StockPricePrediction.ipynb

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        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas_datareader as data\n",
        "import yfinance as yf\n",
        "from pandas_datareader import data as pdr\n",
        "\n"
      ]
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        "yf.pdr_override()\n",
        "\n",
        "start = '2010-01-01'\n",
        "end = '2019-12-31'\n",
        "\n",
        "df = pdr.get_data_yahoo('AAPL', start, end)\n",
        "df.head()"
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              "                Open      High       Low     Close  Adj Close     Volume\n",
              "Date                                                                    \n",
              "2010-01-04  7.622500  7.660714  7.585000  7.643214   6.487535  493729600\n",
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              "2010-01-07  7.562500  7.571429  7.466071  7.520714   6.383557  477131200\n",
              "2010-01-08  7.510714  7.571429  7.466429  7.570714   6.425996  447610800"
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              "                 Open       High        Low      Close  Adj Close     Volume\n",
              "Date                                                                        \n",
              "2019-12-23  70.132500  71.062500  70.092499  71.000000  69.261604   98572000\n",
              "2019-12-24  71.172501  71.222504  70.730003  71.067497  69.327454   48478800\n",
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              "        document.querySelector('#df-06fc1f96-3522-4fc8-ae8b-da2594d35a16 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-06fc1f96-3522-4fc8-ae8b-da2594d35a16');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-05ac69af-7df4-4c80-bf5b-b03485ce7d04\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-05ac69af-7df4-4c80-bf5b-b03485ce7d04')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-05ac69af-7df4-4c80-bf5b-b03485ce7d04 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df=df.reset_index()\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "NtrHJqXpoMns",
        "outputId": "b1afc7ea-558f-422c-a65e-c5e5cd5f7265"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        Date      Open      High       Low     Close  Adj Close     Volume\n",
              "0 2010-01-04  7.622500  7.660714  7.585000  7.643214   6.487535  493729600\n",
              "1 2010-01-05  7.664286  7.699643  7.616071  7.656429   6.498751  601904800\n",
              "2 2010-01-06  7.656429  7.686786  7.526786  7.534643   6.395377  552160000\n",
              "3 2010-01-07  7.562500  7.571429  7.466071  7.520714   6.383557  477131200\n",
              "4 2010-01-08  7.510714  7.571429  7.466429  7.570714   6.425996  447610800"
            ],
            "text/html": [
              "\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Date</th>\n",
              "      <th>Open</th>\n",
              "      <th>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Close</th>\n",
              "      <th>Adj Close</th>\n",
              "      <th>Volume</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2010-01-04</td>\n",
              "      <td>7.622500</td>\n",
              "      <td>7.660714</td>\n",
              "      <td>7.585000</td>\n",
              "      <td>7.643214</td>\n",
              "      <td>6.487535</td>\n",
              "      <td>493729600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2010-01-05</td>\n",
              "      <td>7.664286</td>\n",
              "      <td>7.699643</td>\n",
              "      <td>7.616071</td>\n",
              "      <td>7.656429</td>\n",
              "      <td>6.498751</td>\n",
              "      <td>601904800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2010-01-06</td>\n",
              "      <td>7.656429</td>\n",
              "      <td>7.686786</td>\n",
              "      <td>7.526786</td>\n",
              "      <td>7.534643</td>\n",
              "      <td>6.395377</td>\n",
              "      <td>552160000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2010-01-07</td>\n",
              "      <td>7.562500</td>\n",
              "      <td>7.571429</td>\n",
              "      <td>7.466071</td>\n",
              "      <td>7.520714</td>\n",
              "      <td>6.383557</td>\n",
              "      <td>477131200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2010-01-08</td>\n",
              "      <td>7.510714</td>\n",
              "      <td>7.571429</td>\n",
              "      <td>7.466429</td>\n",
              "      <td>7.570714</td>\n",
              "      <td>6.425996</td>\n",
              "      <td>447610800</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c8ab70c3-8a6d-471e-bcba-3934b16f1a5f')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-c8ab70c3-8a6d-471e-bcba-3934b16f1a5f button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-c8ab70c3-8a6d-471e-bcba-3934b16f1a5f');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-6376ee4d-3500-4810-a4d7-f479586af328\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-6376ee4d-3500-4810-a4d7-f479586af328')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-6376ee4d-3500-4810-a4d7-f479586af328 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df=df.drop(['Date','Adj Close'],axis=1)\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "6eCNwUvfoMo9",
        "outputId": "f2ffd7e6-ab74-4d5e-d674-66e90ef4d921"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       Open      High       Low     Close     Volume\n",
              "0  7.622500  7.660714  7.585000  7.643214  493729600\n",
              "1  7.664286  7.699643  7.616071  7.656429  601904800\n",
              "2  7.656429  7.686786  7.526786  7.534643  552160000\n",
              "3  7.562500  7.571429  7.466071  7.520714  477131200\n",
              "4  7.510714  7.571429  7.466429  7.570714  447610800"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-5051a8af-0f95-40c5-af26-d9b6e966c3d6\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Open</th>\n",
              "      <th>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Close</th>\n",
              "      <th>Volume</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>7.622500</td>\n",
              "      <td>7.660714</td>\n",
              "      <td>7.585000</td>\n",
              "      <td>7.643214</td>\n",
              "      <td>493729600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>7.664286</td>\n",
              "      <td>7.699643</td>\n",
              "      <td>7.616071</td>\n",
              "      <td>7.656429</td>\n",
              "      <td>601904800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>7.656429</td>\n",
              "      <td>7.686786</td>\n",
              "      <td>7.526786</td>\n",
              "      <td>7.534643</td>\n",
              "      <td>552160000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>7.562500</td>\n",
              "      <td>7.571429</td>\n",
              "      <td>7.466071</td>\n",
              "      <td>7.520714</td>\n",
              "      <td>477131200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>7.510714</td>\n",
              "      <td>7.571429</td>\n",
              "      <td>7.466429</td>\n",
              "      <td>7.570714</td>\n",
              "      <td>447610800</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-5051a8af-0f95-40c5-af26-d9b6e966c3d6')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
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              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
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              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-5051a8af-0f95-40c5-af26-d9b6e966c3d6 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-5051a8af-0f95-40c5-af26-d9b6e966c3d6');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-bccda323-486e-4b4e-8bed-1b10d163559c\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-bccda323-486e-4b4e-8bed-1b10d163559c')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-bccda323-486e-4b4e-8bed-1b10d163559c button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(df.Close)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "id": "8CedfLIto6ak",
        "outputId": "072140d1-1e65-4a6e-a596-1996a98008de"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7e4d18972f50>]"
            ]
          },
          "metadata": {},
          "execution_count": 12
        },
        {
          "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": [
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "dGFRlDizo6s3",
        "outputId": "06f8fc0e-bcfd-44b8-9f33-98e92552a22c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           Open       High        Low      Close     Volume\n",
              "0      7.622500   7.660714   7.585000   7.643214  493729600\n",
              "1      7.664286   7.699643   7.616071   7.656429  601904800\n",
              "2      7.656429   7.686786   7.526786   7.534643  552160000\n",
              "3      7.562500   7.571429   7.466071   7.520714  477131200\n",
              "4      7.510714   7.571429   7.466429   7.570714  447610800\n",
              "...         ...        ...        ...        ...        ...\n",
              "2510  70.132500  71.062500  70.092499  71.000000   98572000\n",
              "2511  71.172501  71.222504  70.730003  71.067497   48478800\n",
              "2512  71.205002  72.495003  71.175003  72.477501   93121200\n",
              "2513  72.779999  73.492500  72.029999  72.449997  146266000\n",
              "2514  72.364998  73.172501  71.305000  72.879997  144114400\n",
              "\n",
              "[2515 rows x 5 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-d57ee6e6-e2fe-4f03-a409-80c348c53728\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Open</th>\n",
              "      <th>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Close</th>\n",
              "      <th>Volume</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>7.622500</td>\n",
              "      <td>7.660714</td>\n",
              "      <td>7.585000</td>\n",
              "      <td>7.643214</td>\n",
              "      <td>493729600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>7.664286</td>\n",
              "      <td>7.699643</td>\n",
              "      <td>7.616071</td>\n",
              "      <td>7.656429</td>\n",
              "      <td>601904800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>7.656429</td>\n",
              "      <td>7.686786</td>\n",
              "      <td>7.526786</td>\n",
              "      <td>7.534643</td>\n",
              "      <td>552160000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>7.562500</td>\n",
              "      <td>7.571429</td>\n",
              "      <td>7.466071</td>\n",
              "      <td>7.520714</td>\n",
              "      <td>477131200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>7.510714</td>\n",
              "      <td>7.571429</td>\n",
              "      <td>7.466429</td>\n",
              "      <td>7.570714</td>\n",
              "      <td>447610800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2510</th>\n",
              "      <td>70.132500</td>\n",
              "      <td>71.062500</td>\n",
              "      <td>70.092499</td>\n",
              "      <td>71.000000</td>\n",
              "      <td>98572000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2511</th>\n",
              "      <td>71.172501</td>\n",
              "      <td>71.222504</td>\n",
              "      <td>70.730003</td>\n",
              "      <td>71.067497</td>\n",
              "      <td>48478800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2512</th>\n",
              "      <td>71.205002</td>\n",
              "      <td>72.495003</td>\n",
              "      <td>71.175003</td>\n",
              "      <td>72.477501</td>\n",
              "      <td>93121200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2513</th>\n",
              "      <td>72.779999</td>\n",
              "      <td>73.492500</td>\n",
              "      <td>72.029999</td>\n",
              "      <td>72.449997</td>\n",
              "      <td>146266000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2514</th>\n",
              "      <td>72.364998</td>\n",
              "      <td>73.172501</td>\n",
              "      <td>71.305000</td>\n",
              "      <td>72.879997</td>\n",
              "      <td>144114400</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2515 rows × 5 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d57ee6e6-e2fe-4f03-a409-80c348c53728')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-d57ee6e6-e2fe-4f03-a409-80c348c53728 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-d57ee6e6-e2fe-4f03-a409-80c348c53728');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-8f73348f-9cf6-4ee8-a5a7-0fdfb763a562\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-8f73348f-9cf6-4ee8-a5a7-0fdfb763a562')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-8f73348f-9cf6-4ee8-a5a7-0fdfb763a562 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ma100=df.Close.rolling(100).mean()\n",
        "ma100"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9cT5OhPUo6uT",
        "outputId": "e95de3d4-d43c-4b31-a146-f3aff491c38d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0             NaN\n",
              "1             NaN\n",
              "2             NaN\n",
              "3             NaN\n",
              "4             NaN\n",
              "          ...    \n",
              "2510    59.201075\n",
              "2511    59.401700\n",
              "2512    59.643125\n",
              "2513    59.875125\n",
              "2514    60.106325\n",
              "Name: Close, Length: 2515, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(12,6))\n",
        "plt.plot(df.Close)\n",
        "plt.plot(ma100,'r')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 522
        },
        "id": "UhpbeMJVo6x8",
        "outputId": "17d0c2d9-d2f3-4fa8-c6c9-a491c670f962"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7e4d198aba60>]"
            ]
          },
          "metadata": {},
          "execution_count": 17
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ma200=df.Close.rolling(200).mean()\n",
        "ma200"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZOFD_7cbo6z5",
        "outputId": "752ab087-979b-4c20-f862-99264a4f274e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0             NaN\n",
              "1             NaN\n",
              "2             NaN\n",
              "3             NaN\n",
              "4             NaN\n",
              "          ...    \n",
              "2510    54.132313\n",
              "2511    54.261513\n",
              "2512    54.396763\n",
              "2513    54.529350\n",
              "2514    54.661100\n",
              "Name: Close, Length: 2515, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(12,6))\n",
        "plt.plot(df.Close)\n",
        "plt.plot(ma100,'r')\n",
        "plt.plot(ma200,'g')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 522
        },
        "id": "G4yZJtQyo6-t",
        "outputId": "286adfb7-d739-4227-bc78-2d34348b7263"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7e4d18a07130>]"
            ]
          },
          "metadata": {},
          "execution_count": 19
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZI0GisjYo7Al",
        "outputId": "5f77f451-2858-4a9d-af55-9c303290f2b3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(2515, 5)"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data_training = pd.DataFrame(df['Close'][0:int(len(df)*0.70)])\n",
        "data_testing = pd.DataFrame(df['Close'][int(len(df)*0.70):int(len(df))])\n",
        "print(data_training.shape)\n",
        "print(data_testing.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yNqpGX5eoMr_",
        "outputId": "0ac82e73-20ec-4c1c-8bc9-0dbbf2177163"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(1760, 1)\n",
            "(755, 1)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data_training.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "YXjVjKNVoMtc",
        "outputId": "ba95c527-3399-4d2b-e9fe-2b97531366ba"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      Close\n",
              "0  7.643214\n",
              "1  7.656429\n",
              "2  7.534643\n",
              "3  7.520714\n",
              "4  7.570714"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-59c6b68e-7d6e-4942-a538-b17c1ce355b1\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Close</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>7.643214</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>7.656429</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>7.534643</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>7.520714</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>7.570714</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-59c6b68e-7d6e-4942-a538-b17c1ce355b1')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
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              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-59c6b68e-7d6e-4942-a538-b17c1ce355b1 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-59c6b68e-7d6e-4942-a538-b17c1ce355b1');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-b779c1d1-b047-4015-a16d-810a99c9ed2e\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-b779c1d1-b047-4015-a16d-810a99c9ed2e')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-b779c1d1-b047-4015-a16d-810a99c9ed2e button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data_testing.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "Rx4tEWZLsnG1",
        "outputId": "219f77bf-b758-4fa9-a895-d30999705f1b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          Close\n",
              "1760  29.182501\n",
              "1761  28.955000\n",
              "1762  29.037500\n",
              "1763  29.004999\n",
              "1764  29.152500"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-410ce849-67e7-4cbb-a6cb-cab701a11f12\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Close</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1760</th>\n",
              "      <td>29.182501</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1761</th>\n",
              "      <td>28.955000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1762</th>\n",
              "      <td>29.037500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1763</th>\n",
              "      <td>29.004999</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1764</th>\n",
              "      <td>29.152500</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-410ce849-67e7-4cbb-a6cb-cab701a11f12')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-410ce849-67e7-4cbb-a6cb-cab701a11f12 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-410ce849-67e7-4cbb-a6cb-cab701a11f12');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-855ee546-20df-455b-8073-9f6459cdcb54\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-855ee546-20df-455b-8073-9f6459cdcb54')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
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              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-855ee546-20df-455b-8073-9f6459cdcb54 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import MinMaxScaler\n",
        "scalar = MinMaxScaler(feature_range=(0,1))"
      ],
      "metadata": {
        "id": "Pk0d4v3fvT2b"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data_training_array=scalar.fit_transform(data_training)\n",
        "data_training_array"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lMl0qvtHvT4Z",
        "outputId": "b06b6e94-546f-4969-bc5c-a51dd257ec6d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.02971782],\n",
              "       [0.03021854],\n",
              "       [0.02560389],\n",
              "       ...,\n",
              "       [0.84388656],\n",
              "       [0.85089656],\n",
              "       [0.84616011]])"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x_train = []\n",
        "y_train = []\n",
        "for i in range(100,data_training_array.shape[0]):\n",
        "  x_train.append(data_training_array[i-100:i])\n",
        "  y_train.append(data_training_array[i,0])\n",
        "x_train,y_train = np.array(x_train),np.array(y_train)"
      ],
      "metadata": {
        "id": "qsbY3a21vT7o"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x_train.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Hnye45HOvT9n",
        "outputId": "50c18dcb-3902-4bd8-c421-88d1e93b0b25"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1660, 100, 1)"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from keras.layers import Dense,Dropout,LSTM\n",
        "from keras.models import Sequential"
      ],
      "metadata": {
        "id": "WRTB5uxovUH8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model  = Sequential()\n",
        "model.add(LSTM(units = 50, activation = 'relu',return_sequences = True, input_shape = (x_train.shape[1],1)))\n",
        "model.add(Dropout(0.2))\n",
        "\n",
        "model.add(LSTM(units = 60, activation = 'relu',return_sequences = True))\n",
        "model.add(Dropout(0.3))\n",
        "\n",
        "model.add(LSTM(units = 80, activation = 'relu',return_sequences = True))\n",
        "model.add(Dropout(0.4))\n",
        "\n",
        "model.add(LSTM(units = 120, activation = 'relu'))\n",
        "model.add(Dropout(0.5))\n",
        "\n",
        "model.add(Dense(units=1))\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "sK9FPyu8vUJ7"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zuJOdENavUNJ",
        "outputId": "b139a343-c86d-4f3c-ebe0-cf1ab9567d16"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " lstm (LSTM)                 (None, 100, 50)           10400     \n",
            "                                                                 \n",
            " dropout (Dropout)           (None, 100, 50)           0         \n",
            "                                                                 \n",
            " lstm_1 (LSTM)               (None, 100, 60)           26640     \n",
            "                                                                 \n",
            " dropout_1 (Dropout)         (None, 100, 60)           0         \n",
            "                                                                 \n",
            " lstm_2 (LSTM)               (None, 100, 80)           45120     \n",
            "                                                                 \n",
            " dropout_2 (Dropout)         (None, 100, 80)           0         \n",
            "                                                                 \n",
            " lstm_3 (LSTM)               (None, 120)               96480     \n",
            "                                                                 \n",
            " dropout_3 (Dropout)         (None, 120)               0         \n",
            "                                                                 \n",
            " dense (Dense)               (None, 1)                 121       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 178761 (698.29 KB)\n",
            "Trainable params: 178761 (698.29 KB)\n",
            "Non-trainable params: 0 (0.00 Byte)\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.compile(optimizer='adam',loss='mean_squared_error')\n",
        "model.fit(x_train,y_train,epochs=50)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vIpGmgjsvUPD",
        "outputId": "dfd90bd7-dd72-44cd-d318-42ba53a66e4c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/50\n",
            "52/52 [==============================] - 24s 325ms/step - loss: 0.0656\n",
            "Epoch 2/50\n",
            "52/52 [==============================] - 16s 315ms/step - loss: 0.0123\n",
            "Epoch 3/50\n",
            "52/52 [==============================] - 19s 373ms/step - loss: 0.0103\n",
            "Epoch 4/50\n",
            "52/52 [==============================] - 17s 329ms/step - loss: 0.0089\n",
            "Epoch 5/50\n",
            "52/52 [==============================] - 18s 339ms/step - loss: 0.0091\n",
            "Epoch 6/50\n",
            "52/52 [==============================] - 17s 317ms/step - loss: 0.0079\n",
            "Epoch 7/50\n",
            "52/52 [==============================] - 17s 327ms/step - loss: 0.0083\n",
            "Epoch 8/50\n",
            "52/52 [==============================] - 16s 316ms/step - loss: 0.0074\n",
            "Epoch 9/50\n",
            "52/52 [==============================] - 16s 316ms/step - loss: 0.0078\n",
            "Epoch 10/50\n",
            "52/52 [==============================] - 19s 375ms/step - loss: 0.0073\n",
            "Epoch 11/50\n",
            "52/52 [==============================] - 18s 338ms/step - loss: 0.0082\n",
            "Epoch 12/50\n",
            "52/52 [==============================] - 19s 369ms/step - loss: 0.0059\n",
            "Epoch 13/50\n",
            "52/52 [==============================] - 16s 317ms/step - loss: 0.0075\n",
            "Epoch 14/50\n",
            "52/52 [==============================] - 17s 319ms/step - loss: 0.0060\n",
            "Epoch 15/50\n",
            "52/52 [==============================] - 17s 322ms/step - loss: 0.0065\n",
            "Epoch 16/50\n",
            "52/52 [==============================] - 16s 315ms/step - loss: 0.0060\n",
            "Epoch 17/50\n",
            "52/52 [==============================] - 16s 317ms/step - loss: 0.0061\n",
            "Epoch 18/50\n",
            "52/52 [==============================] - 17s 320ms/step - loss: 0.0061\n",
            "Epoch 19/50\n",
            "52/52 [==============================] - 18s 339ms/step - loss: 0.0054\n",
            "Epoch 20/50\n",
            "52/52 [==============================] - 17s 324ms/step - loss: 0.0052\n",
            "Epoch 21/50\n",
            "52/52 [==============================] - 16s 317ms/step - loss: 0.0051\n",
            "Epoch 22/50\n",
            "52/52 [==============================] - 16s 316ms/step - loss: 0.0048\n",
            "Epoch 23/50\n",
            "52/52 [==============================] - 16s 316ms/step - loss: 0.0045\n",
            "Epoch 24/50\n",
            "52/52 [==============================] - 16s 316ms/step - loss: 0.0044\n",
            "Epoch 25/50\n",
            "52/52 [==============================] - 16s 316ms/step - loss: 0.0046\n",
            "Epoch 26/50\n",
            "52/52 [==============================] - 17s 335ms/step - loss: 0.0044\n",
            "Epoch 27/50\n",
            "52/52 [==============================] - 18s 337ms/step - loss: 0.0040\n",
            "Epoch 28/50\n",
            "52/52 [==============================] - 17s 317ms/step - loss: 0.0050\n",
            "Epoch 29/50\n",
            "52/52 [==============================] - 16s 317ms/step - loss: 0.0047\n",
            "Epoch 30/50\n",
            "52/52 [==============================] - 16s 315ms/step - loss: 0.0041\n",
            "Epoch 31/50\n",
            "52/52 [==============================] - 16s 317ms/step - loss: 0.0040\n",
            "Epoch 32/50\n",
            "52/52 [==============================] - 16s 316ms/step - loss: 0.0038\n",
            "Epoch 33/50\n",
            "52/52 [==============================] - 16s 317ms/step - loss: 0.0041\n",
            "Epoch 34/50\n",
            "52/52 [==============================] - 17s 336ms/step - loss: 0.0036\n",
            "Epoch 35/50\n",
            "52/52 [==============================] - 18s 335ms/step - loss: 0.0038\n",
            "Epoch 36/50\n",
            "52/52 [==============================] - 16s 314ms/step - loss: 0.0036\n",
            "Epoch 37/50\n",
            "52/52 [==============================] - 16s 317ms/step - loss: 0.0035\n",
            "Epoch 38/50\n",
            "52/52 [==============================] - 16s 316ms/step - loss: 0.0034\n",
            "Epoch 39/50\n",
            "52/52 [==============================] - 16s 316ms/step - loss: 0.0037\n",
            "Epoch 40/50\n",
            "52/52 [==============================] - 16s 316ms/step - loss: 0.0037\n",
            "Epoch 41/50\n",
            "52/52 [==============================] - 16s 315ms/step - loss: 0.0033\n",
            "Epoch 42/50\n",
            "52/52 [==============================] - 18s 338ms/step - loss: 0.0030\n",
            "Epoch 43/50\n",
            "52/52 [==============================] - 17s 330ms/step - loss: 0.0033\n",
            "Epoch 44/50\n",
            "52/52 [==============================] - 16s 318ms/step - loss: 0.0034\n",
            "Epoch 45/50\n",
            "52/52 [==============================] - 16s 316ms/step - loss: 0.0031\n",
            "Epoch 46/50\n",
            "52/52 [==============================] - 17s 319ms/step - loss: 0.0028\n",
            "Epoch 47/50\n",
            "52/52 [==============================] - 17s 320ms/step - loss: 0.0027\n",
            "Epoch 48/50\n",
            "52/52 [==============================] - 17s 318ms/step - loss: 0.0028\n",
            "Epoch 49/50\n",
            "52/52 [==============================] - 17s 337ms/step - loss: 0.0031\n",
            "Epoch 50/50\n",
            "52/52 [==============================] - 18s 338ms/step - loss: 0.0032\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.src.callbacks.History at 0x7e4cb152d990>"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.save('keras_model.h5')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RjsF6-3EsrCR",
        "outputId": "c2393de8-4d21-4840-c6ec-e99c57a5d77c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3079: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.\n",
            "  saving_api.save_model(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data_testing.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "lEPFbLOPsrEJ",
        "outputId": "bd86c1f5-251f-40a7-d12d-ed3955018395"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          Close\n",
              "1760  29.182501\n",
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              "1762  29.037500\n",
              "1763  29.004999\n",
              "1764  29.152500"
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              "      try {\n",
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          },
          "metadata": {},
          "execution_count": 36
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "past_100_days=data_training.tail(100)"
      ],
      "metadata": {
        "id": "r2qoeMbXsrHn"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "final_df=past_100_days.append(data_testing,ignore_index=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "56-gWBpCsrPM",
        "outputId": "bcfec63e-7841-4875-a938-7e56ab0ee8a7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-40-46277fdece45>:1: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  final_df=past_100_days.append(data_testing,ignore_index=True)\n"
          ]
        }
      ]
    },
    {
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        "final_df.head()"
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        "outputId": "409d55e1-19a7-41c1-b711-310555b0d7ed"
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              "       [0.8623069 ],\n",
              "       [0.88953767],\n",
              "       [0.86941994],\n",
              "       [0.87770063],\n",
              "       [0.88985614],\n",
              "       [0.89351877],\n",
              "       [0.91310581],\n",
              "       [0.9381071 ],\n",
              "       [0.94102667],\n",
              "       [0.93747015],\n",
              "       [0.93895642],\n",
              "       [0.93587777],\n",
              "       [0.96008286],\n",
              "       [0.961516  ],\n",
              "       [0.99145398],\n",
              "       [0.99087   ],\n",
              "       [1.        ]])"
            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "input_data.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GYXwHwkJ9Wi5",
        "outputId": "336c2481-9292-48d1-8062-595f672fc4b1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(855, 1)"
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x_test = []\n",
        "y_test = []\n",
        "for i in range(100,input_data.shape[0]):\n",
        "  x_test.append(input_data[i-100:i])\n",
        "  y_test.append(input_data[i,0])\n",
        "x_test,y_test = np.array(x_test),np.array(y_test)\n",
        "print(x_test.shape)\n",
        "print(y_test.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ORXnPsWX9WkX",
        "outputId": "870b4891-ebbc-4115-9837-2140f4e12dca"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(755, 100, 1)\n",
            "(755,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_predicted=model.predict(x_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FDihUUok9Wm0",
        "outputId": "ed42eb28-b9ac-4706-ca78-36ccc8ca4e53"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "24/24 [==============================] - 4s 143ms/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_predicted.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bf3Z9zRToM0z",
        "outputId": "286a7f2a-8fbd-470c-caae-c6f49c12ab26"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(755, 1)"
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_test"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hZtihZNZoM2u",
        "outputId": "3c7c17c6-0c5e-483a-fd63-aea613498b32"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
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              "       0.19316316, 0.19040291, 0.18870427, 0.19114605, 0.19146452,\n",
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              "       0.25171192, 0.26545997, 0.27124582, 0.26620311, 0.27066193,\n",
              "       0.26901642, 0.27257286, 0.26726472, 0.27740328, 0.27692557,\n",
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              "       0.30123685, 0.30436863, 0.30059982, 0.28053506, 0.28196828,\n",
              "       0.28154362, 0.28648023, 0.28281761, 0.28812574, 0.31806364,\n",
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              "       0.37751476, 0.38048731, 0.38807798, 0.38616705, 0.37974417,\n",
              "       0.37602849, 0.36206806, 0.3500717 , 0.36079416, 0.35575136,\n",
              "       0.35484899, 0.37162273, 0.38128357, 0.38133663, 0.37666544,\n",
              "       0.3712512 , 0.35219492, 0.36477526, 0.36052874, 0.35389357,\n",
              "       0.35304424, 0.34970009, 0.35134568, 0.35161102, 0.36912789,\n",
              "       0.36397899, 0.36700467, 0.36673925, 0.37602849, 0.38903341,\n",
              "       0.37905409, 0.37804561, 0.38154892, 0.38154892, 0.35798085,\n",
              "       0.35814009, 0.36068797, 0.35086789, 0.36695154, 0.3667923 ,\n",
              "       0.37103883, 0.38149586, 0.37804561, 0.37793942, 0.37772705,\n",
              "       0.38298214, 0.39258985, 0.38781256, 0.40325926, 0.4041085 ,\n",
              "       0.39986206, 0.39211214, 0.39232443, 0.37735552, 0.36084721,\n",
              "       0.36297043, 0.34412661, 0.33887153, 0.34131322, 0.3431711 ,\n",
              "       0.30452787, 0.28324227, 0.31795745, 0.29943202, 0.2761293 ,\n",
              "       0.28281761, 0.31625889, 0.3249111 , 0.34099475, 0.37082654,\n",
              "       0.36785391, 0.36477526, 0.36063492, 0.36822552, 0.38414993,\n",
              "       0.40256918, 0.39949045, 0.39805723, 0.38149586, 0.38791875,\n",
              "       0.39115671, 0.39036044, 0.3816551 , 0.39179366, 0.40793037,\n",
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              "       0.34640907, 0.36350128, 0.36981798, 0.34635602, 0.3552206 ,\n",
              "       0.37220662, 0.36790705, 0.37693086, 0.38006264, 0.38584858,\n",
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              "       0.31747974, 0.32124847, 0.32427415, 0.31418872, 0.32979457,\n",
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              "       0.44604284, 0.45241259, 0.45129784, 0.45358038, 0.44997081,\n",
              "       0.44784758, 0.44450343, 0.46239191, 0.47083183, 0.47868785,\n",
              "       0.48224429, 0.47948412, 0.47014175, 0.46764692, 0.47322048,\n",
              "       0.46483361, 0.46536445, 0.45496047, 0.4544297 , 0.43823987,\n",
              "       0.44253945, 0.43701902, 0.43415258, 0.4195552 , 0.43155156,\n",
              "       0.43011842, 0.43723131, 0.43516114, 0.44614894, 0.42884444,\n",
              "       0.43670046, 0.45034242, 0.46419666, 0.46297581, 0.4498647 ,\n",
              "       0.4665853 , 0.46817776, 0.46594835, 0.46881471, 0.46324115,\n",
              "       0.47109725, 0.46876166, 0.46966403, 0.47704234, 0.48670319,\n",
              "       0.48346522, 0.46631988, 0.46064022, 0.46265725, 0.52216151,\n",
              "       0.55342644, 0.55661135, 0.56234415, 0.55194017, 0.5526833 ,\n",
              "       0.56133559, 0.55416957, 0.56128246, 0.56595365, 0.56855466,\n",
              "       0.58490374, 0.60751637, 0.59626315, 0.59403366, 0.59408679,\n",
              "       0.59642239, 0.59997883, 0.60942731, 0.6187696 , 0.63618028,\n",
              "       0.64706198, 0.66086317, 0.66473809, 0.65682894, 0.63681732,\n",
              "       0.62726265, 0.61149748, 0.64079842, 0.6260418 , 0.65438724,\n",
              "       0.64074529, 0.60910883, 0.61101976, 0.61170977, 0.62052129,\n",
              "       0.60794103, 0.62455545, 0.63198689, 0.62259146, 0.64663732,\n",
              "       0.6508308 , 0.65889911, 0.66962157, 0.68443132, 0.66277411,\n",
              "       0.64313393, 0.64037376, 0.65682894, 0.60104044, 0.59090188,\n",
              "       0.63156223, 0.60634858, 0.63177452, 0.62667875, 0.59923569,\n",
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              "       0.34768305, 0.35017788, 0.35999789, 0.33096237, 0.32278788,\n",
              "       0.33409423, 0.30659804, 0.28504701, 0.25266734, 0.23196564,\n",
              "       0.28685176, 0.28143744, 0.2818621 , 0.28987744, 0.29083286,\n",
              "       0.20733588, 0.23955624, 0.23780454, 0.25277353, 0.26636235,\n",
              "       0.26896336, 0.26094802, 0.24879242, 0.26508845, 0.27501464,\n",
              "       0.27989811, 0.28499396, 0.26630929, 0.26960031, 0.26312438,\n",
              "       0.28998354, 0.28223371, 0.27363447, 0.32974152, 0.33605822,\n",
              "       0.33648288, 0.36159035, 0.37714315, 0.37746171, 0.35994484,\n",
              "       0.35713153, 0.3519295 , 0.35967942, 0.3559106 , 0.3592017 ,\n",
              "       0.35718458, 0.35989171, 0.36573069, 0.36058179, 0.37072035,\n",
              "       0.37740858, 0.37793942, 0.38080578, 0.37167578, 0.38133663,\n",
              "       0.38600781, 0.38430917, 0.37894798, 0.36822552, 0.37040188,\n",
              "       0.40219757, 0.41286698, 0.4171135 , 0.42783588, 0.44052233,\n",
              "       0.45060784, 0.44269868, 0.45135097, 0.48813633, 0.46669149,\n",
              "       0.4544297 , 0.44407877, 0.45299648, 0.45432352, 0.46085251,\n",
              "       0.46770005, 0.48245666, 0.4895165 , 0.49132124, 0.49827489,\n",
              "       0.51473015, 0.51154524, 0.51749032, 0.50862574, 0.50820108,\n",
              "       0.51011202, 0.5102182 , 0.5308138 , 0.53468872, 0.53824516,\n",
              "       0.55390415, 0.55220559, 0.54222626, 0.53702431, 0.53866982,\n",
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          "metadata": {},
          "execution_count": 47
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    {
      "cell_type": "code",
      "source": [
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      "metadata": {
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        "outputId": "24f541fa-322a-4006-9aab-b74d7873f79f"
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      "execution_count": null,
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            ]
          },
          "metadata": {},
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "scalar.scale_"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "W7i4VDhT_M9Z",
        "outputId": "fd6b95f1-7050-434e-e06e-e05bfa57a50e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.02123255])"
            ]
          },
          "metadata": {},
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "scale_factor=1/0.02123255\n",
        "y_predicted =y_predicted*scale_factor\n",
        "y_test = y_test*scale_factor"
      ],
      "metadata": {
        "id": "d4HAoKfg_M_L"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(12,6))\n",
        "plt.plot(y_test,'b',label = 'Original Price')\n",
        "plt.plot(y_predicted,'r',label ='Predicted Price')\n",
        "plt.xlabel('Time')\n",
        "plt.ylabel('Price')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 514
        },
        "id": "daAGg5b8_NCW",
        "outputId": "8051dd0e-e12b-4e6f-a7c9-ef9bedf53593"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 1 Axes>"
            ],
            "image/png": 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hw4a+xx9//HFeffVV3/eoTp06LF++nBEjRiggFxERERHJRr16wdq1tv/4qaeGezY5QwF5HjV+/HiKFy9OfHw8SUlJ9O/fnyeeeML3eIsWLXzBOMDSpUtZs2ZNqvXfx48fZ+3atRw4cIBt27Zx+umn+x6Lioqibdu2qcrWHUuWLCEyMpJOnTpleN5r1qzh6NGjdO3aNeB8XFwcp5xyCgArVqwImAfgC95P5IEHHuDRRx/l+PHjFC9enBdeeIELL7zQ93jbtm3T/fwlS5Zw1lln+YJxf0eOHGHt2rUMHjyYG264wXc+ISGBUqVKZWh+IiIiIiJyYklJsHq1Hc+eDU2ahHc+OUUBub+YGMtUh+u1M+Hcc89l+PDhFCpUiKpVq6ZqSlYsRbb98OHDtGnThi+++CLVc1XI4oIMpwQ9Mw4nf39//vlnqlWrFvBY4cKFszQPf/fddx/XXHMNxYsXp1KlSr5qAEfK70tK6X1Nztw/+OCDVDcMIiMjszhjERERERFJadcuSEiwVb2tW4d7NjlHAbk/jydLZePhUKxYMerXr5/h60899VS++eYbKlasSMmSJYNeU6VKFRYsWMDZZ58NWOZ38eLFnJpGfUiLFi1ISkpixowZvpJ1f06GPjEx0XeuadOmFC5cmI0bN6aZWW/SpImvQZ1j/vz5J/4igfLly2fq+5JSy5Yt+eSTT4iPj0+VJa9UqRJVq1blv//+Y8CAAVl+DRERERERSd/WrTZWqgT5eUMkdVkvIAYMGED58uW5+OKLmTVrFuvWrWP69OncfvvtbN68GYA77riDF154gR9//JF///2XW265Jd09xGvXrs2gQYO47rrr+PHHH33P+e233wJQq1YtPB4P48ePZ9euXRw+fJgSJUpw7733ctddd/HJJ5+wdu1a/vjjD9566y0++eQTAG666SZWr17Nfffdx8qVK/nyyy/5+OOPc/pbBMCtt97KwYMHueKKK1i0aBGrV6/ms88+Y+XKlQA8+eSTPP/887z55pusWrWKZcuWMWrUKF577bWQzE9EREREpCDYssXGFEW1+Y4C8gIiJiaGmTNnUrNmTfr06UOTJk0YPHgwx48f92XM77nnHq6++moGDRpE+/btKVGiBL179073eYcPH86ll17KLbfcQuPGjbnhhhs4cuQIANWqVePJJ5/kwQcfpFKlStx6660APP300/zvf//j+eefp0mTJpx//vn8/PPP1KlTB4CaNWvyww8/8OOPP9KqVSvee+89nnvuuRz87rjKlSvHtGnTOHz4MJ06daJNmzZ88MEHvmz59ddfz8iRIxk1ahQtWrSgU6dOfPzxx765i4iIiIjIyXMy5FWrhnceOc3jTatjVz5x8OBBSpUqxYEDB1KVah8/fpx169ZRp04dihQpEqYZSn6mf2MiIiIiIpn3xBPw5JNw443w3nvhnk3mpReH+lOGXERERERERHIVlayLiIiIiIiIhMD69TBoEEyZYh8XlJL1fNyvTkRERERERHK7/fvhggvg33/h00+hZUv46y97LL8H5MqQi4iIiIiISNiMGGHBuMMJxkEl6yIiIiIiIiI5Zu1aG6+9Fk4/PfCx6tVDP59QUsk6kM8bzUsY6d+WiIiIiEj6nPXi7dvDRx/Z8ciRNpYtG545hUqBDsidvaWPHj1K0aJFwzwbyY/i4uIAiIyMDPNMRERERERyp23bbKxSxT13/fXhmUuoFeiAPDIyktKlS7Nz504AYmJi8Hg8YZ6V5BdJSUns2rWLmJgYoqIK9I+aiIiIiEiaCkpH9WAKfJRQuXJlAF9QLpKdIiIiqFmzpm70iIiIiIgEkZAATijmnyEvKAp8QO7xeKhSpQoVK1YkPj4+3NORfKZQoUJERKh3ooiIiIhIMDt3QlISRERAxYrhnk3oFfiA3BEZGal1viIiIiIiIiHkrB+vVAkKYjim1J2IiIiIiIiEhROQZ2j9+KFD8PLLsGtXjs4plBSQi4iIiIiISFg4Dd0ytH7888/h/vuha9ccnVMoKSAXERERERGRsMhwhtzrheHD7fjaa3N0TqGkgFxERERERETCIsMZ8rlzYdkyKFoUBg3K8XmFigJyERERERERCYsNG2ysUeMEFzrZ8SuvhNKlc3JKIaWAXERERERERMJi/Xob69RJ56Jdu+C77+z45ptzekohpYBcREREREREQi4pKYMB+ahREBcH7dpB27ahmFrIKCAXERERERGRkNuxA2JjISICqldP46KkJHjvPTvOZ9lxUEAuIiIiIiIiYbBunY01akB0dBoXTZpkF5YuDZdfHqqphYwCchEREREREQk5JyCvXTudi0aMsPGaayAmJodnFHoKyEVERERERCTkTrh+fM8e+PlnO77hhlBMKeQUkIuIiIiIiEjIORnyNAPyH36AhARo3RqaNg3VtEJKAbmIiIiIiIiEVGwszJplx2kG5F9+aWP//iGZUzgoIBcREREREZEc5fXCG2/AnDn28VNPwapVUKEC9OgR5BN27YKZM+04HzZzc0SFewIiIiIiIiKSv/38M9x5px3772T2zjtQrlyQT/jlF4viTzkFatYM1TRDThlyERERERERyVGrVrnHa9bA3r1QqBBcfHEanzB2rI29euX43MJJAbmIiIiIiIjkqAi/yHPcOBtbtbKgPJXjx23/cYCLLsrxuYWTAnIRERERERHJUbt3u8fff29j27ZpXDx9Ohw5AlWrwqmn5vTUwkoBuYiIiIiIiOSo7dvd43nzbEwzIPcvV/d4cnRe4aaAXERERERERHLUjh2pz7VrF+RCr9cNyPN5uTooIBcREREREZFsMnIkPPwwJCQEnk8ZkJcvD02bBnmCP/+ELVsgJgY6d86xeeYW2vZMRERERERETlpCAtxwgx3v3etubQaBJetge49HRgZ5EqfjW/fuUKRIjswzN1GGXERERERERDLkqaesz9qGDakf27zZPR4xApYts2OvN3WGPM1q9AJUrg4KyEVERERERCQDDh6Exx+3qvLrrgt8bOdOWL488NzixTbu3w9xcYGPdesW5AU2b4Y//rBGbhdemF3TztVUsi4iIiIiIiIn9NNP7vG0aTB3Lpx5JmzbBnXqQGxs4PWrVtnoZMdLloQPP4TSpe04lfHjbWzfHipWzO7p50oKyEVEREREROSEvvwy8OPffrOAfPr01ME4wMqVNjrrxytXhksvTecFCli5OqhkXURERERERE4gKckCb4D+/W10Au7jxwOv7dIl8HEnQ16pUjovcPgwTJ1qx716nex08wwF5CIiIiIiIpKujRst8I6OduNlpyR969bAay+4wMY1ayAx0T4XoEaNdF5g4kRbaF6vHjRpkq1zz81yTUD+wgsv4PF4uPPOO33njh8/ztChQylXrhzFixenb9++7Ai2o7yIiIiIiIhkO68XXn0VXnnFPq5f390/fOVKezxlQH7OOVC4sJWxb9gA69fb+Vq10nmhzz+3sU8fa+pWQOSKgHzhwoWMGDGCli1bBpy/6667GDduHN999x0zZsxg69at9OnTJ0yzFBERERERKVhmz4Z774V33rGPGzWyoNzjse7pu3alDsjr1YMGDex45Up3i7Q0A/Jdu+Dnn+140KDs/hJytbAH5IcPH2bAgAF88MEHlClTxnf+wIEDfPjhh7z22mt07tyZNm3aMGrUKObOncv8+fPDOGMREREREZGC4Z9/Aj9u1AiKFoWaNe3jVavcgLxaNXjoIShVyoJysOy4E5DXrp3Gi3z+OSQkQNu20KxZNn8FuVvYA/KhQ4dy4YUX0sVZ+Z9s8eLFxMfHB5xv3LgxNWvWZN68eaGepoiIiIiISIHz11+BHzdsaGOjRjauXAlbttjxjz/Cc8/ZcdWqNm7deoIMeUICvPGGHV9/fXZNO88I67ZnX3/9NX/88QcLFy5M9dj27dspVKgQpUuXDjhfqVIltjt984OIjY0l1q/n/sGDB7NtviIiIiIiIgXJkiWBHzuBeJMmMHkyzJjhbmvmBOEAVarYuHw5HDpkx05WPcB331nEXqECDByYnVPPE8KWId+0aRN33HEHX3zxBUWKFMm2533++ecpVaqU70+NdFv5iYiIiIiISDAbNkDK3KmTIe/Xz8bPPrNO6hERULGie50TnDvFzRUqQExMiheIi4PHHrPj226zWvgCJmwB+eLFi9m5cyennnoqUVFRREVFMWPGDN58802ioqKoVKkScXFx7N+/P+DzduzYQeXKldN83oceeogDBw74/mzatCmHvxIREREREZH8ZeJEW/OdkGDB9oMPwpNPWmAN0L594HLvsmUhyq/+2smQb9tmY9D142++aXujVaoEfrttFSRhK1k/77zzWLZsWcC5a6+9lsaNG/PAAw9Qo0YNoqOjmTp1Kn379gVg5cqVbNy4kfbt26f5vIULF6Zw4cI5OncREREREZH8bPRo9/icc+D55wMf93jg9tvh/hv3cz0jaRR9EMafZpuQR0YGlK9DkPXjf/wBjzxix889ByVKZPeXkCeELSAvUaIEzZs3DzhXrFgxypUr5zs/ePBg7r77bsqWLUvJkiW57bbbaN++PWeccUY4piwiIiIiIlIgzJ1r4/XXwxNPBL/mhjZ/cFXpnsTs3wbbgIuwTcrffJMqzToDHsBLKQ7QtGFxfOHnwoVw8cVWsn7RRXDttTn95eRaYW3qdiLDhg0jIiKCvn37EhsbS/fu3Xn33XfDPS0REREREZF8a98+d7uzZ58NXBvus3Mnnt6XWDDesCGcdZal1Zcvhy5dqNisGbMoQ2NWUJ49eJ/3wJjGtpB88WJ7jubN4ZNPLN1eQHm8Xq833JPISQcPHqRUqVIcOHCAkiVLhns6IiIiIiIiudqECdCjB9SvD6tXp3HRZZfB999b2/UFC2zz8X37rEnbBx+A385XqXg8cOmldl2pUjnyNYRbRuPQXJ0hFxERERERkdD6/nsbzzwzjQumTrWLIiLgm2/coLpMGXjrLXj0UZg/nysvOcpKGrGSRhzZcsD2UIuLg5YtoW7dUHwpuZ4CchEREREREQHgt9/go4/s+Lrrglzg9cL999vxLbdAq1apr6lUCS6+mK/9z1UtRqpObxK+bc9EREREREQkd3n9dRtvvBE6dQpywfjx1iG9WDF4/PF0n+vKK2285ZZsnWK+ogy5iIiIiIiI4PXC/Pl2PGhQGhc9+6yNt94K5cun+3zDh0Pv3tCzZ/bNMb9RUzcRERERERFh/XqoUweio+HgQShSJMUFCxbAGWdAoUKwaVMa7dcFMh6HqmRdREREREREWLDAxlatggTjYA3bwGrRFYxnC5Wsi4iIiIiIFFBJSdCtG+zeDXv22LnTTw9y4b598N13dnzrrSGbX36ngFxERERERKSA+usv28XMX9CA/PvvbcuyFi2gbduQzK0gUEAuIiIiIiJSQE2c6B5Xr26d1Xv3DnLhZ5/ZeNVVIZlXQaGAXEREREREpIByAvK334ahQ9O4aPNmmDULPB7o3z9kcysI1NRNRERERESkADp0CObMsePzz0/nwrFjbWzf3tLokm0UkIuIiIiIiBRA69ZBQgKUKwf16qVz4U8/2XjxxSGZV0GigFxERERERKQA2rfPxnLl0rnowAH47Tc7vuSSnJ5SgaOAXEREREREpAByAvIyZdK5aOpUiI+Hhg3tj2QrBeQiIiIiIiIFUIYC8smTbUx3kblklQJyERERERGRAuiEAbnXC5Mm2XG3biGZU0GjgFxERERERKQAOmFAvnYtrF8P0dG2QblkOwXkIiIiIiIiBdAJA3KnXL1DByhePCRzKmgUkIuIiIiIiBRAGQ7IVa6eYxSQi4iIiIiIFEDpBuTx8TBtmh137x6yORU0CshFREREREQKoHQD8vnz4dAhKF8eWrcO5bQKFAXkIiIiIiIiBVC6AblTrt61K0QobMwp+s6KiIiIiIgUQBkKyLV+PEcpIBcRERERESlgvN50AvK9e2HhQjvu2jWk8ypoFJCLiIiIiIgUMEePWt82CBKQT51qEXuzZlCtWsjnVpAoIBcRERERESlgnOx4ZGSQLcYnTrRR5eo5TgG5iIiIiIhIATBzJvTsCWvXBparezx+F3m9bkB+wQUhn2NBExXuCYiIiIiIiEjO69TJxogIuPdeO05Vrv7XX7B1K8TEwNlnh3R+BZEy5CIiIiIiIgXI1q2we7cdlyuX4sEJE2zs3BkKFw7pvAoiBeQiIiIiIiL5nBOAA1StCrt22XGlSikudAJylauHhAJyERERERGRfO7PP93j48dh5047rljR76IDB2DOHDtWQB4SCshFRERERETyuSVL3OPdu92AvEIFv4t+/RUSE6FRI6hTJ5TTK7AUkIuIiIiIiORz/hnyXbvSyJA75ernnx+yeRV0CshFRERERETyub//do+DBuQJCTBunB336BHSuRVkCshFRERERETyscREWLXK/Tg2Fv77z459AfnMmRally0L554b8jkWVArIRURERERE8rENGywIL1wYChWycxs32ugLyL/5xsY+fSA6OuRzLKgUkIuIiIiIiORj//5rY8OGqbc5q1ABa7v+/fd2ol+/kM6toFNALiIiIiIiko+tXGlj48YpuqoD5csDP/wAe/dCjRrQuXPI51eQRYV7AiIiIiIiIpJznAx548Zw8KB7vlw5iIoC3n/fTlx/PURGhnx+BZky5CIiIiIiIvmU1wtLl9pxygx5xYrA4sXW0C0yEgYPDsscCzIF5CIiIiIiIvnUXXfBggV23KZNkID8xRftgyuugGrVQj6/gk4BuYiIiIiISD505Ai8+aYdDx8OjRpBly5up/U+zVe5zdwefDA8kyzgFJCLiIiIiIjkQ2vWWMl6uXJw0012rkcP69+2YwfcHvuyXXDRRdC8eXgnW0ApIBcREREREcmHVq+2sUGDwPPFikHFhK3wySd2QtnxsFFALiIiIiIikg+tWmVjyoAcsM7q8fHQsSOceWZI5yUuBeQiIiIiIiL5UFoZcuLj3a3Ohg4N6ZwkkAJyERERERGRfGbmTJg+3Y5TBeTjx8O2bdZmvU+fUE9N/ESFewIiIiIiIiKSfZYvh06d3I8bNkxxgbN2fNAgt+W6hIUy5CIiIiIiIvnIsmWBHwdkyPfsgV9+seOBA0M2JwlOAbmIiIiIiEg+smmTe9y7N5Qo4ffgN9/YGvLWrbXVWS6ggFxERERERCQf2bjRxgcegNGjUzz4+ec2Xn11SOckwSkgFxERERERyUecDHmNGikeWLMG5s2DiAi48sqQz0tSU0AuIiIiIiKSj6QZkH/2mY1du0KVKiGdkwSngFxERERERCQfcUrWa9b0O5mQAB9+aMeDBoV8ThKcAnIREREREZF84vhx2LXLjgMy5OPHw5YtUKGC9h7PRRSQi4iIiIiI5BObN9tYtCiULev3wPDhNl53HRQuHPJ5SXAKyEVERERERPKgxETbwcyfs368Zk3weJJPrlkDkyfbiSFDQjpHSZ8CchERERERkTzo3HOtN9sPP7jnFiywsXZtvwvff9/G7t2hbt1QTU8yQAG5iIiIiIhIHpOQALNmwZ49cOmltr14YqIbe/frl3xhbCx89JEd33xzWOYqaYsK9wREREREREQkc/buDfx48GDYsAHWrYMyZeCKK5IfGD3aovbq1aFHj5DPU9KnDLmIiIiIiEges3u3jaVLw0UXQVwcPPqonRs8GGJiki8cMcLG66+HKOVjcxsF5CIiIiIiInnMnj02VqgAr74KEcmRXUwM3Htv8kX//gszZtiDgweHZZ6SPgXkIiIiIiIieYyTIS9fHho0gKuvto/vuAMqVUq+yFlQ3rOnlaxLrqOaBRERERERkTzGPyAH22b8ssvg/POTL4iNhU8+sWNtdZZrKSAXERERERHJY5yS9XLlbCxaFC680O+CSZOs81u1an5RuuQ2KlkXERERERHJY1JmyFP57jsbL70UIiNDMifJPAXkIiIiIidh9244ciTcsxCRgsYJyJ0MeYDYWBg71o4vuyxkc5LMU0AuIiIikkVONWj79uGeiYgUNE7JetAM+eTJcPCgfkHlAQrIRURERLLot99s799ly+Do0XDPRkQKknQz5E65et++7n5okivpb0dEREQkizZvdo83bgzfPESk4ElzDXlsLPz0kx2rXD3XU0AuIiIikkX//OMeb9gQvnmISMGTssu6z9SpVq5etSqceWbI5yWZo4BcREREJIsUkItIOCQkwL59dpwqQz5hgo0XXaRy9TxAf0MiIiIiWeD1KiAXkfDYtctGjwfKlk3x4OTJNnbvHtI5SdYoIBcRERHJgq1b4cAB92MF5CISKqtX21irFkRF+T2wfj2sWmX7jp97bjimJpmkgFxEREQkC/76K/BjBeQiEiorV9rYqFGKB6ZMsfH006F06VBOSbJIAbmIiIjka0ePwowZVmKenebMsbFxYxvVZV1EQuXff210fv/4OOXq3bqFdD6SdQrIRUREJN/yeq2v0TnnwFdfZe9zz55tY//+Nm7ZYo2WRERyWtAMeWIi/PqrHWv9eJ6hgFxERETyrQ8/hGnT7HjEiOx73rg4+P13O+7bFwoVsvfCW7Zk32uIiKQlaEC+aBHs32+l6m3bhmFWkhUKyEVERCTfeuMN93j+fDh0KHue988/4dgx2/+3SROoUcPOB6wj37fPbYUsInKStm613hVHj8K6dXYuICB3ytXPOy9FpzfJzRSQi4iISL50/DisWGHHMTGW1Xa25z1ZTlVohw627VCtWvbxhg0wcya83/od24uoYkW4777seVERKbBGjYJq1aBVK7j6aqvIKV4cqlb1u2jSJBu1fjxPUUAuIiIi+dKKFfamtWxZuOEGOzd3burrfv4Z7r03c+u/f/rJxp49bfQPyG/u9A8Dl97jXjxsmHtnQEQkC2bNco9Hj7axSRO7IQjYHozz59tx164hnZucHAXkIiIiki8525K1bAkNG9pxsK3JevaEV1+FkSMz9rxbt8LChfZG+KKL7Jx/QP4K91KEWOaVvRB69bK7Ao8+enJfjIgUaLt3pz7XqpXfB7/9Zr9rGjSAOnVCNi85eQrIRUREJF/yD8idgHn9+rSvnzcvY8/77bc2nn46VK5sx87zxy1byQVMJAkP33V8A5591h4YN86aLYmIZMGePanPtWjh94G2O8uzFJCLiIhIvrR0qY0tW0Lt2nacMiA/ftw93r79xM+5dy8884wdX3WVe94JyNsueAeAn7mQ2Or1oHlzaNYM4uPdOncRkUwKliFv2TL5wOu1tTeg7c7yIAXkIiIiku94vYEBuRMw798fmKj2b4IerJw9pddes0xVs2YwZIh7vlYtiCSBK7DNzt9hKEeOJD942WU2fvddVr4UESmgtm1z2084GfLWrd3HfRnyv/6CjRuhaFHrsC55SlgD8uHDh9OyZUtKlixJyZIlad++PRP82p8eP36coUOHUq5cOYoXL07fvn3ZsWNHGGcsIiIiecGaNZZRKlzYAvLixaF8eXvMP/D2f1uxerVtZZYepwx+6FCIjnbPV68OZzOLCuxmD2WZynluQN63r42//nriFxARweLr6tXhlFPs99S+fXb+3HNtrFrVtl0EYOxYG7t2tS0lJE8Ja0BevXp1XnjhBRYvXsyiRYvo3LkzF198Mf/88w8Ad911F+PGjeO7775jxowZbN26lT59+oRzyiIiIpIHzJljY9u2FpSDW7buBOTj3t7A8atv4Hv6cjrzSUqCv/9O/3m3brXR2XfcUagQXFvyBwB+4mISiObw4eQHmzWz/YpiY92JiYikweuFAQMgKcl+bSxaZMcA/fvbFuPODg8A/Pijjb16hXqqkg3CGpBfdNFF9OjRgwYNGtCwYUOeffZZihcvzvz58zlw4AAffvghr732Gp07d6ZNmzaMGjWKuXPnMt9p6S8iIiIShLO92Zlnuuf815HvXbaFDredQsd/R9KX0cynPTfwPsuXp/+8W7bYWK1aigeSkrg0agwAP2AZcV+G3OOBLl3s2NnAXETyvTFjoGlT+OWXzH3e7t0we7b78b//2liypN1k3LYN3nor+cGVK+GPPyAy0t32QfKUXLOGPDExka+//pojR47Qvn17Fi9eTHx8PF2c/4EBjRs3pmbNmsxLpw1qbGwsBw8eDPgjIiIiBYuTiO7QwT3nH5BH3nsnZdnHMprzFVcA8D43UmXc+2k+Z0KCW+JetWqKBxctoujercQXKc6/1ey9iy8gBwXkIgXQu+/aGvALL7StEjMqZYNJZx25U6JevrxV5QDwxRc2du8OFSue1HwlPMIekC9btozixYtTuHBhbrrpJsaMGUPTpk3Zvn07hQoVonTp0gHXV6pUie3ptEF9/vnnKVWqlO9PjZQ1ZSIiIpKvHTqEL9Pdvr17vn59G4/NWkSpyd+TQCQD+IL+fMlr3AVAtx9uhM8/D/q827dbKWlUFFSokOLBMZYdj+7Vg1FfWo28r2Qd3EZLf/wRsH/RihXQrp27BFRE8o/4ePd45EgbvV5rK9Gzp20bHsyJAnKfpCQ3IB8w4KTnK+ER9oC8UaNGLFmyhAULFnDzzTczaNAglp+oXiwdDz30EAcOHPD92bRpUzbOVkRERHK7NWtsrFAhMGF09tk2tvzjYwC+4XKW0RLw8EzpV3mdO+yCwYNhwYKA55w1Cx591I6rVIGIlO+gkgNyevemeHE7PHLE3nxPngxbkqpwpHYzO/Hbb75P69XL1odefHHwr2XVKrjjDrdUXkTyjkOH3GMnJFm9GkaPtl3K/vgj+Oel7GHtBOROY0qfyZPhv/+slj2tXyKS64U9IC9UqBD169enTZs2PP/887Rq1Yo33niDypUrExcXx37/vUmAHTt2ULly5TSfr3Dhwr6u7c4fERERKThWr7bRyYg7GjeGWpVj6ZdkW5N9zDW+x5o283A3rzG/8sUQFwdXXul7Nx0fb8H8J5/YtanWj//7r63jjI6GHj0oVsxOHz4MkyZZJWmNGvD++q4A7PveLVt3bh6k5bTT4M034cYbM/zli0gu4XRGB9i82UanvwXAtGnBPy9lQO48T6oM+dtv23jttfh+8UieE/aAPKWkpCRiY2Np06YN0dHRTJ061ffYypUr2bhxI+39689ERERE/DgBeYMGgec9Hri90STKsZfNVGManX2PNWsGXiJ4rOYntqn4unVwzz1AQEIbCLJ+3MmOn3celCzpe1985AhMmWLHXi/8iq0jj/vFAvLYWPcpUqzQ8zlwwEZnuzURyTv27nWPnSoX/4DcL8wJ4JSsly0beD4gIP/3X7db3NChJzVPCa+wBuQPPfQQM2fOZP369SxbtoyHHnqI6dOnM2DAAEqVKsXgwYO5++67+e2331i8eDHXXnst7du354wzzgjntEVERCQXc7LOKQNygJ6JPwHWCT2JSN9535Zo+0u5qfAPPoDZs/n228DnSJUhd7Yc6t0bwFeynpBg680dMzmbeKKodGgtuxes5c8/3cdSlaKmUL16+o+LSO6SmOjeUAMLzo8eDQzIZ892b8xt3Qo9ethNPCdD3qJF4HMG/J544QW709erV/BfdpJnhDUg37lzJwMHDqRRo0acd955LFy4kEmTJtG1q5V0DRs2jJ49e9K3b1/OPvtsKleuzOjRo8M5ZREREcnl0ipZJymJOsvHAzAWd7/esmXdnmu7dwOdOtk6csB70038PCYu4Gmcfc0B29T8998t/Z68B7B/5ah/06bDlGAWZwFw6IuxAW/M/d+4O/y7tCsgF8lb/H+mo6Nt/PNP+OcfOy5eHI4dS65+SUxkxuPT8E6YwN1DDrN1q13TvHngc/oy5GvXus0nH3kkp74ECZGwBuQffvgh69evJzY2lp07d/Lrr7/6gnGAIkWK8M4777B3716OHDnC6NGj010/LiIiIpJWyTq//0703p0coKQvMP78c6tOdzLk+/ZZZpsXXySuVHk8//zDwL3DiHST6YEd1t9P3ibtvPMg+T1KdLS7JVHKtaA/cgkAxaf+GNBZff9+S3b5++8/9zgm5gRftIjkKk65evHiUKeOHV93nY3t2sGpp9rxmuVxcPHFXDnyPCbQg3HrmxM33e7WpcyQt2qVfPDww3a3r3t3azQheVquW0MuIiIiklUHD8LOnXacKkM+bhwAs2LOJx6LmGvUsAbFzlpNr9eC8hl/l+OGA68A8DhPclb1dUyfDrfcArfdlvx8cXHuXkY33xzwUk6WPGV39J+wTsjlVsxm+YxdvvPx8XDOOfD00+61/g3fjh3LyFcvIrmF04itTBm3wmXVKhtfe829YVjn3fus5Tqwi/LUZgMTErtyFjNTBeTt2gEzZsC331pVzksv5fwXIjlOAbmIiIjkG07zs0qVoFSpFA8mB+TL61/kO+Vku6Oi7I0zwK5d8OWX8CkDmU4nYjjGS0dvpdPZXt55B4oWTf7kDz6w6L9qVbjoIvw568hTBuQbqcViTiXCm8RlfEvHju4WajNnwmOPuZnytWvdzzt+PJPfCBEJKydDXrZsYN+J3r2hY0cLyKuzibaLhgNwXbmfqMt/TOB8inGUX+hBw11zfJ9Xty4UOrofBg2yEzfcAC1bhuirkZykgFxERETyDadrcadOKR5Yvx6WLYOICPa0u8B32n+fcqdh0u7dtjc4eLiJ94gjmna7foF33nEv3rsXHn/cjh95xF0kmszJkDtbHTnq1LFAH2AwH9KvX+obB3v22OifIVdALpK3+GfI/X/GnQqbBg3gPl4mKimeA607MWpPLw5TgpdOH80UulCcI5S7+gJOZz4A59VeC507W9+KunXhlVdC/BVJTlFALiIiIvnGr8lbfDtN2nySs+N06EDR6u7eQU5WHNyA/Pvv4Y8/7Hgljbmf5LLQu+6C4cPtDfHFF1vk3KwZDBmSah5OQH70aOD51q3hc64ilkKcyp+cWeSPVFueOWvH161zzykgF8lb/DPk/i2wzjnHxoa1YrmazwC4bMnDgAXujzxTlIv5ibmFz8Vz6BCzPWexgsaMmN7QusJVqAA//AAlSoTwq5GcpIBcRERE8oVDh2C+JZPo0iXFgz/8YOPFF9OsmXs6wu+dkJPFeuutwE99gzvY1vVq6/Z2yy3WAW72bPuEL74I3NssmVOyntIpp8BeyjGaPgA0Hf1M6oB8ZTzMn0/Ntb8RgbVpV0Aukrf4Z8hvu80qzOfNs6XfAA3+m0QZ9rOFqkzF7iDWqWO/uz78MobIX8ZB9+5EeRNozEo8SUlw/vn2JK1bh+eLkhyhgFxERETylNGj4euvU5+fOdNi5jp1rKLTZ9s2exDg0kvp2xfuuAM++yzw8/3XeXbvDg8+6HzkIemjT+Dll91UV/v2tmGwr+1xIP+tz/w576Of5n8kEkHRiWPoyGzf4+fxK93vaATt2/PB2s4spymn8IcCcpE8xj8gL1nSNmQ44wz38cJj7JfYN1xOEraNw67kPo9XXgmndy4GEydaJ7hJk2zZzYQJUK9eCL8KCYXUt3RFREREcqnjx6FvXzvu0MG6pDuccvWg2XGv194N16pFBPD666mf+4YbYPt2uP126NbN3hy/8IJlu6tW88C998I999gG4WmlwJMFe7hwYWjc2I5X0JSfylxDn30f8fQ/vTnOs5zNTK7iC9gH3jJl2L/PSyNWMYNO3LZnDJDyCxOR3Mq/ZD2VuDjfMpqfCl/OJRfAjz/63wT006BBkD0cJT9RQC4iIiJ5hn/X8oULAwNyp6FbqoD8009t7Ncv3ec+/XQYP979uEIFW89duLBbZorHc8JgHFJnyMuUsSC/UiX33GetX6PPwaWUWryY97nRd350tVvpPO856tRM5Af6ch7TeH9LD/jyY+jf/4SvLSLh558hT2X2bDh8GG+lSoxf2Y7iJWH5cveGnRQsCshFREQkz/DvWj5vHvSxpdhs325N1MEaEfssXGh/ChWCq67K9OvVqZO1eaaM2XfssEbszpZmAJFlS8H3k5na7UVYvIi11OMjrmNrxBlMPQ4HgB78wicM4gq+gQEDbEPywYOzNikRCZmdO20MmiH/5RcAPOefT4lStoLYv7eFFCwKyEVERCTP8A/InQZuAN99Z2Pr1m63dADeftvGfv3cTcdDwD8rVry4uyuaL9OOrSulbFmmdHmRFxe75z2b3a8zjsL050v2RVfi5vg34e67bSPjoO/yRSQ38Hrh77/tOGjWe8IEG3v0CNmcJPdSUzcRERHJMzZtco8XLbKlmKtXw0MP2blBg/wuXrXKuqAD3HpryOYIVv7uSLnPeNeuNg4dauOxY+5jhQvbm/klS+zjatXASwR3eodBy5Zw8KD2HxbJ5davtx/VQoWCBORbtlh9ekSE+8tACjQF5CIiIpJn+GfInQZv555rfdY6dbLthXwefRQSE6Fnz8AIOQQ6dnSPjxwJfGzsWFub3qaNfey/V3nNmjYuWhT4cVxCBIlPPGUfDB9udyJEJFdybqg1bWpBeYAZM2w85ZQ0FphLQaOAXERERPIMJyA/5xzb/nv8eEs4NW0KX30FkZHJF/78s9Wxezzw7LMhn2fJku7x/v2BjxUpErg2/ZxzbIyIcJvULU4uYXcCcoDYLj1t27X9+23LNRHJlZYutTHorohOQO784EuBp4BcRERE8gwnIL/nHpg+3bYoe/JJ22a8SpXki3btgiFD7Pjuu63UOwz8g+n0XHklfPklrFnjfs7KlTb6d5E/FhcJl11mH3zzTfZNVESylZMhb906yIPTp9uogFySqambiIiI5BnOGvLq1e3NbocOKS5ISoKBA2HrVmjUCJ5+OtRT9PnpJ7jgArthkJ6ICAvKITAAB6ha1SoBEhKsRJ/LL4e33rJNi+PigtTDikg4HT0Kc+bYcaqAfOtW620RERG4rkUKNGXIRUREJE+IjXW3EqpePY2LXnsNJk60uvDvvoOiRUM2v5Rat4Zt29xkfUakzKpXqGBfCiQH5O3b28lDh2zfNxHJNdatgyuugN27bVnKmWemuMApV2/dGkqXDvHsJLdSQC4iIiJ5wpYtNhYuDOXKBblg8WJ4+GE7fuMNaNEiZHPLLikz5BUrpgjIIyKgWzc7MXFiSOcmkpd4vfDAA/Dhh6F5vf377VfOuHH28eOPBylgUbm6BKGAXERERPKEVatsrF8/cD9vAA4ftrrv+Hjo0wduuCHk88sO/hnyEiXgjDNSBOQA559vowJykTQtXw4vvQR33mnBeVYcPgzXXGOBvf8OD8GsWuXuqNC/PwwYEOQiBeQShNaQi4iISJ7gNDpr1CjIg48/bhuSV68OH3wQJGLPG/wz5Jdfbt3aUwXkToZ8yRLYvt06r4tIgI0bbTx8GA4cyFqF+FdfwSef2PHUqe52hOm9Xvv28MUXQS7Yts2ido8Hzjor85ORfEsZchEREckT0gzI//nHStTBgvGyZUM6r+xUvDg0a2aN3B580M6lCsgrVoRTT7XjyZNDPkfJnTZsgHvvdQPDgs4/o+0sd8mshQvd4xN9X52GkymXnfj47z+u9ePiRwG5iIiI5AlBA3KvF269FRIT4ZJL3HLuPGzGDPjvP6hXzz52+tIdO+Z3kfN1TpoU0rlJ7tW3L7z6qm0yIIEB+datWXsO/4D88OH0r3UC8jS3O1S5uqRBAbmIiIjkCUED8m++sTe6RYrAsGHhmFa2K1cuMMuWKkMObkA+ebJt9SYF3uLFNjqJ2ILuZDPkx47BsmWBHycmpn39CTPkCsglDQrIRUREJNc7fNh9U+0LyA8dgnvuseOHH4batcMxtRwXNCA/4wzr+rZ7N/zxR1jmJbnDtGnWdEwCOQEyZD4gT0iw+3uJidbHwZFeltwpaQ8akG/bZncUtX5cglBALiIiIrne7Nk2VqgAZcokn3z6aatFrVcP7rsvbHPLaSkD8rVrYfOOaOjSxU6o23qBdt551k3cX0JCeOaSm5xMyfrLL8Mjj9jxWWdBZKQdpxeQp1uy7mTHtf+4BKGAXERERHK1Vavgiivs2LdEfMUKt0T9jTfcqDUf8g/IDx2CJk0sCxffubs9oHXkksKGDeGeQfgcPAhDh9qvCEdmM+RO+T9Yc8USJew4rYA8Ls42PIA0MuTOTTPnJpqIHwXkIiIikivs2mXbE6V0//12vn17GD4ca+R2++2WBrzoIrjwwpDPNZT8A/ING2yrdYDZxZID8nnzYP/+sMxNcqc1a8I9g/AZNgzefTfwXGYz5M4NjR9/hI4dbfcDSB2Qx8XZuGWL/VoqXNiqeAIkJcGECXbco0fmJiIFwkkF5HFxcaxcuZIE1cWIiIjISdi+HRo3htNPt/ev48fDgAH2PvannyAiAj78EIoVA8aMgV9/tXe/r78e7qnnOP+AfO9e9/wXc2rbNy0x0TZJlgLJyd76W7069PMIl59+svtzTjO7WbNSX5NehtzrtcR1jx52DLB+vY1OW4pgAfn770NMDFx1Ffz9t52rXt2WiQdYvNjuNpYoAR06ZOIrk4IiSwH50aNHGTx4MDExMTRr1oyNyV0MbrvtNl544YVsnaCIiIjkf2+/bcHmypWwfLklvr/80k0oDRxopdocO+Y2crvvPqhbN2xzDhX/bc9273bP//gjJHVV2XpB5vW6QeKVV8JNN9lxQQnI4+Nh0CB46y1rXj5lCixa5D7uBNLbt6e9rn7LFrufNWEC7NsHR464P2e1agU+z6FD7uf98ovdC/viCxgyxM4F/XX00082du0K0dFZ+TIln8tSQP7QQw+xdOlSpk+fThG/NVtdunThm2++ybbJiYiISP53/Dh88IH78ahRqa9x3vDyyiuWvqpe3RZ3FgD+GfI9e9zze/bA1pbJi+onTnTTe1JgHDvm/rW//z60bGnHBWUN+Zw5gctcnnrKPo6JseUtM2ZYQ7akJNi15gDMn+/WmSfz371g3z63W3rJkm7/tVQZ8lWr6Df7Nr7mci5kPNu3219CvXopJpiYCJ98YseXX37SX6/kT1kKyH/88UfefvttOnbsiMevLqNZs2asXbs22yYnIiIi+d+dd8LOne7Hr70W+Hjt2rbLFzt2wPPP28mXX06uX8//0grIAVZXOdtK9zdtgn//Df3kJCSOHIGvv0799+9fQh0TAxUr2vGuXaGbWzj98ouNTuDs7MZw1llWLXDqqVC5MjRhOWU7t7JGFFWrwty5vuc4eNB9vt273ZsZTnYcUjR127oVb6dO9N/zNpfzLeO5iE8ZSDRxqTPkkydbu/dy5eDii7Pry5Z8JksB+a5du6jo/MT7OXLkSECALiIiIpKeWbNgxAhbd5nWzmUDBiSvy3z3XUsJnnZagco2OQH50aOBJesA63fGQKdO9oG2P8u33njDStJr1IAFC9zzTkBerJj1WXAaihW0gPzllwPP9+7tHressI2pnEfhbcmR9p49Vud+7BgQGJDv2eOuH/cPyAMy5Ndcg2f7dv6mGcO4kwQiuZrPGcdFNKh+zP0krxdefNGOr7rKbpyJBJGlgLxt27b8/PPPvo+dIHzkyJG0b98+e2YmIiIi+Z7TDKlHD3jiCXe9dESEZbuGDUveD/j48eQW69ga8gKUAChb1sa9e1NnSDduxN0LTuvI8y1nXfSxY4ErNZw1zU7AWJAy5MeOwT//2PHFF7vl+hUqwPXXJ18UH8/Lmy6nCtvZU6UZrF1rGfI1a3zbJqYMyINlyJ3vb9FVS2HKFJIio+jNGF6vOYxB5X7mMMXozmTOe6OX3TkDK2mYMcN+qd11V858EyRfyFJA/txzz/Hwww9z8803k5CQwBtvvEG3bt0YNWoUzz77bHbPUURERPIpJ+NbpYqV3I4bZ9nAVausIfGddyYH6T/9ZFFGjRrQp084pxxyTtZz9273++XsdbxxI9A9ubHbjBm+rJ/kLytXusfz5rnrnp0MuRMwOv9W9u9PtVQ639m2zcaiRaF8eXjvPbjiClsmHhmZfNHDD9NszywOUoJPeo22rmvOspcRIyAxMUMl6873N3L4WwCsO6Uva2hAvXqwvVV3LmAChyhOiQW/Wne5Z56B666zT3roocAnE0khSwF5x44dWbJkCQkJCbRo0YLJkydTsWJF5s2bR5s2bbJ7jiIiIpJPOZk8J5A47zzbwihVcySnaexVV0FUVMjmlxuUL2/jrl1uhvyUU2zctAlrP1+jhkVpzt5Pkm8kJAR2TY+NdcvWnYDcWeNcpowbjB76chz07Gl7eo0Yke+a/jl7i1etagUz7dvDV1/5dTofM8aaQALX8DHLYhva+X797Bu1cSNMmRLQFG7PHneLNOemF9j3txiH6c+XADx/+DbAXqtxY5jNWVxRaqJ1glu4EP73P/t57NUL7r8/p74Fkk9k+f9o9erV4wP/lqgiIiIimeRkfJ2gM6iDB93FoldckeNzym2c743/+vFTT4WxY5Mz5B6Pla1/8IE1kXJK2CVfWLvWtveKibHtAL/5BqZPt9YBKTPkERFQvpyXO3Y+TLlr/bYinjrVIs2nngr5/LPb9u3Qtq1bGV61apCLVq+Ga64B4J/udzNmUh+6OnuRFyli+yi+8QZ8+CEHT3V/XvbscQP9KlXcpyteHM5nIjEcYy11+fDfMwG7cej0ltzTuAP8sBxefdXS7GeeCXfcUeBuIErmZSlD/ssvvzApyDqlSZMmMWHChJOelIiIiBQMTpDpZMiD+vlnSws2bgwtWoRkXrmJ873Zt88azYObId+4MTnx2bmznZg1K+Tzk5zlNM9v1Mj9a3b696UMyImL472463iI5GD8jjvg0Uft+OmnLZLPg776ygLkn3+Gd96xewv79tljqQLyo0fh0kvtRl7Hjmy/074XTuYbsKZuAOPHc3yXu7n47t1uKXzKgLwvPwDwA30B62HRuDFccon9PA4ZAlSrZttE/PCD9bpQMC4ZkKWA/MEHHyQxMTHVea/Xy4MFZE9QEREROXlOyXq6GfIpU2y86KIC1czNUbas+2U7611bt7bx2LHkMvaOHe3En38G7oUled6KFTY2aQIXXgjR0bZO+qabYORIe6xEsSRr6nfGGVyy/2MSiWD+DSPh9dctEB8yxC584YWgr5EbJCWlXVX/wAOWGe/Z020E6QgIyBMT4eqr4a+/oFIl+OYbqtaKBtzMN2A/QA0bwvHj1Fk21nd6wwbbYg4CA/KShY7Tk/EAjMZ6WMTEQLduULMm/PGHu2RcJLOyFJCvXr2apk2bpjrfuHFj1qxZc9KTEhERkYLhhBlyr9fKbcEWmBdAkZFup3VHlSoWb0By2Xr16tY4KjHRojXJF5KS3NUaTZpYAvbqq+3jESNg9mwv1/IRb01uaEsV/vyTQ4XK0ouxzG862H2iBx+0evZJkyxYzWWmTYNChSxOnjYt8LHjx20rb8ePPwY+7h84c999MHq0Pdl330HVqr6Aff9+t8wdj8e3/KXViq98n75smY3Fi7vr8gHq/vcrJTjMZqrxO6cBtmTAKVcXORlZCshLlSrFf//9l+r8mjVrKKZ/mSIiIpIBXm8G1pD/959FnNHRbha4APL//pQqZd+OcuXs4/37kx846ywbVbaeb4wYYX+dRYtC//527sEHLWCMIp4x9OYjBlPp0Fr7h3H77bww4G9+4cLArc/q1HE35/7ss5B/HScycqTdS/rrL3j88cDHfv89/X50VatiFzz/vG8rMz75xPfzULKkGzgHlK0nB+Qttk6iDHsBt3t9QJAP1Fpk5epj6I03OXzq1y9TX6JImrIUkF988cXceeedrF271nduzZo13HPPPfTq1SvbJiciIiL515Ej7hvgNANyJzt+xhkFOh3lX0HgHDvrhp0SW98Ni9mzQzYvyTnr1lnCF+DFF93u4Q0a2LryT0vcyiX8xDGKMOHclyzafOMNCte2aHLnzhRP6DRE/OGHXNVxPTHRehE6nG3HnAZrzsYBl14a/PNrFtsD114LDz9sJ154IaD5o8fjVpg4N6+2boX+TzfhcP1WRHkT6MPogOcMCMjj46kw38raf6Avd94Jb7/tLkMXOVlZCshfeuklihUrRuPGjalTpw516tShSZMmlCtXjleStxcQERERSY+THS9SJJ1Y26lfLaDl6g7/GxZOQzcnIPctGXcy5PPnW1tuydNuv91utnTqBEOHBj5WbeZXXHnofRKJoB/f8meX+3w/RBUr2jUjR1pLAZ8LLrAftnXrYOnS0HwRGfDHH+52fmDBckKCNSlv1gy+/97On3MOPPKIe11DVvIOt9Chf03LiIN1OH/ggVSv4fx+cW5ePfSQNYp7Zo0F7lfyVcD1AevSf/2V6IN72UkFZnEWV15pfx8FsJ2F5JAstf4rVaoUc+fOZcqUKSxdupSiRYvSsmVLzj777Oyen4iIiORT/g3dgr65TUpSQJ7MPyA//XQbnSDDF5A3bmypwL17LRI77bSQzlGyj9frNkQfNsyWf/ts2AA33wzA0/yP8VxE1+Luw/7Z3V69bMWHx4P9gzn/fFuE/eOPbmfAMHM2aOrVy7qoJybaj/2qVXbeyWqffz7Urg0tCv1L65+eosEfXxOBF45hX8vbb0OHDkFfI2U1idPy6hsu5wUe4lx+ozLb2I598wIy5MnB/jdcThKR1KqVHV+1iCtLGXIAj8dDt27duO+++7j11lsVjIuIiEimnLCh299/W9QeE1Pgg8tgAXmqkvWICDcg0TryPG3fPvdGS+PGfg8kJlqt9IEDbKt5Os9gW5oV9wvIu3d3O35v3gzr1/t9/oUX2ujsXBBmSUnw6ad23Lu3m5n++uvA65o2hXqe/4gcdBWXP9mMRn98RQRexnKRRe9//JFmMA6pM+ROCft66jCPM4jAy9VFv/dd7wvI9+/3dZH7hEFERbkVCCLZJcMZ8jfffJMhQ4ZQpEgR3nzzzXSvvf322096YiIiIpK/nbChm5MdP/ts65pcgMXGusennmpjqpJ1sLL1ceMsIL/nnpDNT7KXs466YkVr6Obz1FO2qLpYMZbe9zmJt9lbef+AvEgR+PBDa5C2aBEsWGA93QDo2tXGBQtsD72SJXP8a0nPb7/B2rU2jcsugw8+gE2brJzcUZp9jCj2NDR+212KccklrO7/OC3btYbaJ36dlNUke/e6j33NFbRnPndV+oqX199mr1k6+cFRo+yHr2lTJk5vQ3QhlapL9stwQD5s2DAGDBhAkSJFGOZ0MAzC4/EoIBcREZETOmFA7jR069w5JPPJzcqUcY9jYmxMVbIObmO3OXOs7lnRQ57kBOQB5dHffGMBOcB771G+cX3fQ/5bdDlOP90NyH09zmrVgvr1rWZ7xgy46KIcmX9GOdnxq66yf8/Vq9vHTrPH05nPt/Sj5sJNdqJ7d3juOTj1VBpk4nVSZsj9t1H7jst4jbupsn4eZxddyMxj7WjXLnkSL79sF911F+Ur6GdJckaGA/J169YFPRYRERHJigMHbPRlo/wlJLjtlQv4+nGA226zTOKVV7rnUpWsg6XPCxWyux1r11rwJXlOqoD8s8+skzjArbfCVVdR06+LerACktNPh3fesYA8QNeuFpD/+mvYA/K//7axWzcba9RwH7swYgJjo/sQEXvc/h2/8457YSb5/6wkJVnjOMc2qvI5VzGIT5l8xv9Y9PREW17/3GuwbZvdJRg4MEuvK5IRmV5DHh8fT7169VixYkVOzEdEREQKCCeQdDK+ARYtgkOHLDWcS5pPhVPp0vDxx5YgdAQtWS9cGNq2teO5c0M0OzkZkyfbmuYvv3TPOQF57ZpJ1lp84EBbPz5wILz+OhDYeyEuLvXzOr0G/vjD7m/5nHuujbmgz4CT43O2dHMy5N2YxBgusWC8Rw9YvDjLwTgEVpPs2pXi+wE8xWMQFUXh3ybRYfLj8MUX8Nhj9uAzzxT4JTOSszIdkEdHR3PcqSMRERERyaKjR20MuuWZs3783HNTtJgWR8oyXJ/27W1UQJ6rjR9v66a7d7cmbgMGuI9t2ACRJHD9jKutRBtsr65RoyAyErDVCHffbasUghWR1K9vcWRsbGCJtq/52dKldtMrxLxeWwp+4IB93eCuca9cGc5iJmPoTXRSHPTta03VTnKtu//PivO9qFIFXnrJjos2q2f7l4MtC7jqKrsB0r+/suOS47L0f7ihQ4fy4osvkpDy9pKIiIhIBjmBZNCA3Fk/rnL1NAXNkINt4Awwb15I5yOZc8cd7h7bDqfZ2Ib1Xj5kMI0WfwlRUVYe8dxzqW5OvfqqJbqDJXAjItxA97///B6oWtUeSEqyPetzUGIiLF9uQbjzcdu2VshRrZqdK1/e/bfc7sh0fuZCYjhG0gU9rGwgOvqk5+EfkG/ZYsfVqsF991m7hXHjsCaIb78NDRrYpB591LrjqQ+D5LAs7UO+cOFCpk6dyuTJk2nRogXFUvyfdPTo0dkyOREREcm/0gzIjx2zd8mggDwdaQbkToZ82bJc0UlbUvN6UwTJycaMsQD2tD+GM4hP8UZF4fn+e7j44iy9Tt26sHKlvVZAb8QOHaxefPZst/N6DnjjDYtz777bbh5s2mQl9OD+/DeudQz+WAHffUe9114D4jh8emeK//B9tpWK+68hdwJypzzeuX8FwNCh9kckhLIUkJcuXZq+fftm91xERESkAHFK1lOtIZ83z+psq1aFhg1DPq+8Is2S9SpVLAO6bp119MrBgEuyZtcu9/iSSyybPXo0XH891GAjK7Et6+KeepHCWQzGwV2bnSr479gRPv/cAvIc5JSEv/YaNGpkyWeAKOK5hXe5llG0XrwU2vh9Up8+FP/iC9u/LZv4ryHfuNGOnQy9SLhlKiBPSkri5ZdfZtWqVcTFxdG5c2eeeOIJigZskCgiIiJyYmlmyP3L1VUumqY0M+Rgab9162wduQLyXGfVKhtr1bKs+J9/wsSJdpNqWNFHKHrsOEfbnk3Mg3ed1OukG5CD3bCJj8+WsnB/n3xived27HDP3XijVd+XYj8TOZ8z8Gv/XqqUVXbceKNVA2Tzz73/zauFC+24efNsfQmRLMvUGvJnn32Whx9+mOLFi1OtWjXefPNNhqqsQ0RERLLghAG59h9PV7oBuVO2rnXkudLKlTY6BSCnnGJZ81U/raDvsc8BiBn+6kkHpmkG5E2aWOv+I0esuVs2u/12WLLEjps2hXvvTX4gIZ6fuZAzWMBeynAz7/L2w1utu9uECVYukAM34ZyflYMH3WXzTm87kXDLVED+6aef8u677zJp0iR+/PFHxo0bxxdffEFSUlJOzU9ERETyqaAl67t3w++/23GXLiGfU14SdB9yh39jN71Py3WcgLxRI/dcTAw0+Pl1++CSS9zt606CE5A724v5RES4EanTryEbHTzoHl95JTz8sB3fzpt0YC7HCpfmXH7jPW6mYacqOV4J49z0W7jQfl5KlYJmzXL0JUUyLFMB+caNG+nRo4fv4y5duuDxeNi6dWu2T0xERETyt6AZ8gkTrONV69Zu1yUJyn9drNPF2qdFC7vg4EHrEia5SrCAnF274NNP7fiee7LldZwu67t3BwbJgBuQZ/M68mPH3OMbbrBseZky0Kzcdp7kcQDmXvoa36xoxddfn9T24hmWsgqnfXvtpii5R6b+KSYkJFAkRYOF6Oho4uPjs3VSIiIikv8FDcjHj7exZ8+QzyevcTLkiYnWAy9AVBScfrodaz/yXMXrdavEAwLy996D48ehXbtsq6cuUcKywQCp8mfOOvI5c4Lc0cm6bdtsLFIERoxwm/zfXXwExTnC77TjcN9BNG4Ml1+ebS+brpQBeUBndZEwy1RTN6/XyzXXXEPhwoV9544fP85NN90UsPWZtj0TERGRE3ECcl/J+rFj1tkKFJBngH+QceRIkKbU7dvDtGlWtj5kSEjnJmn7/XfYsMH+3TtL/Tl+3PbABtsjLBtLuIsXhwMH3CUiPm3b2o2bbdus9XitWtnyev77fPu+jLg4+u58D4Bh3MV9tUKbnnZuXjmaNg3py4ukK1MB+aBBg1Kdu+qqq7JtMiIiIlIwJCW5pa2+wHLMGKurrVXLsoSSrshIC8KPH7ey9XLlUlzgpAGVIc9VPreebfTu7RcofvUV7NxpyzSyeWth54ZXql4DRYva0pBFi+ymTTYF5E4mvmpVv5PjxlHq2Ha2UZkf6MsbIV6NkjJD7pTyi+QGmQrIR40alVPzEBERkQLEf52p783yhx/aeO21WuCZQcWLuwF5KmecYeOqVbaIuHz5kM5NUvv2W/joIzv25bS8XtuoG2zBdTZvQeb8fKXKkIOl6J2A/IorsuX1ggbkX38NwGdcTTyFQv5PUQG55Gb6v52IiIjkuKNHAwMC/2xd0aJYHe+0aVbjeu21IZ9fXpVup/WyZd1FyosWhWxOBU1CAgwYAG+8kf5169dD//72c9Cli98mAlOmwN9/W9R4ww3ZPr80M+SQLdvjHToUuATdv2Tdd0Fyb4gJJa+ge/fQ328L2MkBazInklsoIBcRESko5syB226Dm26yutkQNWU9etT2W27aFLZvd8+BBeMRhw64a5yvvhpq1gzJvPID/07rQZ16qo1//BGS+RREEyfCl1/CnXemf91XX1kDvg4dbDOBqCgskn3cOo8zeLDtDZ7NTpghB/jzz8CylQyaM8eaxg0e7AblqTLk48dbGUeDBvyy7RR++SXTL3PS/G8AOE3mRHILBeQiIiL5yOHDQbadjo2F66+3rspvv22tj6++Gk45xdJ2OWz2bMuabdgA916yBu9zz1PhsnPYQE3+imsEtWtb2+myZeGVV3J8PvmJkyFXQB4+/h3uU20t5ufLL2289trkYBxg7FiYP9/uTD34YI7ML90Mea1aULmypfkXL870c3/3nQXio0ZZxn/ixDQCcoA+fSga4wn7apSKFcP7+iIpKSAXERHJJ5YsseRy27awb1/yycREuPJKW58dEQHXXAMPPAAVKsA//1jjr02bcm5SXi+rPv+d53mQf2jK5wsa4HnkYYotmkFNNlE/cRXs3w8NGsDPP9u8JMOchKqz1RRY4YOvhNgJyP/8M5TTKlD8C03S+lH691+rSi9UyK9n2969MHSoHd95J1SpkiPzSzdD7vGcVNn6smXu8bRpcOml1rIAkkvWExPdnRMuvDDTz58TFJBLbqOAXEREJB84ftzWse7bZ7HXpZcmB2WPPmrdywsVskzVqFHwwgt2UdOmFsldcok9QXZbtgw6dODWz07nQV6kKSuIJ4qdrbrw790jOIN5XF1rhq0f//tvtwmZZJjTjH7OHBs3b7aAw7fL2Smn2Pjff353aSQ77d3rHqcVkDsFCu3aJd9EOXbMFpRv2WI3ox55JMfml26GHNyfu0wG5F6vm1R/6in3NbZts3t/jRphP9t791pdu2+Pt/BSQC65jQJyERGRfODrr2H5cnuzGR1t2aptX0yDF1+0Cz7+GC64wP2EatUsI12+vEULzzyTvRP6/nuLPubNI5ZCfM3lPNHoKyqwi0+vmsLazkNYwBn8W+Fsu65Qoex9/QLi7LNtnDnTxscft4KDkSOTLyhTxpYEgJVQSLbzv8+xcWPwa/75B8DLWbU3wTffWHA6aZKVqn/xReo24Nko3Qw5BGbI/buzncDatba/eeHCVm1/333uYxddBJUqYYvlAbp396vTDw9neUefPmGdhkgqCshFRETygZ9+svHmm+39dWn2UfK2geD14r1hiJWtp1S7Nrz3nh2/+KJF9Nlh7FjbQik2li2te1Cb9TzZ+GsSL7uCA5Rm9Wo3W5ey+7FkTvv2th/5hg0WDFrgl4LWkeeoEwbkhw/T+ruH2UAtnv+ipv1sLF1qqfJJk9wyhxxywgx527YWLG/fnvYdhSCc7HirVnYT8LLL3Mduvjn54LffbOzaNVNzzgl//WU3Ln3bzYnkEgrIRURE8rhjx2DyZDu++GLocp6XEdxI8f1b2F2mAWU/fi3tWKxPH0tnJSTAQw+d/GRWrbJ3vImJcPXV3FZzLNupwqWXWmUuEBCQ52BisEAoXhzatLHjmTNhxQr3sVTryBWQ5wj/kvVU8ey2bdChA5etfp6abCIpItKWEdx3n/2snHVWjs/vhBnyokWhdWs7zkTZurOTXtu27njddfbj37Vr8gsuWGAPnntuZqed7erUgcsvt2XzIrmJAnIREZE8bto0e+9bvbq9r756/1v04zviieKCfV+wP74YL7yQxid7PPDSS7boc+xYdzFyFnjjE0i6cgAcOsRMzqLPvg8ZPyESgH79AgNyJzhQQH7ynHh7/vzALt++jKgC8hzlnyEPWEPu9cKgQfDXX+ygIpfxLbvWHLS/h5deClkDwxNmyCFLjd2cDLlzQ8jjsd6Rn32WvM3Y3LnW8a5GDahbN9PzFikoFJCLiIjkJYcOWdA8aZKtCd65k7E/WSr04ouS8Ax/l1pv3AXAvbzCIqwcduHCdJaHNm5sezGBu+Y8gzZutIzY4MHwQYs3iPhjEfsozRV8zZjx0cTHQ5Mm0Ly57UUO1nhs1y47Vsn6yatUyUZndynH/v3JB05jt5UrTxCVSVakWbI+ahRMmUJS4SKcw3R+K3cZFWuH/h/8CTPkkOmAPCnJDcidDHkqTrn6OecoLS2SDgXkIiIieUFiIjz5pDVj69gRzj/fAq1KlXhxZFl+px0v/1AHhg7Fk5TE5p43MqLQ7b5PX7/emjpv3pzG8zsdmcaPtxR2Bhw4AKedZj2pfv5oO1eufMKeipfZhm1CfO65tm7T44Fy5azHGLi7cClDfvKcrtEbNgSeP3Ag+aByZdtSy+u1tcuSrVJ2WU9MxP7z7LMAzOn+NP/ShBYtwhOX+mfI582Dzp2DbDnuBOR//mlrYE5g7VqrxihSxDZrCGrWLBs7dcrSvEUKCgXkIiIiuZ3XCzfeCE88YRnyatWgZUuoVAmvx0Np737asYiiOzdCyZLw/PNUHzucufM8Ac3Vv/46nd2VGjWCnj3ttd54I82pTJ5sjdn79bPYfccOq7x9pfCjlOAwCziNj7iOhg3tqaZNs6k6TjvNxrFjbVRAfvKcDHlKvgw5qGw9B/lnyOPjrRCBsWNtq7myZXlgvXU48+0/HmL+GfI777TEddu2yTcOHLVq2Y2bhAQrpzkBZ/1469ZpNE+Pj3cv6tDhJGYvkv8pIBcREQmTAwesCdLs2Se48NNPbXFmRAR89JGl4ZYuhe3beeahozRnGS+d+SP8+qulwB98EDweTj3VlrA++aT1bQK3ijSou6zUnVGjAtN+KaayZw98953brfjKpksZEPcRAG/VHYaXCG69NfhLDB0a+HHJkif42uWE0tpXOSAgd+6KLFuW09MpcJyAvKoVhVgcOnw4ADv63MS8v4oRHR18o4NQ8M+Q+zf9++QTv4s8ntR76KUj5frxVJYutUx7mTLuWhURCUoBuYiISJg8+6zFvuk2Wj561E1rP/usrfX2q3tdurII/9Ccwv0uhvPOgxIlUj1Fu3a2ZjsqymL5lKXNPueea3sYHT0KH3wQ9JK//kp5xsut6+7G4/VCv368PPtMPvoIbrkl+EtceCHUr2/HJUtqC6LskFZA7itZB2jRwsa//87x+RQkcXHusvxu3Wz8d9YuKw0BRiYNBmwjg3LlwjFDN0O+YYMV2DhefNHWgvucc46N06ef8DnXrrWxWbM0LnDWop9xRnKHNxFJi35CREREwsR/qfaaNWlc9OGHsGWL7Rl+552pHnbeGDtBblqKFXOrlp2lnal4PG6W/O23rXzVT2xsYIYN4GJ+osHGaVC4MLz4IlWq2D2DyMjgLxERAd9/Dw88YNue16mT/rzlxFIG5M46/YAMefPmNv79dzrd/SSznOy4x2NrswFKTRsDiYl4T23DWz9bd/FBg8I0QdwMufPvoWpVuxm2apX1hvRxAvK5c+2HPR3bttlYpUoaF8yda6OzNl1E0qSAXEREJEy2bnWPR49O4yKnrvTuu6FIEfbts5JxsLjKCeRPFJCDm4m/+mr45Zc0LrriCovwNm+GMWMCHvr3X4vRS5eGmjWhMMd5jbvtwXvvtZsGGdCqFbzwgi2Fl5NXunTgOt4mTWwMCMgbNbKLDh5MsTeXnAwnIC9Vyu2P0Hb9dwAsbXiZr8eC08chHFLuZNC5sy2VARg50u+Bxo3tZ//YsROuI9++3cbKldO4wPn8M87I9HxFChoF5CIiImGQlAT//ON+/NZbFlzv3m29oABLRy9ejDcqigf+vILzzrP3y40b2/ZKO3fC4cOWnctILNyzp3uc1hpvChe2BnIAw4YFZFOdcvWWLa1U9W5eoy7rSKxc1datS1h4PIHN8Zyu1wEl64UKWVAOWkeejZyAvEwZaNAAKpU4SsckW4N9+deXAHYDLDo6TBMkdePEtm3d8nrf7xqwf0gZKFv3ek+QId+/3y3dSXORuYg4FJCLiIiEwbp17trTunUtId2zJ3TtagHVv/9ibdGBdQ3P56VRFZg2zTLUu3dbCaxTPl6zpsXRJ3LOOe777HXr0tmX+Oab7QnnzQt4Y+7smNWyJdSO2szDPAdAxMsvQfHimfnyJZv5d8yuUcPGgAw5aB15Npo92zqM//STfVymjC3HuK7RHAoTxyaqs4qGXHGFNVUMp5QZ8hYtLGsP9rskgLNFWToB+f79tnYe0ujw7+xpWLs2lC2bucmKFEAKyEVERMLASVKecoottyxSxLZLWrLElm8OGwb7vp0CwDPLe/s+r0sXqzyePt16sAHUq5fx1+3UyX2PnOZ241WqwPXX2/GTT/qy5L4MeQsvj667juIcYS5n4hnQP+MTkBzhv9y/dGkbUwXkzjpyZchPWt++doPqxRftY6eJ/UUxUwGYynlER3v44IPw36tKmSFv0sS2LgQLyANaCmRgHblTrl66tP3eSsXZWs9pWiEi6VJALiIiEgbOFr0tWliWydlxyDHpu4OU+Pd3wN7cg5UgT5li+3/7N1PPyPpxf40b2/jvv+lc9MADeAsXhhkzeL/TF3i9bkDeY+GTVP17CgnRRag4/qOAru8SHsEC8oCSdVCGPJt4vbZcxN+FF9rYfKcbkJ91VviDcUhdPVO5spshP37crdQBLFqvUCHddeQnbOh2wj3RRMSfAnIREZEQ83rhm2/suGtXG501nY7m+2YSRSJrqMdGanHLLe6e3d27w7hx7rXVq2fu9Z2lxDNnptPfq0YN9t36GACXzbqNFe/+xrEdB3iVe6g20mpwo95+g/oXNsrci0uO8O9qf8IM+YoVEB8fglnlT8uXB34cGZn8c7xvH8VXWjA6lfPo3j30cwvG/35ZhQr2cUyMm93+8EPfLm2B68h9JwOdsKGbMuQimaKAXEREJMQWLrQGbjExcMklds4/IL/5ZuiMvRn+q/x5rFkDr78e+BydOsHw4Zb07J/JinEnIH/3XWvO5t/t3d/EFvcxmw6UYT9Nb+3MAUpzN6/Zg089BUOGZO6FJcd8/731bfvoI+v4DUEy5LVrW/1yXFw6++zJicyYEfhxhw7J3/Pp0/F4vWwt1ZijparSr19YppeuWrVs9HjcsvU774RevfyqLJy7hBMnBn0OJ0MeNCA/dMj2UwMF5CIZpIBcREQkxD780Mbevd2S1ubNbcex3r2t4/qFZecBUOXys6lXL3iX5ptusjLyzKwhB7dkHez981tvBb9uzu/RdGMynzCQOGwCm0s0tvT8//6XuReVHNWzp/1dXnttOhnyiAi7AwNaR34SZs2y8dZbrbniK68kPzDVytWrDDiPPXsyvAtgSNWs6R47ZetgZeu+G3Pnn2/jggWwd2+q53Ay5EFL1pcssRKg6tVtSwgROSEF5CIiIiG0dq1lMcHdXQwsY/XVV7YfeWRSPA2PLgGg/e3tsn0O/gE5wHvvBb7v3rPH9ix/9104RgzX8AmlOEBltjHqvhWB+6dJrlGokI1OhjxVQA5aR54NnP4PvXrBxx9DO+dHNDkg93Q5L2AJQW7i36vCyZA71q9PPqhRw27cJCXBr7+meo50M+QqVxfJNAXkIiIiIfTMM1Ya2r27Bb1BLV+O5/hxWzSe2Y5tGdCwoSW4X37ZgvP9+6FPH3cro08+sW2d/B2nKDuozCmnZPt0JJs5vQaOHw9s9ga468iTA/KdO1M3KJO0HTjgVvsH/Cxs2WJdEiMi3DXYucjEiXDvvTB0qHsuZUC+YYPfB06WfMKEVM/lBORBtzxTQC6SaVHhnoCIiEhBcfgwfPutHT/+eDoXOim4Nm3sDX4283hsCTjY2vWOHW1d7OjRlvX64IPgn1ekiLvVmuRe/h34Dx2yPbJ9nAz5smXExblBVWysm2GXtC1ZYmPNmikCWqcB2qmnpviG5w7du5OqyZx/yTr4ZcgBLrgAXn3VInmvN6Az3MqVNgZdKuME5OqwLpJhYc2QP//887Rr144SJUpQsWJFLrnkElY6P+XJjh8/ztChQylXrhzFixenb9++7NixI0wzFhERybqxY+HoUUt6n3FGOhc6AXnbtjk+p5Yt4cor7fj66y3gdrZD27AB/vvPvfa881LvaSy5T3S020H74MEUDzoZ8rVr2b3xqO90kKXCEoSzo1eqBLBT2n3eeSGdz8lIN0PesaN1ndy+3d3vEKum2bLFjp12BD5Hj7ot6JUhF8mwsAbkM2bMYOjQocyfP58pU6YQHx9Pt27dOOK3IeJdd93FuHHj+O6775gxYwZbt26lT58+YZy1iIhI1nz5pY39+59g6+4QZ5mcN9b++xH36mVZQKcrM9h7dMkbnCz5oUMpHqhUyVKjXi9HF7v7d6W6ToIK+qPp9frWj+flgDwgQ164MHTubMd+ZetOvF21qts80Oevv2zdeaVK6WxSLiIphTUgnzhxItdccw3NmjWjVatWfPzxx2zcuJHFybcfDxw4wIcffshrr71G586dadOmDaNGjWLu3LnMnz8/nFMXERHJNOfN/IUXpnNRUhL8848dt2yZ43MCN2nqeOQRa1YFVjF/553Qvj3ccktIpiPZwFlHnipDDr6y9YQ/3cZuqbZIk6B+/93GgATwqlWWNi5UyPZAyyNSVrsEZMjBXUc+bpzvlPOrKVV2HALXj6d7x1FE/OWqpm4Hkv9vULZsWQAWL15MfHw8Xbp08V3TuHFjatasybx584I+R2xsLAcPHgz4IyIikhvs22djursBbdxoqero6Bxp6BZMyoD84YcDl8EOGwZz57pBnuR+aWbIwfcXHrHc3fpMAfmJ7dwJq1fbccCSE2e/bqfMO4/wegM/3rjR7gf6XHKJjXPn+urUnYA85e8MwK3n1/pxkUzJNQF5UlISd955Jx06dKB58k/59u3bKVSoEKVT1MRUqlSJ7c4miCk8//zzlCpVyvenRo0aOT11ERHJTY4dg7fftvXXRYpY+eRjj9n5ME/r+HE7Trfnk/OOt1Gj4JuP54CUNwjyUEwhachIhjxmjQLyzJg718ZmzSA5d2ScgPyCC0I+p5PRty+0bm0VMJGRtsuC00EdgGrV3Iz/998DmciQi0iG5ZqAfOjQofz99998/fXXJ/U8Dz30EAcOHPD92bRpUzbNUEREcjWv1940NmgAt91m2ZrYWEtrPf00XH11ivRPaDnZ8cjIE2Sa033Hm/OKFw/Ly0o2SzdD3qoVAGU3/AFYmlQFhSfmbAUYUJV+7BhMn27HeSwgL14c/vzTKmBq17Zzq1aluKhfPxuTt4dI89dTbKy7t70CcpFMyRUB+a233sr48eP57bffqF69uu985cqViYuLY//+/QHX79ixg8qVKwd9rsKFC1OyZMmAPyIikn+tXAl9OuxgT4eL4LLLrLSyVi146y3bMPiLLyzT/MMPdi5MnIC8dOkTLK8MU0A+bJiNyYkwyePSzZC3bAnR0cQc3UMtbOGwMuQnNmuWjQHNDWfMsNKX6tWhadOwzCs7NGpkY6qAvG9f+4U1dy77/97sy6Cn+lKXLrVN78uVs26QIpJhYQ3IvV4vt956K2PGjGHatGnUqVMn4PE2bdoQHR3NVKdzJbBy5Uo2btxI+/btQz1dERHJhb58dDlvzG1LuXk/W+D92GO2b9ett9pGuf37w2uv2cXPPx+20nVnW6kTblHstDEOcUB+xx2WTU25V7HkTelmyAsX9jUMbMdCQAH5ifz2mzV0i4iATp38HnDK1c8/P083MmvY0MYUuw8HlK3vfd/u1tWoEaTKx+ntdMYZefr7IBIOYQ3Ihw4dyueff86XX35JiRIl2L59O9u3b+dY8pulUqVKMXjwYO6++25+++03Fi9ezLXXXkv79u05I90NXEVEpCDw7trN4B97UoPNrPQ0InbBEnjySXcTZseNN1rWfMcO+OijsMzVyZAHrD1NyeuFFSvsOMTZNo9H5er5SVoZ8lWroG5dWFGiHaCAPCO8XltnDXDTTSkSwHl0/XhKaWbIwVe2Xmz8N0Aa9wqdBfZKmIlkWlgD8uHDh3PgwAHOOeccqlSp4vvzzTff+K4ZNmwYPXv2pG/fvpx99tlUrlyZ0aNHh3HWIiKSWxwacjc1E9axlrp08M5m9t40gtjoaLjvPjseMSJ0E/TjBOTpZsi3bbMO65GRFjWJZFFaGfJXXoF16+Dl6YEBudaQp23VKttiu3BheOopvwfWrbOUcmRkntp/PJg0M+RgZesREVRaN596rAkekDsZ8jPPzKkpiuRbYS9ZD/bnmmuu8V1TpEgR3nnnHfbu3cuRI0cYPXp0muvHRUSkAFm1iuI/fQHAlXzFHsrz66/pXN+/vwXmy5a567RDKEMBuZOeqlPH9jQWyaK0MuSlStm4EAvI27KIKOKVIU+H08zt9NNtibTPzz/beOaZ7jc2j3Iy5OvWWbf1AFWrQvIWxAP5NHVAvmULbNpk9fzt2uX4XEXym1zR1E1ERPIgrxfeew+eecYa+oTaiy8S4U1iHD051Pg0ACZMSOf6MmVsnSfASe7okRXOGvJ0S9adgNxJV4lkUVoB+dGjNv5DM/ZQlhIcpg2LFZCnY84cGwOauQF8952NF18c0vnkhKpVoVgxSExMI0uenCwbyKc0a5Jitwqny3yrVlr3IpIFCshFRCRN334Lp50GzZvD5s1+DziLKm++Gf73P2jTxl1DmA2OH3d7mwUVG+trB/4S93PPPZacWboUNmxI5/OuvNLG5C18QilTGXIF5HKS0ipZd24MeYngN84FoDPTFJCnI+h2Z1u3um3XL7ss5HPKbh6Pu/z7kUfsV7y/4+dfwn5KUZsNNNk4KfDBceNszOPr6EXCRQG5iIgElZBgSZGFC63C21mCDcDYsfDmm3ZcvrylVfr3h8OHs+W177nHGgc9/XQaF/z6Kxw8yFZPVebQgTPPdN8s9+0L06al8XkXXmhl66tWWSf2EFJALqGUVobc+XcIMI3OgALy9OzaBatXBwasgGXHvV47mU+2+Ro2zH49jhuXuohoz9GifMR1ABT/2G/7yPh4t7HdRReFaKYi+YsCchERCWrt2sAdwr7+GhYswN6EPvGEnbz/fruwVi1LTY8alS2v7TRCf+wxeOEFe88XILlU9HtvXyIiI6hXz60aXbzYtu5yegwFKFkSOlsQwtixWZrb4cP2xnXt2sx9nkrWJZROlCE/+2x8GfIOzCF+/5EQzi7vWL/exmrV/G6meb1uc8j+/cMxrRzRvDk8+qgd33YbrFkD995rv/f37IF3GEoSHjwTJrh9OGbMsBb9FSpo/bhIFikgFxGRoJzdt049Fa6+2o4//xxbqL1kia0VvP9+C3Lvv98ueOstSEoK9nQZ5vUG9jN76CHbPjzgguSMzGj6UKeOdT++4gpwen4mJNj75FSBPECvXjZmMSB//324+26oXz+NoD8NJ8yQJyS4Ub4CcjlJaWXInYD8qaeg132N2VOmHkU5zjl7tYNNMLt321ihgt/JqVPtF2Tx4jBwYFjmlVMefNC2qN+zBxo0gFdftd+le/bAf9RjSok+duG999r48ss29u5t3eZFJNMUkIuISFBOQN6kiQW7AGPGgHfkh/bBDTe4LYcHDrQIYPVqmDLlpF537143iLjnHhsDStDXrYMdO0iMjGY+Z9C4sZ2uVs12DTtwwKayfn0a69CdgHzuXNi5M9Pzmz/fPXaySRlxwoB8/XoLyosWtS9G5CScKENeqRK8+JKHIjdYQHlF3CckJIRwgnmEE5CXL598IinJrRC69lr3zkc+UagQDB8eeO6//ywgB/iowQtW1z5xIjRuDJMnQ1SURfIikiUKyEVEJEBcHFx/PTz8sH3cpIltsVuiBBzfspuEH5Mb+Fx7rftJxYvDVVfZ8ZdfntTrO0niKlXg8svt2KnkPnoUYn+z5nEbK7QhliK+7XocJUtahgfg77+DvED16pb293rdbYsyYckS93jjxox/3gkDcueLbNDAOtSJnAQnTjx+3P23l5gI+/fbsfPvMHqwBeSdmcbhhStCO8k8IFVAPmyYtV0vVszNEuczZ55pvTj8Ob/rjlev727G7rRjHzLEtmoUkSzR//FFRPKJgweD7B+bBT//DB9+6H7cpImVhPfqZft9R3vjWVeuDbRoEfiJTgfzMWMCF59n0n//2Vivnlu5vW2bBdf16sHHQywgXxh9JkCqgBzcqQUNyMHNkv/0U6bmdvCgFQE4tm/P2Oe9/bY1hwK/N/Ypaf24ZKMSJdylH/XqWVB+4IDbPdsJyAs1rM24iIuJwEuR24dY1C4+u3dDVbbQ+cAYu+noBOEvvJBvmrkFM3y4LRdy/P67jeXKYdnwkSPhjDOsZH3YsLDMUSS/UEAuIpKNli2DSy+1JYYnlJiYem+ZLFqzxkpQk7eKPSnOlrKOpk1tfPllGBrzMQDfFg3yQmeeCTVqWI3sL79k+fWdDHm9elCqlH1dYFnv7dvhtCRbuP3NJgvIzzgj9XM0b25jmgG50wFu8uRM3TxwsuNOMHP48Ikby+/caTvEeb3291O1ahoXKiCXbBQVBR9/bJnyfftsS0CnXL148cA+DS9WfYMjxFBk0Ww46yz47DM4oiZv/Pkn13x0FluozvW/9IEvvrDzDz8MQ4eGd245rEIFeO459/ers1THWaXE4MHWROPeewP/MYlIpikgFxHJRk89BT/8AF26uO/dUtm+3RrgREVB27buXrYnYeRIK0396isbT4b/zYRixSwwBqiy6y8aH/2DOKJ5c9eVqRNpERHuYvOvvsry6zsZ8rp1bXTiU68XapQ+REv+AmAe7Slb1rZHS+mEAXmrVpbdOnYsg3dPzJ9/2njWWRATY8c7dqT/Odu3272XChXc7vFBKSCXbHblldCmjR1v2ZJ2p/9aZ9fiaj4jtlAJC7IGDrSlHf6lMgXAl1/azl3z52M/rKedRoPts0nCw+5qreCmm6xc/dlnbR+0AqBBAxs3bLAx3V0iRCRLFJCLiGSTI0ds/1bH//6XRsPxa6+FH3+04z/+gK5dTyqjDO46RwhsOpZZO3a4u9n8+KPtaBMdnfzgxx8DMD6iF1tjy/m2AwrglK2PH5+6vXMG+WfIIbAk/ckLfyeSJNZTi21U5ayzgi+3dgLy9evd7YIDeDxZKlsfM8bG0093O7r7ytYXLrR28L/8EvCC/mt2030Pr4BccoDTHzC9gPyss2AMfbim7d+W/a1f3/7hXn89vPRSSOcbLtu2WZ/K8eO9jG3/nGWAExKYWa43NdjEtNeWWB33mWeGe6oh5QTkDl+GXESyjQJyEZFsMnEixMZaoFaihDUDT1n+zfTpdmFUFMfGT2VJzV4QG0tir0syvZ7Zn5O5dV7i8cfhlFOgZ8/04+LPP4cBA9wOuqOTdz5q3dqqup3sGvHxyXuewW81rwHS6GDeurVF0LGx7k2HTHLWaDsBealS7mOXVLT143OxN8WdOgV/jrJlrXoeoF+/FF3aHU5APm5chrZqW7rUblBERsKgQW5AvmMHdhfhvPMsmLnwQnj6ad/nOQF56dLpPPnRo7Bpkx0rIJdslJGAvGNHG8cuqUn8E89as67HHrOTDz0EM2eGZrJhMH68rY9+5hk4fjSRN7md53gEAO+DD3FzxR/YSrW0ez/kcyl/HSkgF8l+CshFRLLBsWPw4ot2PHCg7dsK8O67KS50sk1DhvDxxs602/g933IZkYnxeC+9FL79NtOvHR8fWJr95JNWOr9kiTVomzQp+Od5vba/+JdfWuD+zz/wyiv2mH8DdcD2Ht+1CypVYv/p3QE3kx7A43Gz5FkoWz94ELZutWNnO7NLL7XxvPOgzAoLyPc3OZMiRWzeaUlO6ANplK536mQLbHfscDsWpcHrdXc6uvRSC3J8GfJtXmv25L+/1JNPWmkrGQzInex42bJ6xyvZyulZMHy43XyD1AF506ZWwXH0aPLNvYgI+zd8zTV2s+qaa+wmWz7i9cIbb1iJerdu8O0nx/iWftzG2wDcwetM7/Ycu/dYWUtBDchT9u7UryeR7KeAXEQkG9x7r1UslykDN99sfyIibD25r4z94EH49Vc7vu02pk2DBKLpz5d8ytV4EhIsmP3ss0y99vLl6XdX37Il+WDbNst6/e9/sGABa9a418yfb2Xe//1nbzyvvz7FkzhrSa+6isYtrIY9aEAObkA+ZYrbWjyDnF10Kld2A9gzzrDX+mlMkq1vBa794EzWr09dTumvc2e3IXLQ7ckKFYILLrDjsWPTndfrr1vCPyoKHnjAnSNA9OL59g0sWtRe6OqrLYhJ3qQ8QwG5U27gdNATySZOhjw+3j3n7FHuiIhwezE4a4UBeOsti+jXrYP33svReWbF44/bUvdmzdwqn4xYvty+L3feaR9HHNjL6CPd6MtovIUK8UGXb3iTO/j0U/d5C2pA3qSJ21gTFJCL5AQF5CIiJykhwW3g9umnULu29Qy7+24759uqdsIEe1fcuDFJDRv7ytlfeyOKaxnFSK63QG7gQLj11hO37062eLGNnTrZ8uWePd2ngOSAfMYMKyV/+mmrzWzfniP3PAbYWudGjSxrFhNj/YqchmWAZW+duwrXX0+TJnb4779pTKhhQ9vnOzERvv8+Q1+DY0XyNshOdtzRtCkU2/Sv7dsUE0PR01sGvElMi7MrUUCQ4c8pW08nIPd63cKG116zpQDgBuRN5oy0g379rE7+2Wdt4f306TBvXqp9n4NSQC45xAnI/Z17bupzTqDl34+C4sXd0pCnn7afv1wiLs5+1LZssR+foMtS0vDmm3Z/EqAMe5nJ2ZzFbI5El8IzeTJV7ugHWIWR07yyoAaiHo/d3HQU1O+DSE5SQC4icpIWLbL3qaVLuwlXcPdwXbUqOUvqrKm+5BL+/tve+BYrZo17z+8RyRBGMKXVPXbNO+9YVJ+BtZvOG9GOHe31x42DTz6BWrXsfOyqDdC3r5VUt20LffqA10vrcU/zFI/x0EMWXO/ZY43phgxJ8QKvvWZR6UUXQePGviZrq1als2tbFsvWnSA/ZUAOwFwrV+e00yxVnQFOQB40Qw72DYuKshS8f8lAsq+/tmXh27fbTQr/702lSlCMw7RZ+42dcMoKatSwLDnAyy9nLEPulBsEaxkvchL8A/LoaPtd5fzz9OdkgFNlmq+91u7Y7dnjrmnJBVavDtwyPciPb1DHj8M3yT+yk8Yc5d/6PWnOP2yhKuPunw2dOvn6tjkFPiVKQOHC2Tf3vKZdO/dYAblI9lNALiJykqZMsbFzZ2v45fBvLPb3Mq9brt6zJzNm2GHHjlY5ff/94CWCXitf4ciYyfaJ//0H55xjdZVHj/qe99gx29saLCB2nrZLl8B5OW/E+8+62d5Mt2ljAf4PP5D45jsA/I9nuHr3sLS/uOXL3XL15FR//fqWNTl4MJ0tvy6/3MZZs2Dz5qCXLFtmNw383+NnKCDPRJfjlAG512vLCHxv3suUse8xwFdfER8PI0ZYf7W9e+2+wgsv2MPnnBP4prxyZejBLxRNPGId6Dp0cB+8J/nGyk8/EblpPaCSdQkPp5IDrPq8ZMng1zmBVqqAPCrKNqQGuzl3on3+QsSppnFkNCAfN85ukNaunkDXD/pRcc089lKGbkym1QDbnqFs2cAfxYJaru645BIbCxUq2DcmRHKKAnIRkZPkBMRdu6Z+zGmIs/HXVZYSL1IE2rXzvZl0upiffba9cT5+HFZU72rR6vXXu52HWreGBQsAq7KuW9eatr3/vr0/jomB9u0DX7t6dTifCZyxb4Klxr76CooWZcsWuOXvW3iEZwBo8sHdllJPKSHBFsMnJNg7srPPBuwNWe3adonTiyyVGjVsLyVw01EpTJhggfJ997m97JyA3CmLD5CFgNypEtixw763EydaU7aAtecDB9r48cfceEMSN90EN97ormd3dOsW+HGlSnApySX5l14auKdZ06Z2hyQpiQ5L7OZHmgF5bKwbTSggl2zm27aQwOA8paAl647eva0y5ehR9w5VmDm/QwsVstHZneFEnB0mR9R4Bs8vP0PRovx293iuealZwO8d//trzjaKBVWdOrZDZ5p9Q0TkpCggFxE5Sc6WY0786a9lSxsTZ8y2g9NOg0KFUm057fFYkA2WLF27uxR88IG9e6xa1d5tnnMOieMn8OuvVlp+yilW7g4WK6fMXFSrGM9r2EJ27223Q4MGbN9uGe7334fneJilne+yi6+7zlLDjsREuO02y6jHxMCwwCy6U7aeMmgNcIKydSfLD9bEecIENyBP9QZ49273xc44I50XDVSmjC0LAMt6B22m3rev1aT+9x9rP5kF2Fz8bzZERkKPHoGfVq7oUS7kZ/vAaQXv7447ADhv/UhiOJJ2QL5qlfUOKFUKqlTJ8Ncmklkpb9r5S7NkHewX1DN2A4/hw/06RYaPE5BfdJGNJ8qQz5wJt99uGfK2LKTrguStCUeOpO+rZ3LffYHXX3ihja1a2Zdc0J1yiv2/Q0SynwJyEZGTcPCgu9uVk4315wTkZZcnB+TJG/6mDMjBDcgHDbI3Pt98g61x/vtviwaPHyeidy8uI3BrtMqV3QZu/mqMH04T/mUnFdh36/8A6wR//Lg9fsklHppNeMWC8aQki+47dbIu7B07Wldlj8c61jkp8WROQP7225Y5CerSSy2SXbw4aPrKWZ9ZtKiV4ffoYQUBp53mbtXkM3++jY0bZ2oRo8cTWLbuBOfgfh+IiYErrgBgKJbNrlDBnXKnTtafLWVH94oLxlGMo6ynFgmt2pBKjx5Qrx4lEvZzNZ+lHZA7XflatAjMsotkkx9+gMsus53M0pJmybqjSxe76xgba93UwixlQL5tW9p9MBMSbN38W2/Bnj1e3uI2PElJdtPQ2aMyhV69YOlSK0wK1hhPRCS7KCAXETkJTqKoVClrSJySU7LeeLcF5PMiO3L0qGVrITAgr1Mn8HOvuCI52C9TxhrCXXklnoQEvuYKruUjIiMt+bxtm5vN8dm8megnbdutR3mG//aUYswY39bYXHEFjBkDUYUiYORIeP55q22dOdMyYfPnW/T62WfuAkI/zrz/+gu6d0+juVuFCm4df5AsuROQP/usu9YeLGGdSvJ2Z+mm+NLgH5BH+P1fz9nvHLBqAKAPo6nBRvbtc7P1vXr57qMEKP6jbU/3BQPYfyBIIB0R4Xve23mT0qXS6IB3El+bSEb06WPLQtJaPw5pB+SxsXa/Do/HOq2D/c5Ic+uCnJeU5BbMnHmmO/e1a4NfP26c20eiH99yBgvs99urr6b5Gh6P3VDVmmkRyWkKyEVEToITkKeVQWnUCGoW3kE97xqS8HDB0+155BF7rGzZwGRvyoAcrNk6YMHyZ5/xT8cbicDLRwxm75NvOondQElJ1g780CGWxpzBhwymXTt7U/7ii3ZJQNM0jwcefNCayD33nH3uSy/ZgsEBA4J+Xf7NwHfvTqfPk1O2/uWXqaJ2p2S9QYPAivigAXkW1o876tWzccWKwF2bAqpuW7Tgz7KdiSKRuxhGQoL7kv43TfwnHzFpIgCfcTX79qXx4tdeyyGK05QVVF3+a/BrnIA8E6X4ItnNKVn3X0O+c6f9buvdO/lEp05w3nm2faMTnIfB9u1WVRMZab83nVLq2bPt11jDhja9+++3vcqdnnTg5WGSP7j/fi0REZFcQQG5iMhJcBqIV68e/PHoaLiylqWl/6Y5ByjN66/bYykDPadk3d+oUX5xbGQkH7YdzqvJ68JLPnqH1aofO+Z+gtdrbzQnTIBChXjv1A9IIjLV8zol5wGqV7e92kaMsE5rwWrwk511lhvcg70JDqp3b8tErVyJr7V8MidDXqGC3Sx46SUrKXUCaJ/4eHfxdxYC8tatbfzzz3QCcuCNQvcDcDPDqcoW3z7FKUvVAVvfn5jIkkLtWEnjNAPypOIl+ZhrAaj49RupLzh40JYkgDLkElbOzcEDB6zEG5wSbxg71u9CJxD/+OOMtzbPZk51S6VK1gTeqRC6+25b8rN6NTz2GLz8Mjz1lG1NWbIkLHh6Cq34i6SYYr7qFRGRcFNALiJyEk6UIQfoHmPl6rMJrHtOGZD7Z8h79rSlzatWBcaxa9Z6uJdX+L3nU3binXcs3f3885aF7tXLLcP86CO63d2cRo2sIY+/oNuKZUJEhMX9zo5haQbkJUq4mx6/+67vtNfrZsgrVrQk/X33BV8Lz19/WXfn0qWzNPFTT7Xxzz/x7QkOgQF5XBx8tqMbs+hIEWJ5iscANwMX4OhR63wPfFP5ToA0A/JDh+BNbiMJD0V+/dla4/v7/Xf7ZtSqpWydhFWZMu7x3r02LlvmnvPd92vf3vojJCZa+jkMnJtlTq+Jhx+2X31xcZYlT6lIEbupcNqCtwCIuOH6wC9YRCSMFJCLiJyEE2XIAVocdAPyqCh3m56U65L9g/r69d3G3Z07wxNP2JvQceMAPBy8438wfry98MaN9o50wAA7Fx1tbYEHDKB3b1sL/ccfgUF50DLsLHCy+uvWpXPRzTfbOGaML7V15IjbVK1ChRO8iHNHon37wEXgGdS8uWXR9uwJDDD8A/L//oMkr4enitqWToP5iE5Mp3Fj9+/L5803Lb1fuza/1+4HpB2Q798Pa2jAdxHJawseeyzwgh9/tDF5SzmRcImKcmPUunVtyYZ/w0anogVwO65/+WXqm0wh4GTInXtYkZHWYNJf165Wqr5vn/1+6tRgq7vnmfM7SUQkF1BALiKSSV6vVWv+/XcGMuRHjlBuo+2LNocOjBhhb2wXLYLBgwMvjfSrLO/WzZqddepkr/fkk4Gdxxs0wOo0V660PcwuvtjqyG+5xVqpO/uh+fEviY+JyfSXHZTznGlmyME6I3XsaHWwH3wABHZY9+98HtSUKTZ26ZKlORYu7K559w8w/ANyp6P67kYdmNXMvndfMIB+Z24OfLLly+3uCMATT1CqXBSQfkAO8EaZJ+xmwrhxtpwArFuW0+wujbX6IqHklK0fOQLnn+82n4TAbQo55RS3P8RDD4Vsfg4nQ+5fVFKjhlUWge0hPnmyTa106eT91z/91PprdOiQxpodEZHwUEAuIpJJU6bAtdfCVVdlICD//Xc8CQlsjajOloianHOOrWVs0yZ4snfpUvj8c3szXL26bbd1553u440awb33+i3vjomBG26wTOvMmVbC3qpV0Km8+KJlwO65J0tfdlBOOXe6ATnA0KE2vv8+xMf73txXqHCCnb5iY+3rArdjexY4Zev+NvvF2v7b0C3o8yL/0JRqbOWeXzq7GcClS+1OSWystZYfONCXUUwrIHcaZO2v2NC3LzlDhlhE8d13VhtctWqWbzaIZCf/LuXOdo6OgAw52FryqCiYOBF++y3H5+YvZcm649NPbdfGTz8N8kmff27jddfl6NxERDIrKtwTEBHJa5zu23/95ZYzp1mynrygMaZ7R6Y96AnauM1fy5bu3uWOF1+05kX16lkZe1a3qq5Xz8q2s3Or6wxlyMG6tlWqZLWmX33FrrIDgQyUq8+fb2u2K1a02vMsato09blgGfIGDeBYdEl68AuzOIuaW1ZbNrBaNZu71wtNmliJhMdzwoDc2RmqZk2szHfcOGuE1bSpmz6/7rrA8giRMClRwvoMOipWtCqWDRtSZMjBfqHcdJPVij/4oP2sZucvl3SkLFl3lCljTdxSWbHCdo2IjvZrGS8ikjsoQy4ikkmLFtno9VqyFNLJkCdv/F26R4csLxMuVMje71522cm/383u98tOQL5li8XNq1fbEuuU2TUKFYK77rLj555j1/ZEwN7wp8sWzVsG+SQmH6xh/MaNltz7+mt3f/aWLa0H3e6YWrx9zWK7AxIRYV+g12t7sk2fnlwDywkD8vXr/V4/JgYmTbJAxgnGzz8fHn00y1+XSHb66iv7Ufv7b+sNOW+e294gVUAO9m+3WDFrTjh6dMjmGaxkPV0//GBjly5q5iYiuY4y5CIimeD1wuLFgeeaNnX38A2QmOim01N2cMsnKlSwtZubNtky9l+Tt9qOirLl7AFuucXS/StXUva3H4B+6WfIExPdNdaXXXZS86xZM/Dj4sXh8GG44AL3nMdjWyyXK2dZQo+nAkR8Z5HI+vX27r9GjYDnyWiG3HdDoG5dW8g+aZK9YM+etshdJBfo0cP+gNt3wblpFjQgr1TJ1sA89ZQ1lrz4Yvvhz2Fplayn6fvvbXQ6ZYqI5CLKkIuIZMLWrbB9e+C5wYPTSN4uXWqp4hIloEWLkMwv1Dwe2/sX3GAcUu/xDdj3IXlBfLtJz+AhiUqV0nnyWbPsG16qVGDknAUpM+QXX5z6mrZt3aZWkZF+a/wrVoTTTksVjEPGA/Latf1OlixpNxguvdT2YxLJxZybZkEDcrCAvHx5a8Tw0Uc5Pp/ERPd3cIYy5KtX2+/iyMjgP/giImGmgFxEJBOccnX/pOZVV6Vx8aRJNp57br5eI3zDDakrBPz3+w5w221QogRV9yyjN2OoXz+dJ07uyE7fviedRU5ZGu90Y/Z3+umZf95MZ8hF8ph0M+RgN5j+9z87fuIJW7uSg7791pqlR0RkYMkLuOXqTvmLiEguo4BcRCQT/v3Xxr594fnnbRveNN8UTpxoY/fuIZlbuBQrZvcePv8cXnnFzqUZkJcp48uSv8x9NKlzPPh1q1fb4m5wO7SfhJQd7c85xz0+/XTr9XTDDZl/3vQC8oQEd9soBeSSV/kH5GvWwE8/2dKdADfeaGUg27ZZE4kcMmEC9O9vx9WrZ7A6XuXqIpLLKSAXEcmEdetsrFvXGq05W/GmcvCgu378/PNDMrdwOvVU20q7bFn7OK2MMcCx2+5nC1WpyzpOmfBs6gu8XluPmpRke60H27MsC/z3Xq9c2aprhw+3yvht21J3t8+I9ALyrVutvDY6OhPNp0RyGf+AvEEDuOQS+5kJULiwbYMG8MILtp1fDnj3XRvLl89gdfy6ddb0IyLCJi4ikgspIBcRyQQnIHf2307TlCmWIq1fnxPudZaPlC5tY5oZcmDN9uLcyesAFHvjOeta7m/UKMtqRUWlsYfRyc3Nce21tmtTdHTWK1krVbJpHjxocYg/p1y9Ro18vWJB8jknIN+82T23ZEmQC/v3t7taBw6k/mHIBps3wy+/2PGsWVaBfkJOufo552Rgj0URkfBQQC4iuZ/Xa4u3n3nGUtLdusFFF8Htt9tWO87eYyHgbGMV0KQrmO++s7GAZWVOtKYarOz/ey7j5/KD8CQl2d/ljz/CsWPw+utu7fhTT2VbdhysN1x2K1XKli6AJfWdEnVwb96oXF3ysqpVrR+jv1Ql62BZaOeH4c033TtS2WT8eCua6dABGjfO4CepXF1E8gAF5CKSu02ZYu2v27WzxkFff23nxo+Ht96yxdy1a8OHH6bxLjH7JCW5AXm6GfIjR9z9sy+/PEfnlNtkJEPurMP/qds70Lmz7T/Wu7fVlN91l32jr7sO7r8/W+fmbIOeocxaJtx7r9038HphwQI75/W6X2ejRtn7eiKhFB2detXNjh1pXHzBBZaNjo11f+CyiVMFn+Gfpw0b7AfS47HfLyIiuZQCchEJi7g4eO89S3KnrFgGrFPvDTdYNvyPP2x7qL59rWvYp5/CiBHWsbtaNdsD5/rroV8/y7LmkG3bbN6RkUF3wHKNH2/zr1sX2rTJsfnkRhnJkP/zj411mheDn3+2iLZYMTtZpYrdaBk5MtvrvAcPhhkzYMyYbH1awO4XgRVyvPGGrXF95x0716RJ9r+eSCj16hX4cZoBucdjP7+RkfaDNmFCts3hyBEbixfP4Cd8+aWN555rTSNERHKpjPSnFBHJdnff7QYsY8e6mWfA9u6+8EJbKOjxWOD92GPBF/q+8oqVOf/vf1aeuH27dTd3Arxs5JQg16hxgu6+w4fb2L9/GhuU519Ohjw2Fo4fT73NdmIiTJ1qx2eeiV3w8su2HOHIEXuClC3Rs0lEBJx9do48NW3b2j2iF19M/ZgCcsnrevQI/DjNgBygeXPbSeHVV+13999/p/5FkAWHD9uYoV/tXq9t+wDWbVJEJBdThlxEQu7zz91gHKyy0Lfc8MAB2yZs1izb3/bXXy3lmFbXrUKFrLR5yhRb0Dt7NvTpkyPryjO0fvyvvywNGxlpWwEVMCVKuPH0vn1WZpqY6D6+aBHs3m1/tWee6feJhQtbi/YcCsZzWtu2aT+mgFzyurJl4f33oXVr+zjdgBzg8cdt8fnatW739ZPkZMgzFJAvXQrLl9vvlb59s+X1RURySt585yMiedbo0TBwoB3fc49b6jtrFrbwuFs3mDfPMqVTp9oa44w4+2wrj4yJgcmTLTvtHwlmgwytH3/5ZRv79LGNcguYiAi3edoff1jptv/yTaeCtVs3W5uaXzRr5h4XLhz4WLVqoZ2LSE644QYLysEKkdJVogS8/bYdv/CC21zhJGSqZP2LL2zs2TNnujmKiGQjBeQiElIvv2zVhIMHw0svwVln2fnFU/Zat63ff7ds+LRp6acdg2nfHn76ybLmo0fD0KHZ2uht2zYbq1ZN44J//nHfCD7wQLa9bl7jrCMfNsy+/U5/O7C/Vsh/W7NHR1v/vlKl7H6SvwK2akHysUqVbNy5MwO/Wnv3thujSUkwaNBJ9/fIcMl6YiJ89ZUdX3XVSb2miEgoKCAXkZDZvBnmz7cA5emnLZt61llQnl0M/rKzm1KdNg1OOSVrL9KlizXz8XhsUe8zz2Tb/HftstHZlzeA1wv33Wdj374FrpmbP2cduf8bdufNtHNTo2HDkE4pJL76yr6+U05xd7tzKkBE8gPnd19cXPo7Kfi89ZY1aly5Eh555KReO8Ml67/9Blu22C+iCy44qdcUEQkFBeQictK++cYy3gcOpH/d6NE2duhg79EAzqm/mRkR59I8YSnbqcSMJ6ezpVxLhg9334BlWt++9kYQrBncyJFZfKJATkBeoUKQB3/4weqxo6Ph2Wez5fXyKicg373bPec0xNuzx8a0WgLkZR4PFC1qx598Ak8+af8sRPKLIkXcCvATriMHW3zu/P59/XWYOTPLr+3c1DthybrToKR//9TrR0REciEF5CKSKV6v7WbjrKf2euGKK+Cjj2zZdHpljM52U74eO0uWULr76TRN+od9RatwDtO59PFm3HYb3HILdO16EhXnQ4fCww/b8Y03uncDTkKaAfmGDXDTTXb84IMFfuNpp2R97Vr33Nq1VknqZNXyY0Dur2RJuxeU7vZ4InmQU7aeoYAcrEX74MH2y/yaa9zIOpMylCFft8627QC49dYsvY6ISKgpIBeRTHnxRQu8Bw+2j33d0bFK88mTg3/egQPWAB2S97T96iurV9+6FZo2JWbJPI7Xaszu3W7gPm+ebTmeZc88Y28Ak5Jsj3JnXWEaThT879xpY0DJ+q5d9gXt2WNl6s5NgALMyZD7Vzj89591XXe+x2XLhnxaIpINMh2QA7z2GtSsaQHzvfdm6XUz1NTtnXfs9323btreQETyDAXkIpJhCQnw0EN27DTnStnAav784J87ebJ9/qkNDlH3yUFWTnj4sHVRnzOHwg1r0b596s9zGoNlicdj5ZKDBll6dsAAePfdoE/4ySdW3fj668GfKinJLbf2Zch//9327vrrL4vSf/ghW/bbzeucDLm/tWvd71/Jkvmrw7pIQeI0tdyyJROfVLIkjBplxyNGwI8/Zvp1T9jU7fBhtzz+9tsz/fwiIuGigFxEMszJXIMbUKUMyFeudI+//97i7cGDYfx4aMfvTNrZ2tLeERFW0ztpki+l2rJl4HNFR9t2sn/8YYGy/+tnWGSk1dPfdJMF4kOHwtVXB5RNLloEQ4ZAfLw1R//nn9RPs3evBeWV2UaFn0fBRRfB6afDmjVWlzxzJtSqlYUJ5j9OBs3ff//l7/XjIgVFzZo2btyYyU/s3NnNjl93Xaaf4IQl659+aqVY9eurmZuI5CkKyEUkwz76yD2Oj7c/c+fax1dcYeOqVTYuXQqXXWYNbz/6yEuxT99lNh0pf+A/e0f3//buOzyqMu3j+G+SkISWhJoQaZEiIhBCixQVqcuyiA0RkQUsWKLSdNVFQXdZUFxdRXxti4IrK1ZQEFBAQFGpgkhZuoBSBQKEnuS8f9xMZoYESDDJzCTfz3XlOnVmnvGYcO7zPM99L1hgWa/CwrLes2FDz/snJXnmmt99tzR4sA2Vdwd1eRISYj3jY8ZYgD5pktS4sfXSOI7GjrWswRERtnz6aa/XpqfbWPthw7RcTbRL8Qq7+w57wuByWXC/cmWxnzfuzX3D7s27h5yAHAhe7t/vHTsu4sX/+IfUooXNX+nVy/4RyYWMDOnECVvPccj6sWPSqFG2PnCg/c0HgCDBXywAubJzZ/b54du3W+At2VRtyXrIHcdGc0tSCZ3SRPXV/ylF4Tot54Yb7EVt2mT7DO8e8ssvl/r3t/UVKzz73WW+88zlsrJk8+dLl1xiEeINN0gNGqjF3FFK1iI92Os3xeigQpctlt54wwpLV6okXXWVKr4xSk20QplyWS2r4cOltWutV4YJ0T5yCsi3bfNkXScgB4KXO1FhnnvIJSk83HJ5REXZ09wBA3I1J8k7H0WOPeQvvmhj6GvUsCe4ABBEwi58CgBIkyfbkO1WraSNGy2X2cyZ1oEcHy+1bWudEmlpVov5xx+lMjqi5dVvVN3tc5ThCtXpkWMU+fhgC45zUK2aldQ5dEiqX99GOFaq5MluLlnnx8yZ1rl9URVt2rSR1q2TRo+Wxo2T1q7VAxqmByRpgvScJG2VdI/Xa8qX1/bLO2vYt110sHlnTV+SUyFyuOUUkJ865Rk9QUAOBK+LHrLudumlFpRfd500YYJUs6Y0YsR5X+IOyF2uHNJ0rFsn/e1vtj5yJKXOAAQdesgB5Io7Q/oNN3iSmrmry7RqZfdACQm2vX69tOWHVH2ldqq7fY5UurRCZ81Q5F+HnDMYl+xQcrKtN2tmo9lvvjn7ebNmZZ+7nidly9rwxu3b9ds/Xtenuk675Zn4vEtxOnVtJ+nJJ60XZ+9efd77v3pXfRRelWD8QuLifLfd98erVtmyYsXCbQ+A/OPuId+zRzp58iLf5I9/tGlEkvTUU9LLL5/3dO8M6z7/hKSl2dD3kydt3njv3hfZIADwHwJyALniHjberJknIJ8/35bu7OjuadRLFxzTE0u6qbmWKT2mgqVk79QpV58zfrwlK3ef3r+/Tfvu2tV6WN3B3U8//f7vpJgYLWk8QNfrU3VquFs6eVI1Yk8oXrv003NfWK9Ly5ZSaOi5a5Ajm5AQ3yzq7upD7oCcHnIgeFWoIJUsaeu//JL9+IoVUufOUp06lszxnAYMkIYNs/WHHrJe8sxMn1NWrrS4/cgR2/YZrn7smCUq+fFHq3Lx5pvnfeALAIGKgBzAeTmOZRj/+WfbbtzYE5S68/G4A/I2baQwndblT/dUq4yFSlW0nNlzLYlPLlWtasnb3PdVzZvbEPnJk+0Gb+hQ258vAblsGrh0JmgMD1d8gkX87u/r5q5BTkCeO96Jl6pWtaX75p2AHAheLlf2YevHj9u0lMxMqXt3yzeyaZP07bcXeLO//91GIkn2APRPf/LJFnfVVVYYwz0iPSsgX79euvZaGy4VGSl9+qnlBgGAIERADvjbyZOW8Wr7drujCSA9eth0v7lzbfvSS61CmXdQGh4uNWli60MHZ2phvbvVTdN1XJEaXGuaSjRL/N3tSEjwBHjuTOz5FZCvW2fL+vVtWbOmLbdts2W/ftbz/847tk1ls9zJKSB3IyAHgpt72PqOHfawslIlGzG+dKlv9vULDml3uSzaHj/ehj/NnCmndm0ri/bZZyqXtl1ldETfTd2jelqnG0+/L912m9SggWUOjYmx6P/KKwvqqwJAgSOpG+APBw5I778v/fe/0uLFnq7myEipQwer8dWunX/bKKsjLkl9+9rSHXh7B+RNm54ZRu44Cn/iL0r+30RlhoTqHw0+UOfHr8r3NrkD8tWrrTfmYqvbvP22Bd3Tptl24pnnBu6A/OefpW++kSZO9LwmKsoSr+PCvIeWnt1xRUAOBDfvTOs//mhzvL/6Sho0yPe8XM8xv+MOfXP6SmXce7/anlpgf6DfflvZ8sZtP/Mjeeah85QUQJCjhxy4gB9/tApX7pJNF81xrCh3jx5SlSrS/fdbprTTp62buUQJK7Q6fbrUvr2d97s/9OJ5d9YfP27LpCRbegfkrVqdWXnuOen55yVJIW+N18gfu2XVJs9Pdevaf6q0NE8vdl44jvSf/1gHzNNPWwb36tVtjrrkubd7+WXp6qt9X3vPPRaU48K6d7dlhQrZA3KSugHBLfZMDsx9+3yD7kWLbBkaakt37fALcRxpyL/r61rN05+iv5HuvVfpl9XX6TP9Rply6ZCitKFsU2nIEGnZMunzzwnGARQJBOTABQwZYtPcEhKsByAXJVN9OY40ZYpNhm7XzrqdT52yLtkXXrCJdidO2F3NqlU2YS401M5LTvaMqS5kBw9m3+fOeO4dkLdsKemtt6RHH7Udzz3n6VIvACVKeJKE5XXY+oYNNvz8z3/23f/ww54kZO5M8W6lSkljx9pX+utfL67NxdGIEfa/96JFvkPWy5f3TA8AEJzcD9X277eHo94qVLA8IFLuA/IZMyzGllz6/FAbHX72Va37cI0idFKldFShylCMDumxDsvswW/Tpvn1VQDA7wjIgfNIT7cgXLKbjvbtpZdeyv3rnWXL9WO5tnZ3sny5MiJKamr8ffrulRWWPnbwYKlWLZtH53LZeOxx42wiXkKCpaht2dIzibsQnR2Q33679U5LvgH5tXsnS3ffbRt/+YtFtwUsr/PIV62SrrjCgvGNG+0/9Q03WFLeBx7wNF+yeup9+lhA+c03NkLiwQetXG5MTD5/kSKsZEn737t2bd8e8ttuo0wwEOzcAflvv3lKklWrZjnWNm60pOdS7oasO45VPvO2dauVVXMUouMqJcmyfJYqlS/NB4CAwhxy4DzWrPGsX3ed1d1euDD7PLls0tOtW33kSCVmZuqYSmr7jYN1/YLBWr+zopQifVH7PJXAkpJsbvmNN9oHdukivfeedNNN+fTNLsw7IL/rLumf//Rs16ljQW3KJVNV/sHbbTL3gAHSM88UStvyGpC//ronm3pCgpUWP7tWtltEhCeBG/KHdw/57bf7rx0A8oc7D8Rvv3l6yFu1snJnkqVDkXLXQz5zpvWOlyolxcfboLEtWzyBfuvWnmztmzbl33cAgEBBDzlwHu75cB06eAKJPXsu8KJffrFaLX/7m1yZmXpPt+oyrdfln/xD6/d7Js9+8skF3qdSJWnOHJtLfvq0dMstluimkLgD8qQk60mOjvYcq1ZN2jB2lsbu7SllZFiX8quvFloN2HMF5MePe/LjSZZ89/bbPcnpbrnFbuzOFYyjYERF2fD1kSPzVAEPQIDKaci6d2WFvATkkyfb8u67pWbNbH3LFmn3blt3J9oEgKKKHnIgBx98YHOK3QH5lVd6kth4B+QzZtgT+549rcdg44crVe/hrnLt3ClFR+ud1q+p7wzfzGZNmkg//GA3MhcUEWE941FRVhbmjjuk1FQbC1zA3AF5uXI5HPzkE9V+pLfNhe/Rw+aQX2y684vgDsjXr7chkRERVmqncWMbmt68uQ2ddPeKu40bRx1xfymE/2UBFJKchqx7B+TuaSk5DVk/fdqe3YaF2XD1+fNtf9eunvVPPvEMT4+Lk77+WnrkkbxNGQOAYEFADpxl+XJPaSt3Ru2cAvLNm20Ye0aGJfvqX22ORv3vBrmUpp9LXa51oz7Xm+/5Zgjr3t3e+7bb8pBAPTTUuqhjYiyZzZAhFi0//XS+9Ug7jj0kaNTIk9wsx4DccaQXX5SGDrX17t2ld9+1O6tCVLWq9dgfOmRBeaNGlhX9wAGb9/3NN9lfU7EiwTgA5Af3kPW0NM/DZe9Sh+fqId+2zUacZWTYlLBdu+xhaokSNuR9+5mSZt9953lNbKwNOnM/IAeAooYh68BZRo3yrB8+bMFex46egPzwYbvJGDnSbiokKfnoXI35XzeVVZrmqp0aH/tOtz6eoIUL7fjIkdKwYVZ63J3sJk8VzVwuy14+cqRt//3v0sCBNnc7HwwaZEMFX3zRsy9bQL5nj/WGDxliwfj999tY8PDwfGlDXrjz30meGrhvvpn9vMaNPett2hRK0wCgyIuO9pQ2cwfROQ1Z9+4hP3XK5phv2mRJ2+bM8fSIt2hhAf2ll2b/LKYYASjqCMgBL9u3+87tjoyUJk60mDM62hN7jh/vSfz149gFmhHaTSV1Qr80/pN2vD5ThxSjw4fteIUK0mOPWSwdEeE71C9PXC6L6seNs+2XX7ZU4e4Pukhz5lhZL8m3ZzkrII9xbNj8FVdIH39sveHPP2/tKOSecW/u+uezZtlc8dRUm9teqZIlBjpwQFqxwjKs9+ol/etffmsqABQpISGeXvKff7aldw+5e8i6dw/5ihU2oslt6lTPvznXXGPLxo2zV7NwPwwHgKKKgBzw4i753bChtHev9L//eXpZXS7PjcEDD1jn9PAbV6vRE9cpIuO4Mrv8UVUXfaR+A8KzktSULGkBvbsnQfINyPNc01yyOuWTJtkdz2efWVm0DRsu5usqNVXq39+z7V3yPDVVitVu3TnjRhtjv3+/1U5futR6yQspgdu5dO9uy88/l5YssfWOHe07rF7t6dlv2FD6739JDAQA+ckdkG/bZssLJXVzJ+F0/9MxbZr9rZYseahkf7d37PDN1eJdpQEAiiICcsCLe+hd9erW01qjhu9x7yf17S7fpaeW/tF6qK++WiGffJzVLdCzpwWKK1ZYohpv7puY9PTf0bl9222W5SY+3jKXNWliGdjzGOH/9a+WFN5dJ3rLljM3UI6jhqsmaY2u0GVrp9oEv6eftmDcexy4HyUn2/U4dMjTw9+okf33zTERHQAg37gfLh8/bssLJXVzB+QpKTbibN8+K3cmSZdd5jmvTBmb2vXll9Ibb0j16xdM+wEgUBCQA168A/KcuAPyUjqqt/d3k2vHDqluXWnKFE+XwBl//KPvTYZbZKTnxiXPw9a9tWhhdzNt29ok6jvukG6+2VMr5gIyM6UPP7T18eNtmGBmprTl213S9ddr8PLbVUEHdCChiX3O8OGejG8BIDTUkupJ0rFjtkxM9F97AKA4qVjRd/tCSd3cveFNmtjALjeXS6pdO/v7d+xopdAAoKgjIAe87Nhhy/MF5CHK0H91m6rvXW53JDNmSOXL5+lzLnoe+dmqVLFJ4KNH23zuTz6RLr/cuhXcGefO8uWX1tG9erV9funS0rXXSvUvd3S7/qPa3etLn32m064SGqaRWj5ukXU9ByB3Nnw3d6I3AEDBco/2csvtkPWGDT31xiUbiVayZMG0EQCCAQE54MXdQ16tWs7HK1aU/qmH1V2fKTM8Qvr0U6lWrTx/Tr4F5JJ1FT/2mEXZTZva5O977rGh5TNm+AxjX7zYstxefbX0yiu276qrpPCVS/T2lqv1H/1Z4UdTpaZN1aXyDxqlYYqpFDi94mdr29Z3++wbRABAwTi7h/x8Q9b37LEh6i6XDUFv3txzbk4jyQCgOCEgB7xcaMh6k/kvaLBetI2J73hSfeeRO3DMl4DcrXFjK9T6wgs2/nz1apvAfsUVlpF95049/bSdeuKENOmNNN2mSXp1c0cpOVl19yzUMZXUW7VHSYsWafHRBpICez52aKj17kvUGAeAwuQu4el2viHr7uHqtWpJpUr59pCf6wE4ABQXBOTAGZmZluBMOkdAPmGCei4ZKkkaHvmsQm695aI/y92zsG/fRb9FzsLCpMGDpc2bpaFD7Q5p3TrpoYekSy7Rv2fGa6UStUm1dEjRmqTbVXPjHMnl0uEb+qquNujOTY9r1dowpaXZWwZyQC5JkydLfftK06f7uyUAUHyc/e/k+eqQew9XlywfqZu7nCgAFFcE5MAZ+/bZzYPL5XuzIEl6/33prrskSWu7Pqw71z3yuz4rX4es56R8eemf/5R+/VUaN06HL2uuTLkUr11K1CrV0haFKlPH4mtJTz0lbd6sqE8mqEk3qy/TsaO9zSWX5Hl6fKGrXFmaMMFy3AEACsfZVUjOV4fcHZA3aOA558knLS/LI7/vn1MACHoE5MAZ7uHq8fFeycQdRxo1Srr1VkuS1r+/6k8boxo1f18N7gIPyN2io7WuXYqq7VqiGKXq0WuXKGPGF9I330g7d6rUr5ukESOkhARJ1qkuWQ12SerVy+/lxgEAAejsHvLcDFn3Trz5t79ZUZCaNQusiQAQFML83QAgUGzZYsusm4wDB6T777fecUkaNMh6nfMhQnXPd96z53e/1QWNHm31zq+6KkpPz2iu0Mhzn3vNNVL37parTpJ69y749gEAgs/ZeTu8h557D1nPzJTWrLFtKmEAQHb0kANnrFxpyxaXHbLAu25dC8ZDQy0l+b/+Zev5wN0jsHVrvrzdOaWlSR9/bOtjxmQrlZ6jl1+2UQLt2lHXGwCQs/M9m3YPWc/IsGlFR49awJ5TvXEAKO7oIUexl3HspELXr1XsZ0v1maapy6Q50ukz4+yuuEIaP15KTs7Xz7z0Ultu3Wqj4gtqWPjHH0vHjkl16uT+K1SrJm3bZs8eGK4OADiX8uVtMNnZvB/+7t9vy4QEyzsKAPDFn0YULZmZ0tq1Vv5rzRqboLZvn3T8uI2dO3nSJrWdWZ44fFIlTqZJytQg93uclhVKHTLE0ncXwB1E9eoW7B47ZvO1Y2Pz/SMkSZ99ZsvevfMWXHPTBAC4kAoVcg7I3T3k3u6+u+DbAwDBiNtuFA1Ll0rvvmtDzPMwMdv9EP+AyulHJWq+q53+uri7Ipo1LNDu4YgIqWpVaccOm7teEAG540gLF9p6+/b5//4AgOKtQgVp48bs+0ND7cFuerptjxsnpaQUbtsAIFgQkCN4OY40Y4ZlLfv2W8/+UqWsBlbTpjYZunJl2xcZaZHwmeXof0Vq/H8jlaYy2qNYSS6Vi5Gebl44zb/0UgvIW7WSnnhC+vvfbf+JEzZk/LLLPOceOyaVLJm3ZwSbN1vve3i41KxZ/rYdAIBrr7UBaTmJjLQ8JlL2BHAAAA8CcgSf9HTpgw+kZ57xFDcND5duvlm67TYrou2d7vUc/r1I2iJp2jRp8mRp0iR7eWG59FJpwQJbHznSErpXqWLB+fPPS//5j/SnP1k5tnbtbG73zJlSXFzu3t/9jKJZs9wlcwMAIC+GD7dn49dfn/1YRIQnIHeX+gQAZEdAjuBx9Kg0YYJFq+705GXKSPfdZyXJ4uNz/VbHj3veonlzqUsX6a67rFO9sJwp/Z1lyhQLyp9/3rb79PE9vn+/tbNNGytLtnixBfA52bZNevttW2/dOn/bDQCAZA97R48+9zG3ChUKpz0AEIwIyBH4du60CWivvSYdPGj7Kla0IPz++6Vy5fL8lhs22FP98uVtRLvLJbVtm6+tvqCzb1A++si+zvmsXCmtWmW566ZPzzlJzurVNozwt9+kkJCcey4AAChI3ond6CEHgHOjDjkC148/WpbzmjXtEfzBg0qLq2WFsrdtk4YNu6hgXJLWrbPl5Zf7r7TXTTdZTdZbb7XtBQssMXzIWb+VL7xg7W3TxrYzM22Z07y9o0elP/zBgvHERGnePJujDgBAYTpxwrNODzkAnJtfA/Kvv/5a3bp1U3x8vFwul6ZOnepz3HEcDR8+XFWqVFHJkiXVoUMHbcwpnSeKDseR5syROnSQGjeW3nlHOn1ap1q0UY+wKYrevV6zaj9gSdryYNs2qVEjm+8m+Qbk/hIba9lp33tPuu46C7T79fME3LGxVrZs8GCpXj2pZUvf1y9enP09X3xR+vVXGw4/b5509dUF/S0AAMjOPX9cIo8JAJyPXwPyo0ePKjExUa+88kqOx8eMGaOxY8fqtdde0+LFi1W6dGl17txZJ7wfu6JocBzp88+tO7djR2nuXKUrVAsv6anV4xdrZOdv9FH69cpUqO6/34LOvHjiCcv/NnKkxfvucmD+DMi9jRplPePLltl2zZpWQr1bN885Z/d0r1kjde8uffihlJxsc8/HjLFjI0de9OABAAB+N++AHABwbi7HcRx/N0KSXC6XpkyZouvPTHh1HEfx8fEaOnSoHn74YUnSoUOHFBsbqwkTJuhW9zjfCzh8+LCio6N16NAhRUVFFVTz8XssWiQNHCgtWSJJSg+L1CvpA/SChmi7auT4kgoVLGmZd8B6LmvWSA0bWsx/thkzLFFaILjmGunrr2396qs9Gdjd9uzJXYb1WrVsjvzZQ98BACgs3tPBAuNOEwAKV27j0IC9Zd+6dat2796tDh06ZO2Ljo5WcnKyvv/++3O+7uTJkzp8+LDPDwLU/v3Sn/9sY7GXLJFKl5Yeflh9r96qQXpJ3VJqqF8/zz/qDRpIa9dKSUn20uuuk6680uptn8+bb9rNQOvWUnS05/3Klg2s+tze2dCrVct+PDZWuvFGmxs+YMC53+fmmwnGAQAAgGAQsLftu3fvliTFxsb67I+Njc06lpPRo0crOjo666daTpEN/O/TT6UrrrBi2y6XdMcd0qZN0nPP6bst1g18yy3WC75tm835Xr7chph//700ZIgUGmrzqM8MoMhRRoaVLJekxx6Tfv7ZErXv3GkfV6lSwX/V3HInbZOk6tVzPufjjy3T+muvSfv22bD2kSNtyr3bTTcVaDMBAAAA5JOADcgv1uOPP65Dhw5l/ezYscPfTYK3U6eklBSrxbVnj1S/vg1ZHz9eiovT0aMWgEt2SLLe4nr1pPBw246IsPnS33xj219+6ZvN1e2996SwMGnXLikmRurUyZbR0Va/u3LlAv6ueeSdtM27XExOXC4rIxMba8nmn3rK9teqFVi9/gAAAADOLWAD8rgzk2X37Nnjs3/Pnj1Zx3ISERGhqKgonx8EiJ07rdj3//2fRZSPPir98IPUokXWKevX2/DyihUvXLf0yiulSy6Rjh2z+t1btkhffWXzwiUL2t1uvtkT0Acq7yRsjRrl7bWtW0uzZ0uzZvmvjBsAAG7uzOpnVwgBAPgK2IA8ISFBcXFxmjt3bta+w4cPa/HixWrJX/fgs3GjpQn//nspOloZU6fpmZhntHx1hKZPl2bOtNPyUo7M5bJ55JINbW/Z0nrBu3aVJk60WF+Shg6Vnn02/79SQVi7VpowwQYQ5FWHDlbXHAAAf/vuO0sT8/77/m4JAAS2MH9+eFpamjZt2pS1vXXrVq1cuVLly5dX9erVNWjQII0cOVJ16tRRQkKCnnzyScXHx2dlYkeQWLlS6txZ2rtXqlNHmjFDExbU1uOPS+PG2ZDyzEwbtb5li70kt+XI7r7bAtjjx+3t3fr1s2WDBtI//5mP36WAXX554JRiAwDgYiUl2cNxAMD5+TUgX7Zsma699tqs7SFDhkiS+vbtqwkTJugvf/mLjh49qgEDBig1NVVt2rTRrFmzFOkeB4XAt3q11K6ddPCgnMTGmnjbF5r6cGUtXWqHveuJ33mnZT6Xch+UJiXZkPU5c6x8ealSUt269gxAktq3z7dvAgAAAAD5KmDqkBcU6pD70ebNljp8924pOVmPNv5CY16PPufpLpfNH69Z0+aCJyTk7eNmz5bKl5eqVrU51Zs32/t4PfMBAAAAgAKX2zjUrz3kCH5ffimtWWNzud9/33q5Y2Nl48c7drRgvGFDnfp0pt6od+5gfPVqK1G2bp3Nn75QlvGcdOzoWV+2zOZjt2qV9/cBAAAAgMJADzkumuNIITmkBYwrd1JLyrZXte3fyqlVSz//Z6EWrI9T//5WamzSJCs73quX9MIL0mWXST/9RHZwAAAAAEUDPeQocGdVpDvD0eiD96jawW91LCJGN2Z8ri9aecrUde9u2cC3b7ft22+38mYE4wAAAACKGwJyXLS1a323hw2Trl3+T7WfNVHpClX3kx9ozs+XKTJSOnHCzunVy/c1DRsWTlsBAAAAINAQkOOirVljy8aNLRi/qeQMuUY9KklafOuLWjS9oxomSF98IaWlSfv2MacbAAAAANwIyHHR3AH5H/8o3dxiu5TUxyaW33OPWr+aon0npRIlpNBQO69OHf+1FQAAAAACTQ4puYDsHEf65RdburkD8gZ1T0k9e0oHDkjNm0tjx0oulyIjPcE4AAAAAMAXATku6ORJqU8fqVo16d57rTRZz57SwoV2vP2cx6VFi6SYGKt9Fh7u1/YCAAAAQDCg7Bku6KGHpJdfzvlY//Kf6q0D19vGlClWRBwAAAAAirHcxqH0kOO8du2SXn/d1tu0saXLJV13nfT9ez9rfGY/2zl4MME4AAAAAOQBSd2Qo1WrbM74xInSqVNS69bS11/bvPEqVaQKZU9JV/WUUlOlFi2kZ57xd5MBAAAAIKgQkCObL7+UunWzQNztqaesZ7xBgzM7hj4uLVli88Y/+IB54wAAAACQRwxZh4+DB6WbbvIE41FR0kcfSR06eJ00fbr0wgu2PmGCVKNGYTcTAAAAAIIePeTwMXeulJZmNcNXrZJCQs7q/P7lF6lvX1sfOFDq3t0v7QQAAACAYEdADh9ffmnLrl2lyMizDqanS716Wb3xpk2lZ58t9PYBAAAAQFHBkHVkcRxPQN6xYw4nPPWUFR8vW9bqjUdEFGbzAAAAAKBIISBHlg0bpG3bpBIlpGuuOevgl19Ko0bZ+htvSLVqFXr7AAAAAKAoISBHlgkTbNm+vVS6tNeBzZulW2+1LvQBA2wdAAAAAPC7EJAXc44jvfiiFB3tKSV+991eJxw5YonbDh6UkpOll17yRzMBAAAAoMghqVsx5jjSI49Izz/v2RcXZzXIJVkSt9tuk9askapUkT75JIdMbwAAAACAi0FAXoyNG+cJxh95RNq0SerXz+aQy3Gke+6xmuORkRaMx8f7s7kAAAAAUKQQkBdT6emeHG3PPiv95S9eBx1HGjZMeustK0Q+ebJ05ZV+aScAAAAAFFUE5MXUrFnS7t1SpUrSoEFeB84ex/766zaHHAAAAACQrwjIi6m33rJlnz5SePiZnadPSykp0ptv2vaLL0p33eWP5gEAAABAkUdAXgzt3StNm2br/fuf2fnrr1bObOFCG6b+5pvSHXf4rY0AAAAAUNRR9qwYevddm0PeooXU4ApHeu89KSnJgvGoKOnjjwnGAQAAAKCA0UNejPz6q+RySa+9JkmOnmz1lXTVCOnbb+2ExETpo4+k2rX92UwAAAAAKBYIyIuJnTulhJqO6qav0U2apjtDJ6j2ixvsYKlS0mOPSQ8/LJUs6d+GAgAAAEAxQUBe1P38szR/vkImz9fG9Hmqoe22P0NSmTKW1W3YMOmSS/zZSgAAAAAodgjIi5KMDGntWmnRIhuGPn++tG2bJCnuzCknFKHTV7VX2T7XWxK3smX91VoAAAAAKNYIyINVZqb1fq9aJS1ZYkH40qVSWprveWFhUvPmWlWhrR6e3lalOrTW1Nml/dJkAAAAAIAHAXkw2LdPWrHCgu81a6TVq60n/Nix7OeWKSM1by5deaXUtq3UqpVUpoy+elGaPV26tWJhNx4AAAAAkBMC8kDiOJZ97YcfPD8rVkg7duR8fni4VK+eBeDJyRaE168vhYZmOzU11ZYxMQXWegAAAABAHhCQB4LMTKlbNxtyvm9fzufUqWNlyRo2lK64QmrQQKpVy4ak54I7IC9XLn+aDAAAAAD4fQjIA0FIiM0H37fPerfr15eSkqQmTewnMVGKivpdH0EPOQAAAAAEFgLyQDF2rGU8b9iwQGqBHzxoSwJyAAAAAAgMBOSBon37An17esgBAAAAILCE+LsBKBwE5AAAAAAQWAjIiwn3kHWSugEAAABAYCAgLyboIQcAAACAwEJAXgykp0tHjtg6ATkAAAAABAYC8mLg8GHPOgE5AAAAAAQGAvJiwD1/vHRpqUQJ/7YFAAAAAGAIyIug8eOlyy6zsublyknDhtl+escBAAAAIHBQh7yIcRzpiSek3bs9+95/35YE5AAAAAAQOOghL2I2bvQE4z/9JH3wgVSlim3HxfmvXQAAAAAAX/SQFzELFtjy6qulBg3sp2NH6a23bAkAAAAACAwE5EHq9Glp3jwpM1Nq2VJat05avFgaNMiOX3ON59yYGGnIEH+0EgAAAABwLgTkQeSdd2wY+tNPSyNHSqNH2/4SJSxA93b11YXfPgAAAABA7hGQB4mZM6W+fW193jxp+XLPsdOnpYgI6Q9/kI4dkypW9O0hBwAAAAAEHgLyAHfihHTTTdKMGZ597mC8SRNp6VLrNY+PlypV8k8bAQAAAAB5R5b1APfuu55g/E9/kubOlVq1kiIjpTFjpJAQKTGRYBwAAAAAgg095AHMcaSXXrL1kSOlYcNsvV07O+Zy+a9tAAAAAIDfhx7yAPbNN9Lq1VLp0lJKiu8xgnEAAAAACG4E5AHsnXdseeutVroMAAAAAFB0EJAHAMeRZs2S+ve3dcmSuX34oa336eO/tgEAAAAACgZzyANAaqrUo4eUliZ162bJ2954Qzp8WKpWTbrqKn+3EAAAAACQ3+ghDwDlykmDB9v6TTdZTfGBA2176FDLpA4AAAAAKFoI9QLEkCHZ54nfcov00EN+aQ4AAAAAoIAxZD1AxMRYzfFJk6QHH7S55C1akE0dAAAAAIoqAvIA0rWr/QAAAAAAij6GrAMAAAAA4AcE5AAAAAAA+AEBOQAAAAAAfkBADgAAAACAHxCQAwAAAADgBwTkAAAAAAD4AQE5AAAAAAB+QEAOAAAAAIAfEJADAAAAAOAHBOQAAAAAAPgBATkAAAAAAH5AQA4AAAAAgB8QkAMAAAAA4AcE5AAAAAAA+AEBOQAAAAAAfkBADgAAAACAHxCQAwAAAADgBwTkAAAAAAD4QZi/G1DQHMeRJB0+fNjPLQEAAAAAFAfu+NMdj55LkQ/Ijxw5IkmqVq2an1sCAAAAAChOjhw5oujo6HMedzkXCtmDXGZmpnbu3KmyZcvK5XL5uznndPjwYVWrVk07duxQVFSUv5uDPOL6BTeuX/DjGgY3rl9w4/oFN65fcOP6BS7HcXTkyBHFx8crJOTcM8WLfA95SEiIqlat6u9m5FpUVBS/TEGM6xfcuH7Bj2sY3Lh+wY3rF9y4fsGN6xeYztcz7kZSNwAAAAAA/ICAHAAAAAAAPyAgDxAREREaMWKEIiIi/N0UXASuX3Dj+gU/rmFw4/oFN65fcOP6BTeuX/Ar8kndAAAAAAAIRPSQAwAAAADgBwTkAAAAAAD4AQE5AAAAAAB+QEAOAAAAAIAfEJAHiFdeeUU1a9ZUZGSkkpOTtWTJEn83CZK+/vprdevWTfHx8XK5XJo6darPccdxNHz4cFWpUkUlS5ZUhw4dtHHjRp9zDhw4oN69eysqKkoxMTG68847lZaWVojfongaPXq0mjdvrrJly6py5cq6/vrrtX79ep9zTpw4oZSUFFWoUEFlypTRTTfdpD179vics337dnXt2lWlSpVS5cqV9cgjjyg9Pb0wv0qx9eqrr6pRo0aKiopSVFSUWrZsqZkzZ2Yd5/oFj2eeeUYul0uDBg3K2sf1C2xPPfWUXC6Xz0+9evWyjnP9At+vv/6q22+/XRUqVFDJkiXVsGFDLVu2LOs49zCBq2bNmtl+/1wul1JSUiTx+1fUEJAHgPfff19DhgzRiBEj9MMPPygxMVGdO3fW3r17/d20Yu/o0aNKTEzUK6+8kuPxMWPGaOzYsXrttde0ePFilS5dWp07d9aJEyeyzundu7fWrFmj2bNna/r06fr66681YMCAwvoKxdaCBQuUkpKiRYsWafbs2Tp9+rQ6deqko0ePZp0zePBgTZs2TR9++KEWLFignTt36sYbb8w6npGRoa5du+rUqVP67rvvNHHiRE2YMEHDhw/3x1cqdqpWrapnnnlGy5cv17Jly9SuXTt1795da9askcT1CxZLly7V66+/rkaNGvns5/oFviuuuEK7du3K+lm4cGHWMa5fYDt48KBat26tEiVKaObMmVq7dq2ef/55lStXLusc7mEC19KlS31+92bPni1J6tGjhyR+/4ocB37XokULJyUlJWs7IyPDiY+Pd0aPHu3HVuFskpwpU6ZkbWdmZjpxcXHOc889l7UvNTXViYiIcN577z3HcRxn7dq1jiRn6dKlWefMnDnTcblczq+//lpobYfj7N2715HkLFiwwHEcu1YlSpRwPvzww6xz1q1b50hyvv/+e8dxHGfGjBlOSEiIs3v37qxzXn31VScqKso5efJk4X4BOI7jOOXKlXP+/e9/c/2CxJEjR5w6deo4s2fPdq655hpn4MCBjuPw+xcMRowY4SQmJuZ4jOsX+B599FGnTZs25zzOPUxwGThwoFOrVi0nMzOT378iiB5yPzt16pSWL1+uDh06ZO0LCQlRhw4d9P333/uxZbiQrVu3avfu3T7XLjo6WsnJyVnX7vvvv1dMTIyaNWuWdU6HDh0UEhKixYsXF3qbi7NDhw5JksqXLy9JWr58uU6fPu1z/erVq6fq1av7XL+GDRsqNjY265zOnTvr8OHDWb20KBwZGRmaPHmyjh49qpYtW3L9gkRKSoq6du3qc50kfv+CxcaNGxUfH69LL71UvXv31vbt2yVx/YLBZ599pmbNmqlHjx6qXLmykpKS9Oabb2Yd5x4meJw6dUrvvvuu7rjjDrlcLn7/iiACcj/77bfflJGR4fMLI0mxsbHavXu3n1qF3HBfn/Ndu927d6ty5co+x8PCwlS+fHmubyHKzMzUoEGD1Lp1azVo0ECSXZvw8HDFxMT4nHv29cvp+rqPoeD99NNPKlOmjCIiInTvvfdqypQpql+/PtcvCEyePFk//PCDRo8ene0Y1y/wJScna8KECZo1a5ZeffVVbd26VVdddZWOHDnC9QsCW7Zs0auvvqo6deroiy++0H333aeHHnpIEydOlMQ9TDCZOnWqUlNT1a9fP0n8/SyKwvzdAAAoaCkpKVq9erXP/EcEh8suu0wrV67UoUOH9NFHH6lv375asGCBv5uFC9ixY4cGDhyo2bNnKzIy0t/NwUXo0qVL1nqjRo2UnJysGjVq6IMPPlDJkiX92DLkRmZmppo1a6ZRo0ZJkpKSkrR69Wq99tpr6tu3r59bh7wYP368unTpovj4eH83BQWEHnI/q1ixokJDQ7NlRtyzZ4/i4uL81Crkhvv6nO/axcXFZUvOl56ergMHDnB9C8kDDzyg6dOna968eapatWrW/ri4OJ06dUqpqak+5599/XK6vu5jKHjh4eGqXbu2mjZtqtGjRysxMVEvvfQS1y/ALV++XHv37lWTJk0UFhamsLAwLViwQGPHjlVYWJhiY2O5fkEmJiZGdevW1aZNm/j9CwJVqlRR/fr1ffZdfvnlWdMOuIcJDtu2bdOcOXN01113Ze3j96/oISD3s/DwcDVt2lRz587N2peZmam5c+eqZcuWfmwZLiQhIUFxcXE+1+7w4cNavHhx1rVr2bKlUlNTtXz58qxzvvrqK2VmZio5ObnQ21ycOI6jBx54QFOmTNFXX32lhIQEn+NNmzZViRIlfK7f+vXrtX37dp/r99NPP/nckMyePVtRUVHZbnRQODIzM3Xy5EmuX4Br3769fvrpJ61cuTLrp1mzZurdu3fWOtcvuKSlpWnz5s2qUqUKv39BoHXr1tlKfW7YsEE1atSQxD1MsHj77bdVuXJlde3aNWsfv39FkL+zysFxJk+e7ERERDgTJkxw1q5d6wwYMMCJiYnxyYwI/zhy5IizYsUKZ8WKFY4k54UXXnBWrFjhbNu2zXEcx3nmmWecmJgY59NPP3VWrVrldO/e3UlISHCOHz+e9R5/+MMfnKSkJGfx4sXOwoULnTp16ji9evXy11cqNu677z4nOjramT9/vrNr166sn2PHjmWdc++99zrVq1d3vvrqK2fZsmVOy5YtnZYtW2YdT09Pdxo0aOB06tTJWblypTNr1iynUqVKzuOPP+6Pr1TsPPbYY86CBQucrVu3OqtWrXIee+wxx+VyOV9++aXjOFy/YOOdZd1xuH6BbujQoc78+fOdrVu3Ot9++63ToUMHp2LFis7evXsdx+H6BbolS5Y4YWFhzj/+8Q9n48aNzqRJk5xSpUo57777btY53MMEtoyMDKd69erOo48+mu0Yv39FCwF5gHj55Zed6tWrO+Hh4U6LFi2cRYsW+btJcBxn3rx5jqRsP3379nUcx8qGPPnkk05sbKwTERHhtG/f3lm/fr3Pe+zfv9/p1auXU6ZMGScqKsrp37+/c+TIET98m+Ilp+smyXn77bezzjl+/Lhz//33O+XKlXNKlSrl3HDDDc6uXbt83ufnn392unTp4pQsWdKpWLGiM3ToUOf06dOF/G2KpzvuuMOpUaOGEx4e7lSqVMlp3759VjDuOFy/YHN2QM71C2w9e/Z0qlSp4oSHhzuXXHKJ07NnT2fTpk1Zx7l+gW/atGlOgwYNnIiICKdevXrOG2+84XOce5jA9sUXXziSsl0Tx+H3r6hxOY7j+KVrHgAAAACAYow55AAAAAAA+AEBOQAAAAAAfkBADgAAAACAHxCQAwAAAADgBwTkAAAAAAD4AQE5AAAAAAB+QEAOAAAAAIAfEJADAFCM9evXT9dff72/mwEAQLEU5u8GAACAguFyuc57fMSIEXrppZfkOE4htQgAAHgjIAcAoIjatWtX1vr777+v4cOHa/369Vn7ypQpozJlyvijaQAAQAxZBwCgyIqLi8v6iY6Olsvl8tlXpkyZbEPW27ZtqwcffFCDBg1SuXLlFBsbqzfffFNHjx5V//79VbZsWdWuXVszZ870+azVq1erS5cuKlOmjGJjY9WnTx/99ttvhfyNAQAILgTkAADAx8SJE1WxYkUtWbJEDz74oO677z716NFDrVq10g8//KBOnTqpT58+OnbsmCQpNTVV7dq1U1JSkpYtW6ZZs2Zpz549uuWWW/z8TQAACGwE5AAAwEdiYqKeeOIJ1alTR48//rgiIyNVsWJF3X333apTp46GDx+u/fv3a9WqVZKkcePGKSkpSaNGjVK9evWUlJSkt956S/PmzdOGDRv8/G0AAAhczCEHAAA+GjVqlLUeGhqqChUqqGHDhln7YmNjJUl79+6VJP3444+aN29ejvPRN2/erLp16xZwiwEACE4E5AAAwEeJEiV8tl0ul88+d/b2zMxMSVJaWpq6deumZ599Ntt7ValSpQBbCgBAcCMgBwAAv0uTJk308ccfq2bNmgoL49YCAIDcYg45AAD4XVJSUnTgwAH16tVLS5cu1ebNm/XFF1+of//+ysjI8HfzAAAIWATkAADgd4mPj9e3336rjIwMderUSQ0bNtSgQYMUExOjkBBuNQAAOBeX4ziOvxsBAAAAAEBxw2NrAAAAAAD8gIAcAAAAAAA/ICAHAAAAAMAPCMgBAAAAAPADAnIAAAAAAPyAgBwAAAAAAD8gIAcAAAAAwA8IyAEAAAAA8AMCcgAAAAAA/ICAHAAAAAAAPyAgBwAAAADADwjIAQAAAADwg/8HiRrrwBMYCaUAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}