHLkCAcOHODTTz+lVatW5MmTJ7GXJyIiIiIiIiYJiwzjy4NfArHzNaUFmewy0aNyDwB8/XxNTiMpYcPZDRy/exwXBxferfqu2XFExEQWi4WsmbJSLnc5mpRoQtdKXfm4/scADP9tOMHhwSYnFBFJfRJdlHrnnXdo3Lgxs2bN4ubNm891ck9PT3x9fVm3bh0vvvgis2bNYvjw4bz00l8ThPbo0YNOnToxcuRIXnnlFcLCwliwYAGOjn99EmnSpEkUL16ct956i549e1K5cmVGjx79XNlEREREREQkZX139DvuP7pPsazFaFGyhdlxEqxX1V7YWGzYemkrx+8cNzuOJLMJuyYAsfd71kxZzQ0jIqmOj7cP7jncufPgDmP+GGN2HBGRVMdiGIaRmB3u3bvH6tWrWbFiBefOnaNGjRq8/PLLNG7cGAcHh+TKmayio6M5dOgQXl5e2NpqrFcREREREZGUZhgGleZW4vDtw0xqMon/1fqf2ZES5eVlL7P85HJ6VenF7Bdnmx1Hksmea3uo+WVN7G3suTjgIgVcC5gdSURSoXVn1vHi9y9ib2PP8T7HKZWjlNmRRESSXULrLInulMqePTtvv/02q1atYtmyZRQtWpRPPvmEunXrMmbMGE6dOvVcwUVERERERCTj2XFlB4dvHyazXWa6VupqdpxE86nmA8C3R74l8FGguWEk2UzYGdsl1cmzkwpSIvJULUu1pHnJ5kTGRDJ482Cz44iIpCqJLkr9Xbly5ejZsyedOnUiLCyMn3/+mfbt2/Pmm29y9uzZpMooIiIiIiIi6Zzvvtj5mDp5diJ75uwmp0m8BkUbUC5XOcIiw/jm0Ddmx5FkcNr/NCtPrQRgcC29ySwiT2exWJjSdAq2FltWn17N5vObzY4kIpJqPFNRKjIyko0bN9KjRw8aNWrEjh07GDlyJDt37uSXX34hf/78DBgwIKmziogAEBkdySe/f8Lyk8vNjiIiIiIiSeB68HV+PvEzEDsXR1pksVis2X33+RJjxJicSJLa5N2TMTBo7d6asrnKmh1HRFK5MrnKWH8vDNw0kKiYKJMTiYikDokuSn366afUqVOHjz/+mKJFi7JixQp++OEHXn31VZycnChYsCDvv/8+Fy5cSI68IiJM2zuNUdtG8fpPr3Pa/7TZcURERETkOc09MJdoI5p6RerhmcfT7DjPrJNnJ9wc3Th37xy/nP/F7DiShG6F3uKbw7EdcO/Xft/kNCKSVnxc/2NyZM7BibsnmLN/jtlxRERShUQXpc6dO8dHH33E9u3b+fDDD3F3d39sm2zZsvHtt98mSUARkb+7GnSVUb+PAiAyJpK+6/tiGIa5oURERETkmYVHhTP3wFzgr3mZ0ipnB2e6esXOh+Xr52tyGklK0/dOJyI6glqFalG7cG2z44hIGpEtczY+bfgpACO3jiQgLMDkRCIi5kt0Ueqbb77hxRdfxMHB4anb2NnZ4e3t/VzBRESeZOCmgTyIfIBXXi8y2WViy8Ut/HD8B7NjiYiIiMgz+unET9x5cIcCLgVoW7qt2XGeW59qfQBYf3Y95++dNzmNJIXg8GBm7ZsFwNBaQ01OIyJpTY8qPaiQuwL3H923fshWRCQje6Y5pSC2Y+qPP/5gy5Yt8b5ERJLL+rPrWX5yObYWW75t+y0f1v0QgEGbBhH0KMjkdCIiIiLyLGb4zQCgV9Ve2Nvam5zm+ZXKUYrmJZtjYFgLGZK2zT8wn6DwIDxyeNDao7XZcUQkjbGzsWNq86kAzN4/m+N3jpsbSETEZHaJ3eHq1av07duXM2fOYLFYrMNmWSwWAE6ePJm0CUVEgIeRD/FZ//8ThNYYSIU8FXDP4c6iI4s4E3CGkVtHMq3FNJNTioiIiEhi7Lu+j73X9+Jg60DPKj3NjpNk+nn3Y+O5jXx16CtGNxxNFocsZkeSZxQRHcEXe74AYEitIdhYnvmzvSKSgTUq1oh2pdux4tQKBm0axKZOm6zvpYqIZDSJ/mtq7NixFCxYkF27dpEpUybWrVvH4sWLKV++PIsWLUqOjCIijNs+jouBFyngUoBRDUYB4GjnyMyWMwHw3efLwZsHTUwoIiIiIonluy923qXXyr1G7iy5TU6TdJqXbE7xbMUJfBTIkqNLzI4jz+H7o99zPeQ6+Zzz0cmzk9lxRCQNm9R0Eg62Dmy+sJk1Z9aYHUdExDSJLkodPHiQ/v37kz17dmxsbLBYLFStWpX33nuPMWPGJEdGEcngTvufZvzO8QBMaz4NZwdn67rGxRvzevnXiTFi6L2uNzFGjFkxRURERCQR7j64y9JjS4HYzqL0xMZiQ99qfQHw9fO1jjAiaUuMEcPEXROB2NEaHO0cTU4kImlZ8WzFea/GewD875f/ER4VbnIiERFzJLooFRMTQ5YssUMPZMuWjTt37gBQoEABLl68mLTpRCTDMwyDvuv7EhkTSYuSLWhfpv1j20xpOgUXBxf2Xt/Lgj8XmJBSRERERBJr/p/ziYiOoFr+angX8DY7TpLr6tUVJ3snjt45yh+X/zA7jjyD9WfXc/zucVwcXHi3yrtmxxGRdGB43eHkdc7LuXvnmL53utlxRERMkeiiVKlSpTh9+jQAFStWZMGCBRw4cICZM2dSqFChJA8oIhnb0mNL2XJxC5nsMuHb0veJYy7nc8nHmEaxnZrDfh3G3Qd3UzqmiIiIiCRCVEwUs/fPBtJfl1ScbJmz0alC7HBvccMUStoyYecEAHpV7YVbJjeT04hIeuDi6MJnL3wGwKd/fMrt0NsmJxIRSXmJLkr17t2bmJjY4bH69+/PtWvX6NixI9u2bePDDz9M8oAiknEFPQrivV9iW9uH1xlO8WzFn7ptn2p98Mrrxf1H9xn669CUiigiIiIiz2DVqVVcC75GLqdcvFbuNbPjJBsfbx8AVpxcwbXgayankcTYfXU3269sx97GngHVB5gdR0TSkS4Vu1A1f1VCIkL48De9lyoiGU+ii1J169aladOmABQpUoSNGzeyZ88edu/eTc2aNZM8oIhkXB9t/Yhbobcolb0UQ2v/e6HJzsaOOa3mYMHCwkML2X55ewqlFBEREZHEiusc6lmlZ7qep6dCngrUL1KfaCOaOfvnmB1HEiFuLqlOnp0o4FrA5DQikp7YWGyY3jx26L6vDn7Fnzf/NDmRiEjKSnRRCmLneLl37x73798HIGvWrE8cUktE5Fn9efNPZu6bCcCsVrMS9GZF9YLV6VG5BwB91vchMjoyWTOKiIiISOIdvX2U3y/9jq3Fll5Ve5kdJ9nFdUvNOzBPk9qnEaf9T7Py1EoAhtQaYm4YEUmXahaqyZsV3sTAYMDGARiGYXYkEZEUk6ii1N27dxk6dCjVqlWjdu3a1KpVi2rVqvHBBx/g7++fXBlFJIOJjomm19pexBgxvF7+dRoXb5zgfT9r/Bk5nXJy7M4xpu2dlowpRURERORZxH3wqF2ZdhR0LWhymuTXxqMNBVwKcDfsLj+e+NHsOJIAk3ZNwsDgJY+XKJOrjNlxRCSdGt94PE72Tuy4soNlx5eZHUdEJMUkuCgVGhrK66+/zvbt22nfvj0ff/wxI0eOpE2bNmzdupWOHTvy4MGD5MwqIhnE/D/ns+/GPlwcXJjSdEqi9s2eOTsTm8QOtTHq91FcDbqaHBFFRERE5Bncf3ifRUcWAeBTzcfkNCnD3tae3lV7AzDDb4bJaeS/3Ay5ybdHvgVgaC3NVSsiyaega0Her/0+AEM2DyEsMszkRCIiKSPBRalvvvkGW1tb1q1bx/Dhw3n99dd54403GDFiBOvWrcMwDBYtWpScWUUkA7jz4A4fbPkAgDGNxpDPJV+ij9GlYhfqFK7Dg8gHDNo0KKkjioiIiMgzWnhoIWGRYVTIXYF6ReqZHSfF9KjSAwdbB/yu++F33c/sOPIvpu+dTkR0BLUK1aJ24dpmxxGRdG5wrcEUdivM1eCrTNo1yew4IiIpIsFFqW3btvHuu++SPXv2x9blyJGDnj17snXr1iQNJyIZz5DNQwh8FEilvJXoU63PMx3DxmLDrJazsLXY8vPJn9lwdkMSpxQRERGRxIoxYqxD9/l4+2SoeYlzZ8lNh3IdgL+GL5TUJzg8mNn7ZwPqkhKRlOFk72Qd7eXzHZ9rtBcRyRASXJS6dOkSlSpVeur6SpUqcfHixSQJJSIZ07ZL2/j28LdYsDC71WzsbOye+VgV8lRgUI3YLimfDT48jHyYVDFFRERE5BlsPLeR8/fPkzVTVjpW6Gh2nBTn4x07XOHSY0u58+COyWnkSeYfmE9QeBClc5amtUdrs+OISAbxatlXqVu4Lg+jHjJsyzCz44iIJLtEzSnl6ur61PWurq6EhoYmSSgRyXgioiPosz62M6pnlZ5UL1j9uY/5cYOPKeBSgAv3L/D5js+f+3giIiIi8ux8/XwB6ObVjSwOWUxOk/K8C3hTLX81IqIjWPDnArPjyD9EREfwxZ4vABhSawg2lgS/XSIi8lwsFgtTm0/FgoXvjn7Hzis7zY4kIpKsEvxXlmEY2Ng8fXOLxYJhGEkSSkQyni92f8GJuyfI5ZSLz174LEmO6ezgzLTm0wD4fOfnnA04myTHFREREZHEORtwlg3nNmDB8sxDNKcH/bz7ATB7/2yiYqJMTiN/993R77gecp18zvkyZCefiJircr7KdKvUDYABGwcQY8SYnEhEJPkkqijVrFkzvL29n/jVokWL5MwpIunY5cDLjP5jNAATm0wkW+ZsSXbs9mXa07xkcyKiI+i7vq+K5yIiIiImmLVvFgAtS7WkRPYSJqcxz6vlXiWXUy6uBV9j1alVZseR/xdjxDBxV+ycLgNrDMTRztHkRCKSEY1tNBYXBxcO3DzAt4e/NTuOiEiySfCELZ99ljSdCyIi/zRg4wDCIsOoW7guXSp2SdJjWywWfFv4Um5WOTZf2MyPJ37ktXKvJek5REREROTpQiNC+erQV8BfnUIZVSa7TPSo3INxO8bhu8+Xl8u+bHYkAdafXc+JuydwdXTl3Srvmh1HRDKoPM55GFl/JEM2D+GDLR/wcpmXcXF0MTuWiEiSS3BRql27dsmZQ0QyqDWn17Dq9CrsbOyY3Wo2Foslyc9RInsJhtcdzse/f8zAjQNpXrI5ro5PnyNPRERERJLO4iOLCQ4PplT2UjQp0cTsOKbrVbUX43eO5/dLv3P09lEq5KlgdqQMb8LOCQD0qtILt0xuJqcRkYysf/X+zD0wl3P3zjFu+zg+a6wmARFJfzRzp4iYJiwyjH4bYj8t+16N9yiXu1yynWto7aGUzF6Sm6E3+Xjrx8l2HhERERH5i2EY+Pr5AuDj7YONRf8FLeRWiLal2wIwc99Mc8MIu6/uZvuV7djb2DOgxgCz44hIBudg68CUplMAmLJnCufvnTc5kYhI0tP/CETENGP+GMPloMsUci3EyPojk/VcmewyMbNl7H/6p/tN59CtQ8l6PhERERGB3y/9zvG7x8lin4W3Kr5ldpxUw8fbB4BFRxYR+CjQ3DAZ3IRdsV1SnT07k98lv8lpRETgRfcXaVK8CRHREQzePNjsOCIiSU5FKRExxcm7J5m0axIA01tMJ4tDlmQ/Z9MSTXmt3GvEGDH0WdeHGCMm2c8pIiIikpHN8JsBwFsV39KwaH9Tv0h9yucuT1hkGF8f/NrsOBnWKf9TrDq1CoDBtfTGr4ikDhaLhS+afYGtxZaVp1ay5cIWsyOJiCSpBBWlQkNDkzuHiGQghmHQZ30fImMiedH9Rdp4tEmxc09pOgVnB2d2X9vNVwe/SrHzioiIiGQ0V4KusOp07Bv+fb37mpwmdbFYLPhUi+2Wmrlvpj4sZZLJuyZjYPCSx0uUyVXG7DgiIlblcpejd9XeAAzcNJComCiTE4mIJJ0EFaWqVatGQEAAAF26dCE4ODhZQ4lI+rbk6BJ+v/Q7me0yM735dCwWS4qdu4BrAT5t+CkA7//6Pv5h/il2bhEREZGMZPa+2cQYMbxQ7AXK5iprdpxUp6NnR9wc3Th//zybzm0yO06GczPkJt8e+RaA92u/b3IaEZHHfdLwE7Jnzs6xO8eYf2C+2XFERJJMgopSTk5OBAYGAuDn50dUlKrzIvJsAh8F8r9f/gfAiHojKJatWIpn8PH2wTOPJ/ce3mPYr8NS/PwiIiIi6d2jqEfM/zP2DbS4+ZMkPmcHZ7pV6gb8NcyhpJzpe6cTER1B7UK1qVWoltlxREQekz1zdkY3GA3AR1s/4t7DeyYnEhFJGhbDMIz/2qhfv378+eefFC9enH379lGpUiXs7e2fuO23336b5CGTW3R0NIcOHcLLywtbW1uz44ika33X9WXW/lmUzlmaw70O42DrYEqOXVd3Ufur2gDs6LqD2oVrm5JDREREJD1aeGghXVd1pbBbYS70v4Ctjf6f9STn7p3DfYY7BgZn+52lZPaSZkfKEILDgyn8RWGCwoNY9foqXvJ4yexIIiJPFBUThdccL47fPU5/7/5MazHN7EgiIk+V0DpLgjqlJk6ciI+PD+XLlwegVKlSlC5d+olfIiJPs//Gfmbvnw3AzJYzTStIAdQqVIt3Kr0DQO91vTU+s4iIiEgSMQzD2vnTp2ofFaT+RcnsJWlRqgUAs/bNMjlNxjHvwDyCwoMonbM0L7q/aHYcEZGnsrOxY2rzqUDsHIQn7p4wN5CISBJIUKfU33Xu3JmZM2fi6uqaXJlSnDqlRJJfdEw01RdU58DNA3Ss0JHF7RebHYmAsAA8fD0IeBjAlKZTGFRzkNmRRERERNK83Vd3U+urWmSyy8S1QdfI4ZTD7Eip2oazG2j5XUvcHN249t41nB2czY6UrkVER1BsWjFuhNzgy5e+tA6hKCKSmrVZ2obVp1fTrEQzNnTckKJzc4uIJFSSdkr93aJFi6wFKcMwSGRNS0QyqDn753Dg5gHcHN2Y1HSS2XEAyOGUg/GNxwMw8veRXAu+ZnIiERERkbTPd58vAG+Uf0MFqQRoVrIZJbOXJCg8iCVHlpgdJ9377uh33Ai5QX6X/HSs0NHsOCIiCTK56WTsbezZdH4T68+uNzuOiMhzSXRRCmDlypW0bt0aT09PPD09ad26NStXrkziaCKSXtwKvcWHv30IwNhGY8nrnNfkRH/pWqkrtQrVIjQilPc2vWd2HBEREZE07VboLX48/iMAPt4+JqdJG2wsNvSt1heILejpg5/JJ8aIYeKuiQAMrD4QRztHkxOJiCRMyewlGVQjdnSXQZsGEREdYXIiEZFnl+ii1Ndff82oUaOoV68eU6dOZerUqdStW5dRo0axcOHCZIgoImnd4F8GExQeRJV8VehVtZfZceKxsdgwu9VsbC22/HjiRzad22R2JBEREZE0a96BeUTGRFKrUC0q56tsdpw0422vt3Gyd+LYnWNsu7zN7Djp1roz6zhx9wSujq70rNLT7DgiIonyYb0PyZMlD2fvnWXG3hlmxxEReWbPNHzfqFGjGDJkCC+88AIvvPACQ4cO5eOPP+bbb7995iDz5s3Dw8ODsWPHWpeFh4fzySefUL16dSpVqkS/fv3w9/ePt9+NGzfo2bMnFStWpGbNmowfP56oqKhnziEiSWvrxa0sOboECxbmvDgnVU507ZnHk/7V+wPgs8GHR1GPTE4kIiIikvZEREcwZ/8cAPp59zM5TdqSNVNWOnt2BsDXz9fkNOnXhF0TAOhVpRdumdxMTiMikjiujq6Me2EcAKP/GM2dB3dMTiQi8mwSXZS6e/culSpVemx5pUqVuHv37jOFOHLkCEuXLsXDwyPe8nHjxrF161amTp3KokWLuHPnDj4+fw0BER0dzbvvvktkZCRLly7l888/Z8WKFUyfPv2ZcohI0oqIjqDP+j4A9K7am6r5q5qc6Ok+afAJ+V3yc+7eOcbvGG92HBEREZE0Z8XJFdwMvUle57y0L9Pe7DhpTtxwhytPreRq0FWT06Q/u67uYseVHdjb2DOgxgCz44iIPJO3vd6mcr7KBIcHM+K3EWbHERF5JokuShUpUoQNGzY8tnz9+vUULVo00QEePHjAkCFDGDNmDG5uf31SKSQkhJ9//plhw4ZRs2ZNypcvz7hx4zh48CCHDh0CYMeOHZw7d46JEydSpkwZ6tevz4ABA1iyZAkRERpbVcRsk3ZN4pT/KXJnyc3YF8b+9w4mcnF0YWqzqQB8tuMzzt07Z24gERERkTRmhl/sUEK9qvTCwdbB5DRpT/nc5WlQtAHRRrS140ySTtxcUp09O5PfJb/JaUREno2NxYZpzacBsODPBRy6dcjcQCIizyDRRal+/foxffp0unfvzsyZM5k5c6b1+/79+yc6wOjRo6lfvz61atWKt/zYsWNERkbGW16iRAny589vLUodOnQId3d3cubMad2mTp06hIaGcu6c3lAWMdPF+xf59I9PAZjcdDJZM2U1N1ACvFL2FZqWaEp4dDg+6300ybSIiIhIAh28eZCdV3diZ2OnuXqeg0+12G6peX/O05DSSeiU/ylWnVoFwJDaQ0xOIyLyfOoUrsPr5V/HwGDAxgF670JE0pxEF6WaNWvGsmXLyJYtG1u2bGHLli1ky5aNH3/8kSZNmiTqWOvWrePEiRP873//e2ydv78/9vb2uLq6xlueI0cO6zCB/v7+8QpSgPX2sw4lKCLPzzAM+m/sz6OoRzQo2oCOFTqaHSlBLBYLM1vOxNHWkU3nN/HzyZ/NjiQiIiKSJsTNg/Rq2VfJ55LP5DRpV5vSbSjoWhD/MH+WHV9mdpx0Y9KuSRgYtPFoQ+mcpc2OIyLy3MY3Hk9mu8z8cfkPvXchImlOootSAOXLl2fSpEksX76c5cuXM2nSJMqWLZuoY9y8eZOxY8cyceJEHB0dnyWGiKRSq06vYu2Ztdjb2DOr5SwsFovZkRKsZPaSDKszDICBGwcSEh5iciIRERGR1C0gLIDvjn0H/DUvkjwbOxs7elftDfxV6JPncyPkBouOLAJgaO2hJqcREUkahd0KW1/TBv8ymIeRD01OJCKScM9UlEoKx48fJyAggPbt21O2bFnKli2Ln58fixYtomzZsuTMmZPIyEiCg4Pj7RcQEECuXLmA2K4of3//eOvjbsdtIyIp60HEA/pviB3Kc3CtwZTJVcbkRIn3fu33KZGtBNdDrjPq91FmxxERERFJ1b48+CWPoh5ROV9lahasaXacNO+dyu/gYOvAvhv78LvuZ3acNG/63ulEREdQu1BtahWq9d87iIikEUNrD6Wga0EuB11m8u7JZscREUkw04pSNWrUYM2aNaxcudL6Vb58eVq3bm393t7ent27d1v3uXDhAjdu3MDLywsALy8vzpw5Q0BAgHWbXbt24ezsTMmSJVP6kkQEGL1tNFeDr1LErQgj6o0wO84zyWyfGd+WsZ9MnbZ3GkduHzE5kYiIiEjqFB0Tzax9s4DY+ZDSUod8apU7S25eL/86ADP8ZpicJm0LDg9m9v7ZgLqkRCT9cbJ3YkLjCQB8tuMzrgdfNzmRiEjCmFaUcnZ2xt3dPd6Xk5MTWbNmxd3dHRcXF15++WU+//xz9uzZw7Fjxxg+fDiVKlWyFqXq1KlDyZIlGTp0KKdOnWL79u1MnTqVjh074uDgYNaliWRYx+8cZ8qeKQDMaDEDJ3snkxM9u+Ylm/NK2VeINqLpva43MUaM2ZFEREREUp21Z9ZyOegyOTLnsBZS5Pn5VIsdBnHZ8WXcDr1tcpq0a96BeQSHB1MmZxledH/R7DgiIknu9fKvU7tQbcIiwxi2ZZjZcUREEsS0olRCDB8+nAYNGtC/f386depEzpw5mTHjr0+K2draMmfOHGxsbOjQoQNDhgyhbdu29O/f38TUIhmTYRj0Wd+HqJgo2ni0obVHa7MjPbcvmn1BFvss7Lq6i4WHFpodR0RERCTV8d0X213+TuV3yGyf2eQ06Ue1AtWoXqA6EdERLPhzgdlx0qTwqHC+2PMFAENqDcHGkqrf/hAReSYWi4WpzacCsPjIYvZc22NuIBGRBLAYhmEkdOPIyEgqVqzIypUrcXd3T85cKSo6OppDhw7h5eWFra2t2XFE0qRvDn3D26vexsneiRN9TlAkaxGzIyWJybsmM3jzYHJkzsFpn9PkcMphdiQRERGRVOHk3ZOUnVUWG4sNF/pfSDd//6UWi48spvOKzhRwKcDFARext7U3O1Ka8vXBr+m2uhv5XfJzof8FHO0czY4kIpJsuq3qxteHvsa7gDe7u+9WIV5ETJHQOkuiXqHs7e3Jly8fMTEaxkpE/nLv4T2GbB4CwMh6I9PVGxL9q/enQu4KBDwM4IMtH5gdR0RERCTVmLlvJgAvebyUrv7+Sy1eLfsqubPk5nrIdVadXmV2nDQlxohh4q6JAAysPlAFKRFJ98a9MA5nB2f8rvux+Mhis+OIiPyrRJfNe/XqxZQpUwgMDEyGOCKSFg3fMpy7YXcpm6ssg2oOMjtOkrK3tWdWq9jJu+f/OZ/dV3ebnEhERETEfMHhwXxz+BsA+nn3MzlN+uRo50jPyj0B8PXzNTlN2rLuzDpO+p/E1dGVnlV6mh1HRCTZ5XXOy4i6IwAY9uswQiNCTU4kIvJ0iS5KLVmyhH379lG3bl2aNWtGu3bt4n2JSMay99pe5h2YB8CslrNwsHUwOVHSq1O4Dl29ugLQe11vomKiTE4kIiIiYq5vDn1DaEQoZXOVpWHRhmbHSbferfouthZbtl3expHbR8yOk2ZM2DUBgN5Ve+OWyc3kNCIiKWNgjYGUyFaCm6E3+Wz7Z2bHERF5KrvE7tC4cePkyCEiaVBUTBS91/XGwKBLxS7UL1rf7EjJZkKTCaw6vYrDtw8z028mA2oMMDuSiIiIiClijBh898V27vhU88FisZicKP0q6FqQdmXa8dOJn5jpN5O5reeaHSnV23V1Fzuu7MDB1oH+1fubHUdEJMU42jkyqekk2v3Qjsm7J/NO5Xcolq2Y2bFERB5jMQzDMDuE2RI6AZeIxDd973QGbBxA1kxZOe1zmtxZcpsdKVnNPzCfnmt74uLgwimfU+R3yW92JBEREZEU98v5X2i2uBmujq5cf+86zg7OZkdK1/64/Af1F9bHyd6Ja4OukS1zNrMjpWptl7Zl1elVdK/UnQUvLTA7johIijIMgyaLmrDl4hZeLvMyP732k9mRRCQDSWidJdHD98U5evQoK1euZOXKlRw7duxZDyMiadTNkJuM+C12vOLPXvgs3RekALpX7k6NgjUIiQjhvU3vmR1HRERExBQz/GYA0NWrqwpSKaBu4bpUyF2BsMgwvj70tdlxUrVT/qdYdXoVAINrDTY5jYhIyrNYLHzR7AtsLDb8fPJnfr/0u9mRREQek+ii1K1bt3jzzTd59dVXGTduHOPGjeOVV17hjTfe4NatW8mRUURSofd+eY+QiBC8C3jTo3IPs+OkCBuLDbNbzcbGYsMPx39g8/nNZkcSERERSVEX7l9g3Zl1APSp1sfkNBmDxWLBx9sHgJn7ZhJjxJicKPWatGsSAG082lA6Z2mT04iImKNCngr0qtILgAEbBxAdE21yIhGR+BJdlPrwww+Jiopi/fr1+Pn54efnx/r16zEMgw8//DA5MopIKvPrhV9ZemyptUhja5Nxhr30yutFP+9+APRd35fwqHCTE4mIiIiknFn7ZmFg0Lxkc9xzuJsdJ8PoWKEjWTNl5cL9C2w4u8HsOKnSjZAbLDqyCID3a79vchoREXONbjiabJmyceT2ERb8qaFMRSR1SXRRat++fYwaNYrixYtblxUvXpwRI0awf//+JA0nIqlPeFQ4fdf3BaBvtb5UzlfZ5EQpb3TD0eRzzsfZe2eZsHOC2XFEREREUkRYZBhfHvwSAJ9qPianyViyOGShm1c3AHz3+ZqcJnWatmcaEdER1Clch5qFapodR0TEVDmccjCqwSgARmwdQeCjQFPziIj8XaKLUvny5SMqKuqx5TExMeTOnf7nlBHJ6CbsnMCZgDPkdc7Lpw0/NTuOKVwdXfmi2RcAjN0+lvP3zpucSERERCT5LTmyhMBHgRTPVpwWpVqYHSfD6VOtDxYsbDy3kbMBZ82Ok6oEPQpizoE5AAytNdTkNCIiqUPvqr0pk7MM/mH+jN422uw4IiJWiS5KDRkyhE8//ZSjR49alx09epSxY8fy/vtqkRdJz87fO8/Y7WMBmNJ0Cm6Z3ExOZJ7Xyr1G4+KNCY8Op//G/hiGYXYkERERkWRjGIa1Q6dvtb7YWBL9X0l5TiWyl6BlqZZA7NxS8pd5B+YRHB5MmZxlaOXeyuw4IiKpgr2tvfUDtTP8ZnDK/5TJiUREYlmMBLyTWq1aNSwWi/V2WFgY0dHR2NrGziMT972TkxN+fn7JlzaZREdHc+jQIby8vKzXJCLxGYZBq+9aseHcBl4o9gKbO2+O97qQEZ0JOEOF2RWIiI7g59d+pn2Z9mZHEhEREUkWf1z+g/oL6+Nk78S1QdfIljmb2ZEypI3nNtJiSQtcHV25/t51nB2czY5kuvCocIpPL86NkBt89dJXdK3U1exIIiKpSuvvW7P2zFpalmrJujfXmR1HRNKxhNZZ7BJysOHDhydZMBFJm1acWsGGcxtwsHVgZsuZGb4gBeCew533a7/Pp398yoCNA2haoqneGBAREZF0ydcvtkuqU4VOKkiZqGmJppTKXoqz986y+MhielXtZXYk03139DtuhNwgv0t+3qzwptlxRERSnclNJ7Pp3CbWn13PhrMbNASviJguQUWpdu3aJXcOEUnFQiNCGbBxABA7RrtHTg+TE6UeH9T5gMVHFnMx8CKjt41mQpMJZkcSERERSVLXgq+x/ORyAHy8fUxOk7HZWGzoW60vAzcNxNfPl3ervJuhPywWY8QwYVfs39+DagzC0c7R5EQiIqmPew53+lfvz+Tdkxm0aRCNizfG3tbe7FgikoFpIHAR+U+jfh/FteBrFMtajOF11Tn5d5ntM+PbMvaTw1/s+YJjd46ZnEhEREQkac3dP5doI5r6RepTIU8Fs+NkeG97vU0W+ywcv3uc3y/9bnYcU609s5ZT/qdwdXSlZ5WeZscREUm1Pqr3EbmccnE64LTmJRQR0yW4KFW6dGnKlCnzr19ly5ZNzqwiYoKjt48ydc9UAHxb+pLZPrO5gVKhlqVa0r5Me6Jioui9rjcJmKpPREREJE0Ijwpn3p/zAHVJpRZumdzoUrELAL77fE1OY64JO2O7pHpX7Y2ro6vJaUREUi+3TG6MbTQWiP3g8d0Hd01OJCIZWYKG7wPw9X36H7uHDh1i0aJFxMTEJEkoEUkdYowYeq/rTbQRTfsy7WlZqqXZkVKtqc2msuncJnZc2cG3h7/lLa+3zI4kIiIi8tx+PPEjdx7coaBrQdqWbmt2HPl/fav1Zfb+2aw8tZIrQVco7FbY7EgpbueVney8uhMHWwf6V+9vdhwRkVSvW6VuzNo/i0O3DjFy60hmvzjb7EgikkEluFOqcePGj30VL16cFStW8NVXX9G8eXM2btyYnFlFJIV9c+gbdl7dSRb7LExtNtXsOKlaIbdCfFz/YwAGbx7MvYf3TE4kIiIi8vxm+M0AoFeVXtjZJPgzjZLMyuUuR8OiDYkxYpizf47ZcUwxcddEADp7dia/S36T04iIpH62NrZMaz4NgHl/zuPI7SMmJxKRjOqZ5pS6ffs2I0aM4KWXXiI6OpqVK1cyfvx4ChQokNT5RMQkAWEBDNk8BIBRDUZRyK2QyYlSv4E1BlIuVzn8w/wZvkVzb4mIiEja5nfdD7/rfjjYOtCjSg+z48g/9PPuB8D8P+fzKOqRyWlS1in/U6w6vQoLFobUGmJ2HBGRNKNekXq8WvZVYowYBm4cqOkHRMQUiSpKhYSEMHHiRJo2bcq5c+dYuHAhc+bMwd3dPbnyiYhJhv06jICHAZTPXZ4B1QeYHSdNsLe1Z3ar2Pb3eQfmsffaXpMTiYiIiDw7X7/YIdw7lOtA7iy5TU4j/9TaozWFXAvhH+bPD8d+MDtOipq4M7ZLqk3pNnjk9DA5jYhI2jKhyQQy2WVi66WtrDi1wuw4IpIBJbgoNX/+fBo3bszvv//O5MmTWbp0KVWrVk3ObCJikt1Xd7Pg4AIAZreajb2tvcmJ0o66ReryVsW3MDDova43UTFRZkcSERERSbQ7D+7ww/HYQkdcR46kLnY2dvSu2huIHWYxo3za/UbIDRYdWQTA0FpDTU4jIpL2FM1alME1BwMw+JfBGa7bVkTMZzES+Jdr6dKlyZQpEzVr1sTW1vap2/n6+iZZuJQSHR3NoUOH8PLy+tdrE8kIomKiqDqvKodvH6arV1e+avOV2ZHSnDsP7lDatzT3H91nevPp9KuuN3JEREQkbRn7x1hGbB2BdwFv9r6j7u/U6u6DuxT6ohDh0eHs7r6bGgVrmB0p2b2/+X0m7JpAncJ12N51u9lxRETSpAcRD/Dw9eB6yHXGNRrHB3U/MDuSiKQDCa2zJLhTqm3btrRo0YKsWbPi4uLy1C8RSdt8/Xw5fPsw2TJlY3zj8WbHSZNyZ8nNZy98BsCIrSO4GXLT5EQiIiIiCRcVE8Xs/bFDEqtLKnXLlSUXr5d/HfhruMX0LOhREHMOzAHUJSUi8jyyOGSxvuczdvtYboTcMDmRiGQkCe6USs/UKSUS63rwdUrPLE1oRCjzXpynCa2fQ3RMNLW+qoXfdT/eKP8G3738ndmRRERERBLkpxM/8eqPr5I7S26uDLyCo52j2ZHkXxy4cYCq86tib2PP1UFXyeOcx+xIyWbizokM/XUoZXOV5Wjvo9hYEjVNtoiI/I1hGNT6qhZ7ru3hrYpvsbDtQrMjiUgal+SdUiKS/g3aNIjQiFBqFKxB98rdzY6Tptna2DK71WxsLDZ8f+x7tlzYYnYkERERkQSJ67jpWbmnClJpQJX8VahRsAaRMZHMOzDP7DjJJjwqnC/2fAHAkFpDVJASEXlOFouFac2nAfDN4W/wu+5nciIRySj0V5yIALDp3CZ+PPEjNhYbazFFnk/lfJXpW60vAH3X9yU8KtzkRCIiIiL/7sjtI2y7vA1biy3vVn3X7DiSQD7VfACYc2AOkdGRJqdJHkuOLuFm6E3yu+TnzQpvmh1HRCRd8C7gTZeKXQAYsHEAGlBLRFKC3nUWER5FPcJnQ+x/ZPt798crr5e5gdKRTxt+Sp4seTgdcJpJuyaZHUdERETkX830mwlA+zLtKeha0OQ0klCvlnuVPFnycCPkBitPrTQ7TpKLMWKYuGsiAINqDMLB1sHkRCIi6cdnL3xGFvss7Lm2h++OauoBEUl+KkqJCON3jOfcvXPkd8nPJw0/MTtOuuKWyY0pzaYAMGb7GC7ev2hyIhEREZEnu//wPouPLgbAx9vH5DSSGA62DvSs0hOAGX4zTE6T9NaeWcsp/1O4ObpZr1NERJJGfpf8fFj3QwDe//V9HkQ8MDmRiKR3KkqJZHDn7p3jsx2fAfBFsy9wdXQ1OVH680b5N2hUrBGPoh7Rb0M/tcOLiIhIqvT1oa8JiwzDM48ndQvXNTuOJNK7Vd7FzsaO7Ve2c/jWYbPjJKkJOycA0Ltqb/1/RUQkGQyqOYhiWYtxPeQ643eONzuOiKRzKkqJZGCGYcTOdRQdTtMSTXm17KtmR0qXLBYLM1vOxN7GnnVn17Hq9CqzI4mIiIjEE2PEMHNf7NB9PtV8sFgsJieSxCrgWoD2ZdoDWO/L9GDnlZ3svLoTB1sH+lfvb3YcEZF0KZNdJiY1jZ1yYOKuiVwOvGxyIhFJz1SUEsnAfjrxE7+c/wVHW0d8W/jqzYdkVDpnaYbUGgJA/w391Q4vIiIiqcqGsxu4cP8CWTNlpaNnR7PjyDPyqRY77OLiI4u59/CeyWmSxoRdsV1SXTy7kM8ln8lpRETSr3al29GgaAMeRT1i6K9DzY4jIumYilIiGVRweDADNw0EYFidYZTKUcrcQBnAh/U+pGjWolwNvsqnf3xqdhwRERERK999vgB0r9QdJ3snk9PIs6pTuA6eeTx5GPWQrw9+bXac53by7klWn16NBQuDaw02O46ISLpmsViY2mwqNhYblh1fxh+X/zA7koikUypKiWRQo34fxY2QG5TIVoJhdYaZHSdDcLJ3YkaL2ImnJ++ezPE7x01OJCIiIgJnAs6w8dxGLFjoU62P2XHkOVgsFvp59wNih/CLjok2OdHzmbQrdiipNqXb4JHTw+Q0IiLpX8W8FelZuScAAzYOSPO/R0QkdVJRSiQDOnzrMNP3TgdgZsuZZLLLZHKijONF9xdp49GGqJgo+qzvg2EYZkcSERGRDG7WvlkAtHJvRfFsxU1OI8/rzQpvkjVTVi4GXmTDuQ1mx3lmN0JusOjIIgDer/2+yWlERDKO0Q1H4+boxqFbh/j6UNrvuhWR1EdFKZEMJsaIofe63kQb0bxa9lWalWxmdqQMZ1rzaTjZO/HH5T9YfGSx2XFEREQkAwuNCLW+4RTXYSNpm5O9E90rdQfA18/X5DTPbuqeqUTGRFK3cF1qFKxhdhwRkQwjV5ZcjGowCoDhW4YT9CjI3EAiku6oKCWSwXx18Ct2X9uNs4MzXzT7wuw4GVKRrEUYWW8kAP/75X/cf3jf5EQiIiKSUS06vIjg8GDcc7jTuHhjs+NIEulTrQ8WLGw6v4nT/qfNjpNoQY+CmLN/DgBDaw81OY2ISMbTt1pfPHJ4cDfsLmP+GGN2HBFJZ1SUEslA/MP8ef/X2KEvRjcYTQHXAiYnyrgG1RxE2VxluRt2lw9/+9DsOCIiIpIBGYaB777YThqfaj7YWPTfw/SieLbitHJvBfw1PGNaMvfAXEIiQiibqywtS7U0O46ISIZjb2tv/SDztL3TOBNwxuREIpKe6H8dIhnI+5vf597De3jm8aRfdQ3PYiYHWwdmtYx9g2DO/jnsu77P5EQiIiKS0Wy9tJUTd0/g7ODMW15vmR1HkljccIwLDy8kJDzE5DQJFx4VztQ9UwEYUmuIiqUiIiZpUaoFLUq2IDImkv/98j+z44hIOmLqX3dz587l5ZdfplKlStSsWZM+ffpw4cKFeNuEh4fzySefUL16dSpVqkS/fv3w9/ePt82NGzfo2bMnFStWpGbNmowfP56oqKiUvBSRVG/nlZ18degrAGa3mo2djZ3JiaR+0fp09uyMgRE7z1dMtNmRREREJAOZ4TcDgLcqvoWro6vJaSSpNS7eGPcc7gSHB7PoyCKz4yTYkqNLuBl6kwIuBXizwptmxxERydCmNJuCnY0da8+sZdO5TWbHEZF0wtSilJ+fHx07dmTZsmV8/fXXREVF0b17d8LCwqzbjBs3jq1btzJ16lQWLVrEnTt38PHxsa6Pjo7m3XffJTIykqVLl/L555+zYsUKpk+fbsYliaRKkdGR9FrXC4B3Kr1DrUK1TE4kcSY2mUjWTFk5cPOAddx8ERERkeR2OfAyq0+vBmLnjZD0x8ZiY71vff18MQzD5ET/LcaIYcLOCQAMqjEIB1sHkxOJiGRspXOWtnbeDto0iMjoSJMTiUh6YGpR6ssvv6R9+/aUKlWK0qVL8/nnn3Pjxg2OHz8OQEhICD///DPDhg2jZs2alC9fnnHjxnHw4EEOHToEwI4dOzh37hwTJ06kTJky1K9fnwEDBrBkyRIiIiJMvDqR1GP63ukcu3OMHJlz8Hnjz82OI3+TxzkP4xqNA+DD3z7kVugtkxOJiIhIRjB7/2xijBgaF29MmVxlzI4jyeRtr7dxdnDmpP9Jtl7aanac/7Tm9BpOB5zGzdGNHlV6mB1HRESAkfVHktMpJyf9T+rDtCKSJFLV4MwhIbHjXLu5uQFw7NgxIiMjqVXrr66OEiVKkD9/fmtR6tChQ7i7u5MzZ07rNnXq1CE0NJRz586lXHiRVOpq0FU+/v1jACY0mUAOpxwmJ5J/6lmlJ1XzVyUoPIghm4eYHUdERETSuYeRD1nw5wIAfKr5/MfWkpa5OrrSxbML8NdwjanZhF2xXVK9q/bWkJIiIqlE1kxZGdNwDAAjfx+Jf5j/f+whIvLvUk1RKiYmhnHjxlG5cmXc3d0B8Pf3x97eHlfX+H+M5siRg7t371q3+XtBCrDejttGJCMbuGkgDyIfUKtQLd72etvsOPIEtja2zG41GwsWFh9ZzNaLqf9TrCIiIpJ2LT22lICHARRxK8KL7i+aHUeSWV/v2CH8Vp9ezeXAyyanebqdV3ay6+ouHGwd6F+9v9lxRETkb96p/A6eeTwJfBTIx1s/NjuOiKRxqaYo9cknn3D27Fm++OILs6OIpBvrz65n+cnl2Fpiix42llTzlJd/qJq/Kr2r9gagz/o+RERr+FERERFJeoZhWDtm+lTrg62NrcmJJLmVzVWWF4q9QIwRk6qHXYrrkuri2YV8LvlMTiMiIn9na2PL1GZTAZhzYA5Hbx81N5CIpGmp4h3q0aNH8/vvv/PNN9+QN29e6/KcOXMSGRlJcHBwvO0DAgLIlSuXdRt///hto3G347YRyYgeRj6k34bYySgH1hiIZx5PkxPJfxn7wlhyZ8nNKf9TTNk9xew4IiIikg7tvrabg7cOkskuE90rdTc7jqQQH+/YYRrn/zmfh5EPTU7zuBN3T7D69GosWBhca7DZcURE5AkaFmvIy2VeJsaIYdCmQRiGYXYkEUmjTC1KGYbB6NGj2bx5M9988w2FChWKt758+fLY29uze/du67ILFy5w48YNvLy8APDy8uLMmTMEBARYt9m1axfOzs6ULFkyRa5DJDX6bMdnXLh/gQIuBfi4vlqr04KsmbIyuelkAEZvG82lwEvmBhIREZF0x9fPF4A3y7+puUYzkBfdX6SwW2ECHgbww/EfzI7zmEm7JgHQtnRbPHJ6mJxGRESeZmKTiTjaOrLl4hZWn15tdhwRSaNMLUp98sknrF69msmTJ5MlSxbu3r3L3bt3efToEQAuLi68/PLLfP755+zZs4djx44xfPhwKlWqZC1K1alTh5IlSzJ06FBOnTrF9u3bmTp1Kh07dsTBwcHEqxMxz5mAM4zfOR6Aac2n4eLoYnIiSaiOFTrSoGgDHkY9ZMDGAWbHERERkXTkZshNfjzxI/BX54xkDHY2dvSp2geAGX4zUtWn268HX2fxkcUADK091OQ0IiLyb4plK8b/av4PgP/98j/Co8JNTiQiaZGpRanvv/+ekJAQOnfuTJ06daxf69evt24zfPhwGjRoQP/+/enUqRM5c+ZkxowZ1vW2trbMmTMHGxsbOnTowJAhQ2jbti39+2tiVMmYDMOgz7rYOYmal2xO+zLtzY4kiWCxWJjVchZ2NnasPr1anzwSERGRJDPvwDyiYqKoXag2lfJVMjuOpLDulbvjaOvInzf/ZM+1PWbHsZq2dxqRMZHULVyXGgVrmB1HRET+wwd1PyCfcz7O3z/PtL3TzI4jImmQxUhNH5EySXR0NIcOHcLLywtbW030K2nb0mNLeePnN8hkl4ljvY9RInsJsyPJM/jg1w/4fOfnFHErwvE+x8nikMXsSCIiIpKGRURHUGRqEW6F3mLpy0vpUL6D2ZHEBF1XdWXhoYW8WeFNlrRfYnYcgh4FUeiLQoREhLDmjTW86P6i2ZFERCQBvj38LW+tfAtnB2fO9jtLXue8ZkcSkVQgoXUWUzulRCRpBT0KYtCmQQAMrzNcBak0bES9ERRxK8LloMuM3T7W7DgiIiKSxi0/uZxbobfI55xPnfQZWD/vfgD8ePxHboXeMjkNzD0wl5CIEMrlKkfLUi3NjiMiIgnUybMT3gW8CY0IZfiW4WbHEZE0RkUpkXRk5NaR3Aq9RanspTQeexqXxSEL01tMB2Infj5596TJiURERCQtm+EXOwR6r6q9sLe1NzmNmKVyvsrULFiTyJhI5h2YZ2qW8Khwpu6ZCsCQWkOwsejtCRGRtMLGYsO05rFD9y08tJD9N/abnEhE0hL91SeSTvx580989/kCMLPlTBztHE1OJM/rJY+XaO3emsiYSPqs75OqJqQWERGRtOPPm3+y6+ou7G3s6Vmlp9lxxGRx3VJz9s8hMjrStByLjyzmZuhNCrgU4I0Kb5iWQ0REnk2NgjXo5NkJA4OBGwfqPQsRSTAVpUTSgeiYaHqv602MEUOHch1oUqKJ2ZEkiUxvMZ3Mdpn5/dLvfHf0O7PjiIiISBrk6xf7waVXy72qOR+El8u+TJ4sebgZepPlJ5ebkiHGiGHirokADKoxCAdbB1NyiIjI8/n8hc9xsndi59Wd/HD8B7PjiEgaoaKUSDqw4M8F+F33w8XBhSnNppgdR5JQ0axF+ajeRwD875f/Efgo0NxAIiIikqYEhAVYP9jiU83H5DSSGjjYOvBulXcBrCMtpLQ1p9dwOuA0bo5u9KjSw5QMIiLy/Aq4FuCDOh8AMGTzEMIiw0xOJCJpgYpSImncnQd3GLZlGABjGo0hv0t+kxNJUvtfrf9ROmdpbj+4zYjfRpgdR0RERNKQBX8uIDw6nCr5qlCjYA2z40gq8W7Vd7GzsWPHlR0cunUoxc8/YdcEAPpU64Oro2uKn19ERJLO/2r+jyJuRbgWfI0JOyeYHUdE0gAVpUTSuKGbhxL4KBCvvF70qdbH7DiSDBxsHZjZciYAs/bN4sCNAyYnEhERkbQgOiaaWftnAeDj7YPFYjE5kaQW+V3y83KZl4G/hndMKTuu7GDX1V042DrQv3r/FD23iIgkvcz2mZnYJHZI1gk7J3Al6IrJiUQktVNRSiQN++PyH3xz+BssWJjTag52NnZmR5Jk0qhYIzpW6IiBQe91vYmOiTY7koiIiKRya86s4UrQFXJkzsHr5V83O46kMj7escM5Ljm6hHsP76XYeeM+Rf9Wxbc0x5mISDrxStlXqFekHg+jHvL+r++bHUdEUjkVpUTSqIjoCHqv6w1Azyo9qV6wusmJJLlNajoJV0dX9t3Yx7wD88yOIyIiIqlcXAdMj8o9yGSXyeQ0ktrULlQbr7xePIp6xFcHv0qRc564e4I1Z9ZgwcL/av4vRc4pIiLJz2KxMLXZVCxYWHpsKTuu7DA7koikYipKiaRRU/dM5cTdE+RyysW4F8aZHUdSQF7nvIxtNBaAD7Z8wO3Q2yYnEhERkdTqxN0TbLm4BRuLDb2r9TY7jqRCFosFn2qx3VIz981MkU78SbsmAdC2dFs8cnok+/lERCTlVMpXiXcqvwPAgI0DiDFiTE4kIqmVilIiadDlwMt8su0TACY2mUj2zNlNTiQppXfV3lTOV5mg8CCG/jrU7DgiIiKSSs30i52Pso1HGwq7FTY5jaRWb1R4g2yZsnEp8BLrz65P1nNdD77O4iOLAXi/toZ2EhFJj8Y0GoOroyt/3vyThYcWmh1HRFIpFaVE0qCBmwYSFhlG3cJ16VKxi9lxJAXZ2tgyu9VsLFj49vC3bLu0zexIIiIiksoEPQrim8PfANDPu5/JaSQ1c7J3sn6qfYbfjGQ919Q9U4mMiaRekXoaelxEJJ3KnSU3I+uNBGD4luEEhwebnEhEUiMVpUTSmLVn1rLy1ErsbOxiixMWi9mRJIV5F/Dm3SrvAtBnfR8ioiNMTiQiIiKpyTeHv+FB5APK5SpHg6INzI4jqVzvqr2xYGHzhc2c8j+VLOcIfBTI3ANzARhaS93+IiLpWb/q/SiVvRS3H9xm7B9jzY4jIqmQilIiaUhYZBj9NsR+2vW9Gu9RLnc5kxOJWca9MI5cTrk4cfcEU/dMNTuOiIiIpBIxRgy+fr4A+Hj76ANM8p+KZSvGi+4vAjBr36xkOcfc/XMJiQihXK5ytCjVIlnOISIiqYODrQNTmk0BYOreqZy7d87kRCKS2qgoJZKGjP1jLJcCL1HItRAf1f/I7DhiomyZszGpaexE0Z9s+4TLgZdNTiQiIiKpwebzmzl77yxujm508uxkdhxJI+KGeVx4aCEh4SFJeuzwqHCm7p0KwJBaQ7Cx6G0IEZH0rlWpVjQr0YyI6AgG/zLY7Dgiksror0GRNOLk3ZNM3DURgOktpuPs4GxyIjFbZ8/O1CtSj7DIMAZuGmh2HBEREUkF4uYF6urVVX8vSoK9UPwFPHJ4EBIRwreHv03SYy8+spj/Y+++w6Oo3jaO35tG70WkKmAShEBCr1IV6QHpvXcEFakiyg8BUaRLr4KCCEgRERAQUJoIUoNKJ9QQSIgQQpJ5/9g3qzEBAplksvD9cHFlM2d39pmz5+xM9tlzzpWwK8qbMa9a+bQydd8AgJTJZrNpYu2JcrW5as3JNdpyeovVIQFIQUhKAU7AMAz12dBH96Pvq95L9dTIq5HVISEFsNls+rzu53JzcdO3Ad9q/R/rrQ4JAABY6FTwKW34c4MkqXeZ3hZHA2fiYnNR37J9JUnT9k+TYRim7DfaiHZ8se6t8m/Jw9XDlP0CAFK+IjmKqE+ZPpKkARsHKDI60uKIAKQUJKUAJ/DlkS+17ew2pXFLo6l1prI2AByK5iyqt8u/LUnq930/3bl/x+KIAACAVT7f/7kMGapTuI5eyvaS1eHAybQv0V7pPdIrIChAP5750ZR9rj25VidvnFSmVJnUrWQ3U/YJAHAeH1T7QNnSZNOx68c069dZVocDIIUgKQWkcLfCb+ntTfakw3uvvKcXs7xocURIaUZUHaF8GfPp7K2zGrNzjNXhAAAAC/wd8bfmH5ovSY4RL8DjyJgqozqU6CBJmrZvWqL3ZxiGPv75Y0n2kXsZUmVI9D4BAM4lS5osGlV9lCTp/e3vK/husMURAUgJSEoBKdx7W9/Ttb+vySubl96p8I7V4SAFSu+RXpNfnyxJGv/zeJ0MOmlxRAAAILktPbJUt8JvqVCWQnq98OtWhwMnFZPQXPfHOp29dTZR+/r5ws/ac3GPPFw99Ga5N02IDgDgjLqX6q5iOYsp+G6wPtj+gdXhAEgBSEoBKdivl37V5/s/lyR9Xu9zpXJLZXFESKn8vf1V76V6uh99X7039DZtHQAAAJDyGYbhGNnSp0wfudj4Mw9Pxju7t2oVrKVoI1oz9s9I1L7G/zxektShRAflSp/LjPAAAE7IzcVNk2pPkmSfavjYtWPWBgTAcvy1AqRQUdFR6vVdLxky1NqntWq8WMPqkJCC2Ww2TakzRandUmvrma1adnSZ1SEBAIBksuPcDh25dkRp3dOqk18nq8OBk+tbxj5aau7Bubp7/+4T7ePYtWNa98c62WTTwIoDzQwPAOCEahasKX9vf0UZUXrrh7f4Ii3wjCMpBaRQsw7M0q+XflWmVJk04bUJVocDJ1AwS0ENrzJckvT2prcVEh5icUQAACA5TNtvHyXVrng7ZU6d2dpg4PTqe9ZXgUwFFHw3+Im/6PTp7k8lSY2LNJZnNk8zwwMAOKlPX/1UHq4e2nx6s9b/sd7qcABYiKQUkAJdCbuiYT8OkyR9VOMjprtAgr1b8V15ZvPUlbArGrFthNXhAACAJHYh5IJWn1gt6Z/1gIDEcHVxVe8yvSVJU/dNfexvs18Mvailh5dKkgZVHGR6fAAA51QoayG9Vf4tSfYv0kZERVgcEQCrkJQCUqB3N7+rkHshKvV8KfUs3dPqcOBEUrml0vS60yVJ0/dP12+Xf7M4IgAAkJRmHZilKCNK1V6opmI5i1kdDp4SXfy6KLVbah28clC7L+5+rMdO3jNZ96Pv65UCr6hc3nJJFCEAwBkNrzJcudLn0l/Bf2nK3ilWhwPAIiSlgBRm25ltWnJ4iWyyaUa9GXJ1cbU6JDiZWgVrqWWxloo2otXru16KNqKtDgkAACSB8MhwzT4wW5LUr2w/i6PB0yRb2mxqXay1JPtoqYS6FX5Lsw7MksQoKQBAXBlSZdDYmmMlSaN+GqWrYVctjgiAFUhKASlIRFSEem+wT5XRs3RPlclTxuKI4Kw+e+0zZfDIoH2B+zT3t7lWhwMAAJLAimMrdP3OdeXLmE8NvRpaHQ6eMn3K9pEkfXP8G12+fTlBj5n560zdjritYjmLqe5LdZMyPACAk2pfor1K5y6t2xG39d7W96wOB4AFSEoBKciEXyYoIChAOdPl1JiaY6wOB07s+QzPa3SN0ZKkIVuG6Nrf1yyOCAAAmC1mBEuv0r3k5uJmcTR42pR8vqQq5quoyOhIx4i8hwmPDNfkvZMl2dc5tdlsSR0iAMAJudhcNPl1+/li3sF5Onj5oMURAUhuJKWAFOLMzTP6347/SZImvDZBmVNntjYgOL3eZXrLN5evbobf1OAtg60OBwAAmGhf4D7tv7RfqVxTqWvJrlaHg6dUzLSQMw/MfOSC9EsOL9GVsCvKmzGvWhZrmRzhAQCcVMV8FdWqWCsZMtR/Y38ZhmF1SACSEUkpIIXov7G/7kbeVbUXqqmNTxurw8FTwM3FTTPrzZRNNi08tFA7z+20OiQAAGCSmFFSLYu1VI50OSyOBk+rJkWaKFf6XLoSdkWrTqx64P2ijWh98ssnkqS3yr8lD1eP5AoRAOCkPq71sdK4pdHO8zu14vgKq8MBkIyY4wFIAdYErNG6P9bJzcVNn9f9nKkuYJpyecupW8lumv3bbPXe0Fu/df9N7q7uVocFwARR0VG6HHZZ50POx/kffDdYuTPkVv5M+eP8z5YmG+cZwMldDbuqr499LUnqW7avxdHgaebh6qGepXrqg58+0LR90x44AmrtybX648Yfypw6s7qV7JbMUQIAnFG+TPk0pPIQjdw+Uu9uflcNPBsojXsaq8MCkAxISgEW+zvib7258U1J0sAKA1UkRxGLI8LTZmytsVoVsEpHrx3V5L2TNbDiQKtDApAAofdCdSHkgs6FnIs38RR4O1CR0ZGPvd80bmniTVbF/M+bMa9Su6VOgiMCYJY5v81RRFSEyuctr9K5S1sdDp5y3Ut11+ido/XzhZ918PJB+T3vF6vcMAx9/PPHkqTepXsrQ6oMVoQJAHBCAysO1Nzf5up8yHl9+sunGlF1hNUhAUgGNoNJOxUVFaVDhw7J19dXrq6uVoeDZ8yQLUP08c8fq0CmAjre57jSuqe1OiQ8hRYeWqhOazopnXs6nehzQvky5bM6JOCZFhkdqcu34xnlFPrP7Vvhtx65HzcXN+XNmPefpFJG+8+sabLqcthlnbt1LtY+r4RdSVB8z6V7TgUyF4i1z3//z542O6OtAIvcj7qvFye/qMDbgVrSeInaFGfaZyS9VitbadnRZers21nzGs2LVbbz3E69svAVpXJNpbMDzipX+lwWRQkAcEbLjy5Xy5UtldY9rU72Pam8GfNaHRKAJ5TQPAtJKZGUgnWOXTsm31m+ioyO1NqWa9XAq4HVIeEpFW1Eq+rCqtp1fpeaFGmilc1XWh0S8FQLCQ95aMIpMDRQUUbUI/eTNU3WOAmnf//PlT6XXF0Sfu1yL/KeLoZefGBs526d093Iu4/cT2q31A+NK1+mfIy2ApLIimMr1Pyb5nou3XM6/9Z51u5Bsvjlwi+qNL+SUrul1sW3Lipb2myOsgZfNdD6P9are8numtVgloVRAgCckWEYemXhK9p1fpfa+LTRkiZLrA4JwBNKaJ6F6fsAixiGod4beisyOlINvRqSkEKScrG56PO6n8tvlp9WnVilDX9uUN2X6lodFuCUIqMjden2pXin1IuZai/0Xugj9+Pm4qZ8GfPFSegUyFTAkdhJ75He1NhTuaVSoayFVChroXjLDcNQ8N3geJNW527Zj+1y2GWFR4brjxt/6I8bfzzwuXKmyxknceUYfZUpv3KkzcFoK+AJTNs/TZJ9SjUSUkguFfJWkF8uPx28clDzDs7ToEqDJNm/ZLf+j/WyyaZ3Kr5jcZQAAGdks9k0qfYklZlTRkuPLFWfMn1UIV8Fq8MCkIQYKSVGSsEai39frA7fdlAatzQ60eeECmQuYHVIeAa8u+ldfbr7UxXMUlBHex1lEVHgPwzDUMi9eEY5/Wctp2gj+pH7ypYm20PXbnou3XOPNcoppbgXeU+BtwMfWD/nQs7pzv07j9xPKtdUD62ffBnz8R4F/Mfhq4dVYmYJubm46dyAc8qdIbfVIeEZMv/gfHVZ20UFMhXQqTdPydXFVZ3WdNLCQwsZiQ8ASLSua7tq3sF5KpO7jPZ03SMXm4vVIQF4TEzf9xhISiG53bx7U17TvHT9znWNrTlWQyoPsTokPCPCIsLkPc1bgbcDNeKVERpVfZTVIQHJ6n7U/YcmVM6HnNftiNuP3I+7i7vyZcrnGNUUX0IlnUe6ZDiilMcwDN0Mv/nQOr50+5IMPfoSNEfaHA9NXOVMl5M/VvFM6b6uu+b8NkfNizbX8qbLrQ4Hz5i79+8q78S8Cr4brDUt16jk8yVVcHJB3Y++rz1d9qhc3nJWhwgAcGJXw67qpakv6XbEbS1stFAdfDtYHRKAx/TMJaWWLl2qefPm6fr16/L29taIESNUvHjxBD2WpBSSW6/1vTTzwEy9nONlHexxkKlXkKxWHl+ppiuaysPVQ0d6HZFnNk+rQwJMYRiGboXfeuhaTpduX0rQKKfsabM/dM2k59I/RzIkESKiIhQYGk9y8F9rW/19/+9H7sfD1cMxBWKBzAXivFb5MuVTWve0yXBEQNK7efem8nyWR3cj72pnp52qnL+y1SHhGTR482CN/2W8ahWspRLPldCE3RNUtUBVbe+43erQAABPgU9+/kSDtgxSrvS59EffP5QhVQarQwLwGJ6ppNSGDRs0aNAgffjhhypRooQWLVqkjRs3auPGjcqWLdsjH09SKmFu3r2pv+//LVebq1xsLo7/ri7/+f0/5awXEdu+wH0qP7e8DBna3mG7qr5Q1eqQ8IwxDEP1vqyn7//6XrUK1tKmtpvop3AKj0pknA85r7CIsEfux8PVI3aSiURGikOCEYhrwi8TNHDzQJV4roQO9jjIuRuWOHvrrApNKaRoI1pp3NLobuRdfdf6O9YqBQCY4l7kPRX9vKhO3TyloZWHakzNMVaHBOAxPFNJqWbNmsnHx0fvv/++JCk6OlpVq1ZVu3bt1L1790c+nqTUo206tUl1l9ZVlBH12I+1yZbgBFZylsdbpqSP5fNfP9fhq4fVrng7LW68OAleLeDRTgWfUtHPi+pe1D3Nqj9L1V6oZnVIgCTp74i/dSH0Qpw1is6HnNfl25cTPeVbgUwFlCNdDpIQT4H7Ufd16faleNtKzO2EJCljpmKML3GVJ2MeRjObwDAMR9+NuR3zJ0jM7cSWm7mvlFo+eudonb11VnMbzFWXkl1MeGWAJ+O/zF9rTq6RJBXLWUyHex4mSQoAMM3ak2vVaFkjebh6aGv7rcqRLofVIQGSpBcyv8Dfh4+Q0DyLWzLGlCQiIiJ07Ngx9ejRw7HNxcVFFStW1MGDBxO0j5g/9KKiHj/h8qzI7JFZ+TLk083wmzIM44mSUw6Gvc6j/v/fsyh3utz6uMbHtDlY5oVML+j9Ku9rzK4xenvj21aHAyRIWre0juna8mbMq7wZ8/5zO5P9dp4MeZTGPc1D92NEG8/s+edp4iIX5c2QV3kz5FXFvBXjlBuGodB7oboQekEXQy/qYuhFXQix347ZdjnssqKNaF29fVVXb1/V/ov7LTgSIOHypMuj5i835xoSlupbpq+2nNoiSRpcYbCiox89ahUAgISqW6iu6heur21nt6n2F7WtDgdweDn7y/qlyy98GechYv5OedQ4KKcfKXX16lW98sorWrZsmfz8/Bzbx48fr/3792vFihWP3EdERISOHDmSlGECAAAAAAAAAAA81Xx8fOTh8eBRZU4/UsoMbm5u8vHxkYsL6x8BAAAAAAAAAAA8DsMwFB0dLTe3h6ednD4plSVLFrm6uurGjRuxtt+4cUPZs2dP0D5cXFwemrkDAAAAAAAAAABA4jj9Kt8eHh4qWrSodu/e7dgWHR2t3bt3x5rODwAAAAAAAAAAANZx+pFSktSpUycNHjxYxYoVU/HixbVo0SLdvXtXTZo0sTo0AAAAAAAAAAAA6ClJStWtW1fBwcGaMmWKrl+/riJFimju3LkJnr4PAAAAAAAAAAAASctmGIZhdRAAAAAAAAAAAAB4ujn9mlIAAAAAAAAAAABI+UhKAQAAAAAAAAAAIMmRlAIAAAAAAAAAAECSIykFAAAAAAAAAACAJEdSCgAAAAAAAAAAAEnOzeoAkHJdv35dv//+u4KCgiRJ2bNnV4kSJZQjRw6LI3M+1CXw9Lt//77c3d2tDgPPuNDQUG3cuFGXLl1Snjx59PrrrytDhgxWhwXgMR09elTFihWzOgzA4caNG/rjjz9UrFgxZciQQUFBQVq9erUMw1DVqlXl5eVldYhOISoqynGOdnFxUUREhLZs2SLDMFSuXDllz57d6hCdAvVoDvq1OWiP5rpw4YIOHDiga9euycXFRfny5VOlSpWUPn16q0MDYCKbYRiG1UEgZblz547ef/99bdiwQTabTZkyZZIkhYSEyDAM1atXT6NGjVKaNGksjjTloy7NFRUVJVdXV8fvv//+uyIiIuTr60syIJGGDh2qAQMG6LnnnrM6lBRvw4YNqlWrljw8PCRJS5Ys0bx583TlyhVlzJhR7dq1U9++fS2O0nndv39fgYGBypYtG8mUBOjbt6/q16+v119/XX/++afatWsnm82mfPnyKTAwUDabTYsWLVKhQoWsDtXpkOCDlby9vZUvXz698cYbaty4MefnJ0Ryzxx79+5Vz549dffuXWXPnl1z585Vjx49lDp1atlsNgUGBmrGjBmqXLmy1aGmaAEBAeratatu3LihwoULa/bs2erevbsuXrwoSXJ3d9fcuXNVvHhxiyNN2ahHc9CvzUF7NM+dO3c0ZMgQbdq0SZJks9mULVs2BQcHK1WqVBo4cKDatGljcZTO4aefftKmTZuUKVMmvfHGG7H+FgwJCVG/fv20ePFiCyMEmL4P8fjoo4905MgRzZo1S4cPH9Yvv/yiX375RYcPH9bs2bN1+PBhffTRR1aH6RSoS3Ncu3ZNrVq1ko+Pj9q2bauQkBD16NFDLVq0ULt27VS/fn1du3bN6jCdQkBAQLz/161bpyNHjjh+x4O98847un37tiRp5cqVGj9+vBo3bqyZM2eqY8eOmjdvnlasWGFxlM5hzpw5Cg8Pl2RPOn/88cfy8/NTnTp1VL58eQ0dOlT379+3OMqUbd++fXrppZckSePHj1elSpX0008/6euvv9b27dtVtWpVjRkzxuIonUPfvn21ceNGSdKff/6p1157TRMnTtQvv/yiSZMmqU6dOjp16pTFUaZ8ERERsfrt+fPnNXHiRL377ruaOHGiLly4YGF0zqV8+fJavHixatSooR49emjLli2KioqyOiyn0rRpU7366quaOXOmrl69anU4Tmvy5Mlq3LixDhw4oE6dOql79+6qWbOmfvjhB23cuFFt27bVtGnTrA4zxfvkk09UsmRJrVmzRuXLl1eXLl1UsGBB7du3T/v371fVqlU1ceJEq8NM8ahHc9CvzUF7NM+4ceN0/fp1rV27Vj/88INeffVVNWrUSAcOHNDw4cP1ySefaN26dVaHmeKtW7dOvXr1UlBQkA4dOqTGjRtr7dq1jvL79+9r//79FkboXJYuXaqOHTuqf//+2r17d6yy4OBg1axZ06LIngIG8B+lS5c2Dhw48MDyX3/91ShdunQyRuS8qEtzvPvuu0aLFi2MH3/80RgwYIDRokULo3Xr1saVK1eMwMBAo2XLlsaHH35odZhOwcvLy/D29ja8vLzi/I/Z7u3tbXWYKZqXl5cRFBRkGIZhNG3a1JgzZ06s8qVLlxr+/v5WhOZ0vL29HXU5d+5co0yZMsY333xj/Pnnn8aaNWuMChUqGLNnz7Y4ypStePHixrlz5wzDMIxKlSoZx44di1V++vRpo1SpUlaE5nTKlClj/PXXX4ZhGEbXrl2Nt99+27h3755hGIYRERFhDBs2zOjcubOVITqFtm3bGt9//71hGPbrnGLFihkNGjQwBgwYYPj7+xslSpQwfvvtN4ujTPlizjX37983Nm7caHTr1s0oUqSIUbFiRWP8+PHG6dOnrQ7RKXh5eRnvvfeeUaFCBePll182unfvbmzevNmIjIy0OjSnUrJkSce55v79+8bLL79sHD9+3FF+5swZzjUJ8O/zzN27d40iRYoYv//+u6P8jz/+MMqWLWtVeE6DejQH/doctEfzlCtXzjhy5Ijj91u3bhk+Pj7GnTt3DMMwjCVLlhiNGjWyKDrn0ahRI2PRokWO37/77jvD19fX+Prrrw3DMIzr16/zmU8CLVq0yChRooTx4YcfGgMHDjSKFi1qzJw501FOXSYOa0ohjujo6IdOhebu7q7o6OhkjMh5UZfm+OWXXzRt2jT5+vqqZMmSKl++vBYsWOCYyubNN9/UiBEjLI7SOXh5eSlXrlwaNGiQUqdOLUkyDEO1a9fWnDlzVKBAAYsjdA42m02Sfb7r/05pUblyZX366adWhOV0jH/NILx+/Xq98847euONNyRJhQsXliTNnj1b3bp1syQ+Z+Dl5aU9e/Yof/78ypEjhy5duqSXX37ZUX7p0iVHX8fD3bt3z3HOPnHihGbPnu2YptPd3V1du3ZVs2bNrAzRKRw/flze3t6SpEmTJql169YaOnSoo3zSpEkaP368vvrqK6tCdCpubm6qXbu2ateuratXr+qbb77R6tWrNX/+fJUsWVJLly61OsQUb8CAARo5cqR+/PFHrVy5Um+++aayZMkif39/NW3aVC+++KLVIaZ47u7uunfvniT7N6yjo6Mdv0tSeHi43Nz4aOFRDMNwTEX+35+S5OLiEuvaCPGjHs1BvzYH7dE8UVFRsdaNSps2raKionT37l2lSZNGlSpV0scff2xhhM7h3Llzql69uuP3unXrKmvWrOrVq5ciIyP16quvWhidc1m+fLn+97//qUGDBpKkVq1aqU+fPgoPD1f//v0tjs75MX0f4qhWrZref/99HT9+PE7Z8ePH9cEHH8R6g8ODUZfmCA0NdSSgMmfOrDRp0ih37tyO8gIFCuj69etWhedUVqxYofz58+vNN99USEiI8uTJo7x580qScubMqTx58ihPnjwWR5ny7dixQz/++KNSpUqlu3fvxiq7d++eI2mFR4upq0uXLsnPzy9WmZ+fn2M+dsSvd+/emjBhglatWqV27dppzJgxWrFihX777TetXLlSw4cPV8OGDa0O0ynEJPgkORJ8/0aCL2Gio6MdU8ydPn1ajRs3jlXepEkTpolNgPjOI88995z69OmjLVu2aP78+cqVK5cFkTmnmOTe7NmztW3bNrVu3Vo//PCD6taty/oUCVCyZElNmDBBBw4c0NixY/Xyyy9rxowZunPnju7evavPP/+ctbsSoGjRopozZ46uXr2qWbNmKW/evFqyZImjfMmSJY4pefFg1KM56NfmoD2ax8fHJ9Y6R4sXL1aWLFmUNWtWSfY1p9KmTWtVeE4jXbp0unHjRqxt5cuX16xZszR+/Hh98cUXFkXmfC5evBjrM4qSJUtq0aJF+vrrrzVhwgQLI3s68LUHxPH+++/rnXfeUZMmTZQpUybHCSA4OFihoaGqXLmy3n//fYujdA7UpTmyZcum69ev6/nnn5cktWnTRpkyZXKUh4aGKk2aNFaF51Q8PDw0fPhw/fTTT+rVq5dat27NKJQnMGTIEMftPXv2xLpQOXTokPLnz29FWE7p66+/Vtq0aeXu7q6QkJBYZX///bdjpAriV61aNY0aNUpjxozRtWvXZBiGY+Soh4eHWrZsqXfeecfiKJ1D7969NXjwYLm5uTkSfDdv3lShQoV05swZTZ06lQRfAhQvXlzbtm1ToUKFlC9fPgUEBDhGTkn2UWiZM2e2LkAn8ahvVVeoUEEVKlRIpmic18OSe3369NHu3bv1zTffWBCZcxk0aJB69OihNm3aqGDBglqwYIE++OADlSlTRpKUMWNGzZ071+IoU763335b3bp106pVq5Q5c2YtXrxYw4cPV+XKlWWz2RQaGqqZM2daHWaKRz2ag35tDtqjed555x117txZmzZtkru7u65fvx5rZNTBgwdVtWpVCyN0DsWLF9eOHTvk6+sba3vZsmU1c+ZM9ezZ05rAnFCWLFl05coVxxe5JcnT01OLFi1Shw4dWNs+kWwG40jxAKdOndKhQ4cUFBQkScqePbt8fX1VqFAhiyNzPtRl4vTq1Uvly5dXhw4d4i1funSpNm3apEWLFiVzZM4tKChIQ4cO1Z07d3To0CGtWbPGMWUanty2bdvk5uamKlWqWB1KilejRo1Yv7dv314dO3Z0/L5o0SJt2LBBy5cvT+bInE9UVJSOHTumixcvKjo6Wjly5FDRokVjTYGBR/vhhx9iJfhixCT4Bg8eHGtKFsR18OBBdevWTR06dFCWLFk0bdo0tWzZ0pHc++KLL9S9e3e+EPEI+/btU8mSJZk6KZG8vb31888/K1u2bFaH8lS4efOmsmTJ4vh99+7dCg8Pl6+vb6zteLA7d+7o9OnTevHFF5UuXTrdu3dPa9eu1b1791SxYkUVLFjQ6hCdAvVoHvp14tEezXPt2jVt375dERERKl++PJ9PPIF9+/bp4MGD6tGjR7zle/bs0Zo1azR27Nhkjsz5vPPOO8qWLZuGDRsWp+zPP/9U+/btdevWLZ04ccKC6JwfSSkATu/w4cNKnTq1PD09rQ7FKS1evFh79+7ViBEjmAoIKcqhQ4fk4eERa40kIKmR4Eu8gwcPaty4cfr9999jbc+ZM6e6dOnywC+ZAGYjuQcAAIAnERAQoGPHjjnWvf6vP/74Q5s2bVLfvn2TObKnA0kpPLaQkBBt27ZN/v7+Vofi9KhL4Olz584dHTt2zDH1BWAlzjOwUnBwsC5cuOBI7v176gskDn0bKQnt0RxcQ5qDejQH/doctEfz0CaBp4uL1QHA+Vy+fFlDhw61OoynAnVpjpCQEH377bdWh/FUoC4T7/z582rfvr3VYTwVaI+Jx3nGPLTHx5c1a1aVKFFCfn5+JKRMRt82B/3aHLRHc3ANaQ7q0Rz0a3PQHs1Dm0yY+/fva/z48Xr11VfVtGnTOGtnBgUFqUiRIhZF55yio6MfuP3SpUvJHM3Tg6QU4ggLC3vkfyQMdZk8uDgxD3WJlIT2+GicZ5IP7dEcQUFBmjZtmtVhpHj07eRBv04Y2iPw9KFfI6WhTZpj5syZWrNmjVq2bKlKlSpp3Lhxev/992Pdh0nTEiYsLEz9+/eXr6+vKlasqMmTJysqKspRHhwcrJo1a1oYoXNjcm3EUbp0adlstgeWG4bx0HL8g7o0x6MuPrg4STjqMvHKli370PJ/X6Tg4WiPicd5xjy0x+QRFBSk6dOnM/f6I9C3zUG/Ngft0RxcQ5qDejQH/doctEfz0CbNsW7dOo0ePVrVq1eXJDVp0kTdunXT0KFDNWbMGEmiHhNo0qRJCggI0Pjx43X79m3NmDFDx48f19SpU+Xh4SGJBF9ikJRCHOnSpVPPnj1VokSJeMvPnTsXJ8uO+FGX5uDixDzUZeJFRESoVatW8vT0jLc8MDBQ06dPT+aonBPtMfE4z5iH9miOgICAh5afPn06mSJxbvRtc9CvzUF7NAfXkOagHs1BvzYH7dE8tElzXL16VS+99JLj9wIFCuiLL75Q+/bt9e677+rdd9+1MDrn8uOPP2rcuHEqV66cJKlmzZrq0aOHevXqpRkzZkgiwZcYJKUQx8svvyzpwd/4yJgxI5ngBKIuzcHFiXmoy8Tz9vZWrly51Lhx43jLAwIC+MMjgWiPicd5xjy0R3P4+/vLZrPF2+5itvPH26PRt81BvzYH7dEcXEOag3o0B/3aHLRH89AmzZE9e3ZduHAh1nquzz33nBYvXqz27dszbfFjCA4OVu7cuR2/Z82aVQsWLFDXrl3VrVs3jR492sLonB9JKcTRoEEDhYeHP7A8e/bsTLmSQNSlObg4MQ91mXjVqlXT7du3H1ieKVMm+fv7J19AToz2mHicZ8xDezRHpkyZ9O6776pChQrxlv/111/q2bNnMkflfOjb5qBfm4P2aA6uIc1BPZqDfm0O2qN5aJPmKF++vNatWxfnWjwmMdWuXTuLInM+zz//vE6fPq18+fI5tqVPn17z5s1Tly5daI+JZDO4CgeQwn399dcKDw9X+/bt4y0PCgrSsmXLOCEkAHWJlIT2iJSE9miOLl26qFSpUurdu3e85QEBAfL393/kNH+AGejXAADgWRIYGKjTp0+rSpUq8ZZfvXpVv/zyywNH9+Efo0eP1rVr1zRlypQ4ZWFhYercubOOHDmiEydOWBCd8yMpBQAAAMAUmzdv1p07d9SoUaN4y0NCQrR161b+EAYAAACQYoWEhOjatWux1uj6t7CwMB0/fvyBI/LxcCSlEK/g4GCtXLlShw4dUlBQkCT7UFk/Pz81adJEWbNmtThC50FdAk+fiIgIbdmyJd5+XbNmTXl4eFgcIZ4lnGeApxN9GykJ7dEcXEOag3o0B/3aHLRH89AmzUGbhDMgKYU4Dh8+rK5duyp16tSqWLGismXLJkm6ceOGdu/erfDwcM2dO1c+Pj4WR5ryUZfm4eLEPNRl4pw7d05dunTRtWvXVKJEiVj9+vfff1euXLk0Z84cFShQwOJInQPtMXE4z5iL9oiUgr5tHvp14tEezcE1pDmoR3PQr81BezQPbdIctElzcR2ZdEhKIY7mzZvL29tbH374oWw2W6wywzA0cuRInTx5UsuXL7coQudBXZqDixPzUJeJ16lTJ6VJk0bjx49X+vTpY5WFhYVp0KBBunfvnubNm2dRhM6D9ph4nGfMQ3s0z19//aUlS5bE+ePN19dXbdu2VeHChS2OMOWjb5uDfm0O2qM5uIY0B/VoDvq1OWiP5qFNmoM2aR6uI5OYAfyHj4+P8ddffz2w/K+//jJ8fHySMSLnRV2ao1mzZsaIESOM6OjoOGXR0dHGiBEjjObNm1sQmfOhLhOvePHixsmTJx9YHhAQYBQvXjwZI3JetMfE4zxjHtqjObZv324ULVrUaN68uTFlyhRj6dKlxtKlS40pU6YYLVq0MIoVK2bs2LHD6jBTPPq2OejX5qA9moNrSHNQj+agX5uD9mge2qQ5aJPm4ToyablYnRRDypM9e3YdOXLkgeVHjhxR9uzZkzEi50VdmiMgIEAdOnSI820ZSbLZbOrQoYNOnDhhQWTOh7pMvAwZMigwMPCB5YGBgcqQIUMyRuS8aI+Jx3nGPLRHc0yYMEHdunXT8uXL1a9fP7Vu3VqtW7dWv379tGzZMnXv3l3jx4+3OswUj75tDvq1OWiP5uAa0hzUozno1+agPZqHNmkO2qR5uI5MWm5WB4CUp0uXLhoxYoSOHj2qChUqON70g4KCtHv3bq1YsUKDBg2yOErnQF2aI+bipFChQvGWc3GScNRl4jVr1kyDBw9W7969Vb58+Vj9es+ePZoxY4batm1rcZTOgfaYeJxnzEN7NMfZs2fVoEGDB5bXq1dPc+bMScaInBN92xz0a3PQHs3BNaQ5qEdz0K/NQXs0D23SHLRJ83AdmbRYUwrx2rBhgxYuXKhjx44pKipKkuTq6qqiRYuqY8eOqlu3rsUROg/qMvGWLl2qcePGqUWLFg+9OGnTpo3FkaZ81KU5Zs+ercWLFysoKMjxrRnDMJQ9e3Z16NBB3bp1szhC50B7NAfnGXPQHs1Rp04dNW/eXJ06dYq3fMGCBVq+fLk2btyYzJE5H/p24tGvzUN7NAfXkOagHs1BvzYH7dE8tElz0CbNwXVk0iIphYe6f/++bt68KUnKkiWL3N3dLY7IeVGXicPFiXmoS/NcuHBBQUFBkuzfosmXL5/FETkf2qN5OM8kHu0x8b7//nsNHDhQVapUUcWKFeP88bZz505NmDBBtWvXtjhS50HfThz6tbloj+bgGtIc1KM56NfmoD2ahzZpDtpk4nEdmXRISgFwKlycmIe6REpCe0RKQntMnN9++01ffPGFDh06pOvXr0uScuTIIV9fX7Vv315+fn4WR4hnEf0aAAAAT4LrSPO5WB0AUqaAgAB9/vnnWrp0qYKDg2OVhYWFaejQoRZF5nyoS3O5u7srZ86cypkzJyeBRKIun9y1a9e0Zs0a/fTTT4qIiIhVdufOHU2bNs2iyJwX7fHJcZ4xH+0xcUqWLKmJEydq27ZtOnr0qI4ePapt27Zp4sSJJKQeA33bXPTrxKE9moNrSHNQj+agX5uD9mge2qQ5aJPm4zrSfIyUQhy7du1Sz5499cILL+jvv//WnTt3NHnyZJUvX16SffqVKlWq6MSJExZHmvJRl+YJCAjQ1q1blSlTJtWpU0dZs2Z1lIWFhemjjz7S2LFjLYzQeVCXiXP48GF16dJF0dHRioyM1HPPPafp06frpZdekkS/fly0x8ThPGMu2iNSCvq2eejXiUd7NAfXkOagHs1BvzYH7dE8tElz0CbNxXVk0mGkFOKYNm2aunTpovXr12vr1q3q2rWrevXqpR07dlgdmtOhLs2xa9cuNW3aVBs2bNDcuXNVp04d7dmzx1EeHh6ub7/91roAnQh1mXgTJ05UrVq1tH//fv3888+qWLGi2rZtq+PHj1sdmtOhPSYe5xnz0B7N89NPP2n48OEaP368Tp06FassJCRE7du3tygy50HfNgf92hy0R3NwDWkO6tEc9Gtz0B7NQ5s0B23SPFxHJjED+I+SJUsa586di7Vt7dq1hq+vr7F161bj+vXrhre3t0XRORfq0hwtWrQwPvvsM8MwDCM6OtqYPXu24evra/z000+GYRjU42OgLhOvTJkyxunTp2NtmzVrllGmTBnj999/pw4fA+0x8TjPmIf2aI61a9caRYoUMbp37260atXK8PHxMdasWeMopx4Thr5tDvq1OWiP5uAa0hzUozno1+agPZqHNmkO2qR5uI5MWm5WJ8WQ8nh4eCg0NDTWtgYNGsjFxUVvvfWWBg8ebFFkzoe6NMeff/6p8ePHS5JsNpu6deumXLlyqX///vrss8/k4+NjcYTOg7o0x71792L93r17d7m6uqpLly4aM2aMRVE5H9pj4nGeMQ/t0Rzz5s3TkCFDHKOhNmzYoOHDh+vevXtq1qyZxdE5D/q2OejX5qA9modrSHNQj4lHvzYP7dEctEnz0CbNwXVk0iIphTiKFCmivXv3qlixYrG216tXT4ZhaMiQIRZF5nyoS3NwcWIe6jLxXnrpJR08eFDe3t6xtsfM2/z2229bFJnzoT0mHucZ89AezXHu3DlVr17d8XvdunWVNWtW9erVS5GRkXr11VctjM550LfNQb82B+3RHFxDmoN6NAf92hy0R/PQJs1BmzQP15FJi6QU4mjVqpX2798fb1n9+vVlGIa+/vrrZI7KOVGX5uDixDzUZeL5+/tr3759atWqVZyybt26yTAMLVu2zILInA/tMfE4z5iH9miOdOnS6caNG8qXL59jW/ny5TVr1iz16NFDV65csTA650HfNgf92hy0R3NwDWkO6tEc9Gtz0B7NQ5s0B23SPFxHJi2bYRiG1UEAwMNs3rxZ+/fv17Bhw+ItX7dunb7++mt98cUXyRyZ86EukZLQHpGS0B7N0bt3b3l7e+vNN9+MU7Z371717NlT4eHhOnHihAXR4VlDvwYAAMCT4DoyaZGUAgAAAGCKffv26eDBg+rRo0e85Xv27NGaNWs0duzYZI4MAAAAAJASuFgdAFK2oUOHauLEibG2ffbZZxo6dKhFETkv6hJ4+kybNk1Lly6NtW3p0qWaNm2aRRHhWcZ5BilB2bJlH5iQkuxT+ZGQejz0baQktEdzcA1pDurRHPRrc9AezUObNAdtEikZSSk81MWLF3X16tVY265evarAwECLInJe1KU5uDgxD3WZeKtWrdKWLVtibdu0aZNWr15tUUTOi/aYeJxnzEN7REpC3zYH/doctEdzcA1pDurRHPRrc9AezUObNAdt0jxcR5rPzeoAkLLFNy/mxx9/bEEkzo+6NMfFixf131lHr169qsuXL1sUkfOiLhNv69atcbYtWrTIgkicH+0x8TjPmIf2aI527dopT548GjdunGPb4MGDdfnyZS1evNjCyJwLfdsc9Gtz0B7NwTWkOahHc9CvzUF7NA9t0hy0SfNwHWk+1pQCAAAAYLqhQ4cqR44cevvttx3bPvvsM12/fp0p/AAAAADgGUVSCvEKCAjQ0aNHVa5cOeXLl09//vmnli5dqujoaL366quqUqWK1SE6ld27d+vAgQO6fv26XFxclC9fPtWoUUMvvPCC1aEBMEn79u01duxY5cmTx+pQ8AziPAM8fbgeh7MICQnRtm3b5O/vb3UoTiE6OlouLnFXUoiOjtaVK1eUO3duC6JyflyLP56IiAjZbDa5u7tLks6fP6+VK1fq0qVLyp07t5o2bap8+fJZHGXK98MPP+iVV15RmjRprA7lqce5JmFok3AWJKUQx6ZNmzRgwABlyJBBERERmj59uvr3769ixYrJxcVFu3fv1scff6wGDRpYHWqKd+PGDfXs2VNHjx6Vi4uLoqOjVaRIEV27dk3BwcHq2LGjBg0aZHWYTiEiIkJbtmzRoUOHFBQUJEnKnj27/Pz8VLNmTXl4eFgcoXO5cuWKMmTIoHTp0sXafv/+fR06dEhlypSxKLKU78cff4x3e79+/TR8+HDlypVLklSzZs3kDMup0R6fHOcZc3GuQUrB9bi5OM8krYCAADVu3FgnTpywOpQULSwsTMOHD9e2bduUPn16tWjRQn379pWrq6skKSgoSFWqVKEeH4FrcXO0a9dObdq00euvv64DBw6oY8eOevHFF1WoUCGdPXtWZ86c0YIFC+Tn52d1qCmat7e30qVLp7p166pp06YqUaKE1SE9tTjXJAxt0nxcRyYN1pRCHDNnzlS/fv3Uq1cvfffdd+rfv786duyoPn36SJLmz5+vefPm8UdwAowePVo5c+bU/v375eHhoY8//lhhYWFatWqVdu/erQEDBui5555Thw4drA41RTt37py6dOmia9euqUSJEsqWLZsk6cSJE1q2bJly5cqlOXPmqECBAhZHmvJdu3ZNvXv31rFjx2Sz2VS/fn2NHDnScXINCQlR+/btudB7iD59+shms8WZT1iS/ve//0mSbDYbdZgAtMfE4zxjHs41ySMoKEjLli1T3759rQ4lReN63BycZ8wRFhaWqHLYTZo0SQEBARo/frxu376tGTNm6Pjx45o6darjSw98Z/jRuBY3x/Hjx+Xt7S3J3jZbt26toUOHOsonTZqk8ePH66uvvrIqRKfRuXNnbdmyRStWrFDhwoXVtGlTNWrUSFmyZLE6NKfCucY8tElzcB2ZtBgphTj8/Py0bt065c2bV4ZhyMfHRytXrpSXl5ck6cKFC2rYsKEOHjxocaQpX6lSpbRs2TK99NJLkqQ7d+6obNmy2rNnj9KnT681a9ZoxowZ2rhxo8WRpmydOnVSmjRpNH78eKVPnz5WWVhYmAYNGqR79+5p3rx5FkXoPAYPHqwzZ85oxIgRun37tj799FPZbDbNnz9fmTJlUlBQkCpXrqyAgACrQ02xunbtKldXV40ZM8bxobUkFS1aVGvWrFHhwoUtjM650B4Tj/OMeTjXJA++5ZowXI+bg/OMOby9vWWz2R5YbhgGSYAEqF69usaNG6dy5cpJkoKDg9WjRw9lzJhRM2bMUGhoKCOlEoBrcXP4+fnpm2++UaFChVSpUiXNmzfPkaSS7NP5NWrUiPPMI3h7e+vnn39WtmzZdPToUX3zzTf67rvvFB4erho1aqh58+aqVKmS1WE6Bc415qBNmofryKTFSCnEkS5dOt26dUt58+ZVaGioIiMjdevWLUf5zZs34wxZRPw8PDxinVRdXFwUFRWlyMhISVLJkiUVGBhoVXhO47ffftOKFSvifEgoSenTp1f//v3VvHlzCyJzPr/88oumT58uHx8fSdKyZcv05ptvqkOHDlq4cKEkPfRCENLcuXO1cOFCvfHGGxo5cqSqV69udUhOi/aYeJxnzMO5xhyP+qPs9OnTyRSJc+N63BycZ8yRLl069ezZ84FTAJ07d07vv/9+MkflfIKDg2OtF5U1a1YtWLBAXbt2Vbdu3TR69GgLo3MeXIubo3jx4tq2bZsKFSqkfPnyKSAgIFZS6sSJE8qcObN1ATqhYsWKqVixYho6dKi+//57rVy5Ul27dtXzzz+vrVu3Wh1eise5xny0ycThOjJpkZRCHBUqVNCoUaPUtm1bbdiwQZUqVdJnn32mMWPGyGaz6ZNPPlHJkiWtDtMplCpVSlOmTNG4cePk7u6uzz77TPny5XNc3AUHBytTpkzWBukEMmTIoMDAQHl6esZbHhgYqAwZMiRzVM4pLCxMGTNmdPzu4eGhadOmqX///mrfvr0++eQTC6NzHh07dlS5cuU0cOBAbdu2LdZUF0g42mPicZ4xD+cac/j7+z9wWqWY7fzx9mhcj5uD84w5Xn75ZUlS2bJl4y3PmDEj084lwPPPP6/Tp08rX758jm3p06fXvHnz1KVLF6Y1fQxciyfegAED1K1bN929e1f169fXuHHjdPbsWRUqVEhnzpzRF198oe7du1sdZooX3zVNqlSp5O/vL39/f507d06rVq2yIDLnw7nGHLRJ83AdmbRcrA4AKc/gwYOVPn16jRw5Uvfv39ekSZNUrFgx1atXT3Xr1tW1a9c0cOBAq8N0CoMHD9aJEydUpkwZ+fn5adWqVRo5cqSj/NSpU2rcuLGFETqHZs2aafDgwVq4cKECAgIUFBSkoKAgBQQEaOHChRo6dKhatGhhdZhOIW/evDp58mSsbW5ubpo8ebLy5cunnj17WhSZ8ylSpIi++eYbSfYPYblAfny0x8TjPGMezjXmyJQpk/73v//pxx9/jPN/y5YtmjVrltUhOgWux83BecYcDRo0cKx5FJ/s2bOTUEmAypUra+XKlXG2p0uXTnPnzlWqVKksiMp5cS2eOH5+fpozZ4527dql0aNH69atW5o5c6beffddffPNN+rbt6+6detmdZgp3qPaXYECBfTWW28lUzTOjXONOWiT5uE6MmmxphQS7MKFC7p7964KFiwoNzcG2SXU3bt39dtvvykiIkK+vr4sLPiEZs+ercWLFysoKMjxzQ/DMJQ9e3Z16NCBC+YE+uSTTxQQEBDvmiiRkZHq16+ftm3bxpy4j2nr1q3as2ePevToEWtuezwc7dEcnGfMw7km8bp06aJSpUqpd+/e8ZYHBATI39+ffv2EuB5/PJxnkJKEhITo2rVrjnUg/yssLEzHjx9/4CgBPBjX4okTHBysCxcuKDo6Wjly5FDevHmtDslpBAYG6vnnn5eLC9/5R8pAmzQP15FJi6QU4hUcHKyVK1fq0KFDCgoKkmT/VoKfn5+aNGmirFmzWhyh86AuzXXhwoVY9fjv6S/waJGRkQoPD493zZSY8qtXrypPnjzJHJlzeVC/9vX11RtvvEG/TiDaozk4z5iPc82T27x5s+7cuaNGjRrFWx4SEqKtW7cygi8B6NuJx3nGPLRHc1CP5uBa3By0R3NQj+ahLs1BPZqD68ikRVIKcRw+fFhdu3ZV6tSpVbFiRcc3jW7cuKHdu3crPDxcc+fOdSz0hgejLpPH5cuXNWXKFI0dO9bqUJwedflo9OvkQ3t8NNpj8qE9IjnRt5MH/TphaI/moB7NQT2ag3o0B/VoHurSHNRj8uE6MnFISiGO5s2by9vbWx9++GGcBfIMw9DIkSN18uRJLV++3KIInQd1mTwCAgLUuHFjnThxwupQnB51+Wj06+RDe3w02mPyoT2agz/eEoa+nTzo1wlDezQH9WgO6tEc1KM5qEfzUJfmoB6TD9eRicNE5IgjICBAY8eOjfPmJUk2m00dOnRgypUEoi7N8eOPPz60/MKFC8kUifOjLhOPfm0e2mPi0R7NQ3tMHiEhIfr2229JSj0Cfdsc9Gtz0B7NQT2ag3o0B/VoDurRPNSlOahH83AdmbRISiGO7Nmz68iRIypUqFC85UeOHFH27NmTOSrnRF2ao0+fPrLZbHrYwM74TriIi7pMPPq1eWiPiUd7NA/t0Rz88WYO+rY56NfmoD2ag3o0B/VoDurRHNSjeahLc1CP5uE6MmmRlEIcXbp00YgRI3T06FFVqFDB8WYVFBSk3bt3a8WKFRo0aJDFUToH6tIcOXLk0MiRI1WrVq14y0+cOKEmTZokc1TOibpMPPq1eWiPiUd7NA/t0Rz88WYO+rY56NfmoD2ag3o0B/VoDurRHNSjeahLc1CP5uE6MmmRlEIcbdq0UZYsWbRw4UJ99dVXioqKkiS5urqqaNGiGjt2rOrWrWtxlM6BujRH0aJFdezYsQeeCB714Rf+QV0mHv3aPLTHxKM9mof2aA7+eDMHfdsc9Gtz0B7NQT2ag3o0B/VoDurRPNSlOahH83AdmbRsBrWHh7h//75u3rwpScqSJYvc3d0tjsh5UZdP7tdff9WdO3f0yiuvxFt+584dHT16VGXLlk3myJwPdWku+nXi0B7NRXtMHNqjOXr27KkiRYqof//+8ZYHBATI399fAQEByRyZ86JvPzn6tfloj+agHs1BPZqDejQH9Wge6tIc1GPicB2ZtEhKAQAAADAFf7wBAAAAAB6GpBQAAAAAAAAAAACSnIvVAQAAAAAAAAAAAODpR1IKAAAAAAAAAAAASY6kFAAAAAAAAAAAAJIcSSkAAAAAwFPr4sWL8vLy0okTJ6wOBQAAAHjmkZQCAAAAkOSGDBkiLy8veXl5qWjRoqpYsaI6deqkb775RtHR0Y+1r1WrVql06dKmxXb8+HG9+eabqlixonx8fPTaa6/pvffe05kzZ0x7jsexY8cOeXl56fr167G2V65cWTVq1Ii1LSbhsnv37iSJ5datW/roo49UvXp1FStWTJUrV9bQoUN16dKlJHk+AAAAAE83klIAAAAAkkWVKlW0a9cubd26VXPmzFG5cuX00UcfqUePHoqMjLQkpm3btql58+aKiIjQp59+qg0bNuiTTz5RhgwZNHnyZEtiKlWqlNzc3LRv3z7HtlOnTik8PFwhISG6ePGiY/vevXvl4eGhkiVLmh7HrVu31KJFC+3evVsffPCBNm3apIkTJ+r8+fNq2rSpLly4YPpzAgAAAHi6kZQCAAAAkCw8PDyUI0cOPffccypatKh69uypzz//XDt27NDq1asd91uwYIEaNGggX19fVa1aVR988IH+/vtvSfYkzNChQ3X79m3HyKupU6dKkiIiIvTxxx+rSpUq8vX1VbNmzbR3794HxnP37l0NHTpUVatW1cyZM1WxYkXly5dPJUqU0ODBgzVq1ChJUlRUlIYNG6YaNWqoePHiql27thYtWhRrX3v37lXTpk3l6+ur0qVLq2XLlgoMDHSUb9myRY0bN5aPj49q1qypadOmPTARly5dOvn4+MSKfe/evSpVqpRKliwZK1m1b98++fr6KlWqVNqxY4datWql0qVLq1y5curRo4fOnz/vuG9ERIRGjRqlypUry8fHR9WrV9esWbMeWD8TJ07UtWvXtGDBAlWtWlW5c+dWmTJlNG/ePLm5uenDDz903Lddu3YaNWqURo0apVKlSqlcuXKaNGmSDMOI9fwPe31iRsDt3LlTderUkZ+fn7p06aJr167FimvFihWqU6eOfHx89Prrr2vp0qWxyg8fPix/f3/5+PioSZMmTNsHAAAApCAkpQAAAABYpkKFCvL29tamTZsc22w2m4YPH67169dr3Lhx2rNnjz755BNJkp+fn4YNG6b06dNr165d2rVrlzp37ixJGjVqlA4ePKiJEydq7dq1ev3119W1a1edPXs23ufetWuXbt68qa5du8ZbnjFjRklSdHS0cuXKpcmTJ+u7775Tnz59NHHiRG3YsEGSFBkZqT59+qhMmTJau3atli9frhYtWshms0mSfv31Vw0ePFjt27fXhg0bNGrUKK1atUozZ858YL2UK1cuTlKqbNmyKlOmTKzt+/btU7ly5STZk2ydOnXSypUrtXDhQtlsNvXp08cxPeIXX3yhrVu3atKkSdq4caM++eQT5cmTJ97nj46O1oYNG9SgQQPlyJEjVlnq1KnVunVr7dq1S7du3XJsX716tVxdXbVixQoNHz5cCxcu1IoVKxzlCXl9wsPDNX/+fI0fP15LlizR5cuX9fHHHzvK165dq8mTJ+utt97Shg0b9Pbbb2vKlCmOpObff/+tHj16qFChQlq1apX69esX6/EAAAAArOVmdQAAAAAAnm0FCxbUyZMnHb937NjRcTtv3rwaMGCARo4cqQ8++EAeHh7KkCGDbDZbrGTJpUuXtGrVKm3btk3PPfecJKlLly7auXOnVq1apbfffjvO88YkQwoWLPjQ+Nzd3fXmm286fs+XL58OHTqkjRs3qm7dugoLC9Pt27dVvXp15c+fX5JUqFAhx/2nTZum7t27q3Hjxo7H9+/fX5988on69u0b73OWK1dOM2fO1LVr15QzZ07t379fXbp0UVRUlJYtWyZJunDhgi5duuRIStWuXTvWPsaMGaMKFSror7/+kqenpy5fvqwCBQqoVKlSstlsD0xISVJwcLBCQ0MfWDeFChWSYRg6f/68MmfOLEl6/vnnNWzYMNlsNhUsWFB//PGHFi5cqObNmyf49bl//74+/PBDRz22adNGn3/+ueN5p06dqiFDhui1115z1OVff/2l5cuXq3Hjxlq/fr2io6M1ZswYpUqVSi+99JKuXLmiDz744IHHCgAAACD5kJQCAAAAYCnDMByjiiTpl19+0axZs3T69GmFhYUpKipK9+7d0927d5UmTZp49/HHH38oKipKr7/+eqztERERjqRJfM+bUEuXLtXKlSt16dIl3bt3T/fv35e3t7ckKXPmzGrSpIm6dOmiSpUqqUKFCqpTp45y5swpSQoICNBvv/0Wa2TUo47Jz89P7u7u2rdvn7y9vRUeHq6iRYsqOjpawcHBunDhgvbu3avUqVOrRIkSkuxJtilTpuj333/XzZs3Hcd3+fJleXp6qnHjxurcubNef/11ValSRdWqVVPlypUTXAePUqJEiVivo6+vrxYsWKCoqKgEvz5p0qRxJKQkKWfOnLpx44Yk6c6dOzp//ryGDx+uESNGOO4TGRmpDBkySLKvveXl5aVUqVI5yv38/Ew7RgAAAACJQ1IKAAAAgKVOnTqlvHnzSpIuXryoHj16qFWrVnrrrbeUKVMmHThwQMOHD9f9+/cfmJS6c+eOXF1dtXLlSrm6usYqS5s2bbyPefHFFyVJp0+ffmji4rvvvtPHH3+swYMHy8/PT+nSpdO8efP0+++/O+4zduxYtWvXTjt37tT333+vSZMmacGCBfL19dWdO3fUr18/x+ief/t38uTf0qRJo+LFi2vv3r0KCQlRqVKl5OrqKldXV/n5+Wnv3r3au3evSpYsKQ8PD0lSz549lSdPHo0ePVo5c+ZUdHS06tevr/v370uSihYtqh9//FE7duzQL7/8ogEDBqhixYqaMmVKnOfPmjWrMmbMqFOnTsUb36lTp2Sz2WIlkB4moa+Pm1vsP1FtNpsjuXbnzh1J0v/+9z9HIi6Giwsz0wMAAADOgKQUAAAAAMvs3r1bf/zxh2PKvmPHjskwDA0ZMsSRaPj+++9jPcbd3V1RUVGxthUpUkRRUVEKDg5W6dKlE/TclSpVUpYsWTR37lxNnz49TnloaKgyZsyo3377TX5+fmrTpo2j7Pz583Hu//LLL+vll19Wjx491KJFC61fv16+vr56+eWXdebMGRUoUCBBccUoV66cNmzYoJCQEJUtW9axvXTp0tq3b5/279+vli1bSpJu3rypM2fOaPTo0Y7j//XXX+PsM3369Kpbt67q1q2r2rVrq2vXrrp161ac0WQuLi6qU6eO1q1bpzfffDPWVInh4eH68ssvVbly5ViPO3z4cKx9/P777ypQoIBcXV2f6PX5r+zZsytnzpy6cOGCGjZsGO99ChUqpDVr1ujevXuOhN+hQ4ee6PkAAAAAmI+vkwEAAABIFhEREbp+/bquXr2qY8eOaebMmerdu7eqV68uf39/SVKBAgV0//59ffHFF7pw4YK+/fZbxxpKMfLkyaM7d+5o9+7dCg4O1t27d/Xiiy+qQYMGGjRokDZt2qQLFy7o8OHDmjVrlrZv3x5vPGnTptXo0aP1008/qWfPnvrll1908eJFHTlyROPHj9fIkSMdMR09elQ7d+7UmTNnNGnSJB05csSxnwsXLmjChAk6ePCgAgMDtWvXLp09e9axHlOfPn20Zs0aTZs2TX/++adOnTql7777ThMnTnxofZUrV05nz57Vrl27YiWlypYtqy1btujy5cuO9aQyZcqkzJkza/ny5Tp37px2796tcePGxdrfggULtH79ep06dUpnzpzRxo0blSNHDmXMmDHe53/rrbeUPXt2de7cWT/99JMuX77sWNsqMjLSUT8xLl26pLFjx+r06dNav369lixZovbt20vSE70+8XnzzTc1e/ZsLV68WGfOnNHJkye1cuVKLViwQJJUv3592Ww2vffee/rrr7/0008/af78+QnePwAAAICkxUgpAAAAAMli586dqly5stzc3JQxY0Z5e3vrvffeU+PGjR2jory9vTV06FDNmTNHn332mUqXLq23335bgwcPduynZMmSatmypQYMGKBbt26pb9++6tevn8aOHasZM2Zo3LhxunbtmjJnzixfX19Vq1btgTHVqlVLX331lWbPnq133nlHYWFhev7551W+fHkNGDBAktSyZUudOHFCb731lmw2m+rVq6fWrVtrx44dkuxT7Z0+fVqrV6/WrVu3lDNnTrVp08YxiqlKlSqaOXOmpk+frjlz5sjNzU0FCxZUs2bNHlpffn5+8vDwkGEYKlq0qGN7iRIlFBkZqbRp08rHx0eSfWTTxIkTNXr0aNWvX18vvvii3nvvPbVr187xuHTp0mnu3Lk6d+6cXFxc5OPjo9mzZz9w6rssWbJo+fLl+vzzzzVy5EgFBQUpU6ZMeuWVV/TJJ58od+7cse7v7++v8PBwNWvWTK6urmrfvr1atGjhKH+S1+e/mjVrptSpU2vevHkaP3680qZNK09PT3Xo0MFxjDNnztTIkSPl7++vwoULa+DAgerXr1+CnwMAAABA0rEZj7O6LwAAAAAA/9GuXTt5e3tr+PDhVocCAAAAIAVj+j4AAAAAAAAAAAAkOZJSAAAAAAAAAAAASHJM3wcAAAAAAAAAAIAkx0gpAAAAAAAAAAAAJDmSUgAAAAAAAAAAAEhyJKUAAAAAAAAAAACQ5EhKAQAAAAAAAAAAIMmRlAIAAAAAAAAAAECSIykFAAAAAAAAAACAJEdSCgAAAAAAAAAAAEmOpBQAAAAAAAAAAACSHEkpAAAAAAAAAAAAJDmSUgAAAAAAAAAAAEhyJKUAAAAAAAAAAACQ5EhKAQAAAAAAAAAAIMmRlAIAAAAAAAAAAECSIykFAAAAAAAAAACAJEdSCgAAAAAAAAAAAEmOpBQAAAAAAAAAAACSHEkpAAAAAAAAAAAAJDmSUgAAAAAAAAAAAEhyJKUAAAAAAAAAAACQ5EhKAQAAAAAAAAAAIMmRlAIAAAAAAAAAAECSIykFAAAAAAAAAACAJEdSCgAAAAAAAAAAAEmOpBQAAAAAAAAAAACSHEkpAAAAAAAAAAAAJDmSUgAAAAAAAAAAAEhyJKUAAAAAAAAAAACQ5EhKAQAAAAAAAAAAIMmRlAIAAAAAAAAAAECSIykFAAAAAAAAAACAJEdSCgAAAAAAAAAAAEmOpBQAAAAAAAAAAACSHEkpAAAAAAAAAAAAJDmSUgAAAAAAAAAAAEhyJKUAAAAAAAAAAACQ5EhKAQAAAAAAAAAAIMmRlAIAAAAAAAAAAECSIykFAAAAAAAAAACAJEdSCgAAAAAAAAAAAEmOpBQAAAAAAAAAAACSHEkpAAAAAAAAAAAAJDmSUgAAAAAAAAAAAEhyJKUAAAAAAAAAAACQ5EhKAQAAAAAAAAAAIMmRlAIAAAAAAAAAAECSIykFAAAAAAAAAACAJEdSCgAAAAAAAAAAAEmOpBQAAAAAAAAAAACSHEkpAAAAAAAAAAAAJDmSUgAAAAAAAAAAAEhyJKUAAAAAAAAAAACQ5EhKAQAAAAAAAAAAIMmRlAIAAAAAAAAAAECSIykFAAAAAAAAAACAJEdSCgAAAA/l5eWlqVOnWh3GM8PLy0ujRo2yOowEiYyM1Pjx41W1alV5e3urd+/eVof0TGvXrp3atWuXLM81depUeXl5JctzJdbevXvl5eWlvXv3Wh0KnmK0MwAAgIRxszoAAAAAxLVq1SoNHTpUkrR06VKVLl06VrlhGKpWrZquXLmiatWqadasWVaE6ZQuXryomjVrSpKmTJmi2rVrxyqfOnWqpk2bpt27dytr1qxWhOg0Vq5cqXnz5qlDhw56+eWXlTt37gfet127dtq3b58kyWazKW3atMqRI4eKFy8uf39/VapUKbnCfmw1atRQYGCg4/c0adKocOHCatu2rfz9/a0LzAmsW7dON27cUMeOHZNk/5s3b9by5ct15MgR/f3338qcObNKlSqlli1bqkKFCknynEkhKChI8+bN07Zt23T58mXZbDYVLFhQtWrVUtu2bZUxY0arQzTVpUuXNHPmTO3atUvXrl1ThgwZ5Ofnpy5duqhUqVJWh+cwZMgQrV69+pH3a9y4sRo3bpwMEQEAADg/klIAAAApWKpUqbR+/fo4Sal9+/bpypUr8vDwSPIYDh8+LFdX1yR/HitMnz5dr732mmw2m9WhOKU9e/boueee07BhwxJ0/1y5cuntt9+WJN29e1fnzp3T5s2btXbtWtWpU0effPKJ3N3dkzLkJ1akSBF16tRJknT9+nWtWLFCgwcPVkREhJo3b25xdHbz5s2zOoQ41q9frz///NP0pJRhGBo2bJhWrVqll19+WZ06dVL27Nl1/fp1bd68WR07dtRXX32lkiVLmvq8SeHw4cPq3r277ty5o4YNG6po0aKSpKNHj2rOnDn69ddfNX/+fIujNM+BAwfUvXt3SVKzZs1UqFAhBQUFafXq1WrTpo2GDx+ebCP+HqVFixaxkpsXL17UlClT1KJFi1jJs/z586tEiRI6fPhwin0PAwAASClISgEAAKRgVatW1caNG/Xee+/Jze2fS7f169eraNGiunXrVpLHkCpVqiR/DisUKVJEJ06c0ObNm/Xaa69ZHU6yunfvntzd3eXikrjZvG/cuPFYIzgyZMigRo0axdo2cOBAjR49Wl9++aXy5Mmjd999N1ExJZXnnnsuVuxNmjRRzZo1tXDhwhSTlEpIktqs195q8+fP16pVq9ShQwcNHTo0VmK5V69e+vbbb2O9Z6ZUoaGh6tu3r1xdXbV69WoVKlQoVvlbb72lr7/+2qLozBcSEqL+/fsrderU+uqrr5Q/f35HWadOndSlSxeNGTNGRYsWTdaE4oP6hZ+fn/z8/By/HzlyRFOmTJGvr2+c9zLp6T1fAgAAmMm5/xIBAAB4ytWrV0+3bt3Szz//7NgWERGhH374QQ0aNIj3MfPmzVPLli1Vrlw5FS9eXE2aNNHGjRtj3WflypXy8vLSN998E2v7zJkz5eXlpZ9++smx7b9rSsWsJXPmzBkNHDhQpUqVUvny5TVp0iQZhqHLly+rV69eKlmypCpVqhTnG/6rVq2Sl5eXLl68GGt7fOtxtGvXTvXr11dAQIDatm2rEiVK6NVXX3Ucz759+9SsWTMVL15ctWvX1i+//JKQapUk1a1bVy+88IKmT58uwzAeet8aNWpoyJAhcbb/dw2fmGPYsGGDpk2bpipVqsjPz09vvvmmbt++rYiICH300UeqUKGC/Pz8NHToUEVERMT7nGvXrlXt2rXl4+OjJk2aaP/+/XHuc/XqVQ0dOlQVK1ZUsWLFVK9evTivaUxM3333nSZOnKgqVaqoRIkSCgsLe+Dx3rlzR+PGjVPVqlVVrFgx1a5dW/PmzXPU08WLFx2v1Z9//ikvL68nXkvF1dVV7733ngoXLqylS5fq9u3bjrKVK1eqffv2qlChgooVK6a6devqyy+/jPX4wYMHq1y5crp//36cfXfu3DnW9Iw///yzWrVqpdKlS8vPz0+1a9fWZ5999tgxS1LWrFlVsGBBnT9/Ptb26OhoLVy4UPXq1ZOPj48qVqyo999/XyEhIbHuZxiGPv/8c73yyisqUaKE2rVrpz///DNOW3vQ2k3x9aMHtccHvfa///67Y7q0EiVKqG3btjpw4ECc5/r111/1xhtvyMfHR7Vq1dKyZcsSVEft2rXT9u3bFRgY6GgjNWrUcJTfuHFDw4YNU8WKFeXj46OGDRsmaKq08PBwzZ49WwULFtTgwYPjHeno7++v4sWLP3Q/33//vZo0aaLixYurXLlyGjhwoK5evRrrPtevX9fQoUP1yiuvqFixYqpcubJ69eoV5/3rp59+UuvWreXr6ys/Pz91795df/755yOPZdmyZbp69aqGDBkSJyElSdmzZ4+1VtuWLVvUvXt3Va5cWcWKFVOtWrU0ffp0RUVFxXrc2bNn1a9fP1WqVEk+Pj565ZVX9NZbb8XqX5K0Zs0aRx2ULVtWb731li5fvvxE+0qI5cuX6/r163r33XdjJaQkKXXq1Bo3bpxsNpumT58uyZ4E8vLyirdd7Ny5U15eXtq2bZtjW1K9JyZEUp3DEnJMAAAAziTlf3UMAADgGZYnTx75+vrqu+++U9WqVSVJO3bs0O3bt1W3bl198cUXcR6zePFi1ahRQw0aNND9+/f13XffqX///po1a5aqVasmSXrjjTe0efNmjRs3TpUqVdLzzz+vkydPatq0aWratKnjuR7mrbfeUqFChfTOO+/op59+0owZM5Q5c2YtW7ZM5cuX18CBA7Vu3Tp9/PHH8vHxUZkyZZ6oDkJCQtSzZ0/VrVtXr7/+ur766iu9/fbbio6O1pgxY9SyZUvVr19f8+bN05tvvqnt27crffr0j9yvq6urevXqpcGDB5s+Wmr27NlKnTq1unfvrnPnzmnJkiVyc3OTzWZzjIz4/ffftWrVKuXJk0d9+/aN9fj9+/drw4YNateunTw8PPTVV1+pa9euWrFihTw9PSXZ16Bp3ry5bDab2rRpo6xZs2rHjh0aPny4wsLC4kyX9vnnn8vd3V1dunRRRETEA6eYMgxDvXr10t69e9W0aVMVKVJEO3fu1Pjx43X16lUNGzZMWbNm1fjx4zVz5kzduXPHMSVffB+qJ4Srq6vq1aunyZMn68CBA452+tVXX+mll15SjRo15Obmpm3btunDDz+UYRhq06aNJKlRo0b69ttvtWvXLlWvXt2xz+vXr2vPnj3q06ePJOnPP/9Ujx495OXlpTfffFMeHh46d+6cfvvttyeKOTIyUlevXlWmTJlibX///fe1evVqNWnSRO3atdPFixe1dOlSHT9+XF999ZWj3idPnqwZM2aoatWqqlq1qo4dO6bOnTvHm1xLrPhe+927d6tbt24qVqyY+vbtK5vN5hh59OWXXzoSOidPnlSXLl2UNWtW9evXT5GRkZo6daqyZcv2yOft2bOnbt++rStXrjjWyEuXLp0ke2KpXbt2On/+vNq0aaO8efNq48aNGjJkiEJDQ9WhQ4cH7vfAgQO6deuW2rdv/8RTi8as2+fj46O3335bN27c0OLFi/Xbb7/p22+/dYwA7Nevn/766y+1bdtWefLkUXBwsH7++WddvnxZefPmlSR9++23GjJkiCpXrqyBAwfq7t27+uqrr9S6dWutXr3acb/4bN26ValTp46ztt2DrF69WmnTplWnTp2UNm1a7dmzR1OmTFFYWJgGDx4syf7FhZjXum3btsqePbuuXr2q7du3KzQ0VBkyZJAkzZgxQ5MnT1adOnXUtGlTBQcHa8mSJWrTpo2jDhK6r4TaunWrUqVKpbp168Zbni9fPpUqVUp79+5VeHi4fHx8lC9fPn3//fdx1mzasGGDMmXKpMqVK0tKuvfExErMOexxjwkAAMApGAAAAEhxVq5caXh6ehqHDx82lixZYvj5+Rl37941DMMw3nzzTaNdu3aGYRhG9erVje7du8d6bMz9YkRERBj169c32rdvH2v7tWvXjLJlyxqdOnUy7t27Z/j7+xvVqlUzbt++Het+np6expQpUxy/T5kyxfD09DRGjBjh2BYZGWm88sorhpeXlzFr1izH9pCQEKN48eLG4MGD4xzbhQsXYj3Pnj17DE9PT2PPnj2ObW3btjU8PT2NdevWObadOnXK8PT0NLy9vY1Dhw45tu/cudPw9PQ0Vq5cGV+VOly4cMHw9PQ05s6da0RGRhqvvfaa0bBhQyM6OjrW8d24ccPxmOrVq8c6hn/H17Zt2zjHUL9+fSMiIsKx/e233za8vLyMrl27xnp8ixYtjOrVq8fa5unpaXh6ehpHjhxxbAsMDDR8fHyMPn36OLYNGzbMqFSpkhEcHBzr8W+99ZZRqlQpRzuIialmzZpx2kZ8Nm/ebHh6ehqff/55rO39+vUzvLy8jHPnzsU6/nr16j1ynwm5b8zzLlq0yLEtvng7d+5s1KxZ0/F7VFSU8corrxgDBgyIdb8FCxYYXl5exvnz5x2///d1Tajq1asbnTt3Nm7cuGHcuHHDOHnypPHuu+8anp6exocffui43/79+w1PT09j7dq1sR6/Y8eOWNtv3LhhFC1a1Ojevbuj3RmGYXz22WeGp6dnrLYW0x7/K75+9KD2+N/XPjo62njttdeMzp07x3r+u3fvGjVq1DA6derk2Na7d2/Dx8fHCAwMdGz766+/jCJFisQb13917949Ths3DMNYuHCh4enpaaxZs8axLSIiwmjRooXh6+sb533o3xYtWmR4enoamzdvfuTzG0bc95aIiAijQoUKRv369Y3w8HDH/bZt22Z4enoakydPNgzD/v4V817xIGFhYUbp0qWN9957L9b269evG6VKlYqz/b/KlCljNGzYMEHHYRjx94kRI0YYJUqUMO7du2cYhmEcP37c8PT0NL7//vsH7ufixYtGkSJFjBkzZsTafvLkSePll192bE/Ivh5H6dKlH3m8//vf/wxPT08jICDAMAzDmDBhglG0aFHj1q1bjvvcu3fPKF26tDF06FDHtqR6T/y3w4cPP/A8kxTnsIQeEwAAgDNh+j4AAIAUrk6dOrp37562bdumsLAwbd++/YFT90n2KZBihISE6Pbt2ypVqpSOHz8e6345cuTQ+++/r59//llt2rTRiRMnNGbMmASNMpKkpk2bOm67urqqWLFiMgwj1vaMGTPqxRdf1IULFxJ6uHGkTZtW9erVc/xesGBBZcyYUYUKFVKJEiUc22NuP85zxYyWCggI0JYtW544xv9q1KhRrG/dFy9eXIZh6I033oh1v+LFi+vy5cuKjIyMtd3Pz0/FihVz/J47d27VrFlTu3btUlRUlAzD0KZNm1SjRg0ZhqHg4GDH/8qVK+v27ds6duxYrH36+/vHahsPsmPHDrm6usaaBk6yT4VnGIZ27NiR4Hp4HGnTppUk/f33345t/4739u3bCg4OVtmyZXXhwgXH1GEuLi5q0KCBtm7dGmv6rbVr18rPz0/58uWTJMfIlx9//FHR0dGPHd+uXbtUoUIFVahQQQ0aNHBMezZo0CDHfTZu3KgMGTKoUqVKsV6TokWLKm3atI5pvX755Rfdv39fbdu2jTX13MNGByXGf1/7EydO6OzZs2rQoIFu3rzpiPPOnTuqUKGC9u/fr+joaEVFRWnXrl2qVauWcufO7Xh8oUKFHKNTntSOHTuUI0cO1a9f37HN3d1d7dq10507d+KdrjJGzOscM+rqcR09elQ3btxQq1atYq0BVK1aNRUsWFDbt2+XZG9/7u7u2rdvX5zpF2P88ssvCg0NVb169WK95i4uLipRosQjp7QMCwt7rOP49+sYFham4OBglS5dWnfv3tXp06clyfEevmvXLt29ezfe/WzevFnR0dGqU6dOrLizZ8+uAgUKOOJOyL4ex99///3I440pj3md69atq/v372vTpk2O+/z8888KDQ11jLhKyvfExHrSc9iTHBMAAIAzYPo+AACAFC5r1qyqUKGC1q9fr/DwcEVFRT10qqdt27ZpxowZOnHiRKz1iuJbd6VevXpau3attm/frhYtWqhChQoJjuvfH1JLUoYMGZQqVSplzZo1zvZbt24leL//lStXrjixZ8iQQbly5YqzTZJCQ0Mfa/8NGjTQ559/runTp6tWrVpPHOe/xVc3kvT888/H2R4dHa3bt28rS5Ysju0FChSIs88XXnhBd+/edXzgHRoaquXLl2v58uXxxhAcHBzr94dNIfZvgYGBypkzZ5zkZMzUfIGBgQnaz+O6c+eOpNiJhgMHDmjq1Kk6dOhQnA/Eb9++7ahXf39/zZkzR1u2bJG/v79Onz6tY8eO6cMPP3Tcv27dulqxYoXee+89TZgwQRUqVNCrr76q119/XS4uj/6uXokSJTRgwABFRUXpzz//1IwZMxQaGhor+Xju3Dndvn37gf3oxo0bkqRLly5Jsr+m/5Y1a9Y40wGa4b+v/dmzZyXJMd1bfGLWQAsPD4+3Pb744oux1p57XIGBgSpQoECcuo9pZzF1FJ+YtvnvBObjiNn3iy++GKesYMGCjnW1PDw8NHDgQH388ceqVKmSSpQooWrVqsnf3185cuSQ9E9dPiih+Kgkf/r06R/rOP78809NmjRJe/bsibMGUkyiNl++fOrUqZMWLFigdevWqXTp0qpRo4YaNmzo6DNnz56VYRgPnLbUzc0twft6HOnSpXvk8caUx7wXeHt7q2DBgvr+++/VrFkzSfap+7JkyaLy5ctLsr/fJdV7YmI96TnsSY4JAADAGZCUAgAAcAL169fXiBEjFBQUpFdeecUx6uO/fv31V/Xq1UtlypTRyJEjlSNHDrm7u2vlypVav359nPvfvHlTR48elST99ddfio6OTtAH9JLivd+D1ncxDMNxO77kmKQHjl550D4T8lwJETNaasiQIfrxxx8f67FRUVHxxvGgOnzQ9seNOaauGjZsGGedlRheXl6xfk+OEQGJ8ccff0j6JyF3/vx5dezYUQULFtSQIUP0/PPPy93dXT/99JMWLlwYq70ULlxYRYsW1dq1a+Xv76+1a9fK3d1dderUcdwnderUWrp0qfbu3avt27dr586d2rBhg5YvX6758+c/cm2iLFmyqGLFipKkKlWqqGDBgurRo4cWL16sTp06SbK/LtmyZdOnn34a7z7+m7BNiAf1l6ioqATv47+vfUx7GzRokIoUKRLvY9KmTRsrqZ2SFCxYUJJ9vSuzEskP0rFjR9WoUUNbtmzRrl27NHnyZM2ePVuLFi3Syy+/7KjL8ePHOxJV//aodlWwYEHHFwg8PDweet/Q0FC1bdtW6dOn15tvvqn8+fMrVapUOnbsmD799NNYfWLIkCFq3LixfvzxR/38888aPXq0Zs2apa+//lq5cuVSdHS0bDab5syZE2+MMSMXE7Kvx1GoUCEdP378ocd78uRJubu7x0ra1q1bVzNnzlRwcLDSp0+vrVu3ql69eo7kWUp+T3zSc9iTHBMAAIAzICkFAADgBF599VWNHDlShw4d0sSJEx94vx9++EGpUqXSvHnzYn3gt3LlynjvP2rUKP3999965513NGHCBC1atMjxAXtSiUmoxXyrP0ZSjcBJiIYNG2rGjBmaNm2aatSoEac8U6ZM8Y7AunTpkmN6ODOdO3cuzrazZ88qTZo0jsRGunTpFB0d7UiUmCVPnjzavXu3wsLCYo3yiJkaLE+ePKY+n2RPsKxfv15p0qRRqVKlJElbt25VRESEZsyYEWvk2YOmQ/P399e4ceN07do1rV+/XtWqVYsz6sjFxcUxBd/QoUM1c+ZMTZw4UXv37n3seqxWrZrKli2rmTNnqkWLFkqbNq3y58+v3bt3q2TJkg/9wDvmeM6ePRur/QQHB8eZJi6mv4SGhsZKRj9sJNGjxDxn+vTpH3rcWbNmVerUqeNtj2fOnEnQcz0oqZYnTx6dPHkyTiI8pp39d7Thv5UqVUqZMmXSd999p549ez4y8fNfMfs+c+ZMnFFtZ86cifPc+fPnV+fOndW5c2edPXtW/v7+mj9/vj799FNHXWbLlu2J+mL16tV18OBBbdq0KdZUhvHZt2+fbt26pWnTpqlMmTKO7RcvXoz3/l5eXvLy8lLv3r3122+/qVWrVvrqq6/01ltvKX/+/DIMQ3nz5o13xNjj7OtxVKtWTQcPHtT333+vRo0axSm/ePGiDhw4oAoVKsTqQ3Xr1tW0adO0adMmZc+eXWFhYbGmxMuaNWuSvSda5Wk8JgAAAEliTSkAAAAnkC5dOn3wwQfq169fvEmTGK6urrLZbLFGUVy8eDHeEUAbN27Uhg0b9M4776h79+6qV6+eJk2alOAPm59U/vz5JSnWmjFRUVH6+uuvk/R5HyZmtNSJEye0devWOOX58uXT77//HmvkyLZt23T58uUkiefgwYOx1gq5fPmyfvzxR1WqVEmurq5ydXVV7dq19cMPPzhGGP1bYqZ0euWVVxQVFaWlS5fG2r5w4ULZbDa98sorT7zv+ERFRWn06NE6deqU2rVr50iExSQa/j2K7Pbt2w9MsNavX182m00fffSRLly4oIYNG8Yqj28KyZhRQk86Iqhr1666deuWo+3WqVNHUVFR+vzzz+PcNzIy0pHYrFixotzd3bVkyZJYx7do0aI4j4uvv9y5c0fffvvtE8UsScWKFVP+/Pk1f/78eKdSi2k/rq6uqly5srZs2RIrCXbq1Cnt2rUrQc+VJk2aOAloyd7Orl+/rg0bNji2RUZG6osvvlDatGljJV3i22fXrl116tQpffrpp/GONFyzZo0OHz4c7+OLFSumbNmyadmyZbFe+59++kmnTp1StWrVJEl3797VvXv3Yj02f/78SpcuneNxVapUUfr06TVr1izdv38/znM9qi+2bNlSOXLk0Lhx4+J9771x44ajPcUk7/59vBEREfryyy9jPSYsLCzOOnWenp5ycXFxxP3aa6/J1dVV06ZNi1N/hmHo5s2bCd7X42jRooWyZcumTz75JM76f/fu3dPQoUNlGIb69OkTq6xQoULy9PTUhg0btGHDBuXIkSNWG0nK90SrPI3HBAAAIDFSCgAAwGk8aPqef6tataoWLFigrl27qn79+rpx44a+/PJL5c+fXydPnnTc78aNG/rggw9Urlw5tW3bVpI0YsQI7d27V0OHDtWXX36Z4Gn8HtdLL70kX19fffbZZwoJCVGmTJm0YcOGOB98JreYtaVOnDgRp6xZs2b64Ycf1LVrV9WpU0fnz5/XunXrHAkDs3l6eqpLly5q166dPDw89NVXX0mS+vXr57jPO++8o71796p58+Zq1qyZChcurJCQEB07dky7d+/Wvn37nui5a9SooXLlymnixIkKDAyUl5eXfv75Z/3444/q0KFDoo759u3bWrNmjSQpPDxc586d0+bNm3X+/HnVq1dP/fv3d9y3UqVKcnd3V8+ePdWyZUv9/fffWrFihbJly6br16/H2XfWrFlVpUoVbdy4URkzZnQkFmJMnz5dv/76q6pWrao8efI4+kauXLkco7MeV9WqVeXp6amFCxeqTZs2Klu2rFq0aKFZs2bpxIkTjmM4e/asNm7cqOHDh+v1119X1qxZ1blzZ82aNUs9evRQ1apVdfz4ce3YsSPW2mIx9ZA7d24NHz5cp0+flqurq1auXKksWbI88WgpFxcXjR49Wt26dVP9+vXVpEkTPffcc7p69ar27t2r9OnTa+bMmZLsbW7nzp1q06aNWrVqpaioKC1ZskSFCxeO9Z7yIEWLFtWGDRs0duxY+fj4KG3atKpRo4ZatGih5cuXa8iQITp27Jjy5MmjH374Qb/99puGDRv2yLWYunbtqr/++kvz58/X3r17Vbt2bWXPnl1BQUHasmWLDh8+rGXLlsX7WHd3dw0cOFBDhw5V27ZtVa9ePd24cUOLFy9Wnjx51LFjR0n2kWwdO3bU66+/rsKFC8vV1VVbtmxRUFCQY5RO+vTp9cEHH2jQoEFq0qSJ6tatq6xZs+rSpUv66aefVLJkSb3//vsPPI5MmTJp+vTp6t69u/z9/dWwYUMVLVpUknT8+HGtX79efn5+kiQ/Pz9lypRJQ4YMUbt27WSz2bRmzZo4SaU9e/Zo1KhRev311/XCCy8oKipKa9ascSQ5JHtybcCAAZowYYICAwNVq1YtpUuXThcvXtSWLVvUvHlzdenSJUH7kqSpU6dq2rRpWrx4scqVK/fA482SJYumTJmi7t27q3HjxmrWrJkKFSqkoKAgrV69WufOndPw4cNVsmTJOI+tW7eupkyZolSpUqlp06ZxzlFJ9Z5opafxmAAAAEhKAQAAPEUqVKigjz76SHPmzNGYMWOUN29eDRw4UIGBgbE+QP7ggw8UERGhsWPHOqbXypIli0aNGqXevXtr3rx56tatW5LF+emnn+r999/X7NmzlTFjRjVt2lTlypVL8qkDH8bNzU29evXS0KFD45RVqVJFQ4YM0YIFCzRmzBgVK1ZMM2fO1Mcff5wksZQpU0a+vr6aPn26Ll26pMKFC2vs2LHy9vZ23Cd79uxasWKFpk+frs2bN+urr75S5syZVbhwYQ0cOPCJn9vFxUUzZszQlClTtGHDBq1atUp58uTRoEGD1Llz50Qd15UrVzRo0CBJ9jVrcubMKV9fX33wwQeqVKlSrPsWLFhQU6ZM0aRJk/Txxx8re/bsatWqlbJmzaphw4bFu/9GjRpp27ZtqlOnTpz1amrUqKHAwECtXLlSN2/eVJYsWVS2bFn169dPGTJkeOJj6ty5s4YMGaJ169apSZMmGjVqlIoVK6Zly5Zp4sSJcnV1VZ48edSwYcNYH7QPGDBAHh4eWrZsmfbu3avixYtr/vz56tGjR6z9u7u7a9q0afrwww81efJk5ciRQx06dFDGjBnjbasJVa5cOS1fvlyff/65lixZojt37ihHjhwqXry4WrRo4bift7e35s2bp7Fjx2rKlCnKlSuX+vXrp+vXrycoKdW6dWudOHFCq1at0sKFC5UnTx7VqFFDqVOn1hdffKFPP/1Uq1evVlhYmF588UWNHTtWTZo0eeR+XVxcNH78eNWsWVNff/215s+fr7CwMGXJkkVlypTRu+++60jmxKdJkyZKnTq15syZo08//VRp06ZVrVq19O677zqmScyVK5fq1aun3bt3a+3atXJ1dVXBggU1adKkWAmZBg0aKGfOnJo9e7bmzZuniIgIPffccypdunSCjqVEiRJat26d5s2bp+3bt2vNmjVycXFRwYIF1b17d8cXB7JkyeJ435k0aZIyZsyohg0bqkKFCurSpYtjf15eXqpcubK2bdumq1evKk2aNPLy8tKcOXPk6+vruF/37t31wgsvaOHChZo+fbrjmCtVquQYkZvQfd25c0c2m03Zs2d/5PGWLl1aa9eu1axZs7Rx40Zdv35d6dOnl5+fnz766COVLl063sfVrVtXkyZN0t27d2OtFxcjqd4TrfQ0HhMAAIDNeNxVlQEAAAAgBdqyZYv69OmjpUuXPvCD7ZSuRo0aKlu2rMaNG2d1KECCNW3aVLlz59aUKVOsDgUAAAApHGtKAQAAAHgqrFixQvny5Xvi6fgAPL6wsDAFBATEmn4TAAAAeBCm7wMAAADg1L777judPHlS27dv1/Dhwx1TUgJIeunTp9fRo0etDgMAAABOgqQUAAAAAKf29ttvK23atGratKlat25tdTgAAAAAgAdgTSkAAAAAAAAAAAAkOdaUAgAAAAAAAAAAQJIjKQUAAAAAAAAAAIAkR1IKAAAAAAAAAAAASc7N6gBSgujoaEVGRsrFxUU2m83qcAAAAAAAAAAAAJyGYRiKjo6Wm5ubXFwePB6KpJSkyMhIHTlyxOowAAAAAAAAAAAAnJaPj488PDweWE5SSnJk7Xx8fOTq6mpxNAAAAAAAAAAAAM4jKipKR44ceegoKYmklCQ5puxzdXUlKQUAAAAAAAAAAPAEHrVE0sNTVgAAAAAAAAAAAIAJSEoBAAAAAAAAAAAgyZGUAgAAAAAAAAAAQJIjKQUAAAAAAAAAAIAkR1IKAAAAAAAAAAAASY6kFAAAAAAAAAAAAJIcSSkAAAAAAAAAAAAkOZJSAAAAAAAAAAAASHIkpQAAAAAAAAAAAJDkSEoBAAAAAAAAAAAgyZGUAgAAAAAAAAAAQJIjKQUAAAAAAAAAAIAkR1IKAAAAAAAAAAAASY6kFAAAAAAAAAAAAJIcSSkAAAAAAAAAAAAkOZJSAAAAAAAAAAAASHKWJqWioqI0adIk1ahRQ8WLF1etWrU0ffp0GYbhuI9hGJo8ebIqV66s4sWLq2PHjjp79mys/dy6dUvvvPOOSpYsqdKlS2vYsGH6+++/k/loAAAAAAAAAAAA8CCWJqXmzJmjr776Su+//742bNiggQMHau7cufriiy9i3eeLL77QBx98oK+//lpp0qRRly5ddO/ePcd9Bg4cqL/++ksLFizQzJkz9euvv+r999+34pAAAAAAAAAAAAAQD0uTUgcPHlTNmjVVrVo15c2bV6+//roqV66sw4cPS7KPklq8eLF69eqlWrVqydvbW+PHj9e1a9e0ZcsWSdKpU6e0c+dOjR49WiVKlFDp0qX13nvv6bvvvtPVq1etPDwAAAAAQEoRHS2NHCnNnWt1JAAAAMAzy9KklJ+fn/bs2aMzZ85IkgICAnTgwAG98sorkqSLFy/q+vXrqlixouMxGTJkUIkSJXTw4EFJ9sRWxowZ5ePj47hPxYoV5eLi4khuAQAAAACecd99J40aJfXoIZ0/b3U0AAAAwDPJzcon7969u8LCwlSnTh25uroqKipKb731lho2bChJun79uiQpW7ZssR6XLVs2BQUFSZKCgoKUNWvWWOVubm7KlCmT4/EAAAAAgGfc1Kn2n9HR0owZ0tix1sYDAAAAPIMsHSn1/fffa926dZowYYJWrVqlcePGaf78+Vq9erWVYQEAAAAAniYBAdLmzf/8PmeOFB5uXTwAAADAM8rSpNT48ePVvXt31atXT15eXvL391eHDh00a9YsSVKOHDkkSTdu3Ij1uBs3bih79uySpOzZsys4ODhWeWRkpEJCQhyPBwAAAAA8w6ZPt/+sV0/Kn1+6cUNatszamAAAAIBnkKVJqfDwcNlstljbXF1dZRiGJClv3rzKkSOHdu/e7SgPCwvT77//Lj8/P0n2dalCQ0N19OhRx3327Nmj6OhoFS9ePBmOAgAAAACQYoWGSgsX2m8PGCD16mW/PXWq9P9/ewIAAABIHpYmpapXr66ZM2dq+/btunjxojZv3qwFCxaoVq1akiSbzab27dtrxowZ+vHHH3Xy5EkNGjRIOXPmdNynUKFCqlKlikaMGKHDhw/rwIED+t///qd69erpueees/LwAAAAAABWW7xYCguTvL2lmjWlrl2lVKmk336T9u61OjoAAADgmWIzDOu+GhYWFqbJkydry5YtunHjhnLmzKl69eqpT58+8vDwkCQZhqEpU6bo66+/VmhoqEqVKqWRI0fqxRdfdOzn1q1b+t///qetW7fKxcVFr732mt577z2lS5cuQXFERUXp0KFD8vX1laura5IcKwAAAAAgmRmGVKSIdPKkNG2a1KePfXunTvbRU61bS0uXWhoiAAAA8DRIaJ7F0qRUSkFSCgAAAACeQps3S6+9JmXIIAUG2n9K9lFSpUpJ7u7S+fNSrlzWxgkAAAA4uYTmWSydvg8AAAAAgCQzbZr9Z8eO/ySkJKlkSalCBen+fWnOHEtCAwAAAJ5FJKUAAAAAAE+fM2ekdevst2Om7fu3vn3tP2fOtCenAAAAACQ5N6sDAAAAAADAdDNm2NeUeu01ycsrbnnTptLbb0uXLkmrV0vNmyd/jAAeLiJCCgmRbt3652fM/3//7uoqNWsmVawo2WxWRgwAAB6BpBQAAAAA4Oly5440d679dsyIqP/y8JB69JBGjbJP80dSCjBfeHjsRFJ8yaWHJZzu3En4c02eLHl7S127Su3bSzlymHssAADAFDbDMAyrg7BaQhfgAgAAAAA4gXnz7B9Mv/ii9Oef9lEU8bl0SSpQQIqMlA4dkkqUSNYwgRTNMKS//3504uhh2yIizIklQwYpc2YpUyb7z3//z5RJunhRWrHinySWu7vUqJH9faBWrQe/BwAAANMkNM/CSCkAAAAAwNPDMOwjnySpd++HfxidO7f0xhvS8uX2x8yZkzwxAskhOloKDX2yZFLM71FRiY/DZos/ofSgBNN/f8+YUXJLwMdXU6ZIy5bZR0nu3y998439f/78UufOUqdO9tsAAMBSjJQSI6UAAAAA4Kmxa5dUpYqUJo199ETWrObeH0gukZGxk0WPk0y6dcuekDLjIx83t4Qljx50n/TpJReXxMfxOH7/3T5icskS6eZN+zabzb7GXNeuUsOG9ik8AQCAaRgpBQAAAAB49sSMkmrTJmEJpkqV7NP2/f67NH++NHBg0sYHxNi+3T7l3IMSTn//bc7zpE79+KOT/v17mjT2hI4zKVHCPnJq/Hhp9Wr76KmtW6UffrD/z5HDvu5Uly5SkSJWRwsAwDOFkVJipBQAAAAAPBWedI2ohK5BBZglNNQ+lVxIyKPvmz59wpJHD0o4pUqVRAfhZE6dsieeFyyQLl/+Z3ulSvb+36yZlC6ddfEBAODkEppnISklklIAAAAA8FQYOVIaNco+Hd+OHQl/3J07Ut689mm+1q6VGjRIuhgBSZowwT4qr2BBqW/fByeYMmVK2HpKSLjISOn77+2jp7777p91szJkkFq3tieoSpVyvtFhAABYjKTUYyApBQAAAABOLiLCPvLk6lVp+XKpefPHe/y770qffmpfc+aHH5ImRkCyt9WCBaXAQHtipEsXqyN6dl26JC1aZB8teerUP9tLlLAnp9q0kbJksS4+AACcSELzLMm80iQAAAAAAEngm2/sCancuaXGjR//8b1720dGbNoknTxpfnxAjC+/tCeknn9eatvW6miebblzS0OHSn/8YV9zqnVr+3SHv/8u9ev3z2u0bZsUHW11tAAAPBVISgEAAAAAnN/UqfafPXtK7u6P//gXX5Tq17ffnj7dvLiAf4uOlj75xH57wADWe0opXFyk6tWlpUvto6emTpWKF5fu3bNvq1FD8vSUxo6NvR4VAAB4bCSlAAAAAADO7ddfpT177Mmo7t2ffD/9+tl/Llwo3b5tSmhALBs2SMeP29cv6tHD6mgQn6xZ7et8HTok7d9vf50yZLBP7zdsmJQvn9SokbRunX19KgAA8FhISgEAAAAAnNu0afafzZtLzz335PupWVPy8rInpL74wpzYgH8bP97+s2dPKVMma2PBw9lsUunS0syZ9tFRCxZIlSpJUVHS2rVSw4b2deyGD4+9HhUAAHgoklIAAAAAAOd1/bq0bJn9dsxIpyfl4iL16WO/PW2aZBiJ2x/wb7t3Szt32kf09e9vdTR4HOnSSR07Srt22Ue6vfOOlD27PVk1ZoxUuLA9qf3VV1J4uNXRAgCQopGUAgAAAAA4r7lz7eu+lC4tlS2b+P116CClTy+dOCFt3Zr4/QExYkZJtWsn5cljbSx4ckWKSJ9+KgUGSitWSLVr20dVbd0qtW4t5c5tTzoePmx1pAAApEgkpQAAAAAAzikyUpoxw367Xz/7B8OJlTGjPTEl/TMtIJBYAQHSmjX22wMHWhsLzOHhITVtKm3cKJ05I40caV9v6uZNacoUqUQJe6J89mwpNNTqaAEASDFISgEAAAAAnNPatdKFC/ZptJo3N2+/MVP4rV0rnTtn3n7x7JowwT4dZMOG9pE2eLoUKCB98IE9ObVxoz1Z5e4u7d8v9eghPf+81Lmz9MsvTAsKAHjmkZQCAAAAADinmJFM3btLqVObt98iRaRataTo6H9GYgFP6vJlafFi++1Bg6yNBUnL1dU+nd+KFdLFi/Zp/ry9pTt3pAULpEqVpKJFpc8+s6+HBwDAM4ikFAAAAADA+Rw9Km3bZv8QuGdP8/fft6/955w50t275u8fz44pU6SICKliRXtSAs+GnDmld96Rjh+Xdu2SOnaU0qSxr1f3zjv2dcWaN5c2bbInwAEAeEaQlAIAAAAAOJ/p0+0//f3t67iYrX59+5RcwcHSsmXm7x/PhtDQf0bbDR5sbSywhs1mT0YuWGAfNTdzplS6tHT/vn1EVe3aUsGC0qhR9ulIAQB4ypGUAgAAAAA4l1u3/pkOLWZEk9lcXaXeve23p05lHRg8mdmzpZAQ+xRu9etbHQ2slimTfY2p/fulgwft71+ZM9vXrhs50p4Ir1tXWrXKProOAICnEEkpAAAAAIBzWbjQvkZLsWJS1apJ9zxdutjXqjp4UNq9O+meB0+niAhp4kT77XfflVz4CAb/4utrT3hfuiQtXSpVr25Pfn//vfTGG1LevPZ2ExBgdaQAAJiKKyIAAAAAgPOIjv5n6r6+fe1TYyWVbNmk1q3tt6dNS7rnwdPpyy/tCYfnn5fatLE6GqRUadLY32e2bpX+/FMaOlTKlUu6fl369FOpSBGpShVp0SLp77+tjhYAgEQjKQUAAAAAcB4//CD99Zd9Gqy2bZP++fr0sf9cscK+HgyQENHR0ief2G8PGCClSmVpOHAShQtLY8bY15Zas0Zq0MA+wm7XLqljRyl3bqlXL+nAAaYUBQA4LZJSAAAAAADnMXWq/WfnzlK6dEn/fCVLShUrSpGR9vWBgIT47jvp+HEpY0b7GkLA43Bzkxo2lNaulc6flz76SCpYUAoNlWbOlEqXtr83TZ8u3bxpdbQAADwWklIAAAAAAOfw11/29VZsNql37+R73n797D9nzrSvEwQ8yvjx9p89e9pH9QFPKk8eadgw+9R+P/4otWoleXhIhw7ZpzDNnVtq10766SdGTwEAnAJJKQAAAACAc4hZS6pOHfs0V8mlSRP7Gi9XrkirVyff88I5/fKLfbo1d3epf3+ro8HTwsVFqlHjn7XKJk+WfHyk8HBpyRKpWjXJ01MaN46pRgEAKRpJKQAAAABAyhcWJi1YYL8dM3IpuXh4/DMFW8z0gcCDxKwl1a6dfRQLYLZs2aQ335R+/13at0/q3l1Kn94+mnToUClfPsnfX1q/3j71KAAAKQhJKQAAAABAyrdkiRQSIr30kvTaa8n//D162Nd5+fln6eDB5H9+OIeAAGnNGvvtgQOtjQVPP5tNKlNGmjXLPjpq/nz7GnhRUfZ22KCBVKCA9N570unTVkcLAIAkklIAAAAAgJTOMKRp0+y3+/SxT2OV3J5/Xmra1H47ZhpB4L8+/dTeXhs1kooUsToaPEvSp5c6dbInzo8dk95+W8qe3T7V30cfSYUKSbVqScuW2af8AwDAIiSlAAAAAAAp2/bt9g9Z06WTOna0Lo6+fe0/ly6VbtywLg6kTJcuSV98Yb89aJC1seDZ9vLL0oQJ0sWL0tdf20eX2mzSjz9KrVpJefJIAwZIR45YHSkA4BlEUgoAAAAAkLLFjJJq317KlMm6OCpWlPz87KMM5s+3Lg6kTFOmSBERUqVK9rYCWC1VKqlZM+mHH6QzZ6SRI+3rTQUHS5MnS8WLS+XLS3PnSrdvWx0tAOAZYTMMw7A6CKtFRUXp0KFD8vX1laurq9XhAAAAAABinD8vvfiiFB0tHT0qFS1qbTzz50tdukgvvCD99ZfE35CQpNBQ+4f9oaH2tXwaNrQ6IiB+UVHS5s32RNSaNVJkpH17unTSG29IxYpJuXPb/+fJY/+ZPr21MQMAnEJC8yxuyRgTAAAAAACPZ+ZMe0KqRg3rE1KSfeqrd9+Vzp6VvvuO5APsZs+2J6S8vaX69a2OBngwV1fp9dft/69etU85OXeudPKktHhx/I/JkOGfBNW/k1X//v355yUPj+Q9FgCAU2KklBgpBQAAAAApUni4ffRJUJC0apXUuLHVEdkNHiyNHy+9+qq0aZPV0cBq9+5JBQva15SaN0/q3NnqiIDHYxjSzz/bE+2Bgfb/ly7Z/4eGJnw/2bPHn7D69+0cORhh6ATx2wABAABJREFUCgBPqYTmWUhKiaQUAAAAAKRIixZJHTtK+fNLp05Jbilkso+zZ6VChewjuE6csI+OwbNrwQJ7Iip3bun0afs6PsDTIizsnwTVpUuxE1b/vn3vXsL25+oq5cr16ORV5sySzZakhwYAMFeyTd8XFhamPXv26MUXX1ShQoUe+/FXr17VJ598op07d+ru3bsqUKCAxowZIx8fH0mSYRiaMmWKVqxYodDQUJUsWVIffPCBXnjhBcc+bt26pf/973/atm2bXFxc9Nprr2n48OFKly5dYg8PAAAAAGAFw5CmTrXf7tUr5SSkJPt6Ug0a2NdjmT79nzjx7ImOlj75xH57wAASUnj6pE8veXra/z+IYUjBwY9OXl25Yl/TKmY01sOkTv3gqQL//Tuf/QGA03nskVL9+/dXmTJl1LZtW4WHh6tRo0YKDAyUYRj67LPPVLt27QTvKyQkRI0bN1a5cuXUqlUrZcmSRefOnVP+/PmVP39+SdLs2bM1e/ZsjRs3Tnnz5tXkyZP1xx9/aMOGDUr1/xd7Xbt21fXr1zVq1Cjdv39fw4YNk4+PjyZMmJCgOBgpBQAAAAApzJ49UoUK9g/5L160TwuVkmzZYp++L316+4erGTNaHRGssG6dfV2xjBml8+elTJmsjghIuaKi7OtYPSp5deNGwveZKdODR1vF3M6Vi/WuACAZJNlIqV9//VW9evWSJG3evFmGYWj//v1avXq1ZsyY8VhJqTlz5ihXrlwaO3asY1u+fPkctw3D0OLFi9WrVy/V+j/27js8iupt4/i9CYTei1T5CQihJyDSVQQVEKSIiiKg0juIgIAIIlKVJl2QrogCUkREighSRUKT0KVDCD20tH3/OG8SIy0hs5nd5Pu5rlw72Ul275ycyU72mXNOzZqSpBEjRqhy5cpavXq1Xn75ZR05ckQbNmzQDz/8ED266qOPPlKbNm3Uq1cvPfbYY/H9EQEAAAAAdosaffTmm+5XkJKkGjXMtH2BgdLs2VKnTnYngh1GjDC37dpRkAIexts7plD0ILdvS2fPPrh4dfq0dOOGdPWq+di//8GPmTPnw4tXOXJIXl7W/bwAgHuKd1Hq+vXryvT/J1obNmzQiy++qDRp0ui5557TyKgh63G0du1aVa1aVV26dNH27dv12GOP6a233tLrr78uSTp16pQuXLigypUrR39PhgwZVKZMGe3cuVMvv/yydu7cqYwZM0YXpCSpcuXK8vLy0u7du/XCCy/E90cEAAAAANjp3Dnp++/NdufO9ma5H4fDFKI6dZLGj5c6dmT9k+Rm0yZp40YzAqNrV7vTAElH6tTSE0+Yjwe5fj12sep+I6/CwqSgIPMREHD/x0uRQsqd++HFq0yZ+HsPAAkQ76JU7ty5tXPnTmXKlEkbNmzQqFGjJEnXrl2TTzyHwp48eVLffvut3n33XbVr10579uzR4MGDlTJlSjVs2FAXLlyQJGXLli3W92XLlk3BwcGSpODgYGXNmjX2D5UihTJlyhT9/QAAAAAADzJ1qnkTsVIlqWxZu9PcX/PmUp8+0oED0po10v/P8IFkImqUVLNmDx/5AcB6GTKYEau+vvf/GqfTTAd4v4JV1Pb581J4uHTypPl4kLRpH7zO1eOPSwUKULgCgPuId1GqefPm6tmzp9KmTas8efKoQoUKkqTt27eryIMWPbwHp9OpkiVL6v3335ckFS9eXIcOHdL8+fPVsGHD+EYDAAAAAHi6sDBp8mSz7a6jpKJkyCC1aGFGSn35JUWp5CQwUFqyxGx/8IG9WQDcn8NhpoDNnl0qXfr+XxceHrPe1YOKV5cvSzdvSocPm4/7GTBAGjjQ8h8HAJKCeBelmjZtqtKlS+vcuXPR0+RJZi2obt26xeuxcuTIoUKFCsW6r2DBgvrll1+i90vSxYsXlTNnzuivuXjxonz//yqI7Nmz69KlS7EeIzw8XFevXo3+fgAAAACAh1i0yKwlkiuX9Oqrdqd5uI4dTVFq2TLpn3+k//3P7kRIDJ9/bm7r13/wKA0AniFFCjPiKW9eqXz5+3/drVsx613dq3h16pR05Ig0dKgZRfmf9z0BAI9QlNqyZYsqVqwYaw0nSXruuefi/eRly5bVsWPHYt33zz//KG/evJKkfPnyKUeOHNq8ebOKFSsmSQoJCdGuXbv05ptvSpL8/f117do17d27VyVLlozOGBkZqdIPugICAAAAAOB+xo83t23bmrV63J2vr/TCC9Kvv0oTJ8ZM6Yak68wZac4cs92rl71ZACSuNGmkggXNx704nVKtWtKqVWYU5eLFiZsPADyAV3y/oVWrVqpZs6YmTpyos2fPJujJW7RooV27dmny5Mk6fvy4li1bpgULFuitt96SJDkcDjVv3lyTJk3SmjVrdODAAfXq1Us5c+ZUzf+fFqFQoUKqVq2a+vfvr927d2vHjh369NNP9fLLL+uxxx5LUD4AAAAAQCIKCJA2bjRXrLdpY3eauOvUydxOm2amdULSNnasFBoqVakiVa5sdxoA7sThkEaPlry9pR9/NOsNAgBicTidTmd8vuHSpUtaunSpFi9erMOHD6tixYp69dVXVbNmTfk8wlVs69at06hRo/TPP/8oX758evfdd/X6669H73c6nRo3bpwWLFiga9euqVy5chowYICeeOKJ6K+5cuWKPv30U61du1ZeXl568cUX9dFHHyldunRxyhAREaGAgAD5+fnJ29s73j8DAAAAAMACrVpJ06dLTZpI335rd5q4i4iQChc20/dNny69957dieAqV69Kjz8uXbsmLV0q1atndyIA7qhLF7PWYMmS0s6d5mILAEji4lpniXdR6t/27dunRYsWafny5ZKkevXqqXHjxtHrPXkKilIAAAAAYLOLF6V8+aTbt81oqSpV7E4UPyNHmqnc/Pykv/4yV8sj6Yn6PRcrJu3dK3nFewIaAMnBpUvSk0+a2wkTpA4d7E4EAC4X1zpLgs6eSpQooTZt2ujtt9/WzZs3tXDhQjVq1EhvvfWWDh06lJCHBgAAAAAkJ19/bQpS/v6eOSVay5ZS6tRmCsJNm+xOA1e4c0caM8Zs9+xJQQrA/WXNKg0aZLY//tgUpwAAkh6xKBUWFqaVK1eqdevWev7557Vx40Z9/PHH+uOPP7Rq1SrlyZNHXbt2tTorAMS4fNm8aQEAAADPFxEhTZxotjt18sxRRlmzSk2bmu3x4+3NAtf45hvpzBkpTx7p/9fCBoD7attWKlHCjAT+5BO70wCA24j39H2ffvpp9HR9r7zyil577TUVKVIk1tdcuHBB1apVU2BgoHVJXYjp+wAPs2ePVKmSObnbuFFKmdLuRAAAAEiIpUul+vVNYefUKSlNGrsTPZqAADPSK0UK6fhxU7xA0hAZadaG2b9fGjHCjJQCgIdZs0aqWVPy9pZ275aKF7c7EQC4jMum7zt8+LD69++vDRs2qF+/fncVpCQpS5Ysmj17dnwfGgAeLjJSatdOunFD2rZNGjfO7kQAAABIqC+/NLetWnluQUoy60lVrSqFh0tTp9qdBlZavtwUpDJmNKMfACAuatQwF11EREjdu0vxGxsAAElSvEdKJUWMlAI8yPTp5s0KLy9ToEqXTvr7b+nxx+1OBgAAgEexf7+5ctzLSzpyRPrf/+xOlDDffSc1aSLlymVGS/n42J0IVqhaVfrjD6l3b2nYMLvTAPAkhw+bmV5CQ6Vly6S6de1OBAAu4bKRUlEOHz6s33//XWvWrIn1AQAuExws9epltkeMMP8Y3rghsYYdAACA55owwdzWq+f5BSlJatRIyp1bOndOWrjQ7jSwwh9/mA8fH6lLF7vTAPA0hQtL3bqZ7fffN8UpAEjGUsT3G06ePKmOHTvq4MGDcjgcihpo5fj/hWj3799vbUIAiNKrl3TpklS6tClEvfSSmbP/xx/NOgSvvGJ3QgAAAMTHtWvSrFlmu3Nne7NYJWVKM930gAHS+PHSm2/anQgJNXKkuW3WjHXCADyafv3M692hQ2bK2h497E4EALaJ90ipzz77TPny5dOmTZuUOnVq/fTTT5o7d65KliypOXPmuCIjAEgbN0ozZpjtSZPM4tElS8acyHXubEZNAQAAwHPMmiWFhEjFiknPP293Guu0aWOKU5s2SX/9ZXcaJERgoLRkieRwSB98YHcaAJ4qY0Zp6FCzPWiQFBRkbx4AsFG8i1I7d+5Uly5dlDVrVnl5ecnhcOipp57S+++/r8GDB7siI4DkLixMat/ebLdqJVWuHLOvf3+pQAHpxAlzYgcAAADPEBlpRhJJUqdO5k3/pCJXLqlxY7Md9TPCM0WNkqpfX/L1tTcLAM/WooVUrpwZJfzRR3anAQDbxLsoFRkZqXTp0kmSsmTJoqD/r+znzZtXx44dszYdAEjSmDHS3r1S9ux3LyqcLl3MP/qjRkl79iR6PAAAADyC1aulgwelDBnMtGhJTdR0hN98I128aG8WPJozZ6SoGWGi1rYFgEfl5SWNHWu2p02Tdu60Nw8A2CTeRaknn3xSBw4ckCSVKVNG06ZN044dOzRhwgTlz5/f8oAAkrkTJ6SBA832yJFStmx3f03dulLDhlJ4uBlRFRmZqBEBAADwCKIuLHr3XVOYSmoqVpTKlpXu3DFvPsLzjB1rZm2oWlWqVMnuNACSgipVpCZNJKdT6tbN3AJAMhPvolT79u0V+f9v+Hbp0kWnTp1S06ZNtX79evXr18/ygACSua5dpZs3pWrVzFD3+xk71oya+uOPmLWnAAAA4J6OHpWWLzfbHTvam8VVHA4zLaEkTZwoRUTYmwfxc/WqNHmy2WaUFAArDR8upUkj/f679MMPdqcBgEQX76JUtWrV9OKLL0qSChQooJUrV2rLli3avHmzKnHlEAArLVsm/fijlCKFNGnSg9cZyJ8/Zk2pXr2kCxcSJSIAAAAewaRJ5urwl16SihSxO43rNGliRvqfOBFThINnmDrVrPtSrJj08st2pwGQlDz+uNS7t9nu2VO6dcvePACQyOJdlJIkp9OpS5cu6fLly5KkzJkzy5GUFqUFYL8bN2Lm4X//falEiYd/T5cuUpky0qVLXM0IAADgrm7elKZPN9tRI4mSqjRppFatzPaXX9qbBXF35440erTZ7tnTrAMDAFbq2dNcXHv8uPTFF3anAYBEFa8zqwsXLqhXr14qX768qlSposqVK6t8+fLq06ePgoODXZURQHI0eLA5OXv8cenjj+P2PSlSmCk2HA5p5kxp/XqXRgQAAMAj+OYb6fJlqWBBqXZtu9O4Xvv2pqixZo20f7/daRAX8+ZJZ89KefJITZvanQZAUpQ2rTRihNkeOlQ6fdrePACQiOJclAoJCVGTJk20YcMGNWrUSAMGDNDHH3+s+vXra926dWratKlu3LjhyqwAkou//5Y+/9xsjxtn1oqKq4oVpTZtzHb79lJoqPX5AAAA8GiczpgRQx06SN7e9uZJDAUKSK+8YrYnTLA3Cx4uMlIaOdJsd+8u+fjYmwdA0vXGG1KVKmYE8Ycf2p0GABKNw+l0OuPyhRMmTNCSJUs0f/58Zc2aNda+ixcv6s0331SjRo3Url07lwR1pYiICAUEBMjPz0/eyeGfIsCdOZ1S9epmlNMrr0hLlsT/MS5flooWNetKffaZ1Lev9TkBAAAQfxs2SM88Y6a1O31aypLF7kSJY80aqWZNKX1683NnzGh3ItzP0qVS/frmd3TyJL8rAK61Y4dUvrx5L2TzZnOhLQB4qLjWWeI8Umr9+vVq27btXQUpScqWLZvatGmjdevWPVpaAIgyZ44pSKVNa0ZJPYosWaRRo8z2p59KR49alw8AAACPLmqU1NtvJ5+ClCQ9/7xUrJgUEiLNmmV3GjxI1HRa7dtTkALgeuXKSe++a7a7djWjNQEgiYtzUeqff/6Rv7//fff7+/vr2LFjloQCkExduiT16GG2P/7YTHXyqJo2NSOubt+WOnY0Vx0BAADAPqdPS4sWme1OnezNktgcjpifefx43nR0V3/8YT58fMybwwCQGD77TMqQQdq2TZo71+40AOBy8VpTKuMDrhLKmDGjQkJCLAkFIJnq00cKDpaKFzfztyeEwyFNmmT+oVy5Ulq40JqMAAAAeDSTJ0sREWb6vtKl7U6T+Jo1M286HjworV5tdxrcS9QoqebNpdy57c0CIPnIlUv66COz/eGHZlQtACRhcS5KOZ1OeXnd/8sdDofiuDwVANxtyxZp6lSzHVVMSqiiRaXevc12167StWsJf0wAAADE3507Med6yW2UVJQMGWKmaBo/3t4suNv+/WY9KYdD+uADu9MASG66dpUKFZLOnpWGDrU7DQC4lMMZx0qSr6+vMmTIIIfDcc/9TqdTISEh2r9/v6UBE0NcF+AC4CLh4dJTT0m7dkktWkgzZ1r32LduSaVKSUeOmJO8MWOse2wAAADEzdy5ZqRQ3rzSsWNSypR2J7LHgQOSr68pfBw5Ij3xhN2JEKVlS+nrr6UGDaTFi+1OAyA5WrLE/A1KlcoUynmNAOBh4lpnSRHXBxxKlR6Aq4wfbwpSWbJII0da+9hp0kgTJki1apmFtZs3l8qWtfY5AAAA8GBRI4PatUu+BSnJjOR/8UVp1Spp4kTrz33xaM6ckebMMdu9etmbBUDy9corUs2aZorXnj2lH36wOxEAuEScR0olZYyUAmx0+rS5WjQkxEzp0rq1a56nSRPpu++k8uWlzZsljnUAAIDEsX279PTTZnrmkyelnDntTmSv5culevXMBVmnTklp09qdCL16mQJh1arShg12pwGQnO3dK5UpI0VGSmvXStWr250IAOIsrnWWOK8pBQAu0a2bKUhVqmSmzHCVUaOkjBnNmyJTprjueQAAABBb1Cip11+nICVJtWubKZkuX5a++cbuNLh6VZo82WxHrUcLAHYpWVJq395sd+smRUTYGgcAXIGiFAD7rFxphqN7e0uTJkleLvyTlCeP9NlnZrtPH+ncOdc9FwAAAIygIGn+fLPdubO9WdyFt7fUsaPZHj9eYvISe02ZIl2/LhUvLtWpY3caAJA++cSMpt29W5o2ze40AGA5ilIA7HHrVsw/4126mOHprta+vVSunHTtmvT++65/PgAAgORu2jQpNNRMofz003ancR/vvmvWPt21S/rjD7vTJF937khjxpjtnj1de5EcAMRVtmymMCVJH30kXbliaxwAsFqczrhCQkJcnQNAcjN0qHT0qJQ3b8zJlqt5e5srIb28pG+/lX79NXGeFwAAIDkKDzej4SVGSf1X1qxS06Zm+8sv7c2SnM2bJ509a2ZVeOstu9MAQIx27cwIzuBgadAgu9MAgKXiVJQqX768Ll68KElq3ry5rl275tJQAJK4gwel4cPN9tixUoYMiffc5crFjNDq0EG6fTvxnhsAACA5WbJEOnVKypHDrCeF2Dp1MreLFkmnT9ubJTmKjJRGjDDb3btLPj725gGAf0uZUho92mx/+aUUGGhvHgCwUJyKUmnTptWV/x8qum3bNoWHh7syE4CkzOk0xaDQULPIc6NGiZ/h00+l3Lmlw4elYcMS//kBAACSg6gRQG3aSKlS2ZvFHZUpI1WrZkaUTZ1qd5rkZ9ky6cABKVMm00cBwN28+KJUr555nWAJAgBJiMPpfPiqqp07d9Zff/2lggULavv27fL391fKlCnv+bWzZ8+2PKSrRUREKCAgQH5+fvL29rY7DpC0ffONmaokdWpp3z6pYEF7cixYIL3xhrkics8eqUgRe3IAAAAkRXv2SKVLm+mT//lHypfP7kTuKeqc9LHHpBMnGK2TmKpUkTZtkj780EwtDgDu6NAhqUQJKSxM+uknqU4duxMBwH3Ftc4Sp5FSI0eOVKdOnVSyZElJ0pNPPilfX997fgDAfV25EnN1T79+9hWkJOm116SXXjIjtjp0MCO4AAAAYI3x481tw4YUpB6kYUOzntH589IPP9idJvn44w9TkPLxkbp0sTsNANzfk09KXbua7fffN8UpAPBwcRop9W/NmjXThAkTlDFjRldlSnSMlAISSadO0oQJUtGi0q5d9k/jcuSIueLozh2zyDGLGwMAACTc5cumEHXzpvTbb9Kzz9qdyL19+qn08cdSpUqmUALXq19fWrpUatVK+uoru9MAwINdvWpmdwkKMutMdetmdyIAuCdLR0r925w5c6ILUk6nU/GsaQFIrv78U5o40WxPnGh/QUqSChWSPvrIbHfvbt5AAQAAQMLMmGEKUqVKSc88Y3ca99e6tVnQfvNmaccOu9Mkffv3m4KUwyF98IHdaQDg4TJlkj77zGwPHChduGBrHABIqHgXpSTpxx9/VL169VS6dGmVLl1a9erV048//mhxNABJRkSE1K6dmSKvaVPp+eftThSjZ08zcisoyEwpCAAAgEcXGWlGxktmlLzDYW8eT5Arl/T662Y7atpDuM7Ikea2QQPzfwAAeIJ335X8/c2oqf797U4DAAkS7+n7ZsyYobFjx6pp06YqW7asJGnHjh365ptv1K1bN73zzjuuyOlSTN8HuNiECeZNiUyZpAMHzELO7uS336Tq1c2bJps3SxUq2J0IAADAM/30k1S3rpQ5s3TqlJQund2JPMOWLWb6vlSppJMnpRw57E6UNJ0+LT3xhFmTZfNmqWJFuxMBQNxt2GBGIHt5SX/9JZUpY3ciAIjFpdP3DRw4UD179lSNGjVUo0YN9erVSwMGDNDs2bMTFBpAEnT2rNS3r9keMsT9ClKS9NxzUvPmZiRXu3ZSeLjdiQAAADxT1Eif996jIBUfFSpI5cqZtU6nT7c7TdI1dqwpSFWrRkEKgOepVs2MrI2MNOtKsaQKAA8V76LUhQsX5O/vf9f9/v7+upCAOU2nTp2qokWL6rOoOVIl3blzR5988okqVKggf39/de7cWcHBwbG+78yZM2rTpo3KlCmjSpUqafjw4QrnDWXAffToIV27Jj31lNS2rd1p7m/kSClLFikggGlTAAAAHsXBg9LKlWb0eYcOdqfxLA6H1Lmz2Z40iYukXOHqVWnyZLPdq5e9WQDgUY0YIaVObWZ8WbzY7jQA8EjiXZQqUKCAfv7557vuX7Fihf73v/89Uojdu3dr/vz5Kvqf+ZyHDBmidevWacyYMZozZ46CgoLUqVOn6P0RERFq27atwsLCNH/+fA0bNkyLFy/WuHHjHikHAIutXi19+60ZWj55suTO02PmzCkNH262+/c3080AAAAg7iZONLd16kiFCtmbxRO98YaUPbt04oS0bJndaZKeKVOk69el4sVNHwUAT1SggFkbWzIXAd++bW8eAHgE8S5Kde7cWePGjVPLli01YcIETZgwIXq7S5cu8Q5w48YN9ezZU4MHD1amTJmi779+/boWLlyoDz/8UJUqVVLJkiU1ZMgQ7dy5UwEBAZKkjRs36vDhwxo5cqSKFSumZ599Vl27dtW8efMUGhoa7ywALHTnjtSxo9nu0MFMR+LuWrY0c/mHhJih8AAAAIibkBBpxgyzHTXiB/GTOrXUurXZZuS+te7ckcaMMdu9epmL5gDAU/XuLeXNK/3zjzRqlN1pACDe4n0m9tJLL2nBggXKkiWL1qxZozVr1ihLliz6/vvv9cILL8Q7wKBBg/Tss8+qcuXKse7fu3evwsLCYt1fqFAh5cmTJ7ooFRAQoCJFiih79uzRX1O1alWFhITo8OHD8c4CwEIjRpgpXHLlkgYPtjtN3Px7RNfChWahbgAAADzcnDlmyuYnn5Qe4f9C/L927cw56dq10t9/250m6Zg716x1mzev9OabdqcBgIRJly5mppchQ6QzZ+zNAwDx9EiXB5UsWVKff/65Fi1apEWLFunzzz9X8eLF4/04P/30k/7++2/16NHjrn3BwcFKmTKlMmbMGOv+bNmyRa9dFRwcHKsgJSn684SsbwUggY4ckaLWhxs9WvrXKEi3V7q01L272e7USbp50948AAAA7s7pjBnZ06kTo1AS4vHHpfr1zTajpawRGWnWj5XMeb6Pj715AMAKb71lZnq5cUPq08fuNAAQL7b9t3D27Fl99tlnGjlypFKlSmVXDABWczrNmxF37kg1a5q58T3NgAFS/vxmKLynjPICAACwy7p1ZlRPunRSixZ2p/F8UdMfzp4tXb1qb5akYNky6cABc6Fc1PSIAODpHA5p7FizPXu2tG2bvXkAIB5sK0rt27dPFy9eVKNGjVS8eHEVL15c27Zt05w5c1S8eHFlz55dYWFhunbtWqzvu3jxonLkyCHJjIoKDg6OtT/q86ivAZDIfvhBWrnSXIE4YYI5UfI06dNLX35ptkeOlPbtszcPAACAO4s6b2rRwrNGyLur556Tihc3V7/PmmV3Gs83YoS5bd9e+s9MLADg0cqXj7kYpEsXMzIUADyAbUWpihUratmyZfrxxx+jP0qWLKl69epFb6dMmVKbN2+O/p6jR4/qzJkz8vPzkyT5+fnp4MGDunjxYvTXbNq0SenTp1fhwoUT+0cCcO2a1K2b2f7wQ6lIEVvjJEj9+tIrr0jh4eYfWKfT7kQAAADu5/hxaelSs92xo71ZkgqHw8w8IJkp/HiT8dFt3Cht2mQumOvSxe40AGC9oUPNhbVbt0rffGN3GgCIE9uKUunTp1eRIkVifaRNm1aZM2dWkSJFlCFDBr366qsaNmyYtmzZor1796pv377y9/ePLkpVrVpVhQsXVq9evRQYGKgNGzZozJgxatq0qXyYJxpIfAMGmAU2CxVKGnMajxsnpU0rbdjAVaoAAAD3MmmSKZrUqGFG98AazZqZUT2HDkm//mp3Gs8VNUqqRQspd257swCAK+TOLfXta7Z795ZCQuzNAwBxEK+iVFhYmIoXL66DBw+6Kk8sffv21XPPPacuXbro7bffVvbs2fVl1NQQkry9vTV58mR5eXnpjTfeUM+ePdWgQQN14QooIPEFBJgijmSm7Uud2tY4lihQQBo40Gx/8IH0r1GZAAAAyd6tW9K0aWY7amQPrJE+vfTuu2Z7/Hh7s3iqv/8260k5HFKPHnanAQDX6d5deuIJc5Hw8OF2pwGAh3I4nfGbk6pGjRqaMGGCfH19XZUp0UVERCggIEB+fn7y9va2Ow7geSIjpSpVpC1bpNdfl777zu5E1gkLk8qWlfbulVq2jHnjBQAAILmbMUN67z3p8celo0cl/pey1sGDUtGipqhy+LBUsKDdiTzLe++ZPtqwobRokd1pAMC1Fi2SXn3VXCC8f7/0v//ZnQhAMhTXOku8p+9r166dRo0apStXriQkH4CkZNo0U5DKkEEaPdruNNZKmVKaPNlsT59u5qUHAABI7pxOKWoWiw4dKEi5QpEiUq1apq0nTrQ7jWc5fVqaO9ds9+plbxYASAwNG0rVq0u3b/N3D4Dbi/dIqQYNGuj48eMKDw9Xnjx5lDZt2lj7Fy9ebGnAxMBIKSABgoLMFZxXrkhjxkhdu9qdyDVatzbFtxIlpJ07TbEKAAAgudq8Wapc2VyRfeqUlC2b3YmSpp9+kurWlTJnNu2cLp3diTxDz57S559L1apJv/9udxoASBy7d0v+/mY2m99+k5591u5EAJKZuNZZUsT3gWvWrJmgYACSmJ49TUHKz0/q2NHuNK4zbJj044/Svn1mNBhXHgEAgOQsapTUm29SkHKl2rXNtH1Hj0rffGMulMKDXbkiTZlitnv3tjUKACSq0qWlNm3MbC/dukl//slIZgBuKd4jpZIiRkoBj2j9eum558w895s3SxUq2J3ItWbNkt55R0qTxiyczBzNAAAgOTp71qwjFR4u7dhh1t+E64waJfXoYd5sDAgw5964v+HDpQ8/NDMc7N4tecV71QIA8FwXLpjpX69ckaZO5WIGAInKZWtKRdmzZ49+/PFH/fjjj9q7d++jPgwATxUaKrVvb7bbtk36BSlJat7cDH+/dUvq3NnM7w8AAJDcTJ1qClKVK1OQSgzvviulTWsKLBs22J3Gvd25Y6YUl8yMDhSkACQ3OXJIAwaY7X79pKtX7c0DAPcQ7zO0c+fO6a233tJrr72mIUOGaMiQIWrcuLHefPNNnTt3zhUZAbijUaOk/fulnDmlIUPsTpM4HA5p0iSzntTy5dKSJXYnAgAASFyhoWZaIMlcpAPXy5JFevttsz1+vL1Z3N3cudK5c1LevGZqSQBIjjp2lHx9zaipTz+1Ow0A3CXeRal+/fopPDxcK1as0LZt27Rt2zatWLFCTqdT/fr1c0VGAO7mn3+kQYPM9uefm3+Uk4tixcxVl5J5IyYkxN48AAAAiWnRIvOmf65cUqNGdqdJPqLWbl20SDp92t4s7ioyUho50mx37y75+NibBwDskjKlWQtbksaOlQ4etDcPAPxHvItS27dv18CBA1WwYMHo+woWLKiPPvpIf/75p6XhALghp9MUY27dMutJRV21mZz06yc98YR06pQ0cKDdaQAAABJP1Eiddu140z8xlS4tPfOMFBERM1INsS1dKh04IGXKJLVpY3caALBXrVpSnTpmut0ePexOAwCxxLsolTt3boWHh991f2RkpHLmzGlJKABubMkSM3VdypTSxInJc6HltGlj3pAZM0batcvWOAAAAIli507pjz+kFCl4098OUdMlTp1q1k5CDKdTGj7cbHfoIGXIYG8eAHAHo0aZ1+zly6VffrE7DQBEi3dRqmfPnvr000+1Z8+e6Pv27Nmjzz77TL1797Y0HAA3ExIidelitnv2NFPZJVd16kiNG5urVdu1M9OFAAAAJGVRF+W89pqUO7e9WZKj+vXNWklBQdIPP9idxr388Ye0ZYsZvRf1/woAJHdFi8Zc0NC9uxQWZm8eAPh/DqfT6XzYF5UvX16Of42GuHnzpiIiIuTt7S1J0dtp06bVtm3bXJfWRSIiIhQQECA/P7/onwnAPfTqZeZp/9//pH37zIih5Oz0abN4aEiINGUKVwwDAICk6+JFKV8+6fZtUwCoXNnuRMnT4MFS//5ShQqmCAPjlVekZcuk1q3NSDIAgHHlivTkk1JwsFlfisI9ABeKa50lTkWpxYsXx/mJGzZsGOevdRcUpYA42LtX8vc38xEvXy69/LLdidzD2LFSt25S5sxmDnumMQUAAEnRiBFS795S2bLSn38mzymc3UFQkJQ/vxQaKm3bJpUvb3ci++3bJ5UsafpkYKBUpIjdiQDAvUydKrVta963OHRIyp7d7kQAkqi41llSxOXBPLHQBMBCkZFS+/amINWwIQWpf+vYUZo1y6yx8MEH0uzZdicCAACwVkSEWUtUkjp1oiBlp5w5pddfl+bONdMpzppldyL7ff65uW3YkIIUANxLy5bmdXzXLmnAAGnCBLsTAUjm4r2mFIBkaOZMaeNGKV06MzIIMVKkkCZPNm/OzJkjrVtndyIAAABrLV8uHT8uZcsmNWlidxp06mRu58+XLlywN4vdTp2S5s0z27162ZsFANyVt7c0ZozZnjxZ2rPH1jgAEKeRUpLk6+sba12pe3E4HPr7778THAqAG7l4MeYfvIEDzXQhiO3pp81IsokTze2uXVKqVHanAgAAsMaXX5rbVq2kNGnszQKznlT58tL27dK0aVKfPnYnss/YsVJYmPTMM6ZdAAD39txz0quvSgsXmiUIVq9m5DMA28RpTSlJWr169X33BQQEaM6cOYqMjNQeD6y2s6YU8ACtWknTp0ulSkk7dkgpU9qdyD1duSL5+krnz0uffip99JHdiQAAABJu/36peHHJy0s6elQqUMDuRJDMlNEtWpgLxo4eNaP3k5srV6THH5euX2fNWwCIi2PHpGLFpDt3pMWLpQYN7E4EIImJa50lztP31axZ866PggULavHixfr6669Vq1YtrVy50pLwANzEH3+YgpQkTZpEQepBMmeWRo8224MHS4cP2xoHAADAEuPHm9tXXqEg5U5ef13KkUM6eVJautTuNPaYMsUUpEqUkGrXtjsNALi/J54wa2FLUo8epjgFADZ4pDWlzp8/r48++kivvPKKIiIi9OOPP2r48OHKmzev1fkA2CUszExFJ5lFMatUsTePJ2jSRKpZ05zYdewoxW0gKgAAgHu6elWaNctsd+5sbxbEljq11Lq12Y4qHCYnt2/HrI/Sq5cZyQcAeLgPP5Ry5zajbKP+jgJAIovXmdv169c1cuRIvfjiizp8+LBmzpypyZMnq0iRIq7KB8Au48aZxS+zZZOGD7c7jWdwOMy6UqlSSatWSd9/b3ciAACARzdrlnTjhpm+r3p1u9Pgv9q1M4vXr1sn7d1rd5rENXeudO6clC+fuTAMABA36dPHvMczeLB09qy9eQAkS3EuSn311VeqWbOmfvvtN33xxReaP3++nnrqKVdmA2CXkyelAQPM9ogRpjCFuHnyyZjFprt1M1cYAwAAeJrISGnCBLPdqROLobuj/Plj1gOJ+l0lB5GR0siRZrt7d8nHx948AOBpmjaVnn5aCgmR+vWzOw2AZMjhdMZtfilfX1+lTp1alSpVeuAiVeM9cOqAuC7ABSQbjRqZRS+rVJF+/53pMOLr9m2pdGnp0CEz1c24cXYnAgAAiJ9ffpFq1ZIyZpROnzZXVsP9/PabGcWWNq35PWXObHci1/vxR6lhQylTJnMxXYYMdicCAM+zZYtUqZLZ3r5dYuABAAvEtc4S53eaGzRooNq1aytz5szKkCHDfT8AeLiffjIFqRQppEmTKEg9itSpzTR+krlq9c8/7c0DAAAQX1EXG777LgUpd/bss1LJktLNm9LMmXancT2nM2baqQ4dKEgBwKOqWFFq1sxsd+3KmtgAElWcR0olZYyUAv7fzZtSiRLSP/9IPXuaqfvw6Jo2lb75RipXTtq61cz5DwAA4O6OHpUKFzZvUB04ILGGsHubMsWsL1W4sPl9JeWLyjZskJ55xqzh+s8/Uq5cdicCAM91+rR5jb9507x38eabdicC4OEsHykFIBn47DPzz13+/NLHH9udxvN98YWZVmTHDjPqDAAAwBNMnGgKUrVqUZDyBE2bmnPOw4elVavsTuNaURfNtWhBQQoAEipvXqlvX7Pdq5d044a9eQAkGxSlABj798csGDxuHNO0WCFXLmnoULPdt6905oy9eQAAAB7mxg1p+nSz3amTvVkQN+nTm2kWJenLL+3N4kr79knLl0sOh9Sjh91pACBpeP99qUAB6dSpmPeEAMDFKEoBMFfCdugghYVJdetK9evbnSjpaNtWqlBBun5d6t7d7jQAAAAP9s030pUrUsGCUu3adqdBXHXsaG5//tmMmEqKPv/c3DZsyAg+ALBKmjQxf1+HD5dOnLA3D4BkgaIUAGnuXOm338zJyJdfmqsPYQ0vL2nyZHO7YIG0cqXdiQAAAO7N6YwZadOxY9JemyipKVzYFBGdTjP9YlJz6pQ0b57Z7tXL3iwAkNS8+qr07LPS7dtS7952pwGQDPBfBpDcXb4cM/3Fxx9L//ufrXGSJD8/qWtXs92xo3Trlq1xAAAA7mnDBmnPHilt2pjp4OA5Onc2t19/nfTWBRkzxszq8MwzZhYCAIB1HA7zd9bhkObPN+cDAOBCFKWA5K5vX+nCBal4cTOXMFzjk0/MIqJHj0pDhtidBgAA4G5Ro6TeflvKksXeLIi/l16SChWSrl6NGVWUFFy5Ik2ZYra5gh8AXMPPT2rd2mx36yZFRtqZBkASR1EKSM62bYv5B2/iRMnHx948SVmGDNK4cWZ7+HApMNDePAAAAP926pS0eLHZ7tTJ3ix4NF5eMWtLffmlmcovKZg8WQoJkUqWZJ0zAHClTz+VMmaU/vpLmjnT7jQAkjCKUkByFR4utWtn/llt3tzMHwzXathQevllM/VI+/ZJ540CAADg+SZPliIizDlhqVJ2p8GjevddM/3i3r3S77/bnSbhbt+Wxo412z17svYtALhSzpzSgAFmu08f6do1e/MASLIoSgHJ1YQJ0s6dZmqWkSPtTpM8OBzmqtU0aaTffpPmzrU7EQAAgHTnjjR1qtlmlJRny5xZatbMbI8fb2sUS8ydK507J+XLJzVpYncaAEj6OnWSihSRgoKkzz6zOw2AJIqiFJAcnTkj9e9vtocONVfDIHE88YT08cdmu0cP6dIle/MAAAAsWGDWGM2XT2rQwO40SKioKfwWL5ZOnrQ3S0JERMRcPNe9O1ONA0Bi8PGRRo0y26NHS4cO2ZsHQJJEUQpIjrp3l65flypWjFnIEonn/fel4sXNmz99+tidBgAAJHdRI2ratZNSpLA3CxKuVCnpuedMUSdq/VhPtHSpdPCgGf3F/ywAkHjq1JFq1TJLD3zwgd1pACRBFKWA5GbVKnM1rJeXNGmSuUXi8vExbS+ZqXI2bbI3DwAASL62bTMfPj688Z+URE3DOHWqmZ7R0zid0vDhZrtDBylDBnvzAEBy4nCY0VLe3uYCgV9/tTsRgCSGd6OB5OT27ZjpPLp0kfz8bI2TrD3zjFmIWjJXJYeF2ZsHAAAkT1GjpN54gymdk5L69c10jBcumAvSPM3GjdLWrVKqVFLnznanAYDkp1ixmAscuneXwsPtzQMgSaEoBSQnw4ZJhw9LefJIgwbZnQYjRkhZs0p79kjjxtmdBgAAJDdBQdJ335lt3vhPWlKkkNq3N9tRhUdPMmKEuW3RQsqVy94sAJBcDRggZcsm7dvn2dPBAnA7thalpkyZoldffVX+/v6qVKmSOnTooKNHj8b6mjt37uiTTz5RhQoV5O/vr86dOys4ODjW15w5c0Zt2rRRmTJlVKlSJQ0fPlzhVPCB2A4elIYONdtjxjAFhjvInj1m8eYBA6QTJ+zNAwAAkpevvpJCQ6Wnn5bKl7c7DazWqpWZljFqikZPsXevtHy5mT6qRw+70wBA8pUli/Tpp2a7f3/p4kV78wBIMmwtSm3btk1NmzbVggULNGPGDIWHh6tly5a6efNm9NcMGTJE69at05gxYzRnzhwFBQWpU9TwUUkRERFq27atwsLCNH/+fA0bNkyLFy/WOEYdADGcTjNtX2io9NJLUuPGdidClHfekapWlW7ckLp2tTsNAABILsLCYta4ZJRU0pQzp5mWUfKs0VKff25uGzWSihSxNwsAJHetW0ulSkmXL0sDB9qdBkAS4XA6nU67Q0S5dOmSKlWqpLlz56p8+fK6fv26KlWqpM8//1y1atWSJB05ckR16tTRd999Jz8/P61fv17t2rXThg0blD17dknSt99+q88//1ybN2+Wj4/PQ583IiJCAQEB8vPzk7e3t0t/RsAW8+dLb75p5mTft08qVMjuRPi3vXslf38zR/OSJdIrr9idCAAAJHU//CC99popXJw4Yc4TkfRs325Gwvn4SCdPuv+6YadOSU88Yc6Lt2yRKlSwOxEAYO1aqUYNydtb2rVLKlHC7kQA3FRc6yxutabU9evXJUmZMmWSJO3du1dhYWGqXLly9NcUKlRIefLkUUBAgCQpICBARYoUiS5ISVLVqlUVEhKiw4cPJ154wF1dvWoWpZSkfv0oSLmjkiVjpibp3NmMmgIAAHClL780t23aUJBKysqXN0Wp0FAzXaO7GzPGFKSefZaCFAC4i+eflxo2lCIizPtL7jO+AYCHcpuiVGRkpIYMGaKyZcuqyP8P0Q8ODlbKlCmVMWPGWF+bLVs2XbhwIfpr/l2QkhT9edTXAMla//7SuXNm6otevexOg/vp318qUMBcqTxokN1pAABAUrZ7t/T77+aK57Zt7U4DV4uannHSJFPwcVdXrkhTppht/m8BAPfy+edm1O2vv5p1/wAgAdymKPXJJ5/o0KFDGj16tN1RgKRjxw5pwgSzPXEiV8G6s3TpYub6HzVK2rPH3jwAACDpijrnaNRIypfP3ixwvddek3LkkE6fNlNFu6tJk6SQEDOLQO3adqcBAPxbwYLS+++b7fffl+7csTcPAI/mFkWpQYMG6bffftOsWbOUK1eu6PuzZ8+usLAwXbt2LdbXX7x4UTly5Ij+muDg4Fj7oz6P+hogWYqIkNq1kyIjzXpSNWrYnQgPU7euGRIfHi61b29+dwAAAFa6fFmaO9dsd+pkbxYkjlSpzDSNUsy0je7m9m1p7Fiz3auX5HDYmwcAcLe+faVcuaTDh6Vx4+xOA8CD2VqUcjqdGjRokH799VfNmjVL+fPnj7W/ZMmSSpkypTZv3hx939GjR3XmzBn5+flJkvz8/HTw4EFdvHgx+ms2bdqk9OnTq3DhwonycwBuacoU6c8/pYwZzcgbeIaxY82oqT/+kGbMsDsNAABIar7+Wrp1SypdWqpWze40SCzt2pnpGtevd88R+XPmSOfPS/nzS02a2J0GAHAvGTJIQ4ea7U8/NX+3AeAR2FqU+uSTT7R06VJ98cUXSpcunS5cuKALFy7o9u3bkqQMGTLo1Vdf1bBhw7Rlyxbt3btXffv2lb+/f3RRqmrVqipcuLB69eqlwMBAbdiwQWPGjFHTpk3l4+Nj408H2OjcOXMFiyQNGWKuZIFnyJ8/Zk2pXr0k1sYDAABWiYgwUzpLZpQUo1GSj3z5zIh8KWZ6b3cREWHWKpGk7t2llCntzQMAuL/mzaWnnpKuX5f69bM7DQAP5XA6nU67nrxo0aL3vH/o0KFq1KiRJOnOnTsaNmyYfvrpJ4WGhqpq1aoaMGBArKn5Tp8+rYEDB2rbtm1KkyaNGjZsqB49eihFihRxyhEREaGAgAD5+fnJ29s74T8YYLe335bmzTMnClu2mKsi4TnCw83vbtcu6Z13GDEFAACssXy5VK+elDmzWV8obVq7EyExrV8vPfec+b2fOiVlyWJ3ImPxYrO+WebM0okT5kp8AID72rRJqlLFXNzy559S2bJ2JwLgJuJaZ7G1KOUuKEohSVm71qwf5XBI27aZ4gY8z5YtUuXKktMp/fab9OyzdicCAACerlYt6ZdfpB49YkamIPlwOqUyZcz0faNGmVFJdnM6pUqVpK1bzUwPn31mdyIAQFw0bSp9841Utar0+++MvgYgKe51Flun7wNgsTt3pA4dzHaHDhSkPFnFijELUrdvL4WG2psHAAB4tgMHTEHK4Yg5X0Ty4nCYaRslM4VfZKS9eSRpwwZTkEqVSurSxe40AIC4Gj7cjLzduFFasMDuNAA8DEUpICkZOdK84fDYY9LgwXanQUINHSrlyCHt3y998YXdaQAAgCeLWkvq5ZelggXtzQL7NG1qpsk7ckRaudLuNNKIEeb2nXfM/zAAAM+QL5/Uu7fZ7tVLunnT3jwAPApFKSCpOHo0ZrqLUaPMP5vwbFmymN+lJA0aZH7HAAAA8XX9eswalZ0725sF9kqXTnrvPbM9fry9WfbulX76yYzg6tHD3iwAgPj74APp8cfNeoBMCwwgHihKAUmB02mm4rh926wn9eabdieCVZo2lapXN7/bTp3M7xoAACA+5swxhakiRaSaNe1OA7t16GAKQT//LB06ZF+OqDcwGzWSnnzSvhwAgEeTNm3MiNdhw6STJ+3NA8BjUJQCkoJFi8w/lT4+ZmoWFphMOhwOadIk87v9+WfzuwYAAIgrpzNmREynTpIX/wIme4UKSXXqmO2oaR0T26lT0rx5ZrtXL3syAAAS7vXXpapVpVu3pA8/tDsNAA/BfySAp7t+Xera1Wz37m2ugEXSUrRozFzNXbqY3zkAAEBcrF1r1qdMn15q0cLuNHAXnTqZ2xkzpJCQxH/+0aOl8HDpueekp59O/OcHAFjD4ZDGjjW333wjbdpkdyIAHoCiFODpBg6UTp82C1b36WN3GrhKnz7mqtYzZ6SPP7Y7DQAA8BRffmluW7SQMma0Nwvcx4svminzrl6V5s5N3Oe+fFmaOtVsM0oKADxf2bIx6xV27SpFRtqbB4DboygFeLJdu8wVKZI0YYKUJo29eeA6adKY37EkjRsn7dxpbx4AAOD+/vlHWrbMbHfsaGsUuBkvr5g+MX584q5bOnmyGZ1VsqRUq1biPS8AwHU++0zKkEH6809p9my70wBwcxSlAE8VGSm1by9FREiNG/MPXXLw0kvSG2+Y333btuZ3DwAAcD+TJpnzhpo1pWLF7E4Dd9OihZQunbRvn7R+feI85+3bMRfV9erFWrgAkFQ89pjUv7/Z7tOHZQcAPBBFKcBTTZ8ubd5s1gcYM8buNEgso0aZqXe2b4+Z9gQAAOC/bt2Spk0z21HrBwH/ljmz1KyZ2Y6a5tHV5syRzp+X8ueXmjRJnOcEACSOLl2kwoWlc+ekIUPsTgPAjVGUAjzRhQtS795m+9NPpbx57c2DxJMnjxkWL5mrj86dszcPAABwT99+K126JBUoINWta3cauKuoguWPP0onTrj2uSIipJEjzXb37lLKlK59PgBA4kqVylxIK5nbI0fszQPAbVGUAjxRr15mgWA/P658TY7at5fKlTMLU/foYXcaAADgbpzOmJEvHTpI3t725oH7KlFCql7dTPM4ZYprn2vJEunQISlLFql1a9c+FwDAHnXrSi+8IIWGSj172p0GgJuiKAV4mg0bpJkzzfzrkyZJKVLYnQiJzdvbvGng5SV98420erXdiQAAgDvZtEkKCJBSp5ZatrQ7Ddxd1EVuU6eaNZ9cwemUhg832x06mCnIAQBJj8MhjR5t3rdYvFhau9buRADcEEUpwJOEhppRMpK5urBiRXvzwD7lykkdO5rtDh1c9wYCAADwPOPHm9u33pKyZbM3C9zfK6+YNZ6Cg6UFC1zzHBs2SNu2mamdOnd2zXMAANxDiRIx71116yaFh9saB4D7oSgFeJLRo6V9+6QcOaShQ+1OA7sNHizlzm2mQYm68hQAACRvZ85IP/xgtpnmGXGRIkXMm4dRBU2rjRhhbt95R3rsMdc8BwDAfXzyiZQ1q7Rnj/TVV3anAeBmKEoBnuL4cWnQILP9+efmxR3JW8aM0tixZnvIEFOcAgAAydvUqeaK5CpVJH9/u9PAU7RqZUYxbd8ubd1q7WPv3Sv99JOZ0on1UAEgeciaNeY9rP79zbroAPD/KEoBnqJLF+nmTenZZ6VmzexOA3fRuLFUq5aZ2rFDBzNfPwAASJ5CQ826kxJTpCF+cuSQmjQx21aPlho50ty++qr05JPWPjYAwH21bWum8rt40YycAoD/R1EK8ARLl5qPFCmkiRPNVYaAZPrC+PFmIfPVq6X58+1OBMBux45Jy5ebK91PnWIOdyA5WbhQOnfOTO/bqJHdaeBpoqZ7/O476fx5ax7z5Enpm2/Mdq9e1jwmAMAzpEghjRljtsePl/bvtzUOAPeRwu4AAB7ixo2YK10/+EAqXtzePHA/hQpJH31kPrp3l2rXljJntjsVgMRy/br022/SL79Iq1bdPZWnl5dZvyNv3gd/ZMxoS3wAFvryS3Pbrp2UMqW9WeB5nnpKqlhR2rLFrP/x0UcJf8wxY8zFEc89J5Uvn/DHAwB4lpo1pVdeMRdad+8u/fwzF1oDkMPpZK6niIgIBQQEyM/PT97e3nbHAWL78ENp+HCpQAHp77+ltGntTgR3dOeO5OcnBQaaafwmTLA7EQBXiYyUdu40BahffpE2bZLCwmL2e3ubCxiuXJHOno37SKn06e9fsMqXz9w+9ph5fADuZ8cOU1RImVI6cULKlcvuRPBE8+ZJb79t/uYfO5aw4ubly9Ljj0shIdKKFebCKQBA8nP4sPn/JCzMzOjw8st2JwLgInGts1CUEkUpuLG9e80C1eHh5qqSevXsTgR39ttvUvXq5qqjLVukp5+2OxEAq5w9a4pQq1ZJv/4qXbgQe3/BgtJLL0kvvig9/3zMqKfISCkoSDp9OvbHqVOxP792LW45vL3NG90PG3WVPr21Pz+Ah3v3XWnmTOmtt0xhAXgUd+6YQlJQkLRggfTaa4/+WEOGSP36SaVKSbt2cWU8ACRnvXtLI0aYtQX37pV8fOxOBMAFKErFA0UpuCWnU3r2WWnDBqlBA2nxYrsTwRO0aCHNnm2Kmdu2mTmcAXie27eljRvNSKhffpH27Im9P316U3yKKkQVLpyw5wsJubtw9d+Ps2dNkSsuMmZ8eOEqZ05GXQFWCQ42Ixrv3DGjJytVsjsRPNnHH0uffio984y0fv2jPcbt22amh6Agac4cM/oKAJB8XbsmFSli1iz84gvp/fftTgTABShKxQNFKbilmTPNFa9p05rFIB9/3O5E8ARBQZKvr5kuZcwYqWtXuxMBiAun0/ytj5qSb/166datmP0Oh1SunClAvfSSWfMjsa8ujIgw/0Ter2gVNfoqJCRuj5cihZQ798OLV0xbCzzcsGFSnz7m78T27YxIQcKcPm0KShERZoRT6dLxf4ypU6W2baX8+aUjR1jjDAAgff211LKluYDt0CFzkRqAJIWiVDxQlILbuXjRFBaCg83w5p497U4ET/LVV1KbNmYkRWCgeVMXgPu5dElavTqmEHXqVOz9uXPHjISqWVPKkcOenPF17drDR12dO2cKcXGROfPDC1c5ckheXi79sQC3FR4uFSpk1pGaMUN65x27EyEpeP116fvvpdatTYEpPiIipGLFzBuOo0dL3bq5JCIAwMNERkrly0t//WXes5gyxe5EACxGUSoeKErB7bRpYwoLJUuaF2uuLER8REZKVatKmzdLjRubNxQA2C88XNq6NWZKvu3bYxdmUqUyUyVFFaJKlky6ox3Cw01h6mHFqxs34vZ4KVPGjLrKl+/ehas8eaQ0aVz7cwF2WLxYatRIypbNFLdTp7Y7EZKCDRvMa1KaNObvcZYscf/eRYukV18133PiBOsMAgBibNwoVatm/s/56y/Jz8/uRAAsFNc6C4uNAO5m0yZTkJKkSZMoSCH+vLykyZOlsmWlH36QVqyQ6tSxOxWQPB07FjMSas0aM4ro30qUiJmSL+rNv+QgRQpTPMqX7/5f43TGHnUVNT3gfz+CgqSwMPPG54kTD37erFkfPuoqe/akWwxE0jR+vLlt3ZqCFKxTtaqZtm/3bjMCL65rfzid0vDhZrtDBwpSAIDYqlaVmjSR5s83yw389hvn3kAyxEgpMVIKbiQ83KwFsHu3WU/q66/tTgRP1rOn9Pnn0hNPSHv3si4LkBhCQqR160wRatUqM3XRv2XNKr3wgilCvfDCg4syiJuwMOns2YePuvr3Gl0P4uNjRlXlzWtGX/n4SN7epuDv5RV7O7l/HrXtcPBmgl327TOjKr28TBGcNUhhpagpoQsWlA4eNMf8w6xfLz33nBn9e+IE64UAAO524oRZsuLWLTOzS+PGdicCYBGm74sHilJwG6NGST16mDctDxwwV2sDjyokRCpeXDp5UurbV/rsM7sTAUlPZKQUEBAzJd+mTaZIEsXbW6pUyRShXnrJjGDkXCPxOZ3SlSt3F6r+O/rqwgW7k3ouh+P+BawUKaQCBcybD8WKxdwWLmzeuMaj69DBjKxv2NBMmQZY6eZNU6C/ckVavlx6+eWHf8/LL5tR+u3amb4JAMC9DBwoffKJOUfcvz/5zBgBJHEUpeKBohTcwqlT5g2akBBp2jSpZUu7EyEpWLJEatDATAMZEGCKVAAS5uxZMwpq1Srp11/vLmQULBizLtTzz0sZM9qTE/F3507sUVfnzplRzJGR5iMiImbb6s9d+diP+rmr/03w9jbHy78LVVG3mTK59rmTgqtXTcHgxg1p7VqpenW7EyEp+uAD6YsvpFq1pJ9/fvDX7tljpvxzOMzIqsKFEycjAMDz3LwpFS1q3gsbPFjq18/uRAAsQFEqHihKwS00biwtXChVrmwWFvbysjsRkor69aWlS816NczXDMTf7dtmQd6oKfl27469P316U3yKKkTxJhySCqfTfCS0yHXnjnT0qLkKdv9+KTDQ3F6/fv/nzpXLFKf+W7DKm5fXsShjx0rdupm16fbsoV3gGkePmtc1p9PM5FCkyP2/tkULafZs83/N998nXkYAgGf69lvprbfMUgMHD5rzPAAejaJUPFCUgu1+/lmqU8dcMfzXX+YKQ8Aqx4+bEVI3b0ozZ5o3DADcn9Np3jBftcoUotavj70ekcNh1v978UVTiKpY0aw7BCDunE4zKu3fRaqo2zNn7v996dPHFKn+XbAqVMiMCk4uIiPN1cWHD5sp0tq1szsRkrJ69cz0fV27SmPG3PtrTp40Ix/Dw6Vt26Ty5RM1IgDAAzmdUrVq0h9/SG+/Lc2ZY3ciAAlEUSoeKErBVrdumStcjx0z60l9/rndiZAUjRwp9epl1ikLDJSyZbM7EeBeLl2SVq+OKUSdOhV7f+7cMSOhataUcuSwJyeQHFy9akZk/LdgdfiwGYF1LylSmNEc/50G0NdXypAhcfMnhpUrpdq1zTSHp06ZYh3gKr/8Yqbvy5jRTG16r/72/vvS6NFmGsm1axM/IwDAM/35Z8yFDJs3mwv+AHgsilLxQFEKtvroI+mzz6R8+cybLrypAFcIC5PKlpX27pVatZK++sruRIC9wsOlrVvNG22//CJt3x57/ZxUqcyUl1GFqJIlmRoLsFtoqHTkyN3TAAYGmnWV7idv3nuvW5Url+ce1y+/LK1YYabvGz3a7jRI6iIjzTFz8KA0caLUvn3s/ZcvS/nzm+Pw559NAQsAgLh67z1pxgzp6adNYYrlLACPRVEqHihKwTaBgWaqvrAwadEiqWFDuxMhKfvjD6lqVbO9caNUpYq9eYDEduxYzEioNWuka9di7y9RImZKvmeekdKksScngPhxOs1ooagi1b8LVufP3//7MmW698iqggXNyCt3dfiwWdfH6TRFgieftDsRkoNx48z0fcWLm4uc/l3QHTLELFBfqpS0a5fnFnsBAPY4d86cz4SEmLUJmzWzOxGAR0RRKh4oSsEWTqdUo4a0bp252nXZMv6Bg+u1bi1Nm2ZGffz1V/JafwPJT0iI+Rv7yy+mGHXoUOz9WbNKL7xgilAvvGBGrAJIWi5fNgWq/xasjh41oz/uJWVK88bIf9etKlpUSpcucfPfS9Q0abVrm9FSQGK4etWMOrxxw1zY8fzz5v7bt6UCBaSgILMWyNtv25sTAOCZhg+XPvzQTJt+8CCzCAEeiqJUPFCUgi3mzTP/tKVJI+3bJz3xhN2JkBxcvGjeXAsOlkaMkHr2tDsRYJ3ISCkgIGZKvk2bzEjUKN7eUqVKpgj10ktmSkte94Hk6fZtM+Lo36Oq9u83a1ndunX/73v88Zgi1b8LVjlyJM7FRTdumMLA1avSTz9Jdeq4/jmBKB07mun7GjY0szxI0tSpUtu25tg4fJgLngAAj+bOHTMa9+hRqW9fs8wFAI9DUSoeKEoh0V25Yq62DQoyL7R9+9qdCMnJrFnSO+9IadNKf/9trm4FPNXZs2YU1KpV0q+/ShcuxN5fsGDMulDPP28WaQeA+4mMlE6ciL1eVdTtf/++/FuWLPdet+p//7O2+D1litSunVSokLmKmDUXkJj+/ttMdevlZabEzZvX9PXDh6UxY8z0fgAAPKoffzQXPqRKZc6/uHgb8DjJrig1b948TZ8+XRcuXJCvr6/69++v0qVLx+l7KUoh0UVdZejra+Zd9/GxOxGSE6dTql5dWr9eqltXWrqUqSPhOW7fNmuiRU3Jt3t37P3p05viU1QhqnBhe3ICSHouXry7ULV/v/TPP+a19V5SpTLrP/23YFW0aPzXrXM6zVqke/dKo0ZJ3bsn+EcC4q1GDWntWjPF0lNPSY0bm6LsiRNMtQQASBin00yrvmaN9Oqr0g8/2J0IQDwlq6LUihUr1KtXL33yyScqU6aMZs2apZUrV2rlypXKli3bQ7+fohQS1bZtUsWK5sV27VpTHAAS2/79UpkyZmqzuXOlmjXNdCspUsTcpkhBsQr2czrNG79RU/KtXx97ai2HQypXzhSgXnrJ/H2l0A8gMd26ZUYt/bdgdeCAmYrmXhwOM4rqvyOrfH2l7Nnv/T2//WbOG9OmlU6fljJndtEPBDxA1FXs2bKZ0fZ//SV99JH06ad2JwMAJAV79kh+fmb0+rp10nPP2Z0IQDwkq6LUa6+9plKlSunjjz+WJEVGRurZZ59Vs2bN1KZNm4d+P0WpOLhwQRo82CwWHfUmtcMR8xHfz614DE99jgkTzOioZs2k2bMf7fcBWKFfP2nIkAd/jbf33cWqpHTr7U3hzR1dumSujosaDXXyZOz9uXPHjISqWdOs5QIA7iYiQjp+PGa9qn+vXXX58v2/L3v2e69b1aOHWcenbVtp8uTE+zmAfwsPN9NHnjhhPk+VymznzGlvLgBA0hE1u1CZMtKOHawDDHiQuNZZUiRiJpcIDQ3Vvn371LZt2+j7vLy8VLlyZe3cudPGZEnMzz9L48bZnSLpyJxZ+vxzu1MguevXz1x59OefZsTUvUREmI+kLGpUWEKLXBS4rHHypLR9u7kyLkqqVNIzz8QUokqWpK0BuD9vb7OuXcGC0ssvx9zvdJoLvv47DeD+/ebN/eBgM03pxo33ftxOnRInP3AvKVJI7dtLffqYz999l4IUAMBan3wiffONuaD7xRfNe2hRYyqczpiPB30en69N6p//m1UX6lv5WJ703LVrSy1bCgnn8UWpy5cvKyIi4q5p+rJly6ajR4/G6TGiBotFJPU3XhOiUSPzxnTUVZ0P+8MXdV9cvjahf1Rd+QJk1eP9e9vLS3rnHTPlBX0OdkqVStqwwWw7naYIEBZmroCN+kjI51HbYWGmryfm5/fK9LDjLTJSCg01H7BXmjRmZMDzz5uRUJUrx1575d8FKwDwRNmySVWqmI9/u3FDOnTIfBw4YKYFPHBAOnzYvJbVqmVGTXEOCTu9955Z1+zOHen99+mPAABrZcliZmvq3VvautXuNECMv/6SWrTgItkHiKqvPGxyPo8vSlkh8v/f3NqzZ4/NSdxc6dJ2J0haAgLsTgA8Oi8vs24Pa/cgMRw4YHcCAEhcTz5pPu6Fc0i4g59/NrfXrtEnAQDWq1jRrCcMuJtdu+xO4BEiH3IxsccXpbJkySJvb29dvHgx1v0XL15U9vstEvwfKVKkUKlSpeTl5SUHlU4AAAAAAAAAAIA4czqdioyMVIoUDy47eXxRysfHRyVKlNDmzZtVs2ZNSaYSt3nzZr399ttxegwvLy/5cLU/AAAAAAAAAACAy3h8UUqS3n33XfXu3VslS5ZU6dKlNWvWLN26dUuNGjWyOxoAAAAAAAAAAACURIpSderU0aVLlzRu3DhduHBBxYoV07Rp0+I8fR8AAAAAAAAAAABcy+F0Op12hwAAAAAAAAAAAEDS5mV3AAAAAAAAAAAAACR9FKUAAAAAAAAAAADgchSlAAAAAAAAAAAA4HIUpQAAAAAAAAAAAOByFKUAAAAAAAAAAADgchSlAAAAAAAAAAAA4HIp7A4A93XhwgXt2rVLwcHBkqTs2bOrTJkyypEjh83JPA9tCSR9YWFhSpkypd0xkMxdu3ZNK1eu1JkzZ5Q3b17VqlVLGTJksDsWgHjau3evSpYsaXcMINrFixd18OBBlSxZUhkyZFBwcLAWL14sp9OpZ599VkWLFrU7okeIiIiIfo328vJSaGioVq9eLafTqQoVKih79ux2R/QItKM1OK6tQX+01smTJ7Vjxw4FBQXJy8tL+fPnV5UqVZQ+fXq7owGwkMPpdDrtDgH3cvPmTX388cdasWKFHA6HMmXKJEm6evWqnE6nXn75ZQ0aNEhp0qSxOan7oy2tFRERIW9v7+jPd+3apdDQUPn5+VEMSKA+ffqoW7dueuyxx+yO4vZWrFihmjVrysfHR5I0d+5cTZ8+XefOnVPGjBnVrFkzderUyeaUnissLEynT59WtmzZKKbEQadOnVS3bl3VqlVLhw4dUrNmzeRwOJQ/f36dPn1aDodDs2bNUqFCheyO6nEo8D2a0NBQORyO6NflEydOaOHChTpz5ozy5Mmjxo0bK3/+/DandH++vr7Knz+/Xn31VTVs2JDX50dEcc8aW7duVbt27XTr1i1lz55d06ZNU9u2bZU6dWo5HA6dPn1akyZNUtWqVe2O6tYCAwPVqlUrXbx4UYULF9bUqVPVpk0bnTp1SpKUMmVKTZs2TaVLl7Y5qXujHa3BcW0N+qN1bt68qQ8//FCrVq2SJDkcDmXLlk2XLl1SqlSp9MEHH6hp06Y2p/QM69ev16pVq5QpUya9+uqrsf4XvHr1qjp37qzZs2fbmBBg+j7cw2effaY9e/ZoypQp2r17tzZt2qRNmzZp9+7dmjp1qnbv3q3PPvvM7pgegba0RlBQkN58802VKlVKb7/9tq5evaq2bdvqjTfeULNmzVS3bl0FBQXZHdMjBAYG3vNj2bJl2rNnT/TnuL8ePXro+vXrkqSFCxdqxIgRatiwoSZPnqx33nlH06dP1/fff29zSs/w1Vdf6fbt25JM0Xn48OHy9/dX7dq1VbFiRfXp00dhYWE2p3Rv27Zt05NPPilJGjFihKpUqaL169drwYIF+u233/Tss89qyJAhNqf0DJ06ddLKlSslSYcOHdKLL76o0aNHa9OmTRozZoxq166tI0eO2JzS/bVs2VJr1qyRJO3YsUMvv/yy1q1bp/DwcP3++++qV6+edu7caXNKz1CxYkXNnj1bzz//vNq2bavVq1crIiLC7lgepXHjxnrhhRc0efJknT9/3u44Hmvs2LFq2LChduzYoXfffVdt2rRRjRo19Msvv2jlypV6++23NX78eLtjur2RI0eqbNmyWrJkiSpWrKiWLVuqYMGC2rZtm7Zv365nn31Wo0ePtjum26MdrcFxbQ36o3WGDRumCxcuaOnSpfrll1/0wgsvqH79+tqxY4f69eunkSNHatmyZXbHdHvLli1T+/btFRwcrICAADVs2FBLly6N3h8WFqbt27fbmNCzzJs3T++88466du2qzZs3x9p36dIl1ahRw6ZkSYAT+I+nnnrKuWPHjvvu//PPP51PPfVUIibyXLSlNXr27Ol84403nGvWrHF269bN+cYbbzjfeust57lz55ynT592NmnSxPnJJ5/YHdMjFC1a1Onr6+ssWrToXR9R9/v6+tod060VLVrUGRwc7HQ6nc7GjRs7v/rqq1j7582b52zQoIEd0TyOr69vdFtOmzbNWb58eecPP/zgPHTokHPJkiXOSpUqOadOnWpzSvdWunRp5/Hjx51Op9NZpUoV5759+2LtP3r0qLNcuXJ2RPM45cuXdx4+fNjpdDqdrVq1cr7//vvOO3fuOJ1OpzM0NNTZt29f53vvvWdnRI9QtmxZ57Fjx5xOp9P59ttvO4cMGRJr/+jRo51NmjSxIZlniXqtCQsLc65cudLZunVrZ7FixZyVK1d2jhgxwnn06FG7I3qEokWLOj/66CNnpUqVnMWLF3e2adPG+euvvzrDw8PtjuZRypYtG/1aExYW5ixevLjz77//jt5/7NgxXmvi4N+vM7du3XIWK1bMuWvXruj9Bw8edD799NN2xfMYtKM1OK6tQX+0ToUKFZx79uyJ/vzKlSvOUqVKOW/evOl0Op3OuXPnOuvXr29TOs9Rv35956xZs6I//+mnn5x+fn7OBQsWOJ1Op/PChQu85xNHs2bNcpYpU8b5ySefOD/44ANniRIlnJMnT47eT1smDGtK4S6RkZEPnAotZcqUioyMTMREnou2tMamTZs0fvx4+fn5qWzZsqpYsaJmzJgRPZVNly5d1L9/f5tTeoaiRYsqV65c6tWrl1KnTi1Jcjqdeumll/TVV1+pQIECNif0DA6HQ5KZ7/q/U1pUrVpVn3/+uR2xPI7zXzMIL1++XD169NCrr74qSSpcuLAkaerUqWrdurUt+TxB0aJFtWXLFj3++OPKkSOHzpw5o+LFi0fvP3PmTPSxjge7c+dO9Gv2/v37NXXq1OhpOlOmTKlWrVrptddeszOiR4iMjIwezXP06FH169cv1v5GjRpp1qxZdkTzSClSpNBLL72kl156SefPn9cPP/ygxYsX6+uvv1bZsmU1b948uyO6vW7dumnAgAFas2aNFi5cqC5duihLlixq0KCBGjdurCeeeMLuiG4vZcqUunPnjiRzhXVkZGT055J0+/ZtpUjBWwsP43Q6o6ci/++tJHl5ecU6N8K90Y7W4Li2Bv3ROhEREbHWjUqbNq0iIiJ069YtpUmTRlWqVNHw4cNtTOgZjh8/rurVq0d/XqdOHWXNmlXt27dXeHi4XnjhBRvTeZbvvvtOn376qerVqydJevPNN9WxY0fdvn1bXbt2tTmd52P6Ptzlueee08cff6y///77rn1///23Bg4cGOsPHO6PtrTGtWvXogtQmTNnVpo0aZQnT57o/QUKFNCFCxfsiudRvv/+ez3++OPq0qWLrl69qrx58ypfvnySpJw5cypv3rzKmzevzSnd3++//641a9YoVapUunXrVqx9d+7ciS5a4eGi2urMmTPy9/ePtc/f3z96PnbcW4cOHfTFF19o0aJFatasmYYMGaLvv/9ef/31lxYuXKh+/frplVdesTumR4gq8EmKLvD9GwW+uCldurTWrVsnScqfP/9dU8Lu379fmTNntiGZZ7nX68hjjz2mjh07avXq1fr666+VK1cuG5J5pqji3tSpU7Vu3Tq99dZb+uWXX1SnTh3Wp4iDsmXL6osvvtCOHTs0dOhQFS9eXJMmTdLNmzd169YtTZw4kbW74qBEiRL66quvdP78eU2ZMkX58uXT3Llzo/fPnTs3ekpe3B/taA2Oa2vQH61TqlSpWOsczZ49W1myZFHWrFklmTWn0qZNa1c8j5EuXTpdvHgx1n0VK1bUlClTNGLECM2ZM8emZJ7n1KlTsd6jKFu2rGbNmqUFCxboiy++sDFZ0sBlD7jLxx9/rB49eqhRo0bKlClT9AvApUuXdO3aNVWtWlUff/yxzSk9A21pjWzZsunChQvKnTu3JKlp06bKlClT9P5r164pTZo0dsXzKD4+PurXr5/Wr1+v9u3b66233mIUyiP48MMPo7e3bNkS60QlICBAjz/+uB2xPNKCBQuUNm1apUyZUlevXo2178aNG9EjVXBvzz33nAYNGqQhQ4YoKChITqczeuSoj4+PmjRpoh49etic0jN06NBBvXv3VooUKaILfJcvX1ahQoV07NgxffnllxT44qBbt25q3bq1bt26pbp162rYsGH6559/ottxzpw5atOmjd0x3d7DrqquVKmSKlWqlEhpPNeDinsdO3bU5s2b9cMPP9iQzLP06tVLbdu2VdOmTVWwYEHNmDFDAwcOVPny5SVJGTNm1LRp02xO6f7ef/99tW7dWosWLVLmzJk1e/Zs9evXT1WrVpXD4dC1a9c0efJku2O6PdrRGhzX1qA/WqdHjx567733tGrVKqVMmVIXLlyINTJq586devbZZ21M6BlKly6t33//XX5+frHuf/rppzV58mS1a9fOnmAeKEuWLDp37lz0hdySVKRIEc2aNUstWrRgbfsEcjgZR4r7OHLkiAICAhQcHCxJyp49u/z8/FSoUCGbk3ke2jJh2rdvr4oVK6pFixb33D9v3jytWrWK6YDiKTg4WH369NHNmzcVEBCgJUuWRE+Zhke3bt06pUiRQtWqVbM7itt7/vnnY33evHlzvfPOO9Gfz5o1SytWrNB3332XyMk8T0REhPbt26dTp04pMjJSOXLkUIkSJWJNgYGH++WXX2IV+KJEFfh69+4da0oW3NvOnTs1bNgw7dq1K9b9OXPmVMuWLe/7eo4Y27ZtU9myZZk6KYF8fX31xx9/KFu2bHZHSRIuX76sLFmyRH++efNm3b59W35+frHux/3dvHlTR48e1RNPPKF06dLpzp07Wrp0qe7cuaPKlSurYMGCdkf0CLSjdTiuE47+aJ2goCD99ttvCg0NVcWKFXl/4hFs27ZNO3fuVNu2be+5f8uWLVqyZImGDh2ayMk8T48ePZQtWzb17dv3rn2HDh1S8+bNdeXKFe3fv9+GdJ6PohQAj7d7926lTp1aRYoUsTuKR5o9e7a2bt2q/v37MxUQ3EpAQIB8fHxirZEEuBoFPutcunRJJ0+ejG7Hf19lCCQGinsAAAB4FIGBgdq3b1/0utf/dfDgQa1atUqdOnVK5GRJA0UpxNvVq1e1bt06NWjQwO4oHo+2BJKemzdvat++fdFTXwB24nUGSJo4tuFO6I/W4BzSGrSjNTiurUF/tA59EkhavOwOAM9z9uxZ9enTx+4YSQJtaY2rV6/qxx9/tDtGkkBbJtyJEyfUvHlzu2MkCfTHhON1xjr0R2sEBwdr/PjxdsfweBzb1uC4tgb90RqcQ1qDdrQGx7U16I/WoU/GTVhYmEaMGKEXXnhBjRs3vmvtzODgYBUrVsymdJ4pMjLyvvefOXMmkdMkHRSlcJeQkJCHfiBuaMvEwcmJdWhLuBP648PxOpN46I/WCA4O1oQJE+yO4fY4thMHx3Xc0B+BpIfjGu6GPmmNyZMna8mSJWrSpImqVKmiYcOG6eOPP471NUyaFjchISHq2rWr/Pz8VLlyZY0dO1YRERHR+y9duqQaNWrYmNCzMbk27vLUU0/J4XDcd7/T6XzgfsSgLa3xsJMPTk7ijrZMuKeffvqB+/99koIHoz8mHK8z1qE/WiMwMPCB+48ePZpISTwbx7Y1OK6tQX+0BueQ1qAdrcFxbQ36o3Xok9ZYtmyZBg8erOrVq0uSGjVqpNatW6tPnz4aMmSIJNGOcTRmzBgFBgZqxIgRun79uiZNmqS///5bX375pXx8fCRR4EsIilK4S7p06dSuXTuVKVPmnvuPHz9+V5Ud90ZbWoOTE+vQlgkXGhqqN998U0WKFLnn/tOnTzMKII7ojwnH64x16I/WaNCggRwOxz3/QYu6n3Z8OI5ta3BcW4P+aA3OIa1BO1qD49oa9Efr0Cetcf78eT355JPRnxcoUEBz5sxR8+bN1bNnT/Xs2dPGdJ5lzZo1GjZsmCpUqCBJqlGjhtq2bav27dtr0qRJkijwJQRFKdylePHiku5/xUfGjBmpBMcRbWkNTk6sQ1smnK+vr3LlyqWGDRvec39gYCD/eMQR/THheJ2xDv3RGpkyZVLPnj1VqVKle+4/fPiw2rVrl8ipPA/HtjU4rq1Bf7QG55DWoB2twXFtDfqjdeiT1siePbtOnjypfPnyRd/32GOPafbs2WrevDnTFsfDpUuXlCdPnujPs2bNqhkzZqhVq1Zq3bq1Bg8ebGM6z0dRCnepV6+ebt++fd/92bNnV6dOnRIxkeeiLa3ByYl1aMuEe+6553T9+vX77s+UKZMaNGiQeIE8GP0x4XidsQ790RolS5ZUUFCQ8ubNe8/9169fpx3jgGPbGhzX1qA/WoNzSGvQjtbguLYG/dE69ElrVKxYUcuWLbvrArGowlSzZs1sSuZ5cufOraNHjyp//vzR96VPn17Tp09Xy5Yt6Y8J5HByFg7AzS1YsEC3b99W8+bN77k/ODhY8+fP5wUhDmhLuBP6I9wJ/dEav/76q27evKn69evfc//Vq1e1du3a+15RDFiJ4xoAACQnp0+f1tGjR1WtWrV77j9//rw2bdrEuXgcDB48WEFBQRo3btxd+0JCQvTee+9pz5492r9/vw3pPB9FKQAAAAAAAAAAAJmL6YKCgmKt0fVvISEh+vvvv+87Ih8PRlEK93Tp0iUtXLhQAQEBCg4OlmSGyvr7+6tRo0bKmjWrzQk9B20JJD2hoaFavXr1PY/rGjVqyMfHx+aESE54nQGSJo5tuBP6ozU4h7QG7WgNjmtr0B+tQ5+0Bn0SnoCiFO6ye/dutWrVSqlTp1blypWVLVs2SdLFixe1efNm3b59W9OmTVOpUqVsTur+aEvrcHJiHdoyYY4fP66WLVsqKChIZcqUiXVc79q1S7ly5dJXX32lAgUK2JzUM9AfE4bXGWvRH61x+PBhzZ0796529PPz09tvv63ChQvbnND9cWxbh+M64eiP1uAc0hq0ozU4rq1Bf7QOfdIa9ElrcR7pOhSlcJfXX39dvr6++uSTT+RwOGLtczqdGjBggA4cOKDvvvvOpoSeg7a0Bicn1qEtE+7dd99VmjRpNGLECKVPnz7WvpCQEPXq1Ut37tzR9OnTbUroOeiPCcfrjHXoj9ZYv369OnbsqBIlSqhq1aqx2vGPP/7Qvn37NHHixPvOcw+DY9saHNfWoD9ag3NIa9CO1uC4tgb90Tr0SWvQJ63DeaSLOYH/KFWqlPPw4cP33X/48GFnqVKlEjGR56ItrfHaa685+/fv74yMjLxrX2RkpLN///7O119/3YZknoe2TLjSpUs7Dxw4cN/9gYGBztKlSydiIs9Ff0w4XmesQ3+0Rr169Zxjxoy57/5x48Y569atm4iJPBPHtjU4rq1Bf7QG55DWoB2twXFtDfqjdeiT1qBPWofzSNfysrsoBveTPXt27dmz57779+zZo+zZsydiIs9FW1ojMDBQLVq0uOtqGUlyOBxq0aKF9u/fb0Myz0NbJlyGDBl0+vTp++4/ffq0MmTIkIiJPBf9MeF4nbEO/dEa//zzj+rVq3ff/S+//LKOHz+eiIk8E8e2NTiurUF/tAbnkNagHa3BcW0N+qN16JPWoE9ah/NI10phdwC4n5YtW6p///7au3evKlWqFP1HPzg4WJs3b9b333+vXr162ZzSM9CW1og6OSlUqNA993NyEne0ZcK99tpr6t27tzp06KCKFSvGOq63bNmiSZMm6e2337Y5pWegPyYcrzPWoT9aI2/evFq/fr0KFix4z/3r169Xnjx5EjmV5+HYtgbHtTXoj9bgHNIatKM1OK6tQX+0Dn3SGvRJ63Ae6VqsKYV7WrFihWbOnKl9+/YpIiJCkuTt7a0SJUronXfeUZ06dWxO6Dloy4SbN2+ehg0bpjfeeOOBJydNmza1Oan7oy2tMXXqVM2ePVvBwcHRV804nU5lz55dLVq0UOvWrW1O6Bnoj9bgdcYa9Edr/Pzzz/rggw9UrVo1Va5c+a523LBhg7744gu99NJLNid1fxzbCcdxbR36ozU4h7QG7WgNjmtr0B+tQ5+0Bn3SGpxHuhZFKTxQWFiYLl++LEnKkiWLUqZMaXMiz0VbJgwnJ9ahLa1z8uRJBQcHSzJX0eTPn9/mRJ6H/mgdXmcSjv5ojb/++ktz5sxRQECALly4IEnKkSOH/Pz81Lx5c/n7+9uc0LNwbCcMx7W16I/W4BzSGrSjNTiurUF/tA590hr0yYTjPNJ1KEoB8CicnFiHtoQ7oT/CndAfgaSH4xoAAACPgvNI63nZHQDuKTAwUBMnTtS8efN06dKlWPtCQkLUp08fm5J5HtrSWilTplTOnDmVM2dOXgQSiLZ8dEFBQVqyZInWr1+v0NDQWPtu3ryp8ePH25TMc9EfHx2vM9ajP8IdcGxbi+M6YeiP1uAc0hq0ozU4rq1Bf7QOfdIa9EnrcR5pPUZK4S4bN25Uu3bt9L///U83btzQzZs3NXbsWFWsWFGSmTuzWrVq2r9/v81J3R9taZ3AwECtXbtWmTJlUu3atZU1a9bofSEhIfrss880dOhQGxN6DtoyYXbv3q2WLVsqMjJS4eHheuyxxzRhwgQ9+eSTkjiu44v+mDC8zliL/miN9evXa9WqVcqUKZNeffXVWIsDX716VZ07d9bs2bNtTOj+OLatw3GdcPRHa3AOaQ3a0Roc19agP1qHPmkN+qS1OI90HUZK4S7jx49Xy5YttXz5cq1du1atWrVS+/bt9fvvv9sdzePQltbYuHGjGjdurBUrVmjatGmqXbu2tmzZEr3/9u3b+vHHH+0L6EFoy4QbPXq0atasqe3bt+uPP/5Q5cqV9fbbb+vvv/+2O5rHoT8mHK8z1qE/WmPZsmVq3769goODFRAQoIYNG2rp0qXR+8PCwrR9+3YbE3oGjm1rcFxbg/5oDc4hrUE7WoPj2hr0R+vQJ61Bn7QO55Eu5gT+o2zZss7jx4/Hum/p0qVOPz8/59q1a50XLlxw+vr62pTOs9CW1njjjTeco0aNcjqdTmdkZKRz6tSpTj8/P+f69eudTqeTdowH2jLhypcv7zx69Gis+6ZMmeIsX768c9euXbRhPNAfE47XGevQH61Rv35956xZs6I//+mnn5x+fn7OBQsWOJ1O2jGuOLatwXFtDfqjNTiHtAbtaA2Oa2vQH61Dn7QGfdI6nEe6Vgq7i2JwPz4+Prp27Vqs++rVqycvLy91795dvXv3timZ56EtrXHo0CGNGDFCkuRwONS6dWvlypVLXbt21ahRo1SqVCmbE3oO2tIad+7cifV5mzZt5O3trZYtW2rIkCE2pfI89MeE43XGOvRHaxw/flzVq1eP/rxOnTrKmjWr2rdvr/DwcL3wwgs2pvMcHNvW4Li2Bv3ROpxDWoN2TDiOa+vQH61Bn7QOfdIanEe6FkUp3KVYsWLaunWrSpYsGev+l19+WU6nUx9++KFNyTwPbWkNTk6sQ1sm3JNPPqmdO3fK19c31v1R8za///77NiXzPPTHhON1xjr0R2ukS5dOFy9eVP78+aPvq1ixoqZMmaK2bdvq3LlzNqbzHBzb1uC4tgb90RqcQ1qDdrQGx7U16I/WoU9agz5pHc4jXYuiFO7y5ptv3neu/7p168rpdGrBggWJnMoz0ZbW4OTEOrRlwjVo0EDbtm3Tm2++ede+1q1by+l0av78+TYk8zz0x4TjdcY69EdrlC5dWr///rv8/Pxi3f/0009r8uTJateunT3BPAzHtjU4rq1Bf7QG55DWoB2twXFtDfqjdeiT1qBPWofzSNdyOJ1Op90hAOBBfv31V23fvl19+/a95/5ly5ZpwYIFmjNnTiIn8zy0JdwJ/RHuhP5ojW3btmnnzp1q27btPfdv2bJFS5Ys0dChQxM5GZIjjmsAAAA8Cs4jXYuiFAAAAAAAAAAAAFzOy+4AcG99+vTR6NGjY903atQo9enTx6ZEnou2BJKe8ePHa968ebHumzdvnsaPH29TIiRnvM4ASRPHNtwJ/dEanENag3a0Bse1NeiP1qFPWoM+CXdGUQoPdOrUKZ0/fz7WfefPn9fp06dtSuS5aEtrcHJiHdoy4RYtWqTVq1fHum/VqlVavHixTYk8F/0x4XidsQ790RrNmjW7a6713r17q3nz5jYl8kwc29bguLYG/dEanENag3a0Bse1NeiP1qFPWoM+aR3OI62Xwu4AcG/3mhdz+PDhNiTxfLSlNU6dOqX/zjp6/vx5nT171qZEnou2TLi1a9fedd+sWbNsSOL56I8Jx+uMdeiP1siXL59y5MgR677HHntMXl5cFxcfHNvW4Li2Bv3RGpxDWoN2tAbHtTXoj9ahT1qDPmkdziOtx5pSAAAAAAAAAAAAcDlGSuGeAgMDtXfvXlWoUEH58+fXoUOHNG/ePEVGRuqFF15QtWrV7I7oUTZv3qwdO3bowoUL8vLyUv78+fX888/rf//7n93RAFikefPmGjp0qPLmzWt3FCRDvM4ASQ/n4/AUV69e1bp169SgQQO7o3iEyMjIe44YjYyM1Llz55QnTx4bUnk+zsXjJzQ0VA6HQylTppQknThxQgsXLtSZM2eUJ08eNW7cWPnz57c5pfv75Zdf9MwzzyhNmjR2R0nyeK2JG/okPAUjpXCXVatWqVu3bsqQIYNCQ0M1YcIEde3aVSVLlpSXl5c2b96s4cOHq169enZHdXsXL15Uu3bttHfvXnl5eSkyMlLFihVTUFCQLl26pHfeeUe9evWyO6ZHCA0N1erVqxUQEKDg4GBJUvbs2eXv768aNWrIx8fH5oSe5dy5c8qQIYPSpUsX6/6wsDAFBASofPnyNiVzf2vWrLnn/Z07d1a/fv2UK1cuSVKNGjUSM5ZHoz8+Ol5nrMVrjesFBwdr/vz56tSpk91R3Brn49bidca1AgMD1bBhQ+3fv9/uKG4tJCRE/fr107p165Q+fXq98cYb6tSpk7y9vSWZv4/VqlWjHR+Cc3FrNGvWTE2bNlWtWrW0Y8cOvfPOO3riiSdUqFAh/fPPPzp27JhmzJghf39/u6O6NV9fX6VLl0516tRR48aNVaZMGbsjJVm81sQNfdJ6nEe6BkUp3KVRo0Z64YUX1L59e/30008aOHCg3nnnHXXs2FGS9PXXX2vp0qX68ccf7Q3qAbp3767Q0FANHz5cPj4+Gj58uEJCQjR8+HBt3rxZ3bp1U4cOHdSiRQu7o7q148ePq2XLlgoKClKZMmWULVs2SebN2F27dilXrlz66quvVKBAAZuTur+goCB16NBB+/btk8PhUN26dTVgwIDoF1f+GX44X19fORyOu+YT/jeHw0EbxgH9MeF4nbEOrzWJgzcU4obzcWvwOmONkJCQB+4PDAxUs2bNaMeHGDx4sDZs2KDu3bvr+vXrmjRpkp588kl9+eWX8vHxUXBwsKpWrarAwEC7o7o1zsWtUa5cOS1cuFD/+9//1KxZMxUvXlx9+vSJ3j9mzBht3bpV3377rY0p3Z+vr686d+6s1atXa//+/SpcuLAaN26s+vXrK0uWLHbH8yi81liDPmkdziNdi6IU7uLv769ly5YpX758cjqdKlWqlBYuXKiiRYtKkk6ePKlXXnlFO3futDmp+ytXrpzmz5+vJ598UpJ08+ZNPf3009qyZYvSp0+vJUuWaNKkSVq5cqXNSd3bu+++qzRp0mjEiBFKnz59rH0hISHq1auX7ty5o+nTp9uU0HP07t1bx44dU//+/XX9+nV9/vnncjgc+vrrr5UpUyb+GY6DVq1aydvbW0OGDIl+01qSSpQooSVLlqhw4cI2pvMs9MeE43XGOrzWWONhx+vRo0fVo0cP/nl7CM7HrcHrjDWiigD343Q6KQLEQfXq1TVs2DBVqFBBknTp0iW1bdtWGTNm1KRJk3Tt2jXe3IoDzsWt4e/vrx9++EGFChVSlSpVNH36dPn6+kbvP3HihOrXr8/rzEP4+vrqjz/+ULZs2bR371798MMP+umnn3T79m09//zzev3111WlShW7Y3oEXmusQZ+0DueRrsWaUrhLunTpdOXKFeXLl0/Xrl1TeHi4rly5Er3/8uXLdw1ZxL35+PjEelH18vJSRESEwsPDJUlly5bV6dOn7YrnMf766y99//33d71JKEnp06dX165d9frrr9uQzPNs2rRJEyZMUKlSpSRJ8+fPV5cuXdSiRQvNnDlTkh54Ighp2rRpmjlzpl599VUNGDBA1atXtzuSx6I/JhyvM9bhtcYaDRo0uO8V7FH3c1w/HOfj1uB1xhrp0qVTu3bt7jsF0PHjx/Xxxx8ncirPc+nSpVjrRWXNmlUzZsxQq1at1Lp1aw0ePNjGdJ6Dc3FrlC5dWuvWrVOhQoWUP39+BQYGxipK7d+/X5kzZ7YvoAcqWbKkSpYsqT59+ujnn3/WwoUL1apVK+XOnVtr1661O57b47XGevTJhOE80rUoSuEulSpV0qBBg/T2229rxYoVqlKlikaNGqUhQ4bI4XBo5MiRKlu2rN0xPUK5cuU0btw4DRs2TClTptSoUaOUP3/+6JO7S5cuKVOmTPaG9AAZMmTQ6dOnVaRIkXvuP336tDJkyJDIqTxTSEiIMmbMGP25j4+Pxo8fr65du6p58+YaOXKkjek8xzvvvKMKFSrogw8+0Lp162JNdYG4oz8mHK8z1uG1xhqZMmVSz549ValSpXvuP3z4sNq1a5fIqTwP5+PW4HXGGsWLF5ckPf300/fcnzFjxgdOpQYjd+7cOnr0qPLnzx99X/r06TV9+nS1bNmStfbigXPxhOvWrZtat26tW7duqW7duho2bJj++ecfFSpUSMeOHdOcOXPUpk0bu2O6vXu9IZ0qVSo1aNBADRo00PHjx7Vo0SIbknkeXmusQZ+0DueRruVldwC4n969eyt9+vQaMGCAwsLCNGbMGJUsWVIvv/yy6tSpo6CgIH3wwQd2x/QIvXv31v79+1W+fHn5+/tr0aJFGjBgQPT+I0eOqGHDhjYm9AyvvfaaevfurZkzZyowMFDBwcEKDg5WYGCgZs6cqT59+uiNN96wO6ZHyJcvnw4cOBDrvhQpUmjs2LHKnz8/bxTGQ7FixfTDDz9IMiMDOEGOP/pjwvE6Yx1ea6xRsmRJBQUFKW/evPf8eOyxx/h7GQecj1uD1xlr1KtXTz4+Pvfdnz17dgoqcVC1alUtXLjwrvvTpUunadOmKVWqVDak8lyciyeMv7+/vvrqK23cuFGDBw/WlStXNHnyZPXs2VM//PCDOnXqpNatW9sd0+09rN8VKFBA3bt3T6Q0no3XGmvQJ63DeaRrsaYU4uzkyZO6deuWChYsqBQpGGQXV7du3dJff/2l0NBQ+fn5sbDgI5o6dapmz56t4ODg6Cs/nE6nsmfPrhYtWnDCHEcjR45UYGDgPddECQ8PV+fOnbVu3TrmxI2ntWvXasuWLWrbtm2sue3xYPRHa/A6Yx1eaxLu119/1c2bN1W/fv177r969arWrl1LsfQRcT4eP7zOwJ1cvXpVQUFB0etA/ldISIj+/vvv+44SwP1xLp4wly5d0smTJxUZGakcOXIoX758dkfyGKdPn1bu3Lnl5cU1/3AP9EnrcB7pWhSlcE+XLl3SwoULFRAQoODgYEnmqgR/f381atRIWbNmtTmh56AtrXXy5MlY7fjv6S/wcOHh4bp9+/Y910yJ2n/+/HnlzZs3kZN5lvsd135+fnr11Vc5ruOI/mgNXmesx2sN3AHHdsLxOmMd+qM1aEdrcC5uDfqjNWhH69CW1qAdrcF5pGtRlMJddu/erVatWil16tSqXLly9JVGFy9e1ObNm3X79m1NmzYteqE33B9tmTjOnj2rcePGaejQoXZH8Xi05cNxXCce+uPD0R8TD/3RGrRj3HBsJw76Y9zQH61BO1qDdrQG7WgN2tE6tKU1aMfEw3lkwlCUwl1ef/11+fr66pNPPrlrgTyn06kBAwbowIED+u6772xK6Dloy8QRGBiohg0bav/+/XZH8Xi05cNxXCce+uPD0R8TD/3RGrRj3HBsJw76Y9zQH61BO1qDdrQG7WgN2tE6tKU1aMfEw3lkwjAROe4SGBiooUOH3vXHS5IcDodatGjBOgBxRFtaY82aNQ/cf/LkyURK4vloy4TjuLYO/THh6I/WoT9ag3a0Bse2NeiP1qA/WoN2tAbtaA3a0Rq0o3VoS2vQjtbhPNK1KErhLtmzZ9eePXtUqFChe+7fs2ePsmfPnsipPBNtaY2OHTvK4XDoQQM77/WCi7vRlgnHcW0d+mPC0R+tQ3+0Bu1oDY5ta9AfrUF/tAbtaA3a0Rq0ozVoR+vQltagHa3DeaRrUZTCXVq2bKn+/ftr7969qlSpUvQfq+DgYG3evFnff/+9evXqZXNKz0BbWiNHjhwaMGCAatasec/9+/fvV6NGjRI5lWeiLROO49o69MeEoz9ah/5oDdrRGhzb1qA/WoP+aA3a0Rq0ozVoR2vQjtahLa1BO1qH80jXoiiFuzRt2lRZsmTRzJkz9e233yoiIkKS5O3trRIlSmjo0KGqU6eOzSk9A21pjRIlSmjfvn33fSF42JULiEFbJhzHtXXojwlHf7QO/dEatKM1OLatQX+0Bv3RGrSjNWhHa9CO1qAdrUNbWoN2tA7nka7lcNJ6eICwsDBdvnxZkpQlSxalTJnS5kSei7Z8dH/++adu3rypZ5555p77b968qb179+rpp59O5GSeh7a0Fsd1wtAfrUV/TBj6ozVoR+txbD86+qP16I/WoB2tQTtag3a0Bu1oHdrSGrRjwnAe6VoUpQAAAAAAAAAAAOByXnYHAAAAAAAAAAAAQNJHUQoAAAAAAAAAAAAuR1EKAAAAAAAAAAAALkdRCgAAAAAAAAAAAC5HUQoAAAAAkGSdOnVKRYsW1f79++2OAgAAACR7FKUAAAAAuNyHH36ookWLqmjRoipRooQqV66sd999Vz/88IMiIyPj9ViLFi3SU089ZVm2v//+W126dFHlypVVqlQpvfjii/roo4907Ngxy54jPn7//XcVLVpUFy5ciHV/1apV9fzzz8e6L6rgsnnzZpdkuXLlij777DNVr15dJUuWVNWqVdWnTx+dOXPGJc8HAAAAIGmjKAUAAAAgUVSrVk0bN27U2rVr9dVXX6lChQr67LPP1LZtW4WHh9uSad26dXr99dcVGhqqzz//XCtWrNDIkSOVIUMGjR071pZM5cqVU4oUKbRt27bo+44cOaLbt2/r6tWrOnXqVPT9W7dulY+Pj8qWLWt5jitXruiNN97Q5s2bNXDgQK1atUqjR4/WiRMn1LhxY508edLy5wQAAACQtFGUAgAAAJAofHx8lCNHDj322GMqUaKE2rVrp4kTJ+r333/X4sWLo79uxowZqlevnvz8/PTss89q4MCBunHjhiRThOnTp4+uX78ePfLqyy+/lCSFhoZq+PDhqlatmvz8/PTaa69p69at981z69Yt9enTR88++6wmT56sypUrK3/+/CpTpox69+6tQYMGSZIiIiLUt29fPf/88ypdurReeuklzZo1K9Zjbd26VY0bN5afn5+eeuopNWnSRKdPn47ev3r1ajVs2FClSpVSjRo1NH78+PsW4tKlS6dSpUrFyr5161aVK1dOZcuWjVWs2rZtm/z8/JQqVSr9/vvvevPNN/XUU0+pQoUKatu2rU6cOBH9taGhoRo0aJCqVq2qUqVKqXr16poyZcp922f06NEKCgrSjBkz9OyzzypPnjwqX768pk+frhQpUuiTTz6J/tpmzZpp0KBBGjRokMqVK6cKFSpozJgxcjqdsZ7/Qb+fqBFwGzZsUO3ateXv76+WLVsqKCgoVq7vv/9etWvXVqlSpVSrVi3Nmzcv1v7du3erQYMGKlWqlBo1asS0fQAAAIAboSgFAAAAwDaVKlWSr6+vVq1aFX2fw+FQv379tHz5cg0bNkxbtmzRyJEjJUn+/v7q27ev0qdPr40bN2rjxo167733JEmDBg3Szp07NXr0aC1dulS1atVSq1at9M8//9zzuTdu3KjLly+rVatW99yfMWNGSVJkZKRy5cqlsWPH6qefflLHjh01evRorVixQpIUHh6ujh07qnz58lq6dKm+++47vfHGG3I4HJKkP//8U71791bz5s21YsUKDRo0SIsWLdLkyZPv2y4VKlS4qyj19NNPq3z58rHu37ZtmypUqCDJFNneffddLVy4UDNnzpTD4VDHjh2jp0ecM2eO1q5dqzFjxmjlypUaOXKk8ubNe8/nj4yM1IoVK1SvXj3lyJEj1r7UqVPrrbfe0saNG3XlypXo+xcvXixvb299//336tevn2bOnKnvv/8+en9cfj+3b9/W119/rREjRmju3Lk6e/ashg8fHr1/6dKlGjt2rLp3764VK1bo/fff17hx46KLmjdu3FDbtm1VqFAhLVq0SJ07d471/QAAAADslcLuAAAAAACSt4IFC+rAgQPRn7/zzjvR2/ny5VO3bt00YMAADRw4UD4+PsqQIYMcDkesYsmZM2e0aNEirVu3To899pgkqWXLltqwYYMWLVqk999//67njSqGFCxY8IH5UqZMqS5dukR/nj9/fgUEBGjlypWqU6eOQkJCdP36dVWvXl2PP/64JKlQoULRXz9+/Hi1adNGDRs2jP7+rl27auTIkerUqdM9n7NChQqaPHmygoKClDNnTm3fvl0tW7ZURESE5s+fL0k6efKkzpw5E12Ueumll2I9xpAhQ1SpUiUdPnxYRYoU0dmzZ1WgQAGVK1dODofjvgUpSbp06ZKuXbt237YpVKiQnE6nTpw4ocyZM0uScufOrb59+8rhcKhgwYI6ePCgZs6cqddffz3Ov5+wsDB98skn0e3YtGlTTZw4Mfp5v/zyS3344Yd68cUXo9vy8OHD+u6779SwYUMtX75ckZGRGjJkiFKlSqUnn3xS586d08CBA+/7swIAAABIPBSlAAAAANjK6XRGjyqSpE2bNmnKlCk6evSoQkJCFBERoTt37ujWrVtKkybNPR/j4MGDioiIUK1atWLdHxoaGl00udfzxtW8efO0cOFCnTlzRnfu3FFYWJh8fX0lSZkzZ1ajRo3UsmVLValSRZUqVVLt2rWVM2dOSVJgYKD++uuvWCOjHvYz+fv7K2XKlNq2bZt8fX11+/ZtlShRQpGRkbp06ZJOnjyprVu3KnXq1CpTpowkU2QbN26cdu3apcuXL0f/fGfPnlWRIkXUsGFDvffee6pVq5aqVaum5557TlWrVo1zGzxMmTJlYv0e/fz8NGPGDEVERMT595MmTZrogpQk5cyZUxcvXpQk3bx5UydOnFC/fv3Uv3//6K8JDw9XhgwZJJm1t4oWLapUqVJF7/f397fsZwQAAACQMBSlAAAAANjqyJEjypcvnyTp1KlTatu2rd588011795dmTJl0o4dO9SvXz+FhYXdtyh18+ZNeXt7a+HChfL29o61L23atPf8nieeeEKSdPTo0QcWLn766ScNHz5cvXv3lr+/v9KlS6fp06dr165d0V8zdOhQNWvWTBs2bNDPP/+sMWPGaMaMGfLz89PNmzfVuXPn6NE9//bv4sm/pUmTRqVLl9bWrVt19epVlStXTt7e3vL29pa/v7+2bt2qrVu3qmzZsvLx8ZEktWvXTnnz5tXgwYOVM2dORUZGqm7dugoLC5MklShRQmvWrNHvv/+uTZs2qVu3bqpcubLGjRt31/NnzZpVGTNm1JEjR+6Z78iRI3I4HLEKSA8S199PihSx/0V1OBzRxbWbN29Kkj799NPoQlwULy9mpgcAAAA8AUUpAAAAALbZvHmzDh48GD1l3759++R0OvXhhx9GFxp+/vnnWN+TMmVKRURExLqvWLFiioiI0KVLl/TUU0/F6bmrVKmiLFmyaNq0aZowYcJd+69du6aMGTPqr7/+kr+/v5o2bRq978SJE3d9ffHixVW8eHG1bdtWb7zxhpYvXy4/Pz8VL15cx44dU4ECBeKUK0qFChW0YsUKXb16VU8//XT0/U899ZS2bdum7du3q0mTJpKky5cv69ixYxo8eHD0z//nn3/e9Zjp06dXnTp1VKdOHb300ktq1aqVrly5ctdoMi8vL9WuXVvLli1Tly5dYk2VePv2bX3zzTeqWrVqrO/bvXt3rMfYtWuXChQoIG9v70f6/fxX9uzZlTNnTp08eVKvvPLKPb+mUKFCWrJkie7cuRNd8AsICHik5wMAAABgPS4nAwAAAJAoQkNDdeHCBZ0/f1779u3T5MmT1aFDB1WvXl0NGjSQJBUoUEBhYWGaM2eOTp48qR9//DF6DaUoefPm1c2bN7V582ZdunRJt27d0hNPPKF69eqpV69eWrVqlU6ePKndu3drypQp+u233+6ZJ23atBo8eLDWr1+vdu3aadOmTTp16pT27NmjESNGaMCAAdGZ9u7dqw0bNujYsWMaM2aM9uzZE/04J0+e1BdffKGdO3fq9OnT2rhxo/7555/o9Zg6duyoJUuWaPz48Tp06JCOHDmin376SaNHj35ge1WoUEH//POPNm7cGKso9fTTT2v16tU6e/Zs9HpSmTJlUubMmfXdd9/p+PHj2rx5s4YNGxbr8WbMmKHly5fryJEjOnbsmFauXKkcOXIoY8aM93z+7t27K3v27Hrvvfe0fv16nT17Nnptq/Dw8Oj2iXLmzBkNHTpUR48e1fLlyzV37lw1b95ckh7p93MvXbp00dSpUzV79mwdO3ZMBw4c0MKFCzVjxgxJUt26deVwOPTRRx/p8OHDWr9+vb7++us4Pz4AAAAA12KkFAAAAIBEsWHDBlWtWlUpUqRQxowZ5evrq48++kgNGzaMHhXl6+urPn366KuvvtKoUaP01FNP6f3331fv3r2jH6ds2bJq0qSJunXrpitXrqhTp07q3Lmzhg4dqkmTJmnYsGEKCgpS5syZ5efnp+eee+6+mWrWrKlvv/1WU6dOVY8ePRQSEqLcuXOrYsWK6tatmySpSZMm2r9/v7p37y6Hw6GXX35Zb731ln7//XdJZqq9o0ePavHixbpy5Ypy5syppk2bRo9iqlatmiZPnqwJEyboq6++UooUKVSwYEG99tprD2wvf39/+fj4yOl0qkSJEtH3lylTRuHh4UqbNq1KlSolyYxsGj16tAYPHqy6devqiSee0EcffaRmzZpFf1+6dOk0bdo0HT9+XF5eXipVqpSmTp1636nvsmTJou+++04TJ07UgAEDFBwcrEyZMumZZ57RyJEjlSdPnlhf36BBA92+fVuvvfaavL291bx5c73xxhvR+x/l9/Nfr732mlKnTq3p06drxIgRSps2rYoUKaIWLVpE/4yTJ0/WgAED1KBBAxUuXFgffPCBOnfuHOfnAAAAAOA6Dmd8VvcFAAAAAOA/mjVrJl9fX/Xr18/uKAAAAADcGNP3AQAAAAAAAAAAwOUoSgEAAAAAAAAAAMDlmL4PAAAAAAAAAAAALsdIKQAAAAAAAAAAALgcRSkAAAAAAAAAAAC4HEUpAAAAAAAAAAAAuBxFKQAAAAAAAAAAALgcRSkAAAAAAAAAAAC4HEUpAAAAAAAAAAAAuBxFKQAAAAAAAAAAALgcRSkAAAAAAAAAAAC4HEUpAAAAAAAAAAAAuBxFKQAAAAAAAAAAALgcRSkAAAAAAAAAAAC4HEUpAAAAAAAAAAAAuBxFKQAAAAAAAAAAALgcRSkAAAAAAAAAAAC4HEUpAAAAAAAAAAAAuBxFKQAAAAAAAAAAALgcRSkAAAAAAAAAAAC4HEUpAAAAAAAAAAAAuBxFKQAAAAAAAAAAALgcRSkAAAAAAAAAAAC4HEUpAAAAAAAAAAAAuBxFKQAAAAAAAAAAALgcRSkAAAAAAAAAAAC4HEUpAAAAAAAAAAAAuBxFKQAAAAAAAAAAALgcRSkAAAAAAAAAAAC4HEUpAAAAAAAAAAAAuBxFKQAAAAAAAAAAALgcRSkAAAAAAAAAAAC4HEUpAAAAAAAAAAAAuBxFKQAAAAAAAAAAALgcRSkAAAAAAAAAAAC4HEUpAAAAAAAAAAAAuBxFKQAAAAAAAAAAALgcRSkAAAAAAAAAAAC4HEUpAAAAAAAAAAAAuBxFKQAAAAAAAAAAALgcRSkAAAAAAAAAAAC4HEUpAAAAAAAAAAAAuBxFKQAAAAAAAAAAALgcRSkAAAAAAAAAAAC4HEUpAAAAAAAAAAAAuBxFKQAAAAAAAAAAALgcRSkAAAAAAAAAAAC4HEUpAAAAAAAAAAAAuBxFKQAAAAAAAAAAALgcRSkAAAAAAAAAAAC4HEUpAAAAAAAAAAAAuBxFKQAAAAAAAAAAALgcRSkAAAAAAAAAAAC4HEUpAAAAAAAAAAAAuBxFKQAAAAAAAAAAALgcRSkAAAAAAAAA+D/27juuyvL/4/gLEAeu3OXKHOAAxVXONFfumZp7ZG7NkVtLzcyVe2v1deVouHKkmZozTcWVO7XUXDhxse7fH9cPkEQFBW4OvJ+PB49zcc597vM+cIDD/bmvzyUiIjFORSkRERERERERERERERGJcSpKiYiIiIhEgxYtWlCzZk27Y0TaihUrqFq1KgUKFKBYsWJ2x0nQpkyZgoeHR6w81u+//46Hhwe///57rDzey/Lw8GDKlCl2x5B4Tq8zERERkdiTyO4AIiIiIhKzfvzxRwYMGADAokWLnihAWJZF+fLluXz5MuXLl2fWrFl2xIyUChUqcPHiRZo3b86QIUPC3fb777/TsmVLJk2aRNWqVW1K6BjOnDnDgAEDKFu2LO3btydp0qRP3XbKlClMnTo19POkSZOSJk0a8ubNS+XKlalVqxaJEyeOjdhR1r9/f5YvXx76uaurK1myZKF69ep07NiRJEmS2Jgubtu/fz87duygVatWpEqVKtr3f+zYMb766iv27t2Lr68vbm5u5M+fn1q1alG3bl1cXFyi/TFjwqNHj1i8eDFr1qzhr7/+wt/fn8yZM1O6dGlatGjBG2+8YXfEaHX//n2++eYb1q9fz99//02iRInw8PCgUaNG1KlTBycnJ7sjAuH/7j1LlixZ+PXXX2MhkYiIiIiEUFFKREREJIFIkiQJP/300xNFqT179nD58uU4W1iIyLJly2jfvj2ZMmWyO4pD2rNnD8HBwQwaNIjXX389UvcZOnQobm5u+Pv7c+XKFbZv387AgQOZN28es2bN4rXXXovh1C8mceLEjBgxAgA/Pz82bdrE9OnT+fvvv/nyyy9tTmd06tSJ9u3b2x0jnAMHDjB16lTq1asX7UWp7777jk8//ZR06dJRp04dXn/9de7du8fu3bsZNGgQ165do2PHjtH6mDHhxo0btGvXjqNHj/LOO+9Qs2ZN3NzcOHv2LGvXrmXZsmUcOXLE7pjR5vr167Ru3ZozZ85QvXp1mjdvzqNHj9iwYQP9+vVj69atjBs3Lk4UFIsXL86YMWPCXTd48GAKFixIo0aNQq9Lnjw5AIcOHYoTuUVEREQSAhWlRERERBKIcuXKsX79egYPHkyiRGFvA3/66ScKFCjArVu37AsXBXny5OHs2bPMmTOHwYMH2x0nVgUHBxMQEPDSM3x8fX0BSJkyZaTv8+6775I2bdrQz7t27cqqVavo168fH330EcuWLXupTDElUaJE1KlTJ/Tzpk2b8v7777NmzRoGDBhA+vTpbUxnJEqUKNzPZESi63tvNx8fHz799FO8vb2ZPXs2KVKkCL2tdevWHD58mFOnTtmYMPIGDBjAsWPHmDx5Mu+++26423r06MGECRNsShYz+vXrx5kzZ5g6dSoVK1YMvb5ly5aMHj2ar7/+mnz58sVqgfVpPxfZsmUjW7Zs4a4bOnQo2bJlC/f7IISj/1yJiIiIOBKtKSUiIiKSQNSoUYNbt26xY8eO0Ov8/f35+eefqVWrVoT3CQ4O5n//+x81atTAy8uLUqVK8cknn3D79u1w2/3yyy+0b9+eMmXK4OnpSaVKlZg2bRpBQUHhtgtZd+n06dO0aNGCQoUKUbZsWebMmRPp55ElSxbq1KnDsmXLuHLlyjO37d+/PxUqVHji+ojW8PHw8GD48OGsW7eO6tWrU7BgQRo3bsyJEycAWLJkCZUrV8bLy4sWLVpw4cKFCB/zyJEjvP/++xQsWJAKFSqwePHiJ7bx9/dn8uTJVK5cGU9PT8qVK8eYMWPw9/ePMNOqVatCvwfbtm175nNetGgRNWrUwNPTkzJlyjBs2DDu3LkTenuFChVC104pWbLkS62lUrt2bRo2bMjBgwfDva7++OMPunfvTvny5UOf38iRI3n48GHoNj/88AMeHh78+eefT+x35syZ5MuXL/T7e+7cObp160bp0qXx8vLi7bffpmfPnty9ezfKmZ2cnChSpAiWZfHPP/+Eu23r1q00bdoUb29vChcuTPv27SMskPzyyy/UrFkTLy8vatasycaNG594rT1t7aYLFy7g4eHBjz/+GHrds16PEX3vr1y5woABAyhVqhSenp7UqFGD77///omcly9fpnPnznh7e1OyZElGjhz5xGssIlOmTAmdZVKxYkU8PDzw8PAIfc0HBgYybdo0KlWqhKenJxUqVGD8+PGR2vfUqVNxcnJi3Lhx4QpSIby8vKhfv/4z9/Hnn3/Srl07ihQpQuHChWnVqhU+Pj7htgkICGDq1KlUqVIFLy8v3nrrLZo0aRLudQqmlWX37t158803Qx9706ZNz30eBw8eZMuWLbz33ntPFKTAzNDr169f6OfHjx+nf//+VKxYES8vL0qXLs2AAQO4efNmuPv5+fnx+eefU6FCBTw9PSlZsiRt2rTh6NGjTzz+Bx98QNGiRSlUqBDNmzdn3759L7SvyPDx8WH79u3Uq1cvXEEqRO/evcmRIwdz587l4cOHBAQE8Oabb0bYQs/Pzw8vLy9Gjx4del1M/k6MjP/+Hgz5mTx79iwff/wxRYsWpUSJEkycOBHLsvj333/p1KkTRYoUoXTp0nz99ddP7DOyz0lEREQkodFMKREREZEEIkuWLHh7e7NmzRrKlSsHwG+//cbdu3epXr06CxYseOI+n3zyCcuXL6d+/fqhhZhFixbx559/snjxYlxdXQFYvnw5bm5utGnTBjc3N3bv3s3kyZPx8/MLd2AW4Pbt27Rr147KlStTrVo1fv75Z8aNG4e7u3torufp1KkTK1eujPbZUn/88Qe//vorTZs2BWD27Nl07NiRdu3a8e2339K0aVNu377N3LlzGThwIPPnz3/iubVv355q1apRo0YN1q1bx9ChQ3F1deW9994DTKGvU6dO7Nu3j0aNGpErVy5OnjzJvHnzOHfuHNOnTw+3z927d7Nu3TqaNWtGmjRpyJIly1Pzh6z/VKpUKZo0acLZs2dZvHgxhw8fDv1+DRw4kBUrVrBx48bQlnz/LYhERe3atVm6dCnbt2+ndOnSAKxfv56HDx/SpEkTXnnlFQ4dOsTChQu5fPkykydPBszMq+HDh7N69Wry588fbp+rV6/mzTffJFOmTPj7+/PBBx/g7+9P8+bNSZ8+PVeuXGHLli3cuXMnSrO9Qly8eBEgXFu6FStW0L9/f8qUKcPHH3/MgwcPWLx4MU2bNmX58uVkzZoVgO3bt9OtWzdy585N7969uXnzJgMGDODVV199oa/fs0T0vb9+/TqNGjXCycmJZs2akTZtWn777TcGDRqEn58frVu3BuDhw4e0atWKf//9lxYtWpAxY0ZWrlzJ7t27n/u4lStX5ty5c/z0008MGDCANGnSAITOlBs8eDDLly/n3XffpU2bNhw6dIhZs2Zx5swZpk2b9tT9PnjwgN27d1OsWDEyZ878Ql+TU6dO0axZM5InT067du1IlCgRS5cupUWLFixcuJBChQoBpvg1a9YsGjZsSMGCBfHz8+PIkSMcPXo09HV66tQpmjRpQqZMmfjwww9xc3Nj3bp1dOnShSlTplC5cuWn5ghZhyiiWTcR2blzJ//88w/169cnQ4YMnDp1imXLlnH69GmWLVsWuhbTp59+ys8//0zz5s3JlSsXt27dYt++fZw5c4YCBQoAsGvXLj788EM8PT3p2rUrTk5O/Pjjj7Rq1Ypvv/2WggULRnpfkbV582YA6tatG+HtiRIlombNmkydOpX9+/dTqlQpKlWqxMaNGxk2bFi49rC//PIL/v7+VK9eHYjZ34kvq2fPnuTKlYvevXuzdetWZsyYwSuvvMKSJUsoUaIEH3/8MatXr2b06NF4eXlRvHjxF3pOIiIiIgmKJSIiIiLx2g8//GC5u7tbhw4dshYuXGgVLlzYevDggWVZltW9e3erRYsWlmVZ1jvvvGO1b98+9H579+613N3drVWrVoXb32+//fbE9SH7e9yQIUOsQoUKWY8ePQq9rnnz5pa7u7u1fPny0OsePXpklS5d2urWrdtzn8vjGfv37295eXlZV65csSzLsnbv3m25u7tb69atC92+X79+1jvvvPPEfiZPnmy5u7uHu87d3d3y9PS0/vnnn9DrlixZYrm7u1ulS5e27t69G3r9l19+abm7u4fbNuS5ff311+GeW506daySJUta/v7+lmVZ1ooVK6y8efNae/fuDff4ixcvttzd3a19+/aFy5Q3b17r1KlTz/3a+Pr6WgUKFLDatm1rBQUFhV6/cOFCy93d3fr++++feP6+vr7P3e/ztr19+7bl7u5udenSJfS6iF4Ps2bNsjw8PKyLFy+GXterVy+rTJky4fIePXrUcnd3t3744QfLsizrzz//fOL7Gln9+vWzvL29LV9fX8vX19c6f/689dVXX1keHh5WzZo1reDgYMuyLMvPz88qVqyYNXjw4HD3v3btmlW0aNFw19epU8cqXbq0defOndDrtm/fbrm7u4d7rYW8Hnfv3h1un//880+452dZT389RvS9HzhwoFW6dGnrxo0b4a7v2bOnVbRo0dCv/f/+9z/L3d3dWrt2beg29+/ftypXrhxhrv+aO3fuE69xy7KsY8eOWe7u7tagQYPCXT9q1CjL3d3d2rVr11P3GXLfESNGPPOxH+fu7m5Nnjw59PPOnTtbBQoUsP7+++/Q665cuWIVLlzYatasWeh1tWvXDvf7LCKtWrWyatasGe53VHBwsNW4cWOrSpUqz7xvly5dLHd3d+v27duReh4R/Uz89NNPlru7e7jfBUWLFrWGDRv21P0EBwdbVapUsdq2bRv6+g3Zf4UKFaw2bdpEel9R0blz5+c+3w0bNlju7u7W/PnzLcuyrG3btlnu7u7Wr7/+Gm67Dz/80KpYsWLo5zH1O/G/vL29rX79+kV4239fZyE/k0OGDAm9LjAw0Hr77bctDw8Pa9asWaHX37592ypYsGC4fUflOYmIiIgkNGrfJyIiIpKAVKtWjUePHrF582b8/PzYsmXLU1v3rV+/npQpU1K6dGlu3LgR+lGgQAHc3NzCtSVLmjRp6NjPz48bN25QrFgxHjx4wF9//RVuv25ubuFmFyROnBgvL68nWqk9T+fOnQkKCmL27NlRut+zlCxZMnRGDBA666JKlSrhWo2FzET4b+ZEiRLRuHHj0M8TJ05M48aN8fX1DW2ZtX79enLlykXOnDnDfV1LlCgB8ES7t+LFi5M7d+7nZt+5cycBAQG0bNkSZ+ewt/kNGzYkRYoUbN26NVJfg6hyc3MD4N69e6HXPf56uH//Pjdu3KBw4cJYlhWuXV+dOnW4evVquOe8evVqkiZNSpUqVQBCv+7bt2/nwYMHUc53//59SpYsScmSJalcuTKjR4+mSJEiTJ8+PXR2ys6dO7lz5w41atQI9z1xdnamUKFCofmuXr3KsWPHqFevXrgZWqVLl47U9yiq/vu9tyyLDRs2UKFCBSzLCpe1TJky3L17N/R19ttvv5EhQwaqVq0aev9kyZLRqFGjl8oU8jpq06ZNuOvbtm0b7vaI+Pn5AZA8efIXeuygoCB27NhBpUqVwq0XlDFjRmrWrMm+fftCHyNVqlScOnWKc+fORbivW7dusXv3bqpVqxb6O+vGjRvcvHmTMmXKcO7cuWe2B43qc3n8Z+LRo0fcuHEj9PfL4+30UqVKxcGDB5/62MeOHePcuXPUqlWLmzdvhuYOeZ3v3buX4ODgSO0rKkJ+vp/1fENuC/nalChRgjRp0rB27drQbW7fvs3OnTtDZ0lBzP1OjA4hM1wBXFxc8PT0xLKscNenSpWKN954I9zfg6g+JxEREZGERO37RERERBKQtGnTUrJkSX766ScePnxIUFBQhOuhAJw/f567d+9SsmTJCG/39fUNHZ86dYqJEyeye/fu0AOSIf677s+rr74aWgwIkTp16tC1myIrW7Zs1K5dm2XLltG+ffso3fdpXnvttXCfhxRE/tuaLaQg8fhaTWAOjocUaULkyJEDMC3jvL29OX/+PGfOnInU1xUIVyR7lkuXLgGQM2fOcNcnTpyYbNmyhbasi273798Hwh+svnTpEpMnT+bXX399Yv2xx18fpUuXJkOGDKxatYqSJUsSHBzMTz/9RMWKFUO/9tmyZaNNmzZ88803rF69mmLFilGhQgVq164dqdZ9SZIkYebMmYBZY2nu3Ln4+vqSJEmS0G1CChetWrWKcB8hWUK+xq+//voT27zxxhsRro/1Mv77vb9x4wZ37txh6dKlLF26NML73LhxAzCvt9dff/2Jn7U33njjpTJdvHgRZ2dnsmfPHu76DBkykCpVqme+zkK+jo8XMKPixo0bPHjwIMLnkCtXLoKDg/n333/JkycP3bt3p3Pnzrz77ru4u7tTpkwZ6tSpQ968eQH4+++/sSyLSZMmMWnSpAgfz9fXl0yZMj33uTzeBvJpbt26xdSpU1m7du0TP+OP/478+OOP6d+/P+XLl6dAgQKUK1eOunXrhhbhQl6r/22L+t/9pU6d+rn7ioqQn+9nPd//Fq4SJUpElSpV+Omnn/D39ydx4sRs2LCBgICAcEWpmPqdGB3+22YyZcqUJEmSJLSV5ePX37p1K/TzqD4nERERkYRERSkRERGRBKZmzZoMGTKE69ev8/bbbz/1AGNwcDDp0qVj3LhxEd4eclDuzp07NG/enBQpUtC9e3eyZ89OkiRJOHr0KOPGjQs9az+Ei4tLtD2XTp06sWrVKubMmUOlSpWeuP2/B+RDBAUFRXj907I97XrLsiKZNExwcDDu7u4MGDAgwtv/WwB7fIZFXHTy5EmA0CJFUFAQbdq0CV07LGfOnLi5uXHlyhX69+8f7vXg4uJCrVq1WLZsGUOHDmX//v1cvXqV2rVrh3uM/v37U69ePTZt2sSOHTsYMWIEs2bNYtmyZc9dy8nFxYVSpUqFfl6mTBmqVavGJ598ElqsCvk+jhkzhgwZMkS4j6h62mvvvz8Pz/Lf733IfWvXrk29evUivM/LrA8WFU97fs/y+uuvkyhRotDXTEwqXrw4GzduDH3NfP/998ybN49hw4bRsGHD0K9l27ZtKVu2bIT7+G/h7XEhxd+TJ09SrFix5+bp0aMHBw4c4IMPPiBfvny4ubkRHBxMu3btwv0eqV69OsWKFWPjxo3s2LGDr776ijlz5jBlyhTKlSsXum3fvn3Jly9fhI8VUhh/3r6iIleuXPzyyy+cOHEidN2k/wo5seDxWUw1atRg6dKl/Pbbb1SqVIn169eTM2fO0OIgxO3fiY/POg0Rmb8HUX1OIiIiIgmJilIiIiIiCUzlypX59NNP8fHxYcKECU/dLnv27OzatYsiRYo88yDgnj17QmcBPH6w8sKFC9Ga+2kZa9euzdKlS0NbYT0uVapUT8xmgrAZL9Ht6tWr3L9/P9xsqZCZDVmyZAnNfPz4cUqWLPlCB/afJuSM/r/++ivcTAh/f38uXLgQrjATnVatWgUQemD/5MmTnDt3jtGjR1O3bt3Q7Xbs2BHh/evUqcPXX3/Nr7/+ym+//UbatGkpU6bME9t5eHjg4eFB586d2b9/P02aNGHx4sX07NkzSnkzZsxI69atmTp1Kj4+Pnh7e4d+vdKlS/fMr1PI1/j8+fNP3Hb27Nlwn4cUe/87U/BlZqylTZuW5MmTExwc/NzvZ5YsWTh58iSWZYV7nf0359M87bWZJUsWgoODOX/+PLly5Qq9/vr169y5cyf0dR6RZMmSUaJECXbv3s2///77xMzE50mbNi3JkiWL8Dn89ddfODs7h9vnK6+8QoMGDWjQoAH37t2jefPmTJkyhYYNG4Z+z11dXV/oZ+Odd95h1qxZrFq16rlFqdu3b7Nr1y66detG165dQ69/WmvBjBkz0qxZM5o1a4avry/16tVj5syZlCtXLjR3ihQpIpX7WfuKivLlyzNr1ixWrFgRYVEqKCiI1atXkzp1aooUKRJ6ffHixcmQIQNr166lSJEi7N69m44dO4a7b0z9TrRTfHxOIiIiItFFa0qJiIiIJDDJkydn6NChdOvWjQoVKjx1u2rVqhEUFMT06dOfuC0wMDC02BNyJvnjZ4n7+/vz7bffRnPyiHXq1InAwEDmzp37xG3Zs2fn7t27HD9+PPS6q1evsnHjxhjJEhgYGK6tmr+/P0uXLiVt2rQUKFAAMF/XK1eusGzZsifu//Dhw9B2eFFVqlQpXF1dWbBgQbjvxffff8/du3ejfBA6MlavXs13331H4cKFQ9tURfR6sCyL+fPnR7iPvHnz4uHhwffff8+GDRuoUaMGiRKFnTvn5+dHYGBguPu4u7vj7OyMv7//C+Vu3rw5yZIlC12PrGzZsqRIkYJZs2YREBDwxPYhLfEyZsxIvnz5WL58ebhi044dOzh9+nS4+2TJkgUXFxf27t0b7vrFixe/UGYwMzTeffddfv755whnG4XkBHj77be5evUq69evD73uwYMHEb7uIpIsWTLgyaJayOto3rx54a7/5ptvwt3+NF26dMGyLPr27RthG78jR46wfPnyCO/r4uJC6dKl2bRpU7ii9/Xr1/npp58oWrRoaFu9mzdvhrtv8uTJyZ49e+hrJl26dLz55pssXbqUq1evPvFYj38tI1K4cGHKli3Ld999xy+//PLE7f7+/owePTo0d0T++zUMCgp64uudLl06MmbMGJrb09OT7Nmz8/XXX0f49QvJHZl9RUWRIkUoVaoUP/74I5s3b37i9gkTJnDu3DnatWsX7iQGZ2dnqlatyubNm1m1ahWBgYHhWvdBzP1OtFN8fE4iIiIi0UUzpUREREQSoKe1/nrcm2++SePGjZk1axbHjh2jdOnSuLq6cu7cOdavX8+gQYOoWrUqhQsXJnXq1PTv358WLVrg5OTEypUrX6i13YsImS0V0YHs6tWrM27cOLp27UqLFi14+PAhixcv5o033uDo0aPRniVjxozMmTOHixcvkiNHDtauXcuxY8f47LPPcHV1BczMoHXr1vHpp5/y+++/U6RIEYKCgvjrr79Yv349c+fOxcvLK8qPnTZtWjp06MDUqVNp164dFSpU4OzZs3z77bd4eXk90RIvqn7++Wfc3NwICAjgypUrbN++nf3795M3b95wa/LkzJmT7NmzM3r0aK5cuUKKFCn4+eefI5yxFqJu3bqhB/D/m3P37t0MHz6cqlWrkiNHDoKCgli5cmVogeZFpEmThvr16/Ptt99y5swZcuXKxdChQ+nbty/169enevXqpE2blkuXLrF161aKFCnCJ598AkCvXr3o0KEDTZs2pUGDBty6dYuFCxeSJ0+ecAeaU6ZMSdWqVVm4cCFOTk5ky5aNLVu2vPRaMr179+b333+nUaNGNGzYkNy5c3P79m2OHj3Krl272LNnDwCNGjVi0aJF9OvXj6NHj5IhQwZWrlwZ6dZnIUXUCRMmUL16dVxdXXnnnXfImzcv9erVY+nSpdy5c4fixYtz+PBhli9fTqVKlShRosQz9xvytRw2bBjVqlWjTp06vP7669y7d489e/bw66+/0qNHj6fev0ePHuzcuZOmTZvStGlTXFxcWLp0Kf7+/vTp0yd0uxo1avDmm29SoEABXnnlFQ4fPszPP/9M8+bNQ7f59NNPadq0KbVq1aJRo0Zky5aN69ev4+Pjw+XLl0NnAT7NmDFjaNu2LV27duWdd96hZMmSJEuWjPPnz7N27VquXr1Kv379SJEiBcWLF2fu3LkEBASQKVMmduzY8cRs0nv37lGuXDneffdd8ubNi5ubGzt37uTw4cP0798fMEWeESNG8OGHH1KzZk3q169PpkyZuHLlCr///jspUqRg5syZkdoXwO+//07Lli3p2rUr3bp1e+bzHT16NK1bt6Zz587UrFmTYsWK4e/vz4YNG9izZw/Vq1fngw8+eOJ+1apVY8GCBUyePBl3d/dwM+wg5n4n2ik+PicRERGR6KKilIiIiIg81fDhw/H09GTJkiVMmDABFxcXsmTJQu3atUNbNKVJk4aZM2cyevRoJk6cSKpUqahduzYlS5aM8ABlTAhZW+q/a0WlSZOGqVOnMmrUKMaOHUvWrFnp1asX58+fj5GiVOrUqRk1ahQjRoxg2bJlpE+fnk8++YRGjRqFbuPs7My0adP43//+x8qVK9m4cSPJkiUja9astGjRgjfeeOOFH79bt26kTZuWhQsX8sUXX5A6dWoaNWpEr169QotiL2ro0KEAJEmShDRp0pAvXz5GjhxJrVq1SJw4ceh2rq6uzJw5M3TdpyRJklC5cmWaNWtGnTp1Itx3rVq1GDduHNmyZaNgwYLhbvPw8KBMmTJs3ryZK1eukCxZMjw8PJgzZw7e3t4v/HzatGnDkiVLmDNnDqNGjaJWrVpkzJiR2bNn89VXX+Hv70+mTJkoVqwY9evXD73f22+/zaRJk5g4cSJffvkl2bNn54svvmDTpk2hBaEQgwcPJjAwkCVLlpA4cWKqVq1K3759qVmz5gvnTp8+Pd999x3Tpk1j48aNLF68mFdeeYXcuXPz8ccfh26XLFky/ve///HZZ5+xcOFCkiZNSq1atXj77bdp167dcx+nYMGCfPTRRyxZsoRt27YRHBzMpk2bcHNzY8SIEWTNmpXly5fzyy+/kD59ejp06BCuNd2zvP/++3h5efH111+zYsUKbt68iZubG/nz5+eLL754ZgE1T548LFq0iC+//JJZs2ZhWRYFCxZk7Nix4Vp4tmjRgl9//ZUdO3bg7+9P5syZ6dGjR7jfSblz5+aHH35g6tSpLF++nFu3bpE2bVry589Ply5dnvs80qZNy5IlS/j2229Zu3YtEyZMICAggCxZslChQgVatmwZuu2XX37JZ599xrfffotlWZQuXZo5c+aEW88qadKkNGnShB07drBhwwYsyyJ79uyhxbMQb731FkuXLmX69OksXLiQ+/fvkyFDBgoWLEjjxo2jtK+QQmpEa6n9V8aMGfnuu+/45ptvWL9+PRs2bMDFxQUPDw9GjRpF3bp1I2xVV6RIEV577TX+/fffJ2ZJQcz+TrRLfHxOIiIiItHFyYqtU1hFREREREQicOPGDcqWLUvnzp0jVQyIi/r37x8600fEUYwZM4Y1a9awcePGcMVlEREREZGYojWlRERERETEVsuXLycoKOipM6lEJGb8/vvvdO7cWQUpEREREYk1at8nIiIiIiK22LVrF2fOnGHmzJlUqlSJrFmz2h1JJEH54Ycf7I4gIiIiIgmMilIiIiIiImKL6dOnc+DAAQoXLsyQIUPsjiMiIiIiIiIxTGtKiYiIiIiIiIiIiIiISIzTmlIiIiIiIiIiIiIiIiIS41SUEhERERERERERERERkRinNaWA4OBgAgMDcXZ2xsnJye44IiIiIiIiIiIiIiIiDsOyLIKDg0mUKBHOzk+fD6WiFBAYGMjhw4ftjiEiIiIiIiIiIiIiIuKwvLy8SJw48VNvV1EKQqt2Xl5euLi42JxGRERERERERERERETEcQQFBXH48OFnzpICFaUAQlv2ubi4qCglIiIiIiIiIiIiIiLyAp63RNKzS1YiIiIiIiIiIiIiIiIi0UBFKREREREREREREREREYlxKkqJiIiIiIiIiIiIiIhIjFNRSkRERERERERERERERGKcilIiIiIiIiIiIiIiIiIS41SUEhERERERERERERERkRinopSIiIiIiIiIiIiIiIjEOBWlREREREREREREREREJMapKCUiIiIiIiIiIiIiIiIxTkUpERERERERERERERERiXEqSomIiIiIiIiIiIiIiEiMU1FKREREREREREREREREYpyKUiIiIiIiIiIiIiIiIhLjVJQSERERERERERERERGRGKeilIiIiIiIiIiIiIiIiMQ4FaVEREREREREREREREQkxqkoJSIiIiIiIiIiIiIiIjHO1qJUUFAQEydOpEKFChQsWJBKlSoxbdo0LMsK3cayLCZNmkSZMmUoWLAgrVu35ty5c+H2c+vWLXr37k2RIkUoVqwYAwcO5N69e7H8bERERERERERERERERORpbC1KzZkzh8WLF/PJJ5+wdu1aPv74Y+bOncuCBQvCbbNgwQKGDh3KsmXLSJYsGR988AGPHj0K3ebjjz/m9OnTfPPNN8ycOZM//viDTz75xI6nJCIiIiIiInHU1atw+7bdKUREREREEi5bi1IHDhygYsWKlC9fnqxZs1K1alXKlCnDoUOHADNLav78+XTq1IlKlSqRN29exowZw9WrV/nll18AOHPmDNu2bWPEiBEUKlSIYsWKMXjwYNasWcOVK1fsfHoiIiIiIiISR1y4AO7uUKIEBAbanUZEREREJGGytShVuHBhdu/ezdmzZwE4fvw4+/bt4+233wbgwoULXLt2jVKlSoXeJ2XKlBQqVIgDBw4AprCVKlUqvLy8QrcpVaoUzs7OocUtERERERERSdhmzDCzpI4fh5Ur7U4jIiIiIpIwJbLzwdu3b4+fnx/VqlXDxcWFoKAgevbsSe3atQG4du0aAOnSpQt3v3Tp0nH9+nUArl+/Ttq0acPdnihRIlKnTh16fxEREREREUm4Hj6E2bPDPp86FRo0sC+PiIiIiEhCZWtRat26daxevZovv/yS3Llzc+zYMb744gsyZsxIvXr17IwmIiIiIiIi8cSyZXD9OmTKZC63bIEjR8DT0+5kIiIiIiIJi63t+8aMGUP79u2pUaMGHh4e1K1bl1atWjFr1iwAMmTIAICvr2+4+/n6+pI+fXoA0qdPz40bN8LdHhgYyO3bt0PvLyIiIiIiIgnX1Knm8qOPoG7d8NeJiIiIiEjssbUo9fDhQ5ycnMJd5+LigmVZAGTNmpUMGTKwa9eu0Nv9/Pw4ePAghQsXBsy6VHfu3OHIkSOh2+zevZvg4GAKFiwYC89CRERERERE4qrff4e9eyFJEmjXDrp2NdcvWAC3btkaTUREREQkwbG1KPXOO+8wc+ZMtmzZwoULF9i4cSPffPMNlSpVAsDJyYmWLVsyY8YMNm3axIkTJ+jbty8ZM2YM3SZXrlyULVuWIUOGcOjQIfbt28dnn31GjRo1yJQpk51PT0RERERERGwWMiPq/fchQwYoV8607bt/H775xt5sIiIiIiIJjZMVMi3JBn5+fkyaNIlffvkFX19fMmbMSI0aNejSpQuJEycGwLIsJk+ezLJly7hz5w5Fixbl008/5Y033gjdz61bt/jss8/49ddfcXZ2pkqVKgwePJjkyZNHKkdQUBA+Pj54e3vj4uISI89VREREREREYteVK5AtGwQEwB9/QNGi5vpZs6BjR8iVC06eBGdbT9cUEREREXF8ka2z2FqUiitUlBIREREREYl/RoyAIUOgRAl4rCs8fn6QNSvcvg1r10K1avZlFBERERGJDyJbZ9H5YCIiIiIiIhLvBATAjBlmHLKOVIgUKaBtWzOeMiV2c4mIiIiIJGQqSomIiIiIiEi8s2IFXLoEmTJBw4ZP3t65Mzg5wbp1cPp0rMcTEREREUmQVJQSERERERGReCdkBlT79vD/SxaHkzt3WNu+6dNjL5eIiIiISEKmopSIiIiIiIjEKwcPwrZtkCgRdOz49O1C2vp9/bVZZ0pERERERGKWilIiIiIiIiISr0ydai7r14fMmZ++3bvvmhlTt2/DokWxk01EREREJCFTUUpERERERETijRs3wgpM3bo9e1tnZ+jSxYynTgXLitlsIiIiIiIJnYpSIiIiIiIiEm98/TU8eACFCkHp0s/fvnVrcHODI0dg69YYjycSyrLMLD1/f7uTiIiIiMSeRHYHEBEREREREYkOQUEwfboZd+sGTk7Pv88rr0CLFjBrlpktVb58TCYUCVOnDqxebcaJEkHy5OE/3NyevC6ij+dtlyyZmRUoIiIiEheoKCUiIiIiIiLxwtq1cPYspEkDTZpE/n5du5qi1IoV8M8/kC1bjEUUAWDHjrCCFEBgoJk1dft2zDyem1vkilwvUghLnDhmMouIiEj8pKKUiIiIiIiIxAtTp5rLdu3MQfPI8vQ0M6S2bIGZM+Hzz2MinUiYMWPMZZs2MH483Lv39I/79599+9O2u38/7PHu3zcf169H/3OJaJZXVIpcz9pGs7xERETiHyfL0lKuQUFB+Pj44O3tjYuLi91xREREREREJIpOnIC8eU3LvjNn4I03onb/H36A996D9OnNbKmkSWMmp8ixY5A/v3mtHjsGHh4x8zjBwWZ9tRcpckVmm8DAmMn9XyFFq2cVr9KnhwoVoFw5U8gSERGR2BfZOotmSomIiIiIiIjDmzbNXNaqFfWCFJj1fbJmhQsXYNkyaNkyevOJhBg3zlzWqRNzBSkwM4xCijYxwd//xWdxPW+biGZ5Pc/YsaYgVb48VK8O1apBrlwx89xFRETkxWmmFJopJSIiIiIi4sju3oUsWczlhg1QufKL7WfkSBg0CIoXhz17ojejCMClS5AjBwQEwM6dULKk3YnipqfN8npagevMGVi/3hSVH5cnT1iBqlw5zYAUERGJSZopJSIiIiIiIgnC/PmmIOXhAZUqvfh+PvwQhg2DvXtNUerNN6MvowjAxImmIFWmjApSz/Iis7wsC44ehXXrYO1a2L4dTp2CSZPMR7Jk8M47pkBVvTrkzBlz+UVEROTptFykiIiIiIiIOCzLgqlTzbhrV7NOz4vKkAHef9+Mp0x5+Wwij7t9G2bONOO+fe3NEh85OYGnJ/TpA5s3g68v/PijKTZnyWJmXq1dC926mbZ+Hh7Qo4eZXfnwod3pRUREEg6170Pt+0RERERERBzVL7+Ydn0pU8LFi+byZezda2ZIJU4Mf/8NmTJFT06RMWOgXz/Inx8OHzazgSR2WBYcORI2i2rHDggMDLvdzS1sFlW1appFJSIi8iIiW2fRWyARERERERFxWCEzmlq1evmCFJj1pN56C/z9Ye7cl9+fCMCjR6Z1H5iZPCpIxS4nJ/DyMjPUtmwxs6h++AHatTOzqO7fhzVrzGzLXLkgb17o2VOzqERERGKCZkqhmVIiIiIiIiKO6Nw5M6PBsuDYMXMgOTosXAgtWpiD1WfPgqtr9OxXEq6vv4YPPoDMmc1rKnFiuxNJCMsyM9fWrTMf27dDUFDY7W5uUKFC2CyqN96wL6uIiEhcpplSIiIiIiIiEq9Nn24OKFeuHH0FKYCGDSFjRtMOcOXK6NuvJEzBwTB2rBn37KmCVFzj5AQFC5rWiiGzqL7/PqyIeP8+/PQTdOliiuD58kGvXrBxo5kBJyIiIlGjopSIiIiIiIg4nPv3w9rrdesWvftOkgTatzfjqVOjd9+S8KxeDcePQ+rUYa8ribtSp4YGDczvlwsXwMcHvvgC3n4bXFzM93LCBKhSBdKmhdq1YcYMM3NTREREnk9FKREREREREXE4ixfDzZuQIwdUrx79++/QwRyA3roVDh2K/v1LwjFmjLns1AlSpbI3i0SNkxMUKgT9+5vfBdevw3ffQdu28Nprpji+ejV07mza+uXLB717wy+/aBaViIjI06goJSIiIiIiIg7FssJmMHXpYopH0S1rVqhXz4ynTYv+/UvCsGMH7NxpWvZ17253GnlZr7wC770HX31l2nv6+MDIkVC2bNgsqvHjTUvRdOnMLKqZM+H8ebuTi4iIxB1OlmVZdoewW2QX4BIRERERERH7bd9uDgInS2baa6VNGzOP89tvUK4cuLmZx0mTJmYeR+KvOnVg1Spo1w7mzLE7jcSkW7fMDKm1a2H9evj33/C358sH1aqZmZ1lypg2oSIiIvFJZOssmiklIiIiIiIiDiVkllSzZjFXkAJT+PLyMi26vvkm5h5H4qdjx0xByskJPv7Y7jQS00JmUX39tZlFdeCAmUVVpoyZRXXsmJlFVamSmUVVp45mUYmISMKkopSIiIiIiIg4jEuX4IcfzLhr15h9LCcn6NbNjKdNg+DgmH08iV/GjjWXdeqAh4e9WSR2OTmBtzcMGADbtpm1qJYtgzZt4NVX4d49U7Ds1Mmsi1eggClcbtoE/v52pxcREYlZat+H2veJiIiIiIg4ik8/heHDzSym336L+ce7d8+sL3XrFvz0E9SoEfOPKY7v4kV44w0ICIBdu6BECbsTSVwRHAwHD8K6dabV365d4QveKVJAxYqm1V+1apA9u31ZRUREokLt+0RERERERCRe8feHWbPMOKZnSYVInhzatjXjkLaBIs8zaZIpSJUtq4KUhOfsDIULw8CBZn2869dh6VJo3drMovLzg5UroWNHeP118PSEPn3g1181i0pEROIHzZRCM6VEREREREQcwbffmnWkMmeGc+fA1TV2HvfMGciTBywLTp40Y5GnuX0bsmWDu3dh9WqoWdPuROIogoPBx8fMolq3LuJZVJUqhc2iypbNtqgiIiJP0EwpERERERERiVemTDGXHTvGXkEKIFcuqF7djKdNi73HFcc0a5YpSOXPH/a6EYkMZ2coUgQGDTKzqK5dgyVLoFUryJTJzKJasQI6dDBt/Tw9oW9f2LxZs6hERMRxaKYUmiklIiIiIiIS1/3xBxQvbopR//xjDtDGpvXrzcyEVKnMekEpUsTu44tjePTIrCX177/wzTemJZtIdAgOhgMHwmZR7d4dfhZVypRmLarq1c3vqqxZ7csqIiIJk2ZKiYiIiIiISLwRsp5To0axX5ACqFLFtO27cwcWLoz9xxfHsHChKUhlyQJNm9qdRuITZ2coWhQGD4YdO8wsqsWLoWVLyJjRzM5bsQLatzdt/by8zCyqLVvM+mYiIiJxhYpSIiIiIiIiEqeFtLAC6NbNngzOztClixlPnWrWlxJ5XHAwjB1rxj17QuLE9uaR+C1tWnj/fZg3zxRC//gDhg+HkiXN76sjR8zr8Z13IF06qF8f5s6FCxfsTi4iIgmd2veh9n0iIiIiIiJx2RdfwMCBUKwY7NkDTk725Lh928yAuXcPfv3VHOwVCbFyJdStC6lTw99/m1aPInbw9YWNG2HtWtN69Nq18Ld7eZkWf9WqQenSsbtGn4iIxF9q3yciIiIiIiIOLzAQZsww427d7CtIgSk2tGxpxlOm2JdD4qYxY8xlp04qSIm90qUzs6jmz4fLl2HvXjOLqkQJ8zv08GHzen3nHUifHho0MLOo/v5bs0BFRCTmaaYUmiklIiIiIiISV/34ozlgmj49/PMPJE1qb56jR8HT07THOnsWsme3N4/EDdu3Q9mypmXfuXPw2mt2JxKJmK8vbNgA69ZFPIsqcWLInDn8R5YsT45TprQnv4iIxF2RrbMkisVMIiIiIiIiIlEydaq5bN/e/oIUQIECUKGCad83cyaMHGl3IokLQmZJtWqlgpTEbenSQZMm5iM4GPbtMwWqdevg99/B398UVs+de/Z+UqR4esHq8Y8kSWLjWYmIiCPRTCk0U0pERERERCQuOnLErH3i4mJmJWXLZnciY/lyqF8/7szeEnv9+acpVjo5wbFj4OFhdyKRF+Pvb9r9XbwIly6FfTz++cWLcOdO5PeZLl3EBavHP8+UyfyeFxERx+YwM6WuXLnC2LFj2bZtGw8ePOD1119n5MiReHl5AWBZFpMnT+a7777jzp07FClShKFDh5IjR47Qfdy6dYvPPvuMzZs34+zsTJUqVRg0aBDJkye36VmJiIiIiIjIy5o2zVzWrRt3ClIAtWqZPP/8A0uXmtkxknCNG2cu69ZVQUocW+LEpiXp89qS+vnBv/8+v3j16JFpF+jra9axehpnZ3j11ecXr9KmtXddQRERiR4vPVPKz8+P3bt388Ybb5ArV64o3ff27dvUq1ePt956iyZNmpAmTRrOnz9P9uzZyf7/fwFnz57N7NmzGTVqFFmzZmXSpEmcPHmStWvXkuT/5wC3a9eOa9euMXz4cAICAhg4cCBeXl58+eWXkcqhmVIiIiIiIiJxy61b5mDk/fuweTOUL293ovBGjYIBA6BoUdi7VwdKE6qLF+GNNyAgAHbtghIl7E4kEjdYFty8+fSCVcj48mUICorcPpMkef5aV5kzm9aCIiIS+yJbZ4lyUeqjjz6iePHiNG/enIcPH1KnTh0uXryIZVmMHz+ed999N9L7GjduHPv37+fbb7+N8HbLsihbtixt2rThgw8+AODu3buUKlWKUaNGUaNGDc6cOUP16tX5/vvvQ2dX/fbbb7Rv356tW7eSKVOm5+ZQUUpERERERCRumTgRevYET084dCjuFX2uXTOzpR49UjEiIevTx8yUKlsWfvvN7jQijicoCK5ejbhg9fjn169Hfp8pUz57rassWczab4kTx9zzEhFJiGKsfd8ff/xBp06dANi4cSOWZbF3716WL1/OjBkzolSU+vXXXylTpgzdu3dn7969ZMqUiaZNm9KoUSMALly4wLVr1yhVqlTofVKmTEmhQoU4cOAANWrU4MCBA6RKlSq0IAVQqlQpnJ2dOXToEJUrV47qUxQREREREREbBQeHte7r2jXuFaQAMmSA99+HefNg6lQVpRKiW7dg1iwz7tfP1igiDsvFxRSIXnvNzDx9mkePTMvAZ7ULvHQJ7t41H8ePm49nSZ/++cWrDBm03pWISHSLclHq7t27pE6dGoBt27ZRpUoVkiVLRvny5Rk7dmyU9vXPP/+wePFi2rRpQ8eOHTl8+DAjRozA1dWVevXqce3aNQDSpUsX7n7p0qXj+v+fInH9+nXSpk0b/kklSkTq1KlD7y8iIiIiIiKO4+ef4fRpSJ0amje3O83TdetmilLLlsGXX0IkGnVIPDJrljn4XaAAVKtmdxqR+C1JEsiRw3w8y927T653FdHsK39/M/vq+nU4ePDp+3NxMetdPWutqyxZ4JVX4uYJFCIicVGUi1KvvfYaBw4cIHXq1Gzbto3x48cDcOfOHRJHcd6rZVl4enrSq1cvAPLnz8+pU6dYsmQJ9erVi2o0ERERERERiQemTDGXbdtC8uT2ZnmWokXNDKndu2H2bBgyxO5EElsePTItJsG08HN2tjWOiPy/lCnNh7v707exLLhx49ntAh9f7+riRfPxLEmThhWpsmeH/v3hsaZOIiLymCgXpVq2bEmfPn1wc3Mjc+bMvPXWWwDs3bsX92f9xo9AhgwZyJUrV7jrcubMyc8//xx6O4Cvry8ZM2YM3cbX15e8efMCkD59em7cuBFuH4GBgdy+fTv0/iIiIiIiIuIYTp+GdevMGeedO9ud5vm6djVFqZkzzUFIV1e7E0lsWLjQHLDOkgWaNLE7jYhEhZMTpEtnPp5VOAoMfPp6V4+PfX3h4UP46y/zAXDkCOzbB4mifORVRCT+i/KvxmbNmlGwYEEuX74cunYTQLZs2ejRo0eU9lWkSBHOnj0b7rpz586RJUsWALJmzUqGDBnYtWsX+fLlA8DPz4+DBw/S5P/f9RUuXJg7d+5w5MgRPD09Adi9ezfBwcEULFgwqk9PREREREREbBSyllS1apA7t71ZIqNhQ+jd2xyYXLHCfC7xW3AwhKxe0LMnRLFpjIg4iESJwmY/PcvDh2HrXV24AJ06waFDMHcudOwYO1lFRBxJlCeY7969Gy8vLypXrkzyx/oolC9fnqLPWpEwAq1ateLgwYPMnDmT8+fPs3r1apYtW0bTpk0BcHJyomXLlsyYMYNNmzZx4sQJ+vbtS8aMGalUqRIAuXLlomzZsgwZMoRDhw6xb98+PvvsM2rUqEEmNfQWERERERFxGH5+8M03Ztytm71ZIitxYmjf3oxD2g5K/LZqFZw4YdY8+/BDu9OIiN2SJoU33oDSpaFxYxg+3Fw/eDDcvGlvNhGRuMjJsiwrKnfw9PTk1VdfpX79+tSrV4/XXnvtpQJs3ryZ8ePHc+7cObJmzUqbNm1o1KhR6O2WZTF58mSWLVvGnTt3KFq0KJ9++ilvvPFG6Da3bt3is88+49dff8XZ2ZkqVaowePDgcEWzZwkKCsLHxwdvb29cXFxe6vmIiIiIiIjIi5k505xhnicPHD/uOOv0XLoEr79uWj35+EChQnYnkphiWVCqlGnZOGAAjBxpdyIRiWsCA83fgT//hB49YMIEuxOJiMSOyNZZolyUunHjBqtWrWL58uWcPn2aEiVK0KBBAypVqkRiB52zrqKUiIiIiIiIvSzLrO1x9ChMnAgffWR3oqhp3BiWLYN27WDOHLvTSEzZvh3KljUz5M6fh1dftTuRiMRFGzdClSqmBeDhw5A3r92JRERiXmTrLFE+7yxt2rS0bt2alStXsmzZMnLkyMGwYcMoW7YsI0aM4Pjx4y8VXERERERERBKeLVtMQSp5cmjd2u40Ude1q7lctAhu3LA3i8ScMWPMZatWKkiJyNNVrgy1a5tZU7162Z1GRCRuealmCAUKFKB9+/Y0b96c+/fv88MPP1C/fn2aNm3KqVOnoiujiIiIiIiIxHNTp5rLli3NWj2OpkwZ067pwYOwdbEkfvnzT1i9GpycoHdvu9OISFw3bhy4usK6dbB2rd1pRETijhcqSgUEBLB+/Xo+/PBDKlSowPbt2/nkk0/YsWMHGzZsIHPmzHzkaL0WRMRh+PtDv37w7bd2JxERERGR6PD337BihRl36WJrlBfm5BQ2W2raNAgKsjePRL+xY81l3brg4WFrFBFxAHnymDWlAHr2NMcyRETkBdaU+uyzz/jpp58AqF27Ng0bNsTd3T3cNteuXaNs2bIO08pPa0qJOJaRI2HQINOb+cAB8PS0O5GIiIiIvIyBA+GLL6BCBdi0ye40L+7+fciaFW7eNDNqata0O5FElwsXIGdOCAiA3bvhrbfsTiQijuDOHVOcunoVxo83xSkRkfgqxtaUOn36NEOGDGHbtm0MGjToiYIUQJo0aZg/f35Udy0i8lxnz8Jnn5lxYCB07mwWxRYRERERx/TwIcyZY8YhM40clZsbfPCBGYe0I5T4YdIkU5B6+20VpEQk8lKlMifWAgwbBteu2ZtHRCQuiHJRat68edSsWZPEiRM/dZtEiRLx5ptvvlQwEZH/sizo1s0cuChe3PzTv20bzJtndzIREREReVFLl8L165A9O9SqZXeal9epk2nl9/PPcOKE3WkkOty6BbNmmXHfvrZGEREH1Lo1FC4Mt2/DkCF2pxERsd8LrSkFZsbUb7/9xqZNm8J9iIjElBUrYM0as1Do/PkwdKi5vk8fuHHDzmQiIiIi8iIsC6ZMMeNOnUx7ZkeXMyfUqGHG06fbm0Wix6xZcPcuFCgA1arZnUZEHI2Li5ltCTB7Nvj42BpHRMR2UV5T6p9//qFLly6cPHkSJycnQu7u5OQEwLFjx6I/ZQzTmlIicZ+fH+TLZ3q5DxwIn39u2mcULgxHj0L79mFnL4qIiIiIY9i9G0qWhCRJzPu89OntThQ9NmyAd981bZsuXICUKe1OJC/q4UN44w24fBn+9z9o1cruRCLiqN5/38wOLlcONm82s2pFROKTGFtT6vPPPydr1qzs3LmTpEmTsmbNGhYuXIinpycLFix4qdAiIk8zbJj5hz5HDhg0yFzn6gozZpjx7NnmoIaIiIiIOI6QWVJNmsSfghRApUrg7m4WuNe/yY5t4UJTkMqa1bxORURe1JgxkDQpbN0KP/5odxoREftEuSh14MABunfvTtq0aXF2dsbJyYlixYrRq1cvRowYERMZRSSBO3wYJkww4ylTzFpSIcqWNf2ZwbR8CQyM9XgiIiIi8gIuX4bvvjPjbt3szRLdnJ2ha1cznjrVtCkUxxMcDGPHmnHPnvCMpbVFRJ4re/awdek+/tjMxBQRSYiiXJQKDg4mefLkAKRJk4arV68CkCVLFs6ePRu96UQkwQsONsWmoCCoWxdq1nxymzFjIE0a05d52rTYTigiIiIiL2L2bNOOuWRJKFLE7jTRr1UrSJECjh0zbZrE8axaBSdPQurU8OGHdqcRkfigb1/IkgXOnYPx4+1OIyJijygXpfLkycOJEycAKFSoEHPnzmXfvn1MmzaNbNmyRXtAEUnY5s2DHTvM7KiQhUH/K0MGGDXKjIcMgUuXYi+fiIiIiERdQADMnGnG8W2WVIhUqaBlSzMOaVMojsOyYPRoM+7cWeuCiUj0SJ7cnFgLMHIkXLxobx4RETtEuSjVqVMngoODAejevTsXLlygWbNmbN26lUEhC72IiEQDX1/o08eMhw41U92fpl07KFEC7t41rTVEREREJO768Uf491949VVo0MDuNDEnpIXfqlVw/ry9WSRqtm83a9YmSQLdu9udRkTikyZNoFQpuHcPBgywO42ISOyLclGqbNmyVKlSBYDXX3+d9evXs3v3bnbt2kXJkiWjPaCIJFz9+5vClKcn9Ojx7G2dnWHGDHO5bBls2BArEUVERETkBUydai47dIjf6/TkywcVK5qW1DNm2J1GoiJkJkOrVqZ4KiISXZycYOJEM16wAH7/3dY4IiKxLspFKQDLsrhx4wY3b94E4JVXXsHJySlag4lIwrZzJ8yda8YzZoCr6/Pv4+0d1v6lSxctGioiIiISF/n4mFkoiRJB+/Z2p4l5IbOl5s6FBw/szSKRc/Qo/PSTOXDcu7fdaUQkPipeHFq3NuOPPjInL4iIJBRRKkpdu3aNvn37Urx4cUqXLk2pUqUoXrw4AwYM4Pr16zGVUUQSmMBA6NTJjNu0gTJlIn/f4cMhc2Y4fTqsB7yIiIiIxB0hs6Tee8+8b4vvatUybah9fWHpUrvTSGSMG2cu69UDd3d7s4hI/DVyJKRIYWZKffut3WlERGKPk2VZVmQ29PPzo06dOty/f59atWqRM2dOLMvizJkz/PTTT6ROnZoff/yR5MmTx3TmaBcUFISPjw/e3t64uLjYHUckwRs/3pyRmDYtnDgB6dNH7f7LlkHjxqb/+5EjkDt3zOQUERERkajx9YWsWc2M9u3boXRpuxPFjtGjTWvqIkXgjz/MDByJmy5cgJw5ISDArCn11lt2JxKR+GzUKLOuVObM5vhHihR2JxIReXGRrbNEeqbUvHnzcHFxYc2aNQwcOJD333+fJk2aMHjwYNasWYNlWSxYsCBawotIwnXhAnz6qRmPHh31ghRAw4ZQuTI8emTapUSu9C4iIiIiMe3rr01BqnBhs8h7QvHBB+aEqf37TaFD4q6JE01B6u23VZASkZjXo4cphF+6ZApUIiIJQaSLUlu3bqVDhw6kTZv2idvSpUtH+/bt2bx5c7SGE5GEp0cP8PODkiWhbdsX24eTE0ybZv7x//ln+P77aI0oIiIiIi8gKAimTzfjrl0T1myh9OmhaVMzDmlfKHHPrVswa5YZ9+tnaxQRSSCSJg1rGTpuHJw7Z2scEZFYEemi1Llz5yhcuPBTby9cuDBnz56NllAikjCtWwc//AAuLjBjBjhHadW78PLkMS1SwBS67t6NlogiIiIi8oLWrDEH29KmhSZN7E4T+7p2NZfffQeXL9ubRSI2c6Y5Qc7TE6pVszuNiCQUdetChQqm20ufPnanERGJeZE+5Ovn50eqVKmeenuqVKnw8/OLllAikvA8eBD2j3r37lCo0Mvvs39/yJXLTIMPaQkoIiIiIvaYMsVctmsHyZLZm8UORYqYloUBATB7tt1p5L8ePoRJk8y4T5+ENZNPROzl5GRahzo7m04vW7fanUhEJGZFuihlWRbOz5i24OTkhKWFW0TkBX3xBfz1F2TJAsOGRc8+kyY1bfwAJk+GgwejZ78iIiIiEjXHjsEvv5gDbp062Z3GPiEnYc2caYpTEncsXGhmsGXNCu+/b3caEUlovLygQwcz/ugj0/JWRCS+ShTZDS3L4t1338XpKacLqSAlIi/q5EkYPdqMJ06ElCmjb9/vvgsNG5o2KZ06wfbtL9cWUERERESiLuREoVq1IEcOW6PYqkEDePVV+Pdf+PFHaNzY7kQC5uDv2LFm3LMnJE5sbx4RSZiGD4fFi80JtV9/DR9+aHciEZGY4WRFspq0fPnySO2wXr16LxXIDkFBQfj4+ODt7Y2Li4vdcUQSFMuCypVh0yaoWhXWro3+VhkXL0LevKY//Jw5pmWMiIiIiMSOO3fMbHg/PzNbqmJFuxPZa+hQ0xmgTBnYts3uNAKwfDnUrw+vvAJ//x29J8mJiETFpElmXewMGcwJvK+8YnciEZHIi2ydJdJFqfhMRSkR+yxeDE2bmlZ7R46YNaBiwoQJ0KuXWVj7xAlInz5mHkdEREREwpsyxawZmi8fHD2qtXouXYLXX4fAQDhwALy97U6UsFkWlCwJv/8OAwfC55/bnUhEErKAAChYEI4fN8cwvvzS7kQiIpEX2TqLmliJiG1u3zZvssD8AxhTBSmAbt2gUCG4cQP69o25xxERERGRMMHBMHWqGXftqoIUQObMpo0fhH1txD7bt5uCVJIk5n8GERE7ubqak2rBrI194oS9eUREYoKKUiJim8GDzWLC7u4xXyhKlAhmzDDjb74x/3yKiIiISMz65RfTfihlSmjRwu40cUdI8WPRInPSlNhnzBhz2aqVWe9LRMRuVatCjRpmRm3v3nanERGJfipKiYgt9u2D6dPNeNo0c2ZiTCtZMmw9qU6dzLR4EREREYk5ITOB2rTROj2PK1XKtO17+BC++sruNAnXkSPw009mBp8O/IpIXPLll+bk2jVrYP16u9OIiESvSBWl/Pz8YjqHiCQgQUGmKBQcDO+/D5Uqxd5jjxpl1pM6csQsICoiIiIiMeOvv8wBf4AuXezNEtc4OZl2hmBO1AoKsjdPQjVunLmsX990bxARiSs8PMx6jAA9e+qkWhGJXyJVlCpevDi+vr4AtGzZkjt37sRoKBGJ32bPhr17IVUqGD8+dh87XbqwFh1Dh8I//8Tu44uIiIgkFDNmgGXBu+/qgH9EmjaFtGnh3DlYu9buNAnPhQumfSJAnz72ZhERiciQIZAhAxw/HtZpRkQkPohUUcrNzY1bt24BsGfPHgIDA2Myk4jEY1euwIABZjxiBLz2WuxnaNUKSpeGe/egR4/Yf3wRERGR+O7+/bC2dCEzgiS8ZMnggw/MeMoUe7MkRBMnmvVaypWDt96yO42IyJNeecUcNwFzUu3163amERGJPk6WZVnP26hbt27s37+fnDlzsnfvXgoXLoyrq2uE286fPz/aQ8a0oKAgfHx88Pb2xsXFxe44IvFaixawcCEUKQJ79oBdP3KHD0PhwqZVypo1UL26PTlERERE4qO5c+HDDyFnTjh50r73fHHduXPma2RZcOwY5M1rd6KE4dYtyJYN/Pz0v4CIxG1BQVC0KBw8aJZB0IwpEYnLIltnidRMqbFjx9K1a1c8PT0ByJMnD3nz5o3wQ0TkaTZvNgUpJyeYOdPegxNeXqYvM5izd+/fty+LiIiISHxiWWEzfzp3VkHqWXLkgFq1zFgHGmPPjBmmIOXpCdWq2Z1GROTpXFzC1sOeNcucYCsi4ugiNVPqcS1atGDatGmkSpUqpjLFOs2UEol5/v5QqJDphRxXzu7x84N8+Uw/+cGD4bPP7E4kIiIi4vi2bYO33zbt6S5ehDRp7E4Ut23cCFWqQMqU5uuVMqXdieK3hw9NMfDKFZg/33RyEBGJ6xo2hO+/h3fegU2bzMm+IiJxTbTOlHrcggULQgtSlmURxZqWiCRQ48aZglTGjDBypN1pjBQpws44Gj0aTpywN4+IiIhIfBAyS6p5cxWkIqNSJfDwgLt3TZFEYtaCBaYglS0bvP++3WlERCJn7FhIksR0oFmxwu40IiIvJ8pFKYAVK1ZQq1YtChYsSMGCBalVqxYr9BtRRJ7i7NmwWUhffmkW64wr6tUzPeQDAkx7GdXZRURERF7cxYvw449m3LWrvVkchZNT2Ndq6lS9H41JQUHmZDkwrbyfslS2iEickyMHfPyxGX/8sZn1KSLiqKJclPrmm28YOnQob7/9NhMnTmTixImULVuWoUOH8r///S8GIoqII7Ms6NbNvGEqXx6aNbM7UXhOTuZs3qRJ4ddfYckSuxOJiIiIOK6ZM82B/7ffhoIF7U7jOFq2NLP4jx83bZkkZqxaBSdPmpPk2rWzO42ISNT07w+ZM8Nff8HEiXanERF5cS/Uvm/o0KH06dOHihUrUrFiRfr27cunn37K/JfoNTB79mw8PDz4/PPPQ6979OgRw4YN46233qJw4cJ069aN69evh7vfpUuXaN++PYUKFaJkyZKMHj2awMDAF84hItFr5UpYs8achTh9etzse5wzJwwaZMa9esHt2/bmEREREXFEjx7B7Nlm3K2bvVkcTapU0Lq1GU+damuUeMuyTMtuMB0StHaXiDiaFClg1Cgz/vxz+Pdfe/OIiLyoKBelrl27RuHChZ+4vnDhwly7du2FQhw6dIglS5bg4eER7vqRI0eyefNmJk6cyIIFC7h69SpdH+sBERQURIcOHQgICGDJkiWMGjWK5cuXM3ny5BfKISLRy88Punc3448/hnz57M3zLH36mF7+ly/D4MF2pxERERFxPN99B1evQpYsUKeO3WkcT5cu5nL1ajh3ztYo8dK2bfD772ZNlpD/UUREHE2zZvDWW+Z4y8CBdqcREXkxUS5Kvf7666xbt+6J69euXUuOHDmiHODevXv06dOHESNGkDp16tDr7969yw8//ED//v0pWbIknp6ejBw5kgMHDuDj4wPA9u3bOX36NGPHjiVfvnyUK1eOjz76iEWLFuHv7x/lLCISvYYPh3/+Mb2P43qhJ0kSmDbNjKdPh3377M0jIiIi4mhCZvh06qS1el5E3rxQqRIEB8OMGXaniX/GjDGXrVtDpky2RhEReWHOzjBpkhn/73+wd6+tcUREXkiUi1LdunVj8uTJfPDBB0ybNo1p06aFjru/wOlGw4cPp1y5cpQqVSrc9UeOHCEgICDc9bly5SJz5syhRSkfHx/c3d1Jnz596DZlypTBz8+P06dPRzmLiESfI0dgwgQznjIF3NzszRMZFStCkybmQECnTmY9BBERERF5vr17zSyUxInhww/tTuO4Qtoezp0LDx7YmyU+OXLEtBR3coLeve1OIyLyct56C1q0MOOPPjLtSUVEHEmUi1Lvvvsuy5YtI02aNGzatIlNmzaRJk0avvvuOypXrhylfa1Zs4Y///yT3hG8K7x+/Tqurq6kSpUq3PXp0qULbRN4/fr1cAUpIPTzF20lKCIvL6SoExgIdetCzZp2J4q88eNNT/+9e8PWRBARERGRZwuZJdW4MWTMaG8WR1ajhukycOMGLF5sd5r4Y9w4c1m/PuTJY28WEZHo8MUXkDw57Nqlvxci4niiXJQC8PT0ZNy4cfz444/8+OOPjBs3jvz580dpH//++y+ff/45Y8eOJUmSJC8SQ0TiqHnzYPt2MzsqZFq5o3j1VbNgKMCAAXDlir15REREROK6q1dhyRIzfmwJYHkBLi7QubMZT5mis9+jw4ULsGiRGffta28WEZHokiWLOWYB0K8f3Ltnbx4Rkah4oaJUdDh69Ci+vr7Ur1+f/Pnzkz9/fvbs2cOCBQvInz8/6dOnJyAggDt37oS7n6+vLxkyZADMrKjr16+Huz3k85BtRCR2+fpCnz5mPHQoZM9ua5wX0qkTFCkCt2+HPRcRERERidjcueDvD2++aT7k5bRtC0mTgo+POQNeXs6ECaaDQ/nyen2KSPzSq5eZXXvhQti6eSIijsC2olSJEiVYvXo1K1asCP3w9PSkVq1aoWNXV1d2PfYu/K+//uLSpUt4e3sD4O3tzcmTJ/H19Q3dZufOnaRIkYLcuXPH9lMSEaB/f1OY8vSEHj3sTvNiXFxg5kzTc37BAtiyxe5EIiIiInFTYCDMmGHGmiUVPdKlg6ZNzXjKFHuzOLqbN8NacmuWlIjEN8mShbUnHTMGzp+3N4+ISGTZVpRKkSIF7u7u4T7c3Nx45ZVXcHd3J2XKlDRo0IBRo0axe/dujhw5wsCBAylcuHBoUapMmTLkzp2bvn37cvz4cbZt28bEiRNp1qwZiRMntuupiSRYO3eaM2XBHJxwdbU3z8soXhw6djTjzp3N2b8iIiIiEt7KleYM7QwZoFEju9PEHyEFvu+/h3//tTeLI5s5E/z8zAlzVavanUZEJPrVrw/lysHDh6aNn4iII7CtKBUZAwcOpHz58nTv3p3mzZuTPn16pjx2qpiLiwszZ87E2dmZxo0b06dPH+rWrUv37t1tTC2SMAUGmrZ3AK1bQ5kytsaJFp9/bhbqPnYMxo+3O42IiIhI3BPy71n79qClgqNP4cJQurR5jx0y00ei5uHDsPVt+/Y1XRBEROIbJyeYOBGcnWHpUti2ze5EIiLP52RZkV86NSAggEKFCrFixQrc3d1jMlesCgoKwsfHB29vb1xcXOyOI+KQJkww/YzTpIETJ8zZsvHBggXQsqWZFv/nn6Zfs4iIiIjA4cNQsKBpfXzuHGTNanei+GXJEmjSBF591bRkUjOQqJkzxxRLs2WDM2ccu4uDiMjzdOhgTmIoXBj27jV/m0VEYltk6yxRminl6urKa6+9RnBw8EsHFJH448IF+OQTMx49Ov4UpACaNzeLIj94AJqEKSIiIhJm6lRzWa+eClIxoX59eO01uHwZfvzR7jSOJSgIxo414169VJASkfhvxAhInRoOHID//c/uNCIizxbl9n0dO3Zk/Pjx3Lp1KwbiiIgj6tnT9GovWRI++MDuNNHLyQmmT4dEiWD1ali1yu5EIiIiIva7eRMWLjTjkPWPJHolTmzOfIewAqBEzsqVcOqU6eLQrp3daUREYl6GDGEnCw8cCHfu2JtHRORZotS+D6Bu3bqcP3+ewMBAMmfOjJubW7jbly9fHq0BY4Pa94m8uPXroVo1MzV83z4oVMjuRDFjwAAYNQqyZzdt/JIntzuRiIiIiH3Gj4fevcHLCw4e1Ho9MeXff837z8BA2L/ftGWSZ7MsKFEC9uyBQYPM7AERkYTA39/8XT55Evr0gTFj7E4kIglNZOssiaK640qVKr1UMBGJPx48gC5dzLh79/hbkAIYMgQWLzb9/D/7zBSoRERERBKi4GCYNs2Mu3ZVQSomvfYaNGxo3odOnQpffWV3orhv2zZTkEqSBLp1szuNiEjsSZzYrPddowZMnAgffgh58tidSkTkSVGeKRUfaaaUyIv55BNToMmSBY4dg5Qp7U4Us1avhtq1TSs/Hx8oUMDuRCIiIiKxb80aqFkTXnnFrC2qGeQxa+dOKF0akiY1X+906exOFLfVrGleox06wMyZdqcREYl91aqZrja1a5t2piIisSWydZYorykV4vDhw6xYsYIVK1Zw5MiRF92NiDiokydh9Ggznjgx/hekAGrVMm/qAgOhc2fTGkREREQkoQlZ36htWxWkYkPJklCkCDx8qJlSz3PkiClIOTnBxx/bnUZExB7jx5uTaVetgg0b7E4jIvKkKBelLl++TNOmTWnYsCEjR45k5MiRvPfeezRp0oTLly/HREYRiWMsy7Tt8/eHqlWhQQO7E8WeyZPBzQ1++w0WLLA7jYiIiEjsOnnSnH3t5GRO0pGY5+Rk2iQCTJ8OQUH25onLxo41lw0aQO7c9mYREbFLvnxhSy307GlOrBURiUuiXJQaNGgQgYGBrF27lj179rBnzx7Wrl2LZVkMGjQoJjKKSByzdCn88ovp0z5lSsJaR+D1103bQjBnX964YW8eERERkdg0fbq5rF4dcuWyN0tC8v77pm3f+fPw0092p4mb/vkHvv3WjPv2tTeLiIjdPv3U/N3480+1MhWRuCfKRam9e/cydOhQcubMGXpdzpw5GTx4MH/88Ue0hhORuOf2bXOmDcDAgQnzDMSePSF/frh2DVSLFxERkYTCzw+++caMu3WzN0tCkywZtGtnxiHtEyW8iRPNbIDy5aF4cbvTiIjYK00aGDHCjD/5BHx97c0jIvK4KBelXnvtNQIjmPcZHBxMxowZoyWUiMRdQ4bA5cuQJw/062d3GnskThx2lvCsWbBnj715RERERGLDggVw5455H1i5st1pEp6OHcHZ2XQsOHbM7jRxy82bMHu2GWuWlIiI0a4deHmZ35FDh9qdRkQkTJSLUn369OGzzz7j8OHDodcdPnyYzz//nH4J9Qi1SAKxfz9Mm2bG06eb9n0JVbly0LKlWV+rY0f1aBYREZH4zbLCZuh07WqKIxK7cuSAWrXMOOQ9uRgzZpiZfF5eZs1bERGBRInMLFIwvyePHLE1johIKCfLsqznbVS8eHGcHls05v79+wQFBeHi4gIQOnZzc2OPA04ZCAoKwsfHB29v79DnJCLhBQVByZKwd6/pab94sd2J7Hf1Knh4wK1bMHmy2tiIiIhI/PXrr1CxIiRPDhcvQurUdidKmDZtgkqVIEUK831IlcruRPZ7+NCs+3r1qpnN17y53YlEROKW+vVh+XLz92PDhoS1LriIxK7I1lkSRWZnAwcOjLZgIuKYZs82BalUqWD8eLvTxA0ZM8IXX0CnTjB4MLz3Hrz2mt2pRERERKLflCnmslUrFaTsVKEC5M0Lx4/D/Plm1lpCN3++KUhlywaNG9udRkQk7hk3DtasMe1fV6+G2rXtTiQiCV2kZkrFd5opJfJsV66YGUG3b2tG0H8FB5sZZHv2aAaZiIiIxE/nz0POnOZ9z9GjkD+/3YkStmnTTDHKwwP+/DNht1IMCoJ8+eDUKZgwAXr0sDuRiEjcNHCgOak2Vy7ztzwhL8cgIjEnsnWWBPz2VUQi6+OPTUGqSBHo3NnuNHGLs7PpzezsDEuWwMaNdicSERERiV4zZpiCVMWKKkjFBS1bQsqUcOKEaeeXkK1caQpSadJAu3Z2pxERibsGDIBXX4UzZ8zJxiIidop0USpv3rzky5fvmR/59R+KSLyzZQssXGh6Ds+YAZpM+KQiRaBLFzPu0gUePbI3j4iIiEh0efAA5s41Y7WKixtSpoTWrc04pK1iQmRZMHq0GXfpYtbZEhGRiKVMCaNGmfFnn8Hly/bmEZGELdLt+3755Zen3ubj48OCBQsIDg7m8OHD0RYutqh9n0jE/P3B2xuOHYOOHU1RSiJ2+7bp73/5MgwfDkOG2J1IRERE5OV98w20bQvZs8Nff+kEpbjixAnz3tPJyZz1/sYbdieKfVu3QvnypgXV33+b9V5FROTpgoOhRAmzXnjbtvDVV3YnEpH4Jtrb91WqVOmJj5w5c7J8+XK+/vprqlatyvr166MlvIjEDV9+aQpSGTPCyJF2p4nbUqc2fewBPv/cHBwQERERcWSWFTYTp3NnFaTiEg8PqFLFfI8S6oljY8aYyzZtVJASEYkMZ2eYNMmMv/kG9u2zN4+IJFwvtKbUlStXGDx4MLVr1yYoKIgVK1YwevRosmTJEt35RMQmZ8+aKd0A48aZPu3ybI0bm7UWHj0y7W0iNw9VREREJG7atQsOHICkSbVeT1wU0k5x7ly4f9/eLLHt8GFYu9bMFOvd2+40IiKOo2RJaNbMHK/46CMdtxARe0SpKHX37l3Gjh1LlSpVOH36NP/73/+YOXMm7u7uMZVPRGxgWdC9u1lDoHx5aN7c7kSOwckJpk+HxIlh/Xr48Ue7E4mIiIi8uKlTzWWTJpAunb1Z5EnVq5u2fTdvwuLFdqeJXePGmcsGDSB3bnuziIg4mlGjwM0NduyAZcvsTiMiCVGki1Jz5syhUqVKbNmyhS+//JIlS5ZQrFixmMwmIjZZuRJ++glcXU2RxcnJ7kSOw90d+vUz448+grt37c0jIiIi8iL+/Re++86MQ2bkSNzi4mLaKoIpICaUs93/+Qe+/daM+/a1N4uIiCPKmhX69zfjPn0S3mxbEbGfk2VF7q1r3rx5SZo0KSVLlnzmIlVTQ06ncyCRXYBLJCHw84P8+c0/ewMGaC2pF/HgAXh6msXAe/cOO5NTRERExFEMGwZDh0KpUuZMaombbtwwBxcfPIBt26BMGbsTxbxevcxaru+8A7/+ancaERHH9OAB5M0Lf/9t/t5/+qndiUQkPohsnSXSM6Xq1q1LtWrVeOWVV0iZMuVTP0TEsQ0fbgpSOXLA4MF2p3FMyZKFtbuZOBEOHbI1joiIiEiU+PvDzJlm3K2bvVnk2dKmNWuDQNj7z/js5k2YPduMNUtKROTFJUsGY8ea8ejR5jiQiEhsifRMqfhMM6VEjCNHoHBhCAyE1auhZk27Ezm2996DH34wZxhv2wbOUVrFT0RERMQeS5aYdaRefRXOnzfrZUrc5eNj3sMnSmS+X5kz250o5owcCYMGgZcXHDyoNuMiIi/DsqBcOXO8okmTsNaoIiIvKtpnSolI/GZZpid9YCDUrauCVHSYOBGSJ4edO+Gbb+xOIyIiIhI5U6aYy44dVZByBN7epm1fYCDMmmV3mpjz8CFMmmTGffuqICUi8rKcnMxxCycnWLxY7XpFJPaoKCUiAMybZ86OcXML+2dPXk7WrGY9BjD/OF+/bm8eERERkefZv9+cUJMoEbRvb3caiayQNouzZpn2i/HR/Plw9Spkzw6NG9udRkQkfihSBD74wIw/+giCg+3NIyIJg4pSIoKvL/TpY8affmr+0ZPo0b27aS9y4wb07293GhEREZFnC1mXqGFDeO01e7NI5NWrZ9r2Xbli2kfHN0FBYWuf9OoFrq725hERiU9GjICUKWHfPnPCsohITFNRSkQYMMDM4ilQAHr2tDtN/OLqCjNmmPFXX2k6vIiIiMRdvr5h60l07WpvFokaV1fTbhHC2i/GJytWwOnTkCZN2Bn9IiISPTJlgk8+MeMBA+DOHXvziEj8p6KUSAK3axfMmWPGM2borMOYULo0tG1rxp06mX7/IiIiInHN3Lnw6JFp5VOypN1pJKo+/NC8l9+1y5ztHl9YFowebcZdukCKFPbmERGJj7p3h9y5zYzbkSPtTiMi8Z2KUiIJWGCgKZIAtG4NZcvaGideGz0a0qaFw4dh8mS704iIiIiEFxQE06ebcdeuZtFzcSyvvmraLkJYG8b44LffYO9eSJo0bO0sERGJXokTw/jxZjxhApw5Y28eEYnfVJQSScCmTIGDB00bjDFj7E4Tv6VPH/Y1/vRTuHDB3jwiIiIij1u9Gv7+G9Klg/fftzuNvKiQos3ixaY9d3wQ8h66TRvImNHeLCIi8VnNmlClCvj7w8cf251GROIzFaVEEqgLF8J6Bo8eDRky2JsnIWjTxrTC8fPT2l0iIiISt4TMrGnXDpIlszeLvLi33oKiRU0bxq++sjvNyzt8GNauBWdn6NXL7jQiIvGbk5OZJeXiYtby27TJ7kQiEl+pKCWSQPXqZYojJUtqseDY4uwMM2eaN3jffw/r19udSERERAT+/NMceHJ2DmvtLI7Jycm0XwTTjtHR1zIdO9ZcNmhg1joREZGYlT8/dO5sxj16OP7fERGJm1SUEkmAfv4ZvvvOFEdmzDAHICR2FCwIH31kxl26wIMH9uYRERERmTbNXNauDa+/bm8WeXnvv2/aMP79N/z0k91pXtzff5s2hAB9+tibRUQkIRk61KyJfeQIzJ5tdxoRiY90KFokgXnwwBRDALp3h0KF7M2TEA0dClmywF9/wRdf2J1GREREErLbt2HePDMOWY9IHFvSpPDhh2Yc0pbREU2caM7Qf+cdKF7c7jQiIglH2rQwfLgZf/IJ3Lhhbx4RiX9UlBJJYEaNgjNnIHNmGDbM7jQJU8qU5p9sMOt5nTxpaxwRERFJwObNg3v3TLued96xO41El44dTTeETZtMe0ZHc/Nm2Nn5/frZm0VEJCHq0AEKFABfXx07EpHop6KUSAJy8qQpSoEpiqRMaWucBK1BA6haFfz9zcw1y7I7kYiIiCQ0wcFhM2m6djXrEUn88PrrUKeOGYe0Z3Qk06ebYmnBglClit1pREQSnkSJwk6mnTbNMU9wEJG4S0UpkQTCskzxw98f3n0X3nvP7kQJm5OTOQiUJAn88gssXWp3IhEREUloNm6EU6cgVSpo0cLuNBLdunY1l/PmmTaNjuLBA5g82Yz79lWxVETELpUqmRMcgoKgZ0+dTCsi0cfWotSsWbNo0KABhQsXpmTJknTu3Jm//vor3DaPHj1i2LBhvPXWWxQuXJhu3bpx/fr1cNtcunSJ9u3bU6hQIUqWLMno0aMJDAyMzaciEuctXWqKH0mSmGKI/rmzX65cMHCgGffs6VgHC0RERMTxTZliLtu0gRQp7M0i0e+dd0xbxnv3wtYNcwTz58PVq5A9OzRqZHcaEZGEbdw4cHWFDRtgzRq704hIfGFrUWrPnj00a9aMZcuW8c033xAYGMgHH3zA/fv3Q7cZOXIkmzdvZuLEiSxYsICrV6/SNeSULyAoKIgOHToQEBDAkiVLGDVqFMuXL2dyyKlVIsLt26boAaYIkju3vXkkTL9+kCcPXL5sFhAVERERiQ1nzsDatWbcubO9WSRmODmFzZaaNs20a4zrgoLMAVCAXr3MgVAREbFP7txhx5N69TLdd0REXpatRamvvvqK+vXrkydPHvLmzcuoUaO4dOkSR48eBeDu3bv88MMP9O/fn5IlS+Lp6cnIkSM5cOAAPj4+AGzfvp3Tp08zduxY8uXLR7ly5fjoo49YtGgR/vpNKQLAkCGm6JEnjxYKjmuSJAnr8z91Kuzfb28eERERSRimTzdteKpWBXd3u9NITGnRwrRnPHnStGuM61asgNOnIW1aaNfO7jQiIgIwaBBkymRa/obMshYReRlxak2pu3fvApA6dWoAjhw5QkBAAKVKlQrdJleuXGTOnDm0KOXj44O7uzvp06cP3aZMmTL4+flx+vTp2AsvEkft3x9W9Jg+3RRBJG6pXBkaNzZnr3bqZM4QFREREYkp9+7B11+b8WNNKCQeSpHCtGcEcwJUXGZZMHq0GXfpAsmT25tHRESMVKlg5EgzHj7ctFgVEXkZcaYoFRwczMiRIylSpAju/3+q3vXr13F1dSVVqlThtk2XLh3Xrl0L3ebxghQQ+nnINiIJVVCQKXIEB8P775tFKiVuGj8eUqaEPXtg7ly704iIiEh8tmgR3LoFOXNCtWp2p5GYFtKecc0a+M8SznHK1q2wdy8kTapiqYhIXNO6NRQtCnfuwODBdqcREUcXZ4pSw4YN49SpU0yYMMHuKCLxxpw5psiRMqUpekjclTkzjBhhxv3768wjERERiRmWFTZjpksXcI4z/xFKTHF3h3ffNd/76dPtTvN0Y8aYyzZtIGNGe7OIiEh4zs4wcaIZz50LBw7YGkdEHFyc+Bdk+PDhbNmyhXnz5vHqq6+GXp8+fXoCAgK4c+dOuO19fX3JkCFD6DbXr18Pd3vI5yHbiCREV67AgAFmPGIEvPaavXnk+Tp3hsKFzZnLffrYnUZERETio99+g8OHwc0trK2bxH/dupnLr76C+/ftzRKRQ4dg3Tpz0LN3b7vTiIhIRMqUMV14LAt69DCXIiIvwtailGVZDB8+nI0bNzJv3jyyZcsW7nZPT09cXV3ZtWtX6HV//fUXly5dwtvbGwBvb29OnjyJr69v6DY7d+4kRYoU5M6dO1aeh0hc1KePKW4ULhzWskPitkSJYMYMcHKC+fNNCxMRERGR6BQyS6p5c0iTxt4sEnuqVjXtGm/dgm+/tTvNk8aNM5fvvQe5ctmbRUREnm70aEiWzJzk8v33dqcREUdla1Fq2LBhrFq1ii+//JLkyZNz7do1rl27xsOHDwFImTIlDRo0YNSoUezevZsjR44wcOBAChcuHFqUKlOmDLlz56Zv374cP36cbdu2MXHiRJo1a0bixIltfHYi9tmyBRYsMMWNmTNNsUMcw1tvQfv2Zty5M/j725tHRERE4o9//oHly81Ya/YkLC4uYSeqTZkSt85u//tvWLzYjNUtQEQkbsueHfr2NeM+feDBA3vziIhjsrUotXjxYu7evUuLFi0oU6ZM6MfatWtDtxk4cCDly5ene/fuNG/enPTp0zNlypTQ211cXJg5cybOzs40btyYPn36ULduXbp3727HUxKxnb9/2D+cHTrAm2/am0ei7osvIEMG+PNP0DJ7IiIiEl1mzYKgIChXDry87E4jsa1tW3N2+6FDsH273WnCTJgAgYFQoQIUK2Z3GhEReZ6+fSFrVjh/Hr780u40IuKInCwrLp0jZY+goCB8fHzw9vbGxcXF7jgiL+WLL2DgQLM48PHjasviqObNg9atzXoPf/4Jr79udyIRERFxZA8fmrObr10z7XYaNLA7kdihfXuYMwcaNYKlS+1OAzdumNflvXuwfj28+67diUREJDIWL4amTc0xi5MnIUsWuxOJSFwQ2TqLrTOlRCR6nTsHn31mxuPGqSDlyFq2hLffNgtRf/SR3WlERETE0X33nSlIZc0KderYnUbsEtK28Ycf4OJFe7OAWU/13j0oVAiqVLE7jYiIRNb770Pp0uaYRf/+dqcREUejopRIPNK9u+nnW768WbxaHJeTE0yfbtYDW7kSVq+2O5GIiIg4spAO6J06ab3RhKxgQXPiU1CQaedopwcPYPJkM+7b17z/FRERx+DkBBMnmvHChbB7t61xRMTBqCglEk+EFC5cXU0xQ//UOb4CBaBXLzPu1s2cRSoiIiISVXv2wN69kDgxtGtndxqxW8hsqVmz4NEj+3LMnw9Xr5o21Q0b2pdDREReTLFi0KaNGX/0EQQH25tHRByHilIi8cC9e2aWFMDHH0O+fPbmkejzySemz/758/D553anEREREUcUMkvq/ffNuqOSsNWta9b+uHrVrC9mh6Ag024czElYrq725BARkZczciSkSGFOgFm40O40IuIoVJQSiQeGD4e//zZnGQ4ebHcaiU7Jk4e1NRk3Do4dszePiIiIOJYrV2DZMjMOmSEjCZurK3TsaMZTp9qTYflyOH0a0qaFDz6wJ4OIiLy8V18NOw7Vvz/4+dmbR0Qcg4pSIg7uyBEYP96Mp0wBNzd780j0q10bataEgADo3Bksy+5EIiIi4ijmzAF/f3jrLShe3O40Eld8+KFp57h7N/zxR+w+tmXBmDFm3KWLOQlLREQcV48ekCsX/PsvfPGF3WlExBGoKCXiwCzLFCkCA6FOHahVy+5EEhOcnEzBMVky2LIFFi2yO5GIiIg4goAAmDnTjDVLSh6XKRM0amTGsT1bautWs8ZZ0qRm3VQREXFsSZKEtWT98ks4e9bePCIS96koJeLA5s2DbdvM7KiQFm8SP+XIAUOGmHHv3nDzpq1xRERExAGsWAEXL5p1pBo2tDuNxDUhhcolS+Datdh73JBZUm3bQoYMsfe4IiISc+rUgYoV4dEj6NPH7jQiEtepKCXioHx9w/7Qf/opZM9ubx6Jeb17Q968ZlHqQYPsTiMiIiJxXcgMmPbtzVnMIo97800oVswcQJw7N3Ye89AhWLcOnJ2hV6/YeUwREYl5Tk4wcaL5/f7DD7B5s92JRCQuU1FKxEENHAjXr0OBAtCzp91pJDYkTgzTp5vxzJmm7YmIiIhIRA4dgt9+AxcX6NjR7jQSFzk5hbXPmzHDtASPaWPHmsv33jPrj4iISPzh6Rn2nqNHDwgKsjWOiMRhKkqJOKDdu2H2bDOeMQNcXe3NI7HnnXegeXOznlinTnqTJyIiIhELmSVVvz5kyWJvFom7GjWC9Onhn39g9eqYfazz52HxYjPu2zdmH0tEROwxfDikSWNOjomtWbgi4nhUlBJxMIGBYWeetG4NZcvaGkdsMG4cpE4N+/aZoqSIiIjI427cgIULzThkJoxIRJImNe0dAaZMidnHmjjRnFBVsSIULRqzjyUiIvZIlw6GDjXjwYPh1i0704hIXKWilIiDmToVDh40Z56ELBIsCUumTDBypBkPGgT//mtvHhEREYlbvvkGHjyAggWhTBm700hc17GjWQNk82Y4ejRmHuPGDZgzx4w1S0pEJH7r1Any5TNLTgwfbncaEYmLVJQScSAXL8KQIWY8ahRkyGBvHrFPhw5mYeo7d+Djj+1OIyIiInFFUBBMm2bG3bqZdYNEniVbNqhb14xDXjvRbfp0uHcPChWCypVj5jFERCRucHWFCRPMeMoUOH7c3jwiEveoKCXiQHr2BD8/KFEC2rWzO43YycUFZs40B5q+/RY2bbI7kYiIiMQF69bB2bNmVn3TpnanEUcR0uZx/ny4fTt69/3gAUyebMZ9+6pQKiKSELz7LtSsaZag6NXL7jQiEteoKCXiIH7+Gb77zrTWmDHDXErCVrQodO5sxp07w6NH9uYRERER+4WsC/TBB+DmZm8WcRzlykGBAmY20//+F737njcPrl2D11+HRo2id98iIhJ3ffmlmTW1bh2sXWt3GhGJS3RYW8QBPHgAXbqYcffu4O1taxyJQ0aMMGtMnTwJ48bZnUZERETsdOIEbNhgZqJ06mR3GnEkTk7QtasZT5sGwcHRs9+goLD3qL17Q6JE0bNfERGJ+9zdzTEsMLOlAgLszSMicYeKUiIOYPRoOHMGMmfWIpES3iuvwPjxZjxiBPz1l61xRERExEYh6wHVrAk5c9qbRRxP8+aQOjWcOmWKm9Fh+XLzf0zatNC2bfTsU0REHMeQIWY99BMnYm7dQhFxPCpKicRxp07BF1+Y8cSJkDKlrXEkDmrSBCpUgIcPzXoAlmV3IhEREYltd++GtV0LmfEiEhUpUkCbNmY8derL78+yzMl1YF6TyZO//D5FRMSxpE4Nn39uxkOHmnauIiIqSonEYZZl2vb5+5tFIt97z+5EEhc5OZkzjlxdTZ/mFSvsTiQiIiKxbf58U5jy8IBKlexOI44qZL3StWvNDKeXsWUL/PEHJE2qQqmISELWtq1ZhuL2bTNzSkRERSmROGzZMti4EZIkMWcrOjnZnUjiqrx5oW9fM+7eHfz87M0jIiIisceywma2dOkCzvovT15QnjxQrZp5TU2f/nL7GjPGXLZta1o3iYhIwuTiApMmmfGcOXDwoL15RMR++ndFJI66cwd69jTjgQMhd25780jcN2gQvPEGXLgAw4bZnUZERERiy6ZNcPy4ab/WqpXdacTRhcxq+vpruHfvxfZx6BCsX28KpL17R182ERFxTG+/DQ0bQnAw9OihZQdEEjoVpUTiqCFD4N9/TTEqZAaMyLMkSwZTppjxhAlw+LC9eURERCR2hMySatUKUqWyN4s4vqpVIVcuuHULFi16sX2MHWsuGzaEnDmjLZqIiDiwMWNMS9ctW2D5crvTiIidVJQSiYP27w87uDB9uvmjLRIZNWpAvXoQFASdOpmzkEQk4fD3Bx8f+OknOHVKvwNEEoJz52D1ajPWuj0SHZydTRtIMP+TRPVs9vPnYfFiM+7TJ3qziYiI48qRAz7+2Ix794aHD22NIyI2UlFKJI55vJjQuDFUrmx3InE0kyZB8uSwYwfMm2d3GhGJKQ8fmgXkZ82CDh2geHFImRIKF4ZatcDd3cyYKFkSOnaEmTNh1y6tOScS38yYYd43Vqpk1pgUiQ5t2oCbm5l5v21b1O47YYL5n6ZiRShaNGbyiYiIY+rfH7JkMSfVjB9vdxoRsYuTZamLZ1BQED4+Pnh7e+Pi4mJ3HEngZs40RamUKc3aAJkz251IHNHYsabtY7p0cOKEuRQRx3X/vlkQeP9+87FvHxw9CoGBT26bOjW8/rqZKfXgwZO3OzmZtkze3lCoUNhHtmzmNhFxHA8eQNascOMGrFwJtWvbnUjik44dzYkP770H330Xufv4+kL27Obv1s8/Q5UqMZtRREQcz6JF0Ly5OZn25Ekd9xKJTyJbZ1FRChWlJO64ehU8PEz/9kmToHt3uxOJowoIgCJF4MgR+PBDmD3b7kQiEll375oWfCHFp/374dixiFvxpUtnzkIvUiTs8o03THEpKMgUpg4eNPs7eNB8XLoU8eOmSRO+SOXtDfnzQ5IkMfhkReSlfP01fPCBaYdz+jToXxmJTocPQ8GC5nV17pwpgD7PiBFmbVxvb/P3Syc7iIjIf1kWlCoFu3dDy5bq8CISn6goFQUqSklc0aoVzJ9vWi/t2QOJEtmdSBzZ9u1QtqwZ79xpWniJSNxy6xYcOBC+AHXyZMTrd2TK9GQB6kVmN127FlagCvn488+IZ10lSmTagYUUqUIKVhkzvsizFazbCvgAAEL0SURBVJHoZFnm94CPj1k4XGv3SEwoXx62boXBg+Gzz5697YMHZqbutWvw7bfQpEmsRBQREQe0Zw+89ZYZ//47vPmmvXlEJHqoKBUFKkpJXLB1q/mnz8nJnC2iP8gSHdq0gf/9zxxE/uMPFTpF7OTrG774tH8/nDkT8bZZs4YvPhUpErNtLR49MrOxQopUITOrbtyIePtXX32y/Z+7u37HiMSmHTugTBlImhQuXFCrXokZP/xg2vdlzAh///3s2bMzZkDnzqYwdfq0/iaIiMizhZyY/dZb5kRaZ2e7E4nIy1JRKgpUlBK7+fubg3vHjpne7TNm2J1I4otr10xLyJs3zaLTPXrYnUgkYbhyJXzxad8+czAvIjlyhC8+FSkSN2YiWRZcvPhk+79TpyKeyZU0KXh6hi9UFSpk1rgSkej3/vuwdKlp3zd3rt1pJL4KDDRtYS9cgAULzBogEQkKMicn/PUXTJ4M3brFbk4REXE8ly6Zvx337j37b4yIOA4VpaJARSmx26hRMGAAZMgAJ06YdT1EosucOdC+PaRIAcePQ5YsdicSiT9CCjePF5/273/6uk158oQVnooWNe1a06aN3cwv6949s87I4+3/Dh0CP7+It8+RI/w6VYUKmet0JqTIi7t0ycxGCQw0LUC9ve1OJPHZ55+b9n1vvmlaLEXku++gUSMzY+/8ebN4vYiIyPN88QUMHGi6Qpw4YY5biIjjUlEqClSUEjudO2cWkn/wwCzu2LKl3YkkvgkOhtKlTVvIhg1h2TK7E4k4JssyB9r+24Lv6tUnt3VyMmsxPd6Cz9s7/s4aCg42Z8c/Xqjy8Xn67LCUKaFgwfAtAD09wc0tNlOLOK5PP4Xhw037vm3b7E4j8d3Vq2YNQ3//iNf9sCwoXtz8bfz0Uxg61JaYIiLigB4+NMfEzp6N3PqFIhK3qSgVBSpKiZ1q14bVq6FcOdi8OeoL1otEho+POTAeHAzr18O779qdSCRuCymy/LcAFdEaSy4u5h+px1vwFSqks/zAtA49dCh8C8CjR80aVv/l7Gzadzze+s/bG157TX8bRR7n7w/Zs5s2oUuWQOPGdieShKBlS9NaqWVLcyLd4zZvhgoVIFkyc/JGhgz2ZBQREcf044/QoIFpB37smOmqICKOSUWpKFBRSuyyciXUrWsWAT540BzUFIkpPXvCxImQKxccOWLe8ImIWQfj1Knwxaf9++HOnSe3dXU1M3pCClBFi4KXlzkQJ5ETEAAnT4Zfp8rHJ+IZZwDp0z/Z/i9vXkicOBZDi8Qh334LzZqZgu358+b3kkhM27vXzJBKnBj++Sf82ofVqpmTnrp0galT7csoIiKOybLMyQ1btqi7i4ijU1EqClSUEjvcu2eKUH//Df37mz66IjHpzh3Il8+sQ6HWKpJQBQaas+8eXwPKx8f8Tv6vJElMAeTxNaAKFDDXS/S7fPnJ9n8nTpii4X+5upq/oY+3/ytUyKxlIhLflSoFu3bBsGHwySd2p5GE5K23YM8es8bUwIHmuoMHze9iZ2dzgkfOnLZGFBERB3XwoPmfKzjYFKfKlbM7kYi8CBWlokBFKbFD//4werRZpPrPP7WOhsSOZctMm5/Eic1sqTx57E4kEnP8/U2ruMdb8B08aPqW/5ebmzmo9vgaUPnyaQaC3R4+NN/Dx9v/HTwIt29HvH2WLE8WqnLnNi0WReKDffugWDHzu+nvv+HVV+1OJAlJSPu+rFnN2h+JEkHz5rBokXl/uWSJ3QlFRMSRdeoEM2ea9/N//KH38CKOSEWpKFBRSmLb0aPmj2xgIKxaBbVq2Z1IEgrLgqpVYcMGqFLFtFrRWi0SHzx8CIcPhy9AHT5sClP/lTIlFC4cfg0oDw/90+MoLMscjH+8SHXwIJw5E/H2bm6mxeLjLQC9vMzrQMTRtGkD//sfNG1qCgEisenRI8iWDa5dg++/NwXSXLnMjNZ9+8zfUxERkRd17Zo5cfb2bZg9Gz780O5EIhJVCa4otWjRIr766iuuXbtG3rx5GTJkCAULFozUfVWUkthkWVC+PPz2G9SpAytW2J1IEprTp82aOI8ewdKl0KiR3YlEoubePTh0KPwaUEePmkL/f6VJE1Z4CpkFlSuXaTMk8cudO6YQ+Xj7v8OH4cGDiLfPlSv8OlWFCkH27CrUS9x17ZopCDx6ZNr3lShhdyJJiAYPNu37ypc3vzcnTYJKlWDjRruTiYhIfDBxolkPO0MG0xY2dWq7E4lIVCSootTatWvp27cvw4YNo1ChQsybN4/169ezfv160kVicQEVpSLn8mW4e9ccrHF2jtnL+GzePGjd2py5feyYOQAmEtuGDjVrUbz2Ghw/DqlS2Z1IJGJ378KBA+HXgDp+3PQa/6/06cNmP4Vc5sgR//+uyNMFBZlC/H/b/128GPH2r7wSvvWfpyckSxbxts96XT3ttqheH5/25exsPlxcwsaPf0R0vYuL2Z9+ho1Ro2DAAPP7be9efV3EHhcumL+tQUFmjcVHj8wM/MqV7U4mIiLxQUCA6Wpw4gT07g3jxtmdSESiIkEVpRo2bIiXlxef/P9Kv8HBwZQrV44WLVrQvn37595fRannW7sWatY0s3xiQ8gBiKcVrWKjMBZTl1u2wM2bZj2pvn1j5+sp8l8PH5o3eqdPQ/Xqpv1KyM+WPl7uQ16Or2/4FnwnT0a83WuvhS8+FSli1rjQ90Ai4/r18K3/Dh406zsGBNidTCIS8j7qRQpbzyp4vew+Ynvf48ebgsD//getWtn9XZGErGFD074PzGzT/fv191dERKLPunXmOEWiRLBmjZk1BWHHJB8/NhnR+Hm3J7R9/dfjf7NDxhFd97K3x8Q+7b7d3d1MMpCni2ydJVEsZooR/v7+HD16lA4dOoRe5+zsTKlSpThw4ECk9hFSlwsKCoqRjPFBpkxmwfebN83Z6ZYV8SWEfR6TLMucneeoihSBbt0c+zmIY3N1henToV492LrVfIjERcmTQ5Ys5qDX423WXn31yW0jmj0lEpE0aUzrqfLlw67z9zdnZB45Ytr+HTliiqIRtYV8kfc5T7vPs/YV1fvExr5e9DGCg8M+Hn/f+KJC3gsmtPdS2bLBe+8lvOctcUvXruaAIUD//vr7KyIi0atKFXOsYsMGqF/f7jQiYfLkUceC5wmprzxvHpTDz5S6cuUKb7/9NkuWLKFw4cKh148ZM4a9e/fy3XffPXcf/v7+HD58OCZjioiIiIiIiIiIiIiIxGteXl4kTpz4qbc7/Eyp6JAoUSK8vLxwdnbGSaVOERERERERERERERGRSLMsi+DgYBIlenbZyeGLUmnSpMHFxQVfX99w1/v6+pI+ffpI7cPZ2fmZlTsRERERERERERERERF5Oc52B3hZiRMnpkCBAuzatSv0uuDgYHbt2hWunZ+IiIiIiIiIiIiIiIjYx+FnSgG0adOGfv364enpScGCBZk3bx4PHjygvlbDExERERERERERERERiRPiRVGqevXq3Lhxg8mTJ3Pt2jXy5cvH3LlzI92+T0RERERERERERERERGKWk2VZlt0hREREREREREREREREJH5z+DWlREREREREREREREREJO5TUUpERERERERERERERERinIpSIiIiIiIiIiIiIiIiEuNUlBIREREREREREREREZEYp6KUiIiIiIiIiIiIiIiIxLhEdgeQuOvatWscPHiQ69evA5A+fXoKFSpEhgwZbE7mePS1FIn/AgICcHV1tTuGJHB37txh/fr1XLp0iSxZslC1alVSpkxpdywRiaIjR47g6elpdwyRUL6+vpw8eRJPT09SpkzJ9evXWb58OZZlUa5cOTw8POyO6BCCgoJC/0Y7Ozvj7+/PL7/8gmVZvPXWW6RPn97uiA5BX8fooZ/r6KHXY/T6559/2LdvH1evXsXZ2Zls2bJRunRpUqRIYXc0EYlGTpZlWXaHkLjl/v37fPLJJ6xduxYnJydSp04NwO3bt7Esixo1ajB8+HCSJUtmc9K4T1/L6BUUFISLi0vo5wcPHsTf3x9vb28VA17SgAED6NGjB5kyZbI7Spy3du1aKlWqROLEiQFYuHAhX331FZcvXyZVqlS0aNGCrl272pzScQUEBHDx4kXSpUunYkokdO3alZo1a1K1alVOnTpFixYtcHJyIlu2bFy8eBEnJyfmzZtHrly57I7qcFTgEzvlzZuXbNmy0aBBA+rVq6e/zy9Ixb3o8fvvv9OxY0cePHhA+vTpmTt3Lh06dCBp0qQ4OTlx8eJFZsyYQZkyZeyOGqcdP36cdu3a4evrS+7cuZk9ezbt27fnwoULALi6ujJ37lwKFixoc9K4TV/H6KGf6+ih12P0uX//Pv3792fDhg0AODk5kS5dOm7cuEGSJEn4+OOPadasmc0pHcPWrVvZsGEDqVOnpkGDBuH+F7x9+zbdunVj/vz5NiYUUfs+icDnn3/O4cOHmTVrFocOHWLnzp3s3LmTQ4cOMXv2bA4dOsTnn39ud0yHoK9l9Lh69SpNmjTBy8uL5s2bc/v2bTp06EDjxo1p0aIFNWvW5OrVq3bHdAjHjx+P8GP16tUcPnw49HN5ut69e3P37l0AfvjhB8aMGUO9evWYOXMmrVu35quvvuK7776zOaVjmDNnDg8fPgRM0Xn06NEULlyYatWqUaJECQYMGEBAQIDNKeO2PXv2kCdPHgDGjBlD6dKl2bp1K8uWLWPLli2UK1eOkSNH2pzSMXTt2pX169cDcOrUKapUqcKECRPYuXMnEydOpFq1apw5c8bmlHGfv79/uJ/bv//+mwkTJtCnTx8mTJjAP//8Y2M6x1KiRAnmz59PhQoV6NChA7/88gtBQUF2x3Io7733HpUrV2bmzJlcuXLF7jgOa9KkSdSrV499+/bRpk0b2rdvT8WKFfn5559Zv349zZs3Z+rUqXbHjPPGjh1LkSJFWLlyJSVKlOCDDz4gZ86c7Nmzh71791KuXDkmTJhgd8w4T1/H6KGf6+ih12P0GTVqFNeuXWPVqlX8/PPPVK5cmTp16rBv3z4GDRrE2LFjWb16td0x47zVq1fTqVMnrl+/jo+PD/Xq1WPVqlWhtwcEBLB3714bEzqWRYsW0bp1az766CN27doV7rYbN25QsWJFm5LFA5bIfxQrVszat2/fU2//448/rGLFisViIselr2X06NOnj9W4cWNr06ZNVo8ePazGjRtbTZs2tS5fvmxdvHjRev/9961hw4bZHdMheHh4WHnz5rU8PDye+Ai5Pm/evHbHjNM8PDys69evW5ZlWe+99541Z86ccLcvWrTIqlu3rh3RHE7evHlDv5Zz5861ihcvbn3//ffWqVOnrJUrV1olS5a0Zs+ebXPKuK1gwYLW+fPnLcuyrNKlS1tHjx4Nd/tff/1lFS1a1I5oDqd48eLW6dOnLcuyrHbt2lm9evWyHj16ZFmWZfn7+1sDBw602rZta2dEh9C8eXNr3bp1lmWZ9zmenp5WrVq1rB49elh169a1ChUqZO3fv9/mlHFfyN+agIAAa/369daHH35o5cuXzypVqpQ1ZswY66+//rI7okPw8PCwBg8ebJUsWdLKnz+/1b59e2vjxo1WYGCg3dEcSpEiRUL/1gQEBFj58+e3/vzzz9Dbz549q781kfD435kHDx5Y+fLlsw4ePBh6+8mTJ60333zz/9q787ga0/4P4J/TZgmJZG08ZIlEZcmSGcvMYxlGjN0oVMq+jqQxYYySsYes2fqNrYytMQaNZSRmyNoxQ5ZkSVI0qdS5f394Oc+cqYhz1d1tPu/X63k9032l8/Xt+3VddZ37uuUKTzGYRzHY12KwHsVxcnKSLl26pP04NTVVsrOzkzIyMiRJkqStW7dKvXr1kik65ejVq5e0adMm7ccHDhyQ7O3tpR07dkiSJEmPHj3i73wKadOmTVKzZs2k2bNnS1OnTpVsbW2lkJAQ7ThzqR8+U4ry0Gg0rz0KzdjYGBqNphgjUi7mUoxTp04hODgY9vb2cHR0ROvWrREaGqo9ymb8+PGYOXOmzFEqQ8OGDVGtWjVMmzYNpUuXBgBIkoQuXbpg7dq1qF27tswRKoNKpQLw8rzrfx5p4ezsjO+++06OsBRH+tsJwvv378eUKVPw+eefAwDq1asHAFizZg08PT1liU8JGjZsiNOnT+ODDz5AlSpVcO/ePTRu3Fg7fu/ePW2v0+tlZWVp5+y4uDisWbNGe0ynsbExPDw80K9fPzlDVISrV6/CxsYGALBkyRIMHjwYvr6+2vElS5YgKCgI33//vVwhKoqRkRG6dOmCLl264OHDh9i1axd2796NDRs2wNHREWFhYXKHWOJNnDgR/v7+OHLkCMLDwzF+/HiYm5vDxcUFffv2RZ06deQOscQzNjZGVlYWgJfvsNZoNNqPASAzMxNGRvzVwptIkqQ9ivyf/w8ABgYGOmsjyh/zKAb7WgzWozi5ubk6z40qW7YscnNz8fz5c5QpUwbt2rXD/PnzZYxQGW7fvo2OHTtqP+7evTsqVaqEUaNGIScnB5988omM0SnL9u3b8c0336Bnz54AgEGDBmHMmDHIzMzEhAkTZI5O+Xh8H+XRoUMHfP3117h69WqesatXr2LWrFk6/8BRwZhLMZ4+fardgKpYsSLKlCmDGjVqaMdr166NR48eyRWeouzcuRMffPABxo8fj7S0NNSsWRO1atUCAFhaWqJmzZqoWbOmzFGWfMePH8eRI0dQqlQpPH/+XGcsKytLu2lFb/YqV/fu3YODg4POmIODg/Y8dsrf6NGjsXDhQkRERGDo0KGYN28edu7ciXPnziE8PBx+fn747LPP5A5TEV5t8AHQbvD9HTf4Ckej0WiPmIuPj0fv3r11xvv06cNjYgshv3mkatWqGDNmDA4fPowNGzagWrVqMkSmTK8299asWYOoqCgMHjwYP/30E7p3787nUxSCo6MjFi5ciN9//x0BAQFo3LgxVq1ahYyMDDx//hwrV67ks7sKwdbWFmvXrsXDhw+xevVq1KpVC1u3btWOb926VXskLxWMeRSDfS0G61EcOzs7neccbd68Gebm5qhUqRKAl8+cKlu2rFzhKYapqSkeP36sc61169ZYvXo1goKCsGXLFpkiU567d+/q/I7C0dERmzZtwo4dO7Bw4UIZI3s/8G0PlMfXX3+NKVOmoE+fPjAzM9NOACkpKXj69CmcnZ3x9ddfyxylMjCXYlSuXBmPHj1C9erVAQBDhgyBmZmZdvzp06coU6aMXOEpiomJCfz8/HDs2DGMGjUKgwcP5l0o72D69Ona/z59+rTOQiU2NhYffPCBHGEp0o4dO1C2bFkYGxsjLS1NZ+yvv/7S3qlC+evQoQPmzJmDefPmISkpCZIkae8cNTExwcCBAzFlyhSZo1SG0aNHw8fHB0ZGRtoNvidPnsDa2ho3b97E8uXLucFXCE2bNkVUVBSsra1hZWUFtVqtvXMKeHkXWsWKFeULUCHe9K7qNm3aoE2bNsUUjXK9bnNvzJgxiI6Oxq5du2SITFmmTZsGLy8vDBkyBHXr1kVoaChmzZqFli1bAgAqVKiAdevWyRxlyTd58mR4enoiIiICFStWxObNm+Hn5wdnZ2eoVCo8ffoUISEhcodZ4jGPYrCvxWA9ijNlyhSMGDEChw4dgrGxMR49eqRzZ9T58+fx0UcfyRihMjRt2hTHjx+Hvb29zvVWrVohJCQE3t7e8gSmQObm5njw4IH2jdwA0KBBA2zatAlubm58tr2eVBLvI6UC3LhxA7GxsUhOTgYAWFhYwN7eHtbW1jJHpjzMpX5GjRqF1q1bw83NLd/xsLAwHDp0CJs2bSrmyJQtOTkZvr6+yMjIQGxsLPbs2aM9Mo3eXVRUFIyMjNC+fXu5QynxOnXqpPOxq6srhg0bpv1406ZNiIyMxPbt24s5MuXJzc3FlStXcPfuXWg0GlSpUgW2trY6R2DQm/300086G3yvvNrg8/Hx0TmShfI6f/48PD094ebmBnNzcwQHB2PgwIHazb0tW7Zg5MiRfEPEG5w5cwaOjo48OklPNjY2+PXXX1G5cmW5Q3kvPHnyBObm5tqPo6OjkZmZCXt7e53rVLCMjAzEx8ejTp06MDU1RVZWFvbu3YusrCy0bdsWdevWlTtERWAexWFf64/1KE5SUhJ++eUXZGdno3Xr1vz9xDs4c+YMzp8/Dy8vr3zHT58+jT179iAgIKCYI1OeKVOmoHLlypgxY0aesT///BOurq5ITU1FXFycDNEpHzeliEjxLl68iNKlS6NBgwZyh6JImzdvRkxMDGbOnMmjgKhEiY2NhYmJic4zkoiKGjf49Hf+/HkEBgbiwoULOtctLS3h7u5e4JtMiETj5h4RERERvQu1Wo0rV65on3v9T3/88QcOHTqEsWPHFnNk7wduStFbS0tLQ1RUFFxcXOQORfGYS6L3T0ZGBq5cuaI9+oJITpxnSE4pKSlISEjQbu79/egL0g97m0oS1qMYXEOKwTyKwb4Wg/UoDmuS6P1iIHcApDz379+Hr6+v3GG8F5hLMdLS0vDDDz/IHcZ7gbnU3507d+Dq6ip3GO8F1qP+OM+Iw3p8e5UqVUKzZs3g4ODADSnB2NtisK/FYD2KwTWkGMyjGOxrMViP4rAmC+fFixcICgrCJ598gr59++Z5dmZycjIaNWokU3TKpNFoCrx+7969Yo7m/cFNKcojPT39jf+jwmEuiwcXJ+Iwl1SSsB7fjPNM8WE9ipGcnIzg4GC5wyjx2NvFg31dOKxHovcP+5pKGtakGCEhIdizZw8GDhyIdu3aITAwEF9//bXO5/DQtMJJT0/HhAkTYG9vj7Zt22Lp0qXIzc3VjqekpKBz584yRqhsPFyb8mjRogVUKlWB45IkvXac/oe5FONNiw8uTgqPudRfq1atXjv+90UKvR7rUX+cZ8RhPRaP5ORkrFixgmevvwF7Wwz2tRisRzG4hhSDeRSDfS0G61Ec1qQY+/btw9y5c9GxY0cAQJ8+feDp6QlfX1/MmzcPAJjHQlqyZAnUajWCgoLw7NkzrFq1ClevXsXy5cthYmICgBt8+uCmFOVhamoKb29vNGvWLN/x27dv59llp/wxl2JwcSIOc6m/7OxsDBo0CA0aNMh3PDExEStWrCjmqJSJ9ag/zjPisB7FUKvVrx2Pj48vpkiUjb0tBvtaDNajGFxDisE8isG+FoP1KA5rUoyHDx+ifv362o9r166NLVu2wNXVFV9++SW+/PJLGaNTliNHjiAwMBBOTk4AgM6dO8PLywujRo3CqlWrAHCDTx/clKI8GjduDKDgd3xUqFCBO8GFxFyKwcWJOMyl/mxsbFCtWjX07t0733G1Ws0fPAqJ9ag/zjPisB7FcHFxgUqlyrfuXl3nD29vxt4Wg30tButRDK4hxWAexWBfi8F6FIc1KYaFhQUSEhJ0nudatWpVbN68Ga6urjy2+C2kpKSgRo0a2o8rVaqE0NBQeHh4wNPTE3PnzpUxOuXjphTl0bNnT2RmZhY4bmFhwSNXCom5FIOLE3GYS/116NABz549K3DczMwMLi4uxReQgrEe9cd5RhzWoxhmZmb48ssv0aZNm3zHr1+/Dm9v72KOSnnY22Kwr8VgPYrBNaQYzKMY7GsxWI/isCbFaN26Nfbt25dnLf5qY2ro0KEyRaY81atXR3x8PKysrLTXypUrh/Xr18Pd3Z31qCeVxFU4EZVwO3bsQGZmJlxdXfMdT05OxrZt2zghFAJzSSUJ65FKEtajGO7u7mjevDlGjx6d77harYaLi8sbj/kjEoF9TURERP8miYmJiI+PR/v27fMdf/jwIU6dOlXg3X30P3PnzkVSUhKWLVuWZyw9PR0jRozApUuXEBcXJ0N0ysdNKSIiIiIiEuLnn39GRkYGevXqle94Wloajh49yh+EiYiIiIioxEpLS0NSUpLOM7r+Lj09HVevXi3wjnx6PW5KUb5SUlIQHh6O2NhYJCcnA3h5q6yDgwP69OmDSpUqyRyhcjCXRO+f7OxsHD58ON++7ty5M0xMTGSOkP5NOM8QvZ/Y21SSsB7F4BpSDOZRDPa1GKxHcViTYrAmSQm4KUV5XLx4ER4eHihdujTatm2LypUrAwAeP36M6OhoZGZmYt26dbCzs5M50pKPuRSHixNxmEv93L59G+7u7khKSkKzZs10+vrChQuoVq0a1q5di9q1a8scqTKwHvXDeUYs1iOVFOxtcdjX+mM9isE1pBjMoxjsazFYj+KwJsVgTYrFdWTR4aYU5dG/f3/Y2Nhg9uzZUKlUOmOSJMHf3x/Xrl3D9u3bZYpQOZhLMbg4EYe51N/w4cNRpkwZBAUFoVy5cjpj6enpmDZtGrKysrB+/XqZIlQO1qP+OM+Iw3oU5/r169i6dWueH97s7e3xxRdfoF69ejJHWPKxt8VgX4vBehSDa0gxmEcx2NdisB7FYU2KwZoUh+vIIiYR/YOdnZ10/fr1AsevX78u2dnZFWNEysVcitGvXz9p5syZkkajyTOm0WikmTNnSv3795chMuVhLvXXtGlT6dq1awWOq9VqqWnTpsUYkXKxHvXHeUYc1qMYv/zyi2Rrayv1799fWrZsmRQWFiaFhYVJy5YtkwYMGCA1adJEOn78uNxhlnjsbTHY12KwHsXgGlIM5lEM9rUYrEdxWJNisCbF4TqyaBnIvSlGJY+FhQUuXbpU4PilS5dgYWFRjBEpF3MphlqthpubW553ywCASqWCm5sb4uLiZIhMeZhL/ZUvXx6JiYkFjicmJqJ8+fLFGJFysR71x3lGHNajGAsXLoSnpye2b9+OcePGYfDgwRg8eDDGjRuHbdu2YeTIkQgKCpI7zBKPvS0G+1oM1qMYXEOKwTyKwb4Wg/UoDmtSDNakOFxHFi0juQOgksfd3R0zZ87E5cuX0aZNG+0/+snJyYiOjsbOnTsxbdo0maNUBuZSjFeLE2tr63zHuTgpPOZSf/369YOPjw9Gjx6N1q1b6/T16dOnsWrVKnzxxRcyR6kMrEf9cZ4Rh/Uoxq1bt9CzZ88Cxz/99FOsXbu2GCNSJva2GOxrMViPYnANKQbzKAb7WgzWozisSTFYk+JwHVm0+EwpyldkZCQ2btyIK1euIDc3FwBgaGgIW1tbDBs2DN27d5c5QuVgLvUXFhaGwMBADBgw4LWLkyFDhsgcacnHXIqxZs0abN68GcnJydp3zUiSBAsLC7i5ucHT01PmCJWB9SgG5xkxWI9idOvWDf3798fw4cPzHQ8NDcX27dtx8ODBYo5Medjb+mNfi8N6FINrSDGYRzHY12KwHsVhTYrBmhSD68iixU0peq0XL17gyZMnAABzc3MYGxvLHJFyMZf64eJEHOZSnISEBCQnJwN4+S4aKysrmSNSHtajOJxn9Md61N+PP/6IqVOnon379mjbtm2eH95OnDiBhQsXokuXLjJHqhzsbf2wr8ViPYrBNaQYzKMY7GsxWI/isCbFYE3qj+vIosNNKSJSFC5OxGEuqSRhPVJJwnrUz7lz57BlyxbExsbi0aNHAIAqVarA3t4erq6ucHBwkDlC+jdiXxMRERHRu+A6UjwDuQOgkkmtVmPlypUICwtDSkqKzlh6ejp8fX1likx5mEuxjI2NYWlpCUtLS04CemIu311SUhL27NmDY8eOITs7W2csIyMDwcHBMkWmXKzHd8d5RjzWo34cHR2xePFiREVF4fLly7h8+TKioqKwePFibki9Bfa2WOxr/bAexeAaUgzmUQz2tRisR3FYk2KwJsXjOlI83ilFeZw8eRLe3t74z3/+g7/++gsZGRlYunQpWrduDeDl8Svt27dHXFyczJGWfMylOGq1GkePHoWZmRm6deuGSpUqacfS09Px7bffIiAgQMYIlYO51M/Fixfh7u4OjUaDnJwcVK1aFStWrED9+vUBsK/fFutRP5xnxGI9UknB3haHfa0/1qMYXEOKwTyKwb4Wg/UoDmtSDNakWFxHFh3eKUV5BAcHw93dHfv378fRo0fh4eGBUaNG4fjx43KHpjjMpRgnT55E3759ERkZiXXr1qFbt244ffq0djwzMxM//PCDfAEqCHOpv8WLF+Pjjz/G2bNn8euvv6Jt27b44osvcPXqVblDUxzWo/44z4jDehTn2LFj8PPzQ1BQEG7cuKEzlpaWBldXV5kiUw72thjsazFYj2JwDSkG8ygG+1oM1qM4rEkxWJPicB1ZxCSif3B0dJRu376tc23v3r2Svb29dPToUenRo0eSjY2NTNEpC3MpxoABA6RFixZJkiRJGo1GWrNmjWRvby8dO3ZMkiSJeXwLzKX+WrZsKcXHx+tcW716tdSyZUvpwoULzOFbYD3qj/OMOKxHMfbu3Ss1atRIGjlypDRo0CDJzs5O2rNnj3aceSwc9rYY7GsxWI9icA0pBvMoBvtaDNajOKxJMViT4nAdWbSM5N4Uo5LHxMQET58+1bnWs2dPGBgYYNKkSfDx8ZEpMuVhLsX4888/ERQUBABQqVTw9PREtWrVMGHCBCxatAh2dnYyR6gczKUYWVlZOh+PHDkShoaGcHd3x7x582SKSnlYj/rjPCMO61GM9evXY/r06dq7oSIjI+Hn54esrCz069dP5uiUg70tBvtaDNajOFxDisE86o99LQ7rUQzWpDisSTG4jixa3JSiPBo1aoSYmBg0adJE5/qnn34KSZIwffp0mSJTHuZSDC5OxGEu9Ve/fn2cP38eNjY2Otdfnds8efJkmSJTHtaj/jjPiMN6FOP27dvo2LGj9uPu3bujUqVKGDVqFHJycvDJJ5/IGJ1ysLfFYF+LwXoUg2tIMZhHMdjXYrAexWFNisGaFIfryKLFTSnKY9CgQTh79my+Yz169IAkSdixY0cxR6VMzKUYXJyIw1zqz8XFBWfOnMGgQYPyjHl6ekKSJGzbtk2GyJSH9ag/zjPisB7FMDU1xePHj2FlZaW91rp1a6xevRpeXl548OCBjNEpB3tbDPa1GKxHMbiGFIN5FIN9LQbrURzWpBisSXG4jixaKkmSJLmDICJ6nZ9//hlnz57FjBkz8h3ft28fduzYgS1bthRzZMrDXFJJwnqkkoT1KMbo0aNhY2OD8ePH5xmLiYmBt7c3MjMzERcXJ0N09G/DviYiIiKid8F1ZNHiphQREREREQlx5swZnD9/Hl5eXvmOnz59Gnv27EFAQEAxR0ZEREREREQlgYHcAVDJ5uvri8WLF+tcW7RoEXx9fWWKSLmYS6L3T3BwMMLCwnSuhYWFITg4WKaI6N+M8wyVBK1atSpwQwp4eZQfN6TeDnubShLWoxhcQ4rBPIrBvhaD9SgOa1IM1iSVZNyUote6e/cuHj58qHPt4cOHSExMlCki5WIuxeDiRBzmUn8RERE4fPiwzrVDhw5h9+7dMkWkXKxH/XGeEYf1SCUJe1sM9rUYrEcxuIYUg3kUg30tButRHNakGKxJcbiOFM9I7gCoZMvvXMz58+fLEInyMZdi3L17F/88dfThw4e4f/++TBEpF3Opv6NHj+a5tmnTJhkiUT7Wo/44z4jDehRj6NChqFmzJgIDA7XXfHx8cP/+fWzevFnGyJSFvS0G+1oM1qMYXEOKwTyKwb4Wg/UoDmtSDNakOFxHisdnShERERERkXC+vr6oUqUKJk+erL22aNEiPHr0iEf4ERERERER/UtxU4rypVarcfnyZTg5OcHKygp//vknwsLCoNFo8Mknn6B9+/Zyh6go0dHR+P333/Ho0SMYGBjAysoKnTp1wn/+8x+5QyMiQVxdXREQEICaNWvKHQr9C3GeIXr/cD1OSpGWloaoqCi4uLjIHYoiaDQaGBjkfZKCRqPBgwcPUKNGDRmiUj6uxd9OdnY2VCoVjI2NAQB37txBeHg47t27hxo1aqBv376wsrKSOcqS76effsKHH36IMmXKyB3Ke49zTeGwJkkpuClFeRw6dAgTJ05E+fLlkZ2djRUrVmDChAlo0qQJDAwMEB0djfnz56Nnz55yh1riPX78GN7e3rh8+TIMDAyg0WjQqFEjJCUlISUlBcOGDcO0adPkDlMRsrOzcfjwYcTGxiI5ORkAYGFhAQcHB3Tu3BkmJiYyR6gsDx48QPny5WFqaqpz/cWLF4iNjUXLli1liqzkO3LkSL7Xx40bBz8/P1SrVg0A0Llz5+IMS9FYj++O84xYnGuopOB6XCzOM0VLrVajd+/eiIuLkzuUEi09PR1+fn6IiopCuXLlMGDAAIwdOxaGhoYAgOTkZLRv3555fAOuxcUYOnQohgwZgq5du+L333/HsGHDUKdOHVhbW+PWrVu4efMmQkND4eDgIHeoJZqNjQ1MTU3RvXt39O3bF82aNZM7pPcW55rCYU2Kx3Vk0eAzpSiPkJAQjBs3DqNGjcKBAwcwYcIEDBs2DGPGjAEAbNiwAevXr+cPwYUwd+5cWFpa4uzZszAxMcH8+fORnp6OiIgIREdHY+LEiahatSrc3NzkDrVEu337Ntzd3ZGUlIRmzZqhcuXKAIC4uDhs27YN1apVw9q1a1G7dm2ZIy35kpKSMHr0aFy5cgUqlQo9evSAv7+/dnJNS0uDq6srF3qvMWbMGKhUqjznCQPAN998AwBQqVTMYSGwHvXHeUYczjXFIzk5Gdu2bcPYsWPlDqVE43pcDM4zYqSnp+s1Ti8tWbIEarUaQUFBePbsGVatWoWrV69i+fLl2jc98D3Db8a1uBhXr16FjY0NgJe1OXjwYPj6+mrHlyxZgqCgIHz//fdyhagYI0aMwOHDh7Fz507Uq1cPffv2Ra9evWBubi53aIrCuUYc1qQYXEcWLd4pRXk4ODhg3759qFWrFiRJgp2dHcLDw9GwYUMAQEJCAj777DOcP39e5khLvubNm2Pbtm2oX78+ACAjIwOtWrXC6dOnUa5cOezZswerVq3CwYMHZY60ZBs+fDjKlCmDoKAglCtXTmcsPT0d06ZNQ1ZWFtavXy9ThMrh4+ODmzdvYubMmXj27Bm+++47qFQqbNiwAWZmZkhOToazszPUarXcoZZYHh4eMDQ0xLx587S/tAYAW1tb7NmzB/Xq1ZMxOmVhPeqP84w4nGuKB9/lWjhcj4vBeUYMGxsbqFSqAsclSeImQCF07NgRgYGBcHJyAgCkpKTAy8sLFSpUwKpVq/D06VPeKVUIXIuL4eDggF27dsHa2hrt2rXD+vXrtZtUwMvj/Hr16sV55g1sbGzw66+/onLlyrh8+TJ27dqFAwcOIDMzE506dUL//v3Rrl07ucNUBM41YrAmxeE6smjxTinKw9TUFKmpqahVqxaePn2KnJwcpKamasefPHmS55ZFyp+JiYnOpGpgYIDc3Fzk5OQAABwdHZGYmChXeIpx7tw57Ny5M88vCQGgXLlymDBhAvr37y9DZMpz6tQprFixAnZ2dgCAbdu2Yfz48XBzc8PGjRsB4LULQQLWrVuHjRs34vPPP4e/vz86duwod0iKxXrUH+cZcTjXiPGmH8ri4+OLKRJl43pcDM4zYpiamsLb27vAI4Bu376Nr7/+upijUp6UlBSd50VVqlQJoaGh8PDwgKenJ+bOnStjdMrBtbgYTZs2RVRUFKytrWFlZQW1Wq2zKRUXF4eKFSvKF6ACNWnSBE2aNIGvry9+/PFHhIeHw8PDA9WrV8fRo0flDq/E41wjHmtSP1xHFi1uSlEebdq0wZw5c/DFF18gMjIS7dq1w6JFizBv3jyoVCosWLAAjo6OcoepCM2bN8eyZcsQGBgIY2NjLFq0CFZWVtrFXUpKCszMzOQNUgHKly+PxMRENGjQIN/xxMRElC9fvpijUqb09HRUqFBB+7GJiQmCg4MxYcIEuLq6YsGCBTJGpxzDhg2Dk5MTpk6diqioKJ2jLqjwWI/64zwjDucaMVxcXAo8VunVdf7w9mZcj4vBeUaMxo0bAwBatWqV73iFChV47FwhVK9eHfHx8bCystJeK1euHNavXw93d3cea/oWuBbX38SJE+Hp6Ynnz5+jR48eCAwMxK1bt2BtbY2bN29iy5YtGDlypNxhlnj5rWlKlSoFFxcXuLi44Pbt24iIiJAhMuXhXCMGa1IcriOLloHcAVDJ4+Pjg3LlysHf3x8vXrzAkiVL0KRJE3z66afo3r07kpKSMHXqVLnDVAQfHx/ExcWhZcuWcHBwQEREBPz9/bXjN27cQO/evWWMUBn69esHHx8fbNy4EWq1GsnJyUhOToZarcbGjRvh6+uLAQMGyB2mItSqVQvXrl3TuWZkZISlS5fCysoK3t7eMkWmPI0aNcKuXbsAvPwlLBfIb4/1qD/OM+JwrhHDzMwM33zzDY4cOZLnf4cPH8bq1avlDlERuB4Xg/OMGD179tQ+8yg/FhYW3FApBGdnZ4SHh+e5bmpqinXr1qFUqVIyRKVcXIvrx8HBAWvXrsXJkycxd+5cpKamIiQkBF9++SV27dqFsWPHwtPTU+4wS7w31V3t2rUxadKkYopG2TjXiMGaFIfryKLFZ0pRoSUkJOD58+eoW7cujIx4k11hPX/+HOfOnUN2djbs7e35YMF3tGbNGmzevBnJycnad35IkgQLCwu4ublxwVxICxYsgFqtzveZKDk5ORg3bhyioqJ4Ju5bOnr0KE6fPg0vLy+ds+3p9ViPYnCeEYdzjf7c3d3RvHlzjB49Ot9xtVoNFxcX9vU74nr87XCeoZIkLS0NSUlJ2udA/lN6ejquXr1a4F0CVDCuxfWTkpKChIQEaDQaVKlSBbVq1ZI7JMVITExE9erVYWDA9/xTycCaFIfryKLFTSnKV0pKCsLDwxEbG4vk5GQAL9+V4ODggD59+qBSpUoyR6gczKVYCQkJOnn8+/EX9GY5OTnIzMzM95kpr8YfPnyImjVrFnNkylJQX9vb2+Pzzz9nXxcS61EMzjPica55dz///DMyMjLQq1evfMfT0tJw9OhR3sFXCOxt/XGeEYf1KAbzKAbX4mKwHsVgHsVhLsVgHsXgOrJocVOK8rh48SI8PDxQunRptG3bVvtOo8ePHyM6OhqZmZlYt26d9kFvVDDmsnjcv38fy5YtQ0BAgNyhKB5z+Wbs6+LDenwz1mPxYT1ScWJvFw/2deGwHsVgHsVgHsVgHsVgHsVhLsVgHosP15H64aYU5dG/f3/Y2Nhg9uzZeR6QJ0kS/P39ce3aNWzfvl2mCJWDuSwearUavXv3RlxcnNyhKB5z+Wbs6+LDenwz1mPxYT2KwR/eCoe9XTzY14XDehSDeRSDeRSDeRSDeRSHuRSDeSw+XEfqhweRUx5qtRoBAQF5/vECAJVKBTc3Nx65UkjMpRhHjhx57XhCQkIxRaJ8zKX+2NfisB71x3oUh/VYPNLS0vDDDz9wU+oN2NtisK/FYD2KwTyKwTyKwTyKwTyKw1yKwTyKw3Vk0eKmFOVhYWGBS5cuwdraOt/xS5cuwcLCopijUibmUowxY8ZApVLhdTd25jfhUl7Mpf7Y1+KwHvXHehSH9SgGf3gTg70tBvtaDNajGMyjGMyjGMyjGMyjOMylGMyjOFxHFi1uSlEe7u7umDlzJi5fvow2bdpo/7FKTk5GdHQ0du7ciWnTpskcpTIwl2JUqVIF/v7++Pjjj/Mdj4uLQ58+fYo5KmViLvXHvhaH9ag/1qM4rEcx+MObGOxtMdjXYrAexWAexWAexWAexWAexWEuxWAexeE6smhxU4ryGDJkCMzNzbFx40Z8//33yM3NBQAYGhrC1tYWAQEB6N69u8xRKgNzKYatrS2uXLlS4ETwpl9+0f8wl/pjX4vDetQf61Ec1qMY/OFNDPa2GOxrMViPYjCPYjCPYjCPYjCP4jCXYjCP4nAdWbRUErNHr/HixQs8efIEAGBubg5jY2OZI1Iu5vLd/fbbb8jIyMCHH36Y73hGRgYuX76MVq1aFXNkysNcisW+1g/rUSzWo35Yj2J4e3ujUaNGmDBhQr7jarUaLi4uUKvVxRyZcrG33x37WjzWoxjMoxjMoxjMoxjMozjMpRjMo364jixa3JQiIiIiIiIh+MMbERERERERvQ43pYiIiIiIiIiIiIiIiKjIGcgdABEREREREREREREREb3/uClFRERERERERERERERERY6bUkRERERERERERERERFTkuClFRERERERE7627d++iYcOGiIuLkzsUIiIiIqJ/PW5KERERERFRkZs+fToaNmyIhg0bwtbWFm3btsXw4cOxa9cuaDSat/paERERaNGihbDYrl69ivHjx6Nt27aws7PDf//7X3z11Ve4efOmsNd4G8ePH0fDhg3x6NEjnevOzs7o1KmTzrVXGy7R0dFFEktqaiq+/fZbdOzYEU2aNIGzszN8fX1x7969Ink9IiIiIiJ6v3FTioiIiIiIikX79u1x8uRJHD16FGvXroWTkxO+/fZbeHl5IScnR5aYoqKi0L9/f2RnZ+O7775DZGQkFixYgPLly2Pp0qWyxNS8eXMYGRnhzJkz2ms3btxAZmYm0tLScPfuXe31mJgYmJiYwNHRUXgcqampGDBgAKKjozFr1iwcOnQIixcvxp07d9C3b18kJCQIf00iIiIiInq/cVOKiIiIiIiKhYmJCapUqYKqVavC1tYW3t7eWLlyJY4fP47du3drPy80NBQ9e/aEvb09PvroI8yaNQt//fUXgJebML6+vnj27Jn2zqvly5cDALKzszF//ny0b98e9vb26NevH2JiYgqM5/nz5/D19cVHH32EkJAQtG3bFlZWVmjWrBl8fHwwZ84cAEBubi5mzJiBTp06oWnTpujSpQs2bdqk87ViYmLQt29f2Nvbo0WLFhg4cCASExO144cPH0bv3r1hZ2eHzp07Izg4uMCNOFNTU9jZ2enEHhMTg+bNm8PR0VFns+rMmTOwt7dHqVKlcPz4cQwaNAgtWrSAk5MTvLy8cOfOHe3nZmdnY86cOXB2doadnR06duyI1atXF5ifxYsXIykpCaGhofjoo49Qo0YNtGzZEuvXr4eRkRFmz56t/dyhQ4dizpw5mDNnDpo3bw4nJycsWbIEkiTpvP7rvj+v7oA7ceIEunXrBgcHB7i7uyMpKUknrp07d6Jbt26ws7ND165dERYWpjN+8eJFuLi4wM7ODn369OGxfUREREREJQg3pYiIiIiISDZt2rSBjY0NDh06pL2mUqng5+eH/fv3IzAwEKdPn8aCBQsAAA4ODpgxYwbKlSuHkydP4uTJkxgxYgQAYM6cOTh//jwWL16MvXv3omvXrvDw8MCtW7fyfe2TJ0/iyZMn8PDwyHe8QoUKAACNRoNq1aph6dKlOHDgAMaMGYPFixcjMjISAJCTk4MxY8agZcuW2Lt3L7Zv344BAwZApVIBAH777Tf4+PjA1dUVkZGRmDNnDiIiIhASElJgXpycnPJsSrVq1QotW7bUuX7mzBk4OTkBeLnJNnz4cISHh2Pjxo1QqVQYM2aM9njELVu24OjRo1iyZAkOHjyIBQsWoGbNmvm+vkajQWRkJHr27IkqVarojJUuXRqDBw/GyZMnkZqaqr2+e/duGBoaYufOnfDz88PGjRuxc+dO7Xhhvj+ZmZnYsGEDgoKCsHXrVty/fx/z58/Xju/duxdLly7FpEmTEBkZicmTJ2PZsmXaTc2//voLXl5esLa2RkREBMaNG6fz54mIiIiISF5GcgdARERERET/bnXr1sW1a9e0Hw8bNkz737Vq1cLEiRPh7++PWbNmwcTEBOXLl4dKpdLZLLl37x4iIiIQFRWFqlWrAgDc3d1x4sQJREREYPLkyXle99VmSN26dV8bn7GxMcaPH6/92MrKCrGxsTh48CC6d++O9PR0PHv2DB07dsQHH3wAALC2ttZ+fnBwMEaOHInevXtr//yECROwYMECjB07Nt/XdHJyQkhICJKSkmBpaYmzZ8/C3d0dubm52LZtGwAgISEB9+7d025KdenSRedrzJs3D23atMH169fRoEED3L9/H7Vr10bz5s2hUqkK3JACgJSUFDx9+rTA3FhbW0OSJNy5cwcVK1YEAFSvXh0zZsyASqVC3bp18ccff2Djxo3o379/ob8/L168wOzZs7V5HDJkCFauXKl93eXLl2P69On473//q83l9evXsX37dvTu3Rv79++HRqPBvHnzUKpUKdSvXx8PHjzArFmzCvy7EhERERFR8eGmFBERERERyUqSJO1dRQBw6tQprF69GvHx8UhPT0dubi6ysrLw/PlzlClTJt+v8ccffyA3Nxddu3bVuZ6dna3dNMnvdQsrLCwM4eHhuHfvHrKysvDixQvY2NgAACpWrIg+ffrA3d0d7dq1Q5s2bdCtWzdYWloCANRqNc6dO6dzZ9Sb/k4ODg4wNjbGmTNnYGNjg8zMTNja2kKj0SAlJQUJCQmIiYlB6dKl0axZMwAvN9mWLVuGCxcu4MmTJ9q/3/3799GgQQP07t0bI0aMQNeuXdG+fXt06NABzs7Ohc7BmzRr1kzn+2hvb4/Q0FDk5uYW+vtTpkwZ7YYUAFhaWuLx48cAgIyMDNy5cwd+fn6YOXOm9nNycnJQvnx5AC+fvdWwYUOUKlVKO+7g4CDs70hERERERPrhphQREREREcnqxo0bqFWrFgDg7t278PLywqBBgzBp0iSYmZnh999/h5+fH168eFHgplRGRgYMDQ0RHh4OQ0NDnbGyZcvm+2fq1KkDAIiPj3/txsWBAwcwf/58+Pj4wMHBAaampli/fj0uXLig/ZyAgAAMHToUJ06cwI8//oglS5YgNDQU9vb2yMjIwLhx47R39/zd3zdP/q5MmTJo2rQpYmJikJaWhubNm8PQ0BCGhoZwcHBATEwMYmJi4OjoCBMTEwCAt7c3atasiblz58LS0hIajQY9evTAixcvAAC2trY4cuQIjh8/jlOnTmHixIlo27Ytli1bluf1K1WqhAoVKuDGjRv5xnfjxg2oVCqdDaTXKez3x8hI90dUlUql3VzLyMgAAHzzzTfajbhXDAx4Mj0RERERkRJwU4qIiIiIiGQTHR2NP/74Q3tk35UrVyBJEqZPn67daPjxxx91/oyxsTFyc3N1rjVq1Ai5ublISUlBixYtCvXa7dq1g7m5OdatW4cVK1bkGX/69CkqVKiAc+fOwcHBAUOGDNGO3blzJ8/nN27cGI0bN4aXlxcGDBiA/fv3w97eHo0bN8bNmzdRu3btQsX1ipOTEyIjI5GWloZWrVppr7do0QJnzpzB2bNnMXDgQADAkydPcPPmTcydO1f79//tt9/yfM1y5cqhe/fu6N69O7p06QIPDw+kpqbmuZvMwMAA3bp1w759+zB+/HidoxIzMzPxf//3f3B2dtb5cxcvXtT5GhcuXEDt2rVhaGj4Tt+ff7KwsIClpSUSEhLw2Wef5fs51tbW2LNnD7KysrQbfrGxse/0ekREREREJB7fTkZERERERMUiOzsbjx49wsOHD3HlyhWEhIRg9OjR6NixI1xcXAAAtWvXxosXL7BlyxYkJCTghx9+0D5D6ZWaNWsiIyMD0dHRSElJwfPnz1GnTh307NkT06ZNw6FDh5CQkICLFy9i9erV+OWXX/KNp2zZspg7dy6OHTsGb29vnDp1Cnfv3sWlS5cQFBQEf39/bUyXL1/GiRMncPPmTSxZsgSXLl3Sfp2EhAQsXLgQ58+fR2JiIk6ePIlbt25pn8c0ZswY7NmzB8HBwfjzzz9x48YNHDhwAIsXL35tvpycnHDr1i2cPHlSZ1OqVatWOHz4MO7fv699npSZmRkqVqyI7du34/bt24iOjkZgYKDO1wsNDcX+/ftx48YN3Lx5EwcPHkSVKlVQoUKFfF9/0qRJsLCwwIgRI3Ds2DHcv39f+2yrnJwcbX5euXfvHgICAhAfH4/9+/dj69atcHV1BYB3+v7kZ/z48VizZg02b96Mmzdv4tq1awgPD0doaCgAoEePHlCpVPjqq69w/fp1HDt2DBs2bCj01yciIiIioqLFO6WIiIiIiKhYnDhxAs7OzjAyMkKFChVgY2ODr776Cr1799beFWVjYwNfX1+sXbsWixYtQosWLTB58mT4+Phov46joyMGDhyIiRMnIjU1FWPHjsW4ceMQEBCAVatWITAwEElJSahYsSLs7e3RoUOHAmP6+OOP8f3332PNmjWYMmUK0tPTUb16dbRu3RoTJ04EAAwcOBBxcXGYNGkSVCoVPv30UwwePBjHjx8H8PKovfj4eOzevRupqamwtLTEkCFDtHcxtW/fHiEhIVixYgXWrl0LIyMj1K1bF/369XttvhwcHGBiYgJJkmBra6u93qxZM+Tk5KBs2bKws7MD8PLOpsWLF2Pu3Lno0aMH6tSpg6+++gpDhw7V/jlTU1OsW7cOt2/fhoGBAezs7LBmzZoCj74zNzfH9u3bsXLlSvj7+yM5ORlmZmb48MMPsWDBAtSoUUPn811cXJCZmYl+/frB0NAQrq6uGDBggHb8Xb4//9SvXz+ULl0a69evR1BQEMqWLYsGDRrAzc1N+3cMCQmBv78/XFxcUK9ePUydOhXjxo0r9GsQEREREVHRUUlv83RfIiIiIiIion8YOnQobGxs4OfnJ3coRERERERUgvH4PiIiIiIiIiIiIiIiIipy3JQiIiIiIiIiIiIiIiKiIsfj+4iIiIiIiIiIiIiIiKjI8U4pIiIiIiIiIiIiIiIiKnLclCIiIiIiIiIiIiIiIqIix00pIiIiIiIiIiIiIiIiKnLclCIiIiIiIiIiIiIiIqIix00pIiIiIiIiIiIiIiIiKnLclCIiIiIiIiIiIiIiIqIix00pIiIiIiIiIiIiIiIiKnLclCIiIiIiIiIiIiIiIqIix00pIiIiIiIiIiIiIiIiKnL/D6HARn3c1kEgAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Create df with aggregate stats for month & year (reset_index to make opened_month_year a col)\n",
        "year_df = df_closed.groupby('opened_year')['processing_days'].agg([np.mean, min, max]).reset_index()\n",
        "\n",
        "# chg col names: 'process_mean' refers to mean nbr of days required to close a 311-case\n",
        "year_df.rename(columns={'mean': 'closing_days_mean', 'min': 'closing_days_min', 'max':'closing_days_max'},inplace=True)\n",
        "year_df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "xNJFgww9mHma",
        "outputId": "4ab82351-58ef-40cf-97d4-17598671cc28"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   opened_year  closing_days_mean  closing_days_min  closing_days_max\n",
              "0         2010           3.666667                 2                 5\n",
              "1         2013         559.000000               559               559\n",
              "2         2014           2.333333                 1                 7\n",
              "3         2015           1.000000                 1                 1\n",
              "4         2016           5.013203                 1                95"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-c4bcf062-6258-44a2-8642-7486fe5f7956\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>opened_year</th>\n",
              "      <th>closing_days_mean</th>\n",
              "      <th>closing_days_min</th>\n",
              "      <th>closing_days_max</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2010</td>\n",
              "      <td>3.666667</td>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2013</td>\n",
              "      <td>559.000000</td>\n",
              "      <td>559</td>\n",
              "      <td>559</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2014</td>\n",
              "      <td>2.333333</td>\n",
              "      <td>1</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2015</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2016</td>\n",
              "      <td>5.013203</td>\n",
              "      <td>1</td>\n",
              "      <td>95</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c4bcf062-6258-44a2-8642-7486fe5f7956')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-c4bcf062-6258-44a2-8642-7486fe5f7956 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-c4bcf062-6258-44a2-8642-7486fe5f7956');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 73
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "del grouped_df"
      ],
      "metadata": {
        "id": "E-BuaehomYzL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# same visualization, but at yearly intervals\n",
        "# NOTE that the y-axis scales are NOT the same\n",
        "\n",
        "#TODO change this to match the current way I populate opened_year\n",
        "\n",
        "#labels = year_df.opened_year.dt.strftime('%Y').astype(str)\n",
        "labels = year_df.opened_year.astype(str)\n",
        "\n",
        "x = year_df.closing_days_max.max()\n",
        "x_max = int(math.ceil(x / 100.0)) * 100\n",
        "\n",
        "fig, ax = plt.subplots(3,1, figsize=(18,12))\n",
        "for i in range(3):\n",
        "  ax[i].set(ylabel='Nbr of Days')\n",
        "  ax[i].set_xticklabels(labels)\n",
        "  ax[i].set(xlabel='Year Case Was Opened')\n",
        "#  ax[i].set_ylim([0, x_max]) # makes y scale same on all 3\n",
        "\n",
        "ax[0].set_title('Minimum Number of Days Required to Close Cases, Over Time')\n",
        "ax[1].set_title('Maximum Number of Days Required to Close Cases, Over Time')\n",
        "ax[2].set_title('Mean Number of Days Required to Close Cases, Over Time')\n",
        "fig.tight_layout(pad=4.0)\n",
        "\n",
        "ax[0].plot(year_df.opened_year.dt.strftime('%Y'), year_df.closing_days_min, color='lightgreen')\n",
        "ax[1].plot(year_df.opened_year.dt.strftime('%Y'), year_df.closing_days_max, color='plum')\n",
        "ax[2].plot(year_df.opened_year.dt.strftime('%Y'), year_df.closing_days_mean, color='lightblue')\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "XcmuPYOCmTYO",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "6482b8a3-7a1b-463a-a9e7-613e83d05c92"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-75-2ba42fe16abd>:15: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
            "  ax[i].set_xticklabels(labels)\n"
          ]
        },
        {
          "output_type": "error",
          "ename": "AttributeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-75-2ba42fe16abd>\u001b[0m in \u001b[0;36m<cell line: 24>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     22\u001b[0m \u001b[0mfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtight_layout\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpad\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m4.0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     23\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 24\u001b[0;31m \u001b[0max\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0myear_df\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mopened_year\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstrftime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'%Y'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0myear_df\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclosing_days_min\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolor\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'lightgreen'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     25\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0myear_df\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mopened_year\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstrftime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'%Y'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0myear_df\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclosing_days_max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolor\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'plum'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     26\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0myear_df\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mopened_year\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstrftime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'%Y'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0myear_df\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclosing_days_mean\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolor\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'lightblue'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.9/dist-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m__getattr__\u001b[0;34m(self, name)\u001b[0m\n\u001b[1;32m   5900\u001b[0m         ):\n\u001b[1;32m   5901\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 5902\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mobject\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__getattribute__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   5903\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5904\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__setattr__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.9/dist-packages/pandas/core/accessor.py\u001b[0m in \u001b[0;36m__get__\u001b[0;34m(self, obj, cls)\u001b[0m\n\u001b[1;32m    180\u001b[0m             \u001b[0;31m# we're accessing the attribute of the class, i.e., Dataset.geo\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    181\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_accessor\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 182\u001b[0;31m         \u001b[0maccessor_obj\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_accessor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    183\u001b[0m         \u001b[0;31m# Replace the property with the accessor object. Inspired by:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    184\u001b[0m         \u001b[0;31m# https://www.pydanny.com/cached-property.html\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.9/dist-packages/pandas/core/indexes/accessors.py\u001b[0m in \u001b[0;36m__new__\u001b[0;34m(cls, data)\u001b[0m\n\u001b[1;32m    510\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mPeriodProperties\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morig\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    511\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 512\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mAttributeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Can only use .dt accessor with datetimelike values\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mAttributeError\u001b[0m: Can only use .dt accessor with datetimelike values"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1800x1200 with 3 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Cck9xXc9c_nT"
      },
      "source": [
        "### Changes over time: has the source changed over time?"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0eeJ1aTrV-9b"
      },
      "source": [
        "### Plot closed cases \n",
        "  as a Series by: \n",
        "\n",
        "*   Responsible Agency\n",
        "*   Category\n",
        "*   Request Type\n",
        "*   Supervisor District\n",
        "*   Neighborhood\n",
        "*   Police Disctrict\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "yUqydSeDgBPN"
      },
      "outputs": [],
      "source": [
        "# plot CLOSED cases (as a Series) by: Responsible Agency, Category, \n",
        "#  Request Type, Supervisor District, Neighborhood, Police District\n",
        "# 'ranking relationship' so using horizontal bar graphs\n",
        "# for Supervisor District, maybe join to diff table to get supervisor name (based on date)?\n",
        "\n",
        "elements = ['Responsible Agency','Category', 'Request Type',\n",
        "            'Supervisor District', 'Neighborhood', 'Police District']\n",
        "\n",
        "for el in elements:\n",
        "  plt.title('Most Common '+ el+' for Closed 311-Cases')\n",
        "  plt.xlabel('Count')\n",
        "  df_closed[el].value_counts().nlargest(n=8).sort_values().plot(kind='barh',color='g')\n",
        "  plt.show()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "p9pa5hWlWleR"
      },
      "source": [
        "### Plot open cases \n",
        " as a Series by: \n",
        "\n",
        "*   Responsible Agency\n",
        "*   Category\n",
        "*   Request Type\n",
        "*   Supervisor District\n",
        "*   Neighborhood\n",
        "*   Police Disctrict"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "PaZv-_04zyJz"
      },
      "outputs": [],
      "source": [
        "#  Supervisor District (could join to a diff table to get supervisor name)\n",
        "# 'ranking relationship' so using horizontal bar graphs\n",
        "\n",
        "# When considering 'open' cases, focus on cases that have remained open for a lengthy period\n",
        "# (If a case opened yesterday is still open, that doesn't indicate an issue)\n",
        "\n",
        "# only consider cases open for at least a year...\n",
        "df_open_long = df_open[df_open['time_open'].astype('timedelta64[D]') > 365]\n",
        "\n",
        "for e in elements:\n",
        "  plt.title('Most Common '+ e+' for Open 311-Cases')\n",
        "  plt.xlabel('Count')\n",
        "  df_open_long[e].value_counts().nlargest(n=8).sort_values().plot(kind='barh', color='r')\n",
        "  plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zcXFVD164NhF"
      },
      "source": [
        "## **Question**: Are the most common Closed case types the same as the most common Open case types? Should I be looking at percentages instead? Relative to total number of cases?"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "m_mheCHfXDnc"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Owh2G2DqcGzG"
      },
      "source": [
        "## Correlation between [something?] and 'closure rate'\n",
        "### must define 'closure rate'"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ltCun2xucE7S"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "3Bzpk8cqcEnA"
      },
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
    "colab": {
      "toc_visible": true,
      "provenance": [],
      "collapsed_sections": [
        "Cck9xXc9c_nT",
        "0eeJ1aTrV-9b",
        "p9pa5hWlWleR",
        "zcXFVD164NhF",
        "Owh2G2DqcGzG"
      ],
      "authorship_tag": "ABX9TyNLFMFfHBqmJ9CdmG5UH+S6",
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}