BTC.ipynb

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            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "be1f6e8c007845a6a5c7d96f4c2ccf89": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        }
      }
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Sohammhatre10/Predicto/blob/main/BTC.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "_ynE6CLEX_B9"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import LSTM, Dense\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "from tensorflow.keras.callbacks import EarlyStopping\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "sns.set_style('darkgrid')\n",
        "plt.style.use('fivethirtyeight')\n",
        "data = ('BTC-INR.csv')\n",
        "df = pd.read_csv(data)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install pycaret"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9G6444XwvCJZ",
        "outputId": "0bc8a10d-f83c-42dc-e14b-b104eef79e08"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "Building wheels for collected packages: pyod\n",
            "  Building wheel for pyod (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for pyod: filename=pyod-1.1.0-py3-none-any.whl size=185330 sha256=3df634d0756991d51f33d0b7127d1c23b5eb84079725d041121643d65b99bed9\n",
            "  Stored in directory: /root/.cache/pip/wheels/36/8e/e2/e932956b10b843eb6be9eefa70b5c1bee7b561be14c423b136\n",
            "Successfully built pyod\n",
            "Installing collected packages: trace-updater, kaleido, dash-table, dash-html-components, dash-core-components, xxhash, wurlitzer, Werkzeug, tsdownsample, scikit-base, schemdraw, retrying, orjson, jedi, importlib-metadata, deprecation, deprecated, ansi2html, sktime, scikit-plot, pyod, dash, pmdarima, plotly-resampler, category-encoders, tbats, pycaret\n",
            "  Attempting uninstall: Werkzeug\n",
            "    Found existing installation: Werkzeug 2.3.6\n",
            "    Uninstalling Werkzeug-2.3.6:\n",
            "      Successfully uninstalled Werkzeug-2.3.6\n",
            "  Attempting uninstall: importlib-metadata\n",
            "    Found existing installation: importlib-metadata 4.6.4\n",
            "    Uninstalling importlib-metadata-4.6.4:\n",
            "      Successfully uninstalled importlib-metadata-4.6.4\n",
            "Successfully installed Werkzeug-2.2.3 ansi2html-1.8.0 category-encoders-2.6.1 dash-2.11.1 dash-core-components-2.0.0 dash-html-components-2.0.0 dash-table-5.0.0 deprecated-1.2.14 deprecation-2.1.0 importlib-metadata-6.8.0 jedi-0.19.0 kaleido-0.2.1 orjson-3.9.2 plotly-resampler-0.9.1 pmdarima-2.0.3 pycaret-3.0.4 pyod-1.1.0 retrying-1.3.4 schemdraw-0.15 scikit-base-0.5.0 scikit-plot-0.3.7 sktime-0.21.0 tbats-1.1.3 trace-updater-0.0.9.1 tsdownsample-0.1.2 wurlitzer-3.0.3 xxhash-3.3.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.info()"
      ],
      "metadata": {
        "id": "jsqQfmi1eizm",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "51318083-9802-41f5-b46e-33672d014ae1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 3239 entries, 0 to 3238\n",
            "Data columns (total 7 columns):\n",
            " #   Column     Non-Null Count  Dtype  \n",
            "---  ------     --------------  -----  \n",
            " 0   Date       3239 non-null   object \n",
            " 1   Open       3239 non-null   float64\n",
            " 2   High       3239 non-null   float64\n",
            " 3   Low        3239 non-null   float64\n",
            " 4   Close      3239 non-null   float64\n",
            " 5   Adj Close  3239 non-null   float64\n",
            " 6   Volume     3239 non-null   int64  \n",
            "dtypes: float64(5), int64(1), object(1)\n",
            "memory usage: 177.3+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.describe()"
      ],
      "metadata": {
        "id": "_9KaQliQenMG",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "outputId": "63fd65d6-7947-474d-fad6-9d6a021446bc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "               Open          High           Low         Close     Adj Close  \\\n",
              "count  3.239000e+03  3.239000e+03  3.239000e+03  3.239000e+03  3.239000e+03   \n",
              "mean   1.027038e+06  1.051518e+06  1.000466e+06  1.027641e+06  1.027641e+06   \n",
              "std    1.211659e+06  1.241012e+06  1.178551e+06  1.211522e+06  1.211522e+06   \n",
              "min    1.098354e+04  1.309874e+04  1.064906e+04  1.105842e+04  1.105842e+04   \n",
              "25%    5.177187e+04  5.223994e+04  5.159637e+04  5.186137e+04  5.186137e+04   \n",
              "50%    5.341527e+05  5.499438e+05  5.226903e+05  5.343747e+05  5.343747e+05   \n",
              "75%    1.651089e+06  1.687922e+06  1.607334e+06  1.655311e+06  1.655311e+06   \n",
              "max    4.993192e+06  5.109432e+06  4.920109e+06  4.994456e+06  4.994456e+06   \n",
              "\n",
              "             Volume  \n",
              "count  3.239000e+03  \n",
              "mean   1.235645e+12  \n",
              "std    1.458528e+12  \n",
              "min    3.628589e+08  \n",
              "25%    8.806387e+09  \n",
              "50%    7.415702e+11  \n",
              "75%    2.088343e+12  \n",
              "max    2.582998e+13  "
            ],
            "text/html": [
              "\n",
              "\n",
              "  <div id=\"df-2a112d15-a9c5-40fc-88c4-4998b31025ce\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>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>count</th>\n",
              "      <td>3.239000e+03</td>\n",
              "      <td>3.239000e+03</td>\n",
              "      <td>3.239000e+03</td>\n",
              "      <td>3.239000e+03</td>\n",
              "      <td>3.239000e+03</td>\n",
              "      <td>3.239000e+03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>1.027038e+06</td>\n",
              "      <td>1.051518e+06</td>\n",
              "      <td>1.000466e+06</td>\n",
              "      <td>1.027641e+06</td>\n",
              "      <td>1.027641e+06</td>\n",
              "      <td>1.235645e+12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>1.211659e+06</td>\n",
              "      <td>1.241012e+06</td>\n",
              "      <td>1.178551e+06</td>\n",
              "      <td>1.211522e+06</td>\n",
              "      <td>1.211522e+06</td>\n",
              "      <td>1.458528e+12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.098354e+04</td>\n",
              "      <td>1.309874e+04</td>\n",
              "      <td>1.064906e+04</td>\n",
              "      <td>1.105842e+04</td>\n",
              "      <td>1.105842e+04</td>\n",
              "      <td>3.628589e+08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>5.177187e+04</td>\n",
              "      <td>5.223994e+04</td>\n",
              "      <td>5.159637e+04</td>\n",
              "      <td>5.186137e+04</td>\n",
              "      <td>5.186137e+04</td>\n",
              "      <td>8.806387e+09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>5.341527e+05</td>\n",
              "      <td>5.499438e+05</td>\n",
              "      <td>5.226903e+05</td>\n",
              "      <td>5.343747e+05</td>\n",
              "      <td>5.343747e+05</td>\n",
              "      <td>7.415702e+11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>1.651089e+06</td>\n",
              "      <td>1.687922e+06</td>\n",
              "      <td>1.607334e+06</td>\n",
              "      <td>1.655311e+06</td>\n",
              "      <td>1.655311e+06</td>\n",
              "      <td>2.088343e+12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>4.993192e+06</td>\n",
              "      <td>5.109432e+06</td>\n",
              "      <td>4.920109e+06</td>\n",
              "      <td>4.994456e+06</td>\n",
              "      <td>4.994456e+06</td>\n",
              "      <td>2.582998e+13</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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              "\n",
              "\n",
              "    <div id=\"df-83cc4886-1a3e-4058-b173-fe0f877397eb\">\n",
              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-83cc4886-1a3e-4058-b173-fe0f877397eb')\"\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",
              "    </div>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\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-quickchart:hover {\n",
              "    background-color: #E2EBFA;\n",
              "    box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: #174EA6;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart: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",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-83cc4886-1a3e-4058-b173-fe0f877397eb button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-2a112d15-a9c5-40fc-88c4-4998b31025ce 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-2a112d15-a9c5-40fc-88c4-4998b31025ce');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install jinja2"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lrH0edU3vxhB",
        "outputId": "05faeeb2-565e-44b4-dac1-b1f51d8b87d8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (3.1.2)\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2) (2.1.3)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "from pycaret.regression import *"
      ],
      "metadata": {
        "id": "V6xJNnTmv6j9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df\n",
        "asset = df"
      ],
      "metadata": {
        "id": "YUhxdkFSwH1X"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "future_days = 1\n",
        "asset['Future Price'] = asset[['Close']].shift(-future_days)\n",
        "asset = asset[['Close', 'Future Price']]\n",
        "asset"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "lB55aU6cwg-v",
        "outputId": "908ace17-bbb2-4c67-945b-1a8fe39e799f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             Close  Future Price\n",
              "0     2.785164e+04  2.577412e+04\n",
              "1     2.577412e+04  2.402334e+04\n",
              "2     2.402334e+04  2.488181e+04\n",
              "3     2.488181e+04  2.426826e+04\n",
              "4     2.426826e+04  2.450312e+04\n",
              "...            ...           ...\n",
              "3234  2.395233e+06  2.406396e+06\n",
              "3235  2.406396e+06  2.406235e+06\n",
              "3236  2.406235e+06  2.411990e+06\n",
              "3237  2.411990e+06  2.416823e+06\n",
              "3238  2.416823e+06           NaN\n",
              "\n",
              "[3239 rows x 2 columns]"
            ],
            "text/html": [
              "\n",
              "\n",
              "  <div id=\"df-df4c8984-76b4-4e27-86d6-b0bb267a6d88\">\n",
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              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Close</th>\n",
              "      <th>Future Price</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2.785164e+04</td>\n",
              "      <td>2.577412e+04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2.577412e+04</td>\n",
              "      <td>2.402334e+04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2.402334e+04</td>\n",
              "      <td>2.488181e+04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2.488181e+04</td>\n",
              "      <td>2.426826e+04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2.426826e+04</td>\n",
              "      <td>2.450312e+04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>3234</th>\n",
              "      <td>2.395233e+06</td>\n",
              "      <td>2.406396e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3235</th>\n",
              "      <td>2.406396e+06</td>\n",
              "      <td>2.406235e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3236</th>\n",
              "      <td>2.406235e+06</td>\n",
              "      <td>2.411990e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3237</th>\n",
              "      <td>2.411990e+06</td>\n",
              "      <td>2.416823e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3238</th>\n",
              "      <td>2.416823e+06</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>3239 rows × 2 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-df4c8984-76b4-4e27-86d6-b0bb267a6d88')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "\n",
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              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-4ca6c146-ed70-47e5-81f2-83cd667596e4')\"\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",
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              "    </g>\n",
              "</svg>\n",
              "      </button>\n",
              "    </div>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\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-quickchart:hover {\n",
              "    background-color: #E2EBFA;\n",
              "    box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: #174EA6;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart: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",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-4ca6c146-ed70-47e5-81f2-83cd667596e4 button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-df4c8984-76b4-4e27-86d6-b0bb267a6d88 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-df4c8984-76b4-4e27-86d6-b0bb267a6d88');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "asset_copy = asset.copy()\n",
        "x_1 = np.array(asset_copy[asset_copy.columns])\n",
        "x_1 = x_1[:len(asset) - future_days]\n",
        "y_1 = np.array(asset_copy['Future Price'])\n",
        "y_1 = y_1[:-future_days]\n",
        "x_1_train, x_1_test, y_1_train, y_1_test = train_test_split(x_1, y_1,test_size=0.25, random_state = 0, shuffle=False)"
      ],
      "metadata": {
        "id": "-x8U9lY_xZLy"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_1_data = pd.DataFrame(x_1_train, columns=asset_copy.columns)"
      ],
      "metadata": {
        "id": "4tVuhRtoybg1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "test_1_data = pd.DataFrame(x_1_test, columns=asset_copy.columns)"
      ],
      "metadata": {
        "id": "859eAHJLy_Oe"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "regression_setup = setup(data=train_1_data, target='Future Price', session_id=123, use_gpu = True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 645
        },
        "id": "WuBW58pvzUUK",
        "outputId": "2ef84611-a66c-4586-c55b-90c40c13da99"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<pandas.io.formats.style.Styler at 0x7fe814b31840>"
            ],
            "text/html": [
              "<style type=\"text/css\">\n",
              "#T_194bf_row8_col1, #T_194bf_row15_col1 {\n",
              "  background-color: lightgreen;\n",
              "}\n",
              "</style>\n",
              "<table id=\"T_194bf\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th class=\"blank level0\" >&nbsp;</th>\n",
              "      <th id=\"T_194bf_level0_col0\" class=\"col_heading level0 col0\" >Description</th>\n",
              "      <th id=\"T_194bf_level0_col1\" class=\"col_heading level0 col1\" >Value</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th id=\"T_194bf_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
              "      <td id=\"T_194bf_row0_col0\" class=\"data row0 col0\" >Session id</td>\n",
              "      <td id=\"T_194bf_row0_col1\" class=\"data row0 col1\" >123</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_194bf_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
              "      <td id=\"T_194bf_row1_col0\" class=\"data row1 col0\" >Target</td>\n",
              "      <td id=\"T_194bf_row1_col1\" class=\"data row1 col1\" >Future Price</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_194bf_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
              "      <td id=\"T_194bf_row2_col0\" class=\"data row2 col0\" >Target type</td>\n",
              "      <td id=\"T_194bf_row2_col1\" class=\"data row2 col1\" >Regression</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_194bf_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
              "      <td id=\"T_194bf_row3_col0\" class=\"data row3 col0\" >Original data shape</td>\n",
              "      <td id=\"T_194bf_row3_col1\" class=\"data row3 col1\" >(2428, 2)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_194bf_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
              "      <td id=\"T_194bf_row4_col0\" class=\"data row4 col0\" >Transformed data shape</td>\n",
              "      <td id=\"T_194bf_row4_col1\" class=\"data row4 col1\" >(2428, 2)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_194bf_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
              "      <td id=\"T_194bf_row5_col0\" class=\"data row5 col0\" >Transformed train set shape</td>\n",
              "      <td id=\"T_194bf_row5_col1\" class=\"data row5 col1\" >(1699, 2)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_194bf_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
              "      <td id=\"T_194bf_row6_col0\" class=\"data row6 col0\" >Transformed test set shape</td>\n",
              "      <td id=\"T_194bf_row6_col1\" class=\"data row6 col1\" >(729, 2)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_194bf_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
              "      <td id=\"T_194bf_row7_col0\" class=\"data row7 col0\" >Numeric features</td>\n",
              "      <td id=\"T_194bf_row7_col1\" class=\"data row7 col1\" >1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_194bf_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
              "      <td id=\"T_194bf_row8_col0\" class=\"data row8 col0\" >Preprocess</td>\n",
              "      <td id=\"T_194bf_row8_col1\" class=\"data row8 col1\" >True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_194bf_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
              "      <td id=\"T_194bf_row9_col0\" class=\"data row9 col0\" >Imputation type</td>\n",
              "      <td id=\"T_194bf_row9_col1\" class=\"data row9 col1\" >simple</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_194bf_level0_row10\" class=\"row_heading level0 row10\" >10</th>\n",
              "      <td id=\"T_194bf_row10_col0\" class=\"data row10 col0\" >Numeric imputation</td>\n",
              "      <td id=\"T_194bf_row10_col1\" class=\"data row10 col1\" >mean</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_194bf_level0_row11\" class=\"row_heading level0 row11\" >11</th>\n",
              "      <td id=\"T_194bf_row11_col0\" class=\"data row11 col0\" >Categorical imputation</td>\n",
              "      <td id=\"T_194bf_row11_col1\" class=\"data row11 col1\" >mode</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_194bf_level0_row12\" class=\"row_heading level0 row12\" >12</th>\n",
              "      <td id=\"T_194bf_row12_col0\" class=\"data row12 col0\" >Fold Generator</td>\n",
              "      <td id=\"T_194bf_row12_col1\" class=\"data row12 col1\" >KFold</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_194bf_level0_row13\" class=\"row_heading level0 row13\" >13</th>\n",
              "      <td id=\"T_194bf_row13_col0\" class=\"data row13 col0\" >Fold Number</td>\n",
              "      <td id=\"T_194bf_row13_col1\" class=\"data row13 col1\" >10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_194bf_level0_row14\" class=\"row_heading level0 row14\" >14</th>\n",
              "      <td id=\"T_194bf_row14_col0\" class=\"data row14 col0\" >CPU Jobs</td>\n",
              "      <td id=\"T_194bf_row14_col1\" class=\"data row14 col1\" >-1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_194bf_level0_row15\" class=\"row_heading level0 row15\" >15</th>\n",
              "      <td id=\"T_194bf_row15_col0\" class=\"data row15 col0\" >Use GPU</td>\n",
              "      <td id=\"T_194bf_row15_col1\" class=\"data row15 col1\" >True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_194bf_level0_row16\" class=\"row_heading level0 row16\" >16</th>\n",
              "      <td id=\"T_194bf_row16_col0\" class=\"data row16 col0\" >Log Experiment</td>\n",
              "      <td id=\"T_194bf_row16_col1\" class=\"data row16 col1\" >False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_194bf_level0_row17\" class=\"row_heading level0 row17\" >17</th>\n",
              "      <td id=\"T_194bf_row17_col0\" class=\"data row17 col0\" >Experiment Name</td>\n",
              "      <td id=\"T_194bf_row17_col1\" class=\"data row17 col1\" >reg-default-name</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_194bf_level0_row18\" class=\"row_heading level0 row18\" >18</th>\n",
              "      <td id=\"T_194bf_row18_col0\" class=\"data row18 col0\" >USI</td>\n",
              "      <td id=\"T_194bf_row18_col1\" class=\"data row18 col1\" >8307</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "best_model=compare_models(sort='rmsle')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 613,
          "referenced_widgets": [
            "de4ca9d88a434ff78c092c6054ba31c4",
            "d4ce192216354a97b99f7ee4e784efb9",
            "3293bda1da98400cbaf5b3b5f6ec814d",
            "cddcf0f6311e49c5bd89124caa566fc0",
            "56ecf212eb844b05aa1e5ce310b12554",
            "cef8028385484fb8809123572b8f8799",
            "42c1ebaa271d403b92ede1485e5f0027",
            "09f1c421e1d84412b699545b63bae227",
            "5a9ff1a9c2d64aec977b2494ddc14fe3",
            "0d8afa9ad02645938a8ea9e20d67e610",
            "4efd16d9f33640faa869f4ae3a90bdfb"
          ]
        },
        "id": "9wVhYkVBzmu6",
        "outputId": "f94faccb-9a03-4094-dbe5-9e519d2c1992"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
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            "text/html": []
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              "#T_5ecda_row0_col0, #T_5ecda_row0_col3, #T_5ecda_row0_col4, #T_5ecda_row1_col0, #T_5ecda_row1_col1, #T_5ecda_row1_col2, #T_5ecda_row1_col3, #T_5ecda_row1_col4, #T_5ecda_row1_col5, #T_5ecda_row1_col6, #T_5ecda_row2_col0, #T_5ecda_row2_col1, #T_5ecda_row2_col2, #T_5ecda_row2_col3, #T_5ecda_row2_col4, #T_5ecda_row2_col5, #T_5ecda_row2_col6, #T_5ecda_row3_col0, #T_5ecda_row3_col1, #T_5ecda_row3_col2, #T_5ecda_row3_col3, #T_5ecda_row3_col4, #T_5ecda_row3_col5, #T_5ecda_row3_col6, #T_5ecda_row4_col0, #T_5ecda_row4_col1, #T_5ecda_row4_col2, #T_5ecda_row4_col3, #T_5ecda_row4_col4, #T_5ecda_row4_col5, #T_5ecda_row4_col6, #T_5ecda_row5_col0, #T_5ecda_row5_col1, #T_5ecda_row5_col2, #T_5ecda_row5_col3, #T_5ecda_row5_col5, #T_5ecda_row5_col6, #T_5ecda_row6_col0, #T_5ecda_row6_col1, #T_5ecda_row6_col2, #T_5ecda_row6_col3, #T_5ecda_row6_col5, #T_5ecda_row6_col6, #T_5ecda_row7_col0, #T_5ecda_row7_col1, #T_5ecda_row7_col2, #T_5ecda_row7_col5, #T_5ecda_row7_col6, #T_5ecda_row8_col0, #T_5ecda_row8_col1, #T_5ecda_row8_col2, #T_5ecda_row8_col3, #T_5ecda_row8_col5, #T_5ecda_row8_col6, #T_5ecda_row9_col0, #T_5ecda_row9_col1, #T_5ecda_row9_col2, #T_5ecda_row9_col3, #T_5ecda_row9_col5, #T_5ecda_row9_col6, #T_5ecda_row10_col0, #T_5ecda_row10_col1, #T_5ecda_row10_col2, #T_5ecda_row10_col5, #T_5ecda_row10_col6, #T_5ecda_row11_col0, #T_5ecda_row11_col1, #T_5ecda_row11_col2, #T_5ecda_row11_col3, #T_5ecda_row11_col5, #T_5ecda_row11_col6, #T_5ecda_row12_col0, #T_5ecda_row12_col1, #T_5ecda_row12_col2, #T_5ecda_row12_col3, #T_5ecda_row12_col5, #T_5ecda_row12_col6, #T_5ecda_row13_col0, #T_5ecda_row13_col1, #T_5ecda_row13_col2, #T_5ecda_row13_col3, #T_5ecda_row13_col4, #T_5ecda_row13_col5, #T_5ecda_row13_col6, #T_5ecda_row14_col0, #T_5ecda_row14_col1, #T_5ecda_row14_col2, #T_5ecda_row14_col3, #T_5ecda_row14_col4, #T_5ecda_row14_col5, #T_5ecda_row14_col6, #T_5ecda_row15_col0, #T_5ecda_row15_col1, #T_5ecda_row15_col2, #T_5ecda_row15_col3, #T_5ecda_row15_col4, #T_5ecda_row15_col5, #T_5ecda_row15_col6, #T_5ecda_row16_col0, #T_5ecda_row16_col1, #T_5ecda_row16_col2, #T_5ecda_row16_col3, #T_5ecda_row16_col4, #T_5ecda_row16_col5, #T_5ecda_row16_col6, #T_5ecda_row17_col0, #T_5ecda_row17_col1, #T_5ecda_row17_col2, #T_5ecda_row17_col3, #T_5ecda_row17_col4, #T_5ecda_row17_col5, #T_5ecda_row17_col6 {\n",
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              "#T_5ecda_row0_col1, #T_5ecda_row0_col2, #T_5ecda_row0_col5, #T_5ecda_row0_col6, #T_5ecda_row5_col4, #T_5ecda_row6_col4, #T_5ecda_row7_col3, #T_5ecda_row7_col4, #T_5ecda_row8_col4, #T_5ecda_row9_col4, #T_5ecda_row10_col3, #T_5ecda_row10_col4, #T_5ecda_row11_col4, #T_5ecda_row12_col4 {\n",
              "  text-align: left;\n",
              "  background-color: yellow;\n",
              "}\n",
              "#T_5ecda_row0_col7, #T_5ecda_row1_col7, #T_5ecda_row2_col7, #T_5ecda_row3_col7, #T_5ecda_row4_col7, #T_5ecda_row5_col7, #T_5ecda_row6_col7, #T_5ecda_row7_col7, #T_5ecda_row8_col7, #T_5ecda_row9_col7, #T_5ecda_row11_col7, #T_5ecda_row12_col7, #T_5ecda_row13_col7, #T_5ecda_row14_col7, #T_5ecda_row15_col7, #T_5ecda_row16_col7 {\n",
              "  text-align: left;\n",
              "  background-color: lightgrey;\n",
              "}\n",
              "#T_5ecda_row10_col7, #T_5ecda_row17_col7 {\n",
              "  text-align: left;\n",
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              "  background-color: lightgrey;\n",
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              "</style>\n",
              "<table id=\"T_5ecda\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th class=\"blank level0\" >&nbsp;</th>\n",
              "      <th id=\"T_5ecda_level0_col0\" class=\"col_heading level0 col0\" >Model</th>\n",
              "      <th id=\"T_5ecda_level0_col1\" class=\"col_heading level0 col1\" >MAE</th>\n",
              "      <th id=\"T_5ecda_level0_col2\" class=\"col_heading level0 col2\" >MSE</th>\n",
              "      <th id=\"T_5ecda_level0_col3\" class=\"col_heading level0 col3\" >RMSE</th>\n",
              "      <th id=\"T_5ecda_level0_col4\" class=\"col_heading level0 col4\" >R2</th>\n",
              "      <th id=\"T_5ecda_level0_col5\" class=\"col_heading level0 col5\" >RMSLE</th>\n",
              "      <th id=\"T_5ecda_level0_col6\" class=\"col_heading level0 col6\" >MAPE</th>\n",
              "      <th id=\"T_5ecda_level0_col7\" class=\"col_heading level0 col7\" >TT (Sec)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th id=\"T_5ecda_level0_row0\" class=\"row_heading level0 row0\" >huber</th>\n",
              "      <td id=\"T_5ecda_row0_col0\" class=\"data row0 col0\" >Huber Regressor</td>\n",
              "      <td id=\"T_5ecda_row0_col1\" class=\"data row0 col1\" >14637.7098</td>\n",
              "      <td id=\"T_5ecda_row0_col2\" class=\"data row0 col2\" >1483251201.1237</td>\n",
              "      <td id=\"T_5ecda_row0_col3\" class=\"data row0 col3\" >36861.1772</td>\n",
              "      <td id=\"T_5ecda_row0_col4\" class=\"data row0 col4\" >0.9978</td>\n",
              "      <td id=\"T_5ecda_row0_col5\" class=\"data row0 col5\" >0.0382</td>\n",
              "      <td id=\"T_5ecda_row0_col6\" class=\"data row0 col6\" >0.0250</td>\n",
              "      <td id=\"T_5ecda_row0_col7\" class=\"data row0 col7\" >0.0570</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_5ecda_level0_row1\" class=\"row_heading level0 row1\" >knn</th>\n",
              "      <td id=\"T_5ecda_row1_col0\" class=\"data row1 col0\" >K Neighbors Regressor</td>\n",
              "      <td id=\"T_5ecda_row1_col1\" class=\"data row1 col1\" >17269.9891</td>\n",
              "      <td id=\"T_5ecda_row1_col2\" class=\"data row1 col2\" >1892814592.0000</td>\n",
              "      <td id=\"T_5ecda_row1_col3\" class=\"data row1 col3\" >41319.3812</td>\n",
              "      <td id=\"T_5ecda_row1_col4\" class=\"data row1 col4\" >0.9972</td>\n",
              "      <td id=\"T_5ecda_row1_col5\" class=\"data row1 col5\" >0.0419</td>\n",
              "      <td id=\"T_5ecda_row1_col6\" class=\"data row1 col6\" >0.0292</td>\n",
              "      <td id=\"T_5ecda_row1_col7\" class=\"data row1 col7\" >0.0650</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_5ecda_level0_row2\" class=\"row_heading level0 row2\" >lightgbm</th>\n",
              "      <td id=\"T_5ecda_row2_col0\" class=\"data row2 col0\" >Light Gradient Boosting Machine</td>\n",
              "      <td id=\"T_5ecda_row2_col1\" class=\"data row2 col1\" >18863.8234</td>\n",
              "      <td id=\"T_5ecda_row2_col2\" class=\"data row2 col2\" >2584424767.5477</td>\n",
              "      <td id=\"T_5ecda_row2_col3\" class=\"data row2 col3\" >49209.7073</td>\n",
              "      <td id=\"T_5ecda_row2_col4\" class=\"data row2 col4\" >0.9962</td>\n",
              "      <td id=\"T_5ecda_row2_col5\" class=\"data row2 col5\" >0.0426</td>\n",
              "      <td id=\"T_5ecda_row2_col6\" class=\"data row2 col6\" >0.0291</td>\n",
              "      <td id=\"T_5ecda_row2_col7\" class=\"data row2 col7\" >0.4480</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_5ecda_level0_row3\" class=\"row_heading level0 row3\" >rf</th>\n",
              "      <td id=\"T_5ecda_row3_col0\" class=\"data row3 col0\" >Random Forest Regressor</td>\n",
              "      <td id=\"T_5ecda_row3_col1\" class=\"data row3 col1\" >19483.9859</td>\n",
              "      <td id=\"T_5ecda_row3_col2\" class=\"data row3 col2\" >2486929419.9940</td>\n",
              "      <td id=\"T_5ecda_row3_col3\" class=\"data row3 col3\" >47884.0162</td>\n",
              "      <td id=\"T_5ecda_row3_col4\" class=\"data row3 col4\" >0.9963</td>\n",
              "      <td id=\"T_5ecda_row3_col5\" class=\"data row3 col5\" >0.0459</td>\n",
              "      <td id=\"T_5ecda_row3_col6\" class=\"data row3 col6\" >0.0323</td>\n",
              "      <td id=\"T_5ecda_row3_col7\" class=\"data row3 col7\" >0.5480</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_5ecda_level0_row4\" class=\"row_heading level0 row4\" >et</th>\n",
              "      <td id=\"T_5ecda_row4_col0\" class=\"data row4 col0\" >Extra Trees Regressor</td>\n",
              "      <td id=\"T_5ecda_row4_col1\" class=\"data row4 col1\" >20469.0538</td>\n",
              "      <td id=\"T_5ecda_row4_col2\" class=\"data row4 col2\" >2810768928.6009</td>\n",
              "      <td id=\"T_5ecda_row4_col3\" class=\"data row4 col3\" >51211.7650</td>\n",
              "      <td id=\"T_5ecda_row4_col4\" class=\"data row4 col4\" >0.9959</td>\n",
              "      <td id=\"T_5ecda_row4_col5\" class=\"data row4 col5\" >0.0485</td>\n",
              "      <td id=\"T_5ecda_row4_col6\" class=\"data row4 col6\" >0.0343</td>\n",
              "      <td id=\"T_5ecda_row4_col7\" class=\"data row4 col7\" >0.7350</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_5ecda_level0_row5\" class=\"row_heading level0 row5\" >lar</th>\n",
              "      <td id=\"T_5ecda_row5_col0\" class=\"data row5 col0\" >Least Angle Regression</td>\n",
              "      <td id=\"T_5ecda_row5_col1\" class=\"data row5 col1\" >14932.2993</td>\n",
              "      <td id=\"T_5ecda_row5_col2\" class=\"data row5 col2\" >1485100268.8000</td>\n",
              "      <td id=\"T_5ecda_row5_col3\" class=\"data row5 col3\" >36791.0793</td>\n",
              "      <td id=\"T_5ecda_row5_col4\" class=\"data row5 col4\" >0.9979</td>\n",
              "      <td id=\"T_5ecda_row5_col5\" class=\"data row5 col5\" >0.0490</td>\n",
              "      <td id=\"T_5ecda_row5_col6\" class=\"data row5 col6\" >0.0353</td>\n",
              "      <td id=\"T_5ecda_row5_col7\" class=\"data row5 col7\" >0.0790</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_5ecda_level0_row6\" class=\"row_heading level0 row6\" >br</th>\n",
              "      <td id=\"T_5ecda_row6_col0\" class=\"data row6 col0\" >Bayesian Ridge</td>\n",
              "      <td id=\"T_5ecda_row6_col1\" class=\"data row6 col1\" >14932.0536</td>\n",
              "      <td id=\"T_5ecda_row6_col2\" class=\"data row6 col2\" >1485100883.2000</td>\n",
              "      <td id=\"T_5ecda_row6_col3\" class=\"data row6 col3\" >36791.1045</td>\n",
              "      <td id=\"T_5ecda_row6_col4\" class=\"data row6 col4\" >0.9979</td>\n",
              "      <td id=\"T_5ecda_row6_col5\" class=\"data row6 col5\" >0.0490</td>\n",
              "      <td id=\"T_5ecda_row6_col6\" class=\"data row6 col6\" >0.0353</td>\n",
              "      <td id=\"T_5ecda_row6_col7\" class=\"data row6 col7\" >0.0750</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_5ecda_level0_row7\" class=\"row_heading level0 row7\" >omp</th>\n",
              "      <td id=\"T_5ecda_row7_col0\" class=\"data row7 col0\" >Orthogonal Matching Pursuit</td>\n",
              "      <td id=\"T_5ecda_row7_col1\" class=\"data row7 col1\" >14932.2904</td>\n",
              "      <td id=\"T_5ecda_row7_col2\" class=\"data row7 col2\" >1485099884.8000</td>\n",
              "      <td id=\"T_5ecda_row7_col3\" class=\"data row7 col3\" >36791.0736</td>\n",
              "      <td id=\"T_5ecda_row7_col4\" class=\"data row7 col4\" >0.9979</td>\n",
              "      <td id=\"T_5ecda_row7_col5\" class=\"data row7 col5\" >0.0490</td>\n",
              "      <td id=\"T_5ecda_row7_col6\" class=\"data row7 col6\" >0.0353</td>\n",
              "      <td id=\"T_5ecda_row7_col7\" class=\"data row7 col7\" >0.0800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_5ecda_level0_row8\" class=\"row_heading level0 row8\" >llar</th>\n",
              "      <td id=\"T_5ecda_row8_col0\" class=\"data row8 col0\" >Lasso Least Angle Regression</td>\n",
              "      <td id=\"T_5ecda_row8_col1\" class=\"data row8 col1\" >14932.2993</td>\n",
              "      <td id=\"T_5ecda_row8_col2\" class=\"data row8 col2\" >1485100268.8000</td>\n",
              "      <td id=\"T_5ecda_row8_col3\" class=\"data row8 col3\" >36791.0793</td>\n",
              "      <td id=\"T_5ecda_row8_col4\" class=\"data row8 col4\" >0.9979</td>\n",
              "      <td id=\"T_5ecda_row8_col5\" class=\"data row8 col5\" >0.0490</td>\n",
              "      <td id=\"T_5ecda_row8_col6\" class=\"data row8 col6\" >0.0353</td>\n",
              "      <td id=\"T_5ecda_row8_col7\" class=\"data row8 col7\" >0.0750</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_5ecda_level0_row9\" class=\"row_heading level0 row9\" >en</th>\n",
              "      <td id=\"T_5ecda_row9_col0\" class=\"data row9 col0\" >Elastic Net</td>\n",
              "      <td id=\"T_5ecda_row9_col1\" class=\"data row9 col1\" >14932.3098</td>\n",
              "      <td id=\"T_5ecda_row9_col2\" class=\"data row9 col2\" >1485100902.4000</td>\n",
              "      <td id=\"T_5ecda_row9_col3\" class=\"data row9 col3\" >36791.0855</td>\n",
              "      <td id=\"T_5ecda_row9_col4\" class=\"data row9 col4\" >0.9979</td>\n",
              "      <td id=\"T_5ecda_row9_col5\" class=\"data row9 col5\" >0.0490</td>\n",
              "      <td id=\"T_5ecda_row9_col6\" class=\"data row9 col6\" >0.0353</td>\n",
              "      <td id=\"T_5ecda_row9_col7\" class=\"data row9 col7\" >0.0500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_5ecda_level0_row10\" class=\"row_heading level0 row10\" >ridge</th>\n",
              "      <td id=\"T_5ecda_row10_col0\" class=\"data row10 col0\" >Ridge Regression</td>\n",
              "      <td id=\"T_5ecda_row10_col1\" class=\"data row10 col1\" >14932.2904</td>\n",
              "      <td id=\"T_5ecda_row10_col2\" class=\"data row10 col2\" >1485099884.8000</td>\n",
              "      <td id=\"T_5ecda_row10_col3\" class=\"data row10 col3\" >36791.0736</td>\n",
              "      <td id=\"T_5ecda_row10_col4\" class=\"data row10 col4\" >0.9979</td>\n",
              "      <td id=\"T_5ecda_row10_col5\" class=\"data row10 col5\" >0.0490</td>\n",
              "      <td id=\"T_5ecda_row10_col6\" class=\"data row10 col6\" >0.0353</td>\n",
              "      <td id=\"T_5ecda_row10_col7\" class=\"data row10 col7\" >0.0470</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_5ecda_level0_row11\" class=\"row_heading level0 row11\" >lasso</th>\n",
              "      <td id=\"T_5ecda_row11_col0\" class=\"data row11 col0\" >Lasso Regression</td>\n",
              "      <td id=\"T_5ecda_row11_col1\" class=\"data row11 col1\" >14932.3098</td>\n",
              "      <td id=\"T_5ecda_row11_col2\" class=\"data row11 col2\" >1485100902.4000</td>\n",
              "      <td id=\"T_5ecda_row11_col3\" class=\"data row11 col3\" >36791.0855</td>\n",
              "      <td id=\"T_5ecda_row11_col4\" class=\"data row11 col4\" >0.9979</td>\n",
              "      <td id=\"T_5ecda_row11_col5\" class=\"data row11 col5\" >0.0490</td>\n",
              "      <td id=\"T_5ecda_row11_col6\" class=\"data row11 col6\" >0.0353</td>\n",
              "      <td id=\"T_5ecda_row11_col7\" class=\"data row11 col7\" >0.0550</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_5ecda_level0_row12\" class=\"row_heading level0 row12\" >lr</th>\n",
              "      <td id=\"T_5ecda_row12_col0\" class=\"data row12 col0\" >Linear Regression</td>\n",
              "      <td id=\"T_5ecda_row12_col1\" class=\"data row12 col1\" >14932.3056</td>\n",
              "      <td id=\"T_5ecda_row12_col2\" class=\"data row12 col2\" >1485101548.8000</td>\n",
              "      <td id=\"T_5ecda_row12_col3\" class=\"data row12 col3\" >36791.0916</td>\n",
              "      <td id=\"T_5ecda_row12_col4\" class=\"data row12 col4\" >0.9979</td>\n",
              "      <td id=\"T_5ecda_row12_col5\" class=\"data row12 col5\" >0.0490</td>\n",
              "      <td id=\"T_5ecda_row12_col6\" class=\"data row12 col6\" >0.0353</td>\n",
              "      <td id=\"T_5ecda_row12_col7\" class=\"data row12 col7\" >0.0500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_5ecda_level0_row13\" class=\"row_heading level0 row13\" >gbr</th>\n",
              "      <td id=\"T_5ecda_row13_col0\" class=\"data row13 col0\" >Gradient Boosting Regressor</td>\n",
              "      <td id=\"T_5ecda_row13_col1\" class=\"data row13 col1\" >18145.0225</td>\n",
              "      <td id=\"T_5ecda_row13_col2\" class=\"data row13 col2\" >2410612771.2528</td>\n",
              "      <td id=\"T_5ecda_row13_col3\" class=\"data row13 col3\" >46953.2741</td>\n",
              "      <td id=\"T_5ecda_row13_col4\" class=\"data row13 col4\" >0.9965</td>\n",
              "      <td id=\"T_5ecda_row13_col5\" class=\"data row13 col5\" >0.0507</td>\n",
              "      <td id=\"T_5ecda_row13_col6\" class=\"data row13 col6\" >0.0366</td>\n",
              "      <td id=\"T_5ecda_row13_col7\" class=\"data row13 col7\" >0.2860</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_5ecda_level0_row14\" class=\"row_heading level0 row14\" >dt</th>\n",
              "      <td id=\"T_5ecda_row14_col0\" class=\"data row14 col0\" >Decision Tree Regressor</td>\n",
              "      <td id=\"T_5ecda_row14_col1\" class=\"data row14 col1\" >22054.5484</td>\n",
              "      <td id=\"T_5ecda_row14_col2\" class=\"data row14 col2\" >3358199006.7205</td>\n",
              "      <td id=\"T_5ecda_row14_col3\" class=\"data row14 col3\" >56293.4282</td>\n",
              "      <td id=\"T_5ecda_row14_col4\" class=\"data row14 col4\" >0.9951</td>\n",
              "      <td id=\"T_5ecda_row14_col5\" class=\"data row14 col5\" >0.0533</td>\n",
              "      <td id=\"T_5ecda_row14_col6\" class=\"data row14 col6\" >0.0371</td>\n",
              "      <td id=\"T_5ecda_row14_col7\" class=\"data row14 col7\" >0.0520</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_5ecda_level0_row15\" class=\"row_heading level0 row15\" >par</th>\n",
              "      <td id=\"T_5ecda_row15_col0\" class=\"data row15 col0\" >Passive Aggressive Regressor</td>\n",
              "      <td id=\"T_5ecda_row15_col1\" class=\"data row15 col1\" >23707.4320</td>\n",
              "      <td id=\"T_5ecda_row15_col2\" class=\"data row15 col2\" >3432371361.4716</td>\n",
              "      <td id=\"T_5ecda_row15_col3\" class=\"data row15 col3\" >53373.2864</td>\n",
              "      <td id=\"T_5ecda_row15_col4\" class=\"data row15 col4\" >0.9952</td>\n",
              "      <td id=\"T_5ecda_row15_col5\" class=\"data row15 col5\" >0.0548</td>\n",
              "      <td id=\"T_5ecda_row15_col6\" class=\"data row15 col6\" >0.0442</td>\n",
              "      <td id=\"T_5ecda_row15_col7\" class=\"data row15 col7\" >0.0490</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_5ecda_level0_row16\" class=\"row_heading level0 row16\" >ada</th>\n",
              "      <td id=\"T_5ecda_row16_col0\" class=\"data row16 col0\" >AdaBoost Regressor</td>\n",
              "      <td id=\"T_5ecda_row16_col1\" class=\"data row16 col1\" >64654.1819</td>\n",
              "      <td id=\"T_5ecda_row16_col2\" class=\"data row16 col2\" >6363412269.9601</td>\n",
              "      <td id=\"T_5ecda_row16_col3\" class=\"data row16 col3\" >79428.4327</td>\n",
              "      <td id=\"T_5ecda_row16_col4\" class=\"data row16 col4\" >0.9901</td>\n",
              "      <td id=\"T_5ecda_row16_col5\" class=\"data row16 col5\" >0.9174</td>\n",
              "      <td id=\"T_5ecda_row16_col6\" class=\"data row16 col6\" >1.3953</td>\n",
              "      <td id=\"T_5ecda_row16_col7\" class=\"data row16 col7\" >0.2040</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_5ecda_level0_row17\" class=\"row_heading level0 row17\" >dummy</th>\n",
              "      <td id=\"T_5ecda_row17_col0\" class=\"data row17 col0\" >Dummy Regressor</td>\n",
              "      <td id=\"T_5ecda_row17_col1\" class=\"data row17 col1\" >479645.1469</td>\n",
              "      <td id=\"T_5ecda_row17_col2\" class=\"data row17 col2\" >691051177574.4000</td>\n",
              "      <td id=\"T_5ecda_row17_col3\" class=\"data row17 col3\" >824239.7250</td>\n",
              "      <td id=\"T_5ecda_row17_col4\" class=\"data row17 col4\" >-0.0039</td>\n",
              "      <td id=\"T_5ecda_row17_col5\" class=\"data row17 col5\" >1.9787</td>\n",
              "      <td id=\"T_5ecda_row17_col6\" class=\"data row17 col6\" >8.3994</td>\n",
              "      <td id=\"T_5ecda_row17_col7\" class=\"data row17 col7\" >0.0470</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Processing:   0%|          | 0/81 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
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          },
          "metadata": {
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              "colab": {
                "custom_widget_manager": {
                  "url": "https://ssl.gstatic.com/colaboratory-static/widgets/colab-cdn-widget-manager/b3e629b1971e1542/manager.min.js"
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          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": []
          },
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        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_1 = create_model(best_model)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 457,
          "referenced_widgets": [
            "fcc62800c0d2419e926250ada67a36d9",
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            "6057b560b0534a50a3e1268c6936ab1c",
            "3eaba4d2aaaa436897972106b3e971f7",
            "94513bc2e1a04e668d2c462381b31a3e",
            "bc125c1f880242babb13c49a01c64b3b",
            "49cad3f0498c4e3890fc8f27a45e329d",
            "f2028fe332cd42aca04eebb2a4297dc1",
            "4f18d4e2b3424e5e81fa73c1087d388b",
            "be1f6e8c007845a6a5c7d96f4c2ccf89"
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        },
        "id": "2pw6JjjYz-rM",
        "outputId": "d5f7a878-40db-4632-e080-ad4510ed2539"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": []
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          "metadata": {}
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<pandas.io.formats.style.Styler at 0x7fe7ff3d6890>"
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            "text/html": [
              "<style type=\"text/css\">\n",
              "#T_d1166_row10_col0, #T_d1166_row10_col1, #T_d1166_row10_col2, #T_d1166_row10_col3, #T_d1166_row10_col4, #T_d1166_row10_col5 {\n",
              "  background: yellow;\n",
              "}\n",
              "</style>\n",
              "<table id=\"T_d1166\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th class=\"blank level0\" >&nbsp;</th>\n",
              "      <th id=\"T_d1166_level0_col0\" class=\"col_heading level0 col0\" >MAE</th>\n",
              "      <th id=\"T_d1166_level0_col1\" class=\"col_heading level0 col1\" >MSE</th>\n",
              "      <th id=\"T_d1166_level0_col2\" class=\"col_heading level0 col2\" >RMSE</th>\n",
              "      <th id=\"T_d1166_level0_col3\" class=\"col_heading level0 col3\" >R2</th>\n",
              "      <th id=\"T_d1166_level0_col4\" class=\"col_heading level0 col4\" >RMSLE</th>\n",
              "      <th id=\"T_d1166_level0_col5\" class=\"col_heading level0 col5\" >MAPE</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th class=\"index_name level0\" >Fold</th>\n",
              "      <th class=\"blank col0\" >&nbsp;</th>\n",
              "      <th class=\"blank col1\" >&nbsp;</th>\n",
              "      <th class=\"blank col2\" >&nbsp;</th>\n",
              "      <th class=\"blank col3\" >&nbsp;</th>\n",
              "      <th class=\"blank col4\" >&nbsp;</th>\n",
              "      <th class=\"blank col5\" >&nbsp;</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th id=\"T_d1166_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
              "      <td id=\"T_d1166_row0_col0\" class=\"data row0 col0\" >15453.5920</td>\n",
              "      <td id=\"T_d1166_row0_col1\" class=\"data row0 col1\" >1227024372.5736</td>\n",
              "      <td id=\"T_d1166_row0_col2\" class=\"data row0 col2\" >35028.9077</td>\n",
              "      <td id=\"T_d1166_row0_col3\" class=\"data row0 col3\" >0.9973</td>\n",
              "      <td id=\"T_d1166_row0_col4\" class=\"data row0 col4\" >0.0426</td>\n",
              "      <td id=\"T_d1166_row0_col5\" class=\"data row0 col5\" >0.0291</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_d1166_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
              "      <td id=\"T_d1166_row1_col0\" class=\"data row1 col0\" >15464.3363</td>\n",
              "      <td id=\"T_d1166_row1_col1\" class=\"data row1 col1\" >1489874203.2788</td>\n",
              "      <td id=\"T_d1166_row1_col2\" class=\"data row1 col2\" >38598.8886</td>\n",
              "      <td id=\"T_d1166_row1_col3\" class=\"data row1 col3\" >0.9983</td>\n",
              "      <td id=\"T_d1166_row1_col4\" class=\"data row1 col4\" >0.0323</td>\n",
              "      <td id=\"T_d1166_row1_col5\" class=\"data row1 col5\" >0.0220</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_d1166_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
              "      <td id=\"T_d1166_row2_col0\" class=\"data row2 col0\" >12636.0520</td>\n",
              "      <td id=\"T_d1166_row2_col1\" class=\"data row2 col1\" >1049200406.6292</td>\n",
              "      <td id=\"T_d1166_row2_col2\" class=\"data row2 col2\" >32391.3631</td>\n",
              "      <td id=\"T_d1166_row2_col3\" class=\"data row2 col3\" >0.9980</td>\n",
              "      <td id=\"T_d1166_row2_col4\" class=\"data row2 col4\" >0.0368</td>\n",
              "      <td id=\"T_d1166_row2_col5\" class=\"data row2 col5\" >0.0245</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_d1166_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
              "      <td id=\"T_d1166_row3_col0\" class=\"data row3 col0\" >14880.3411</td>\n",
              "      <td id=\"T_d1166_row3_col1\" class=\"data row3 col1\" >1380183598.4411</td>\n",
              "      <td id=\"T_d1166_row3_col2\" class=\"data row3 col2\" >37150.8223</td>\n",
              "      <td id=\"T_d1166_row3_col3\" class=\"data row3 col3\" >0.9983</td>\n",
              "      <td id=\"T_d1166_row3_col4\" class=\"data row3 col4\" >0.0400</td>\n",
              "      <td id=\"T_d1166_row3_col5\" class=\"data row3 col5\" >0.0263</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_d1166_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
              "      <td id=\"T_d1166_row4_col0\" class=\"data row4 col0\" >19005.9821</td>\n",
              "      <td id=\"T_d1166_row4_col1\" class=\"data row4 col1\" >3099270401.8776</td>\n",
              "      <td id=\"T_d1166_row4_col2\" class=\"data row4 col2\" >55671.0913</td>\n",
              "      <td id=\"T_d1166_row4_col3\" class=\"data row4 col3\" >0.9959</td>\n",
              "      <td id=\"T_d1166_row4_col4\" class=\"data row4 col4\" >0.0405</td>\n",
              "      <td id=\"T_d1166_row4_col5\" class=\"data row4 col5\" >0.0269</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_d1166_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
              "      <td id=\"T_d1166_row5_col0\" class=\"data row5 col0\" >13004.1812</td>\n",
              "      <td id=\"T_d1166_row5_col1\" class=\"data row5 col1\" >949105116.3498</td>\n",
              "      <td id=\"T_d1166_row5_col2\" class=\"data row5 col2\" >30807.5497</td>\n",
              "      <td id=\"T_d1166_row5_col3\" class=\"data row5 col3\" >0.9976</td>\n",
              "      <td id=\"T_d1166_row5_col4\" class=\"data row5 col4\" >0.0480</td>\n",
              "      <td id=\"T_d1166_row5_col5\" class=\"data row5 col5\" >0.0249</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_d1166_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
              "      <td id=\"T_d1166_row6_col0\" class=\"data row6 col0\" >19658.4114</td>\n",
              "      <td id=\"T_d1166_row6_col1\" class=\"data row6 col1\" >3501388406.2637</td>\n",
              "      <td id=\"T_d1166_row6_col2\" class=\"data row6 col2\" >59172.5308</td>\n",
              "      <td id=\"T_d1166_row6_col3\" class=\"data row6 col3\" >0.9962</td>\n",
              "      <td id=\"T_d1166_row6_col4\" class=\"data row6 col4\" >0.0334</td>\n",
              "      <td id=\"T_d1166_row6_col5\" class=\"data row6 col5\" >0.0233</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_d1166_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
              "      <td id=\"T_d1166_row7_col0\" class=\"data row7 col0\" >11708.6946</td>\n",
              "      <td id=\"T_d1166_row7_col1\" class=\"data row7 col1\" >737219238.2640</td>\n",
              "      <td id=\"T_d1166_row7_col2\" class=\"data row7 col2\" >27151.7815</td>\n",
              "      <td id=\"T_d1166_row7_col3\" class=\"data row7 col3\" >0.9991</td>\n",
              "      <td id=\"T_d1166_row7_col4\" class=\"data row7 col4\" >0.0328</td>\n",
              "      <td id=\"T_d1166_row7_col5\" class=\"data row7 col5\" >0.0229</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_d1166_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
              "      <td id=\"T_d1166_row8_col0\" class=\"data row8 col0\" >13737.7644</td>\n",
              "      <td id=\"T_d1166_row8_col1\" class=\"data row8 col1\" >838007652.7644</td>\n",
              "      <td id=\"T_d1166_row8_col2\" class=\"data row8 col2\" >28948.3618</td>\n",
              "      <td id=\"T_d1166_row8_col3\" class=\"data row8 col3\" >0.9986</td>\n",
              "      <td id=\"T_d1166_row8_col4\" class=\"data row8 col4\" >0.0373</td>\n",
              "      <td id=\"T_d1166_row8_col5\" class=\"data row8 col5\" >0.0255</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_d1166_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
              "      <td id=\"T_d1166_row9_col0\" class=\"data row9 col0\" >10827.7426</td>\n",
              "      <td id=\"T_d1166_row9_col1\" class=\"data row9 col1\" >561238614.7949</td>\n",
              "      <td id=\"T_d1166_row9_col2\" class=\"data row9 col2\" >23690.4752</td>\n",
              "      <td id=\"T_d1166_row9_col3\" class=\"data row9 col3\" >0.9993</td>\n",
              "      <td id=\"T_d1166_row9_col4\" class=\"data row9 col4\" >0.0386</td>\n",
              "      <td id=\"T_d1166_row9_col5\" class=\"data row9 col5\" >0.0242</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_d1166_level0_row10\" class=\"row_heading level0 row10\" >Mean</th>\n",
              "      <td id=\"T_d1166_row10_col0\" class=\"data row10 col0\" >14637.7098</td>\n",
              "      <td id=\"T_d1166_row10_col1\" class=\"data row10 col1\" >1483251201.1237</td>\n",
              "      <td id=\"T_d1166_row10_col2\" class=\"data row10 col2\" >36861.1772</td>\n",
              "      <td id=\"T_d1166_row10_col3\" class=\"data row10 col3\" >0.9978</td>\n",
              "      <td id=\"T_d1166_row10_col4\" class=\"data row10 col4\" >0.0382</td>\n",
              "      <td id=\"T_d1166_row10_col5\" class=\"data row10 col5\" >0.0250</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_d1166_level0_row11\" class=\"row_heading level0 row11\" >Std</th>\n",
              "      <td id=\"T_d1166_row11_col0\" class=\"data row11 col0\" >2761.8296</td>\n",
              "      <td id=\"T_d1166_row11_col1\" class=\"data row11 col1\" >951705683.8257</td>\n",
              "      <td id=\"T_d1166_row11_col2\" class=\"data row11 col2\" >11158.1726</td>\n",
              "      <td id=\"T_d1166_row11_col3\" class=\"data row11 col3\" >0.0011</td>\n",
              "      <td id=\"T_d1166_row11_col4\" class=\"data row11 col4\" >0.0046</td>\n",
              "      <td id=\"T_d1166_row11_col5\" class=\"data row11 col5\" >0.0020</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n"
            ]
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              "Processing:   0%|          | 0/4 [00:00<?, ?it/s]"
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    {
      "cell_type": "code",
      "source": [
        "unseen_predictions = predict_model(model_1, data=test_1_data)\n",
        "unseen_predictions"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "id": "kas6TnAX0LMb",
        "outputId": "d44b3b62-3759-425a-c0be-f7374440ac3b"
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
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            "text/plain": [
              "<pandas.io.formats.style.Styler at 0x7fe7ff3d7c70>"
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              "<style type=\"text/css\">\n",
              "</style>\n",
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              "    <tr>\n",
              "      <th class=\"blank level0\" >&nbsp;</th>\n",
              "      <th id=\"T_ccbd3_level0_col0\" class=\"col_heading level0 col0\" >Model</th>\n",
              "      <th id=\"T_ccbd3_level0_col1\" class=\"col_heading level0 col1\" >MAE</th>\n",
              "      <th id=\"T_ccbd3_level0_col2\" class=\"col_heading level0 col2\" >MSE</th>\n",
              "      <th id=\"T_ccbd3_level0_col3\" class=\"col_heading level0 col3\" >RMSE</th>\n",
              "      <th id=\"T_ccbd3_level0_col4\" class=\"col_heading level0 col4\" >R2</th>\n",
              "      <th id=\"T_ccbd3_level0_col5\" class=\"col_heading level0 col5\" >RMSLE</th>\n",
              "      <th id=\"T_ccbd3_level0_col6\" class=\"col_heading level0 col6\" >MAPE</th>\n",
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              "  </thead>\n",
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              "      <th id=\"T_ccbd3_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
              "      <td id=\"T_ccbd3_row0_col0\" class=\"data row0 col0\" >Huber Regressor</td>\n",
              "      <td id=\"T_ccbd3_row0_col1\" class=\"data row0 col1\" >61062.4829</td>\n",
              "      <td id=\"T_ccbd3_row0_col2\" class=\"data row0 col2\" >8608225801.5655</td>\n",
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              "      <td id=\"T_ccbd3_row0_col4\" class=\"data row0 col4\" >0.9886</td>\n",
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            "text/plain": [
              "          Close  Future Price  prediction_label\n",
              "0    4160718.00    3621502.00      4.174798e+06\n",
              "1    3621502.00    3651423.00      3.633757e+06\n",
              "2    3651423.00    3655200.50      3.663779e+06\n",
              "3    3655200.50    3426544.50      3.667570e+06\n",
              "4    3426544.50    3404260.75      3.438140e+06\n",
              "..          ...           ...               ...\n",
              "805  2387096.25    2395233.00      2.395174e+06\n",
              "806  2395233.00    2406395.75      2.403338e+06\n",
              "807  2406395.75    2406234.75      2.414539e+06\n",
              "808  2406234.75    2411990.25      2.414377e+06\n",
              "809  2411990.25    2416823.25      2.420152e+06\n",
              "\n",
              "[810 rows x 3 columns]"
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              "                                                     [key], {});\n",
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              "\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",
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          },
          "metadata": {},
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(15,6))\n",
        "unseen_predictions[r'Future Price'].plot()\n",
        "unseen_predictions[r'prediction_label'].plot()\n",
        "unseen_predictions[r'Close'].plot()\n",
        "plt.ylabel(None)\n",
        "plt.xlabel(None)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 490
        },
        "id": "EbNfh179tY45",
        "outputId": "a7b8de3b-8f8f-45e0-a30d-69fad90387ec"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x600 with 1 Axes>"
            ],
            "image/png": 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eA9VUZAiSBAC4YHE9AOCoVlto+4tzrsKQJwAAEEPxGatyJSIiIiIiIpqvGJQSEU1CMJFBvR4rHPd4Rgal4Rl5ZkLPQstXlFoOpXB+cY0b5y+uw2/OeicAwH3u+Xhu7Rsx6LSXA5DDCaT1+Iz0iYiIiIiIiGi+YlBKRDQJwUQGtXqxorR7RFBqxEorSjsGtuLpXXchng5N+3nZnAk9Z8IxvEapUyu8JggC/vTJt+Hnt/47Tnt9N1Y//AQa/C4MaHZQKqUMJCID0342ERERERER0UIkV7oDRERzwWAig5p0sUqz111f+Dl7zNT7jR0PIqlHEXA14Kwlb5/w3vv6NiFnGljdfCEEQQAAJA170yjNsKfQWw615BqPpsCjKUCdvcFTk8eJHjVQeD3ZfQhoOmnyb5CIiIiIiIhogWNQSkQ0CcFkBv58UJp1ahhS3YXXjJhdaRpO6bjuV8/gHzqeRdPTO5H6VjswQVAaSw/hhX33AgBU2YXlDWcAsKfdA4CqD1eUOsa9T6PXgd4RQane3zv5N0dEREREREREnHpPRDSRpJ5FysjBk0oAAAyfG1GhGFxm4/bapeu2d+LFQ91o+O2LcO3sBtY9Me599w5E8c1HnkP9PS+j8Y7nse3wk4XXCkGpYW/mNHLq/ViaRgWlfZN+f0RERERERETEilIiogkNJuyqTnfCrig1vB5EJWfh9WzCrijtj6XhVQzIkRQAwAxHMZ7/98dXkd37Kj50+3MAgO46N3JnfwaSKCOp5wAAim5PvRcmqCgNOFRElGKVay4aGac1ERERERERER2LFaVERBMI5oNSZzIJAMj6vCUVpbm4HaAGkxk0mjEIpgUAsGLj7zy/fkcXWlLBwnHdA5vRHd4HoFhRquh2Rangco6+wQheh4KkMqJPsdjEb4yIiIiIiIiIChiUEhFNYDCRBgA4YsNBqR/pkaFkvtJ0MJFGU3ZEFWk8Me59V9R5ETCKbbSjIRzq3Ahg5BqldlAqudyjbzCCR5WRlWSYsmT3Kc6glIiIiIiIiGgqGJQSEU1gMJGBLJqQY/aU+pw/gIykwBLtHeqHg9JgIoMGY0RAGU+Oe9+saaI2Uxpopp/4s31pxoAkmJDT+aDU4x33Xh5Nse/pVAEAZnz8alYiIiIiIiIiKsWglIhoAkPJDDxqDnLUDkqtQC0gCMjlw0kzkci301GnF4NPIZ4quU/aSCCc7C8cDyYy8KdKg1L56VcBAH3xNHxaDnLYDlvVhqZx++jN98VwaCV9IiIiIiIiIqLJ4WZOREQTGExk0KIkIGbs6fBCTQOQArKaAjmlw8yvXRpMZlAzIigVExlYlglBEGFaJh7e+hPEUkFcvvYv0eBbjYSehTdVGmhKnf3ImVn0x9Kok1OQ8uujqk2t4/bRo9kf54ZmV5RaDEqJiIiIiIiIpoQVpUREExhMZHCaY7BwLNcvAQBktdLqzcFEGoFMccq7mMzAyOnImSbu37oH6b174Np0EE/u+nVh3VN3sjTQVAZjSOpR9MZSaDdChfPO1kXj9nG4olTP98lKjD/tn4iIiIiIiIhKsaKUiGgCB4IxvMnoKhxrzYuAzih01a7ezCXisCwLQ0kdvlQxKJWSOnQ9gd9v78K3H34Gv7r991CCcXR/+q042LQdAOA6pvJTjqQQD/ehN5ZC64ig1N2yZNw+DleUDvfJSqTGa05EREREREREx2BFKRHROHKmiQ0d/VjbeQAAYMkyXGeeBQBIqg4AgBmJIpI2kDMteI6ZSp8OB3HXqwex2AhCCdohauuPn0BX/wsAAC2/4ZOeX1sUAOJH9qM/lkZjOlw452iZXEVpRrP7hCSDUiIiIiIiIqKpYFBKRDSO17tD8KpxBHZ1AgCU009Ge1MdACDorQEAiP0h9MfsENR1zJR3PTwIv1NFqx4qOe/809MIOAwo+Q2ijrYU1yBNHDmA3lgKDelI4Zza1DxuP4eD0pRqB65CMjO1N0pERERERES0wDEoJSIaQzwdwnN7f4cNB7dgdU0Mzl3dAIDAmy5Fi88JSRTQ664FACgDUfTFggAAR7I0KM2EBtEXTaM5UxqU1t23CRcuikDKB6WHm4oVo5muw+iNpVCTjAIATKcKyeMZt7/DU+9Til1RKiTT03rfRERERERERAsV1yglIhrDxo6HcTi4DW4RuCARg5Q2AAA1b74csiSize9Cl6segL2uaO9gB05pjEOLHVNRGhlCT8yH+lSk5Lzz4ADeIh0o3PdITTNyigTJyCHd1YWkdTb8CTsoRV1gwv66FBmCUFwOQEhlYFkWBEE4kV8GIiIiIiIiogWDFaVERMf4l4e3oPvlp7Ds879F4NFtOLtvLwDAEgR4LnwjAGCR34UOraFwTarjXnz+jYchx0rXBjXCQ+iNplGXKA1KAWDNK68Vfu5X/EgE/ACAXHcvAMAbi9kv1tdM2GdRFOBWZSRle+q9mDKQNY1JvmMiIiIiIiIiYkUpEdEIKSOL7zy5HQ898xRce7rh2tmN6BtWAgCk1Ssg19ih5aIaNzY56gvXuV/vhGfzIShDpZs5pYaGkM42wB+3q0MjTXVI5GS0DvbB/+SOQrt+xYewPwDfwBCEvkGgBXBG7XVPpfp6TIZXU5DIT72X0gZ0IwVFUqf5K0FERERERES0sLCilIhohHgmCwDwHe4rnPO+ctA+d9HFhXOLA270eYoBZuvNf0LLLU+Nul9iaAgA4MlXh8a9PjzbdgYAQO2PFdr1ST4EvQEAgNwfhlPOQQ3boavc1DipvntUGXHJUTg2YqFxWhMRERERERHRSAxKiYhGSOp2UBpa2lI4J+RMAEDtxW8pnFtc40ZMdcFwjF+xaYTDACw4I3YoatTV4vn2M0a1Czv86MtvDiUPxrAkkIIcsoNStallVPuxeB0KYrKzcJyJDE3qOiIiIiIiIiJiUEpEVCJp5AAAOZc26jXvG99c+HlRjRsQBMRqAuPeLxuNwqmYUPKhp6u1Fa+1nISIq3QXe1dTIw7np/LLsTQu8vYUNnrSmlsn1XevpiAqFYNSnUEpERERERER0aQxKCUiGmG4olQxSjdCEha1Qm0pBpaLA24AQMgfGPM+Vn6zeSsWh181CtWhi1etgNPlxP0nXVLSvrXWh31ScYr9pYndhZ+dLYsn1Xe3KpcEpdnY6A2kiIiIiIiIiGhsDEqJiEZIGnZQKuulQan/TaXB5uIaOyiN1wbGvk/ABwAQEgm0WRGIWXv6vrd9Ma45YzHuP/mykvantARwoLa9cNz4YjEo9bavmFTfvZqCiDiiojQWntR1RERERERERMSglIioRFK3p94fG5QG3nx5ybHfoQAA9pp1hXPPrLkQGX8tXm4/BeEmexq9mEig3ShuquRqW4rPvHkt+rx12J8PRr2XXIZ3rm1Dj68B3fVNAADfs3sK12jtiybVd48mlwSl2Vh0UtcREREREREREYNSIqISwxWlUqYYlNZe80HUXfuhknaCYM+tjxjFzZz+5sbPonHLPvzTO7+AlGbvPi8n0mjNFNcKdbcvxemtNfjDDZcgfPOdaP5/X8WSm36Ey1c2Q5NF/Ln9TACAmF8r1XXhG0qm/I/HqykIS67CMYNSIiIiIiIiosmTK90BIqJqMrxGqZSvKJX++hqsuPmuMdt+8dKTcWcsiL/e/hicTU0IXPFOZHMCIAjFoDSZQkMqXLhGbbJDz/edthg4bTFwVXGDqEtXNuO5I2fiw689VjjX/HefmnTfvZqCpFKsKM0xKCUiIiIiIiKaNFaUEhGNYO96b0HMV5QKTtdx23733Wdjy02fwLkHunDa5h2QXC74HXaFaUK1r5OTGdQm7cDSkiXIdXXHvd+717bj9aaViKl22CnXN6Dmqqsn3XePJiMjKbBEu9o1F49P+loiIiIiIiKihW7aQem3v/1trF69eszXbr75ZqxduxannXZayf8GBwen3VEiotmQNrJQRAtiJl9Z6jp+UCoIApq8TsiBAESHXUHqUCSokohYvrJTTOqoT9m7z1s13sKU/bFcc8ZiNNZ48cS5VwIA2r76DYiaNum+ezQFEARk82FtLh6b9LVEREREREREC920pt7v2rUL69atG7fNVVddhe9+97vT6hQRUaUk9Rw0KQtxeAq+yz3le/idCkKyfZ2UzKAmlZ8CX+sf97pGrxMd/3I1cub7oOgZSO6pPduj2R/pWYcKJZmBlUhMue9EREREREREC9WUK0pN08Q3vvEN3HDDDTPQHSKiykoaWXgsvXA8naC00eNAn2qHooJpwXU0aP9cVzPhtYokwqHIUw5JAXuNUgAwHHYVqhlnUEpEREREREQ0WVOuKL377ruhaRre85734Ac/+MFx2+3Zswcf+tCHsHfvXrS0tOArX/kK3vSmN03pWZZlIZlMTrWLc04qlSr5L9F0cSyduEgyDa+ZLhybijblz6EGl4pupRiKaofzQWlt7Yx+pqnIAQAMzZ56nwz34ZHXf4Y3rvgQZFGZ0r04lqgcOI6oXDiWqFw4lqgcOI6oXDiWqFw4lsZnWda4y+CNNKWgdHBwEDfffDPuvPPOcds1Nzdj0aJF+MIXvoDGxkbcc889+OQnP4n169dj+fLlk36eYRjYtWvXVLo4px06dKjSXaB5gmNp+rr7B0uC0qFEEqkpfg45TB0HlNrC8fA0/rTDMaOfaQOD9l+KGdWuKJViKXQP7ceWzAvwSE3TuifHEpUDxxGVC8cSlQvHEpUDxxGVC8cSlQvH0vGpqjqpdlMKSr/zne/g6quvxsqVK9HV1XXcdtdeey2uvfbawvENN9yAhx56COvXr8dnP/vZST9PURSsXLlyKl2ck1KpFA4dOoSlS5fC6XRWujs0h3EsnThtVwIeszj1vmXJMrSsXTule6w6bOClXaPXIw0sXYYVU7zXVAj9UeDxDvRZHrQC8G46hFUf/RlqH7kSi5dP7bkcS1QOHEdULhxLVC4cS1QOHEdULhxLVC4cS+Pbv3//pNtOOijdsGEDtmzZggcffHBanWpra0N/f/+UrhEEAa5xdpyeb5xO54J6vzRzOJamTzcB94iKUldN3ZR/LRfVeBFxeGCKAkTTKt6rddGM/r6019lTCTqFAM7Kn1NCSVi7dsF16uXTuifHEpUDxxGVC8cSlQvHEpUDxxGVC8cSlQvH0tgmO+0emMJmTuvXr0cwGMRll12GCy64AFdffTUA4IILLsBDDz1U0vaWW27Bhg0bSs4dOHAAixYtmnTHiIgqIaln4TYzhWPZ7Z3yPRq9TliCiIyvdEMmZ1PrCfdvoud+511n4f9Ofwe2Na0onM/GYzP6XCIiIiIiIqL5YNJB6Ze//GU89thjWLduHdatW4dbb70VALBu3TpcfvnluOKKK7Bp0yYAQDgcxr/+67/i4MGDyGQyuO2223DkyBG8//3vn5l3QURUJikjC1euGJQqbt+U79HsdQAAEt7SkNXVvPjEOjcJX7r8VFx3zdvxuSs/XziXi0cBANmcgd7IQeTM7Iz3g4iIiIiIiGiumfTUe7/fD7+/uOZeNmv/Q7u5uRkA0NHRUdjN+Qtf+AIAe23ScDiMlStX4o477ii0JSKqVkk9VxqUeqYRlPrsNWGiXi/q0VM472hqO/EOToJHU5CSiwtV52J2RemGA/fjQP9mnL7oMpy95B2z0hciIiIiIiKiuWJKmzmN1N7ejj179hSOR/6saRpuvPFG3HjjjSfWOyKiWZY0snDmips5Ta+i1A5Kw+7SDZ2UhsYT69wkeVQZOUmGqUgQjRzMRBx3vXoQOPgU6p7fix1vTTAoJSIiIiIiIjrGtINSIqL5KKnn4MoWK0olt2fK96hzaZBEAUFnMSg1HSokt3ucq8rHrdkf7VmHCtVIIRaK4KO/fR5P/2kdnAcH4Nl8GNa7fwhBmPTqK0RERERERETzHv+VTEQ0QtLIwpEtVpSKDseU7yGKApo8DvQ7ikGpFZj6plDT5dUUAEDWoQEAUpEonLIJ58EBAIBny2HEM+FZ6w8RERERERHRXMCglIhohKSehZYPSk1NgSBO72Oy2edEr1pTOBbq/OO0Li9PPig1NHudUjGVRsChl7QJRbpmrT9EREREREREcwGDUiKiEZJGrlBRauUDx+lo8jrRVRKU1p5w3ybLo9pT7/V8UCokk1hkhkvaDO3cPGv9ISIiIiIiIpoLGJQSEeUZORNGzixUlFoOdYIrjq/Z68BRpRiUynV1J9y/yfLk1yjVVXvqvZhKY7E+VNImsf21WesPERERERER0VzAoJSIKC9lZAEAmpGfpn5CQakTg65A4bhm0eoT6dqUDE+9z6j5qffpNNrSwZI2mV27Z60/RERERERERHMBg1IiorykngMAqIWgVJv2vU5q9CGpODDkDgAAPKtPPdHuTdrw1Pt0vqJUSuloSpVWlGL/YZhmbtb6RERERERERFTt5Ep3gIioWiTzFaWKYdgnTiAo/dCZSxFNGfBd9lss7tyDug9eX44uTsrwrvdp1QEAkDIZ1MfDJW3UjgEk9DC8jtlbEoCIiIiIiIiomjEoJSLKS+rDQaldUSo4HdO+lypL+PSb1wBYA+DtZejd5LlUCQCQVOygV04bCMQipf3rDSMVCTIoJSIiIiIiIsrj1HsiWvASmQhyZhZJw56Kruj5itITCEorSRJFuFQJKdnuv5jS4YuUBqWCBSSPHJzwXolMGM/tuQdHQ3tnpK9ERERERERE1YJBKREtaH2RQ/j9xu/i4a0/QSJjB6RyPig9kYrSSvOoChJKMSh1DMUAAMHGhkKbzNDAhPfZ3vMMDgxswYb9989MR4mIiIiIiIiqBINSIlqw9g9G8fDr98D/1A7Et76GWHoQn77gCLSsPfVedDor3MPp82gyEpIdlAoWoHaHAQCdDa2FNkaw/7jXW5aFr7/Yha7HH8FJH/kpHL96FCk9NqN9JiIiIiIiIqokBqVEtGC94YePIHf/BrR//2Gs/NSvEI4+jbOaoxDzlaWCY+5WlHo1BTG52H8xv/7q/tr2wjl9aPC41+/qj2JPpBdt616CMhhH/b2vYDDWNXMdJiIiIiIiIqowBqVEtGANJXUsffjlwrFrw3NYc83NUHvs9TxFl6tSXTthHlVGXBpdEbvHVwxKs0NDx73+kT29OLt2CO7XOwEAUjyDwV0by99RIiIiIiIioirBoJSIFqScaQIAMiOqRltueRJSUi8cW5oy6/0qF7emICaNrog9oDbCcNvnc6HQca9/bG8PLh3YXqhEBYDoxpcwEDuCbE4/7nVEREREREREcxWDUiJakKJpe3p91qUVzqn90ZI2Ll/9rPapnDyajMgYFaXd3npk3Pb5XDiMzqHdCCX6StoMJTPYcnQAK3btLjmffG0zHnr9FrzIjZ2IiIiIiIhoHmJQSkQLUiQflFqO41eN+kTfbHWn7DyqjKhYGpSaDhVhhxdpl30+NdiDJ3fegUe3/QxZ0yi0e2x3N5xyFp5XOkqu9750AA13bUDX5idhWebMvwkiIiIiIiKiWcSglIgWpEjanj6u6pkxXxdUFYEr3jWbXSorr6YgdExQmmttBAQBSae99qoSjKP5lifhfOhlxFJBJDIR5MwstvWE0GDGCxW2MY8XAKAdDaHxzhfQ+sPHEU0HZ/cNEREREREREc0wudIdICKqhEjKrqDUUulRrzXcdBPaPvAXUOrn9tT72DFT78X2VgBAwukGALi3dsK9tROWKGDvB57BrvhrqPcswlDyHDTmYoXrXl16Ci7d/lLh2L29CwODB+Ff1DDzb4SIiIiIiIholrCilIgWpOGKUiU1uqK09oxz53RICgAeTUFK1krOOZYtBwDEHK6S84Jp4dArD6Pmka2Ib9mEcCqNumy88PrLi05FThBKrglufn6Gek5ERERERERUGawoJaIFKZI2IMCCnBwdlDqXraxAj8rLo8rISaUf8Z4Vq6D0iohqnlHtW3/4GByH7en0sZuuQYtRrCg9/62X4u66Bhjbt+KG1x4CACRe3QhcNYNvgIiIiIiIiGiWsaKUiBakaMqAJpsQE6VBqeVQITc2VqhX5ePWRn8P5luxFrUuFWHVPeq14ZAUAPRUFDVGonD8T+95My7+1Mfx0wuuwVB9HQAgt3U3cmZ2BnpOREREREREVBkMSoloQYqkdbglA1LaKH2hvQnCMdPM5yKPqow6516xBrUuDUPq6IrSkax4GD69GJQqNbU4f7EdkO5tXgoAcO7pRjjZV74OExEREREREVUYg1IiWpAiaQP1ucSo8/KSJRXoTfl5HaODUsfS5ah1qggqvvEvTkThT9u/NqYqQ3Q60eZ3ocXnxLZ6e51TrXMI8cGjZe83ERERERERUaUwKCWiBSmc0lFrjg5Ka9ecOfudmQGaNPrjXfJ4UOPSMCB5x71WTsfgydi/NpbX3vhJEASc015XCEoBILV9exl7TERERERERFRZDEqJaEGKpHXUjFFR6rvokgr0pvwM0xzzfK1LRb88flDqy6XgSicBAJavuJ5pm9+FLq2u+IzgQBl6SkRERERERFQdGJQS0YIUSRsIZJOF45q77sHap19AzXvfV7lOldFlK5uxusGHW9/2N4AsY9H3bgIA1Lo0BJXx1yj1ZVNwJVP2gbfYNuBUEFGKwakRCZW/40REREREREQVMnpbZCKiBSCaMrBsxM7uLaeeBveKkyrYo/LSZAlb//k9AN4DMXszRE0DAHg1BdExdr0fyZNNw5Gyg1LBX1zPNOBUkVQdsARAsIBcmEEpERERERERzR+sKCWiBSmS1uEfUVGqBerGaT03yZIIWRILISkAxHUDOan0O7KkopUce7IpqAk7KBVHBKV+pwpLEJF1OQAAuUhkprpORERERERENOsYlBLRghRJG/DoqcKx6JtgJ/h5QhKEUeduP/s9OFjTWjh2Z9NQhoPSgL9wPuBQAQB6ISiNzmRXiYiIiIiIiGYVg9IqkMiEEczuRyabmrgxEZVFJK3Do9sVpaZDgagoFe7R7PjMxWvhcyh44pwrAEHA8tvvwnn/+k1cf923kNPsXwNfLgUpngEAyIHawrUBZz4oddpBqRVlUEpERERERETzB9corQKbOx9Bj7EPe/qcuND33kp3h2jey5km4pks3Jk0AMByOyvco9nT5neh8+sfgCpdCzEegxwIwP3KAQBA1qFCyhgI6AlISR0AIAdqCtcGnHaQmnI6UQPAisVnvf9EREREREREM4UVpdXg6Zew9Ev3IP3665XuCdGCEE0bAABnJr9GqcdVwd7MPo+mQJUlyIEAAMDrsL8zMxz2WqX+SLjQVq4prt06XFGactgVpYgWN8MiIiIiIiIimusYlFYBz/89A/fWTih/eKrSXSGal/b3vYp7N34fnUO7AdjrkwKAM21XlMK7sILSY3nzU+4NzQ5CPUPhwmtqbUPh5+GgNKnZv15CvLgZFhEREREREdFcx6C0CgiWZf8wHNoQUdkcHorj+V13I7ttJ57cdhsAIJiI4J/ecLiw673g9VSyixU3HJTqml1Rqg7ECq+ptY2Fn/35zZzihaA0BWv484uIiIiIiIhojuMapdUgv4mMZRgV7gjR/NIbTWHNd9fhgY4nUfvIVgx86EKYb7awo3sLznL0Qwnaa2wKPm+Fe1pZXs3+qyCTD0qHf10AQKspBqUORYJDlhBT7TVdpXgGWdOAIqmz2FsiIiIiIiKimcGgtBoo+d8GI1vZfhDNM/e+fhh6zkTtI1sBAA13v4SPvXUDajqewHXf+ynErAkAcI6omlyIPPmK0nQ+KB1JrakvOQ44VUQVNwBATGag60koTgalRERERERENPdx6n01GA5KdQalROXky+/SPtIfdxzCGft2FEJSAAi0rJzNblWd4an3KdUx6jWppqbkOOBUEM4HpYIFpMP9M99BIiIiIiIiolnAoLQayPmgNMup90TlpEqjP+IUIYHm7u7CsefCN6D+w385m92qOsNT71NKaVBqOlSIamm1aMCpIpQPSgEgMzQw8x0kIiIiIiIimgWcel8FhPwapTByle0I0TyT1HMASjcbOtMThKvDroJ0XPterL39vgr0rLqosgRVEpFUSqfem7W+UW39ThVDEoNSIiIiIiIimn9YUVoN8kGpwDVKicoqqWchCaVB6UW5DqjdIQCA78zzK9GtquTVFCSOrShtaRrVLuBQEZQ8hWM9NDjjfSMiIiIiIiKaDQxKq4Cg5itKs6woJSqnhJ6FQyr9c3VWxw4MZ6c1Z7+hAr2qTh5NRlx2lpyzWltHtQs4VQyOqCjVw8EZ7xsRERERERHRbGBQWg2Gp95nWVFKVE5JIwuvlSk55335YOFn12lnzHaXqpZXUxA7JigVW9pHtQs4FUSUYkVpNhya8b4RERERERERzQYGpVVA4NR7ohmR0LPwWumSc84D+V3aWxshBwKz36kqNWZQ2rZkVLuAU0VSdcAS7GMGpURERERERDRfMCitAsLwrtLczImorJJ6Dj4zPeZrztNZTTqSR5MRFUuDUql12ah2fqcKSxCRddnrmeYikVnpHxEREREREdFMY1BaBQoVpVyjlKisEnoWbjMz5mt1F79tlntT3bwOBRGpNCgVWtpGtQs47C929HxQakajM985IiIiIiIiolnAoLQayMNT7xmUEpVTQs/Ce5yK0tqrrp7l3lQ3pyIhfExFqeD1jmoXcOaDUmc+KI3EZr5zRERERERERLOAQWkVEPNT71lRSlReSSMLd250Ral8+snQliyd/Q5VsUjKQEhwTdgu4LS/2Enng1LEEzPZLSIiIiIiIqJZw6C0ChTWKM3mYFpmZTtDNI8k9SzcudEVpfXveG8FelPdBhNpxJSJg1KvZgeluqbZJxKpmewWERERERER0ayZdlD67W9/G6tXrz7u67/+9a/xjne8A2effTY+/OEPY/v27dN91LwnqHbwIGZN5HLc+Z6oXJJ6Fq4xKkrrP/SRCvSmumVNCylFKxyLfv+Y7ZyKBAAwlPwXPGl9xvtGRERERERENBumFZTu2rUL69atO+7rTz31FG6++WZ8//vfx4svvojLLrsMn/zkJ5FMJqfd0flMGBFOmMbYG88Q0dQl9Cxc2eKfqcA3v41V9/0RztVrKtir6vT995wDiCIOLF4LAFh022/GbOdSZQBAZnht5QyDUiIiIiIiIpof5KleYJomvvGNb+CGG27AD37wgzHb3HPPPbj66qtxxhlnAAA+9rGP4de//jWefvppvOtd75r0syzLWhDhqikW8+pYeAg5S6pgb2guS6VSJf9d6OIZA84RQWnrxz8BQVEWxOfKVJ3X4sXuL74TdV9+F8RwENmaWuDQoVFjyTIMAIAuFytK+etJx8PPJCoXjiUqF44lKgeOIyoXjiUqF46l8VmWBUEQJtV2ykHp3XffDU3T8J73vOe4QemOHTvwzne+s3AsiiLWrl2Lbdu2TSkoNQwDu3btmmoX55xEOoPhvab3794JxddY0f7Q3Hfo0KFKd6EqxNJ6oaLUkkTs3r+/wj2qfpHCD1EAo8dS1rQAjKwoXRif03Ri+JlE5cKxROXCsUTlwHFE5cKxROXCsXR86vD+QBOYUlA6ODiIm2++GXfeeee47cLhMPzHrG/n9/sRCoWm8jgoioKVK1dO6Zq5aN8r9RievLq0rRW+9lUV7Q/NXalUCocOHcLSpUvhdDonvmCey+R2Q8vaf7osh4q1a9dWuEdzx3hjSRZ3I5OvKBUyBn9d6bj4mUTlwrFE5cKxROXAcUTlwrFE5cKxNL79UyiamlJQ+p3vfAdXX301Vq5cia6urnHbWpY1lVuPSRAEuFwT78I816lOVyEolUVrQbxnmllOp3PBjyPTtJDOmnDk1/21HMqC/zWZjrHGklORkc4HpWLGgMOpQRS4ZAgdHz+TqFw4lqhcOJaoHDiOqFw4lqhcOJbGNtlp98AUgtINGzZgy5YtePDBBydsW1NTg3A4XHIuHA5j1SpWSo5F1EZs5pRJV7AnRPNH0sgCALSsvaam5dTGa05T4FIlpKR8RameRTZrQFUYlBIREREREdHcNuld79evX49gMIjLLrsMF1xwAa6++moAwAUXXICHHnqopO2pp56KHTt2FI5zuRx27txZ2NyJSglqMcDJMSglKoukng9K9fxmTgxKy8apSEgPB6UWYKRiFe4RERERERER0YmbdFD65S9/GY899hjWrVuHdevW4dZbbwUArFu3DpdffjmuuOIKbNq0CQDw4Q9/GA888ABee+01pFIp/OQnP4Gqqrj00ktn5E3MdeLIoFTPjNOSiCYrkQ9KVSO/sIXDUcHezC8uRS5UlAKAkWRQSkRERERERHPfpKfe+/3+kg2aslk7hGhubgYAdHR0IJlMAgAuvvhifP7zn8dnP/tZBINBnHbaabj11lvhYFAxJmlEUGrqrCglKofhoFTR7an3gpOfP+XiVCQkpeLnVjYRrWBviIiIiIiIiMpjSps5jdTe3o49e/YUjkf+DADXX389rr/++un3bAER1WKAw4pSovJIGjkAgKLnK0oZlJaNSz22ojRewd4QERERERERlcekp97TzBk59d7MMCglKofhilI5H5QKLmcluzOvOGQJSXFERWmcQSkRERERERHNfQxKq4CocOo9UbkNb+YkZ+yp96LLVcnuzCtORUJiZFDKzZyIiIiIiIhoHmBQWgUkrTgl2OTUe6KySBwTlApOVpSWi0uVER8RlOaSiQr2hoiIiIiIiKg8GJRWgZFrlHLqPVF5DAelos6K0nJzKhISwoiKUq5RSkRERERERPMAg9IqUFpRqlewJ0TzR0rPQRIsiGk7KJVc7gr3aP5wKTISYnEzp1wyWcHeEBEREREREZUHg9IqIKnFKcGmwaCUqBwSehaqmIM4vEapm0FpuTgVCVGh+AUPp94TERERERHRfMCgtAqM3PXe4hqlRGWRNLLwChkIln0suzyV7dA84lJlpKURm9ClWFFKREREREREcx+D0irAqfc015hmDqFELyzLqnRXjiuhZ+E104Vjyc2gtFycioSMpBSOcwxKiYiIiIiIaB5gUFoNZLnwI4NSmgtePvhHrNvyA7ze+WSlu3JcCT0Lb64YlMpubwV7M784FQkQBORU+7PLTKUq3CMiIiIiIiKiE8egtAoIggBLtn8rOPWeqpllWfjGo6/hyDP3o+2/HsauZ/+v0l06rqSehdcq/nmSWVFaNk4lH5Dmg1KrgkFpNmcgpccr9nwiIiIiIiKaP+SJm9BssGQJQtaExc2cqIrdu/UI/uvp1/DM/94FAHDs7QU+UuFOHUdCz8JvFQM81ROoXGfmGaciAQBymgIlni6pKDVyOiLJftR52iAIwoz2w7IsPL7jlxiIHsHbTv0btAZWzujziIiIiIiIaH5jUFolLFkCYMDSjUp3hei4Htl1FO3eYijmODJUwd6ML5bJoiFXXDtT89dUsDfziytfSZpV7HVKrbS9xMFgrAtP774TiUwEq5svxBtWvq/szzZyOjoGXkM4GcQjeyScsvNJNOw4ip3/sASt5zAoJSIiIiIiouljUFolrHyFFitKqZrtH4jitFxPpbsxKeFUBquzicKxo66lgr2ZX4YrSrNqPihNpXFwsBdP77gFdX96He2bOnDoIwNo8C7CyqZzyvbcWDqIh17/CdJ6DLCARjOHRd/6I0Q9iwFZQurH18Opci1aIiIiIiIimh4GpVXCrigFLIMVpVS99g3GcFnsSOHY9Lsq2JvxDSV1BEasXanWNVSwN/OLSymtKNWTKXzm3vvwmUcfQs0TOwAAzr092NRQh+VvOxOiKJ3wMw8MxvDz5x7CRRuew6L7N0EeiCH0zjMg6lkAgP/JHTjQtwWnLrr4hJ9FRERERERECxM3c6oSVn7ne069p2plWRb642ksC3YVzmUbApXr0ARCSR2+jF1RakkiRA83cyqXQkVpPihNRhNYtf/1QkgKAGp/DA3fX4f0iKreWHoIj2+/DXt7N075mR/97fMwn34GLbc8CbUnAjFrom79luLz+qLoevL+6b4lIiIiIiIiIgalVUNhRSlVt8OhBAALzX29xZOmWbH+jMc0LYRSOrxpO6Qzva4Z31hoIRleo1TPB6VCRsfSIXtJBksAnj/jAgCA7+UDSA31AwBCyQweev2XGNr4HDZs/78pP3PfQAytof5x28gPPjvl+xIRERERERENY1BaJYYrSsGKUqpS23pCCDiycB8ZEVZV6XiNZgyYlgV3Kr+Zk89d2Q7NM8MVpUY+KJV0HQ2RIAAgXePHKyvOKLTNBPthWRYu/d/HoT3yLFb8451Y8rX7pvS8bM5EMJmBPxUDAOQUGRlFLbwez1cLuzYegGVZ039jREREREREtKAxKK0WMjdzouq2vSeMVi0BrStUOCdkqjMoDSUzAABXOh+U+rnBTzkNB6V6PqyUdQM1IXtcJBrqMCQXg2k9PIiDwTh29w+h/b8eAQC4t3UhZ2Yn/bzBhP376U3Ya85GmuvxyIo3AABygoBXz7I3jJJiaWTN6hyTREREREREVP24mVOVKG7mNPnwgGg2be8N46R4NwSzWLEnVOl4HUraXzg4kikAgBDwVbI7887wZk4ZOV9RahhwD4YBAIn6RgyJI4LSoUFsPDyAJYF0yT10IwWnNrkAuz9uX+uO20Gp0lCL/e/7e2gvePGTbD08DrvSVNSz0NNxKO7a6b85IiIiIiIiWrBYUVot8sEDuEYpVamjkSRaU8GSc4JerUGpXYGoJeygVPQzKC2n4YrSjFysKFX7IwCAVGMrgnJx4yw9PISXDw/iTKG75B5GLDzp5w0Hpc6oHZT62ppwz+evwel/eADPv/G9iMvOQttMZHDqb4iIiIiIiIgIDEqrRmGN0iqt0CPqjabQkIqUnBOMHEwrV6EeHV8opQOwoCTsgE2qqalsh+YZRRIhiQL04aB0KAEpbofTmabFCEmuQttsJISXDg/gnKH9JffIRIcm/by+WAqSYEKN2EspyPUNhde8moJoSVA6+fsSERERERERjcSgtFoMV5RmGZRSdeqNpVF3TFAqGjlkc9VXBT2U1KFJJuSYXVEq13AqdjkJggCnIiEt2UGpmDULr+VaViChFoPSzNAQXu8OYVV3R8k99Gh40s8biKfhVbOQ8kGp0thceM2jyYhKxaDUiIRGXU9EREREREQ0GQxKqwUrSqmKJTIGYhkDgXh01GvZVKICPRpfKJmBRzYg5dcqZVBafi5FRlpWR51XFq1ATpSQddqvxYJDsCwTtfuPlrQzonag+dqRJ/Dc3t+Nu7lTfzyNJiEO0bCrl7URQanXoSAyooLVmEIAS0RERERERDQSg9Iqwan3VM368mtEeuOxUa9VZVCa0tGYixeO1dqGcVrTdDgVCSmpNCi1BAHa4sUAAMOpAQAyoTDcigHngb6Stno0hJc6DmLb1nXofO1J7Dj6/HGf1R9Po80IF5/d3Fb42aspCIsjKkpjpVXPRERERERERJPFXe+rhcKglKpXb9Sewu6Kzo2gdCiZQUOuWP2q1TVWsDfzkx2UaiXnrIYAAj57Iyfd5YRzKAbEYmhEAmKm9LNt894j+MYrD+OOX94BKZrCwG8XA4suHfNZA/EkmvVw8dnNiwo/ezUFIXHEmqisKCUiIiIiIqJpYkVptZAYlFL16o2lIcCCI2JXaSYDxV3Nc+lkpbp1XENJHfXZYkWpo755nNY0HS5VRkpUSs6pixcjkJ9yn3E4AABSIoHm3OglG17ZdRArwkchR1IQLED9yR/GfE4yE8UVy/+M9zUfLj67JCiVERmxRmk2yopSIiIiIiIimh5WlFaLfEWpwM2cqAr1xVLwajnIIbt6NFJTC1fYDiKrsaI0nNKxyhgZlDZVsDfzUzZnIQxHybmaMy6A4rDD05TTDi+lRAqNxujw0mOk4XXphWMhNnoc7ewN46Gtj2DNIxvg2XiwcF4dsUapz6EgpRSD0twYy0MQERERERERTQaD0mqh5Cuz8puVEFWT3lgKjUIcUsre4X4wUIsWHAEA5NKpSnZtTEPJDAIjglKtlkFpuamyiK6Mp+Rc65duRH++ojSp2SGqnEihXh8dXrqyadSOqPpFbHRl8j/e9wpW73sOl/78mcI506VBdBaDUa+mwBRF5DQFUsZALsaglIiIiIiIiKaHU++rRX4zJ4FBKc2AeDqM9IjgcKp6Yym0Z0PFY09d4edcqjqn3gcyxfer1HLX+3L7i7OXYU/jcqT8tVDa2nHqxq1Q29rhd9hBaUKz1w2Vk2nUjRGUerIZBPRiFamQSMGyzJI2zxzow/LuwyXnzJrScNaj2V8yZfPPNVlRSkRERERERNPEitJqIecrSnMMSunE6GYSO3r+jDVt50ORHNh48EEcGNgCTXbhmvO+DOWYnconozeaRqteDEqPuIu7yGercI3SUCoDb8bul+nWIMj8qCu3z1y8Fn9zwUq4broBACCq9rjyaDJEQUBCtas+5UQatRk7vLREARmfC45wAm4jA1+mGJSKsTSSegxuzV/yHFW2So4Nb+kGUt58UGo4NGiRBMxE9S0FQURERERERHMD04NqoRQrSk0zh2CiGx6tBk7VM8GFREVGzkSvsR3Rwwewt/9lpHP1cB3agrZ7XoHR4EX01L9Fnadtyvfti6WwKhMuHB9yFneRN6ssKE0bOST1HLxpOzCzvO4K92j+Gq7mHEkQBPgdCqKqXVEqJnXUZuzNnHSPC1mnBoQTUDJp+ORiqCnH04gGu+ButYPSTNb+0sifLK0QdfaUHns1+7NTd9gBqhVnUEpERERERETTw6C0WuTXKBWyOdz98r9Dz6Xh1gK45twvQRC4QgJNTM/mcMGP/4TvRddh7Y+fwMBH3gij3oO2/3ms0Cb2j7tQd9bUglLLMvGG9h24uLu7cK7bVV/4OZdOn3jnyyiUygBAISiFn182zLaAU0VUyQelRg61UbsaOe1xIafaf+1omTQ8klRyXXT/DrS0ngIAGEzYv4+eZOmSEd6zzy89zm8eVQxKqyu4JyIiIiIiormDQWm1yE+9FzNZ+H7zDNyvHUby1HYYZ30WquyY4GIiYHtvGPsG42j5+ZMQLKDxzheQ9TlL2qS6OoCzJn/PlJHF+m0bcUFsJ/xP7QQAZN0ORPIhGADkqqyidCBeGrBJgZpKdmdBCjhVhJViJa+vfwgAkHS7Mfy9j6rrgCiUXJc4uAe42P55MGEH8K4RFaLuc8/Hon/7dsk1w1PvM2p+Sn6iusYjERERERERzR0MSqvEoAEszv/c9KvnAQDubV3IfC8ENdBSuY7RnBFN2zvSC2ZxTUc5WrojfbrnKEzLhAABglAaUo3lm4++ju49j+LT3/otxPxGY3rAj4RQnHJtZqqrovRgMAaHnIOrLwwAUJYsHv8CKju/Q0FYKobpWm8YABB3uaGaWQCAms5AOmbzpsyhjsLPw4G3Y7hC9F2X4OR7nhz1LF8+KE1r+aA0WV3jkYiIiIiIiOYOzumuEgcT5qhzgmkhHQpWoDc0F4VSOgAg3VpaQTnkCxR+TnR34HevfBuPbf85LKt0k5yxPLizCxd1byuEpACQqFuKlFDcEMpMp8a6tGIODMbQ7ExB6YsAAJwrTqpwjxYev1PFkFxc8kCO2GMk6vQUAk01k4HjmOpP40hn4eeBeBoCLKgxu6JUrqvHWIan3qdUu/JeYFBKRERERERE08SgtAp0DMUxmB17t3s9MjjLvaG5KpTUIQoW5ERpUHT3mrdA99hT8KNH9sFz15NIPPIY4pnQWLcp6I4kcTAYxvL9+wvnAu+/Bh1/8SkY0oiK0ipbo3R/MIbV2X6IWfvLB++qkyvco4XH71AwJI7eRCvs9BYCTTmtQz0m1LSO9hZ+DiYycKu5QsiqNDSM+SxPfs3TpDIclGZO/A0QERERERHRgsSgtArctrEDQwPF435fbeFnPTJ+mEU0LJzS4ZSzkOLFoCjX3IY/rrkYSZ9d3dfwu1fQdNtzWPxv65AK9qI7vA+x9NCY93t6fy9W1CTg3XoEAOC67mqsuvNuZE45CzlRginZHx9mprqCqQODMaxMFQM33+rTKtibhSngVDEojd5EK6R5kVLsilI5rUOOlwalYk+wUOk8kEijXkhCzNhT9ZX6pjGfJUsinIqERD4oFZMZmObYXzwRERERERERjYdBaRXYPRDFdveiwvGvL3hv4WcjGq5Aj2guCqd01CEJIZevpPy3GyE9+iJCTh/iXu+o9gd3PY3Ht/8SD2z+HxwN7R31+tP7e3ERDkMZtDdFanjLuwAADtneqdxS8v+tsorSA8EYFsf6CsfO5asq2JuFye8o7no/UtjhK1R+SikdUj4otfLL5crBGPSsXUE6EM+gNRsuXOtoOP5azV5NQUKxq6bFlA49W11jkoiIiIiIiOYGbuZUBf797afhZ2IGnjO8WBeVsSkqFV7LsqKUJimU0lGfK+4QrjW1wF0XAABE3aOD0v1/fhorHngB6RUNeOofM1ix5EKc3n4ZPA57jdOn9/fi6wM7Cu0Dl74FAKDK+UpSRYaUNqpqM6dMNocjoSRaQv0AALPWB2mMkJhmVsCpICOrMCURYq64/nLMWYNEfskHKVGsRA62NKO+uxdyJIVEbABa7RIMJNJozEYLbZxNrcd9nldTEJftoFSwAD06BEf96Kn/tPD0RQ9hf98mrGw8B03+ZZXuDhERERERVTlWlFaBNY0+/N1ZLVjyuc8j9Ka3ITRibT9WlNJkhZIZ1OXihWO1tgGNXrt6b8jlH9W+/bFX4OgYQOCJnVj0lXtw6JVH8KcdtwEA9vRHkNKH0L7DXp9UWLYIals7AMCRryTNKfb3LJauz9ybmqJDQ3GYloXaoL0JmrD4+OEazZx6jwMQBERcpSF13F2PhKSNat9ZX6wWjXbaYy6YyKBRjxTOu5oXjbpumFeTEZMdheNMdOzlJGjhsCwTL+x7EI+89hMceeVRPLntdmSy1bXxHBERERERVR8GpVXG5yhOIQWAbCwyTmuiolBKR222GJQ6ahuhyRJqnCoGHb5R7R0Hiwvjuncexaq/ux2OH/4eppnDA9s6cUHtIDyvHgIA1L6zuBzE8NR7szD1vnrWKN0/GIMimvD02UGZsnRJhXu0ML375HYEnCr+uOKikvNJdz1isnNU+701xRA02dkBwN71vi4TK5zXxpt671AQHXFfru28sGVzOfzihdtx+NX1WPrFu7HyE3eg+Wv/h22dz1S6a0REREREVOUYlFYZn0NBVpKRy+/kbEajE1xBZIukDNQaI4LSOnvzmyavA/2OwKj2Utoo/JyV7NCz9sHXkNLjuG/bEbx94DWIur2RTsP7P1hoq+Wn3ufkfEVplWzm9MK+e9ET/D3W1seg9thfMDhWcH3SSgg4Vfy/y0/BnWe+q+S8VVuPmDx67dIdvsWFn5NdHRiIdSKYTKEmbX/+WaIAubZ21HXDvJqCiDQiKI0yKF2ocqaJf3nod/DtfAbLP/MbuHceBQB4N3bgyAO/QlKPTXAHIiIiIiJayBiUVhmPpgAAcg7V/m+M/6ijyQmlMvDrxTVK1doGAECT14lutea41x0542T8+h3XAQBEPYuOwwexq7cPK1/Pr0/aUAvP+RcW2g9XlGYVe6yeyNT7rZ3PYGPHw7Asc8K2x5MysrjjlW3oeno9ajY8jy+ctLMQ8NauPXva96UT8+k3rYEcqMHPz7kKAKAtWw7V40FUGl1RutvbBku0d3Tq2fMyHnr9f/HXZ+1BXcYOSk2fG4J4/L+uvJqC8DhLlphWDkauOgJ9mlmfuX8j0js3YsmN90JKln42NfzyGfSF9leoZ0RERERENBdwM6cq43PY4ZPh1KBGkzDjiQmuoIUing5jKNGNRbVrIAijQ6NQSodPTwKwdxGXAwEAdkXpTvn4QWkqUIdurVit98rm19CgpeF9+QAAwH3lO0pCqsIapfnAdDoVpTnTxEsd+9Cx/V4IehZtgVVorZle9eePnt2NnzzxLO659XcQM1kkTmkrvOY787xp3ZNOnEuV8fo/vwcvXXsBlnX+FTxr1sLzQje6RceotkHVh7TfA2cohsBj2+B7bg/8V50Ndy5fUV8z/oZcXoeMkFisVDVGTL03chk8sPm/YVom3nn638PrOH5lKs1tRs7Ez1/ah58cfBVS2oAlS/ifD3wJem8PvvznO+A4NIjYc88C15xV6a4SEREREVGVYlBaZXz5ilLdocENwIrHx7+AFow/7fglIqkBnLfs3Til7U0lr1mWhVBShzdjB+uW21EIN5u8TjypBo5730xNPbqUYpAa7jyM5RmhUI3lu+ytJe214YrS/NR7ZCZXUWpkM9jT+xLaa9fiZxv68dhLD+PbP7wNYiaLwUcvResbpheU/uLlfWhIRyBm7CpS9w57qq100go416yd1j2pPNr8LnzgjKXAGUsBAN5NAwhLpVPvLVFAQnUi5vfBGYrBccTeiMu1+6FCG7H2+EE/YFeUhsRipWo6Yq+/u6c/glueewZve/pxiGkdL/xDLa446+9P/I1RVQolM8iZFjwJ+3Mwt6QZK6+5Gj94eCO+/Oc7AACZgwcq2EMiIiIiIqp2nHpfZYYrSjMOu+rKYkXpgndgMIaTvn0/HD/4HVb95c+w9anbR7VJZ3PQcybcabui1PQWw6hmrwNhpw+WMPb9s/UtOKoECsdisA/tmeKu4Z5lJ5W0P3bqPXQDE7EsC5uPPI1Xd6/H+ld/gKf2deKs0H5ISR1CzkTihecnvMfxnNNeB481uqq14drrp31PmhleTcaQUBqUml4XIAiI+PzHvS6waPW49613a4ipbliCPcgzPXZY/o1HX0f3qy+j6Y7n0HD3y8j+8ndIZLhB3nw1lP9yx5WyPweFgBdtfjeSqhOGUwMA6D3dJdf0RQ8hGD86ux0lIiIiIqKqxaC0yvjya5Omtfz01Hiygr2havD9p7ejMxxF/f2vQh2IYcm//AGWZZW0CQ0HBOmUfcJbXK+x0eNETpSQcrsxFqmpHSFnMaRSw4NoTheDUmfr4pL2w1Pvs9LkKkoty8JltzyO/b+/HSe/74douelBeJUDWBEqhhOZPXvHvcd4cpYFjzlGUHrNh6d9T5oZdS4NUcWNnFBM7Z1tSwEAQXdgVPusrEBbtgLNH//UuPddVutFTpKRqLPHcfZIJwDg968fxvJIMRhr/NVz2Hnk2RN8F1SthpL254AjYf+9Kfr9aA/YwXyixgcAyPX0FtoH4914ZOvP8MfXfoy9va/Mcm+JiIiIiKgacep9lfFq9m9JWrWrX5BIVbA3VA2Seg4eoRgEKsE4IqkBBFyNhXOhlB1WOoeDUp+n8NrKent9xwPuZpwWHz3t1NG6DNnBNHSPE2o8BWdkCI2aHS5YogC1samkvSbZ368Yw1PvJ6goPRJK4LmD/fjGn+yq0cATO3Hl5ZvQECwGFtaBw7AsC4JwnLLXcST1LDzHbNTjPu98OFaddJwrqFJuOH8lNnYG8cgVf43z97yEZcva0fbZz0N+uBd9ruLaoZZTw3k9EQjy5P6KWl5nj/eh2jp4BsOwunqRzelYWiOjZX+w0E6wgPD9fwC+9J7yvjGqCsOfg2oyDQAQa/xo99ufZRFfAIHuAaB3APv7XsWzB3vxwuvb8Vc/+RUsTcYrN0ahvcmFJfWnVqz/RERERERUeVMOSnfv3o3vfOc72L59OzRNw/nnn4+vfvWraGhoKGl3880345ZbboF8zD90n376adTX159Yr+ex4an3SdWuKBUS6Up2h6pAi8+JGrO0srivaxsCJ72lcBzOBwRa0g5KBV9x85s3L2/Ef733HPjOuwkP3nEn4moOH3rqgcLrgSXLgK27kPR5oMZTcMeiEA07/DRrfaPCquGKUkOe3NT77b1huJUstGjxPSy/9zmIqeJ16uEBpPQYXJpv3HuNJWXk0DQiKK392MfR/vkvT/k+NPMW17jxx49dDnzs8pLz2QfvRJ8rUDgWVy6ZdEgKAMvr7PHe56/HYhyA1B3E7zd+D19+cxJ12wdL2soPvQDzn02IY2yIRnPbUFKHAAtK3P4clAI1WBSwK+kHfTVYAkDoD+HF1++CbJq48sg+OA/0AwCW/9NdOHjHKiy5jEEpEREREdFCNqV/Keq6jr/5m7/B+eefjw0bNuDBBx9EMBjEN7/5zTHbX3XVVdi2bVvJ/xiSjs+jDgel9sYkYjI9apo1LSwJPQu/WVpZPPjsn0qOC5VU+Qpk0V8MHAVBwOcuORnXfOjdeP69f4s9NYsKr1migKYl9nHCa1fleWIx+GL5dRwbRu8QPrxGqSFNrqJ0R28Yi9wJyKHieruezYfh2lWcEq32RTEU7Bj3PseT1LNwjghKmz73RWiLl0zrXlQ5+43ihk2BNWdO6dpalwqvpqDLY39hp/RF4LvtMSz5n4cR6CsNSrWOfsRHLC1B80comYFTzkGK2V8wKrV1qHWpcMgS+l32+HIcGcLKv/0FVn3sNtQ/taNwrRKMA7/4Q0X6TURERERE1WNKQWkqlcLnPvc5fOITn4CqqqitrcXb3vY27Nu3b6b6t+CIogCvpiCeD0qFpI6cla1wr6iSYhkD/lxpUBp94Vk8uu1WpPQ4gOE1Si0o+Qpk0R8Y8171Hgd65OJ6pGatF00BOyCNuO1w1RmNwxOJ2vdpHP3FxvCu98MVpYI+/vjc3hvG6fpRCDlz3Hah7a+O+/rxJI0sXLniOqmqe+pVqVRZX7rsFGxvWIGsZI+p1i98ZUrXC4KA5XUeHHDZy0SIRg6Nv30JNU/sgHdjaQCvDCUQ7Npdno5TVRlK6qgV0xCNHABAqa2HIAhoD7hw1FVXaKeEkpDDSbi3d5VcL+49DNPKzWqfiYiIiIioukxp6r3f78e1115bOD548CDuv/9+XHnllWO237NnDz70oQ9h7969aGlpwVe+8hW86U1vmvTzLMtCMjn/NzNKpVIl//VqMuKKPfVeShuIhINwOhj+LFThRHpUUOp7djf6Vj6M3devxOqmC9EficMhm5DidmWl4PWO+Wen1iGhWw4UT9TVwCvZAWbIZY8xLZyAkl8rV2xsGHWf4QrnjFQMSsf7c7rt6BCujhwpHL+6aC3O6dw1ql1022tIvmnqf94TGaOkolQXJWQXwOfGTDv2c2kmffHNq3BOqw8tHzodHtEClq+Y8mf/Yr8Te7Tm476+f9kKrOyw1+gd3LwBTW1nn1CfaXJmcxz1RxNozEYLx4I3gGQyiVavA4e1489mGaipQ0MoCLVrCIPhHni00ZX0VHmzOZZofuNYonLgOKJy4ViicuFYGt9U9kSZ1mZOR48exTve8Q5ks1lcd911+MxnPjOqTXNzMxYtWoQvfOELaGxsxD333INPfvKTWL9+PZYvXz6p5xiGgV27Rgcq89WhQ4cAACpyiMmuwvk9r78KzXf8AIDmt75QBGuPCUrVvigWfedBDCgtMN/ix/7OAQSENMR8dWdSlMf8s2MlYwg5i6G7EfDh4L698CoiBvM730vhBMR81WjK5RnzPqoowBgRlB7vz2nWtLC7P4qlQXuafdah4idnX41fdH6r2AeHCiWtI7Vz17T+vEdTGTiydkWpJQC7Dx6c1qZQNLbhz6WZtgxA0ONGEACmMQ58VhpHPE3Hff3VpacWgtLQ5k3YtXLh/N1SDWZjHB3qHUBDLlY4HsxkEdu1C25Lxz5l7KDUEgQ8fPol+Ks/3wc5lsaOTc/CV7t6xvtK0zdbn0k0/3EsUTlwHFG5cCxRuXAsHZ+qqpNqN62gtK2tDdu2bcPhw4fx9a9/HV/60pdw0003lbS59tprS6pPb7jhBjz00ENYv349PvvZz07qOYqiYOXKldPp4pySSqVw6NAhLF26FE6nE/XP9iAmOQuvt9X60LBqbQV7SBX1bA882eKmXk+uPA+XHt4MychBPdCBtZ9eC6VDR4NZDAhq2pdg+drRY+bkmApj2wDSmgOOTBp1y0/G0rVr0eLvRJ8WAACIWRPI2lWm9avXYskY93Eoe5HJr1EqGFmsWbNmzHBy70AMurkTjb32DvfG0kXY3rSipM1QWxOaDnRCOzqAtWM8ayKGtbcYlGoKTj755Cnfg0Y79nOp2p0TVXHXriBMTYaYGb0cxC7fIkTrAvAFw1COdE1rrNHUzeY4MjcG0ZAtfg4uO/VM+NeuxcldOTy7p2fMa3rbV+ClmpPwV/ljfyyINW/k2KhGc+0ziaoXxxKVA8cRlQvHEpULx9L49u/fP+m20wpKAXtNuKVLl+Jzn/scPvShD+GrX/0qamvHn67W1taG/v7+KT3D5XJN3HCecDqdcLlc8Ds1REZUlArpxIL6dVjoLMtCJpuEQ7F3a04YJjzZYkXpd9/0UZyc7UXLoU7gUBdcLhdihokmM15o425sGXPMXLyqDcBr2NW6Bmd1vIbGS98Bl8uFFr8LvWpgVHvf0lVj3sehyNCHK0qNHFSHAkUa/e3MgcgAHLIJd+eAfb/T1uLaM5fi5hv+Df/4xC/w3WWX4rr4Jvs+seSUx7llWUgauWJQ6lD5Z6XMhj+Xqt3qllpAEMYMSQFgv9aInsYW+IJhiAe6oDlUSOK0/wqkKZqNcRTJZLFaL34OBtqXwulyYVm9Hwm19P8sbj3/rTgvehT73/txHOwtjoPMwb1zYrwvZHPlM4mqH8cSlQPHEZULxxKVC8fS2KYy63RKmzlt2LAB73jHO2CaxU1ZRNG+haIoJW1vueUWbNiwoeTcgQMHsGjRItD4fA4VIak4sI0wd2heSF4+uB53v/zv2Ne7EQAQSxtwG8WK0qTiRHetPcVY6uyHnk1jIJFBoxEptHE0to157zPbarHnK1fhA39+Eqe8/Brqrv9LAECT14FOZfQXHZ72sZfJcCgSMvlgVDAt5PT0mO329EcRkDJQe8IAAG3NGtz90Yvx6x/fiLaXt+OBky+DruTvk8qMeY/xGDkTOdOCZhSDUlqYltV6xn19wNeI/bXtAADt0AAGYp2z0S2aRaGkjoCeKByrtQ0AgPaACzjm/xj95W9vx+mbtsH39isRcXqRcdtBqjGFb5qJiIiIiGj+mVJQeuqppyIej+M///M/kUqlMDQ0hJtvvhnnnnsuvF4vrrjiCmzaZFeHhcNh/Ou//isOHjyITCaD2267DUeOHMH73//+GXkj84nPoSAkFoNSPRauXGdo1hg5E3/3uw3oWncXFv3bA9j05M8A2Lveu/JBqanK8Lgc6PC3AADU7jAi8V4MxNNo0CcOSgFgZb0PdbV+uE45tfCtSpPXiR4lMKqtq23JmPfQJBGZEdV42WR8zHb98TRashEIVv66tuIXJS7Vvn44KEVGP/byCSXzu1ur+YpSMChdsJbVetDkdeDHF9hLvrzauqbk9cXtTdjsXAoAkJI6Dr/2xGx3kWbYUFKHLx+UWpII0WOH5/Vux6i2arP9Gbq60V6zOdhoh6pmBwN0IiIiIqKFbEpBqdfrxW233Ybt27fjwgsvxLve9S54vV7893//NwCgo6OjsFPxF77wBVx88cW44YYbcN555+HBBx/EHXfcgeZmbko0EZ9DwdCIitJsNFy5ztCs+cPWw/jly/ux5Ov3wffifiz90j0wTRNxPVsISi2nhtWNPuxy2UGomDUR3rsNA/E06tLFtfmU+sYpPfu0lgBCTh+GRmz0BAByQ8OY7R2KhIxYrCI30okx2w3G02jKjQhw64t//p2KvWFUZngKf8aYUp8BIJnfvEo1hoNSbcr3oPlBlSW88I9X4G/+9z9h/vQu3PjWT5W8fuGSBmxpKK55PfTQOpiWeextaI4yTQuhlA5v/rPI9LkKXwSd3V6L5XUe/PiSjwIAmr/wpcJ1qxvsz7zhKn35SD+M7NSr24mIiIiIaH6Y8gJtq1evxp133jnma3v27Cn8rGkabrzxRtx4443T790C5dVkxEYGpZFw5TpDs+ZoOAnAKhzLkRQGE/3ImRachv0Pd8ul4aQGP15wFStGo7u3YiCxDIF0FABgOlSIU1y8+S/PWY7Hdnfjm91/hx899F8AAGXxYgji2N+lOGQJIbNYpTUwsA81batGtQsmdTQaxQDXOaLSVZVEiIKAzPDU+/R0KkqHg9J8yMqgdEFbVufFsjovrNXX4WplA37u/A4+/eLdaPv8l3DdmUvw85f24VDrIizt7oTrqdfRFzmIlsD83zBwIYhmDJiWBU8qX1Hqcxde02QJ27/0Xhi5d8MR/0/IdXWF1xo8DgScKo74m3EuAKUnjGQqBL+XX+oSERERES1E3MmiCvk0FSlFgyUAggUM9u6FaeUgClKlu0YzyKFI0KTSCreuju32a5n8GqBuJ1Y3+nCPvwmWIECwLCT37EJSXwxffvp7zu/GVKmyhP/7yzfj2y01+M0py/CJrQ+jOb9+6Vi8moLDRrH6tHPbM2hb+0aosgOKVAwrg4k01uojgtKmYlAqCAJcqoS0bLcXMgYsy4QgTL7QPanbU+8VPR+yOkdPsaWFRxAE/OKDFwEfvAjAPwMALjFNNHud+OPSN+Afuzvh2t2Dw68/iZZLGJTOB0NJ+8skV8qe1SL4vSWva7IETZYAra7kvCAIWFnvxWGfXYUvZk0kOjvgP5lBKRERERHRQjSlqfc0O2pcKixBRNJhV5UK2/Zhb35jH5q/QikdPqm0qjK88TkAgKbnp4K6nDipwQdDUhCrrwEAGAc6AACehB2UmoHSgGCyJFHE195+On749U/g5HvXofbqa47b9m2rW7CtaSV01a4Gza5/Ar/f+B2s3/JDmFau0C6YzKAmUwxK1brSqfxORUI6P/VezGSRzU1t+v1wRamSrygVHAxKaWySKOLaM5fg8WUXwMrv62M8+GRlO0VlM5S0PzsdyRQAQAj4xmteosHjQJ8aKByn+4+WtW9ERERERDR3MCitQu8+uR1uVcYjyy8EAPif3YM9f/pNhXtFMy2U1NGQi5Wcy27dAgBQ8xsdCfmKUgDoz69DKh7uBmDBFc9POZ1CQDBdf3HOcmRUJ55deQ4AoPaRrVj6xbvhuvk+xNPhQrvBRAaBdD7AdagQjwkyXYqMVL4CVTAtGJnklPoxvEaprOeDUlaU0jjec3I7Bjy1CC5uBQAIuw5WuEdULsMVpVrC/gwRA4FJX1vv1tAj+QvH6YHesvaNiIiIiIjmDgalVajZ58Q/X3YKfnHOVUhrdojku/Vhbjwyz4VTOupzpbvHyzv2wqXkoGbsEEBwu7Gq3gdBALpq7M1H1K4h+LQsHDE7ILACfsy0Nr8Lb1nVjPuXv6lwzr29C/V/2IRY134AQDQdQ70zBl9+cxVrjCUBXKqMpFycqp9NRKfUj+Fd76V8UDrVtVlpYWnx2eMj7rOrrq1ofLzmNIeEkjqaPRmoCXuZEqkmMOlr690a+pXiF0z6YH+5u0dERERERHMEg9Iq9flL1sLw1+L5C+0gyr2tC3o2XeFe0UwKpXTUZkuDG8feHnzqvCNQ0nZQKnrccCgSFgXcOOix19BTQgks1WJQovlqTH9gVvr7V+etwObWNejxlq75Fz+0D5Zl4fHtv8DXLjuI1uFd78dYEsCpSEgJSuHYSMRGtRnPcEWplBmuKGVQSsdX67JD+YQzv6wJg9J5oz/Wh69ctB9S1J5672lcPOlr690aopqncGwEB8rePyIiIiIimhu4mVOVcmsKGj0O9PnqAQBCzoQeC8NR66pwz2imhFM6VmQTJeeUoQTe9NyfC0Gp4LH/Mb+yzovdztZCu3NynZCjw2vz1cxKfz945lLsG4jia32fxF9v+SPeeGQrAGBg/158dPsf8M2tv0PzhgMQ8uuICjWjK11diozkiM2fjKlWlOrHVJS6+OeDjq/GZa+pG9Ps6mYxmoRlWRAEoZLdohP03ME+7DvyBK66435IafuzoHb1WZO+vt7tQE6UoHucUOMpZAcHZ6qrRERERERU5RiUVjG/Q0FULlbIZcIDQG3rOFfQXBZO6QjoxaA07PAgkI6j6Zd/hmDZ56R8ULqi3osHXcVdmc8YOgAhZy/NIARKKzxniigK+Po7zoAkCvhywxI894u/AwD07D0A1Neg7g+bIJhWsX3N6ADXqUhISMV1RbOJqVX4pYwsBFgQMwxKaWKaLMGlSogOB6XxNLKmAUVSK9yzuSlrGugJH0CTbylUuTLrA6eMLD5+9xP4/tE/wffSAQCA7/K3ovb9x9+M7lj1bvvLmozHBTWeQm5oaEb6SkRERERE1Y9T76uY36kiJBfXdcyEWOUyn4VSOnwjgtJPvPdGZJyOQkgKAJLHnr6+qt6LPk89TMn+I7y881ChjThLQemwNr8bhqQgPbwGaX8vzo0fLAlJAUCqqx11rVORkBCLIVU2ObWgNKlnoYgmxEx+Cr6TQSmNr9apIazaXzhISR2Z1NSqmOcyy7JgWdbEDSd5ry2HHseTO27HC/t+X5Z7Tsedmw7i7LrDqF+/GQDgOO88rPr9OoiaNsGVRfVuO+RNuu3PDysUKX9HiYiIiIhoTmBQWsV8DgUhaURQGg5WsDc000JJHT7dXmfU1BQcrmlB59KlJW1kj73hyIp6L0xRRLTBDh/9e48W2og1DbPT4bxFATtciOU3kXIMDOLkocOj2sm1owNclyojIY7czGmKa5QaObgFvRDKSq7RG0YRjVTr0jCkFNejTA72VLA3sydnZrH+tR/hgc3/c8LrXedME5f+7+PovPvnWPOBmxH/+a9g5PQy9XTyTNPCD5/difcc2gAlZH92LvrK16cUkgLFitKEO//5McSglIiIiIhooWJQWsX8DhVhqVghZ0Q4HXC+yuZMxDIG3Jn8DvFeJ0RBwJGa5pJ2ss8OSlfW25Wlg7X2GrZaZ3FsSIHG2ehyQXs+KA3nN5FyhSJY3NM1qp1SPzrAdSkyYiOC0tw0Kkq9ZjH0ERmU0gRqXCqCI4LS9AIISvf0R3Djg3+EdNsDUH/xAHZ2v3BC99veG8bzHf1o/97DkJI6mm99Bt1De8vU28l7Yl8PeiJDaHv4VQCAuGoZ/G+7Ysr3afDYFaVRp/25KkS4yRcRERER0ULFoLSK+RwKBsVi8GOEQxXsDc2kSH4DEnfK3pDJ8rqxrNaDvd7SNWllbwAAsLzO/gd99xihqFAzu1Pv2/12UDrosdcgdQSj8B8aHT5p9U2jzrlUCQlhxBqlqcSoNuNJGll4zEzhWHZ7xmlNZAelg7K3cKwvgB3O//buDTjw7Etouv05NN75IqLr153Q/bojKQBWYV1kAOh+8ZET7OXUvdgxgOWOKJwH+gEAgeuvn9bGXAGnAlEQEHHanx9iJAHTzJW1r0RERERENDcwKK1ifoeCyIg1SrPRcOU6QzMqlLLDPmfaDkrh8+CkRh92ONtL2inDVZuqjHa/Cwc9pRWnpqZAcDoxm9yaghqnih6XHdCq/VE4Do1eT9fR0DzqnFOREROKFaVmcopBqZ4rDUpdDEppfLVODf1SMSjNDM3/oHTD4QEsivUXjq2nXj6h+3WGE/CoOVhiMZSMPfZ42dY/naztvWGckSwu81F77pundR9JFFHrUhHS7HEhR1JI6awqJSIiIiJaiBiUVjG/Q0VSccDK/1s0F+G6afNVOGVXlDryFaWi34s1jT7s97WVtFM8gcLPK+u92OVoKb1RjX9G+3k87QEXupzFStZjN3ICAEdD66hzLkVCcsSu97lEsvBz19BuPL3rNwgn+0ddNyxpZOGxilPvJVaU0gRqXSoGRlSUGkNzf5M8y7Lw3J57cO/G7yOU6B2zTW12xJcQR/uQNY1pP68rnESLFC/5c+7YsBPhZN+07zkd23tCWB0pLvPhOfWMad+r3q0hqOan3psWUsH5vyQDERERERGNxqC0ivmcCiAIyDrtirtcbOHszrzQ9EY68MnzOuHKV5SKPi9WN/oRcnhL2qn+msLPKxu82OcuDR89K9bOfGfH0B5w45BaP24bV1Pb6HOqDF2SC18GmGk7KP3Tnm4889yPEbrvXjz72h3HvWdSz8JjFjeRUdy+qXeeFpQal4q4OqJSf2hub5IXTev41mPr0f/A7+C+5X68umvsafV1qeIXberhQQSjo9cRnqyuSBLLjNJKXOe+PoQO75r2PacqZWSxPxjD4sFuAIBZ64PSMP2N7Bo8DvSrxc+Pgc6dJ7zpFRERERERzT0MSquYT1MAAIbLDkrNKIPS+eiFjn70H/4d3nLwFSgD9u+x6A9gTaMPOGa9Pc1XrNr8+4tWw79yJV4+921Ity1B3V/9DVpv+tGs9n1Yu981Kijd2bK85FipG72eqlORAEGAqdpj3Uym8EJHP6689Qks+eq9WPSdB6Hcev9xn5s0cnDlimGG7PYety0RANS4NJiiCMNtVzJnw+HKdugEfXHdK6jpehSLvrUe9b/fCOPm20uqSmP59Y8D8WJQKsfT6Ns6/Q2dusIJLE6PDphjr22a9j2naldfBLAsNPTaVaziqmUndL86t4Y+uViRv3Xr/Xh46y2wLHOcq4iIiIiIaL5hUFrF/E4VAGA47X/Qm7FYJbtDM+DQUByX/u/jWPTTR7Do2w9CjtgVpVIggDWN9j/aTbH4x3RkRemZbbXY/v+uwqefeQRv3nMAy//3VmjLV8zuG8gLOFX0u2tLzv34nGtw1Fes8JJcrlHXORUZAJDT7P+aqSR++uJe+B3Zwjqn9fduPO5zU3oWrlxxjVLVw4pSGl9t/osn3ZX/XJ3DQenr3UPoCm3E2rueKEyDr3nodWzb8WChTU/M/kzxHvP3R/Tl6QelneEkWpOjg9L04YPTvudUbe8No85lwHnYrmx1rD2xavp6t4YeqRiULv/8/8Hx33cjqfPvXSIiIiKihYRBaRXzO+wqu4zD/ge9FZvaRjdU/TYcGoBpmQg8XTplVQnUot5tBzovLD69cF7yVmcQeHprDbKSjIjDntIsNzbhzR9+P350zZcgtC1C8+e+OOZ1LlUCAOSGK0pTacQyBhaLk1uP164oLU69lzn1niZQm/8CKu22Nz0zw3Nz7WfLsvDVBzfg+sRGeDd2FM5LaQOJO35b2FipJ2oHpe5IaeBnvTa9afKWZaErkkBjYggAkNUUZLz2lyDGkc5p3XM6dvSEcZI8BGXI/nvRe+rZJ3S/ercDg2rp50fdA5sR7jlwQvclIiIiIqK5Ra50B+j4hoPStJbf7CbOoHS+SehZOOTRUzuV2joIgoCfXnshHmhQcckTP0HLW98KUVEq0MuJXXP6Yjx/8CR0N3wby/e/grZv/gf+u30RcNV5AD533OsKFaX5oNRKpZDIZLEqM/aGNMdK6lk4syMqShmU0gRqXHZQmnLmK5wjc7Ni8KFdR1GjbMOS258CAERcXgw2NGDF4YPwPbIFKSMOl+pFbzQFUbCghUt3cRe6B8a67YRCKR1JPYe6aBgAkKjxIeNwoDGWRK5r9jZA2t4bxpmJQ4XjmjPOP6H71bk0JFTnqPPRfTvQtvTEQlgiIiIiIpo7GJRWMb8j/w96x3BQmqpgb2gm9MZSqJNH/76qAXst0o9fuAofv3AV8OUPznbXpkSVJdxyzQUALgDwD5O+zqXYFaXZfABspVOIpHWcdcxO96aZgyhKo65PGlk4R1SUii73qDZEIw1PvU8MB6XR+Ditq5ORM/Gl9a/im+HnC0tU3H3xh6GYEaw4fBDqQAzhgQ642k5HbywFn2pADh3zRVtyehsVdYbt+/jySxZE/QHEHS404iiEngFYlgXhmLWVZ8K2njDeFTlSOHaffNoJ3S/gVEetCQ0AiYN7Tui+REREREQ0t3DqfRXz5StKk/kqF4FB6bzTE01hcTY06rxaO/4O8vOFS7W/qzGGK0rTGRwOJdAeL612S+ljh1lJPQdHPii1ZLFqK26petTmK0rjTjtUF6Jzr1J/3fZOHA6F0PqHlwAA4uoV6Lz8vdjlbC20iex6HYD9GdNiRSEaOQCAKdphoJCc3t8nXeEkAAuukL3x3JCvBr0++/NK6Q0jk01O675T0RNN4mgkiSVH7an+1qJmyH7/BFeN7/2nLcIZrTX48QXXosdT3DRP75i9dVeJiIiIiKjyGJRWMa+mQBCAuGZXPomJdGHdOZofemMptOtDo877atsr0JvZ58xXlBrDFaWpNAYTGTSGSzeKSQXHntKbNLJwGPbUe1NTZ7CnNF94NQWSKCCq2kGpGEvBtHLjXmPkdCQy1bOW6e7+CBqVJLSeMADA86FrsKqpBnvdbYU28b07AeS/jDGKnzHRRjsEFFLFJSumojOchEfNQR20lyyIBBpwxGVv2iaHk4iGuqd136nY1BlEwGHAd9B+lnrm6RNcMbEal4bNX3g33veD7+H9f/FfiDTZv06Zjg48t+ceHOjfcsLPICIiIiKi6segtIqJogCvpiCu2FPvxUQGOdOocK+onHqjKbRkSitK5cYmONeeUqEezS5Xfo1SQ7FDTjMf3gSGSsPjZF/XqGtN00LKyBWn3jtYTUoTEwQBNU4VEc0OSqVYGunM8affm5aJP275EX6/8bsIxo/OVjfH1RlOYHlusHDsXroKJzX40O+pQTZfpZ3Zvw+A/WVMq178jBmos0NNIalP64u3rnACdVYCUsL+s6o3NOOgs7HwevTgzqm/oSna1BnEalcIWqf9OeE/9w1lu/eKOi8AYKg2X1Xa1YuB++7GxqdvhZGdXrhMRERERERzB4PSKud3KIgr+YpSPYt0am5uPEJj64mm0JAKF45P23kAp23eAcnlqlynZtHw1Ht9eMp8OgNRsOAeKA2PM/2jq9QSeta+h5Ffa9HpmLmO0rxS69LQI9hTtQXTQmfn5jHbbe0O4ZKb74X7P/8Py//x1+g+sHE2u3lcneEklqaLy1N4l63BqgYfLEFEpNGeBp/rOAzA/jKmKR0utO2psUNNMaUjZ2Wn/Oz+eBp16WjhWG5owj61qXCcOLh3yvecqo2dQVwQOwAhn/PWXnBx2e69rNYDAOjx24Gy5/VOLPrWH7HoK3eje2jm3xsREREREVUWg9Iq53eoiCjF0EwPTW+nYqo+lmWhJ5pCXdKe0mt6HHAsXgI5EKhsx2bR8NR7Xc5Pm0/rqHXoUPujJe3SA72jrg0m7eoudzK/xmTAO3MdpXml1efEHhTXAT78zH1jtrv9lf2wBjpQ+9DrcO7rQ+LBR2eri+PqDCXQliwuT+FavBwnNfgAAH21+YrRw70wLRM90RTq81/GmJKIfn8tAEA0cjDSU9/IKp3NwWkWN1DzBXw46mkovn5oZtf0tCwLm44EcfJAh30sCvCcdW7Z7u91KGjwaOj0NpacV/tj6H7+kbI9h4iIiIiIqhOD0irncygIy8WdvNPhwXFa01wSSunQcyYCcTsoteoDle1QBQzvep/JT70XMzqWW0MQM6WVbsZA36hrB/NTf50Je/MYoSYwgz2l+eSaM5Zgc+taJPKV2+IfnkQwPrpqef9gDKtTxfPGoUOz1cXjsiwLR8IJNMftoNRSZcgNDVhS44YiiTjibwYAqEeHEIoPIpjMoDllV2jrfg9iI754y0RHr488kUzWhBPFJWACAT8yioa0z/57yjjSOe33NhmHhuIIJjNo78o/Z/kiSG73+BdN0Yo6L/Y7m0edjz/xJ64TTkREREQ0zzEorXI+h4KI5Cwc6yEGpfNFb9TeddoTs5dTEOprK9mdinDm1yjN5CtKhUwWSxKjQ1FjcPS4D+aDUkfcDkpFBqU0SdefvQyS04GHT3ojAMC3YT8O7n5mVLuDwRiWxYvj0ey0Q9OcmUV3eD86Bl7H0BgB60yKpA3EM1nUxsJ2nxprIQgCZEnEijoP9nlaAABSIoM9uzbBq2bRELFD1ZgvgNiIv08ykdCo+08kkx2xLjCAuhp7CYN4XcA+0T36z285HOx/DZ3Bndh8dAiqZMLbYVeZK6eXfz3nJTUe7NFGB6XqS7sQTfHvYCIiIiKi+UyudAdofH6Hiv2Sp3CsT+MftlSdevJBqStiT38VGxvGaz4viaIATRaRkew1SsWMgfrU6N3Fc8HgqHODiTQAC0rMDkrl4c1XiCbgd6r44JlLcW//Zbj2tT9ByJnI3P8wcO71hTY508TBYBytkf7COfHoAA70b8GmjoeRykQhpnRYLgfef+4X4HPWj/WosusM20tN+ENhAIDQWpwivqrBh+2OtmLbV+/BB1evhOfn9nqlh5ecUhKUGrGp/32SNnIIjNhUMOD3AxhEwuuxFzMIH386f2/kILpD+3Bq+yVQ5cmtKZwzTbxw4GUc2X4noCjocX4YLY4U1F77c8I1AxvfpbO5UVPvAcC5rxfdHa/Cf8oVZX8mERERERFVB1aUVjm/U0FQKk6VzIanPlWSqlNPLAVJMKHlgwWlqWmCK+YnlyIjJQ9PvTfgzyYLr8UbAgAAMxhC2kjgyZ2/xs6jzwMAhpIZOOUc5Ji9mZNcx6CUJu8DZyzB4ZoWhNrsQMzaU7q25tFICnrORH2wWEGo9Ibx8tM/Re2378Haq36AtR+4GStv+Bl6OrfOWr+PhBIQYME9ZAeFcmtL4bVTmgPo8LXCFAQAQOv/PoYr922AlLaDzT2nX14yQ8GIhqf8fD1nQrOKQanHa68NnFLzwWcyNeoay7KwqWsIz75yO/Y/eif29rw86ef90/0bsfWZX2Plx2/Hik/chljfbpyU7YNg2lPgPavKH5T+9fkrkFIcOFLbCgBYt8beLEqwgNRzL5T9eUREREREVD0YlFY5n6YgJBbXX0uF+sdpTXNJbzSFgGpADtkVYmpLa4V7VBkuVUZa1gDYG8wEjGJFWqTWXo4gNxTCb178FeJ3/hbbHv05sjkdg4kM6sQURN1ez1SpH10BRnQ87X77C6io3546joHSL6H2D0YhwIJvsHheShlY+fHbEHhyJ0QjBwBQ+6IIPfnY7HQa9o73Xi0HdcDe8ExdtLjw2qfeuBr+5kb8+ox3AgAchwbR9sPH7RcDXoROPgtRcWRF6ejq7YmkjRwcuWJQ6vPZm0ilNDsoFRKjg9LN/Ulc9rOn0PTl27Hsi3cj8cCDk3pWTzSJ+7Zuw6m3PQIpbUCOpODbtQmrkj2FNoHVp0/5PUzkXWvbsf5vL8PaBx7G7htvwvfe/FcwZXs95VzX7C61QEREREREs4tBaZWrcamIq8WK0nh3RwV7Q+XUG0uhzYwUKqMcze0V7lFlOBUJKUktHNcm7QAo61AR9djVauZQFE0P3IfWm5/A4n/5A0KxbgQTGTRni0GPVr8wK3Jpelp8dmAY9thBqRAMl7x+IBhHwJGF1l16fvjP6/rVby6cy2ybvYrSrnACrWIUUtJeJ9S5aGnhtTa/C3/+h3fgT1fegNvOfk/Jdd4rroTH5URYLP59YkSnHpRmsjloI6bee/NBaVIZDkozo67ZG0pDk0w4Dg4AAMydeyf1rF+8tB9vb+yGa8fRwrma7g4sGbFurGP5yim/h4mIooB3ndyOk88+FcZb3gVTFJF12V/m5Kbwa9Y5tAt/2nE7BmIzu8EVERERERGVD4PSKndqSw1yooTBZrtaLvfaDphmrsK9onLoiabQrher1Zxti8dpPX+5FBnJEUGpL25vbpV1aYg67fV51WgSzT9/BgAgx9IY2rUZg4kMGrPRwnWO+tGbrxAdT51LgywKGHTZQak0lICRK4Z8BwZjWCqGIMfTo67NBXz4ziU3oHP5UgCAuesAOga2onNo94z3+0g4gRWZ4swC99JVJa8vqfXgmU+/A8+9+69xy/kfKJxvvu4v4VFlhEdMvc9Np6L0mKBUdXngVmUkFPu+YiqDnJktuSZumPAJmUIVrhmJYiJGzsStG/biLTteLITTANDQcxTNoXzgWu8v+473x/I77fWTdacdBJv5zfcmcs+WQ3jhqduQuu23eOT5m5DSj792KxERERERVQ8GpVXujUsbIAjAjjb7H8OOHZ0Ixo9OcBXNBf2xNJr0cOHY1bJAg1JVQlLSCsfemB2i6E4Hgg67Wk3tLw1Wots3YyiZQYNRDC2cjQtz6QKaHlEU0OR1YsARAADI0RSS8eKmYQeCMazJ9Ix5rfbmiwBRxMH6Rfbx/j688PzP8OcXbpnxXdG7wkks1YvP8C5bM6pNq9+FZz/9Dpz1zW/Cdds9WPbzO+B/2xXwOhTE5RFBaXzq4V0ma0IbMfVedrrhcyiIq/Z9pZQBXU+WXJMwcqgzE4VjKzpxUPry4UEMxBNYtGlXyflAVz8aQvb7Fxa1jHVpWQUc9pc4GVc+KI1O/Gv2Ykc/Pn7P02i+6fdo+dnTWPIv9+KFXf83o/0kIiIiIqLyYFBa5WpcGk5tDuD52rUAACWURO82biYxFxxbVXWswUQGdfqIoK9p4U69T4rFoNQZsYOIrNOBXfrYGzSld+4c/evHoJSmqMXnRLdWUziOHS1u6HRgMIYVibGD0pZ3XIWVdV68HlgKAFBCCaz+y59hxSdux9GDr85onzuG4liWKk49Hzn1fqRal4bPXLwWp1z3AdR/+CMQBAEeVUZWkmHK9l/9ufjEgeWx0tkc1BEVpaKmwacpiI3YxT4TLV3vNW6YqMkVg1JEJg4btxwNYpE3CeeBvpLzWmcQ7qN2oC0vWzrl/k+V35kPSh359xebuO/PHOjDSm8M7m1dAADXnl7oP7oDsTQ3YyQiIiIiqnYMSueANy5rxMaG1YXj0PNPVbA3NBn90cP4v5f+Dc/s/u1x2wwk0qhNF4MKpa5+NrpWdZyKjAiKIYsasoMIwevG822nQ5fkUdfk9h1EMJFBTaYYlCp1DTPfWZpXmr1OdKm1heNk12EAgGlaOBCMYVmsFwCQU0rHYO3brsSZbbV41bO85LwcTWHoqUdnrL+xtIEjoQTaB+wA12ysheTxTPp6j2ZPI8/lqyTNeGK85mPKZHPQsnZQaioSBFGE16EgJhfXPtUjpYFgwjBRY46oMo1N/NzNXUM4x+iEmLG/cHr65AsB2Bu+aUdDAADHshVT7v9U+R32r1lKy1fijuh72kjAsqxR12w8MogLEwdKztX//hUMdO6YuY4SEREREVFZMCidA960rBG93nrEauy19LIbX69wj2g8tzy/B3et+xlab/wtIrf/Ckl9dNWWZVkYTGQQSNlBn+lzQZBHB4ILQa1LQ5/oKxzLEXvXbF99AH/1/svwyau+gm5faQgqdfQglk7Cn7ZDVdOlQVRVEE1Fi8+JTqUYlKZ7jgAA9g5EkcgYaDtqL3My0NyEO856FwBAaW6B2taOM9tqcLC2bdQ9M5tmrqJ0Z18YAizU9dhrlEonLZ/gilIezf6MyeaD0ulMvU9nc1CGq+UVeyd4n6YgLI0MSoMl18T1HPxGMWAUY6lxn5HJJpHRd+H8eDFsvHf5m0e186w6ecr9n6pAvqI0qeW/zEnYfd/f9yrufvnf8dKBB0raW5aFV44EcdrgwZLzgmkhtPH5Ge8vERERERGdGAalc8Cbl9sbOR1ZvgwAoO04XMnu0AT+8f5X0Hb3n+DddAit//skuo+O3hE7kjZg5Ex4k3ZQYQW8s93NqlHv1tAr+0adl3w+fOudZ+GRH30Rl+7ehxv+6ib8/jJ7J2+tK4QGLQpv2g5frMDkq+qIhrX4nOh3FqfeZ3q6AQAvHxlEmzcN9177OHD+eTj3m9/A4v/6AdY8alf0n9FaC0NSRt1T3noAaWNmNu7Z3htGvUuH84i9Rqdj7dopXe/NV5QaDnupCyuRHK/5mDJZE2q+otRS7ft5HQoiIzaJ0sOhkmsShglfrhiOirHUcZcmSWQMPLfnEbzL8yrWDh4CAGS9LmxuXQNDK/0ypGb1mVPu/1QNV5QmNDsIFmMp/ON9r+Chu27H8k/9CqHf/Kak/dFIEr2xJJYctUP3aG0ApijY93h9y4z3l4iIiIiITgyD0jmgPeCGJAoI1diVTxNV41DlpAz7H//eQ8V19XqffHBUu4H8TtqefFCKWv/Md65KNXg0RByjg2LJZ4en7QE3/D4P6tasxlb3EgCAkDNxeuYo3Ml8lZp/4QbNNH3NPicysgrDZQeH2b5eGNkMDg+8iA/U7ocSsoPEFW95O/76LWej6ZOfhmOlvbHeJSuacEqzH7e9/WPIihJCbnu8Og70o69394z0d0dvGGvFAcjRfNX1aWdP6frhqfd6PnC0ElObep/NmciZFtSc/Tln5Zck8GoKwmKxotQYNfU+B9+IDZ6kpI5MZnSYnDZyOPl76zF0y09x0l/9HPX329W55ppVsAQRr9UXp9qLXi9ca2a+olSVJThkqbBZlZhI42cv7sbyx5+F8+AA/Hc9AyObKbR/5UgQtU4Dvn12NfKexSsx1NwEAMjt2DPj/SUiIiIiohOzMOf6zkGaLEIX7d8uQR9/kyCqnGDC/gdzsrkGOGxXo6X+/Bysv7UgCEKh3XBQ6syvdyfWBGa3o1Wk3u1ATpSge5xQ48UvASRvaZXpyc0BPOhdXDyOdcKVr4gTahZu0EzT1+K1w6+k3wt/MgO9tw/3b/45TurZCGd+ejsA1Fx4yahrXaqMrf/8XuTMd+Oqn1+Nvj//Gbes/x4E00L/hqewZNG5Ze/v9p4wTosfKfbr9AumdL03P/Ve1/IVpcmpfemWyeYAAEq+ohT5ilKfQ8GQ5C60y8YiJdfFDRNeo/RZqWAvXO2BknO7+iLoiiTR+ERpFX7NOefgnr+8GIfObkDfxqdx+qJ61L/97ZC8s/MFid+pIKbmK0pTOryKDn/3AABA6YsiEu1Bfe1SAPb6pGvUILTuMABgS+0KQBRQ390LZV83knoMLpVf7BARERERVSsGpXOEQ5aQyU/zFIwcTMuEKLAguNoMJXUAgCOdLpxzbtqHULIXte6WwrmBfKCqxe2gT6yrxUJV77ZDm4zHVRKUyv5ASbtTmv34hbcOWVWBrBtYOnQUjjiDZpq+Zp8dlMa9Xvh7BpHr74X3f3+D+j9sKrSxfO5CFelYJFHEG5Y341s7lsGURIg5E6lXXgGuK29fM9kkLmx7Befu318451p7ypTuMTz1PqOWrrc5WemsCQDFNUpV+/9C+BwKwmJx6n02WhqUJgwT7mOC0nSwD2hfU3KuJz9bQrLMkvONF1yOk89YApyxBPiLK6bU53IIOFREZfv9CRaw1hmG1mUvLyCYFrq3P49Di3dhaf1peLUriDPCHYVrO1pPgYIczsNGaEdDGOzZi8VLzim8fnDgdQxED+PsJe+AImuz+8aIiIiIiGgUJm1zhCZLhd2/RSOHXM6ocI9oLMGkHYA6ksVpplrnEHr3vFLSbiCehiyaUPJTaOX6hbtje4PHDm2SHnfJedlfU3J8SnMAliAi2Gyv2dvc11sIVqXaulnoKc03wxWlEY9dvezefLgkJAUA6eTVEMTx/6q8cEkDMoqGwbZmAIC1Y29Z+7mpM4gv3/sALnjwUbT89GkAgNnWCMntnuDKUsObOaVVO5ATUunxmo8yXFEq55cYsfLBq1eTkVLGDkpzpoVk1oQ7UxqUZob6cayeaBKqZEJOFPslNzQicPnbptTPcvM7FYSV4jrI56UOQhwxs2P/C3/AoftuxyMv/Ddc0gEsj/QUXvOedgY2eYubbgU32xs6ZXMm7t68Hy8/fjOO/u52bDvy1Cy8EyIiIiIimgiD0jnCIUvIiMWNLLLpqa0tR7NjKB+UjqyMBIDk8y+UHA8m0vDJBqR8BZXa0Dg7HaxCw0Fp/JjQR/EFSo5PabaPu+rtylxv1yCkfNCs1NXPbCdpXmry2mNvyG0v3SAadhAYU4uhX+MlE1cwDo/Now322BQP9yFbxi+z/t8fX0X8uefReNeGwjnnyVNfn3O4ojSVD0qRmFpQmh4OSoffm5Kfeq+pMEURWYf9d5QZjxWuiWbstk699Fn60OCo+/dGU6hT0pAj9p9rz5f/CWfsOQS5rrJfhPgcKkJScQ3WU/sOlLy+6Ft/xJKv34fln7oD18kvY3nMDkrNhhqctrwNu/LT8gEgtmUjAODm53fjK+uewMpP3IFF3/ojgvffO/NvhIiIiIiIJsSgdI7QZLFQUQoA2fTUdyummRdMZCAJJuRYaShgHOksOR5MZNBiRiBY9rHW0DxbXaw6w1PvY87SnevVmtLlCBo9DtS5NOwNtAMAtK4hSGk7hPG1rgDRVKmyhHq3hj0YUdHd2ISffvEnOHTdx+F98yVo+vjfT3ifJq8DfoeCgz47KFW7Q4jEe8vWz4FEGr5U6eZHjW9595Tv48pvvvT/2bvPwDiqqwHD72zvu+rdlnvFBmOKTe8JJAQIJJBK8qVAKqEmpJIQCOkEEhJSSKgJvXcMBoNtXHDvtiSrd2l7n+/HrHa1SLIlW8Wyz/MHzcydO2flYVd75tx7Q0YtQawLRfbVvI9Iaui9Ia5VUyqpRaGcqZXh41ZtO+HLJEp9PYnScPbDo2hn9oJPAI3eEBWJzH7r5GnoTKY+7Uabx2qi05B5kFNRu7ffdqZmL5Nv+h+527X3e6WynGPKcwmarHQWasne+LpNqGqS659dw+x4r8rTh98cwVcghBBCCCGEGCyZo3ScMBv0hHXG9HY8JBWlh6LOYBSnIYb+Q5VaiZbsYaat/gjF8czwVEtR6ajEdyjyWEzodQpd1uwFTkye7CpRRVGYW+Jhw64JXPGhPvL7WWxHiMFYVFnAit0z+D9A7/Yw89mXeHTuPOBzg+5DURRmFrrZ6tCS+Lp4ks7t68k7vmJYYgxGE1gTmaTm9OdfxXXaGUPuR6dTcJgNBFOJUiUUJakm0Cn6QZ0fTlXc9iRKSSUxXalEacxixoKfpC+T1PWGtbbmcHZSNt7Z3qf/Rl+IikgmUWovqxxUXCPNbTGyTpdJlDq2NwzYVh+MYt2tvd+bpk5hcaWWhN9eOpkTW9oxb9pLd0irpp3lr8+cmCsL0gkhhBBCCHEokIrScUIbep9JlCZCUlF6KGoPRihUfelK0R7J5uxhpq2BMEWx3onS8tEI75Ck0ynk2810mLNXuTd7+g6nn13kZkuvle8BVLcD2/xjRjRGcfh68LMn87fbvs3s5Ws4av02bHPnHVA/0wtdbLaWpbe9W9cNU4QQjMWxxrWF4lQFXKedgaIoB9SXw2QkkFo0SB+KEYsNvqo0ksieozRdUZqa+zRq0fpVA5kHeT1D783hDz086urs03+TN0RJJLPfXjZp0LGNJLfFRJsuU/Fu7Bjcg0rb9JkUOa1MzXeyIk9buMrU4k3PWT2p11ymidQUJEIIIYQQQoixJYnSccJs0BHuPUdpZGirFYvR0RGMUBT3prejttSiKW3ZSYE2f5j8SKadvbCMI1mB3ULbhxOlOX0TpXOKPTQ7cklYM/8vWE5ehKIfXEWcEB/mMBs5YWIB9qPmY8w/8LluZxa6qHMVkdRpCczwjm3DFSKhWAJzT6LUbDzgJCloSU2/ITMHa9TXN2E5kJ6KUn2filLtv1FzKlHqzzzI86amxzB+aJh/vKurT/+N3hCFocx+c+nwVOQeLI/ViM9k67M/Zso8vAzkuVk/d37WcddMbXtxZUE6UQrQvPQ1AEraMtMzqAHtd9YdauWtbQ9T3zm8C4IJIYQQQgghBkcSpeOE6UND7xMy9P6Q1B6IkB/PDDttLdbmHtW3eUkkM6sktwYi5EUy8/iZCo/cOUpBm6e0xZidKDV5+i7gMrvYA4qSlaDIP2foczUKMdxmFLqJ6Y34CrS5dWO7qoat72A0jjmRSZQeDIfZiN+QqV4Md/ddVGkgPXOU6tMVpVpitGfofdiS6rffRGl2RWmyuzt7O5nEpGunMNylbdst6G19k5NjwW0xkdAbSJiyZyvaOLFX8tNVyJr8adnnzdSqk0+aVEi1p4SwQ3s9kdVrUFDJaWrNNO72sXR3M08v/RPef9zH0td+N0KvRgghhBBCCLEvkigdJywGPWGlV6I0LBWlh6LOUJS8aCYBujdfm3vU0BkgGMokBlr9YTxhrV3SakJnObKHXeY7LDQZMnP0qQZdOgnT25wirY1PzRzLO+eCkQ9QiP2YWajdmy35hdqO6npUVd3HGYMTSySJJ1UssVSi1HJwixs5zQaakplh5J0tewZ9bs+q9/rUfz889D5kSr2PBTOfT75IHAUVfTCa1ZfanXmfTCZVVlW/xfcX72BCXJujVM0/dObsdFlTi1XZst+TnpqwKP3zngXn8G5OJnGq6nVYJmmLzJ00qRAUhT0VlQBYNlVx7pQ2LHWZ+ViVbj9n/eUVSu9+ipK/vknZb19CVZMj9ZKEEEIIIYQQA5BE6TihDb3vlSiVofeHpPZAhJxYpqJ0h1ube1RJqvibqgEIRGKEYgncIS1RoOY4+/RzpCmwm2nQZxIjSbu13+HF+Q4Lx5bn8vsTLgfAfsKJmCdWjlaYQgxoSp4DvU6hNkerDjfVthOK+fdz1v4Fo6nFkFJD7zEfXKK0zG1jl6Egvd2x4f1BnxtJJUh1H64oTVW59iRKlV6L2XWHY9gMcXTBD82F6tV+N6qqcuY9r7Lnkb8w6+I/kfvSBu14Qd+K8rHi7plawJZ5oJU0G3F/4pMEvnoNBV/6KubPfZnteROJG1NVpxXFKAbt55mFLvJsZpZ6ZgNg2d3Ml0PLMfgzvyedL8QkTxDPW9qUDbYtDQR6Tc/SWyIZp8PfMCyJeCGEEEIIIUQ2SZSOExajnmCvOUplMadDU3swgieSSgAosM2VWaQpUKtVbrUGtISBO6i1U3I8oxvkISjfbqG91xylqn3gCts3v3Eud//1F8xds4kZz74yGuEJsV8mg57JuY50otTgDRFpbdzPWfsXSs0LaoxpQ9g5yIrSRZUF7MkpJ2rVkpyhVasGfW5PRakuNUepLpUotZkM6BSFoEmb+1QXCLOtcQWReAhfJEZOMthngTu82vQx9d1B3tnTQul9S9GlXiuAvvDA54sdbp7UnMhRa+Z9STd7Gv/78lmc/offUnnXPZw6s4K43sD6Im34vWf+8em2iqJw1eLpvDBlMSGTGUWFiT99Kusael+Yc03Z1b3djbv6xJJIJnl35+M8u+aPbK5/Z9heoxBCCCGEEEIz5ETptm3b+OIXv8ixxx7L4sWLueaaa2htbe237f333895553HggULuOKKK9i0adNBB3ykMuv1hJGK0kOZqqp0BKO4IloCIGG3UG3KfNkP1dcAsKfdh4KKq10bdqkvLRn9YA8xBQ4zEaOZRM/8i46B5ya0m43MLHJjnTETvd0+ShEKsX9FTivt1kzCP9o5+Pk/BxJMVW+aUkPvsfSdkmIoFlcWktTp2FsxEQDd+h1E4+H9nKXpmaNUF+0Zep+qIFUUbZEoo7at94VZ99JfWb/nFbyRGLlq5sFeyKMN+1d8QZLJBE0+7dqGDy32ZCgqOtCXOOzcqTlYsWeS1LZjj81qs7Aij7suPh7dj26n6NvXMOGW27OO33z2UeRMnMDdx13W7zWUpMoZu1dn7fPu3Jy13RmMMO22p/HeeQ+zLvkT2x+680BfkhBCCCGEEGIAQ0qURqNRvvzlL3P88cezfPlynn/+edrb2/nZz37Wp+2SJUu46667+PWvf817773HGWecwVVXXUUwKJWQB8Js0GVVlCZDkig91PgjcWKJJM7UQltxl51mS076eKSxHoDtLV7ybdH0/HTWGTP7dnaEyU9VkIadWoJUcTr21VyIQ5LVqCeoyyQyY4HhG3qfrii1Htx8xkeVeHCYDawrnAqAZWczjy77GaurXtzvuZFYAlDTQ+91veYRdlmM+I1aRamSVJnynQdpvftuvOE4OYnM4oPtHm2xK703SG3HVhq92t8ECZc161r6osIDf5HDrCdRagtkXkfuCaf1afeNk2fwtc99jAm3/xbL1OyFnSxGPf/49GJenH82K8vn9Hudove2ZG0Hd23P2v7D0q3UdPopuv9ddJE4E37xzAG9HiGEEEIIIcTADPtvkhEKhfje977HxRdfjMFgIDc3l3POOYcHH3ywT9v//e9/XHLJJcyfPx+Ar3zlK9x///28+eabXHDB4BZfUVX1iEishlJJz9A+kp96VEK9FnOKBPxHxO9mPKnv0r5E21LTIiQcdoImK3GrCUMoSqShnmAwyMb6dmapLenFTSxTZw3bv+Vg7qVDkSt1a7cY3UyiG1tOsdzfY2y83ktjyaxX8PdKlAY72w/6Pu7wae8rxqiWKFVNpoPu87jyXN7ZM4tLeRFdLEHpzx6n9qytVH57ATaTa8DzfKEwekVFSQ2RTxqM6VjMel06Udoj98n36ZodIieeiXddzhTKq/eiD0bZ/PzfaZxyArMK/Bg7A1nnOh2lh8x7gInUIla1nel91vnHDTm+BcUOdv/g41R/7Qz0TXX87cX32Vtfxc+f+BMAlursCuTQnl1Z19jb4cNqyF7g6VD5HY0GeU8Sw0XuJTEc5D4Sw0XuJTFc5F7aN1VV+10HpT9DSpS63W4uuywzbGzPnj089dRTfPSjH+3TdvPmzZx//vnpbZ1Ox6xZs9i4ceOgE6WxWIytW7cOJcRxrbq6esBj/q4Ogr2G3re31PHmhkdx68ux6nIGPE+Mnm0dIYodEdxhrYosnho+HvQ4cYXaidTVs3XrVj6oaWJBsC59Xoc1h65hvs/3dS8dilzxJJPdZt6ecwaV3seJnnTWEfX//qFsvN1LYykW9OPXZSo+m2qq8B3kfby9WUsgGqLag5WYXnfQ/29MsSZ5uGBKetu5uhrH2hq2LnoHW87kAc+rbWzFosRRktqEo75IJB3LrnY/M6LZC9MpsQSBUCdFiUwy78XpJ3HulncxRaIYH3+DvNPq+cHU/KyFjdDraZ80g45D5D0gkXq9b008lnN3rgBgVzCEcoDxGQDVaqCpbCI17QNXHUf31GT9Wze3d1Cgz04oH4nvk/KeJIaL3EtiOMh9JIaL3EtiuMi9NDCTaXDrPQwpUdqjvr6e8847j3g8zqc+9Sm+853v9GnT1dWF2+3O2ud2u+ns7OzTdiBGo5GpU6ceSIjjSigUorq6msrKSqxWa79tShtVQvrMwiABbzWxt9rh+ONYcOJ3RytUsQ/V29bwi7N2YnpaSwoY8rQhpn6nC1djO4ZOL7NmzaL++Sou9Takz5t5zkfQO4ZnqPlg7qVD1bq5s4ELQL0TRSfrzI218XwvjZXi7UFqelWU5tptTJ4166D6rNU3ATUYUsPdzW4Xsw6yz4vNefxjUxtbiyYxq7kK0IbL5yejTNxH3+76JNZkVXo7p7CQqan2VxztZ0VHfVZ7fSBCsdpOvppJBjZZc3lz+vGct3EZnre24XlrG3F35v5y/PIWSj/2aYwlpQf1GofbvZ+0s36KHfuaZ8i78BM4Z88+6D4n1cR4d/PePvvbiwrIa27F0NSa9W9tXNtJRSz7b6iDvRfGE3lPEsNF7iUxHOQ+EsNF7iUxXORe2rddu/oulDqQA0qUlpWVsXHjRmpqavjJT37CjTfeyO9+97s+7VT1w8vcDo2iKNhsAy/qcrixWq0Dvl6nzUJMl6koLfjvCgzdIYInbMP2xg9GK8T96gq28N7OJ6jIm81R5X3ncDtcXf34CnKrH+fjd9yLPqQNkTXnFwDQ4XBTCtDWSUzRUe8NUd7eDECyKBdn4fDPxbeve0mIoZB7afBcNgu+XhWlSixy0L+7hE4PgD419F5vtx90n+fNmchvPh7ktrYr+frqJzm5Zr12rdamffadUHTY1Fh62+Rwptv/9hPH88zUEv5tjxHdsoGvva+t6l7Stpupanv6nJPmzeB/3Wdw3sZl6X2G7szwoPwFx+Gecug9IP2/xbNg8SzgkmHrs8hlx2vOfkgWNxnYPmk6i5tbMTR0YjTpMRq05HskCeXRjqz2R+L/m/KeJIaL3EtiOMh9JIaL3EtiuMi91L/BDruHA1j1vvdFKisr+d73vsfzzz9PR0f2H+85OTl0dXVl7evq6iI3N/dAL3lEMxv0oCgkjdqX5p4vlsa9rWMZVpanNlTz3Ju3ovvVP9nx8J9Q1eT+TzoMtPnD/Pv9HZy0fFk6SQpQUjkXg06hzpoPgKmxkw31ewDIa24BQDd5wugHLIQYETajHr+SSZQmAoF9tB6cUGo+UH1Ee2/RWQ5uMSfQPr+vPX02l11+Pj858+vp/T0Lzg0kEk9gUzOr0+vNmSfVxS4rX180Hc/5H+N/s89O75/YVs2k3bu1jUnl3PDRhTRNnMEbkxf2ew17aeUBvKLxKdduJqHTE7Nn/k27iwupcRUDYGj30d2l/ZuoqkpXKEJRtCurjyPlc1YIIYQQQojRMqRE6fLlyznvvPNIJjN/mOtSQ2SNRmNW27lz57J58+b0diKRYMuWLenFncTQmAza7zlpzC4CVkKR/pqPus1NXfxp6TNU3vA/8p5bR9nvXiYcOzIWmXhuSx0zcr143t+Z3mc/4USKL/88c4s9rHFOAkDvj1C3/j0cpji2Bq3CyjRtWr99CiHGH5vJQECfGXqfDB18orRn1XtdqqJUZx2+p8PFTqu24JxZ+/yONjX22y6eiLGzeTU6OrD2qijVWfsmbWcXuQmYbQRztEWhZnXvxbm+BgDH6WdwbEUeLb/4NHt+9Acuu/z2PuebS8oP+nWNF3k27V6JOTK/x87SCnZYtUSpokLXro3sbF7Nsx/cydcXvMOJ1ux/o1g8OnoBCyGEEEIIcQQYUqJ07ty5+P1+fvOb3xAKhejo6OCuu+5i4cKFOJ1OPvKRj7B69WoArrjiCp5++mnWrVtHKBTinnvuwWQycfrpp4/E6zjsWQxaJWky9d8eulCUZDIxFiGlqarKt55YyRcCK7Ds1RKA+kCEYGfTmMY1Wp7ZVMtHwlswdmqJ4ckP/JfZbyzDMmUqCyfksSYnM4w0uWElFXovxjZtzj77zDljErMQYvjZjAbiOj2qThvWkRiGFclDsQR6RUUXSSVMh3EYTbFLqwgNebRFmBItLX3adAQjvL7lVda8eDdHW17msumZ+ZX15r6xzC7yANBcVASA5/XN6INaMq/wIxcBWkXrjAIXDc4CkvrMnyFJuwX9ETRMKNemTSaftGYmlfdXTGWbuTi9vfadl1nz/F3YbvkXU799P8XvbM7qI+LLHs0jhBBCCCGEODhDSpQ6nU7+9a9/sWnTJk488UQuuOACnE4nv//97wGoqqoimPpieOqpp3LttddyzTXXcPzxx/Pee+9x7733YhmGYYNHInNPotSUXVGqi8SJxkL9nTJqXtrWwJq6BqY++GbW/kB91QBnHD4CkRivbW9k4c4NAKgmI55zPpI+flxFPg3OAsIO7cu/Y8c2poYyCWT79INfEEQIcWiwmrQpUhKpCs3kMCRKg9E4ZiWOrmcIvs1+0H32KHFqiVK/S0uUqi3tWceTSZWT/vQyVf/4B1O+/SDTv/QPjn5refq4wdJ3kvgZhS4UBWpySgDSCV5Vr8Nz6pnpdtMLXSR0eryFeel9an72ApCHuzy7VlFqCmQ+ww0z5lPrKk4nkF3vr2Ty9x7G88YWrLtbsG1tyOoj0t02egELIYQQQghxBBjyYk4zZszggQce6PfY9u3bs7Y/85nP8JnPfObAIhNZeipKEx+qKAWIejuxFAzPqukHYkV1K5VGL5bq7C9soYZaWDBGQY2St3Y3E44nKPxAW0FNt3hB1gr2x03IA0Whrqycqdt34N5dw8SCzCrFzonTRz1mIcTIsKWmRkmYjRhCUZKh4akodfSeF3QYh96XpCpKvQ4XJQBt2Suqb2vpZndbN9M2bAHA4A+T++L6TCyWvrHYTAYm5Tp431zJ6byd3m88Zj56pzO9PaNQS4o25xfiaUzNtZ2fMxwva9zoGXpv6MhM0VB67InEX95NR0kh+XVN5LyxBSU58MKYkY42mDTioQohhBBCCHHEOODFnMToMqfmKE0Y+ua2o77OPvtG0/ZWL3MjdX32hxv77jvc1HYFMeqSmNq8ABiPyq4QnVPkwWrUs7lgMgDW3S3MTmQqSq3lE0cvWCHEiLKlKv4TJq2iVA0dfLV/MBbHqYbT27rhrCh1aYnODruWtNS3e0n2WhxoWVULE1xB7Fv7X+RpoKTtd06ZyTsTj6XWVZjeV/iZL2e1mZSrPVCq9RSl96n5nqG/iHEsN5UobXFkFrmcu3AeAHsLywDQpeaoHUi0W4beCyGEEEIIMZwkUTpO9Ay9Txj7Jkpj3jFOlLZ0M93b94t0tPnwn6O0PRDGo4+k5+Az5RdkHTfodSwoy+V9tzZPqS4SZ8r2HQAkbeasCishxPhmM2rv0/F0ojS8r+aDEowmcCQzFaUG+/CNHrAY9XisJlqtHgD0nQFCUX/6+LtVrSyOV2Hw9/86+pujFODbp8yi6o9fZ8qqTRQtX4/yn//i+ewXstoYU0PLdzlL0/uSysG8mvHHZjLw6aMr+cN5V4PLTdF3vkepx06ezcx2T0VW28aS0n77iHZJolQIIYQQQojhNOSh92JsZIbe91NR6u0a5WgyEskkO1q9VHQ3AxC3mEga9Jj8IeItzWMW12hpD0YoSnjT26b8oj5tjq3I46GcKelt5wfVAKiFR9YwUyEOd/ZURWncpC3OczAVpfWdO9hQ+yYmXTH23olS6/BOs1ListJk0d6LdPEkgaYa7BOPAmBZVTPfbc1MqdNVmIen1zymhn0svGQ1Gphflkswx0JrNIyi9J8F3WQpS//sSBx5c5g//PlTSH72ZBR+jKLTksfzSj18sHsSV/Rqt3T6Qi7pfhVTMDtpHR/jB6VCCCGEEEIcbqSidJzoGXof7ydRGvd1j3Y4aXs7A0TiSYpataRoZ2E+IZf2RT7RevgvMtEWiFCcyPz+zQXFfdrMLHLTbnMTTQ2z7FncRCnMH50ghRCjomfofaynojQ89IpSVVX5w9JNvP38HehuvZc53W+OWEUpaAs61ZsyD216FuGr6wpQ3RFgyl5tu6sgl9qKCVnnGiwHF8sl8yawy5GplCz+6CUH1d94pdMp6SQpwLzSHDa5s6dlec81HX8/UxPEurtGODohhBBCCCGOLFJROk70DL3vL1Ea83v77Bst21q86BUVV5NWZdSYX0ResBt3Qytq6+E/JLAtECE/5ktvWwr6Do+cUeACRaGzsICi6sy8rfrivtWnQojxq2fofdSoJUoJRfbRun///aCaJ9Yu4Q8//g8Ahq4A9mkXpI8b7MM7XUexy8oHxszK88H6vYA27N5qSODZVgvA7oopdDrdHNXr3INdWOqeT57A7R474Vl3M6m9joIrv3JQ/R0ujirJocPqIuy0Y/EFUBWFTfmTabTmkkv2lDYJ79g9KBVCCCGEEOJwJInSccLSM/edvp+KUv/wfVFSVXXAIZL92dHqpcAWxlyvDf+rdpdgUJKUAUp717DFdajqCESYFs0kSm2FfROl0wtcADTlFmYlSo2l/c85J4QYn9IVpUZt6D3hoSdKf/LyB1yXXJnedr23C9uUaHrbaHcdXJAfUuKy8rw5U1EaSS3Ct7mpixKDH1Oz9iCufsI0OiOJrHONloNbWCrfYeF3n1gILDyofg4380pyQFHYkVvOPN92jNOnEjRZeSNnLnPYktU27h27B6VCCCGEEEIcjiRROk6YUwtfxPtZzCnh8/XZ12OwiU9VVXljy3/oDDZy/rxvYDe7BxXXtpZu5iSa0ivzbreXkpfQFgPRdfpIqkl0yuE7w0NbIEJOr0SpuaCkT5sSlxWH2UCNu5j5vfabyyr6tBVCjF+21PtzNJ0oje6jdbY9revZ2tRArrmBmY+/m94fd1qy5yi1Df8cpV6zg6RBjy6eINrUQDKZoMXXxtRYa7pdsHAi1W3ZnzU6y5E3p+hoOKYsl08dPZFV0c+w8L3/Uv79H/Hc3JP57Ss5VMVbWBdQ+UjLWqztPpI+SZQKIYQQQggxnCRROk70VJTG+q0o7T9RuqH2TTbWvcXJ0y5jYv7cAfve0tTF9556i2uW30P+nlbq/nUCM6afOai4drR4meFrTG/X5kymLabNV2roDBKO+LBZBpd0HY/agxE8YS0xnDQZ0PezuImiKEwvcLHdnl1BaiuvHI0QhRCjxGbS3qcjQ6goDccSPLJmFbZN96DvDnKNzYJ9Y68pOgIR8tRAetvoGP6KUhSFkMeBva2bWEMDr22+jxOLdjDF1pJpWDqFXd0NWef2nldTDB+dTuGRz58Knz8V+CEA5wPnzyrjhXOP5o5/vslZL2zBig91Hw9KhRBCCCGEEEMnidJxomeO0pje2OdY0u/P2g7F4lz676VcWfdnKp5dye7ro0z87O0D9v3Plbto27uT/CfXaOe//iYMMlFa1eHnlO7Mok3R8kk0d2lDA5VEkkBzLbaJh2eiNBpP4A3HcKUSpapn4Eqvafku1lvLsvbZK6aOaHxCiNFlTVWUhg1aolQJx/Z7znXPrsZZ9xgX//QRdPEkcZc167iSVCkJZFaaH+6h9xNztPet7pwc7G3dBKrrUH72Z2Yu3UbpydPT7VyVU6ivGXyFrBgZLov2N0AkVc2b9Af21VwIIYQQQggxRFIOMk70rHof62cxp2Qg+4vS81vqWVFdQ+nDS7HubEb3n2dRVXXAvuu6g0yMZL6Ix9taB2z7Yb5IDFtUW9lZNerJzfXQ2Gu+u0BDzaD7Gm86glrSwBHUfv+qe+BE6fQCF9XO7GH5jgmTRy44IcSoM+p1GPU6IulE6b4Ti43eIM9t2szpy95EF08CYPCGAFg/cWa6XWF35v1Zbzu4eUE/bFKu9r7V4tYWdHI0dZDz+mb04Rg5r28GIGm3UFJWRNBkHbAfMTrcFu3eCvdMe+CTRKkQQgghhBDDSRKl44RZn6oo1e2/onTV3jbmmFoxNWqLPJl2NBCMDrzgU2N3kNJwZoX6eMfgV6v3ReLYYlqiNGk1UeS0UGfMTR/fs/VNvKG2gU4f19oC2uu2BYMAKDkDV85OL3QRNpoJ5mrVYKpOh6mo73ymQojxzWbUE0olSnWRGKqaHLDtn97exrk51emEZI+gO4e/Hn9JetvTrr0nJ436YR/uXuS0YDXqaXAWAGCu60BJZMesFudR7hneBK04MD0VpWFzKlHqD45hNEIIIYQQQhx+JFE6Tuh0Cka9jmg/FaVqIPNFqc1fh0l9h/MTW9P7jG0+Wqo3DNh3oy9EUbgzvZ3s6hpUTNF4glgiiSWqzcOnWs0Uu6zUGTMVpYlnX+WtlfcOqr/xpj1VUWpNDX1UPPtIlBZoCdKm3EJtR74bJZX8FkIcPmwmA2G9GQBdJE4s3n9VaSgW52/Lt3PeB0vRxbTV5N+9+me4L/kUK79zG7ttxZk+W7T3ZzVVTTicFEWhMtdBja1wwDa60iKKHLJw06HAnUqUBlOJUsUfHstwhBBCCCGEOOxIonQcMRt0RPuZo1QNakM1n9q4l1ff+DcnLHmOo3dvyWrTseqdfvtUVZWG7hD5wa70vmTX4FbR9adWurfEUguW2KxMK3DRbXbitWjVR57Xt+D+wd8H1d9401NRakpV9BjycgdsO6vQhcdq4k3XLAA8xy4e+QCFEKPOZjSkK0oB4sH+30+X7GzCbvRTvGo7AIaTjuea3/yI6fc/jOGEk/CZ7SRTU66YmrU+VEvf9//hUJnrYKelaMDjhrKy9MOeV6adqMU0SaYOGQtOs3YPBFLTICiB0FiGI4QQQgghxGFHFnMaRywGPRFdPxWlwRBV7T4u+89SXtr2XzxvbevTJvDBWvhc3z67QlHC8QQ5vl5D87sHmSiNpBKl0Z5EqYUvHz8VbyjG9zqu45aV/6K8rg7znhaSyQQ63eFVQdkeiKBXkhh92hdVfW7egG3tZiPLvv0RGj99HFNrvoRz0UmjFaYQYhTZTHqCqYpSgKjfi83dt1rzuc11nGaqwVKtTU1SeNGn0seKXVZURUfE5cDa0ev92Dz8FaWgzVP62D4SpabyCTgtRlZ896PsueRoJu58H/eZZ49ILGLfDHoddpOBgFFLlOqCEZJqAp1yeH2+CiGEEEIIMVYkUTqOmA36fitKCYSo6QygqvSbJAVIbNqOqqooipK1vyG1cIjT1+vLeHf2nKcD8Ue0FZ1N0UxFqVGv44Yz53DnO1tZETqOS+vq0AcihMM+bDbPoPodL9oDEZyGKHq/9vqNeQX7bD+ryM2sIjccJZVYQhyubEZDVqI0Fuj74CmZVHlhSx23NH2Q3pf/8U+mfy52asOqAx9KlI7E0HvQEqVt9hySBj26eKLPcdtE7T3ruAn5HDchH046akTiEIPjthjxmWwA6EMxgqEuHLaBH9QJIYQQQgghBk+G3o8jOgUi/SzmpITCBFNz3AVLcrKOhaxa1YlpRz2ReN/VcRu6tWHj9i5fpj/v4BaH8PUkSiPaHHyKLbMicq7NRKfRmYmjvXFQfY4n7cEIJYlMEsOcP/Acf0KII4PNpCegzyQ0+0uUrq3vIBrvZtJGbS5pZdYUzOUV6ePFTu291Od0ZZ2nWMyMhMpcB6qiw5/X/zzLjsppI3JdcWBcFiN1iczn6/atr49hNEIIIYQQQhxeJFE6jsQSKpF+KkqVYIRAar5QnapmHXt7/iIATK0+gi0Nfc5t8IbQK0lMnZlEqd4bJJGM7zeenqH3xrBWUanYbeljeTYz7b0Tpa2HX6K0LRChKJH5vZkLivfRWghxJLAaDQSUzMJHiUDfCv3H1lXzsfwa7BvrAMi74OKs48UuLVHaZc9OlDJCidJJuQ4AOgeYPsQ5acaIXFccmDnFHtbaKtPbzQ/8k2hcFnUSQgghhBBiOEiidByJJZIDVJRGCaSSlvqwVt2pWsw8/Knr2FCR+YIb7idZ2eQNkWOKYOjKVJHqg1Eiof3PU9qzmJMhVVGqc9jTx3JsZlp7JUojbc377W+8aQtEKIhnfk+W/JIxjEYIcSiwmQwEsobe+7KOByIx/vfBVj6+8S2URBKA4su/mNUmx2rCqNfRbvtQhedIJUrztERpizuTKN1dPEH7QVEwl00YkeuKA3PNqbPYVjCJbRVTAPA8sZIde98d46iEEEIIIYQ4PEiidByJJ/tPlOpCUYLRKAoqup7h8F//LLsWn0enPlPlGe1s73NugzfIxEQnSnYhKsH2/Sc2e+YoNaSSszp7JlGaazPRrO+VKO1o2W9/401HIEJ+NJMotRWVj2E0QohDgc2ox6/LJDTjvRKlqqry31X3ccvJ6yh6SZuf1HLGqVhnzsrqQ1EUihwWWi3ZiVKzwzMiMXusJnKsJjYZStP7Hj7ls1BcSuFXr0Zn7GdubDFmFk8q5LQpRfxt7oUAGDsDtDx43xhHJYQQQgghxOFBEqXjSDypEu4vURqNEwz5MOqS6MJa8lJvd+C2GOnUZ5KX0e7+EqUhJsT67g+37X+ovFZRqqLvuaYjkxjNs5lp7ZUojbW37re/8aY1ECY/0quitFAqSoU40tlMBny9h94Htbmhd7Z6+di9D1P6z/uYcflfMHRrC+lVfPf7/faTZzezg/ysfYVHnzJCUcPFR03gjZKjSZpM2I87nmf+eQvH7drLxN//acSuKQ7ctafPZnnFUbQUFwGgvLaccKzvPORCCCGEEEKIoZFE6TgSSyQJK/1X9kR9nbjUcLoyVG934LaaaO+VKI11dfY5r7E7REmoo8/+wQyV94djmHRJdKFo+po9cm1mfCY7qpK6dkffZOx41+IPkxPWqsWSFiN6h2M/ZwghDnc2owGfrm+i9Ccvr+N433LyH1uFPqi9Z9qOPgb3Wef028/JkwpZ55lCQtGh6vWU3HgzpTf9cMTivvdTJ7LyL9ezsKaZWa+/g6KTPw8OZedML8FuNrJ00rEA2DbWUrd37RhHJYQQQgghxPgn34TGkXhSJdRrNeXeypUXOLkoU7WptzvwWE206TPJu3h330RpgzdIcbirz/5I+76HyquqCsltnFDYgZLUsrN6Z6aCNMdmIqnTEbdpCYNEZ99rj2fBaBx/JI4ntaK1muPczxlCiCOBzaTHr2SG3ieCfqrafSzbvYPFb70FQNJiZvK/HmDG86+hKEq//fzuEwt57ObPMWvVBuZv2E75T34+okPgFUXBYzWhdzpR9PoRu44YHmaDnnNmlPBS8QIAdPEkjS89McZRCSGEEEIIMf4ZxjoAMXhJVR2wonTyTQ9z+WUnprcNDidui5GQ0YKqgKJCvLs7u7+kSqM3RElYqyhN6hR0qaRnrKPvUHlVVdnVsoZAVM/6+iYq1z7Ct4yZL9SGXis059m1REHEYcMYCB92idIWv7bCsLNnRetc9z5aCyGOFDajgbAh80ArEQzyh6VbOd++G/fb2wHI/8KV5H3qin32Y9TrWFRZABSMZLhiHPvY7HK+umESQacdmy9A5I23Ub+ZRFHkGbgQQgghhBAHShKl40xogESpuaGL8v+8nd422F24LCZURUfCasYQjJDwZidKqzr8RBNJilu1YfbtpcXk1zeiqH2HyrcHIjy36QMcy/9AwmbC7g0z4Zans9oYXJlkYa5NS5SG7VYcQLIr+9rjXU+i1O7XEqVKXu5YhiOEOETYTHpURUfSZEAXjRMPBLh/9S7+Wf8uSlJF1eso/+4NYx2mOAycP6sMVdGxYdocTlz7Pvb3dxKM+rGbXfs/WQghhBBCCNEvSZSOM1F9JlEas5oxhiLpbUNXMP2z0enGbdHaxmw9idLMwkMA6+prcJnjuGq1YfY1xeV4ujox+vtWgF7z9CriW57n2ju1oX1Jc99bx+jsnSjVKqpCVpu2o8vXp/141pMotXi1RKm+IG8swxFCHCKsRu29MZFKlHZ0dFNY5qVo+RYATKcswjyxcgwjFIeLIqeVWUVuNpRO58S172PoDuFv2I190jFjHZoQQgghhBDjlozPGmfCyUyCstvtJjHAEDuj04XbqiUro1ZtnlDVl1p4KKmyvWkTgaZ/cucZGzE1aEnR7Z4JROxaYjPR2ZXV38NrqyjfU53e1kXi/VzTk/45L1VR6relFpPqPrwSpc2+EHpFxeTVktOGfBkeK4TQVr0HiJu1B1Xeri7OV7dibugCoOTTXxir0MRhqMJjp8pSmN721ewaw2iEEEIIIYQY/6SidByZkueka29Xets37yTuTBTyg+UPYAsGs9oaHR7cJu2LesSiJUqTPq368dL/LOX4hgc54zf3o5oMKNq0pKx1VHJeaqi82t13qHzcZu6zrzdTr0Rpz9B7v1lLvCreYH+njFut/jAuQwS9NwSAqbB4jCMSQhwKXKlK/qjZghUfSncXp2xfA4BqNJD38U+OZXjiMFPutrHEmJ/eDu3dM4bRCCGEEEIIMf5JRek48tSXT2fKrCk0Xfx5nCefyiX/vJctx57JS2ec16etqdfQ+3AqUYovQDKp8symWqa9tAJdPIk+GE2fs9k1IT1UXu3MHqafazNhi0fYF6MrJ/2zzWTAYtDTbXEAoPeFSCYTQ3/Rh6gWf5jyZFc6yWwuKh3bgIQQh4QKj/Ye2uXxAODu7qJolbaIk+GU4zGk9gsxHCo8NhptmalfwnV7xzAaIYQQQgghxj+pKB1H5hR7WP7d84Hz0/s8ViPdRnuftiaHB3dC++cNWazaTn+QtoA2t6ZOn90+keeiy+rC35Mo7c5OlBr1uv0mSs2u7AWNcm0musypRGkgQjjsw2bz7LOP8aLFF6Y0nqm6tRSVjWE0QohDxQSP9n7c5sphImBv7MDYqr2f2k8+ZQwjE4ejMo+NiMFE1GHF5A8Rq68f65CEEEIIIYQY16SidJxzW0x09pMo1dsduK1aRWnQpCVKFX+IhtRQcUs8mtVerdQqItssHu38Ni/xRCx9PBCNY42H9xmLwZm90m6uzUyH0ZHeDrY1DuYl9RFPROkKNqOq6gGdPxJa/GGKol3pbXtJxdgFI4Q4ZHisJhxmA012rcrP1OJNV57bp80aw8jE4agilZj352qLKSYam8cyHCGEEEIIIcY9SZSOc26riS59dqI0aTKg6PU4TEYUBQJmLVGqC4Sp79bmCrV1ZVeMOnNLKXVZqXNoc50ZOwJ4u7XEpqqqWqI0lp1c7U016tGZTFn78uxm2o3O9Hb4ABKlzb4QL3/wN55/7Rfsbd885PNHSos/TEE08zu0lU4cw2iEEIcKRVGo8Nipt+b1OeaeMW8MIhKHs3K3Ngqk2+XRdjS1jl0wQgghhBBCHAYkUTrOeSxG2vSOrH2qRUtY6nQKLrMRn7EnURqhsduHUZfE1BnInKAoTLzxp0zKdbDHmlk917tnKwChWAJVBXNMG3qftJr44dlXs/WYzJf+pDU7SQqQYzPRasgkSoMt9cQTMaL7qUztsbKmlcpfPIb9e39kxmf/yqoHfj6o80ZDiz9Mfkgbeq8qYMyTVe+FEJoPr0Tew1Y5dQyiEYezil5TPQDoWrtQ1eRYhiSEEEIIIcS4JonScc5tNdH+oYpStdfq9B6rCa9JO66LJWjraCXPEMKQGoJvvvabzNu6B+eik5iY62CHKbN6u3/PNkAbdg9gTlWUJkvzeWPK8TR5MslB1Zq5Zo9cq5lGXWY4/tZNL/HflT/nsfduJRDp7tP+w37x2kZyDGEcH9QAUHHL0/s9ZzQkkyqt/giekE/bdttR9Pr9nCWEOFJMyLGx25j98CSR70Jvs41RROJw5bQYcVmMNNu0RKmhzUcw6h/jqIQQQgghhBi/JFE6znmsJjp0H5qj1JJJWrotJrqNmS/n3tZmJiY60tuu2XMwl2vza1bm2qm1Zb7ch6p2A70SpdHUYk5WMxaDntbUfKagDb3/sCn5DhrNuUSN2lypBXe/yJTL/sjkz/yJlqr1+31tiaTK1ERmGKEunhx0NepI6ghGSKoqroCWKFVTc8MJIQRoCzo12rOH3ivlJWMUjTjcVXhs1Fm1aXN0sQT+hj1jHJEQQgghhBDjlyRKxzmPxUTAZEVVeu3sVd3pthrp0mcSpYHOFiqi7elte/nk9M+VuQ6CJitRhzZUP1q7VzsnlSg1RVNzlFotOC0GmsyezDXDfecv/fqi6UyfUMydx38aAGObH0N3CENXkMjaD/b72nQKVIaz51trbdqWtd3QtYsnVv+abY3L99vfcGn2a8lah19LlCp5nlG7thDi0FeRYyemNxL2ZKZFMUwoH8OIxOGszG2nxpyf3vbV7BrDaIQQQgghhBjfJFE6zrmsRlRFR9xmyey0Zn52WYx0GTIVp4muDkrDnelte/mk9M+VOdqXem+eNoQvUVtPi7cGX1gbpm9MJUoVmxW7yUC9OTd9ri4Y6RNbjs3MK18/i5qPfoo/LLoi61g8VY25L3VdQcpDbVn7WlcuBbQFpr75xEre+d8tFPzfH9n611/ut7/h0uwLASoOnza8UZ+fv+8ThBBHlJ55I32eTLW5qXLyQM2FOCgVHht7TJnPodDe3WMYjRBCCCGEEOObJErHOU9q4aZYr3lJ6ZU0dVuyh+YnvZ0UhLrS25aSTJVTZa6WKG3P0RKgan0Tbzzzc+qaXgXAGOlJlNpwmIzsNeSkz9WFY/3GV+i0svw7H2XKtddy9pV3p/cnAvufQ21vV4DiQEfWPt+alQB8UN/BP1ZsY/J1j2Dd3UL5717eb3/DZU+7H5cpjrmpCwBzhax4L4TImJBKlHZ6Mu+R9qkzxioccZir8NhptGYSpdH6ujGMRgghhBBCiPFNEqXjnCe12ny0VxWpYrOmf3ZbjFmLPemD3eQHuwBIuG3ozJkEa4XHhk5RaHJpX7hs2xqZevV/cNz9ZwAM0Vi6f4fZQJM5kwRQkuqAMep0CnaTgaAxE1ciGNzn6+oORfGGY+R7sxOliXWbAXh7dzPT3dlVqaM1f+n2Fi/TdO0Yeobgzz5qVK4rhBgfyj3adCctjkzVvWvqnLEKRxzmCp0WogYj8dQD02Rbx37OEEIIIYQQQgxEEqXjnNuqLZQUsWaSkDqrrddxU1ai1BkN4vF5tY2CTKITwGTQU+a2UmfPHkqe9/RaAAypilKdzY7dZCBktDBYdpOBpE5H0mQAIBkM7LN9bZd23NPVmbXfuG0v8USM13Y0clZgS9Yxf0fDoOM5GNtaupkbylTseOYcOyrXFUKMD2aDnmKnlS26ovQ+5zRJlIqR4Uh9rsZTI0wSoX0/iBRCCCGEEEIMTBKl41zP0PuQJZMoVeyZxKjbYiRktKQXe3JGQzhTiVKlIHtVZgBvOMZuc1HWPlWv3SY9w+t1NhvfOnkmAD6zlpQtuvb6fcZpS32RS5i1xG5yPxWle7u04/ZOb9Z+U4uPjo563t7TzHF1W7OO+WtHZwGLbS3dTPPWp7ddM+eNynWFEOPHF4+bzNuVx5N05+A+9yOYimXVezEyHKnP1Z5EqRqQRKkQQgghxOFCVZPUtG2iO9i6/8ZiWBjGOgBxcHqG3gfNmepOnS1TUeqymFAVHQmrGUMwgjMWxNalJR/1RYV9+vvIzDKWN5Vl7UuaDRh0SfSpRKnebufjc8p5/MrTcF72NpOqN5P7iUv2GafNmKp4MRkxEuqTKI3EQ+gUHUa9NnSwttNPiTOMuVWLtbWshIL6RgDWrVmDmoxSvjU7MRqsq4ERLu4MxxJUdfgp72wCIJnrwpCTs5+zhBBHmtsuWMAvPno0uru+g6LXj3U44jBmTz2IjJlSidL9PIgUQgghhBDjQ6M3yHs7l6O+cy+6SRO4+MI7UBSpdxxp8hse53qG3gfMmeSovldFaU4qkRpOzVtanOjG1K4tpGQqyU6IAvzwnKOYcfwCHpj/0Ux/wSiFxiC6aDzVvwNFUbj4qAkcd+LR5F/+WXS9hv73x/6hilI1FEofq+1o5vGlP+Op924nnojhDUcJh17jjrlr0AciAGwpm5Juv3fzBmbaOrDuaMq6Rrhh7z5jGA4727yoKhS0tACgTCrfzxlCiCOVXqeTJKkYcQ5zKlFq7kmUhvbVXAghhBBCjBOX3PcW6+6/n4k/eoKi7/2TQMS7/5PEQZOK0nHOYtBj0uvwmzOJSr3dkf55Sr4TgM7cXBxtXRTuqs0sQjRtVp/+5hR7ePnrZ3OML0xwciFff+o/AEyNZ8q8e/c/WLY+FS/aF7mfvLSOrt2P8pmf30XCYWb9K5/g1H9s5elt9+J5IzMH6arCmZzGMgAie6tYbLD3WUCqu3obb2//L7NKT6LAWTHkGAdjW4sXkz6Js7EdAOPUKfs5QwghhBg5PQ8io0bt85Xg6CxsKIQQQgghRk40nmB1bQdfr68CwNTipbtmK44Zi8Y4ssOfVJSOc4qi4LYa8Zl6V5RmEpkzC10A1OUVA2Dd3ZI+ljP3uAH7zbObaDc409uVkV6JUodryHFmhgamKkpTX+R++fpGTnn5NfTBKKYWH7veWk44Hs9KkgJscEwgnqqONbU0clTbHgCSeh0Rp/baEys+IPyDX/HGEz8hmUxknZ9Ixlmx+xm2Nrw35Nh7297STbnJj7G5GwDbzNkH1Z8QQghxMHo+XyOpB5GEJFEqhBBCCHEoSaoJXt/8b55a83sCke5BnVPTGSCpquS1t6f3eXduHqkQRS+SKD0MeCwmuo2Z4fa9E6UOs5Fyt43drtI+5zlnzx+wzxybmZZeidLyYOZ/ToPD2d8p+/ThOdQIhwlEtDlPc5oySdhQLIJFn+hzfp01H3+eBwBPZwcTamsAaCstxluQq72eNdXkvLqJyd9+gD1Na9PnvlvVws+e/R+dt9zGjrtvJRT1DTn+Htuau1mQqEVJFbO6Zy844L6EEEKIg+VIJ0q1Ob4lUSqEEEIIceh4r6qFs/70b5Rr7yDvy79l0wdPD+q83e0+QMXV2pHeF9i1bWSCFFkkUXoY8FhNdBkyFaUGZ3bF54xCF5tt2XNpJvPdGDyeAfvMs5lp0Wf6Kfb3TpQOvaLUZtLm6UsPDQyF2dTUBajY2rrS7eLeTvLIXojC8O0bCJqsdHq0RZPyvR04dzYAsKN0Et0ud1Z7RYXdD96FqiYBuPrxFdhff5b8J9dQctertO/dOuT4e6xr6GSBd09623PUwgPuSwghhDhYPQ8iw0YtUaqEImMZjhBCCCGE6OWXr2/k+JZVuN7diXV3C1333Esssf+/13a3+XCZYpibutL7olVVIxip6CGJ0sOAy2KkSclUeVpd+VnHZxW52WTPnrNTmTxhn33m2kw096oozfN2pn82HcTQ+0hPojQcZUNjFzmWGMY2f7pdwtdNQSKznfu3PzHlp7cA0ObSKkftm+owdmrJ1NU50+iwZydKAWwPvEabr442f5jNTd3MqN0NaEnUrk2rhxw/QJs/zNbmbmbUpYb9F+Zgrtj371EIIYQYSVajAUWBUE+iNBwd44iEEEIIIQSANxxlyc5GTty9Pr3P/co6dlXve0pAVVVp8e7gFGcD+nAsvT9eUztisYoMSZQeBipzHWw3FKe3cybNyTo+s9BNqy2HuMWU3meaPm2ffebZzARMNlSdAoC7qyt9zOjom5jcH5vxQ4nSUIQNDZ0cpcteuV7xe8lPZIbGm/MKcFlMeKwmGu15WpteizhtKJxNiy2nz/UsVa20rnyT17c+yWfnN5C/c2/6WGDrgc3r8V51Kw5TnPwdWl+mhcegKMoB9SWEEEIMB51OwWY0EDZon69KKIqqqvs5SwghhBBCDJUv3MGmurf7zDPa6qvlg5rXiCWyH1i/uKWeSrePwlXb0/v0/gg7//U79rSupz/N3iAPrXyEuTse4v8Cy7OOKXVN/Z4jhteQV72vr6/ntttuY/Xq1ej1ek499VRuvvlmXK7sKsMnn3ySm2++GaPRmLX/oYceYt68eQcXtcjyk3PnUeGxY1vootBmxDozezX7GYUuUBQ6iwooqKkHwD5z7j77zLGZQVGIOayYvEHsnZk3AqPzABKlHx4aGI6yoaGTY73VWe10QT955kxFqSW3EICJOXZqbAVZbVWnnV32ApqsmUSpqigoqS+IG994Gnd9A59JJLHszczrEd21a8jxgzbX6XxLC+ZarS/3SacfUD9CCCHEcLKbDAQNFgB04RjxRBSjwTzGUQkhhBBCHD4eX19DrOp+HG8vZetZCzjr9O/hsRZz97LNFO2+C8f720h8O8DC2RcB0OFvpaH9UX6WvwlTsxeAhE6HPpmk8G9vsM5hwvPtu2nx7SUc8zMx7yi8ETs/eOY+Pr/0YfIfX9UnBkNDJ9F4GFPq7z4xMoacKL3qqquYO3cuS5Yswefz8c1vfpM77riDX/7yl33aHnfccTzwwAPDEqgYWLnHzo/PnQf0n4CeVaQlNhvyi9KJUvfcY/fZZ55d+4IVsWuJUlOrN33M5Oxbwbk/Rr0Oo15HpKfiJRxlQ2Mnl3dml47rgwFydL0SpfklAEzIsbPHXJjVtuCzXyCahHpTbnpf7cSJFLc1Y/KHKHvm3XRSszd1z94++wbj3apWTu3KTJ5ceNp5B9SPEEIIMZwcZgOBVGJUUSEW6MboLtzPWUIIIYQ4VCWTCRRF12cEYyjqx2K0oSgyOHg0bWjo5NP3L+W1tffjXFVF7LFVvParLgKVJ/LUqi5u+8MjGLqC+FQrzXcczfObXoO6dSz4wwvYN9UBkNTpuOX0r/Djpf/CGI5RfvtzPFs8kZwd2zB0Bdl68jTW26Zz+bpX+k2SAhi8IbpbqigondXvcTE8hpQo9Xq9zJ07l+uuuw673Y7dbufiiy+WZOghrthpxWUxssFawXy01eDdc47Z5zm5Ni2hGbbbcNKOPpSZF+NAht5DT8WL9kVOF47hC0cpbWzIamMMBsgxBdLblrwibb9exy5T9pe+iptv4Zh/L2d7R2ZO1heKjuUi/WpK/DX9JkkB9DXNxBNRDHpTv8f7E4rFWV3XzlX1WjWqarNgnzt/0OcLIYQQI6V3RSlAxNeFTRKlQgghxLi0p3UTK7Y/So6zlLPmXInJYCGeSLKtaR0ffPBvJkw9kdNmfmaswzyivFfdSr41inOVtpiSsTNA5bf+Q/dZq/ju7DIMXdoaKvoVG3ntkZ8x4aW1OFfsRh/ILNqU96nPUHDmZ7jKVci9z9+OPp5g0t2PYduuDacvfOBdCk6ZjuvdnfuMpXb9W3iKJmPUy+ihkTKkRKnL5eL222/P2tfY2EhhYf9/jDc2NvKlL32JTZs24XK5+M53vsMnPvGJQV9PVVWCweD+G45zoVAo678j4UsLJ/FKx0l8duNS3ItOIu5yk9jH79au04avB622rP2qAmFVRTmAfxebUU+oJ1EaS2DVxfE0tWW1MYaCuGxaolTVKUQMBqLBIB+bUcxT6z20uvMp6G6j5Fe/I2qxcPcnjuHHZj1dic9TnAjycMF5LNxQR8numgHjMLV4aWzYSV7epEHHvqq2g1giSVGN9jRIPWo6oWgUoofWohmjcS+JI4PcS2I4yH00OqwGHX59JlHqb2vBnFs+hhENP7mXxHCRe0kMB7mPxHAJhULE1BA7mlZT4pnNz159nTOrH2PSna8Qqczntd8EKC8/luteaOfsqqc466//xXvmUoL/vWisQz+irK5p4Riyi7x0sQQ5L28k5+WN6X2W3c1M/elj6QSpqtPh/Ob3KLr0MswzZ/GdFi8nrKlix5TpzNq+NZ0k7eF+ZwcAIbOFR+eezRfXPN8nlsanH2arqYaJ5ccxt+R0TAardo68L+2TqqqDXmNmyEPve9u4cSMPPvgg99xzT59jubm5VFZWcu211zJ16lRee+01brzxRgoLC1m0aNGg+o/FYmzduvVgQhxXqqurR6zvz04wcPmXT0X3pZMJ6PVs27Ztn+3bQnEA/DZ71n7VYtrvuQPRqwlCvZ56lBn9mBs7s9qYwmGcYS1RmnRY0teabVC588xKjIvvQelqpfmYY2neuhUj8KsTC+DEbxEFnE9uZ4+7lOM+dO0kCktmncjZW7XJkHe9/SotM08ddOzLqrsx6JLYmrsAiJYWH9L35kjeS+LIIveSGA5yH42wWCQrUVqzYwstCesYBjRy5F4Sw0XuJTEc5D4SB+u9Bh/uyOs4n1/PsmPmcMbe3Uy8/TmURBLDxjq4/i5qTpnOx+fOZMImbVFi+/u72LxlEzpFP8bRHzner2rk/O6q9PatF3ybk7a+xxl71mS1U1TSSdL4medi/ORlBOfOowpg2zZQVSqcJl4oO45Z2zP5hPdmLcTe2c38Jq2a9O3LruY+04x0ovSJo8/mwk1vYYzHKXxoOXlPraHrrNm89MXVVOSdhVWXmR5R3pcGZjINblTxASdK16xZw9VXX811113H4sWL+xw//fTTOf3009PbF1xwAa+99hpPPvnkoBOlRqORqVOnHmiI40YoFKK6uprKykqs1kPji000noSnduCz9E2Uzpp1YPNh5CypT8+hBjAv0YA+HMtqYw6HsUe0alXVac+61pzZ+7/GnJJWNtdkV9Hk/uF3/M5yFM+8sz6dKLV1Ng/pdTzXvJUi03aM7T7ttcyey+QD/D2MpEPxXhLjk9xLYjjIfTQ6CtZ24tNlEqWFLiflY/AZ5Y90sr15OQadkXllZw/6qf1gyL0khovcS2I4yH0khsOq2g6+v2wjzy97CMf6WgpKl2Ns8aIkkiRR0KFi31SHfVMduaVrSTi0z3q9P0JFaR4uT8kYv4IjQzyRZPej25jerq11krSY+MYPv83H75vPf3y3MLVVW3clodejTyS0kyZPYN4jj/X7t9CldQnuazmW65QH0gtRL/zZrTwbMHHc1jcpqCjlG5+8nJbXNrF94t3M3/Y+u6ady5+cxXxxzXPkB7vRB6PkPbcO58o9tNzi5ayLbyMWifd5X2oP1LGrdRWzik/BZcnvE8uRZNcQFvU+oETpkiVLuOGGG/jxj3/MRRddNOjzysrK2LRp06DbK4qCzWbbf8PDhNVqPWRerw1tcQiv6UOJUqv5gGN0WEwEe1WUzu3ODI/35efgbOvEFI5g6ykVdzuGfK05JTk8Y6vI2ld66rnMaoF7P6glbjJiiMbwbVyFxWpBN8hJsGu9EWbGmlG09zE80+ceMv9W/TmU7iUxvsm9JIaD3Ecjy22z0KTPfFFXIqFR/X0nkkkeXPU6xponyHt8NUmLgdCfjyXfOfzD/+VeEsNF7iUxHOQ+EgfjzaqdzLF34livJdrMDV0AqHY7t116M3Pef5WLty5NH0saMt9dE2312EqnjHrMR6Jlu1byszO3MfHX2jR8yvSJnDdvMvdeofJQ/q1cv/TfPBG0sjBSw+ytWtVvyVeuxm6399vf5cdO5Q/v7GBD+XTm127HPH8ex11wNicBkBn1+qdLFwGLgKt4HOj81sX8d+1N/PGBJzl9+fOcvWcVphYvRT9+iN0nnE+eZwpJNYHVasVoMnPLqx9wVN2fyH3uA7Z+fS9nfOwHI/lrOuQN5QH+kBOla9eu5aabbuLOO+/k5JNPHrDdI488gtvt5vzzz0/v2717NxUVFQOeIw4teTYznWZH1j7VduATBttMBgK9EqWTWzMr3jeWl+Fs68QYimBNJUoVt3PI15hd5OZvjjziZiOGSIykzYJl2gwW2TpRFR27yyqZUbUT62sfUN+2lYqCOYPqt7rDz/RQc3rbNX3ukGMTQgghRoLdZMCrZCpKEwHfqF07nkhy+p9f5ZN77+Pkvz6R3u/98gryT7901OIQQgghxpt39jRzundL1j6dw8GMZ17imRMW8ZkHFnPlkrf492O3aMfiyXS7QF0VzBv8VHJi6BLJJBf8fQkfqfoni//3MqZW7e8r0xwth3Dl8VO48vgpqDd/hv+77Smalj3HbDajWCwUf/bLA/a7sCKPey49gejC31O49iWKvvaNQcWTYzNz9cmz+PSC6/novcew6pXH+cE792Ns89Pwu1+x8+gJJKZPwV+1k6r2rZToYky+9mEAvKF/wxGeKB2KISVK4/E4P/rRj7j++uv7TZJ+8Ytf5NOf/jTnn38+0WiUX/ziF1RUVDBz5kxeeeUV3n77bR599NFhC16MrFybmQ5jdqIUq6X/xoNgNxnw9hoaWNTYCEDMZqbdkwuAIRTBklooSnG7hnyNWUVuVEVHfV4xExtqMc+bi6LTcWx5LseU5fLglLP4RdVOTC1eVt7/M949aR7nHfVVcuzF++x3T4efs/wtmdcyafqQYxNCCCFGgsNsoFuXqSiNj2KidHurl+U1rdz0/oas/eGG2n7bJ5JxPtj7GvmOcirzjxqNEIUQQohDTiSeYEVNG1+u1xbvSZqNHPX2+xiLSjDma0Okjy7L4Ql3OUm9Dl0imXV+uH7vqMd8pNnY2MVrOxq5ZsXqdJIUwL3ghKx2iqLw+BdP540TplJywdG4FxyLIS9vn31/bdF0WDQdrjhvyHHl2sz8/hMLOXVvG5dUvcuMut0UPrQcHlpOpNRDaHoxc5bvQu1VQelasZukmpB5bQdpSInSdevWsXv3bm699VZuvfXWrGMvv/wytbW1dHd3A/CFL3yBQCDAd7/7XVpbWykvL+fPf/4zc+dKJd54kWszUad4svbpD2Joic2op6FXotRRoyUeuwvy8Ju0fvWhCKaAUfvZ7R7yNU6ZXMQFs8tYFf0iM1Y+RcWNPwW0N6+rFk/nm3tb8DsdOHx+Jv70KeKul9n1DzfHnf+dAfuMxhPUdQUp8bYBkMhxoHc4BmwvhBBCjCa7yZA1R2nC7x+1a7enFiywBIJZ+6OtLX3aLtnZyLtb3mDhE3exd0YJzu/+mTxH2ajEKYQQQhxK3t/bRlKNUbZLWyBId/RsbHOyHyDOK80hrjfQWVJAXl1z1rFIY/2oxXqkavJpI11tLV1Z+wsXn92n7THluRxTngtnzxuN0DhpUiEfm1PBb5s/zd/rbkvvNzd0padw6E3V6/D5W3E7910gJjRDSpQuXLiQ7du3D3h8yZIl6Z8VReEb3/gG3/jG4MqIxaFnRqGbJy1l6YmkAXKnzj/g/uxmAz4lM/Te0K298XTmF+I3piamDsVQ9dp+vSenbyf7YdTrePb/zgTOBH6WdeyKYyq58bk1PDnjdL6wWls9zuANEX7qRdhHonRvV4CkqpLX2a7tKCsaclxCCCHESLGbDIQNmVU8E4GBE6Ut3ho+2Psac8tOpSzn4EdHtAe1RKnZH8raH2vrmyi9+vGVfKn6GfKf1FaIXTNtDudcduuwLvokhBBCjAdv725mur0b244mAGyL+o7YPbpUG3W5t6i8T6I01tQ48kEe4Zp9YRRUDOFIel/FHb/HPv+YMYwq4x+fXsSvC1zsnuagfvVa1od0fH3NU+gTcZqc+ZR5W9NtlUSSjh3rcB/7kTGMePw44FXvxeHvFx89mukFTpZNsjK3fhtzS3Io+NJXDrg/mzF76H2PtrxifMZMparBH9b+mzP0ROm+2M1GHvrcKdxkAmsoyGWbtcS+uqtmn+ftade+cLraOgDQTxj+xSmEEEKIA+UwGVEVHQmLEX04RrKfRKmqqnznqVXMqXmAmf97jq1Xfoyyq+4+6Gt3BCOAivFDidJ4e3vWdiKZZFebj9LauvQ+028fpPbMzzAhf3DzhQshhBCHi/eqWzmmcxdKakh9zql9qxSLXVYKHRa25UzgGNZkHUu2tPZpPxziiRj1ndspck/CYux/MaIjRYsvjNMQSxd4WW/+LsXfHLjAarQVOCz85sJj4cJjSSZVPqhpJrr9i/jNbp6u8jH54XuwtNRzxh7t3vFuXgcfSpRGYkEMehN6naQGe5PfhhiQx2ri26fMglNmDUt/2hyl1j772wom4I0k+uw35ux7Xo8D8dFZZZx00yXkeGNUuMKcuPw99DVNxBIRjPr+F6qq6vBjNSawtGjTSpgnThz2uIQQQogDZTNpf84lzD2J0mCfNlubu/nLu9t5btVr2DfVEb7vBRiGRGlnMIpNH0efesjZI9nZmbXdkjpuTWSqMuxb6ml86mEmfPWXBx2HEEIIMZ5Ud/hZ4G9LbztmH91vu3mlOazdPYkrPrRfbekY1HXCsQCvb74PhyWHxVM/ickw8Joj6+o72LHnBQxLn8B16mmcfer3BnWNw1WzP0RZsgslqY2utRSVjnFEA9PpFGYVudjakc/8WbM4+wwbt08o5ifPr2Jp9dfRJVVCO7eRTCbY3foBXcE2tjTuRt+6lah9Il8++4axfgmHFEmUilFjMxkI9ZOMbMmdTHdr3zlWTLkFIxKHy2Ki3G1jp7uMEwFTi4/OlioKS2b2276q3c8idzN6v/blzjpZFnISQghx6HCYtT/n4hYTpu4gYW8btR1bKc+ZgaLoANjV5kNBxZn6YqVv7CCRjB90BUFHMEI+wfSXiB5qR1fWdn2qGsOZmsu+R+TdFfDVgwpBCCGEGFdUVaW2K0hRQBt9kTQb0Ofk9tv26NIc7nVPSm8nFQWdqqK0dvbbvrc/L9vGyjXP89W//5ZokZuXf1zDWYu+gd3s6dO2OxTl9D+/yl+r7qXiueX4X9lI/K1vYdAbD+xFHgaafWHKE13pbUvx+JpXfUahm5jeSKAgB2dzB8HtO/jr63+k7I1XMTV1M3FHE/aNdXTMnACrJVHam26sAxBHDrvJQEKnJ2nIXmnNVzGLLl3fRaJGKlEKMKvIzUZrRXq7Y9OqftsFo3Her9nB171vp/flz1gwYnEJIYQQQ2VPVZTGTdo8perGHaz6683sbd2UblPV4cdjjmJq1hKVxo4APm/feUSHqj0YoSjhTW8njKnEa6c3q119t1blauv0Ze1n576nvxFCCCEON93hGIFonDxfFwCJAs+A83V/ddE0pk+bSPUlV7K5aAqrFx4PgKHdRywR3ed1/rB0KzM2rsRS3YZr5W5yr/4jzz/3Y55f/2de2vA3ApGudNuVe9uIxCOUbNkNgH1DLc0tA69PcyRo9oUojmYS0rbS8TWydGahC4CWgkIAjFurmPXzP1P65zfIf2I19o3adEh288jlXcYrSZSKUZMeGmjJPJVKluRhzs2hU983UWrJKxyxWGYXu9nkyCRK/Vs29NvuG0+s5ETvMir++joAhopy3ItPHbG4hBBCiKHqqSiNmrVEqW17ExW3Pceu/96TbrOn3cdUtQ1dLDPVja9q60FfuyMYpSCeSX52F+cDoHRlz5Pa0B3EYkhg6shOlBqrmglGPpQ8FUIIIQ5jtV0BANxdXQAk8vuvJgWYmu9i5TXnc9n9/+DWL/+KHYVask4fjBLoGHhBp0g8QVWHn7KuzCJQ5toOKr96L+7P/gL7F3/O9tXPpY+trGljmq0La5U296mSSNLyzisH/BoPBy3+MIXhzEgY+zhLlE7JdwJQl6MtRm2pasWxbi8AAbuL+MTJlP7sVha99NqYxXiokkSpGDU2o1ZJmjBlEqW6GVNwWYx06PpOFO0oGLlFk2YVeeiwuok6tDlTw9syXxa7g600de+htjPAA6v3cOyby9DFEqhmEzP+9zQ6y8DzugghhBCjraeiNJaqKO3heOyd9M972v1MDzVlHfdV7Tjoa3cGI+THMonO5nztIae+O0gymUnK1ncHKVF86EMxAOoqtS8bBm+IjppNCCGEEEeKui5tlIU9NfpCLcwf1HklLiv1lkxS1V+3Z8C2VakFiQvaMvOgxnV6dLEEluo2rDubCT/6DADJZIL6zg18JLY1ayod33vvcCRr9oXJD6f+jXQKpvyiMY5oaMwGPVPynGywTMjan//5KzmtvoVFm3dQdv330btcYxThoUsSpWLU9HyRw5wZem+dOw+X2dinolR12DCXVTBSZhe5QVFoL9LKzBO7qonGEzy8Zi0vP34zy/5xEztatC+Qzk7tKZK6eD62eUePWExCCCHEgej5fI1+KFGaLM+MzKjq8FMZyB5qH64Z+AvWYLUHouRGM4nSWrf2JUIfiBAMdqX313cHqYxlvqytqJib/rlz/fsHHYcQQggxXtR2BTDpk5jbU9PUFAxuJGWJy0qtMbPgsb9+94Btd7Z5U3OTa/OgdpxzKl//7G08MzMzOjJRW8faunYeXPYQH13/EGftyP48Tq7eSFLtu+jykSCRTNIWiJAT1P6Nkm47il6/n7MOPRcdVcEbBfOImiwobg+T/v5vKv/ydxSDLFe0L/LbEaOmZ+i9MTXUAMB99HG4LEZCBguqAkrqAZb59JNH9H/eWUVuAPbmllKyey+G3fXc/NwzFNW/zqk/+g+6eJIGxyxgGma/tgCFLsczYvEIIYQQB8ph1kZqREzZCyYmY9oihMmkSlW7n1LfhxKle6tp89WS6yhDp2Q/O/eFO3l9078o9kxm0dSLB7x2RzCCO5r5XK92Fad/XrnmAYqmLGBu+anUdweZEGlPH3u76Cg+qbyAokJg8wa4bIgvWgghhBin6ruDlOh96cWClcLi/ZyhKXPbeMuQSZR27xx4RMauNh85lijmxi7t3HlzeeJbX2XmrwqZbW5j2votqHVNnH/vi/xj158penZNnz6sW+tp66ymMHfKEF7d4aEtECGpqrgCqYrSXPcYR3Rg7vjYAq47fTZ5v/kyOrNZRscOklSUilHTU/HSM+wOIG/BSTgtRlCUdJIUoOgTnx7RWHJtZqblO3nLrq10b2rxsWj7g5xw71Po4klt3/LlgIoxlSjVe3JGNCYhhBDiQKRHbFizHzCqHdqIiCZfiHA8QUFne9bx4NbNvPnvG1mz63lCUR97WtcTS0T5yUvruOFPf6Do0lvxf+8nROPhAa/dEYrgjmjD+xJWE43WzGel/sY/UPOLH9Lhb6TRG6I4klkQYYetBF+BNnwwvHXrfhekEEIIIQ4XtV1BJsda09v6gtJBnXd0WS7tNg8hlwOA0Oq+yc0eu9p8zEo0p+cmd0yfzYQcO4oCzS5tqL+hsZOzcqsoeGV91rntuVoyVheJU7P0mcG/sMNIi1/728cR0P7GUfLHZy5AURSKnFYMbrckSYdAEqVi1FgMfUvVrVOm9bs//yMXjng8N5w5hxemLKbbpT0dmnDrs1j3ZD6wzLurMesS6H1aotSYl9dvP0IIIcRY6kmUevzZK83TpW1XdWh/5LvbO7MOu5ftoPIHj9H281/ywst/YuPvv8/6zc9x5ztbOWb9CoxtPjxvbsXbXN3vdcOxBMFoAmdYqyiNOW006TMVF/bN9RQ+tJz6FS9T3x2kKKBdXzXq6bI4aSrSKmj0m3bz5Os/pSvY0vciQgghxCGusWsXq6tepMW7d1Dt67oCWaMs9EWDm3LupEkFoChUTajUzlu/c8CHmTtbvcwMNaS3PTOPwWzQU+ayUetIrYLeGeCzG15GF4lnnfuvueeTSH1H737uWVRV5UjT7NN+r1av9jeULl9yAUcSSZSKUeNMrXbf5so8jVH0ekz6vrehIWfkn9h8ceEUJhTncu/R/SdlrTsbmW7rSleYGvIGN8m2EEIIMZqMqc/RcEMoa7+uW0tg7mn3Y9AlsbV09jkXIP+pNbjvuI/Su17D+6e/4Y/EKW3OfLnyVm3r97yOoDZk0B7SrpNw2Ggw9B2a1vnm63SHY+T6uwBI5ruxmQ2stk0CwFLTzsTP30nVu0dm1YoQQoixl0jGqe/cQTQeJhT1s6V+GYFI14Dtm7urWb7rOW567m2W/f1HtN/4I97+1w28ve2/+00s1nUFKQtmEqW6QVaUTslzUuiwsKZoBgDmmjaaazf223ZXm49Jvswijs7pswGYlOdgjyUzJ2rBo9q8pM3z5hG84RZMX/kmT08/lc2p9tY319PmqxtUfIeTZl8IUDGn/pYyFBSMbUBiVEmiVIya6QUufnzOPN679ne4P34RM19eAsCFc8o5ZXIhL332OszTpjPj+VdHJR6DXscfLjqOl2adzq6yaah6Pa+cdBF/Ou8L2nF/mDPDmRWBzbmDm2RbCCGEGG0nTszn7qMuytqn6w4ST8aoavdRYfJj7ND+2A/kZK9uqipg36tVc+o37sSgS+Kpy1R3Bqt39XvNnkSpLaglaONOBx2mviunxt//AIMuSY6vS9tRmMdvL1zIS/M+wtqS6dp1g1GCL782tBcthBBCDINNjZ08teIxVt5zI4+9eiePvnAPVT//Ae+/eW+ftpF4guueWc3S+27Bd+OPmRN4mAm/eprclzYw8UdPEP/mT9haN/Bq8aqqUtcdpCjYAUDSYkRxDW7+S0VROGlSIW+7tenjFBValvX97hyOJdDTyhS/lihN5jrRO50AVOY62GYu6XPO8Tf9gNN++kPm//FO5lYU8HT5IgBMrT6qlzw2qPiGWyIZ33+jEdLiD2MzJDB0BwEwFo6vFe/FwZHFnMSo+tlH5sNH5gNfSO+zm4289c3zgPOAO0Y1nvNnlVH/yyuw33Y5SjLJ359dxzuvLeM7qePH1W5Ot7UUDG6SbSGEEGK0vfL1s9n1yRP47h2VnLz3LS56/Xn04RghbzvbW70siNem226pmMZxnZl5zVSDHmOn9kXAVNPKHFcX5oZM9Wmotrrfa3YEtXlFLUHtXNXtIqY39mln21jD36/7APcL2qr3+uJCvr5oOlPynJync/D8Sz8kf28D8W07D+6XIIQQQgxBPBFlfd0KrnhwD3fu+Cv5T6zGv2ATXruDwnc2EdjegnreDSiKkj7n6Y213L1sM5c+9DgGb4icV7MXVHKu3EPVr2+h9I5HUBQFb6idIlclJoM2P2RXKEogGiffq33OJgtyMPTqf39OmlTAs7mVxE0GDNE4gRXL4fOZ453BCH9ZtoLbnG9SvCn1oHNipmJ1Uq6DR+3ZBUCqTiH39HPT25fNn8gva47hZqMBQyyO/5nn4aJrBx3jwfJHYqzd8zS1Ve9x6olfoyJ31qhdu0ezL0yp6k2PLjUXDa7qVxwepKJUHPFcFhN6sxmd1cqxFXnUuQqJ2rSVg8s2Z6pozAV9n7wJIYQQhwKH2cjRZbmEJ0+n1pWpegi11LOuvoN53dXpfY+VLM46t2ehB9BGU3zCvw4lmRk2GKvtf8619lRFqTm16CHu/iti9P4Isy69G8terXomZ9p8AI4q8QBQW6h9vup31RFPxvrtQwghhBhOS3Y28vdXH6LpVz/m56Vvkf/EagAca6vJrdamn7FsrSMQ0RZGbOjcSYu3huU1rRSbghi82dPdRGwWdhZPBKDg/nd4+Ym/8PL/bmb1n27g8edvYEPtm4A2b7jVmKCgWxt6rysZWqXiSZMKSegNNFRO0Has25p1/KrHV7Ljreeo/OHjWPZq18iZd3z6eGWug4jBRNjjSO9T5kzF0Osz/JJ5EwgZLeyaoQ3xN6zeOmqfzztbvRT8+H9EbriFaZffzap7fjAmc6Q2+0KUJzIPja0lg5tHVhweJFEqRC8LK/JQFR3N5WUAWHdnhh7a8iVRKoQQ4tCWZzfT0mv4e2djLdtbvUxp1pKdiVwnFZdcwoMLL2RHSWW/fZywJXsV3XhdAxtq32Rv+5asLyva0HsVYypRqro9+43PsfhkKr55PQBFTiuFDgs7crQvH6aGTjrbagb7UoUQQogDEo0nuPTfS/E89gCFD7zLlG8/mHXcUqs92NMHIuxct5w/v7OUFffcwDv/uolgxMusaGOfPhtPOo3vn/4NYiYjSiJJwf3/pfK6Byj/7UtM+9w91P/w+2xrXMGTG/ZymqcexyZtpIfzmOP79LUvR5fmoFMUqoq0RKmxuiVrQad3q1qY35gZoZF76aeZeNPP0tuTcrUEaSwvkyi1n3Jq1jWmF7jItZnYUDQV0OZCbW+tGlKcB+rBNXsw6aJ43tqGklSpuP156tv7nyt9JDV4Q1T0WnDLUTF51GMQY0cSpUL0MrfYg6LA3sKyPsfMMvReCCHEIS7fbqa518rzdburQFUpqqsHQD97Ovd/9hQ6v3Ytvzrjyn77yH07+wuJsmsv9T/6AWv/eAMvffBnQlFtBdjOYBSrPo7er31BKygroTLXzoNnfA5Vr+fXJ3+ePcXaFznF42H6My8y69W3MFdMSPd9VImHdfZKrY0K7etXDMvvQQghhBhITWeA7nCM4mWb99v2lWeeJ7TkPipue56KnzyBvmkzU4NNfdrN/+JXafAUsnrm0QC4lu/KWk0+/8k17Lr9Zuo73uay2nfTQ7qLrvjKkGI3GfRMynWw26kNBTd0h+hs3A1Aqz9MozfEhFbtMz9ZXsiUfz+EecLE9Pk9iVKlK1MRW3R29uLGiqJwbHkeK51aolRJqrStfmtIcR6oQDTOJLIXn1z3wO28s/1/hGOBUYkBoNEbpDiSicNeLonSI4nMUSpELxajnnK3jRpnESf12p+0mNCZzWMWlxBCCDEY+XYzDfpMRWl7fR0FNge2Km2EhHX+0QCUuWw8a8rrtw8lkczatuztSA+bDz+xmuV3xzjjjGtoD0QoVfzpYfrWgkK2ffUi9MrFqNG/ctbavSgNl1O6axX5n/1CVoK0x1ElOdzvzHyB821cA+d87sB/AUIIIcR+7GrzASo60/7rxnL27mTSHi0RqSRV8vZsxBVozWpjrJzI1PPP57SGt3i09mQWbViVPtbhycGvtzKhvYGif73N5Y1d2DZriUzd3BlYZ8+BrdnD5/dnWoGTLbbMnJl1H7zOjtBmfLFpKKjk12uJXP3MqX3OLXVbAVhTMJ3T2rU4PSed3qfdcRPyuNOTSQ56V6+EC742pDgPRE1ngKnR5qx9eb9+ktCzy9n0qxgLTxidvxEaukMUBrVEqWrQY8yXVe+PJJIoFeJDpuQ52WnNHmafdNvHKBohhBBi8ArsFjpNzvR2sLmZhU4b+rA2t1jOQm1+0lK3FZ/JTsJkQB/tf1XZiNmEORLN2mfZ2078J3dR/dSZ1HYFOM2amb/UXTQJoz71pdNi4arF04HpaIs19m9uiYcOm4ew04bFFyS8ZcsBvGohhBBi8Ha3+XAa41gaO/fbdmpbLdaGzBDskvY6XH4vAPF8N/OeW4KpsBidycQVCybxjZ1zCDrt2Hxa9WPd127iJ3V6/vHsL8n3d5H70oZ0X8Vf+OoBxT+j0MUD1swIyNZHHsDU1E3g4mOZ5jkVa422eKJ17rw+5+p1Ov7wiYW8nfsNznnn3xRfeBF6e9/vuseW5xEw2+guysPd3E5o3QfUtG2i1DMNo2HkCohqOvycEmzJ2mfwhXGuqsL3t3/BKCRKQ7E4naEoeX5tftpkvjtrQS9x+JOh90J8yOQ8J1vNH5qP1CWJUiGEEIe+PLuZhN5AzK6trpvobGehb0/m+HGnAFDqtoGiEPI4++0nZjbx2OILsvYlFO3PRse6vWz78608v6WWj+xeCYCq15G36PQhxzuvJAeAllLtcze5fc+YLNoghBDiyJBIxmn3b+IUR0N6aLzv4kv40VlXsWfOjD7tXe/uxNjuT2+XtjWS25FaiGlCCfaj5mMs0hZkunT+RMwWEy9M1h5K6ouL+NIPvss937mUa7/wa96YvDDdj2XmLIo++6UDeg3TClx0WN3ErFrCMu+5dThXVVF58+P83LkUXeoBqOeYE/o9/zunzuLxH36e+S+/QdE3vt1vm4UV2qiT6jJtNIjj9Q2s+931PPfGLTR07er3nOFQ0xmg1J9JTP/l+E+mfza+twl/uGvErt2jMbVQl9unJUopyBnxa4pDiyRKhfiQKfkO6uwFqLpeT408roFPEEIIIQ4R+akEacRhA8AV6uD4Zm3O0aTLhnniJABKXdpxf6/Pt7ghM9DorYuuYnnRnKy+P3vZLTQXaF8G3f9+nWMLmyh5S6uMMZ9+MqbioS96OLvYjV6nsCevHADTrkZ84Y4h9yOEEELsz4qaVv7yyuMseOUevta6JL1/9le/xpvTT2Rvbuk+ztbkNrbgbNUqUfUTs6eU8VhNvPjVs5h8y+2U3forZj79MjqjkQvnVrDpV1dy5bI3mV3VyDF1bcxdtSFrpfmhmFHgAkWhq6jvFDqVP3oiE+sxiw6of4Ayt40ip4VNudrwe0WF0rtep+SLf+L9v/6AYNQ34LmqqvLerqd49oM7B0xsqqrK+r1vsH7vG+kHpIFIjLZAhEKflihNFOXwzlmXc9dZnwXAXNfB3nWvH/BrGqyGbi1R6ujWKod1RTLs/kgjiVIhPmRKvou43kAgL/PBpcs5sA8xIYQQYjTl27XqknAqUVqyeQ95r2jJTNcZZ6WHjpW5teNdTk/63KozTkG5+NOU3vwT3j3hfPaY8rP6ts2cwwMLzgfA2Objm7tewNipDS0s/0r/FSn7YzUaOKrYw+ocbR41gy9M88b3DqgvIYQQYl8u/tdb2F58iOJ/LKX4n2+n9+fOms+0fCfbXZnh7LsmTem3D8vedsypIfuWSX0X+DllchFXnX00pddcj23uUen9ep2OYpcVe0EBBo/noIZyTy/QHnK25BUO2EbV67BUHvgCRIqicOLEAt50zMzab/CHKfnVM9Rtfrvf897f28bRv76P0NU/IP/Cn7D1vf8BEI4FWL/3DbqCLSyvbuWbjzxA40030XjTTbR5tWl8alJ/U+R2pqZEKCvigtllvFq8IN1/2wtPo6pJGrt2E42Hs67tC3ewtvoVukPZc8gOVYM3CKhYurRKYn1x0UH1J8YfSZQK8SFT8rSVADvzMk/odB7PGEUjhBBCDF5PojSQmm/MXN+pLbZkNTP59j+m2xU7tcrTdnvmQeDRi45l4QMPUXbzTzhlShGdlky1qeOcczhzWjFLCzJf+soefhcANd9Dzkc+dsAxnzCxgHfdmeGOHcvfOuC+hBBCiP4kkyot/jDFSzZk73dYMObnM7PIzRpzZnHBTSd9nKWLPk7rlDlsnTKfp44/FwBdLJFesd4+pe9Q/dHQ87Czxl2c3hc3G1lTlklq6o6aiaI7uHTP7RccwykXfZSOq27g7fM+x81nX631HU/S+vKzfdoHoz7+9u5Szg6sxbV8F8bOAL77HuQfy3fw0JO30XTjTSx74pecctdLHL3iCfKeW0fec+toXfqy9npSiVJHexcA+rISzp9VTqsjl5ZybdRKYukKHl3xOMvvupYXX7uVcCxAIhnnt29u5on/3krbtTfx5hO3HNTrbugOYjUkMHZoiVJTSdl+zhCHG1nMSYgPmZKnzdfWnJNPBdoKh/rc3LEMSQghhBiUnkSp15w9t3bZj3+BeULmC6DJoKfQYaHZnvl8s1ZMSv989eIZ7Gz1UZ3/WxbsXsuEO37PGY1B7l6WQ2dRPjnNbek50HIu/ww6o/GAYz5hYj5/c+QTctmxegOE16w54L6EEEKI/ngj2qKGXRVFFNbUp/er5UUoisKXj5/Kl3Y00DX7aIoifm657WYMqe+AX3rkXZa98Q4Xv/9qVp/uGfNH7wX00lONutuZmfImOW0Svznvx1z12t85feu7TPjygY306G1GoZu7Lz0RLj2RwIa9LPnPUro2PIqnpZ3oshWoqoqiKKiqymPr96Du/AsX7NmNY3NNug/LO5tYctqTXHXXfVh3txBduZsTvjmZ6Y+sTbcJbNcWcqzpDGDRxzG3anODmiomcNqUImwmPSsmzuPCukZsG2tx/fX35D+xmljOWzxzl594PMKKzkl8747/YGrqxvPaJvjc3Qf8uhu8ISroQhdLaK+htOKA+xLjkyRKhfgQt9UEQIs7M+RQtY3cyn5CCCHEcMmzm3GajXygL+cotC8hxd+7gZJvXdOnbanLyi5d5rPOM2l2+me31cQ/L18MLE7vO80WQVFg84TpnNzclt5fcYDD7nucMCEfFIXaiolM37wFZcNOEsk4ep38mSqEEGJ4dAYjAFgi2cO1E0UeAC6YXU7zL69Aue0z6QRgjznFHh5yfGj4tdmEa2bfVeVHy7R8J+taMpWOFR+/lO3fvwzlp5ehxOPoTKZhvd6Z04rRKQobJ87klJZ3sazdTaevgVxXGXcv28Ybq5/mpt/9HUNXMOs8Y5uPLz3zENbd2kr2plYfN7a+iG1nU7pNZJe2OFRNh58J+m70wSgA1klTsRj1nD2thOdrj+VCXkEXS5D/xGqt784Ak77wZ5SkymfPOQZTk5ZgVVQIRnzYzP0vWLk/Dd1BJsYy86XbyioPqB8xfsnQeyEG0KzPDEfURxJjGIkQQggxOHqdjv9+4RTKrr6GnGu/z4znXqHiF7f3O/zurOklLPPMJGE0oi8owHXUgn56zMixmVlQlstb+XMz1zvuaCxTpx1UzNMLXHisJjYWavPBWfa00Na6+6D6FEIIIXrrCmkVpdZwdqLUocsk03qSox+eP/ToslwSegPVJZnKwkl3/Q3DGE7P9q/LF2M6eiFdZ38c19nnUnbNTRj0OvQ63bAnSUFbqOq4ijxeL9CSwwZfmJp3n8MbjvKbJev44qaX+iRJ0+e+uTVru/TPb2jTAqUkd1cDWkXpMWpjer+jUvv74vzZ5WwqmJKef723nn4KNu7J2t9RtaFP28Fq9IYoi2QSpfaKA5/rVYxPkigVoh8ui5EV+swqhsUzjh/DaIQQQojB+8jMMr7/yVOZ+vNbcZ1x1oDt7vjYAt697f9YsL2G+eu3o7fbB2zb44LZ5bxTMJeoUfsSNuHqaw86Xp1O4fgJ+bznmQ5oX3o6Vr1z0P0KIYQQPbrCWpWiKRjK2j/hG9fv99wzpxbz03Pn0XHdbeR99SrmLF9D/mc+PyJxDtbiSYWsvu5jnPP0U8x4+sVBfYYfrLOmF/Nu4VzUVCK56ZF/8493H+dLEz6g5NlVAEQM2lQ8ze48tk+enj43YjTx9rRj++1XX9NMRyBIh38XnwquTu93T9EezJ4/q4ykTsfGqZmRL6pO4f6jz09vG9t8WX12bFh1wK+zwRuiJNwrUVouidIjjYxpEqIfS64+lxufzcVnbmJCIkDh57401iEJIYQQw0pRFCbnOYHBD037/llzKXFZyf340xQFu8m55NJhieWYshz+7M7MkRreuWNY+hVCCCEAOoM9iVKtojRx+vHM+dFvcZywaL/n6nQKPzlvPjAfuGIEozy0XTingtte38SmiukctXc7+U+uZnbs99g31qFPVex2/fVRFtuT3LklQMvGTfy84S48J53M+5/6Dre/u4d5jbvw+LtZPnUhvgIX5y5fgrGpi+8+8jBfbniW0vuWAmCYWIFt8lRAW7zqmLJcXilawHFoiVT9wnm8c/5XCU3M5+vP3J9eYKtHYOM6+NSBvc4Gb5CCYBcASbNxTCuHxdiQRKkQ/TimPJfXvnEucO5YhyKEEEIcMswGPV9bNB2Yvt+2Q1HmtuE32UiYjegjMaJNjfs/SQghhBikrlAUUDEGtESpacZUnCcu3vdJIstxE/L59ccW8BPfV7jnhTso7m4j77l16eNF37ue4y7/OAAPfhySSRWFW1F0OmaoKued56Pop5diVpPc+MQGbCte4lyWoKhw6a7HKblH+1lXkM/MJ19CMWTSVRcdVcFv9sznRqMRUyxG2eX/R5lio9qU/+EwAYhs3Upd53byHeVYjIOvtvWGo/gjcUpCWkWpmu/pMxWDOPxJolQIIYQQQoypYpcVFIWQx4GjuZNEU/NYhySEEOIw0h2OYtIl0Qe0RZ2kSvDAXHfGHHLtZn6W4+Tzz/2F4+u3YEzEKb7++5T/9BdZbXU6BcjM+zo13wW4AJhZVMML1sycr6V3v679YLcx+4U3sM6YmdXXDWfMwazXYzjlYSraain6v69T8eQqXjEV9hunaeU21vz6GkKzyig9/mxOmfHpQSU836tuxW6KU7xnLwDGqZP2c4Y4HEmiVAghhBBCjKkSpxWAgMuFo7mTZGvbGEckhBDicNIZjJJLECWhDdE2evLGOKLx60vHT+XK46ZQ/4MrcKlxzP5uzBUT9n9iLzML3fzD3rcadOo/H8Q6e06f/WaDnhvOnANkjlV4bDRac/vt39gZoPSu1wAIzHmNtpeOpyB333ONxhIRVlUt42M51Vj3tAKQc875+zxHHJ4kUSqEEEIIIcZUqVtbybbb6aIIoKVjn+2FEEKIoegKRclP+tPbxpz+E2xicBRFodyTGtKe4x7y+TML3SR1OppKSyhu0KbbmfDbP5LzsQsH3Ue5x05MbyTitmPuDgzYzr65nsZ3XqbgE98YsM1TG6po3/0fjn3+NU7oDqb3l5x/2aDjEYcPWfVeCCGEEEKMqeJURWmHXfuypWv3oqrqWIYkhBDiMNIVjpKfyCTTzDn9z20pRsfpU4s4bUoRb510OfqKCUy88y8UXfWtIfVR4dEesvp7JWrDbjtBh4uI3clPz/waSZ023N777tIB+/nfB9U8+96DzPjxXyn430pyXt4IQLKsAMu04Z2TXYwPUlEqhBBCCCHGlMWoJ8dqos3mAUDf6ScSC2AxOcY2MCGEEIeFzmCU3FimotSc1//clmJ0mA16lqQXT/7NAfVRkapo7XJ7yKMBAP2MShY/v5y3q1p45V/v8I3dL1BUU0989XpUVe0zT+nmpi6++fg7PPLBf7FUZ0/7Yzv9NFnI6QglFaVCCCGEEGLMlbisNFlyANDFk/gb945xREIIIQ4X3aEonnivRGmuJErHu/JURWmbKye9z1hUiN5m44Rp5Rj1OjaXTQPAsmkvS7c8xNaGd4kloun2/35/N1OVRjxvbwdgW8UUkjotTTbh018ZrZciDjFSUSqEEEIIIcZcictKvSnzZSdQv4f8ibPHMCIhhBCHi85QlJxYZui9NadoDKMRw8FqNJBnM9Nky8w3ayguBsBmMnBseS7L9s7iTN5CH4yiv+xaao6pZP2FC1Fy3UyeejJr6oyc4NudPt/2kzvIL8kjL9SN+8yzR/01iUODJEqFEEIIIcSYK3HZWGvKrEIcbKgZw2iEEEIcTrpCUVzRzCI9spjT4aHCY6POkvnbwVRSlv755EmF/H3brPS2ZW8Hlr0d5D2zFgDvoqfYvOhHfLqtCgDVqOfiT56PzmQapejFoUqG3gshhBBCiDFX4rJSZ8x8cQ3X141hNEIIIQ4nXeEozlSiNGkzoxikZuxwUO6xs92QqQ52T5yR/vmMacV0W53UFpYCkFQUEkomBeZavovyRBMV9bUAqNMmSpJUAJIoFUIIIYQQh4BSlxWvyU7SqAcg2tQwxhEJIYQ4HETjCYLRBPZwKlHqsI5xRGK4nDO9hI3uSsJON4rFTOHJ56SPnTejlN9eeCyBW+6i4Nob8T62hEuvuIMn552ZbrMosgdPdSMApnlzRz1+cWiSxyhCCCGEEGLMFbusoCiEPE7srV0kmpvHOiQhhBCHga6QtniPPZIaeu+yjWE0Yjh98+QZnD+7jIpbLoN4DGNBQfqYoih877TZwGzgE1QC/4eT31kcXLJhCQCnNG/C2KYt8uU6dtGoxy8OTVJRKoQQQgghxlyJS6vwCbidACRbWscyHCGEEIeJrnAMAGs4rO1wOsYwGjGcFEVhcp4TY44nK0k6kLklOYSMFvwFHgAq3t6QPlZwwukjFKUYbyRRKoQQQgghxlxPotTndGk7WrvGLhghhBCHjc5gBABLUKsoVVySKD1SzS32ANBcVAKAsV2rJlVNBhxz549VWOIQI4lSIYQQQggx5vJsZgCCNm1IpOIP7qu5EEIIMShdIa2i1BzSKkoVt2sswxFjaGq+E5NeR1VeadZ+40knyEJOIk0SpUIIIYQQYsxZjdrU+RGD9kVFicTGMhwhhBCHCW2OUhVjUEuU6l3usQ1IjBmjXsfMQjebXROy9pddedUYRSQORZIoFUIIIYQQY85s0KEoEDUYAUmUCiHEeJZIxtnTso5Q1DfWodDsC1FsDWPs1kYqWPKKxjgiMZZybCY+sFVm7cu/4OKxCUYckiRRKoQQQgghxpyiKFiNesJSUSqEEOPa0xv38s/n72Xb97/BW8/8ElVVxzSelXvbOClRhS4SByB3gaxufiQ7riKPXc7M0HvLCcehs1jGMCJxqDGMdQBCCCGEEEIAWA0GwnotUaqLxEmqCXSKfoyjEkIIsT+RWIhN9e+wp/EDHlyv5ysvPkTBko1E39xK4xlfojR/xpjE5Qt34A9t5bT2bel9+SefMyaxiEPDNafN4oP6DrZd+T0W7lrD5HvvG+uQxCFGEqVCCCGEEOKQYDPpCRu0RZ2URJJ4OITJKqsTCyHEoSqZVPn7ih1EttzNnMdep/LdnXzr9Jk4VlcDYGr1sevhuyn9zl2jGlcimeT2NzYyedc9XLViHZaqNi3eiiJMRcWjGos4tJS4bLx61TmAJMxF/yRRKoQQQgghDglWo4GQPrPqbDTglUSpEEIcwu5fvYcfPPcOzz94P8bOAACet7ZltUk++Czer/4UlzV/1OJ6aG0V97yzksf+81+M7f70fuPCo0ctBiHE+DTkOUrr6+v55je/yQknnMDixYv5/ve/j9fr7bftiy++yMc//nGOOeYYLrnkEpYtW3bQAQshhBBCiMOTzajPSpTGA/3/jSmEEGLs+cLt7G5Zx1FqczpJ2h/7lnrWPvOnUYwMnli/l/Ni27KSpACexaeNahxCiPFnyInSq666CpfLxZIlS3jyySfZuXMnd9xxR592W7du5aabbuL6669nxYoVXHnllXzrW9+iqalpWAIXQgghhBCHF6vRQFBnTm/Hgv59tBZCCDEWwrEEP3t5Pa/88/ssevJ3XGlZlz62bcLU9M87p04jbNUWyVF+/S/qO7aPSnyxRJK39zRzSv2GPseKTv3oqMQghBi/hpQo9Xq9zJ07l+uuuw673U5xcTEXX3wxq1ev7tP2scce47TTTuO0007DbDZz4YUXMn36dJ599tlhC14IIYQQQhw+bCY9wayKUt8YRiOEEKI//1q5i1tfW8/Em+6j4NH3mfvbp9LHfnbilST02iJ8L849n38c+wkAbDua2PKXW1BVdcTje7eqhUgszKRNmcRs0mTCMn8+tllzRvz6QojxbUhzlLpcLm6//fasfY2NjRQWFvZpu3nzZk47Lbusffbs2WzcuHHQ11NVlWAwOJQQx6VQKJT1XyEOlNxLYrjIvSSGg9xHYqiMOrIqSgNd7QSDQbmXxLCRe0kMhyP9PnpuUw12fazP/kShh+9+4QKMH5tNia+NC2adzBcffpfLdrxFUUsz9n+8xK7PvU9Z4VEjGt8zG6o51tqMbWcjALprr2HGV76DzukkFA6P6LWH6ki/l8TwkXtp31RVRVGUQbU9qMWcNm7cyIMPPsg999zT51hXVxdutztrn9vtZteuXYPuPxaLsXXr1oMJcVyprq4e6xDEYULuJTFc5F4Sw0HuIzFYsVCQsC5TUdpQXUV3TuZvQbmXxHCRe0kMhyP1PnISpTjZdw7peHkxJzmj4CyjkTKmqV5mFTr544JLuf3lP2Ns87Hz77+m++IfDDphcSBe31rL6U07UFLFq4m5x7KztRVaW0fsmgfrSL2XxPCTe2lgJpNp/404iETpmjVruPrqq7nuuutYvHhxv20OtqzeaDQyderU/Tcc50KhENXV1VRWVmK1Wsc6HDGOyb0khovcS2I4yH0khqpoa4AtOkt6O99pp3LWLLmXxLCRe0kMhyP9PrJsDVCU6JsotUyfxqxZs7L2/cyQw2VtARpLSihpbMT93zcp+uYvyPNUjlh8/heryQtn4pt11rnoD9F/pyP9XhLDR+6lfRtK0eYBJUqXLFnCDTfcwI9//GMuuuiiftvk5OTQ1dWVta+rq4vc3NxBX0dRFGw224GEOC5ZrdYj6vWKkSP3khguci+J4SD3kRgsp8WMX8kMvdfFo1n3jtxLYrjIvSSGw5F6H3mjCfLjfeeQdsyc0+f3cdH8ySyo2Mbf5n2CnzX+FVOzl873XqXi0mtGLL7OUAxXOABA0mLEmZc3YtcaLkfqvSSGn9xL/RtKFfuQV71fu3YtN910E3feeeeASVKAuXPnsmnTpqx9GzduZP78+UO9pBBCCCGEOALYTHp8veYoTQQDYxiNEEKI/nQEIuRF+yZKXbP6ftdXFIVPzpvIkvIFJPVa+sG7/D0A2v31rK56EW+obdhiiyWS+CIxnJFUotQpWtSYSgAAVeZJREFUCSMhxNAMKVEaj8f50Y9+xPXXX8/JJ5/c5/gXv/hFXnzxRQA+9alP8d577/HWW28RiUR4/PHHqa6u5sILLxyeyIUQQgghxGHFatQTUHonSg//RT3HSnewlW2NK9hY9xbdoUN33j4hxKGnPRghN+bvs98zZ0G/7RdVFhA1GGmZUAZAYu0GLr9/Ka/+/jr8n/kGS/7yLfa0bmZzYw1PPHcjr6/6K4lk/IBi6wxGAHCEUg/aXPYD6kcIceQa0tD7devWsXv3bm699VZuvfXWrGMvv/wytbW1dHd3AzB9+nR++9vfcvvtt1NfX8/UqVP529/+RkFBwfBFL4QQQgghDhs2oyFr1fukVJQOuxZfiLuXfcBRa3+Le9Vu9MEIu8+cy/QvfI/ZpSeNdXhCiGHS0LmTqrb1HD3hbOxmD+GYnxW7n6HINYmZJYvoCDTgsuZj1Jv339mHdASjeCIfSpQ67VgnTOq3/cKKPAw6hW2lkymuqsW8pZbG5jVU/u0FDN0h7BsfZ/OuJtaXTuPc3/ybWIGTlQ+bWXzClw4oNgBbKPWgze0cch9CiCPbkBKlCxcuZPv27QMeX7JkSdb2ueeey7nnnntgkQkhhBBCiCOK1agnqdORNOjRxRMkw6GxDumwc8Nza2H7s3z8d8+m97ne20X9sm0Y//pPppWfMIbRCSEOVmcwwgV/X8J11XdR+MoaNt12I48qp6Fsf5xL/vI36o4qZ9XXLsTzzmqSx8zgpI/dQIGzAtAWY44mwpj0lgHn81NVlY5gBFfP0HaDjpz/u5rCs89B0fU/YNVmMnBMWS4r6mZwOkvRByJct+tpDN2Z9/jiB5bhnrwdJaliavYSuOYW9j59LBOK5g3t9Ye0RKklqPWtuF1DOl8IIQ541XshhBBCCCGGk9Wk/WmaNBvQxRMy9H6YBaNxntq4l7/WrAAgqVPotLnI83fjeWsbu398Lfl3P0mOvWiMIxVCHKhH19ewramBskeWovdHCD38FH+sKOR/u9/E2ObD8+ZWXO9sRxdPkjS8yfINuznlR/+k2W+lpmEZvjf/Q+VHPsPC2Rf12783HCOeVHGmhrYni/OY/rs79xvXosoCHt42I71d8tB76Z+TOgVdUsW6JzMNiH1jHc33/5MJN+y/7946UkPvzQEtUarzuId0vhBCDHkxJyGEEEIIIUaC1agHIGEyAqCGw2MZzmHn+S112AwBJr6vLbgaOeFYLv/0r9hcMQ2AvMdWsu7B28cyRCHEQVq9t52F1KH3awlDdU8tNmOC4k270m108WT6vyV3vcL6/93Noj+9ROBXtzLxJ0/i/c73CUX7zkEKmUSkvWdqFI9jUHEtqiygzZ6LL8+Ttf+JxR9l3ez+F3yObtk2qL6z49MqSo2pRKk+J2fIfQghjmySKBVCCCGEEIcEq1GrKE30VJaGZOj9cPrfumqu0G3A1KStKTDlC//HDz+xmBtO/yZ+p5bs0P/lUbqDw7cCtRBidK2pa+e4rkxS1Ly3lbNtVZhT/98DdJdV8puTPkfIoa0IH3/0Kdz6bkqWaQ9RnCt2sX3Zo/32355KRFp7hrYPsmLz7Okl5NvNPDNxcWan2cye0z/N/ZPOSu/y57honjYBgGRj86D67q0zGMGgJDD4tAdthpzcIfchhDiySaJUCCGEEEIcEmypitJ4T0Vp6MiuKA1F47y7/VHeWP9PglHvAfWxt30zH9S8xorqJgLeD/j4spcBUM1Gii+6nOtOn427vIx/L7wQAOvuFna8cv+wvQYhxOgJxeJsaupiRkt1ep8+GOVTe95Obx+1YRvHrdvCC0efyztztDmJnSt3cY1+WdacoW3/+DvJZKLPNXoqSi0BbWqUwSZKc21mdt58ERf98280PPAiZfc9xJyly7ni46eysmQ2TXnalB/PTjmZDreW3FSahv7QpiMYJUcJoYtpsRtz84bchxDiyCaJUiGEEEIIcUjoGXofN8rQ+4fW7GHhbX9FvehbWD/2PZY8dDOBSPf+T0zZ1ebl7L+8wMabrqLrM1/jnmf+yTVLH8C5cg8AOZ++Ar3TiU6n8LVF03iy8mRiZhMA3v88SCIZH5HXJYQYXolknBZvDYlknPUNnSSSSUrr6rLaVL6yCoBkWQGWyVNxW018fE45D1ecCoAuluCEe5/OOsf5ygc012/pc732gJYoNR3A0HaXxcQZ00r4xMXnUnrZp7HNncfFR03gyS+fSdF/n2PiPf/gbwsuocWpJUr1zV1Dfi/qCEYoSmamDTDlFQzpfCGEkESpEEIIIYQ4JNhSQ+7jMkcpD6zew6KubZhrOzB2Bsm/4Z8s+c+N+MIdgzr/L+9uJ1S/kcL/LsexvpZPLX2CvFc2AGA5eRFTfv+XdNsvHT+VuNXOO0dp1WWOpZupq149/C9KCDGs/vdBNY88/Qfe/+GVPPf0D1m78xH+fcJ7OHc1ZrXrGYZuOTkz7P1zCyezLXcircVaJWdPNak/tUq8LhKna+XbfFhHMIJOUTH4tPaG3IMf2n7h3AqOX7SAws9fSUmei3pbXjpuf0fDkPrqDEXJj/vS26ZcSZQKIYZGEqVCCCGEEOKQ0FNRGjNoiVLC0TGMZvi1+vYOOtG5obGT2Z1709v6cIzC7/+Hpf+8nkh8/3O3Lq9u5eTu7ent/CWb0j9P++t/0Fks6e0Ch4WLjqrgP5XaPIG6WIL6px4cVJxCiNEXjYd5besSvv3EG5T84a8U3fcOJV+5k6O//GNmfvkf6CL9V2FWXP6V9M/nzSjlmPI8/jL7wqw2Tx51ZvrnYNUuPqwjGMWtC6MPxwAw5uYPx0tKm+CxU2PNJDe91UNb0KkjGMlKlFpSQ/qFEGKwJFEqhBBCCCEOCbbUYk5RkzYEnHBkDKMZPusbOvjlSy+z4kdX8sZv/o9Nde+gquqA7Zu8IZp9YSa2aMNnww4rUaMRXTROwY8eYsvyx/Z5vVAsztq6DuY29V3lOlmci6Vycp9zPjlvIttzJ9BdoFWHRV9eQlLtOz+hEGLsqKrKb9/czH0P/BJu/C73OF/E9UENALpoHGNHINNWUWifWJI5eVI5OWd/JL1p1Ot465vnsuDqr/PTy39C96x5hKbM4MEpZxF1WgGI1lT3iaE9GKEo0atiM69wWF9jRY6dPaZMn4GavsnafekMRsmLZYbeW/NL9tFaCCH6kkSpEEIIIYQ4JPSpKI0cHonSrz26Av/zj1D8z7cpu+M5mq/8Cu+s+deAc++ta+hAQSW/Vhs+2z53Nt/+6PUk9Tp0sQTtd/6JaLz/aQniiSir9raQSCYpq97b57jx+AX9nveRmaVYjAZWTDsGAOvq3by5+h90BYe+6rQQYmS8vaeFX72xkhl//jeeN7cy4RfPpI8F8otpP/Ykln/xev6/vfuOj6pK/zj+uVMzM+mdEkIPJRTpBBWwIBYs2LuuXdeOq+tPV13X1V11Xde2Yl07thWwgAXFAlIF6UIgkISE9N4z9/fHDROyBCQQSDDf9+vly9xz23OHk0nyzHPOcV98BQ+O/x2bwzsH9sdfeyOGYTS5XrDbyZ9PHMqnL/6J45Ysp/a9LykJCqY0ypp3tD49c7cY8suriatvXFzOHd26Q9u7hftIdTdes3LrlhadX1BRTXhNY6I0KEaJUhFpGSVKRURERKRd2DlHaY1zZ0VpbRtG0zqq6+pZmp5PUnbjH/uhP6ZiXHI38+c92Wxl6c/bC+kUVEFQRj4AXceMYl3Xfnw7aIx1/hc/8+O306mqbaweyyyu4KOVy/jo6UvZvPRpunnL8KVm73bt8KOO2a0NrITJCf0682GcNU+prbae4ONv5PvHb9DCTiLtQGlVAZ+vmcX98QvxrW86B6nZpxsT0jKYPH8+Nz3zCIOfe566U8/h1QTr+90WF0vnS67+1Xv0iAoGID+iYd7RjN0/KNltaHsrV2x2DfdR4fJS53UDUJ2x+wc+e1NQUUNYjfXeaNptOEJCWzU+Efntc7R1ACIiIiIi0FhRWt1QUWpUH/5zlK7bYa1UH5/dNLERtC0f44ZHyF90NtHh3ZrsW5FZyMjqrYHh8p1Hj+OKmh78q/A0jv55IbZ6P8bv/8IXd2xg8iX/wGl3c9ar33Baxlsc+9x71IV5uOGqqdhqraHzfpuBzW8lZOOPOWWPsU4d3I3LVvWiOCyMsOJibHV+Yh6fRfl1WYRGJ7TaayIi+y6/vJonvv6IQStmcPzsZfjW7F7lGX7a1N3a3rzoSNYcl0z/O6bi6tQZe0jIr94rIdyH3WaQHRZDX8CeXUC9vw67rTFtUFBRTWJNY0Wpp5UrNrtF+AAojwwjrCKH+sztmKZJbmk6OSVpGIZBv05jm8S0k99vUlhZQ2iVlSj1h3p2q6IVEfk1qigVERERkXYhyGElSmvsVkWpUX34V5T+nFWI215P6LYcAFaMP5pXR1mLp7gzC9ky69VmzxlUkhbYjhx+JHcek0x1fAIzkycCELStgNhpL5G25hvyyqpYvC2fQT8sAazVq8e8PhsA04Af+w+3LhQTiadv/z3Geu7Q7tx49ADuOfKqQJutzk/ljoz9fn4R2X+rsgoZ/o//ctTLD9PzkVlNkqSrevaj1uPB8HlJvOyG3c6N9Lo5qmccwcNH4urcZZ/u57TbSAj3kh5sDX13FFdSmt/4/W+aJlkllfTy5wfaPDGdd7vOgegW7gWgKDwcgJqMDP793/tY/MdLyLnod+y4+Armz3+62Ur3kupa/KZJcEOi1Az1tWpsItIxKFEqIiIiIu2CYRh4nHaqdlaU/gaG3q/aXkQ/ex7OQusP94gjhvPi4ClUBVvJgNL3P2yyaFJlbR0bckro0bCQU314MO5OXUiI8LHstpNZdMFtPHz0pQDYquvIevsVlqTnYzdMQkqKA9dx7bAqvkpHjGLrpXcSds4F9Pn3q3utrnLabTxx+khijp/EvRfcFmivys9ppVdDRFritSWb8ZZmEfn5agBKgkOoc7qwhYcx7N+vMnj5WgYvX4u7e49Wu2ePyGA2BzWuFF+0cVXg6/U5JWQUlZO8eR0A/oQ4HKFhrXZvaKwozQ+1hv8Hr0zniGseJe4/3xOyNI2QxZtxX/0gixe8stu5hRXWvNbeygqrIfTXq2hFRP6XEqUiIiIi0m5YidKGitKaWkzT38YRHZiV2wsYXtE4P+nISZMxXC4WNlR5+r5fT/q25YH9GUUV+E2T8KJCAMzE+EByMyHCx/0nHcHM/hPI6N4wXP+T+azIWE+Sr5CgbY1VXgCEBnPk6zP4x/Vn0Pfl1wg/4aR9ijnC6yLP3phgqFGiVKRNZBZXkFiTF9hOeP4ZRmbkMfSXdIamjCA4IQFXl66tes/ukcGsd8cHtstSNwS+nrU6nX4hxYSvTAPAN/mEVr03QGiQi/gQD8tcjdN92CutD81So6xndWUVU/3Hv+1WVVpQYU3X4qmotBrCgls9PhH57dMcpSIiIiLSbnidDqrs1iIetjo/tTXNr+5+uFiVVcTkwnQATMOg64ixnLJ5CW9kj2fiku+w1dSx9rmH+OXMEynPTiM06XcA+EqsxVJsMdFNrjeueyzdInx80iuFa9K24dmcyxEv30lyXBhGwzykNS43LgN6v/A67q4tn1s0wuMiZ9dEaUHeXo4WkYMlq6SCXtUFge2Ynkk4fN6Des8ekcG84YnBNMAwoTJtExmFG/glezFrs+o5s3QptmorQdll6kUHJYbXLhjHm12CWWAz6VORT+9unQj63bVMnpPOrQte4ITF3+BduZWsrSvo2mNE4Lz8horSoAqrotTWMHxfRKQllCgVERERkXbD47RT2TBHKUBteQlG0OG5avGO0kpyyqrolmvNK+hPiMXu9XL12D6c+PNWMjp3oev2TCJe+QLzjXmEF1WQ/lQE0ImgEmuovv1/EqU2m8EFw3rw3PYUrrK/i63eT9TsFYH9ps3GyLQsHC4XtqCg/Yo70usmz95YiVVbWLCXo0XkYMkqqWRcVVFgOzih50G/Z/fIYGrtTqoiQvAUlJK+6mey77iSmI+WclFKHxz51oc4/vBgwlPGH5QYju3biWP7doKrJjdpn7TlW97OHM8Ji7/B8JtkzfmArtc1JkqzSysBk6CiMgCcsbEHJT4R+W3T0HsRERERaTe8LgeVDRWlAHXlJXs5un1bnVUEQOT2HQDYkqwkx6Skzpw2qBt/HmnNNWovr8ZRZFVAOZYuwWHz4yyyEqXO2Dj+15Wje2NGRPJZ/yN32+ePDccVGrrfSVKwKkqrHW78Lqumoq5IiVKRQ800TbaXVBJTYU3D4Q9y4ggLP+j37R1tVZPviLAWdOr87Uri31mIvaqW8HlrCV5pVcgHH388ht1+0OPZ1QlJXVgfkUhlw5D68q/mNdmfXVJJiL0WR7H1fuqM73RI4xOR3wYlSkVERESk3bAqSp2B7drysjaM5sBsL7Gqm9yFVgWWPaFx5eknTx9Jarf+zBl0VJNz7NlZxBjl2BsWsnLF776idI+oELbffxZXzpnNpdc9x18m/i6wzzdxwgHHHe61KnprfVaytb6w6ICv2Rrq/LXsKN7S7GrXIr81pdW1lNfUEVlmLdLmjw7b62JsrWVkQjTjusfwbO+TAbCXWcPZq4Lc1Nnt+B0OQo+fRO/7/n7QY/lfk/t1BsNgTe9+ALgWraOssvGDnKySSrr5CwPTkAR1avnUIyIiSpSKiIiISLvhdToo36WitLaitA2jOTC5ZVUE2epwFlsLi7hiGqtDEyJ8fHHtcWy59l7+NeZcaoKsZ3bl5pFQVxg4Lii+C83xOB10Dffx2R/Pp+zEs/jp7GsIGjKE3rfed8BxR3isRGmN10qU+ouLD/iaB+q1pam88f4TLLvtIpZ880JbhyNy0G1veN8ILWuoqo+OPCT3tdkM3r9sPNuGHcVLo04LtPd48XVG7ShmZFYhSf/9FHf3Hocknl3Fh3o4okskX8cPBcBZWEH24saq0qySShJqGxOn3i6JhzpEEfkN0BylIiIiItJuBDntVNh2GXpfUUppVT7V/sMvYZpTVkU8ZRj1fgBcsfFN9o9OjKHXmSnE/ZzJBDOVwYuWEpRTSJeaxkSpp3O3vd4jMTKYr66fBExqtbgjvdbrX+X1EAL4i9t2+oMFW3K44p0fmP/eX3Hml1G2MRuOua5NYxI52LJKrURpcMP3ny0uem+Ht6rYEA/fXH8C74zpS+S2U4iMjSbi5CmH7P57c2TPWN7d2I87GrYr1q2G8WcBVqI0aZfFr4K7Hvpkrogc/pQoFREREZF2w+tykL1LovSTpUvp/Mmz4PdT2fc/eDm4Kz63ph2llXTdtTo0bvdh9FE+Nx6nneyQKAYDrtxiutY3nhPcqfshiLSpnRWlFd6G17q4bZPUry3dTLIvH2e+NQ1D8PKtbRqPyKGwvbiCXRcmcsTH7/2EVpYQ4eOOY5OB5EN631/TLdxHjicCv8OGrc5PdUZ6YF9WSSVH77L4la+LEqUi0nJKlIqIiIhIu+FzOdhR7wts9535JiFLtgCQf8pCoo46vY0ia7mcsiria4sC25743atDDcMgMcJHhs+qFrNV19GvuPEPf08bzLEX0TBHaUVQQ6K0pG3nif1xay5TCn5q0xgOd5U1pazbvoDOEX2IDzv4K6fLgcsqqcRrr8NZYH3/ubQwEQBdwryYho2qiBC8ucXUZmYC1uJXWaUVRFc2Ln5lDw1ry1BF5DClOUpFREREpN04pk88m33xlESEAwSSpAA1W9PaJqj9lFtWRVx14/yevk7Nz5fXLSKYNHdMYDsxYxsAfl8Qdo/n4AbZjAiPVdFb7rYSpUZpxSGPYaftxRWsyipkxOZVgTa/24HfrG+zmA4nVbX13PzhF7z75HWUn3IpK+6+itr6mrYOS/ZBVkkl3cyixoWJfmUajo4iIdx6XyoNt5Kg/qwd1nZ1LRU19UQ2zOl6qBa/EpHfHiVKRURERKTdOHdodxKiQ3ljyOTd9tUWFzRzxqFTXl2E37/vCbqcsiqiqhrn92xu6D1AYoSPja7GhZ7CN28HwB8Zup+RHpggpx2P005JQ6LUVlqJafr3eHxxRS47itNaPQ7TNHlz6WruSl5L7IpNgXZbdR2VJW3bFw4HFTV1nPzClwxaPp0BD7xDUFoeka9/y6YVn7V1aNIM0zTZtOMn5q1+lZXp8ygoz6dbTX5gvxYmsiSEWyMOisLCrYYd1muUVfK/i19FHOrQROQ3QkPvRURERKTdcNptTJs4kNtzCrl02Wx8peWBfXX5+Xs58+D5MS2btb98RPSX/8UY0p8JZ95LSFDUXs8xTZOcsioiGxKlfl8Q9qCgZo/tFuEjPSga0wDDBEdRQwVndHhrPkaLRHhcFLuDAbBX1LC9oIDsgqXkZK5i5PDzKK7MJb1gOx+vg6MXPo0zp5gR/3ybmPDurXJ/v9/kgje+44itrzLlqXewVzStgqzIzcQXFrOHswXglcWbWJu+leSXPgm0GX6TrOefpt/zp6rarh1Zm13EidM/YfqyvxPx9VqK+sRx3GkpfFPT+D7j66opEwA6hXqwGQa5wZEkAfacIvymP5Ao9TXMqWyLPXSLX4nIb4sSpSIiIiLSrlw+sjez12Tw162XcNuPbxNVVgRAfdGhrSI0TZNHv15D/sqXmTL9DdzpBZj2r5mftp1jpr2I173nis/S6lqq6/yEVVh/tPsjQvZ4bLcIH3V2B9URIQQVNC6cZIuObL2HaaFIr5siZ+NcsWc/+QoPz3mKqPVZLLpgNu60XNwZhRw7LJHoD5cBkDXxbWIu/WOr3P/dlWms2rqKG1+bGUiSbo+Mo3OBNcy2akcm9B7aKvc6XGWXVJKes4islZ8y+OiL6R4zqMn+BWm5jK5L2y3J7Pn4R7J2rKNz/IBDGa7sxd+/XsPRdauI/GoNAN4N2fR+chZFZxwXOCY4QYlSAIfdRqdQD1k+K4nsKK6goiQvkCj1FFnvoY64uD1eQ0RkbzT0XkRERETalSCnnU+vOparHpjGyRc+QUVsOAD+wqJDGsec9du597NlnPzSW7jTrSStUe8n7uEP+WXua3s9N6esCoCQ8oaFkCL3vKhIYoSVkCxtmJd1J3tM21VMRnhdFDiDA9uX5n+Hd+12DL9J7BsLCPt+I0FpeYEkKUD1Lxtb5d519X4emPszN+Z+gSvLmuP1y8vu4C8nXRM4piovu1XudbgwTZP88koWrH6Hb396lWXbcjjm6Tcpvv46Ol36D365/jJyS7Y1OWd5RgFHFKUGth867goAHCWVpP7nyUMavzQvNa+UeStnMbb4VS5Mnddkn626jkGLre8vf5BLCxPtIiHcS0ZQ4wdJJds2kF1aidtWh6PIGoXg1OJXIrKflCgVERERkXYpqmH19SqfNVemWVS8t8Nb3fdbckiwl+BJywNg4ZCR1LmdAJR/Omev52aXVBAfXI2vzEqUGlF7ni8vMcJKSOaHNz3GGRe/37EfqHCPiwJHY6J00Mff7naM39b0T4na7KxWuffbP6WRU5TLoNnzATCSepJzzOlk2RsTRdV5O1rlXoeL5xduZPKjz2Keeh3OKTfx0ocvM63gY8IWWMnpiM9WsuS285n51s18uWw6azKX8ktuCX12WMnTusgQZvccR358LADmW7Mpqypqq8cR4OfthSQ98hF1N9/BsGmvEDVzudU+cDBVIdZ7nmdzLgD2/r01VcIuuob7SHM1fpBUlraJrJJKulKMrc6aTzmoU5e2Ck9EDnNKlIqIiIhIuxTptVZfr9yZKC0u2dvhzSooz6K2vnq/7p+aV8qA6u2B7Tl9U8jqbq08Xf/zOkzTbPa8d37awo/fv8jz+W8QktGQ6Ije85ymXcI8RHnd/BjUq0m7p1PCfsXdGiI8LnIcjdMFOPOthG/mxOPw3nAbc256hFsm30pqTGOM/u2tU+X5/ZYd9KzOwZVrDaENv+4aokM85Dob46nNzWmVex0uXlj4C5NzV+DKKcVRVMGp899hwGufNzkm5p1FdL76GUIn3kDWVZeSEFJBfEYGAPaBfUmMDObdAccC4EnNYcOcVw/1Y8guvtu8gyBbHeHLNzdp/0+v41jVd2CTtq7X33YoQ2v3uoZ52eSKDWxXbttCVkklPeoa57H2dNbiVyKyf5QoFREREZF2KaohUVoeZCVKKS7dy9FNfbYuk2c/fomFt53Dtx/8Zb/un5pfSu/yxirJTd6ubIjvDoB7QyblFbvPmfrVL1nc8N48Rj7xHHGvfo+jYQi+Yy/D6O02G19edzyrT7yUF4afRp3TCXY7Xcafsl9xt4ZIr5tcW3CTNtMwmPyvZxn4t79z71+n8d70P3Hh1D+zbuRQ64CsvFa5d355DfF1jdXD3t5JRPrc1Nmd1DX0ibqCtlnYqy3klVWxYnshQ7atD7RFfbEae0UNpmHwl2OvIj2ysfrY8JuEfbuBP1XNxZdmJeo9Q4dyXUoS7yUeRW2QVald9uobh/ZBpImNeaX0IQ/D3/iBiydlLP5R4/ms04hAmxkTQexZF7ZFiO1WQriXfHcofqcdgJqMrWzIKWZM/dbAMWGJ/doqPBE5zClRKiIiIiLtUpTPSoqVeqw5PG3F5ft03uqsQi57ay5JTz5K7JsL8fzhKUrKW1aBaJomqXmldCu1zjMddmL79GFJRB8rluo6diyfv9t5z/ywgfOMlXg3Nh0a7ord+8IigztH8M3NJ3PLjJcYsmErg1dvxDdwcItibk0RXhd5jqYLUHlOPZGgHo0LynQK9WC3GeSGWHMFWqtP1x/wvfPLq4iubUyKe6I7Ee0LAqAm2ANAXX7HSZR+nboDj6Oe+HWbd9sXfM5UZrz/ND0WrOTr6Z9y80m3UdhQvTzo6VnYauoAiBhxJJeP6kWFy8PaoVa/cq5OO2TPILvblFdK/6rGivXE2bMYOPdbpg7twbzYIVQHWX0+7trfY3M62yrMdqlruA8Mg8pIa0G90gU/MDBsBSnrG+Z0jQ4nZEDbvX+KyOFNiVIRERERaZc8Tjtuh42SIKuy0VZaucfh7juZpsmtHy3hJuN7QhduAsCZV8aWz99p0b0LKmoorqolrsiqyKvvHMXI7nH8ENy38ZhFTeft3FZYzqdr0zlpxe4JVHdc51+9p2EYDIgPxxsbizuhW4vibW0Om0GdvWlypvef/9Fk226z0TXMS6a3YfXpkkrKCg58+H1+RTVRtWWBbW9cF6IbkuaVwVbS3F9QdMD32R9lVUXU+WsP6T2/3pjNWEc67uymc/Sabhe9H/wHQU47R3SNYtp5x1OfMoF/Dj97t2tEj5lAdHAQQQ47+SHWXLi2kgpM039InkF2l5pXSq+yxu+X8OQRGIbBTUf149FzjsL9ygckPvE03abd04ZRtk8J4dYog+Iwa97ikGVpXPPHvxK5YAMAviknYdjtbRafiBzelCgVERERkXbJMAyivG6K3Fai1F5eTU313qtKX12SSkTNEsbPmN2kvfCjD1t079R8q6IxsqBhtfuEzozsFk2eL4KKhiqmqmXLm5zz9PfrOTtmI9EL1vO/9iVR2p6MTrSmCtjaxUrYhpx1Jp5evXc7rluEj22e6MB2Sdruz95SeeXVRFRZ89GaBrgiYxoTpd6G+WoLiw74Pi2xLD2fJz//jK//eCafTr+ewvLWmY91X8zbmMWEvFWB7c+OPhNCQuj2yOO4OjcuWGOzGTxx2gjm9RzDil2GHbuHH4G7c1cAIr0uind+P1XUUFO1b1Xa0rrq6v1sKSgjocSqPPcHe3BGW99zDruN649MYuSUE4i96lol/JoxIC6cCI+L+REDAm328sa5qBMuvrYtwhKR3whHWwcgIiIiIrInUT43Bc7GuTIrcjNwJzQ/99znG7bzzJef8djsF3BnFjbZ5/r6J4rLcwnz7Xmu0F2l5pViYBKcYyVKnd0TGd3NSgimd0skqWAVxppNmKaJYRgsTc/nwxXLeP7Lt7DV1mMasK5LbwZkWFWt4V36tPjZ29KEXnG8d+l4Eqe+S+K29USdfV6zxyWE+1jj3HX16Y0w/Nj9vq9pmuSXVxPWkBD3h3gw7PZAorTUa/UFo7hsj9dobaZpcs5r87lmyxvEvzMfmM/CtVtwXXQW/fscQ5eIvr96jf2VWVzBxrxSBmy1Vrf3d4riT5/O2OPxKT1iybz/bDJvnkzojgxi3XaCkvoFVkyP9LopdO3y/ZSfhbtryJ4uJwfJtqJyauv9xBRY8/r6E+K0qn0LhAQ5WfWHKSzbOoa/PT+WiLU/cfVi68Mws1M0oSPHtnGEInI4U0WpiIiIiLRbkV43+a7GRE5lbvOVfKZpcs17P3L2pq/xrbJW+l4w9gQeOukaAJwF5ax859F9vu/m/FJiHJU486zK0qCevekZFUy4x8WGGGs1ZdfWPEpKsqmpq+fSt77n9pxPCV2WBkDRORfz3IX3UzNyDGFnTCX4MJsvzzAMpg7uxvBxI4g+/yIMR/P1Fd0ifKTuuvr01t3n0WyJkqpa6vwmoZVWItQMs5J6Oxf22jlfrVF06BKl6UUVpBWU03frpkBbzMvfEHrs71l906VU1R68qswFabk4bX5iNm0DwLFz4ay9iPK5Gdw1mu7Dh+JNHtRkfstIr4v8XT54qMzLau4ScpBtanhfCc+z5tq1J3Zty3AOS51CvZwyKJF/PXQzP51yKbMmXYwREU73+/+mpLOIHBBVlIqIiIhIuxXpdZPrCA1sV+U1nyjNL69mW2E5iVnpANR3isL+f3/n8w8XcluwF09ZBbZHXmbLpLPokTDqV++bml9G/9osjIYpUUN6W1V5gzuFs3JbIqcCRr2fvJULWBIyjOKyHQz+7Dvr4F7dOO6Z55gUFAT3Nl+J+VuREO6jzO2jzuvGUVFNTca2wL7q2gowDNwOzz5fL7/CGj7rq2hIPoZbSXKXw06I20lhkLVtL6nA76/HZjv4w5IXbcvDYfMTvSWzSbthQuR/l7D6vFcYcfLvD869t+YyyJkbmJ80ZMyRB3S9CK+b/F0W6arO37GXo+VgSc0rxWWrx5NlVay7evZq44gOX53DvCy97WS47eS2DkVEfiNUUSoiIiIi7VaUz0W2vTFRWpO3e2Kntq6ahZs/JzmulMg8ayir0TuRa47qT//EeB4fcwEA7qwiNt13B3X1v74Yz+b8UgbWNq5IHZY0FLBWp1/ia1z5vXjFEp5f8AuXVC3C1ZDM6nrXn7A1rFj9W9ctwqrwLI+0FlWpy9xORlE5f/tyMXMevpC5j15CVe2+V3/mN8wz6C1vSJRGhAX2RfvcFLqtJJ+tuo7KkrzWeIRftXhrHsmuxmTlykuv5Lopd1Ib5AKg9B9PU1FT0ur3NU2T9dlbOap0XaAt7sgTDuiakV4XOfbGRGlN/qF5DaWpTXml9DHysVda70W+Ps1PJyIiIoeeEqUiIiIi0m5Fetzk7JooLcgNfO33m9z+0UKef/pGIs69kocXv0RwdsOcoj174LTbeOm8FD7rPY6f+1iLfoR+uJC1iz/Y6z2zSipYnZXDpKyfADBtBiG9+wNWojTHE0F1SMOqyytWsCAtmwlLv7eOjQwl7swLWunp279uDatPF4WHA1C/cTP3ffoVK+e+R+e/z6bTQ/8lbcHsvVyhqbyGRGlQeSUA9obrgpUozdtlGoaflr5LbX01B9vibXmMK/4lsD35ggvY0mswHw+dCEDwok18/dTv+W7t2yza+BH1/rp9um5xRe4ek8ifrsvkmVmvcv2SJ5iy4hsA/B4XoYOHHdCzRHrd7NjD95McOhvzSuhf3fhBTGjSoDaMRkREdqVEqYiIiIi0W1E+N8VOL6bNmnOuNt+a02/djjwen/UER6z+B6PveRl3egExc1biLLQqET29kwAY2iWSq8b25c/DL8Zvt2Gr85P78CN7nVfygTmLuCJsGXGfrwDAe+pJgQrRwZ0iwDDI6xwPgH9DKoOcOYQvTQUg+Pxzsblcrf9CtFO9o0OJ9Lr4Icx6vT2/ZDN+6YuMzVkbOKbgs9msyMhh7qJXmfXSNWzKWrrH6+0ceu8qrQDAHhkZ2BcX4mGjGR3Ytl1+D0s+forqunpWZGTw+dyHWfTTW5im2WrPV1vvZ1lGPsm51r+v6XLQedRY7pg4gOeSTqHSY00rEHP/W7jGXYZ/wsVsWLb3xPDCtFwenPke3992Jl8+dgVlVUVN9qcXlnPmK/Po+dKTxLz9I8E/bbV2JPfZ41yx+yrS66LQ0ThHaW1B/gFdT/ZPal4pvcsa54cN7zekDaMREZFdKVEqIiIiIu1WpNcNhkFtsJWQKv7mS158+x7+8PTTHHntn0h65L/Nnrdrhda9xw+mMKYrXw6x5ncMnbeaRR/8fbeE2rbCcm58fy7H/vQ4pz7/OobfxHS76PPI04FjkjuFYzMM0mKtxVe8W7IZVbY5MJdp/FkXttqzHw6CnHaenjqa55NOJifGWtQp6T+fM+TnFYFjqr/7kW9n/JGQc26h080vse2iC9m4bSGVNWUsSZ3Nuu0LqPNbQ5Dzy6uxU4+j1KoodURFBa5zx8SBpHdL5ps+IwBwFpZT+/DTXPD613z6yn2En3MftefdTGlp8/PY/pqiyhrWZi5l7ry/s2nHckzTZHVWEZW19SRmWMlKc0BPbE4n0yYM5LyJR3DjpNso93oxTLDV1uMoriRn+nOBa5qmn/VZP/JL9mJM009WSQVn/+dzBr/zOLFvLCDuoQ9YOP0PFFXkBPrj9B9/YZRvOzHzVjeJzzNixH49164ivG78Nht1DYtj1RcWHPA1pWXq/X4255fRtTQHAL/XjSsmvo2jEhGRnbSYk4iIiIi0W1E+K6FT4w3CVVJByJItJK96nOpTjsFRVgVApcuNp6bpEOyIAY1DlONDPdwxcSD/LJrK+HWLcVdW4bjnaTaNOIY+vccHjrv0re8ZuOFDur88J9AWe9vtuLsmBLY9Tgd9Y0JYE96NcYCjpJJxOWsAMA0IG3hgQ6MPR+ce0Z1Za5KYVnwd//nwAWzVdXg2Ns4lG7xyG2NX/iewHbIolfTLLmXBxKH0fX4OOXGhbLpyMuMve4T88mqijApstfUAOKNiAucd2TOW1XefSR/sFC9+jdN+mIN3dQb16T8w8dkZGPV+3JmFZC36itDjL2rRM8xP3cFDs9/hzlnPELl4M+nDppPx55tY65hIjLuSsI3WMOmgI44AwGG38eyZo3mxaxTP9utJr1mvM3XdNwC45iyisGQ7M9dXMv/H97n0jWfBbrDj4Wn8K20gl9l/oNPHjVW1IX95gwUr12A7eQLDj7+RxVsWc1Xml7vFmHDSgS8MFtmQIK0J9uCoqKa+sOiAryktk1FUQU29n7h8a9oDf0KcVmkXEWlHVFEqIiIiIu1WTEOitNjXOK+ivaqWXhutOSPrvW6Ov+RptvXrHdjvdznwdOvR5Dp3HjOQ7n178PdxFwPgyi5m2z13UldfY53jN1makc+AnC3WttNBjxdeJfHuP+8W06BOESzzNl6/++JV1jmdorB7vQf8zIejW47uzy/R3dnae8+rd9e6XazrYu0PWbyZAX/7EEdRBd4N2cTf8Sob33mO/IpqOtc3Lozkjo5rco0wj4uzhybyauIxgbYbln8QSJoDFC38brd7V9SUklua3uyw/CXb8vj9u7P4w6f/JnTxZgCCl6fhPu9uQn58lLNsa7BXWP0k5tjGlbUNw+CqMX14995L+e7iadx38g2AVem67J1nuO/Tb7hg9qv41mbiW5WB/bJ7GV/0JpNffw/DhBq3C7/Nhr2ihpgZi4i44jGW3Homd773GH0+sJ6hbFA/Qt75mF6vvUPExOP3+Nruq0iPNS1Etc+q0PYXFR3wNaVlNuaVAhCeZ017YE/s2pbhiIjI/1CiVERERETarZHdojkxqRNP9TyZsqDGJGRYqjW/nxkTjsvjZl2nxpXo/V1jMWxNf811Oey8ceGRfJl0JIsaqj5DPl3G2oXvA5BWWEZFTT2dcxuu26sL0edf1Gyl14iEKNaEdKPeaQ3OcuVYiQ+jZ7fWeuzDzoiEKJJiQpnZc9yeD7r/Ue48416+6zM80FTjclHvsl7H6h8XkVdeTWzdLonSZoYkXzCsB1khMWT2TAQg/Mu1TfZXL/8p8HVOaSU3fLCQOX+5lGVXn8HXs/5Ehd9KUJVX17J4w8e89cPL3Jv7IWELNgKQHt0J0zBwlFWR9MBbnDP7PQBMm424Y05p9tEuHtmTrzoNpSrY6qPO6a/xrx2vE75wY+AYZ14po+95FXdmIQDLLr6Fx6fewerOvalzOLDV1hPzziJClmzBVm0tCJV85/30O2UykVPP2vPr2gI7K0orGxL6ZnHJ3g6Xg2BTXilOox5vttUPXD17/soZIiJyKClRKiIiIiLtltNu492LUhhwxkmccNGTmA15y0AFYWwUoxKiWRieFDjHltil2WslxYZxzdi+PDzkPPx2G4bfJO/vj1JbX82a7CLAJHK7NW+gvc+ekxdH9Yylzu4gu3tCk3ZX7957OOO3zzAMzhjcjZmdxzRp/2awlTgNPfccUm68gXm3TuHzqx/kpzN+R92wEfz+xGnkdrf+vepTt1JQXk1MXWngfE/s7v+WYxNjSIzwMbvH2GZjsa9OZUvuz5RU5vH8wo18smQ5XZ/8mKjZK4i76knKXnyYV5duYtxfn6X25Eu54Kq76TH9C+s5+vXi0z//h5tPuo1KrwfDBE+q1SfMQb2xh4Y2e89zh3YnNMTLrAFHA+Bdn0Xn178HoCQmmn8ceSH1dnvg+OCzpnLjEw/xzst/YeMTb3HZ6fdSEBoBQGWXrgRfcyN9Z31GzOmtkyDdKcJrVZRWeBo+dCgua9Xry69LzSulp60Qe7k1XYi3T782jkhERHalOUpFREREpN2L8TiotzuoCfPhLmpcsd4eF8OY7jE8vbYxUbpzxfvm3HVsMi8u2sjXR4zj2KXfEfz1Krb+9CVrixOJcVYRtN2q8vL0H7jHawzrGoXXZWd1lz502bgl0O7byzkdwTVj+/LsDxv4fOjRTFrxLY7zzuWcB/9FdHYa3qFWFW//uDDmXDcJrptEQUU1P9/7Llk5ccSzFdu2bPLKq0iuaaxy9MR23u0+NpvBM2eO5p8hTjaW5dLNqGaWoxO4qjl73ixrWoWTT2HDMUNZP/B2BlZvx6j3B86PeucH3u33JaflLcOVXRxoNw2DpGde4YVRY0kprOIRfwUPfNa4MJPv6KP3+OzhHhdfXHs8Z9rBVVnN6au+BsA5oD8jX3qTuWm1PNCjH3cvfJ2o7t3o+8wrGIaBYcBfTx7GVWP6EGZej7cwF3fvPgdtzsrIhkTpzupso6R8b4fLQbApr4QB1dsD26F9B+3laBEROdSUKBURERGRdi/W6wSgPCKsSaLU0Smei4f35KVFG1l75CQGrllMj4t/v8frxIV4+N2o3vyr4DSOXfodhgk5H81gzYCrGVqbEUiohSbveVEmp93G2MQYvs3ozwl8HmgPSx6+x3M6gm4RPhbfchI11x5LwpY1+EaMsuZs7RTV7PGRXjfRPjfbwuI5AnBmF1FfXUREdWOVozsqrtlzT+zfhRP7d4GbpwDw7OvfseabbzibWQB4127Hsz6LqgdPp09FVpNzjXo/E5d/THhOfpP2mJtuJnR0CgD3nzCUk7bmca99Oo56a2GpTiecsdfnH9olkh+nncb7k4bi2fYznRwmkadPxXA4+PsgYMpw4I/NntsjKsT6Ijpir/c4UCFuJ3abQWmQDwCjtOKg3k92tymvlJPLG/tkeL8hbRiNiIj8LyVKRURERKTdi/Vav7YWh4URSWM1lrtzV/rFhZF1/9kYxjmYpvmr1XinJSfw9PdR5CR2IXZrJnXzfmBd5DmML98WOCZqSPPDunc6skcsf1/bD9NmYPitBYIik0fu7+P9ZvSJaRianjBhn45Pigllg8+qGjX8Jpe75pOctw6Ael8Qdpdrn64zJjGa90MTqA1y4ayqCVyvR9oSOpdaQ+f9dhtbevak18ZN9Jm3BKNhHlDbuacy5LGXcEQ0JiknJXVicv8u3H7iLfzzs3/i6NaFiJSJvxpHTHAQ16UkQcqeq5rbkmEYRHpdFLusRKm9tIra2mqcTncbR9Yx+P0mqfmlJJTssLY9LtzxzU8VIiIibUNzlIqIiIhIu7ezojQ/pGnFXVBnawGlncnRfRmyfFTPOMKCnPzYY7B1jdXbcJRsoldhJgD+ICfeHntevR3gqF5xVDqDyO1mJfnqI4JxRkW34IkErMTqanfjqt9HPvAq4d//AoAtPmafr3PxiJ4c068r/xx5Lms7Nf7bDcxYR3yRlSitio3glaTJADiKK7FX1QLQ5fTzmyRJwepH/718Ai/+848MXrmewd8tx7aPSdv2LtLjpsgVDFjJ5Kqi3DaOqOPILK6gus5PXGEeAP6EuIM2zYKIiOwfJUpFREREpN3zOGxEeJzk+JomtLwJLV8x2mm3cUK/zsyMsSpADb/Jnekf0H/zBgDMHl0wbHv/NXlMYjRdw7zMTrAqT8OOO6HFcQgkxYaS6o3Db2/6ejsHJZP0zxf3+ToRXjdzrjmOu154jN+deg/b+lnJ0sTNm4nKt4bYGwmd+L7rcFYn9m1ybuxxza9k73LYGRAfTlD3HjjCw1vwVO1bpNdNgTM4sF2Zu30vR0tr+iXXmn83MtdKTtu7qZpURKS9afHQ+++++44777yT0aNH88QTT+zxuLvuuotZs2Zh32V1R7fbzdKlS/cvUhERERHp0DqHetjuiWzSFpKwfyvNTxmYwLs/dac8IhRfYQldZiwM7PMmD/7V8z1OByvvmEJJ5QnE5N5PUK+Ou+L9gegbE0q93UFFbDjBWQUA+DvHMOSH5b+arG7OkM4RhLid/Ny5L93WpxK8KZt6nzWs3Ne7J7OuGM+FtnrumDedo9cvJvTiC7F5PK36TO1dhNdFniMksF2Vn92G0XQsq7IKCXdW49tmVZQGdfAF4ERE2qMWJUpfeOEF3n//fRITE/fp+Ouuu44bb7xxvwITEREREdlVl1AP29xNh7d7u+zb76X/67SBXRmVGM2zQ07nlu/fxllXi2kY+I46kl7/97d9uka4x0W4xwWR/fYrBrESpQDmLhWl4RdeuF9JUgC7zca4HjHMzxzAKXyGUe/HUVIJQFDP3oxNjOb9MwfS7Y5PiKsuw9W541X0RXrdrNklUVqdn9OG0XQsK7YXMqIuHVuNNT9u2LBRbRyRiIj8rxYlSt1uN++//z4PPfQQ1dXVByumANM0qaj47a/EWFlZ2eT/IvtLfUlai/qStAb1I2ktO/tQrM/FImdjorQ+Ipiqujqoq2vxNQ3gq6smkH/hWLIL7qd7WR7uyEgcsbH4oUP8DtoedPJao882RHZjRIZVZRd34TUH9PqPTYjkr+F9qXfasdfWB9pd3XtTWVmJx2HDZ/NTHxlFZVXVgT3AYSjEaZBnb0yUVubsUH9vof39+fZTej4nlG4JbAcPHK3XvoPT70rSWtSX9m5fFvvcqUWJ0ksuuaRFgfz444989dVXbN26lV69enH//feTnJy8z+fX1taybt26Ft3zcJaWltbWIchvhPqStBb1JWkN6kfSWjz1VWxzRQW26yJDW+V3RRuwDSA/3/pPDqkrk6P5oP58BlWU4Bo/kdTiEigu2e/rjQyuwxbkZt7gcRy/7NtAe4EzhJKG96OO/L5UXVpMgd0b2C7I2Nah/uZqTS3pR7X1Jutzi7kxPx2Ael8QWyqqMPTaCx37PUlal/rSnrn2cVHGFs9Ruq8SEhKw2WzcfPPN+Hw+nn76aX73u98xd+5cIv5nVck9cTqd9O7925/vqbKykrS0NLp3746ng82RJK1LfUlai/qStAb1I2ktO/vSgIR4qlflUhPiwVVaiT0+hv79+7d1eHKAnujfH84dD1zTate8vdjB46VnNkmUDph4AjWm2eHfl3rm2ylfnYtpMzD8Jj7Q91EL7c/Pt5+ziqjzr6NLViYAZt9EBgwYcDDDlMOAfleS1qK+tHebNm3a52MPWqL0hhtuaLJ9xx138PHHH/Pll19y9tln79M1DMPA6/X++oG/ER6Pp0M9rxw86kvSWtSXpDWoH0lrGdTFqibNjO5Ej9LNhCcPV9+SZt15/BBm/JzOU0efz43fvo1zzAhCIiMDw5w78vtSVIgXDIM6rxtnWRVUlB/2r8W2gnyWb/kG17ofCOnak3Hjr8dm7N88ty3Rkn60oSALh81P2LYdALgHDjjsX3dpPR35PUlal/pS8/Z12D0cxETp/7Lb7XTq1ImcHE0WLiIiIiItNyohkhfOGYv3mOl0/WUJ0Rdf3tYhSTvldTn48ZaT2HbpeHrnT8PVrXtbh9RuhAVZQw/rPFaitL5k/6c5aGvVdfVc/Ob3jNkwnTFvfIyzoJy6UA/p84eT2GdsW4fXxMrtBQy05+IsLAcgZMiwNo5IRESac1A+ZjNNk4cffpj169cH2mpqati2bRsJCQkH45YiIiIi8htnGAa/G92bU06eQKdb78AZHf3rJ0mHFe5xMbhzBN5BQ3CEhbV1OO1GaJATgBqPGwCztKwtwzkgs9dk8MmazYx6dw7OAisB6SipZNurT7VxZLtbtDWPYyvWBrajRxzdhtGIiMietFqidMeOHUyePJn09HQMwyAjI4MHHniAHTt2UF5ezmOPPYbT6eS4445rrVuKiIiIiIhIC4TtTJQGBQGHd6L0s3WZTLBtwZ1d3KTdeP8LyquK93DWoZddUsmibXmMTV0JgD88mPBhY9o4KhERaU6LEqWDBg1i0KBBzJw5kzlz5gS2wVqhfsuWLdTU1ADw0EMP0b17d6ZOnUpKSgrr1q3jP//5j+ZKEBERERERaSNhHmvofdXORGnZ4ZkoNU2TuRu2c1z2ikDba0eeDoA7s5CNn7zSNoE1Y/baDCKd1XRa8QsAzmOOxLDb2zgqERFpTovmKF21atUe93Xt2pUNGzYEtsPDw3n44Yf3PzIRERERERFpVaFuq6K0ym0lSimrbMNo9t/K7YVklVQwcNM6ACp6duWlpBM5b/kcXBVVlM36BM68pW2DbDBrdTqn1qzCUWy91vGnn9fGEYmIyJ4c/KUARUREREREpF0I81iJ0oqGRKlRXkl28Wa2F25sy7D2akdJBT+t/5Tvl71KVtEmqmrL+GxdOikxuYSt2QaAa8J4qh0uMnp3B8Bc80sbRtwovbCcX3akcvqmbwHwu53ETZ7axlGJiMieHLJV70VERERERKRtBbucGAZUuDwAGKUVrD5vCkZNHfa3ZxMX27eNI2yUVbSJuevW88ScTTzz9l9wZRWxdnQvqrvH0D/My+hlW7DV1gPQ+8yL8M3JY2NMIj1Zjys1m/LyfHy+qDaLf8GW7bzy5Rv8c8UMor+yRmcGHXM0dk1HJyLSbilRKiIiIiIi0kHYbAYhbiflDYlSR1EFYd9ZU6j98vifiPvbO4cslrr6Ghx2127t81N3cN+sj7lnzmP0Si/ggZF9cG8vAiD0x1T4MbXJ8d4TTyD66OMY+vNclmf25ATAVlvPjp++o2fDvKU7FZbvwOUIwucO2+2+a7OLyMyYR+3Sz+l36lX07Dx8v56rqraeWz76loEbXuHy6TNxFpQDYO/bi/5Pvrxf1xQRkUNDiVIREREREZEOJNTtpMTZTFXj/CUH/d6mafLiok1EbfsA1/df0Of3/0dkpz5sL9zEhoLOGBvfo27VMm7dlEXYD9Z0AF225QfOz4uIIqi6iuAKK/noO/lE+r3+AYZhMCwhihlr+wSOzV/8LcGDhxHujeXDVVmsWfMlR330LPU+N1Xd43HU1hI8fiIhSeNYumYxpRvWMPbfHxK+vYit735N9Cc/EOqJ3uvzpBeWszTtZxwLXqHaG0yVzYF33WpOzSsh+r3FGPV+ALzHH0vSy+/giIho7ZdURERakRKlIiIiIiIiHUiYx0mp07NbuzN1OxVVJXiDQg/avT9bv50b3l/AV3P+SVBaHtuXbSYtJpSQ5WmYZ6XQ7Y352Ctqmj034r67GTHtAbYVlhNUW0VoUR5BfZMwDAOAYV0jecobQVV4MEFFZdQ/8m9+/mIO1d2jWdJnNCO/+56IL5ouUOx/9gsqEqMZnprTpD14cSrLnvwD4+98EUwTwzAwjMYlPkzTZGn6Bh76fAG3fjGdqHlrmo3ZDPbS56U3iDj51AN52URE5BBRolRERERERKQDCXW7KHb4dmu3V9aS/sPHJB17wUG79+cbtjPImUNQWh4AwSvTA/t6Tv+iybFVniBMhx1PaTmm3Ua3S3+PYRgkRgYDwRDXtNpzeFdrPtLsrl3oXrQBe0UNYd/9At/9wpkRK6AhoborW50fzy5JUr/NRkWwj+CSUjz/nMFb0WGEbt6Ezesm5rRzcIYl8/WqH+m09FNiF6/j/7YX4dm4Y7fr+m02XEl96PvyW/gGDdnfl0tERA4xJUpFREREREQ6kFCPk2L77hWlADmffBBIlP64NZfKrG+p+ulz+p1+NT06WXN2+s16auoqsRtOnA53i+69aGsu44vX7fWYnFPOYNCoUfxfYSjVWzYybe6/ibrqKlyxsXs9LynGqoTdEJ1Id6x5V2vcLlzVNTgLKwLHhT/5BD8kjGbuwjVM+e4dOtcUYx9/Av1HD+f29ZWsXbGKZ2b+DXtZNUm3PBU4z//ExxSO6MHYddsD847uVNU/CffNf6JnqJuwseNwxsS06HUREZH2QYlSERERERGRDiTU7SSzmYpSAPt7X7L5hh/4pbY7V7zxCW99/ADRm3aw9T9zWXzB8ThzcnCWluIoqcTvdTP8vueJj00KnF9UsYPc0nQcNiedwnsT5Gy8T0VNHcszCrgme1OgrT4oiLzYTrzWezy3f/cmQUcM5sTX3sTmcvF64KjH9um5HHYbl4zoydsFKRzv+Ir6+HjOPepmXvrmMSKzrKpR02Gnx1mX0CcsjMsmjQIub3KNp4+r5eQXwvhLaR73zHsRw2zcZ6uuI7Rh3lSA8ohIbMmDSTziCLpM+yOOyMh9ilNERNovJUpFREREREQ6kDCPk9W2pos5Vfm8BJVX4CwsZ8sfbuaelHu4rfRLvJusYeW+1Rn0vPuV3a61Kfg+4v/2DisyC7jjo++45Ye/EroyDdPlYN24fsRdfyNJPccTHBTBkvR86vx+um1NA8A/dgijPv0RE4MJfhNP3ZPYgoIw7Pb9fraXz0shf8pwwp/4PYbHi/fxT3k37ziuzXoLAGP0EBxhu694v1NIkJNPrjqWP3QK55vk3hyz5ivSxp7Ex4UmfX+cw7htK/B27YJx9kWMvPwKfMHB+x2riIi0P0qUioiIiIiIdCChbhf5tqYVpStGj8TMq2Dsz0sInbuClMHfMGb2F82eX2e3YxgG9ro6mDWPqr9UcPenP9F7yxfEzloWOM6zcQc1Hyzi+wn9qBvQne0DxtLN7cGXmg2Ab8wYbE4nAMEAbucBP5thGEQHBwFBAEybOIBbsvO4KOwTgouL6XrZdb96jZAgJ8+dNQbOGgNMYwRwFgA3AVBRUcG6deswbLY9X0RERA5LSpSKiIiIiIh0IGEeJwX2ponSiMREbuk6jE/WLMNW7+ect97ElVMKQPXjzxA7fAyx9ZX4+vRl0L/nk7L8I66b8ybu9AJWzn2bLza4eG/dgsD1UuO70St7G47SKqJmr4DZK4j2fsojZ47DVucHIPqoSQf9Wa8a04deUSF0v2ocUXnbCZ147EG/p4iIHL70EZiIiIiIiEgHEup2UmdvWr3Zo19fCj1hbO3bGwB3ZiEAZq8Exl19NX1HDCF89BickZFcOKIXH8SPpd5pDZHPe+d1ensKiV9kLdJkO3sK21+YzaOXPsj8pJFUu6wFn+wVNfR8/WvrumHBxBx5/EF/VsMwOLZvJ3oM6q8kqYiI/ColSkVERERERDqQ0CDXbm2JSUn0iw3l0+6jm7THXnsDhmE0abtgWA/K3D7WDxgIQMT85VxTOB97VS0APa66mdsmDuTdZ/7Ild/P5+JbX+PVI89oco1ujz+J3dt0nlQREZG2pkSpiIiIiIhIBxIatPtcoMEJvTlnaHf+Gzcav8OqFPWHeEi4ZPc5PXtFhzCqWxSzEscC4CysYPRH1nym/sRORIwdHzg23OPi+XPG8uKAk0nrmgiAZ8qJxJ17Sas/l4iIyIFSolRERERERKQDCWsuUdq1B5eM6Ik9LJwfhowDIPamW7H7fLsdCzB1UCJzoo+grmEBJkdRhXXt88/brQJ1UlJnFtw6haRZX9Pn3f8y4D8f7HaMiIhIe6DFnERERERERDqQkGYSpQ5fMD18sOGPp0PtyYQU5uDu0XOP15g6uBt3fbKcjf360X/lKgBMm0H3y29q9vjhCVFAFPTt1hqPICIiclCoolREREREREQAiAkOIiYihKCevfZa9dkrOoQjukTyacKoQJsx9giCuiQcijBFREQOCiVKRUREREREOpChnSPoFuHj5YnnABBy+cX7dZ3LRvbik5hhVIYEA5B4/R2tFqOIiEhb0NB7ERERERGRDsTlsLP6jilU1J5EaMHDuBL2bzj8DUcm0ScmlB5XphBeVkTIuKNaOVIREZFDS4lSERERERGRDsbnduJzOyE4cb+vYRgGJ/TrDHRuvcBERETakIbei4iIiIiIiIiISIenRKmIiIiIiIiIiIh0eEqUioiIiIiIiIiISIenRKmIiIiIiIiIiIh0eEqUioiIiIiIiIiISIenRKmIiIiIiIiIiIh0eEqUioiIiIiIiIiISIenRKmIiIiIiIiIiIh0eEqUioiIiIiIiIiISIenRKmIiIiIiIiIiIh0eEqUioiIiIiIiIiISIenRKmIiIiIiIiIiIh0eEqUioiIiIiIiIiISIenRKmIiIiIiIiIiIh0eEqUioiIiIiIiIiISIenRKmIiIiIiIiIiIh0eEqUioiIiIiIiIiISIenRKmIiIiIiIiIiIh0eIZpmmZbB9Gc5cuXY5omLperrUM56EzTpLa2FqfTiWEYbR2OHMbUl6S1qC9Ja1A/ktaiviStRX1JWoP6kbQW9SVpLepLe1dTU4NhGAwbNuxXj3Ucgnj2S0f6hzUMo0MkhOXgU1+S1qK+JK1B/Uhai/qStBb1JWkN6kfSWtSXpLWoL+2dYRj7nGdstxWlIiIiIiIiIiIiIoeK5igVERERERERERGRDk+JUhEREREREREREenwlCgVERERERERERGRDk+JUhEREREREREREenwlCgVERERERERERGRDk+JUhEREREREREREenwlCgVERERERERERGRDk+JUhEREREREREREenwlCgVERERERERERGRDk+JUhEREREREREREenwlChtY5mZmVx99dWMHj2aiRMn8uijj+L3+9s6LGmHvvvuO1JSUrj11lt32/fpp58yZcoUjjjiCKZOncr3338f2Of3+3niiSc49thjGTlyJFdccQXp6emHMnRpZzIzM7nhhhsYPXo0KSkp3HXXXZSUlACwbt06LrroIoYPH86kSZN4+eWXm5y7t74mHcv69eu59NJLGT58OCkpKdxyyy3k5uYCsHDhQs466yyGDRvGySefzKxZs5qc+9prr3HCCScwbNgwzj//fFavXt0WjyDt0F//+leSkpIC2+pL0hJJSUkkJyczaNCgwH8PPvggoL4kLfPcc89x5JFHMnToUC677DIyMjIA9SPZd0uWLGnyXjRo0CCSk5MDP+PUl6Ql1q5dyyWXXMKIESMYN24c06ZNo6CgAFBfOihMaVNnnHGGec8995glJSXmli1bzEmTJpkvv/xyW4cl7cz06dPNSZMmmeedd555yy23NNm3du1aMzk52fzmm2/Mqqoqc+bMmeaQIUPMrKws0zRN87XXXjMnTpxobtq0ySwtLTX//Oc/m1OmTDH9fn9bPIq0A6eccop51113mWVlZWZWVpY5depU8+677zYrKyvNo446ynzqqafM8vJyc/Xq1eaoUaPMuXPnmqb5631NOo7q6mpz7Nix5tNPP21WV1eb+fn55kUXXWRef/315o4dO8yhQ4ea7733nllVVWX+8MMP5uDBg82ff/7ZNE3T/Oqrr8wRI0aYK1asMCsrK83nn3/eHDdunFleXt7GTyVtbe3ateaoUaPMvn37mqZpqi9Ji/Xt29dMT0/frV19SVrijTfeMCdPnmympqaapaWl5oMPPmg++OCD6kdywJ577jnz5ptvVl+SFqmtrTXHjRtnPv7442Z1dbVZUFBgXn755eaNN96ovnSQqKK0Da1atYr169czbdo0QkJC6N69O5dddhkzZsxo69CknXG73bz//vskJibutu+9995j/PjxjB8/Hrfbzamnnkrfvn0DnyTNmDGDyy67jF69ehEcHMytt95KamoqK1euPNSPIe1ASUkJycnJ3H777fh8PuLj4znjjDNYunQp33zzDbW1tVx33XV4vV4GDhzI2WefHXhP+rW+Jh1HZWUlt956K9dccw0ul4vIyEiOP/54Nm7cyOzZs+nevTtnnXUWbreblJQUjjnmGN577z3Aek+aOnUqQ4YMISgoiCuvvBKAr7/+ui0fSdqY3+/nvvvu47LLLgu0qS9Ja1FfkpZ4+eWXufXWW+nZsyfBwcHcc8893HPPPepHckC2b9/OK6+8wh/+8Af1JWmR3NxccnNzOe2003C5XERERHD88cezbt069aWDRInSNrRmzRq6dOlCWFhYoG3gwIFs2bKFsrKyNoxM2ptLLrmEkJCQZvetWbOGAQMGNGkbMGAAq1atoqqqik2bNjXZHxwcTGJiIqtWrTqoMUv7FBoaysMPP0x0dHSgLSsri9jYWNasWUNSUhJ2uz2wb8CAAYHhGXvra9KxhIWFcfbZZ+NwOADYvHkz//3vfznxxBP32E/21I9sNhv9+/dXP+rg3nnnHdxuN1OmTAm0qS/J/nj88ceZMGECI0aM4N5776W8vFx9SfbZjh07yMjIoLi4mJNOOonRo0dz0003UVBQoH4kB+TJJ5/kzDPPpHPnzupL0iJxcXH079+fGTNmUF5eTn5+Pp9//jkTJkxQXzpIlChtQ0VFRYSGhjZp25k0LSwsbIuQ5DBUVFTUJNkOVj8qLCykuLgY0zT3uF9k1apVvPHGG1x33XXNvieFh4dTVFSE3+/fa1+TjikzM5Pk5GROOukkBg0axE033bTHfrSzn6gfyf/Ky8vjqaee4r777mvSrr4kLTV06FBSUlL4/PPPmTFjBitWrOCBBx5QX5J9lp2dDcCcOXN45ZVXmDlzJtnZ2dxzzz3qR7LfMjIy+Pzzz7n88ssB/XyTlrHZbDz11FN89dVXDBs2jJSUFOrq6rj99tvVlw4SJUrbmGmabR2C/Ab8Wj9SP5PmLFu2jCuuuILbb7+dlJSUPR5nGEbga/Ul2VWXLl1YtWoVc+bMIS0tjT/84Q/7dJ76kezq4YcfZurUqfTu3bvF56ovya5mzJjB2WefjcvlolevXkybNo2PP/6Y2traXz1XfUmgsR9ceeWVxMXFER8fz4033si8efNadL7Irt58800mTZpETEzMPp+jviQ71dTUcO211zJ58mSWLl3Kt99+S0hICNOmTdun89WXWk6J0jYUGRlJUVFRk7aioiIMwyAyMrJtgpLDTkRERLP9KDIykvDwcGw2W7P7o6KiDl2Q0u7MmzePq6++mrvvvptLLrkEsN6T/vfTxaKiokA/2ltfk47LMAy6d+/Orbfeyscff4zD4ditnxQWFgb6ifqR7GrhwoX89NNP3HDDDbvta66vqC9JS3Tt2pX6+vpmfxdSX5Lm7JyaaNcKrS5dumCaJrW1tepHsl/mzp3LMcccE9jWzzdpiYULF5KRkcFtt91GSEgIcXFx3HTTTXzxxRf6+XaQKFHahpKTk8nKyqKgoCDQtmrVKnr37o3P52vDyORwkpycHJiDZKdVq1YxZMgQ3G43ffr0Yc2aNYF9JSUlbNu2jcGDBx/qUKWdWL58OXfeeSdPPvkkp59+eqA9OTmZDRs2UFdXF2jb2Zd27t9TX5OOZeHChZxwwgn4/f5Am81m/UoxePDg3frJ6tWrm/SjXd+T6uvrWbt2rfpRBzVr1izy8/OZOHEio0ePZurUqQCMHj2avn37qi/JPlu7di2PPPJIk7bU1FRcLhfjx49XX5J9Eh8fT3BwMOvWrQu0ZWZm4nQ61Y9kv6xbt47MzEzGjRsXaBs0aJD6kuyz+vp6/H5/k8rQmpoaAFJSUtSXDgIlStvQgAEDGDRoEI8//jhlZWWkpqbyyiuvcP7557d1aHIYOeecc1iwYAHffPMN1dXVvP/++6SlpXHqqacCcP755/Paa6+RmppKWVkZjz32GP3792fQoEFtHLm0hbq6Ou655x6mTZvGkUce2WTf+PHjCQ4O5rnnnqOyspKVK1fy/vvvB96Tfq2vSceRnJxMWVkZjz76KJWVlRQUFPDUU08xYsQIzj//fDIzM3nvvfeorq5m/vz5zJ8/n3POOQew3pM++ugjVqxYQWVlJc899xwul4sJEya07UNJm7jrrruYO3cuM2fOZObMmUyfPh2AmTNnMmXKFPUl2WdRUVHMmDGD6dOnU1NTw5YtW3jyySc599xzOe2009SXZJ84HA7OOuss/v3vf7N161by8/N55plnmDJlCmeccYb6kbTY2rVrCQ8PJzg4ONCmn2/SEkcccQRer5ennnqKyspKCgsLee655xg5cqR+vh0sprSprKws88orrzQHDx5spqSkmP/6179Mv9/f1mFJO5OcnGwmJyeb/fr1M/v16xfY3mnu3LnmpEmTzIEDB5qnnXaauXjx4sA+v99vPvnkk+bYsWPNwYMHm1dddZWZlZXVFo8h7cCSJUvMvn37BvrQrv9lZGSYGzZsMM877zwzOTnZnDBhgvnmm282OX9vfU06lvXr15sXXXSROXjwYHPMmDHmLbfcYmZnZ5umaZqLFy82Tz31VHPgwIHmpEmTzLlz5zY598033zTHjx9vJicnm+eff765YcOGtngEaYfS09PNvn37BrbVl6QlFi9ebJ577rnm0KFDzVGjRpkPP/ywWVVVFdinviT7orq62rz//vvNkSNHmkOHDjXvvPNOs6yszDRN9SNpuX//+9/mySefvFu7+pK0xKpVq8yLLrrIHDFihJmSkqLfuw8ywzQ1s6uIiIiIiIiIiIh0bBp6LyIiIiIiIiIiIh2eEqUiIiIiIiIiIiLS4SlRKiIiIiIiIiIiIh2eEqUiIiIiIiIiIiLS4SlRKiIiIiIiIiIiIh2eEqUiIiIiIiIiIiLS4SlRKiIiIiIiIiIiIh2eEqUiIiIiIiIiIiLS4SlRKiIiIiIiIiIiIh2eEqUiIiIiIiIiIiLS4SlRKiIiIiIiIiIiIh3e/wOEgY9Y1UtitgAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(15,6))\n",
        "plt.plot(df[r'Open'], linewidth=1,color='green')\n",
        "plt.plot(df[r'Close'], linewidth=1, color='red')\n",
        "plt.ylabel(None)\n",
        "plt.xlabel(None)\n",
        "plt.title('Opening & close price of price')\n",
        "plt.legend(['Open Price','Close Price'])\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 598
        },
        "id": "yLpTPF08erwC",
        "outputId": "27542a8d-09bf-4197-c600-e970e989fbd8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "s1 = pd.Series([None, None])\n",
        "for i in range(0,len(train_data)):\n",
        "  unseen_predictions = unseen_predictions.append(s1,ignore_index=True)\n",
        "unseen_predictions\n",
        "unseen_predictions.shift(len(train_data))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "Dd-EnOrzSzmP",
        "outputId": "8642f387-83e0-4c70-f833-002fc1bdf58b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      Close  Future Price  prediction_label     0     1\n",
              "0       NaN           NaN               NaN  None  None\n",
              "1       NaN           NaN               NaN  None  None\n",
              "2       NaN           NaN               NaN  None  None\n",
              "3       NaN           NaN               NaN  None  None\n",
              "4       NaN           NaN               NaN  None  None\n",
              "...     ...           ...               ...   ...   ...\n",
              "7450    NaN           NaN               NaN   NaN   NaN\n",
              "7451    NaN           NaN               NaN   NaN   NaN\n",
              "7452    NaN           NaN               NaN   NaN   NaN\n",
              "7453    NaN           NaN               NaN   NaN   NaN\n",
              "7454    NaN           NaN               NaN   NaN   NaN\n",
              "\n",
              "[7455 rows x 5 columns]"
            ],
            "text/html": [
              "\n",
              "\n",
              "  <div id=\"df-69ba3a6b-3aab-44ff-9300-94563bc5539f\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Close</th>\n",
              "      <th>Future Price</th>\n",
              "      <th>prediction_label</th>\n",
              "      <th>0</th>\n",
              "      <th>1</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>None</td>\n",
              "      <td>None</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>7450</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7451</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7452</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7453</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7454</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>7455 rows × 5 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-69ba3a6b-3aab-44ff-9300-94563bc5539f')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "\n",
              "\n",
              "\n",
              "    <div id=\"df-3e4992ce-5849-4a7c-8275-ffbdf3579994\">\n",
              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-3e4992ce-5849-4a7c-8275-ffbdf3579994')\"\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",
              "    </div>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\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-quickchart:hover {\n",
              "    background-color: #E2EBFA;\n",
              "    box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: #174EA6;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart: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",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-3e4992ce-5849-4a7c-8275-ffbdf3579994 button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-69ba3a6b-3aab-44ff-9300-94563bc5539f 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-69ba3a6b-3aab-44ff-9300-94563bc5539f');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 75
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(15,6))\n",
        "df[r'High'].plot()\n",
        "df[r'Low'].plot()\n",
        "plt.ylabel(None)\n",
        "plt.xlabel(None)\n",
        "plt.title('High & Low price of Bitcoin')\n",
        "plt.legend(['High Price','Low Price'])\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "_ii4RGNnh6Mg",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 480
        },
        "outputId": "b3006fdd-19e7-4e94-814e-d3b5f7b14ce1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x600 with 1 Axes>"
            ],
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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "n_cols = 1\n",
        "dataset = df[r'Close']\n",
        "dataset = pd.DataFrame(dataset)\n",
        "data = dataset.values\n",
        "\n",
        "data.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-mM9qbFpi8Qm",
        "outputId": "9d7b9b78-d217-4f51-89ed-03c4cf4c3d67"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(3239, 1)"
            ]
          },
          "metadata": {},
          "execution_count": 64
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import MinMaxScaler\n",
        "scaler = MinMaxScaler(feature_range=(0,1))\n",
        "scaled_data = scaler.fit_transform(np.array(data))\n",
        "train_size = int(len(data) * (0.75))\n",
        "test_size = len(data) - train_size\n",
        "print(train_size, test_size)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4mkHsHRCjOUG",
        "outputId": "b5f3f22f-4d5e-4b61-a311-5f68b3c5deb1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2429 810\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_data = scaled_data[0:train_size, :]\n",
        "train_data.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6JedEEgFkCfH",
        "outputId": "f28a1805-f7d3-4e22-d9c3-1b070f6c1a71"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(2429, 1)"
            ]
          },
          "metadata": {},
          "execution_count": 66
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x_train = []\n",
        "y_train = []\n",
        "time_steps = 60\n",
        "n_cols = 1\n",
        "for i in range(time_steps, len(train_data)):\n",
        "  x_train.append(train_data[i-time_steps:i, :n_cols])\n",
        "  y_train.append(train_data[i, :n_cols])\n",
        "  if i<= time_steps:\n",
        "    print(x_train)\n",
        "    print(y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QLDku115kLMU",
        "outputId": "cba6c4ec-8918-46a6-e165-b8add6d582c2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[array([[0.00336983],\n",
            "       [0.00295295],\n",
            "       [0.00260162],\n",
            "       [0.00277389],\n",
            "       [0.00265077],\n",
            "       [0.0026979 ],\n",
            "       [0.00311881],\n",
            "       [0.00295531],\n",
            "       [0.00284613],\n",
            "       [0.00274273],\n",
            "       [0.00268255],\n",
            "       [0.00240848],\n",
            "       [0.00241797],\n",
            "       [0.00258922],\n",
            "       [0.0025313 ],\n",
            "       [0.00241234],\n",
            "       [0.0022202 ],\n",
            "       [0.00184179],\n",
            "       [0.00173861],\n",
            "       [0.00185676],\n",
            "       [0.00191566],\n",
            "       [0.00212595],\n",
            "       [0.00225203],\n",
            "       [0.00222629],\n",
            "       [0.00223535],\n",
            "       [0.00243514],\n",
            "       [0.00255948],\n",
            "       [0.00271883],\n",
            "       [0.00264254],\n",
            "       [0.00251895],\n",
            "       [0.00250535],\n",
            "       [0.00259994],\n",
            "       [0.0025766 ],\n",
            "       [0.00248412],\n",
            "       [0.00252212],\n",
            "       [0.00248527],\n",
            "       [0.00217971],\n",
            "       [0.00217883],\n",
            "       [0.00204292],\n",
            "       [0.00213414],\n",
            "       [0.0021223 ],\n",
            "       [0.00217084],\n",
            "       [0.00190732],\n",
            "       [0.00203715],\n",
            "       [0.00194869],\n",
            "       [0.00179382],\n",
            "       [0.00179558],\n",
            "       [0.00181737],\n",
            "       [0.00184528],\n",
            "       [0.00196509],\n",
            "       [0.00209083],\n",
            "       [0.00200703],\n",
            "       [0.00204496],\n",
            "       [0.00226435],\n",
            "       [0.00230694],\n",
            "       [0.00231903],\n",
            "       [0.00299621],\n",
            "       [0.00298083],\n",
            "       [0.00270678],\n",
            "       [0.00243827]])]\n",
            "[array([0.00258375])]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x_train, y_train = np.array(x_train), np.array(y_train)"
      ],
      "metadata": {
        "id": "0Hf94OnukLvs"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x_train = np.reshape(x_train, (x_train.shape[0], x_train.shape[1], n_cols))\n",
        "x_train.shape, y_train.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CfjQHSDakL8U",
        "outputId": "bc5ffa47-8af1-4278-ea16-d66162966a3c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((2369, 60, 1), (2369, 1))"
            ]
          },
          "metadata": {},
          "execution_count": 69
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model = Sequential([\n",
        "    LSTM(50,return_sequences = True, input_shape = (x_train.shape[1], n_cols)),\n",
        "    LSTM(64, return_sequences= False),\n",
        "    Dense(32),\n",
        "    Dense(16),\n",
        "    Dense(n_cols)\n",
        "])\n",
        "\n",
        "model.compile(optimizer = 'adam', loss = 'mse', metrics = 'mean_absolute_error')"
      ],
      "metadata": {
        "id": "DUIiVLyombKD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "x9xy_4lhmbSJ",
        "outputId": "984dfc06-618a-472b-81e7-837046dafaf6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " lstm (LSTM)                 (None, 60, 50)            10400     \n",
            "                                                                 \n",
            " lstm_1 (LSTM)               (None, 64)                29440     \n",
            "                                                                 \n",
            " dense (Dense)               (None, 32)                2080      \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 16)                528       \n",
            "                                                                 \n",
            " dense_2 (Dense)             (None, 1)                 17        \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 42,465\n",
            "Trainable params: 42,465\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "history = model.fit(x_train, y_train, epochs = 100, batch_size = 32)"
      ],
      "metadata": {
        "id": "UvtQgnDdn1CP",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4460be5b-0308-477d-847b-bc2a2f504ab0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n",
            "75/75 [==============================] - 6s 84ms/step - loss: 4.1551e-04 - mean_absolute_error: 0.0117\n",
            "Epoch 2/100\n",
            "75/75 [==============================] - 5s 68ms/step - loss: 4.0109e-04 - mean_absolute_error: 0.0117\n",
            "Epoch 3/100\n",
            "75/75 [==============================] - 6s 80ms/step - loss: 0.0011 - mean_absolute_error: 0.0187\n",
            "Epoch 4/100\n",
            "75/75 [==============================] - 5s 64ms/step - loss: 2.7534e-04 - mean_absolute_error: 0.0086\n",
            "Epoch 5/100\n",
            "75/75 [==============================] - 6s 80ms/step - loss: 2.7851e-04 - mean_absolute_error: 0.0093\n",
            "Epoch 6/100\n",
            "75/75 [==============================] - 6s 74ms/step - loss: 3.0018e-04 - mean_absolute_error: 0.0096\n",
            "Epoch 7/100\n",
            "75/75 [==============================] - 5s 62ms/step - loss: 7.3699e-04 - mean_absolute_error: 0.0162\n",
            "Epoch 8/100\n",
            "75/75 [==============================] - 7s 92ms/step - loss: 2.6101e-04 - mean_absolute_error: 0.0093\n",
            "Epoch 9/100\n",
            "75/75 [==============================] - 5s 61ms/step - loss: 2.0224e-04 - mean_absolute_error: 0.0078\n",
            "Epoch 10/100\n",
            "75/75 [==============================] - 5s 63ms/step - loss: 2.1754e-04 - mean_absolute_error: 0.0079\n",
            "Epoch 11/100\n",
            "75/75 [==============================] - 6s 85ms/step - loss: 1.8062e-04 - mean_absolute_error: 0.0070\n",
            "Epoch 12/100\n",
            "75/75 [==============================] - 5s 68ms/step - loss: 1.7438e-04 - mean_absolute_error: 0.0072\n",
            "Epoch 13/100\n",
            "75/75 [==============================] - 6s 81ms/step - loss: 1.5180e-04 - mean_absolute_error: 0.0062\n",
            "Epoch 14/100\n",
            "75/75 [==============================] - 5s 69ms/step - loss: 1.8128e-04 - mean_absolute_error: 0.0081\n",
            "Epoch 15/100\n",
            "75/75 [==============================] - 5s 67ms/step - loss: 1.5844e-04 - mean_absolute_error: 0.0074\n",
            "Epoch 16/100\n",
            "75/75 [==============================] - 7s 91ms/step - loss: 1.4602e-04 - mean_absolute_error: 0.0064\n",
            "Epoch 17/100\n",
            "75/75 [==============================] - 5s 64ms/step - loss: 1.5018e-04 - mean_absolute_error: 0.0067\n",
            "Epoch 18/100\n",
            "75/75 [==============================] - 5s 73ms/step - loss: 1.2604e-04 - mean_absolute_error: 0.0060\n",
            "Epoch 19/100\n",
            "75/75 [==============================] - 6s 81ms/step - loss: 1.4638e-04 - mean_absolute_error: 0.0071\n",
            "Epoch 20/100\n",
            "75/75 [==============================] - 5s 61ms/step - loss: 1.2582e-04 - mean_absolute_error: 0.0059\n",
            "Epoch 21/100\n",
            "75/75 [==============================] - 6s 85ms/step - loss: 1.1840e-04 - mean_absolute_error: 0.0057\n",
            "Epoch 22/100\n",
            "75/75 [==============================] - 6s 76ms/step - loss: 1.5354e-04 - mean_absolute_error: 0.0072\n",
            "Epoch 23/100\n",
            "75/75 [==============================] - 5s 68ms/step - loss: 1.1961e-04 - mean_absolute_error: 0.0062\n",
            "Epoch 24/100\n",
            "75/75 [==============================] - 7s 89ms/step - loss: 1.4020e-04 - mean_absolute_error: 0.0075\n",
            "Epoch 25/100\n",
            "75/75 [==============================] - 5s 63ms/step - loss: 1.5777e-04 - mean_absolute_error: 0.0073\n",
            "Epoch 26/100\n",
            "75/75 [==============================] - 5s 73ms/step - loss: 1.2844e-04 - mean_absolute_error: 0.0065\n",
            "Epoch 27/100\n",
            "75/75 [==============================] - 6s 77ms/step - loss: 1.2374e-04 - mean_absolute_error: 0.0064\n",
            "Epoch 28/100\n",
            "75/75 [==============================] - 5s 61ms/step - loss: 9.5053e-05 - mean_absolute_error: 0.0053\n",
            "Epoch 29/100\n",
            "75/75 [==============================] - 7s 87ms/step - loss: 1.1961e-04 - mean_absolute_error: 0.0063\n",
            "Epoch 30/100\n",
            "75/75 [==============================] - 5s 65ms/step - loss: 1.1742e-04 - mean_absolute_error: 0.0064\n",
            "Epoch 31/100\n",
            "75/75 [==============================] - 5s 60ms/step - loss: 1.1258e-04 - mean_absolute_error: 0.0062\n",
            "Epoch 32/100\n",
            "75/75 [==============================] - 6s 87ms/step - loss: 1.1257e-04 - mean_absolute_error: 0.0064\n",
            "Epoch 33/100\n",
            "75/75 [==============================] - 5s 65ms/step - loss: 1.0747e-04 - mean_absolute_error: 0.0060\n",
            "Epoch 34/100\n",
            "75/75 [==============================] - 5s 60ms/step - loss: 1.4857e-04 - mean_absolute_error: 0.0073\n",
            "Epoch 35/100\n",
            "75/75 [==============================] - 6s 86ms/step - loss: 1.3278e-04 - mean_absolute_error: 0.0068\n",
            "Epoch 36/100\n",
            "75/75 [==============================] - 5s 62ms/step - loss: 1.4183e-04 - mean_absolute_error: 0.0074\n",
            "Epoch 37/100\n",
            "75/75 [==============================] - 5s 71ms/step - loss: 1.2201e-04 - mean_absolute_error: 0.0065\n",
            "Epoch 38/100\n",
            "75/75 [==============================] - 6s 76ms/step - loss: 9.6531e-05 - mean_absolute_error: 0.0055\n",
            "Epoch 39/100\n",
            "75/75 [==============================] - 5s 60ms/step - loss: 8.6626e-05 - mean_absolute_error: 0.0048\n",
            "Epoch 40/100\n",
            "75/75 [==============================] - 6s 82ms/step - loss: 1.4036e-04 - mean_absolute_error: 0.0077\n",
            "Epoch 41/100\n",
            "75/75 [==============================] - 5s 71ms/step - loss: 4.5434e-04 - mean_absolute_error: 0.0131\n",
            "Epoch 42/100\n",
            "75/75 [==============================] - 5s 60ms/step - loss: 9.3737e-05 - mean_absolute_error: 0.0051\n",
            "Epoch 43/100\n",
            "75/75 [==============================] - 6s 87ms/step - loss: 3.9043e-04 - mean_absolute_error: 0.0118\n",
            "Epoch 44/100\n",
            "75/75 [==============================] - 5s 61ms/step - loss: 1.0349e-04 - mean_absolute_error: 0.0056\n",
            "Epoch 45/100\n",
            "75/75 [==============================] - 5s 61ms/step - loss: 9.4777e-05 - mean_absolute_error: 0.0052\n",
            "Epoch 46/100\n",
            "75/75 [==============================] - 7s 91ms/step - loss: 9.0150e-05 - mean_absolute_error: 0.0052\n",
            "Epoch 47/100\n",
            "75/75 [==============================] - 5s 64ms/step - loss: 9.9740e-05 - mean_absolute_error: 0.0056\n",
            "Epoch 48/100\n",
            "75/75 [==============================] - 6s 83ms/step - loss: 9.9028e-05 - mean_absolute_error: 0.0053\n",
            "Epoch 49/100\n",
            "75/75 [==============================] - 5s 64ms/step - loss: 9.7734e-05 - mean_absolute_error: 0.0055\n",
            "Epoch 50/100\n",
            "75/75 [==============================] - 5s 62ms/step - loss: 1.0070e-04 - mean_absolute_error: 0.0058\n",
            "Epoch 51/100\n",
            "75/75 [==============================] - 6s 83ms/step - loss: 8.9272e-05 - mean_absolute_error: 0.0050\n",
            "Epoch 52/100\n",
            "75/75 [==============================] - 5s 66ms/step - loss: 8.7123e-05 - mean_absolute_error: 0.0055\n",
            "Epoch 53/100\n",
            "75/75 [==============================] - 6s 81ms/step - loss: 1.3305e-04 - mean_absolute_error: 0.0067\n",
            "Epoch 54/100\n",
            "75/75 [==============================] - 5s 67ms/step - loss: 8.1095e-05 - mean_absolute_error: 0.0045\n",
            "Epoch 55/100\n",
            "75/75 [==============================] - 5s 66ms/step - loss: 1.0460e-04 - mean_absolute_error: 0.0060\n",
            "Epoch 56/100\n",
            "75/75 [==============================] - 6s 81ms/step - loss: 8.5705e-05 - mean_absolute_error: 0.0050\n",
            "Epoch 57/100\n",
            "75/75 [==============================] - 5s 63ms/step - loss: 9.0969e-05 - mean_absolute_error: 0.0053\n",
            "Epoch 58/100\n",
            "75/75 [==============================] - 6s 74ms/step - loss: 9.9773e-05 - mean_absolute_error: 0.0057\n",
            "Epoch 59/100\n",
            "75/75 [==============================] - 6s 74ms/step - loss: 9.0980e-05 - mean_absolute_error: 0.0051\n",
            "Epoch 60/100\n",
            "75/75 [==============================] - 5s 63ms/step - loss: 1.0703e-04 - mean_absolute_error: 0.0059\n",
            "Epoch 61/100\n",
            "75/75 [==============================] - 6s 83ms/step - loss: 9.8206e-05 - mean_absolute_error: 0.0058\n",
            "Epoch 62/100\n",
            "75/75 [==============================] - 5s 67ms/step - loss: 1.1987e-04 - mean_absolute_error: 0.0062\n",
            "Epoch 63/100\n",
            "75/75 [==============================] - 6s 78ms/step - loss: 1.3004e-04 - mean_absolute_error: 0.0071\n",
            "Epoch 64/100\n",
            "75/75 [==============================] - 5s 70ms/step - loss: 1.1012e-04 - mean_absolute_error: 0.0058\n",
            "Epoch 65/100\n",
            "75/75 [==============================] - 5s 64ms/step - loss: 9.1213e-05 - mean_absolute_error: 0.0054\n",
            "Epoch 66/100\n",
            "75/75 [==============================] - 6s 86ms/step - loss: 9.2631e-05 - mean_absolute_error: 0.0052\n",
            "Epoch 67/100\n",
            "75/75 [==============================] - 4s 60ms/step - loss: 8.9494e-05 - mean_absolute_error: 0.0049\n",
            "Epoch 68/100\n",
            "75/75 [==============================] - 5s 64ms/step - loss: 1.0413e-04 - mean_absolute_error: 0.0060\n",
            "Epoch 69/100\n",
            "75/75 [==============================] - 6s 81ms/step - loss: 9.4740e-05 - mean_absolute_error: 0.0057\n",
            "Epoch 70/100\n",
            "75/75 [==============================] - 5s 65ms/step - loss: 1.4322e-04 - mean_absolute_error: 0.0070\n",
            "Epoch 71/100\n",
            "75/75 [==============================] - 6s 82ms/step - loss: 1.4789e-04 - mean_absolute_error: 0.0073\n",
            "Epoch 72/100\n",
            "75/75 [==============================] - 5s 62ms/step - loss: 1.2624e-04 - mean_absolute_error: 0.0063\n",
            "Epoch 73/100\n",
            "75/75 [==============================] - 5s 62ms/step - loss: 7.7701e-05 - mean_absolute_error: 0.0043\n",
            "Epoch 74/100\n",
            "75/75 [==============================] - 7s 88ms/step - loss: 8.5480e-05 - mean_absolute_error: 0.0048\n",
            "Epoch 75/100\n",
            "75/75 [==============================] - 5s 63ms/step - loss: 9.9778e-05 - mean_absolute_error: 0.0059\n",
            "Epoch 76/100\n",
            "75/75 [==============================] - 6s 80ms/step - loss: 1.0218e-04 - mean_absolute_error: 0.0057\n",
            "Epoch 77/100\n",
            "75/75 [==============================] - 5s 64ms/step - loss: 8.5069e-05 - mean_absolute_error: 0.0048\n",
            "Epoch 78/100\n",
            "75/75 [==============================] - 5s 65ms/step - loss: 1.0196e-04 - mean_absolute_error: 0.0058\n",
            "Epoch 79/100\n",
            "75/75 [==============================] - 6s 80ms/step - loss: 1.6400e-04 - mean_absolute_error: 0.0076\n",
            "Epoch 80/100\n",
            "75/75 [==============================] - 5s 62ms/step - loss: 9.0813e-05 - mean_absolute_error: 0.0052\n",
            "Epoch 81/100\n",
            "75/75 [==============================] - 5s 69ms/step - loss: 9.5500e-05 - mean_absolute_error: 0.0056\n",
            "Epoch 82/100\n",
            "75/75 [==============================] - 6s 74ms/step - loss: 9.5329e-05 - mean_absolute_error: 0.0051\n",
            "Epoch 83/100\n",
            "75/75 [==============================] - 4s 60ms/step - loss: 1.0330e-04 - mean_absolute_error: 0.0057\n",
            "Epoch 84/100\n",
            "75/75 [==============================] - 6s 85ms/step - loss: 1.3077e-04 - mean_absolute_error: 0.0068\n",
            "Epoch 85/100\n",
            "75/75 [==============================] - 5s 62ms/step - loss: 1.1042e-04 - mean_absolute_error: 0.0059\n",
            "Epoch 86/100\n",
            "75/75 [==============================] - 5s 60ms/step - loss: 1.1141e-04 - mean_absolute_error: 0.0060\n",
            "Epoch 87/100\n",
            "75/75 [==============================] - 6s 81ms/step - loss: 8.0004e-05 - mean_absolute_error: 0.0047\n",
            "Epoch 88/100\n",
            "75/75 [==============================] - 5s 60ms/step - loss: 9.3956e-05 - mean_absolute_error: 0.0058\n",
            "Epoch 89/100\n",
            "75/75 [==============================] - 5s 72ms/step - loss: 8.7127e-05 - mean_absolute_error: 0.0051\n",
            "Epoch 90/100\n",
            "75/75 [==============================] - 5s 71ms/step - loss: 9.8267e-05 - mean_absolute_error: 0.0060\n",
            "Epoch 91/100\n",
            "75/75 [==============================] - 5s 65ms/step - loss: 1.0516e-04 - mean_absolute_error: 0.0060\n",
            "Epoch 92/100\n",
            "75/75 [==============================] - 6s 82ms/step - loss: 1.1964e-04 - mean_absolute_error: 0.0063\n",
            "Epoch 93/100\n",
            "75/75 [==============================] - 5s 60ms/step - loss: 8.6802e-05 - mean_absolute_error: 0.0050\n",
            "Epoch 94/100\n",
            "75/75 [==============================] - 5s 60ms/step - loss: 9.5705e-05 - mean_absolute_error: 0.0059\n",
            "Epoch 95/100\n",
            "75/75 [==============================] - 6s 85ms/step - loss: 3.0516e-04 - mean_absolute_error: 0.0107\n",
            "Epoch 96/100\n",
            "75/75 [==============================] - 5s 64ms/step - loss: 9.0966e-05 - mean_absolute_error: 0.0053\n",
            "Epoch 97/100\n",
            "75/75 [==============================] - 6s 78ms/step - loss: 8.0824e-05 - mean_absolute_error: 0.0048\n",
            "Epoch 98/100\n",
            "75/75 [==============================] - 5s 66ms/step - loss: 8.2634e-05 - mean_absolute_error: 0.0046\n",
            "Epoch 99/100\n",
            "75/75 [==============================] - 5s 60ms/step - loss: 9.3919e-05 - mean_absolute_error: 0.0055\n",
            "Epoch 100/100\n",
            "75/75 [==============================] - 6s 80ms/step - loss: 9.3475e-05 - mean_absolute_error: 0.0053\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(12,8))\n",
        "plt.plot(history.history[\"loss\"])\n",
        "plt.plot(history.history[\"mean_absolute_error\"])\n",
        "plt.ylabel('loss')\n",
        "plt.xlabel('epochs')\n",
        "plt.title('Losses')\n",
        "plt.legend(['Mean Squared Error','Mean Absolute Error'])\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "2e7817f8-6e73-4154-ee2c-7a4455dfb900",
        "id": "YQGmR6gHqSVG"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x800 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x800 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "time_steps = 60\n",
        "test_data = scaled_data[train_size - time_steps:, :]\n",
        "\n",
        "x_test = []\n",
        "y_test = []\n",
        "n_cols = 1\n",
        "\n",
        "for i in range(time_steps, len(test_data)):\n",
        "  x_test.append(test_data[i-time_steps:i, 0:n_cols])\n",
        "  y_test.append(test_data[i,0:n_cols])\n",
        "\n",
        "x_test,y_test = np.array(x_test), np.array(y_test)\n",
        "x_test = np.reshape(x_test, (x_test.shape[0], x_test.shape[1], n_cols))"
      ],
      "metadata": {
        "id": "Hx1DWCzYrBwd"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x_test.shape, y_test.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4cjiwYXcsl8u",
        "outputId": "0ae88794-1241-4aad-8331-406fc0b0c2b7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((810, 60, 1), (810, 1))"
            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "predictions = model.predict(x_test)\n",
        "predictions = scaler.inverse_transform(predictions)\n",
        "predictions.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MCJSigycsqfX",
        "outputId": "10c46704-743d-46ce-ef82-21ac535a937a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "26/26 [==============================] - 7s 62ms/step\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(810, 1)"
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_test = scaler.inverse_transform(y_test)\n",
        "rmse = np.sqrt(np.mean(y_test - predictions)**2).round(2)\n",
        "rmse"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wGbeEsxntVoJ",
        "outputId": "2d5cb440-a01b-47b4-84bf-a01a31696248"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "69713.79"
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "preds_acts = pd.DataFrame(data = {'Predictions': predictions.flatten(), 'Actuals': y_test.flatten()})\n",
        "\n",
        "s = pd.Series([None, None])\n",
        "for i in range(0,len(train_data)):\n",
        "  preds_acts = preds_acts.append(s,ignore_index=True)\n",
        "preds_acts"
      ],
      "metadata": {
        "id": "jY0qiS5Dtqc_",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "outputId": "c559aa91-432b-4c93-97e4-207a88000f6d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      Predictions     Actuals    0    1\n",
              "0      4250409.00  3621502.00  NaN  NaN\n",
              "1      3705586.00  3651423.00  NaN  NaN\n",
              "2      3743096.00  3655200.50  NaN  NaN\n",
              "3      3740341.00  3426544.50  NaN  NaN\n",
              "4      3523701.25  3404260.75  NaN  NaN\n",
              "...           ...         ...  ...  ...\n",
              "3234          NaN         NaN  NaN  NaN\n",
              "3235          NaN         NaN  NaN  NaN\n",
              "3236          NaN         NaN  NaN  NaN\n",
              "3237          NaN         NaN  NaN  NaN\n",
              "3238          NaN         NaN  NaN  NaN\n",
              "\n",
              "[3239 rows x 4 columns]"
            ],
            "text/html": [
              "\n",
              "\n",
              "  <div id=\"df-7de6a687-58e6-4fcb-a039-24bf4993631d\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Predictions</th>\n",
              "      <th>Actuals</th>\n",
              "      <th>0</th>\n",
              "      <th>1</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>4250409.00</td>\n",
              "      <td>3621502.00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>3705586.00</td>\n",
              "      <td>3651423.00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3743096.00</td>\n",
              "      <td>3655200.50</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3740341.00</td>\n",
              "      <td>3426544.50</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>3523701.25</td>\n",
              "      <td>3404260.75</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3234</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3235</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3236</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3237</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3238</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>3239 rows × 4 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-7de6a687-58e6-4fcb-a039-24bf4993631d')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "\n",
              "\n",
              "\n",
              "    <div id=\"df-1442cff4-346a-4019-bcd8-b93e92ae2635\">\n",
              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-1442cff4-346a-4019-bcd8-b93e92ae2635')\"\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",
              "    </div>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\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-quickchart:hover {\n",
              "    background-color: #E2EBFA;\n",
              "    box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: #174EA6;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart: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",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-1442cff4-346a-4019-bcd8-b93e92ae2635 button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-7de6a687-58e6-4fcb-a039-24bf4993631d 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-7de6a687-58e6-4fcb-a039-24bf4993631d');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "preds_acts['Predictions'].shift(len(train_data))"
      ],
      "metadata": {
        "id": "9pl7LmfL5FA5",
        "outputId": "a916505a-0ad9-4243-950a-9e3f4557bd32",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "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",
              "3234    2466901.50\n",
              "3235    2465906.00\n",
              "3236    2475657.50\n",
              "3237    2477431.75\n",
              "3238    2483442.00\n",
              "Name: Predictions, Length: 3239, dtype: float32"
            ]
          },
          "metadata": {},
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize = (16,6))\n",
        "plt.plot(unseen_predictions['prediction_label'])\n",
        "plt.plot(preds_acts['Predictions'])\n",
        "plt.plot(preds_acts['Actuals'])\n",
        "plt.legend(['PredictionsLSTM', 'Actuals','prediction_labeljinja'])\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "j5MoWJ_LuIQl",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 561
        },
        "outputId": "c55daf52-0a6b-43b0-8001-88ca4e70a449"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1600x600 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(16, 6))\n",
        "plt.title(\"Bitcoin stock predictions\", fontsize=20)\n",
        "plt.xlabel('Date', fontsize=20)\n",
        "plt.ylabel('Close Price', fontsize=18)\n",
        "plt.plot(dataset.iloc[:train_size]['Close'], linewidth=1)\n",
        "plt.plot(dataset.iloc[train_size:]['Close'], linewidth=1,color='black')\n",
        "plt.plot(unseen_predictions[r'prediction_label'].shift(len(train_data)), linewidth = 1, color='pink')\n",
        "plt.plot(preds_acts['Predictions'].shift(len(train_data)), linewidth = 1, color='green')\n",
        "plt.legend(['Train', 'Test', 'Predictions','prediction_label'])"
      ],
      "metadata": {
        "id": "Y9H2V8wRu_1j",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 510
        },
        "outputId": "9c002b00-917c-4025-b2fe-806a8342e936"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7fe7ff31b820>"
            ]
          },
          "metadata": {},
          "execution_count": 86
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1600x600 with 1 Axes>"
            ],
            "image/png": 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    }
  ]
}