Speech.ipynb

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        "<a href=\"https://colab.research.google.com/github/JessemanGray/Syn-Aesthetics/blob/main/Speech.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "source": [
        "# Import packages\n",
        "\n",
        "import os\n",
        "import seaborn as sns\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "from sklearn.feature_extraction.text import TfidfVectorizer\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.metrics import accuracy_score, classification_report\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n"
      ],
      "metadata": {
        "id": "h8sF3C9lpcuO"
      },
      "execution_count": null,
      "outputs": []
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    {
      "cell_type": "code",
      "source": [
        "# Turn file path into DataFrame, show first five rows\n",
        "url = \"https://raw.githubusercontent.com/JessemanGray/Syn-Aesthetics/main/ChillsDB%201.0.1%20-%20Speech%20Stimuli%20(2).csv\"\n",
        "df = pd.read_csv(url)\n",
        "df.head()"
      ],
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          "height": 273
        },
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        "outputId": "0e6cbf67-cd1e-40ad-dcd4-cbecfa639320"
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      "execution_count": null,
      "outputs": [
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              "      Video ID  Age  Gender Ethnicity  chills Binary  chills Binary.1  \\\n",
              "0  Bg_Q7KYWG1g   26  female     White              1             True   \n",
              "1  g-jwWYX7Jlo   51  female     White              1             True   \n",
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              "4  g-jwWYX7Jlo   26  female     White              1             True   \n",
              "\n",
              "   Chills #  Chill Intensity  \\\n",
              "0         1                2   \n",
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              "4         4                2   \n",
              "\n",
              "                                            Timings  Daily Chills  ...  \\\n",
              "0                                            441.44             2  ...   \n",
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              "2                                            359.27             1  ...   \n",
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              "   Emotion drift      Direction  Unnamed: 27  Unnamed: 28  Tan inverse  \\\n",
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          "metadata": {},
          "execution_count": 71
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Fill missing values with the mean of the column\n",
        "df = df.fillna(df.mean())"
      ],
      "metadata": {
        "colab": {
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        },
        "id": "3pvqQIZwtd4w",
        "outputId": "7a40c96a-e455-43a9-8977-badc73676c39"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-72-15288beba849>:2: FutureWarning: The default value of numeric_only in DataFrame.mean is deprecated. In a future version, it will default to False. In addition, specifying 'numeric_only=None' is deprecated. Select only valid columns or specify the value of numeric_only to silence this warning.\n",
            "  df = df.fillna(df.mean())\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Check for missing values in the entire DataFrame\n",
        "missing_values = df.isnull().sum()\n",
        "\n",
        "# Display columns with missing values\n",
        "columns_with_missing_values = missing_values[missing_values > 0]\n",
        "print(columns_with_missing_values)"
      ],
      "metadata": {
        "id": "0IPWk-kJIBdr",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9f10eddd-8ee1-414b-d07b-78caaf0c1955"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Timings        24\n",
            "Unnamed: 27    80\n",
            "Unnamed: 28    80\n",
            "dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Chenck types before dropping\n",
        "df['Timings'].dtypes"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5jt7AtCnAR6I",
        "outputId": "066d18a7-8eb5-4545-ead1-2d037efc27cc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dtype('O')"
            ]
          },
          "metadata": {},
          "execution_count": 74
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Drop duplicate or unnecessary columns\n",
        "df = df.drop(columns=['chills Binary.1', 'Unnamed: 32', 'Unnamed: 34', 'Unnamed: 28', 'Unnamed: 27', 'Timings'])\n",
        "\n",
        "\n",
        "# Convert data types\n",
        "df['chills Binary'] = df['chills Binary'].astype(bool)\n",
        "\n",
        "# Check for correlation between 'Direction' and 'Tan inverse.1'\n",
        "print(df[['Direction', 'Tan inverse.1']].corr())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FqydgFwYwApl",
        "outputId": "8cae5034-772b-451e-e9f4-6e9e349a489a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "               Tan inverse.1\n",
            "Tan inverse.1            1.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-75-e36ad79d19f0>:9: FutureWarning: The default value of numeric_only in DataFrame.corr is deprecated. In a future version, it will default to False. Select only valid columns or specify the value of numeric_only to silence this warning.\n",
            "  print(df[['Direction', 'Tan inverse.1']].corr())\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Columns with zero variance\n",
        "zero_variance_columns = df.loc[:, df.nunique() == 1]\n",
        "print(zero_variance_columns)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "chWBKxTRujwh",
        "outputId": "1d335a0e-fb7b-4f42-d278-d0dae3e0ccb7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "   Prior exposure\n",
            "0              No\n",
            "1              No\n",
            "2              No\n",
            "3              No\n",
            "4              No\n",
            "..            ...\n",
            "75             No\n",
            "76             No\n",
            "77             No\n",
            "78             No\n",
            "79             No\n",
            "\n",
            "[80 rows x 1 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def score(val):\n",
        "    if val >= 1:\n",
        "        return 3\n",
        "    elif val >= 0.5:\n",
        "        return 2\n",
        "    elif val >= 0:\n",
        "        return 1\n",
        "    else:\n",
        "        return 0\n",
        "\n",
        "def classify_chill(row):\n",
        "    emotion_score = score(row['Emotion drift'])\n",
        "    calm_score = score(row['Calm Diff'])\n",
        "    pleasure_score = score(row['Pleasure Diff'])\n",
        "\n",
        "    # Sum the scores and normalize to a 0-10 scale\n",
        "    total_score = emotion_score + calm_score + pleasure_score\n",
        "    chill_rating = (total_score / 9) * 10  # 9 is the maximum total_score\n",
        "\n",
        "    return str(round(chill_rating, 1))  # Round to 1 decimal place\n",
        "\n",
        "df['Chill Rating'] = df.apply(classify_chill, axis=1)"
      ],
      "metadata": {
        "id": "5ASvyKTtgqUt"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 273
        },
        "id": "0Gz8u4FBhCRp",
        "outputId": "a2c28b48-454e-481e-ff19-71f96cd7eede"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      Video ID  Age  Gender Ethnicity  chills Binary  Chills #  \\\n",
              "0  Bg_Q7KYWG1g   26  female     White           True         1   \n",
              "1  g-jwWYX7Jlo   51  female     White           True         5   \n",
              "2  g-jwWYX7Jlo   20  female     White           True         1   \n",
              "3  Bg_Q7KYWG1g   51  female     White           True         7   \n",
              "4  g-jwWYX7Jlo   26  female     White           True         4   \n",
              "\n",
              "   Chill Intensity  Daily Chills  NIO_Openess Prior exposure  ...  Post NEO  \\\n",
              "0                2             2            2             No  ...        15   \n",
              "1                2             2            3             No  ...         9   \n",
              "2                1             1            3             No  ...        10   \n",
              "3                2             2            2             No  ...         9   \n",
              "4                2             3            3             No  ...         8   \n",
              "\n",
              "   Diff NEO  Total NEO  Emotion drift      Direction  Tan inverse  Calm Diff  \\\n",
              "0         3         27       1.118034  -0.5463024898    -0.463648        1.0   \n",
              "1         3         15       0.707107    1.557407725     0.785398       -0.5   \n",
              "2         6         14       1.414214   -1.557407725    -0.785398        1.0   \n",
              "3         4         14       1.802776   -14.10141995    -0.982794        1.0   \n",
              "4         5         11       1.000000              0     0.000000        1.0   \n",
              "\n",
              "   Pleasure Diff  Tan inverse.1  Chill Rating  \n",
              "0           -0.5      -0.463648           6.7  \n",
              "1           -0.5       0.785398           2.2  \n",
              "2           -1.0      -0.785398           6.7  \n",
              "3           -1.5      -0.982794           6.7  \n",
              "4            0.0       0.000000           7.8  \n",
              "\n",
              "[5 rows x 30 columns]"
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              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Video ID</th>\n",
              "      <th>Age</th>\n",
              "      <th>Gender</th>\n",
              "      <th>Ethnicity</th>\n",
              "      <th>chills Binary</th>\n",
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              "      <th>Total NEO</th>\n",
              "      <th>Emotion drift</th>\n",
              "      <th>Direction</th>\n",
              "      <th>Tan inverse</th>\n",
              "      <th>Calm Diff</th>\n",
              "      <th>Pleasure Diff</th>\n",
              "      <th>Tan inverse.1</th>\n",
              "      <th>Chill Rating</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Bg_Q7KYWG1g</td>\n",
              "      <td>26</td>\n",
              "      <td>female</td>\n",
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              "      <td>2</td>\n",
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              "      <td>1.118034</td>\n",
              "      <td>-0.5463024898</td>\n",
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              "      <td>1.0</td>\n",
              "      <td>-0.5</td>\n",
              "      <td>-0.463648</td>\n",
              "      <td>6.7</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>g-jwWYX7Jlo</td>\n",
              "      <td>51</td>\n",
              "      <td>female</td>\n",
              "      <td>White</td>\n",
              "      <td>True</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>No</td>\n",
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              "      <td>9</td>\n",
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              "      <td>0.707107</td>\n",
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              "      <td>-0.5</td>\n",
              "      <td>-0.5</td>\n",
              "      <td>0.785398</td>\n",
              "      <td>2.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>g-jwWYX7Jlo</td>\n",
              "      <td>20</td>\n",
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              "      <td>White</td>\n",
              "      <td>True</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>No</td>\n",
              "      <td>...</td>\n",
              "      <td>10</td>\n",
              "      <td>6</td>\n",
              "      <td>14</td>\n",
              "      <td>1.414214</td>\n",
              "      <td>-1.557407725</td>\n",
              "      <td>-0.785398</td>\n",
              "      <td>1.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.785398</td>\n",
              "      <td>6.7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Bg_Q7KYWG1g</td>\n",
              "      <td>51</td>\n",
              "      <td>female</td>\n",
              "      <td>White</td>\n",
              "      <td>True</td>\n",
              "      <td>7</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
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              "      <td>1.802776</td>\n",
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              "      <td>6.7</td>\n",
              "    </tr>\n",
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              "      <th>4</th>\n",
              "      <td>g-jwWYX7Jlo</td>\n",
              "      <td>26</td>\n",
              "      <td>female</td>\n",
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              "      <td>4</td>\n",
              "      <td>2</td>\n",
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              "      <td>8</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 30 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
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              "      display:flex;\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "      border-radius: 50%;\n",
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              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "          + ' to learn more about interactive tables.';\n",
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              "\n",
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              "      spin 1s steps(1) infinite;\n",
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              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
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              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
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              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
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              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-96ecb074-c8ea-4a69-b827-6203c4511f05 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 78
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# import visualization library\n",
        "\n",
        "from bokeh.plotting import curdoc, figure, show\n",
        "from bokeh.models import HoverTool\n",
        "from bokeh.io import output_notebook\n",
        "from bokeh.palettes import Category10\n",
        "from bokeh.transform import factor_cmap\n",
        "\n",
        "# Create a list of unique colors for each circle\n",
        "colors = [Category10[10][i % 10] for i in range(len(df))]\n",
        "\n",
        "# Add the colors to your DataFrame\n",
        "df['colors'] = colors\n",
        "\n",
        "# Create a new plot with the HoverTool\n",
        "hover = HoverTool(tooltips=None, mode=\"vline\")\n",
        "p = figure(tools=[hover, 'wheel_zoom', 'pan'], title=\"Chill # and Chill Intensity\")\n",
        "\n",
        "# Add a circle renderer with color mapped to the new 'colors' column\n",
        "p.circle('Chills #', 'Chill Intensity', radius=1, fill_color='colors', fill_alpha=0.6,\n",
        "         line_color=None, source=df)\n",
        "\n",
        "curdoc().theme = 'dark_minimal'\n",
        "output_notebook()\n",
        "show(p)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 617
        },
        "id": "aj3UTkmXHTXr",
        "outputId": "639ef29d-f0af-45c9-e796-9dd8acc856d5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "(function(root) {\n",
              "  function now() {\n",
              "    return new Date();\n",
              "  }\n",
              "\n",
              "  const force = true;\n",
              "\n",
              "  if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n",
              "    root._bokeh_onload_callbacks = [];\n",
              "    root._bokeh_is_loading = undefined;\n",
              "  }\n",
              "\n",
              "const JS_MIME_TYPE = 'application/javascript';\n",
              "  const HTML_MIME_TYPE = 'text/html';\n",
              "  const EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n",
              "  const CLASS_NAME = 'output_bokeh rendered_html';\n",
              "\n",
              "  /**\n",
              "   * Render data to the DOM node\n",
              "   */\n",
              "  function render(props, node) {\n",
              "    const script = document.createElement(\"script\");\n",
              "    node.appendChild(script);\n",
              "  }\n",
              "\n",
              "  /**\n",
              "   * Handle when an output is cleared or removed\n",
              "   */\n",
              "  function handleClearOutput(event, handle) {\n",
              "    function drop(id) {\n",
              "      const view = Bokeh.index.get_by_id(id)\n",
              "      if (view != null) {\n",
              "        view.model.document.clear()\n",
              "        Bokeh.index.delete(view)\n",
              "      }\n",
              "    }\n",
              "\n",
              "    const cell = handle.cell;\n",
              "\n",
              "    const id = cell.output_area._bokeh_element_id;\n",
              "    const server_id = cell.output_area._bokeh_server_id;\n",
              "\n",
              "    // Clean up Bokeh references\n",
              "    if (id != null) {\n",
              "      drop(id)\n",
              "    }\n",
              "\n",
              "    if (server_id !== undefined) {\n",
              "      // Clean up Bokeh references\n",
              "      const cmd_clean = \"from bokeh.io.state import curstate; print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n",
              "      cell.notebook.kernel.execute(cmd_clean, {\n",
              "        iopub: {\n",
              "          output: function(msg) {\n",
              "            const id = msg.content.text.trim()\n",
              "            drop(id)\n",
              "          }\n",
              "        }\n",
              "      });\n",
              "      // Destroy server and session\n",
              "      const cmd_destroy = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n",
              "      cell.notebook.kernel.execute(cmd_destroy);\n",
              "    }\n",
              "  }\n",
              "\n",
              "  /**\n",
              "   * Handle when a new output is added\n",
              "   */\n",
              "  function handleAddOutput(event, handle) {\n",
              "    const output_area = handle.output_area;\n",
              "    const output = handle.output;\n",
              "\n",
              "    // limit handleAddOutput to display_data with EXEC_MIME_TYPE content only\n",
              "    if ((output.output_type != \"display_data\") || (!Object.prototype.hasOwnProperty.call(output.data, EXEC_MIME_TYPE))) {\n",
              "      return\n",
              "    }\n",
              "\n",
              "    const toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n",
              "\n",
              "    if (output.metadata[EXEC_MIME_TYPE][\"id\"] !== undefined) {\n",
              "      toinsert[toinsert.length - 1].firstChild.textContent = output.data[JS_MIME_TYPE];\n",
              "      // store reference to embed id on output_area\n",
              "      output_area._bokeh_element_id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n",
              "    }\n",
              "    if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n",
              "      const bk_div = document.createElement(\"div\");\n",
              "      bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n",
              "      const script_attrs = bk_div.children[0].attributes;\n",
              "      for (let i = 0; i < script_attrs.length; i++) {\n",
              "        toinsert[toinsert.length - 1].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n",
              "        toinsert[toinsert.length - 1].firstChild.textContent = bk_div.children[0].textContent\n",
              "      }\n",
              "      // store reference to server id on output_area\n",
              "      output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n",
              "    }\n",
              "  }\n",
              "\n",
              "  function register_renderer(events, OutputArea) {\n",
              "\n",
              "    function append_mime(data, metadata, element) {\n",
              "      // create a DOM node to render to\n",
              "      const toinsert = this.create_output_subarea(\n",
              "        metadata,\n",
              "        CLASS_NAME,\n",
              "        EXEC_MIME_TYPE\n",
              "      );\n",
              "      this.keyboard_manager.register_events(toinsert);\n",
              "      // Render to node\n",
              "      const props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n",
              "      render(props, toinsert[toinsert.length - 1]);\n",
              "      element.append(toinsert);\n",
              "      return toinsert\n",
              "    }\n",
              "\n",
              "    /* Handle when an output is cleared or removed */\n",
              "    events.on('clear_output.CodeCell', handleClearOutput);\n",
              "    events.on('delete.Cell', handleClearOutput);\n",
              "\n",
              "    /* Handle when a new output is added */\n",
              "    events.on('output_added.OutputArea', handleAddOutput);\n",
              "\n",
              "    /**\n",
              "     * Register the mime type and append_mime function with output_area\n",
              "     */\n",
              "    OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n",
              "      /* Is output safe? */\n",
              "      safe: true,\n",
              "      /* Index of renderer in `output_area.display_order` */\n",
              "      index: 0\n",
              "    });\n",
              "  }\n",
              "\n",
              "  // register the mime type if in Jupyter Notebook environment and previously unregistered\n",
              "  if (root.Jupyter !== undefined) {\n",
              "    const events = require('base/js/events');\n",
              "    const OutputArea = require('notebook/js/outputarea').OutputArea;\n",
              "\n",
              "    if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n",
              "      register_renderer(events, OutputArea);\n",
              "    }\n",
              "  }\n",
              "  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n",
              "    root._bokeh_timeout = Date.now() + 5000;\n",
              "    root._bokeh_failed_load = false;\n",
              "  }\n",
              "\n",
              "  const NB_LOAD_WARNING = {'data': {'text/html':\n",
              "     \"<div style='background-color: #fdd'>\\n\"+\n",
              "     \"<p>\\n\"+\n",
              "     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n",
              "     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n",
              "     \"</p>\\n\"+\n",
              "     \"<ul>\\n\"+\n",
              "     \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n",
              "     \"<li>use INLINE resources instead, as so:</li>\\n\"+\n",
              "     \"</ul>\\n\"+\n",
              "     \"<code>\\n\"+\n",
              "     \"from bokeh.resources import INLINE\\n\"+\n",
              "     \"output_notebook(resources=INLINE)\\n\"+\n",
              "     \"</code>\\n\"+\n",
              "     \"</div>\"}};\n",
              "\n",
              "  function display_loaded() {\n",
              "    const el = document.getElementById(null);\n",
              "    if (el != null) {\n",
              "      el.textContent = \"BokehJS is loading...\";\n",
              "    }\n",
              "    if (root.Bokeh !== undefined) {\n",
              "      if (el != null) {\n",
              "        el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n",
              "      }\n",
              "    } else if (Date.now() < root._bokeh_timeout) {\n",
              "      setTimeout(display_loaded, 100)\n",
              "    }\n",
              "  }\n",
              "\n",
              "  function run_callbacks() {\n",
              "    try {\n",
              "      root._bokeh_onload_callbacks.forEach(function(callback) {\n",
              "        if (callback != null)\n",
              "          callback();\n",
              "      });\n",
              "    } finally {\n",
              "      delete root._bokeh_onload_callbacks\n",
              "    }\n",
              "    console.debug(\"Bokeh: all callbacks have finished\");\n",
              "  }\n",
              "\n",
              "  function load_libs(css_urls, js_urls, callback) {\n",
              "    if (css_urls == null) css_urls = [];\n",
              "    if (js_urls == null) js_urls = [];\n",
              "\n",
              "    root._bokeh_onload_callbacks.push(callback);\n",
              "    if (root._bokeh_is_loading > 0) {\n",
              "      console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n",
              "      return null;\n",
              "    }\n",
              "    if (js_urls == null || js_urls.length === 0) {\n",
              "      run_callbacks();\n",
              "      return null;\n",
              "    }\n",
              "    console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n",
              "    root._bokeh_is_loading = css_urls.length + js_urls.length;\n",
              "\n",
              "    function on_load() {\n",
              "      root._bokeh_is_loading--;\n",
              "      if (root._bokeh_is_loading === 0) {\n",
              "        console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n",
              "        run_callbacks()\n",
              "      }\n",
              "    }\n",
              "\n",
              "    function on_error(url) {\n",
              "      console.error(\"failed to load \" + url);\n",
              "    }\n",
              "\n",
              "    for (let i = 0; i < css_urls.length; i++) {\n",
              "      const url = css_urls[i];\n",
              "      const element = document.createElement(\"link\");\n",
              "      element.onload = on_load;\n",
              "      element.onerror = on_error.bind(null, url);\n",
              "      element.rel = \"stylesheet\";\n",
              "      element.type = \"text/css\";\n",
              "      element.href = url;\n",
              "      console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n",
              "      document.body.appendChild(element);\n",
              "    }\n",
              "\n",
              "    for (let i = 0; i < js_urls.length; i++) {\n",
              "      const url = js_urls[i];\n",
              "      const element = document.createElement('script');\n",
              "      element.onload = on_load;\n",
              "      element.onerror = on_error.bind(null, url);\n",
              "      element.async = false;\n",
              "      element.src = url;\n",
              "      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
              "      document.head.appendChild(element);\n",
              "    }\n",
              "  };\n",
              "\n",
              "  function inject_raw_css(css) {\n",
              "    const element = document.createElement(\"style\");\n",
              "    element.appendChild(document.createTextNode(css));\n",
              "    document.body.appendChild(element);\n",
              "  }\n",
              "\n",
              "  const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.3.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.3.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.3.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.3.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-3.3.2.min.js\"];\n",
              "  const css_urls = [];\n",
              "\n",
              "  const inline_js = [    function(Bokeh) {\n",
              "      Bokeh.set_log_level(\"info\");\n",
              "    },\n",
              "function(Bokeh) {\n",
              "    }\n",
              "  ];\n",
              "\n",
              "  function run_inline_js() {\n",
              "    if (root.Bokeh !== undefined || force === true) {\n",
              "          for (let i = 0; i < inline_js.length; i++) {\n",
              "      inline_js[i].call(root, root.Bokeh);\n",
              "    }\n",
              "} else if (Date.now() < root._bokeh_timeout) {\n",
              "      setTimeout(run_inline_js, 100);\n",
              "    } else if (!root._bokeh_failed_load) {\n",
              "      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n",
              "      root._bokeh_failed_load = true;\n",
              "    } else if (force !== true) {\n",
              "      const cell = $(document.getElementById(null)).parents('.cell').data().cell;\n",
              "      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n",
              "    }\n",
              "  }\n",
              "\n",
              "  if (root._bokeh_is_loading === 0) {\n",
              "    console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n",
              "    run_inline_js();\n",
              "  } else {\n",
              "    load_libs(css_urls, js_urls, function() {\n",
              "      console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n",
              "      run_inline_js();\n",
              "    });\n",
              "  }\n",
              "}(window));"
            ],
            "application/vnd.bokehjs_load.v0+json": "(function(root) {\n  function now() {\n    return new Date();\n  }\n\n  const force = true;\n\n  if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n    root._bokeh_onload_callbacks = [];\n    root._bokeh_is_loading = undefined;\n  }\n\n\n  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n    root._bokeh_timeout = Date.now() + 5000;\n    root._bokeh_failed_load = false;\n  }\n\n  const NB_LOAD_WARNING = {'data': {'text/html':\n     \"<div style='background-color: #fdd'>\\n\"+\n     \"<p>\\n\"+\n     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n     \"</p>\\n\"+\n     \"<ul>\\n\"+\n     \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n     \"<li>use INLINE resources instead, as so:</li>\\n\"+\n     \"</ul>\\n\"+\n     \"<code>\\n\"+\n     \"from bokeh.resources import INLINE\\n\"+\n     \"output_notebook(resources=INLINE)\\n\"+\n     \"</code>\\n\"+\n     \"</div>\"}};\n\n  function display_loaded() {\n    const el = document.getElementById(null);\n    if (el != null) {\n      el.textContent = \"BokehJS is loading...\";\n    }\n    if (root.Bokeh !== undefined) {\n      if (el != null) {\n        el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n      }\n    } else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(display_loaded, 100)\n    }\n  }\n\n  function run_callbacks() {\n    try {\n      root._bokeh_onload_callbacks.forEach(function(callback) {\n        if (callback != null)\n          callback();\n      });\n    } finally {\n      delete root._bokeh_onload_callbacks\n    }\n    console.debug(\"Bokeh: all callbacks have finished\");\n  }\n\n  function load_libs(css_urls, js_urls, callback) {\n    if (css_urls == null) css_urls = [];\n    if (js_urls == null) js_urls = [];\n\n    root._bokeh_onload_callbacks.push(callback);\n    if (root._bokeh_is_loading > 0) {\n      console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n      return null;\n    }\n    if (js_urls == null || js_urls.length === 0) {\n      run_callbacks();\n      return null;\n    }\n    console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n    root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n    function on_load() {\n      root._bokeh_is_loading--;\n      if (root._bokeh_is_loading === 0) {\n        console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n        run_callbacks()\n      }\n    }\n\n    function on_error(url) {\n      console.error(\"failed to load \" + url);\n    }\n\n    for (let i = 0; i < css_urls.length; i++) {\n      const url = css_urls[i];\n      const element = document.createElement(\"link\");\n      element.onload = on_load;\n      element.onerror = on_error.bind(null, url);\n      element.rel = \"stylesheet\";\n      element.type = \"text/css\";\n      element.href = url;\n      console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n      document.body.appendChild(element);\n    }\n\n    for (let i = 0; i < js_urls.length; i++) {\n      const url = js_urls[i];\n      const element = document.createElement('script');\n      element.onload = on_load;\n      element.onerror = on_error.bind(null, url);\n      element.async = false;\n      element.src = url;\n      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      document.head.appendChild(element);\n    }\n  };\n\n  function inject_raw_css(css) {\n    const element = document.createElement(\"style\");\n    element.appendChild(document.createTextNode(css));\n    document.body.appendChild(element);\n  }\n\n  const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.3.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.3.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.3.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.3.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-3.3.2.min.js\"];\n  const css_urls = [];\n\n  const inline_js = [    function(Bokeh) {\n      Bokeh.set_log_level(\"info\");\n    },\nfunction(Bokeh) {\n    }\n  ];\n\n  function run_inline_js() {\n    if (root.Bokeh !== undefined || force === true) {\n          for (let i = 0; i < inline_js.length; i++) {\n      inline_js[i].call(root, root.Bokeh);\n    }\n} else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(run_inline_js, 100);\n    } else if (!root._bokeh_failed_load) {\n      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n      root._bokeh_failed_load = true;\n    } else if (force !== true) {\n      const cell = $(document.getElementById(null)).parents('.cell').data().cell;\n      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n    }\n  }\n\n  if (root._bokeh_is_loading === 0) {\n    console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n    run_inline_js();\n  } else {\n    load_libs(css_urls, js_urls, function() {\n      console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n      run_inline_js();\n    });\n  }\n}(window));"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"c8f838d1-bd37-43a0-bdf1-8e3f7c92ef0a\" data-root-id=\"p1126\" style=\"display: contents;\"></div>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "(function(root) {\n",
              "  function embed_document(root) {\n",
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              "  const render_items = [{\"docid\":\"b915ec90-1578-44df-9c59-bd66c6204f09\",\"roots\":{\"p1126\":\"c8f838d1-bd37-43a0-bdf1-8e3f7c92ef0a\"},\"root_ids\":[\"p1126\"]}];\n",
              "  root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
              "  }\n",
              "  if (root.Bokeh !== undefined) {\n",
              "    embed_document(root);\n",
              "  } else {\n",
              "    let attempts = 0;\n",
              "    const timer = setInterval(function(root) {\n",
              "      if (root.Bokeh !== undefined) {\n",
              "        clearInterval(timer);\n",
              "        embed_document(root);\n",
              "      } else {\n",
              "        attempts++;\n",
              "        if (attempts > 100) {\n",
              "          clearInterval(timer);\n",
              "          console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n",
              "        }\n",
              "      }\n",
              "    }, 10, root)\n",
              "  }\n",
              "})(window);"
            ],
            "application/vnd.bokehjs_exec.v0+json": ""
          },
          "metadata": {
            "application/vnd.bokehjs_exec.v0+json": {
              "id": "p1126"
            }
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# a few visualizations to explore the data\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "sns.countplot(x= 'Pleasure Diff', data=df)\n",
        "plt.style.use('dark_background')\n",
        "plt.title('Range of Pleasure Diff')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "Ft8_lsmXhqqt",
        "outputId": "aa425357-00d3-4e65-a85c-50feb03b9e48"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Chills # and Chill Rating\n",
        "\n",
        "sns.countplot(x='Chills #', data=df)\n",
        "plt.style.use('dark_background')\n",
        "plt.title('Range of Chills #')\n",
        "plt.show()\n",
        "\n",
        "sns.countplot(x='Chill Rating', data=df)\n",
        "plt.style.use('dark_background')\n",
        "plt.title('Range of Chill Ratings')\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 927
        },
        "id": "YdUyFLCpmV6I",
        "outputId": "22969510-0b76-45f6-b8e1-1595cc61950e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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Kff7550pMTFSLFi00YsQIJSUladq0aVaXhyo2f/58HTlyRL/++qv8/f314IMP6sYbb9SgQYOsLg24bJZf36LRaBXf/Pz8zH/+8x9z9OhRk5uba06cOGG+/vpr55oN2h+rjRo1ysTGxprMzEyTnZ1ttmzZYgYMGGB5XTTa5TbWyAAAANtijQwAALAtggwAALCtP8Ri32uvvbbMn6QLAACs5XA4Lvmquis+yFx77bV80BkAADZ1qfc5uuKDzPkzMYGBgZyVAQDAJhwOhxISEi75u/uKDzLnZWZmEmQAALjCsNgXAADYFkEGAADYFkEGAADYFkEGAADYFkEGAADYFkEGAADYFkEGAADYFkEGAADYFkEGAADYFkEGAADYlqVBZsSIEdq+fbvS09OVnp6u9evXKywszLk9JiZGxhiX9uGHH1pYMQAAcCeWftbSsWPHFB4erv3798vDw0NDhgzRd999p1tuuUW7d++WJH300Uf617/+5dwnOzvbqnIBAICbsTTIfP/99y63X3rpJT3xxBPq0KGDM8hkZ2crJSXFivIAAICbc5s1Mp6enho4cKB8fX21YcMGZ//gwYOVmpqq2NhYjR8/XldddZWFVQIAAHdi6RkZSWrZsqU2bNigGjVq6MyZM+rXr5/27NkjSfryyy8VHx+vxMREtWrVSm+99ZaaN2+u/v37l3g8b29v+fj4OG87HI5KnwMAALCGhyRjZQFeXl66/vrr5e/vr/vuu0/Dhw9XSEiIM8z83p133qlVq1apcePGOnToULHHi4yM1Msvv1yk38/PT5mZmRVdPuD2UqLesLqEClF/zFirSwBQhRwOhzIyMi75+9vyS0sFBQU6ePCgfvnlF7344ovavn27Ro0aVezYjRs3SpKaNGlS4vEmTJggPz8/ZwsMDKyUugEAgPUsv7R0IU9PT5dLQ7/XunVrSVJSUlKJ++fn5ys/P78ySgMAAG7G0iAzfvx4LVmyREeOHJHD4dCgQYPUpUsX3X333WrUqJEGDRqkxYsX68SJE2rVqpUmTZqk1atXKzY21sqyAQCAm7A0yNSrV0+fffaZGjRooPT0dO3YsUN33323VqxYoeuuu07dunXT6NGj5evrq6NHj2revHl6/fXXrSwZAAC4EUuDzPDhw0vcduzYMXXp0qXqigEAALZj+WJfAACA8iLIAAAA2yLIAAAA2yLIAAAA2yLIAAAA2yLIAAAA2yLIAAAA2yLIAAAA2yLIAAAA2yLIAAAA2yLIAAAA2yLIAAAA2yLIAAAA2yLIAAAA2yLIAAAA2yLIAAAA2yLIAAAA2yLIAAAA2yLIAAAA2yLIAAAA2yLIAAAA2yLIAAAA2yLIAAAA2yLIAAAA2yLIAAAA2yLIAAAA2yLIAAAA2yLIAAAA2yLIAAAA2yLIAAAA2yLIAAAA2yLIAAAA2yLIAAAA2yLIAAAA2yLIAAAA2yLIAAAA2yLIAAAA2yLIAAAA2yLIAAAA2yLIAAAA27I0yIwYMULbt29Xenq60tPTtX79eoWFhTm3+/j46IMPPlBaWpoyMzM1d+5c1atXz8KKAQCAO7E0yBw7dkzh4eFq27at2rVrp1WrVum7777TTTfdJEmaNGmSevXqpfvvv18hISG69tprNX/+fCtLBgAAbsRDkrG6iN87ceKEnnvuOc2dO1epqakaNGiQ5s2bJ0lq3ry59u7dqw4dOmjjxo2lOp7D4VBGRob8/PyUmZlZmaUDbikl6g2rS6gQ9ceMtboEAFWotL+/3WaNjKenpwYOHChfX19t2LBBbdu2lbe3t1asWOEcs2/fPsXHx6tjx44lHsfb21sOh8OlAQCAK5PlQaZly5bKzMxUXl6epk2bpn79+mnPnj0KCAhQXl6e0tPTXcanpKQoICCgxONFREQoIyPD2RISEip7CgAAwCKWB5l9+/apdevWuu222/Thhx9q9uzZuvHGG8t9vAkTJsjPz8/ZAgMDK7BaAADgTqpbXUBBQYEOHjwoSfrll1/Uvn17jRo1Sl999ZV8fHzk7+/vclamfv36Sk5OLvF4+fn5ys/Pr/S6AQCA9Sw/I3MhT09P+fj4aOvWrcrPz1doaKhzW7NmzRQUFKQNGzZYWCEAAHAXlp6RGT9+vJYsWaIjR47I4XBo0KBB6tKli+6++25lZGRoxowZioqK0smTJ5WRkaHJkydr/fr1pX7FEgAAuLJZGmTq1aunzz77TA0aNFB6erp27Nihu+++2/lKpWeeeUbnzp3TvHnz5OPjo6VLl2rkyJFWlgwAANyI272PTEXjfWTwR8f7yACwI9u9jwwAAEBZEWQAAIBtEWQAAIBtEWQAAIBtEWQAAIBtEWQAAIBtEWQAAIBtEWQAAIBtEWQAAIBtEWQAAIBtEWQAAIBtEWQAAIBtEWQAAIBtEWQAAIBtEWQAAIBtEWQAAIBtEWQAAIBtEWQAAIBtEWQAAIBtEWQAAIBtEWQAAIBtEWQAAIBtEWQAAIBtEWQAAIBtEWQAAIBtEWQAAIBtEWQAAIBtEWQAAIBtEWQAAIBtEWQAAIBtEWQAAIBtEWQAAIBtEWQAAIBtEWQAAIBtEWQAAIBtEWQAAIBtEWQAAIBtEWQAAIBtEWQAAIBtWRpkwsPDtWnTJmVkZCglJUULFixQs2bNXMbExMTIGOPSPvzwQ4sqBgAA7sTSIBMSEqIpU6aoQ4cOuuuuu+Tl5aVly5apZs2aLuM++ugjBQQEONvzzz9vUcUAAMCdVLfyzu+55x6X20OHDlVqaqratm2rn376ydmfnZ2tlJSUqi4PAAC4ObdaI+Pv7y9JOnnypEv/4MGDlZqaqtjYWI0fP15XXXVVicfw9vaWw+FwaQAA4Mpk6RmZ3/Pw8NC7776rtWvXateuXc7+L7/8UvHx8UpMTFSrVq301ltvqXnz5urfv3+xx4mIiNDLL79cRVUDAAAreUgyVhchSVOnTtU999yjO+64QwkJCSWOu/POO7Vq1So1btxYhw4dKrLd29tbPj4+ztsOh0MJCQny8/NTZmZmpdQOuLOUqDesLqFC1B8z1uoSAFQhh8OhjIyMS/7+doszMpMnT1bPnj3VuXPni4YYSdq4caMkqUmTJsUGmfz8fOXn51dKnQAAwL1YHmQmT56sfv36qUuXLjp8+PAlx7du3VqSlJSUVLmFAQAAt2dpkJkyZYoGDRqkPn36KDMzU/Xr15ckpaenKzc3V40aNdKgQYO0ePFinThxQq1atdKkSZO0evVqxcbGWlk6AABwA5YGmZEjR0qSVq9e7dI/dOhQzZ49W/n5+erWrZtGjx4tX19fHT16VPPmzdPrr79uRbkAAMDNWBpkPDw8Lrr92LFj6tKlS9UUAwAAbMet3kcGAACgLAgyAADAtggyAADAtggyAADAtggyAADAtggyAADAtggyAADAtggyAADAtggyAADAtggyAADAtggyAADAtggyAADAtggyAADAtggyAADAtggyAADAtggyAADAtggyAADAtggyAADAtggyAADAtggyAADAtggyAADAtggyAADAtggyAADAtggyAADAtggyAADAtggyAADAtggyAADAtggyAADAtggyAADAtggyAADAtggyAADAtggyAADAtggyAADAtggyAADAtqpbXYCVpm5abXUJFWLkrSFWlwAAgCU4IwMAAGyLIAMAAGyLIAMAAGyLIAMAAGzL0iATHh6uTZs2KSMjQykpKVqwYIGaNWvmMsbHx0cffPCB0tLSlJmZqblz56pevXoWVQwAANyJpUEmJCREU6ZMUYcOHXTXXXfJy8tLy5YtU82aNZ1jJk2apF69eun+++9XSEiIrr32Ws2fP9/CqgEAgLuw9OXX99xzj8vtoUOHKjU1VW3bttVPP/0kPz8/DRs2TIMGDVJMTIwk6ZFHHtHevXt12223aePGjVaUDQAA3IRbrZHx9/eXJJ08eVKS1LZtW3l7e2vFihXOMfv27VN8fLw6duxoSY0AAMB9lCvIrFy50hk6fs/hcGjlypXlKsTDw0Pvvvuu1q5dq127dkmSAgIClJeXp/T0dJexKSkpCggIKPY43t7ecjgcLg0AAFyZyhVkunTpIm9v7yL9NWrUUKdOncpVyJQpU9SyZUv97W9/K9f+50VERCgjI8PZEhISLut4AADAfZVpjczNN9/s/P9NN93kvAQkSdWqVVNYWFi5gsPkyZPVs2dPde7c2WX/5ORk+fj4yN/f3+WsTP369ZWcnFzssSZMmKCoqCjnbYfDQZgBAOAKVaYg8+uvv8oYI2OMVq1aVWR7Tk6Onn766TIVMHnyZPXr109dunTR4cOHXbZt3bpV+fn5Cg0Ndb5SqVmzZgoKCtKGDRuKPV5+fr7y8/PLVAMAALCnMgWZhg0bysPDQ4cOHdKtt96q1NRU57b8/HwdP35c586dK/XxpkyZokGDBqlPnz7KzMxU/fr1JUnp6enKzc1VRkaGZsyYoaioKJ08eVIZGRmaPHmy1q9fzyuWAABA2YLMkSNHJP33MlJFGDlypCRp9WrXT6EeOnSoZs+eLUl65plndO7cOc2bN08+Pj5aunSpcz8AAPDHVu73kWnSpInuvPNO1atXT56ermuGX3vttVIdw8PD45Jj8vLy9NRTT+mpp54qV50AAODKVa4gM3z4cH344YdKS0tTcnKyjDHObcaYUgcZAACAy1GuIPPSSy9p7Nixevvttyu6HgAAgFIr1/vI1K5dW998801F1wIAAFAm5Qoy33zzjbp3717RtQAAAJRJuS4tHThwQK+99po6dOig2NhYFRQUuGyfPHlyhRQHAABwMeUKMn//+9915swZhYSEKCQkxGWbMYYgAwAAqkS5gkyjRo0qug4AAIAyK9caGQAAAHdQrjMyM2bMuOj2YcOGlasYAACAsihXkKldu7bLbS8vL7Vs2VK1atUq9sMkAQAAKkO5gsy9995bpM/Dw0MffvihDh48eNlFAQAAlEaFrZExxigqKkrPPPNMRR0SAADgoip0sW/jxo1VvXq5P4cSAACgTMqVOt555x2X2x4eHmrQoIH++te/avbs2RVSGAAAwKWUK8jccsstLrfPnTun1NRUPfvss/r0008rpDAAAIBLKVeQ6dq1a0XXAQAAUGaXtaClbt26at68uSRp3759SktLq5CiAAAASqNci31r1qypGTNmKCkpSWvWrNGaNWuUmJioTz75RFdddVVF1wgAAFCscgWZqKgohYSEqFevXqpVq5Zq1aqlPn36KCQkpMhCYAAAgMpSriDTv39/DRs2TNHR0crMzFRmZqaWLFmixx57TPfdd19F1wgAAFCscl9aSklJKdJ//Phx1axZ87KLAgAAKI1yBZkNGzbolVdekY+Pj7OvRo0aioyM1IYNGyqsOAAAgIsp16uWRo8erejoaB07dkzbt2+XJAUHBysvL0/du3ev0AIBAABKUq4gs3PnTjVt2lSDBw9WixYtJElz5szRF198odzc3AotEAAAoCTlCjLh4eFKSUnRJ5984tL/yCOP6JprrtHbb79dIcUBAABcTLnWyDz++OPau3dvkf5du3ZpxIgRl10UAABAaZQryAQEBCgpKalIf2pqqho0aHDZRQEAAJRGuYLM0aNHdfvttxfpv/3225WYmHjZRQEAAJRGudbIfPzxx3r33Xfl5eWlVatWSZJCQ0P19ttv886+AACgypQryEycOFF16tTR1KlT5e3tLUnKzc3VW2+9pTfffLNCCwQAAChJuT/9Ojw8XK+99ppuvPFG5eTkaP/+/crPz6/I2gAAAC6q3EFGkrKysrRly5aKqgUAAKBMyrXYFwAAwB0QZAAAgG0RZAAAgG0RZAAAgG0RZAAAgG0RZAAAgG0RZAAAgG0RZAAAgG1ZGmQ6deqkhQsXKiEhQcYY9enTx2X7zJkzZYxxaUuWLLGoWgAA4G4sDTK+vr7avn27nnzyyRLHLFmyRAEBAc72wAMPVGGFAADAnV3WRxRcrujoaEVHR190TF5enlJSUqqoIgAAYCduv0amS5cuSklJ0d69ezV16lT96U9/uuh4b29vORwOlwYAAK5Mbh1koqOj9fDDDys0NFQvvPCCQkJCtGTJEnl6llx2RESEMjIynC0hIaEKKwYAAFXJ0ktLl/LVV185/79z507t2LFDhw4dUpcuXbRq1api95kwYYKioqKctx0OB2EGAIArlFufkblQXFycUlNT1aRJkxLH5OfnKzMz06UBAIArk62CTGBgoOrUqaOkpCSrSwEAAG7A0ktLvr6+LmdXGjZsqODgYJ08eVInT55UZGSk5s2bp+TkZDVu3Fhvv/22Dhw4oKVLl1pYNQAAcBeWBpl27drpxx9/dN6eNGmSJGnWrFl64okn1KpVKw0ZMkS1atVSYmKili1bpnHjxik/P9+iigEAgDuxNMisXr1aHh4eJW4PCwurwmoAAIDd2GqNDAAAwO8RZAAAgG0RZAAAgG0RZAAAgG0RZAAAgG0RZAAAgG0RZAAAgG0RZAAAgG0RZAAAgG0RZAAAgG0RZAAAgG0RZAAAgG0RZAAAgG0RZAAAgG0RZAAAgG0RZAAAgG0RZAAAgG0RZAAAgG0RZAAAgG0RZAAAgG0RZAAAgG0RZAAAgG0RZAAAgG0RZAAAgG0RZAAAgG0RZAAAgG0RZAAAgG0RZAAAgG0RZAAAgG0RZAAAgG0RZAAAgG0RZAAAgG0RZAAAgG0RZAAAgG0RZAAAgG0RZAAAgG0RZAAAgG0RZAAAgG0RZAAAgG1ZGmQ6deqkhQsXKiEhQcYY9enTp8iYV155RYmJicrOztby5cvVpEkTCyoFAADuyNIg4+vrq+3bt+vJJ58sdvvzzz+vf/zjHxoxYoRuu+02ZWVlaenSpfLx8aniSgEAgDuqbuWdR0dHKzo6usTto0eP1uuvv66FCxdKkh5++GGlpKSob9+++uqrr6qqTAAA4Kbcdo1Mw4YN1aBBA61YscLZl5GRoY0bN6pjx44l7uft7S2Hw+HSAADAlcltg0xAQIAkKSUlxaU/JSXFua04ERERysjIcLaEhIRKrRMAAFjHbYNMeU2YMEF+fn7OFhgYaHVJAACgkrhtkElOTpYk1a9f36W/fv36zm3Fyc/PV2ZmpksDAABXJrcNMnFxcUpKSlJoaKizz+Fw6LbbbtOGDRssrAwAALgLS1+15Ovr6/K+MA0bNlRwcLBOnjypo0eP6t1339VLL72k/fv3Ky4uTq+99poSExP17bffWlc0AABwG5YGmXbt2unHH3903p40aZIkadasWXrkkUf09ttvy9fXVx999JFq1aqltWvXKiwsTHl5eRZVDAAA3ImlQWb16tXy8PC46JjIyEhFRkZWUUUAAMBO3HaNDAAAwKUQZAAAgG0RZAAAgG0RZAAAgG0RZAAAgG0RZAAAgG0RZAAAgG0RZAAAgG0RZAAAgG0RZAAAgG0RZAAAgG0RZAAAgG0RZAAAgG0RZAAAgG0RZAAAgG0RZAAAgG0RZAAAgG0RZAAAgG0RZAAAgG0RZAAAgG0RZAAAgG0RZAAAgG0RZAAAgG1Vt7oAAKgMExf8bHUJFeK5fh3KNP74oucqqZKqVa/XRKtLgE1wRgYAANgWQQYAANgWQQYAANgWQQYAANgWQQYAANgWQQYAANgWQQYAANgWQQYAANgWQQYAANgWQQYAANgWQQYAANgWQQYAANgWQQYAANgWQQYAANgWQQYAANiWWweZyMhIGWNc2p49e6wuCwAAuInqVhdwKTt37lS3bt2ct8+ePWthNQAAwJ24fZA5e/asUlJSrC4DAAC4Ibe+tCRJTZs2VUJCgg4ePKjPP/9cf/7zny863tvbWw6Hw6UBAIArk1ufkdm4caOGDh2qffv2qUGDBoqMjNRPP/2kli1b6syZM8XuExERoZdffrlqC7WZrOwVVpdQIXxrdrv0IADAFc2tz8hER0dr7ty5io2N1bJly9SjRw/VqlVLAwYMKHGfCRMmyM/Pz9kCAwOrsGIAAFCV3PqMzIXS09P122+/qUmTJiWOyc/PV35+fhVWBQAArOLWZ2Qu5Ovrq8aNGyspKcnqUgAAgBtw6yAzceJEde7cWUFBQerYsaMWLFigwsJCzZkzx+rSAACAG3DrS0vXXXed5syZozp16ig1NVVr165Vhw4dlJaWZnVpAADADbh1kHnggQesLgEAALgxt760BAAAcDEEGQAAYFsEGQAAYFsEGQAAYFsEGQAAYFsEGQAAYFsEGQAAYFsEGQAAYFsEGQAAYFsEGQAAYFsEGQAAYFsEGQAAYFsEGQAAYFsEGQAAYFvVrS4AqCprYnZZXUKF6Hzn/1hdAgC4Dc7IAAAA2yLIAAAA2yLIAAAA2yLIAAAA2yLIAAAA2yLIAAAA2yLIAAAA2yLIAAAA2yLIAAAA2yLIAAAA2yLIAAAA2yLIAAAA2yLIAAAA2yLIAAAA2yLIAAAA2yLIAAAA2yLIAAAA2yLIAAAA2yLIAAAA26pudQEAAFyuBdvCrS6hQvS75c0yjf/hrcWVVEnV+usLPcq9L2dkAACAbRFkAACAbRFkAACAbRFkAACAbdkiyIwcOVJxcXHKycnRzz//rPbt21tdEgAAcANuH2QGDBigqKgovfLKK2rTpo22b9+upUuX6pprrrG6NAAAYDG3DzJjxozRxx9/rFmzZmnPnj0aMWKEsrOz9eijj1pdGgAAsJhbBxkvLy+1bdtWK1ascPYZY7RixQp17NjRwsoAAIA7cOs3xKtbt66qV6+ulJQUl/6UlBS1aNGi2H28vb3l4+PjvO1wOFz+/T0vz2oVWK11ipvbxf0x512tmlvn9lIr67w9vL0rqZKqVdZ5e1f/Y36de1T3ufQgGyjrvKt7/kHn7e3Wv8ZLrbh5l/axuDIegd+JiIjQyy+/XKQ/ISGh6oupIsMzMqwuwRIZzPsPJePJf1pdgiX+8Ud9vjNes7oES2RkvGx1CZbIGF3y17nD4VBmZmaJ2906yKSlpens2bOqX7++S3/9+vWVnJxc7D4TJkxQVFSUS9+f/vQnnTx5stLqLInD4VBCQoICAwMv+iRcaZg38/4jYN7M+4/A6nk7HA4lJiZedIxbB5mCggJt3bpVoaGh+u677yRJHh4eCg0N1QcffFDsPvn5+crPz3fps/qLLjMz0/IarMC8/1iY9x8L8/5jsWrepblPtw4ykhQVFaXZs2dry5Yt2rRpk0aPHi1fX1/NnDnT6tIAAIDF3D7IfP3117rmmmv06quvKiAgQL/++qvCwsJ0/Phxq0sDAABuwNAqp3l7e5vIyEjj7e1teS3Mm3kzb+bNvJn3lThvj//9DwAAgO1cGW+sAQAA/pAIMgAAwLYIMgAAwLYIMgAAwLYIMpfh2muv1b///W+lpaUpOztbO3bsUNu2bUscP3PmTBljirSdO3dWYdWXJzw8XJs2bVJGRoZSUlK0YMECNWvW7KL7DB8+XGvWrNHJkyd18uRJLV++XO3bt6+iiitGXFxcsc9dSW/MKEmjRo3S3r17lZ2drSNHjigqKsrlc8DsYMSIEdq+fbvS09OVnp6u9evXKywsrMTx/fr10+bNm3Xq1CmdOXNG27Zt04MPPliFFVeMyMjIIs/1nj17LrqPv7+/PvjgAyUmJio3N1f79u3TPffcU0UVV44XXnhBxhhNmjSpVOMHDhwoY4wWLFhQyZVVLE9PT7366qs6dOiQsrOzdeDAAb300kuX3G/kyJHavXu3srOztXfvXj300ENVUG35derUSQsXLlRCQoKMMerTp0+RMa+88ooSExOVnZ2t5cuXq0mTJpc87siRIxUXF6ecnBz9/PPPlvx8t/ylU3ZstWrVMnFxcebTTz817du3NzfccIO56667TKNGjUrcx8/Pz9SvX9/ZAgMDTVpamomMjLR8PqVtS5YsMUOGDDE33XSTadWqlfn+++/N4cOHTc2aNUvc5/PPPzdPPPGECQ4ONs2bNzeffvqpOXXqlLn22mstn09pW926dV2eu9DQUGOMMSEhIcWOf+CBB0xOTo554IEHTFBQkLnrrrtMQkKCeeeddyyfS1laz549zT333GOaNGlimjZtal5//XWTl5dnbrrppmLHh4SEmL59+5oWLVqYRo0amX/84x+moKDAdO/e3fK5lKVFRkaa2NhYl+e8Tp06JY738vIymzZtMt9//735y1/+YoKCgkznzp1Nq1atLJ9LeVu7du3MoUOHzK+//momTZp0yfFBQUHm6NGjZvXq1WbBggWW11+WFhERYVJTU02PHj1MUFCQ6d+/v8nIyDBPP/10ifuMGDHCpKenmwEDBpiGDRuagQMHmoyMDNOzZ0/L51NSCwsLM6+99prp27evMcaYPn36uGx//vnnzalTp0zv3r3NzTffbL799ltz8OBB4+PjU+IxBwwYYHJzc83QoUPNjTfeaKZPn25Onjxprrnmmqqcm/UPrh3bhAkTzJo1ay7rGH369DGFhYXm+uuvt3w+5W1169Y1xhjTqVOnUu/j6elp0tPTzUMPPWR5/eVtkyZNMvv37y9x++TJk82KFStc+v7f//t/5qeffrK89sttJ06cMI8++mipx2/dutW8+uqrltddlhYZGWm2bdtW6vGPP/64OXDggKlevbrltVdE8/X1Nfv27TOhoaEmJibmkkHG09PTrF271jz66KNm5syZtgsyixYtMp988olL39y5c82///3vEvdZt26defvtt1367PQ9XlyQSUxMNM8++6zztp+fn8nJyTEDBw4s8Tg///yzmTx5svO2h4eHOXbsmHnhhReqbC5cWiqn3r17a8uWLfr666+VkpKiX375RcOHDy/TMYYNG6YVK1boyJEjlVRl5fP395ekMn0oZ82aNeXl5WXJB3lWBC8vLz344IP69NNPSxyzfv16tW3b1nmKtWHDhurRo4cWL15cVWVWOE9PTw0cOFC+vr7asGFDqfbp2rWrmjdvrjVr1lRydRWvadOmSkhI0MGDB/X555/rz3/+c4lje/furQ0bNmjKlClKTk5WbGysIiIi5Olpzx+xU6ZM0Q8//KCVK1eWavy//vUvHT9+/KLfE+5s/fr1Cg0NVdOmTSVJrVq10h133KElS5aUuI+Pj49yc3Nd+nJycnTrrbeqenW3f9P8Iho2bKgGDRpoxYoVzr6MjAxt3LhRHTt2LHYfLy8vtW3b1mUfY4xWrFhR4j6VxfJkaMeWk5NjcnJyzBtvvGFat25tHnvsMZOdnW0efvjhUu3foEEDU1BQYO6//37L51Le5uHhYRYtWlTmv0CmTJliDhw4cNHTle7c7r//flNQUGAaNGhw0XFPP/20ycvLM/n5+cYYY6ZOnWp57eVpLVu2NJmZmaagoMCcOnXK3HPPPRcd7+fnZzIzM01+fr7JyckxjzzyiOVzKGsLCwsz9913n7n55ptN9+7dzbp168zhw4fN1VdfXez4PXv2mJycHPPJJ5+YNm3amAEDBpi0tDTzr3/9y/K5lLUNHDjQ7Nixw/n9eakzMrfffrs5evSo89KbHc/IeHh4mAkTJpjCwkKTn59vCgsLTXh4+EX3eeONN0xiYqJp06aNkWTatm1rkpKSjDHGBAQEWD6nS7ULz8h07Nix2Nq/+uor85///KfYYzRo0MAYY0yHDh1c+t966y3z888/V+V8rH9A7djy8vLMunXrXPree+89s379+lLtHx4eblJTU42Xl5flcylvmzp1qomLizOBgYGl3ueFF14wJ06cMDfffLPl9Ze3RUdHm4ULF150TEhIiElKSjLDhg0zLVu2NH379jXx8fHmpZdesrz+sjYvLy/TuHFj06ZNGzN+/Hhz/Phxc+ONN5Y43sPDwzRu3NgEBwebMWPGmFOnTpW4lsguzd/f35w+fbrES2r79u0z8fHxxtPT09n3zDPPmMTERMtrL0u77rrrTHJyssv358WCzNVXX20OHTpkwsLCnH12DDIDBw40R44cMQMHDjQtW7Y0Dz74oElLS7voH6Y1atQwM2bMMPn5+aagoMAcO3bMvPnmm8YYY+rVq2f5nC7VCDI0c/jwYfPxxx+79I0YMcIcO3asVPv/9ttvJioqyvJ5lLdNnjzZHDlyxNxwww2l3ufZZ581p06dMm3btrW8/vK266+/3pw9e9b07t37ouPWrFlT5Pr54MGDTVZWlvHw8LB8HpfTli9fbqZNm1bq8R9//LGJjo62vO7LbZs2bTLjx48vdtuPP/5oli9f7tIXFhZmjDG2+mOlT58+xhhjCgoKnM0YYwoLC01BQYFLUJNkgoODi4wvLCx0jr/Yix/cqR05csSMHDnSpW/s2LFmz549l9y3evXqJjAw0Hh6ejoXANvhe/zCINOwYUNjjDHBwcEu43788Ufz7rvvFnsMLy8vU1BQUGStzaxZs8y3335bZXOx5wVcN7Bu3To1b97cpa9Zs2aKj4+/5L4hISFq2rSpZsyYUVnlVarJkyerX79+6tq1qw4fPlyqfZ577jmNGzdOYWFh2rp1a+UWWIkeeeQRHT9+XD/88MNFx9WsWVPnzp1z6SssLJQkeXh4VFp9VcHT07NMLyMv63h35Ovrq8aNGyspKanY7evWrVOTJk1cnttmzZopMTFRBQUFVVXmZVu5cqVatmyp1q1bO9vmzZv1xRdfqHXr1kW+pvfu3Vtk/MKFCxUTE6PWrVvr6NGjFs2kbEr6fi3NGqezZ88qISFB586d09/+9jd9//33MsZUVqmVJi4uTklJSQoNDXX2ORwO3XbbbSWuiSsoKNDWrVtd9vHw8FBoaGip19FVFMuToR1bu3btTH5+vomIiDCNGzc2DzzwgDlz5owZNGiQc8z48ePN7Nmzi+z72WefmQ0bNlg+h/K0KVOmmFOnTpnOnTu7vDS1Ro0azjGzZ892+cv1+eefN7m5uebee+912cfX19fy+ZSleXh4mMOHD5sJEyYU2XbhnCMjI016eroZOHCgueGGG0y3bt3M/v37SzxF665t/PjxplOnTiYoKMi0bNnSjB8/3hQWFppu3boVO+/w8HDTrVs307BhQ9OiRQszZswYk5+fb4YNG2b5XMrSJk6caDp37myCgoJMx44dzbJly8zx48dN3bp1i533ddddZ9LT0837779vmjZtanr06GGSk5PNiy++aPlcLrddeGnpwrlf2Ox4aWnmzJnm6NGjzpdf9+3b1xw/fty8+eabzjEX/jxv2rSpGTx4sGnSpIlp3769mTNnjklLSzNBQUGWz6ek5uvra4KDg51n0kaPHm2Cg4PNn//8ZyP992f1yZMnTa9evUzLli3NggULirz8esWKFebJJ5903h4wYIDJyckxDz/8sGnRooWZNm2aOXnyZFVfXrP+wbVr++tf/2p27NhhcnJyzO7du83w4cNdts+cOdPExMS49Pn5+ZmsrKwiY+3SSjJkyBDnmJiYGDNz5kzn7bi4uGL3sdP750gyd911lzHGmKZNmxbZduGcq1WrZv71r3+Z/fv3m+zsbBMfH28++OAD4+/vb/k8ytI++eQTExcXZ3Jzc01KSopZvny5M8QUN+/XXnvN/PbbbyY7O9ucOHHCrFu3zgwYMMDyeZS1zZkzxyQkJJjc3Fxz9OhRM2fOHJfLJBfOW5Lp0KGD2bBhg8nJyTEHDhwwERERRS7F2LFdGGSKm/vvmx2DzNVXX20mTZpkDh8+bLKzs82BAwfMa6+95nJZ8MKf5y1atDC//PKLycrKMqdPnzYLFiwwzZo1s3wuF2shISHF/iz+/fP5yiuvmKSkJJOTk2OWL19e5OddXFxckZ/dTz75pDl8+LDJzc01P//8s7n11lurdF4e//sfAAAA22GNDAAAsC2CDAAAsC2CDAAAsC2CDAAAsC2CDAAAsC2CDAAAsC2CDAAAsC2CDIAKY4xRnz59StweEhIiY4z8/f0lSUOGDNGpU6ec2yMjI7Vt27ZKr7MkMTExmjRpkmX3D6DsCDIASqV+/fp6//33dfDgQeXm5urIkSNauHChunbtWupjrF+/XgEBAUpPTy9XDUFBQTLGONuJEyf0448/6o477ijTcS4MVOfde++9GjduXLlqA2ANggyASwoKCtLWrVvVtWtXPffcc7r55psVFhammJgYTZkypdTHKSgoUEpKymXXExoaqoCAAHXu3FmJiYn6/vvvVa9evcs+7qlTp3TmzJnLPg6AqkOQAXBJU6dOlTFGt956q+bPn6/9+/dr9+7dmjRpkjp06OAytm7dupo/f76ysrL022+/qVevXs5tJZ0JKasTJ04oJSVFu3bt0vjx4+Xv76/bbrvNuf3BBx/U5s2blZGRoaSkJH3xxRe65pprJP03lP3444+SpNOnT8sYo5kzZ0oqemkpLi5OERERmjFjhjIyMhQfH6/HHnvMpZaOHTtq27ZtysnJ0ebNm9WnTx8ZYxQcHHxZcwRQOgQZABdVu3ZthYWFacqUKcrOzi6y/cLLRJGRkfr666/VqlUrLV68WF988YVq165dKbXVqFFDDz/8sCQpPz/f2e/l5aVx48YpODhYffv21Q033KBZs2ZJko4ePap7771XktSsWTMFBARo1KhRJd7Hs88+qy1btuiWW27R1KlT9eGHH6pZs2aSJIfDoUWLFik2NlZt2rTRuHHj9NZbb1XKXAGUzPJP5KTRaO7b2rdvb4wxpm/fvpcca4wxr776qvN2zZo1jTHG3H333Ub6v0/fPf8p4EOGDDGnTp1yjo+MjDTbtm0r8fhBQUHGGGOysrJMZmamKSwsNMYYs3nzZlO9evUS92vbtq0xxhhfX99i6zjfLvyk57i4OPPZZ5+5jElOTjaPP/64kWQef/xxk5qaanx8fJzbhw0bZowxJjg42PLnjkb7IzTOyAC4KA8PjzKN37Fjh/P/2dnZSk9Pr5D1K783cOBA3XLLLerfv7/279+voUOH6uzZs87tbdq00cKFCxUfH6+MjAytXr1aknT99deX+b5+Px9JSk5Ods6nefPm2rFjh/Ly8pzbN23aVJ4pASin6lYXAMC97d+/X+fOnVOLFi1KNb6goMDltjFGnp4V+zfT0aNHdeDAAR04cEDVq1fXggUL1LJlS+Xn56tmzZpaunSpli5dqsGDBys1NVXXX3+9li1bJm9v7zLfV1XMB0D58d0I4KJOnTqlpUuX6sknn1TNmjWLbL/chbuXa+7cuTp79qxGjhwpSWrRooXq1q2r8PBwrV27Vvv27StyRuj8eppq1apd1n3v27dPN998s0tAat++/WUdE0DZEGQAXNKTTz6patWqadOmTbr33nvVpEkTtWjRQk8//bQ2bNhgdXl6//33FR4erquuukpHjhxRXl6enn76aTVs2FC9evUq8t4w8fHxOnfunHr27Km6devK19e3XPf75ZdfytPTUx999JFatGih7t2765///Kek/565AVD5CDIALikuLk5t2rRRTEyM3nnnHe3cuVPLly9XaGionnjiCavL0+zZs+Xl5aWnnnpKaWlpGjp0qO6//37t3r1b4eHhznBxXmJioiIjI/Xmm28qJSVFH3zwQbnuNzMzU7169VLr1q3166+/6o033tCrr74qScrNzb3seQG4NA/9d9UvAKACDBo0SDNnzpS/vz9hBqgCLPYFgMvw0EMP6dChQ0pISFBwcLDeeustff3114QYoIoQZADgMgQEBOjVV19VQECAkpKS9M0332js2LFWlwX8YXBpCQAA2BaLfQEAgG0RZAAAgG0RZAAAgG0RZAAAgG0RZAAAgG0RZAAAgG0RZAAAgG0RZAAAgG0RZAAAgG39f2eN0k9BfzGGAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# A few more\n",
        "sns.countplot(x='Chill Intensity', data=df)\n",
        "plt.style.use('dark_background')\n",
        "plt.title('Range of Chill Intensity')\n",
        "plt.show()\n",
        "\n",
        "sns.countplot(x='Daily Chills', data=df)\n",
        "plt.style.use('dark_background')\n",
        "plt.title('Range of Daily Chills')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 927
        },
        "id": "pOZiU9ifppN-",
        "outputId": "a7e004b6-f1dd-4494-9a31-cd0aad23f6f3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Calculate the correlation matrix for your selected columns\n",
        "selected_columns = ['Chills #', 'Chill Intensity', 'Daily Chills', 'Pleasure Diff', 'Emotion drift', 'Chill Rating']\n",
        "corr = df[selected_columns].corr()\n",
        "\n",
        "print(corr)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "V2HH01jn43m7",
        "outputId": "787b81ae-a9bc-4cb6-d346-251b6b2780bd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                     Chills #  Chill Intensity  Daily Chills  Pleasure Diff  \\\n",
            "Chills #         1.000000e+00         0.692152 -7.125541e-17       0.243660   \n",
            "Chill Intensity  6.921519e-01         1.000000  2.041475e-01       0.280296   \n",
            "Daily Chills    -7.125541e-17         0.204147  1.000000e+00      -0.277070   \n",
            "Pleasure Diff    2.436602e-01         0.280296 -2.770701e-01       1.000000   \n",
            "Emotion drift   -7.990091e-02        -0.155129  6.265514e-02      -0.564080   \n",
            "\n",
            "                 Emotion drift  \n",
            "Chills #             -0.079901  \n",
            "Chill Intensity      -0.155129  \n",
            "Daily Chills          0.062655  \n",
            "Pleasure Diff        -0.564080  \n",
            "Emotion drift         1.000000  \n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-83-a7164c62bda3>:3: FutureWarning: The default value of numeric_only in DataFrame.corr is deprecated. In a future version, it will default to False. Select only valid columns or specify the value of numeric_only to silence this warning.\n",
            "  corr = df[selected_columns].corr()\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Create a grcontour graph of matrix\n",
        "# Create a mask for the upper triangle\n",
        "mask = np.triu(np.ones_like(corr, dtype=bool))\n",
        "\n",
        "# Set up the matplotlib figure\n",
        "f, ax = plt.subplots(figsize=(13, 3))\n",
        "\n",
        "# Generate a custom diverging colormap\n",
        "cmap = sns.diverging_palette(230, 20, as_cmap=True)\n",
        "\n",
        "# Draw the heatmap with the mask and correct aspect ratio\n",
        "sns.heatmap(corr, mask=mask, cmap=cmap, vmax=.3, center=0,\n",
        "            square=True, linewidths=.5, cbar_kws={\"shrink\": .5})\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 372
        },
        "id": "ToyPl2LHr0qm",
        "outputId": "b1e85e47-78ed-491c-a6d1-7d7ec5747842"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1300x300 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from bokeh.palettes import Category10\n",
        "from bokeh.transform import factor_cmap\n",
        "\n",
        "# Create a list of unique colors for each circle\n",
        "colors = [Category10[10][i % 10] for i in range(len(df))]\n",
        "\n",
        "# Add the colors to your DataFrame\n",
        "df['colors'] = colors\n",
        "\n",
        "# Create a new plot with the HoverTool\n",
        "hover = HoverTool(tooltips=None, mode=\"vline\")\n",
        "p = figure(tools=[hover, 'wheel_zoom', 'pan'], title=\"Pleasure Diff and Chill Intensity\")\n",
        "\n",
        "# Add a circle renderer with color mapped to the new 'colors' column\n",
        "p.circle('Pleasure Diff', 'Chill Intensity', radius=1, fill_color='colors', fill_alpha=0.6,\n",
        "         line_color=None, source=df)\n",
        "\n",
        "curdoc().theme = 'dark_minimal'\n",
        "output_notebook()\n",
        "show(p)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 617
        },
        "id": "wAte3H-GHiRr",
        "outputId": "7b5a35c3-2065-44d0-c9df-7844ff3bfd4d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "(function(root) {\n",
              "  function now() {\n",
              "    return new Date();\n",
              "  }\n",
              "\n",
              "  const force = true;\n",
              "\n",
              "  if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n",
              "    root._bokeh_onload_callbacks = [];\n",
              "    root._bokeh_is_loading = undefined;\n",
              "  }\n",
              "\n",
              "const JS_MIME_TYPE = 'application/javascript';\n",
              "  const HTML_MIME_TYPE = 'text/html';\n",
              "  const EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n",
              "  const CLASS_NAME = 'output_bokeh rendered_html';\n",
              "\n",
              "  /**\n",
              "   * Render data to the DOM node\n",
              "   */\n",
              "  function render(props, node) {\n",
              "    const script = document.createElement(\"script\");\n",
              "    node.appendChild(script);\n",
              "  }\n",
              "\n",
              "  /**\n",
              "   * Handle when an output is cleared or removed\n",
              "   */\n",
              "  function handleClearOutput(event, handle) {\n",
              "    function drop(id) {\n",
              "      const view = Bokeh.index.get_by_id(id)\n",
              "      if (view != null) {\n",
              "        view.model.document.clear()\n",
              "        Bokeh.index.delete(view)\n",
              "      }\n",
              "    }\n",
              "\n",
              "    const cell = handle.cell;\n",
              "\n",
              "    const id = cell.output_area._bokeh_element_id;\n",
              "    const server_id = cell.output_area._bokeh_server_id;\n",
              "\n",
              "    // Clean up Bokeh references\n",
              "    if (id != null) {\n",
              "      drop(id)\n",
              "    }\n",
              "\n",
              "    if (server_id !== undefined) {\n",
              "      // Clean up Bokeh references\n",
              "      const cmd_clean = \"from bokeh.io.state import curstate; print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n",
              "      cell.notebook.kernel.execute(cmd_clean, {\n",
              "        iopub: {\n",
              "          output: function(msg) {\n",
              "            const id = msg.content.text.trim()\n",
              "            drop(id)\n",
              "          }\n",
              "        }\n",
              "      });\n",
              "      // Destroy server and session\n",
              "      const cmd_destroy = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n",
              "      cell.notebook.kernel.execute(cmd_destroy);\n",
              "    }\n",
              "  }\n",
              "\n",
              "  /**\n",
              "   * Handle when a new output is added\n",
              "   */\n",
              "  function handleAddOutput(event, handle) {\n",
              "    const output_area = handle.output_area;\n",
              "    const output = handle.output;\n",
              "\n",
              "    // limit handleAddOutput to display_data with EXEC_MIME_TYPE content only\n",
              "    if ((output.output_type != \"display_data\") || (!Object.prototype.hasOwnProperty.call(output.data, EXEC_MIME_TYPE))) {\n",
              "      return\n",
              "    }\n",
              "\n",
              "    const toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n",
              "\n",
              "    if (output.metadata[EXEC_MIME_TYPE][\"id\"] !== undefined) {\n",
              "      toinsert[toinsert.length - 1].firstChild.textContent = output.data[JS_MIME_TYPE];\n",
              "      // store reference to embed id on output_area\n",
              "      output_area._bokeh_element_id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n",
              "    }\n",
              "    if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n",
              "      const bk_div = document.createElement(\"div\");\n",
              "      bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n",
              "      const script_attrs = bk_div.children[0].attributes;\n",
              "      for (let i = 0; i < script_attrs.length; i++) {\n",
              "        toinsert[toinsert.length - 1].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n",
              "        toinsert[toinsert.length - 1].firstChild.textContent = bk_div.children[0].textContent\n",
              "      }\n",
              "      // store reference to server id on output_area\n",
              "      output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n",
              "    }\n",
              "  }\n",
              "\n",
              "  function register_renderer(events, OutputArea) {\n",
              "\n",
              "    function append_mime(data, metadata, element) {\n",
              "      // create a DOM node to render to\n",
              "      const toinsert = this.create_output_subarea(\n",
              "        metadata,\n",
              "        CLASS_NAME,\n",
              "        EXEC_MIME_TYPE\n",
              "      );\n",
              "      this.keyboard_manager.register_events(toinsert);\n",
              "      // Render to node\n",
              "      const props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n",
              "      render(props, toinsert[toinsert.length - 1]);\n",
              "      element.append(toinsert);\n",
              "      return toinsert\n",
              "    }\n",
              "\n",
              "    /* Handle when an output is cleared or removed */\n",
              "    events.on('clear_output.CodeCell', handleClearOutput);\n",
              "    events.on('delete.Cell', handleClearOutput);\n",
              "\n",
              "    /* Handle when a new output is added */\n",
              "    events.on('output_added.OutputArea', handleAddOutput);\n",
              "\n",
              "    /**\n",
              "     * Register the mime type and append_mime function with output_area\n",
              "     */\n",
              "    OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n",
              "      /* Is output safe? */\n",
              "      safe: true,\n",
              "      /* Index of renderer in `output_area.display_order` */\n",
              "      index: 0\n",
              "    });\n",
              "  }\n",
              "\n",
              "  // register the mime type if in Jupyter Notebook environment and previously unregistered\n",
              "  if (root.Jupyter !== undefined) {\n",
              "    const events = require('base/js/events');\n",
              "    const OutputArea = require('notebook/js/outputarea').OutputArea;\n",
              "\n",
              "    if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n",
              "      register_renderer(events, OutputArea);\n",
              "    }\n",
              "  }\n",
              "  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n",
              "    root._bokeh_timeout = Date.now() + 5000;\n",
              "    root._bokeh_failed_load = false;\n",
              "  }\n",
              "\n",
              "  const NB_LOAD_WARNING = {'data': {'text/html':\n",
              "     \"<div style='background-color: #fdd'>\\n\"+\n",
              "     \"<p>\\n\"+\n",
              "     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n",
              "     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n",
              "     \"</p>\\n\"+\n",
              "     \"<ul>\\n\"+\n",
              "     \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n",
              "     \"<li>use INLINE resources instead, as so:</li>\\n\"+\n",
              "     \"</ul>\\n\"+\n",
              "     \"<code>\\n\"+\n",
              "     \"from bokeh.resources import INLINE\\n\"+\n",
              "     \"output_notebook(resources=INLINE)\\n\"+\n",
              "     \"</code>\\n\"+\n",
              "     \"</div>\"}};\n",
              "\n",
              "  function display_loaded() {\n",
              "    const el = document.getElementById(null);\n",
              "    if (el != null) {\n",
              "      el.textContent = \"BokehJS is loading...\";\n",
              "    }\n",
              "    if (root.Bokeh !== undefined) {\n",
              "      if (el != null) {\n",
              "        el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n",
              "      }\n",
              "    } else if (Date.now() < root._bokeh_timeout) {\n",
              "      setTimeout(display_loaded, 100)\n",
              "    }\n",
              "  }\n",
              "\n",
              "  function run_callbacks() {\n",
              "    try {\n",
              "      root._bokeh_onload_callbacks.forEach(function(callback) {\n",
              "        if (callback != null)\n",
              "          callback();\n",
              "      });\n",
              "    } finally {\n",
              "      delete root._bokeh_onload_callbacks\n",
              "    }\n",
              "    console.debug(\"Bokeh: all callbacks have finished\");\n",
              "  }\n",
              "\n",
              "  function load_libs(css_urls, js_urls, callback) {\n",
              "    if (css_urls == null) css_urls = [];\n",
              "    if (js_urls == null) js_urls = [];\n",
              "\n",
              "    root._bokeh_onload_callbacks.push(callback);\n",
              "    if (root._bokeh_is_loading > 0) {\n",
              "      console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n",
              "      return null;\n",
              "    }\n",
              "    if (js_urls == null || js_urls.length === 0) {\n",
              "      run_callbacks();\n",
              "      return null;\n",
              "    }\n",
              "    console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n",
              "    root._bokeh_is_loading = css_urls.length + js_urls.length;\n",
              "\n",
              "    function on_load() {\n",
              "      root._bokeh_is_loading--;\n",
              "      if (root._bokeh_is_loading === 0) {\n",
              "        console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n",
              "        run_callbacks()\n",
              "      }\n",
              "    }\n",
              "\n",
              "    function on_error(url) {\n",
              "      console.error(\"failed to load \" + url);\n",
              "    }\n",
              "\n",
              "    for (let i = 0; i < css_urls.length; i++) {\n",
              "      const url = css_urls[i];\n",
              "      const element = document.createElement(\"link\");\n",
              "      element.onload = on_load;\n",
              "      element.onerror = on_error.bind(null, url);\n",
              "      element.rel = \"stylesheet\";\n",
              "      element.type = \"text/css\";\n",
              "      element.href = url;\n",
              "      console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n",
              "      document.body.appendChild(element);\n",
              "    }\n",
              "\n",
              "    for (let i = 0; i < js_urls.length; i++) {\n",
              "      const url = js_urls[i];\n",
              "      const element = document.createElement('script');\n",
              "      element.onload = on_load;\n",
              "      element.onerror = on_error.bind(null, url);\n",
              "      element.async = false;\n",
              "      element.src = url;\n",
              "      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
              "      document.head.appendChild(element);\n",
              "    }\n",
              "  };\n",
              "\n",
              "  function inject_raw_css(css) {\n",
              "    const element = document.createElement(\"style\");\n",
              "    element.appendChild(document.createTextNode(css));\n",
              "    document.body.appendChild(element);\n",
              "  }\n",
              "\n",
              "  const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.3.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.3.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.3.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.3.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-3.3.2.min.js\"];\n",
              "  const css_urls = [];\n",
              "\n",
              "  const inline_js = [    function(Bokeh) {\n",
              "      Bokeh.set_log_level(\"info\");\n",
              "    },\n",
              "function(Bokeh) {\n",
              "    }\n",
              "  ];\n",
              "\n",
              "  function run_inline_js() {\n",
              "    if (root.Bokeh !== undefined || force === true) {\n",
              "          for (let i = 0; i < inline_js.length; i++) {\n",
              "      inline_js[i].call(root, root.Bokeh);\n",
              "    }\n",
              "} else if (Date.now() < root._bokeh_timeout) {\n",
              "      setTimeout(run_inline_js, 100);\n",
              "    } else if (!root._bokeh_failed_load) {\n",
              "      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n",
              "      root._bokeh_failed_load = true;\n",
              "    } else if (force !== true) {\n",
              "      const cell = $(document.getElementById(null)).parents('.cell').data().cell;\n",
              "      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n",
              "    }\n",
              "  }\n",
              "\n",
              "  if (root._bokeh_is_loading === 0) {\n",
              "    console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n",
              "    run_inline_js();\n",
              "  } else {\n",
              "    load_libs(css_urls, js_urls, function() {\n",
              "      console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n",
              "      run_inline_js();\n",
              "    });\n",
              "  }\n",
              "}(window));"
            ],
            "application/vnd.bokehjs_load.v0+json": "(function(root) {\n  function now() {\n    return new Date();\n  }\n\n  const force = true;\n\n  if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n    root._bokeh_onload_callbacks = [];\n    root._bokeh_is_loading = undefined;\n  }\n\n\n  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n    root._bokeh_timeout = Date.now() + 5000;\n    root._bokeh_failed_load = false;\n  }\n\n  const NB_LOAD_WARNING = {'data': {'text/html':\n     \"<div style='background-color: #fdd'>\\n\"+\n     \"<p>\\n\"+\n     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n     \"</p>\\n\"+\n     \"<ul>\\n\"+\n     \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n     \"<li>use INLINE resources instead, as so:</li>\\n\"+\n     \"</ul>\\n\"+\n     \"<code>\\n\"+\n     \"from bokeh.resources import INLINE\\n\"+\n     \"output_notebook(resources=INLINE)\\n\"+\n     \"</code>\\n\"+\n     \"</div>\"}};\n\n  function display_loaded() {\n    const el = document.getElementById(null);\n    if (el != null) {\n      el.textContent = \"BokehJS is loading...\";\n    }\n    if (root.Bokeh !== undefined) {\n      if (el != null) {\n        el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n      }\n    } else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(display_loaded, 100)\n    }\n  }\n\n  function run_callbacks() {\n    try {\n      root._bokeh_onload_callbacks.forEach(function(callback) {\n        if (callback != null)\n          callback();\n      });\n    } finally {\n      delete root._bokeh_onload_callbacks\n    }\n    console.debug(\"Bokeh: all callbacks have finished\");\n  }\n\n  function load_libs(css_urls, js_urls, callback) {\n    if (css_urls == null) css_urls = [];\n    if (js_urls == null) js_urls = [];\n\n    root._bokeh_onload_callbacks.push(callback);\n    if (root._bokeh_is_loading > 0) {\n      console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n      return null;\n    }\n    if (js_urls == null || js_urls.length === 0) {\n      run_callbacks();\n      return null;\n    }\n    console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n    root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n    function on_load() {\n      root._bokeh_is_loading--;\n      if (root._bokeh_is_loading === 0) {\n        console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n        run_callbacks()\n      }\n    }\n\n    function on_error(url) {\n      console.error(\"failed to load \" + url);\n    }\n\n    for (let i = 0; i < css_urls.length; i++) {\n      const url = css_urls[i];\n      const element = document.createElement(\"link\");\n      element.onload = on_load;\n      element.onerror = on_error.bind(null, url);\n      element.rel = \"stylesheet\";\n      element.type = \"text/css\";\n      element.href = url;\n      console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n      document.body.appendChild(element);\n    }\n\n    for (let i = 0; i < js_urls.length; i++) {\n      const url = js_urls[i];\n      const element = document.createElement('script');\n      element.onload = on_load;\n      element.onerror = on_error.bind(null, url);\n      element.async = false;\n      element.src = url;\n      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      document.head.appendChild(element);\n    }\n  };\n\n  function inject_raw_css(css) {\n    const element = document.createElement(\"style\");\n    element.appendChild(document.createTextNode(css));\n    document.body.appendChild(element);\n  }\n\n  const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.3.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.3.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.3.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.3.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-3.3.2.min.js\"];\n  const css_urls = [];\n\n  const inline_js = [    function(Bokeh) {\n      Bokeh.set_log_level(\"info\");\n    },\nfunction(Bokeh) {\n    }\n  ];\n\n  function run_inline_js() {\n    if (root.Bokeh !== undefined || force === true) {\n          for (let i = 0; i < inline_js.length; i++) {\n      inline_js[i].call(root, root.Bokeh);\n    }\n} else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(run_inline_js, 100);\n    } else if (!root._bokeh_failed_load) {\n      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n      root._bokeh_failed_load = true;\n    } else if (force !== true) {\n      const cell = $(document.getElementById(null)).parents('.cell').data().cell;\n      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n    }\n  }\n\n  if (root._bokeh_is_loading === 0) {\n    console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n    run_inline_js();\n  } else {\n    load_libs(css_urls, js_urls, function() {\n      console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n      run_inline_js();\n    });\n  }\n}(window));"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"d35cfeea-2f9d-483d-9830-b64f1670a731\" data-root-id=\"p1188\" style=\"display: contents;\"></div>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "(function(root) {\n",
              "  function embed_document(root) {\n",
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              "  const render_items = [{\"docid\":\"4d26a466-6944-43fe-9f0a-04c4d90bdc45\",\"roots\":{\"p1188\":\"d35cfeea-2f9d-483d-9830-b64f1670a731\"},\"root_ids\":[\"p1188\"]}];\n",
              "  root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
              "  }\n",
              "  if (root.Bokeh !== undefined) {\n",
              "    embed_document(root);\n",
              "  } else {\n",
              "    let attempts = 0;\n",
              "    const timer = setInterval(function(root) {\n",
              "      if (root.Bokeh !== undefined) {\n",
              "        clearInterval(timer);\n",
              "        embed_document(root);\n",
              "      } else {\n",
              "        attempts++;\n",
              "        if (attempts > 100) {\n",
              "          clearInterval(timer);\n",
              "          console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n",
              "        }\n",
              "      }\n",
              "    }, 10, root)\n",
              "  }\n",
              "})(window);"
            ],
            "application/vnd.bokehjs_exec.v0+json": ""
          },
          "metadata": {
            "application/vnd.bokehjs_exec.v0+json": {
              "id": "p1188"
            }
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Assuming Pleasure Diff is binary where 1 represents good chills and 0 represents bad chills\n",
        "good_chills = df[df['Pleasure Diff'] == 1]\n",
        "bad_chills = df[df['Pleasure Diff'] == 0]\n",
        "\n",
        "print(\"Good Chills - Mean Intensity: \", good_chills['Chill Intensity'].mean())\n",
        "print(\"Bad Chills - Mean Intensity: \", bad_chills['Chill Intensity'].mean())\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OOyOgKUp7N2t",
        "outputId": "b0230b6b-4a29-480b-fd18-033942896dd2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Good Chills - Mean Intensity:  2.0\n",
            "Bad Chills - Mean Intensity:  1.6666666666666667\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "gx8C6tXnJvdZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Assuming 'df' is your DataFrame\n",
        "print(\"Dataset Overview:\")\n",
        "print(df.info())\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hsbo4iqgBsvb",
        "outputId": "4e422056-ea70-4be8-c491-c24ee28c1dcd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dataset Overview:\n",
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 80 entries, 0 to 79\n",
            "Data columns (total 31 columns):\n",
            " #   Column                  Non-Null Count  Dtype  \n",
            "---  ------                  --------------  -----  \n",
            " 0   Video ID                80 non-null     object \n",
            " 1   Age                     80 non-null     int64  \n",
            " 2   Gender                  80 non-null     object \n",
            " 3   Ethnicity               80 non-null     object \n",
            " 4   chills Binary           80 non-null     bool   \n",
            " 5   Chills #                80 non-null     int64  \n",
            " 6   Chill Intensity         80 non-null     int64  \n",
            " 7   Daily Chills            80 non-null     int64  \n",
            " 8   NIO_Openess             80 non-null     int64  \n",
            " 9   Prior exposure          80 non-null     object \n",
            " 10  Sleppiness              80 non-null     int64  \n",
            " 11  Pre-Pleasure            80 non-null     int64  \n",
            " 12  Prepleasure /2 arrondi  80 non-null     int64  \n",
            " 13  Prepleasure /2          80 non-null     float64\n",
            " 14  Post-Pleasure           80 non-null     int64  \n",
            " 15  Pre Calm                80 non-null     int64  \n",
            " 16  Pre-calm/2              80 non-null     float64\n",
            " 17  Pre-calm/2 (rounded)    80 non-null     int64  \n",
            " 18  Post Calm               80 non-null     int64  \n",
            " 19  Pre NEO                 80 non-null     int64  \n",
            " 20  Post NEO                80 non-null     int64  \n",
            " 21  Diff NEO                80 non-null     int64  \n",
            " 22  Total NEO               80 non-null     int64  \n",
            " 23  Emotion drift           80 non-null     float64\n",
            " 24  Direction               80 non-null     object \n",
            " 25  Tan inverse             80 non-null     float64\n",
            " 26  Calm Diff               80 non-null     float64\n",
            " 27  Pleasure Diff           80 non-null     float64\n",
            " 28  Tan inverse.1           80 non-null     float64\n",
            " 29  Chill Rating            80 non-null     object \n",
            " 30  colors                  80 non-null     object \n",
            "dtypes: bool(1), float64(7), int64(16), object(7)\n",
            "memory usage: 19.0+ KB\n",
            "None\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "x14brhbj4S6J",
        "outputId": "b94140ba-5511-4429-a80f-32acc49ab0db"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Video ID                  0\n",
              "Age                       0\n",
              "Gender                    0\n",
              "Ethnicity                 0\n",
              "chills Binary             0\n",
              "Chills #                  0\n",
              "Chill Intensity           0\n",
              "Daily Chills              0\n",
              "NIO_Openess               0\n",
              "Prior exposure            0\n",
              "Sleppiness                0\n",
              "Pre-Pleasure              0\n",
              "Prepleasure /2 arrondi    0\n",
              "Prepleasure /2            0\n",
              "Post-Pleasure             0\n",
              "Pre Calm                  0\n",
              "Pre-calm/2                0\n",
              "Pre-calm/2 (rounded)      0\n",
              "Post Calm                 0\n",
              "Pre NEO                   0\n",
              "Post NEO                  0\n",
              "Diff NEO                  0\n",
              "Total NEO                 0\n",
              "Emotion drift             0\n",
              "Direction                 0\n",
              "Tan inverse               0\n",
              "Calm Diff                 0\n",
              "Pleasure Diff             0\n",
              "Tan inverse.1             0\n",
              "Chill Rating              0\n",
              "colors                    0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 88
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import OneHotEncoder\n",
        "\n",
        "# Create your features matrix (X) by dropping all target variables at once\n",
        "X = df.drop('Pleasure Diff', axis=1)\n",
        "\n",
        "# Create your labels matrix (y) by selecting all target variables\n",
        "y = df['Pleasure Diff']\n",
        "# Create a OneHotEncoder object\n",
        "encoder = OneHotEncoder()\n",
        "\n",
        "# Fit the encoder and transform the categorical data\n",
        "X_categorical = encoder.fit_transform(X.select_dtypes(include=['object'])).toarray()\n",
        "\n",
        "# Combine the categorical and numerical data\n",
        "X_numerical = X.select_dtypes(include=['int64', 'float64']).values\n",
        "X_preprocessed = np.concatenate([X_numerical, X_categorical], axis=1)"
      ],
      "metadata": {
        "id": "J_VAUjMm6nxS"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "Ghq06BgJBBsk"
      }
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "lIf_yzg_-RLB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.linear_model import SGDRegressor\n",
        "\n",
        "# Split the preprocessed data into training and testing sets\n",
        "X_train_pre, X_test_pre, y_train, y_test = train_test_split(X_preprocessed, y, test_size=0.2, random_state=42, shuffle=True)\n",
        "\n",
        "# Train a stochastic gradient descent model\n",
        "model = SGDRegressor()\n",
        "\n",
        "# Fit the model to your preprocessed training data\n",
        "model.fit(X_train_pre, y_train)\n",
        "\n",
        "# Predict the target for the preprocessed testing set\n",
        "y_pred = model.predict(X_test_pre)\n",
        "print(y_test)\n",
        "print(y_pred)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GnkmDBPy5RqJ",
        "outputId": "97ac9ecc-3e47-476c-a08f-ced70327bdda"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "30    0.0\n",
            "0    -0.5\n",
            "22    0.0\n",
            "31   -1.0\n",
            "18    0.5\n",
            "28   -3.5\n",
            "10   -0.5\n",
            "70   -0.5\n",
            "4     0.0\n",
            "12   -1.0\n",
            "49   -2.0\n",
            "33   -1.0\n",
            "67   -0.5\n",
            "35   -1.0\n",
            "68   -0.5\n",
            "45   -0.5\n",
            "Name: Pleasure Diff, dtype: float64\n",
            "[-1.04336083e+12 -5.15138622e+11 -5.52899140e+11 -1.09161453e+12\n",
            " -3.28484490e+12 -1.42957521e+12 -2.05150070e+12 -1.72898542e+12\n",
            " -1.03245924e+12 -1.91858715e+12 -4.93494741e+11 -9.68937796e+11\n",
            " -2.97277130e+12 -1.16257034e+12 -3.05269076e+12 -1.39887295e+12]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Shape of X_preprocessed:\", X_preprocessed.shape)\n",
        "print(\"Shape of y:\", y.shape)\n",
        "print(\"Shape of X_train_pre:\", X_train_pre.shape)\n",
        "print(\"Shape of X_test_pre:\", X_test_pre.shape)\n",
        "print(\"Shape of y_train:\", y_train.shape)\n",
        "print(\"Shape of y_test:\", y_test.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JBEameY4BsCu",
        "outputId": "26ed58b1-9622-4edf-d444-a4cd8608df3f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Shape of X_preprocessed: (80, 91)\n",
            "Shape of y: (80,)\n",
            "Shape of X_train_pre: (64, 91)\n",
            "Shape of X_test_pre: (16, 91)\n",
            "Shape of y_train: (64,)\n",
            "Shape of y_test: (16,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "small_subset = X_test_pre[:5]\n",
        "small_pred = model.predict(small_subset)\n",
        "print(\"Shape of small_subset:\", small_subset.shape)\n",
        "print(\"Shape of small_pred:\", small_pred.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OQBKK9DSBzpz",
        "outputId": "ff342868-1d5d-4208-d91f-f98573c6b5ed"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Shape of small_subset: (5, 91)\n",
            "Shape of small_pred: (5,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = model.predict(X_test_pre)\n",
        "print(\"Shape of y_pred:\", y_pred.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6mu_at02CFhx",
        "outputId": "be4d33c3-e3bc-486d-9779-52da5649e436"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Shape of y_pred: (16,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(y.describe())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nOdFSWJKJeEQ",
        "outputId": "80979f52-2922-4f1b-ee29-b1376e29b2c8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "count    80.000000\n",
            "mean     -0.968750\n",
            "std       1.074279\n",
            "min      -4.000000\n",
            "25%      -2.000000\n",
            "50%      -1.000000\n",
            "75%      -0.500000\n",
            "max       1.500000\n",
            "Name: Pleasure Diff, dtype: float64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Shape of y_test:\", y_test.shape)\n",
        "print(\"Shape of y_pred:\", y_pred.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bRB4AIwiBD_G",
        "outputId": "d4c83a5e-c4a3-481c-90f5-31495e2b039d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Shape of y_test: (16,)\n",
            "Shape of y_pred: (16,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Extract the 'Pleasure Diff' values from y_test\n",
        "y_test_values = y_test.values\n",
        "\n",
        "print(y_test_values)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xYeE3kyvJ_uf",
        "outputId": "e81cfc39-2743-42b2-c76c-14a2c69f88bc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[ 0.  -0.5  0.  -1.   0.5 -3.5 -0.5 -0.5  0.  -1.  -2.  -1.  -0.5 -1.\n",
            " -0.5 -0.5]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Create an array for the sample number or index\n",
        "index = np.arange(len(y_test_values))\n",
        "\n",
        "# Scatter plot of the true values\n",
        "plt.scatter(index, y_test_values, color='hotpink', label='True Values')\n",
        "\n",
        "# Scatter plot of the predicted values\n",
        "plt.scatter(index, y_pred, color='limegreen', label='Predicted Values')\n",
        "\n",
        "plt.xlabel('Observation Index')\n",
        "plt.ylabel('Predicted/True Values')\n",
        "\n",
        "plt.title('True vs. Predicted Values')\n",
        "plt.legend()\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "MwMaMMwCmT9Y",
        "outputId": "1b8388bc-c6c6-4386-ca91-85120ef18744"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.scatter(index, y_test_values, color='hotpink', label='True Values')\n",
        "plt.xlabel('Observation Index')\n",
        "plt.ylabel('True Values')\n",
        "plt.title('True Values')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "52t3kW8YKYF9",
        "outputId": "90ac7ed9-7de6-46b4-a476-d6e341e71f41"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.scatter(index, y_pred, color='limegreen', label='True Values')\n",
        "plt.xlabel('Observation Index')\n",
        "plt.ylabel('Predicted Values')\n",
        "plt.title('Predicted Values')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "gyRrxt7aKkyt",
        "outputId": "28268ec5-1109-47db-b886-af1eeec25321"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(y_train.values)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1LLKHUppLR3y",
        "outputId": "8d598474-e5a5-412b-d9e4-f971e134b9eb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[ 1.5  0.5 -1.  -2.  -2.  -1.  -2.  -2.5  0.  -1.5 -0.5 -1.  -2.   0.\n",
            " -1.5  1.  -1.  -0.5 -0.5 -1.  -0.5 -2.  -1.  -4.  -1.  -1.5 -2.  -1.5\n",
            " -2.  -2.   0.  -2.  -3.5  1.5 -1.5  0.5 -2.   0.  -1.5 -2.   0.5  0.5\n",
            " -3.  -1.  -1.   0.5 -2.   0.  -2.  -1.  -0.5 -0.5 -1.  -0.5 -2.  -2.5\n",
            " -1.  -0.5 -1.   0.  -1.  -1.   0.5 -1. ]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "residuals = y_test_values - y_pred\n",
        "plt.scatter(y_pred, residuals, color='limegreen')\n",
        "plt.xlabel('Predicted Values')\n",
        "plt.ylabel('Residuals')\n",
        "plt.title('Residual Plot')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "iELkAE1g-35K",
        "outputId": "c72f34f7-5526-428e-e7c3-f9b823520034"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.hist(residuals, bins=20, color='hotpink')\n",
        "plt.xlabel('Residuals')\n",
        "plt.ylabel('Frequency')\n",
        "plt.title('Histogram of Residuals')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "4oc52l7r--x-",
        "outputId": "f4765fa9-d993-4c35-e794-b0ccc248c363"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "ijFeD75mKcTb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import mean_absolute_error, mean_squared_error\n",
        "import numpy as np\n",
        "\n",
        "# Calculate MAE, MSE, RMSE\n",
        "mae = mean_absolute_error(y_test_values, y_pred)\n",
        "mse = mean_squared_error(y_test_values, y_pred)\n",
        "rmse = np.sqrt(mse)\n",
        "\n",
        "print(f\"Mean Absolute Error (MAE): {mae}\")\n",
        "print(f\"Mean Squared Error (MSE): {mse}\")\n",
        "print(f\"Root Mean Squared Error (RMSE): {rmse}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kYNwRHKa_TVz",
        "outputId": "dd9847dd-e94d-4025-9dc3-4433ea8ed9ec"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean Absolute Error (MAE): 1543643976942.469\n",
            "Mean Squared Error (MSE): 3.142328625821149e+24\n",
            "Root Mean Squared Error (RMSE): 1772661452681.01\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import MinMaxScaler\n",
        "\n",
        "# Select relevant columns\n",
        "df_selected = df[['Pleasure Diff']]\n",
        "\n",
        "# Fill any missing values\n",
        "df_selected.fillna(method='ffill', inplace=True)\n",
        "\n",
        "# Normalize the data to be between 0 and 1\n",
        "scaler = MinMaxScaler(feature_range=(0, 1))\n",
        "df_scaled = scaler.fit_transform(df_selected)\n",
        "\n",
        "# Convert the DataFrame to a 3D array for LSTM\n",
        "# The shape should be (number of samples, 1, number of features)\n",
        "df_reshaped = np.reshape(df_scaled, (df_scaled.shape[0], 1, df_scaled.shape[1]))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cX1W1reLE5B7",
        "outputId": "cb350480-faa9-451e-e51f-b6120b8c944b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-104-6548cf1f7f5a>:7: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df_selected.fillna(method='ffill', inplace=True)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "from keras.models import Sequential\n",
        "from keras.layers import LSTM, Dense\n",
        "\n",
        "# Define the LSTM model\n",
        "model = Sequential()\n",
        "model.add(LSTM(50, return_sequences=True, input_shape=(df_reshaped.shape[1], df_reshaped.shape[2])))\n",
        "model.add(LSTM(50, return_sequences=True))\n",
        "model.add(LSTM(50))\n",
        "model.add(Dense(1))\n",
        "\n",
        "# Compile the model\n",
        "model.compile(loss='mae', optimizer='adam', metrics='accuracy')\n",
        "\n",
        "# Train the model\n",
        "history = model.fit(df_reshaped, df['Pleasure Diff'], epochs=50, batch_size=72, validation_split=0.2, verbose=0, shuffle=False)"
      ],
      "metadata": {
        "id": "EHWUujQYFPe9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import MinMaxScaler\n",
        "\n",
        "# Create a MinMaxScaler for the target variable\n",
        "scaler_target = MinMaxScaler(feature_range=(0, 1))\n",
        "df_target_scaled = scaler_target.fit_transform(df[['Pleasure Diff']])\n",
        "\n",
        "# Train the model\n",
        "history = model.fit(df_reshaped, df_target_scaled, epochs=50, batch_size=72, validation_split=0.2, verbose=0, shuffle=False)\n",
        "\n",
        "# Make predictions\n",
        "predictions = model.predict(df_reshaped)\n",
        "\n",
        "# Invert scaling for the predictions\n",
        "predictions_rescaled = scaler_target.inverse_transform(predictions)\n",
        "\n",
        "# Plot the actual values vs the predictions\n",
        "plt.plot(df['Pleasure Diff'].values, label='Actual values')\n",
        "plt.plot(predictions_rescaled, label='Predictions',color='hotpink')\n",
        "plt.title('Pleasure Difference')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "eFcZveX0BNBk",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 507
        },
        "outputId": "afdbd8f8-c5e0-4c3b-88a7-e7111556d575"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:5 out of the last 13 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7da3b66a5ab0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "3/3 [==============================] - 1s 6ms/step\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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+MkQIgiAIogIhsSpBEARBECWDDBGCIAiCIEoGGSIEQRAEQZSMEaERMcLj8aCxsRF1dXXweDyl3h2izJEkCfv370c0Gi31rhAEQRAY4YbImDFj8JWvfAVz584t9a4QI4hkMolbbrkFH3zwQal3hSAIYtTjASCVeif0qKurQyQSQX19fV7WjN/vx29+8xsMDAzgwQcfxL59+5BOp0u0p8RIwe/34+yzz8a8efPw9a9/nTwjBEEQRcBo/lYzYj0iEyZMQDgcxi9+8Qts2LCh1LtDjCD+/ve/49BDD8WYMWOwffv2Uu8OQRDEqGbEilW93uyux+PxEu8JMdJIpVIAQJoigiCIMmDEGiIEQRAEQYx8yBAhCIIgCKJkkCFC5CFJEs4888yi/kZHRweuueaaov4GQRAEUf6QIVJCPv7xjyOVSuHxxx+3/V2ayAmCIIhKgAyREnLZZZfh9ttvxwknnIAJEyaUencIgiDyOPY/z8WUBfNLvRsVReO4sTjxkotQVV9X6l0pCyrOEAlWhUvysktNTQ0uuOAC/Pa3v8UTTzyBL3/5y3mfOf300/HGG28gGo1i//79eOSRRwAAL7zwAqZOnYpbb70VkiRBkrKlYG688Ua88847im1cc8016Ojo4P8+8sgj8fTTT2P//v3o6+vDiy++iMMOO8zyfn/lK19BZ2dnXsbJo48+irvvvhsAMH36dDz66KPYs2cP+vv78cYbb2Dx4sW625wyZQokScKCBQv4ew0NDZAkCYsWLeLvHXzwwXjyySfR39+PPXv24L777kNLSwv/+7nnnov3338fQ0ND6OrqwjPPPIPq6mrLx0YQhJL2ubNxzg3X4XPf+3apd6Wi+OQlF+H0b34dR55xaql3pSwYsXVEtAhWhXHTGy+U5LeXHH0iEtGY5c+ff/75WLduHTZs2IA//elPuPXWW3HTTTfxv5966qn4+9//jp/85Cf40pe+hGAwiFNPzd6055xzDt577z384Q9/wJ133mlrP+vq6nDvvffi6quvhsfjwbXXXosnn3wSs2bNwsDAgOn3//a3v+H222/HiSeeiOeffx4A0NTUhFNOOYXvX21tLZ588knccMMNiMfj+NKXvoTHHnsMc+bMwY4dO2ztL6OhoQHPP/887rrrLnzjG99AVVUVfvazn+HBBx/E4sWLMX78ePzlL3/Bt7/9bfz9739HXV0djj/+eErRJYgCqGlqAABaubtMVV32fNY0NpR4T8qDijJERhKXXXYZ/vSnPwEAnnrqKTQ0NGDRokV46aWXAAA33HADHnjgASxdupR/5/333wcA9Pb2Ip1Oo7+/H3v37rX1uy+8oDTUvvrVr6Kvrw+LFi3CE088Yfr9vr4+rFixAp///Oe5IfK5z30OXV1dfNvvv/8+31cA+P73v4+zzz4bZ5xxBu644w5b+8v4+te/jnfeeQc33HADf+/SSy/Fzp07MWvWLNTW1iIQCOCRRx7hRcqohDtBFEYgnPX2+oPBEu9JZeHz+wAAgXCoxHtSHlSUIZKIxrDk6BNL9ttWmT17No4++micffbZAIB0Oo2//vWvuOyyy7ghsnDhQtveDiuMHTsWP/7xj/HJT34SY8eOhc/nQ3V1NSZPnmx5G/fffz/uvPNOXHXVVUgkErjooovwwAMP8BBRTU0Nli5ditNOOw0TJkyA3+9HVVWVrd9Qs2DBApx44omapYJnzJiBp59+Gs8++yzWrFmDlStX4umnn8ZDDz2Evr4+x79JEKOdQCg7UfoDgRLvSWXh9WenXnZ+RzsVZYgA9gyCUnHZZZchEAhg165d/D2Px4N4PI6vf/3riEQijnqgZDKZvFBEQDWA3HvvvWhpacE111yDbdu2IR6PY9WqVQjaWPE89thj8Hg8OO200/Dmm2/i+OOPxze+8Q3+91/84hc46aSTcN1112HTpk2IRqN46KGHdH8jk8kAUFY6Ve93bW0tHnvsMXznO9/J+/7u3buRyWRw0kkn4ROf+AROPvlkXH311fjJT36Cj33sY9i6davlYyMIQiZIHpGiwDwiTvSFlUjFiVXLHZ/Phy996Uv45je/iYULF/LXggULsGvXLlx44YUAsuENI4FnIpGAz+dTvLd//36MHz9e8d7ChQsV/z722GNx2223YcWKFfjoo48Qj8cxZswYW8cQj8fxyCOP4KKLLsKFF16I9evXK0Syxx57LJYvX45HH30UH3zwAfbs2YOpU6fqbm///v0AoMgcUu/322+/jYMPPhhbt27F5s2bFa+hoSH+uVdffRVLly7FYYcdhkQiwb1OBEHYJ5CbKH1B8oi4CfeIhMkQAcgQGXZOP/10NDU14e6778aHH36oeD388MO47LLLAAA/+MEPcOGFF2Lp0qWYO3cu5s+fj29/W1aub926FSeccALa2tp45siLL76IMWPG4Nvf/jamT5+Oq666Cv/xH/+h+P2NGzfii1/8IubOnYujjz4a999/v2Iit8r999+P0047DZdeeinuv//+vN8455xzsGDBAhx66KH485//zHsDaRGLxbBq1Spcf/31mDt3Lk444QT8+Mc/VnzmjjvuQHNzM/7yl7/gyCOPxPTp03HyySfjnnvugdfrxdFHH40lS5bgiCOOwKRJk3DOOedgzJgxWLt2re1jIwgiCwsdeL1eeP0+k08TVvFRaEYBGSLDzGWXXYZnn30WkUgk728PP/wwjjrqKBxyyCF46aWXcN555+GMM87Au+++i+effx5HH300/+z3v/99TJ06FZs3b0ZXVxcAYN26dbjqqqvwta99De+99x6OPvpo/OIXv8j7/aamJrz99tv44x//iNtuuw379u2zfRzPP/88enp6MHfuXPz5z39W/O2b3/wment78eqrr+Kxxx7DypUr8fbbbxtu79JLL4Xf78fq1atx66234n//938Vf9+9ezeOPfZY+Hw+PP3001izZg1uvfVW9PX1IZPJIBKJ4IQTTsCTTz6JDRs24Mc//jGuvfZaPPXUU7aPjSCILEFBTOkPUHjGLZghEiSPCEcq11ddXZ0kSZJUV1eX97cpU6ZI9913nzRlypSS7ye9RtaL7h160cva67RvXCX9cs0q6ZdrVknVDfUl359KeX393t9Jv1yzSrr6T38o+b4U62U0f6tf5BEhCIIgNBFDByRYdQ/KmlFChghBEAShiRg68AUqLsmyZFBoRgkZIgRBEIQmASG9lDwi7sGEvwFK3wVAhghBEAShgzI0Qym8bkFZM0rIECEIgiA0oayZ4kChGSVkiBAEQRCaiAW3yCPiHl6h1ww15iRDhCAIgtBBbMpGGhH3YB4RAPCH6LySIUIQBEFoInpEqMy7e4iGCIVnyBAhCIIgdBAnSdKIuIdYLp/6zZAhUtEsW7YMf//73/m/X3jhBdxyyy0FbdONbRAEMTJQhmbII+IWokdEPMejFTJESsCyZcsgSRIkSUI8HsfGjRvxve99L6+brtucc845+N73vmfps4sWLYIkSWhoaHC8DYIgRjakESkOFJpRQqXySsSKFStwySWXIBQK4dRTT8Udd9yBZDKJm2++WfG5QCCAZDLpym/29vaWxTYIghgZBClrpih4FR4RMkQqziNS7Q+V5GWXeDyOvXv3Yvv27fjd736HZ599FmeccQYPp3z3u99FZ2cn1q9fDwCYOHEi/vrXv6K3txfd3d149NFHMWXKFL49r9eLX/7yl+jt7UVXVxd+9rOf5aWFqcMqwWAQN998M7Zv345YLIaNGzfi0ksvxZQpU/Diiy8CAPr6+iBJEpYtW6a5jcbGRtx7773o6enB4OAgnnzyScycOZP//eKLL0Zvby9OPvlkfPTRR+jv78eKFSswfvx4/plFixbh9ddfx8DAAHp7e/Hyyy9j8uTJts8pQRDu4fP74RW8tGSIuIPH64XXK0+9FJqpMI9ItT+Ewa88XJLfrrnzXAyl4o6/H41G0dLSAgBYvHgxIpEITjrpJACA3+/HypUrsWrVKhx//PFIpVL43//9Xzz11FM49NBDkUwmce211+LLX/4yLr30UqxduxbXXnstzj77bDz//PO6v3nffffhmGOOwX//93/jvffew7Rp09Da2oodO3bgnHPOwSOPPILZs2cjEokgGo1qbmP58uWYNWsWzjjjDEQiEfzsZz/Dk08+iYMOOgipVAoAUF1djeuuuw5f/OIXkclk8Kc//Qm/+MUv8IUvfAE+nw+PPvoo7rzzTlx44YUIBoM4+uijIUmS43NJEEThqMuP+/xkiLiBGJYBlEXjRisVZYiMVBYvXozPfOYzuP322zFmzBgMDg7i8ssv5yGZiy66CF6vF5dffjn/ziWXXIK+vj588pOfxDPPPIP/+Z//wU033cTFqVdccQU+85nP6P7mrFmzcMEFF+DTn/40nnvuOQBAR0cH/3tPTw8AYN++fThw4IDmNmbOnIkzzzwTn/jEJ7Bq1Sq+rzt27MBZZ52Fhx56CEDW83LFFVdgy5YtAIBf//rX+P73vw8AqK+vR2NjIx5//HH+93Xr1tk8gwRBuI1au0AaEXfwqrSAFJqpMENkKBVHzZ3nluy37XD66aejv78fgUAAXq8Xf/7zn7F06VLccccdWLNmjUIXsmDBAsycORP9/f2KbYTDYcyYMQOvv/462tra8Prrr/O/pdNpvPXWW7pV+xYuXIhUKoWXXnrJ1n6LzJs3D8lkUvG7PT09WL9+PebNm8ffGxwc5EYGAOzevRtjx44FkNWcLFu2DCtXrsQzzzyDZ599Fg8++CD27NnjeL8IgigcdR8UCs24g7qLMYVmKswQAewbBKXihRdewJVXXolEIoFdu3YhnU7zvw0ODio+W1tbi9WrV+Oiiy7K287+/fsd/b5eqKUYqMW2kiQpYqSXXnopbrvtNpxyyim44IIL8OMf/xgnnXSSwsAhCGJ4UYdmyBBxh/zQDHlEKk6sOlIYHBzE5s2bsWPHDoURosXbb7+NWbNmYd++fdi8ebPiFYlEEIlEsGvXLnzsYx/j3/H5fDjiiCN0t7lmzRp4vV4sWrRI8++JRIJvR4+1a9ciEAgofre5uRlz5szBRx99ZHhMat59913cfPPNOPbYY/HBBx/g85//vK3vEwThLmrtgo9CM67g9as9ImSIFNUQOf744/HPf/4TnZ2dkCQJZ555ZjF/rmK5//770dXVhX/84x847rjjMHXqVCxatAj/93//h/b2dgDA//3f/+H666/HmWeeiTlz5uA3v/kNGhsbdbe5bds23Hvvvbjnnntw5pln8m2ed955/O+ZTAann346WltbUVNTk7eNTZs2caHpsccei0MPPRR/+tOf0NnZiX/84x+Wjm3q1Kn46U9/io9//OOYPHkyTjrpJMyaNQtr1661f6IIgnCNvNBMgDwibuDzqzUiFJopqiFSU1OD9957D1/72teK+TMVTzQaxQknnIDt27fjkUcewdq1a3H33XcjHA4jEokAAH75y1/ij3/8I+69916sWrUK/f39iqqqWlx55ZV46KGH8Jvf/Abr1q3DnXfeyQ2OXbt24cYbb8TNN9+MvXv34te//rXmNi655BKsXr0ajz/+OFatWgWPx4NTTz2VZ8yYMTQ0hLlz5+Lhhx/Ghg0b8Ic//AF33HEHfv/739s4QwRBuE1+aIY8Im5AWTPaSMPxkiRJOvPMM219p66uTpIkSaqrq8v725QpU6T77rtPmjJlyrDsP70q50X3Dr3oZf469KQTpV+uWcVfn7/pxpLvUyW8xk2fqjivZ13/jZLtS8O4MdKxF35OmnzIQa5v22j+Vr/KSiMSDAZRV1eneBEEQRDDj1q7QB4Rd/CqQzOh0nlEph9xGM757rU441vXlGwfgDITqy5ZsoSLLyORCDo7O0u9SwRBEKMStXaBNCLukBeaqSqdWLV97mwAwK71G0u2D0CZGSI33XQT6uvr+YsJMQmCIIjhJb+gGRkiblBOWTPtc2cBADrXri/ZPgBlVkckkUjwtFGCIAiidDCPSGxgEOHaGvjII+IKao9IKUMzbXNyhsg68og4gvUi8fvLypYiRgCsNgr1syEIfZghMpTLzCONiDvkGSJVpTFEGsaNQW1zE9KpFPZs2mL+hSJS1Fm8pqZG0Yl12rRpWLBgAXp6erBjx46Ctt3d3Q0AmDt3LjZv3lzQtojRBSsvz1KfCYLIh4VmYv0DAAAfhWZcoVwqq7bNyepD9nVsQ6rEkYiiGiJHHnkkbycPgLePX758OS655JKCtj04OIgXX3wR559/PoBsozSrtSuI0UsoFML555+PdevW6TbzIwhC1i4MRbI9rkis6g55Te9KFJppn5c1RDrXbSjJ74sU1RB56aWXdJuuucGyZcsAABdccEHRfoOoPGKxGG666SYKzRCEAWyCjDJDhEIzrsCa3qVTKfj8/pKJVdu5PqTCDZFiI0kS7rnnHjzwwANobW0tqtFDVAbpdBp79uwh7xlBmMDSSqP9zBAhj4gbsKyZ+OAQqhvqS5a+25bLmNlVYqEqMMINEcbQ0BC2b99e6t0gCIKoGJhYNZrTiJBHxB2YRiTaP4DqhvqShGbCtTVonTQRQOkzZoARnDVDEARBFI+80AxpRFzBxz0igwBK0/SOpe327NqNaBmI9skQIQiCIPJg2RykEXEX1n03OpDLRvL78zJpik25VFRlkCFCEARB5MG67zJDhNJ33cHry3lEBob4e8PtFWnjFVVLL1QFyBAhCIIgNGChGZa+6/V68xq2EfbxBbLnMBGNIpNOAxj+Mu/tc8gjQhAEQZQ5ckGzfv6eP0DhmUJhWTPpVAqJWAzA8BY18/n9GDdzGgDyiBAEQRBlDCs9zjwiAKjfjAv4BEMkGYsDkMNgw8G4GdPgDwQwFImgd/eeYftdI8gQIQiCIPJgoZn44BAPIVAtkcLRNESGMYWXVVQth/ohDDJECIIgCAUer5dPjslYDKlEEgAZIm7ADJFMKi2EZobPEGkro4qqDDJECIIgCAXiCj0RiyGVzDZFoxTewuEakWQKyfjwh2ZY6m45FDJjkCFCEARBKBDTSVPxBHlEXMSXa3qXSaeRjGY9IsMVmvF4POQRIQiCIMoflsWRjMUhSRLSyZwhQlkzBeMNiBqR4c2aaWqfgKq6WqQSCezr2Dosv2kFMkQIgiAIBcwjwjQM5BFxD4VYdZhDMywss3vTFmRS6WH5TSuQIUIQBEEoYIYImyhTCdKIuIVPUUdkeLNmeGn3MqkfwiBDhCAIglDAQzM5DUMqF5qhMu+FI2fNpPj5Ha7QDNeHlElFVQYZIgRBEIQCVnKchWbSCdKIuAUrk6/Mmhkmj0iuhki5VFRlkCFCEARBKOChmZg6NEMekUIRs2aYoTccoZmapkY0jhsLANi9YVPRf88OZIgQBEEQCsSsGUAUq5JHpFDEXjPDGZppz3Xc3b9tB+JDQyafHl7IECEIgiAUqEMzrKCZL5d6SjjHF9DImhkGQ6RtDitkVl5hGYAMEYIgCEKFHJpRp++SR6RQRLEqz5oZhhLvzCNSTj1mGGSIjEJqGhsK3kZ1Q70LezL827ZDVX09PB5PqXeDKGMq9R4J6qXvOuy+G6yqIiMmh69EoZk2Xtp9fdF/yy5kiIwyDj35U/jhv5/CcZ8/z/E2Js0/CD946Ul89tqrXdyzLEeecSp+9PJKHH32Z13fth1ap0zCD158Ahf86IaS7gdRvoydNgU/ePEJnHfj9aXeFdfhoZmoKmvGgTHhD4Ww5Mm/4b/vv9O9HRzByBoRQaxaZI+IPxTC2KmTAZRXjxkGGSKjjKkL5gMAJs2f53gbE2ZOh9fn48Vx3IRtc9LBc13fth0mz58HX8BflGMkKoPxM6fDF/CjbV7l3SN5WTNJ55VVG8ePRX1rC9rnzobHS1MOz5pRaESKa4jUNjXC6/MhlUigv6u7qL/lBLorRhl1rS0AgKq6Osfb8OXcs8XIfWfbrKqrdX3bdqhryZ6n4RCRESMTfyjrHRiuYlTDCc+aiRduiIhjTXAYu8yWK6JHZLh6zVTVZ8fToUh/UX/HKWSIjDLquSHifKL3BbIWfTFy39k2w/XODSU3qB/TCmB4RGTEyCSQC1MMV3nu4SQ/NJPLmnEQmhHHmlB1tQt7N7JRlHiPDk/WDDMGY/0DRf0dp5AhMsrgHpECJnpm0RfDimfbLLlHpLUZQGWudgl38OcMkEo0VnWzZhyIVcWxJlhd5cLejWxY+m42NDM8GhE2nkbJI0KUA2yCLWSi9/PQjPuTNNtmIaEjN2AGWyVOMoQ7MI9IJYYb8rNmnItVlR4RMkQUWTM5DU6xFzzh3HgaJY8IUWr8wSCq67OpseECDJFiekSYm7vkHpGWrMEWCIVIYEdowjQilagjyitoVkCJdwrNKOG9ZlIpHvoaNo9IP3lEiBLDJlcACNfU8AfCLtwjUoTYOA/NlFojkvOIAEAgRPUPiHyYIeL1ermAu1Jgz7a6+64jj0i9XBeIQjOAl2fNpOWsmVCoqPVoquvJI0KUCXVjWhT/rqp15nXw5QyYQNj9h4etDAKhUMkKIPn8ftQ0NQr7VHkrXqJwAkHZEK+08AwLkbpR0Ez0iITJI6IKzcT4+8X0ijDxf4w8IkSpEVf5gBw3tAsLzQDuPzzigF6q8IzoOQJIsEpo4xc8ZZVmrAZ5aEapEXHi+RGf4yAZIpoaEaC49xC7BpS+S5QcVhuD4XSiF1dFbodnxO2VKjxTpzLYSLBKaBEQPHaVlsLLs2ZU6btONCJiKj6JVZWGiCRJivBMsagisSpRLrCMGYbTiV7pEXHXihe3V4igthDIECGsIHpEgkUo7ldKuEYkr6BZYVkzpBGRx89MKgVArl5bzPAeuwYx8ogQpUY9wTr1iPgEQ8Tth0cMg5QqhVdtsFFohtBCXMFWXGimSjtrxldgZVXKmpHriKRzhshw9JshjwhRNtSrxapODZGA4BFx0Z3o9fsU2y6VRoRVVWWQR4TQQqERqbjQTE6sykIzyeykWahYlUIz8jlMqzwigVDxjNkwpe8S5QLTiMQGBgE49zj4ihSaUQ/mJfOIqMSqlbbaJdxBkTVTQfeILxCAN1c7Jy9rxlFohiqrMsSaRJlUGoBcvbaY4T3Wa4Y8IkTJYSGHfR3bADjXiIjKeTcfHvVgXi5i1UqaZAj3UHhEKih9Vwy3JtQl3m2GZvzBoMKjONpDM+IiLj80U5x7yOPxIFxLHhGiTGDpu3u3bAVQgCHiL05oRh0CYVb8cMPOUyaTAVB5bnfCHSo1NMMmxHQyxVftqaQzj4g6vDraPSJKQ4R5RIqbNROqreEermiEPCJECamqr+ODCPeIuCBWdTU0o/aIlFisGtnfBaCyVruEe4jpu5XkNVNnzADOm96pFzuhqtFtiIgZh3lZM0W6h9g4n4jGkM5lP5UbZIiMEtgqf+hABAPdPQCcp8eKglI3H5680EypxKqtWbFq985OAHIDMIIQUYZmKuceYeHWhFD106lGRD3GhGpGeWgmILfVyKSzHhEeminSgkfOmCnPsAxAhsiogekeIl3d/IZ0R6xaxNBMCQyRcG0N34+enbuz+1VBbnfCPUSxaiXdIzxjRmGI5Cqr2tSIqMcY0ohkx86U4JkodmhGbnhXnmEZgAyRUQMzRPq7uvkNWe6hGacl6AuBnafYwCAGe/sAUGiG0EZR0KwSQzOx/NCM1+vlTduswMYY9iyNdo2I2PCOwbNmiuR5reJ9ZsgQIUoM0z30d/cU7hERs2ZcfHjYtphItBQeEe452t/FY+SVNMkQ7iGGKSrJWJX7zMgekXROrArYy5xhY0zf3n0AqI6IWN6dUfzQTHlnzABkiIwa6lvkCZZ5RNzQiBQjNDPQ0wugNOm79S2ywZbIFXOqJLc74R6BSs2aYZ13NTwiAOALWNeJsGf4wN79ALKhmWK2uy93fKry7gCK3msmXOZVVQEyREYNdWNYaKaHp3CFqqsUYRarFDs0E9mXzVYphSFSl6uq2t/VzV2mlbTaJdzB6/cpQhSV5DXTCs1k0mkurrTlEcml4DOPCDC6CwR6NTwiyWhxPa/VuXE0WqZ9ZgAyREYN9YJGJDYgW8ZOJvtiZ82wtFl/IDDs5dXrWepuV7cQmqmc1S7hDqJQFaisNgBaoRnAWVEzFprp39/FQ66jOTxTitBMmMSqRLnAxard3ZAymYLCM8XOmhno7eWrr+GuJSKLenvkAaKC3O6EO4hCVaC4nVOHG5aKnFQZImkHHXiZPmEo0o/EUBQAEBzFmTPqhneAfJ6LlzVDHhGiTGD9UyJd2RoihQhWRbFqMUIziWis4Mwep7B+PP1dXbzh12h2JRPaBFSTcSUZq1qhGUBOObXnEcmtxiP9iA8OARjdHhHtrJniel7lPjOj3BC56qqr0NHRgWg0itdeew1HHXXUcPwskcPr96G2uQlA1kUKyKlcTiZ60SPibtaM3PGz0Mwep/Cqql09soisgla7hDuoPSKVZKzqh2ZyRc1siFVFoWR8KGeIjOKiZoahmaJVViWxKs4//3z86le/wg9+8AMcfvjheO+997By5UqMGTOm2D9N5GDekHQyhaEDEQDyTelEI+L1yyI9dz0icmlpHjoa5n4z9YJYNVHk0svEyMWv8oBUVmgmP2sGcKoRkVfj8Wg2NDOaPSJy1kx+HZFi6YzYNYiNZo/IN7/5Tdx5551Yvnw51q5diyuuuAJDQ0O49NJLi/3TRA4ebujpgSRJAMTQjP2J3q8IzbivEUnEYohFmMdm+DwiXp8PNU2NAHJZM5S+S+igvicq6R7R6jUDOCvzzhY60f4B0ohAJ2uGVVYt0oKHi1XLtOEdUGRDJBAI4IgjjsCzzz7L35MkCc8++yyOOeaYvM8Hg0HU1dUpXiONiQfNwQ1PPYLD/uMkS5+/9Lb/h6/89pai5taLVVUZ0Yjz0Iy3SOm7QaG09FAk67kx2j+v34ev3/s7XPiT71va/jHnnY0bnnoEY6dN0fx7TVMjvF4vMuk0Bnr75NCMQ2PLFwjgfx64B+fccJ2j7zMu/80vcdkdvyhoG1qEqqtx3SN/whnf+m/Xt63HUWeeiu+ueBjjZ04ftt8sBoG80EzlGCJiiFSEaUTslHnnVT0j/XJohjwiqtCMsec1WBXGtQ/dhzO/8z+OfnPU95ppbW2F3+/H3r17Fe/v3bsX48ePz/v8kiVLEIlE+Kuzs7OYu1cU5nzi42hun4D5ixeZfjZUXY2DTzwec4/7OGpz4ZNiwFJS+3NCVUDwiNTX29qW1+fjLaUBd8MWokguZiF0NGbyJEw7fIFlo++QxSeguX0CDln8Sc2/sxTngZ5eSJkMj906dbuPmz4Vkw6eZ3n/tKhtacK84z+Bg044lntr3GLS/HmYMGsGDj/tM65u14hDT/4UWia2YdbHjhy23ywGzCvAit5VUvhO9EyKpHkHXmsekUA4xL2nokdkVBsiRlkzOsbsxIPmom3OLBz52f+w/Xser5d6zdjlpptuQn19PX+1t7eXepdswy66lclL7LtQnys4VgzEsuUMlspl1yPiVRVAczU0U8VEcnEha0bfEGF6Dl/Ar9Ct6G4/N1m0zZ2l+fc6lcFWaGiGDbg+m63TRVgnYMD9e4Rtr66l2dL5cwN2PYMjvB0884iw56iSPCKiVktEFqtaK4LIrnUmnUZ8aAhxFpqpGr2hGV/uOWPlCQAxNKN9D7EFUnVDvf3ux7Xy+D5qPSJdXV1IpVIYN26c4v1x48Zhz549eZ9PJBLo7+9XvEYaTFxpRRkufoYZC8WACzC7RY+Is9CMTzVhuTkAi6EZKxoWZjgA1owFZui0z52t+Xc26Ue6syEsNhB7fT5HxgS7vnbEfWrE+4JpfdxC3F5tc/E8ciLseo70zAkmVmUhRH8waKsZXDmjG5rhHXitTYbqlTiFZgCvTz9rRs+rphwD7D2nLHU3PjSkEMiWG0U1RJLJJFavXo3Fixfz9zweDxYvXoxVq1YV86dLBlsFhCxY/eIDWe/yJCPCbl5RI8IU1GGbWTN+1YRcrNCMlawecSK1YhCx7Y+ZMkmzHblaSyO6pp2EZ5goz+f3w+N19qjVC8aW28aquD3xd4oJfz5G+GTEVqYsCw2oHMEqr+eTV0eEiVWtGdbqQlqyWHVkX/tC4B6RZH5oxuvzabbcUBgiNp/TkRCWAYYhNPOrX/0KX/nKV/ClL30Jc+fOxW9/+1vU1NRg2bJlxf7pksBXfBYeNnEyLKZHhIdmRLGqQ4+IOjSj9/A4QYxNW/OIyOfMikEkfmbC7Jka21OGZjKpNNK5AcPJJCPeA069IsU0FsRBzW1vix5MwT/SJyMemhG8tpUSnuGhGd0S79Y8ImFVIa0414iMbG9YIRhlzQDaNYvqCliMjISqqgDgzgxiwIMPPogxY8bghz/8IcaPH493330Xp5xyCvbt22f+5REIj4FbGGjFz9i1dO2gnmABUSPizCOSSae5KzpQFUbaBYs7WCWEZiLmJegVoRkLhog4UbTPm42t776v2l6+liYZj8MX8DvKDlIaIsG8ugxWEA0Et43V+tbibVsLn9/Pz8lIn4zYZJyMxZGIxhCsCleMISKHSJX3Ky/xbjFMqS6kRaEZ7ayZdCqFdCoFn9+PYDjMhfqMugKeU7mGSHl7RIpuiADAHXfcgTvuuGM4fqrkyB4RK6EZ+TNMx1EM5LLl7nlEErEYAqGQ7sPjBOZ1SERjlgwl8ZxZ0oiIhsicfMEqN9gELU0iFkO4tgbBKvuTjCjKszp4qxEFqvXFDM0UUSzNEI3KSvGIpBIJJGNZQ6RSMmf0smbsekTE8u4AkKDKqpqGCJA1+ny1fs1xrL7V+RgwEqqqAmWWNVMJME2DlYE2JGQO2BUhWSVUU81XIP3doiHizCPCYpzpZEqus+FSbJx5HbKVVS2EZoRzZilLSZgo2ublC1aZWFU02AppSKX2iDihkNWQGcPtERGvZWiEZ834edGvRMH1ZsoNfUPEpkaE1RDhHhHSiLD0XTFrBoBhO4lCxgB1eKxcIUPEZdjE7vV6TQcmZWimOBMB225sYJDXPABkCzkQDtmaJFn2SDqVktPOXCpvHRRKS1sRq4oTqVnoxBcIKLIaJsycnpeyKveZEQ0R51UPxevrs5jyqEY0tty8R3x+v6IuSbEMYRGxZk2lhGZS8QR/riql3wwz2FMFNr3LD83kNCKjOH2XjUFqj4hcj0Y5Z3i8XtQKz6ldnVh17pkjj8goIlgVVkw4ZoOtUqxanImgvjU/LAMA8YFBZDIZAPbCM9y1mEzqPjxOEHt3JKIxntXj8/s1a06oJ1Izj4VoFMaHhuAPBjF22lT+XrCqCuGaGgDKc2WWWmeEeH3d8Yi4d4/UtjQp/u122EeLqooMzcSF7qkj3xDxeL1ysba8gmZZj4j99F1VaGaEX/tC0Oo1A+h7XmubGhULKLuicvU1KFfIEHGRsCrMYTbYin8P19QUpcgTF2B2Kw0RSZIQG7Df+E6sDFhI2EKNaMwk41kBIMtYqdJofKdewZtpONgkkU6msPOj9QCU9UTY9uJDUd6uHDAvNmREoVkzgXBIMXlX19svaKSHekAbltCMcJ+N9MlIFKtWUmhGfJbz0ncTNsWq9doekZFuhBYCz5pJqjQiOqEZ9XNpOzTDDJEDZIiMGtSeBbPBVv33YkwGcg2Rnry/WclMUcNCM5lU2jCuaRfuDk4kIOU8NUY6FvW5MnOLi/qTznUbAADtQoVVXkNEZbC5FZqx0zpdvU/iZOeWV4QJfVlBruHWiIz0yYjXvEkkit7GfTgRtVapvMqqTsWq2XuM0nf1xap6nlcmImfPqV1ROYlVRyHqCdMsFqpWjxejqBSbcMSUVEbMQhl1NexBSiWTBYUt1Pg1BHJGmT15hojF0EwiFsOu9RsBqDwiGinO4v44Cs0I19dOozAGK3IX6erm++WWwcCOd9e67LkIVVcVfYIQr2O4pqaojR6LjT8ka0SSLoYoSw1/TlRVVQH7YlV111cWmhnpRmgh6GbNRLWF/+x5Z8+pPxi05cGm0MwoJM8jYpKmph74i+IR0ZlgAVjKTFGjCM3oPDxO0KpdwNL+1CEvIN8zYJY1ExSKNHWuzXpExJ4zvAx+l9ojYtyQygjREHUSUpHTibv5frml5WD3Wtf2nYgNDip+r1ior+NI9iDw0EyFeUTk6sYahgjrvuu4jkjWI2JFyF+p2M2aYSHUnl27Ze+lDWG5OnOpXCFDxEXUWgY7GhGgWIaIdsgBgKXGcmpEsWoxQjMKQ4R3CLbgEbEamonFsXdzB1LJJKrr69E0YXxue/k1RMT9KYVGRC6w1s0zedyqgMoMmmJ4W/SwG7osZwKiR6SCNCI8c00VlgHse0SquUYk+xyLxs1oDc/oZc0kdTyv4kIysj83Bth4TskjMgoR0xMBC1kzOXEqC5sUY0Var1HMjGE00eshuhb1Hh4naNUuMDKU1J4BO6GZdCqFvZs6AGQrrAJCGEQVwiokNBMssI6I2PuGXT+37hGx/5C87SIbIvVqMffInYzY/ZaKV1bWDO8zoxmaYWJViyXeVX1OJEni3rfRaojIWTPa6bvqcaxeYwyw4xVVh8fKFTJEXMS+WDX7MHZt3wlA2fLdLQxDM6x6qa2smZxYNZnWfXicEDD0iOiLVZkHwzw0o9x+5/pceCZXYVXd8I5RmEeksMqq9cIx8kHIpQq8YiiKncNip/Cqr+NI9ogwjUgyUVl1RMTGk2rSNjwiwaoqPunGhNX4aG985xUKQorI3mWVRmSMYIh02/Ncev0+XpKAPCKjCPXK3WpohhkibntEPF4vapuz9SK0xKqFhGZSqaTuw+OEoEajrVhEX6zKJs2ezt3ZfTAxFNSGDhN/MY+IXMxMFZrhbvdCPSLOQzP9Xd2I8EHIJY+IYKAOm0ekkkIzQeYRGUWhmdzkacW7x7ys6WRK4V0Z7Zkz+iXedbJmmKdWXIxYfE6rauXnLTYw6GyHhwkyRFwkf6A1E6uqDRF3JwJWDCeTTmOgty/v73I/FzuhGbmNtauhmSrWetxaaIZNpD07O7PftxGaAcBTeJlHRKu8OwAhI8LeMQbCIXi98uNVkFhVDJ+4pBGp4wNcN489F90jkmeoj9zJSPSIuPkclBoemtEUq7KCZuZGtSxUVa7EE9wQGblGaCHop+8aZ81E9nfZXjAwcXhsYDBPHFtukCHiIuqYqGWPyI5caMblVuzshh3o7eO1OUScNL4TS7wnCqixoUbLJWyU1cMm0q6cIWI2CcihmewAy1J4m9smoKaxgVcaVYt6Ew6zZtRGqNVMA5F6Uay63z2NSKimmq98i6E/0aNK9XyM5MlILvEeF0IzFeARYZ5JDY1I2kZBM3XDO0Z8lKfwco2IOmtGYywNVoURrpWrPdsdA0aKUBUgQ8RVWAz8wN59AIwbe3k8Hl5JtWv7DgDZsttu1lbQ0z0wWOxWKz1WD0XWjI4V7wTtrBltj0i2G2728z07c6EZE41IIKw0dGIDg9wAnPWxI/lx5WXNOHS7qwdau6EZj8eDWg1BqRteC3ZfRPsHkIzFedVdt7wterDryJ+PETwZsayZSmt6x58Tw6wZc+9eWKeQVjw6ukMzTCOiFqtyr5owjrHnPxGNIT44xBdJVj0i6sq25QwZIi7CLFArA63ouu/e0YlMJgOf34/qxgbX9qfeQKgKFOoRSWs+PE7RDM3k8ubDqqwecSIdOnAguw8WPSLi9plOZM5xHwcADPT05veAcBiaUV97u2LV6oZ6/p3+nl5uINktaKSFuv/QcHhE/MEgn+TY81EJoZnUaArNJKw3vavS6fpKoRnjyqqiMcvDxTkDhOnXrNYRIY/IKIVNEH179wMwHmjZiiCTySA2MIjBnIbDbglfI+pyN3KkK1+oChhnpeihaHpXQLEvNTw0E9fyiGgbIv1d3ZYzd9QeEQDozIVn5h6bNUTU3hAAjsNP6qq6VhuFMdgxDvYdQDqZRDqZxNCBXJnnAr0i9aqMo35hgPN4izMkhOuyLuZMJsNroozkyUgUqyai7oUoS42WZ5LBNCJWPCLVOqvx0R6a4b1mdBY84jimzniUdWLNeZ3DtWDjZqzMU3cBMkRcxY7rmRkpbIXAU7NcdI8bpe4Ccm65E7Fqto6Ie6GZgEZsWhbTKg2lehay6O6x7BYXe80wWIVVvaqq4udta0RUVXXthma0wmoRl7Jb1Nse6OlFJpOB1+dDTZN7HjmRKi6cG+AK/pHsnuf3a0JoeufCc1BqjNJ37TS9C5uKVUfutS8En0nTO9GrVi8IVQFgqO8A96TUNpt7RUZKnxmADBFXYRN6H3c96xsizEhhK4RipFCqXfBq2EQvus3NEMWqbrqktUInokdE1M6ISnKr4SG+0hMMnV25WiKMiJYh4jA0k68RsesRYenE8j65dY/w85fbXiad5h65YulEeLw6MjDiu7CKwuNUPOFqiLLUaIVIGXY0Inqr8ZF+7QvFNGtGuIfUtZIkScJAdy8Aa33Jquq1jcFyhAwRlwhVV/PyvcwjworJaH8++yByj0gXKyrlXpzeTKwaHxri6m2rtUREi17r4XGKlkiOGSJen08xcNXzIj89tkMz4gB7YO9+PgEDQP/+/PPkVtaMXY2IVkVct7QcYlqwetvFSuHlHpH+gRG/KmZCVSDrOaisrBlldpkI84iwfilG6K3G44PZhVd4hF77QtGrrKoVmqlXLRiy/5+rwm1hwSAb/2SIjBrYCiCVSGCgpw+AsdXPQjNxboi47xFhoqaIhvaBYVew6hWb3rkammEeC9kQScXjfBUmGkpi/xyrBcf0sgFYPREAPHtExHFoplCPiGBs8f1jxkKBXgstA7XYRc3Y/TUUiXAv4EjViDChaiaTyT4HFRWayWUDGYRmvD4fX3TpoSeUJI1ILmtGp+md6HnVfk6tV1dVp8uXM2SIuERYEGfxgdYgfZeHZqLZz0aKsCJl2getqqoMuVaHNY+IfxhDM9n9yxlKQuZMneAt4H0+HIRmAKAzlzmT3V6+weZeaMamR8RoECpQ0CyvtEQjJ+eRc1EsLSKukEd6mW9RqAqIHZorOzSTzolVAfP7mV9v1Wp8pHvDCsU0a6ZKX6ya/X/W6sH8OQ1T1szoQyzgYyUOmh+acXdFqi6Goweb6NUpsnrIvRKSmg+PU/Q8FlqCVbEcu1g+2m+wImWr1URM3yOidZ7YMfqDQVsZJWygZa3TbYtVhcqn6v0r1FjVXmlZd/k6gRmSsf6BEV9LQkzdBeR7ZKR6eEQMQzO5exkAfCaN78I66bvs2o9UI7RQvILYX0Q7aya/EWfERi0R2Rgsf4+IebCvApl8yEE48zv/o/m3xFAUj/7sVuzd3GFrm+KKL57rMOnz++EPBvmAJaIOzbiVEcFQF8PRQ+7n4sQjUtysGUC7DL1CrCoYLsFwCCmNQkyAvNJTGzq7xNCMhudI/HwgFEIiN5CawQbaob4DqB/T6iB9N7caEnQrdgsaaeHxelHb1JjdniL27LyXzWevuxpDByJ47s57dT8juurZ8+F0MpowewY+c9VXsOL239t+ThmN48birCXfxL/+9FdseesdW99lYTZ2b4hhDL3nHQA+ft5ZmHLIwfjbD252XHJ7/qcW4bBTT8KDN/7U8Ll2ilHWTCaVzmZXeb0WPCLaYYHhCsu1z52N06/9etEExAM9vfjr937CU+qtYpY1EwiFsgseSUJdM8sO1PCKWqglMpLqiIxKQyRcW4upCw7R/fux/3kuHvnJL2xtkwmDYv39ilV6qLpKc2DS94i4I1Zlze4GenoNP2dURl0L8UHiLuncw6NVRt4qZqEZlg7o9fn4sfV3d0PKZJBKJOAPBhEMh3UHBr3QzP5tOxDZ34VgdRX69uzN+x5zvwNZL5NVQ4SF5QZzhohdsao6swWAXOLZYkEjLfT6Dzn1yNW1tuCTF38emUwGLy7/M9LCqllErLTJG58ZhC6N+Pi5Z+KQxYuwf+s2PHHrbx1t46izT8chixdBkiTbhkhA5RFRGKvhsK4hcvIVl6Jh7BisfvwpbHpjtaP9PvHSizB1wSFY//JreOPRxx1twwi5oJm2QZ9OJOENh0wNker6egClC80cffbpmP3xo4r6G+tPeR2v/vURW98xC80A2fE0EA5xUTDLlAHkxZIVr6goEC93RqUhsmv9Rtxz9bfy3p+68BB86rIv8UZodhBXAFImg0Q0hmBVGMHqKgz2Hcj7fFCdvpsTlFbX1xuuqqzvjzXFtN0OvF7hQVI/PFYnaS10QzM5Q4kVSKppaoTX682lnGbPayIWgz8YhN9AUCqHZpSGSCadxq0XXgp/IKgwIBmSJPFraUewygxNZhjZMUR8gQBqchV2tQSltc1N8Pp9eVVgrcD7D/X0KgxHp2Eftp9erxd1zU08dV2NGLosdDJi2qdCKrO2z52d25Z975JcfC/7jGZSaaSSSfgDAQSrQojqLJLZuWqbO8uxIcIm+La59scoK/DuuxqhGSBrfAXCIUPxtcfj4WHhUhU0YxWqX3voH/jopZdd3fbC/zgJh596sqNroJc1Iy54AuGQXNCwt09htNgRq44kjcioNEQGenrx4Yv5N2fX9p05Q2QmPB4PJEmyvE11Xf/40BCCVWHdwTakCs1EI/1IxuMIhEKoa21G7649to4pb38s3oQ89GGxuqpfyJpRPzyFGSLaA6A6q6deYyJNxuJAvbGglOlYtHpoHMhVwtUjGY9nDREbISg2STIj1ErHUgbzeKSSSYUhOXQggnQyBV/Aj7qWZtP91qJeIxsHEArq2TRE1CEzPUOkWkPM7XQyYvtYiEiaLTacaGKYFkm8/5OxOPyBgO494g/Jk3f7nNm2f5PBntNiGSJ+g6wZwJrmKVhdxbNq8g2RwrxhVmH35dZ339cc6wvBFwzi8FNPdnQd+UJOFZqTJAnJWByBcAjBcJiXcVDXNrLqOff5/XwxRFkzI4x9W7cjEY0hVF2NlskTbX1XPfEnTERZ3BAR4rx2rF3z/bFWVc9uaMYrlHhnDw9QeOYMD81ElQMgb8yXG4C1lORW9kEvNGMFJwWrWGXVoZwhYid9l3stunsUxrAkSejvKawCLw/5qFKVmcu3qq7WlucnrJFWbfS5WL8s5naqE2D3gFjPww5V9XVomdgGwJnwVx2aAcwzZ8Tnq31eAYZIbjvtc2a72iCToRciZfCiZgZiVTb2pBKJPM3WcGVMFbOqKKvIPGHWDNM0ZjXs82qPCKCsWVSnUUcIkBcMoepqQ48iM1gzmcyICM2QISIgZTLYvXEzANl1axV1KCRu4n7mGpGoaIi4l8Kr13RKjZwea7OgWe5BcqvfjFavGcX+5QZgWTshC0vN9sHj9XJDQG+ANUIuWGXDEOGhmZwhYiM0w1dDGgXWCs2u0hvgYgOD3KCzo0FRpFUbrNLkOiJyaMbr8zm6b9gxOC2k1zZ7Jv//QDjEwwhWUYtVAfm+0jNWRUNk7LQptuvKAEqvSri2Bs3tbba3YYaeZ5JhpfGdPPbkT4DMG8aE/MUiLIQC3aZnZydig4MIhEMYM3Wyre/6AtoaEUA0ZkOaGjEgu8CN5cTeRs8bO/74wKAtz36pIENExa5cIzS7OhH5xs8+fGZdJmWNiBzO6HexHXuVxQfRtliVZc3kVN9JFwwRr9/HH1D1ADikSt/lHSk1PCJ6hoLoLtcKzZjhpGAVM0CZjsWJR0QrnbjQCrxG/YecZG6J2iKm3dD8nBCaEY1BuzqRUE01f6aceuHaVIsMo/3WQtsjYuyVEw19n9+P8TOn2fpNIP8ZLUZ4Rqs5pIiVMu9G+jSFkL+meILVYhbzkiQJu9dvAgC027wGYtahGvEe0guhiu8ZPacjqc8MQIZIHsztZtd9qg7NyGlq1jQiQGEplGrCVkMzEZaVYj9rBrAWFjFD/K5aMBpTpe9q9WCRC6tpGwriKlWM61vFatE0xW/mJstB5hGxoRGp1wmfAHIIxalHRN1IS8SJIazQiBh4UuTeI/2QMhnHPUfE43aaNq72dtrNQvIHlWJVQL5H9GrZqI0Iux5XzW0UEOLRwh8MwpurlaMbmkmal3k3WgSJ176YKbxVOk333ILVH2qfO8fyd8QwTjqZLzRPCOE9o8UIL/NuaIiMHKEqQIZIHp25RmjtNj0iPF2Ni1WNB9ogT98VQjMsNcvmCk17f6xlzcRsVlZVuxYTLlSVZBNKJpPJS/9UZ/VoPaBmoRmeMRONOXJTOjlGJsYrRCOiuRrqZhVQnd0jvHS8Rtn/fgfVVbVK76sRQwrseiaiziYjMWzp9J5jq1hWy8OuUacuaAZYCc0ony+1V8YK6vBpIaJXLcTzqec5TPPQjIFHRDUWqpH1c8XxiPgCAX4diuURYBWZ7XilmL4O0NaIyJ7dEDeO+zUWI1a8ourkiXKHDBEVezZuRiaTQf2YVlsrJXWqlFmKIpuoFB6Rbvc8IlZdk3Z7zfgEsSqgfHicEjBIGWTnM6zyiIgTqWloRic12CpOCrdxj0hfNpfTZ0MjYhyaKayWiFYzPYYTb4t43+hpm9hnMuk09xTKbRDsTUbivjkpVuULBDBuejYssn3NR3nbtAIPzcTzxap6Xjl2/zID3pFHJDe5FLINI9hzkk6mdFPDZbGqgUbEZDVe7KJm/H7LZBAfGCzKb7DO3Xaugc8veEQ0CtqJ3mW5aKORTswgFMo8kOQRGZkkojHs37odgL1Vi1jCGjB/2JiBkhA1Il32XeN6iFkKRqg1GGaoxapuhma06ngwQ4l5eNhEKoYWzPbBrDaCGXLWjDVDxB8M8vM05CA0U2cgVi20Aq+eCA4QU3jtiFXzS++rqdZYnbFsMduhGcEA8zvImhk/cxp8AT8G+w5g+wdZQ8Su3kZLrGoWmmEe0x0frgUAXiLADuwZ3fnRemQyGTSMG8OL+7kBM6KMBN1sAWJUKdhsEWSmnysUXlxyYKBoQs09mzqQTqZQ09iAxnFjLX3H6zP2iIieV61eUwzZI2IlNEMekRHLLh7/s+Z2yxbwUcZFzWolqAuaAe5WV7V6IzJDxRfwI2ght5+t7DNFCM1oeSzYqipUWwOPxyN0pc0PzegVNBNDM06we4ziADsUyXlE/H7LvWp4ZouBW9bJPWLWf4hnbdnSiAhiVZ0Vmla83ulkJIaknBi/LJyxa91GSytLLXiHWq2sGZP03e3vfySXCJjUbut32Tb69uxF9/adALIGjVuYZcwA1rJmwvXGiyAetrbpDbNKlSpxoBikEgns7dgKwPqC1ReQPSJaJf7Zea+ur0N1Q9ZwjWiKVc3nCR4eGwF9ZgAyRDSRhUjWbrBQTTUXeak1Imbpu4qsGQuWrlXkGKGxRyQRjXHhaZWFxndspZ9yMWtGDs1oGCK5B8nr9aJ+bCvCNWwiFUMzxpMAH2CHKTTD9T/RGJJCXRSrXpF6DWOLESmgOR3rPxQfimr2KXGSGiw2S9RLhQ1rpHPKGiq7oRl58HVi/DKBZ+f6DUK6vDOxqrqgGaCfUszOweCBA3KJgHnWhY6AsmR3Zy67z83wTMCCR0QuaFaIR6TYoZnhKW2+K6cTsSoalsdO7TYIbHxqztW4yRY0zC/Ta2UMkI0xe71wSgUZIhpwIZJFwSq78ZOxuNyR02DFFwiHuIJaEZrJucb9waDluh76+2R9VRC1IVhVlyhmD49ebNwKRi7hdDLJPRljp04BkJtIBU+SmU4laGDoWMFu+EnOiBpSCBqt6ETCdbXc4NESlA7wgkZVtlNf6w08LYCzrC21tkjLiNFK53SqExC372QiY89057oNcu8eh2LVpEb6rp6xWiWESp2WCBAn+F1cLOmeISJ33tV/TqxpRIy7vjrNmLLKcGWMsAWr1euo1/COwcY5ZojodU234hWl0EwFwAaK1imTLIUrtGr6G4VmxAlELIueSiR4b5JCvCKKLAULBX3sCFZZ1gyz6p0U+1LDQzM6oRN2XsdOyxoi6gfUctaMQ40I375FcaTsEYkq6gVY8Yiw6x7tH9CcEBLRGGI5AV6dzT4pdQa1CQA5a8tJHRF2nNqGSH46J8+asVlLQh02spON5PF4+KSxa91Gx3obLbGq1dBMNDIglwiwWYMiLBQp7Fy3HoB9Y8aIgCVDxIZHRGc1buYtLpTwMGWMdNoM4Xt1+sww2HlvyRWq09KIAYJOrKVZN9w7XOfALcgQ0WCgpxcH9u6H1+tVVGHUQ0uMxz0iGnFQUR+iFlMVKkYEtLMUjJAzU6x7RNwUq/LQjE7ohJ1XPUPEbB+sDLBG2D3GcI2yRgw7LqOy2AyetqezGhL/ZtdYNRLAAUB/rlOzPxDgMWoz2L3WvaNTd5+0XOVOJyP1KtCOAdw8sR3h2hok43Hs27qNe4Zqch2JrcIm4VRCFKsyY9XYIxLtH5BLBDit3tw/wL22Y6dOLriqMcNSaIYXNLPgETFJ3y1WQbPh8oiwBWvLxHZLdZi4N1lDHwIIoZn2CQD0PZeDvX3IZDLw+nyoaWrQ/AzVEakQ2GBhJU9cS49h5H7U0ocwCi3hDdh3y7EJwpJGRCVW5VVHCwrNGIvk2P6NnT4VQH7Gh1n6Lst2KTRrxmpvEybCYzoMKwI/hlFWC4MVOrN7j/C0YI2QD5ANg7EmfVa2Haqu5hP4vq3bdL+ndT8mHDS+83i9eVkiVjOZAHnlunvjZmRS2e7NmXQaXo3tGiGLVe2EZmQvgdMSAdXCONPf1Y1IVze8Ph8mzJpheRtGBC1oqaw0vTPTpxU/fXd4vAHRSD96OncDsOaZUi/i1DDvMjN09TyX2c7jfQD0dSJUWbVC4EIkC6sWrYHW6GFjXpKEliHiQi0RubiaNWtYXTTMCLVY1c3QTELHY5EXmlFNpGZucbZvwxWaCamK1Vkpi80wqiHC4DFim7VE2OeNjBw73hZ236cSCT4gawk/wxp9j5x0Ya3NeS4ymQx/vuzUdmHPMnu2pUwGAzkvkJ3nTUusanYPhoUxwmmJAL6NA/2K47BT3dMIXm/HILssnbuXC0nfdZq6bRWxim+xsVNPhBnteoaIeqFUiFfUaouPcoEMER24EMmCR4TX7BBj4EP67ket1F2GkxTKvP0xaDqlBUsxtaMR4R4Rk0JOVjALnbDjYPn6+aEZaxoRx6GZuL3QjLqPUNrCKpJh1GOC4bTMOzdyNMq7M+ykB1c1yKsuI0+euuowIIZmrE9GbNuDvX18MrNjALNnmT3bABwJVg3FqjrGqnpxYLdEAJC/yt1lw2trBWuhmdy9rCNW9Xg8COUyp/Q9IsXViDCPzNAwTMJ29D5GDe+A/PNu6BU1uW+HK3PILcgQ0YHFYK20ejb2iOQ/bPKKOd8jwm8wm0JE5f4ww8hiaCZirQOvTyhRnHYzNGNScExt1atFXKZZM4VqRJjXx+LqW91HSB68rWhEjDNbAGel2MXPa9UmYLDUQL2aICJiNgy/bzUMaK14tRyasT4ZydUmu0xLqmsh1hBhsPNsx/Bn94EifdegMWKwKswnIZZJYjfjAsg/j3wSdEmwaik0Y2KIhGpreCmDWL92VVOevmvDG2aH4QxLdNrIgJIzDo01IgxDjwi7bzXGAH8wyMdC0oiMcHp2diI2MIhAKMRDAnqwG39IIz1Ry/0Y1Gh4x3AqRFTujz2hktXQjNjoiq3y3SxoprcSUw8oelkzuqGZqkJDM8YaFDUhIWsGkEMzPktZM/pVVRn9hWpErIR9bIRmopF++b7VGBjVIQVAds/b8YjUC52D7dZ2qW1uQsO4MchkMti1YRN/387xMuTuu0JBs6j+PRgWMovYPdFpI/TLUE+wbBKcMHum5WJ5RnDPZNTIEDG+l9k9IZYyUDNc6bvDUd6cGbXjZ0w3Tc8304ioQ2JWQqhGhn8mndasF1SOkCGigyRJcr6/idtNLu+eHwP3BwIKTwIgilU1QjMOJxnF/hi04dZCriNiHJoRmzbx7rsGA7BVzFqPq49D7S0YttCMzfTd+JBarOqWRsT+Kt7j8aCuWb+RlnrbVkIzYodno2wvrRWqk8mIVUDt7+42FSirYSvWrm07FCnzERvHy5BLvOf3mtG6B+WJUT5+uyUCRK8KG2e6tu9EfGgIwaowxkyZZHn/9XCjoJmVrrfDFZoZDo9I7+49GDoQgS/gx/gZ0ww/6zXxiKg1ckZjgFHNH7nE/WDRSty7DRkiBrDBwqzLpWZWgDDYqd3P6hWzSMShENFsf4yw6hER3bHq0IxeeXUrmIZmVIOaOrQgh4d0sma4oeMwa4ap2S1mzahDM3Y0IlayZpyUea9uqOcT2UB3r+7n7GTkiJ43o1RYLQ9dPGp/MuIND7u6bYdmWAyfPdMM2ZNjvcy7X6vpnUGIUssQE0sETJhtnvXCvSrJFPe+SJkMdm9wVqVVCyuhGbN7mV1rI32Gk4wpO2jVdSomVvWEph4RdWjG4Dk10mQN9/G7ARkiBshCJBNDREOMl0ml+Y2ldj+HcmXKjUIztc1N8Pp9eX+3glaWghG8johJ+q5XozJgwo06IqZZM8J5zWQw0KPKmoma1BEpODRjz+uj1gBZKYsNIFcXoBGAsdeCiVVrm5us96/JDVgDPb26AyFgLyNHXHmKqbDsGPjnNCZiJ2W+67mR1iOkVFszgNkz3LlOaYiIxaGswj1sQujBKDSjFyq1U09Edxvr3NOJWMmaMdM78dW4wSLIiVDZDsPRa0bEaksQ06wZ4bxHI/1IGRiEEYMQvlll23KEDBEDrNYS0UuV0ivzHlSld4oM9R3gE71Tr4hdsRYXq5qEZvwaqm8u5Cxi1owY8hrs7ctzbZpNSkZN9ayQtKmDyQ/NmJfFBnLGp9ebqxNwQPdzA7192Unf50OtatLXg2fj6NQQYTAjx4qHQIzFS5kM37Y4OGqFFACnoRk568dMoKymjRsiGxTvO6nbwwuaKbrv6t8jLHSrHh/sZOZpFU20uw0zrFQgNitoZqXHVcKBN8wq/mCQH8dweQTkcvsmHhEbWTNGHlFA1IhohGbII1JZKFo9jx+n+7mwTihEr7EXU4treUQkSUJ/D1uVOtOJVFtseMew2mtG9ojITZsSNlemWpgVNBMte62JlBkYvoBf04vEt++w+67dSY+HZlQFzczEbCz0MNDTCymT0f2clMlggBU0sjiB8mwciwNcTWOD6f6qDV7uTREEq1ohBcCZToDXQenuseWlClaFMWbqZABy2izDScdr7RLv+l45vYWBnVpFYR3dl51tmMHCXEYrcTO9k5VFkFFGYaGIQk2trMRi0CmE8D0ej+7nfLmxSbfEu3DerT6nVfV18KvG3pFWzAwgQ8SQdDKJvVs6ABjniesJtPSKmnGxqo6iudDqqmGbrkkWzzXziGjFOO0KObUwE8mJ51XrARUnOK2JQC5o5swjwvbL6/VaEpzmp++al8UGxPRU40EIsH+PWNGeADmXcM7QrDOpNspXv7n7TCsVVs8oZt5An99vuV+MKOS1YxyOnzUDXq8Xkf1deYYsM57CNTWWRKMej0cQq4oeEX1jOKzjMbVVIkAn3Lp70xakUynUNjehfuwY0/03wspzkkpay5oxEsqz58IX8FtqBGkHMVw4XELNfR1bkYzHEa6tQXOuT4wWdrJmzAyR2MAgv+fURQTl5AkyRCqGTpMulx6vV1MVDxiFZvTTdwGhToTD6qpip08rMMvZ6/MZ9n9goQXt0EwRs2aE86pV6CudTPL+DVqeGb59p6EZ4XtWjlMdektb1IiYdccVsXuP1Ampr0ZIksQ7/JoZOermZlqpsHrCadF4tKIVCIRDfFtKQ8T8ejCxeadKqApkFwvsObRi1IkVRbWyZoD8e1CroBugLBHAPDZ66K1yU/E49nVky+sX6hXhoRnDyqrGdUT0vMMioqfCbZ2IldCQ22RSaezZtAWAcXjGPGtGCM2YhFAB/b5kpTgHhVI0Q+S73/0uXnnlFQwODqK3V1/9W+6YCZHCuSqCQP4qgGUG6GbN6DSkK9QjYjdrJhWXc/6NwjPaYlXZW+B0dWMamjHxiABC1oKGZ8aKCM+ITCrNj9nKClzdS8hqrxmz7rgidtO8zRreiVhtvKieHLVSYfVCCpl0Wm4maGEyYvvCug/bCc20z8svZCYi1+4xN+rEfkNinYxUIoFMLpym3ie9mL1YIoDtox5GcX+7XWD1sBaaMW5XYGURJF57t8Mzdsc+t+AhMoPraCdrxspzyscAVQhfq65VuVM0QyQYDOJvf/sbfvvb3xbrJ4aFXSYVENmNHx+K5t1gPDNA5WVQlwBXE3GQUqjcJ/s3Ik/hNcicMQrNAM7DM2ahmUxK7iKsF1owyloIcpezM0NE/K6VUvZscFUXNDMTq9ZbDJ8A9kuTWw3NAED/fv3iZCLqNMH+rvzS83ohBcCeVkDtLTKrHSPCnl21UJVhx/BnE3A6lcrroqoXLgrreEwBOyUC9Kslc7FkgZkztkq864pVrRkCRgUfC8FuVWm3kLOX9K+jWdaMuOCxEp6VxwBVaGYYC7q5hd/8I85YunQpAODiiy+2/J1gMIiQ4Nass9CErdiwKowtE9sQrqvNG0yM0tVirHpklVojkgvNaNQRAYwV0WboZSmYEY30o66l2dAjwlXfgliVPTy+gB+BcNhRkyWzrBkgO7CFqqv1PSIGlTYDBdYRYd+tqqu1FZpRFzQzahQGWCtmxrBbgdfOtq3WElFPOpGu/KwZI+FcfGgItc1NFj0iytBSwqIh4vX50DZ7JgB9Q8SqBwgQq6rmVw1NxmIIVVfl3SNGrnJWIsByZp6BMTPzqMNxzg3XKf6WTqXwxt8f4/VGjLASmmEaETOPiNkiKD4URW1zk2ZoZuy0KTjmvLMVlZwZOz74CG/+40nd7ZaqhkanhcwZM48IkL2vqwK1tsaAo848TdGBefKhBwMYWem7RTNEnLBkyRJuwJQL0Ug/unfuQsvENrTPmYXNb72j+LtRJUE9jYhZaKYQj4heloIZsYFsX4hwrT2PCCA/PE4zZ6z0gjmwdz8ax41Fd+cuzb8bh2YK6zWj2L7JxOfz+7nng4dmktbEqqwVPesIawQ3Vi32m+GhGQux534Ng0KNx+Ph94rsEcmf0I1CCnYyZ9QenaTF+jWNE8YhEA4hGYuje0en5mfsdLyWU3fzDRG9cJFRkS+20Bk/c7rh7xoZMzvXbshm9zU14tj/PDfv722zZ+K3l33dcPtZrVv2N7QqPjPkDDDtqaO6IauHMVsEGaXwfvbaq3HQomN1vnku1r38mu59XKqMkd0bs9excdxYhKqrNc+h3DBUWyMCZJ/9qrpa9OiMcyI9O7OfmXLowZiSMz5EDuzbb2nfy4GyMkRuuukm/OpXv+L/rqurQ2en9uAxnOxavxEtE9vQNne2hiGiv1LRS981C82wiaC2xThrQQunOeS8QqpB9VCfhliVfbeqrhbBKvuGiMfjseQSfvDGn2LiQXOx/f0PNf+uFzrxB4O8CVdBoRmLpezFay2HZqyJVdl5sNIfgonZWNl2I3yBAJ8g7GTk1Bp45EI11XnNzbRSYY1c5XqGuhbMKO9XGSJmhmE4VzxwKBLRTYmWvUvmhr9WMTMGM07U+2R0Dvr27gWQqyHj8+WFe9Tb0BpnopEI7vnvb2HKofMV74franHCFy7AxIPmwuPxGGaRtE6eiGBVGPGhKHp37dH9nBxm1L6XeYq1ic7JKDQz8eC5AIBX//qIwig/7qLzUF1fj4ZxY/UNEZvFHN0iPjiETCYDr9ebO4/5z7DXgkfk/u/ciOaJbejavtP0N1c99CgyUgZVGovHvr37sPnNt20cQWmxZYjcdNNNuP766w0/M3fuXKxfv97RziQSCSR0GiWVks51G3DI4kWaglVjj4he+q5Z1oy2CMkKeoWPzOADu8Fk6dMQq2a/6zxzRjR8jDwWezZt4cp0LfQ65IqTgtOsGXHfzI6RXetkPM5XPjxrxkQjYqXENsOOroFNDqlEgme4WNm2kUeE3fdiczN1KmwiGjUx1K3rBOQJLrtvVpstGnW6ZujF2rXQKu/OSOhkkBktDnhF2lxVXT2XPK+WrHP91r38Gta9/JriPa/fh2POO4unlXbv1F/UscqsuzduMqxhw559Le+e1+/jXj2z0IKeEVrX2oL61hZk0mn88xe3KcaEeSd8AtUH1xve86WsoZENzVVremUBofuujrEJADs+XIsdH6619HvxwSH8674H7O9oGWLLEPnlL3+J5cuXG35myxb9yWKksstAlR42yNnWqh7pDwb5DambNZOL0YeqqxCqqbbVQTFsYBgZwTqJGntEdEIzOkaAFUQPQ2GGQm4fVIMAmxTSyZShS9R0+xZDM1oTn3WPiHVRLauAmtWthAyNOCY6tSJUFT9nOOBrhAqyqbBDCFVXo66lGd07Ow1DCjw0U2U9NKP2iFj1UOkZ/eI27YhVte7VpI5XziilVcpkMNDTi/oxrahvbdGdwJ1kg7C00kkHz0Pb3FmGhggrT6CXWcRgRqfX58vz4NTmvHPpVApDffqVgQExLFejeJ+Nsfu2bs+7pyMWRNRybZvhF2omojlDRGcc1FvIETYNka6uLnTllPGjCSZEGjd9GnyBgEKsaSjG04iDiisAPQ0HS1EM19agrrXFliHitJgNq4lgZEzIMc780AzgrN9MQPACGK3EzNDbh6CFsI8VrKaLsokvJlwzqwXN7Ihq44NDSERjCFaFUdfawuPFWsiTuLk+JPs5Kx4Rbd1Df1cPQpOrUT+mJWuIGDwffFVsULuGoRarWs2a4anUUf1nSKsQmx7s9zTFqhrGaqi6mk9AeouDyP5u1I9pzep91mn/rt2O2oxd6zZi0sHz0D53NtY8+6Lu53gvHo1aKyLMqAay93MiKhsiLP15oLvXtJiYXrHHtlzWibo5IWDNYCxleXOzcdAsa2Y0U7T03UmTJmHBggWYPHkyfD4fFixYgAULFqCmpsb8y2VG3569GOw7kG31PFPZ6lkekPNdplqhGeYdScbihi46u1kR8v44c02ygTVg5BHJDagpwRADhLCFg/TdQouNMRI6WTNuZMwA1nvqhGuUqbuAfL7M6qxYEe2KWJ1AuSFioVBa9nPZyT4QDinq5IjoZYupJwujkILeZKSFug6K1awZS6EZQZNlVKIbkEOXRmJVMTTDxodUMql7Xa1cR6f1Maw2ZGPZHiyLRw8mvAYAn0onwkLJEQv3WUKn1xCrw6G1H1q9jNSUsuGb2ThoJWtmtFI0Q+SHP/wh3n33Xfzwhz9EXV0d3n33Xbz77rs48sgji/WTRYUXrFHliRsJ0bRCMyG+Yh40/D077diV+6NvGBnBPCLqvgUibCJVe0TsdqcVkfvAFGaI6FV4DdjQXRhu36LXR526CwBpmx4Rq6Xo5UqmxtqG+hZrVVUZyVicT3h695/eylMd1jGaQPUmIzUejyevV46boZmBnh5kMhn4/P68zsFq/AZi1aSGoDlsIVSgVZFWJFRdzVfT9g0R87RSUZexZ5Nxmm8mleaF29T3M2+saEEQzby86qwZplXZtV7DELFQ1qCUNTTM+m7peZSJIhoil1xyCTweT97rpZdeKtZPFhW9TrxGMfCERnoiL/+tU0OEYacdu2J/HBb0YSs8Ix2D7BFRi1X1a3iYYUcXYYRezxtezMxhVVVGIStws9bpQFbox86vVe+N1foXdmqIMMzc4GEdz5s6c8YopKAVutSiuqGeD+JsVWxVIG3FI5JJpTHImwgaP29GHpGkRtaMld4rWhVpRdgYk0okDKuearF7wyZkMhk0jhura2QZ6TK0SOsUNbNzn8lVp5WLtJbJEwHIBpSIlfvdSon5YpHixrH2GMF6EJFHJB/qNWMRPRenpayAqnyPiNEKDbDXjt3q/hjBxKpWQjN5GhEuFHViiLgVmtHJmmEr2AJqiACyx8Zs4tNagVvRiChEu5Y9ItbqzXCxqoWVKiNiEhrUm2D591rMPSJWQzNs4hns7eODeMJi+q6Wh0oLXkvEJMzFxNxa96t2aMY8VGpm9FmtVqpFfGgI3blUUL0S8O1z5wDI70ysh16Zd17rxUJoRuvaT5g9E16vF31793HDUMRKxd9S9llJ6AjmGRSa0YcMEYuIZZTFOHKVQboscz+Ga/LFqmYtqq263dU4rSyYtOIR0cuaKSg0U1gfGIaehoMNCsMVmmFVdMWJT66sqm+IsMkrk8loCiG1iFhwVWf/bk8jAliYHHU9Irn7dkyLaUiBPR9moRmt8vTMWPMHAnndbkVYRo6Z4W81c8ZvVFlVIzRjJVRgbvQVlgnCBKhtOuXHuT7EJGOGkUrqeERshAC10neZoaSXuRPR6a3CCIRDPEW+NBoR43HQZ9L0bjRDhohF9nVsk1s9T2zn7xu5XrU0ItZXaM40InqdPs2QxaoGGhEzsWo5h2ZcyppxFJqx0H3XiajW6uRpp8+MvG3j7r5ydpY6a0beJy7U1AkpJCym7/KMGaGIlVG3WxGWkWPV8DdrfBcI5rJmNEMz+V4aK94Mc6PPPLxjBBN+6jVkazfpxaNGr6iZncaKWsUezXoCsa7QwaqwZqYVM9jSqZRp6LsYmI2D5BHRhwwRi2TSaezemBVyiS5Oo5gke9gCoRBftdldoVlJKRQJO6wsKItVrYRmlBZ9WWXN5IlVi+txUWMYmjHImnGiZbHqNZNTX20YIiaGsJ4nUMwAqWowDktoGepasIqn4v6L3W6NroncCdlYHC6XzDcOc1kLzcj7o6el0fxtnetoZRtGMOGnVlO8UHU1xkydnPucRY+InkbERgiQZxQKYWueMaNjiLCyBoD2fVnKGiKA/hjEoPRdfcgQscEulQLd6/PxEtJa6YliwTKmE7G7QrMbmuGu4GKIVVmJ92QRsmYK1nAYV1YdttCMRh8hKwXNnGT3WEnxDtfJPYD6u8172Ki3recG12tuxiah2pYm2TunMzFY14g0K7bNsJI5Y9ZOge83N/ytiVWTWh4Rjeeg2oJmQV2RVk2htTFYyGXs1Ml5RtuEXENAPV2GFsywVqejqzObjFAboV6/j/fbMSqqZnTPl7KqKiBcf7PKqmSI5EGGiA3UglWxxgKz1EXSqRR3y7PBNmQxNMMGxtrmJni81i+TPEHYS9+1I1ZNq0IzKTdCM4V6LHQLmrkU+rEYmmEDa0xhiJiLVeXQjHVDJJIrLljX0qJb/4IN2NFIv62MC95JV0cYqKd9GOjt5amwLZMm8t/Wwmr6rp7L30rmjFVxuCsaEY0VsZW6FtmKtNn909L7GOnQrNDf1Y1IVze8Pp+iSytgXLdDDy2NSKi6mo9tVhorxlXF7MZOnYJAKIRo/4BhwzcjPU0pi5kB5qEZL9fYkUZEDRkiNlDXEqnKrfhig4O6xcnUKbxBi2LVgZ5e3oOi1qS2gYjTrBk3xKpmk7QWrmfNVKk9Iu7UKTFzuzLYdRavb9qCRsRJBdiBnIfDF/DzpnZqnOhDADlrSz99l+kWlPeZmAo7bvrU7Gd0QzPatSTU6Iltzeo2AOLzZs3wt2qIaIdm8jUiVsXjRoaQXDzO+QQre3OVOhG5boe1sAygfT8zr1VscNCSPiOhuvZscbdrw0bDqqzG58lZVWm3MBsHSSOiDxkiNtiVy8lvGDcGtc1NwqrQeLUDCKEZtkIzeVilTAYDvLaBNZ1IIYWP7IhV1R4RM5ekEUG3Kp/qpe+6FZqxWECLrfDiGmJVo8qqzMBJ2fCIpFMpof6F9j1iR0Aowib9mqZGfk+JGDV7ZCvisdOm6H4GsF7iXV3encHCiUb3nZzFZNUjUoBYVSM0Y1VoKqdiG630nU+weuUH5Iqq1huVynVx5PvZbhuBuCprxmplVyODccSEZgwqao9WyBCxQSIaRde2HQCywi8rLlP1A8dDMxb6x9hpxgXANEvBCEseEb+2a1GvvLoVXMua0TEUhjs0wyY+TY2IoSHirCeO2UreSTEzQOgK6/XmFcLyeL2GkyP7LdkQ0fGI5IxxfyDA7y0tWJ0UtVdHr9utiJaHSgtmPFXX1xs+A0ahmYSGoNnq5Ghpgi0gJZU37hQEq16/HKox6zEjwioF+4TzJF8ja73I2AKNNQFtN+gxI2KknQsXmF1UKDxripre2YYMEZuwB6V97ixLq52EKk3NavouYD2lkBEuIJbMDBfj7rs5sWpeQTNrYQst3Co4pldMyK2sGavhJy1xJB+4A35dLYcs2rW3n2YreS70tBC3F2FdYYH8eLxCG6VxrzFRaXP7hNxnjD0igDKNU8QXCPCwU75Y1Xpoxux5i0b6+URi5BUxFKtqTERVFrPYeEE1jd92Q/vADI0Js2dyzdnYaVPhDwYR7R9Ab+duy9vSypqxU0MEyL/2TKti1v3XKJur1B6RhEnRQ8qa0YcMEZswF2fb3NmWXKbqzACWvmu2QgPEh85addXqAtLX5EHUyCOSe5DcDM2wgmOFZs3oNb0LDXNoRjN9Vz5fPp3VdsBh9pAs3tO+R7RSX60iFicTYZ7A+FBUc1Bl961ZmDCdSnHPgl7mDJvgUslkXmaaXDvGSvquhefNpOcLIHpE8q+T1nPAWy6YTI5G6fqFFjQDgK7tOxEfGkKwKowxUyYBEHQZ6411GWq06uLY9byJ137c9KmobqhHKpnEnk1bDL/HjFEtsaqVDKViYr2gGRkiasgQsQlLhWufO9swTs5QGyJW0wkB+aGzmsJbSCzZSmjGqyO2siIa1KPooZkqZ54GNVaPUTt9V14962XOyCEke4aIWZq3XuqrFZibXT05mmmj1Ktiw4yRQeXzocZogtMqqS7iD4W4MWTJ8LeQDi2LVY1CM9n98Xg83HtkZkTIz3r+bzutliwiZTLYvYHVQZqd+6+9QmYMrbo49TY1IoA8Bk4/YgEAYO/mDlNvgVG4utShGTOvKfWa0YcMEZuwWOuYqZNRP24MAHuhGavpu4C1gVEkzFdf9h9EK2JVP68jolNZ1UnWjNuhmXBIEf4IOJzg1SR1Qj9qNEMzwsCjZ4g4qawKmN8jTjUi2e9oewjMUsTVv6VVY4ehVWFTpF5HqAro145hiK0VrGRy9JuUEAcsVlbN7U+oxrp4XC6opp8NUmjIQS1YbXOQMQNo18VxUjSPjYHTDltgeT/Y9mubGvPKGhSa5lwoZuMgZc3oQ4aITfq7exDZ3wWv14vph2cfIEti1Rp76buAc7GqusiUFdjAamSI6OXBF1LQjKXbupU1Ayh1Lq41vRN7m2hkkQDZFQ/7PbXHi01Seh14ndQRAcwnTx67t9FnhqHXFdbMI6IWldoJXaphlU61JrikSdaMqA+xEnqIWCgiyK6TUR2RYFUYHo+HG2zJeNy0f5BYkVYk61Vxpz6GHFbOGiBOaogA2nVx2P1nVawKyGPg1MMOsbwfA719umUNSl9HxCQ0E6CsGT3IEHEAE361z8t2rbQTmmGVWK14RCK8I6jF0EwBK4Jkwtyr4ffriVWd1xFxL6tFnsDFgcC90Iy8fT1jTayKqa5boVcWm3/X4XkwCt95/T6e8WLHZc7QM4TNRIF5HhGD50Or+ZkIq3SqVQfFLDRjRx8CAP0WOl77DcSqYlE+fyhoK1TAjKDaliaFRy9UWwNvbuUf6zcuU2+G2LizqW08quuzuoy9mztsbUeroBlLO3YSmmEVeK1k7hiVNeB6nBI0vANs1BGhrJk8yBBxALPc+QBhcOM7LWgGWG/zzrDS6VMP0dWsV+/CG9ATq+ZW+8GgrrdAD+6xKDCrRZIkoemYPDE5TYtVI6ZDq4umMdjEl0ok8ow1vdbp6v10HJrRuEdqm5vh9XoV9UZsbbubZW2psmZMMkFseUSixqEZo9CSVrdbETvCcEAw/I08IkZi1bjSWLWzMBjoyf62z+9HdWMDf58908mYuVfFjN2btiCdSqGupRnzjv8EAGDvJnNdhpq0qsS7x+tFbXMTAHuF89TXZZdFrYpezZXSe0Qoa8YpZIg4QB3LNPaI5Abaqir4/H6uszAraAbI1S3DtTWWMlIKSV8TV3h6mTN6MU4r3gI9nPRY0UMrRutWaAaQV7y6E59BOfG0Tut0vp8OzwNbSVc31OcZkExfMdDdaysrgm9bp7qq2X0W6x9QHIexR8SaWFVrgtPqditiJ1UeMO+vAxh7RKRMhhsLwXDY1sSYSaV5urR4vq0I4q2Sisexr2MbAOCos04DAHSutxeWAfI1IjVNDfD6fMhkMrYM3nhUvi7dOzs122RooVdzxagB6XBglllHWTP6kCHiALXK3GoMXFz1WVmlxQeH+ORXayE8o1d22wqil0OvloheHRGFt8BmeIYZWIWKSQHtgcCt0E92+8biSNkQyZ/4uDtbRyMid9+1dx6ikQjftjqEx+P2DvQhgH6NEisTrOiiNwopxFUeQzV6VVUBc1d4yIb3Mfsb5llqPH1XwxBR7FNV2PbCQE7Fln/fjaqqImwRNXn+Qdl/m9Tt0EK+l7PjAbvPBnP6DauI16XTxn5oGYxsoQeUziOiJ5hn6GUdEmSIOKI7l5PPsBoDN3Ld66EnYtOiUNck9ygE7cc4tapKWsGt0AygrRkIuKQR0du+iFHYja0ifXoekZCz0Aygr+UoJGMm+z3trrC894mBwct+0yykoK48rIbXQdEwpszEgUGLDe/kfdYORYmwZ0PPcyXWs5E7YVt7HuXrKIfZqlyujaEWhNpN3QWAtOpeZueLedCsEhOqS9vZD62UdVZDJJVMuuL9dIL4u1qLOcqa0YcMEQdIkoRd6zfxf1vJmgkK3SmtlHdnmNWJEOFt1x2unphgVdcjYvAgmU0KerjVC0bcB7ZNr8/HV21uDE5iVoQWhqEZjZRHEZ495OA8yEWeVB4RA2+CFfS6wloJF7DVvdkEqk5vVyPXQcmf5HglS10Plb3QDNtnfzDIG1qKeH0+nvmgFZoBlF45u94MrUrKVnvVWEUdVrabugsAqaRS7+REqAooDXY7nhmtlPVwAcUc3UJ8drXGQeo1ow8ZIg4RLXjDOiLR/NCM1RUaILhrLQhWCy18lDIpasa776rEqoC5t0Bze34/fzjdCJ3IacTZiUmcoNwM/ZhlaWh7RFgRKO2eKnJoxoFHhKXwqu6ReoPUV7vbFoWBVtLE2W+apZIbpe+G62r5NezPdRoW4RoRXcMwdz0s6LGA7H09dCBb80SrrYKo79ESqwKq0IzNhYGWZ8vuNswQx62uHTst6zJE1Blg3PNmMwQoGoid66w33dOqueK2weYEKZMx7Dej172cIEPEMaLC2+hhZu7HoBCasbpCA+zVEuGFjxw+jEleS8TEI6IRmnFS1Ez8rBuhGbWhIGa32G0CqIVZdVUujozqa0R0S7wXFJrJraTzNCL6qa+Wt61R8VNuS28emjErbR4Xng81bMUbjfRrXj+3QzOAcfM50UAXy/Yr90kIzVjsM8N/W6OPitsT7NCBCHp37wHgTB8CiE0cs+eDaTXsGrzMYB/s7cOBvfstf48vzlrE81TaYmYMPeOYVVUFgHSSPCJqyBBxCFtZDB2IQMpkdD8npu/aFc8BwsBoIlZ1o/ARX7XrdY/UEasC1nuxiDCDIZNOu7JKUBtDbHJ34mXQ3L5JbxOjTq9aRaBEuJbFgcGkZ6zWF6gRAbQnZitapAP7shPLYN8Bw+2z7DEtsWrD2Gzl4n6dhn0Jk8qqzkKh2qmhgPxcpJJJ3Wc+KXjl7BoR3OgTnvViZIJ0rs16H5zoQ4D8e5mHz2yGZtj9Y3c/jO5JJ6UL3CQZ1R4HvT7ZE0pZM/no994mDNn50Xo884dl6N6+0/BzzPsRrBJCMxZdxYB1j0ioprrgwke8uqpeaMbvbmjGzYwWcTtsu0472uqRNGk7b9RHSL2KzPtuAaEZvVV8oWJVQLsrrJXsrPeffRETD56H1f9cYbh9o/Td8bkW9Xu3bNX8rln6rlEWkx7seLWy1NhzoZcxAyhDM2GnWTNCiK2qgLYNeqy4/Q/o27sfrzzwsKPvq9N3+X1mU6y65rmXMPHgeXjzH0/Y+h67n1lZg0Q05loZ/ELR85r6RI8IGSJ5kCFSAE/d/gfTz4hZM6wBlrrqphFWxaq8nHQBhY+si1XzXYtmk4IWTsua65EXmimSoeNkBW7qESmg3ole2qnTlarWtpkb3Ovz8erARh6RaKQfD//o/5luXxZz5xsivDuszorZrOuzIw8kq66q0c1Ybninf420smYse0Q0rmMxJtg9m7bg7z/9pePvM7FqXtaMjudKj6EDEUv3iBpW1iBYFUZtSzN6du6ybfQVC73QDBs7ATJEtKDQTJERV8fsgbUTs7ZaXdWNDp1y+q6xWFXLtShXubRjiDCPhVuGiDJrxnVDh4dmzLJm8g2RtEbrdIbH4ykoe0iuPyHfI9lQYHZ/CvKIqISBzJgGjLVRVlFXHhbh3WF1MjvMQjNONCJGhr9cVVXf0FdmzdibHJk3prq+nt8n5aJ9EOEZYAGVWLWA+8wuXESdu+dLXVWVoTcOioaIUSh/tEKGSJERa4bUFWCI1DU3axbJYbjRedJUI5J7mFKuhWbcKb+u3ge23aDrho5x1gyrtWFUR0TLIyJ6kRxlzWispNn/x4eGLGeNaBFRdeBlWRyxwUFX0hDF0KWILxDAuOnTAOg3Q9Mq6S8ie0QcaEQ0xar6nXcZyoJm9ibHaKSfHxPTiZRDNogasV1BQNDCDKchou6xxEsXlKjPDENvHDQaOwkyRIYFNjGxh8aOq5itknwBP6ob8msbMKpdKHzEsma0QjMer5f3StD0iBQUmnFXTFq00IxVcaRh+q6GISJsz5FYNZfaGgiFuGeMl0bfX9jk0J/rpspCM2add+3CzlW4RukRGT9zGnwBP4YORNC3Z6/md83quhhpdvTo18hcYTCPSNLII5K7fqGqKoRy3iM750rtkakqg/oYanhxvoCfG0zJWNwVD5lV1AajnQaDxURvHOSdy6nhnSZkiAwDTJzK0tzsiOfEhmVGglU3SkGzyVIrNCO6FrWs+kKyZlzzWKgU68ECioRpbt8sNFPDxMj20nfZeUglEo7ctql4nA/AbGB2y12u7grLJkaz+iBWSehoRNrnmLeoV3a7zTcOQw7E4dwDpCFW9VsQqzLXfF1rCxeP25kc1eJ0N3vNuEVKCDPKvYDsCVULRRZRKw3kUp8nvXHQaBFHkCEyLBTiEQFENb2+IcLFWgVMEDxrRsOroRRb5bvkEyYZJVq4ndWi7j3iZvl4IF+DosYofTdtEJqR++043091mjcryKWX+moVdVdYtwd8ZpQHQiFFrYX2eTlDxKApm7KSpZYh4iA0kxOr1jY3Ke55QBCr6hQzA+RrWD82q12ID0VtiRNF7YPX5+OanLLSiAgNHOUaIoXdZ3ZR3+/loqXRGwepvLsxZIgMA2ywZavVmI2BERCsf4N+M27EktnAriWo9AkVQTVDMyaTtBYBrhFx12NRqtCMYfqugVhVNpicnwe1qJn1KynUIyJ2ha0f0yL0T3E3NAModSJtc7JCVaOiW+put2qchGaGDkS4+7y2pUnxN3a/GnpEcvcyuw52Q1gRITQTqhGEwWVkiMhhxiBfHBVSNM8J6vvdbvG4YqEXmiFDxBgyRIYBls7JJiE7KzTAWi0RN1YEPDRj0LApk8loihR52MJJaMat0IlKsR4soEiY5vZNjtGoci47tz6NEu9unAf1PcJTKl2YIERD2O2VZzqZ5EYaO38ej4cbImbFrpiR6dfyiFTZz5qRJAn9PdqGv5U6IrIhkqsKa3NiFK8jm1ztelWKjSi8LkXGTPb3VFoaXm+lxOm7KsE8g/rMGEOGyDCgHgjtDIxAvkJcCzZoFVL4iItVNbrv8gdJZ0B00n2Xh2ZcCp2oJ6VCanNokRQyIrSwlDWjUdCMaVkKCs2oCo/xhncFilUBubZGXWsLwkVYeapTeJsntiNcW4NkPI59W7cZflcvJu8LBLjRFx+0J6LUM/yZDsVIrMquodMGlHIqdrPrwmC3YEa11+dD47ixAAoPAdpFvCezVaVzIawSi1XNsmbKyaAsJ8gQGQbUHhC7GhGjlEKGm2JVzRbWBuXdgUJDMy6XYFeHZtwydEy7veZW4DYLmrmhZeGTZ24Vz/4bsdmITHvbclfYYsTi1Sm8rH7I7o2bkdHQI4noGSJiFo7d66/VBReQPSJGhq1a7+TYI6LwPpWPUBVQ9tlpmjAegHZ35GLCs5uamxGqreFi0FJrRJI6Y4SX12Aij4gWZIgMA/keEbsaEfPQjBuVBVljMc3QjEn6mbPQjMtZLSqh2HCGZjxeL/89zdCMkUbEBS0L85rVqz0iboRmBA+BG2niauJC9WEAaJtrrg9hJHS8VEHeeTdmOxOJr7ZVRQT9NgqaMex6M8QCcm4sLooBq6wKAE1tWUNkuMWqA7mUdV/Aj9ZJ7QCyz6fTqtJuoZdZ58sZSuQR0YYMkWFAnT5oNzTTryoqpUW1C/UG5NBM/mTpNXEtcm+Bk6wZ10IzytVIIR1ttTDy+ogDj9b1TRt4RIJuakTGtMLj9fJsAjcmiEi37JELuyxWBcQU3qwXg5d216moKpKMaa9AnfSZYbAwg9oDaaXEu9qYtGtEyN6YFl43qNThBjWZVBqZnHHXOD4XmhlmjYhY1mDs9KkAyuM86YVmvAadywkyRIYFdSjGcfquoUek8KwZI7Gqn3tE9NqfG2eUaCF7AopT54OHflwLzegbImziSydTmufISCPiRil67jVraUZNUwO8Ph8ymQzPeCkE0RBm4QK36ogAsrHAPCJWaogw9MSBskfEflXZflVqKCPAKqsaekRUhojN88SMIH8wiKb2CdltlFloBhDT0bP383AbIoA8Lo6dNgVAeXiOkjqZdaQRMYYMkWFAvSqzu0pjbvfqhnqu1VDjRjzZSKxq5hExK/alRfFCM6oS78MQmuGpohrFzAATjYgLWhZmLNQ0NaJx3DgAwGBvnysqfVEYWIzCUWJjyNrmJjSMG4NMJoPdGzebf1cnNBNy0GeGodfNmIdmjLJmVPeaXUMklUhg6EAEADB2avlMsGrUxthwi1UB2fiRz1PpDTa56Z12913KmtGGDJFhQO0BsTs4RiMRrjHQqvjo8XqLLlZlpcn1XIvllDXDQzNul5A36KfDxJF63i5eFlszNFO4QTbY24d0KgWv14sJs2cAcG+VKvayKUaaJAtdhmpqeNpu9/adlgx2/dCM/c67DL3Gd1ysatRrRlULxsnkmL/SL/0Eq0asrszuveGmHD0icr8rypqxAxkiw4BoeOi57s3oN6iuquiI6oZY1aDEu2nWjK3QTHGyZnx+P3x+v+BpcMcjwgcZDa+PWafXdMpIrFq4wSRJEhfwscncNUNE6Apb09wIwG2xai5rprpK7rhrUj+EoZc1EzSo6WIGK1cudjMGrIpVC9OIAPJ1GzNlEgB39ThuIZ6D4S5mxmAGo3yeSm+w6YVmvAadywkyRIYFcTDUc92bYSRYZd6QQgsfGTW9MxNbmXVC1aJYoRkg66p3PTQjaDjUvU1CVcYTX0rVOl3ErQqwTFTKxJ4RlzIZopH+vKZ9bq4+xdAM2/dOCxkzgEFoxkExM8ZAzvAKVoV5/yBANiKN7qe80IwTQ0TQiWS3UfoJVo04DpQiLAPIBpt8nkpvsOmNg+QRMYYMkWFAdA9r1ZiwglF1Vbfc5fwh0vBqcLGqXtZMbkIIqZqXGeF2r5l0KsX3LxAKFa27L5AvjjQLBYit09XI56Ewg4ndI9wj4kINEYZ61etmp9W4UNCsjRsiFj0iOvdsIaGZRDTGj0983thvGHlE1DofJ+JxtSerHCZYNeI5KIVQFci/v8vhPCV0OkJ7KX3XEDJEhgHR+HCyQgOEzBkNjUjYJQFhykr6rl7WTFTfW6CH26ETQCkodTtrJpNK89h4fihAv5gZoCyLrcaNrBlA9poxD1nEhaqqfNvCtqL9A466BOvBzlltcxPGTJ0MANhl1RDR0SYVEpoBtIsIWhGrppNJhSDRyeSovm7lMMGqEYuaDXcNEUb+eSq958g0a4bSdzUhQ2QYEMMxTlZogLFHRC4yVaBHxKiyqsWsGcC6TkQOzbhjKACCeLEIoRlx++reJrzPjE66qNxrpoihGVUrdjdXquLq0+0BnxkLUxccAq/Xi0hXt2V3v544sJA6IoAc5lJ4RCyIVbN/l+83Jy0X8jwikYjtbRQbsajZcFdVZeSfpzIwREya3pFGRBsyRIYB0QvieGA0EKvy1N0CH0RDsapJ1kwmLXgLqqwZIm6FJETEDrl+lwuaiduyK45MJfU9IkGXPSLyv90MzcjbdnvAZ8Z5w7gxAKyHZQDB8FQbhjXONSKAsrAYQxarGl8n0QPnTCOinmDL3CPiYgjQDmoNVDl4jphhzATzDL6Qo/RdTcgQGQZEL4hzj4h2R1AArjUiYy5nrRRcK2IrdYl1M9wOnQDKeiZcJ+CiIWK2AjdL39UqaOZ3ScuiNjzcFBGK23Z7wFcbC1ZKuzNkQ0R9PZxrRABl7RSG36pHJLdPscFBR3Uj8ifY0q/01aQVGpHShGbEsgbZf5f+PCkE88I46uUeETJEtCBDZBhQZs04NEQM+s241YgsKQgqPR6P4m8+E7EqYCx2VePxeOQS7K6GTlj30zrhPfdDM3krcO4R0Unf5aEZf965DboUmlEbIm6mVYqTTSEdnrVIqDLJ7HhE9AzDwjUi+bVE2DU362fC7men4nH1dXRTGOwWokekVOm7QHENZCekUylufIrGsZXxczRDhsgwkIzFeW+GQsVz6iJLAFyrdimK8Hyq8IzPRKwKGNfZUCNO5G6GTngb9sYGeftF0IioV+C8pLhJ+i6Qf24DLoWoxJV0Mh53teiYUiNSXI+IvdCMtliVpe869kBqakRyhoiJR4Tdg07P01DfAR4CdepVKTaiJ6JUWTOAUrBaLp4jLeOYsmaMIUNkmGADomNXcW6SCYRCPEuGwdN3C4wlJxOi4FTHEDHyiOgUl9JCnMhd9VjkMnBqGhsBZAdMN92h8iCj12TNWKwK5OtE3GrOJ04IbmbMZLcnCxLd1iyoNVTd23da/q6ehypo4qEyg2fNtORrRMwMW+aedxoqkCQJ/T3Z591NY9JN2P2cSiZ5SfpSIBrI5XKutO5JqiNiTNEMkSlTpuCuu+7Cli1bMDQ0hE2bNmHp0qUI6PRKqXSYJ8TpwJiKx/nApm5+55ZHJJNK89WXOoWXiVWNJnWeUWIhNCOGZSRJcrS/mvsQZ4ZIg2KfXNu+3grctLKqPACpDRHmQSq0+V8iGkVsMOvGd1tAKIZm3F55il6kXes32bof9DU7LoVmxmhpREwMkdzfC/EcMUOoHMINWjAPXym9IdnfF+/L8jhXWl5TnjVD6bua+M0/4oy5c+fC6/Xiv/7rv7Bp0ybMnz8fd955J2pqavCtb32rWD9btsgeEWcDI5AVH1bV16GutQX7Orbx92WxauEPYjKeQKi6Kt8jkotxplwKzbDPFMtQqG6sV/zbve2biSP1r28yHs9m8wTUoRl3smaA7MAcrqlxXUAoCl+LGZrZtd66UBXQNwyDLolVa5sa4fF6IWUy/JkwC80keWjGucHGrl85CDC1YB6RUglVGcwQSkRjjlpnFAMtr6k31/SOsma0KZohsnLlSqxcuZL/u6OjA7/4xS9w5ZVXjkpDhA22Tj0iQFYUNnbaFEw+5CDFAMXcx25MEKlE1hDJ84hYyINnA/DYaVN4dU89WKMqNzNaADkDh4VmXN9+bnutkycqjrGmKft7Rtc3nUxlDZEihWaA7MA8Zsok11eqqUQCQ5EIquvrXZ8cxXNmRx8C6JfULrSOyECuc7HX50NtUyMGDxzgcf6kiViVebYKCRVwj0iZGiJMw1Jqj0ikDM+TlnFMdUSMKZohokVDQwN6evQt6GAwiJDg1q+rq9P97EiDh2YclngH5If+9G98Dad/42t5f3fjYeQpvOrKgFY8Ijkj4JSvfQWnfO0rln7Prc67fHssNNNUrNBMdnuLvnQhFn3pwry/G0182VVkjcLIE+sNuGE0Mc9FMSaI/q4eVNfXIzbgrkckFY8jnUrB5/ejc609Q4RV5XU7NCNlMhjo7UN9awvqWlsUKeamHhGmESnAEGEF1aIun2u3YLVUIiWqIcLgnqMyOk9GoRnSiGgzbIbIjBkzcPXVV+O6667T/cySJUuwdOnS4dqlYeWNvz8OfyCAja+/5Xgbb/3zSUw6eJ5meuzeLR3YvWFTIbsIQBasqqurWrHo335iJSYeNEezVoYWkpTBaw/9w+GeasNWIzUNxTFE3n3qWcw86nAEq/J76uzftt1wItUqaiY2a3NjX9989Ak0t0/A+8+9VPC21LzywMM4/NSTsfmtd1zf9qt/fQRNbeNdCc34/H5u7BXigezv6kZ9awvqx7TgwL79/H2z9N13Vz6LtrmzsObZFx3/9nsrn8fcYz+Ot5942vE2isma517CrI8fVfL92/TmanS8/R7ee/r5ku6HiFbRQx6aIUNEE9uGyE033YTrr7/e8DNz587F+vXr+b/b2trw1FNP4W9/+xvuuusuw23/6le/4v+uq6tDZ2en3V0sS97655N4659PFrSNdS+/hptOO8+lPdJGr98Mr6xqIFb94Pl/4YPn/1W8nbMAc4uz9F23QzOb3liNmz97gaPvapV5Z0ZlJp12Jca99t+vYu2/Xy14O1q88peH8MpfHirKth+9+RZH32MeMJ/fD6/fh0wqzfv+AFkBr1PEjtcBi0JVANiw6k1sWPWm498FgN0bNuHW/7y0oG0Uk+1rPiqL/Yv1D+DXF19R6t1QkBDaTDDII2KMbUPkl7/8JZYvX274mS1btvD/nzBhAl544QW8+uqr+OpXv2r4vUQigYTJaoMoLiz+rZe+axSaKQeYV6EqV9DMzRoihSJXV5UNEbeKmY1WxJBJMBxGbGCQh2WS8XhBqdusd09dS4ulhncEAQheuhBpRKxi2xDp6upCl6q5lh5tbW144YUXsHr1alxyySWupmkSxSHFUnCDI7NpExsEvN5sZrrbGpRCSPMOvLKRFyhS9tBogXW79fp8CKgMEacZMwyxuirvW0QLJcIErXpKvHs5lXjXpGgakba2Nrz44ovYtm0brrvuOowZM4b/be/evcX6WaJAdD0irERxmefBqz0L5eRpSPES+vmhGTJEnJOMxxGqruY6kaBJTRer8KJmQmiGPCKEGWzMCVSRR8QqRTNETjrpJMyaNQuzZs3K03moe20Q5QPXiOhWVh0ZoRm9f5cSFtYSS7yzWgPlZDCNNBLRGELV1XwFWmjGDIO3VRgjhGbII0KYYBSaIY+INkWrrHrvvffC4/FovojyhWkqdMWqyfJ+kNS1OMrKENHyiLDQTBlpWUYa6nRJt0IzkVwqdH1LC/cQ0nUizNAOzVDWjBHUa4ZQkEro1BGx0PSuHFCXSS8nT4MsVhU0Iiw0U0ZalpGGXNRMFZopIGMGkKur1rW2cM0UhWYIMxJUR8Q2ZIgQCnTTd0eIRZ8fmimfCV7LIxLknXfLZz9HGuqS2jw0U0DxQEAOzYRra1DDWgZQaIYwgSqr2ocMEUIBF6uG1ZVVWR2R8n6Qyjk0w86dVtZMoQ3vRjPMm8RWoEGXNCLxwSGeHtw0YTwA8ogQ5hiHZso7tF0qyBAhFPAS7zq9Zso/NDNCs2ZIe+AYdWiG9ZkpVCMCyF2Mmye2AZBLmxOEHgnyiNiGDBFCgV6J95GSB1/WWTMGBc1II+IcdUntUBXziLhgiORqiTS3t+V+q3zuJ6I8MQrNlLtHuVSQIUIo0Evf9fOsmfL2iOSFZsrI06BZ4r2K0ncLRS0ODNUwj0hhoRlA7u7a1JYNzZBGhDCDjTlBEqtahgwRQoEcmlFqREZK+lleaKaMPA1alVW5R6SMDKaRBk/fDbGsGTc9IllDpHHcWACkESHMYR2hA5S+axkyRAgFbMWn6xEp8wdJPVGUUzaKZvddSt8tGB6aqWJ1RNxJ3wVkj4jXl51IyCNCmEFZM/YhQ4RQkGLCv6CORqTMQzOSJCni+OXkaeChGdEQCVPWTKGoxYFyQbPCQzPMI8IgjwhhhlZoZqRo7EoFGSKEAj2PCO81MwIsejE8U06hGa2CZkGqrFowxQ3N9Cj+naLrRJiQiJJHxC5kiBAKzHvNlL9FL07qZRWaMUrfLSODaaShF5pxJX1X5RGh0AxhhthrhrU0IbGqMWSIEAqSOmJVuftueYdmAOWkXk6eBlkjIhQ0o6Z3BaPOmgnm0ndjBVZWBYBIV5fi3xSaIcwQxxz2fJMhYgwZIoQC5nrW94iU/4OkDM2UjyGSNirxXkYG00iDV1YNua8RGejuVf4WeUQIE0SNGjOOmdh5JHiUSwEZIoSCpF5l1RFSRwRQDgRlFZpJ5kq8B/I9IuW0nyMNtTiQZ824EJpJp1IY7O3j/yaPCGGGKJhnxjHzKJNGRBsyRAgFKZ3KqiMlfReQDZFMJsN1GeWApkaEZc2UkedmpMGzZqpC8Pp83LhzwxAB5BRegMSqhDW4cVwV5jVEAPKI6EGGCKEgqdF9V/EgJcv/QWITU7mV42ZZMz7N0Ax5RJwilnhnGTOAO6EZAOjvljNnKDRDWEFMKWdhbYA8InqQIUIo4JVVQ/mpZ8BICc3EFP8tF9LJfCNPDs2Ul9E0khDd4EwfkkomXfPeRfbLglUKzRBWkHVLYeX4SYaIJmSIEAq06oiMtAeJuUXLTQCq1fSOZ81Q+q5j5NBM2NXUXYZYS4Q8IoQVEjylPDTixs9SQIYIoYBnzQQC8Hizt4fYpG0kuBbZpF5uXgZ10zuP1yvXESkzo2kkwa5zMBzmqbtxl8IygLKWCGlECCuw5zkQDvOMmUwmAymTKeVulS1kiBAKRHFnIOcVEVN3JUkqyX7ZgU1M5eZl4B6RnEYkIHidyi2MNJLQCs24JVQFgP5u2RAhg5GwQjIqG8dUVdUcMkQIBWyyBGQtg/wglb9QFSjn0IxSIyJ25yw3781IQhQGhmtrAABxF4qZMRShGdKIEBZICNVVvSOoPUapIEOEUJBJp5Fm9S5UOfCpESBUBcTQTHl5GdTdd8ViZiPB01SuiNe5pqkJgLsaERKrEnbhoZmq8IhbyJUCMkSIPJIJZQfekeZaZBNTuZVNVze9o4wZdxDPX21zIwAgHnXPIxIRPCLlVJeGKF/klPLQiKpKXSr85h8hRhupeAKoqeGZM74RVMwMALat+RDJWBxb3nq31LuiIM3Fqn54PB7qM+MSkiQhGY8jEAqhttl9j0g0EsH2Dz5CsKoKg30HXNsuUbkkhLYDZIiYQ4YIkQdb9anFqiMlNLP9/Q9xwzGfLrsHXzx/vmBQDs2QR6RgkjGlIeKmWBUAbvv85fB4vZT1QFhCDM14yRAxhUIzRB5ydVVl58iRFOMsx4deKQQOUJ8ZF2HGXG1TIwB303eBrNclkx459z9RWsSUch9L3x1B4+dwQ4YIkUeeR4RU364gVqXNGiK5PjNkiBQMO4e1Lc3Zf7vsESEIO4ihGcqaMYcMESIPZs3z9F32II2Q0Ew5w1N4AxSacRPmVSpWaIYg7KCZNUMeNV3IECHySKo68FKM0z1447tAQK6qSoZIwcihGWaIuBuaIQg7KLNmsqEZVhaByIcMESIPufFd1hBhvVHoQSocuahZAIEqCs24BTuHzHtHoRmilIgFzShrxhwyRIg8UiqxKnlE3EMsahYksaprqL1K5BEhSglvOxCWs2ZGSh2mUkCGCJFHMqH2iJAh4hZimXcKzbiHupw/aUSIUpIUOkKzrBkaP/UhQ4TIQ/aIqJrekVi1YOTqqhSacRN1g0PyiBClJCE0YqSsGXPIECHyILFq8UjzDryUNeMm6vAWaUSIUqKoI0JZM6aQIULkwcWqOQ0DiVXdQyFWpdCMa+SHZsgjQpSOpNARmsSq5pAhQuTBDRESq7oOE6v6gkEKzbiI+hySR4QoJQktQ4QWcrqQIULkwcSq/pBaI0IPUqGkKWumKCSjJFYlygcxNENZM+aQIULkIXtEqLKq28hiVcqacRPRI5JOpXgIjCBKAVtceH0+BHOezzT1mtGFDBEiD7VYlWKc7qHQiFCvGdcQNSIUliFKjfhMh2trAND4aQQZIkQeeem7TKxKD1LByAXNZI0IhWYKJymk78ajZIgQpSWTSvNQdri2NvseZc3oQoYIkYdc4j0bOiCNiHswj4gv4KfQjIuIK9D4IGXMEKWHeenII2IOGSJEHrpiVXqQCialUUckQYZIwYihGUrdJcoBZhyTIWIOGSJEHinWwlolViXVd+FwjUggwOu0UGimcMTQDGlEiHKAPddVLDRDHmVdyBAh8mCrS7VHJEVZMwUjVlaVDRHyiBSK6FWi1F2iHGDPdYg8IqaQIULkkcwTq5JHxC2U3Xcpa8YtlFkzFJohSg8PzdSQIWIGGSJEHqmESqyay5pJkWuxYNi5DdVUw5vrykmhmcJRZM2QR4QoA5hHJFyXNUQoa0YfMkSIPHj6rio0Qx6RwmFi1ar6Ov4ehWYKR5E1Q+m7RBnADRHyiJhChgiRR1JHrEoPUuGw6rTMEEmnUnReXYAKmhHlRkKorgrQ+GkEGSJEHindXjMkVi0U7hGpyxoi5A1xB2VohjQiROlRh1zJo6xPUQ2Rf/zjH9i2bRui0Sh27dqF++67DxMmTCjmTxIukCdWpToirpFKZs9tdc4jom5fTzhD9CyRR4QoB9SLjHSSNCJ6FNUQeeGFF3D++edjzpw5OPfcczFjxgw89NBDxfxJwgV49U+/H16fj8SqLqLWiCSiJFR1C2bUxaiyKlEGqLPhaCGnj7+YG7/11lv5/2/fvh0333wzHn30Ufj9fqToopQtzCMCZDNnSKzqHjxrproaAGXMuEkyFke4pobSd4myQO0RoawZfYpqiIg0NTXhoosuwquvvqprhASDQYRyKaMAUFdXp/k5orikhRbq/lAQXn9ObEUekYJhHhEGhWbcg3mXKH2XKAfUzzZ5RPQpulj15ptvxsDAAHp6ejB58mSceeaZup9dsmQJIpEIf3V2dhZ79wgNJEmSa4kEg/Dz7rskVi2UdDKh+DcVM3OP7h07c/+lcYMoPckohWasYtsQuemmmyBJkuFrzpw5/PM///nPcdhhh+Gkk05COp3GfffdZ7jt+vp6/mpvb3d2VETBJIVaIl7qvusaeR6RKHlE3GL5N5bgZ2f8J7p3kiFClB71IoNC2/rYDs388pe/xPLlyw0/s2XLFv7/3d3d6O7uxsaNG7F27Vrs3LkTH//4x/Haa6/lfS+RSCCRSOS9Tww/cgpvSPCIUIyzUFKq+5tCM+4RHxzCvo5tpd4NggCgEZqhhZwutg2Rrq4udHV1OfoxrzfrgBF1IER5IhY1kzUiFJoplDyPCIVmCKIiodCMdYomVj366KNx1FFH4eWXX0Zvby9mzJiBH/3oR9i0aRNWrVpVrJ8lXEIs8051RNxD7RGh9F2CqEwSlDVjmaKJVYeGhnDOOefgueeew/r163H33Xfj/fffx6JFiyj8MgLgHpGQIFYlj0jBqN2zJFYliMqEsmasUzSPyAcffIDFixcXa/NEkeEekWBIFqvSg1QwKVXWTIo0IgRRkVBoxjrUa4bQJJmg0EwxUGtE1O5bgiAqA8qasQ4ZIoQmzCMSCAbl7ruk+i6YvKwZ0ogQREVCoRnrkCFCaJJK5DQiVWHyiLgIVVYliNFBXmiGFnK6kCFCaMIKmoVzPVEAMkTcQC34JbEqQVQm+U3vKGtGDzJECE14c7YawRChrBlXEMMzFJohiMokP32XFnJ6kCFCaMI8IgpDhDwiriCGZyg0QxCViTojjjwi+pAhQmjCxKrhmhr+XoYeJFcQPSKUNUMQlYtYsJCyZvQhQ4TQhIlVQ7VZQyRFYRnXEM8lhWYIonIRWziQR1kfMkQITZIqjwgpvt1DoRGh0AxBVCzi801jqD5kiBCaqMWq6RR5RNxCHJAoa4YgKhcxNEMeEX3IECE0UYtVyZp3D8qaIYjRQVLQgFHTO33IECE0UYtVSajqHpQ1QxCjA/Z8ZzIZSJlMifemfCFDhNCEi1VzHhESq7qH2PiOQjMEUbmw55syZowhQ4TQRB2aoQfJPdKCR4R5ngiCqDxY6JX0IcaQIUJowlyK/kAAAHlE3ISFZpKxOCRJKvHeEARRLNg4SoaIMWSIEJqoV+qkEXEPJlalsAxBVDbsGSexvzFkiBCaJFXt6smidw/mXUqSIUIQFQ3LmqGMGWPIECE0yeuTQKEZ12AekSSVdyeIioY947SQM4YMEUKTZJw8IsWCaUQoNEMQlY2cNUMeESPIECE0SVFopmiQR4QgRgcs/ErjpzFkiBCaqMWqJLZyj7SQNUMQROVCoRlrkCFCaJJMqDQi9CC5RipFoRmCGA0kyCNiCTJECE3yPSIkVnULuY4IGSIEUckkSSNiCTJECE1IrFo8Bnp6AQD93T0l3hOCIIpJf1f2GR/s6yvtjpQ5/lLvAFGeqD0gZIi4x+rHViCTTOGjf79S6l0hCKKIbHrzbfz5uz/E1nfeL/WulDVkiBC6JGNxBMIhACRWdZNkLI43Hn281LtBEESRkTIZrH5sRal3o+yh0AyhiyhYJY8IQRAEUQzIECF0EQWrZIgQBEEQxYAMEUIXUbBKWTMEQRBEMSBDhNAlGafQDEEQBFFcyBAhdFGEZkisShAEQRQBMkQIXcR+M2SIEARBEMWADBFCFzE0k6HQDEEQBFEEyBAhdBE9IikSqxIEQRBFgAwRQpckpe8SBEEQRYYMEUIX0ogQBEEQxYYMEUIXKmhGEARBFBsyRAhdSKxKEARBFBsyRAhdSKxKEARBFBsyRAhdRLEqeUQIgiCIYkCGCKGLQqxKhghBEARRBMgQIXRJib1mKGuGIAiCKAJkiBC6UB0RgiAIotiQIULoQmJVgiAIotiQIULoQmJVgiAIotiQIULoItYRodAMQRAEUQzIECF0UVRWJbEqQRAEUQTIECF0SSUoa4YgCIIoLmSIELpQ1gxBEARRbMgQIXRRhmYoa4YgCIJwn2ExRILBIN555x1IkoQFCxYMx08SLpCkyqoEQRBEkRkWQ+T//b//h127dg3HTxEuQpVVCYIgiGJTdEPklFNOwcknn4zrrruu2D9FuEyKNCIEQRBEkfEXc+Njx47FnXfeibPOOgtDQ0Omnw8GgwiFQvzfdXV1xdw9woR4NAog6w0hQ4QgCIIoBkU1RJYvX47f/e53WL16NaZMmWL6+SVLlmDp0qXF3KWS4vf6kMqkC9pGlT8Ej8b7iUyq4G0z5jZOxLETDsKx4+fhU7/egqF0At0LzsFfN/0bW/v3Oto/PYZScfMP2aQ5VIc5je3oHOzG9oH9rm8/7AvC68k/SrNr4IEHC1qn4dMTF+KoMbOwc7Ab73RtxjtdW7CudwfSUsaV/Qt6/Uhk3DccPfDA7/UhWaRtB31+xNP2RdF+rw8HNU3GwtZpaA7VYdOB3VjftxMd/XsLeia8Hi+aQrVoCdehNVyPgWQMH/RsQ8bCdXLjWS/WdXQLN47RDQJef1HuyUKYWNOKw8ZMx2GtMzCvcRI+7N2O+9Y/V5TxqBLwAJDsfOGmm27C9ddfb/iZuXPn4uSTT8b555+PRYsWIZPJYMqUKdi6dSsWLlyI9957T/N7Wh6Rzs5O1NfXo7+/385uGtJe04JTJx+p+Tevx4sqfxBV/iCq/SFU+UKoDoRQ5cv92x9Cde5V5Q9iMBnDjoEu7Bjcj50D3dgxsB97o31or2nBnMaJmN3QhtmN7ZjT2I7GUC229e/D+r6d2NC3K/vfA7swkIyiJVyHllB99r9h9t/sANgSrkdLKPvvoC+gud8ZKYO9Q318X3YMdGHHQBf2DPWiOxZBd6wf3bEIeuID6E9G0Rqux6TaVkysac3+t7YVBzVNxifGz0VLuF733L22dx3+uunfeGjzK6jyB7GwdXr21TIdC1unoa2mxda16I0P4N2uLdlX9xa819WBdX070RSqVezbpNpW1AWq0RPv58fC/juuugmH8f2Yhsl1Y/n2e2L9eLd7C97t6sC7XVuwvm8nAl4/v37seqaldG578rYPJAYxpW6scHzZY5xUO0bzWFKZNDoie7HhQCc29HVifV8nNh3Yhan14/Dp9oVYPHEBxlQ1aH43mopjTfc2rOnZin3RA/LxxSPoikYAAK1V9Xn3SGtY+Ldwj0RTcfl4ctvojovnrR9dsQi6YxGEfAH5ngtlt9Uq3IPs382hWvi8PuyL5u6zAfk+S2XSyvs1XIfGYA16EwPYMdCFnbnP7RjYj0gyiun14zC7oT37jDS2YVZDG6r8IWSkDIZScURTCQyl4hhKxdEXH8zut3Dto6kEDm6ejIWt0zG/eQpCGs9FMp3C5shurO/rxO6h3tx243y78XQS9cFqxTlsFc5Dc7gOXo8yet2fGMLr+zZg1Z51eHXvWryxdwOaQrX83mD3ycTaVvTFB7BdOEc7BvajNz6QN4ZU+0NoDNYorm9ruB5hfxD9iSH5+7nnen/0AMJ8PJLHJb83P9LugQdhnzyesVfQ50csleTnIpqOYygZx/5YBO93d+D97q34sGebwhCqC1ThhLb5WNy+AJ9qX4AFrdMQTyf5PdWV++++aB92Dnbx8XDnYDd2DnSh2h/C7MZ2zG5sy42N7ZhWPw6SJMn7kbvusXQCksbUVO0PoSVUnztXdWitqkdtoAqxVAI7B7sU53rXYE/RDBSf15udG/zK+WFa/Tgc1jpD9zl/bud7WLbuGTzSsQrR3CLMAw9mNkzAgpZpWJAzpuVxrp/f+wGvnx8ze04bQzWai6JkJo3OgW7FvLRzsJv/5nBQV1eHSCRiaf62bYi0traipcV4stmyZQsefPBBfPazn4UkyZv3+/1IpVK4//778eUvf9n0t+wciB1OmngYnv7sj1zbXqURTcXxxr4NeHn3R3h1z1q01bTgP2eegE+2zYfP6yv17lli50AXxlU1IuArqtPPNv2JIby46wO8sucjtFU347AxM7CwZRrqgtWl3rURTV98AO92dWBftA+zcsZ/TSDs2ra7Y/0YU9WA+lF0nVKZNNb17cQH3dswtX4sjhwzC/4R8vyXmlQmjbW9O/BObvHzqfYFWDxRzhiNJIbw9I53MLG2BYc0T3XtXjVjKBnLGZ4JRFMJbvit2rsO1756l6u/VVRDxCqTJk1Cfb28sm5ra8PTTz+Nc889F6+//jo6OztNt1EsQ2Rh63TceOSFmn+TJGRXB4J1nr1gcQxyq13+e0OwOrdiH4NJuZX7+Kom7BrqyfN8dEUPYEbDhNxKsJ17SsK+IF+5ilZwl2Klkf3/vsSgpgu/NhDGpBpxP7L/HVPVwFfKLeF6fsNnpAx2D/byVcTOwS50RPbitb3r8E7XFs2VxLiqRnxuxnG4YObxOH7CwRhKxvB+z1bubXivuwNre3dYdif7PF7MqB+v8KosaJ2GplAt0pk0dg8J+zfQhUhyCE3BWoXHqCVczyehd7o2493uDrzf3YFIYghBrx8HNU/mq9TDWqdjat1YxNLJ7DUVrrPf41Nsk004sVQCH/RsU3hVPurdjphGCKExWMOvKbvGsxrbsD96AM/ufA/P7nwXr+9bn+fOZiuiw1pnYHZju9LTkVshA1DcI12CR0h9j/Qno2gM1eRWTw18ha/0qMhelEQmlbcN9e+wvyUzaUysacm7z7weT95+9SUG0Ryq48/FpNoxmFjTgsZQLfccic9IVyyi6Xlk4RHRY1EbCGND367sNe/agg5VyNADDybWtmJOY9br0hKuy9t22B9AJDGk+8x1xSLoiffz6+X1eHFQ0yR8Yvy87GvcPMxqbEMslcCa3HPwXnf2Htl4YBdawnWKcWFSzRjUB6vzPDPRVAIHEoOKc9cViyCSGMLY6sa857opVItYOqEao+JI6oRJ1J8dSsWRzKQQ8gUUnpkafxgTa1uzK/OWaWgO52v0NvbtwvOd7+H5zvfx8p6P4PN4FZ6w1nA9xlU1YmJtC/doTqodw8ed7f37sOHALu4x3BzZjbSUUXhrqvxBVPlCeb/NjkW8H7tiEfTGB9AQrFHcj5NqWzGhulnTW+AGmZwXRzz/Q6k49gz14p2uLfigZxti6YTiO1PqxuKLsz+FL89ZjBkNExR/i6bi+KBnG97r7sCuwR408zE7/zkVvYO98QHN8FiVP4iJNa2YWCs/q7WBKt3jeXLbWzjtyaWunBtGWRgiaqyEZtQUyxAZzYR9QdQHqxUDrBPqAlUYTMUtxcvtwowLtzQTTgh4/WgM1eg+6AQBAPXBagwmYyW9V4vFxJpWLGidhoObJmNvtA/Pd76PHQ41Dg3BGiQyqWENDZQzx004GMeNPwgd/XvxXs5wLfY91BCsQVOoNmfoyWHpKn92IfzKno9c/T0yRAiCIAiCKBl25u9hC6Bv27YNniK5yQiCIAiCGJlQrxmCIAiCIEoGGSIEQRAEQZQMMkQIgiAIgigZZIgQBEEQBFEyyBAhCIIgCKJkkCFCEARBEETJIEOEIAiCIIiSQYYIQRAEQRAlgwwRgiAIgiBKBhkiBEEQBEGUDDJECIIgCIIoGWSIEARBEARRMoat6V0h1NXVlXoXCIIgCIKwiJ15u6wNEXYgnZ2dJd4TgiAIgiDsUldXh/7+fsPPeABIw7M7zmhrazM9CCfU1dWhs7MT7e3tRdl+OUDHWBnQMVYGdIyVAR2jve3s2rXL9HNl7REBYOkgCqG/v79ibyYGHWNlQMdYGdAxVgZ0jNa+bwUSqxIEQRAEUTLIECEIgiAIomSMWkMkHo9j6dKliMfjpd6VokHHWBnQMVYGdIyVAR2j+5S9WJUgCIIgiMpl1HpECIIgCIIoPWSIEARBEARRMsgQIQiCIAiiZJAhQhAEQRBEySBDhCAIgiCIkjEqDZGrrroKHR0diEajeO2113DUUUeVepcK4vjjj8c///lPdHZ2QpIknHnmmXmf+cEPfoBdu3ZhaGgIzzzzDGbOnFmCPXXG9ddfjzfeeAORSAR79+7F3//+d8yePVvxmVAohF//+tfo6upCf38/HnroIYwdO7ZEe2yfK664Au+99x4OHDiAAwcO4NVXX8Upp5zC/z7Sj0+L73znO5AkCbfccgt/b6Qf54033ghJkhSvtWvX8r+P9ONjtLW14Y9//CO6urowNDSE999/H0cccYTiMyN5zAGAjo6OvGspSRJ+/etfA6iMa+n1evHDH/4QW7ZswdDQEDZt2oT//d//zfvccFxLaTS9zj//fCkWi0lf/vKXpXnz5km///3vpZ6eHmnMmDEl3zenr1NOOUX60Y9+JJ111lmSJEnSmWeeqfj7t7/9bam3t1c644wzpEMOOUR69NFHpc2bN0uhUKjk+27ltWLFCuniiy+WDjroIOnQQw+VHn/8cWnr1q1SdXU1/8xvfvMbadu2bdKJJ54oHX744dKrr74qvfzyyyXfd6uv008/XfqP//gPaebMmdKsWbOkH//4x1I8HpcOOuigijg+9evII4+UtmzZIr377rvSLbfcUjHX8cYbb5TWrFkjjRs3jr9aWloq5vgASI2NjVJHR4d0zz33SEcddZQ0depU6aSTTpKmT5/OPzPSxxwAUmtrq+I6Ll68WJIkSVq0aFHFXMslS5ZI+/fvl0499VRpypQp0rnnnitFIhHp6quvHu5rWfqTMZyv1157Tbr99tv5vz0ej7Rz507pO9/5Tsn3zY2XliGya9cu6dprr+X/rq+vl6LRqHTBBReUfH+dvFpbWyVJkqTjjz+eH088HpfOPfdc/pk5c+ZIkiRJH/vYx0q+v05f3d3d0qWXXlpxx1dTUyOtX79eWrx4sfTCCy9wQ6QSjvPGG2+U3nnnHc2/VcLxAZBuuukm6V//+pfhZyptzAEg3XLLLdLGjRsr6lo+9thj0l133aV476GHHpL++Mc/Duu1HFWhmUAggCOOOALPPvssf0+SJDz77LM45phjSrhnxWPatGmYMGGC4pgjkQhef/31EXvMDQ0NAICenh4AwBFHHIFgMKg4xvXr12Pbtm0j8hi9Xi8uuOAC1NTUYNWqVRV3fHfccQeeeOIJPPfcc4r3K+U4Z82ahc7OTmzevBl/+tOfMGnSJACVc3xnnHEG3nrrLTz44IPYu3cv3n77bVx++eX875U45gQCAXzhC1/APffcA6ByruWrr76KxYsXY9asWQCAQw89FMcddxxWrFgBYPiuZdl333WT1tZW+P1+7N27V/H+3r17MXfu3BLtVXEZP348AGgeM/vbSMLj8eDWW2/Fyy+/jA8//BBA9hjj8TgOHDig+OxIO8b58+dj1apVCIfDGBgYwNlnn421a9di4cKFFXF8AHDBBRfg8MMP19RlVcJ1fP311/HlL38Z69evx4QJE3DjjTfi3//+N+bPn18RxwcA06dPx5VXXolf/epX+OlPf4qjjjoKt912GxKJBO67776KG3MA4KyzzkJjYyOWL18OoDLuVQC4+eabUV9fj3Xr1iGdTsPn8+GGG27An//8ZwDDN3+MKkOEGPnccccdmD9/Po477rhS74rrrF+/HgsXLkRDQwM+97nP4d5778WiRYtKvVuuMXHiRPzf//0fTjrppIrt0/HUU0/x/1+zZg1ef/11bNu2Deeffz6i0WgJ98w9vF4v3nrrLdxwww0AgHfffRfz58/HFVdcgfvuu6/Ee1ccLrvsMqxYsQK7d+8u9a64yvnnn4+LLroIn//85/Hhhx9i4cKFuPXWW7Fr165hvZajKjTT1dWFVCqFcePGKd4fN24c9uzZU6K9Ki7suCrhmG+//XacfvrpOPHEE9HZ2cnf37NnD0KhEA/ZMEbaMSaTSWzevBlvv/02vvvd7+K9997DNddcUzHHd8QRR2DcuHF4++23kUwmkUwm8clPfhL//d//jWQyib1791bEcYocOHAAGzZswMyZMyvmOu7evRsfffSR4r21a9di8uTJACprzAGAyZMn49Of/jTuuusu/l6lXMuf//znuPnmm/HXv/4VH3zwAf70pz/hlltuwZIlSwAM37UcVYZIMpnE6tWrsXjxYv6ex+PB4sWLsWrVqhLuWfHo6OjA7t27FcdcV1eHj33sYyPqmG+//XacffbZ+NSnPoWtW7cq/rZ69WokEgnFMc6ePRtTpkwZUceoxuv1IhQKVczxPffcc5g/fz4WLlzIX2+++Sbuv/9+LFy4EG+99VZFHKdITU0NZsyYgd27d1fMdXzllVcwZ84cxXuzZ8/Gtm3bAFTOmMO45JJLsG/fPjzxxBP8vUq5ltXV1chkMor30uk0vN6saTCc17Lkyt3hfJ1//vlSNBqVvvSlL0lz586Vfve730k9PT3S2LFjS75vTl81NTXSggULpAULFkiSJEn/8z//Iy1YsECaNGmSBGTTr3p6eqTPfvaz0vz586W///3vIyqV7o477pB6e3ulE044QZFOFw6H+Wd+85vfSFu3bpU++clPSocffrj0yiuvSK+88krJ993q66c//al0/PHHS1OmTJHmz58v/fSnP5XS6bT06U9/uiKOT+8lZs1UwnH+/Oc/l0444QRpypQp0jHHHCM9/fTT0r59+6TW1taKOD4gm3qdSCSkJUuWSDNmzJAuvPBCaWBgQPr85z/PPzPSxxz28ng80tatW6Wbbrop72+VcC2XLVsm7dixg6fvnnXWWdK+ffukm2++ebivZelPxnC/vva1r0lbt26VYrGY9Nprr0lHH310yfepkNeiRYskLZYtW8Y/84Mf/EDavXu3FI1GpWeeeUaaNWtWyffb6kuPiy++mH8mFApJv/71r6Xu7m5pYGBAevjhh6Vx48aVfN+tvu666y6po6NDisVi0t69e6VnnnmGGyGVcHx6L7UhMtKP8y9/+YvU2dkpxWIxaceOHdJf/vIXRX2NkX587HXaaadJ77//vhSNRqWPPvpIuvzyy/M+M5LHHPY66aSTJEmSNPe9Eq5lbW2tdMstt0hbt26VhoaGpE2bNkk/+tGPpEAgMKzX0pP7H4IgCIIgiGFnVGlECIIgCIIoL8gQIQiCIAiiZJAhQhAEQRBEySBDhCAIgiCIkkGGCEEQBEEQJYMMEYIgCIIgSgYZIgRBEARBlAwyRAiCIAiCKBlkiBAEQRAEUTLIECEIgiAIomSQIUIQBEEQRMn4/+yDbWEikG8qAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "81geChQYEksA"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}