Supervised_Capstone_Google_Play_Store_App_Arun_Nair (2).ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Supervised Capstone Google Play Store App Arun Nair",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    }
  },
  "cells": [
    {
      "metadata": {
        "colab_type": "text",
        "id": "4XpQWG1RFZa4"
      },
      "cell_type": "markdown",
      "source": [
        "# Unit 3 Capstone - Supervised Learning - Analysis of Reviews and Ratings in Google App Store\n",
        "\n",
        "\n",
        "![google play store](http://idaay.org/wp-content/uploads/2018/07/kisspng-google-play-android-app-store-google-play-5ac7a0bf108fc6.0418072915230322550678.jpg)"
      ]
    },
    {
      "metadata": {
        "colab_type": "text",
        "id": "od20DxgEFZa7"
      },
      "cell_type": "markdown",
      "source": [
        "## Topic"
      ]
    },
    {
      "metadata": {
        "colab_type": "text",
        "id": "enctFO-4FZa9"
      },
      "cell_type": "markdown",
      "source": [
        "The data for the capstone has been taken from [Kaggle](https://www.kaggle.com/lava18/google-play-store-apps).\n",
        "\n",
        "The scope of this supervised machine learning capstone is to explore features related to the sucess of apps within the Google Play Store Apps.  We aim to see the relationship these features have against ratings for these apps. We will split our modeling between accurately being able to classify our data and predicting the features of the apps against their ratings.\n",
        "\n",
        "We have to deal with a lot of dirty data and need to binarize the parameters properly. \n",
        "\n",
        "[Github Repository](https://raw.githubusercontent.com/nairjr/Datasets/master/googleplaystore.csv) \n",
        "\n"
      ]
    },
    {
      "metadata": {
        "colab_type": "text",
        "id": "SVOfkoUwFZa9"
      },
      "cell_type": "markdown",
      "source": [
        "## Import Libraries"
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "Qv6gVXZ9FZa_",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "%matplotlib inline"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "colab_type": "text",
        "id": "DWuamuN3FZbD"
      },
      "cell_type": "markdown",
      "source": [
        "## Loading Data"
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "3-yl4f8EFZbE",
        "outputId": "0e0cd17a-2091-4d7e-ab12-642ff123988b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        }
      },
      "cell_type": "code",
      "source": [
        "google = pd.read_csv('https://raw.githubusercontent.com/andandandand/CSV-datasets/master/googleplaystore.csv')\n",
        "google.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>App</th>\n",
              "      <th>Category</th>\n",
              "      <th>Rating</th>\n",
              "      <th>Reviews</th>\n",
              "      <th>Size</th>\n",
              "      <th>Installs</th>\n",
              "      <th>Type</th>\n",
              "      <th>Price</th>\n",
              "      <th>Content Rating</th>\n",
              "      <th>Genres</th>\n",
              "      <th>Last Updated</th>\n",
              "      <th>Current Ver</th>\n",
              "      <th>Android Ver</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Photo Editor &amp; Candy Camera &amp; Grid &amp; ScrapBook</td>\n",
              "      <td>ART_AND_DESIGN</td>\n",
              "      <td>4.1</td>\n",
              "      <td>159</td>\n",
              "      <td>19M</td>\n",
              "      <td>10,000+</td>\n",
              "      <td>Free</td>\n",
              "      <td>0</td>\n",
              "      <td>Everyone</td>\n",
              "      <td>Art &amp; Design</td>\n",
              "      <td>January 7, 2018</td>\n",
              "      <td>1.0.0</td>\n",
              "      <td>4.0.3 and up</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Coloring book moana</td>\n",
              "      <td>ART_AND_DESIGN</td>\n",
              "      <td>3.9</td>\n",
              "      <td>967</td>\n",
              "      <td>14M</td>\n",
              "      <td>500,000+</td>\n",
              "      <td>Free</td>\n",
              "      <td>0</td>\n",
              "      <td>Everyone</td>\n",
              "      <td>Art &amp; Design;Pretend Play</td>\n",
              "      <td>January 15, 2018</td>\n",
              "      <td>2.0.0</td>\n",
              "      <td>4.0.3 and up</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>U Launcher Lite – FREE Live Cool Themes, Hide ...</td>\n",
              "      <td>ART_AND_DESIGN</td>\n",
              "      <td>4.7</td>\n",
              "      <td>87510</td>\n",
              "      <td>8.7M</td>\n",
              "      <td>5,000,000+</td>\n",
              "      <td>Free</td>\n",
              "      <td>0</td>\n",
              "      <td>Everyone</td>\n",
              "      <td>Art &amp; Design</td>\n",
              "      <td>August 1, 2018</td>\n",
              "      <td>1.2.4</td>\n",
              "      <td>4.0.3 and up</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Sketch - Draw &amp; Paint</td>\n",
              "      <td>ART_AND_DESIGN</td>\n",
              "      <td>4.5</td>\n",
              "      <td>215644</td>\n",
              "      <td>25M</td>\n",
              "      <td>50,000,000+</td>\n",
              "      <td>Free</td>\n",
              "      <td>0</td>\n",
              "      <td>Teen</td>\n",
              "      <td>Art &amp; Design</td>\n",
              "      <td>June 8, 2018</td>\n",
              "      <td>Varies with device</td>\n",
              "      <td>4.2 and up</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Pixel Draw - Number Art Coloring Book</td>\n",
              "      <td>ART_AND_DESIGN</td>\n",
              "      <td>4.3</td>\n",
              "      <td>967</td>\n",
              "      <td>2.8M</td>\n",
              "      <td>100,000+</td>\n",
              "      <td>Free</td>\n",
              "      <td>0</td>\n",
              "      <td>Everyone</td>\n",
              "      <td>Art &amp; Design;Creativity</td>\n",
              "      <td>June 20, 2018</td>\n",
              "      <td>1.1</td>\n",
              "      <td>4.4 and up</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                                 App        Category  Rating  \\\n",
              "0     Photo Editor & Candy Camera & Grid & ScrapBook  ART_AND_DESIGN     4.1   \n",
              "1                                Coloring book moana  ART_AND_DESIGN     3.9   \n",
              "2  U Launcher Lite – FREE Live Cool Themes, Hide ...  ART_AND_DESIGN     4.7   \n",
              "3                              Sketch - Draw & Paint  ART_AND_DESIGN     4.5   \n",
              "4              Pixel Draw - Number Art Coloring Book  ART_AND_DESIGN     4.3   \n",
              "\n",
              "  Reviews  Size     Installs  Type Price Content Rating  \\\n",
              "0     159   19M      10,000+  Free     0       Everyone   \n",
              "1     967   14M     500,000+  Free     0       Everyone   \n",
              "2   87510  8.7M   5,000,000+  Free     0       Everyone   \n",
              "3  215644   25M  50,000,000+  Free     0           Teen   \n",
              "4     967  2.8M     100,000+  Free     0       Everyone   \n",
              "\n",
              "                      Genres      Last Updated         Current Ver  \\\n",
              "0               Art & Design   January 7, 2018               1.0.0   \n",
              "1  Art & Design;Pretend Play  January 15, 2018               2.0.0   \n",
              "2               Art & Design    August 1, 2018               1.2.4   \n",
              "3               Art & Design      June 8, 2018  Varies with device   \n",
              "4    Art & Design;Creativity     June 20, 2018                 1.1   \n",
              "\n",
              "    Android Ver  \n",
              "0  4.0.3 and up  \n",
              "1  4.0.3 and up  \n",
              "2  4.0.3 and up  \n",
              "3    4.2 and up  \n",
              "4    4.4 and up  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 2
        }
      ]
    },
    {
      "metadata": {
        "colab_type": "text",
        "id": "wpbaGsAGFZbI"
      },
      "cell_type": "markdown",
      "source": [
        "## Data Cleaning"
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "Jdjox-tyFZbK",
        "outputId": "5216d465-7586-4c98-a0e3-794e9373056a",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "cell_type": "code",
      "source": [
        "google.info()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 10841 entries, 0 to 10840\n",
            "Data columns (total 13 columns):\n",
            "App               10841 non-null object\n",
            "Category          10841 non-null object\n",
            "Rating            9367 non-null float64\n",
            "Reviews           10841 non-null object\n",
            "Size              10841 non-null object\n",
            "Installs          10841 non-null object\n",
            "Type              10840 non-null object\n",
            "Price             10841 non-null object\n",
            "Content Rating    10840 non-null object\n",
            "Genres            10841 non-null object\n",
            "Last Updated      10841 non-null object\n",
            "Current Ver       10833 non-null object\n",
            "Android Ver       10838 non-null object\n",
            "dtypes: float64(1), object(12)\n",
            "memory usage: 1.1+ MB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "colab_type": "text",
        "id": "-gj8Vf3FFZbO"
      },
      "cell_type": "markdown",
      "source": [
        "We notice that most of the variables in this dataset are objects. Let's change these to floats for our machine learning purposes.\n",
        "We can eliminate the variables that are not important to this report."
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "sY-4XOyxFZbP",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "google.drop(['App','Genres',\n",
        "             'Last Updated',\n",
        "             'Current Ver','Android Ver'], \n",
        "              axis=1, inplace=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "colab_type": "text",
        "id": "GvIELmCsFZbR"
      },
      "cell_type": "markdown",
      "source": [
        "Change column names for simplicity."
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "P15RJxvWFZbS",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "google = google.rename(index= str, columns={\"Content Rating\":\"content_rating\"})"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "colab_type": "text",
        "id": "QGl0D0o6FZbX"
      },
      "cell_type": "markdown",
      "source": [
        "### Ratings"
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "f9pb0DAmFZbX",
        "outputId": "c4533180-dff4-49a7-aa3c-e3cf66108280",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "cell_type": "code",
      "source": [
        "google.Rating.unique()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 4.1,  3.9,  4.7,  4.5,  4.3,  4.4,  3.8,  4.2,  4.6,  3.2,  4. ,\n",
              "        nan,  4.8,  4.9,  3.6,  3.7,  3.3,  3.4,  3.5,  3.1,  5. ,  2.6,\n",
              "        3. ,  1.9,  2.5,  2.8,  2.7,  1. ,  2.9,  2.3,  2.2,  1.7,  2. ,\n",
              "        1.8,  2.4,  1.6,  2.1,  1.4,  1.5,  1.2, 19. ])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "metadata": {
        "id": "dot1A42IJmQ2",
        "colab_type": "code",
        "outputId": "f59450c4-b6f1-4039-d376-e7ac44a6c729",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "cell_type": "code",
      "source": [
        "import statistics\n",
        "\n",
        "statistics.median(google['Rating'])\n",
        "\n",
        "statistics.mode(google['Rating'])\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "4.4"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "uV8j81lBFZbc",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "google.Rating.dropna(inplace = True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "colab_type": "text",
        "id": "Kojkn4mbFZbf"
      },
      "cell_type": "markdown",
      "source": [
        "### Reviews"
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "TNaKotUzFZbg",
        "outputId": "e2c01eb1-d1d9-434c-fba6-6a5340569453",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "cell_type": "code",
      "source": [
        "google.Reviews.unique()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['159', '967', '87510', ..., '603', '1195', '398307'], dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "P7FnbOaYFZbj",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "google.replace('3.0M', 3000000, inplace=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "wJ1sQnI2FZbm",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "google['Reviews'] = google['Reviews'].str.strip(\"-\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "XRQCX0I5FZbq",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "google.Reviews = pd.to_numeric(google['Reviews'], errors='coerce')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "G1RIvveMMYDI",
        "colab_type": "code",
        "outputId": "cb1dab67-826c-4f0f-d5f3-e9770b9dcea6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "cell_type": "code",
      "source": [
        "import statistics\n",
        "statistics.median(google['Reviews'])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "382.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "metadata": {
        "id": "B7KA_Rt0L6Ho",
        "colab_type": "code",
        "outputId": "17bdb9a0-f5f6-4ff0-c449-cc2ecd9e5d08",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "cell_type": "code",
      "source": [
        "google.Reviews.count"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<bound method Series.count of 0           159.0\n",
              "1           967.0\n",
              "2         87510.0\n",
              "3        215644.0\n",
              "4           967.0\n",
              "5           167.0\n",
              "6           178.0\n",
              "7         36815.0\n",
              "8         13791.0\n",
              "9           121.0\n",
              "10        13880.0\n",
              "11         8788.0\n",
              "12        44829.0\n",
              "13         4326.0\n",
              "14         1518.0\n",
              "15           55.0\n",
              "16         3632.0\n",
              "17           27.0\n",
              "18       194216.0\n",
              "19       224399.0\n",
              "20          450.0\n",
              "21          654.0\n",
              "22         7699.0\n",
              "23           61.0\n",
              "24          118.0\n",
              "25          192.0\n",
              "26        20260.0\n",
              "27          203.0\n",
              "28          136.0\n",
              "29          223.0\n",
              "           ...   \n",
              "10811         4.0\n",
              "10812        80.0\n",
              "10813        20.0\n",
              "10814       785.0\n",
              "10815      5775.0\n",
              "10816         2.0\n",
              "10817       885.0\n",
              "10818        96.0\n",
              "10819        52.0\n",
              "10820        22.0\n",
              "10821         6.0\n",
              "10822         0.0\n",
              "10823         1.0\n",
              "10824        67.0\n",
              "10825         7.0\n",
              "10826     88486.0\n",
              "10827       117.0\n",
              "10828       291.0\n",
              "10829       603.0\n",
              "10830       881.0\n",
              "10831        38.0\n",
              "10832      1195.0\n",
              "10833        44.0\n",
              "10834         7.0\n",
              "10835         0.0\n",
              "10836        38.0\n",
              "10837         4.0\n",
              "10838         3.0\n",
              "10839       114.0\n",
              "10840    398307.0\n",
              "Name: Reviews, Length: 10841, dtype: float64>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "metadata": {
        "colab_type": "text",
        "id": "FpNrV5Y6FZbs"
      },
      "cell_type": "markdown",
      "source": [
        "### Price"
      ]
    },
    {
      "metadata": {
        "id": "6nCRm6E65ZOM",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "We need to get rid of the dollar sign and dirty data to convert the string to a float."
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "pECEtuJfFZbt",
        "outputId": "0f93606f-9bc1-4984-f246-af0530953772",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "cell_type": "code",
      "source": [
        "google.Price.unique()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['0', '$4.99', '$3.99', '$6.99', '$1.49', '$2.99', '$7.99', '$5.99',\n",
              "       '$3.49', '$1.99', '$9.99', '$7.49', '$0.99', '$9.00', '$5.49',\n",
              "       '$10.00', '$24.99', '$11.99', '$79.99', '$16.99', '$14.99',\n",
              "       '$1.00', '$29.99', '$12.99', '$2.49', '$10.99', '$1.50', '$19.99',\n",
              "       '$15.99', '$33.99', '$74.99', '$39.99', '$3.95', '$4.49', '$1.70',\n",
              "       '$8.99', '$2.00', '$3.88', '$25.99', '$399.99', '$17.99',\n",
              "       '$400.00', '$3.02', '$1.76', '$4.84', '$4.77', '$1.61', '$2.50',\n",
              "       '$1.59', '$6.49', '$1.29', '$5.00', '$13.99', '$299.99', '$379.99',\n",
              "       '$37.99', '$18.99', '$389.99', '$19.90', '$8.49', '$1.75',\n",
              "       '$14.00', '$4.85', '$46.99', '$109.99', '$154.99', '$3.08',\n",
              "       '$2.59', '$4.80', '$1.96', '$19.40', '$3.90', '$4.59', '$15.46',\n",
              "       '$3.04', '$4.29', '$2.60', '$3.28', '$4.60', '$28.99', '$2.95',\n",
              "       '$2.90', '$1.97', '$200.00', '$89.99', '$2.56', '$30.99', '$3.61',\n",
              "       '$394.99', '$1.26', 'Everyone', '$1.20', '$1.04'], dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "nOsriW7mFZbv",
        "outputId": "4c98f92f-e7fc-4879-e8a1-714d1bcd41c0",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "cell_type": "code",
      "source": [
        "google.Price = google.Price.str.strip('$ ')\n",
        "google.Price.unique()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['0', '4.99', '3.99', '6.99', '1.49', '2.99', '7.99', '5.99',\n",
              "       '3.49', '1.99', '9.99', '7.49', '0.99', '9.00', '5.49', '10.00',\n",
              "       '24.99', '11.99', '79.99', '16.99', '14.99', '1.00', '29.99',\n",
              "       '12.99', '2.49', '10.99', '1.50', '19.99', '15.99', '33.99',\n",
              "       '74.99', '39.99', '3.95', '4.49', '1.70', '8.99', '2.00', '3.88',\n",
              "       '25.99', '399.99', '17.99', '400.00', '3.02', '1.76', '4.84',\n",
              "       '4.77', '1.61', '2.50', '1.59', '6.49', '1.29', '5.00', '13.99',\n",
              "       '299.99', '379.99', '37.99', '18.99', '389.99', '19.90', '8.49',\n",
              "       '1.75', '14.00', '4.85', '46.99', '109.99', '154.99', '3.08',\n",
              "       '2.59', '4.80', '1.96', '19.40', '3.90', '4.59', '15.46', '3.04',\n",
              "       '4.29', '2.60', '3.28', '4.60', '28.99', '2.95', '2.90', '1.97',\n",
              "       '200.00', '89.99', '2.56', '30.99', '3.61', '394.99', '1.26',\n",
              "       'Everyone', '1.20', '1.04'], dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "GAwl84tRFZbz",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "google.Price = pd.to_numeric(google['Price'], errors='coerce')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "OHmxJ-AxFZb3",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "google.Price.dropna(inplace = True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "colab_type": "text",
        "id": "imrfVCpLFZb5"
      },
      "cell_type": "markdown",
      "source": [
        "### Category"
      ]
    },
    {
      "metadata": {
        "id": "PFoziFT_5ZOq",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "We will be using the categories aas a parameter to compare the correlation between other variables. Let's use the labelEncoder package from sklearn to convert these to numeric classifications."
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "kA_LpIhVFZb6",
        "outputId": "eb277a1d-28c2-4447-b2a9-edca6e10f71a",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "cell_type": "code",
      "source": [
        "google.Category.unique()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['ART_AND_DESIGN', 'AUTO_AND_VEHICLES', 'BEAUTY',\n",
              "       'BOOKS_AND_REFERENCE', 'BUSINESS', 'COMICS', 'COMMUNICATION',\n",
              "       'DATING', 'EDUCATION', 'ENTERTAINMENT', 'EVENTS', 'FINANCE',\n",
              "       'FOOD_AND_DRINK', 'HEALTH_AND_FITNESS', 'HOUSE_AND_HOME',\n",
              "       'LIBRARIES_AND_DEMO', 'LIFESTYLE', 'GAME', 'FAMILY', 'MEDICAL',\n",
              "       'SOCIAL', 'SHOPPING', 'PHOTOGRAPHY', 'SPORTS', 'TRAVEL_AND_LOCAL',\n",
              "       'TOOLS', 'PERSONALIZATION', 'PRODUCTIVITY', 'PARENTING', 'WEATHER',\n",
              "       'VIDEO_PLAYERS', 'NEWS_AND_MAGAZINES', 'MAPS_AND_NAVIGATION',\n",
              "       '1.9'], dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "uLM1sJX-FZb-",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "#Fix misplaced value\n",
        "google = google.replace(to_replace = '1.9', value = 'ART_AND_DESIGN')\n",
        "#inplace?\n",
        "google = google.replace(19, 1.9)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "KGs_lq0oFZcB",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import LabelEncoder\n",
        "\n",
        "LE = LabelEncoder()\n",
        "google['category_code'] = LE.fit_transform(google['Category'])\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "fM4gnE9eFZcC",
        "outputId": "51071ea8-c0b8-47fe-99f7-82dfa2d8b4ba",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "cell_type": "code",
      "source": [
        "google['category_code'].unique()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 12, 13, 15, 16, 17, 18,\n",
              "       14, 11, 20, 27, 26, 24, 28, 30, 29, 23, 25, 22, 32, 31, 21, 19])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "faXHJmJrFZcF",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "google.drop(['Category'], axis=1, inplace= True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "colab_type": "text",
        "id": "qPiVRBbLFZcI"
      },
      "cell_type": "markdown",
      "source": [
        "### Installs"
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "QO21RmZ3FZcI",
        "outputId": "d94db318-70c9-42a7-f9f3-090a9f124360",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "cell_type": "code",
      "source": [
        "google.Installs.unique()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['10,000+', '500,000+', '5,000,000+', '50,000,000+', '100,000+',\n",
              "       '50,000+', '1,000,000+', '10,000,000+', '5,000+', '100,000,000+',\n",
              "       '1,000,000,000+', '1,000+', '500,000,000+', '50+', '100+', '500+',\n",
              "       '10+', '1+', '5+', '0+', '0', 'Free'], dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "rI5bkn3RFZcN",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "google.Installs = google.Installs.str.strip('+')\n",
        "google.Installs = google.Installs.str.replace(',','')\n",
        "google.Installs = pd.to_numeric(google['Installs'], errors='coerce')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "ZXMtsw9WFZcP",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "google.Installs.dropna(inplace=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "P8irI46pFZcR",
        "outputId": "79d098c6-9e45-409c-ee89-0966e4abe304",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "cell_type": "code",
      "source": [
        "google.describe()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Rating</th>\n",
              "      <th>Reviews</th>\n",
              "      <th>Installs</th>\n",
              "      <th>Price</th>\n",
              "      <th>category_code</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>9367.000000</td>\n",
              "      <td>1.084000e+04</td>\n",
              "      <td>1.084000e+04</td>\n",
              "      <td>10840.000000</td>\n",
              "      <td>10841.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>4.191513</td>\n",
              "      <td>4.441528e+05</td>\n",
              "      <td>1.546434e+07</td>\n",
              "      <td>1.027368</td>\n",
              "      <td>16.723826</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.515735</td>\n",
              "      <td>2.927761e+06</td>\n",
              "      <td>8.502936e+07</td>\n",
              "      <td>15.949703</td>\n",
              "      <td>8.354469</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>4.000000</td>\n",
              "      <td>3.800000e+01</td>\n",
              "      <td>1.000000e+03</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>11.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>4.300000</td>\n",
              "      <td>2.094000e+03</td>\n",
              "      <td>1.000000e+05</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>14.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>4.500000</td>\n",
              "      <td>5.477550e+04</td>\n",
              "      <td>5.000000e+06</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>25.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>5.000000</td>\n",
              "      <td>7.815831e+07</td>\n",
              "      <td>1.000000e+09</td>\n",
              "      <td>400.000000</td>\n",
              "      <td>32.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            Rating       Reviews      Installs         Price  category_code\n",
              "count  9367.000000  1.084000e+04  1.084000e+04  10840.000000   10841.000000\n",
              "mean      4.191513  4.441528e+05  1.546434e+07      1.027368      16.723826\n",
              "std       0.515735  2.927761e+06  8.502936e+07     15.949703       8.354469\n",
              "min       1.000000  0.000000e+00  0.000000e+00      0.000000       0.000000\n",
              "25%       4.000000  3.800000e+01  1.000000e+03      0.000000      11.000000\n",
              "50%       4.300000  2.094000e+03  1.000000e+05      0.000000      14.000000\n",
              "75%       4.500000  5.477550e+04  5.000000e+06      0.000000      25.000000\n",
              "max       5.000000  7.815831e+07  1.000000e+09    400.000000      32.000000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "metadata": {
        "colab_type": "text",
        "id": "TJ20Q697FZcT"
      },
      "cell_type": "markdown",
      "source": [
        "### Content Ratings"
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "CwGpYb6fFZcU",
        "outputId": "864ca582-6d8a-418a-be5f-ec7c826134fc",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "cell_type": "code",
      "source": [
        "google.content_rating.unique()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Everyone', 'Teen', 'Everyone 10+', 'Mature 17+',\n",
              "       'Adults only 18+', 'Unrated', nan], dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "OMexLF6UFZcZ",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# Changing variables to integers\n",
        "content_rating_code = {\"content_rating\": {\"Everyone\":0, \"Teen\":1, \"Everyone 10+\":2, \"Mature 17+\":3, \"Adults only 18+\":4, \"Unrated\":5, \"nan\":\"\" }}"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "cGejSEScFZcc",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "google.replace(content_rating_code, inplace= True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "IauyCU9rFZcd",
        "outputId": "7e5e18e4-10b7-4382-d1ed-946bc2aec2bd",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "cell_type": "code",
      "source": [
        "google.tail()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Rating</th>\n",
              "      <th>Reviews</th>\n",
              "      <th>Size</th>\n",
              "      <th>Installs</th>\n",
              "      <th>Type</th>\n",
              "      <th>Price</th>\n",
              "      <th>content_rating</th>\n",
              "      <th>category_code</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>10836</th>\n",
              "      <td>4.5</td>\n",
              "      <td>38.0</td>\n",
              "      <td>53M</td>\n",
              "      <td>5000.0</td>\n",
              "      <td>Free</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10837</th>\n",
              "      <td>5.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>3.6M</td>\n",
              "      <td>100.0</td>\n",
              "      <td>Free</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10838</th>\n",
              "      <td>NaN</td>\n",
              "      <td>3.0</td>\n",
              "      <td>9.5M</td>\n",
              "      <td>1000.0</td>\n",
              "      <td>Free</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>20</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10839</th>\n",
              "      <td>4.5</td>\n",
              "      <td>114.0</td>\n",
              "      <td>Varies with device</td>\n",
              "      <td>1000.0</td>\n",
              "      <td>Free</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10840</th>\n",
              "      <td>4.5</td>\n",
              "      <td>398307.0</td>\n",
              "      <td>19M</td>\n",
              "      <td>10000000.0</td>\n",
              "      <td>Free</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>18</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       Rating   Reviews                Size    Installs  Type  Price  \\\n",
              "10836     4.5      38.0                 53M      5000.0  Free    0.0   \n",
              "10837     5.0       4.0                3.6M       100.0  Free    0.0   \n",
              "10838     NaN       3.0                9.5M      1000.0  Free    0.0   \n",
              "10839     4.5     114.0  Varies with device      1000.0  Free    0.0   \n",
              "10840     4.5  398307.0                 19M  10000000.0  Free    0.0   \n",
              "\n",
              "       content_rating  category_code  \n",
              "10836             0.0             11  \n",
              "10837             0.0             11  \n",
              "10838             0.0             20  \n",
              "10839             3.0              3  \n",
              "10840             0.0             18  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 31
        }
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "-oO7GQ7kFZci",
        "outputId": "8768d679-ea6a-47c3-8dd7-3d01635cb098",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "cell_type": "code",
      "source": [
        "google.describe()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Rating</th>\n",
              "      <th>Reviews</th>\n",
              "      <th>Installs</th>\n",
              "      <th>Price</th>\n",
              "      <th>content_rating</th>\n",
              "      <th>category_code</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>9367.000000</td>\n",
              "      <td>1.084000e+04</td>\n",
              "      <td>1.084000e+04</td>\n",
              "      <td>10840.000000</td>\n",
              "      <td>10840.000000</td>\n",
              "      <td>10841.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>4.191513</td>\n",
              "      <td>4.441528e+05</td>\n",
              "      <td>1.546434e+07</td>\n",
              "      <td>1.027368</td>\n",
              "      <td>0.327952</td>\n",
              "      <td>16.723826</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.515735</td>\n",
              "      <td>2.927761e+06</td>\n",
              "      <td>8.502936e+07</td>\n",
              "      <td>15.949703</td>\n",
              "      <td>0.761608</td>\n",
              "      <td>8.354469</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>4.000000</td>\n",
              "      <td>3.800000e+01</td>\n",
              "      <td>1.000000e+03</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>11.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>4.300000</td>\n",
              "      <td>2.094000e+03</td>\n",
              "      <td>1.000000e+05</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>14.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>4.500000</td>\n",
              "      <td>5.477550e+04</td>\n",
              "      <td>5.000000e+06</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>25.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>5.000000</td>\n",
              "      <td>7.815831e+07</td>\n",
              "      <td>1.000000e+09</td>\n",
              "      <td>400.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>32.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            Rating       Reviews      Installs         Price  content_rating  \\\n",
              "count  9367.000000  1.084000e+04  1.084000e+04  10840.000000    10840.000000   \n",
              "mean      4.191513  4.441528e+05  1.546434e+07      1.027368        0.327952   \n",
              "std       0.515735  2.927761e+06  8.502936e+07     15.949703        0.761608   \n",
              "min       1.000000  0.000000e+00  0.000000e+00      0.000000        0.000000   \n",
              "25%       4.000000  3.800000e+01  1.000000e+03      0.000000        0.000000   \n",
              "50%       4.300000  2.094000e+03  1.000000e+05      0.000000        0.000000   \n",
              "75%       4.500000  5.477550e+04  5.000000e+06      0.000000        0.000000   \n",
              "max       5.000000  7.815831e+07  1.000000e+09    400.000000        5.000000   \n",
              "\n",
              "       category_code  \n",
              "count   10841.000000  \n",
              "mean       16.723826  \n",
              "std         8.354469  \n",
              "min         0.000000  \n",
              "25%        11.000000  \n",
              "50%        14.000000  \n",
              "75%        25.000000  \n",
              "max        32.000000  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        }
      ]
    },
    {
      "metadata": {
        "colab_type": "text",
        "id": "aAS1WoTjFZcm"
      },
      "cell_type": "markdown",
      "source": [
        "### Size"
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "XON0dRv5FZcm",
        "outputId": "b49f5b9e-c40a-425a-f48c-d70c4fade5db",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "cell_type": "code",
      "source": [
        "google.Size.unique()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['19M', '14M', '8.7M', '25M', '2.8M', '5.6M', '29M', '33M', '3.1M',\n",
              "       '28M', '12M', '20M', '21M', '37M', '2.7M', '5.5M', '17M', '39M',\n",
              "       '31M', '4.2M', '7.0M', '23M', '6.0M', '6.1M', '4.6M', '9.2M',\n",
              "       '5.2M', '11M', '24M', 'Varies with device', '9.4M', '15M', '10M',\n",
              "       '1.2M', '26M', '8.0M', '7.9M', '56M', '57M', '35M', '54M', '201k',\n",
              "       '3.6M', '5.7M', '8.6M', '2.4M', '27M', '2.5M', '16M', '3.4M',\n",
              "       '8.9M', '3.9M', '2.9M', '38M', '32M', '5.4M', '18M', '1.1M',\n",
              "       '2.2M', '4.5M', '9.8M', '52M', '9.0M', '6.7M', '30M', '2.6M',\n",
              "       '7.1M', '3.7M', '22M', '7.4M', '6.4M', '3.2M', '8.2M', '9.9M',\n",
              "       '4.9M', '9.5M', '5.0M', '5.9M', '13M', '73M', '6.8M', '3.5M',\n",
              "       '4.0M', '2.3M', '7.2M', '2.1M', '42M', '7.3M', '9.1M', '55M',\n",
              "       '23k', '6.5M', '1.5M', '7.5M', '51M', '41M', '48M', '8.5M', '46M',\n",
              "       '8.3M', '4.3M', '4.7M', '3.3M', '40M', '7.8M', '8.8M', '6.6M',\n",
              "       '5.1M', '61M', '66M', '79k', '8.4M', '118k', '44M', '695k', '1.6M',\n",
              "       '6.2M', '18k', '53M', '1.4M', 3000000, '5.8M', '3.8M', '9.6M',\n",
              "       '45M', '63M', '49M', '77M', '4.4M', '4.8M', '70M', '6.9M', '9.3M',\n",
              "       '10.0M', '8.1M', '36M', '84M', '97M', '2.0M', '1.9M', '1.8M',\n",
              "       '5.3M', '47M', '556k', '526k', '76M', '7.6M', '59M', '9.7M', '78M',\n",
              "       '72M', '43M', '7.7M', '6.3M', '334k', '34M', '93M', '65M', '79M',\n",
              "       '100M', '58M', '50M', '68M', '64M', '67M', '60M', '94M', '232k',\n",
              "       '99M', '624k', '95M', '8.5k', '41k', '292k', '11k', '80M', '1.7M',\n",
              "       '74M', '62M', '69M', '75M', '98M', '85M', '82M', '96M', '87M',\n",
              "       '71M', '86M', '91M', '81M', '92M', '83M', '88M', '704k', '862k',\n",
              "       '899k', '378k', '266k', '375k', '1.3M', '975k', '980k', '4.1M',\n",
              "       '89M', '696k', '544k', '525k', '920k', '779k', '853k', '720k',\n",
              "       '713k', '772k', '318k', '58k', '241k', '196k', '857k', '51k',\n",
              "       '953k', '865k', '251k', '930k', '540k', '313k', '746k', '203k',\n",
              "       '26k', '314k', '239k', '371k', '220k', '730k', '756k', '91k',\n",
              "       '293k', '17k', '74k', '14k', '317k', '78k', '924k', '902k', '818k',\n",
              "       '81k', '939k', '169k', '45k', '475k', '965k', '90M', '545k', '61k',\n",
              "       '283k', '655k', '714k', '93k', '872k', '121k', '322k', '1.0M',\n",
              "       '976k', '172k', '238k', '549k', '206k', '954k', '444k', '717k',\n",
              "       '210k', '609k', '308k', '705k', '306k', '904k', '473k', '175k',\n",
              "       '350k', '383k', '454k', '421k', '70k', '812k', '442k', '842k',\n",
              "       '417k', '412k', '459k', '478k', '335k', '782k', '721k', '430k',\n",
              "       '429k', '192k', '200k', '460k', '728k', '496k', '816k', '414k',\n",
              "       '506k', '887k', '613k', '243k', '569k', '778k', '683k', '592k',\n",
              "       '319k', '186k', '840k', '647k', '191k', '373k', '437k', '598k',\n",
              "       '716k', '585k', '982k', '222k', '219k', '55k', '948k', '323k',\n",
              "       '691k', '511k', '951k', '963k', '25k', '554k', '351k', '27k',\n",
              "       '82k', '208k', '913k', '514k', '551k', '29k', '103k', '898k',\n",
              "       '743k', '116k', '153k', '209k', '353k', '499k', '173k', '597k',\n",
              "       '809k', '122k', '411k', '400k', '801k', '787k', '237k', '50k',\n",
              "       '643k', '986k', '97k', '516k', '837k', '780k', '961k', '269k',\n",
              "       '20k', '498k', '600k', '749k', '642k', '881k', '72k', '656k',\n",
              "       '601k', '221k', '228k', '108k', '940k', '176k', '33k', '663k',\n",
              "       '34k', '942k', '259k', '164k', '458k', '245k', '629k', '28k',\n",
              "       '288k', '775k', '785k', '636k', '916k', '994k', '309k', '485k',\n",
              "       '914k', '903k', '608k', '500k', '54k', '562k', '847k', '957k',\n",
              "       '688k', '811k', '270k', '48k', '329k', '523k', '921k', '874k',\n",
              "       '981k', '784k', '280k', '24k', '518k', '754k', '892k', '154k',\n",
              "       '860k', '364k', '387k', '626k', '161k', '879k', '39k', '970k',\n",
              "       '170k', '141k', '160k', '144k', '143k', '190k', '376k', '193k',\n",
              "       '246k', '73k', '658k', '992k', '253k', '420k', '404k', '1,000+',\n",
              "       '470k', '226k', '240k', '89k', '234k', '257k', '861k', '467k',\n",
              "       '157k', '44k', '676k', '67k', '552k', '885k', '1020k', '582k',\n",
              "       '619k'], dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 33
        }
      ]
    },
    {
      "metadata": {
        "colab_type": "text",
        "id": "g_FuK-nTFZcq"
      },
      "cell_type": "markdown",
      "source": [
        "# this cell should only be run once\n",
        "\n",
        "```\n",
        "mega = google['Size'].str.contains('M')\n",
        "kilo = google['Size'].str.contains('k')\n",
        "```"
      ]
    },
    {
      "metadata": {
        "colab_type": "text",
        "id": "9IsKocJKFZcq"
      },
      "cell_type": "markdown",
      "source": [
        "```\n",
        "google['Size'] = google['Size'].str.strip('k')\n",
        "google['Size'] = google['Size'].str.strip('M')\n",
        "google['Size'] = pd.to_numeric(google['Size'], errors='coerce')\n",
        "```"
      ]
    },
    {
      "metadata": {
        "colab_type": "text",
        "id": "48RsCrGMFZcr"
      },
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        "```\n",
        "google['Size'].loc[mega] = google['Size'].loc[mega] * 1000000\n",
        "google['Size'].loc[kilo] = google['Size'].loc[kilo] * 1000\n",
        "```\n"
      ]
    },
    {
      "metadata": {
        "colab_type": "text",
        "id": "4B_LcucNFZcr"
      },
      "cell_type": "markdown",
      "source": [
        "### Type"
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "NO7fKqyrFZcs",
        "outputId": "48604c3e-1f8d-41fd-a322-e30e55ae16c3",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "cell_type": "code",
      "source": [
        "google.Type.unique()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Free', 'Paid', nan, '0'], dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        }
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "gvKt5AuwFZcv",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "google.dropna(inplace=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "QLEX0QMOFZc3",
        "outputId": "a95e50a8-97ef-4c8b-f809-30a30c085ec6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "cell_type": "code",
      "source": [
        "google.Type.unique()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Free', 'Paid'], dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 36
        }
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "_1F5JcwYFZc5",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# Changing variables to integers\n",
        "type_code = {\"Type\": {\"Free\": 0, \"Paid\":1}}"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "fsogAYhWFZc7",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "google.replace(type_code, inplace=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "colab_type": "text",
        "id": "EJIdAamsFZc8"
      },
      "cell_type": "markdown",
      "source": [
        "## Visualizations"
      ]
    },
    {
      "metadata": {
        "id": "oL_zGagM5ZP6",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "We will be utilisizing matplotlib and seaborn packages to conduct an exploratory data analysis on our dataframe."
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "MBHp6P2GFZc9",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "sns.set_palette(\"GnBu_d\")\n",
        "sns.set_style('whitegrid')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "DhzpFxjZFZc_",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "google.drop(\"Size\",axis=1, inplace=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "B4OPM7915ZQA",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "In order to avoid skewed results, we should scale our variables.\n",
        "Check if y needs to be scaled."
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "cn-iWCx5FZdA",
        "outputId": "0b125e32-6cb5-406d-ac24-ba297ef1bb6e",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import MinMaxScaler\n",
        "scaler = MinMaxScaler()\n",
        "print(scaler.fit(google))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "MinMaxScaler(copy=True, feature_range=(0, 1))\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/preprocessing/data.py:323: DataConversionWarning: Data with input dtype int64, float64 were all converted to float64 by MinMaxScaler.\n",
            "  return self.partial_fit(X, y)\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "5R5znxatFZdD",
        "outputId": "3301bc6d-e767-49a5-bf5a-16ee0588831b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        }
      },
      "cell_type": "code",
      "source": [
        "google_scaled = pd.DataFrame(scaler.fit_transform(google), columns=google.columns)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/preprocessing/data.py:323: DataConversionWarning: Data with input dtype int64, float64 were all converted to float64 by MinMaxScaler.\n",
            "  return self.partial_fit(X, y)\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "metadata": {
        "colab_type": "text",
        "id": "s30P0o_GFZdF"
      },
      "cell_type": "markdown",
      "source": [
        "### Pairplot"
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "ZGuE1caNFZdF",
        "outputId": "db263bf0-28ed-4f70-db34-c7b217091143",
        "scrolled": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1304
        }
      },
      "cell_type": "code",
      "source": [
        "sns.pairplot(google_scaled)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.PairGrid at 0x7f7b6179b390>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 43
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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uYk1aMj4+nW/tXrp9M0qr69Ch0VI6Hz97AX2DN6g6H/3yLDKT4mndNc1upGYk\nQj4q6+X4zu5v4Y8fnbB69x56djc++vysRX1bO1V4Mmc9jk7RTyKidw/dvWUD8q+VISEqgtHFt6Gl\nHbHhIbTnA/xMLsDmXXTT9Z/uIjzbUBTEvZRAsCPq7h4qo42mp5f6e9RgxMoVMQDub2Xl+nhb3Mtm\nsbCEzYJ+eARd3bqHcnV7WJfNclkDpQvTlumc1RnfqO4PYvOazBm3ahuMRlyXNcypDgR6VNpuRrsN\n5vNw584wtTUduL+dfTrmbeULkflwZSHYnp7+AVrb1fXT/wJKePSQt9K7oMzWZWQ+mMlu1T098F7O\ngaurC4b0enT39s/6HU3c5gm2Zr7GXO2UsqfK0fb02UyGOdvjVAxGI6NHyGwxu/1Nl0M3j/omkDAu\nzCRGR4LNYmFIr0dDSxtu6fWMroshIgGVjXS6/XWotbh9Z5g6VrQpESLgWbhSTi1P2qBAcmwk3Jew\nkBgdQc0z4yNCLe7h+/tZuFhifBz+vr7081J/roUbqjmUQvekntN10Pb2YfDWLUpneasSy5Z6WNQ5\nShxE9e3p9091ZzW7m3pyPFCroM/YKpO3oEPTjeTYKEpPP+/lVJ82lzOT+6zBOAZ+AJfxu3FIr38o\nF2A6/ae7CPMDZheKgux0IxDsSEunaSeXJ4eD6DAJbt66jQ6NFrq+fqxLT8H7J76ApqcXSrUWh/fn\norJODkVbB3y8PMHxcIeivRNiAQ8d6m6rstksFgwGy8GJyWVzuqvo1Iw45hTUU7cf56zOmPNfwRKi\nI/DW66+ioKgMVQ0KBHJ94b6EjatTJv8kO5t9mGq3YcEiqLp1YLm5YfDmLTS2tWPz6kxEh0qg1vVQ\nKbnN29vZLDdszMrAxeIytKs0VFkLjSKpjNrWr+sboOyzSCqzmSvLQiE6OtreKjw0zcouAADXxxtJ\nMVGoblSgb/AG9X/Co898ZDm0NTPZbVZyAtgsFn70m7fBZrEwPDqKb+/aNqt3NHGbJ9gas+2a5wut\nnSoM6fU2H3PNcwk2iwU/by9q7tHSobKZjPnOxDq9LraWY/4mmC5nrsPHLATOXi2h3KKHR0bRw+Ca\n2NM/gKe3bsTxsxcBWLdlh6YbYcEiVDXIAQDtKi1+/NILOHrGdL0w0B9ZKUkollZDrTN97/34pRcQ\nIuLj717/JaXDrk2PUR5IbBYLe7bl4MPPz1HzUB8fHxRPzkvHxycwajBiCZsFFxdnFEtlWJOWiMGb\nQ3Bfwsa1yhpszMrABYbF1ZYOFbIzV+LUhcuU3T25ORtvT+6yu3FrCBtXZ+JvXxYw1FmLDatWQn9n\nGK6uLmhoacP/2f8M3jv+Oa3fx0IQAAAgAElEQVQ8pVqL55/agY/PXMChZ3dD29uPLWtW4d/ffpcq\nPzY8FIM3h2jv1/T0Yeu6LFTUNiIrJQGjBiN6+gcgCPCHRMhHa6cKEqEABoMRudtyoNRooe7ugZAX\nAFcXF4vvSgDo0GixMWsl9HdGIBbw0KnRQiIUUPP8axU1+Ptnvn4oCrLoRiDYkbAgAdatTMb4xASU\nag2MY2NYm5YEQYA/iipqcOPWEFycnbE2LQlXyqVo79JAEMhFeHAQmpWdcHVxgfsStkU2NhdnZ6zP\nSEWAny+6tN3Y948/gkTERxCfZxp8aCbP4WJLl7ipW3zHJyZQWFoBNouFHevX4AcvfBsA8Gn+JVTW\nNaJD3Q2xkIfU+Bg8tXmDTdsnIToCCdEReOPdD3D6cpHVLxwSof0yDp66cBnlsnqq/ukJcYvG1TU8\nWIi1aUlQaXXw9ORgOWcpOrXdCOT6IlQkRFuXChwPdyolt/sSNtguLoiJDENVvQIXisqoWG+KZtv+\ncjtfBPEDqH7h5+1FbU3Pzkizt2rzxnyMAbYmIliIxx/Lsoop0kYW3RYNTFnp7Pk+eRAz2W27Sg3j\n2BjWZ6TizuTHYXN7J85cLsK29au/lpwQEX12xtAgoa2qQlhkhAUJsDYtySo+WbcNd6ABJtuViPhW\n8amc4WQzGfM1dsxHXQAgIToSQQKelRyf5cttKmchwvf3w+//dhxcH288t3MrauUtaGfI1Nml1TE+\nM6d7QEW9nLpeIuTji0tXESUWYeemdahpbMZ1WT3Cg4Pw9NaNaGpR4ouLV5CRnABhgB/kbUqw3dyg\n6raUUVRRbTEPHRwcRIiQB36AP7q03egdGIRYEAhRYABcnZwxcPMmqhtMiRnWZ6TiYnEZoz2HCPlo\nbGm3sLvrsnqkxkVPxlrT4ZVf/nbGOrd1quHv44Xo0DA0trbjDx+egFjIgyCQS8XBm9omggAuQngB\ncHZ2Rm//IP7fO3+xKj+YH2hxv4uzM9alp+DevXu4LquHWMCDKDAAnZpuJEVHYujOHXxw8jQ1T69u\nbAKbzcISFgtcHx8scWPh7sQ47TNt7VCD72/yXHF2MjmGmuf5G1atnJVNkUU3AsGOJMZGoaaxCV9V\nVFv5qK9NS0JGUjxcnJ2tfNjNq/nmFMabstKpLcbr0lPg5ORkkZa5fYqfPrUVeRI2i4W7d+/idOFX\n2J69FgCQnhCHkqpaq0WusEk/9k/zL+GtDz62ikUAYE4+uqcvCpr1XpkQa3NZD4M55h1d/RfDwltC\nTCTe+uBjZKUk4NzVEotYgqXVdTictxcfnDwD4L49H96fa20z1bU4vD/XbvX4JqTGx1B9xBxclc1i\nISV+4ez6+ibM9xhgK1ZM2u6jYoeEr8/UvmvG0fsuo93m7cUE7uHtI8exNi0JpdWmPtiu1uBqRRXu\n3Zug3usPQ3ZGGoqkMqu2WZ+eYtsKERYNibFRVrZrnhPYkuS46DmXM19jx3zUBQCSYyKtYjiyWSz8\n+MWDNpWzEEmNj0ZJlQx9gzfw2z9/hFcP5NGOjXACjp7Ox6sH8qxitLFZLBzcsxOXyiqo61Nio/DG\n+0dorzeP6W+8fwQnL16xuOb8tRKre149kEfZ466X/5VRB3OZZh2SY6NwuawST+Vk09pzUmwU3pxy\nvfn+7Mw0i+9RJnm523JwqawCGYnbreKnTo2DN7VNDv7oZ1blZSTG0cZwM9+/Lj0FxVOeifl8VkoC\nTuRfwqsH8nD1ehU1T8/OTLO43ix/uj4iXgAKyyrQpOygnmFheaVF+80GsuhGINgRmbwF+uERWh91\ng9EId/YSODk70Z4fNRghEQqg7e1DYWkFcrdvhlpn+vhnKrO1U4Vnv7UV8jalhUtcYWkFxsfvT853\nbloPN1dXNCk7IZO3QBDAxcqEWGpBqbJOTh+LoE4+Jx/cZrnXZQ1QqrWQCPkW+sw35ph3UzHHmFsM\ni26VdaZf7ZjiK8iaWhAVIsbtO8Pov3ETbBZr3m1mrqmuV9C6l1bXKxZkfb4uC/V5LlS9CbZD1tCE\n3G056O4bwNBtPTyXccDj+kLW0OSwNsBotw0KrM9ImXQrNVj8qGYwGlFaXfu1Ft3MyRIul0vR1qVG\naJAQ69NTSBIFB4M1LS6RIzNfY650Us50VzdbyqmXt+DQs7vR0KLErdt6LF/GQWy4BA3yFpvWpYqh\nzaps3WaN9HHFqhqbFn2fr5qc4zk7OUMY4I/qxibKdbG7tx9iIR9OTqYQPKlx0dQuqKmY46FFScTg\nBXARxAuATN6Ml7+9l4rPZmWvDQo898QWLOcsg3TKNbs2PUYdA0CISICqSZ3GxyfwyvO5+O1fjzO+\nJ9akJuHu+Djcl7Aha2zGM9tzqPvv3h2HwTgGNssNrq4uqG9uQ0pcNOqb26j74yPCMWowWsiXMtS5\nuaMLXB9vdGnp443fuwesTkmCs7MTXF1dUN3YhONv/T/8z0efWsROa+7oom2je/fuIT4iHPcm7jF8\nP4/hiU3r0NqpwqFnd6OxRYnh0VHgHujLm7iHSIkYPl6ecF/CRluXmvq+NhiNaOtU48DTT0Db0w/D\nmHHW83yy6EYg2JFbt/XoHRikPafW9cLf14dxM7mutx/+vj7Ux365rB7+Pt4YNdxjLFPT04exu+Oo\nkTdZuMQBljEpprqNhYgESI2LslhMmq+4FlPZuWm9wyxoMdW/Q6PFP/78TbgvYSMrOeFrfewsJDom\n47Mxpd9u61IjOTYKlXVyJMdGIUwsQlFFDe21jhxHaSbaVBrqV7WpfUkiFNhbtXnBHmOALVioehNs\nh1KrAy+Ai7ExI3oGBuG+hEX931GZyW7XO6Vi67pVqKyTw8/bi9p5CwBK1de36w1Z6Yv+g9tRkcmb\nkX+tFDJ5ExKiq7B5TabDZ5mcrzG3U6tDdmaalatbp8Z2/bqrtw8xkaEYH7+LvsFBcDyWwNnZGZ29\ntnWVbWdoG6b/z5aHjfO8GFH39GHjqjQ0trSj98YNKDXdCBYEwtXFBf6+PgDuAZNfaLu3bsI7Rz+l\nLadDo0VUqAT6O8NQ6XrQqdHhpf25OHulhNFe83Zuw+eXrmD5smXYuGol7oyMYuem9fjhm29T5W7M\nysDlskqsSopHl7Yby5cvn7GvTY3JJhEK8K9//zy+9/ovESwIxNjdu+gdGEQg1xcSoSnEwI1bQ5RO\nXVodIiTBkLcqLeQzxYTT9Q0gKSYKLR30YTs61VpwfX2oRTelphsBvj4W+vt5e6F34AZtG3VpdZRH\nFh3dvX24e3cc7ktYEAOYGB/H2N270Pb2M7b58mUcah4fIhTA39cHfH8/hAWL0K7SoLC0EmIBD4kx\nETh/rZxW7oMgi24Egh1ZvmwpnJ2daH3qhYH+WLZ0qYXf+1QCuNYpjm/d1mNIfweBXF/aMvkBXHiw\n2RYucWbMMSno3MaKpDWYwH23sYUYE8eWMNV/atpp88vgUVx4Ewt5KKmqRXJsFEN68iB8cemryQCs\nGjS0tCM9IfaRsplwsQhKtcaqL0VIrF2hH0XM9Z+Oo9d/sY9dBCBndTqty8vBPTvtrBkzM9ntO0c/\nRd/gTezeuhEnzl20PC8idv2oIJM34/DP3qDstk2lwZkrRXjr9VcdeuFtvsbcNamJOHo636pfP7Nj\ns81kbF2Tid9NcX9rV2lQJJXh5by9NpMBmN6jdG0WGRJsUzkroiJo4zwnRDmuPc0Xj6/LxB8+PAGD\n0QhPDge7ctbj4yn2Bdx3TTxx7gLjnCjAzxeXSq5T32r7dmzBkVOnkZWSgGM0rpO523Lwv59+jq8q\nqqlQQ6XVtVjOWWoRv/ticRm2rsui3mWHBwdn7GsnCwopWRIRH8dO52NNWiKOfmnZZ6oaTDv82lUa\ntKvu63TywhXEhodYyGeqc3iwENWNCiRERdC3ybTv19xtOegZGLSoX/+Nm9i7LYe2jXZv2YAvC68h\nJY7+G2Tqt1hpdR1yt+Xg4zMF2L1lg0XopanlTf2/Wb+slAQr99aS6lq8lLfH2mAeAudZ3UUgEGxC\nuDgIHA932hTGIl4A/P18EOBHnwKaLsXxkP4O2Cw3xjI9lrAZUyqvSl4BYGZXADOZCXG0ZThyTBxb\nks5Q/+nPhOlXmIVOTFgIADCm3w4WBMKTs5T635BeD0GgP+21MeEhc6/wHMDj+oHNYsGTw0FybDQ8\nORxTanY/X3urNi+Y689msai09Quh/onREbR2mBAdbieNCPONvFVJ+45TtCkZ7rA/M9lt36DJRUY3\nbbcNm8VCZtKK+VSTMIfkF5VSH6nmMddgNKKAYbeJozBfY662p5e2fbQ2TNhgdqeb+t43GI2oalDY\nTAYAxnl/gK+PTeVsXpNJKydndYZN5SxEauQtlD3xuH7oGxikfW+MjBqgHx6h5kRTYbNY4Hi4w8/b\ni7JHja4HEgEfvf305fUMDEIQwKWO9cMjVLlmuzZjfpeZ/8fc1yKwIjKcmqdGh0rA4XhAq6PvMyOT\noQrMOnT39oHH9YMnZyk113NzdbPSxyyPH+CPQK4vgviBD/Wt1DMwiMEbNy3KY7NY6J5075yuX3dv\nPwCA70//PWv1fdzXj6zkBOj6+mnbvLu3nyrLfD/AHEKnuqEJs4HsdCMQ7EhhmRRb12aA589Fh1oD\nta4XEeJgcDge2PbYGvzkrXewNi0Jzz+5HfK2Dqh1PRAG+sPV1dUqxbFSrUWAnw8mxseREBMJvj8X\nyskyw4JF4PtzMaTXo7unD4f356KqXo52lSk+2qrkFdSOrJlcJ3/z3l9RWa9AiEiAw/tzUV2vQJtK\nA4mQj5T4aIeNh2NrpsaY61BrIeQxP5NHkYslFTi4ZycUbR3Yum4Vbt2+gy6NFpIgIdJXxGJ41Iik\nmCgUFJVS91yrqMGhZ59GjbwZHWotxEI+YsIkuFB8Hc898bgdazM7quRNeDlvL6QNcnSou5ESF4WU\n2GjkFzv2B5CtKJn89dCckS4zMR7BAh5KquvwD88+bW/1GCkovo6X8vagqqGJssPk2Eicv1aGvdts\ntyOC4Lgwjcvts3DFnC9msluzS2mHWosd69dA2qBAhCQImUkrsGXtKnurTrAR9U2ttK5Rdc2t9lZt\nRs5fK8ehZ3ejRt5C2W5idDjOXyu36ZjbptLStk9bl/VOmNmi1vVi/67tVplYS6rrbCYDAIqlMtqY\nsUVSmU3frwnREXjr9VdRUFQGmaIZCVERyFmd4dA7J+eLrmnuykN37iA7M80q82ZP/wCef3IHjnx+\nDrnbctCl1aFT0w0hLwASIR+tnSqw3Nzu26NKi3/53vP45Hwhrdzm9k789vUf4OjpAgAmV00/by/s\ne+Jx/Mcf/oyDe3aisVWJtPgYfJpfSOno4+ND29eSYyNRU69Ah7YbKbFRlHvk2z95FV9cKmJ0t5wa\nqqC1Sw2ujw/0d4ZN8lva0KnVQalSTx63o0PTDbGAh5jwEDQ0t+LO8AhaOrqQuy0HrZ0qaHv7IWL4\nVmpq74Qoj4c33v+Qsnvv5Z5oau9k1C8sWISymnqLfjK9fHN20+HRUdwdG2cMvdSh0cLf1wdiQSCC\n+Dwc/fI8uL4+jCF0ZvttRxbdCAQ7kp2Rgt//7RPTLw9hEri5uqGkuhZ5Ox9HuawOgX6++OTcJaxK\nWgGJkI+QIAG6NN1UlpWpBHJNv4x5eXLw3vFTAEy/AgBAWU09Xjt0AOtWJlPXMy2QMW1PDuT64YvC\na5TLYJG0Bj9+6QX8/JUXbdEUC46pMeZ++t9/xPlrpVbXPKoua+kJsXjv+CmwWSyEBYug6taB4+EB\nlqsrpPUKxEeEorrR8pffsGAh3v/k88m/RZDWy1FSJcO+HVvsUYVvzOasDAs3E3P2Tlu7mTgqmUnx\ntFmlcrfl2FmzmXksPRlvHzkOT85SJMVEobpRgZIqGV7Yu8veqhHmiYXoGs1ktwf37KTe94H+XFyt\nqMK+HVug0unIgtsjxpq0JPzt83NWY66j/2iVFh+NP3x4gpovmN/9tn5X0LnKmd35bMXmNRl499hJ\nKxm2fn8E8QNQWFphFTM2OyPNpnIA08IbWWSzhsn9c2qmSwAICxJifGyMmhMBpm8viZBPO0fat2ML\nPssvZHQhjggJxsdfnqOOAyddHT/6/Cw2Za3Eu8dOAgBGRkctdLxe24D0FTG0fS0rJQFtKg3aVCb3\nyIN7duJ04VWsTUvCR1+et3bJ3r4ZR0/nUzqY3TWzUhIsQjOIhXzq/ePn7YWS6lqUVNdauKder23A\n2rSkyZKcaL9fIyRB+NvnZxDED6TsXiwwbQg5fvYCo3tpbHgIdb3pe9ey/KnZTc0eXe00bS4W8FFc\ndf+61WlJKJbKGEPozPbbjriXEgh2xLw1eEivR3lNHZqUHRjS6yFvVaJL2wM/by8M6fW4PTwMXd8A\nWG5ucHFxod1Ou5zDAZvlhp7JLcvmxTGlWoMhvZ4x4OV0UuNjaMtfwmZZbLM1GI24XFb5zRvhEYCp\nzcwuu48a5m3xQ3o9qhrk6Bu8YQq8OnQbw6OjqG1qha/Xcup6s9vh1HuG9Hpqq/1ChClrk63dTByV\nnv4B2vrr+ul/GXQU5C2mMbdv8AYKikrRN3gDBqMRjS3t9laNME8wuQE5sms0k93KW5Xg+phcl5Z5\neKBv8Abkre3ITHw03z2LGVV3D+2Yq+p27Heo+V0x/d1v63eF2VVuKraeYzS2tNPKsPX7IzE68r47\n4hS3WbI4Nn8wzXGmul6yWSzw/Lm4XFFFzYsNRiO0vX3oYuivZnucyYWY4+5OHXM83GEwGhEhCaLs\nz2A0oq1LbeGi6uriTLlYT+9r091F5W1KsFxdodbR66jt6aVCxDC5W7JZLOp4qp3SydMPj0Db2wdX\nV/rv10A/XwRyfRETJqHsvkOjRc8M7qAGo5EKcWP+3p1a/lT9zDC5o/L8/ayeMcAcQiclNgqzYU53\nuv3iF7+ATCaDk5MTfvSjH2HFivuTgA8//BBffPEFnJ2dERcXh9dee20uVSEQHBLzFlVPDgdhwSK0\ndqowpNdDqdYiK2UFxifuAQC0PX0I8POBtqcPivZOq23ngX4+8FruiUvF5XBxoe/WbTTBUukw74CT\n1smhVGsRLhbBw30Jvrz01azLfNQxu+aWVNVCqbZ22X3UaFJ2ArC2W13fAPx9fWAwjuHA00/gDx+e\nsHCLmJ5ZDwBaOhemDS32LJjNSvqsVEz/dxRmGnMJi4OiSdetu3fHYTCOgc1yg6uri81dt2zJTHb7\nwp4nMXZ3jNpt3dKpxr9/P3mm4ggLkJYOFcP/HXvMNb8Tptuurd8V5rmEeXdY/w1TrENbzjGY3hO2\nfn+cvVqCg3t2okPdjXu4Byc4QSzk4ezVEux18N3kjwpM9tnTN4CkmEi4uriAxXJDkVSGH/3Dd/Hz\n378HwGR/seGhVCxBOnv85+89j+/9318gKyUBowbTIhLP3w9L2CwUSWV452c/xC39HYzdncDtO3pk\nJq3Azk3r8WVhEaVHUkwUNRcHgOTYaLz11+MA6OfmU+ffSpUWP3npBRw/V8io45a1mejUdFPumoFc\nPwt3Sz9vL0b3y+nyevoGkBgdCYPBiNxtOVCqtVB191jU+U//8Rr+/vVfIndbDvQjwwgViXDi/EXa\n8js0WmxYtRKjowaqPE1PLybGJybdb9sxPGJAzzR9ze6oLs4ucHNzxdjYXYxPjKOsph6x4aFoaGkz\nxZib1NdoHLMoP0QkQGJUBC6WX8dTWzfOYD30zNmi2/Xr19HZ2Yljx46hra0NP/rRj3Ds2DEAgF6v\nx/vvv4+CggK4urriu9/9LmpqapCYmDhX6hAIDklYkABr05Ks4kN09/ThzugoqutNu2b4AVx4cZbC\n6R7QOzAIVbfOYtt5WnwMRg1j6Bm4wbgdNjRI+NB6PbV5g4X76Wu/eZs2i+rXKfNRZ3v22kd2kW06\n4cFCWrtt71LDydkZ7mw2jpw6C+PYmIVbRHGVzKqsheqCu9izYC7U+s805hIWB2JBIO4BGLt7F70D\ngwjk+sLV1QUSAc/eqjHCZLe63j4MDt1Cl6Ybfl5ecHF2hkTIxy//+Gc4OztjOWcpegYG0aLswoqo\nCGxek0l2yyxQQkT8bzy3swfhQfTzBZ2Nx9wQER8SEd8q/pMznGwmY77ee2KePwBgeHQEHepuiIWm\nsUnCC7CpHAIzMz1rw9hdeCxhI1wSBDcnZ5y7WmRlf8H8QGQkxlHxzKba499OnqHeQ64uLvD39YGr\ni4upfAEPH35+Fv03byE6VIJ3P/4KTs7OuHCt1EKn6kYF4sJDqeOqBjnje6K9S43rdY0WdZC3tM3Y\nZ5a4scF2Y+FCSTkAUzbRqd+X04+nYnaJpY79/dDd2w9tbx+Kq2TIycqAMNAfTk5OVJ1PF15FuIgP\nPx8vdDXoUN901aS7it4dtK1TDX6AHzhLPeDp4QGPYBEAQN6mhLOTMwK4PljqsYRyJ+2/cROrEuKQ\nEBWOGnkzWjtVEAt5puQTLi5o1+io+jvBCQbjGJydnaDS9cBjyRLw/bn39Q2cXX+fs0W30tJSbNxo\nWgUMDQ3FrVu3oNfrweFw4ObmBjc3NwwPD8PDwwMjIyNYvnz5A0okEB49EmOj8NYHH1v5qx/O24u7\nE+Pw9FiK7jMF8FjChvHuXcRFhKJgcgA0b+dls1gIDRJCO5m5bOp2WzNsFgvr01NmrWd2RhqKJv3i\nbVUmYeGSEBNJa7eHnt2NG7dug+vthfwpSRTMmW2nL7ot5Iy3qfExKKmqteoTC7U+X5eFWn/GMXd/\nrp01I8wXyXHRC84GZpor3ANw8sIVsFksZGemIUISjN//7Tg2ZaXj7JVi6p7WLjXOXCnCW6+/Shbe\nFiALdR6WEEs/X7B1f5uPfj1f770VNHOskqpahx6jHjWYnrWQF4APTp4GAFytqMKhZ3fjt//7EV49\nkIe3psX5ZbNYyEpJoEL9mMfsN94/YnW9uXzzecCUUGPft7biL599iV/+6QO8eiCP0qlv8AaS46JQ\nUm06/v4vfsuow8E9O3FNWkPJSImNwt//9FeM15t1yM5Mo74np7tzTj+eWoep2UPZLBbCg0UomZz/\ns1ks3J0Yx7XKaqs6T9cnIzGOtnwRLwCFZRVoUnaY+gXD/ft3bbfQN2VFLGMs5nOTIZjMsYnNz3j6\n8dXrVbPuh3O26Nbf34/Y2Fjq2MfHB319feBwOGCz2XjxxRexceNGsNlsbNu2DRKJZK5UIRAclso6\nOa2/urRBgQNPP4G/njyDvCceR4dGi8tllRgfn8DGVSsxeGsIPf0DkAj58PJcBrWuB6XVdchZk4Eb\nN4dMGSWH9OjUdiMsWIR1acnYkJU+az3N914ul6KtS43QICHWp6d8ozIJ1nA4HHur8FAw2a1M3oJA\nri/aulTYsX4t6lvaKPfS6noFXvnOPpROZnwVC/lIiY2CvLkNWIBZb+sam6ksUlOzsdY1Ni+KLL41\n9Qpa14iaBoVD159xzK2TO7TeBNshXYA2MNNcIZgfCK6PN/oGb8DJyQmfnL0ATw4HIwYD7T0FRWVk\n0W0BYp5vXbl+fx722ErHn4fN15jL1K+rbCinevK9Nz2raHW9bd97j/J7ijUtRpajwvSsWztVFgs5\nNfIW5D2xlTHOrzm+mfl6aYMC2x9bQ10/3RW0qlGBl/L24tiZAvQN3kCXVocta1YhOzMNF4rKqAyp\n/TduoU7egtxtOVB192Dv1g34+NwlWh3krUqsiAyHr7cXgngBkMmb8ZMXD+CatJbxvfLct7bis4LL\nyEpJwL1799Ch7oYTgNxtOWjpVKG7tx9OcELuthw0d3RRbRQhDoK6uwcSoYBqM7WuF5ESMfj+fnBy\ndkJ1gwLJsdFT6tyEH0/Th81iobVT9VDPQNqgwOH9uZRnDd39ri4uVJtPd6eVNigQIhKgXaWBwWhE\nc0eXRfnTj2fbD+cte+m9e/eov/V6Pd555x2cP38eHA4H+/fvh0KhQFTUzIHppFLpjOdne+2jyGKv\nP/BwbZCSMj+/ENLpEhcXN2NcKGGgP27d1uPmbT3KZXXU/72Xe6KhpQ3JsVH4lxe+DaPRiPc//RIJ\n0RGob2qDiBcAVxcX1MibECER47s7t2JgYOAb24TXElc8tT4THh4eGB4exsTEhM3tbLHarfbGbZTK\n6qFUaSER8ZGZEAe+9zLG6x3ZbqNDxei4rYeX5zIL91KJUABnFxd09/bhyc3ZOHP5Gt4sq4BEKIDB\nYEB9ff1cV8dmhIaGoqlThfPFZeD6eFPZBC9P1ufmzZtoa2uzt5pzBpfLRbumG8GCQCvXiHZ1N7q6\nutDXR+865Mi2u9Ds0BYstjE3Li5uxrhMTDbgyHb7WHoKkmKiUFBUipYOFXZsWIue/kE0MAR3r5E3\noa6uDsZpH1sLjcVmuwAwPDICZ2dncH184OzsjOGRkQe2w3zYLpMO8zXmztSv220kJy4uDm2q+zuW\npmYV/TrzmAc9r0f1PTWoH0FZXSMUbR2ICi1FRnwMfDjujNfbc8wNDg6e8VlPjVfWodbilQPP4eV/\n+zVt+Vbx1NRa/Ov3nseb7/8N+3dtt3IFLa2pw/DIKOLCQxETHoKLJRVYGR+N9IRY/M9HJ3ChpBye\nHA62rstCVYMCvAAuXJydERUWgg71EVodlGotvv+dfThx/hJUuh50anR47cUD+Ovn5xmv/7vcJ9E2\n6drZ0zeIVckrUNXQRLnErkpegco6OS6VXrdoo9LqWkRKxPBe7mnRZisT4lDf3IrUuGhMjE9Y1VnI\n24QO9X19/Ly9oO3tf6hnYP72aJ/Sb6bf/1LeXpy6cAXZmWlW7rSdGh02ZmXgTx9/SvvM6J7hyMgI\nGhvvu+xOhcl252zRzd/fH/39/dRxb28vuFwuAKCtrQ0ikQg+Pj4AgNTUVNTX1z9w0e1hO6BUKp23\nzuqILPb6A47XBky6zBQzQK3rhecyDspq6u5fL+BRK/VLPdzh6emJkwWXcfZqCe2WZtwDxGIxxGLx\nnNTLljjaM5svLhWX43ihYMAAACAASURBVM0/f2jx/IqlMvz4xYN2/wV7NnY7cOsWnJ2ccPW61CJp\ngljIR3WjAn2DN6Bo77C4h81mL7hnb24DczZBMxIhH15eXguuPl+X1amJ+Ph0vtW2/2e2b0ZQUBCC\ngoLsqN3sbHch2uE3YbGOuY5sA7OxW6VKg+pGBXX8xaWvMKS/wxhvJzE6EvHx8bZVfJ5ZjLY7fa4A\nAFfKpQ49VwBM735a2xXZtr+FBgtp5YQHC20mJ1wsglKtocK7mImQBD2UjIe1W0ceo2aDTN6Mn//m\nj5Tttqk0uFhS4RCu7g8ac6c/6+nxysRCPo6dPk/ZxnSmXx8eLMRfPvsCOWvS8d6xU7SuoO8dN/2/\npLoWh57djfc/+QJ8f19KpyG9HueuFiNv5+PUtYee2YUwpj4gFuG946dQ29RCzdMKS8pntLO/fPoF\nSqtrKffKkxeuYEf2Gpw4f4naobdyRSyalB1WbeTj5Wmx60ws4KGw9Dp2b92AP3/yBW2d//fTLyz0\nmRoz7mGeweeXrkLEC2S8/2RBIVanJuHYmQIr+bnbcnCxuIyxfImQbxEeRyLkw93d/Wv3Q+evdfXX\nICsrC/n5+QCAhoYG+Pv7U65TAoEAbW1tGB0dBQDU19cviEUBAsHWxISFgM1igc1iQRDgT/0dHS6B\ncWwMokB/DOn1AEwftEH8QAzp9WCzWEiNM8WQuF7bQLs9eNRgxJqVJIOZo1NYXkn7/C6XO+6v+Ix2\nGyaBr5cXokJN4QKSY6PhyeGY7DU2CkP6OxblfJPU2/YmNjyUNpV4THiInTSaX7on08NPxWA0UrEl\nHZWZxlzC4iAxOpK279r7428mZrLb6PAQDOnvgM1iISpUjL7BGxbxdqbCZrGQszrDTrUgfBPMc4Wp\nNuDocwUAiAmT0NtuqG3HXFFgAK0cQaDtkg/wuH60MgL9fG0mA7g/v5guZ6HOL/KLShld3R2V5Ngo\n2vHTk7MUft5e1DNJiAqDgMulbGP69dPjmwkCA3BneBTyFiWjK6gnZyl1LJO3gM1yQ7tai9TYaEoG\nm+UGeatlGRZ2P+Vvvj8XISIBNWZoe/swajTOOI9tnsyKbL6ezXKDtrefkmcwGsEP4D5UGwXxA2Ew\njkHR1slY5xu3biNlsn7mnW2enKUP1aYxYRKodb3UO4/u/tt3htHTP0A7hur6B3D7zjBj+Tx/Pwv5\nybP8bpmznW7JycmIjY1Fbm4unJyc8JOf/ASfffYZli1bhk2bNuHAgQP49re/DRcXFyQlJSE1NXWu\nVCEQHJavKqvxct5eSBvk6FB3Y1XyCqTERiO/uAx7tmxEc6cKISIBJEI+IkPEuFhyHVvWrkJSTASe\n2LgeABi3oev6+rF17Sqr/5+6cBnlsnoqI1J6Qhx2blo/p/V0NE4XfoXiKhnVBlnJCXbLPNreZf0r\nEwC0ddkuzb2tuXK9Ci/l7UFVg8Jkt0nxiIkIRW//DYQFCeDluQzh4mB0qLVIiYtCamw0Cisq8IOD\nz6Gkqo6KgZYaG4WC0vJZpd62N4WlFbQx3QrLKvHcE4/bW705p6WT3j5bOlTzrMnXY6YxN++JbfZW\njzAP5F8rw6Fnd6NG3kL13cTocOQXlWHvthx7q0fLTHb79usb8cTGdQgLFuFq2f0FmKvlUqxLT6Fc\nacLFpviujry4SGCmQ91N6xrF5FbpKBSWSSf7WzNlu4nRETh/rRx5O2035pZU15neyS3t6NB0Y1XS\nCso973vPPGUzGbnbciiXwMzEeAQLeCiprsM/PPu0TWQAwKXSSsY2W4jzi1p5M+3/ZQr6/zsCBcXl\nk2OuAh1qLcKCBEiMjkRlgxwsNzesTklAckwUpA0KuC9hQ9HeaRF/TMQLgETIR2unyiK+2VcV1Xj2\nia04cvIMrVylWkuFC5h6/MyOx/Fvb7+Dw3l7UdmgAN/fDyVVtdR9Pj4+KK2pZ+wDGzJTqTGjrUuD\n7+U+if/7X+/QvgvPXyu3cLds7VQjKSYKLZMLcYDJfbNYKrOoM9/fD4kxkWhsaQPLzQ2rklcgNTYa\ndYoWrE5NhLxVyVjnJzdnI7+4zOI9Zxwbw8t5e1FeUwd1Tx/4/n7ISIpHTWMzJEIBNe/+8/FTAICi\nimo8s2MLurTd6NDoYDSO4aW8PZA1NiOA64uymnraMbS1U40tazLQqe2hfWblsnokRUfC3X0JkmMj\nUVBSht2z+G6Z05huP/jBDyyOp7qP5ubmIjeXZGEhLG5ystJpM6m8lLcHV0orcFt/B16enmhSdiLA\nzxcv7N2JNamWu9eYtgeLBdYpjU9duIzf/u9HVvIALJqFt9OFX+GN947QtoE9Ft5CRPSuF6FBwnnX\n5WHJyVqJt48ct2zD6jqsTUvCzSFPHPn8LNS63vvnqkxb5P/08UksW+qBTaszcLG4DCVVMuQ66Efu\ng0hPiMN7x0+BzWIhLFgEab0cJVUy7PvWFnurNi8wuVJESOzrVvogZhpzCYuD9IRY/OHDE9Z9d4fj\n9t2Z7La5vQOiwACotDq4uLpQ94xPTKCwtAJsFgubstLxVUU1rl6vgp+PN1l4W4CsTknAx3SuUdsd\n+x2anZGCP3x4wsp2D+7ZaVM5G1elUa52lJzqWhzcazs5mUnx9M/AxvOYDZmptG32wt5dNpUzX6yI\nikArzQ/JCVGOOw6tTU3C744cA2BaYBLyAi0yY0pEfOrYk8NBckwkNd7y/bngeLjjg5OnreKRbV6T\niVMFl2d0uzaHCwBM7qjVjQoc/fIskqIj8daRYxAEcLEiOtzCpXpwcHDGPvD5hStQ63ope/3Lp19S\nfXP6u/Dgnp04d7XYSoewYJGV+6a5zn7eXvBwX2Itv6r2/7P35gFtXWfe/xcQEtgEmx0kgRCrQGB2\nA8ZLjPfYSZzE8RLHSad1J23SZDLTZmbedtr+2k7btJm8v47btJ2M09Z1FtuJYyexnZjYxgs7SCBA\nSKwSIAmJ1cYYkMDw/iF0kdC9MsEykuPz+QvpLuc59zznnMvR+T4P1udmY2zM6LDOZ4uvYWVaCu08\ntzYnA6aJCdQ0KlAqlWHfji3Ysb4AV6tr8da7J6j7rM7JsOufZbXm6+uVbSjISmOUlw4M3QDH25u2\nzTbk5yApLgbnLpeguKIa29YVLMSl7p28lEAg3BmpvJl2q22tvBnjJhNaVJ1o6+yGRt8LlUaHCyVV\ndvfISU2m3X6bTZPCvFJGL0WtksmdUJv7g7kpwAHzM7D+xWgxKczLoW2/9bnuG7NDpmilfYYjo2PQ\n9fWjb/C63TGZohUA0NGtxdvHTlJZggwDg4tmtzPR6g0wmkzmzEtyBYZHRsxxJ3oMrjZtUWCSUjhb\nZuNsHI25hAcDDVPf1btv33Xkt106PXS9vbg5OgoO29uuXwLA6Pg4VU93lnQRmNHPSKOsMZpMMPS5\n9xw6VwIHzEjK2ul3vTi9nFbnldPbP0jfBv3ObYOm1g7acpoYkqO4O1vW5N93UneLPxlNJty8NQq1\nRmcjORwbn80OPTwyAgEvgpIrDly/geDAAOqzdiYch+UdKTaKR8murbHIro2mCeozNzwUwyO3MD5u\ngk5vvk9HtxafXbhCSarn2myNpQ+wvb2pz/r+AUxMTlLnz50LFW2z53PYbHDDQmE0TdiELLAOYWA0\nmdA/dB0jo2O05d8YGUFPX5/DOpsmJij559zrR0bHqEyjHDYb3t7eePMv71Hha+jaZO71nboexvsb\nBgZRWd9IyWXntllCjAD/e/wUOrrNi3ShgQGMfuOIRcteSiAQ7HGUoej733oWlyqliBNEQipXQN83\ngKmpabtzn5xJW1zToIBKo4OQz0V2ahL1/XzLe1BwlLnOFVgCIBdXStDepUFsFB/rc7NcHhjZEUzP\nSt83AA/AJsuP9TUZySJcqZLYpOpu63RvOSITjPJKhu+/bpTMyAomJ2/DaJoAh+0NFssLJRKZU2U2\nzoaMgYS2mT5q+TXbMha5c9915Lf/9I19YHmzMXjjOuStHXhk3Wp4enpAKldS8pgrVnG/3FnSRWCm\nRdVF+32zqnORLflqML5zdTt3zF2MdzumZ+3sNnC399S7JS0pAYd+/BqKSipQp2hGelIiNq/Oc+sd\nt9bPOk4QCbW2B4B53hDHx2Lw+jD1OThgOT4+fwl7t29GT18/QgID7KSXlrG4RCLDm//nVbz2m0PY\nu30zunr0VKibqIhwFJVUYm1OOgauD5vPr6nDjvWrsW/HVrz2m/8GAIQEBuAf9z6F/z1+iiojMDDQ\nod88uaUQf5jZRdai6sLPXv0Ofvq7PzOe/40nH8UX18rg68PBtZo6PL5hDfR9g3hx/y7UK1uh1vZg\n6vZt7N2+GS3qLkxNTUPfN0B7P33fAJ7cUohPL14111mnh1rbg2heBKK45jo/vW0jPjx3gfZ6Q/8A\nNqxaiZFbo2CxvFBd34Q927fgbyc/pZ65tzeLcYFd3zeAvPRU5jG0oxNbVudBZ+jDi/t3Qd6qwvDN\nEfg/5IeIkCBcLKtGelIivLw84cNhL/g9lyy6EQguxFHmmJJqKZY9tBQVdebU4OEhQfBfupT2Pk9u\n2UC7yPZVyntQcMdnsKEgFxsKctHc3IzExESX2TFfmLaIh4cEIXD5Mkissv5YiI3kwZvlhUyxyCaW\nQuCyZYthstNxRz9aTKJ54ZgGMDE5id6BQYSHBIHF8oKQF+Fq0xzyoLcbwSzpF0Zy7eK6eMLD1aYx\n4shvL5ZVYWDoOrr1BrBYLNwaHcXP//m7ePuDj/HeZ5/b/bLvzpIuAjP3YygKYPHG3MUoh6mMmEWq\ni7PLWUzSkhKQlpSAhoaG+yJ7snUbtHV2I1ssspk3BNxw5KWnoK2zG7refqQkxgEApqamIGlUIIob\nZiO9tEgVC/NyUFVXj6iIUHTrDfD08ERSnBBGowndegMEEWGYmLhNnZ+fsQJXq2sRGhSEBAEfjzxc\ngKbWDpwtvoaoiNkyBgcHHfaBUkkdNdd5AHj/k3MOz79QVknZsD4vB9X1CnDDQmCamIQ3i4XMpETc\nuHWLkmNyQ0MQHhLE+L/B2eJr4IeH4MipM/D38zPLWeVKFFfWoDAvB7yQIERHctFBc72AG4H2Tg2C\nA5ZByOdienoaH5+/hNSEOPgtXYLb+il0aTUQ8CNor4/mRaCntx8CbrjDMcKHw4FpYhITE+bdb74+\n5l100RHhGDWa4OlpfkdY6HsukZcSCC4km0EamiUWoUauRERIMJWt1G+JLzLEd7cgk5uWQlveyjTx\nXd33fqIgM432GazKXOEii2YZmclU6+5kpybRPkO/Jb6Ij460O5/DZiMjRYQLZVUor62HSqNFeW09\nSiUyxETeny+RD3pfykxJQqlEZteeGSnunY2WccylkeMTvp7cj77r6F3h0N+P4aPzl8BisVAqkVFj\nUG56it193F3SRWDmfgxFASzemLsY5TCVkblIdXF2Oa7ANOdHAHfFOnTP8MgIkuJjbOaNy1USHDtb\nBA6HTc0jx84WYWp6GsoONTw8PGililliEX71P0eQmSxCqUSGi+VVOHe5BBfLq8zzUHIile3V8l7d\nNziEP73/IVaIEnD4xGkUV9bgWk0tMlNEVBlPvPzvNtlNLXDYbGSIE1FR10DNdenJifjiWjnj+Vli\nEa7V1FI2eHgASpUalyqqcfjEaZgmJ/Hh+YtIS4ynyldptIwZs82JJtRUdlJrOaulvFd++X+RlUyf\nMdbD0wPNKjU4HDaOnS1CcUUN1QaHT5zGNKahVM0+c7rrG1pa4enlyVjfdz/9HIHL/WeerwQqjRbF\nleY2FifGokRSi6vV0rt6VyA73QgEF6JsVeGVA3sgbWqGSqOFkM9DZnIimtvV2L11A9756BOszcmA\nkM8DNywEj298+K7KsyRLqJLJKSnqyjTxA5NEAZhNllAmraeewarMFS7LXno/omzpwCvP7YVM2Yq2\nzi5ERoQhJpIPLw8PdHRr8fJzeyBpVFLZkFITYyFtUNDGUqhpUMxrl6a78aD3JQlDe0oaFHjKjduT\nccxtVQFbXG0dYTGwjEVz5aVSN/ZdR+8KMZE8aA19WOLjAw6bDUW7GjsK19pIumTKFqSJEtxe0kVg\nhgpFUSVBR6cGMQI+1q9071AUANCoaJ2RpLVRvrtCFAe5otWp/a2puR0vH9gDqVxJzcmZYhEUze2A\nk8qRNippJYO1jUqn1mWxyiEwI1O0zsg/Dejp7WOMlzY2bqQWnqw/W7JHs7xYuHFzBMse8kN8dCQa\nm1vx8oG9kMzsIpt7P4lcifyMVCzx8UVURBimAazPzUZKQixq5lzz9gcf43sHdqOxuR3f2fM43j7+\nCW02Uml9k42NtfJmbFuzCjJFC63EtV7RgixxEvz9lsLD08MmPIGljvzwUNTNXN/TN4DhmyNgs7xx\ncPdOKNpUUGl0VDbT9k4N1mRnQNHagb3bN2Pw+g1MYRqe8EDg8mWoV7Tgpf1PQ9rUjIKsNBhNE+gx\n9CGazwU8zJm4HcVsm/vMp6emzfJVPhceM9cDsxm9p6fNIRss/aqtS4NvPLkDXToDfUy8NhV2bdmA\nK9VS9A0OLfhdgSy6EQgupKVLgyh+BLy8PBESGAgvL0+MGo1o7tLg+zHR+O7+p5EYIwQAyBQt+O3b\nR1CvbMEKUQK2rMlf0Mvzzk3rH5iFASZ2FK4li2x3QUu3FlGRXADTCAkMBIfNxtIlvviytArxAh68\nPL0QFhwIbmgwBoZuQGfoQ8fXLEYJMNuXurq6EBXl3lk7nc39GnNGqe5CYMAyeHl6mMdcTw/0Dg5B\noaaP9UH4+qHW6VGYn2MnL1Vr9a42jREmv23u0mBH4VpU18shb+3AClEcpqZnY79aJF0E5yBTtOD8\ntfK7fg9bKGNGIzw9PBESFAhPD0+MGY2LVvZCadPoEBoSBM8Z3/X09MDg9WG0OnmuaOnSIDgoAL4+\nHCTHmd+b+2b6iLPo6NZSGQ+tJYNCPs9pZSxmOYDrfdpd6ZhZlPH28sQ3dz2OP73/Ee15+r4BmzjG\nls/6vn4AwNj4OPoGB8FieUHRrkKnVo+8zHSH71C/ePW7uFIlxYWySqg0Wvj7+eHfvvMNnCkusTl3\ndHwctU3NmJqaQmBgIJTqLnxeUg5+eCgKsjJQKqlFcUU1hHyejY0qjQ6vv/Y9/PDNP2JyespO4tqp\n1WPDqpW4UilBs0pNW+eCrAxI5Upww0Ks5JgcAICnhwdCgwLhzWJh5NYoRo3j0Bp64RURjqTAAJtF\nvoQYAWrkSvzghefx4k9/g1UZqdAZ+hASZP6fmBsaAi9Pz5lnyhwzzlK/S+XVSBRGY2VaCuqamm3s\nt2T0ThRGIz46ClV1Ddi/8xE0tXZA19uPvjmJ3bw8PbEuNwu3xsbQrOpCSnwskuNjcKGsmtaOO0EW\n3QgEF/LIunybtOAAcKVKihf370JLhxqNbSokxgghU7TglV+8QZ3X1qXB2cslOPTj18jkSFh0HPlt\nY0sHbt66hVNFxdRxfz8/5KaJaWMpxAvcOx7NfOjr63vgFt3u19hom1fn2qS0B8zygoO7d7rQKsJi\nsjo7HcfOnKd8wPLP7b4d7rvVkclvX9y/y2YsttRlK/nH2em4+j3szKWreOPw0TnzrnkHhzv/iLhY\nY+5ilJMgjIJKo6UkgxYSYwROKwOYnV/nluPs+dXVPu3OWPtTV4+B8Z0nPCQIUqs4xpbP63KzUCqR\nUc/Wkvly344t+ODMF4iPjqS9X4IwCm8fP4k6RSvy0lOg0mgxPDKC906ftbPBuoz2zm4q3rJG34vj\nZ88z2ijkc/H2sY+xNicD73/2hV2feebRrTh3+RoiI8Jpn014SBBKJbV4fOPDNn1OpdGirLYeBVlp\nqJQ1oDA/B8fOFlHHo/lc/H4mmQN1vrQeLx/Yg9NFxYx9eN+jW3HsbBEyxaJ5tUHgcn9cKq+COC6G\ndtGQGxqMiroGHNj5CFUeh81GplhkExNubhta6vfiApOFkZhuBIILqVO00m5llSlaEbjcH+L4WACg\n9P1zzysqqVg0WwkEC478NijAH+1dGpu4CcMjI+CFhdLGUuBFhC2KzQTnksMQcybbzWPOMElEFO30\nWa8IXz96ZuLrWGM0maDr7XORRXeGyW9lila7fkjeDe4Nrn4PK5PW05ZfJq1flPIXymKNuUzlKJ1Y\nTlhwEO28FxYU6LQyACBTTB/b6m7jOs/F1T7tzlj7k0qjo+KRWWOJV2Y5z/IZAKMUUjeziBoREkx7\nv/DgIIQGBSI4YJnNOdUNCqQnJVKf58oth27eRHKccF42pori4e+3FBo9vZxSqzfAb8kShzHa2N7e\njH1u3GhCojAa40aTTblMz0QqV6K9S8N4vy6dnip3vm3QNzgEAS+C9vwobjg4bG80WZVnNJls7u/I\nXpliYRnAyU43AsGFqB1sLw4ODMTwrXEAQD1DB5cpF9bxCYS7wZHfJsZEoVomR5wgElK5gjp2raYO\nm9fkoX/wuk2MklKJDC/sfWqxTCc4iSe3bMA0zLHdLPFzslKT3D4+H6Oko9u9ZbEE59HaSS83a1V3\nL7Il88eRFGnuWAuQd4N7gavfw+5XSf9ijblM5XQ4sZxSiYw21lqJRIbvLnD3Cx2Xq2vwyoE9qJHP\nxsbNFotwqboaT2/b5LRyXO3T7oy1PwUHLMfFyiqqTTo1OvAjwpCaGAdlmwoxkTzww8Mg5HPR1tmN\n9KRERilka6cGT24pxJniEhzcvRNNbSqqjZPjhLhYXo2Ah/wg4EWAzWZjXU4Gbo6OYeuaVTh27ksq\nZtvo2DgMVmVkpyTjzx+cMsdo0+lnYppFIDkuBvKWdsRE8iDgmWO8fXGtEj//pxfwr7/5b0Yb/+Gp\nR3H+Wjn2bt8MlUaH7h4DIq3qvGvrRnz4+QXa6/W9/RDFCaFom13wdiQPVWl0+PUPvof/819/oD2u\n1vZgdXY6bgzfpOzR9BgQzeciQ5wISaPCrg2EfB66NDp878BuSOXNUFveU8UiyBQt2Lw6z+4HC0vM\nt8nJSUxMTDq0dyGQnW4EgguJ5tOnHRbyuRgcHMSHn3+JemUrVojot3mnMXxPINxLHPnt+WsViI7k\noa3T9h/YKG44iq5VQCpXwjQxAalciUvl1YgX2Gc7Jbg/MkULDh05hhZ1J1ampaBF3YlDR44t+BfA\nxcKR7xIeDKIZ2tqdMykz+W30zD8YcyHvBs7H1e9h9+vYtVh2M/VroRP7dRQ3DJfKq+3eYwRcehne\nQlmfk4NDR4+jsaUN8dFRaGxpw6Gjx7E+J8ep5bjap90Za7/tH7qOvLRUHDp6HGVSGaZh9t/DJ06j\nRCqDN8sbPhw2jpw6g5pGBXoHBhHHEDolQRiFs8XXsLEgB4dPnEaZVAbTxATKpDIcPnEa6/OyIG1q\nRnFFDf5+6gwiQkMglStxoawSeWli/PG9jyBpVAAegMDK52samyDkR+DIqTOQyJUIWOYPSaMSb717\nAn5Ll8Cb5Y0yqQx/fO8jrExNwvufnkMsg43xAj5a1V2oaVTgyKkzAAC2t7dNna9WSxmvDwsJQklN\nHcJDgmyeofVna4R8Lk5/Wczch/lctHdqbOyZBlAqleEPR08gJpKPpUt88dDSJVQbmCYmIIjk4Q9H\nT0DSqEDAMn/UNCpw6OhxxAgi4cPm2I1NlphvvhwfJAqjnT52kUU3AsGF3GkLOdvbGxfLqrBlTT7t\neZtX5y2arQSCBUd+2z90A5HhYRgeGbE5lp4UDwB26dODAwMW1XaCc7DIUiyxQzT63vtClmItz7DA\nYbMf+Pg1DxJMEhxRrNBFFt0ZJr/NFCfayV/Iu8G9wdXvYZsKcuHv52dX/qrMFYtS/kJZrDGXqV8n\nObFfZzNIDLPEIqeVAYDKbNk3OISiknL0DQ5RMjxn4mqfdmcKMtNspIbK9k4q+6eutw9dPQbqs1qr\nQ6yAT2UIHbh+AxGhIYzyUb8lvlC0qqj3YIts2Wgyobm9k7rOHE/OLK1ke7MoOejwyAjqmprBDZ2V\nn3Zqeyg5qtFkgqF/YPb+VvJni/2KdjUiw8Pow76Eh6G4soa6nsXyglqrs6lze5eG8Xp/v6V4aOkS\n+C3xtakLkzw0UyxC3+AQYx9OjBVArdXZ2GOJeWh+DzWAzfJGUMByqv43b42iU9tDPS+pXIHhkRHz\nM+5Q49NLV7BClEBbXnK8EOV1DUiOi2EYu+Lv7EA0EHkpgeBCikor8fKBPZBYbSHPEotwvrQCjxWu\nBTcsBHWKFqQlJeDQj19DUUkFZMoWpIkSsHl1HvlHkeASmPy2qKwCr7/2Mi6WVWFzQS5aOzWI5kUg\nihuOc1fKse/RrWjuUNvKMmrq8N1ndrm6SoSvyP0qSykqrbSRG0TzucgUJ+KLaxXYs32zq80jLAJF\nJZW2EpyZMaqopBIHdm53tXm0MPltUWklvrNvF5o71GhRd0IcH4sd69eQd4N7gKvew6yzS67KXIGw\noECUSGQQcMOxMk3s1kkUAOCLaxWUJM7iu2aJm3PH3Atl1bRyvQtl1U7r1xcoiaECak0PVmWuQLY4\nCRcqq/DUto1OKQNwHMLDmZD/LZix9KsyaT18fDh2Ukn1HPnphdJqal5Z4uvjUIr86jf24tDfT+D5\nJ3agU9uDTp0emcmJEPAiUFbbYBMyQK3pwdNbN2B9Xg5+/Ls/2ZRZUddIlbFz03q88ONfY+/2zdQ9\n89NTIU6IRWNzG0wTE1Sm7o5uHZ7buR1/P32W1sar1bV4fMM6NLWrkJ6UgHplG9KTEtFpVeeMZBEq\nZHKbvh0TycMKURwq6hrB9vbG6Nj4rBxUb8DU1BRemfnfQTXnf95/O/g8fvaH/6Wdmy+WVc8s3rPA\nYrFwpVJi01Ztnd3YsmYVLlXUUGOA75w2s0at7UFGsghfXC2nHTM+v1qO5f4PoVHZajd2JccJce5K\nOfZs/+qJl8iiG4HgQtbmZOD3R4/D328pMpJFqG1Sokwqw7f3PIHBwUHoDH2IDDcHmk9LSiATIcEt\ncOS3A0NDGDMa6Ft+UgAAIABJREFU0dSuQmhQECRyJYora7C5IBdni69heOQWggOWQzrzS+7GVbmu\nrg5hAawQJaCtyz42lrvLUh7OzcQfjp4Ah81GnCASkkYF5buEBwN+eAiOnDoDfz8/sw/MjFGFec6V\nbjkTR35769Yt+PpwMD1tziK9Y/0aV5v7tWWx38Poskty2Gzs3PQwPr9ShlKpDEI+z63fDXPTkvHH\n9z6ye1945tGtTi5HjMMnTtv1kb1OXNhbkZCAQzTZF52d+Xgxs4OT/y2Y2VG4FjsK10KtVuPtk2eo\nNukfum6TSbN/6DoyueHUvLI2OxNR3HBcKq82Kzqs3nkL83Og1ursMnVaMk8f3L0TR0+fo2wQ8rn4\n9t4nAdj6hcUGSxlduh7kZ6TaZAtVabQor2tAQVYaVBotVcae7ZtxoawScQI+zl8rt7Nx65pVGLxx\nA2XSepRJ6/HsY9ug0ujgL+BT2T1rm5R49vFt+ON7HwEwLwJ6enrYZdOuaVTg2ce2wdA/CAE3AoeO\nHgcvLASbVufhQmkF1UdPXyhGNC+cdm7eXJCLnr5BBC0PxbkrpXbtFBfFx2eXrlDZVAEgmsdFbBSf\nth/FCfiobVIiThCJt949gZDAAGpsKq6oRn7GCupZVNTL8a2nd2KZ31KUSmUorqhe8LsCkZcSCC7E\nsr147hbyptYOAMCtsTEU5rvvPwKEBxNHfqvV9yFeEAmjaYLazm3Zrm40TdjJS9fnZrm6OoQFcL/K\nUiy+O1duYBlzCV9/LPJ4ax+4F5kBnYkjvxUlxGB0fBwqjRbDIyNuL/EmzB+m7JJdOj0lrXL39tbN\nhB6Y+76g1RucWo5FTje3j+j76YOhL4TFyny8WNlLCfNjcHAQKQmxjFJJ68/DIyNoam+npJJz33mT\nYoW4UillzuprtTuLw2YjKW5WHm0tb7Yu02gy4dbILfT2D9Lec2zcaHOdvrcPN26O2MharW0MCw7E\n0I2b1Bij0uhQ06iwkcw+tHQJ6hSt1Dn9Q9cxMjpGP1716DF44wYlT+3o1uLtYyfR0W2WiBoGBqHW\n6MALC6WdmyNCQ6Dv70dwYAC9ZDc0BMBsxlmL5JcbRi/x5YaGYHjkFvX8rMemudlQjSYT2jq7UCqV\nUcfF8bHzdR0byE43AsGFOMpGFRgYiF1bN5JfoAhuhyO/3btjM26MjGD3to04d6XMZrv6z/7pBZy9\nXIKOTg1iBHysX5mFDQVkp9v9iLUspU7RjPSkxPtClnK/ZgAkOI9zV8pp5W7nrpQ7NTOgM3Hktzni\nJBz66/vUd+4u8SbMHyYZv75vANzQEKg0Wrdvb8ZswQzfL5QWVddX+n4hLFbm4/txjPo64+3tjaJr\nFdi7fTP6B69j2gPwggcO7t6JhpY26Ax9CFy2DD956SA6urTIzUjBG4ffNUslewzo1Ogg4HMRFRGG\nopJK/ODgs3j9z3+jLUul0WFNTjpGx4zU+RZ59BelFTahXTzggVcO7EFVgxwpogT8118/oL2nvm8A\nwQHLoTX0AjD78YEntuO9Tz6nlZdW1Tfh9X99GY2t7dDoe6HW6LA6O91GMrsmO8Mme6mj7KRtnRpk\nJIvQqmboox2d+LcXvoHf/u/fUZCVNhOfbRkGhm7ANDmBUqkMu7ZuRFFJBQqy0jA5eRtG0wQ4bG+w\nWF4zdmWgStZoYw+TxLe6oQlPb9uA/oHrc7KzhoLFYqGitgG8sFD0D12H0WRCq7obu7ZuRHtnNyJC\ng/FlWSX2P77tjn4zF7LoRiC4EEdbyDkcjtv/A0t4MHHkt3WKFoyPG1FUWoHpaczKSAtysdTXF4H+\n/hgNCUKgvz9JonCfY5GlNDQ0IDU11dXmzIvFlO0Q3BN+aBAGrw/Df6kvVmWuwMitWxi8PozI0GBX\nm8aII79VtqvQP3Rj9lweF+cul6BcKkNblxYxkVwU5uWQHzjuQ5hk/JERoQA8IIzkInDZssU37Cuw\nWGPuYpSzWHXhhgRCpmyFn68vtq5dBU2PATJlK3gM2R8J95aJiQmkJMQBMCuQOnV6CLjhGLk1Cl5o\nKH77r/8EAPj4/EV0aLUokcoQFRGKbr0Bt2/fRkhQIG7fvo1uvQGCiDCc+uKSQ18aHTXC29sLfkuX\nYHVWGn779hF0dGkQvHwZ/uudoxDFROPJLYU4W3wNF8ursG1tATgcDuM9w0OCbJJwhIcE4XRRMZWN\n1yIvlSlakJeRivCQQLz6i/9CvCAKT2/bCJm8Ge2dGoQGBVDnT09P08pd6cqP5kdQck7641z86b0P\nERfJRZxQgKbWDpRJ6xHNi0ByfAzYnl4oqalDNC8c0wAmJifROzCI8JAgsFheiOaFo1RSi7ioSEb5\nrXX9woIDUSqpRzQ/AsGBy2Ew9IHt7Q1PD7MANC0pAfq+ASoGngc8UFxejcCAZejWG5CywJ1uRF5K\nILiQ7NRk+kxIqUkusohAuDOMfis2/5IVsNwfGn2vzXb1vLQUvPzz3+LE51+irLYeJz7/Eq/84g3I\nGH7JJ9w/mObICdwZMuYS0sUiHDtbhE8uXsV7n36OTy5exbGzRUhzY+mWozH3lf98E3kZqdR3U9NT\n+M3bR3B7ehoqjRYXy6vx87cO42JppStMJ9wFTDJ+FouFq9VSlEpkyHDzH2cXa8xdjHIWqy6ZKUko\nlcjwWfFV/On9D/FZ8VVzW6c4N0sqYX5MT09DFCPAsbNFuFwlgUqjxeUqCY6dLUJsFA+AecHtv48c\nQ3FFDZpVavDDw1AqkeFqtRSVsobZ/pqciC9KypHFkAk3LSkexZXVKCqpwOETp6HRG/Bx0SXUKVuo\nbJrKDjXefOcolB1qs//N+AWTf1rLJS2f65tbKbmqRV6al5GKUokMxRU1UGm0KK6swR/f+wgZqUlo\nVqmREB1FnV9R18Aod51bfnpSAvoGh6jr5x6PjAhDnbIFqaIEHD5xGsWVs+UfPnEaKaJ4NLS0QhQr\nRKlEhvLaenO8utp6lEpkWCGKh0bfCwEvglF+S1u/ihr84egJpCQloFmlhoAXQXt/fngolCo1yqQy\nlEpkEMUIFuRHZKcbgeBCZPJm7N2+GT19Axi+OQL/h/wQERIEmbwZT23Z4GrzCARaGP1W0YLrN29C\n2a7GYxvWoqG5HbFRfKzPy4a0SUkb66GopILs6CQsGmTMJdQ0KGjHIkmDwm19wNGYu7kgFzdHR7Fu\nZRa8vDxxpVKC21NTVBwfS4yb4koJ2e12n2Et469tUiIs2CyPsmTvM5pMkDY1u3W71jYqaSVhtY1K\np/Y3RXM7Xj6wB9KZzIhCPheZYhEUze2Ak8qpm+mHXT16qDU9iOZHICoi3Onzh7TRPEZZB3jvGxyC\ntNF9x6ivMz4+Pii/UkE7b1TUNWJH4VpUW80rHDYbLeou2vPrm9uwJjsDTa0d2Lt9M7p7DFBrdYjm\ncREZEYZGZRv8/fyomIQjo2PUQlK9ooXW/5pVXdiB2XnCWtKaHCdEc7saMZE8RIQEIz05ER2dGmwu\nyEddUzPVN6enp8Hy9KK1uVbejG8+8Sj0Q0MoyErDuNEEk8kEmcWemWyjHtOYyQbaAbWmBxGhwfDh\nsNGgbEOWOAkafS+t3LOtsxuimGhI5PT/J0jlShTm56BVRf9MZYpWpCbEo8fQhxf370KLqgvTALw8\nPLF3+2a0dnZjYOgGpqemGe9/cNdOqLQ62uMt6i6bubRC1rigrNH3dKfbr371K+zZswd79+5FfX29\nzbGenh7s27cPu3btwk9+8pN7aQaB4LaodHoAwMSEOZDkxITJ5nsCwR1x5LdPbd2IZpUaP/zut/DB\n736F//yXF7Fh1UrImppp7+Xu8WgIXy/ImEtQ34dx/Rz57TOPP4IunR4p8THw9PCgrrHE8bHQTiNT\nJLg/aUkJeO3bzyE82CwRu1RejdtTU9Rxd59DVdoeALOSsInJSZvvnYVC3QVpkxI+HA4KMlfAh8OB\ntEkJBUMcqYWgnumHvmwOkuKE8GVzADh//tAY+vHSs7uREh+LVnUXUuJj8dKzu6Ex9Du1HIJjZIoW\n/PbtIzh1uQwdXfaySGB2XLWeV+jim3l5eqIwPwe3xsegNfTi5ugo/JYugbeXF0ICA+HlaV6SUfcY\nECeIpK6zjOPBAcuhHxgCYO9/DS1tZls0OjtJq6JdBW1vP777zC54eXlh5NYoxoxGhAUHoMOqb2r0\nvRg1jqMwP4eyxYJKo4Pp9iRujtxCZHgY2CwWXnp2D1q7NDhy6gwkciUClvmjulGBt949gR5DP5Li\nhGB5eZmv1/Zgw6qVUHVrcam8GlK5EqaJCWo80/X2Y/v6NQ7nZm5osEP7tj28Ct7e5r1ko+PjULSp\nMDJ6C35Ll4DNYiFWwIeaYcxRaXQYnzCio5u+je3m0gXGo7xnO92qqqrQ2dmJ48ePo729HT/84Q9x\n/Phx6vjrr7+Ob37zm9i0aRN+9rOfQafTgcslMVUIDxaMKaP37HSxZQQCM4789qPPv0SiMNruGqbY\nNGkissuNsHiQMZcQH00fVyZBGOUCa+aHI799/5NzCAsOwtFPPkdumhjrcrNwqbzaLo5PbBTfVeYT\nnEB4SLDdLgzA/efQNdnp+ODMeTvf3ffoFqeWU5CVhuNni2yeEYfNxt7tm51Wxtx+aCnj4G7nzh+P\nrMvHH9/7yOaZldXW48X9u5xaDoEZmaIFr/ziDRhNJvj7+SEzOZF23rCMq7ECvsP4Zutys1AqkVFt\n2tGtRVW9HAVZaaiUNQCY9aWjp89R11mP49/cuBZ/+fBTO/97Yd+TAIA1Oen44LPzdsefeXQr3vnw\nEyg71KiUNaIgKw1fXCvHtnUFNn3G0jctc4gFIZ+L8yUV6Bscomx++/hJKqabJduohYDl/rhYVkWF\nl9m7fTP+evJTpCTEokOjpeSe1vc/W3yNMSZdNJ+L89cqqOyhc+2L5nPx148+xe5HNtH0mwYUZKXh\nYlkVY8w54cz9mWLOOWsuvWc73crLy7Fx40YAQGxsLG7cuIGRkREAwNTUFCQSCQoLCwEAP/3pT8mC\nG+GBhDFldKuK4QoCwfU48ttlD/mBGxZidw1TbJrNq/Puqa0EgjVkzCVEhATTjkXhwe4bpNyR305M\nTsJviS+GR0YwMjqGycnb8Pfzs4vjsz4v2xWmE5zE/TqH6mZiu1pjNJmgM/Q5tZze/kHqn3xeWCgl\nBzMMDDqtDEs/nFuGot2580edopVRRkdYHM6XlFNtMDwyYhMvzAKHzcb63CwAQGR4GDhsNhW0399v\nKfVZyOdh3GiibVNLGADLZ0Wbyub+fkt8KZ9TtnfS3kPe2gEA0Onp+5pWb4BpYsKmzIiQYKrPOLLJ\nEmfOaLK9vrvHwBiXznruMZpM0Pebd/1xQ0OoZ2LpPxw2GxGhwdD19iOT4X7JcUL0DQ4x2ieOj0F4\ncDCaGObJsXEjADDGnEuMFWB45Bbjcbu5dKbNvyrz2ulWXl5ufyGLhaioKISFhdFe09/fD7FYTH0O\nDAxEX18f/Pz8MDg4iKVLl+LXv/415HI5srOz8f3vf/+OdkgkkvmY+5XP/TryoNcfmN8zyMpaWMf5\nqtDZkpKSwihnUWl0MBqNaGxspD3+dYX47f3vt2/9f/+GDz77Av39/ejs7LQ5/h8vPI+qRgWa2tVI\njo3GypQkTI7e/Fq0+9ehDnfDfOvvzr5LxtyvPwkJCSirbbCJQxPNi0AUNxxltQ149rGtaGmxl+u5\ns9/+/ic/wEs/ewOAWQYTFhSI55/YDqlcCSGfh/CQIIQHB4IftOxr1d5fp7rMl4XMoYvhu0zlp6Sk\noJVBitWq7nbamBsbG4vWzm4U5udQ8aIsmQdb1d24fv062tvb76qMiIgIdOr0tGV0avXQ6XTo6bmz\nZPZOfhsbG+tQZueMurgad3/PZbPZdiFRPvjsC+x7dCu6egxQa3SIieQiLy0Fy31Y6OrqQlltgzme\nWWsH1NoemCYm8PKBPahtUmJkdBz6XnppsEW6aNn5pdLosCYnHaNjRiTHCaHvG8DjG9fh9u0panFt\nLh1dWoyPjzP3tU4NNq3Ow9vHTlJlHnhiO46eOkt7vqF/AOlJiVji64PkOCHOXSlHnCCS2s2m7xtA\nQVYGLlZW4ZUDe1AjV6JTowM/IhQsFouKOWmhRdWFjGQRquqbsHf7ZnRqe9Cp0yM/PRUCXgTK6xqw\nLicD50vK8b0DuyGVN0Ot0SF6JibdX06cZrQvPSkeNfVNiOSG2SxY0j3jK5USrMvNwuTkJDQ9vVTM\nuwtl1fbH9b2I5nGRJRahRq5ATCQP0TwuclYkY7kPa0Fj7rwW3f785z9DIpFAKBTCy8sLKpUKYrEY\nGo0GL7zwAvbv33/He0xPT9v8bTAY8Nxzz4HH4+Ef//EfcfnyZTz88MMO7zHfDiiRSBats7ojD3r9\nAfd7Bky2WG9HtiZewMfg4KBb1eFe425t5grc7RksxG/rm9sxahxHcHAwgoOD7c7ZtG610+10Ne7W\nbouNO9Z/Ib7L4XDcrh73Endst8UgPyMVx84WATDH3imrrUdZbT32bt+Mhx56yKXPZCF++4ejJ5AQ\nHYlOrQ7hIUFge3vj7eOnAJjrJ5UrsSY7A4mJ7pud9avyoPouYJ5DGxoakJqa6mpTKBy1BZNkTMjn\nOnXMnSuts0jlnnl0K5YvX+6UcuZKWC1l7N2+GVwu946qrfn6raNn5qy6uAp367tMtqQlSdFuFePL\nNDmJI6fOYP+j2/DrH3zP7vyNq3LsQgCUSetRkJWGOkUzMsUidMxDuijkczFw/Tpq5S0ok8qwd/tm\nfHf/0wCAH735B0aJq4+Pj8PQCRdKK2zKPF1UzOxnkTxEhofi7OVSFFdUY1NBLiplcpvrSyW1eHrb\nRhw6ehwcNhv5GSkAPHD+mv1GrWheBGqblNi2rgDHaPrPvke34Dv7duE/3zqMN995FzGRPGxdtwqG\n/n689e4J+/pEC+C31BcBDz1EyUkdSYAtz/j21BQulVdjy+p8hAQFQtKoQJlUhl1bN+CjLy7aHF+T\nk4mLZZUolcrwT8/vhc7Qj1KpDKVSGQ79+LUFJYCbl7yUy+Xi1KlT+Oyzz3D69GmcPHkS8fHx+PLL\nL3H69Gnaa0JDQ9HfP7uq29vbi5AQs+QoICAAXC4XUVFR8PLyQn5+PlpbyZZZwoOHZTuyNRw2G7zw\nMEy6yCYC4U448tuq+nrww+l3QBMIrsaR7xIeDCySGktcGcvfzpShORtHfts/dB0jo2Pw9/OD3xJf\n+HI4NvUDsGA5DME9MdHEdnNXMsUiWt/NEDt3EdiRtM5Z9A7Qy/GcPXakJyXQPrO0pHinlkNghknO\nvXZlJu35TCEA7iRtnCtdzBAnQtMzOy9ZpJkAUJiX41DiOp/QCZYy1doeiONjac9PihXianUtFUON\nGxpiY6OvjzmBg0UGPTwygsuVUsQJImnvF8UNh9E0gZ7ePodS84gZ+WlHtxbvffI5/JYspb1fWHAg\nKuoa0NatnZcEeO4zDg5cjjpFM5UhtsdqFyKHzUaMgIdjZ85DMzOm1DQocGtslGqTopIKLIR57XTr\n7OxEfPxsR4+Li0N7ezs4HA68ZjJTzKWgoAC///3vsXfvXsjlcoSGhsLPz89cKIuFyMhIqNVqREdH\nQy6XY/v27QuqAIFwP3O1upY2ffLV6lp848kdrjaPQKDFkd+uykiBz5xJj0BwFxz57gv7nnK1eYRF\noFnVSf99B/337oAjv/32nifw9rGTePbxbdD3D2J15gpM3r6N9i4NYqP4WJ+bhQ0Fua6uAuEB5dyV\ncry4fxfqFK2UZCw9KR7nrpTj6W2bnFaOI2mds2hjyITa1tnttDIA8zM7uHsnmtpUNjK7c1fKsWe7\ncxNQEOhJS0rAoR+/hqKSCsgUzUhLSsTm1XmMO5yYQgDQShv1veBHhCEnNRl1TS0Q8nk2/aIgKwPH\nz54HYJZmWrCM48WVEtrxvUQiQ0FWGsaN5oWkiNBg+HDYKJHI8PS2jSivbYCQz0VbZzdWZ6fj/LUK\n2lALxRU1eP6JR3GxvAoslhdKpTJkzMg5V6aJodb0ICdVjBPnvgRgiasWi8uVUrv7JcfHQNHajjXZ\n6WhikH+2qs3950plDQqy0mA0TWC5vx9KZ+ozd94rkcjwr99+Hn9870Ob+8xKgPXo1PSAHxFG1dcS\nasHXhwOpvBni+FjIW9vNi369fdi4aiUmJm8jIiQIF0qr7SS/1m2y0IzR81p08/X1xW9+8xusXLkS\nnp6ekEqlmJiYwLVr17BkyRLaazIzMyEWi7F37154eHjgpz/9KT7++GM89NBD2LRpE374wx/i3//9\n3zE9PY2EhAQqqQKB8CARxQ3DpfJqKvCmVK6E0WRCYV4OegcHERkR4WoTCQQ7mPx2fV4Obo2Po0un\nd7WJBAItjsZcwoOBI+mWu+LIbz/6/EsII3n48PMLWJuTiVVZ6ViVle5qkwkEAAA/NAiD14cR4O8H\n0VqzZGzw+jCiQu3DT9wNi9GvFysLOzc0CG+9ewIhgQHISBahtkmJ4opqMk+5AC9PT4QEBsCbxUJ3\njx4nzhWho9sc060wL4da8GLyPzppY7wgClNTU3jznXfBDw9FQVYGSiW1KK6oxvq8HJRKaqnr5/rv\nhoJcxh9RonnhmAbA8vJCaFAgWDMbo4S8CAwMXgfH2xtHTp0Bh81GQrQA/PAQHDl1Bv5+fogTRKJO\n0QwPTw+EBgXgbx9/hvCQILBYXhDyInDwqR0QCASQKVrQourCtWopYiK5EEZyqUUxATcc3XoDahUt\niInkQSJXoriyBltW5+OJTesxOm50mAF2hSgBS319oDP0YXTU6PB/jf89fhoRIcHghoXgSqUEt6em\nKAnw9ofXoCAzDYbBIaq+wQHLIVO0IC8jFWFBAejU6ZEpFiEhOgoavQFNbSpE88LNbRYUgKqG2fiM\nQj7Xpk0W2t/ntej25ptv4m9/+xuOHz+OqakpxMTE4NChQxgdHcVvf/tbxut+8IMf2HwWiUTU3wKB\nAB988MGCjCYQvi5kpyajTFpvIwPhsNnIEosgU7SSRTeCW8Lkt9liEaYBeHh6uNZAAoEBxjE3NcnF\nlhEWC2sfsODuPuDoXeHQ0ePYu30zSmrq3D6TJeHBI10swqEjx+z62yvP73VqOYvRr7esycfZyyV2\nZTi731nq0jc4hKKScqocdx6jvm7IFC145RdvUG2dIBTgjcNHbeKRlUhkAMwLYTkM/pcQHYXy2nrq\nc3K8EP//X99HYb5ZKqrR91I7qDhsNjLFiSiuqKY+Z3+FNs9MSaLvawf24I13jlJlGk0mNLS04p//\n4RmUSesxPDICqVyBwvwclEpkdjHX/vkfnkF/fz+ujxptnslr3zqAQ0eP251fkJWGS+WzdUiOF+Lg\nj36Bl57djTKpzM6+vPQUAIAoxvYZP//EDspe63kvKiLM5hmty82yKW/MOI6jn17DS8/uxpUqCXU9\nXf2kciUKstKg0mgp+w/u3olrkjrqfhniRFyyKm+h/X1ei27Lly/Hq6++iunpaZuECJ6e8woJRyAQ\nGKiTN9Nu7ZUpWvDzf3nR1eYRCLQ48tvCgpV4ODfb1SYSCLQw+q68GU9t2eBq8wiLgLy5HS/u34X6\n5jaourUQRvKwIjEOTS3tbusDjsbc1197CUVXKxYc3JlAuJfUNCho4zhJGhRO7W9Nze14+cAeSOVK\nSpKZKRZB0dwOOKkcG8mhsgVpogSHksOFUtuopJXV1TYq3XaM+rpxvqSc8lt/Pz906fQwmkzUrqn+\noeswmkworpJgQ0Eu2ru1tG2m6TEgURiNwOX+8PXhoF7ZCn8/P0pu6u3FwvWbI1j2kB/E8UIomtuR\nKIwGLywE2alJePIrtLeUoa9Jm5SIieTZlDk6No7G5jbK5sHrw5iemqa9vlXdDUFwKvVMOGw2UuJj\nIZnZeTb3fEyDqkNaUjw+Ol+M7Q+vgby5DXu3b0Z3jwFqrQ7RPC4iI8LQrOrCDgDldQ0297PIRbtn\nMsZG883nf/DZFzblsbxYWJOdAV8fH0zenqSyp/7lxGkc3L0TijYVtIY+TE+DMe6eZXHPaDJB0abC\n+twceLO8EBEaDHlLO5JihUiOi7mr/j6vRbfDhw/jz3/+M27dugXAnH3Uw8MDCoViQYUSCAQzbV0a\nfFlWSW3ttWzFFfJ5rjaNQGDEkd++9o/PoXdgEH87+RnqlS1YIUrAljX55J9BgltAxlyCSqdHYqxg\nRjYUCK+ZH5A7tO4ri3fktz9MToJU3kLGWIJbomaIdcUUA2uhtGl0iI7iwdubhdCgQHh7szBmNKLV\nyeWkJSXc877W3j2768ZaVkfmqcWjXjEbtytOEImuHgMK83OoRbVMscickECjwy/fOgx5Wwc6uunb\nLGCZv83nOEEkZIpmAMDo+Dj6Bgfh7e0FmbIVXTo9ntu5Hd06A2KjIu3skilacP5aOe37dQeDr3d0\n67BpdR7eOXGKKnPi9iQ0Xb2Un4njY6HW9tBeL1O24LG1uWhsbqOeQXpSIr64WkZ7vlqrQ0FmGvqH\nbgAAEqL46B+6jqEbwxDwuXZzr0zZgreOHkdHl730FABYLC+EBAWCxbLNI+Dl6Yl1uVkYHR+DztAH\nXlgovL1nl7ZMk5NQtKvgzWLh8U3r8OG5C7T3t8Tds47hlhQnxPXhm+jWG6DR9+JHB/ffdQbweS26\nnTx5Ep9++ukdUyETCISvhiUGgGVrrwV3ji1DIDjy2+oGBQyDg/i46BIA8z+LZy+XkF0YBLeAjLmE\nR9bl4w9HT9j84n2lSorvHdjtQqsc48hvyyQynDj3JVZnpZMxluB2LFYMxUfW5eOP731k068vV0rw\n4v5dTi1nMYjmc6HSaG1kdQCZpxYT6/h9bZ3deHzjOpw496WdlHLX1g347NI1ZKWI0NFt32aWmG6z\nMtUolNc2YF1ulo3U0bJgt2vrBvzyT3+B0WTC3z85a/PuPFfyOvf9Oj46kravJQijcKG0wqZMs5RV\nRPmZvLUhdug1AAAgAElEQVSd+jyXNFECRkdHsS43G38/dQZGkwndPXokxQppz4/mcXHi8wswmky4\nVFGNgqw01CmacXD3Thw+cdpOXvrNpx/DR59fREpCrM399j26FcfOFtmdv+/RrThy6gzjM7TITa2P\nS5uUSEtKcBh3z4KQz0VJTR2GR0bAYbPx7GPbqI1nd8O89KECgYAsuBEI94AscRI4bDY4bDZ4YaHU\n35li0Z0vJhBchCO/LZfWo79/EP4z2aoB3FWKbQLBmZAxlyCVN1P/dFh8wGgyoVbe7GrTGHHkt2Pj\n4wBAxliCW2LxXWvuxZhbp2illY7JFK1OLWcxSImPpe3v4vhYV5v2wLBlTT7lt0aTCYa+flr/6unt\nBwBwQ0No/XyZnx/E8bHw9/MDh81GeHAQOGxvjI0bae83cP0GvrnrccRE8uzena3lndZzl+WciJBg\nWhu4oSEQ8nmYnLxNlWk0meDrw6F8KzhgOfyW+NJev3l1Hqanp6Gf8wwsNsw9nxcWQj0Pi3wzJHA5\nFG0q2jo3t5szh4tio6n7WUt6557f1WNAakK8TX2sj4+NG+Hv50c9Yw6bDb8lS8APp7fX14dD3YfD\nZiMxVmDznLr1BqeEVJvXTrfExER8//vfx8qVK+HlNbu1b9eu++/XAwLBnSiursYrB/agRq6AWtOD\nVZkrkC1OwqXqauzattHV5hEItDjyW44XG9mpyYjr6LTZkbHQFNsEgjMhYy6hS6enlQl1urG81JHf\n/uqfv4cjp86QMZbgllysrJrx3dlYa9liES5UVjl1zF0sGevF0kpcqqimzWDpLEprpbT9/WJVFfY/\nvs2pZRHosY7fp+vto81aC5j9K04QiYq6RpuYbpHcMOSkJqNO3oyhG8PIEouQnpyAL65VYvPqPJRL\n623uY5FK3hodxRdXyxDNi8ATm9fjy7JK6hxreaf13NXQ0gYAKKttsI39yY9AclwMGpvboNEbwA0L\nQWF+DpXts6S6Fvse3YouXQ/UWj1uT97Gwd070dDSBo3OgJgoPtbnZiEtKQENDQ1QtHVQthRkZaC8\nrhEHd+9EU5uK6tvpSfGQNZl/wLKeWwuyMlAla2R8hhnJIlworabs9/X1gaJNRXt+p1aH0MBATE9P\n2dTHgqF/AKuz09HS0Uk9r6mpaZTX2tor5HORIU5EXVMzhHweFSv1Qmm1jdy0Vd0Nf/8td3KZOzKv\nRbfe3l6w2WzU1dXZfE8W3QiEu+PhnGy7zC9l0nq3lrkQCI78tlXdhfauLrR1dttcs9AU2wSCMyFj\nLmFNTjo++Oy8nUzomUe3utgyZhz5bVVdI5LiYuC3ZImLrSQQ7Hk4JwuHjh4HLywEm1bn4UJpBcqk\nMqfLPhdDxnqxtBI/f+swYwZLZ1GYm0vb318+sMdpZRDujCV+X2NjI05fqUBHN71/1TQqII6PwaXy\nampn2crUZPzeKoyBSqNFWW09Xtz/NC6UViEsJAgdVv46VyppOd+6za3lnZZzOGw2nn9iBwAgPyMV\nx84WAQCCA5aD7e1tI+dsnyO/XJ2TYSPfVGm0KK2tNyc70BlwraYW12pqERwYgNsTEzaS21JJLR7f\n+DAOnzhNlVcmlaFMKsPe7ZtxqVJC2bd3+2ZcLK9CfLSAsY/WNikRJ4jEkVNn4O/nhxWieMQK+LTn\nhwXPSnbnZi8FzDH4hkduoSArjaqfRcZqbW+pVIZSqQwHHn8EQ8MjVKzU/IwVkOp6bOwbHh524Cnz\nY16Lbr/+9a/vuiACAQDydj3/la+p+OjIPbDEPbDIXKyxyFye3rbJRVYRCI5x5LcF6akYunXLLgbD\nQlNsEwjOhIy5BJ2+l9YHtHqDiyy6M478dnXmChhNE3hqS76LrCMQmLHIPju6tXj72Enqe5miFXu2\n3/3uEQvpSQkok9bbvXukJcU7rYxLlTW0/bC4UuLURTemrJBSuZLsyHYBJpMJEVZySQscNhsRocE2\nUk2jyYSJyQlIGMZsmaIFE5MT8PVZTp3PYbMZ5abWbT5X3mk5p6fPLHE19A9Qx/uHrmN45Bbt+XPl\nl3OPd/cYoOvto44VlVRgQ1YKtqzJx9nLJTCaTPBbssRGLmodx65Lp4e/nx+GR0ZgNJmg6+0DACTH\nCVEmldk9w6Q4IWoaFdQzHB4ZQb2yFU9uXk/7zK3loHOzj3LYbISHBONSeRV8ObPXcthsKNs7ae1t\n79KgrbObiuE2V24qjo/FlNVOuoXicNHt1Vdfxe9+9zusW7cOHh4edscvX7581wYQCA8ylu3wc1NQ\nO3s7PIHgTBz57Yv7n8blqho8+9g2XKmWIk2UcFcptgkEZ0LGXEJrp/mXeksmUMvLtuV7d8SR337/\nW8+iqKwKK0TOW1wgEJyFxXdDAgOQkSxCbZMSfYNDTh9zz10pt5O6JccJce5KudMW95iyK7YzSA8X\nymJJZQnzw9vbG1cqa2zko+EhQfD14aBS1oislCQELluGn7x0ENKmZoSHBOHc5VIA9GP2k1sK8bu/\nvod1uVmYnJyE0TQJfd8AbdnWbW4t77TG4n8tqi6qTHF8LAavD9PaYOgfwJqcdCha6eWbKo0O4vhY\nyFvbzQuFyhZszctAqpXkNk0Uj7+e/Iz2erW2B3npqbhSJYHRZEJbpwaPFq5DUUmlrfx1Rs5ZVFKJ\ndSszcXPkFvZu34wWdRcClvnjWk0dCrLSMG40x86LjAgFi8VCrVyJTHESNXcb+gaQnpQIT08P+Ppw\nUF3fhEceXoMvreLhxQkiqX7FDw9FQVYGSiW10Oh7odb2YE1OOsbGjMgQJ0LW1IIYPg8CPhdREWH4\nsqwSK559itE/5ovDRbf/+I//AAC8//77dsfGxsbuunAC4UEnJpILYSTXTp/vCftFbgLBXXDktxV1\n9RgbNeLb+57Et/c+6WpTCQQbyJhLiIviYW1OBjq1PejU6ZGZnAgBLwI9hj5Xm8aII7+trpcjaJk/\nvvMfv0RMFB9b1uSTHznuc2SKFpy/Vo56ZQtWiBLu6zZNEPDxyMMFaGrtQKu6CynxsUiOj0H7zAKB\ns+CGBuGtd0/YLO4VV1SjMC/HaWXERHJp5W6xUXynlQEwS2VjSPZSlzAxMYGUhDh8XHSJWsCyyBuf\n2rIBr337Oepcy45HZbuKccw+W3wNt6emcKm8GmtzMnF9+CbCQ4LuKI+2lncCs3HgPOCB//7b+3bz\nhJAXgbz0FLSou2xs8PLwxNTkFIRRPBuJq4VoXgR0vf3U+YHLlmFiYgLArOQWAIora+izl/K56LG6\n3gNAlawRvNBgSj4aJ4ik5JwbC3Lxoxe/hf/7zrs4cuoMOGw2EqIFiOaFYxoAy8sL/PBQLOX4IDQk\nCFO3p2zmblW3Ft36Xhj6B5CXkYrw4EBcqZQgmjfbj9o6u7FqhRg7N61DXVMLqmSNiBdE4eltG9Hc\nqkLI8gB83lAGeAAsLxZCggJx+/ZtdOsNSImPpep/NzhcdAsODgYA/OQnP8E777xjc+ypp57CyZMn\n6S4jEAjzJDMlCYeOHLPT57/y/F4XW0YgMMPotwf2YArTWJme4mILCQR6yJhLSBeL7jsfcDTm/vp/\n/oYX9+/C2SulqFO24OzlEhz68Wv37SLNg45M0YJXfvEG1dZtXZr7uk1XJCfa+W5Zbb3T+1tOajLK\npPXoGxxCUUk5APMOn+zUJKeVUZiXgxKJvTxufW6W08oAgOyZuswtJ9OJdSHMn+npaRtppUWa6Ch0\niqMx+0J5FXU9i+WFZpUahfk5tFLKLKs2t7YBsI8D99q3DtjEAhRGcu1itllseOOdo4xlenh6oFml\npj7/8z88g+npabs6MvlpVEQYiiuqqc8Hd+/EW++ewEvP7kZ5XQOGR0aoRGscNht5aeb/GTasWolP\nLl6B0WRCQ0srfvCtA/i9VX1eena3TYw6S30O7t6JqzW1KMzPsXkevPBZSfDwyAhSkhJs7keNRVbP\no5Smf//kpYO09f+qOFx0+/TTT/HWW29Bp9Ph4Ycfpr6fnJxEUFDQXRdOIDzoSBoUtHp6SYMCT23Z\n4CKrCATHMPqtXIn9j26BOJHInAjuCRlzCRYfmCu5cWcfcDTmJgoFqGtqsYmhU1RScV8u0BCA8yXl\ntG19v7ZpzSKNuWqdHi/u3wWZohWqmcyEaUnx6NQ5L1ajZRdTcaUE7V0axM5kd3R29tLaRiWtlLG2\nUem2Y9TXHetspjJlyx1Dp0gb6f1eKlciZ0Uylj30EHihIWjr7IaQz4PRaDJLL3v0UGt6qDZXdc/K\nS61tULSr4OHhSZURE8mjdt8BcBgnTiJXQhQTjSuVEqzLzYKXpydUGh0EXPPOsiuVEpvza+XNSNi8\n1q6ODU0tOLh7J5TtKqi6zZLuyIgwfFZcYiP/VLarsD43B83talq/VnZ0Ykehbf26enps6uPv54em\n1g7a+ijaVMgSJwHTsDluqd/0NDA2Ps4YK1EiV2JNdgamZ66f+24gbWrGhqy730zgcNHtsccew/bt\n2/GjH/0IL7/8MvW9p6cnwsLC7rpwAuFBhyk+A4nbQHBnHPltXHTUIltDIMwfMuYSOnV6FObn2Ml+\nOrV6V5vGiCO/fe3gAbz5l/cQJ4ikdg/IlC2LaR7BidQr6Nvufm3TxYpPVidXon8wDJ6enkiKE8Jk\nmoBM2QqNvvfOF38FNhTkOn2RbS7t3VpqF4+1lFHI593TcgmOsZZW3omObnr/7tDo8Mi6VVC0q9Gt\nN6C2qRkBy/xR06hAqVSGRGE0Apb5U20eJ4hktOHZf/kR9f3GgjybGGbBAcsdxol7ckshfn/kA0SG\nh0HX24eg5csxNT1tE8PfIl+9NT6GXx5+F2miRBupu3ZgACmieHh5sRAaFAgPDw/4LV2C9MQ4qLQ9\nlPyzvLYBcdGRULSpaP3auo7W9dv36g+p7+MEkVBrZzOKzq3PB7/7lc3zAEBJeBOF0fiHXY/hfz6g\nV2harn/uBz+mfTdoaGnD1rwM2mu/CnfMXurl5YXXX38dt27dwo0bNwAARqMRu3fvxkcffXTXBhAI\nDzKLkeKcQHA2jvy2VdWJFNH992s84cGAjLmEgqw0HKeR3OzdvtnFljHjyG//8tEnKMhKw+kvL1Pf\np5Ex+L5lbtwmC/drmy7WmFuQlY73P/vCThr2zGNbnVrOYmB5ZtZSRoDMU/cTTH4fzefixOcX0Dc4\nBA6bjYKsNFwqr6aOBy73t9nhFe/gh2zrseJCaQXiBHyqzP6h68gUixj73tnia9j36FYb+Slg7jPr\ncrNwqbzaTr7a3qW1kbpvLcizkbMCQInEnPxApdHayD+Pnj6H3DQxrV8nCgUMz3A2hmJbZzeyUujr\nExPFs3se1gQu98ffTn56x7FoXW42/n7qjN27wfNP7HBKTDfP+Zx0+PBhrFu3Dlu3bsWTTz6JJ554\nAsnJyXddOIHwoJMlTgKHzbb5jsNmI1MscpFFBMKdceS3Xl5eLrKKQLgzZMwlGPoHaCUm+n76XQHu\ngCO/1fcPQN/XbyMrYoozRHB/tqzJp23r+7VNF2vM1eoNtP1a2+M8eeliUZCZRvvMVmWucJFFhK9K\nQWY6bRsmxwnRNzgEwOyfk5O3IeTzwGGzwWGz4evDsRnLQwMDGMuYO1bwwkKpz0aTCb4+HFob0pIS\nMDI6ii6dnrbPTE7eRqIwGuNGE6PUHQBqGOSaY+NGGzsUbSoEByxDRGgIrT3BDHVMiY+1uY8oNpr2\nenF8DO3zsBz3W+ILATcC/4+9e4+L6r7zx/8CdFCDF5A7w2UAuV+UUQHxEvHaaNu0SSuxpnZzabtN\nN70k2Tb2m6SbxKTb3PZrNv01XfNNU9tNbLMmmzVdtVUTlbsDDMhV7gwgFzUaggIqvz/InAzMmWEG\nZuacw7yej0cecWbOzHmfc96fz8CH8/58kk0+z/T1pM/fb/o9ano83X39ovHZa9I73QDgyJEjKCgo\nwP33348DBw7g+PHj6OpiKQbRdB3NL8I/3bsDuuo6YYlzbXICjuYX4e4vbZQ6PCJR1vL2y7lrpA6P\nyCL2udRgYdVES8/LgbW8zdEuQ2nlOWhTEhES4G91niGSP3vnjpI7V/W559vM73Cx9vxUuWJl2e25\nazE0MoKzVbXCOVuemojtuebzapF8mOaGNjUZD+/OG38NkxPw//7rfwB8UbppvINq1bJUJC2JQWVt\nAzTqMGG+szM6Pf7xW98Q3Z9pXxEbGY53jxwfN2eacZ64FkMXDN09iFSHIilWg79+XICvbrwdf/0o\nf9znmca0aMF8dPeKDzYZS90tlY5f6LsIf99Fwt1sLYYubFqdheMFJaJzup05W4F/3Hm32ef8Lb9o\nbJ67rgsYvD6E4/mlwuPWzm5EhYUgIjQYx84UY+eXvyTadybGRKG44hw25KzEG+9+gAe+eSdqGluE\na5IUq8Hf8kuw66t3oLaxWfR4apta8JU1K0Vfs4dNg2633XYbVCqVkBgbNmzAd77zHdx7773TDoDI\nnS1LjMerBw4CGKu/LyjTo6BML+syFyJredvU3oGkJbESR0gkjn0umZbgmFoSFS6ytTxYy9vjhSVY\nlpSAX/zgfomjJEexZ+4ouXNVn+uKMlZXrSx7+MQp7HvrHXirVIiNDIfuXC0KyvTwnj2bA28yNTE3\ntqxdhX1vvQNgfN7/8N5v4qU3/mhWujm2kmYV1q5YhuGREaHEdMuabKv7Ne0rSirP4URhqTBnmnGe\nuK1rspGRnICPS8twsqgUm1dn4947t6G2qWVcmzGNyXg3qlibMpa6W2pzwQGLUVZdJzzWqEPx9/wi\nRIQEj4vPeIwbV4nPkagJD8Vb7x3GAh8fJMZq4O+3SHgcGxkOXXUdThafHbe4iOn5qKw7j396+tcY\nGh5Gz8VLyFqagv1/ft/smtyzfQsA66X9LisvXbhwIT744APExcXh8ccfx/79+9Hb69iJKYncUW//\nJQwNDwv17cZ/91y8JHVoRBZZy9smkS8sIrlgn0thwYGiJSahQQESRTQ5i3nbfwlDwyMICfCXOkQi\nUa7qczPTU0Tb9cr0ZIftw9rKso5UUFaJoeFhXB0YQFl1rbAqcUFZpUP3Q45jmhsBfr6oaWwRzfuy\n6nokREdZXFl0YPCasGqmt0qFYP/FNsdgLOU23ae3SoWYyHC8e/S4MI9ceMjYYpgpcV+UW05c7dRa\neaqx1N1S6fjEEtnEWA06e/oQGRYiGt/6TK3o8QQu9oO3SoWrAwMorqjCHG+V8NjYLqyV3h85XSDE\ncenKFXSbXAfTa9LV2wfAemn/6OiobRfBCpvudHv88cdx69YtbNq0CW+99RZ6enrw0ksvTXvnRO6u\nvqVN/Plm8eeJ5MBa3n7/nrtcHA2R7djn0unSCtESl9OlFfj+PeYlLnJgMW9b2vCVDWvxUYkO933j\nqy6Oimhyrupz79y0HgBQoq9Gi6ELGnUoVqYnC887gqtWluUq28pjmhvLkhJwvlV8uoJWQxf+4a4v\n4/eH/kf09Z6+i1iaGA9PT49Jy0sn+t/Thfjhvd9EeXW9UD65LDkeZ/XV40pWT58tx4M7voZjp8eX\nb/ZMWO3042Id1mVqcePGTXT19CI9MX5cqfux/LH9lX2+P406FBnJCSivqYNGHSaUf/4tvxh3b92A\nC739eOCbd6LV0I1RjMLL0xNZ6SkWVwM+c1aPHG06POABb28VRoZHkLdtM1o7u9DZ0zdp6b3xmnir\nVLjj9jXjVnc1db61A4D10n6dTmfTNbDG6qDb2bNn8ZOf/ATDw8Pw9fXF7373O3z/+9/HH//4R3zv\ne9/DqVOnrH74c889B71eDw8PD+zZswdpaeYTQL700kuoqKjAgQMHpnckRArElfRIiazlbUV1Hbbe\nvlqCqIgmxz6XosKCcdyOEhc5sLwSXgj+8r/HsSItCZV155GWsESC6Igsc2Wfe+em9Q4dZJvIVSvL\n8ntKnqzN52eaG+U1dUhZEmPxGm5ek42K2gY0d4iUZgb6o7u3H129fXZ/L4X6L0Z5TT1GMYpAfz/M\nmjUL5TX1KD1XC9+FX6yKaizHTIqNHle+GRoUgGaTmG/euoUThaXYccdmfHvbBqSkpIzbX2xEBF56\n44+IDg/D1rWrYOjuwYtvHDAr/1yftQJnq2oRHhQA1exZGLx+Da2GbkSHW8/n2PBQBAcGoK2zG7WN\nLYgMC4bfogXo6b+IxYsW4dPPPkNH9wWLg27pifGICAvBtetDqKxrsNquvv9/9iI6Qo0ta7Lx2IPf\ntvmc28Nqeekrr7yC3//+9yguLsZjjz2GJ554Avfeey+Kiorwl7/8xeoHl5SUoK2tDQcPHsTevXux\nd+9es20aGxtRWloq8m4i97A8NUn0VlZtaqJEERFNzmLeJicgJChQoqiIJsc+l1akJYuWuCyXcQ5Y\nytuk2GgMDQ9jdHQU//T0r6G3cCcOkVRmUp/rqpVluXqp/BjnbDt07AQa2w04dOwEHn7mBaHPNc2N\nvkuXsTQpzmreW8qlOd4qtBg6he+lrKXjB7qs0aYmIl+nx8misyiqqMKxM4XI1+mRtSx13HedMV+N\nMRjLNzOXipdoR4YFY2hoyHx/yQnwVqnQ3NGJ/+8//4LPrl8TLf/08ADqW1oRFhKE3/zpXZwsOosW\nQyeOF5bi6df243h+sejxLEtJxDsfHsNHJTq0GDrxUbEOv/nTuxi+cQPF+iocO1OEF/YfwOET4jeB\nZSTFI1+nR2F5JYoqqpDxebwTjy8xVoOKugaza+poVu908/T0RExMDICxxROef/55/OxnP8OmTZsm\n/eDCwkJs3Di2Kk1MTAyuXLmCgYEB+Pj4CNv86le/wk9+8hP8+7//+3SOgUixys/VIUebjqHhEXT1\n9CE0KADeqtmoOFc3bmJIIjmxmLc19biLK0CSjFnK3XL2uW6jvLpOdAW0iup6p94lMx2W8ra+qRUP\n37sDL7/5J9y8dQvHzhTNmAn4aWaYST/numplWeNiCaWVNfjk0wEsmu+DFWlJXERBIh4eHlbn8zNO\n3m/MjdqmFtSebxn7nunuQZuhC5HqUESEBKHV0A3APJei1KGI10SirqllrDRTHYKIkGDUt7Rju41x\nllbVisboAWDdSi2C/RcjN3uFkK/GGE4UlmLw+jVUNzSLTr1Qdq4OMRvNK1jKautFV0vt7O1Dq6EL\n4SHB8PAYK1Nd4OOD9q4LovGdLNaJlphW1DaIbn/t+pDwhzPjXIdibUNXUzdubrnSinPI27YZHd09\naO3sQlRYKMJDglB7vhkadZhwd6GzvketDrp5eHiMexwSEmLTgBsA9Pf3Izn5i8kr/fz80NfXJwy6\nHTp0CCtXrkRYWJjNwdpTT+uI2lslm0nHP9VjseV9Wq345I2OJhZLSkoK2rp7sGpZKrp6+hC42A/z\nvL0RGhSAgvIqDA0N4dy5cy6JTy5mUt5OFfNWmdw9d209fuauvLhb3vr4+KChtQNHzxSZlcBo1GGo\nr6/HwMCA2fvknLeP/+A+vPDG2BQt+tp6VFVVYXjCLyozkbvl7kRy6nMtxTJT+9wN2hRszVqGkZER\n3Bj81Cm/nw6NjODGzRvou3QJPvPmYGhkZMbkvNx/zp1IpVJBX1Mv+lrFhD53gzYFX1mbiWd/dwDD\nN2/g5s2bCFjsh5s3b6LjQg86untEt3/5wLuI10RijsobibEaeIwCPrfNQ0lVjU19uo+PD5otLGTW\nYuhCkL8fBgY/Q29vL3SDnwqvXRq4hoHBz1DX3IaQQH+oZs+GvrZhXDmqRh0GX1/fcedKpVKhoroO\nTR2dZqulJsZo8NI//xP++cXfoL6lFQAQGxmO1s5u0fia2g3Cd++lgWsoqqpBXXMrQgIDkJu9Ah8X\n63Dz1i1h+wt9F+HvuwidPb3C8TU3N+Py5csWr5m/7yJ0X7wMTYQaXp6eCPDzg5enJ3xum4dbo2Of\nnZGcgLlzvKGva8BPnn0RfosWICs1CX4+cwFMv8+1aSEFo4mDcPYwXfXhk08+waFDh/Dmm2+ip6fH\n5s+wtQHqdDqXNVY5mmnHP5Vjkds5sBTL5jWZ2H/w/XEj+d4qFR7c8TV4e3vL6hicTW7XTApyOwdT\nyVtD70VZHYMryO26uZocj5997uTkeN1cYUlkOFoMnUIJjPB8VATi4+MljGxqeXv4xMfCc+mJ8UhN\nTXV6nFJz19w1ktvxW4uFfe4XbL1u7//tJPa99Y5wzpo7OnFGp8dP/mGnbO/GtZWScteorKwM6Ynx\naBKZg22phT739qwV+MN7h83yfvfXtotuv2HVCrz2x7+Ybf/Qrm/Y3Kcbv9smCvJfLAyg/b2gFPue\neAzpiXHQ1zbg2Zd+K+yzqd0Ab5UKOdp0nCj8YvovjToUly9fNjtX6YllaOroFKZqMEqKjUZQUBBC\ng/yFQbfGtg5kLU0RjS9eE4n4+HiReMYG9NZlas3iyS/Tj3scHR1t9rnG+ACg//IneGDT7dj/5/F9\n0amz5cjbthkflZShxTC2v7xtm/HOh8fGna8bg59OO2+tzulWXl6O22+/XfjP+HjdunW4/fbbrX5w\nYGAg+vv7hce9vb0ICBhbjr2oqAiXLl3Ct771Lfzwhz9EdXU1nnvuuWkdCJES1Z5vEb11tuZ8s0QR\nEU3OWt5W1dZJFBXR5NjnUnhoMLxVKizw8UFGciIW+PjAW6VCeEiQ1KFZZC1v+y99AsA5c0sRTRf7\nXPsV66tFz1mJvlqiiNzb6Oio3fP5XejrF72G3X39ottX1IiXUlbU2D6/mPG7bWKMPvPmwt93kVCS\neezzVTwtlczevHkLmempwnfjyvRk3Lhxw2x/k52T5CUx8Fap4K1SISTAHyEB/qLbBwX4WY3HWE5q\n3D4k0H/c+y3Ndbh17Sph/3FRkahtFO+L2rsuYMHnlZhDw8Po6u0T9mc8X9O58czI6p1uR44cmfIH\n5+Tk4NVXX0VeXh6qq6sRGBgolJZu3boVW7duBQAYDAY8/vjj2LNnz5T3RaRUXBaclMha3t7/ja+6\nOKifR68AACAASURBVBoi27HPpcLySjzwzTtRc74ZrZ3d0CYnIGlJNE4W6/Dgjq9JHZ4oa3n72IPf\nRkd3L+758lbO50ay48o+93h+MU4UlaK5owvR4aHIzVohOleU3LXye0p27J3Pr7ZRfFC5tqlF9HlH\ntJPTpWXj5lgLDwmCRh2KxrYOqGbPFsonqxoaAQCVExYM8PL0xLpMLUZGRtB78RK0KQlYnpqEOzet\nFy2tnOycHC88ix98625U1DZg8NoQzuj0onPGnTmrxz/u/IZZPEY9fRexNDEenp4emDvHG8X6c1ia\nGI/b5s7ByvTkcfO5ma4wm54Yj8ceuBeF5VXw9lahtlH83Ld2diM2Mly4872xzTDusb6uAVuzltl8\nHSyxOuhmz3xrE2VkZCA5ORl5eXnw8PDAU089hUOHDmH+/Pk2zwtHNNNxWXBSImt5W15Th5jIcAmi\nIpoc+1zamLMSr799SPiLd4uhEwXllfjePV+XODLLrOXtb/74ZyTERHHAjWTJVX3u8fxiPP3a/nHt\n+oxurARNaQNvMZFq0XO2JFItQTRkZFwwwRZLNBFoFJljLV4TKbq9I9pJdEQYjpwqhLdKhdDAAGjU\noUKZJAChfHLXV+8AACTEaMbFuC5Ti3ydfvx3Y1klvGfPRsjC20T3ae2c5GZp8Zs/vSusmpqRnIAT\nhaXCHHDGkteNq8baZ1pCnOg5Cw70R3dvv7DQwWrtUiycPx8lleeQX6aHRh0mlMs+/MwLQvwRYSF4\nYf8BDA0Pj93ZnhQveo6jwkKgq66z+Dg9IQ4jIyOWT7yNrJaXTtejjz6Kd955B2+//TYSEhLw9a9/\n3WzATa1W48CBA84Mg0i2LC1fvCxZ2nlliKyxlre+CxZIFBXR5NjnUnVDs2iJSbWMy92s5e3g9evo\n7OmTKDIi61zV554oPmtxZUSlCQ8OEj1nYTIugafxAhf7iV5Dfz9f0e1zMtJFt7dUOim+z8VCCWlX\nbx/au3tE20RH99h8+iGBAePKNq9dHxLdvqCsEl5eXjbHYVTX9EU559DwMObO8Rbi6+zpFQbj1meO\nzZVmqVx1jrcKLYZOYXuVajY+/Og0+i5dtlguO/F4rg4MIDIsRPTzI0KDcfXzBZTEHm9enTVubYKp\nsmshBSJyrL9+XPj5rbfn0WroQpQ6FEsTl+CvHxfiG1/iHaEkT9by9vlHfyB1eEQWsc+l5s8nVjau\nXtrY1oGrAwNobjf/C7hcWMvbR+/fhed/+/+kDpFIlKv6XEvtt8nCio5ylq+rEC3Dy9fp8b28u6QO\nj2xw5qylUsoK/OPOu822N5ZIFpRVosXQBY06FKsy0saVTk4m//PyzZs3b+G2eXNRZ6Gc8nxrOwDg\n4+KzyNGm4/rQ2DxuF/ouim7fYujCgin8Qb25Y6w01nhnW1F5FXK06bhx4wYM3b2IjlBjfaZWuBNV\nrFw1Iyke5bUNiI0MR2hQAGZ5eaGovAphQYHov/wJhoaHhZJd0/JUf99FZsfz9v8cwT1f3or27gto\nNXQjKiwESUui0djShqylqfBduADa5ATUt7QjNjJ8XLmsI1YO5qAbkYQigvwxPHIDfgvnIyF6FXov\nXsTwyA1EBgVIHRqRRdbytuJcHbbevlrqEIlEqQMX49InV+G7wAcJa1ehp78flz65inCTiXlpZouL\nVGPtimVo6+xGW9cFZCTFIzIsBD0WfuGQA3XgYly+8imSYqORtTQFJ4vO4lxDEzLTknDw8BGkJ7C0\nlOTJVT/nRoeHiZaOxTi4JNN0zqi0hDhsWZPt8NLulLhYHDp2wqwM764tGxy6H3KeqLAQHC8sMS+l\nNCl1FpuD8LlHfzjlfUarQxAaFIj2rm40tRkQGRaCZrE2ETHWJlLjl+Dy1auY5eUFTw9PqyWuV69e\nFd2ntXkUYyPCoAkPFQYeje3E57bb8J//Jr6A5sRyVX1tAzw9PBDo54t53nMQ5O+HoaRhdPX2Y3lK\nImIjw2G40Iv7fvZLRIaFCOWp/Zc/QUZywrjjGb5xA2+9dxgbslcicLEfKmrr4eE5tkBCT/8l3DZv\nLuZ6e+OR+3dNeq6ngoNuRBJKS4oftyw4MPYXgYd350kYFZF1FvP23h0ICQqUMDIi65YmJ7DPdXPL\nUhLxypv/aTbPzU/+YafEkVm2NDkBFTX1OFVabpa792zfguxltpcgEbmSq37OzV6WijO6CrP9ZKWn\nOGwfE+eMamw34MOPzmDfE485dOBty5psfPjRGaEMD+DqxEpjmo+m19CYj86Yg3DizzdhwQFCOaeR\nt0qFrKVjMWQkxY+LITd7hej2y1MTcfPmTbP9TXYM2tSkaX3XTmxvxnhytOloMXRCEz5+zrrgQH8h\n/onlrKbvH8UoivVVyM1eYTaH3ZmzFUL8jubUOd2IyLqzVbVCjXpYUKDQOeiqaqUOjcgii3lbXcfJ\nvEnW2OdSaVWN6Lw1Z2WcA/ra8xgYvAYAQt4CEObuYb9LcuWqPre2uRU52nRkL0uDRh2G7GVpyNGm\no665zWH7MM4ZNfFYjHNKOYqxzO6uLRsQGxmOu7ZscPjAHjnXZPnojDkIdRPamrGcMzdrBTTqMKzP\nXI68bZtR3zJWXqqrqRsXw8fFOuRo07HeuH3W2PaVdefh6Wk+ZDTZMZw9Vyv6etk529q+6Rxtpu+/\ndn0IC3x8zOagM8a/cVUmYiPD4bdwIZ586AGhHW3MyUTets0YvTWKNcuXYZbXLJfOA8k73Ygk1N51\nAbnZK4Rbb43LObd1XpA6NCKLmLekVMxdUuLcT58OfIagAD9kJCeMy9uPi3WyjpvIVX2uvqYeje0G\ns3K+WAeupn6uvlH0WKoaGh22DyN7Vsok+ZksH53xPdQ2oa0Z86e77yI2rc7CO4eP4mTxWSEG0znQ\nAODmrVs4UViKhOgorMpIw4W+i+i40IO2zguYN2+92f4mO4amNvFjaWw34EfPvAifeXPGlaNONDE+\nowt9FxEbGW42Z5sx/qTYaPzxpWeF542f/91fPIOwwAAAo+js6UVoUABys1fg42Idbt66NS7+va/t\nx+zZsx1aPs5BNyIJ5WjTcVBkOee8bZsljozIMmt5W1ZVjYzUZIkjJBLHPpeiw0PF536KcOzcT44U\nHxOJt//nqFnersvUYvYs/ihP8uWqPjctIQ6N7YZx5XwAHDrf4brM5fjDe4fNjmX317Y7bB80M0yW\njxoL30PREWFT3qeltrZj22a8d+yEsCKnMQZjjBP5LlyAd48cF+6a2/nlrRgcHDSPdZLvUkuvB/kv\nFgYhrZXUWoovOGAxqs83I3lJtOjnJ8ZozJ4DgLUrtfiPg+8J56e544vv0ROFpcJ2UWEh+Li0HFcH\nBoTycUdgeSmRhHr7L2FoeBgLfHyQkZyIBT4+GBoeRs/FS1KHRmSRtbz9W0Hp5B9AJBH2uZSbtUIo\nzzTyVqmwPlMrUUSTu9B7UTRvrw8NY33WCqnDI7LIVX3uljXZou3akfOgXejrFy1H6+7rd9g+aGaY\nLB9T4mJEX09eEj3lffZdvCSan5c+uQKfefPgrVKNi8FSjHPneAufMzZo2AMPDw+z/Rm/S03LrU2/\nSy191078fEvlnNbiuzowIMzZNvF1S22+5nyzxXJV4+d4q1SICA0WBiiN5eNix28v/nmMSEJNHZ3Y\n/bXtZquoFZRXSR0akUXW8jZeEyF1eEQWsc8l41/UT35emhkTocb6TK1TJk52FEt5W1hRhdtlPFhI\nZDF3HdznGudBO3amCPq6BqQnxGHz6iyHlmjWNjaLP9/U4rB9kHzZs3JtemIcnnzoAbPvGeP2x04X\nIW/bZrR3XUBrZzeiwkIQERqMY2eKsfPLX5pSfOdb28c99vL0xLpMLa58+ikAYLV2KXKzlgsxTGwz\noYH+mDVrFj6eMAh2vtWABQsWmO1vQ04mPh0cRLH+HFoN3ViVkYbM9BThu9T0u7a5wwB1cKDo51sq\nqRVr0xlJ8SivbRg3Z1tZTb1Nbb7F0CX6fE//RSxNjMe8ud6ICAnG2/9zZNzr+roGbM1aJvpee3DQ\njUhCm9dkYv/B981uBX5wx9ckjozIMmt523mhd5J3E0mHfS4BY78MbMjJRH19PeLj46UOZ1LMW1Kq\nLWuyxpV0OTN3nT0PmqVyN0eWsJI82btyrb62AU+/th8A4O+7CKfPluP02XL4+/kiPTEOSbHReOu9\nw1jg44PYyHDoqutwsvgs7tqyYcoxTszPdZla89U5dRVCDMD4NvP0q7/DXz/ON/vcJVHhuHr1qtnz\nx/OLzVYnLSirxPx588YNvBn//cv/+1scOV1o9jnWpnYQa9MT/0Bm6x/MYiLVouWosZERmDtXhYGB\na3jrvcPmMSTEYWRkxKZ9WMPyUiIJ1Z5vEb3Vtea8+F/TiOTAWt6uWzn9vwYROQv7XDI18HkJidwx\nb0mpLJV0KTF3XVHCSvLj4eFhcSVNSyvXGrc3zulm/Ldxe2MuXR0YQFl1La4ODEw7l0zz01ulMlvd\nc7KYgwMWi+Z3eHAQbpksNGBk7wqsa5Yvk3Rqh/DgINH9hwUF4GShDqMYtdi+R0dHp71/3ulGJCHj\nra7G1W36L3+CoeFhi7fAEsmBtbzNWpYucXRElrHPJVOqCT9gyxXzlpTKUo4qMXddUcJK8jN79myL\nK2nq68SfN24/sc82bu+MXDJ+5onCUgxev25WbjpZzGfOViBHmy6sfhocsBhz53jjtK4CWclLzLa3\ndwVWqad2OFVaLnp8p0rLsW5lBrxVKovlqjqd+ECiPTjoRiSh6PBQaMJDzZYf98T0J2wkchbmLSkV\nc5eAL+bm0dfWIz2xzOrcPHJgLW9rGluQFCu+WhuR1KLUIaIlXRp1qMP3Zc+cW1Pl7BJWkp+RkRG7\nS4vTE+MRERZi1mf7LVxoso1zcmnkxg3UNjYjMiwEoUEB+LhYh5smd6pZijklLhaHjp0QBgqNK4ze\ntWWDaHmlLSuBi7XJZ3/6g3Gv//p3b1lss45s01FhwTheWGp2fBtXZeIXP7hf2M5Zg4AcdCOSUEZK\nIva99Y7ZXBcP786TODIiyyzm7b07oK9t4A+kJFvsc2ni3DxNHZ1W5+aRA2t97j8++ZysYyf3tjw1\nCQVllePK0LxVKmhTEx26H3vn3CKy1ejoKLasycaHH50xy2NL5aAZSfF4+rX9Zn32kw894LQ4xdqA\nt0qFdZlanCgsFWJOjIkSfb82OUE4xs6eXmH7jKR40fLKFWnJOGMyZ5xx++Wft+3J2uR0X7eXabym\nx7fcwX2RJZzTjUhCuqpa0Xr4sqpaiSIimpzFvK2uQ31LqzRBEdmAfS7ZOzePHFjrc8OCAmQdO7m3\nypoGPPDNO7E+awU06jCsz1qBB755J6pqxEvcpkqJ7ZqUw1i6edeWDYiNDMddWzZYHfzR1dSJ99k1\n9U6L0VIbGL01inhNFLKXpSFHm44SfbXo+8tq65GjTUf2sjRo1GHC9uW1DfDwMK8GKK+uQ962zVif\nuXysbWcuR962zaiqbwIAHC8ssdomJ2uzjm7Txnhzs1YgOjwMuVkrkLdtMyqqnXdNTPFONyIJWZrT\nolmBc12Q+7CWtzu2b3ZxNES2Y59L9s7NIwfW8nbT6iycKCxxcUREtmns6MTIrZvwnj0bqzLScPHy\nFdQ2taCt84JD96PEdm3N4ROnkF+mR6uhG1HqEORkpGN77lqpw3Jr9pSDTiUfp3vNLe2ztbMbvgsX\nCOWUGnWYeGw19cLdcabll7GR4diSudRs+4bWDhw9UySswFpRWw8PTw8Mj4xg109/gZDAAORmrzAr\nbzWeg8nOkaXXy2vq8NO9LyE4wN+uctRmQzeCA/zh5emJAD8/eHmO3XvWZPL96swSdQ66EUnI0vLF\nSyItL59MJDVreVvX3Io0C/NFEEmNfS7ZOzePHFjL27/nF2FZUoIEURFNLkebjoMfHjMrQcvb5tg/\n0CmxXVty+MQpvLD/wLjSxIKySgDgwJtC2JuPjrjmsVERovvUqEORX/ZFGajpnGtiMZuWXxpjFpvT\nTfP5nG7GFVhzs1cg36TcVKy81fh5pvubaLLXg/wXQ/f5gKA95aibV2di/5/fN+uLHvjmnQCsl8M6\nAstLiSRkcfni4CCJIiKanLW8TUuIlygqosmxz6Uta7JFc8DS3DxyYC1vP/1sUNaxk3vr6b8oWiJ2\nof+iQ/ejxHZtycQ58ICxc2YchCH5szcfHXHNgxb7ie4zJNB/3OP1mVq7Yxab0y0lLkbY3lulwrXr\nQ6LHcO360LjtjOdgsnNk6XWfeXPh77sI3iqVXeWodU0toq/XNbWMe7+3SoWwoMBxny9WXmsvp97p\n9txzz0Gv18PDwwN79uxBWlqa8FpRURFefvlleHp6QqPRYO/evfD05BgguRdryxd/7567pA6PSBTz\nlpSKuUvpiXF48qEHcLJYh6Z2A2Ii1FifqZX1ZOvW8vaVPY8gNipc6hCJRDW0tNv1/FQZ59w6dqYI\n+roGpCfEYfPqLIe3a1eskGqpnNzS8yQ/9uajI675GZ1e9HuiWH8OSxPjMf+2echamoLy2ga8+V8f\nmOWvtZh1Op3Z/v6eX4IHvnknahpbMHjtOnr6xAfSe/ovCvs3/a6d7BxNfD00MAAadSga2zqgmj1b\nWBG2qqERwOTlqs0dFqZp+Pz5c/WNyM1eYbbibFVDI7ZmLbP1MljktEG3kpIStLW14eDBg2hqasKe\nPXtw8OBB4fUnn3wSf/jDHxAcHIyHH34Yp0+fxrp165wVDpEsxUSE4diZIrP6+S1rsqUOjcgi5i0p\nFXOX9LUNePq1/QAAf99FOH22HKfPlsPfz1e2A2/W8nbUAX+BJ3KWJVHhoqXRcZoIh+/Lnjm3psJV\nK6RGqUNEz5lGHeqwfZDz2ZOPjrjmEaHBOFFYavY9sWHVSuz96UOoqm/ED//lX63mrz0x52iXYv+f\n3x9bETVWg+BAfzSLHENwgD+6e/tRUVtv9l072f5MX//9ocN4893/NlsRdvfXtgOYvFzVWA47UXTE\n2Bx36zKX4w/vHRb9fLHyWns57daywsJCbNy4EQAQExODK1euYGBgQHj90KFDCA4OBgD4+fnh8uXL\nzgqFSLZCAvyF21c7e3qF21qD/RdLHRqRRcxbUirmLhlLSExzQO6rHFrL2+JylpyRfBlz15RS+1xX\nrZCak5Eues5WZaRZeAcpnSOuedISjej3REKMBgDwv6fyHZq/F/r6MTQ8jKsDAyiuqMIcb5XoMczx\nVqHF0Dnt79rOCz2i8Xf39QOYvFzVtBzW9PXkJdHjjsfS50+X0+506+/vR3JysvDYz88PfX198PHx\nAQDh/729vcjPz8ePfvSjST9T7NZGR2w7E82k45/qsdjyPq1WvK7d0cRi0Wg0Fm8FPqPTY8eXNqCl\npcUl8cnFTMrbqWLeKpO7566txy9l7kZGRlrN3W9sWY+2tjaXxCcX7pa3KpUK+pp6ABDuBui//AmG\nhodRUVuPqqoqDE/4oRuQd95+/56vW4x7JnO33J1ITn2upVhmUp9r2ndMZK3vmMiW6xay8Db8+Dv3\noLSyBi2GLmjUoViRloSQhbfNiLyX+8+509luIg8PD8yePRsjIyOi86IZTfea+/j44NjpYuRt24z2\n7h60GboQqQ5FREgQPio6i3XLUlB7vhmAfd99pkzjUKlUqDnfNO71j4t1WJepxY0bN9HV04vQoADM\nmjULHxePj7+msRn19fX47LPPrJ4TU2L7M/28qqoq3Bgexv/53m6UnKtFTVMrkmKisDIlETcGP0VV\nVRWOnipE3rbN6O7tx5WBz7DQ5zaEBPrj6OkiaOOirX7+V9asnHaf67LVS8VO6sWLF/H9738fTz31\nFHx9fSf9DFsboE6nc1ljlaOZdvxTORa5nQNLsUSEBoneCpybtQJ+fn7w8/NzcaTSkds1k4LczgHz\n1jZyu26uJsfjn0ru+vv7w9/fX/R9M5Ecr5srLE0qR6Q61GzeFr+FC5GamippbFPJ257+i1jzpU0u\njlRa7pq7RnI7fmuxzKQ+Nz2xDE0d5uVpSxPjbeo77LluWgBf25xrb4iyp6TcNZpqzPbO/zfdax4V\nGoyOCz24efMmAhb74ebNmwCAYH8/7Pm31xEZFoqV6SlobOtAV2+/Xd99YudgYnu4eesWThSWYscd\nm/Hrn/0IL7/xR/z5f/8mvO7l6Yl1mVp4wAPPvP4Hu+dEtLX9bVq3WvT9KXHl6LjQg+tDw+i9eAle\nXp7ouNCD1LhYxMfHIz0x3uLnj4yMTDtvnVZeGhgYiP7+L27H6+3tRUBAgPB4YGAADz74IH784x9j\n9Wrxk0M00y1PTRK9FVibmih1aEQWMW9JqZi7lJEUj3ydHoXllWgxdKKwvBL5Oj2WyXQ+N8By3i5L\njserB/4MvYUJpImkNpP63Jm0Qio5l3H+v0PHTqCx3YBDx07g4WdecGpfrU1NRL5Oj1OlZSjWV+FU\naRne+fAYRjE2f9vxwhK88+ExeH9e7jnd7z5tcoJoe0iLjwUAbFi1ctzr6zK1yNfpcbywZErnZLrt\nz/jdX1CmR4uhEwVl+nHHb+/qrfZy2p1uOTk5ePXVV5GXl4fq6moEBgYKJaUA8Ktf/Qq7d+/G2rVr\nnRUCkeyVnasTve2+/Fwd7tqyQerwiEQxb0mpmLukq6kTnbelrKYeG3IyJYrKOkt5W1HTgAU+t+HY\nmSLZLgJB7m0m9bmuWiGVlM/a/H/OypfSqlrRfV67PiQMfIs9nup3X1ltPXK06bhx4yaGhkfgrZqN\nWbO8UF7bgA05mePaS21TCzw8PKd1Tqbb/ib77rd39VZ7OW3QLSMjA8nJycjLy4OHhweeeuopHDp0\nCPPnz8fq1avx/vvvo62tDe+++y4AYPv27dixY4ezwiGSpeaOTmF1FNPb7jXqMKlDI7KIeUtKxdyl\nSgt/VdfXyfduMWt5uywpQdaxk3ubaX2us1dIpZlBiu+ZZpGVOwHgQt9F+PsuQmdPr+jjqcZUVXce\n6pAgjNy4gd6LlxAcsBizZnmhsv68sI1pe9n101+Ifo49+5/Y/vS1Dfj1796yqYTXlmvizPbt1Dnd\nHn300XGPExIShH+fO3fOmbsmUgTjEtHG2+6NuCw4yRnzlpSKuUtpCXFoFPnlJD1Bvr9IW8vb8po6\nrF2RIWF0RJaxzyV3JMX3jCY8FC0G8znJggMWo6y6zuLjqca0LnM5/vDeYeHuMePg+u6vbRfd3tHn\nxFjCa9x/Y7sBH350BvueeEx04CwhRiO6/8TPV3d1NqfN6UZEk0uKjYa3amyJ5bCgQOHfiUtc0wEQ\nTQXzlpTKYu7GMnfdhRLnZZosb+UcO7k3/rxA7kiK75mUuBjRffrMmwt/30VC25s7x1sYqJpOTBf6\n+oU5Go1te2h4GN19/aLbO/qcWCvhFaMOCRbdf0iAaxZzcdnqpURk7u8FpXjgm3ei5nwzWju7sWpZ\nGpKWROPv+aW496vbpA6PSBTzlpTKYu4WlOLeO5m77sB03paK2nosTYyX/bxM1vL2lT2PIDYqXOoQ\niUTx5wVyR1LM/3fsdBHytm1Ge9cFtHZ2I0odgqWJcaisbYBq9mys1i5F9rJU1Le0IzYyfNox1Te3\nIjd7hdlK4PUtbaLbO/qc2FIuarqCbEhgAPK2bRZWbzXOL/lRiQ73feOrU4rBHhx0I5JQZnoy9v/5\n/XG35haUVyJv22aJIyOyjHlLSsXcJeCLeVuqqqqQmpoqdTiTspa3HHAjOWOfS+7K1fP/JcVG4633\nDmOBjw9iI8Mx19sbv/nTu+Pa3hldBfY98RgeuX/XtPdnb3kp4NhzMlm5qlj5aUllNdauWIbhkRFh\nfklXLejC8lIiCfX0XxS9NfZC/0WJIiKaHPOWlIq5S6aGJ+SCXDFvSamYu0SuYSzfvDowgOrzTbh8\n9VO7yi/tZSwvnfj5lspLHW2yclVL5acDg9fQf/kToTTWVdMz8E43Igk1tLTb9TyRHDBvSamYu6RE\nzFtSKuYukWuYlm929/XjgoXBL0etoFrb2Cz+fFOLQz5/MpOVq1oqP+3pvwhtSiJCAvxdOrUEB91o\nyrLu3i11CIpnXNVpomiu6kQyxrwlpWLukhIxb0mpmLtErmNavvnr372F5g7ztme6WqjpnGdpCXHQ\nJiegrKYe+tp6pCXEYcuabIuDUnJYCdxauaql+KIj1BgZHoGXp2sLPlleSiSh5alJorfGZqQmShQR\n0eSYt6RUzF1SIuYtKRVzl0gak5VfGuc8O3TsBBrbDTh07ASefm0/Ll+9Kjx++JkXoLdwx5jcVwK3\nFN+tW7fwcWkZ/vy/f7N6fI7GO92IJFR+rg452nRh5RfjSirl5+pcNrEjkb2Yt6RUzF1SIuYtKRVz\nl0gak5VfWprz7Nr1IXirVBgaHhbmgNugTbH786U2Mb6osFDcGr2Fj4t1wjbG43NFzBx0I5JQU0en\nsNqLv+8iYSUVjTpM6tCILGLeklIxd0mJmLekVMxdIulYK7+0NOfZhb6L8PddhM6eXgBjc8BtzVpm\n9+fLgWl89/3sl6gRmW/OUXPcTYblpUQSilKHABgbae/s6RX+4qDhXBckY8xbUirmLikR85aUirlL\nJE9pFuZeCw5YjP7LnwiP0xPiMDIy4qqwnCYhRiP6vKvmoOOgG5GEcjLS4a1SwVulQlhQoPDvVRlp\nUodGZBHzlpSKuUtKxLwlpWLuEsmTpTnP5s7xFgbHjXO0jY6OShGiQ0k9Bx3LS4kktD13LW7cvIli\n/Tm0GrqxKiMNmekp2J67VurQiCxi3pJSMXdJiZi3pFTMXSLpTFyd1HQ1UrE52TKS4lFe24DYyPBx\nc7TpdLpJ9iR/Us9Bx0E3Ignpaxvwypv/KfxFocXQiYKySmjUYbKukSf3xrwlpWLukhIxb0mpmLtE\n0jCuTmpse43tBnz40Rnse+KxcQNvE9vhhpxMl8fqKlLOQcfyUiIJWVo55tiZIokiIpoc85aUJ3WZ\nvQAAIABJREFUirlLSsS8JaVi7hJJg21PXninG8le1t277X7Pa48/7IRIHM/SyjGuWkmFaCqYt6RU\nzF1SIuYtKRVzl0gabHvywjvdiCRkaeUYV62kQjQVzFtSKuYuKRHzlpSKuUskDbY9eeGgG5GEpF5J\nhWgqmLekVMxdUiLmLSkVc5dIGmx78sLyUiIJGVdSKdDpcQu34AlPrNKmc3JZkjXmLSkVc5dM+fj4\nSB2CTZi3pFTMXSJpSL1aJ43n1EG35557Dnq9Hh4eHtizZw/S0tKE1woKCvDyyy/Dy8sLa9euxUMP\nPeTMUGgSU5k3jRyjsb0D7RcuoNXQjSh1CALb/dghkuwxb0mpmLt0PL8YJ4pK0dzRhejwUORmrZD9\nim1medvqi08uXcY6mcdNxD7XfvraBhw9XYjKugakJcRhy5psnjOFmewaTnxdm5yAspp66Gvrp3zN\nD584hfwyvdDWVqQm4dq1a1i8aBE+GxxE/6XLdsU4XRPjyclIx/bctVP+vOnG6+h47OG0QbeSkhK0\ntbXh4MGDaGpqwp49e3Dw4EHh9WeffRZvvPEGgoKCsGvXLmzZsgWxsbHOCodIlv7r6HHse+sds6XU\nAeCuLRukDI3IIuYtKRVzl47nF+Pp1/aPy4EzOj0AyHbgzVLePnzvDhzPL5Zt3ETsc+2nr23Aw8+8\nIJyzxnYDPvzoDPY98RgH3hRismto6fUcbToa2w1TuuaHT5zCC/sPmLW1HG06ivVVAICTxToAY991\nzs4zS/EAmNJA13TjdXQ89nLanG6FhYXYuHEjACAmJgZXrlzBwMAAAKCjowMLFy5ESEgIPD09sW7d\nOhQWFjorFCLZOltVK7qcs66qVqKIiCbHvCWlYu7SieKzojlg/GVEjizl7dnqOvRf+USiqIgmxz7X\nfkfPFIqes2NniiSKiOw12TW09Pq160PCPGz2XvOCskqbPtP4XefsPLMUj3Ggy17TjdfR8djLaXe6\n9ff3Izk5WXjs5+eHvr4++Pj4oK+vD35+fuNe6+jomPQzdTrbfyCyZ9uZyN2PH7DtHGi1WhdEIh5L\ncnIyWg1dotu3GLpw/fp1VFdXOzs0WWHeMm+Vyt1z19bjlzJ3ExMTrebu4OAgamvd6xdBd8tbHx8f\nNLd3ir7W1G5AfX298AdiU3LN21ZDF76X93WLcc9k7pa7E8mpz7UUS1JSktU+99q1a6ipqXFmaLIz\n2XVTqVTQ19SLvlZRW4+qqioMTxg4UBq5/5w7ne2Aya9hfX29xdcv9F2Ev+8idPb0Ctvbcs19fX3R\nYqGtTfzMpnYDmpub7c4ze86BtXhaDF1obm7G5cuXRV8XM9124Yh4ptvnumwhhdHR0Wl/hq0NUKfT\nuayxypG7H7+RnM6BpVii1CFoMZj/AqBRh2LOnDmyOgZnY97K7xwwb20jt+vmanI8/qnk7rx582R3\nHM4kx+vmCtHhoaI5EBOhRnx8vAQRfcHevI1Sh6K4sgo77tji7NBkxV1z10hux28tFmt97ty5c2V1\nHM5m63VLTyxDU4f5OVuaGI/U1FRnhOYySspdo6nEbO0axsfHIz0xXvT14IDFKKuuG7e9rdfcUlub\n+JkxEWpER0dbjEFsn1M5B9bafnR0tF2fBUy/XUwnHkfkrdPKSwMDA9Hf3y887u3tRUBAgOhrPT09\nCAwMdFYoRLK1PDVJdDlnbWqiRBERTY55S0rF3KXcrBWiObA+Uz6/CE5kKW+XJyfAf+EiiaIimhz7\nXPttWZMtes42r86SKCKy12TX0NLrc+d4CyWQ9l7znIx0mz7T+F3n7DyzFM+qjDQL77BuuvE6Oh57\nOe1Ot5ycHLz66qvIy8tDdXU1AgMDhaXZ1Wo1BgYGYDAYEBwcjJMnT+LFF190Vihu56Hn90kdAtnI\nOImsrqoWLYYuaNSh0KYmcnJZkjXmLSkVc5eMiw6cLNahqd2AmAg11mdqZb0YwcS8jVKHjg24LZjP\n1UtJ1tjn2i89MQ77nngMx84UQV/XgPSEOGxencVFFBRksmso9npGUjzKaxsQGxk+pWtuXAygoKwS\nLYYuRH/e1ipqGqBRh5l91zk7zybGo1GHYlVG2pQXLZhuvI6Ox15OG3TLyMhAcnIy8vLy4OHhgaee\negqHDh3C/PnzsWnTJvzyl7/EI488AgC44447oNFonBUKkazdtWUD7tqyAdevX8ecOXOkDofIJsxb\nUipj7g4ODmLevHlSh0MS2JCTiQ05maivr5e8pNRWxry9du0a5s6dK3U4RDZj7tovPTGOg2wKN9k1\nFHt9un/82Z671mwQ6etWBridnWdi8UzHdON1dDz2cOqcbo8++ui4xwkJCcK/V6xYgYMHDzpz90SK\nUl1dLat5DohswbwlpaqtrWXuujklLj5QU1PDvCVFYu4Skbty2pxuRERERERERERE7oqDbkRERERE\nRERERA7m1PJSmr6su3dLHQIREREREREREdmJg24uxAE0IiIiIiIiIiL34DE6OjoqdRC20Ol0UodA\nM5SzJ3Vl7pIzMG9JqZi7pETMW1IqZ+Yu85achX0uKZVY7ipm0I2IiIiIiIiIiEgpuJACERERERER\nERGRg3HQjYiIiIiIiIiIyME46EZERERERERERORgHHQjIiIiIiIiIiJyMA66ERERERERERERORgH\n3YiIiIiIiIiIiByMg25EREREREREREQOxkE3IiIiIiIiIiIiB+OgGxERERERERERkYNx0I2IiIiI\niIiIiMjBOOhGRERERERERETkYBx0IyIiIiIiIiIicjAOuhERERERERERETkYB92IiIiIiIiIiIgc\njINuREREREREREREDsZBNyIiIiIiIiIiIgfjoBsREREREREREZGDcdCNiIiIiIiIiIjIwTjoRkRE\nRERERERE5GCKGXTT6XQ2b1tdXe3ESOTP3Y8fkNc5sDV35RSzFNz9+AF5nQPmre3c/RzI7fiZu7Zx\n9+MH5HUOmLe2c/dzIKfj5+9ntnP34wfkdQ7Y59rO3c+BI45fMYNu9rh+/brUIUjK3Y8fUOY5UGLM\njuTuxw8o8xwoMWZHc/dzoNTjV2rcjuLuxw8o8xwoMWZHc/dzoNTjV2rcjuLuxw8o8xwoMWZHc/dz\n4Ijjn5GDbkRERERERERERFLioBsREREREREREZGDcdCNiIiIiIiIiIjIwTjoRkRERERERERE5GAc\ndCMiIiIiIiIiInIwDroRERHRlPn4+EgdwpQoNW5yb8xbIiIiZZnlzA9vaGjAD37wA3znO9/Brl27\nxr1WUFCAl19+GV5eXli7di0eeughZ4ZCEjieX4wTRaVo7uhCdHgocrNWYENOptRhERGRAyi1j1dq\n3OTemLdE1rGNELk3OfcBTht0GxwcxDPPPIPs7GzR15999lm88cYbCAoKwq5du7BlyxbExsY6Kxxy\nseP5xXj6tf0YGh4GALQYOnFGpwcA2SS/3Dz0/D6731P07ltOiISIyDql9vFKjZvcG/OWyDq2ESL3\nJvc+wGnlpSqVCv/xH/+BwMBAs9c6OjqwcOFChISEwNPTE+vWrUNhYaGzQiEJnCg+KyS90dDwME4W\n6ySKiIiIHEWpfbxS4yb3xrwlso5thMi9yb0PcNqdbrNmzcKsWeIf39fXBz8/P+Gxn58fOjo6Jv1M\nnc72k2bPtjORlMfv4+OD5vZO0dea2g2or6/HwMCA0+Ow5RxotVqnxwE473rMtDyfacczFUrMW143\n9zoH0+njpcxduXw3yYk75a0lcu9zmbfi3D13bT1+V+Su1L+fKamNuHveAvLvc6ez3Uwm53Pgij5g\nun2uU+d0czRbG6BOp3NZY5UjORx/dHgoWgzmyR8ToUZ8fLzT9y+Hc2DKWbHI6RinS27XTApyOwe2\nxCK3mKXgjudA6j5+Mpauh9zjdiV3zNuJ5HYOmLe2kdt1czW5Hb8cfj9TQhuR23WTgtzOAX/OtY0S\nzoEz+wBHHL8kq5cGBgaiv79feNzT0yNahkrKlZu1At4q1bjnvFUqrM+Ud4MlIqLJKbWPV2rc5N6Y\nt0TWsY0QuTe59wGS3OmmVqsxMDAAg8GA4OBgnDx5Ei+++KIUoZCTGCcsPFmsQ1O7ATERaqzP1Mpi\nIkMiIpoepfbxSo2b3Bvzlsg6thEi9yb3PsBpg27nzp3Dv/7rv6KzsxOzZs3C0aNHkZubC7VajU2b\nNuGXv/wlHnnkEQDAHXfcAY1G46xQSCIbcjJlk+hERORYxj6+vr5eNuU7tlBq3OTemLdE1vH3DiL3\nJuc+wGmDbikpKThw4IDF11esWIGDBw86a/dERETkAnKZoNpeSo2b3BvzloiISFkkmdONiIiIiIiI\niIhoJuOgGxERERERERERkYNx0I2IiIiIiIiIiMjBOOhGRERERERERETkYBx0IyIiIiIiIiIicjAO\nuhEREdGU+fr6Sh3ClCg1bnJvzFtSKuYuEbmrWVIHQO7t8IlTyC/To9XQjSh1CHIy0rE9d63UYRER\n0SSU2n8rNW5yb8xbUirmLpE5tgv3wkE3kszhE6fwwv4DGBoeBgC0GDpRUFYJAOx0iIhkTKn9t1Lj\nJvfGvCWlYu4SmWO7cD8sLyXJFJRVCp2N0dDwsNDpEBGRPCm1/1Zq3OTemLekVMxdInNsF+6Hg24k\nmRZDl13PExGRPCi1/1Zq3OTemLekVMxdInNsF+6Hg24kmSh1iNlz3ioVspamShANERHZyth/e6tU\nCAsKhLdKBQDQqEOlDGtSSo2b3BvzlpRK7Gd9gLlL7o3twv1wTjeSTE5GunB7rZenJ9ZlanHt+hCK\n9VV4/MV+ZKan4M5N66UOk4iIJlijTccsLy8MDF7Dhb6LyEhOgM+8uchemiJ1aFYpNW5yb8xbUirT\nn/WNvFUqrMpIc/i+3v/bSRTrzwkT0/P3CJIrV7YLdyLnPoCDbiQZ40SRBWWVWDj/Nvz14wKh82nu\n+GJCSbk0FiIiGjN84yZOlZaPmwTYW6WCNiVR4sisU2rc5N6Yt6RUpj/rtxi6oFGHYlVGmsMni3//\nbyfxypv/KToxPX+PILlxVbtwJ3LvAzjoRpLanrsW23PX4v+8/BvRCSVL9NWyaChERPSFYn21Ivts\npcZN7o15S0pm/Fm/ubkZ0dHRTtkH2wgpjbFdkGPIvQ/gnG4kC03tBtHnOaEkEZH8tCp0EmClxk3u\njXlLM8Hly5ed9tlsI0TuTe59AAfdSBY4oSQRkXIotc9Watzk3pi3RNaxjRC5N7n3ARx0I1nITE8R\nVuMy8lapsDI9WaKIiIjIEqX22UqNm9wb85bIOrYRIvcm9z7AqXO6Pffcc9Dr9fDw8MCePXuQlvbF\nihx/+tOf8MEHH8DT0xMpKSn4xS9+4cxQSOaMtdYl+mphQsmV6cmyqMEmIqLxlNpnKzVucm/MWyLr\n2EaI3Jvc+wCnDbqVlJSgra0NBw8eRFNTE/bs2YODBw8CAAYGBvDGG2/g2LFjmDVrFu677z5UVFRg\n6dKlzgqHFODOTetl0zCIiMg6Y5/d3t6OiIgIqcOxmVLjJvfGvCWyjr9HELk3OfcBTisvLSwsxMaN\nGwEAMTExuHLlCgYGBgAAs2fPxuzZszE4OIgbN27g2rVrWLhwobNCISIiIifp6+uTOoQpUWrc5N6Y\nt0RERMriMTo6OuqMD37iiSewbt06YeBt586d2Lt3LzQaDQDggw8+wLPPPgtvb29s27YNP//5z61+\nnk6nc0aY5Oa0Wq3T92Fr7j70/D67P/u1xx+2+z2kfHLKWyJ7MHdJiZi3pFTOzl3mLTkD+1xSKku5\n69Q53UyZju0NDAzg9ddfx5EjR+Dj44Pdu3ejrq4OCQkJVj/D1gao0+lc0ljlyt2PH5DfOXBWLHI6\nxumS2zWTgtzOgS2xyC1mKbj7OZDj8TN3J+fuxw/I7xwwb23j7udAbsfP389s4+7HD8jvHLDPtY27\nnwNHHL/TyksDAwPR398vPO7t7UVAQAAAoKmpCeHh4fDz84NKpcLy5ctx7tw5Z4VCRERERERERETk\nUk4bdMvJycHRo0cBANXV1QgMDISPjw8AICwsDE1NTbh+/ToA4Ny5c4iKinJWKERERERERERERC7l\ntPLSjIwMJCcnIy8vDx4eHnjqqadw6NAhzJ8/H5s2bcL999+Pb3/72/Dy8sKyZcuwfPlyZ4VCRERE\nRERERETkUk6d0+3RRx8d99h0zra8vDzk5eU5c/dERERERERERESScFp5KREREc18vr6+UocwJUqN\nm9wb85aUirlLRO7KZauX0sx3+MQp5Jfp0WroRpQ6BDkZ6dieu1bqsIiIyAmU2ucrNW5yb8xbUirm\nLpE5tgv3wkE3cojDJ07hhf0HMDQ8DABoMXSioKwSANiBEBHNMErt85UaN7k35i0pFXOXyBzbhfth\neSk5REFZpdBxGA0NDwsdCBERzRxK7fOVGje5N+YtKRVzl8gc24X74aAbOUSLocuu54mISLmU2ucr\nNW5yb8xbUirmLpE5tgv3w0E3cogodYjo8xp1qIsjISIiZ1Nqn6/UuMm9MW9JqZi7RObYLtwPB93I\nIXIy0uGtUo17zlulwqqMNIkiIiIiZ1Fqn6/UuMm9MW9JqZi7RObYLtwPF1IghzBO+lhQVokWQxc0\n6lCsykjjZJBERDOQUvt8pcZN7o15S0rF3CUyx3bhfjjoRg6zPXctOwsiIjdh7PObm5sRHR0tdTg2\nU2rc5N6Yt6RUzF0ic/y92b2wvJSIiIim7PLly1KHMCVKjZvcG/OWlIq5S0TuioNuRERERERERERE\nDsZBNyIiIiIiIiIiIgfjoBsREREREREREZGDcdCNiIiIiIiIiIjIwTjoRkRERERERERE5GAcdCMi\nIiIiIiIiInIwuwfdhoeH0d3d7YxYiIiIiIiIiIiIZoRZtmz0+uuvY968ebj77rtx11134bbbbkNO\nTg5+/OMfOzs+IiIiIiIiIiIixbHpTreTJ09i165dOHLkCNavX4+//OUvKCsrm/R9zz33HHbs2IG8\nvDxUVlaOe627uxv33HMP7r77bjz55JNTi56IiIiIiIiIiEiGbBp0mzVrFjw8PHDq1Cls3LgRAHDr\n1i2r7ykpKUFbWxsOHjyIvXv3Yu/eveNe/9WvfoX77rsP7777Lry8vNDV1TXFQyAiIiIiIiIiIpIX\nmwbd5s+fj+9+97toamrCsmXLcPLkSXh4eFh9T2FhoTBAFxMTgytXrmBgYADA2ICdTqdDbm4uAOCp\np55CaGjodI6DiIiIyGYBAQFSh0BkN+YtERGRsniMjo6OTrbR4OAgCgoKkJGRAT8/P+Tn50Oj0Vgd\nKHviiSewbt06YeBt586d2Lt3LzQaDfr7+/Gtb30La9asQXV1NZYvX45HHnnEagw6nc7OQyOla+3/\nBGeratBq6EaUOgTLU5MQ5b/IofvQarUO/TwxtubuQ8/vs/uzX3v8YbvfQ8onp7y1hyvaNMmbHHKX\neUj2Yt6SUjk7d93t9zO2Q9eQQ5+rRJcGrqGoqgZ1Ta1IiIlCVmoS/HzmWty+/eIVlFRWC/m8Mi0Z\nEYsXujDimcdS7tq0kMKXvvQlpKen49KlS1i9ejVycnLsDsB0bG90dBQ9PT349re/jbCwMHz3u9/F\nRx99hNtvv93qZ9jaAHU6nUsaq1zNhON//28nse+tdzA0PAwAaDF0oqCsEj/5h524c9P6Sd8vt3Pg\nrFjkdIzTJbdrJgW5nQNbYrE15um2aTmT23VzNTkev6V4ZnIe2kuO183V5HYOmLe2kdt1czW5Hb+7\n/H420343kYLczoEjf86Vmr62Ac++9FshP5s6OvH3glLse+IxpCfGmW1/+MQp/Nvv3zbL58ceuBfb\nc9eO21Yp58BZHHH8NpWXnjhxAvfddx8uX76MJ554Al//+tfN5mibKDAwEP39/cLj3t5e4ZZ4X19f\nhIaGIiIiAl5eXsjOzsb58+encRg00xTrq4VOwGhoeBgl+mqJIiKi6WCbJjlgHpISMW+JpMd2SHJ2\n9EyhaH4eO1Mkun1BWaXo9gVllaLb0/TYNOjm5eWF5ORkaLVaLF++HAsXLpx09dKcnBwcPXoUAFBd\nXY3AwED4+PgAGFuYITw8HK2trcLrGo1mGodBM02rwXxhDW+VCp9duy5BNEQ0XWJtGgBaLDyvJCqV\nSuoQyEbGPPRWqRAWFAjvz6/dTMhDmrmYtzQTKP27cib/HEPKV1nbIPq8vk78eUt5y3x2DpvKS7/z\nne/A09MTycnJWL58OXbt2oX58+dbfU9GRgaSk5ORl5cHDw8PPPXUUzh06BDmz5+PTZs2Yc+ePfj5\nz3+O0dFRxMXFCYsqEAFAlDoELYZOAICXpyfWZWpx7foQevou4te/ewtb1mSL3ipLRPJk2qZNadTK\nXURHX9uAo6cLoa+tR3piGfslBYgOD4UmPBTXrg/hQt9FZCQnYO4cb3jC+uJQRFJi3pKSzZTvypn4\ncwzNHGkJcWhsN5g9n54g3tbcMZ+NfVFlXQPSEuJc2hfZNOiWmJiIc+fOoa6uDvPnz8fChQuRmpoK\nLy8vq+979NFHxz1OSEgQ/h0ZGYm33357CiGTO8hMTxFue12XqUW+Ti/cAtts6MSHH52xWKNORPJj\n2qaNvFUqrExPljCqqdPXNuDhZ14YN3cG+yX506Yk4v9OmJPHW6XCj3bnSRwZkWXMW1KqmfRdOdN+\njqGZZcuabHz40Rmz/Ny8Okt0+5yMdNF8XpWR5vRYpTCxL2psN7i0L7Jp0O1nP/sZAODTTz9FSUkJ\nfvvb36KkpGTSElOiqTJOSFpeXY/BoesWa9SV9oVN5K6MbbpEX40WQxc06lCsTE9W7CTg1ubOYL8k\nX43tBtHr1tRu/tdeIrlg3pJSzaTvypn2cwzNLOmJcdj3xGM4dqYI+roGpCfEYfPqLIvtzLhYQkFZ\npZDPqzLSzBZRmCmk7otsGnRraGhAaWkpSktLcf78ecTExAgDcUTOcuem9bhz03rs+ukvRF+3VKNO\nRPJkbNMzgb1zZ5A88LqREjFvSalmWu7OpJ9jaOZJT4yzawBpe+7aGTvINpHUfZFNCyk888wz+PTT\nT3Hffffh8OHD2LdvH3bs2OHs2IgAjNWoi7FUo05E5Gzsl5SJ142UiHlLSsXcJSI5kLovsmnQbf/+\n/fDx8cGxY8fg4eEBvV6PoaEhZ8dGBGCsRt17wopH1mrUiYicjf2SMvG6kRIxb0mpmLtEJAdS90U2\nlZf+y7/8C+bPny/M4VZdXY3f//73eOWVV5waHBFgf406EZGzmfZLFbX1WJoYz35JAXjdSImYt6RU\nzF0ikgOpxxNsGnRrbm7GO++8g3vvvRcAsHPnTnz44YdODYzIlL016kREzmbsl6qqqpCamip1OGQj\nXjdSIuYtKRVzl4jkQMrxBJvKS2fNGhub8/DwAAAMDg7i+vXrzouKiIhIIYYnrIZEysDrRkrEvCWl\nYu4Skbuy6U63rVu3Yvfu3TAYDHj22Wdx6tQp7Ny509mxERERERERERERKZJNg267du1CWloaSkpK\noFKp8PLLLyMlJcXZsRERERERERERESmS1fLSmpoaAEBhYSE+++wzJCcnY8mSJfj0009RWFjokgCJ\niIiIiIiIiIiUxuqdbv/93/+NpKQk/OY3vzF7zcPDA9nZ2U4LjIiIiIiIiIiISKmsDro9/vjjAIAH\nH3wQa9asERZSICIiIiIiIiIiIstsWr30zTffxO23347nn39eKDklIiIiIiIiIiIicTYtpPDmm2/i\n4sWLOHr0KJ5//nlcuXIF27dvx3e/+11nx0dERERERERERKQ4Nt3pBgCLFy/Gzp078dhjj2Hp0qV4\n/fXXnRkXERERERERERGRYtl0p1tFRQWOHDmC48ePIyIiAl/+8pfxz//8z86OjYiIiIiIiIiISJFs\nGnR79tln8ZWvfAVvv/02/P39nR0TEREREf3/7N15QFvXmT/8r1gkDGLfhdgXs2PAGDBesQ1O7DR2\nNttJ3GTaZKbTZDLt/JLOJPOm6TRL+2vSaSfLvO+06TRpmthOE8eJ7STY8c5iDAIEZt+FECAWs4PE\n9v4BkiV0BQK0oufzj83VXc69es659z669xxCCCGEEGLRdHq99LPPPkNgYCC+/fZbAIBIJMLc3Nyy\ny73xxhs4cuQIjh49isrKSsZ5fvvb3+L48eMrKDIhhBBCCCGEEEIIIeZNp6Tbm2++ic8//xynT58G\nAJw9exavvfbaksvcunUL7e3tOHXqFF5//XW8/vrrGvM0NTWhpKRkFcUmhBBCCCGEEEIIIcR86ZR0\nKykpwbvvvgsnJycAwDPPPIPq6uollykqKsLevXsBAOHh4RgaGsLo6KjaPL/+9a/x05/+dDXlJoQQ\nQgghhBBCCCHEbOmUdONwOAAAFosFAJiZmcHMzMySy/T19cHd3V35t4eHB3p7e5V/nz59Glu2bEFA\nQMCKC00IIYQQQgghhBBCiDljzenQOdvvfvc79PT0oKKiAkeOHEFeXh5SU1PxwgsvaF3m5Zdfxs6d\nO5VPux07dgxvvPEGQkNDMTg4iGeffRZ//vOf0dPTgxdffBEfffTRkmUQCAQr3DVClpeammrwbega\nu8/86u0Vr/u9F59b8TLE8plT3BKyEhS7xBJR3BJLZejYpbglhkBtLrFU2mJXp9FLn3zySRQXF2PD\nhg3o7u7GD37wA8TExCy5jI+PD/r6+pR/S6VSeHt7AwBu3ryJgYEBPPbYY5DL5RCJRHjjjTfw0ksv\nrWonFhMIBEaprObK2vcfML9jYKiymNM+rpW5fWemYG7HQJeymFuZTcHaj4E57j/F7vKsff8B8zsG\nFLe6sfZjYG77T/dnurH2/QfM7xhQm6sbaz8G+tj/JV8vLS0txfbt27F//378/ve/x5NPPokXX3wR\nUqkUjz322JIrzsrKQl5eHgCguroaPj4+4HK5AID9+/fj66+/xqeffop3330XcXFxyyai7w+mAAAg\nAElEQVTcCCGEEGJ++Hy+qYuwKpZabmLdKG4JIYQQy7Lkk26/+93v8MEHHyA8PByXLl3Cz3/+c8zO\nzsLV1RV/+9vfllxxSkoK4uLicPToUbBYLLzyyis4ffo0nJ2dsW/fPr3uBFkfPs+7hNKqGrSJuxDC\n98fmhFg8mLvH1MUihBDCwFLbbEstN7FuFLeEmB7VQ7KeUDwbz5JJNxsbG4SHhwMA9uzZg1/96lf4\n13/9V52TZs8//7za39HR0Rrz8Pn8ZftzI+vf53mX8PaHJyGTywEAreJOFJZVAgBVfkIIMTOW2mZb\narmJdaO4JcT0qB6S9YTi2biWfL1UMVqpgr+/Pz2lRgyitKpWWekVZHI5BFW1JioRIYQQbRRtNofN\nRoCvDzhstkW02ZZabmLdKG4JMT26VyHrCcWzcek0kILC4iQcIatxqaAYl2+WoKVDgrBAHvZmpUMk\n6Wact1UsMXLpCCFkZdhstqmLYHQiSTeyM9MwMSlDd28/UuKiscGBg/ZO5rbcXCjKPT09A5l8CuFB\nfNjZ2Zp9uYl1o7gl64Ghz5WL7y+yM9KwJytdb+tv03JPQvcqxBJRPGsyZBuyZNKtvLwcu3btUv7d\n39+PXbt2YW5uDiwWC1evXtVLIYj1uFRQjF++977ao6z5AiGOHsxFs6hDY/5QPg8AIKxtQN6NIlTW\nNSAxOgq52zORFBNl1LITQogqRbskrK1HUkyZVbVL2zZvwslzeWptOYfNxrGDuSYu2dK2pyWjs7sH\nE9MySPsH4OftCQ7bHjvSkk1dNEK0orgllswY50pt9xcA9HbTHML3R6u4U2O64l6FEEtijfG8VD5h\nqTbEzWFFz6kxWnIN33777Zo3QIiqy8WljI+ydkl74cLlYnh0VDmdw2YjNSEGwtoGPPfqm8rlmkRi\nnL+aj7dffsFqbnAJIeZlcbvU3NFpVe1SV4+UsS2XSHtNVCIdzc3hekm5ZrLwPvNOFhIrR3FLLJSx\nzpXa7i+uFAv0lnTbnBCLwrJKte0o7lUIsTTWFs/L5ROWakMe3J255u0vmXQLCAhY8wYIUdUi0syo\nA0BjWwd+9vffx+XCErSKJQjl85CaEIMHc/fgN3/8kLESXMi/aRU3t4QQ85OXX2TV7VJju5h5epvm\nE8vmpLlDzPi9tYiY94cQc0BxSyyVsc6V2u4vmvVYRxSdy1fWNmJwZBRuzlwkxkRSp/PEIiniVlBV\nq3HvvR4t1xYt1YY4Ojqueftrf1aOkBUIC+QxPsoaHsTH3q3p2Lv17q9RZy5ewZt//BDl1fWM6xLW\nNRisnIQQ/Ttz8QqKhbeVQ5OnJ8Xj0L7dpi7WqlTWMrc/1tIuLdWWm7POLinjdHE383RCzAHFLbFU\nxjpXGuucZGtjg6mZafT2D8DJ0QG2NkuOSUiIUa20OyaOvT0cNzggNiIULBYLHHt7I5bWuJZri5Zq\nQ8bHx9e8fUq6EaPKzkhDvkColml24XJx786tavOduXgFv/vzJwCAlLhoxkqQFL3+nyYhZL1Q1Gmm\nocktMfGWGB2FJoZf0K2lXWJqyzlsNnanp5qwVMuLDAtCC8P5JCo02ASlIUQ3FLfEUhnrXGmMc9Li\n65gWcScKFvp8ssTrGLK+KF6fBAAvdzecv5q/5Kvc5y5fx5vvf6Scv+/OIC4WFAMADmbvMF7BjWS5\ntmipNmR2dnbN26ekGzEqRb8KV4oFaBVLsC01CV29fXjno09x9soNbE6IxYO5e1AsrFYG/QYHDjhs\ntkYlyNmWYZJ9IISsnGqdVpDJ5bglrLbIi9Xc7Zk4fzXfatsl1ba8WSRGeBAfu9NT9TpSnCH4e3oy\nnk/8PD1MWCpClkZxSyyVsc6VxjgnrbfrGLK+XCi4iazUJI1R5S8WFDMm3YrLqxjnL66oWpdJt+Xa\noqXaEIFAsObtU9KNGN2erHTsyUrH+av5+M0fPtR48oVjb682jPG1YgF2pqeqNQo52zKsot8kQtaL\n9TY0eXOHGDvSkjE6PoHu3n74eXuC67gBzR1iq2mbFG15fX09Nm7caOri6GR4bIzxexseGzN10QjR\niuKWWCpjnisV5yRDWW/XMWR9mZ2ZRYHKk1qKAXfu3bWNcX4u1xFfXyvUnH/R22frRVJMFN5++QVc\nyL8JYV0DkqKjNPIJhmxDKOlGTCa/tILxF6PCskpEhgQpXymdmZ3F5aIScNhs3Ld7O55/+vumKC4h\nZA3W29DkJZU1uHJzvl3ycndDWXUdZHI5ZmZm8UBOtqmLZ1SjKqNOm7s7w6OM31t2Rpqpi0aIVhS3\nxFKtp3PleruOIevL8NgY4331yCjzjzODI8zzD42s3x9zkmKiTPbDOPX+SExmqV+M9mSmgcNma3wW\nERJo6GIRQgwgPSleo05z2GxsSYozUYnWRtF+yeRydPZIVX4ppF+8zVl7J/P31tZJ3xsxXxS3xFKt\np3PleruOIetLG0NCGABjohi4e17RWA+dVwyCnnQjJrPUL0Y701Px0797FLeE1cphjLckxVGfCYRY\nKEXdXS91mn7xtkzBAf5o6dD83kIC6Hsj5ovilliq9XSuXG/XMWR9SYreiGaRZl3bFMPc/QedV4yL\nkm7EZDYnxKKwrFKjQ8PUhBgA8yc3OpERsn6spzq9XPtFzBN9b8QSUdwSS7XeYnc9XceQ9WWlg5as\nt7pp7ijpRkzmwdw9AABBVa3yF6PUhBjldEIIMVfUflkm+t6IJaK4JZaKYpcQ49BloABVVDeNi5Ju\nxKQezN1DlZsQYpEU7dfw8DBcXFxMXRyiI/reiCWiuCWWShG7fX198PLyMnVxCFm3VjpQAN2HGw8l\n3YjZOHf5OgrKhGgTdyGE74+slCQczN5h6mIRQgijc5evo6iiCq0dnQgNDEDmpgRqsyzAucvXUXq7\nFoPDI3Bzccbm+Bj63ojZo7gllq6rq8ugSTc6JxNLQve91oWSbkRvzly8gmLhbWXjkZ4Ur3O/B+cu\nX8eb73+kfK9cJOmCna0tSm/XoqlNhMToKORuzzTZML+EkLUT1jYg70YRKusaLL5On1/UZrV0dCK/\ntAIsAAfooslsfX3lOoqFtzE6PoGevgGwWCwUC2/DhgXcu5u+N2KeKG6JJVOc+4W19UiKKTPIuZ/O\nycSSLL7vbRV3orCsEgAo8bYGy+UiTHkfQkk3ohdnLl7B7/78CWPjoUvibXFHjjvTU3G9pFw5rUkk\nxvmr+Xj75Rcs9iadEGsmrG3Ac6++uW7qdElVrVqbBQAyuRwlt2vpAt+MdXT3qp1bWsWd4LDZ4Pv5\nmrhkhGhHcUss1eJzf3NHp0HO/XROJpZk8X0vMB+vhWWVlHRbpeVyEaa+D7Ex5MrfeOMNHDlyBEeP\nHkVlZaXaZzdv3sQjjzyCo0eP4sUXX8Ts7Kwhi0IMrFhYzdh43BJW67R8q1ii/D+HzcbEpIxxfRfy\nb669sIQQo8vLL1pXdbqhtX1F04l5aBZ1MMZhs6jDRCUiZHkUt8RSGevcT+dkYklU73t1mU6Wt1wu\nwtT3IQZLut26dQvt7e04deoUXn/9dbz++utqn//85z/H22+/jZMnT2JsbAw3btwwVFGIEbStsfEI\n4fsr/+/l7obu3n7G+YR1DSsvHCHE5CprmeuupdZpnp8343S+n4+RS0JWoqNLyjhd3M08nRBzQHFL\nLJWxzv10TiaWRPW+V1Uon2fkkqwfy+UiTH0fYrCkW1FREfbu3QsACA8Px9DQEEZHR5Wfnz59Gn5+\nfgAADw8P3Llzx1BFIUaw1sZjW0oSOGw2AKDvziD8vD0Z50uKtrzX0AghQKKWumupdTo8kK9ssxQ4\nbDZC+QEmKhHRRUQwn3l6EPN0QswBxS2xVMY694fROZlYkCyV+14FDpuNrSmJa1qvsLYBv/nDh3j8\nX/4dv/nDhxBqSTQpXCooxn+88wf85LW38B/v/AGXCorXtH1T0paLiAoJAmD6+xDW3NzcnCFW/PLL\nL2Pnzp3KxNujjz6K119/HaGhoWrzSaVSPPbYY/j000/h7u6udX0CgcAQxSR60tY3iLc/PKn22CaH\nzcZzTxxFiJfbsst33hmFRNoLSU8vhkbHEBcZho+/+kZjff/PPzwBD+4GvZU7NTVVb+vSRtfYfeZX\nb6943e+9+NyKlyGWz5ziVlcDoxN47X8+BDD/NGvfnUEA0HudNpb2/iGUV9dhdHwC3b398PP2BNdx\nA5LjohHs6Wrq4pktU8eu4lwFqMehrucqYp0obomlMnTsLnetIOobwu8/PAFAPXZ/8sQxBHnp71xJ\n5+T1xdRtrjGI+odQUlmDVrEEoXwe0hJjEbSGWFVcZ+t679w9OIqbldUadSYjMQ5+btxVl8NUFuci\nbG1skJ2ZBhtbGzS1irArPRV//epbAIa9D9EWu0YbSIEpt9ff348f/ehHeOWVV5ZMuCnoWgEFAoFR\nKqu5MsX+p2I+uG8Jq5WNx5akOJ1HLy098Tk6e6QYm5iEtH8AdrY2OHYwF929fWgSiZEUHYWcbRk6\nd3RobjFgqLKY0z6ulbl9Z6ZgbsdAl7LoWuYvLl7BsYO5aJd0oV3cha0piQjm+WNUPoV9qdv0UVyj\nOv3bd3GpqAQcNhte7m4oq66DTC7H7OwsHvg/z5q6eEZlbnELaI9dwanTjHHYPziMB3P3GLmUpmWO\n35uxmdsxoLjVjbl9b8Zmbvu/VFkEJ5ljt3dwEIdzs/VXBgA2LBbKq+sxOzsHFycnJMdt1Pk+xBjM\n7XszBXM7Bvq8zl3xtgEcztFfHfjNHz9k7LOsoqEFLzz9fY35/+fE51oH6DmwZ6favOb2vTFZnIvY\nkZaMk+cvKPevo6sHj37vHnR0daOlQ4JtmzchO30z9mSlL7tufey/wZJuPj4+6OvrU/4tlUrh7X33\nffvR0VE8/fTT+MlPfoJt2yzvhotoOrRvt04nt0sFxbh8swQtHRKEBfJwYNd2zMzOaFT80tu1OHYw\nB7/45x8ZuuiEEAPr6e3HiXN5yjresnByP3Yw18QlW52Wjvk+ImRyOTp7pBrTiXmanp7GqfMXNeLw\n6IF9Ji4ZIdpR3BJLNT1jvNjV9T6EEHOw+H44OyNNpwSQNivts2w9DtCj2gYsTkJuT0tRuw9pFXci\nv7QCXh7uRhm91GBJt6ysLLzzzjs4evQoqqur4ePjAy737qOKv/71r/HEE09gxw4aFteaXCooxi/f\ne18t4EuqapGxKZ6x4ndQJ8GErAsSaS9jHZdIe01UorUJ4fsrfxVUPKYuk8upE1wz193bzxiHXVoG\n7yHEHFDcEktFsUuIJqb74XyBEABWnXhLjI5Ck0isMV1bn2XrfYAe1SQkh83G7NwcY1v01aVrlp10\nS0lJQVxcHI4ePQoWi4VXXnkFp0+fhrOzM7Zt24YzZ86gvb0dn332GQDg4MGDOHLkiKGKQ8zEjdIK\njYD3dHNFUxtzVr21o9MYxSKEGJi2Ot7Ubpm/qG1PTYKdrS1mZ+fA4bAhk8lhY8NC5qZ4UxfN6FSf\nYjd36y0OiXWguCWWimJ3dc5dvo6CMiHaxF0I4fsjKyUJB7PpQZX14nJxKWMC6EqxYNVJt9ztmTh/\nNV+jT7ecbRmM84cH8xl/PDbkAD3GjGvVJCTPx1vr6Ka1za0G2f5iBu3T7fnnn1f7Ozo6Wvn/27dv\nG3LTxMx8c70Qt+ubUN/azvg5z88bLWLNBBvfzwfP/+r34LDt1vzYLSHEdJaq4xaJBQT4+qBNLEFt\nUyuCeX4I9OcBLFMXzHjOXLyCYmE12sQShPB5SF9BP56msu7ikFgFiltiqdZb7F4qKMaVWwK0iMQI\nC+Jj95ZUvd+bnLt8HW++/5HaU1CFZZUAQIm3daJFxPxQSTPDk2q6SoqJwtsvv4DLRSXo6O5BoJ8v\nsjPTtD7FlRoXDRagHEghJS5aOfiIIawmruevM28rk3TpSfE6X2cuTkIG+Hmj1YRtkdEGUiDW53Te\nJZRU1aC9swu+Xp5w4TopX8lSJZH2YntaMkoqazSy84H+fvjs20uQyeVrfuyWEGI6EUGBjHU8LNBw\nv6gZUv/giEbfEJyKKjz1yCETl8w4zly8gt/9+ZNFF0/zbbQ5J960xqEBf9klZK0obomlCgvkM8Zu\nKD/AhKVancWvBLZ0dOJGSTkA/d6bFJZVMj4FVVhWSUm3dSIqNIgxAbQxLHhN620Vd6Lvzh109/Zh\nA4eNVnGn1qSbi5MT40AKu7YYZsCElcY183XmfJJOl+tM1SRkd18/QgL8mdsiI92HUNKNGMTpvEv4\nL5Vhe1s6OuHC5eLQvl3gsNkalc6F64Qdackawxb39N3tC0Iml+PKrdU/dksIMR0bGxvGOm5jY2Pq\noq1KTVML48VDTVOLiUpkXGW3axn3v+x2rVkn3VgsFmMcsqzpEUVicShuiaXSdn3vzHXS+7aEtQ3I\nu1GEyroGJEZHIXd7pl77amLqIkcml+NGaYVe701atbwGp206sTz+3l4a98McNht+Xp6rXueXi5JU\nLR3z/cSxANzPcF127ZaAMZ6vlZQZ5F57pXFdLKxmLN8tYfWKrjOnpqfR2d2DyJBAxrZoY0iQcl5D\ntiGWebdDzF5JleYNmbOTIwoEQmSlJiEzORGh/ABkJiciZ3sG8m7cxMzsLOzt7ODj6QFHBw5mZmdx\n5Wap2jpa1vDYLSHEdK6XlKnVcXs7O8zMzuJGSZmpi7YqbeKuFU1fb5ramdtipk58zcn1knLGOLy+\n8LQCIeaI4pZYqrwbN8Hz8YazoyN8PD3g7OgIno838m7c1Ot2hLUNeO7VN3H6wmU0icQ4feEynnv1\nTQi1jOi4Gg1aushpaGOevlohfH/G6TRQ0/pxo7RC4344KzUJ+YKKVa+zrKae+cfQmnrG+Ru1XccZ\nqL/Flca1tj7YdE0+V9Y1qrUJf/7sKwCAh6srfDw94ObijOTYjbhUdAvHfvIS/vvjvxm0DaEn3YhB\nMFWUvjuDSEuMxeWiEmWnjWXVdeCw2diXlY5zV26Aw2YjJjwUU9MzuM5wMx4SQCccQixRoL+vWt1X\ndNi6OyPN1EVbFaZX5QHruSgOsNB+eoJ46ysOiXWguCWWKsDXCx9+cQ7eHu5Ijo1GeU0dLhTcxO6M\nzXrdTl5+EWRyuUYduZB/U29Pqhirf7qslCSNV/E4bDa2piTqdTvEdHg+nhr3wzK5HDvSkle9zsZW\nEfP0NubpfD8fLX2c+a66DEtZaVyv5jr7UkExLt8sQUuHZH6ghtQkXCsWYGZ2FjOzs7hYUIwnDh3E\ny88+hYsFt/Dae39UthuNbSLGpOWF/JvYk7r2QdIo6UYMgqmiyORyBPP8UcKef5+6u7cPO9NTMT09\ng67ePhw9kANRVzfaxF3YmpLI+Niti7MTzl66jiCen1GG9yWE6Ie7iws4bLbaNA6bDXcXZxOVaG02\nxWxkvHiwlnYpNDAAtxj6xggx8356IoODlH2CKHDYbEQGB5qoRIQsj+KWWKr4qAjY29lBJp/C0Mgo\nYsJDsSkmChvDQvS6naq6RmRnpmFiUqbsFH6DAweV9Y1624ax+qdT9G9VVF6J1g4JQgN5yExOpP7c\n1pGwQL7yGqqzRwpA0b/Y6mNJkRRenHjWlhTeFLMRtyqrNa9jow1zHauI38KySrSKJQjl87A1RXtc\nZyQlMF5nb0mKY5x/cZ+Lij7qdqan4nJRCdh2djh2336Iurpx7CcvIYTPw9EDOThx9lt4ubuhu7ef\ncb3Cugbsz1h9MlSBkm7EINISYhkrysTkJB7avwd9A4MI8POGpKcPE5My9PT2Y25uDhscOBBJuiCS\ndCE7Mw2zs7No6ZCA5+sNDtseo6Pj+MOp0xgeHcPbL79gNTe4hFi6AD8fHDuYizaxBO2SbmRuSkAI\nn2eQfl2MoaG1HU89cgg1Ta3K0TtjI0K1vn6y3jhw2NiRlgyZfAoy+RQ4bHtw2PZw4NibumhL2uDA\nYYzDDQ4cUxeNEK0obomlsrefPydMTU9D2j8AP29PcNj2yun6kpmShFPnL4DDZiMiOBDVjfP9rh49\nkKO3bQT4ejP2CcXz9dbbNhRkU1OwW3id3M7ODrKpKb1vg5iOnZ0djh3MRXtnF9o6uxAS4I/gAH/Y\n2tquep3hQYHYwOFojEYa4MucdGOxwNxXqAG7Cj2YvWPJ5LGwtgGXCm9B3N2DQH8/PPfEUQiqatG6\ncJ29OSFGa39ul4tLGZ9Um5iUgcNm4+jBHJw8d0EjKXfse/tx8twFpMRFMz5ZlxQdhSk91D9KuhGD\neCB3DwCgdKGihPJ52JmWgov5N7HB0QHjkzLcGRrGFZUK0rIoI32xoBi52zLB9/OBA5uN4AB/BAX4\nQVjfqPdHxgkhhjU8MoqT5xed7Cqq9HpBbExsezu8/+kZAICXuxsKy4QoLBPi3p1bTVwy42gTzf8Y\nIpJ0Qdo/gJAAP/B8vNEmMu+OngeHR9ZVHBLrQHFLLNXg0DDjCIn6foWtr38ARw/koL2zC+2SbqTE\nbkRwgD96+wf0to379+7C3NwcyqvrMTs7BxcnJyTHbcT9e3fpbRsA8HneJbytMhgdAFwtFgAAHly4\nvyKWzcvdFb//4MTC/91QWF6JwvJK/OTJY0sut1RH/0F+Pjh5Lk+jrv3sqeOM66ptbsXFgmKNV1zX\nknNTfb0zLJCH7Iw0nQdlENY24LNvv1MmAecADAwNgcO2h3xqSnmdzbG3Z0zctYg0E2YA0N3bj5AA\nHiQ9vYxJOUlPLzhsNjY4cBjfssvZloHp8RHdD4IWlHQjBvNA7h5l8k2hRdyJk+cvAABS4qI1gh8A\nHB0c4MLlYnh0FA1tIkzPzCAyJBDVTS24fLMEiRsjEB0eiisLJyBCiPlrFUsY+1ux1NG4BkdGle2X\n4tWA+eljpiqSUTk5bdBMArDZZp90XG9xSKwDxS2xVM0iMeONbrOeB90J8PfBX774GsB8EqOoogpF\nFVX4/uF79bqdQ/t2G3yE7tKFwegW13dBVS0l3daJ0tt1jNeQguo6HM7JVv595uIVFAtvo03che1p\nyTilct3VJBLj/NV85ZtfFXWNjHWtoq4RBxiSVPoeEIvp9c58gRAAdEq83aqsZkzQP7R/j7IOAPOv\npyqSbqfzLqH0di0k3b3Krq0W15tQPg9e7m4oqaph3G6zSIyY8FDkl5Tj0e/dA5GkW/lkXXpSHJJi\noiAQrD3nQEk3smJrGU5X2j8AmVyOAF8ftXenbW1ssDM9FROTMlQ3tiAxOgJse3vIZHI4bnBQq4SK\nIZB//NjDBtk/QsjaLG4j7t+7ExJpL2N/KyJJt6mLuyrtncyjlIo6reMmeHBkjPHibsjMk47rLQ6J\ndaC4JZZK3N2zoumr1SKSICs1SaOOtOj56eu13APpSiTpZqzv7Z1U39eLloXE1uIEUbNKIuzMxSv4\n3Z8/0ejoX3VQkt6BO8o3v6obmxm3pW16RFAAQgN5GnFma2Ozqn3S9nrnlWKBTkm3xrZ2xuU7urrB\n8/FWvvqp+LHp7HdX8V8qT4Ty/X2wLytd4/Xa3embkb11C/79t+8yD8wQGIC52Tk8dv+9+OTstwDU\n32Cxs7WFv+vau8KhpBtZEcWQ3Nqy7Np8nncJLSIx6lrm+zvquzOo9u70zvRUFAiEGtntR+/bj7qW\nNsZKKKxtwBF6tYIQs6JoIxT9qnxXeAuNbR3YutDfyuI6bqmvRwUH8NDSoXnyDraS0UvbtSQX28w8\n6bje4pBYB4pbYqmCA/wZz5UhAfo9VzpzHfHNtUKNOnKPHp++VlzfAPM35eev5ut0D7RSWalU39e7\niCA+Y8LLTqVPt2Lh3UEOvNzdMDA0gmcefwQ1jS1obBMhPjIcsZFhyje/VlrXosJC8P6nZzTi7KlH\nDq1qn7S93qnrU63iLqlO00P4PDz90mvg+Xqp5Qfm5oAbpZpPyu3akgpgflRWptdH+b4++MfHHsZL\nv32X8enDwrJKPLJ3m077sJTVpTKJ1VIMya1K0b+aNoq+Ca6XlCMxOkI5gqGXhxs4bPbCAAsyxvU2\ntXdgYHCYcb2tYgnOXroOYW3DGveKEKIvFwuLcfRADjKS4mBvZ4eMpDjkbEtHZ4+UsY6Le5hPsubO\nzdmJcTRWVwsdGGKltI2wFR7EN3JJVma9xSGxDhS3xFK5OnMZz5Uuzvo9Vw5pefp6WI9PX3/53VVk\npSYhJS4abHt7pMRFIys1CV9euqq3bQB33wrisNkI8PVRJgp69Ng/HTGtTXEbUSAQoqy6DvKpKZRV\n16FAIFRL3rapdB/Qd2cQ9+7MxPufnsGV4lK0ijtxpbgU7396BrnbMgAAblrqmquWuqbtoZb6lnYU\nlJbhN3/4cEX32JGhzKNpbwwN1mn50EDm5GAInweJtBfA/P6wWIB8agqNrSLlPBw2G+OTk4z1Jr9s\n/hXXkqoaZKUmITM5EaH8AGQmJyIrNQmFC5+3dkiU61IsD8znG1xcXHTah6XQk25kRSq1VD5hnfZK\nKayuV3ZuWlXfhK0pieD5eKP0dh1+8PD30Nd/B6W36xiXFXdLERcZhvrWNo3PQvk8GsmUEDPj4eqC\n1o5OyOTzI/1MyqfQ3duHVi2/gLUy/CpnCUbHxtVeZfHz9sQGh/lRo6xBSlw08ksrNH4x3BS70YSl\nWt56i0NiHShuiaWanJTh6IEciCTdylEag3h+6O3rX37hFdD29LW26avB4XBwqagEAJQdzwPAgV1r\nfwpGVbOog/H1Un33g0dMp6K6jvF16IrqOmW/fYo+ygDA28MNFbXMfbYp3vyamJAxXpdOTMoYy6Dt\n/NHSIUZYEB//59f/pXySUxf+Xl6MT5L5envotLy2J9GCA/wxNjEJV64TpmdncLO8Crsz0jA0OoKW\nhePj5e6G3v47avVmc3wMIoID0dLRicf/5d8RyPMHMP/Eqruri3LgiN0ZaQCAsEAe49OHNmBheJj5\nAaCVoKQbWZHE6CjGDhaToqNw9tI1lFTVYGhkDK7OTkiJi8ahfbsRHOiPv3zxtYMktXMAACAASURB\nVFqfbC5cLn748P2ob2mDTD6FIJ4f43vWYYE8RIUGw6WkHMOjo8rpHDYbqQkxKCgT0kimhJiRqanp\n+X+npyHtH4CftyfuDI8gMjRIeXJUFRWi2y9g5obn66McFEb14ttaXv+oWPgxRdTVg3axBMF8HoL8\nfSGsrjfrjp7XWxwS60BxSyxVeHCg+kjfC6M0rvYVNq3bCeIzvloXHsz89M1qjI+PMyZKxsfH9bYN\nAMhMnn+9FLDO6wtr4OS0/OvQ6UnxKCyrhEwux84tqSgsq2Rcl6KPMz8fL5w4l6fs3qW6sQUyuRzH\nDuYyLqdISC0WGRIEV2dnAHffZtuTGr/sPuULhIxJv/xSIf7x0eX7Yfd0d8OOtGRMTMogkfaC5+ON\niOBAiCTdkPYPgMO2Ryifh9nEWUxMyhAVEoySyhrI5HKMjI0jOzMNn359UXlMQwN5agN+NYnE4LDZ\n2Je1BfKpGZTX1GF4FMonAdMS45R96M0f1/nv5Kd/9yhmZmaWLf9yKOlGViR3eybOX83XyEJHBPNx\nq7Iao+MTkPYPwMaGBcHtWni5u6KpvVM5v2LAhOnpGRSVV4Jtb48NDhxwHTcwv2ft54szF68gPSke\nAb7euFFagfCgAIQH83Hm4jXlCa+qocnox4IQArT1DeKzt95Fm1iCqNAghAfxUSAQYmZ2Vu1i8alH\nDuFqsUCjjvt5e5qq6GtSUCZkvPguKBPiH61gkBcu1xESaS9mZmbg7emBmZkZSKS94HIdTV20JUWE\nBDHGYXiI/m7MiOXgcrmmLoJOKG6JpapqaIJMLocLlwtfL0+MjI1jeHRU79ft2p6SCfD10ds2HDc4\n4GuGRIm+R+3ukvYxXl90Sfv0uh2iX5cKinG5uBQtok6EBQUgO32z1gEEhhdeh148kILq69CKUXJv\nCasxNDyKkAB/xgdUQhb6Ei4oq5x/s0zShfbObqTEbUQwzx+F5ZX40aMPaSwXFRqMa7fKNOpMZEgQ\nJsYnldOEdQ3Yn5HMuB+KgUXqmlsRxPPF5aIS5T4pniTbu3X5QRQAwNfVBTbRUahtboWLMxcBvj74\nyxfnAMwnn4uFt1EsvI2jB3JQ19KO7t4+HDuYi8Y2EVgsG0ikvcp9Ue26avExHp+QobOnF/FREdgU\nE4nm1g4A8w8FMQ7kIOlBUkSITvuwFEq6kRVJionC2y+/gAv5NyGsa0BSdBRytmWgsLyScZjfLYlx\nau+k787YDBaLhckZOXr6BhAauDCMb2UNHtq/B/13hjA0MgJPdzfI5HL89cuvMTM7i5aO+fX95MlH\n8fFXX+ParTIc2rdL+SvBE4cPmuqQEGK1TuddxtsqIwepdvbb0zcAT3dX9N8Zgnx6CmPjEzh2MBeS\nnl4MjY7BlesEnq83Riz0dcwgnh/jxcWezDRTF80onJ2c8PXVAo2LNXP/JZ4FYEdaMmTyKcjkU+Cw\n7cFh24Nl6oIRo7pUUIzLN0vQ0iFBWCAP2RlpOo2uZipLxW17pwRNbR1mXX5ivbp7+/HE4YPouzME\nsObg7+0FL3dX3Kyo0ut2hsfGsCMtWTlyoZ+3J7iOGzA8pr8+3QZHRhlv4gf1PGo319E4yT2iP5cK\nivHL995X+87ySysAgLFtFnUxj1C7eETqQ/t2K5Nvp85fQGF5pWa3HgtvemVuilN7suvuNfk+xjKP\njI7h2MFctHd2KV/9Dg7wx8joGDY4OoDv5wNxtxRJ0VGYmprSWH7x4Ip+PndfL1UMRMBhs7E7PVWn\nYygdHkZlXaOyDk9OTKrdT9wZHIa3pztEXd3oHRiAnZ0tpqansTEsBP13hlDV0Khcl5e7G6QLr5tO\nT89AJp9CeBAfdna26JL2w8lxA4orqmBnawMbGxs8+tOXEODrjezMNFwrFmBmdhZsOzscu28/2iVd\neOm//ogQvj+yUpJwMHuHTvuzGCXdiE6EtQ24VizAyNgYnJ2ckLs9Ey88/X1U1jViYmICLBaUgyJE\nBAeiqb0Dw6OjqKxvQlpiLCTSXnh7uGF7Wgr+eOo0egcG4eXuhgKBUHkiYdvbQzYlR3ffABwcHMBa\ndBckk8shrGtAckw05jAHJwcH7NqSAm8Pd8gYGgNCiGFV1NYzDl/e3dcPbw838Hx8MDM7A083N9jY\n2KCuuVXtadjOHin4/r6m3o1VSY2PUY64rHpxkRIfY+KSGUebuJPx5oPpV1hz0tzegZnZWdjZ2kDO\nAuxsbTAzO4vm9g5TF40YCePNkWC+I2VzTVwtFbfxUeH45XvvAzDf8hPrdc+OrRgaGcWEbBJtYglC\n+DywWG7Yv0O/CaTRsXFcLCjWuB7J3a6/7XR09TDexHcsSpSs1ZCWp6CG9JzcI/pzraRM+USn6n3w\nzYrbjO1y5qYEfPrNdxqvgj5yz16t2/Bzc8WPH3sIFbWNyrq0KSYSfm6uAADpwCDjk1o9/XcY1zc7\nO4tPv/kOG0ODcfzwAZy5cAWF5ZV45J69GBufQGRIEB6+Zy+iw0IxPT6isbxicEXFPpcv9FOHuflX\nXkP5PGxNSdT5vNQ7MKj2AE90SBC4To4QdXWjsKwSoYE8sFgslFTWwNWZi9KqGgDA9w8dQHgIHxMy\nmfIhHba9PbZv3oTOHimmcffVUFsbG2xLTcL45CSSoiPxydlv1bq/4rDZ2JmeistFJTh2336NJKbi\nFd/VJN4o6UaWVVXfhKLySsimZOD7+0Ha34ey6jqwWEBTewdKqqoh7u7F4/ffi/bOLsxhDv5enggJ\n5EHaPwBHBw5+/NjDqG5sxrfXCrF3azr6B4fQO3AHCRsj4OXuhvGJCXxx8SqAhT4fFkYS2ZeVAenA\nHTS1d8z/mjQ0gm2bN6G2qRU1za3wdHXF1MwMCsuEGBkdQ+72TOrbjRAjEXX14CdPPor+wSGMjI4h\nIykeQQH+mJmdQe/AHYyMjWNqehoOHDampmcYn4Zd6gLDnE1MyhifPNHWYe1609nTp9OvtOaGy3XC\n9NAw7O3swfPxxsDQCKamp8C1klFnCXC5uJTxhvZKscBsk1ZLxW2gn4/Zl59YrznM4cS5PAT4emNv\nVga+K7iJAoEQ/3DsAb1up1UswROHD0Ik6UZjmwjxkeEI4vmhSI9P1GWlboKkRwp7ji2cuY6YnprG\n7NwcslI36W0bwN2noGZmZjEpkyMiOBC2tjZmf35dr9zd3Zedp03chScOH0T/4BDmMIcAX28E8fxQ\n29SKf371Lbg6OyEpJgrCmgZ0dPcglOeHowdy0N3Xj6GRUWQmJ8DPyxPyKfWk2ZmLV1BV3wxbOxt4\nuLrgxNk8/NvTx/GLf3oalwuL8es/foTH778XOwE0tLYDADbHx+Che/bhs28uovR2LRrb2lFceRsF\nJeUI4fPQ0dWDHx17EP19A/in40dQWd+IvOtF8PP2RM62DDS1dcCF64QuaR8Kyyrx8jNPIYHh9cqm\ntg7Gp1hrm1tx/PABfHr+At58/yME+vsp780/z7sEYW0jhkZG4erMRVJMpLIfYHF3j/LczPPxRkxk\nON77+FMAUD6oUwAhfvjw/fjmWqFyoITmDjHaxBJEBgfiyQfuQ7ukCxOTMkj7BxDg64PObim4To6Q\nyeaPrb29PRzmZtHZ3cuYpJybA95/42XcXOhPb3EitbCskpJuCnFxcaYugknFxy/d2eG5y9eRFBMJ\nHw8PSAcGYAfA399fbZ6uri4AgIeHB7xcnODp7or7d2fBw8MDLe0dmJicRFRIEEbHxvBI7h7ERoXj\n2i0BHDj22JoyP/zuwOAw7tu9HcEB/riYX4S4yDBkJCfguxvFGJ+YhIebK2QyOQYGh7AnMw0ce3uI\ne6To7OnFlsRY2Nvbw9XVCS88fRzXbpWhpLIaDW0i1Da3IjE6Ats2J+P6LQHAAt75+c/wly/O4rlX\n38TJ3/5y2WNACFmbrh4p/u3p4xgZn0RdSxsaWtoRFsTH+MQEfL28UN/cjrLb9QgJ8EdoAA8Nre2M\nJ7d2C72IrG1qwRzUB4yws7NFbVOLqYtmFDvSktV+IVQkUR+9b7+JS7Y0dxdn+Hi4o0UkRlFFFaJC\nghAVGoyp6WlTF40YSZu4C9mZafinRx+Eh4cHBgYG8M4nnys7ozZHTHEbHRYClo0NOBwOTr/zf9HY\nTiMbWouNG817lGhVbeIuPHv8EVQ3tuBSYTGiQoNxOGc3GvX8dHHutkz84dRppMRtxD8/eQynzufh\n5PkL+Puj+kvuse3sEODrA2n/AAbuDMHd1QX+nh6wt9Pv7fSOzcno6O5BgI830hLjUFJZje6+fuxI\nS9HrdkyBzWabugg6O3f5OkbGx7ElIQ4nzn2LDRwO0uKj4eXhgb6BAfQNjiiTSQ/fswedPb1IjApH\n1uZklFXV4LqgAuGBAdiaugmFggpU1NQjLjIMzz99HAWl5Sgsr8L3dm9DYsxGVNbW46sr+YgOD8GH\np89BOtCPsEA+7gyNwNvDFUnRUSi7XY/njh/BdUEl/vLltwjh8/Dc8SNI3zSf90iJi8aDOdkYGB6C\nrQ0LSTGR2J2+GSE8H8RvjISzgwNqm9swhzmcvnAF8XEbUV5dBy93N+zdmo7ymjoI6xqQGhcDYX0j\ntqduwn27t6Ortw/enu74Nv8WuFxHNLeLUVnXgNjIMHCdHDE4PILqxmbERYaDxWLhxaePw8PDA8lR\nYcivuA0ft/lBGSqqqlFRU4+cbRnYkhiHW5XVuJB/E2PjE2ABcHPm4onDB3F/9jZ4enjgT3/7Elmp\nSZiUydEl7UNqfAwcOGx09/Xhg//7CpraRPjxf/xGmdAfGR1FZ48U25ITkZGShJtlQtysrMaPjx1W\nnuvzK27D3tYWyVuSUV5dB/n0FBKiwnHfnp346rurePsvp+Dn5Y7I4EDMzczA3dUFsik5UuJiIKiu\nxfDIGArLV5fIN2jS7Y033oBQKASLxcJLL72ExMRE5WeFhYX4z//8T9ja2mLHjh145pln1ry9z/Mu\nobSqBm3iLoTw/bE5IRYP5u5RdvJXWdeAxOgo5dNQ5y5fR0GZEG3iLoQF8pASH6NcPjIkEH7eXigW\nVmPv1jTUNLVAJOlBVmoSunv70NjWobaMSNKD7WmbIO6WorldjKjQIPh6eaJAIEQQzxexkWG4cKMY\nYYE8tembE2JRWVMPUU8f7t2ZibLqOmX5YyPD8F1BCbYmJ6CjuwfN7WKE8nlITYhB2e1adPffwT3b\nMyGorkWbuAvhwXzw/Xxwo6QCQTxfpMRF42pJKXanpSnniQgKwKaYjfjvjz+DSNKDHWnJynWH8P0R\nGxGGyzcFyM5IRU1Ty91jGReD/z5xGmJpv7Kcb/7vJwjh+2NTTBQ++PIsdqSmorymHl9cuKpc5sRX\n36BJ3IWcbemoaWrBmYvXlNu5kF8Mvp83gvn+KBLextlL1zUe8Tx2IAdP//tr8PWaHwFF0i3FttRN\nKCqvwsnzFxHC90dcZDiee/UthAX44bnjR/D2x59pxAAhRL8KK6rQ0zeAUyqPXrcsJF6ePnIYguo6\nDI+OolXcibbOLo3XxRXE3T1GLLX+OHOdlh15aj3rXPhFUpVMLkenmX+fvm4u+M3/3h2dqnlhNKuf\n/eBRE5eMGMvf378PfeNTePuvnyn7sdkcH4PczQmmLppWS8XtJ199i84eKRrbRMgruAlXrhMmZXI4\nbtiA0bEx+PvMD0IVEuCnU991ltbfnTFpu58w1HZuNzRhZ/pmdEl70dTegb1Z6ahubLaoa9ykyFCN\n2L1ys1Tvba64uwfPHT+C0tu1+K8PTiAkwB/PHT+CJobRGVfL2ZmLqrqG+T6n+gYAFgtT09NIiNZv\nDGxw3IBN0VEovV2LfIFQ2UaNLzrn6oPqvfBa+6xaiiKmhbX1SIopM/s3k65euw6ZTI7K2kZ89d11\nxIWHICYiDO+q3mPGxeCzby6iSdSJhMgwbAwLQZHwNk4s3J9ujotBdUMTfvnuHxHG5yElLhrChkZ8\n+d11RIcFY1N0FM58dx3/+cFJ5fy365tQ1yZCSIA/gvyn4e3pjptllbgzNIro8CD8/oO7/SinRIcD\nAN77+DO0d3bhnl3bAAAtIgkuFwkQEuAPj3hXVLd04K0/n1TGUUwoH9/cKMGm2GhlnP2/n3ym/Hxw\nZBRfXLwKno8XNsfHQCTuwtdXC7AjLRn/+/lXKqODBuD9T89ojA4KzOHarfL5PElstPK+XPH319cK\n8N8f392e/QZ7fH7+Mvbv2QX23AzeWZg/PJgPWxsbFFdUYWZ2FiLJ/A9mQ8OjePJf/wMh/Pk6XlZT\nh4v5NxEVzMem6Cjkl1fir2fzlMf0/zv5BRraxbh393Y4su1RLKzGqa+/U25/YmoKT7/0GsICeXjh\nh8chqKnDk//6H8qcSVVDM85ezlfOf+Tg6tpcgyXdbt26hfb2dpw6dQrNzc146aWXcOrUKeXnr732\nGv70pz/B19cXjz/+OHJzcxEREbHq7X2ed0mjQ2/Fe7eq05tEYpy/mo+f/t2jasPChgbyGDsEf+qR\nQ8qAys5MU7vBVF0mOzMNJ87maSyflZqEy0UlKFwYUeTDL85pTH/qkUOIjQrHux99qlF+1e0rpitG\nzdu/PQPvfHRqyW0+d/wI3laZZ0dasvLv7Mw0jScVCssq8ePHHsJ/f/yZxvSjB3IQvzGCsZyLt6O6\nLh9vL419UD0egtt12JqcyHgDJ5H2orOnV5mEYzoeinUBYCwDALO/KCHEknyedwn/c+I04iLDGOtt\nTWMLsjM248x3VwEAEmkvtqclMw5NHhFsmaPvKfpcUbV45Kn1TNtTNeb+tE1JdQPj91ZS04ADe3eb\nqFTEmPrGpzSvFcrnr2PM1VJxu4HDQd6NIgB3f7DMSk3C5ZulyEpNwolzechKTcKlopJl+66zxP7u\njGVxp+GK+4m3X35Br8kD1e1kZ6bhL1+cUw4Y9sdTX1jcNa6x2tyIIL7B6/WdwSHGbjL4fvrtm9aR\nzTZKG3Xu8nW8+f5HeuuzSpvFdae5o9MgdUef+sflat/BQ7nZjPeYRw/k4GLhLa2fP3v8EXx9o2g+\nZ7Doc0Ub3SruVLvv/ya/SPmdP3v8ETS2i/DCDx/Hl5duqNWlsOBg5To5bPaScSOR9qr9nZWaBBuw\ntM4vn5rCleJS5d8XCosh6rr7YyuHzYaNjQ3zvXtPLyTSXo19ZjoGqnHNnpvR+Fy1j7Wd6aka9a+w\nrFJ5DO/dlcX4HTz1yCFcLSmHI9t+yeOzuHyqOROm8q6UzaqW0kFRURH27p3vqyc8PBxDQ0MYHR0F\nAHR0dMDV1RX+/v6wsbHBzp07UVRUtKbtlVbVMn7xpVW1cFnUV4tMLsctYbXyb9VhZRfPV9PUyjjP\n4qFotS0/MSlTjuQhknTDhcvVmN7d14+K2kaN5QGgpqmVcb2YA8qr65fdZml1nfIzFy4XIkn3smWu\nqG3EYjK5HIMjo1rLWbowct/iZYS1jeju7Wf8THE8/L29lO+hL9YkEiMkgLfs8ejq7VdrDFQ/E1TV\nMq6bELI6pVW1cHZyRHdvP+PnbZ0SJMdFq03z8/Jc+AXsLg6bDV9PD4OV05DaO5lfRdM2fb0J4fsz\nTg/l8xinmwtt5xpt08n6o+16RfV6ydwsFbcP5e5Wa1sV14EAIJfPDzKlel14pVigdTuK/u5ULbeM\ntVB0Gq5KJpfjQv5Ng2xH9Tpd9fp98fbN/RrXWG2uMep1s6iDcRvNIv2+KmusNqqwrJJxO4rEm74Y\nq+7ok+p3kLgxUut3IpJ0Y0dastbPy6rrER0WsmyeQPF3TVMrvD3c1ZZ/8J492BQbrdYFwh9ffUlt\nmw/v37Nk3Dx6MFft75ytW5acPyslUX3+rHS0qWw/KjQIja0ixmPXJJp/409b3oRpe7/+lx9pLc/E\npAwuXO6Sx5Dv56M1R1DT1IonDx9ccn+P379fbf1LtbmrrYcGe9Ktr69PrW81Dw8P9Pb2gsvlore3\nFx4eHmqfdXQs32AJBMwn/bi4OLVAUNUmliA5NhoX8tWTeq1iCbzc3dDZI4WXu5v2m8eF+QCozaO6\nzFLLd/f2K7fT1tmFiOBAlFXXqk338fBA2W3NL9DL3U3rfo1PTqKnb2D5barsZ0RwINo6u5Yts+oy\nqpwdN6CyTjMht1Q528QS+Gi5qVYcD3t7O9jb2aKFYdQ7vp8PZFPTy25neGQUPf3Mx6NVLIFMJsPt\n27c1PktN1W0Y47XSFrvmul5TWW/7sxq6HANTxm18fDzaxBL03RlESlw042iVMeGhahehXu5uuFle\nhazUJGXH+37e86+MF1VU4ckHDqKuznxveBezsbFBZEgQ45N7kaHBKC8vx+zsrAlKZjybE2I1Ltg5\nbDZSE2KWjGFTxy7Pz1v7uUbLeWI9s7Y2V9F+MWkz02uF5eKW7++ncc2muA6USHuV13uKeZpFYtTX\n1yt/CFfgcrloETG/iqdtGVMyZuyy2WwIa+oZP6uorUdVVRXkDD9Ir2U7qtfpqtfvi7WKJRgdHUV9\nPXP5jBG72r4LY7W5q63XK91GR5eU8TNxt1TnbSwXt8bYF2B+cABt/Vi2iiVoaWnBnTvMo16uxGrr\njqnbXNXv4FDObnz0xXnG5ds6u/Czv38Cv/nDh8yfiyV4IDcbp/MuM36u2jYr5lfNW7SJJcjdngEA\nCA7wU15zR4WFqJXxYPYOvPjWu1rL8MzjD+PPp88q/3ZwcFgyzn748P3461ffKP/+6Q8exZ2REeX2\ng/z9MTQ6orVuOztxUaPSx/FyeQcPDw+t5enu7UdEcOCSuZas1GTcEjLXizaxBD/9u0dx4dW3tH7+\n3PeP4lLR3VhYqs1drh5qi12jDaQwNze35nUsVQFD+P6MN38hfB7KazRv5kL4POUImUvdPKrOpzqP\n6jJLLe/n7YmyhYxoSIA/BAv/V50uHRhASIBm+fvuDGJrciLjeh0dHBDC5y2/TZXyN7V3ICV247Jl\nVl1G1cj4hPZypjCXM4TPg52drcb0xcfj0L5dKKms0biBC+EH4MzCqKZLHQ8XZy42OLAZPwvl88Dh\ncIzWgDMx1LZNuU/6JhAI1tX+rIa5HQNtZVG0txscOMqnJxQ4bDY2xWzEH06dVk4bGRtH/MZw5F0v\nUo4YWLbwi9P+HVvh5ORkVvuti4FxOa4WCzT2fXvqJiQnJ5uwZMaRCoCF+aceFUPDb06IwQNm8pqT\ntngKC+QznmtC+QEmP08Ym7m1N8ai7XrRnK8Vlorb+uZW9N0ZVJtfcR2YlhCLkqoapMRFK68Lw4P4\nWjviDwtkvq5cahlTMEXsJsWUoZnhh5ZNMRuRkKC//gAV21G9Tle9fl8slM8Dl8s1y7gFjNfmGqNe\nhwfzGbcREcTXaRu6xq2x2qilthMWFqaXbQDGqzursdx1LgCcuXCF8f4XmL+XPXH2G+2f83k4f+UG\n/Lw9l71nV8yvmrcI5fPwyVffYmNQAOIiw1FUXgWZXI6Glja1bX785ddLfp8ff/m12t8DAwNLzv/X\nM3eTjCF8Hk6c/Xa+LrPn6/Ktytu4Z2eW1nv3b64VICI4kDFvwrS9AS25EMUxqm5sQVxkmNbPCwTl\niAwO0vodnDj7zZL7+8nZr9W+o+Xa3NXUQ4O9Xurj44O+vj7l31KpFN7e3oyf9fT0wMfHZ03b25wQ\ny/jq0uaEGAyPjmlMT0+6+xSeTC5X3jwuni82IpRxHtW/l1p+gwNH+Zh4EM8Pw6OjGtP9vDyxKTZK\nY3kAiI0MY1wvWEBK3MZlt7lZ5RWv4dFRBAf4L1vmTTGRGuXgsNlwc+ZqLefmuBjGdSXFRMLf24vx\nM8XxkMnl8HJ3xY60ZGQmJyKUH4DM5ETsSEuGDYuFYZVfVhOiIxjX5e/tiSCeP+NnqQkxGuUlhKye\nor29VixAVmqSst7u37EVz33/KOZYc0iIikAoPwDZGWn4hyOHsTl2o7Lt6eyRKtsoc+3PYzl7stLx\n82eeQk5WOsICA5Cz8Lc19Xv0QO4evPH8s/jtz57FG88/azYJt6X4eXsynmt8vT1NXTRiJNquF835\nWkFr3Hp54m95lzVuejY4cAAAbLY9AKhdF+5O136jkJ2RxnhsllrGWuRuz2Q8NjnbMgyyHdXrdNXr\n98XbN+e4BYzX5hqjXifHMt93JcXqNyFtrDYqKyWJcTtbUxK1LLE6xqo7+qT6HVTWN2JzPPN9bhDP\nD9dLyrV+nhK3EXUtbcvmCRR/x0aEonfgjvLv1LhoVNY34vA//Ru4To44djAXu9M3443/+VBtm2ev\n3MDmeC1xEx+DLxYeYFGs8/A//Zv2OIuPxrcL/YQqcglF5VXo6etX1mWuoyO83N203rv3DtzRmjfR\n2F5CzHx5tBzDqJAgeLq5IiwwQOsxFHdLteZMYiNCceLcBa25itS4aPztm0tq5TNEm2v7i1/84her\nWnIZ9vb2+OSTT3Do0CFUV1dDIBDg0UfnR6pxcXHBBx98gJ07d8LR0RFvvvkmnnrqKbi7u2tdX1dX\nF3g87X3FxEaEwYXrBAc2GwALKbHROJyzGw/m7kFKbDQ4bDampqexOyMNzx4/gl3pm+Ht7gY7WzsA\nLHi6uuBQzi5sWFg+PSkeGZsScL20Ag/dswfOjo5ol3Tj3p1Z8PP2xOzsnHIZBzYb7ZJuHNy9HT4e\nbpidAzKSEpCxKR5V9c1IjtmI/Tu24mpxGbYkxiEj6e70wzm7UdfYAkFNAx773n5s4HCU5d+/Yyu+\nKyzBvTuzlOtNjY3GoZzd6JH2o6KuEY/dtx8OC8tsSYhFVuomVNY1ITlmI+7fuxNXSkpx5J4c5Txs\nOzvcv4+5zCmx0cjdkYmLBSV4cH82nB0d1Y5lU6sIZbWNGuW8L3s78svK8cj+fcrtKJaprW+GsKEZ\nD9+7V219uTsycbW4DMkxUcjZPr/NzQkxGBufgK2tLVy5XCTFRqGpVYTZOWBLYhy2p25CdX0z7t29\nDVwnR7XjdLW4DGw7W3xv707GGDCl5WJX4f1Pz6x43U89cng1RTJLuh6nEAv5wwAAIABJREFU9cyc\njsFSZVG0t2x7e7SKJfD38cZ9u7chlOcFOzsWuDaALdsB9+/dgdmZWXAc2DiUkw0XrhOcNjiAhfkL\nmvv37cIDOdnG3TE9CgviY3dmGuJD+Xj44D0IC+Kbukgm0dDQYDZxCywduzHhobgzPIzBoRG1c42p\nzxOmYE7tjTEtdb1oSiuO2+hIOG2ww+wsC37eXpibnUVKXDTio8LBmmMhLjIcszMzyExJRGVdE1Lj\nYvDk4YNL/jAQFsRHkJ8vbGxsMDcHbI6PXXYZUzBF7Pp5ezLeT+j7hyPV7TS1d+DQ3l3g+XqjsqEJ\nD9+zF9xF1+bmHLeA8dpcY9Rr1W2wwELyCreha9waq42KCg1WuxdOiY3G0QM5eh+9VDWm5VNTyDZQ\n3VkpXa5zFd+BDYADe7ZrfCeK+1RbGxYOZGt+XlvfjOnZWWXOwN3ZCdMzs8hITsD39uxAj7Qfc4By\n/oaWNkzPzCr/5jjYw85m/vvZHh8JFpuDoeFReHm4gcf3QVp8rHKbzlwn5GzL0ChDq6gTEzK58u/R\nySn0D9zBjoQoJKgsr/i8RSTBpMr89U1tkE/PwMPFBclx0cq67LTBEeHBfI26rTgmqnkS1VzL4u1N\nTs+ht68f4eFhyEqO1/i8rUMCGxsbYG4O9+3dofG54hjazAHf26eZB6hrbMHM3Bwc2Gzs37WV4fhI\nMCGT380FcRZ+tFLJmeijHrLm9PHepxZvvfUWSktLwWKx8Morr6CmpgbOzs7Yt28fSkpK8NZb8+/W\n5uTk4Ic//OGS61rJo+STk5NwcHBYc/ktlUwmA2chYKyVOR0DXWM346EnVrzum58x9yFgiaz1VSdV\n5nQMdC2LOdU1UzGn780UzG3/KXZ1Y27fmymYUwxQ3OrO2mN3dHQUXC7X1MUAsLLvwtpj19rjFgCq\nqqpM/kqpArW5urP2Y6CP/TfY66UA8Pzzz+PkyZM4ceIEoqOj8cADD2Dfvn0AgLS0NJw6dQqnTp1a\nNuG2UtXV1cvPtI5ZW0fQTOgYEGIcVNeIpaLYJZYYA5ZYZqJf2gZNMHcUu0QfA44YG8UtHQN97L9B\nk26EEEIIIYQQQgghhFgjSroRQgghhBBCCCGEEKJnlHQjhBBCCCGEEEIIIUTP7ExdAELI6ln74AuE\nEEIIIYQQQoi5MujopfokEAhMXQSyThl6JCGKXWIIFLfEUlHsEktEcUsslSFjl+KWGAq1ucRSMcWu\nxSTdCCGEEEIIIYQQQgixFNSnGyGEEEIIIYQQQgghekZJN0IIIYQQQgghhBBC9IySboQQQgghhBBC\nCCGE6Bkl3QghhBBCCCGEEEII0TNKuhFCCCGEEEIIIYQQomeUdCOEEEIIIYQQQgghRM8o6UYIIYQQ\nQgghhBBCiJ5R0o0QQgghhBBCCCGEED2jpBshhBBCCCGEEEIIIXpGSTdCCCGEEEIIIYQQQvSMkm6E\nEEIIIYQQQgghhOgZJd0IIYQQQgghhBBCCNEzSroRQgghhBBCCCGEEKJnlHQjhBBCCCGEEEIIIUTP\nKOlGCCGEEEIIIYQQQoieUdKNEEIIIYQQQgghhBA9o6QbIYQQQgghhBBCCCF6Rkk3QgghhBBCCCGE\nEEL0jJJuhBBCCCGEEEIIIYTomcUk3QQCgc7zVldXG7Ak5s/a9x8wr2Oga+yaU5lNwdr3HzCvY0Bx\nqztrPwbmtv8Uu7qx9v0HzOsYUNzqztqPgTntP92f6c7a9x8wr2NAba7urP0Y6GP/LSbpthKTk5Om\nLoJJWfv+A5Z5DCyxzPpk7fsPWOYxsMQy65u1HwNL3X9LLbe+WPv+A5Z5DCyxzPpm7cfAUvffUsut\nL9a+/4BlHgNLLLO+Wfsx0Mf+r8ukGyGEEEIIIYQQQgghpkRJN0IIIYQQQgghhBBC9IySboQQQggh\nhBBCCCGE6Bkl3QghhBBCCCGEEEII0TNKuhFCCCGEEEIIIYQQomcGTbo1NDRg7969+Otf/6rxWWFh\nIR566CEcOXIE7733niGLQYhFsLe3N3URiIlxuVxTF4EQqxEfH2/qIhCyYhS3xFJR7BJCrJWdoVY8\nPj6OV199FZmZmYyfv/baa/jTn/4EX19fPP7448jNzUVERIShikPWSFjbgLwbRaisa0BidBRyt2ci\nKSYKp/MuoaSqBm3iLkSGBMLP2wsFAiFCAvyQEh+D0oXPIoL54Pn6oLCsEttSk9DR3YPmdrFymaLy\nKmQmJ6C7tw+NbR0I4ftjc0IsHszdY+pdNwrFcfzV/55EeDAfgX6+uFFajkB/X7g5czE8OgZnrhPG\nxsaxKS4awrpGNLWKEOD3/7N35+FNXfe+8L+yseRBtrHxKMvzPGPLDhgDBocpgbSQpIHMvSdpbp+k\nyW0b2rflvGl4m5CmzWl7mzSnJ21yc1reDKQJoSEkMQnzYCYZPGEbz7Y8T4DNYDH4/gFSJGtLlm0N\n2/b38zw8j7f23mv91rC39lpIWoGICVfC3V2KK1eH0djahrbOHsRGKqFKS8ba5UttFuOeI8ex99hJ\nNLS2IyY8DNlpSSipqEJDa/uUa6/P9x7EkZJSNGk6EKUMRX52JtYULh5XGju+3ofjpRX6NHLSU3C6\nohr1rW3jTtO4bhUonJ+LO/PnTaRoRDSGT4r26N+bptq9i2Yu9luaqhzVdx2RD69DMsfcWNmcifSl\nsfJwRAyTMXrsNC8zzeJY1fD4aKUCqvRko7Hn6PPHKo81Y1nD7YmMD82x26SbVCrF3/72N/ztb38z\n2dfa2gpfX1+EhoYCAAoKClBcXMxJN5EqrTqH5156DcNaLQCgrkWDXfsP47nHN+D1v3+of71R0waZ\nVIp8VSZGAMF9G1avwPs7vxJ8/cNdu41eP1pSBgDT/s1se9Ee/MlMPe4tPqn/+8sDR7Fh9Qqjem3Q\ntOFE2Vk8uGYltu362uj1gydPA4BNJt72HDmOX7/5tlGMh9VnkK/KRKOmbUq11+d7D+K1t7cK9jVr\nb6w7vt6HP777vkkaQvUxVprCdVsKAFNi4u2Z37w+7nOOffx3O0RCNLZPivaYvDdNlXsXzVzstzRV\nOarvOiIfXodkjrmx8usv/Exw0msifWmsPBwRw2SYGzsBwmNVoeOPlJQKjrXWLl86ZnmsHcsKpR/q\n6zXp8tvt66WzZs2Cu7u74L6enh74+/vrt/39/dHT02OvUGiSig4X6zuoTqD/bJwqrzJ5fVirxdVh\nLa5fv2GyDwBa2jsFz2lp7zQ5dlirhbq8ygYlELeTZurxytVhyKRSo7/N1V9zW4dJusNaLU6UVtok\nxr3HT1mMUbc9FdrraEmZYFl0N1ZrHC+ttKo+rEnTXN3uO662Oh4iso65962pcO+imYv9lqYqR/Vd\nR+TD65DMERorD2u12H34mODxE+lLY+XhiBgmw9zYydxY1dqxlu78scpj7VhWaCzn6uo6kSIbsdsn\n3exBrbZ+EDqeY6cjW5VfKpWi9GyNyev5qiycKK0QPKejuxdBc/xNXg/wm40mgckhAGhq60CA32y0\ndXUbvd6oacfly5dRVTX+G4A1daBSqcad7kSYiyU0NBRNmnbBfZ09ffo66ezpQ1xk+Ljrr0nTjpaW\nlklNasvlcjS0tI0ZI3Crvbq6uqDRaCaUl72vWz8/PzSaqe9GTTsaGhowMDBgMY3AwECr2syaNC3V\nbX2LBjU1NRgaGjLZ5+x+K9Z0nWk6lmk8rC2/M/tuWlqa2Wu3UdOO4eFhVFQIv69NVzO93wLif1Zg\nvxU20/uumO655mJxVN91RD5KpdJiHtY++870fguI/5473uPMjZUB4ExVDcrLy6E1mOyZSH8dK4+a\nmppxxTCR/jyZvmtp7NQoMFYd71hraGjIYvoXL160eiwrlL6Pj8+k77lOmXQLCgpCb2+vfrurqwtB\nQUFjnmftBahWqx12sYqRrcufmVyC+lbjjnpEfRrxURFo1Jh24NCgAMwSmBHuHTiPvLnpgudEhYXi\n6GnTTwVFKxXw9PQcd3nE1gcsxRKlDBWsk5DAOSiprNb/XVnbgOyUxHHVX5RSgYiICEREREwieiAm\nXDFmjMCt9goODkZwcPC483BUm5mr72ilAjExMZNKQ6g+xkrTXN3GRiiRmJhoVTz2Yq/2ENO1aQti\nu984mhjLby4eS9e/TCYTXTnsSYzt5mhiqwP2W+uIrd0cTWzln8gzrq37riPysZSHNc++Yms3ZxBb\nHVgTizUxC42VAWBuciLS09NNXp9If7WUR2JiIjKTE20Ww+j+bIt2s5Sf0Fh1PGMtuVxuMX0fHx+r\nx7JC6V+8eHHS5bfr6qXmKJVKDA0NQaPR4Pr169i3bx/y8/OdEQpZYeWiPP3HLHV6+s8jJz3F5HWZ\nVAp3mRSzZrma7AOAyLBQwXMiFCEmx8qkUqjSk21QAnHLNVOPHu4yDGu1Rn+bq7/IsFCTdGVSKe7I\nTLVJjIXzcy3GqNueCu2Vn50pWJYF2RlWpzEvM82q+rAmTXN1u3SeeB5KiKYLc+9bU+HeRTMX+y1N\nVY7qu47Ih9chmSM0VpZJpVixcL7g8RPpS2Pl4YgYJsPc2MncWNXasZbu/LHKY+1YVmgsd+PGjYkU\n2Yjr5s2bN086FQEVFRV4/vnnceLECZSXl2P37t24cOECent7ERsbi4SEBGzevBnbt2/HqlWrUFhY\naDG9jo4OKBQKq/Iez7HTka3LHxI4B9kpSZBJpbh2/TqWzs/Fjx5dj6Xzc+Er97rdgSWYl5mG+XPT\nUV5TD38fb6xdsQTut/fdkZGKBVkZOHq6HKuXLESQ/2zcHIH+nKOny3HX4gUICZyDmzdHkJ2ShHUr\nlk74hxzF1AfGiiU5LsaoHu9IT0G+ai7Ka+pu/e9EQiyuaa8jNSEGAwMX8N3lS+Dj5YWbIzeRnhiH\nRTlZcHeXIjEmCr7eXnB1dUVuegruXVlos9VLYyKUiAgJhouLC0ZGbt3Y1i5fgq7uPowAk24vwHFt\nlhAdiUC/2ZjlOguABNkpSdiwesW4VqdJio2Gn483pG5u+jTWrViK7p4+jIxgXGma1G1aCr6/bo3T\nF1Gwtj3e/mjHuNN+8oF1EwlJtMR0v3EGsZXfUjwpcTHwkXvp35tsce+aqsTWbs4gpjpgv7WemNrN\nGcRU/rFicVTfdUQ+k81DTO3mLGKqA2tjseY4c2NlcyuHTqQvjZWHPWOwRbsJjZ3uX3Wn2bHq6ONV\nKUlYu2Kp0djT8PyxyjPWWFYofd1Yzhbll4yMjIxMKgUHGc/HGsX20VVHm+nlB8RVB9bGUlZWhowM\n6z9tNd2Iqc2cpaamxulfKdWxtj3m3//4uNOebquXzvS+K7byWxvP8PAwZDKZAyISJ7G1mzOIqQ7Y\nb60npnZzBjGVfzyxzPS+K6Z2cxYx1YG1sYgpZmeZ6XVgi/I75eulRGTq2rVrzg6BnExo0QQiso+Z\n+OPzNPWx39JUxb5LRDMVJ92IiIiIiIiIiIhsjJNuRERERERERERENsZJNyIiIiIiIiIiIhvjpBsR\nEREREREREZGNcdKNiIiIiIiIiIjIxjjpRkREREREREREZGOcdCMiIiIiIiIiIrIxTroRERERERER\nERHZGCfdiIiIiIiIiIiIbIyTbkRERERERERERDbGSTciIiIiIiIiIiIb46QbERERERERERGRjXHS\njYiIiIiIiIiIyMY46UZERERERERERGRjnHQjIiIiIiIiIiKyMU66ERERERERERER2Rgn3YiIiIiI\niIiIiGxslj0Tf+WVV1BaWgqJRIJNmzYhIyNDv++9997DZ599BhcXF6SlpeHf//3f7RkKERERERER\nERGRw9jtk24nTpxAc3Mztm3bhi1btmDLli36fUNDQ3jnnXfw3nvv4YMPPkB9fT3OnDljr1CIiIiI\niIiIiIgcym6TbsXFxVi2bBkAIDY2FhcuXMDQ0BAAwM3NDW5ubrh8+TKuX7+OK1euwNfX116hEBER\nEREREREROZRkZGRkxB4Jv/DCCygoKNBPvD300EPYsmULoqOjAQCfffYZXn75ZchkMqxevRq/+MUv\nLKanVqvtESbNcCqVyu55sO+SrYmp3z7zm9fHnfabv3xu3OfQ9CCmvktkLfZbmqrs3XfZb8keeM+l\nqcpc37Xrb7oZMpzbGxoawltvvYWvvvoKcrkcjz/+OKqrq5GUlGQxDWsvQLVa7ZCLVaxmevkB8dWB\nNbGILWZHm+nlB8RXB/aKRUxltAWxtZujibH8vOeObaaXHxBfHbDfWmem14HYys/xmXVmevkB8dUB\n77nWmel1YIvy2+3rpUFBQejt7dVvd3d3IzAwEABQX1+P8PBw+Pv7QyqVIicnBxUVFfYKhYiIiIiI\niIiIyKHsNumWn5+PoqIiAEBlZSWCgoIgl8sBAGFhYaivr8fVq1cBABUVFYiKirJXKERERERERERE\nRA5lt6+XZmdnIzU1FRs2bIBEIsGLL76I7du3w9vbG8uXL8cTTzyBxx57DK6ursjKykJOTo69QiEi\nIiIiIiIiInIou/6m28aNG422DX+zbcOGDdiwYYM9syciIiIiIiIiInIKu329lIiIiIiIiIiIaKbi\npBsREREREREREZGNcdKNiIiIiIiIiIjIxjjpRkREREREREREZGOcdCMiIiIiIiIiIrIxTroRERER\nERERERHZGCfdiIiIiIiIiIiIbIyTbkRERERERERERDbGSTciIiIiIiIiIiIb46QbERERERERERGR\njXHSjYiIiIiIiIiIyMY46UZERERERERERGRjnHQjIiIiIiIiIiKyMU66ERERERERERER2Rgn3YiI\niIiIiIiIiGyMk25EREREREREREQ2xkk3IiIiIiIiIiIiG+OkGxERERERERERkY3Nsmfir7zyCkpL\nSyGRSLBp0yZkZGTo93V0dOCnP/0prl27hpSUFPz617+2ZyhEREREREREREQOY7dPup04cQLNzc3Y\ntm0btmzZgi1bthjtf/XVV/Fv//Zv+Pjjj+Hq6or29nZ7hUJERERERERERORQVk26HThwAP/6178A\nAM8//zxWrFiB3bt3WzynuLgYy5YtAwDExsbiwoULGBoaAgDcvHkTarUahYWFAIAXX3wRCoViwoUg\nIiIiIiIiIiISE8nIyMjIWAetX78ef/nLX1BeXo4dO3bg5Zdfxg9/+ENs3brV7DkvvPACCgoK9BNv\nDz30ELZs2YLo6Gj09vbi4YcfxqJFi1BZWYmcnBw8//zzFmNQq9XjLBrR2FQqld3zYN8lWxNTv33m\nN6+PO+03f/ncuM+h6UFMfZfIWuy3NFXZu++y35I98J5LU5W5vmvVb7q5u7vD398fBw4cwHe/+114\neXnBxWV830w1nNsbGRlBV1cXHnvsMYSFheGpp57C/v37sWTJEotpWHsBqtVqh1ysYjXTyw+Irw6s\niUVsMTvaTC8/IL46sFcsYiqjLYit3RxNjOXnPXdsM738gPjqgP3WOjO9DsRWfo7PrDPTyw+Irw54\nz7XOTK8DW5Tfqpmz4eFhvP322zh06BDy8vLQ1NSEwcFBi+cEBQWht7dXv93d3Y3AwEAAgJ+fHxQK\nBSIiIuDq6oq8vDzU1tZOohhERERERERERETiYdWk20svvYSuri785je/gUwmw+HDh7Fx40aL5+Tn\n56OoqAgAUFlZiaCgIMjlcgDArFmzEB4ejqamJv3+6OjoSRSDiIiIiIiIiIhIPCx+vbS1tRXAra+X\nPvbYY/rXCgoKxkw4Ozsbqamp2LBhAyQSCV588UVs374d3t7eWL58OTZt2oRf/OIXGBkZQUJCgn5R\nBSIiIiIiIiIioqnO4qTb448/bnafRCLBnj17LCY++tNwSUlJ+r8jIyPxwQcfWBMjERERERERERHR\nlGJx0m3v3r2OioOIiIiIiIiIiGjasDjp9vOf/9ziyb/73e9sGgwREREREREREdF0YHHSLS8vz+w+\niURi82CIiIiIiIiIiIimA4uTbuvWrRN8XavVYuPGjVi7dq1dgiKaiTw9PZ0dAhHRjJGWlubsEIjG\njf2Wpir2XSKaqSxOuuns2LEDr776Ki5cuAAAcHFxwfz58+0aGE09O77eh+OlFWjSdCBKGYrc9BTU\nt7ShsrYey/LnobK2Hu3dfbi7IA9nqs7pj1OlJqPoyDFkJSeis6cXtU2tiAlXIDstGafKz6KlvQv5\nqkyjfYXzc3Fn/jxnF9kmSqvO4URZBTSd3aj961ZEKkLh6yPH4NAlqNJTIHOR4HhFNRpa2jA/Kx09\nff3w9HDH+cEhNLd1ICw4CHGR4fCRe+H8xUE0tGqg6exBbKQSWSmJuG/lnfh870EcKSlFk6YDSTGR\niIlQoqy6Fm2d3YiPjsAd6SlYXbhYH9MnRXtwqvwsmjQd+raob9agrPocMpISsHJRHjKTExxSN0WH\nih2eLxFNX4b3tyhlKHLSU3DfyjudHRaRRey3NFU5qu86Ih/D5+koZSjyszOxxuD5earlQ85jj/46\n3n7j6PHeZMs81vmT3W/P686qSbetW7di586d+OlPf4q33noLO3fuhLe3t00CoOlhx9f78Md338ew\nVgsAaNS04WhJGfJVmZiXmYa/bfsUw1otnnnkAfznex+bHPfso+vxxtZt+tejwxV4/e8fYlirRWFe\nLrbt2m10zmF1KQBM+Ym30qpz+Pirb3Dw5Gl9+Rpa2yCTSpGvysTpymr9vsK8XPzzy2+Qr8rEFweO\nGh1/oqwSD65ZiQ9H1dOhk6cBQF+XwK26ffujHd+er2nD/uNqjABYU7gYnxTtMTnecLuuRYNd+w/j\n9Rd+ZtcJsNKqc3jupdccni8RTV+j72+69yAAnMAg0WK/panKUX3XEfl8vvcgXnt7q2AetpwQc1Q+\n5Dz26K/j7TfjGe/ZwmTLPNb5k91vqf5Cfb0mXX4Xaw7y9vZGYGAgbty4AU9PT6xfvx6ffPLJpDOn\n6eN4aaW+k+oMa7WQQIKW9k4Ma7UI9PfD2bpGwePUldWQSaUAAJlUiitXhzGs1Rr9PfqcfcfV9i2U\nA+wpPmG2fFeHtRjWXjOqBwCCxwNAc1uHcN2WVwnW7ejjjpVWAABOlVfp91s6fvfhY5Mo+diKDhc7\nJV8imr4M7286uvskkVix39JU5ai+64h8jpaUCeahG5hPtXzIeezRX8fbb8Yz3rPFb/lPtsxjnT/Z\n/Zbqz9XV1aoYLbFq0s3V1RX79u1DaGgo3njjDXz55Zdoa2ubdOY0fTRp2gVfl8mkaGrrAABkpSSZ\nPa5J0464yHAAQIDfbHT29Jn8PVp9i2ayYTudpqML7V09gvs6unsxrL0G4Nt6MFcfAX6z9fU8mrm6\nHa3xdn0atpGl40urz5kplW2UVQmnb+98iWj6Mvce1GjmdSIxYL+lqcpRfdcR+ZhLy9ZlcVQ+5Dz2\n6K/j7TfjGe+5ublNOC6h/KyJb7znT3a/pfrz8fGxKkZLrJp0+93vfoeQkBBs2rQJ3d3d+Oyzz/Cr\nX/1q0pnT9BGlDBV8fXhYi0hFCADg9NlqRIUJHxelVKCuuRUA0DtwHiGBc0z+Hi02QjnZsJ1OGRKM\n0OBAwX2hQQGQSW/d5HT1YK4+egfO6+t5NHN1O1r07fo0bEtLx2cm2fcrnhlm0rd3vkQ0fZl7r4pW\nKhwcCZH12G9pqnJU33VEPtOpLORc9mjj8aY5nvHetWvXJhzXROMb7/n23H/x4kWrYrTEqkm3nTt3\nIjk5GXPmzMFLL72Ev/zlLzh16tSkM6fpY15mmv4rjDoyqRQjGEFkWChkUil6+geQEh8jeJwqNUn/\nkc5hrRYe7jLIpFKjv0efs3Seyr6FcoA7F9wBTzPlc5dJIZO6GdUDAMH6AG5NrgnWbXqyYN2OPm5+\n5q1VpXLSU/T7LR2/YqF9F1NZuSjPKfkS0fRleH/T0d0nicSK/ZamKkf1XUfkk5+dKZjHguwMm+Xh\nyHzIeezRX8fbb8Yz3hsZGZlwXEL5GaZvbZnHOn+y+y3V340bN6yK0RKLCykcO3YMx44dw2effaZf\nuRQArl+/ju3bt+O5556bdAA0PaxdvhQAcKK0Eo2adkQrFchJT0ZDazuOl1XiB+vX4WxtA745ehJP\nP3w/Sqtq9cdlpyah6MgxbFi9Ap29fTjX2AIXSPDc4xugLq9Cc1un0b7YCCWWzlNN+UUUAOgXBIhQ\nhKKtqxu1jc2ICFPA19sLQ0OXkZWahPnpyThRWYP6Fg0euGsZevr6cXfBAlwYvISmtnYoQ4IRF6GE\nt9wLG9asRGOrBprObsRFKJF5e/VSmZsbjpaUoVHTDnepFE8+sBblNXXQdHYhIToSuWnJ+tVLdT9m\nqS6vQqOmXd8WDS1tKK0+h8ykBKxYON/uixlkJifg9Rd+ht2Hjzk0XyKavkbf36KVCqjSk/lj9CRq\n7Lc0VTmq7zoiH92P0euep6OVCizIzrD54gaOyoecxx79dbz9ZjzjPbV68r+jPtkyj3X+ZPdbqj9b\nlF8yYmHqsru7G8ePH8cf//hH3Hvvvd+eJJEgJycH8+Y5btJDrVZDpbLuk03jOXY6munlB8RVB9bG\nUlVVheTkmfu/1mJqM2cRUx1YG8v8+x8fd9rHPv77REISLTG1mzOIrfzWxjM8PAyZTOaAiMRJbO3m\nDGKqA/Zb64mp3ZxBTOUfTywzve+Kqd2cRUx1YG0sYorZWWZ6Hdii/BY/6RYUFIR77rkHWVlZUCpv\n/d6TVqtFX18fQkOFv/dKRBNz+fJlZ4dARDRjVFRUzOiHSJqa2G9pqmLfJaKZyuKkm86uXbvg6emJ\n733ve7j33nvh5eWF/Px8/PjHP7Z3fERERERERERERFOOVQsp7Nu3D4888gi+/PJLLF26FP/85z9R\nUlJi79iIiIiIiIiIiIimJKsm3WbNmgWJRIKDBw9i2bJlAICbN2/aNTAiIiIiIiIiIqKpyqqvl3p7\ne+Opp55CZ2cnsrKysG/fPkgkEnvHRkRERERERERENCVZNen2+995SA0ZAAAgAElEQVT/HkePHkV2\ndjYAQCqV4re//e2Y573yyisoLS2FRCLBpk2bkJGRIZj2mTNnsHXr1nGGTkREREREREREJE5WTbq5\nuroCuPXbbiMjIwCAjo4O3H///WbPOXHiBJqbm7Ft2zbU19dj06ZN2LZtm9ExdXV1OHnyJNzc3CYa\nPxERERERERERkehYNen2xBNPwMXFBWFhYUavW5p0Ky4u1v/+W2xsLC5cuIChoSHI5XL9Ma+++ip+\n8pOf4M9//vNEYiciIiIiIiIiIhIlqybdrl+/jg8//HBcCff29iI1NVW/7e/vj56eHv2k2/bt23HH\nHXeYTOQRERERERERERFNdVZNusXFxWFgYAB+fn4Tzkj3tVQAOH/+PLZv3453330XXV1dVqehVqvt\ncux0NNPLD1hXByqVygGRWN8eM73dZnr5ganZb8WSrjNNxzKNh7XlF1vfZbvN7PIDU/Oey3ZjHYjp\nnsvxmfVmevkB3nOnqpleB5O951o16dbZ2YkVK1YgNjZW//tuAPDee++ZPScoKAi9vb367e7ubgQG\nBgIAjh07hv7+fjz88MPQarVoaWnBK6+8gk2bNk2oEKOp1WqHXaxiNNPLD4ivDqyJRWwxO9pMLz8g\nvjqwVyxiKqMtiK3dHE2M5ec9d2wzvfyA+OqA/dY6M70OxFZ+js+sM9PLD4ivDnjPtc5MrwNblN+q\nSbennnpq3Ann5+fjjTfewIYNG1BZWYmgoCD9V0tXrVqFVatWAQA0Gg1++ctfjjnhRkRERERERERE\nNFVYnHS7efMmACAnJ2fcCWdnZyM1NRUbNmyARCLBiy++iO3bt8Pb2xvLly+fWLRERERERERERERT\ngMVJt5SUFEgkEpPXR0ZGIJFIUFVVZTHxjRs3Gm0nJSWZHKNUKrF161ZrYiUiIiIiIiIiIpoSLE66\nVVdXOyoOIiIiIiIiIiKiacPF2QEQERERERERERFNN5x0IyIiIiIiIiIisjFOuhEREREREREREdkY\nJ92IiIiIiIiIiIhsjJNuRERERERERERENsZJNyIiIiIiIiIiIhvjpBsREREREREREZGNcdKNiIiI\niIiIiIjIxjjpRkREREREREREZGOcdCMiIiIiIiIiIrIxTroRERERERERERHZGCfdiIiIiIiIiIiI\nbIyTbkRERERERERERDbGSTciIiIiIiIiIiIb46QbERERERERERGRjXHSjYiIiIiIiIiIyMY46UZE\nRERERERERGRjs+yZ+CuvvILS0lJIJBJs2rQJGRkZ+n3Hjh3DH/7wB7i4uCA6OhpbtmyBiwvnAImI\niIiIiIiIaOqz2yzXiRMn0NzcjG3btmHLli3YsmWL0f5f/epXeP311/Hhhx/i0qVLOHTokL1CISIi\nIiIiIiIicii7TboVFxdj2bJlAIDY2FhcuHABQ0ND+v3bt29HSEgIAMDf3x8DAwP2CoWIiIiIiIiI\niMihJCMjIyP2SPiFF15AQUGBfuLtoYcewpYtWxAdHW10XHd3Nx5++GF89NFH8PPzM5ueWq22R5g0\nw6lUKrvnwb5LtiamfvvMb14fd9pv/vK5cZ9D04OY+i6Rtdhvaaqyd99lvyV74D2Xpipzfdeuv+lm\nSGhur6+vDz/84Q/x4osvWpxw07H2AlSr1Q65WMVqppcfEF8dWBOL2GJ2tJlefkB8dWCvWMRURlsQ\nW7s5mhjLz3vu2GZ6+QHx1QH7rXVmeh2Irfwcn1lnppcfEF8d8J5rnZleB7Yov92+XhoUFITe3l79\ndnd3NwIDA/XbQ0ND+MEPfoAf//jHWLhwob3CICIiIiIiIiIicji7Tbrl5+ejqKgIAFBZWYmgoCDI\n5XL9/ldffRWPP/44Fi9ebK8QiIiIiIiIiIiInMJuXy/Nzs5GamoqNmzYAIlEghdffBHbt2+Ht7c3\nFi5ciB07dqC5uRkff/wxAGDNmjVYv369vcIhIiIiIiIiIiJyGLv+ptvGjRuNtpOSkvR/V1RU2DNr\nIiIiIiIiIiIip7Hb10uJiIiIiIiIiIhmKk66ERERERERERER2Rgn3YiIiIiIiIiIiGyMk25EIpGS\nkuLsEIiIZoy0tDRnh0A0buy3NFWx7xLRTGXXhRRoaimtOoeiQ8Uoqz6HjKQErFyUh8zkBADAniPH\nsffYSTS1dWKhKhPd/f2ob9YgXzUXrZ1dqG/WIDZSifCQYBxRn0FaQhxiI5UoqahC98B5rMqfj1OV\nVeg9f0H/d5OmA7GRSihDglB8ugLL8+9AZW09mjQdiFKGIic9BZXVtahqbNGnfejkaYQrgpGTmox9\nJ08gIiTMKM6p6NOv96Gs6hzcZVIMXBxES3snYiOUkHt6of/8AKLDlfD28kRtcyvONTQjLCQIGUnx\nqG1qQW1TKyLDQpAaH4tr16+jtqkFrR1dWJybjZs3b6KupRXtnT2IiwyHIjgAwYEBiFGGmW1nQzu+\n3ofjpRX69piXmYa1y5c6oYbEGQ8RTU2fFO3BqfKzRu81962809lhEVnEfktTlaP6riPymU5lIdsy\nbLOYcAWy05KhLq9Co6YdUcpQ5GdnYk3hYsHjzbWx4dgnIyEWCbFRFs8RSvPsuXqcrW8SHDsJHR/g\nPgt/+9dui2NEHcO5g7kpSYgJV+CkQXrjHasZxpMQqURGSqJ+O1qpgCo9GSUVVWhobf+2fDV1ONvQ\nbHL86DZIjo5AalK8yX5derpte113nHQjALcumudeeg3DWi0AoK5Fg137D+P1F36G3v4B/PrNtzGs\n1aIwLxcf7tqt//v9nV/pz2nUtEEmlSJflYntu/fq/16ZPx+vb92GYa0WG594VP+34TlPPrAWf9v2\nqdHrR0vK8KNHH8AXh4qN0t5bfBJHS8rw3KPr8do7W/VxTsWJt0+K9uD1v394q1zHTunL39D6bXnb\nurpx8OTpb/dp2nCirBL5qkw0atrQqGlD8elyLM7Nwr5jp1CYl4vWjk6Tc2RSKR5csxL/+933BdvZ\nsP52fL0PfzQ4TtceAJwy0SW2eIhoatLdc4XuJRzQkFix39JU5ai+64h8plNZyLZGt1l0uMJsG64p\nXGxVG48e+9y/stDiOebSfPrh+7HrwBGTsZO54597dD3qWjRGY0Qho+cO8lVz8Scz8VkzVhsdz91L\n8k3iO1JSajT+PVpShmcfXY9dB4+aHD+6DdYuL7C431KbRQXMHjP+sfDrpQQAKDpcrO9kOsNaLfYW\nn8S+E2oMa7WQSaW4cnXY5O/R51y5OgyZVIphrRa+3nKoK6sxrNUiKSZK/7chmVSKs3WNgmmVVNYg\nJjxMMO1TldXISUvGsFaL3YeP2aFW7O/M2RoAMFuXw9prt/+Zr2fd9tDlK/CRy3H9+g0MXb4ieE5L\ne6dJDEL1d7y0UvD8E6WVEyvoJIktHiKamk6VVwneS9TlVU6KiGhs7Lc0VTmq7zoin+lUFrItwzaz\nNEbWTeJY08aGY5/FuVk4JTCGNjzHXJpnqmqhDAnSb+vGTuaOP1VZjU/feFW/vfvwMUgkEpMyG84d\n+MjlaGnvnNRYzTCeQH8/s3MDo8e/6spq5GWlGx0/ug2UIUE4U1Vrdr+lNlOXV8Hd3d2qMljCSTcC\nAJRVnRN8XffVUQAI8JuNzp4+k79H6+zpQ4DfrRnh+MgINGnaAQCrly7S/20oLjJc8HUAaNK0Y1n+\nfMG0mzTtuP+u5QCA0mrh+MWuoUVjsS7bu3owrL0muM+wLnTbcZHhGNZeM5teU1u70Tk6o+vPXHs0\nmnnd3sQWDxFNTbyX0FTEfktTlaP6riPymU5lIdsybDNL4zpdG1rTxobHPHjPXWOeY2ksna/KGtfx\n/v7++u3S6nNwc3MzOc5w7iAuMhxNbR0W4xuLYTxZKUlm4xs9/m3StGP96pUW2yBflWVx/1htFhoa\nalUZLOGkGwEAMpKEv5oZHhKMmIhbnzTrHTiPkMA5Jn+PFhI4B70D5wEAtc0tiAq71VF37Tuk/9tQ\nXXOr4OsAEKVU4Jsj334KyzDtKKUCH3/5NQAg00z8YhcdHmaxLhXBgZBJTW90gHFd6Lbrmlshk7qZ\nTS8qTGF0js7o+otSCrdHtFIh+Lq9iS0eIpqaeC+hqYj9lqYqR/VdR+QzncpCtmXYZpbGdbo2tKaN\nDY/5YOeXZsfKY6UZpVTgiPr0uI7v7+/Xb2cmJeDaNdMPgBjOHdQ1tyJSEWIxvrEYxnP6bLXZ8o4e\n/0YpFdi2q8jo+NFtcER92uL+sdqso0N4QnE8OOlGAICVi/L0H9XUkUmlKMzLReG8HP1XOj3cZSZ/\njz7Hw12m/wrqhcEhqNKSIZNKUd3QpP/b0LBWi5T4GMG0slMT0dDaJph2TmoSTlVUQSaVYsXC+ZiK\nslKTAMBsXcqkbrf/ma9n3bbc0wMXh4Ywa5Yr5J4egudECNwQhepvXmaa4Pl3ZKZOrKCTJLZ4iGhq\nyklPEbyXqNKTnRQR0djYb2mqclTfdUQ+06ksZFuGbWZpjLwgO8PkeMP9hm1sOPY5ePI0cgTG0Ibn\nmEtzbnI8NJ3d+m3d2Mnc8TmpSVj37C/02ysWzsfIyIhJmQ3nDi4ODSEyLHRSYzXDeHr6B8zODYwe\n/6pSk1B8utzo+NFtoOnsxtyUBLP7LbWZKj0ZV69etaoMlrhu3rx586RTcYCOjg4oFNbNlI7n2Olo\nIuUPCZyD7JQkyKRSXLt+HUvn5+LZxzYgMzkBMRFKRIQEw9dXjgBfX6jSkxEeGoyhS5dxZ14uAvxn\nY+TmCHIzUrF0fg483d0xNykBhQtycfHiJQxdvoK7FuchJNAfEhcJlufdAU8PDwT4+WFucjwWqrJQ\nVdeI1YUL4eXpAUCC7JQkrFuxFLUNLfDz9UFmUgLmz01HRU0dslKTsG75EpTX1kKVmoKnH/6eySIK\nYuoDlmJJiYvBbB9vDJy/iKSYSCiCAuEikeCOzDSkxMfiypUriAhTYEFWOub4+eLmzZtIT4zHXQUL\ncPHiIG7cHEFWShJWLsqDj7ccvnI5Wjs6kZ4Yj7T4WPjIveDq6orcjFQsyMpAcOAcPHTPKqN2/tGj\n603qLyk2Gn4+3pC6uUHXHvevutOuixZYqidnxOMMU6XfGnr7ox3jTvvJB9ZNJCTRElO7OYPYyj/W\nPddH7gV3qRSG7zUz8cepxdZuziCmOmC/tZ6Y2s0ZxFT+sWJxVN/V5ePnLUeAvx8yE+Ow5s7FNs1H\nl0ekIgR3L1mIiNAQLF843+o8rG236Xy9T6W+q9PX14fg4GCLx4xuszm+Pli7Ygk8b/8WWHZaMtbf\nvVy/eqnueA+ZDBIJkJ2ajLUrlhi18eixj5urK+5eutBsv9ClGRLoj5y0ZMRFheOugnzU1Dfh2vUb\nJmMn3fHBt4+PvX18gIcbEhPikREfhyfXr0NmcoJgXY2eOwjw88OqxXmQe3oiwM8PafGxWLdiqdVj\ntdF16DICfGd5ATzcZZAAUKWnYO3yJejqvvU10Kzb5W9oaUN0eBguXbpsVD+6Ngjw80V8VDguX76M\nVQX58L4dn7eHB+5ZthieHu7ACDBntg++u3wJPGRSSCDRp6+rX29vb6vKYQ5XL52mDJfwtWbJXwDI\nTE7QH7Pn6Als21WE3/zXu4iLVGJuSiLOXxhERU0DViych8FLl9HW1QMPdxmilApcu34dGYnxUFdW\n6ZfZDQr0h1Y7jIa2dkSFhaL/wiDOVNUhX5UJSIC+8+eRHBuFprYOtHR0QeYuRUx4GDzd3fUz6peu\nXkFXbz883N0R4D8b4YpQuLq44PLVYTS1diIlztPudWlvurK2d/fB19sLdy1ZCA+ZDK6urpBIolBe\nUwcJAG9PT+RnZSIsNAhJMdF4dO3qCedpzUqva5dbf6N0BLHFQ0RTl4e7DMlx0TD9aWAi8WK/panK\nUX338rAWnT19cJfJ7JZHZ28fjp2pQJQyFMpQy5Mxk+Hr7YV8VQYuXb5itzzIPN1YurSqBpnJJWOO\npe9beafRpNknRXvg4iJBoL8/XF1cMCzwFU1XV9db+11dBdMcPfbZXrQHoUFzUHCHCueamgSvp4EL\ngzhdeU7/dc2U1ASMSAAXiQu8PU3HzecvDOKMwfEA8MX+I4hShmKOv++YY0ZXFxcE+fvB1cUFUjc3\n3Lh5Az39/fD28jDJz5r5CU8Pd6TER8Ntlps+/UB/f7jcXsxBJpMiKMAf7rd/fun84CAaW9v1X2P1\n9HBHclw0Zt2u08FLl9Gk6UBMuALRAK7fuI6e/n74eHncSs/NDUEB/ph1+3frpG5uCJrjDze3W9Nk\n//77P6OhtR1RylDkpqfg3glOfnPSbRrqH7qCl3//X/qPXhou+WvNZMueI8fx6zffFlxC9/F1a/D2\nRzuMltOVSaV4+uH78cbWbSbL7D75wFqkJcXr9xXm5WLbrt36vz/4vMgkrXxVJvYWn8Q3R0+MWha4\nFM8+uh7/8c5WHDhRgg2rV+Dvn34+rrKJzejlkQHgwIkSLM7NQkJ0JDxkMkSGheDtj3ZgcW4Wbty8\niY+++gaLc7Nw/6plU7LMRETOInTP/eboCQCYFp8ioOmJ/ZamKkf13dH56MYNtsxHOI8ym+YhlA8A\n/dfeeL07RmnVOTz30mv6NqhvbRvXeNPc+A641YbC+9X6/UK2F+3Bn8z0i3sF0tT1z+ceXY9d+w4D\nAHYfvvU76Xfmz7N4/Ldj71v9OzJAYBG+UXUUHxWB197eqt9uaG3DoVNn9PmNPn70/MToeH7yPx4S\nrMMNq1dg1/7DKMzLNYn/SEmpfh7hmUceMNpvOJ8B3PotuNHp7y0+iXxVJo6VlgMA9h9Xj5qLKNPX\n93jxN92moeNmlgDWXWhj2Xv8lOASuuaWA/aRexktw2uYZ1VdI2rqm/W/w6ZLy9LSvLqlgM0tC5wU\nE4VhrRYt7Z3wkcvHVTaxKakQbquhy1dQ29SKuuZWnL84CB+5Fy5duYrr128AAC5fHcbe4pPOCJmI\naMo6Zeb9UV1e5aSIiMbGfktTlaP6riPymU5lIcuKDhdPaiw9VhtOpI1Pmjnn1Bhpnqqsxn9t/n/0\n2/uOq8c8/tM3XjVKX2j1UsM6MjdHYJjfWHVqGI8yJMjs3EJLeycC/f0sziMoQ4Jwtq5RcD5DaHv0\n+YZzD6O3T03wOuSk2zRUVd8o+Hpp9TnB10draGnT/224hK655YAtLevbqGnHTYyYpGVpaV7DpYCF\nlgVevXTRrb/bOhAXGT6usolNY6v55ZAvDA5B7umBhKhIZKUkoaO7F8Paawjwm432rh60dnY5OFoi\noqnN0nsVkVix39JU5ai+64h8plNZyLKyKuFxpbXjzbHacCJtPNE0mzTtSI6P1W/Xt2jGPN7f398o\n/YCAAJPjDOvI3ByBYX5j1alhPPmqLPPxtXUgKyXJ4jzC6PNHzztYOw8htD3R65CTbtNQXlY6ZFIp\nfORyZKcmw0cuB3BryV9rLF80Hz5yOQL9/ZCZnIDE6EgAQHdfH2IjlQBglPbZunqLSx/HRITBRy6H\nTOqGB+5eBmVIEObM9sXTD9+PpJgoBPr7YcXCPAT6+0EmlSI3IwXeXre+Ax4THgZlSLC+DClx0Wjv\n6kJGYjx+9uSjiI1QwEcux5rbE3FTTVT4re+fZyTGY8vzP8L/XH8v0hPiER4aDF9vOS5duYpzTc04\nfbYaoUEBkEnd0DtwHorgQISH2O93JIiIpiPde9Wdebl45zcv4s68XADWL2lP04ufn5+zQ7AK+y1N\nVbq+m5EYj189+xQyEuMB2L7v6vKJCQ/DUxvuQ0x4mM3z0eVhOG6xdR6G+axalId//MdLWLUozy75\nkHkZt8fMo/vTvIw0q85PiI4QfF3XhuauizSDybHRdOe8+cJGHHjvr3jzhY2CacqkUoQFB+k/nRWt\nVKC/v1+fx2PfvcvoeKH+rNVq9cevWboQPj4+JvHccbsuHv3uXXjpxz/E3NQEwfRiI27NHejqdOMT\nD2Pv//8WNj7xMABAlXpr9dX4qHD9+RKD7dFtEKVUIDw0GJFhwnMPGYlxmJsSj+y0RP35372zQP9B\nnZjwMHxnWQGSYqIE6ysmXIFXf/6c/rpLjI5EZnLCpK93yYjQGrAipFaroVKpbH7sdFJWXYv9x05h\n6PJlBAcGQNPRhf4LF+Hj5YnoCCUK5+cgSqnAJ0V7UFpVi8FLl5EaF4327h4MXBhEYmwUOrp7UNvU\ngvioCAQHzMERdSmiwkIwNyURp8/WwM/HG95eXmhua0dTWyfiIpWIVoZBGRqMN/7xIS4OXUKA32z0\nDpwHADz5wFp8c/Qk7i7Iw5mqc+jsHcDdBXkoqazWL7iQk5qMMxXVCAsLQXt3D+qaWxGlVECVkoSa\nxmbcuHkTABAc4A9fb2/McnHBKYMFG+YmJ+KLA0ehCJqD/OxMrClcLKo+YCmWrw4cwOXhG+jtH0B9\nSxta2zsRG6lEekIcZrnNQt/ABfjIPfHWB9uRr8rEjZs3cURdOi1/001MbeYsYqoDa2OZf//j4077\n2Md/n0hIoiWmdnMGsZXfUjxFBw5g6PI1nKqsRpOmHVFKBXJSkyD3dMPKggIHR+pcYms3R/p870Ec\nKSnVP0fonh2caaL99uCpChTOz8Gd+fMcHLHzzOS+C4ir/GPF8vn+Axi+cs3ouT0nNRkyDzesWWK7\ne+7n+w/gyhWt0fgiOzUJHh5Sm+Xz2Z79uDB4CWdrG9DU1oGosFCkxMfA19sL37lzyZjnW9tu+w4c\nQP/lazhb34gRABIAKbHR8Pd0w9Ip/j41VfpuadU5nGtq1venmHAFslOSoD5bjcbWdsSEhyEvKx21\nTa1o7eiEam4m3GdJUFp1DhcGLyElLhrv7/zK5PfXHvnu3aisrceSvFzc0JpeF1V1Daisb7q1nZ6C\nWbNmQV1+FhcGL+HexbnovWx6ToiPB/Lz87Hj6/1QV5zFsPYahrXXIJO6QSZ1gyotBWU1tZjl4oLE\n6Ej9+Q/esxIXBodM+rOnhwc++fIbRIXfGoufqapBXUsb4iLCMDc1CafKz6JJ04HYiDBkJScaxaNK\nTcaZqho0tLQhKlyB5NhoRAQHoSAvF2XVtahtbDZ5H6tpasHZ2nokxkQhIToCZdW1aNS0Izk2Gsmx\n0UaLNGanJuHi0GV8c+Q44m5/EOiIuhR+vj64cuUKHr/vHjS0tkHu6YlLV64gPiIcJWdr0KhpQ2yE\nEnOTEkbNISTgVNlZaLp6EBI4B3JPDyiCg3DwRIm+fitq69F/YRC+ci/ER0dYfb2P5rp58+bNE+mo\njjaeJYb9/f0xa5bj14iobmjGnqMncPPmDew/roabmxsC/HztmmdHRwda2rvg6+2FxvZ2SDCC1UsX\no7e/DxnJ8fj+ujVQhgQhJS4KwQFz0NjahqJDxYiNCMPdBQvQ3T+ABVnpuP+uZfhi/2EMXLiIR9et\ngbr8LNxlUrz0k6cR4OuDl//yf5A3Nw2L78jBG1u34dr161i1eD4kLhK0dfWgq68PqtQkhAQFwG3W\nLKTGxWDe3DREhYVi3fJC/OPTXZB7euBnTz6C9z77EhXnGhAWHITK2gaoK6tx76pC/HXbpzjX2IyB\ni4No1LRDXVmN+1YW4sF7VsLV1RWzveVYnj8P9S0tuHHzJjb+4HE0adpwfnAQ/+/TT6CuuQXv/PMz\nrFqQi8jISKf0ASGW+m7ZuQZ09/bj/Z1FqG/RYODiIBpa21BytgbpCXG4OjyMdz/ZiScfWAutVoub\nN29iYU4WlufPn1YTboC4lhF3FjHVgbWxvP3RjnGn/eQD6yYSkmiJqd2cQWzltxRPaU09Xv/HNv3v\nZTbdfq/JSktBSlyMgyN1LrG1m6N8vvcgXnt7K17b+Awe/M5dyIiPxk9f/RMC/WYj4fYn+51hIv02\nOy0VD9y1DHN8vXHpymXIvbwcHLVzzNS+q+Pj4wPp7U9lONtYbVFupu/m2PieW15Tjzf+sQ2B/rPx\nxANrUVpVg399c9Cm+VTU1uMv73+M1s4uyKRS1DTempTJzUi1Kg9r++2Z2kZ09w3g4tAlnL84CKmb\nG1xcXDDL3QPJsdG2KIrT9PX1IThYHN/UsdQeR0vO4I1/bEOkIgTPPLoel4Yu4a//3IHZ3nI8tm4N\nTldW4dOv9yNfNRdeHu4Inu0LdWUVMpPi8eja1bhx/QZGRkawbsUS/Pj7DyEyLATuMhmUocG4OqxF\nkN9s/OkfH+KBVYV4/slHoR2+iv/99w/xvZV3wt3DHWeqarDv2Cmo0pJw48YNFOblonfostG11N7Z\njdjIcKQmJcLbyxP1LRq0d/fgjvQUPLZuDW7euIGOnj7EhivwyHfuQsBsH1Q3tuB4aSWSY6MROMcP\nf3n/Y3h6uGP9mhU4XVmNr48cx7y5aViUm43TldXYue8Q7lqSj1WLF8DVxQX/+d7H+vyjlAp8/NUe\nk2t7WV4ufvT4BjS1avDeZ1/h3hUFmO3jg5b2dmz5r3dx34oleP4Hj+HKlcv40z+2IV+VgTl+fgic\n44e3P9qB84NDSI2PRYQiBH/d9qlR+iWV1Viefwf+1/94COcvDqK7bwCpCTGIj4xAlFIBH7kXWju6\nUH6uDt6eXujs7cO+Y6fQf+GiPt64CCWe+/6DqKptwD+/3IPvLl+CB7+zCrWNzfrFDB+79x7U1Dfh\n/c+LsHZZAQrzcnFxcAiNmnYEB8yZ0HVo10+6vfLKKygtLYVEIsGmTZuQkZGh33f06FH84Q9/gKur\nKxYvXoxnnnnGYlrWzIx/UrRHP/uqmyG+b+WdZpenNfyfzphwBbLTkvXnx0eFIyQwAMdLK7FsQS7O\n1jWgpb0L+apMdPb0orap1eiclvYuLMqdC01nN+qbNUiI/vaTYhGKYKTEx2D3oeOICVcYvZ6TnoKy\nszVo6eo1+QRYSnwMvjlyEguy0tHa2YX6Zg2ilQqo0pNRUlGFzr4B3LUoTz8DHBuphDIkCIdOnkGE\nIhjZqUnYf/IUlubm6o+JiwjD3Nuz0i3tXVicm6VPO0oZitRQUSUAACAASURBVJS4GOw9pkbhfBXO\n1jUYzaSXVp9Dq0Ccc5MT0NnXj96+AQxdvoLOnj79bLEyJAiH1WVYsXCeUXopcTHYffg4lCGBSE2I\nRVl1LQ7eXtXFUOH8XLR19SAyLGRUezTeniUPRWp8LIoOHUNMWAgyRs1g6/qAM1nqu//aewiny8/i\nq0NHTfbdmZcLuZcX9h07hdz0FCzMmYu7CvLtHa7TiOl/v5xFTHXAT7pZT0zt5gxiK7+leDa//ha+\nOmh6v121eAE2P/c/7R2aqIit3RzlwIEDtz4tUFGl/9/9nLRkBHi6ocCJnyKZSL+9u2ABopRK1Le0\norapBVFKBXzlXrg6rIWnhweGLl1CaFAgDp06g6iwEBTOzx3zE3F7jhzH3mMn0dDajphwhVXnOJqz\n+q658YS98qk4V4eCeTnouP0tkGX581BZWz9lnnEBYPMbb+GrA6Z9966CBXjxWdvdc3/z5jtIiIk0\nua7PNTbjl08/YZM8Xvj9m7iBEVy5Oqwf63i4y+AqkeCln1oexwLW99v3d36FqroGkzFVclwMHrpn\nlS2KoueoT/3q+nRpVQ0ykxPtdu2Mh6X2+O1/vYu4yHB9f0qNjUJyXMy3/Uv3Ta3bnwJLj49BYkyU\nyafQKs/VoaqxRf9JudKaWtQ1tyI2Ihxzk+JRUlmNhtvj2ZzUZFTU1KG6qQVRYaGYm5KAmtpGeHh5\nYO2yJfg/n+zEvmPfLqL322f/Tf9e1tzeAVVaKqIUwSbXQHNHF06WVeq3fTzdcK6lA+fPDyIpLtrk\n+Oq6RjR1dCHAz/fWdn0jzjVrEBYciL2385dJpVClJetXCDakG7srQwKRnZKkrxNdHejr6HZ+vReH\nUHa2GmHBAQhXhH77yTtlKCQSCQ4cV+PGzZtwdXFBwTwVRkZGjOpYV4dL5qkEP124KGcuDp06gxXz\ncpCUEGtS3vpmDSrrG6EIDkBOajJqGppQXtuApKhwpCXGm8Q7ePUqvr9uzbj7m90+CnTixAk0Nzdj\n27ZtqK+vx6ZNm7Bt2zb9/pdffhnvvPMOgoOD8cgjj2DlypWIi4ubcH6WlnE2fF23PO1P/sdD+OO7\n75tdRrZR0waZVIonH1iLtz/agWGtFoV5udi2a7fgOYV5ufhgZ5HJ+bpla4+WlGHD6hX4+6efm7z+\n5ANrkZIQiz9v/cgkfsP8da/rlsNdtWg+3ti6zWKezz26Hq8bHLM4N0u/XZiXa9Q5dXk+/fD9+M/3\nPjZ5fcPqFUg1E+fTD38P27/aaxLLg2tWYqEq06QMhvXRpOmARCLcrk1t7RjWXsPuw8dM2mN0WgCM\nymqvpbxtSebqgprGZsF9jZp2LMjKQFxkOBpa26BKT3ZwdERE08s5M/fbWjOv0/TTe/ma6bPC6VvP\nS2Jlrt/WNDQjNDgQRYeKAQANrQbPgcdOIV+ViQ8+L0K+KhN7ik/isPrWAMncJNqeI8fx6zffNqqb\nsc6ZKUqrzuG5l14zGU+8/sLPbDp5YJhPYV4u/vHp5xjWavH4ujX427ZPp9QzLgCcaxDuu+Zen6iE\nmEi7X9dyuSe+OHDUZKxzd8ECm+UBAINDl3Dw5GmTfJQ2/i1n3ad+hfqULSfeRl879a1tdrl2bCku\nMtyoP92/slBwjLlh9Qp8ffSE2f0/evQBfHGo+NacgcH+htY2HDxZgnxVJho1bUbj/i8PF+v77zOP\nfA9tXb2IUIQYLQzgI5ebvJfdt0IghtvXwEdf7DbaXreiEMUlZWaPb+vtw77jp/TbJVXnBBYm6BWs\nO93YfQTG4/LRdWCYX0psNPz8fPDWB9tN+n3BPBX2Fp9EwTwVjqhLTeo4X5WJ9u4etHV2Ca5G6iKR\nQBEUiKSEWLP5Hy+vxL5jp/RzJ5/tO4S1ywtsek+x20IKxcXFWLZsGQAgNjYWFy5cwNDQEACgtbUV\nvr6+CA0NhYuLCwoKClBcXDyp/Mwue1teBR+5l8nrJ0or9duWlo09W9coeIzhtjXLzuqWuPWRy01e\n7+ztE1wWF4DRcreG6WIEOF1ZM2aepyqr9fsMl/O1FPOZqlqTOIa1WpwfHDIb55mqc4JpNWja0NHT\na3bJXx+5HEOXL5v9McTIMAW0166NWR8dPX1o6RC+2MS8xPbR0+UICZwjuC85NhpXtFrUNbciWqlA\na3ung6MjIppezL3XRITN3K+qzTSnKquFnxUMnpfExlK/XZiTrf8BaODb50AA0GpvPT8ZPhfuO642\nm8/e46cE68bSOTNF0eFiwbrZffiYXfIxfE43fH4fnb+Yn3EBx91zzV3Xp2x4XZ8fvCSYx/nBSzbL\nAwDqW1oF86lvabVpPkdLygTz0U282Yqjrh1bMuxPGYnxZvtXS3snFudmmd1fUlmDpJioMecJdNtn\n6xr1P9g/rNXi9Nlz6O3vx40bN4wWLXzjhY1Gea5bvsTiNfC9u+402vaQulk8PlIRbLQdGxGGCEWI\n/jiJxPy1HRmmgEQCs/MmQvndu3wxymvqzdaRj1xusQ4ToiNQ16wRjKeuRYOVi+ZZLO+C7HSj7XuW\nLhKc85jMPcVun3Tr7e1Famqqftvf3x89PT2Qy+Xo6ekxWorW398fra1j30jUauE3/dTUVIvL3mal\nJGH3YeNJvUZNOwL8ZqOtq9visrFNt48DYHapWWuWnW3r6kZTWwfiIsNRUlll9HqQvz9KKkwbMMBv\nttlyXb56FV29/WPnaVBOw+V8rSlzW1e30evenh4oqzadkLMUp6ajC4EGbW2Uz+36AABfb7n+gVBH\nJpXC19sL4aEh6OkfsJjPxcEhdPUJ10ejph3Dw8OoqKgw2eeorycI9d20tDTUNrUgOlwhWPaVi/JQ\n13zrjTcrNRGzXF3NXgPTxXQvnzWsqQNn9lsxp+tM07FM42Ft+Z19z7X0XmPufWI6m2n9Ni0tzexz\nhJifFSz125iwUJNnNt1zYHt3j/55T3dMfYsGNTU1+v8I15HL5WhoaROMy9w5zuTIviuVSlF6tkZw\n35mqGpSXl0Mr8B/Sk8nH8Dnd8Pl9tEZNO4aGhlBTIxyfI/quubZw1D3X0nXdZOG6Hm8ezW3CeTS3\nWZ/HWP02LS0NrR3dgvs0nd02qzM/Pz80WrgXNjQ0YGBgYNL5TPTacfY917A/rV2xFFs/3SV4flNb\nB37+1OP43V+FfzalSdOOe1cWYnvRXsH9hvdm3fGG8xZNmnY8um41XF1dERep1E+URikVRjE+9J27\n8fPf/slsDM8+tgH//HKPftvT09PiNfP//fiH+PTr/frt//X9B7H/+Cn9tZwSFwtPD5nZazslLha1\nTS3618ead/D39zcbT2dPH+Iiwy3OtaTExeDC0CAaNKbvYcqQIKxclI+iQ38wm//TD3/PqH5e2fgj\nbPqPP5s93tJ1aK7vOuyX5m3x03GWLsAoZSgaBSo6SqnA6bOmE1pRSoX+e8i9A+eRnZpk9nzdcYbH\nGJ5j6fyQwDkouT0jGhUWqv+fVMPXu/v7ERVmGn/vwHksyMoQTNfT3R1RSsXYeRrEX9fciuyUxDFj\nNjzH0ODlK+bjzBaOMy4yHDDT9ob14ectR74q0+R3EgYHL6GuuXXM+vDxlsPDXSq4L1qpgEwmc+pv\n15jLO0oZigPH1SiYpzIqe0JUBAYHh9DY2obnHl0Pb08pVhQ4d2U1e5upvy9kSGx1YK9YxFRGWxBb\nuzmaGMtvLp7BoUuC7zVDQ5ed/j7haGJsN0cw97wo5mcFS/22pr5Rv2K8ju45MDc9BSfLzyI7NUn/\nXBgboURiYqJgPjHhws+Vls5xBmf03czkEtS3mtbN3OREpKen2zwfw+d0w+f30aKVCsjlclH2W8Bx\n91xHXNeRYaFoEOgDUWHW5WFtv42NVAqPqSKUDquzmBjbLXLhqGtnIiyNz3R1s2P3PsHxL3BrLPvB\nzi/N71cqsGvfIYQEzhlzzK473nDeIlqpwI7d+5CVEIOm1nZsWL0CLe2dqG9uNYrx/c++sBjDBzu/\nMEqzv7/fYvu//68vjM7ftqsILhIX/bXcf/48btwwP3YvO1eHuMhwwXkTofwsxROlVKC3/zwiFSFm\n6/BEWQVWLV6Ak2VnTSYBo5Vh+OrAEcvl/cy4vO//y3x9TvSeYrevlwYFBaG399vv+nZ3dyMwMFBw\nX1dXF4KCgiaVX056itHH64FbFZ2TnoyLQ5dMXp+X+e2n8Ia1Wni4ywTPT4mLFjzGcNvS+R7uMv3H\nxCMUIbg4NGTyekjAHMxNSTA5HwBS4mME04UEyE5NHDPPnNQk/b6LQ0OIDAsdM+a5yfEmccikUsz2\nlpuNMydNuP7joyIQGhQouE9XH8NaLTKS43FEXYqSympor11DSWU1jqhLoQwNxkWD/1k1Vx+hgXMQ\noQgV3Cfm30LTLQe9t/ikvuyVtQ0ImuOP6zdv4t+feRL33bVs2k+4ERE5QnZasuB7TVZa0tgn07Rg\n7nlRzM8KZvttahL+WbTXZJDh4S4DAEilbgBg9Fy4dJ75gULh/FzBurF0zkyxclGeYN2sWDjfLvkY\nPqcbPr+Pzl/M/RZw3D3XEde1o+4dWSnC47vMFNtOfOdnZwrmsyA7w8wZE+Ooa8eWDNu6rKYWOWnJ\nZseyB0+eNrs/OzUR1Q1NY84T6LZT4qLR0z+g31alJqGsphbrnv0F5qYm4cNdu6GurMaf3/sYOanf\n5vnp1/vNxpCTmqT/FJcuzXXP/sJ8f05Nwle3fydUd37x6XLIZFL9tdzR02f22laGBqOnf8DsvIlJ\nfunJFuOJCgtF/4ULcHObZbYOe/oH4OrigsW5WcjLykC0Mgx5WRlYnJuFOX6zcaa61qi+RpdX96k+\nXXl37jskOOcxmevddfPmzZsndOYY3Nzc8P7772Pt2rWorKyEWq3GQw89BODWUtf//d//jYKCAnh6\neuK1117Dk/+XvTuPj+K8833/FYgWi1gkkJCEJNCC9gUkMGAZxA42XmMnMF7izCSZOxMnPjNjchMz\n44NPYsjmybnD2HnNHMevubkexyZjE4+3RCQGbPZFAiGEJJDQ1hIgCTC2DEiAdP+Abneru6UWqt7o\nz/svqqvqeX7PU78qVf3o5VvfUkREhMv2BvqZ5azUZI0LH6ORJpOkEBVkZeih5Yv08IolKsjKUJjJ\npKvXrmnR3Nn67hOrtXDOLEVFTFDo8FBJIZo4fpweXL5Qo27uPyc/R3Nn5OqTQ0f0yN1LNHb0aDW2\nntE9xUWKiZqonp5e6z4jTSY1tp7RvYvmKzpygnp6pbn5uZo7I0cVNXWamZmulQvu1I79ZbojL1tz\n8798/aHli1R98pRKj5/QY/ev1KiwMGv8KxfcqT/vOah7ious7RZmZejB5Yt0tu2cjlSf1GP3rdTI\nm/vckZulosIZOlpdq5mZ6XpgabG2Hzyk1Xcvt25jCg3VA8ucx1yQlaEVC+bpT7sP6uGVizV29Gi7\nuaytb1JZ1UmHOO9bPF+flJZa+wlRiGbe3Kf65CkdPVGnr96z1K69FQvmacf+Ms3MTNPy+fP0pz0H\n9fCKxRoVFqbPv7ikvPTp1j57emV/PFYu0bgxo+3macf+MplCh+v+pcVOc8CX+std27y9fr1HqVMT\ndN+i+Zo02qQlxXd5OVLfcven1G9n/jQH7sby69+9M+i2v/W1h24lJL/lT8fNF/xt/O5cc0OHD7f7\nW+PrvxO+4G/HzVv6u1/0pUHn7dKFGjN6hHp6QhQTNUm9PT0qyM5QTlqKQnpDlD09RT3Xr2teQZ6O\nVteqMDtT33jo3n5/ECE5MV6JMZM1bNgw9fbe+A/VgfbxBV/kbkzURKfPE0Z/EbxtP7WNzXpw6ULF\nTY7S0RO1+urdSxXe597cn/NW8t411xvn9VD7cDdvbfuxfaYyes7SkqbaPQsXZGVozarlhv96qW1O\nd1+9qsUeOncGy93nMylEwyStWjLf4dhbnlOHDwvRqsWO66tq6nStp8daM4gYO0bXrvdo7oxc3b9k\ngc62nVOvZN3+xKkGXbveY10OGzlCocNuHJ8FuWnKzclSiKTT7R1KS03VvJnZ1j7HjQvXsrvmOMTQ\n0Hxal7u6rMudl6/q3IULKshNU2FOlsP2p5padaWr27pcU9ug7mvXrWOwnMsTxo3X0qI7HM5ty5zY\n1klsay19+7vUfV0d585ramKi5hfmOZnjRn36+ReKGDfW6f6WOTQ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032ZWdns+axNraxm5KSgk6O\nMdSpVGHHpnVo6uhm2t8/NIIgfz+D/tD1jy4PG3PGo73gOscXE3P6wNbjluva1z80Aq1WiwBfCXZs\n3ogvz5cA4L/edChVUKvVqKurs17gc2y21/+FyNxz19Zjl+866Wjj0BoW2zV3pmPAXsdtp1KFF7+5\nB//7xSlmm+2Zca6ehW1psY1dY/Z0zZ3LY7HQjvNCa89M2Ptz7nTpjNcdpnvGvXHjhsE13HjeHBcZ\njs7ePtb8nUoV/mnvY3ATCfEff/6Asw61Wm3RvW26+8o//+OTOPi3L5j8ISEhBn0QGxvLnV9vrg5M\nzQ2mW3d45dlv4Z/+7TXO/Y9u3oCjRadZ9/cPjeD/PPENHPj3/+LMv2nVCvzhw4+Z9MZzab74jNNP\ndz/lGrtmLbr19/fj0KFD2LdvH95++2309vbiD3/4A3bu3MmZJygoCMPDw8z24OAgAgMDAUxNWMRi\nMSIiIgAAeXl5aGlpmXbRje8ETE+qQFtPr8nrGUmJSE1N5S13pqIkoehQmtYZLREjJiZm2vy6mIev\nXkNWspS1rCiJGBcrqk1edxIImPrNza+frrKhESnxsZx5Khum3iGn7BtAVJhpOysbGpGSEDt9/WFi\nODuxv6EySiKGvG5qlZwrf0igPyr03q0XFRYK+b3tKIkYx06c4Y0TmDoeXl5e83YBZ8NVN9cYipKI\nUV7bAJHQFcNXrwGY6gtXFxdmW/earn9WZqXNajzamlwut+kxsgf21gfc41bMeb4u9fTErTt3cOhv\nnzOv618vjEVLxBCJRHbVbnPM9vq/kNjbuAUsv+Y66jicDXs8bvPBnsfATJ4VPvj0c4PtYpZnxrl8\nFraFxTp2deyt/XMZiz21c7bs7bjZgr31gTmxsMWsv+7AN6eOlojh7e1tcA03nje3dvUgS5bIeY0/\n8sVJuLq68M5zRSKRxfc2vvvK4eNfGuTv7+83P7/eXB2YmhtUNjQiOS6Gs76jX57ibF+URIzPzpxH\nSKA/59zj0N8+583/VfElg/QVFsRnnH6m91OzPl6qMz4+DrVajbCwMLS2tvKmzc/PR1FREQCgvr4e\nQUFB8Lz3VkMXFxeEh4ejs7OT2R8dHW1x8Po2r85j3vqnIxIKUbhqxazK5ZOflc5a58qsNLPy62JW\nazRwdxOxlpWTmmSSTyQUIj0pgamfN3+ylNnWTzd05Spk8TGseWRx0Ri6chUioRC37t5BhizBJN2N\n0VvISU6atv7w0GCIgwNZ92UnS6HWaHjzu7uJDN6OGyEOwY3RUaZtNU0tvHGKhEJks/ShvchJlXEe\nt76hYbi4ODMf4fX0cGe2den0+4erLHPHIyHmyklNYh1rnh7uiI2UIC4inHmXG3DvesExPu35/OQz\n2+s/sY2FNg6J5RxxDPA9K+je5SYSCrEsPdkk71w/CxNCyGKjv+7AN6fWPRPqX8ON5703RkcRGRbK\neY0vr1OgpLIWOSnsz966e5el9za++4ruXW66/Gq12uz8urm6btvdTYShK1d52/hfHx7lbF9WciIa\n2zt51wq+PF/Cm7/93gIp29rCdPEZp5/p/dT51VdffXW6RL29vaipqUFsbCxeffVVnD17Fmq1mved\nbqGhoWhtbcXbb7+N8+fP48CBAzh37hyUSiViY2ORlpaGV155BUeOHIFYLMbTTz8NgUDAWV5fXx/v\nr5uGBPojSyaFSCiEZmwMG1bk4oV9u+f0i2MToiMR6OsDF2cXAAJkyaTYs7XQ7C/R1o+5tasHW9ev\nQpCvDya1QJZMip0PbMSjhRs469Cvv0vVjy1r8xEaGICJyUlkyaTYXrAOpy9fxo6CDfD0cAcggP9S\nb2wvXAc3oRDVja14/KHN8HBzY8p+aMNqFF0oRW6qDCvSU1Db1AZMTOKhgrVwEwqZdNsL1uFMWRl2\nPbgJ7iIRa/07CtejraMbNS3t2LVlEzw9PPTqWYOTF0uxe0shPNzc7ucPCsDExCSyZVJsL1yPgcER\naDHVHw+sWYmvSyuQIU3AjoJ1OFFSivSEOORnZ6CmsRUCANs2rjGIc0fhept/MTLf2JXFxcDbcwkT\nc6ZMih0F63Bbo4GHmxtqmlqQkhCLtcuyEeDrixs3RwEBsDw9hTk+mUmJTDtnMx5tbbpzfDGwpz4w\nZ9x6urtBCyA1MQ5rl2UjShIGD3d3+Lg5Q+t0fxzqxqf+WLeX83OmZnv9X0jsadwCll1zHX0czoa9\nHbf5Yq9jwOJxW7AOXX0DuHv3LnLTkrHrwQI8XLCOea4cGx/H+nl4FraFxTp2deyp/ZbE8u5Hxywu\n/5ldOyzOY6/s6bjZij31gbmxsKXTn8OPjY9jqZcXtqxdCaGrK9ieCY2v4QE+S/HIpnXMtsjVBQ/r\nbevuS0vdhcC952kXJyds2bCK895l6b2NK3133yBu37lrkJ+tD7jyD125Bs3YGNavyMUDa1cyc3mR\n0BUPG60n7Chcj6FrNzF66xa8vbywKX+ZyX5FUxvGJyeZNYzQwADm/rZ1/Sr0DQxDC0Do4oIH1+eb\n5G9q7cDY+ASzrVaPMfHt2VqI6zdvQTM2hgBfXzy8cQ2ErkJotVOLiru3bMKtO3enfvRxlvdTgVb/\ny9Z4TExMwNnZGRUVFRgZGUF+fj48PDxmVOlMWPJ21Nra2gX1NnpLqVQqu7mg2crNmzfh5eVl6zAA\nmD921Wo16xdVLhb29pZzW7CnPqBxa7729vZF95FSffY0bgEau+ayt+NmC/Y0Bmjcmm+xj117ar8l\nsazY+ZTF5V868r7FeeyVPR03W7GnPjA3FnuK2VYWex9YY23JrI+Xfu9734OzszMAICsrC5s2bZrX\nBTdLLaQvip2Jvj72L2NcTNh+NMHeLbYv8CYLA41bLLofTVgoaOwSRxwDjhgzIYQQ4qissbZk1g8p\nSCQSHDlyBJmZmRDqfdY1PDx81gEQQgghhBBCCCGEELLQmLXo9vnnn5u8JhAIcOrUKasHRAghhBBC\nCCGEEEKIozNr0e306dOc+44dO4bt27dbLSBCCCGEEEIIIYQQQhydWd/pxufo0aPWiIMQQgghhBBC\nCCGEkAVj1otuZv74KSGEEEIIIYQQQgghi8asF90EAoE14iCEEEIIIYQQQgghZMGY9aIbIYQQQggh\nhBBCCCHEEC26EUIIIYQQQgghhBBiZWYtut24ccPktZ6eHgCAp6endSMihBBCCCGEEEIIIcTBTbvo\nNjk5ieeffx5arRaTk5OYnJyERqPBc889BwD43e9+N+dBEkIIIYQQQgghhBDiSFz4dh4/fhzvvPMO\nurq6kJSUxLzu5OSEVatWzXlwhBBCCCGEEEIIIYQ4It5Ft23btmHbtm1455138OKLL85XTIQQQggh\nhBBCCCGEODTeRTed73znO/jqq69w/fp1aLVa5vWdO3fOWWCEEEIIIYQQQgghhDgqsxbdnnnmGQgE\nAoSFhRm8TotuhBBCCCGEEEIIIYSYMmvRbWxsDB9++OFcx0IIIYQQQgghhBBCyIIw7a+XAkBcXByu\nXr0617EQQgghhBBCCCGEELIgmPVOt/7+fhQWFiI2NhbOzs7M63/961/nLDAyc56enrYOweYcsQ9S\nUlJsHQIhFqNxC/j6+to6BJuSSCS2DmFGaOwSRxwDjhgzIYQ4KqFQaOsQiI1ZYwyY/UMKM/Haa6+h\nuroaAoEA+/fvR1pamkmaX/3qV6iqqsLBgwdnVMd8q1Y0o+h8CWoam5EmTcDm1XlIT0owO480Nhqh\nQYE4W1qOlIQ4i/Pz1XmquBSnL5WhvUeFmHAxNqzIxcb85bzlxUWFI8jfH8XyakSIQyCLi8bpS3Js\nWJGNhtYOdCpViJKEIidVhoo6BYavX0dh3nKU1yvQrRpAfnY6BoZH0NqlZP5u7ug2yNPZ24/87HT0\nDw2jrbsXq3MzoOwfRFuXElGSUCTHx6Lo/CVIQgKRnZyEL86XICIoAClJCSivbUCnsg9RklBkJUtx\norgUa3IyDWLLSEpE0fkSyOJiII2JREllzbR9YE8+Ljp1v51hochJSUKAhyua+q/gXKkcMRFhCPb3\nQ0llLSLDQhAZForl6akGY+BvX32N3oEhtPcooeofQnx0BNbkZM6q7cdOnkFpdR3T/8vTU7B903r2\nuO8d78c2b5xVX8wmHjK/dMe/q7cPkeJQ+Hh74q5agzRpPJydnVBSWWsyNuZ7zMy146fPobiimmlP\nflY6tm1YY+uw5o2jHk9HjZtYjyOOAa5nhfc+P4OI0GAE+fvhUlUtpDFReGjDmmmfLQkhhHDTzZer\nFU3IkFUiS5YIeX0j53z8aNEplOndV3JTZXhU775i7jxdn/51Pyk6AsnSeGY7LiIMGclSVNQp0N6j\nwrM7NmP47jjvvU0/Bq70mkkB/l70lVn3xunarB+/NCoCKUnxvPFNV950ayL67YuPCkd6UgLTP+a0\nRz9/XEQYlmekoqG1A9WKJqQnVZi1bsPFrEW3ZcuW4euvv4ZSqcSTTz6J7u5uhIeH8+a5fPkyurq6\ncPjwYbS1tWH//v04fPiwQZrW1laUlZXB1dV1RsHPt2pFM1762RtQazQAgNZuJT77+gLe/tEPOA8A\nWx6RUIj87HQcPXF6RvnZ8pwqLsVPf/suk65D2YsL8moAMDih+eI5XVKGixXVeO6JnfjdX48YlHWx\nogb52ekozFuOtw8ehlqjwYa8XBz+7ITJ38Z5IsNCDNJ98GmRSbo9Wwvx/ifHcbGiBi/s24XJyUm8\n/f6HJule3Lcb79yrX//1Z3Ztx+it23jj3YPT9oE9+bjolGk7K2vw0r7d8PX2RERYCE5cuMQco1Ml\nZRAJhejpGwAApCcl4NjJM5DXKXCurJIpp13Zi69L5QBm1vZjJ8/g1+8dMulnANi+aT173Pf2z8XE\nZbp4yPwyPv7tPb3MGP33P3+AZ3Ztx5lL5QAMj9V8jpm5dvz0OZPrja49i2Hhbb6vAdbiqHET63HE\nMcD3rLAxLxe//ctHzDX4+JnzOFlcyvtsSQghhJvxfDlSIjaYZxvPx48WncJ/cNxXHt280ex5uj7j\n6/6jm9cbbK/JzTTYHr49xszRjWN4jCUGrvQv7duNDmWvQf6oAB+T+KZrs3H82zet5b33TlfedGsi\nbH189nIF8rPTTdrDdq83zh8dLjZ4zm/r6Z123YaPWd/p9sYbb+DIkSM4evQoAODTTz/Fz3/+c948\nJSUlKCgoAADExsbi+vXrGB0dNUjz+uuv4/vf/77FQdtK0YUSpuN11BoNTly4ZHGeO3fVEAmFM85v\nnOd0aTlrujP3Fl7MjUckFKJK0cKaZmJiAtVNrVBrNBAJhbhzV23yt3Geu2oNxscnpk3XreqHt6cn\n1BoNqhTNqG/pYE0nr2+Et+cSk9dbOnvQ0zdgVh/Yk/JaBWvM5fWN8PH0xFIvT2ac3FVrmL9v3bmL\n0yVlAICK+iaM3r7DWs7ZyzNre2l1PWt5l6vreeOW1ypmVN9s4yHzi+v437mrBgA0tnci0M/XYF95\nrQJhwYEmeeZqzMy1ixU1rH2gu6EvdPN9DbAWR42bWI8jjgG+Z4X0xFgE+vla9GxJCCGEm/58mW/+\nqrvOlnFdo+/dV8ydp+vTv+7HhIehor6J2fb29ES3qp/Z/uSd11Fe38h7b9OPgS99eX0j/uNf/69B\nfraPV07XZv34JSFBnOsLuvimK2+6NRGuPtbdF43rM6af35xjbimz3ulWVlaGjz76CPv27QMAPP/8\n89izZw9vnuHhYSQnJzPbfn5+GBoaYr5r6+jRo1i2bBnCwsLMDlYuN38BwZK05hAKhahuaGLdV6Vo\nQm1tLTRGB4YvT//QCAJ8fdA7MDij/Pp5PD090d7dy5qurVuJpqYmjI6OmhVPcIA/OpUq1jTK/gEE\n+PoBAAJ8fdA/NGLyt7G+wWEE+ZvmMdbZ24e4yHBU1CvQ0dOLQD8/9nRKFTJlUpy4UGLw+vWboxga\nucKaR78PjGVnZ7PmsTa28ZiSksLZ151KFVY8+y3cvH0HAb716B0YRN/gMDNm+gaHAa0WKpUK12+O\nYpCj7a08becSGBjIGVeHUoUbN27w7h8YGIBSqTS7Pn1s/TRdPN3d3RgaGppRffbInGuXvY5b3XWk\no6fX5DztVKqwadUK/OHDjw3ydChVUKvVqKurs27wc8jX1xcdPGOyvb19Qf/4kEQimfE1wF7HriOO\nQ2uw9rOSvZvpGLDXcdupVCExJpq53uo/W1ZzPFsuFItt7Bozt/3zMXbn8lgstOO80NozE/b+nGs8\nX+abv1YpmjAwMMB7XxkeHjZrnq4vOjraoMyC/BU4qbfYExcZjs7ePmbbz89v2nubfgx86TuVKmTI\npAb5g4KCDPoqNDSUt75bt24Z7M/PzsTlavbnK1180z1X8q2JtLe3c/ax/n1Rvz79e73xWsp0x5zv\n3so1ds1adBOJRAAAgUAAAJiYmMDExIQ5WRlarZb5+9q1azh69Cjee+89DAwMmF2GuSegXC6fk5M1\nPakCbT2mBzQjKRGpqakW5QkJ9EdFfeOM8xvniQkXo0Npmi42QoLExESz47l56zayk6WsZUlCgiFy\nnVopHr56DVn30un/bSw0KAAu9358gy9dVFgo5Pf6Izo8DM4C9jdhRknEqGxoNHl9qZcnPNxFaDej\nD2yBazxGSULZ+0MixqXKWrR0dWP46jUAU30pr1Mwf4eHBEMsFmOp1xI4OQlYy4mbYdu54oqWiOHt\n7c27Pzg4GMHBwRbXyXfe8tUXERGBiIgIi+uzR3N17ZopS4+H7jqyKifD5DyNkojxVbHpvw5FS8QQ\niUR21W5z8I3JmJgYG0Q0v+biGmBNM7mWOOI4nA17u97MF3seAzN5Vmhq72Cut/rPluk8z5aObrGO\nXR17a/9cxmJP7ZwteztutmBvfcAVi/58mW/+mpGUiODgYN77SkBAgNnzdH36ZX5VfAlRYfe3W7t6\nkCVLZLavXLlisN84BpFIZBADX/ooiRhVes/w0RIxBgcHTfqKr81Lliwx2F8sr0R8ZARvfNM9V6Yn\nJXKuicTExHD2sf59Ub8+4/bo55/umM/k3mrWx0uzsrLwyiuvYHBwEO+99x6efPJJ5Obm8uYJCgrC\n8PAwsz04OIjAwKmPFl26dAlXrlzBE088gRdeeAH19fV47bXXLA5+vm1ence8PVFHJBSicNUKi/O4\nu4mYj1zOJL9xng0rclnTrV9uOKCmi0et0SBDlsCaxtnZGenSeOajC+5uIpO/jfO4iYRwcXGeNl2E\nOAQ3RkchEgqRkZSA5IQY1nTZyVLcGL1l8np8VDgiQkPM6gN7kpMqY405J1mKa6OjuH5zlBknbiIh\n8/cSdzdsyJs6B7OSpfD0cGctZ+2ymbV9eXoKa3nL0pN5485OTZpRfbONh8wvruPv7jb1DzTSmCgM\nXblqsC8nNQm9A0MmeeZqzMy1/Kx01j5YmWX6g0EL0XxfA6zFUeMm1uOIY4DvWaG6qQ1DV65a9GxJ\nCCGEm/58mW/+qrvO5nJdo+/dV8ydp+vTv+639/QiK0XKbN8YHUVkWCizvePFHyInJYn33qYfA1/6\nnGQpvvuLtwzys72ra7o268ev7B/kXF/QxTddedOtiXD1se6+aFyfMf385hxzSzm/+uqrr06XKC8v\nD+Pj4xAIBLhz5w62bNmCb37zm7x5XF1dcejQIWzfvh319fWQy+V4/PHHAQBxcXF44okn8I1vfAOr\nVq1CTU0Nfv3rX/OW19fXB7FYbFajLElriZBAf2TJpgb82Pg41q/IxQv7dvN+mZ5xnvzsDKzKzkBN\nUyvWLc+xOD9XnTEREkSEBMPJyQlaLZCTIsO3dmwz+XJG4/KWpadgRXoKapvakClLxObVeTh5sQyP\nbd4AryUeAATIkkmxo3A9BgZHUNvahse3boabSIQuVT+2rM1HaFAA2ntUU38HBmBichLZMim238vT\n2TuVLiTQHx09KmxbvxpBfj6Y1AJZMikeWLMSX5dWIEOagO0F6/Dl+UsYV2vwUMFauAmFTAyPFKzF\nieJS7Ni0Ht56sT20YQ2+KimDr7c3Ht64BkJXIW8fzDe+8SiLi4G35xKmnZkyKXYUrEOAhyt6r1xH\nTWMLctOSsSw9GfUt7chMlmJ1TgYKV93/9RRpbDRu3rqFSHEolnotgbOzM5anp+Kb27fMuO3S2Gj4\nentB6OoKXT/vfGAj86MFxnHrxshsvoSar5+mi2ehmKtr10yYO24FAgEyZFKkJsRCAAEeKVgLdzcR\nXF3uHyvd2LD2mLGlhOhIBPr6wMXZBbr27NlauCh+RAGYm2uAtVhyzbWnuOebPV1v5pO9joGZPit8\ncqYY2SkyLEuTob6lHfnZGfjuU3sX9I8oLNaxq2NP7bcklnc/OmZx+c/s2mFxHntlT8fNVuypD/hi\n0Z8va8bGEBMuwZ4tm+C71Jt1Pp4UF4OlnkvuLdJM3VceLVzP/PKmufN0fcbXfcGkFtsK1jDbQhcX\nPLJpHTzc3KDVAmvSEpCaIuO8txnHwJV+DE4YGBw2yM/WV9O1mS1+47m9fnzTlTfdmohx+5anp+Lh\ngjUYGByBFpj2Xm+cP9DXB7u2FCDQzw+asTFsMGPdh49Aq/+5Tw5vvvkmXn75ZYPX/vVf/xW/+MUv\nps1XXl4OgUCAAwcOoKGhAV5eXti0aROTRqlU4pVXXsHBgwd5y7Lk7aj29tbV+dbU1GTzj1Pamj31\ngbnjUa1WMx/lXowW+3kL2Fcf0Lg1X3t7+6L4SCmXgYEBm3+cVB+NXfPY0/XGVuxpDNC4Nd9iH7v2\n1H5LYlmx8ymLy7905H2L89grezputmJPfWBuLLW1tQv2o/rmsqfjZgvWGAO83+l28uRJnDhxAiUl\nJRgcHGReHx8fR1lZ2bSFGy/USaVSkzQSiWTaBTdiGUu+NH+hcsQ+qKurW9QXNOKYaNxiQf9ogjmU\nSqVdLbqZi8YuccQx4IgxE0KIo1qoP0ZDzGeNMcC76LZ69Wr4+fmhrq4OeXl5zOsCgQAvvPDCrCsn\nhBBCCCGEEEIIIWQh4l10c3NzQ3Z2No4dOwaRSAStVgszPo1KCCGEEEIIIYQQQsiixrvopvOXv/wF\n//mf/4lbt6Z+NVKr1UIgEEChUMxpcIQQQgghhBBCCCGEOCKzFt2OHDmCv//973bzayOEEEIIIYQQ\nQgghhNgzJ3MSRUZG0oIbIYQQQgghhBBCCCFmMuudbomJifjnf/5nLFu2DM7OzszrO3funLPACCGE\nEEIIIYQQQghxVGYtug0ODkIoFKKqqsrgdVp0I4QQQgghhBBCCCHElFmLbr/85S8BANeuXYNAIMDS\npUvnNChCCCGEEEIIIYQQQhyZWYtuFRUV+Jd/+RfcunULWq0WPj4+eOONN5CamjrX8RFCCCGEEEII\nIYQQ4nDMWnT71a9+hd/97ndISEgAADQ0NOAXv/gF/vrXv85pcIQQQgghhBBCCCGEOCKzfr3UycmJ\nWXADAJlMZvCDCoQQQgghhBBCCCGEkPvMXnQrKirC6OgoRkdH8fnnn9OiGyGEEEIIIYQQQgghHMz6\neOlPfvIT/OxnP8O//du/wcnJCVKpFD//+c/nOjZCCCGEEEIIIYQQQhySWe90Ky4uhlAoRFlZGUpL\nS6HVanH27Nm5jo0QQgghhBBCCCGEEIdk1qLb3//+d/zmN79htv/7v/8bx48fn7OgCCGEEEIIIYQQ\nQghxZGYtuk1MTBh8h5tAIIBWq52zoAghhBBCCCGEEEIIcWRmfafbhg0bsGfPHmRnZ2Nyc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9K89+C//14VFmO1MmRYo0AW++9wGTLys5CW//z0fM9t6HHsT/+wP7uzU7lSr82/PP4NS9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\nrng6/FhsaBBtHae1FlgYniY6dCYyNxeD+bMLlUglEk6dPkNsWLDedOY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3cSJQ4U1rewfZ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\n/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29v54033hB+L1myhE2bNo2ZU1fDlerrjIUeT0RwoNFV6+55+jmt3xp9tvkxUbg4OpKr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\nTkSuQmCC\n",
            "text/plain": [
              "<Figure size 1260x1260 with 56 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "G5f9IsTS5ZQJ",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "We can see that from the above pairplot that we do not have any linear relationships amongst the variables. "
      ]
    },
    {
      "metadata": {
        "colab_type": "text",
        "id": "LrmMLGjPFZdH"
      },
      "cell_type": "markdown",
      "source": [
        "### Distribution Plots"
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "JQ_RUT1CFZdI",
        "outputId": "4c34efc4-43be-46b2-9852-fad81b9df777",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 352
        }
      },
      "cell_type": "code",
      "source": [
        "sns.distplot(google_scaled[\"Rating\"])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/matplotlib/axes/_axes.py:6521: MatplotlibDeprecationWarning: \n",
            "The 'normed' kwarg was deprecated in Matplotlib 2.1 and will be removed in 3.1. Use 'density' instead.\n",
            "  alternative=\"'density'\", removal=\"3.1\")\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f7b5e92dac8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 44
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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AIAhI1Glh0CdAISjgdDnR2W1BS1s7THUNMNU1DHqs9JQkzJo2GdMmZEHNraxC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\n2P8H0XcCfsSJ91cAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "colab_type": "text",
        "id": "iSLsXKpwFZdM"
      },
      "cell_type": "markdown",
      "source": [
        "### Heatmap"
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "ThvIsjmpFZdN",
        "outputId": "4dacec85-61a8-4fc1-b32e-a26b318d67dd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 258
        }
      },
      "cell_type": "code",
      "source": [
        "google_scaled.corr()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Rating</th>\n",
              "      <th>Reviews</th>\n",
              "      <th>Installs</th>\n",
              "      <th>Type</th>\n",
              "      <th>Price</th>\n",
              "      <th>content_rating</th>\n",
              "      <th>category_code</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Rating</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.068141</td>\n",
              "      <td>0.051355</td>\n",
              "      <td>0.039581</td>\n",
              "      <td>-0.021903</td>\n",
              "      <td>0.000931</td>\n",
              "      <td>-0.034603</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Reviews</th>\n",
              "      <td>0.068141</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.641612</td>\n",
              "      <td>-0.043290</td>\n",
              "      <td>-0.009820</td>\n",
              "      <td>0.060971</td>\n",
              "      <td>0.000883</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Installs</th>\n",
              "      <td>0.051355</td>\n",
              "      <td>0.641612</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.053102</td>\n",
              "      <td>-0.011890</td>\n",
              "      <td>0.028532</td>\n",
              "      <td>0.022763</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Type</th>\n",
              "      <td>0.039581</td>\n",
              "      <td>-0.043290</td>\n",
              "      <td>-0.053102</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.223040</td>\n",
              "      <td>-0.034638</td>\n",
              "      <td>0.023227</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Price</th>\n",
              "      <td>-0.021903</td>\n",
              "      <td>-0.009820</td>\n",
              "      <td>-0.011890</td>\n",
              "      <td>0.223040</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.016128</td>\n",
              "      <td>-0.012913</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>content_rating</th>\n",
              "      <td>0.000931</td>\n",
              "      <td>0.060971</td>\n",
              "      <td>0.028532</td>\n",
              "      <td>-0.034638</td>\n",
              "      <td>-0.016128</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.124740</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>category_code</th>\n",
              "      <td>-0.034603</td>\n",
              "      <td>0.000883</td>\n",
              "      <td>0.022763</td>\n",
              "      <td>0.023227</td>\n",
              "      <td>-0.012913</td>\n",
              "      <td>-0.124740</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                  Rating   Reviews  Installs      Type     Price  \\\n",
              "Rating          1.000000  0.068141  0.051355  0.039581 -0.021903   \n",
              "Reviews         0.068141  1.000000  0.641612 -0.043290 -0.009820   \n",
              "Installs        0.051355  0.641612  1.000000 -0.053102 -0.011890   \n",
              "Type            0.039581 -0.043290 -0.053102  1.000000  0.223040   \n",
              "Price          -0.021903 -0.009820 -0.011890  0.223040  1.000000   \n",
              "content_rating  0.000931  0.060971  0.028532 -0.034638 -0.016128   \n",
              "category_code  -0.034603  0.000883  0.022763  0.023227 -0.012913   \n",
              "\n",
              "                content_rating  category_code  \n",
              "Rating                0.000931      -0.034603  \n",
              "Reviews               0.060971       0.000883  \n",
              "Installs              0.028532       0.022763  \n",
              "Type                 -0.034638       0.023227  \n",
              "Price                -0.016128      -0.012913  \n",
              "content_rating        1.000000      -0.124740  \n",
              "category_code        -0.124740       1.000000  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 45
        }
      ]
    },
    {
      "metadata": {
        "colab_type": "text",
        "id": "MJ7w5X-vPlcN"
      },
      "cell_type": "markdown",
      "source": [
        "\n",
        "We observe a high correlation ($r = 0.64$) between the numbers of reviews and installs. Intuitively this makes sense, as apps that have been installed more often would have a higher number of reviews. These is also a modest correlation ($r=0.22$) between type and price of an app.  \n",
        "\n"
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "8oxAQ0kIFZdO",
        "outputId": "64acc81d-6f0f-426d-c835-ff9f53125efe",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 339
        }
      },
      "cell_type": "code",
      "source": [
        "sns.heatmap(google_scaled.corr(),annot=True)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f7b5c9eb588>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 46
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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ovPrZ06hhA5RmtXHzmcfJn34mKzsbb7cZ6OrqErxlO+cio1Cpkxg/ZTrt27Zh2uSJr9dw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\nVDIiIrJo/gBkOfOxVY/LBwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "hSC5KupM5ZQX",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "We see that installs and reviews have the highest correlation. Negative correlations such as price vs. most variables could indicate that high prices are a putoff for users."
      ]
    },
    {
      "metadata": {
        "colab_type": "text",
        "id": "RYss8whIFZdS"
      },
      "cell_type": "markdown",
      "source": [
        "### Scatterplot"
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "5DnK37_cFZdT",
        "outputId": "b1b42791-f320-4cea-b12b-610370ce323a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        }
      },
      "cell_type": "code",
      "source": [
        "plt.scatter(google_scaled.Rating, google_scaled.Reviews)\n",
        "plt.xlabel('Rating')\n",
        "plt.ylabel('Reviews')\n",
        "plt.axhline(y=0)\n",
        "plt.title('Rating vs. Reviews')\n",
        "plt.show()\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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BA82VChER6WDWawBLly6t9fr+KZ6goCAEBQWZ8+uJiMgA7gVERCRQLABERALFAkBEJFAs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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "colab_type": "text",
        "id": "2Pezh0VmFZdU"
      },
      "cell_type": "markdown",
      "source": [
        "The scatterplot shows higher ratings with higher reviews, which makes sense. Apps with good ratings might have users leaving a good reviews for others to view.\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "YfdGs_ruFZdU",
        "outputId": "064401e2-54a8-4604-fdb6-69ca7fc85f54",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        }
      },
      "cell_type": "code",
      "source": [
        "plt.scatter(google_scaled.Rating, google_scaled.category_code)\n",
        "plt.xlabel('Rating')\n",
        "plt.ylabel('Category')\n",
        "plt.axhline(y=0)\n",
        "plt.title('Rating vs Category')\n",
        "plt.show()\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Pz8hAwq07BVQ+VpaWaOBQl5nbwKEuPNuyK391cmsFxzq18+5vF8aqqgU0GhNmTKMxgWfb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\nSUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "colab_type": "text",
        "id": "W0I0rUP-FZdX"
      },
      "cell_type": "markdown",
      "source": [
        "## Data Modeling"
      ]
    },
    {
      "metadata": {
        "id": "-l9MAKjA5ZQg",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "**Splitting the data modeling in two parts: Classification and Regression. The classification models should help us correctly classify the categorical variables against ratings.**"
      ]
    },
    {
      "metadata": {
        "colab_type": "text",
        "id": "nkWBWzkxrMPn"
      },
      "cell_type": "markdown",
      "source": [
        "\n",
        "## Regression Analyses"
      ]
    },
    {
      "metadata": {
        "colab_type": "text",
        "id": "32EYs_tdFZdY"
      },
      "cell_type": "markdown",
      "source": [
        "Lets begin by training our regression model. We aim at predicting the features that would lead to a given rating. "
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "TfRBT9KBFZdY",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "#Assigning my x and y\n",
        "\n",
        "features = ['Reviews','Installs','Type','Price',\n",
        "            'category_code','content_rating']\n",
        "X = google_scaled[features]\n",
        "y = google_scaled[['Rating']]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "LwbexrAVFZda",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "#Train Test Split\n",
        "from sklearn.model_selection import train_test_split"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "hMfCKgMfFZdc",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X, y, \n",
        "                                                    test_size=0.3,\n",
        "                                                    random_state=101)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "colab_type": "text",
        "id": "08q55kMrFZeG"
      },
      "cell_type": "markdown",
      "source": [
        "### Ridge Regression\n",
        "With ridge regression, we try to predict the rating as a continuous value. The low $r^2$ value indicates that these features have low explanatory power. We're likely underfitting the dataset. \n",
        "\n",
        "**TO DO: add polynomial features and use a lower value for alpha **"
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "I2psI1koFZeH",
        "outputId": "6c4cb26f-7603-440f-8e38-a3d66b556fba",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 90
        }
      },
      "cell_type": "code",
      "source": [
        "from sklearn import linear_model\n",
        "from sklearn.linear_model import Ridge\n",
        "\n",
        "ridgeregr = linear_model.Ridge(alpha=0.5, fit_intercept=True) \n",
        "ridgeregr.fit(X_train, y_train)\n",
        "print(\"The r^2 value is = {}\".format(ridgeregr.score(X_train, y_train)))\n",
        "origparams = ridgeregr.coef_[0]\n",
        "print(\"The parameters of features {} \\n for the ridge regression model are \\n{}\".format(features, origparams))\n",
        "\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "The r^2 value is = 0.008564418905254056\n",
            "The parameters of features ['Reviews', 'Installs', 'Type', 'Price', 'category_code', 'content_rating'] \n",
            " for the ridge regression model are \n",
            "[ 0.1618027   0.02650054  0.02595659 -0.08714568 -0.01947737  0.00234455]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "AQNYDKVpo3PT",
        "colab_type": "code",
        "outputId": "0ec9a592-306b-48fd-ed00-ba65dc9ca256",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 399
        }
      },
      "cell_type": "code",
      "source": [
        "#Polynomial Ridge Regression\n",
        "from sklearn.preprocessing import PolynomialFeatures\n",
        "\n",
        "print('\\nNow we transform the original input data to add\\n\\\n",
        "polynomial features up to degree 2 (quadratic)\\n')\n",
        "poly = PolynomialFeatures(degree=2)\n",
        "X_poly = poly.fit_transform(X)\n",
        "\n",
        "\n",
        "print('\\nAddition of many polynomial features often leads to\\n\\\n",
        "overfitting, so we often use polynomial features in combination\\n\\\n",
        "with regression that has a regularization penalty, like ridge\\n\\\n",
        "regression.\\n')\n",
        "\n",
        "\n",
        "X_train_poly, X_test_poly, y_train, y_test = train_test_split(X_poly, \n",
        "                                                              y,\n",
        "                                                              random_state = 0)\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "ridgereg = Ridge().fit(X_train_poly, y_train)\n",
        "\n",
        "print('(poly deg 2 + ridge) linear model coeff (w):\\n{}'.format(ridgereg.coef_))\n",
        "print('(poly deg 2 + ridge) linear model intercept (b): {}'.format(ridgereg.intercept_))\n",
        "print('(poly deg 2 + ridge) R-squared score (training): {}'.format(ridgereg.score(X_train_poly, y_train)))\n",
        "print('(poly deg 2 + ridge) R-squared score (test): {}'.format(ridgereg.score(X_test_poly, y_test)))\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Now we transform the original input data to add\n",
            "polynomial features up to degree 2 (quadratic)\n",
            "\n",
            "\n",
            "Addition of many polynomial features often leads to\n",
            "overfitting, so we often use polynomial features in combination\n",
            "with regression that has a regularization penalty, like ridge\n",
            "regression.\n",
            "\n",
            "(poly deg 2 + ridge) linear model coeff (w):\n",
            "[[ 0.00000000e+00  2.78950506e-01  2.16420903e-01  6.63800203e-03\n",
            "  -3.40277212e-02  5.28231530e-02 -1.86844155e-02 -1.56101414e-01\n",
            "  -1.54042240e-01  3.61885309e-03  4.40025691e-05  1.12582907e-01\n",
            "   2.39671586e-02 -2.62963228e-01  2.17005644e-03  1.88825852e-05\n",
            "   5.51066201e-02 -2.02420052e-02  6.63800203e-03 -3.40277212e-02\n",
            "   1.85762907e-02  6.29007300e-02 -1.33086994e-03 -5.11846340e-02\n",
            "   1.17069304e-02 -7.25191588e-02  1.25360705e-01 -1.13356854e-01]]\n",
            "(poly deg 2 + ridge) linear model intercept (b): [0.79159375]\n",
            "(poly deg 2 + ridge) R-squared score (training): 0.022443572637663722\n",
            "(poly deg 2 + ridge) R-squared score (test): 0.024319713337627835\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "colab_type": "text",
        "id": "kwjIxZn-FZdd"
      },
      "cell_type": "markdown",
      "source": [
        "### Linear Regression"
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "SsP8K2QrFZde",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LinearRegression"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "Bd3SgWUqFZdf",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "lm = LinearRegression()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "rJ-8ZwGhFZdg",
        "outputId": "590167c0-79d3-4f70-a955-fb0ee335a34e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        }
      },
      "cell_type": "code",
      "source": [
        "#Train\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, \n",
        "                                                    test_size=0.3,\n",
        "                                                    random_state=101)\n",
        "\n",
        "lm.fit(X_train,y_train)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None,\n",
              "         normalize=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 56
        }
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "ErhCgUY6FZdk",
        "outputId": "a2baa2c6-9c98-4a13-8621-3705bba7b89f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "cell_type": "code",
      "source": [
        "#Evaluate by checking coefficients and intercept\n",
        "print(lm.intercept_)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[0.80417688]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "sRhWzhGbFZdm",
        "outputId": "5a43808a-559d-4128-b0ff-3c8c78038489",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 72
        }
      },
      "cell_type": "code",
      "source": [
        "print (\"Features: {}\".format(features))\n",
        "# The coefficients are relatively low \n",
        "print('Coefficients: \\n', lm.coef_)\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Features: ['Reviews', 'Installs', 'Type', 'Price', 'category_code', 'content_rating']\n",
            "Coefficients: \n",
            " [[ 0.17311278  0.02341905  0.02615527 -0.09135575 -0.01950176  0.00222041]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "xjLFsk0nFZdo",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "#Test\n",
        "predictions = lm.predict(X_test)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "mRsqcStjFZdq",
        "outputId": "983ec195-35d4-4a4b-facb-5724f9a0ef35",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "cell_type": "code",
      "source": [
        "plt.scatter(y_test,predictions)\n",
        "#Why does it not show a straight line? \n",
        "#Is corr low?"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7f7b5b8b3320>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 60
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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C0siRXXrJ3ds7N+dVXNcb7a+tDIPreiPe3fk5/vHmGofr0Hk4qMVsxl8+3o1qlgAfExWJ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\nCaLgTgghEvT/AfuV00HQ8ABIAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "9mdleRxZFZdr",
        "outputId": "dc0a0ab8-64ef-4e5b-bc83-a513bf592987",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 72
        }
      },
      "cell_type": "code",
      "source": [
        "#Evaluate\n",
        "#Mean Squared Error = MSE\n",
        "#Mean Absolute Error = MAE\n",
        "#Root Mean Squared = RSME\n",
        "#from sklearn.cross_decomposition import PLSRegression\n",
        "from sklearn import metrics\n",
        "\n",
        "print('MAE:', metrics.mean_absolute_error(y_test, predictions))\n",
        "print('MSE:', metrics.mean_squared_error(y_test, predictions))\n",
        "print('RMSE:', np.sqrt(metrics.mean_squared_error(y_test, predictions)))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "MAE: 0.08931495536371267\n",
            "MSE: 0.016007898366191792\n",
            "RMSE: 0.12652232358833673\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "iKWLugx2FZdu",
        "outputId": "65d3453f-af0f-4763-8b1c-62e47a0486e9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 320
        }
      },
      "cell_type": "code",
      "source": [
        "#Residuals\n",
        "sns.distplot((y_test-predictions),bins=50);"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/matplotlib/axes/_axes.py:6521: MatplotlibDeprecationWarning: \n",
            "The 'normed' kwarg was deprecated in Matplotlib 2.1 and will be removed in 3.1. Use 'density' instead.\n",
            "  alternative=\"'density'\", removal=\"3.1\")\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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jViuOnzmH0xcrIZPJsG3DWmiiIjiNLgRsLV6DN9/7GB9+ekLyjWwZ0hQ0evsH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\nYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "kt0fJA8XtBwk",
        "colab_type": "code",
        "outputId": "206d16b4-4de7-4f52-cfc3-22c26a7f4f98",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "cell_type": "code",
      "source": [
        "#Polynomial Linear Regression\n",
        "print('\\nNow we transform the original input data to add\\n\\\n",
        "polynomial features up to degree 2 (quadratic)\\n')\n",
        "poly = PolynomialFeatures(degree=2)\n",
        "X_poly = poly.fit_transform(X)\n",
        "\n",
        "\n",
        "print('\\nAddition of many polynomial features often leads to\\n\\\n",
        "overfitting, so we often use polynomial features in combination\\n\\\n",
        "with regression that has a regularization penalty, like ridge\\n\\\n",
        "regression.\\n')\n",
        "\n",
        "\n",
        "X_train_poly, X_test_poly, y_train, y_test = train_test_split(X_poly, \n",
        "                                                              y,\n",
        "                                                              random_state = 0)\n",
        "\n",
        "\n",
        "linreg = LinearRegression().fit(X_train_poly, y_train)\n",
        "\n",
        "print('(poly deg 2) linear model coeff (w):\\n{}'\n",
        "     .format(linreg.coef_))\n",
        "print('(poly deg 2) linear model intercept (b): {}'\n",
        "     .format(linreg.intercept_))\n",
        "print('(poly deg 2) R-squared score (training): {}'\n",
        "     .format(linreg.score(X_train_poly, y_train)))\n",
        "print('(poly deg 2) R-squared score (test): {}'\n",
        "     .format(linreg.score(X_test_poly, y_test)))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Now we transform the original input data to add\n",
            "polynomial features up to degree 2 (quadratic)\n",
            "\n",
            "\n",
            "Addition of many polynomial features often leads to\n",
            "overfitting, so we often use polynomial features in combination\n",
            "with regression that has a regularization penalty, like ridge\n",
            "regression.\n",
            "\n",
            "(poly deg 2) linear model coeff (w):\n",
            "[[-2.99929554e-10  6.25553178e-01  1.67036670e-01  2.59858914e-03\n",
            "   1.25359652e-01  5.28265136e-02 -1.96160289e-02 -5.77619384e-01\n",
            "  -1.91590625e-01  1.15677932e+02 -6.97730971e+03  1.52056961e-01\n",
            "  -1.31894196e-01 -2.34068348e-01 -5.35502263e+01  4.33039973e+03\n",
            "   4.99606814e-02 -6.50233243e-02  2.59857542e-03  1.25359640e-01\n",
            "   3.02228585e-02  5.66468808e-02 -6.51806378e-02 -7.17823386e-01\n",
            "   3.41221307e-01 -7.29336406e-02  1.38232585e-01 -1.24788282e-01]]\n",
            "(poly deg 2) linear model intercept (b): [0.79089785]\n",
            "(poly deg 2) R-squared score (training): 0.024710738145733546\n",
            "(poly deg 2) R-squared score (test): 0.02647794979104035\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "bC_WDaP25ZRg",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Classifier Analyses"
      ]
    },
    {
      "metadata": {
        "colab_type": "text",
        "id": "R1c6VEKMFZdw"
      },
      "cell_type": "markdown",
      "source": [
        "### Logistic Regression "
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "DBPhx3M3FZdx",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# Logistic regression is a classification model \n",
        "from sklearn.linear_model import LogisticRegression"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "R3KLeH1c0giM",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X, y, \n",
        "                                                    test_size=0.3,\n",
        "                                                    random_state=101)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "etwpbYe3FZd1",
        "outputId": "79967377-61eb-4ae4-c4a7-f5c9d19112d9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        }
      },
      "cell_type": "code",
      "source": [
        "from sklearn import preprocessing\n",
        "from sklearn import utils\n",
        "\n",
        "lab_enc = preprocessing.LabelEncoder()\n",
        "encoded = lab_enc.fit_transform(y_train)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/preprocessing/label.py:235: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "8YbTgvPCTFR4",
        "outputId": "fd8be7c4-007d-40f4-a64e-57a75917db3b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 90
        }
      },
      "cell_type": "code",
      "source": [
        "print('median rating = {}, min rating = {}, max rating = {}'.format(y_train.median()\n",
        ", y_train.min(), y.max()))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "median rating = Rating    0.825\n",
            "dtype: float64, min rating = Rating    0.0\n",
            "dtype: float64, max rating = Rating    1.0\n",
            "dtype: float64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "colab_type": "text",
        "id": "Fb1ni1HbYT2w"
      },
      "cell_type": "markdown",
      "source": [
        "To apply logistic regression, we binarize ratings as being above or below the median. "
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "Kc_xS9s1Vn3L",
        "outputId": "cd4df127-1742-4d0f-c1da-e9fa4de2b46b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 138
        }
      },
      "cell_type": "code",
      "source": [
        "y_train_binary = (y_train > y_train.median()).astype(np.int)\n",
        "y_train_binary.head(3)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Rating</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>558</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1891</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5626</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      Rating\n",
              "558        1\n",
              "1891       1\n",
              "5626       0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 68
        }
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "HY7zUHnVFZd5",
        "outputId": "b60deff7-de97-42a3-d23c-c5b8bde92ad8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "cell_type": "code",
      "source": [
        "print(utils.multiclass.type_of_target(y_train_binary))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "binary\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "s2PHVefdFZd_",
        "outputId": "36aa5bda-6e5e-4b94-f64b-6ce57dc2d7fc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 183
        }
      },
      "cell_type": "code",
      "source": [
        "logmodel = LogisticRegression()\n",
        "logmodel.fit(X_train,y_train_binary)\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
            "  FutureWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
              "          intercept_scaling=1, max_iter=100, multi_class='warn',\n",
              "          n_jobs=None, penalty='l2', random_state=None, solver='warn',\n",
              "          tol=0.0001, verbose=0, warm_start=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 70
        }
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "lEI-mz1nFZeB",
        "outputId": "410efbc7-73e2-47cc-ba09-f4bf1b02b450",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "cell_type": "code",
      "source": [
        "predictions = logmodel.predict(X_train)\n",
        "print(predictions.shape, y_train_binary.shape)\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(6556,) (6556, 1)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "DiBkTeeDW3KY",
        "outputId": "a181780b-209c-4f99-c867-aae88557d159",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 181
        }
      },
      "cell_type": "code",
      "source": [
        "#Evaluate\n",
        "from sklearn.metrics import classification_report\n",
        "print(classification_report(y_train_binary,predictions))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.58      0.94      0.72      3707\n",
            "           1       0.60      0.11      0.19      2849\n",
            "\n",
            "   micro avg       0.58      0.58      0.58      6556\n",
            "   macro avg       0.59      0.53      0.45      6556\n",
            "weighted avg       0.59      0.58      0.49      6556\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "-Fv17Mp3FZeC",
        "outputId": "8ca0f6d1-569a-4cea-cbe9-0196a944e5e4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        }
      },
      "cell_type": "code",
      "source": [
        "#Encode ytest\n",
        "lab_enc = preprocessing.LabelEncoder()\n",
        "encoded_test = lab_enc.fit_transform(y_test)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/preprocessing/label.py:235: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "0lb_u-jyd9u1",
        "outputId": "84e96cc9-c77b-4515-e04b-aa5f75069ba0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 138
        }
      },
      "cell_type": "code",
      "source": [
        "y_test_binary = (y_test > y_test.median()).astype(np.int)\n",
        "y_test_binary.head(3)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Rating</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>7693</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3053</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3387</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      Rating\n",
              "7693       1\n",
              "3053       1\n",
              "3387       0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 74
        }
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "mFcphqGSFZeC",
        "outputId": "bc89f189-1254-4720-9ab8-78cb855a8aa3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "cell_type": "code",
      "source": [
        "print(utils.multiclass.type_of_target(y_test_binary))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "binary\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "Ims0OvRtd9u7",
        "outputId": "e3607ba6-fe25-4213-d66e-428a2f183bed",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 183
        }
      },
      "cell_type": "code",
      "source": [
        "logmodel = LogisticRegression()\n",
        "logmodel.fit(X_test,y_test_binary)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
            "  FutureWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
              "          intercept_scaling=1, max_iter=100, multi_class='warn',\n",
              "          n_jobs=None, penalty='l2', random_state=None, solver='warn',\n",
              "          tol=0.0001, verbose=0, warm_start=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 76
        }
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "gidSyMM-d9u-",
        "outputId": "f414aeb6-b10f-4543-d722-14785b4a27c4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "cell_type": "code",
      "source": [
        "predictions_test = logmodel.predict(X_test)\n",
        "print(predictions_test.shape, y_test_binary.shape)\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(2810,) (2810, 1)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "W7S_si8VFZeE",
        "outputId": "01051560-9a09-45c6-f986-90fb93a70f40",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 181
        }
      },
      "cell_type": "code",
      "source": [
        "print(classification_report(y_test_binary,predictions_test))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.58      0.94      0.72      1595\n",
            "           1       0.57      0.10      0.16      1215\n",
            "\n",
            "   micro avg       0.58      0.58      0.58      2810\n",
            "   macro avg       0.57      0.52      0.44      2810\n",
            "weighted avg       0.58      0.58      0.48      2810\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "colab_type": "text",
        "id": "cNBBnDqBd9vD"
      },
      "cell_type": "markdown",
      "source": [
        "Test is close to train (a little lower)."
      ]
    },
    {
      "metadata": {
        "colab_type": "text",
        "id": "0UAhv_rVFZeI"
      },
      "cell_type": "markdown",
      "source": [
        "### Decision Trees"
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "32ujy5kZFZeI",
        "outputId": "ebbc80d8-4fb2-4f59-c0ac-dccd5974f63b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 774
        }
      },
      "cell_type": "code",
      "source": [
        "from sklearn import tree\n",
        "\n",
        "# A convenience for displaying visualizations.\n",
        "from IPython.display import Image\n",
        "\n",
        "# Packages for rendering our tree.\n",
        "import pydotplus\n",
        "import graphviz\n",
        "\n",
        "# Initialize and train our tree.\n",
        "decision_tree = tree.DecisionTreeClassifier(\n",
        "    criterion='entropy',\n",
        "    max_features=1,\n",
        "    max_depth=4,\n",
        "    random_state = 1337\n",
        ")\n",
        "decision_tree.fit(X_train, y_train_binary)\n",
        "\n",
        "# Render our tree.\n",
        "dot_data = tree.export_graphviz(\n",
        "    decision_tree, out_file=None,\n",
        "    feature_names= X_train.columns,\n",
        "    class_names=['High Rating','Low Rating'],\n",
        "    filled=True\n",
        ")\n",
        "graph = pydotplus.graph_from_dot_data(dot_data)\n",
        "Image(graph.create_png())\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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+uQ8Eh7F0+/XgN/2a4pLy9v2KD+w4QEAAD3661X1VYq5aK1W6WvP27f28OvnaWF+Z17GhsZCSHK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\nmQAAACCb22awt7Oz8/X1XbBgQ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Abz6NGjns4FZWVljRkzhkgkKisr+/n5NTc3t9eSxWJt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\ntZ2lvMgvZrE57vZWmqoKeBwWoU5twduZyK3gcVh3e6usw9v6a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QouG5vouS8L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\nkRFCCP1t8Lw5hBBC9VSVlSaOGZ1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P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\nupsWTeke0H1b4H6D/wGLV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Bk/islk5t7nfJCApabxM0FU8OtVuoQwAKCq4dNAck/tdMXz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\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655++BsbenjXGuIeKkHlkEQRBkE6XX1rlEnwuLae4ns15n5GXXVRh1EtVRU4SAO6+S6+r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qiu+O8ySYXO0Gnxq6nj0nZ9rL/5P9qltTN6NmfiWggkAfVMIDYNrGdSbCH7ox49zd/g918HxVou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\nv3/+/PmzZ8/ev3/funVrrOwgR8RyBb558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XqS27zN+8/lPcjwcRlfXlbi84WvuW0rF58/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o3Lx2zafMiKq8FwhmZtOjTgCAARRq0/4TG3XUm0qSylNDZouL4HA4ssJx2pDMcHRFPtnj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\nnJaScmzVEXSeFp0NVS09AGDRquU3qy0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1BMBVrQfhNY2KHx4j6nfDUNTVegzFqeoUPzhE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\nAAAAAAAAIBT/BTLYCYPAR5HyAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 79
        }
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "RhnycwJ3FZeK",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "predictions_test = decision_tree.predict(X_test)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "ZiFrUrJvFZeL",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import classification_report,confusion_matrix"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "BCnfYTHvFZeM",
        "outputId": "f89be8ca-29fb-49f7-dfe0-c644dc1e46e6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 181
        }
      },
      "cell_type": "code",
      "source": [
        "print(classification_report(y_test_binary,predictions_test))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.59      0.93      0.72      1595\n",
            "           1       0.61      0.14      0.23      1215\n",
            "\n",
            "   micro avg       0.59      0.59      0.59      2810\n",
            "   macro avg       0.60      0.54      0.48      2810\n",
            "weighted avg       0.60      0.59      0.51      2810\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "colab_type": "text",
        "id": "jABnW1PHd9vl"
      },
      "cell_type": "markdown",
      "source": [
        "Awesome, its performing marginally better!"
      ]
    },
    {
      "metadata": {
        "colab_type": "text",
        "id": "3novD_lGFZeN"
      },
      "cell_type": "markdown",
      "source": [
        "### Random Forest Classifier"
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "BmCVCplkFZeO",
        "outputId": "c4cf2985-bf8f-4cc7-e7d1-e81e2800f745",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 201
        }
      },
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import RandomForestClassifier\n",
        "rfc = RandomForestClassifier(n_estimators=100)\n",
        "rfc.fit(X_train, y_train_binary)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:3: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  This is separate from the ipykernel package so we can avoid doing imports until\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
              "            max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
              "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
              "            min_samples_leaf=1, min_samples_split=2,\n",
              "            min_weight_fraction_leaf=0.0, n_estimators=100, n_jobs=None,\n",
              "            oob_score=False, random_state=None, verbose=0,\n",
              "            warm_start=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 83
        }
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "1f0A4sQHFZeQ",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "rfc_pred = rfc.predict(X_test)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "JZXEKzpgFZeR",
        "outputId": "9e554f51-6795-49ce-ff9d-a4b32b6d4df9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        }
      },
      "cell_type": "code",
      "source": [
        "print(confusion_matrix(y_test_binary,rfc_pred))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[1195  400]\n",
            " [ 462  753]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "-kFhZm7jnPBs",
        "outputId": "ee54fc0d-9698-4d21-d4de-c472a84f6a02",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 181
        }
      },
      "cell_type": "code",
      "source": [
        "print(classification_report(y_test_binary, rfc_pred))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.72      0.75      0.73      1595\n",
            "           1       0.65      0.62      0.64      1215\n",
            "\n",
            "   micro avg       0.69      0.69      0.69      2810\n",
            "   macro avg       0.69      0.68      0.69      2810\n",
            "weighted avg       0.69      0.69      0.69      2810\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "colab_type": "text",
        "id": "_IzUA9obn1PS"
      },
      "cell_type": "markdown",
      "source": [
        "The random forest classifier outperforms the other two. We care the most about the f1-score metric which represents the **harmonic mean** of precision and recall. Both precision and recall have to be high for the $f_1$ measure to be high. \n",
        "\n",
        "$$\\textit{precision} = \\frac{TP}{TP + FP}$$\n",
        "\n",
        "\n",
        "$$\\textit{recall} = \\frac{TP}{TP + FN}$$\n",
        "\n",
        "\n",
        "$$f_{1} = \\frac{2 \\times \\textit{precision} \\times{recall}}{\\textit{precision} + {recall}}$$"
      ]
    }
  ]
}