KDD_Project.ipynb

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    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "7ivu75srYb6P"
      },
      "source": [
        "# Analyzing the Impact of Skycrapers on Climate Change"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BWnoI4jtpmlO",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 498
        },
        "outputId": "6ccd393a-2a12-41e2-86ab-cc88a304b4ab"
      },
      "source": [
        "from IPython.display import Image\n",
        "from IPython.core.display import HTML \n",
        "Image(url= \"https://v1019.com/wp-content/uploads/sites/51/2018/01/Charlotte-Skyline.jpg\", width=1024, height=478)"
      ],
      "execution_count": 171,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<img src=\"https://v1019.com/wp-content/uploads/sites/51/2018/01/Charlotte-Skyline.jpg\" width=\"1024\" height=\"478\"/>"
            ],
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
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          "metadata": {
            "tags": []
          },
          "execution_count": 171
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CIR0dDhBopLy",
        "colab_type": "text"
      },
      "source": [
        "## Description\n",
        "Project Description:<br>\n",
        "<ul>\n",
        "<li>The project focuses on analyzing the effect of infrastructure modifications from tall building units/skyscrapers on climate change.</li>\n",
        "<li>The project includes a study of the energy usage by the building. Counterfactual models are required to measure the energy consumption done depending on four energy types. The consumption is based on historic usage rates and observed weather.</li>\n",
        "<li>It can be then predicted how far will it affect and result in climate change in later years. A goal is to develop a predictive model for climage change based on energy consumption from skyscrapers.</li>\n",
        "<li>The project has a data of over one thousand building's hourly meter reading for three years.</li>\n",
        "</ul>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "eO_CvYZqYb6R"
      },
      "source": [
        "Team Members:<br>\n",
        "<ul>\n",
        "    <li>Ravina Gaikawad</li>\n",
        "    <li>Sahithi Priya Gutta</li>\n",
        "    <li>Uma Sai Madhuri Jetty</li>\n",
        "    <li>Kevin Thomas</li>\n",
        "</ul>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pAkyCDZSqUMy",
        "colab_type": "text"
      },
      "source": [
        "## Installing Dependancies, Importing Libraries"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "yeILTw6gA7_e",
        "outputId": "76942ad5-64aa-4448-e500-23e4d5c37565",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 193
        }
      },
      "source": [
        "! pip install windrose"
      ],
      "execution_count": 172,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: windrose in /usr/local/lib/python3.6/dist-packages (1.6.7)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.6/dist-packages (from windrose) (1.17.4)\n",
            "Requirement already satisfied: matplotlib in /usr/local/lib/python3.6/dist-packages (from windrose) (3.1.2)\n",
            "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib->windrose) (2.4.5)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib->windrose) (1.1.0)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.6/dist-packages (from matplotlib->windrose) (0.10.0)\n",
            "Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib->windrose) (2.6.1)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.6/dist-packages (from kiwisolver>=1.0.1->matplotlib->windrose) (42.0.2)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.6/dist-packages (from cycler>=0.10->matplotlib->windrose) (1.12.0)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "4iLF_rljczAt",
        "outputId": "38271b12-22a2-46b6-a691-52899e40a8fa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 104
        }
      },
      "source": [
        "! pip install lightgbm"
      ],
      "execution_count": 173,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: lightgbm in /usr/local/lib/python3.6/dist-packages (2.2.3)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.6/dist-packages (from lightgbm) (1.3.3)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.6/dist-packages (from lightgbm) (1.17.4)\n",
            "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.6/dist-packages (from lightgbm) (0.21.3)\n",
            "Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.6/dist-packages (from scikit-learn->lightgbm) (0.14.0)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "taI_7QNpYb6T",
        "colab": {}
      },
      "source": [
        "#Importing Libraries\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "import random\n",
        "import scipy.stats as stats\n",
        "from datetime import datetime\n",
        "import missingno as msno \n",
        "import seaborn as sns\n",
        "from windrose import WindroseAxes"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "dLftiT-iYb6R"
      },
      "source": [
        "## Importing Data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "63SygZAOqcip",
        "colab_type": "text"
      },
      "source": [
        "This notebook was developed using Google's online IPython application: Colaboratory.<br>\n",
        "The notebook can be run using Jupyter Notebooks as well."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xDhftjDvq4b6",
        "colab_type": "text"
      },
      "source": [
        "### Google Colab Implementation"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "H53wDlDaZDw6"
      },
      "source": [
        "Run the following cells if the notebook is being run on Google Colab"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "SciEosK6Ynu7",
        "outputId": "4d17861c-aa93-4d35-a434-fe07dfcb10c4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": 175,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qZ4TdiVyrFHk",
        "colab_type": "text"
      },
      "source": [
        "Change the following path to where the data is located."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "46UpshFiYyfT",
        "colab": {}
      },
      "source": [
        "PATH=\"/content/drive/My Drive/KDD Project/Data\""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7f8WilygrU4H",
        "colab_type": "text"
      },
      "source": [
        "### Jupyter Notebook Implementation"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "gkcIsJ60Y5i7"
      },
      "source": [
        "Run the following cell if the notebook is being run on local machine. Data should be placed in a folder called data in the same directory as the IPython file."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "ZiM9r993ZJgK",
        "colab": {}
      },
      "source": [
        "PATH=\"./Data\""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "hSYO6qgiYb6V",
        "colab": {}
      },
      "source": [
        "train_data = pd.read_csv(PATH+\"/train.csv\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "nk8TwHekYb6X",
        "colab": {}
      },
      "source": [
        "meta_data = pd.read_csv(PATH+\"/building_metadata.csv\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "j6kvprD4Yb6Z",
        "colab": {}
      },
      "source": [
        "weather_train_data = pd.read_csv(PATH+\"/weather_train.csv\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "VdToeoAVYb6d"
      },
      "source": [
        "## Preparing and Visualising the Data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "Xng2jNsYARYx"
      },
      "source": [
        "### Train Data\n",
        "This table consists energy usage of buildings.\n",
        "\n",
        "<ul>\n",
        "  <li>building_id - Foreign key for the building metadata.\n",
        "  <li>meter - The meter id code. Read as {0: electricity, 1: chilledwater, 2: steam, 3: hotwater}. Not every building has all meter types.\n",
        "  <li>timestamp - When the measurement was taken\n",
        "  <li>meter_reading - The target variable. Energy consumption in kWh (or equivalent). Note that this is real data with measurement error, which we expect will impose a baseline level of modeling error. UPDATE: as discussed here, the site 0 electric meter readings are in kBTU.\n",
        "</ul>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "DtRkBQpDYb6d",
        "outputId": "9b47313c-3f01-4092-b4ca-992e06b1dcb6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 288
        }
      },
      "source": [
        "train_data.describe()"
      ],
      "execution_count": 183,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>building_id</th>\n",
              "      <th>meter</th>\n",
              "      <th>meter_reading</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>2.021610e+07</td>\n",
              "      <td>2.021610e+07</td>\n",
              "      <td>2.021610e+07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>7.992780e+02</td>\n",
              "      <td>6.624412e-01</td>\n",
              "      <td>2.117121e+03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>4.269133e+02</td>\n",
              "      <td>9.309921e-01</td>\n",
              "      <td>1.532356e+05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>3.930000e+02</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>1.830000e+01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>8.950000e+02</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>7.877500e+01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>1.179000e+03</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>2.679840e+02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>1.448000e+03</td>\n",
              "      <td>3.000000e+00</td>\n",
              "      <td>2.190470e+07</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        building_id         meter  meter_reading\n",
              "count  2.021610e+07  2.021610e+07   2.021610e+07\n",
              "mean   7.992780e+02  6.624412e-01   2.117121e+03\n",
              "std    4.269133e+02  9.309921e-01   1.532356e+05\n",
              "min    0.000000e+00  0.000000e+00   0.000000e+00\n",
              "25%    3.930000e+02  0.000000e+00   1.830000e+01\n",
              "50%    8.950000e+02  0.000000e+00   7.877500e+01\n",
              "75%    1.179000e+03  1.000000e+00   2.679840e+02\n",
              "max    1.448000e+03  3.000000e+00   2.190470e+07"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 183
        }
      ]
    },
    {
      "cell_type": "code",
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        "colab_type": "code",
        "id": "LWrntvQcYb6g",
        "outputId": "08455035-9d0f-43d0-87af-b794b45b33be",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 173
        }
      },
      "source": [
        "train_data.info()"
      ],
      "execution_count": 184,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 20216100 entries, 0 to 20216099\n",
            "Data columns (total 4 columns):\n",
            "building_id      int64\n",
            "meter            int64\n",
            "timestamp        object\n",
            "meter_reading    float64\n",
            "dtypes: float64(1), int64(2), object(1)\n",
            "memory usage: 616.9+ MB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "4b-3rF5ZYb6i",
        "outputId": "7e3a8763-4662-4f62-ee83-388f0b135960",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 198
        }
      },
      "source": [
        "train_data.head()"
      ],
      "execution_count": 185,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>building_id</th>\n",
              "      <th>meter</th>\n",
              "      <th>timestamp</th>\n",
              "      <th>meter_reading</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2016-01-01 00:00:00</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2016-01-01 00:00:00</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>2016-01-01 00:00:00</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>2016-01-01 00:00:00</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>2016-01-01 00:00:00</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   building_id  meter            timestamp  meter_reading\n",
              "0            0      0  2016-01-01 00:00:00            0.0\n",
              "1            1      0  2016-01-01 00:00:00            0.0\n",
              "2            2      0  2016-01-01 00:00:00            0.0\n",
              "3            3      0  2016-01-01 00:00:00            0.0\n",
              "4            4      0  2016-01-01 00:00:00            0.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 185
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NlWlFZPBmAQ8",
        "colab_type": "text"
      },
      "source": [
        "Visualizing Meter Distribution"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "4Ppx4EhiM6gS",
        "outputId": "2e082251-0274-4e18-9271-68a75a8ed60b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 458
        }
      },
      "source": [
        "plt.rcParams['figure.figsize'] = (14, 7)\n",
        "sns.countplot(data = train_data, x ='meter', palette = 'Set1', alpha = 0.5)\n",
        "plt.title(\"Meter Count\")\n",
        "plt.xlabel(\"Meter\")\n",
        "plt.ylabel(\"Count\")\n",
        "plt.xticks(ticks = [0, 1, 2, 3], labels = [\"Electricity\", \"Chilled Water\", \"Steam\", \"Hot Water\"])\n",
        "plt.show()"
      ],
      "execution_count": 186,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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rqh63yMIAAAB2t7XeBOE5SR7Y3U/p7icneVCS39zZSlX16Kq6oqqurKpn76DPE6rq8qq6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\nAACGIQABAADDEIAAAIBhCEAAAMAw/j/e9yO0PegthwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 1008x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "XIGoqTkfAVjx"
      },
      "source": [
        "### Building Meta-Data\n",
        "<ul>\n",
        "  <li>site_id - Foreign key for the weather files.\n",
        "  <li>building_id - Foreign key for training.csv\n",
        "  <li>primary_use - Indicator of the primary category of activities for the building based on EnergyStar property type definitions\n",
        "  <li>square_feet - Gross floor area of the building\n",
        "  <li>year_built - Year building was opened\n",
        "  <li>floor_count - Number of floors of the building\n",
        "</ul>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "e_dH-zwrYb6m",
        "outputId": "7b7afa07-54b9-42ca-853b-68ab9685410e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 207
        }
      },
      "source": [
        "meta_data.info()"
      ],
      "execution_count": 187,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 1449 entries, 0 to 1448\n",
            "Data columns (total 6 columns):\n",
            "site_id        1449 non-null int64\n",
            "building_id    1449 non-null int64\n",
            "primary_use    1449 non-null object\n",
            "square_feet    1449 non-null int64\n",
            "year_built     675 non-null float64\n",
            "floor_count    355 non-null float64\n",
            "dtypes: float64(2), int64(3), object(1)\n",
            "memory usage: 68.0+ KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "fPM8SuA1Yb6o",
        "outputId": "09c971d8-f2a0-4c4d-e15c-fef0f0cfa296",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 198
        }
      },
      "source": [
        "meta_data.head()"
      ],
      "execution_count": 188,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>site_id</th>\n",
              "      <th>building_id</th>\n",
              "      <th>primary_use</th>\n",
              "      <th>square_feet</th>\n",
              "      <th>year_built</th>\n",
              "      <th>floor_count</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Education</td>\n",
              "      <td>7432</td>\n",
              "      <td>2008.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>Education</td>\n",
              "      <td>2720</td>\n",
              "      <td>2004.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>Education</td>\n",
              "      <td>5376</td>\n",
              "      <td>1991.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Education</td>\n",
              "      <td>23685</td>\n",
              "      <td>2002.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>Education</td>\n",
              "      <td>116607</td>\n",
              "      <td>1975.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   site_id  building_id primary_use  square_feet  year_built  floor_count\n",
              "0        0            0   Education         7432      2008.0          NaN\n",
              "1        0            1   Education         2720      2004.0          NaN\n",
              "2        0            2   Education         5376      1991.0          NaN\n",
              "3        0            3   Education        23685      2002.0          NaN\n",
              "4        0            4   Education       116607      1975.0          NaN"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 188
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "c0_y52FnmJbn",
        "colab_type": "text"
      },
      "source": [
        "Visualizing primary use of buildings"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "bfz4hHg_Yb6q",
        "outputId": "d697b60e-253c-4cdc-fa9a-05c4bf947a03",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 458
        }
      },
      "source": [
        "plt.figure(figsize=(12, 7))\n",
        "sns.countplot(data = meta_data, y ='primary_use', palette = 'Set1', alpha = 0.5)\n",
        "# meta_data.primary_use.value_counts().plot(kind='barh', color='#0504aa',alpha=0.7, width = 0.5)\n",
        "plt.title(\"Primary Use Count\")\n",
        "plt.xlabel(\"Count\")\n",
        "plt.ylabel(\"Primary Use\")\n",
        "plt.show()"
      ],
      "execution_count": 189,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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XF2KM64BnJJnaPs9c2CAlSZKkiWTCpfFyGrBCkl8CB9NbVjia99NbnvjpsQxQ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\nSZIkSR35/xh6xiafnpbMAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 864x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "Uf0fpmhMYb6s"
      },
      "source": [
        "Due to the large amount of missing values in the \"year_built\" and \"floor_count\" columns in the building metadata table, we dropped these two columns. The use of square_feet enables us to ignore floor_count as they both represent the size of a building. While the age of a building will impact its power consumption, the sheer number of missing values in the dataset coupled with the inability to impute the values has prompted the dropping of this column."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "oyBkim5mYb6s",
        "colab": {}
      },
      "source": [
        "meta_data = meta_data.drop(['year_built', 'floor_count'], axis=1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "giheMZSEYb6u",
        "outputId": "7eb97787-a113-442e-b266-6623cf229e29",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 173
        }
      },
      "source": [
        "meta_data.info()"
      ],
      "execution_count": 191,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 1449 entries, 0 to 1448\n",
            "Data columns (total 4 columns):\n",
            "site_id        1449 non-null int64\n",
            "building_id    1449 non-null int64\n",
            "primary_use    1449 non-null object\n",
            "square_feet    1449 non-null int64\n",
            "dtypes: int64(3), object(1)\n",
            "memory usage: 45.4+ KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "xpwlOrri4qcT",
        "outputId": "427928bc-7cba-4da3-bc93-7214f9a19a6c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 288
        }
      },
      "source": [
        "meta_data.describe()"
      ],
      "execution_count": 192,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>site_id</th>\n",
              "      <th>building_id</th>\n",
              "      <th>square_feet</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>1449.000000</td>\n",
              "      <td>1449.000000</td>\n",
              "      <td>1449.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>6.952381</td>\n",
              "      <td>724.000000</td>\n",
              "      <td>92111.776398</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>5.003432</td>\n",
              "      <td>418.434583</td>\n",
              "      <td>110769.950997</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>283.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>3.000000</td>\n",
              "      <td>362.000000</td>\n",
              "      <td>23012.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>5.000000</td>\n",
              "      <td>724.000000</td>\n",
              "      <td>57673.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>13.000000</td>\n",
              "      <td>1086.000000</td>\n",
              "      <td>115676.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>15.000000</td>\n",
              "      <td>1448.000000</td>\n",
              "      <td>875000.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "           site_id  building_id    square_feet\n",
              "count  1449.000000  1449.000000    1449.000000\n",
              "mean      6.952381   724.000000   92111.776398\n",
              "std       5.003432   418.434583  110769.950997\n",
              "min       0.000000     0.000000     283.000000\n",
              "25%       3.000000   362.000000   23012.000000\n",
              "50%       5.000000   724.000000   57673.000000\n",
              "75%      13.000000  1086.000000  115676.000000\n",
              "max      15.000000  1448.000000  875000.000000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 192
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "kI42B2t-Aay0"
      },
      "source": [
        "### Weather Data\n",
        "<ul>\n",
        "<li>site_id\n",
        "<li>air_temperature - Degrees Celsius\n",
        "<li>cloud_coverage - Portion of the sky covered in clouds, in oktas\n",
        "<li>dew_temperature - Degrees Celsius\n",
        "<li>precip_depth_1_hr - Millimeters\n",
        "<li>sea_level_pressure - Millibar/hectopascals\n",
        "<li>wind_direction - Compass direction (0-360)\n",
        "<li>wind_speed - Meters per second"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "UKjoP4weYb6w",
        "outputId": "48061a98-2367-4f36-cff3-c6f6dfe7e3cc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 259
        }
      },
      "source": [
        "weather_train_data.info()"
      ],
      "execution_count": 193,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 139773 entries, 0 to 139772\n",
            "Data columns (total 9 columns):\n",
            "site_id               139773 non-null int64\n",
            "timestamp             139773 non-null object\n",
            "air_temperature       139718 non-null float64\n",
            "cloud_coverage        70600 non-null float64\n",
            "dew_temperature       139660 non-null float64\n",
            "precip_depth_1_hr     89484 non-null float64\n",
            "sea_level_pressure    129155 non-null float64\n",
            "wind_direction        133505 non-null float64\n",
            "wind_speed            139469 non-null float64\n",
            "dtypes: float64(7), int64(1), object(1)\n",
            "memory usage: 9.6+ MB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "cjW1LbMjYb6y",
        "outputId": "59027a27-d0fa-492a-8167-87b2d54bb5f2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 285
        }
      },
      "source": [
        "weather_train_data.head()"
      ],
      "execution_count": 194,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>site_id</th>\n",
              "      <th>timestamp</th>\n",
              "      <th>air_temperature</th>\n",
              "      <th>cloud_coverage</th>\n",
              "      <th>dew_temperature</th>\n",
              "      <th>precip_depth_1_hr</th>\n",
              "      <th>sea_level_pressure</th>\n",
              "      <th>wind_direction</th>\n",
              "      <th>wind_speed</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>2016-01-01 00:00:00</td>\n",
              "      <td>25.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>20.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1019.7</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0</td>\n",
              "      <td>2016-01-01 01:00:00</td>\n",
              "      <td>24.4</td>\n",
              "      <td>NaN</td>\n",
              "      <td>21.1</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>1020.2</td>\n",
              "      <td>70.0</td>\n",
              "      <td>1.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0</td>\n",
              "      <td>2016-01-01 02:00:00</td>\n",
              "      <td>22.8</td>\n",
              "      <td>2.0</td>\n",
              "      <td>21.1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1020.2</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0</td>\n",
              "      <td>2016-01-01 03:00:00</td>\n",
              "      <td>21.1</td>\n",
              "      <td>2.0</td>\n",
              "      <td>20.6</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1020.1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>2016-01-01 04:00:00</td>\n",
              "      <td>20.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>20.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>1020.0</td>\n",
              "      <td>250.0</td>\n",
              "      <td>2.6</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   site_id            timestamp  ...  wind_direction  wind_speed\n",
              "0        0  2016-01-01 00:00:00  ...             0.0         0.0\n",
              "1        0  2016-01-01 01:00:00  ...            70.0         1.5\n",
              "2        0  2016-01-01 02:00:00  ...             0.0         0.0\n",
              "3        0  2016-01-01 03:00:00  ...             0.0         0.0\n",
              "4        0  2016-01-01 04:00:00  ...           250.0         2.6\n",
              "\n",
              "[5 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 194
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "uF8_VWOnYb6z",
        "outputId": "e615546d-8b3b-47f8-e313-a4fb09402252",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 259
        }
      },
      "source": [
        "weather_train_data.info()"
      ],
      "execution_count": 195,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 139773 entries, 0 to 139772\n",
            "Data columns (total 9 columns):\n",
            "site_id               139773 non-null int64\n",
            "timestamp             139773 non-null object\n",
            "air_temperature       139718 non-null float64\n",
            "cloud_coverage        70600 non-null float64\n",
            "dew_temperature       139660 non-null float64\n",
            "precip_depth_1_hr     89484 non-null float64\n",
            "sea_level_pressure    129155 non-null float64\n",
            "wind_direction        133505 non-null float64\n",
            "wind_speed            139469 non-null float64\n",
            "dtypes: float64(7), int64(1), object(1)\n",
            "memory usage: 9.6+ MB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Mhlr56LEmO4j",
        "colab_type": "text"
      },
      "source": [
        "Visualizing wind speed and direction using WindRose"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "fXc4dO-fBArB",
        "outputId": "45b2e368-35d4-431e-ebcf-ac9c3a6f0435",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 603
        }
      },
      "source": [
        "plt.figure(figsize=(8, 8))\n",
        "ax = WindroseAxes.from_ax()\n",
        "bins_range = np.arange(1,15,1)\n",
        "ax.bar(weather_train_data.wind_direction, weather_train_data.wind_speed, opening=0.8, edgecolor='white', bins=bins_range)\n",
        "ax.set_legend(loc=1)\n",
        "plt.show()"
      ],
      "execution_count": 196,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x576 with 0 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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V+9Dr9UiShMFgQJKkWt9XVVVx+PBhZDIZcrm8zlcLCwvjw9LSstb3VlZWWFtb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\nAS64uNS9NLnqVbPPB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N596LSOtZDvS4DFy+t3O+l1MHIw/P3ahsdrRJmYwWd4Yf5uXlyw\nG71WZvnKwSxYehWf/NCPb9cH4O5e9+YcEKDBYGgeGb20zdzdTcvwGf7EzOrNxz9MwCyP4MaBWr68\neh8/TdnBnVEZ6Btppx/ZDWYPqU9Kq8zw6V742f41mrOlMPw/kFsAx0coPCapaal3ddEXyI0NfHkW\n3joN70nBdKIuuwxAgw8aZAS0aBAdqC42WlRyY7RAhcU+uanBGm0vJkpZeCt1ayAkRS04niJ7XWLI\nYBuHMXEcE3cw5pL1mVRiYQgzKOIMBZxGUWRkofVrLgRBICQkpM1vns5GTTdVQ0hKSkIQBKKiouzu\n4+npWatBU1xc3Kx7UZcuXQgICEAQBIKDgykubnro3M3NDaPR2KaFxm1OcCoqKqioqGhzzwpnoqCg\noF0RHEGSqsX9DCCoN5Aq9iLijw4fTmND0ATozij2kg5AyFkXsoMdu3jNYBBpmMlwknLxHMmb97Ko\n5xjuLFwtZRDaKYiEXFe2OdCt8+0h1cBy5T5VYdeWm/elcNVDV1848DjMj2l8/xp09YXtD8IPG+DB\n5wRW/RJF+pmmkZx/vacWBJsuIWQuevjgFfBwrz/G3dXMjKtO8O6zfxAdlcc3v0by4MsT+W5TBLnn\nLygGVlTC8bS6Y61WCA9vehWzr68GL6+6ZKCkTE2jFZfAd+vg7/e7MmhaT1b8MAFPjwH8c2QpP17z\nB6+MTqaDvn7SRSPCZ7Pre01ZJcgqhofXND6vkioY/5ZCwoHDXN8RvnZtmNzU4KkTAvtKYK3UpXbd\nVDz4g1A8EHGn8SicLKuF7j8nQY8lEPEyeGvgWftepQBUCTo2aMK43ppWb9vqXCi2wAs4ViP4X4qI\npDPiRbcLBYXNHKEHaudrEpuQBREB/WVpGQ8ODm6SB9dfAY5EcA4ePEhKSgrXXHMNX331FcuWLSMh\noW7+e+zYsbW1MevWrbPpJt4YLs0+2CIpJ0+epG/fvnaPIYoiWq22TRWN25zgJCYmEh4e3tbTcBoU\nRaGsrAwvr0Zc/i4TVO+p6vSUJQfFaxJV7AKsdOAeKim2I+4n4kkg+8lAkMGvUEe+n2Nf1N50ZbWT\niov7oCdI1PDhmdZJT+kVKzdI6YyM6sXjaxvfvwalRvjHd+rT/dFcx6I5GlFNWf37etjwQOOaOR4u\ncOAR2HMA7nqs5r7RdJKjKHDTA2pX1cXXKUEAdzdY+sSFdQa9lZmTU1j+5HZcDVaeejOGt78aREqG\nL/b6mDdth4uFWE0mmcjIphOcyEg9VVUXLvCKoraiX4oqI3y7Dibe4s2NC/qTkTee6f21fDMpjuXj\njuCpvfBhPDQOQjvU72iTFLh+hW3/rkshonBXj8NsSgbRP5ojIwU8HLhyysCNCQKCVcOXcifeJIA3\nCcQVsTY11RAqzZBZDJPfh5tXwrkyKKiAp9bBE13q2oTYwnptN0ZKOXRQ6hI/SYElJwWmSl4O3QA2\nUUEVCjOo61q+nRRccSeYPmRwoPo1G1EuQ7Gxp6cnJpOpVbp0TIDRiYujM6yRFKkhOT/99BOxsbFk\nZWURGxvL4cOHufPOO4mLi2Pjxo3MmTOHRYsWMXDgQBISEli+fDkACxcuZOXKlcTExODr62uT4Nx+\n++1Nfl8uxYYNG7jxxhsb3MfV1bVN9XAEpY0b1d9++23S09N5880323IaTkNJSQlHjx61W+F+uaHI\nMoIiqVQ2/yNkv1nka16ljG/pzBbSSGcPq+qN60UMI7mT+/kcvzwt3U67sndo46SlEx14hRsZQQa5\nTnAO/1IJhiJXJhxoHYIzzZLGgiAjkltfrnm/eccQBZg3Gl6frt5IHanPMVqgzKT6Vm2v/5ANQMrT\nYCqGEdPrG1uCwuzrkuneuYRlK4ZhsTYeDbh2Ivz4Uf0WcYsFhk2VCfbPYNqEk+w40JmftvSkosqx\nupBpk2DVO+BRTdhMJplXXy1gyZLzNvdfsSKde++tH4K4554OvPlmAB4e6mupqISFr8B7DZcLAODb\nAR6bZ+X6q05x+nQGuwtC+Sq1J8nPaup9HkYLPLQGPt7d+HFFFOZHVaelkqNxNwjsehg8vKDnbhwS\nQJgbAh/1UZ28DQ5QCrNVbZx/cSO8vkX9/8XQipC2GHYaYc6Rho811XoSf7mKz/R1n7T1Apwdr/C5\nrpDlDpjg/hMf7sKdR/iyzvqHuZpOmPmFlxjNHYQzEhEB8TIUGyclJakaM6GhTR4rSRKpqamEh4ej\nqS5Qq9HBybdRs9JSdPT1JdOOo3gN4uPjueeee7j11lt55plnnD4HZ8JisTBz5kzWrFnToNZbbm4u\nVqu1TobG1nvfWmjzCM7BgwcZPHhwW0/DaWhv9TeCKIJsAdkMVYkIog+V1YrFGrqQSaLNceHEkMQZ\nJBQ65bo4rH0zjYGkYHYKuQEYLLvyXlbrkButIjFdPsWwyJ48+2vzjyMr8MFO6P4irP1TvSGifcI2\nAAAgAElEQVQ1Vit4sWbOK9fVjzD8fC/46OHqObbIDTQnkrN+C3z0NVRdcrzi4kKW/nMH3UJKeeqN\nGL7+NcphcgOwc7/amVUDFxeR6GhHKjzqondvPYaLwhJ6HcTZ1p6sh8JieOY1LeNu6cWpvHHM7G/l\n45htFJzPq7Of0aKqIjeH3MgIlBlVp3LRDPsc8AX+Zyj8N0pNYzZGbhRFnd/2NIh8GV7ZXJ/cgOp0\n/sgPcENH8G/kY9qs6cpIOQcPpW6I0azA6xkwR3JMx2A1pXTAhV7U9db7g2P4EYaIlhS2I6C/bMrG\nQUFBTlU1NhgMZGZnU1JS4vSlMXIDMGLECA4dOtQk7Zq2gk6n48cff2xUyNbd3b1NIzhtTnCSk5OJ\njo5ufMe/CNobwcFiVgXPRAMY+mAlG4lcXJmIiJZcUmwO60BXDpABCgT9P/beOzyqMn//f50zvaR3\nahJKEjooWBAUFxW7a3eta2+71l3bx+7q2nd1xa6svayiYqNjQKoC0hJ6gJDeJ9PnnPP740kbMklm\nwiRBvr/bay515rTMnPM89/Mu911hDNs5PJeBfBml4uLLiEXS4Juumz26heOVYtJTUvitzMwv7WVD\nIkaFAy75r0gp7KkVKYbOIMui5fz2E2D1XaLeBuDOaXByLpx2BZRVdHaEyEnOXU9A0X5h5RAIBNiw\nYQObNv3GuHGj8WjjqG2InJjU1MGe4uD3Ro6M3HRz/Hgzen0rmfX5YVPo27NDVNXALf9n5J0vRzJp\n0kR2bi/k119/xefzoaqinuaKD7o+Tihy04xqpyA5Q03wYQclCMkGmHcEPDVMONB3Jfro9kFVo7h/\nTp4JRV0EEWZvgO0V8N7IzrfzSnoW6gZxWmB3u89eL5aIkyROJUQR1gEoR2EpLs5nYtD7m9iPD4Ux\nnEoVRbioRdMCvaJsnJSURG1tbVSduM1mM7GxsVF/dUVumnG4KRpbrdY+Ffvrc4Kzb98+cnJy+voy\nooa6urqwPIx6A5qmgaEpH+HagBY7DSeLAIjlAirZiRKiEDiODCyY2UgxMQ4dHpOK39j1iiwOC/GY\nmEt0GPsVahyflAn9jmhD0jTOVXZyVM5QHv0husdesl0Ugz4xV9R4hFqFt4XFCKMyYPP9Qm/nidPg\n1gfhlw3hnC0ykuPzwTnXQm1tPfn5+RiNRo4//nj6ZSTx4sOQ1M1bd15+sD3EoEGGdirHXSEvL5gU\nrVjbvUCA2QR33wAxMbFMmTKFhIQE8vPzKSurYnNZF6addE5umrG7Gk5/Hc5PFVGatjgxEQonw9QE\nYcjaGQKKKFR/dRlkPSoigOHi7q9hWgKkd1Hu9L0+mz8oezEe4Fpe44ePS+EuNSms8/2XegaTGjRp\naGgsZRvDmnyrClmCkD7qeV8lWZZJSkqiurq6x8/VW5BlGVVV+9ziIFpoTkFFk4RGgj4lONXV1djt\ndgyG8MPhhzL8fj+aph06f4+mQcALqhcavkMzZ+KWhGmRkSM77J4ayUmUUY8DD2mVRspTw4venMoY\nSglEpXvKDGRh5L8lPZOeGqtWkhpro8pnZ+G26B/fr8BT80WqYckOQXQ6G7OMerAZ4dFTwWKCjyIo\neI6M5GgMG7CL+QvWkDdiLLm5uS1hZoMOXnw4kvMK6PUQYw82+NTpYPDg8J8Ds1kiNbWVDbjconi5\nO3jgL5AYJ4qoJUkiOzubMWMnsXb9RhLdBWz4u4a1A1IQDrlpxs+74JbP4Z9D4MgYYbz69DD4YQIk\nGsDUVdTGD+v2w4Rn4K6vaBGXDBfzC6GgDN4e0fl2DsnIWjmVKUpxu8/+tVciG0O7VvZQWIKLAHAy\nwaKsP7MdO0mYsbOTlegwo6EelEN6uEhJSaGyMvIQb3OX0KFGJCRJaiE5hwuMRmNQFKf5O+8Nnbg+\nJTjbt29ncHOBmK9ROF0fYjdcJGhoaCAuLq6vL6MVYoQHSQeSGYkY3KwC9OhJooSCkLtlMIq1CInb\n1EojFSnhjbxjyeRbomMUdT0JOALwcw8pF58e2MURQ7P454KeOX4zimrglJlw0btQ2dh1t5VOJ7qE\nNi2AMR1LXYRA1yRHr1O59U/rGZdXwQ2PTGXFuqQgKwezGS46EyZPbLdrO9jt8OTfYftPKo4ClQtP\n1zC3CcC43Vq7iExnGD7cSNvIvCyHX3/TFsOy4O83QVv9N58PdhfHcsFtU/j0GzdlW1ex8z4/ww7o\nko6E3DTjrRXw+TpYOAHWHQ1/GSRSUp2N3W5/cBfepoPodn7gOxHFie9CsvU7fZZIUx0wvq53CEf0\nB7Su7RsCwJc4OJ7gG3MvNdThYhxn4aCSekpB01B7IU3VXYIjyzJms5n9+/fj8XhQFOWQeUmShN/v\n7/PriNbLYDDgcrlQFIVAIEB1dTUGg6FXjKj7VMl427ZtZDfLbfud4KqClJHg2QrGwSCbUTU3SBoS\nZqS+z6h1irq6ukOK4EiSBLJeCPyl340EpPEvNBR0GKghdOGJjSQ2sxZJhbh6PbXxXQ9UMpCInYVE\nR5vij2oMH5USFSXkA5GuOhmhd5GYmMLHoR0qoo6vN8L8rfDYaaJ1GTq2fLCYYWAGrJkjamb+Myvc\nswiSc+kZBdx77eqg7qoYm5d7rlnD9j0JzPx4HKom8ee7oXAxpBlbTTP1Opj1POSdCKFKAc6YDo/f\npZE7RKOmRuGDD+qZPbuR1avd7N8/jPR0MaTIsmj77sSqJgi5uUa8XhWjUVyvLAntnkjx9rPBzeya\nJmp5zr8RPF49/3xzPDv37uBS5zJ+vvkozv/ASv7O7pGbZizeAZdPghwDGDoZohRVFKR/uhbumi3E\nIg8WP2yBfbXwSi5c2klHVbEcS6NkIE+toUAXnJJ6aa/Es7l20IWSiwjGFzj4EwOwYMRNK1tfxjam\nMYmVfMR2fma8dAa6Hnl6g2G1WlEUBa/Xi8kUWd3X4MGDqaiooKio6JCK5AQCgUMrE3CQqKuro7Ky\nsmVuNBgM3ep86w76lOAUFBQwfOgQCPhg/RuQNgEMu2DnWYCENmIjfrOOBj7GSA4mRmIgCx2xaPgJ\nEECHETmM8GpvoL6+nv79+/f1ZQRDkkHf6oll5US0JmWGK3mdSnZRzEZK2Uolu0glGz06tlFOYq2B\nuvhAWOrFR5CFBPwaBe8pIzAAA5+V90wIc7qyh2HZg3lrhRSWDkq04PLB3V/Bu6vg8z9DZoJIR4VC\ns3TRM/fDmdPhT3+B6q67eQlFcmLtPh66aQVzFg9hwcrBLVs2OOCP18HS/7USHFmG/ulwx7Xw7Gut\nR735SnjgFpXkRIkvvmjg1htq+fnn4N960SInF18ciyxLWCwSo0eHP+Hk5ZkwGlt/77WbQhOsznDx\nWTBxLLSdF3x+uPx2KG7h3RKfzx3GnhI7fwss53+XTuLaL2MYrERObmxGeO0iuKCpR6IzcuPxw95a\noYK9on2970Hh8bkw80KxyOgssfGjPouTlaJ2BOezMnglD07Fxg9d1M+tx0stCqczhv/R6qy+mt2c\nyXiMWCniF47kfIR1g9Z5OCsKSE5OpqqqKuKxV5Zl0tPTSUtLQ9O0Q4bkOBwONm/ezNFHH93XlxIV\nfPHFF/zwww+89dZbLSm43kKfhkQKNm9mZF6eqBGp2QaJ2eBublvW0PRxNEifUM87VHIPxZzBbkay\ni1E4mY8BCxuZy34246QWDRWFAH48IYtnexr19fWHVAQnFCQk5CaRdj1GMshlPGdzGn/nz7zJ6dyP\nDpnhpJFRZaYiObz01GSGsQZPVL71K4jDrcCKHkhPyZrKiep+RmYN5LWfo3/8cLC5FOpdIoLT0Nje\n8bstLGaYehRsXQLTwpZWak1XPXTTCh69dTkffZcbRG6asWodPPJisJWDxQyP3QUD+8ElZ0PpLyrP\n3qcy650aBgzYzp/+VNKO3AAsXOjE5WrNr0fSKj5unAlTU9GKxws/LAl7VwBiY+CVJwhOk3mEq/pX\nc9tvv3pjBg+/PJ7FS1Zxz+hVDE6MjNyMHwBbHoDzxgkrjo7Qoh6twhHPRp/cgIgI+QLwYBfqxmvk\ndEaq1Vi14KfUocDXFXCzFl6F+WwcTCTYKb2YWhpwM5pTqacMJzVN3VQ9X1za3TRVM5onXZ1Od0i8\nYmNjaWxsPKSu6WBeY8aMYdOmTeh0ul4lN9DHBGfb9u2MOeIIkA2C4FgTwdOm4lMfi7/JKqAtVOqR\nMLGHtazmE77naT7iNt7lOmbzEPm8xTq+YTdrqKcMhQAaKgG8+PCh9UDoVFEU/H5/2O2AhxL0GNE3\nRcJkZPwo3MbJHFc9iEuSpnIpRzOBwdjpeEU+iDQWRKl76o9aDF9V9Ex6arxaQXx8PJvKjezoofbz\nrnDiMDhiIBxxOmQdCx9/I8T2Omo0MJuEkN2P78M/722N7nQOifkrBpHZvwG3R8faLWkdbvnPmbBu\nU7CVgyzBjnyVWc9rfPBeLQMGbOeBByqprOx4wlq61B3kQZWdHb6a8ahRrc+NqkZef/PUPYKYNSMQ\ngOIyuK2TounCXYls2GqnrKyKy6ZlcXJe1+RGkuCOabDyLugfB5YOsgiaJmp/5v4EmcfC/jJ477LI\n/qZw4VfgpZ/g+i58zgKSzApdP45T2ns4vbNfYrhqCmtC+I5GkrBjPCABsIpdZCLEgXazGk2S0Iiw\ncrobSExMDGko+XtFc32Q+0DBqt8phg4dyr59+/rk3H1GcFRVpbq6mn79B4DeDLU7wGAHb7Osqx4J\nG35CL3l0ZFJD8Jem4KeWYnaxmnV8zQJe5jP+zjtcwyfcTQW7MGIkn20UUdWSQ/YRwIMf5SBaGxsb\nG1scXH/vMKDDqOlwOBwMjR3AdEZwI9N4hct5gYu5mikcwxASm/QzjMjEYWJ5FNJTAFmqidkVPRPW\nPjGwjzGZg3h1WY8cPiy8cwl8MBs2FAgNmavvhqkXwI6i9qaYzZAkIXz316tFbU7mwNDbNSPO7uXB\nG1bx4nsTWF+Y2ml3larC+TcJgtMcpTcawWiUufPOcv72twpqa7t+NrZv99HY2LpdTIxMSkrX6WNZ\nFm3lzTAZYeXaLndrwYTRcN0lwQRHUUU7vKcDCQ5Z0rjlT+toaDRx3wvHkr/0F96/0MmETizxUuww\n/2b4xxlg1HWsbeNyQ0k5nH0tnH2N0Ai68X44LQ+GdF3L2y28sRxSTaI1vTMs1A3ixED72rsFNRDQ\n4Aq6tpj5DS9ONE4iWIRnLXuwkwLI7GU9Ygw39XjjiNlsRlXVPvU8ijZiY2NpaIiO3U1fQ6/XY7fb\n+6Sdv88ITklJSYtbKX6nWK7rbODdKTawiVYOP+1bGwEkEqkP6aEUChqNVKGhspKdvE0+DzGbG/gv\nN/Mez/A9H7KCeWymgBLqcKGhEUDBgx9/GKq8DocDu93e5Xa/FzQ0NBATEyPCpOgwY0BCIpkYpjKc\nPzOFF7iEl7mMf3ABBiQCUYi5HIcFiwQLe2BBZtb8jJVq6Z+WwhehBZx7HH8+CtJi4P5ngt9fuRZG\nTof7nxYkpyNtLIsZRgyHzQtEx1MomI0BHrhxJV8sGMbaLelhtZCXlsPCZeDxtPWC0njggWQslvDJ\n5tKlrpZaBpcrPE+qwYMNtFVs37ytY6J3IGQZ3n2eIM0djxduewS2dND+30xuAF75aDzb9iTy5Otj\nyV+6kh+v9ZAeYo6fngOFD8Dk7I6jNn6/qPl54U0YOgV+XNL62YKlsPwX+OTgLYBCoqwB5hbCE0M6\n326vHIsZhVQ1ONqqaPB5GVysdp1i14AfaeSoA9JU2xGqwkOYRBnCtVbF0+M1OHD4RXHaOoIfDsjM\nzGT79jCcjKOMPiM427Zta20RbyiGgVNA9UCgSbrVdgwK1YjmxPbQYe3AJLJjWElh/wG+K4142UY5\nP7GVj1nJU3zHX/mQa3mXh/mKt8nnW9azliLKaUBBRUXFgx8vgZZ01+EUwYHOFZllZMwYkJGIw0Iq\nMbhQWcAgNpPFu2RwNXGMxBjxDfYnYlndAK4eSN1PUspISklj7lYZR5gTaLTx5Gnwr7dDKxQrivhs\n6BSYmy8iKqHkMIwGYY3w3xfhvX8Jw8xmSJLGXVf9wsrfMliyurlToesW8h/f1zhtmkpNjdJieClJ\nEvHxMg89FH7YYd681jocVW0v3hcKwmRT7OPzC/G7cDO91/8JhmXSQpA8XkHUXu9ArfhAcqNqYvJd\nX5jKfz4czqqVq1l5m9JCmAw6ePZs+O5GSLCG9hnTNHHeVeth1HR48LnQkaM7HoOxA+DIHmogmbkU\nJti77hxZpuvPZKWk3fuflElkaeE9s3NxkkJ80HsKGgWUMIwpaChNKumSIDk9jMON4NjtdhyO6CjC\nHwrIzMxk27YeEBzrAn1GcAoLC8lsbhGv3QHpE8DX5qGzjCFA6LydTBx6TDTQqY59OxiwU0Z9WNv6\nUdhHDavYxWzW8i/m8zc+5Wre4e98xiss5At+YRnbUdBaIjhuVJyoKL3QItmTiMRyQkLCiowOiVh0\n/AEr95DENwykkGw+ox+3kMBEzHS1np+gWPi6h9JTU5T9jBjUn/e6oa8SDdwwGeIs8EwXpp4l5SK1\ncfY14r9De1GJVM75p4lozrimbMEFJ2/D6THw5fxhB2wdmuTY7bA9X2VsjsJRRxUxZMhO9uzxEwiI\n+9dikbnzzqSw3cGXLnVhMEhN+0qMGNE1wcnLM7UQClVRmTAK6jZq7MhXeeJvHZOd1GR47v9aU1Oq\nKjrDLr899PYdkZtmLF41kDkLk9i/YxM/3gDZyfDL3+CWKSIlFSoQ4fZAbb1we59yHmzvpIj4ty3C\nD+ytizv7NrqPuYWi2PjOzM63W6brz5QQdThLakV05jy6Xqgtx40JmZEEF/6sYy8JiAvYy3pU6BV5\nj8ON4BxuEZwhQ4ZQWFjY6+ftM4Kza9cusgYPBlWB6kJIygNf6+igmbLxsTPkvmYmohDAHSZZaYYJ\nIxUcXF5TQ6MCB7+xjx/ZyJv8RC1edjTWcVZMLZdTwuNUMYt6VuGmqikC5UPDiYqvFyTMo4G6ujri\n4+O73jAEmgmPEQkLMsdg5XYS+Jh+FDKEbxnA3SQyFQu2Nl0reiBZ0rGgB1K1Zi1AntRAWnISP2yJ\n/vHDwYMnwUvvigkxHMz9CYZNheffFJENf4gWNYsZBmTAqq/hsdvLOWZcCa9+MhZCdgMFk5x+aQo7\nflJxO3yMHbuL337z4vVqnHNOMYrSStAlCd5+OyOsa9640YvfL/bV6SQmTOg6FDN6tKklDWY0SiQm\nbmXy5CI+/6SGay4IUPObyk+fa4zKDd7v348QlNoKKHDejaG/367ITTPe+iKPVWsbGW7dw86HIC9V\nWGkcCEURv8eszyFrMnzyTZd/JgD/9yyMSIcxXRQEdweKCu+vgau7+KnKZBsSGqlqsBBPQIMfq+BC\nres6HDcav+HheIJ/lC2UYMWGESslbEGHCZB7vA4nJiYGp9N5yLR6HyysVisul+uw+XuGDh3K7t09\n0ELYBfqM4OwvLmbQgH5Cvbi+COIzwdPK8DRjP/xSUch9TYzERWQ9xFYSMKCn/CAJTiiYNT34/Owz\nwio8fEgDj1DF+exnPEUMZydnsY97qeBV6liAk334CaChNBEfN2qPdHd1B4FAAFVVMRrD74LpCmZk\nTMgYkBiLmRuJ5y0y2EI2ixjEwyTxJClICHXVaGOcWkFcUgoLt0m4e19BgLNHQ0qMqM+IBB4vPPSc\nSH2sXCf+/8AxT6cDVfVw1KgNjBg1kdiYzpIUguRU1MTx8fOr2Vbo5JhjiqioaE1bbd3q45Zbylvq\ncQwGQVQuvbTriU9VYfXq1pDT8OFd30PjxplbZNt37/ZTW6vy668e7ruvkn79tnP22cX4G12s/U5j\n7XcaeUPh+KPh3FNb28LdHtENtixEdC5ccgNgtcikDziSbdu243A4MIT4Kt0e2Lwdjj4Hbn5ARI3C\nxaatsPBneO3C8PeJBB+sgUwL2LsY2VfpMpiothfl/LJcIkcJLz84DyfZBLOpMurx4GcyVzGUyU1x\nXT2a2rOCU5IktZCCwwGSJGE0Gg+bwunMzExKStqnRXsafVdkvH8/gwcNBlRo2Ae2ZPAVtW6gjyVA\n+zAqgJHhOIisxzeNYXjx44py26IRPZrLQ7XV0GExnRuNzfj4ikaeo4Y/U8qx7GEoOzmBvdxKGS9Q\nw2wcFOLF1RTl6at0V29YTpiQsSAjIzEMI1cSz3nEYJQldk6BN0bAJekwIHJT6pCYpJQxvF86n4W2\n3+pxPHW68JeqqOre/tt3w9Tz4ao7RYSibRGupmls2LCBnJzhHH+0ncLF8IfjOj5WfKzEY/fm4HJZ\nWbhwFT5f+5qct9+u48cfnS0kx2yW+c9/0omL63rI+PFHZ0sdT1qaHqu185TjkCGCBAUCGvPmBRe/\nahrMn+9k+vS9HHnkbipKnPw2V2PRJxr6puiNzyeIw2P/an/sSMjNhNGwZSGcc4qJMWNGs27duqAV\ntMcLThfc+RiMnwFru6G0DPCPl2HCAEjtgZ6ENXuh1gV3Z3axnS6dSUpZu/fnVkOcDobTtYructzE\nYSYeC0eSyeUcy9NcgBUTWUxkNKe0bCvpel6VNy4ujrq6HvJ26QMcTnU4mZmZlJZGR+U+EvQZwSkt\nKyNz2HChgdOwF0x28LWpuZGt+AnN+PT0p572D2dnSGIwtVHSaWmLYaTS6HRSYov8AVaAIvwswMVr\n1HEbFZzEPnLYxQR2cyWl/IMq3qeeNbipaerm8jYRH38PEZ++sJwwILWUJGda4Or+guTsmQqlx8PH\no+Ga/jDc2sWBQkDSNMZrVWT2S+GH0PZbPYohyTA0FZ57/eCP9ekckRJ559NW7Zzi4mIURWHQoEGY\nTJAQB9//F555oL1mjl4PG+aq7C3yce65RnbsMHHvvSUYDO1Tp1ddVUJdnYqqivvMZJJ45pnULq9x\n6VJXC9f3elVycjpmqcnJOmJimgT+PBqLF3f8jG7Y4GXGjH0sXuzE7daQ5VYrhvNuaK8jFC65kSS4\n63pY8RX0SxNpv7S0NGJiYtixYweqKs7x9TzIPg5e+yB08Xe4WLYadu6BF8/t/jE6wydr4aKOZY8A\n2CnFkaE5MWvBkZVqP2x1CrHNjjAAPecSw1XEYULmJS7jek7gRPLIaCo81qFDFwZJiibi4uKor4+s\nbOFQhs1mO2wiUklJSX3S9t5nBKe+vp6U1FShgeMoBr0FfM0t4TISFgId+BpJpNBIZEvheNKpJPps\nOJtUKlyNVHdkT9xNVKKwAjfv08CDVHEu+xnLbnLYybns534qeY3almiPokFAhcYAuJWDS3kfCorM\nOgnseiE4l26CC9PhXzmweTLUToM544Wx4biYrm/iwVoDFquNHdV6KvpgQfTcOUJIb3OUmggaHHDr\ng3DU2bCh0EdBQSHjxo1rSfM0a+bceiX88h1ktenaWf6lhuIPcPLJe3G74cMPk9i1yxyS5NTXq5x7\nbnGLZYLFInPVVfEceWTnKYxffnEjy+JafD46LVDOyzPiconzWq0Sy5Z1rqU0eLCB44+3YrOJX13T\nNO5+AvYdsBYKl9ykJsPCT+Dxu8V31ramZ9SoUezevYftu5ycchlcfEv3I3AH4tnX4PTcrrfrDr78\nDQabu+imkiQ2ycmMVNv/QXMqYbLaupIYhoFLieU10lhPFssYzJOkcE6bYmQzBnR97BUYHx9/WEVw\nDqeUmyzLGI3GXhcv7JM70uPxYDQaxYCsBsBTB7IV/E0pKfMIJElG6SBKIxGDk8gq5m0kUdYD9TcD\nSKDS7aSmI3GMKMOFxga8fImDZ6hho+blvRIN8wIYuRwu2wiP7oRPymBLYyvZcSrgDAizv67gcDiI\nje263qI3ITcRHr0E8QY4PRmeGgqrjgLHH2DxkXBvFhwbD8YD5rKxSiUDU1P4ppsphYPFtGz49zvR\nP+66TTDz7a2s25qJqlnatSZbzDBiKGyaLywXXnpMI2+oxvTpe9sI90mdkpwVK9w88URlS8pJp4NZ\ns/rRmeK63w/r14scmsnUeat4bq6pJRpSWhqgtLTzWo0330xviQ653SqyLPHsAyozTmjdJlxyc/JU\n2LoYjp0QLBIIgpipmoFNO3OY9dFmlqzo9LIixqdzRAv61cdE97gAy3eLguPLuyhk3iCnMFYJTvXr\nJGgISAzGyHtkUEA28xjEgyRzGnaS0KFDwtZUXXMowW63H1adR4cTwQFITU3t9TRVnxCcsrIykpOb\ntDU8tZCUI0wh/U3LMMtoVK2xxRTyQMiYIiY4RuKoJvo3fzIx1Lmc1Fj7xvk1XTWwuVEioME2F3xd\nCU8XwZ82CsJjXQj98+GsdXDPdnh9H6ysg5qmQluPAo4A+JsmGU3TcLvdWCyWPvl7woUkgU0PRhms\nOjghER7KhoVHCMKz+ih4bAhMT4QJWiWZaSks2Nr713nlJDGRfflD9I89IL2BsTkV/N+L2WRNFp5L\nPn9w+sTQpJkz6wX4y1US11xTws6dB1ZZd05y/vGPatav9+D1quh0EtnZBm66qXPJ3B9/bGxyB5cZ\nN65jgjNypBGLRUJVNRYt6nwwP/tsO1OnWjEaZQIBjZKSAHZ7Ia/OrOWrNzVuuzo8cmMwwPMPwZx3\nIS5WkLBmaJoQWfxpJeROg7ueGkB8jIdRw6Lr6+H2wEdfw51To3pYQJCbBVvhsi66qX7TpTBWrWJK\nAjyQBfkTofFEeGQIeDWNE7BiR27SlJeRDjFCcyCavY78odoNf4ewWCyHjV0DCM+w/ycITmlpKSnN\nBMdZAckjQWkErakA2JyD0gmB0WHCGWEXlR5zj9TgxGDD7/JQE+UUVdjn13Ts6ILkl3phUQ28sg9u\nLoRjVkPSYohbBFPXwK2F8PwemFsFRQ1ejCYTAU3CERAE6PfSqWjRgVknSM/EOPh7Fnw1TuMoQwPp\nyXGcP050MyV2o46nu7jtePjf9x1bBhwMLj+zgA/m5BEI6KishktuhZMvhaLi9to5RtSfh+wAACAA\nSURBVINQKX722bQOTDA7JjmqChdcsB+fTzguWywyzzyTSnp6x0mQ/HxXS03MqFEdE5zx4y3odBIu\nl8qiRR0/nzabxBtvZLQ4jisKnHNOMU6nxj33VHDzzaU8fZ/Km//onNwMGQxrv4ebLhPfSdu+AI8X\nKqvholvg5MugaJ/4Xt7+cjRXnbOZaLujvfMpDE0Be5QK6dtiziYYHWKNEqODU5LgyaGw8BgzI6wK\n347y8UA2TEkQz49eBrtOOuQJTTP8KHgJ4COA3W6nobH+9zNodYLDLYKTkpLS651UfUdwUpuKFRtL\nIWEYBNrkgo2ZBDoQ8dPTHxldxG3iegzU9ADBMWFEcnuotYTlgBh12GTY2U2S3xCANQ3wXgnctx1m\nrIWzljv4vtHO2BVw5SZ4fBf8rxwKnYLsqM3pLiW8dFdfwiRDwNlqOXHNMfDBFVD5FOx6GF6/GC45\nAvp3T+6nS+hlyEmB976I/rGz+teTnOBm1YbgZXpz5OGJl4USctsuU7NZpn9/PStXZnLnnYkhmv46\nJjn79we49NKSFi0eWYaXX+64knXlSjcmkzjBgAGtVgyxsTKy3GqtkJMjFgYWi0x+fseD+aOPphAT\nIyNJEh6Pyh13lLNpUytrnDWrjv/8ZzljcsEeH5rcXHYubJwPOUOCU1LNuj1mk/CP+npe8H7b9yRQ\n22DmiBGRCYt2hRW/Qm0d3DM9qocFYOE2SDTDCBucmwr/zoHCyVB3InwxTogBjo8VAnmu+hosXVuG\n9Sk0tJauUrWJaAZQ+JUi5rCe5U3WEPUxRTQ0Hh4eTjqdDlVVDxstnLS0tP93CE56etPg6NgHcYNb\nLRoADP06LDA2MByFAP4IjR2N6Kkj+mxYr0oogNLLNvAAA9FjkiV2RzGKOUBrZK8UQ4ETZlfAk7vh\nwg2Q9zNYFsLgfDhnnSBEb+2H1fVQ1zTpuRVwBYTg2qGC6urqFkVmg06slmUJspLgmqPhjYth76NQ\n+gR8dCVccwwMS4nOua87VrhaR7t+A+CCU7byv7nDCSXo5/fDU/+BnBNg8YpgE02dTsJolHjiiRTm\nzx8UwgyzY5IzZ04j77xTh9utYjbLnHVWDCeeGDoc5nRqFBR4m67Hj8s1HEXJpb4+h3799Pj9uXg8\nw4iJEcevr1fZtSt0amHkSBN//WsiFouMx6OyeLGLV19ttVyRZY1bbilnzx4/L7yQyiN3wBXnte4f\nY4dPXoG3nhbEpq22jdutsn69h1GjdvH11w7eeTZ0e9RnPw7nghlbiXYU58Ov4MKx0TveoAS4bCI8\nPEOYgW6eDP8dBTcNhBybuPdtOkH+4dBTAFY00Sjhb7phG1HZjJfPcfAcNdxEGcexh19ws5rd/Jv5\nfMVavmEdBnT47LU4Ha5e8b/qDRxOaap+/fr1OsHpk7BD8b59ZKSlgeIHRwmkjQV/K6HRDMkEpDUh\n9zUwGF+EkRg7ycjI1EfJ7boZMiB5/ThMfRO9GY8Jp6LREIjew5yhOdkhdRzSKPaK14IDxsR4PeTa\n4PvxogZg1Q7IS4eMWPH/Hr8gGKG8fHoSdXV19O/fP+RnOrk1PZAeCxdNgDNHiWts9MDSnTB/Kyzd\nBRv2Rx6xunISzJnfvn35YNEv1UH/tEZWbui8yGJPMcy4HM46Cb56S8Pp1Fq6jywWmeOOs7B16xAu\nvHA/Cxa0faYEybn00mruvbeEf/6zH36/2O/228s58UQr2dlG9Hp4551+DB++E5+v/Zczb56TnBwT\nkiTxwgsreOONX3E4fDzzTBbZ2f9mxoyhvPbaGWiaxk8/hX6mJQneeScDSQJV1XA4VC67rHWQbCY3\nAK+8koaqOsjIqOC1J1PYUCij18HsNyEpIbjWxuNR8fk0brutnFmzRGvxHXeUs3XrEM6YDt8uCL6O\n7XsS8Xj1jBpWxabtUWLAiPTlLVeCUS9sFiJFXjpMyRZmoCcME+lXtz/YENTeyfCUmJjI/v2h9cZ6\nEl4VfKogWnoJKn2iPX2tQ6TKsmwa03S7qelA+X0zXo5r08pegxMFFZPdgLvCjYaCxCEelgoDZrMZ\nj8eD1dqLefUeQv/+/Vm7dm2vnrNvCE5xMWPyckH1g7sKrKng+6Xlc00f12S02R4GBuCJsFg4gf4t\nzuDRRBpxBLw+asx98yDlYqIkyrUdqaqL5frIdeTrArCyXgyuj86BN5aL9416ERHJS4PcNBjXH0b3\ng8GJYJDB5RerSkvHOokHhbq6OkaOHBnWtrLUSnjirXDGKDhxuCBmARVWFQm/n6U74Zd9XU9Iucnw\nzI8Hd/2hMOO4Ir7Lz0brRLSuLVavB69XY88eP8OGiZSQwSBhMskYjRrffTeQl1+u4b77KtrYQYQm\nOc1WDuvWZaHXy6Sm6rj33iQee6x9u/GSJS6uvz4BnQ5qatzs3i3SyqqqsWdPPQ0NXhwOL5JkYMGC\n0NHVK66IY/RoE3q9hM+ncf75+6mpEYyxPbkR38cLL9RwxBFmVsyOQdbJ6OTW9m9F0VBV+OSTBu66\nq6LlWCBUlN99t46XHonj2wXtI7LfLsnm1OOKokpwVq0TRc3XHg0zl3W+rU4Wz8+UIXBSLhzX5Gzu\nDYDN2Pr8RFLT09x5pGlai8xAtKBpYjyQZOHlpWqwzwObGoVaeYFTpL63OsHdhsecmgwfj5E6JDcA\nu/BzGrbWcwF1uIi1JOByOYl2pK2vYDKZ8Hp7oICvDzBgwIBeJ9N9QnBKS0vp36+f8KFyV4E5Fjxt\nUlQ6K2oHRcZ60nFE2O4dQwrOKCsYA/QjHofXTb2xbwhOJgb2Rjl6maK5qJC7v1qI0cOONnOdLwCb\nS8WrLSQJBsaLFWhuGozOgPEDhChenAXcPhGuNutpUayNFIqioCgKJlP3qjglCWxNuxqBacPh6Ezx\nvizBb/vhhwJBeFbsBmebW2ziILBbYUEXk1akMBoUjh5byl+fDF9E5ZNXNDZs8HLUUUWMGGHk3Xf7\nNfk/yU2S8HDzzQmcdJKNc88tbtNlFZrkFBT4+MtfynnppTQsFpn770/mgw/q26WYli1zYbVKSJKe\n0aPbCwTm5iZjMumQJClk/U1ioq7lHG63yvPPV7ds1xG5AUhL0zF0qBFZljC2iWS43Sp79/q56qpS\nVq4M/eA88UQVf/5zPCdNgflLgz9buyWNa87bRFyMh3pHmHbnXUDT4MefRC3YgQTHpIdJgwWhmZEn\n7ilZEh2PbYXTDQcx/Miy3JIG6W6UQFEFkZElsBqh0Qu7qsTzMXEwpMXB6BWw201YTnw7XGDXiQh5\nR9vvxY/pAOveShwkm/uxw1OH1DdTW9RxOBGcgQMHUlYWmUDvwaJP7oL6+nqSU1PEE+quBqMNGttE\nbCQjSgdFxDqSIjbZtJNEI56uN4wQacRS5/X0WYoqQ9OzNsoEJ17zUkf3CIFZBpsBdoYhhqZpsLdW\nvOYeoC6cYBURn7x08e8JAyE3FdJiRX2PNyAiQ1197Q0NDVHX82lrvDhxMIzpD3efKK6lsFz8LT/t\nhEsmwKbCyHyKwsExY0tYuyUNjze8XN+QwXDMBDjhBEEGtmzxcdRRRVx5ZRwvvZSGwSBhschYLDJ5\neSY2bszmuutK+fDD5kVEaJLz5pt1nH66nVNOsSHL8MYbGUyfvjfo3LW1Knv2+MnKMjJuXHq7axs/\nPh2jUU9jo8rmze0H8eeeS8VolPD5xOePPCJurM7IzYwZNj75pD9ms9TSceX1qqgq3H9/JS+/XNNp\nynDfvgBffNHAcw/EMnZGcERD1SQWrxrI9KP38sX84Z1/8RHgu0XwwlSIMcOxWXD8UDglV0Q6A00z\nfNuUU7T7NZstDroiOL6AeBmanr3KRthaDmuLYUuZuP8LygkS0zx9JHxwZWSNEEVuUVk2GhO/dSAV\nUkwAywEpqFLqGCmnosk1KIqCTvf7T1EdTgQnJSWl160n+mRmbmxsJCEpGSSd0MHRm0BpE7HphODI\nxOHqwMKhI1hJ6BGCk0wM9X1IcBI0PcWe6IWVzVoAr6RD62ao+shYERguPkgx0VqXECtbfoD5rNkA\nw1NExCc3TUR8RmWIwkqd3LqKbE53ORwOYmJiQp8kSmj704/MgOGpcMNkcQ2yDK89JYqMl66G/VFY\nvEw9spjP5uaEvf1bz2isWuVmxYrgGea//63nm28cPPdcGpdfHocsi7SVwSDx9tsZnHGGneuuK6Ox\nUaUjknPVVSUUFg4hJUXH5MkWzj03hi+/DB7A5s1zcs01BrKzE9td28iRIqqzYoWrXVfv0UdbuOyy\nOAwGCadT5bzzilGUjsmN0Sjx9NOp3HxzAgaDMCvUNA2/H+bOdXLTTWWUlIRX5PLcczUsXx5LegqU\nHSB/s3j1QO6/YVVUCE5KEhw3Ec45GZLjoOEZEQXUy6331cFEZ8JFTExMy8SjaaJeTtFa6+WKawWB\nWVssCExhuSA2zjCC4jsqwW7oPBpzIPya0OkaZ+yM4PgxIJGMnaqmkoUqGjGRis6q4XK5evzZ7w2Y\nTKbDxo/KbDYTCHSj0Owg0DcEx+kkITlFJMbdtaAzQKC1K0LCiNphG3gM3ghrcMzYKesBghOHhWpv\nPY74viE4VlWmLIrkPllzUy11X+BvfCxUOUXIuifg8cOGEvFqC1kSNT25aSLiM6op3aXVOkhKiMHl\nE/n/g0l3hQuDLnhSuuYiuPQcIbZXUSW6mhYsE4Rn++6OjxMKNouf/mmNbCvqXGSvGbExcNR4jTPP\nCC1SV1urcs01pbzxRi2zZvVj0CADVquMySRz9tkxTJ5s5Y9/LObXXz2EIjl1dXDeecUsWjQYk0ni\ntdfSmTfP2USKBJYscXHppXHY7QbS0myUl4tiYp1OYuDAWNxutZ3BplBLzkCWRe3QlVeWsHdvoENy\nM2yYkdmzB5CdbWiJ2rhcKjU1CtdeW8rcuZE1Jfz6q4edO3388z4TV90Z/FllrZVAQCYjpZHSysjc\nMgf1hymT4MRjhRnqoH7CvLNtBtXWS3JaqirEBjUEwdlVtJdNpSKttKFEkJjCchGNPZjnuahGPHOj\n7LAhgmF7nweGGTv+MhrR8KCSSXILwanBiR4LWHw0uh3E2O2/+24qk8l02DiKA73e8t4nM7Pf58Ns\nbsphe+uE4abSTGj0SJIRtcM0lBlfhO3eRmzU90CLeAwWir0VOEzRycdHCjMy5VG89+M1L7VS9/+W\nPBvs7wMrGFWD3dXi9cOW1vfvG9PIgvJ+mO2C/IxMF23gzUWYPp9Q/jUaoJOx9KCg17d2saSnwkVn\nwpnTheZKoxOWroF5+YLwbCzs3MRx4qgyft2cFnZx8dP3QVlpgIULO7/3V60SbdK33prAU0+lIsui\ny6pfP4nlyzN54IEKnn++Bk1rT3J+/tnNk09Wcc89SdjtMo8/nsIdd5S3ObqG3S7jcvnJy0tpITiZ\nmfHIsoQs067+5i9/SWTQIAM+n8YnnzTwxReODsnNFVfE8frr6eh0Ivrk92toGjz/fDX/+Ec1Xm/3\nBtRXX63l/x5MJZSSxorfMjhmXAlfdhHFyR0qCM304+CEo0Unl9sjWtWbsyd2W6eHOGj4/eDxibSS\n2QTVtbB1F6zbDFu2wStPwMPf2enncXD3+9E/vzcgIrKT4iIjOEVuGBjbeRq2CoX+xNPcnlKLEz0G\nFJMHrzf6C9q+wOGUogLQ6/X4fD6MPTXgHni+XjlLZ/DWg2QEpSnnrxe5erWDKI2EMWKCY8CCswd8\nqCwY8fl8OI2RreSiBaMEFVEkOHGal/qDyPAPNsPe3lXi7hT9rI1sqbbhLBeFwCCKmCmHC28Sgm+5\nQyBvKIwfBSOHw8B+IiLk9ogUk8Uc3UWgLLdOavFxcMYfxIreYAB/AFavE0WnS1fDLxugrer8xFFl\nzP05M+xznXuKylNP1na9IaKV/d//ruXzzx3MnJnOjBl2DAaR+nnssRROP93ORRftp6JCaUdyHn+8\nilNPtTN+vIlbbkng/ffruPrqeC6+2EZcnJikJEkUFS9ZUgRAXl4KbncAg0HP2rWtk1G/fnqefDIF\no1Fizx4/t9xSFpLcxMTIvP12BmeeacdsltE0DY9HZc0aD9dcU8r27Qf3YHz2WQMvvpjGuJGwfnPw\nZ6s2ZHDrn9YHERydDsaNhCkT4aSpcNyRImrn8YLN2qbDqQcITbO9REARJEaSoKRcmLuu2wwFO6Bg\nuyA2jgOG1VuuhAFJZuIqvYh4TvQjHsV1MDLCIXKnW+JUTd/p5VQQIIXWGrt63BjQo5hceL2HR9TD\naDQeVhEcm81GY2NjizZZT6PXCU4gEEBuFsVTFQj4QDK0Ehyj0PbomOAY8EWoZyNjwtVBLvdgYMSI\nGlDw6PvGRdcsQ1UUO9/j8FIndV83Pt0I+YeMZpiGTe/HGQgmbJkJ8G2+IDDrN7efvGQZsgaK1Xfe\nUBiTC2NHiGJdm1VYIKhq8Cr8YCBJ4rggIknTjoWjJzR1asni+n5cAvmrNXKya/n3BxPCOu6RYyAp\nUeKjjyIryC8pCXDOOcWcfLKNt9/OIClJh8Uic8wxQjPnoov2M2+esx3JOf/8YrZsGYLForFixWBq\natw89NBCPvxwA6+8cjoXXzySkSNb26vz8pKRZYm1az20TcvPnJmOTtdqxeD1qu3IzaRJZr78cgCJ\niTrMZtFh5XZr3HxzGZ9+Gp2FTHm5wvr1Hu663sLltwV/tr/cTlK8m2nHBjhmgp4ZJ8DEMeL38gcE\nsWkhNFEcYZvTSiDO4fXC7n3wWwFsKBBEpnAn7CiCcEsdtu2C3IESO0oNIZ+XaGBfLWRF2Flf6oVY\nVUdnUjalBBhIa2F0PW7haG4K4G/w/+7TUyAiHr1dt9KTsFqtOByOw5fgNDY2YrM1LWMCHrAmixtR\nbSI0hjRUzQtS6NCyhC5iFWMZA+4oa+AA6NFDINAnBCcRGZ0ktZhmRgPxmpdyqfst4nEylB4iKumx\nBh/1vvZkLdEMu/aG2KEJqgo794jXdwuDP0tNFqQnbyiMGAYTRsPwLFEs6m1Kd5mikO5qayMwaRyM\nzYMbL3WwtdDKr9/rmJcPS1bCz2ugpoOU4N9vgtWrPVRUdE9lcN48J0OH7uT++5O4995kJEkiLk5i\nzpyBvPJKDffcU9GO5Fx++X6++moggYDKY4/9xKuviuTBokW7OfvsHMaPb+2kGj06FaNRh92uYLfL\nNDaqnHyyjVNPtaOqGnffXc7mzZ4gcqNpEvfck8Rjj6W0kEu/X+Pdd+u4775KGhqiW/z1+ecObvmL\nCZCJscOxR8DUo2DGCRKKO4lTT6ohKTk16PeKRuTdHxCRH4MOzGbxG2/fDeu3wKatgsQUbI9O0Xrh\nTjhjNCz3WEk1u9ndGH2Cs7saJg6IbJ9yH5i0zsfVMhRG0Foz6MKLiobBpMPn9aOh/W78tDqCLMuo\nneWtf2ew2Wy9WjTd6wTH4XC0tiMG3GBr0sdoJji6JLROCoIFwYksGiOji7rIH4AOHQQUvH1AcAY0\n/XT1UST3cZqXbXJ4BayhYJVF6+ihgBSzi0pPe7JmNcLebmpNVVSJ108rg9+3WUW6K2+oSHlNGAUj\nhsOAdEACj0d0eZm7me4ymcDZWE1ychJ5OZCTDddfKlbxRfuEXsuSFZC/SqQmAI47QuXpf0YWvTkQ\nXq/Gww9X8f779bz9dgaTJlkwmSRuvFFo5vzxj8VBJGf6dDGLKYrK009P57PPNlNd7SY/fw8Wi56c\nnOSWY48bl46maYwebaaycghr1ngYOdJCIBDAaNTx5ps1QeQmNdXAZ5/158gjzRiNEm63yvbtPq66\nqpR163qgQzJZh90uM7C/TOFiGJYloid6PZiMUFSUhMNRzYAB7fV9woGmCVLsD4i0kixDaYUgLms3\niX8X7hSv+h5cNOwrgXgTVHospJpd7G6M63qnCFHaAAkRDpGVPjB1QU5qUIK0cDTAgx+DydBUtxIA\nelk6/f9Hp2hOUfUW+iSCE0RwzImgKaA1ERBdQpcEJxAxwZF7iOBISKpKQNf7BCcNA25FQwmz4DQc\n2PDjPIgBwSRD1SFCcBJNXmpDRHCsZiiOcp2Q0wVrN4pXW+h0IrWVOwTyhsHoXBg3QqTALGZweQBN\n/HdXVmb19fWkp4sIiMEgXgDZg4I7tcqrYMVayEiXO/R2ihQ7dvg5/vi9XHhhDDNnZmC1SuTkmNiw\nIZsbbijj/fdhwQI/mraRiRM389FH5zNgQCwvvngKV1zxFTt21NDQ4CU52YrdLiakIUMSkCQJm+1J\njj56AAsXXoHPp6DX66mpcTN/vpPZs+Gll9I45RQ7H38stG00DZxOlbvvLueNN+o6LcqOBAMH6pk6\n1cq0aVamT7czaJAel0vD51PJGSJ+HFsbvhwfH09padc3kqpquN0iGi1EDyVKK0T0bf2WpmjMDpFW\n6otSi/1lYNVDrddMvKlnCnMrG8XiJxJU+USNYWeoRcFA8CLGjQ+zwYo/EP3xvq8gy/Jho+vTnKLq\nLfRJBKc1ReUGcwJobQiLLgatEwIjdyOCIyHjJfp5TB1ynwmCJ6PDFWWPI4sWwCV1/5YwyqJj4lBA\nnMFL3QEEJ9Uu6lxKo2sK3SEURdQ4bNsF38wP/iwjrbXAecRwmDAShmeLThuPV6zsLSYRMQAhWjh8\neOiunbadWhmpQlfF6VSZPXsADQ0qS5e6mD/fSX6+i02bvN0mBZ995uCHH5w89VQK11+fgNEo8cYb\n6cycmQYEuPfeTzn1VCvnn/8pq1dfz0UXjeL113/l55/3kZ+/l9NOG0pOThI6nYTVamTDhnJcLj97\n99bj9ytIEkyf/h5HHCFx+unDuPHGo8jO9nDDDQnodBqKIjF7dgO33Vbe7dRbM3JzjUyZYmX6dBsn\nnGAlOVmHy6VhsUjodGJmtdk6nmFjYmKCVqKBgChy1umEcGJdncL27T7Wr/ewaZOXwkIfBQVe1q/P\n5tF/6Xj9g4O6/KihtALMRqjzmYg39Uy3TpUTTBGuw2oDrYagHaEBFf0BRTpufFj1MVQH/HRawPM7\nQnMdzuFAcA77FFVwBMcDxhhQ2yxdOiU4ViQklAijMTIy/h4gOCgqAblvcryJyLiinJq1EMB9ELeE\nQQd1h4jxbZzR264GJzdNkI6O6lZ6E6Xl4rV4efD7MXZBfHKHwqtPqLhcGnV1Ci6XG73eTGOjgl4v\nYTJJHXoHyTItppoJCTrOPNPOH/5ga2mjXrnSzbx5TpYudfHLL+6gTq2u4HCo3HprOW+9VcesWf0Y\nPtyIJKnYbCZWr/YyfLjK2WfHcMcdP/Dii6fy7rtnM2LETBYs2MW0aYPJzU1Gr5dwufz8+OMOAGbN\nOhu/X+Xf/17BqFEKXi889tg2Fi06hqwsM5qmsWdPgKuvLuWnnyJn0DodjB1rZsoUCyefbGfyZAs2\nm4zHo2GztX6PdnvXz7LXK0w6TSYZWZbZu9fFjh0aa9d6KCjwUlAgiExdXeiHs6jIz7gRh85EVVkj\nUmQNAROD7T2TC6txCS+qSFDnB1mSSEamqgOJwAZUUVTcBi58WPQ2AgEXUogW/98jmglOdy1nDiVY\nrdbDO0UVVIOjeAXB0doQHNnaIcGREcqUkRMcCT9RDncAmqLg74P0FEAsMs4eiOC4pe6nqIw6aDhE\n5CdiDT7KD6jByUpq3yZ7qMHRCGt+E69Xn4DTTtvH1q0N3HMP3HHHbnJzTeTlGRk71sSYMWaysgxN\nNSkilmixSMgHkG6RCmpV/P3DH2wce6ylqVNLYv16Dz/80Eh+vouVK924XF3HJdev9zJ+/G6uuy6e\n11/PwOsNsGzZ1Tz44CJ2795FVhbMn7+DadOyuf32o5g3bxdms4ERI1IwGGpQVY38/D1cdNFIJkzI\nYMuWSsrKRC+/w5HE99+fjsfjx9wkp3veecWsXx9ehMFkkpg40cyUKVZmzLAxcaIFnU6Qu+Y0EXRM\naDRNpJVUVXyfigJ79vjZuNHD+vVeCgsFkTnrLLjllkK2bg1fHHPXLh9ZA/tGNysUqmsFITYajMQZ\neyaCU+8W0d1I4Gga29LQU9WBj6ATFd0BdTpufMTJNlT1EH/QI8Dh1El12EdwPB5PKxMNeMFgaa2/\nAZDNnRAcIaYQKcGReojgyBp9FsGxIuOONsE5iAhOcpM9QuMhokll0QdwBYLJWkYcNPxOxj2zWdRs\n7NnjJykpQFWVnsJCH4WFPr76Knjb/v315OWZyM01MmqUiXHjzAwfbiQhQYfHoxIIiEnfYGi9Vy2W\n1hln0iQLY8aY+NvfkjCZJAoKvMyb52TJEhfLlrmorQ29gtY0eOONOp59Ngm9Xhz/kUdOYPXqYXz0\n0WLWrfuVY44ZwOOPn8hnn23B71c5/vjBlJfXY7Ua2Lixgk2bbkJRVL74YhF6vcyxxx7JmWfmoGnw\n0097uf76Obz55pn85z/pHHdc6PY3u13m2GMtTJ0qCM2YMWYCgWbC1/p3Gg8o6lAUDbe7Na3U0KCw\nc6ePdeu8bNrkpaBApJb27PG3s5IAOPpoPYmJkU08u3f7OXlEz+jNdAcNDtE5mGjXY9H1zCTq8EZu\nORHQwK9qJMgd7+hCC0Fw/CRhBLQecUjvC8iy3OsKwD0Fk8mEy9V7dQy9TnCCiqUUL+jNwQRHMqF1\nQGBkxMpHjTDdJCMR6AGCg6qi9NEDZEHCHeUUlV5TCUjdi0glNXGJcPxpegNWvR93IPj2Trb9fgjO\n6BxBIEpLA0yaFKC6uuNHdf/+APv3B1iwINiSIC5OJjfXSF6eiPpMmGAhN9dIRoYeVQWPR8NgALNZ\nxmxu/d1HjTKTk2PihhsSsFgkior8LYRn6VJXkKfTXXclomkqSUnPM2lSf2bNOpsjj+zH2LEX8+KL\n3/Leez9w3XVnMnPmaaxaVcy4cenU1enYubOGxx+fhsEgM3PmHCwWA7fffg42m5HaWg/XX/8t33yz\nFYCnn/6ZOXMuwWgUhbjJyTqOO87K8cdbOOUUOzk5RjweDZ0OTE2FG23JnM+nLgaSigAAIABJREFU\n4vWKtJJeD5WVCgUFXtat87Bli6+FyFRXRzZGVFfrSUqKbCyqqAgQazt0CA6A2wsxVgMWuWcIjtMr\nugiNMvgiGLO8KsR1Un3vCRHB8eJHjwkkDhuCI0nSYdMqrtPpepWsdUlwHn30Ufbt28dbb70FwLJl\ny5gyZQqLFy/mhBNOAODGG28kJSWFxx9/vMsTqqraKvSn+EFnOoDgGDokOBJmtKZ/IoGMjBK21Vv4\nx5Q0UPoozWtGjmiwCAsHMRbENt1J3kMkkmrRBXArwbd3vAUaD5Ei6K4wNBOcTg1FgcTEADU1ka9F\n6utVVq3ysGpVcN7QaJQYOtTQEvUZN87M6NEmMjMNGAxSu66f7Gwj115r4LLL4rBaJcrLAyxa5GLh\nQie33RbPu++uw+MJkJ+/h5yc/3DXXcfy6KPHc//9f+TTT5fw7beLOeusE1mzphSTSYdOJ1FZ6eK8\n83KZMyef+HgzN998JrIs89JLq3j44SW4XK1jwI4dNVgsBjZuzMZkkhk0SI/TqQVFpSwWcLs1GhsV\nLBYZVYW9e/1s2uRl/XoPBQU+Cgu9bNvma/n7DhY1NXoyMyMLWdbUKJhNh9Zq3OMBu1mPRemZh9fd\n9FMm6qEsggWQTwNbJ3U0HrR2ZcReAuiwIsn/H3vnHd5Web/9zxnakle8Y2c4iePsSfaCkIQwWhIK\nLZsApS1hlBYKHdAFLVBaKGWVVSAFygqUFggzgbAKISbTznYSx4731JbOef945KFYsi1v/Hvv69KV\nWNKRzjk653nu5zvuu+99j3oLTcaxgwFd1fUZMWIEJpMJi6UlHbxu3TomTZrU7nYdjponn3wyl19+\nefPfGzduZPbs2WzatKmZ4GzcuJFHHnmkUzsaRnD0oPCh0lutnCQDepQIjYQRPUaiIodukGAP9zsZ\nET9Uf0VwVKSeJzjdgENIAg0YGGQNnxY+ONqM0IfR0W4hIxUaQoUINptGXV3PFab6fDq7d/vYvTt8\ntpEk0S7dVOczYYKJadPMjB5tJCFBAfy43ZCSonL++XGsXu1A14P8+MdzGDkykXffPcDmzUe4666P\nef75Haxbt4pzzllIfv5OvvpqCwsWzGnu4DrppEzy8/NJS7NxxhkL2b69nDVr/s3u3RWMHTuEhQuH\nc+qpI1myZAQpKTY8ngCjRxvRdR2n048sSxgMBhobfdjtRqqqgtx5Z1UzkSkq8vdYC3k0OJ0yVmts\nX1JfrzW3+A8UeH1gM8sYezrn3fT5oeHcESPB8WuirSQaAujISMjIaKF5IUAQCQUkMdcMhs6jwZSi\nUhSFYLBr19kLL7zA1KlTY9qmQ4IzZ84cSkpKKC4uJisri02bNnHbbbdx9913A1BaWsqRI0eYO3du\np74wjOBoAZBVCCMtygl/t0BCgZijN+IQY436dAQVcdH1Vw2OCvgH0DVvlCE4gPZHkXSCJxAckyoG\n828C4uNEBAfAaNTw+Xo/m6zrcORIgCNHAm0cvidONLFjRw4//ek7ZGbamTYtg7y8ZNLT7fj9QVas\nyOGUU0ZgMqn4/UE+//wY//nPXj79tJgf/WgGxcUH+fzz/zFnzmwkSWLHjm1IEkydOpXHH8/n0KEa\nHnhgJbNmDcVsVvF6gwSDGkajgixL1Nd72bOnkq1bS9m9u5LCwkoKCiqoqHDx2mvfQ1Ey+fOf+9Yn\nxOeTMRpjIzgul47ST2NGNPj9YFIllCjq8d2FLzSfWWKMdgd0MLRDcJpifEZkPKE5Q0NHQkaSBlcE\nZzClqPryWDocNY1GI/PmzWPjxo2cd955HDp0iNNPP53rrrsOj8fDxo0bmTt3bos7eAcIq8HRNURe\npNUBS1I7URqlCzRF3FVaDxMcOURwtH4aq2SkAUUoVImIhZj9BUXW2oggqorQl/kmQHgNiRNqMun4\nfP3b8pqaqhAIaPzlL5+FPW8yKeTmDiEvL5lx41KYOjWNGTMyWbx4OHPmDEVRZMxmlXHjxlFQUMDn\nn3+OxyNSZjNmTMfrDbJmzVQsFgO6rlNcXM/u3RVs3Xq8mcTs2VMVlrI6EWVljUyc2PcrdZ9PwmiM\n7aL3+/UORR37GoEgqLKMIvfODayFPrYj4b422yEi1dFfFx8st0pjiefk5hqcwYDBlqLyx6JL0Qrf\n/e53w1JUn332WdjfkSDpnThzd9xxBwcPHuTSSy/loYce4l//+hcXXXQRV1xxBc8//zzZ2dnceuut\nndrJxx9/nI0bN3LJJZcIoT8tAAYzBEIa80oyugxB2rogS5hQSMJJ51dqEhJWEqnF1aMkR0YiTjPj\nDPhpjFXkoQeQhIKuSVT2YEQiEQ81dK2F1aJAkgGODQCNGRA6OA0+I1qrATLZJrKhVZ0z2O5XJMSB\nyahTVhbEZgvi90v9SnIsFomkJIVjxzqnlaIoEiaTisViwGIJRVF1PSShLxZOTZFcrzeAyxXA4wkQ\nDMa+uktIMGM0Grot/hcrTCYNh0OjsrLz0TWzWWLIEKVHfKR6Cump4PKDEQ+Vnt5pYc9KgHJfbEXG\nGSZwSRqNURa8MqKNvPXYbsWIERmfxx92jX2T4fF4UBQFw0DLbXYBN910E4cPH6auLjYbmREjRvDa\na6/1fIoKRB3OE088wbBhw5rrbhYvXszGjRvZuHEjTz31VKe/UJZl4uPjWbFiBex5FWqKYMppsPss\n8YZR/8YZb6VUuqbNthYWk85TPMnlbV6LBhUzq3iUtayjoR0LiFgRh5k/1C/l77u+5P65w3vsczuL\nJ8nAXW7lyq97LoT0uOdtrjSv6NK2ZybDS5PA8tMe251u4c4ZH3HPzplUelu0cF6/CvRSuPLaftyx\nTuJPv4KV872cddZBLr+8nD17LHzyiaPf9mfRIgsffDAMVW1pJJBlieHD45ujNxMnpjBtWgajRiXi\ncJhwOn1omo7JpGIwyOTn56NpGsePHychIYEZM2YiSTKKItPY6MNiUdE0nUOHavnqq1K2by9rjuIc\nPFhDMErI8rHHzmLcuDy+9a3DfXU6AMjLc3PWWTX86U+Znd5m2TIbL72URcKkgTPx7t8M63bqDG14\nl6s+Xd7jny9JoP0VTvoMtsQggXJkkc46cxUPE3nVlIjMdnL4IU/jCmnlfIeZzCKVLRu/Zsm8UwaF\nOF5+fj6ZmZmkpaX19650G1u2bOlyDU5X0CmCc9JJJ1FeXs6zzz7L66+/DgiCc+aZZ1JaWsqsWbM6\n/YWyLKM1HaCshAqMW93szWmrSAh2odFHsH+5h9syNTQkSULR+id0qKGjDqBUfkDvmpFkbyGoy6gn\nhNwDQTB/QxZBTpfQrgFR62Ey9V8O3myWUFUJRZG5//6VZGfHMXFiKsOGxaMoEm53ANAxGBQMBoXj\nxxt55ZXdvP9+EePHp3DttSexbdtOFEVixowZvP3228THx/PVV1uYMGEqL71UwPvvH2Lu3CyWLx9F\nbu4QsrPjWLUqD1mWUENmtsXF9ezaVUF+fikFBaIOZ8+eSjIy7DG3ePcEjEYNrzc2omI0SgT7acyI\nBlUBf1BD60Ffu9YIOV/gi/GwZUQhcdTPDY3pgVYRHrmp5EFnULSIw+BpdwdRotKXUbVOERyDwcCC\nBQvYtm0beXl5AOTm5tLQ0MCCBQtiCp2FtYlJiiA0UusUjwZRWgNFd1VsJ6dJM6enCY4PUSyt9NNY\n5UeP2d+lN+HRoB9M1aMiqEvIUjgp8ASEc/I3ATW1QsAOulbr0RUkJSkn6OaYycszkZamEAhAQ4OP\nq66aHiokFgKCiiJx5IjOqFEKzz+/kxtvfIeqKjczZmTw1FNnM3JkAv/73xYsFgNTp4qaG0mSGDdu\nHDt27OLDDz/le99bwFln5XL++eu57roNxMWZmDcvm0WLhnHaaaOZNi2DYFAnMzOOESMSWLYsB69X\nuI6bTGIIc7s17r8/jV27WnyfejtlJWqjYrsJrVahjDyQYDSCJ6AT0HvnBm4S+YtVeV2VwdcOwWma\nvAKtum5lZHQ0dJ1BkZ4CQXAGy7Fomoaqdq1h4sQanHvvvZeTTz653W06/U0bNmxo81xn3HRPRBjB\nkQ0hH6pWBEf3I0XZLR1vWEFZZ6A1R3B69gIJEBARnH4q/vKj4xhA17wzKMS8pAFSbOzTFExy+Ijq\n9Iri3W8CSsvBEfqBGxtl7PaemRWbWsGbNHAmTRKt4KNGGYmPV3C7NYJBHbNZRm0VIgwGNQwGFUmS\n2LLFzVtvCS+rTz914XTqfPRRNmlpdgIBjYcfPoMrrpiGpuk8/fQGHA4TZ521iA8+OMTChcPRNJ3t\n28vJy8vj+PFG1q17i4suOo0337yAN97Yx9VXv8GGDfvZsGE/Tz21jV27rub880sYO9bIaafZmTlT\n1NwEAmA0itWtxSJz9dWJuN2iiNdqlXE6NQ4cEIaXO3Z4m1vIDx3qmRZyuz1IY2NsN2F8vILfP4BW\nJoDJCC5vEL/WOwNKU9S0IcZL2CgJteJoMCChndCSoqKgEQRt8ERwNE0bNMfS1QhOUVFRl76vz5WM\nw9rEZFXYNbT2P9L9SESOCOmhPKuEFFPbtxbBlK270AAkCaWfMgde9A7ddmNGN4hJbWgRZVLB07Ui\n+R6FK6BiUcNbpmrcYE/upx2KEXsPCcNMg0Eo5mZlxSbgYzRKjBljZNw4I3l5JqZONTFpkonhw4WY\nn8ulI0mEeTOBsDbQdR2XS3gxBQI6X3zh5u23hRv5li0efBFyDT/5SQVffjma2tpbcDp9lJc7eeyx\nN7DZjFxyyQreeGMf6el2nE4ffr9GdraDu+76hJ/8ZA67dr3DAw+8xsqVi1mxYhSHDl3PTTe9y8MP\nb+EHP5hBcbGXl18WxRt33FGFosC0acJravlyG/PnWzCbZbxeDZtNbj4em01m8mQhYii8pfRmEcCj\nR9uKAO7ZE5sI4JAhsQswJiUpuAeInUkTLGZwugO45N7J31pCH1sbYwejUSZqgTGACamNPr0JFQ3/\n/4/gDFCEycT0Afqc4Miy3FJkpJhEJ1UYwfEiYYy4rRYqEpZRY/Kj0tBRe8FZVpf7L4LjRsfcw81b\nAUlG1YMEpNg/uCrUzWUzDgyC4wm29dapbIS4/qvTjQm794Gm6WRmGqiujm4JEB8vN0djxo83hdJK\nwo4hGASPR8NgkMKsGCDcaFLTBKExmyUaGzU+/tjFu+862bzZzbZtng6jHWPHGrnvvjQCAQ1d13n1\n1QK2bs3HaFT4yU9WoWk6a9e+SVHRj/n006P4/UHi4kw8/vhW1qyZytq1K7n55n+xfv17JCSMYu3a\n2fz5z8u5+eb5ZGfHc8UVJWHfFwzCli0etmzxcO+91UgSjBtnZOFCK6eeamPJEiuJiQput47FIqEo\nElZry/EqCuTkGMnJMbJihQ2fTxBCk0moNO/Z4yM/382uXb5mh/BINT5JSQGOHrW2eb49pKUp1DcO\nnNW4JIHJBLWuAG5L70wHNiMEtNg6qEBEcOrbITgW5DYCrmZUArgGVQ3OYIvg9OWx9DnBsVgseEM6\nGKhG8J9AcIJuJCIXSmiIVZyCISaCo6NjaCPq3X3osoTaTwWDjWgxC2d1BBcGrASo78K5qg4IvQu7\nCaqcHb+/t+EKqNhOiOCU1EGcvZ92KEb4fEIUbvhwAzt2GMjICLJsmY28vBaF4dxcoTAs0kotxcBN\nkGUwRHA5bDKZtFplysuDbNrk5L33hM/U3r2d1x0wmyVuvTWZG28cgq5r+HxBrrzydZKT65AkiW9/\newmqqnDttW8xbFg8kgSbNx9m7FgNp9PPzJmZXHHF6/znP+dz3XWn89vfvoKuH+DWWxu5667lpKba\n8XgCzJpl4ZVXGqiri2762aTM/Pe/i46bESMMLFxo5eSTrSxdaiM7W9g7nHiOTCaZ1o02aWkqaWkq\n8+ZZ8Hha0l0NDUEOHPCTny/SXYWFXoYN09i4MbaoR06OgdKKgTNZxTlAlqDO6cdl6J3pIM4M/hjT\nUwYJDLJEVTsegjakCATHiJ8aBpLXV3cxmCI4Ho8Hm83WZ9/X5wTH4XDgbNLLV8zgbwC5VcRGd7ZD\ncIQGhxIlhRUNWi8RHBQFYxd0O3oC9WhYe/iQ3JKKRQ9QL3WtEtcfhPjekdGIGXV+Ew5DeC7gUDU4\nbAOnTuhEGAzCgypvFIwbLfbzww+H43YH+eijEtavHxqqN5GQ5fC0Unvw+4WCs9Gg4/Pp/OtfdXzw\ngSA0xcVdUz5cudLG449nkJioIEk6RqPC/PlPMWOG2K/4+OFMmZLOnj2V/OMf+fzkJ3PxeoMUFFQy\napSMwaCwZMkIrr9+A2+9tZ/ly3NYtGgG27bt4pxzbHz0UR3BoMqcORYuuyye7343jrVrj/Pcc53T\n4Skq8lNUVMe6dUJvIyVFYeFCK0uWiLTWmDFG3G5hNmo0tj1/qiqFRbkcDoWpUxWmTGlKd8Fnn+3h\nhhsmUlKis3Onh/x8QXwKCnzs3etrFmpsjZwcIx9+1ZUz3jtIThQRMb/PR70/cuS8u4i3xE5wmrzt\njrdjrGyLEMGxYsAbdKK040L+TUMgEBgUlhMALpcLh6Pvwuj9QnBcztASX7WArwFaT6jBxqgpKmGv\npqFGfT0yNDSMvXGocv+1idcSxNbD17wLFWuMTu2t4QtCQmwR+15Dnc9IhiU8lLSrVEQ1hiRCZd+q\n+ofBYQ+RmDHi3+kTYUIuZKQhXL69ovOkyRnbYlGwWq2AG5ut4xCU1ycmLZMJ9hyAdz6CTZ/BE3fp\n/OIXZTz6aNfVGLOyVB5+OJ1ly2wYDBAISNx3XzVnn23h9tvH8eab+/nsswAffLAYSYJLLnkNXYfl\ny0dhtRooLKxkxYpkrFYDK1eO5vrr4eqr36Ck5KdceulUZHkab7+9nU8//YK77srkmmuS+eMfU3E4\nJJ54IoO1axO5/PJS9uyJTeGyoiLI+vUNrF8vosBxcTLz5llYvNjKLbckEwjo+AMSEtCeKLskiXRX\nICDuE6vVwOjRMHq0kZUrNXy+FtfysrIghYVetm71sHu36O7KyzNx3zNdPfs9j9Rk8PiEMGadr3da\nDJOssaenElQI6jq1UvQNHchhLeIAVkyUBxpRVTN6ky/VNxyBQGBQiPwBOJ3OwU1w7HY7rqYIjsEC\n7poTUlQNSO2o6WpdIDg6GqZeONRgk06u3vciMJUEsfZw1LIpgtNV+DRIHCBdSnU+M3nx4ZLFtW4x\n+Wem9Q3ByUyDvNEiGjMhF6ZNgDEjBcHyeIVthNkErTMDihz+dxPi4uKor6/Hbm9LcNwecfkpMmwr\ngA2b4KP/wWdbobEVx/vBhRLnnOPoEsFRVbj++iRuvz2l+VKvrdU599yjbNrkpKSkjNWr7dx002oS\nEy1oms6DD37Jzp3lSBLMnZuFLEvs2VOFpg2hvt7LmDFDOOOMMVx88WRApM5kWeKqqywsWmTm5ptL\nuPNO2LTJxWuvZZGcrHDSSRa2b8/hL3+p4ne/q+yyM3h9vcaGDU42bHCydKmNg8ctPPQMLJwFpy2B\nmZPFOQ0GRRHuibd3fX09cXFxYc+dmO7KyFDJyFBZuNCK262hqhIGAzzyR7j6Evh6N+zcAwX7oPAA\nHC3p+8hiRmoLwan29k74NdkOEYJZ7SLRECJF7fCTeBSCJ6SwLBhwB5wY1DhEK8jgIDhdba0eaHA6\nnRHHsN5C/6SomiM4ZnBXCR0cyQi6D4I17RIcnSBqlBRWNGhomGNMa3UGQXSQZVRNJ6D0LcEpJYBZ\nkVAlIbLXE3BiwE7XvR+8mhjMBgKqvSaGmNxtnnd5ISsDthf0zPeoKuQMEyQmbzRMGQeTx4nnzCZw\nhXbBYibMg8hsEo/OIiEhgZqaGjIyMnG5BQkKBOHLbbDhQ9j8hfi/r52f7/5/SLz+hA2HQ6ahofNL\n6nnzLDz1VAZZWQbMZhm3W+OTT1ycf34J1dUB1q4tIxiEU0+10dhowecL4nL5ufXWjQCMH5+C2axS\nXu7E4xEEev/+aiZOTOX1189n+3Y3y5cf5v7708nJMbB+fTZz5gT43vequOWWEu68M5Px4w/y6KPp\nrF7twGSSuf76JC69NIErryzlzTcbO38i25xXmenTzVz/HfjsK0EM7/ib+F2njodFs2H5Ipg3Q0R2\nvF6wWaG2tpaEhIROfYeqSjgcLROt0SAI1IxJ4vrQAatZEN7DxbCjUJCfgv1QuB/2FbX/u3YHQ9OF\nTUOSycOB+s4dT6xIsYMzxghOsrFjUpSAjP+EWkwLRtx+F7ZBEvGAvu886k3834jguEOjvmoBZ8iD\nSrZDsBoClcgdEBxDjH5JGgHMMUZ9OoMgQTSDijmg0aj07QVYHEolJahQ2UNdS3WSiTi96yOpS4PU\nAUJwKjxWUsyRCc7wobF/ns3aEo1pSiuNHyPIkq6LiIyiCNLSerVv62bKTteFqnFCQjIHDh6mqEyk\nnDZ/Adt2E5Oey4ZN0Nioc+65Dp58smMvmCFDFP7yl1S+9714VFXsi8+n8/Ofl/PXv9Ygyzpr14r7\n98EH07j88kSCQaE8/IMf/BenU1yYCxcOx+sNUlhY2fzZX399nMmT07jnnipuvrkCgMsuK2Hz5hFM\nnGji179O4bbb4MILW0jOBReUcMEFcTz+eAaqKpGerrB+/VDee8/JD394vEv1RN/6loO6ep3Pvgpf\noAQCsGW7ePzlMfGbjhsjIjzLFkBaXCW5Y3NodAptpa7MP5IUfn2YjJCbIx5nLA1F+IziuiotF4Qn\nf6fosCvYL6I+dZ0rSYqK7Ayo9kCq2UWFp3fyy0PjoDrGGpxUY/sifwBDUPDQUmcnIWHCgN/rD1k0\nDI6ox2DCoK/BCUtRqWbwVAs1Y8URIjjlSJIRoVjcdvTWCWAktjyIhh9LL0Rw/PiRVEUQnD5WyK1D\nI6DpJBmkHiU4CXrX/bpqg5Ae1/H7+gKNAQN2gw+xPm6ZvCo9MKod67C0lBYSMyEXpk2EsTmQnCRW\n776AmIiMJ1xO9h66k4IauP2g6iJi8OJ/4b2PYfMXVn5ysZfr/+jH4+36tfz2Zonrr09ql+BIEqxZ\nE89996WHinAl3G6NsrIAq1YV8/XX3jbkJjfXxAMPpKEooKoqQ4fGYbGouN0Bli3LwWJR2bq1RRh0\nx45yfL4gp55qAwTB+d//PKxbV8f558dx883JvPuuk2efDSc5zz1Xz+efu3n11SzGjDFisciceqqN\nfftGcdttFdx7bzWBGHjOFVck8NnWjqOvug6794rHo8/qPPbbGtb8IpG5M+CUebB0viC7Tpcgud3N\nKJwY4RuaLh6L54jrUFXE9VFTB/sOQf4u2LW3JepT3EkN1nFj4EgtpJjdlHt6J788YgiUxKj9k2YE\ndzv1NwCZqDTQYm5lw4iMRMAbwGgyIA2CTqq+tjbobQz6FFWY0J8kg8EKuh/kEKvzFQMgY2/ummoN\nnQCGGAlOEC/WXojg+PCjqCrmWFsEegheHVKMsDc2DbioqJVMZGpdD/eX+mDkkJ7Zl+5DosFvJM7g\no97fMlMcrIIp40PdSqGIzORxIrWUMxxsFpE20DSRVmrdvGAyQU979/mD4A0IrZAjNfBuIWzcD3/9\nNtxyF/z1iZbj2X0giYmjq9iyK73L3/fT30sc+sTEnDkWPv+8bYRr0iQT//hHBuPHm5q7s7xejZde\nqudHPzqOy6W3ITcGg8xrr2WhKBLBoM7TT9fy+98v5c47l/Lii7tYvXocbrefgoKWCE5BQQWapjN5\nshmTSWruOLrxxjLOOceBwyHx8stZ5OUd4Nlnh4SRnIMH/cyceYg77kjl+uuTMBqFWOFvfpPCVVcl\nctllJXzySdtjOxHDhqnMn29h5pmxTYQjMuspqbCzv0hhfxGse0U8n5osIjwvPyIIkQ64faJg3NhD\nI61BDa/RSoyHWVNFyssdWptYzCKldahYpGK37RbRnoL9cOCw6KprQu5IePWATooSwB3snbTOsCR4\no+OfIwyZJp16uf1xNR2ValrGq3isBAiC14jBpPZLbWRPYzDV38D/gRRVE5oNxIxxoPlBiRcvBMpF\n37/kiEJwvBiJLZQawI0txrqdzsCFF4PRiN3XfwQntQd5Wx0m4um6zGqRBxYl9tz+dBdlHjtLRjRi\nizMxLg2mZsFZE8VrhRvFhBAprdSbdg7egIjSmFTYWw5vF8KH+2HzgXD9oHOnwreXtSY48MX2DOZM\nKe0WwSkpE6v93/42mRUrjjY/b7fL/O53yVxzTRKSJOpGfD6dQEBnzZoSXnxRrJRPJDeaJnHvvWkM\nG2bA79f5wx8quf32Kq68Er7//QR+9rNxuFx+JEmQmiYUFlZitRrwenVmzTKzebOYAWtqNK67roxH\nHkknLk5m3bpMzjyzuA3J8ftlfvazct5+u5EXX8zCapWwWmVycgx88MFwXnihnhtuKGvXhHPt2kSO\nHdf5eldsk+CcKSV8sb3tb1BeCevfElGV296BZ76E+TmwaBScNg4mZoq0kyT1vOmrLIenu8zmlkjk\nt5e3FLTLEpSUi0hU/i6RDvPtdlPj772LPjMOdpbHtk2OBcqk9kNxKSjsaRXBScCCnwCq14opwfSN\nJzcAPp8Po7F32vf7Ay6Xa3BHcABMJhMulwub2QjmRFFcrDYVuGmAD5k44FiErT2YYiQ4PpzE0/O5\nkwbcWE0m7N6udx51Bx40Uo091yVQK5lI6kaKqqARzh/WY7vTaaTYIS8NxqXB+HSYng1jU6Gq2IHR\n1Ehq5hBMavgqWlHA3gd6U26/SJApMmw7Bm8XwEcH4NND0NgOl3zsU3j1CrEab1qZby1I5fLVO5Fl\nDa0bvkFX3CSx9U0bs2eb+d//PKxe7eCRR9Kx22UMBjEpuN0ahYU+Vq8upqhILPkjkZszz7RzxRUJ\nqCocPRrg7rtb2tMee6wWr1fnwQfTsNuVsBqcI0fqCASEQuuiRdZmggPwzDN1XH11ItOnm1m61MYP\nf5jAI4/URiQ577/vIi/vAM89N5T58y1YLDJGI5x7roOzz3Zwww1lPPnh6snWAAAgAElEQVRkbZvu\nJIdDZu3aJG65M/bzOGdKKb9/eG7E16aMF0Tjyf+Bywdv7RaPn/9HkJrZw2FhiPDMyA51amnCzqA3\n5mNJapvuys4Qj1PmQ4ML1kxvoPy4ncpVgnTnF8Ou41BYBgXHobQbdT5Wo9DB+SLGzxhhgS0diLkm\no3KMlo7ARGwE8CN7zZhN5kERwfF6vaF6osGBYDDYpy3v/UJwbDYbtZUV2DJTBcEJ+kFpWfrr+FGI\nXNGvU4+J2BigmwbiSOvWPkdCLS4cJjMOX9d1RboDlxwk3dRzBKdSspCsxxhLboUt9ZBkEyH5ns7a\nyZLI5eelwrh0mJgB07IgJxkcJjGZaLqYKJrqvb12O3V1dTj6SHxQ14WrukURxOaLw4LQbD4AXxwR\n9TudxZu7RUv7isXw2tviOa9PZf+RBCaOrmL73pQu7+euvaKF/PXXs9m508v8+ZbmNI+m6QQC8Ne/\nVnPrrRXN9SyRyE1mpsqzz2ZiMIi0x5o1JW18qpYutWK1ytTXe6mqarm2dB2KimrJzR3CihV27rij\nKmy7yy8vIT8/B7NZ5r770ti82c2uXd6IJKeiIsjy5Uf48Y+TuPPOVBGVM8uYzfC3v6Vx9dWJrFlT\nwvbtLYzy2msTcXvhgadjO3dD0xrw+hQqayNHPM5cCqW14no8ER6/iNZ9uB9uf1u4ZU/Lgi9uhFo/\nKBKYZdGNaFXENd+baEp3lZc24nA4GGKDuSMFCXP7AQmsBrHfB6sEQd9eEiI+ZXCgUpCz9jByiBgL\nCmJUN882wz/b6eiMR8aIxCFaooKC4LjBZcBqsX/jyQ0MPoLT15YT/UJwHHY7NVWVDM0YApZEYbip\nJrW8QfchS9EITh3WKOQnGlzUkhZj51VnUIWTLFM6jvr+SVFVS0GGm8OLaLsDn6SgoiHrOloXLsT8\nejFxDUsUg19XYDZAboogMXlpMG2oIDPDksSA7/aLf09c8VojRHEdDgfFxcVd25FOQNeFi7pZFv9+\nXAs1frgoE5J/HnmSiwU7yuDi1S0EB2DTl1mcMudItwiO0QhfbZeYNUVmwQJLs5Kvx6PR2Khx3nnH\n2LixpbArErmRZXjppaGYzaJ+5j//aQzbpgmnnGJDliX2728rPLRjRxm5uUM46SQziiI0Z5qwe7eP\n+++vZu3aRAwGiddey2LSpIN4PEQkOboO995bzaZNLl59dSipqSoWi4zFIjN5soktW0by0EM1/OpX\nFRiNEr/4RTK3/SX26M3S2Uf4cEtW1NcvWgUb9nbuswIaFNcKYj71MzjigfF2WJgApw6BxYmhdmk0\nDEjIvVQ0W19fT0ZGRvPfsgy2VnOqxQgTMkR0dNVkIUvRZKB5rBZ2H4etRwXpKQw9nKFrf3QyNMR4\nH5hlSDRIbCN6NHkoKj40amm55lJw4KWWoFvCahkgiqPdxGAiOG63u88FC/uF4MQnJFBZWQmMBcsQ\n8DlBaVWdqnlR5MjFHAEqsTAhpu9rpIpRvVCDU0Y9CaaROGJZmvcgSqUAI3s4dV6NmSTdQ6UU+wcH\ngEYfjErumOAkWVtIzPh0mJ4l/p/qEPou3oBIKZlOuELtMfyMTeJ4zfVe3URQB3dQrK4rfLCpBt6v\ngo9qYE+ruX1VMswZAR90cqKLhj9vhOcuFqm0JsG+LTvTWbNqFw6blwZn7Nf0KfPhyT9BypBwiwe3\nW2PzZhcXXFASVrcSidwA/OIXQ5g2zYzRKONyaVx77fE235WWppCZqaLrOl9/3fb1bdvKOPPMXCRJ\nZupUM199FT6h/frXFVx8cTypqQpDh6r87W9pfP/7xwEpIskByM/3MGHCQR55JINzz3WEVIXFPl91\nVQIXXhhHfb1GXYNo/44FqhpkwfRj3HDXyRFfHz0CRo2A5TEoFS/PgxoPHA4d+q5G8XgkxMtLF+tU\nmnwU4GMBVjJQ8RNARkbpIQPhuro68vLyOnyfJAmy0xrDk8RjaW74PVvZCHvKRarYIAuyVuiEsk6Q\nnZEW8Gs6u+Xobx6GAfcJIn8ZxFOvHURCHjTWBl6vN6Ri/s1HWVlZG3HM3ka/EJzMoUM5euyYEPiz\nJIO7FoytUkhBF0rriE4rBDmOhTkxfV89ZVh7geAUU43NaCLB2z8RnIP4OLuHCU65bCVFd1EZY6da\nExqCMDoF3ikUA+KwRFEbk5cGkzJEoe/oFGHA5/YJ0mBWRdtrE4xqz3SdyLKMyWTC4/FgsXSFsOm4\ng+BQJA674b0q2FgjCM3RdkqVjnph5bjuE5z128B9Hpx7BvzjRfFcUJP5aEsWp8w+yr8/GN3pz0pL\ngQd+D99aJs51U+dpICB8lX72s3IeeCBc+TkauZkzx8Ktt6Y0t4/fdFM5ZWVt74FFi6yhrqsgZ5+d\nx+HDdTQ0eHE4PPzqV4u49NIp+P0akiSzcKG1DcFxuXSuuqqUF18cisUic8klCbzxhpPXXmugPZLj\ndOpcfHEJb7wRxxNPCM0co1HCYpExmyXMFpnaBpmc4XDwcKdPIXMml7J9bwpOd+RV6EWroKRGdMN1\nFqumwPYoWeEkg2giuJhyCvGhAAcYxSfsZyrZWDChoKEENSSlaxHqYDCI1+vF3J4/RSdw4j2bbBeP\noCZSbm9OFwsDZxAOuuDrBtjRKFJXhU445BZjAcBoa0gYsB3+NgwD3hNSWCk4KHQfw2pJR8eP1AvS\nIH0Nn89HYuIA6tzoBo4ePRoWKewL9EuDfdbQoZSUHhcmm9YUcFWAIbP5dSlQi6InR9zWT3HMNTg1\nFKMg93ireAUNqCYjif1UZLwbH+k9XGBfIVlJ1WPrOzfJMNEO30mDRBM8eC7s+RV4/gwHb4OXLoc7\nzoQ1c0QBcFxoLLUYRURG7cXFVnx8PLW1nauR8qHhQkNDZz8+nqYOFYm1BTBiM1y5G54tbZ/cAPy3\nCs6Z2gM7D7yzD66/4oTnPh3O8vlFKHLHKn+yDGsvhYMfi/oQo6GF3LjdGkeP+jnppEOdJjfx8TLr\n12ehqsJa4cABH488EnlGX7zYitksIcsKTqfCBRfM5rrrFmG3m7j44jkEgzbsdiNWq8SKFZErvl9/\nvZFNm1z4fBoGA6xbl0lWVtNMKkjOwYNmbrmlBIMh/Hz861/1TJx4EKNRwhWydJAkCbtNJnUI7H4f\nbvuxSNl1DJ2zlhzkrc0jIr4qSfCDi+DZrZ35LAGjCsvHwuNRsqhLk6A+CIWhiXwKZgLoPMVmDlGJ\nTBDQu0xuABoaGoiLi+u12ghFBqtKszGwTYFJDrgoA347Cp6fBIXzwb0UDi6A16fBA3nifE7AiDlK\nWm4UBupoKeyRkIjHSoOrLpSeGhzaMR6PZ9CkqP7PEJyhWVmUlpWDooI9ExpLwdBy4FKgApXIJ8LP\nIUzE1v7iopYgGvFdjEq0B81kwOHpH4KTjxuLIpHYg3G4EslGph5ZCydBhbnxsCYT7h4D782AY4vA\ntRS+mgNPToBQNoDcVDGAy7IgMVZj/9T8JSUlUVMTeQL2oOFBw4/ODjz8nVqu4jjjOcjJHOE3VLJP\n9jDNEZsXxr1FInI1KjJHjwk/fU20+86c3PJcZY2VggNDWHxS+/VFMyfD9rfhT78Ure+tO2m8Xo0X\nXhAEoHXxLUQnNwBPPplBYqKMLEtoGlx2WWlUNeXly+3N0ZOFC4+Ql1fEyJFFlJYGGTu2iEmTDqFp\nIn04b170e/MHPzgeaoiRMBrhlVeyWukTtU9yUlMVfD6dvYckfP4WryejQQg23vwj2PshLF3Q7qlk\nytgKXB6VA0cjr6aXL4J4B/xuQ/uf0xqn5kIQeK5t9g6AVak6B5WW32YRFmpxoSMxjkxAF7Y13TCw\nqq6u7pcIgSSJYny7KoqrDTKMtMJZKZBuAqOs8zrZ7GMU+YzgZYbyG5K5gDhmY2Y2FkpbdVAlY0dC\nwucMYLVbBoXJJoi6le5G1wYKjh07RlZW9Pq13kC/EJzMzEzKy0IWDY4sqCsCNbXlDf5jqETW+vCy\nFxklZpLjJ0BCjNt0BgFZR5Zl1I7aCXoBZWh4gjo5PZiiPSY5GCM1smwIXDcM/j4evpgNNSdDzSnw\n/ky4Pw9uGA5Lh0CmWRT9GmVwqNCDTV09gqSkJKqrq9HR8eInQBAfAQLoeNC5kBLGcZDTKeZuqvkQ\nF85WEvHvSE7OiLGet8QHZe6eieIUh4o4b1kb/vzL74xh1an7kKW2k1t8nDB0/PRVIWZoaTU++kKd\nt7ffXsWaNaW4XOHbt0duLr88ntNPtzf7UT35ZG2btFLzPsTLjBolUgSBABw50rbl1+fTOX5cLA4s\nFplx4yKHUg4f9vOb31TicmkYjTKTJpm49dbW7DEyycnOVnnzzWH85z2YdhqsvASqa4WtRhOsFtEy\n/eZT8PLfIT2ViDh3xV5e3DA28ovAjVfBl0chlrXOxSfB7ijdRYoEZ6TAy1JLf/Xp2CmkmBxSUJBR\nMKDTvcVVdXU1SUmRywH6C0YZLJLokgLRDj4bC5cRz20ks45MRmFkEbn8ntV8n8VcwnwCBLE1ZGKz\nW/vetbSX0BPpw4GC0tJShg7tgk9ON9AvBCcjI4Oy8pDykz0DqveC2qrI2FuEQuRZRaOcIIGYO6kC\n+EmMUT+nM/DiRbeYSHL3kF9CjHDqOqO6EJgySDDeBqtT4Zcj4cXJUDAfPltiZ7G5gVenwB9Gw5VD\n4aR4SAils5tWXeoAjwDr6PjxYLSbcTqdbNMO8zJb+B2vcxVP8Q47qCXIF3jwtuN5s446UowwJsZL\n560aWDO7mwcRwrWviNqZnFYWE8cr7Rw4ksCSWUfC3nvB2XDoE7jkHDAYwpWY3W5h5Lhzj85ll8Vj\nt4f/iO2Rm7FjjTz0UDpmc1PHlc7NN0dXb5s/39rs9F1U5Is63xQUiPSL16uxcGH0k/znP1dx/HgA\nTdOxWGR+/vNk5s9vfeGHk5y0NIlNm4ZzrEziOz8Ux/HBJ5B3svDxcrfiZbIs0lRnngIHNsM1l4V7\nS00eW46uSxQcjCzTnZsDS+bC9euj7n4bWI1w9mS472jk1xckiHv0+ZDYaQoKYzGygZ1MJRuNABpB\nQOlWaLSmpuYbU+OhIGFDxtJq2hrOEBYwhjwyAA1LYyoOxwDxi+kmAoEAiqL0eWt1b6GsrOz/Rooq\nIyODyoqQfoEtFSoLQLFBU+eOZy8K0TX/hfVCrATHTVIvRHAacGKyWkly9Q/BqZeC7U6+cSrMiodL\nM+GPY+Cd6XB0EbhPha/nwlMT4Vc5cG465NkgzmLC7/djVXRsau9rcfQUdIL4Qy2jbuo5xBd8znME\npQANCRpP12zibXZSRCUaOhspYBgGkjsIZVejUalpnBljFOd3B0WLbF4PyC99fBCKKuHXPw5//p//\nHcd5K/ZiNvkZOwo+eRWe+JOQ728dtdE0oVNz7xMw+1sw4wwJu0Pl5ZeHNk/k7ZEbk0m0acuhi8Hj\n0bjmmuPU1UWPWi5ebKGpI3TnzuiKhvn5HgIBHZtNDvlSRYbQ2Slt1uVRVVi/PouEhNZDmCA5JSUW\n1q2rQlIlpp8RPsRVVsPyC+Hndwo7g9at6SaTiOjc/QvY8Y5I8cmyxuWrdvHUa9E7N395LRyogK8j\n6ZJGweop4A3C81HSUxek6xyWfM3xmWXYqMfPMWqYzShkdGQUJL3rDQ4ulwuDwdDnrbs9DWGyqWLE\nSGNjI3G2+EGhgeNyuQZNBxVARUXF/w2Ck5aWRlV1SBfDlADV+0APttThuPKRJStSFO0aHS82Ygur\neqklmZ73wCinnnirlaTuip50ESWyn4l2nUyTKEq8JhseHgefzYKqk6HuFPhwJvwtD346HJYlQ5a5\nJe/tUMF8whxvtVpxOmNU5upr6H4INqLhQSdAPS8hEeAgX/JPruF9HqSQTVSxn9SUVFIrwtMfZdTT\nQICFnajL+kJxcUF6bCHvIx446obLeiiK86NX4Pxvw5iRLc9V1lj55OtsHvj1Pra/AydNCa+zAZGO\nqaqFZRfCL+8WE7rPB7NXycybZ+Xhh9PbJTcA99yTyvDhBgwGCb9fZ+tWD88917407YoVdoxGGa9X\n4+uvoxOc3bu9eDxC0XjJkvYH848+crF+fT0ej4YsS8TFyTzzTGbYe9LTDdx440wccQk88/wWJCIT\ngL8+CbO+BUdLw6M5IMjh2FEixffU3UUcLk3gwNHIC6rhWeJ3uf7Vdne9Da5dBBuj+J2qEnw3HZ6X\nW87xd3BwgBJScZCKAynUKE43ak0qKipITY2Sk/sGIhBiv4PFu8ntdnep+3OgoqKigszMzI7f2IPo\nF4JjNBoJBALoug6yArYU0NxgDBUg+fah68GohcY69TETnEaqSO8Fu4Ziqkm12PskRaUiugdOw8Za\nEnmQNOZLVr6XIVG8CP49De4cA1dlwZwE0WYKgsA4VEFoOoMm/ZiBhIDPh+5qBH+ow0syQNFlEKyn\nij9SwU008jJJhBexHeRzJqTkkFbRtr6jlEpWdCKq9xA1TIuL3ffr8VL4/ryeSee9vwf2lMG9t7U8\nd/opcMcvRjMsrQSvpyHMhBHExL3pM8hbAh9+Hv7a4aOw4hKZCy+M46mnxEwbidycfrqdq65KbNbM\n0XW4/PL2raotFokJEwTT8vl0CgujE5yCAh+KIr4zKUlh+PD2ownXXVfWHHUxm2WWLbNx1VWCfEyY\nYGLr1pHIRiMnXzyefYfjueXKLzCokUnOtt0wYSm89IZQjW4NITzoITXhALdcP44LV0Xen9tvEtG1\ntwvb3e0wjAvpPv1sX+TXVwwR18xTiN8lGYXpmPkPXzOLHHz4kZDQCbaTXO0Y5eXlpKR0XTByoKGh\noaFPjRx7G4MxgpOe3nUfva6g3yopEhMTOV5yDAJucAyFgAuM2c2v67ijEhyNChxRanSioY7SXong\n7KeCVKuN5B5MUdmQmIKJ1Ti4mSSeIYPPGc5+RvEew7iPNH5MIme1apeXJNGC2RNppfj4eOrqoiwv\n+wmKwYAky0JaYOvfQfOC7xBy/WdY9VMAqGMd8aSHyQjs5xMcdjt2jwGDP/zEfMw+lmDrcA28Cx81\nQZ1zYkw33X0IjAp8a1Js20XD956GZQvh4nPgv0/B+kchK0Nl8uRJ5Ofno4XamZqiNDfe3lJYGwn/\n26pz/2PbyMszMXr0ZHQ9/PxkZKg891xmc6rJ7da4554q9uxpP1o5e7YFv19MvSaT3FxnEwmFhd5m\n8uR26yxc2P6KtaIiyE9/WobbLY7VbJa5//40brstmS+/HEHhIYWxS2Q8Holn/zuOg8XtkxyXGy69\nAS7+sfh/k9O2ruts27aNvLG5pCabefwu+PQ1UbTdhAm58N2z4MoX2t3lNrhhidCC2RdFjeFH2To7\nZQ9NCcBvYaceHwepYBFjQ9erhNQNAUtd1wdkgXF3UFdXR3x8fH/vRo9hMBEcTdMIBAJ93vLebwQn\nIz2dQ/v2gRaAuGHgaQwjOGguVCJXXAc4QnyULqtoqKSIhF4oMj5IOTabjczG2AlOKgrzsHAJcdxO\nMq8ylO2MpJBRvMJQ/kAKPyCRk7ExFAMKEmqo0M6MjNRL0u0DkeAI53kzejAAI08FdwU4lkL1PzEz\nAzDgZz9+GsliYvN2GhouqQ5Tqo208vAQzMfsQQFmdyJN9Yni5Iqhsa2XA8BH9XDT0pg2i4o95UL1\n9pl74dQFos1ZkkTKNy4ujv379+P2QFExzDwTHmpHUVeWdNZekE9pGdz852lccUUizz+fidEorilJ\nghdfFFYMkiSh6zo1NUFuv71jD46FC63NJRCqCvv2RSc4tbUatbWCfFgsEqec0nFE7bHHatm710cw\npAynKBK//W0KDz4jc8r3Wt8TnSM5AC/9F8YvhZ17ReSruLgYTdMYNky4x5rNoiZn29tw5y0ijfXg\n7bD9mDBP7SySrHDJLLjtYOTX040ignOP1OLNdQnx7KCIbJJIwRFKTql0Jz1VXV1NfHz8oFH8Bait\nrSUhIbbazIEMp9OJzdYHjsB9gLKysn4pZu83gpM5dCiHjxwRasZxw6CxDIytCgwCtVEJjo992Nsp\nQo6EMvZjRMXRw55UATQ0i4lEjz9ia6ICjMDAMqz8iATuJ413yWYvOWxhBP8gg1+SzMXEMxMLiaFB\ny4SMDRlDL5GY9jAQU1R+lwtkWRCdhJFQtgvivw21LwM6FmYAEGQvw5ketu0xvmZseg4Zx8NXDxpQ\nTh1ndkI48s9SFVMckBNjSvy6QuEaPaWb3ZHzc6DglzQbh5pOSJeNHz+Bw0eO8OLrtUxaJrqloqGJ\n3AA8+Nw0Nn4qMWeVzPIVDj7+eDipqQq33DKEGTPMmExiiPD5dL7//ePNnVHtYeVKW3O31fHjAbze\n9rdpIkCKIrF0accDuqaJguMm/R1VlXB7wBHxZ+w8yTlcDLPOgoeedlJQUMjkyVPDIiQGVejnXLsG\nXHth3kw45x8d7m4Yrl0MtT54oSzy61cO1anSNT4P+TBNwsQwDLzElyxiLAH86OjoaN1KTx0/frzP\n0wW9jcEWwWloaMBuj03UdqDi0KFDfV5gDP1IcLKyszlaUgKqGeJHQN1hMLfoTEi+Eoz6yIjbetke\n6qLq/OTvoR4fAVJ7oQ7HK/mQTEZmeWTOxs6NJPEUGXwWSittYhh/I50bSOJs7ORhwhKKwFiRsSL3\nmpFeV6AoCgaDAY+nA8nePoRsNKK53SDJ4GuEmv1gnwWyFXxHseoiTNLAK2QzJSy6tZ03GZU8jNRK\nI9IJjT+fsIezsHd4IxwiQJkWZE1mbNPKATfsboRfLo9ps2YMscEzF8EH10LOkMimoj4f+AMGXn53\nBs7aLajtePicSG60UFpqZyGMXiSTlGKirCyXP/whtTl15PVqvPeekzffjCwA2RqqCtOntywiCgs7\nLr7Pz/egaeK8ZmerpKR0HFXIz/fw6KM1zakqixnWnCva6dui8yRHloJIvi/511uTcHstYZo5TbBa\nxFpGluCh8yC7kwtThxluPAXujtIabpDgx8PheaUlp3gZ8RynDideFpGLhIaCiqRr3WofHmwER9O0\nLluyDEQ0pXSMnZPZHvA4cuRInxcYQz8SnNGjR3Oo6LCYsIbkQcUuMI5ofl3y7sfAqIjbuvkKCRlb\njK3iPrykdZPgxGFhLOmczDguYi6/4iyGYGG0I5G/NTr4I6n8iESWYiMLAypSmH5Db6WVehoJCQmd\ntjjoCyiqimyxQMCPLhnBlgFBDzhOQa75D3ZOB6CeF1BQSGVM87aNVOJRXKhJFlIrwweMd9mFGYm5\nnUhTvajU8cNs0YEWC67ZIzRPcmJQNpYkuGIuHPo1nDtN1PLIEe5Wtwe2F4r0yqPPJ/LvjaO54dKv\nIgoARiM3TaiuhelnyGia3pz+ATGZ//CHUfqZT0BrchMIiI6rjrBrl7c5MuR06u3q4bTGL35Rgcej\ni2YFhO7PP/8KQyPO250hOTo/OG8bW3al8Y9X0sk7WRRnRyI5kiQKkZfmwt5fiTRkR8Xk1y8GjwZ/\nieJ/9d108Tvfi1DejkfmbBz8my2cxEhkJKFc3KR/00XU19djMBgGjYAciGNyOByDRjNmMKWnAPbt\n28eoUZHn895EvxGc3Nxcig4dEn8kjobjW0Jt4qEL1LUNA9FknV0E8OIgthZHP42dIjgSEqk4mMow\nVjKJq1jM7azmUS7jAS7iRlZyPrM5lfHkko6MhN1ux9PQiJ0WBc5vMpoUgAcczCGV0pwVUHUAElZB\n2V9QGYqBEYAPP8cYycywzY6zgzFDR5JVHJ6mCqBRSg3ndaIA/QFqsChwRowWDB/XwiEX/GZl594/\nKRO+vBH+9h2x6jdHaCzSNKFKfM+jMOfbcCSkwfLOJ8OprjOzZvVOaJXE6IjcNOGJP4HXJzV3Nrnd\nGrfeWkFxcecUcxcutDZ3OXk8Grt3R++gakLrImSzueN28SZMnWomMVFpToFJkkjdvfxIZDLYEcn5\n9ikHSHB4eeEt4axdVQOnXSyKtU/UzGmCSRW/z29WihTigpzI+5pohVuWwR1RyI0E3Jaj857c0Fxc\nfD5xuPDzBYdYyWRkNDQCwkSyGxN5f0jm9zYGW8F0Y2PjoOoIO3DgAGPHRlcC7y30K8E5fDh0tzuG\nQvGnovW3SQun8VNkkiCKQaaGM+ZC40bKyG7VXm5EYRhJzCaHVUznxyznHr7Lk1zO3ZzH1ZzCamYy\nnzEMYwjmkDutCRUzBuRWp8/hcNDY2HEI/5uCgUhwfC4XejCIZDCJyF/5dkg4GwKl6MFybKwAwMmr\njGZu2LZbeY1xaTmkV5qQT5ioNrCd07Fj6YCY+oAtspubRsRe/fCjQvjedBjTTvOf3QT3roavboIp\nmWCJ0jHt8YrJ99Tz4bZ7Tpx4JR7+1xSGpjayaul+oPPk5rJzhSFnk0hgMKhTXOznvvs6fx2sWGHD\nahX3haJInUpRiU4qsU+qKrF8efsr11mzzLz33jDefXcYGzbpbCuQmy0ojEaYMl6I70VGZJKzeOZR\nFkw/xp/+MbPN+XnwaTjpLDhS0lYzpwlWo0ghvn8trLtYOGm3xm2nQX0A7jsSefszU4Q+1S8lUcRt\nAH5EIh+zm5Ekk0UiMjJqNw2DdV3n2LFj/ZIu6E0MNoIzmOpvAIqKisjNze3z7+03gpOWlkZlVZUI\nL6tm8dCcYAqFsdxfA0EMDIu4vU4V8XSub9eEnXRyMeNgJiP5OWfwNy7kMdbwG87mChZyJlOZznBS\niUNBRkbGjAETaqfSSna7nYaGhs4e/oCH3W7H6XQ2tx4PBBibWia9bhHFCXhFDY5tLnLtOzj0cwCo\n4TGMWEltleKspwyP4sKQbCO9LDyK8xn78aN1qtj411I5s+NhQoxjzwc1sNcJ95wd+fXVU0Q66gfz\nwKBEd1j3eIXlwNglwnIgEoKazN1PnMTcqSUsnX24U+RmzEh4+A/hQoHBoDDTDHTS7kiSYO7cluiL\nxSJTUNBxBOfo0QA+XwtpHDPGSFxc+NCkqrB6tYPPPx/B5s0jsAI3N3sAACAASURBVCVambhMYuUl\nEhddH/55FjP86jpRBBxlT8NIzkkTS/nO8r3c/sgcPN7IrHJ7gdDMqW8EVxSSI8sixfSdqeK3vHKu\nOCejU2DtQrhmb/RzcOcYnY9kJ03xm1U4MAEv8yXfYhpBAqHCYq05JdcV1NbWYrFYBlV6Cr5ZlhOd\nwWCL4Bw+fJgxY8Z0/MYeRr8RHEmSSE9L4/DBg0K8LXE0+BrA1CQ0oaHrToxEjvkGKCIpjPxIOEgm\nm8lM4jQWcSWr+B2X8ncu4SFW8jMSyEBDYxyZxGNFQkJFwYwRQzfdZ+12O42Njd0afAYSJEkacHU4\nAJKigMmCrsswdhXUFkHid6H09xjJRSUbaMTPUUYzL2zbIj5nyrBxjDjSdnAv4AhX0HEHxgECHMLP\nLSNj/50v2gWnjYe5rWrnc5Lhg2vg2UtEQbElygI9EBSmlWYTnHU51HTQxe/xqfzh0dlctmoXGSmN\n7ZIboxFeezzct8rj0XjhhTo+/dTd6eObMMGEydTyHbW1QWprO0eQDx9ukVlwu3XmzbMgy7BggYUH\nHkijsjKXp5/OpMppJu9kiblnS+wLZbj3F8FdD4dHV1QFXvk7JET9SQXJcXsVfnrZV9z5xEnUNbav\n0TFqOCQ4YOMBcHrBF4X4mQ0iGnf/d0Q0buO1UNgI66NYd61KhZEWuFESb1CAn5LEFvaRTBxTGYYM\nKBi7pX0DotizqfV9sMDtdg8Ky4nWaKopGgzw+Xx4vd5+6XDrV8vE3LFj2Z6/FXQNknLBWQXmVmGs\nYC0GwjupJEwYyUPGQjaTOZXrOI8/cTlP8F3uYSnXMINzyGUhyYzAGCoeVTFiwByWVupJyLKMyWQa\nUJ1H3UVSUhJVVVUdv7EP4XM6Rbu4agCjA0q2wJALwXcQPViOnbMAaOCfjGE+UiviupX1ZA/JIKnR\nhNkdfh08///YO+/4KOr8/z9ntmTTK+mdAEkgQELoTYqKevbzznIoX3vH3k5FT+zt7J7lTu8siCJ6\nYkEE6SWUQCAkkF4J6YVks23m98ckgSS7KSRhN3u/5+PhQ91M+Wyys/Oad3m92cloXEjoQwpguVjF\nn4MgvJ+eVfubYEsdvP9n5Sb45CI4/JjSAq7roaxCb4LCWjj3PWhqgWce6P1coiCz+KLD7MsagVYt\nc+6sQpvbvvwYxETQyQnZZIJ777U9TNMas2e7dYrE9OR/05WMDEPHw4FaDT//HEl9/Rh+/TWS+ef4\n8Lc3VXiPE7lgiUCBlS6kZ99S0nbtzxeiqIiRT161fc7pE44RFdrE+p0RXH9pZo/dVT7e8PO/YXsR\n/OEfkPgcHDwG+h7eoqsGkkLA3w2yWsDDyjOUSoBXx8j8Kp6gvi16cwme+CDyH7ZxMcmY25yLwYI8\ngO8vs9lsl4GHQ01NTY1TpacsFotTTRE/cuQIUVFRvW84BNhV4CQmJpKZnQ0qF0Xg1OaB60Tlhypf\nBMmCj3wz/vyVUFYQzV5iOUoEP6EjBQtGYkjFmyCldRIRTVtiyR7dSj4+Pg5nkDcQAgICqK7u3djt\nTKJxc0M2m8HYChaz4oQtuoHHHMTab/GWrwaggY8REYngpI2wET2NQjm+EYFElXT+8qihmeM0cmMf\nOvN+R0+FbOHR04ji/DEDRo8A/avwyEKlSFVrY3SOLCtRgs93Q9ILsCEHFn8OD9wCF/fQdn5qzc0b\n/05l2TszmJtawpXnZUMX95Tz5sGtf+k8nFOvl7jnngpqavo3yHHhQjfc3JTrTpJk9u/vu9jfv7+1\no1jYxUVElmXu+ZuI11iRxAUir30IPWVLDQa44UE6anFAMec7dy7ceFX37c+dWcCV52ez7O0ZfPjN\n+B67q9Rq+P4jwAUWvqu8VlwHU1+F134Hk8X22tQqJSp3YSAUzKabG/aNYTBCCw8IijGOBngEf3Zx\nFG/cmUFcW/RGjSAzoOhNeXk5QUFBTjOrqZ2qqiqnGznh5eXlNB1hGRkZdikwBjsLnPiEBHJy80Cl\ngSn3QlAyeC+CCXUwsRZBG9HWDH4jbsxETSACIgIaRNxRDbDgbrDx9vZ2uJTOQPDx8aGxsRGLtfYR\nOyEIAoJaraSpECH+T1CTAwHXw7GnUBOKC0mAhJEDJNDZRngfq5keOZ6YYtdunjir2MUleOLXh8vi\nWVUVN4ZDaD+iOMFa+HCsImgkyXY6ChRh02yEP38CN62A1rYb9/cH4fWNsOIdpZi2K9YKipv1Gp58\newbhwU3cv2QvWo3y9wwOhC/fglMj+2azjKuryNdf97+ebO5c944vZb1e7nGKeFeys40dYxIALBaB\nL76jz/U/AL9uhl82KmKnHZ0LvP23kyMWRFHihssPMjO5nL++MYvaBld666768EWYMBYmv9ZZyFgk\nePxHWPgO1LSc/BtZw1UF/hr4LAk2pCqGkd5qeHG0zD9VtbRLwavxxhP4lO38kdS26I2IYks5sDR6\nQUEB0dHRAzqGoyHLMtXV1QQE9LO10YFxNsPCrKwsEhIS7HJu+6aoRo+moKAA2WgAyQJebaMa1G1P\n0YIGUXBHYHg8cTjiiIOBIAgCvr6+jtdN1dyMDAgarSKOS3eC7xUgtSAbcvFqi+LU8hLhjMP1lNqa\nIvYiuIKLjxshxzsrjL0UcQIj1/WhFudHmjkuW1gW23sURwTuiIC82fCHAMUgznobs4LeCPvLIOFZ\n+C6j+88f+QE25sFvX0DEKc0wPXVLGU1qXv0klbLjHiy/eyuB/i189Y4S5Tj1QVGtFmholCgsjOOm\nm3zo68N+TIwGH5/Ob6qnGVRdyc42oNOdXIjJDFMm9nn3Dm7/K0hd/iQqlVJjFOBn4PFbdqFVW3j6\n3emcaDn1729d5Cx/EP70B1j4PlTYMPfenAvxy+H3nJ5FjiCAToSZPnB4JhTOljEKEi+3+d54IfIw\nfqwjgxC8mUJsW/RGhSAzoNbwuro6VCqVU904QfGLcXFxcar6G2cbOZGbm/u/GcEZNWoURSUlCFoX\n0LoP6AJ2BLy9vR1uxMFACQwMpKqqyt7L6IS23QDL0IosCeAZChYj+FyOWPEanlyGgI5W0jBTRzxn\nddo/ny1MikliZH53v5V1HOAmfHptGQd4XHWc/wuDkT14BKZ6QcYMeHk0uKmUye62kCQwWuCl9TD9\nNSjtIRh43vtwrAU2fQ0Bfn1rBZdlgRU/x7Nq3SjefGwbEUFlnUY+6Fvh7x+DzziRj1eqePXVIAoL\n47jhBp+OGVW2mDPHrcNVGJS5Uj1NEe9KTo6xm+ibPaXPu3dQfhwefbFLwbEa3F2qeP/JLew4EMJ7\nX03AIln76ussch681cL9N8MVn8IeG+3d7dS2wPnvw32rleibpYd0mlYEl7aOK2+VyOy2OsF78cOM\nmW/Zy2JmnFJ7IyEPMHqTn59PTIx1Z/jhjLOlp8D5Ijj2ahEHOwscb29vWltbMRj6/kXoyKhUKlQq\nldO8H4ARI0Y4nMABlMniru6AALGLoGQXBN4NNR+BbMaDiwBo4gvGcnZbmF8hjW8I9huBt1mHd33n\nEMWPHEBG4uo+GEJuQE8BJl4b0z2K46OGfyTA9ikQ76akKHqi1QTVzbDgLXjq5+5RCGukvAKCDjau\nlLl3Se+t4O1IQiiLzp1JaUk+6enpmEwmZBlOtMATryjbPPQcBCSLrNmo4bXXgjh2bBTLlgUQGmo9\npDN/vluH/w0os6v6ag4ISlHzsWMnt3fVwaKz+rx7J97+BIrKlDZ3i8VCZmYmOUcPcdbcKbi4RdPz\niBdF5IwI8Obis9K4cYWFnw73/dzvbYVJLytF4T0VIAO4iUps+hNCWUsEN+LDp2xiPOGMIgh1m++N\nIAsDqsfQ6/XU1dU5XXExQGVlpVMJHEmS0Ov1TjNFHJTOPXu4GIOdBQ5AdHQ0hw/34xvEwfH19aWu\nrs7eyxg03N3dMZvNDtcdZjYYkFpbFaFj0kPNYXCbALpEhPof8ZFvBqCO19Gg7eRsLGGkQjjEmFGj\nGJPb/YtkE4dYih+6PkRx7hIrWBQAs06JKF8TohSULg4FjQiqXq6yVhP8dgTGLIetNqZMW8MswbgX\nZepq0jl/Hnz+Y+/ixtMDVn8IHu5uzJw5Ew8PDzZu3EhhURm3PipzovnktkYj3Poo+I4X+fu/VNx8\nqz9FRXH8/nskixd74+198o0tWOCOKJ48d1GRydrs2R7p6pmTOr5z63pfsVhgyX1w/HglGzduBGDO\nnDn4+Xnx+ZsQ2ot91uN3C9x0XQKbS7yJNaeh6eoM2QuHjkHS8/DFXjCYrc7g7UBEQIvASDQYkQjC\ni+uZjdwxVFMeUOcUKC6yMTExiD3lRYchFouF+vp6p+qgampqcqqRE3V1dWg0Glxc+tlyOkjY/ROf\nmJhIenq6vZcxaDiiA/BACQ4OpqKib7OIzhRqFxdEnQ4QkAUNxF8B9QUQeDdCyX1oiUFHCmBGz0Ym\ntrWPt7ONT0gIiWNEgw735s6XwdfsQY3MNX2I4hzGyDZa+CBRJtFdidh8lAg+mt6jNmaLcgNcugou\n/ADq+245A4CIzPUj0/k2A0aOTWbHdwJBvTzMfvwS+PkowkEURUaNGsXkKTPIPFzMuOidRIV0T7FK\nEjzzBoRNEZlxqYBeduONN4KpqRnNjh3RvP56EGFhJ2sgZFkmI6P/Ucy9e1sxmU6qAUGA5LH9PgxB\n/s3MmrCH7buySRo/mbFjx6JSqZQaGC18bWOUgyjCW3+Dx+6Eaz4TeGxbAvlN3jyS1H+RozfBjV/C\n1Z8qAqen2hwAF0S0iFzBFDxxRYuuLXozsM4po9FIeXm53dp0h5L24mJnEm7O1vK+b98+4uPj7XZ+\nu38yUlNT2bt3r72XMWj8f4Fz5jC1tIBGg6DSgM4PCjaA/3Ug66HlID7ybQBU8Tg+hBJ0ygDOFuqp\nFvKJiotmTE730QDryeA+/HDvQxTnPvE4sW4CmTOVmpue6mza0ZugoBZSXoIPtvf9PbcjInNHQlsr\neGYy8c8LCG6w90eIi7a+z+LL4cKzO7eEA7i6urP0+Wms3RbNfUv2sHTxXgL9m60eY/cBOP86Ab8J\nInOvECisdOX6G3xobDxZdGIwyBw40P+IX1aWkdbWk8exWPpXh+Pt2cpNV2Sw7PYdpGcFcsuy2eh0\nnUWqVquIpsfu7Lyvmyt8/zEs+RMs+gd8ewBA4PP80xc5AKszIKfNaUHfh93ViKgAC22KaIBP8vn5\n+URGRjpdazg430R0cL6RE7t27WLy5Ml2O7/dBc6kSZM4ePCgvZcxaDjiiIOB4ufnR2NjI+b+9Oye\nAdSursgWC5gMyBIQkACmE+B/A0LJPbizEDURSBzHwCFS6DwnYQsfkhKRSEi1a7cozrfsRcbCLb34\n4izAjbVEIovK31vTyxXVnq5w1cD8t+DwaejGU8XNO1nJSAi0miH+BShugb0/dRcGcdHwj+c7j2IA\n0Oth+ZuQXySwKyOEe1+Yx8GjI3jq9h3c/Zd9xITZ7grctgeuuhM+Wy12qr8xmfrXQdVOdrYBjebk\nDd1VB+fM7X2/kBEnuPmKA7x0/xaOVbpz9/Pz2LArktp6gbuXdZ8f5aqDJ5fCtBTl/6PCYc+PMDUV\nJr4Cm/NO3XpgIufJRRDmA2O2w8uFYJR6r68SUaFi4F1BRqPRrvUPQ4ksy1RWVhIY2L+By46Os42c\n2L9/P6mpNmemDDl2FzhJSUnk5ub+/xEHDowgCAQEBDhcsbEgCMroBp0bgqiC8BmQ9xsEPwzNO5FN\nJfjKtwJQzUOEkogfER37N3KcGrGI6FExxB/pHsVZxU5ux5dAKx0soaj5lBA+IIQRqND14VIySnDC\nApekQ1Ez/Htx/x/QrYmbdiQJZvwdvstUWshvvkZ5XaNR6m66PsRLElTWwkvvn/KaLLBhVyR3PTuf\n9KxAbrtqP3+7cxszJpbZdPo9Z07nY+t0/eugaic724hOd/L3KAgwc5L1bVWixKSxFTx60y4euTGN\ngjJv7np2Pms2jcRsPvn3+vQbOJittJ132l8Fqz+AKy+Cg7+CUQfRz0CeVV/L0xM55yXCY2fD/x2G\nYgMsy4OFe6HaBK1nwFoqLy+PqKgop2qhbqe+vh53d3enem/OOHLi8OHDTJpk4yI+A9hd4Oh0Ovz9\n/cnP70d1pYPjjGmq0NBQysvL7b2MbkhmM5LRqLgam4xKiERwBb8rEUsfxYsrUeGPkWwMHCGVyzvt\nv4n3SI5IILjOFc+mzkJmI0doQM9j+He8pgZuxYfNRDILV7QIfXLN1ltgXyMkbIPvq2DWHkiNhGfO\n7/t77UncnMp1n8MDP8Dfn4LP3lDqSkZGdR7FAEoN0PUPKMXEXbFIIlv2hvPQK3P48qd4UhIrefeJ\n9dx+1X6SRlehaotYeXtBbJfyDlHs35iGdhobJWprO9/5dTpIaMssioLMmOhabrj8IO89+RtnTS7l\n5y0x3PPCPNZtj8Zosp4bvP4Buho4K6McvOHLt+E/6TDxJWjpccn9EzljAuHr6+H9MvjmlIkXW+og\nfiusrx1akWMwGCgtLSU21vosv+FOeXm503WFOVv9TWtrK7W1tYSHh9ttDQ6RmE1NTWXr1q1OE0r1\n8/MjNzeXuLi43jceJgQGBpKRkYHZbHaofL7YvhZZg2wyIMT/EfJ/gYinIHMUcuRr+Khuo0ZYThUP\nEMEP+BFBLcpAoyaqOC5mk5AQT+3hA+yY2jkl8w/W8wgX8i8a0CLwGkEEocKlj88GkgxmGV4ogOX5\n0J64LDXA4ixYuQD2lii1Gj2+zz6Km3be2gTb8mDzXeBuxaen1QBr1iuTyXtGICvfn6x8f7QaM1OS\nKjh/TgFL/7KPnCJfTHIQ1dV+jAjw6CiGPXbM3MmVuD8cPWpk2jRlwbIsU1/XwpUX1FJfW8nYuGqK\nyr3YsT+UL3+Mp6W1b0+6mUfhzX/B7dd2rj/SuShF3nutzLayjiJyronN4pGkNF44OAWT1F1UBXjA\n+jshvQmWHul+lDoz/CEdbgmH9xPBJMloxMHtmsnOziY2NtapogHtyLLMsWPHmD17tr2XMqjU1tbi\n7+/f+4bDhF27djF27Fi7doQ5xJ1q8uTJ7N69m+uuu87eSxkU2lNU8gAn/zoSoigSGBjI8ePHCQsL\ns/dyOmE2GBBlWemMMRug5ThoFoDPJYglj+Md/R51vIuRgxjIYgp/5hde6dh/I+/xp5C/k513lIDq\nFqoDTt6dczhOEVWsIQJTm92a2Mc5Z60WaDDDZQdgu5WM5feV8GoxfHEdzHgd0kutH6e/4qadsgYw\n0xbU6rKLJMFdT/bpMB0YTWq27gtn675w1CqJ+Jha7r3hONlZJaTrm/H09MTb25viYjXjxrVQUaGh\nvl6F2dyzGFSrZby8zAQHmzhypBBPT5mmpkYaGhpwdXVlXJwvy98J550vJ9qM0vTGstdg8WWKqDn1\nd+GihneugG35cKRPs0V7FjmuGvj1djBpYG5az0daUwWSLKMWBFqR+pTm7AtNTU1UV1eTlJTU+8bD\nkLq6Otzd3e3WejxUVFdX280QbyjYuXOnXQuMwUEETmpqKitXrrT3MgYNlUqFp6cn9fX1TlUwFhYW\nRkFBgcMJHHX7F50kIZtkhHHXQtEGCHseMhMgYjm+qruoEZ6mkqWEs5ZA4qgkF4BWTlAg7GDa2Eno\nM3awfm4tCIod3FzGEIU/Zixo+uEm22qBtTWw5BDU91Cb/VgujPeAdXdA8ktQ0sVC6XTFjSDAiiXW\np5TrWxUjv4r+DQvvhNkicig3gEnJAUxIVAzKmpqaqKtroKysmoULGwgJMeHtbUGtlpVW6VYRWQYf\nHzNvvVWIi4uEKCrdUo2NKioqtNTWSmi1gYwZE4K3tzeiKFJdC+l39r6mnmhugZsfga/ega73RbUI\n390EE15UXIh7x7rIUYvw35sh3B/idpyM1llDLcB3E2XyZTPnicU8TQB/xAsNDHhQcGZmJgkJCU7V\nPn0qpaWlDvcdNFD0esUjwlkmiAOkp6fz5z//2a5rcAiBM378eI4ePWrvZQwq7SMOnEngBAQEdDjf\nOlro26TXoxIERJUIZiM0lEDUHPC5DLH4AXxiPqGef2DiKAb2MIPFfMeyjv238S+u9nsPL09PYgpb\nMMe4cRNzCcWnX8LGLIEFuCsbPirr2z5/2A8ZU2HjXTD5FcX2H05f3ADcPw+mRisRilOxWCC/GN77\nT58PZRNXHYxrGzEjimKbIPFk61Y1n37aOS8mijI6nXLLf+utQh5+OAKDQcRi6fyeFixw4+abI3B3\nP3lz9vVROp2KbES4+sr3v8KmXXDWNKVdvB21CqJ84bVL4M5v+nq0ziLnpUNT+GKJitRoSNoJjb0I\npTfGyIzygFliCa3IPEwVv9PCh4Rg6qeYPpWKigosFovT1ae0I8syx48ft9vwxqGiurraqRyZQRHa\n9iwwBgcoMgZFtfr5+VFYWGjvpQwajjriYCAIgkBISAhlZX28c59BNK6uivGfqFYSSeOvU3xxwl+G\nupXIpnL85YcBqOA2fAklhpPhUwmJ3XzJeWPnMDnXn8cM5xOBH9p+PAPoLZCnh+QdfRc37aTsApUr\n/H4XeLgMTNxMioBnL1TSJV2RJMXldzBcDKalgLFLrY1G092RWDmvQEuLipYWFbKs/HdXcQNKe7mb\nW+fX9XqYM3Xg6wUlimOtYdNVCzfNgAv6ZSyoiJyCE958sSiNeaMsTNut1Ff1xJJQuD4cblSVUX9K\nnKcUMxIyJswY6b8lg8Vi4dChQ4wfP95pUuNdqaysxMfHx+EesAaKs7W8GwwGampqiIiI6H3jIcQh\nBA4oaaotW7bYexmDhpeXF83NzQ7nHTNQoqKiKC7uZfKgnTDp9UgmE4IggtkE+lpQjQD/JYiFN+DJ\nJWgZg0Q1zXzHDBZ38hsx0oyoUzM6dhRHD2ej6uPlIbe16KyrgQk7IMu6R16PmIFxOyDQF9bdIbN0\n7OmJGw8XWH0jqKzsom+Ff30Ne3opaO4rs6d0T3/pdCLZ2f3voGqnvNxMa2tnBeKqg3nTT/uQnSgq\nhaffUERTVzQq+PxaCOndwLoDlShw0ewEYkO9+XBTGvnNPbdGzfaFfyTKvCRWs5OTBj1eiPyLEA5R\nxm38h90UACD3mOjqzNGjRwkJCcHT07Pvb2CYUVxcTGRkpL2XMajIskxNTY3TFRgnJibaXWg7jMCZ\nPHkyaWm9VOUNIwRBcMp2cS8vL2RZdsip6R1RHLUGWVBB4lVwdA2EvwjNO6HlACPk5QBU8QgaVIzn\nAjwJ5AIeYR63okFFXMxIGhsb+xSBMyLRjEy9JBHiImMegJ3TCQnG7ZCRqtI5NwH+ldc/cQPw4ZVK\nF4+1+VcGo1J7M1gsmtvdOLC21tLJ1fh0KCjoHBZSqWDhrAEdshOv/AMqqrtHsQRBqVn6+vq++RO5\nqOG/N8E5YwX+0pBAhuTNI8Y0NLJ1kTPaDX5MlvlBaOJDTnbrCcB7BKPBxGv8jBqRKPzRcwIBEUnu\n3RW6sbGR8vJyxowZ0/vChykGg4H6+nqninSAUhTu5ubmUN2pA2X79u12LzAGBxM4Bw4csPcyBpUR\nI0ZQWTmASk4HJTIykpKSPvfWnlHMBgOS0Yggy2AygIu3UhQT9DBC/uW4kIwbCwEzdTxPKpfxZ14m\niNGo0CAgIIoiycnJHW3xtmhF4iAG5lHEbLGQMR7w4qjTVziiLHO1Pp3vqmBiajIblwp4W2nxtsU1\nqXDpeOupqVYD3PE4NAySLlWrIcVKk87p+N905cCB1m7Gn+EhEBgw4EMDitPy/92v+AB1xUUNKeHw\n8MKej+Hdlk6cNhKSd0Fmi8Dn6gTyResiJ1gLv0+WOSK2co/Q+TvhIfxIxYVn+Q4JWMwM/HGllmLM\nmEAAWbIdGZIkifT0dJKSkpzqJtmV0tJSwsPD7R4VGGyqqqqcrv5m3759TJnSjzkrQ4TDCJzk5GSO\nHj2K6XQNNByQoKAgpxQ44eHhlJWVOeQ4io4hnC6uoNJC9AI4tEJxN5YlhNqvCZSfx40F+HIHFowI\nCKi61Np4eXkRFhZGVlZWt3NIbbOe36KOyyijAgv1SNyuKufOSLi8l2nV1hBlmTtMSlrqVVUy8TsF\ngv1hx70Q1IeMw8gA+PAqcLEibkxm2HcIvviu/+uyRco4upnnSZJMevrAp85nZBi6pamaW/o3l6o3\nNu2E1b8owq8rrlp4+jylSNsaUX6w5wGIDIIxO6Cg/S0L1kWOjxo2TZbRa8xcLnYuzroED27Gl/dZ\nRzUnmMUoZhJHCXsJIg4VIqIkKk7dNsjLy8PLy8vpIhunIsuyU6anAI4fP05Q0Gl8aTgwe/fuZdas\nQQy7niYOI3A0Gg3jxo1zqjocnU6HSqXixIkT9l7KoKLRaAgICODYsWP2XopVLCYTsskEFjOy2QLR\nC6GxGCLeQih7DFHwJZRPUBOCCq3N44wePZqamppOIrUViWos/JEy3qSuU4XEJvR8INbxn3EySR59\nX++p4uYdTTKSIFBvhrjtoPGAvQ/B6B7uXRqVUnejtnE1y3Kbm+8gMmcqWLroW71eJjOz/yMaupKd\nbcTcJdenc4G50wZ86E4sf7N7iq0dtQjf3gBeXbp2Z8RA+kPQrIXY7crYhU50ETl+ooXfU2U8dBbO\nEYs6fV4mo+NVgviePeynhFhGcD2zKSSNkUxTpomjQhZsF9Q2NDRQVFTEuHHjTut3MFyora1Fq9Xi\n7t59pMpwxmQy0dzcjLe3t72XMmgUFBSg1WodIirlMAIHYMGCBaxbt87eyxhUHHUS90CJiYlx2PEa\nKo1GyaHo3BDUWvCMgJyfwPdiGF9M25QBhF4+/qIokpKSQkZGBkajEQMSG2lhHsXsw3qk4iVq2SPo\nWTtJJtC2djp5Divipp1WSRnSWGiBvQ/CXBvG2M9fCHEBitDpil4Pr30IR/K6/2wgnDNHmcDdFWsd\nVP0lK8uAq2vnv41aDecMknGtVgsP3AK7/gvNemWye1dEIWVlugAAIABJREFUEXzd4J9Xn3ztlplK\nWmptA0zcpcwWs0qbyClVefO5SxpBOhMLVIWcmryLQ8O/CSWNXH5gP/648yDncZyjRJOCiEopMJYl\nmykZi8XCvn37mDhxotN1FXUlPz/fKcdOtHdPOVPa7eeff2bevHn2XgbgYAJn3rx57Nixw97LGFRC\nQkKcUuD4+voqVvoOPFRUliRlRpXWDSYvVVyOAcS+O6B6eXkRHRvD/v37+atcyU1U0NhLZ8vVYjkG\ntZm1KTKuPVxhPYmbdiSUuVVfVysOubfM7Pzzs+PhrrlKWqUrsgz1TfC3v/f2LvuHIMD0lO6vu7kJ\nA+qgaicvz2i1yHdUDHgNoEFIFGHx5VC4HR5fCk+uBa9HIKeqezQKlFqmC8bCHbPhy+vg9cvggVy4\n6mDv5/LWCNwxM54APy/+s3cLraecIBQ1XxNGCZV8wEZc0fAw52OkDk/8UKFGxoQgCyDY/gAdPnyY\nwMBAAgIGqTjJQdHr9TQ0NBAcHGzvpQw6FRUVTve+Nm/ezIIFC+y9DMDBBM6kSZPIzs52qtZqT09P\nWltbMRgG/mTraMTExFBQUGDvZVhFEATMBgOyqAKDXnG4U/ff2t2ImYCYYCyyTEjB8T7vd76qhAh3\nmW8myFYvsr6Im1O5PhPuParcZD/9i9LtE+gJXy0BjY2r2GKBGx9S2sMHk7GjwcWKoDIYZMrKBn7t\nms1YPY6+FWachm+YWg1/uQzyt8K7z8J/cyDgcXh1gzIrbMlnyr+todPA21fA+eNg2m54qw8OCQEa\n2D5FJsxD5tpEHYXeOm5KK0FtkQhAxSrCaKGJ5/kBNSL3cS4eiBhpwg0/wIwoq3ps5SovL6eurs7p\nDO+sUVhYSFRUlFNFOUApDq+pqXE6gbpnzx7mzJlj72UADiZw2utwtm/fbu+lDBqCIBAUFMTx432/\nOQ4XwsLCqK6udljxpnF1VQqhXVyVXuN+ICNjxsJmjnC/8BWFyRbG51cxp65vN/AmJC5SFTPbDz4a\n2/nu2V9x0867pTBnD1wwHg48DLvuBzcroxhAGTmgVsNFZ4OfT58O32fmTAWDlbROfv7gNQhYq+XR\nqGFuPwz/vDzhnhugZBe8+xz8XgZBT8KtXymO0+2kl8KH20FvI/hkluCYGbL74G8UpYM902Q8XM3M\nVRXQJMj8kBBIqbeO29NKWGUJRkUrT/INAgK3s4BIfKghD3+ilFlnkgoE25/X5uZmMjMzSU1Nddpx\nDO1YLBZKS0uJiorqfeNhRk1NDT4+Pqj6+d3kyJSUlKDRaByi/gYcTOAALFy4kF9++cXeyxhUnLUO\nRxRFIiMjHTaKA6DS9qEQpgsmLLRi4k1+499sx4SFn7WZ+E6K5Nq9pYQY+tY9VoyZq1Sl/CkYXh6t\niJzTFTft7GmCyK0Q6AMjPKx3TYESkbjuM7jwPCjaAffe2H0G0+mycHb3CeWyLJORMXihovT0Voxd\nily0Wjj3rN73TUmCj16C4/vg8Xvhy0Pg9xj83+fQYkPEPPqDUotjzeVYLUKkDl7uZQ5iiifsmy5z\nwsXIXFURLe0/EAS2J4Qw0zuIkrS9PGFZiQTcwGzGEUo5BwhjrFJULIvIgu1Wb7PZzO7du0lKSsLN\nza33X8Ywp7S0lBEjRqA9jevY0XHG9NSaNWscpv4GHFDgzJs3z6kiOAD+/v7U19c7VQt8OzExMRQX\nFztNWtGAERmZh/ma/XTOR7ztu4mwuCiW7TmO2lZOowsHMHCjqpw7IuDpWGlA4qadeHdwU4O7je/8\nFiM8+RP8Ow3CnoLnNsCT90HRdrjhSiWyMxDmWHEwbm2VOXBg8CJ5WVkGDIbuv+OkMdY7n6LC4aHb\nIGcz7PgOps+Bm75SUlH3re4csbFGYyvc8TUYbHyMXVVwawScZyObcGkgbJsis1fVwiKxpNOghWBU\n/FeIIDYhijTvEianebLYMo2pxFJMGtFMQt3WzScj2kzFyLLMgQMHCA4OdrobozVkWSY3N5e4OBvV\n9cMYWZapqKhwuplhmzZtcpj6G3BAgZOSkkJ2djZG48CLFR0FURQJCgpyyiiOVqslNDTUYcc39BUJ\nCQkzemqQweqwQwn4MDqNYA9Pnsvse3H1VvTcIR7jvMb9TPQcmLhxV8HqidgcxShJUN4Ar/9+8rXn\n14H/47DyMLy+TEnZ3LZYGYHQX2KjwMfKKAOLRR6UAuN2srONaDTdf0cmM0yZqAislCR4Yilkroe8\nLbD0FvitFCKWwdgX4LM9/Tvnin2wuxhMNjz1NAJ8maSY9rUjAMtGyqwYL/ORqo7rxc7WCdFoWEME\nIi08IqzkYMIJwr2DcU9rIt+yg5FM7RgXIku2O6YAcnNzMZvNTu1WfCrHjh3Dy8sLD49+eC4ME2pq\navDy8nK67rfdu3c7TP0NOKDA0Wg0JCUlOZUfDij1Ko44pHIwGDlyJPn5+Q5p/NcXzJjQ08APPMvX\nPEwD5dzLOVZFTh0trE8qI7zRwNKCFitH644gywSl57ATPVfOmsjyUae/1n8kwgit9VEMAGYZlnze\nPWIhSXD3KvD7K3x1GJ57BI7theUPQmg/PMZmT7ZetKzMoBq8CE52thGdrvubFID1X0J9JmxfDTdc\nC1urYNRyJVp120qoHIDt1A1fWE9TQdsoBxV8NUFZh7cafkqReSAa7hQreJHOY1nG48IawjlBHY+x\nEhmJJcIsFiRMw+LdRF2aGiyiMstMkhB6qKc5duwYpaWlpKSkOF2xrTVkWSYnJ4dRowZwsTgwZWVl\nhIWF2XsZg0peXp7D+N+043ACBxQ/nLVr19p7GYOKv78/TU1NThWZasfV1RU/P79hKeAkDKjRkMFP\nVKKYxfzIcvxxZQkzre5zSCynZLJIckE1V1T0fFMXZJlr0ssBeDk5kNtUx7gnCt6Ol/s5ZQquCoY/\nBinpEmu0muCrfbCtB3siswT3rALfv8Kz6+G6q6FoJ/z4KVywoPda7AWzrPvfCALk5g7eZ/vECYnq\n6u75Ip1OWeMDa8DnEYh+Bm5ZAQU1g3PenCp4ab3tWh0XESZ7wcrxcHiGzERfiXNVRfxM5wrkhbjx\nLWHkUMLTfIcKgVuZx0xGUizsYnrCfHy8fUhLS0MymZUedhvU1dWRmZnJ1KlTne6J3xbV1dWo1Wp8\nfAa5Qt4BkCSJ48ePO12a8aeffmL+/Pn2XkYnHFLgOKMfjiAIhISEOKz770AZPXo0R48eHT5RHFm5\nedbyMtW8wBT+hC/KE5WZVtbyItMYyVnEW939F20mTA1g0cFy5tRbL9w4Vdx8nhyKLAhsQM+1qjKu\nC4MV42WsZGGsEusKH41VbrC2MFmUepO+8vIGiHgaznpL8UL86l2oPgBvPwMzUq13Z82fYf1erFIJ\nzJjhRni4uqd7da8IAoSEqJkxwxVPT+sHMpjhnzuhdYjKvp79FepabEdyXFVwaZBMvdbIVFUBxXRe\nyM348AEhrOcQb7IOLWruZxETCaeEvYpLsaAhPiEeby8v0vbswWKxnhdrbm5mz549pKam/k8UFYMS\nvTly5IjTpuKqqqrw8/NzurlhmzdvdjiBI8hdp9o5ABaLhcjISA4fPuxUFtZ1dXUcPnyYmTOtRwaG\nO/v27SMgIMDx58UYW0DrhiS30MpeyoVrCGElEiNZxV8xtbkUj+VspnA1L/AjR7He5n9X3Rxq9xTz\n9rQI9nue/MKyJm5OZTQaVlkiyWiEC9MFGnu4WasF2DMNEt1te97oTXDXN/DxAJ4L1CIsPQtumAIx\n/tCih+9/Vf7ZsB3c3aAszbrAaWmRkCRwdxeQJKivVyIwNTUW6uqUCeMtLRJGo0xQ0C5qa6fh5ibi\n6Sni56fCz09FQIAKX18VarVAc7MilHU6sVtUyWCGWX+HPUNY9nVugjIpXGvjHmRBpgoL8yjiRNtQ\nLh0CLxPIItz5lE1sIxcvXHmI8/BHRyVZRDABdVvNDZKELAhkZWXR0NDAlClTOrUMt7a2sm3bNsaN\nG+d0s4p6orKykpycHKf+ngwJCXGqAmNJkoiKiuLQoUMOdc92yAiOSqVi/vz5fPfdIE4HdAB8fHzQ\n6/Xo9Xp7L2VIGDNmjMNHcWRZBpUGJCOisRIdk/CRb+MYV+GCyDxu69g2k3UUsJP7WEQA1i103/Ld\nTMjEWO7cVcqoFuUpvDdxA3AUEwtUBYz2ktg7TSa6h6nhz8XBaDfb4sZsgawKJaoxEMySYn6X+AL4\nPgrP/g7jp8Gnf4e6Q0pxsrXhlCYT/P2fIp6JIh4JAnOvEHj0ZRXfrnMhu8QNs8aTwAhvxiT5MnGq\nHxqtSMIEX4KjvJF1nuQec+O/G1146k01Z18j4D0WPBJEHnxORG/lfBYJZo8c2HvtjbVZ8EuW7a4q\nFQI+iLyMMiQsEjU/Ec48XHiG1WwjlzB8Wc6leAP1FBHB+A5xI8sSiErHVEJCAt7e3qSlpXVEcoxG\nIzt27CA+Pv5/StzIskx2drbTGhiazWaqq6udbjDqli1biIuLcyhxAw4awQFYtWoVX375Jd988429\nlzKoHDlyBEEQGD26F1ONYcr+/fvx8fEhOjra3kuxiSxJCJIRRBlajyK7JlIuXIOJCsL5lf38SDrf\nd2x/Mc8AvjzJalroXpwhAg8em09pViHPTg9nZpbSLWdL3JyKFvivFEG4pOXCdIEtdZ1/vsAPfkoB\nbS+pqdSXIaO8j7+A02BqNPxwM/i6grpLRKWpGe54HP6zqm/H+uhva7nxyXN73e6s6fDjJ91rfmQZ\nfs2GRe/17XynS5gP5D6huBnbon0+2WzcqKSO5fwXI2ZSiOJ25lNPCWo0eBGIChWCLCDLdCsolmW5\nI5KTnJxMWloakZGRDn0dDQXHjx8nPz+f6dOn23spQ0JJSQm1tbVMmDDB3ksZVO6++25GjhzJ0qVL\n7b2UTjhkBAfgnHPOYceOHTZz08OVyMhISkpKcFBdOWBGjx5Nbm6uQ//dBFEEQQOiK+gSoPUowfJH\ngIEq7iWZi4ni5LClH1iGKxIPsAi1lUtGAl4O2Uj4mCge3JCLu0Xuk7gBMAKLxBJ+UzXx2ySZuyNP\nfi5GaGHlBHqs09Eb4Z0tQytuAHYVQoO+u7gBxWE4K2fwz5mdZ7ugeUZMj5MMBoWyesUA0FbBMdAW\n9XNjP3k8ybeYsHA5k7iTBZSxH08C8CYIERlBlpARrHZLtUdyPDw8WL9+PaGhof9z4qZd5MXHW697\ncwaKi4sdP4V/Gvz2229cdNFF9l5GNxxW4Hh6epKYmMjvv//e+8bDCFdXV9zc3Kitre1942GIm5sb\nwcHBDjtpvIP2WgdRi+AyEtHcRKj8H1r4jQY+YD534I9iDy8h8R2PEYYXd7AAwUr/kyRLbDi+Fw9v\nD1KbLES39k/A3iNU8oRYxfOjYNUEGU8VfJEEbqLtG7ksQ7MRnvixX6c6LXxcIdaGyZ3OBY4MwZ+7\nolKZ9m31nBpIOAOZm7c2Q0m99WGc7YhAFAH44s6jXMAixpHHNiKZiAvuCEiIkiKqe2rxNpvN1NXV\n4evrS1VVlUM/JAwFpaWluLu74+vra++lDAnNzc0YjUan6wzLzc0FFNNXR8NhBQ7AJZdcwqpVfYx7\nDyMiIyMpKiqy9zKGjNGjR1NYWOiwM6rakSQJJAugRkCDWg4kSH6bWl6glW2cz0O44wdAKyf4L08y\nllD+j1mdDyTDpHRPJGT+MzOXiPgoHtteTEJz/25QX9DIBapiZgVINC6AWT6K74otDGa4dSWcOAO/\n5pmxtmc11dRB0wC8Z3oi38ZlYjAPfR0OKMKmp2GcAGpERuDJG1xNKC6Uk0Ec01GhVsSNrAGx5/Zu\ng8HAtm3bCA8PZ/r06d1qcpwdi8XCkSNHSExMtPdShoz26I2z+RitWLGCiy++2N7LsIpDC5yLLrqI\nDRs22HsZg05wcDDV1dVOOboBFHfj2NhYjhw5Yu+l9IgoisiCCJIZVN6IxlrcmI2f/BAVLAYquIBH\n0KK05zZynF94nhmM5Erapj62iRuAvclNmAWJl0N/J3xcDPfvKGZKT+1RVsjFxPWqcmS5Z3FjNMP2\nAli1/3Teef+ZMxI0NtZzJG/oznsgy3q7trsWzj5DXcQ7C+HzPYrPkC20KO1WHngTwfgOd2JBEkDo\n+Wu2paWFbdu2MXLkSGJjY20WHjsz+fn5BAcH4+7ubu+lDAmyLFNaWkp4eLi9lzLorF27lksuucTe\ny7CKQwuciIgItFotWVlZ9l7KoKJSqQgJCaG8fIgLJ+xIdHQ0VVVVnDgxRI/2g4QgCMiiVimk0YYg\n6vPx5WY8+TOlLMIVkfN4oOOGVUUe63iNhSRyuZzSSdy0Z64k4KWgDQQmx3DzrhLOq+67AZ4bAu8T\njCT0nOKSgZtWnMYbPk0WJVpvmZYkSD88dOc9mA2tVpyTBQHmnsERRfetBmMvOkNuaxcXkUA2I0sS\niD07JzY2NrJ9+3YSEhKIiIjoeP1/SeQYDAYKCwudtvEClNZ3b29vXAZr4q2DUF9fT35+PpMnT7b3\nUqzi0AIHlDTVV199Ze9lDDpRUVEUFBQ4bbGxSqUiISGBQ4cOOfx7FEQRRHVb0XEcgv4wgfLzuDGT\nUhbgwwjOZilC2+iGcjJZL79JWDpE4d9J3JzK6/6/4z41jCv2l3N9ad8mbT/HCAKUfhub27QY4blf\nIb/6tN5uv3HVwFgbpqstesgcwkBdVq7t+hdfN4j2G7pzn0pdCyxdpfgN2UJAQLSYlJSUoO5x9AIo\nN71du3aRkpJi1RPlf0XkZGVlERsb65QTw9spLCwkKirK3ssYdL755hvOOeccxIG4ew4hjrmqU7jk\nkkucbmwDgJeXF2q1mrq6ut43HqaEhIQgSdLwGDIqqsBiAVGndFbpMwmWP0RLLOWcRzCxzOc2pcBY\nhoZ0FRVkcX7yWVwh2H56+cBrG/pZPszLq+GJI0227XGBi/DgQjzR9XBZShJUN8MLvw3o3faLadFK\nSswagqiIkKEiO1cZz2ANvenM1OG082kaFNUqvkM2UelA6GXeBVBUVMTBgweZPn06fn62VZqzi5y6\nujrq6uocskB1sGhpaeHEiRNO530DsGbNGi699FJ7L8MmDi9wUlJSKC0tpaqqyt5LGXRiY2MpKCiw\n9zKGDEEQSEpKIjMzc1h8McvtTyGiC4IuEaE1l1D5C0TcKedCIhjHHPkmPNNTATiW/CvrhTc4l7H8\nhek2Yy7f6NLJnmlhfIOJt/ZW42nuLnIiUPMKgWh7mVAlCHD957YFx1Awe6TtTi43ndLOPVTkF9vW\nhG4amH+GshqiANdPg/ggMA3Ax1KSJDIyMigtLWXWrFl9mpTtrCJHlmUyMjJISkpy2AjAYFBQUEB0\ndLTTFRebTCbS0tJYuHChvZdiE4f/VAmCwKJFi1ix4gwWHJwhgoODqa2tpdVakYGT4OnpSUhICDk5\nQ2CUMsgIgqDUTchyW/t4HIKhnDD5G0CmXL6cpnQXghnNieR9IEApGazlJeYymluYZzO1tF2dx5rJ\npcR6+vDa1lLiW07eJdXAh4Sg7kXcGCyK0LhpBoR4DeIb74XzEmyb3elblXbuocJigVIb49tEERac\nAYEzNw4OPgpvXA4vFMC7ZaA/DY1hMBg6ZuxNnz69X/UYzihyioqK8PDwICDAhv+AE2CxWCgrK3NK\n75tffvmF8ePHO/SMNIcXOABXX301q1f3Y4rgMEEURaKiopy6ZRyUEQ6lpaUOX3AMSj2OJMtKLkjQ\nImgjEI21hEmrKE6fRQsbmJE8h3nCrQhtl08FR1jD0yQTzv0s6uio6UqxUMPbY3YSNiaCB7YVceUx\npb/7QfyIRYOmF4FjAf6wD6aMhvxl8NdzlPqYoUQtQnKE7Z/nD+E8qHYOH7X9s3AfCLQ+RWPAjA+F\ndXfA2tuhSITQrfBoDizLgyZLj9nGbtTU1LBlyxbCw8MZP378aUUsnEnkGAwGcnJyGDt2rL2XMqSU\nlpYSFBTklFPgv/jiC66++mp7L6NHhoXAmTt3Lrm5uU7ZdRQVFUVxcbFDz28aKGq1mrFjx7J//36H\nLziGtvZxANkCghbUQWTsO4wbC4hI/okK4WIimcBC7kZsKzyupYTVPEoMPizjYryxPlyqFTMvh/yO\nekYQFxyt5pNDJ7hR8sa1l0tRb4GHj8KP1RC7He7PgfsXQtHTcMN0UA3RlTyphwdPWYb9Q9hB1c6+\nQ2C00YjWbBz8OpzEYFh9I+x5EHyDYMJOOH8/HQNRmy1wcyYY+/BRlmWZnJwcDhw4wOTJkwdcaOos\nIufQoUOMHDkSna0CKydAlmUKCgqcsr5Ir9ezefNmLrvsMnsvpUeGhcARRZE//elP/POf/7T3UgYd\nFxcX/P39nVK8nUpISAguLi7DJloliCIyIrJkIf1ANog6khODiOB7ZCyUsYgwRnEeD6FGSTU0U8NK\n7sMTM8u5jHBsO7J+6r6LI7Ms+MoiO7Zspampyea2FgkK9PBuycnX3i2FgM3weQ28fpkS0bkmdfCF\nzuxY211Mra1KG/dQk50HBhvdSzo1zBukdvHJkbDmFtj/MMRGwczdkJoGR1q6b/t9FWyuA2MPzyV6\nvZ4dO3bQ0NDA7NmzB20Q4XAXORUVFbS0tDjljf9Uamtr0Wg0eHmdwXzyGWLlypXMnj27TzVk9mRY\nCByAxYsXO2WaCmDkyJHk5uYOi+jGQBg/fjw5OTnDZ5q6IJB+4CAgkzwxBVEbjGisJVz+HhUelDCP\nAIK4iCfQtU0bN9PK1zxAIwU8xSWkYPuJfYZqNElJY4mPj2fXrl02bQMk4LpDyr+7vn7vEQjcAutb\n4P0roeApJaJjzbPmdDg3AdxsdO9apKHtoGonKxe0NiL8ahWcPYDRRaIAl4yHtPth670QGArT0mDC\nLtjd2PO+Nx8GW1dsWVkZW7duJTIyktTU1EFPUQxXkWMymTh06BATJ050uqLbruTk5BAXdwbNms4g\nK1as4Nprr7X3Mnpl2Aic8ePHo9fryc4+A4+MZ5h2Ayhn7BQ7FRcXF+Lj4zlw4IDDizlZlklPTwdg\n4oSJCMgg6BA0YYiGMsLkVbgQSzEz8UDkUp7Bi5PDkX7hRQ6zljtZwGVM6lZdczZjGUMwGlQEBQUx\ne/Zsqqqq2L59O83NzR3b6S3wrzLY08PNtlWC6zNhxGb4+QS8dhkcWw6PnQP+AzCGFQSlRdwWrjql\njXuoOZIHLj1YpMSNAK9+ZjqCvZQapvLl8Nm1UOUGo7fBlN2wz3YwrROFevhbXueC49bWVnbv3k1h\nYSEzZ84cUufa4ShyDh8+TGRkJJ6eQ1Q45SA0Njai1+udsjW8traWgwcPcvbZZ9t7Kb0ybASOIAhc\ne+21fPTRR/ZeypAwatSoYdFpNFDav/BLSkp62dJ+nCpukpOTEVUqZESQ2wuPY9tayD/DnbmUMBOR\nIi7lbwRzcn7Abr5iA29zHuN4iPNxb0tlReLHVUztVIzs4uLClClTiImJYfv27eTl5SHLMgYJHu7j\nx6JVglsOg+8meKsc7p5/8gY+Nbr/v4exweDSQyRIks9MkXGLHiprbP9cb1JmZfWGixoumwC/3g4l\nf4M75sO/qsFvM1yQDkWn0cz4ciFUGMEiyRQXF7NlyxZGjBjBjBkzzkh3yXASOZWVldTV1TltVONU\ncnNziYuLc8oo1b///W8uuuiiYVE4PWwEDijdVN9//73DP/2fDv7+/lgsFqc2/gPlC3nixIkcOXKk\nU6TCUegqbtq/oJTuKoD2FvIxCPpMguU38ZFvpJyLaGUN5/MwY5jbcbwi9rKKh4nEjRf4I4mEspRz\nrE4kBwgNDWXu3Lk0NDSwcdMmlu2vpb6fnjcS8FQ+BG+FPx6ApFGw6W7IeQLundf3rqM5cT377ZRV\nKG3cZ4Ke5l1pRGVWljW0ajgvEf6zGGpegE/+AqoAmLP7ZFdUT3U0vWGS4YF0ZdzCsWPHmDVr1hn3\nPBkOIsdoNJKRkcGkSZOc2vMGFGO/2tpawsLC7L2UIWHlypVcd9119l5GnxhWn7TIyEiCgoLYtm2b\nvZcy6AiCQFxcXMfoeWdGp9MxduxY0tPTHUqs2hI37YgqldIa3OaTgy4RQZ+FHw8yQn6RSh6ilmeY\nybXMYklHh1UzNaxgKbVk8QgX4I0rqh4uPa1WS9LEFMSYJKKPZXCHMR1v+fRGhv9QrdSThG+BDQZ4\n+Fwof0YRPEumKuMObHH2GNv1NzC0Ixq6su+QbTGlVcOihJP/H+gJiyfDdzdB/Yuw8nqIHwV3HQWv\nTbBgH+xoGPiaXGUT15kymV+9mwr/kUxInYqrq/XuuaHGkUWOLMscOHCA2NhYp09NAeTl5REbG+uU\nQq6goIDKykqmTJli76X0iWH3F7j22mv5+OOP7b2MISEkJISmpqZh4RczUEJDQ3F3d3eYtFxv4qYd\nob2FXJJAdAHdaERDIZ5cSJj8FU18yzGuJI4pXMQTuOHTsW8+O5Cw2PTJ6bQe4NZSfx5wmUOh6MVL\nhs1cbjqKVj69G1e1SUlfBW9VuoMaveDVy6DqOdh6D9w1B0Z28Vub04ODsdEI6ZmntZTTIitXMRW0\nxcRw+MeVcPQJpf7o9T+CVxjckAWeG2FyGvxrkBoVVbLEInMBrxs2okfNfS5ncefxYOwtKRxV5JSW\nlmI2m52+awqUSFVFRYVTGvsBfPTRR1x99dXDJvU27ATOFVdcwbp16zCZeph6N0xpj+IcPdqDs5kT\nMW7cOEpKSuyeluuruGlHEATFRtdiAdSgjUI0NeAixRIp/4pEIyVMxxOBP/I8oYzFgwDmclNHVKcn\n9BZ4vQiym0ESRH5Uj+Q+l7l4YuTvht9ZYC5ClE8/r7KrAS7cD/5bYPZuOO4Oj50PRx6H0mfggyvh\n4YXg10OBssF0Zjqo2snKUX7ltjhhgIuTYU0zxG+DgC0wfw98OYhj0ARZZrqlnNcNG4mVGnjUZTYr\nNWMwCipqTVCglzlhsW9E0tFETnNzM9nZ2f8TXVOMikFtAAAgAElEQVSgRG8iIyNRqwepjdGBkGWZ\n1atXs3jxYnsvpc8MO4Hj6+vLpEmT+Pbbb+29lCEhPDycurq6/4kojkajISUlhX379tlNsPZX3HRC\npQJUIAmgDkREi8rYQri8BjfOopQ5tLKGRdzPVbzWMY285/VAvRme7lJz0ixo+UQzjqe000mQannD\n8DtzzSWIA0zx7WiAyw9AyFYI3QwfVUFKIiw7Dxp7iJhoNWdW4GTngVsP2R+1Gt4ug/uOQo4V35oB\nIctMthzjFcMm5phLeUWbyrvaidQJSuvW2f5wZKZMlCu4qMBis3n8zOAoIsdisbBnzx6SkpLslro7\nkxgMBkpLS4mN7UPF+zBk586duLq6MmrUKHsvpc8MO4EDsGTJEj777DN7L2NIEEWR0aNHc+TIGSxw\nsCO+vr5ER0fbpR5nQOKmHVFEFrVKTkn0QNBEIOqzCZJfYYT8AtX8FQO7kDCi6kNqyijDTZmgtxGg\nqRTdeVubzHPaqSRLlbxh2MBCcxHq00xddTq2USlOTk2Db6rAo4dRSS5aOJo/4FP2fW3VcKKHmnSd\nChb5D+45RVliprmMVw2bONtczNvaibzoMoViUTFuS3SHdZNkfkiWOeLaTIoqj4sp7eZXZA8cQeRk\nZmYSEBBAcHDwGT+3PcjNzSU6OnpYdBedDh9//PGw8L45lWEpcC644AL27/9/7J15eFT1vf9fZ/bs\nIfsKCQFCSCAbJIGE1R17RbTaTW211v5E23q1am+v3WzdbbX0arVKLbVaW5eqrRasFUJYkhACCZCw\nhCwkIXvIOvvM+f0xJAUk+8yc2V7PMw8Pycw57zOZc857Pt/Pcshju/8mJCTQ39/PwMAEncY8hJFv\nPPX1zrtj2sXcnEOQyRCFc+ZFpgJNKoK+niDxv0gWj+DHcmSMk617DqMoopbBp70T77NNFsjzqlwe\nU+Uzz9rH/xk+Y6PpJIHiGDMNpshlYbYmeGPR1QPD9o6UTMCpCZpg5waDwg6rIBrRzDXmBn5j+Iw8\nazu/UWXzuDqfBpktnypRA1szRA4tF4mZZeQKWRN3Ce3ogcMY+DMD6F3A5khpclpbW+nv7yctLW3i\nJ3sAer2eM2fOeGyekVarZdu2bW61PAVuanBUKhVf+9rXeOGFF6SW4hAEQSA1NdVrojiCIJCdnU1j\nYyO9vZO4u88Qe5qbEWzJx8J/8nLUychEAYSJl6VGUAkCAxaR+iKRDZGTe027LJCXVJn8QL0SP8z8\nyrCTTcZDzLFOv0woVg0xEwy6Pu7E6M0IVbXjD7gUBMieQZFOjHWIO4yH2Wz4jDjrED9TLec5VS6N\nMtuIhXg1/DZN5FSRyKpoM1+TtXKNrJkGLqylf5Ie9BIvU40ghckZGhqipqaGpUuXemQl0aWoq6sj\nOTnZI3NvALZu3cqaNWsICwuTWsqUcNtP37e//W3+8pe/eOyQyri4OIaGhujvt0M9qxugVCrJzc2l\nsrISvX4aHdcmiSPMzQiCINjycgS5rRmNLACZxTQ6dXw8dFh5nG4y5afYox7krUyR3ctEMid5w+4T\nNLypTGOT+jJqZOHcYzzEU/pdXGluxF+cWn7TylDQjnMftFhsZdvOprp2/Eoqiwgrxx7/dUlUooVV\n5mYeNezhf4zlnJEF8l31OraoFtMps2VZz/WDVxeJNKwUuTbOwiZZOyvlTezj0mIGsfIIXRhcIIoD\nzjU5JpOJ/fv3s2TJEq/IuwHbzLG2tjaSkpKkluIwtm7dyqZNm6SWMWXc1uAkJyeTnJzM3//+d6ml\nOISRKI4njqYYi9DQUBYuXMj+/fsdchF2pLm5AEFAlKsRzWaQT9zN1opIBxZeoQ8zcL/QySpZI34h\nBvbni7y1RCRpkvcKsyBnpyKRhzSreVGVRbx1iOcNO/hv4wGWWtpRTKL6ak0YaMa5Muj0UCNBdf+x\nU7bq/LHwk8GVk8jDkYkiSyxdbDIe4gXDv0m39vC6Mo3vqdeyTZGM/txyY14IvJ8lcrxIZF2smW/L\n2lkhb2QbEzeorMWIGhkWLzI5oihSWVlJQkIC0dHRE7/AQzh58iRz58712OjNwYMHGRwcZPny5VJL\nmTJua3AA7r33Xl5++WWpZTiM2NhYDAYD3d3dUktxGgkJCYSFhXH48GG7Jh07zdycQxAEBOXEeTcA\nFuABOi5Y6GjDwgZZCzfJWlgaZeZEocjvFokkTLB0dD5NsmBeU2WwSX05JfJ4iiyt/NbwKfcaD7LM\n0o5KvHSb4ivDQTHOlUEmc84MqouprbPNvxoLQYAVoVyyR7RCtJJp6eRbxmpeMvyLa8wNVMkiuUe9\njt+qsjgpCwNBQCXA12Lh0HKRkmUiyREGbhJaWSVv4l+TMDYhyHiUCLaRSBsDyMboWC0FjjY5x48f\nRy6Xe8UohhEGBwfp6ury6OjN5s2b2bRpk1uW+QuiK7WSnSJms5mUlBSKi4s99gM2Mths1apVbvkB\nmw6iKFJaWkpMTIxdkvacbW6mgh4r/2SY79Ix7vPW4cfPLdHECnL+eAZ+0SDQOI2h7CrRTI61kzxL\nO4ut3dQLIVTKo6iSRdIuBBCqFOhZO36CMUB0jq2yaTq8+uh27vzxVVN+nUwG+pMwXpGKyQpZ+6Bm\nGMJFHZmWTrKtXaRZezgmC6NcFkOFPAatcOFG0gLgrgSRO+IBAUpkw/xE6KRjkhEYJfA1QniYcIwY\n+QM7OUQzN5LLNSyZVHNHZyGKIrW1tfT395OXl4dcPvk8sbE4c+YMJ06coKioyGMjGZeirKyMhIQE\njx3LMDg4yMKFC6mpqSEkJERqOVPGrQ0OwM9//nN6enp4/vnnpZbiMMrLy4mLi3PoZGJXw2g0snv3\nbjIyMmY0kdeVzQ3AMFZW0EjvJG+kVxDAjy0RxAsK3u+ExxsEDk1y+vXFyEQrC629ZFm7yLJ0EoiJ\nQf9wNqaEExsZRkBAwCXfr6FhCJpBccx0DQ5AXQmkzPn8z0VRRKfTcaarl48bepANdGNGRpU8kkOy\nKI7IwjFflPAdpYKbo+HbiSIL/aFFNPOavI/fM/m8NwH4AoE8QjghCGynivepHP29EjnP8iVC8R9z\n/pgU2NPk9PX1UVFR4bQBo65Cd3c3NTU1rFy50uWuK/bi6aefpq6ujt/97ndSS5kWbm9wOjs7Wbp0\nKSdOnECjGSd+7cYMDQ1RWlrK2rVr7fJty10YHh5m79695OXlTevbg6ubGz1Wfkw3f2bq7QAK0PBT\nayQLUFE5CE81CHzYZUu0nS4BopGno3soUvTS19uDXq8nODiYkJCQ0Ye/vz+HjwlkTs+fADMzOB9s\ngf+6XESv19Pf309/fz99fX0MDAygVCoJCwunThHGHc3h9Aufvx5Eq2BDFNwWJ1IQAmetIsXyIZ6h\nl1amNtX0SgL4IeHEoaCCOl6jBPNFRjWSIB5lIwrkqF0oigP2MTlarZa9e/eSm5vLrFlTzPB2Y0RR\nZNeuXSxevNjtKosmi9VqJSMjg7fffpv09HSp5UwL1zrjpkFUVBQrV67ktdde4+6775ZajkMIDAwk\nOjqa+vp6t+oiOVMCAgLIzc1l//79FBYWTqkqw9XNjRmRU5h4axrmBqAUPVfLmpmNgkeDI3l9sT9a\nC/zfadjSKtA6jdmcw4KKlSmxLA6KBWydaAcGBujv76erq4u6ujp0Oh3DOhX3fyOIjm5/Onv96er1\np+usH30DaoZ1Si6dBTMVRPw0ZkKDDETO0hEZpiUyTEdM+DDDZ4fYvl2Hn5/fqPmaP38+wcHBo0sj\ncUbob7FtSQCyguCaCLg5RiQj0GZqKmRaNghnqZJP7Y2SAVcTwIOEk4CCwzTxNLvQcWH/ITUKriOL\na1hCPx2Y0RLBHOS4ThO4kZyc2tpaysvLp2xyTCYTZWVlZGRkeJW5AVufH39/f481NwD//Oc/iYiI\ncFtzAx4QwQHbEs7dd99NRUWFy93I7IXRaKS4uJhVq1ahVk8h09QDGFnfLywsnFSXUFc3NyM8TQ+/\nwT5zuFTA95jFTZZQImUySs7CS822qI5+koU8/nIYWAfycd6uYa3IY5sN/P2TIaIjhokM0xEVpiVi\nlo6QQAMBfiZEUUCrV6DVK9HpFegNcswWGRargMUioyinlb2H4lDIrcjlIhq1GT+1GT+NmQCNCblc\nRKtX0D+oprvPj84em4Hq6PEnd0kQz/9MQ3Dg+H/TJxsg1V9kXbitIqxLtLBXPszL9HGCqY8FUSNw\nA0F8j1mEIecwjfyeErQXGRsBgZXM58vkI8NKGa/TQDnLuYU01k55v85gOpEcq9XKvn37iI2N9djR\nBGNhsVjYsWMHBQUFBAYGSi3HYVxzzTXcddddbNy4UWop08YjDA5Abm4uzz33HKtWrZJaisM4deoU\ng4ODZGVlSS3F6Zw6dYr29nYKCgrGvQC7g7kxYsaIGSUqytHzQ7ponMZNdyyWoOJBMYIcqx9K4P1O\neL1N4NMeMI1ztq8Ng39k24zOWAwNw5fvhY/+PfZzZDIr/hqbafHXmNGozcjlVhRyEZlM5L9vq+CX\nf1iGxSpgtsjQG+ToDAp0egVanRKLdewSrsxFUPahbVTEWAxYROTAIZmOj4Qh3mZgjI41ExONnFsJ\n4XZCkCFSQR1/Yh/GSyxnZTObr1JAKH6c4DNKeZM55FDIbahQoSQQq6hFJrhenspUTI4oilRUVODv\n7+/W3+6ny7Fjx7BYLB597KdOneKyyy6jrq7OrZPG3Vf5Rdx///08++yzHm1wkpOTKS4upre316ND\no5ciJSUFo9FIRUUFy5Ytu2SHVHcwNyIiBsw8wFuE4Md3uJLPmM1W+nmeXvrt0DelGiO3CmdADjcQ\nyO0xobwdZYv6fdQFb3cIbO+GwYuqhFfNmrhvhJ9m4hJxq1XGkFbFkPbSLsRkllN9YpKtmi/i+Knx\nq6gAAuXwLoPcT+e09iEAhfhxByGsJYB+9GzjAP+g6pLPzyCeL5NPDCG0UMnH/I4QYrmOHxHOHMyc\nRCAcK3JkuGb0dbLLVaIoUlVVhUKhYNGiRRIolZbh4WFaWlpYvXq11FIcytNPP83dd9/t1uYGPCiC\nYzabSU1N5R//+IdHzz/p6enhyJEjXlU2PoIoihw+fBiTyUROTs4Fx+8O5gbAhIWX2MF+GkZ/lssc\nbmUValT8il620o/BAa3+byKIr4rBzLdoCJRDxQD8rUPg0144OAB78qAgdAL9JtDMH7/h3kTMJMkY\noP0ARE/gj9owk0fjlLabiIIbCeIWQghBRjPd/JUyjtN+yednksgXWUocobRxlGJeRokfy7iZZJZi\n4jSIOpRCCgIqBNE2k9VVP5swcSTn6NGjDA8Ps2zZMpc+DkdRWlpKYmKix5aFg606LCsri5qaGoKD\ng6WWMyM8xuAAvPjii+zZs4c33nhDaikOpbKyklmzZnnsYLfxGOmWqlQqWbx4MYIguI25MWPhJB08\nwUeX/P1/kcXVZGMEnqaXtxmw48LVheSh4TZCyLX4ESnIMYsQMF7yzTlONcG8lTPb90wNzo6/wpqC\n8Z8jIpJLI12M38wuAjnrCeBLBJOOml70lHOCv1F5yWUoOQJ5zGUjOYQRSBtHKGELMmTksJEFFGGi\nC6vYikawLSULou2VuOjn8mLGMjknT56kq6uL/Px8r6rmHKG9vZ1Tp06xYsUKl73G2IMHH3wQhULB\nE088IbWUGeNRBkev17NgwQL27NlDYmKi1HIchl6vp6SkxCsTjsGW4Lh//34CAgJYtGgRhw4dAlzb\n3IDN4PyAt+lk7MY1MuArFFBIGlrgWXp5x4FGB2zr1A8SziYmroT58F+w4Zsz299MDc4vfwTfu8M2\n9msshrHyAB18dInuwwkouJIANhLEEtT0Y+I4zfyNA7SN0QMnEDWrWcg1LEaFnBYq2MMfUeFHFteR\nykrM9GIRm9AIWYhYkYkybMbG/RrGX2xyGhsbOXPmDMuXL3f7ZYvpYLFY2LlzJ3l5eQQFzWCiq4sz\nNDTEwoULqaysnFH/MVfBoz6pGo2GTZs28fjjj/Pb3/5WajkOQ6PRkJKSQk1NDdnZ2VLLcToymYxl\ny5axf/9+du7cSUhIyOeWrFwNI0YUKLBMsPRkBd6glD9Tzi0U8GNSeZgwfsNZ/swAWgcsXZmBs1jQ\nYcVvnCwcoxEOHrX77qdMbZ1tHlZgwNjPUSOwHD8+Yhg1AsvQsBp/1hNIPAr6MXKSFn5KFY30jLmd\nuURyJenkMRc9Ok7yKZW8SwixFPJ15pKHmR704gH8hGwUQjCCKEMQZSBz3yjH+Tk5O3fuRKlUeq25\nAVv0KiYmxqPNDcBzzz3Hhg0bPMLcgIcZHIBNmzaxaNEiHnvsMY9OxE1KSqKkpISuri4iI6eXsOnO\nCIKAQqHAbDa7fBRLxApY6Ocsz3AzH3KIj6nCOM7yiRUrf2Qvf2IvN5HH/SziQcL5A338gX7aJ1h6\nmSqr8R/X3AAYTNLMoLqYY6fGj94AKBD4OqEsRsNi1JgR6WWQamr5JYc5i3bM1wahYTkpXEE64QTS\nxxk+4zc0cZAEMriGh4ghFTPtGMQDaIQsFEImgqhAEEWbsXFdrz1pBEFApVJhNpvRaDSXTOz3BgYG\nBmhpaWHNmjVSS3EoJpOJLVu2UFxcLLUUu+FRS1Qj/M///A9Wq5WnnnpKaikOpb+/n4qKClavXu1V\n36zOz7nJzMykoqKCgIAA0tPTXTKKY8HMdn5JK0dJZRVL+Rpm4A32UcqpScdkriSDq8kiFA3/YphX\n6aN82gXQ/0EAjjEX/4kMjhHyr4Oqmpntb6ZLVOGzoPvSBU0XYEakiiZ2UEs1LeM+V4WCbGazilQW\nEYceLc0cYD9/wYqV+RSymGvwIxgLLVjFPjTCEkQsyEQ5iIJbR2wuRV1dHW1tbRQUFHDy5Em7zq5y\nF0RRpKSkhIULF3pMVGMsNm/eTHl5OX/605+klmI3PNLgdHZ2kpuby7FjxwgIGCeO7QHU1tZisVjI\nyMiQWopTuFRCsdVq5cCBAyiVSjIzM13K5FgxIkPF+/yULurP/VRGIbeSwiq6GORP7KOGM5PeZhpx\nfIl8EgijDQu/p4/3GKRvmiXmC1HxMYkoJwg7WEUITLUtD82EmRocgP6jEDzBaoEBE//Hv6mi+ZK/\n16Akk0SWk8JiEjFjpoc6DvI+7RwnhgWksY5k8rCgRaQFASVKkgDBFq1x0xyb8RBFkWPHjtHb20te\nXh5KpdIhAzrdgVOnTtHf309OTo7UUhyKxWIhLS2NDz74wKOqkD3S4ADcc889xMbG8sgjj0gtxaFY\nLBaKi4vJzs72+Hbp41VLiaLIoUOHMJvN5Obmukw4XcSEniOoWcwpyijjLXTnEllV+LOWu4kjg1N0\n8mfKqKdr0tsORMVN5JFNCoEo2cEwbzJAMdopJSV/nRB+SPiEEZyOLojJncKGx8AeBqfyY8iewNOb\nsbCNI/yV8tGfRRPMEhLJI5l5RGPEyFkaOcJ2mjhAMNHMYwULWY2GIMy0YxHbUQsLEVAgoEEQrYDM\nbaqipsJIKwadTsfSpUsvMDLeZnJGZuGtXr0alWqczpIewNatW3n33Xf58MMPpZZiVzzW4Jw+fZpV\nq1Zx4sQJj/9w9vb2UlVVxerVq13mxm5vJlMKLooiNTU1DAwMsGzZMsmX7aziMFa0NAmrULOQSF5A\nTgSVvM8RtmM5Z0OCiGQ1dxNJMsdp5232T8nogK3Z3AZySCQSKwL/YJAPGGIfugmzdV4lhisJmHDa\n9a4yWH3TlGRden92MDhbnoHbb57YY/QyxFuUk0E8S0gkGA3DDNPNcWr4F2eoJYhIklnGAlYSQgxm\nzmKlHQElKlJsy1AjDfpE0SONDdiqE88/x8ZqpukNJkcURfbu3UtycjJxcXFSy3EoVquVrKwstmzZ\nwrJly6SWY1fkP/3pT38qtQhHEBISwoEDB2htbaWgYIKmGW6On58fQ0ND9PX1ERERIbUcuzPZPjeC\nIBAZGYlOp6O2tpaYmBjpTI7VgiBTg2ghlDsxCIfo4gGsDJLM10nnSnQMcpZmDAxzgmIaKSeNTK4m\nnwzi6WKQboYmtbtOBinhBB9zEB16iojgFiL5f8xiAUpE4AzmS87LfozICROMLRb4YDtst0P+4XVr\nT/Hhjnkz2kbKHFiVP3FXYz9UpBNLEFoaKGEHL3CIDxikiwQWs5LbyWEj0cShpA+z2IxamIOcYBTE\nIIggnN/DxkPNjdlsZv/+/Wg0GrKyssb8oiQIAhEREQwODlJfX09cXJxHfqk6ffo0Op2OhQsXSi3F\n4bz33nscPnyY//3f/5Vait3x2AgO2PJTrrvuOo4ePerxURyz2UxxcTE5OTketVQ13SZ+TU1N1NXV\nkZ+fL81APLMeRi785k6synBMNNIl/BA9lUTwKIF8CS39lPJnmqgcfWkw0azkTiKZRzv9fMBBKmjA\nOsUScRmwljSKSCWaMDTIqETPdobZhZZjGElCyU5mI58gejM0DPf/HF55c6pvxOexRwTn6jXw9m/H\nLxUHMGNkG88yQBdxpDGbLBJYjBwFZnqBXqyiHo2QgYjp3BKUBZAjIrhUPpej0Ov1lJWVERsby/z5\n8yd1zJ4cyRlZmlq5ciUajUZqOQ5FFEUKCgp4/PHHueyyy6SWY3c82uAAfPWrXyU7O5sHH3xQaikO\np7e3l0OHDrFq1SrJl2fswUw7FHd2dlJVVUVOTg7h4eGOkDgmoijaLIMggFULognRagDFLLTspFv4\nOSY6iOZZ/LiSQbop5y+c5tDoNjQEU8Q3iGMJesz8k8Ps4jhDGKalKYN41pJGMjGEoEF3LilZgTBh\n/o1WB1fdCrvLx33apLCHwUlKhLoSkE8QPDBjRIEKEStGBhE4i5U+BPxQMR8RHQJ+NlMjKD16CepS\nDAwMUF5ezsKFC0lISJjSaz3R5IiiyO7du5k3bx6xsbFSy3E4b731Fi+++CLFxcUeaeY93uA0NDSw\nZs0aampqPL6iCmyTbg0GA5mZmVJLmRH2Gr8wcgFPS0uTZH6MKIoIVjPIFCDqwTKAKMhBHsIA79Er\nPIuIniieRcNahuimgndppALxXMRGhoKl3Mg8VqPGj/008C+OUjfNYZJgK4suZD63UIAcGbIJx2xC\nRCb0nJ32Lkexh8ERBNCfhMkGZnXiwXOJwnJbl2E0IJqxxbkErzI1I3R1dXHo0KEZfQHwNJNz4sQJ\nhoeHvaKBqsViYcmSJbz22mvk5eVJLccheN7i6UUkJydz3XXX8fjjj0stxSksWLCAvr4+Ojo6pJYy\nbew5Wyo4OJjCwkLq6uqora3F2X5eEASQn4sMWGWgiESQBSOYOgiyXkGSuJcw8QG6+F9Ok4OcStbw\nLb7Mr0hjLXKUWDFTzl94k018xmbmo+KHfIFn+RLXsJgQ/Kasy4iZHdQyhGFS5mZwyD7mxl6IIjRN\nsrLeihE/IRuZqERAhUw8lzAsKGwl3l5mbkRRpL6+nsOHD1NQUDCj6OZIx+OQkBDKy8uxWOzbgNKZ\n9PX10dzc7DUtN1555RUWLFjgseYGvCCCA//pi3Po0CGnL1VIwdDQEPv27XPLWVWOGpxpsVhGy8hz\ncnJQTpSd6iisFrCaz7XiFcHUjlUegCALYoC3OCu8gJkeIvkx/mxERE4Nn1LDpwzzH4ehIpBl3Mhs\n8vAngOO0U8xxKmlCP8ki8VD8+TVfnbB6CuDgEchZP92DvhB7RHAA3v0d3HD1xM8TrRYQZB4Zgp8q\nFouF6upq9Ho9S5cutdt54O6RHLPZzK5du8jKyvLoDvgjGI1G0tLS+Oijjzw6kdpjq6jOJyAggL6+\nPj7++GOuvfZaqeU4HJVKhUwm49SpU8THx7vNhd2RU8FlMhmxsbEYDAaqq6uJjIyUJvFckNmWqxDA\nagV5IIJMjWDqQCWkEyrcjYpk+oRX6OVnCBiJ5zoy2Ugkc9ExyCDdWDDSTBVH+JgWDjOH2awgiw3k\nMI8oBAR6GMI0TpF4JoksIREF49+MRBG2FcMHn9jnLbBHFRVA6lxYkQsTpZsJPnMD2JKJS0tLCQoK\nIjs7264mxN2rqw4fPkxoaCizZ8+WWopTePrppwkKCuKb35zh5FwXxysiOACDg4MsWrSIPXv2eMWH\nWBRF9u/fT3h4OCkpKVLLmRBHmpuLGUk+Tk9Pl7THhSiKtuO0mP6zWGzqRJSpQR6CnkrOCi+gZSca\nlhHOD1GyGAND1PBvTrL7gqgOQCKZZHA1YaSgQcUpOimngSqaab9oUvY3KGI1qcgnkWD8yDPw3Kv2\nOW57RXBu+oKtH06Q56fWzZju7m4OHTo0rWTiqeCOkZzW1lbq6+spLCx0K1M2XQYGBsjIyGDfvn2S\n5CU6E68xOAC//vWvKSsr48037VDr6gaYTCZ27drl8qXjzjQ3I2i1WioqKpg1axbp6emSXthGK66s\nxnPJyGaw9CEiQ1QEYeUsZ4XfMcg7WDESzgP4sxEl4XRyklp20EQlpovmUkUznwyuIpKF+BPIIHqq\naOYwLdRyhh+zgWiCJ9Q3NAw3b4J/7rDP8drL4CxeCBUfgUqi1UZ3QBRFTpw4QWtrK0uXLiU4eOK/\ntz326S4mZ2Q5v6ioCD+/qeeyuSMPPPAAMpmMZ555RmopDserDI7BYGDRokW89957bl9lNFnOnj3L\ngQMHWLVqlUv2ApLC3IxgtVqpqamht7eXpUuX4u/v77R9jy3KAlYTyNUg6kC0gGUIq8IfQfBniI/p\nF15HTxkqFhPGA6jIR46GFqqpYy+nqcJ8USm5ikDSuYw55BJADP5Mvr+HxQrzVkLjpUc6TRl7GRy1\nGrTH/9NuyMeFGAwGKisrUalUZGZmOrV1hDuYHIvFQklJCWlpaURHR0stxym0tbWRl5dHdXW1S3/p\ntRdeZXAAXn/9dd58803++c9/Si3FadTX19PV1UVeXp5L5SJIaW7Op62tjSNHjki+ZHUBVqvN3MiV\nYNXZerSY2hBlfiAPwkIv/bzBoPAeZpoJ4AvKOT0AACAASURBVApCuAsli5Gj4Qw11FPOaQ6Nzr46\nnzQuYzlfRc7kwh9/fBeOnoC6RmhohqYW6O2b3qFN1+CEBMOceEhKgHlJsHAefOsr09Pg6XR1dVFV\nVcX8+fOZPXu2JOeWq5ucqqoqlEolixYtklqK07jjjjtYsGABP/jBD6SW4hS8zuBYLBYyMzPZvHkz\n69atk1qOUxBFcXQ5Zt68mSd32gNXMTcjaLVaKisr8ff3Z/HixdJVWV3E6PKVaAUBEI22h2UYq9wP\nQRaMkWMMCG8xxD+x0IEf6wjldpRkoySIftpp5AAtHKaDOqyYyecrpHMFcib3rX73KYgPgmA1BGhA\nowaD0VY63t4F7Z3Q2g6dPdA3AP2DtqWtYS3oDWA0gdli8213f3E7L717FQq5bXnJTwP+frauxMGB\nEBoMEWEQHwNx0RAdCRGzbM8zGEGrhwEDnBmCnERQu39PS7thsViora2lu7ubnJwcpyxJjYermpyW\nlhYaGxtZsWKFV+TdgK1H2lVXXUVtba1rRKudgNcZHICPPvqIRx99lH379nnNh3skHycrK0vyUnlX\nMzcjiKJIXV0dp0+fdon36XNYLeeiOiqw6m19XEzdIMiwylUIQhAGahgS3meYTzDRiIIUQrkdDWts\ns5WQ00U9MSyY9G7/VgU3bLnwZ1GBNnORHgsp4RAXCpEBEKIGfwWoFKCU2ToNy2UgE2wPBNi9aztF\nK6/Cis3wWK1gPvcwWkBrthmY7mE4MwANPXC0DQ62QMtFUaNnNsB9a0DhGvdNSRkYGODAgQNERUWx\ncOFClzETrmZyBgYGKCsr86q8G4ANGzawfv16vv3tb0stxWl4pcERRZGrrrqKL37xi9x1111Sy3Ea\nIyd2YWGhZA7eVc3N+fT19XHw4EGXu1GMMFp9ZdaDQgOWYZBpwNQBgvyc2QnGTBtD/BOt8G907AeM\n+HM5wdyMP1cim6A8HMBggsf/BY9us5/+Vwu3c+eemefgANxRAL++EQLdq92TXbFarZw6dYqmpiay\nsrJccuCuq5gco9FISUkJmZmZLvk+OYpt27bx0EMPUVlZ6RFjfCaLd4QvLkIQBF544QUee+wxBgYG\npJbjNIKDg0lPT6e8vByz+VJzpR2LO5gbgNDQUFatWgVAcXExPT09Eiu6kNH3TaGxdWaW+dtKzVVx\nIA9CJgQjmHtQmuSEWG8kVnyNFLGWePEdVMzDQBVMshmg0QLHXLgpdm3HxPOoPJn+/n5KSkoYHh5m\n9erVLnvTdoWOxyNL9cnJyS77PjkCs9nM/fffz0svveRV5ga81OAAzJ8/n6997Ws89NBDUktxKnFx\nccTExFBVVeXUsQXuYm5GkMvlpKenk5OTw+HDh6mursZkmpwpcCaCcG6O0vlmx2wEZRQowpEJfshE\nAcHUhZ95LmGWuwnnB0x2MLlaYTMRrsqxDvBzjXQppzKSa1NRUcGiRYvIyspymbyxsZDa5NTU1ODn\n50dycrJT9ys1Tz75JEuXLmXFihVSS3E6XmtwAB555BG2b99OZWWl1FKcSmpqKmazmbq6Oqfsz93M\nzfmMRHPUajXFxcW0tbU5fZ7VZBl9X5V+No2CEkSFbflKGQcyf2SyIABkwuTKxBVyODH9mZ4O56wW\n+nRSq3Au3d3dFBcXY7FYWL16NZGRkVJLmjRSmZyWlhZ6e3tZsmSJW11/Zkpraysvv/wyzz77rNRS\nJMGrDY6/vz+//OUv+c53voPVapVajtMQBIGcnBxaWlocPpTTnc3NCDKZjNTUVPLz82loaKCsrIyh\noSGpZY3LaGRHft63enkAWM7NwpokHQNgcP5q5pQ42SW1Aueg0+k4cOAAR48eJTs7m4yMDLdccnC2\nyenr6+PYsWMsW7bM5fLpHM13v/tdHnroIaKioqSWIglebXAANm7ciL+/P6+99prUUpyKUqlk2bJl\nVFdXOywPyRPMzfkEBQWxfPlyZs+eTWlpKbW1tZLkMk2L8/J2bLOwJocrL0+NcKjFNi/LU7FardTV\n1bF7927Cw8NZtWqV2zdpc5bJ0el0VFRUkJubi0Yz+eaWnsAnn3zCyZMnufvuu6WWIhleb3BGEo4f\nffRR+vs/3xDNkwkMDCQnJ4fy8nJ0OvvG+T3N3IwgCAJxcXGsWbMGURTZuXMnLS0tLrtsNRPMFltp\ntqtztB20RqlV2B9RFGlvb2fnzp0MDQ2xevVqkpKSPOpccqTJMZlMlJaWkp6e7vaGcKqYzWb++7//\nm9/+9rduGeWzF15vcAAWLFjAV7/6Va/p7ng+4eHhpKWlUVZWZrckWk81N+ejUChYtGgRy5cvp62t\njV27dtHd3S21LLuiN0NNu9QqJqbWDTROlb6+Pvbu3Ut9fT25ublkZWW55KiVmeIok2O1Wtm/fz+z\nZ88mNjbWLtt0J5588klycnIoLCyUWoqkeGUfnEsxPDxMRkYG7733HtnZ2VLLcTonT56ku7ub/Pz8\nGTU/9AZzcyl6e3s5cuQIarWatLQ0yTvI2gOdCS7/P9jbYN/t2rMPDkDiLGj8iWfMpNJqtdTW1jIw\nMEB6errX5E7Ys0+OKIocOnQIhULB4sWL7ajSPWhpaWH58uVUVFR4zYytsfCAS4J9CAgI4Nlnn+We\ne+7xqoTjEebNm4e/vz/V1dXTXm7xVnMDEBYWxsqVK0lMTKSiooIDBw64fCLyRPgp3SMHp6UPTG5+\nyup0Oqqqqti7dy8RERGsWbPGa8wN2DeSc+LECYxGIxkZGXZU6D5873vf48EHH/R6cwM+g3MBN9xw\nA4GBgbzwwgtSS3E6giCwePFi9Ho9J06cmPLrvdncjDCSn7N27VqioqIoKyujsrKS4eFhqaVNi36d\nrQzb1RFFaHStXoyTRq/XU11dze7duwkODmbt2rXMmTPHa8+fmZqcpqYmOjo6yM3N9cr38P333+fk\nyZNs2rRJaikugc/gnIcgCLz66qs8/fTTNDY2Si3H6chkMpYuXUpnZyf19fWTfp3P3FyIIAgkJiay\ndu1aIiIi2LdvH5WVlW7XNdudyq8Pt0mtYGoMDw9TXV1NSUkJAQEBrFu3juTkZK8rY76YmZicM2fO\nUF9fT35+vlcm1vb19XH//ffzxz/+0SuP/1L4DM5FzJ49mx/96EfceeedXrlUpVAoKCgo4PTp0zQ3\nN0/4fJ+5GRuZTMbs2bNZt24dUVFRHDhwgLKyMpcb/XApRBEOtUqtYvIcagG96zWa/hz9/f1UVFRQ\nWlpKcHAw69atIyUlxeuNzflMx+R0dnZSU1NDQUEBarV3Dia79957ueWWW8jKypJaisvgMziX4Fvf\n+hYAL730ksRKpEGpVFJQUMDJkydpbx+7RMVnbiaHTCYjISGBNWvWkJSURG1tLSUlJbS2trqsidaa\n4IgbRUWOdYLJ+eONJoUoinR0dLBv3z4OHjxIbGws69atIykpyWdsxmAqJqe3t5fq6moKCgq8ajr4\n+Xz44YdUV1fzyCOPSC3FpfDFsS6BIAj8/ve/p7CwkGuvvZY5c+ZILcnpaDQaCgoK2Lt3LwqF4nPD\n6XzmZuoIgkB0dDTR0dH09vbS0NBATU0Ns2fPJikpybW+eYruVX5d2w4aFxvFZDKZOH36NI2NjQQG\nBjJv3jwiIiJ858okGTE5tbW1lJeXX7K6qr+/nwMHDpCXl0dgYKBESqVlYGCA++67j/fee88jWwnM\nBF+Z+Di88sorvPXWW3z66adee1EaHByktLSU3NxcwsLCAJ+5sSd6vZ6mpiZOnz5NWFgYSUlJhIWF\nSf6eWq2Q/DM4fdb+27Z3mTiASgHaZ6WfLC6KIv39/aPJrvHx8SQnJ+Pv7y+tMDdmrBLyoaEhSktL\nycnJGb02eSO33norc+fO5Wc/+5nUUlwOn8EZB1EUueKKK7jhhhu8Oiu9v7+f8vJycnNzmTVrls/c\nOACr1UpbWxunT59Gq9WSmJhIYmKiZCF3gxn8HnDMCARHGByA5kchIdTum50URqOR5uZmmpubUSgU\nzJ49m/j4eN8SlJ242OTodDpKS0vJzMx0q2Gj9ubvf/87jzzyCBUVFS4/TV4KfAZnApqbm1mxYgUl\nJSUkJSVJLUcyRkxOYGAgarXaZ24ciE6nG03yDgwMJCEhgZiYGKdWRpzohNRfOGbbjjI4n2yCKxba\nfbNjYrFY6OjooLW1lb6+PuLj45k9e7bXLpU4mhGT09PTg16vJysry6vNTV9fHzk5Ofztb38jMzNT\najkuiS8HZwISExP5yU9+wh133MGnn346oy6/7kxwcDBBQUF0d3ezYsUKn7lxIH5+fqSmprJgwQJ6\nenpobW2lpqaGWbNmER8fT3R0tMMjA9VnHLp5h3CwBdbMB6UD3xqr1UpXVxetra10d3cTERHB7Nmz\nyc3N9dprg7MYab/Q2NhIQECAVy9LAWzatInbbrvNZ27GwWdwJsE3v/lN/vrXv7J582buu+8+qeU4\nnZGcG5VKRVFREfv3778gJ8eHYxAEgYiICCIiIrBarXR3d9PS0sKRI0eYNWsWMTExREdH2z2xUG+C\nKjcqER+hpt2m3d4Gx2w209nZSXt7O93d3YSFhREfH09mZqZvCcqJDA0NUVZWxtKlS+nu7h4z8dgb\nePfdd6mpqWHr1q1SS3FpfAZnEoxUVS1fvpxrr72W+fPnSy3JaVwqoTgvL280J8dncpyDTCYjKiqK\nqKioUbPT3t7OsWPH8Pf3JyYmhqioKAIDA2ccXTNZ3WNEw8Uc67SfuRkeHh41NYODg0RGRhIXF8eS\nJUt8TdQkYGBggPLy8tGcm8jIyHGrqzyZrq4uvv/97/P+++/78m4mwJeDMwXeeOMNnn/+efbu3esV\nH6zxqqVGLjhLlizxqpk5roYoigwMDNDe3k5nZyd6vZ7w8PDRm4BGo5nyNk0WyH4ajjqoD46jcnBC\n/KDvqem91mg00t3dTVdXF11dXSiVSiIjI4mJiWHWrFm+JVkJOXv2LBUVFWRnZ1/QrsKeAzrdBavV\nyrXXXsuaNWt4+OGHpZbj8vgMzhS54447CAwMZPPmzVJLcSiTKQUfCRmnpaURFxfnbIk+LoHJZKKn\np2f0Ri2KIuHh4YSFhREWFkZAQMCEN2uraKugMpodo9FRBgeg90mYNUFFtiiK6HQ6ent76enpobe3\nF4vFQkRExKgx9PUTcQ26u7s5ePAgy5YtIzT08yVy3mZynnrqKf7973+zbds2X87XJPAZnCmi1WpZ\nunQpTzzxBBs2bJBajkOYSp+bkXLNuXPnemVDRFfHYDDQ29s7+tDpdISEhIw+QkND0Wg0F/yNW/sh\n4UeO0+RIg1N6P+QnXfgzvV5Pf38//f399PX10d/fj0qlGjV9YWFhXtsB15Vpb2/nyJEj5OfnExQU\nNObzvMXklJeXc/PNN7N//36vrh6bCr7F5Cni7+/P22+/zfr168nNzSUhIUFqSXZlqk38/Pz8KCws\nZN++fZhMJubNm+cMmT4miVqtJjY2ltjYWMBW2jxys+/o6OD48eMYDAaCgoIICgoiMDCQE2eDCFEF\n0m9UA+6zNBOkMHK0YZAohhgeGmRoaIiBgQGUSuWomUtJSSEkJMSXR+PiNDc3c/LkSVasWDFhk8TJ\ndDx2dwYGBrjlllvYunWrz9xMAV8EZ5q8+uqrvPbaa+zatctjTqaZdCg2mUyUl5cTGhrKokWLfDkL\nboTVamVw0GYI+gcGOXBqiJ6+QYKVRvqMajr1/raHzo8uvT89Bg39Rg19RhVmceqf/elEcBSClWCV\ngRClgTC1nig/LVEaHVEaLZEaLWFqPcNmJRr/QArmBzErJHDUtHnK+ektnDp1iubmZpYvXz6l8SWe\nHMm58cYbWbx4MT/96U+lluJW+AzONBFFka985SvMmTOHp56aZmajC2GP8QsWi4WDBw8iiiI5OTke\ndYHxFoYM8J134A9lICASqjIQqdES5acl8pyhCFPrCVEZCFUaUMisGKxyBo0qtBYlOrMCnUWB9ty/\nJqsMiyjDYhWwiAIWUcYtKTX8uT4NuWBFLojIZVYUgoifwoyf3Iy/woSf3Iyfwkyw0oif3IxZlNFv\nVNFvVHPWqKFT50+n3ma4OvX+nDVosCJw2QL44C4I8KXQuB2iKHLkyBEGBwdZtmzZtAo5PNHkbN68\nmXfeeYcdO3Z4xPE4E5/BmQGDg4Pk5uayefNmrr76aqnlTBt7zpY6v9tofn6+L1nTzdCbYM1voKxx\n8q9Ry8yEqGxGRKMw4y83jZoVxTnzIhNEFOcMzcY5J3mnccGo4bGIAmarDJ3FZop0ZgXac/8OmFTo\nLQomu1QWHwqnfwYyXwDRrTCbzRw4cACVSkVmZuaMEmg9yeQcPHiQ66+/ntLS0tFlZh+Tx2dwZsjB\ngwfZuHEjpaWlxMTESC1nyjhqcGZjYyP19fXk5+cTEBBgl236cA6hD0O/znHbd2SSMYDul643WdzH\n2Oj1esrKyoiJiWHBggV2uQZ5gskZHh5m6dKlPPfcc279BVpKfHVmMyQ7O5sHHniAr3zlK1itVqnl\nTAlHTgVPSkoiIyODvXv30tvba7ft+nAsZ7WONTfOoKFHagU+Jsvg4CB79uxh7ty5pKam2u0aNJJ4\nHBISQnl5ORaLxS7bdSZ33nkn1113nc/czACfwbED9957L8HBwfzoRw6srbUzjjQ3I0RFRZGXl0dl\nZSWnT5+2+/Z92J+TXVIrmDlVrY6Zgu7DvnR0dIxOBE9MTLT79t3Z5Lz88ss0NDTwi184aOKtl+Az\nOHZAEAS2bt3Ku+++y7vvviu1nAlxhrkZISQkhKKiIpqamjhy5IjbRbm8CatoG1jp7lSfAYODmhT6\nmDmiKHLy5ElqampYsWLFBd2J7Y07mpzdu3fz+OOP8/bbb3tFx3xH4jM4diI0NJT333+f++67j+rq\naqnljIkzzc0IGo2GwsJCLBYLpaWlGI1Gh+/Tx9TRGR03nsGZ1HaA2eejXZKRZOKzZ89SVFTklPw8\ndzI5LS0t3Hrrrbz55psOiWp5Gz6DY0cWLlzIyy+/zBe/+EV6elwvEUAKczOCTCYjMzOT2NhYSkpK\nGBgYcNq+fUwedxyyeTHHOkDj6+Pncmi1Wnbv3k1QUNC0y8CnizuYHL1ez/XXX88jjzxCYWGh1HI8\nAp/BsTPr16/nW9/6FjfeeKNLnURSmpvzSU5OJisri7KyMlpbWyXR4OPS+Ks8w+DUdYGvz6Rr0dXV\nxZ49e0hNTbVrMvFUcHWT841vfIMVK1bwzW9+U2opHoPP4DiA73//+8THx3PvvfdKLQVwHXMzQnh4\nOEVFRdTX11NdXe1yFxpvxWCG1j6pVcwcsxXO9EutwgfYrj3Hjx/nyJEjFBQUSN7LxVVNzi9+8Qs6\nOzv51a9+JbUUj8JncByAIAhs2bKFiooKXnzxRUm1uJq5GWFkhpVcLmf37t0MDw9LLcnr8aTy6qPt\nUivwYTAY2LdvH8PDw6xcuXLcgZnOxNVMzgcffMAf//hH3nnnHd+MNDvjMzgOQqPR8MEHH/DMM89Q\nXFwsiQZXNTcjyGQy0tPTSU1NZe/evZw5c0ZqSV6LKNqqjzyFymYwucaXc6+ku7ubkpISEhISyMnJ\ncbkbt6uYnJqaGr7zne/wwQcfEBYWJokGT8ZncBxIXFwcf/3rX7nttttobGx06r5d3dycT0xMDIWF\nhdTV1XH48GHJv1F5IwazrX+Mp3CswzZ2wodzGVmSqq6uJi8vj9mzZ0staUykNjm9vb1s3LiRF198\nkbS0NKfu21vwGRwHs2zZMh577DE2bNjgtGUYdzI3I/j7+1NUVIQgCL4qKwmwWOF4p9Qq7MexDlC7\nVtDA49FqtezZs4fh4WFWrVpFcHCw1JImRCqTYzabufHGG7njjjv4whe+4JR9eiO+WVRO4qGHHuLo\n0aN88MEHDg3XuqO5uZiuri6qqqpITk5m7ty5bnkM7obOBH5KWyfj451w5IwtJ6ehF5p6oaUPtHZq\nX2SvWVRqBSSEwpwwSAqD5HDIiIXUKEiNtj3HN3TT8YiiSEtLC8eOHSM9PZ24uDipJU0ZZ86uEkWR\nO++8E61Wy5tvvum7vjkQn8FxElarlS996UsEBQWxZcsWh3yoPcHcjGA0GqmursZoNJKdnY2fn5/U\nkjyejQchLwQWB0GSGsLkECgHfyUo5DBsgK4haBuA1n5bxVXXEPRooU8LfToYNMCgHrQmm2nSm8Bo\nseXDWKy2bsm/W76db++7CoUcFDJQyW3DMf2UtlL1IDUEaSBEA6F+EB5geySE2h4xwRAVaHuOxQrD\nJhg2Q58FmoxwZAj29cEfMiDIF8VxKEajkaqqKkwmk9ufp84yOY888gglJSV88sknqNVqh+zDhw2f\nwXEiBoOBq6++moKCAp544gm7btuTzM35tLS0UFtby6JFi4iPj5dajsfSrIfZu8b+fao/ZAZBagAk\naSBOAxEKCJGDnwAqGSgFUMpspkUpB/kYC+Dbt2/nqqs+H8GxWG0l3qbz/jVYQS/CoBV6THDGCI06\nOD4M1UNQMwRjNS3ekwcrQqf+XviYHJ2dnVRXV3tUpNXRJueFF17glVdeobi4mJCQELtu28fn8X2/\ncSJqtZoPP/yQVatWERcXx3e+8x27bNdTzQ1AQkICYWFhHDx4kNbWVpYsWYJGo5FalsdRMzT+749r\nbY+poACCFRCoAD+ZzfzIgAeMsHivzcDorDBkhgEz2Ht81MEBKAjxLVPZG5PJxNGjR+nr6yMvL88t\ncm0my0hOTm1tLeXl5XY1Oe+88w7PP/88JSUlPnPjJHwGx8kEBQWxfft2ioqKiI6O5uabb57R9jzZ\n3Izg7+/PihUrOH36NCUlJaSmppKYmOiRxyoFJitUOiCn2wz0mm2Pi/d3ZAJDZQ9qhkFngQDfVc5u\ntLW1cfToUebMmcOSJUuQyTyvTsURJmfnzp18//vf59NPPyUmJsZOSn1MhO/Ul4CoqCi2bdvGunXr\nCA8P57LLLpvWdrzB3IwgCAJz5swhKiqK6upqWlpayMrKwt/fX2ppbo/eCrUe2Gexdhjw3FPCqRgM\nBg4fPoxer6egoIDAwECpJTkUe5qcqqoqbrvtNv72t78xb948Oyv1MR6eZ7/dhLlz5/L+++9z++23\nU1lZOeXXe5O5OR8/Pz/y8vKYM2cOe/bs4dSpU/jSyGaGUoBjHmhwjg2Dv+8KNyNEUaS5uZldu3YR\nHh5OYWGhx5ubEexRQt7Q0MDGjRvZsmULubm5DlDpYzx8p7+EZGVlsXXrVm688Ubq6uom/TpvNTcj\nCIJAfHw8q1evpr+/n+LiYnp7e6WW5bZo5J5pcNoMthwfH9NjYGBgtMN4UVERycnJXnmtma7J6erq\nYv369Tz++ONcccUVDlTpYyx8Bkdi1q5dy9NPP821115LR8fEo5y93dycj0qlIicnh8WLF1NdXc3B\ngwcxGAxSy3I7eoww6KHNo+t1UitwP0aSiPfv309KSgr5+fluXf49U6ZjcoaGhli/fj133303X/7y\nl52g0sel8BkcF+Cmm27iu9/9Ltdccw39/WOPQfaZm0sTHh4+2jl1165dNDQ0+JatpsCJKVZHuRNV\ng7Y5Wz4mZqRhX3FxMQqFgjVr1vgSYs8xFZNjMBi4/vrrufzyy/nud7/rRJU+LsZncFyEe+65hw0b\nNnDVVVcxNPT5EhOfuRkfmUxGSkoKK1eu5OzZs+zatYueHg8aj+0grKKtnNpTqR60JVH7GJ/+/n72\n7t1La2sry5cvJzU11aHdfN2RyZgcs9nMDTfcwNy5c3n88cclUOnjfHxVVC7ET37yE0wmE1dffTXb\nt28nICAA8JmbqaDRaMjJyaGnp4ejR4+iVqtJT0/3msTIqaKzwFEPzL8Z4dgwWHwRnDHR6XTU1tbS\n19dHeno60dHRUktyacarrhqZLxUdHc1LL73ku067AL4Ijovx85//nKKiIq655hq0Wq3P3EyT8PBw\nVq5cSUJCAqWlpVRXV/vycy6Fh1ZQjXBs2JZE7eNCTCYTtbW17N69m7CwMNasWeMzN5PkUpEci8XC\nzTffTGhoKK+++qpH9gdyR3yjGlwQURR58MEHqaio4IknnkCtVvvMzQywWCw0NDTQ0NBAUlISc+fO\n9YXfzyGKEL/LVnHkLF7VbedOv5kP25wMcgH0l4PCd+oAtpl4p0+f5uTJkyQmJpKSkoJSqZRallsy\nMtaht7eX5557DrVazeuvv+67trgQPoPjooiiyH333ceBAwf45JNPfA3t7IDRaOTEiRO0tbWRkpLC\nnDlzvP5ipLVAwL+du09nGhyAhpWQ5L1FQMB/EohPnDhBWFgYCxcu9OrKKHsxsiylVCr5y1/+4vXX\nE1fDF0dzUQRB4PnnnycvL290ucrHzFCpVGRkZFBUVMTg4CA7duygsbERq9V7s1AbvKCMeqI5W57M\niLHZsWMHnZ2d5Ofnu/3Ub1dhZFnKz8+Pt956y2duXBCfwXFhBEHgl7/8Jfn5+Vx55ZUMD3twsoQT\n8fPzIzMzkxUrVtDX18dnn31GU1OT1xkdUbSVUXs6lYNg9K4/LaIocubMGXbu3El7ezvLli0jNzfX\nl2xvJ0aqpfz9/XnjjTdQKHz1Oq6Iz+C4OIIg8NRTT7Fq1SquuOIKBge94I7kJPz9/cnKymL58uX0\n9PTw2Wef0dDQMK2W7O6I3moro/Z0aoe8x+BYrVaam5vZuXMnLS0t5ObmsnTpUoKCgqSW5jEYjUau\nv/56wsLC2Lp1qy9y48L4DI4bIAgCjz32GFdccQWXX365byyBnQkICCAnJ4fly5czODjIZ599xokT\nJzCZTFJLcygW0TOHbF7MsWFQeviVzmw2U19fz2effUZXVxe5ubnk5eURHBwstTSPQqvVct111xEb\nG8uWLVt85sbF8cXV3ARBEPjZz35GQEAAq1ev5uOPPyYxMVFqWR5FQEAAS5YswWAwUF9fz44dO4iP\nj2fu3LkembOgkXuHwTmuBbWHGhyjZ2EnIAAAIABJREFU0UhDQwOnT58mJiaGFStW+AoSHMTZs2dZ\nv349eXl5PPfcc75ScDfAZ3DcjIceeojo6GjWrl3Lhx9+yKJFi6SW5HGo1WrS0tKYN28eTU1No71C\n5s6dy6xZs6SWZ1e8YVbTsAW6jBCpklqJ/RgYGKChoYGOjg7mzJnD6tWrUak86ABdjJaWFq655hpu\nueUWHnroIV/LDjfBZ0HtzHvvvUdubi5ZWVksXLiQdevWfS55devWrVx++eWj/29paUEQBP7whz+M\n/uzJJ5/k1ltvveQ+vv71r/PrX/+a9evXs3fvXocchw9QKpXMmzePyy67jJiYGI4cOcKuXbtobm72\niDydVr33dPk94QGRKlEUaWtrY8+ePVRWVhIaGspll11Gamqqz9w4kGPHjrFmzRruv/9+Hn744UmZ\nm8ncBwDWrFlDcnIyWVlZo4/t27c74jC8El8Ex460tbVx1113ceDAAebMmQNAZWXl506ItWvXsmnT\nJoxGIyqVih07dpCfn8/OnTv5xje+AcCOHTvGnUJ77bXXEh4ezpe+9CVeeOEFvvCFLzjsuLwdmUxG\nfHw88fHx9PX10dDQwLFjx0hISCApKcltl6+OelH5dOUgFITaGv+5G0ajkaamJpqamggNDWXhwoWE\nhYX5oghOoLS0dMrX2MneB0Z47rnnuP766+2m2cd/8BkcO9LR0YFcLicsLGz0Zzk5OZ973uzZs4mJ\niaG8vJyioiJ27tzJww8/zA9+8APA1kZ9z549vPzyy+Pur6CggG3btnHttdfS1dXF7bffbt8D8vE5\nQkNDyc7OHr3p7Nmzh8DAwNG/qbusyxutcNALKqhGODpkm7sV6CZXPFEU6erqoqmpib6+PhITEyks\nLHRbM+2O/OMf/+Cee+7hrbfeYvny5ZN+3WTvAz4cj3tcjd2EJUuWUFRUxJw5c9i4cSPPPPMMra2t\nl3zu2rVr2bFjBwC7d+/miiuuICoqioaGBvbv309UVBRJSUkT7jMtLY1du3bx/9u78+io6vv/4897\nZ0syMwlZIRDQGAiLCghEj4KCBg1QlLghYmssKrbSumBtxW9dKjatlWPdPfpTLCottIKgrYgGAZeK\ngMjikoBIkGxk32aSmblz7++PGaIUUANJbjJ5Pzj3DBNmMu9JQuY178/nfj4PP/yw7F7bhex2O0OG\nDCE7O5uMjAzKy8spKCjgs88+o7Gx+2/P7dN7xwTjQwo9oPaAhofH4+HLL7+koKCA4uJiBg4cSHZ2\ntqw83MVeeOEFbrnlFtasWdOucAPtex0AuP322w8botq7d++Jli/CZKuGTlBYWMjGjRtZs2YNGzZs\nYOvWrQwePPiw2yxdupTFixfz4osvctVVV/HRRx/x+9//vu3Fcu/evbzwwgs/+jFra2v5yU9+wrhx\n43jsscd6TCchkgQCAUpKSvjmm29QFIWBAwfSv39/HA6H2aUdoSVoEG1R2NxgsKsJirwKe72hScfF\nLVCvdd5jd+ZWDW5LaFuG9GjIiIHMGIPT3XCGG6JVuuWwTiAQoLy8nAMHDuDz+Rg0aBADBw7slj83\nvcEf//hHli5dytq1a0/oTNUf8zowadIkbrvtNhmi6iQScDrZlClTuOCCC/j73/8OwJVXXsn//d//\nUVZWxuDBg3n00Uf5+uuv+fOf/8w777zDyy+/THl5Oddddx3XXHNNux7L6/Vy5ZVX4nQ6eeWVV2Ti\noYkaGxs5cOAA5eXlOJ1OBgwYQGpqarfa2PAPVDMMGxmGnRTdhsuw4FTBoSq0BA0O+kMTkfe1wjct\nChV+OOiDqgBU+6EmAHUBaGnnInrtDTgOFRJsoSPJFjobKsUO/ewwMMogPRrSoqCfA5wWBb9u4NGh\nWQlSrWp8rfjZiY8/kNzOr1Dn0TSNgwcPUlJSQkNDA/369SMtLY34+PhuGcJ6A13XueWWW9i6dStv\nvvnmYUNMJ+pYrwMScDpXDxmR7hlKS0spLi5m/PjxQGjdhH379jF06FC2b99+2G379+/PwIEDWbRo\nEY8//jgA55xzDnPnzqWqqoolS5a0+/FjYmJYvXo1N910E9nZ2axcuZLk5O7zS703iY2N5dRTT2XE\niBHU1dVRWlpKYWEhcXFxDBgwgL59+5oadmoI8jz1oSsK8J31yvqiMtoSzbBoO+nRNjKwkaWrOHUr\nUSjYUXCEgxCAZhi0BEOnYx86WnTwHbrUQTNChwE4tsHykQZWJbTLt10NdVeiLKHLGAs4LaHLGBVs\n4cfx6wY+A/yGQSsGXjVIrRqkAo1P8VNEKMh8o2pHHXyfRSzDMa8rEgwGqayspLS0lNraWlJSUjjl\nlFNISkqSUGOy5uZmZs+eja7rvPvuuye0llB7XgdE55IOTgfav38/c+fOZd++fcTExKBpGrNnz+bu\nu+8+6u1/8Ytf8MILL1BXV9e2R8zZZ59NXV0dhYWFx12HYRg8+uijPP3007z66quMGjXquD+X6DiG\nYVBdXU1paSmVlZW43W769etHv379unx+xRZauIxjzwv4MezAAKz0w0oKVhKxEIeKGwtOFKJQcKBi\nR8ECWFBQgPq17xGXcx5BDHTAh4EfPRRaMGgiSAM6tQSpJBRgSgjQeoLP+a+kcDnucBVdw+fzUVFR\nQUVFBfX19SQlJTFgwABSUlJkGLmbKC4uZsaMGUydOpU//vGPJ7w6cXteByZNmsT+/fuJi4tr+9id\nd97Z7u69ODoJOBFs7dq13Hjjjfz1r3/l8ssvN7sc8R2GYVBXV9f24qeqalvYiYuL69R39EEMXqKB\ne6nutMf4PgvXFnFPztAuf9yb6MMdJBDdiedWGIZBc3Nz2/fV5/O1fV8TEhIk1HQzGzZsIC8vjz/9\n6U/Mnj3b7HJEB5MhqgiWk5NDQUEBl1xyCbt27eK+++6TVng3oSgKCQkJJCQkMGLECDweDxUVFXz+\n+ed4PB4SExNJTk4mOTm5w7s7rRjswd+hn7Mn+Ao/nbHnps/no6qqiqqqKqqrq4mKiqJv376MGjUK\nt9st/+e6qaeffpqHH36YFStWMG7cOLPLEZ1AOji9QENDAzNnzsTtdvPyyy/L6abdnKZp1NbWUllZ\nSVVVFbqut4WdxMTEE5487kUnj3I2Yc4+DWZ1cE7GxgYGYTnBIapAIEBdXR1VVVVUVlYe9v1JSkrq\nVhPJxZGCwSDz5s1j27ZtrF69mtTUVLNLEp1EOji9QFxcHG+++Sa/+93vOOecc1i1alXbCpui+7Fa\nraSkpJCSkgJAa2sr1dXVVFRU8MUXXwCQmJjY1gFyOp3t6hLEoLK3F3ZwviGAzmHzqX+QYRi0tLRQ\nW1tLTU0NtbW1BINB4uPjSUpKIj09XTa37EFqamq4/PLLSUtL47333iMqKsrskkQnkoDTS1gsFhYt\nWsRLL73ExIkTWbJkCRMnTjS7LPEjREVFkZaWRlpaGhAaEqmtraW2tpb9+/fT0tJCXFwcffr0oU+f\nPsTFxX3vL24vOlX0/L202ksHytEYxLE7LD6fj4aGBhoaGqivr6e+vh673U5CQgJJSUlkZmZKB7SH\n2rlzJ1dccQU33XQT8+fPl6HDXkACTi9z7bXXMnToUK666ip++9vfcvPNN5tdkmgnh8NBampqW2s9\nGAy2vSCXlZXx5Zdf4vf7iY2NJS4ujri4ONxuN06nE1VV2U/A5GdgniJ8DMKGrut4vV6amppobGyk\nvr6exsZGLBZLW1hMT0+nT58+WK3ya7KnW7FiBbfffjvPPvssU6dONbsc0UVkDk4vVVpaSm5uLsOG\nDePZZ5+VNnuECQaDNDU1tQWf5uZmPB4PFouFRlcUBW6ocDk46LZTE2On2W6BLnpH22VzcAyDmECQ\nBG+Afs0++jb5Oa/ZoG+TDy0QwOl04nK52kJgbGyshJkIo2kad955J2vWrGH16tUMHdr1c7+EeeR/\ncy81YMAAPvzwQ+666y6ysrL4xz/+wciRI80uS3QQi8XSNmT13flWjQEf/6+pjIPN1aQ2tTK6vJEE\nrx+XP0iL1UJtjI26GBs10TZqY+w0Oqw0OSw0Oax47RaM7tLWNwyiAzqxPg2XTyPWp5HgDZDQEiDB\n6yfRGyAmEMRrCz2ngy4HB112vkiJZ6orjXiHDDNFuuLiYmbNmsXgwYPZunVr21pjoveQgNOL2e12\nHnnkEd544w2mT5/OggUL+OUvf2l2WaITWWw2Pktw8HFC/BH/Fh3udiR4/SR4AwyqbyHWp+EOhwin\nPzRvx2O30Oyw4rVZaLWqtFpVfFYLrTY1fN1CQFXQVYWgArqiEFSV8CVYdIP0Wi8W3UA1DCy6gcUA\nVTew6QYOLUiUprcdDi1IVEAnWtNxh+tQDIMWWyh4HQpgtTE2dic5qYnpQ12MDY/tyK5UA3acyMTS\nSPfqq68yf/58HnjgAa677jqzyxEmkSEqAUBJSQkzZ84kLS2NxYsXy7udCLYeD+/iZR8BivFTgvaj\npxwrhkGMP4jbpxF9KIQEgjjaAkkonFiDBhbjUIDhO0HGYMTBZnb1c4cDkIKu0BaANFUJhSRbKCiF\nwlP47zaVRocVTzs6SSqQipWTsZGOjdNwcA1xP3g/0TMFAgFuvfVW3nvvPf71r38xfPhws0sSJpKA\nI9pomsZ9993HihUrWLp0KWPHjjW7JNEJvqYaN06isRODikFob6oSAuwjwH40ytGoCB9VBKklSEf9\noujIOTh9UEnBSl8spGIlFSuDwmEmDSvJWFGBFnRaCNCMh5NJ7JDHFt3LV199xVVXXcWYMWN4/PHH\n5Ww3IQFHHKmgoIDrr7+e2267jdtvv93sckQHqqSR37C87boVlXSSySCFNOJJxk0sThxE4cCCAwt2\nFIIYNKNTT5A6dGoIth1N6DSj40HHi0FLeF8pPwaB8BEktEWEAVy/9nMW55yKBQUVsKFgC2/iGYVC\nNAoxqMSg4g4f8agkYSEhfPTBghsVKwoBDHzo+NDw4aMJL1U0Uko9X1PFXioOW8P4bqYzDFncLZK8\n8sorLFiwgIcffphZs2aZXY7oJmQOjjjC5MmT+fjjj5k9ezbr16/nb3/7GwkJCWaXJTpAKXWHXdfQ\n2cNB9nDwmPeJwU4aCfQlliTcxBNDPFGkEUUUNmzYsGLFgiW8oabatrGmGg4xCrRtcvkuhaxlEAZg\nhDfcPHSpY6BjEEQnGP7jJ4CPFjz4aKKVEjzU0MxBGimljsZ2bsNZTDVD6IulE/ekEl3D6/Uyb948\nduzYwYYNG8jIyDC7JNGNSMARR9WvXz8KCgrIz8/nzDPP5Omnn+aiiy4yuyxxAjSC7Kem3ffz4mc3\nFeymokPqmEoi1/N8h3yu41FGPQGCEnB6uC1btnD99ddz/vnn89FHH+FwOMwuSXQzEnDEMamqyu9/\n/3uys7O57rrrmDhxIo8++qismdNDaeiUU292GaYrox71BPejEubRNI177rmHZcuW8dRTTzFt2jSz\nSxLdlLyFET/o7LPPZvv27bhcLs444ww2bNhgdkniOFhRKZOAQzn12OW9XY+0fft2srKyKC0t5dNP\nP5VwI76XBBzxo0RHR/PII4+wePFi5s6dy80330xLizm7UYvjY8XCYPqSSV/cvXAtGCcOTiGZ00kz\nuxTRToe6Nrm5uSxcuJCXXnqJPn36mF2W6ObkbYxol/Hjx7N9+3buvvtuRo8ezXPPPSebdvYQQXQu\nYxR2HDiw4UejhmYqaaSMeqpppiZ81OGliZYOOzW8K7hwEI+TRFwk4iQRN/3pQ19iScRFVPg5+/Gh\nEcTarn3FhVm2b9/OnDlzOPXUU/n000+Jjz9ykUohjkZOExfH7YMPPmDOnDlMnjyZRYsWydycbq6c\nQv5NPgBRxJLKUJJIpw/9cZGEnVisONrOi9LR8eCjkVYa8FKPlzq8NOPDQyse/Hjx0UKA1vDhQ8OP\nRuB7lg6cujaRNTlHn+xsRcUeriIKK1HYiMKOEzsxOHCFjzhiiCeGPsTgJhoXDiyoBAgSIICGjwBN\neKimnnJqKKaMQrzhs8jO4wYyObftzC7R/Wiaxv33388rr7zCE088wcUXX2x2SaKHkQ6OOG4TJkxg\n+/btLFiwgDFjxvDMM89w/vnnm12WOAodnWr2t11vpZF9bGEfW456ezsxJHISfehPLMnEkMDJxDGU\nBCxEo2LHghU1fHK4BfWIs5JCp3rr6Bjh08BD76XeZz3P8JPwCeWHDjW8JN+R9w+GP4seji9BWvDT\nTAtl1FHLfippoIIaimml+Ud9PeooIYgfK3LmTXf06aefMmfOHEaMGCFdG3HcpIMjOsT777/PnDlz\nmDRpEosWLSIuTpbD704C+DjADj5mGR5qwkvudTSVKFw4wv0WG1HYsIejiwUVGwrQsDaR2JwaDLRw\neAkQwI+GDz8efHjx0YT+ncX5OpKTeE5jCiPIxoq9Ux5DHJ/W1lbuvfdeli9fzhNPPMEll1xidkmi\nB5MOjugQ5557Ljt27GDhwoWMHDmSe+65hzlz5qCqMo+9O1AwGMQo0hmHgY6Heho5SB2lNFJJM9U0\nUY2HWnw/sgtyJJ1WGmml8XtvlchU9vD+cT7GD7MRjSs8E8dNEm5SiGcAcfTFSQIqVoIEoEfNMIp8\nK1eu5He/+x2TJ09m+/bt0rURJ0w6OKLD7d69m3nz5uH1ennyySc544wzzC5JAF8zCh0v0WQRxTgc\nDMXKSUASKk4sRGHBRhCNVprwUo+HWpqpoYUGWmgKryUcmoXjx4OfFgL4aE9YSFw7lZqcNT/69lYc\n4Rk4MeG9wF3hI5ZoYsNTihOIoQ9RuLFiJ4hGkFZ0vEANGt/gowgfn9LKx+h4yOArFGzt/jqKjrVn\nzx5+/etfU1NTw7PPPsuYMWPMLklECAk4olMYhsFrr73GHXfcQU5ODg899JAMW5mswVhGQNmDn30E\n2IfGAQx8h91GJQUHp+EgExsnY6UfFpKBOBScgB0VGyo2LOEGsIERnhtzaHrxt4ceHoYyCLbNwtm5\ntphROSeHZ99YUFGxYMWCLXzYsWDDGr6uhOfmBNHQ0dDxAwEMvEADQarD24Lux88efHyGRun/PHsb\nNgZg42RsnILdGEKc8tNO/oqL79Pa2sp9993HP/7xD+6//36uu+466fiKDiUBR3Qqr9fLgw8+yNKl\nS2XYymSG/wCGagM1GkVxAhZ06tAow08xAaUYjTI0yglSgcZBgtTAMefCWMP7eKdgJQWVuPDhQsUZ\n3jrTgYI13CkJfd8/XKszPkcltPNUAAMtvDVnKzoeDDzoNBKkLhxeKtGoAPzHqEMJb8GZgpV+4T3F\nU7EZ6eGQNgALCYSimAeCXhQ9gGIf2MFfYfFjfXc4Kj8/X4ajRKeQgCO6xJ49e5g3bx7Nzc08+eST\n0obuSroORgsoCqgxoLeAvwR0D6CCGgWKA0O1YqhWUBygRKMqDgxDD+8VXkeQWjSqCVJJUKlDpx79\nO3uJh4JJKwYtGPjCRyjAEO7ggEHh2rsZlvOncGfGgoIVsKLgQMURDkYxqESHw5IrvKd4HKoRj5UU\nLCSGjwRUXCiKBcPwYxgtYPhQdA1F18Dwge4DNFCiwJ4GFlfoa2AEwbCBVc6k6iqHhqNqa2t59tln\nZfhadCoJOKLLGIbBqlWruOOOO8jOzuahhx6SXcq7gB4IoFqtEPSBoYPtO+sVGVo4ABig2EG1g94K\nWhVoteEbKaBYQbG1XRqKiqEooKigWABr6N+woGAJXSpHX0hv7dq15OTkHPFxwwhCeDgLgqEAghaq\n0dBRDCN8qYERCH88PFnYMMAaD7bkUIgzAuHnRSiwqd+ZaxMIr8BtjUL3+1AdvW9V567m8Xi4//77\nWb58OX/4wx/Iy8uTTq7odHIWlegyiqJw6aWXkpOTQ35+PqNGjWLOnDncddddREdHm11exFJt4Rd3\n6/+8kBt6KJRYrBDUQGsFNQjWaLAPDB2Hbmf4Q4ECA1BRFBuKYgt1hSAUioKtYDSH/q63hu6DHp5/\nbMChRfWMAHi2f/sxBUBBUeygOlCU6FBXSY0CxQ2HXgh1fzjQ6OH7WcLdpv95odR8oAfA4gBL+Lnr\n+refx/btz5qEm84VCAR48skneeyxx5g+fTo7duyQ4SjRZaSDI0xTXl7OfffdxzvvvMP8+fO5+eab\nsVhk+XyzHfqVoChKKBgoyrdBxjAg6AddA8IByXqUkPE9jtXBOUYxoc6TrgEKqBaw2A9/vHB4MQwj\nVLMwna7rLF26lAcffJDRo0eTn59PRkaG2WWJXkZ6hMI0qampPPfcc7z99tts3LiR0047jeXLlyOZ\n21yKonwbFFT123AT+sdQoLE7we4OdUMU9Yjv2fF8D4/6ORQl1Hmyu0KPaY06MkyFOzMSbrqHt99+\nmzPPPJMlS5awbNkyli9fLuFGmEI6OKLb+OSTT7jzzjtpamoiPz+fCy+80OySRCdoVwdH9BibN2/m\nrrvuoqGhgUWLFsm2LcJ0MgdHdBtjx47l3XffZd26dfzmN78hMTGR/Px8zjzzTLNLE0IcQ2FhIQsW\nLOCLL74gPz+fyy67TLppoluQISrR7WRnZ7Nt2zbmzp3LT3/6Uy677DI+++wzs8sSQnxHcXExeXl5\nTJkyhWnTpvH5559z+eWXS7gR3YYEHNEtKYrCzJkz+fzzz8nJyeGSSy5hxowZbN261ezShOjVioqK\nuOaaazjvvPM49dRT+fLLL7nxxhuxWmVAQHQvEnBEt2az2bjpppvYvXs3V1xxBddeey0XXngh69ev\nN7s0IXqVrVu3kpuby7Rp0xg/fjxFRUX89re/lSUeRLclk4xFj6LrOv/+979ZuHAhVquVO++8k9zc\nXFk0rAeRScY9y7p168jPz6eiooIFCxYwa9Ys6daIHkECjuiRDMNg48aNLFy4kKqqKm677Tby8vJk\nHZ0eQAJO96frOitXrmTRokUEg0Huuecepk+fLm8kRI8iP62iR1IUhUmTJrFu3TpefPFF3njjDYYP\nH84jjzxCa2ur2eUJ0SMFg0FeeOEFRo0axTPPPMOf//xnNm/ezCWXXCLhRvQ48hMreryxY8fy2muv\n8frrr7Njxw4yMzOZP38+Bw4cMLs0IXqE6upq7r33XjIzM/nPf/7D3/72N9atW8ekSZPkrCjRY0nA\nERFj2LBhLFmyhI8//pjY2FjGjx/PjBkzWLdunayOLMRRbN68mauvvprRo0fj9XopKChg5cqVjB07\n1uzShDhhMgdHRCy/38+KFSt47LHHaGlp4ec//zk33ngjTqfT7NJ6NZmDYy6/38/LL7/M888/T3Nz\nM7feeiuzZ88mJibmh+8sRA8iHRwRsex2O1dffTWbNm3ixRdfZOfOnWRmZjJnzhy2bdtmdnlCdKnd\nu3fzq1/9ioyMDN5++23+8pe/sHPnTm644QYJNyIiybl+olcYM2YMixcvpqGhgVdeeYWf/exnuFwu\n8vLyyMvLk66OiEh+v59ly5axePFiSkpKmDt3Ltu2bSM5Odns0oTodDJEJXolwzDYtGkTTz31FOvX\nr2fChAlty87L2SKdS4aoOpeu67z//vu8+OKLFBQUkJWVxbx587jgggvkZ1v0KhJwRK/n8XhYtWoV\nS5YsoaioiOnTp3P99dczZswYs0uLSBJwOkdhYSHPP/88q1evpm/fvuTl5XHFFVcQHx9vdmlCmEIC\njhDfUVlZybJly1iyZAmtra3k5uZyww03kJ6ebnZpEUMCTscpLy9n8eLFrFixgtbWVq699lpmz57N\noEGDzC5NCNNJwBHiGPbs2cPLL7/M0qVLSUlJ4YorriAvL4+kpCSzS+vRJOCcmMbGRpYuXco///lP\n9u7dy8yZM7n22msZOXKk2aUJ0a1IwBHiBxiGwebNm3nppZdYvXo1mZmZTJkyhVmzZsk75eMgAaf9\nKisrWb58OW+++SY7duzgoosuIi8vj/POO0+2JxHiGCTgCNEOmqbx3//+l1WrVvH666/jcrnIzs5m\n5syZZGVlySTOH0ECzo+zc+dOli9fzjvvvMPBgweZNm0al156KRMnTsThcJhdnhDdngQcIU5AUVER\nq1evZtWqVZSWljJx4kRyc3OZNm0aUVFRZpfXLUnAOTpN03jnnXdYuXIl69evx+12k5ubS25uLiNH\njpQtE4RoJwk4QnSQ6upq3nzzTV577TU2bdrE6NGjycnJYcaMGTJJ+Tsk4HyrrKyM119/nbfeeovN\nmzczatQocnNzufjii+nfv7/Z5QnRo0nAEaIT+Hw+Nm7cyKpVq1i3bh3BYJCsrCzOP/98pk2bRlpa\nmtklmqY3B5zKykrWrFnD+vXr2bx5My0tLZx//vnMmDGDyZMny4KTQnQgCThCdIGDBw+yceNG1q1b\nx4YNG9B1nbPOOotJkyYxdepUBgwYYHaJXaY3BZyqqireeust1q9fz8cff4zX62XixIlkZ2czceJE\nmaQuRCeSgCOECSoqKg4LPIZhkJWVxbnnnsuFF15IRkZGxM65iNSAYxgGBw4coKCggPfee48tW7bg\n8Xg477zzmDx5MpMmTZJAI0QXkoAjRDdwKPAUFBSwadMmampqyMzM5PTTTycrK4sJEyaQnp4eEaEn\nUgJOSUkJH3zwAZs3b2bnzp0UFhbidDo566yzmDx5MhMnTuSkk04yu0whei0JOEJ0Q62trezatYut\nW7eyZcsWtm7dSnV1NUOGDOG0005j3LhxTJgwgYyMjB53anpPDDj79+/ngw8+YMuWLezatYuioiJc\nLhfjxo0jKyuLcePGMXr0aJlDI0Q3IgFHiB7C5/MdEXqqqqpIT08nPT2djIwMRowYwciRIxkyZAhW\nq9Xsko+quwYcXdfZu3cvu3bt4osvvmDPnj0UFxfz1VdfERcXx9ixYznzzDMZO3Yso0aNkjAjRDcn\nAUeIHszn81FUVMTu3bspKiqisLCQwsJCSkpKcLlcnHzyyZxyyikMHjyY4cOHc9pppzFw4EBTV781\nM+AYhkFZWRmfffYZX375Jbt2Gko6AAADcUlEQVR372bfvn0UFxdTV1dHamoqQ4cOZfjw4QwdOpQh\nQ4YwfPhwYmJiTKlXCHH8JOAIEaFqamrYs2cPu3fvbgs+u3fvprq6GrfbTXJyMikpKaSkpJCamkpa\nWhppaWkMHDiQQYMGERsb2yl1dVbA8Xg8fPPNN+zfv5/S0lJKS0spLy+noqKCqqoqqqqqqK+vJz4+\nnszMTIYNG8awYcPIzMwkMzOT5OTkiJjjJIQIkYAjRC9jGAZ1dXWUl5e3HWVlZZSUlFBWVkZZWRkV\nFRW0tLTgdDpJSkoiPj4el8uF0+nE6XTicrmIjY3F7XYTGxtLbGwscXFxxMXF4Xa7cbvd2O12rFYr\nFovlsMu3336bCy64gGAwSDAYRNM0gsEgfr+f5uZmGhsbaWxspKGhgYaGBpqamto+5vF4aG5uxuv1\n0tzcTF1dHVVVVTQ1NREVFUXfvn0ZMGAA/fv3b7tMTU1tu0xISOhxc5aEEMdHAo4Q4piampooKytr\nCxHNzc1tl42NjYeFj6amJpqamvB4PDQ1NaFpGrquEwwG0XUdwzDarttsNhRFQVXVtsNqteJyuXC5\nXG0hKTY2FpfLdVhwcrvdbbdLTk4mNTWVuLg46b4IIQ4jAUcIIYQQEUd6tUIIIYSIOBJwhBBCCBFx\nJOAIIYQQIuJIwBFCCCFExJGAI4QQQoiIIwFHCCGEEBFHAo4QQgghIo4EHCGEEEJEHAk4QgghhIg4\nEnCEEF1i5cqVjB07ltGjRzNs2DAuuOACdF03uywhRISSrRqEEJ2uvLyc008/nU8++YSTTjoJgG3b\ntnHGGWfIHlJCiE5hNbsAIUTkO3jwIBaLhYSEhLaPjRkzxsSKhBCRToaohBCdbuTIkUyYMIGTTjqJ\nSy+9lIcffpjS0lKzyxJCRDAZohJCdJnCwkI2btzImjVr2LBhA1u3bmXw4MFmlyWEiEAScIQQppgy\nZQoXXXQR8+fPN7sUIUQEkiEqIUSnKy0t5cMPP2y7XldXx759+8jIyDCxKiFEJJNJxkKITqdpGg88\n8AD79u0jJiYGTdPIy8tjxowZZpcmhIhQMkQlhBBCiIgjQ1RCCCGEiDgScIQQQggRcSTgCCGEECLi\nSMARQgghRMSRgCOEEEKIiCMBRwghhBARRwKOEEIIISKOBBwhhBBCRBwJOEIIIYSIOP8fhYdglQmG\nQ40AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 640x640 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LXZBp0d_spdx",
        "colab_type": "text"
      },
      "source": [
        "We drop some variables that are irrelevant and have large amounts of missing data."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "n85CY4XLYb61",
        "colab": {}
      },
      "source": [
        "weather_train_data = weather_train_data.drop(['precip_depth_1_hr', 'cloud_coverage', 'sea_level_pressure', 'wind_direction'], axis=1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "2ToR4u_N4W7d",
        "colab": {}
      },
      "source": [
        "weather_train_data.dropna(axis=0, inplace=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "YXb7nZoNYb63",
        "outputId": "3d550afa-faaa-4897-c5b2-e14220cb6904",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 190
        }
      },
      "source": [
        "weather_train_data.info()"
      ],
      "execution_count": 199,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 139393 entries, 0 to 139772\n",
            "Data columns (total 5 columns):\n",
            "site_id            139393 non-null int64\n",
            "timestamp          139393 non-null object\n",
            "air_temperature    139393 non-null float64\n",
            "dew_temperature    139393 non-null float64\n",
            "wind_speed         139393 non-null float64\n",
            "dtypes: float64(3), int64(1), object(1)\n",
            "memory usage: 6.4+ MB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "0UkjLffV4_AY",
        "outputId": "43de7b33-7081-40d0-e669-4029cbe0002b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 288
        }
      },
      "source": [
        "weather_train_data.describe()"
      ],
      "execution_count": 200,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>site_id</th>\n",
              "      <th>air_temperature</th>\n",
              "      <th>dew_temperature</th>\n",
              "      <th>wind_speed</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>139393.000000</td>\n",
              "      <td>139393.000000</td>\n",
              "      <td>139393.000000</td>\n",
              "      <td>139393.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>7.473926</td>\n",
              "      <td>14.407688</td>\n",
              "      <td>7.347791</td>\n",
              "      <td>3.560702</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>4.607432</td>\n",
              "      <td>10.622384</td>\n",
              "      <td>9.790117</td>\n",
              "      <td>2.336138</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>-28.900000</td>\n",
              "      <td>-35.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>3.000000</td>\n",
              "      <td>7.200000</td>\n",
              "      <td>0.600000</td>\n",
              "      <td>2.100000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>7.000000</td>\n",
              "      <td>15.000000</td>\n",
              "      <td>8.300000</td>\n",
              "      <td>3.100000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>11.000000</td>\n",
              "      <td>22.200000</td>\n",
              "      <td>14.400000</td>\n",
              "      <td>5.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>15.000000</td>\n",
              "      <td>47.200000</td>\n",
              "      <td>26.100000</td>\n",
              "      <td>19.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "             site_id  air_temperature  dew_temperature     wind_speed\n",
              "count  139393.000000    139393.000000    139393.000000  139393.000000\n",
              "mean        7.473926        14.407688         7.347791       3.560702\n",
              "std         4.607432        10.622384         9.790117       2.336138\n",
              "min         0.000000       -28.900000       -35.000000       0.000000\n",
              "25%         3.000000         7.200000         0.600000       2.100000\n",
              "50%         7.000000        15.000000         8.300000       3.100000\n",
              "75%        11.000000        22.200000        14.400000       5.000000\n",
              "max        15.000000        47.200000        26.100000      19.000000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 200
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lCQFMMEBswys",
        "colab_type": "text"
      },
      "source": [
        "Changing the timestamp variable from object to timestamp data type"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "5NEAprFnUETG",
        "colab": {}
      },
      "source": [
        "weather_train_data['timestamp'] = pd.to_datetime(weather_train_data['timestamp'])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vvxZyyNbmZhP",
        "colab_type": "text"
      },
      "source": [
        "Visualizing air temperature over time"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "Uo0ZVj_sQSKU",
        "outputId": "04a19bad-7925-42a5-f683-38322ebefa1e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 617
        }
      },
      "source": [
        "import plotly.express as px\n",
        "fig = px.line(weather_train_data[weather_train_data.site_id==1], x='timestamp', y='air_temperature')\n",
        "fig.show()"
      ],
      "execution_count": 202,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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        "id": "9_tWPN3aYb6-"
      },
      "source": [
        "Splitting the timestamp variable in the train dataset into two columns: date and time. Both columns are of tiype datetime."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "BQVTiZcQYb6-",
        "colab": {}
      },
      "source": [
        "train_data['timestamp'] = pd.to_datetime(train_data['timestamp'])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "oyIJgUBxYb7A",
        "colab": {}
      },
      "source": [
        "# train_data['date'] = train_data['timestamp'].dt.date\n",
        "train_data['date'] = train_data['timestamp'].dt.normalize()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "0Hyru6NsYb7C",
        "colab": {}
      },
      "source": [
        "train_data['time'] = train_data['timestamp'].dt.time\n",
        "train_data['hour'] = pd.to_datetime(train_data['time'], format='%H:%M:%S').dt.hour"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "L9rSuAl5Yb7E",
        "colab": {}
      },
      "source": [
        "train_data = train_data.drop(['time'], axis = 1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "xrP2rVi-Yb7F",
        "outputId": "2d7d7d66-15b0-45ae-ec72-8f927d17c0fc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 207
        }
      },
      "source": [
        "train_data.info()"
      ],
      "execution_count": 207,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 20216100 entries, 0 to 20216099\n",
            "Data columns (total 6 columns):\n",
            "building_id      int64\n",
            "meter            int64\n",
            "timestamp        datetime64[ns]\n",
            "meter_reading    float64\n",
            "date             datetime64[ns]\n",
            "hour             int64\n",
            "dtypes: datetime64[ns](2), float64(1), int64(3)\n",
            "memory usage: 925.4 MB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "tzPq5a_d5J2V",
        "outputId": "d5ccacc2-d824-4d37-eb95-98c797028f53",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 288
        }
      },
      "source": [
        "train_data.describe()"
      ],
      "execution_count": 208,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>building_id</th>\n",
              "      <th>meter</th>\n",
              "      <th>meter_reading</th>\n",
              "      <th>hour</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>2.021610e+07</td>\n",
              "      <td>2.021610e+07</td>\n",
              "      <td>2.021610e+07</td>\n",
              "      <td>2.021610e+07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>7.992780e+02</td>\n",
              "      <td>6.624412e-01</td>\n",
              "      <td>2.117121e+03</td>\n",
              "      <td>1.150232e+01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>4.269133e+02</td>\n",
              "      <td>9.309921e-01</td>\n",
              "      <td>1.532356e+05</td>\n",
              "      <td>6.922017e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>3.930000e+02</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>1.830000e+01</td>\n",
              "      <td>6.000000e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>8.950000e+02</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>7.877500e+01</td>\n",
              "      <td>1.200000e+01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>1.179000e+03</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>2.679840e+02</td>\n",
              "      <td>1.800000e+01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>1.448000e+03</td>\n",
              "      <td>3.000000e+00</td>\n",
              "      <td>2.190470e+07</td>\n",
              "      <td>2.300000e+01</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        building_id         meter  meter_reading          hour\n",
              "count  2.021610e+07  2.021610e+07   2.021610e+07  2.021610e+07\n",
              "mean   7.992780e+02  6.624412e-01   2.117121e+03  1.150232e+01\n",
              "std    4.269133e+02  9.309921e-01   1.532356e+05  6.922017e+00\n",
              "min    0.000000e+00  0.000000e+00   0.000000e+00  0.000000e+00\n",
              "25%    3.930000e+02  0.000000e+00   1.830000e+01  6.000000e+00\n",
              "50%    8.950000e+02  0.000000e+00   7.877500e+01  1.200000e+01\n",
              "75%    1.179000e+03  1.000000e+00   2.679840e+02  1.800000e+01\n",
              "max    1.448000e+03  3.000000e+00   2.190470e+07  2.300000e+01"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 208
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "vwbMhzjmYb7H"
      },
      "source": [
        "Splitting the timestamp variable in the weather dataset into two columns: date and time. Both columns are of type datetime."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "iOXqoMEbYb7H",
        "colab": {}
      },
      "source": [
        "weather_train_data['timestamp'] = pd.to_datetime(weather_train_data['timestamp'])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "psT7JyPWYb7J",
        "colab": {}
      },
      "source": [
        "weather_train_data['date'] = weather_train_data['timestamp'].dt.normalize()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "bCxWgrcpYb7L",
        "colab": {}
      },
      "source": [
        "weather_train_data['time'] = weather_train_data['timestamp'].dt.time\n",
        "weather_train_data['hour'] = pd.to_datetime(weather_train_data['time'], format='%H:%M:%S').dt.hour"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "tDhj8kMAYb7N",
        "colab": {}
      },
      "source": [
        "weather_train_data = weather_train_data.drop(['time'], axis = 1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "XCxtvXFUYb7Q",
        "outputId": "30fc73f4-f48b-4bf8-b9f0-0679aec6a0e1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 224
        }
      },
      "source": [
        "weather_train_data.info()"
      ],
      "execution_count": 213,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 139393 entries, 0 to 139772\n",
            "Data columns (total 7 columns):\n",
            "site_id            139393 non-null int64\n",
            "timestamp          139393 non-null datetime64[ns]\n",
            "air_temperature    139393 non-null float64\n",
            "dew_temperature    139393 non-null float64\n",
            "wind_speed         139393 non-null float64\n",
            "date               139393 non-null datetime64[ns]\n",
            "hour               139393 non-null int64\n",
            "dtypes: datetime64[ns](2), float64(3), int64(2)\n",
            "memory usage: 8.5 MB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "6WaSeTQFYb7S",
        "outputId": "ef7463d0-0bbe-4109-9569-9bf74411c2ed",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 173
        }
      },
      "source": [
        "meta_data.info()"
      ],
      "execution_count": 214,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 1449 entries, 0 to 1448\n",
            "Data columns (total 4 columns):\n",
            "site_id        1449 non-null int64\n",
            "building_id    1449 non-null int64\n",
            "primary_use    1449 non-null object\n",
            "square_feet    1449 non-null int64\n",
            "dtypes: int64(3), object(1)\n",
            "memory usage: 45.4+ KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "z5iLZTKUYb7U"
      },
      "source": [
        "Combining all the tables into one big dataset"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "qMCQqZ0EYb7V"
      },
      "source": [
        "Merging Building metadata and train dataset on the basis on building_id."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "4F1r3GhPYb7X",
        "colab": {}
      },
      "source": [
        "temp_merge = pd.merge(left=train_data, right=meta_data, left_on='building_id', right_on='building_id', how='outer')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "SzHP4b45Yb7Y",
        "outputId": "874d47be-7967-4629-9a47-5a5e7398af40",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 198
        }
      },
      "source": [
        "temp_merge.head()"
      ],
      "execution_count": 216,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>building_id</th>\n",
              "      <th>meter</th>\n",
              "      <th>timestamp</th>\n",
              "      <th>meter_reading</th>\n",
              "      <th>date</th>\n",
              "      <th>hour</th>\n",
              "      <th>site_id</th>\n",
              "      <th>primary_use</th>\n",
              "      <th>square_feet</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2016-01-01 00:00:00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2016-01-01</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Education</td>\n",
              "      <td>7432</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2016-01-01 01:00:00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2016-01-01</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>Education</td>\n",
              "      <td>7432</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2016-01-01 02:00:00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2016-01-01</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>Education</td>\n",
              "      <td>7432</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2016-01-01 03:00:00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2016-01-01</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>Education</td>\n",
              "      <td>7432</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2016-01-01 04:00:00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2016-01-01</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>Education</td>\n",
              "      <td>7432</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   building_id  meter           timestamp  ...  site_id primary_use  square_feet\n",
              "0            0      0 2016-01-01 00:00:00  ...        0   Education         7432\n",
              "1            0      0 2016-01-01 01:00:00  ...        0   Education         7432\n",
              "2            0      0 2016-01-01 02:00:00  ...        0   Education         7432\n",
              "3            0      0 2016-01-01 03:00:00  ...        0   Education         7432\n",
              "4            0      0 2016-01-01 04:00:00  ...        0   Education         7432\n",
              "\n",
              "[5 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 216
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "C1zOy0ljYb7d"
      },
      "source": [
        "Merging weather data and the combined table from the last merge step into the final table. This is done on the basis of site_id, date and hour."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "9Y6BqMIfYb7d",
        "colab": {}
      },
      "source": [
        "train = pd.merge(left=temp_merge, right=weather_train_data, left_on=['site_id', 'date', 'hour'], right_on=['site_id', 'date', 'hour'], how='outer')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "12zS1ukvYb7f"
      },
      "source": [
        "Deleting the unnecessary variables to save memory."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "c2SnYUPQYb7f",
        "colab": {}
      },
      "source": [
        "# del temp_merge, train_data, meta_data, weather_train_data"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "uZxXQm9fYb7k",
        "outputId": "605aa4c2-eb05-4608-a022-380ed3b08ffe",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 305
        }
      },
      "source": [
        "train.head()"
      ],
      "execution_count": 219,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>building_id</th>\n",
              "      <th>meter</th>\n",
              "      <th>timestamp_x</th>\n",
              "      <th>meter_reading</th>\n",
              "      <th>date</th>\n",
              "      <th>hour</th>\n",
              "      <th>site_id</th>\n",
              "      <th>primary_use</th>\n",
              "      <th>square_feet</th>\n",
              "      <th>timestamp_y</th>\n",
              "      <th>air_temperature</th>\n",
              "      <th>dew_temperature</th>\n",
              "      <th>wind_speed</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2016-01-01</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2016-01-01</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Education</td>\n",
              "      <td>7432.0</td>\n",
              "      <td>2016-01-01</td>\n",
              "      <td>25.0</td>\n",
              "      <td>20.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2016-01-01</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2016-01-01</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Education</td>\n",
              "      <td>2720.0</td>\n",
              "      <td>2016-01-01</td>\n",
              "      <td>25.0</td>\n",
              "      <td>20.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2016-01-01</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2016-01-01</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Education</td>\n",
              "      <td>5376.0</td>\n",
              "      <td>2016-01-01</td>\n",
              "      <td>25.0</td>\n",
              "      <td>20.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2016-01-01</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2016-01-01</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Education</td>\n",
              "      <td>23685.0</td>\n",
              "      <td>2016-01-01</td>\n",
              "      <td>25.0</td>\n",
              "      <td>20.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2016-01-01</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2016-01-01</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Education</td>\n",
              "      <td>116607.0</td>\n",
              "      <td>2016-01-01</td>\n",
              "      <td>25.0</td>\n",
              "      <td>20.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   building_id  meter timestamp_x  ...  air_temperature dew_temperature  wind_speed\n",
              "0          0.0    0.0  2016-01-01  ...             25.0            20.0         0.0\n",
              "1          1.0    0.0  2016-01-01  ...             25.0            20.0         0.0\n",
              "2          2.0    0.0  2016-01-01  ...             25.0            20.0         0.0\n",
              "3          3.0    0.0  2016-01-01  ...             25.0            20.0         0.0\n",
              "4          4.0    0.0  2016-01-01  ...             25.0            20.0         0.0\n",
              "\n",
              "[5 rows x 13 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 219
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "NXnL1Jt4Yb7m",
        "outputId": "f7719888-0ec5-42b5-fa0a-d8af960d0467",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 259
        }
      },
      "source": [
        "train.isna().sum()"
      ],
      "execution_count": 220,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "building_id           135\n",
              "meter                 135\n",
              "timestamp_x           135\n",
              "meter_reading         135\n",
              "date                    0\n",
              "hour                    0\n",
              "site_id                 0\n",
              "primary_use           135\n",
              "square_feet           135\n",
              "timestamp_y        148395\n",
              "air_temperature    148395\n",
              "dew_temperature    148395\n",
              "wind_speed         148395\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 220
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "BtUIqnnZYb7p",
        "colab": {}
      },
      "source": [
        "train = train.dropna(axis=0, subset=['building_id'])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "oazGzU146NN0",
        "outputId": "c9a5cb6b-473c-47c9-8ce5-64271ae87f79",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 259
        }
      },
      "source": [
        "train.isna().sum()"
      ],
      "execution_count": 222,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "building_id             0\n",
              "meter                   0\n",
              "timestamp_x             0\n",
              "meter_reading           0\n",
              "date                    0\n",
              "hour                    0\n",
              "site_id                 0\n",
              "primary_use             0\n",
              "square_feet             0\n",
              "timestamp_y        148395\n",
              "air_temperature    148395\n",
              "dew_temperature    148395\n",
              "wind_speed         148395\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 222
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "dXDOlP_rYb7r"
      },
      "source": [
        "Dropping some column artifacts."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "_IFVPNMVYb7r",
        "colab": {}
      },
      "source": [
        "train = train.drop(['timestamp_x', 'timestamp_y'], axis=1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jK2HZyhRkCxb",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train = train.dropna(axis=0, subset=['air_temperature','dew_temperature'])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Xrcg5jG7lxFq",
        "colab_type": "code",
        "outputId": "0bab5fb3-7fbf-4e41-dbca-a905fc876dc2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 294
        }
      },
      "source": [
        "train.info()"
      ],
      "execution_count": 225,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 20067705 entries, 0 to 20216099\n",
            "Data columns (total 11 columns):\n",
            "building_id        float64\n",
            "meter              float64\n",
            "meter_reading      float64\n",
            "date               datetime64[ns]\n",
            "hour               int64\n",
            "site_id            int64\n",
            "primary_use        object\n",
            "square_feet        float64\n",
            "air_temperature    float64\n",
            "dew_temperature    float64\n",
            "wind_speed         float64\n",
            "dtypes: datetime64[ns](1), float64(7), int64(2), object(1)\n",
            "memory usage: 1.8+ GB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EmQm8nwsm0x1",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "data = np.random.randint(low=0, high=20067705, size=20067705//2)\n",
        "train.drop(train.index[data],0,inplace=True)\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "U3jNf6KVovzY",
        "colab_type": "code",
        "outputId": "f102ed34-6c9d-42ab-9692-3eba2ee31226",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 580
        }
      },
      "source": [
        "train.reset_index(drop=True)"
      ],
      "execution_count": 227,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>building_id</th>\n",
              "      <th>meter</th>\n",
              "      <th>meter_reading</th>\n",
              "      <th>date</th>\n",
              "      <th>hour</th>\n",
              "      <th>site_id</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
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              "      <td>Education</td>\n",
              "      <td>7432.0</td>\n",
              "      <td>25.0</td>\n",
              "      <td>20.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>2016-01-01</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Education</td>\n",
              "      <td>2720.0</td>\n",
              "      <td>25.0</td>\n",
              "      <td>20.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>2016-01-01</td>\n",
              "      <td>0</td>\n",
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              "      <td>Education</td>\n",
              "      <td>5376.0</td>\n",
              "      <td>25.0</td>\n",
              "      <td>20.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>2016-01-01</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Education</td>\n",
              "      <td>116607.0</td>\n",
              "      <td>25.0</td>\n",
              "      <td>20.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>2016-01-01</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Education</td>\n",
              "      <td>8000.0</td>\n",
              "      <td>25.0</td>\n",
              "      <td>20.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12173157</th>\n",
              "      <td>1400.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>4.6566</td>\n",
              "      <td>2016-03-24</td>\n",
              "      <td>11</td>\n",
              "      <td>15</td>\n",
              "      <td>Lodging/residential</td>\n",
              "      <td>21168.0</td>\n",
              "      <td>1.7</td>\n",
              "      <td>1.1</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12173158</th>\n",
              "      <td>1400.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>25.0848</td>\n",
              "      <td>2016-03-24</td>\n",
              "      <td>13</td>\n",
              "      <td>15</td>\n",
              "      <td>Lodging/residential</td>\n",
              "      <td>21168.0</td>\n",
              "      <td>2.8</td>\n",
              "      <td>2.2</td>\n",
              "      <td>1.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12173159</th>\n",
              "      <td>1400.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>32.3439</td>\n",
              "      <td>2016-03-24</td>\n",
              "      <td>14</td>\n",
              "      <td>15</td>\n",
              "      <td>Lodging/residential</td>\n",
              "      <td>21168.0</td>\n",
              "      <td>5.6</td>\n",
              "      <td>4.4</td>\n",
              "      <td>2.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12173160</th>\n",
              "      <td>1400.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>24.2214</td>\n",
              "      <td>2016-03-24</td>\n",
              "      <td>15</td>\n",
              "      <td>15</td>\n",
              "      <td>Lodging/residential</td>\n",
              "      <td>21168.0</td>\n",
              "      <td>11.1</td>\n",
              "      <td>4.4</td>\n",
              "      <td>5.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12173161</th>\n",
              "      <td>1400.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>16.3553</td>\n",
              "      <td>2016-03-24</td>\n",
              "      <td>16</td>\n",
              "      <td>15</td>\n",
              "      <td>Lodging/residential</td>\n",
              "      <td>21168.0</td>\n",
              "      <td>15.0</td>\n",
              "      <td>4.4</td>\n",
              "      <td>8.2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>12173162 rows × 11 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "          building_id  meter  ...  dew_temperature wind_speed\n",
              "0                 0.0    0.0  ...             20.0        0.0\n",
              "1                 1.0    0.0  ...             20.0        0.0\n",
              "2                 2.0    0.0  ...             20.0        0.0\n",
              "3                 4.0    0.0  ...             20.0        0.0\n",
              "4                 5.0    0.0  ...             20.0        0.0\n",
              "...               ...    ...  ...              ...        ...\n",
              "12173157       1400.0    1.0  ...              1.1        0.0\n",
              "12173158       1400.0    1.0  ...              2.2        1.5\n",
              "12173159       1400.0    1.0  ...              4.4        2.1\n",
              "12173160       1400.0    1.0  ...              4.4        5.1\n",
              "12173161       1400.0    1.0  ...              4.4        8.2\n",
              "\n",
              "[12173162 rows x 11 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 227
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Dybfwu6qmnxr",
        "colab_type": "text"
      },
      "source": [
        "Removing outliers using interquartile range"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Yex_xm5auibj",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "Q1 = train['meter_reading'].quantile(0.25)\n",
        "Q3 = train['meter_reading'].quantile(0.75)\n",
        "IQR = Q3 - Q1\n",
        "train_final = train[train['meter_reading'] > (Q1 - 1.5 * IQR)][:]\n",
        "train_final = train_final[train_final['meter_reading'] < (Q3 + 1.5 * IQR)][:]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xefxnngun6MW",
        "colab_type": "code",
        "outputId": "2d2a1d55-1e14-476d-806b-119b62ff6bfc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 294
        }
      },
      "source": [
        "train_final.info()"
      ],
      "execution_count": 229,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 10682582 entries, 0 to 20216099\n",
            "Data columns (total 11 columns):\n",
            "building_id        float64\n",
            "meter              float64\n",
            "meter_reading      float64\n",
            "date               datetime64[ns]\n",
            "hour               int64\n",
            "site_id            int64\n",
            "primary_use        object\n",
            "square_feet        float64\n",
            "air_temperature    float64\n",
            "dew_temperature    float64\n",
            "wind_speed         float64\n",
            "dtypes: datetime64[ns](1), float64(7), int64(2), object(1)\n",
            "memory usage: 978.0+ MB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Z5C2sPBXTcD-",
        "colab_type": "code",
        "outputId": "b39abfd0-bed8-4b47-b102-d3a69fe1154b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 224
        }
      },
      "source": [
        "train_final.isna().sum()"
      ],
      "execution_count": 230,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "building_id        0\n",
              "meter              0\n",
              "meter_reading      0\n",
              "date               0\n",
              "hour               0\n",
              "site_id            0\n",
              "primary_use        0\n",
              "square_feet        0\n",
              "air_temperature    0\n",
              "dew_temperature    0\n",
              "wind_speed         0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 230
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "YTErFlT8XOu4",
        "outputId": "7a186e30-1514-4beb-f4d1-9e3e042956cc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 288
        }
      },
      "source": [
        "train_final.describe()"
      ],
      "execution_count": 231,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>building_id</th>\n",
              "      <th>meter</th>\n",
              "      <th>meter_reading</th>\n",
              "      <th>hour</th>\n",
              "      <th>site_id</th>\n",
              "      <th>square_feet</th>\n",
              "      <th>air_temperature</th>\n",
              "      <th>dew_temperature</th>\n",
              "      <th>wind_speed</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>1.068258e+07</td>\n",
              "      <td>1.068258e+07</td>\n",
              "      <td>1.068258e+07</td>\n",
              "      <td>1.068258e+07</td>\n",
              "      <td>1.068258e+07</td>\n",
              "      <td>1.068258e+07</td>\n",
              "      <td>1.068258e+07</td>\n",
              "      <td>1.068258e+07</td>\n",
              "      <td>1.068258e+07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>7.756834e+02</td>\n",
              "      <td>5.803126e-01</td>\n",
              "      <td>1.176209e+02</td>\n",
              "      <td>1.145972e+01</td>\n",
              "      <td>7.687434e+00</td>\n",
              "      <td>9.115281e+04</td>\n",
              "      <td>1.620857e+01</td>\n",
              "      <td>7.856638e+00</td>\n",
              "      <td>3.376340e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>4.249695e+02</td>\n",
              "      <td>9.070368e-01</td>\n",
              "      <td>1.453428e+02</td>\n",
              "      <td>6.945406e+00</td>\n",
              "      <td>5.057196e+00</td>\n",
              "      <td>9.516882e+04</td>\n",
              "      <td>1.067366e+01</td>\n",
              "      <td>9.941448e+00</td>\n",
              "      <td>2.269172e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>2.830000e+02</td>\n",
              "      <td>-2.890000e+01</td>\n",
              "      <td>-3.500000e+01</td>\n",
              "      <td>0.000000e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>3.620000e+02</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>1.382780e+01</td>\n",
              "      <td>5.000000e+00</td>\n",
              "      <td>3.000000e+00</td>\n",
              "      <td>2.799500e+04</td>\n",
              "      <td>8.900000e+00</td>\n",
              "      <td>6.000000e-01</td>\n",
              "      <td>2.100000e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>8.640000e+02</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>5.805000e+01</td>\n",
              "      <td>1.100000e+01</td>\n",
              "      <td>8.000000e+00</td>\n",
              "      <td>6.456000e+04</td>\n",
              "      <td>1.670000e+01</td>\n",
              "      <td>8.900000e+00</td>\n",
              "      <td>3.100000e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>1.153000e+03</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>1.652530e+02</td>\n",
              "      <td>1.700000e+01</td>\n",
              "      <td>1.300000e+01</td>\n",
              "      <td>1.189660e+05</td>\n",
              "      <td>2.390000e+01</td>\n",
              "      <td>1.560000e+01</td>\n",
              "      <td>4.600000e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>1.448000e+03</td>\n",
              "      <td>3.000000e+00</td>\n",
              "      <td>6.414920e+02</td>\n",
              "      <td>2.300000e+01</td>\n",
              "      <td>1.500000e+01</td>\n",
              "      <td>8.750000e+05</td>\n",
              "      <td>4.720000e+01</td>\n",
              "      <td>2.610000e+01</td>\n",
              "      <td>1.900000e+01</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        building_id         meter  ...  dew_temperature    wind_speed\n",
              "count  1.068258e+07  1.068258e+07  ...     1.068258e+07  1.068258e+07\n",
              "mean   7.756834e+02  5.803126e-01  ...     7.856638e+00  3.376340e+00\n",
              "std    4.249695e+02  9.070368e-01  ...     9.941448e+00  2.269172e+00\n",
              "min    0.000000e+00  0.000000e+00  ...    -3.500000e+01  0.000000e+00\n",
              "25%    3.620000e+02  0.000000e+00  ...     6.000000e-01  2.100000e+00\n",
              "50%    8.640000e+02  0.000000e+00  ...     8.900000e+00  3.100000e+00\n",
              "75%    1.153000e+03  1.000000e+00  ...     1.560000e+01  4.600000e+00\n",
              "max    1.448000e+03  3.000000e+00  ...     2.610000e+01  1.900000e+01\n",
              "\n",
              "[8 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 231
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "2y0LfjuKYb7w"
      },
      "source": [
        "We can see that the above dataframe is very large in size, thus occupying a good portion of a system's memory. To counter this, we downgrade the datatypes into their lowest memory variants. This reduces the size greatly."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "-B2Btsu4Yb70",
        "colab": {}
      },
      "source": [
        "train_final.primary_use = train.primary_use.astype(\"category\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "0A5bHnrrYb72",
        "outputId": "26ba6401-d963-464a-feb6-cdfa4c637ab7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 294
        }
      },
      "source": [
        "train_final.info()"
      ],
      "execution_count": 233,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 10682582 entries, 0 to 20216099\n",
            "Data columns (total 11 columns):\n",
            "building_id        float64\n",
            "meter              float64\n",
            "meter_reading      float64\n",
            "date               datetime64[ns]\n",
            "hour               int64\n",
            "site_id            int64\n",
            "primary_use        category\n",
            "square_feet        float64\n",
            "air_temperature    float64\n",
            "dew_temperature    float64\n",
            "wind_speed         float64\n",
            "dtypes: category(1), datetime64[ns](1), float64(7), int64(2)\n",
            "memory usage: 906.7 MB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "GHtws4dRYb73"
      },
      "source": [
        "To save compute time, we store the dataset in a pickle file"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "scxnnvJdYb74",
        "colab": {}
      },
      "source": [
        "train_final.to_pickle(\"train_data.pkl\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "-KNjl-MkYb75",
        "colab": {}
      },
      "source": [
        "del train_final"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "JHSZGUWOYb76"
      },
      "source": [
        "The data can be read from the pickle file using the following command."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "bzhmo4kHYb77",
        "colab": {}
      },
      "source": [
        "train = pd.read_pickle(\"train_data.pkl\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dCCVAp6wmzaJ",
        "colab_type": "text"
      },
      "source": [
        "Correlation matrix for our dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "q_IxwGPWYb8J",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 561
        },
        "outputId": "d6ac1e9e-877a-47df-b4cd-91093c612c73"
      },
      "source": [
        "plt.rcParams['figure.figsize'] = (14,8)\n",
        "sns.heatmap(train.corr(), vmin=-1, vmax=1, center=0,\n",
        "            square=True, cmap = sns.diverging_palette(240, 10, n=9))\n",
        "plt.show()"
      ],
      "execution_count": 240,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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zagjNFPJ4TgX+duQzJnlEkk2m+JkkSZKmZaaB72vAoiRX0NwNeipTt4OWA5cm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\nEREz3L8AuPXagBp4wl8AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 1008x576 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "wcnPE1y6dhgA"
      },
      "source": [
        "## Data Preparation"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pzSpshknm_3x",
        "colab_type": "text"
      },
      "source": [
        "### Encoding Categorical Variables\n",
        "##### Primary Use:\n",
        "We use Label Encoding to convert Primary use into numerical values."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0n6Vf1Y0ApG8",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.preprocessing import LabelEncoder\n",
        "\n",
        "encoder = LabelEncoder()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Y9vayHimBHzp",
        "colab_type": "code",
        "outputId": "cf14daef-f06f-4ee1-9542-c13dca7796bb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 276
        }
      },
      "source": [
        "train['primary_use']"
      ],
      "execution_count": 242,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0                     Education\n",
              "1                     Education\n",
              "2                     Education\n",
              "4                     Education\n",
              "5                     Education\n",
              "                   ...         \n",
              "20216094    Lodging/residential\n",
              "20216096    Lodging/residential\n",
              "20216097    Lodging/residential\n",
              "20216098    Lodging/residential\n",
              "20216099    Lodging/residential\n",
              "Name: primary_use, Length: 10682582, dtype: category\n",
              "Categories (16, object): [Education, Entertainment/public assembly, Food sales and service,\n",
              "                          Healthcare, ..., Services, Technology/science, Utility,\n",
              "                          Warehouse/storage]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 242
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "can_g2puAwko",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train['primary_use'] = encoder.fit_transform(train['primary_use'])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VeJu98-ttmVu",
        "colab_type": "text"
      },
      "source": [
        "##### Meter\n",
        "The meter variable is converted into four separate binary columns using One Hot Encoding."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "i-XFesU272yb",
        "colab": {}
      },
      "source": [
        "dummy = pd.get_dummies(train['meter'])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "vOZh7e7K8Wuk",
        "colab": {}
      },
      "source": [
        "train = pd.concat([train, dummy], axis=1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oNAEZar63ZR6",
        "colab_type": "code",
        "outputId": "002fe33a-9a31-4f7f-9dd3-aae7256c079b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 305
        }
      },
      "source": [
        "train.head()"
      ],
      "execution_count": 246,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
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              "    .dataframe thead th {\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>building_id</th>\n",
              "      <th>meter</th>\n",
              "      <th>meter_reading</th>\n",
              "      <th>date</th>\n",
              "      <th>hour</th>\n",
              "      <th>site_id</th>\n",
              "      <th>primary_use</th>\n",
              "      <th>square_feet</th>\n",
              "      <th>air_temperature</th>\n",
              "      <th>dew_temperature</th>\n",
              "      <th>wind_speed</th>\n",
              "      <th>0.0</th>\n",
              "      <th>1.0</th>\n",
              "      <th>2.0</th>\n",
              "      <th>3.0</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2016-01-01</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>7432.0</td>\n",
              "      <td>25.0</td>\n",
              "      <td>20.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2016-01-01</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2720.0</td>\n",
              "      <td>25.0</td>\n",
              "      <td>20.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2016-01-01</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>5376.0</td>\n",
              "      <td>25.0</td>\n",
              "      <td>20.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2016-01-01</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>116607.0</td>\n",
              "      <td>25.0</td>\n",
              "      <td>20.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>5.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2016-01-01</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>8000.0</td>\n",
              "      <td>25.0</td>\n",
              "      <td>20.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   building_id  meter  meter_reading       date  ...  0.0  1.0  2.0  3.0\n",
              "0          0.0    0.0            0.0 2016-01-01  ...    1    0    0    0\n",
              "1          1.0    0.0            0.0 2016-01-01  ...    1    0    0    0\n",
              "2          2.0    0.0            0.0 2016-01-01  ...    1    0    0    0\n",
              "4          4.0    0.0            0.0 2016-01-01  ...    1    0    0    0\n",
              "5          5.0    0.0            0.0 2016-01-01  ...    1    0    0    0\n",
              "\n",
              "[5 rows x 15 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 246
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_0lqVjs3nF4H",
        "colab_type": "text"
      },
      "source": [
        "### Scaling\n",
        "We use standard scaler to scale square feet. We do this because it has the most variance in the dataset."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Zte7yvwr39RW",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train['square_feet'] = np.log1p(train['square_feet'])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "r4kYASVuGhBH",
        "colab_type": "code",
        "outputId": "cc8bb90f-21b9-443a-ba87-cf6b0e07be34",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 305
        }
      },
      "source": [
        "train.head()"
      ],
      "execution_count": 248,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "  <thead>\n",
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              "      <th></th>\n",
              "      <th>building_id</th>\n",
              "      <th>meter</th>\n",
              "      <th>meter_reading</th>\n",
              "      <th>date</th>\n",
              "      <th>hour</th>\n",
              "      <th>site_id</th>\n",
              "      <th>primary_use</th>\n",
              "      <th>square_feet</th>\n",
              "      <th>air_temperature</th>\n",
              "      <th>dew_temperature</th>\n",
              "      <th>wind_speed</th>\n",
              "      <th>0.0</th>\n",
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              "      <th>2.0</th>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>7.908755</td>\n",
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              "      <td>20.0</td>\n",
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              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2016-01-01</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>8.589886</td>\n",
              "      <td>25.0</td>\n",
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              "      <td>1</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <th>4</th>\n",
              "      <td>4.0</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>2016-01-01</td>\n",
              "      <td>0</td>\n",
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              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>5.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2016-01-01</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>8.987322</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   building_id  meter  meter_reading       date  ...  0.0  1.0  2.0  3.0\n",
              "0          0.0    0.0            0.0 2016-01-01  ...    1    0    0    0\n",
              "1          1.0    0.0            0.0 2016-01-01  ...    1    0    0    0\n",
              "2          2.0    0.0            0.0 2016-01-01  ...    1    0    0    0\n",
              "4          4.0    0.0            0.0 2016-01-01  ...    1    0    0    0\n",
              "5          5.0    0.0            0.0 2016-01-01  ...    1    0    0    0\n",
              "\n",
              "[5 rows x 15 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 248
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EgqCmbxbneUc",
        "colab_type": "text"
      },
      "source": [
        "### Feature Reduction"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZpbJC6CpaLHE",
        "colab_type": "text"
      },
      "source": [
        "We select the important features from our data and split it into two Dataframes with independant variables and dependant variables."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "FIxwnx9ldzt4",
        "colab": {}
      },
      "source": [
        "X = train.drop(columns=\"meter_reading\")\n",
        "Y = train['meter_reading']"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HxtsM0aVY8NL",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X.drop(columns=['building_id', 'site_id', 'date', 'meter'], axis=1, inplace=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7FFafFOla-jr",
        "colab_type": "text"
      },
      "source": [
        "Splitting the data into training and testing set. The test set percentage is set to 20%"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "Xao89o-9d1b4",
        "colab": {}
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "x_train, x_test, y_train, y_test = train_test_split(X, Y, test_size = 0.20, random_state = 0)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DTANxO6T3HkD",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# x_train, x_test, y_train, y_test = train_test_split(temp, Y, test_size = 0.20, random_state = 0)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "msNuolnY819q",
        "outputId": "c887d243-0bef-4ea5-8b40-18b69f6d5d53",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 198
        }
      },
      "source": [
        "x_train.head()"
      ],
      "execution_count": 253,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>hour</th>\n",
              "      <th>primary_use</th>\n",
              "      <th>square_feet</th>\n",
              "      <th>air_temperature</th>\n",
              "      <th>dew_temperature</th>\n",
              "      <th>wind_speed</th>\n",
              "      <th>0.0</th>\n",
              "      <th>1.0</th>\n",
              "      <th>2.0</th>\n",
              "      <th>3.0</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>5567959</th>\n",
              "      <td>9</td>\n",
              "      <td>0</td>\n",
              "      <td>11.150534</td>\n",
              "      <td>24.4</td>\n",
              "      <td>23.3</td>\n",
              "      <td>3.1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4553853</th>\n",
              "      <td>18</td>\n",
              "      <td>0</td>\n",
              "      <td>11.000448</td>\n",
              "      <td>17.2</td>\n",
              "      <td>1.1</td>\n",
              "      <td>5.7</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4266592</th>\n",
              "      <td>10</td>\n",
              "      <td>0</td>\n",
              "      <td>11.100451</td>\n",
              "      <td>2.8</td>\n",
              "      <td>-3.9</td>\n",
              "      <td>4.6</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14597762</th>\n",
              "      <td>8</td>\n",
              "      <td>0</td>\n",
              "      <td>9.560011</td>\n",
              "      <td>19.4</td>\n",
              "      <td>15.6</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15297494</th>\n",
              "      <td>19</td>\n",
              "      <td>6</td>\n",
              "      <td>10.957712</td>\n",
              "      <td>21.1</td>\n",
              "      <td>11.7</td>\n",
              "      <td>3.6</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          hour  primary_use  square_feet  air_temperature  ...  0.0  1.0  2.0  3.0\n",
              "5567959      9            0    11.150534             24.4  ...    1    0    0    0\n",
              "4553853     18            0    11.000448             17.2  ...    1    0    0    0\n",
              "4266592     10            0    11.100451              2.8  ...    1    0    0    0\n",
              "14597762     8            0     9.560011             19.4  ...    0    1    0    0\n",
              "15297494    19            6    10.957712             21.1  ...    1    0    0    0\n",
              "\n",
              "[5 rows x 10 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 253
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Q1w3beJtXBE1",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 121
        },
        "outputId": "ffd2c299-1c45-4f13-b2ee-2ec2128d1bb7"
      },
      "source": [
        "y_train.head()"
      ],
      "execution_count": 254,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "5567959      93.690\n",
              "4553853      75.470\n",
              "4266592     124.220\n",
              "14597762      0.000\n",
              "15297494     97.667\n",
              "Name: meter_reading, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 254
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_U1IHAMPVaUC",
        "colab_type": "text"
      },
      "source": [
        "## Modelling"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3BfZs80TbRbF",
        "colab_type": "text"
      },
      "source": [
        "LightGBM<br>\n",
        "Light GBM is a gradient boosting framework that uses tree based learning algorithm. How it differs from other tree based algorithm?\n",
        "Light GBM grows tree vertically while other algorithm grows trees horizontally meaning that Light GBM grows tree leaf-wise while other algorithm grows level-wise. It will choose the leaf with max delta loss to grow. When growing the same leaf, Leaf-wise algorithm can reduce more loss than a level-wise algorithm.\n",
        "\n",
        "Reference: https://medium.com/@pushkarmandot/https-medium-com-pushkarmandot-what-is-lightgbm-how-to-implement-it-how-to-fine-tune-the-parameters-60347819b7fc"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gzs-_raqbgt-",
        "colab_type": "text"
      },
      "source": [
        "### Training"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "CizLDz6AeSNK",
        "colab": {}
      },
      "source": [
        "import lightgbm as lgb"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RLag6CvdT-Su",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "lgb_train = lgb.Dataset(x_train, y_train)\n",
        "lgb_test = lgb.Dataset(x_test, y_test)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yYJORXjD_D_t",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "params = {\n",
        "          'boosting_type': 'gbdt',\n",
        "          'objective': 'regression',\n",
        "          'metric': {'rmse'},\n",
        "          'learning_rate': 0.8,\n",
        "          'feature_fraction': 0.8,\n",
        "          'bagging_fraction': 0.8,\n",
        "          'bagging_freq' : 5\n",
        "          }"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JHmR5AfF_3-3",
        "colab_type": "code",
        "outputId": "9952f258-b5e7-48f0-a00f-3802c7df30e7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "  gbm_regress = lgb.train(params,\n",
        "              lgb_train,\n",
        "              num_boost_round=4000,\n",
        "              valid_sets=(lgb_train, lgb_test),\n",
        "            early_stopping_rounds=20,\n",
        "            verbose_eval = 20)"
      ],
      "execution_count": 258,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Training until validation scores don't improve for 20 rounds.\n",
            "[20]\ttraining's rmse: 106.47\tvalid_1's rmse: 106.528\n",
            "[40]\ttraining's rmse: 99.3353\tvalid_1's rmse: 99.4038\n",
            "[60]\ttraining's rmse: 95.6981\tvalid_1's rmse: 95.8043\n",
            "[80]\ttraining's rmse: 92.6527\tvalid_1's rmse: 92.7721\n",
            "[100]\ttraining's rmse: 90.2338\tvalid_1's rmse: 90.3859\n",
            "[120]\ttraining's rmse: 88.0032\tvalid_1's rmse: 88.193\n",
            "[140]\ttraining's rmse: 86.7461\tvalid_1's rmse: 86.9801\n",
            "[160]\ttraining's rmse: 85.5623\tvalid_1's rmse: 85.8154\n",
            "[180]\ttraining's rmse: 84.7424\tvalid_1's rmse: 84.9963\n",
            "[200]\ttraining's rmse: 83.8994\tvalid_1's rmse: 84.1948\n",
            "[220]\ttraining's rmse: 83.3142\tvalid_1's rmse: 83.6405\n",
            "[240]\ttraining's rmse: 82.8916\tvalid_1's rmse: 83.2482\n",
            "[260]\ttraining's rmse: 82.4007\tvalid_1's rmse: 82.775\n",
            "[280]\ttraining's rmse: 81.9287\tvalid_1's rmse: 82.3342\n",
            "[300]\ttraining's rmse: 81.4677\tvalid_1's rmse: 81.8977\n",
            "[320]\ttraining's rmse: 81.1472\tvalid_1's rmse: 81.6051\n",
            "[340]\ttraining's rmse: 80.755\tvalid_1's rmse: 81.2644\n",
            "[360]\ttraining's rmse: 80.3499\tvalid_1's rmse: 80.9009\n",
            "[380]\ttraining's rmse: 80.1321\tvalid_1's rmse: 80.7131\n",
            "[400]\ttraining's rmse: 79.7771\tvalid_1's rmse: 80.379\n",
            "[420]\ttraining's rmse: 79.4967\tvalid_1's rmse: 80.1257\n",
            "[440]\ttraining's rmse: 79.1323\tvalid_1's rmse: 79.7842\n",
            "[460]\ttraining's rmse: 78.8545\tvalid_1's rmse: 79.5453\n",
            "[480]\ttraining's rmse: 78.6431\tvalid_1's rmse: 79.3627\n",
            "[500]\ttraining's rmse: 78.4328\tvalid_1's rmse: 79.1853\n",
            "[520]\ttraining's rmse: 78.2601\tvalid_1's rmse: 79.0426\n",
            "[540]\ttraining's rmse: 78.0935\tvalid_1's rmse: 78.898\n",
            "[560]\ttraining's rmse: 77.8635\tvalid_1's rmse: 78.7012\n",
            "[580]\ttraining's rmse: 77.6973\tvalid_1's rmse: 78.5615\n",
            "[600]\ttraining's rmse: 77.5641\tvalid_1's rmse: 78.4508\n",
            "[620]\ttraining's rmse: 77.3815\tvalid_1's rmse: 78.2941\n",
            "[640]\ttraining's rmse: 77.2287\tvalid_1's rmse: 78.1687\n",
            "[660]\ttraining's rmse: 77.0433\tvalid_1's rmse: 78.0094\n",
            "[680]\ttraining's rmse: 76.9\tvalid_1's rmse: 77.8992\n",
            "[700]\ttraining's rmse: 76.7401\tvalid_1's rmse: 77.7676\n",
            "[720]\ttraining's rmse: 76.5736\tvalid_1's rmse: 77.6327\n",
            "[740]\ttraining's rmse: 76.4349\tvalid_1's rmse: 77.5222\n",
            "[760]\ttraining's rmse: 76.3057\tvalid_1's rmse: 77.4182\n",
            "[780]\ttraining's rmse: 76.1902\tvalid_1's rmse: 77.3283\n",
            "[800]\ttraining's rmse: 76.0394\tvalid_1's rmse: 77.2153\n",
            "[820]\ttraining's rmse: 75.9273\tvalid_1's rmse: 77.1256\n",
            "[840]\ttraining's rmse: 75.7987\tvalid_1's rmse: 77.0273\n",
            "[860]\ttraining's rmse: 75.7014\tvalid_1's rmse: 76.9459\n",
            "[880]\ttraining's rmse: 75.5992\tvalid_1's rmse: 76.8701\n",
            "[900]\ttraining's rmse: 75.503\tvalid_1's rmse: 76.8051\n",
            "[920]\ttraining's rmse: 75.3659\tvalid_1's rmse: 76.6987\n",
            "[940]\ttraining's rmse: 75.2446\tvalid_1's rmse: 76.6004\n",
            "[960]\ttraining's rmse: 75.1328\tvalid_1's rmse: 76.5166\n",
            "[980]\ttraining's rmse: 75.0579\tvalid_1's rmse: 76.4653\n",
            "[1000]\ttraining's rmse: 74.9467\tvalid_1's rmse: 76.3729\n",
            "[1020]\ttraining's rmse: 74.8496\tvalid_1's rmse: 76.3102\n",
            "[1040]\ttraining's rmse: 74.7739\tvalid_1's rmse: 76.259\n",
            "[1060]\ttraining's rmse: 74.6722\tvalid_1's rmse: 76.1949\n",
            "[1080]\ttraining's rmse: 74.5887\tvalid_1's rmse: 76.1376\n",
            "[1100]\ttraining's rmse: 74.5097\tvalid_1's rmse: 76.0883\n",
            "[1120]\ttraining's rmse: 74.4401\tvalid_1's rmse: 76.0456\n",
            "[1140]\ttraining's rmse: 74.364\tvalid_1's rmse: 75.9995\n",
            "[1160]\ttraining's rmse: 74.2745\tvalid_1's rmse: 75.9329\n",
            "[1180]\ttraining's rmse: 74.1813\tvalid_1's rmse: 75.869\n",
            "[1200]\ttraining's rmse: 74.1035\tvalid_1's rmse: 75.8107\n",
            "[1220]\ttraining's rmse: 74.028\tvalid_1's rmse: 75.766\n",
            "[1240]\ttraining's rmse: 73.96\tvalid_1's rmse: 75.7165\n",
            "[1260]\ttraining's rmse: 73.8762\tvalid_1's rmse: 75.6527\n",
            "[1280]\ttraining's rmse: 73.8098\tvalid_1's rmse: 75.6129\n",
            "[1300]\ttraining's rmse: 73.7367\tvalid_1's rmse: 75.5677\n",
            "[1320]\ttraining's rmse: 73.6602\tvalid_1's rmse: 75.5183\n",
            "[1340]\ttraining's rmse: 73.5908\tvalid_1's rmse: 75.4741\n",
            "[1360]\ttraining's rmse: 73.5155\tvalid_1's rmse: 75.4287\n",
            "[1380]\ttraining's rmse: 73.4551\tvalid_1's rmse: 75.3963\n",
            "[1400]\ttraining's rmse: 73.3798\tvalid_1's rmse: 75.3509\n",
            "[1420]\ttraining's rmse: 73.3206\tvalid_1's rmse: 75.3203\n",
            "[1440]\ttraining's rmse: 73.2641\tvalid_1's rmse: 75.2913\n",
            "[1460]\ttraining's rmse: 73.1926\tvalid_1's rmse: 75.2434\n",
            "[1480]\ttraining's rmse: 73.1368\tvalid_1's rmse: 75.2165\n",
            "[1500]\ttraining's rmse: 73.0873\tvalid_1's rmse: 75.1902\n",
            "[1520]\ttraining's rmse: 73.0325\tvalid_1's rmse: 75.1584\n",
            "[1540]\ttraining's rmse: 72.9624\tvalid_1's rmse: 75.1148\n",
            "[1560]\ttraining's rmse: 72.9081\tvalid_1's rmse: 75.0838\n",
            "[1580]\ttraining's rmse: 72.8562\tvalid_1's rmse: 75.0513\n",
            "[1600]\ttraining's rmse: 72.8135\tvalid_1's rmse: 75.0337\n",
            "[1620]\ttraining's rmse: 72.7566\tvalid_1's rmse: 75.0038\n",
            "[1640]\ttraining's rmse: 72.6994\tvalid_1's rmse: 74.9616\n",
            "[1660]\ttraining's rmse: 72.6431\tvalid_1's rmse: 74.9326\n",
            "[1680]\ttraining's rmse: 72.5903\tvalid_1's rmse: 74.8998\n",
            "[1700]\ttraining's rmse: 72.542\tvalid_1's rmse: 74.8655\n",
            "[1720]\ttraining's rmse: 72.4988\tvalid_1's rmse: 74.838\n",
            "[1740]\ttraining's rmse: 72.4393\tvalid_1's rmse: 74.8036\n",
            "[1760]\ttraining's rmse: 72.3889\tvalid_1's rmse: 74.7778\n",
            "[1780]\ttraining's rmse: 72.3291\tvalid_1's rmse: 74.7358\n",
            "[1800]\ttraining's rmse: 72.2701\tvalid_1's rmse: 74.7005\n",
            "[1820]\ttraining's rmse: 72.2178\tvalid_1's rmse: 74.6722\n",
            "[1840]\ttraining's rmse: 72.1612\tvalid_1's rmse: 74.6346\n",
            "[1860]\ttraining's rmse: 72.1131\tvalid_1's rmse: 74.6075\n",
            "[1880]\ttraining's rmse: 72.0597\tvalid_1's rmse: 74.5833\n",
            "[1900]\ttraining's rmse: 72.0163\tvalid_1's rmse: 74.5643\n",
            "[1920]\ttraining's rmse: 71.9713\tvalid_1's rmse: 74.5412\n",
            "[1940]\ttraining's rmse: 71.9317\tvalid_1's rmse: 74.5219\n",
            "[1960]\ttraining's rmse: 71.8888\tvalid_1's rmse: 74.5023\n",
            "[1980]\ttraining's rmse: 71.8451\tvalid_1's rmse: 74.485\n",
            "[2000]\ttraining's rmse: 71.8017\tvalid_1's rmse: 74.4677\n",
            "[2020]\ttraining's rmse: 71.7458\tvalid_1's rmse: 74.4356\n",
            "[2040]\ttraining's rmse: 71.6967\tvalid_1's rmse: 74.4148\n",
            "[2060]\ttraining's rmse: 71.635\tvalid_1's rmse: 74.367\n",
            "[2080]\ttraining's rmse: 71.5925\tvalid_1's rmse: 74.3473\n",
            "[2100]\ttraining's rmse: 71.5427\tvalid_1's rmse: 74.3202\n",
            "[2120]\ttraining's rmse: 71.5016\tvalid_1's rmse: 74.2962\n",
            "[2140]\ttraining's rmse: 71.4562\tvalid_1's rmse: 74.2764\n",
            "[2160]\ttraining's rmse: 71.4053\tvalid_1's rmse: 74.2475\n",
            "[2180]\ttraining's rmse: 71.3698\tvalid_1's rmse: 74.2322\n",
            "[2200]\ttraining's rmse: 71.3216\tvalid_1's rmse: 74.2048\n",
            "[2220]\ttraining's rmse: 71.282\tvalid_1's rmse: 74.185\n",
            "[2240]\ttraining's rmse: 71.2525\tvalid_1's rmse: 74.1736\n",
            "[2260]\ttraining's rmse: 71.2188\tvalid_1's rmse: 74.1573\n",
            "[2280]\ttraining's rmse: 71.1851\tvalid_1's rmse: 74.1445\n",
            "[2300]\ttraining's rmse: 71.1524\tvalid_1's rmse: 74.13\n",
            "[2320]\ttraining's rmse: 71.1176\tvalid_1's rmse: 74.1146\n",
            "[2340]\ttraining's rmse: 71.0764\tvalid_1's rmse: 74.0925\n",
            "[2360]\ttraining's rmse: 71.0361\tvalid_1's rmse: 74.0771\n",
            "[2380]\ttraining's rmse: 70.9931\tvalid_1's rmse: 74.0558\n",
            "[2400]\ttraining's rmse: 70.9504\tvalid_1's rmse: 74.0346\n",
            "[2420]\ttraining's rmse: 70.912\tvalid_1's rmse: 74.0227\n",
            "[2440]\ttraining's rmse: 70.8773\tvalid_1's rmse: 74.0014\n",
            "[2460]\ttraining's rmse: 70.8396\tvalid_1's rmse: 73.9868\n",
            "[2480]\ttraining's rmse: 70.8011\tvalid_1's rmse: 73.9746\n",
            "[2500]\ttraining's rmse: 70.76\tvalid_1's rmse: 73.9624\n",
            "[2520]\ttraining's rmse: 70.7286\tvalid_1's rmse: 73.9524\n",
            "[2540]\ttraining's rmse: 70.691\tvalid_1's rmse: 73.9359\n",
            "[2560]\ttraining's rmse: 70.6495\tvalid_1's rmse: 73.916\n",
            "[2580]\ttraining's rmse: 70.604\tvalid_1's rmse: 73.8907\n",
            "[2600]\ttraining's rmse: 70.5626\tvalid_1's rmse: 73.8666\n",
            "[2620]\ttraining's rmse: 70.5247\tvalid_1's rmse: 73.8566\n",
            "[2640]\ttraining's rmse: 70.4892\tvalid_1's rmse: 73.8474\n",
            "[2660]\ttraining's rmse: 70.4548\tvalid_1's rmse: 73.8423\n",
            "[2680]\ttraining's rmse: 70.4127\tvalid_1's rmse: 73.8177\n",
            "[2700]\ttraining's rmse: 70.3813\tvalid_1's rmse: 73.8041\n",
            "[2720]\ttraining's rmse: 70.3384\tvalid_1's rmse: 73.7743\n",
            "[2740]\ttraining's rmse: 70.303\tvalid_1's rmse: 73.7644\n",
            "[2760]\ttraining's rmse: 70.2679\tvalid_1's rmse: 73.7481\n",
            "[2780]\ttraining's rmse: 70.2391\tvalid_1's rmse: 73.7407\n",
            "[2800]\ttraining's rmse: 70.2067\tvalid_1's rmse: 73.729\n",
            "[2820]\ttraining's rmse: 70.1773\tvalid_1's rmse: 73.7186\n",
            "[2840]\ttraining's rmse: 70.1534\tvalid_1's rmse: 73.7082\n",
            "[2860]\ttraining's rmse: 70.127\tvalid_1's rmse: 73.7043\n",
            "[2880]\ttraining's rmse: 70.0935\tvalid_1's rmse: 73.6908\n",
            "[2900]\ttraining's rmse: 70.0598\tvalid_1's rmse: 73.679\n",
            "[2920]\ttraining's rmse: 70.0242\tvalid_1's rmse: 73.6611\n",
            "[2940]\ttraining's rmse: 69.9891\tvalid_1's rmse: 73.6479\n",
            "[2960]\ttraining's rmse: 69.9534\tvalid_1's rmse: 73.6289\n",
            "[2980]\ttraining's rmse: 69.9204\tvalid_1's rmse: 73.6109\n",
            "[3000]\ttraining's rmse: 69.8881\tvalid_1's rmse: 73.5929\n",
            "[3020]\ttraining's rmse: 69.8572\tvalid_1's rmse: 73.5869\n",
            "[3040]\ttraining's rmse: 69.8275\tvalid_1's rmse: 73.5704\n",
            "[3060]\ttraining's rmse: 69.7967\tvalid_1's rmse: 73.5612\n",
            "[3080]\ttraining's rmse: 69.7656\tvalid_1's rmse: 73.5459\n",
            "[3100]\ttraining's rmse: 69.7321\tvalid_1's rmse: 73.5342\n",
            "[3120]\ttraining's rmse: 69.6998\tvalid_1's rmse: 73.5334\n",
            "[3140]\ttraining's rmse: 69.6676\tvalid_1's rmse: 73.5185\n",
            "[3160]\ttraining's rmse: 69.6284\tvalid_1's rmse: 73.506\n",
            "[3180]\ttraining's rmse: 69.5989\tvalid_1's rmse: 73.4922\n",
            "[3200]\ttraining's rmse: 69.57\tvalid_1's rmse: 73.4816\n",
            "[3220]\ttraining's rmse: 69.5473\tvalid_1's rmse: 73.4749\n",
            "[3240]\ttraining's rmse: 69.5286\tvalid_1's rmse: 73.4696\n",
            "[3260]\ttraining's rmse: 69.4899\tvalid_1's rmse: 73.4504\n",
            "[3280]\ttraining's rmse: 69.4574\tvalid_1's rmse: 73.4353\n",
            "[3300]\ttraining's rmse: 69.437\tvalid_1's rmse: 73.4276\n",
            "[3320]\ttraining's rmse: 69.4046\tvalid_1's rmse: 73.4147\n",
            "[3340]\ttraining's rmse: 69.3723\tvalid_1's rmse: 73.4003\n",
            "[3360]\ttraining's rmse: 69.3343\tvalid_1's rmse: 73.3805\n",
            "[3380]\ttraining's rmse: 69.3025\tvalid_1's rmse: 73.3638\n",
            "[3400]\ttraining's rmse: 69.2699\tvalid_1's rmse: 73.3531\n",
            "[3420]\ttraining's rmse: 69.2452\tvalid_1's rmse: 73.3488\n",
            "[3440]\ttraining's rmse: 69.2163\tvalid_1's rmse: 73.3422\n",
            "[3460]\ttraining's rmse: 69.1873\tvalid_1's rmse: 73.3347\n",
            "[3480]\ttraining's rmse: 69.1644\tvalid_1's rmse: 73.3254\n",
            "[3500]\ttraining's rmse: 69.1362\tvalid_1's rmse: 73.3181\n",
            "[3520]\ttraining's rmse: 69.1061\tvalid_1's rmse: 73.3108\n",
            "[3540]\ttraining's rmse: 69.0766\tvalid_1's rmse: 73.3002\n",
            "[3560]\ttraining's rmse: 69.0463\tvalid_1's rmse: 73.2901\n",
            "[3580]\ttraining's rmse: 69.018\tvalid_1's rmse: 73.2749\n",
            "[3600]\ttraining's rmse: 68.99\tvalid_1's rmse: 73.2664\n",
            "[3620]\ttraining's rmse: 68.9671\tvalid_1's rmse: 73.2644\n",
            "[3640]\ttraining's rmse: 68.9405\tvalid_1's rmse: 73.2606\n",
            "[3660]\ttraining's rmse: 68.9121\tvalid_1's rmse: 73.2492\n",
            "[3680]\ttraining's rmse: 68.8881\tvalid_1's rmse: 73.2439\n",
            "[3700]\ttraining's rmse: 68.858\tvalid_1's rmse: 73.2377\n",
            "[3720]\ttraining's rmse: 68.836\tvalid_1's rmse: 73.237\n",
            "[3740]\ttraining's rmse: 68.8127\tvalid_1's rmse: 73.2337\n",
            "[3760]\ttraining's rmse: 68.7883\tvalid_1's rmse: 73.2243\n",
            "[3780]\ttraining's rmse: 68.7629\tvalid_1's rmse: 73.2177\n",
            "[3800]\ttraining's rmse: 68.7307\tvalid_1's rmse: 73.204\n",
            "[3820]\ttraining's rmse: 68.7024\tvalid_1's rmse: 73.1947\n",
            "[3840]\ttraining's rmse: 68.6723\tvalid_1's rmse: 73.1837\n",
            "[3860]\ttraining's rmse: 68.6421\tvalid_1's rmse: 73.171\n",
            "[3880]\ttraining's rmse: 68.6113\tvalid_1's rmse: 73.1583\n",
            "[3900]\ttraining's rmse: 68.5908\tvalid_1's rmse: 73.1587\n",
            "Early stopping, best iteration is:\n",
            "[3885]\ttraining's rmse: 68.6036\tvalid_1's rmse: 73.156\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tc63M-s2bsZQ",
        "colab_type": "text"
      },
      "source": [
        "### Testing"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "8oZybEpq7s-l",
        "colab": {}
      },
      "source": [
        "y_pred=gbm_regress.predict(x_test)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "PzUxt-BB9yqt",
        "colab": {}
      },
      "source": [
        "from sklearn.metrics import mean_squared_error\n",
        "\n",
        "score = np.sqrt(mean_squared_error(y_test, y_pred))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "qPTc_gWE-NfY",
        "outputId": "1e25e3ff-274f-4c43-e01e-acc2e55cfa09",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "print(\"The Test RMSE score for our model:\", score)"
      ],
      "execution_count": 261,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "The Test RMSE score for our model: 73.15604209334575\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NCLIKUOLhf8Z",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}