Polina.ipynb

{
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  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "name": "Exploring factor analysis",
      "authorship_tag": "ABX9TyOuGuodIUHo4avZRAEk74i8",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Nadezda-Sut/statistics_in_science/blob/main/Polina.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "from IPython.display import display, HTML\n"
      ],
      "metadata": {
        "id": "tdFbj5d25com"
      },
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "metadata": {
        "id": "pyYPADm441iT",
        "outputId": "1975df70-b3b8-4a22-9de3-c562792418a6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/gdrive; to attempt to forcibly remount, call drive.mount(\"/content/gdrive\", force_remount=True).\n"
          ]
        }
      ],
      "source": [
        "#link google colab and google drive\n",
        "\n",
        "from google.colab import drive\n",
        "drive.mount('/content/gdrive')"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# path to file in google drive\n",
        "\n",
        "file_path = \"/content/gdrive/My Drive/Colab Notebooks/Polina.csv\"\n",
        "data = pd.read_csv(file_path)\n",
        "data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 530
        },
        "id": "sOPJ9e7b5MN1",
        "outputId": "23e313c8-1cfb-44ee-f475-5f1acdca7adb"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "     номер  возраст  зависимость: 0 - нет, 1 - да  пол 1 -муж 0 - жен  \\\n",
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              "     диагноз 1 - алко, 2 - опиоиды, 3 - стимуляторы, 4 - все, 5 - седативные  \\\n",
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              "\n",
              "             F          K  СТРЕЛЯУ выносливость  СТРЕЛЯУ эмоц реактивность  \\\n",
              "0    61.916667  46.222222                   2.0                       12.0   \n",
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              "\n",
              "     СТРЕЛЯУ настойчивость  СТРЕЛЯУ активность  СТРЕЛЯУ сенс чувст  \\\n",
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              "\n",
              "[232 rows x 60 columns]"
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              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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-2589fe49-f57b-436e-98ae-09bb60c92f23 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-2589fe49-f57b-436e-98ae-09bb60c92f23');\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",
              "\n",
              "\n",
              "<div id=\"df-5dd3ddfa-63c4-41a5-a356-fb8625947f22\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-5dd3ddfa-63c4-41a5-a356-fb8625947f22')\"\n",
              "            title=\"Suggest charts.\"\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",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-5dd3ddfa-63c4-41a5-a356-fb8625947f22 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.keys()"
      ],
      "metadata": {
        "id": "0YAIAbZ_MLQa",
        "outputId": "a82e97c8-f03f-43c0-8fe6-586814c83d96",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['номер', 'возраст', 'зависимость: 0 - нет, 1 - да',\n",
              "       'пол 1 -муж 0 - жен',\n",
              "       'диагноз 1 - алко, 2 - опиоиды, 3 - стимуляторы, 4 - все, 5 - седативные',\n",
              "       'длит употр', 'возраст 1го употребления', '0 серия ср время  01',\n",
              "       '0 серия ср время 02', '0 серия пропуск 01', '0 серия пропуск 02',\n",
              "       'среднее время 11', 'среднее время 12', 'пропуск 11', 'пропуск 12',\n",
              "       'среднее время 21', 'среднее время 22', 'пропуск 21', 'ошибка 21',\n",
              "       'пропуск 22', 'ошибка 22', 'результат1', 'результат2', 'результат3',\n",
              "       'повыш 1, более 8 объектов - 2', 'повыш 1,  более 8 объектов, то 2',\n",
              "       'стаж употр',\n",
              "       'образование. 0 - отсут1 - ср 2 - ср спец. 3 - выс 4 - кн',\n",
              "       'сколько детей в семье', 'какой вы посчету ребенок',\n",
              "       'образ отца. 0 - отсутствует 1 - ср 2 - ср спец. 3 - выс 4 - кн',\n",
              "       'образ матери. 0 - отсутствует 1 - ср 2 - ср спец. 3 - выс 4 - кн',\n",
              "       'возраст отца', 'возраст матери', 'был ли опыт употр 1 - ДА 2 - НЕТ',\n",
              "       'возраст 1 употр, 0 нет зависимости', 'равен A', 'равен B', 'равен C',\n",
              "       'равен D', 'равен E', 'равен сумм', 'менделевич', 'СМИЛ hs', 'D', 'HY',\n",
              "       'PD', 'PA', 'PT', 'SC', 'MA', 'L', 'F', 'K', 'СТРЕЛЯУ выносливость',\n",
              "       'СТРЕЛЯУ эмоц реактивность', 'СТРЕЛЯУ настойчивость',\n",
              "       'СТРЕЛЯУ активность', 'СТРЕЛЯУ сенс чувст', 'СТРЕЛЯУ динамичность'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fig,ax = plt.subplots(1,figsize=(60, 20))\n",
        "\n",
        "ax = sns.boxplot(data=data)\n",
        "ax.set_xticklabels(ax.get_xticklabels(),rotation=45)\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "Wt_IfK2Lnbv8",
        "outputId": "2e844e2e-6555-4697-ee71-39f353efbf46",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 640
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 6000x2000 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "\n",
        "# Create an empty DataFrame to store the outliers\n",
        "outliers_df = pd.DataFrame(columns=['Variable', 'Outlier Values'])\n",
        "\n",
        "# Calculate and store the outliers for each variable\n",
        "for column in data.columns:\n",
        "    q1 = data[column].quantile(0.25)\n",
        "    q3 = data[column].quantile(0.75)\n",
        "    iqr = q3 - q1\n",
        "    lower_bound = q1 - 1.5 * iqr\n",
        "    upper_bound = q3 + 1.5 * iqr\n",
        "    outliers = data[(data[column] < lower_bound) | (data[column] > upper_bound)][column]\n",
        "    if not outliers.empty:\n",
        "        outliers_df = outliers_df.append({'Variable': column, 'Outlier Values': outliers.tolist()}, ignore_index=True)\n",
        "\n",
        "# Display the table with outliers\n",
        "print(\"Outliers Table:\")\n",
        "display(HTML(outliers_df.to_html()))\n"
      ],
      "metadata": {
        "id": "-lpqKI5p8bls",
        "outputId": "358d8f13-5c8f-4557-e835-bb83f3dbf6c2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-6-2443add9f9b2>:15: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  outliers_df = outliers_df.append({'Variable': column, 'Outlier Values': outliers.tolist()}, ignore_index=True)\n",
            "<ipython-input-6-2443add9f9b2>:15: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  outliers_df = outliers_df.append({'Variable': column, 'Outlier Values': outliers.tolist()}, ignore_index=True)\n",
            "<ipython-input-6-2443add9f9b2>:15: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  outliers_df = outliers_df.append({'Variable': column, 'Outlier Values': outliers.tolist()}, ignore_index=True)\n",
            "<ipython-input-6-2443add9f9b2>:15: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  outliers_df = outliers_df.append({'Variable': column, 'Outlier Values': outliers.tolist()}, ignore_index=True)\n",
            "<ipython-input-6-2443add9f9b2>:15: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  outliers_df = outliers_df.append({'Variable': column, 'Outlier Values': outliers.tolist()}, ignore_index=True)\n",
            "<ipython-input-6-2443add9f9b2>:15: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  outliers_df = outliers_df.append({'Variable': column, 'Outlier Values': outliers.tolist()}, ignore_index=True)\n",
            "<ipython-input-6-2443add9f9b2>:15: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  outliers_df = outliers_df.append({'Variable': column, 'Outlier Values': outliers.tolist()}, ignore_index=True)\n",
            "<ipython-input-6-2443add9f9b2>:15: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  outliers_df = outliers_df.append({'Variable': column, 'Outlier Values': outliers.tolist()}, ignore_index=True)\n",
            "<ipython-input-6-2443add9f9b2>:15: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  outliers_df = outliers_df.append({'Variable': column, 'Outlier Values': outliers.tolist()}, ignore_index=True)\n",
            "<ipython-input-6-2443add9f9b2>:15: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  outliers_df = outliers_df.append({'Variable': column, 'Outlier Values': outliers.tolist()}, ignore_index=True)\n",
            "<ipython-input-6-2443add9f9b2>:15: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  outliers_df = outliers_df.append({'Variable': column, 'Outlier Values': outliers.tolist()}, ignore_index=True)\n",
            "<ipython-input-6-2443add9f9b2>:15: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  outliers_df = outliers_df.append({'Variable': column, 'Outlier Values': outliers.tolist()}, ignore_index=True)\n",
            "<ipython-input-6-2443add9f9b2>:15: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  outliers_df = outliers_df.append({'Variable': column, 'Outlier Values': outliers.tolist()}, ignore_index=True)\n",
            "<ipython-input-6-2443add9f9b2>:15: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  outliers_df = outliers_df.append({'Variable': column, 'Outlier Values': outliers.tolist()}, ignore_index=True)\n",
            "<ipython-input-6-2443add9f9b2>:15: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  outliers_df = outliers_df.append({'Variable': column, 'Outlier Values': outliers.tolist()}, ignore_index=True)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Outliers Table:\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-6-2443add9f9b2>:15: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  outliers_df = outliers_df.append({'Variable': column, 'Outlier Values': outliers.tolist()}, ignore_index=True)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Variable</th>\n",
              "      <th>Outlier Values</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Time-test: Среднее время реакции, серия 1 (Часть 2)</td>\n",
              "      <td>[386.9]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Time-test: Среднее время реакции, серия 2 (Часть 1)</td>\n",
              "      <td>[465.5, 450.4]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Time-test: Проп 1 1</td>\n",
              "      <td>[3, 5, 8, 7, 3, 3, 4]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Time-test: Количество пропусков, серия 2 (Часть 1)</td>\n",
              "      <td>[10, 10]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Time-test: Количество пропусков, серия 2 (Часть 2)</td>\n",
              "      <td>[15, 9, 8]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Time-test: Ош 2 1</td>\n",
              "      <td>[18, 19]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Time-test: Количество ошибок, серия 2 (Часть 2)</td>\n",
              "      <td>[19]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>WMem: Время серии 2</td>\n",
              "      <td>[162343]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>WMem: Время серии 3</td>\n",
              "      <td>[160699, 183638, 164476, 186743]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Время 1</td>\n",
              "      <td>[11102.84615, 11131.33333, 13185.95]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>Время 2</td>\n",
              "      <td>[1976.2, 8513.0, 10956.75, 8909.058824, 1699.333333]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>Время 3</td>\n",
              "      <td>[10202.11111, 9828.578947]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>Время фиксации на области интереса (тексте)</td>\n",
              "      <td>[55.655716, 54.2406, 53.394531]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>Длительность первой фиксации на области интереса</td>\n",
              "      <td>[0.899536, 1.550644, 0.909882]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>Среднее время фиксации</td>\n",
              "      <td>[0.762794, 0.507381, 0.710798, 0.755799, 1.011922, 0.753342]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>Количество саккад</td>\n",
              "      <td>[165, 166]</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "\n",
        "# Create an empty dictionary to store the count of outliers for each variable\n",
        "outlier_counts = {}\n",
        "\n",
        "# Calculate and store the count of outliers for each variable\n",
        "for column in data.columns:\n",
        "    q1 = data[column].quantile(0.25)\n",
        "    q3 = data[column].quantile(0.75)\n",
        "    iqr = q3 - q1\n",
        "    lower_bound = q1 - 1.5 * iqr\n",
        "    upper_bound = q3 + 1.5 * iqr\n",
        "    outliers = data[(data[column] < lower_bound) | (data[column] > upper_bound)][column]\n",
        "    outlier_counts[column] = len(outliers)\n",
        "\n",
        "# Create a DataFrame with variable names and outlier counts\n",
        "outliers_df = pd.DataFrame(list(outlier_counts.items()), columns=['Variable', 'Outlier Count'])\n",
        "\n",
        "# Sort the DataFrame by outlier count in descending order\n",
        "outliers_df = outliers_df.sort_values(by='Outlier Count', ascending=False)\n",
        "\n",
        "# Display the sorted table\n",
        "print(\"Variables with Outlier Counts (Sorted):\")\n",
        "display(outliers_df)\n"
      ],
      "metadata": {
        "id": "N5skyPaYBeNL",
        "outputId": "1868669f-0215-4797-ede0-477e7534d98e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 405
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "TypeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-166-1de549a310b5>\u001b[0m in \u001b[0;36m<cell line: 7>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0;31m# Calculate and store the count of outliers for each variable\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mcolumn\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 8\u001b[0;31m     \u001b[0mq1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mcolumn\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mquantile\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0.25\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      9\u001b[0m     \u001b[0mq3\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mcolumn\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mquantile\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0.75\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m     \u001b[0miqr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mq3\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mq1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/series.py\u001b[0m in \u001b[0;36mquantile\u001b[0;34m(self, q, interpolation)\u001b[0m\n\u001b[1;32m   2681\u001b[0m         \u001b[0mdf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_frame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2682\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2683\u001b[0;31m         \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mquantile\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mq\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mq\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minterpolation\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minterpolation\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnumeric_only\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\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   2684\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2685\u001b[0m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\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/python3.10/dist-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36mquantile\u001b[0;34m(self, q, axis, numeric_only, interpolation, method)\u001b[0m\n\u001b[1;32m  11272\u001b[0m             \u001b[0;31m# ExtensionArray, ndarray[Any, Any]], Index, Series], Sequence[float]]\";\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m  11273\u001b[0m             \u001b[0;31m# expected \"float\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m> 11274\u001b[0;31m             res_df = self.quantile(  # type: ignore[call-overload]\n\u001b[0m\u001b[1;32m  11275\u001b[0m                 \u001b[0;34m[\u001b[0m\u001b[0mq\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m  11276\u001b[0m                 \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maxis\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/python3.10/dist-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36mquantile\u001b[0;34m(self, q, axis, numeric_only, interpolation, method)\u001b[0m\n\u001b[1;32m  11319\u001b[0m             \u001b[0;31m# error: Argument \"qs\" to \"quantile\" of \"BlockManager\" has incompatible type\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m  11320\u001b[0m             \u001b[0;31m# \"Index\"; expected \"Float64Index\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m> 11321\u001b[0;31m             res = data._mgr.quantile(\n\u001b[0m\u001b[1;32m  11322\u001b[0m                 \u001b[0mqs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mq\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minterpolation\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minterpolation\u001b[0m  \u001b[0;31m# type: ignore[arg-type]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m  11323\u001b[0m             )\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/internals/managers.py\u001b[0m in \u001b[0;36mquantile\u001b[0;34m(self, qs, axis, interpolation)\u001b[0m\n\u001b[1;32m   1629\u001b[0m         \u001b[0mnew_axes\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mFloat64Index\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mqs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1630\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1631\u001b[0;31m         blocks = [\n\u001b[0m\u001b[1;32m   1632\u001b[0m             \u001b[0mblk\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mquantile\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mqs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mqs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minterpolation\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minterpolation\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1633\u001b[0m             \u001b[0;32mfor\u001b[0m \u001b[0mblk\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mblocks\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/internals/managers.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m   1630\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1631\u001b[0m         blocks = [\n\u001b[0;32m-> 1632\u001b[0;31m             \u001b[0mblk\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mquantile\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mqs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mqs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minterpolation\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minterpolation\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   1633\u001b[0m             \u001b[0;32mfor\u001b[0m \u001b[0mblk\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mblocks\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1634\u001b[0m         ]\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/internals/blocks.py\u001b[0m in \u001b[0;36mquantile\u001b[0;34m(self, qs, interpolation, axis)\u001b[0m\n\u001b[1;32m   1333\u001b[0m         \u001b[0;32massert\u001b[0m \u001b[0mis_list_like\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mqs\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# caller is responsible for this\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1334\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1335\u001b[0;31m         \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mquantile_compat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mqs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_values\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minterpolation\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   1336\u001b[0m         \u001b[0;31m# ensure_block_shape needed for cases where we start with EA and result\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1337\u001b[0m         \u001b[0;31m#  is ndarray, e.g. IntegerArray, SparseArray\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/array_algos/quantile.py\u001b[0m in \u001b[0;36mquantile_compat\u001b[0;34m(values, qs, interpolation)\u001b[0m\n\u001b[1;32m     35\u001b[0m         \u001b[0mfill_value\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mna_value_for_dtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcompat\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     36\u001b[0m         \u001b[0mmask\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0misna\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 37\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mquantile_with_mask\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmask\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfill_value\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mqs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minterpolation\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     38\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     39\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mvalues\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_quantile\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mqs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minterpolation\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/python3.10/dist-packages/pandas/core/array_algos/quantile.py\u001b[0m in \u001b[0;36mquantile_with_mask\u001b[0;34m(values, mask, fill_value, qs, interpolation)\u001b[0m\n\u001b[1;32m     93\u001b[0m         \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrepeat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mflat\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mqs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     94\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 95\u001b[0;31m         result = _nanpercentile(\n\u001b[0m\u001b[1;32m     96\u001b[0m             \u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     97\u001b[0m             \u001b[0mqs\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0;36m100.0\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/python3.10/dist-packages/pandas/core/array_algos/quantile.py\u001b[0m in \u001b[0;36m_nanpercentile\u001b[0;34m(values, qs, na_value, mask, interpolation)\u001b[0m\n\u001b[1;32m    214\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    215\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 216\u001b[0;31m         return np.percentile(\n\u001b[0m\u001b[1;32m    217\u001b[0m             \u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    218\u001b[0m             \u001b[0mqs\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/python3.10/dist-packages/numpy/core/overrides.py\u001b[0m in \u001b[0;36mpercentile\u001b[0;34m(*args, **kwargs)\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/numpy/lib/function_base.py\u001b[0m in \u001b[0;36mpercentile\u001b[0;34m(a, q, axis, out, overwrite_input, method, keepdims, interpolation)\u001b[0m\n\u001b[1;32m   4164\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0m_quantile_is_valid\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mq\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4165\u001b[0m         \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Percentiles must be in the range [0, 100]\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4166\u001b[0;31m     return _quantile_unchecked(\n\u001b[0m\u001b[1;32m   4167\u001b[0m         a, q, axis, out, overwrite_input, method, keepdims)\n\u001b[1;32m   4168\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/numpy/lib/function_base.py\u001b[0m in \u001b[0;36m_quantile_unchecked\u001b[0;34m(a, q, axis, out, overwrite_input, method, keepdims)\u001b[0m\n\u001b[1;32m   4422\u001b[0m                         keepdims=False):\n\u001b[1;32m   4423\u001b[0m     \u001b[0;34m\"\"\"Assumes that q is in [0, 1], and is an ndarray\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4424\u001b[0;31m     r, k = _ureduce(a,\n\u001b[0m\u001b[1;32m   4425\u001b[0m                     \u001b[0mfunc\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0m_quantile_ureduce_func\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4426\u001b[0m                     \u001b[0mq\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mq\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/python3.10/dist-packages/numpy/lib/function_base.py\u001b[0m in \u001b[0;36m_ureduce\u001b[0;34m(a, func, **kwargs)\u001b[0m\n\u001b[1;32m   3723\u001b[0m         \u001b[0mkeepdim\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0ma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3724\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3725\u001b[0;31m     \u001b[0mr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\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   3726\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkeepdim\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3727\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/numpy/lib/function_base.py\u001b[0m in \u001b[0;36m_quantile_ureduce_func\u001b[0;34m(a, q, axis, out, overwrite_input, method)\u001b[0m\n\u001b[1;32m   4591\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4592\u001b[0m             \u001b[0marr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0ma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4593\u001b[0;31m     result = _quantile(arr,\n\u001b[0m\u001b[1;32m   4594\u001b[0m                        \u001b[0mquantiles\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mq\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4595\u001b[0m                        \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maxis\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/python3.10/dist-packages/numpy/lib/function_base.py\u001b[0m in \u001b[0;36m_quantile\u001b[0;34m(arr, quantiles, axis, method, out)\u001b[0m\n\u001b[1;32m   4708\u001b[0m         \u001b[0mresult_shape\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mvirtual_indexes\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0marr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4709\u001b[0m         \u001b[0mgamma\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgamma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresult_shape\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4710\u001b[0;31m         result = _lerp(previous,\n\u001b[0m\u001b[1;32m   4711\u001b[0m                        \u001b[0mnext\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4712\u001b[0m                        \u001b[0mgamma\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/python3.10/dist-packages/numpy/lib/function_base.py\u001b[0m in \u001b[0;36m_lerp\u001b[0;34m(a, b, t, out)\u001b[0m\n\u001b[1;32m   4525\u001b[0m         \u001b[0mOutput\u001b[0m \u001b[0marray\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4526\u001b[0m     \"\"\"\n\u001b[0;32m-> 4527\u001b[0;31m     \u001b[0mdiff_b_a\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msubtract\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ma\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   4528\u001b[0m     \u001b[0;31m# asanyarray is a stop-gap until gh-13105\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4529\u001b[0m     \u001b[0mlerp_interpolation\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0masanyarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0madd\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdiff_b_a\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mTypeError\u001b[0m: unsupported operand type(s) for -: 'str' and 'str'"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "! pip install factor-analyzer\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ggo6W0h8-yTG",
        "outputId": "a1d0dc4d-c5a1-4fad-8b24-9fb338fdc9e1"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: factor-analyzer in /usr/local/lib/python3.10/dist-packages (0.5.0)\n",
            "Requirement already satisfied: pandas in /usr/local/lib/python3.10/dist-packages (from factor-analyzer) (1.5.3)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.10/dist-packages (from factor-analyzer) (1.11.2)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from factor-analyzer) (1.23.5)\n",
            "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.10/dist-packages (from factor-analyzer) (1.2.2)\n",
            "Requirement already satisfied: pre-commit in /usr/local/lib/python3.10/dist-packages (from factor-analyzer) (3.4.0)\n",
            "Requirement already satisfied: python-dateutil>=2.8.1 in /usr/local/lib/python3.10/dist-packages (from pandas->factor-analyzer) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas->factor-analyzer) (2023.3.post1)\n",
            "Requirement already satisfied: cfgv>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from pre-commit->factor-analyzer) (3.4.0)\n",
            "Requirement already satisfied: identify>=1.0.0 in /usr/local/lib/python3.10/dist-packages (from pre-commit->factor-analyzer) (2.5.29)\n",
            "Requirement already satisfied: nodeenv>=0.11.1 in /usr/local/lib/python3.10/dist-packages (from pre-commit->factor-analyzer) (1.8.0)\n",
            "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.10/dist-packages (from pre-commit->factor-analyzer) (6.0.1)\n",
            "Requirement already satisfied: virtualenv>=20.10.0 in /usr/local/lib/python3.10/dist-packages (from pre-commit->factor-analyzer) (20.24.5)\n",
            "Requirement already satisfied: joblib>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from scikit-learn->factor-analyzer) (1.3.2)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn->factor-analyzer) (3.2.0)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.10/dist-packages (from nodeenv>=0.11.1->pre-commit->factor-analyzer) (67.7.2)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.1->pandas->factor-analyzer) (1.16.0)\n",
            "Requirement already satisfied: distlib<1,>=0.3.7 in /usr/local/lib/python3.10/dist-packages (from virtualenv>=20.10.0->pre-commit->factor-analyzer) (0.3.7)\n",
            "Requirement already satisfied: filelock<4,>=3.12.2 in /usr/local/lib/python3.10/dist-packages (from virtualenv>=20.10.0->pre-commit->factor-analyzer) (3.12.2)\n",
            "Requirement already satisfied: platformdirs<4,>=3.9.1 in /usr/local/lib/python3.10/dist-packages (from virtualenv>=20.10.0->pre-commit->factor-analyzer) (3.10.0)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "from factor_analyzer import FactorAnalyzer\n",
        "from factor_analyzer.factor_analyzer import calculate_bartlett_sphericity, calculate_kmo\n",
        "from IPython.display import display, HTML\n",
        "\n",
        "# Assume you have loaded your data into the 'data' DataFrame\n",
        "\n",
        "\n",
        "#Specify the variables you want to include in the factor analysis\n",
        "selected_variables = [\n",
        "       'возраст', 'зависимость: 0 - нет, 1 - да',\n",
        "       'пол 1 -муж 0 - жен',\n",
        "       'диагноз 1 - алко, 2 - опиоиды, 3 - стимуляторы, 4 - все, 5 - седативные',\n",
        "       'длит употр', 'возраст 1го употребления',\n",
        "       #'0 серия ср время  01',\n",
        "       #'0 серия ср время 02', '0 серия пропуск 01', '0 серия пропуск 02',\n",
        "       #'среднее время 11', 'среднее время 12',\n",
        "       #'пропуск 11', 'пропуск 12',\n",
        "       #'среднее время 21',\n",
        "       'среднее время 22',\n",
        "       #'пропуск 21',\n",
        "       'ошибка 21',\n",
        "       #'пропуск 22', 'ошибка 22',\n",
        "       'результат1',\n",
        "       'результат2',\n",
        "       'результат3',\n",
        "       'повыш 1, более 8 объектов - 2',\n",
        "       'повыш 1,  более 8 объектов, то 2',\n",
        "       'стаж употр',\n",
        "       #'образование. 0 - отсут1 - ср 2 - ср спец. 3 - выс 4 - кн',\n",
        "       #'сколько детей в семье', 'какой вы посчету ребенок',\n",
        "       #'образ отца. 0 - отсутствует 1 - ср 2 - ср спец. 3 - выс 4 - кн',\n",
        "       #'образ матери. 0 - отсутствует 1 - ср 2 - ср спец. 3 - выс 4 - кн',\n",
        "       #'возраст отца', 'возраст матери',\n",
        "       #'был ли опыт употр 1 - ДА 2 - НЕТ',\n",
        "       #'возраст 1 употр, 0 нет зависимости',\n",
        "       #'равен A', 'равен B',\n",
        "       'равен C',\n",
        "       'равен D', 'равен E',\n",
        "       #'равен сумм',\n",
        "       #'менделевич',\n",
        "       'СМИЛ hs', 'D', 'HY',\n",
        "       'PD', 'PA',\n",
        "       #'PT',\n",
        "       #'SC',\n",
        "       'MA',\n",
        "       #'L', 'F', 'K',\n",
        "       'СТРЕЛЯУ выносливость',\n",
        "       'СТРЕЛЯУ эмоц реактивность', 'СТРЕЛЯУ настойчивость',\n",
        "       #'СТРЕЛЯУ активность',\n",
        "       #'СТРЕЛЯУ сенс чувст',\n",
        "       'СТРЕЛЯУ динамичность',\n",
        "]  # Replace with the actual variable names you want to include\n",
        "\n",
        "\n",
        "selected_data = data[selected_variables]\n",
        "\n",
        "# Create a new DataFrame with only the selected variables\n",
        "#selected_data = data[selected_variables]\n",
        "\n",
        "# Specify the number of factors you want to extract\n",
        "num_factors = 3\n",
        "\n",
        "# Calculate the cumulative variance explained by factors\n",
        "cumulative_variance_explained = []\n",
        "for i in range(1, len(selected_variables) + 1):\n",
        "    factor_analyzer = FactorAnalyzer(rotation='geomin_obl', method='uls', n_factors=i)\n",
        "    factor_analyzer.fit(selected_data)\n",
        "    fa_var = factor_analyzer.get_factor_variance()\n",
        "    total_cum_variance = fa_var[-1][-1]  # Access the last element of the array\n",
        "    cumulative_variance_explained.append(total_cum_variance)\n",
        "\n",
        "# Print the cumulative variance explained by factors\n",
        "for i, variance_explained in enumerate(cumulative_variance_explained):\n",
        "    print(f\"Total {variance_explained * 100:.2f}% Cumulative Variance explained by the {i + 1} factors\")\n",
        "\n",
        "# Initialize the factor analyzer with the desired number of factors\n",
        "factor_analyzer = FactorAnalyzer(rotation='geomin_obl', n_factors=num_factors, method='uls')\n",
        "\n",
        "# Fit the model to your selected data\n",
        "factor_analyzer.fit(selected_data)\n",
        "\n",
        "# Get the factor loadings\n",
        "factor_loadings = factor_analyzer.loadings_\n",
        "\n",
        "# Create a pandas DataFrame from the factor loadings\n",
        "factor_loading_df = pd.DataFrame(factor_loadings, columns=[f'Factor {i+1}' for i in range(num_factors)], index=selected_data.columns)\n",
        "\n",
        "# Print the factor loadings table with grid lines using HTML\n",
        "print(\"Factor Loadings Table:\")\n",
        "display(HTML(factor_loading_df.to_html()))\n",
        "\n",
        "# Bartlett’s Test\n",
        "chi_square_value, p_value = calculate_bartlett_sphericity(selected_data)\n",
        "print(\"Bartlett’s Test:\")\n",
        "print(\"Chi-Square Value:\", chi_square_value)\n",
        "print(\"P-value:\", p_value)\n",
        "\n",
        "# Kaiser-Meyer-Olkin (KMO) Test\n",
        "kmo_all, kmo_model = calculate_kmo(selected_data)\n",
        "print(\"\\nKaiser-Meyer-Olkin (KMO) Test:\")\n",
        "print(\"KMO Overall:\", kmo_all)\n",
        "print(\"KMO Model:\", kmo_model)\n",
        "\n",
        "# Scree Plot\n",
        "ev, v = factor_analyzer.get_eigenvalues()\n",
        "plt.plot(range(1, len(ev) + 1), ev, marker='o')\n",
        "plt.title('Scree Plot')\n",
        "plt.xlabel('Factor')\n",
        "plt.ylabel('Eigenvalue')\n",
        "plt.show()\n",
        "\n",
        "\n",
        "# Calculate basic descriptive statistics for the selected data\n",
        "descriptive_stats = selected_data.describe()\n",
        "\n",
        "# Calculate skewness for each variable\n",
        "skewness = selected_data.skew()\n",
        "\n",
        "# Calculate kurtosis for each variable\n",
        "kurtosis_value = selected_data.kurtosis()\n",
        "\n",
        "# Add skewness and kurtosis to the descriptive statistics DataFrame\n",
        "descriptive_stats.loc['Skewness'] = skewness\n",
        "descriptive_stats.loc['Kurtosis'] = kurtosis_value\n",
        "\n",
        "# Print the descriptive statistics\n",
        "print(\"Descriptive Statistics:\")\n",
        "display(HTML(descriptive_stats.to_html()))"
      ],
      "metadata": {
        "id": "OcOekVIS9rP2",
        "outputId": "510f6610-acff-4199-dab9-37912442faf2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "execution_count": 76,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/factor_analyzer/utils.py:166: RuntimeWarning: divide by zero encountered in divide\n",
            "  smc = 1 - 1 / np.diag(corr_inv)\n",
            "/usr/local/lib/python3.10/dist-packages/factor_analyzer/factor_analyzer.py:663: UserWarning: No rotation will be performed when the number of factors equals 1.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/factor_analyzer/utils.py:166: RuntimeWarning: divide by zero encountered in divide\n",
            "  smc = 1 - 1 / np.diag(corr_inv)\n",
            "/usr/local/lib/python3.10/dist-packages/factor_analyzer/utils.py:166: RuntimeWarning: divide by zero encountered in divide\n",
            "  smc = 1 - 1 / np.diag(corr_inv)\n",
            "/usr/local/lib/python3.10/dist-packages/factor_analyzer/utils.py:166: RuntimeWarning: divide by zero encountered in divide\n",
            "  smc = 1 - 1 / np.diag(corr_inv)\n",
            "/usr/local/lib/python3.10/dist-packages/factor_analyzer/utils.py:166: RuntimeWarning: divide by zero encountered in divide\n",
            "  smc = 1 - 1 / np.diag(corr_inv)\n",
            "/usr/local/lib/python3.10/dist-packages/factor_analyzer/utils.py:166: RuntimeWarning: divide by zero encountered in divide\n",
            "  smc = 1 - 1 / np.diag(corr_inv)\n",
            "/usr/local/lib/python3.10/dist-packages/factor_analyzer/utils.py:166: RuntimeWarning: divide by zero encountered in divide\n",
            "  smc = 1 - 1 / np.diag(corr_inv)\n",
            "/usr/local/lib/python3.10/dist-packages/factor_analyzer/utils.py:166: RuntimeWarning: divide by zero encountered in divide\n",
            "  smc = 1 - 1 / np.diag(corr_inv)\n",
            "/usr/local/lib/python3.10/dist-packages/factor_analyzer/utils.py:166: RuntimeWarning: divide by zero encountered in divide\n",
            "  smc = 1 - 1 / np.diag(corr_inv)\n",
            "/usr/local/lib/python3.10/dist-packages/factor_analyzer/utils.py:166: RuntimeWarning: divide by zero encountered in divide\n",
            "  smc = 1 - 1 / np.diag(corr_inv)\n",
            "/usr/local/lib/python3.10/dist-packages/factor_analyzer/utils.py:166: RuntimeWarning: divide by zero encountered in divide\n",
            "  smc = 1 - 1 / np.diag(corr_inv)\n",
            "/usr/local/lib/python3.10/dist-packages/factor_analyzer/utils.py:166: RuntimeWarning: divide by zero encountered in divide\n",
            "  smc = 1 - 1 / np.diag(corr_inv)\n",
            "/usr/local/lib/python3.10/dist-packages/factor_analyzer/utils.py:166: RuntimeWarning: divide by zero encountered in divide\n",
            "  smc = 1 - 1 / np.diag(corr_inv)\n",
            "/usr/local/lib/python3.10/dist-packages/factor_analyzer/utils.py:166: RuntimeWarning: divide by zero encountered in divide\n",
            "  smc = 1 - 1 / np.diag(corr_inv)\n",
            "/usr/local/lib/python3.10/dist-packages/factor_analyzer/utils.py:166: RuntimeWarning: divide by zero encountered in divide\n",
            "  smc = 1 - 1 / np.diag(corr_inv)\n",
            "/usr/local/lib/python3.10/dist-packages/factor_analyzer/utils.py:166: RuntimeWarning: divide by zero encountered in divide\n",
            "  smc = 1 - 1 / np.diag(corr_inv)\n",
            "/usr/local/lib/python3.10/dist-packages/factor_analyzer/utils.py:166: RuntimeWarning: divide by zero encountered in divide\n",
            "  smc = 1 - 1 / np.diag(corr_inv)\n",
            "/usr/local/lib/python3.10/dist-packages/factor_analyzer/utils.py:166: RuntimeWarning: divide by zero encountered in divide\n",
            "  smc = 1 - 1 / np.diag(corr_inv)\n",
            "/usr/local/lib/python3.10/dist-packages/factor_analyzer/utils.py:166: RuntimeWarning: divide by zero encountered in divide\n",
            "  smc = 1 - 1 / np.diag(corr_inv)\n",
            "/usr/local/lib/python3.10/dist-packages/factor_analyzer/utils.py:166: RuntimeWarning: divide by zero encountered in divide\n",
            "  smc = 1 - 1 / np.diag(corr_inv)\n",
            "/usr/local/lib/python3.10/dist-packages/factor_analyzer/utils.py:166: RuntimeWarning: divide by zero encountered in divide\n",
            "  smc = 1 - 1 / np.diag(corr_inv)\n",
            "/usr/local/lib/python3.10/dist-packages/factor_analyzer/utils.py:166: RuntimeWarning: divide by zero encountered in divide\n",
            "  smc = 1 - 1 / np.diag(corr_inv)\n",
            "/usr/local/lib/python3.10/dist-packages/factor_analyzer/utils.py:166: RuntimeWarning: divide by zero encountered in divide\n",
            "  smc = 1 - 1 / np.diag(corr_inv)\n",
            "/usr/local/lib/python3.10/dist-packages/factor_analyzer/utils.py:166: RuntimeWarning: divide by zero encountered in divide\n",
            "  smc = 1 - 1 / np.diag(corr_inv)\n",
            "/usr/local/lib/python3.10/dist-packages/factor_analyzer/utils.py:166: RuntimeWarning: divide by zero encountered in divide\n",
            "  smc = 1 - 1 / np.diag(corr_inv)\n",
            "/usr/local/lib/python3.10/dist-packages/factor_analyzer/utils.py:166: RuntimeWarning: divide by zero encountered in divide\n",
            "  smc = 1 - 1 / np.diag(corr_inv)\n",
            "/usr/local/lib/python3.10/dist-packages/factor_analyzer/utils.py:166: RuntimeWarning: divide by zero encountered in divide\n",
            "  smc = 1 - 1 / np.diag(corr_inv)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Total 23.55% Cumulative Variance explained by the 1 factors\n",
            "Total 32.82% Cumulative Variance explained by the 2 factors\n",
            "Total 37.90% Cumulative Variance explained by the 3 factors\n",
            "Total 42.05% Cumulative Variance explained by the 4 factors\n",
            "Total 45.23% Cumulative Variance explained by the 5 factors\n",
            "Total 48.28% Cumulative Variance explained by the 6 factors\n",
            "Total 51.85% Cumulative Variance explained by the 7 factors\n",
            "Total 55.26% Cumulative Variance explained by the 8 factors\n",
            "Total 59.14% Cumulative Variance explained by the 9 factors\n",
            "Total 59.48% Cumulative Variance explained by the 10 factors\n",
            "Total 61.51% Cumulative Variance explained by the 11 factors\n",
            "Total 63.74% Cumulative Variance explained by the 12 factors\n",
            "Total 65.41% Cumulative Variance explained by the 13 factors\n",
            "Total 67.66% Cumulative Variance explained by the 14 factors\n",
            "Total 69.11% Cumulative Variance explained by the 15 factors\n",
            "Total 70.51% Cumulative Variance explained by the 16 factors\n",
            "Total 76.68% Cumulative Variance explained by the 17 factors\n",
            "Total 73.11% Cumulative Variance explained by the 18 factors\n",
            "Total 73.10% Cumulative Variance explained by the 19 factors\n",
            "Total 76.59% Cumulative Variance explained by the 20 factors\n",
            "Total 84.50% Cumulative Variance explained by the 21 factors\n",
            "Total 77.62% Cumulative Variance explained by the 22 factors\n",
            "Total 80.34% Cumulative Variance explained by the 23 factors\n",
            "Total 76.27% Cumulative Variance explained by the 24 factors\n",
            "Total 74.77% Cumulative Variance explained by the 25 factors\n",
            "Total 74.60% Cumulative Variance explained by the 26 factors\n",
            "Total 74.63% Cumulative Variance explained by the 27 factors\n",
            "Factor Loadings Table:\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/factor_analyzer/utils.py:166: RuntimeWarning: divide by zero encountered in divide\n",
            "  smc = 1 - 1 / np.diag(corr_inv)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Factor 1</th>\n",
              "      <th>Factor 2</th>\n",
              "      <th>Factor 3</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>возраст</th>\n",
              "      <td>0.359160</td>\n",
              "      <td>-0.038624</td>\n",
              "      <td>-0.554445</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>зависимость: 0 - нет, 1 - да</th>\n",
              "      <td>0.882326</td>\n",
              "      <td>0.019892</td>\n",
              "      <td>0.290555</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>пол 1 -муж 0 - жен</th>\n",
              "      <td>0.104748</td>\n",
              "      <td>-0.231826</td>\n",
              "      <td>0.126614</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>диагноз 1 - алко, 2 - опиоиды, 3 - стимуляторы, 4 - все, 5 - седативные</th>\n",
              "      <td>0.622725</td>\n",
              "      <td>-0.033014</td>\n",
              "      <td>0.409612</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>длит употр</th>\n",
              "      <td>0.833120</td>\n",
              "      <td>0.022313</td>\n",
              "      <td>-0.055433</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>возраст 1го употребления</th>\n",
              "      <td>0.706063</td>\n",
              "      <td>0.011703</td>\n",
              "      <td>0.131696</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>среднее время 22</th>\n",
              "      <td>0.116251</td>\n",
              "      <td>0.129312</td>\n",
              "      <td>-0.426517</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ошибка 21</th>\n",
              "      <td>0.018683</td>\n",
              "      <td>0.035480</td>\n",
              "      <td>0.306377</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>результат1</th>\n",
              "      <td>-0.242082</td>\n",
              "      <td>-0.060524</td>\n",
              "      <td>0.201902</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>результат2</th>\n",
              "      <td>-0.349094</td>\n",
              "      <td>0.039611</td>\n",
              "      <td>-0.028343</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>результат3</th>\n",
              "      <td>-0.378286</td>\n",
              "      <td>0.065958</td>\n",
              "      <td>-0.127960</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>повыш 1, более 8 объектов - 2</th>\n",
              "      <td>-0.189246</td>\n",
              "      <td>-0.042744</td>\n",
              "      <td>0.138444</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>повыш 1,  более 8 объектов, то 2</th>\n",
              "      <td>-0.199615</td>\n",
              "      <td>0.125279</td>\n",
              "      <td>-0.241390</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>стаж употр</th>\n",
              "      <td>0.833120</td>\n",
              "      <td>0.022313</td>\n",
              "      <td>-0.055433</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>равен C</th>\n",
              "      <td>-0.640939</td>\n",
              "      <td>0.024746</td>\n",
              "      <td>-0.009987</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>равен D</th>\n",
              "      <td>-0.512081</td>\n",
              "      <td>-0.135182</td>\n",
              "      <td>0.095022</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>равен E</th>\n",
              "      <td>-0.693406</td>\n",
              "      <td>-0.027599</td>\n",
              "      <td>0.185139</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>СМИЛ hs</th>\n",
              "      <td>-0.069591</td>\n",
              "      <td>0.686073</td>\n",
              "      <td>-0.155920</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>D</th>\n",
              "      <td>0.135491</td>\n",
              "      <td>0.774599</td>\n",
              "      <td>0.035503</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>HY</th>\n",
              "      <td>-0.033049</td>\n",
              "      <td>0.841701</td>\n",
              "      <td>-0.109403</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PD</th>\n",
              "      <td>0.241874</td>\n",
              "      <td>0.555184</td>\n",
              "      <td>-0.023542</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PA</th>\n",
              "      <td>0.194309</td>\n",
              "      <td>0.482033</td>\n",
              "      <td>0.234888</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>MA</th>\n",
              "      <td>0.203159</td>\n",
              "      <td>0.245548</td>\n",
              "      <td>0.357441</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>СТРЕЛЯУ выносливость</th>\n",
              "      <td>-0.003858</td>\n",
              "      <td>-0.565169</td>\n",
              "      <td>0.047406</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>СТРЕЛЯУ эмоц реактивность</th>\n",
              "      <td>0.112507</td>\n",
              "      <td>0.581400</td>\n",
              "      <td>0.222594</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>СТРЕЛЯУ настойчивость</th>\n",
              "      <td>-0.050902</td>\n",
              "      <td>0.555861</td>\n",
              "      <td>0.404313</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>СТРЕЛЯУ динамичность</th>\n",
              "      <td>0.002776</td>\n",
              "      <td>-0.574828</td>\n",
              "      <td>-0.088006</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Bartlett’s Test:\n",
            "Chi-Square Value: inf\n",
            "P-value: 0.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/factor_analyzer/factor_analyzer.py:109: RuntimeWarning: divide by zero encountered in log\n",
            "  statistic = -np.log(corr_det) * (n - 1 - (2 * p + 5) / 6)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Kaiser-Meyer-Olkin (KMO) Test:\n",
            "KMO Overall: [0.40411525 0.74836457 0.55062489 0.86350108 0.78414452 0.69752965\n",
            " 0.67551514 0.58754605 0.35592372 0.85954323 0.45183844 0.29637526\n",
            " 0.32824558 0.78414452 0.90451789 0.85275578 0.85968978 0.72965947\n",
            " 0.91293231 0.74833684 0.84948727 0.89776248 0.85802282 0.82014689\n",
            " 0.83116541 0.79065495 0.83828692]\n",
            "KMO Model: 0.7409101829980753\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Descriptive Statistics:\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>возраст</th>\n",
              "      <th>зависимость: 0 - нет, 1 - да</th>\n",
              "      <th>пол 1 -муж 0 - жен</th>\n",
              "      <th>диагноз 1 - алко, 2 - опиоиды, 3 - стимуляторы, 4 - все, 5 - седативные</th>\n",
              "      <th>длит употр</th>\n",
              "      <th>возраст 1го употребления</th>\n",
              "      <th>среднее время 22</th>\n",
              "      <th>ошибка 21</th>\n",
              "      <th>результат1</th>\n",
              "      <th>результат2</th>\n",
              "      <th>результат3</th>\n",
              "      <th>повыш 1, более 8 объектов - 2</th>\n",
              "      <th>повыш 1,  более 8 объектов, то 2</th>\n",
              "      <th>стаж употр</th>\n",
              "      <th>равен C</th>\n",
              "      <th>равен D</th>\n",
              "      <th>равен E</th>\n",
              "      <th>СМИЛ hs</th>\n",
              "      <th>D</th>\n",
              "      <th>HY</th>\n",
              "      <th>PD</th>\n",
              "      <th>PA</th>\n",
              "      <th>MA</th>\n",
              "      <th>СТРЕЛЯУ выносливость</th>\n",
              "      <th>СТРЕЛЯУ эмоц реактивность</th>\n",
              "      <th>СТРЕЛЯУ настойчивость</th>\n",
              "      <th>СТРЕЛЯУ динамичность</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>232.000000</td>\n",
              "      <td>232.000000</td>\n",
              "      <td>232.000000</td>\n",
              "      <td>232.000000</td>\n",
              "      <td>232.000000</td>\n",
              "      <td>232.000000</td>\n",
              "      <td>232.000000</td>\n",
              "      <td>232.000000</td>\n",
              "      <td>232.000000</td>\n",
              "      <td>232.000000</td>\n",
              "      <td>232.000000</td>\n",
              "      <td>232.000000</td>\n",
              "      <td>232.000000</td>\n",
              "      <td>232.000000</td>\n",
              "      <td>232.000000</td>\n",
              "      <td>232.000000</td>\n",
              "      <td>232.000000</td>\n",
              "      <td>232.000000</td>\n",
              "      <td>232.000000</td>\n",
              "      <td>232.000000</td>\n",
              "      <td>232.000000</td>\n",
              "      <td>232.000000</td>\n",
              "      <td>232.000000</td>\n",
              "      <td>232.000000</td>\n",
              "      <td>232.000000</td>\n",
              "      <td>232.000000</td>\n",
              "      <td>232.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>35.301724</td>\n",
              "      <td>0.646552</td>\n",
              "      <td>0.581897</td>\n",
              "      <td>1.646552</td>\n",
              "      <td>11.232759</td>\n",
              "      <td>11.625000</td>\n",
              "      <td>406.610945</td>\n",
              "      <td>7.181358</td>\n",
              "      <td>13.419322</td>\n",
              "      <td>10.484424</td>\n",
              "      <td>9.604747</td>\n",
              "      <td>1.284483</td>\n",
              "      <td>0.875000</td>\n",
              "      <td>11.232759</td>\n",
              "      <td>8.452586</td>\n",
              "      <td>8.586207</td>\n",
              "      <td>3.952586</td>\n",
              "      <td>49.673549</td>\n",
              "      <td>47.677937</td>\n",
              "      <td>49.135816</td>\n",
              "      <td>53.069053</td>\n",
              "      <td>52.783679</td>\n",
              "      <td>57.680555</td>\n",
              "      <td>9.830300</td>\n",
              "      <td>10.436303</td>\n",
              "      <td>11.651554</td>\n",
              "      <td>13.216635</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>8.400907</td>\n",
              "      <td>0.479074</td>\n",
              "      <td>0.494314</td>\n",
              "      <td>1.591396</td>\n",
              "      <td>10.581253</td>\n",
              "      <td>10.214237</td>\n",
              "      <td>61.231271</td>\n",
              "      <td>3.736409</td>\n",
              "      <td>6.829746</td>\n",
              "      <td>6.254444</td>\n",
              "      <td>6.011747</td>\n",
              "      <td>0.693895</td>\n",
              "      <td>0.765532</td>\n",
              "      <td>10.581253</td>\n",
              "      <td>2.482387</td>\n",
              "      <td>2.845423</td>\n",
              "      <td>3.458147</td>\n",
              "      <td>7.781196</td>\n",
              "      <td>10.852949</td>\n",
              "      <td>8.680377</td>\n",
              "      <td>14.574940</td>\n",
              "      <td>12.132601</td>\n",
              "      <td>12.455825</td>\n",
              "      <td>4.797959</td>\n",
              "      <td>4.491453</td>\n",
              "      <td>4.164484</td>\n",
              "      <td>4.191156</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>18.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>288.800000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>28.000000</td>\n",
              "      <td>27.000000</td>\n",
              "      <td>28.000000</td>\n",
              "      <td>20.000000</td>\n",
              "      <td>27.000000</td>\n",
              "      <td>23.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>29.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>363.375000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>45.000000</td>\n",
              "      <td>40.000000</td>\n",
              "      <td>42.750000</td>\n",
              "      <td>41.000000</td>\n",
              "      <td>44.000000</td>\n",
              "      <td>48.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>9.000000</td>\n",
              "      <td>11.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>36.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>11.000000</td>\n",
              "      <td>14.000000</td>\n",
              "      <td>398.200000</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>12.400000</td>\n",
              "      <td>8.850000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>11.000000</td>\n",
              "      <td>9.000000</td>\n",
              "      <td>9.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>49.000000</td>\n",
              "      <td>47.000000</td>\n",
              "      <td>48.000000</td>\n",
              "      <td>53.000000</td>\n",
              "      <td>54.444444</td>\n",
              "      <td>59.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>11.000000</td>\n",
              "      <td>12.000000</td>\n",
              "      <td>14.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>41.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>20.000000</td>\n",
              "      <td>17.000000</td>\n",
              "      <td>433.550000</td>\n",
              "      <td>9.000000</td>\n",
              "      <td>18.000000</td>\n",
              "      <td>14.000000</td>\n",
              "      <td>12.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>20.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>11.000000</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>53.222222</td>\n",
              "      <td>54.000000</td>\n",
              "      <td>54.000000</td>\n",
              "      <td>62.000000</td>\n",
              "      <td>58.088235</td>\n",
              "      <td>64.393382</td>\n",
              "      <td>13.000000</td>\n",
              "      <td>14.000000</td>\n",
              "      <td>15.000000</td>\n",
              "      <td>16.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>60.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>40.000000</td>\n",
              "      <td>50.000000</td>\n",
              "      <td>646.700000</td>\n",
              "      <td>22.000000</td>\n",
              "      <td>28.000000</td>\n",
              "      <td>28.000000</td>\n",
              "      <td>28.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>40.000000</td>\n",
              "      <td>12.000000</td>\n",
              "      <td>12.000000</td>\n",
              "      <td>12.000000</td>\n",
              "      <td>76.000000</td>\n",
              "      <td>80.000000</td>\n",
              "      <td>75.000000</td>\n",
              "      <td>93.000000</td>\n",
              "      <td>96.000000</td>\n",
              "      <td>87.000000</td>\n",
              "      <td>20.000000</td>\n",
              "      <td>20.000000</td>\n",
              "      <td>20.000000</td>\n",
              "      <td>20.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Skewness</th>\n",
              "      <td>0.148483</td>\n",
              "      <td>-0.617133</td>\n",
              "      <td>-0.334236</td>\n",
              "      <td>0.444830</td>\n",
              "      <td>0.387778</td>\n",
              "      <td>0.499368</td>\n",
              "      <td>1.185852</td>\n",
              "      <td>0.855939</td>\n",
              "      <td>0.397981</td>\n",
              "      <td>0.944717</td>\n",
              "      <td>1.207379</td>\n",
              "      <td>-0.447841</td>\n",
              "      <td>0.216261</td>\n",
              "      <td>0.387778</td>\n",
              "      <td>-0.801918</td>\n",
              "      <td>-1.253607</td>\n",
              "      <td>0.456562</td>\n",
              "      <td>0.505648</td>\n",
              "      <td>0.310590</td>\n",
              "      <td>0.431763</td>\n",
              "      <td>0.168924</td>\n",
              "      <td>0.420811</td>\n",
              "      <td>0.136644</td>\n",
              "      <td>0.184257</td>\n",
              "      <td>-0.207492</td>\n",
              "      <td>-0.169260</td>\n",
              "      <td>-0.647484</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Kurtosis</th>\n",
              "      <td>-0.084553</td>\n",
              "      <td>-1.633303</td>\n",
              "      <td>-1.904782</td>\n",
              "      <td>-1.279631</td>\n",
              "      <td>-1.064751</td>\n",
              "      <td>0.169282</td>\n",
              "      <td>1.991884</td>\n",
              "      <td>1.655847</td>\n",
              "      <td>-0.655825</td>\n",
              "      <td>0.253412</td>\n",
              "      <td>1.054630</td>\n",
              "      <td>-0.864899</td>\n",
              "      <td>-1.262388</td>\n",
              "      <td>-1.064751</td>\n",
              "      <td>0.453542</td>\n",
              "      <td>1.329992</td>\n",
              "      <td>-0.968646</td>\n",
              "      <td>0.503470</td>\n",
              "      <td>-0.055170</td>\n",
              "      <td>0.172964</td>\n",
              "      <td>-0.132872</td>\n",
              "      <td>0.650999</td>\n",
              "      <td>-0.115255</td>\n",
              "      <td>-0.568202</td>\n",
              "      <td>-0.604614</td>\n",
              "      <td>-0.524388</td>\n",
              "      <td>0.124424</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Filter factor loadings to keep values higher than 0.3\n",
        "filtered_factor_loading_df = factor_loading_df[factor_loading_df.abs() > 0.3]\n",
        "\n",
        "# Replace NaN values with empty strings for better readability\n",
        "filtered_factor_loading_df = filtered_factor_loading_df.fillna(\"\")\n",
        "\n",
        "# Print the filtered factor loadings table with grid lines using HTML\n",
        "print(\"Filtered Factor Loadings Table (Values > 0.3):\")\n",
        "display(HTML(filtered_factor_loading_df.to_html()))\n"
      ],
      "metadata": {
        "id": "-vp5KPB-h_6Z",
        "outputId": "30478793-b418-490d-fbbe-a2a40e0f7c99",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 913
        }
      },
      "execution_count": 77,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Filtered Factor Loadings Table (Values > 0.3):\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Factor 1</th>\n",
              "      <th>Factor 2</th>\n",
              "      <th>Factor 3</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>возраст</th>\n",
              "      <td>0.35916</td>\n",
              "      <td></td>\n",
              "      <td>-0.554445</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>зависимость: 0 - нет, 1 - да</th>\n",
              "      <td>0.882326</td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>пол 1 -муж 0 - жен</th>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>диагноз 1 - алко, 2 - опиоиды, 3 - стимуляторы, 4 - все, 5 - седативные</th>\n",
              "      <td>0.622725</td>\n",
              "      <td></td>\n",
              "      <td>0.409612</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>длит употр</th>\n",
              "      <td>0.83312</td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>возраст 1го употребления</th>\n",
              "      <td>0.706063</td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>среднее время 22</th>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td>-0.426517</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ошибка 21</th>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td>0.306377</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>результат1</th>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>результат2</th>\n",
              "      <td>-0.349094</td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>результат3</th>\n",
              "      <td>-0.378286</td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>повыш 1, более 8 объектов - 2</th>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>повыш 1,  более 8 объектов, то 2</th>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>стаж употр</th>\n",
              "      <td>0.83312</td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>равен C</th>\n",
              "      <td>-0.640939</td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>равен D</th>\n",
              "      <td>-0.512081</td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>равен E</th>\n",
              "      <td>-0.693406</td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>СМИЛ hs</th>\n",
              "      <td></td>\n",
              "      <td>0.686073</td>\n",
              "      <td></td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>D</th>\n",
              "      <td></td>\n",
              "      <td>0.774599</td>\n",
              "      <td></td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>HY</th>\n",
              "      <td></td>\n",
              "      <td>0.841701</td>\n",
              "      <td></td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PD</th>\n",
              "      <td></td>\n",
              "      <td>0.555184</td>\n",
              "      <td></td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PA</th>\n",
              "      <td></td>\n",
              "      <td>0.482033</td>\n",
              "      <td></td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>MA</th>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td>0.357441</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>СТРЕЛЯУ выносливость</th>\n",
              "      <td></td>\n",
              "      <td>-0.565169</td>\n",
              "      <td></td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>СТРЕЛЯУ эмоц реактивность</th>\n",
              "      <td></td>\n",
              "      <td>0.5814</td>\n",
              "      <td></td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>СТРЕЛЯУ настойчивость</th>\n",
              "      <td></td>\n",
              "      <td>0.555861</td>\n",
              "      <td>0.404313</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>СТРЕЛЯУ динамичность</th>\n",
              "      <td></td>\n",
              "      <td>-0.574828</td>\n",
              "      <td></td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Calculate Communalities from factor analysis results\n",
        "communalities = factor_analyzer.get_communalities()\n",
        "communalities_df = pd.DataFrame(communalities, columns=[\"Communalities\"], index=selected_data.columns)\n",
        "\n",
        "# Print the Communalities table with grid lines using HTML\n",
        "print(\"Communalities Table:\")\n",
        "display(HTML(communalities_df.to_html()))"
      ],
      "metadata": {
        "id": "sYZkfYIVuXeT",
        "outputId": "4a4c4f70-cade-4c28-a6b5-403fb2d642ad",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 913
        }
      },
      "execution_count": 78,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Communalities Table:\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Communalities</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>возраст</th>\n",
              "      <td>0.437897</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>зависимость: 0 - нет, 1 - да</th>\n",
              "      <td>0.863318</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>пол 1 -муж 0 - жен</th>\n",
              "      <td>0.080747</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>диагноз 1 - алко, 2 - опиоиды, 3 - стимуляторы, 4 - все, 5 - седативные</th>\n",
              "      <td>0.556658</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>длит употр</th>\n",
              "      <td>0.697659</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>возраст 1го употребления</th>\n",
              "      <td>0.516006</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>среднее время 22</th>\n",
              "      <td>0.212153</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ошибка 21</th>\n",
              "      <td>0.095475</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>результат1</th>\n",
              "      <td>0.103031</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>результат2</th>\n",
              "      <td>0.124239</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>результат3</th>\n",
              "      <td>0.163824</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>повыш 1, более 8 объектов - 2</th>\n",
              "      <td>0.056808</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>повыш 1,  более 8 объектов, то 2</th>\n",
              "      <td>0.113810</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>стаж употр</th>\n",
              "      <td>0.697659</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>равен C</th>\n",
              "      <td>0.411515</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>равен D</th>\n",
              "      <td>0.289530</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>равен E</th>\n",
              "      <td>0.515850</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>СМИЛ hs</th>\n",
              "      <td>0.499850</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>D</th>\n",
              "      <td>0.619623</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>HY</th>\n",
              "      <td>0.721521</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PD</th>\n",
              "      <td>0.367287</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PA</th>\n",
              "      <td>0.325284</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>MA</th>\n",
              "      <td>0.229332</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>СТРЕЛЯУ выносливость</th>\n",
              "      <td>0.321678</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>СТРЕЛЯУ эмоц реактивность</th>\n",
              "      <td>0.400232</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>СТРЕЛЯУ настойчивость</th>\n",
              "      <td>0.475041</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>СТРЕЛЯУ динамичность</th>\n",
              "      <td>0.338180</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import statsmodels.api as sm\n"
      ],
      "metadata": {
        "id": "xeviFiDQq2_H"
      },
      "execution_count": 98,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np"
      ],
      "metadata": {
        "id": "aRulcd04rN5-"
      },
      "execution_count": 99,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# binarize phone_1/comp_0 (the subject used a computer or telephone)\n",
        "# binarization threshold\n",
        "\n",
        "binthresh = np.mean(data['зависимость: 0 - нет, 1 - да'])\n",
        "\n",
        "# 'binquality' column with Boolean type True or False\n",
        "\n",
        "data['binquality'] = data['зависимость: 0 - нет, 1 - да'] > binthresh\n",
        "data"
      ],
      "metadata": {
        "id": "dz5Hx-xVq6lx",
        "outputId": "0f8a3507-a183-476e-81d6-360771edaba4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 530
        }
      },
      "execution_count": 85,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     номер  возраст  зависимость: 0 - нет, 1 - да  пол 1 -муж 0 - жен  \\\n",
              "0        1       42                             1                   1   \n",
              "1        2       32                             1                   1   \n",
              "2        3       30                             1                   1   \n",
              "3        4       27                             1                   0   \n",
              "4        5       33                             1                   0   \n",
              "..     ...      ...                           ...                 ...   \n",
              "227     78       28                             0                   0   \n",
              "228     79       42                             0                   1   \n",
              "229     80       43                             0                   1   \n",
              "230     81       31                             0                   0   \n",
              "231     82       37                             0                   0   \n",
              "\n",
              "     диагноз 1 - алко, 2 - опиоиды, 3 - стимуляторы, 4 - все, 5 - седативные  \\\n",
              "0                                                    1                         \n",
              "1                                                    1                         \n",
              "2                                                    1                         \n",
              "3                                                    1                         \n",
              "4                                                    1                         \n",
              "..                                                 ...                         \n",
              "227                                                  0                         \n",
              "228                                                  0                         \n",
              "229                                                  0                         \n",
              "230                                                  0                         \n",
              "231                                                  0                         \n",
              "\n",
              "     длит употр  возраст 1го употребления  0 серия ср время  01  \\\n",
              "0            21                        21                 367.2   \n",
              "1            21                        11                 539.0   \n",
              "2            21                         9                 318.3   \n",
              "3            21                         6                 381.5   \n",
              "4            21                        12                 494.3   \n",
              "..          ...                       ...                   ...   \n",
              "227           0                         0                 456.5   \n",
              "228           0                         0                 484.8   \n",
              "229           0                         0                 510.8   \n",
              "230           0                         0                 423.8   \n",
              "231           0                         0                 413.5   \n",
              "\n",
              "     0 серия ср время 02  0 серия пропуск 01  ...          L          F  \\\n",
              "0                  328.8                 0.0  ...  45.833333  61.916667   \n",
              "1                  333.0                 0.0  ...  45.833333  61.916667   \n",
              "2                  328.2                 1.0  ...  45.833333  61.916667   \n",
              "3                  324.2                 0.0  ...  45.833333  61.916667   \n",
              "4                  405.2                 1.0  ...  45.833333  61.916667   \n",
              "..                   ...                 ...  ...        ...        ...   \n",
              "227                288.0                 0.0  ...  46.000000  40.000000   \n",
              "228                395.5                 0.0  ...  54.000000  45.000000   \n",
              "229                421.0                 0.0  ...  38.000000  45.000000   \n",
              "230                378.0                 0.0  ...  54.000000  52.000000   \n",
              "231                372.2                 0.0  ...  46.000000  40.000000   \n",
              "\n",
              "             K  СТРЕЛЯУ выносливость  СТРЕЛЯУ эмоц реактивность  \\\n",
              "0    46.222222                   2.0                       12.0   \n",
              "1    46.222222                   9.0                       15.0   \n",
              "2    46.222222                   5.0                       11.0   \n",
              "3    46.222222                  11.0                       16.0   \n",
              "4    46.222222                   3.0                       18.0   \n",
              "..         ...                   ...                        ...   \n",
              "227  39.000000                   8.0                        6.0   \n",
              "228  45.000000                  13.0                        5.0   \n",
              "229  63.000000                   7.0                        7.0   \n",
              "230  47.000000                   9.0                       10.0   \n",
              "231  62.000000                  19.0                        2.0   \n",
              "\n",
              "     СТРЕЛЯУ настойчивость  СТРЕЛЯУ активность  СТРЕЛЯУ сенс чувст  \\\n",
              "0                     10.0                13.0                16.0   \n",
              "1                     12.0                17.0                14.0   \n",
              "2                     16.0                 8.0                14.0   \n",
              "3                     16.0                18.0                19.0   \n",
              "4                     15.0                 8.0                 6.0   \n",
              "..                     ...                 ...                 ...   \n",
              "227                   10.0                 7.0                16.0   \n",
              "228                    6.0                14.0                11.0   \n",
              "229                   11.0                12.0                15.0   \n",
              "230                   16.0                14.0                20.0   \n",
              "231                    5.0                18.0                18.0   \n",
              "\n",
              "     СТРЕЛЯУ динамичность  binquality  \n",
              "0                    13.0        True  \n",
              "1                    12.0        True  \n",
              "2                     2.0        True  \n",
              "3                    15.0        True  \n",
              "4                     8.0        True  \n",
              "..                    ...         ...  \n",
              "227                  16.0       False  \n",
              "228                  16.0       False  \n",
              "229                  10.0       False  \n",
              "230                  16.0       False  \n",
              "231                  19.0       False  \n",
              "\n",
              "[232 rows x 61 columns]"
            ],
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              "      <th>номер</th>\n",
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              "        const charts = await google.colab.kernel.invokeFunction(\n",
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            ]
          },
          "metadata": {},
          "execution_count": 85
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "xcols = []\n",
        "\n",
        "\n",
        "for key in data.keys():\n",
        "  if key in [\n",
        "      #'возраст',\n",
        "      #'зависимость: 0 - нет, 1 - да',\n",
        "       #'пол 1 -муж 0 - жен',\n",
        "       #'диагноз 1 - алко, 2 - опиоиды, 3 - стимуляторы, 4 - все, 5 - седативные',\n",
        "       #'длит употр', 'возраст 1го употребления',\n",
        "       #'0 серия ср время  01',\n",
        "       #'0 серия ср время 02', '0 серия пропуск 01', '0 серия пропуск 02',\n",
        "       #'среднее время 11', 'среднее время 12',\n",
        "       'пропуск 11', 'пропуск 12',\n",
        "       #'среднее время 21',\n",
        "       #'среднее время 22',\n",
        "       'пропуск 21',\n",
        "       'ошибка 21',\n",
        "       'пропуск 22', 'ошибка 22',\n",
        "       'результат1',\n",
        "       'результат2',\n",
        "       'результат3',\n",
        "       'повыш 1, более 8 объектов - 2',\n",
        "       'повыш 1,  более 8 объектов, то 2',\n",
        "       #'стаж употр',\n",
        "       'образование. 0 - отсут1 - ср 2 - ср спец. 3 - выс 4 - кн',\n",
        "       'сколько детей в семье', 'какой вы посчету ребенок',\n",
        "       'образ отца. 0 - отсутствует 1 - ср 2 - ср спец. 3 - выс 4 - кн',\n",
        "       'образ матери. 0 - отсутствует 1 - ср 2 - ср спец. 3 - выс 4 - кн',\n",
        "       'возраст отца', 'возраст матери',\n",
        "       #'был ли опыт употр 1 - ДА 2 - НЕТ',\n",
        "       #'возраст 1 употр, 0 нет зависимости',\n",
        "       #'равен A', 'равен B',\n",
        "       'равен C',\n",
        "       'равен D', 'равен E',\n",
        "       #'равен сумм',\n",
        "       #'менделевич',\n",
        "       #'СМИЛ hs', 'D', 'HY',\n",
        "       #'PD', 'PA',\n",
        "       #'PT',\n",
        "       #'SC',\n",
        "       #'MA',\n",
        "       #'L', 'F', 'K',\n",
        "       #'СТРЕЛЯУ выносливость',\n",
        "       #'СТРЕЛЯУ эмоц реактивность', 'СТРЕЛЯУ настойчивость',\n",
        "       #'СТРЕЛЯУ активность',\n",
        "       #'СТРЕЛЯУ сенс чувст',\n",
        "       #'СТРЕЛЯУ динамичность',\n",
        "]:\n",
        "                     xcols.append(key)\n",
        "\n",
        "xcols"
      ],
      "metadata": {
        "id": "_9M-2uUyrnC3",
        "outputId": "36153a29-23cf-40a8-a7c4-3dc52d686690",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 126,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['пропуск 11',\n",
              " 'пропуск 12',\n",
              " 'пропуск 21',\n",
              " 'ошибка 21',\n",
              " 'пропуск 22',\n",
              " 'ошибка 22',\n",
              " 'результат1',\n",
              " 'результат2',\n",
              " 'результат3',\n",
              " 'повыш 1, более 8 объектов - 2',\n",
              " 'повыш 1,  более 8 объектов, то 2',\n",
              " 'образование. 0 - отсут1 - ср 2 - ср спец. 3 - выс 4 - кн',\n",
              " 'сколько детей в семье',\n",
              " 'какой вы посчету ребенок',\n",
              " 'образ отца. 0 - отсутствует 1 - ср 2 - ср спец. 3 - выс 4 - кн',\n",
              " 'образ матери. 0 - отсутствует 1 - ср 2 - ср спец. 3 - выс 4 - кн',\n",
              " 'возраст отца',\n",
              " 'возраст матери',\n",
              " 'равен C',\n",
              " 'равен D',\n",
              " 'равен E']"
            ]
          },
          "metadata": {},
          "execution_count": 126
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# setup and run the logistic regression model\n",
        "model = sm.Logit(data['binquality'],data[xcols])\n",
        "results = model.fit(method='cg')\n",
        "\n",
        "# have a look at the results!\n",
        "results.summary()"
      ],
      "metadata": {
        "id": "MYzlFrp1rw0B",
        "outputId": "b5fcf84f-7194-4046-ad46-16f8b12d0b10",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 786
        }
      },
      "execution_count": 127,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "         Current function value: 0.425012\n",
            "         Iterations: 35\n",
            "         Function evaluations: 83\n",
            "         Gradient evaluations: 83\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/scipy/optimize/_optimize.py:1698: OptimizeWarning: Maximum number of iterations has been exceeded.\n",
            "  res = _minimize_cg(f, x0, args, fprime, callback=callback, **opts)\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/base/model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
            "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<class 'statsmodels.iolib.summary.Summary'>\n",
              "\"\"\"\n",
              "                           Logit Regression Results                           \n",
              "==============================================================================\n",
              "Dep. Variable:             binquality   No. Observations:                  232\n",
              "Model:                          Logit   Df Residuals:                      211\n",
              "Method:                           MLE   Df Model:                           20\n",
              "Date:                Tue, 26 Sep 2023   Pseudo R-squ.:                  0.3457\n",
              "Time:                        14:49:13   Log-Likelihood:                -98.603\n",
              "converged:                      False   LL-Null:                       -150.70\n",
              "Covariance Type:            nonrobust   LLR p-value:                 2.226e-13\n",
              "====================================================================================================================================\n",
              "                                                                       coef    std err          z      P>|z|      [0.025      0.975]\n",
              "------------------------------------------------------------------------------------------------------------------------------------\n",
              "пропуск 11                                                          -0.1658      0.115     -1.441      0.150      -0.391       0.060\n",
              "пропуск 12                                                           0.2842      0.123      2.316      0.021       0.044       0.525\n",
              "пропуск 21                                                           0.0811      0.076      1.066      0.286      -0.068       0.230\n",
              "ошибка 21                                                           -0.0019      0.083     -0.022      0.982      -0.164       0.161\n",
              "пропуск 22                                                          -0.2118      0.076     -2.800      0.005      -0.360      -0.064\n",
              "ошибка 22                                                            0.2312      0.092      2.513      0.012       0.051       0.412\n",
              "результат1                                                           0.0005      0.044      0.011      0.991      -0.087       0.088\n",
              "результат2                                                          -0.0904      0.033     -2.752      0.006      -0.155      -0.026\n",
              "результат3                                                          -0.0586      0.050     -1.176      0.240      -0.156       0.039\n",
              "повыш 1, более 8 объектов - 2                                       -0.2683      0.392     -0.684      0.494      -1.037       0.501\n",
              "повыш 1,  более 8 объектов, то 2                                     0.1378      0.388      0.355      0.723      -0.623       0.899\n",
              "образование. 0 - отсут1 - ср 2 - ср спец. 3 - выс 4 - кн            -0.5303      0.249     -2.128      0.033      -1.019      -0.042\n",
              "сколько детей в семье                                                0.3826      0.303      1.264      0.206      -0.211       0.976\n",
              "какой вы посчету ребенок                                            -0.0829      0.393     -0.211      0.833      -0.853       0.687\n",
              "образ отца. 0 - отсутствует 1 - ср 2 - ср спец. 3 - выс 4 - кн       0.5929      0.304      1.952      0.051      -0.002       1.188\n",
              "образ матери. 0 - отсутствует 1 - ср 2 - ср спец. 3 - выс 4 - кн     0.5030      0.311      1.618      0.106      -0.106       1.112\n",
              "возраст отца                                                         0.0070      0.035      0.201      0.841      -0.061       0.075\n",
              "возраст матери                                                       0.0555      0.043      1.281      0.200      -0.029       0.140\n",
              "равен C                                                             -0.2297      0.109     -2.116      0.034      -0.442      -0.017\n",
              "равен D                                                              0.1327      0.092      1.447      0.148      -0.047       0.313\n",
              "равен E                                                             -0.3604      0.073     -4.965      0.000      -0.503      -0.218\n",
              "====================================================================================================================================\n",
              "\"\"\""
            ],
            "text/html": [
              "<table class=\"simpletable\">\n",
              "<caption>Logit Regression Results</caption>\n",
              "<tr>\n",
              "  <th>Dep. Variable:</th>      <td>binquality</td>    <th>  No. Observations:  </th>  <td>   232</td>  \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Model:</th>                 <td>Logit</td>      <th>  Df Residuals:      </th>  <td>   211</td>  \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Method:</th>                 <td>MLE</td>       <th>  Df Model:          </th>  <td>    20</td>  \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Date:</th>            <td>Tue, 26 Sep 2023</td> <th>  Pseudo R-squ.:     </th>  <td>0.3457</td>  \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Time:</th>                <td>14:49:13</td>     <th>  Log-Likelihood:    </th> <td> -98.603</td> \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>converged:</th>             <td>False</td>      <th>  LL-Null:           </th> <td> -150.70</td> \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Covariance Type:</th>     <td>nonrobust</td>    <th>  LLR p-value:       </th> <td>2.226e-13</td>\n",
              "</tr>\n",
              "</table>\n",
              "<table class=\"simpletable\">\n",
              "<tr>\n",
              "                                  <td></td>                                    <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>пропуск 11</th>                                                       <td>   -0.1658</td> <td>    0.115</td> <td>   -1.441</td> <td> 0.150</td> <td>   -0.391</td> <td>    0.060</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>пропуск 12</th>                                                       <td>    0.2842</td> <td>    0.123</td> <td>    2.316</td> <td> 0.021</td> <td>    0.044</td> <td>    0.525</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>пропуск 21</th>                                                       <td>    0.0811</td> <td>    0.076</td> <td>    1.066</td> <td> 0.286</td> <td>   -0.068</td> <td>    0.230</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>ошибка 21</th>                                                        <td>   -0.0019</td> <td>    0.083</td> <td>   -0.022</td> <td> 0.982</td> <td>   -0.164</td> <td>    0.161</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>пропуск 22</th>                                                       <td>   -0.2118</td> <td>    0.076</td> <td>   -2.800</td> <td> 0.005</td> <td>   -0.360</td> <td>   -0.064</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>ошибка 22</th>                                                        <td>    0.2312</td> <td>    0.092</td> <td>    2.513</td> <td> 0.012</td> <td>    0.051</td> <td>    0.412</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>результат1</th>                                                       <td>    0.0005</td> <td>    0.044</td> <td>    0.011</td> <td> 0.991</td> <td>   -0.087</td> <td>    0.088</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>результат2</th>                                                       <td>   -0.0904</td> <td>    0.033</td> <td>   -2.752</td> <td> 0.006</td> <td>   -0.155</td> <td>   -0.026</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>результат3</th>                                                       <td>   -0.0586</td> <td>    0.050</td> <td>   -1.176</td> <td> 0.240</td> <td>   -0.156</td> <td>    0.039</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>повыш 1, более 8 объектов - 2</th>                                    <td>   -0.2683</td> <td>    0.392</td> <td>   -0.684</td> <td> 0.494</td> <td>   -1.037</td> <td>    0.501</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>повыш 1,  более 8 объектов, то 2</th>                                 <td>    0.1378</td> <td>    0.388</td> <td>    0.355</td> <td> 0.723</td> <td>   -0.623</td> <td>    0.899</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>образование. 0 - отсут1 - ср 2 - ср спец. 3 - выс 4 - кн</th>         <td>   -0.5303</td> <td>    0.249</td> <td>   -2.128</td> <td> 0.033</td> <td>   -1.019</td> <td>   -0.042</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>сколько детей в семье</th>                                            <td>    0.3826</td> <td>    0.303</td> <td>    1.264</td> <td> 0.206</td> <td>   -0.211</td> <td>    0.976</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>какой вы посчету ребенок</th>                                         <td>   -0.0829</td> <td>    0.393</td> <td>   -0.211</td> <td> 0.833</td> <td>   -0.853</td> <td>    0.687</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>образ отца. 0 - отсутствует 1 - ср 2 - ср спец. 3 - выс 4 - кн</th>   <td>    0.5929</td> <td>    0.304</td> <td>    1.952</td> <td> 0.051</td> <td>   -0.002</td> <td>    1.188</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>образ матери. 0 - отсутствует 1 - ср 2 - ср спец. 3 - выс 4 - кн</th> <td>    0.5030</td> <td>    0.311</td> <td>    1.618</td> <td> 0.106</td> <td>   -0.106</td> <td>    1.112</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>возраст отца</th>                                                     <td>    0.0070</td> <td>    0.035</td> <td>    0.201</td> <td> 0.841</td> <td>   -0.061</td> <td>    0.075</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>возраст матери</th>                                                   <td>    0.0555</td> <td>    0.043</td> <td>    1.281</td> <td> 0.200</td> <td>   -0.029</td> <td>    0.140</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>равен C</th>                                                          <td>   -0.2297</td> <td>    0.109</td> <td>   -2.116</td> <td> 0.034</td> <td>   -0.442</td> <td>   -0.017</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>равен D</th>                                                          <td>    0.1327</td> <td>    0.092</td> <td>    1.447</td> <td> 0.148</td> <td>   -0.047</td> <td>    0.313</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>равен E</th>                                                          <td>   -0.3604</td> <td>    0.073</td> <td>   -4.965</td> <td> 0.000</td> <td>   -0.503</td> <td>   -0.218</td>\n",
              "</tr>\n",
              "</table>"
            ],
            "text/latex": "\\begin{center}\n\\begin{tabular}{lclc}\n\\toprule\n\\textbf{Dep. Variable:}                                                   &    binquality    & \\textbf{  No. Observations:  } &      232    \\\\\n\\textbf{Model:}                                                           &      Logit       & \\textbf{  Df Residuals:      } &      211    \\\\\n\\textbf{Method:}                                                          &       MLE        & \\textbf{  Df Model:          } &       20    \\\\\n\\textbf{Date:}                                                            & Tue, 26 Sep 2023 & \\textbf{  Pseudo R-squ.:     } &   0.3457    \\\\\n\\textbf{Time:}                                                            &     14:49:13     & \\textbf{  Log-Likelihood:    } &   -98.603   \\\\\n\\textbf{converged:}                                                       &      False       & \\textbf{  LL-Null:           } &   -150.70   \\\\\n\\textbf{Covariance Type:}                                                 &    nonrobust     & \\textbf{  LLR p-value:       } & 2.226e-13   \\\\\n\\bottomrule\n\\end{tabular}\n\\begin{tabular}{lcccccc}\n                                                                          & \\textbf{coef} & \\textbf{std err} & \\textbf{z} & \\textbf{P$> |$z$|$} & \\textbf{[0.025} & \\textbf{0.975]}  \\\\\n\\midrule\n\\textbf{пропуск 11}                                                       &      -0.1658  &        0.115     &    -1.441  &         0.150        &       -0.391    &        0.060     \\\\\n\\textbf{пропуск 12}                                                       &       0.2842  &        0.123     &     2.316  &         0.021        &        0.044    &        0.525     \\\\\n\\textbf{пропуск 21}                                                       &       0.0811  &        0.076     &     1.066  &         0.286        &       -0.068    &        0.230     \\\\\n\\textbf{ошибка 21}                                                        &      -0.0019  &        0.083     &    -0.022  &         0.982        &       -0.164    &        0.161     \\\\\n\\textbf{пропуск 22}                                                       &      -0.2118  &        0.076     &    -2.800  &         0.005        &       -0.360    &       -0.064     \\\\\n\\textbf{ошибка 22}                                                        &       0.2312  &        0.092     &     2.513  &         0.012        &        0.051    &        0.412     \\\\\n\\textbf{результат1}                                                       &       0.0005  &        0.044     &     0.011  &         0.991        &       -0.087    &        0.088     \\\\\n\\textbf{результат2}                                                       &      -0.0904  &        0.033     &    -2.752  &         0.006        &       -0.155    &       -0.026     \\\\\n\\textbf{результат3}                                                       &      -0.0586  &        0.050     &    -1.176  &         0.240        &       -0.156    &        0.039     \\\\\n\\textbf{повыш 1, более 8 объектов - 2}                                    &      -0.2683  &        0.392     &    -0.684  &         0.494        &       -1.037    &        0.501     \\\\\n\\textbf{повыш 1,  более 8 объектов, то 2}                                 &       0.1378  &        0.388     &     0.355  &         0.723        &       -0.623    &        0.899     \\\\\n\\textbf{образование. 0 - отсут1 - ср 2 - ср спец. 3 - выс 4 - кн}         &      -0.5303  &        0.249     &    -2.128  &         0.033        &       -1.019    &       -0.042     \\\\\n\\textbf{сколько детей в семье}                                            &       0.3826  &        0.303     &     1.264  &         0.206        &       -0.211    &        0.976     \\\\\n\\textbf{какой вы посчету ребенок}                                         &      -0.0829  &        0.393     &    -0.211  &         0.833        &       -0.853    &        0.687     \\\\\n\\textbf{образ отца. 0 - отсутствует 1 - ср 2 - ср спец. 3 - выс 4 - кн}   &       0.5929  &        0.304     &     1.952  &         0.051        &       -0.002    &        1.188     \\\\\n\\textbf{образ матери. 0 - отсутствует 1 - ср 2 - ср спец. 3 - выс 4 - кн} &       0.5030  &        0.311     &     1.618  &         0.106        &       -0.106    &        1.112     \\\\\n\\textbf{возраст отца}                                                     &       0.0070  &        0.035     &     0.201  &         0.841        &       -0.061    &        0.075     \\\\\n\\textbf{возраст матери}                                                   &       0.0555  &        0.043     &     1.281  &         0.200        &       -0.029    &        0.140     \\\\\n\\textbf{равен C}                                                          &      -0.2297  &        0.109     &    -2.116  &         0.034        &       -0.442    &       -0.017     \\\\\n\\textbf{равен D}                                                          &       0.1327  &        0.092     &     1.447  &         0.148        &       -0.047    &        0.313     \\\\\n\\textbf{равен E}                                                          &      -0.3604  &        0.073     &    -4.965  &         0.000        &       -0.503    &       -0.218     \\\\\n\\bottomrule\n\\end{tabular}\n%\\caption{Logit Regression Results}\n\\end{center}"
          },
          "metadata": {},
          "execution_count": 127
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "# Assuming 'data' contains your dataset\n",
        "font_size = 15\n",
        "axis_title_font = {'fontsize': 16}\n",
        "plt.figure(figsize=(17, 8))\n",
        "\n",
        "# First Plot\n",
        "plt.subplot(1, 3, 1)\n",
        "sns.violinplot(x=data['зависимость: 0 - нет, 1 - да'], y=data['ошибка 22'], palette=\"Set2\")\n",
        "#plt.title(\"Violin Plot of Knowledge Component\")\n",
        "plt.xlabel(\"зависимость\", fontdict=axis_title_font)\n",
        "plt.ylabel('ошибка 22', fontdict=axis_title_font)\n",
        "plt.xticks(fontsize=font_size)  # Set tick label font size\n",
        "plt.yticks(fontsize=font_size)  # Set tick label font size\n",
        "\n",
        "# Second Plot\n",
        "plt.subplot(1, 3, 2)\n",
        "sns.violinplot(x=data['зависимость: 0 - нет, 1 - да'], y=data['результат2'], palette=\"Set2\")\n",
        "#plt.title(\"Violin Plot of Security Responsibility Component\")\n",
        "plt.xlabel(\"зависимость\", fontdict=axis_title_font)\n",
        "plt.ylabel('результат2', fontdict=axis_title_font)\n",
        "plt.xticks(fontsize=font_size)  # Set tick label font size\n",
        "plt.yticks(fontsize=font_size)  # Set tick label font size\n",
        "\n",
        "# Second Plot\n",
        "plt.subplot(1, 3, 3)\n",
        "sns.violinplot(x=data['зависимость: 0 - нет, 1 - да'], y=data['образование. 0 - отсут1 - ср 2 - ср спец. 3 - выс 4 - кн'], palette=\"Set2\")\n",
        "#plt.title(\"Violin Plot of Security Responsibility Component\")\n",
        "plt.xlabel(\"зависимость\", fontdict=axis_title_font)\n",
        "plt.ylabel('образование', fontdict=axis_title_font)\n",
        "plt.xticks(fontsize=font_size)  # Set tick label font size\n",
        "plt.yticks(fontsize=font_size)  # Set tick label font size\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "1dVEIfOG2_bK",
        "outputId": "8fc955bd-0694-4137-f116-6ccd3057f7a3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 625
        }
      },
      "execution_count": 128,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1700x800 with 3 Axes>"
            ],
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vvfSSgsGgzjnnHM2ZM0ennXaaA88E2WZZVipoGqSOJknyd6zTREcTAABwg2SoREcT4DmWZXUanWd1HJPhNZzfCJoAAJ6SHJ2X1tJkf0dHE9xiwoQJuvXWWwd021mzZmnWrFmDWxA8o7q6Wk1NTZJhJNZWGiR2aEXQBAAA3CDV0WQkgya6IQBvSH+t+kxD8S4uR/5hjSYAgKckR+elXWbPAyZoAuB1yfWZyopk+AZvV90ew7d37141NzcP2v0CAAAMRHpHkz2igm4IwBvSj734DMnX8b1lWRyXyWMETQAAT0pfowkAckVqfabBG5snSWYoKLMwLEnatm3boN43AABAf9mdD4aP0XmA16R3LpmGkTz5V+IE4HxG0AQA8KT05ZgssTYTgNyQ7GgaxPWZbHZXE+PzAACA09JH58lH0AR4SeegKfkSPug65BeCJgCApxg9tDL1dB0AeEGqo6lk0O+bdZoAAIAbWJbVaXQeHU2At3QKmqROHU0ETfmLoAkAAABwgebmZu3du1eSFKgY/KDJ3xFebdmyZdDvGwAAoK9isZisjhEVhmkmuppE0AR4hT0ezzQSJ/yaxsHXIf8QNAEAAAAuYHca+YrCMkPBQb9/u6Npx44dHMgBAACO6bQfYhoSHU2Ap9hdS3awYHZxHfIPQRMAwFMYnQcgV2VyfSZJ8hUXyAj4FY1GtWvXrow8BgAAQG/SAyXDZ8pgjSbAU1IdTYljMIZhyDjgOuQfgiYAgCdZslLfWz1sCAAekcn1maTEB0B7JB/j8wAAgFM6BUpGqqMpFotxkBrwALtrKf1cX3t8Hq/h/EXQBADwFLtrKZ4WLtnfmiZ/1gB4lx3+BDLU0SRJ/iGJ+7ZDLQAAMqm6ulqHHXaYDMPQxIkTnS4HLtHe3p74xjQTnRBpn+PoagLcL32NJhtBEzgiBwDwFDtMSu9iskMngiYAXhWNRrVjxw5Jkj9DHU2Skh1NBE0AgGz48Y9/rH379jldBlzGDpPskXn2f6W0EAqAa1kdB2RMpZIme3geQVP+4ogcAMBT7DApfdclbu/kEDQB8KgdO3YoFovJCPrlKyrI2OMEOjqatmzZkvyACABAJvztb3/Tww8/rO9+97tOlwKXscOkZCdTWlsEHU2A+9lhEqPzkI4jcgAAT0l1NKWt0XTAdQDgNcn1mSpKkyNCM8FfViyZhpqbmznDHHCB1tZWzt5HTmppadH3v/99HX300frJT37idDlwmWSYZHc0GUbyKDXviYD7MToPXeGIHADAU7ruaEr8N5MHZwEgk5LrM2VwbJ6UGE3jLyvu9JgAnLFhwwZ9//vf19VXX636+nqnywEG1c9//nNt3rxZ999/vwKBgNPlwGWSHU1pI/Ps7iaCJsD9kkFTF6PzmJqQvwiaAACe0tUaTRaj8wB4nN3R5K8ozfhj2ePzWKcJcNamTZvU3t6uxsbG5BptQC5YvXq1fv3rX2v27NmaMWOG0+XAhZJrNKV/fusInRidB7iffQwm/Vxfg46mvMcROQCAp9hhUiwtabJ3Y3w+nwMVAcChsSwrNTpvSBaCpopE1xRBE+Cs9IOpHFhFrojH47riiitUXl6uX/3qV06XA5dqa2uTJFmmoXh7VJZlJbub6GgC3C/ZtWRZisTisiwrGTLQ0ZS/CJoAAJ5ih0nxtH2XmNX5OgDwkqqqKrW0tEimIX9pUcYfz09HE+AKBE3IRb/73e/01ltv6Y477lBlZWW/bx+JRNTQ0NDpC7nHDpNitfu1588vy4rGZHR8lrNDKADuZYdJtW1xXb9il9riVrKjiaApfxE0AQA8JRU0pXU0dXxP0ATAi5Jj88pLOq1VkCl2R9O+ffvU2NiY8ccD0DWCJuSabdu26aabbtLMmTM1a9asAd3HbbfdprKysuTXmDFjBrdIuEJXYRIdTYB3dBUmsUYTCJoAAJ5ih0mxtH0Xu7uJNZoAeFFybF5HAJRpZjAgX1GBJGn79u1ZeUwAByNoQq65+uqr1dbWpvvvv3/A93HjjTeqvr4++cXfqdzUVZhERxPgHV2tw0RHE/xOFwAAQH/YYVJ6R1OMjiYAHpbtoEmS/ENKFGtq0ZYtWzRlypSsPS6AlPQDrZzBj1zwzDPPqLy8XFdeeWWny1tbWyVJO3fu1BlnnCFJ+vOf/6zhw4cfdB+hUEihUCjjtcJZdDQB3tZTmNRVCIX8QNAEAPCULjua4p2vAwAvSY7O61g7KRsCFaWKbN/LOk2AgwiakIvq6uq0bNmyLq9rbW1NXmeHT8hPXQZNfjqaAC8znC4AjmPGEADAU5JBUzyto0mJ7xmdB8BrGhsbtW/fPknZ7WiyH4ugCXAOQRNyjWVZXX598sknkqQjjzwyedn48eOdLRaO6vI9r6OjiaAJcL8u12hidF7e44gcAMBT7KApTkcTgBxgrz3hKwrLDAay9rj+jqBp586drA0DOISgCUC+6qqjze5oikQi2S4HQD91GTT1cB3yA0ETAMBTkkGTDl6jye9nIiwAb0mOzavI3tg8SfIVF8gI+BWNRrVr166sPjaABIImAPmqqzDJ7PgsR9AEuF/XYZLRw3XIBwRNAABPSY3OS11mdzcxOg+A12zbtk2SFBjSv7F5lmUp3h5VvD06oA9zhmEkx+fZNQDIrvTxUARNAPJJlx1NAV+31wFwF0bnoSsckQMAeEpqdB4dTQC8L9XR1M+gKRrTnj+/rD1/fllWNDagx/azThPgqPRwiTVJkMvGjx8vy7K0adMmp0uBS3TVtWSPziNoAgBvImgCAHhKsqMp7SSZGB1NADwoFotpx44dkqRAlkfnJR6TjibASekHWhkVBSCfdL1Gk7/b6wC4n9H7Jshxrjwi19zcrMWLF+vyyy/X5MmTFQ6HVVRUpKlTp+rmm29WY2Njt7d96KGHdNJJJ6m4uFhDhgzRF7/4Ra1YsSKL1QMAMikVNB3c0WRfBwBesHv3brW3t8vw++QrLsj649vrQtHRBDiDoAlAvuo6aEp8luP9EHC/nsbjMTovf7kyaHr00Uf11a9+VX/84x/l8/n05S9/WTNmzNAnn3yin/3sZzrxxBO1d+/eg2537bXXavbs2frggw90zjnn6KSTTtJLL72kL3zhC1q8eHH2nwgAYNClRuelLmONJgBeZHcS+StKZBjZPwcwUF4sGVJDQ4Pq6+uz/vhAviNoApCvWlpaDrrMDCQ6mpqbm7NdDoB+IkxCV1x5RC4QCOh73/ue1q1bp3Xr1unxxx/XkiVLtHHjRh1//PHasGGDrr322k63efnllzVv3jxVVlbq/fff1+LFi7VkyRItX75cPp9Ps2fPVl1dnSPPBwAweOwwKb2jKc4aTQA8yA6aAv1cn2mwGH6ffCVFnWoBkD0ETQDyVVdhkhEkaAK8zD5tjhAqf7kyaPr2t7+t+fPna8qUKZ0uHzFihO69915J0qJFizotmHrXXXdJkm666SZNmjQpefmpp56qK6+8UnV1dXrggQeyUD0AIJPsjiaLjiYAHrd9+3ZJkr/cmaBJ6uhqEkET4ASCJgD5qqmp6aDLzGCg2+sAuEtXYZI9oIGgKX957ojc1KlTJSV2xKurqyUlWm6XLl0qSbrkkksOuo192dNPP52lKgEAmZLqaEpdFmeNJgAe5HRHk5QY25deC4DsaG9vVywWS/67q/VKACAXxWKxrtdo6gia6GgCAG/yXNC0efNmSYnxekOGDJEkbdy4UZFIRMOGDdPo0aMPus0JJ5wgSVq9enX2CgUAZERqjaZU0hSjowmAxzQ1NSVPmvJ3dBU5wQ657O4qANlx4PokBE0A8kV3QZLZMTqvqamJjgjA5brsaOrhOuQHzx2RmzdvniTpggsuUCgUkpQ6A7OrkEmSioqKVF5ertraWu3fvz87hQIAMsIOk+Jpl9mj8+hoAuAVdrDjKwonR8U4wR7bt2PHjk7dFQAy68Bg6cDgCQByVXI0nq/zIUl7f8iyLN4TAZfrOkwyergO+cBTQdNzzz2nBx54QIFAQLfcckvy8sbGRklSYWFht7ctKkosdNxT0BSJRNTQ0NDpCwDgLnaYFIundl7sHRk6mgB4hX2ilJPrM0mSr7hAht+naDSq3bt3O1oLkE8ODJroaAKQL+ygyTjwRBufKZlGp20AuFNPazQhf3nmiNyGDRt02WWXybIs3XHHHcm1mgbTbbfdprKysuTXmDFjBv0xAACHxuhi78XubiJoAuAVdkeT38H1maTEe6o9um/Hjh2O1gLkEztYKvJ3rD0Zi6mtrc3JkgAgK+rr6yVJvnCw0+WGYcgMhzptA8B76GjKX544Irdz505dcMEFqq2t1XXXXadrrrmm0/XFxYkPxz0tGGifDVFS0v2H+RtvvFH19fXJL2bVA4D72EFT+ug8ez+mqxAKANzI3s8MOLg+k83uqrK7rABknv3ZtSyUGvvLqCgA+cAOkexQKZ2vINhpGwDuFI/HD7rMDhkImvKX3+kCelNTU6PzzjtPW7du1ezZs3XnnXcetM3YsWMldX8WZlNTk+rq6lRRUdFj0BQKhZLrPgEA3MkOk9L3XawDrgMAN7MsK7nf6vToPCkRdrWIjiYgm+ygqdBvKuwz1Bqz1NzcrLKyMocrA4DMSgVNB69RSUcT4GEdh2MImvKXqzuaGhsbdeGFF2rdunW6+OKL9Yc//KHLg4iTJ09WKBRSVVWVdu7cedD1q1atkiQdd9xxGa8ZAJBZ9ni89F0XS6zRBMA7ampqEgeZDUP+0iKny0mGXXTzA9ljB01hv6Fwx/i8niZ0AECuqKurkySZBQef6G1fZm8DwJ26XKOph+uQH1x7RC4SiegrX/mK3nzzTZ1//vl67LHHkgvAH6igoEBnnXWWJOmJJ5446PqFCxdKkr70pS9lrmAAQFakOppSOy+MzgPgJcluptIiGT7nd8ftNZr27t2rSCTicDVAfrBDpQKfqQIfQROA/JFcoykUPOg6e90mOpoAd+tqdJ59OKar65AfnP9k24VYLKavf/3rWrp0qWbMmKFFixYpGDz4D1C66667TpL0i1/8Qh999FHy8tdff13z589XeXm5Lr/88ozWDQDIvGTQlHYZo/MAeIm9FpLfBeszSZKvICQzFJRlWV1OBwAw+JJBk99Ugd/odBkA5LLk6LyCg4/z2R1NBE2Au3W9RpPR7XXID65co+mee+7RU089JUkaOnSorrrqqi63u/POOzV06FBJ0jnnnKNrrrlG8+bN07Rp03Tuueeqra1NL730kizL0oMPPqjy8vJsPQUAQIZ0uUYTHU0APMQOc9wSNEmJWtr21GjHjh064ogjnC4HyHlNTU2SpAK/oQJG5wHII9XV1ZIkX0H4oOvMwlCnbQC4U5dBE2s05T1XBk21tbXJ7+3AqStz585NBk2S9Jvf/EbTpk3TPffco5deeknBYFDnnHOO5syZo9NOOy2jNQMAsqOnnRZ2aAB4QXJ0nkuDJgCZZwdNhX5ThR1BU2Njo5MlAUDGxWKxVNBUXHDQ9f7iQklSVVVVVusC0D+MzkNXXBk0zZ07V3Pnzh3QbWfNmqVZs2YNaj0AAPdJb16ikQmAV8Tj8WRHU6DMRUFTRy0ETUB22KFSUYCgCUD+qK6uThyENk0ZXYzOs8OnhoYGtba2Khw+uOsJgPMYnYeuuHKNJgAAekO2BMCL9u3bp0gkIpmGfCWFTpeTZHdXETQB2WGHSoV+U4UBgiYA+WHv3r2SEoGS0cUnOjMYkBEMdNoWgPt0NU2GjiYQNAEAPIXReQC8LDk2r7RIhumeXfFAR9C0b98+tba2OlwNkPu6Gp1nXwYAucoeiefvYmyezb6O8XmAe3UVJvk6kqZYLJbtcuAS7vl0CwBAH3R55kwP1wGAm9hj89y0PpMkmaGgzHBihI1dI4DM6dTRxOg8AHliz549kiRfcfdd3fb4PDqaAPeKRqMHXebrSBkImvIXQRMAwJMYnQfAi5IdTWUlDldyMMbnAdnR3t6ulpYWSVJRwKfijtF5+/fvd7IsAMi49NF53bFHC9uhFAD36Spo8nd0NHV1HfIDQRMAwFPsFm3TSEVNdos2s4ABuF2yo6msyOFKDuYvSwRNu3btcrgSILfZnUumpAK/oSKCJgB5Yvv27ZJ67uy290c48QVwry47mgia8h5BEwDAU+w2bDOtpcn+nhZtAG5mWZZ2794tyX2j86TUgR1G5wGZZQdKhQFTpmF06mhiDDCAXNXW1pbcDwpUdN/ZbV+3detW3hMBl+ppdB5BU/4iaAIAeEpXHU0mHU0APKCmpiYxLssw5C/pfm0Cp9hdVgRNQGY1NDRIUjJgKgr4JCVOmGlubnasLgDIpB07digej8sIBWQWhLrdzl9WLBmGmpqaVFNTk8UKAfQVo/PQFYImAICn2F1LPjqaAHiMHeD4SgplmO7bDbc7mvbu3au2tjaHqwFyl93RZI/MC5iGQh07NnYIBQC5Ztu2bZISHUuG0f2Ku4bPTJ78snXr1qzUBqB/uu5oImjKd+77hAsAQA9So/PS12jqfB0AuFFqfSb3jc2TJDMclBH0y7Isffrpp06XA+QsO0wq6ehkktRpfB4A5KL0oKk3/vKSTrcB4C6RSOSgy4IdQVNX1yE/EDQBADyl6zWajE7XAYAb7dq1S1JqRJ3bGIbBOk1AFtTX10tKhUuJ732drgOAXGOHRv6K0l63DQwhaALcrKswKUDQlPcImgAAnpIanUdHEwBvcXvQJEn+UtZpAjLNDpNKgqmOppJg4qM5o/MA5KJYLKbNmzdLkgJD+hA0dYRRmzZtymhdAAamqzDJHgPMCO78RdAEAPCU9vZ2SZI/7S+Yv+PMGfs6AHCjZNBU6s7ReVIqBNu9e7fDlQC5yw6T0juaSuhoApDDtmzZotbWVhnBgPzlve8HBYaVS4ahffv2qaqqKvMFAugXOprQFYImAICnpIKmVEcTQRMAt2tubk4eQLa7htzIDsEImoDMSXY0pa3RVNIROhE0AchF69atkyQFD6uQkTaZojtmwK9AZaKraf369RmtDUD/ddW1FPQRNOU7giYAgKdEo1FJqbNlJMnf8WHFvg4A3MbuZjILQjKDfoer6Z6/tFBSImiKx+MOVwPkptTovLSOpiAdTQBylx0WhYYP6fNtgocntrVDKgDu0VWYFKSjKe8RNAEAPCXZ0ZR2JpwdOjELGIBbpcbmubebSZJ8JYWSYSgSiai2ttbpcoCcY1mW6urqJEmlwYM7muzrACBXxGIxbdiwQVIqPOoLe1s6mgD3aW5uPuiycMf6Bl1dh/xA0AQA8JSuR+cl/ktHEwC3skfR2WsguZVhmomwSalwDMDgaWlpSe7LpI/OK6WjCUCO2rp1a8f6TH75y0v6fLvgYRWSYaiqqop1mgCXaWpqOuiywo4DM11dh/xA0AQA8JRU0JS6jDWaALidVzqapFSNBE3A4LM7BcM+I7mWgZQKmurq6mRZliO1AUAmfPDBB5Kk4GFDZJi9r89kS1+nae3atRmpDUD/WZbVZZhUQNCU9wiaAACeYo/HC5qpP2GMzgPgdp9++qkkyeeFoKmj68quGcDgsTuW0sfmSan1miKRiFpbW7NeFwBkyltvvSVJCo0c2u/b2rex7wOA81pbW7tcy7XQn1qjiWkz+YmgCQDgKfbCkulnAQd9ZqfrAMBN4vF4MrTxREdTSaJGe9wfgMFjr8FkB0u2kM9UqGPfhnWaAOSKqqoqffzxx5Kk8NjD+3378LjhkqQ1a9aw7gvgEnbH0oGhQkHa2Bm6mvITQRMAwFOSQVPa2AX7e4ImAG5UXV2dGO1pGvIVFThdTq/8pYk1mgiagMFnj847sKMp/TKCJgC54s0335QkBQ+vkK8g1O/bB8pL5C8rUjQa1apVqwa7PAADYIdIBf7OozBNw1C446QZgqb8RNAEAPCUrjuaCJoAuFeym6mksF9rEzjFHu+3b98+1r4DBpkdIpV1GTQlPp7bYRQAeN0bb7whKdWZNBD2be37AuCsxsZGSVKh/+BYoShgdtoG+YWgCQDgGfF4PHnQk44mAF5hdwb5Stw/Nk+SzHBQRsAvy7K0Z88ep8sBckpfOpoImgDkgurqam3atEmSFB57CEFTx21Xr17N+DzABXralykJ0J2dzwiaAACe0dbWlvy+q46m9OsBwC3soMkL6zNJkmEYjM8DMqSnjqYyRucByCF2B1LwsIGNzbP5y4vlKy1Se3s74/MAF0itN9n9vgwnzeQngiYAgGekdyz50zqaAh3ft7a2Zr0mAOhNcnReR3jjBXb3lV07gMGROgv44I/idDQByBWWZWn58uWSDm1snpQ4Aaag4z6WLVt2yLUBODTJfZlAV/syjAHOZwRNAADPaGlpkSSFfIZMIxU0hX2JP2cETQDcyA5rfCXeCZrsUIygCRhc9lnAXY2boaMJQK7YsGGDtm3bJsPnU8GEkYd8fwUTR0uGtHbtWu3YsWMQKgQwUMl9mVAXY4BD7MvkM4ImAIBn2EFSKG1sXvq/CZrgFs3NzVq8eLEuv/xyTZ48WeFwWEVFRZo6dapuvvnmLhdHnTt3rgzD6PbrhhtucOCZ4FBFo1Ht27dPkuT3yBpNUioUY40mYPC0tLQkT5rpanQeZwEDyBVLliyRJBUcOVJmKHDI9+cvLlB4zOGSpBdeeOGQ7w/AwCVH5wW6X2+SoCk/+Z0uAACAvkp1NHU+T8IOmuzrAac9+uij+u53vytJmjJlir785S+roaFBK1as0M9+9jM99thjWrZsmQ477LCDbnv66adr4sSJB10+ffr0jNeNwbdv3z7FYjHJZ8osHPj6BNnmL6GjCRhs9kGXkM9Q2H/wOZ+sawAgF1RVVentt9+WJBVOHjdo91v4mXFq3bZH/3j1VV166aUqLi4etPsG0HepMcCs0YTOCJoAAJ5hdyyFD+hoYnQe3CYQCOh73/uerr32Wk2ZMiV5+e7du3XRRRfp3Xff1bXXXqtHH330oNteccUVmjVrVharRSbZHUH+kkIZhtHL1u5hd1/V1NSora1NwWDQ4YoA7+vpwEz65a2trWppaVFBQUHWagOAwfLyyy/LsiwFh1cqUD54YVDwsAr5K0rUVrtfy5Yt00UXXTRo9w2gb+LxuKqqqiRJFaGDT5qp6Bidt3fvXlmW5anPPzh0jM4DAHhGanTeAR1Nfjqa4C7f/va3NX/+/E4hkySNGDFC9957ryRp0aJFamtrc6I8ZJEX12eSJCMUkBFInJPG+DxgcNhBU1dj8yQp7DeTXdqcCQzAiyKRiJYuXSpJKvrM4HUzSZJhGCqaPFaS9OKLLyoejw/q/QPoXXV1tWKxmHyGVNbFGk2VYb8MJd4LGhoasl8gHEXQBADwjO7XaEr8OYvFYmpvb896XUB/TJ06VVJi57u6utrhapBpdtDk91rQZBiMzwMGWaqjqfuP4YycAeBl//jHP9TU1CRfcYFCo4YN+v0XTBgpIxjoNJ4PQPbYJ6BVhv0yu+hW8ptGsquJzxD5h9F5AADPaG5ulpQalWdLH6XX0tKiQODQF5wFMmXz5s2SEuP1hgwZctD1S5cu1XvvvafW1laNHj1aF154IeszedjevXslea+jSUrU3F7TkHwOAA5Nbx1N9nV7W6IETQA8JxKJaNGiRZKkoinjZZiDPzLL8PtUNHmsGtd8rCeeeELTp0+Xz9f9eyqAwWUHTcMKuo8Uhhb4VROJac+ePZo8eXK2SoMLEDQBADzDHo0X9nf+0GIahoKmoba4pZaWFpWWljpRHtAn8+bNkyRdcMEFCoVCB12/YMGCTv+eM2eOvva1r+mhhx5i0WMPSl+jyWvscIzRecDgSAZNXYyasdnXETQB8Jrnn39edXV18hUXqHDSmIw9TtHR49W0cZt27typ5cuX68wzz8zYYwHozO5Sqgz3EDSF/fpQETqa8hCj8wAAnpEMmnwH//kKs04TPOC5557TAw88oEAgoFtuuaXTdRMnTtSdd96ptWvXqrGxUdu3b9f//u//atSoUXryySf1zW9+s9f7t2dhp3/BOemL5fqKvRc0+YsJmoDBVFdXJ6nnjiZ7rJ69LQB4QUNDg55++mlJUsm0STK6+Lw2WMxgQMXHHilJWrhwoSKRSMYeC0Bnfe1oSt8W+YOgCQDgGd11NEmp8Mkerwe4zYYNG3TZZZfJsizdcccdybWabJdddpl+/OMf6+ijj1ZRUZFGjx6tb3zjG3rrrbdUWVmpxYsXa+XKlT0+xm233aaysrLk15gxmTubFL2rra1NrBtnGPIVhZ0up998JQWSxOg8YJD0dXRe+rYA4AV/+ctf1NLSIv+QUoXHj8j44xVNHitfUVi1tbVasmRJxh8PQMLOnTslpcKkrhzWcZ29LfIHQRMAwDO6W6NJoqMJ7rZz505dcMEFqq2t1XXXXadrrrmmz7cdMWKEZs+eLUm9fpC+8cYbVV9fn/zavn37IdWNQ5Ncn6m4QIbpvd1ue9zfvn37FIvFHK4G8DbLspLhUWkfgqaampqs1AUAh2rv3r168cUXJUmlxx8lwxj8tZkOZPhMFU87SpL017/+Vfv378/4YwL5rrW1NTkOb3RR9+tij+y4bufOnYpGo1mpDe7gvU+8AIC8lRqd131HE0ET3KampkbnnXeetm7dqtmzZ+vOO+/s931MmjRJkrR79+4etwuFQiotLe30BefY4yJ8xQUOVzIwZmFYMk3FYjFVV1c7XQ7gac3NzWpra5OUGo/XFTuEYnQeAK9YuHChYrGYgsMrFRo5NGuPWzBhhPwVJWppadHixYuz9rhAvtq+fbssy1Jp0FRJDyfNVIR8KvQbisVi2rFjRxYrhNMImgAAnpEanddFRxOj8+BCjY2NuvDCC7Vu3TpdfPHF+sMf/jCgszzts+CLiooGu0RkkN3RZHcGeY1hGMmQjBnrwKGx38cL/IaCPaxdUhZKjc6zLCsrtQHAQK1bt06vvvqqJKn0hKOy+tiGYSQf88UXX9TWrVuz+vhAvrFfY6OKgj1uZxhGchtel/mFoAkA4Bk9djQxOg8uE4lE9JWvfEVvvvmmzj//fD322GPy+bo/86s7lmXpqaeekiSdcMIJg10mMig1Os+bQZOUCsmqqqocrgTwNrtDqaf1maRUR1N7ezsnzwBwtdbWVs2fP1+SVDhptAKVZVmvIThiqEJjDlcsFtP8+fMZ0wVk0JYtWyRJo4q7H5tnG9mxjX0b5AeCJgCAZ9gHXAq66GgqoKMJLhKLxfT1r39dS5cu1YwZM7Ro0SIFg92f+VVVVaV77733oPnyjY2N+sEPfqA33nhDw4cP18UXX5zp0jGI0tdo8iq7dvu5ABiYvqzPJEkB01Bhx8kz9m0AwI3+7//+T1VVVfIVhVVywmccqcEwDJWdfLSMYEBbtmzR008/7UgdQD5IdTT1HjTZ29DRlF/8ThcAAEBf2SFSuIuRM3ZHE0ET3OCee+5JdiENHTpUV111VZfb3XnnnRo6dKiampr0wx/+UDfccINOPPFEjRgxQlVVVVq1apWqq6tVXl6uhQsXqrDQu50x+cjuAvLnQNBERxNwaPoaNNnbNEejqq2t1ejRozNdGgD02/r16/XCCy9IkspO+azMoHOHF30FIZWdNEV1r67WokWLNH36dI0dO9axeoBcFI1GtW3bNknSyD4ETaOLU0FTPB6XadLrkg8ImgAAnhCPxxWJRCSlQqV0dvjE6Dy4QfpZ6Hbg1JW5c+dq6NChqqys1PXXX6+VK1fqww8/1IoVK+Tz+TRhwgTNmjVLP/rRjzRq1KhslI5BEolEVF9fL8nbo/MImoDBYY/OKw32fqClNOjTp81ROpoAuFIkEtHvf/97SVLBxNEKjRzqcEVSePwIhbZ8qsiOvZo/f75uvvnmAY2sBtC1Tz75RO3t7Sr0mxpW0HuccHhhQCGfoZaWFm3btk3jx4/PfJFwHEETAMAT0juVuu5oImiCe8ydO1dz587t8/YlJSW6/fbbM1cQss4OZoyAX2ao97P+3MrfEZIxOg84NKmgqW8dTZKSYTUAuMnjjz+uPXv2yCwMq3T6ZKfLkZQaoVe1t0affPKJnnnmGX3lK19xuiwgZ2zYsEGSdERZUKZx8Im/B/IZhiaUBrWhNqKNGzcSNOUJ+tYAAJ5gB0gBU/KbB+/YFPiMTtsBgJNyYX0mKVV/Q0ODWltbHa4G8K7+BU1mp9sAgFusW7dOS5YskSSVnXKMzKB7TqbxFYZVeuIUSdKTTz6pLVu2OFsQkEPsoOnI0lCfb2Nvu379+ozUBPchaAIAeIIdIIW66GaSpFBHRxNrNAFwA7ujyetBkxkMyOhYd2Hfvn0OVwN4VzJoCvQeNJV0bEPQBMBNqqur9dvf/laWZalg4miFRw1zuqSDFEwYqdDoYYpGo7r77rvV2NjodEmA58XjcW3cuFGSdGRZP4Kmjm03bNggy7IyUhvchaAJAOAJdtDU1di8xOV0NAFwDzuU8Rd5O2iSJF8R6zQBh8oeg9fXNZrSbwMATmtvb9dvfvMbNTQ0yF9RorKOziG3MQxD5acdK19xgaqqqnTPPfcoHo87XRbgadu3b1dzc7NCPkOjivvexTi2JCi/kZiMsHv37gxWCLcgaAIAeII9sins73oesB1AMdoJgBvYQZPXO5qkVNBERxMwMK2trckTYfozOq+2tjajdQFAXz300EP6+OOPZQQDqph5vAx/7+9lTjFDwUSNPlOrV6/WwoULnS4J8DR7bN6E0qB8fVifyeY3DY0rDUpifF6+IGgCAHhCanRed0ETHU0A3CM5Oi8nOprCkgiagIGyO5MCptHtfkw6OpoAuMnSpUv1yiuvSJIqZhwnf0mhwxX1LjCkVGWnfFaStHjxYr311lsOVwR41/vvvy9JmtiPsXm2SWWJzxGrV68e1JrgTgRNAABP6G10nr1GUzQaVXt7e9bqAoCu5FRHUzGj84BDkT42z+jDmcD2Gk3Nzc1qa2vLaG0A0JNNmzbpoYcekiSVTJuk0Ej3rcvUnYIjRqrwM+MkSf/93/+tnTt3OlwR4D2tra1au3atJOmYIf3/XHP0kETQtGbNGo7T5AGCJgCAJ6SCpq4P0KSfIUxXEwAntbW1qaGhQVKqG8jLGJ0HHJq6ujpJUnGgb6OmCvyG7N0aupoAOKW+vl533323otGoQmMOV9Fnj3C6pH4rnT5ZwcMq1NraqrvvvlvNzc1OlwR4ytq1a9Xe3q4hIZ+GF/r7ffvRxQGVBk21trYyPi8PEDQBADwhNTqv6z9dPsNQ0GR8HgDn2YGM4ffJCPZ9wVy3sjuaCJqAgUnvaOoLwzAYnwfAUS0tLbrjjjtUW1srX2mRyk87tk8dmW5jmKbKvzBNZmFIu3bt0rx58xSNRp0uC/CMVatWSZKOGRIe0HuAaRg6uqMTyr4v5C6CJgCAJ0QiEUndr9EkScGO6+xtAcAJ1dXVkhKdQF48KHMgX2GiK6u+vp6DM8AA2GFRSR87mhLbmp1uCwDZ0t7errvvvlubN2+WEQpoyBnHywz2v5PBLXwFIVXMPEGG36c1a9bovvvuUzwed7oswPXi8bjeffddSdIxlQMfB/7ZjvF57777rizLGpTa4E4ETQAAT2htbZWUCpO6YodQ9rYA4IRU0OT9sXmSZIaDkmnKsizV1NQ4XQ7gOcmgqY8dTYltE6GUPXYPALIhHo/rvvvu0wcffCDD79OQs6bLX1bsdFmHLDi0TBUzj5dMQytXrtTDDz/MAW+gF1u2bFFdXZ1CPkMTy0IDvp9J5SH5jcR6rzt27BjECuE2BE0AAE9IdTR1/6craJqdtgUAJ9gj5szC3AiaDMNIhmZ2iAag7+w12wbS0bR///6M1AQAB7IsSw8++KDeeOMNyTRUccbxCg4td7qsQRMaOVTlpx8nSXrppZf05JNPOlwR4G5vvfWWJGlyeVh+c+BTGkI+U0eVhzvdJ3ITQRMAwBOSHU097OAE6WgC4AJ214+vaOAjJtyGoAkYOLujqTjQ94/fxazRBCDLFi5cqL/97W+SpPLPT1VoxFCHKxp8BeNHqPSkoyVJixYt0gsvvOBwRYA7xeNxrVixQpI0bdihf6ax7+O1116jmzCHETQBADyhP6Pz6GgC4KRcG50npdZpImgC+i/Z0RTse0eTHUrZtwWATFqyZImeeuopSVLpyUerYNxwhyvKnKLJY1U8daIk6ZFHHkkeTAeQ8uGHH6qqqkohn5FcY+lQHFdZoIBpaPfu3frkk08GoUK4EUETAMAT+jQ6j6AJgAvYo/NyKmjq6M6ynxuAvhtIR5M9Zo+OJgCZtnz5cj3yyCOSpOKpk1R01FiHK8q84mOPVOHksbIsS//93/+tVatWOV0S4CqvvfaapERAFOzhGExfhf1mMrCy7xu5h6AJAOAJbW1tknoZnWcSNAFwlmVZqdF5ObJGk5R6LvZzA9A37e3tamlpkTSwNZroaAKQSUuXLtX8+fMlSYWfGafiY49wuKLsMAxDpSdOUXj8CMViMd1999168803nS4LcIVoNJpYq03S5w4rHLT7te9rxYoVisVig3a/cA+CJgCAJ9hBU6CHoMm+rr29PSs1AcCBmpubk2F3LgVNZhFBEzAQdlBkGlKBv+8LaRcHCZoAZNYLL7yg//mf/5FlWSqcPFaln/uMDKPv71NeZxiGyk8/VuHxwxWLxfTb3/6WTgtA0vvvv6/GxkaVBkxNKg8N2v1+piKsIr+p+vp6rV27dtDuF+5B0AQA8AQ7PPL38JeLoAmA02prayVJRjAgw9/37gW38xUkPmTazw9A39hBUXHA7NcB3KKO7qf9+/crHo9npDYA+evpp5/Www8/LEkqOnq8Sk+cklchk80wTZWfPlUFR4xUPB7Xfffdp7///e9OlwU46h//+Ick6YTDCmUO4vuCzzR0/LDEOO5XX3110O4X7kHQBADwBDs86ktHk939BADZlhqbN3hn/7mB3Z3V0NBAmA/0Qypo6l/wXNxxZo1lWWpsbBz0ugDkJ8uytGjRIj322GOSEmsVlZwwOS9DJpthGio77VgVHjVGlmXp97//vV566SWnywIcUVdXp3feeUeSdOIgjs2z2ePz3njjDe3fv3/Q7x/OImgCAHhCX0bn+eloAuCwXFyfSZKMUEAyEx8d6urqnC0G8BD7IEpxoH8fvX2mocKOUXsciAEwGCzL0v/93/9p4cKFkqSSaZNUMm1SXodMNsMwVHrS0Sr6zDhJ0oMPPqjnnnvO4aqA7Pv73/+uWCym8SVBjSoODvr9jysJanRRQO3t7Vq+fPmg3z+cRdAEAPAEOpoAeIEdNJm5FjQZRrJLq7q62uFqAO+wO5qK+hk0JW7j63QfADBQlmXpT3/6k/76179Kkkqmf0bFxx7pcFXuYhiGSj73GRV99ghJ0p/+9CctXrzY2aKALIrH4/rb3/4mSTp9RFFGHsMwjOR9v/zyy4wHzjEETQAA17MsK22Npt6DJjqaADjFXsMo10bnSakuLdZpAvouOTqvp0Umu2F3QdHRBOBQRKNR3X///Xr++eclSaUnHa3io8c7W5RLGYahkmmTVDx1oiTp8ccf14IFCzgYjrzw7rvvqrq6WkV+U9OGDf7YPNsJhxUq7DO0Z88effDBBxl7HGQfQRMAwPVisVjye18PQZOvY+xDNBrNeE0A0BV7rJxZkHtBk/2cGJ0H9J0dEhX1c42mxG0ImgAcmubmZt1xxx36xz/+IRmGyk79rIomj3W6LFczDEMlx01UyfTJkqTnn39ev/vd75iagZxnr0128vDCHifJHKqQz9SJhxd1ekzkBoImAIDrdQqaetjfsfeF0rcHgGxKdjQV5NboPEnyETQB/dbY2Cip/2s0JW7D6DwAA1dbW6tbbrlFa9askeH3qeLME1Q4cbTTZXlG8dETVP754yTT0BtvvKHbbrst+Z4O5Jo9e/Zo9erVMiSdNrw4449nj89btWoVY7lzCEETAMD1OgdNPXQ0dSRNjDYA4JSc7mjqGAfI6Dyg75Kj8wayRpOfjiYAA7Njxw799Kc/1datW2WGg6o87ySFRw1zuizPKZgwUkPO/pyMgF8bN27U3LlzVVVV5XRZwKBbsmSJJGlyRUhDC/wZf7zhhQFNKgvJsiy9+OKLGX88ZAdBEwDA9dJH4fXUwW1fx+g8AE6Ix+Oqr6+XlOr+ySWMzgP6LzU6b+BrNNHRBKA/1q9fr7lz56q6ulq+0iJVXnCKApVlTpflWaHhlao8/2SZhWHt2rVLP/vZz7RlyxanywIGzf79+/X3v78iSTpzVEnWHnfmqETn1Msvv6zm5uasPS4yh6AJAOB6doeSIcnsqaOp4zpG5wFwQmNjY/L9xwwHHa5m8PnCBE1Af9lBUzFrNAHIghUrVui2225Tc3OzAsPKNfSCk+UvKXS6LM8LVJRo6AWnyF9erLq6Ot188816//33nS4LGBQvv/yyIpE2jSoK6Kjy7J0sd/SQsA4v8KulpUVLly7N2uMicwiaAACulzxw28t6lPb1jM4D4ITk2LxQQIYv93az6WgC+icejx/iGk0ETQD6xrIsPf3007rnnnsUjUYVHnu4Ks85UWYo9058cYqvKKzK809WcHilWltbdccdd+iVV15xuizgkLS1temFF16QJJ01ukRGDyf2DjbTMHTm6EQH1ZIlS5hMkwNy7xMwACDnpIKmnnd6fEbn7QEgm3J5fSZJ8nWs0dTY2MgHQaAPmpqaZFmWJKnQP4A1mjq6oFh8HkBP2tvbNX/+fD322GOSpMLPjFP5jGky/P3vpETPzGBAQ86aroIJIxWPx/WHP/xBf/rTnzjREZ71j3/8Qw0NDaoI+TRtaEHWH/9zhxWqNGiqpqZGK1asyPrjY3ARNAEAPKP3c2sSW9gHdQAgm+z1mcxwbgZNRjAgdQT+9nMF0D27EynsM+TrrS27C0V+OpoA9Ky+vl633nqrli9fLhlS6YlTVHbiFBkDeM9B3xg+U2WnH6vi4yZKkp577jndcccdrDEDz4nH43rmmWckSWeMKh7Qvsqh8puGZo5MdDU988wzHMvxOIImAIDrsbMBwAsaGhokSWZBbo6pMQwjufaU/VwBdM9+nQxkbF767SKRiCKRyKDVBSA3bNu2TXPmzNGHH34oI+DXkLM+p6LPjHO6rLxgGIZKpk5U+RemyfCZev/99/Wzn/1Me/bscbo0oM/eeust7dmzR4V+Q6cML3KsjtNGFCnkM7Rjxw69++67jtWBQ0fQBADIGZy3B8BJdpePL5ybQZOkZNBERxPQO7sTqXCAQVPIZyTHAtPVBCDdO++8o7lz52rfvn3ylRRq6IWnKDRyqNNl5Z2CccM15PyTZRaEtHPnTs2ZM0fr1693uiygV/F4XE899ZQk6fMjihVycH3ZAr+p00ckgq6nnnqKE409jKAJAOAZWVyXEgD6LddH50mSr2P9KYImoHf22krFgYGtk2IYhoo6QirWaQIgJSY9/PWvf9Vdd92l1tZWBYcP0dALT5G/rNjp0vJWsLJMQ794qgKVZWpsbNQvf/lLLV261OmygB6988472rZtm0I+QzNHlThdjs4cVaKAaejjjz/W6tWrnS4HA0TQhLxVVVWlxYsX65VXXmHhRiDHcAYMACckg6YcHZ0n0dEE9IfdhTTQ0XmJ2yZCKsZVAmhra9P999+vP//5z7IsS4VHjdGQsz8nM5S7+x1e4SsMq/K8kxQeN1yxWEz/8z//o0ceeUSxWMzp0oCDWJaV7Gb6wsji5EktTioJ+pJdTYsWLeKYjkf5nS4AcMpjjz2mlStXSpKGDx+uKVOmOFwRgMHCTgkAJyTXaMrhjiaCJqDv7PeEIv/AD+DYt2V0HpDfamtrdffdd2vTpk2SYaj0xCkqmjzW6bKQxvD7VD5jqhrLi9X4/iYtWbJEO3fu1P/3//1/Ki6m4wzusWrVKm3ZskVB09DMUe753TxrdIle292kjz76SB988IGOPfZYp0tCPzkfWQIOST9AwhmCgDf0lh/ZV5smf94AZF8qaMrdM4vtEI2D3kDvUh1NAxudJyl5ljGfV4D8tWnTJt10003atGmTjKBfQ86eTsjkUoZhqOS4iSr/wjQZfp/WrFmjOXPmaMeOHU6XBkjq3M00Y2TxIe2jDLbSoE+nDqerycs4Eoe8FYlEuvwegPvYwZGlnnc07B0RgiYA2WZZVvKgshkKOFxN5tjPjaAJ6J39OjmUkTT22D1ec0B+Wr58uW6+5RbV1tbKX1asoReeqtCIoU6XhV4UjBuuyvNPlq8orD179uinP/2p3nnnHafLAvT+++9r8+bNCpqGznBRN5Pt7NEl8hvSxo0btW7dOqfLQT9xJA55q62trcvvAbiPHRzFezmhxb6eoAlAtkUiEbW3t0tSTq+VYHdrcdAb6J3dhXQoazTR0QTkp1gspgULFuj+++9XtL1dodGHqfLCU+QvLXK6NPRRYEipKr94moKHV6i1tVV33XWXnnrqKbo04BjLsrRw4UJJ0ukjilQSdE83k60s5NMpHV1NTz75JK8Xj+FIHPIWHU2Ad/h8iR2gXoOmjv8SNAHItmTw4jNl+N33oW2w2CEaQRPQu8EImuyRNrzmgPzR2Nio//qv/9Lzzz8vSSo+9khVnHG8zADLrHuNLxzUkHNOVOHksbIsS0888YTmzZun1tZWp0tDHlq1alWym+ms0SVOl9Otc8aUym9IGzZs0AcffOB0OegHjsQhbxE0Ad6RGp2nHs9oiTM6D4BDkuszhYIyDMPhajLHHp1HdwXQu8FYo6mEjiYgr+zYsUM33XSTPvjgAxl+n8q/ME0l0ybl9L5FrjNMU2UnHa2yU46RTENvvvmm5s6dq7179zpdGvJIPB5PdjPNGFnsym4mW3nIp9NGJMb6PfHEE3Q1eQhH4pC30s8g4WwSwN3Sg6N4D9tZjM4D4JDk+kzh3B2bJ6WeXyQSYfQw0IPW1tbkyWyMzgPQF2+//bZ++tOfau/evfIVFajyglNUMG6402VhkBROGqPKc0+SGQ5q27Ztuummm7R27Vqny0KeePvtt7V161aFfIbOGu2+tZkOdM6YEgVMQ5s2bdJ7773ndDnoI47EIS/F4/FOB0cImgB36xQ09XAyC6PzADglGTQFAw5XkllGwC91nFXNKC+ge/brI2BKId/BnQiWZSkSiysSi/d4pm5JRzcUQROQuyzL0uLFi3XXXXeptbVVwcOHaOgXT1Wgwr2jrTAwwcMqEv9vh5SqsbFRt99+u1566SWny0KOi8fjevLJJyVJM0cWq+gQOq2zpTTo04yRibWaFi5cSFeTR3AkDnnpwGCJoAlwN78/NY881kPSZF+Xvj0AZENjY6Ok1Gi5XGUYRvI5NjU1OVwN4F719fWSpKKAr8uRV21xS9ev2KXrV+xSWw/7NnY3VFNTk6LRaGaKBeCYSCSie+65R48//rgkqXDyWA0553M53yGdz3xFBao8/2SFx49QLBbTgw8+qD/+8Y+8xyNj3njjDW3fvl1hn6EzRnknwD5rVImCpqFPPvlE77zzjtPloA8ImpCXDgyWWlpaHKoEQF90Cpp6OJMlahE0AXCGHbrketAkSUZH15YdrgE4mB00lRzC2DxJKgyYyQ/tdDUBuaWmpkY333yzXn/9dckwVHry0So76WgZTGfIeYbfp/LPH6eSaZMkSS+//LJuv/12usUx6NK7mc4cXaLCQ9wvyabioE8zRyXG/C1cuFDxeE8LKcANvPPbBQyi5ubmTv8maALczTTN5Di8aA8d01E6mgA4JNnRlOOj86RUmEbQBHTPDoUOdbFt0zCSXU12eAXA+zZt2qSbbrpJn3zyiYxQQEPOPVFFR411uixkkWEYKj72SFWccYIMv0/r1q3TnDlztGPHDqdLQw5ZsWKFdu3apUK/qZkj3b8204HOGFWisM/Qtm3b9PbbbztdDnpB0IS8dGDQdOC/AbiPHR71ODqv46pAIPcP9AJwFzt0MUK5P+qG0XlA7+rq6iRJpYNw5rAdVhE0Abnh1Vdf1S233KK6ujr5y4s19MJTFTp8iNNlwSHhMYep8oJT5Csq0N69e/XTn/5Uq1atcros5IBYLKZFixZJks4cXayw33sxQFHATHY1Pfnkk3Q1uZz3fsOAQUDQBHiPHTRFWaMJgAvlyxpNUqpri/EuQPeSo/MOsaMpcR+Jj+12eAXAm+LxuB577DHdd999am9vV2h0ImDwlxQ6XRocFqgo0dAvnqrg4RVqbW3Vr3/9a/31r3+V1cPYeKA3r732mj799FMV+U3NGOG9bibbzI6upu3bt+vNN990uhz0gKAJeckOluwFNgmaAPdLBk09jc5jjSYADmF0HoB0dihUEhiEoKnjPlijCfCu1tZW3X333Xr66aclSUWfPUIVZxwvM8DnFiSY4aCGnH2iCieNkWVZ+vOf/6z58+crGo06XRo8KBaL6amnnpIknTW6xJPdTLZCv6kzRpVIoqvJ7bz7WwYcAnvUi6+4IPlv3qgAd+vL6Lxox8vY5zv0gzoA0B/2vkU+dDQZQUbnAb1JdTQd+kfu0o6uqNra2kO+LwDZV19fr1/84hd65513JNNU+eePU+nxR8kwDKdLg8sYPlOlJx+t0pOmSIah5cuX67/+6784ORr99o9//EN79uxRccDU50cWOV3OIZs5qlgFfkM7d+7UypUrnS4H3SBoQl6yD4z4SxJvtpZlqaWlxcmSAPTCXnepvcegKXFdMJj7a6QAcBf7AIARzP0zk+2uLQ56AN2zQ6GyQRidV9oRVhE0Ad6za9cu/fSnP9XmzZtlhgKqPPdEFUwY6XRZcDHDMFQ0eZwqzjxBht+ntWvX6uc//7mqq6udLg0eEY1GO3UzhXzeP/xf4Dd1Jl1Nruf93zRgAOw1BczCkAx/4sMf418Ad7PDo57WaLJDKDuUAoBssCwrNZY3D0bnGQRNQI8sy0oFTaFDD5rssIo1mgBv2bBhg+bOnauqqir5igtUecEpCh5W4XRZ8IjwqGGqPO8kmQUhbd++XT/96U+1detWp8uCB6xYsUJVVVUqDpg6fYT3u5lsXxhZrEK/od27d7NWk0sRNCEvJcfbBAOMfwE8wg6Poj0siBolaALggEgkolgsJkky8mCtBbOja4t9J6BrTU1Nam9vl5Qae3co7LCKjibAO1auXKlf3nabGhsbFRhapsoLTpG/NHcO+CI7ApUdvztlxaqtrdXPf/5zrV692umy4GLxeFx//etfJUlnjCrOiW4mW9hv6gsjE11Nf/nLX2T1cGwIzsid3zagH5IdTaFAci0F+zIA7pQandf9Nu0WQROA7EsGLqaR7JTOZSYn6QA9sjuPCv2mAuahr8GSvkYTB1UAd7MsS88884x++9vfKtrertCYw1R57knyFYScLg0e5S8uUOX5Jyt4+BC1trbqjjvu0N///neny4JLvf3229q1a5fCPkOfH1HsdDmDbsbIYgVNQ1u3btV7773ndDk4AEET8lJDQ4MkyQwHZYaDnS4D4E7JjqY+rNFE0AQgm5Jj8wKBvFjY216HitF5QNdqamokSWXBwfm4bY/Oi0ajnBwHuFg8HtfDDz+sRx99VJJUOHmsKr5wfF6chILMMkMBDTn7cwqPH6FYLKbf//73WrhwIScfoBPLsvSXv/xFUmLMXNife4f9i9LGAdqdW3CP3PuNA/qgU9AUImgCvCDV0dRT0NR5WwDIBruzxw5gcl16RxMHOICD2Qu2l4cG5z3BbxoqDiQ+utshFgB3icfjuv/++/Xiiy9KkkqmT1bpiVNkDEJXIyBJhs9U+eePU9Fnj5AkLVq0SH/605/YF0PSmjVr9MknnyhoGvrCqNzrZrKdMbpEPkPauHGjNmzY4HQ5SEPQhLyUDJpCQfnoaAI8oS8dTXYIFQwGs1ITAEhpHU15EjTZ61DF43FFIhGHqwHcxw6aKkKD18Vg39e+ffsG7T4BDI5YLKb77rtPr776qmQYKv/8VBUfPSEvupyRXYZhqPT4o1R60tGSpOeff16PPPIIYRMkKdnNdOrwIhUHcreTsizo08mHJ7qa7OcMdyBoQt6JRCLJgyJmOMToPMAj7PCo544mRucByD47aDLy5L3H8PukjoNnLS0tDlcDuI8dBpUPatCUCHjtEAuAO0SjUd17771asWJFImSaMVUFE0Y4XRZyXNHksSo75RhJ0gsvvKAHH3xQ8XgPixkj523atEnr16+Xz5DOHJ273Uy2s0aXyDSk999/X9u2bXO6HHQgaELeqa+vT3xjmjICPpnhUOfLAbhSfzqaCJoAZJMdtuRNR5NhJLuaWKcJOFhqdN7gBU32fTE6D3CPaDSq3/3ud1q5cqVkGqqYOU0F44Y7XRbyROGkMSo79bOSpJdfflkPPPAAYVMes8d2Hj+scNBG97rZ0AK/jqsskJR67nAeQRPyTl1dnSTJVxCSYRgyC0OdLgfgTn1Zo4mgCYATUh1Nuf+hzmYSNAHdyuTovKqqqkG7TwADF41GNW/ePL311luSaapi5vEKjznc6bKQZwonjlbZ6cdKhvTKK6/oD3/4A2FTHqqvr08E3pK+MDL3u5lsMzqe62uvvarGxkaHq4FE0IQ8ZAdKdsBkr9FE0AS4W1+CpihrNAFwQLKjKY+CJqOje4vReUBn8Xg8OTpvSHjw3hOGhFmjCXCL9vZ23X333XrnnXcSIdMZxys8+jCny0KeKjxilMpPP04ypGXLlun+++8nbMozr7zyiqLRqMaWBDS2JH+OhRxRGtTIooAikTYtW7bM6XIgFwdN77zzjm6//XZdfPHFGj16dGJERw8LKc6dOze5TVdfN9xwQxarh5uldzRJklmQGp3HH2PAvRidB8Ctkh1NeTI6T6KjCehObW2totGoTA3u6LzKjtBq7969g3afAPovFovpN7/5jd59913JZ2rImScoPGqY02UhzxVMGKnyGdMkw9Crr76q+fPny7K6/9yM3BGLxfTyyy9LkmaMyJ9uJikxznvGiCJJ0ksvvcQxXRdw7afhW265RX/5y1/6fbvTTz9dEydOPOjy6dOnD0ZZyAG1tbWSlFybyQyHJEOyLEv19fWqqKhwsjwA3Uh2NPWwvxzr2Jn2+1375w1ADsrH0Xl0NAFds0fbVYR98vVwomR/2d1RDQ0Nam1tVTgcHrT7BtB3f/rTn/Tuu+/K8JmqOHO6QiMqnS4JkKTE+mCGVLf8ff3jH//Q8OHD9dWvftXpspBh77zzjmpqalQcMHX8sEKny8m6Ew4r1F+31Gvv3r16//33dfzxxztdUl5z7afhU089Vccdd5xOPPFEnXjiiRo/frwikUivt7viiis0a9aszBcIz7KDJl/H6DzDNGSGQ4q3RFRbW0vQBLiUHR7FuulosixL0Y4TWOhoApBNqdF5+fPeY9DRBHTJ7jiqHMSxeZJU6DdV6DfUHLVUVVWlMWPGDOr9A+jdSy+9pBdeeEGSVPb54wiZ4DoFY4fLOqld9W+s1RNPPKERI0bolFNOcbosZNCLL74oSTp1eJH85uCd4OIVIZ+pUw4v0is7G/Xiiy8SNDnMtUHT9ddf73QJyFE1NTWSJLMwdRagrzCseEtENTU1OuKII5wqDUAP7KAp2s0IgLgk64BtASAb7KApH0fn0dEEdJapoElKdDU1N7Zr7969BE1Alq1Zs0YPP/ywJKlk2iQVjB3ucEVA1wqPGqNofaOaNmzVf//3f2vYsGE68sgjnS4LGVBdXa1169bJkHTa8CKny3HMaSOK9crORq1evVr19fUqKytzuqS85do1moBMsYMmX1rQZIdO9nUA3MfuUop1M3Y3fe0mgiYA2ZTqaBq89VjcziBoArq0Z88eSVJlePDfD+zwyn4MANmxc+dOzZs3T/F4XAVHjFTRZzk5Fe5WMv0zCo0apvb2dv36179WdXW10yUhA1auXClJOqI0qIoMnODiFcMK/BpbEpBlWXrjjTecLiev5VzQtHTpUl177bW68sor9Ytf/ELvvPOO0yXBZQ4cnZf+PUET4F69dTRF0wIoRucByKZkR1MerdFERxPQtU8//VSSNKxg8PdFDitIvO5279496PcNoGv79+/XnXfeqebmZgUOq1DZKZ+VMYjrrwGZYJiGyj8/Vf7yYtXV1enXv/61WltbnS4Lg2zFihWSlJdrMx3ohI6fgf0zgTNyLmhasGCB5s2bp/nz52vOnDn63Oc+p0suuUSNjY1OlwYXaG5uTp11nD46r4iOJsDtelujKdYRQBmGIdPMuT9vAFwsH4MmOpqAg1mWlQyB7FBoMA3ruE87zAKQWdFoVHfffbf27NkjX3GBKmYeL8PH5wx4gxn0q+LME2SGgtqyZYvuu+8+xePdjAeB5+zevVuffPKJTEnThhY4XY7jpg0tlCHpww8/1L59+5wuJ2/lzF/IiRMn6s4779TatWvV2Nio7du363//9381atQoPfnkk/rmN7/Z631EIhE1NDR0+kJusduFjWAgeSaulBqjx5sR4F7JjqbugqaOy/1+P2cZAsiq5uZmSeq0b5Hr7KDJfu4AEp0Pzc3NMpSZNZroaAKy68knn9SGDRtkBBIH7H3hoNMlAf3iLy5UxRnHS6apt99+Wy+88ILTJWGQ2GPzjqoIqTiYP+O7u1Me8umIssR7tP2zQfblTNB02WWX6cc//rGOPvpoFRUVafTo0frGN76ht956S5WVlVq8eHGvv2i33XabysrKkl8ssJp77CDJ7mCy+YoS6T9zawH38vkSO0/dnYMVszpvBwDZ0N7erlgsJim/OprsUI0xLECKHQCVh3wK+gb/pBd7HF9NTQ2vPSDDduzYoWeeeUaSVHbqZxUoL3G4ImBggodVqPTEz0iSnnjiCY575QDLshib1wXG5zkvZ4Km7owYMUKzZ8+WJC1ZsqTHbW+88UbV19cnv7Zv356NEpFF9h9UO1iypY/Oo5UYcCd7HF43DU2Kd4zOY2we3KC5uVmLFy/W5ZdfrsmTJyscDquoqEhTp07VzTff3ONI34ceekgnnXSSiouLNWTIEH3xi19kZ9nF0kfHGf78CZoYnQcczA6ahmVgbJ4kFQVMFfkT+zmMzwMyJx6P649//KNisZhCow9TwbjhTpcEHJLCSWMUGFau1tZWPfLII06Xg0P06aefaufOnfIZ0rGVjM2zTa0skClpy5YtTKxySF4cjZs0aZKk3kcMhEIhlZaWdvpCbumuo8ksCEmGoVgsprq6OgcqA9CbVNDUddIUP2A7wEmPPvqovvrVr+qPf/yjfD6fvvzlL2vGjBn65JNP9LOf/Uwnnnii9u7de9Dtrr32Ws2ePVsffPCBzjnnHJ100kl66aWX9IUvfEGLFy/O/hNBr5LrM/l9Msz8GdtpBAmagAPt3LlTkjS8MJCxxzi8MPHa27VrV8YeA8h3y5cvT4zM8/tUdtIUp8sBDplhGCo7+RjJMPTWW29p1apVTpeEQ7B+/XpJ0viSoAr9HP+wFQd9Gl2S2Aezf0bIrrz4baytrZUkFRUVOVwJnJYKmjon/oZpylcY6rQNAHfpvaOp83aAkwKBgL73ve9p3bp1WrdunR5//HEtWbJEGzdu1PHHH68NGzbo2muv7XSbl19+WfPmzVNlZaXef/99LV68WEuWLNHy5cvl8/k0e/ZsToZwoWTQlEdj8yTJ9CfGlBI0ASk7duyQlAqDMuHwjhDLfiwAg6uhoUGPPvqoJKn4uIkHHTsAvCpQUaKiKeMlJSYoMILVu9atWydJmlgecrgS95lYlviZEDQ5I+ePxlmWpaeeekqSdMIJJzhcDZzWXUdT4rKCTtsAcJdeO5oYnQcX+fa3v6358+drypTOZ8GOGDFC9957ryRp0aJFamtrS1531113SZJuuummZDe2JJ166qm68sorVVdXpwceeCAL1aM/8jVoSh+dZ3Xzvgzkm2x0NA3vCLHsxwIwuB599FE1NjbKX1GioinjnC4HGFTFxx0pX1FY+/btSx4rhbdYlqUNGzZIko4sI2g6EEGTs3LiaFxVVZXuvfde7d+/v9PljY2N+sEPfqA33nhDw4cP18UXX+xQhXCL7jqaJMlXTNAEuJnPlzh7vruOJvs4p70d4FZTp06VJEUikeTagS0tLVq6dKkk6ZJLLjnoNvZlTz/9dJaqRF/ZQZOZp0GTZVmKRCIOVwM4r7W1Nfk5YngGO5qG09EEZMyGDRu0fPlySVLZycfI4AQ25Bgz4FfpSUdLkp577jn+lnjQnj17VFNTI5+RGJ2Hzo4oDclQ4udkf9ZG9rj2r+azzz6rU045Jflln/Gbftmzzz4rSWpqatIPf/hDjRw5UmeddZb+5V/+Reedd57Gjx+v+fPnq7y8XAsXLlRhYaGTTwkOi0ajyTGKdqiUzg6fqqqqsloXgL4xjMTaJ/FurrcO2A5wq82bN0tKjNcbMmSIJGnjxo2KRCIaNmyYRo8efdBt7K7s1atXZ69Q9Emqoym/Qm7D75M63m4ZnwekOoxKAqaKMvh+YAdNe/bsUXt7e8YeB8hHzz33nCSpYOJoBYeVO1sMkCHh0YcpNPowxWIxvfDCC06Xg36yO3XGlQQV9Ln2sL5jwn5To4tZp8kprj31sqqqSm+88cZBl6dfZgcClZWVuv7667Vy5Up9+OGHWrFihXw+nyZMmKBZs2bpRz/6kUaNGpW12uFO1dXVidEuPlNm+ODUn9F5gDcQI8Hr5s2bJ0m64IILFAolWvu3bdsmSV2GTFJincny8nLV1tZq//79KikpyU6x6FUqaMrcqCw3MgxDRsAvqy2qlpYWVVRUOF0S4Kjt27dLyuzYPEkqDZoq8Btqica1a9cujRvHaC9gMOzfv1/vvvuuJKnoM7yukNuKPjNOkR17tXLlSn3rW99SIM/2Y73MDk8mMjavWxPLQtre2K7169fr85//vNPl5BXXBk2zZs3SrFmz+rRtSUmJbr/99swWBM9LH5vXVceD3eVERxPgTn1dA4S1QuBmzz33nB544AEFAgHdcsstycsbGxslqcfu66KiItXV1fUYNEUikU5jzBoaGgapcnQnNTovvzqapMT4lVhH0ATkO/uEgZFFmT1YZxiGRhYG9HFDm7Zt20bQBAySlStXKhaLyV9RokAFJ/QgtwUPHyKzMKSmpia9++67Oumkk5wuCX1kjzscw9i8bo3t+NkwGjL76LFD3rADJH8XY/OkzkETB6oB97Ffl911NBkHbAe4zYYNG3TZZZfJsizdcccdybWaBtNtt92msrKy5NeYMWMG/THQWbKjKZh/Z4La6zQRNAFpQVNx5t8L7DDLfkzAbe666y5dfPHFmjRpksrKyhQKhTRu3Dh961vf0po1a5wur0uvvvqqJKngiJEOVwJknmEaKpiQ+F23f/fhfpZlac+ePZKkoWHX9o44zv7Z2D8rZA9BE/KGHTR1tT6TJPkKw5Kk9vZ2zgAHXKy7JZhYmglutnPnTl1wwQWqra3Vddddp2uuuabT9cXFxZKk5ubmbu+jqalJknocm3fjjTeqvr4++WWPckLm2P/PzED+fdizg6aefm+BfGBZVtY6mtIfg6AJbvXLX/5Szz//vIYMGaKzzz5bF110kcLhsBYsWKDp06frmWeecbrETj799FN99NFHkiEVjB/hdDlAVthB07vvvqv9+/c7XA36Yv/+/ckTvCoJmrpVWZD42TQ0NHBCXJbxW4m8kQyairoOmgyfKbMwrHhzq/bu3auysrJslgegF711NNni8XjmiwH6oaamRuedd562bt2q2bNn68477zxom7Fjx0rqvr2/qalJdXV1qqio6DFoCoVCyXWfkB2pNZryb7fapKMJkCTV1taqsbFRhjK/RpMkjSBogsv95S9/0fTp0xUOhztdft999+nqq6/WFVdcoR07dsjvd8ffTrujIzRiaPIEVCDXBSpK5B9SqmhNg1auXKlzzz3X6ZLQi71790qSyoKmgj7OtO1Ood9Uod9UczSuqqqq5GdtZB4dTcgb6Ws0dcdXFO60LQD3SI3E63qHit0suFFjY6MuvPBCrVu3ThdffLH+8Ic/dLlO4OTJkxUKhVRVVaWdO3cedP2qVaskSccdd1zGa0b/pIKm/FujiY4mIGHr1q2SpMMK/AqYmd8jGVEUkCElu1cBtzn99NMPCpkk6aqrrtKRRx6pPXv2aN26dQ5U1rU33nhDUqrDA8gX9u/866+/7nAl6As7aKKbqXdDw4nPZozPyy6CJuSN3kbnpV9H0AS4j92p5OvmL5ev4+B9LBbLVklAjyKRiL7yla/ozTff1Pnnn6/HHntMPl/XYURBQYHOOussSdITTzxx0PULFy6UJH3pS1/KXMEYEEbn0dEE2EHT6CyszyRJIZ+pYR1jYbZs2ZKVxwQGSyCQeJ0Eg+5YyN6yLH366aeSpODhFQ5Xkx+seFzRxmbFGlP7D7HGFkUbm2UxnSKr7N95O8CAuyXXZyrIv88d/WX/jAiasougCXkhGo2qpqZGUs9Bk7+j28kOpQC4RzQalZQKlA7k6ziD2N4OcFIsFtPXv/51LV26VDNmzNCiRYt6PaBy3XXXSZJ+8YtfJNYJ6PD6669r/vz5Ki8v1+WXX57RutF/dtBkBLNzgNlNzCBBEyClwp5Rxdk7cD6qI9QiaIKXLFiwQBs3btSkSZM0adIkp8uRlBhPbJ+oZobdEX7lulhzq6qeWq59z7yWvGzfM6+p6qnlijW3OlhZ/vF1/M7X19enTRCBW9nHNStC+TdJob8qQonPKdXV1Q5Xkl+IQJEXampqEn80fWaPO492CEXQBLhPe3u7JMnfzUQa+3KCJrjBPffco6eeekqSNHToUF111VVdbnfnnXdq6NChkqRzzjlH11xzjebNm6dp06bp3HPPVVtbm1566SVZlqUHH3xQ5eXl2XoK6CM7ZMnPjqbEgW5G5yHfffLJJ5Kk0UXZC5zHFAf1blVL8rEBN7rjjju0du1aNTU1af369Vq7dq1GjhzZY5d3ttnjJ42AX4ZLagKyxQwn1naNxWJqampScXGxwxWhJ/axjmyM6fU6f0drDRNvsiv/PhEjL6XWZwp3uTaGzV6/icQbcJ9kR1M3O1XpHU2WZfX4Wgcyrba2Nvm9HTh1Ze7cucmgSZJ+85vfaNq0abrnnnv00ksvKRgM6pxzztGcOXN02mmnZbRmDEyqoyn/dqvtjiaCJuSzpqam5ElqWe1o6gi17LF9gBu98MIL+tvf/pb897hx4/TII49o+vTp3d4mEokoEokk/93Q0JDRGu37NwvoZnLCmWeeqYsuukjPPvusXnnlFcVaIvIXFzpdVt4wfKaMoF9WW1T19fUETS5nhybdTXlBCksrOIPRecgLqaCp+7F5Uuc1mmgbBtzFDpr83Y3O67jcsqzkek6AU+bOnSvLsnr9Gj9+/EG3nTVrlt5++201NTWptrZWzz//PCGTS1mWlbZGU/6NzjMImoBk0FMR8qkokL2P1/Z6UHv27OE1CNd6+eWXZVmWamtrtXz5ck2aNEkzZ87Urbfe2u1tbrvtNpWVlSW/xowZk9Ea7Y4mu7MD2XXRRRdp5MiRuuiiiySp07pNyA576k+mQ10cOvuYCA1NvSNocgZBE/JCn4OmwrAkqbW1VU1NTRmvC0Df2TsI/m7+cqVfzvg8ANnQ1taWfG/Ky44mRucBqbF5xdkNm4sCvuQaDazTBLcrLy/XjBkz9Nxzz2n69OmaM2eO3nrrrS63vfHGG1VfX5/82r59e0Zrs4MmH+szOeLZZ5/Vrl279Oyzz0rqeU1tZIavI2S1XwtwLzqa+s5kaQVHEDQhL6SPzuuJ4fclz+awbwPAHdra2iR1v1PlTzutx17PCQAyKRmwGIl9iHxDRxOQCprGZHFsnm1MR7jFOk3wikAgoEsvvVSWZenpp5/ucptQKKTS0tJOX5mUHNNncnjMCa+88op+8pOf6JVXXpEk+QroLMu6jt/91tZWhwtBb1JBk8OFeAAdTc7gLynyQl+DpvRtCJoAd7E/BIa62avyGUZyhyt9rjsAZEpyfaZAIC/XhTODdDQBTgZNozsek6AJXmKvTWmvbea0UaNGSZKidfsdrgRwhv27b78W4F52aGLm4eeO/rLPQyZoyi6CJuSFmpoaSZKvsPc2bLNjG/s2ANzBPsMq6Ov+T1fIx9lYALLHHrNr5uHYPCn1vJuamljbEnmpublZu3fvlpTqLsqmMQRN8KBly5ZJko488kiHK0mw18uM1jfJ4oAk8kysJaJ4a5sMw9DYsWOdLge9KChIHK9sibEmdW/sn1FhYaHDleQXgibkPMuyVF1dLamPHU2FiVZtgibAXXrraJKkYMd1BE0AsiEVNGX/ALMbGB3POxqNJsebAvlk69atkqTykE/FweyPz7TDrd27d9NZCNd47bXXtGTJEsXjnQ+Etre363e/+50WLFiggoICXXrppQ5V2NmQIUNUXFwsWZaidY1OlwNkVXtNgyRpxIgRCoUYW+h2FRUVkqSGNkLx3jS0Jf4GlZeXO1tInsnP0y+RV5qbm5MHqH2FfRmdlzhDwA6nALiD/ToO9hA0hTr6oxmdByAb7KDJyNegye+TDEOyLDU3N3OAAnln8+bNkpzpZpKk4qBP5SGf6iIxbd26VVOmTHGkDnjXxx9/rPnz52vFihWqqqrSV77yFf3qV7+SJL3xxht6//33demll6qsrKzP9/nRRx9p9uzZGjp0qKZPn67Kykrt27dPa9as0e7duxUOh/XQQw9pzJgxmXpa/WIYhsaNG6e1a9eqvWa/ApV9f66A10U7gia7sw/uZocmdoiC7tlhHEFTdhE0IefZnUlGMNCnhbrtMIqgCXAXu0sp1MNCvSE6mgBkUb6PzjMMQ0bQLyvSrqampuRZlkC+cHJ9JtvY4oDqIjFt3ryZoAn98vDDD+vKK69MnqBlGEandYqbm5v1gx/8QMFgULNmzerz/c6cOVP/8R//oWXLlmn16tXat2+fgsGgxo8fr0suuUT/+q//qokTJw720zkk48ePTwRNtQ1OlwJkVXttYn2mcePGOVwJ+iIVNNHR1Jv9BE2OyM9PxcgrqfWZ+naWrb1dbW1txmoC0H+pNZp6Gp3HGk0AsiffO5qkxNjAWKRdjY2MG0L+cUPQNLo4qNXVrazThH5ZuXKlrrjiChUWFuqWW27RzJkzdfLJJ3faZubMmSorK9PTTz/dr6BpwoQJuvXWWwe54syyD7K376t3uBIgeyzLUnt14neejiZvIGjqOzqanEHQhJxXV1cnSTIL+hY02dvV1dXJsiwZRvcHtQFkj732QLiHoCnsT1zX0tKSlZoA5LdkR1Moz4MmpX4WQL5obm7W7t27JTk3Oi/x2ImQi6AJ/fGrX/1KlmXp2Wef1ec///kutzFNU9OmTdO6deuyXF32TZkyRaZpqr26Xu01DQoMKXW6JCDj2nZXK9bYolAopCOPPNLpctAHqTWaGJ3XE8uykj8jJi5kV/fzh4AcYQdNfVmfSUoFTZFIhIPVgIvYBzELA93/6Sr0J67jzHoA2WC/1+R10NTx3HnfRb7ZunWrJKki5FNxsPfx3Jlih1y7d+9OnpQD9Oa1117TSSed1G3IZBs+fHgyUM1llZWVOuWUUyRJjesIbZEf7N/1M888U4WFhQ5Xg74YMmSIJKk5GldTO2FTdxra42qLJxoHCJqyi6AJOc8egdfnjqaAX0Yg0exnh1QAnJcMmvzd/+kq6LiOM+sBZEMyaMrj0Xn22ECCJuSbzZs3S3K2m0mSioM+VYQSQdeWLVscrQXeUVdXp7Fjx/a6XUtLi9ra2rJQkfMuuugiSVLrlk8Va+KEU+S29poGte2ulmmauvDCC50uB31UWFioww8/XJK0ozE/3psHYsf+xM9m9OjRCgadG2+cjwiakPOSHU19DJqkVCjFOk2AO1iW1aegqYigCUAW2eGKQUcTQRPyjj2qbrSD6zPZ7LCL8Xnoq8rKymRXXk82bdqk4cOHZ6Ei502YMEHHHHOMZFlqWt/7zwbwsqZ1WyRJJ598soYNG+ZsMeiXI444QpK0naCpW9s6fjYTJkxwuJL8Q9CEnNfQ0CBJMsN9/xDo69h2//79GakJQP+0tbWpvb1dEh1NANwjNTrP+QPNTiFoQr6yu4dGO9zRlKgh8R5ERxP66pRTTtHbb7+ttWvXdrvNa6+9prVr1/Y6Xi+X2F1NzR9tV7yt3eFqgMyINbWoZUtiJKb9Ow/vSAZN+3mP6s72xsTPxv5ZIXsImpDzBhI02dvW19dnpCYA/WMHR6akkM/odrtCgiYAWZR8b8rj0Xn2c+d9F/mkpaUluW7NGBd0NI2mown9dPXVVysWi+lrX/ua3nvvvYOuX79+vb7zne/IMAxdddVV2S/QIVOnTtXo0aNlRWNq/nC70+UAGdG0YatkWTr66KM5EO9B9v+zbXQ0dcmyLG3rGJ3H73f2ETQh56WCpn6MzuvY1r4tAGfZZ8oX+E0ZRu9BE2fW40CWZWnFihX6v//7Py1fvlzRaLTH7VesWKFHHnkkS9XBi+LxeFpHUx4HTSG6wJF/tm7dKsuyVBb0qSToc7qcZNi1e/dutba2OlwNvODss8/Wddddpw8//FDTp0/XUUcdJcMw9MILL+i4447Tscceq48++kj/9m//plNOOcXpcrPGMIxkh0fj2s2KNfN6Qm5pr29U08Ztkuhm8qpx48bJMAzVRmLa3xZzuhzXqWuLqbE9Lp/P16e1CDG4CJqQ06LRaOogUL86mhIHjAiaAHewX4slwZ7/bBV3XM9rF+lWr16tKVOmaMaMGfrGN76hM888UxMmTNDjjz/e7W3+8Ic/aPbs2VmsEl7T3NyseDwuKc9H53XsMxE0IZ+4aWyeJJUEfSoLmrIsq0/r7gCSdOedd2r+/PkaPny4Nm3aJMuytHv3bn3wwQcaMmSIfve73+n22293usysO/3003XEEUfIaouqbsUaWZbldEnAoLDicdW/ulqKxXXcccdp2rRpTpeEASgsLNSIESMkSVv309V0oK0NiZ/J6NGjFQzm72c0pxA0Iaelj3Hpz1gbkzWaAFexg6PiQM9nDZcEUkETHwohSfv27dO5556rDz/8UJZlqbKyUqZpaufOnfr617+uH/3oR06XCI+y9xGMgF+GL393qeloQj5yW9AksU4TBua73/2uduzYobfffluPP/64/vznP+u1117Trl27dPXVVztdniP8fr9+8IMfKBAIqG13tZo/3OZ0ScCgaFzzsdprGlRUVKTvf//7PU4KgbtNmTJFkrShlq7LA9k/k6OPPtrhSvJTvz8V79q1S2+88Ya2bev9j+2HH36o5cuXD6gwYDDY3UxG0C/D7PsfUdYbANzFXi/NDpK6YwdRsViM1y8kSXfffbeqqqp05plnavv27dq7d68+/fRT/fu//7tM09Rvf/tbffe733W6THhQcjRvHo/Nk1In5xDwI5/YXUOjitxzpuyoosR7ER1N6C/DMHTCCSfokksu0T//8z/r1FNPld/vd7osR40aNUpf//rXJUn739moaD1jueFtbfvq1LhmsyTpO9/5jioqKhyuCIfC7kZbV9PK/ncay7K0riNoomPPGX0Omnbs2KFzzjlHY8aM0WmnnaYJEybopJNO0uuvv97tbW677TadeeaZg1IoMBADXaTbIGgCXMUOmop7WQfBbxoq8BudboP89uyzz6qsrEyPP/64Ro0aJUmqrKzU7bffrueff15lZWX64x//qO985zsOVwqvSa0B6Z4DzU6wO5pisZhaWlocrgbIvGg0qh07dkiSRrmoo8muhaAJGBznnXeejjnmGFmxuOpeWyOrY1wu4DVWNKa611ZLlqXTTjtNp556qtMl4RAdc8wxCgQCqonEtKel57WH88nOpnY1tMUVCoX0mc98xuly8lKfTlNpamrSmWeeqc2bN3dKSt9++23NnDlTd9xxh6655pqMFQkM1EAX6ba3t28PwFnJNZp66WiSEl1NLdGoGhoaksEC8tfHH3+s008/XZWVlQddd84552jZsmU655xz9PDDDysej+uhhx7KfpHwpNQ+Rn4HTYbfJ8PvkxWNaf/+/SosLHS6JCCjdu7cqVgsprDP0JBQzyfAZJPdXbVjxw5Fo9G870hBzx555JF+bf+tb30rQ5W4l2mauvLKK3X99derubpejR9sVslxE50uC+i3hlUbFWtoVkVFhWbNmuV0ORgE4XBYU6ZM0erVq7W+plXDC91z4ouT1tUkupk++9nPKhDgZ+KEPu19/u53v9PHH3+sY445Rvfff7+OP/54ffTRR7rtttv0+OOP67rrrlNDQ4PmzJmT6XqBfkmNzutn0ERHE+AqfR2dZ29T1UJHExLa29s1ZMiQbq8/9thjtWzZMp111llasGCBJBE2oU/oaEoxQwHFojE1NDTo8MMPd7ocIKOSY/OKA65a32JI2KeQz1CkvV27d+/WmDFjnC4JLjZr1qx+/f7mY9AkJbrgZ82apfvuu0+Nqz9WaHilgocxcgze0bp9r5o3JpY++f73v6/i4mKHK8JgmTZtmlavXq11Na06c3SJ0+W4wnrG5jmuT0HTokWLVFBQoGeffVZjx46VJE2dOlV//vOfdcEFF+jKK6/U3LlzFY1G9fOf/zyjBQP9YY9wMQP9O6PP6Ni+paVF8Xhcppm/i3wDblBXVydJKulldF76NvZtkN+GDx+uzZs397jNZz7zGf3973/XmWeemQybmHWN3jgVNFnxuGLNrbLaY8nLYo0tigd88hWGZTiwz2KGQ4o1tSZ/JkAuc+P6TJJkGoZGFQW0uaFNW7duJWhCn4wdO1ZnnHGG02W42umnn653331Xr7/+umpeWaWhF5wsfxkH6+F+bfvqVPeP9yRJ559/vo477jhnC8KgmjZtmh555BF93BBRazSusD+/j1s2tce0paFNEkGTk/p09H3jxo069dRTkyFTulmzZmnMmDH6p3/6J/3iF7+QZVm6+eabB71QYCDsoMnoZ9CUHky1trYyBgZwWE1NjSSprA8jaso6gib7NshvJ5xwgp599lnV1tb2uOjtUUcd1SlsCoVCWawSXmR3TWY7aIo1t6rqqeWdLtv3zGuSpGFf/YL8xdnfZ7F/BnSSIh9s25Y4M3xkkftGsozoCJq2b9/udClwuTlz5uhXv/qVtm3bptraWs2bN0/jxo1zuixXMgxD3/ve91RVVaVNmzap5m/vqPLCU+QrYF8R7hVtaFLt0lWyYnFNnTpV//Iv/+J0SRhkw4cP14gRI7R79259UNOizx1W5HRJjlpd3SpL0rhx47ocm4/s6FPc2dra2uMYjLPPPlvPP/+8ioqKdOutt+qnP/3poBUIHIrW1kTbZH87muQzpY5RAvZ9AHBGtGO9JUkq70NHU3mIoAkp559/vtrb2/WnP/2p120nTZqkZcuWadSoUbz3o1d2qOILc6DJ7DjYRtCEfGCHOCPcGDR1rNFgh2FAd37+85/rgw8+0Pnnn6+//vWvOuaYY3Trrbeqvb3d6dJcKRQK6Sc/+YkOP/xwxZpaVLP0HcXbo06XBXQp1tqW+B2NtGn8+PG65pprWLcvR5122mmSpHf2tjhcifNW7W2WJJ166qkOV5Lf+hQ0DRs2TDt27Ohxm89//vNasmRJMmxivSa4wUA7mgzDSN6mubl50OsC0Hd1dXWyLEs+QyrqwxpNdkdTbW1tpkuDB3zxi1/UaaedphUrVvRp+yOPPFLLli3TtGnTuuzkBmzJjqYCd43PcgIdTcgX9fX1amhokCFpeKH7DtrZXVa9fXYHpMQ+z3PPPacnn3xSQ4cO1Zw5c3TsscfqxRdfdLo0VyotLdUNN9yg0tJSRWsaVLf8PVnxuNNlAZ1Y0Zhql76j2P5mDRs2TP/+7/+ucDjsdFnIEDto2ljbqsa2WC9b5666SEyb6iOSCJqc1qegaerUqXr77bd7PeB+2mmn6cUXX1RJSYl++ctf6q9//eugFAkMVCSSeKMx/L13QRzIvk1bW9ug1gSgf+zOpNKgT2YfFi2mownpxowZo1dffVWPPfZYn28zYcIErVq1Sp988kkGK4PXpUbn0dHkI2hCnrADnMqwTyGf+9ZCGN4RNFVXV6upqcnhauAVX/3qV7V+/XrdcMMN2rJliy688EJdcskljGDswuGHH65/+7d/UzAYVGTXPtWvXMu6nnANKx5X7T/eU3t1vYqLi3X99dervLzc6bKQQSNGjNARRxyhuKT39uVvV9O7Vc2yJE2ePFnDhg1zupy81qe943PPPVetra164oknet32lFNO0YsvvqjS0lLOJofjBiNosu8DgDPswKi8D+szSZ3XaOKDH4BMiMViamxslJT9NZrciNF5yBf2SDo3js2TpEK/mdxfIiRAfxQUFOiXv/ylVq9erbPPPluLFi3S0Ucfrdtvv13RKCPi0h155JH613/9VxmGoZaPd6rx/U1OlwTIsiw1vLVekR1VCgQC+slPfqKRI0c6XRaywO7gWVWVv9OY7Odud3jBOX0Kmr74xS9qxIgRfVrfQJJOOukkvfTSSyTncJzdjURHE+BddtBUlrY+k2VZisTiisTiB4VJZR0HWNrb25MHggHbEUccoeuvv97pMuBxDQ0NifceQzJDBE2MzkO+2LlzpyRpeKE7gyYptU6TXSvQH0cddZRefPFFPf7446qoqNB//ud/6thjj3W6LNc54YQTdPnll0uSGtd8rMY1HztcEfKZZVna//YGNX+4XYZh6Ic//KGOOuoop8tClpx66qkyDEObG9pU05p/JwbsaW7X9sZ2maapk08+2ely8l6fBksfddRR/Z7z/LnPfY6xRXBcsqPJN4CgqWMcBh1NgLP27dsnSRoSTr2O2+KWrl+xS5L0X6eNVMiXGqkXMA2VBEztb49r3759KikpyW7BcLUtW7aoqqrK6TLgcXV1dZISY/MMs/eRnrnO19HRZP9cgFzlhaDp8EK/1tcSNKFnvj5+PrYsSx9++GGGq/Gms846Sw0NDXr88ce1/72PZMXjKj5uoow+jPoGBotlWWp4c72aP0x03H7nO9/RiSee6HBVyKYhQ4ZoypQpWrdund6pata5Y0qdLimr3tmb6GY69thjVVqaX8/djdw3WBoYRKmOpv7/qtvhFB1NgLPsUGBIqO+Lbg8J+zvdFgAGUzJoKmB9JkkyCxKLTLe0tHCCDnKWZVnJ8Obwwr7vk2Tb4XQ0oQ8sy+rTF3r2T//0T/r6178uSWpc/bH2v/shPzdkjWVZql+5Vs0fbpNhGPre976ns88+2+my4IAZM2ZIkt7c05xX70Fxy9KbHUGT/TOAswiakNOS86TNgQRNZuf7AOCIZNAU7ntnor0tQROATLCDJh9BkyTJCPiSJ+jQ1YRc1dDQoMbGRhmSDitwb9A0vCMEI2hCT+LxeJ+/YrGY0+W62pe+9CV985vflCQ1rf1E+9/ZkFcHeuEMK26pfsUatWzaIcMw9IMf/EBnnHGG02XBISeffLLC4bCqWqL6pCF/Tpb/qC6iukhMhYWF+tznPud0OVAfR+eli8Vi2rdvn4YMGaJAoOeRATU1NWpsbNTYsWMHXCBwKOyQyBhA0GSHU+3t7YNZEoB+sCwrLWjqR0dTiI4mdK+xsTG5oHtv2IdBV2prayXR0WQzDENmYUix/c2qra3V4Ycf7nRJwKDbtSsxsndI2Kegz73nax5ekPiMXlNTo+bmZhUWFjpcEZD7LrzwQvn9fj344INqWr9VVtxS6YlTGKOHjLDicdW9tlqtWz6VaZq6+uqrdeqppzpdFhwUDod1yimn6O9//7ve2NOkI8ry4zPKG3uaJEmnn366gkHWzXWDPu8hNzQ06IorrlBZWZlGjhyp0tJS/fM//7M+/rj7RQ9//OMf64gjjhiUQoGBsEMiYwAfBo2ONV/oaAKc09TUpJaWFknSkFDfO5oq6WhCD5588klNmDCh1y/2YdAdO2iioynFZJ0m5Ljk2LwC967PJEmFAVOlgcRnHzscA5B55557rr773e/KMAw1b9ym+pVr6WzCoLNicdX94321bvlUPp9P11xzDSETJEkzZ86UJL1b1aJILO5wNZnX3B7X6n2JY0X2c4fz+nR6eDQa1Xnnnae33nor+YcyEolo4cKFeuGFF/Tggw/q4osv7vK2/GGFk5Jt/gMZnWcyOg9wmh0UFQfMfp09zBpN6ElhYaGGDh3qdBnwsOQaTYUETTZfQUjtSoVwQK7ZvXu3JOkwF6/PZBtW6FdDfZs+/fRTTZw40elygLxx5plnyufzaf78+WrZtEOKx1V26mcHNmEFOIAVjal2+XuK7KyS3+/XtddeqxNOOMHpsuASRx11lEaMGKHdu3frvaoWnTy8yOmSMmpVVbOiljRmzBhNmDDB6XLQoU9/7X7/+9/rzTff1PDhw7VgwQJ98MEHeuqpp3TKKado//79uvTSS/Xwww9nulag31Kj8wbQst6xM8hMasA5e/fulSRV9mNsXvr2e/fuVTz+/7N33+FRllkfx7/PPFOSSZs0AiSE0EGkC4KgFFmxAqIs7lrWsrvqvq7rutZ1Laur67qCuhbsvdB7R0BAmhTpNYSQAoT0kDaTKe8fkxlAWkiZZ8r5XNdclyaTyW9CyjP3uc+5g383j7g4Y8eO5dChQ3W6CXE20tF0Js/XQgpNIlh5Ck2Jfnw+k0ez2q4rT2YhfklV1Trf9Hr//573J1dddRX/93//h06noyrjCMUrtuCskc2romGcVhuF32/EmpuPwWDgb3/7mxSZxGkURfF29nhGyjUVh8tFYbWdouqTv9uKqu0UVttx+Kjh5Kfa5zhkyBAZU+pH6lRomjRpEnq9niVLlnD77bdzySWXMGrUKNauXctzzz2H0+nkvvvu49NPP23qvEJcFO8Ccz0KTZ7ilBSahNBOfRd14kwqOgVsNpssegohGl1RUREAqjlM4yT+Q1f7tfB8bYQINt6OpoAoNLkzSqFJnIvL5arzTTZtXbwrrriCRx99FKPRiPVIAUVLN+KotmkdSwQoR0UVhYs3UJNfgtls5umnn6ZHjx5axxJ+6Morr0RRFDLKbORXNV2Bu8Tq4KWNx/jPluPet/1ny3Fe2niMEmvTr6Eeraghq7wGVVUZOHBgk38+UXd1KjTt3LmTAQMG0LVr1zPe98ILL/DZZ5+hKAp//OMf+fjjjxs9pBD15SkS1au6rUihSQitHTt2DLj4QpOqU7xdTZ7HEEKIxmC32yktLQVOFlfEyaKbFPdFMLLb7d4u62Z+fkYTSKFJXJjT6TzjBnD33Xef833i4vTu3ZtnnnmGyMhIagpLKVy0HvuJSq1jiQBTU3yCgkXrsZdWEBcXxwsvvEDnzp21jiX8VGxsLN27dwdOdvwEI89z69WrF9HR0RqnEaeqU6GpvLyclJSUc77/rrvu4ttvv0Wn0/HAAw9IsUn4jZMdTfU4o6m20CQX1kJop76FJoBEKTQJIZpAaWmp+wxSnYIuzKh1HL+h1p5XJR1NIhjl5eXhcrkwqQrRRv8/a8VzjtSxY8fktYwQGurQoQPPP/888fHxOE5UurtSisq0jiUChDWviMLFG3BWWklOTuaf//zneddmhQC84/M25lXi9NEYO19yOF1sOu4u2nueq/AfdbpKjouLIy8v77z3GTt2LJMmTUJVVSk2Cb/h7Uaqz7hOGZ0nhOYaVGgKl0KTONPgwYNlF6BoEO/YvHCTzAM/xamj81xB+KJWhDbvKN8wfUD83MeZ9OgUsFqt0mUohMaSk5N58cUXadWqFc4qK4VLNmA9Wqh1LOHnqrKOUfT9Jlw1djp16uQtWApxIb1798ZsNlNic3CgxKp1nEa3p7iaEzVOoqOjZYSkH6pToenSSy9l48aN1NTUnPd+Y8aMYfLkyd5i0/LlyxslpBD15dnB15DRebILUAhtVFRUUFbm3vHn6U66GIkyNkacxYoVK3jiiSe0jiECmKfQJGPzTucZnVdTU0N5ebnGaYRoXA3Z+KIFVacQZ5INN0L4i9jYWJ577jm6dOmCq8ZB0fJNVGXKaxRxdhX7sihZuRWcTvr06cPTTz9NZGSk1rFEgDAajd5zi4JxfN5Pee5upkGDBqHXB8Z1WSipU6Hp6quvpry8nLlz517wvqNHj2batGno9XpycnIaHFCIhvAWiRpQaJJduUJow7MwEm3QEaa/+DE1UmgSQjQFb0eTFJpOo6g6dCb3KEEZnyeCTaAVmuDkOU1SaBLCP0RERPDkk0/Sr18/cLooWb2N8t2HZL1BeLlcLsp+3k/ZT7sBGDZsGI888ghGo4xqFhfnqquuAmB7YTWV9uDZPF9e42BXURVw8jkK/1KnK+XrrruOZ555hldffZUxY8Zc8P433XQTM2bMYMyYMRfsghKiKTWk0CRnNAmhLc9mhSRz/Q7dTqo9nyAvL4+amhoMBv8/vFv4ztGjR5k9ezb79u2jrKzsrC/yFUXhk08+0SCd8GeFhe5xN2qEFJp+SRcRhtNqo7CwkNatW2sdR4hG4ynWJARQoSkhXA/FUmgSwp8YjUYefvhhvvzyS5YsWcKJzftwlFcRfVkXFJ3/j+UUTcflcFKybgfVh9ybJG+55RbGjBkTEONahf9p27YtKSkp5OTksL2giv7NI7SO1Ch+zq/C4YK0tDRSU1O1jiPOok5Xyj179rzogtH1119PdnY2lZWV9QomREOdWiCq3+i8Mx9HCOE7nkJT84j6FYhijCphqkK1w8nRo0flQkR4vf322zz++OOnXdt4Ck3KKd2sUmgSZ1NQUABIR9PZqOYw7EVl3mKcEMHCU6xpFkCFJjmrUpzPiy++eNa3b9269Yz3KYrCs88+64tYIUGn0/G73/2OhIQEvv32Wyr3ZeGotBI7qDuKXtU6ntCA01ZD8Q8/Y8srQlVV7rvvPoYMGaJ1LBHAFEXhiiuuYMqUKfycXxk0haat+e4ag2c0oPA/db5SVtWL/4OXmJh40R8jRGM5rUAkZzQJEXC8hSZz/RZ1FEWhRYSBQ2U2cnJypNAkAFi2bBl/+ctfiI6O5m9/+xsrV65k3bp1fPDBB+zfv58ZM2aQmZnJI488IoeLirPyjs6LCNc4if/xdHlJoUkEk+rqau/PfSCNzpMRwuJ8XnjhBRRFOaOje+vWrWzduhXA+34pNDU+RVG48cYbiY+PZ+LEiViz8yhc+hOxQ/ughsmYtFDiqKiiaNlm7KXlhIWF8cgjj9C9e3etY4kgcPnllzNlyhQOlFgpr3EQaQjsQnap1UFGmQ1wPzfhnwLnSlmIi9TQQpOMzhNCW55CU4t6js4DaG52F5qys7MbK5YIcG+99RaKorB48WIuv/xy7rnnHtatW8cf/vAHAP71r3/x4IMP8umnn7JlyxaN0wp/5Olo0snovDN4im+er5EQwSAvLw8As15HRAAt0ngKTcePH8fpdKLTXfx5lyJ4Pf/881pHEMCAAQOwWCyMHz+eyoJSChetJ25YH/TRwdF9IM6vpqiMouWbcVZZsVgsPPHEE6SlpWkdSwSJFi1akJaWRmZmJtsLqriiRaTWkRpka0ElLqBDhw4kJCRoHUecgxSaRNA6vdBUjweQQpMQmqmsrPTuiK/v6Dw42Q3lKVoJ8dNPP9G7d+9z7oIymUxMnDiRBQsW8OKLL/L555/7NqDwa3a7ndLSUkBG552Np6PJ0/0hRDDwjJ4LpG4mgFiTil5x/94qKCigWbNmWkcSfkQKTf6jS5cu/POf/+Q///kPBQUFFC5aT+zQPhgTLVpHE03IeqSA4pU/47I7SElJ4YknnpDFc9Ho+vfvT2ZmJluDotBUBbifk/Bfsq1JBK2Gn9EkhSYhtOIpDMUYVcz6+v+palFbpJJCk/AoLi6mXbt23v83GNzfI1VVVd63mUwmrrzySpYtW+bzfMK/FRUVuccM6XToZLTNGTzFN+loEsHEM3oukM5nAtApCgkyPk+IgJCcnMyLL75IWloaTmsNRUt/ojo7T+tYoolUHsyhaPlmXHYHl1xyCc8//7wUmUST8BRlDpRYOWFzaJym/oqtdg6V2VAURQpNfk4KTSJoNXh0nk4KTUJoxTPqrkVEwxZ1PGP3jh8/flohQYSuuLg4KioqvP8fGxsLQFZW1mn3czgccs6MOIPne0KNCKvfJpYg5xmdV1RUhMMRuC9mhThVoHY0wcnimOc5CCH8l8Vi4bnnnqNnz564HE6KV/5M5QEZ/x1MXC4X5TsOUrp2J7hcDBw4kCeffJKICBmVKJpGs2bNaNeuHS5ge2Hgrodsq+1m6tSpk/f1u/BPgXe1LEQdnbbAoat/R5MslAjhexkZGQCkRDasYyDKqBJjVCm1OTh8+DCdO3dujHgigKWmpp52Ztell16Ky+Vi3rx5dOrUCYDy8nJWr15NSkqKVjGFn8rPzwdOFlTE6XThJlAUnE4nxcXFsjtXBAVPN1AgFpo8HU1HjhzROInwV1arlRUrVrBv3z7KysrcXbu/oCgKzz77rAbpQk9YWBh/+9vf+OSTT/jhhx8oXb8LR5WVyG7tZINLgHO5XJRt3EPlPvfmtptuuonbbrtN/l1Fk7vssss4ePAge4urGRig4/P2FlcD7uci/FvgXS0LUUd2u939H4pSrz/eSu2Bud7HEUL4zKFDhwBoFVn/85k8WkUaKC1ycOjQISk0CQYPHswbb7xBXl4eSUlJ3HDDDURERPD3v/+dY8eOkZqayhdffEFRURG33Xab1nGFn/GMhFMjpdB0NopOQY0Iw1FeRUFBgRSaRMBzuVzk5uYCkGQOvJfOSeHu6ygpNImzmTlzJvfff/95O7hdLpcUmnxMVVX+8Ic/YLFYmDVrFuXb0nFWWYnue4l36ooILC6Hk5I126k+7O4uvfPOO7nuuus0TiVCRbdu3Zg8eTIHSqw4XC7UACtu2p0uDpbaAPdzEf4t8K6WhagjbydSfS/GdNLRJIQWampqvB0nDe1o8jzGzqJqb/FKhLaxY8fy888/s3XrVkaMGEFcXBwTJkzggQceYMKECYB7USUtLY1//vOfGqcV/sZbaIoI0ziJ/1Ijwr2FJiECXWlpKZWVlShAYnjDN7/4mqc4JoUm8UubNm1i3LhxANx2223s2rWLHTt28NRTT3HgwAGWLl1KWVkZ9913n3R4a0BRFH79619jsVj44osvqNyfjaPKSuygHih6Vet44iI4bTUU//AztrwiVFXlwQcf5IorrtA6lgghaWlpREREUFFRQfYJG2nRJq0jXZRDZVZqnC5iYmLk71EAkEKTCFqeApGnM+lULpcLl732/Xr17B1PtR8nhSYhfCs7OxuHw4FZryPO1PAXUq2i3AtDUmgSAH379mXp0qWnve0Pf/gDffr0YerUqRQVFdGlSxfuueceYmJiNEop/JV3dJ50NJ2TGhkOeSe/VkIEMk83U3yYiiEAOwmSas+qLC4uprKyErPZrHEi4S9ef/11HA4HM2fOZOTIkdxzzz3s2LGDl19+GXD/Dr/rrrtYuHAhP//8s8ZpQ9c111xDTEwM7777Ltbs4xQt20TskN7oTIFX+A5FjspqipZvxl58grCwMB599FEuvfRSrWOJEKPT6ejatSs//fQT+0usAVdo2l9iBdwj72XUpP9rlEJTaWnpOef5gvs8BCF8raamBgBFPUuhye4gb9L3ACTdNhzFcOaPgufjPI8jhPCNzMxMAFIiDY1yIeHpijpy5AjV1dWEhUkngjhT79696d27t9YxhJ872dEkhaZz8XxtpKNJBANPJ5CnYBNowvU6Yow6Sm1Ojhw5Qvv27bWOJPzEmjVruOSSSxg5cuRZ35+YmMikSZO8Hd7vvPOOjxMKj8svv5zo6Ghef/11qo4XU7hkA3HDLpPuaj9nL6ug6PtNOCqqiImJ4cknnyQtLU3rWCJEXXrppd5C0zUBtkR/aqFJ+L8zV+DrqLi4mD//+c80b96cuLg40tLSaNOmzRm3tm3bNmZeIerMZnPP8DxboakuPB/neRwhhG+cPJ+p4WPzAGKMKtFGHS6Xy1vEEqHryy+/ZO3atVrHEAHI6XR6iyd66Wg6J0+3l3Q0iWCQk5MDBOb5TB6eIpnnuQgB7t/Rp55dqte7v8erq6u9b4uJiWHw4MEsWLDA5/nE6bp06cLzzz+PxWLBXlJO4eL12E9Uah1LnENNURmFizbgqKgiKSmJF154QYpMQlOeIs2hMitWh1PjNHVXaXeSdcK9JiuFpsBQrxX40tJS+vfvz3vvvUdRURHh4eG4XC6aN28O4O1sSk1NpVWrVo2XVoiL4C0QqfUbvaXUfpwUmoTwrf379wOQGtU4hSaA1rWPdeDAgUZ7TBGY7r77bj7++GOtY4gAVFxc7B6nqyjowgNr5IQvSaFJBBNPcaZ5gHY0wcnsUmgSp4qKisJut3v/3zMu+JfneRkMBo4dO+bTbOLsUlNT+ec//0mLFi1wVFRTuHgD9tJyrWOJX7AVlFK4dCNOq420tDReeOEFkpKStI4lQlxSUhJxcXE4XJB9InCmNmWdsOHCnT8+Pl7rOKIO6lVo+u9//8uBAwe46667KC0t5dZbb0VRFHJzczlx4gQTJ07EYrEwePBgORNDaMbb0VTPwzKlo0kI3ysvL/cuhLSNbrxCU5vaOcSeIpYQQlys48ePA6BGhJ31/Efh5un2KigowOkMnB2TQvySy+Xi8OHDALSMCNxCkye757kIAZCSkkJ2drb3/z3dTStWrPC+raamhvXr18siuR9JTEzk2WefJSUlBWeVlcIlP1FTfELrWKKW7XgxRd9vxGWroUOHDvzjH/+QM1+FX1AUxXusTV5V4BSajlW6s7Zu3VrjJKKu6vUqec6cOSQkJDBx4kTCw8NPO0PDbDZz//33M3/+fL7++ms+/PDDRgsrxMVo8Oi82gKV1WpttExCiPM7cOAALpeLxHA9Ucb6FYnPxlO02rdv3znPExRCiPPxdOiokWaNk/g3XXgY6BQcDgdFRUVaxxGi3goKCqisrESnBHZHU/IphSa5BhIegwYNYteuXZSWlgJwww03oNfrefTRR3n33XeZO3cuY8aM4ciRI1x55ZUapxWnslgs/OMf/6B169Y4q23uYlNhqdaxQp71WCFFyzbhqrHTuXNnnnrqKcxmuWYU/iM5ORmAY5X2C9zTf+TVZm3ZsqXGSURd1WsFPiMjgz59+ngPVPcUmhwOh/c+/fv3Z8CAAXzyySeNEFOIi1dZ6Z5ZrDPUb6a6UvtxVVVVjZZJCHF+no6jNo3YzQSQEmnEoHN3TP1yJIgQQtSFt6NJzmc6L0WnoEbI+DwR+LKysgBICjeg1ykXuLf/ah5hQIf7Gqi4uFjrOMJPjB49mpSUFFauXAlAixYt+Pvf/86JEyd4+OGHGT16NPPnz8disfCvf/1L47Til6Kjo3nmmWdo164dLlsNhUs3Yssv0TpWyLIeyado+WZcdgfdunXjySefJDxcrheFf/EUmvIqA6+jKSUlReMkoq7qfappbGys9789Vfri4mISEhK8b09NTWXevHkNiCdE/XkKTYqxft/mutqPs9ls2O127wGpQoims2/fPgDaRjfu+Sd6nUJqpJGDZTb279/vvcgSoenHH3/k3nvvveD9FEWRDTPCy1No0kuh6YLUyHAcJyrJy8ujS5cuWscRol48o+aSIwO3mwnAoFNoZtZzrNLO4cOHiYuL0zqS8ANXX331GWeXPv/883Tr1o2pU6dSVFREly5deOSRR7zjloR/iYyM5Omnn+a///0v+/bto+j7jcQN64MxSX7Gfak6+zjFq7aC00mvXr34y1/+gtHYuJsmhWgMnmLNsQApNLlcLm9RTNZvAke9Vs5btmxJbm6u9/89Fx7bt29n2LBh3rdnZGTI4rzQjLejyVi/F4fKKZ1QlZWVREdHN0ouIcTZ1dTUcPDgQaDxO5oA2saYOFhmY9++fQwdOrTRH18EjoMHD5Kenn7B+0mhSZxKRufVnT4yHBvS0SQCm7fQFMDnM3m0jDB4C029evXSOo7wY2PGjGHMmDFaxxB1ZDabefLJJxk/fjy7du2iaPlmYof0xtQiXutoIaEq6xglq7aBy0W/fv146KGHZA1U+C3P+Lkym5PKGidmg3+fOXuixkml3YWiKLRo0ULrOKKO6vUbsFu3bqxZs8b7/1deeSUul4vnn3+evn37EhUVxddff82GDRsYOHBgo4UV4mJ4Rt4p9R2dp9Oh6FVcdgdVVVVSaBKiiR08eJCamhoi9DqahTf+BbqneLVnzx5cLtdp5wuK0DJw4EDuu+8+rWOIAHOy0CQdTRfiKcZ5usCECDQul8vb7ZEaFfg701OjjGzJr6rTJgshRGAJCwvj8ccf54033mDbtm0U/7CFuOF9MSZatI4W1KxHCihZ7S4yXXHFFTz44IOoauOdMSxEYzObzcTFxVFUVMTxqhrSDI07RaaxebqZmjVrJl2CAaReK3nXXnstM2fOZMWKFQwdOpQBAwYwcOBA1qxZQ1xcHNHR0ZSUlKAoCk888URjZxaiTk6cOAGAzlT/X0g6kwGH3cGJEydISkpqrGhCiLPYsWMHAB1jTU1SBGoXY0JV3IvFeXl5NG/evNE/hwgM7du353e/+53WMUQAsdlsFBUVAaCPko6mC1GjpNAkAltRURElJSXogFYBPjoPIK22WJaeni6bbcQFbd68mVWrVpGQkMCtt94qZ80EAKPRyKOPPsrrr7/Ojh07KFq+mfhrL8cQE6l1tKBkyy+heOXP4HTRv39//vSnP6HT+Xd3iBDgPt+tqKiIKrtL6ygX5Mkom/4DS71+E/7mN79h9erVdOzY0fu2GTNmcOONNwLus5osFgsTJkzgpptuapykQlyk0tJSAHRhDSg0hZlOeywhRNPxFJo6W8Ka5PFNqo42tWc/eT6XEELUhadgohj0KKbAX3Ruap5zrPLy8jROIkT9eLqZWkYaMKqBv3iYEmlEVaCsrEwKwMLr+eefp1mzZiQkJPDQQw/hcrmYMGECl19+OY899hh33303l19+OWVlZVpHFXVgMBj461//Srt27XDZaij6fhOOiiqtYwWdmpJyipZvxmV30K1bNykyiYDi6QyyOf2/0FRTm1G6mQJLvX4bRkZGMnDgwNMO40pMTGTOnDmUlpaSm5tLfn4+f/nLXxotqBAXy1McUsMbUmgynvZYQoimUV5e7j2fqVNs07Vwex57+/btTfY5hBDBx1MwUaPM0glQB56OprKyMu8oYyECiWfEXFoQjM0D0OsUUiJPdjUJMWXKFF566SUKCgooKipi4sSJvPzyyzz11FMkJSVx66230rx5c3bt2sX48eO1jivqKCwsjCeeeILk5GScldUUfr8JR7VN61hBw15eRdGyjbhsNbRv356//vWvciaTCCieok2NFJpEE2n0srvZbKZFixZS0ReaO9nRVP9Fa124dDQJ4Qs7d+7E5XLR3KzHYmq6i/XOse5uqV27dmG325vs8wghgounA0Av5zPVic5o8HZ+SfeECESejqbWQVJogpNFM89zE6Htww8/RFEUJk+eTEFBAaNHj+bll1/GYDCwadMmJk+ezE8//YTJZGL27NlaxxUXISoqiqeeeor4+HgcZRUUL9+Es0Ze9zSUo9pG0bKNOCutJCcn8/jjjxMW1jSTOIRoKiaTe42zxuHUOMmF2RzuQpMnswgM9aoGXczi3IQJE+rzKYRoEIfD4W3xb8joPLX2Y4uLixsllxDi7Dyj7Do10dg8j+QIAxF6HdXV1bKjN0StWLGCJ598UusYIsCc2tEk6kYf6f5ayfg8EWiqq6vJyMgA8I7cDQZtY9yva3bv3q1xEuEP9u7dS79+/Rg7dixxcXG8/PLLWK1WrrrqKlq0aAFAcnIygwcP9k4dEIEjPj6ep556iqioKGoKyyj+4WdcAbCw7K+cNXaKl23CUVZ52tdWiEBjMLg3ggXC6Dyb0/07SzqaAku9Ck1jx47F4XCc9z7Hjh3jmmuu4fHHH69XMCEaorCwEJfLBTqdtyupPtTancsFBQWNFU0I8Qsul8s7yq4px+YB6BRFxueFuMGDB9OpUyetY4gA4ymW6KXQVGeeopwUmkSg2bNnDw6Hg/gwlYTw4BmJ1D7GhALk5ORQUlKidRyhsby8PNq3b+/9/w4dOgCQlJR02v2aNWtGZWWlT7OJxpGcnMyTTz5JWFgYtmOFlKzd4V4jERfF5XRRvPJnaorKiIqK4u9//zvx8fFaxxKiXrwdTQFQaJLReYGpXoWm2bNn8+tf//qcxaY5c+bQvXt3vv/+e6644ooGBRSiPvLz8wFQI8MadJaCp9DkeTwhROM7fPgwhYWFGHQK7WOafudwl9rxeZs3b27yzyUCx549e3jggQe47rrrePDBB9m7d6/WkYQf8XY0RUqhqa70UmgSAWrXrl0AdGjiLmtfizCoJEe6dzLv3LlT4zRCaw6Hw7uzHUBVVYAzjkCQIxECW9u2bXn00UdRVZXqzKNU7j2sdaSAc2LbAWxHCzGajDz55JPejj8hApHTGTidjQrutdwLNboI/1Kvq4brr7+emTNnMm7cuNP+waurq3nwwQe5+eabKS4u5p///CcrV65stLBC1JWnMKRv4IKQGnGy0CS7f4RoGj/99BMAXWJNGNWmfzHbNS4cnQLZ2dkcPXq0yT+f8H8HDhxgwIABfPTRRyxevJgPPviAAQMGNHi84ubNm3n11VcZM2YMKSkpKIpy3s0PL7zwgvc+Z7s99dRTDcoj6sfhcJy8roiWQlNdSUeTCFSeIkxHS/CMzfPwPCdPMU0IEfwuvfRSbr/9dgDKNu/DdlyOBair6uzjVOx0j1L94x/+SNu2bTVOJETDFBUVAWAxqRonubCY2ozShR1Y6jULYMaMGYwZM4YZM2Ywbtw4Jk+ezI4dO/jtb3/L3r17adu2LV9//TX9+/dv7LxC1Inn4GlPoai+PB9vs9koKysjJiamwdmEEKfbuHEjAN0TGvbzWldmg44OMSb2lVjZuHEjI0eO9MnnFf7rtddeo6ysjCFDhjB27FgWLlzIvHnzeO211/jwww/r/bgvvfRSvQ7QHjhw4GnjbDz69OlT7yyi/goKCtwbq1QdOnNwdTg0JU9H07FjxzROIkTdlZaWkpWVBUBHH3RZ+1oHSxjLc8rZuXMnLperQZMfROBbtGgRw4YNO+/bpMM7OIwYMYL09HTWrl1L8aqtJNxwBWoDjhgIBfayCkrWuEetjxgxQqY1iaDgKTTFGAOg0GR0b0L2ZBaBoV6FJqPR6C02zZw5k4EDB7Jt2zasVit33nkn7777LpGRkY2dVYg6O3LkCABqA3ceK6oONTIcR3kVR44ckUKTEI0sNzeX3NxcdApcEuebQhO4i1pSaBIeK1asoFmzZixatAij0ciDDz5Ily5dWL58eYMed8CAAXTv3p2+ffvSt29f0tLSsFqtF/y43//+99x9990N+tyi8XgKJfpIsyzKXgQ1OgJwn5tps9lkvroICJ7zG1tGGIgMgEWYi9Uu2oiquH8uc3NzSUlJ0TqS0NCxY8fO2AxwtrfJ377ApygKv//97zl8+DC5ubmUrN5K3PC+KDIa8axcdgfFq7biqrHTsWNHb0eYEIHM5XIFVEeTxeQuWUihKbDU+3TTU4tNCxYsQFEUvvzyS+64447GzCdEveTk5ABgsDS84Km3ROIoryI7O5suXbo0+PGEECd5upk6WkyY9b57odMtPpxp6SUcPHiQwsJCOdA1xOXm5jJixIjTFsIHDx7M559/3qDHffLJJxuYTPgD7/lMUTI272LoTAYUgx5XjZ3jx4/LgrYICJs2bQLg0rjg7F40qjo6WcLYXVzNpk2b5OcyhK1YsULrCMLHwsLC+Otf/8o//vEPqvOKOfHzfqL7dNY6lt9xuVyUbtiFvfgE0dHRPPzww+j19V46FcJvVFVVeTc9BkZHkzvjiRMnZNNaAGnQb8tfFptmzJjBbbfdJr+EhaZqampO7j6OiWrw4+ljIrHm5HuLV0KIxuMpNPWI9103E0C0UaVNtJGMMhsbN27k2muv9ennF/7FarUSFxd32ttiY2OpqanRKJHwJ95rCjmf6aIoioI+ykxNURnHjh2TBW3h92w2m7ejqZuPxvlqoVtCuLfQNHr0aK3jCI0MHjxY6whCAy1btuSBBx7gzTffpGJ3JoYEC+Gtm2sdy69UHsimKuMIOp2Ohx9++IzXCEIEKk9nkFmv+ORs7IYy6xUMOoUap4vi4mKSkpK0jiTqoF7fWVlZWd7bsWPHmDBhApdddhmzZ8/m17/+NYcPHz7tPkL40tGjR3E6nSgGPTpzw+cO6y3uYpUUmoRoXHl5eRw6dAgFuNTHhSY4eSbU+vXrff65hTif5cuX88gjj/DAAw/wr3/9i82bN2sdKaR5Ck3S0XTxVDmnSQSQHTt2YLVaiTWppEQYtI7TZC6NC0MBMjIyKCws1DqOEMLH+vXrxw033ABA6fqdOKouPNY5VNhPVFK2yX0u2bhx47jkkks0TiRE4/GcZR8I3Uzg3rTmGfHnyS78X71aj9LS0s46p9flcjF79uzTDr5WFAW73V7/hEJcJE9xU2+JbJR50p7xe1lZWTidTnQyx1iIRrF69WrAPTYvSoOLnV4JZmZnlLJ//36OHTtG8+aym0/4h6+++uq0/3/22We55ZZb+Pzzz+UMTA2c7GiK0DhJ4PF8zaTQJAKBZ2xet/jwoD6TJsqokhZt5FCZjU2bNjFixAitIwmNHTlyhJUrV5KbmwtAcnIyV111FcnJyRonE03ltttuY9euXWRmZnJi8z4sg7prHUlznpF5OJx07dqVG2+8UetIQjSqPXv2AJAaFTgj6FIjDeRX2dmzZw/dunXTOo6og3oVmlJTU4P64lsEtvT0dACM8TGN8nh6SySoOiorKzl27BgtW7ZslMcVIpQ5nU5voalfkjaLtzEmlU6xJvYWW1m9ejVjx47VJIfwLVU9s6ipKApffPEFX3zxhQaJTmrfvj2vv/461113Ha1bt6a4uJhVq1bxxBNPMH36dBwOBzNnzjzvY1itVu/sbYCysrKmjh3U7Ha7dwedFJouniqFJhEg7Ha7t3u0W3xwns90qu7x4RyqHR8shabQVVpaykMPPcSkSZNwOp2nvU+n0/Gb3/yGt99+m5iYxnldLfyHqqrce++9PP/881QdOkJ4u2RMLUL7zNrqw8ewHS3EYDBw7733ypqnCDq7du0CoKMlcK5zOljC2Jxf5c0u/F+9Ck2ZmZmNHEOIxuMpNBkSLI3yeIpOhyE+hprjxaSnp0uhSYhGsHfvXvLz8zGpiqYLOv2aRXgLTbfccot0LIYAl8tV5/v6+gXmHXfccdr/R0RE8Nvf/pahQ4fSrVs3Zs2axfr16+nfv/85H+Pf//43//znP5s6asjIz893j+NVVXThDR/HG2r0taPzjh49qnESIc5v27ZtlJeXE2XQ0TYm+H/WeySEM/tQKXv27KGgoICEhAStIwkfq66uZvjw4WzZsgWXy0WPHj1o164d4B6ruHXrVr755hv27t3L6tWrMZmC/+ci1LRv357hw4ezdOlSSn/aReKNg1AC4NyWpuC01XhH5o0aNYoWLVponEiIxnXixAkOHz4MQHtL4Pw+71Cb9eDBg1RWVmI2yyhzfxeaf0VE0LLZbN5fnoaExtt5Zax9LE8RSwjRMKtWrQKgV0K4pgdRXhofTpiqUFBQ4G0lF8HN6XTW+eZwOLSOC0CLFi245557AFi0aNF57/v0009TWlrqvWVnZ/siYtDyFEjUaLPsbK0HfbT7xWBxcTHV1dUapxHi3Dxd1n2amVFD4Gc9LkxP+xgTLpeLNWvWaB1HaODtt99m8+bN9OrVi02bNvHzzz8zbdo0pk2bxpYtW9i8eTN9+vRh8+bNvP3221rHFU1k3LhxWCwWHGWVlO/M0DqOZk78fABnlZUWLVpw0003aR1HiEa3e/duXC4Xzc36gDmjCSA+TE98mIrT6WTv3r1axxF1IIUmEVQyMzNxOBzowoyokeGN9rie7igpNAnRcNXV1fz000+AdmPzPIyqQq9E90KoZ5FJCH/UoUMH4MKdISaTiejo6NNuov48X28Zm1c/OpMRnckAyPg84b/Ky8vZsmULAP2SQmenbN9m7ue6atWqi+r2FcFh8uTJREdHs3jxYnr37n3G+3v16sWCBQuIiopi0qRJGiQUvmA2m7nzzjsBKN+Zgb2sQuNEvmcrKKFyv/uc73vvvReDwaBxIiEa386dOwH3KLpA4xn1J+PzAkODCk0lJSWMHz+eG2+8kZ49e9KzZ09uvPFG3njjDUpLSxsroxB1tnv3bgAMibGNuvPYmGgB4PDhw5SXlzfa4woRijZu3Eh1dTUJYSptorU/iNKz0LJhwwbZcS/8VnFxMeAepyd8x1Mc8YyAExfPc07TkSNHNE4ixNmtX78eu91OywgDLSO0vy7xlR4J4Rh0CkePHuXgwYNaxxE+tn//foYOHUp8/LnP5UlISGDo0KHs27fPh8mEr/Xv35/u3buD00npT7u1juNTLpeL0g3u5zxo0CC6du2qcSIhGp/L5fIWmjoG0Ng8D8/4PM9zEP7tgoWmt956i6uvvvqMt69YsYKOHTvyxBNPsGDBArZv38727dtZsGABjz32GJ06dZLd4cLnPL94GvsgS9Uchj4mEpfL5S1mCSHq5/vvvwfc3Uz+MIqqTbSRxHA9VqtV/m6FgKysrIu6+QOXy8XMmTMBzrrrWDSdk6PzpMBXX55uMOloEv7KM87Xs/EkVITpdXSPd0+AkOuf0ONwOOrUuWEwGHA6nT5IJLSiKAr33HMPqqpiO1qILb9E60g+Y83Jx15URnh4OLfffrvWcYRoEgcOHCAvLw+DTqF9AJ5D2dFiQlUgOzubQ4cOaR1HXMAFC02rV6/mhx9+OO1thw4dYtSoURQUFNC5c2f++9//MmPGDGbMmMF///tfunTpwvHjxxk5cqT3vBwhmprVamX//v1A4xeaAIy1jylVdCHqLyMjgwMHDqAq0L+5fyzcKorCoBbuLEuWLJHxMUEuLS2NNm3a1OnWtm1bn+XKz8/n3Xff5cSJE6e9vby8nAcffJANGzbQvHlzxowZ47NM4mQXjj7GP35fBSK9dDQJP5aZmUl6ejo6xX0+U6jxjAr88ccfpas7xLRp04ZVq1ZRVVV1zvtUVVWxatUq2rRp48NkQgtJSUkMHDgQgIrdobOQW177XIcPH05MTOOd8S2EP1m6dCkAvRLDCdcH3gk6kQaVHgnujTGe5yL81wW/w8422/8///kP5eXlPPnkk+zcuZO//e1vjB49mtGjR/O3v/2NHTt28NRTT1FaWsprr73WJMGF+KV9+/Zht9vRmcNQm2DEjam5FJqEaKglS5YA0DPBTLQfHULZLykCo04hNzdXuhZDgNlsJjU19YK3Vq1aNejzzJ8/n/79+3tvNpsN4LS3zZ8/H4CKigoeeughWrZsybBhw7j99tu55pprSEtL44MPPsBisTBt2jTM5tBbCNVKZWUlJSUlgJzR1BBSaBL+zLNg0SM+3K+uS3ylg8VEYrieqqoqfvzxR63jCB8aOXIkx48f5/bbbyc/P/+M9+fn53vfN3r0aN8HFD534403AlCdlRcSZzXZ8kuoOV6Mqqpce+21WscRokmUlpayYcMGAAa1iNQ4Tf15sq9du1aOM/Fz+gvd4WwLGkuXLqV9+/b8+9//PuvHKIrCyy+/zNSpU1m8eHHDUwpRB9u2bQPc3UxNMY7LmBQLisKxY8fIy8sjKSmp0T+HEMGsrKyMdevWAXBlS/9atA3X6+ibZGbN0QoWL14s87mD3NixY/n000+b/PPk5+d7L+xPderbPIs78fHxPPnkk6xfv579+/ezdu1aVFWlTZs23H333fz1r38lOTm5yTOLkzyFEV24CZ1RDoauL0832NGjR3G5XH4xMlUIcHeMrlmzBoArWwbu4ktD6Gq7umdmlLJkyRKuvvpq+RkNEY8//jjffvsts2fPZunSpVx77bXezqWMjAwWLVpEVVUVrVu35rHHHtM4rfCFlJQUevbsydatW6nYk0nM5cH9esjTuTVw4EBiY2M1TiNE01i5ciV2u53UKAOpUYF7DmWbaCMtzAaOVtpYvXo11113ndaRxDlcsKOpefPmwOm7EI8cOcJll1123o9TFIW+ffuSm5vbwIhCXJjL5WLTpk0AhKU0a5LPoTMaMCbFAXg/lxCi7lasWEFNTQ2tIg209sOLnCtrd8ls3rz5rDs7hbhYd999Ny6X67y3u+++G4CoqCheffVVfvjhB3JycqiurqaiooKdO3fy+uuvS5FJA57zmaSbqWHUKDMoClarlaKiIq3jCOG1cuVKbDYbLSMMtIn2v+sSX+lb29Wdk5PDnj17tI4jfCQ2NpYVK1bQt29fKioqmD59OuPHj2f8+PHMmDGDyspK+vXrx7Jly7BYLFrHFT5y0003AVB5MBdHlVXjNE3HXlZBdVYecLKTS4hg43Q6vedjB3I3E9Qed1C7WXnp0qVydqAfu2ChqXv37rhcLpYtW+Z9m8lkori4+IIPXlJSgskUeAeNicCTlZXlXhhWdd6zlJpCWCt3EUsKTUJcHIfD4b3IubJlpF/ulm0eYaCjxYTL5fJmFUKELu/5TFJoahBFp0ONcs9Vl/F5wl84nU7vOF9/vS7xFbNex2W151PJNJLQ0qZNG9avX8/q1at56aWXuP/++7n//vt56aWXWL16NevWrZPzmUJM586d3WeUOpxU7s/SOk6TqdiTCUCvXr1ISUnRNowQTeTnn3+moKAAs15Hz4TAH79+WTMzJtU9ZWrXrl1axxHncMFC09ChQzGbzbz44otUVLjntHbt2pWVK1eSmZl5zo87dOgQK1as4JJLLmm0sEKci6fwY2qRgM5wwYmQ9eYpNO3fv5/S0tIm+zxCBJuNGzdSWFhIhF5Hr0T/vcjx7PRZsWKFHIotRIjzdDSpUmhqMH20+3erFJqEv/B0L5v1Cn0Sw7WOoznP6MBNmzaRl5encRrhawMHDuSZZ57hvffe47333uOZZ55h4MCBWscSGlAUxdvhU7kvG5fTpXGixuessVN50D15SbqZRLByuVzMnj0bgMuTzBjVwN9QY1J19KvdGDNr1ixcruD7/RQMLlhoioqK4qGHHuLgwYOMHj2a0tJS7rnnHqqrqxk+fDjz5s0742PmzJnDr371K2w2G7fffnuTBBfiVN6xealNMzbPQ40IxxAXjcvlYvPmzU36uYQIFqde5AxqGYFB578XOZfGh5EQpqe8vJzly5drHUc0kWnTptGuXTs6d+5Mjx49GDJkCL/97W95+eWXWblypdbxhJ/wjH/2nDEk6s/zNZRCk/AHp16XXNEiEqN6wZfEQa9FhIHOse6u7rO9vhdChI6+ffsSGRmJ02qjpqBE6ziNznqkABxOkpKS6Ny5s9ZxhGgS69evJz09HaNOYUhylNZxGs2wlCgMOoU9e/bImqyfqlPrx7/+9S+2b9/OokWL6NChAzfccAPh4eEcOnSIUaNGERcXd9rBkcXFxbhcLgYPHsz999/fpE9AiJycHA4fPgw6pcnOZzpVWOvm1BSVsXbtWoYNG9bkn0+IQLd9+3YOHz6MUadwlZ8ftq1TFK5uFcXkA8XMnz+fX/3qVxgMBq1jiUZWXl5OeXn5GW/3jE5q3749X375JZdffrmvowk/YbfbOXbsGAAGi3//3goE+hj311DObhX+YNeuXWRkZGDQKQz28+sSXxqeEs3e4nxWrVrFLbfcIufyhICioiImT56MTqfj9ttvJzIykqKiIl555RX27NlD165defzxx0lMTNQ6qvAhVVXp0aMHa9asoTr7OMZmsVpHalTWnOMA9O7dO6THporgZbPZmDRpEuAuzMSYVI0TNZ7YMD2DkyP5PvsE3333HT179kSvb7qpVuLi1Wn7ll6vZ968ebzyyisAfPHFF1RVVXkPsi4sLGTTpk1s2rSJoqIiIiIi+Nvf/sbChQvlH1w0uR9//BEAU8tEdKamP8g3vE0LAPbs2UNhYWGTfz4hAt3JXcMRRBj8/yKnbzMzMUaV4uJiVq9erXUc0cgOHTrEoUOHSE9PZ8+ePWzZsoWlS5fy+eef8/jjj9OjRw8OHDjA9ddfT1ZW8M6mF+eXl5eHw+FA0avozGFaxwl40tEk/Mmpo2SijP5/XeIr7WKMpEUZqampYeHChVrHEU0sPz+fXr168dBDD/GnP/2J/v37U1ZWxq9+9SsmTJjAwoULGT9+PAMHDuTEiRNaxxU+1qdPHwCqa4sywcLldGHNzQdOPkchgs3ixYvJz88nxqhjaErwbagZnhJFpEHH0aNHWbZsmdZxxC/UuQqk0+l46qmnePzxx/npp5/IycmhsrLS+35FUQgPDyclJYXevXtjMpmaJLAQp3I6naxduxaA8LYtffI51YhwjEmx2PKKWbt2LTfddJNPPq8QgWjv3r3s3bsXVSFgWrb1OoWhKZHMyihl7ty5DB48GFWVhahg0bp16/O+/9VXX+Xll1/m2Wef5a233mL8+PE+Sib8iacgoo+JkN2ujcDT0VRSUkJ5eTmRkcH3olcEhoMHD7Jr1y50wNCUwLgu8RVFURjeKoqPdxeydOlSRo4cSUSEjA4NVu+88w7Z2dkMGTIEnU7HihUr+P3vf8+BAwf43//+R2JiIi+++CJ79+7l7bff5u9//7vWkYUPde/eHVVVcZRVYC+rQB8k51XWFJTgtNZgNpvp2LGj1nGEaHRlZWXMmjULgOvTYjAF4XjgML2O61pHMzW9hOnTpzNw4EB5beFHLvo7TlVVBgwYwNixY/nd737nvd11112MHTuWAQMGSJFJ+My+ffsoKChAMegJS/FdS394G3dRy9NNJYQ4O8+u4X5JEVgCqGV7QPMIIvQ68vLy2LBhg9ZxhI/9/e9/p3Xr1ixevFjrKEIjJ89nkhctjUFn0Hs7w6SrSWjJc13Su5mZ+DCZvPFLl8SF0cKsp7q6Wv4GBrlZs2YRFxfHwoULWbBgAYmJiUyfPp033niDhx56iHHjxrFw4UJUVWXu3LlaxxU+ZjabvecXBVNXk+e5yLgtEaymT59OVVUVyREG+jYzax2nyfRvHkFzs/tsbc+1nfAPflva3Lx5M6+++ipjxowhJSUFRVHqtKP0888/p1+/fkRGRhIXF8f111/v7XgRwcdzaHtY6+YoPuw4CGvdHHQ6srOzycjI8NnnFSKQZGZmsm3bNhTg6gDbNWxSdVyV7F5gnj17Nk6nU+NEwpcUReHdd9/lT3/6k9ZRhEa8haYg2cHrDzzj8+ScJqGVjIwMNm3ahIJ77Io4k05RGN4qGoAFCxZQUVGhcSLRVLKzsxk4cCAmkwmj0ciAAQMATpvWkZqayoABA9i3b59WMYWGPKPlrEFUaDr1fCYhgk1WVpZ3lNyotjHogngqg6oojGwTA7hHBcrrC/9RrxL+qlWrLur+V1111UV/jpdeeumiq5KPPPIIb731FuHh4VxzzTVUV1ezdOlSlixZwrRp0xg9evRF5xD+q7y8nHXr1gFgbp/i08+tMxoIa51E9SH3TNC2bdv69PMLEQimTZsGQK/EcBLCA2/H2JUtIlmec4Ls7Gw2btzI5ZdfrnUk0QicTic63YX32Vx//fU+SCP8lbfQZJGOpsaij4nEdrSQnJwcraOIEOW5LumdaKZ5hEHjNP6rV2I4S7P1HKusZMGCBYwdO1brSKIJVFVVER0d7f1/i8UCQLNmzU67X3Jysvc1twgtPXr0AMCWX4LL6USpw/WzP3NabdhL3cXz7t27a5xGiMZlt9uZOHEiTqeTbvFhdLQE/xmzXWLD6BIbxp7iat5//31eeOEFOfLAD9Rr5W/IkCF1nlevKAp2u/2iP8eAAQPo3r07ffv2pW/fvqSlpWG1Ws95/++//5633nqL+Ph41q1bR4cOHQBYt24dQ4YM4Z577mHIkCHeCygR+FavXk1NTQ362CgMCTE+//wRHVpRfegoa9eu5fbbb8dsDt62VCEu1oEDB9iyZQsKMCI1+oL390dmg44hyZEszjrB1KlT6du3b50KFMK/tW7dmgceeIA//OEPZyymCAHuYqSMzmt8htqinRSahBb279/P1q1b0SlwbevAvC7xFZ2icF3rGD7bU8jChQsZMWLEaQUJERxiY2MpLS31/r9nIswvlZWVERUlHYChKCkpCaPRiM1mw1FeFfBd3p4iU3x8vJznIoLOjBkzOHz4MBF6HWPbx2odxycURWFcBwuvbs7j4MGDzJkzh5tvvlnrWCGvQStmLVu25Kqrrjrv7corr6zXYz/55JO8+OKL3HTTTTRv3vyC958wYQIA//jHP7xFJnAXrB544AFKSkr45JNP6pVF+B+Xy8Xy5csBMHdopclB3YZmsehjIrBaraxZs8bnn18IfzZlyhQA+iaZSTIH7q7hIclRmPU6jhw5ImeyBYnc3Fyee+45UlNTufPOO1m/fr3WkYSfycvLo6amBlQdaqRsImksnu4wGW0htDB16lTAfWZkYgB2Wfta9/gwUiIMVFdXM2/ePK3jiCbQrl07Dh486P3/t99+m/z8/DPul5mZSUqKb6eHCP+g0+lo2dJ9NrW9tFzjNA3neQ7JyckaJxGicaWnp3sngo1tbyHaGDpdPRaTnlvbWwB3sS0zM1PTPKKehaY777wTl8tFXl4ePXr0YM6cOaxYseKct6ZWVVXlLTrceuutZ7zf8zY5xDJ47N27l9zcXBRVJbxNC00yKIqCuUMrwN1R53K5NMkhhL/ZuXMnu3btQlXg2gDtZvII1+u850tNnz69Xh26wr9s2LCBO+64A0VR+Oabbxg4cCB9+/bliy++OG/ntAgdno4bfUwkii54Z5v7mqc7rKioSM59ET61a9cu73XJiFbSmVEXiqJwfZr7Gm7JkiUUFxdrnEg0tkGDBlFRUeHeWHEO6enp7N6923t+kwg9JwtNgf93WwpNIhhZrVYmTpyIy+WiT2I4PRNDb5Ncn0Qz3ePDcTgcTJw48bx/10TTq1eh6YsvvmDVqlV07tyZ//3vf3Tu3JlvvvmmsbPV2b59+7BarSQmJp51t43noL/t27f7OppoIosWLQIgvG0LdEbtuiXC2yaj6FWys7PZvXu3ZjmE8Bcul8vbzXRF8wjiwgJ/1/CVLSOINujIz8/3yeYJ0bQ8RaWcnBxeeeUVWrVqxebNm7n33ntJSUnh6aefJisrS+uYQkOeQpNBzmdqVDqjAZ3ZPS9exucJX3E6nXz33XcADGgeQWwQXJf4SpfYMNKi3GOzZsyYoXUc0cj+/e9/k5mZicFw7tfSJpOJmTNn8sQTT/gwmfAnnqJMcHQ0uYtlnuKZEMFg0qRJHD16lBijjlvahcbIvF9SFIVft7cQadCRnZ3tPZNTaKPeo/MGDRrEzz//zPjx46moqOCuu+5i6NChmiy2exaEztXSHRERgcViobi4mBMnTvgymmgCeXl5bNq0CQBz5zRNs+hMBsLbuS++FixYoGkWIfzBli1bSE9Px6BT+FWAdzN5GFWd97nMnDlTul6CRHx8PE899RSHDh1i1qxZDB8+nKKiIv7zn//Qrl07br75ZpYtW6Z1TKEBb0eTFJoanZzTJHxtw4YNZGRkYNQpAXtmpFYUReGmNu5zcFesWCFjL0NQq1atGDVqFG3bttU6itBIcBWapKNJBJedO3eyePFiAG7rEIfZELrnSUcaVcZ1cBfa5s2bx969ezVOFLoa9F2oqip//etf2bt3L+PGjWPlypX06tWLxx57jPJy3/0h8nwus/ncLYIREe6DC89XaLJarZSVlZ12E/5nyZIluFwuTC0T/GK3cUTn1gD8/PPPHD16VOM0QmjH6XR6u5muahkZVLOBBzSPIM6kUlJS4r2YE8FBURRGjhzJ4sWL2bt3L3/84x9xOBzMmTOHa665hq5du/LJJ5/gdDq1jip85NTReaJx6aXQJHyopqaGSZMmATAsJYqoILou8ZV2MSYujQ/D6XR6v5ZCiNBxstAU2KPzXHYHjopqQApNIjgUFxfz7rvvAu5JMl3iwjROpL1u8eH0bWbG5XLx9ttvU1paqnWkkNQo5c7mzZvz7bffsnz5ctq3b8+ECRPo3LlzwF2M/vvf/yYmJsZ7a9WqldaRxC9UVlZ6R1dFdGmtcRo3fXQEpuRE4ORIPyFC0cqVK8nOzsasVxiWElxnIOh1Cte1du+EnjVrlly0BKHDhw/z8ccfM336dMA9BjIpKYk9e/bwxz/+kT59+sjieAiw2+0cOXIEAL0luH6P+QNPoSk7O1vjJCIULF26lPz8fKKNOoamSOG4vm5Ki0GnwObNm9mzZ4/WcUQTqKioYNu2baxevZpVq1ad9SZCU0yMu6vRZXfgcgTupiun1X1mi6qqREXJ9Z0IbHa7nbfeeovS0lJamPWMahujdSS/cUs7C83C9RQXF/POO+/gcDi0jhRyGrWvbsiQIWzbto3XXnuNsrIybr/9dq6++urG/BRnFRnpfuFQWVl5zvt4Dh0+3x+Vp59+mtLSUu9NXgT7n2XLllFdXY0+JhJjiwSt43hFdEkD3Avt0gknQlFVVRVTp04F4JrUaCKCsG27TzMzKZEGqqurvcUIEfiWLFnCyJEjad++Pf/973+pqKjg3nvvZevWrRw5coQlS5bQv39/tm3bxl//+let44omduTIERwOB4pBjxohOwMbm6G2eJeVlYXL5dI4jQhm5eXlzJw5E4DrWsdgUoPvusRXkswG+ie5p4N888030uEbRDIyMrjpppuwWCz07t2bIUOGMHTo0DNuw4YN0zqq0EhY2MlrIZc9cBdsXXY74D53TIhA991337F//37CVIV7L4mXa5xThOl13NslHqNOYdeuXd41KuE79ToNtS4zeg0GAy6Xix9++KE+n+KipKamAucew1FRUUFJSQmxsbHnLTSZTCb5w+PHbDYbCxcuBCCiaxqKomic6CRj8zgMcdHYispYtGgRv/71r7WOJIRPzZ07l5KSEhLC9AxqEZy7hnWKwug2Ft7Zkc+yZcu45pprznk2oPBvZWVlfPbZZ0ycOJEDBw7gcrlITk7mwQcf5P777yc+Pt573+HDhzNs2DB69uzJ8uXLNUwtfMFz7qfeEulX1xnBQh8TCYq7COC5NheiKcyePZuKigqam/X0Szr3eHVRN9e1jmZzfiUZGRmsX7+eK664QutIooGOHj3KgAEDyM/Pp2XLltjtdo4fP86AAQM4cOAABQUFKIrCgAEDMBgMWscVGtHr9aiqisPhcBeaTIH5veApksl6nwh069ev966L3t4pjsTwwPyZbErNIwz8pmMsX+wtYs6cObRv357LLrtM61gho15lz8zMzAveiouLGzvrOXXq1AmTyUR+fv5ZDyndsmULAN27d/dZJtH4Vq9eTUlJCTpzGOFpLbWOcxpFUYi41F2AXbJkyXm764QINoWFhcyfPx+AkW1i0OuCd3G2vcVEt/gwXC4X33zzjdZxRD08+OCDpKSk8Oijj7J//3769+/Pd999R2ZmJn//+99PKzJ56HQ6LrvsMkpKSnwfWPiUp5vdECtjVZqColdRo9ydEZ6inhCN7ejRo95x1iPbxKBK0bjBoowqV9eORf7uu++wWq0aJxIN9eqrr5Kfn8/f//53cnJyuO6661AUhTVr1nD8+HEWLlxI69atCQ8PZ+nSpVrHFRryFGc8XUGBSApNIhjk5OTwwQcfAHB1ShTd4sM1TuS/eiWaGdzSvQF64sSJHD16VONEoaNehaZDhw7V+ZaRkdHYmc8QHh7ubec+W1vctGnTALjpppuaPItoGg6Hg7lz5wIQeUkaih+2hoalJqFGR1BZWcmyZcu0jiOEz0yePJmamhraRRvpFh/8o6ZGtnGfVbBt2za2b9+udRxxkT744ANsNhu//e1v+emnn1izZg3jxo1DVc9/SPxVV13FXXfd5aOUQivejiYpNDUZTxFPCk2iqXz77bc4HA46x5roEhv81yW+MiQ5iliTetoGIxG4Fi9eTHJyMv/85z/P+v4RI0awcOFCVq1axfjx432cTviTk4WmwB2d55RCkwhwVVVVvPnmm1itVjrEmLg+LVrrSH5vZJsY2kYbvV+76upqrSOFhHqt1rdu3fqibr7w6KOPAvCvf/2LAwcOeN++bt06PvjgAywWC/fdd59PsojGt379eo4fP47OZMDcoZXWcc5KURQiu7YBYOHChdhsNo0TCdH0MjIy+PHHHwEY1dYSEqOmEsMN3vGAclZB4Hnuuec4fPgwX3311UW10N9999189tlnTZhM+ANvR5NFCk1NRV/7tZWzUEVT2LlzJ5s3b0anwOg2oXFd4itGVeGmNu4Dx+fOmUNRUZHGiURDZGVl0bNnT+9GG53OvTRkP6VrpVOnTlx55ZV8++23mmQU/iEYCk3S0SQCmdPp5P333+fIkSPEGFXu6hwn3dp1oOoUftclniiDjuzsbD766CM5I9YH/K8tpNb8+fPp37+/9+ZZtD/1bafupBo+fDh/+ctfKCwspGfPnowePZrrr7+eq666CrvdzmeffYbFYtHo2YiGcDqd3sN8zV3SUPTn33WupfA2LVEjwigpKZGzPETQczqdfPHFFwBc1sxMapRR40S+MyI1GrNeITs7WzoYA8wLL7xAUlKS1jGEHyovL/cunOotwXnWnD+QjibRVBwOB1999RUAA5tH0DxCzi1obL0SwmkTbcRqszFp0iSt44gGMBgMREREeP/f898FBQWn3a9Zs2Y+mVIj/JfnjC5XIG+uq80u542JQDR16lQ2btyIqsA9XeKIMvrvmqi/iTGq3N0lHp3ibkTxrC2LpuO3hab8/Hw2bNjgvXmqjqe+LT8//7SPefPNN/nss8/o0qULS5cuZd26dQwfPpxVq1YxevRoDZ6FaAzr16/nyJEjKEYDEZ180yFXX4qq857VNHfuXOlqEkHtxx9/5MCBAxh1CjemxWgdx6ciDDqua+1+zlOmTKGsrEzjRKIxFBUVsXHjRjmLKUQdPnwYADUyHJ1RFiKaimcsYW5u7mk754VoqB9++IHs7GzMeoVrW8tImaagKAo3t7UA7uvA9PR0bQOJemvZsuVpnaVt2rgnc2zatOm0++3atQuz2ezTbMK/eMZN+fOG3wtRajv35Hw5EWh+/PFHZs+eDcBvOsaSFi1deRerXYyJse1jAffROhs2bNA4UXCrV6Gpbdu2db61a9euXsHuvvtuXC7XeW933333WT9u06ZNVFRUUFxczMKFC7niiivqlUFo79RupogurdEZ9RonujBzuxR05jCKi4tZsWKF1nGEaBKVlZV89913AFyTGoXFFLgvPOrrihYRtIwwUFFRcdbzAYX/eu2110hKSqJly5Z89NFHgPvspuTkZPr370/z5s2ZMGGCximFr3kKTfpYWaBuSmpEGIpRj8PhICcnR+s4IkiUl5czefJkwN11HGEIvesSX0mNMtK3mbvw8OWXX8oI4QDVp08f9uzZ4y34X3311bhcLp566il27drFiRMneOWVV9ixYwc9evTQOK3Qkqc4o+j9fy3mXBSDO7uc0SICyYEDB/joow8BuDolisuaRVzgI8S5DGgeweCW7okVEydOlE7dJlSvQlNmZuZF3YSorw0bNpCbm4ti1BPR2b+7mTwUVUdkN3dX05w5c6SrSQSlGTNmUFpaSmK4niHJoXmWiaoo3NLOAsDy5cvlYiVALFy4kKeeeor8/HyOHTvGgw8+yKxZs/jzn/9MbGwsHTt2xGaz8fjjj7Ny5Uqt4wof8hSaDHGh+TvNVxRFwVBbzPN8zYVoqOnTp1NeXk5zs957jqJoOje2icGkKqSnp3vP6hSB5dprr6WkpIRFixYB0L17d0aPHs3u3bvp3r07FouFZ599Fp1Ox/PPP69xWqGlqqoqAJQALuB7urGk0CQCRUFBARMmTKCmxs6l8WHckCYb4RpqZNsYOseasNlsjB8/nuLiYq0jBaV6FZoOHTpUp9v999/f2HlFCHE6ncyYMQOAiC5pATXGRrqaRDDLyclh8eLFAIxpa0GvC92DKNvFmOiTGI7L5eKLL76QXb0BYOLEiSiKwsKFC1myZAkADzzwACNGjCArK4s9e/Ywfvx4XC4XH3zwgcZphS95C03S0dTkPOc0SaFJNIbs7GyWLl0KwM1tLaghfF3iKzFGlWtauX9XTpo0icrKSo0TiYt12223kZ2dzZAhQ7xv+/rrr3nooYdo1qwZer2ebt26MXXqVAYOHKhdUKEph8NBTU0NADqDdDQJ4QvV1dWMHz+e0tJSWkYYuLNTHDpFrm0aSlUUftc5nqRwPcXFxYwfP14aA5pAvQpNrVu3rtPNYrE0clwRStauXVvbzWQImG4mj1O7mmbNmiWzgEXQ8BRUHA4Hl8aH0SUuTOtImhvZxoJJVThw4IDs6g0A27dv57LLLmPEiBEMHz6cyy+/nPz8fF588UX0tSNBHnnkEVq1asX69es1Tit8xW63k5ubC5w8Q0g0HX1t11hWVpbGSUSgc7lc3vFt3ePD6BQr1yW+Mjg5koQwPSUlJcyaNUvrOOIi6fV6kpOTiYw82QFoNpv53//+x9GjR7FarWzdupWbb75Zw5RCa6cWZgJ5dJ7OIB1NIjA4nU7ee+89Dh8+TKRBxx8uicek1mvpXpxFuF7HH7omYNbryMjI4P3338flcmkdK6jId6vwS3a7nenTpwMQ2bVNQHUzeZjbpaBGhlNaWurdNS9EoNuwYQO7du1Cr8DoNhat4/iFGNPJXb3fffcdFRUVGicS53Ps2LHTzo/0HH7dpUsX79sURaFnz54cPXrU5/mENnJycnA4HChGPWqELFQ3NU/XWGZmpry4Ew2yadMm73XJqLYWreOEFL1O4eZ2MYB7LK38zRQi+HgLMzoFJYAXuz1FMrvd7j2XTAh/NHnyZDZt2oSqwH2XxBMbFrgFXn+VEK7n3i7x6BRYv369d+1ZNI7A/Ushgtrq1avJy8tDF2bE3ClV6zj1oqg6Iru7FzPnzp3rnW0sRKCqrKzkq6++AuDqVlEkhMtFj8fg5EiahespLS1lypQpWscR5xEeHn7aC0yDwb2RISzs9OKC2WxGkREFIcNzpqghNlr+3X1AHxMJOoXKykry8/O1jiMClM1m4+uvvwZgWEoU8bIY43Nd48LpEhuGw+HwXiOKwGOz2Vi/fj3Tpk1j6tSprFu3TsYJCQDKysoA0JmMGidpGMWgh9rrO89zEsLf/PDDD8ydOxeA33SMpU20SeNEwau9xcTY9rGA+/zxNWvWaJwoeEihSfidmpoa79lMkZe2DehZwOFtWqJGmykvL2fhwoVaxxGiQaZPn05xcTEJYSrDW8kZJqfS6xRubW8B4PvvvycjI0PbQOKckpKSTlvYvvPOO/nf//53xv2OHDlCQkKCL6MJDR06dAgAQ7z8bvMFRdWht7jH53mKfEJcrHnz5pGfn4/FqHJ1Kxl5qZWb28agKrB161Z+/vlnreOIi2Cz2XjmmWdISkpi4MCBjBs3jttuu41BgwbRrFkznn76aRkBH+IKCwsBUM2B3e2t6BRUs3vR3vOchPAne/bs4ZNPPgHgmlZRXNYsQuNEwW9A8wiGJrvHx3744Yfs379f40TBoV4r+MOGDavT/WShTdTHsmXLKCwsRGc2Ye7QSus4DaLodER170DJj9uYP38+11xzzWlzsIUIFJmZmSxatAiAW9rHYpCDts/Q0RJGn0Qzm/Mr+fjjj/nXv/6FTif7OfxN165dWblypff/hw0bdsZ1jc1mY8eOHVx++eW+jic04u1oipNCk68Y4qKxF5Vx6NAh+vXrp3UcEWAKCwuZM2cOACPbxsj5BRpqZjYwODmS5TnlfPXVV1x66aXebmHhv6qrq7nmmmtYs2YNLpeL+Ph40tLSAPffxMLCQl577TV+/PFHli5dekbntwgNRUVFAOgCvNAE7ufgqKj2Pich/MWxY8d44403cDgc9EwI59rW8nrEV25qE0N+lZ2dRdVMmDCBl156icTERK1jBbR6XZH/8MMPdbrJAb/iYlVXV3sPk43q1h5Fr2obqBGEpTVHHxtFVVWVtw1WiEDidDr59NNPcblc9Exwj0gRZze6bQxhqkJmZiZLly7VOo44iyeffJL//Oc/1NTUnPM+GzdupHXr1nIAdohwOp0cPnwYkEKTL3m+1tLRJOrjm2++wWaz0S7aSK+EcK3jhLxrWkUTZdBx7Ngx78Yk4d/+85//8OOPP9KhQwfmzp1Lfn4+GzduZOPGjeTn5zNv3jw6duzI2rVree2117SOKzTiKcp4uoECmacrSwpNwp+Ul5fz3//+l/LyclIjDfy2Yyw6GePtMzpF4c7OcbSMMFBWVsbrr79OZWWl1rECWr06mlasWFGn+3344YdMmjSpPp9ChKhFixZRVlaGGmUmvH2y1nEahaIoRPXsQPGKLSxevJhrr72W2NhYrWMJUWcrVqwgPT0dk6pwsxy0fV5RRpUb02KYdrCEyZMn069fP/l59zP9+vW7YPfEwIEDZfxPCDly5Ag2mw1Fr6JGy5gKX/GMKTx06BAul0vOxhJ1tmfPHtavX48C3NzOIt87fiBMr+OmNjF8u7+YmTNnMmjQILn+8XPffvstkZGRLF++nJYtW57x/uuvv56ePXvSqVMnvvnmG5577jkNUgqteQtNEYFf0FcjpNAk/Ivdbuett97i6NGjWIwq93VNwCgd2j5nUnX8oWs8b/x8nOzsbN5++20ee+wxVDXwGx+0UK/v4MGDB9fplpqa2th5RRArLy9n3rx5AET1aI8SRCOnTMmJGBIt2Gw2Zs6cqXUcIeqstLSU7777DoDrW0cTY5I/thdyRYsIUiMNVFdXy8HYQgQAz/lM+rhoWbD2IYMlChSFsrIyWfQRdeZwOPjiiy8A99/blMjAPqA+mFzWzEzrKCPV1dWy2TQAZGVlMXTo0LMWmTxatmzJsGHDZFJNCAvGjiY5o0n4i6+//ppdu3Zh1Cn8vms8MUZZa9FKrEnP77smYNApbNu2Ta5jGiB4VvJFwJs3bx6VlZXoLZGEpbXQOk6jUhSFqF4dAXd3SF5ensaJhKibr776isrKSlIiDQxqKeeL1YVOURjbIRYFWL9+Pdu2bdM6kjgLl8vFggUL+Mc//sH999/Pp59+6n1ffn4++/fvx+FwaJhQ+Ir3fKbYKG2DhBhFr6KPcXeQeYp9QlzIihUryMrKwqxXuF7OMPArOkXhlnYWAFavXk16erq2gcR5xcbGEh5+4S6VsLAw6U4LYQUFBcDJIk0g85wz5XlOQmjphx9+YMmSJQDc2TlONs74gdQoI7/t6P57N3/+fNasWaNxosAkhSbhF4qLi73zvKN6dgzKHcWmpDiMLeJxOBxMnz5d6zhCXNC2bdtYu3YtCvDr9rGoQfhz2VRaRRq5qrYw9+mnn1JdXa1xInGqbdu20aVLF2666SZeeeUVPv74Y3788Ufv+5cuXUqXLl1YsGCBhimFrxw8eBAAQ0KMxklCjyHe/TXPyMjQOIkIBOXl5UyZMgWAa1NjiDDIzl9/kxplpG8zMwBffvklTqdT40TiXIYPH86qVauwWq3nvE91dTWrV69m2LBhPkwm/IXdbic/Px8ANcqscZqG09c+h+PHj2ucRIS69PR07ybHa1Oj6RYf+KMpg0WvRDPDW7k3H3744Ydylmw9SKFJ+IVZs2Zhs9kwJFowpSRqHafJRNd2Na1Zs0ZGEAi/Vl1d7b34uaplJKlRssPmYl2fFk2sSSU/P1+Ky34kJyeH4cOHs3//fq677jpee+01XC7XafcZPXo0BoOB2bNna5RS+IrD4fC+gDDGS6HJ16TQJC7GzJkzKS8vp7lZz8AWcp6av7qxTQwmVSE9PZ21a9dqHUecw7/+9S9qamr47W9/e9aF94KCAu644w5qamp4+eWXNUgotJafn+8+Q1FV0YUHwei82kJTWVkZlZWVGqcRoaqkpIQ33ngDu91Ot/gwrkmViQr+5vrW0XSJDaOmpobx48dTVlamdaSAoq/PB9177711ut+mTZvq8/AixOTl5bF8+XIAonoFZzeThyE+hrDUJKqz8pg6dSp/+9vftI4kxFnNmDGD/Px8LCaV69NkNE19mFQdt7a38NGuQhYuXMjAgQNJS0vTOlbIe+WVVygsLOTNN9/k4YcfBuCJJ5447T5ms5kePXqwceNGLSIKH8rNzcVms6EYVNRoWbj2tVMLTS6XK6ivAUXD5ObmekfMjG5rQdXJ94q/ijGqDG8VxfzMMr777jsuu+wywsICf+xWsPn888+54YYb+PLLL1m8eDHXXHMNbdq0AdzjTJcsWUJVVRV33XWX91w0D0VRePbZZ7WILXzo2LFjAKjR5qD4+6wz6NGFGXFW2zh27Bht27bVOpIIMXa7nTfffJPi4mKam/Xc3jEOXRD8bAUbnaJwZ+c43th6nPzCQv73v//x1FNPodfXq4QScur1Vfr888/rfN9g+IMkmtb06dNxOByYWiZgSorTOk6Ti+rZgersPDZv3syBAwfo0KGD1pGEOE1mZqZ3ZNit7SyYVGl+ra+uceH0TAhna0EVH3/8MS+++CI6nXw9tbRo0SI6d+7sLTKdS1paGitWrPBRKqEVTyeNIS5Grlk1YIiNAp1CeXk5+fn5NGvWTOtIwk99/fXXOBwOusaF0Tk2sIsWDpeLEqsDm+PkWLmiajtGVYfFpAbFqOIhyVGsP1ZBYXExc+bM4de//rXWkcQvvPDCC96/e5WVlcyaNeus9/vyyy+9nd+Kong3BUihKfh5zpXWB8HYPA81OkIKTUIzX3zxBfv37ydMVbjvknjC9LIu4K/Meh33dYnnjW3H2b17N99++y133XWX1rECQr0KTZ999llj5xAhKisry3vAWlTP0Ci46GMiCW+bTNXBXCZPnswzzzwji1vCbzidTj7++GOcTic9E8K5VOYFN9iYdhb2FleTkZHB4sWLue6667SOFNKOHDnCqFGjLng/RVGkTT4EeM9nkrF5mlBUHYbYKGoKy8jIyJBCkzirbdu2sW3bNlQFRrcN/J/VEquDlzYeO+1t/9niHl32bN/mxIcF/o5Zg05hVBsLn+4pZP78+QwdOpTExOAdjx6InnvuOXkNKs7LU2gKhvOZPPRRZmqOF3ufmxC+smLFCpYtW4YC3NU5jsRwg9aRxAU0jzBwe8c4Pt1TyKJFi2jbti2DBg3SOpbfq9dV7O9+97vGziFC1NSpU3G5XISlJoXUIk9Uj/ZUHTrK7t272blzJ926ddM6khAALF68mIyMDMJUhZvbWbSOExSijSoj28QwJb2EKVOm0LdvXxISErSOFbIiIiK8Bxufz6FDh4iLC/4u21B36NAhAAzxMiJUK4b4GGoKyzh48CD9+/fXOo7wMw6Hg2+++QZwnxkpCzOBo1t8GO1jTKSXWpk8eTIPPfSQ1pHEKV544QWtIwg/5xmdF1QdTbXPxfPchPCF3Nxc7wjS61tHc0mcbOYNFN0TwhmRGsXirBN8+umntG/fnubNm2sdy69Jn57QzIEDB9i8eTMoodPN5KFGhGPu2AqAyZMnn3EQvRBayM/PZ8rkyQDc1CaGGKOqcaLg0b95BG2jjVitVj755BP5mddQt27d2Lx5MwUFBee8z+HDh9m2bRt9+vTxYTLhazabjcOHDwOB29E0dOhQXn/9dYYOHQqAo8qqcaKL5/nae7rLhDjVihUryMnJIUKv41epUhAOJIqiMKptDAqwdu1a0tPTtY4khLgI3jOagqjQpJdCk/Cxmpoa3nnnHWw2G50sJq5uFaV1JHGRRqRG0y7GSHV1Ne+++y52u13rSH6twYUmm83GunXrmDZtGtOmTWPdunXYbLbGyCaC3JQpUwAIb5uMPiZS4zS+F3lpWxS9SkZGhhw4LzTncrn4+OOPsdpstIs2MqB5hNaRgopOURjXIRZVcY8A8owMFb53xx13cOLECX7/+99TWVl5xvttNht/+tOfqKmp4Y477tAgofCVw4cP43A40JmMqJGBubPwhhtuoGXLltxwww0AOMqrNE508QwJ7kLToUOHcDgcGqcR/qSyspJp06YBcG3raMxylkHAaRVppG+Se2H366+/lo02fqygoICtW7eydevW827GEaHBbrd7JwDoo4PndaHnuUihSfjKlClTOHz4MBF6Hb/tFIdORpYGHJ2icEenOML1CgcPHmTGjBlaR/Jr9b5at9vtPPvsszRr1oxBgwYxbtw4xo0bx6BBg2jWrBnPPfecVPnEOe3cuZNdu3aBTkdk9/Zax9GEGm4ioksa4B4h6HQ6z/8BQjSh1atXs2PHDvQKjOsQKxdATSDJbGBE7W7sL7/8Us7/0cg999zD4MGDmTNnDp07d+aPf/wj4C4APvzww3Ts2JGFCxdy9dVXM27cOI3Tiqbk2V1vSIgJ2HMq5s+fz5EjR5g/fz5AQBbM9NGRKAYVq9VKbm6u1nGEH5k9ezZlZWU0C9dzhWyACVjXt47BqFPYv38/GzZs0DpOyNq7d+9Z3/7VV1/RrVs3kpKS6NOnD3369CEpKYkePXrw3Xff+Til8Bf5+fk4nU4UVUUXbtI6TqPxdGedOHGCiooKjdOIYLdjxw7vNfpvOsbKxJgAFmvSM659LOC+Pt2zZ4/GifxXvQpNTqeTkSNH8sorr1BWVobFYqFXr1706tULi8VCWVkZL7/8MqNGjZLFc3EGl8vl7WYyd0hBH4CLIo0l4pI0FKOB3Nxc6XAQmiktLeXrr78GYETraJqZ5fyDpnJ1ShQtIwyUl5fz1VdfaR0nJKmqyty5c/nNb35Dbm4uH3/8MQA///wz77zzDllZWdxyyy2yUykEeEa1GRIs2gZpgBUrVvDYY4+xYsUKwL2JJdAoOsU7Pk9GawmP/Px8Fi1aBMCoNjGousAsBguwmFSGpbhHBX333XfU1NRonCj0jBw5kn79+p3x9oceeoi7776bXbt24XK5iIqKIjo6GpfLxY4dO7jjjjv461//qkFioTXv2Lxoc8BuxjkbnUHvLZxJV5NoSmVlZUycOBGAgS0iuDQ+dNc9g0XPRDP9ksy4XC7ee+89ysvLtY7kl+pVaPr4449ZtGgRrVu3Ztq0aRQWFrJp0yY2bdpEYWEh06dPp3Xr1ixatIhPPvmksTOLALdlyxbS09NR9CqR3dppHUdTOqOByK5tAJg+fbp0AQpNfPnll5SXl5McYWBYsswMbkqqTuG2DrEowJo1a9i6davWkUJSZGQk33zzDbt27eL111/nT3/6Ew888AAvv/wyW7ZsYerUqURGht5I11DjKWoYEwLzfKZg4in2SaFJeEydOpWamho6xJi4JC5M6ziigYamRBJt1JGfn8/333+vdZyQExUVdUb3xuzZs3nvvfeIiorilVdeIT8/n5KSEoqLi8nPz+eVV14hOjqa//3vf8ybN0+j5EIrniKMPojOZ/KQc5pEU3O5XHz00UeUlJSQFK5nVBt5rREsbmlnISFMT2FhIZ9++qmMBD6LehWavvzyS8LDw1m+fDljxow54/0333wzy5Ytw2Qy8cUXXzQ4pAgeTqeTqVOnAmDulBqQO28bm7lTKrowI8ePH2flypVaxxEhZsuWLaxbtw4FuK1DrOwY9oHUKCODk91FjE8++YSqqsA7UyVYdO7cmUcffZR33nmH9957j6effpqePXtqHUv4QFlZGcePHwdOnhEktOMp9nm6zERoy8zM9Hb6j2wTuKMtxUkmVcd1teODZ86cKSOrfCwi4szRk++//z6qqrJo0SKeeuop4uPjve+Lj4/nqaeeYtGiRSiK4t2VL0LH0aNHAVCD6HwmD8/4PM9zFKKxrVu3js2bN6MqcFfnOIyqnDEZLEyqjrs6x6FTYP369WzatEnrSH6nXt/tO3fuZMiQIaSlpZ3zPm3atGHYsGHs3LmzvtlEENqwYQNZWVkoBr23kyfU6Qx6Ii9tC8CMGTOw2WwaJxKhorKy0tt1OjQlklZRRo0ThY7rWkcTH6ZSWFjIpEmTtI4jRMjxFDTU6Ah0RhkXqjVPR1NOTo4U3wXfffcdLpeL3onhcm0SRPo1jyApXE95eTlz587VOk5IiYpyTyw4dXrGpk2bGDRoEP379z/nx11++eVcddVVbNy4sckzCv+Sl5cHBGlHU23xzPMchWhMVquVb7/9FoBftYomOVKuY4JNapSRq2tHAn/zzTeyhvsL9So0Wa1WYmIuvPszKioKq9Van08hgpDD4WDatGmA+2winUl+4XqYO6aiM4dRXFzMsmXLtI4jQsS3335LcXExCWF6rq3dZSp8w6TqvIdJLl269JwHNIumU1BQwKuvvsq1117LpZdeSteuXRkxYgSvvvqqt9NFBK8DBw4AYEy0aBtEAO6zpdSIMFwul3Q1hbgdO3awY8cOVAVuSJNuw2CiKgo31Y4PWrhwIYWFhRonCh2tW7cGOO16s6ysjOTk5At+bMuWLSktLW2ybMI/5efnAye7f4KJWntGuOc5CtGY5s6dS1FREbGnnE8ogs/wVlHEGHUcP36chQsXah3Hr9Sr0NSqVSvWrVuHw+E4530cDgfr168nJSWl3uFEcFmzZg1Hjx5FMRmI6JKmdRy/oqg6orq7z6uaPXs21dXVGicSwW7Xrl0sX74cgN90jJV2bg10jA2jf3P3jroPPvhANmb40OzZs+nYsSPPPPMMS5YsYffu3ezZs4elS5fyzDPP0LFjR2bOnKl1TNGEvIWm2k4aoT1DbdHP828jQo/T6eS7774DYGCLSOLD9BonEo2ta1wYbaON1NTUeMepi6bXv39/XC4XX331lfdtCQkJ7Nq164Ifu2vXrtPG6ong53Q6KSgoAECNCL4z8tQId6HJ8xyFaCz5+fnejt1RbWIwqjL6N1iZVJ1388ysWbMoLi7WOJH/qNfK4ogRI8jKyuIvf/kLNTU1Z7zfZrPx8MMPk5WVxXXXXdfgkCLw2e12pk+fDkBk17boDPLC8ZfC2yWjRpkpKytjyZIlWscRQay6upoPP/wQgEEtImgXI2elaWVUmxhijCp5eXnejk/RtDZs2MDYsWMpKSmhT58+/O9//2Pu3LnMnTuXt99+m8suu4yysjLGjRvHhg0btI4rmoDT6fR2zRiko8lveIp+UmgKXevXryczM5MwVeGaVNkFHIwURWFk7cLM6tWrycnJ0ThRaOjXrx/t27fnzTffZP369QBceeWVbN++/bxnan/++eds27aNgQMH+iqq8AOlpaXuMYsKqOYgLDTVdjQVFxefNk5SiIb69ttvqampoV2MkR4J4VrHEU2sd6KZ1lFGrFard6OUqGeh6amnnsJisTBx4kTatm3L448/znvvvcd7773HY489Rrt27Xj//feJi4vjySefbOzMIgD98MMP5OfnowszEtEpVes4fknR6Yjs3h5wt9tWVlZqnEgEqylTppCfn0+sSeVGGUujqXC9jl+3twCwYMEC0tPTtQ0UAl588UUcDgf//e9/+emnn3jooYe44YYbuOGGG/i///s/NmzYwPjx47Hb7bz00ktaxxVNIDs7m+rqahSDij4mUus4opan6Jeeno7L5dI2jPA5u93u7XAZlhJFpEHVOJFoKmnRJrrFu0dlSleT77z11ls4HA6uu+46vvnmGx5++GF0Oh333nsvt956K/PmzWP37t3s3r2buXPnMmbMGO677z50Oh2PPvqo1vGFD3nH5pnDUHTBN/VCF2YEVYfL5ZIRnqLR7Nmzhw0bNqAAY9paUBTpZgp2OkXhlnYWAH788UfZLFerXn81kpOTWbRoEa1atSI3N5cJEybw5z//mT//+c+88cYb5Obm0qpVKxYuXFinub8iuNlsNu8IoshL26Lo5YXjuYSntUAfE0FFRYXM+RRNYv/+/SxevBiAX3eIJUwffC8eAk3X+HD6JJpxuVx8+OGHZ+0UFo1n7dq1XHrppfztb387533++te/0q1bN9asWePDZMJXPC8CDPEWFJ28CPQXhthoUHWUl5dz9OhRreMIH1u5ciV5eXlEGnQMTpYCcLC7vnUMCrBx40Y5l81HrrvuOj777DOsVit33XUXw4cPR1Xdr8tnzpzJqFGj6NatG926dWP06NHMmjULnU7HW2+9xYABAzROL3zp5Ni84OzIUBTFOxJQxueJxnDqaNIBzSNIjpTz6ENFapSRfknus+y++uor2SxHPQtNAH379mX//v189dVX3HvvvYwYMYIRI0Zw77338tVXX7F//34uu+yyxswqAtSyZcsoLi5GZw7D3LGV1nH8mqJTiOzRAXB3N5SXl2ucSAQTm83Ghx9+iMvlol+SmS6xwTcKIVDd3C6GSIOOnJwcORuoidXU1NCtW7cL3u/SSy+Vol+Q8nQOGmVsnl9RVB2GuGhAxueFGpvNxowZMwD4VasoTHJuZNBrEWHgsmbuhZkpU6ZonCZ03HnnnezevZs///nPtGrVClVVcblcp93CwsJo37499913H5s2beL//u//tI4tfMxbaIoMzkITnCyiebq3hGiIffv2kZmZiVGncH3raK3jCB+7IS0GveJ+jZmRkaF1HM016KAco9HI7bffzu23395YeUSQsVqtzJkzB4Cobu1QVOlmupCw1CT0sVFUFZ9g3rx53HbbbVpHEkFi+vTpHDlyhGiDjtFtLFrHEaeINKjc2s7C53uLmDt3Lv369SMtLU3rWEGpc+fOZGdnX/B+ubm5dOrUyQeJhK95O5qk0OR3jIkWavJLSE9PZ/DgwVrHET6ydOlSiouLiTWpDGwh3Uyh4trW0WzJr2THjh3s2rWLrl27ah0pJKSlpfHmm2/y5ptvah1F+KmioiIAdEF4PpOH5+yp4uJijZOIYPD9998D0LuZmUijrHmGmhijSs9EM5uOV7Js2TLatWundSRNyXYx0aSWLFlCaWkpamQ44e1ljGJdKIpCVA/3WU2LFy+mrKxM40QiGKSnpzNv3jzAPTLPbJBf//6mR0I4PeLDcTgcfPDBB3I4bRO5//77+fHHH1m5cuU577Ny5UpWr17N/fff78NkwhfKysq8Y9mMCXJGnb8xJsYC7jGvIjRUVVUxe/ZsAK5NjUYv4yxDRnyYngHNIwB3V5OMm9GOzWZj3bp1TJs2jWnTprFu3TpsNpvWsYRGTpw4AYDOFLzjv3Rh7ufmea5C1FdZWRk//fQTAANr/6aJ0HNF7b/92rVrqaio0DiNtmSlUTSZqqoq5s6dC0Bk93ZBeZBkUzGlNMMQH31aR5gQ9WWz2fjggw9wuVz0STRzaXzwjkEIZIqicGt7CxF6HYcPH/YuvInG9Yc//IFHHnmEG264gccee4zt27dz4sQJTpw4wfbt23n88ce54YYbeOSRR/jjH/+odVzRyDzdTPqYiKBeQAlUni6znJyckH+RFioWL15MeXk5ieF6LqudcS9CxzWp0Rh0CgcOHGD79u1axwk5drudZ599lmbNmjFo0CDGjRvHuHHjGDRoEM2aNeO5556TjU8hyFtoCgve6yTPc5NNvaKhVq5cid1up1WkgVZRwfszI86vTbSRFmY9NpuNH3/8Ues4mpKVf9FkFi5cSHl5OWq0mfA2LbWOE1AU5eRZTZ5xIkLU14wZM8jNzSXKoGNMO9nB78+ijCq3tLMAMGvWLA4fPqxtoCCkqipvvvkmVVVVvPHGG/Tq1QuLxYLFYqFXr15MmDCBqqoq3nzzTVRVPe2m1zdo4rDwA55OGUNt54zwL2q4CTXKjMvl8p6lJYJXZWWlt9v62tRoVEW6mUJNtFFlUAv3LuCpU6dKV5MPOZ1ORo4cySuvvEJZWZn3OshzXVRWVsbLL7/MqFGjcDqdWscVPnSyo8mgcZKm49lsJB1NoiGcTifLly8HkNG/IU5RFK6o/R74/vvvQ/p6RgpNokmUl5czf/58AKK6d5BupnowtUzA0CyWmpoaZs2apXUcEaAyMjK8izhj28cSYZCZwf6uV2I43ePDcDgcvP/++7KTtJH98tDri7nJQkvg83Q0GeV8Jr/l+bfx/FuJ4LVw4UIqKytJCtfTK1G6rUPVsJQojDqFjIwMtmzZonWckPHxxx+zaNEiWrduzbRp0ygsLGTTpk1s2rSJwsJCpk+fTuvWrVm0aBGffPKJ1nGFD4VSR5MUmkRD7Nq1i7y8PMJURa5jBJc1M2PUKeTm5rJv3z6t42hGVv9Fk5g/fz5VVVXoLZGEpTXXOk5AUhSFqJ7urqbly5eTn5+vcSIRaGpqavjggw9wOp3u4kWCXPwEAvcIvVjMtSP0PCNIReNwOp0NuonAZbfbOXjwICCFJn/mGZ8n5zQFt/LychYsWADAta2j0Uk3U8iKMqpc2dK9C3jatGkhvQvYl7788kvCw8NZvnw5Y8aMOeP9N998M8uWLcNkMvHFF19okFBoweVyhUhHk/u5SaFJNMT69esBd4HBpMryeqgL1+u8BUfP90Yokp8E0ehKS0tZtGgRAFE9O6DIC8d6MyXFYWwej8PhYMaMGVrHEQFm5syZZGdnE2nQecexicAQfcoIvRkzZpCVlaVtICGCQGZmJjU1NShGA2q0HNbrr4y1Yw3T09OluBvEFixYQFVVFS3MBnrIRpiQNywlEpOqcPjwYTZu3Kh1nJCwc+dOhgwZQlpa2jnv06ZNG4YNG8bOnTvr/LiVlZXMmjWL++67j06dOhEWFkZERAQ9evTgxRdfpLy8vBHSi6ZitVqpqakBCOqzLGV0nmgMGRkZAHS0hGmcRPgLz/eC53sjFEmhSTS62bNnY7VaMcRHY0pppnWcgOfpalq1ahW5ubkapxGBIiMjgzlz5gAwtr2FSBmZF3B6J4ZzqYzQE6LReDpkjIkW2QTjx/QxkSgGPdXV1VJkD1Ll5eXeTWnSzSQAIgwqV9V2Nc2cOVO6mnzAarUSE3Phs1ujoqKwWq11ftxvv/2Wm2++mU8//RRVVRk5ciRXXnklhw4d4vnnn6dv374cP368IdFFE7LZbN7/VvTB+/rR89xsNpv8vhH1YrPZyM7OBiA1Kni7/8TFSY1yF7EPHz4csus3UmgSjSo/P5/vv/8egKheHWUhpxEYEy2YUprhcrmYOnWq1nFEAKipqWHixInekXk9EsxaRxL1oCgKv64doZeZmektHAoh6sdbaGoWq3EScT6KTsGQ4F78lHOagtOiRYuorq6mhdlAt3jZBSzchiSf7GqSs5qaXqtWrVi3bh0Oh+Oc93E4HKxfv56UlJQ6P67BYOCPf/wju3fvZvfu3UyZMoVFixaxb98+evXqxd69e3nkkUca4RmIpuD9flCUoF7LUXQnn5t0T4v6OHz4ME6nkyiDjhhj8BZlxcVJCFMJUxVqamrIycnROo4mpNAkGtX06dOx2+0Ym8dhapGgdZygEdXL3dX0008/ec+XEOJcpk+fTm5urozMCwKnjtCbOXMmmZmZmuYRIlC5XC5v0cIg5zP5Pc/4PDmnKfhUVFR4u5lGpEZJN5PwijCcPKtp+vTp0mXQxEaMGEFWVhZ/+ctfvKPSTmWz2Xj44YfJysriuuuuq/Pj/u53v+ODDz6gS5cup729RYsWvPvuu4B7LPSpnTPCf3h34OuC/Hez7uRSaKh2HYiG8YxGaxVlDOqirLg4iqJ4u5pCdXyeFJpEo8nJyWH16tWAu5tJNB6DJYrwti0BmDx5ssZphD9LT09n7ty5L3d1KgAAye1JREFUAPy6fayMzAsCvRPD6R4fjsPh4IMPPpAXQ0LUQ0FBAcXFxaAoGOMvPCpIaMtYWwyUQlPwWbx4MZWVlTQ36+kuZzOJXxiSHIlRp5CZmcnWrVu1jhPUnnrqKSwWCxMnTqRt27Y8/vjjvPfee7z33ns89thjtGvXjvfff5+4uDiefPLJRvmcPXr0ANxj+woLCxvlMUXj8rzOUHTBvVR46vM7X1efEOfiKSKkRgbvWWaiflpFhnahSa91ABE8Jk+ejMvlIiw1CWOCRes4QSeyR3uqMo+yc+dOduzYQbdu3bSOJPyMzWbj/fffx+Vy0SfRLAs4QUJRFMa2t3Cw1Mrhw4eZNWsWt956q9axhAgonoKFIS46qM8cCBaGBAso7pHMxcXFxMbKuMNgUFlZyYIFCwC4JlXOZhJnijSoDGoZwfKccmbMmEHPnj1lp3gTSU5OZtGiRYwdO5asrCwmTJhw2vtdLhepqalMmzaN5OTkRvmcnkU3g8FAXFxcozymaFyeoosS9B1NJ5+fbOIT9XHo0CEAWsn5TOIXPGd2hWqhKbi3KQif2bNnD5s3bwZFIbJnB63jBCV9pBlzx1QAvvnmG5klLM4wffp0jhw5QrRBxxgZmRdUoowqt7a3ADB79mzvha0Qom685zPJ2LyAoDPq0VuiADmnKZgsXbqUyspKmoXr6SmbYcQ5DE2OwqBTOHjwINu3b9c6TlDr27cv+/fv56uvvuLee+9lxIgRjBgxgnvvvZevvvqK/fv3c9lllzXa53vrrbcAuPbaazGZTI32uKLxnBydF9xLhYqiQG0RWzqaRH1UVlYC7lH3QpzK8z1RVVWlcRJtBPdfD+ETTqeTb775BgBzhxQMMZEaJwpeUd3boRj0ZGVleccUCgHuRdR58+YB8OsOsUQY5Nd7sOmVaKZngnuE3vvvv3/WefpCiLPzns/UTDpjAoWnKLhv3z5tg4hGUV1dfUo3k5zNJM4tyqhyRYsIwL25RjQto9HI7bffzkcffcSCBQtYsGABH330EbfffjtGY+ONhFqwYAGffPIJBoOBl1566bz3tVqtlJWVnXYTvuE9Gy2EfkXLBl5RH7raYqwcJwhDhw7l9ddfZ+jQoQCU2UK7eOus/Z7QBXnB/lxC81mLRrV+/XoyMjJQ9CqR3dtrHSeo6UxGIru1A2DKlClYrVaNEwl/YLVavSPz+jYzc2m87BIOVre2sxBp0JGdnc2MGTO0jiNEQKiurubw4cMAMto3gBgS3UVB6WgKDsuXL+fEiRPEh6n0SjRrHUf4uWHJUagK7N27lz179mgdRzTQ3r17ueOOO3C5XPz3v//1ntV0Lv/+97+JiYnx3lq1auWjpMLTaeayB3fxxeVweisE0l0n6sMz1tUplSZuuOEGWrZsyQ033ABAUXVoj6P0fE+E6uhfKTSJBrHZbEyaNAmAiEvboobLH+mmFtE5FTUijOLiYu/OUBHapkyZwrFjx4gx6rhZRuYFtUijytjaEXpz5szh4MGD2gYS57V582ZeffVVxowZQ0pKCoqi1OmC8/PPP6dfv35ERkYSFxfH9ddfz9q1a32QODgdPHgQl8uFGhGGGhGmdRxRR56OpszMTGw2m7ZhRIPYbDbmz58PwPBW0agh+sJb1F2MSeXyJHdX06xZs7QNIxokNzeXa6+9luLiYh599FH+8pe/XPBjnn76aUpLS7237OxsHyQVcGqhKbgXil32kx0XYWFybSgunrejSeMc/mD+/PkcOXLEe60XF6bXOJG2PN8T0tEkRD0sWLCAgoICdGYTkV3StI4TEhRVJapXRwBmz5lDYWGhxomElvbu3cuiRYsAGNchFrNefq0Hux4JZnonhuNyuXj//fdlAdaPvfTSSzz99NPMnDmT3NzcOn3MI488wj333MPOnTsZPnw4/fr1Y+nSpVx11VWy2FZP3rF50s0UUNTIcHRhRux2O5mZmVrHEQ2watUqiouLsRhV+jaTbiZRN1e3ikIH7Nixg/T0dK3jiHooKirimmuu4fDhw9xzzz28/vrrdfo4k8lEdHT0aTfhG96ii9Pl7voJUp5Ck6qq6PWhvSgu6sdTRHBKpYkVK1bw2GOPsWLFCkDOrXLJ6Dwh6qegoMC76BXduxOKPrR/mfhSWFoLDIkWbFYr3377rdZxhEaqq6u9I/P6J5m5JE5G5oWKW9pZiDLoyM3NZfr06VrHEecwYMAAnn32WebMmcPRo0cvOJrj+++/56233iI+Pp5t27Yxa9YsFi1axKpVq1BVlXvuuYeSkhLfhA8i+/fvB052yIjAoCgKhtp/M8+/oQg8druduXPnAjAsJQq9TrqZRN3Eh+m5LMldmJSzmgJPeXk51113Hbt372bMmDF89NFHITtGKJCceq3alF1NqjmMxJuvIuHGgd63Jdw4kMSbr0I1N32Hkee5ydg8UV8nC01SaRKnkzOahKinb775BpvNhrFZLGFpLbSOE1IURSGm3yUArFu3jt27d2ucSGhh0qRJHD9+HItJZVRbi9ZxhA9FGFTGdXCfXzJv3jxZhPVTTz75JC+++CI33XQTzZs3v+D9J0yYAMA//vEPOnTo4H37gAEDeOCBBygpKeGTTz5psrzByOl0enfCG5rFapxGXCyjnNMU8NatW0d+fj6RBh39m0doHUcEmKtTolBwj6KV8WmBw2q1MmrUKH766SdGjBjBd999h6rKptRAoNfrvR0+p46Xa2yKToc+0owaeXKjpBoZjj7SjOKDxVln7XOTsXmivjxFysogP89MXLyK2u8Jo9GocRJtSKFJ1MuuXbvYsGEDKBDdt4vsTtKAIS4ac0f3wahffPEFDkfTXQgK/7Nr1y6WLFkCwG86xBIuI/NCzqXx4fRtZsblcvHBBx/ICL0AV1VVxfLlywG49dZbz3i/522ezgBRN0ePHqW8vBxUHYbYKK3jiIvk6UI7cOAALtkxGnCcTidz5swBYEhyJEZVXi+Ii5NkNtAjwb0Q7fleEv7N4XDwm9/8huXLl3PllVcyY8aMkF1sC1Se4ouzJnjXFzxFNOloEvXVpk0bADLL5DW4ON3hMisAbdu21TiJNmRlUly0mpoaPv/8cwDMHVMxxMnMZK1E9eyAYjSQnZ3N4sWLtY4jfKS6upoPP/wQgCuaR9ApVnZihaqb21qIMeo4evQoU6dO1TqOaIB9+/ZhtVpJTEwkJSXljPf37t0bgO3bt/s6WkA7ePAgAMb4GJ/skBWNyxAXDYpCSUmJnEkZgDZv3kxubi5hqsLAFpFaxxEBangr9yaBtWvXkpeXp3EacSHvvPMOM2fOBCAhIYE//elP3H333WfcCgoKNE4qziUqyv0z56y2apyk6Tir3M9Nzv8S9eWZPpF5QgpN4nSe74mOHTtqnEQbcuqduGhz5swhNzcXXZiRqB7ttY4T0nQmI9G9OlK6YRdTp06lb9++JCYmah1LNLHvvvuO/Px8Yk0qI9vEaB1HaMhs0PHrDrF8tKuQBQsW0Ldv35C9oAl0WVlZAGctMgFERERgsVgoLi7mxIkT3kUAcX6ekWuGBPldGYgUvYohNoqaojLS09NJSEjQOpKoI5fL5T1X58qWkdJ5LeotJdJI51gTe4utzJs3j/vuu0/rSOI8iouLvf/tKTidzQsvvCC/0/1UXFwcR48exVFRrXWUJuN5bvHx8RonEYHKU2jKPmHD7nTJGZQCAKvDSW55DcBpo/BDiVzxi4uSk5PDrFmzAPfIPJ1J2uC1Ft4hBWOzWKxWK59++qmMlglyu3btYunSpQDc1iGWMFm4CXld42SEXjAoLy8HwGw2n/M+ERHu801OnDhxzvtYrVbKyspOu4UyT0eTIcGibRBRb55/O89ZWyIw7Ny5k4yMDAw6hcEtpZtJNMyvWrm7DlauXHlaIUP4nxdeeAGXy3XBW1pamtZRxTl4CoCOyiAuNFVKoUk0TPPmzYmKisLugpxyef0t3LJO2HDiLtiH6u8XWaEUdeZ0Ovn4449xOByYkhMJa33hg81F01MUhZj+XUGnY9u2baxdu1brSKKJyMg8cS43t7UQLSP0BPDvf/+bmJgY761Vq1ZaR9KM1Wr1dooZpdAUsAyJ7m40KTQFFk8304DmEUQaVY3TiEDXLsZEm2gjdrudBQsWaB1HiKDmWRx1VlRpnKTpOGqfW6guBIuGUxRFxueJM3i+F0K1mwmk0CQuwrJly9i/fz+KXiXm8ktQFGkN9Rf6mEgiu7UD4Msvvwz5HezBSkbmiXMxG3SMax8LwIIFC9i/f7/GicTFiox07/ivrKw8530qKioAzjs27+mnn6a0tNR7y87ObtygAeTQoUM4nU504SbUCCnMBypPkfDQoUPY7XZtw4g6SU9PZ/fu3egUGJos3UyicXjOalq2bJm3C1gI0fg8xZdgHp3nlNF5ohF4RtYfKAne88zExUmv/V4I5eMMpNAk6uTYsWN8++23AET16ogaEa5xIvFLkV3boLdEcuLECT755BMZoRdkdu/eLSPzxHl1jZcReoEsNTUVcI+oPZuKigpKSkqIjY09b6HJZDIRHR192i1UeTpgZGxeYFOjzChGAzU1Nd4ONeHf5syZA8BlzczEhsmRwKJxXBIbRssIA9XV1d5rYiFE4/MWmmR0nhDn1bt3bwD2FFVTbJXNUKGuoMrO/tpCU69evTROox1ZqRQX5HA4eO+997BarRiT4jB3StU6kjgLRdVhuaIbKAobN25k9erVWkcSjcRqtXpH5g2QkXniPE4doTd9+nSt44iL0KlTJ0wmE/n5+eTm5p7x/i1btgDQvXt3X0cLWJ7zmYwJ0gEayBRF8f4bev5Nhf/Kyclh06ZNKMCwlHMXxYW4WIqicHXt99SiRYuwWmUHuRBNwVtoKq8Kys2rzho7zmr3hjzPeVRC1EdKSgqXXHIJTmDt0Qqt4wiNrTlajgvo0aMHzZuH7lEzUmgSFzR79mzS09NRDHosA7vJyDw/ZoiPIapHewA+//xz8vPzNU4kGsO0adM4fvw4FqPKKBmZJ87DbNAxtnaE3vz58zl06JDGiURdhYeHM2zYMICznrM1bdo0AG666Saf5gpkGRkZgPtvowhshjh3Z578TvN/c+fOBaBbfBjNzQaN04hg0zMxnPgwlRMnTrBixQqt4wgRlJKSktDr9bjsDhzlwXdOk73EPXrTYrF4R1cLUV/XXHMNAOuOVWB3Bl9hVtSNzeFkfZ672Oj5nghVUmgS53Xw4EFmzJgBQEy/S2RkXgCI6NoWQ6KF6upqJk6ciNPp1DqSaICMjAzvocdj21tCfmSew+WisNpOUfXJ1vSiajuF1XYcQbjjrj66xYfTMyEcp9PJRx99hMPh0DqSqKNHH30UgH/9618cOHDA+/Z169bxwQcfYLFYuO+++7SKF1DKy8u9my08RQoRuDzFQik0+bf8/HzWrl0LwPBW8nMnGp+qKN5Oufnz58u5bUI0Ab1eT3JyMgD2khMap2l8nufUqlUrjZOIYNCnTx/i4uIor3GyteDcZ+2K4LY5v4oqu4tmzZrRo0cPreNoKrRXLMV5VVZW8u677+J0Oglr3ZywNi20jiTqQNEpWAZ2R9Gr7N271zsnXwQeu93Ohx9+iMvloldiOF3jpdBbYnXw0sZj/GfLce/b/rPlOC9tPEaJVQoqHre0s2DW68jMzGT+/PlaxwlZ8+fPp3///t6b59ysU9926r/P8OHD+ctf/kJhYSE9e/Zk9OjRXH/99Vx11VXY7XY+++wzLBaLRs8msHgKEmqUGZ1JuioCnSHeXbTIycmR8+f82IIFC3A4HHSIMZEaZdQ6jghS/ZIiiDLoKCws9BY2hRCNy1OEqSkOvkKT5zl5zkcVoiFUVWX48OEArDpSrnEaoQWXy8Xq2n/7X/3qV+h0oV1qCe1nL87J5XLx8ccfc+zYMXTmMGIuv0RG5gUQfZSZ6H5dAPcIpt27d2ucSNTHvHnzyMrKwqzXMaatRes4IoBEGVVGt3V3AEyfPp2jR49qnCg05efns2HDBu/NM+f+1Lf9csTpm2++yWeffUaXLl1YunQp69atY/jw4axatYrRo0dr8CwCk6fQJN1MwUFnDkNnMuJwOMjKytI6jjiLsrIy7yiz4a3kbCbRdAw6hcHJ7nFXc+fOlekNQjQBTxHGM2YumEhHk2hsQ4cORa/Xk3WihswyOT8w1GSU2ThSUYPRaGTw4MFax9GcFJrEWX3//fesX78eFIXYq3qgM8muxEBjbpdCeLtkXC4X77zzDiUlJVpHEhfhyJEjzJw5E4Cb28YQZVQ1TiQCTd9mZjpZTNTU1PDxxx/LQowG7r77blwu13lvd99991k/btOmTVRUVFBcXMzChQu54oorfP8EAtjJ85mk0BQMFEXx/lvK+Dz/tGTJEmw2G60iDXS0mLSOI4LcwBaRhKkKubm5/Pzzz1rHESLoeApNwdbR5HK5qCl2F8+ko0k0lpiYGO9rtbmHSr2bC0Xwc7pczDlUCsCVV14p574hhSZxFhkZGXz11VcARPXuhDExVuNEor5i+l2C3hJJSUkJ77zzjiw0BwhPR2FNTQ2dY01c1sysdSQRgBRF4dcdYjHqFPbs2cMPP/ygdSQhfOZkR1OMxklEY/F0p0mhyf9UV1ezZMkSAK5OiZIpCKLJhet1DGzhXsyZPXu2LOoJ0cg8RRjHiQpc9uAZT+6stOKy1aDT6bznUAnRGG699VaMRiMHy2z8nF+ldRzhIxuPV3L4hI2wsDDGjBmjdRy/IIUmcZry8nLeeust7HY7plbNiOjSWutIogEUvUrsVT1R9Cq7d+9m2rRpWkcSdbB69Wr27t2LUacwtn2sLNiIeosP03N9mntxdtKkSZSVlWmcSIimV1lZ6R1JaIiTEV7BQl/b0XT48GGNk4hfWrFiBeXl5SSG6+meIOdJCt8YnByJXoH09HT27t2rdRwhgkpMTIz7XFAX1BSWah2n0dgKSgBISUnBYJAzPEXjSUhIYNSoUQDMPlSK1SGbvINdld3J3NpupjFjxhAbK00aIIUmcQqHw8H//vc/8vPzUSPDsVzRTRa4g4A+JpKY/pcCMGvWLDZs2KBxInE+FRUVfPvttwBckxpFfJhe40Qi0F3ZMpKWEQbKy8uZNGmS1nGEaHI5OTkA6MwmGf0bRAwWd9EwJydHOrT9iN1uZ/78+QAMS45EJ68dhI9EG1X6JUUAMGfOHI3TCBFcFEWhY8eOANiOF2ucpvHU1D6XTp06aZxEBKMbbriBxMRESm0OlmYH19hJcaZFWWWU1zhp0aIF1157rdZx/IYUmoTXN998w86dO91dMEN6ozPKDo9gEd6mBRFd0gCYOHEimZmZmuYR5zZlyhTKyspICtczJFl24ouGUxWFse0tAPzwww/s379f20BCNLHs7GzgZGFCBAc10gyqjpqaGvLy8rSOI2qtXbuWoqIiog06Lqtd9BfCV4amRKEA27ZtIysrS+s4QgQVTzHGll+ibZBGZMuXQpNoOkajkTvvvBOAFTknyK+ya5xINJVjFTWsPuI+7+2uu+5Cr5cN4h5SaBKAe/Fx0aJFAMQM7IYhVhZngk1U746YWiZgs9kYP348paXB0wIfLA4dOsT3338PwK3tLeh1sitYNI420Sb6JbnP+vrss89wOIJn1roQv+RZbNRLoSmoKDoFg8V9JounmCi05XQ6mTt3LgBXJUdikOsW4WOJ4Xp61I5rnDdvnsZphAgu3kLT8eKgOAfNWWOnpsjdZSKFJtFU+vTpQ/fu3XG4YGZGidZxRBNwuVzMyCjB6XL/e/fo0UPrSH5FCk2Cffv28cknnwAQ2aM94anNNU4kmoKi02EZ1AM1ykxhYSFvvvkmdrvssPAXTqeTTz/9FJfLRe/EcDpYwrSOJILMyLQYzHodhw8f9hY0hQhGniKEPjZS4ySisXmKh9K54B9+/vlncnNzCVMVBraQnzehjatT3L8X1q5d6z2fTwjRcK1bt8ZkMuGqsWMvKdc6ToPVFJSCy0VCQgLx8fFaxxFBSlEU7rrrLlRVZXdRNVuOV2odSTSyn45Xsr/EisFg4I477tA6jt+RQlOIy8vL44033sDhcBCWmkRkt3ZaRxJNSGcyEDe0N4pBz759+/j444+DYndSMFixYgUHDx7EpCqMamvROo4IQpFGlRvSogH3iMaSkhJtAwnRBFwul4zOC2KeQpN0NPkHTzfTFS0iCNfLy0qhjVZRRjpaTDidThYsWKB1HCGChqqqdOjQAQiOc5o8z8Fz9pQQTaVly5aMGjUKgCnpxRRWywbvYJFfZWd6egkAY8aMISkpSdtAfkheEYSw8vJyXnvtNcrKytDHRRNzRTcUOcA36OljIrFc2QMUhVWrVjFjxgytI4W8yspKpkyZAsB1raOJMaoaJxLBakDzCFpFGqiqqvJ+zwkRTEpKSqioqABFQR8jHRbBxlDbpZaTk6NxErF//37279+PqsDgllLUFdrydDX98MMKTpyQA9iFaCze8Xl5RRonaTjbcfdzkLF5whduvvlmOnToQLXDxdf7inDIBu+A53C6+GpvITaniy5dunDTTTdpHckvSaEpRNlsNiZMmMDRo0fRmcOIG9obnUEOLwsVYcmJRP8/e/cdHtV95v3/faZpVFAXAomOwBTTe7EBY2xTbcAFd9yNCXbixJvYGzt+nOyV7D7Zze4+TzZPfkk2m2RT7DiJN+4l7g1jMGB6F0IIIWk0M5pezvn9MToDJNimaOY75X5dl64rAaPzkTQzOvO9v/f9nToSgD/84Q+8/fbbihPlt2effZauri56F9q4qE4WRkXqWDSNFUPLAXjrrbdksVbknJaWFgCsJYVoVrnNzTW20sTvyOPHj8tZc4qZ3UxTehdRViAbZIRaw8sL6FdsJxyO8Oqrr6qOI0TOGD16NACRYx1ZPQlFj8aSHU3m1yREKlmtVtauXYvT6eSgN8Krh2UTRLZ78bCXw74oxcXF3HfffVgs8l7zdOS7kod0XefHP/4xu3btQrPbqLxkEtYiOQ8m3xQPH0Dx6MEA/OQnP2Hbtm2KE+Unl8vFiy++CMCSQWVYpatQpNjg0gLGVhViGAa//e1vVccRokcdO3YMAFuvIsVJRCpYigrAaiEej8tZLAodPXqUTZs2ATC3n3QzCfU0TWNe92PxlVdeIRKJKE4kRG5oaGjA6XSih6PEXF7Vcc5Z5Hgn6Inzmfr27as6jsgTvXv35vbbbwfg5cNeDnrDihOJc7XXHeIvTYli4V133SXnvH0OKTTloSeffJIPPvgALBoVcyZgr5A3iPmq14ThOAf1IR6P84Mf/EAO11bg6aefJhKJMLjUwZgqKfiK9FgyqBSLljjIfefOnarjCNFjzEKTtbRYcRKRCpqmJYuI5s9apN/zzz+PYRiMrnTSp8iuOo4QAIyvKaSiwIrX6+Wdd95RHUeInGCz2ZIdQOGj7YrTnLtId/YxY+S4CJFes2fPZvbs2RjAr3a5CMZ01ZHEWfJHdf57dycGMG/ePKZOnao6UkaTQlOeefHFF5OjLsqmX0hBX6nC5jNN0yifORZH7wqCwSDf+973ZIdwGjU1NfHWW28BsGxwmdz0irTpXWRnZp/EQvyvf/3rrB6FIcTJpKMp99m6i4hSaFLD4/Hw7rvvAifOxREiE1g1jbn1ifGazz33HLoui3lC9ISxY8cCEG7J3kKTmd38WoRIp9WrV1NTU4MrHOepvZ3y3juLGIbB7/a68ETi9O3bl5tvvll1pIwnhaY88u677/KrX/0KgF7jh1E0tF5xIpEJNKuFirkTsZWV4Ha7+d73vofXm71t8dnkd7/7HYZhMLaqkMGlBarjiDxz+YBSCqwaBw4cYP369arjCNEjkoWmUik05SqrdDQp9fLLLxONRhnYy8HgUofqOEKcYnqfYopsGq2trXz88ceq4wiRE8aMGQNApM2NHo0pTnP24v4gMY8fTdPkfCahRFFREWvXrsVqtfJJe5C3jvpURxJn6LUjXXzaETrlzC3x+aTQlCe2bNnCj3/8YwCKRgyk+MIhihOJTGIpsFN56WSsxU5aWlr4p3/6J0KhkOpYOW3nzp188sknWLTEGDMh0q2Xw8q8+sRu9CeffJJYLPveOApxMl3XaW1tBcDaS0bn5SpbL+loUiUcDvPaa68BcEm/EunEFhmnwGphVt9EV9MLL7ygOI0QuaG2tpaamhrQDSLHXKrjnLVwSwcAQ4cOpaSkRHEaka+GDx/OTTfdBMCfD3rY65b1tky30xXihUOJTfi33XYbQ4bIOvqZkEJTHti3bx//+q//SjwexzmoL6WTR8gbQ/E3rEVOKudPxlJg58CBA/zgBz+QhecUeuaZZwCYXltMbznfQCgyr18JJXYLra2tfPjhh6rjCHFePB4P0WgUNA1rsew2y1XWXoUAMupXgbfffhufz0eV08qYqkLVcYQ4rdl1JVg12LNnD/v27VMdR4isp2naifF5R7Pvd2+4OZFZxuYJ1S677DJmz56NbsB/7XTRGZL1tkzVHozxy90dyXOZLrnkEtWRsoYUmnJcc3Mz//RP/0Q4HMbRt4rymXL4ofhstrISKuZNQrNZ+fTTT/nRj34k881T4ODBg3z66adYNJjfX843EOoUWC3M6T7P4Nlnn5V50SKruVyJXbaWQgeaRW5xc5W1KFFEdLlc8pqVRrqu8+KLLwIwp64XFnk/ITJUmcPKxJrEiE3pahKiZ0ycOBGA0JG2rPrda8R1wkcT5zOZX4MQqmiaxp133smgQYPwx3T+c2cHkXj2PJ/yRTiu87MdHQRjBg0NDaxevVp1pKwi78JzWHt7O9/97nfx+XzYq0qpmDMBzSo/cvH5HDXlVMwZDxaNDz74gF/84hdZdTOZDZ577jkAxlcXUuW0KU4j8t2sPiUUWDWamprYvHmz6jhCnDOz0GQtlG6mXGYpTJxpGA6HCQaDitPkj08++YRjx47htGpM6yNnoInMNrd7E81HH30k3Y9C9IDRo0fjcDjQAyFinV2q45yxSKsLIxanvLycQYMGqY4jBA6Hg6985SuUlJTQ5Ivy+32dst6WQQzD4Hd7O2kJRCkrK+PLX/4ydrtMIDobUnXIUV6vl+9+97u4XC6spcVUXjIZi10WtMWZKairoXxmorX81Vdf5Q9/+IPiRLnj5BFll/STbiahXpHdwsw+iTNPnn32WcVphDh3nZ2dAFiKChQnEalksdvQuu9pzeKiSD2zm2lm32IKZOOayHD1JQ6Glxeg6zqvvPKK6jhCZD2Hw8GYMWOARFdTtggdOQ7AhAkTsEi3u8gQNTU13H///WiaxobjAd5t8auOJLq92ezjk7YgVquVBx54gMrKStWRso680uagQCDAP/7jP9LS0oKlyEnVpZOxOB2qY4ksUzi4L6VTRwHwxz/+kZdeeklxotzwwgsvYBgGIyoK6Fciz0uRGebU98Kqwa5du9i7d6/qOEKck2RHU5F0NOU682dsFhdFajU2NrJjxw4sGlxcJwepi+xgdjW9/vrrhEJy6LoQ52vChAkAhJuPK05yZgzDINxdFJOxeSLTXHjhhVx//fUA/OmAm71u+T2l2s7OEH8+6AHgpptuYsSIEYoTZScpNOWYSCTCv/zLv3Dw4EEsBXaqLp2MtVgO6zUZuk7MFyDuOzFqJe4LEvMFMOQsor9RfMEASsYNA+CXv/wl7777ruJE2c3j8fDmm28CMF+6mUQGKS+wMql3YhSSdDWJbOV2uwGwFkpHU64zu9ak0JQeL7/8MgDjqgopL5AJCSI7jKhwUlNoIxgM8s4776iOI0TWMwtN0XYP8WBYcZovFnP7iPuD2O12Ro8erTqOEH9j8eLFzJw5E92A/9rpoj0YUx0pbx0PRPnFzg4M4OKLL+ayyy5THSlrSaEph+i6zg9/+EN27NiBZrdSOX8ytjLZdXiyeCBE25/epv2595J/1v7ce7T96W3iAdlBcDolY4ZQPGIgAD/+8Y/lDJfz8Je//IVoNMqAEjsNZbIQKjKLOcrx448/prW1VXEaIc7eidF50tGU68yOJhmdl3pdXV28917ivvki6WYSWcSiaczumxgN/Morr8gZGEKcp4qKCoYMGQJAuDnzx+eFu8fmjR49GqdT7g1F5tE0jbvvvpshQ4bgj+n8bEc7oZhsgE+3QEznpzs6CMUNhg8fzh133IGmaapjZS0pNOUIwzD4+c9/zoYNG8BioWLuROxVZapjiRygaRq9Jo+gcHAd8Xicf/3Xf5XRWufAMIzkbsqL6krkF5fIOH2K7FxQniiAmouKQmQTn88HgKVADmzNdRZH4mfs98tM+1R78803iUaj9Cu2M7hURv6K7DKttpgCq0ZzczPbt29XHUeIrGeOoDPPPspkoe5imNmJJUQmcjgcPPjgg5SXl9MSiPHfe1zosjEibXTD4Je7OjgejFFZWclXvvIV7HZ5L3k+pNCUI/7whz/wl7/8BYDy2WMp6FOlOJHIJZqmUTbzQgrqqolEIvzv//2/aW5uVh0rq+zdu5fW1lYcFo2x1TLOUmSmKbWJ8XnvvPOO7PwVWScYTIzFtdhltFeu0xyJn7H5Mxepoes6r776KgCzZZOMyEJOm4Up3aOBzRGQQohzZxaaIkc7MGJxxWk+WzwYJtrmBuR8JpH5KisrefDBB7HbbWzrCPFSo1d1pLzx7EEPuzrDOBwOvva1r1FWJg0b50sKTTng1Vdf5Y9//CMApdNGUTiwj+JEIhdpFgvlF4/HXl2Gz+fju9/9Lh0dHapjZQ3zfKux1YUUWOWlV2SmMVWFOCwara2t0rkosk4gEABAc8gutFxnFhPNn7lIjU2bNtHe3k6xzcLEmiLVcYQ4J7O7Rz5u2rSJtrbMH/clRCYbOHAgVVVVGPE44WOZuxZgjvYbPHgwVVWyCVtkvoaGBu688y4AXmnqYlOb3OOm2ketft5oTkzEuPfeexk0aJDaQDlCVjuz3IYNG/iv//ovAErGNVA8fIDaQCKnWew2Ki+ZhLW0GJfLxfe+9z0ZW3MGotEoH3zwAUByV6UQmajAakl23MnB2SLbmN0tmt2qOIlINc0uHU3p8PrrrwMwrU8RDqt0M6XCvHnz+P73v8+8efMA8EYyt0MgW/UpsjOsrADDMHjzzTdVxxEiq2madmJ8XlPmjs8zs02aNElxEiHO3EUXXcSSJUsA+O2eTo74IooT5a5D3jBP7k2c77t8+XKmT5+uOFHukEJTFtu3bx8//OEPMQyDouH9KRkzVHUkkQcsBQ6qLp2MpbCA5uZm/vVf/5VYLKY6VkbbvHkzfr+fMoeFYd1n4AiRqcxi6Icffkg0GlWcRogzE41Gk49Xi8zVznkyOi/1Ojo62LJlCwDTa4sVp8ldixcvpq6ujsWLFwPgCsk9dSrM6JN4DL/11lvouhy0LsT5MIs34ea2jBy1bcTihFvaARmbJ7LPqlWrGD9+PFHd4Gc7OvBFZQNKT/NG4vx8ZwdxAyZPnszKlStVR8opUmjKUq2trXz/+98nEolQUF9D6ZSRMjddpI21uJDKSyah2axs376dn/70pxl5k5kpzLF5k3oXYZHnqchww8oLKHNY8Pv9fPLJJ6rjCHFGTi44SEdT7pPRean31ltvYRgGQ8sc9C6S4m2qPP/88xw9epTnn38egEqnnDGXCmOrCym2WXC5XMkCqhDi3IwcORKn04keDBNt96iO8zfCxzogrlNVVcXAgQNVxxHirFgsFtauXUttbS2d4Ti/2OkiLmttPSamG/zXzg48EZ36+nrWrFmDxSKlkZ4k380s5PP5+Kd/+ie8Xi+2ylLKLxqHJk8MkWb2ylLKLx4Pmsbbb7/Nn/70J9WRMlI0Gk2+oZXzDUQ2sGha8rG6adMmxWmEODPhcDjxPywWuSfKA5otsRif/LmLHqXrenLEmNkJIlLjjTfe4Gtf+xpvvPEGAKUOKZSngs2iMbk2cW9jjoQUQpwbu93OuHHjgBNnIWWS8JHE2LyJEyfKZmyRlYqLi/nqV79KQUEBez1hnjuYeQXdbPXMATcHvBEKCwt58MEHKSwsVB0p58g78SwTi8X4wQ9+QEtLC5YiJ5XzJiZ3dQqRbs76GkqnjgTg6aefTnbuiBP2799PJBKhxG6hvlh2BIvsMLzCCcCOHTsUJxHizCS7amU9Ia9IN3VqfPrpp7S3t1No0xhbJZtkRG4wi6affPIJnZ2ditMIkd0mTJgAQCjDCk2GYRBqTozNMzMKkY369evHmjVrAHij2cfG49LFf74+PObn3ZbEGfNr166lb9++ihPlJik0ZZlf/epX7Ny5E81uo/KSSViLnKojiTxXPHwAxaMHA/CTn/yEgwcPKk6UWcyF+oayAtlRJbLGkFIHFg3a29s5fjxzD/oV4q9pUmnKD/JjTql33nkHgMk1RTis8s0WuaFPkZ3BpQ50Xef9999XHUeIrDZu3Dg0TSPm8hIPhFTHSYp1dqEHQjgcDkaNGqU6jhDnZerUqSxbtgyA3+3t5IgvojhR9mrsivD7fYlNJldffbWc35ZCUmjKIm+99RavvvoqAOWzx2Kv6KU4kRAJvSYMp6C+hmg0yr/8y7/g9XpVR8oYJxeahMgWBVYLA0ocgHQ1iewgHU35Rn7QqRIKhdi4cSOQOFtSiFwyqXs0sBSahDg/ZWVlDBkyBMis8XlmltGjR+NwOBSnEeL8XXvttYwbN46obvCfOzrwR+OqI2Wdrkic/9zRQdyASZMmcdVVV6mOlNOk0JQlDhw4wH/+538CUDK2AWe/3ooTCXGCpmmUzx6LtVcRHR0d/J//83+Ix+UXYCQSYe/evQAMK5dCk8gu5mNWCk1CCJE/Nm3aRDgcpsppZWAvWaQTuWV8dSEW4ODBg7S0tKiOI0RWOzE+r11xkhPMUX4yNk/kCovFwtq1a+nduzeucJzf7umU0dFnQTcMfr3HhScSp2/fvqxZswaLnOebUvLdzQJer5cf/OAHRKNRCvrVUDJ2qOpIQvwNi8NOxdwJaDYr27dv53e/+53qSMrt27ePaDRKqd1C70I5S01kF7MLb8eOHXIzK4TILN0NTfLa1PM++OADACbWFMnIX5FzShxWhlck7m/Mx7oQ4tyMHz8egEhLO0ZcVxsG0MMRou1u4EQ2IXJBSUkJX/7yl7HZbGxzhZLnDIkv9lazj12dYex2O1/5ylcoKpJu/VSTQlOGMwyDH/3oR3R0dGAtLaJ81lh50ycylr28F2UzxwDw/PPPJ0ev5Ktdu3YBMLRczmcS2WdwqQOrBi6XS85pEkJkFPM3qhSaepbP52Pz5s1AotAkRC6aeNL4PHkNEeLcDRo0iPLycoxYnMhxl+o4hI+2gwH9+/enurpadRwhetSgQYO4/vrrAfifA26O+uW8pi9yuCvCc4c8ANxyyy3069dPcaL8IIWmDPfaa6+xZcsWsFqomDMBi8OuOpIQn6twYB+KRw4C4Cc/+Ulen9fU3NwMQP8SGT0jso/DaqG2KPE7R8bLiExntyceq0Zcl4XDPGDEEuN55fyFnrVp0ybi8Th9i2z0LZb3HCI3ja0qxKbB0aNHOXLkiOo4QmQti8XC2LFjAQi3dChOcyLDuHHjFCcRIjWuuOIKJkyYQMyAX+x0EcmATsJMFYrp/HKXi7gBU6ZM4ZJLLlEdKW9IoSmDtbS08Otf/xqA0gnDsZf3UpxIiDPTa8IwbOUleL1efvrTn+btop/ZBVLllLF5IjtVOa0A0tEkMl5yDIJhJIsQInfp0RiAjL/oYWY305iqQrVBhEghp83C8HIncOIxL4Q4NxdeeCEAEcWFJsMwkoUmM5MQuUbTNO655x7Ky8tpDcb40wGP6kgZ6w/73bSHYlRVVXHXXXfJhKE0kkJThorH4/zHf/wHkUgER58qikYMVB1JiDOmWa2UzxoLFo2PP/6Yt99+W3UkJU4UmqyKkwhxbswiqRSaRKYrKChIHuxqdBchRO4yIomfcWGhFER6Sjwe59NPPwVgZKVTcRohUst8jG/ZskVxEiGy2+jRowGIurzoYXWjvOJdAfRACJvNxgUXXKAshxCpVlpayn333YemaXxwzM/mtoDqSBnn4+N+NhwPoGkaa9eupaSkRHWkvCKFpgz15z//mf3796PZbZTPvFCqryLr2CtL6TVuGAC/+MUvaGtrU5wovYLBIF1dXYB0NInsJYUmkS00TUsWHXQpNOU86Wjqefv378fv91Nk0xjYS0YSitw2oiJRaNq9ezeBgCzSCXGuKioqqK+vByB8TN05TWY30/DhwykoKFCWQ4h0uPDCC1m6dCkAT+7rxBuRaQ4mdzjG7/e5AVixYgUjRoxQGygPSaEpA7W1tfHMM88AUDp1FNZi2a0pslPxqMHYa8oJhUL85je/UR0nrczCWrHNQqFNXmpFdpLReSKbmEUHs9tF5C4jEgWk0NSTzBFiF5Q7scgGN5HjagptVDttxONxtm/frjqOEFktOT7vmLrxeea1ZWyeyBdXX301gwYNIhgz+J8DbtVxMsYf93sIxw0aGhq46qqrVMfJSzm1+jl37lw0TfvMj5deekl1xDPy29/+lmg0iqO2ksLBfVXHEeKcaRaNsmmjQYP169eza9cu1ZHSprW1FZCxeSK7ndzRlK9nrYnsYXY0GdGo4iQi1cyOJhmd13O2bt0KyNg8kT/Mx7qc0yTE+TGLO2FF5zQZukG4u9BkjvITItfZbDbuvPNONE1jY1uQ3Z0h1ZGU2+4KsrUjiMVi4c4778RqlbU4FXJyntPKlStPO4PRbOnNZLt37+bDDz8EoHTyCBmZJ7KevaIXRQ39Cext4pe//CXf+c53kudo5DKXKzE6oLwgJ19mRZ6o6H78hkIhgsGgdA+IjFZcXAyAHpZCU64zf8bmz1ycn3A4zMGDBwEYXi4jh0R+uKC8gHeO+ti7d6/qKEJktZEjR6JpGvGuAPFACGtRejcsxNxdGJEYTqeTIUOGpPXaQqg0ZMgQFixYwCuvvMLT+9383cRa7Jb8XEOOxHX+0D0yb9GiRQwYMEBtoDyWkyug3//+9xk0aJDqGGdN13V++ctfAlDY0A97ZaniREL0jJLxwwgeauHQoUO8/fbbzJ07V3WklIvHE3Ny7blfUxM57OSpj7GYjCMTma2qqgqAuF929OW6uD8InPiZi/PT2NiIYRiUOiyyQUbkjf4libPImpubCYVCOJ3SzSfEuSgqKmLAgAE0NjYSaXNTOLBPWq8faXMDMGzYMOlgEHnn2muv5aOPPqLN7eYvTV1cMTA/15FfaerCFY5TVVXFihUrVMfJa7IEmkE++ugjDh48iGa30mv8MNVxhOgxVqeDkrFDAfj973+fFwvWuq4DSFeiyGonn9Mho/NEpquurgZOFCFE7jKLiTU1NYqT5IYDBw4AJxbehcgHZQVWyhwWDMOgsbFRdRwhslpDQwMA0XZ32q8dPanQJES+KSoq4uabbwbgtSYvbcH8m+zQ4o/y+pEuAFavXi0bRxSTQlMGeeGFFwAoHjkIa6GMrRC5pfiCgVgKC+js7EyOh8xlZqEpTzuXRQ4xH8Nml54QmcosOsR9UmjKZYZhJIuJZnFRnB9zbJ4UmkS+6df9mDeLrUKIc2MWeczuonSKdBe3pNAk8tX06dMZM2YMMQOe3ufOqw2ihmHw9L5OdAMmTZrEpEmTVEfKezlZaPrZz37Gfffdx5e+9CX+/d//ncOHD6uO9IX27t3Lvn37wKJRNFxmSYrco1ktFF2QeGy/9NJLOf/Lzyw0WaWjSWQ580bBfEwLkamkoyk/6KEIxHU0TZPReT3kREeTXXESIdLLLK6axVYhxLkxizzRDi9GPH3vGeKhCPGuAABDhw5N23WFyCSapnHbbbdhs9nY7Q6z3xNRHSltdrnD7PdGcDgc3HrrrarjCHK00PSd73yHH/3oR/zwhz/kgQceoKGhgW9/+9uqY32ul156CYDCQX2lm0nkrKJh/cFq4cCBA+zZs0d1nJQyuz+kzCSynTk+TzqaRKZLFpp8oZzezGAtclKz/GKql8xK/ln1klnULL847Qdwq2AWEisqKrDZ5Dyh8xWLxTh69ChwortDiHxhFlezYWOqEJmsT58+lJSUgK4T7fSm7brm2Ly6urrE9YXIU3369GHOnDkAvHYkfc9B1V5rSnyt8+fPl0kHGSKnCk0XX3wxv/rVr9i/fz+BQIDdu3fzD//wD9hsNh577DH+7d/+7XP/fTgcxuv1nvKRDh0dHaxfvx5IjM0TIldZnQ4KB9cB8OKLLypOk1oyOi/15s2bx/e//33mzZsHgDcihZBUMB/D0tEkMp3Z3WLE4+jh3J1Prlks2EqKsJYUJv/MWlKIraQIzZJTt/anZZ7PJG8me4bbnRixYtWg1JH7jx8hTlbhTBSrXS6X4iRCZDdN0050NaVxfJ6MzRPihCVLlmCxWNjVGaapK/e7mg56E91bVquVRYsWqY4juuXUu4knnniCm266iSFDhlBYWMjw4cN55JFHeOaZZwB4/PHHCQY/e5zKd7/7XcrKypIf/fv3T0vu9evXo+s69t4V2CtL03JNIVQpHjEQgE2bNn3u8zHbFRQkOhPD8dzdVa/a4sWLqaurY/HixQC4QjHFiXKPbhhEuh/D5mNaiEzlcDiSxYeYx6c4jUiVmDtx2G+fPn0UJ8kNnZ2dAJQ6rGgy7lfkGbO46vP5iEZzd4OCEOkwePBgAKKdXWm7Zqz7WkOGDEnbNYXIVLW1tcyYMQOA146k73moymtNia/x4osvlnHaGSSnCk2f5bLLLmPy5Mm43e5k59DpPPzww3g8nuRHU1NTWvJt2rQJgMIBtWm5nhAq2cpLsPYqIhaL8emnn6qOkzLl5eWAdNmk0vPPP8/Ro0d5/vnnAah0ygilnuaL6ugkdimWlZWpjiPEFxo4MLGZIebKn5ER+SbqSrypNH/W4vy43W4gUWgSIt8U2yxYu+ur5nNBCHFu+vXrB6R3s0/M7Tvl2kLku2XLlgGwtT1IayB3N1A0+yJsd4XQNI0lS5aojiNOkheFJjjRStvS0vKZ/01BQQGlpaWnfKSa3+9n9+7diev3653y6wmhmqZpOPvVAPDJJ58oTpM6FRUVgBSaUumNN97ga1/7Gm+88QYgi2SpYD5+S0tLsVrl+ysy36BBg4D07qYV6RXrPvvB/FmL82MurpfJ2DyRhzRNS94/SqFJiPOTLDS5fWk5K1OPxpLnNtbX16f8ekJkg/79+zNp0iQM4C853NVkdmxNmzaNvn37Kk4jTpY37yjMsRDFxcWKk5xq69atxONxrKXF2HoVqY4jRFoU1CeKqp988knOnvtidjR5Irn59Yn8YBaazMKpEJnO7HKJSkdTTtLD0eQZTdLR1DNOHp0nRD4yH/vmc0EIcW769OmD1WrFiMWTv6tTyeycStcmcSGyxZVXXgnAx8cD+HJw47MnHGdzW6LIbHZwicyRF4WmtrY23nnnHQAmTpyoOM2ptmzZApDs8BAiHzhqK9DsNrxeL4cOHVIdJyXMQlMgphPT5ZwmkZ283YVS8/EsRKZLjs7z+DDiUujPNdHubqaamhqKimSDVk8wz6WxW+R8JpGfzMd+LCZnfQpxPmw2G3V1dUB6xueZY/PSdba6ENmioaGBQYMGoRuwzZX6om+6fdoRxCAxuUwmHGSenCk0vf/++zzzzDPE46dWaw8dOsTy5cvx+/0sW7Ys42a3NjY2AuDoLbvFRf7QLBbs1YnzXsznQK4pKSnBZkucGSTj80S28nQ/dqXQJLJFdXV1ogChG2k9I0Ckh3k+k7yp7DnmvUpc9sSIPBXvHvFlPheEEOfOHGFnFoFSybzPy7Q1PiEyweTJk4FEUSbXmF+T+TWKzJIzhaY9e/awfPly+vXrx+LFi7nxxhuZPXs2I0eO5L333mP06NH85Cc/UR3zFLquc/ToUQBsZSWK0wiRXvbux/yRI0cUJ0kNTdOS48Y6w1JoEtnJfOzK6DyRLTRNO3FOU4dHbRjR48yfqYzN6znm+XvSfS3yVbz7sS9nUQpx/pLnNKWjo8njB+R8JiFOxyzC7O4MEc6hKQ+BmM5eTxiQQlOmyplC07Rp01izZg11dXVs2LCBp556im3btjF+/Hj++Z//mQ0bNtC7d2/VMU/R2tqaGFdhtWAtkfEf6TZv3jy+//3vM2/ePADiwbDiRPnFVp7bhSY40cZ/xBdRnESIc9Pc/diVkRQim1xwwQUAhI+5FCcRPckwDCKtiZ+p+TMW58/s4tDTcHC7EJnI7OaTjiYhzl91dTUA8UDqx3WZ1zCvKYQ4oX///tTW1hIzYFdn7qx17nSF0I1EUbtv376q44jTyJlC08iRI/mP//gPNm7cyPHjx4lGo7jdbj744AMefPBBCgsLVUf8G+YCu62sBE3moqfd4sWLqaurY/HixQDEfbnXUprJ8qHQNHjwYACafFHFSYQ4ezHd4Kg/8dgdMmSI4jRCnLlRo0YBEGl1Ycjiec6Ie/3owTB2u51hw4apjpMzkh1N8lQReUpG5wnRcyorKwHQA6lf2DYLTeY1hRAnaJqWk+PzZGxe5suZQlM2am9vB8DWS7qZVHj++ec5evQozz//PADWkswrRuYyW69iADo7O3P28F1zcb6pSzqaRPZp8UeJG1BcXExNTY3qOEKcsWHDhmG329GDYeJev+o4ooeEu7uZhg0bhsPhUJwmdzidTgCCsdwZqyLE2Qh2V1kLCgoUJxEi+5lFn1R3NBmxOEY4eso1hRCnmjRpEgA7XMHkmNhsFtMNdnYmXluk0JS5ZNuOQsFgohJrcciPQYU33niDN954I/n/rYXy5iKdtJMe96FQiJKS3DunzOxoOh6MEY7rFFilti+yR1P32LzBgwejadJ1K7KHw+Fg2LBh7Nixg3CrS87BzBGR7lGIZsea6Bnm2JHjwdzc9CPE5wnFdDyRxHmUffr0UZxGiOxnFn2MaAw9EkvZWpd57IHD4aC4uDgl1xAi2w0fPpzCwkICwSDHgzH6FttVRzovLf4o4bhBSUlJcq1NZB5Z9VTILDRpdik0ifyjWSzQXXgxnwu5pry8nIqKCgygWcbniSxjjnyUsXkiGyXH58k5TTnBMAwpNKVIXV0dAB3BGLEc2O0qxNkwC6ylpaU5uelNiHRzOp0UFSUm9sSDqetqOnlsnmyIE+L0LBZLchNFeyj7NxS1dX8NdXV18rzPYFJoUkgKTSLfWbof+7laaIITXU2HfTI+T2SXkzuahMg2Jxea5Jym7Bdz+9DDEQoKCmhoaFAdJ6dUVFRQWFiIDrRLV5PIM62BxKaa+vp6xUmEyB0nzmlKXaFJl/OZhDgjtbW1QG7c45lfg3QgZzYpNCkUCiV+OVqk0CTylJYHhSZzQWyfO/UHogrRU/xRPdmFJ4u6Ihs1NDTgdDrRwxGi7R7VccR5Cje3ATBixAhsNrlv7kmapiXH57XmwCKEEGfD7GgyO/uEEOevtLQUAD2Uuo2W5uc2ryWEOD2zKNOWA/d45tdgFs9EZpJCk0IWS+LbLzttRd7qHtFitVoVB0md8ePHA7DHHZaRNCJr7O4MYQD9+/enqqpKdRwhzprNZku+/oaaWtWGEectdDjxM5SDf1PD7OY46pfua5FfjvoTm2qk0CREz3E6nQAYsXjKrmF+bvNaQojTy6XReebXIB1NmU0KTQoVFBQAqf0FLEQmMx/75nMhFw0cOJDy8nIiusF+j3Q1ieywozPRcTtu3DjFSYQ4d1OnTgUSRQrZ1JO94v4Q0Q4PmqYxadIk1XFy0vDhwwHY1Sn3KSJ/RHWDvd0TBy644ALFaYTIHekoNOlSaBLijJjdP7nQ0dQuHU1ZQQpNCjkcDkAKTSJ/GfHEY998LuQiTdOSi/U7O1M3p1qInqIbBru6H6tmR4gQ2WjcuHHY7XbiXQFiHp/qOOIcmR1pw4YNo7y8XG2YHDVx4kQAGrsieCPyvkTkh73uEBHdoKKiQs6jFKIHmZtI9WgKO5qiiQVnKTQJ8fl69+4NgDscR8/ijXeRuEFXVAdOfE0iM0mhSSHpaBL5zDCMvOhoghOL9TtcUmgSme+IL4ovquN0OpO73IXIRoWFhVx44YXAidFrIvuYP7spU6YoTpK7KioqGDJkCADb5V5F5IltHYnH+sSJE9E0TXEaIXLHiY6m1HVQyOg8Ic5M8sgWIJt/0538a9r8mkRmkp+OQoWFhQAYkexvYRTibJm7kCD3bxDHjBmDxWLheDBGRw7MxhW5zSyIjhkzBpvNpjiNEOfHLE6Emo4rTiLOhR6KEDneCUihKdXMsYTbOoKKkwiReoZhsN2VeKzLSE4helY6NlTny4ZVIc5XNJo4i9CmkdWbKmwnRTe/JpGZpNCkUEVFBQDxgOwcFPknHkjMRC8qKsr5QlNRUVGyM0QWcESmMxdeZGyeyAXmTvWYy0vM61cdR5ylUFMrGAYDBw6UMRkpZi6273GHCMd1xWmESK0mXxRPRKegoIBRo0apjiNETpFCkxCZI1losmRvkQkSRTJr95cQS2G3pDh/UmhSqKqqCoB4UApNIv/o3QXWyspKxUnSY9q0aQBsbAsoTiLEZ2sNRGnyRbFYLLLDV+SE0tJSxo4dC0DwwFHFacTZCuxvBmDGjBmKk+S+/v37U1tbS1SHDa1yryJy23stiXP7JkyYkNNnxQohhMhvZlEm2wtNcOJrkI6mzCaFJoXMjibdH8bI4kPZhDgXZief+TzIddOnT8disXC4K8rxgPxiFJlp4/HE4uK4ceMoLS1VnCZ/zZ07F03TPvPjpZdeUh0xq1x88cVAomgh91vZI+b1E21zo2kaF110keo4OU/TNK644goA3mzuyuoDo4X4PJ5InI+773fMx7wQoufoendXbCrHdHV/7uS1hBCnlSsdTQB2KTRlBTl8QSGzk8OIxzEiUbQC2U0l8kc8zzqaysrKGDt2LJs3b2ZjW4CFA8tURxLiFIZhJBdeZs2apTiNAFi5ciUlJSV/8+f19fUK0mSvSZMmUVRURCAQIHKsg4K+1aojiTNgdjONHTs2bzalqDZnzhz+8Ic/0O7zsbU9yPiaItWRhOhxbzf7iBswfPjw5GhrIUTPiccTY+20FC5sm5/bvJYQ4vROnNGU/YUm82uQQlNmk0KTQg6Hg/LyctxuN7GuAA4pNIk8Eu9KLGjX1tYqTpI+s2fPZvPmzXx8PMAVA0qz+jBGkXsOeiO4wnGcTqeMzcsQ3//+9xk0aJDqGFnP4XAwc+ZMXnvtNQL7m6XQlAUM3UiOOpwzZ47iNPnD6XSyYMEC/vSnP/F6cxfjqgvlXkXklFBMT47NW7JkieI0QuQm6WgSInN0dHQAUGLP/oFmJXYL7kicjo4OhgwZojqO+AzZ/0jLcnV1dQDEvDILXeQX81D2vn37Kk6SPpMmTcLpdNIRinOoK6I6jhCnMLuZpk6dKgfripxjjs8LH25Fj8guuEwXOdaBHghRXFzMxIkTVcfJK5dddhl2u53DXVEOeOVeReSWD1v9hOIGffv2ldcWIVLELP6kdKOCFJqEOCONjY0A1BXbFSc5f3Ulia/B/JpEZpJCk2J9+vQBIN696C5EPjAMIy8LTQUFBUyZMgU4sagvRCaI6Qab24OAjM0TuWno0KHU19djxHVCjcdUxxFfwBybN3PmTBwO6fhPp7KysuSZWC8c8si5ZiJnhGI6rzd1AbBo0SIsFlkKESIVksUfGZ0nhHJmUaa+JPsLTfXFUmjKBnJ3pZi5yB6TQpPII3o4ihGJoWlastiaL2bPng3AxuMBwnHZgSUyw5b2IIGYTmVlJaNHj1YdR3T72c9+xn333ceXvvQl/v3f/53Dhw+rjpS1NE1LjmDz72mSxfMMFg+GCR1OFANlbJ4aV111FQ6Hg/3eCBvbZGOMyA0vNnrxRnX69OmT7HIVQvS8SCTRDaulspjb/bnNawkhTi9ZaMqBjqb64sTmMyk0ZTYpNCmWHJ3n8SlOIkT6xLsf79XV1Xm3U3n06NHU1tYSihtslK4mkSHe7T6v4JJLLpEdvhnkO9/5Dj/60Y/44Q9/yAMPPEBDQwPf/va3VcfKWnPmzMFutxNzeYm2uVXHEZ8hsLcJdIOGhgaZv65IdXU1y5cvB+B/DngIxmRjjMhuR/0R3um+17n11lux27N/wU2ITOX3JzZRawWpe55Zuj+3eS0hxN9yu914PB40cmt0Xnt7uzz3M5isJinWr18/INHRZMh8WZEnou7EGz3z8Z9PLBYLCxYsAOCdo37ZVS+Ua/ZFOOiNYLVamTdvnuo4gsR5Qr/61a/Yv38/gUCA3bt38w//8A/YbDYee+wx/u3f/u0z/204HMbr9Z7yIRJ69eqV7Cr175KdcJnI0HUCe5oAuPzyyxWnyW+LFi2ib9++dEV1XmyU1xGRvQzD4Ol9bnQjcQ7luHHjVEcSIqf5fIn3+hZHCgtNDik0CfFFzM6fmkIbDmv2L/8X2SxUFlgBZNJHBsv+R1qWq66uxul0gm4Q65LuBpEfYt2Fpv79+ytOosbFF1+Mw+GgJSAHbQv13m1JvEGbMmUKFRUVitMIgCeeeIKbbrqJIUOGUFhYyPDhw3nkkUd45plnAHj88ccJBoOn/bff/e53KSsrS37k6+vsZzGLF6HDrcT9IcVpxF8LNbaiB8OUl5czbdo01XHymt1u59ZbbwXgnaM+mn1yvyKy04bjAQ54IxQ4HNx0002q4wiR88zij0U6moRQas+ePUBunM9k6tf9tezevVtxEvFZpNCkmKZpJ7qa3DI+T+SHmDtxEG8+djQBlJSUMGvWLADePSrPe6FOIKonRzhedtllitOIL3LZZZcxefJk3G4369evP+1/8/DDD+PxeJIfTU1NaU6Z2QYMGMDIkSPBMPDvkZ1wmca/O7Hzcv78+dhsNsVpxNixY5k6dSoG8NQ+N3HpwhZZpisS588HPQAsX7GC6upqxYmEyH3J0Xkp7GgyP7fZPSWE+Fsff/wxACMrnIqT9JwR3V+L+bWJzCOFpgyQLDR1dilOIkTqGYaR16PzTOb4vC0dQTyRuOI0Il99dNxPRDcYMGAAF1xwgeo44gwMGzYMgJaWltP+fUFBAaWlpad8iFOZXU3BvU0YcXn9zRSRDg/RNjdWq5X58+erjiO63XzzzRQWFtLYFeElGaEnsohuGPx6jwtfVKe+vp5FixapjiREXkjn6DwpNAlxei0tLTQ1NWHR4MLKQtVxesyYqkI04MCBA7S1tamOI05DCk0ZwBxrE3VLoUnkPj0YxohEsVgs1NfXq46jzKBBgxg+fDi6Ae9JV5NQQDeMZEfdggUL0DRNcSJxJjo7OwEoLi5WnCR7TZo0iaqqKvRwlOCB0xfsRPr5dx4CYPr06ZSXlyvNIk6oqqrizjvvBOC1pi52d8rIyfNVXmDl0Sl9+PrE3sk/+/rE3jw6pQ/l3WcPiPP3ZrOPXZ1h7HY769atky5JIdJA13UZnSdEBvjoo48AGF5eQJE9d5b+ezmsDClzALBhwwbFacTp5M6jLYsNGDAAkI4mkR+i3Y/zvn37YrfnzqzYc3HFFVcAiTNywnFdcRqRb7a2B2kPxU8Z5SgyW1tbG++88w4AEydOVJwme1mt1mRXk2/HAQxdxoGpFusKEDp0DICFCxcqTiP+2owZM5g3bx4G8N+7XXRJJ/Z5sWoaVU4blc4ThY9Kp40qpw2rbProEYe8YZ47lBiZd8sttyTfbwshUsvr9aLrife1FqcjZdexFBYA4PF4iMViKbuOENnKLDSNrcqdbiaT+TWZX6PILFJoygBmR1PcF0SPyi9JkdvM85nkgHqYOnUqtbW1BGI6Hx6T3VgifQzD4C9HEs/Fyy67DKczd+Y2Z7v333+fZ555hvhfjXQ7dOgQy5cvx+/3s2zZsrwePdoT5s+fT3FxMXFvgFBTq+o4ec+/4xAYBmPGjGHIkCGq44jTuOWWW+jXrx9dUZ3/3u1Cl/OaRIYKxHR+ucuFbsC0adO45JJLVEcSIm90dHQAYCkqQLOkbrnR4nSARcMwjGS3vxAioa2tjYMHD6KRGDWXa8ZVJ76mvXv3yvM/A0mhKQOUlpYmR4TE3DJCS+Q2s6NJdhaCxWJhyZIlALzR7CMuu+pFmuxxh2nyRSkocHDZZZepjiNOsmfPHpYvX06/fv1YvHgxN954I7Nnz2bkyJG89957jB49mp/85CeqY2a9wsLC5GPft+0AhiyaKxMPhgnsPwLAsmXLFKcRn6WgoID7778fh8PBbnc4uVlBiExiGAZP7u3EFY5TU1PDXXfdJaOBhUgjs9BkLUrtJjZN05LXMK8phEhYv349AEPKHPRy5N5I3vICGwN7OTAMQ7qaMpAUmjKE2d0h4/NErjOLqdLRlHDRRRdRXl6OOxxnY1tAdRyRJ8wFwrlz51FaWqo4jTjZtGnTWLNmDXV1dWzYsIGnnnqKbdu2MX78eP75n/+ZDRs20Lt37y/+ROILXX755TgcDmIuL5EWWaRQxb+rEeI6DQ0NjBo1SnUc8Tn69evHrbfeCsALh7xs6wgqTiTEqV4+3MWW9iBWq5V169ZRVFSkOpIQeSVZaCpOfReFWWhyuVwpv5YQ2ULXdf7yl78AMLEmd38HTqxJvMa89tprsmEww0ihKUOY3R1RtxSaRO4ydJ2YJ1Foko6mBIfDkTyr6fUjXTKKRqTc4a4Ie9xhLBYLixYtUh1H/JWRI0fyH//xH2zcuJHjx48TjUZxu9188MEHPPjggxQW5t74A1VKS0uZN28ekOhqEumnR6IEdh8GYOnSpdJ5kAXmzp3L/PnzMYBf7nLR5IuojiQEABuPB3jpsBeA1atX09DQoDiREPknXR1NAJZi6WgS4q9t3ryZ1tZWCm0ak3vnbqFpam0xBVaN5uZmtm3bpjqOOIkUmjJEsqNJCk0ih8U8ftANCgsLqa6uVh0nY1x66aUUFhZyLBBjhyukOo7IcWY308yZM6mpqVGcRgi1Fi9ejNVqJdLqItLmVh0n7wT2NGFEY9TX1zNp0iTVccQZ0DSNW2+9lTFjxhDRDX66vR13WM6YFWod8IT5zZ5EV8PixYuZP3++4kRC5KcTHU2pLzSZXVNSaBLihJdffhmA6bXFFFhzd8m/0GZhanchzfyaRWbI3Uddlkl2NHX6pO1P5CyzkNq/f3/ZtXySoqIiFixYAMDLh73yGiBSpsUfZWt7YtTR0qVLFacRQr3q6mpmz54NgO/T/YrT5Bc9GsO/4yCQeD2ypPDQcNGzbDYbDzzwAPX19XgiOj/Z3kE4rquOJfJUezDGz3Z0EDdg8uTJXH/99aojCZG32tragBPdRqlkFrPMawqR744cOcKnn36KBsyuK1EdJ+Uu6v4aP/nkE1pbWxWnESZ5R5ch6urqsFgsGJEoeiCsOo4QKRHtPFFoEqdatGgRBQUFNPmibJeuJpEiLx/2YgBTp06V56EQ3a688kosFgvh5jbpakqjwO7D6OEotbW1zJo1S3UccZaKior4u7/7O0pLS2n2R/nlLpeM/xVpF4jq/H/b2/HHdAYPHsx9990nRWshFDEMg2PHjgFg61Wc8uuZ1zCvKUS+Mzt7xlQ5qXLaFKdJvd5FdkZWODEMg1deeUV1HNFN7sIyhMPhoG/fvoCc0yRyV8wt5zN9ltLSUi6//HIAXmqUribR8476I2xuD6JpGitXrlQdR4iM0adPHy666CIAfFv3KU6TH/RIDN/2RDfTihUrsFqtihOJc1FTU8NXv/pV7HY7210hfr/PLfcvIm0icZ2f7mjneDBGZWUlX/va13A6U99FIYQ4va6uLgKBAAC2Xqk/G8ZWmrjG8ePHicfjKb+eEJnM5/Px7rvvACc6ffLBxd1f65tvvkkwGFScRoAUmjKKufge65RCk8hNZkeTFJpOb/HixTidTo74o3zaIV1Nome91Jg4IHvatGnSzSTEX7nqqquwWq2Ej7YTOd6pOk7O8+9uxIhE6du3LzNnzlQdR5yHYcOGcd9996FpGh8c8/Pngx4pNomUi+kGP9vRwQFvhMLCQh566CEqKipUxxIir5mdRdZiJ5ot9RtILEVOsFqIx+MyPk/kvVdeeYVwOEJdsZ2GsgLVcdLmgooCehfaCAaDvPbaa6rjCKTQlFFOnNMkhSaRe/RwFD2QKJ7IIvfp9erViyuuuAKAlw57ZQSN6DFHfBG2doSkm0mIz1BbW8vFF18MQNcW6WpKJT0Sxb/jECDdTLli2rRp3HnnnQC80ezj5cPyXkakTtww+OWuDna7wxQ4HHz9619n4MCBqmMJkfdaWloAsKZhbB6ApmnJzinz2kLkI7/fzwsvvADAgv698uo8dIumcWn/XgA899xzhEKyYVs1KTRlkGRHk4zOEznIHAlZU1NDUVHqW+mz1aJFiygsLOSoP8rWdmn9FT3D7GaaMWMG9fX1itMIkZnMrqbIsQ7CrS7VcXKWf1eim6m+vp4ZM2aojiN6yLx587j55puBxGaZN5vl/Yzoebph8Ns9nWztCGGz2Xjwq19l+PDhqmMJIThR7DFH2qWDrVTOaRLipZdeIhAI0KfIxrjqQtVx0m5S7yJqCm10dXXJWU0ZQApNGSRZaPL4MWTGrMgxMVdioVvG5n2+kpISFi5cCEhXk+gZjV0RtrkS3UwrVqxQHUeIjFVTU8PcuXMB8G3ZK+O/UkAPn9rNZLHIW5FcsnDhQq6++moAnjng4YNjfsWJRC4xDIM/7nfz8fEAFouF+++/nzFjxqiOJYToZhZ7zOJPOli7ryUdTSJf+f1+XnzxRQCuGFCKJY+6mUxWTeOyk7qa5KwmteTdXQaprKykpKQEDIOo26c6jhA9yhwJKaMtvtjChQspKSnhWCDGR60B1XFEFjMMgz8fdANw0UUXUVdXpzaQEBnuqquuwmazEWntJNLSrjpOzvFtP4ARjdG/f3+mTZumOo5IgeXLl7NkyRIAntrbycbjch8jzp9hGDx7yMO7LX40TWPNmjVMnjxZdSwhxEmam5uBE8WfdDCLWkePHk3bNYXIJC+++CKBQIC+RTbG5mE3k2lid1eTz+fj1VdfVR0nr0mhKYNomsagQYOAE90fQuSKaPdj2nyMi89WXFzMlVdeCcCLjR4icV1xIpGtdnSG2O+JYLfbk7vMhRCfraqqigULFgDg/US6mnpSPBDCv6sRgGuvvVa6mXKUpmlcf/31zJ8/HwP4790uPj4unU3i3CU2zXh4/UhiI+btt9/OrFmzFKcSQpwsFoslO5rs5SVpu66t+1pHjhxJ2zWFyBQ+ny/ZzXR5nnYzmayaxuUDpKspE8g7vAxjLsJHpdAkcogR14l1d+lJoenMXHbZZVRXV+OJ6Lx1VDocxdnTDYPnDnoAuPzyy6murlacSIjscOWVV+J0Oom5vIQaZeZ/T/F9uh/iOsOHD2fixImq44gU0jSN2267jXnz5mEAv97dyUetUmwSZ88wDJ454OGN5sS98G233cb8+fMVpxJC/LVjx44Rj8fRbFYsRc60XdfsaPJ6vXi9soYm8ssLL7xAMBikb5E9r7uZTBNrTnQ1vfTSS6rj5C0pNGUYKTSJXBR1d4FhUFJSQlVVleo4WcFut3PttdcC8JemLnxRObdNnJ0NrQFaAjGKi4tZtmyZ6jhCZI3S0tLk6K+uzXsxdOkqPV8xr5/A3sRu41WrVqHl8Y7LfGGxWLjjjjuSnU2/3dPJh3JmkzgL5plM5oarO+64I9lxKoTILGZHka2sJK2/4y12G9bixAK7ObpPiHzg8Xh48YUXAFg4ML+7mUwWTeOKAaVAoqupq6tLcaL8JIWmDDN06FAgUWgyZFyWyBHRdjeQeHzL4tKZmzlzJgMHDiQUN3j1sPySBCgvsPLolD58fWLv5J99fWJvHp3Sh/ICq8JkmSUSN3ixMbFh4aqrrkqc/yeEOGMLFy6ktLSUeFeA4D5ZuDhfXVv2gmEwfvx4RowYoTqOSBOLxcLtt9/OZZddhgH8bm8n77dIl7b4Yrph8PR+N+90n8l01113SSeTEBnMLPLY0jg2z2ReUwpNIp/88Y9/JByJMLCXgzFV6esizHQTagqpL7YTDAb585//rDpOXpJCU4bp3bs3paWloBtEXR7VcYToEZE2NwDDhg1TGyTLWCwWrr/+egDebfHREYopTqSeVdOoctqodNqSf1bptFHltGGVImbS20e7cEfiVFdXy+5fIc5BYWFh8qy8rq37MGLSVXquoi4voUOJEYRmp67IH5qmceutt3LFFVcA8NQ+N+/KSGDxOXTD4Pf73LzXXWS6++67mTdvnupYQojPcXJHU7qZ15RzmkS+aG1t5fXXXwdgyaBS2cx9EoumsWRQGQCvvPIKHR0dihPlHyk0ZRhN05KL8ZE2KTSJ3BDtLjQ1NDSoDZKFxo4dy4UXXkjcgGcPymuC+GJdkTivNSU64K655hocDofiREJkp0svvZTq6mr0YBj/rkbVcbJW1yd7AJgxY4ac05inNE3j5ptvZtGiRQA8vd/N60ekU1v8rbhh8Js9nXxwLFFkuvfee5kzZ47qWEKIL5AsNCnpaCo+JYMQue73v/898XicERUFDCuXbqa/NqKigIayAqLRKH/4wx9Ux8k7UmjKQOZivDluTIhsFg+GifuCaJomhaZzdOONN6JpGpvbg+xzh1XHERnu+UYvobjB4MGDmTVrluo4QmQtu93O1VdfDYBv2wH0cERxouwTbukgfLQdq9Uq3Ux5TtM0brzxxuSZgX8+6OHFRg+GYShOJjJFTDf4xU4XHx8PYLFYWLt2LRdddJHqWEKILxCLxTh2LNG5bFdSaOoFSKFJ5IdDhw7x/vvvAyQ7d8SpNE1jyaDEWU1vvfWWjNVMMyk0ZaDhw4cDEGl1yZsvkfUixzsB6NevH0VFRYrTZKeBAwdyySWXAPCnA250eV0Qn+GIL8L67sPWb731ViwW+TUvxPmYPXs2AwYMwIjG8H16QHWcrGIYBt5Nu4FEd1htba3iREI1TdNYtWpVsuj48uEunjkoxSYBkbjOT3e0s7UjiM1m48tf/jIzZ85UHUsIcQZaWlqIx+NodiuWovR3V9hKEx1NXq8Xr9eb9usLkU5PPvkkABNrCulXIpNLPsug0gLGVjkxDIOnnnpKdZy8IitQGWjYsGEUFBSghyLEOmWshMhu4aPtAFx44YWKk2S3a665hqKiIpr90WQhQYiTGYbBH/e7MYCZM2cmNy0IIc7dyWfl+XcfJuYLKE6UPUKHWoi5vDidTpYvX646jsggV111FbfccgsAbzX7eGqfbKLJZ6GYzv/b1s6uzjAFDgcPPfQQkydPVh1LCHGGTj6fScVZMRa7DWtJ4SlZhMhFu3btYsuWLVg0WDhQupm+yKJBZWjAhg0b2L9/v+o4eUMKTRnIZrMxatQoIDFyRIhsZRgGkZZEoWnMmDGK02S30tJSVq5cCSRGowViuuJEItNsbg9ywBuhwOFILowLIc7f2LFjGT16NOg6vs37VMfJCkZcp2vzXgCWLl1KaWmp4kQi01xxxRXcfffdaJrGB8f8/Hq3i7guxaZ844/G+Y9P2zjgjVBYWMg3Hn5Y3jMIkWWampqAEyPsVDDPhjKzCJFrDMNIdjNNry2mptCmOFHm61NkZ0ptYqqSdDWljxSaMpR5g212gwiRjeJdAeL+EDabjREjRqiOk/UWLFhAXV0dvqjOK4dlLIA4IRLX+fNBDwBLly2jqqpKcSIhcoemacnibfDgUaIuef39IoE9h4n7gpSXl7Nw4ULVcUSGmjt3LuvWrcNqtbKxLcjPd3YQlWJT3vBE4vzfrW0c9kUpKSnhm9/8JhdccIHqWEKIs2Sef6LifCaTnNMkct3WrVvZvXs3dovGZQNkA9eZunxAKVYNPv30U3bs2KE6Tl6QQlOGMgtNkeOd6NGY4jRCnJtwcxuQOHfM6Uz/vOZcY7PZuPnmmwF4+6iP1kBUcSKRKV4/4qMzHKe6upolS5aojiNEzhkyZAgzZswASJ47JE5Pj0TxfZoYT7Fy5Ur5/S8+1/Tp03nwwQex2+1sc4X4/7a1E5Ku7ZzXEYrx71uO0xKIUV5ezmOPPcbgwYNVxxJCnIMTHU3qCk1mkUsKTSIXndzNNLtvMeUFVsWJskeV08aMPolz3J566ik5FzQNpNCUoerq6ujbty/oOqGm46rjCHFOgodaAGTOeg8aN24cEyZMQDfg6X1u+UUpaA/GeK0p0WFxww034HDIoaBCpMK1116L1Wol0tIhHeefw7f9IHo4Sl1dHXPnzlUdR2SBCRMm8I1vfAOn08leT5j/+LQNfzSuOpZIkWP+KP++5TgdoTi9e/fmW9/6Fv369VMdSwhxDqLRKK2trUDijCZVzGs3NzfL+2ORczZs2MChQ4cosGrM769uRGW2WjCgFLtFY8+ePWzevFl1nJwnhaYMpWlacudsqHuxXohsEusKEG33oGka06ZNUx0np9xyyy3Y7Xb2esJsaguqjiMUMgyDp/d3EjMSnbDyXBMidWpra1mwYAEA3k/2yELGacQDIQI7DwGwatUqrFbZcSnOzMiRI/nmN79JSUkJh31R/s/WNjxhKTblmsNdkcTPNqLTr18/vvWtb1FbW6s6lhDiHB07dgzDMNDsNiyFBcpy2EoTHQs+n4+uri5lOYToabqu8/vf/x6AufUllNjl3vpslTmsXFR3oqtJ16VzPpWk0JTBzEJT+Gg7ejiiOI0QZyfUeAyAUaNGUVFRoThNbqmtrWX58uUAPHPATVBGzOStrR1BdnWGsdlsrF69Gk3TVEcSIqddddVVFBYWEnN5CR2UjUB/rWvLPoy4zgUXXMCkSZNUxxFZZsiQITz22GNUVFRwLBDj37cepz0oI8RzxV53iB9+2oY/pjN06FAeffRReY8gRJYzz2eylRUrfR+i2axYSwpPySRELvjggw9obm6myKYxt166mc7V/H69cFo1Ghsb+fjjj1XHyWlSaMpg9fX1DBw4EAyDUGOr6jhCnDHDMJJj88yCqehZixcvpm/fvnRFdV5olIPp81EopvPH/R4Ali1blhi3KoRIqdLSUq688koAujbvwYhLx4Up2tlFcH/ibIQbbrhBCt/inJzc5dIRivPvW4/T4pczKbPdto4gP97WTjhuMHr0aB555BF69ZIFMyGy3dGjRwG1Y/NMZleTmUmIbGcYBn/+858BmFvfi0KbLOGfq2K7lYvrEq9Tf/7zn2UyRQrJozTDzZw5EwD/3iZ5IoisEW13E+vswm63M2XKFNVxcpLdbue2224D4N2jPpp80vWYb14+7MUTSZxvsGzZMtVxhMgbV1xxBZWVlcT9Ify7D6uOkzG6PtkDBkydOpVhw4apjiOyWO/evXnsscfo378/3ojO/9naxuEuuc/JVhuPB/jPnR3EDJg0aRIPPfQQhYWFqmMJIXpAstDUXeRRySx2SaFJ5IrNmzfT1NREgVVjdl/1xdxsd3FdCXaLxoEDB9i+fbvqODlLCk0Zbu7cuTgcDmIuL5HjnarjCHFG/DsbAZg1a5bsVkyhCy+8kJkzZ2IAv9/biS7F6Lxx1B/lraM+AFavXo3D4VCcSIj84XA4uOaaawDwfXoAPSzdFuFjHYSb27BarVx33XWq44gcUFFRwaOPPkpDQwOBmM4PP21jrzukOpY4S++1+Pjv3S50A2bPns0DDzwg9yxC5JCM6mgqSxS7ZHSeyBVmN9OsvsUU2WX5/nyVOKxM75N4nTC/t6LnySM1w/Xq1YuLLroIAH/34cpCZLKYL0jocOJ8piuuuEJxmtx34403UlhYyGFflPda/KrjiDTQDYMn93aid3cOjB8/XnUkIfLORRddRP/+/TEiUXzbDqiOo5RhGHRt2gPAJZdcImM8RY8pKSnhkUceYfTo0YTjBj/e1s72jqDqWOIMvdbk5ff73BjAggULuPfee7HZbKpjCSF6iGEYtLQkxuWbRR6VpKNJ5JJdu3axe/durBrMqZPN2z1lXn0JFmDbtm0cOJDf7+FSRQpNWcBcrA83HSfWFVCcRojPF9jdCAaMHj2aAQMGqI6T8yoqKli1ahUAzx3y0BmSQ7Nz3TtHfTR2RXA6ndx8882q4wiRlywWS/K117+rkbg/fxe/Q4dbiXZ4cDqdrFixQnUckWOcTicPPfQQkyZNImbAz3Z2sOm4vB/KZIZh8NwhD88dSpwhumzZMlavXo3FIksPQuQSt9tNOBwGDawl6sdhWnsVAdDR0UEsJu+JRXZ79tlnAZhaW0xZgVVxmtxR6bQxsXfitUK6mlJD7vayQH19PePGjQPAv10qriJz6aEIgb2Jg8AXLlyoOE3+mD9/PsOHDyccNxI7R2WEXs5yhWI8371wc8MNN1BVVaU4kRD5a/z48YwcORJ0na4t+1THUcLQ9cTZTMDixYspKytTnEjkIofDwQMPPMCsWbPQDfjVbhfvt/hUxxKnoRsGf9jv5rWmLgBWrVrFqlWr0DRNcTIhRE87fvw4ANaiQrQMKCRbnA40qxXDMGhra1MdR4hz1tTUxCeffIIGXNJP/VjKXDO/X6JDbMOGDcmuTNFz1P82EGfkyiuvBCCwr5moR95YiczU9el+jGiMgQMHyjivNLJYLNx1113YbDZ2dIbY1Ja/O+tzmWEYPLWvk4huMGLECC655BLVkYTIa5qmcf311wMQPNBMtLNLcaL0C+w9QrwrQGlpKYsWLVIdR+Qwm83GmjVrmD9/Pgbw1D43bxzJv+dcJosbBr/d08m7LX40TeP2229n2bJlqmMJIVIkWWjqpb6bCRL3ZWYWM5sQ2ej1118HYExVITWFdsVpck/fYjujKp0YhsGbb76pOk7OkUJTlhgxYgSTJk2Ck+bgC5FJYl4/gd2HgUSnhYzHSK/6+nqWL18OwB/3u/FF44oTiZ728fEAuzrD2O127rrrLnmOCZEBGhoamDp1KhjQtTm/7s/0aAzf1kQn14oVKygszIyFJpG7LBYLt99+O0uXLgXgfw56eOWwV3EqARDXDX61y8WG4wEsFgv33Xcfl156qepYQogUShaaSooUJznBzCKFJpGtotEo7733HgAz+qg/+yxXTa9NfG/feecd4nFZO+tJskqVRVatWoXFYiF85DjhVpfqOEKcomvzXjAMxo0bx5gxY1THyUtLly6lf//++GM6f9rvUR1H9KCuSJw/HUj8TFesWEHfvn0VJxJCmK699tru+7M2Inl0f+bfeQg9FKG2tlY6LEXaaJrGqlWruPrqqwF4odHL84c8MjZYoZhu8PNdHWxuD2K1WpNjDoUQua21tRUAW6/MKTTZSqSjSWS3TZs24fP5KHNYuaCiQHWcnDW60kmJ3YLb7WbLli2q4+QUKTRlkfr6eubNmwdA18ZdGLq8oRKZIXK8k1DjsVPGCIn0s9ls3H333Wiaxsa2ANtdMkIvV/xxv5tATGfgwIEsXrxYdRwhxEnq6uqS92feT/bkxYJ3PBTBv/0gkCi02Ww2xYlEPtE0jRUrVnDDDTcA8GpTF/9zUIpNKkTiOj/d0c62jhB2u50HH3yQKVOmqI4lhEiDEx1NmdPRbO0lHU0iu7311lsATKktwiLnG6aM1aIxqXfi9cL8noueIYWmLHP11VfjdDqJdnjx7zqkOo4QGPE47g+2ATBnzhwGDBigOFF+Gzp0aPKcjKf2dhKI6ooTifO1uS3AJ+3BU87iEkJklhUrVuBwOIi2uQk35/4B1P5tBzBicQYNGsS0adNUxxF5asmSJdx6660AvNns4+n9bnQpNqVNOK7z/23vYFdnmAKHg4ceeogJEyaojiWESJOOjg4ArMUZVGjqztLe3q44iRBnz+VyJbtrpvbOnE7BXDWte3zepk2b8HplFHNPkUJTlikrK+Omm24CEqPKYh6f4kTZxVrkpGb5xVQvOTHOoXrJLGqWX4y1yKkwWfbq2ryPuNdPeXl5cmepUOuaa66hb9++eCI6fzzgVh1HnIeuSJzf73MDsGzZMoYMGaI2kBDitCoqKrj88suBxP1ZLndWxP0h/N1nMl533XVyXpxQ6vLLL+euu+5C0zTea/Hzu72dUmxKg1BM5/9ta2efJ4zT6eTr3/gGF154oepYQog0MQwDt9sNgLUoc8Z7WbqzmNmEyCbvvvsuhmEwuNRB7yK76jg5r67YTv8SO/F4PHkuljh/8s4wC82bNy9xBk5cx/3BNhmhdxY0iwVbSdEp7d3WkkJsJUVoslBy1iJtbvw7E6Nz7rjjDkpKShQnEgAOh4M1a9agaRofHw+wtV1G6GUjwzD4/b5O/DGdAQMGsGLFCtWRhBCfY+nSpRQWFhLr7CJ06JjqOCnj+3Qf6DojRoxg7NixquMIwbx581izZg0Wi4WPWgNSbEoxs8h00BuhqKiIRx55hBEjRqiOJYRII5/PRzweB8DizJxCk7UwkcXj8aDrMtlDZJcPPvgAgKndnTYi9czvtfm9F+dPVtazkKZp3H333RQWFhJtc+PfeUh1JJGHjFgc9/ufggGzZ89m0qRJqiOJkzQ0NLB06VIAntrXiS8aV5xInK2NbUG2doSwWq3ce++9MjJPiAxXUlLCkiVLAOjashcjBxc4Yl4/gX3NQOJsJk1mx4sMMXv2bL70pS9JsSnFQjGdH29v51BXhOLiYv7+7/+ehoYG1bGEEGlmdgxpBXY0a+YsK1qcDgB0Xaerq0txGiHOnNfrpbGxEYALK2XaUrqY3+v9+/cTCAQUp8kNmfMbQZyVqqoqbr75ZiAxoiXS7lYbSOQdz4adyZF5t9xyi+o44jRWrlxJ//798UV1nu4evyaygycc5w/7OwFYvnw5gwYNUhtICHFGrrjiCkpLS4l3BQjub1Ydp8d1bdkHhsH48eOlg0FknOnTp7N27VopNqWIWWQ66E0UmR555BEGDx6sOpYQQoHk2LwM6maCxAQbS0Gi2CTj80Q22blzJwB9i2z0clgVp8kfFU4b1U4bhmGwa9cu1XFyghSasticOXOYMmUK6Dqdb20mHgyrjiTyRGBvE8F9R9A0jXvvvVdG5mUou93Ovffei9VqZXN7kE1tskMjGxiGwZN7OwnGDAYPHsyyZctURxJCnKHCwkKuvPJKALq27seI5043abSzi9ChFiDRzSREJpoxYwZr165F0zQ+ag3wpBSbesTJRSZzXJ4UmYTIX2YRx1KYWYUmkHOaRHbavn07AMPKpZsp3YaVJ14zzJ+BOD9SaMpimqZxzz330LdvX/RACPc7m3NyTIvILJE2N56PErstrr32WjmfIcMNHjw4uej59D437nDuLHrmqg+P+dnRGcJut7NmzRoZmSdElpk/fz6VlZXogRCBPU2q4/SYri37gETXiHRZikx2crFpfWuAp6TYdF6kyCSE+GvJjqYMLDSZmaTQJLLJiUJT5j2ncp35Pd+xY4fiJLlBCk1ZrqioiAcffBCn00mktZOuTbtVRxI5LB4M0/n2J6DrTJkyRTotssRVV13F4MGDCcR0frPHJYstGex4IMqfDngAuOaaa+jXr5/iREKIs+VwOFi+fDkAvu0Hc6KrKeryEm5qRdM0Vq5cqTqOEF9o5syZyWLTh60B/rDfjSH3P2ctEtf5yY5Ti0xDhgxRHUsIoZh5lonmyLwNcZo9kUnOWxHZwuVy0dLSggYMLZNCU7oN6/6eNzY24vV6FafJflJoygH19fWsWbMGAP/ORgI5eCaAUM+Ix3G/vRk9EKauro577rlHDgHPEjabjfvuuw+Hw8Eed5i3j/pURxKnEdcN/nu3i4huMGrUKBYtWqQ6khDiHM2ZM4fq6mr0YJjA3iOq45y3rq37gUSnSH19veI0QpyZmTNnct9996FpGu+1+Hn2kEeKTWchphv8fKeL/Z4IhYWFUmQSQiSFQiHgRFEnk5iZzIxCZDqzk6Z/iZ0imyzTp1svh5W+RXbgxFlZ4tzJIzhHTJkyJTkey/PBNkLNbYoTiVxi6Abud7cSOd6J0+nkK1/5CkVFRapjibNQX1/PTTfdBMBzBz0c9UcVJxJ/7eXDXg77ohQVFbFmzRosFvkVLUS2stlsyfsy37YDKelq0mxWalddSu2qS9FsqTs0+ORuJrNTS4hsMWvWLO644w4AXj/i47WmLsWJskPcMPjVbhc7O0M4HA4eeughKTKJM7Zx40a+973vsWLFCvr164emabJBMccEg0EALBlYaLLYE/dEUmgS2eLIkcSmtAG9HIqT5K8BvRKFpuZmadw4X7KKlUOuueYaZs6cCYaB++3NRNrcqiOJHGAYBt4NOwgdbsVms/HVr35VdjNnqfnz5zNhwgRiBvxql4uoLrt6M8UBT5hXuxe/7rzzTqqqqhQnEkKcrzlz5lBVVZWyriZN07DYbVjstpQu4Ek3k8h2l1xyCTfeeCMAzzd6ebtZOrs/j24YPLm3ky3tQWw2Gw8++CAjRoxQHUtkkW9/+9s8/PDD/OlPf5JFuxx1oqMpdRtdzpVmk44mkV1cLhcAFQWZV7jNF+b3vqOjQ3GS7CeFphxisVi49957GTNmDEYsTucbG4l55I2UOD++T/cT2NOEpmncd999jB49WnUkcY40TePuu++mtLSUlkCU5w55VEcSJA7Z/u/dLgxg9uzZTJ8+XXUkIUQPsNlsXHXVVUDquppSTbqZRK5YvHgxK1asAOCPB9ysb/UrTpSZDMPgmQMePmoNYLFYWLduHWPHjlUdS2SZGTNm8Oijj/LnP/+ZlpYWCgrkzJFckyw02TJvYdwcnWd2XQmR6drb2wEoL8i8wm2+qOj+3ps/C3HupNCUY2w2G1/5ylcYMmQIejiK6y8fEw/ITg5xbvx7DuPbsg+A1atXywJ4DigrK+Puu+8G4K1mH7s75fVBtT/sd+MKx6mpqWH16tWq4wghetApXU17mlTHOWvSzSRyycqVK5PnH/5uT6JjR5zqpcPe5Fme9957L1OmTFGcSGSjr3/96zzxxBMsXbqUPn36qI4jUsAsNGXi6DxNRueJLGN20VRIoUkZs8hndpeJcyeFphzkdDr5u7/7O/r27UvcH6Lj1Y+k2CTOWmDfEbzrE4cSrlixggULFihOJHrKxIkTufTSSwH49W4XXZHs22WfKzYeD7DheABN01izZo2cfSZEjjnlrKYdhzDiuuJEZy7q7kp2M5mdWUJkM03TuPHGG5k3bx4G8KtdHRzwhFXHyhjvtfh4+XBijO9tt93G7NmzFScSQmSqSCSS+B/WzFtS1KyJBeNwWF7fRebTdf2k0XlSaFLF/N53dHRgGHLExPnIvN8KokeUlpbyjW98g+rqauLeAB2vfETcL8UmcWYCe5vwfLANgAULFrBy5UrFiURPu/HGG+nXrx/eqM5v9nSiyy/TtGsPxnhqXycAy5cvl/MPhMhRc+bMoby8HD0QInjoqOo4Z8y//SAAU6ZMoV+/forTCNEzNE3jjjvuYNKkScQM+OmODo4FoqpjKbetI8jT+9yAbDATQpy5FB4Red5SeX6lED3F4/EQj8fRgFIpNCljdjSFQiH8fhmvfD6k0JTDampqePTRR6mpqSHeFUh0NvllRIT4fIE9TXg+3A7A5ZdfzurVq+UmLQcVFBSwbt067HY7OztDyTEpIj1iusEvd3UQjhtccMEFcvaJEDnMbrezcOFCAHzbDmbFLrmYL0jwYAsAS5cuVZxGiJ5lsVj40pe+RENDA4GYzo+3teMJ52939yFvmF/sSpwVOXfuXNlgJoQ4Y1lwSyNERvN6vQAU2y1YZd1NGYfVgtOa+P6bPxNxbqTQlOPMYlPv3r0TxaZXpNgkPpt/92E86xNFpoULF3LLLbdIkSmH9e/fn5tuugmAZw96aPJFFCfKHy80ejjsi1JcXMzatWuxWmX3khC5bP78+RQVFRH3+gk3HVcd5wv5dxwEw2D06NEMHTpUdRwhelxBQQFf+9rX6NOnD53hOD/e3k4olj2jLXtKWzDKT7Z3ENUNxo0bx+233y73/kK5cDiM1+s95UOIsyWvZSIb2GyJc85kwox68e4fgd1uVxsky0mhKQ9UV1fz6KOPUltbS9wXpOPlj4h5pRVQnMq/8xDejxJnMi1evJibbrpJbs7ywKWXXsqUKVOIG/DLXS7CWXR+SLba1Rni9SOJDrK77rqL6upqxYmEEKlWVFTEZZddBoBv24GM7mqKhyIE9x0BSJ4vJUQuKi0t5etf/zplZWUc9Uf5z50dxPTMfW72tK5InP+3rR1/TGfIkCE88MADyQUvIVT67ne/S1lZWfKjf//+qiOJrJI/r+Mi+xUUFAAQjsvjViXdMIh23wOaPxNxbqTQlCeqqqp49NFH6dOnD3F/kI6X1xPt8KiOJTKAYRh4P9mD9+NdQGJEzg033CBFpjyhaRp33XUXVVVVtAVjyfn8IjW6InF+vTtx2Of8+fOZOnWq4kRCiHS5/PLLsdvtRDs8RI65VMf5TIFdjRjxxMLz6NGjVccRIqVqa2t56KGHKCgoYI87zB/2uzO6ENxTYrrBz3Z00BGK07t3bx566CGcTqfqWEIA8PDDD+PxeJIfTU1NqiOJv9JTawWazUrtqkupXXUpmk0mPIj8YxY14gbE8+D+I1NFT9po5HA4FCbJflJoyiOVlZV861vfYtCgQeihCB2vfkS4pUN1LKGQoet4PtyOf9sBAK677jpWrVolRaY8U1JSwtq1a9E0jQ3HA3x8XDoeU0E3DH69x0VXVKdfv37cfPPNqiMJIdKorKyMuXPnAuDbfkBtmM+gR2P4dzcCsGzZMrkfEHlhyJAh3H///WiaxgfH/Lzbktv3QYZh8NS+Tg51RSgqKkp2dQmRKQoKCigtLT3lQ+QmTdOw2G1Y7Laeu+eQtXqRRU7unolKV5MyJ3/vpdB0fqTQlGfKysr45je/yahRozCicVyvbyTYeEx1LKGAEYvT+dZmgvuOJLtarrzySllUylMjRoxIHv781F43xwNRxYlyz+tHutjVGcbhcLBu3Tq5gREiDy1evBhN04i0dBB1+1TH+RvBA0cxIjFqa2uZPHmy6jhCpM2ECRNYtWoVAH864GZPZ0hxotR566iPj1oDaJrG/fffT9++fVVHEkJkGfN9jBGLK07yt4zuUfDyXktkA7vdnlyDi2TR+N7yAiuPTulz2o/yguzrTgx3f+8dDgcWi5RKzod89/JQUVERf/d3f5cY2aTruN/ejH/PYdWxRBrpkSiuv3xM+MhxbHY7X/7yl5k3b57qWEKxq666ilGjRhHRDf5rl+uU9mFxfg56w7zQmDjI+NZbb5VZ80Lkqd69eycLOIFdjYrTnMowDPzdma644gp5kyXyzpIlS5g9eza6Af+1y0V7MKY6Uo/b2Rnifw4kxqffdNNNjB07VnEiIUQ2KioqAsCIZt7rpBFJbJg0MwqRyTRNSxZFs+mcJqumUeW0nfbDmoWb1yPxE4UmcX7kHWSecjgc3H///cyfPx8A7/odeDftzouZ5Pku5gvQ8dJ6Isc7KSws5OFvfIMpU6aojiUygMViYe3atZSWlnLUH+V/DrhVR8oJ/qjOL3a50A2YOXNmcnSWECI/XXHFFQAEDzSjhyOK05wQPtpO3OunsLCQiy++WHUcIdJO0zTuvPNOGhoaCMR0frK9nVBMVx2rx7QGovxiZwcGMHfu3ORrkRBCnK3CwkIgMwtNencmM6MQma6qqgqAtqBMlVGlrXtzkfmzEOdOCk15zGKxcPvttyfHZfm3H8T9zhaMeOa1P4ueEWn30PHih8Q8PioqKnjssccYOXKk6lgig1RUVHDfffcB8G6Ln81tAcWJspthGPx2jwt3OE5tbS133HGHjKcUIs+NGDGCgQMHYsR1AnuPqI6TZHZYzZ07VxZnRN5yOBx85StfoaKigtZgjP/e7cqJjXihmM7PdnQQihsMHz6c2267Te5HRMo8//zzTJ8+PfkRiSQ2VZz8Z88//7zilOJ8mPcJeiTzCk1GNLGeJfcyIlsMGjQIgCM+KTSpcsSX+D01ePBgxUmynxSa8pymaaxcuZJ7770Xq9VKqPEYHa9uIB7KnB22omeEDrfiemU9eijCwIEDeeKJJxg4cKDqWCIDjR07lmXLlgHwu72dOTk6Jl3eOupjmyuEzWbjgQcekDc8Qgg0TUt2Evh3H8bQ1XdMRD0+wkfb0TSNyy67THUcIZSqqKjgq1/9Kna7nW2uEG82Z955amfDMAye3NfJ8WCMyspKvvKVr2C321XHEjmsra2N9evXJz/MYu3Jf9bW1qY4pTgfmTw6T4/K6DyRXczihhSa1DG/92bRT5w7KTQJAC6++GIefvhhioqKiLa56Xgp0fUisp9hGPh2HKLzrU8w4jrjxo3jsccek5ZQ8bmuueYahg8fTihu8ItdHcTkvKazdrgrwrMHT5yDIDctQgjTjBkzKC0tRQ+ECB1uVR0n2c00adIkamtrFacRQr0hQ4Zw8803A/DsIQ8HvWHFic7d+8f8fNIWxGq1cv/991NWVqY6kshxq1evxjCMz/1YvXq16pjiPCQ7mjKw0GTI6DyRZU50NMmGf1XM772s2Zw/KTSJpFGjRvG//tf/oqamhnhXgPaX1hM+1qE6ljgPhq7j/WgnXRt3ATB//ny+9rWvyU2X+EJWq5V169ZRUlJCky+aLJiIMxOI6fxiVwdxA6ZOncqCBQtURxJCZBCHw8Gll14KQGD3YaVZ9EiM4P6jAHJmixAnmT9/PjNmzEA34Bc7XfiiPTte3GHR+MeZdfzjzDocltSMsTvii/Cn/W4ArrvuOoYPH56S6wgh8ovZLaSHM68DwxznJx1NIluYxQ1XOI4/qn7SQb7xROJ4ozqapjFgwADVcbKeFJrEKerr63niiSdoaGjAiERxvfYxgb1NqmOJc6CHo7j+spHAnsQC1g033MDtt9+O1WpVnExki6qqKu69914gMQLu046g4kTZwTAMntzTSUcoTk1NDXfddZecgyCE+Bvz5s1D0zQixzuVdpEHDx3FiMepq6uTcxuFOImmadx555307dsXdyTOr3d3ovfgeU2aplFgtVBgtaTkPiEY0/mvnR3EDJg4cSKLFy/u8WsIIfJTRUUFAHowpDjJ39IDiUxmRiEyXXFxMb179wagWbqa0s7sZqqrq8PpdCpOk/2k0CT+RllZGd/85jeZPn06GAaeD7fj/XgnhozOyhoxr5/2lz4kcqwjeajxkiVLZLFbnLWTFyZ+s8eFK5R54xEyzXstfrZ0nBhRU1xcrDqSECIDVVVVMWHCBAAC+44oyxHYm7j2JZdcIvcJQvyVwsJCHnjgAex2Ozs7Q7x+pEt1pDNiGAZP7u2kPRSnurqae++9V57fQogeU1lZCUA8kFmFJsMwkpnMjEJkgyFDhgCwx5O9o3qz1R534ns+dOhQxUlygxSaxGk5HA7WrVvH1VdfDYB/ZyOdb25KtiGLzBU+1kH7ix8S9/qprKzk8ccfZ8qUKapjiSx23XXXMXToUIIxg1/schGXovNnOuKL8KcDbgCuv/56uVkRQnyuSy65BIDA/maMePpHZUQ7PMRcXmw2GxdddFHary9ENhgwYEDyPJkXGr1ZcYbChuMBNref2PRSUlKiOpIQIoeYRRw9GMHQM2fUlx6OQvd7VeloEtnEXLP7pC2A0YPd0+Lz6YbB5rbE5J7JkycrTpMbpNAkPpOmaaxYsYL7778fh8NBuLmNjpc+JNYVUB1NfIbAniZcr32MEYnS0NDAd77zHTnMTpw3m83G/fffT1FREY1dEZ5vlPOaTicU0/mvnS7iBkyaNImFCxeqjiSEyHDjxo2jsrISIxwldLg17dc3xyNPnTqVXr16pf36QmSLuXPnMnXqVHQDfr3bRSyDN910hmP8sftcpquvvpqGhga1gYQQOaesrAyLxQKGgR7KnOK7OTavrKwMm82mOI0QZ27ChAkUFBTQEYrT5Mu8s89y1SFvBE8kTmFhIePGjVMdJydIoUl8oenTp/PYY49RUVFBzOOj/cUPCLe6VMcSJzF0Hc9HO/Cs3w6GwcyZM/nmN79JeXm56mgiR9TU1HDPPfcA8PoRH9tdcl7TyQzD4Ml9nbSHYlRVVXHPPffIiBohxBeyWq3MnTsXIO1nYurRGMGDLcCJziohxOlpmsbtt99OaWkpLYEYL2TophvDMPjtnk5CcYOGhgaWLFmiOpIQIgdZLJZkx1Dcnznj88wsMjZPZBun05kcqf1Jm2zuTxfzez158mTsdrviNLlBCk3ijAwZMoRvf/vbDB48GCMcxfXqhrQviIjT08NRXK9vJLD7MJDYubh27VocDofiZCLXTJkyhcsvvxyA3+zuxB2WUZqmD4/5+aQtiMViYd26dTKiRghxxubOnYumaURaXcS8/rRdN3SoBSMWp0+fPowcOTJt1xUiW5WWlnLnnXcC8MYRHwcy8ByF91r87HGHcTgcrFmzBqvVqjqSECJHJQtNGXROUzwohSaRvaZPnw7AJ+1BdBmfl3K6YbC5PbGB2vzei/MnhSZxxiorK3nssccST0DDwPPhdrwbdmbUTN58E/P6aX/pAyItHTgcDr785S+zYsUK6aQQKXPDDTcwePBg/DGdX+1yyQ0Q0OKP8scDiZ3N1113HcOHD1ecSAiRTaqrqxkzZgwAwYNH03bd4IHEtcxClxDii02ePJmLL74YA/jNHhdhBWerfZa2YIw/H0zcj1x//fX07dtXcSIhRC7r3bs3AHFf5nRfxLuPeaiurlacRIizN378eJxOJ+5wnMauzBlJmav2ecJ0RXWKi4uT78XE+ZNCkzgrBQUFrFu3jquvvhoA/65GOt/YhB6RGaLpFm5pp/3FD4h7A1RVVfH4448zdepU1bFEjrPb7axbtw6n08l+b4RXDnepjqRUJK7zi10dRHWDsWPHsnjxYtWRRA4IBoM89thjDB8+HKfTSV1dHbfffjvNzc2qo4kUmT17NgDBgy1pOQA45gsSOd6JpmnMmjUr5dcTIpfcfPPNVFZW0h6K8/whr+o4QGJk3u/2uIjoBqNGjWLBggWqIwkhclxdXR0AMU/6urG/iJmlvr5ecRIhzp7D4WDSpEkAbGjNnAJurvr4eOJ7PGXKFDnTrQdJoUmcNU3TWLFiBQ888AAOh4Pw0XbaX/wwreNe8p1/dyOuv2zEiMQYNmwY3/nOdxg0aJDqWCJP9OnThzvuuAOAlw972efOvNEx6fKnAx6OBWKUl5ezZs2axKG4QpyHUCjEJZdcwre//W18Ph9XXnkl/fv35+c//zkTJkzgwIEDqiOKFJg8eTIFBQXEuwJE21N/9kuou3Nq5MiRVFVVpfx6QuSS4uJi7rrrLgDeOerjiE/9ruMNxwPs90YocDi455575H5ECJFyZtdkJq0DmVmko1NkK/Ps1g3HA3RF4mrD5DB3OM7G7kKT+T0XPUPuQMU5mzZtGo8//jiVlZXEvX7aX/yQ8LEO1bFymqHreNbvwPvRTjAMZs+ezd///d9TVlamOprIM7NmzWLOnDkYwK92u/BF8+8maHNbgA+O+dE0jTVr1sjzUPSI73znO3z44YfMmDGDPXv28OSTT7J+/Xr++Z//mba2Nm6//XbVEUUKOJ1OpkyZAqR+fJ5hGAQPtgAnOqmEEGdn3LhxTJ8+HQP4/T630lHCgaieHJm3YuVKampqlGURQuSPkzua0tGN/UWMuJ4c42dmEyLbjBo1iiFDhhDVDd4+6lMdJ2e91dxF3IARI0bI0Qc9LOcKTTJuJr0GDRrEt7/9bYYOHYoRieJ67WMCe5tUx8pJeiSK6/WNBPYcRtM0Vq1axZo1a3A4HKqjiTx166230rdvXzyROL/d05kRbzDSpSMU43d7OwFYtmyZzPQVPSISifB//+//BeCHP/whJSUlyb978MEHGTt2LG+99RYbN25UFVGkkDnCLnSoJaXnX8Y6u4h5fNjtdhm5K8R5uOmmm3A6nTR2RVivcMTNc4c8+KI69fX1LFy4UFkOIUR+6dOnD5qmYUSi6GH1RynEuvxgQGFhIeXl5arjCHFONE1j2bJlALzb4iMUy5yzIHNFIKrz/rFE9+PSpUsVp8k9OVVoknEzalRUVPDoo48yffp0MAw8H27H+/EuDD1/Fp1TLdbdMRZp6cDhcPDlL3+ZZcuWyeHdQimn08n999+P3W5nuyvEW3my4yauG/xyl4tQ3GD48OGsXLlSdSSRI9577z08Hg9Dhw5lwoQJf/P35vmIzz77bLqjiTS48MILKS0tRQ9HCR9tT9l1zI6pCRMmUFRUlLLrCJHrKisrT7wuH/Qo6e5u7IrwQfdiyW233SZnDAgh0qagoIDq6moA4hkwPs88n6murk7WSURWmzx5Mn379iUYM5K/40XPea/FRzhu0L9/f8aPH686Ts7JqUKTjJtRx+FwsG7duuSCq3/nITrf3IQejSlOlv3CrS7aX/yQuNdPZWUljz/+eHK8jhCqDRw4kJtuuglILLI0Z8A5Ban20mEvjV0RioqKWLt2rSzqiB6zZcsWACZOnHjavzf/fOvWrWnLJNLHarUyc+ZMAIKHWlJyDcMwCB46BpzooBJCnLvLL7+cAQMGEIjpPHcw9eernUw3DJ7e14lBYgzmqFGj0np9IYQwR9RF3V2Kk0DMk9j0KGPzRLazWCwsWbIEgDebfcRkE3+PicSN5AbppUuXSlE6BXKm0CTjZtTTNI2VK1eybt067HY74eY2Ol5eTzwQUh0tawUONON6bQNGJMrQoUP59re/zaBBg1THEuIUl156KZMmTSJuJM5risRz90ZovyfMa02JN1J33nmnnIMgetThw4cB6Nev32n/3vzzxsbGtGUS6TVt2jQAwkfaMOI9Pyoj2uFBD4RwOp2MGzeuxz+/EPnGarVy2223AbC+NUCLP33joza1BWjyRSksLOSGG25I23WFEMI0YMAAIDGWV7WoK5HBzCRENps9ezbl5eV4InE+Pq5uPG+u+ajVjy+qU11dnZjKJXpczhSaZNxM5pgxYwaPPvooZWVlxDq7aH/xA6Iur+pYWcUwDLq27MPz3qegG0ybNo1HH32UiooK1dGE+BuapnHXXXdRXl7OsUCM5w6ld0dvugRjOv+924UBXHzxxXJjInqcz5fYXfVZ48yKi4sB6Or67Dfz4XAYr9d7yofIHsOGDaOsrAwjGiPS6urxzx863ArA+PHj5YxHIXrIBRdcwNSpUzEgbfdAMd3ghUOJ1/elS5fKeSRCCCUGDx4MQLRD/f1mzJV4/TUzCZHN7HY7ixYtAuClRi+RFGxAyzehmM4rhxOvVYsXL5bJNCmSM4UmGTeTWRoaGnjiiSeor69HD4TpeHk9oeY21bGyghHX8bz/Kb6t+4DEm8d169bJgpDIaKWlpdxzzz0AvH3Ux87O3OtkfHqfm85wnN69e3PrrbeqjiPEaX33u9+lrKws+dG/f3/VkcRZsFgsTJ48GThRFOophmEQakp8ThnBK0TPuvbaa7FYLGx3hTjgCaf8eu+1+HCF45SXl3PFFVek/HpCCHE65rSVaGcXhq5uIVwPR4j7E+8/Bw4cqCyHED3psssuo7q6GnckzpvN+XEediq9fqQLb1SntraWSy65RHWcnJUzhSYZN5N5ampqePzxxxk9ejRGLE7nG5vw7zmsOlZG08NRXH/5mOCBo1gsFu68806uv/56LJaceaqKHDZu3Dguv/xyAH6724Uvkv5DsVNl4/EAG9sCWCwW7rvvPgoLC1VHEjnIHPsbCJx+PILfnzgMtlevXp/5OR5++GE8Hk/yo6mpqeeDipRKFpqaWjF6cCZ7zOMn7g1gs9lkbJ4QPayuro65c+cC8OwhD4aRujHCiR25ic7WlStX4nQ6U3YtIYT4PL179068L9J1Yh6/shzmBJ3a2trkBAAhsp3D4WDVqlUAvNbUhTeH1lfSrTMc443uYt0NN9yA3W5XnCh35czqtYybyUzFxcV8/etf5+KLLwbDwLt+B95Nu1P65itbxf1BOl7+kEirC6fTyUMPPSRVdpF1rr/+eurr6/FGdX63rzMnnuuuUIzf7+sE4KqrrmL48OGKE4lcZc6UP3LkyGn/3vzzz9upWVBQQGlp6SkfIruMHj2aoqIi9FCEaLu7xz6v2SF14YUXfub9shDi3K1cuRKHw8FBb4TtrtR1dr/R3IU/ptOnTx/mzJmTsusIIcQXsVgsJ7qaFB6XYJ7PJOdZi1wzY8YMGhoaiOgGL+ToEQXp8NxBL1HdYMSIEclNfSI1cqbQ1BNk3Exq2Gw27rnnnuQ5Wf7tB/F8sE1pa3Wmibq7aH9pPTGPn4qKCr71rW/JbmORlRwOB2vXrsVqtbKtI8SHrdl9cKVuGPx6j4tQ3KChoYHly5erjiRymPm6v2nTptP+vfnnY8eOTVsmkX42my153qg56q4nyNg8IVKroqIiOcbuhUZvSjbbBKJ6cnzOddddJ+cLCCGUSxaaOtQtgkflfCaRozRN46abbgJgfWuAZl9EcaLs09gVYWNbIPm91DRNdaScljOFJhk3k9k0TWPFihXcfffdaJpGcH8znW99ghFT0/qp2azUrrqU2lWXotmsSjKYIsc76Xj5I/RAiPr6ep544gmZKyyy2qBBg7juuusAeOaAm85QrMev4bBo/OPMOv5xZh0OS+puFN496me/J0JBQUGygCZEqsyaNYuysjL279/P5s2b/+bvn376aSBxdp/IbZMmTQIg3EPnW8YDIWIuL5qmfeZ5pkKI87dkyRIKCgo46o+yy93zZzW91+IjHDfo378/U6dO7fHPL4QQZ8ss7igtNLVLoUnkruHDhzN9+nQM4JkDqR3Pm2sMw+CZA24ALrroIoYMGaI2UB7ImUKTjJvJDnPnzuXBBx/EbrcTPtJGx2sb0MPRtOfQNA2L3YbFblNazQ4dOY7rtQ0YkSgNDQ089thjVFVVKcsjRE9ZtGgRw4cPJxw3+N3enh+hp2kaBVYLBVZLyp7D7cEYz3W3p99www3U1tam5DpCmBwOB1/60pcAWLt2bXKTDMC//Mu/sHXrVubMmZMsQojcNWbMGCwWS+JcJX/wvD9fuKUDSCzAlJWVnffnE0KcXklJSXL09etNnz2y/VxE4gZvHU10My1dulR25AohMsKwYcOAxOg8I57+jcTxYJi4L4imaQwdOjTt1xciHa6//nrsdjt7PWG2tJ//e4N8sbEtwEFvhAKHg2uvvVZ1nLyQM4UmGTeTPSZNmsTDDz9MUVER0TY3HS+vJx5I3RzzTBXY30znm59gxHXGjx/PI4888rkdd0JkE4vFwt13343dbme3O8z6LBuhpxsGv93rIqIbjBo1ivnz56uOJPLEN7/5TaZNm8b777/PsGHDuO6665g+fTpf/epXqamp4T//8z9VRxRpUFxcTENDAwDho+3n/fnMzyH3wUKk3qJFi7Barez1hDnc1XMjbjYc9+OL6lRXVzN9+vQe+7xCCHE+evfundjEohtEO9J/TlO0zQ1Av3795AxKkbNqamqSUy3+eMBNICZHkXwRfzTOn/YnNg5fedVVVFZWKk6UH3Km0CTjZrLLiBEjeOyxx6ioqCDm8dHx8npivvypyvt3H8bz/qdgGFx00UU8+OCDOJ1O1bGE6FF1dXXJXSPPHHDTGe75EXqp8l7LiZF5d999NxZLzvy6FBnO6XTyxhtv8Oijj1JUVMQzzzxDY2Mjq1evZtOmTdLun0fMotD5FpoM3SDcIoUmIdKlqqqKmTNnAvCXIz3T1aQbBm90f65FixbJ2UxCiIyhaVqyqynSXfRJp0hbJ3Cis0qIXLVs2TL69u2LN6Lz3EF1oyqzxf8c9OCP6fTv358lS5aojpM3cmblTMbNZJ8BAwbw+OOPU1tbS9wXxPXKemJd2dX1cC78Ow/h/WgHAAsXLuSee+6RN4siZy1cuJBhw4YRihs8mYIReqnQHozxbPeN2/XXX0/v3r0VJxL5prCwkCeeeIJ9+/YRDodpaWnh5z//Of369VMdTaRRstDU0oGhn/uuxajLixGOUlhYmOySEkKklrm5cWt7kLbg+W+0+bQjSHsoTklJCXPnzj3vzyeEED1JbaEpcc3hw4en/dpCpJPD4eDOO+8E4P1jfg54ev4syFyxxx3io9YAmqZxxx13yJprGuVMoQlk3Ew2qqmp4dFHH6Vv377E/SE6XllPzONTHStlfNsP4P14F5DYjXDTTTdJp4TIaRaLhXvuuQe73c6uzswfoacbiTOlzJF5l156qepIQog8NWTIEEpKSjCiseQh1+fC7Ii68MIL5U2WEGnSr18/xo0bhwF8cOz839u815LYRHnppZfKFAQhRMZJntPU7k7rxkIjrifH9UlHk8gHI0eOTG44eXJvJzE98zfyplskbvDUXjeQuG+SInR65dQKt4ybyU6VlZU8+uij1NfXowfCdLzyEVF37hWburbuo2vTHgBWrFjBddddJ4f4irxQV1fHNddcAyRG6HVF0n9I7Jla3xpgnydMgcPBXXfdJYVgIYQyFouFCy+8ECA5+u5cRGRsnhBKXHLJJQB81Bo4r4Wg9mCMPe4wmqYxb968noonhBA9ZsiQIVitVvRgmHgaj0SIujyg65SUlNCnT5+0XVcIlW644QZKS0tpDcZ6bERvLnm1yUt7KEZFRQXXXXed6jh5J+dW0GTcTHYqLy/n0UcfZcCAAeihCK4cKzZ1bdmHb8s+AK699lquvvpqKTKJvLJo0SIGDx5MKG7wPxk6T9gXjSdH5l1z7bXU1tYqTiSEyHejR48GINLaeU7/3ojHibS7ARg1alRPxRJCnIEJEyZQXl6OL6qz3XXuC68ftia6mcaMGUNNTU1PxRNCiB7jcDiSG7sjx11pu27keOL+6IILLpD1FZE3SkpKuOWWWwB45bCX1kBUcaLMcdQfSRbfVq9eTVFRkeJE+SfnCk0ie5WWlvL3f//3DBo0CD0cwfXahpw4s8m34yC+rYki0w033MBVV12lNpAQClgsFm6//XY0TePj4wH2uTNvnvBzBz0EYjoDBgzg8ssvVx1HCCEYMWIEAJF2N0b87M9pirR7QDcoLy+Xnb5CpJnNZuPiiy8G4INj/i/4r08vrht81P1vzQ4pIYTIRCNHjgTOfXPMuTCvZV5biHwxY8YMxo0bR9yA3+zpRM+Cs7BTLa4b/GZ3J7oBU6ZMYcqUKaoj5SUpNImM0qtXLx5++GH69euHHgzjem0D8UBIdaxzFtjbRNfG3UCik2nJkiWKEwmhztChQ5k/fz4AT+/PrHnCB71hPuw+P+r222/HarUqTiSEEInRo6WlpRDXiXacfTeouQAzYsQI2ekrhALmqLvdnWFcodhZ//sdnSG8UZ3S0lImTpzY0/GEEKLHJDfHHE9PocnQjeS1zGsLkS80TePOO++ksLCQxq4IbzbnzkSoc/XakS6O+KOUlJRw2223qY6Tt6TQJDKOWWyqra0l7gsmik2hiOpYZy14sAXPh9sBWLp0KVdeeaXiREKod+2111JaWsqxQIy3MuRmKG4Y/H6fG4C5c+fKYZFCiIyhaRoXXHABcG4LN+b4GvNzCCHSq7a2ltGjR2MAHx8/+0kNG7o3wVx00UXYbLYeTieEED1n+PDhaJpGvCuQls3CMXcXRjSG0+lk4MCBKb+eEJmmqqqKm2++GYAXDnk4lscj9I76I7xy2AvArbfeSnl5udpAeUwKTSIjVVRU8Mgjj1BRUUHM48f1l4/RI9nzohk6chz3e1sBmD9/PqtWrZKdxEKQmCd8ww03APDyYS+d57C7t6e9c9TH0e6dL6tWrVIdRwghTpHcIdx6dmceGLpOtM0NyEgZIVSaOXMmAFvbz+6cpnBcZ2dn4t/MmjWrx3MJIURPKioqYtCgQcDZ37OcC/MaF1xwgUyjEHlrzpw5jBs3jpgBv83TEXrmyLy4AZMnT07edwk1pNAkMlZNTQ2PPPIIvXr1Iuby0vnWZgz97M8nSLdIuwf325vBMJg1axa33XabFJmEOMlFF13EiBEjiOgGfzrgVprFE4nzYmNi58uqVasSI6qEECKDJAtNbW6Msxg5GnV1YcTiFBUV0a9fv1TFE0J8gUmTJmGxWDjij9IePPMNNjtdIaI69O7dW3brCyGywonNMakfnxeW85mE+JsRem8cyYypMen0atOJkXnmueBCHSk0iYxWX1/PN77xDRwFDiLHOvB+tBMjgyv0cX+Qzjc3YcR1xo0bxz333IPFIk8zIU6maRq33XYbFouFrR0h9nvCyrK81OglHDcYOnQoc+fOVZZDCCE+y8CBA3E6nRjRGDHPmb95jLYlFmCGDx8u9yJCKFRaWppcCN1yFl1Nm7v/22nTpsmiiRAiK5ivdeEUdzQZhpEcDyznM4l8d/IIvRcbPRzzZ880qPPV7IvwSlNi4/Dq1atlZF4GkHedIuMNHjyYdV9ah6ZpBPY24d95SHWk09KjMVyvb0QPhunfvz/r1q2TWepCfIb+/fsnD8j+80GPkgLysUCUD4/5AbjppptkIVYIkZEsFguDBw8GINrhOeN/F3Ul3nQNHTo0JbmEEGdu2rRpAGxpP7NzmiJxnR2uxBknU6dOTVkuIYToSSNGjEic0+T1p/ScppjbhxGOUlBQwJAhQ1J2HSGyxckj9H6z10U8gzfo95SYbvCbPZ3oBkyZMoUZM2aojiSQQpPIEpMmTeLGG28EoGvjbkJNrYoTncrQddzvbCHm9lFWVsZDDz1EUVGR6lhCZLQVK1ZQ4HDQ2BVha0fqD4z9a88d8mCQeH254IIL0n59IYQ4U+YiytkUmiLtnlP+rRBCnSlTpqBpGod90TM6n3K3O0xEN6iurpbnsBAia5SUlCRHfaZyfF7kWAeQOJ9JNvcKcWKEXlFREYe7orxxpEt1pJR7tclLs4zMyzhSaBJZY+HChcyfPx8A97tbiXZmzgtn16Y9hJvbcDgcfO1rX6O6ulp1JCEyXkVFBYsWLwYSRZ/4WZw9cr4OeMJs6wihaRqrVq1K23WFEOJcnCg0ec/ov9cjMeJe/yn/VgihTllZGQ0NDUCiiPRFdnUmNuBMnDhRFk6EEFnFHJ8Xae1I2TXM0XyjRo1K2TWEyDZVVVXccsstALzY6KUlh0foNfkivNqUWBO+/fbbKSsrU5xImKTQJLKGpmnceuutjBkzBiMWp/PtT9CjZ36gbqoEDx9LjvNbs2aNjKgR4iwsWbKE0tJS2oIxPmz1p+WahmHw7KHETv+5c+dSX1+flusKIcS5ShaaOr0Ycf0L//uoK/EaV11dLW+8hMgQY8aMAWC3+4u7uPd0F6PMfyOEENnCLP6k6pwmwzCS3VJSaBLiVBdddBETJkwgbsCv97jSupk3XWK6wW92u9CNxGji6dOnq44kTiKFJpFVbDYba9eupbKykrg3gOfDbUrOdjHFugJ43t8GJBbMzfnrQogzU1hYyPLlywF4qdFL+AwWUM/Xpx0hDnojOBwOrr766pRfTwghzlfv3r0pKSkB3SDm/uKObrPzyTzbSQih3oUXXgjAns4w+ue8f3GFYrQFY1gslmRngBBCZIsT5zQFUnJOU6yzCyMSxel0MmjQoB7//EJkM3OEXnFxMUd8UV7LwRF6Lx/20hKIUVpaym233aY6jvgrUmgSWae0tJT7778fq9VK6NAxAnublOQw4nE6396MEY0xfPhwrr32WiU5hMh28+fPp7a2lq6ozttHfSm9lm4YvNCY2Om/cOFCKioqUno9IYToCZqmJYtGkTM4p8k8y0nG5gmRORoaGnA6nfhjOkc/Z5yN2c3U0NAgZ74KIbJOcXFxsgAUOdbzXU2R7k4pOZ9JiNOrqKhg9erVQKIoc8QXURuoBx3uivDaSSPzSktLFScSf00KTSIrDR8+nOuuuw4A74ZdZ3U4dk/xfrybmMtLSUkJ69atk5scIc6RzWZj5cqVALzV7CMST12X4nZXiGOBGIWFhSxdujRl1xFCiJ5mLtrE3F9ckDf/G9npK0TmsNlsyQ6lPZ9zTpM5Ws/sgBJCiGyTyvF54WNyPpMQX2TmzJlMmTIF3YDf7OnMiRF6Md3gN3tcGCS+vqlTp6qOJE5DCk0iay1evJiJEyeCrtP57laMeDxt1w4dOU5gz2EA1q5dS1VVVdquLUQumjFjBjU1NfiiOh+l6KwmwzCSu18WLFggu4SFEFmlX79+QGJkzOcx4joxr/+UfyOEyAyjR48GYL/nswtNB7r/zvxvhRAi25hFoEgPF5oMwyByXM5nEuKLaJrG7bffTklJCUf9Uf6SAyP0Xjns5VggRllZGbfeeqvqOOIzSKFJZC1N07j33nspLy8n7vXTtXV/Wq6rR6J41m8HEqO3xo0bl5brCpHLrFYrixcvBuCN5i7iKTh77YA3QmNXBLvdzsKFC3v88wshRCr1798fgKjb97nnU8a8fjAMioqKqKysTFc8IcQZaGhoAKCxK3La57E7HMcT0bFYLDL6UgiRtS644ILEOU1dPXtOk5zPJMSZKysr45ZbbgESI/SOBT57bG+ma/ZFkudN3XbbbfTq1UtxIvFZpNAkslpJSUny8Df/9oNpGaHn3bgbPRCmtrZWzmUSogfNmTOH0tJSOkJxNrcFe/zzm91Mc+bMoaysrMc/vxBCpFJdXR2apmFEoujBz+6GiLkTr3X9+/dH07R0xRNCnIFBgwZhtVrxRXU6w387jaGxK3GOQv/+/SkoKEh3PCGE6BFFRUUnzpbswa4m83ONGDECq9XaY59XiFw1a9Ysxo8fT9yA3+3pRE/Bht5UixsGv93biW7AlClTZGRehpNCk8h6U6ZMYfr06WAYuN//FCOup+xa4ZZ2gvuOAHD33XfLG0AhelBBQQGXX345AH850vW5O/bPVrMvws7OEJqmJTunhBAimzgcDvr06QN8/jlN0e7RejI2T4jM43A4GDBgAHCiqHSyw91/NnTo0LTmEkKInmaeSWeeqdQTzM9lfm4hxOfTNI077rgDp9PJoa4I7xz94rNeM80bR7o44otSVFSUbDQQmUsKTSInrF69ml69ehFz+/BtP5CSaxixOJ4PEiPzFixYIDc3QqTAggULcDqdHPVH2dnZc2MWzJnE06dPp7a2tsc+rxBCpNOJ8XmfPWfdLEKZ/60QIrOYRaTDpyk0NUqhSQiRI3r6nCY5n0mIc1NVVcUNN9wAwPOHvHSEYooTnbnWQJSXGr0A3HzzzZSXl6sNJL6QFJpETigtLU0eBuffdqBH5wCbfDsO8f+3d+/RUdZ3Hsc/k8llEgKBxJgLgdyAghoVRAWDXFU43NZWLMK6itblrOuCu2ura49VabdrXbfVo6219ZzWul21LtS7eF26C0VQREClCkqC4WrIdXKZ+7N/TJ4JQ+6TSeb2fp3DMfk9t+/E80y+me/z/f28re3KycnRqlWrwn5+AP7pMOfPny9J+r8wPW3T5Oqcim/ZsmVhOScARILZpeRpau1xH09TS9C+AKKLuU7TmYUmn2GopoVCE4D4EO51mlifCQjd/PnzNWXKFLl8hv5wsCGss8cMFZ/hj9VjSBUVFZo9e3akQ0I/UGhC3Jg5c6YmTZokw+uT/aMDYT23t82h1o5OqVWrVslms4X1/AA6XXHFFZKkzxucagjD0zYfnGyVT9KkSZP4owRATDOnzvM2d19oMrw+eVv9hfWCgoJhiwtA/5lT5x1v8wR90NPo9MrpNWS1WjV27NhIhQcAYZGRkaHi4mJJCnQiDYZ5jkmTJrE+EzBASUlJuuWWW5SSkqIDjU7tHoI1scPt/ZNtOtTsUlpamm655RbWno0RFJoQNywWi/7mb/5GktR+6JhcdU1hO7d97xcyPF5NmDBBM2fODNt5AXSVn5+vc845R4akHSd7fmq/P3yGoR0n/OeYO3fu4IMDgAgKrNFkb+t2u7e1XTL8a94xtQQQnQoLC2WxWNTm8anF3bm27Ik2d2A7H6ICiAeTJ0+WFKZCU8cUfOY5AQxMQUGBvvnNb0qSXjzUqHbP0K1vP1itbq9eqfJ/prtixQrl5uZGOCL0F4UmxJXy8nLNmjVLkmTf9VlY2kHd9c1q/+KIJP+coFTRgaFnTp+382SbfIO4j79scuqUwyubzaYZM2aEKzwAiAiz0ORrd8rn7trx6enodMrLyyNfAaJUamqqzj77bEmdxSX/1/57mm4mAPEiUGga5DpNp6/PRKEJCN2SJUtUUFAgu9unzR1rH0WjV6ub1erxady4cVq4cGGkw8EAUGhC3Fm5cqVSUlLk+rpBzmOnBn0++56DkvxT802cOHHQ5wPQt+nTp2vEiBFqdHr1WUPoc3q/19HNVFlZyZSXAGJeZmamMjMzJUnelq5TXng7Op3MghSA6GSuoWYWl/xfu4O2AUCs+8Y3viFJ8jS2yOd097F3z7z2NvkcLiUnJ6usrCxc4QEJJyUlRWvWrJEkbT3WoiMtrt4PiIDqZmdgVpqbbrpJycnJEY4IA0GhCXEnJydHV155pSSp9ZNDgzqXu8Eu59FaWSwWrVixIhzhAeiH1NTUQHfijhPdTxHVl1a3V3tP+T+InTdvXthiA4BIysvLk9TZvXQ6c0o9cx8A0cnsWgruaHIHbQOAWJeVlRVYM9JVG/r0ea6T/mMnTJig1NTUsMQGJKqKigrNmDFDhqSNXzQOagaZcPMZhjZ+2ShD0uWXX04HYwyi0IS4tHjxYlmtVrm+bhjUfMAtHYWqSy+9lEW1gWFmFoc+qW+X3eUd8PEfft0uryEVFxertLQ03OEBQESY3UrebtZpoqMJiA3m3xWn2js7mk61e4O2AUA86Jw+bxCFpq/9U++ZHVIABuf666+XzWZTtd2l90+G9mDvUPjz8VYdaXErIyNDq1evjnQ4CAGFJsSl7OxszZ49W1JnsWigPPY2OQ4flyQtX748bLEB6J/x48erpKREPkP6pK7rFFF92XPKnzDNnj2btUoAxA1zbRdva9f3RU/HdHrmPgCik3mP1jv9xSWHx6e2jkW5WfAaQDwxi0OD6miqbQw6F4DByc7O1jXXXCNJeqWqKZCDRFKr26vXDzdJkr797W8rKysrwhEhFBSaELeWLl0qi8Ui59FauRvsAz6+dX+VZEgXXHCBSkpKwh8ggD5dcsklkqR9dQNbp8nu8qqq2T/f8MUXXxz2uAAgUs466yxJkrc1+H3RMIxA8cncB0B0Mu/ReodHPsNQvdPf2ZSZman09PRIhgYAYWWuc+2ua5bhHfiH2T6HK9CxPWHChLDGBiSyhQsXauzYsWr1+PRuzcA/Mw23t76yq91jqLi4WFdccUWkw0GIKDQhbhUUFAQ+YG47WDOgY31uj9oPHZPkL1gBiAzzHj7Q6FD7AJ6y+aTeIUNSaWkpH7gCiCs5OTmSunY0GU631PEBTnZ29rDHBaD/srOzlZSUJK8hNbt8qnf4O5voZgIQb/Lz85WZmSn5fHI3NA/4eNepRklSYWGh/zwAwiI5OVnXXXedJOn/jrWowenp44ihc6rdo23HWyRJq1evVlIS5YpYxf85xLUFCxZIktoPHZPh6f8aL47qEzI8XhUUFOicc84ZqvAA9GHs2LEqKCiQ15D21/e/q2nfKf8HsHQzAYg3nYWm4PdEs/A0evRopaSkDHtcAPrParUG7uV6h0d1Dv+HOxSaAMQbi8XS2dXUMQXeQJjT5pnnABA+06ZN0+TJk+X2Gdp8eOCF4HB57XCTvIZUUVGhioqKiMWBwaPQhLh27rnnKjc3V4bbo/avTvb7uLYv/B1Qc+fOZW0XIMLMYtHH/Vynqd3j04FGR9CxABAvzA+nDbdHPpc7MG4WnsztAKKb2XHd4PSqoWOtJrqwAcQjs0jkCqHQ5KbQBAwZi8Wi1atXS5I+ONmmY62uYY/hK7tLH9W2y2KxaNWqVcN+fYQXhSbEtaSkJM2dO1eS1P5F/6bPczfY5T7VJKvVqtmzZw9hdAD6wywW7a93yOU1+tx/f71DXsM/vcLYsWOHOjwAGFY2my0wdczpXU3eNgpNQCwZM2aMJKnZ5ZXd5Q0aA4B4Euho6pgGr78Mn0/uuqagcwAIrwkTJmjGjBkyJL1c1TSs1zYMQy9VNUqSZs2apZKSkmG9PsKPQhPi3uzZs2WxWOQ62SBPS1uf+5trM02bNk1ZWVlDHR6APpSVlSk7O1sun6Evm5x97r+/3t/5NH369KEODQAiIjB9XttphSY6moCYYv6dYXd7ZXf7gsYAIJ6Ul5fLYrHI2+oIyl364mlqleHxKj09nQcIgSG0cuVKWa1Wfdbg1IGG/t+jg7W/waEvm1xKSUnRt7/97WG7LoYOhSbEvZycHE2ePFmS5Kz5utd9DcOQo8Y/xd7MmTOHPDYAfbNYLDrvvPMkSV/0UWgyDCOwD3P7AohXo0ePliT52jvfE82vzW0Aopt5rza7fGru6Gji/gUQj2w2mwoLCyVJ7vr+rwNjdjOVlpYqKYmPL4GhkpeXpyuuuEKS9MZXw7NWk2EYeqNjXahFixbxsFyc4J0aCcHsbHD0sU6Tp7FFXnubUlJSdOGFFw5DZAD645xzzpHUd6HplMOjJpdPycnJTK8AIG51V2jyUmgCYorZvdTg8KjZRUcTgPhWVlYmqbN41B+nF5oADK3ly5crOTlZh5pd/ZpJZrAONDpV0+JWWmqqlixZMuTXw/Cg0ISEYBaaXLUN8jp6XtzO7GY677zzZLPZhiU2AH2bMmWKJKnG7pLD4+txvy8a/QnRhAkTlJqaOiyxAcBwM4tJXjqagJhlFpW+bHapzUOhCUB8M4tF7rqBdDQ1Bx0LYOiMGTNGc+bMkSS9XTP0XU1vdVxj/oIFGjVq1JBfD8ODQhMSQm5urn9ROaP36fMcHdsuvvjiYYoMQH/k5uYqNzdXPklVzT0Xi82OJ7MDCgDiUbcdTQ4KTUAsmThxosaOHauUlBSlpKRo2rRpGjlyZKTDAoAhESg09XPqPMPnk7vBHnQsgKG1dOlSJSUl6bMGp2rsPX/uMliHmpz6ssklq9WqxYsXD9l1MPySIx0AMFymTZum6upqOY+fUsbEoi7bvQ6XPB1Jz9SpU4c7PAB9mDJlimpra/VFk1NTsrt2HJ6+PpPZAQUA8cjsejC7tA2vT4bTHbQNQHTLyMjQQw89FOkwAGBYFBcXy2KxyNfulLfNIWtG7zPIeBpbJJ9PGRkZysvLG6YogcSWl5enyy67TNu2bdPbNXbdfM7QrJv0To2/iDx79mzWZoozdDQhYZx77rmSJNfJehmG0WW762S9JGncuHF8SANEob7WaWJ9JgCJ4syOJl9HN5PValVmZmakwgIAAOiWzWbT2LFjJfVv+jxzn5KSEiUl8dElMFyWL18uSdpX164Tre6wn/9Ii0v7GxyyWCxatmxZ2M+PyOLdGgmjvLxcKSkp8jlc8ja3dtluFpqYcguITt/4xjck+RMTr69rsfiw3Z8ElZaWsj4TgLhmzmPu6+hoMv87cuRIPowBAABRqaSkRJICU+L1xt3QWWgCMHyKiooCy4lsOdr3vTpQW474zzlz5kzl5+eH/fyILP4SRcJITU0NdDk4O4pKp3Od8I8x5RYQnXJzc5Weni6vIZ1s7/pkzdEW/wet/DECIN6ZXUuG2yPD55OvY9o81ncBAADRaty4cZIkT2PfH157GlskSePHjx/SmAB0Za6btLu2XW0eX9jO2+Lyas+p9qBrIL5QaEJCMbuVXCcbgsZ9Tpc8Tf5EZvLkycMeF4C+JSUlqbi4WJJ0pKWbQlNHWzeFJgDxLjMzUxaLRZLkc7rlc3Z2NAEAAEQjs2jUV6HJMAy5O/Yxi1MAhs+kSZM0btw4uX2Gdp1sC9t53/+6TV5DKisrU1lZWdjOi+hBoQkJpby8XJLkrg+eE9j8Pi8vLzAdDYDoYxaajp5RaDIMIzBm7gMA8SopKUkjRoyQ5H9YxuxoYn0mAAAQrQIdTc1tMrw9d0n42p0ynG5ZLJbAuk4Aho/FYtEVV1whSdp+oqXbde4HymcY2n7c/4C/eW7EHwpNSChmp4O3uVU+tycwbhaa6IQAopt5jx5pdQWNN7q8avX4ZLVaVVRUFIHIAGB4mUUlf0cThSYAABDdxowZ439QxjACM8p0x5w2Lz8/n7V3gQiprKxUWlqaTrR5dKjZ1fcBfTjY6NQph1cZGRmaMWNGGCJENKLQhISSlZWl7OxsSZLntAUo3XX+QlNpaWlE4gLQP6d3NJ3+VI3ZzVRYWMgfIwASQmCdptOmzqPQBAAAopXFYgl0Nbl7mT7P3cC0eUCkZWRk6LLLLpOkQCfSYPz5eKsk6fLLL5fNZhv0+RCdKDQh4ZgdEe66psAYHU1AbCgqKpLVapXDa6jB6Q2MH2tl2jwAicVcj8nndMnncgeNAQAARKPA9HmNvXQ0dXQ7UWgCImvBggWSpD2n2tXi8vaxd8+anF59UtcedE7EJwpNSDiBBSib/NV0w+uT194WtA1AdEpOTtbZZ58tSTrV3jn9ZW3H14WFhRGJCwCGW0ZGhiTJ5/bIcHmCxgAAAKKR+feap7m1x33MbazPBERWWVmZSkpK5DX8xaZQfXSqTT5JkyZNYqmDOEehCQmnoKBAUmfy4ukoMqWnpysrKyticQHon/z8fElSraOz0HSq42tzGwDEO7OoZLg8gXUnR4wYEcmQAAAAemV+HuPtrdDU8VAwf9sBkTdz5kxJ0t5BFJr21LYHnQvxi0ITEk6g0GT3Jy9mglNQUCCLxRKxuAD0j9nRVHd6oamjo8ncBgDxLtDR5HLL6Jg6Lz09PZIhAQAA9Krz85g2GT6jy3af0xXIayg0AZE3Y8YMSdIXTU41hzB9XoPDo2q7SxaLRZdcckm4w0OUodCEhGMmK742p+x7D6rtYE3QOIDoZt6rZnHJ4fHJ7vZJkvLy8iIWFwAMp0BHk9sjH1PnAQCAGJCTk6OUlBTJZ8jb2rVDwuxmys7Ols1mG+7wAJwhNzdX5eXlMiTtC6GryZxyb/LkyRozZkyYo0O0odCEhJOZmanRo0dLklr2fSnnsVOSxDyhQIwwu5Zq2/1P05idTZmZmUwbBSBhdHY0eWS4KTQBAIDol5SUFHhwsLt1msyZZ8zOJwCRZ3Y1fXSqbcDHmoUm8xyIb8mRDgCIhFtvvVUffPCBDMPfqp2RkaEFCxZEOCoA/WH+YVLn8MgwDNZnApCQzGnyDJebQhMAAIgZBQUFqqmp8S9jMDY3aJunuS2wD4DocMkll+i//uu/dKjJpXdqmpXUz2VHPD5Dhzumzbv44ouHOEpEAwpNSEgVFRWqqKiIdBgAQpCb6/9jxOUz1Orxqd7h72w666yzIhkWAAwrs9DkbXd2GQMAAIhWgY4me9fuCG/HGFOiA9EjNzdXEyZM0BdffKFXq5sHfPyUKVMCM0shvlFoAgDElOTkZI0cOVJ2u13NLl9gQcrs7OwIRwYAw8dct8DncEmSLBaLf80DAACAKGY+ONjdGk3eFv+YOV06gOhw00036e2335bH4xnQccnJyVq0aNEQRYVoQ6EJABBzRo8eLbvdriaXN1Bo4gkZAInELDSZ0+bZbDZZ+jmNBQAAQKSYM1GYRaXTmcUnsxgFIDqUlpZq7dq1kQ4DUS4p0gEAADBQZlGp2eVVs8sXNAYAiSAtLa3X7wEAAKLR6R1N5rrZkmR4vIFObaZFB4DYQ6EJABBzxowZI8lfaGrq6GgyxwAgEZgdTT19DwAAEI3MIpLh9spwuQPjno5upvT0dI0YMSIisQEAQsfUeQCAmGN2L1U3uwKFJjqaACQSOpoAAEAsSk1N1ejRo9XY2Kj2Q8dkzUyXJLnr7ZL8HU9MBwwAsYdCEwAg5pjdS5/WO7qMAUAiMNdkMqecSU9Pj3BEAAAA/ZObm6vGxkY17/qs220AgNhDoQkAEHMuvfRS7du3T3a7/6m3iooKZWRkRDgqABg+VqtV1157rXbv3i2r1aolS5ZEOiQAAIB+Wb58uV599VV5vd6g8ZSUFC1evDhCUQEABsNinL7yHoI0NzcrKytLTU1NGjVqVKTDAQAgofB7ePD4GQIAEDn8Hh48foYAAETOQH4PJw1TTAAAAAAAAAAAAIgzFJoAAAAAAAAAAAAQEgpNAAAAAAAAAAAACAmFJgAAAAAAAAAAAISEQhMAAAAAAAAAAABCQqEJAAAgCvzpT3+SxWLp8d+MGTMiHSIAAAAAAEAXyZEOAAAAAJ3Ky8s1a9asbscBAAAAAACiDYUmAACAKDJr1iw99dRTkQ4DAAAAAACgX5g6DwAAAAAAAAAAACGh0AQAAAAAAAAAAICQMHUeAABAFDl48KDuvvtu1dXV6ayzztKsWbO0aNEiJSXxfBAAAAAAAIg+FJoAAACiyPbt27V9+/agsYqKCm3atEkTJ06MUFQAAAAAAADd49FYAACAKJCVlaXvfe972rFjh+rq6lRXV6d3331XM2bM0Mcff6yrrrpKTU1NvZ7D6XSqubk56B8AAAAAAMBQoqMJAAAgDL75zW/qL3/5y4COefrpp3XJJZdIkqZOnaqpU6cGbZ8/f762bdumefPmaevWrXr88cd1991393i+Bx54QBs2bBh48AAAAAAAACGi0AQAABAGVVVV+vzzzwd0TFtbW5/7WK1W3XXXXdq6davefPPNXgtNd999t/75n/858H1zc7P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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "# Assuming 'data' contains your dataset\n",
        "font_size = 15\n",
        "axis_title_font = {'fontsize': 16}\n",
        "plt.figure(figsize=(17, 8))\n",
        "\n",
        "# First Plot\n",
        "plt.subplot(1, 2, 1)\n",
        "sns.violinplot(x=data['зависимость: 0 - нет, 1 - да'], y=data['равен C'], palette=\"Set2\")\n",
        "#plt.title(\"Violin Plot of Knowledge Component\")\n",
        "plt.xlabel(\"зависимость\", fontdict=axis_title_font)\n",
        "plt.ylabel('равен C', fontdict=axis_title_font)\n",
        "plt.xticks(fontsize=font_size)  # Set tick label font size\n",
        "plt.yticks(fontsize=font_size)  # Set tick label font size\n",
        "\n",
        "# Second Plot\n",
        "plt.subplot(1, 2, 2)\n",
        "sns.violinplot(x=data['зависимость: 0 - нет, 1 - да'], y=data['равен E'], palette=\"Set2\")\n",
        "#plt.title(\"Violin Plot of Security Responsibility Component\")\n",
        "plt.xlabel(\"зависимость\", fontdict=axis_title_font)\n",
        "plt.ylabel('равен E', fontdict=axis_title_font)\n",
        "plt.xticks(fontsize=font_size)  # Set tick label font size\n",
        "plt.yticks(fontsize=font_size)  # Set tick label font size\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "qO5aNXBB_CaN",
        "outputId": "38a8234d-5cc2-456e-88cd-81ba5ae11578",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 625
        }
      },
      "execution_count": 130,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1700x800 with 2 Axes>"
            ],
            "image/png": 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}