Clefts_BNC.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyMU5r3It/6YRduYfd3vd9QG",
      "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/cnserge/corpus/blob/main/Clefts_BNC.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 matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "data = pd.read_csv('/content/sample_data/Clefts BNC.csv', delimiter='\\t',  on_bad_lines='skip')\n"
      ],
      "metadata": {
        "id": "jZCMgbLRqA49"
      },
      "execution_count": 95,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dcZRDqCfr_GF",
        "outputId": "2fdc0baf-58d2-44d7-b601-b47b09a49741"
      },
      "execution_count": 96,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['nr', 'previous_turns', 'speaker_id', 'pre', 'match', 'match+1',\n",
              "       'match+2', 'match+3', 'post', 'id', 'url', 'role', 'category',\n",
              "       'subcategory', 'notes', 'type', 'focus', 'sentiment', 'generalization',\n",
              "       'hedging', 'age_cohort', 'gender', 'name', 'age', 'occupation',\n",
              "       'location'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 96
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#drop values where sentiment is nan\n",
        "data = data.dropna(subset=['sentiment'])\n",
        "data.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7MGCsDELtLqF",
        "outputId": "ab10be10-18ee-4326-fb19-3f429e322333"
      },
      "execution_count": 97,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(189, 26)"
            ]
          },
          "metadata": {},
          "execution_count": 97
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data[\"age_cohort\"].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 331
        },
        "id": "321LSgt27sp8",
        "outputId": "b8e571f2-ec6e-475c-8e36-dfa839b89876"
      },
      "execution_count": 98,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "age_cohort\n",
              "Ag3    48\n",
              "Ag5    34\n",
              "Ag4    28\n",
              "Ag2    26\n",
              "X      23\n",
              "Ag1    18\n",
              "Ag0    12\n",
              "Name: count, dtype: int64"
            ],
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>count</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>age_cohort</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Ag3</th>\n",
              "      <td>48</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Ag5</th>\n",
              "      <td>34</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Ag4</th>\n",
              "      <td>28</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Ag2</th>\n",
              "      <td>26</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>X</th>\n",
              "      <td>23</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Ag1</th>\n",
              "      <td>18</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Ag0</th>\n",
              "      <td>12</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 98
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Ensure that 'x' is removed from the 'age_cohort' column\n",
        "data = data[data[\"age_cohort\"].str.strip().str.lower() != \"x\"]\n",
        "\n",
        "# Verify the changes\n",
        "data[\"age_cohort\"].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "BdeL9jjC8H21",
        "outputId": "952955b0-a4d1-4975-ee37-1936d7a35ca9"
      },
      "execution_count": 99,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "age_cohort\n",
              "Ag3    48\n",
              "Ag5    34\n",
              "Ag4    28\n",
              "Ag2    26\n",
              "Ag1    18\n",
              "Ag0    12\n",
              "Name: count, dtype: int64"
            ],
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>count</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>age_cohort</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Ag3</th>\n",
              "      <td>48</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Ag5</th>\n",
              "      <td>34</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Ag4</th>\n",
              "      <td>28</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Ag2</th>\n",
              "      <td>26</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Ag1</th>\n",
              "      <td>18</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Ag0</th>\n",
              "      <td>12</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 99
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data[\"age_cohort\"].value_counts().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hTPdmA_t9tar",
        "outputId": "657c85bf-0a5d-4bfd-9212-05d0864e132e"
      },
      "execution_count": 100,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "166"
            ]
          },
          "metadata": {},
          "execution_count": 100
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data[\"sentiment\"].value_counts().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GAMoea5YzDsL",
        "outputId": "6ab8a801-636c-489c-91a2-ad19cb5ce365"
      },
      "execution_count": 101,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "166"
            ]
          },
          "metadata": {},
          "execution_count": 101
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Analyze the distribution of the 'sentiment' column\n",
        "sentiment_distribution = data['sentiment'].value_counts(normalize = True)\n",
        "\n",
        "# Plot the sentiment distribution\n",
        "plt.figure(figsize=(8, 6))\n",
        "sentiment_distribution.plot(kind='bar')\n",
        "plt.title('Distribution of Sentiment Categories', fontsize=16)\n",
        "plt.xlabel('Sentiment', fontsize=14)\n",
        "plt.ylabel('Ratio', fontsize=14)\n",
        "plt.xticks(rotation=45, fontsize=12)\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 607
        },
        "id": "gXNySJHozI7u",
        "outputId": "ab884fbf-c49b-452a-9e8e-60abe63e0933"
      },
      "execution_count": 102,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data['gender_binary'] = data['gender'].map({'m': 0, 'f': 1}).fillna(-1)\n",
        "\n",
        "# Perform One-Hot Encoding for the 'sentiment' column\n",
        "sentiment_ohe = pd.get_dummies(data['sentiment'], prefix='sentiment')\n",
        "\n",
        "# Add the one-hot encoded sentiment columns back to the dataset\n",
        "data = pd.concat([data, sentiment_ohe], axis=1)\n",
        "\n",
        "# Display the updated dataset\n",
        "print(data.columns)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "collapsed": true,
        "id": "VcncXpBI-Xxt",
        "outputId": "b17094fc-db4a-4ee3-b2d6-5e58f7a46d97"
      },
      "execution_count": 103,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Index(['nr', 'previous_turns', 'speaker_id', 'pre', 'match', 'match+1',\n",
            "       'match+2', 'match+3', 'post', 'id', 'url', 'role', 'category',\n",
            "       'subcategory', 'notes', 'type', 'focus', 'sentiment', 'generalization',\n",
            "       'hedging', 'age_cohort', 'gender', 'name', 'age', 'occupation',\n",
            "       'location', 'gender_binary', 'sentiment_negative', 'sentiment_neutral',\n",
            "       'sentiment_positive', 'sentiment_unclear'],\n",
            "      dtype='object')\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Select only the specified columns\n",
        "selected_columns = [\n",
        "    'age_cohort', 'gender_binary', 'sentiment_negative',\n",
        "    'sentiment_neutral', 'sentiment_positive', 'sentiment_unclear'\n",
        "]\n",
        "data_selected = data[selected_columns]\n",
        "\n",
        "\n",
        "\n",
        "data_selected.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "Z3q_Xxx3FD-u",
        "outputId": "301763f0-db59-4079-db02-1a7fab6c0863"
      },
      "execution_count": 106,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   age_cohort  gender_binary  sentiment_negative  sentiment_neutral  \\\n",
              "29        Ag5              1               False               True   \n",
              "44        Ag5              1               False               True   \n",
              "47        Ag3              1               False               True   \n",
              "55        Ag2              0               False              False   \n",
              "75        Ag3              0                True              False   \n",
              "\n",
              "    sentiment_positive  sentiment_unclear  \n",
              "29               False              False  \n",
              "44               False              False  \n",
              "47               False              False  \n",
              "55                True              False  \n",
              "75               False              False  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-252eaaa1-1479-4a42-9fc1-d54e78f714d4\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>age_cohort</th>\n",
              "      <th>gender_binary</th>\n",
              "      <th>sentiment_negative</th>\n",
              "      <th>sentiment_neutral</th>\n",
              "      <th>sentiment_positive</th>\n",
              "      <th>sentiment_unclear</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>Ag5</td>\n",
              "      <td>1</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>44</th>\n",
              "      <td>Ag5</td>\n",
              "      <td>1</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>47</th>\n",
              "      <td>Ag3</td>\n",
              "      <td>1</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>55</th>\n",
              "      <td>Ag2</td>\n",
              "      <td>0</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75</th>\n",
              "      <td>Ag3</td>\n",
              "      <td>0</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-252eaaa1-1479-4a42-9fc1-d54e78f714d4')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .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-252eaaa1-1479-4a42-9fc1-d54e78f714d4 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-252eaaa1-1479-4a42-9fc1-d54e78f714d4');\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-0ab2877d-8e39-4f34-8d4d-adaa91841e05\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-0ab2877d-8e39-4f34-8d4d-adaa91841e05')\"\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-0ab2877d-8e39-4f34-8d4d-adaa91841e05 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "data_selected",
              "summary": "{\n  \"name\": \"data_selected\",\n  \"rows\": 166,\n  \"fields\": [\n    {\n      \"column\": \"age_cohort\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 6,\n        \"samples\": [\n          \"Ag5\",\n          \"Ag3\",\n          \"Ag0\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"gender_binary\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sentiment_negative\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          true,\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sentiment_neutral\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          false,\n          true\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sentiment_positive\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          true,\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sentiment_unclear\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          true,\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 106
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Calculate the mean sentiment proportions by age cohort\n",
        "sentiment_trends_by_age = data_selected.groupby('age_cohort')[\n",
        "    ['sentiment_negative', 'sentiment_neutral', 'sentiment_positive', 'sentiment_unclear']\n",
        "].mean()\n",
        "\n",
        "# Plot sentiment trends for each age cohort\n",
        "sentiment_trends_by_age.plot(kind='bar', figsize=(12, 8))\n",
        "plt.title('Sentiment Trends by Age Cohort', fontsize=16)\n",
        "plt.xlabel('Age Cohort', fontsize=14)\n",
        "plt.ylabel('Average Proportion', fontsize=14)\n",
        "plt.xticks(rotation=45, fontsize=12)\n",
        "plt.legend(title='Sentiment', fontsize=12)\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 807
        },
        "id": "LY8Ny4hDHBRZ",
        "outputId": "9261ceec-6167-408d-87df-e765c4e0b3c9"
      },
      "execution_count": 107,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x800 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Here are the important p-values (statistically significant):\n",
        "\n",
        "Sentiment Neutral:\n",
        "P-value: 0.045\n",
        "Indicates a significant difference in neutral sentiment across age cohorts.\n",
        "Sentiment Positive:\n",
        "P-value: 0.011\n",
        "Indicates a significant difference in positive sentiment across age cohorts."
      ],
      "metadata": {
        "id": "3jHlAz-5JD0E"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Calculate the mean sentiment proportions by gender\n",
        "sentiment_distribution_by_gender = data_selected.groupby('gender_binary')[\n",
        "    ['sentiment_negative', 'sentiment_neutral', 'sentiment_positive', 'sentiment_unclear']\n",
        "].mean()\n",
        "\n",
        "# Rename gender_binary for better visualization\n",
        "sentiment_distribution_by_gender.index = sentiment_distribution_by_gender.index.map({0: 'Male', 1: 'Female'})\n",
        "\n",
        "# Plot sentiment distribution for each gender\n",
        "sentiment_distribution_by_gender.plot(kind='bar', figsize=(10, 6))\n",
        "plt.title('Sentiment Distribution by Gender', fontsize=16)\n",
        "plt.xlabel('Gender', fontsize=14)\n",
        "plt.ylabel('Average Proportion', fontsize=14)\n",
        "plt.xticks(rotation=0, fontsize=12)\n",
        "plt.legend(title='Sentiment', fontsize=12)\n",
        "plt.tight_layout()\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 607
        },
        "id": "3Le4MBeCHrei",
        "outputId": "61294ed6-aa96-4ba9-92bc-fa3dd9b8e1de"
      },
      "execution_count": 108,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAA90AAAJOCAYAAACqS2TfAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy81sbWrAAAACXBIWXMAAA9hAAAPYQGoP6dpAACNYUlEQVR4nOzdd3jN9+P+8fskIUMkRiR2E7sxIypVQszQqt1QRWjFyIdSH58uu5RSVLWU1oiirVnaGjVjVSlKi1Az9kiQRGiQnN8ffjlfRxLiJCeD5+O6cl3Oe97vc0Lceb2HwWg0GgUAAAAAADKdTXYHAAAAAADgaUXpBgAAAADASijdAAAAAABYCaUbAAAAAAAroXQDAAAAAGAllG4AAAAAAKyE0g0AAAAAgJVQugEAAAAAsBJKNwAAAAAAVkLpBoAMOnbsmPr16ydvb2/ly5dPDg4OKlmypF544QX169dPy5Yty+6IjxUeHi6DwaCAgIDsjpIjBQQEyGAwmH3ly5dPxYoVU926ddW/f39t2rRJRqMxzW10795dBoNBYWFhWRf8EZKPKTw83Gx6TsspSSNHjpTBYNDIkSOzO0qacmJGo9GolStXqmvXripfvrxcXFyUN29eFSlSRPXq1dP//vc//f7779kdM12S/94BQG5E6QaADFi+fLmqVq2qadOm6cqVK6pbt67at2+vatWq6fz585o2bZp69+6d3TGf+f+wZlaRrF69uoKDgxUcHKzWrVurWrVqOnHihL788ks1btxYNWrU0J9//pk5odOQVlnOrfiFj3WcOnVKL7zwgtq0aaMFCxYoKSlJDRs21GuvvaZatWrpxIkTmjhxourUqaN27dpld1wAeKrZZXcAAMitLl++rODgYCUkJOi///2vxowZIwcHB7Nl9u7dq6VLl2ZTwvSrXbu2IiIi5OTklN1RcrQ2bdqkOpK5bds2DR48WLt371a9evW0ZcsW1apVy2yZcePG6f3331exYsWyKO2jffvtt7p165ZKly6d3VEeq1+/furUqZPc3NyyO0quEBkZqRdffFFXrlxRnTp19OWXX6pmzZoplvv999/16aef6vDhw9mQEgCeHZRuALDQL7/8ops3b6p48eKaOHFiqsv4+vrK19c3i5M9OScnJ1WqVCm7Y+Ra/v7+2rZtmxo3bqzt27erc+fOioiIkK2trWmZYsWK5ZjCLSlXlO1kbm5uFO4n0KVLF1Ph3rx5s+zt7VNd7sUXX9SyZcu0e/fuLE4IAM8WTi8HAAtdvnxZklSkSBGL1r93755mzZqlgIAAFSpUSPb29vLy8lLfvn119uzZFMs/eBru3bt3NX78eFWuXFmOjo4qXLiw2rVrp4iICLN1kq8zTfbwdcmnT59Ose0HnT59WgaDQZ6enkpKStLUqVNVrVo1OTk5qVixYurTp4+uXbsmSUpISNDo0aNVqVIlOTo6qnjx4howYIDi4+PTfA/27t2rN954Q6VLl5a9vb0KFSqkwMBArV69OtXlPT09Tbk3b96sZs2aqWDBgnJ0dFTNmjX17bffppp/3rx5kqQePXqYHX9mXn+bN29ezZgxQ9L96/xXrFhhNj+tU9yTkpL09ddfq27duipQoIDy5Mkjd3d3Va9eXf3790/xGW3ZskWS1LBhQ7NjSd7ug59ZYmKiJk+eLB8fHzk7O5t9L6TnNPUDBw6oXbt2KlKkiBwdHVWtWjV9/vnnSkxMTLHs407hDwsLk8FgUPfu3c0yNGzYUJK0ZcsWs+Px9PQ0Lfe466V//fVXtWzZUu7u7sqbN6+KFy+ujh07as+ePaku/+Cx79+/X+3atZObm5vs7e3l7e2tSZMmPfL6/MeJjIxUt27dVKxYMTk4OKhChQoaOXKkbt++bbbciBEjZDAYHnkJyu7du2UwGFSiRAndu3fvsfsODw/X9u3bJUkzZsxIs3A/qHbt2qlOz4p/ox60c+dOtWjRQgUKFJCzs7Nq1aqlOXPmPDb/7du3NWnSJL344osqUKCAHBwcVLFiRb377ruKjo5OsfyD34vXrl3TwIEDVbZsWdnb23OZAwCrYKQbACyUPFJ48OBBbdy4UY0bN073unFxcWrVqpXCw8Pl7OwsX19fFSlSRH///bdmzJihJUuWaP369fLx8Umx7t27d/Xyyy/rt99+U/369fX8889r9+7d+vHHH7V582b9+eefpsJSo0YNBQcHm0pncHCw2bacnZ3TnblLly5asWKFGjRooLJly+q3337TzJkztXv3bm3btk3NmzfXX3/9pYCAAJUvX17btm3T1KlTdezYsVRL9Oeff65BgwYpKSlJNWrUkJ+fny5duqTw8HCtW7dOo0aN0vDhw1PNMmfOHI0ZM0Y1a9ZU8+bNdfr0af3+++8KDg42/Sc6+fiCg4O1fft2nThxQnXr1lW5cuVM26lRo0a6jz89KleuLB8fH/35559av3692rdv/9h1evbsqblz58rBwUH16tVTkSJFdO3aNZ08edJ0rbinp6eKFi2q4OBgrV27VpcvX1ZgYKCKFi1q2s6DxyXdv4lWu3bttHbtWvn7++v555/XoUOH0n0su3fvVt++fVW0aFE1btxY169fV3h4uAYOHKjt27dr8eLFGb5PQPPmzeXg4KBff/1VHh4eat68uWleeke2hw0bpjFjxshgMOill15S6dKlFRERocWLF2vZsmX6+uuv9eabb6a67q+//qrJkyerbNmyatq0qS5evKjt27dr8ODBOnv2rKZMmfLEx3Tq1Cn5+vrKzs5O9evX1+3bt7V582aNGjVKGzZs0IYNG0yXofTt21effPKJFi5cqPHjx6tAgQIptjdt2jRJUu/evWVn9/j/tq1cuVKSVK1aNVWrVu2J8yfLqn+jki1ZskSvv/66EhMTVaVKFVWtWlVnz55Vz549H/l9e+HCBTVv3lx///23ChUqpBdeeEH58+fXvn379Omnn2rJkiUKDw/Xc889l2LdqKgo1apVSzdu3JC/v798fX2VN29ei98zAEiTEQBgkbi4OGOJEiWMkowGg8EYEBBgHD16tHHVqlXGK1euPHLdzp07GyUZW7Zsabx8+bLZvM8++8woyVi+fHnjvXv3TNM3b95slGSUZPTx8TFevHjRNO/27dvGwMBAoyRjr169Uuwveb20JG+7QYMGZtNPnTplWrds2bLG06dPm+ZFRUUZy5cvb5RkrFq1qrF27drGqKgo0/yTJ08aCxYsaJRk3L59u9l2165dazQYDEY3Nzfjli1bzOb99ddfxpIlSxolGcPDw83mPffcc0ZJxjx58hh//vlns3lz5841SjK6uroab926ZTYvODjYKMk4d+7cNN+DR2nQoIFRknHEiBGPXbZnz55GScZ69eo9NkNkZKRRkrFkyZJmn2eyw4cPGyMjI1PNsnnz5lT3/+BnVrJkSePRo0cfeUwPbyc5pyRjaGio8e7du6Z5Bw8eNBYpUsQoyThjxozHHt+Dkj+f4OBgs+lpfe89aMSIEam+/2vWrDFKMjo4OBjXrVtnNm/WrFmm75WDBw+meuypHcfGjRuNBoPBaGtrazx79myamdLKKMnYunVrs+/Bs2fPGitUqGCUZHz//ffN1nvjjTeMkoyTJ09Osc2rV68a7e3tjXny5En1+yM1/v7+RknGt956K93ZU5OV/0ZdvHjRmD9//lTfhw0bNhgdHBxS/TcsKSnJWLduXdPxxsbGmubdvXvX+N///tcoydiwYUOz9ZK/FyUZGzdubIyJibHsTQKAdOL0cgCwkLOzszZu3Cg/Pz8ZjUaFh4dr2LBheuWVV+Tu7i4fHx/NmDEjxam4ERER+v7771W8eHF99913cnd3N5s/cOBAvfzyyzp27JjWrFmTYr8Gg0Fz5841G+V0cHDQqFGjJEkbNmywwtFKU6dONRstKly4sPr27Svp/mj/7NmzVbhwYdN8Ly8vdenSRZK0ceNGs22NGDFCRqNRM2bMUP369c3mVa1aVZMnT5YkffHFF6lm6d+/v1q2bGk2rXv37qpUqZJiYmLSPK04KySP0KZ2WuvDki9RqFmzptnnmez555/P0LXXY8eOVYUKFSxat1ixYpo0aZLZ6GrlypVNZx9MmjTJ4lyZJfleCqGhoWratKnZvLfeekstW7bU3bt39fnnn6e6frt27VKc2t2oUSMFBgYqMTFRmzdvfuJMjo6OmjFjhhwdHU3TSpYsaXq/pk+frn///dc0b8CAAZKkr776KsUp7bNmzVJCQoI6dOiQ6vdHaqKioiSlfdnLxo0b1b179xRfR44cMS2T1f9GzZ49W3FxcXrxxRf1zjvvmM1r3Lhxmqff//rrr9qxY4dq1KihGTNmKH/+/KZ5dnZ2mjBhgqpUqaLNmzfr4MGDKdbPkyePvv76a7m4uKS6fQDILJRuAMiAihUr6vfff9euXbs0fPhwBQYGmv6zu3//fvXt21fNmzfXnTt3TOusXr1aRqNRLVq0MPtP4oOSryv87bffUswrXbq0qlevnmL6888/L0k6f/58Rg8rBTs7OzVr1izF9PLly5syValSJc35Fy5cME2LiorS7t275ejoqFdffTXV/T3q+CWluZ4134P0SkpKkqR0nXpdqVIl5c+fX6tXr9bHH3+sU6dOZWqW9JzenpagoKAUd+OX/u8ShWPHjpl9rlnt3r172rFjhySZXSf+oLfeekuS0izP1vg+atasWaoFuWXLlipcuLBiY2O1b98+0/QXXnhBderU0bFjx/Trr7+apiclJZnuEdCvX78nzpGWiIgIzZs3L8XXpUuXTMtk9b9RyfcVeOONN1Ld18OXxSRbtWqVpPvf56mdem9jY2P6pV5qOX18fFSmTJlUtw0AmYnSDQCZoHbt2ho1apTpetu9e/eqU6dOku6P6jw40nby5ElJ90d3Hr6xWfLXu+++K0m6evVqin2lNfKZPFqTkJCQqccm3R/1TO0/tcnXhKeVKfk/7A+O7J06dUpGo1G3b9+Wvb19qsefPLKW2vE/an/J78GD+8tqySONhQoVeuyy+fPn19y5c+Xo6KihQ4eqTJkyKl68uNq1a6evv/5aN2/etDiHu7t7hh4B5+Xller0/Pnzm85oOHfunMXbz6jo6GjT55xW1rJly0pKuzxb4/sorSySTNcxP/y+vf3225KkL7/80jTtl19+UWRkpHx8fPTSSy+le//JZ1qk9XenX79+MhqNpq/k9+hBWf1vVPL7kdZ7l9b05JzDhg1LM+f06dPTzPnwdeUAYC3cSA0AMpnBYFDNmjX1/fff69atW/rpp5+0YsUK/e9//5P0fyOhNWrUSHU06EF+fn4pptnYZP3vSx+3zyfJlHz8zs7OFo/EZsd7kF7Jo5hVq1ZN1/Lt27dXkyZN9NNPP2nbtm3asWOHfvzxR/34448aPny41q9fn+5tPejB05ut5eHToR8l+XPPSbLr++jh961Dhw4aPHiw1qxZo1OnTsnLy8t0A7UnHeWuWbOmtm3blqFLLHLLv1HJOevVq5fqLw8eVLly5RTTsuLvCABIlG4AsKpmzZrpp59+Mo1+SlKpUqUkSXXr1jUb2XpWJB+/wWDQnDlzcnSBflKHDh3S/v37JSnV0/HT4urqqq5du6pr166SpLNnz6p///5auXKl+vXrZ3pMWFZK61T3uLg40/XqJUuWNE1PvutzXFxcqutFRkZmar7ChQvL3t5eCQkJOnnyZKp36k4eCS1RokSm7vtRHnWJQPLj3x5836T7l2/07dtXQ4cO1fTp0xUSEqL169erUKFCev31159o/61atdLnn3+uAwcO6ODBg6le9vE4Wf1vVIkSJXTkyBHT+/OwtKYn52zdurUGDx5spXQAkHFPz/90ACCLpWeU78yZM5LM/5PdokULSdJPP/2UZadB58mTR5LS9ZxfaytevLiqVaumuLg4rV27Nkv2mVwIrXn8d+7cUZ8+fSTdv1a7VatWFm+rVKlSpptOJZf4ZFlxLNL9RzildqnC/PnzJd1/RNmDZTb5z6k9h9loNKZ6wy3J8uOxs7NTvXr1JCnNZ4MnP+M5+VngWWHdunW6cuVKiumrV69WdHS08ufPL19f3xTze/fuLQcHB82ZM8f0nPC33nrriUdjGzVqpDp16kiS+vTpY3Y/ifTK6n+jGjRoIElauHBhqvO//fbbVKcn51yyZEmGnqsOANZG6QYAC02fPl3BwcGp3qDHaDRq+fLlplGi5Ou7pfs372nfvr3Onj2rdu3apTqKEx8fr4ULF5rubp1RyaX/SZ7TbE1jxoyRJPXo0UM///xzivlGo1G7du3SunXrMmV/1j7+HTt2yN/fX9u3b5ezs7MWLlyYrhH8P//8U4sWLdLt27dTzEt+Xx5+vnBWfZYXLlzQ4MGDze6+HxERoY8++kiSUtxlukmTJpLul/LDhw+bpt+9e1fvvfee/vjjj1T3k3w8x44d0927d58o43//+19J9+/8/fAd8sPCwvTTTz8pT548pjuEZ4Xbt2+rb9++Zp/phQsXTFn79OmT6g3q3Nzc1LlzZ127dk1ff/21bGxsFBoaalGGhQsXys3NTTt27FDjxo1T/OIm2cGDB3Xjxo0U07P636i33npLzs7O2rlzp6ZOnWo2Lzw83HRDuYe1bt1aL7zwgnbv3q0ePXqket329evXNWPGjBzxC0cAzy5OLwcAC929e1fffvutvv32WxUpUkQ+Pj5yc3PTjRs3dPjwYdN/VLt06WK6i3KyuXPn6saNG1qzZo0qVqyo6tWry8vLS0ajUadPn9aBAwd0584dRUREyMPDI8NZ27dvr4kTJ6pJkyZq1KiR6QZn48ePN3vMV1Z59dVX9fnnn+u///2vWrVqpXLlyqlixYpydXXV1atXdeDAAV25ckXvvffeE52mnZY2bdpo1KhRmjp1qg4ePKhSpUrJxsZGrVq1eqIR6RUrVpg+17t37+ratWvav3+/6c7P1atXV1hYmGrUqJGu7UVGRqpTp05ydHRUzZo1VapUKd27d09///23jh49qrx582rChAlm67Rv315z587Vu+++qw0bNsjd3V0Gg0FvvvnmE91w63H69OmjWbNmadWqVfLz89P169e1efNm3blzR23btjU9Li5Z3bp11bp1a61cuVK1atVSvXr15OjoqH379ik2NlYDBgxI9dFdpUuXVq1atbRnzx5VrVpVtWrVkoODg9zc3PTJJ588MmOLFi00dOhQjRkzRk2bNlXdunVVunRpHTlyRPv27ZOtra1mzJiR6vW81tKtWzf98ssvKlOmjPz9/fXvv/9q06ZNio+PV506dUxnMKTm7bffNo3Ov/LKKxbf6MvLy0s7d+7Ua6+9pu3bt8vHx0flypVT5cqVlT9/ft28eVMRERE6evSopPvXRCc/aSBZVv4bVbx4cX3zzTfq0qWLBgwYoFmzZqlKlSo6f/68tm3bpoEDB+qzzz5LsZ6NjY1WrFihV155RfPmzdPSpUtVvXp1lS5dWnfu3NHJkyf1999/KzExUd27d0/1ZpAAkBX41wcALPTWW2/Jy8tLGzdu1K5du3T48GFdvnxZdnZ2Kl68uF5//XV169ZNzZs3T7Fu/vz5tW7dOi1atEgLFizQ3r17tX//frm4uKhYsWJ644031KpVq8feHCi9Ro8eLRsbGy1fvlwrVqwwnXI6dOjQbCnd0v2C0ahRI33xxRfavHmzNm7cKBsbGxUtWlQ+Pj565ZVXMvTIqwdVq1ZNy5Yt08SJE7Vr1y5t3LhRRqNRJUuWfKLSfeDAAR04cEDS/Zswubq6ysvLSx06dFDbtm3VsGHDdD0qLNmLL76oTz75RFu3blVERIT+/PNP2dnZqWTJkvrPf/6j/v37q2LFimbrvPLKK/rmm2/01VdfadOmTbp165ak+8UpM0u3n5+fevXqpREjRmj9+vW6efOmypcvr7feekv9+/dP9TgXLVqkMWPG6LvvvlN4eLgKFiyoxo0ba/To0dq2bVua+1q2bJk++OADbd68WYsWLdK9e/f03HPPPbZ0S/e/t+vWrasvvvhCu3bt0u+//y43Nze99tprGjx4sGrXrp2h9+FJeXl5ac+ePRoyZIg2bdqk69evq3Tp0urcubPee++9R54uXr16dRUtWlSXLl3K8GPCypUrp3379mnFihVaunSp6fs+ISFBrq6uKleunAYOHKigoCDT6egPyup/ozp16qSSJUtqzJgx2rlzp06cOKGKFStqxowZ6tWrV6qlW7pf2H///XeFhYVp0aJF+uuvv7R7924VKlRIxYsXV58+fdSqVatUzy4AgKxiMHIRDAAAQLbbsGGDmjZtqooVKyoiIuKJfoEDAMi5uKYbAAAgmyUmJmrEiBGSpEGDBlG4AeApwkg3AABANpk7d662bt2qPXv26ODBg6patar27dvH9ccA8BRhpBsAACCbbNmyRWFhYTp37pzatm2rX375hcINAE8ZRroBAAAAALASRroBAAAAALASSjcAAAAAAFbCRUOSkpKSdOHCBeXPn5+7hQIAAAAAHstoNCouLk7FixeXjU3a49mUbkkXLlxQqVKlsjsGAAAAACCXOXv2rEqWLJnmfEq3pPz580u6/2a5uLhkcxoAAAAAQE4XGxurUqVKmfpkWijdkumUchcXF0o3AAAAACDdHneJMjdSAwAAAADASijdAAAAAABYCaUbAAAAAAAroXQDAAAAAGAllG4AAAAAAKyE0g0AAAAAgJVQugEAAAAAsBJKNwAAAAAAVkLpBgAAAADASijdAAAAAABYCaUbAAAAAAAroXQDAAAAAGAllG4AAAAAAKyE0g0AAAAAgJVQugEAAAAAsBJKNwAAAAAAVkLpBgAAAADASijdAAAAAABYCaUbAAAAAAAroXQDAAAAAGAllG4AAAAAAKzELrsDALDASNfsTmB9I2OyOwEAAACQYYx0AwAAAABgJZRuAAAAAACshNINAAAAAICVULoBAAAAALASSjcAAAAAAFZC6QYAAAAAwEoo3QAAAAAAWAmlGwAAAAAAK6F0AwAAAABgJZRuAAAAAACshNINAAAAAICVULoBAAAAALASSjcAAAAAAFZC6QYAAAAAwEoo3QAAAAAAWAmlGwAAAAAAK6F0AwAAAABgJXbZHQAAAADIFCNdsztB1hgZk90JADwBRroBAAAAALASSjcAAAAAAFZC6QYAAAAAwEoo3QAAAAAAWAmlGwAAAAAAK6F0AwAAAABgJZRuAAAAAACshNINAAAAAICVULoBAAAAALCSHFm6p02bJk9PTzk4OMjPz0+7d+9+5PI3btzQf/7zHxUrVkz29vaqUKGCVq9enUVpAQAAAABInV12B3jYokWLNGjQIM2YMUN+fn6aMmWKAgMDdfToUbm7u6dY/s6dO2ratKnc3d21dOlSlShRQpGRkSpQoEDWhwcAAAAA4AE5rnRPnjxZISEh6tGjhyRpxowZWrVqlebMmaP3338/xfJz5szRtWvX9NtvvylPnjySJE9Pz6yMDAAAAABAqnLU6eV37tzR3r171aRJE9M0GxsbNWnSRDt37kx1nZ9++kl16tTRf/7zH3l4eKhKlSoaO3asEhMT09xPQkKCYmNjzb4AAAAAAMhsOap0R0VFKTExUR4eHmbTPTw8dOnSpVTXOXnypJYuXarExEStXr1aw4YN06RJkzRmzJg09zNu3Di5urqavkqVKpWpxwEAAAAAgJTDSrclkpKS5O7urq+//lq+vr7q2LGjhgwZohkzZqS5zgcffKCYmBjT19mzZ7MwMQAAAADgWZGjrul2c3OTra2tLl++bDb98uXLKlq0aKrrFCtWTHny5JGtra1p2vPPP69Lly7pzp07yps3b4p17O3tZW9vn7nhAQAAAAB4SI4a6c6bN698fX21ceNG07SkpCRt3LhRderUSXWdunXr6vjx40pKSjJN++eff1SsWLFUCzcAAAAAAFklR5VuSRo0aJC++eYbzZs3TxEREerbt6/i4+NNdzPv1q2bPvjgA9Pyffv21bVr1zRgwAD9888/WrVqlcaOHav//Oc/2XUIAAAAAABIymGnl0tSx44ddfXqVQ0fPlyXLl1SjRo1tHbtWtPN1c6cOSMbm//7XUGpUqX066+/6p133lG1atVUokQJDRgwQO+99152HQIAAAAAAJIkg9FoNGZ3iOwWGxsrV1dXxcTEyMXFJbvjAI830jW7E1jfyJjsTgAAyG2ehZ+PEj8jgRwivT0yx51eDgAAAADA04LSDQAAAACAlVC6AQAAAACwEko3AAAAAABWQukGAAAAAMBKKN0AAAAAAFgJpRsAAAAAACuhdAMAAAAAYCWUbgAAAAAArITSDQAAAACAlVC6AQAAAACwEko3AAAAAABWQukGAAAAAMBKKN0AAAAAAFgJpRsAAAAAACuhdAMAAAAAYCWUbgAAAAAArITSDQAAAACAlVC6AQAAAACwEko3AAAAAABWQukGAAAAAMBKKN0AAAAAAFgJpRsAAAAAACuhdAMAAAAAYCWUbgAAAAAArITSDQAAAACAlVC6AQAAAACwEko3AAAAAABWQukGAAAAAMBKKN0AAAAAAFgJpRsAAAAAACuhdAMAAAAAYCWUbgAAAAAArITSDQAAAACAlVC6AQAAAACwEko3AAAAAABWQukGAAAAAMBKKN0AAAAAAFgJpRsAAAAAACuhdAMAAAAAYCWUbgAAAAAArITSDQAAAACAlVC6AQAAAACwEko3AAAAAABWQukGAAAAAMBKKN0AAAAAAFgJpRsAAAAAACuhdAMAAAAAYCWUbgAAAAAArITSDQAAAACAlVC6AQAAAACwEko3AAAAAABWQukGAAAAAMBKKN0AAAAAAFgJpRsAAAAAACuhdAMAAAAAYCWUbgAAAAAArITSDQAAAACAlVC6AQAAAACwEko3AAAAAABWQukGAAAAAMBKKN0AAAAAAFhJji3d06ZNk6enpxwcHOTn56fdu3enuWxYWJgMBoPZl4ODQxamBQAAAAAgpRxZuhctWqRBgwZpxIgR2rdvn6pXr67AwEBduXIlzXVcXFx08eJF01dkZGQWJgYAAAAAIKUcWbonT56skJAQ9ejRQ97e3poxY4acnJw0Z86cNNcxGAwqWrSo6cvDwyMLEwMAAAAAkFKOK9137tzR3r171aRJE9M0GxsbNWnSRDt37kxzvZs3b+q5555TqVKl1Lp1ax06dCjNZRMSEhQbG2v2BQAAAABAZstxpTsqKkqJiYkpRqo9PDx06dKlVNepWLGi5syZo5UrV2rBggVKSkrSSy+9pHPnzqW6/Lhx4+Tq6mr6KlWqVKYfBwAAAAAAOa50W6JOnTrq1q2batSooQYNGmj58uUqUqSIZs6cmeryH3zwgWJiYkxfZ8+ezeLEAAAAAIBngV12B3iYm5ubbG1tdfnyZbPply9fVtGiRdO1jTx58sjHx0fHjx9Pdb69vb3s7e0znBUAAAAAgEfJcSPdefPmla+vrzZu3GialpSUpI0bN6pOnTrp2kZiYqL+/vtvFStWzFoxAQAAAAB4rBw30i1JgwYNUnBwsGrVqqXatWtrypQpio+PV48ePSRJ3bp1U4kSJTRu3DhJ0kcffaQXX3xR5cqV040bN/Tpp58qMjJSPXv2zM7DAAAAAAA843Jk6e7YsaOuXr2q4cOH69KlS6pRo4bWrl1rurnamTNnZGPzf4P0169fV0hIiC5duqSCBQvK19dXv/32m7y9vbPrEAAAAAAAkMFoNBqzO0R2i42Nlaurq2JiYuTi4pLdcYDHG+ma3Qmsb2RMdicAAOQ2z8LPR4mfkUAOkd4emeOu6QYAAAAA4GlB6QYAAAAAwEoo3QAAAAAAWAmlGwAAAAAAK6F0AwAAAABgJZRuAAAAAACshNINAAAAAICVULoBAAAAALASSjcAAAAAAFZC6QYAAAAAwEoo3QAAAAAAWAmlGwAAAAAAK6F0AwAAAABgJZRuAAAAAACshNINAAAAAICVULoBAAAAALASSjcAAAAAAFZC6QYAAAAAwEoo3QAAAAAAWAmlGwAAAAAAK7GzdMU7d+5oxYoV+uOPP3Tjxg0lJiamWMZgMGj27NkZCggAAAAAQG5lUemOjIxU06ZNdeLECRmNxjSXo3QDAAAAAJ5lFpXud955R8ePH1fXrl315ptvqmTJkrKzs3jQHAAAAACAp5JFTXnTpk1q3Lix5s2bl9l5AAAAAAB4alh0I7WkpCT5+PhkdhYAAAAAAJ4qFpVuPz8/RUREZHYWAAAAAACeKhaV7k8++USbNm3S0qVLMzsPAAAAAABPDYuu6V61apUaNmyojh07qkGDBqpZs6ZcXFxSLGcwGDRs2LAMhwQAAAAAIDcyGB/1zK802Nikb4DcYDCk+vzunCY2Nlaurq6KiYlJ9ZcHQI4z0jW7E1jfyJjsTgAAyG2ehZ+PEj8jgRwivT3SopHuzZs3WxwMAAAAAIBnhUWlu0GDBpmdAwAAAACAp45FN1IDAAAAAACPZ9FId7IdO3YoLCxM+/fvV2xsrFxcXOTj46Nu3bqpXr16mZURAAAAAIBcyeLS/c4772jq1KlKvg+bwWCQ0WjU3r17NXv2bA0YMECTJ0/OtKAAAAAAAOQ2Fp1ePm/ePH3++ecqX768Fi5cqAsXLujevXu6ePGivvvuO1WoUEGff/65vv3228zOCwAAAABArmHRI8NefPFFXbhwQX///bdcXVM+miEmJkZVq1ZV8eLF9fvvv2dKUGvikWHIdZ6FR6LwOBQAwJN6Fn4+SvyMBHKI9PZIi0a6Dx06pPbt26dauCXJ1dVV7du316FDhyzZPAAAAAAATwWr3b3cYDBYa9MAAAAAAOQKFpXuypUra9myZbp582aq8+Pi4rRs2TJVrlw5Q+EAAAAAAMjNLCrdvXv31rlz51SnTh0tW7ZMUVFRkqSoqCgtXbpUL730ks6dO6e+fftmalgAAAAAAHITix4Z1qNHD/3555/68ssvFRQUJEmysbFRUlKSJMloNKp///4KDg7OvKQAAAAAAOQyFj+ne+rUqXrttdcUFham/fv3KzY2Vi4uLvLx8VFwcLD8/f0zMycAAAAAALmOxaVbkvz9/SnXAAAAAACkwWp3LwcAAAAA4FmXrpHuM2fOSJJKlCghW1tb0+v0KF26tGXJAAAAAADI5dJVuj09PWUwGBQREaEKFSqYXj+OwWDQvXv3MhwSAAAAAIDcKF2lu1u3bjIYDHJ1dTV7DQAAAAAA0pau0h0WFvbI1wAAAAAAICVupAYAAAAAgJVYVLptbW01evToRy7z8ccfy84uQ08kAwAAAAAgV7OodBuNRhmNxnQtBwAAAADAs8pqp5dfvXpVjo6O1to8AAAAAAA5XrrP//7222/NXu/fvz/FNElKTEzU2bNn9e2336pKlSoZTwgAAAAAQC6V7tLdvXt302PCDAaDVq5cqZUrV6ZYLvmUckdHR40cOTJzUgIAAAAAkAulu3TPnTtX0v1S/eabb6p169Zq06ZNiuVsbW1VqFAh1alTRwULFsy0oAAAAAAA5DbpLt3BwcGmP2/ZskVt27ZVq1atrBIKAAAAAICngUU3Ujt9+rT++OOPzM4CAAAAAMBTxaLSvXv3biUmJmZ2FgAAAAAAnioWle5KlSopMjIys7MAAAAAAPBUsah09+/fXytXrtThw4czOw8AAAAAAE+NdN9I7UFlypRRQECAXnzxRfXu3VsvvPCCPDw8TI8Ue1D9+vUzHBIAAAAAgNzIotIdEBAgg8Ego9GoSZMmpVq2k3HtNwAAAADgWWVR6R4+fPgji3ZmmDZtmj799FNdunRJ1atX1xdffKHatWs/dr0ffvhBr7/+ulq3bq0VK1ZYNSMAAAAAAI9iUekeOXJkJscwt2jRIg0aNEgzZsyQn5+fpkyZosDAQB09elTu7u5prnf69GkNHjxY/v7+Vs0HAAAAAEB6WHQjNWubPHmyQkJC1KNHD3l7e2vGjBlycnLSnDlz0lwnMTFRb7zxhkaNGqUyZcpkYVoAAAAAAFJn0Uh3svj4eK1YsUL79+9XbGysXFxcVKNGDbVp00b58uWzaJt37tzR3r179cEHH5im2djYqEmTJtq5c2ea63300Udyd3fXW2+9pW3btj1yHwkJCUpISDC9jo2NtSgrAAAAAACPYnHpXrZsmXr16qUbN27IaDSaphsMBhUoUEDffPON2rVr98TbjYqKUmJiojw8PMyme3h46MiRI6mus337ds2ePVv79+9P1z7GjRunUaNGPXE2AAAAAACehEWnl//222/q1KmT4uPj1bNnT3333XfavHmzvv/+e4WEhOjWrVvq1KnTI0emM0tcXJy6du2qb775Rm5ubula54MPPlBMTIzp6+zZs1ZOCQAAAAB4Flk00j127FjZ29trx44dql69utm8jh07KjQ0VC+99JLGjh2rn3/++Ym27ebmJltbW12+fNls+uXLl1W0aNEUy584cUKnT5/Wq6++apqWlJQkSbKzs9PRo0dVtmxZs3Xs7e1lb2//RLkAAAAAAHhSFo1079y5Ux07dkxRuJNVq1ZNQUFB+u23355423nz5pWvr682btxompaUlKSNGzeqTp06KZavVKmS/v77b+3fv9/01apVKzVs2FD79+9XqVKlnjgDAAAAAACZwaKR7lu3bqW45vphHh4eunXrlkWhBg0apODgYNWqVUu1a9fWlClTFB8frx49ekiSunXrphIlSmjcuHFycHBQlSpVzNYvUKCAJKWYDgAAAABAVrKodHt6emr9+vUaO3Zsmsts3LhRnp6eFoXq2LGjrl69quHDh+vSpUuqUaOG1q5dayr6Z86ckY1NjnzaGQAAAAAAJhY116CgIO3du1fBwcG6cOGC2byLFy+qe/fu2rt3rzp27GhxsH79+ikyMlIJCQnatWuX/Pz8TPPCw8MVFhaW5rphYWFasWKFxfsGAAAAACAzWDTS/d5772nt2rWaP3++Fi1apHLlysnDw0OXL1/W8ePHdefOHdWuXVvvvfdeZucFAAAAACDXsGik28nJSVu3btXIkSNVsmRJHT58WJs3b9bhw4dVsmRJjRo1Slu2bJGjo2Nm5wUAAAAAINewaKRbuv/YreHDh2v48OGKi4tTbGysXFxclD9//szMBwAAAABArmVx6X5Q/vz5KdsAAAAAADwkQ6X74sWL+uGHH/Tnn38qJiZGrq6u8vHxUadOnVSsWLHMyggAAAAAQK5kcemeNm2a/ve//ykhIUFGo9E0fcGCBRoyZIgmTpyo0NDQTAkJAAAAAEBuZFHp/uGHH9S/f3+5ublpyJAh8vf3N929fOvWrfr8889N84OCgjI7MwAAAAAAuYLB+OAwdTrVrFlT586d0/79+1W8ePEU88+dOycfHx+VLl1ae/fuzZSg1hQbGytXV1fFxMTIxcUlu+MAjzfSNbsTWN/ImOxOAADIbZ6Fn48SPyOBHCK9PdKiR4ZFREQoKCgo1cItSSVLltRrr72miIgISzYPAAAAAMBTwaLSXaBAAeXLl++Ryzg7O6tAgQKWbB4AAAAAgKeCRaW7VatW+vnnn3Xv3r1U59+9e1c///yzWrdunaFwAAAAAADkZhaV7gkTJihfvnxq1qyZfv/9d7N5O3fuVLNmzZQ/f3598sknmRISAAAAAIDcyKK7l/v4+OjOnTvat2+f6tatKzs7O7m5uSkqKso0+l2sWDH5+PiYrWcwGHTixImMpwYAAAAAIBewqHQnJSUpT548Kl26tNn0h2+s9vCN0S24UToAAAAAALmWRaX79OnTmRwDAAAAAICnj0XXdAMAAAAAgMezaKT7Qffu3dPRo0cVGxsrFxcXVaxYUXZ2Gd4sAAAAAAC5nsUj3deuXVNISIhcXV1VrVo11atXT9WqVVOBAgXUq1cvRUdHZ2ZOAAAAAAByHYuGpK9du6YXX3xRx48fV6FCheTv769ixYrp0qVL2rNnj2bNmqUtW7Zo586dKlSoUGZnBgAAAAAgV7BopHv06NE6fvy4/ve//ykyMlJr167V3LlztWbNGkVGRuq9997TsWPH9PHHH2d2XgAAAAAAcg2LSvfKlSsVEBCg8ePHK1++fGbznJycNG7cOAUEBOjHH3/MlJAAAAAAAORGFpXuCxcuqE6dOo9cpk6dOrpw4YJFoQAAAAAAeBpYVLpdXV0VGRn5yGUiIyPl6upqUSgAAAAAAJ4GFpXuBg0aaMmSJdqwYUOq8zdu3KglS5YoICAgI9kAAAAAAMjVLLp7+YgRI7Rq1SoFBgbq5ZdfVoMGDeTh4aHLly8rPDxca9askZOTk4YPH57ZeQEAAAAAyDUsKt2VK1fWr7/+qu7du2vVqlVatWqVDAaDjEajJKls2bIKCwtT5cqVMzUsAAAAAAC5iUWlW5Lq1aunY8eOaceOHfrzzz8VGxsrFxcX+fj4qG7dujIYDJmZEwAAAACAXMei0v3mm2+qatWqeuedd1SvXj3Vq1cvs3MBAAAAAJDrWXQjte+++05XrlzJ7CwAAAAAADxVLCrdZcuW1cWLFzM7CwAAAAAATxWLSvebb76pVatW6fz585mdBwAAAACAp4ZF13S3b99emzdv1ksvvaR3331XL7zwgjw8PFK9eVrp0qUzHBIAAAAAgNzIotJdpkwZ0yPC3n777TSXMxgMunfvnsXhAAAAAADIzSwq3d26deORYAAAAAAAPIZFpTssLCyTYwAAAAAA8PR54tIdGRmpPXv2yGAwqFatWlyzDQAAAABAGp6odA8YMEDTpk2T0WiUdP+a7bfffluTJ0+2SjgAAAAAAHKzdD8ybN68efriiy9kb2+vwMBABQYGyt7eXp9//rkWLFhgzYwAAAAAAORK6S7ds2fPlqOjo3bv3q3Vq1dr9erV2r17txwcHDRr1ixrZgQAAAAAIFdKd+k+ePCg2rVrp8qVK5umVa5cWW3bttVff/1llXAAAAAAAORm6S7dMTExKlOmTIrpZcqUUWxsbKaGAgAAAADgaZDu0m00GmVra5tiuq2trenGagAAAAAA4P880d3L4+PjdeXKFbNpN2/elCRdvXo11fLt7u6egXgAAAAAAOReT1S6J06cqIkTJ6aYbjQaVbRo0RTTDQaD7t27Z3k6AAAAAABysXSX7vr168tgMFgzCwAAAAAAT5V0l+7w8HArxgAAAAAA4OmT7hupAQAAAACAJ0PpBgAAAADASijdAAAAAABYCaUbAAAAAAAroXQDAAAAAGAllG4AAAAAAKyE0g0AAAAAgJVkSum+du2azp49mxmbAgAAAADgqWFx6Y6JidGAAQPk4eGhIkWKyMvLyzRv165devnll7V3795MCQkAAAAAQG5kUem+du2a/Pz89MUXX6hUqVJ6/vnnZTQaTfOrVaumHTt2aOHChZkWFAAAAACA3Mai0j1y5Ej9888/+uGHH7Rnzx699tprZvMdHR3VoEEDbdq0KVNCAgAAAACQG1lUun/66Se1bNlSQUFBaS7j6empc+fOWRwMAAAAAIDczqLSffHiRXl7ez9yGXt7e8XHx1sUCgAAAACAp4FFpbtw4cKPvVv5kSNHVKxYMYtCAQAAAADwNLCodNevX18rV65M8/Txw4cPa+3atWrSpEmGwgEAAAAAkJtZVLqHDBmixMRE1a1bVwsXLlRUVJQkKSIiQrNnz1ajRo1kb2+v//3vfxYHmzZtmjw9PeXg4CA/Pz/t3r07zWWXL1+uWrVqqUCBAsqXL59q1Kih+fPnW7xvAAAAAAAyg50lK1WtWlWLFi1S165d1a1bN0mS0WhUlSpVZDQalT9/fi1evFjly5e3KNSiRYs0aNAgzZgxQ35+fpoyZYoCAwN19OhRubu7p1i+UKFCGjJkiCpVqqS8efPql19+UY8ePeTu7q7AwECLMgAAAAAAkFEG44MP2H5C165d07x587Rr1y5du3ZNLi4u8vPzU48ePeTm5mZxKD8/P73wwgv68ssvJUlJSUkqVaqU+vfvr/fffz9d26hZs6ZeeeUVjR49+rHLxsbGytXVVTExMXJxcbE4N5BlRrpmdwLrGxmT3QkAALnNs/DzUeJnJJBDpLdHWjTSnaxQoUJ65513MrKJFO7cuaO9e/fqgw8+ME2zsbFRkyZNtHPnzseubzQatWnTJh09elTjx4/P1GwAAAAAADyJDJVua4iKilJiYqI8PDzMpnt4eOjIkSNprhcTE6MSJUooISFBtra2mj59upo2bZrqsgkJCUpISDC9jo2NzZzwAAAAAAA8wKLS/e233z52GRsbG7m4uKhixYqqWLGiJbt5Ivnz59f+/ft18+ZNbdy4UYMGDVKZMmUUEBCQYtlx48Zp1KhRVs8EAAAAAHi2WVS6u3fvLoPBkO7lK1WqpC+++EKNGjV67LJubm6ytbXV5cuXzaZfvnxZRYsWTXM9GxsblStXTpJUo0YNRUREaNy4camW7g8++ECDBg0yvY6NjVWpUqXSeTQAAAAAAKSPRaV77ty5Wr58uX7++Wc1a9ZMdevWlYeHhy5fvqwdO3Zo3bp1atWqlerXr699+/Zp0aJFevnll7Vt2za98MILj9x23rx55evrq40bN6pNmzaS7t9IbePGjerXr1+6MyYlJZmdQv4ge3t72dvbp3tbAAAAAABYwqLS7erqqnXr1mnjxo1q2LBhivnh4eF6+eWX9eabb2rQoEEKCQlR48aN9cknn2jZsmWP3f6gQYMUHBysWrVqqXbt2poyZYri4+PVo0cPSVK3bt1UokQJjRs3TtL908Vr1aqlsmXLKiEhQatXr9b8+fP11VdfWXJ4AAAAAABkCotK99ixYxUUFJRq4ZakgIAAvfbaaxozZoxatWqlBg0aqHnz5tq+fXu6tt+xY0ddvXpVw4cP16VLl1SjRg2tXbvWdHO1M2fOyMbGxrR8fHy8QkNDde7cOTk6OqpSpUpasGCBOnbsaMnhAQAAAACQKSwq3YcOHUrzzuDJSpYsqaVLl5pee3t7a/369eneR79+/dI8nTw8PNzs9ZgxYzRmzJh0bxsAAAAAgKxg8/hFUnJ2dta2bdseucy2bdvk7Oxseh0fH6/8+fNbsjsAAAAAAHIli0p369attWPHDoWGhurq1atm86KiovSf//xHO3bsUOvWrU3T9+/fr7Jly2YsLQAAAAAAuYhFp5ePGzdOO3bs0IwZMzR37lyVK1dO7u7uunLlio4fP66EhARVqlTJdKOzS5cu6fbt2+revXtmZgcAAAAAIEezqHQXLlxYu3fv1ieffKKFCxfq0KFDOnTokCTJ09NTb7zxht577z3T6eVFixbVvn37Mi81AAAAAAC5gEWlW5Ly5cun0aNHa/To0YqLi1NsbKxcXFy4bhsAAAAAgP/P4tL9oPz581O2AQAAAAB4iEU3UgMAAAAAAI9ncek+e/asevfurbJly8rR0VG2trYpvuzsMmUgHQAAAACAXMmiVnzy5En5+fnp+vXrqly5shISEvTcc8/JwcFBJ0+e1N27d1W9enUVKFAgk+MCAAAAAJB7WDTSPWrUKMXExGjjxo06cOCAJKlHjx6KiIjQ6dOn1apVK8XHx2vp0qWZGhYAAAAAgNzEotK9YcMGvfzyy2rQoIFpmtFolCQVK1ZMixYtkiR9+OGHmRARAAAAAIDcyaLSHRUVpUqVKple29nZ6datW6bX9vb2atq0qX755ZeMJwQAAAAAIJeyqHS7ubkpPj7e7PXp06fNlrGzs9ONGzcykg0AAAAAgFzNotJdvnx5nThxwvS6du3a+vXXX3Xy5ElJ0tWrV7V06VKVLVs2c1ICAAAAAJALWVS6W7Rooc2bN5tGsgcOHKi4uDhVq1ZNL7zwgipUqKBLly6pf//+mZkVAAAAAIBcxaLS3bdvX4WHh8vW1laSFBAQoB9++EHPPfecDh48KA8PD02dOlUhISGZGhYAAAAAgNzEoud0u7i4yM/Pz2zaa6+9ptdeey1TQgEAAAAA8DSwaKS7UaNGGjZsWGZnAQAAAADgqWJR6d61a5cSExMzOwsAAAAAAE8Vi0p3pUqVFBkZmdlZAAAAAAB4qlhUuvv376+VK1fq8OHDmZ0HAAAAAICnhkU3UitTpowCAgL04osvqnfv3nrhhRfk4eEhg8GQYtn69etnOCQAAAAAALmRRaU7ICBABoNBRqNRkyZNSrVsJ+PabwAAAADAs8qi0j18+PBHFm0AAAAAAGBh6R45cmQmxwAAAAAA4Olj0Y3UAAAAAADA41k00p3szz//1Pfff68jR47o1q1b2rBhgyQpMjJSu3btUpMmTVSoUKFMCQoAAADLeb6/KrsjWN1ph+xOAAApWVy63333XU2aNElGo1GSzK7xNhqN6ty5syZNmqQBAwZkPCUAAAAAALmQRaeXz507VxMnTlTLli31119/6YMPPjCb7+npqdq1a+unn37KlJAAAAAAAORGFo10T58+Xc8//7yWLVsmOzs75c2bN8UylSpVMp1uDgAAAADAs8iike7Dhw+radOmsrNLu7N7eHjoypUrFgcDAAAAACC3s6h029nZ6c6dO49c5sKFC3J2drYoFAAAAAAATwOLSnfVqlW1adMmJSYmpjo/+U7mvr6+GQoHAAAAAEBuZlHpfvPNN/XPP/+oT58+SkhIMJsXGxur7t2769KlSwoJCcmUkAAAAAAA5EYW3UjtzTff1IYNGzR79mwtWrRIBQoUkCTVrl1bERERio+PV/fu3dWhQ4fMzAo81rPwDFKJ55ACAAAAuYVFI92S9N1332nmzJny8vLS+fPnZTQatWfPHpUuXVpfffWV5syZk5k5AQAAAADIdSwa6U4WEhKikJAQ3b59W9evX5eLiws3TwMAAAAA4P+zqHTfvHnTrFw7OjrK0dEx00IBAAAAAPA0sOj0cg8PD73xxhtau3atkpKSMjsTAAAAAABPBYtKd9myZfX999/rlVdeUfHixfXOO+9o7969mZ0NAAAAAIBczaLS/ddff2n//v165513ZGtrq88//1y1a9eWt7e3xo0bpzNnzmR2TgAAAAAAch2L715erVo1TZw4UefOndOvv/6qN954Q+fOndOQIUNUpkwZBQQEaPbs2ZmZFQAAAACAXMXi0p3MYDCoadOm+vbbb3X58mUtWLBATZs21Y4dO9S7d+/MyAgAAAAAQK6UoUeGPezevXtKSEhQQkICN1gDAAAAADzzMly6ExMTtXr1ai1YsEC//PKL/v33X9nY2KhZs2bq2rVrZmQEAAAAACBXsrh0//7771qwYIEWL16s6OhoGY1G1ahRQ127dlXnzp3l4eGRmTkBAAAAAMh1LCrd5cuX18mTJ2U0GlWiRAn973//U9euXVW5cuXMzgcAAAAAQK5lUem+dOmSunXrpq5du6phw4YyGAypLpeQkCB7e/sMBQQAAAAAILeyqHRfuXJFjo6Oac7ft2+fZs+erR9++EHR0dEWhwMAAAAAIDezqHSnVrhv3LihBQsWaPbs2frrr79kNBofWcwBAAAAAHjaZfju5Rs2bNDs2bO1cuVKJSQkyGg0qk6dOurRo4c6duyYGRkBAAAAAMiVLCrdZ8+e1dy5czV37lydOXPGdEO18+fPq3v37pozZ05m5wQAAAAAINdJd+m+e/euVqxYodmzZ2vjxo1KTExUvnz59MYbb6hbt25q1KiR7OzsZGeX4cFzAAAAAACeCuluyMWLF9e1a9dkMBjUsGFDdevWTe3atVO+fPmsmQ8AAAAAgFwr3aU7OjpaNjY2euedd/Tuu++qSJEi1swFAAAAAECuZ5PeBbt37y5HR0dNnjxZJUuWVKtWrbRkyRLduXPHmvkAAAAAAMi10l2658yZo4sXL2rmzJmqWbOmfvnlF3Xq1EkeHh7q3bu3tm/fbs2cAAAAAADkOuku3ZLk7Oysnj17aufOnTp06JAGDhyovHnz6ptvvlGDBg1kMBh09OhRRUZGWisvAAAAAAC5xhOV7gc9//zzmjRpks6fP6/FixerWbNmMhgM2rZtm8qWLavGjRtr/vz5mZkVAAAAAIBcxeLSnczOzk4dOnTQmjVrdPr0aY0aNUrPPfecNm/erO7du2dCRAAAAAAAcqcMl+4HlSxZUsOGDdOJEye0fv16derUKTM3DwAAAABArpLuR4Y9qcaNG6tx48bW2jwAAAAAADlepo50Z6Zp06bJ09NTDg4O8vPz0+7du9Nc9ptvvpG/v78KFiyoggULqkmTJo9cHgAAAACArJAjS/eiRYs0aNAgjRgxQvv27VP16tUVGBioK1eupLp8eHi4Xn/9dW3evFk7d+5UqVKl1KxZM50/fz6LkwMAAAAA8H9yZOmePHmyQkJC1KNHD3l7e2vGjBlycnLSnDlzUl1+4cKFCg0NVY0aNVSpUiXNmjVLSUlJ2rhxYxYnBwAAAADg/+S40n3nzh3t3btXTZo0MU2zsbFRkyZNtHPnznRt49atW7p7964KFSpkrZgAAAAAADyW1W6kZqmoqCglJibKw8PDbLqHh4eOHDmSrm289957Kl68uFlxf1BCQoISEhJMr2NjYy0PDAAAAABAGnLcSHdGffLJJ/rhhx/0448/ysHBIdVlxo0bJ1dXV9NXqVKlsjglAAAAAOBZkONKt5ubm2xtbXX58mWz6ZcvX1bRokUfue7EiRP1ySefaN26dapWrVqay33wwQeKiYkxfZ09ezZTsgMAAAAA8KAcV7rz5s0rX19fs5ugJd8UrU6dOmmuN2HCBI0ePVpr165VrVq1HrkPe3t7ubi4mH0BAAAAAJDZctw13ZI0aNAgBQcHq1atWqpdu7amTJmi+Ph49ejRQ5LUrVs3lShRQuPGjZMkjR8/XsOHD9d3330nT09PXbp0SZLk7OwsZ2fnbDsOAIC5xMRE3b17N7tjAM8EOzs72draymAwZHcUAHim5cjS3bFjR129elXDhw/XpUuXVKNGDa1du9Z0c7UzZ87Ixub/Bum/+uor3blzRx06dDDbzogRIzRy5MisjA4ASIXRaNSlS5d048aN7I4CPFNsbW3l7u4uV1fX7I4CAM+sHFm6Jalfv37q169fqvPCw8PNXp8+fdr6gQAAFksu3O7u7nJycmLkDbAyo9Goe/fuKTY2VhcvXtTt27ezOxIAPLNybOkGADwdEhMTTYW7cOHC2R0HeKbkz59f9vb2ioqKkqOdQbfvGbM7EgA8c3LcjdQAAE+X5Gu4nZycsjkJ8GzKly+fjEajCjjw3z4AyA786wsAyBKcUg5kj+S/ezb8FQSAbEHpBgAAAADASijdAADATHh4uAwGA3ebBwAgE1C6AQDIoa5evaq+ffuqdOnSsre3V9GiRRUYGKgdO3Zk2j4CAgI0cOBAs2kvvfSSLl68mCMeM9W9e3e1adMmu2MAAGAx7l4OAEAO1b59e925c0fz5s1TmTJldPnyZW3cuFHR0dFW3W/evHlVtGhRq+4DAIBnBSPdAADkQDdu3NC2bds0fvx4NWzYUM8995xq166tDz74QK1atTIt07NnTxUpUkQuLi5q1KiRDhw4YNrGyJEjVaNGDc2fP1+enp5ydXVVp06dFBcXJ+n+KPKWLVv0+eefy2AwyGAw6PTp0ylOLw8LC1OBAgX0yy+/qGLFinJyclKHDh1069YtzZs3T56enipYsKDefvttJSYmmvafkJCgwYMHq0SJEsqXL5/8/PwUHh5ump+83V9//VXPP/+8nJ2d1bx5c128eNGUf968eVq5cqUp34PrAwCQG1C6AQDIgZydneXs7KwVK1YoISEh1WVee+01XblyRWvWrNHevXtVs2ZNNW7cWNeuXTMtc+LECa1YsUK//PKLfvnlF23ZskWffPKJJOnzzz9XnTp1FBISoosXL+rixYsqVapUqvu6deuWpk6dqh9++EFr165VeHi42rZtq9WrV2v16tWaP3++Zs6cqaVLl5rW6devn3bu3KkffvhBf/31l1577TU1b95cx44dM9vuxIkTNX/+fG3dulVnzpzR4MGDJUmDBw9WUFCQqYhfvHhRL730UobfWwAAshKlGwCAHMjOzk5hYWGaN2+eChQooLp16+rDDz/UX3/9JUnavn27du/erSVLlqhWrVoqX768Jk6cqAIFCpgV36SkJIWFhalKlSry9/dX165dtXHjRkmSq6ur8ubNKycnJxUtWlRFixaVra1tqnnu3r2rr776Sj4+Pqpfv746dOig7du3a/bs2fL29lbLli3VsGFDbd68WZJ05swZzZ07V0uWLJG/v7/Kli2rwYMHq169epo7d67ZdmfMmKFatWqpZs2a6tevnymfs7OzHB0dTdezFy1aVHnz5rXK+w0AgLVwTTcAADlU+/bt9corr2jbtm36/ffftWbNGk2YMEGzZs1SfHy8bt68qcKFC5utc/v2bZ04ccL02tPTU/nz5ze9LlasmK5cufLEWZycnFS2bFnTaw8PD3l6esrZ2dlsWvK2//77byUmJqpChQpm20lISDDL/PB2Lc0HAEBORekGACAHc3BwUNOmTdW0aVMNGzZMPXv21IgRIxQaGqpixYqleo1zgQIFTH/OkyeP2TyDwaCkpKQnzpHadh617Zs3b8rW1lZ79+5NMXr+YFFPbRtGo/GJ8wEAkFNRugEAyEW8vb21YsUK1axZU5cuXZKdnZ08PT0t3l7evHnNbn6WWXx8fJSYmKgrV67I39/f4u1YKx8AAFmFa7oBAMiBoqOj1ahRIy1YsEB//fWXTp06pSVLlmjChAlq3bq1mjRpojp16qhNmzZat26dTp8+rd9++01DhgzRnj170r0fT09P7dq1S6dPn1ZUVJRFo+CpqVChgt544w1169ZNy5cv16lTp7R7926NGzdOq1ateqJ8f/31l44ePaqoqCjdvXs3U/IBAJBVKN0AAORAzs7O8vPz02effab69eurSpUqGjZsmEJCQvTll1/KYDBo9erVql+/vnr06KEKFSqoU6dOioyMlIeHR7r3M3jwYNna2srb21tFihTRmTNnMu0Y5s6dq27duum///2vKlasqDZt2uiPP/5Q6dKl072NkJAQVaxYUbVq1VKRIkW0Y8eOTMsHAEBWMBi5cEqxsbFydXVVTEyMXFxcsjsOMsDz/fSPnuRmpx06Z3cE6xsZk90JkEn+/fdfnTp1Sl5eXnJwcMjuOMAzJ/nvYM/lkTof93Sfqv9M/HyU+BkJ5BDp7ZGMdAMAAAAAYCWUbgAAAAAArITSDQAAAACAlVC6AQAAAACwEko3AAAAAABWQukGAAAAAMBKKN0AAAAAAFgJpRsAAAAAACuhdAMAAAAAYCWUbgAAAAAArITSDQAAAACAlVC6AQB4SoSFhclgMOj06dPZHQVpMBgMGjlyZHbHAABkIUo3AAC5zNixY7VixYrsjmGx6dOnKywsLLtjWM3q1asp1gAAE7vsDgAAeHZ5vr8quyNIkk5/8kp2R3giY8eOVYcOHdSmTRuz6V27dlWnTp1kb2+fPcHSafr06XJzc1P37t2zO4pVrF69WtOmTUu1eN++fVt2dvz3CwCeJfyrDwDAU8LW1la2trbZHQOP4ODgkN0RAABZjNPLAQDIgLi4OA0cOFCenp6yt7eXu7u7mjZtqn379pmW2bVrl5o3by5XV1c5OTmpQYMG2rFjh9l2Ro4cKYPBoOPHj6t79+4qUKCAXF1d1aNHD926dcu0nMFgUHx8vObNmyeDwSCDwWAaMU7tmm5PT0+1bNlS4eHhqlWrlhwdHVW1alWFh4dLkpYvX66qVavKwcFBvr6++vPPP1Mc45EjR9ShQwcVKlRIDg4OqlWrln766SezZZL3vWPHDg0aNEhFihRRvnz51LZtW129etUsz6FDh7RlyxZT/oCAgHS/3waDQf369dOKFStUpUoV2dvbq3Llylq7dm2KZc+fP68333xTHh4epuXmzJmTYrnIyEi1atVK+fLlk7u7u9555x39+uuvMhgMpvdJkrZt26bXXntNpUuXlr29vUqVKqV33nlHt2/fNi3TvXt3TZs2zZQ1+evB/Mkj4EuXLpXBYNCWLVtSZJo5c6YMBoMOHjxompaezwEAkPMw0g0AQAb06dNHS5cuVb9+/eTt7a3o6Ght375dERERqlmzpjZt2qQWLVrI19dXI0aMkI2NjebOnatGjRpp27Ztql27ttn2goKC5OXlpXHjxmnfvn2aNWuW3N3dNX78eEnS/Pnz1bNnT9WuXVu9evWSJJUtW/aRGY8fP67OnTurd+/e6tKliyZOnKhXX31VM2bM0IcffqjQ0FBJ0rhx4xQUFKSjR4/Kxub+7+UPHTqkunXrqkSJEnr//feVL18+LV68WG3atNGyZcvUtm1bs331799fBQsW1IgRI3T69GlNmTJF/fr106JFiyRJU6ZMUf/+/eXs7KwhQ4ZIkjw8PJ7oPd++fbuWL1+u0NBQ5c+fX1OnTlX79u115swZFS5cWJJ0+fJlvfjii6aSXqRIEa1Zs0ZvvfWWYmNjNXDgQElSfHy8GjVqpIsXL2rAgAEqWrSovvvuO23evDnFfpcsWaJbt26pb9++Kly4sHbv3q0vvvhC586d05IlSyRJvXv31oULF7R+/XrNnz//kcfxyiuvyNnZWYsXL1aDBg3M5i1atEiVK1dWlSpVLPocAAA5B6UbAIAMWLVqlUJCQjRp0iTTtHfffVeSZDQa1adPHzVs2FBr1qwxjXj27t1blStX1tChQ7Vu3Tqz7fn4+Gj27Nmm19HR0Zo9e7apdHfp0kV9+vRRmTJl1KVLl3RlPHr0qH777TfVqVNHkuTt7a3AwECFhIToyJEjKl26tCSpYMGC6t27t7Zu3WoafR4wYIBKly6tP/74w3SteGhoqOrVq6f33nsvRdkrXLiw1q1bZzrWpKQkTZ06VTExMXJ1dVWbNm00dOhQubm5pTv/wyIiInT48GHTLxsaNmyo6tWr6/vvv1e/fv0kSUOGDFFiYqL+/vtvUxHv06ePXn/9dY0cOVK9e/eWo6OjZs6cqZMnT2rFihVq3bq1pPufj4+PT4r9jh8/Xo6OjqbXvXr1Urly5fThhx/qzJkzKl26tOrUqaMKFSpo/fr1jz0+R0dHvfrqq1q6dKmmTp1qujTg0qVL2rJli9k14U/6OQAAcg5OLwcAIAMKFCigXbt26cKFCynm7d+/X8eOHVPnzp0VHR2tqKgoRUVFKT4+Xo0bN9bWrVuVlJRktk6fPn3MXvv7+ys6OlqxsbEWZ/T29jYVbkny8/OTJDVq1MhUuB+cfvLkSUnStWvXtGnTJgUFBSkuLs6UPzo6WoGBgTp27JjOnz9vtq9evXqZnU7t7++vxMRERUZGWpz/YU2aNDEb3a9WrZpcXFxMuY1Go5YtW6ZXX31VRqPRlDsqKkqBgYGKiYkxnf6/du1alShRQq1atTJtz8HBQSEhISn2+2Dhjo+PV1RUlF566SUZjcZUT8tPj44dO+rKlStmp7EvXbpUSUlJ6tixoyTLPgcAQM7BSDcAABkwYcIEBQcHq1SpUvL19dXLL7+sbt26qUyZMjp27JgkKTg4OM31Y2JiVLBgQdPrB0uwJNO869evy8XFxaKMD2/T1dVVklSqVKlUp1+/fl3S/dPSjUajhg0bpmHDhqW67StXrqhEiRLpyp9ZHt5H8n6S93H16lXduHFDX3/9tb7++utUt3HlyhVJ96/nLlu2rNkvCiSpXLlyKdY5c+aMhg8frp9++inF8cTExFh0LMnX+i9atEiNGzeWdP/U8ho1aqhChQqSLPscAAA5B6UbAIAMCAoKkr+/v3788UetW7dOn376qcaPH6/ly5ebRrE//fRT1ahRI9X1nZ2dzV6ndfdxo9Focca0tvm4fSXnHzx4sAIDA1Nd9uFyao38D0tv7i5duqT5C49q1ao90T4TExPVtGlTXbt2Te+9954qVaqkfPny6fz58+revXuKMxbSy97eXm3atNGPP/6o6dOn6/Lly9qxY4fGjh1rWsaSzwEAkHNQugEAyKBixYopNDRUoaGhunLlimrWrKmPP/5Yn332mSTJxcVFTZo0ybT9PTwqay1lypSRJOXJkydX5S9SpIjy58+vxMTEx+Z+7rnndPjwYRmNRrNcx48fN1vu77//1j///KN58+apW7dupunr169Psc0nPb6OHTtq3rx52rhxoyIiImQ0Gk2nlkvW+xwAAFmDa7oBALBQYmJiitOK3d3dVbx4cSUkJMjX11dly5bVxIkTdfPmzRTrP/gorSeRL18+3bhxw6J1n4S7u7sCAgI0c+ZMXbx4McX8nJrf1tZW7du317Jly8weuZXswdyBgYE6f/682aO3/v33X33zzTcptimZj9gbjUZ9/vnnKbafL18+SUr3MTZp0kSFChXSokWLtGjRItWuXVteXl6m+db6HAAAWYORbgAALBQXF6eSJUuqQ4cOql69upydnbVhwwb98ccfmjRpkmxsbDRr1iy1aNFClStXVo8ePVSiRAmdP39emzdvlouLi37++ecn3q+vr682bNigyZMnq3jx4vLy8jLdBC2zTZs2TfXq1VPVqlUVEhKiMmXK6PLly9q5c6fOnTunAwcOPPE2fX199dVXX2nMmDEqV66c3N3d1ahRo0zN/cknn2jz5s3y8/NTSEiIvL29de3aNe3bt08bNmzQtWvXJN2/U/mXX36p119/XQMGDFCxYsW0cOFCOTg4SPq/UetKlSqpbNmyGjx4sM6fPy8XFxctW7Ys1WvVfX19JUlvv/22AgMDZWtrq06dOqWZNU+ePGrXrp1++OEHxcfHa+LEiSmWscbnAADIGpRuAAAs5OTkpNDQUK1bt850DXe5cuU0ffp09e3bV5IUEBCgnTt3avTo0fryyy918+ZNFS1aVH5+furdu7dF+508ebJ69eqloUOH6vbt2woODrZa6fb29taePXs0atQohYWFKTo6Wu7u7vLx8dHw4cMt2ubw4cMVGRmpCRMmKC4uTg0aNMj00u3h4aHdu3fro48+0vLlyzV9+nQVLlxYlStXNj1+Tbp/Tf2mTZvUv39/ff7553J2dla3bt300ksvqX379qbynSdPHv388896++23NW7cODk4OKht27bq16+fqlevbrbvdu3aqX///vrhhx+0YMECGY3GR5Zu6f4p5rNmzZLBYFBQUFCK+db4HAAAWcNgzMw7m+RSsbGxcnV1VUxMjMV3hkXO4Pn+quyOkCVOO3TO7gjWN9KyOwEj5/n333916tQpeXl5mQoMkNNNmTJF77zzjs6dO5fr7wqe/Hew5/JInY9LzO44VvVM/HyU+BkJ5BDp7ZFc0w0AAJ5pt2/fNnv977//aubMmSpfvnyuL9wAgOzH6eUAACDbXbp06ZHzHR0dTc8Rz2zt2rVT6dKlVaNGDcXExGjBggU6cuSIFi5caJX9AQCeLZRuAACQ7YoVK/bI+cHBwQoLC7PKvgMDAzVr1iwtXLhQiYmJ8vb21g8//GD22C4AACxF6QYAANkuteddP6h48eJW2/fAgQM1cOBAq20fAPBso3QDAIBs16RJk+yOAACAVXAjNQAAAAAArITSDQAAAACAlVC6AQAAAACwEko3AAAAAABWQukGAAAAAMBKKN0AAAAAAFgJpRsAAAAAACuhdAMA8JQICwuTwWDQ6dOnszsKntDp06dlMBgUFhaW3VEAAJmM0g0AQC4zduxYrVixIrtjWGz69Om5slx+9913mjJlSnbHAADkMnbZHQAA8Awb6ZrdCe4bGZPdCZ7I2LFj1aFDB7Vp08ZseteuXdWpUyfZ29tnT7B0mj59utzc3NS9e/fsjvJEvvvuOx08eFADBw7M7igAgFyEkW4AAJ4Stra2cnBwkMFgyO4oz7x///1XSUlJ2R0DAJADULoBAMiAuLg4DRw4UJ6enrK3t5e7u7uaNm2qffv2mZbZtWuXmjdvLldXVzk5OalBgwbasWOH2XZGjhwpg8Gg48ePq3v37ipQoIBcXV3Vo0cP3bp1y7ScwWBQfHy85s2bJ4PBIIPBYBoxTu2abk9PT7Vs2VLh4eGqVauWHB0dVbVqVYWHh0uSli9frqpVq8rBwUG+vr76888/UxzjkSNH1KFDBxUqVEgODg6qVauWfvrpJ7Nlkve9Y8cODRo0SEWKFFG+fPnUtm1bXb161SzPoUOHtGXLFlP+gICAdL/fBoNB/fr104oVK1SlShXZ29urcuXKWrt2bYplz58/rzfffFMeHh6m5ebMmZNq7oevgw8PD5fBYDC9TwEBAVq1apUiIyNNuT09Pc2W/eGHHzR06FCVKFFCTk5Oio2N1bVr1zR48GBVrVpVzs7OcnFxUYsWLXTgwIF0HzMAIHfj9HIAADKgT58+Wrp0qfr16ydvb29FR0dr+/btioiIUM2aNbVp0ya1aNFCvr6+GjFihGxsbDR37lw1atRI27ZtU+3atc22FxQUJC8vL40bN0779u3TrFmz5O7urvHjx0uS5s+fr549e6p27drq1auXJKls2bKPzHj8+HF17txZvXv3VpcuXTRx4kS9+uqrmjFjhj788EOFhoZKksaNG6egoCAdPXpUNjb3fy9/6NAh1a1bVyVKlND777+vfPnyafHixWrTpo2WLVumtm3bmu2rf//+KliwoEaMGKHTp09rypQp6tevnxYtWiRJmjJlivr37y9nZ2cNGTJEkuTh4fFE7/n27du1fPlyhYaGKn/+/Jo6darat2+vM2fOqHDhwpKky5cv68UXXzSV9CJFimjNmjV66623FBsb+8SniA8ZMkQxMTE6d+6cPvvsM0mSs7Oz2TKjR49W3rx5NXjwYCUkJChv3rw6fPiwVqxYoddee01eXl66fPmyZs6cqQYNGujw4cMqXrz4E+UAAOQ+lG4AADJg1apVCgkJ0aRJk0zT3n33XUmS0WhUnz591LBhQ61Zs8Z02nfv3r1VuXJlDR06VOvWrTPbno+Pj2bPnm16HR0drdmzZ5tKd5cuXdSnTx+VKVNGXbp0SVfGo0eP6rffflOdOnUkSd7e3goMDFRISIiOHDmi0qVLS5IKFiyo3r17a+vWrabR5wEDBqh06dL6448/TNeKh4aGql69enrvvfdSlO7ChQtr3bp1pmNNSkrS1KlTFRMTI1dXV7Vp00ZDhw6Vm5tbuvM/LCIiQocPHzb9sqFhw4aqXr26vv/+e/Xr10/S/ZKcmJiov//+21TE+/Tpo9dff10jR45U79695ejomO59Nm3aVCVKlND169fTzP3vv/9qz549ZtutWrWq/vnnH9MvMaT7195XqlRJs2fP1rBhw574+AEAuQunlwMAkAEFChTQrl27dOHChRTz9u/fr2PHjqlz586Kjo5WVFSUoqKiFB8fr8aNG2vr1q0prvvt06eP2Wt/f39FR0crNjbW4oze3t6mwi1Jfn5+kqRGjRqZCveD00+ePClJunbtmjZt2qSgoCDFxcWZ8kdHRyswMFDHjh3T+fPnzfbVq1cvs2vK/f39lZiYqMjISIvzP6xJkyZmo/vVqlWTi4uLKbfRaNSyZcv06quvymg0mnJHRUUpMDBQMTExZqf/Z5bg4OAURd7e3t5UuBMTExUdHS1nZ2dVrFjRKhkAADlPjizd06ZNk6enpxwcHOTn56fdu3enueyhQ4fUvn17eXp6ymAw8CgPAECWmjBhgg4ePKhSpUqpdu3aGjlypKn8HTt2TNL9MlakSBGzr1mzZikhIUExMeZ3Tn+wBEv3R58l6fr16xZnfHibrq737xpfqlSpVKcn7+v48eMyGo0aNmxYivwjRoyQJF25csXq+R/28D6S95O8j6tXr+rGjRv6+uuvU+Tu0aNHqrkzg5eXV4ppSUlJ+uyzz1S+fHnZ29vLzc1NRYoU0V9//ZXiswcAPJ1y3OnlixYt0qBBgzRjxgz5+flpypQpCgwM1NGjR+Xu7p5i+Vu3bqlMmTJ67bXX9M4772RDYgDAsywoKEj+/v768ccftW7dOn366acaP368li9fbhrF/vTTT1WjRo1U13/4umBbW9tUlzMajRZnTGubj9tXcv7BgwcrMDAw1WXLlSv3RNvMDOnN3aVLFwUHB6e6bLVq1SQpzTu9JyYmPnGu1E5XHzt2rIYNG6Y333xTo0ePVqFChWRjY6OBAwdyd3MAeEbkuNI9efJkhYSEmH4TPWPGDK1atUpz5szR+++/n2L5F154QS+88IIkpTofAABrK1asmEJDQxUaGqorV66oZs2a+vjjj0033HJxcVGTJk0ybX9Z9UiwMmXKSJLy5MmTq/IXKVJE+fPnV2Ji4mNzJ4/E37hxw2x6aqfDW5J76dKlatiwodl1+sn7c3Nze+LtAQBynxx1evmdO3e0d+9esx+QNjY2atKkiXbu3JmNyQAASCkxMTHFKcLu7u4qXry4EhIS5Ovrq7Jly2rixIm6efNmivUffJTWk8iXL1+KkmgN7u7uCggI0MyZM3Xx4sUU83NqfltbW7Vv317Lli3TwYMHU8x/MHfyteFbt241TUtMTNTXX3+dYr18+fI98Snhtra2KUb5lyxZkuJaeADA0ytHjXRHRUUpMTExxaNDPDw8dOTIkUzbT0JCghISEkyvM3JzGgDAsysuLk4lS5ZUhw4dVL16dTk7O2vDhg36448/NGnSJNnY2GjWrFlq0aKFKleurB49eqhEiRI6f/68Nm/eLBcXF/38889PvF9fX19t2LBBkydPVvHixeXl5WW6CVpmmzZtmurVq6eqVasqJCREZcqU0eXLl7Vz506dO3fOoudN+/r66quvvtKYMWNUrlw5ubu7q1GjRpma+5NPPtHmzZvl5+enkJAQeXt769q1a9q3b582bNiga9euSZIqV66sF198UR988IGuXbumQoUK6YcfftC9e/dSzZ18GdwLL7wgZ2dnvfrqq4/M0bJlS3300Ufq0aOHXnrpJf39999auHCh6SwCAMDTL0eV7qwybtw4jRo1KrtjAABG5u4bSTk5OSk0NFTr1q0zXcNdrlw5TZ8+XX379pUkBQQEaOfOnRo9erS+/PJL3bx5U0WLFpWfn5969+5t0X4nT56sXr16aejQobp9+7aCg4OtVrq9vb21Z88ejRo1SmFhYYqOjpa7u7t8fHw0fPhwi7Y5fPhwRUZGasKECYqLi1ODBg0yvXR7eHho9+7d+uijj7R8+XJNnz5dhQsXVuXKlU2PX0u2cOFC9e7dW5988okKFCigt956Sw0bNlTTpk3NlgsNDdX+/fs1d+5cffbZZ3ruueceW7o//PBDxcfH67vvvtOiRYtUs2ZNrVq1ikvikCFV51XN7ghW93fw39kdAcg0BmNm3tkkg+7cuSMnJyctXbpUbdq0MU0PDg7WjRs3tHLlykeu7+npqYEDB2rgwIGPXC61ke5SpUopJiZGLi4uGTkEZDPP91dld4Qscdqhc3ZHsL5cXsbwf/7991+dOnVKXl5ecnBwyO44wDMn+e9gz+WROh/35DeIy02eiZ+Pkqp6pbyD/9OG0o3cIDY2Vq6uro/tkTnqmu68efPK19dXGzduNE1LSkrSxo0bzZ4vmlH29vZycXEx+wIAAAAAILPluNPLBw0apODgYNWqVUu1a9fWlClTFB8fb7qbebdu3VSiRAmNGzdO0v3R8cOHD5v+fP78ee3fv1/Ozs4pHmMCAABypkuXLj1yvqOjo+k54gAA5CY5rnR37NhRV69e1fDhw3Xp0iXVqFFDa9euNd1c7cyZM7Kx+b8B+gsXLsjHx8f0euLEiZo4caIaNGig8PDwrI4PAAAsUKxYsUfODw4OVlhYWNaEAQAgE+W40i1J/fr1U79+/VKd93CR9vT0TPEoDgAAkLusX7/+kfOLFy+eRUkAAMhcObJ0AwCAZ0uTJk2yOwIAAFaRo26kBgAAAADA04SRbgA5Es8gBQAAwNOAkW4AAAAAAKyE0g0AAAAAgJVQugEAAAAAsBJKNwAAAAAAVkLpBgDgKREWFiaDwaDTp09nd5SnysiRI2UwGNK1LJ8BAOBhlG4AAHKZsWPHasWKFdkdw2LTp09XWFhYdsfIkNz+GQAAso7BaDQasztEdouNjZWrq6tiYmLk4uKS3XGQAZ7vr8ruCFnitEPn7I5gdVW9Smd3BKt7Vh4Z9u+//+rUqVPy8vKSg4OD2byc8mi43PZZODs7q0OHDimKa2Jiou7evSt7e/t0j8xmhypVqsjNzU3h4eHZHSVd7t27p3v37pl9/+amzyD572DP5ZE6H5eY3XGs6ln4+SjxMxLIKdLbI3lONwAATwlbW1vZ2tpmd4ynjp2dnezs0vdfJj4DAMDDOL0cAIAMiIuL08CBA+Xp6Sl7e3u5u7uradOm2rdvn2mZXbt2qXnz5nJ1dZWTk5MaNGigHTt2mG0n+brh48ePq3v37ipQoIBcXV3Vo0cP3bp1y7ScwWBQfHy85s2bJ4PBIIPBoO7du0tK/XpiT09PtWzZUuHh4apVq5YcHR1VtWpV0yjz8uXLVbVqVTk4OMjX11d//vlnimM8cuSIOnTooEKFCsnBwUG1atXSTz/9ZLZM8r537NihQYMGqUiRIsqXL5/atm2rq1evmuU5dOiQtmzZYsofEBCQ7vfbYDCoX79+WrhwoSpWrGjKvXXr1hTL/vnnn2rRooVcXFzk7Oysxo0b6/fffzdb5u7duxo1apTKly8vBwcHFS5cWPXq1dP69etNyzx8TfeTfAYtW7ZUmTJlUj2WOnXqqFatWmbTFixYIF9fXzk6OqpQoULq1KmTzp49m+73BwCQ81C6AQDIgD59+uirr75S+/btNX36dA0ePFiOjo6KiIiQJG3atEn169dXbGysRowYobFjx+rGjRtq1KiRdu/enWJ7QUFBiouL07hx4xQUFKSwsDCNGjXKNH/+/Pmyt7eXv7+/5s+fr/nz56t3796PzHj8+HF17txZr776qsaNG6fr16/r1Vdf1cKFC/XOO++oS5cuGjVqlE6cOKGgoCAlJSWZ1j106JBefPFFRURE6P3339ekSZOUL18+tWnTRj/++GOKffXv318HDhzQiBEj1LdvX/3888/q16+faf6UKVNUsmRJVapUyZR/yJAhT/Seb9myRQMHDlSXLl300UcfKTo6Ws2bN9fBgwfNcvv7++vAgQN69913NWzYMJ06dUoBAQHatWuXabmRI0dq1KhRatiwob788ksNGTJEpUuXNvulycOe5DPo2LGjTp06pT/++MNsemRkpH7//Xd16tTJNO3jjz9Wt27dVL58eU2ePFkDBw7Uxo0bVb9+fd24ceOJ3iMAQM7B6eUAAGTAqlWrFBISokmTJpmmvfvuu5Iko9GoPn36qGHDhlqzZo1ptLR3796qXLmyhg4dqnXr1pltz8fHR7Nnzza9jo6O1uzZszV+/HhJUpcuXdSnTx+VKVNGXbp0SVfGo0eP6rffflOdOnUkSd7e3goMDFRISIiOHDmi0qXvXx9asGBB9e7dW1u3bjWNPg8YMEClS5fWH3/8IXt7e0lSaGio6tWrp/fee09t27Y121fhwoW1bt0607EmJSVp6tSpiomJkaurq9q0aaOhQ4fKzc0t3fkfdvDgQe3Zs0e+vr6SpE6dOqlixYoaPny4li9fLkkaOnSo7t69q+3bt5tGmrt166aKFSvq3Xff1ZYtWyTd//xefvllff311+ne/5N8Bq1bt5a9vb0WLVqkF154wTR98eLFMhgMCgoKknS/hI8YMUJjxozRhx9+aFquXbt28vHx0fTp082mAwByD0a6AQDIgAIFCmjXrl26cOFCinn79+/XsWPH1LlzZ0VHRysqKkpRUVGKj49X48aNtXXrVrNRZen+yPmD/P39FR0drdjYWIszent7mwq3JPn5+UmSGjVqZCrcD04/efKkJOnatWvatGmTafQ9OX90dLQCAwN17NgxnT9/3mxfvXr1MjsV29/fX4mJiYqMjLQ4/8Pq1KljKtySVLp0abVu3Vq//vqrEhMTlZiYqHXr1qlNmzZmp3YXK1ZMnTt31vbt203vZ4ECBXTo0CEdO3Ys0/I9yMXFRS1atNDixYv14L1rFy1apBdffNH0/i9fvlxJSUkKCgoyvc9RUVEqWrSoypcvr82bN1slHwDA+hjpBgAgAyZMmKDg4GCVKlVKvr6+evnll9WtWzeVKVPGVOSCg4PTXD8mJkYFCxY0vX6wBEsyzbt+/brFT9h4eJuurq6SpFKlSqU6/fr165Lun5ZuNBo1bNgwDRs2LNVtX7lyRSVKlEhX/sxSvnz5FNMqVKigW7duma4fv3XrlipWrJhiueeff15JSUk6e/asKleurI8++kitW7dWhQoVVKVKFTVv3lxdu3ZVtWrVMi1vx44dtWLFCu3cuVMvvfSSTpw4ob1792rKlCmmZY4dOyaj0ZjqsUlSnjx5Mi0PACBrUboBAMiAoKAg+fv768cff9S6dev06aefavz48aaRS0n69NNPVaNGjVTXd3Z2Nnud1p2vM/KEz7S2+bh9JecfPHiwAgMDU122XLlyT7TNnKZ+/fo6ceKEVq5cqXXr1mnWrFn67LPPNGPGDPXs2TNT9vHqq6/KyclJixcv1ksvvaTFixfLxsZGr732mmmZpKQkGQwGrVmzJtX38OHvEwBA7kHpBgAgg4oVK6bQ0FCFhobqypUrqlmzpj7++GN99tlnku6fYtykSZNM219WPf85+dTsPHny5Kj8qZ0K/s8//8jJyUlFihSRJDk5Oeno0aMpljty5IhsbGzMRvkLFSqkHj16qEePHrp586bq16+vkSNHPrJ0P8kx5MuXTy1bttSSJUs0efJkLVq0SP7+/ipevLhpmbJly8poNMrLy0sVKlRI97YBADkf13QDAGChxMRExcTEmE1zd3dX8eLFlZCQIF9fX5UtW1YTJ07UzZs3U6z/4KO0nkS+fPmy5G7W7u7uCggI0MyZM3Xx4sUU87Mr/86dO83uLn727FmtXLlSzZo1Mz0nu1mzZlq5cqXZ49MuX76s7777TvXq1TOdqh8dHW22bWdnZ5UrV04JCQmZegwdO3bUhQsXNGvWLB04cEAdO3Y0m9+uXTvZ2tpq1KhRKc4KMBqNKXICAHIPRroBALBQXFycSpYsqQ4dOqh69epydnbWhg0b9Mcff2jSpEmysbHRrFmz1KJFC1WuXFk9evRQiRIldP78eW3evFkuLi76+eefn3i/vr6+2rBhgyZPnqzixYvLy8vLdBO0zDZt2jTVq1dPVatWVUhIiMqUKaPLly9r586dOnfunA4cOPDE2/T19dVXX32lMWPGqFy5cnJ3d1ejRo3SvX6VKlUUGBiot99+W/b29po+fbokmT1abcyYMVq/fr3q1aun0NBQ2dnZaebMmUpISNCECRNMy3l7eysgIEC+vr4qVKiQ9uzZo6VLl5o95iytY3iSz+Dll19W/vz5NXjwYNna2qp9+/Zm88uWLasxY8bogw8+0OnTp9WmTRvlz59fp06d0o8//qhevXpp8ODB6X6PAAA5B6UbAAALOTk5KTQ0VOvWrTNdw12uXDlNnz5dffv2lSQFBARo586dGj16tL788kvdvHlTRYsWlZ+f32Ofr52WyZMnq1evXho6dKhu376t4OBgq5Vub29v7dmzR6NGjVJYWJiio6Pl7u4uHx8fDR8+3KJtDh8+XJGRkZowYYLi4uLUoEGDJyrdDRo0UJ06dTRq1CidOXNG3t7eCgsLM7v5WeXKlbVt2zZ98MEHGjdunJKSkuTn56cFCxaYvVdvv/22fvrpJ61bt04JCQl67rnnNGbMGP3vf/97ZIYn/QwcHBzUqlUrLVy4UE2aNJG7u3uKZd5//31VqFBBn332mekXCKVKlVKzZs3UqlWrdL8/AICcxWDMqXc2yUKxsbFydXVVTEyMxXeGRc7g+f6q7I6QJU47dM7uCFZX1av04xfK5f4O/ju7I2SJf//9V6dOnZKXl5ccHByyOw5yOYPBoP/85z/68ssvsztKrpH8d7Dn8kidj0vM7jhW9Sz8fJT4GQnkFOntkVzTDQAAAACAlXB6OQAAyHaXLl165HxHR0fTc8QBAMhNKN0AACDbFStW7JHzg4ODFRYWljVhAADIRJRuAACQ7davX//I+cnPtOZWNACA3IbSDQAAsl2TJk2yOwIAAFbBjdQAAAAAALASSjcAAAAAAFZC6QYAAAAAwEoo3QAAAAAAWAmlGwAAAAAAK6F0AwAAAABgJZRuAAAAAACshNINAMBTIiwsTAaDQadPn87uKE+dgIAABQQEZHcMAEAuROkGACCXGTt2rFasWJHdMSw2ffp0hYWFZXcMAACyhF12BwAAPLsiKj2f3REkSc8ficjuCE9k7Nix6tChg9q0aWM2vWvXrurUqZPs7e2zJ1g6TZ8+XW5uburevXt2RwEAwOoY6QYA4Clha2srBwcHGQyG7I6CTGA0GnX79u3sjgEAyCBKNwAAGRAXF6eBAwfK09NT9vb2cnd3V9OmTbVv3z7TMrt27VLz5s3l6uoqJycnNWjQQDt27DDbzsiRI2UwGHT8+HF1795dBQoUkKurq3r06KFbt26ZljMYDIqPj9e8efNkMBhkMBhMI8apXdPt6empli1bKjw8XLVq1ZKjo6OqVq2q8PBwSdLy5ctVtWpVOTg4yNfXV3/++WeKYzxy5Ig6dOigQoUKycHBQbVq1dJPP/1ktkzyvnfs2KFBgwapSJEiypcvn9q2baurV6+a5Tl06JC2bNliyp/ea6VPnz4tg8GQ6qnpBoNBI0eOfOL3M9mCBQtUu3ZtOTk5qWDBgqpfv77WrVv3yDwJCQkaMWKEypUrJ3t7e5UqVUrvvvuuEhISzJabO3euGjVqJHd3d9nb28vb21tfffVViu0lf1a//vqr6bOaOXNmut4bAEDOxenlAABkQJ8+fbR06VL169dP3t7eio6O1vbt2xUREaGaNWtq06ZNatGihXx9fTVixAjZ2NiYSti2bdtUu3Zts+0FBQXJy8tL48aN0759+zRr1iy5u7tr/PjxkqT58+erZ8+eql27tnr16iVJKlu27CMzHj9+XJ07d1bv3r3VpUsXTZw4Ua+++qpmzJihDz/8UKGhoZKkcePGKSgoSEePHpWNzf3fyx86dEh169ZViRIl9P777ytfvnxavHix2rRpo2XLlqlt27Zm++rfv78KFiyoESNG6PTp05oyZYr69eunRYsWSZKmTJmi/v37y9nZWUOGDJEkeXh4ZPBTSNvj3k9JGjVqlEaOHKmXXnpJH330kfLmzatdu3Zp06ZNatasWarbTUpKUqtWrbR9+3b16tVLzz//vP7++2999tln+ueff8yuuf/qq69UuXJltWrVSnZ2dvr5558VGhqqpKQk/ec//zHb7tGjR/X666+rd+/eCgkJUcWKFa3yvgAAsg6lGwCADFi1apVCQkI0adIk07R3331X0v3Tg/v06aOGDRtqzZo1ptO+e/furcqVK2vo0KEpRlN9fHw0e/Zs0+vo6GjNnj3bVBK7dOmiPn36qEyZMurSpUu6Mh49elS//fab6tSpI0ny9vZWYGCgQkJCdOTIEZUuXVqSVLBgQfXu3Vtbt241jT4PGDBApUuX1h9//GG6Vjw0NFT16tXTe++9l6J0Fy5cWOvWrTMda1JSkqZOnaqYmBi5urqqTZs2Gjp0qNzc3NKdPyMe934eP35cH330kdq2baulS5eaftkg3f/80vLdd99pw4YN2rJli+rVq2eaXqVKFfXp00e//fabXnrpJUnSli1b5OjoaFqmX79+at68uSZPnpyidB8/flxr165VYGBgxg4cAJBjcHo5AAAZUKBAAe3atUsXLlxIMW///v06duyYOnfurOjoaEVFRSkqKkrx8fFq3Lixtm7dqqSkJLN1+vTpY/ba399f0dHRio2NtTijt7e3qXBLkp+fnySpUaNGpsL94PSTJ09Kkq5du6ZNmzYpKChIcXFxpvzR0dEKDAzUsWPHdP78ebN99erVy+yacn9/fyUmJioyMtLi/BnxuPdzxYoVSkpK0vDhw80Kt6RHXhu/ZMkSPf/886pUqZLpfYmKilKjRo0kSZs3bzYt+2DhjomJUVRUlBo0aKCTJ08qJibGbLteXl4UbgB4yjDSDQBABkyYMEHBwcEqVaqUfH199fLLL6tbt24qU6aMjh07JkkKDg5Oc/2YmBgVLFjQ9PrBEizJNO/69etycXGxKOPD23R1dZUklSpVKtXp169fl3R/1NVoNGrYsGEaNmxYqtu+cuWKSpQoka782eFx7+eJEydkY2Mjb2/vJ9rusWPHFBERoSJFiqQ6/8qVK6Y/79ixQyNGjNDOnTtTXE+efAZAMi8vryfKAQDI+SjdAABkQFBQkPz9/fXjjz9q3bp1+vTTTzV+/HgtX77cNIr96aefqkaNGqmu7+zsbPba1tY21eUedarz46S1zcftKzn/4MGD0xx9LVeu3BNtMyPSGnlOTExMcx1r5UlKSlLVqlU1efLkVOcn/0LjxIkTaty4sSpVqqTJkyerVKlSyps3r1avXq3PPvssxZkOD46KAwCeDpRuAAAyqFixYgoNDVVoaKiuXLmimjVr6uOPP9Znn30mSXJxcVGTJk0ybX9Z9UiwMmXKSJLy5MmTI/Inj1LfuHHDbHpGTl0vW7askpKSdPjw4TR/MZLWegcOHFDjxo0feTw///yzEhIS9NNPP5mNuj94+jkA4OnGNd0AAFgoMTExxTW57u7uKl68uBISEuTr66uyZctq4sSJunnzZor1H3yU1pPIly9fiuJpDe7u7goICNDMmTN18eLFFPOzOr+Li4vc3Ny0detWs+nTp0+3KIcktWnTRjY2Nvroo49SjDo/ajQ8KChI58+f1zfffJNi3u3btxUfHy/p/0baH9xWTEyM5s6da3FmAEDuwkg3AAAWiouLU8mSJdWhQwdVr15dzs7O2rBhg/744w9NmjRJNjY2mjVrllq0aKHKlSurR48eKlGihM6fP6/NmzfLxcVFP//88xPv19fXVxs2bNDkyZNVvHhxeXl5mW6CltmmTZumevXqqWrVqgoJCVGZMmV0+fJl7dy5U+fOndOBAweeeJu+vr766quvNGbMGJUrV07u7u6mG5A9Ts+ePfXJJ5+oZ8+eqlWrlrZu3ap//vnniTMkK1eunIYMGaLRo0fL399f7dq1k729vf744w8VL15c48aNS3W9rl27avHixerTp482b96sunXrKjExUUeOHNHixYtNz9pu1qyZ8ubNq1dffVW9e/fWzZs39c0338jd3T3VX2QAAJ4+lG4AQLZ5/khEdkfIECcnJ4WGhmrdunWma7jLlSun6dOnq2/fvpKkgIAA7dy5U6NHj9aXX36pmzdvqmjRovLz81Pv3r0t2u/kyZPVq1cvDR06VLdv31ZwcLDVSre3t7f27NmjUaNGKSwsTNHR0XJ3d5ePj4+GDx9u0TaHDx+uyMhITZgwQXFxcWrQoEG6S/fw4cN19epVLV26VIsXL1aLFi20Zs0aubu7W5RFkj766CN5eXnpiy++0JAhQ+Tk5KRq1aqpa9euaa5jY2OjFStW6LPPPtO3336rH3/8UU5OTipTpowGDBigChUqSJIqVqyopUuXaujQoRo8eLCKFi2qvn37qkiRInrzzTctzgwAyD0Mxsy4s0kuFxsbK1dXV8XExFh8Z1jkDJ7vr8ruCFnitEPn7I5gdVW9Sj9+oVzu7+C/sztClvj333916tQpeXl5ycHBIbvjAM+c5L+DPZdH6nxc2jedexo8Cz8fJX5GAjlFenskI90AAAAAcpSISs9ndwSry+1neyH9KN0AACDbXbp06ZHzHR0dzZ5nDQBAbkHpBoBswm/xgf9TrFixR84PDg5WWFhY1oQBACATUboBAEC2W79+/SPnFy9ePIuSAACQuSjdAAAg2zVp0iS7IwAAYBU22R0AAAAAAICnFaUbAJAleEIlkD2S/+4l8VcQALIFpRsAYFV58uSRJN26dSubkwDPpvj4eBkMBt34Nym7owDAM4lrugEAVmVra6sCBQroypUrkiQnJycZDIZsTgU83YxGo+7du6fY2FjFxsaqQIECun2PoW4AyA6UbgCA1RUtWlSSTMUbQNawtbVVsWLFeMY5AGQjSjcAwOoMBoOKFSsmd3d33b17N7vjAM8EOzs72dracmYJAGQzSjcAIMvY2trK1tY2u2MAAABkGW6kBgAAAACAleTY0j1t2jR5enrKwcFBfn5+2r179yOXX7JkiSpVqiQHBwdVrVpVq1evzqKkAAAAAACkLkeW7kWLFmnQoEEaMWKE9u3bp+rVqyswMDDNG/D89ttvev311/XWW2/pzz//VJs2bdSmTRsdPHgwi5MDAAAAAPB/cmTpnjx5skJCQvT/2rv/oKjrPI7jrwUFZWHBH6CBDGj+yjINGEsERdtOEbpqJlG5DqTO8/7w7hwnPTUtf1wgIU42UJ03KdUd6ODNeQ5OOhyp6Q16el3BqFyZ7NkvQFAQRaFbvvdH49a2K5axsurzMbMz7Pv7/ny/7+/q7Ic3319ZWVkaM2aMXn/9dQUEBGjLli1u8zdt2qQZM2ZoyZIluueee7Ru3TrFxMSooKDgJlcOAAAAAMA3vK7p7ujo0L/+9S9ZrVZHzMfHR1arVZWVlW7HVFZWOuVL0vTp06+ZDwAAAADAzeB1dy9vbGyU3W7XoEGDnOKDBg1STU2N2zF1dXVu8+vq6tzmt7e3q7293fG+paVFknThwoUfUzq8QGd7W0+XcFNcMBk9XYLH2S/be7oEj7tov/33ke9VwHvcCXPknTA/SsyRtwvmyFvf1X9Dw+j6u8frmu6bIScnR2vWrHGJR0ZG9kA1wA8X3NMF3BQne7oAj5vQ0wXcDMF3xv9WAN7hzvnGYY68LTBH3jZaW1sV3MW/p9c13QMHDpSvr6/q6+ud4vX19Ro8eLDbMYMHD/5B+cuXL9fixYsd7zs7O3Xu3DkNGDBAJpPpR+4BgB/rwoULioyM1KeffiqLxdLT5QAA4DWYIwHvYRiGWltbFR4e3mWe1zXdfn5+io2NVUVFhR5//HFJXzfFFRUVWrhwodsxEydOVEVFhRYtWuSIlZeXa+LEiW7z/f395e/v7xQLCQnpjvIBdCOLxcIvFAAAuMEcCXiHro5wX+V1TbckLV68WJmZmYqLi9OECRP08ssv69KlS8rKypIkZWRkKCIiQjk5OZKk3/72t5oyZYry8/OVkpKibdu26dixY9q8eXNP7gYAAAAA4A7nlU337NmzdfbsWT3//POqq6vT+PHjtWfPHsfN0s6cOSMfn29uvB4fH6/i4mKtXLlSK1as0IgRI7Rz507dd999PbULAAAAAADIZFzvVmsAcJO1t7crJydHy5cvd7kUBACAOxlzJHDroekGAAAAAMBDfK6fAgAAAAAAbgRNNwAAAAAAHkLTDeCWEh0drXnz5vV0GQAA3JJsNptMJpOKiop6uhTgjkHTDaBbFRUVyWQyyWQy6dChQy7LDcNQZGSkTCaTUlNTe6BCAAC6z7fnve++li1b1tPlAfACXvnIMAC3vj59+qi4uFgJCQlO8QMHDuizzz7jjqsAgNvK2rVrNXToUKcYj68FINF0A/CQmTNnqrS0VK+88op69frmq6a4uFixsbFqbGzsweoAAOheycnJiouL6+kyAHghTi8H4BFz585VU1OTysvLHbGOjg7t2LFD6enpLvkbNmxQfHy8BgwYoL59+yo2NlY7duz4Xttqbm7WokWLFBkZKX9/fw0fPly5ubnq7Ozstv0BAOBGvfPOO0pMTJTZbFZQUJBSUlJ0/Phxp5x58+YpMDBQZ86cUWpqqgIDAxUREaHCwkJJUnV1taZNmyaz2ayoqCgVFxc7jT937pyeffZZjR07VoGBgbJYLEpOTtaHH374vWqsqanRk08+qf79+6tPnz6Ki4vTrl27uucDAO5wNN0APCI6OloTJ05USUmJI/bOO++opaVFc+bMccnftGmTHnjgAa1du1bZ2dnq1auXZs2apd27d3e5nba2Nk2ZMkV/+tOflJGRoVdeeUWTJk3S8uXLtXjx4m7fLwAA3GlpaVFjY6PTS5LefvttpaSkKDAwULm5uVq1apVOnDihhIQE2Ww2p3XY7XYlJycrMjJSL730kqKjo7Vw4UIVFRVpxowZiouLU25uroKCgpSRkaHa2lrH2NOnT2vnzp1KTU3Vxo0btWTJElVXV2vKlCn64osvuqz9+PHjeuihh3Ty5EktW7ZM+fn5MpvNevzxx/XXv/612z8r4I5jAEA32rp1qyHJOHr0qFFQUGAEBQUZbW1thmEYxqxZs4ypU6cahmEYUVFRRkpKimPc1ZyrOjo6jPvuu8+YNm2aUzwqKsrIzMx0vF+3bp1hNpuNjz76yClv2bJlhq+vr3HmzJnu3D0AAJxcnffcvVpbW42QkBBj/vz5TmPq6uqM4OBgp3hmZqYhycjOznbEzp8/b/Tt29cwmUzGtm3bHPGamhpDkvHCCy84YleuXDHsdrvTdmpraw1/f39j7dq1TjFJxtatWx2xhx9+2Bg7dqxx5coVR6yzs9OIj483RowYccOfDYCvcaQbgMekpaXp8uXLKisrU2trq8rKytyeWi5Jffv2dfx8/vx5tbS0KDExUe+//36X2ygtLVViYqL69evndHTBarXKbrfrvffe69Z9AgDAncLCQpWXl7u8mpubNXfuXKc5ytfXVw8++KD27dvnsp5f/OIXjp9DQkI0atQomc1mpaWlOeKjRo1SSEiITp8+7Yj5+/vLx+frX+3tdruampoUGBioUaNGdTmXnjt3Tu+++67S0tLU2trqqLGpqUnTp0/Xxx9/rM8//7w7PiLgjsWN1AB4TGhoqKxWq4qLi9XW1ia73a4nn3zSbW5ZWZl+//vf64MPPlB7e7sjbjKZutzGxx9/rKqqKoWGhrpd3tDQcOM7AADA9zRhwgSXG6m99NJLkqRp06a5HWOxWJze9+nTx2U+Cw4O1pAhQ1zmw+DgYJ0/f97xvrOzU5s2bdKrr76q2tpa2e12x7IBAwZcs+5Tp07JMAytWrVKq1atcpvT0NCgiIiIa64DQNdougF4VHp6uubPn6+6ujolJycrJCTEJefgwYP66U9/qsmTJ+vVV1/VXXfdpd69e2vr1q0uN4r5rs7OTj3yyCNaunSp2+UjR47sjt0AAOAHu3pDz7fffluDBw92Wf7tp3tIkq+vr9v1XCtuGIbj5+zsbK1atUpPP/201q1bp/79+8vHx0eLFi3q8saiV5c9++yzmj59utuc4cOHX3M8gOuj6QbgUU888YQWLFigw4cPa/v27W5z/vKXv6hPnz7au3ev0/O7t27det3133333bp48aKsVmu31QwAQHe4++67JUlhYWEen6d27NihqVOn6o033nCKNzc3a+DAgdccN2zYMElS7969mUsBD+GabgAeFRgYqNdee02rV6/Wo48+6jbH19dXJpPJ6VQ4m82mnTt3Xnf9aWlpqqys1N69e12WNTc363//+98N1w4AwI8xffp0WSwWZWdn66uvvnJZfvbs2W7blq+vr9ORb+nr+55c73rssLAwJSUl6Q9/+IO+/PJLj9YI3Kk40g3A4zIzM7tcnpKSoo0bN2rGjBlKT09XQ0ODCgsLNXz4cFVVVXU5dsmSJdq1a5dSU1M1b948xcbG6tKlS6qurtaOHTtks9m6/As/AACeYrFY9Nprr+nnP/+5YmJiNGfOHIWGhurMmTPavXu3Jk2apIKCgm7ZVmpqqtauXausrCzFx8erurpaf/7znx1HsrtSWFiohIQEjR07VvPnz9ewYcNUX1+vyspKffbZZ9/7Wd8A3KPpBtDjpk2bpjfeeEPr16/XokWLNHToUOXm5spms1236Q4ICNCBAweUnZ2t0tJSvfXWW7JYLBo5cqTWrFmj4ODgm7QXAAC4Sk9PV3h4uNavX6+8vDy1t7crIiJCiYmJysrK6rbtrFixQpcuXVJxcbG2b9+umJgY7d69W8uWLbvu2DFjxujYsWNas2aNioqK1NTUpLCwMD3wwAN6/vnnu61G4E5lMr57HgoAAAAAAOgWXNMNAAAAAICH0HQDAAAAAOAhNN0AAAAAAHgITTcAAAAAAB5C0w0AAAAAgIfQdAMAAAAA4CE03QAAAAAAeAhNNwAAAAAAHkLTDQAAAACAh9B0AwCAbpOUlCSTydTTZQAA4DVougEA8HIffPCBfvWrX2nMmDGyWCzy8/PT4MGD9cgjjyg/P19nz57t6RIBAMA1mAzDMHq6CAAA4Kqzs1NLly5Vfn6+fH19NXnyZN1///0ym81qaGhQZWWljh8/LrPZrP/85z+KiIjo6ZKVlJSkAwcOiF8vAAD4Wq+eLgAAALj33HPPKT8/XzExMdq+fbuGDx/ukvP+++/rd7/7nS5fvtwDFQIAgOvh9HIAALzQRx99pLy8PIWGhmrPnj1uG25JiomJUXl5uaKjo53iVVVVmjNnju666y75+fkpKipKv/71r9XU1OSUZ7PZZDKZNG/ePJ06dUpPPPGE+vXrJ7PZLKvVqg8//NDtdg8dOqQpU6bIbDZrwIABmj17tj799NNr7o9hGNqyZYsmTZoki8WigIAAxcXFacuWLS65q1evlslk0v79+1VUVKSYmBgFBAQoKSmp6w8NAAAvxJFuAAC80Jtvvim73a4FCxYoNDT0uvm9en0zpe/atUtpaWny8fHRY489psjISJ04cUIFBQXau3evjhw5on79+jmNt9lseuihh3Tvvffq6aef1ieffKK//e1vmjp1qk6ePKlBgwY5cisqKpScnCwfHx/Nnj1b4eHhqqio0KRJk1zWK33dcP/sZz9TSUmJRowYofT0dPn5+am8vFzPPPOMTpw4oQ0bNriMy8vL0759+/TYY4/pJz/5iXx9fX/IRwgAgHcwAACA15k6daohyaioqPhB4xobGw2LxWJEREQYNpvNaVlJSYkhyVi4cKEjVltba0gyJBnr1693yl+5cqUhycjJyXHE7Ha7MWzYMMNkMhkHDx50xDs7O4309HTHur5t8+bNhiQjKyvL6OjocMTb29uNRx991JBkHDt2zBF/4YUXDEmG2Ww2qqqqftD+AwDgbTi9HAAAL1RXVydJCg8Pd1m2f/9+rV692um1f/9+SdJbb72lCxcuKCcnR1FRUU7j5syZo5iYGG3bts1lnUOHDtWSJUucYs8884wk6ejRo47YoUOHdPr0aaWmpiohIcERN5lMys7Odns0uqCgQGazWYWFherdu7cj7ufnpxdffFGSVFJS4jLul7/8pcaOHesSBwDgVsLp5QAA3GL279+vNWvWuMSTkpJ0+PBhSdKRI0f0ySefuORcuXJFjY2Namxs1MCBAx3x8ePHy8fH+W/xQ4YMkSQ1Nzc7Ylev8U5MTHRZd1RUlCIjI2Wz2RyxtrY2VVdXKzw8XLm5uS5jvvrqK0lSTU2Ny7IJEya4xAAAuNXQdAMA4IUGDRqkkydP6osvvtDo0aOdll09ui1J27Zt09y5cx3Lzp07J0kqLCzscv2XLl1yarotFotLztXrxO12uyPW0tIiSQoLC7tm3d9uus+fPy/DMPT555+7/UPBt+txty4AAG51nF4OAIAXio+PlyTt27fvB4272jxXV1fLMIxrvr576vn3FRwcLElqaGhwu7y+vt5tPbGxsV3W424/TSbTDdUIAIA3oekGAMALZWZmysfHR5s3b1ZjY+P3Hvfggw9KkiorKz1S17hx4yRJBw8edFn23//+1+WxYUFBQbrnnnt08uRJp9PUAQC4U9B0AwDghUaOHKmlS5eqoaFBycnJOnXqlNu87zayWVlZCgoK0nPPPafjx4+75Le1tTmu+74RCQkJGjp0qMrKynTo0CFH3DAMrVixwulU9Kt+85vfqK2tTfPnz3d7Gnltba3TKekAANxOuKYbAAAv9eKLL6qjo0MbN27U6NGjNXnyZI0bN04BAQFqaGhQVVWV/vnPfyowMFDjx4+XJIWGhqqkpESzZs3SuHHjNGPGDI0ePVrt7e2y2Ww6cOCA4uPjtWfPnhuq6erR95kzZ8pqtTqe0/3uu+/qyy+/1P3336+qqiqnMQsWLNDhw4f15ptv6h//+IesVqvCw8NVX1+vmpoaHTlyRMXFxYqOjv6RnxgAAN6HphsAAC/l4+Oj/Px8PfXUU3r99df13nvv6ejRo2pvb1f//v117733Ki8vTxkZGU43NktJSdG///1v5eXl6e9//7vKy8tlNps1ZMgQZWVl6amnnvpRdVmtVlVUVGjlypUqLS1V37599fDDD6u0tFQZGRku+SaTSUVFRZo5c6b++Mc/qqysTBcvXlRYWJhGjBihDRs2yGq1/qiaAADwVibDMIyeLgIAAAAAgNsR13QDAAAAAOAhNN0AAAAAAHgITTcAAAAAAB5C0w0AAAAAgIfQdAMAAAAA4CE03QAAAAAAeAhNNwAAAAAAHkLTDQAAAACAh9B0AwAAAADgITTdAAAAAAB4CE03AAAAAAAeQtMNAAAAAICH0HQDAAAAAOAh/wdUoyDlKfK/gAAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}