notebooks/data_analysis_and_processing.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "_kg_hide-input": false,
        "_uuid": "928ed469171a0ae01e24043f2753b488b59f205f",
        "id": "z5CX7T8CfPUa"
      },
      "source": [
        "**Data Processing and Analysis Notebook**\n",
        "\n",
        "This notebook is bifurcated into two sections:\n",
        "\n",
        "1.  The first section entails conducting an in-depth Exploratory Data Analysis (EDA) to gain insights into the underlying patterns and relationships within the data.\n",
        "\n",
        "2.  The second section pertains to the preservation of processed data. This involves removing duplicated rows, deleting global outliers, and other such measures to ensure data integrity. The processed data is then saved to a separate CSV file, thus ensuring that the original data remains unaltered."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pvH-cmTydRB-"
      },
      "source": [
        "Import Libraries"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "_uuid": "a2d92f36f38765c5fbed4d672ea8f746cb073202",
        "id": "n9ARbn-JfPUc"
      },
      "outputs": [],
      "source": [
        "# import required libraries\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import seaborn as sns"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "_uuid": "98d81f72eded731792db8202cd9ffb7fb2a4c6c2",
        "id": "rSlIzeX8fPUd"
      },
      "source": [
        "**PART 1: Exploratory Data Analysis (EDA)**\n",
        "\n",
        "Import CSV data as Pandas DataFrame"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "_uuid": "573a8353e37dfbfd0c193a7118199528c38650f7",
        "id": "a6AoB_zRfPUe"
      },
      "outputs": [],
      "source": [
        "df = pd.read_csv(r\"E:\\NYC Taxi Trip Duration Prediction\\data\\raw.csv\") # import dataset"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "_uuid": "8bcf7a799ff0f92329de70fff29e31c9a62d97cd",
        "id": "PcfAJkacfPUb"
      },
      "source": [
        "**I. Data Overview**\n",
        "\n",
        "a. Overview few Records"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "_uuid": "e4a398e7c1f4003a0d288edb01ed1942865a3803",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BMNwcVhZfPUf",
        "outputId": "e647ef8e-50ac-4799-9552-13417e381b71"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Top 5 records from train dataset:\n",
            "           id  vendor_id      pickup_datetime     dropoff_datetime  \\\n",
            "0  id2875421          2  2016-03-14 17:24:55  2016-03-14 17:32:30   \n",
            "1  id2377394          1  2016-06-12 00:43:35  2016-06-12 00:54:38   \n",
            "2  id3858529          2  2016-01-19 11:35:24  2016-01-19 12:10:48   \n",
            "3  id3504673          2  2016-04-06 19:32:31  2016-04-06 19:39:40   \n",
            "4  id2181028          2  2016-03-26 13:30:55  2016-03-26 13:38:10   \n",
            "\n",
            "   passenger_count  pickup_longitude  pickup_latitude  dropoff_longitude  \\\n",
            "0                1        -73.982155        40.767937         -73.964630   \n",
            "1                1        -73.980415        40.738564         -73.999481   \n",
            "2                1        -73.979027        40.763939         -74.005333   \n",
            "3                1        -74.010040        40.719971         -74.012268   \n",
            "4                1        -73.973053        40.793209         -73.972923   \n",
            "\n",
            "   dropoff_latitude store_and_fwd_flag  trip_duration  \n",
            "0         40.765602                  N            455  \n",
            "1         40.731152                  N            663  \n",
            "2         40.710087                  N           2124  \n",
            "3         40.706718                  N            429  \n",
            "4         40.782520                  N            435  \n"
          ]
        }
      ],
      "source": [
        "print('Top 5 records from train dataset:\\n', df.head())"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nm0C18rBjwVG"
      },
      "source": [
        "b. Information about Dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "_uuid": "81df01ab44c6cb343641fe6835f13c31140d1eff",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DU2kze36fPUj",
        "outputId": "33c818b5-1bec-491b-b960-f04fbcd26cda"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 1458644 entries, 0 to 1458643\n",
            "Data columns (total 11 columns):\n",
            " #   Column              Non-Null Count    Dtype  \n",
            "---  ------              --------------    -----  \n",
            " 0   id                  1458644 non-null  object \n",
            " 1   vendor_id           1458644 non-null  int64  \n",
            " 2   pickup_datetime     1458644 non-null  object \n",
            " 3   dropoff_datetime    1458644 non-null  object \n",
            " 4   passenger_count     1458644 non-null  int64  \n",
            " 5   pickup_longitude    1458644 non-null  float64\n",
            " 6   pickup_latitude     1458644 non-null  float64\n",
            " 7   dropoff_longitude   1458644 non-null  float64\n",
            " 8   dropoff_latitude    1458644 non-null  float64\n",
            " 9   store_and_fwd_flag  1458644 non-null  object \n",
            " 10  trip_duration       1458644 non-null  int64  \n",
            "dtypes: float64(4), int64(3), object(4)\n",
            "memory usage: 122.4+ MB\n",
            "Information about train dataset:\n",
            " None\n"
          ]
        }
      ],
      "source": [
        "print('Information about train dataset:\\n', df.info())"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GCDtB6hG5dPk"
      },
      "source": [
        "Observations:\n",
        "\n",
        "1.  Total columns: 11\n",
        "\n",
        "2.  *`id`* feature identifies each row uniquely.\n",
        "\n",
        "3.  *`trip_duration`* is the target variable.\n",
        "\n",
        "4.  *`dropoff_datetime`*: It needs to be dropped, because if we have *`dropoff_datetime`* and *`pickup_datetime`*,  then *`trip_duration`* is merely the difference of the two.\n",
        "\n",
        "5.  Feature data types automatically detected - object, float64, int64. These could be changed in case size is the issue e.g. if possible change float64 to float32, or object to categorical data type.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "_uuid": "d85baac0eff4e6525df407b262596a72e8a5fecb",
        "id": "B2j9LkhHfPUk"
      },
      "source": [
        "**II. Data Checks**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "_uuid": "d74e5da2bb632a78ada91ef797bf1a562e9df80c",
        "id": "4VMeGuOrfPUl"
      },
      "source": [
        "a. Check for Duplicated Data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "_uuid": "b4da4373e43625132d7df1723ed12c8253b2a007",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KYV11OEBfPUl",
        "outputId": "fe01c7b1-c611-432c-bdd1-cd695df9ca2f"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Number of duplicated rows: 7\n"
          ]
        }
      ],
      "source": [
        "# ignore id column while checking duplicates as id is unique identifier\n",
        "print('Number of duplicated rows:', df.loc[:, df.columns != 'id'].duplicated().sum())"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AJDvufA10een"
      },
      "source": [
        "Obervations:\n",
        "\n",
        "1.  7 duplicates found. This could cause data leak and needs to be deleted."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "C5BlKn_Ol9-I",
        "outputId": "6ed446cd-c861-45c0-c24a-19da9ac90fe4"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Row count before performing action: 1458644\n",
            "Row count before performing action: 1458637\n"
          ]
        }
      ],
      "source": [
        "# global outlier\n",
        "# drop duplicates\n",
        "print(f'Row count before performing action: {df.shape[0]}')\n",
        "df = df.drop_duplicates(subset=df.loc[:, df.columns != 'id'])\n",
        "print(f'Row count before performing action: {df.shape[0]}')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DENYjLgXfFmJ"
      },
      "source": [
        "b. Check for Missing Values in Each Column"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "_uuid": "daf4dc10c14b5191a0a5b9d4576bcab57160f726",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1cySSbF_fPUm",
        "outputId": "0270272d-8c5a-4561-f7ea-6bb50f5a7d96"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Number of NaN values for each column:\n",
            "id                    0\n",
            "vendor_id             0\n",
            "pickup_datetime       0\n",
            "dropoff_datetime      0\n",
            "passenger_count       0\n",
            "pickup_longitude      0\n",
            "pickup_latitude       0\n",
            "dropoff_longitude     0\n",
            "dropoff_latitude      0\n",
            "store_and_fwd_flag    0\n",
            "trip_duration         0\n",
            "dtype: int64\n"
          ]
        }
      ],
      "source": [
        "print(f'Number of NaN values for each column:\\n{df.isna().sum()}')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gFH_In_O0lq3"
      },
      "source": [
        "Observations:\n",
        "\n",
        "1.  No missing values found. Therefore, no imputation required."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VbXpzb80fdn5"
      },
      "source": [
        "c. Check Number of Unique Values"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ggb7ccjTfeFl",
        "outputId": "55f5a56c-3fd5-4334-9a3b-5f6acb11dd33"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Number of unique values for each column:\n",
            "id                    1458637\n",
            "vendor_id                   2\n",
            "pickup_datetime       1380222\n",
            "dropoff_datetime      1380377\n",
            "passenger_count            10\n",
            "pickup_longitude        23047\n",
            "pickup_latitude         45245\n",
            "dropoff_longitude       33821\n",
            "dropoff_latitude        62519\n",
            "store_and_fwd_flag          2\n",
            "trip_duration            7417\n",
            "dtype: int64\n"
          ]
        }
      ],
      "source": [
        "print(f'Number of unique values for each column:\\n{df.nunique()}')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "_uuid": "810ae616deaec1e0b1ea42e7bf90a253e6676ce4",
        "id": "D1oWNIqyfPUn"
      },
      "source": [
        "d. Check Statistics for Numerical Features "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "_uuid": "f1af4b544b6100c54dd0f4a066b84ec7b5712304",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qDTNj3y8fPUn",
        "outputId": "29c45f97-0e10-410f-fa00-8886e2904e11"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Statistics for numerical columns:\n",
            "           vendor_id  passenger_count  pickup_longitude  pickup_latitude  \\\n",
            "count  1.458637e+06     1.458637e+06      1.458637e+06     1.458637e+06   \n",
            "mean   1.534948e+00     1.664525e+00     -7.397349e+01     4.075092e+01   \n",
            "std    4.987773e-01     1.314242e+00      7.090187e-02     3.288107e-02   \n",
            "min    1.000000e+00     0.000000e+00     -1.219333e+02     3.435970e+01   \n",
            "25%    1.000000e+00     1.000000e+00     -7.399187e+01     4.073735e+01   \n",
            "50%    2.000000e+00     1.000000e+00     -7.398174e+01     4.075410e+01   \n",
            "75%    2.000000e+00     2.000000e+00     -7.396733e+01     4.076836e+01   \n",
            "max    2.000000e+00     9.000000e+00     -6.133553e+01     5.188108e+01   \n",
            "\n",
            "       dropoff_longitude  dropoff_latitude  trip_duration  \n",
            "count       1.458637e+06      1.458637e+06   1.458637e+06  \n",
            "mean       -7.397342e+01      4.075180e+01   9.594335e+02  \n",
            "std         7.064342e-02      3.589055e-02   5.236970e+03  \n",
            "min        -1.219333e+02      3.218114e+01   1.000000e+00  \n",
            "25%        -7.399133e+01      4.073589e+01   3.970000e+02  \n",
            "50%        -7.397975e+01      4.075452e+01   6.620000e+02  \n",
            "75%        -7.396301e+01      4.076981e+01   1.075000e+03  \n",
            "max        -6.133553e+01      4.392103e+01   3.526282e+06  \n"
          ]
        }
      ],
      "source": [
        "print('Statistics for numerical columns:\\n', df.describe())"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "S1kHUffP04EX"
      },
      "source": [
        "Observations:\n",
        "\n",
        "1.  *`pickup_longitude`*,\t*`pickup_latitude`*,\t*`dropoff_longitude`*,\t*`dropoff_latitude`*, and\t*`trip_duration`* may have outliers (confirm through visualization)."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XqhtWN3WkkWr"
      },
      "source": [
        "e. Check Categories Present in the Different Categorical Columns"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UvEkN5ErkvkD",
        "outputId": "6888995e-1168-4d0b-f639-f52797bdccb0"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Categories in passenger_count feature:\n",
            "[1 6 4 2 3 5 0 7 9 8]\n"
          ]
        }
      ],
      "source": [
        "print(f'Categories in passenger_count feature:\\n{df.passenger_count.unique()}')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Dbr4H1cPz6Nm"
      },
      "source": [
        "Observations:\n",
        "\n",
        "1.  Categories in passenger_count feature: [1 6 4 2 3 5 0 7 9 8]\n",
        "\n",
        "2.  Min value of *`passenger_count`* is zero - These are outliers as with zero passengers it cannot be a taxi trip.\n",
        "\n",
        "Delete these records."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yVMgY3B718Ou",
        "outputId": "b2226f7b-8a41-4f95-9810-1d5073124833"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Row count before performing action: 1458637\n",
            "Row count after performing action: 1458577\n"
          ]
        }
      ],
      "source": [
        "# global outlier\n",
        "# delete rows with passenger_count equal to zero\n",
        "print(f'Row count before performing action: {df.shape[0]}')\n",
        "df = df[(df.passenger_count > 0)]\n",
        "print(f'Row count after performing action: {df.shape[0]}')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cNghLSkXlfnS",
        "outputId": "dde2f034-8f80-41d0-c108-189c2e9eb706"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Categories in store_and_fwd_flag feature:\n",
            "['N' 'Y']\n"
          ]
        }
      ],
      "source": [
        "print(f'Categories in store_and_fwd_flag feature:\\n{df.store_and_fwd_flag.unique()}')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "g206L_nY0W14"
      },
      "source": [
        "Obervsations:\n",
        "\n",
        "1.  Categories in store_and_fwd_flag feature: ['N' 'Y']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LVWVMmrLnUTX",
        "outputId": "d4407d73-201c-4980-a6d6-7e228a0c096a"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Categories in vendor_id feature:\n",
            "[2 1]\n"
          ]
        }
      ],
      "source": [
        "print(f'Categories in vendor_id feature:\\n{df.vendor_id.unique()}')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LLPUtylindae"
      },
      "source": [
        "Obervsations:\n",
        "\n",
        "1.  Categories in vendor_id feature: [1 2]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8VzOqXbcqqas"
      },
      "source": [
        "**III. Analyzing Features**\n",
        "\n",
        "Analyzing features for outliers, skewness, imbalance, etc.\n",
        "\n",
        "**Note: NYC location is at 40.7128° N, 74.0060° W (source: Google).**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VLuvOQ-duB0M"
      },
      "source": [
        "a. Pickup Latitude & Pickup Longitude"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 384
        },
        "id": "1Vqs7n3wuH1R",
        "outputId": "fc3695a2-8f7e-43fc-a857-15b94f0f03ec"
      },
      "outputs": [
        {
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            "text/plain": [
              "<Figure size 1800x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# plot pickup location\n",
        "pickup_longitude = list(df.pickup_longitude)\n",
        "pickup_latitude = list(df.pickup_latitude)\n",
        "plt.subplots(figsize=(18,6))\n",
        "plt.plot(pickup_longitude, pickup_latitude, '.', markersize=10)\n",
        "plt.plot(-74.0060, 40.7128, label='NYC Location', marker='x', color='red', markersize=15, mew=10)\n",
        "plt.title(\"Pickup Location\")\n",
        "plt.xlabel('pickup_longitude')\n",
        "plt.ylabel('pickup_latitude')\n",
        "plt.legend(loc=\"upper right\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QDPAFpldpuaV"
      },
      "source": [
        "Observations: \n",
        "\n",
        "Cluster isn't too packed, i.e., few points are little too far from the cluster. However, this may or may not be the outliers, because there could be few instances when ride locations are at far away distance.\n",
        "\n",
        "(Global Outliers)\n",
        "Remove the pickup locations are too far from the cluster. \n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GPb7R9fapOlN",
        "outputId": "cfc12c1d-9195-4024-f5ef-8b9d69b62109"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Row count before performing action: 1458574\n",
            "Row count after performing action: 1458574\n"
          ]
        }
      ],
      "source": [
        "# global outlier\n",
        "# drop outliers\n",
        "print(f'Row count before performing action: {df.shape[0]}')\n",
        "df = df[(df.pickup_longitude > -100) & (df.pickup_latitude < 50)]\n",
        "print(f'Row count after performing action: {df.shape[0]}')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 387
        },
        "id": "bs_jXQCR-SE0",
        "outputId": "3485ee29-61fe-4339-998d-5c9417253229"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 1800x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# again plot pickup location\n",
        "pickup_longitude = list(df.pickup_longitude)\n",
        "pickup_latitude = list(df.pickup_latitude)\n",
        "plt.subplots(figsize=(18,6))\n",
        "plt.plot(pickup_longitude, pickup_latitude, '.', markersize=10)\n",
        "plt.plot(-74.0060, 40.7128,  label='NYC Location', marker='x', color='red', markersize=15, mew=10)\n",
        "plt.title(\"Pickup Location\")\n",
        "plt.xlabel('pickup_longitude')\n",
        "plt.ylabel('pickup_latitude')\n",
        "plt.legend(loc=\"upper right\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1sPS8jy1DTrq"
      },
      "source": [
        "Observations:\n",
        "\n",
        "There are still few points that are away from the cluster. These could be outliers or could be few intracity trips.\n",
        "\n",
        "(Global Outliers)\n",
        "Assumming only intercity trips comprise the dataset, remove those outliers."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "J_-fXD5NDSxD",
        "outputId": "dc063c5e-6981-4570-806d-ae2ded71486a"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Row count before performing action: 1458574\n",
            "Row count after performing action: 1458550\n"
          ]
        }
      ],
      "source": [
        "print(f'Row count before performing action: {df.shape[0]}')\n",
        "df = df[(df.pickup_longitude > -76) & (df.pickup_longitude < -72.5) &\n",
        "                    (df.pickup_latitude > 39) & (df.pickup_latitude < 42)]\n",
        "print(f'Row count after performing action: {df.shape[0]}')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 384
        },
        "id": "eZYxTrSzEuqM",
        "outputId": "555e1220-0555-41dc-b546-cc6d1b43ca94"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 1800x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# again plot pickup location\n",
        "pickup_longitude = list(df.pickup_longitude)\n",
        "pickup_latitude = list(df.pickup_latitude)\n",
        "plt.subplots(figsize=(18,6))\n",
        "plt.plot(pickup_longitude, pickup_latitude, '.', markersize=10)\n",
        "plt.plot(-74.0060, 40.7128,  label='NYC Location', marker='x', color='red', markersize=15, mew=10)\n",
        "plt.title(\"Pickup Location\")\n",
        "plt.xlabel('pickup_longitude')\n",
        "plt.ylabel('pickup_latitude')\n",
        "plt.legend(loc=\"upper right\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tOzFZpopFEDI"
      },
      "source": [
        "Observations:\n",
        "\n",
        "(Global Outliers)\n",
        "Remove those four points with *`pickup_longitude`*<-75."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "B1OLB5HoFhuy",
        "outputId": "8e6d90ca-58c1-4cf2-a24e-308ea170c0e6"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Row count before performing action: 1458550\n",
            "Row count after performing action: 1458546\n"
          ]
        }
      ],
      "source": [
        "print(f'Row count before performing action: {df.shape[0]}')\n",
        "df = df[(df.pickup_longitude > -75)]\n",
        "print(f'Row count after performing action: {df.shape[0]}')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 384
        },
        "id": "nmxPgXYxFtqN",
        "outputId": "6ba70ec9-b9b9-41cc-c305-a7b2cbb80f53"
      },
      "outputs": [
        {
          "data": {
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            "text/plain": [
              "<Figure size 1800x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# again plot pickup location\n",
        "pickup_longitude = list(df.pickup_longitude)\n",
        "pickup_latitude = list(df.pickup_latitude)\n",
        "plt.subplots(figsize=(18,6))\n",
        "plt.plot(pickup_longitude, pickup_latitude, '.', markersize=10)\n",
        "plt.plot(-74.0060, 40.7128,  label='NYC Location', marker='x', color='red', markersize=15, mew=10)\n",
        "plt.title(\"Pickup Location\")\n",
        "plt.xlabel('pickup_longitude')\n",
        "plt.ylabel('pickup_latitude')\n",
        "plt.legend(loc=\"upper right\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BvkGSe7nFyr9"
      },
      "source": [
        "Observations:\n",
        "\n",
        "Pickup locations look to belong from the same cluster now."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-SMC4wU8ua0h"
      },
      "source": [
        "b. Dropoff Latitude & Dropoff Longitude"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 387
        },
        "id": "y956rZPlufpo",
        "outputId": "807bfdf6-c72c-4968-b203-f81c19032e9d"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 1800x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# plot dropoff location\n",
        "dropoff_longitude = list(df.dropoff_longitude)\n",
        "dropoff_latitude = list(df.dropoff_latitude)\n",
        "plt.subplots(figsize=(18,6))\n",
        "plt.plot(dropoff_longitude, dropoff_latitude, '.', markersize=10)\n",
        "plt.plot(-74.0060, 40.7128, label='NYC Location', marker='x', color='red', markersize=15, mew=10)\n",
        "plt.title(\"Dropoff Location\")\n",
        "plt.xlabel('dropoff_longitude')\n",
        "plt.ylabel('dropoff_latitude')\n",
        "plt.legend(loc=\"upper right\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wiTB_AgBq5Wu"
      },
      "source": [
        "Observations:\n",
        " \n",
        "Similar to pickup locations, even in dropoff locations few points are away from the cluster. And similar to pickup locations, this could again may or may not be outliers and could be the real dropoff locations.\n",
        "\n",
        "(Global Outliers)\n",
        "Remove the points that are too away from the cluster."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Xm00tHS-q8-z",
        "outputId": "4df8ae81-5310-4562-e093-718de601444e"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Row count before performing action: 1458546\n",
            "Row count after performing action: 1458538\n"
          ]
        }
      ],
      "source": [
        "# drop outliers\n",
        "print(f'Row count before performing action: {df.shape[0]}')\n",
        "df = df[(df.dropoff_longitude > -75) & (df.dropoff_latitude > 39)]\n",
        "print(f'Row count after performing action: {df.shape[0]}')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 384
        },
        "id": "2DRbubK_GeJo",
        "outputId": "b05f030e-a405-42ed-cf8f-ef9ce85a61ab"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 1800x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# again plot dropoff location\n",
        "dropoff_longitude = list(df.dropoff_longitude)\n",
        "dropoff_latitude = list(df.dropoff_latitude)\n",
        "plt.subplots(figsize=(18,6))\n",
        "plt.plot(dropoff_longitude, dropoff_latitude, '.', markersize=10)\n",
        "plt.plot(-74.0060, 40.7128, label='NYC Location', marker='x', color='red', markersize=15, mew=10)\n",
        "plt.title(\"Dropoff Location\")\n",
        "plt.xlabel('dropoff_longitude')\n",
        "plt.ylabel('dropoff_latitude')\n",
        "plt.legend(loc=\"upper right\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uxuPOpXuGo7S"
      },
      "source": [
        "Observations: \n",
        "\n",
        "There are still few points away from the cluster. Let's keep them now."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Mh1l4oq4vSc3"
      },
      "source": [
        "c. Trip Duration"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "_uuid": "80d09dbb000f6c53f79054839537ba518167cd5c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 410
        },
        "id": "n6uJ9abTfPUq",
        "outputId": "4cdbdb78-499d-4f69-db91-079d613acaa2"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1000x400 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# plot trip duration using box plot\n",
        "fig = plt.figure(figsize=(10,4))\n",
        "sns.boxplot(df.trip_duration, orient=\"h\", palette='coolwarm')\n",
        "plt.title('Trip Duration Outliers')\n",
        "plt.ylabel('trip_duration')\n",
        "plt.xlabel('seconds')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qiINifsSru8K"
      },
      "source": [
        "Observations:\n",
        "\n",
        "4 points that are far away from the cluster, and rest of the points are near the cluster.\n",
        "\n",
        "(Global Outliers)\n",
        "Remove these 4 points."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fLSM70qg1u-F",
        "outputId": "db3feb31-10c0-46ae-f062-2b68cc2d5746"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Row count before performing action: 1458538\n",
            "Row count after performing action: 1458534\n"
          ]
        }
      ],
      "source": [
        "# drop outliers\n",
        "print(f'Row count before performing action: {df.shape[0]}')\n",
        "df = df[(df.trip_duration < 500000)]\n",
        "print(f'Row count after performing action: {df.shape[0]}')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 410
        },
        "id": "uVot_Pnm5cTM",
        "outputId": "1e57d7d9-a895-475e-cea9-8e7dcb38ca6d"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1000x400 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# again plot trip duration using box plot\n",
        "fig = plt.figure(figsize=(10,4))\n",
        "sns.boxplot(df.trip_duration, orient=\"h\", palette='coolwarm')\n",
        "plt.title('Trip Duration Outliers')\n",
        "plt.ylabel('trip_duration')\n",
        "plt.xlabel('seconds')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nEBhJgrQsgdI"
      },
      "source": [
        "Observations:\n",
        "\n",
        "This is a right skewed feature. Too many points outside the box. These points could be outliers, however, on rare occasions, *`trip_duration`* could really exceed.\n",
        "\n",
        "Plotting a histogram to verify if the feature is right skewed."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 366
        },
        "id": "NQsD6csW4TGu",
        "outputId": "18307b00-e3fe-4563-c013-385178565155"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1800x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# plot histogram for skewness detection\n",
        "plt.subplots(figsize=(18,6))\n",
        "sns.histplot(data=df, x='trip_duration', kde=True, bins=10000)\n",
        "plt.xlabel('trip_duration')\n",
        "plt.ylabel('number of train records')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "stJoqHri3Yzz"
      },
      "source": [
        "Observations:\n",
        "\n",
        "The target variable, i.e., *`trip_duration`*, is indeed right skewed. This gives us two options:\n",
        "\n",
        "*   Either perform log-transformation\n",
        "*   Or use proper metric for evaluation\n",
        "\n",
        "Certain trips have *`trip_duration`* equal to 1 second, 2 seconds, 4 seconds, etc. and certain distance travelled."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 368
        },
        "id": "y8Ak5VTuIFMZ",
        "outputId": "37df98d0-699e-4de5-a81c-0054f2094257"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1800x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# zoom the above histogram on the left part\n",
        "plt.subplots(figsize=(18,6))\n",
        "sns.histplot(data=df,x='trip_duration', kde=True, bins=50000)\n",
        "plt.xlabel('trip_duration')\n",
        "plt.ylabel('number of train records')\n",
        "plt.axis([0, 5000, 0, 3000]) # zooming co-ordinates\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Vglw8SbpPHHb"
      },
      "source": [
        "Observations:\n",
        "\n",
        "There are points with *`trip_duration`* of few seconds as well. These may or may not be outliers."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "67VgEQidrvP6"
      },
      "source": [
        "d. Vendor ID"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 465
        },
        "id": "m-oP8kMpS8Vf",
        "outputId": "c97733af-1249-4817-c315-594a1061cff1"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 500x500 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# plot countplot\n",
        "fig, ax = plt.subplots(figsize=(5,5))\n",
        "sns.countplot(x=df['vendor_id'], data=df, palette='coolwarm', ax=ax)\n",
        "for container in ax.containers:\n",
        "    ax.bar_label(container, color='black', size=10)\n",
        "plt.xlabel('vendor_id')\n",
        "plt.ylabel('number of train records')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tqiub16Ryp9e"
      },
      "source": [
        "Observations:\n",
        "\n",
        "1.  Two vendors: *`vendor_id`* 1 and *`vendor_id`* 2\n",
        "\n",
        "2.  Distribution of training records - \n",
        "\n",
        "    *   *`vendor_id`* 1 having 678278 training records\n",
        "\n",
        "    *   *`vendor_id`* 2 having 780263 training records"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 364
        },
        "id": "tntNwOes1siV",
        "outputId": "12a2d42a-e97d-4990-8b72-6ebe4872759e"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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YSFZWlj/LCxoF7CIiIiIiIiIiIiJhJjk5Gbs9iqnP/KHFzmm3R5GcnNzs8UuWLOGkk05q+n7ixIkAXHHFFUybNs3f5QWFAnaRAPjtrOoZeancEcR6miMcahQRERERERERkT2ysrJYt24txcXFLXbO5ORkn2aen3jiiXi93gBWFHwK2EX8qDkhtYLs/6f2Ls2zYfIUuk26JdhliIiIiIiIiEiIycrKajWtVsKVFjkVCXHhvCBoONcuIiIiIiIiIiJyKArYRQLsSELmcAuow63eI7W/690weUoQKhERERERERERkWBQwC4iIelIw/rWdmPjt8G9gnwRERERERERkeBTwC4i+9VSIXMohtkiIiIiIiIiIiLNoYBdREKKAveD+/XMdc1iFxEREREREREJLgXsIvuhkDd0+LPVy/6O1Vp+1/sL2xXAi4iIiIiIiIgElgJ2kSPQWsLZUOPrz/Vwfw/6/YmIiIiIiIiIyJGwBLsAkXAzIy+VczIKgl2GiIiIiIiIiIi0cdu3b6e4uLjFzpecnExWVlaLnS8cKGCXNmPD5Cl0m3RLsMuQEBYuN0/U+kVEREREREREtm/fTs8ePaiprW2xc0bZ7axdt04h+68oYJc2RcFkeJqRl8odwS5CRERERERERCSEFBcXU1Nby7/Pv4wuyWkBP9/m4nz+/MHrFBcXNztgf/DBB/nwww9Zt24ddrud4447joceeoju3bsHuNqWo4Bd5AiFy6xnkQPRpztEREREREREwleX5DR6p2cGu4z9mjNnDjfddBNHH300LpeLv/71r5x22mn89NNPREdHB7s8v9Aip9IqhcNMdS2w2bKO5Od9qH3D4XcZDq8JEREREREREWldZs6cyZVXXknv3r3p378/06ZNY/v27SxdujTYpfmNAnYRPwmHkFWkOUG7wngRERERERERCYSKigoAEhMTg1yJ/yhgFzkMCtNbP/2ORURERERERET8x+PxcPvttzNs2DD69OkT7HL8Rj3YRURERERERERERCSgbrrpJlavXs38+fODXYpfaQa7SIjQjOnQoN+DiIiIiIiIiIh/3XzzzXzyySd88803dOjQIdjl+JVmsIuIiIiIiIiIiIiI33m9Xm655RY++ugjvv32W7Kzs4Ndkt8pYBcREREREREREREJU5uL80P2PDfddBNvvvkmH3/8MTExMeTn7zlGXFwcdrvd3yUGhQJ2CVsbJk+h26Rbgl0GoLYi4WZGXip3BLsIPwml14GIiIiIiIiItJzk5GSi7Hb+/MHrLXbOKLud5OTkZo9/9tlnATjxxBP32v7KK69w5ZVX+rGy4FHALm1WoEPW1hTihhrd0BARERERERGRti4rK4u169ZRXFzcYudMTk4mKyur2eO9Xm8AqwkNCthFRIJMs9BFRERERERE5HBkZWX5FHiL/5mCXYCIrzZMntIi+4iIiIiIiIiIiIgcjAJ2kQNQGxJpzXTTSURERERERETkyClglzavuUG6AncR3ynIFxEREREREZHWTAG7hD0FeCLNo9eKiIiIiIiISOhpCwuB+lso/cwUsIuIiIiIiIiIiIi0MKvVCkBNTU2QKwk/DQ0NAJjN5iBXApZgFyASDtQeRkRERERERERE/MlsNhMfH09hYSEAUVFRGIYR5KpCn8fjoaioiKioKCyW4Mfbwa9A5Aj4q+XFjLxU7vBhu0i42DB5Ct0m3dLqziUiIiIiIiLSGqSlpQE0hezSPCaTiaysrJC4IaGAXeQ3NFtdRERERERERERagmEYtG/fnpSUFBobG4NdTtiw2WyYTKHR/VwBu4iIiIiIiIiIiEgQmc3mkOgnLr4LjZhfRCQMNOfTDa3xExD+asUkIiIiIiIiItLaKGAXEfFBawzQRURERERERETk8ChgF5FWr62H4pqBLiIiIiIiIiISGArYpc1p62Gr7F9LPi/0HBQRERERERERaR0UsIuI+JkCdBERERERERGRtkEBu7QpCj6lJbX0883frWDUWkZERERERERE5OAUsIuItGFHGqIrhBcRERERERGRtkwBu4hICNKnLUREREREREREQp8CdhERAZo/G12z1kVERERERERE9lDALiIiTQ4UnitUFxERERERERHZlwJ2EREREREREREREZHDENSAfe7cuYwdO5b09HQMw2DGjBlNjzU2NnLnnXfSt29foqOjSU9P5/LLL2fXrl17HaO0tJTx48cTGxtLfHw811xzDU6nc68xK1eu5PjjjycyMpLMzEwefvjhlrg8EREREREREREREWnFghqwV1dX079/f/7zn//s81hNTQ3Lli3j7rvvZtmyZXz44YesX7+es846a69x48ePZ82aNcyaNYtPPvmEuXPnMmHChKbHKysrOe200+jYsSNLly7l3//+N/fccw8vvPBCwK9PQocWjJTD1dznjp5jzaNWMyIiIiIiIiLSmliCefLRo0czevTo/T4WFxfHrFmz9tr29NNPc8wxx7B9+3aysrJYu3YtM2fO5IcffmDw4MEATJkyhTFjxvDII4+Qnp7O9OnTaWho4OWXX8Zms9G7d2+WL1/OY489tlcQLyLiC38F6jPyUjkno8AvxxIRERERERERkZYVVj3YKyoqMAyD+Ph4ABYuXEh8fHxTuA4wcuRITCYTixYtahpzwgknYLPZmsaMGjWK9evXU1ZWtt/z1NfXU1lZudeXiMjh0Mx2EREREREREZHWK2wC9rq6Ou68804uvvhiYmNjAcjPzyclJWWvcRaLhcTERPLz85vGpKbuHXD98v0vY37rwQcfJC4urukrMzPT35cjPlJbCZFD82eYr9eciIiIiIiIiMihhUXA3tjYyLhx4/B6vTz77LMBP99f/vIXKioqmr527NgR8HOKtAb1bgOvN9hViIiIiIiIiIiItIyQD9h/Cde3bdvGrFmzmmavA6SlpVFYWLjXeJfLRWlpKWlpaU1jCgr27m/8y/e/jPmtiIgIYmNj9/qSlqOZs+Fp6YoyZhW045uiJDblOoNdTlj57czzI52JHsjXkD+Prde6iIiIiIiIiIS7kA7YfwnXN27cyFdffUVSUtJejw8dOpTy8nKWLl3atO3rr7/G4/EwZMiQpjFz586lsbGxacysWbPo3r07CQkJLXMh0mJeenNrsEtokzxeePTZjXgwcLosTPjTj2yttms2+680NzRvjT3bFaSLiIiIiIiISGsV1IDd6XSyfPlyli9fDkBubi7Lly9n+/btNDY2csEFF7BkyRKmT5+O2+0mPz+f/Px8GhoaAOjZsyenn3461113HYsXL2bBggXcfPPNXHTRRaSnpwNwySWXYLPZuOaaa1izZg3vvPMOTz75JBMnTgzWZYu0Oluqo9ieV4vN5CYlop6GBg8rK2JZWhaHs9oV7PLarNYY1ouIiIiIiIiIhJKgBuxLlixhwIABDBgwAICJEycyYMAAJk2aRF5eHv/973/ZuXMnRx11FO3bt2/6+u6775qOMX36dHr06MEpp5zCmDFjGD58OC+88ELT43FxcXz55Zfk5uYyaNAg/vjHPzJp0iQmTJjQ4tcroU+BpO/q3CbWV0UD0CvWyZDEcm68qjMGXnbVRXL17Uspb7AEuUoRERERERERERH/C2rqdeKJJ+I9SA+Jgz32i8TERN58882DjunXrx/z5s3zuT4Jrg2Tp9Bt0i1+OQ4oOA+UNZUO3F4TvbvHkFlZgGHAJedlsvJ/S1haFseu/Dp2k0ivWGezXtPSeh2sVYy/Xu8iIiIiIiIiIi0ppHuwixwpzUgPrJJ6K3m1dsDLH37fFcP4/8cSbY2MaFfCiKHJeDFYUxnDXf9cQ4PHOODxREREREREREREwokCdhE5LB4vrKqIAaBjVC09cmL2GWMzefnnX3rRN64SE14WLC7h28IkSuqtLV2uiIiIiIiIiIiI36kxsogclm01dipdVqyGhx4xzgOOMwyD7OhaEm2NbLZ2YOeuWr4rSeD197bj9bLXrHcREREREREREZFwohnsIuKzerfBukoHAD1inUSYD91bPc7q4uXHB5Jhr8WLwfOv5fJ9aTx1br0NHUggWhwFqm3Swfqr+2O8iIiIiIiIiEgoUrIlrUrJnMXBLqFNWFfloNFrItbSSKeo2mbvFxVlYWB8JUfFVxBhM1FUH8GcokR+WF4WwGolUPwdkit0FxEREREREZFwo4BdRHyydkMl22rsAPSNq/K5xYthQFZUHVMfH0iMpZF6j5mJk1ayrjIaz6EnwrdqWpRXRERERERERCS8KGAXkWbzeuGx5zcBBh3stSRFNB72sbKzojk+uZSOUTV4vbDB6eC7kgRq1TJGfqYZ7SIiIiIiIiIS6pRkiUiz7aiNZO2GKiyGh16xB17YtLksJugfX8U9f+6JxfBQ2mBjTlES+XU2P1TbdoXCTHh/heMK2UVEREREREQklClgF5FmafQY/FQZA0C3mGoizR6/HXvkCSmc0K6UOGsjDR4Ti0sTWF3hoLHRf+dojUIhSBcRERERERERacsUsEtYU8DYctZVOWjwmOiUGUXn6Bq/H99hcTM8uZTsn4+9pTqaG+9cTrXL7PdziYiIiIiIiIiI+IMCdgk6tYAIfZtyneRW71nY9LYJOZh8XNi0uczGnoVTj04ox2p4WLuxijlFieyqjQjMCUVERERERERERI6AAnYROSivF574eWHT9pF1HH1UQsDP2d5ez4h2JfTpEYvLa2JJWTyPPLMRtzfgpw55h/OpjZb+pIdumomIiIiIiIhIW6GAXUQOalddBMvXVGA2vPSOrWqx80ZZPDz9YH9yHNUAzPh8F/OKEtm2w//taVqKP4NutUcSEREREREREQk+BezS6imIPHwuj8Gaij0Lm3Z1VBNladlFRy0WE71inRybWEZ8nJVKl5VrJy5lR01ki9YhIiIiIiIiIiKyPwrYReSANjijqfOYSU+LpMvPM8mDISWygWlPDSLZ1kBtnYcfy+P4sSwWlydAzeDDUHNuJIXzzSa1nRERERERERGRUKSAXUT2y+kys9kZBcCt13XBHOQsOzkxgqFJZVxzSUfAy45aO3OLE6lotAS3sBbw22C8pYLyljrP4YbnCt1FREREREREJNgUsIvIPrxeWFURgxeDlIh6hh2dFOySADAMuOriThyXVEakyY3TZWFeUSJbq+142+gCqOE8K11EREREREREJNwpYJewodmqLSe/LoKi+ghMeOkTV4VhhFYrluSIRka0KyEloh4PBisrYllaFoez2hXs0kREREREREREpA1RwC5thmb6No/bC2sq9yxs2sVRjcPiDnJF+xdh9jIksZxesVUYeNlVF8nVty+lrKH1t4wREREREREREZHQoIBdRPayyRlNjdtMpMlN1yAubNochgE5jhqGJ5diN7vZlV/H/OJE3pmxM+RbxuiGz4Hp0yoiIiIiIiIiEi4UsEtIU9DWsnYX1LGxKhqA3nFVWMLkHSLB5mJEuxJGDE3Gi8GUlzazuDSeBk9otbYREREREREREZHWJUziMxFpCVNe2owHg2RbA+mR9cEuxyc2k5d//qUXfeMqsVoMCuoj+LYwiRVrKoJdml/4e8a7ZtCLiIiIiIiIiBw5BezSaigwPDKFdTbmLizGwEufuEpCbF3TZjEMg+zoWl54dCDRZhd1HjO3/nU5G6qiQr5ljIiIiIiIiIiIhB8F7CKCxwurf17YNDu6hlhraC5s2lxdOzsY0a6UDHstbg+sq4phYUk8dW695cn/UwsqERERERERETlSSpsk7O1v5rpms/tmS3UUTpeFhHgr3WNCe2HT5rKYvAyMr+Qvt3XHbHgpbohgTlEiPywvC3ZprZZedyIiIiIiIiLS1ihgF2njikvqWf/zwqY3XtkZq6n19FIxDDhjZBonJJcQY2mk3mNm4qSVrK2MxuVuPdcpIiIiIiIiIiLBoYBdQopaNrS8Z6Ztwe01kWBtYNRJrXMGcozVzfHJpXSMqsHrhY1OB7f+dTm1ahnjs1Cepa73DxERERERERFpaUqXRNqwknorX35bCHjpG1eFyRSGK5s2k8UE/eOruOfPPbEYHlb+VMm3hUnk19mCXVpICeUA/RcK0kVEREREREQkVChgF2mjXG4vqyr2LGzaMaqWeJsryBW1jJEnpHBCu1K6dXHQ6DWxuDSBKS9txtPKO8YcKDj3dbuIiIiIiIiIiPw/BewibdTHn++i0mUlNsZCz1hnsMtpUQ6Lm+f+PYDO0XsWdH1nxk7mFydS7TIHuTIREREREREREQknCthF2qB6t8GLb2wF4LpLs7G1ooVNm8tmNdEnzsnRCeXEOCyUN1qZU5TIrtqIYJfmV77MRNesdRERERERERER3yhgF2mD1lY5cFa7iLU0ctao9sEuJ6ja2+t55clBJFgbcHlNLCmLZ0V5DO62d89BRERERERERER8pIBdpI0pa7CwvcYOQN+4Kszm1ruwaXOlpUQyLLmMro49LWO21UQxryiRbTtqglyZiIiIiIiIiIiEMgXsEtLUssK/vF5YVRELGIw6KZWkiMZglxQyTAb0jHVybGIZNpOHSpeVaycuZUdNZLBLC7pQfx1umDylTZ5bRERERERERIJPAbuEpJfe3BrsElqlHbWRlDdasRgebrgyO9jlhKSUyAZObFdCsq2e2joPP5bHsawslppad7BLExERERERERGREKOAXYJCsz5bXoPH4KfKGAC6x1STnNi6FvP0p0izh6FJ5Vw7vhPgZWetnWv/sJSKRkuwSwuIUJ+h7k967xERERERERERf1LALmGnLYWB/rS+ykGDx4TD4iI7Wr3FD8Uw4MqLOjIsqYxIk5vtebXMK0pka7Udr7d1r4Dqy2ustbweFbyLiIiIiIiIyOFQwC7SBlQ0Wsit/v+FTU1a17TZkiIaGdGuhKGDE/FgsLIilkkPraXRox+iiIiIiIiIiEhbp4BdpJXzemF1RQxg0D6yjnYRDcEuKexEmL08dHcfesVWYeDlmwVFzClKpKyhdbaMERERERERERGR5lE6JGGrtbSmCLS82khKGmyYDS+9Y6uCXU7YMpkMchw1JNka2GTKYHdhHfOLE3lnxk683j0tZUREREREREREpG3RDHZpUQfqc6z+x4FRU+vmp0oHAF0d1URZPEGuKPwl2Fy8/OQg2kfW4cVgykubWVwaT0MbbBkTzJtces8QERERERERkVCggF2kFXv1nW3UecxEmV10cVQHu5xWI8ZhYXBCBX3jKrFaDArqI/i2MIkVayqCXZr8TAG8iIiIiIiIiLQEBewSstQC5sg4XWbe+XgnAH3iqjC3vQnWAWUYkB1dywuPDiTa7KLOY+bWvy5nQ1U0Xm+wqwtdLf26PtKgXUG9iIiIiIiIiByMAnaRVsjr9bKqIgaXy0tqRD1pkVrYNFC6dnYwol0pHey1uD2wrsrBwpJ4Ssr0Mz8cCrRFREREREREJJwoYBdpheYvKqGoPgKrxaB3nBY2DTSLycuA+Er+elt3zIaX4oYIrrp1CUX1tmCXJiIiIiIiIiIiAaSAXaSVcXvhqRc3A3DRuZk4LO4gV9Q2GAaMGZnGCcklxFgaKS1vZGFJPGsro3G5W1fPGC1uKiIiIiIiIiKyhwJ2kVZmkzOa3YV1RJrcXD4uK9jltDkxVjcntCvlrFHtAYONTge3/nU5tW693f5WoIJ6hfAiIiIiIiIi0lKU+Ii0IjUuExurooE9C5vaI81BrqhtMhtwx83dGJRQjsXwsPKnSr4tTCK/rm20jAm1BYoVuIuIiIiIiIhIoChgF2lFVlfG4MFgYL942kfWB7ucNi/DXs8J7UrpnuOg0WticWkCU6ZuwtO6OsaIiIiIiIiIiLRZCthFWolFy0rJr4vEwMvtE3IwjGBXJAAOi5tnHx5A5+hqAN75OI/5xYlUu1rXpwsONms9kDPa/T07XbPdRURERERERMQXRxywu91uli9fTllZmT/qEZHD4PHCEy9sAiA7uobOHaODXJH8ms1qok+ck6MTyolxWChvtDKnKJHZ8wqDXVqLCrXWMSIiIiIiIiIiR8rngP3222/npZdeAvaE6yNGjGDgwIFkZmby7bff+nSsuXPnMnbsWNLT0zEMgxkzZuz1uNfrZdKkSbRv3x673c7IkSPZuHHjXmNKS0sZP348sbGxxMfHc8011+B0Ovcas3LlSo4//ngiIyPJzMzk4Ycf9vWyRULaZmcUO/JqiTC56R5THexy5ADa2+t55clBJFgbcHlN/OPhtawoj8GtljEiIiIiIiIiImHJ54D9/fffp3///gD873//Izc3l3Xr1vGHP/yBv/3tbz4dq7q6mv79+/Of//xnv48//PDDPPXUUzz33HMsWrSI6OhoRo0aRV1dXdOY8ePHs2bNGmbNmsUnn3zC3LlzmTBhQtPjlZWVnHbaaXTs2JGlS5fy73//m3vuuYcXXnjB10sXCUm1bhMbnHtmrPeMdWI1Ka0NZWkpkQxLLqOrY8+NkG01UcwrSmTbjpogV+ZfoTZb/betX9QKRkRERERERET8weeAvbi4mLS0NAA+++wzLrzwQrp168bVV1/NqlWrfDrW6NGj+ec//8m55567z2Ner5cnnniCv//975x99tn069eP1157jV27djXNdF+7di0zZ85k6tSpDBkyhOHDhzNlyhTefvttdu3aBcD06dNpaGjg5Zdfpnfv3lx00UXceuutPPbYY75eukhI+qnSgdtrok+PWDLtdYfeIUBqd+bT4Zt3SVv4CQWffI2pUYusHojJ2HMz5LF7+2Izual0WbnmD0vZXhMZ7NLkABTIi4iIiIiIiMj++Bywp6am8tNPP+F2u5k5cyannnoqADU1NZjN/lu0Lzc3l/z8fEaOHNm0LS4ujiFDhrBw4UIAFi5cSHx8PIMHD24aM3LkSEwmE4sWLWoac8IJJ2Cz2ZrGjBo1ivXr1x+wb3x9fT2VlZV7fYmEopJ6K3m1dsDLH64P3sKmtvJCvjvhIuJyV5G0dhFLzr2BHm88QKfPXmLjg88SWZwHHk9wigthxwxM5MR2pSTb6qmr97C8PI5lZbG4PKG3Qm2ozUg/HAcKyZsbnitkFxEREREREZHfsvi6w1VXXcW4ceNo3749hmE0BeCLFi2iR48efissPz8f2BPo/1pqamrTY/n5+aSkpOz1uMViITExca8x2dnZ+xzjl8cSEhL2OfeDDz7Ivffe658LEQkQjxdWVcQA0DGqlu45MUGpw164g6xZr1NXX0t9XDLV7bPp0FhAzebtROdvZcOkJ+gCuCKiWFaziPhdNpwZOUGpNRRFmj0MTSon9ZQhTH0jl521dsobrQxKqCDO6gp2eWFhRl4qdwS7CBERERERERFpk3wO2O+55x769OnDjh07uPDCC4mIiADAbDZz1113+b3AYPjLX/7CxIkTm76vrKwkMzMziBW1ThsmT6HbpFuCXUbY2lZjp9JlxWp46BnrPPQOAeDYsYHMr9/G5G4k/pj+fJ8zGndkNJc9P47HL34Ox65NDGhXy67P5mGpr2H3u5+R8fO+c1Z+RJopBWd6Dq7q1tV/3FeGAVf+riNrP1/G0rI4nC4L84oS6RNXhdfbdnvqz8hL5ZyMgmCXISIiIiIiIiJyQD4H7AAXXHDBPtuuuOKKIy7m137p815QUED79u2bthcUFHDUUUc1jSksLNxrP5fLRWlpadP+aWlpFBTsHdD88v0vY34rIiKi6caBSCiqdxusq3QAe3p524KwsGncxh/JmD8Dw+uhqkNXRn05jQUTP2l6vDE2kbLYYxj8/Dgevu4t7EU7GTUgghXP/xd78U6cazeTxGaS1ixkVsrbdEzKpDqjC86MHOoS08DwuYNV2EuKaGREuxJ+LI+jsD6ClRWxTHpoLZEeQ4vXhgHdNBQRERERERFpe5oVsD/11FPNPuCtt9562MX8WnZ2NmlpacyePbspUK+srGTRokXccMMNAAwdOpTy8nKWLl3KoEGDAPj666/xeDwMGTKkaczf/vY3GhsbsVqtAMyaNYvu3bvvtz2MSDhYWxVDo9dEnLWRjlG1LX7+pFXzSfvhCwDKu/Qn7/hzsURHHXgHk5na1I50+8c4ZuxKw1Rfy/iz0vjq7tdw5G3EVl2BY/cWHLu3kLpkFq7IaJwZXdj5ug1LTRWuqOC0vwmGCLOXIYnlbK6OYm2lg28WFBFlTmRQQkWwSxMRERERERERkd9oVsD++OOP7/V9UVERNTU1xMfHA1BeXk5UVBQpKSk+BexOp5NNmzY1fZ+bm8vy5ctJTEwkKyuL22+/nX/+85907dqV7Oxs7r77btLT0znnnHMA6NmzJ6effjrXXXcdzz33HI2Njdx8881cdNFFpKenA3DJJZdw7733cs0113DnnXeyevVqnnzyyX2uSSRc/LShku01kQD0jatq2YVNvR5+uuOhpnC9uM8wCo4+zefZ5p4IO+3PG8XuLyrA6+XGPx3D29dMITpvE9G7c7HUVRO/eSUrrl5Jd6AuIZWf4nKJznPjrq0LwIWFFsOAHEcNSbYGNpky2F1Yx/ziRN6ZsROvl6AtZttW+Gsm+q+Po9ntIiIiIiIiIq1TswL23Nzcpj+/+eabPPPMM7z00kt0794dgPXr13Pddddx/fXX+3TyJUuWcNJJJzV9/0vf8yuuuIJp06Zxxx13UF1dzYQJEygvL2f48OHMnDmTyMjIpn2mT5/OzTffzCmnnILJZOL888/fa8Z9XFwcX375JTfddBODBg0iOTmZSZMmMWHCBJ9qlcPnj2BJvZj38Hrh8ec2AQYd7LUk2hpb7uQeNxnzPiJ38woA8o8eRUnf4Ud+XMPA0S2b0l7HUtrrWAy3C3vhDhx5m8ixllK+dA2RZQXkPv4ynYAvf24n40zPwZnRhfqE1FabOCfYXLz86CCuvmo2u+simfLSZlIj4hmQUBGUtkCHY0Ze6qEHhSEF5iIiIiIiIiICh9GD/e677+b9999vCtcBunfvzuOPP84FF1zA+PHjm32sE0888aAL+BmGweTJk5k8efIBxyQmJvLmm28e9Dz9+vVj3rx5za5LWsaMvFTuCHYRYWZ7TSRrd1dhMTz0asGFTV3OarJmTScmbyOGxcKO486mIueogJzLa7ZQ0z6bmvbZXPn8OB694hWid21haCc3ue/OwlpTiSNvE468TfADNNpjmnq31xeeEpCaginGYWFwQgVbaxpYXxNHQX0E3xYmMSihgqSIFrzBEiT7e5/QDTcRERERERERCRU+ryK4e/duXC7XPtvdbvc+i4mKiP80eAzWVu3pRd49pppIs6dFzmuuq2bRaVcSk7cRj8XK4I+eCVi4vj/uyGgqO/el/4sPsOF3f+KEFZ+ye8hoqjp0xWO2Yq2tIn7TcjrMeZ+vMo6j88fPsO6vjxC9awuGe9/3qnBkGJAdXcvzjwwk2uyizmNmQUkCG6qiOcg9yrAUDjPeN0yeEuwSRERERERERCRE+DyD/ZRTTuH6669n6tSpDBw4EIClS5dyww03MHLkSL8XKCJ7rK9y0OAx0SkzimxXy9zMsjrL6fjFq5RXFOOKsLP91MsYe/oI+OjdFjn/PgyDmF45lPY+jtLex2G4Gokq3EF03iYceRuxl+ZjL9nN5n+/SCfAY7GyuOAbEsscODO60BDXLjh1+0nXzg5GtCtlZUUMO2vtrKtyUFxvpaSsIdiliYiIiIiIiIi0ST7PYH/55ZdJS0tj8ODBREREEBERwTHHHENqaipTp04NRI0iQHjMbA2UTblOcqvtANx+fQ6mFmg5HlFWQPYnLxBRUUxkZntyz7iO2pTMwJ/YB16Ller0zhQefRpbzrmJkTsXsHPEBWRceg6NdgcmVyNFM+fSftFndP1wCt3efYQV1/2V2C2raCgpC3b5h8Vi8jIgvpKj4iswG16KGyK48tYlFNXbgl1aiwuX94QDzXjXTHgRERERERGR8OfTDHav10ttbS0ffPABO3fuZO3atQD06NGDbt26BaRAkbbO64XHn9+zsGl6ZB2D+yfwdYDPGZW/jayv3sDcUEddfAonz32bpffNDfBZj1xEajIVXfpz1PPj+NL6DhFlBZw5NJolT35AVME2rNWV7Jz2AZnArPbv0TkpHWdGDiXzsjHcLrxmnz/UExSGAVlRdSRYG1lSFkdZOSwknq6Oalzu8OwZEy5h+f74e8FTLaAqIiIiIiIiEj58DthzcnJYs2YNXbt2pWvXroGqS9q4kjmLg11CyMirjWTFmj2zlXvFVgX8fDHb19Lhm3cxuV1Up2Sx/dRLsXdIC/h5/c4wqE9Mo8vEcXywPmZPO5mCbZzUy8zaaZ8SWVaAvTgPe3Ee3588h+4WG9Xts6nOyMGZkUNDbFKwr+CQYqxuTmhXirnfUfz3i91sdDq49a/LSXX7/OGkVkWLoIqIiIiIiIhIS/EpYDeZTHTt2pWSkhKF6yItwOUx+KnSAUBXRzVRlsAubLr9pffInP0WhtdLZWZ3dp40Dq+ldbQe8VqsVGfk0OvhcXxSkc2t/xjBq5c+gSNvEylVO2koKiV2x3pid6wHoMERzyrzGmK2GlS37xzk6g/MbMAdN3cjb/FaVpTHsvKnSqxGEqcsLg52adIMmq0uIiIiIiIiEt58nub4r3/9iz//+c+sXr06EPVImGpOL2H1G/bdBmc0dR4zGe0j6eKoDtyJvF6SV8xh1e//juH1UtZ1IDtOubjVhOv7E5meSkXXAeSdeCEjdy5g89k3UDD4VJzts/GYzNic5Wyf+g5ZX79NjzcfZMHw39Fu2WzsBdvxuFzBLn8fGfZ6RrQrpXuOg0avibvuW8PqCgeeEO0YEw4tYfSeJSIiIiIiIiKH4nPD4csvv5yamhr69++PzWbDbrfv9XhpaanfihNpy7bvrGGzMwqA267LYf7UbQE5j9fjIW3RZyT99D0ARf1OoHDQyD2NvtsIw2SiLimduqR0ivudgNHYQHT+Vkb0MFj3+udElhdRvmg5KUDK8m+ZlfoWmQmZODNyqNkyNNjlN4m2uHn24QFce9lMtlRHs6U6mtIGG4MSKoi2uINd3gGFQ9guIiIiIiIiIrI/PgfsTzzxRADKEPl/Ctv2LGz6xAub8GKQGlHPcUcnMX+q/89juF38eNkfm8L1Xo/9jfdWR/r/RGHGa7XhzOxG78fG8Wl1VyzOCi4cmcC8B98ietdmqHQSW7mW2G1r+ab7/8iJSaQ6I4f8jxMxNdThsQXvZ2izmugT52TCH47h7vtXUd5oZU5RIv3jK8mw1wetLhERERERERGR1sjngP2KK64IRB0igML1X+TXRfDDj2WY8NI7LjALm7qqnGR9+Tq7d2/BYzKTd8J5nHHL5XD9uwE5XzhzOeLIvOoCdn7vAY+HCRN68eHNz+HYtRlH8U4iqkqJWLeYpRcspodhoiYlk433F2EvrKM2OSMoNQ8fksyIdiUsLYujrNHG0rJ4iutr6BOg51Oo0oKnIiIiIiIiIhJIPgfsAG63mxkzZrB27VoAevfuzVlnnYXZbPZrcSJtkdsLqytjAOjiqMYRgNYe5lonC0+5HMfuLZijo9g6/EKqM3L8fp5WyWQi/uh+FB91IsVHncjER8bwwrjHcORtIstVQPXGrUQXbGPDPU/RGXDZ7Cyt+o74/MgW/xlHWTwMSy5jfZWDjc4ottVEUdpg5ZIdNS1ahz/9chNON+MOTounioiIiIiIiLQMnwP2TZs2MWbMGPLy8ujevTsADz74IJmZmXz66ad06dLF70WKtCUbq6KpdZtJSY6gq8X/M2+tlaV0/PJVKitLcUVGM+yrV1n54lq/n6etsMQ4cGb1wJnVg8ufH8fjlzyPI28TA9PqyftkDpaGWvI/+IJf5rF/u+JD0sypONNzcDkDuHDtz0wG9Ix1kmRrYFl5LFUuK9f8YSk97KHbCmhGXip3+DBWRERERERERCRYTL7ucOutt9KlSxd27NjBsmXLWLZsGdu3byc7O5tbb701EDWKtBm78mvZ5IwG4OZrumDx+RV6cJElu8n+9EUiKkuxd8og94zriB/c178naeMaYxIo63E0g955inWX3MWWM6+j66RbqEnJwmuYqF6fS9JP39Pxqzf4MmUInT57meQVc6hYuhq8noDVlRLZwIntSkm21VNX72F5eRz/fHwdLk/bWcy2JWyYPCXYJYiIiIiIiIhIC/J5BvucOXP4/vvvSUxMbNqWlJTEv/71L4YNG+bX4qT10qzT/Zvy0mY8GCTb6jlpWDJLXvffsaN255L11XTMjfXUJaRyyty3WXLPt/47gezLZKY2JYtud49jxs4UTPW1XHpOOrPufhXHzk3YnGVE5+cSnZ/L/GO/ontkFNXtu+DMyMGZkYMrOtav5USaPQxNKif1lCFMfSOXmV8X4LAkMiihwq/nERERERERERFpK3wO2CMiIqiq2neRPKfTic1m80tRIm1RYZ2N778vwcBL37gqDMN/M4tjc1eTMed9TB431Wmd2D5yPJHtU/x2fGkeT4SdtHNOZffnZeD1ctMdx/Lm1U/hyNtEYtkOqKomLncVcbmrAKiLT+GnmM04dnqoTuvolxoMA678XUfWfr6MDaRRVALzihKZ8fkuvN49j4uIiIiIiIiISPP43IDizDPPZMKECSxatAiv14vX6+X777/n97//PWeddVYgahRp9TxeWFWxZ2HT7OgaYqz+W9g0Yd1iOnzzLiaPm8qOvdh22uV4bKHbf7vNMAyiczpS1nMIO0aO59SCReSOuZbCo06kJrkDXgwiywvJfXIaHb98jR5vPMD3o64kaeU8Ikp2H3E7maSIRl55cjCpEfV4MHjkmY0sKYujMYxbxuiTMSIiIiIiIiLS0nyewf7UU09xxRVXMHToUKxWKwAul4uzzjqLJ5980u8FirQFm51RVLstJMZb6R7pp4UvvV42TJ5C+nf/A6C0+9HsHnommPzc2F38wmS1UpPWkZq0jhQNPAVzfQ3Ru7ZwXLaHze98ia26gpKvF5IGsORLGu0OqtP3tJOpyz/psM4ZH2flmMRytlRHsa46lt11kVQUWRiUUEGCzeXX6xMRERERERERaY18Dtjj4+P5+OOP2bRpE2vXrgWgZ8+e5OTk+L04CU8bJk+h26Rbgl1G2CgqqWfDzwub3nBVZ1a9s/OIj+l1u2m/8H9sXPcDAIVHnUTRgJPU/yOMuCOiqMzuQ7/nxzHT2xdbZTFnD49l0WPvEr07F2utk/jNK4jfvILZmR/QJSEVZ4euFM/ugOFqbPZ5DAO6OGq47R8ncPsdS6hxW5hfnEivWCedo2sCeIUiIiIiIiIiIuHP54D9Fzk5OQrVRfzgPy9vwe01kWBtYNSJqax658iOZ7gaWXbx7SSu+wEMg13HnklZz2P8U6wEh2HQENeO7JvH8d6qCAy3C3vhDhx5m3DkbcJesovIsgIiywpYdPp8epgtLM7/mqRyB86MHOrjUw55c6VXt1hGtCthefmemexrKmMorrdRUdn8sF4OTTcgRURERERERFoXn3tFnH/++Tz00EP7bH/44Ye58MIL/VKUtC1tuW9ySb2Vr+YWws8Lm5pMRzbD3NRQR8cvXyP/oy/xmMwMfOsJheutkNdsoaZ9NoWDT2XL2TcwctdCdoy4kLKuA4hIT8HkdlH05XzSFs8k56On6fbOv0mf9yGxm1dirj1wCyKrycvghAr6xlViwktBfQRX3baUknprC15dywjF950Nk6cEuwQRERERERER8ZHPAfvcuXMZM2bMPttHjx7N3Llz/VKUSFvg8cLKnxc27RhVS/wR9ryu211Ip89eIjp/K5ZYB9tGXU7780/3R6kS4iLaJVLZpR+7jj+PU7bOZdO5N9Pz33+hKqMrHrMVa00VCRt/JHPOe/R461/MO+Y8UpZ8SfG332O4937eGQZkR9dyfLtSos0uCovrWVCSwIaqaNxub5CuUEREREREREQkNPncIsbpdGKz2fbZbrVaqays9EtR0nrNyEvlDh+2t2Zbq+1UuazExljoGe08omPZKkr4bsTF2EvzabQ7GD77DVY8u8pPlUo4MQyD+oRUOt8+jvfXRmG4Gokq2I5j1yai8zZhL82n8sc1tAMWnTqP7hYbNe07kTulDlt5FQ1xyWAYxFldjGhXirtnX774poB1VQ4m/mMlSW4TkWZPi1+Xr+8R+xs/Iy+VczIK/FmWiIiIiIiIiLRxPs9g79u3L++8s2+T6LfffptevXr5pSiR1q6svIF1VQ4AJlyWjc10+DODI4vzyP70RWpzd1Ifk0juGdcRd1RPf5UqYc5rsVKd0YWCo0ex5ZybWH/RHfSf9jDlXfoTkZqM2dVAzI4N/DTxfrp++BRd332U9PkziN2yiojGau6e2IOj4iswG16Wrijn26JECuv2vcnaWjS3dUygWsz80ibmUO1i1E5GREREREREJDT4PIP97rvv5rzzzmPz5s2cfPLJAMyePZu33nqL9957z+8FirRGz7+Wi8trIs7ayNjT2vPojMM7TnTeJjJnv4XZ1UDsgN4s7nEmbrvDr7VK6+KKiqHD+LPJm1vPJc9ewFPjpuDYtZk+sVUUfrsYW3UFtg1LSdiwFC8GCzZ8yuCGBNqn92Fd1lByt9fwfWk8z7+Wi8cLR7hsQFC1xU/OiIiIiIiIiIh/+Rywjx07lhkzZvDAAw/w/vvvY7fb6devH1999RUjRowIRI0irUpZg4V5s/IB6BtXhdl8eAll7JaVZMz9EJPHjbN9Z0776jUW/vkzf5YqrZxhMlGf1J76pPYMeX4c/77mDaLyt+HI24QjbxOR5YWU/7CSdkC7FXPoHzed/3Ufz5rE/rz+3nYSrQkMSqwI9mU0SyguaioiIiIiIiIi4c/ngB3gjDPO4IwzzvB3LSKtntcLqypiAci015Joazys4ySuWUj7RXvC9IrsPuSdcD7WWM1clyPjtdio7tCV6g5dKQAs1ZVceGoicx94E8euzVgqyjlr8X/ISRvM530uoxQ7c3fF8L8nv8JUX4snwh7sS9ivYIbrGyZPUe93ERERERERkVbM5x7sAOXl5UydOpW//vWvlJaWArBs2TLy8vL8WpyEr7bQH/hwrnF7TSTljVai7GZ6xh7GwqZeL+vufrwpXC/pdSw7T7wQr/mw7pWJHJQrOpbMK84j78RxrL/4ToZ//wEFg0bSkRKuWfwv0iq2Um+O5KGvrOQt3UTWpy+x4b6nsRduB4872OXvV0uF7eHwHhgONYqIiIiIiIiEOp9TuZUrVzJy5Eji4uLYunUr1157LYmJiXz44Yds376d1157LRB1SphSgPP/Kp2NrK2KAeCaSzqx7fNdvh3A4yZ9wX/ZvHEZAAWDRlLc7wQwwrgJtoQPw0TcoD4U9x9Bcf8RTHz0DErHPc6aSierY/vwQ6eR7EjIwfXvF+lcW4TbFsnSiu9IKIjEmZET7OpbrQ2Tp9Bt0i3BLkNERERERESkzfJ5BvvEiRO58sor2bhxI5GRkU3bx4wZw9y5c/1anEhr8tL0rTR4TMRYXJx/ZrpP+7prasmc/RYJG5eByUTesLMp7j9C4boEjcURTW1mNzr3aMe//t4bK27y4zoxbcQ9rO4wFHNDHfkffUn6d/+l23uP8W2vUaQt/ISY7WtprDyMT28EmHq0i4iIiIiIiMjh8HkG+w8//MDzzz+/z/aMjAzy8/P9UpRIa1PRaGHeZ3tmrPeJq8Riaf69LVN9LYtGX03sjvV4zBaOfvdJ1nxaGqhSRXw2fEgyI1JKWVYeR2mDjf/1uYqf+p3JHYOLWPP8R0QV7qB641aS2ErS2kXMSn2XTkkZODNyKP+hB3g8YDqsjmUiIiIiIiIiIkHlc8AeERFBZWXlPts3bNhAu3bt/FKUtE5tdYbonoVNY/B4ID2yjnYRzV/Y1FJdSccvXqWsvBC3LZLtI8cz9qyR8Om7AaxYxHdRFg/HJZWRMPxoXn9vG5s97Xi4sBNZp6UQ563m0nPTmfW3V4netYmIylKiC7YRXbCNBcfNpnuEner0LjgzcnCm5+ByxAX1WmbkpXJHUCtoHrXgEhEREREREQk+nwP2s846i8mTJ/Puu3sCPsMw2L59O3feeSfnn3++3wsUCXd5tZGUNtiIjDDRK7aq2fs5120m+5MXsFVXEJGewppjLqQ+MS2AlYocGZMB11+ezcbZK1hWHsvmrdVsNZLoG2cl7ayR7P75kxc33zmU6Vc9hSNvI4llO6DSSVzuauJyVwNQH5fMmuiNOHZ4qU7rFMQrav3Uw11ERERERETkyPj8mfxHH30Up9NJSkoKtbW1jBgxgpycHGJiYrj//vsDUaNI2KqpcbGm0gHA5eOyiLJ4gEPPPLUX7uC7Ey/BVl1BfVwyx819W+G6hI2UyAZObFfKoP7xuL0Gy8vjuO+xdbg8e9YMiOqcSVmPo9lxyiWcWrCILWdcR+FRJ1GTkonXMIioKGbrlNfoOOt1ekx/gO9PvZzkFXOpWLYGvJ4gX52IiIiIiIiIyP/zeQZ7XFwcs2bNYsGCBaxYsQKn08nAgQMZOXJkIOoTCWvT3tlGvcdMlNnFRedm8sTsRYfcx7FjA5nfvE2jq5Ga5A5sP+1SojpmtEC1Iv4Tafbw2L39uP6yz1hXFc0X3xTgsCQyKKFir3Emi4Xa1CxqU7MoGngypvpaondvYXhnL5ve/gKbs5ySbxeRCswfMovukVE407vgzOhKdUYXXFGxwbnAn83IS+WcjIKDPv5bvs4aP9R4zUIXERERERERCR6fAvbGxkbsdjvLly9n2LBhDBs2LFB1iYS9qkYzn/03D4C+cVXYrIf+wMjON2aQ9dV0DK+HdqcNZ27qiXisEYEuVSQgzGaDbjHVJNka2EAaRSUwryiRjz7bhdcLhrHvPp4IO1WdetP32XF87u6NrbKUc06IZeGj75JQuh2cNcRvWUX8llUA1CWk4EzPoejL9hiu5q9v0By/Dc8D0Zv9UMdUn3URERERERGR0OZTixir1UpWVhZutztQ9Yi0Cl4vrK6MweXykhpRT2pkwyH3SVq1gBVX3Ynh9VDepT+DP3pW4bq0CkkRjbzy5GBSI+rxYPDosxtZUhZHo2c/CfuvGQYNcUl0unE8O0aO57SCReSOuYai/iOoSc7Ai0FkWSHJa75j8RnX0mP6AywafTVJq+YTUZqP1+ttmQv0g+YE6QcboyBeREREREREJDh8bhHzt7/9jb/+9a+8/vrrJCYmBqImkbCXXxdBUX0EVotBn7iDL2zq9XhIXTyT5NULACjufRwFx4zCZLO1RKkiLSI+zsoxieVsqY5iXXUsu+siqSiycu6GymYfw2SzUZPWiZq0TjBoJOa6GqJ3bcaxazPta/Ko25lP8VcLSAP44QtmZ71LRnQ6zowcnBk5uO2O/R53f21cWgu1jxEREREREREJLJ8D9qeffppNmzaRnp5Ox44diY6O3uvxZcuW+a04kXBUX+9mdWUMABefl0n5/PwDD/a4WXH1XU3heo9//ZkPNuzpKb1nRmrrDf6k7TEM6OKo4bZ/nMDtd/xAjdvCDXcsp0d0FJ2ja/bbMuZg3JFRVHbuS2Xnvox/7kKevOg/nD0shkWPv090fi71+UXEU0T85hUA1CamUZ2RQ/HXmRiuRrwWq9+vMRBtZEREREREREQkdPkcsJ9zzjkBKEOk9Xjjgx3Uus3YzW4uuzCLKfN/2O84o7GBzG/eJm/nRryGibzh53DGH6+F699t4YpFWlavbrGMaFfK8vI9M9nXVMZQXG/jqPgKIsyH19bFMAwa4lPIvnUc762xY7hdXHVpZz6ZOBXHrk3YS3ZjL83HXprPolHz6WG2Up3Widwna4goc1If326/x/UlMA/VmfC/zGLXbHYRERERERER//M5YP/HP/4RiDpEWoVql5mZ728HoHdsFfZI837HuWtq6TRzBlFFOzHZI8kdfgHOzO4tWapIUFlNXgYnVND93L489swGCuojmFOUxKCECpIijnyxUq/ZQvKJx1J49HYKOQ1zrRPHrs1E520mrXon9buLiMnbyE9/epAcoDEqlhWe5cRuM1Od3uXIL/Aw/XZhVREREREREREJbT4tciriq1Cd0RkoayodNDR6SbbV0z6yfr9jrM5ydkz7kKiinbgi7Bz75asK16VNMgw4Z3Q6x7crJdrsos5jZkFJAhuqonG7/btAqdvuoKJLf3adcB6nbJvHpnNuIv/o00k+dTgeswVrTSU7X/2QzG/fo/ubDzF/yHmkLJlF1O5cPA2HXqT4tw703tfW3hNFREREREREWjsF7BJwbSVQKqyzkV8Xidls0Deuar/9pCPKCsj+5EUaS8poiI4j94xrSTj2qBavVSSUxFldjGhXSgd7LWCwrsrBxH+spM4dmL+iDMOgPjGNkr7DGPLZS6wb/1e2jrqCzhOvpi4hFQMvFcvW0G7lXLI/f5kvU4aw6+1PSPzpe5zrt+D1+jf8D0V71oAQERERERERkUPxuUWMiOzL7YVVFXsWNr1gbAa1i/dd2HTFtX8h+9PPMDfUYUtOZP3JV+NyxLV0qSIhyWLyMjChkuSIBlZVxLJ0RTk2UyID4ysDfm6vxUp1Rg49HxrH/8qzsdRUMe60JObcPx1H3maorqZ641bab9zKnD6fYomLoX1qd3Z/EIupvjbg9YmIiIiIiIhI6FLALuIHW5xRVLstRJjcXH1xR/6zeOlejzu2ryPv248xu9zUpGTR+fLT+LFU4brIb2VF1ZFgbWR7dEc2b63m+9J4nn8tF08LThp3RcXQ4bJzyJvfAF4P19/Yn/dveAZH3iZiS3bgqqgisWIJyy5aQg/DYEd6Cl6Ph6j8BmpSOjQdp618ekdERERERESkLVOLGJEjVOs2scEZDUCvWCfRUXvft4rfsJSs2W/hdbmpyuzO1tOvwGyPDEapImEhxurmhUcG0DGqBjB4/b3tfFeSQEFRXcsXY5iIO6onJf2OZ9voqzi1cDHpF4+lpPdQHL1yMLxe6vIK2HT/M2R/NpUe0x9kyQU3kbB2MdbK0hYv99etXXxp86KWMCIiIiIiIiKHx+cZ7G63m2nTpjF79mwKCwvxeDx7Pf7111/7rTiRcPBTpQO310SCtYEO9l8FgF4vmx56noz5MwCI7d+TNQN+ByZzcAo9AnvCN83GlZYTEWGmf3wVyRENrKtPorTGxlW3LaWH1UZapO+LjvqLJTqK6JyO5NuP4fLnx/HYpS9yfM0yzPZIdnz8DZb6Wgo+/or0n8c3xCTgzMjB2bcdjRVVfqkhUGH4hslT6DbploAcW0RERERERKS18jlgv+2225g2bRpnnHEGffr0wdjfSo7SZrT1QKa43kperR3w0i/+Vwubej2kLfqc9a98D0BRv+PJGdsPdoVfuC4STBn2eu54eBDX37iA8ipYTAKdo6vpFevEFAJ//bii4/gm+iTueH4cX133NpGluxlztJ1l//mIqILt2KrKSFz3A7vXQf6HX9ApKYOSnmnU5RXscyy1lBEREREREREJPz4H7G+//TbvvvsuY8aMCUQ9ImHj1wubdoqqJc7qAsDT0ECHb98nLncVALuHjKa093EYxr6BWjjSbHZpaRlpdoYnlxJ9zEDe+TiPLdXRlDbYGJRQQbTFHezy/p/JRF1yBjl3jePD3ERMjfVE7c7FkbcJR94mIipLiC7cTmnhdr7tfToJxx6F0eHUYFctIiIiIiIiIkfA5x7sNpuNnJycQNQiEla+KUyiymXFanjoEesEwNRYzw9nXU9c7iq8homjXnuE0t7HNfuYB2r9oP7I0taZDLjl2hyOSSzDangob7QypyiRvNqIYJd2QB5rBM6sHuQPPZNNF9zOSRtns2vY2UR2SMNdXUPx7O/o/PEzFH+zMNilioiIiIiIiMhh8jlg/+Mf/8iTTz6J1+sNRD0iYaHebfBRXhoAPWOd2ExezLVOOn3+CsWzv8NtsbHttMvIuHhsi9UUDiF8ONQooS0tsoETU0pItDXg8ppYWhbPivIY6utDaCb7AUR16kBZ98F0uPJ8+r/0L8xRdiLLi1h02pXs/vALLNWVATv34b72juQ1q9e7iIiIiIiItAU+B+zz589n+vTpdOnShbFjx3Leeeft9eVPbrebu+++m+zsbOx2O126dOG+++7bK9z3er1MmjSJ9u3bY7fbGTlyJBs3btzrOKWlpYwfP57Y2Fji4+O55pprcDqdfq1V2pa1VTHUus3EWRvpGFWLtaqM7E+nYi/Ow5acwNbRV1Gd0bxPeiiEEvGN3ezhuKQyujqcgJdtNVFM+NOPVDWGxxoHH+9Ko8Pl59LxxvGU9BwCJhPONRvJ+eBJklYvwOsO/ZsFv6b3MBEREREREWnLfA7Y4+PjOffccxkxYgTJycnExcXt9eVPDz30EM8++yxPP/00a9eu5aGHHuLhhx9mypT//8f8ww8/zFNPPcVzzz3HokWLiI6OZtSoUdTV1TWNGT9+PGvWrGHWrFl88sknzJ07lwkTJvi1Vmk7yhosbK+xA9A3rorI0t1kf/ICEZUlNDjiGTrnLeradQhylSKtm8mAnrHVHJtYjs3kZvPWauYWJ/H57Pxgl9ZsZnsk+UPPZPjC94nMSMXsaiBt8Uy2v/gONdvygl2eiIiIiIiIiDSDz4ucvvLKK4GoY7++++47zj77bM444wwAOnXqxFtvvcXixYuBPbPXn3jiCf7+979z9tlnA/Daa6+RmprKjBkzuOiii1i7di0zZ87khx9+YPDgwQBMmTKFMWPG8Mgjj5Cent5i1yPhz+PxsqoiFoBhSaV0KNlA1lfTMTfWU5eQyrZRl+Polg38ENxCRdqIlMgGTmxXSklaV5auKOf+J9bTwR5Lv7iqYJfWbHEDe9PhqguY820eqUu+hKJS8l77CG+ji4jUZCwx0S1Wy4bJU+g26ZYWO5+IiIiIiIhIuPN5BntLOu6445g9ezYbNmwAYMWKFcyfP5/Ro0cDkJubS35+PiNHjmzaJy4ujiFDhrBw4Z5F4xYuXEh8fHxTuA4wcuRITCYTixYt2u956+vrqays3OtLDqwttQf4dFY+5Y1WLIaH0bXf0/HL1zA31lOd1oncMdfgiooNdokibU6k2cNj9/ajR4wTkwl21tqZU5TIxtzwaQVmGAbl3Qax6fzbiBvUB4Bdb/2Pbc+8Qdmi5XhcriBXuEdber8XERERERERaY5mzWAfOHAgs2fPJiEhgQEDBmAYxgHHLlu2zG/F3XXXXVRWVtKjRw/MZjNut5v777+f8ePHA5Cfv6cVQGpq6l77paamNj2Wn59PSkrKXo9bLBYSExObxvzWgw8+yL333uu365DWocFj8PxruQAMqF1L9cyPMXm9VHbsyc4RF+K1WINcYXjZE9SlHnKcSHOYzQbdYqq55W/D+ONfl1HttnD9H5fRPcpOp6haDvLXVkhxR0TxXf/fcfpRPalauZ7yH1ZS/OV85h99blPw/osZeamck1HQYrUpXBcRERERERHZV7MC9rPPPpuIiAgAzjnnnEDWs5d3332X6dOn8+abb9K7d2+WL1/O7bffTnp6OldccUXAzvuXv/yFiRMnNn1fWVlJZmZmwM4n4WF9lYPy6kbiXRWcNOdp8Hop7T6Y3UPHgik0PwyiEFvamv694xnRroTl5XEU1EewqiKW4nobR8VXYjV5D32AEBGZnkrfZyaz5PwbKZ69kKrVG6havQGvy40tJRGLo+XaxoiIiIiIiIjIgTUrYP/HP/6x3z8H2p///GfuuusuLrroIgD69u3Ltm3bePDBB7niiitIS0sDoKCggPbt2zftV1BQwFFHHQVAWloahYWFex3X5XJRWlratP9vRURENN1QEAGoaLSQW71nYdPRy6Zi9rpJPP5o1uSMJRSnxipYl7YswuzlmMRyOo4Zyn9e2szuukgqiqwMSignwRYarVYOZUZeKneYTMQN6I2je2dclU62v/gOedM/xhRhI+nEIdD+lGCXKSIiIiIiItLmhea025/V1NRg+s3MYLPZjMfjASA7O5u0tDRmz57d9HhlZSWLFi1i6NChAAwdOpTy8nKWLl3aNObrr7/G4/EwZMiQFriKtqO1tg9Yf+8UVpU7AIOeu3+gY+kGdh975p6Aaz/herj9HAJdb7j9PKR1MAy46JxMhieXEmV2UeM2M784kU3OKDye0JvJPiPvwDfEzFF2+j4zmcxrLiRuUB889Q0UfTGPLh8/S+2O3S1YpYiIiIiIiIj8ls8Bu9vt5pFHHuGYY44hLS2NxMTEvb78aezYsdx///18+umnbN26lY8++ojHHnuMc889F9izKNztt9/OP//5T/773/+yatUqLr/8ctLT05ta2fTs2ZPTTz+d6667jsWLF7NgwQJuvvlmLrroItLT0/1ar7RO3+VHU9oYgdVVz8gtM9h50jhKe+nmTEtQOC9HKsHmYkS7UtpH1uHF4KfKGO66bzX17sP/5MnBwvBAHicyPZVhC94lZcyJmCIjiCwrYOe0D8j/+Ctc1TV+qUlEREREREREfONzwH7vvffy2GOP8bvf/Y6KigomTpzIeeedh8lk4p577vFrcVOmTOGCCy7gxhtvpGfPnvzpT3/i+uuv57777msac8cdd3DLLbcwYcIEjj76aJxOJzNnziQyMrJpzPTp0+nRowennHIKY8aMYfjw4bzwwgt+rVVaJ2+1k3dy2wFw7NYvOeWDf1OZ3ecQe4WHQIbXCsYllFhNXgYnVNAvrhITXr5bUsqcoiRK6sNjYeJfB/GG2UzcoD50uulSyroNAqBq5Tq2/ecNtv7nDbw/f8KrpQXiNa/3EREREREREQkHzerB/mvTp0/nxRdf5IwzzuCee+7h4osvpkuXLvTr14/vv/+eW2+91W/FxcTE8MQTT/DEE08ccIxhGEyePJnJkycfcExiYiJvvvmm3+qStsFWWcLWdYU4O3QhvqaIlF6ZJJ80FN7eEezSRMRHhgGdomtJsDWSa+vA9rxaFpQkMO2dbXi9IbmUwj5m5KVyx8//PSejgF3Dz6Hf8GwKP5tDfX4Ra26/j4jUZFJGnRDUOjdMnkK3SbcEtQYRERERERGRluLzDPb8/Hz69u0LgMPhoKKiAoAzzzyTTz/91L/ViQRJZHEesV9/zLL04wHolVhLY3L7Q+y1L83AbB79nKSlxFldTH18EB3stYDB1De2srAknjp3SC9JckCRGWlkXnMh7UaPwBIfS31BMd+dcBEF/5uNq7rWL+fQ61NERERERETkwHxOFDp06MDu3XsWVevSpQtffvklAD/88AMRERH+rU4kCKLzNtPxs5f5uvPZeExm+jnKSE7Uc1uktYiymxmYUMlR8RVERpgobojg26JEfvixNNilHRbDZCJ+cF9O/OkLYo/qCUDl8rVse+YNypesatG2MQrjRUREREREpK3xOWA/99xzmT17NgC33HILd999N127duXyyy/n6quv9nuBIi0pdssqsma9zubEXmxN7oUJL5dkFwS7LBEJgKyoOl56fBAxlkYaPGYm/mMVaysdeLzBruzwRLRLJHXsKRw3920iUpPx1NVT9Pkcdrz0HnV5+X4/n8J0ERERERERkcPowf6vf/2r6c+/+93v6NixI9999x1du3Zl7Nixfi1OpCVt/c8bdPj2PVwmK7P6XgpAjqOalMiGIFcmR2JPCJja7O3StnTMjOKEdqWsrohhW00UG53RlDRYKSiqC3Zphy1h6AAyrx1HxdLVlHzzPfX5Rex4+X1iB/Si043jsSUnHnT/5gTnzQ3X1Y9dREREREREWjufZrA3NjZy9dVXk5ub27Tt2GOPZeLEiQrXJXx5vaQs/Yo1t9+HgZevj7maKmssdrObHEd1sKsTkQAzG9A/vorJd/bCYngobbBx1W1Lya8LfmuoGXmHdxPIMJmIP7ofHW+8lJj+PQCo/PEnvu11OtteeBuv2+3PMkVERERERETaLJ8CdqvVygcffBCoWkRanMflIn3Bx7RbMQeAjYPPYnn8AAB6x1ZhCc91D+Ug/N3WQm0yWo+Th7djRLtS4q2NVFa5WFwaz+oKBw2NLdfD3N8sjijSzhpJhyvPpy4hlcayClbf9A8WDBtH3S61vxIRERERERE5Uj7Hh+eccw4zZswIQCnSmhzurMuWZLgaWfa7W0nYsBSvYdD32fuY32EkHgySbfW0j6wPdoki0sKiLW6GJ5fyu7MzANhSHc0Nd/xItcsc5MoOrDnvt/bM9mw++wZ6PfY3TBE2KpauZsdL71Hw6Tc0lJQFvEbdiBIREREREZHWyuce7F27dmXy5MksWLCAQYMGER0dvdfjt956q9+KEwmUxrIKOn7xKgUF2/CYLew88UKSjjqF/I9XY+Clb1wVhhHsKuXXfumZrt7pEmgmA265Nofceav5sSyO9ZucbDYS6R9fGezSDmhGXip3HGqQyUz2LRdTu2MXDYWl5E3/mMpla/i21+kkDB1A7IBeGC3wxqe+7CIiIiIiItKa+Bywv/TSS8THx7N06VKWLl2612OGYShgl5Bnqalk4UmXEl2wDUtcDBuHjaMqtRNPvrgZgOzoGmKsodufWAFzy9HPum1Li2zgxJQS8hO7sGptJUvL4nn46Q24vXv6tocriyOaXg/fhWE2Ufj5HBqKSin89BsqfvyJlDEjiGyfEuwSRURERERERMKGzwH7rxc4FQk3tooiOn7xGlXOchrtMRz/9Rus+M9KtlRFsbOqlgiTm+4xWthURPawmz1MefAorrv0MzY6o/nvF7uJsSQyOKEi2KX5ZH9tZOwdM8i67nfYEuNZ99dHqN9VwI6p7xI3qA9JJx3rt3OrPYyIiIiIiIi0Zj73YJ88eTI1NTX7bK+trWXy5Ml+KUokEOxFO8n+ZCo2ZznR3TqRe+Z1xPbrQa3bxAbnnlZHvWKdWE3eIFcqIqHEYjboGVvNsYnlJMRbqXJZmVucxGdf5eMN87cLw2wm+7Yr6XjjpcT06QZAxdLVbHvmDXa88j7ecL9AERERERERkQDzOWC/9957cTqd+2yvqanh3nvv9UtRIv7m2LmRTp+/jKW+hprkDIZ++xaNMQkA/FQZg9trom/PWDrY64Jc6f6Fw6KxIq1dSmQD054aTLKtHrfX4IEn1/NjeSwuT8v3iznUe4Kv7xmWmGjSzj2NjMvPxdYuEXdNHSsn/I2dr7xP3e7CIylVREREREREpFXzOWD3er37XQRtxYoVJCYm+qUoEX+K27yCrFlvYHI14szIYdvoq4hot+e5umxVOXm1kYCXP1yf02oWNg1GIK82ENIWJCXYGJpUTo8YJyYT7Ky1M6cokY1b9r3x3BL8/VqP+rltTPKpwzA7oqjLK2DHS++x+tbJuGv9dwNS7xciIiIiIiLSWjQ7YE9ISCAxMRHDMOjWrRuJiYlNX3FxcZx66qmMGzcukLVKiAvFWdZJqxfQYc77GF4P5Z37sX3keDzWCAA8Xnji+U0AdIqqpVuXmGCWGhCh+DsRCXeGAd1iqpnyQH8iTW6q3Rau/9MycqvtQW8Z44/XvGE2k3DsAE5cMxNH767g9bLt2else2Y6lSvW4vV4/FDp4VEwLyIiIiIiIqGm2YucPvHEE3i9Xq6++mruvfde4uLimh6z2Wx06tSJoUOHBqRICR0bJk+h26Rbgl3GoXm9rL3rYdIWzwSgpPdQ8o85HYz/v6e0tdrOlt3V2EweesQGZ/apiISv/r3jGdGuhOXlcRTUR7CqIpbiehtVTlewSztsM/JSOSejAIDI9FTanzeKmgG9qVi6CufazRT8dzYLT7yEJSlDOb2fzx+C20fY/J0iIiIiIiIicgDNDtivuOIKALKzsxk2bBgWS7N3lVbqQDMJgz1r2tPYSPq8j9iy6UcACgafSnHf4/l1/5ey8gbWVTkA6BHjxKaFTcUHe577+nSAQITZyzGJ5XQacxxPv7SJ3XWRXH3bUnI8FhJs4Ru0/1pUdgf6PX8fP5x1PSVzf6Bs4Y90NpZTuKsv2bdeccD9fh3Wg8J0ERERERERaZ18nn42YsQIhesSsozGBpacfxMJm37EMJvJO/5civudwG+bqz/3Wi4ur4luXRx0jKoNUrUi0hoYBvzunA4MTy4lyuxid2Ed84sT2eSMCnrLmP05nJugJpuNhOMG0vHG8bS/cDSG10vFDyuZ02c0lSvX4T3CC1XrFxEREREREQlXR/75bmnzgj1j/Rfmuho6zZxG0edz8JitDPrgP5R3HbjPuLIGC5/Oygdg4u9bz8KmIhJcCTYXI9qVctKwdngx+KkyhkWl8dS7W8+bjDXWwcA3n2DrqCuxJsVTX1BMwcdfsfPVD6lcua7F61EwLyIiIiIiIsGmgF1ahdodu+n02VSiinZgTYhj6+lXknrGSfuM83phZUUsAJn2Wvr0iNtnTDCFys2KcKfQTYLFavIy+c6e9IurxISXwvoI5hQlUVJvDXZpflWd0YWO119Mjwf+iGG1ULdjN/OPOY+iL+fhrqsPdnkiIiIiIiIiLaZZAfvKlSvxeDyBrkXksESUFfDdCRcRWV5EY3QsQ799k9rUrP2O3V5jp6LRSnSUmZ5a2FREAsAwDDpF13J8u1IcFhd1HjMLShKY9s62kGwZcygHuvFnmM10+fMEOt4wHkfPLnjdbsoXrWDbs9PJe/O/BPJiD+cmmm68iYiIiIiISCA0K2AfMGAAxcXFAHTu3JmSkpKAFiXhJ1gzr+0F2+n06UvU7cynLr4dW86YQEyvnP2ObfAYrP15YdNrLulEpFk3jX5LAVTw6Gff+sRZXZyQXEqmvRYwmPrGVhaWxFPnDp0Pj/njvdsaF0P7C0ZzzGcvYU2Mx+2sYfkVf6bT5y9TX+i//1/Qa0RERERERERCUbP+lR8fH09ubi4AW7du1Wx2CQmO7evpNHMaloZa4occxdYx1+JyHLjly7pKBw0eEzEWF+edkb7P4wpvjoza24jsy2LyMiChkqPiK4iMMFHcEMG3RYl8u6Ao6LPZD/SaPdzXcrtTh5N1/cUknXQsJnsk0flb2f7C2xTNmo+7vuFIShUREREREREJWc0K2M8//3xGjBhBdnY2hmEwePBgOnfuvN8vkZaw49UPyZr9JiZ3I1WZ3Tj2y2m4I6MOOL6i0cLWGjsAfeMqsVgObwZpIEL4UAqmm1NLKNUban79/NANG/m1rKg6Xnp8ELGWRho8Zv7+r5/4riSBikZLQM/b0q9Xk8VM4vDBjFj1GZUde4LXS/n3y9n2zBtUrd6AN9h3FURERERERET8rFn/sn/hhRc477zz2LRpE7feeivXXXcdMTExga5NZB9er5fklXNZ+fIsDKAsZwC7hp+NOcp+kH1gVUUMYJAeWUdyRGOL1duSFHz/P/0sJBR1zIzi+HalbKyKZmtDLCUNNuYUJfKvp9bT4DaFRduqGXmp3NGMcVEdM9hxyiWcWruYoi/m0lhaQf5HX/L9qZfj6JlDRLvEgNcqIiIiIiIi0hKaPXXu9NNPB2Dp0qXcdtttCtil5Xk9rP3Tg6QumQVAcd/hFAw+DQzjoLvl1UZS2mDDbHjpHVfVEpWGlUCE0ZoJL7J/ZgN6xFbzwH2ncNvNc8irjeSTWfmYjSS6Oarp7KjBfPC3tJDRnLA9Oqcj9k4XU77wR0rnL6F0zmJK5y0hfkh/Ov/hqhapE/SJEhEREREREQkcn/tkvPLKK03h+s6dO9m5c6ffi5LQFoygwnC7yJjzAblPvQpAz4fvouDoUYcM1xs9Bmsq9yxs2s3hxB4GM0RFpPVLS4lkUEIFw5NL6dktBrfXxNqqGL4pTGZXbUTI9mc/HCaLhcTjj6bjDeNJPesU8HgoX/gjc/qOoWrNxkO2jVE4LiIiIiIiIqHM54Dd4/EwefJk4uLi6NixIx07diQ+Pp777rtPi59KQLic1WR9NZ34LSsxLBZ2jrig2TMfN1RFU+8xE2120dlRs+/jbTi40QxyORJt+bXjT4m2Rp7/9wAGxFcQaXJT4zazpCyeBSUJrNsU/E/c+PN9whofy+APniH9ojOxJsRSl1dA/odfsOj0q2goLgvac0rPZRERERERETkSPq+u9re//Y2XXnqJf/3rXwwbNgyA+fPnc88991BXV8f999/v9yKl7TLXVvP9qVfgyNuE22Jj6MfPsPqD3c3ad9uOGrZU71n4tE9cVdi0XZDWZ0+Apxsasn8mk0FmVB3tI+vY5Ixmc3U0pQ02rpu4jA6RsfSMdQa7RL+K7toJe3YHzDYrG+9/hpKvF1Ly7SISjj2KzhOvxuKIDnaJIiIiIiIiIs3m8wz2V199lalTp3LDDTfQr18/+vXrx4033siLL77ItGnTAlCiBMuhZvXNyEsN6Cxoa1UZ2Z++SMWSVbgiotg2+iranXZ8s/b1euHxFzbixSA1op7UyIaA1dnSNPNcpHWymPb0Zz85pZgMey1eL+yotTO7MIlp72zDfZhtY0JxTQSTxULXv99Mx99fQsqZJ4HHQ9l3y5jTdwy7P5h5yLYxIiIiIiIiIqHC54C9tLSUHj167LO9R48elJaW+qUokYjSfLI/eZGIyhLsWenknnktte06NHv/3XURLFlejgkvfbSwqcghqU1G6LCbPQxKqOT5RwaQYG3A7TUx9Y2tfF2YzFdzC4Pen92frAlxHP3Rc7T/3RlY4mOp25nPsotu49sep9JQXBbs8kREREREREQOyeeAvX///jz99NP7bH/66afp37+/X4qSti0qfyvZn72EtbaKuoQUjpv7Ng1x7Zq9f12dmzWVexbizXFUE21xB6pUkSOiUFsOpnf3WIYnlzEwvoKU5Ahq3Wbu+fda5hcnUNZw6A5vvs5KP9T4I53lfrD9Hd2y6fj7S0g8/mhMETZqtuxg2/Nvse7vj+FpaDyi84qIiIiIiIgEks892B9++GHOOOMMvvrqK4YOHQrAwoUL2bFjB5999pnfC5S2JWbbT3T49j1MbhfVqR3ZPnI8kRm+hTrTP9hBrdtMarsIcswFAaq0bVFbGpHgMAzoEFXHw08ezU1Xz2RbQyxl9TbmFSfRwV5Lz1gndnPrWGDcZLWQdOIQ+r34AIvPvJaaTdvY/NDzWGIdxA3qg9frxTC0mIaIiIiIiIiEFp9nsI8YMYINGzZw7rnnUl5eTnl5Oeeddx7r16/n+OOb1x9bZH+2v/gOmV+/jcntojKrB9tGXYEnwu7TMapdZqZ/sB2AW67pgsXnZ3joaC2hdmu5jmDRLHcBiIw00z2mmreeP4YO9loAdtba+bowmfVV0bhaR8YOQHSXLNIvOpP248Zg75iBq9LJsnG3sOut/9FQUu7z8fQaEhERERERkUDyeQY7QHp6Ovfff7+/a5G2yuul3YpvWfXy1xhAWbdB7DpuLJjMB9xlT2Cyb3C7ptJBQ6OXZFs9I45LZvFrgStbQs+BnhehfmyR5mqXFMHAhEqyo2tYUxlDaYON9VUOtlXbGfxtQUj0Z5+Rl8odR3gMwzBwdO9M/xcfYPEZ11K+eAU1m7ez/fk3qVy5jsThg/xSq4iIiIiIiMiRCuP5vdKSAjUD0Ot2k/b9p6Qs+xqAov4j2DXs7IOG6wdSUGcjvy4Ss9mgb1yVz60ENNM68AL9Mz7c42uGq4SbBJuLYUllDEoox252U+cxM/nRdcwvTmD1usoWrSWQr2tzlJ2kk47lhOWfENUlC6/bQ9n8JWx79k3yP/4KbyjcURAREREREZE2TQG7BI3hdvHjpRNJWrsILwa9n7ibwkEj9zQd9lFDo4fVFXsWNr1wbAYxVi1sGk50c8N/dLOg7TAMyLDXc3JKMT1iqrBHmihrtPH7P//I0rJYat2t56/46K6dSL94LO0vHI0lLgZXRRVLL7iJXW9/QvWmbXrei4iIiIiISNC0nn99S1gxNdSR9eXr7H5/Jh6TmZ0nXkinmy497OO9M2Mn1W4LESY3x2+f6cdKJVS0lhBeQaD4m9mAbjE1vPX8MWTaazEMyPu5P/u6ytbTn90wDBw9utDx95eQMGwQhtVKzaZtzO1/BiXfLsJdW7fXeL3WREREREREpCX4FLB7vV62b99OXV3doQeLHEBdfhGdPn8Zx+4tmB1RbD/tMio79z3s49W6TbzyxmYAesU6sZtbSZokIuKD5MQIBiRUMvWxgSTaGnB7DTY4HXxdmMyOmsiA9mdvyRtgJpuV5JOHcsLy/xHVORNPQyOl835gTr8xFHzytYJ1ERERERERaVE+B+w5OTns2LEjUPVIK2etLGXhiIuxl+zGFRnN0NlvUJ3e5YiO+VNlDA0eE4m2BjrYdfNHRNq27jkxDEsqY3BCOVE/92f/sTyOecWJrPypokVrmZGXGrDwfdfbn5B+yVkMfOcpLLEOarfmseTcG9j19ifUbNnz/ykK20VERERERCTQfArYTSYTXbt2paSkJFD1SCsWWbKLzp+8QM2WHTTEJJB75nXEDex9RMcsrreSVxuJgffnhU39VKyISBgzDEi313NSSjE9Y6owGx7KG63ceOdylpTGkV8YPjcjDxbQG4ZB+/NG0fGGS+hyxwQMq5XqjVuZ028MJXMW42l0tWClIiIiIiIi0hb53IP9X//6F3/+859ZvXp1IOqRVqr4m4V0+uxlLHXVxPbvSe4Z19EQm3REx/R4YdXPC5uemFJCnDX0gpRfB0OtpYe4iIQPswFdY2o4JaWErKgaDAN21UVyye8Xs7YyGpfHv3clg/U+Z7LZ6HH/Hzlh2cfYszvgqW+gdO5itj//JtUbtzbrGJrtLiIiIiIiIofD54D98ssvZ/HixfTv3x+73U5iYuJeXyK/FbtlFT+ceR3mxnqc7bM59us3cEXFHPFxt1bbqXJZsZk8nJtR4IdKpS1pTTc8FAzKoUSaPRwVX8XLTwwiydZAQ6OXjU4HswuT+Oyr/ID2Z29Jjh5dyBh/NgPefBxzTDSNZZXsevsTdr3zKY1llU3j9JoRERERERERf7H4usMTTzwRgDKktUr8aRFp33+KBy8VnXqTd8L5WGMdR3zcikYL66r2HKdHjBOHxX3Ex/SX1hTcSmjZEwrq+SWHr2tnB8cllTH0qpP550MrqHFbeODJ9cRZE+kTW0VSRGOwSzxihmGQfuEYKpevpXTeD5QtWkH1hlxqtmzHlhxP5z9eG+wSRUREREREpBXxOWC/4oorAlGHtDZeL+v/8QTtv/8EgKzrL+bz+h5g8vlDE/v1wc40XF4TcdZGOkbV+uWYzaHwXETCnWHACUOTOSmlhNzqKLa54qmogQUlibSPrKNXbBXRFk9Qa5yRl8o5B/lk0oEe//XMdFOEjeSRw4jp35OimXOo3ZrHhnueYufrM4g/pn+za9kweQrdJt3S7O0iIiIiIiLSthxW2rl582b+/ve/c/HFF1NYWAjA559/zpo1a/xanIQpj5v2C/7LpgeeBaBwwMn0mfIPv4XrpQ1W5hfvaUcUrgubKqgXkWAzG5DjqOHt54+hY1QN4GV3XSTfFCbzU6WD6prArGvR0u9/Ee0Sybj0HNLOPY2I9u2o2bydXW/9jyUX3ETNtryAnlutaERERERERFo/nxPPOXPm0LdvXxYtWsSHH36I0+kEYMWKFfzjH//we4ESXL6GA+66ejK/fofEDUvAZGLXcWdRNOAkDD+l4G63t2lh00x7LYm28G9nIBIICvakuRLibfSPr2JEu1KSbfV4MNjkjObi6xezrToyIP3ZWzpkNwyDmD7dGLF6JvHHHgWGQcHHXzGn7xhK5y/BXd+wzz56DYmIiIiIiEhz+Byw33XXXfzzn/9k1qxZ2Gy2pu0nn3wy33//vV+Lk/Biqq9l8eirid2+Fo/ZwsC3n6Ssx9F+PcenX+VT0WjFbnbTM9bp12P7SrPQRaQ1ibO6GJpUzjGJ5USbXZSWN7KiIo45RYksW1Ue7PL8whrroN2pw8macBGJJxyDp7aOkm++Z96AsVRv3tY0LhDhugJ7ERERERGR1snngH3VqlWce+65+2xPSUmhuLjYL0VJ8BxuAGCpqST7s5f2zAS0RrDttMtpf+5pfq2twWPw/KtbADgnPZ9Ic3B7BIuEOwV+8luGAWmR9ZyUUsIt13TBYniodFm59a8rWFwaR7XLHNDzt9SNy4iUJI796jWOeu0RzI4oqjduZdeb/2Pp726ldvuuFqlBREREREREWgefA/b4+Hh27969z/Yff/yRjIwMvxQloe23AYitopjsT14ksqyAiLR25I65hpr22X4/77pKBxVVLmIsjZycWuL340t4ONIAzt8Bnj7JIK2RyYDfndOBU1KK6RRVg8kE+XWRfFOYxDOvbKbRE7zFL/z1mjMMg4yLx9LxxkvJvu1KMAzyP/xiT9uYBUvxut373U83pkREREREROTXfA7YL7roIu68807y8/MxDAOPx8OCBQv405/+xOWXXx6IGiWERRbtJPvTF7E5y6mPTeK4uW9Rn9Te7+epaLSwtcYO7FnY1NyMbEchiBypIwnymrNvsML5YLw29HoMTxFmL/3iq5j21GDaRezpz/7mhzuZXZjM1mo7bnfzGrQf6Lnu79fYjLxUn49pjrDR65G/kHXd70gYNgh3TS0lXy9k2/NvUTz7u8OuT0RERERERNoGnwP2Bx54gB49epCZmYnT6aRXr16ccMIJHHfccfz9738PRI0SoqLzNtHp81ew1NVQm5xB7hnXEZWd6ffzeL2/LGxqcMrx7UiOaNmFTTVDWZor0M+VcHsuKlRvPTp3jObYxHKGJJaRmWGnwWNiZUUsV9++lKJ626EPEAYiUpMZ+s10+r/yEOboKBpLyll0+lXsfn8mtTvzD7rvhslTjvj5rteLiIiIiIhIePI5YLfZbLz44ots3ryZTz75hDfeeIN169bx+uuvYzb7vzdrXl4el156KUlJSdjtdvr27cuSJUuaHvd6vUyaNIn27dtjt9sZOXIkGzdu3OsYpaWljB8/ntjYWOLj47nmmmtwOoO7QGa4y3vrf3T88nXMrgac6V3YOvoq3PbogJzry28LKW2wYTa83HR1l4CcQ9o2X4PrQI8XCUWGAamRDbz+9GD6xFZiNTxs3lrNwpIEFpfGsWNXTbBLPGKGYdDh0nPoeON44o/pByYTzrWb+KbbSDY/8iKehoZmHUdhuYiIiIiISNvhc8D+i6ysLEaPHs2FF15I165d/VlTk7KyMoYNG4bVauXzzz/np59+4tFHHyUhIaFpzMMPP8xTTz3Fc889x6JFi4iOjmbUqFHU1dU1jRk/fjxr1qxh1qxZfPLJJ8ydO5cJEyYEpOa2IHHNdyy//E8YXg/lnfuy/dRL8VgjAnKuRo/BM6/sWdi0m8NJSnJgziMiIs1jsZjo7KjllNRiLjgzAwMv+XWRXHbTElZXOILan91fzJERtBt1Ascv/ojIDml4GxtZ95dH+CrrBGpydwa7PBEREREREQkhhxWwv/TSS/Tp04fIyEgiIyPp06cPU6dO9XdtPPTQQ2RmZvLKK69wzDHHkJ2dzWmnnUaXLntmMXu9Xp544gn+/ve/c/bZZ9OvXz9ee+01du3axYwZMwBYu3YtM2fOZOrUqQwZMoThw4czZcoU3n77bXbt2uX3mlszr9fLdzNW0H7R5wCU9DqWvBEX4DVbAnbODVXRlJQ1EG120dkR/rMjpeW1tdnjbe16JXhsJi+3X5/Die1KSImox+XysqU6mtmFyXz02S48zWvPHtJi+/egw5Xnk3rWKdjaJdJYUkbeGzP48dKJuCqdmqkuIiIiIiIivgfskyZN4rbbbmPs2LG89957vPfee4wdO5Y//OEPTJo0ya/F/fe//2Xw4MFceOGFpKSkMGDAAF588cWmx3Nzc8nPz2fkyJFN2+Li4hgyZAgLFy4EYOHChcTHxzN48OCmMSNHjsRkMrFo0aL9nre+vp7Kysq9vto6r8fDymv/SrtV8wDofv8fyR8yBozD/hDEIVU1mtlSHQVAn2YubNpcL7251X8HE2nFQiFADIUa5MBirG6OTSrn3//og8PiosFj4tFnNzKnKInCusPvzx4qCwUbhkFs/56c+NMXxB3dFwyDXe98ytZnp1O28Ec8jS27LoiIiIiIiIiEFp/T0WeffZYXX3yRBx98kLPOOouzzjqLBx98kBdeeIFnnnnGr8Vt2bKFZ599lq5du/LFF19www03cOutt/Lqq68CkJ+/Z9Gx1NS9/4Gdmpra9Fh+fj4pKSl7PW6xWEhMTGwa81sPPvggcXFxTV+Zmf5fuDOcuGtq2f3uZ+x87UO8hom84eeSc8eEPQ15A8TrhVUVMXgxGD4kidTI5vW99ZVm+4Y2/X7CgwJwARg6OIkT25XQN66S2BgLVS4L35cmsKgkHqfL/2u0NIc/30Os8bGknD6CzGvGEZmRirehkeKvFjBv8DnUbPW9bYxeNyIiIiIiIq2DzwF7Y2PjXrPBfzFo0CBcLpdfivqFx+Nh4MCBPPDAAwwYMIAJEyZw3XXX8dxzz/n1PL/1l7/8hYqKiqavHTt2BPR8oeiXf/i7a+v4ftRVVG/ciikygu2nXEx5t4EBP//uugiKGyIw4eWWa7WwqUhromCx9TIZkB1dy9svHEPn6GoMvBTUR/BNYRKrKxxUVu1/tveRzlb3Zf8jDd0j27ejw1UXkDL2ZMxRkTh/2kTe6zPI/+hL6nYVHNGxD0WvHRERERERkdDjc8B+2WWX8eyzz+6z/YUXXmD8+PF+KeoX7du3p1evXntt69mzJ9u3bwcgLS0NgIKCvf9BW1BQ0PRYWloahYWFez3ucrkoLS1tGvNbERERxMbG7vXVFjVWVLFz2geUf/8jblskQ2a+gjOrR8DP6/LAmsoYAHIc1WSk2QN+TpG2RJ8MkECLdVjpE+fkpJQSUiPq8WKwpTqai65fTG61Pez7sxuGQdxRveh446VkXX8xAFWrNzCnz2i2PDENr9sd5ApFRERERESkpTRrdcqJEyc2/dkwDKZOncqXX37JscceC8CiRYvYvn07l19+uV+LGzZsGOvXr99r24YNG+jYsSMA2dnZpKWlMXv2bI466igAKisrWbRoETfccAMAQ4cOpby8nKVLlzJo0CAAvv76azweD0OGDPFrva1JQ1Epm17/BGt1JZEZqaw+5kLOGjYIXtsc8HNvckZT6zZjN7vJcVQH5BwlcxYH5LgiIvL/HBY3Q5LKKayzsaYyhsoqWEXs/7F33/FN1fsfx18nO2m6Jy17liFLlOHAgROvojhxT1TE67hyr/cqXnHjz4l63VtUHOBWNsgse5RRKJRC917Zyfn9UVpbaKEjbdryeT4efdgmJ+d8Uk5s8s4nny/7KyyctaEw0OU1m9Zsov9zj6B6veT9tgxHRg47H3kOQ3QEUeNOC3R5QgghhBBCCCFaQYMC9k2bNtX6uSqoTk2tDFujoqKIiooiOTnZr8U9+OCDjBkzhmeffZarr76apKQk3n33Xd59912gMux/4IEHePrpp+nTpw89evTg8ccfJz4+ngkTJgCVHe8XXnhh9WgZt9vNfffdx7XXXkt8fLxf6+0oilZv4uAn36G3O9FHhjNm+VdseGZFqxy7wqNlb3kQAANDytC13BqqQohGqhxPId3vovFiTC6ijAUkXn46r7+9m3KPjn/8dxsxxjDSDrbMG6mtydQphs63Xom5cyd2/ef/cOUVsuacGwg+qR9d77oWU1y0X4+XMmMWfadP9es+hRBCCCGEEEI0TYMC9iVLlrR0HXU65ZRTmDt3Lo8++igzZsygR48evPrqq7VG0UybNo2KigruuusuiouLOf300/n9998xmUzV23zxxRfcd999nHvuuWg0GiZOnMjrr78eiLvU5lXsSSP1/97HZ3dii+7MoJsuwNy19d6ISC614kMhyuCkk8l51PUS8IlAkbEqLUMe0ycOjQITLoon+fuVpJQFccBpJddp5Ob71tPVHIzLp2DQHHt2TFt+HCqKQtfbryJuwjjWXnw7pZt2ULZtN8sGXkjfJ+5H9fnqvJ2E5UIIIYQQQgjRvjUoYA+kSy65hEsuuaTe6xVFYcaMGcyYMaPebSIiIpg9e3ZLlNehHPp0Lplf/wKqiqV3N3aMvokhlqJWO/7q9QVkO0woqJwUWoaitNqhhQi4thwcCuFPeo3KwNBynn7iLB78+59kO0zsr7BwyGaiX3A5Hk/dQXRTzcuIZUJC3YuPtsSioYbIcGLHn81Jb/yX9ROn4MzKZcfDz2KIicRxKJsh7z/n92MKIYQQQgghhAicRgfsDoeDWbNmsWTJEnJzc/Ed0ZG1ceNGvxUnWoeqqux76X12Pfp/ACRcfxnmbvGo2YZWq8GrwmvvVo4c6hlkI1gvC8QJIURH1iXewqkRJZxz76k8/t8NlHn0bC8N4ab71tPJYSDG6PLbsRryBta8jFim+e2IEHbKYLrcdiXmhDh2PfYyrtwCDn3yPfhUEp/7R61tawb90tEuhBBCCCGEEO1LowP222+/nfnz53PllVdy6qmnokibcbum+nzs/OdM9r/6EQBho4cx5MPn2fP0m61ax75yC4fK7Bg1XvoGt/95vEL4S0ODwbZCRr6IxhoxJJyzogs5YDOzq8xKeoaddMKJNjrZd6D9/T2oGZYrGg1d77yGuMvPqx4bc+izuWT/uJDw0cPweTwBrFQIIYQQQgghhD80OmD/+eef+fXXXznttNNaoh7RinwuF1vu+DeZX/4EQP+Z/8RTVoGi8c/Kog0N2uxeDSnlVgAGhJSjP84M3rq0pYBRCCFE4ygKdA+yk2B2EH7ayXz1XTp5TiO33L+erqZgikr8183e2qo60mMvOYfQYQMo3byTko3J5P2+nJWjriRk2ADMXTpVbyuEEEIIIYQQon1pdJKakJBAcHBwS9QiWpGnvIL1l99D5pc/oeh0DPl4Jj0fvC0gtSSXBONVFQYPCKGz2RGQGoQQQgSeXqNy7y09OTumgE4mBz4fpNksXDc5idRyC77Gv//appgS4jht1TcMeuO/aExGSrfs5NDH35Hz4yI8FbZAlyeEEEIIIYQQogkaHbC/9NJL/POf/+TAgQMtUY9oBc68QtacdzN581egtZgZMfctOl9/WUBq2bi1iEyHCVB5cHIfWdhUdHjyaQshji9I5+WUiBJef3YIITo35RVekkuDWZIbSbbDiNrMoL0lH4fH27ei1dJt8nV0u/cGOt8yEYDSLTs58NYXFK/fhnp4bZuW7GaXTnkhhBBCCCGE8J9Gj4gZMWIEDoeDnj17YrFY0Ov1ta4vLCz0W3HC/2wHMki6+DYqUtLQR4Zxyg/vEj5ySEBq8anw6uGFTbtb7PTpaQ1IHUIICf5F2zT8pDDGRhcy8MozeOXNnVR4dSQVhhFlcDIwtJxQffudYa4LMjPghWmgQN6vy3Dm5JP32zJKN+0g5sKxgS5PCCGEEEIIIUQDNTpgv+6668jIyODZZ58lNjZWFjltR0q37SbpkjtwZuZi7hrPqb+8jzWx11HbtVZnW1qFmX1ZFRg0PhJDyht0GwkBhRDixKIocMn5ndjyzQr2lAexr9xCvsvIsjwD3Sx2iorb73x2AHPnTnS542pKNmynYMkanNl5rDrjGkKGDqDbPddjjI4IdIlCCCGEEEIIIY6h0QH7qlWrWL16NUOGBKbrWTRN4Yr1rJtwN56SMoIH9uXUX97HlFA7rK4K1udlxDIhIadF63F4Newqq+xY7x9cjqEJC5sKIQJH3uwSrU2vURkQUk43i40dpcFkOUwcsFm4dnIS3XQWXG5fre3b0zmqaDSEnTIY64De5C9aRdmWXZRu3sGygRfS76kH6XrH1ShabaDLFEIIIYQQQghRh0bPYE9MTMRut7dELaKFZP+4kLUX3YanpIzwMcMZveTzo8L11raz1IpH1dDNYqOrpfJ88lfnfFWo0p7CFSGEf8hs6Y4vSOfjlIgSTossJFTvpsLmZUdpMDfcu44se+Pmszf270RL/13RBVmIu3Qco5fOxhAbhbuohO33/ZdFPc6iOGkrIOe4EEIIIYQQQrQ1jQ7Yn3/+eR5++GGWLl1KQUEBpaWltb5E25L+wTdsuGoqPoeToL49GPn7R+jDQwNa0/ZdpRy0mwG4oVumLGwqRIC0tzehJFgUNUUa3ZwZVci//94Po8ZLZraDdUVhrCoIZ8++ho0dg7b5OIg47WS63nE10RecgS7EijMrl5WnX03Oz4vx2qTJQQghhBBCCCHakkaPiLnwwgsBOPfcc2tdrqoqiqLg9Xr9U5loFlVV2fv826RMfxWAzrdMxJQQi9ZsatDtWypwUFV45Z09AHQx2+lltbGtJLhFjiWEaB1tMaAUJwZFgYvHxbH56z+JOnMEn885QIHLwG0PbKCLOYTE4HJMWt/xd9QGKRoNYacOYfD7z5E0/nbKtu6mdNMOynelEnXOaFSfD0XT6D4JIYQQQgghhBB+1uhXZkuWLGHJkiUsXry41lfVZSLwVJ+P5Aeerg7Xe/3rbga/+0ybeCF+wGZm995ydIqP/g1c2FQI0X50xLBdOufbPp1G5a4be3BOTD7xJgeqCuk2M4tyI9lTZsHbwLEx/vy39te+THHRxF12HqOXfIEhJhKf3UnuL0tZedrVFK/f5vf65HwXQgghhBBCiMZpdAf72LFjW6IO4Sdep4stt04j65vfQFEY8PJ/6HHfjYEuCwCXT6le2LQ9dxUKIYRomyw6HyMiSrjw76P492PrKHbr2VkWzAGbhQEhZaiNGdB+2JELf7fGQuB1iTh9BF3vvIbidVspXLqWkvXbWDnmKrreeQ39nnoQQ0SY34+ZMmMWfadP9ft+hRBCCCGEEKIjaXTAvnz58mNef+aZZza5GNE87tJyNlw5hYIla1D0eoZ+9ALx14wPdFnVdpVacfk09OwWRHdX88KJjtglK4QIrMrOXfl/S0cweEAoZ0QVcshuYmepFZtXy/qiMO57dAuhLh1hBk+dt5uXEcu0Or5viuMF8U3Zv6LRED5yKMED+uDMySfzy59If/crsr77ncRn/0GXWyb67dNqNTvZJWgXQgghhBBCiPo1OmA/66yzjrpMqbFKpcxgDwxnTj5Jf7uL0k3JaK0WRnz7JlHnjgl0WdVSUstIs1UubPrA5N4sfGNfgCsSQrRlrRV2S6jecSkKdLE46GRysLc8iL3lQWxJLgEi6GJ2kF/oDHSJQNM64nXBQQx48V8oeh0lG7ZTnryHbZMf4+AH3zBo1hOEDh/YQtUKIYQQQgghhDhSo9ucioqKan3l5uby+++/c8oppzB//vyWqFEch23fQVaNvY7STckYoiMYteDTBoXrrTVntXJh072AQoLZwfCTwlrluEII/5JPjoj2SKeBxJAKzonJ57yxMYDCQbuZ6yYnkVIW1KD57Eee+1U/+/Mx0ZR9WbolEHfZOPrP/Bdaq4XipC2sGDWR7VOfxF1U4rfahBBCCCGEEELUr9EBe2hoaK2vqKgozjvvPF544QWmTWvOh6lFU5Rs3smqM6/FlpqOuUdnxiz7krARJwW6rFoO2U1s21mKVlEZEFLW6NvLgmtCCCGay6Lz8cQ/+nN6VCHhehd2h49dZVYW50ax6M9cmjCevZaaAXlrvhmlaLX0fPBWzkr+nfhrLwFV5cDbs1k68EIOfvwdu598vVXqkL/VQgghhBBCiBOVfwZ1ArGxsezevdtfuxMNkL90DWvOuR5nTj4hgxMZs+xLgvp09+sxmhsSuH0KO0orFzbtay3HLAubCtEqpNtciLpFGNycHlXE9IcTMWm82L1anpi5kxX54RS5Gj05r80wxccy7LOXGLngE6z9e+HKK2Trnf/m0Mffse3e6YEuTwghhBBCCCE6rEa/kty6dWutn1VVJSsri+eff56hQ4f6qy5xHFnf/c7mm/6Bz+Um4sxTGfH9W+hDgwNd1lFSyoJw+rR0jjfTU23ewqZCCCGEPygKnH9WLOu/WEZqRRAHXCEUOQ38mR/J06/swpve6bhvCDfnTayWnP0fddYozlg/j/1vfMaeGW/gOJRN+vtzUPR6+j35d/RhIS1yXCGEEEIIIYQ4UTW6g33o0KEMGzaMoUOHVn9/8cUX43K5eP/991uiRnGEA2/PZuN1D+BzuYm7/HxO/eX9NhmuZ9qN7KuwAPD3O3uhVY5zAyFEmyOd8G2DjN9oGToN9AuuYPbbp9LZbAfg98U5LM6NYndZEA5H6yzc7s/HWcqMWWgMBno9dDtjt/+GdUCfyrExb33O0oEXcujTuajNnYcjhBBCCCGEEKJaowP2/fv3s2/fPvbv38/+/fs5cOAANpuNVatWkZiY2BI1isNUVSXlydfZPvVJUFW63nUtw798Fa3JGOjSjqKqMDs9HhWFOJOD0SMiA12SEKIDkwBaNEdMlJHh4aWcEVXASf1D8KoKu8usTLpnHYdspmbPZ2+oxgTtxzrnq64zd46j08QLSLjhMoISe+LKLWDL7f/i0Cff48zJb/D+hBBCCCGEEELUr9EBe7du3Wp9denSBZPJ1BK1iRpUr5ftU55gz9NvAtBn+lQGvfFfFK02wJXVLcthZEdpMBpUBoaUB7ocIcQJqL7AUIJEUZ9wg4e3XhjKyeHFmLVecvOdbCwOZUV+ONt3ldZ7u5b6pIe/9mvp0YUzN/xA4rMPo7WYcRzMIv29r0l+6BncJbL4uBBCCCGEEEI0R5NW81q0aBGLFi0iNzcXn6/2jNIPP/zQL4WJv3gdTjbf9A+y584HRWHQrCfoNvk6vx9nXkYsExKaPyfd44Pk0sqRNb2tFQTpWucj9kIIIURzKYpCgtlJnMlJ/Lkj+fCLfRS5Ddz9yCYSzCEMCGnagt3++hvbVBqDgV6P3EX8tX8jafztlO9MJW3Wp2TN+ZXEF6Y1eWxMyoxZ9J0+1c/VCiGEEEIIIUT70egO9ieffJLzzz+fRYsWkZ+fT1FRUa0v4V/u4lKSLr6d7Lnz0Rj0DP/qtRYJ1/1pb3kQdq+WSIOL3taKQJcjhBBtjnQAt31aBW6+phvnxhTQ5fB89gy7mcW5UewqDcLT+Iy9WqDWNkiZMQtzl050uvIi4iddSlDf7jhz8tlyyzQOfTqX0m27A1KXEEIIIYQQQrRnjQ7Y3377bT7++GPWrl3LvHnzmDt3bq0v4T+OrFxWn3MDhX+uQxdi5dRfP6DTFRe06DGb+6I/I8vO3vIgAK7tkomu0WeY/2sSQojGak4ALuF5x2LS+hgWXsr7rwwnwuDCqyqklFtZnBvFQZsJn6/+zu+29verZj1BvbpyxsafiDx7FIpOhyM9kxWnXE7e/D9xlwZutJs8foQQQgghhBDtTaPjT5fLxZgxY1qiFlFDxZ40Vp15LWXbdmOMi2bUos+JHDsy0GUd1+vvp+JDIdroZHh4/fNqhRBCiPYksXcwp0UWMSK8GIvWi8OnZVNxKJP/sYlClz7Q5VWrL9SvK7jWGg1EnD6CbvdeT1BiT1Svl+K1W1g28EJKt+2uc2yMBOBCCCGEEEIIUVujA/Y77riD2bNnt0Qt4rDi9dtYNfY67GkZWHp3Y8yyLwkd2j/QZR1XjsPAyqQCFFQGhZShKIGuSAghhPAfRYF4s5OzY/LpH1yGVvGxc08ZK/Ij2FAUSnauI9AlNok+NJj4qy7mlJ/fQx8RijM7j5x5C8j4bC5lyXsCXZ4QQgghhBBCtGmNXuTU4XDw7rvvsnDhQgYPHoxeX7tr6+WXX/ZbcSeivIUr2XDlfXgrbIQOH8gpP72HMSYy0GUdl1eF7SWVC5v2DLIRrJeFTYUQ/tHWxmwIoVWgT7CNLhYHhsEn8fOCLDLsJibds45uhiD6WG3oNE1bNLSp/LHYaMwFZ9J18iS0ZiN7ZryB/UAmf46YQOiIQficLhKfedhP1QohhBBCCCFEx9HogH3r1q0MHToUgO3bt9e6TpGW5WbJ+Opnttz2L1S3m8hzRjPi2zfQBVtb/LgfzE7j9kndm7WPfeUWKrw6IiMM9DXk+qcwIYRoJyrHZsgbAScak9bHtPv7UbY5me0lwRS4DOxxWUm3mekfUk4Xc/vraNfotPR59B5ceYXkzV9Bxe59FK/ZTFnyHkKG9EdV1UY/3/NH+C+EEEIIIYQQbVWjA/YlS5a0RB0nvP2vf8KOh58FoNPVFzPkwxfQGg0Brqph7F4NKeWVbwRMubUnm788GOCKhBDtnXSti/YkVO9hTGQRo245m2dmbsXm1bG5OJT9FRYKXXpeCHSBDVRzvro+LIT4qy8m7JTBbL75EdxFJWy6/kHM3TsTc+GZAaxSCCGEEEIIIdqWRs9gF/6lqiq7Hnu5OlzvPuVGhn32UouH6/5cpCy5JBivqhBhcHHe2Bi/7VcIIWpqa6G7LPYoalIUGDsmmrNjChgQUoZO8VHi1rOyIILHn99BhUfjl3O4Mfvwxzkac9FYut59HRFjR6IxGbGnHeLAu1+x89EX8ZRXNHv/QgghhBBCCNHeScAeQD6Ph613/YfUF94BoN/TDzHglf+gaNrPP0u+U0+mwwSonBRaJmOChBBCnNC0CvS22jgnpoBuFhugsmRlHktyo9hZasVm8wS6xEbT6HREnnkKY7f8QlCf7uDzse//3mfZoIso27EXVa09b17efBJCCCGEEEKcSNpPktvBeG12Nlx5H4c+/g40Gk5652l6/3NyQALqgmVJTbqdT4VtJSEAdLfYCdW3v9BACNFy2lrH+fE0pF4JDkVDmbQ+hoSVMTa6kOGDw/ChsKc8iGsnJ3GgwoRazxqoxzoPj7yutR9jlp5diL/2EjpdMx5zj844MnLI/u53ki66DVd+UavWIoQQQgghhBBthQTsAeAqLGbtRbeR+8sSNCYjI759g663XdVqx/dXQLS/wkKZR4dB4yMxpLzZx2xvYZwQouFqPr7lsS5OJKF6D689PZhTwouxaD0UFrvZUhLKsrwINm0rrvd2DQ3aq75vzceYtW8Pxm75hT6P34ei1ZK/aBUH3vmSXf95CU+FrXo7eUNKCCGEEEIIcSKQgL2V2Q9ls/qcGyhatRFdWAgjf/uQ2L+dG+iyGq2wyMXusiAA+geXY9DU04onhBDHIGG7OBEoikIns5OzYwqYcltPdIqPUo+eqf/ewrrCUCo82kCXWK2hj0mt2UTf6VPpevckoi8aCz4fqTPfZdlJF1O28+ixMUIIIYQQQgjRUUnA3orKdqay6sxrKU/egzE+hjFLviDi9BGBLqtJ3v5kHx5VQ5jeTVeLvcn7kXBNCNERtMT/y6T7t+PRKnDd5V04Nyaf7hYbGg1kOUwsyY1kR6kVt69lxsS15N9aQ0Qop/zwDp2uHo+5WwKOg1lkf/s7SePvwFVQJOexEEIIIYQQosOTgL2VFK3ZzOqzJuE4mEVQvx6MWf4VwYP6BrqsJil06fl1UQ4AJ4WWIuuaCiGEEA1n1KoMDivj49dHEGVw4kNhb3kQi3IjOVBhxus9fve3v0Lz5uynKjxXFAVrvx6M3foLvf9zL4pWQ/6CFaS/8yX5i1fjc7uPuo0QQgghhBBCdBQSsLeC3N+Wseb8m3EXFhN2ymDGLJ2NpVtCoMtq0otqVYVtJcEAdDHbCTfIwqZCiLaprX1CRoJFcaSe3YIYHVnMqRFFBGk9uHxatpSEcNsDG8hz6gNdXqNpLWb6/ffvdJ08CUuvrqheH0UrN3Dgf7PJnrfgmGNjUmbMOuZjRB4/QgghhBBCiLZKAvYWduizeay//B58dgfRF5zByAWfYIiKCHRZTXbAZqbErccapKX/cRY2FUKI9qqthfOi41IUiDO5ODumgIEhZegVH6lpFawuiCCpMJSDmbbj7+QYGnIu++t8rwrBDZFhxF/3NzpddTG60GA8JWVsuOo+1l16F67C4jpvI4QQQgghhBDtlQTsLSj15Q/Ycts/Ub1eEiZdyoi5/0MXZAl0WU1WUupmZ6kVgNsndcek9QW4IiGEEKJj0CjQy2rj3Nh8Jl4Sj4JKtsPEjVPW88yOXpSV+/cTYy39JpKiKFgTe9Lt7kmEn3Yyil5P3u/LSX97Nrv/+xpe21/rt0jILoQQQgghhGjPJGBvAarPx45pL7DrnzMB6PHgbQz56AU0+vb3ce+a3vs8DbeqIVjn5vLxgR9xI4QQTdHYYLEtdbNLENnxGTQqD07uw1nRBUQbnXg8KqkVQVw3OYm0CjO+449nb1M0Bj1R54zmzM0/EXXe6aheH3ufeYtlQ8aT89OiY46NEUIIIYQQQoj2QAJ2P/O53Wy59Z/sf+VDABJfmMaAmf9E0bTvX3WJW8cPv2cCcFJoGTpt3SubSvgjhAiEpobgbSk8F6KmYL2X0ZHFvPjEIKw6D8WlbraWhLAsL4Jch6FVa/HH48Tatwen/vI+cVdeiKlzHPa0DNZfcS+ZX/+Mq7DED1UKIYQQQgghRGC079S3jfFU2Fh/+T1kzP4RRatlyIcv0Ouh2wNdVrOpqsrW4mBUFRLMDqKM7kCXJIQQHYa8MSmOZfSISM6KLuDByb3RKz7KPHrWFIaztiCMco820OU1yp6n3iC4f2/Gbv+NXtPuQtHrse05QPrbsylYthafWxZOF0IIIYQQQrQ/ErD7iSu/kLXn3UzeH3+iMZsYMfd/dL5xQqDL8os/luRS5DZgNmkYEFIW6HKEEEKIE4pGgYmXJHBubD49gypQUMlxGlmSG8n2Eiul5W3vje9jdb3rgiwkPvMwZ278AXOPLqheL4XL13Hg7dmUp+xvxSqFEEIIIYQQovkkYPcD24EMVp01ieJ1W9FHhDFq/ifEXDQ20GX5hdun8NZHqQDcfE03zLKwqRBCtEnSCd/xGTQqg0LLOSu6gFijExWFfRVBXHtXEvsrzHi8teeZ1wy52+I4JGtiLxKuv5S4iReiCw7CU1xK1te/sO7yu7HtPxjo8oQQQgghhBCiQSRgb6ay7SmsOvNaKnbvx9SlE6OXziZ81NBAl3VMjXmRvbssiMJiN0FaD1df1tkvx/9gdppf9iOEECcKf4ejEsa3b8F6LyMjixkVUUSwzkNpmYdtJSHcMnV9g+azt1bY3pDjKIpC8IDedLv3evJPOh00GnJ/XsKywePZdNM/yPllCT6Xq0HHk/NaCCGEEEIIEQgSsDdD4Yr1rDr7epyZuVgH9GbM8q8I7t8r0GX5TZlby/4KC1C5sKlBL6eLEEII0VbEmFyMjS7gobt7Y9D4SDtoY01hOGsKwkg7WNGofQW6w11jMJBzygV0vetaIs8aic/hJPPLn1g/4W4Wdj6dnJ8Xk794NarXW30bCdSFEEIIIYQQbYEkpk2U89Mi1l50G57iUsJHD2P0ki8wd44LdFnVjvei83gvpFUVtpUEo6JwxshIYkwN6x4TQghxfC0ZZh7r//8SSHY8GgWuGJ/AOTH5XDOhMwoquU4jN9+3nm0lwbh8SoP3FeiQHcAYHcHI+Z8wZvlXhJ06GGNcNO6iEko37WDtBbewqNuZbP/7UxSu2oiqqsffoRBCCCGEEEK0MAnYmyD9w29Yf+V9+BxOYsafzcjfP8IQERboshqkoeFKlsNIvsuIBpX77ug4XflCCCFER2TQqEy9vRdnxxQQZ3Lg9cH+CguLcqLYV27G10JZdEuE8nueeoPw0cOIvuBMzk1bxsj5HxMybAD68FCcOfkceOtzVo+9jrRZn7LzXzMp2ZhcZ9gubygJIYQQQgghWoME7I2gqip7X3iHbZMfA5+PzjdfwcnfvoHWYg50aX7l8UFyaTAAva0VJMT59/4VLEvy6/6EEEIEhgSYbY9V5+XUiBJeeWowwTo3blXD9tIQluZFkuMwtGjXd0uE7YpWS9TZo4m95BzGHVrBKT++S8KkS9FaLXhKytj30gesGHkFC+JGUbB0LWU7U/1egxBCCCGEEEIcS7sK2J9//nkUReGBBx6ovszhcDBlyhQiIyOxWq1MnDiRnJycWrdLT09n/PjxWCwWYmJieOSRR/B4PI0+/q5HX2T3Yy8D0Oufkxn83rNodLpm3ae2aG95EHavFrPWS29r42a4CiFEILXmiIu2ME4jECRUbx9OGRrO2OhCBoeWYtD4KPfoWFsYzsNPbKPUrW30/mqe780596tue+SC5w3Zp8ZgIOaisVh6deW8zNXEXXkhcRMvQGMy4i4spvDPdSwffDHLh1/K3hfewV1UctQ+5PwVQgghhBBC+Fu7CdjXrVvHO++8w+DBg2td/uCDD/LTTz/xzTffsGzZMjIzM7niiiuqr/d6vYwfPx6Xy8WqVav45JNP+Pjjj5k+fXqja0h/9ysABrz0bxKffghFafhc0/ai3KNlb3kQAINCytC1mzNECCGEEDVpFOgeZOfcmHx6BVWgoJK0qYhleZFsLQ6muMTd4jXUF5xXfZqtIcF6XaG41mwiuH9vggf04bzMVcRedh6WPt1QdDrKtu1m92Mvk/bGZ6w87Wr2v/YxjoycOvbccBLMCyGEEEIIIerTLuLT8vJyrr/+et577z3Cw8OrLy8pKeGDDz7g5Zdf5pxzzuHkk0/mo48+YtWqVaxZswaA+fPns2PHDj7//HOGDh3KRRddxFNPPcWbb76Jy9W4hTsVvZ6hn71Ej/tv9uv9a0uSS4LxoRBtdBJncga6HCGEEEI0k16jMjC0nLNjCjhjVCQqCmk2C9dOXktquaXO+ext9dMgdQXdumArIYP7kXDt3xiXsZKT3n6ayLNHAVCctIUd/3iORT3Gsrj32RRv2I4rv7BZ9UrYLoQQQgghhKipXQTsU6ZMYfz48YwbN67W5Rs2bMDtdte6PDExka5du7J69WoAVq9ezUknnURs7F8v3i644AJKS0tJTk6u83hOp5PS0tJaXwDDv3yVhGsv8ffdazNyHAZynEYUVAaFlNEBG/SFEKJZTtSxMKJjsOq8PPefQYyJLCRE56a8wktyaTBLciPJdhhoznj2Yz02WuJxU1/IbYgIw5GRzaj5n9DjgVuJvuAMwkcPA1XFfiCTvF+XsrDz6SRdcgeHPp2Lu6TMr8cXQgghhBBCnHjafMD+1VdfsXHjRp577rmjrsvOzsZgMBAWFlbr8tjYWLKzs6u3qRmuV11fdV1dnnvuOUJDQ6u/unTpAkDUOaObe3faLKfLx7aSyoVNewbZCNZ7A1yREEJ0LG01nJeg8MQTZXQzNrqQf97XF4PGS4VXR1JhOKsLwih1N3xtmfrO6bZyruuCgwg7dQhjln/F2XsWEXnuGIxx0aheL3l//MmW2//FwoQxrL/qPjK/+RWvzR7okoUQQgghhBDtUJsO2A8ePMjf//53vvjiC0wmU6sd99FHH6WkpKT66+DBg6127ED5et5BbF4dRo2XfsGysKkQQgjRkSkK/O2CTpwbU0BvawUaVPJdRpbmRfDimyk4vXV/jM0fC5wGgqV7ZyLGDKfrndcwdvtv9H1iKvrIcHxOFznzFrBp0oMsiB/DphsfJufnxXidLnnzSQghhBBCCNEgbTpg37BhA7m5uQwfPhydTodOp2PZsmW8/vrr6HQ6YmNjcblcFBcX17pdTk4OcXFxAMTFxZGTk3PU9VXX1cVoNBISElLrqyOzezV8OicdgIEh5eg0zfiMeD3aSjebEEK0tMb+/07+/1ibhJqtS69RGRBSOZ+9k8kBKPzwexaLcqP4v909cLl9AautpR4b1n496fPYfXS7ZxJnrP+BXtPuwtw9AW+Fjcyvfmb95fewsPNp5Py0iLyFK/F5PM0+ppzXQgghhBBCdFxtOmA/99xz2bZtG5s3b67+GjFiBNdff33193q9nkWLFlXfZvfu3aSnpzN6dOU4l9GjR7Nt2zZyc3Ort1mwYAEhISEMGDCg1e9TW5RcEozD6SPC4CLB7Ah0OUIIIZDgXbSuIJ2XUyJKOC2ykL49rXhUDTtKg7lxyjqy7MZmzWdvrNY69xVFIWRIIonPPEzCDRMYs2IOYacOwdgpGk9xKaWbd5J00W0s6nYmub8to3DFelRf4N5wEEIIIYQQQrRNDR+0GQDBwcEMGjSo1mVBQUFERkZWX3777bfz0EMPERERQUhICFOnTmX06NGMGjUKgPPPP58BAwZw4403MnPmTLKzs3nssceYMmUKRqOx1e9ToFR1Th35onXDliIyHSY0Gjgp1L8Lm0o4JIQQbZP8/1nUJ9Lo5tmXh/P3W35iZ6mVjCwHGYQRZXCxZ395QGqalxHLhISc429Yj5QZs+g7feoxt1EUhfCRQ4i+4Az6/OdeClesZ8e0F7AfyMCVW4Art4DVZ1+PqXMcps5xBA/sg6qqKLIivBBCCCGEECe8Nt3B3hCvvPIKl1xyCRMnTuTMM88kLi6O77//vvp6rVbLzz//jFarZfTo0dxwww3cdNNNzJgxI4BVtw0+FV55Zy8AEy6KJ1Tf/I9ACyGEEDIOo33TahW6WhycG1PAjVd1PTyf3cBtf9/AluJgHN7APX1sjTeHFK2WyLEjiR1/NuMOruCUn94leHA/dMFBOA5lU7xmMwc/+IalAy5g9dnXU5a8xy/HlceNEEIIIYQQ7VOb7mCvy9KlS2v9bDKZePPNN3nzzTfrvU23bt349ddfW7iy9md/hYW0UhsGjY87ru/O25s21bld5Qs+6XYUQoj2TjrXRWPoNCqTb+pB1rIN7CgNJtNh4oDNQobdRM9v0/GqoG2DDdzzMmKZ5qd9afR6Yi4cS3HSVnpNu4u835ez+4lXqUhJw7b3ALa9B1g+9BKCB/al0zUXE3/1eIJ6da21DwnOhRBCCCGE6NjafQe7aBqHV8PusiAA+geXExKsD3BFQgghhGiLLDofIyJKePP5oYTp3XhUDW9/sp8luZFk2o2orTmgvZmq3mRqSuitNRmJm3AenSZeSM+HbmPop/9HUJ/uKHo9ZckppEx/laWJ57Fi9JXse+Uj7IeyG7RfCeCFEEIIIYRo3yRg7+Bqvmir2bm4s7RyAbP+fYLparEHojQhhBCN1Jod6BL6iSMNGRjKGVGFDAsrITLCgM2rY31RGFMf3UKxK/Afimzs46Mh29f3ONAYDSRc9zfir72E8zJWMvjdZ4g6dwxoNJSs38bOac+zuOdZrD7nBorXb8NTcfRzLXmMCSGEEEII0TFIwH4CKnTpOWg3A/DA5N5+XdhUCCFEYMj4F9EaFAW6WBx8+fap9LWWo0Flc3IJy/Mj2FwcQn6hs8Vr8Pe53tygWx8eSpdbr2Tk7x8xLv1PBr42HVOXTqCqFP65jrzflrH/lQ9Ze/HtHPzke9zFpX6qXAghhBBCCNEWSMB+glFV2FYSDEBXi52B/UICXJEQQoj2qmbQKd24JxaLWUtiSAXnxOQz7swYQCHdZua6u9eRUmbB28anxjQlpG/IOW6MjaL7vdfT5ZaJnJO6hMQXpmHsFA2qSv6CFWy941EWJoxh/cR7KduegqfC1pTyhRBCCCGEEG2IBOwnmAM2MyVuPTrFR//gskCXI4QQoo1qbx3xEvAHhkXn47+P9Of0qELC9G7sdi+7yoJZnBtFht1Ia41nP/J8bc3zob5jmbvG0+uh2+l6xzV0u/cG+v73fqz9e+Fzucn5cRHZc+ezMH4MWd//QfaPC/F5vE2qXc59IYQQQgghAivwAzNFi5iXEcu0Iy5z+RR2lloBSAwux6ht4+1lQggh2oQPZqf5ZT+VQWD7Cu5Fw0QY3JXz2SeNZeYrydi9WjYUhbHf4GJQSGDf0K/rOVFrqBl8GyLD6POfKfg8XjpdcQGZc34l/Z3ZuItKKU/ew4aJU9AYDTgzcvB53Pg8HjQ63VH76zt9amvfDSGEEEIIIcRxSMB+gpiXEcuuUituVUOwzk33IFnYVAghOqL21nkuOg5FgfPPimX9F8tIrQhib3kQhS4Dy/MjefqVXXi9Gsxa3zH3EYjzd15GLBMSchq0bXO7xRVFIeSkfoSc1A9FpyX2orNIfuhp7AezcGbmcujT7wHIn7+CuIkXEn/NeCJOOxlFIx86FUIIIYQQoq2SZ+sniGKXjjRb5cKmJ4WWoWmhhU0l2BFCiI6nYFlS9fdt7f/zMh6j7dFpoF9w5Xz2zubKN/R/X5zD4twodpcF4XB4m32MeRmxxzwXm3qe+uP8bsg5mTJjFoqiEHbqYKLPP4Nz9y+j802X0/Wua9FaTLjyi0h/50vWnHMDi3uexY5/PIcjMwe1iTN3qmqSx4sQQgghhBD+JwH7CcDnUw8vbKqQYLYTZXQHuiQhhBCiwSQUbJ/MWh/Dw0s5I6qAgf2C8aoKu8usTLpnHYdspiaHxW1Vc0YpKRoN5m4JnPTmk/R44FZO/eV9Ot90BboQK46MHPa/9jEHP/iGpYnnsevxVyjbniKPCyGEEEIIIdoICdhPAH8szaHIbUCr+BgQUh7ocoQQQohq/prvLtqucIOHt18cxsnhxZi1XnLznWwsDuWeaZspcrXctMK29mmLhlK0WqLPP4MhHzzHuIxVnPztm3S6+mIUnQ7bvoOkPv82y4f9jQNvz2bPs2/hKiwOdMlCCCGEEEKc0CRg7+DsXg3/+2gfAH2tFcedfSqEEOLEEqgQMhDdtzWPKd2/rUtRFBLMTs6OzueOG7qjVVS27yrlz/xINhaFYPce+ympP87Tlj7Xa45Saq6q81NrMhJ32TiGf/EKPR++jWGfv0zspeeiMehx5RWS8sRrHHjzc1aMvIJ9r3yI/WCW32oQQgghhBBCNIwE7B3cDxmxFBa7CdJ66Gm1+W2/EkwIIYQQorF0Grjlmm6cG5PPRedUBt6H7GYW50axqzQITxvoA6j6VEV9gXyg3pTSGAzEXzOeEd+9xbiMVcT87RyizjsdFIWSjcnsnPYCi3uexaqzJpH21hc4c/JbpA55DiiEEEIIIURtErB3YPsOVLAwJwqoXNhU20ILmwohhBBtiQSAbZ9J6+M/DyZyZlQBEQYXXlUhpdzK4twoDtpM+HwNm8/eEmF3QzrRAz1+Rh8WQujQAYz89QN6PHgrg2Y9QcTpIwAoWrmB5L/PYGHXM1h70W0c/OhbvHZHQOsVQgghhBCiI5OAvQOpOcdWVeG1d/fiQ+GMkZHEmFyBK0wIIYRohySob3lhBg+nRRZVz2d3+LRsKg5l8iObSKswk5FtD3SJx1VX2F51WXOD+CPPwbrOSV2QhW53T2L0ki84Z/8y+s/8F6EjTgKfj/yFK9l613/Y9/KHrLv8bsq2p+Apr2hWTUIIIYQQQojaJGDvoLIcRjZsLUaDytQ7egW6HCGEEB1coDt6RfulKJBgdnJOTD79g8vQKj52ppSxtSSEa+5MYmFOJFuLg1m+Oh+3r2U+jtfWxsE01cEPv6Hng7dy+upvOWvnfPrOeIDggX3B5yP35yVkz53PgvgxbJz0ANnzFuB1OANdshBCCCGEEO2eBOwd0LN3ziG5NBiA3tYK4uPMftmvdPIJIYTwN38uDNmS5G9gy9Mq0CfYxrkxBdxxQ3ciDC60WgWbV0eazcK/n03m9+xoVuSHs7ssiEKXnuNNkmkLAfmxamjJ8yqodzf6PHoPZ27+ia6Tr6P3o3ejDw/FZ3eQ9c1vbLjqPhYmjGHLbf8i94/l+NzuVqlLCCGEEEKIjkYX6AKE/+0pD8Lu1RIXY6SPNifQ5QghhBDisMrgMvChb1tm0vq45Zpu5C5ey5RXruDxe34h12HAExbJoUw7hS4DhS4Du8tAr/gofy6ZwgozMcaW78ZuTGDf2uF+zVA8ZcYs+k6fWv2zMSay8metFmdWLrqQYLK++RXHoWwOfTaXQ5/NRR8ZRqcrLiD+2ktQfW1gtVkhhBBCCCHaCelg72AOZdpJLQ8CYOodvWVhUyGEEOI4pFu37Qqy6IgzORkcVsZX75zKuTF5DA4tpZPJgV7x4VY1LF2Vz9aSEBbmRnPt5CS2FgeT7TC22DiZltBS5+CR+1UUBVN8LANm/pPOt0xk9JIv6Hb3JAzREbgLikl/72vWnHsj+1//hB0PP0vR2i2o6vEXnJXHkBBCCCGEOJFJB3sjfTA7jdsndQ90GXUqWJbE63v740Mh2ujkzFGRrP040FUJIYQQorV11E75IJ2PIJ2d7kF2fCoUu/X0O38Yc7/dQ5FLz6FMO2AhzWZBQSXc4CbG6GJHSimqWjnvXVRSFIWI00cQcfoIBrzyHwqWriXz61/InjsfT0kZ+1//hP2vf4K5R2firx5P/NUXE3xSv0CXLYQQQgghRJsjHewdSLbDwKp1hSionBRahtKAV5HScSSEECIQ2sJsbNG+aRSIMLi59brunB5VxIVxeTz774F0t9gI0npQUSh0GdhVZuWuhzfxe3Y06wtD+emPLGyetvMU+MjHgr8eG1XP8RryXE+j0xE97jSGvPcs4zJW0enq8cRfewlaixn7/kOkvvAOf558GcuHjKdgeRLlKfuPe1whhBBCCCFOFG3n1YVospQZs3C6fGwvqVzYtGeQDavOG+CqhBBCiLarZogpYX/HoNeonDk6isFhZZwbW8C5MfnV42SsQVrcqoZMh4kX3khhYW40i3MjefWdvWQ7DHiaOE6mpc6d4+23JUPs1BfewdqvB8M+e4lxmasYNvsVYi8bh8agp3xnKoXLklg28EL+PPUKUl96H3t6ZpOPJWG8EEIIIYToCGRETAfx9byD2Lw6IiMM9DPkBrocIYQQotlmTp4T6BJaVEcd49JWBOm81eNkHnrrfB67Yx55TgOGzvFs21lCuUfHtz9nAOEoqGQ+uhlHmYUYowuf7/hzxzuCIxdDPZIuyEL8VRcTf9XFuEvKyPlhIXuefQt7Wgalm5Ip3ZTMrn+9iKlzHIbIcOImXtCK1QshhBBCCNE2SAd7B1Dg1PPJ1+kATLm1JzpNy7wolA4/IYQQzdGQvyOB/lvT2I5a6cBtH3RahQiDm37BFfxv5jAuisvjlPBiJlzUCcvhcTKbt5ewqyyY5fmR/O2GVawvDCXdZiInz+G3Oo51fjf03A/UOacPDabzTZeTMOlSzj24guiLzyLizFNBUXAcyib5gadY1O1MDn0+j/QPvsFVWByQOoUQQgghhGhtErB3AF8f7ITT5SPC4OK8sTGBLkcIIcQJzt8heaBDd9Hx6DUqncxO/nFvX8YdHifz8D19iDM50Ck+Sso8ZDpMbC4OZeJta/nPtr48vaMXOQ4DHp9/amiN87qljmGMjiDs5EGMXvQZ5+5fRtT5pxN26hDw+bDvP8S2ux9jYefTWTfhbjJm/4inrLxF6hBCCCGEEKItkIC9nVu/pYj1RWFoNDR4YdO6NLYbSsIOIYQQ7Y387RL1CdJ5ufzieE6NKOHCuDzeemEofa3lhOtdaDSQ5TCxryKItYXh/JYdw6r8cD77Jp1ilw41QNNkjnc++/t8r++5oikhlvCRQzlt5RzO3r2QyLNHEXxSP1S3m9xflrD55kdYED+GDdfeT9b3f+C1O465PyGEEEIIIdobCdjbMY/Hx6vv7AXg8ovjCdV7AlyREEIIIUT7plFg8IBQEkMqOCO6iJ+/GMOI8GK6WWyYtV5UFPJdBt75dD/L8yP5Iyea/764k3SbibwCZ4vVVV9g3tQgvebt/BV2W3p2IeL0EZy58Ue63j2JPo9NIahPd3wOJ9nf/cHGa+5nYcIYNt8yjYo9aaher1+OK4QQQgghRCDJIqft2Hc/Z5J20IZV5+H267vz9sZNgS5JCCGEEM0ki5+2LSFWPfFmJ/FmJ6paRoVXS57TQGhiD1Yn5eHyaVi4PBcI5fJb1hCsG0C00UmM0UWEwYWujbWzzMuIZVodlx9vwdPGMkZH0Hf6VPpMn0rpph1kzvmFzDm/4jiYRcYXPwCgMRtx5RbidToZ8t6zKFqt344vhBBCCCFEa5GAvZ0qKHLxwew0AK7snE2IVd9ix5KP1AshhBDH19hgvCF/XxuyTwnkW4+igFXnxaqzM+2xQTx/1xyKXHp6jxvKD9+nUuLRU+bRUebRsa8iCA0qEQYXRo0PZV/5UeNkGrewaev9G9cXwtfleN3viqIQOnwgocMHkvjsPyhavYnMOb9y6JPv8FbYSX//awDyF6yk05UXEn/NJYSNHNLksYdCCCGEEEK0tjbWUyMa6u2P92Gze+nfJ5jTowoDXY4QQgghxAlHo0Ck0c2dN/TgzOhCfv68cpxM18PjZHwo5LuMfHOoE7f+fQN/5EQx46WdrMwPw+E9/tPwqgD+yP8eb/uWdrxQva7rU2bMQtFoiDjtZAa99jg9HriVhBsuo8utV6IxGXFm55H2xmesOuMalvQ5l13//j9KNu9ErfGuhMxtF0IIIYQQbZF0sLdD23aW8NviHAAevLs3mq9WBrgiIYQQomOQT22J5ggNOXqcTK7DgFaBFGcYdgfMX5oLdAUgWOcmxuiit7UCbzMXSz3WuduYjvSa/D02piZFo8HSowt9p0/F2CmaitR0FI2GnB8XYT+QQeqL75H64nsEJfYk/urxxF99cUDqFEIIIYQQ4nikg72d8XpVXnm7cmHTS86LY0DfEEA6eoQQQggh2pKqcTI9rXa6Bdn5dfZpjIks5MaruhKmdwMqZR49qRVBvJzSk9+yYnjw8a3sLbdQ4tbV6txurLbyRlFDn58qWi3Wvj0Y9un/cV7mKuImXkjc5eejMRqo2LWPPTNmsWzQRaS/+xWpL76LLe1Qs44nhBBCCCGEP0nA3kRV889b20/zs0jZV441SMvkm3pUX95WXkgJIYQQHZH8nRXNpddriDK6mXxT5TiZC2LzODm8mK4WO+F6Fz4U1m0uYkdpMMvyIrnsptVsLArh98U5DRon01KqQmt/PQaOFYKnzJiF1mImeEBvTp4zi3GZqxny0QtEX3gmik6HMyefXf9+iSV9zmXlGdeyf9anOLJy/VKXEEIIIYQQTSUBeztSUurm3c/2A3DH9T0IDzMEuCIhhBAisBo6m7o5+24LGtKZ669tROswalUSzE6GhpXyf0N2cXZ0Pvff2YsYoxOtolJY7OaQ3czTr+xifk40S3MjSC6xklxixeVr+AKgzTmPA3XbKvoQK51vmMCpP73HuIN/EjP+LCLPGgmKQvGaTex46BkWdTuTQ5/NJf29r/Ha7M06njw+hBBCCCFEU0jA3o689/l+Sss89OoexISL4+VFgBBCCCFEB/BDZizBei9XX9qZUZHFXBiXy+vPDKa3tYK+vawAlB4eJ/NSSk/u3TCI1QVhfDn3IKXNHCcDLf9mkj8++WmIiiB0+CBGLfiUHn+/mQEv/ZuwkUNBVbGnZbDt3unse+UjlvQ/n0Ofz8NdWt7sYwohhBBCCNEQErC3E7v3lvHD71kAPDi5NzptwzuXhBBCCNHy2lLHu2jftAoMHxzOgJByPnz1ZC6IzWV4WAldzHZMGi8+FPKcRt78cB9L8yKZcMsaNhaFcMhmorDI1eL11Xeu+3ucTH3H0AVb6XH/zZy24mvOTllI5DmjCRnSH3w+bHsPsOXWf7IwYQwbrrmfrO9+x2t3tFg9QgghhBBCSMDeDvh8Kq+8sxdVhXFnxjB0UFigSxJCCCHarLYcdLfl2kTbZdSqdLY4GBZeynmx+ZwVnc/AkDJGDg9Hq6gUFLo4ZDezsTiUS29azdLcCHaUWslzGvA2r7ndLwqWJR11WUMeCzU/rVnfJzctPboQcdrJnLF+Ht3unkTEmacQ1Lc7PoeT7O//YOO1f2dB/Gg23fQPcn5dis/V8m9ACCGEEEKIE4sE7O3AH0tz2L6rFLNJw5Tbega6HCGEEEI0Qnsb6dbe6j3RKAqE6L30stp46cnBXBiXy6tPV46TCdG5gcpxMnvLg1hdEM7v2TGsKQjj63mHyLAbaew0mbb2ptCx6jFERxA5diRjt//O6evm0euROzF3S8BbbiPzy59Yf9lkFnY+na2THyN/8WpUr7cVKxdCCCGEEB2VBOxtXHmFh/99tA+AW67tRnSkMcAVCSGEEKJKQ8LHxgaUNbdva+Hm8Ug43/q0CowYUjlO5qyYQn78dDTDw0robLZj1Hjxqgq5TiOzPkjl8e39WJATxbOv7uKQzUSpW9uoY6XMmHXMc3JeRmyjz9mmnjPHup2iKIQO7U/is//g7D2L6HzrRMJOHYwxLhp3UQkHP/yGtRfcwqJuZ5L8wNMUrtqI6vM1qQ4hhBBCCCF0gS5AHNtHX6ZRWOymS4KZqy/tXOc27e3FtxBCCCH8R54HiJoiwg10tjjobHGgqlDm0ZHrNGDt3ZVNmwpw+LT8uigHCGXj5lD6PrABX6mVfKeep92+Nnc+pcyYRd/pU5t8e0VRMHfuhLlzJ/r8514KlieR+fWvZH//B86cfNLe/Iy0Nz/D3DUeU0IssZecQ/ZPi+j3xP1+vBdCCCGEEKIjk4C9Ddt3oIJvf8oA4IG7eqPXywcOhBBCCCFEw1SOk/EQovcw7cnBJP/3DT5O60yPsSfx84/7KfXoSUktB4LYWx7ExdetJIQwYoxO0g5WoKqV++goFK2WqLNHE3X2aAa9/jj5i1aT+dXPZP+4EHt6Jvb0TFaMvAJ9RBgK0Onq8WR982uzAn4hhBBCCNHxSWLbRqmqymvv7sXrgzNGRTJyeESLHq+tdSsJIYQQQgj/+iUrhmiji3tv7cVZMYWcH5vHYw8mVo+TcTh95DqNbC8N4YZ717MgJ4rNxSEs+jMXp7d20t6c0Uet5VhjZDQGAzEXjWXoJy9yXuZqhn/9Otb+vdCYjLgLi9nz9JssH3wxB975kr0vvINt/8FmHU8IIYQQQnRcErC3UUtW5rNhazEGg4apt/cKdDlCCCGE6GAkDBQmrY8Lz4lleHgp58fm89HrJzMgpIxooxODXsHh05JuM/PEzJ38kRPNjOTe7CwNIt+px9fIxVKr1Be0t2YAf+S5rzWb6HTFBXS68iLOy1xF7GXnEXPxWSg6Ha7cAnY/9jJL+o5j5WlXs/+1j3Fk5vjt2EIIIYQQov2TgL0Nsju8vPFBKgDXT+xCfJy5zu3mZcQyc/Kc1ixNCCGEEAEQqE+ayfOME4eiQJ8eVnpbbYyOLOa3L09jVEQRvYIq6NU9CFBIs1nYU25lVUEEv2VHM23GNhbmRPL5gXhUtYmJux/VfJw0NMg+cjtdsJWQwf0IPXkQ4zJWEnPJ2USeMxo0GoqTtrDjH8+xqPtYVo+7kQPvfoUrv7DZobmE7kIIIYQQ7ZvMYG+DPvsmndx8J51iTNwwsUuLHktGwwghhBBCiCMZjVpiTC5iTC6mzbqYGXd8S57TQK7TQJ7TiMunYdW6QlaRAEDy7WsxVgQTY3QRZXRh0LRO4N6S4bQhIozQYQPpO30qjuw8sr79naw5v1C0ehOFy5IoXJZE8v0zMHdPwNKjC16Hs8VqEUIIIYQQbZcE7G3MoUw7X35fOeNx6h29MBq1Aa5ICCGEECeS1nrzvTIYlTf62wuT1kcXi4MuFgeqCqUeHQPGj2Dp91vZXWolJ88JWEi3WQCVML2HXaVB/D25GJ8KmjoWS52XEcuEhKaPW2mKpgbyprhoetx3Iz3uuxHbgQyyvv2NzK9+oXTzDmyp6Wy57Z8oWi0Ve9KIv/piYsefjdZS96dQhRBCCCFExyIjYtqY19/bi9ujcurwcM4YFRnocoQQQgghjtKcrmEZh9H+KQqE6j1cP7Erfaw2LozL5f/+exI9gyoI1nkAhWK3npRyK1P+tYXfsqNJKgzlu18yKPdojxonU/WmTn3/bWss3RLwVtg5Y91cxm7/jYixpxKU2BPV6yVn3gI2TXqQBfFj2HTjw+T8vBiv01XnfuSxIIQQQgjRMUgHexuyMqmAVesL0ekUHrizN4pSR6tPE0iHmBBCCCGaq77nE/I8Q+g0MOrkCJaHlgPl2L0a8pwG8pwGKvQhFJe6yXaYeOXtvUAUO+9IwlAeTLTRRYWnfX9a09qvJ5Fnnkqfx+9j+33/RR8WQuacX7CnZZD51c9kfvUzurAQ4iacR8K1l6D6fIEuWQghhBBC+Jl0sLcRTpeP19/fC8DVl3Wma2dLvdt+MDutlaoSQgghhBCiccxaH10tDk4OL+XHz0ZzZlQB/YPLGD44DAWVrFwHB2wW1heFcf+mAfyZF86u0iAKnHo8npYJoOvqhq/ZQd6QbvJjbaMoCsbYKBKfeZiEGyYwZsUcetx/M8ZO0XiKSzn08XesvfBW9r/6Edvvn0HhivVtYmFYIYQQQgjRfBKwtxFfzT1IRpaDqAgDt1zdNdDlCCGEEKKdaavjNJrDXyM0ZBRH4Gg0CmEGD32Cbbz+zBAuisvjxScG0TOoAqvOg4pCkdtASrmVlQURjL9+FUmFocz9NbNWd3tTzu+mPiaa+1hSFIW8P5Yz4KV/c+7+ZYxa+CkhwweijwzDW2HnwP++YPXZ15P2+ifs/OcLOLJyJWwXQgghhGjHJGBvA7JzHXw6Jx2Ae2/ticVy7Mk9BcuSWqMsIYQQQgi/kIBbVNFpVEaPiGRQaDnnxBTwf4N3MjSshHiTA73io8LmJdth4qX/7WFRbhT3bRzA/72VQpbdiM1T90uXugLxtvCGU8qMWShaLZFjRxI7/mzGHVxB/HV/I3hwP3TBQXhKy9n38occfH8OC+JGsvuJVynbsbfefQkhhBBCiLZJAvY24M0P9+F0+RgyMJTzxsbUuY08qRZCCCGEEB1NhNFNV4uDERElXBiXx/svDycxuIyhg0JRULF5dcz7LYt1RWHcv2kgf+aF80NGDHvLLXi8zev6bo0QvuZoR41eT1DvbsRddh7jMlfT6aqLsA7ojaLT4S4sYe+z/2P5kPEsH/Y39j7/NhWp6bX21RKvB+Q1hhBCCCFE87XpgP25557jlFNOITg4mJiYGCZMmMDu3btrbeNwOJgyZQqRkZFYrVYmTpxITk5OrW3S09MZP348FouFmJgYHnnkETweT2velXqt31LEkpV5aDTw4GT/LWwqhBBCCNGRSLjYMdUMuRUFEvsE0zfYxhvPDeWiuDxOjSjiyksSsOo8+A6Pk/khM45nd/bmkutX8ubebqRVmMnItrdYjQ09TxoT2GtNRqyJveg08UJ6PnQbsRPOI6hPdxS9nrLtKex+/BWWJp7HyjFXsfbi2/GUlje1fCGEEEII0cLadMC+bNkypkyZwpo1a1iwYAFut5vzzz+fioqK6m0efPBBfvrpJ7755huWLVtGZmYmV1xxRfX1Xq+X8ePH43K5WLVqFZ988gkff/wx06dPD8RdqsXj8fHqO5UfA73i4gR697Ae9zbyQlAIIYQQwv/kOVbbo9OoxJlcPDC5N+fEFDAuJo8hoaXEmxwEaT2UV3jZUBTK1pIQrrkziYU5kcxI7s3y1fm4fXU3rTS2a72x2zflPNIYDYSc1I/4ay/hvIyVxPztHKLOHQMaDcXrtpK/YAX7X/uYQ598z4G3Z+PMK2z0MYQQQgghRMtp0wH777//zi233MLAgQMZMmQIH3/8Menp6WzYsAGAkpISPvjgA15++WXOOeccTj75ZD766CNWrVrFmjVrAJg/fz47duzg888/Z+jQoVx00UU89dRTvPnmm7hcrkDePb79OYO0gzbCQvTcdn23gNYihBBCiI6jvlCw5uXNGY/RFuZbnygk+P+LReejW5CdERElvDZsB+++NIzE4HIiDC60WgWbV0eazcK/n03m9+xoVuSH89GXaaSWW/DVmCbTkJntR/6cMmMW8zJi/f7vceRx9OGhhA4dwMjfP2Jc+p8MfG06pi6dALCnZ7J96pMs6nI6SeNvZ93ld1OyYTsffrSr3v03pF45x4QQQgghmufYq2m2MSUlJQBEREQAsGHDBtxuN+PGjaveJjExka5du7J69WpGjRrF6tWrOemkk4iN/evJ6wUXXMA999xDcnIyw4YNO+o4TqcTp9NZ/XNpaanf70tBkYsPZx8A4O6bexBi1TfodvKCVgghhBCB0JDQPhAqw0Gp4USjUSCxbwh9gyvoG1zBlFeu4PF7fiHXYcATFsmhTDuFLgMfzD4A9Eav+Ch/LpnCCjMxRmed+5yXEcu01r0b9UqZMYu+06fS/d7rceUX4i4po3zHHsqS9+DMyiNv/goAcn9eQiyw+JkErIk9sSb2OvzVE2v/XoG9E0IIIYQQJ4h2E7D7fD4eeOABTjvtNAYNGgRAdnY2BoOBsLCwWtvGxsaSnZ1dvU3NcL3q+qrr6vLcc8/x5JNP+vke1Pa/j/dhs3vp3yeYi8fFteixhBBCCCHak6YG1hJ0n7iCLDriTE7iTE6mvXMRT9z+LXlOIxEDe5K0NhubV8fSVflACAC7JiehKw6mm8VOZ1vD12YKVAivDw0mfPRwwkcPx1VQjDE2koMffIPX7sCVV4j9QAb2Axnk/fFnrdtpg8wULEuqDt+DB/Qm97dl9H/xX41a+6kq8K/qdu87fapf758QQgghRHvWbgL2KVOmsH37dlasWNHix3r00Ud56KGHqn8uLS2lS5cuR233wew0bp/UvdH737qjhN8XVy7E+uDdvdFoZGFTIYQQQojWICF8x3C8oDtI5yNIZ2faowPZ+eRiPknrTL/zhzH32z0UufQcyrQDFmbt7c5bk1YRqg0nxuhiR0opqlq54Gqgaq+prvEthsgw+vz7XlSPl77Tp/LSzR8xTp9C+MihlO9KpXxXKkVrNuMpLcdbYadweRKFy5Nq7ePgR99i7dcTr9OFISqcnJ8XY03shaVHZxSt1g/3UgghhBDixNEuAvb77ruPn3/+meXLl9O5c+fqy+Pi4nC5XBQXF9fqYs/JySEuLq56m6Sk2k8oc3Jyqq+ri9FoxGg0+vleVPJ6VV45vLDpJefFMaBvSL3bVnWKQMOfiMuLRiGEEEIESiDGxQR6RI1o+7QKRBjc3Hpdd/KWJuH2KZxx29m8++o6bF4tuU4jhV4DhS4Ddz28Cb0STbTRxU9/ZGHzVC5ZVdd5Vl+zzbyMWCYk5DAvI5bI3g2vs+bz/caE8F5TEOaEeLreeU31ZSkzZuFzuXDlFxFx+gjKd+2jfOdeynelUrHnAJ7ScorXba3evmDxaqBywdWgPt2x9u+FtX/v6s53n8fb8DviBzVfBwkhhBBCtHVtOmBXVZWpU6cyd+5cli5dSo8ePWpdf/LJJ6PX61m0aBETJ04EYPfu3aSnpzN69GgARo8ezTPPPENubi4xMTEALFiwgJCQEAYMGNC6dwj4aX4We/aVYw3SMfnmHse/gRBCCCGEaJaWCOGlqaH90mtUzhwdxZqPy5iQkEPQnXfwzCMLyXUaKNMEUWGDTIeJF95IAaLZXhqMWesj2ugkyuCu3k/BsiQ4zqdZj9zGH2sZNDR81xgMmOJj6XzDhFqX75r+KgnXjKd8Vypp/5uNK78QjVZL2Y49+JwuyranULY9pfbOFIWsb35FURQM0RFYunfGmtiLoMSe6EOsDa5dCCGEEKIjatMB+5QpU5g9ezY//PADwcHB1TPTQ0NDMZvNhIaGcvvtt/PQQw8RERFBSEgIU6dOZfTo0YwaNQqA888/nwEDBnDjjTcyc+ZMsrOzeeyxx5gyZUqLdanXp6TUzbuf7Qfgzhu6Ex5qaNXjCyGEEEK0FW2t87y+elozSJfQvmUc61yblxHLtE5mugfZ6R5k56G3zuexO+aR6zBg6BzP9l0lZDtMAOyvsKCgkvnoZnoWRVPs0uHzqQ2u4chQvKGPgaqO+KaoedyqrnCNTkvwwD4ED+xDWfIeoHKm+u7/vkaXm66gbFdqZbf7ztTKzvddqXhKyrDtPQBAxZ40ilZtrD6GKSG2Vrd7UdIW+j/3CIboiDrnvEt3uhBCCCE6mjYdsP/vf/8D4Kyzzqp1+UcffcQtt9wCwCuvvIJGo2HixIk4nU4uuOAC3nrrrepttVotP//8M/fccw+jR48mKCiIm2++mRkzZvi93uPNZH/3s/2Ulnno1T2Iyy6K9/vxhRBCCCHaosaG6U0N3/0V2ssiqycunVYhwuAmwuBm2ovDeObOb+gZZOPnrBjynEZsXi2bt5ewmU4A/O2GVfTTd+WcBVnYvZomHbM553tdnex1zW2va5sjQ25Fo8HSswuWnl0oWb+NIe8/B1R+qnjntBeIuXgs+17+EFd+ETqrhfJd+3Bm5+HIyMGRkUP+wpXV+8r4dC76yDCsib3w2uwkTLoMa//KAF5VVQnZhRBCCNGhtOmAXVWP3xFiMpl48803efPNN+vdplu3bvz666/+LK3Rdu8t48c/sgB4cHJvdFpZ2FQIIYQQ4lhausu9rXXRi7ZHr1E5OaKUg3YzqlpGhVfL0CtGseSrDWwrCaakzEMSYSS9fnicTEkwZq2XHIeBSIMLXdMy92qNPUfrC9c/mJ3GGQ28/VHBu6KgCw6i8M/1hJ0yGKjseE+ZMQuv3UHcpeMo27m3utu9aPUmPCVluAuKKVq5AYDSTTv+2p9ejyEqDFtqOo6sXLrfewPWxJ5YenVFo9c36v4KIYQQQrQFbTpg7yh8vsqFTVUVzhsbw9BBYce9TUM6T4QQQgghROO0ZKjekH1Ll3v7U/Xvqihg1Xm5YnwCgzZ8z/eHYrn4gQt49akV6BM6kby7lKzD42T2VQShoBJpcPPZN+mNGifT1BprLpBa10iZxiyc2lBas4nw0cMIHz2s+rKUGbPwud24C4qJOOMU0t/9CkNMZPUCq6rbjTMrj4zZPwJQsGQNUBm8B/XphjWxF67cAjL6dK8cPdOvB1qzqXrf0vkuhBBCiLZGAvZW8MeSHLbvKsVs1jLl1p4Nvl1znwR/MDutGbcWQgghhBBC5tDXZV5GLBoFBg8IJTGkgmn/N5ynD4+T+SUrhlynEbtXS77LwDuf7gciufTG1QS5Q4g2usgrcAa8/mO9zpiXEcuEZoTZGr0eY1w0Cdf9jYo9adX78bnd7Hj4OVz5hYQMTiRzzq9o9DrKd+3Da7NTvmMv5Tv2AlC4Yn3lzhQFc/cErIm9cBeVYO7SCWtiL6z9e6EPC2lSfUIIIYQQ/iQBu5/UF2aXV3h46+N9ANxyTVeiIhu2sGrNDqjmBuXy8WchhBBCiIaT506iKQwalRERpRyqMU4mz2kgNLEHq5PyKC51U4yZDLuZy29ZQ7Aukmijkxiji4ga42T8cf41Z2HUIzXnk7VH3laj12OICscQFU7vf92Nz+Wm7/SpqD4f9oNZlO9KpXznPjJm/4DWYqZ8ZyruwmLs+w9h338IgOI1m6v3Z4yLrl5c1dq/d/Wcd2NcdJ0LrDalfumYF0IIIcTxSMDewj76Mo2iYjddEsxcfWnnQJcjhBBCCBFQJ2p4XdWZ3RL3v710fZ8IjhwnY9XZmfbYIJ6/aw4X/f18Xnt6JblOIyUePWUeHWUeHfsqgtCgEmFw8cV36ZS4dYToPH6rpT1QNBos3RKwdEsg5oIz8ZSWVQbvqoorr5DyXamkvvQB7vxCdMFWynel4sjIwZmdhzM7j4Kla2vtT2M0EDJ0ANb+lZ3u1sReBCf24uBnc+n3xP0BupdCCCGE6KgkYG8BH8xO4/ZJ3dl3oIJvf8oA4MG7eqPXN3OVIyGEEEIIIY5h5uQ5jdpewvnWoVFgyMAwEkMqSKSCyS9ezhP3/Uqu00Be9TgZI//7uHKcjEHjxfnSTvJsJqKNrgYfpzVD9Zpd8lWd3nV1u1eNo6mvG/xYXeKKomCMicQYE1kdoldt6y4tp+Lwwqrlu1LJnrcAVVWxpabjc7ooXruZ4rWba+9PpyX3x0VYB/T+q/M9sRdBfbqhMRia8dsQQgghxIlMAvZG+HTOAcyWYAAKliUROfbUerdVVZVX392L1wdnjIrk1OERrVWmEEIIIYTwk/bUBSzaj9AQPfFmJ/FmZ/U4mVyHgZB+3Vm7Ph+XT8v8pblAKACrC8KIMbq4LCEHbxPWSi1YllTvdVUB+JFjZeq6vKGjZ/wxombm5DlMe+fqeq/Xh1gJO3UwYacOBkBjMtJ3+lR2TX8Fd0ExkWeNpHxn6uGxM6lU7EnD53RRunUXpVt31dqXotVi6d21OnCvCt99roa9uSGjZIQQQogTmwTsflDXjPQlK/PZuLUYg0HD1Nt7VXe1CyGEEEKIxpOgu+2R7nf/qB4nY7UzbfpJPH/XHApdenqdM5Sf5qVS7NZR5tFT5tHzckpPNKjkPr6V8jILS1bmUezSYdZ6UdUmJO8toDmPVX8E1RqdDmNsFPFXXVzrctXrJfnhZ4kedxr7Z32KOSGuuvvdU1ZBxe79VOzeT84PC2vdLvPLn2t1u1eNnTFEhrf4fRFCCCFE+yABeyMVLEuC4wTlbo+PNz5IBeCGK7sQH2du/jEbSF58CiGEEEK0vJrPudrbXPW6mkNE26FRIMroZvJNPShZuQ6nVyHfVTlKpsytpchtYN3mIiCYx5/fAUQCsOyqFei8kVi0XsxaLxatF4vOy/ZdpTi8GowaX5Nr8ueiqVX7Y/IcJiRU/nxkM1LNx9TxOtmPpWbIrWi1GCLCiL3kHEo2JldfrqoqzsxcynelUrZj7+HQvXL0jCu3AHt6Jvb0TPLmr6i1b0N0RHW3u+1AJnkLV2JN7IUpIdYvC6wKIYQQov2QgL0FbE0uITffiTVIy/VXdGnUbaXTXQghhBDixNSSjRINCexrbiPd6W2HUauSYHaSYHaiqlDu0XLShJF893ky4d1i2JNShNOnxeH0ATrKPbVf4m14ZBMQjQaVTZOTcOT3w6Lz8vHXB1Dywyhw6snJc9CUBviaY2Sm1XN9U/Z3pLpmuze0Q/x42ymKgikhFlNCLFHnjql13c5/zcSVX0T4qGHV3e7lu/ZhP5CBK6+QwrxCCv9cB0D+/D8B0AUHEdSvJz6XC43JSPDhRVbNPTqj0cnLbyGEEKIjkr/wzVBXN/uhTDvbd5UCcOrwCIxGbQAqE0IIIYQQQnQ0igLBei9XX9qZtF9WMe3FC5g5eQ5eFW747yW8/u/52Dxa7F4NNq8Wm1eLPjSYnDwHPhQOZdoBI7jg/c/TgK4ArLxtLQoxrC4Mo8u/N5NfFIJZ6+WXhdnkO/WYtT48TW+AP6Z5GbGQcfxPCdd1uwktPIZFazFj7mqm6x21O+g95ZUjZcoOB+7Zc+eDArY9B/CUVVCyfhsAu7furr6NxqAnqE8PrP0Pj5vpX7nQalDfHmhNxha7D0IIIYRoeRKw+9nr7+3F54NTh4fTNaF5o2GEEEIIIUTHEOgxMjWP35jxg+3Fid5xr1WgS7yFaKMLjshqp71zHs/fNQeHV8OlD4zjw5nLsXu19BrZm6Tl6di8Gpzo8Xoh32kkf1sJUPk65rnXdgMRACzOjcSo8bGz1EqPl3ayvzQIi9bHuk2FlHu0OF2+VhlX2ZzZ5nV1yNfc35Hf13dMnTWI0JMHEXryIAA0eh19p0/F53JRsTedlKdm4c4vwhgXXbnQasp+fHYHZckplCWn1C5Ao8HSo/Nf891rzHrf/+pHx72vMutdCCGECDwJ2P3oYIaNVesL0WjggTt7s2B5bqBLEkIIIYQQ7VCg19Xx1/FP9OC7rdAoYNH5GH5SGAstDgCm/b0fM3dsAeDht67i6cnfYfNoOfeWMcx5dz12r4bYfgkkb83D7tXiQ8Hh07KnPIg9S3MBKwAPTt8GRLF44p8YNVGHZ8D7cPsUDtlNWLRe9qdX4PQePZe8oedZze2aOgv+yHPxWMH6sQLrY12vMRgIHtCb4P69Af6a8+7zYT+QUT3bvXxnKmU7UyndvAOfw4ktNR1bajq5vyyptT9tcBBFqzbiLi2ny02XVwfwhphIFEXhg9lpnNHI34GE8UIIIYT/ScDuJx6vytqNRQAM7BdC186WY25fNWu95sx1WXBKCCGEEEI0VnPC8NYM0psatvsrpJewv35arYJZ68Os9XH+WbFs/rICgGkzBjNz8hxUFc6JLeD7Q7HYvFpOvWwYv81JxubVYokJ5+ChcryqBqdPi9OnpcgNmdmm6v2vnbIeOImw61cR6umNy6epsRCrD7PWS3mFp9F1V3Wk1zWj/cht6nKs2zVVXftUNBosPbpg6dGFmIvGkjJjFoPffYbdT75Ot8nXsevf/0fYKYMrA/idlQutOrPy8JZVkL9oFQAl67ZW708fHoq1fy9C9DEUGcrI/X1Z5Zz3rvEoGo3f71NTSJgvhBDiRCIBexPUFYRv31lCWbmHqAgDQwaGNur2NZ981DXXXQghhBBCnJgC3cl+LG25tioSqvuHokCo3kO4wUM4Hq6f2JWM+WsAmPbW+bxw1xwmv3g5Mx/4CZtXi/3w/He7V4PNo8VnNFFe4aW41E0xdTciLbt2JXolmh33r6e8MBSz1sdX8w6SaTceDuK96JW6V2KdlxFbq7P9WKG6P9V8HVezcaq+bY6kKAqmuGgs3TvT/Z7rSZkxi1HzPwHAXVzKjmkvEDFmOOkffoMrvxC8Pmz7D+EuKqFo1UYsQD6Qv2AlUDkzPqhfT6yJPau73YP798LSqysavb4lfgVCCCGEQAJ2vyiv8LB1R+XCplNu60V6hq1Rt2+tJ4BCCCGEEEI0VnsI0muSUL31KQqEheoJM3gI4+hO9GnvXM0zd37D1f++iI2vf8eS3IgjgngtLp8Gt6ph7/4KoLL7/Y0P9gFh1fvRKj42FofS9cltZBUHY9Z6WfRnLkUuHSVuHapadwBfnyOD+WNtV/P1Wn2h+bE64quuO/J29d1GHxaCuXMcXW6ZiD09s/q2XruDipQ0ynel8uc364lLWQsaDRUpaXhtdko3JVO6KbnWvhSdjqA+3VBVFdXj+WuR1X490Fpabt0w6WIXQghxopCA3Q/WbSrC61WJjTYy7sxoPvzyQPV1R46AqauroUpHXHBKCCGEEEJ0fI0J4WdOntOClbS8EzXAb+4bLXqNSp8eVubazPS02o+6/r5XJ/LslHmMv/csPn11FXavlm7DurNxTQY2b+XoGa+qIcNuImN9IRzuhH9i5k4gkj/zIzFcuYJwTT98KrzwRgp7dvXAovWxdUcJNpeeqvy9ocF6TU0Ni+trpmrs/PQqqS++B1SG7eXegXTaG0Hf6VPZ/d/XSJh0aeWiqofnvJfvSqV0625Ut5vynakA7H3u7b92piiYu8XXWli1qutdH37sT2ULIYQQ4i8SsDfT+i1FpB20oSgw6uQIFOXoxXuEEEIIIYTwt/bQWd7UGk/UEPtEZjFrCdF7GT0ikj+DKgP4adMGMHPydgC8KpUd7x4tZ04aydyPN2LzaonoHsue3UU4fBpcLh85GAH46Y8sIBiAe/+5GeiPgsqGO9fiKgyj0KUnz2lgZX44O0uDeDDHwfeHYrmic+MXUZ2XEUtk78bdpmBZEvPw32gbRaPB2rcH1r494LJx1ZfvfvJ1ut56JeW7Utn32se48ovQWcyU70rFlV+EPS0De1oGeb8vr7U/Y2wUQYk9sSb2wn4gg54P3oY1sSfGTjHHfM0rXetCCCFORBKwN4NPhVff2QtAYp9gIsINLXo8eaEhhBBCCCFaSnsI7MWJS6uAVefFqvNy6QWd2PX94YVYZ17IzMlz8Klw44y/8dq/fsfm1TLsosEsnLcLm1eLISyEnFw7PhQysx2AkRX5lUH87jIrAKvuWAvEsLognM7/2kzBvj4MDSslyugiz2kgx2Ggu9tXb30Fy5IgoWnd8cfT0H1Whds1Q25FUTB3jcfcNZ6iNZuBv8bU7PjnTGIvOfuvrvdd+yheuxlPaTnOnHycOfkUHv6UdVUArwsNxtrv8Iz3wx3v1sReWLonoGi1fr3fQgghRHshAXsz7K+wkFZqw2TUMOyksECXI4QQQgghRJvl7wBfmk9ETRoFEuLMRBndgJvbJ3WvHsE57Z3z2PnkLL5O78TlD43j/eeXH57/XrkIq92rxaXocXugwGWgILkEMHPI/td88tUF4SgT/yRU159Ig4vuL+7AeDCOAb9lkuswYNZ6+fZgLDpN5fYN7UZviUC+IVJmzEIXZCbyjFMoWLKGgS//p/ryng/eSvmufYe/UsmetwBQqdhzAE9JGcVJWyhO2lJrfxqTkaA+3VF9PhSNQtmOvRiiwvE6XWiNLduIJoQQQgSaBOxN5PBq2F0WBMDJQ8IxGjTV1xUsS4JjzFpvLTLTXQghhBBCiGOrGdQ3NrSXkL/90Cpg0fkYMjCMLhbHUdf/439X8fRd32Hzahl362l8/XYSsSYXO0qt1QuxelWFYreeYree1OV5QAy/vrUHCK/ej0HjY3leBGatlzc/TGV/hZmVSQX8lpbAVV2yGlzvsRZMrVLVNQ9HB/U1u9gbMral5ja6YCthpwwm7JTBAGgOB+Q+j5eEqy8+3O1e2fGev2QNnpIyfA4nZdt2V+4reU/1fg++PwdLry5/zXlP7IU1sSfWxJ7ogq0N/G0IIYQQbZsE7E20o9SKR9UQFWkg4uAO6HVqg273wey0li1MCCGEEEKIE0B9HfEN6ZTvKMF4R7kfreF454VGo2DS+jBpfYw7M4aNX9iYkJBTfTtVhbtenEDSM5/yW3Y0p04Yzm9zthM/sAtbN2Zj92rxqBpcvsqvYreeL+ceAkL451PbgUiW5kUSfN1Kwn19cPkULFovZq2PDUUhkFqGy6egqmr1jPPmzGRvCRqdluBBfQke1JeUGbMY9vnLpMyYRZ//3Evyw8/hyi/EnV+EqXMcKb8mYS3Nxed0UZGSRkVKGjk/Lqq1P12IlbCRQw8H7pUjZ4L798IQFdGi90PmxAshhPA3CdibICfPcfjjgmrlwqbbD7Z6DTMnz2n1YwohhBBCCNHaWno2fH37b0xjzLG2bczz9tZsxqkvnJfQ/i81zw1FgfBQA1tKQog3O5l0RRcO/bGaaY8PYubkHagquFWleiFWu1fDgLP6s3x+KiHxUezfX4pb1VBW7qEMc63jJJcGwwMbgRiWTlhCfJcQgou7U+LWMfv7g2TajewrNxNd7EJVK2tp7P1oTFB/ZPf78ShaLYaIUAwRodC3B32nT+WPyXO4LD6bbndeUzlqZmcqZTv3kvf7cnwOJ86cfDyl5eQvWEH+ghW19qe1mAgdPqi6271443YSn34YU+e4Yy6w2pokpBdCCFGTBOyNpKqwZn0hAF0tdqIjjRRQ+fG8yLH1d7EfeX1TxsjIwlNCCCGEEOJE0NGf99YXpDdnxKME44GlKGBQVAwaD6F6DwD339kbx/qNTHvtAmZOnoPHp3DtYxez4bVvWJwbWT3/3ebVogRZKCp24/Bp2XegAggB4K2P9gFhrC8KgxtXY9AMItLgossTW8kpDkav+DiwLJdCl54ilw6fT61VV0MeS/XNga8vXD9e6F51LiqKgik+FlN8LFHnjK6+ru/0qbiLStgx7QXCRw2rMXImFXtaBl6bg8IV6ylcsb56n5mzf0JrtVQusHq4271qkVVLzy5odLqjaujoAfiJcB+FEKK9kIC9kdJsZgpL3OgVH/2Dy5u9v+M9iZYnykIIIYQQQhxbQ0PExmzTkusZNXffgf40a0u8RjkRXvfoNCo9uwXhCStjf4Wl1nXT3hnHs3fOwe7Vcsl9Z/PJyyuxe7V0G96DDasPYfdqcapaXD4NWQ4TWRuLAAsHbBa++7+dQAQr8iPQT/yTcG0/fCqMfH03e8uCsGi9bN5ejM2pJ9zgBv461+vqbK/Z8d6YRVgbs60+PBRzl044MrIZ8OK/qi/32uwkP/wckWeMqJ7zXvjnOtzFZXjLbZRs2E7Jhu219qUx6LH06Y41sSfB/XtjTeyFMzufDz7eze239GtQPTU1Nrhu7Ix7UUl+J0KIjkQC9kZwuLzsKq1ciCUxpByjVj3OLdrOgqdCCCGEEEK0da3Vud6c49R125YM45urLQXXbamWQKrv/NNpIFjjZeTwCJYF2QGY9kh/Zk7eBsADb1zJ2ife48fMWMZeP5LvP9yIzaslqmccu3cV4vBqcHsg12ME4JcF2UDl69f7Ht0C9EeDSszta3AXh2PWevngizTSbSZ2lFoJyrLj8f01gqVmnccK5Jvig9lpnHH4+5pBa+r/vY+pUzQJky6t3jZlxix6P3o3ttR0ynamcuCtL3DlF6Lo9RQn7wWXk/LkPZQn7yGbP6pvF/v+1yyaEYchMhz94RE2FfsOojWb0AUHoQ8PPXx5GPqIsOpt2hIJ74UQon2QgL0Rtmwvwa1qCA/T081sD3Q5QgghhBBCiGMI1Pz2urTmfPX6tLeAW+bE12bQa4g1uYg2urjkvE7s+LYCgGnPX8jMyXPwqXDL05eybuZs5udEMfziwSyYtwubR4MxMpTsbBteVUN2rhMwAPDRVweAUDYXh8JdSSgMwqjxsX/aJgqLQrBofYTp3eQ6DFh0XpxOb7PuQ1V3fMGyJOYRW72Q7IR6QuKqcTR7n3ubvtOnYk3sRdm23QD0nT6VDz7fx3WnGyq73Xfuqx41U7IxGZ/DieNgFo6DWUftt2jlhnprVPR6Dn3yPfrwUNwlZYSePLB2CB8eWtlZv2I9GV/9jNZswut0kfrCOxJ0CyHECUoC9kbYs68CnT6IUSdHoNl56Ljbt+VOFiGEEEIIIUTraexrg9YcA9OWAuu2VEt7o1EgLsZEv5AKdpZZufW67uQtrTzvpr1zPi/cNQeHT8MVD5/He88uw+7V0GdMX1YvTUOrqBT6LLhcPhw+Ldt2lsLhxVj3lAdVH2PxlSswagYSb3bS44UdHNjRi8t+ycBbHEypu3JxV7PWB/wVpje1w3rm5DlMSKj987R3rj7iTms49Olc+k6fSnHSVkwJsQx+52l2P/k6P6dauW3KyaS9+Rk7QgYwJlFP9tz5+BwOLL264S4qoWzbbrQWM67CYlyFJSiqiup2Y0/PxJ6eCYA9re7X/lnf/Fr9/f5XP0LR6zj48XcYDnfG6yPCDgfzoZRs2E76h9/81U1fYxutyVjnwrIS1gshRPshAXsjJZjtxMWYKNh59HUNGQcjobsQQgghhBDCH+oa4eHPfTak614C8banvnNBUcCs9XFS/1A6WxwATLuvLzO3bWZCQg59Hr+Pp+78DrtXw7jbTufrt5OwebSE6D3srzBj82rxqhqcPi37KyzsX5EHBPHy23uBHgAszYsiSOtBp1Exa73Men8vmuxIctbkExdjwl1jBM2RNdc3fuZYs90LliVBwtGXK4qC12wlfOQQ3vtwN5FjTqXHpO64C4sBaoXZfadPJWXGLOYdiubBFy5i95Oz6Hz9pfz03W76py7D53DitTsIGZxIwdK1mDrHUbZ1F9ogM/aD2fgcTlBVVLen3o55gPxFq+q8XGsxo+i0ZM9bgD4iFFdOARqzEZ/DSfHGZNI/+AZDZGVQrw8PrQ7ta+poY2I62v0RQnR8ErA3gk6nMDCk+QubHk9b+PioEEIIIYQQ4sTWmOagYwXtgQjhJfhvPEVRMGl9mLQ+zj0jhg2f26qv6x5kR1Xhnpcu5/n7f+KksDI0Y8/ll6+2kjCoK1s2ZGP3anGrGiq8OvBCiVvP1z9kAAl8+Uzy4T3FsPyaFWhdEZi1PraVBGPWerFovezaU8Y36XFc3SW7+rhVbxYc+abBkQuxNnQ2fNV+ImencfuRzXGK5nCAHUrYKYNx7QlhcZFaHe7/2ftvnBEWUh3IV/1XVVV6/v0WUp58nYRJl+IuKsFVWIK7sAR3UTG5vy7Da3dg7tIJV2EJFSn7UTQKrsIS8Pnw2irHz1aNvqlSumkHAAX1BPOKTsfBD7/B5/agNRsp255SY658KPrwMPIXraLb3dcdvjwMQ2QYWrOpRQJsCcWFECcyCdgbYdhJYZgKywJdhhBCCCGEEEI0Wn0d721pVn1jaznRgvSW+rdqSEitKBASrCfM4OGgzcy0CZ058Nsqpj02iJmTK8Ngt0/B5q0cFWPzaBl0zgCW/b6X0M7R7NtXgsunocLmBfSUegDnX/u/46GNQAwLc6Ppcu86bAVh1eG7RefFrPWRX+jEpzb//jbk0+cNpSgK+rAQ9OGVwfyRVG/lyJwju+ZVn49dj71MtzuvYe8L7+C1O4m9+CwOffEDXpuD0CGJ5C9dg7lLPO7CYlyFxZWhfWEJqteL6vHgyPirs99+ILPO+rK+/a3WzxqTEUWvI/v7+ZVd8ZFhdY61qRxlc3jufGQYGrMJRan7EwhHkrBdCHGikYC9Efr1smIrbN0xLy39ZFcIIYQQQgjRcZ2oryfaUlBfc98d/Q0BvUYlVOMhVF/5832398KWtIFpr1zAzMlz8Phg0vTxzHpsAXav9nAYr8Xm0aAJtlJQ6MKrKqQdtAHGo/a/4uY1aDgJs9ZL5uNbyC0OxqL18ceSHNxlFjYUhfKY9+gEvimBetXr/mONqDnSB3V1xtdD0WjQmoxYenTBFF95TsRfM57y3fsAanXK16SqKp6yClKeeI3ON1zGvlmf4LM7iTp7FK7CYnJ/XYq1b09cRcWUbt6J1mrBcTALn9ON6vFUjrRxOClLTmngb6KSxmg4OoQ/3PGvjwijZON2sr77HX1EKM7sPOzpmegjwypH4DQwmG8t8gaAEMLfJGBvIXWNeZH560IIIYQQQghxtKa+ERCoIN3fQXlHD96r6DTQvUsQsSbXUddNe+c8nrtrDnavlr9NPYeP/29FZSd8jSDeqWrx+RQqvDrWby4GLAA89fIuoDcAiyf+SbiuH6qqMOOlnaQUB6PTqHzy9QH2lZvRaVR0isrchz9ibWFnJqdX8GV6J3SKiter1tvNX7AsiXlUXnesjv+6FmOtueBrY9R1HEVR2P/qR+jDQwg9eRBBPbtWbnf31ZWLIw+8svr4NUfZ9Hn8PrzlFex+4jW8dgc+u5OY8Wdx6LN5hI8eRu6vS/HaHVi6xuMqKMZdVNkt78wrBJ8Pn9OFMysPZ1ZevfXm/rK0+vv0974GQGPQo48IQ/V4sSb2rA7my3fvq76uKqTXh1d2y/tcLlRVrTOYb+3xNjJORwjREBKwCyGEEEIIIYRosEB3xdd3/PpC6saG1ydK2N0WaRWw6rycMjScJUH2o65/6M0reWry99i9Ws66YTTffbgBm0dLdJ9O7ErOx+7V4vVCvrey+33+0lyqQvi9n6cBIdX7Wl8UBsCKKeuBaAB+m7AcrRLDyptW4yiNrA7j1xaEodP40Ckqb32USkpZEHN+PERZXjhmrY+ijYUUlFsoKnZh82goLXeT8dLb9H+i/tC2poZ0yTc3lN3z1Bv0nT61cpxNWOXvoXxnKmEjTqLPo/cwNy2SCQk5tY5RNWNedbnpete1vP/wN0y6d2TlyJrDIXzOL0vw2SsXgjVER1Cxez9eh6OyY97txudy48yuDOULVxTWqql4zeZ669338keHg/fQv0L48DDKUw4H89Xz5iuDeX1k5TZaa1CzO+b9GYBLmC7EiUECdj/zxwKl0ukuhBBCCCGEaE9aInT31z79tZ+awXtrvcnQnPEyLf1GQSDeiNDpNATpfATpfFw8Lo7t31QAMO2ZC5k5eQ6qCrc8cykvP/Ibdq+WUZcPY+E3W/GoCgPG9GLjn2l4VAWPqmDReil0GdBZjJSVuVGpDGW9qkJBkQuoXKz1SAe+PwRY2fVeKtCl8sIntgG9YWcWEM3C61YBgzFO/BOjrz9eVWHX3zeg5PXEpPFh0lbOlD9oM3H2VwdILbewPC+cQyvy+DW1C+fEFKBNK68M68vcWMxa4K/u+MaMrTlSQ8P8KoqioBgNWLol4IiMJ+qc0bW29bnctX6uur7P4/fhrbDhLqxc9HX/ax8Te7hj3mt34HU4sfbrifvwbHlXUUnl9wXF+FyV4bwzJx9nTv5R9R0rmFd0OjRGPeau8X+F8IdnyVfNmy9L3kPewpUYIsI49Pk8NGZTvR3zQgjREBKwt4L6nnjUDOOlS0IIIYQQQgjR3rTkAqaNFejO+oZobI2N3d4fDV/Qfl+fKgrERpuINLoBN5Ou6MKhP1YDMO3+fsxM3nLUbaa9UzlaxavC3S9O4NWHfuKG/1zA+zMWVIfxHp8Gt6rg8SkMG5fI6vkp9Dm5G9uSDhGq9+CLiiHrYAkeVYPHp+A7HNY7XT6cVA6kT9lXDliPOn7qF2lAMMmlwfDCDiCcNYXhMHUDEM3CSasA0CuDUBTYcNdayvIi2FUaRNST2/Ds60qOw8AbH6SyuywInaLy0x9ZWCxaioqDsSeXkG8zYdJ6iS11H3OR2KqRNFUhfF1hfFXIX7Mzu67tFEVBZw1CZw3C3DUeS4/OdLryIsp27K3e5sjO7qpRNrsffwWv3UGXm67AVVQZuleF8Lk/LyaoX0+KVm/CZ3egC7ZWb+NzulA9HrweD+U7U+u/o0D293/U+nn/Sx+gMRowde2Et9xGycZk9OGhVOzZj6LTHp43H/ZX1/zhznpdiPWECealG1+I+knA3kwN6Tav70mRdKoLIYQQQgghhKipvYbb7Z1WgfBQA0E6L316Wg+H9Eebensv7EkbmDZtADMnbwdg2qyLK+efH/bAG1eybcbbdJpyK2/++zc8PoUJ953JV6+vwOPT1AjuFQac3puNy/cTaXChdO7C/t35eFQFg9VMaYmrOqx3qxpQISPLAegpLdeTsr4QCAPgwLzKznqA5DeqFjDtAf/aDPSt/PH6VUAsv2dHY9L68KkQaXAT/s9NZBeEoVNUXpi1m+SSyv2klltYlhfB9hIr164voMCpR6dRyci283V6HP90+476/dQVth9r9nzNOfFQGcxrDHo0Bj0hQxKPCnV9Dmet7WvOmPfZHeya/io+u4NOV12Eu7CEQ5/OJeK04eT8tBhrYi8KV23E53DgtTtRdFpcOQWoXi+q14vXZqdiV+Uis45D2dXHLF579BszVRSt9nDYXjnCxlVQiNZkIursUZRs2YXWbCTjq58xRIbxa5KNq67rj9fhZPeTr9Pviftr/Q4ay1+B95G/SwnRhWg8CdiFEEIIIYQQQpwQmtPlXtdtjxWGNyYob83ue3/Nqm/u7Zqyn/by5oNBryFY7yU+zkyo3gPA6BGR/Gl2HrXttPv6MnPb5srvnx9aHdRXddb7VLj3pcvZ/vwH/JoVwzWPnMsnzy/BoyqMu+FUfvpkAx5VYfi4RFb9kYJHVeg2KIH8XenkOIwER4WQm1OOV9XgVSvDeo+qodyjAcBm13FwRylQObc+c342EMS+iiCAys56YMOT24EIAJbdmQTE8McVf6JVBqFVYFV+OCatF5tXy74KMzEv7MBi1mIxa3FkxGLSeNn5eyYlBWGYtV7WF4Zye2oZuQ4D0cUuvjsUiwb45zF+rykzZtW7wGzVjPlfynoxISGHqLNGMXPyHCYMH0ivR+7iu71hTEjIIf7qi6tvUxUo+9weut89iXce+Job7hvJgbe/xOtwEHHayeT8uJgMQwzROSkYY6NwF1aOsnHmFKB6PKheL668Qlx5tefLl23bXf193u/LAYgClvz38IWKQvrbs9FHhOKtsFO8fhuGGrPki9ZuIWHSpRgiD8+YPzyHXhcajKLRHOO35F8NCdxrBvRCnKgkYBdCCCGEEEIIcUJrS+NlWqKWmt3VzdHYETStFdq3l+C9KTQKhATriTK6CdF7OKl/KDEmFwCXnN+JHd/ZAJhyWy8q1m4AYNr0C0mZsbQyjH5nXPW//0NvXcXzd3+Lx6dwZnQh83Oi8fgUxt91Gt+9vRqPT2HUpYNZMje51ngcj6oQ2SWCjLRiPKqCotfjcFZ2r1cG95DvMlTXXOgywIq8Gvfi8L/Nm3uArtWXrnxgI5AIN64GYlFQWXrdSgyuRExaLxHTNqFm9CDq+WSCLDqc6Z1IrzAz+/uDlOVGYNb6yE3Kp6g0CJPWhyXTjsOrwelVUNW6Z+HUHH9TFdRr9DpMCbE4I+KIHDuSgmVJldf/42q8Ngc7e/+NIXt/qhUgz5w8B8XjZupT46oXfJ3zzK+MsOTgtTsIGz6QvIUr8dmdGOOicBUUU5xRgMFRgddmB1XFlV+EK78IAEfG0XPxq4L5mlRFg9ZkwNw5Dq/NTsaXPxF+6pDqkTWVo2yq5s2HcWj2D9WL2yoaTaM61I/1yYPGaMgxP5idxu2TuvvleM0lXfyiKSRgbyPa0hM6IYQQQgghhBD+0dKv9WqGy80ZQ1qzTnl92nz1dVq35vGh9vmh0yoYNCoGjcr6ojAiDJVjcM4+LZp1nzoAuOWabuQuXnvU/qa9el6t7vrn75rDlJevIPm59/k5K5rToopZlBOJR1UYHFZG8LizSP91JZZRp7Dyt914VIXugzuza1MWHlXB7VOqQ3yvWtmRraJQVu4BKsP6jJ2lQDCsrFroNLryPn20D+hcedFTyUCvyu8nJwHRzM+JRjthORo1mpX54YRMWUdxXjip5RYKXXoKXXoy7Cbe/Ww/9qxoTFovaUtzyHYY2bi1iPwKM+UeLQVFrqPC+prhq6rTY4qPxRRf+fst+yKN0IRIoLKbW2MyVn+fMmMWe3r/jdsndWfX9Ffw2R10uXki+175EK/dSfR5p+EqLCbnx0UED+hD4cr1eO0O9CHB2NIO4bU7Ud1uFNWHz+6gYk9adU22vQeOeS4cePNzUBT04aGASt7vy3EVFlMQ05v+wxMo2bITjU7Hvlc+pHj9Ng5+9C0as4nyXftQdFoKlieR8cUPKDod5Sn70ZpNaExGtGZj9X1sbRKC/0V+F22DBOx+JnPVhRBCCCGEEKL9aksLt9Z3rIZ0kjektuZ0fjf1vjfkmM35vda3//q6+FuiW76pv9ea/65H7uN4v5PW7uLXKBBs1RFhdBOi99LLamNbSeU4mXyngdsu7czMXxTuuaUnZavXAzDtsUHMnLzjqH09XNVZryrc/MRF7Jz1JYtzo7hk8ul889YqvKrC6EsHs3huMh6fQr+RPdiy+gAen0J0tygO7i/C41PQGA3Y7F4AvD7woqHAZaAg3QYYKHJXBvdZDhMA++ekA50qi3hpFxBG0n+2An0AWHzTauAklE3pfK0ZSPCta7AXD6DLPzaSd3hm/TOv7MK1PRmT1su+cguLcyMxabzkrMmnsDSItQVh3H3IRpFLR86SdajXdePHnITK38egvvyhG86ExBy63HolAN+lhDDtf1cfNRd9XkYsD78+gdfu+IzzQg+ScO0lpP3vC9ZnGjj3b31xF5awLekgvSJ97FmZglGvEIIdR3YeqssNqoq7sBiA4sKtAJhT00lb/de/Q8HSNQDk/bas1r9P5pc/VX+f/u5XdZwMGhSdlgNvz64O3t0l5Vi6JZBTquIqs1O6bTdaU2UgrzUZ0ZhNFK/bSvT5p6M1GfHNTeagczQas+nw9UYy5/xKt7uuqw7yawb7qu/o+f/1qTnn/8g58/4Op5uzv0DdVviHBOyNULhyAyaDJdBlCCGEEEIIIYRo41oyfPdXp3praYnwtyFjbxp73MaOwGkt9d2PtlBv1fnUnPNKq1XQa1T0qHTvEsSckhCijS7Gjo5i7ceVnfU3Xd2N7EWVnfUTyhdgjKg83rSXz6/VWf/CXXO4sFMe8fffweuP/IxHVZj4wNl88dJyBoeVklQYhsen4FE1DD6rD2sXpeJRFTonxrE3OY+wuFCyM8qqu+tBQVXB7tViz3cCOnbsLqNqZn3W4hwqp6tX2llW+SYDz/zVWb/8nnXAAAB+u2w5WqLRKSpb7llHSV4Ee8otRD+bjCclhSxHMO98uo/1u3qg06hcujibkqIQ8pwGdh9wUKQP43tHKI8MH4ql72qKrbF4y3NIfPohfpw8h6veuZqFNX4fKTNm8UN6JFOfPg93YQmfPDqXMdYs1mcaCO4cxbDuWvIXrSStxMDAk+PZuTqV3v2jKd+5F9XjRXV78Hm8lfPmPV4UrQafw4nPVWMRYJ8P1eU7ag69Kycf4+HfVE7Gnjr/7YtWVL75Eg9sXf3zUddnfDq33vMm9YV30ZgM4FPRR4ZVBvvFZVi6d0Zj/ivIr9iThv1gFmXbU/CU20hanIqi/x9ak5FlC9Mwd/m2VrBvSztE0dotaM1G0j+YQ69/3FmrY1+ja3yU2tgAvKUCc3/WIaF+bRKwCyGEEEIIIYQQ7ZS/F25tCf4aP9OQ2zZ2/zW3r3rjoiMtuNoe+PN3qSjwR3Y00yKNBOsru9lHDAlnsdnJaVHF5Dn/Gmny4OQ+zNy4CYBpT13AzMlzmPbWXzPrVRW86l+jbCb963w+fGYhf7v7DOa8uQqPqnDahCEcnL8Gh1dLSlkQHlUhwuBG7RTPwdQiPKqC1mSgvMJDVVjvQYNHhQOHbICeYreenavzqVpEdv83B4HKoH7bK7uB7gCsfngTVWH+gmtWAoPRKj6W50VguuoPHN4IMv6zhYzCUHSKykv/24PzYBz7bGZ+Xudi6Zwd6EL7khAdxX5DKFGnnsS5V3XFrhhJz4ri2revYv493zDp3b866OGvufXw10KmqtfL/931JX+fOZ69z73N/Iwwzo3Mocv/t3fn8VFV9//HX3cmmQlJyEZW9h1kCwKSRgVRkGCphWpbXGpBrYpiv1IEhX4rIH4f1cJPK1XUarX4+Gpxq8C3VkUEEgoGkB0krIJhSQIkJCF7Zub8/ggZMyRAiNmA9/PxyCMz956595yZfLjDZ858zvjb8JSW8d0b7xN3exJLXkrB5i4naVw87uJSPKWl/OejzfxoeBeyV28gpG9P3MUl3sS+p6SUwn2HcLQKp/hIJsblwh7gpDw3H4zxSewblwt3QcUiwu6iYu/2suPZ1f4uCr6pSPDnbdxBFLB3W8Us/XMm9v93iff2d6+86/s35ucHNgv/0JbYApwUlNsx/v5ExYVQmnWC3A3bvIn9sGv6cXrnXiw/O8bl+j6RX2Umf7XEfoCTsuxTFB/OuOjE/t45L/GfM6WIzt5eUzK8tkny+kqmX85JeSXYRURERERERKROVK/93AnihkjCn6vmfkOU1Wmox9Wnqn2o7/5YFvhZBj8qarD37NaSSGc5Q34USerfK2bW/+rn7Zm7fB1Bfm6uDs8HYGybLLrP/Em1mfVuAw/NHcv8qf/C5bH4+ZSbeOf/peDyWNx01yD+/b+bcRuLfjf2YP2X+3EZi3ZXxbFvZxYuYyMwPIick8W4jIXBAioWmc0tt0G5PwCbtucCFSVwjnx6DIgGYPfr+4FQADaeCqsY4KfHWPzpMaB3xd2xq4Folt3+H+zu3riNhQ2DzTJ8fSZpH/z4ZlzHMgnp0obDedFs+cM+Ygp7keF2klUYS+TGFvj7BXLaMZiY/C5sjCrFZhnC4/qzYcspep7cxraeo3GW5GJPuBX/e27F389i+5+TGfDfI/D3syh7/V26Pjaewy/9HT+boeeMhzj0p1e5atYk8HjYM3s+ptxFp9+OZ/+811lxLIywft25dVgrFj33BT9/KIEj7yzFuFxEjRxC5tIvaXXDYE4s+w9hg/vx9ac76Z/Yvlpi311ciqeklOLDGd7EflHOafwtT7XEPuCdsV+ZWM09XPG7+Ltj3raViX2A3HVbL/g3d77EfmUpnqqJ/fCoIMpzcrH87ORu2EbRoaOEhu9k28qIipI6LQKwBzg4lbq1Wo19e4CTwv3fkZ2yHlvA94n+7/66iK7TJ150KZ4LqY9Fc5tzgl4J9gaiWuwiIiIiIiJyJWusevb1neT/IbPgz7W9NiVtfkgfqqqaaK6v56Y+ktc/5JsB9bHuwNn7azpXfZTdqWmB24pkPbQKdxDsVzGzfkDfML4MKAWg19f/ZG/Lir5MfrArczdtBuCJOUlVEvXDmfvQBxgDk1+6nT9NWnxmkdjvF40ddW8iS/62AZexGHxLb1Z/kobL2OhydXt2bjxypjxORYkcl8fCY7PjdpuqPaWszAPYffpf5D6TPtx7GgiCbbmAE44Uk04YAEeKW8CnlcnlaHg/HQgGYNcbBwBIpR0Am3Mrkv3M2nGmfQRrfrf5zO2e8Jv1VJbV4ZdrgL7w09X4+1kY99XYLAj5/bdQ8CNKPDb8d7dg+Y4T5DmuYc+6SA67f0KbwBLCT3Vlb3A4/Vt0orBjFFFd+rG5Vx9Kh1zFuk93YbtmGH7j+rPqwy2M/vUgvvo6m14nNuNvGeyWYX1OKDdE5dD5/ts5+rdFdBo/lmNvLMLylNPpnjEcfXMR6zKDsXvKuTb0JMblImb0jXz25lr6BZ3yJvaNy0XI1b05lboZU+4iqHsn8rfv5pQjnJgQy5vY92sZiKe4lKKc0/h5yjHuKgnus0rx+AGnM7/fXZnYb8G3HFlHNbWpsV/p0F/e9t4+8KfXwWbx3Wv/wFNahjM2EpvTSXlOLtkpGyg5monl50fo1b3Yf7SMkhO5DBzVu2LmfZUZ+3lbd2Hz8yNzyfLvS/gEBHDk7Y/pNHkCHy47wV13dMUe4MDWIqDWM/abS8JdCXYRERERERERuSBNJKudpk74n8u5kvwXe/zmUPu9vjWH2fi1ZVngdNoJsFefXTxyWAxbF1WUS/nNrzqR85+vAXhiei/mPrSzWvtpr/2CPz30IR4Dj77wM1783VIe/ONPeGXGv/EY8BgLD5b39s8mDeHDl9fw4/t+xP+9ub5ivwEPFj1bFuAyNkKuHcRXn+2mY1Ax+wsC8RiLHoPas+9gASUncvEYiHCUc6LUQWS7CDLTc3EbCA4PoiS3gCK3Hbu/H6VlbjgzU79SucsANjBwMqcMqFi4lrxyTlGxZuKp7bmAs6Ic0NoTQAuOLs8EWsH/HQWCOFAl+Z/2twNACDte2gvAajr4nDM1OwKmbQN6we/3AgMrdvxPFnDTmRcFPjxd8XoEfe6gNOzHfOLnpsW3oZjIDhS4/GjnaEVGux8RG1BKaI8OlDq/JbPESfy1HfH3t1GweQfR1/XHz8/Ghs93YcMw/Bf98MOD3bjI+SwZu3ERe/O15Hy2kl05AcQHn8LudmH3lBFzwyDWfrQJP3c5vVrkYHeV0WpQL/LWrMdyzlKv8wAAGyxJREFUuQnu2YW01APE+RVyvNBO2w6hZOzLJIQS/FoGUnbiFJafHXdBoU9i/+wZ+678Au++kqNZ3tuFe74lEAgEDu4+97Uic/EX1balv/Ee0cCXU77fZtntFYn4Kovbluee5sSy/3jvFx06SsbHywgb1NennS3ASZFlqp2nISnBLiIiIiIiIiJSC/WdPD9XyZcfcv4f0sdzJc9rqlV/vvNcSgnrptIQpYUu9rGWZWGzqJgR3tKfALuH2OgA7yz7s103uBVrW5SeSeSX+OxzGRsAE8d3Jv+rjYxtk+X9G3niiV4VH/C0qmhbue+J+Tf7zNDfO+eliu1//aV3tv5jf7md53/7MSNjT9L+v+5lwfR/Ywzc898jOfDX90k+3srnQ4DRv0lk6Rvr8BiLm+4cwBf/2ML1Y/uRvHiHt03/G7uxceX+iuT/NR34ZkM6nfq2Zt/2DCIc5RwvcZw5ZsVxgyOCyM0uwhHopKiwDE+VMj2V3GfGn3/aBdgpLbPDsWKgBQB5u/MBB9llDvg6B87M/D+6ojJJHQmLj5y5fSb5/9bBKmfoVvHrnVPA1QAkn66yexnQcvj3951AGtBqMDYMfnkGcxW0sLspNzZaRQeTF3macEc5we2icR3LIKRLWw6nZWFhsOOmfUAR4X27smf9QeIT2lGwdScR8d0p3Lwdu8dNRL9uFGzejt3j4mRgDO5jGdg95fTpH4NVVoa9vBSrtISS3fuwuUrJLYS2bQIpTz+MMySQwhP5BBTnYfOzU3a6CJvn+78743bjLijCXVDk8zyfr8Z+VUWm5r/hhqIEu4iIiIiIiIjIRaivRLtq2Puq+nzUpt58bZ6/+ppxX5kIrvpNgB9S0qY2qh6/Nuetqmr75vatg8rX7ULjsCwICLDjbzOsOt6KJ6K+T/5379KSJXkhtHKW+zxmxNBoNr9bkfy/fXQbDvzfWhLSlpLR8vvz+CxwO+0q5j60gydmJjH3oTSfDwYqVZbnqUz8A0QMvYYTKV/7zOL3GIt7Z4/ijVnLGBKZQ9z4X/DOvFV4DIyZeD3/fHUtHmMRH5bPplOheIzFVSEFuIzFN3nBXDOyJ2Xlhq9X7Dvz7YGKDwS6xLdhz9ZjGGMR1yWS9P3ZeAwE+nk4Xe6Hv80DAS0oKCjDnPnWQVUeLMo8FdvKXRUfBBzJKAb8OO3ygz2ngWDYmktFZr7C0dIgWJMHRHBgTSHQCdaWA1dVNPga4JqK24VAaGcAln931gvZZqjv/b5nfncAC4O/w46n3EVwiBN/Pwvy8ylxW0TFBpObkUds2xD8bR7Kj2WSX2rRrlM4WfuziPIvxm5cBLeJpvxQOkFtIrG7XZR9dxhbeAAsmUZjUYJdRERERERERETqTU0J1NrUqq/N9tq42G8G1Leq56zal4utJd9cP4Cpr28oVP9ApUJtnqdztalp4dvaPI/na1vTeC3Lwm6B3VuKpOJ3x3ZBhPq76BxcTPfeoSxzlgEwNDGSdQsrau4PjTpFTllFeZsfx51gydEYrgop5JF7uzD3oQ+ID/M9/1jPXpa0OvNtgLnfz/qv/DCgYmHd33q3j2mdxeKjMfzXi7fxwmOLuTnmJG5j8XlmFMOicvgyqxXXR54i+UQEP2qVS/Qvb+WjBWtrLP1TeXtQ0lWcWLOZoAHxbE45gMdA94Ht2bXxMFHOMjKKnd72kW3DyDych8dAUFggJXlFuIxFqdtGRfGZ7z8AMN66/zZy8yo/LKlYqPf0sXIgkJzDrjPbo8EGR74D/NvwbeWhjgGOCDhxplnLjriKC8/3ctc7W6OeTURERERERESkETTX5GRzVt/J6Ob8GjRF3+rr+a2vDyUaYpZ7Tcepmjw/nwv1v7bHqW+V/ao6tnO9lpV9XHI0xqe/tel7Xcd39rmsM2V/AgP9cNgMKSdaseZkBMF+bjaeCiXM4aJryyIineX0CS3g+oRIWp8p/dM+sISOQcV0Di6ma3AR3VsW0jOkkEcmdKaF3cPUR7rRPyyfAeH5zHmyF4Mj8ugUVMy1kblcH3mKoVE5LPzLIG6KzmZETDaLFyZyY3Q2CwZ8w09aH+fWuOP8JC6L5R9ez6jY44yMOcFHbyZwU/RJhkWdZFavfVwfmcO1rXKY0v1bBkecYlB4LrOmXkX/sDz6heYz+cGu9Ao5Tc+Wp/lp6yy6BhfSOaiQ20a3ZmhUNm1bFDMwPK9Oz2VdaQa7iIiIiIiIiIhcNi420VzZ/mIT4PVVE7+hk/21GVdt2lQm5M/X35pq9F/sc3CubwCcq01tjnku9V0+pzbjruvfJ5x7seKLVXnMJUdjaFWL56C+vrVgWRWTzg/PfQWHLQYw5L/2BsF+FcfekhtChKNiJnuf0AL2FwQBcPMN0Wz5R0Xpn28/WUvX4O+P2SukYuHVKRO7sff45yw5GsOIVod55Qf3tvY0g11ERERERERERKQRNdfZ/U01S7wx1ZSkrs3r0dDlhhpmEeX6bV99LYDqqn5oUXn77LFd7Fhr80FI5SK5AJ9kRF/U8X+oKyrBvmDBAjp27EhAQAAJCQls2ND4dbhERERERERERESao+aW+P8hs71ro7Fq9DfEWgMXOmZtj13Ttw5qc676+lCiIZ6bxnbFJNjff/99pkyZwqxZs9i8eTPx8fEkJSVx/Pjxpu6aiIiIiIiIiIiISJ00p2R0Q3xoUV8lgRrKFZNgf+GFF3jggQe499576dWrF6+99hqBgYG89dZbTd01EREREREREREREbkEXRGLnJaVlbFp0yZmzJjh3Waz2RgxYgSpqanV2peWllJaWuq9n5dXsfJsSVlxw3dWREREREREREREROqkModrjGmU810RCfaTJ0/idruJifH9qkBMTAy7d++u1v7ZZ5/l6aefrr79H480WB9FREREREREREREpH5kZ2cTGhra4Oe5IhLsF2vGjBlMmTLFez83N5cOHTqQnp7eKC+KiFyc/Px82rVrx+HDhwkJCWnq7ohIFYpPkeZNMSrSfCk+RZo3xahI85WXl0f79u2JiIholPNdEQn2yMhI7HY7WVlZPtuzsrKIjY2t1t7pdOJ0OqttDw0N1T+aIs1YSEiIYlSkmVJ8ijRvilGR5kvxKdK8KUZFmi+brXGWH70iFjl1OBwMHDiQFStWeLd5PB5WrFhBYmJiE/ZMRERERERERERERC5VV8QMdoApU6Ywfvx4Bg0axODBg3nxxRcpLCzk3nvvbequiYiIiIiIiIiIiMgl6IpJsI8bN44TJ04wc+ZMMjMz6d+/P59//nm1hU9r4nQ6mTVrVo1lY0Sk6SlGRZovxadI86YYFWm+FJ8izZtiVKT5auz4tIwxplHOJCIiIiIiIiIiIiJyGbkiarCLiIiIiIiIiIiIiNQ3JdhFREREREREREREROpACXYRERERERERERERkTpQgl1EREREREREREREpA6UYK+FBQsW0LFjRwICAkhISGDDhg1N3SWRy97s2bOxLMvnp2fPnt79JSUlTJo0iVatWhEcHMztt99OVlaWzzHS09MZPXo0gYGBREdHM23aNFwuV2MPReSSt3r1am699VZat26NZVksWbLEZ78xhpkzZxIXF0eLFi0YMWIE+/bt82mTk5PD3XffTUhICGFhYdx///0UFBT4tNm+fTtDhgwhICCAdu3aMXfu3IYemshl4UIxOmHChGrX1FGjRvm0UYyKNIxnn32Wa665hpYtWxIdHc3YsWPZs2ePT5v6el+bnJzMgAEDcDqddO3alYULFzb08EQuabWJz2HDhlW7hk6cONGnjeJTpGG8+uqr9OvXj5CQEEJCQkhMTOSzzz7z7m9O108l2C/g/fffZ8qUKcyaNYvNmzcTHx9PUlISx48fb+quiVz2evfuTUZGhvdnzZo13n2/+93v+Ne//sWHH35ISkoKx44d47bbbvPud7vdjB49mrKyMr766ivefvttFi5cyMyZM5tiKCKXtMLCQuLj41mwYEGN++fOnctf/vIXXnvtNdavX09QUBBJSUmUlJR429x999188803LF++nE8++YTVq1fz4IMPevfn5+czcuRIOnTowKZNm5g3bx6zZ8/m9ddfb/DxiVzqLhSjAKNGjfK5pi5atMhnv2JUpGGkpKQwadIk1q1bx/LlyykvL2fkyJEUFhZ629TH+9qDBw8yevRobrzxRrZu3crkyZP5zW9+w7Jlyxp1vCKXktrEJ8ADDzzgcw2t+gGz4lOk4bRt25bnnnuOTZs2sXHjRm666SbGjBnDN998AzSz66eR8xo8eLCZNGmS977b7TatW7c2zz77bBP2SuTyN2vWLBMfH1/jvtzcXOPv728+/PBD77a0tDQDmNTUVGOMMZ9++qmx2WwmMzPT2+bVV181ISEhprS0tEH7LnI5A8zixYu99z0ej4mNjTXz5s3zbsvNzTVOp9MsWrTIGGPMrl27DGC+/vprb5vPPvvMWJZljh49aowx5pVXXjHh4eE+8fnkk0+aHj16NPCIRC4vZ8eoMcaMHz/ejBkz5pyPUYyKNJ7jx48bwKSkpBhj6u997RNPPGF69+7tc65x48aZpKSkhh6SyGXj7Pg0xpgbbrjBPPbYY+d8jOJTpHGFh4ebv/3tb83u+qkZ7OdRVlbGpk2bGDFihHebzWZjxIgRpKamNmHPRK4M+/bto3Xr1nTu3Jm7776b9PR0ADZt2kR5eblPbPbs2ZP27dt7YzM1NZW+ffsSExPjbZOUlER+fr73004R+eEOHjxIZmamTzyGhoaSkJDgE49hYWEMGjTI22bEiBHYbDbWr1/vbTN06FAcDoe3TVJSEnv27OHUqVONNBqRy1dycjLR0dH06NGDhx9+mOzsbO8+xahI48nLywMgIiICqL/3tampqT7HqGyj/7eK1N7Z8Vnp3XffJTIykj59+jBjxgyKioq8+xSfIo3D7Xbz3nvvUVhYSGJiYrO7fvrVdWBXgpMnT+J2u31eCICYmBh2797dRL0SuTIkJCSwcOFCevToQUZGBk8//TRDhgxh586dZGZm4nA4CAsL83lMTEwMmZmZAGRmZtYYu5X7RKR+VMZTTfFWNR6jo6N99vv5+REREeHTplOnTtWOUbkvPDy8QfovciUYNWoUt912G506deLAgQP8/ve/55ZbbiE1NRW73a4YFWkkHo+HyZMnc91119GnTx+Aentfe642+fn5FBcX06JFi4YYkshlo6b4BLjrrrvo0KEDrVu3Zvv27Tz55JPs2bOHjz/+GFB8ijS0HTt2kJiYSElJCcHBwSxevJhevXqxdevWZnX9VIJdRJqlW265xXu7X79+JCQk0KFDBz744AO9AREREbkId9xxh/d237596devH126dCE5OZnhw4c3Yc9EriyTJk1i586dPusKiUjzcK74rLoeSd++fYmLi2P48OEcOHCALl26NHY3Ra44PXr0YOvWreTl5fHRRx8xfvx4UlJSmrpb1ahEzHlERkZit9urrUCblZVFbGxsE/VK5MoUFhZG9+7d2b9/P7GxsZSVlZGbm+vTpmpsxsbG1hi7lftEpH5UxtP5rpWxsbHVFgd3uVzk5OQoZkWaQOfOnYmMjGT//v2AYlSkMTz66KN88sknrFq1irZt23q319f72nO1CQkJ0eQUkQs4V3zWJCEhAcDnGqr4FGk4DoeDrl27MnDgQJ599lni4+OZP39+s7t+KsF+Hg6Hg4EDB7JixQrvNo/Hw4oVK0hMTGzCnolceQoKCjhw4ABxcXEMHDgQf39/n9jcs2cP6enp3thMTExkx44dPgmD5cuXExISQq9evRq9/yKXq06dOhEbG+sTj/n5+axfv94nHnNzc9m0aZO3zcqVK/F4PN7/pCQmJrJ69WrKy8u9bZYvX06PHj1UekKknh05coTs7Gzi4uIAxahIQzLG8Oijj7J48WJWrlxZrdRSfb2vTUxM9DlGZRv9v1Xk3C4UnzXZunUrgM81VPEp0ng8Hg+lpaXN7/pZtzVbrxzvvfeecTqdZuHChWbXrl3mwQcfNGFhYT4r0IpI/Xv88cdNcnKyOXjwoFm7dq0ZMWKEiYyMNMePHzfGGDNx4kTTvn17s3LlSrNx40aTmJhoEhMTvY93uVymT58+ZuTIkWbr1q3m888/N1FRUWbGjBlNNSSRS9bp06fNli1bzJYtWwxgXnjhBbNlyxbz3XffGWOMee6550xYWJhZunSp2b59uxkzZozp1KmTKS4u9h5j1KhR5uqrrzbr1683a9asMd26dTN33nmnd39ubq6JiYkx99xzj9m5c6d57733TGBgoPnrX//a6OMVudScL0ZPnz5tpk6dalJTU83BgwfNl19+aQYMGGC6detmSkpKvMdQjIo0jIcfftiEhoaa5ORkk5GR4f0pKirytqmP97XffvutCQwMNNOmTTNpaWlmwYIFxm63m88//7xRxytyKblQfO7fv9/MmTPHbNy40Rw8eNAsXbrUdO7c2QwdOtR7DMWnSMOZPn26SUlJMQcPHjTbt28306dPN5ZlmS+++MIY07yun0qw18JLL71k2rdvbxwOhxk8eLBZt25dU3dJ5LI3btw4ExcXZxwOh2nTpo0ZN26c2b9/v3d/cXGxeeSRR0x4eLgJDAw0P/vZz0xGRobPMQ4dOmRuueUW06JFCxMZGWkef/xxU15e3thDEbnkrVq1ygDVfsaPH2+MMcbj8ZinnnrKxMTEGKfTaYYPH2727Nnjc4zs7Gxz5513muDgYBMSEmLuvfdec/r0aZ8227ZtM9dff71xOp2mTZs25rnnnmusIYpc0s4Xo0VFRWbkyJEmKirK+Pv7mw4dOpgHHnig2mQRxahIw6gpNgHz97//3dumvt7Xrlq1yvTv3984HA7TuXNnn3OISHUXis/09HQzdOhQExERYZxOp+natauZNm2aycvL8zmO4lOkYdx3332mQ4cOxuFwmKioKDN8+HBvct2Y5nX9tIwx5uLmvIuIiIiIiIiIiIiIiGqwi4iIiIiIiIiIiIjUgRLsIiIiIiIiIiIiIiJ1oAS7iIiIiIiIiIiIiEgdKMEuIiIiIiIiIiIiIlIHSrCLiIiIiIiIiIiIiNSBEuwiIiIiIiIiIiIiInWgBLuIiIiIiIiIiIiISB0owS4iIiIi0gRmz55N//79G/Qcw4YNY/LkyQ16jvNJTk7Gsixyc3ObrA8iIiIiIg1JCXYRERERkXpU26T21KlTWbFiRcN3qJHUNO5rr72WjIwMQkNDm6ZTIiIiIiINzK+pOyAiIiIiciUxxuB2uwkODiY4OLipu3NB5eXl+Pv71+mxDoeD2NjYeu6RiIiIiEjzoRnsIiIiIiL1ZMKECaSkpDB//nwsy8KyLBYuXIhlWXz22WcMHDgQp9PJmjVrqpWImTBhAmPHjuXpp58mKiqKkJAQJk6cSFlZWa3OXVhYyK9//WuCg4OJi4vj+eefr9bGsiyWLFnisy0sLIyFCxcCcOjQISzL4v333+eGG24gICCAd999l+zsbO68807atGlDYGAgffv2ZdGiRecd96FDh2osEfPPf/6T3r1743Q66dixY7V+duzYkT/+8Y/cd999tGzZkvbt2/P666/X6jkQEREREWlsSrCLiIiIiNST+fPnk5iYyAMPPEBGRgYZGRm0a9cOgOnTp/Pcc8+RlpZGv379anz8ihUrSEtLIzk5mUWLFvHxxx/z9NNP1+rc06ZNIyUlhaVLl/LFF1+QnJzM5s2b6zSO6dOn89hjj5GWlkZSUhIlJSUMHDiQf//73+zcuZMHH3yQe+65hw0bNlxw3FVt2rSJX/7yl9xxxx3s2LGD2bNn89RTT3kT/JWef/55Bg0axJYtW3jkkUd4+OGH2bNnT53GIiIiIiLSkFQiRkRERESknoSGhuJwOAgMDPSWRtm9ezcAc+bM4eabbz7v4x0OB2+99RaBgYH07t2bOXPmMG3aNJ555hlstnPPjSkoKODNN9/knXfeYfjw4QC8/fbbtG3btk7jmDx5MrfddpvPtqlTp3pv//a3v2XZsmV88MEHDB48uMZx1+SFF15g+PDhPPXUUwB0796dXbt2MW/ePCZMmOBt9+Mf/5hHHnkEgCeffJI///nPrFq1ih49etRpPCIiIiIiDUUz2EVEREREGsGgQYMu2CY+Pp7AwEDv/cTERAoKCjh8+PB5H3fgwAHKyspISEjwbouIiKhzQvrsvrrdbp555hn69u1LREQEwcHBLFu2jPT09Is6blpaGtddd53Ptuuuu459+/bhdru926rO8Lcsi9jYWI4fP16HkYiIiIiINCzNYBcRERERaQRBQUFN3QUsy8IY47OtvLy8Wruz+zpv3jzmz5/Piy++SN++fQkKCmLy5Mm1rg9/sc5eVNWyLDweT4OcS0RERETkh9AMdhERERGReuRwOHxmY1+Mbdu2UVxc7L2/bt06goODa6xnXlWXLl3w9/dn/fr13m2nTp1i7969Pu2ioqLIyMjw3t+3bx9FRUUX7NfatWsZM2YMv/rVr4iPj6dz587Vjl2bcV911VWsXbu22rG7d++O3W6/YD9ERERERJobJdhFREREROpRx44dWb9+PYcOHeLkyZMXNfO6rKyM+++/n127dvHpp58ya9YsHn300fPWXwcIDg7m/vvvZ9q0aaxcuZKdO3cyYcKEao+76aabePnll9myZQsbN25k4sSJ1WaL16Rbt24sX76cr776irS0NB566CGysrIuetyPP/44K1as4JlnnmHv3r28/fbbvPzyyz713UVERERELiVKsIuIiIiI1KOpU6dit9vp1asXUVFRF1WnfPjw4XTr1o2hQ4cybtw4fvrTnzJ79uxaPXbevHkMGTKEW2+9lREjRnD99dczcOBAnzbPP/887dq1Y8iQIdx1111MnTrVp+b7ufzhD39gwIABJCUlMWzYMGJjYxk7dqxPm9qMe8CAAXzwwQe899579OnTh5kzZzJnzhyfBU5FRERERC4lljm7CKOIiIiIiDS6CRMmkJuby5IlS5q6KyIiIiIiUkuawS4iIiIiIiIiIiIiUgdKsIuIiIiINHPp6ekEBwef8+diytCIiIiIiEj9UYkYEREREZFmzuVycejQoXPu79ixI35+fo3XIRERERERAZRgFxERERERERERERGpE5WIERERERERERERERGpAyXYRURERERERERERETqQAl2EREREREREREREZE6UIJdRERERERERERERKQOlGAXEREREREREREREakDJdhFREREREREREREROpACXYRERERERERERERkTpQgl1EREREREREREREpA7+Pwf8iPzqdjZ5AAAAAElFTkSuQmCC",
            "text/plain": [
              "<Figure size 1800x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# plot zoomed histplot\n",
        "plt.subplots(figsize=(18,6))\n",
        "sns.histplot(data=df, x='trip_duration', palette='coolwarm', kde=True, hue='vendor_id', bins=50000)\n",
        "plt.xlabel('trip_duration')\n",
        "plt.ylabel('number of train records')\n",
        "plt.axis([0, 3000, 0, 1500]) # zooming co-ordinates\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Hmher1f64H42"
      },
      "source": [
        "Observations:\n",
        "\n",
        "Almost equal distribution of *`trip_duration`* per *`vendor_id`*."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jdwd9CHm0Fo2"
      },
      "source": [
        "e. Store and Forward Flag"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 480
        },
        "id": "thQFKGsdx4U2",
        "outputId": "42d57d43-f1de-4962-f737-54449224b0a4"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 500x500 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# plot countplot\n",
        "fig, ax = plt.subplots(figsize=(5,5))\n",
        "sns.countplot(x=df['store_and_fwd_flag'], data=df, palette='coolwarm', ax=ax)\n",
        "for container in ax.containers:\n",
        "    ax.bar_label(container, color='black', size=10)\n",
        "plt.xlabel('store_and_fwd_flag')\n",
        "plt.ylabel('number of train records')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Aoh8Z-C10gN1"
      },
      "source": [
        "Observations:\n",
        "\n",
        "1.  *`store_and_fwd_flag`* has two values: N and Y\n",
        "\n",
        "2.  This feature is highly imbalanced with majority of records having value N.\n",
        "\n",
        "2.  Distribution of training records with value - \n",
        "\n",
        "    *   N having 1450500 training records\n",
        "\n",
        "    *   Y having 8042 training records"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QTxKrACg1h21"
      },
      "source": [
        "f. Passenger Count"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 327
        },
        "id": "lyHLYSkf4eLE",
        "outputId": "3ab46a8b-edbe-4c9c-bb75-0686248eff5b"
      },
      "outputs": [
        {
          "data": {
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",
            "text/plain": [
              "<Figure size 1500x400 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# plot countplot\n",
        "fig, ax = plt.subplots(figsize=(15,4))\n",
        "sns.countplot(x=df['passenger_count'], data=df, palette='coolwarm', ax=ax)\n",
        "for container in ax.containers:\n",
        "    ax.bar_label(container, color='black', size=10)\n",
        "plt.xlabel('passenger_count')\n",
        "plt.ylabel('number of train records')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4CwEvAiH5V9Q"
      },
      "source": [
        "Observations:\n",
        "\n",
        "1.  Majority of times a taxi is taken by when *`passenger_count`* is 1.\n",
        "\n",
        "2.  *`passenger_count`* distribution - \n",
        "\n",
        "    *   1: total training records count is 1033510\n",
        "\n",
        "    *   2: total training records count is 210303\n",
        "\n",
        "    *   3: total training records count is 59893\n",
        "\n",
        "    *   4: total training records count is 28401\n",
        "\n",
        "    *   5: total training records count is 78087\n",
        "\n",
        "    *   6: total training records count is 48333\n",
        "\n",
        "    *   7: total training records count is 3\n",
        "\n",
        "    *   8: total training records count is 1\n",
        "\n",
        "    *   9: total training records count is 1\n",
        "\n",
        "(Global Outliers)\n",
        "Drop records with *`passenger_count`* 7, 8, and 9, because these look like rare instances and could mess up with the model performance. A normal taxi doesn't accomodate more than 6 passengers."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "j0jPzm4G5Nps",
        "outputId": "d16b2f61-cfa4-4748-a11c-be33eb94241b"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Row count before performing action: 1458534\n",
            "Row count after performing action: 1458529\n"
          ]
        }
      ],
      "source": [
        "# drop records with passenger_count>6\n",
        "print(f'Row count before performing action: {df.shape[0]}')\n",
        "df = df[(df.passenger_count < 7)]\n",
        "print(f'Row count after performing action: {df.shape[0]}')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 368
        },
        "id": "zClzzfdl89Vl",
        "outputId": "9e77ba4c-b28c-46e2-a2d6-3ca88633d433"
      },
      "outputs": [
        {
          "data": {
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",
            "text/plain": [
              "<Figure size 1800x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# plot zoomed histplot\n",
        "plt.subplots(figsize=(18,6))\n",
        "sns.histplot(data=df, x='trip_duration', palette='coolwarm', kde=True, hue='passenger_count', bins=50000)\n",
        "plt.xlabel('trip_duration')\n",
        "plt.ylabel('number of train records')\n",
        "plt.axis([0, 3000, 0, 3000]) # zooming co-ordinates\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7mMP-Sg1CJTY"
      },
      "source": [
        "Observations:\n",
        "\n",
        "When *`passenger_count`* is more than 1, then almost uniform distribution of *`trip_duration`*.\n",
        "\n",
        "Plot another histogram with *`passenger_count`* equal to 1, and *`passenger_count`* more than 1 to get clear picture."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 368
        },
        "id": "EuRr7FsI_1IQ",
        "outputId": "906bb22f-67d1-4499-93bd-c3bd1661deb0"
      },
      "outputs": [
        {
          "data": {
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",
            "text/plain": [
              "<Figure size 1800x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# plot zoomed histplot\n",
        "plt.subplots(figsize=(18,6))\n",
        "passenger_count_df = np.where(df['passenger_count']>1, 1, 0)\n",
        "sns.histplot(data=df, x='trip_duration', palette='coolwarm', kde=True, hue=passenger_count_df, bins=50000)\n",
        "plt.xlabel('trip_duration')\n",
        "plt.ylabel('number of train records')\n",
        "plt.axis([0, 3000, 0, 3000]) # zooming co-ordinates\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "K4YXhkVdDXPi"
      },
      "source": [
        "Observations:\n",
        "\n",
        "Even with *`passenger_count`* > 1, the distribution looks right skewed.\n",
        "\n",
        "Plot median *`trip_duration`* for taxi's with each *`passenger_count`*."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 407
        },
        "id": "JK5BFdFcCcei",
        "outputId": "5efb8247-46e1-45b2-aa89-07be1aebc34d"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 600x400 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# plot passenger_count Vs median trip duration\n",
        "grouped_passenger_count_df = df.groupby('passenger_count')\n",
        "\n",
        "fig,ax = plt.subplots(figsize=(6,4))\n",
        "sns.barplot(x=grouped_passenger_count_df['trip_duration'].median().index, \n",
        "            y=grouped_passenger_count_df['trip_duration'].median().values, palette='coolwarm', ax=ax)\n",
        "ax.set_title('Median trip_duration Vs passenger_count', color='black', size=10)\n",
        "plt.xlabel('passenger_count')\n",
        "plt.ylabel('median trip_duration')\n",
        "for container in ax.containers:\n",
        "    ax.bar_label(container, color='black', size=10)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "j6_s7MoTGiPk"
      },
      "source": [
        "Observations:\n",
        "\n",
        "Median *`trip_duration`* is almost same for taxi's with each *`passenger_count`*."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ni2Cm9Pps--Z"
      },
      "source": [
        "g. DataTime"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Md8Z9JdstEsw",
        "outputId": "3129779b-0f1e-4c2d-e733-cf087b4e72d0"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Start DateTime: 2016-01-01 00:00:17\n",
            "End DateTime: 2016-06-30 23:59:39\n"
          ]
        }
      ],
      "source": [
        "# convert to datetime object\n",
        "df['pickup_datetime'] = pd.to_datetime(df.pickup_datetime)\n",
        "\n",
        "print(f'Start DateTime: {min(df.pickup_datetime)}')\n",
        "print(f'End DateTime: {max(df.pickup_datetime)}')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pMz-8bv9z_gN"
      },
      "source": [
        "Observations:\n",
        "\n",
        "The dataset is from first 6 months of 2016.\n",
        "\n",
        "> Essential to do 'TimeSeries' split instead of random train_test_split.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 407
        },
        "id": "mRyrYJAZt7_a",
        "outputId": "ad2b4242-fa2d-4595-b1ab-89ffbde752b3"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 600x400 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# plot month Vs median trip duration\n",
        "grouped_month_df = df.groupby(df.pickup_datetime.dt.month)\n",
        "\n",
        "fig,ax = plt.subplots(figsize=(6,4))\n",
        "sns.barplot(x=grouped_month_df['trip_duration'].median().index, \n",
        "            y=grouped_month_df['trip_duration'].median().values, palette='coolwarm', ax=ax)\n",
        "ax.set_title('Median trip_duration Vs month', color='black', size=10)\n",
        "plt.xlabel('month')\n",
        "plt.ylabel('median trip_duration')\n",
        "for container in ax.containers:\n",
        "    ax.bar_label(container, color='black', size=10)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vjj02pBJuJx9"
      },
      "source": [
        "Observations:\n",
        "\n",
        "Median *`trip_duration`* is almost same in all the six months."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 407
        },
        "id": "ea0Ps41-ufft",
        "outputId": "a20e533a-97cc-4795-ea44-d4cbb81b9c10"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 600x400 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# plot weekday Vs median trip duration\n",
        "grouped_weekday_df = df.groupby(df.pickup_datetime.dt.weekday)\n",
        "\n",
        "fig,ax = plt.subplots(figsize=(6,4))\n",
        "sns.barplot(x=grouped_weekday_df['trip_duration'].median().index, \n",
        "            y=grouped_weekday_df['trip_duration'].median().values, palette='coolwarm', ax=ax)\n",
        "ax.set_title('Median trip_duration Vs weekday', color='black', size=10)\n",
        "plt.xlabel('weekday')\n",
        "plt.ylabel('median trip_duration')\n",
        "for container in ax.containers:\n",
        "    ax.bar_label(container, color='black', size=10)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IiUjCMhJunrX"
      },
      "source": [
        "Observations:\n",
        "\n",
        "Note: Monday starts from 0 in pandas.\n",
        "\n",
        "Little variation in median *`trip_duration`* at each day of the week."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 280
        },
        "id": "-X5-9D_sus8E",
        "outputId": "683e73c3-1749-4516-8658-4d1d535a4fad"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 1800x400 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# plot hour Vs median trip duration\n",
        "grouped_hour_df = df.groupby(df.pickup_datetime.dt.hour)\n",
        "\n",
        "fig,ax = plt.subplots(figsize=(18,4))\n",
        "sns.barplot(x=grouped_hour_df['trip_duration'].median().index, \n",
        "            y=grouped_hour_df['trip_duration'].median().values, palette='coolwarm', ax=ax)\n",
        "ax.set_title('Median trip_duration Vs hour', color='black', size=10)\n",
        "plt.xlabel('hour')\n",
        "plt.ylabel('median trip_duration')\n",
        "for container in ax.containers:\n",
        "    ax.bar_label(container, color='black', size=10)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Xthd_-rGu32Q"
      },
      "source": [
        "Observations:\n",
        "\n",
        "From hour 2 to hour 7, *`trip_duration`* required is less. This could be due to the fact that traffic is minimum from hour 2 to hour 7 which is the night time. \n",
        "\n",
        "Divide the hours from hour 2 to 7 & rest of the hours into two categories, and check it's impact on median *`trip_duration`*"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "hbof37h-vHpw",
        "outputId": "e816215d-7960-47c5-e147-bb8d0a1cf825"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "<pandas.core.groupby.generic.DataFrameGroupBy object at 0x7fe81cb9c610>\n"
          ]
        },
        {
          "data": {
            "image/png": 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Qo82NGzfw6NEjvPnmm7C0tJRe69atQ1JSEgAgPj4enTt31phPrVZXuuycnBzMmDEDnp6esLGxgaWlJeLj45GSkqLR7umxNQqFAk5OTuV+bVEdPj4+VbZp27YtzM3NpWm1Wo2cnJwyl1OflZSUhIKCAo3tYGtrK10aft5Yi4uLsWjRInh7e8PW1haWlpbYu3dvme1Vlfj4eHTp0kWjrEuXLkhMTERxcbFUpu12b9WqFV599VWsWbMGwJP95tixYxpfXU2YMAEbN25Eu3btMGvWLJw4caLKeLt37464uDiN1/fffy9rnaqjOvtIqezsbAQEBMDLy0vj64/WrVtLx0nv3r3LzLd161Y8fPgQgYGBlfZfennazMys0naxsbHo168f3NzcYGVlhW7dugFAmX3j6WOydN+Mj48H8GRwbFRUlMYx7u/vj5KSEiQnJ1e6/NL5b9y4ASsrK2l+W1tb5OXlISkpCVlZWUhNTdU4PoyMjKrc3lXt923btkXPnj3h7e2NwYMH47vvvsODBw+qjLc6lEolHj16VG5dSUkJ5syZg0WLFmHOnDmYN28eXn/9dZw8eRIAcOnSJXTt2rXS/r29vWFiYiJNV7UNbW1tMXr0aPj7+6Nfv3748ssvkZqaqtU6ZWdn486dO+UeL6X7gi48/f7h7OwMANL7x4ULF7Bw4UKNfW3cuHFITU2tcHsDT44rQ0NDjX5L+4yPj4eRkZHG/mVnZ6exj1fXs/+XN998E+7u7mjatClGjhyJ9evXVxrn48ePYWpqWuW41AMHDqBnz5546aWXYGVlhZEjR+LevXsafRsZGaFTp07SdKtWrWBjY6Nx3MrZlqUuXLiAQ4cOaczfqlUrAE/OaXLPa1Wd37Xdptqo7LitSNVfxldh7NixmDhxIgBgxYoVZepLv7P97bff8NJLL2nUmZqayl7ujBkzsH//fnz++edo3rw5lEol3nnnnTLjkYyNjTWmFQoFSkpKZC2zqjuk/gkKhaLMuJDyBjU/G+tnn32GL7/8EsuWLZO+V586dareBqnK2e5BQUGYNGkSVqxYgcjISDRr1kw6qQNA7969cevWLezatQv79+9Hz549ERwcjM8//7zCPi0sLNC8eXONsvIGtVdF7navyMOHD/HWW2/BysoKW7du1dheu3btkvpWKpVl5v3+++/Rt29fODo6VrqM0g81Dx48gL29fbltcnNz4e/vD39/f6xfvx729vZISUmBv7+/VvtGTk4O/v3vf5d7l1F1bj7JyclBx44dyx1XVlHs1VHVfm9oaIj9+/fjxIkT2LdvH7766iv85z//walTp9CkSZMy/Tk5OeH06dMaZenp6VLd0+7fv19h7Hfv3kVaWpo0njQoKAgPHz6En58fvv/+e2zevLnKO4Kf3deqsw0jIyMxefJk7NmzB5s2bcLcuXOxf/9++Pr6Vrqsf9rTx0NpQlH6/pGTk4OwsDAMHDiwzHyVfWh43nOBgYGBrPcAKysrnDt3DocPH8a+ffswb948LFiwAGfOnCn38QQNGzbEo0ePUFBQoJGQPe3mzZvo27cvJkyYgI8//hi2trb4/fffERQUhIKCAo0P85WRuy2fnr9fv3749NNPy9Q5Ozvjxo0b1YrjWVWd37Xdptqo7LityHM/R+qtt95CQUEBCgsL4e/vX6bey8sLpqamSElJQfPmzTVerq6uAABPT88yb06ln8wqcvz4cYwePRr/+te/4O3tDScnpzID1OUwMTHR+irE0y5cuKAxUO3kyZOwtLSU1rUizZo1g7GxMU6dOiWVPXjwoMwjH+zt7TU+RSYmJlYrez5+/Dj69++PESNGoG3btmjatGmZvquz7p6enjh+/HiZvlu0aKHxaU+Od999FwYGBtiwYQPWrVuHsWPHlvlUZm9vj8DAQPz4449YtmxZpYPYq6s66yR3u5cnOzsbvXr1gomJCXbs2FHmDcvd3V06Rp798JGcnIxDhw5VOcgceLJPqVQqXL16tcI2165dw7179/DJJ5+ga9euaNWqVYVXDp8+Jkv3TU9PTwBPbj65evVqmWO8efPmFZ4MntahQwckJibCwcGhzPzW1tawtraGs7OzxvFRVFSE2NjYSvutzn6vUCjQpUsXhIWF4fz58zAxMcHWrVvL7U+tVuPSpUsa22j//v1QqVTw8vKSypKSkpCXl1fmxptSDRo0gFKpxNGjR6WyqVOnYvbs2Rg6dCh69OiBV155pdJ1e1ZV27BU+/btERoaihMnTuDll1/Ghg0bAFTv+FepVHBxcSn3eHl6/atD7ntthw4dkJCQUO6+ZmAg75Tm6emJoqIijf3r3r17SEhIkNbL3t4eaWlpGslUdZ+nZGRkBD8/P0RERODixYu4efMmDh48WG7b0gHtlR23sbGxKCkpwRdffAFfX1+0aNECd+7cKdOuqKgIZ8+elaYTEhKQmZmpcdw+z7bs0KEDrly5gsaNG5eZ38LCotrntWdV5/yuzTatrqqO24o8dyJlaGiI+Ph4XL16tdwTqZWVFWbMmIFp06Zh7dq1SEpKwrlz5/DVV19h7dq1AID3338fiYmJmDlzJhISErBhw4YK7xgp5eHhgS1btiAuLg4XLlzAsGHDZF9pelrjxo1x6tQp3Lx5E3///bfWfRYUFCAoKAhXr17Frl27MH/+fEycOLHKndLS0hJBQUGYOXMmDh48iMuXL2P06NFl5uvRowe+/vprnD9/HmfPnsX7779f5pNWeTw8PKRP3vHx8fj3v/8tfZLWZt2nT5+O6OhoLFq0CNevX8fatWvx9ddfY8aMGdXYOpWztLTEkCFDEBoaitTU1DLPIps3bx62b9+OGzdu4MqVK9i5c6f0hvA8qrNOcrf7s0qTqNzcXKxevRrZ2dlIS0tDWlpatU4qa9asgbOzc7lf+T3LwMAAfn5++P333yts4+bmBhMTE3z11Vf4448/sGPHDixatKjctgsXLkR0dLS0bzZs2FB6Hsvs2bNx4sQJTJw4EXFxcUhMTMT27dulq9VVGT58OBo2bIj+/fvj2LFjSE5OxuHDhzF58mTpCuKUKVPwySefYNu2bbh27Ro++OCDKp8FVtV+f+rUKSxevBhnz55FSkoKtmzZgoyMjAr3q169esHLywsjR47EhQsXsHfvXsydOxfBwcEaV9iPHTuGpk2bolmzZuX2Y2pqiilTpiAsLAxfffUVEhMTcezYMcTFxcHCwgLHjh0rc6fa827D5ORkhIaGIiYmBrdu3cK+ffuQmJgorWvjxo2RnJyMuLg4/P3338jPzy93OTNnzsSnn36KTZs2ISEhAXPmzEFcXBymTJmiVbxy32vnzZuHdevWISwsDFeuXEF8fDw2btyIuXPnarX8p3l4eKB///4YN24cfv/9d1y4cAEjRozASy+9hP79+wN48tyrjIwMREREICkpCStWrMDu3bur7Hvnzp1Yvnw54uLicOvWLaxbtw4lJSUVfr1lb2+PDh06VHrcNm/eHIWFhdJx+8MPP2DVqlVl2hkbG2PSpEk4deoUYmNjMXr0aPj6+kpJ+vNuy+DgYNy/fx9Dhw7FmTNnkJSUhL1792LMmDEoLi6u9nntWVWd37XdpsCTxDQuLg73799HVlaWNNTjaVUdtxXSeqRWNQZrPXvXXklJiVi2bJlo2bKlMDY2Fvb29sLf318cOXJEavPrr7+K5s2bC1NTU9G1a1exZs2aSgebJycni+7duwulUilcXV3F119/XWbwYnmD86oaBJmQkCB8fX2FUqkUAERycnKZAdelyhts3r9/fzFv3jxhZ2cnLC0txbhx40ReXl6Fy3vaw4cPxYgRI4S5ublwdHQUERERZdbpr7/+Er169RIWFhbCw8ND7Nq1q9xBz6WDAUvdu3dP9O/fX1haWgoHBwcxd+5cMWrUKI3/Y3XX/ZdffhFeXl7C2NhYuLm5ic8++0xjWXK2e6kTJ04IAKJPnz5l6hYtWiQ8PT2FUqkUtra2on///uKPP/6osK/q3rVXnXWSu90rWnZ5r6ru3CouLhaNGjXSamD6rl27xEsvvaRxs8Oz+9SGDRtE48aNhampqVCr1WLHjh0a61Ia86+//ipat24tTExMxCuvvCIuXLigsazTp0+LN998U1haWgoLCwvRpk0bjUHJlQ02F+LJ3V6jRo0SDRs2FKampqJp06Zi3LhxIisrSwjxZHD5lClThEqlEjY2NiIkJKTMPvysqvb7q1evCn9/f2Fvby9MTU1FixYtNG5YKc/NmzdF7969hVKpFA0bNhTTp08XhYWFGm169eolwsPDK+2nuLhYrFq1Srz88svCzMxMNGrUSEyZMkVkZGQIf39/0axZM5GRkVHuvBXt25Vtw7S0NDFgwADh7OwsTExMhLu7u5g3b560b+Tl5YlBgwYJGxsbAUDjrtVn416wYIF46aWXhLGxsWjbtq3YvXu3RpvqHO/Vfb8pvWni6eNjz5494tVXXxVKpVKoVCrxyiuvaNyBV53tNWXKFNGtWzdp+v79+2LkyJHC2tpaKJVK4e/vL92JWGrlypXC1dVVWFhYiFGjRomPP/64zGDzZ5dz7Ngx0a1bN9GgQQOhVCpFmzZtpJs0KvLNN98IX19fjbJnj58lS5YIZ2dnKdZ169aVe87cvHmzaNq0qTA1NRV+fn4ad7YLUfW2RCWDzYV4MlD/X//6l7CxsRFKpVK0atVKTJ06VRosX53z2rOqOr/L2abu7u7lvu8+rTrHbXkUQlTxIBaqttGjRyMzM/O5f/aCSFeEEOjcuTOmTZuGoUOH1nQ49cKVK1fQo0cPXL9+Xa+/CEAvrsePH6Nly5bYtGlTlTdekW48z3Fba39rj4ien0KhwLfffltjPztSH6WmpmLdunVMokg2pVKJdevWaTzYkvTreY5bXpHSocquSKWkpFQ6GPPq1at6+WkdIiIi0h8mUv+QoqKiSu8qbNy4cbV+GoKIiIhqDyZSRERERDJxjBQRERGRTEykiIiIiGTioBw8+emBO3fuwMrKqsrfNyIiIqL/I4TAw4cP4eLiIvvp8nUZEykAd+7cqfInXIiIiKhit2/fRqNGjWo6jH9cjSZSjRs3xq1bt8qUf/DBB1ixYgXy8vIwffp0bNy4Efn5+fD398c333yj8WOtKSkpmDBhgvQL1IGBgQgPD9fqDjgrKysAT3YClUr1/CtGRERUT2RnZ8PV1VU6l9Y3NZpInTlzRuP3xS5fvow333wTgwcPBgBMmzYNv/32G37++WdYW1tj4sSJGDhwoPSDmcXFxQgICICTkxNOnDiB1NRUjBo1CsbGxli8eHG14yj9Ok+lUjGRIiKS4a+//sLs2bOxe/duPHr0CM2bN0dkZCR8fHwAoMJhExEREZg5cyYOHz6M7t27l9vm9OnT6NSpk95iJ92or0NjatXjD6ZOnYqdO3ciMTER2dnZsLe3x4YNG/DOO+8AePJL9Z6enoiJiYGvry92796Nvn374s6dO9JVqlWrVmH27NnIyMio8Ffn8/PzNX6QszSbzsrKYiJFRKSlBw8eoH379ujevTsmTJgAe3t7JCYmolmzZtIPwKalpWnMs3v3bgQFBeHGjRto2rQpCgoKcP/+fY02H374IaKjo5GUlFRvT9J1QXZ2NqytrevtObTWjJEqKCjAjz/+iJCQECgUCsTGxqKwsBB+fn5Sm1atWsHNzU1KpGJiYuDt7a3xVZ+/vz8mTJiAK1euoH379uUuKzw8HGFhYXpfJyKi+uDTTz+Fq6srIiMjpbImTZpotHFyctKY3r59O7p3746mTZsCAExMTDTaFBYWYvv27Zg0aRKTKKrVas3w+m3btiEzMxOjR48G8OTTi4mJCWxsbDTaOTo6Sp9s0tLSNJKo0vrSuoqEhoYiKytLet2+fVt3K0JEVM/s2LEDPj4+GDx4MBwcHNC+fXt89913FbZPT0/Hb7/9hqCgoEr7vHfvHsaMGaOPkIl0ptYkUqtXr0bv3r3h4uKi92WZmppK46E4LoqI6Pn88ccfWLlyJTw8PLB3715MmDABkydPxtq1a8ttv3btWlhZWWHgwIEV9rl69Wr4+/vXy7vAqG6pFV/t3bp1CwcOHMCWLVukMicnJxQUFCAzM1PjqlR6erp0+dfJyQmnT5/W6Cs9PV2qIyIi/SspKYGPj490k0/79u1x+fJlrFq1CoGBgWXar1mzBsOHD4eZmVm5/f3555/Yu3cvfvrpJ73GTaQLteKKVGRkJBwcHBAQECCVdezYEcbGxoiOjpbKEhISkJKSArVaDQBQq9W4dOkS7t69K7XZv38/VCoVvLy8/rkVICKqx5ydncu853p6eiIlJaVM22PHjiEhIQH/8z//U2F/kZGRsLOzw9tvv63zWIl0rcavSJWUlCAyMhKBgYEaz36ytrZGUFAQQkJCYGtrC5VKhUmTJkGtVsPX1xcA0KtXL3h5eWHkyJGIiIhAWloa5s6di+DgYJiamtbUKhER1StdunRBQkKCRtn169fh7u5epu3q1avRsWNHtG3btty+hBCIjIyUHmVDVOuJGrZ3714BQCQkJJSpe/z4sfjggw9EgwYNhLm5ufjXv/4lUlNTNdrcvHlT9O7dWyiVStGwYUMxffp0UVhYqFUMWVlZAoDIysp6rnUhIqqPTp8+LYyMjMTHH38sEhMTxfr164W5ubn48ccfNdplZWUJc3NzsXLlygr7OnDggAAg4uPj9R026Uh9P4fWqudI1ZT6/gwMIqLntXPnToSGhiIxMRFNmjRBSEgIxo0bp9Hm22+/xdSpU5Gamgpra+ty+xk2bBhu3bolPXiZar/6fg5lIgXuBERERHLV93NorRhsTkRERFQX1fhgcyKimnYvemNNh0Ckd3Y936vpEF5IvCJFREREJBMTKSIiIiKZmEgRERERycREioiIiEgmJlJEREREMjGRIiIiIpKJiRQRERGRTEykiIiIiGRiIkVEREQkExMpIiIiIpmYSBERERHJxESKiIiISCYmUkREREQyMZEiIiIikomJFBEREZFMTKSIiIiIZGIiRURERCQTEykiIiIimZhIEREREcnERIqIiIhIJiZSRERERDIxkSIiIiKSiYkUERERkUxMpIiIiIhkYiJFREREJBMTKSIiIiKZmEgRERERycREioiIiEgmJlJEREREMtV4IvXXX39hxIgRsLOzg1KphLe3N86ePSvVCyEwb948ODs7Q6lUws/PD4mJiRp93L9/H8OHD4dKpYKNjQ2CgoKQk5PzT68KERER1TM1mkg9ePAAXbp0gbGxMXbv3o2rV6/iiy++QIMGDaQ2ERERWL58OVatWoVTp07BwsIC/v7+yMvLk9oMHz4cV65cwf79+7Fz504cPXoU48ePr4lVIiIionpEIYQQNbXwOXPm4Pjx4zh27Fi59UIIuLi4YPr06ZgxYwYAICsrC46OjoiKisJ7772H+Ph4eHl54cyZM/Dx8QEA7NmzB3369MGff/4JFxeXKuPIzs6GtbU1srKyoFKpdLeCRFQn3IveWNMhEOmdXc/39NJvfT+H1ugVqR07dsDHxweDBw+Gg4MD2rdvj++++06qT05ORlpaGvz8/KQya2trdO7cGTExMQCAmJgY2NjYSEkUAPj5+cHAwACnTp0qd7n5+fnIzs7WeBERERFpq0YTqT/++AMrV66Eh4cH9u7diwkTJmDy5MlYu3YtACAtLQ0A4OjoqDGfo6OjVJeWlgYHBweNeiMjI9ja2kptnhUeHg5ra2vp5erqqutVIyIionqgRhOpkpISdOjQAYsXL0b79u0xfvx4jBs3DqtWrdLrckNDQ5GVlSW9bt++rdflERER0YupRhMpZ2dneHl5aZR5enoiJSUFAODk5AQASE9P12iTnp4u1Tk5OeHu3bsa9UVFRbh//77U5lmmpqZQqVQaLyIiIiJt1Wgi1aVLFyQkJGiUXb9+He7u7gCAJk2awMnJCdHR0VJ9dnY2Tp06BbVaDQBQq9XIzMxEbGys1ObgwYMoKSlB586d/4G1ICIiovrKqCYXPm3aNLz66qtYvHgx3n33XZw+fRrffvstvv32WwCAQqHA1KlT8dFHH8HDwwNNmjTBhx9+CBcXFwwYMADAkytYb731lvSVYGFhISZOnIj33nuvWnfsEREREclVo4lUp06dsHXrVoSGhmLhwoVo0qQJli1bhuHDh0ttZs2ahdzcXIwfPx6ZmZl47bXXsGfPHpiZmUlt1q9fj4kTJ6Jnz54wMDDAoEGDsHz58ppYJSIiIqpHavQ5UrVFfX8GRl2wYMEChIWFaZS1bNkS165dAwC88cYbOHLkiEb9v//9b+nGhaioKIwZM6bcvtPT08vc+Un1C58jRfUBnyOlHzV6RYpIG61bt8aBAwekaSMjzd133LhxWLhwoTRtbm4u/T1kyBC89dZbGu1Hjx6NvLw8JlFERCQbEymqM4yMjCq8ExN4kjhVVK9UKqFUKqXpjIwMHDx4EKtXr9Z5nEREVH/U+I8WE1VXYmIiXFxc0LRpUwwfPlx6TEap9evXo2HDhnj55ZcRGhqKR48eVdjXunXrYG5ujnfeeUffYRMR0QuMV6SoTujcuTOioqLQsmVLpKamIiwsDF27dsXly5dhZWWFYcOGwd3dHS4uLrh48SJmz56NhIQEbNmypdz+Vq9ejWHDhmlcpSIiItIWEymqE3r37i393aZNG3Tu3Bnu7u746aefEBQUhPHjx0v13t7ecHZ2Rs+ePZGUlIRmzZpp9BUTE4P4+Hj88MMP/1j8RET0YuJXe1Qn2djYoEWLFrhx40a59aUPYy2v/vvvv0e7du3QsWNHvcZIREQvPiZSVCfl5OQgKSkJzs7O5dbHxcUBQJn6nJwc6SoWERHR8+JXe1QnzJgxA/369YO7uzvu3LmD+fPnw9DQEEOHDkVSUhI2bNiAPn36wM7ODhcvXsS0adPw+uuvo02bNhr9bNq0CUVFRRgxYkQNrQkREb1ImEhRnfDnn39i6NChuHfvHuzt7fHaa6/h5MmTsLe3R15eHg4cOIBly5YhNzcXrq6uGDRoEObOnVumn9WrV2PgwIGwsbH551eCiIheOHyyOfhUVqL6jk82p/qATzbXD46RIiIiIpKJX+39A3adyanpEIj0rk8ny5oOgYjoH8crUkREREQyMZEiIiIikomJFBEREZFMTKSIiIiIZGIiRURERCQTEykiIiIimZhIEREREcnERIqIiIhIJiZSRERERDIxkSIiIiKSiYkUERERkUxMpIiIiIhkYiJFREREJBMTKSIiIiKZmEgRERERycREioiIiEgmrROp9PR0jBw5Ei4uLjAyMoKhoaHGi4iIiKi+MNJ2htGjRyMlJQUffvghnJ2doVAo9BEXERERUa2ndSL1+++/49ixY2jXrp0ewiEiIiKqO7T+as/V1RVCCH3EQkRERFSnaJ1ILVu2DHPmzMHNmzefe+ELFiyAQqHQeLVq1Uqqz8vLQ3BwMOzs7GBpaYlBgwYhPT1do4+UlBQEBATA3NwcDg4OmDlzJoqKip47NiIiIqKqaP3V3pAhQ/Do0SM0a9YM5ubmMDY21qi/f/++Vv21bt0aBw4c+L+AjP4vpGnTpuG3337Dzz//DGtra0ycOBEDBw7E8ePHAQDFxcUICAiAk5MTTpw4gdTUVIwaNQrGxsZYvHixtqtGREREpBWtE6lly5bpNgAjIzg5OZUpz8rKwurVq7Fhwwb06NEDABAZGQlPT0+cPHkSvr6+2LdvH65evYoDBw7A0dER7dq1w6JFizB79mwsWLAAJiYmOo2ViIiI6GlaJ1KBgYE6DSAxMREuLi4wMzODWq1GeHg43NzcEBsbi8LCQvj5+UltW7VqBTc3N8TExMDX1xcxMTHw9vaGo6Oj1Mbf3x8TJkzAlStX0L59+3KXmZ+fj/z8fGk6Oztbp+tERERE9YPWiRTw5Cu1bdu2IT4+HsCTr+fefvttrZ8j1blzZ0RFRaFly5ZITU1FWFgYunbtisuXLyMtLQ0mJiawsbHRmMfR0RFpaWkAgLS0NI0kqrS+tK4i4eHhCAsL0ypWIiIiomdpnUjduHEDffr0wV9//YWWLVsCeJKYuLq64rfffkOzZs2q3Vfv3r2lv9u0aYPOnTvD3d0dP/30E5RKpbahVVtoaChCQkKk6ezsbLi6uupteURERPRi0vquvcmTJ6NZs2a4ffs2zp07h3PnziElJQVNmjTB5MmTnysYGxsbtGjRAjdu3ICTkxMKCgqQmZmp0SY9PV0aU+Xk5FTmLr7S6fLGXZUyNTWFSqXSeBERERFpS+tE6siRI4iIiICtra1UZmdnh08++QRHjhx5rmBycnKQlJQEZ2dndOzYEcbGxoiOjpbqExISkJKSArVaDQBQq9W4dOkS7t69K7XZv38/VCoVvLy8nisWIiIioqpo/dWeqakpHj58WKY8JydH67vkZsyYgX79+sHd3R137tzB/PnzYWhoiKFDh8La2hpBQUEICQmBra0tVCoVJk2aBLVaDV9fXwBAr1694OXlhZEjRyIiIgJpaWmYO3cugoODYWpqqu2qEREREWlF6ytSffv2xfjx43Hq1CkIISCEwMmTJ/H+++/j7bff1qqvP//8E0OHDkXLli3x7rvvws7ODidPnoS9vT0AYOnSpejbty8GDRqE119/HU5OTtiyZYs0v6GhIXbu3AlDQ0Oo1WqMGDECo0aNwsKFC7VdLSIiIiKtKYSWv/eSmZmJwMBA/Prrr9LDOIuKivD2228jKioK1tbWeglUn7Kzs2FtbY2srCy9jJfadSZH530S1TZ9OlnWdAiy3YveWNMhEOmdXc/39NKvvs+htZ3WX+3Z2Nhg+/btSExMxLVr1wAAnp6eaN68uc6DIyIiIqrNZD1HCgA8PDzg4eGhy1iIiIiI6pRqJVIhISFYtGgRLCwsNJ6/VJ4lS5boJDAiIiKi2q5aidT58+dRWFgo/U1ERERE1UykDh06VO7fRERERPWZ1o8/GDt2bLnPkcrNzcXYsWN1EhQRERFRXaB1IrV27Vo8fvy4TPnjx4+xbt06nQRFREREVBdU+6697Oxs6QGcDx8+hJmZmVRXXFyMXbt2wcHBQS9BEhEREdVG1U6kbGxsoFAooFAo0KJFizL1CoUCYWFhOg2OiIiIqDardiJ16NAhCCHQo0cPbN68WeNHi01MTODu7g4XFxe9BElERERUG1U7kerWrRsAIDk5Ga6urjAw0Hp4FREREdELResnm7u7uwMAHj16hJSUFBQUFGjUt2nTRjeREREREdVyWidSGRkZGDNmDHbv3l1ufXFx8XMHRURERFQXaP393NSpU5GZmYlTp05BqVRiz549WLt2LTw8PLBjxw59xEhERERUK2l9RergwYPYvn07fHx8YGBgAHd3d7z55ptQqVQIDw9HQECAPuIkIiIiqnW0viKVm5srPS+qQYMGyMjIAAB4e3vj3Llzuo2OiIiIqBbTOpFq2bIlEhISAABt27bF//7v/+Kvv/7CqlWr4OzsrPMAiYiIiGorrb/amzJlClJTUwEA8+fPx1tvvYX169fDxMQEUVFRuo6PiIiIqNbSOpEaMWKE9HfHjh1x69YtXLt2DW5ubmjYsKFOgyMiIiKqzbT6aq+wsBDNmjVDfHy8VGZubo4OHTowiSIiIqJ6R6tEytjYGHl5efqKhYiIiKhO0XqweXBwMD799FMUFRXpIx4iIiKiOkPrMVJnzpxBdHQ09u3bB29vb1hYWGjUb9myRWfBEREREdVmWidSNjY2GDRokD5iISIiIqpTtE6kIiMj9REHERERUZ2j9RgpIiIiInpC6ytSTZo0gUKhqLD+jz/+eK6AiIiIiOoKrROpqVOnakwXFhbi/Pnz2LNnD2bOnKmruIiIiIhqPVk/EVOeFStW4OzZs88dEBEREVFdobMxUr1798bmzZt11R0RERFRraezROqXX36Bra2trrojIiIiqvW0/mqvffv2GoPNhRBIS0tDRkYGvvnmG50GR0RERFSbaZ1IDRgwQGPawMAA9vb2eOONN9CqVStdxUVERERU62mdSM2fP18fcQAAPvnkE4SGhmLKlClYtmwZACAvLw/Tp0/Hxo0bkZ+fD39/f3zzzTdwdHSU5ktJScGECRNw6NAhWFpaIjAwEOHh4TAy0nr1iIiIiKqtWplGdnZ2tTtUqVSyAjlz5gz+93//F23atNEonzZtGn777Tf8/PPPsLa2xsSJEzFw4EAcP34cAFBcXIyAgAA4OTnhxIkTSE1NxahRo2BsbIzFixfLioWIiIioOqqVSNnY2FT6EM6nFRcXax1ETk4Ohg8fju+++w4fffSRVJ6VlYXVq1djw4YN6NGjB4AnP1Hj6emJkydPwtfXF/v27cPVq1dx4MABODo6ol27dli0aBFmz56NBQsWwMTEpMzy8vPzkZ+fL01rkygSERERlarWXXuHDh3CwYMHcfDgQaxZswYODg6YNWsWtm7diq1bt2LWrFlwdHTEmjVrZAURHByMgIAA+Pn5aZTHxsaisLBQo7xVq1Zwc3NDTEwMACAmJgbe3t4aX/X5+/sjOzsbV65cKXd54eHhsLa2ll6urq6y4iYiIqL6rVpXpLp16yb9vXDhQixZsgRDhw6Vyt5++214e3vj22+/RWBgoFYBbNy4EefOncOZM2fK1KWlpcHExAQ2NjYa5Y6OjkhLS5PaPJ1EldaX1pUnNDQUISEh0nR2djaTKSIiItKa1s+RiomJgY+PT5lyHx8fnD59Wqu+bt++jSlTpmD9+vUwMzPTNhTZTE1NoVKpNF5ERERE2tI6kXJ1dcV3331Xpvz777/X+qpObGws7t69iw4dOsDIyAhGRkY4cuQIli9fDiMjIzg6OqKgoACZmZka86Wnp8PJyQkA4OTkhPT09DL1pXVERERE+qL18wGWLl2KQYMGYffu3ejcuTMA4PTp00hMTNT6J2J69uyJS5cuaZSNGTMGrVq1wuzZs+Hq6gpjY2NER0dj0KBBAICEhASkpKRArVYDANRqNT7++GPcvXsXDg4OAID9+/dDpVLBy8tL29UjIiIiqjatE6k+ffogMTERK1euRHx8PACgX79+eP/997W+ImVlZYWXX35Zo8zCwgJ2dnZSeVBQEEJCQmBrawuVSoVJkyZBrVbD19cXANCrVy94eXlh5MiRiIiIQFpaGubOnYvg4GCYmppqu3pERERE1SbriZWNGjXCxx9/XGmbDz74AAsXLkTDhg1lBVZq6dKlMDAwwKBBgzQeyFnK0NAQO3fuxIQJE6BWq2FhYYHAwEAsXLjwuZZLREREVBWFEELoo2OVSoW4uDg0bdpUH93rVHZ2NqytrZGVlaWXgee7zuTovE+i2qZPJ8uaDkG2e9EbazoEIr2z6/meXvrV9zm0ttN6sHl16Sk/IyIiIqo19JZIEREREb3omEgRERERycREioiIiEgmJlJEREREMuktkRoxYkS9HL1PRERE9Yes50g9ePAAq1evlh7I6enpibFjx8LW1lZqs3LlSt1ESERERFRLaX1F6ujRo2jSpAmWL1+OBw8e4MGDB/jqq6/QpEkTHD16VB8xEhEREdVKWl+RCg4OxrvvvouVK1fC0NAQAFBcXIwPPvgAwcHBZX47j4iIiOhFpfUVqRs3bmD69OlSEgU8+ZmWkJAQ3LhxQ6fBEREREdVmWidSHTp0kMZGPS0+Ph5t27bVSVBEREREdYHWX+1NnjwZU6ZMwY0bN+Dr6wsAOHnyJFasWIFPPvkEFy9elNq2adNGd5ESERER1TJaJ1JDhw4FAMyaNavcOoVCASEEFAoFiouLnz9CIiIiolpK60QqOTlZH3EQERER1TlaJ1Lu7u76iIOIiIiozqlWIrVjxw707t0bxsbG2LFjR6Vt3377bZ0ERkRERFTbVSuRGjBgANLS0uDg4IABAwZU2I7jooiIiKg+qVYiVVJSUu7fRERERPWZVs+RKiwsRM+ePZGYmKiveIiIiIjqDK0SKWNjY43nRBERERHVZ1o/2XzEiBFYvXq1PmIhIiIiqlO0fvxBUVER1qxZgwMHDqBjx46wsLDQqF+yZInOgiMiIiKqzbROpC5fvowOHToAAK5fv67zgIiIiIjqCq0TqUOHDukjDiIiIqI6R+sxUmPHjsXDhw/LlOfm5mLs2LE6CYqIiIioLtA6kVq7di0eP35cpvzx48dYt26dToIiIiIiqguq/dVednY2hBAQQuDhw4cwMzOT6oqLi7Fr1y44ODjoJUgiIiKi2qjaiZSNjQ0UCgUUCgVatGhRpl6hUCAsLEynwRERERHVZtVOpA4dOgQhBHr06IHNmzfD1tZWqjMxMYG7uztcXFz0EiQRERFRbVTtRKpbt24AgOTkZLi5uUGhUOgtKCIiIqK6QOvHH7i7u+sjDiIiIqI6R+u79oiIiIjoiRpNpFauXIk2bdpApVJBpVJBrVZj9+7dUn1eXh6Cg4NhZ2cHS0tLDBo0COnp6Rp9pKSkICAgAObm5nBwcMDMmTNRVFT0T68KERER1UM1mkg1atQIn3zyCWJjY3H27Fn06NED/fv3x5UrVwAA06ZNw6+//oqff/4ZR44cwZ07dzBw4EBp/uLiYgQEBKCgoAAnTpzA2rVrERUVhXnz5tXUKhEREVE9ohBCiJoO4mm2trb47LPP8M4778De3h4bNmzAO++8AwC4du0aPD09ERMTA19fX+zevRt9+/bFnTt34OjoCABYtWoVZs+ejYyMDJiYmFRrmdnZ2bC2tkZWVhZUKpXO12nXmRyd90lU2/TpZFnTIch2L3pjTYdApHd2Pd/TS7/6PofWdlpfkUpPT8fIkSPh4uICIyMjGBoaarzkKi4uxsaNG5Gbmwu1Wo3Y2FgUFhbCz89PatOqVSu4ubkhJiYGABATEwNvb28piQIAf39/ZGdnS1e1ypOfn4/s7GyNFxEREZG2tL5rb/To0UhJScGHH34IZ2fn534MwqVLl6BWq5GXlwdLS0ts3boVXl5eiIuLg4mJCWxsbDTaOzo6Ii0tDQCQlpamkUSV1pfWVSQ8PJwPDyUiIqLnpnUi9fvvv+PYsWNo166dTgJo2bIl4uLikJWVhV9++QWBgYE4cuSITvquSGhoKEJCQqTp7OxsuLq66nWZRERE9OLROpFydXWFLodVmZiYoHnz5gCAjh074syZM/jyyy8xZMgQFBQUIDMzU+OqVHp6OpycnAAATk5OOH36tEZ/pXf1lbYpj6mpKUxNTXW2DkRERFQ/aT1GatmyZZgzZw5u3ryph3CAkpIS5Ofno2PHjjA2NkZ0dLRUl5CQgJSUFKjVagCAWq3GpUuXcPfuXanN/v37oVKp4OXlpZf4iIiIiEppfUVqyJAhePToEZo1awZzc3MYGxtr1N+/f7/afYWGhqJ3795wc3PDw4cPsWHDBhw+fBh79+6FtbU1goKCEBISAltbW6hUKkyaNAlqtRq+vr4AgF69esHLywsjR45EREQE0tLSMHfuXAQHB/OKExEREemd1onUsmXLdLbwu3fvYtSoUUhNTYW1tTXatGmDvXv34s033wQALF26FAYGBhg0aBDy8/Ph7++Pb775Rprf0NAQO3fuxIQJE6BWq2FhYYHAwEAsXLhQZzESERERVaTWPUeqJvA5UkTPj8+RIqrd+Bwp/dD6itTT8vLyUFBQoFFWHzciERER1U9aDzbPzc3FxIkT4eDgAAsLCzRo0EDjRURERFRfaJ1IzZo1CwcPHsTKlSthamqK77//HmFhYXBxccG6dev0ESMRERFRraT1V3u//vor1q1bhzfeeANjxoxB165d0bx5c7i7u2P9+vUYPny4PuIkIiIiqnW0viJ1//59NG3aFMCT8VCljzt47bXXcPToUd1GR0RERFSLaZ1INW3aFMnJyQCe/IjwTz/9BODJlapnfxePiIiI6EWmdSI1ZswYXLhwAQAwZ84crFixAmZmZpg2bRpmzpyp8wCJiIiIaiutx0hNmzZN+tvPzw/Xrl1DbGwsmjdvjjZt2ug0OCIiIqLa7LmeIwUA7u7ucHd310UsRERERHVKtRKp5cuXY/z48TAzM8Py5csrbTt58mSdBEZERERU21UrkVq6dCmGDx8OMzMzLF26tMJ2CoWCiRQRERHVG9VKpErv0nv2byIiIqL6TOu79oiIiIjoiWpdkQoJCal2h0uWLJEdDBEREVFdUq1E6vz58xrT586dQ1FREVq2bAkAuH79OgwNDdGxY0fdR0hERERUS1UrkTp06JD095IlS2BlZYW1a9eiQYMGAIAHDx5Iv7tHREREVF9oPUbqiy++QHh4uJREAUCDBg3w0Ucf4YsvvtBpcERERES1mdaJVHZ2NjIyMsqUZ2Rk4OHDhzoJioiIiKgu0DqR+te//oUxY8Zgy5Yt+PPPP/Hnn39i8+bNCAoKwsCBA/URIxEREVGtpPVPxKxatQozZszAsGHDUFhY+KQTIyMEBQXhs88+03mARERERLWV1omUubk5vvnmG3z22WdISkoCADRr1gwWFhY6D46IiIioNpP9QM7U1FSkpqbCw8MDFhYWEELoMi4iIiKiWk/rROrevXvo2bMnWrRogT59+iA1NRUAEBQUhOnTp+s8QCIiIqLaSutEatq0aTA2NkZKSgrMzc2l8iFDhmDPnj06DY6IiIioNtN6jNS+ffuwd+9eNGrUSKPcw8MDt27d0llgRERERLWd1lekcnNzNa5Elbp//z5MTU11EhQRERFRXaB1ItW1a1esW7dOmlYoFCgpKUFERAS6d++u0+CIiIiIajOtv9qLiIhAz549cfbsWRQUFGDWrFm4cuUK7t+/j+PHj+sjRiIiIqJaSesrUi+//DKuX7+O1157Df3790dubi4GDhyI8+fPo1mzZvqIkYiIiKhW0vqKFABYW1vjP//5j65jISIiIqpTZCVSeXl5uHjxIu7evYuSkhKNurffflsngRERERHVdlonUnv27MGoUaPw999/l6lTKBQoLi7WSWBEREREtZ3WY6QmTZqEwYMHIzU1FSUlJRovbZOo8PBwdOrUCVZWVnBwcMCAAQOQkJCg0SYvLw/BwcGws7ODpaUlBg0ahPT0dI02KSkpCAgIgLm5ORwcHDBz5kwUFRVpu2pEREREWtE6kUpPT0dISAgcHR2fe+FHjhxBcHAwTp48if3796OwsBC9evVCbm6u1GbatGn49ddf8fPPP+PIkSO4c+cOBg4cKNUXFxcjICAABQUFOHHiBNauXYuoqCjMmzfvueMjIiIiqoxCaPlrw2PHjkWXLl0QFBSk82AyMjLg4OCAI0eO4PXXX0dWVhbs7e2xYcMGvPPOOwCAa9euwdPTEzExMfD19cXu3bvRt29f3LlzR0ruVq1ahdmzZyMjIwMmJiZVLjc7OxvW1tbIysqCSqXS+XrtOpOj8z6Japs+nSxrOgTZ7kVvrOkQiPTOrud7eulX3+fQ2k7rMVJff/01Bg8ejGPHjsHb2xvGxsYa9ZMnT5YdTFZWFgDA1tYWABAbG4vCwkL4+flJbVq1agU3NzcpkYqJiYG3t7fGFTJ/f39MmDABV65cQfv27cssJz8/H/n5+dJ0dna27JiJiIio/tI6kfrvf/+Lffv2wczMDIcPH4ZCoZDqFAqF7ESqpKQEU6dORZcuXfDyyy8DANLS0mBiYgIbGxuNto6OjkhLS5PaPPs1Y+l0aZtnhYeHIywsTFacRERERKW0TqT+85//ICwsDHPmzIGBgdZDrCoUHByMy5cv4/fff9dZnxUJDQ1FSEiINJ2dnQ1XV1e9L5eIiIheLFonUgUFBRgyZIhOk6iJEydi586dOHr0KBo1aiSVOzk5oaCgAJmZmRpXpdLT0+Hk5CS1OX36tEZ/pXf1lbZ5lqmpKX9gmYiIiJ6b1tlQYGAgNm3apJOFCyEwceJEbN26FQcPHkSTJk006jt27AhjY2NER0dLZQkJCUhJSYFarQYAqNVqXLp0CXfv3pXa7N+/HyqVCl5eXjqJk4iIiKg8Wl+RKi4uRkREBPbu3Ys2bdqUGWy+ZMmSavcVHByMDRs2YPv27bCyspLGNFlbW0OpVMLa2hpBQUEICQmBra0tVCoVJk2aBLVaDV9fXwBAr1694OXlhZEjRyIiIgJpaWmYO3cugoODedWJiIiI9ErrROrSpUvSnXCXL1/WqHt64Hl1rFy5EgDwxhtvaJRHRkZi9OjRAIClS5fCwMAAgwYNQn5+Pvz9/fHNN99IbQ0NDbFz505MmDABarUaFhYWCAwMxMKFC7VcMyIiIiLtaP0cqRcRnyNF9Pz4HCmi2o3PkdIP3Y0YJyIiIqpnmEgRERERycREioiIiEgmJlJEREREMjGRIiIiIpKJiRQRERGRTEykiIiIiGRiIkVEREQkExMpIiIiIpmYSBERERHJxESKiIiISCYmUkREREQyMZEiIiIikomJFBEREZFMTKSIiIiIZGIiRURERCQTEykiIiIimZhIEREREcnERIqIiIhIJiZSRERERDIxkSIiIiKSiYkUERERkUxMpIiIiIhkYiJFREREJBMTKSIiIiKZmEgRERERycREioiIiEgmJlJEREREMjGRIiIiIpKJiRQRERGRTEykiIiIiGRiIkVEREQkExMpIiIiIplqNJE6evQo+vXrBxcXFygUCmzbtk2jXgiBefPmwdnZGUqlEn5+fkhMTNRoc//+fQwfPhwqlQo2NjYICgpCTk7OP7gWREREVF/VaCKVm5uLtm3bYsWKFeXWR0REYPny5Vi1ahVOnToFCwsL+Pv7Iy8vT2ozfPhwXLlyBfv378fOnTtx9OhRjB8//p9aBSIiIqrHjGpy4b1790bv3r3LrRNCYNmyZZg7dy769+8PAFi3bh0cHR2xbds2vPfee4iPj8eePXtw5swZ+Pj4AAC++uor9OnTB59//jlcXFzK7Ts/Px/5+fnSdHZ2to7XjIiIiOqDWjtGKjk5GWlpafDz85PKrK2t0blzZ8TExAAAYmJiYGNjIyVRAODn5wcDAwOcOnWqwr7Dw8NhbW0tvVxdXfW3IkRERPTCqrWJVFpaGgDA0dFRo9zR0VGqS0tLg4ODg0a9kZERbG1tpTblCQ0NRVZWlvS6ffu2jqMnIiKi+qBGv9qrKaampjA1Na3pMIiIiKiOq7VXpJycnAAA6enpGuXp6elSnZOTE+7evatRX1RUhPv370ttiIiIiPSl1iZSTZo0gZOTE6Kjo6Wy7OxsnDp1Cmq1GgCgVquRmZmJ2NhYqc3BgwdRUlKCzp07/+MxExERUf1So1/t5eTk4MaNG9J0cnIy4uLiYGtrCzc3N0ydOhUfffQRPDw80KRJE3z44YdwcXHBgAEDAACenp546623MG7cOKxatQqFhYWYOHEi3nvvvQrv2CMiIiLSlRpNpM6ePYvu3btL0yEhIQCAwMBAREVFYdasWcjNzcX48eORmZmJ1157DXv27IGZmZk0z/r16zFx4kT07NkTBgYGGDRoEJYvX/6PrwsRERHVPwohhKjpIGpadnY2rK2tkZWVBZVKpfP+d53hk9bpxdenk2VNhyDbveiNNR0Ckd7Z9XxPL/3q+xxa29XaMVJEREREtR0TKSIiIiKZmEgRERERycREioiIiEgmJlJEREREMjGRIiIiIpKJiRQRERGRTEykiIiIiGRiIkVEREQkExMpIiIiIpmYSBERERHJxESKiIiISCYmUkREREQyMZEiIiIikomJFBEREZFMTKSIiIiIZGIiRURERCQTEykiIiIimZhIEREREcnERIqIiIhIJiZSRERERDIxkSIiIiKSiYkUERERkUxMpIiIiIhkYiJFREREJBMTKSIiIiKZmEgRERERycREioiIiEgmJlJEREREMjGRIiIiIpKJiRQRERGRTEykiIiIiGR6YRKpFStWoHHjxjAzM0Pnzp1x+vTpmg6JiIiIXnAvRCK1adMmhISEYP78+Th37hzatm0Lf39/3L17t6ZDIyIiohfYC5FILVmyBOPGjcOYMWPg5eWFVatWwdzcHGvWrKnp0IiIiOgFZlTTATyvgoICxMbGIjQ0VCozMDCAn58fYmJiyp0nPz8f+fn50nRWVhYAIDs7Wy8xPsrJ0Uu/RLVJdnZJTYcg28PcRzUdApHeGevpHFd67hRC6KX/2q7OJ1J///03iouL4ejoqFHu6OiIa9eulTtPeHg4wsLCypS7urrqJUYiIqKaF6TX3h8+fAhra2u9LqM2qvOJlByhoaEICQmRpktKSnD//n3Y2dlBoVDUYGSkC9nZ2XB1dcXt27ehUqlqOhwiegaP0ReLEAIPHz6Ei4tLTYdSI+p8ItWwYUMYGhoiPT1dozw9PR1OTk7lzmNqagpTU1ONMhsbG32FSDVEpVLxTZqoFuMx+uKoj1eiStX5weYmJibo2LEjoqOjpbKSkhJER0dDrVbXYGRERET0oqvzV6QAICQkBIGBgfDx8cErr7yCZcuWITc3F2PGjKnp0IiIiOgF9kIkUkOGDEFGRgbmzZuHtLQ0tGvXDnv27CkzAJ3qB1NTU8yfP7/M17dEVDvwGKUXiULU1/sViYiIiJ5TnR8jRURERFRTmEgRERERycREioiIiEgmJlJEREREMjGRohfOihUr0LhxY5iZmaFz5844ffp0TYdERACOHj2Kfv36wcXFBQqFAtu2bavpkIieGxMpeqFs2rQJISEhmD9/Ps6dO4e2bdvC398fd+/erenQiOq93NxctG3bFitWrKjpUIh0ho8/oBdK586d0alTJ3z99dcAnjzl3tXVFZMmTcKcOXNqODoiKqVQKLB161YMGDCgpkMhei68IkUvjIKCAsTGxsLPz08qMzAwgJ+fH2JiYmowMiIielExkaIXxt9//43i4uIyT7R3dHREWlpaDUVFREQvMiZSRERERDIxkaIXRsOGDWFoaIj09HSN8vT0dDg5OdVQVERE9CJjIkUvDBMTE3Ts2BHR0dFSWUlJCaKjo6FWq2swMiIielEZ1XQARLoUEhKCwMBA+Pj44JVXXsGyZcuQm5uLMWPG1HRoRPVeTk4Obty4IU0nJycjLi4Otra2cHNzq8HIiOTj4w/ohfP111/js88+Q1paGtq1a4fly5ejc+fONR0WUb13+PBhdO/evUx5YGAgoqKi/vmAiHSAiRQRERGRTBwjRURERCQTEykiIiIimZhIEREREcnERIqIiIhIJiZSRERERDIxkSIiIiKSiYkUERERkUxMpIiIiIhkYiJFRP+YN954A1OnTq3pMIiIdIaJFBEREZFMTKSI6IVWUFBQ0yEQ0QuMiRQR/aNKSkowa9Ys2NrawsnJCQsWLJDqUlJS0L9/f1haWkKlUuHdd99Fenq6VD969GgMGDBAo7+pU6fijTfekKbfeOMNTJw4EVOnTkXDhg3h7++v5zUiovqMiRQR/aPWrl0LCwsLnDp1ChEREVi4cCH279+PkpIS9O/fH/fv38eRI0ewf/9+/PHHHxgyZIisZZiYmOD48eNYtWqVHtaCiOgJo5oOgIjqlzZt2mD+/PkAAA8PD3z99deIjo4GAFy6dAnJyclwdXUFAKxbtw6tW7fGmTNn0KlTp2ovw8PDAxEREboPnojoGbwiRUT/qDZt2mhMOzs74+7du4iPj4erq6uURAGAl5cXbGxsEB8fr9UyOnbsqJNYiYiqwkSKiP5RxsbGGtMKhQIlJSXVmtfAwABCCI2ywsLCMu0sLCzkB0hEpAUmUkRUK3h6euL27du4ffu2VHb16lVkZmbCy8sLAGBvb4/U1FSN+eLi4v7JMImINDCRIqJawc/PD97e3hg+fDjOnTuH06dPY9SoUejWrRt8fHwAAD169MDZs2exbt06JCYmYv78+bh8+XINR05E9RkTKSKqFRQKBbZv344GDRrg9ddfh5+fH5o2bYpNmzZJbfz9/fHhhx9i1qxZ6NSpEx4+fIhRo0bVYNREVN8pxLMDDoiIiIioWnhFioiIiEgmJlJEREREMjGRIiIiIpKJiRQRERGRTEykiIiIiGRiIkVEREQkExMpIiIiIpmYSBERERHJxESKiIiISCYmUkREREQyMZEiIiIikun/A3PTbgsKhTpiAAAAAElFTkSuQmCC",
            "text/plain": [
              "<Figure size 600x400 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# plot hour 2-7 & rest of the hours Vs median trip duration\n",
        "hour_before7_after7_df = np.where((df.pickup_datetime.dt.hour == 0) | \n",
        "                                  (df.pickup_datetime.dt.hour == 1) | \n",
        "                                  (df.pickup_datetime.dt.hour > 7), 1, 0)\n",
        "\n",
        "grouped_hour_before7_after7_df = df.groupby(hour_before7_after7_df)\n",
        "print(grouped_hour_before7_after7_df)\n",
        "fig,ax = plt.subplots(figsize=(6,4))\n",
        "sns.barplot(x=grouped_hour_before7_after7_df['trip_duration'].median().index, \n",
        "            y=grouped_hour_before7_after7_df['trip_duration'].median().values, palette='coolwarm', ax=ax)\n",
        "ax.set_title('Median trip_duration Vs Hour 2-7 (labelled as 0) & rest of the hours (labelled as 1)', color='black', size=10)\n",
        "plt.xlabel('hour')\n",
        "plt.ylabel('median trip_duration')\n",
        "for container in ax.containers:\n",
        "    ax.bar_label(container, color='black', size=10)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ccqrx0OEvS0R"
      },
      "source": [
        "Observations:\n",
        "\n",
        "Difference between the median *`trip_duration`* from hour 2 to 7 and rest of the hours."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bfOf56zrZAXf"
      },
      "source": [
        "**PART 2: Preservation of Processed Data**\n",
        "\n",
        "1.   Drop duplicated rows. \n",
        "\n",
        "2.   Delete rows with *`passenger_count`* equal to zero and more than six.\n",
        "\n",
        "3.   Delete global outliers from pickup location, dropoff location and trip duration.\n",
        "\n",
        "4.   Drop column *`droppoff_datetime`*."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "n3uPUz0FZTXV"
      },
      "outputs": [],
      "source": [
        "df = pd.read_csv(\"raw.csv\") # import dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mt3v-PWPZLyS",
        "outputId": "1bd1c45f-427f-4ffa-ed45-ff1a41d071bf"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Row count before performing action: 1458644\n",
            "Row count after performing action: 1458529\n"
          ]
        }
      ],
      "source": [
        "print(f'Row count before performing action: {df.shape[0]}')\n",
        "\n",
        "# drop duplicated rows\n",
        "df = df.drop_duplicates(subset=df.loc[:, df.columns != 'id'])\n",
        "# drop rows with passenger_count equal to zero\n",
        "df = df[(df.passenger_count > 0) & (df.passenger_count < 7)]\n",
        "# drop global outliers from pickup location \n",
        "df = df[(df.pickup_longitude > -75) & (df.pickup_longitude < -72.5) &\n",
        "                    (df.pickup_latitude > 39) & (df.pickup_latitude < 42)]\n",
        "# drop global outliers from dropoff location                   \n",
        "df = df[(df.dropoff_longitude > -75) & (df.dropoff_latitude > 39)]\n",
        "# drop global outliers from trip duration\n",
        "df = df[(df.trip_duration < 500000)]\n",
        "# drop column droppoff_datetime\n",
        "df = df.drop(columns=['dropoff_datetime'])\n",
        "\n",
        "print(f'Row count after performing action: {df.shape[0]}')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "mfJtW7BtZZf4"
      },
      "outputs": [],
      "source": [
        "df.to_csv('data/processed.csv', index=False) # save processed df to .csv"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3.7.16 (conda)",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.7.16"
    },
    "vscode": {
      "interpreter": {
        "hash": "ccc6ac5cc4b1c6ba965de596f06cd93599e015a19c516c5ed1fcc75f56229d88"
      }
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}