MSc_Dissertation_Linear_Regression(ver2).ipynb

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    "colab": {
      "name": "MSc Dissertation - Linear Regression(ver2).ipynb",
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      "collapsed_sections": []
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      "name": "python"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vK5jCIf0ULTY"
      },
      "source": [
        "## Import Libraries"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wd7ilEq6UVXt"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "from datetime import datetime\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "%matplotlib inline\n",
        "sns.set_style(\"darkgrid\")\n",
        "\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import r2_score, mean_squared_error"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "txXqRt4NNqXH"
      },
      "source": [
        "plt.rcParams['figure.figsize'] = (12,6)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UKfqkbXVVl8e"
      },
      "source": [
        "## Import Data"
      ]
    },
    {
      "cell_type": "code",
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          "base_uri": "https://localhost:8080/",
          "height": 206
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        "id": "CoVI6dYzUdCs",
        "outputId": "068da59d-2ccc-4650-ae38-e31b5bf613ba"
      },
      "source": [
        "df = pd.read_csv('/content/drive/MyDrive/data/ADBL_data.csv')\n",
        "df['Date'] = pd.to_datetime(df.Date)\n",
        "# df = df[df['Date'] >= '2015-01-01']\n",
        "df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        Date   Open   High    Low  Close\n",
              "0 2012-01-01  115.0  115.0  113.0  114.0\n",
              "1 2012-01-02  114.0  118.0  115.0  118.0\n",
              "2 2012-01-03  118.0  116.0  114.0  115.0\n",
              "3 2012-01-04  115.0  115.0  113.0  114.0\n",
              "4 2012-01-05  114.0  114.0  112.0  113.0"
            ],
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              "      <th></th>\n",
              "      <th>Date</th>\n",
              "      <th>Open</th>\n",
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              "      <th>Low</th>\n",
              "      <th>Close</th>\n",
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              "  <tbody>\n",
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              "      <th>0</th>\n",
              "      <td>2012-01-01</td>\n",
              "      <td>115.0</td>\n",
              "      <td>115.0</td>\n",
              "      <td>113.0</td>\n",
              "      <td>114.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2012-01-02</td>\n",
              "      <td>114.0</td>\n",
              "      <td>118.0</td>\n",
              "      <td>115.0</td>\n",
              "      <td>118.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2012-01-03</td>\n",
              "      <td>118.0</td>\n",
              "      <td>116.0</td>\n",
              "      <td>114.0</td>\n",
              "      <td>115.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2012-01-04</td>\n",
              "      <td>115.0</td>\n",
              "      <td>115.0</td>\n",
              "      <td>113.0</td>\n",
              "      <td>114.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2012-01-05</td>\n",
              "      <td>114.0</td>\n",
              "      <td>114.0</td>\n",
              "      <td>112.0</td>\n",
              "      <td>113.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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          "metadata": {},
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      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DeceZu4gWUlO"
      },
      "source": [
        "## Time Series chart of ADBL"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 409
        },
        "id": "-0XflDtaWRbm",
        "outputId": "bcd5425c-6fd5-4f95-ff04-ddd8caae6278"
      },
      "source": [
        "plt.title('ADBL Stock Trend', fontsize=14)\n",
        "plt.xlabel('Year', fontsize=14)\n",
        "plt.ylabel('Closing Price', fontsize=14)\n",
        "plt.plot(df['Date'], df['Close'])\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x432 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KGq917vxoQ2F"
      },
      "source": [
        "##Generate Features from Closing price"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Wa6K0C5GoN13"
      },
      "source": [
        "df = df[['Date','Close']]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7ZeCpA6fmpUR"
      },
      "source": [
        "# Generate Simple moving averages over the period of 7, 15 and 30 days\n",
        "for period in [7, 15, 30]:\n",
        "    column = f'sma_{period}'\n",
        "    df[column] = df[['Close']].rolling(period).mean()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hwhdElhdnda-"
      },
      "source": [
        "# Generate log returns\n",
        "df['log_returns'] = np.log(df.Close.pct_change() + 1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "ca8T0G2GqlMa",
        "outputId": "64667fa5-d054-455a-d9d0-23483df15413"
      },
      "source": [
        "df"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           Date  Close       sma_7      sma_15      sma_30  log_returns\n",
              "0    2012-01-01  114.0         NaN         NaN         NaN          NaN\n",
              "1    2012-01-02  118.0         NaN         NaN         NaN     0.034486\n",
              "2    2012-01-03  115.0         NaN         NaN         NaN    -0.025752\n",
              "3    2012-01-04  114.0         NaN         NaN         NaN    -0.008734\n",
              "4    2012-01-05  113.0         NaN         NaN         NaN    -0.008811\n",
              "...         ...    ...         ...         ...         ...          ...\n",
              "1906 2020-03-16  382.0  395.428571  418.866667  403.800000     0.000000\n",
              "1907 2020-03-17  378.0  390.000000  415.333333  403.400000    -0.010526\n",
              "1908 2020-03-18  374.0  385.571429  411.466667  402.933333    -0.010638\n",
              "1909 2020-03-19  376.0  385.000000  407.533333  402.600000     0.005333\n",
              "1910 2020-03-22  368.0  379.571429  401.866667  402.033333    -0.021506\n",
              "\n",
              "[1911 rows x 6 columns]"
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              "      <td>NaN</td>\n",
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              "      <th>3</th>\n",
              "      <td>2012-01-04</td>\n",
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              "      <td>-0.008734</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2012-01-05</td>\n",
              "      <td>113.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "      <th>1906</th>\n",
              "      <td>2020-03-16</td>\n",
              "      <td>382.0</td>\n",
              "      <td>395.428571</td>\n",
              "      <td>418.866667</td>\n",
              "      <td>403.800000</td>\n",
              "      <td>0.000000</td>\n",
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              "    <tr>\n",
              "      <th>1907</th>\n",
              "      <td>2020-03-17</td>\n",
              "      <td>378.0</td>\n",
              "      <td>390.000000</td>\n",
              "      <td>415.333333</td>\n",
              "      <td>403.400000</td>\n",
              "      <td>-0.010526</td>\n",
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              "    <tr>\n",
              "      <th>1908</th>\n",
              "      <td>2020-03-18</td>\n",
              "      <td>374.0</td>\n",
              "      <td>385.571429</td>\n",
              "      <td>411.466667</td>\n",
              "      <td>402.933333</td>\n",
              "      <td>-0.010638</td>\n",
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              "    <tr>\n",
              "      <th>1909</th>\n",
              "      <td>2020-03-19</td>\n",
              "      <td>376.0</td>\n",
              "      <td>385.000000</td>\n",
              "      <td>407.533333</td>\n",
              "      <td>402.600000</td>\n",
              "      <td>0.005333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1910</th>\n",
              "      <td>2020-03-22</td>\n",
              "      <td>368.0</td>\n",
              "      <td>379.571429</td>\n",
              "      <td>401.866667</td>\n",
              "      <td>402.033333</td>\n",
              "      <td>-0.021506</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1911 rows × 6 columns</p>\n",
              "</div>\n",
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              "\n",
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              "\n",
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              "          document.querySelector('#df-2033692c-442e-4334-9441-d22e9ea6d9b0 button.colab-df-convert');\n",
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              "          const element = document.querySelector('#df-2033692c-442e-4334-9441-d22e9ea6d9b0');\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",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "LxkoeBzsTpsY",
        "outputId": "57e17a97-6534-48a9-996f-9c718068ee17"
      },
      "source": [
        "# Lag days of -1 is actually 1.\n",
        "LAG_DAYS = -1\n",
        "\n",
        "df['close_future'] = df['Close'].shift(LAG_DAYS)\n",
        "df.dropna(inplace=True)\n",
        "df.reset_index(drop=True, inplace=True)\n",
        "df"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           Date  Close       sma_7      sma_15      sma_30  log_returns  \\\n",
              "0    2012-02-14  109.0  109.428571  110.533333  112.300000     0.009217   \n",
              "1    2012-02-15  111.0  109.428571  110.466667  112.200000     0.018182   \n",
              "2    2012-02-16  108.0  109.142857  110.066667  111.866667    -0.027399   \n",
              "3    2012-02-21  109.0  109.000000  109.866667  111.666667     0.009217   \n",
              "4    2012-02-23  109.0  109.000000  109.666667  111.500000     0.000000   \n",
              "...         ...    ...         ...         ...         ...          ...   \n",
              "1876 2020-03-15  382.0  401.571429  419.933333  403.866667    -0.038516   \n",
              "1877 2020-03-16  382.0  395.428571  418.866667  403.800000     0.000000   \n",
              "1878 2020-03-17  378.0  390.000000  415.333333  403.400000    -0.010526   \n",
              "1879 2020-03-18  374.0  385.571429  411.466667  402.933333    -0.010638   \n",
              "1880 2020-03-19  376.0  385.000000  407.533333  402.600000     0.005333   \n",
              "\n",
              "      close_future  \n",
              "0            111.0  \n",
              "1            108.0  \n",
              "2            109.0  \n",
              "3            109.0  \n",
              "4            107.0  \n",
              "...            ...  \n",
              "1876         382.0  \n",
              "1877         378.0  \n",
              "1878         374.0  \n",
              "1879         376.0  \n",
              "1880         368.0  \n",
              "\n",
              "[1881 rows x 7 columns]"
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              "      <td>112.300000</td>\n",
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              "      <th>1</th>\n",
              "      <td>2012-02-15</td>\n",
              "      <td>111.0</td>\n",
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              "      <th>2</th>\n",
              "      <td>2012-02-16</td>\n",
              "      <td>108.0</td>\n",
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              "      <td>109.0</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2012-02-23</td>\n",
              "      <td>109.0</td>\n",
              "      <td>109.000000</td>\n",
              "      <td>109.666667</td>\n",
              "      <td>111.500000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>107.0</td>\n",
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              "    <tr>\n",
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              "    <tr>\n",
              "      <th>1876</th>\n",
              "      <td>2020-03-15</td>\n",
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              "      <td>-0.038516</td>\n",
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              "    <tr>\n",
              "      <th>1877</th>\n",
              "      <td>2020-03-16</td>\n",
              "      <td>382.0</td>\n",
              "      <td>395.428571</td>\n",
              "      <td>418.866667</td>\n",
              "      <td>403.800000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>378.0</td>\n",
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              "      <th>1878</th>\n",
              "      <td>2020-03-17</td>\n",
              "      <td>378.0</td>\n",
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              "      <td>374.0</td>\n",
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              "      <td>0.005333</td>\n",
              "      <td>368.0</td>\n",
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              "    }\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-47e14cf0-06c7-4e53-997e-d43855f393b1 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-47e14cf0-06c7-4e53-997e-d43855f393b1');\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": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Tzb_5iivV2GB"
      },
      "source": [
        "X = df[['Close', 'sma_7', 'sma_15', 'sma_30', 'log_returns']]\n",
        "y = df[['close_future']]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JFy8F0HXonI6"
      },
      "source": [
        "# Split Train and Test set into 70/30 percent\n",
        "X_train, X_test, Y_train, Y_test = train_test_split(X, y, test_size=0.30, shuffle=False)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CTeu2XzoWCpJ"
      },
      "source": [
        "## Scaling Train and Test Data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8GAIyUL9WBxs"
      },
      "source": [
        "scaler = StandardScaler()\n",
        "X_train = scaler.fit_transform(X_train)\n",
        "X_test = scaler.transform(X_test)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OHSMLW7Jp3v3"
      },
      "source": [
        "## Linear Regression"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Dcq5nC60phAu"
      },
      "source": [
        "# Train the model on the training set\n",
        "model = LinearRegression().fit(X_train, Y_train)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lGNUrIQJqNPK",
        "outputId": "b92b9c5f-fb08-401f-9dd0-b89dfbb3020f"
      },
      "source": [
        "# Linear model coefficients(Open, High, Low) and Intercept\n",
        "print('LR Coefficients: \\n', model.coef_)\n",
        "print('LR Intercept: \\n', model.intercept_)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "LR Coefficients: \n",
            " [[212.97538838  -4.05199217  -1.7792257    3.26349589   1.36966548]]\n",
            "LR Intercept: \n",
            " [575.15329341]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "B50wo-hUsBNY"
      },
      "source": [
        "## Model Evaluation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qLtOFyg1r88O",
        "outputId": "7eb239e9-0a8b-445f-f823-207446c4fc8b"
      },
      "source": [
        "#  coefficient of determination (R²)\n",
        "#  Its maximum is 1. The higher the R² value, the better the fit.\n",
        "print(\"Performance on training set (R²): \", model.score(X_train, Y_train))\n",
        "print(\"Performance on test set (R²): \", model.score(X_test, Y_test))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Performance on training set (R²):  0.9923030733225079\n",
            "Performance on test set (R²):  0.9655031427974509\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vErC-NwRsW5g"
      },
      "source": [
        "## Stock Prediction"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Q_gMm9dMsFHZ"
      },
      "source": [
        "yhat_test = model.predict(X_test)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wedg2ugCaTgX",
        "outputId": "f6aca159-d8c3-40fc-b71e-5919cd22bc3f"
      },
      "source": [
        "print('Test Actual:', Y_test[:6].values)\n",
        "print('Test Predicted:', yhat_test[:6])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Test Actual: [[425.]\n",
            " [422.]\n",
            " [427.]\n",
            " [428.]\n",
            " [427.]\n",
            " [431.]]\n",
            "Test Predicted: [[420.37393754]\n",
            " [424.82996442]\n",
            " [421.17596259]\n",
            " [427.27230596]\n",
            " [427.88222716]\n",
            " [426.70586731]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pOe57cu-sdsl",
        "outputId": "775131aa-e057-4f1f-cdaa-69c6517929d2"
      },
      "source": [
        "# The mean squared error\n",
        "print(\"Root mean squared error: %.2f\"\n",
        "      % mean_squared_error(Y_test, yhat_test, squared=False))\n",
        "# Explained variance score: 1 is perfect prediction\n",
        "print('Variance score: %.2f' % r2_score(Y_test, yhat_test))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Root mean squared error: 9.40\n",
            "Variance score: 0.97\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OzyOhKcKsk6Y"
      },
      "source": [
        "# Preparing predicted data for plotting\n",
        "date_train = df['Date'][LAG_DAYS:len(Y_train) + LAG_DAYS]\n",
        "date_test = df['Date'][-len(Y_test):]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XPVw6F5gsxpz"
      },
      "source": [
        "## Prediction Visualization"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 409
        },
        "id": "Prl8DoYedak0",
        "outputId": "2e1c1ecf-5494-4252-d9ca-ff105b20360f"
      },
      "source": [
        "plt.title('Prediction on Test set', fontsize=14)\n",
        "plt.xlabel('Year', fontsize=14)\n",
        "plt.ylabel('Price', fontsize=14)\n",
        "plt.plot(date_test, Y_test, label='actual')\n",
        "plt.plot(date_test, yhat_test, label='predicted')\n",
        "plt.legend(fontsize=14)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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9QV+fPNRqsH2/5+93u8FU65ULGo+uNCbd07j0TGPTPY1LV0N5TGKJDFEyJL0wscIxFDf+nYZ1jVQOqxg0Y1JTU9bjvnxOdy4DlllrX8u+fxiYZq1dba3NWGs94G42lIEsB8Z1On9sdpuIiIiI9GPtJSJJQnjl43H8DG7Lir4OK2/ylmBba1cBS40xJrvpCGC2Maa202GfBz7Ivn4MONUYU2CMmQRMAV7PV7wiIiIism1SXjbB9kNkKsYDQ6sXdr67iFwI/CnbQWQh8FXgdmPM3gQlIouAbwJYa2cZYx4CZgNp4Hx1EBERERHp/1IZn4iTJuEXkCkPWuqGmhb3cVT5k9cE21r7Dl37BZ6xmeOvB67foUGJiIiISE6lMx5R0iS8YrySUfhuZEjNYA+dlhMiIiIikhcpzydMhrgXAjdEpmws7hBazVEJtnS4/PJLuP76azreX3DBOfzsZzfmPY6GhgamT9+HmTPfzPu9RUREZPu1L5Ue84NU06sYWr2wlWBLj378459y7rkXbNWxM2e+yfTp+9DQsLm1g0RERGQoaO8ikvBCpD2fTPmEIVWDrQR7kEmlUjm7Vnl5BcXFJTm7noiIiAwNqYxH1EmTIkxrIk2mfDxuohFiQ2MiLt9dRKSXLrjgHCZMmEgkEuXJJx8H4IQTTuS88y7EdV1OPvkEjjnmeFavXsV//vMC++yzP9dddyPvv/8ud975S+bMmUVZWTnTpx/CeeddSElJKQDxeJxbbvkJL774HIWFRXzhC6d2e++ddtqZSy+9AgiS93vvvYunn/4X69evY/jwEZxyypeYPv0QLrroXACOP/7TABxzzPFcddU1+L7P/ff/gUcf/Rtr165l7NixnHbaWXzmM8d23GfOnFn89Kc3sGjRQiZMmMg3vvG/O3RMRUREZMfxfZ9UxqcgkiHph2lOpKkpD1r10bAYiqb0bYB5MKQT7IK5D1M454G83jO+66kkdjm5V+c8/fSTHHvs8dx55++YP/9DbrrpOoYNG8app54OwIMP3s9ZZ32Ne+/9I5mMx4IF87n00gv42te+yRVXXE1TUxO3334LN9zwI6677iYA7rjjVt544zWuu+4mampquPfeu3nnnbf51KcO6zGO6677Ae+99w4XX/xtpkwxrF69itWrVzFixEiuv/4mrrrqcu677yHKy8spKCgE4K67fsWLLz7HpZdewfjxE/jgg/e56abrKCsr58ADp9PW1sZll13C3ntP4+qrr6Guro7bb79lG0dXRERE+lrGD3ovR50MKcKsb0sxIduqz2lYpARb+odhw4ZzySWX4TgOEyZMZOnSxTz44P0dCfbee0/jtNPO6lgq/dpr/4/DDz+SL33p9I5rfOc73+WrXz2N+vr1FBQU8vjjj/K97/0f++//SQCuvPIHnHTSsd3eH2Dp0iU899zT3Hzz7RxwwIEAjBkztmN/WVk5AFVV1VRWVgIQi8V48MH7+fnP72CvvT4OwOjRY5gzZxZ/+9tDHHjgdJ555knS6RRXXvkDiouL2WmnyZx55te49tr/y+EIioiISL6kMx4AUYISkbUtCbwJweLcTsNiqN3c2YPDkE6wE7uc3OvZ5L6w++574DhOx/s99vgYv/3tb2htbQFgl1123eh4a+eyfPlSnn/+mY5tvu8DsHz5MgoKCkmlUuy++54d+9uT257Mm2dxXZdp0zZtY96zRYsWkkwm+Pa3L9wo/nQ6zahRwf9dixd/xM47T6G4uHij5xMREZGBKZUJco4waVKEqGtJ4hfU4EVKoXllH0eXH0M6wR4sCguLNnrv+x7HH38iX/zil7scW1NTw5Il+WmT43nB/2A33vhzRo4ctdG+cFh/9ERERAajlBfMYIf8NBknzNrWZLAjXATpRB9Glj/KcgaA2bM/wPf9jlngWbPeZ/jwmo4PLG5q6tRd+OijBYwdO67b/WPGjCUcDjNr1gcdZR6xWIyFCxdsVPbR2ZQpU/E8j5kz3+woEeksEokA4HkbVrOfNGkS0WiUVatW8olP7NvtdSdMmMQTT/yTWCxGUVFRx/OJiIjIwJRMe7gEX+FIAXXZBNsPF+BkhkaCrTZ9A8DatWu57bZbWLJkES+88Cx//vN9nHJK19npdqeddla2M8ePmTdvLsuWLeW//32Jm24KVp0vLi7m+OM/x29+8wveeGMGCxcu4IYbfoSX/YmzO+PHT+Dww4/kxhuv48UXn2PFiuW8++7bHZ1NRo2qxXEcXnnlZerr62lra6O4uIRTTz2dX/7yNv75z0dZtmwpH35o+fvfH+bRR/8GwJFHHk04HOKGG37EwoULeOONGfzhD/fmcPREREQkn9JesMgMQEG0gLUtQVLthwogHe/L0PJGM9gDwFFHHY3nZTjnnK8ADscd97luyz/aTZ48hV/+8m7uuuvXXHDBN/G8DKNHj+GQQzZ0CDn//EuIxWJceeVlFBQUcvLJXyQej202jquv/iG//e2vufXWm2lsbKCmZkRHHDU1Izj77G9y992/4sYbr+Poo4/jqquu4RvfOI/q6moeeOCP3HLLTygpKWHy5KmcdtqZQJDs33jjrdxyyw2cffbpjB8/kXPPvZDvfvfS7R43ERERyb/2VRwBCgoKN5SIhAqGTImI0/7ht8Eglcr4DQ1t3e5btWoxo0ZNyHNE22/TXtSb095FZKjYmu9pZWUxPf2ZGIo0Hl1pTLqncemZxqZ7GpeuhuqYzFvTwgX3vcjbhefyyIgLuWbNwTx7/oFUPnwCoZIq1h3zh74OMSdqasreArrt/qASERERERHJmZTnEyH4TFZRYSGN8TTJtIcfLhwyJSJKsEVEREQkZ9LZZdIhSLCBoExENdjSX9xxx119HYKIiIjIVutcg11SFKxzUdeSYJdQAU58aNRgawZbRERERHImmfEIZ0tESrMteNe1JrMlIkqwB53B9IHOoU7fSxERkf4plfGJZmewy4qDBLuuZWiViAyZBDsUCpNKJfs6DMmRVCpJKKQKJxERkf4m7XkdJSLFRYWEXIe61mTQB1sLzQwupaWVNDTUkUwmNPs5gPm+TzKZoKGhjtLSyr4OR0RERDbRuQbbCUUZXhJlbXuJSGrza24MFkNmCrCoqASAxsa1ZDLpPo5mx3AcZ0j88BAKhSkrq+r4noqIiEj/kcp4RLJdRAhFggS7JQEVQ2ehmSGTYEOQZA/mpGyoNrQXERGR/qNzH2zfjVBTGmVJfQw/XIDjpcDLgBvq4yh3rCFTIiIiIiIiO146s6EGm1CUYSXRoItIqCDYlhn8n4lTgi0iIiIiOZPsVIPtu2FqSqM0xtOknSgATmbwdxJRgi0iIiIiOZPqPIPtRqkpCWaumzNBWYgzBDqJKMEWERERkZxJZ/yOpdL9UIRhpcHMdVMqW3c9BHphK8EWERERkZxJeR5RJ/iQI26EmpIgwW5IB701nCHQSUQJtoiIiIjkTCrjU+Rmu4iEotRkZ7DXJ4K0UyUiIiIiIiK9kMp4FLjtM9hhKooihFyH9cls2qkEW0RERERk66U9nwKnvQ92FNdxGF4SZV3cAVQiIiIiIiLSK8m0R2F7DXYoAhCs5pjIJtiawRYRERER2Xopz6fQSePjgBN0DqkpjbI6lj0gHev55EFCCbaIiIiI5EwsmaHCbcMvKAcnmLUeXhJldUwz2CIiIiIivdYQSzHcbcUvqOzYNrw0ytp4kHbOW7m+r0LLGyXYIiIiIpIz9bEUVU4zXmFVx7aakgISBPXYK957muiCJ/oqvLxQgi0iIiIiOdMYS1Hht+AVVXdsG14aJUHQD/uzoVepePKcvgovL5Rgi4iIiEhOpD2fxniaUr8Jv9MM9vCSaMcM9lCgBFtEREREcqIpngKgON20cYlIaZQMob4KK+/C+byZMaYS+C2wB+ADXwMs8CAwEVgEnGKtrTfGOMBtwLFAG/AVa+3MfMYrIiIiIluvvi1FhDRRr41UpwS7oihCJOT0YWT5le8Z7NuAJ621uwB7AXOA7wLPWWunAM9l3wMcA0zJfp0D/DrPsYqIiIhILzTEUlTSDLDRDLbrOFxztNn4YN/PZ2h5lbcE2xhTARwC3ANgrU1aaxuAzwG/zx72e+DE7OvPAX+w1vrW2hlApTGmNl/xioiIiEjvNMRSVDktAPgFVRvtO2qXERsf7CXzFVbe5bNEZBJQB/zOGLMX8BZwMTDSWrsye8wqYGT29Rhgaafzl2W3rURERERE+pUC+zDO2pFUESTYnWewu+Ok4/ihgnyElnf5TLDDwDTgQmvta8aY29hQDgKAtdY3xmzz7wtCIYfKyuLtDHPgCoXcIf383dGYbEzj0ZXGpHsal55pbLqncelqSI2J7xN+4Qo+VnUYzzu7AFBSUwubef6KYgfKBuf45DPBXgYss9a+ln3/MEGCvdoYU2utXZktAVmT3b8cGNfp/LHZbT3KZHwaGtpyHPbAUVlZPKSfvzsak41pPLrSmHRP49IzjU33NC5dDaUxcZItDM8kqG6ay6jIeACaUkV4mzx/3eiTqVn+DDVOI03r6/Ey5X0Rbk7U1JT1uC9vNdjW2lXAUmNMe4X7EcBs4DHgrOy2s4BHs68fA840xjjGmAOAxk6lJCIiIiLShzKez4d1Lfi+jxNbB0BNcgkTw/VA9yUib+3xfa5JBWmfk47nL9g8y2ubPuBC4E/GmCiwEPgqQZL/kDHmbGAxcEr22CcIWvTNJ2jT99U8xyoiIiIiPXhpwToue2w2e9aW8f2PtTAMcPHYjw+C2upIUZdziiIh4tkFZ5yMEuycsNa+A+zTza4jujnWB87f4UGJiIiISK+tjwWLyiypj/GrZ2ayX7ASOrtm5uKVjur2nMKISzy7ZPpgnsHWSo4iIiIi0muptAfAH8+YxslTgqQ5g4uLT3JCl7lTAAojIeJ+NhMfxAl2vktERERERGQQSGaCBLusMMyhY1xYDPF9LgbHoW2fi7s9pzASGhIz2EqwRURERKTX2hPsgpCLG1+H70Zo2+9ScHpeEr2oc4I9iGuwVSIiIiIiIr2WzPg4QMh1cGLr8QqrN5tcAxRFXBIM/hIRJdgiIiIi0muptEc07OI4Dm68Hr+oeovnFEY31GAP5hIRJdgiIiIi0mvJjEc0FKSSbmxdMIO9BRu16VOCLSIiIiKyQTLjEQkFJSFOfD1e0bAtnlOoGmwRERERke4lM/5GM9h+UdeVGzdVGHZJE8YjBOnEjg6xzyjBFhEREZFeS2ZrsPHSuInGrSoRCYdcIiGHlBtViYiIiIiISGepbA22E28A2KoSEYDCcIiUU6ASERERERGRztprsN3YOgD8rZjBhmC59KSjGWwRERERkY0k0x4FYRc3vh7Y+hnsgrBLkgL1wRYRERER6SyZ8YmEXJz2BLtwyx9yhKBEJKEZbBERERGRjbXXYLuxIMH2t7YGO7uao2qwRUREREQ6SWS7iLi9nsF2ifsRzWCLiIiIiHQWzGA7OLF1eNEyCEW36rzCSIgYUdVgi4iIiIh01l6D7cbWb3UHEYCygjCN6TB+KrYDo+tbSrBFREREpNdSmfYuIvV4RVufYJ86bQytmTAtrS07MLq+pQRbRERERHotkfaCLiKxdVvdog9gt1Fl1FSWk05qBltEREREpEN7DbYb712JCEBBYQkjnAYKZv0JfH8HRdh3lGCLiIiISK/4vk8y4xN1g5Uct7aDSLt0qAiA8hevINSwYEeE2KeUYIuIiIhIr6QywaxziZvEySR6VSICMGvU//CX9CEAuM3Lcx5fX1OCLSIiIiK9ksx4AJT5TQC9LhFJFNdyW+YkAEItK7oekEngtK3dviD7kBJsEREREemVVDbBLvcaAXo9gx0JOaz2q/FxcFtWdtlf9N7vqH7g0wO2PlsJtoiIiIj0SiKdncHOtCfYvZvBjoQcUoRJFw7H7WYG221dgxtbC5nE9gfbB5Rgi4iIiEivtNdgl2ZnsP1efsgx4gYpaLJ4JKFNZrBf+HAt81fXA+CkWrc31D6hBFtEREREeqW9BrskvY0lIuEgBY0X1XaZwX7w7eUsXNMAgJNs3t5Q+4QSbBERERHplfYEuyjTiO+G8aPlvTo/4joAxApHdanBXlIfw88kAXCTA3O1RyXYIiIiItIryWwNdie5TmcAACAASURBVHGqHq+wGhynV+dHQsHxbYUjcVMtOImgG0lbMkNdSxLHSwGawRYRERGRIaK9Brsg1dDr+muASChIQdsKRgB0lIksbQiWT4+QBsDRDLaIiIiIDAWJbIlIYaqh1x1EYEOC3VIwEtjQC3tpfZBgR8kA4KSUYIuIiIjIEJDKlohEk/V4hb37gCNsKBFpigYJdnsd9pKOBLu9REQJtoiIiIgMAe0fcowk6/G3ZQY726avOdy+2Ewwg72kS4mIarBFREREZAhIZjxcPMKJhuBDjr3UPoOd9MN4JSM6emEvWR9jRGmUiKMabBEREREZQpIZnwpacPC3qwY7lfHwSkd3lIgsbYix5+hyzWCLiIiIyNCSSntUO0Hy62/HDHbK87MJ9gqa4ikaYil2G1nW8SFHVys5ioiIiMhQkMx4VBMk2L1dxRE6zWCnPTKltYRaVrB0fRsAE6qLKXA1gy0iIiIiQ0gys2EGe3tqsNtnsJ10jJV1qwEYX1VEgZNt06cabBEREREZCpriacaG1gPglYzs9fntXURSGY9M6WgAmuuW4DowpqKQ6ACvwQ7n82bGmEVAM5AB0tbafYwx1wDfAOqyh11prX0ie/z3gLOzx19krX0qn/GKiIiISFcfrWvjy4Vr8NwK/G0oEQm3z2BnPLzSWgAS65cxqnw3omGXqJMGf+AuNJPXBDvrMGvt2k22/dxae3PnDcaY3YBTgd2B0cCzxpip1tpMnuIUERERkW4sXNfG1NBKMpWTwXF6fb7rOIRdh1TG70iwaV7O+KpPABDWUuk7zOeAB6y1CWvtR8B8YL8+jklERERkSGtJpFndnGBMZhnpysnbfJ1IKJtgF4/Ed0JEY6sYX1kU7PMHdoKd7xlsH3jaGOMDd1pr78puv8AYcybwJvBta209MAaY0encZdltPQqFHCori3dA2ANDKOQO6efvjsZkYxqPrjQm3dO49Exj0z2NS1eDdUwWf/gRxllCaWotmdG79uoZO49JNBzCDbtUVpfhlYxkeONawmMqqKwsxs0ule6mWqisKASnP88Jd5XvBHu6tXa5MWYE8IwxZi7wa+BaguT7WuAW4GvbcvFMxqehoS1nwQ40lZXFQ/r5u6Mx2ZjGoyuNSfc0Lj3T2HRP49LVYByTp+asYfRLl/LP6MsAtBSOJ9mLZ+w8JiEHWmJJGhraiERGUMs6IoUhGupbqPEzNPtFlDkxGuvq8KNlO+R5tkdNTc8x5fXHAWvt8ux/1wCPAPtZa1dbazPWWg+4mw1lIMuBcZ1OH5vdJiIiIiJ94Oon5jI+8SGRbBu9TNW2l4hEQy6pjA/A+nANtc46xlUWgRfMXtf7pQA4iYHXSSRvCbYxpsQYU9b+GjgK+MAYU9vpsM8DH2RfPwacaowpMMZMAqYAr+crXhERERHZIOP5FLkpJjqrSLjFeNFyMmXjtnxiD4IabA+Alf4wap311JYV4GSCBHslQXcSt23N9gefZ/ksERkJPGKMab/v/dbaJ40x9xlj9iYoEVkEfBPAWjvLGPMQMBtIA+erg4iIiIhI36hrSTDRX0HY8Wg7/Ce0jD0IQpFtvl640wz24nQVBzkpwsmGjq4kS7wR7O/OxW1dCeydi0fIm7wl2NbahcBe3Ww/YzPnXA9cvyPjEhEREZEtW94YZ6qzFID08N23aYGZzoISkWAGe148qGcOtazAKx4OwBJ/BABuy6rtuk9fGFgfyRQRERGRPrGiMc5UdxmeGyFTOWm7rxcJOcFS6b7PBy3lALgtKyBbIrKKajwnTKh15XbfK9+UYIuIiIjIFi1vjGOcZWQqJkEout3Xi7hBDfaa5gSL01UAuC0rcbIfckz4EeKFIzWDLSIiIiKD04rGOONC9XgV43NyvfYa7CX1MdZREcxWt6yATBKAFGFihSOyNdgDixJsEREREdmiFY1xSkNJ/EhJTq7XXoO9pD6Gh0u6eCRuy4qOGewUYVqiNZrBFhEREZHBaUVTnGKS+OHCnFyvfan0pQ0xCsMulI/GbVm50Qx2c6QmqMH2/ZzcM1+UYIuIiIjIZsVTGepakhSSgHBRTq4ZdjfMYI+rKsIrHU2oUw12kjBNkRqcdBwn0ZiTe+aLEmwRERER2axVTQkAon4cP0cJdjQcdBFZUh9jfFURXmltMIOdDu6V8kM0hIOWfQOtDlsJtoiIiIhs1vKmOC4eIS+JHynOyTUjrks8lWF5Y5zxVUVkSkfjeEnc1qDmOkWY9aEaYOD1ws7nSo4iIiIiMgCtaIxTSFAbnasZ7HDIYX1bUA4yrrIIr3g0AKHGxQCknAhrnWC59FDrSlI5uWt+KMEWERERkc1a3hCnIhykuLlKsCOhDYUU46uK8CK1AISaggTbDUVZ61Th42gGW0REREQGlxVNcSaU+hDPXYIdDTkdr8dXFZGhfQZ7EQBOKEos4+IV16gGW0REREQGlxWNccaVBq9zVYMdzs5glxWEqSyK4BcNw3ejhJqWAOBGosRTGbySUQNuBlsJtoiIiIhs1orGOGPb15fJVYmIG8xgj6sqwnEccNygk0i8HoBQuIBE2sMrrQ16YQ8gSrBFREREpEdN8RTNiTS1xR5ADheaCdLQ8VUbEvZM6aiO1244SjztZWewlWCLiIiIyCCxojEOwKjCDJDLDzkGM9jjKzdczysd3fE6lC0RyZTW4iYaIRXLyX3zQQm2iIiIiPSoPcGuKcgm2Lnqg93NDPZGCXa4MCgRKQlmtQdSmYgSbBERERHp0fJsgj28IMcz2J1qsNtlsgm2j0MkHA5KREqD9n3tC9AMBEqwRURERKRHyxvjlBeGc77QzP4Tq/jC3qOZWlPSsa09mSYUpTAaCrqItCfYA6gOW32wRURERKRHKxrjjC4vxEm1BRsiuUmwa8sLufyIyRttay8R8d0IBWGXRNojky0R0Qy2iIiIiAwKKxrjjK4oxEkHHzLM1Qx2d9pLRAhFKAyHiKc9iBTjFVQQGkAz2EqwRURERKRbnu+zoinOmGyC7btRcHdcAYRfWIUfKsB3oxRGghlsgGTRCN6aM5d3XvgzkRUzdtj9c6XXI2SMKQSOB3YG7rTWNhhjdgbqrbXrcx2giIiIiPSNtS1JUhmf0RWF0BzDz1F5SI8ch0xpLY6X7igRaYqnWNRcSmlqDbvN+ynFLbvROPqAHRvHdurVDLYxZjIwB/gNcD1Qnd11HnBTbkMTERERkb7U3qJvdEVQg52rRWY2xysdje8GJSIA33pkFh8lK5jkrqY6vQYnu9Jjf9bbEpFbgWeAkUDnbt+PAYflKigRERER6XsrmoIEu6NEJJybHtibk5h8Asmdj6EwEqSp761owuw8hQpaATqWUu/PelsiciBwgLU2Y4zpvH0JMLr7U0RERERkIFreEMch6PjhpOOwAz/g2C6+xxkAlM9ZA8DFn9qJnYqWwKJgvxPr/xXJ21KlHulm23igcTtjEREREZF+ZHlTnJrSKNGwG8xg7+ga7E4OnzKccV/em91ry/EW1XZsd1MtkElCKJq3WHqrtyUiTwOXdnrvG2PKgR8Cj+csKhERERHpc+0t+oBsDXb+Euxo2GX32nIAMqW1G+3r72UivU2wLwWmG2MsUAg8SDBhPwr4bm5DExEREZG+tKIxaNEHQJ5qsLvTvpqj5wfLqzvx/l0m0qsE21q7AtgbuBG4E3gTuByYZq2ty314IiIiItIXkmmPNc2JDTPYeS4R6cwvqCQRqeBdf2cA3H5eh93rGmxrbQy4N/slIiIiIoPQquYEPmycYOehTV+3HId3DnuAG//xGn8ruKbft+rrbR/s640x53az/VxjzLW5C0tERERE+tKKxqAj8+iKQsikcOON+JGSPouncMQUlvo1wOCrwT4DeLub7W8BZ25/OCIiIiLSHyxraO+BXUR08XM46TZS4w7ps3iqiiM0UAaAO5hqsIERQHe11usIFp8RERERkUFgcX2MoojLiNIohXMeIFM8kuT4Q/ssnqJIiHAkStwtprF+Da+8+BjVv98X0vE+i6knvU2wlwAHd7P9EGDZ9ocjIiIiIn1pxqL1zHrqN5w/70wmVhYQaltNdPHzJHb5ArjbsoRK7lQVR2lxy1m+cjmL330Gt3U1OL1NZ3e83o7SncDPjTFR4PnstiOAGwg6i4iI9BsLly6h6PkrqJ32Wdjji/3yL2ERkf7m7leXcM26hxjnLGbf0nUUzH0Nx/eI7frFvg6N0eUFrF9fihtbzwSnjVTJmH654EyvEmxr7S3GmOHA7UD70ySB26y1N+U6OBGRbWXXtPDAI3/ljtBL8J+XaI2tpG2/S7d8oojIEBZPZVi1ajl7Rz4EYFpkEYVzHiA5+gC8ykl9HB2YEWWsWl1CldPABAdai8f1dUjd6vV0jrX2e8Bw4IDsV421VovMiEi/8vrieoq8ZgA+KtyN4rd+QWj9h30clYhI/zZrVTMH8zau4wMwvf6vhBsXEd/t1D6OLLDbqFI+8kawE8vZyVlFfeEgSbABrLWt1to3sl8tuQ5KRGR7NcTSDHNbAbjSvwA/UkLZi5eD7/VxZCIi/dfbyxo5MDSLdVTytjeZ4c1z8KJlJHY6rq9DA2CXkWW84e1CiZOg3GljTWR0X4fUrS2WiBhjHgNOt9Y2ZV/3yFr72S1caxHQDGSAtLV2H2NMNcGS6xMJll0/xVpbb4xxgNuAY4E24CvW2plbildEBKAxlmJiuI0MIV5trGD1p6+k9uXLKZz1J+J7nNHX4YmI9EvvLm/iyGgzzeFRvNc6jo+780lMORH6aAXHTY2tLOSD8G4d71c4tezch/H0ZGtmsNcBfvb1+uz7nr62xmHW2r2ttftk338XeM5aOwV4Lvse4BhgSvbrHODXW3l9EREa4ylqQm1kopWAw7/cw0iOOYiSV3+M27Kyr8MTEemXVjbFGR5qpap6JON2D3pex3f7Uh9HtYHrOAwfOZ5l2e7QH/n9s0v0FmewrbVf7fT2f4GEtTaTwxg+Bxyaff174EXgiuz2P1hrfWCGMabSGFNrrdW/jCKyRY2xFMNCrbhFVUyJlvCbVxbzmZN+zOi/HkXJK9fTfNQdfR2iiEi/0xBLUR5ppqBsGHsefibrpx1EpnpqX4e1kYs/tRP+jAPILPsH81PD+zqcbm11DbYxJgQ0AmY77ucDTxtj3jLGnJPdNrJT0ryKDQvWjAGWdjp3WXabiMgWNcTTVNKKX1jJFUdMZk1LkrvmusSnnkh06b/B97d8ERGRISTj+TTF05RkmvAKq8AN9bvkGoI67OJDL+dnpZexJtbX0XRvq9v0WWszxpjFbGjPty2mW2uXG2NGAM8YY+Zucg/fGLPN/+qFQg6VlcXbEd7AFgq5Q/r5u6Mx2dhQGo+meJqKSAuh0p341O61fP7jddz/1jLOO+xjjJrzIJVuPVSM3aoxaYylqCiKAOD+9+eQasM79Kp8PEafGUp/VnpLY9M9jUtXA21M1rUkCJOmwGsjUzmCyA6IPWdjUrkrC0bGqV/d0i/HuLcLzVwL/MQYc7q1dm1vb2atXZ797xpjzCPAfsDq9tIPY0wtsCZ7+HKgc++VsdltPcpkfBoa2nob1qBRWVk8pJ+/OxqTjQ2V8fB9n4ZYihK3mWSojOaGNr65/3iemb2a22cV8WOgbcHrJHeq3uKY1LUk+Ozdr3Ptsbvw6cmVDHv1dryi4TTs/a38PVAfGCp/VraFxqZ7GpeuBtqYLF7XSiVBc7g2SonvgNhzOSblkRBrWxJ9NsY1NWU97uttm77vANOB5caYBcaY9zp/be5EY0yJMaas/TVwFPAB8BhwVvaws4BHs68fA840xjjGmAOARtVfi8jWaE1myHg+RZkmvMJKAIaVRDnvoIn8dWU1nhMiXPd+t+c2tKXwZ9zO8Dungu/x0bo20p7PE7NXE1n5Om6iETfW6/kFEZF+r74tRaUTJNh+YXUfR7Nl1SURmuJpkun+1361tzPYDxPUUTvbcK+RwCPGmPb73m+tfdIY8wbwkDHmbGAxcEr2+CcIWvTNJ2jT99WulxQR2YTv0dCW7Pg1Z2tBZceuk/YazWMfrGZ+42jKF79NaP+up1/9xBz+tPoWHDJEVr7OqqaJAMxYXI9T9TQAbqIR0nEIF+bjiURE8qIxlqKKYIEur7Cqj6PZsonVxURCDinPI7ptS7vsMFuVYBtjioGfAicCEYJ2ehf2pkzEWrsQ2Kub7euAI7rZ7gPnb+31RWSISccI131AunZfZi5rYM/aciIhl9IXLmNc/Woq+CJAxww2QNh1+NmJu7Po/ikcvOZlkq/eDEdf3bG/LZnhraWNvBWewv7uXAo+/Acrw98AIJXxCC14Ct9xcXwPN7YOr0yfuxaRwaM+lqIqO4M9EBLsw6cMZ8+z96Mk2tv54h1va9P9HwJfAR4H/gx8GvWlFpE+VDjv71T+7STWrvyIbz74Hn9+azl4GQoWPklxw5wNv+bsNIMNMKKsAPfg7/CqtxsVM2/FmfW3jn1vLm0g7flUhINOpN7cR4nVL2N4SZRPlqykNL6C5IRPA7Dq3X9R9ty3IJPM0xOLiGyf8IrXcVtW9Li/Ida5RKT/J9iO4zCirKCvw+jW1ibYJwFnW2vPsdZeDBwHnJht3SciklermuIsW7EMB5/WlXMAeHz2akJr3sNNNFKQWNfp15yVXc7fdeoeXMgVLCuYQuiFHwXlHsCMRfUUhl12LolTFx6Fk4px+eKvs3tpK1+tmo3nOzRODWbGQ2/fQ+Hcv1Aw/595emoRkd5pTaZ5YvZq0p4PqTYqH/sSJa/9tMfj69tSjAy1AgNjBrs/29oEexzwUvsba+3rQBronwvAi8igdtcri3l+9mIA/HULAFi4ro2meS8AEPLTTHJXBfu7+UciGnbZb+Iwfpz6Mk7TMoreuxeAGYvWs8/4SkLxetI7fYYvJ6+ighYOC7/H/qkZvOPvzCtttQDs4gZt+guz54qI9DcvfriOH/zL8pNnPySy9CWcTILw6rd7PL4hlmJkpA0/VNBvlkYfqLY2wQ4Bm/4eNE3vPyQpIrLd3l/ZRLEfrC4QaVoIgOtAeuF/Oo6Z6gRdPb2CrjPYAAfvVM0TbYbGcYdT/NYdrFy1nKUNcQ4cV4KbaqGscgSLCqbS6BdzYOK/VDTO5uXQfvx9frrjGgk/THTNOxS+9CPIpHbU44qIbJM1LQkAHn1/FQteD5q0hevn4ySauj2+IZaiJtSq2esc2NoE2QH+aIxJdNpWCNxtjOloPmit/WwugxMR2VRTPMWi9TFKwkFZR3HLYgrCLtPHlzB2xXukavYkUvc+H48sAh+8ou6X0T1op2oc4JHqczhr6QskX78bOJyDRgX7/aJh7DWmireXTOHQlhkAxCYcxYtzW2gqKKbcaWPO6JP5YGkdp793F25hOW37XrLjB0BEZCutbUlSWhDiiMnDGT3/Zdqi1RSn1hOue5/U2IO6HF/flqLabRkQ9df93dbOYP8eWAGs6/T1R4KlzDtvExHZoWavCmqrS90gwa6OL2FYSZTTaldSSJJ5NccA8DF/HpnSWoiWdHud6uIoe9SW8ddlpSQnHsHk5X9lQpnL2IJgZtwrquLjYyuY6U0BIF0xiT33mAZAnV8BwNjJH+f76bOZW/kpimf+Cqd1Tbf3EhHpC3WtSWpKC7h6WopaZz0/bwv+fuyuTMT3fda3BW36NIO9/bZqBttaqx7UItIvvL+yGQeYWOJBDKrTqxlT5rOP9z5p3+Xuho9zKxAlRbJy8mavdfDOw/jVy4tYcdxpTFr0LOeMeI9QIugm6hdWc8TU4Tw27+NQ/zDJSUfx8XFVVBZFaPQrgZWEhk9hfJXDPQVncVPTfyl542e0HPqTHT4GIiJbY21LgpqSKMVLngfgP4WH8XXnBSrXvEtsk2P/9t5K1rXGGVvyEZnKz+c/2EGmf3XlFhHZgg9WNrHT8GJqCoKaZxef3QrWUrTivywt2oW/L/Rp8YMFYDJVO2/2WgfvNAyAmxeOYYFXy9Ftj+HG1gPgFVYzqryQc075IrFdTyW2x5mEXYdzD5pAafXo7PUns8vIUv69rpzY7mdQOPt+Quvn9e6BfJ+CD/8B6U3/uRMR2T5rW5MML40SXfwcqRF7MXLUOKw/vsvfUwvWtvLzFxfyP2OaKci0khq1Tx9FPHgowRaRAcP3fT5Y2cweteWUuQkWeEFHj3389wiveRdvXFBTuMYPPtiYrtr8DPbOw4sZU1nI43PW8ofMZxjW9AHRxc8B4BVllwkOF9Jy+M14FRMA+J+9RjNmpz3IlI/HL6pmt1FlrGlJsmz38/EjJZS8+uNNgu55Cd+VTXHueOivlD99HsUztbSAiOSO7/vUtSQZXxAjvGomyQlHsPPwYt5L1hJqXNTRwz+R9rj68bkUR0J829QDkB41rQ8jHxyUYIvIgLGkPkZTPM2etWVE0m0sjExhvV/KoesfwvEzVO16BKPKCmhwg+Q4s4USEcdxONyMAMCOPBYvUkrBvGDhmU0XqOmsbZ+LqT/lSQB2GVkKwI/+s5a3xn6FgkXPEln2X2YsWs8Nv72Xql9NouL3BxCue7/LdZ61dYRWvgVA0Qf3QSbR5RgRkW3RGEuT9nympd7EwSc58Qh2HlbCvMxoHD9DqOEjAH7xn4XMX9vKD442VK5/F6+wmkzFpD6OfuBTgi0iA8YHK4MPOO5RW46TaqG4tII3PUNZqg4/VECmdh++++kpVI8YC2y5RATgsF2CBHvvSWOI73oKju/hRcshFOn5pFAUv6AcgD1ry/ncHqOYvaqZ02d/gmX+cAoe+wqxJy7na4nfU+dX4CVaKZlxY5fLzFzWyN7ufDKEcGN1FMz/R2+HRESkW2tbgxlq0/wqXlEN6Zo92Xl4CfP9MQCE6j9kxqL1PPj2Ck6dNoaDa2JEl71EatQ0cJy+DH1QUIItIgPG+yubKImGmDSsGCfVSkV5Fa97uwCQqt0XwoUctFM1NeN3xSuqwSsZtcVrfnJSNRcePImTPlZLfM+vAL1bIjgScrn6M1P517kHcM/pB/D4brfxeHo/TvaeYg8WcGv6f5hR8wWiS14ktHZ2x3me7/Pu8ib2dubzTvGBpKsmU/TuveD7vRsUERlSrn3KMuuBy6h45OTNHlfXmiBEhtHrZ5CYeDg4LhOqi1jgj8bzHeY89ztan7yKqZUOV1S/RNWfD8eN1xPf48w8PcngpoViRGTA+GBlM7uPKsP1MzjpOONHDmeXyt3h/T+RHDu947i2T1wQ/COxFbMw4ZDLmfuNAyBTvBOJnY/bpkVjXMfBjCzFjPwUs3abxr8XPs/+3tvMmHskMTfEIZE/UTzzVzQe+Qvi65bQvOC/TE22Mr6gjifSxzF132Mo+/eVhFe9Sbp2317fX0QGvxWNcZ7/YBE3FDxC1InjtK7BLxnR7bFrW5JMcz4kkmqibcIRQDAhEI4WscwfzgHp1zgAOMn9N9GXm0iOO4TmQ3+CVz4+j080eCnBFpEBIZbKML+uhbP2G4eTagXALSjjU/sdRfOwn5CYfMKGg8NFeOFtW+a36ahfbfevR3cfVQajPkcrn2Pi2lm8vz5GfLfTKHrvHi5beRxfbLyb40Kv89eC4PjnWidx4uSTKJlxI0Xv3kOzEmwR6caj76/k+NCrlDjBOgDRFa+RmHJCt8fWtSQx7lJg4w8t/uoLH6P45d3wV/8HO+EMptT/m6aDryGxyxdUGpJDSrBFZECYs7qZjJ+tv04GCbYfLQHHIb776bm7kRvK3bUIOpW8vHAdr404hen+PRzX/CCfjs5ifvmnWDP2/7N333FSlVcDx3/33ukzuzvb2cIuLGXoRUCKCIISUezdqK8lMe01GhNTjInl1RSjiRpNYo/Ghl0joFiRjoBLX4ayLNt7md7uve8fs6wgHbbv8/18+HzYue3cC7tz9sx5nmcuQcnG2q/S2OuXSBx+FdaNz+L3VqIlZLdrHIIg9FyhqMor68t5c/1ePrQsZmcsl/5KA8bK1YdNsKu9IUYYPeiSjGZNb3t9RL8ElNPvwNtyOalDL6Kxs26ijxE92IIg9AhbKvcNcExoq2DrxkOv0tidDEqzo+pw0we1fKzM4ErpU8yqn/Qp1zFkxnVYh50NSGyt8hIcfSOgY93yQhdHLQhCd6DrOgu2VnPp82t5ckUJf0heRK5axgepN7NBd2GsWHXYY6s9YfKMLWi29IMKB7HM8YSHXtTR4fdpIsEWBKFH2FzlIddpIdlmQor6ANCNji6O6uiGZSYgARP6JzHywt8CoMtGoq0944PS7GQlmvnYXYeWmEs05zSMpV92YcSCIHQXr31dwX0fuTnXvJGN/f7EBd5XCQ25EH3Qd1gaGYqhaQdSuOWQx1Z7wmQpzcc02Ftof6JFRBCEbm/fAjMT8+JzU7e1iBhtXRnWMclLtvLWTZPITjRjUGRCw64ALYZuiv9yIEsS5wzP4IWvyqj3R3AkDcB8iDmze6VoEHPxQsJDLxW9n4LwLZ7SDQxbdT9LHHUM8BejJvTHO+svhFyXM6zcxxt6fK5qQ/02ojlT246r8YYxhRswekpItzeh2Y+8HoDQMUQFWxCEbq/GG6beH2F0VgLANy0ipu5fwYZ4km1Q4j9uvWf+De+cvx+wfe7wTDQdPt5ei5qQgxxuhmigK0LtNL5wjMav3yLx058dMH2hIAhxxave5GxpDelJDjyz/0rjNUsJjfguKEaGZTrYpsVXlzXUbz3guNvf3ULNW7fyvPIATrUezZ7ZFeH3eSLBFgSh29t/gRlgvxaR7t+DfSwGptoYnungw221aI744EbFV9nFUXWs1wsr+HjNWgCUlj1dHI0gdC+hqEpZXRNRjASuWkR4+JUHLH6VaDFiSupHs5xyQIJd0RKktK6JCbFCcqV6rLEWkWB3EZFgC4LQ7W2u8mA2yAxJjyfUPWmQ47GaOzyD7bU+yrX4Mu+yt6J9TqzrmIoXQyzU2gap1AAAIABJREFUPudrJyWNQbKoB0Dx7O3iaAShe/lsRz2yGkI3Hn660eGZDor0fAx1W9peW7a7kanyNmxSuO01zSYS7K4gEmxBELoV27rHcSz5zQGvbanyMizDgbG1zWJfD7bWAwY5Hquzh2WgSPBxlQlonwp2KKqycvlHJH34PexfPXzS52tPFc0hcqU6AJQWkWALwv7WljbhNKoopsMn2CP6JfB1tD9K405Q4wn10t0NnGfZRFA3oerxcQ2qQwxy7AoiwRYEoVsxlXyCxf1W22qKgYjK9hrvN+0hYQ+KtxwdCY5Q3elpUu0mTs1P5q1dOjoSgcL5JH1w7QmtKrnPoqJadq1fDIB143MozcUH7qCpJxPySaloCYoEWxAOIxDVcMhRUCyH3WdcThKF2hAkPYZl6yu8UVjJ2tJmZhu3sMEwhmJyAESLSBcRCbYgCN2K7KtAioUwNLrxhKL8+LW13CG9zB1Vt5H6/DjSnh2BdcuL8bldpd71I+ycERmUe1VqdCeZzesxlS7B0Lj9hM9XWN7CZLmIRlM2usGCffm9AGyp8lD84cOk/ns8UrDzl5kIRFRaAiH6tS5xccQWEV3vpKgEofsIRlVschTdcPgEe1img+XyRIocU7Atv5+FX3zK+QMkUsLlRHOm0pgwDEBM09dFete7kyAIPY6m6/xrRQkrihvRY2Fkfy0AhppC3t1UzfimRfzAsBCHSSE84Cx8U39LyznP0Hz5wi6OvP2dMTgNq1GmUk9te81Qs+GEz7e5rJ4J8k5WyRMITLod897PUXd+zF/eW8ao3f9CDjVi3fRce4R+XCpb4sm1QdKoIxnZVwlq5IB9dtX5WfThWyT/ZwqGmsJOj1EQulI4qmIljG44/Kd0RkVmdHYStwRupl6z87T1n9zrio/dGD1pDq5ZNxIedC662dlZYQv7EfNgC4LQpSpbQjy/uhSAeTkh/kG8Ymmo+ZqFxQN5y/w+0YyJNF/ybq+fK9lqVLhxch7NhZmg7kJVLBhrCgmNuu64z1XlCZHp347VHOEj/2AGFXyX7ML/oH/2O34ezcKgaGyWRzBy078Jjv8RuimhA+7o0Mqbg+RI8QGOy9URXKysQPGWozoL2vb5+9JifljxbwxKBfI7/0M4czLpFg1JDRMeeA6hUdd2WryC0NmCUQ2LFEU3HHkg9/icJJ4ubebPzl/wt9DdsOL36AYrsfTRoBiJ5s3spIiFbxMVbEEQulQwGu8DPnNoGsGGeKIdlm3EyteT5yskVWsgMOGnvT653ufGyXlknnoF/4nNoSb51BOuYBeWtzBFLgJgRdTF3Ge+5qfNV5KlVjJHWc+Ggv/l/4KXIUc8nb5yZEXLNwMcV2ojAVAaitq2V7aEKNu7gxnKJpaaZlClOQlXbKSqbCda/U4cy+9B9vbuaQyFvi0UU7EQgSO0iADMGZbOzEGp3HT51QQm3ooUCxLNPOWAKf2EriEq2IIgdKlgVAPgglH9mDogEZbCUn0cZ/lWUSDFk6hY+siuDLHTmYZdwN1fpDPEuoSssmVIEe9xV5i/Lm/hcsN2IslDmZQ0iBynlan5Y/HvKEPRo6RM/SkbilYSkh2YSpcQGXxeB93NwUoaA8w0bkeXDCxSJ3Ov/V2sW14mMmgeAO9vruIq5QskSWLkVQ8TtvXjnY1VPLNqLwm+KpZY78C29m/4ZnevmVEEob0EoxpmKXLEHmyAASk2Hr4o/vMxMOl2ZF81kfxZnRGicBSigi0IQpfaV8G2GhXs4WoAvoy4kNCZadqBLpvQbBldGWKnS7IasBhktstDkNAx1G467nNsLmtkgrwDNWcqD8wbzo9PG8C43CQCs/+C98xHcNrMjMxO5it5DKbSJZ02mLCiJUhl0QoulpYQGH0DEcXGyuSLMZUvQ2nYTkzVWLi5gmtNXxLJn42WkI1RkbnylBze/d6pOLMK+FSZjrn4o06JVxC6QiiqYtbD6EeYReQgsgHfmX/t1F+WhcMTCbYgCF0q1JZgy8jeCkLGZLZr/QGYyFbUhOxeN1vI0UiSRL9EM+ujAwEw1B5fm0iDP0JCyzasepBo9pTD7jdjUCoLgiNR/NUoh5itRGkuxvn2hZh2LTi+GziMrdVefvv+Fu5VniNqzSQ4+Q5ynVbek+egGyxYNz7LsuJGxoXW4NSaCI245oDjEywGJvZ3sjaUixxuRgo2tEtcgtDdhGIaRj18xIVmhO6tb71rCYLQ7exrEbEYFRRfBTFHNqV6fN5Wh+5DS+jfleF1mX6JFnb7TcSSBmI8zlk0NlS0MFHeAUAke/Jh9xuXk8QX6nhUyYh1y0sHbFu8tZonX3oOY/V6khb/COvGZ4//JlpVeUL8bmERN76ynvO88xkhlRCYcR+6yUF+spVtLUZCrssw73iHN1ds5CrzClR75iE/6h6UZmeXFl9O3tC064TikcItyP7qE74fQehIqqYTjmkY9cjxVbCFbkUk2IIgdKn9W0RkbyVKUi51OAnq8RUN1cTcrgyvy2QlmqnyhIlljjvugY4bKjwMV8pRreno9sO31wxMtVGHk81p87Bsm4/sq2rbtmxXPXl6JT7dQnHKTOwr7sdYtvy478Nd4+Pyf69F3r2YVc57+AmvEx54dlu/dV6yjbLmINtyvousRpjlfY/p8lYiA+aAfPAwoUFptrYEWzmBBFvXdRzL74sv4iMI3VA4Fi86GLXwUQc5Ct2XSLAFQehSbQm2QUL2VaAn5pKVaKFUjyeGWkJfTbAtNAej+FPGoPirD0h+j2ZnnY+RxmrUlMFH3M9hNpCVaOYN06WAhrXwybZtO2p8jLHUUir35/8Mt6I6B5H48Y+RPaXHHIem6zzxSSFvGO7hX8rDpJlVPGf9Hc/cp9tmhclPthLTdK5a2MIKaTw/UhZgVP1E8s445Dnzk23USKlEJfMJJdi3vr2F2j0b48dqseM+XhA6WjCqIqNh0I8+yFHovkSCLQhClwq1tojYdB9y1I+WkENeiq0twVb7aII9Kis+a8gm4kmysfzA6rGu60Sqt2DZ+spBS54X1/nJ18tRk4cc9TqD0uys9SQSGnop1q0vIwXq0HWdHbVe8vRKwokFrKiIUD3nadA1khZ9H6KBY7qHz3fUM71hPmPYifeMB2n67hLCrktAVva7vg2AIekOcs+8DVmPostGornTD3lOk0Gmf7KDSkPucSfYO+t8rN7bRGK4EkmLIXsrjut4QegMwaiKmfjCS0daaEbo3kSCLQhClwpGVRRZwhyI98SqjmwGp9mpkuPL+6p9tAd7THYSZoPMxy25xFKHY1/7aNtqh75wjJdfe5qct+eSsOTXGMuWth3XGIhgCNVj03zEko9cwQYoSLVT0hjAO+4noEWxbXiKGm8YLezHGavDkTWUqKqzsMqOZ84TKI3bcax84JjuYXGhmxsNHxMeNI/QyGsO2fIxMiuRJ68Yw1NXjME29ExiqSOI5p6GbnIc9ryD0mzsUPthaN59THHs8+6mahwEcOIFjrJEuyB0kVBMi8+BDaKC3YOJBFsQhC4VjKpYjTJKazVRS8jhpsl5TJ80FV2S0ZLyuzjCrmE2yIzLSWRNaQv+qXeiePZi2foKUVXj5+9uIb1uFT7dgioZMe1X3d5d72ewHH+Wx1bBthFVdT6scVCZPRfTphfZvXUVFyorAcjIG8G4nET+tbyE+ozphAvOxbT386Oed29jgJSqJdgJEjzlx0fcd0J/JxajApJE88Vv4jn7X0fcf3R2IhvD2cieMqRwy1Fjgfj/s0XbahhpbWp7TWk59nYXQegsoaiKhWj8C5Fg91giwRYEoUuFolp8gKOvNSl05JBgMZAw8RqaL1uAZs/s4gi7zuT8ZIobAlSkTCOSMxX7ukf552dbKKzwcFZaC3ukXHaaRh7QPrK7PsBgaV+CffQK9rDMeKX4ng/dXF98BnIsxEVfX8efjc+2nmMQvzpzMN5QlH+tKCGWMQbFW44UajrSaflsRz0D5Wp0SSaWOvyY71k3Jx11UZ1xOUms1YYhoWOs/OqYzvuJuw5/ROX7+4WieEqOOS5B6CyhqIZVCgOIWUR6MJFgC4LQpeIVbAXFW4Eum9BtafENiolYxpiuDa6LnZqXDMDashb8U+9CDjaQVvQs10zIJSNaRiBhIItDwzDWb22bE3p3vZ8xxgo0owPN3u+o1yhItfPKdafw1JVj+OUV57Kn4DoW65PZLeejSzKqcyBD0h1cPj6HdzZWUWwoAMBQt/WI5y1u8OMy1aM5ckAxneSTONDQDAduw1CikhFjxapjOubdTVUMTLEx2RlvD/HKTkI7PyP03o+wrX4QKeJr1xgF4UQFo+o3LSJiHuweSyyVLghClwpGVSwGGdlXiebI6nOLyhzJkAw7TquRr/Y2kZ9cQECbzI+Mi/CM/RnK1mrsQy7j0/p+3GaGhC/v5P3+d/FhUQ2/NxUSzT2tbaaOoxmasV+/c+4fGaNqmG0mGpproHWQ1Q+n5fPx9lr+vMnCq4ChfgvR/oceiAhQ2hRkoFyL2gEtPgZZYlh2GlvrXYysPHqCvaPWx5YqL7efUYDBW4YPK+ui+czybcTu3YOpQkXSovin/a7dYxWE43VAgi0q2D1WpyfYLpdLAdYBFW63+zyXy/UCMBPY10h3g9vt3uByuSTgMeBcIND6+tedHa8gCB0rGNPaKthqQk5Xh9OtyJLEpDwnq0qaWFvazADztZwTW0fy5z8HIHvgKCp2p/Ka4/tcuft5Ru/czM1JV5Hqr8dTcM4JX9egyCRYTTSHU9tec5gN3DazgHs+dONLzMBQt+Wwx+u6TmlTkGxjNWrSqSccx5Gc0j+JL8pdjK17BynUjG5xHnbfdzdVYVIk5o3IRPm8jBZLNkow/ovc72I38cPcCgZt+jfB0TehJWQfXyC6fsy/yAjCsQjFNMyS6MHu6bqiVHQbUPSt137pdrvHtf7Zt6LCOcCQ1j8/AI486qUXUeq2Yt7+VleHIQidItzaIiL7KtBEgn2QU/OcNAaitIRi3HbRHEIjr8FYvS6+MWUI54/qx521s/mfyK/JMzRzh/8hdNlAZMCZ7R7LOcMzGJ2VSGE0D0NNYTy5PISGQBQ54sWuelATO2aQ6mkDU1iujUJCx1T25WH3C0ZVPiyq5cyh6SQpEYxVa3H2H8OoK/+Md9IvWCDP4s2E60DXsa396zFfv66umsDb3yf1+bHInrL2uCVBACAUiWGltQdbJNg9Vqcm2C6XKxeYBxzLmrsXAv9xu9262+1eDThdLldWhwbYier9EZ5fXcqakqa2VZsAZH81zg+uIfGzn4kf2kKfEIxq2A0asr8G1XGc1cM+4LSCFDIcJu6d62JohgP/xJ+hG6yt/dEDuHRsFrlOC6dMv4Dgdz8imjGO8KB56Jbkdo9FkiSunpDDB5HxGDx7MZavOOR+pU0B8qQagA5pEQEYkm6n0j4Sj5yEqXhx69zdPl5ZV07p8v9gW/sI6BobK1rwR1TOHZGBdduryOEWQmNuQE0fSejU23FlJLCywUFw9P9g2f4mSuOOo15b1XQK33+Y/lWLIezBVthn6j9CB1uwtZrTNtzO341PAGIe7J6ss1tEHgV+BXx7iPgfXC7X3cBnwG/cbncYyAH2zzDLW1877HJmiiLhdNraN+IO8uaWGv61ogQAi1Hm1AEpzBjk5Lvbb0GKxRdxcJYuQJv+i2M+p6LIPeb+O4t4Jgfqjs8jrGr0N3uRdA1zxkBMnRxfd3wm+3M6baz49ez9XhiANvtepMp1OFOTcabCF78445vtN3+Oous4T7Jt4XDP5cIJ/fn757Pw8CaJW59FHf2dg/ap39VAfmuCbcsdjq2Dnu/sEdks3nAK5xV/yu+ffJ7EcAVjpGImGD4FwBKtpzLpVgBOLXBif+EZtLxpOIad3naOcXnJzF9XhnTdL6FoPs71f0W9/KUjXvetrysw+KupU5ysVSYxr+h1TP2GoOXPgMyRfXYcQXf/XuoKx/NMnl+xh7cXf8aN5lXQ+u2bkJIMveyZ9pX/J52WYLtcrvOAWrfbvd7lcp2x36Y7gWrABDwN/Br4vxO5hqrqNDcf2wpjXW1XlYcki4F7z3GxuqSJVSVNTN3zODbDV9yj3MYNtiXkbpxP84ibD7k4w6E4nbYec/+dRTyTA3XH5+EPx0iNxJMxn5JOtJPj647P5KiGXBP/04FxH+m5zB6ewwubzuTW3W/TVFF80Gwl28pbmKzsQEeiWcrosDjnDEllwbYpXB77gqe4B4ygSwaWmWdRETRwVeGLFA+4kFS7Ceu2t5G8lXhm/InIfvEMdFoIRTUeX9nAta7vkb35MVq2LyPWb8Jhr/vJtmpuMvmx2tP5c8P5DDO7Gfzp71EAn5yAV3Zik2OoyYP4zHgGFwXexHPu871+Tvce+b3UwY71mXxYVMOfPnLzgPGTA173BHQ0qXc90970/yQ9/fBTinZmBfs04AKXy3UuYAESXS7Xy263+9rW7WGXy/Vv4I7WryuA/Zdwy219rVeoaAmS67QyvSCV6QWpmEo+JWnhB2zrdzElyrk8sifG303/IOH9q/HOewFM9q4OWRA6RCiqkaHXAYge7B5ixqBUHv16Ercqb2Pa+wWhEVe3bWsORtm+9SvuUz4hNPyKDv3ZNbJfAiN/8GM8u/LQzAmoSQPREnLYvqWOpZ+9zVWmT5DqihicMhbb1/8iljqMSP7sA84xKc9JXrKVfy4v4QXGsszixPH2FbTY8knKyMdz7vMHDWKs90VIV/xYkjL43axZ/PTLfGzhWsarmxmvbcGmBWgKK5wT/orLpfgc5YZGN5FenmALJ2Z1SSP3fbSDSbkOLm1cjUe1kigFATGLSE/WaQm22+2+k3i1mtYK9h1ut/tal8uV5Xa7q1pnDbkI2Dc0/b/ALS6Xaz4wGWhxu92HbQ/paSpaQozIjP/mI3vKSPj0NqJpI0m/8CEeNlj4sCiNOxbHeLjyKdSvnyAw5dddHLEgtD9d1wlGVdK1eIIterB7hrE5SVSaB9KkpGPb+1lbgh1TNR76bBffV99AtyZ0zrR3kkx4yPkHvDQ8M4GntHgym+hxc26KhKFsB56zHjsoWU53mHn7pkmUNQVZVdLEQ7seYkLtG0wIbCOj5BOkUBO6NeWAY+p8YZy0oFkGM6G/k/9ce0rrlrlt+7yzsZLrP/uA3xpfZZy8W8yzLRxSWVOQX/13GwWpNh6dLmN9z8cb6hyuN8Qr2WIe7J6rOzSKveJyuTYDm4E04IHW1xcBxcAu4BngJ10TXvuLaTpVnjA5TguoERIX/wh0Dc/cp9qm5DlneCbNgy7lfe00DOufQnnveoyVq7s4ckFoX+GYhg6kxGrRLMlg7P19eb2BQZaYOiCFxdGxSMWfk/jKLOTVj/DztwtZ7i7jLMMGYq6LOmSg5bEoSLPRojhpkZ0M0Us4u+V1VEcO4cEXHPaY/slWrhifzS8uP4fyyffzh8hVACievQfsp+s69b4wCZrnoMR7f5eMzeaieZdwcyQ+jkaKeNvhzoTeprC8hWBU44F5w0iqWwvAG+qsb3aQ23eRJqHzdMlCM263ewmwpPXvsw+zjw78b+dF1XlqvWFUTScnyYJ9xf0YazfSMvdptKQBB+z3i1mDePHTn3BK2c/IrlxCdGsS0ewpXRKzIHSEUDQ+g44zWoPqEO0hPck1E3NZ7p9LtHYZNX4oWP9XHtBfIzh4FsbyCP5B87osNqMiMyTdwabG/pytrCPRE8A3/T5QjMd0fH6ylUV6JgBKy15imePbtnnDMTQ1hk314rccPsEGMMoSXuIVSJFgC4fSEIgvKJOTZMW4ZjWxxHy21u7XSiTmWO+xukMFu88pb473Vk3wL8W2+d8Exn6fyKBzD9ovzWHmtvNP5/TIY+xKnBafd1YQepFgTAUgMVoj+q97mOGZCdx8xVU8NPZTZnvv4xZ+S5ZdYVT5K2jWdKJZHbPAzLHH52CblkeiFCDmHERw5DXHfGxespUyPR0AxVN6wLYGf5Rk4u0emjX1oGP3Z1AkQpjQJAOyaBERDqHBH8FhVjArYKxcQzRnCm1TiAg9mlgqvQtUtIToL9UwZtPdRDNPwT/1t4fd16DIJJgNFBtduOqXxfsBu+hjV0Fob8FoPMF2hGqIOWZ0cTTCifjelDxsJoWzh52K334j2qZ/oybmgax0aVzXTsylXJ+Jvmsx3lkPHdeKeDlJFqKyBa8hFcO3WkQa/BGSpXg1Wj9KBdsgS4BEzGBHiooKthBnW/MwoBOY/Esa/FFSbSaUhu3I4Wai2VP5e8EoWNjVUQonSyTYXWBLlYcrDMuQY0Gav/NPUI7cY5VsM7JNHsI5gKF2I9G8MzolTkHoaMGoRiJ+TKqPiKhg90gWo8KNk/Pavg6e0j2Gy+Q6reSedTUNp59z3EUJgyKTk2ShWu1HfsteNF1HliRQI5jKlzFO3gWAdoQebIi3qgDEjA7ksEiwhXgPv7n4Q2RvOYGJt9IQiJBiN7WNsYpmT2ZqYgoNN6xDCjZ2cbTCyRAJdidbuaeR/26p4dOUYmL2UWiJuUc9JsliZIM2CB0JY02hSLCFXsMbijJD3gRALH10F0cj9DqSdMKf+OUnW9lTk06/+q3c//hj3J69lYFNyzkz4mWmIZ44Hy3BjlewIWpwYBEtIn2erutc93Ihb/urcah+TOUraPQnMCTdgalyNaojBy0xPjuxZu8H35pfXuhZRA92J2rwR7jvIzeuVCMF4SKiWZOO6bhkm5HqsBE1eQiGmg0dHKUgdJ61pc1cblhKzJ5NNGdqV4cjCG0GptooCqeQEKnjCeVhkquXUZZxFtsSZ2CQ4oNzj9oi0lrBjiqiRUSAvU1Bdtc2Y4t5ADAVL6YhECHVZtiv/1roLUSC3Uk0Xee+j9z4wjEenqIiq2Gi2cc2CCjZaqQpECWaOR5jTSHoegdHKwidY9vOXZwubyY8/PI+u7y00D1dN7E/Q8edAUDDxF/x3YT/MHfvlbyintW2j3ZMPdgQUexIokWkz1td0kQKXmRJJ4aCqeQTfOEYQ5VK5GCDmCWslxHvaJ1k/tcVrCpp4raZgyjwrgM45lH2SVYjzcEo0YyxyKFG5G+NaheEnqi8OUiqZxMyGpGB3+nqcAThAE6bkYkzL6HuR8Vok2/lkcvGkWwz8XZDf6IY0EyJR532ry3BNjiQxTR9fd7qkibGJMVnEVuijkUJ1OKSyhgW3gxARCTYvYpIsDtBOKbxz+UlTC9I4aqBYWxf/5NI/5notvRjOj7ZZiSm6XhTxgDEq9iC0MOtLW0mT6oFQP3WHPCC0G20DkJPs5t4/NLRWK0Oiq2j0I7h5/e+QY5h2YYUFT3YfVk4prGurJlpGTEA3lZPB2CGvIk8/wZUe+ZBa2EIPZsY5NgJtlZ7CMc0Lh6ZTuJnP0BXjPFpo46R0xr/Z6q1DiLbYMFQu4Hw0Is6KlxB6BQVLSEGy3VopiR0c1JXhyMIR5WXbGX+DRPI0P+B11t/1P33VbDDokWkz9tQ0UI4pjEuKQRlsNc0lB16LjPkTWQ01RDtP00sKtPLiAp2JygsbwHgjIZXMVavxzfjD2gJ2cd8fLI1XkFpCunE0seICrbQK1R7QgwyNqAm5R19Z0HoJlJsJhw5w4n1m3DUfY1KPGEKyQ4kLQJquKPDE7qp1SVNGBWJAqsfgNGDB7FEHcs0eSvmUB3RbDHIu7cRCXYn2FDu4dzkSlI2PEZoyIXHXX3eV8GO92GPw1C3BdRIR4QqCJ2m2hMmX6ptm5ZKEHqbfRXskGwDQBJT9fVZq0uaGJuThClUj2ZK5H9nD2dl5jV8IU1CV8xE8sRCW72NSLA7WEzT2VTZwt3qE2jWNHwz/nDc53Da4gNpmoJRYpnjkdQwhoYiAGRPebvGKwidpbolQIZWg5ogEmyhdzLI8bfY4L4EO+zpynCELlLnC7Or3s/U/GTkQB2aLR2rUeGhq2Yw/IdvUX/zdrRE8UlebyMS7A62tcpDVqyMfpESApN+hm5xHvc59rWINLdO1QcQKVvHqmULSX1pCkprsi0IPUVM1ZADtRj1KGpSfleHIwgdYl+LSFCKJ9iyGOjYJ60uaQJgyoBvEux9TAb5qLPRCD2TSLA72NLdjUxXtgEQ6X9iHwFZjTJZiWYWb68las9Gs6ZTs2MVG9cvBUBpKWmvcAWhU9T6IuTQOoOIqGALvdS+FpFAW4uIGOjYF60uaSLVbmJIuh05UItmy+jqkIROIBLsDrK12st/Fi8hq+gZLrFuQE3of8IfAUmSxG0zC9hR5+ftTdVEM8eT5tnCYKkSADlw9NHsgtCdVHlC9JfqAMRHo0KvpexLsNnXIiIS7L5G1XTW7G1iSr4TKRZC8ZaJn3l9hJimrwMsL27gnQ/e5AXlDyhSfNXF4OArT+qcs4ekcWqek3+t2MMVE8eSXfIxp8oqAHKg9qRjFoTOVO0JkyrF+1E1u6jmCL2TJEkYZAl/a4uIWC6979la6aElFGPKgBSMtYVIWoxo1qSuDkvoBKKC3c6qPSHuXuTmEttGdMXMMym/QjUlEB503kmdV5Ikfjl7MKGoxhMl/QAYJFcBJ17BlltKSHrnUmTRYiJ0sipPiGTJiy4b0I2Org5HEDqMUZHwYwfELCJ90a66+L/5yH4JGKvWAhA9hikehZ5PJNgnSdd17lpQhG/lU8gVX3H3ou1ous6cpCr0jJFcdPWtNH5/K9H8WSd9rQGpNq6ZmMurFekEdVPb6ydSwXbX+vhw4XxMVWtI+OJXoOsnHZ8gHKsGf4QMQwDd7BSLKwi9mkGW8WMBQBYtIn1Ogz8+pW6q3YSxai2xFNcJTXYg9DwiwT5Jqg579u5iYOH9JL5/FZaqVfxqdgG25iJi6aPjO0nt95i/NyWPlAQ7hfoQAGIYkAN1h9xXCjbgWPo7pGDjQdsWbq1BqdsKgKliJaaST9otRkE4mpZQjFQlgCbeaIReziBLhHT5lbEmAAAgAElEQVQjumwULSJ9UIMvjNkgY1V0DNVfE+03satDEjqJSLBPkkGW+GH2XgDqtAReND/MJXyGHPUT3ZdgtyOrUeEP84Yh5U0DwG1wIQcPbhFZV9rMwvmPYd38Agmf33FQhXp7jZcxhlLWaMOISSaMFavbthkrV2PZ8lK7xy4I+7QEo6RKPnRLcleHIggdyqhIxDTQTQmiRaQn0GLt+oluoz9Cis2IoWkHcsRDNFv0X/cVIsFuB7OMW6nTk/iB+SFIyiNhyW8Avqlgt7OxOUkMmXs7T2XexwatIN4i8q0fCO9sqmKkbzmqbMZc8jGWrS+3bVM1nV21HoZKpWyXB1NiGsyeLctYtK0GQ/V6kj64Fsfye0XbiNBhmoNRkvCjmUUFW+jdDLJEVNPiCbZYaKZ702Ikz5+DfeUD7XbKBn8Ep9WIsXodANF+IsHuK0SCfbJ0jeTaVfiypnHvpdPxXPQ6seTB6AYbavKQjrusOZHqfrMpizjiU/80uiEWBuKLeOwoKeYUaSeb8m4g0n8mjuX3ojTuAKC0KUi/WDlGPUooeQRL/PkMiu3iy9XLSFpwPcTCSGoYKXRwa4kgtIeWUIxEvKIXUej1DIpMTNXRTA4ksdBMt7Oj1sf5T69hcVEt5l0LMDTtxLzrg3YrMDX4I6TY4v3Xqi1DTNHXh4gE+2RpUdCipJxyBfkpNnR7Bs2XvEfTpe91+OpMGQ4ztVoSACnzz0L+7B4ACitaODW2HlnSWWGYgufMR9BNDhI/vgXUMEU1XkZKJQBYc8dRqA3BIkV5JPBboij4p90FgOKt6ND4hb6rJRjFrnnRzKJFROjd4hVsvbVFRPRgH4191R8xF73eaddbs7eJam+Y3y0qwr/87+hIKL5KlKad7XL+Rn+EZJsxPsAxa5IY1N2HiAT7ZClmGm7aRGTgnLaXdIsTNW1Eh186x2mhjm8qgPKGl5BCTSzaVstIuZQgZlb5MtDtGXhn/xVDwzbqFt7N0yv3Mt1QhGZKZMCQ0RRqgwEwoPHX1AeI5sT7u2VfZYffg9D3hKIqWiyMWQuKCrbQ6xlkiZiqiR7sI2gJRtF1HSnUjLXwSRwrH4BosFOuvbshQIrNyC8HVZEV3MF/E68BwLT3i5M+t67rNPgj5BubUbzlYv7rPkYk2O1BVrrkslPyk7l59jfzaUqxIJ5Vz7Bgaw3TkpqoM/VnV0MIVdN5Pziad5RzGVH2CjOkjcyzbSXa/3SGZyXz64tn4h33v7yQ9yAv7HXSbEiP35ZIsIUO0BKKkYQfQMwiIvR6RkUmpunoJgeyqGAfZHOlh7P+uYrznl7Dfxe8jqRryKEmLO63OuX6xfV+hqTb+b78X/zGVH5ddxZVpgGYSk8+wfZHVCIxjRGxIgAxg0gfIxLsHkySJIaPnsTHCZdwuelJ1IKzSC56kRw7DKQCn2MgNd4wV7ywjrsXuXnOegPNjsH8Qf0r1lAtkbwzAJhWkErotDsZN+VsIqrOf4tj6IoZZV+CrcW67iaFXqclGMUpxSt5umgREXq5b1pEEkWLyCEUN8R/2R6UZielehnNup0y02AsndAmomo6xQ0BptmrMJctRZ9wMzNdOXwcGY2x8is8e77CtubhE+7HbgpEASgIbkY3WImljWzP8IVuTiTYPZ1sYN3gX7DOk8iHiZeRrLfw6KANGHwVGNLigyzNBpkHLxjB8/8zFe28J5G0+Dd9JG/mAacamuFgVFYC726uRrX3Q/ZWsmfPTtKeGY6x9MtOvzWhd2oJRXEST7BFBVvo7YyKhKpq6CZHvEVEzM50gPrWhVgeumA459u3sc06gZXaCAwNRaCpHXrtyuYgxEKc63sL3WAjNPJaTi9I5aPIGCQtQvKiG7Cve5Rdm5ezdHcDK9xlNHx4D8bP78RQ+dVRz98YiN9blncT0czxHT4uS+heRILdCwxKs6EDP/sqkWLDICaUPImETr8Bo3jzxom8fN0pzB6ShixJqKnD8M56iOCo69Ec2Qed6+IxWZQ0BvGYMvHW7eXt919FigUxi4VohHbSEox9U8EWCbbQyxlkmaimo5kS4sUNNdTVIXUrdb4ISRYD9ubtKIFaGjJPZ20wC0kNo7SUdNh1G/wRypY9x1bzTQyuXUxw5HfRLU6mDkxmgz4Uv24mhfi0ipu+eJln3v+QUR9fytDdz2MqehPne5dj2fbqEa/RFIhiJ0iSxy36r/sgkWD3AgVp9ta/SWgTf4wcif9QiCUPZkCKDflbo5bDrkvwzfzDIc81x5WO3aTgDiWh+CqZIm8DwHgMv60LwrFo3q9FRMwiIvR2BkUipsZnEQF63kDHiB/7ygdQGoo65PT1vghpDlPboELDoLMo0uJT2XXUNQH+9sVuEvd+hA8rkbRRBMf9AIBEi5HxAzLYaBxHBAOF2mCuMX7JIuvd9LdFWDPlGc43PssydRQJX/wK2+q/gK4d8hqNwShT5G1IaKL/ug8SCXYvkOu0kmo3cfPpA0kadymqIxsdCTVp4HGfy2pUOG9kJl+32EmMNTBN3oqGhNJQhBRq7oDohb5m/xYRUcEWejuDLLUNcgR63EBHU/kybIVPkvz6XOwr7oeIv13PX++PkG43Yyr9gmj6aAbm5bNLz0FDjreJdICoqvHVnlqmGdww4lI8Vy464BPdv1wwkgGXPcyikY/xpvQdHLqPaO5peK/+lMET5/LPa6fxaOp9vBabhX3930lY/BNQIwdd56vd1dxlfI1YQn+iOVM65F6E7ksk2L2AQZb44OZTuf3MIaAY8Z12N6HR/wNG6wmd76YpeRTKo5HRyZSaWahORkJvW4kKoLLJT82H95Py7wnIntL2uhWhD2gJxshQ/OiyAd1oP/oBgtCDGWSZaOs0fUCPGujYHIyyrbgYgEjBXGwbniLltVmYij9st17yOl+YXGsEQ/V6InmzSLWbSHI4qDbkYqjvmAR7Z9HXXKYuwqyHkAbMPGi72SBjTB3ItJkX8JMf/pqmyxbgOe9FdFsaACk2E49fcQqfF9zJH6NXY9m9AMfSuw54Jiv2NDJg73wKpEp8Z/wJDCf2fiz0XCLB7iWMiozU2goSGXwevhmHbgE5Fik2E9POuJD7jLcRSBrK32KXE5IdOL68C6VmI8t2N/DFq/cxqvgplEANppLPDnke2V9Nwse3QDRwwrEIvU9LKEq2oQXNliEWXRB6PaOyr4Ld81pEnl21l5Vb4isAe+Y8TtMl76Gbk0j68GYSF95w0sUVTddpCEQ5VduIpKtE8mcBMHVACuvD2VC6HOv6J9p1YOin7jqylv+K3xlfQZcUojlTD7uvJEmYjQqxzHEgHZgumQwyP54+kKfV81mbcwPWba9h2fxC2/bnVu7lBtPnqLlTiLbO2CX0LSLBFg7p/FH9uOWHd+C/9nOyBozgB9LviKoajrcu4qsPHuc6PmSpOppmczamihUHHV/tCeF976dYdr6HqWJlF9yB0B3pus62ai/9lWY0R1ZXhyMIHc4gSz22gq3rkCp58MsOUEzEsibSdMWH+E67G1PFKlJem41t3eOHbI84Fs3BKKqmMyq0Fs2cRCxzPAC/OnMw6zOvwh3rh2P1n5G95Yc/yXHMNLJyTyN3L9jMgFgxxZZR+E+7G92ceEKxA/RPtjIgxcrDkcsIDzwbx/J7MZYtZ1edH0ftavrrVeinXH/C5xd6NpFgC0d18egslvrzmNZ0DxsZyp+Nz5JKEx8mXMZaRmGsWHXQD7nvvbYBY1O88qGZRZ+tELe91kdJY5A8QxOqXSTYQu9naK1ga6092D0pwfZHYqRJLdSoCXziriMUVUE2EBz3AxqvWUIkfzb2NQ+S/Pp3UBrcx33+Ol8E0BnQsppI/xkgG4B4dXjouJncE40np4fqxd5Y0cK2RY+S+vxYpEDd4S+iqZj2fEKDx8d9H7k5M7kOkxQjbcb/Ehz7veOO+dtOL0hlfYWX4il/QU0eTOLiH7J0/TouNyxFNSWgD7vgpK8h9EwiwRaOanpBCmcMTmXexBGk3vg+gVNuITT0YuxDZrPQNwQ53IKhbvMBx9T6IuRIDQBt824LwqJttRgVSIzWiQq20CcYZRm1daEZ6FkJdkMgSqbipUV28tsFRcx9cjX3feRmzd4morYsPHOfpmXei0hhD853Lkbac3zrJdT7IgyTyrCF64jkzTpg2/BMB269P3Bwgu0Lx/jjf79iXPE/kcPN2DY+c8jz76zz8fn7T5G06EaU+ReSEK7mlyPjgzSjGWOOK9bDOXdkJgZZ4vo3d1J46hPowLRdD3G2YQPRgWef8FgooecTCbZwVAZF5qELR3LrzAISbBb8U3+Dd87jnD08k+XaaEKSFfvqB9v65HRdR2G/irZYCVIAYprOx9trmTPAghwLiARb6BMMikRU1dFN8QG9cg/qwW7wR8g2eBk+cCD/uGw0s4ak8cXOem55azPnPb2GR5bsZpP1VJoufR/NkYUy/3LMRW8c8/nr/WFGy/FBlN+eJzonyYJidlBnzEbZb7Cjqum8/dFCHovdh0MKsYkhyIXPobx6HsmvzCD1uTFtqy++uW4PculyAljpFyllsfUu8sreRTMnoSXmtcszGpxm59mrxyFJEtctaGCx9TxOlzZg17yEC+a2yzWEnkkk2MIJG5BqY8744fxf5LuYypdh2foyABUtIQZKVW37SSfYnyf0Ll/tbaIxEOXC/PicsZpoERH6AIMsE9M0UMzoihmpdZ2CnqDBH8GptaDb0jk1P5l75rr46EdT+NN5wxnRL4E3Ciu57uVCrnq3lm1nvYaefxqJn/8cy5aXjnheQ+1GDFVrKW0KMViuQpdNaIn9D9hHkiSGZTrYoee1VbA1XeeuhUWcVfo3BppacE/8E8+l/JIdDGBbfZSvQrn4bP0xr38C30sX8/PiG5imbOVLdRT3ZT6BkpSDsaaQWMbYdh1g7cpw8MI14xmSbuf+6imoyOgGK5H+B89QIvQdhq4OQOjZvjcljzlfz+bmlI0MWHE/kbyZFNVYGSHt/WYnUcEWgEXbaki0GJjgDAKgOvp1cUSC0PH2VbCBb5ZL7wFUTccXDGI3e/BbU9tetxgVznKlc5YrneZglM931PH4sj3c9UkFr39/PsYXzsZSNJ/QqOsOOmckpvHAxzu4v/ZnOE1QbH2cuaYa1MQBbf3X+xue6WB9VQ7T1K8gGmT+pga+3FHNU9YyIqNuJH3yNfxuMkRic/liYyXPrS7F1lzDEvPPGehtnVZWgnXZ1/DDc2fTbJqBbf0/iGZPbvfnlWY38a8rxvL8aid7W66mX3KCaA/p4zo9wXa5XAqwDqhwu93nuVyugcB8IBVYD1zndrsjLpfLDPwHmAA0AFe63e6Szo5XOLIkq5Eh6Q7+YrqFf4R/iuOzn/OpfC9zWleABED0YPd5/kiMJbsamDciE3Mw/sYnKthCX2CUJXTiCatmSugxPdjNwShOPR6r1jr/87c5rUYuGZuNw2zgroXbeWJpKbf0n451w9MQCx4w97Om69z7kZtC9y7SLDvRgwb2+popkKpQk0cd8vyn5DpZsH4AkqLhXf0sj68bx5V5AQy1EQLp3xxjMsh8d0Iu80Zk8urXFawJ/Z7VO8qYGVvBFHkbp886H9VmBIwEJt/Rfg/pW8ytU/fBg7TvcjxCT9QVLSK3AfuPWHgQeMTtdg8GmoB9w3q/BzS1vv5I635CNzQ+N4nPayzUTLoLc+Vq8ve8wneMGynS4h/5SaKC3fPEQph2LYh/1PutZYCNFaswVq45rtN9uauBcEzj3BEZyL4qdCQ0e0Z7RiwI3ZJBjrci7JsLu6ck2A3+CGlSCwCa9dAJ9j7fGZbB+SMz+dfS3WxXhiFpMQy13wx813WdR5YU84m7jnn27UD8fcHh20N6rBLVOeiQ553QP4mV8ilstE+nYNODXG9Zyk+Hxj8BiKUdnJQnWY38+LQBjDjzJiZdeDvlp96Lf+JtqCmuE3oGgnAyOjXBdrlcucA84NnWryVgNvBW6y4vAhe1/v3C1q9p3X5m6/5CNzMuJ4lwTGPeygKWaOP4vfFVUvVGPtZaB62IBLtHicQ0Ct+4h6TFPyLhyzvx7lzKRc9+xfzPlpOw6Cac711O4qKbQD32TyYWbashO9HMmOxEFE8pmi0dFFMH3oUgdA8GJf42G58L29FjBjk2Bo49wQa4Y/Zg8lNs3Pl1fDCnsXo9kq8ay5aX+M9Xpcz/uoKrxmdzfZobTY+/lc+WC1FQiSUfOsG2GBUm5qdxecPNLFHH8jvtSdKKnkc3WFGdBUeMZ3R2IjMnTyMw+ZcHLRIjCJ2hs//XPQr8CthXEksFmt1u974MrBzIaf17DlAG0Lq9pXV/oZsZl5sEgIaENPdvYLKjI/GxOgEASevBgxx1DfP2NyEW6upIOs29H7mx1G9it5ZFTLawd+V8rvL/hx8UXYtesgx/wfnxqRlLPsVYvuKoq6yVNgVZW9rMJUNNJH56Gxb3W20LSghCb2c8oIKd2IMq2FFSiQ/I1A/TIrI/m0nhkcvHsjtoo96YRWXRMj556V4SvryTxNV/5LHMxTxQdRMFNR+xUJuMhsTZylqAw1awAWYMSiGCkWVj/0o0ZwqGhm3E0kaCrLTPjQpCB+m0HmyXy3UeUOt2u9e7XK4zOuIaiiLhdNo64tQ9gqLIXXL/TqeNF26YSEGanawkK1rKs1S611K7KhkAm1nG0kX/LifzTHRdJ1S8ksTPbkeVA2in/rido+t8R3sey3bV84m7lscc5Xymn8IeLcBs30JmGXR2Zp7LdaXzyGjI5A3pE+wf/RgTMWJXvo4+eE7bOcJRlYY9hTiKFxJJHsLyr2v4vqmMn+96HyniQ51+B/JpP8dpsHTGLR9VV33fdHfiuRze8TybBIcZAJvDjNHhRGrc1iOeq1/TyZHqAUjIHgCmo8ecmipz2YRcPtk4kouiS0nU7URR+IFhIbSAlj+d6LTbuHdhMmO0YsbIe9AVE44BY8By6PNfNXUgzkQr547qhxSbj/bezcgFs3vEMwTxfXQofeWZdOYgx9OAC1wu17mABUgEHgOcLpfL0FqlzgUqWvevAPoD5S6XywAkER/seFiqqtPcHOio+Ls9p9PWZfc/MtUGeuvzT53GnoLRRFctBSDo8xPsqLjUKLa1fyM47gfoluSDNp/MM1ld0siy9xfwFwNoG9+geWjPX/L2SM8jEtO4979bOcUZxBZqwtJ/DG/v1jjLtAr/sKtwzn6I35Q0cdeCIj7Vx3GespqYYkP/4o80p05D1WHh1hqeWr6bF2O/JF8uBeKDLpAgmjwJ7xkPoqYMBZ8GdI/v1a78vunOxHM5vON5NrFw/APaxqYAKVgwhzw94rmW1fuZrlSgJuTSHJAhcPSYnU4b57nSeWDdWVxt/hSrFGHDiN8yMCuTWM4UtIT4B9SDMjexuzqXfGoJjP8JgZABQoc//8x8J35vCFDg7OfjL/aAZwji++hQetMzSU9POOy2Tkuw3W73ncCdAK0V7Dvcbvc1LpfrTeAy4jOJXA+833rIf1u/XtW6/XO3233kz6KFbsOoSMRo/QivA3uwDfVbsK9/HC0xn9CIq9r13FurveRRDYCxdiNK027Uw/QK9gavrC+ntCnIX6aHYB2MP+U0GD+MJn0ysZwpIEmcNjCF166fgLfuj9y24D1Oz7ZyWeWDFK95h7vcA9hT7+Ee52KGa6UsH/5/NCa4cJgUxuSmoKcMFr2QQp9jUL5pEdFMifFp+nQdJAnZV4Vl6ysEJt3e7VoeShoC3GyoIpY85LiOc2U6UDJHUtgygnFsJ3fKVUSsKQfsc89cFwnbLyZSbiQw4Zb2DFsQuo3uMA/2r4H5LpfrAaAQeK719eeAl1wu1y6gEWjf7EnoUEZZJrrvv1cHTdOn6TrVNVUkA7Jn71H3P16lTUHOlWpo0BNwykHsy+/FM++FbvdG2B6qPSGeW13KrCFpjFY2xF/MGMk4UwIxZhywb1aihazEodT1P4cHq1qYLGWTuPZh5hm/w83ORThCVUTyZuKadWPbYg7iN2Ohr9o3i0h8NUcHkq5CLERAN6FseovUwkeJFJxNLH10F0d6oD31HvL0cqIp3znuYx++aCRKw1/xhkvQv5VcA2QkmGHSjbRMurE9QhWEbqlLEmy3270EWNL692Lg1EPsEwIu79TAhHZjUKS2BFtq5wTbUP01Sksxnxtn88VnG3jMBEpL+yfYexuDDDfXURQbxN6UM7im9FHsK+/HP/3edr9WV/vbkvhyxb8fXodlxYvEkgejmw7/0RfAmUPTWbmniVeSr+Y3wb8yXH2OaMYkWsb/kciAM9t1pTRB6Kn2zSIS07S27yk54uHFwgDDCtdxhQxK/bZulWC3BKNYAhUYzVFCx1nBhviiK9hHE6b73JMgdLbuUMEWeiGjIqMhoyO1e4uIfc1DGKvXUzHpC5Kl+JRXSjtXsHVdp7QpQLZSTVniKP7SMI2LRnuwb3wWNXkwoZHXHvbYPQ0BEvcsoKDsDVounH/IFcq6E3eNj+U7q3lxwCcM/Phl1KR8vGf9/ajHnT8yk1PznPRLmI53cxKx9NHEsiZ1QsSC0HMcWMGOJ9hSxMfOuiBz9Kr4Pg1FhLsswoMVNwQYIsWHQ8VShnZxNILQM3Xvd36hxzK29h1qkqFdKtjLixsY1S8Rp0nFWPUVkhom4qnGuS/Bbik56WvsrykYxRhuwmrxk9BvKC21MZbn38qslmIcS3+HmjQQb+YUVq5bwzlbf4YxJQ9v1uk8Uz+Sl3ZKfGH6AyapgT1fPMP42rcJTL0zXtXtht7fVMbr5vs5pXonwRFX4zvtXjDZj3qcJEn0S4zPBBIac1MHRykIPZNROXChGQAp4mVvU5QBcnyMh6F+22GP7wq76/1tCbaacvwVbEEQumYlR6EPMMrx/1qqZAD15CrYvnCMn7+7lXs+2s7/t3ffYVZV9/7H3/uU6b3B0PsCBEEFNAIiGLuJMUbFEkvUXzTxqokx5ZprSeLVRFO9SbwqtlwTe9SYoGIngCgiIMgsmvQ+vc9pvz/2ZmiDDHDOzGH4vJ5nnpmzz95rr/V9zpzzPWuvtXZg0zyciNvXE6xZQx5ugu1rrsZpqjzoczjN1but57y2opF+jvvh17PfMLJTA9w2fQVzR/+KSG5/smfcyB/eXUbRR/fgNFWyZvNWSubfx21rr2Ru+s2UOuVE8DF66b0EK8rIfv160hc+gr9qVZvrRvvqNu51x8SO0NASYW3ZhxzrLKdu/B3UTb6vXcm1iLTPzjs5ujeaAYg2VVNeXU2pU0EUx02w97Oe/BcKNcajqq1Wbq9naGADkczS/Q4VE5G2KcGWhNjRaxNxAod8o5ntdS3EgNmfV7Ll0zdat6fVr20dIgIHNw67JRzlvXdeofDRY8j4+IHW7WsqGxjmLTMXLB7MQ1NHkeJ3uObFVczscwP+hi2M/ewXnOL/hHXDruMy36+4tcdTrBt7J2k9RzLDP4nXImNIcSK8Hh3Hikg3sv59JwVPnUTBX04kY+79EA3TEo7y8pxPKPjLBDLm3ndAdXdaat2b4BxCYj5vXRUDIu746+b+Bz6ZSUS+WH66e8fS9ZVNRFNyAKisqqB3zP0CPz9m8DVX4avffFDlB7YuouhhQ/YbN+A0bI9LnVeWNzA8sMldUlNEDooSbEmIHRN7ok7goMZgp6x6jbxnTodIiG31bo91etBHZPVMmotHE3P85DauJY866mPujRz8VasO+DzPzl7MlCU/xIm2kD7/T6294J9uqmVy4FMi2b2I5vZjUFEmj196DMO7ZXH13ELWxYq5MPAezYXDKZ70Xf717eP54XmTSBt3DTVf/SsbT/oNs7NOJxpIJ/vU27nMfx8Tm3/LT0NXMb+xhMx5vyPw2EQCfz2b+g8fxYm2kPHJg+1uw6ryepa+8Wdy3voeKavfOuB271C2pZYRvtVEU3KI5vQ56HJEpG0DizIozUnl7eXbW3uwq6oqW6+Q/T18IgApq2cccNnb65r5dM4/cWJRUlf+k4K/TiLts78d0pfuWCzG59tr6RNdT1jDQ0QOmhJsSQi/Aw7eEJEDHIP90dpKtn34NMHtS/DVbWBbndsD/uPxxQyNruAD3zFEcnpT2LKBHqmNLI4NoN6XTfbbPyD7zZsJbF3YrvNsrG5i2SdvUeDU8ULJTTihetIXPEQ0FuODlVuY4FtMS5+TW1fDyM9I4Y8XHM15o3ryz5yLacnqTd1Zj0AgDWePFTPOGt6Nm7/1bcqvXcrgoaN55drj+fnFp5N5/DX8Iucufsp3+Kw+h8LaJfyH/yW2BUqJBdLImnn7fi8V+ytX8tZ7M2heNROA9EWPHlB8d7V0Sx3HBNcRLhquVT9EEsBxHKYMLmbumkpqo+kA1NXuTLBfjpzIttyj3StokQOb6vjCwk3UrfmIhszeVE6dQbhwKNnv3Eru3y/AX7H8oOpbXt9CVvNmUmNNRA5iBRERcSnBloRwHIeg3yFCAOcAerA3Vjfxw1eWkLN9HgD+2g2U17sJ9plZy/E7MR7c0If6jD50D2+kwKkns7A35zTeydo+55Oyajr5z51N9lvf3++53l9ZzpDYaiL4eKp5As2DziF90aOsWLuO/k1LSIs1ugn2LoJ+Hz/+8mDOv/yHVF8+e/+9vt4KIn6fw1GlOVzzpb48cvFobrj+J/ws/x6eC0/C58T4R2wiDWO/T8rad92erGgYX/UaCDUSjsb47bsreaNsK4ENc8h77ixu2vgDxvnKqCeNlPUzD/rDdNnmagbFVhMuHnFQx4vI/p0ypIhwNMb7690EuqGuikHB7UTSCmh0Mni98Er8dZtI++zpAyp3wYZqRvtWsCJgiOQPovprz1E7+T4CFWXkP3OaOxQt3NTmsb66TaTP/yOZs+8mdekzEHE7QlZu1woiIvGgBFsSJuj3EXH87R4iEo7G+K9/lVHUsoESpwoAX+16ttW1kIcZYEoAABvkSURBVBH0k71lDtFAOkt9Q5hTlUtvNpMZraFvz540ZvXju9WXUH75hzQN+gqpy/6+3/OWballdHAt5Sm9WLw9TO2xN+GEGqh9//ec4fuIqC+FUK8J+y7gEHp8/T6Hn542mFk9r2FZzolMqx/PZz0voDpzAKE3bmPRY9dS+H/jKXxiDAsWfcxfP97A69OfIf2lS6l1ssl0mshxGvl16AKijp+0pQf2wQywra6ZvMbVpMRalGCLJNBRpdmUZKXw5opqYoE0WuqrGRgoJ5rTmz75GcxoGk6odKzbi72PhHhP0crVHLP5GXo4FbzX4H3Rd3w0Db+Yikveo3nQOWTO+x35z5xGcMPsvY7f8uZ9ZM25h5RPHibn7VvInP0LAFaW1zPYWQ9AJH9QfAIgcgRSgi0JE/A5hAngRNo3yXHanDUs2ljDdX02tW7z16xje30LRVkpBNf9m1CPEzh3dB9m1hST7TSSFqnDl57PNV/qw+JNtczcEKalz2ScaAh/zdovPN/SLXWM8K+hIW8oLZEYDyxNZTrjOan6JS5KmUXLwDNbx0wmwtBu2dzxjUlsPHUaGyjmwicXcV3lxRSFN3FK0wxeiXyJcChE/rz7mZrxMY+l/oY19GByzZ3Mjh4FwMLsScwNjCXNvtDaA9Ven26q5UTfEgBC3cfEvX0i4vI5DlOGFPPB6goiwSzCTdX0dLYTye7NwKIMVlY0UD/2Fvz1m90x1O0QnvU7bvM/CcA7dX34eF1V63OxjCJqT32Aqq88hRONkPfShbvN1Xhkzhq2rS2jjAEManqCVf0uI2PRNFKXvcTK7fWMCG4kklFCLC0/voEQOYIowZaECfp9hPcxydFpqtrtDf+T9dU8OnctXxlWwLnhN9gSy6MupQR/3Qa21zVj0moIVK0k1HsiY3vn8WF0aOux0bR8zh7ejV55aTw4azXhvIGAO1YZgFiM4PpZ0FLfekxjKEJFxVaKI1sJlLp3G3vyo3W8mnsZ6U6I9GgdTcMuTkRY9jK0JIsLR/fg6hP6cPWFl9Aw9CJCRUfxTPdbeSh8Jic0/5t7o78mWjKSzCte4ZKJo1h1zE+pnfhzJo4awSP14/E1bidl7TvtPmc4GuOROWs4PWURobyBRHP7Ja6BIsIpg4toicSoiWWQFqmjILyFaHZPBhRlsqGqiZpuJxAqHUfG/P/Zby/20/M3ULXmE9ZGi9k64AK2Zw/jxhc+5bWlW3fbL9RnEhVT3yRcYMh678c4LXXMX1/FQ7PXMDS1nN79h5Ea8PNQ6pWESseR+fat1K/+iMnOfEKle91gWUQOgBJsSZig3yGMv80bzaQvfJjcf16B01hBOBrjjull9MxN4+c5L5Nevoh7olexzV/iDhGpb+FE32IAWnpNYGSPHD6nR2tZsbQ8An4f136pL8u21fNORR4A/soVzF2+kZUPX0Deyxe5Ewg9y7bWMcxxl/XL6j2Kb5/Yl19+dTh3Xno2TcOnEi4whHqdmMjwtPL7HG49ZRDXje/HqJ651E+5n6oL/sWPzjia1YOu4vW8i9k8/h6qzn2a1KwCrhjXm9MnTKTp6KsY1zeP96KjCPvSCK6b2e5zPr9gI+u3lTPO+YxQ3ykJbJ2IABzdM4eizBSWN+VynG85gVgLkZzeDCzKJAbMX19D/bhb8NdvIX3JU19Y1j8WbWAQa9nc/RScM3/LtEvHcVRpDv/1rzL+9O/Pie46UTqYTu3kX+Gr20zK7Hu5a7qlV06QkuhWnPx+jOuTxzurqrkz5QeUh1P5Y/i/yI7V0nT0lQmNh0hXpwRbEibo9xFm7zHYf/loHZ9/+j7g3oFx2dY6NtU0c8fQLeQufJDG4ZdSlj+ZDbEi/DXr2V7XwujwAqLphUQKh5KR4mdIt1y2xdw1ZaPeZczTh5bQvyCD//mwkkh6MZXrP2PmX+/hhNAHbM4cRpp9rnUZvMWbajnNN4+oL4Vwt2O55kt9mTK4CMdxqDv5Xioveh2cTvr3cBzw+SnNSeNHZ47m2Evvwz/6mxDM2GvXgowUwgTYnH0Uwc3z2lX89voWHpy1mm93W44/2kKLEmyRhPM5DpMHFzE3PJBSpwKAaHZvxvXJo09+OrdPL6MsdRSh7seRtuT/9rmaUFMogq9yBamEGHr0CQDkZQT54zdGcu7I7jw2dx0/euUzGloirceEux9H48gryVryBN1rP+Wek/NwoiEiOX2YOLCQLbXNPLsiwt8H3E2qEyNcOJxQ6fGJD4pIF6YEWxIm4HMIEdxtmb7t9S08PPtz+jaVAeCvWcMn66sppJpJ9nYi+YOom3AnfQvSWdlSgL92HXfxIMPq5tDSc3xr0ntMz1w+2jFMxBd0y/I5fHt8Xz6vaGBLsBfh9fP4TuAV5jij+WXeXeBPJePDXxOLxXhjyTrOD8yiZcAZxNLydq+442td/SPZ5aYHcYDVaSPcu8HtMgymLcu31nHry0tID1fznZZphPOHEOpxQsdUVuQId8qQIuZHdy59F8nuRVZqgD+cP4Kg38eNLy5ma5+vEqhcjr98aZtlLN9WzxDcq2/hoqNatwf9Pm47dTDfO3kA768s55qnF7C5ZudQk7e6X8umWAEP5jzOiBR3icBIbj/OHFbCjSf159krx/D1s86l6rznqT7jf7Vsp8ghUoItCbOjB9vZZfLdEx+uo1dkPdmOe2tff81aFmyo5g8ZjxBoqaHm9D9BMJ2+BRnYZrdn+qLAuzSnFtM07MLWcr45thcNU35F/djvEeqxs6dl8uAihhRnMqsim/6xdWQ7Dbxeci0flQdoPPpq0pa/zGo7j/4VM8mhjqZhF3VQNBIj4HPITQ/yWXA4TixCcMsnbe7XFIrw51mrOfdPs1hb2cALvZ4l2FxJzakPgD/YwbUWOTKN7pnL6tRhrY8j2b0A6Jmbzh++PoL6lgjfWdyfmBMg+82byJ5x41492Uu31DLct4aoL0gkb/dVPhzH4ZLjevGb80awsbqJK576hEUba6hqDHHn2xv5U/p3KGleTdasn7nnz+lLWtDPN8f2pleeu0Z3uHQM0bz+iQyDyBFBCbYkTNDvENpliMjmmiZeWLiRrxV6S0A5AXzVq9mw/nPGRz+m4bj/IFLofvicP6oUM+Y01meP5tmRj1F92buEdlmTujAzhUkjBtEw7pbdept9jsPd5wwjZdhZ1KeVEr3kBTJ6H8P6qia2DL+GlkA2ze/cw9Tge4Qze3zxMnyHifyMIAtjQ4jhENz04W7PVW5cge+ps6l6+DRO/vh6nsv/M7P6TKPv1jepP/4HRIqP2kepIhJvfp/DneedQGN2f6JpBZCS2frckJIsfv21o/isOsiM8CiC5UtJW/Yiga0Lditj6ZY6xgcskaLh+/xyPL5/AY9dcgzpQT/XP7uQm15cTHVjiLO+cilNg88lULmcmC9INKs0oe0VOZIpwZaECfocwrGdkxwfm7uOWAwu6r6F6lgm69KHESr/nL4t7k1SWnZJdgsyUjhrwnhSL3+VySedSnrQ3+7z9ivI4MTTLqPh6o+I9TuJYd3cpfa+9eJq/tB4BhOjHzLBWUTzsAvA1/5yk1VBRpCNTUEihUN3G4f9wsKNrHn+FtIry2hy0hhZ6DAqbTMZFZ/SNOirNI6+rhNrLXJkOqo0h8iIi2kedM5ezx3XO49pF4/mLfMzJjX/hogvhdRlL7U+v3hTDatWfsZIltM88KwvPE//wgwev/QYRvbI4bPNtfy/E/sypCSLugl3EU3Nc3vPu8D7n0iyOjwGmsphKeD3EYq5Pdgbqht5efFmzhvZndzqFSwN9Kcy2o2i6kWM9H1OzPG5t+tOgKHdsgn4HCobQ+RMuJ7IwrfwN1XQNPTC/R98GMhPT2H5tjpCA8eSal+EaITtjREWvf881/k/ZsHgmyidcgvhgI9wXgZVVQ2dXWWRI1rjsd/Z53PDumXTZ/JR/MPWsSj9eEYtf4WGY7/L9DUxfvXGYm5OmwkRaB507n7Pk5ce5IHzR7JwQw3H9s4F3DWyq89+HCfcGLf2iMjelGBLwgT9Di3ejWYembOWgM/hW8f3xv83S2XmKZTVpTEpupUTAsvdsYRtrJIRD3npQZ66/FhKslLJSg1Ql/ffBMrLiOb2Tcj5OlpBRpDKxhCh7mNIX/wk/vIyps2t5w7nIRpzh9DzlJvAr4tVIoeLzJQAZw4v4b7FJ/OXlDnkPz6WqTH4ZjACEffGUNGcXu0qK+j3MabP7hO5w6W6sZRIoinBloQJ+twe7Fg0zPTPtvD1UT3oRjm+UB2RXoYl2+shBcawhFDJeQmty4DCnWMdWwadQ0sbl2cPV/kZQWqawjSWHEcOsGXpe/RaWUZxoIbq058Gf0pnV1FEDtANE/tzW82JnLL6l0z1v8vA4myOH9wLJzWbll3mo4hIclKCLQkT9Du0xHzEIiEiMXdMYMBbemroUeNYsKaBsvArDPWto7F4ZCfX9vBVkOFOdCoPdKMgoxtrF7/H2GAj4fwhhItHdHLtRORgZKUG+O15I1i+rT8B3zkMKMygWUvniRw2lGBLwgT8Plpi/tZ1sHNSA/grLAAp3Ydx3ZQWrpr+n/xj8Ns4A87szKoe1vIz3B7q1ZWNbGkexNHRMgoyAkSLxndyzUTkUPgcB1OS1dnVEJGDoARbEiboc9wEO+Iu05eTHiCwvoxIVimx1FxONXDSwLOJBb5C2/csk/bY0YN926tlfD08iMn+WdAIdQmaNCoiIiJfTDOfJGGCXg+2s0cPdqTAtO6TGtBL8FDlpbsJdlVjiHEnnta6PVGrsoiIiMgXUw+2JEzQ79Ac8+OLuQl2dopDoHIFjV3g5i7JpHtOGgMKM7jwmB4cO7KE2McZOOEGwoVKsEVERDqDEmxJmIDP7cH2xSJAjMKWDTiRZsKFQzu7al1KasDHM1fuXHYr1P1Y/BXLiWUUdWKtREREjlxKsCVhgn6H5qgPfBAgQm7dSoDdhohI/NVNuAtfU0VnV0NEROSIpQRbEsYdIuK+xHJTIKXSEsMhXDC4k2vWtUUKDZHOroSIiMgRTDPMJGGCfh9h/ADkp0CgwhLJ7QeB9M6tmIiIiEgCKcGWhAn6fYS8BDsvFXcFkUINDxEREZGuTQm2JEzQ5xD2RiGVpLbgr/qcsMZfi4iISBenBFsSJuh3Wnuwh/rW48QiRAq0goiIiIh0bUqwJWECfh8hb5LjkMgqAMIaIiIiIiJdnBJsSZig32md5Ng3vIKYL0gkt38n10pEREQksZRgS8IEfTsnOfZsWkEkfxD4g51cKxEREZHEUoItCZOR4m/twc4JbdUERxERETkiKMGWhBnXJw/TPb/1sW6RLiIiIkcCJdiSMAG/j2snDGp9HC45uhNrIyIiItIxlGBLQjm7jLkOF4/sxJqIiIiIdAwl2JJQMV9g599p+V+wp4iIiEjXENj/LvFhjEkD3gdSvfM+b629wxjzODAJqPZ2vdJau8AY4wC/B84CGrzt8zuqvhInPrcHOxZI7+SKiIiIiHSMDkuwgWZgirW2zhgTBP5tjJnuPXertfb5PfY/Exjs/RwP/Nn7LYeTcBMAIY2/FhERkSNEhw0RsdbGrLV13sOg9xP7gkPOBZ70jvsAyDPGlCa6nhJf4W6jaRx+KbWn/k9nV0VERESkQ3RkDzbGGD/wMTAI+KO1dq4x5nrgbmPM7cBbwI+ttc1AT2DdLoev97Zt2lf5fr9DXl5Gwuqf7Px+XxK2PwPOe4CcTjp7csak8ygee1NM2qa47Jti0zbFZW+Kyd6OlJh0aIJtrY0Ao40xecDfjTEjgJ8Am4EU4CHgR8DPDqb8SCRGVVVDvKp72MnLyzii298WxWR3isfeFJO2KS77pti0TXHZm2Kyt64Uk+Li7H0+1ymriFhrq4B3gDOstZu8YSDNwGPAOG+3DUDvXQ7r5W0TEREREUlaHZZgG2OKvZ5rjDHpwKlA2Y5x1d6qIV8DFnuHvAJcboxxjDEnANXW2n0ODxERERERSQYdOUSkFHjCG4ftA5611r5qjHnbGFMMOMAC4Dpv/3/hLtG3AneZvqs6sK4iIiIiIgelwxJsa+0i4Jg2tk/Zx/4x4LuJrpeIiIiISDzpTo4iIiIiInGkBFtEREREJI6UYIuIiIiIxJESbBERERGROFKCLSIiIiISR0qwRURERETiSAm2iIiIiEgcKcEWEREREYkjJxaLdXYd4mkbsKazKyEiIiIiXV5foLitJ7pagi0iIiIi0qk0REREREREJI6UYIuIiIiIxJESbBERERGROFKCLSIiIiISR0qwRURERETiKNDZFTiSGWN6A08C3YAY8JC19vfGmALgGaAfsBq40FpbaYwZCjwGHAvcZq29f5eyvgdc45XzKXCVtbapjXO+BpwA/Ntae84u228AbgYGAsXW2u3xb/H+xTkmNwHXAg7wsLX2d/s45xnA7wE/8Ii19l5ve6fHJMniMQ0Y4x2/DLjSWlsX90bvR5LF5HFgElDt7XqltXZBXBvcTkkWl5lAtrdbCfChtfZr8W1x+yVZbKYA9wMpwMfA1dbacNwbvR+dFJNHgXOArdbaEbtsvwC4ExgGjLPWzotva9vvIOJyKfAj3LbXAtdbaxd6ZbX5GmjjnFcAP/Ue/sJa+4S3/W7gciDfWpuVgOa2S5LF5DWgFDd/nQl811obiXuj40A92J0rDNxirR2Om/R+1xgzHPgx8Ja1djDwlvcYoAK4EffNuZUxpqe3fYz3puUHpu7jnPcB32xj+yzgy3T+OuLxiskI3Df8ccAo4BxjzKA9T2aM8QN/BM4EhgMXe+eD5IhJMsXje9baUdbao4G1wA1xbWn7JVNMAG611o72fjolufYkTVystRN3xASYA7wY78YeoKSIjTHGBzwBTPXeq9cAV8S7se3UoTHxPA6c0cb2xcDXgfcPpUFxcqBx+RyYZK0dCfwceAja9b6Bt18BcAdwPG4M7zDG5HtP/8Pb1tmSKSYXWmtHASNw15++IAHtjQsl2J3IWrvJWjvf+7sWWAr0BM7FfRPG+/01b5+t1tqPgFAbxQWAdGNMAMgANu7jnG/hfqPcc/sn1trVh9SgOIhjTIYBc621DV7v0Hu4b+B7GgessNausta2AE9750qKmCRZPGoAjDEOkI7bk9HhkikmySQZ42KMyQGmAC/FoYkHLYliUwi0WGuXefvNAM6PUzMPSCfEBGvt+7iJ+p7bl1pr7aG36tAdRFxmW2srve0fAL28v9v7vnE6MMNaW+GVMwPvS4i19gNr7aZ4t/FAJVlMarx9ArhXgZL2Zi5KsJOEMaYfcAwwF+i2yz/VZtzLMvtkrd2A26uwFtgEVFtr30hcbTvGocQEt0dkojGm0BiTAZwF9G5jv57Aul0er/e2JZ1kiIcx5jHvfEOBBw6iGXGVDDEB7jbGLDLG/NYYk3oQzYi7JIkLuB+4b+3yodjpOjk224GAMWaMt/0b+zi+Q3VQTA47BxGXq4Hp3t/t/Ww5bD6DIDliYox5HdiK21n4/AE3ooMowU4Cxpgs4AXg5j0/iKy1MfbzDc27dHIu0B/oAWQaYy5LUHU7xKHGxFq7FPgl8AbwGrAASMpxWu2RLPGw1l6F+xpbClx0oMfHU5LE5Ce4XzbGAgW44w47VZLEZYeLgb8d5LFx19mx8c4xFfitMeZD3AShU9+XOjsmyepA42KMmYybTHb6e0CiJEtMrLWn447DTsW9QpaUNMmxkxljgrgv2KestTvGKW4xxpRaazcZY0pxv6l9kS8Dn1trt3llvgicaIxZDvyvt8/t1tpXEtCEuItTTLDWTgOmeWX+N7Dem6zxD2+XB4GF7N7b0gvYEJ+WxEeyxcNaGzHGPA38EHfSU4dLlpjs0nvT7PXu/+DQWnZokiUu3nFFuJeEzzu0VsVHssTGWjsHmOgdfxow5FDbdrA6MibW2gfj34LEONC4GGOOBh4BzrTWlnubN9DGa8AYczy7fC57+528x37vxrdFhy7ZYmKtbTLGvIzbuTjj0FsYf0qwO5E3lnUasNRa+5tdnnoFd+LLvd7vl/dT1FrgBO/yXCNwCjDPWjsXGB33iidQHGOCMabEWrvVGNMHd0zgCdbaKnaJiTdmfbAxpj/uP/VU4JJ4tedQJUs8vHoMtNau8P7+KlAWl0YeoGSJiffcjg8XB3c4xOJ4tPFgJFNcPN8AXrVtrGbU0ZIpNrscn4rbs3d3PNp4oDo6JoeLA42L1+YXgW/anWPrAT6ijdeAtXYJu79WCoD/Njsn8Z2Ge2UsaSRLTLwe9GzvPTcAnI27kkhScmKxpB0f3uUZYybgvjg+BaLe5v/EHdv0LNAHd5b5hdbaCmNMd2AekOPtXwcMt9bWGGPuwr1kHwY+Aa6x1ja3cc6ZuJe0s4By3CWiXjfG3IjbI9kd91vov6y11ySm5fsW55jMxJ1UFAK+b90Jnm2d8yzgd7irrzxqrb3b297pMUmWeBh39YOZXrkObg/d9Z0xtjZZYuJtfxt3JruDe2n8OtsJSxd6dUmauHjPvQvca619Ld5tPVDJFBtjzH24S9X5gD/bfSxpl2idFJO/4fZMFgFbgDustdOMMefhzukoBqqABd4wgA53EHF5BHei6o7VpsLW2jFeWfv8/9jjnN/yzgFwt7X2MW/7r3C/mPXAXbjgEWvtnfFrbfskS0yMMd2AV3GHhviAd3BXt+rwZS7bQwm2iIiIiEgcaZKjiIiIiEgcKcEWEREREYkjJdgiIiIiInGkBFtEREREJI6UYIuIiIiIxJESbBERERGRONKNZkREugBjzP8BI4Bx1tqWXbafgnsL60nW2tmdVT8RkSOJerBFRLqGG3Bv9nHHjg3GmBzgUeC+eCfXxpiUeJYnItKV6EYzIiJdhDHmy8B0YLy19kNjzDTgOOBc4JfAjrvjzQZuttYu944bCPwGOB7IBixwu7X21V3KXg08jnvXtq8DM6y1FyS+VSIihx/1YIuIdBHW2jeBPwNPGGO+AVwKXAHMAJqAScCXgE3Am8aYDO/QLNzE/FRgFPAC8KIxZugep/g+UAaMYedtjEVEZA/qwRYR6UKMMenAJ8Bg4MdAOfATYIi1Nubt4we2Atdba5/dRzkfAK9aa3/hPV4NfGqt/Uqi2yAicrjTJEcRkS7EWttojLkf+APwa+ABoD9Qa4zZddcMYCCAMSYTd+z2OUApEATSgEV7FD8voZUXEekilGCLiHQ9YSBqrY0aY3zAAmBqG/tVeL/vB84AfgAsBxqAJ4E9JzLWJ6a6IiJdixJsEZGubT5wMbDdWlu1j30mAE9aa18AMMak4fZuL+uYKoqIdC1KsEVEurancHumXzbG3A6sBXrjrizyoLeSyDLgPGPMy0AId7hIWifVV0TksKdVREREujBrbQNwErAKeA53FZAngHyg0tvt+7iTHmfiribygfe3iIgcBK0iIiIiIiISR+rBFhERERGJIyXYIiIiIiJxpARbRERERCSOlGCLiIiIiMSREmwRERERkThSgi0iIiIiEkdKsEVERERE4kgJtoiIiIhIHCnBFhERERGJo/8PR9cNuBxtb80AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 864x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}