Seattle Airbnb Open Data.ipynb

seattle-airbnb-open-data.ipynb

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  "metadata": {
    "colab": {
      "name": "Seattle Airbnb Open Data.ipynb",
      "version": "0.3.2",
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      "include_colab_link": true
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    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/gist/jinglescode/08d21b680bd11008c73083f9645d6b1d/seattle-airbnb-open-data.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "V6Rnha-lfvNB",
        "colab_type": "text"
      },
      "source": [
        "# Introduction\n",
        "As a prospective Airbnb host in Seattle, I have questions like:\n",
        "* When to rent to maximise revenue, and for maintenance during off-peak?\n",
        "* Common group size of Seattle travelers, is it 2 or family or 4 or larger?\n",
        "* Bedroom configurations to maximise booking rates?\n",
        "* Main factors to achieve good rating?\n",
        "* Does higher ratings hosts has higher revenue?\n",
        "* Common amenities to include?"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "b85lo5i1juLc",
        "colab_type": "code",
        "outputId": "19ed0265-d05c-4342-bc02-6e5468eafd37",
        "cellView": "form",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "#@title Load Packages\n",
        "import pandas as pd\n",
        "pd.options.mode.chained_assignment = None  # default='warn'\n",
        "import numpy as np\n",
        "import seaborn as sns\n",
        "import matplotlib as mpl\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "\n",
        "print(\"Packages loaded\")"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Packages loaded\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QT3CxlNM_C9j",
        "colab_type": "text"
      },
      "source": [
        "# Fetch and Prepare Data\n",
        "Fetch listing and reviews data from csv files, and perform data cleaning\n",
        "* review_score_xx that are empty are assumed that they do not have reviews, so set as zero\n",
        "* bathrooms, bedrooms, and beds that are empty are assumed that they do not have, so set as zero\n",
        "* price has dollar sign, remove it and cast as float\n",
        "* covert date to pandas datatime type\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BwNKcG7elfHn",
        "colab_type": "code",
        "cellView": "both",
        "outputId": "e3aad258-ae2a-42fa-a638-9b5e74712017",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "#@title Load data from listings.csv\n",
        "# get listings data\n",
        "pd_listings = pd.read_csv(\"https://raw.githubusercontent.com/jinglescode/notebooks/master/kaggle/seattle_airbnb_open_data/listings.csv\")\n",
        "\n",
        "# select columns from pd_listings\n",
        "pd_listings = pd_listings[['id','name','neighbourhood_group_cleansed','latitude','longitude','property_type','room_type','accommodates','bathrooms','bedrooms','beds','amenities','price','guests_included','minimum_nights','number_of_reviews','review_scores_rating','review_scores_accuracy','review_scores_cleanliness','review_scores_checkin','review_scores_communication','review_scores_location','review_scores_value']]\n",
        "\n",
        "# basic data cleaning\n",
        "pd_listings['price'] = pd_listings['price'].str.replace(\"[$, ]\", \"\").astype(\"float\")\n",
        "\n",
        "pd_listings.at[pd_listings['bathrooms'].isnull(), 'bathrooms'] = 0\n",
        "pd_listings.at[pd_listings['bedrooms'].isnull(), 'bedrooms'] = 0 # yea there are 6 that has no bedrooms, but they do have 1 bathrooms\n",
        "pd_listings.at[pd_listings['beds'].isnull(), 'beds'] = 0 # there's one listing for 1 guest, without any beds\n",
        "\n",
        "pd_listings.at[pd_listings['review_scores_rating'].isnull(), 'review_scores_rating'] = 0\n",
        "pd_listings.at[pd_listings['review_scores_accuracy'].isnull(), 'review_scores_accuracy'] = 0\n",
        "pd_listings.at[pd_listings['review_scores_cleanliness'].isnull(), 'review_scores_cleanliness'] = 0\n",
        "pd_listings.at[pd_listings['review_scores_checkin'].isnull(), 'review_scores_checkin'] = 0\n",
        "pd_listings.at[pd_listings['review_scores_communication'].isnull(), 'review_scores_communication'] = 0\n",
        "pd_listings.at[pd_listings['review_scores_location'].isnull(), 'review_scores_location'] = 0\n",
        "pd_listings.at[pd_listings['review_scores_value'].isnull(), 'review_scores_value'] = 0\n",
        "\n",
        "pd_listings.rename(columns={'id':'listing_id'}, inplace=True)\n",
        "\n",
        "# pd_listings.describe()\n",
        "\n",
        "print('listings.csv loaded into pd_listings')"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "listings.csv loaded into pd_listings\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6kayoy_tQb-3",
        "colab_type": "code",
        "cellView": "both",
        "outputId": "c3affeec-592d-4851-c0e5-9597c2af1ada",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "#@title Load data from reviews.csv\n",
        "pd_reviews = pd.read_csv(\"https://raw.githubusercontent.com/jinglescode/notebooks/master/kaggle/seattle_airbnb_open_data/reviews.csv\")\n",
        "\n",
        "pd_reviews = pd_reviews[['id','listing_id','date']]\n",
        "\n",
        "# basic conversions\n",
        "pd_reviews['date'] = pd.to_datetime(pd_reviews['date'])\n",
        "\n",
        "# pd_reviews.head()\n",
        "print('reviews.csv loaded into pd_reviews')"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "reviews.csv loaded into pd_reviews\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8wDWQ1CxJ2xC",
        "colab_type": "text"
      },
      "source": [
        "**Check if reviews.csv tally with listings.csv**\n",
        "\n",
        "In listings.csv, it has 'number_of_reviews' column, so just to check if number of reviews in reviews.csv is equal to SUM(number_of_reviews). Result shows empty result (0 rows), means that the number of reviews in reviews.csv matches the sum of number_of_reviews in listings.csv. \n",
        "\n",
        "**Create bookings DataFrame**\n",
        "\n",
        "With this, I shall create a dataframe pd_bookings using each review as one booking, and estimate revenue by (price * minimum_nights)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0BPSPsyN8v8n",
        "colab_type": "code",
        "outputId": "2aa8dd4d-2408-4620-e66d-31f81697a9a2",
        "cellView": "form",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 86
        }
      },
      "source": [
        "#@title \n",
        "pd_listing_count_reviws = pd_reviews[['listing_id','id']].groupby(['listing_id']).count()\n",
        "pd_listing_count_reviws.columns = ['# of reviews']\n",
        "# pd_listing_count_reviws['listing_id'] = pd_listing_count_reviws.index\n",
        "\n",
        "pd_listings_plus_reviews = pd.merge(pd_listings, pd_listing_count_reviws, on='listing_id')\n",
        "\n",
        "pd_listings_plus_reviews.at[pd_listings_plus_reviews['# of reviews'].isnull(), '# of reviews'] = 0\n",
        "\n",
        "pd_listings_plus_reviews[ pd_listings_plus_reviews['# of reviews'] != pd_listings_plus_reviews['number_of_reviews']]"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "    }\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>listing_id</th>\n",
              "      <th>name</th>\n",
              "      <th>neighbourhood_group_cleansed</th>\n",
              "      <th>latitude</th>\n",
              "      <th>longitude</th>\n",
              "      <th>property_type</th>\n",
              "      <th>room_type</th>\n",
              "      <th>accommodates</th>\n",
              "      <th>bathrooms</th>\n",
              "      <th>bedrooms</th>\n",
              "      <th>beds</th>\n",
              "      <th>amenities</th>\n",
              "      <th>price</th>\n",
              "      <th>guests_included</th>\n",
              "      <th>minimum_nights</th>\n",
              "      <th>number_of_reviews</th>\n",
              "      <th>review_scores_rating</th>\n",
              "      <th>review_scores_accuracy</th>\n",
              "      <th>review_scores_cleanliness</th>\n",
              "      <th>review_scores_checkin</th>\n",
              "      <th>review_scores_communication</th>\n",
              "      <th>review_scores_location</th>\n",
              "      <th>review_scores_value</th>\n",
              "      <th># of reviews</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "Empty DataFrame\n",
              "Columns: [listing_id, name, neighbourhood_group_cleansed, latitude, longitude, property_type, room_type, accommodates, bathrooms, bedrooms, beds, amenities, price, guests_included, minimum_nights, number_of_reviews, review_scores_rating, review_scores_accuracy, review_scores_cleanliness, review_scores_checkin, review_scores_communication, review_scores_location, review_scores_value, # of reviews]\n",
              "Index: []"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nRz7ivxnMlAz",
        "colab_type": "code",
        "cellView": "form",
        "colab": {}
      },
      "source": [
        "#@title Calculate estimated revenue for each listing\n",
        "\n",
        "# get estimated bookings base on reviews\n",
        "pd_bookings = pd.merge(pd_reviews, pd_listings, on='listing_id')\n",
        "pd_bookings['estimated_revenue'] = pd_bookings['price'] * pd_bookings['minimum_nights']\n",
        "\n",
        "# get revenue by listings\n",
        "pd_listings_revenue = pd_bookings[['listing_id','estimated_revenue']].groupby(['listing_id']).sum()\n",
        "# pd_listings_revenue['listing_id'] = pd_listings_revenue.index\n",
        "\n",
        "pd_listings = pd.merge(pd_listings, pd_listings_revenue, on='listing_id', how='left')\n",
        "pd_listings.at[pd_listings['estimated_revenue'].isnull(), 'estimated_revenue'] = 0"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bwyT9Q88KCTm",
        "colab_type": "text"
      },
      "source": [
        "# Time to answer some questions"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xvALlL-zY80R",
        "colab_type": "code",
        "outputId": "6be70bce-a0fc-4ff6-d2b6-75df0fc2bd2d",
        "cellView": "form",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "#@title Showing 5 highest revenue listings\n",
        "pd_listings[['listing_id','number_of_reviews','minimum_nights','accommodates','bedrooms','beds','estimated_revenue']].sort_values('estimated_revenue', ascending=False).head()"
      ],
      "execution_count": 6,
      "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>listing_id</th>\n",
              "      <th>number_of_reviews</th>\n",
              "      <th>minimum_nights</th>\n",
              "      <th>accommodates</th>\n",
              "      <th>bedrooms</th>\n",
              "      <th>beds</th>\n",
              "      <th>estimated_revenue</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2617</th>\n",
              "      <td>3594885</td>\n",
              "      <td>8</td>\n",
              "      <td>1000</td>\n",
              "      <td>4</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1200000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2107</th>\n",
              "      <td>5056580</td>\n",
              "      <td>100</td>\n",
              "      <td>31</td>\n",
              "      <td>2</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>306900.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1500</th>\n",
              "      <td>4009508</td>\n",
              "      <td>38</td>\n",
              "      <td>20</td>\n",
              "      <td>5</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>266000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1537</th>\n",
              "      <td>1954452</td>\n",
              "      <td>71</td>\n",
              "      <td>14</td>\n",
              "      <td>2</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>218680.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1519</th>\n",
              "      <td>3971934</td>\n",
              "      <td>48</td>\n",
              "      <td>20</td>\n",
              "      <td>3</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>171840.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      listing_id  number_of_reviews  ...  beds  estimated_revenue\n",
              "2617     3594885                  8  ...   1.0          1200000.0\n",
              "2107     5056580                100  ...   1.0           306900.0\n",
              "1500     4009508                 38  ...   2.0           266000.0\n",
              "1537     1954452                 71  ...   1.0           218680.0\n",
              "1519     3971934                 48  ...   1.0           171840.0\n",
              "\n",
              "[5 rows x 7 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BSzM5ZcvSwGI",
        "colab_type": "text"
      },
      "source": [
        "Wow! Looks like our top earners are hosts have minimum nights of 1000. But it might be data anomaly because 1000 nights are kind of extreme, so let's look at the proportion of listings with different minimum_nights."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TI798sgQSwYN",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 762
        },
        "outputId": "474ace48-c2db-41a7-e2ed-8af8c513a0ed"
      },
      "source": [
        "pd_listings[['listing_id','minimum_nights']].groupby(['minimum_nights']).count().sort_values('minimum_nights')"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
              "        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>listing_id</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>minimum_nights</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1610</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1423</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>480</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>122</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>67</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>50</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1000</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                listing_id\n",
              "minimum_nights            \n",
              "1                     1610\n",
              "2                     1423\n",
              "3                      480\n",
              "4                      122\n",
              "5                       67\n",
              "6                       16\n",
              "7                       50\n",
              "8                        1\n",
              "10                       5\n",
              "11                       1\n",
              "12                       1\n",
              "13                       2\n",
              "14                      16\n",
              "15                       1\n",
              "20                      11\n",
              "21                       1\n",
              "26                       1\n",
              "28                       1\n",
              "29                       1\n",
              "30                       6\n",
              "31                       1\n",
              "1000                     1"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tK-6IUkCLEFO",
        "colab_type": "text"
      },
      "source": [
        "Most hosts have minimum_nights of up to a month, the host with 1000 nights, gotta filter it away."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gkg8Q5j_LYii",
        "colab_type": "code",
        "outputId": "7517a978-06f8-4c76-b9d8-9bd698a3aa09",
        "cellView": "form",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "#@title Showing 5 highest revenue listings (minimum_nights <= 7)\n",
        "pd_listings.loc[pd_listings['minimum_nights']<=7, ['listing_id','number_of_reviews','minimum_nights','accommodates','bedrooms','beds','estimated_revenue']].sort_values('estimated_revenue', ascending=False).head()"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
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              "\n",
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              "        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>listing_id</th>\n",
              "      <th>number_of_reviews</th>\n",
              "      <th>minimum_nights</th>\n",
              "      <th>accommodates</th>\n",
              "      <th>bedrooms</th>\n",
              "      <th>beds</th>\n",
              "      <th>estimated_revenue</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1070</th>\n",
              "      <td>3385421</td>\n",
              "      <td>31</td>\n",
              "      <td>6</td>\n",
              "      <td>4</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>103602.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3308979</td>\n",
              "      <td>20</td>\n",
              "      <td>4</td>\n",
              "      <td>11</td>\n",
              "      <td>5.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>78000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3667</th>\n",
              "      <td>53803</td>\n",
              "      <td>41</td>\n",
              "      <td>7</td>\n",
              "      <td>4</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>71750.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1592</th>\n",
              "      <td>9460</td>\n",
              "      <td>240</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>71280.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3165</th>\n",
              "      <td>3040278</td>\n",
              "      <td>156</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>67704.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      listing_id  number_of_reviews  ...  beds  estimated_revenue\n",
              "1070     3385421                 31  ...   2.0           103602.0\n",
              "2        3308979                 20  ...   7.0            78000.0\n",
              "3667       53803                 41  ...   3.0            71750.0\n",
              "1592        9460                240  ...   1.0            71280.0\n",
              "3165     3040278                156  ...   2.0            67704.0\n",
              "\n",
              "[5 rows x 7 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1IhfK2hBV0Zp",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "cellView": "form",
        "outputId": "6a602e63-2b79-46d0-bd11-05c035243283"
      },
      "source": [
        "#@title Showing 5 highest revenue listings (minimum_nights <= 4)\n",
        "pd_listings.loc[pd_listings['minimum_nights']<=4, ['listing_id','number_of_reviews','minimum_nights','accommodates','bedrooms','beds','estimated_revenue']].sort_values('estimated_revenue', ascending=False).head()"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "    }\n",
              "\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>listing_id</th>\n",
              "      <th>number_of_reviews</th>\n",
              "      <th>minimum_nights</th>\n",
              "      <th>accommodates</th>\n",
              "      <th>bedrooms</th>\n",
              "      <th>beds</th>\n",
              "      <th>estimated_revenue</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3308979</td>\n",
              "      <td>20</td>\n",
              "      <td>4</td>\n",
              "      <td>11</td>\n",
              "      <td>5.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>78000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1592</th>\n",
              "      <td>9460</td>\n",
              "      <td>240</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>71280.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3165</th>\n",
              "      <td>3040278</td>\n",
              "      <td>156</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>67704.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3216</th>\n",
              "      <td>481220</td>\n",
              "      <td>164</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>63960.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2922</th>\n",
              "      <td>699596</td>\n",
              "      <td>136</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>61200.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      listing_id  number_of_reviews  ...  beds  estimated_revenue\n",
              "2        3308979                 20  ...   7.0            78000.0\n",
              "1592        9460                240  ...   1.0            71280.0\n",
              "3165     3040278                156  ...   2.0            67704.0\n",
              "3216      481220                164  ...   3.0            63960.0\n",
              "2922      699596                136  ...   1.0            61200.0\n",
              "\n",
              "[5 rows x 7 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PLjh8P2BNCqJ",
        "colab_type": "text"
      },
      "source": [
        "Short term hosts has lower revenue compared to our long term hosts. What is the correlation between minimum nights and estimated revenue?"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3-leZ0k3WO-k",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 111
        },
        "cellView": "form",
        "outputId": "19c58940-955a-4099-ff78-220ddd9357c5"
      },
      "source": [
        "#@title Correlation between minimum nights and estimated revenue (not filtering min night 1000)\n",
        "pd_listings[['minimum_nights','estimated_revenue']].corr()"
      ],
      "execution_count": 10,
      "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>minimum_nights</th>\n",
              "      <th>estimated_revenue</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>minimum_nights</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.872084</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>estimated_revenue</th>\n",
              "      <td>0.872084</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                   minimum_nights  estimated_revenue\n",
              "minimum_nights           1.000000           0.872084\n",
              "estimated_revenue        0.872084           1.000000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yRYYjq5WNX38",
        "colab_type": "code",
        "outputId": "6fd2bf29-4205-4dae-dd1e-a6fc2c7ae8c9",
        "cellView": "form",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 111
        }
      },
      "source": [
        "#@title Correlation between minimum nights and estimated revenue (filter min night 1000)\n",
        "pd_listings.loc[pd_listings['minimum_nights']<=7, ['minimum_nights','estimated_revenue']].corr()"
      ],
      "execution_count": 11,
      "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>minimum_nights</th>\n",
              "      <th>estimated_revenue</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>minimum_nights</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.199189</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>estimated_revenue</th>\n",
              "      <td>0.199189</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                   minimum_nights  estimated_revenue\n",
              "minimum_nights           1.000000           0.199189\n",
              "estimated_revenue        0.199189           1.000000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oJMHyV6LOTN5",
        "colab_type": "text"
      },
      "source": [
        "Host with 1000 minimum nights has influenced the correlation for revenue. But after removing that host, minimum nights and estimated revenue are not highly correlated."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "324oYXKzRg9C",
        "colab_type": "text"
      },
      "source": [
        "It would be useful to know the most popular time of the year to rent in Seattle (so dont have to rent all year round and do maintenance during offpeak wont hurt revenue)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "C-V2FvyPOt55",
        "colab_type": "code",
        "cellView": "form",
        "outputId": "598fe0ae-e885-4472-f273-aa772c705967",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 336
        }
      },
      "source": [
        "#@title Best months for rental?\n",
        "\n",
        "plt.figure(figsize=(15, 5))\n",
        "\n",
        "# # bookings by month\n",
        "plotdata = pd_reviews[['date']].groupby(pd_reviews[\"date\"].dt.month).count()\n",
        "plotdata.rename(columns={'date':'# of bookings'}, inplace=True)\n",
        "\n",
        "ax = plt.subplot(1, 3, 1)\n",
        "ax.set_title(\"# bookings by month\")\n",
        "plt.bar(plotdata.index, plotdata['# of bookings'])\n",
        "\n",
        "# revenue by month\n",
        "plotdata2 = pd_bookings[['date','estimated_revenue']].groupby(pd_bookings[\"date\"].dt.month).sum()\n",
        "plotdata2.rename(columns={'estimated_revenue':'revenue'}, inplace=True)\n",
        "\n",
        "ax = plt.subplot(1, 3, 2)\n",
        "ax.set_title(\"revenue by month\")\n",
        "plt.bar(plotdata2.index, plotdata2['revenue'])\n",
        "\n",
        "# avg booking price by month\n",
        "plotdata3 = pd.concat([plotdata, plotdata2], axis=1)\n",
        "plotdata3['avg booking price'] = plotdata3['revenue'] / plotdata3['# of bookings']\n",
        "plotdata3.head()\n",
        "\n",
        "ax = plt.subplot(1, 3, 3)\n",
        "ax.set_title(\"avg booking price by month\")\n",
        "plt.bar(plotdata3.index, plotdata3['avg booking price'])\n",
        "\n",
        "_ = plt.plot()"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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J0rwzk2dPYct6/tQ7fZIkSZI0xEz6JEmSJGmI2b1TGiAOkS1JkqTZZtInSZLU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\nKQHJuK6mQsIAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 1080x360 with 3 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KwVrOl4wKZcp",
        "colab_type": "text"
      },
      "source": [
        "Jul, Aug and Sep are the best period to maximise revenue.\n",
        "Before May are the best time for maintenance work\n",
        "Oct to Dec is a good time to take a break and enjoy the holidays.\n",
        "\n",
        "Dive into the price, what are the range, and where are the expensive listings? To normalise price to compare between listings, we usually look at per person per night. That's what we usually do when we travel."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GO-W4iuF7YMK",
        "colab_type": "code",
        "cellView": "form",
        "outputId": "78d0a252-d2d8-4e24-b6b4-15dba9e37fb5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 422
        }
      },
      "source": [
        "#@title Price distribution of listings, per person, per night\n",
        "pd_listings['price_per_person_per_night'] = pd_listings['price'] / pd_listings['accommodates'] / pd_listings['minimum_nights']\n",
        "n, bins, patches = plt.hist(pd_listings['price_per_person_per_night'], 100, facecolor='blue', alpha=0.5)\n",
        "print(pd_listings['price_per_person_per_night'].describe())"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "count    3818.000000\n",
            "mean       27.137816\n",
            "std        19.962500\n",
            "min         0.037500\n",
            "25%        14.375000\n",
            "50%        22.500000\n",
            "75%        34.500000\n",
            "max       275.000000\n",
            "Name: price_per_person_per_night, dtype: float64\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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0qjP6A8D6vvvrurZfqKodwI5hnyjJTFVND/s4x6uWx+fYJlfL42txbKO6vPKbwMYkZyR5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\nqdxpyqciRD0AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "B95w9Qed7bMf",
        "colab_type": "code",
        "cellView": "form",
        "outputId": "e9f5708a-93ca-4850-8af2-bd3f73acd10b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 607
        }
      },
      "source": [
        "#@title Pricier than average Airbnbs\n",
        "\n",
        "filter_price_gt = 75\n",
        "\n",
        "plt.figure(figsize=(14, 10))\n",
        "\n",
        "ax = plt.subplot(1, 2, 1)\n",
        "ax.set_title(\"Price per person per night\")\n",
        "\n",
        "ax.set_autoscaley_on(False)\n",
        "ax.set_ylim([47.4, 47.8])\n",
        "ax.set_autoscalex_on(False)\n",
        "ax.set_xlim([-122.5, -122.2])\n",
        "plt.scatter(pd_listings[\"longitude\"],\n",
        "            pd_listings[\"latitude\"],\n",
        "            cmap=\"Oranges\",\n",
        "            c=pd_listings[\"price_per_person_per_night\"] / pd_listings[\"price_per_person_per_night\"].max())\n",
        "\n",
        "\n",
        "ax = plt.subplot(1, 2, 2)\n",
        "ax.set_title(\"Pricier than average Airbnbs\")\n",
        "\n",
        "ax.set_autoscaley_on(False)\n",
        "ax.set_ylim([47.4, 47.8])\n",
        "ax.set_autoscalex_on(False)\n",
        "ax.set_xlim([-122.5, -122.2])\n",
        "plt.scatter(pd_listings[pd_listings['price_per_person_per_night'] > filter_price_gt][\"longitude\"],\n",
        "            pd_listings[pd_listings['price_per_person_per_night'] > filter_price_gt][\"latitude\"],\n",
        "            cmap=\"coolwarm\",\n",
        "            c=pd_listings[pd_listings['price_per_person_per_night'] > filter_price_gt][\"price\"] / pd_listings[pd_listings['price_per_person_per_night'] > filter_price_gt][\"price\"].max())\n",
        "\n",
        "_ = plt.plot()"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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IiIjIDimgEhERERER2SEFVCIiIiIiIjukgEpERERERGSHFFCJiIiIiIjskAIqERERERGR\nHVJAJSIiIiIiskMKqERERERERHZIAZWIiIiIiMgOKaASERERERHZIQVUIiIiIiIiO6SASkRERERE\nZIcUUImIiIiIiOyQAioREREREZEdUkAlIiIiIiKyQwqoREREREREdkgBlYiIiIiIyA4poBIRERER\nEdkhBVQiIiIiIiI7pIBKRERERERkhxRQiYiIiIiI7JACKhERERERkR1SQCUiIiIiIrJDCqhERERE\nRER2SAGViIiIiIjIDimgEhERERER2SEFVCIiIiIiIjukgEpERERERGSHFFCJiIiIiIjskAIqERER\nERGRHVJAJSIiIiIiskMKqERERERERHZIAZWIiIiIiMgOKaASERERERHZIQVUIiIiIiIiO6SASkRE\nREREZIcUUImIiIiIiOyQAioREREREZEdUkAlIiIiIiKyQwqoREREREREdkgBlYiIiIiIyA4poBIR\nEREREdkhBVQiIiIiIiI7pIBKRERERERkhxRQiYiIiIiI7JACKhERERERkR1SQCUiIiIiIrJDCqhE\nRERERER2SAGViIiIiIjIDimgEhERERER2SEFVCIiIiIiIjukgEpERERERGSHFFCJiIiIiIjskAIq\nERERERGRHVJAJSIiIiIiskMKqERERERERHZIAZWIiIiIiMgObTmgMrOKmX3SzN6Zvv+QmX0qPR40\ns3eMec/Xl7b5lJltmNnL02tvNrO7Sq897dydloiIXGp0nxIRkd1Q3ca2PwZ8AZgHcPfnFS+Y2duA\nPx5+g7t/AHha2mYfcAfwntIm/9Ld/3D7wxYRERmh+5SIiFxwW8pQmdk1wEuBN415bR54ITDyl78h\n3wn8ubuvbXeQIiIim9F9SkREdstWS/5+EfhXQD7mtZcD73P3pTPs43uB3x167nVm9hkze72ZNca9\nycx+xMxuM7Pbjh49usXhiojIJUb3KRER2RVnDKjM7GXAQ+7+iQmbfB+jN6DhfVwJ3Ay8u/T0a4An\nAM8E9gE/Ne697v4Gd7/F3W85ePDgmYYrIiKXGN2nRERkN20lQ/Vc4FvN7G7grcALzewtAGZ2APhq\n4M/OsI/vBt7u7u3iCXc/5KEJ/Ebaj4iIyHbpPiUiIrvmjAGVu7/G3a9x9xuIcoj3u/sr08vfCbzT\n3TfOsJuRvw6mvwZiZkaUY/ztNscuIiKi+5SIiOyqs12HaqTe3MxuMbM3lb6/AbgW+ODQe3/bzD4L\nfBY4ALz2LMciIiIyTPcpERE5r8zdd3sMW3bLLbf4bbfdttvDEBG5pJnZJ9z9lt0ex8OR7lMiIrvv\nQt+nzjZDJSIiIiIicslSQCUiIiIiIrJDCqhERERERER2SAGViIiIiIjIDimgEhERERER2SEFVCIi\nIiIiIjukgEpERERERGSHFFCJiIiIiIjskAIqERERERGRHVJAJSIiIiIiskMKqERERERERHZIAZWI\niIiIiMgOKaASERERERHZoepuD0DkEccdWivQWgUzaMxDbbr/eqcZr3sXKg2oz0FW2b3xioiIiMh5\no4BKZDvcYeVQBE14PNdeg6lFmN4bQdbGqf5r3Ra0V2H28q0FVXkHNk5DZwOyKkwtQHXqfJ3NtrjH\nOZnZLo9ERERE5OFDAZXIdrRXB4MpiK83TkUmqhxM9V7OobUcQddm8g4sPxjbA+RtWNmA6f3QmDuH\nJ7E93l7HH7wtxmaG77kau+oZ2MMk0BMRERHZTZpDJbIdrVVGAqZCexX3HO+28LzTy+hACkp8wvsK\nG6f6wVT/nbBxIjJju8DzLv6V90Qwhcf4lu7H73wvPjJWERERkUuPMlQi27FJ2Z5vLMHGScAAB8vw\nxjyWVaDbhONfwmcOwPS+8WVznY0JO/bIXlVq5+QUtmXpvsiU9YLIdG6dDVg+BPNXb/p277Zh7WiU\nRWJQ3wOzBzDT33JERETk4qCASmQ76nugucxwlsq7LWivR5OK4jXPobmMN+ah247vV4/GazP7R/c9\nMcjwTV47t7zbjnlflTpWqUWQ6A6LN8YcMSyCo1P3QHMJmBxQuecRkPUyWQ6tJeg28flrNBdLRERE\nLgoKqES2o9qIOU3rx4EUOnm3FEwN8W5kc7xbPAFrx/BxWarGAqwdY6SksDp93rsERvDzQASLZuCO\nT81jjT34/sdCbaaXVfLaDBx4PNRmN99pEYwN67bimpQ7I4qIiIg8QqnuRmS7puZh8XqYS537Nk4z\ncV4VpJK5Es/Hb1+fja5+WP9RnYLZg+ds6BOtPNTPvBXj21iCrDYQTEHq8mcGtWhK4e54axVvrQzO\nExvXoKPQbZ23UxERERG5kJShEtkJy6A20+/45x7/ji1jG3ouq44+V5hajHWtuu0I1rJt/l/UPRpn\ntFdjjI09W2u7vnGS0eDHobWMVeojr5ll0G3jzRX86BciQMoqWFbF9z8u5ox1mzGGcdekUt/eeYmI\niIg8TCmgEjkb9dk0lygHsghoUslcBBPDAYXBzMHN5w9ZFqWF2+UejSK6LXoBUGs19lWtQ2UqgsCh\nY7s7o90FixcndfIzPKvBXbfC+rH+5tP7Ic+x6cX+vC8vBZru0VxDLddFRETkIqGSP5GzMXOwFDh0\nIgDxPDJL+x4Ne2+ASgOwyMrMXxXBxvnQWh0MpmJQMV+p04yGEOvHRuY1mdnkAKc2neY6DQWAZnD8\niymY8v5j/Ti0V0b34ymL123D1KIaUoiIiMhFQxkqkbNglRp+8CY4fV9kqiwyUMxfhWERXO29cUIp\n4DnW3mSNLM/BKtF+vb0WmbWyPVfCybsH328ZzF0Zwdb68Tg/98hyzRyAez485ngea1YtXDt+DN1m\nLIAsIiIicpFQQCVylqw6BfsfO/hkez069uUdYv2l2WiVfj7bn291352NkYDKajP4vkfHmDsb0Vlw\nZj9WlB7OHIhHEmWCXcYabsLRPwrMHIh1uUREREQuEgqoRM61ThNWjtDP3qRGEXkn1nLKauenDXpj\nTxynOG5WiawUlOZ12cTAy6qNMy7U29vWDM+qKWAcMq6dunusbTUuc1VskncjoGutQn0m5pop+BIR\nEZGHOQVUIueId5oxh6m5go0rhetsRHtyIxYInlo8t6WA1akI2NZPQiV1Ehzef1Gydy4s3ggn7mC4\nTND2PzaCxrxLNOuwyM5N7524K+804dDfRIDmeQR9J+/Cr3w6dJr4vR+NxYQrdbjyqdiVTxto5S4i\nIrJbTi21edNv380H//I4tarx0m+8gr/3XddSr+k+dalQQCVyltzz+LDfXEmZoDyyN9WpMc0XHDBo\nrUTjisaeczuYqYXoordxavzr1emddRAcw65+Jt5cjpbr1UYEibOXYUXgVEm/Xqb3nfk8T3x5cG0q\nz6Hbwh/6HNz/1/329J0NuPcv8bXj2GNedE7OQ0REZKeazS4/8hOf5OiJFp1O/IHxd95+P397+xKv\n/7mbd3l0cqEodBY5W0sPRjBFqf143jnD4rUeQdX5MLHVOWc/hyvvRAYsLQRsC9dhVzwFO/AEbP5q\nrFrHy+ddaWytCcXaifHPH/tydAYsZ8HyDhz9YgRzIiIiu+h9Hz7GqaV2L5gCaLVy/vb2JW7/su5T\nlwoFVCJjeLeFn7wLP/xp/PBn8NP3RyZqeDv3CDDGddfrjmnOUM5YbRb4nI3NFg6u1Ha+324LP/0A\nvn4C76xHt8CstNZWcW55G+928Eodn953dmWNrZXxzS/M8BNf2fl+RUREzoG/vf006xtjPh8AX7rz\nPP3hVB52VPInMsTzLhz74mDDhbVj0F7D9z92qIyvWINp7J6IwCb9OxxYnK/FbSuNCHKGAxHLdnxM\n9xyOfDYtYpxM75scoHU3YKMF6yfxxh6Yv3rzOU8zB2Dt6Ojzw402egPKwR1vr2O16W2fj4iIyLlw\n7VUzNOoZzdZgUFXJjCsu0yL2lwplqESGrZ9IDRXKHIqsTIltFqTU52Jh37krUse9UkBlWcx3Osfc\nHe9sRHYoq/XDkEo9gpadlvwd+2IKpkqL+DaXJ8eSkDJwabuVhzbf//w10ciil1mz+P7ym8eMOYPG\nAtaYO39lkyIiIlvwzd9wOdXq4B9MswwWF2rc8tTFXRqVXGgKqESGtdcYGyl4aoowbP6a0Q/9lsH8\nVfF1bRrmLo/uepU6NOZjwdxsewli77QiWPLxUYx7Dkv3w/KhKENsLkPexWcui2Bqm8fr7Tfvwmq5\nDXySt0afi4EMPwHrx/H2+sRjWFaBxRtigeGZA/Hv4g1k04tww9elzoQWj7mDcM0tOzoXERGRc2lx\nvsYv//un8JgbZ6lWjGrFePrNi/zKv38KWXYOO/nKw5pK/kSGVafol+qVmEU53RCrz+D7HwurxyKL\nVZuB2QNYpd7fqMgQ7YB3mnDyrhTMGWQVfPF6bLhz3uqx0UYY3RasH4+AbqfGrTVVaC4NZtomBHu4\nw30fw6+6Bas1oNvGsTin1lJ6n0FtFis1sXAHm9mLX/fsWDDYqrFelqXg6lx3SRQREdmmx9w4x2/8\n4tNZXmlTyYyZGX28vtToJy4ybGZ/LMw7bg7ShIVmrdqAha0tirsd7g7HvxzBRDwDeQ4n78QP3jQY\ntE0qf2ut4H7ZmBbuW1SpxwLB45poZNVYj2rteMyb6rbHb5d34npunIDuTGTT2qujwVrexrM6VGox\n3vYKdNvxdXGueRuyOszsxc7XPDQREZFt2jN3Fo2f5BFNJX9yaes0IxhYfQg2lsBzLKvC/sfFmk2F\nSgOm92IbJ8eX/Z0vreXxXe7cY9znkbvjrdVYZ2r+2vFljQdvigCns95fkLcYH1GG6J7jK4fxqcUU\nFHmc09jMl0PejAYglfpoy/RCYw82s/9cnq6IiIjIjihDJZeu1mpaADd9YO+2ImsyexlWrcPUPO5R\nUjaQ3dk4DXNTeN6JeUpWiQ/4Z9MefJJxrdchxjxc3lebifEPq05ve2yed2HpvlJAYzBzMALQ7gY0\nFmLOU20aTnyF3jU0g6waa1F1mhF8rp+EvI1N7425UrB5GWFxfq0VxgZToGYUIiIi8rChgEouTe6D\nwVTv+W4ESd0mwPhAxLv48hE4fU+/c59l+P7HYd1mzHnCo0vdzIF+ELET9dnx85IsAzf85N3xdW0m\n5hN1Nvrd9YpW7bMHt3/cteH5WB4t0hvz2MK1/Wdbq4zMNzOLFugbpyfvfyvdBjdbGHlc1k5ERERk\nFyigkktTPinzA3TWJr8GeLcDqw8QpWspkPAcjn4Bnz3YC8I878DSffj0/tEGEltk1akolds4DRRz\nk1JL8U4pG9Veg6n5aHzh3cgOVSLLtun6T5O0Jqzu3lnHPe/v02xs/w7GdPTz1SNQm4kAs9cifbO+\n6zZ5GzuLIFVERETkHNIcKrk0WcbED/Obrq3kUVY3rvECDGRVrPjfk3dNbHW+JYvXw/zVMaer0ois\n1UjmzKG1inXWsMY8NncZNr24vWDKfXw2bDPVCYvqZmMm5q4cgeZS/1rUZiZkqtK51eci6zawjUGl\nFvOy7vsEfuwrZ3dtRUTkkrC2kfORz2zwvr9e59Cx81/lsLbR5W8+t8ztX1kjz3WfutgpQyWXpqwa\nneLy4bIyg2qjV/I3oAg4NpvXNPTh3vBortA8DVM7W+DPzGD2QDwAP3r7+A3zTrQZzztxfnk7Fiiu\n1Cd2JwTifNZP9IPB6hTU5qKdOaTzTmWEtdk4q/ZaPFedxjdWoFItBktcw5lUfli6aVmGrz6EtZbj\nGHk35kLNHowArDevKoImZg/G+5fa0dkQj318/t3wxffhlVrsY8/l8JJ/i82qSYWIiIy6/e42v/wH\ncU/LHd72gTW+7mkNvudFs+dl/vOff/AEb/y9Q1QqRp7DntkKP/cvrue6q9SZ9mKlgEouXTP7Y65Q\n74O8R0akNhNd/8p6gZJHMNBtjs9SlduYx9Yxp6rb4Zz9yjbbJJOUgrqBtu+esj0Lo5ktz+Ncy+fS\n2YiSOqtFoFRkiPJONPI4fkcvQPP2OvzNb8fr+66PDNrp+6G1Bk98aZQlFtd37kosb0V5YrtUVrn6\nEFzx9Bhntx3BVG0m3eQq+ML1sd5Vt4k/+Hn40gdiuyKwPXU//u7XYt/x+h1eUBGRS1c3d1ZWO8zN\nVKlULr6FaNsd57+/bZnm0N9CP/zpJjc/ps6THlUf/8Yd+vLd67zx9w7RbDlFyUuzmfMz//Vu3vzz\nj9divxcpBVRy6coqseBttx3I3FyOAAAgAElEQVTBR6XeDx6m90XWphy4FEFHfU8EVO11BuoD63NY\nNlzClsGn/xDIyZ/wUrIbnnP2455ajLENq9RjXaqNk+BDXfRaqxHs1KZHnx8TnJnneDVuMr05YVkV\nGvOxf5uO4Oz4nfGGvAPHvlLeA5y6H67/mmg3P7UY3QBXD8fLWS2uf96J69pawuauGHu6llVgem+M\n4Uv/X8wPK/McTt6HLx3C5q8cuw8RERn1J+85wv/8/fvZ2Mip1YzvftmVvPI7rjo/XWt3yRfvbY8t\nDW+24SOfaZ7zgOrPbj1Oqz14PCdKDj/35TVufvzsOT2ePDwooBKp1IChOT/VKZjaFxmsgUlVhs3s\nw6cWYOXByMSYQW12JJhywLsbcNnj4YG/gc//MXm1QXbNM85uvLMHI4tUDuiyCkztjflVq2Nap+PR\naGI4oOrN+bKhrT1V2PXPyczi+Uo9AlCrTp5LRpQJmgHtNWxmP55V4jhTe0sliBaliVttMrExoVlG\nVoGmWqmLiGzVez98jF97y300W/F7vN1x3vonh6hkxvd/+1W7PLpzJ89J1RmjQVW3e+7nNi0td8c3\n5wVW1tSh9mKlphQik9SmIoNVm4m1larTeGMBtwyrTWG1WWxqASsyU1ktlcpVIptjGdZeh8ufAFhk\nwr70nrMellmGLV4Pe2+AuSth/hpYfBQ2s6/YYvwbxwU/vbbvNvjAJvyF0gabROy7YXz5YVaFA4+O\nw3Y2Yl0qsshwpWsTj9SxcGJgNuSGrxnf8AKLsYiIyJb85h8+0AumChvNnLf+6aGLqonC46+rjT2f\nRg2e9eTGOT/es79qnqn66P2z3XGe9NiZc348eXhQQCWXHvdUaraFG0alBjP78awGK4dj7amjt+Mn\n7465UUUg4MVCu9E4YeBXaVbpZ2Q2TvWH0VrFlx/El+7H10/hk4IKz6Nt+srhtD5UFIJbdSo6+TX2\nYEVTiLHBRjKuI1+ej+kYWJzShOuTd3oZJWvMwQ3PjSCpOOu912PPeCW258oozzt1D/6ld+Gt5Sg7\nHD6eWcyR2gK7+Vthdm9prlpqIvK1/xCrbHLuIiIy4NiJ8Wv9rW90abW3+EeuR4BG3fjBl81Rq0I1\n3YobNXjSo+s87XHnttwP4AXPWuCaKxs0SkFVo2684tsuY35OhWEXK/1k5dJRLObbLJWNNeZhakyz\nhvLbmsuwfIiBcoFOMz7ID78v78afKSyjt45Sa63UmCHmCfna8cGFhTtNaC3j89cMZoY6G6PHbi7F\nfmpj/tJlqaRu4+Tge6wSDTe2xXH3/hyqIhAlK523Ydc9E7/+2XDvx6BSwy6/qX8OtRnYeyN+8q7N\nF+PdYutza8zB3/0l/Av/G+79BMwdwJ78MuzgY7d5biIil7brr5nhS3eOlojvXajRqF9cf2+/5aYG\nN15V5a8+12Rtw7n50XUed131vMwVq9UyfuH/fhTv++hJPnTbEntmKrz06/fxlCfMnfNjycOHAiq5\ndDSXUjDV//DuzaVYq2lqfvL7Vh9ipPbassm/iN1Tssbxbhvu/Xg8n9WwJ35LLPhbDqbiTZHhai1H\nkAcx1rWjsbvh5nwrR7DFG8YHgvWZ6M7XSh32shp0NvCl+9PErrRDq/SSSmPPpL0GlXq0J3dSBi6P\ndbGaSxEg1eegPkdmBk9+OX7qXmwocDLLYP5qfOUwPjeFOaPjrm99kq7VZ7Cnfgc89Tu2/B4RERn0\nI6+4lp/+T18aKPtr1DP+4SuuvaiaUhT2L1R4yXMuTMldvZbxzc/fzzc/X8t5XCoUUMmlo7nEcGBk\nOL5xEuqz0U1unG5n9DnvphK9tHxv+eZjhrvjG6fhvr+GtZOw9wbsppdh+x8VGa+xYnFeGvNR5rd2\nPCVufEz5nUWnweqENS0qdZiuR+Zr+UEc7wVN7g7tVr8TYG021soyw4qtGnMRUOWdVOLXz0jRXsHm\nLht72OFgqj+eRgSR1Wl8ahErR4lWgdnx+xvLoxWtd9uQmmRsawFjERHhq540z398zeN40+/ez933\nrXPFZXVe/d3X8Jxn7N3toYk84iigkktDb3HaCa8tPwAL141/vTEHa2MW+qW/xkTEO6kULi1Ya2bw\n2BfGppUp2JPagm+2yK5lsHa81H1vchc9b61hkwKqwvoJKAVTkLr1VWvQTgFVezVKC2vT0UGwOo1l\nlQhYmqeGduiwcTqComzMrw/Lxl/nouRx5XDvGHg3FgqeWpgczA4c2qG5FHOx2uvpONFEw2cvw6YW\nzrwPERHpecpN8/zSzz1xt4ch8oinP+vKpcEsNU4Yx6G9hq8exTtjJunOXjbY1jurpFbrQyo1bOG6\n6LxXmx5c5Le7Ec0Z8k4EE0VGpTfXKn3dbUaA021FBqwo0eu2odOKJhJ5BzptaJ6e3Diid9xxgSCM\nFPl5BIHU5/oBXz50Ldz7c506G+N3O7U4sm/Pu/jK4Xi+Ng0LN2DzV2EL12Iz+7YWTEGcb3M55qT1\ngrYUKK8ciUWGRURERC4wBVRy8fEc2hulLE8yvS/WUSpv6h7rILnHXKnjX8KXHhgIVKxSgwOPg5n9\nUbo2rhkFRBBkWQQiYwMdh+UHI9yozcS+sloEZ1kNsiqWxuRF8JJ3YWMJ1k7A+qmUTWrGMZpLcOKO\nyd0BY/CbvHaG//sXAah7lD0W5X/d9vgySEhNPiKoilgwx1cfgubpaEF/3deRVUbH5O54ew3fOI2P\nC9Y8x5tLEfSNPV9PjThERERELiyV/MnFZeN0PApZFeYui39r01BpRCajKE3LO/TK6ooP6usnI1NT\nKiGzSg3mr47NTnyFcQsEgsPSIaA74XUiGEklfb15V+649WYvpQV00/4q1ZQhS+eWDwUy7XVYOQJ7\nrhx/vMYCrB8fGI/3AqQ8slGex+tWgdVj8Vx9Nt7bXE7HHGqgsXIYr89glaGWs2YwvRhlfJ7HdV68\nEfCJmSjPu3D6voEA2GvTMH91b26U552YXxYHGX+uk4I8ERERkfNIGSq5eLTXUzDl/UfehpWj8bp7\nZIZqjZQVMiAfnV9lGSwfxttrA7v3ThM/cWd8uB/HHVqnobkyeYxmeGdjIJiCCSFCEex0mzGm6b1j\n5l95mic1JO/C6lHYOIVjqVovZb66beisx7nn7WJg0Va924xmFKvHsLwD0/uYGDyujTlu6TzJKtHo\nIss2L+tbOZxKE0s/t/Z6BJ6FM5bz2bY6BYqIiIicK8pQycVjTBc/IIKGTgtaS1jejuDEwC2V3G2c\nSoGNQW2KXnizchivzmBzl+PrJ/AH/joCnGodX7gOsMHAKC863Hm/NfkIS2PcvCVtZKl6ffniuJZF\nV7+hQG+EewRTeTuNIUttzovFjItrVI3XU/vzwU550XyCxp7JjSY6zX4watmE8z3TUPMo4xt9JY4/\nezB92x18baT1emqW4Tk2tTh+jpuIiIjIeaCASi4em80lylsj5XK9oCWrRblZtcFAkAR4Zw1fPowf\n/dv+h/pOE07dCzP78dpMbJ+nsrnYcQoyqv3nyGKOVH0O76yNCQiGTmU4a+aOZYaPJJUtzVkq6Q6d\na3tttMFEnBy9oCqf0O7csgnzwYAsg9P39LvtTS3EosLnbP2S0nFrs/SD0fTa8DX0DmycxFur2PxV\ng01BRERERM4TlfzJxaM6vcmL5cxMn1mWmi/Y2MV6DWDjxOj8nG6s78Tp+1IwMmadKKtEUJXVovxt\najEaW2T12DplsaIUL+81wug1xCgHOVkW2Zxyow3LImiYu2Lw0MMliZM68g1eiLHNLaxSj/lk5Yxa\nkc3rNge77W2cTgsWb51ZFs05ximV8Fm1kRZfHlzvq/eAuF55Fzrr+NKDZ+6AKCIiInIOKEMlD2ve\nbUXThW4zmiTM7J+8iGtjvtQFrvgwbWnuUQqahrv8QZTR9RoejGE27q3918bJKv2MVtH6fPUwvn4c\npg/A2gpeqUI3H2ht7pZFAFYucbNKjD3vxDlWG1HOV5uGxsLoivYj5W5bCCw6zdSlsAK1mZjzVLSH\nn786AsfmUr+1e21mzM8hBVVTi9vLUu25IvbfK0eM4JaZg4PbzV0Zwd36qbg+3VaUcnoeP9/yMTtF\ns46hYFNERETkHNtyQGVmFeA24AF3f5mZfQjYk16+DPi4u7986D1fD7y+9NQTgO9193eY2Y3AW4H9\nwCeAv+fuY+qS5FLlzSV46HP9D9qrR2Hpfvzym8cvKptlMH9VdKZrr0dA0NgTAZM7cHpg9lIkMDw6\n220276nSmDCPyGB6fwQ8AwFQNrovM/AIinztKOBYp53K7sonnRpFZCkoqtSg0sDqM1H2tpVApVKP\noKvITFWnot36JFbp7zetR+VTe7GZ/Wnohq+fgLVj/ffs3TNmRxAdE888R2zg8NUpfO+NEYx1mjHe\n0mK/RebMLIuAsjGPt9bgxJf74x9n4xQ+s2+0E6FctHSfEhGR3bCdDNWPAV8A5gHc/XnFC2b2NuCP\nh9/g7h8Anpa22QfcAbwnvfyfgNe7+1vN7NeAvw/86g7OQS5C7g7HvthveODE1+11WHoQFq/rryGF\nRVtziG2nFgZanvcZVsrWmKWgKqtE9qe9jtdn0mtRimcWc6p8/uqYN9V7E9DYg03vxeqzeHUaTt/b\nO874wCcaN0RGyUZL83onn8Oeq7Da1LavW8/MgcgotVYiEOtsDDV2MKjtSY0rxry/PgNZNVrMN5dG\nG2F0WxH4DCuyaSOn1I129J31NLfJoLUc51qpQWMRpvcNzV/bSNnJ+PzqtVmYuzwCrXJnQ5t0vYHl\nw/HfQmP+HM7tkocx3adEROSC21JAZWbXAC8FXgf8+NBr88ALgR88w26+E/hzd1+z+NT0QuD702v/\nC/hZdKO6ZLl7fOBuLqUP2fWYfzSz0F+AttuKOTprR/FqHZYeoMiGuFUiq1OfhT1Xxpybst7co3Lt\nXvqAXZ+N7Ih3o+V5pYabpdK5aHPOkc9GADRzMDrPNeYj4KkvwvR8fPA/k5EP/puU4q0eiZbl9bmd\nBQJmvcDSSMHI0n3xfKO4pg5dG10A2XNoreKegiA8fh7loGrtBL7nitGyv+l9I+P1bhtO3hX79W60\nbS/LKmldrFasuQXRmv70/Qxco/YqLD2AL1w7mHErMpgj18njPNtr8W9jUlZNLga6T4mIyG7ZalOK\nXwT+Fb0VUAe8HHifuy+dYR/fC/xu+no/cMq9V+90P3D1uDeZ2Y+Y2W1mdtvRo0e3OFx5RCjWRPIc\nVh9K7ctTg4duMz4Ap7WMMIsP9dP74n1LxYft1BYcItuycQqOfTGCoMGDlZoUpAwR9LNFpe1ibk6z\n97wfvyOem9oLizdgU2neUqcJaw/hzeXBoMTz0c544xokbBYomfUzTCOXzQcaWExqvuDu+Pop/MSd\n0Vij0oCpRSyrpMxbaggxriRu7Viaq+RxnkXZY6GzEWt1dZr942fV8UHL6kOln+uYrFye5kM1lyOT\nBaX1xIYUP5usNE9sUndHq/TXv9psjpxcLHSfEhGRXXHGDJWZvQx4yN0/YWYvGLPJ9wFvOsM+rgRu\nBt693QG6+xuANwDccsstatt1sdg4nbIf4MX8pTM1T+gtFlsBivK1MesfeQ7Lh2Dvjf2n1k8Rn7MG\n/4bgno/vglep9cr+2IhxMr0fTtwRjTKmFiMTltVg7ShgEVD0Ov6lYK8INrw72jzBKuODgayWMj8e\nQVXKUrl7LIK7+hC44719GV6bie2mF/vzy9LCvr3rWh9tJGGWsnu9C0IsfpwCLvfUBbG1EufcXk/X\nK2XwKvUIboqMXlGiWdZawVePweG/jfOpTcOBx2HlhhF5B5iKgCmbHs2aleVtmLsMTqz0r3XeSY1H\nSP+d1OI8yic2cW0weaTTfUpEztY9D7b53Xcvcef9LeZnM771+XM87+kzo42fRMbYSsnfc4FvNbOX\nAFPAvJm9xd1faWYHgK8Gvv0M+/hu4O3uXtT6HAcWzaya/vp3DfDAzk5BHnFaq/1SMspFeEOt9CpF\nZz5S1ietd5Rlk9dNKh8j6QUilTo+vQhpraleG/LVo6lFd5EJa6Q1qYacvKMfALVWYiHZhWtjhEVQ\nUbxem47n2mspOEjlZ1m1/8Hesmj1nrfjfIos3EDDjVJwtvxgv4FGsU0RwDVPR7Cy/CC+94aYN1UO\npnrXd5B3mv01qoqgqNQ+vhdU1qYjKGzsiWDGsggIU7YrxtKJYG/28oHAxVePwd0f6c/hai7Dg5/E\nr7gZW7hm8DyL8+p1Xhwaf+qCaLUpfP6auCZealtfn00B6dC5DncBlIuN7lMismP3H2nzc288RrMV\n95K1jS7/60+XOLmc820vULm4nNkZS/7c/TXufo2730CUQ7zf3V+ZXv5O4J3ufqaFbr6PfhkFHjVC\nH0jvB3gVYyYLy0Vq5IM+Yz8AW/pgb0XwYRU2XWh24P2lkrC8HR+626uw8lBkTFprEdStHIlAYONU\n/Du1iNVT23Cy+HdqX+ynnE1q7IG5y6NkMe/GMbqtlInq9tu310prY3kez+fp85plWJZh1QZWn41H\nZSgYsAxIwd/qUSDvl0GOrK2Vvj91TzR/GDenqPyd54PBVO+6D63FZVZqNuFQm4uApzK6LXl3tKPg\nkc8NNcRI1+Kh20vlgjWoTfebizTmS5kui8CzNgPVKWzjFLTWsJl9sP+xMSessQerTWHexbrNKBnN\nO/2fR30euXjpPiUiZ+OP3r9Mqz14j2y2nT+5dWXkeZFxznZh33K9OQBmdouZvan0/Q3AtcAHh977\nU8CPm9kdRK36r5/lWOSR4kzZpTHrTFkxj6o2O7SQrY8GWJZFsNP7Pi2wC/15VuvHU5MFi3lZB26C\n2ctLJXGpvHDP1XDlVw2PBmYu6wV8A/O8ylorKSBbgEoDJ8MrdahG4GBZ+TzHnAcWgYBZBG69/RdZ\nu01+ybebkZWpz8ajUo91psrvOdPPYVhx/NoUk8szfbSEcn3CYr9FAGpZrBWWfq7uaR5bfS61gJ/u\nZc2sCCSbp/H1k3D8S1GGODCXzCNgK3dRbJ3eWiAuFyPdp0S2qNnK6XQmzEu9iN15f3viVOdjp7Z5\nr5RL0rYW9nX3W4FbS9+/YMw2twH/oPT93YyZyOvudxJlGHKpqTZG23BDv2Su98F5zOtT86ldd5on\n1ZsXZf1AbO4KbHqx/7asEqV+qw+N7nNqMQKPzjoAbtU0h6gaccvG6VQuVlqHaqvrGuXtaAyxfBju\n/evYlxm+/9Fw3VdHNmZgXo/34xTLYgx5J4KpkcV6z6C9CpV67zp6UUrY2cCx0cWDe2Pu4CMlc6Wm\nEykDZRNWOo5W9vlgcWFjz2Cb80JWhcUbYXpvL7jslWe20vwoy6A6WsLn0A/Uiilp4zJyxfXtpgzi\nuFJOuajoPiWyfXfdu8Yv/Pc7+MIdK2RmPOeZe/nJH300C3u2ee95hLp8f2Vs4NTNncU9Z5t7kEvB\ntgIqkXNiem9kFYbmx9jMfry9Bu01HB8tPYM0x8dgz1X43JX9JgjejeCjUh9ovODdNqwdj0Cgvidl\nM7qxt9p0ZJfK48jb0Gz352sBlndinamilM27W5yPY/jaCbjjA/1siTscvxNvr2GPe1F6qpRZ6QU7\nOWY53l6F9ho2vRefvTxKFIuGFeU1scp68836zSbMDCdah1uxrpPV++WH5fO3DKcydIrpOLXp3nXp\nj9gGt9k4hRtYaoHODc+DL75r8FhZDa5/Dtns/sHjt1f7wVRvTB18qBTRAC8yfKU5X2OvhaWfcd4G\nFFCJiJSdWmrzT3/6s6yudXGHHOejf32Sf/Hg5/ifr3/qJdGU4du/fg9fvvfEQHlfvQbPeeo0M1MK\nqOTM9F+JXHiVOsxflTJBlWgCMXdZZDJaK2MzJ+4eQU2pYYOZ9VqAA3Dybvwr7yW/56PRLrzbgpN3\nRpe+bpOY/zMNC9fBzL7J2QpPgY1ZfAjvNGOuTm2mP4ervdEPhMa1SYfIpD30hdHSOu/C0mHy9dMR\nNmychtWj+MqR2I/nvXbfRVmhrx2HE1+KRh1ZWnOryMwNy6rgHeis99uQx876HQDT9aM63T/n3iMf\nHy+mjJelfVG0n0/ljl50NfRurFOVgsjsiifDY74xle5V4ud/7bOwy26KphIrD/XLBHvlgWnf7jHP\nq7M2cC4O/U6Am7SOHyiTzPT3IxGRYe96/0O02/nAbazTdQ49tMFnvrC8ewO7gJ5wY4Mf/a5F9s5n\nVCoRTL3gllle/a2LZ36zCMpQyW6p1AfnOfWk3+h5B8+KRghE0DK9f8z2kYXyz78DWsspE2T4sdvh\niqdi9ZnhraP87EwldGaxZpJ7r4U6l92MFwEWGeYdBroPYv34JqvG3Kz1CespZVl05csyfOn+yLLM\nXh5NIbLK6PYQwcja0ZjXZQZZoxcAFefdWxS50G3iTEXzC1IHw1J20LJKLPqbpwxf3oas1lvhC4pY\nsZwxTKWJZgPhnJnFz6zICLXXe+tSZVc/Hb/qqyJwyirYypFS84oOrDbxqcUU+DI+A9ht4jbdb1LS\nSdvmnU1+nkU9YFpzS0REBtx971qvu12ZO9x/aJ2nPvHSaOrzzCdNc8sTp1jbcBo1o1q9+DNzcu4o\noJJd4912mi+TPlg39kRWp7MBxdpCherMUBOH0n6OpPWNepmt9N7Dn8avfdbI2kuTGyrQnyeV52NK\n+zzWnapVU/lYtZ+dKo6RpW6EjT3x3rnLouRweL2pvBvzmZYeiMzP4nURTE0alxm9taxWDkVQleab\n9bsfdolau+HOex2wVCpZn43nNk5SdO0zM5g92At+fP0UrJ9IJXVFBqr4VeFpTld9aP5XSaUKnVYv\nI+SeQ2strll1asKivd6fZzWxvCR1Mazvweqz0amxncoDOxt4tUGvTLNoUV+MffbgFss0RUQuLU98\n7Bwf/MvjbDRHm1E85obZXRjR7jEzZqfP373ivkMt3vquE3z5niYH91X5rhfv5Wk3Df/hVx6JVPIn\nu8I9h1N3l+bLpEVs2xvE3J9Stz3L4gPxJCfuHN9gwT0+yI819AvT8wjsWivxaK+NBkGx4dBussEu\nf5alBW9TVuvqr0rztMrvqcDcZZE1qtSw6X1glRiRd0bKB91T57zePKw0L6jIyhQZKvfJZW3uMHMg\nxtfYA3uuioYcU3vj60ZpnY2phQi8um2K69/rsljM8SoHu+OOValBdSqC5lP3RkOQ9RPRSKS5PL5E\n70xd+NyjbLCxJ85j8dqUdUqZufZ6BHKVepRzWhY/rZn9oz8DEREB4O+84DJmpiuU/2ZZrxlPeMwc\nj3/03O4N7CJzz4MtXvNfH+Djn13jxOkuX7yryS/8+hFu/filUVZ5sVNAJbujuTQ6t8gqkcGoTcVc\np9psfOBfuL6/PtE4m7028kE6Wq9bbbqfuXKPYGogKEtNDMZ9yN90Lk7MSzKP/WaNOezmvwspA0V1\nCvZeB/tvjG1nLoPZg/1yulJw5Hk3LT7chOXSeqKeR3ljewM6HVi4IRbTrdTHZGGs39q9fJ2yKjT2\n4NUG3lzC10/25jyZGTZ3eWTB0hw1L8ZDPnm+UvkaLFwXx105wsDCu0XwPC5Y3cofBacW+ptn1Zjb\ntvcGup/6M/IjX4lFfbMK7jne3iC//YPb75AoInIJmZmu8IZfeApf/5wDzExXWNhT5TteciU//zM3\n7fbQLiq/887jNFs+8LGi2XZ+8x3H6eZa1uORTiV/sjs6qUlET5SjjZTndVNQ0233S7iG2OVPwteO\njWZNGvPYwrVRclfOHpHWOcoqWGUaHxlL4h6Bi3tkbIpAqtqIgKCY71PoZaoyop17Bc9zmFogu+kl\n+MZSLLpbmifG6hGoTw/MWcI7MX8r78RivsPHaSxEdql418ohmL82Wr+n9u/9i5MCyiy1YC+aP1Qb\nUdq3dixey2qwsYQ35lLGzNKlNryzXrq2qQFFlq5jr8279a/5fCodzLujY++d42AXQgCrzcYctXJL\n/XJZ4cyBkfllZgb1OfK/eiu4kz/qWWQ3vRDyLvnn/wK/55Nkz/z+S6JLlYjITh3c3+Bf//jjdnsY\nF7Uv39McO+Fgo+UsLXfZu6CP5I9k+unJ7ujNd0m/Xio1xqco8sjOFPOFpvcPlqcB7H0ULB+Bo1/o\nz2WqNrDHvhibmsen90FzGZqnsSJDAqmDXKdfNtbrXFc6NgZ5EzZa0FjsB331WTxvpCwWKZgq9tnG\nzaC93Mt6eVaLbM3UfAREBmwsxXyipUMwf+XAKXnRcS9L87WKxhdTCzB9IAUIaY0pMuhsYPNX40uH\noFssrFtkp2rpeCf7+++sR+BSbUBlKrY2wztNfP1ktGm3Smoz3xkMZD2PMS1cG9ei24prWmmMzDmb\nyModClMp38z+eG7jVJpnlVri16Zgev/mWcqZRVg9gd/5Mbp3fqz//PSigikREdl1e+erLK20xr42\nM62CsUc6BVSyO+pzDKyjNGktoaKFebFIa5FRqU33NjEz7Prn4Fc8JYKW2jTsubK/v9YqrJ8Y29TC\nu+0ImIoAZWAMpcV8IQKQxkLKmkSHvCIbVSRSHI9Sxl5Qk+Tt6PpXn+ll4Xy2Do05OP3AwKZe/G9r\nLc61Ph/7BWzm4Mh1MjO8tRL73nM5rJ3od9Cr1KNJxBADvDoFeXdwfSczvNuMZhmd9fElj5Z+Ht1m\ntJKf0H7esmosKNwdvoEYNOZjPbK8E1m0ok08xPPTe8fuc5LsOT9IfuuvpDl4SW2K7Dmv3tZ+RERE\nzofvfPEiv/zbRwc6KtZrxgu+eo5GXQHVI50CKrng3HM4eXeU8RWBTLeDV0YX8403lOfbeJTBLVw7\nUv5njbkIUMpbbyzFWkebZTfyovnCmGNXG9BKAUHewigtmmtFhqj/1ghI2qP7SZmWckmjWYZXGlFO\nmOe4Zb3A0q0CU/MR9By7PUr1qlMwM75bXa+Fu2X4zH7ozAGOtVeZnCma8HyxlhSkCCeVMRad/opF\nlIfnwI0ze3nKMBaZQZ8cl/UAACAASURBVIufxfTeVIJZP9MetqTy7B+A9dPkf/WWOIY72Ve/gspz\nX31O9i8iInI2nv20OU6c6vLWd50g92gm/Lxb5vjB7ziw20OTc0ABlVx466f6rdGLDEi3C3kDr9RK\nc4mieYF7rLHUb9zQjaxTYwvdh1YORybFwCdmwYbboydm0W68Oh3ZmhTw9JsqVIB8TKHiuKxOaU2t\ngeezKOMrsj7exS3mivn0Abj/L/vBTWeDKEMc/EuWp9JFW3oQn1qIjnpFcNWYP3O38PJaWuVGHYWs\nClbtXTv3rN9AZGRfqSOhd6MdfKWGL14PzZV+2/TazMDPIdq0pzlwWRVmL8Ma21v3xMyofsM/x7/u\nR2D5KOw5gJWymINDzCMYzCpjWuqLiIicHy99wQJ/52vnOX6qw8Jchekp3YMuFgqo5MJrTljsduNU\ntNqeWoyStM5aagoRGSq3DGrTmFWhvRrrDqWFbEcCpbyLr59MHfO6qalFDSfrda0DUtOETSKO1PSA\nzkaa+2S9REsEWiul7AvxQlaJAHHAJlmi4SxN3o31rE58Gab3YbWZaJyxfhw/fR8sXk8RYHpxbE8d\nAVcODe5nM3k3ml8wtF1WH2zwUQqm4pJEBs26rcHMX7cNa8ejGUVnLfZfmYoW8Y25frBWPvv1kxEA\nlht1LD+ILx+OUsG5K7DZrf/1zmpTsO/asa95UTJarHcFMb9u5sD4QFtEROQcq1WNKw6o++zFRgGV\nXHi2yZpArVXYcyVem4aHDjMQiKQFYn1qb3x4P/GVtL8M33MlVjSryDupXXdanLcInjrrkFUjA0TR\nnjyt6eTG2KCn6OzX2BPzfqCfzeqs9gKaUr6FsasRpOYX7mPKGlurMRep/HyeY7MHAYu5Wp7D7EH8\n5Ffw41/G5q/Fq/V+m/Ws3s9kuUcXRe9CpYbXZ0eO6Z5HkDj8PPSbTMDEn5VBXM9ioWB3WD+Jd1sR\nGFMKWE8uRZDcWIhr2Mt0OawdZdx1d+/CoU9Ct43PX4M95hvPPpu0fiIFU6XjrR/v/Tc3aS6YiIiI\nyGaUa5QLb3pxtNlB7/s8MjxrJ0beljaM0rFuK/7NOxFcnb4PXz6Mt1bxtfjQ3OtwVxgo2SvKDbOh\n10rbFus6mWHT+8ZmMWwgmOo9GQFO+f9eVolxdtt4ez266XXbEfg1Tw104AMi82OVXptwswzLKtj8\ntdBp4nkrXYOiHDKLOME9deZLwdXGqd5Cuu5AnlqdW6kJxNDQN+3ON7BxKdjybjyaS6PvL9b5KhZM\n7r/A2PWoCgvXx/mdvh8/evvWxrSZ4WCq0N2I0tDOhBbvIiLysOR5zn1vfhsffs538n+e/q3c8fNv\noLu2fuY3ipxjylDJBeXucOreCAYGSt0cOq3IElSnU0A1qWlCt/ThN5W85Tks3w9pwXHParFWU302\nys+ySmofXnSPSB36itK2ojnGuLWubHv/NzEsuttV6qmng0UZXHt1MIDwvN8Br7MeAWJWg6yKbdYk\no7EH6127rH9eWWXM+l5EkNNcguo0Xm1g9bnRNbsmKcotRzJrqRSyvGm5mcWwbjvG1VruZ7UoWr+P\n/pwN8MZc/Aw3TsHR2+GyJ25tzONOY7OxxRZxnLnLd3wMERG5sD79Qz/F4bf/RS+I+vId93DoD/6c\n537098lqKquTC0cZKrmwiixF3ol/O80oPWunvyjtf2xarHV27JwbILXGLpcCpqxVWd7uz5Wpzw8G\nU2VDC8VGmWCRvTKoNLAxbccnsgjcLEvZpawSOaz2+mg2xjKoFUGJpcWGq2eYz2NwzdfAwg1QmcYq\n9X47eMtiTlSnGcfrbAzOoyoCivpslBgCnnfxbhvPO/15ZcNSCWC33aK1ukye59GlrzR/ytsb4987\nrHQNzCxayU86U+/C3GXxzZnmg3Vbsa5Xc3lssGhmm3QUTNd70iLEIiLysLP8+Ts49EfvHshI5esb\nrN5xN4ff8Re7ODK5FClDJRdWp/zB2wazBlOL/c5sUwupQ1/KbJRllcgkeR5zdlIHvpFApNqIwKhS\nH/9huWgwMaz40J9F2Z2P7eQ35m1pgV+zLNaNKhanwsEnZITKAV2lUZonVJTmDbWGr031F7htzEWA\nWsjb6fvSwsXdJnjaPu+CVfGsHovwdh8cCUR9XOc+nNbyST7127/KQ1/8DNc949nc/KIXgaf3Fov0\nNuaxmX3js1/FfitD85RmDkTmrr3eP1f3+LkXXf8A9j1qzLiSjdODpYStlZir1cuEJbOXw9L9jP73\nVB38V0REHvZOfvRvxs6t7a6scfz9H+Oq73rJLoxKLlXKUMmFVZ1KC/POxAK8tZkUuGQDH4DNMtj/\n2JTBKIIKS/N2ioyUxYdpGJPVMajNpecnlM8Bk+cLWXT167ainDDv9h/eW3oX9xiTF4vT5l28sxGB\nYzdl3zYrr/v/2XvzaMvOs7zzeb89nPnO99Y8a7YkT5Id2xiDCRhoQpsACU0cxsQ0kA5rMSRNr3Sa\nkHZIA93NSjqYZjAGDDRDYrzigA02NjbY1mQLyZJsySqVqlTjrTufaY9v//F+3x7O2edWlaS6VaX6\nfmvdpapz9tn72/uU7tnPed73ebMSRCWljmPLM5GCuhyxXhh46/iFawOJox87H9ICUZe8BRvA8hMT\nnRyJPGekhZTCJAoxWF3GEx/8baw8/QTqMwtlgWjCP4brWrSNuH7Kywcxj8ShExFQm83dyiSUfisz\nmDiSIBHMHxtfKyDbl/qyNMHmmKtFfkvml2V9deJaSZ8aSXmhxWKxWG4IarvmAWf8NlbVfNT32/Jt\ny85iv5K17Agc9sAXngC2zokQaC/lTovjASkB7SUpO4sHIiTcOtCc16lxmuL8oiQUl0bSFsoHdHyY\nxD1SBGZnvIeGodP9zBr7wNopYO150NG3An5fry3WSenGQZFZUEQEVkpCKxgAUknPGwtlMEmCFWVr\naSKuTWtXhShkKVd0xSnLQjIMXlMEpdlvpQunRsSknhO1dWZ8nYVt0jhG2N0ASOG5T30ED//mLyGN\nI7i1Gg6/9l6MXW/9Og62QLOHRXAlEv9OxvkZW4tcG4p74NqURJqbh00c/HAD2H0vaLhRLXgmlhqS\nCDS/WX7UawKzR6Uc1Lh7RCJUvWbFfiwWi+X6oRcwnj6T4uIW0KwBt+1RWJy6Occ+LL7jrXAadSTd\nfinoilwH+7/326/hyiw3I1ZQWa463LsI/tJ/1UlwqdzAbp0B774X5Nbk715DxMXmyXwbZnEnwh6y\nm3/l5uVjnGi7vyItbvTvygMSI7yMK6SfI1cEU60D7Lkb2HsPjHziREerO96I/GCQqoHSPBxDfp9P\nECmOB8QVgqqzF2TcnmymlHal6tMS1z4JIglRGK7rkjlnpLRwmw/Z7dL1ALj1Orrnz+Kzv/xvceHJ\nLwAA2rv24uv+l5+HN7tXZkeZQI0i8VD2rbxCcIY+JAAada9MSqHfkjljgzVJMDTu2cJt8m/kUj1U\nVUw6fSIR6o3ZXOzaOVQWi+U6pzdkfOLJFEkiv097AbDaTfGaQ4QDCzdfwZHyfbzpLz+Ah7/9RzE8\ndQZQCk6zgdd+4P9EY//ua708y02GFVSWqw6f+FS5V4d1T9Ha88DibfJYmgCbp1GapQTITTup/PE0\nFvHgt0GkHyMC4CErayseG7pgjghMVHKksteyLj0rGTkp2Ag5t6k3LbhZDHAaGtlV6P+ZcBFISQlh\nNASgy+KULwOKTWQ7KRGIpMv7RlL0KlGOiANAXrP+XEEsTUpJZF2Wl1YLK68FcIKZI7fim37ht5EM\nB+iePo6p216nL5EIOV5+qlxuRwo0d4uUSI6EfbDui+KwKymDxVJHvU7yGjK42WyfxnnP3ST3yGvk\n5YHlI4qjF/VlvW69Ir1xJCrfYrFYrmOeOp2LKUOSAo+dZOyfr5hxeBPQvv0o3vb4n6L/leeRDAN0\nXnVrHtRksewgVlBZriqcxkB/pfrJ4uwlx5/smrj18k0zJ9JvNSw4JGb2U/EDJZLBs5zqnie+vHAJ\nACIAjfAgGvugIiLkI30rZlGNQSI4atInxnEIQH8yFkIw2GtIWdrooN9LwEkkjksSiDDNBvNW9JbV\np8uCJ020cNVzu4yIcVwQKTj1JqZvuTebiaUXCyzeCT7zeWSytbkk75PjAeyAUQjYSCI5zmBNhGpb\nlzgqV4eGVLhdSSH0wojGURwP8DsSx27ODww4dWDzTOG0CWgt2eG9FovlhuXiVvXXZCkD/RBo3aS/\n3ogIrVsPX+tlWG5yrKCyXGVUXr43CjkiltJYxIAOmChvUxEoQUr3VWnnyjyfxmDlZrOfABaHwpSe\nxUOwUxsTR8bFKpEmOk1Q9z4x8jQ7ynyaUlKguGCqWhgqV5wbx5E+qyxynPMkwzQC/MU8wOEy4DgA\nNk7ps9DJgCZy3px3sJmvqdaWcypeA+UA5Ms6qJC8GCdyPR1PpxamZUeHlPQ1BZvianXPgKMOaOH2\nUvISm4HD2QMpsHZcZoU150GNOZk7loYAqHwsvy3bONvME6m15ZrFQ0h4hyMDk0ej9bvnRZhxKv/u\nttunxWKxXGfUPGAYjT/ODPjO+OMWi2XnsILKclUhpcCzR4C158pCgxQwtVdu4B1/8s0tc9m9UG65\nBBAoiB3OXQ9AuzCmFC8Buhck5W10/1XHJMoivjPpwbr/KusDIoxKMVKOJP9lPT/6+f5FoDYtFYel\niHc9BFjPekLUvyxBxZxKwMdgRV87LfQI+etJ6U/aTn69quZxEQHkiXgJ+ygJESNSq9wyItDUPnC6\nG7j4JJBGoJlDYzG2RAR2PAmJKAZUxANg8zS4Pg0y1xYkr28vbDM3qngh0ryHDJBziEfmlBUZrCB7\nT/yWhFHchGUyFovlxuO2PQqffy5FUvgoVQTsmQU898b+PcbM6AUS2tfw83NZWQvx6c9dRJoy3vKG\neexarBrtYbFce6ygslx16NBXgYNNYLCWBy80ZoG5I3oLnebWmNPDeAtzlNI4F1RZr9GoIADQmBGh\nk8b5zT+zlPkRgWPtAmWCxbhaEYh5vBRMi6bK5D0juMxaWKGceqdFlwnhAEScDVaBzu5qcaJk7WOR\n4xUwp8DKV+Q1bj0P8RjbMM3L5vLVb1OeSHIdjNOTnW8KJidPOcy2JsD18zW5jerod7Nv5ct7IQO7\n5GHX11HrxeOxOFbtpe17nJhlVlkx/j3YnHyGuTKW/4Q9uX423c9isdwA7Jsj9APCl85ImXnKwK5p\n4LWHb+yeoXPrjL94FOjK1A7smmG84zXAp/7mPH7xvc9A6SKX/+d9x/HD33sE3/mt+6/1ki2WMayg\nslx1yK2B7vo2pN3zwOpXJFBidOhqGoOac2CvIY5D2EMmUpSnh7xOSGPTDhUnIWi4Bm7M5W6SwSmE\nVlTNLYoSEQNGKE1yZeSMyslwypHYdyTg1AQ96FI+4zwpTwbLVu2NSFwtQMrXtoGZgfVT+cyu7dyY\npCoZb4KkIkfHwLuFOU3ZQYE0BXOQz/9SrgwxNs+7DdDiHdusnECOq/8EsHIAqotbV32mEuDhbyN2\nol5F+l8hJXFiQkhh27BnBZXFYrlhuHWPwpElcXPqHlDzbmxnqhcwPvSAfAQbzq0Dv/OxAH/0/mcQ\nhuUS+vf+1nP4O/fN4cBe+3vbcn1hBZVlx6DWkoiZqnlM2iEirwF2akB4XB5XTi4yTCR3FWEPWH4K\nnAzzwcFJDNp1NwACKRfsNir1RPZQoofwKh9wfDCPpyYxA4QEgFMuF1SONt+0IxQH5blQSSACob04\ntvRs5pLXzmc2jW5jht5GgQ6LkHVxoYer4lVjj1A0BHv1kbI8Giu5LCcaFvq8zONxLCLWxN435yGe\nle7jGn09m1h5kmvBiQzV3Tba/RJR6XFQOkdJBozka1u3licwmnOsPMb28fEWi8VyveE6hOlXiJ54\n6pT8yi7CDDzzpZXKz7YkYXzir5fxPf/g0M4s0GK5TKygsuwYRARuzgO9ZZR/U5L0F/UuimiKB9U7\nUG51GhwgzpBygYTkJjnsAlMHgJTBSsdyd/bITCwy/+yl74iUr12mUJwnE9VeMUdJWzIiCkx5nvJk\nwK85JebxIbtpLAIg7IP9ZiZo2Igypy4lbqPnxSmw8UIe/61U2VEx7l31VRl/iBSwcUb61xzdj6a8\nvBQvTaqdOcccp5geKOWARC7QmJMUwGyWllxbBqScc7gh+zCzn4olhZVLZ3EqvUbedzWKcsGmXJBT\nIOjm+4sHMizYbcj5JGXxpU8id/osFovFsuOs91HqCTOkKSOt6HFmZiTJpaoPLJadxwoqy45CtSkw\nOdJPlMZys8wsg2KZkQ3eNZHlRZeCHJ2WNyIgvIYIlIXbwVFP3BS3KQNhwUASZwKAap1yz5FOGiQi\ncKrDIVSSDZzlonMTDSQpsL0kTkwWeBGDEukzKvUsMYtANGV/IDnPdEZu9knJPqf2gyaVtnUvaDGl\nhQpGAh+UI47eqIDDhJp6HdrBG6dAc0e1KGQRU0EXfPKzYKRAew9o4VaQLn1kIO/zKsIM1GdAaQIe\nrOjrYo6lyyL7qwDHQGf3yOtNj5squVqcJgAnIE6A3rKEdqw+m8XgY/52gBh89gtAsC7X1a0DzUUd\n7a7FXrAlfXn1KQnbGKyg5Fi5NUkntFgsFss1Yd8c8OzZcskfAOw/MofHHngOo1+Eea7CV79pYecW\naLFcJlZQWXaUbGCr48tN8HBzpBpLD3o1jk8xdIFcXdIVa5EEnfqXh1CQo2+S01jHgCt9f23mE1Wk\nCeokO5CS4wFynCSRm/AklB/jrHT2jJejse6digd6zSyzkUrbaUEUDvOIeFLA9MHJFyzYkNelSWHI\nbSFUA5BYcx3IAU61UKXK+U4Mkl615afApx4EmnNAbUpmNvWX8w23ToODLWD//fogE0rmah0ROVtn\ny2IKAHMCwAUWb88dNohQBekkROWC0zgb5svrJ3QkPkuJZmMWVHSehuvgFz4n5Zmc5PtyGxKe4XWk\n1I9IrsPgoggqvykBGGFXrpHbqBz2y5zK3LTBmuy7MQc0527KgZkWi8Vytbl1D/DwV4CtQV765yrg\nzqMN7HnXYbzvd08gjlMwREz9w3fux7HDlzH03mLZYaygsuwYnETAxS/lQ3MBERR+B6V5U8W2l1Q7\nPkwAiaAhxwMcL+vNKSXsOXW52TavR1rYn4k5HykXSCM9ZFi7Fo4vYQleDdztjYuiJBLBN3aCLL1E\nSSLuSFUPEGsh6DZ0aWOelFd90XQpW7CBTETFAXj2kFw7cypKAU5TlyLqtZCSdEFdqsdQ4gYFGxJ3\nzqkI2tXngKg7fi5hFzxcF1cvDirEKMkcqmgwuUzTJCuacxklFfcwZQZWnimHbMQDYGsA9lrlnq80\nkrLHxjyKqY8ElSclGjez1OfmynonrZQZWD2uI9j167bOiDCePTzxdRaLxWJ5cbgO4TvfwnjoGeDZ\ncxKb/qoDwKuPAM59B/Dm++bwl3+9jDRlfM1bFnHrESumLNcnVlBZdo71E3JznabioOhyM8T9UukV\nA+I2FZwnCUQoJ+9lpWgoDNOlbWqrlatLBgtleXFQLgWLunKT3tmbpf2Np+Vtc4zICIJttuFEO2sO\nMHt4bG4TgHzOVBJqMVU4bjwAlr8MzN+iwzz0DC5d9sdF0Uh54AQxS6y4Lruj1iLgt8BRHwirwhlY\n3BrWQ47dWuE6K6C9CzRYGYtmN5A5j+JcrUg7lMXgCtaia1JiYRIBbi0P73DrMux37Lqlsi+9RnJc\n/W9pZCBxFebajK1BnEaO+iCbBmixWCwvO3WP8Na7gLfeNf7ckYMt/OB329Jsy/XPjT28wHLDwHEo\n/UBJJDfoiQ5pqJiVJKVzoz1B1SVXZFwnhu652kbIuA3tBhX2pcvLxo5vysZ0el2JNMrDJMovzPdF\n7sQ1Zzf39WlQZ0/1NhsvSJ9ZWBHxnq19AwgHUqo3XMuPXzXLanSmFyDnFw+lZG+S4HBMSSVJaWJj\nRlIX69OgsLdNIIZ+S5ReSxrLsUiJi2jWZH7i4eSyQuWISIx68lOfqRSh5SOn+j2iimj1EaKBOFHB\nhpQwuiODI5l1jL/FYrFYLBbLONahsuwM6ycw2X1QKA3CNSVk0UAHBzS0yKkWCtIiRRL2tt13BMoB\nkQL7LREWcSAleNtAtQ44CcWpydYcA26jlMDOphTQ4DeBcLNqj5LMZ/p+KuB4qI+XAI4DpFWhEzpJ\nMDbnqwMdTC9XKdBD92ANR9Yz7OalfFRw+cz+nBrg6/KKWgc0WAFzLD1gSQSud0TQEo0FiGSBh3FQ\nmPtVKOtsLGgRqFHeBDFM0uPVLyRDTkr9kwug+6dSQGnBbUobScn7UhRjcQD0L8L4eiDKo+AjXcZo\nnEqLxWJ5mYlixkNPDHDybIQ9ix7eeE8ddd9+122x3GjYuwTLzlAUJEWYC4NkFRD3QYl2K8wQ3jQW\n10A5ug+oWAoIAJyV/2XDaUedEzLJgfom261Jgl93wgDYsCv9NkSg5jy4MZuHXOj1loIKGFIWZ8oD\nSQHNBQk3MKKNHMBr66Q9rzLZjzkFLn4ZQGE/npz72EDiYk+TuYbRQJcTjpTwKQV4NS0S9LrDDX0I\nB7z71TJ0eah7tRozwMyB3AFUNRF6ww0RKEqXa5p9KRdg6WFicNaHll2caJAlJwIQMWVCRpJQ5oR5\nzUKioVm3Pk5RbKXRWKR9RqkkVMlMse75fK2DVaC9K5t7loV+lHZBkiyYlW8SUJ+uPp7FYrG8SDa6\nCX7mVy5iq5diGDJqHuEPPrqJf/PDC1ictbdnFsuNhP0axLJDbFOK59Ryp6Mq3ICUlFwNVoFoAE7T\nQsldvt+sZ0e5+oabAGgBpDwJcyjcmBOpCbHZIuaYU3Ca5KVjjp/d2I+mvpFJISze6Ds+0N4DTB8C\nmktAbUbW4dbF+WnMjR/axMmXd567SAa/XXBNCBwPkZ57DBx2q680kbymNi2BDmm5zJJcH7T7XuDQ\nVwEH3wxauA3k1GW9Tl10SlQYrDxaRkckgtfxxdkiEb7yo2dnkcqFURpLOWGqI+qZpaSwuahDJRRQ\nnwUt3FmIndf0L8r7vB1eS0RQcXaWKYnMIvqxbcliNots7uglSgwtFovlyvn9P9vE6kaCYSi/j4KI\nsdVP8esfXL/GK7NYLFeK/QrEsjO4Del/GaWY7jcRU8YV58EB9amxvh9m1vHmSd6b4zUL4QeJCDK3\nlr+2MSulc8Uba68h4iMd7e1iQLmTOqPEBYrTrHyOmUGtOVAa5VHnzDI3qj5VHeEebMn5Or4e2pvk\nyX1eU59TXQssR7ZJQnG16h1QPAAwjbH+LWZxyoh0ymI6ftz6jLxKKQBaGCpXShyB8aHKwy1wfQoA\n8sHA4ExMjeH48t4ZfdM9X+pXIscHTe2VocNmhhUA9ju6XFGvOeoD3XPgxjzg+PmxzHva3g1qzhec\nqRHMvxOT6FglqoiAuSP5YGCLxWJ5mXnoyeHYUFtm4KnjIeKE4Tr2d4/FcqNgBZVlZ5jeD1x8GuUb\nXMqEAzPnMeKjJV6JzIkiFG7smcHg8ZvdNILMnEJ1WRgnecCAeW1rSfZp0ueUyntxsteZ7bnUO1WC\nlAQ2DNbBnIA6B0D1TuHQkR5Q2wM2IMN9Zw5LDLxBueJkaWeHmUWMBZuA55V7eZQra6VaHraRxhK0\noePBZa26j4hcXX6Yjg/pNdHxXlPEr3L0HKsoL48jKr99SSAljV5DnCnWKXsVMfBEBC6WAJqQDFOe\nOdKjVLzG1FwA984DXhM0vV9Hzg/B/RWQcjH2byot9I5NRD9Xmy7HpOt9UK0j18JisViuEmpSbhFN\njDSyWCzXKbaOxbIzNOelL8fxdUmeJzfebiPvlxpuYkxMASIulCoLpMG6doHyH4RdSJMU9M/luF8a\nryHhB/UpERCjrzN/DXqALgNkTsFJCE4iPfbJkQ39lkSU98+DUxEZnCbA+cf1GjXDDWD5yXJioC53\nMyVmWQ+T38ZYeh8pfZoEKpYPhl1g66wIq8GahC4kobhBNS3wKgccayHit2Rb83cjqEbT7wBwGoP7\nq+DBqgjGqtlbQD7QOVtjT5cyml6r/BpwmsgMLGZx+ZQDLL5KyhD9Nkg5IL8FmtqHcdHEUhIYDXSg\nRsX7Tzpww1yH9i79dx2uUZ8RoWWxWCxXkTfd24A78mvdUcC9t9XgWHfKYrmhsA6VZYcgYGq/lGGF\nXXFESLsJg1UpBXN8wG3KjXcaitPk1kQENOaB4SrQK6S9BRt5aRh5GLt5TpNS6Vi+FAJau8QpCrta\nqFDmdk30NZhFMATrEnYA3a9FCkhCMJoyDNgMm+UU2DyVH185+Sh4s44kkvNvzus1R5X9WTyacOjW\nytvVpnRJZaG8MezKmp0aoHxxvIhEGMVDudZJINuSEgFSn5XrxrqvSUlJHBuHy7w2S74jLYgodw1T\n7Sia62lKAUcj21OdRqj8cophrHu1hhuA60sQid8Y72MaLcnM3yigvwLu7AW5o/OtSMJCitfO8UVU\nWSwWyw7yXd84hWdOhVheTRAlDM8ldJoKP/jOyQPIb0bOXgjwVw+sI4gYb3rtFG47YqsHLNcfVlBZ\nrjrMnJe6mRlPUHomVSA35G5NhzcsiKAilc8wMtQ7Uia3drzQF6XDDAZr44EWJoYdqnwD7TVBtbaU\n50XT4N75EREzIfnP9B0ZYVV0snSgBieBFg5aLKYDsFcHoiH4hUeBtRPy3OwBYNedUrI23JDzCrvj\nvU1F0kT27bfGxYUrx8BocqDXlj4xrwGTcMj1GaB3AXzqEdCh15fnUBVFj3IBjrIsB3nbXBG42i2T\ns2/oFL9IHD7tuLFxuNIIiAv7ZR3jTkoGKWdR5yLE8mvA+Tyy2hUOdgw2JFhk6gAoCeXfBjlyrqP/\nriwWi+Ua0KwrvOdHF/HEswFOnY+xa97Fa66hOzUYpvijj3fxuceHSFPgdXfU8F3f0MFU+9oVM330\nU6t47++eQZIyYh3dJAAAIABJREFU0hT4kz+/iL/7lln8yLv22v5Wy3WFFVSWq89gVZe6maG30ANj\n68D8LVqcNHIBVVWOpqHGLLj2GiDckhvx2pTEfm+drX5BEmQODZQjTk5tqrABj/dLjfYX6c2ym/us\nVK3C+RoTRAwOusCTH9ZDevU1WD0BDDbAx74GFG7JnC7t2hTdHUALUk61ztNR8l69/DwItOteOd+o\nJ2LFa4McFxxsAUEkqXlgkHKRpgCe/kvw5gXQ674jXxcK6+ekIBizswEV3LxsU68BOG7eD8apTHYK\ntjBGGsnxvEZ+3dyGCF3lyL7Cbj4HSu9vrOTR8QFMGnxM8vrBKtBaKF0vi8ViuV5QinDPrXXcc+u1\nXUeaMn7u/Ws4sxwj1t+FPfDFIZ4+GeLf/egCfG/nxcvGZoz3/u4ZhFH+BWcQMj72N2t42xtncPdt\nV/hFm8VyFbGCynL16a+gupBOhtCSGR57mZBystCFDOVOKAEjCX6oCEoAADZDdouvIAVWfu5wZW6Y\nERtX0JsFABsvgPbcA8weBOIQfPYxYOW4lLRFA+ktM26OTuIrl/hpp8ak7aVJfjVN/5XSyXpeIxcq\nELFFpIMbOJGyPQB05otSxnfmUfDaCeDg/aBDrwdnpXXV57dtUWQpWEKHavhtOUfTWxUPtTBVeamg\n8gC/nYk0Ug641pHnzOytsAeudcadxJGBwvlT2mUM1kVQWSwWi2UiXzoR4sJqkokpAEhSYKvPePjJ\nId786upB9FeTh7+4JaGzI4Qh41MPrltBZbmusILKcg2gvPco2AS7NZ3WduVwEgJbZ3RfVixOROZk\nENBanCimAIiDEw/Abj0ro2MTua7XREQSuECOuD+ThsqOnCMPN4Dlp0EH3yg9T8aBa70dmDsK/son\ngf4q0FnU6YJu3leWBMhKD1Mt7IxgVE4udzKBsY3Ic3SSXhKJa6R0L5XXAPbdC6pPg7fOy/tgSv8m\nBXpsJySLLpIO0yBFIo6iUMe/u0C9oUsUB+JGeo1xRy7swQhukBIRRgrsN/PrEvblOPV5caKq1ljU\nftFARGway3tYn84DNywWi+Um5PS5IX75/Sfw1MkUncW5seqDIGQ8fy7Gm1+982tTylRDjAZVAc6k\niESL5RphBZXl6tNcADYHOlYbOqJ7HZkDEWyBt3GRJsFRHzjzsN6v7s2KBxKsoFwJemgtXcb6FoHN\n03KzTkr6feK+7M9rIE1i6dGK+lJi6NZE5KiRwAtmCVpwG+BgUwI4DtwPeLlYA7QDM38U5Ld0YIIS\n0aNL6dhxRGgEW5kQKrlvVY4ep2CeMP8pG2JrgkAIWLwFdOh+6VVzfSAcgJHKuocbcu4NSbsbFTsT\n69ajAdhvlbcnArlNoL1b9r11Vt6bqKeDPVAq5eOwD6w9h+wDlFPA70hvWDzU18t8wJKESTTmRYCl\nI3OyQHmqYdiT99DsNwkk4KS1aEWVxWK5KVldD/Hun/pb9HoJ6p0m2vMMGunfqnnA3oVr03d6/70d\npOl4RYTvEb72TTa4w3J9YQWV5erTmJWQgMG69D4Vgw+GGyKC3Dowc/DK9rvyzEivU0E47L53PLhh\nAtScB5MDbJyStRRqDHjrbF52BgDDNbByZE6REVWAdnS0MCICts4B0/tBRdeneEwA3JgVwQGATO8S\nA0SuOEleS2783XourpqzIOWDkY4U5UlyhLSDFdL1OC2XxOkZXtRZKoV+MBjontXOkL6OUQ+obQHT\nB/L9gYHhJrg+mwdZZPtNgIjAxflNTj0TgFSbAveWtWBLgbrua9LOFnMqgSOjfWjhlsSY+808fdBr\nAc35rGeLp/YCGye1eDQfwAwMVsUpJCfv78oEH4uwtwl/FovlJuRXP/A8uj35fBhs9ZHECUjlPbJE\nIl7eePe16UFtNx381D89gJ//1VPyXSDLZ+e3f+OiTfqzXHdYQWW56hAReOYwMPiCuC0lWByh4TqA\nKxRUw7Xqx8OKIIRLrbExI44MtHDongNvnMrFlNcUJ8TxpA8q7IGGG0Bnt56lNeIOtXcVQiaqXR0y\nA4TjoaTbmbh1AsjxwMoRNwkQpyUJRHhknywj+0zEgZIhuyzbJxGyckAjsLKoc+TuVRKWxZQ8KSWZ\nYV/KA5MAWH4KHA8l4GLxVeA0AtIkX0ocyI/S/VFzu8rrnD0K9C4AfRN/T3L+XrM6wCJbSgzaRnCT\n1wDP3SLCfbhZTnzkRBL/dCBHKfVx7N+jxWKxvPJhZnz8r1dKj5199jTm9y2iOdWEUoQ7Dnn4/m+d\nQr127VL+3vz6afzWL7bw2c9vIoxS3H9vB3uWbFWB5frDCirLzhDoAIbKQAM9oygeSkDB5cZak5oQ\nM06YFKpwWbslAjp7wIM1ufH320B7d95j5TkA6iIEveaIA1WIUQf0vKdyKSMzg8DaQdJR6EmIbLYT\nkbg15ubfuFdGcGmXiUevUzyQ55pL2j3zxSEyz6Vm1lMhch76HSnNaiqtVsTuytP5rCjSgRLT+4DN\ns0C8oQWhuG0i3gC0d1XO1EJ7l/wM1iQynlO99nj04IUXXvoDnZQjzllvuXqDNNb/tkwGPGyEusVi\nuSk5eXo4Vk6XxAkuPH8ORMBv/F+vxrFD10fow3THxTe+ba70WLef4hOP9PH08xH2Ljj4ujc0sTRn\nb2kt1w77r8+yM4Rd7e5U3biT9LF0L8hztSlJ8btUkl57L7D5AkpR37qv5mWdT9FcKvdA6cAFNKZ1\n5Hrh2LJBnj6nB+yy38q34VTCNMx18BoihEz4BDnaXQpz8ZKJAAdItPvCenCxCa5IdS+Zjoo34oaZ\nR5wYcaqYVO5aeU2gsSipeKW0RJKSw+nDIqySAHDqoM5ukONLOeCgCQTdvIfJrUsYiHeJkoz6DJDG\noLAnS3K83DHLDq9EZI+mOk6Ck/F9FM8bKLxnNBKhb7FYLDcHUqHuII7Hv8iq151txdTF9Rh/9ulN\nHH8hxJF9Pr75rVNYmN2528nVjQQ/86srGASMKAaePA588pEBfuJds7jt0JX1YlssLxdWUFl2BnNT\nXFXWRaT7bPQNb7CVD5DdjtkjMhjWBFyAJcBg/raXZcnU2Stlf5NcDOVJ8lwW1FBwPvxCCVsSgftr\nUm7m+ACNigZTpjcEcQqYyHQjDhwt5tx6WexkYRxmP0pmbpHKnSLZECXRSY6sI01EBHEq/UWNWaA5\nJ2WFwzWge062P/cUEHZBe++VUr7WIrB4hz6k/J2bC/pUrkDIkqQwojELJJGUORLp4cf6vXTr8ufe\nMjgaALOHxwc+l/bp5OWN40+Wr1VtSgb9WiwWy03GgT11LM77OH0uBSf554PjEn7guw5MfN3zZ0L8\n6/90DlHMSBLg6RMBPv5AFz/7o7txaO/OiJk//ngX3T7DGGxJKj+/8aFN/Pv/ad4O/LVcE65dYazl\n5qI+Kw7V9D5dyqZdHuUBU/tGyuZ4+34aDSkHtPs1wN77gYU7gD2vB+19/YuOYB/bf2MGmD2EiXOX\ndHx6OQUvlRv/4UY+WwokQsjxJlYikt4fj4qBYvnfRKGA3GlS3qXDOMzMpzFBZkoBE1BzCTR7DKjN\nAFNLwNZ5YM9rgENvBe26Z0zUEI0P+71slCsunXJBc8fkz66OVjdhH0QSkrF5ettdEZGkSlZdaPPv\nrrUIdPZeWrBbLBbLKxQiws/+5G2YartotT14vkKj6eKNr5vDt3/znomve98HVzEMREwBQJwAw4Dx\nvg+uTnzNy83jXwlQEf6HlY0E3f6kCgWL5epiHSrLVYHTWAc6kPQZafeB3Dp45lBhppI3Ieq7Yljr\nBMhvXTWnQS3cAd48IzfzpYOKaBlbe7ABRIWyxkTKGcmp5THraUWvEBEICuzW9cwp7U6lIXDm88D+\nN4AdX45HrqTdjbzefD/CnIKoKHh0qSCSPKocqF5H8SW1DigegN2aiLAdECCchHrY8Vx1yafuudpW\nNDbm5P3pLet/RyRiTbnWlbJYLBbNsUNN/OGvvBaffnANq2sR7rmjjTtvbVd+JveHKZQiPH0iqNgT\n8OUTAc4vB5ieclGvXd3e1JpP2KoQTgxJJbRYrgVWUFleXtIY3F+VnqkC3FoCajPgYF1+WWt3Z+J3\nSW4jv7kmBfgtkC6vM8NeX05bn5NIh2K4gFsv77uzB+ivgIfrmaNTdmQkRY9THTAxelZJBNQaOhTB\nm7wGEBANJcLc1al6xz8JpAl4sAGaPQjTg4V4WDhWPscqG5YL1hWIeraV15TyyOJ5VcxLLD3JLO9T\n0gN23325l/LlYeJ7yyN9a1UvJSkjbMyKMI36Omq9oa+NxWKxWACgXnPw9W9dmPj8Cxdi/OZ/7eL0\nsnzJ2ezU0dsK9BiNnDRJ8T3//AtgBr7l63fhR77vMFzn6oibr7u/iQ9+oouw8J2go4B7b6mh5ltB\nZbk2WEFleXlgSYPjcEvf8CpxodJYRErvAjB9UNLtgk2YfiPyWmPuD7Mu+Qs29CMkPTScAr3z4joo\nF1yfE7FTm3rR4oqZZZ/FEkPlgqf2Z3OLiAhoLYhQjLraqZGUPjJCxpSmdfYAvYsSHV6fkp4m01uV\nRpL4FyUYVTLMDKyfLDtznb3y3113g2YP5edIjggkE1Zh3Jo01g7ZlvQ0OTVk/VhpKGIuLexf+UBa\niBfPoHzfaSrndsvXvoire+WQ40uARxLqfrCR99Wpbd9DNYpybGmfxWKxbMMwZHzu8SEurie447CH\nu476UETo9lP8/O9sYhDkn1fKddGaUuhu5PMZOWX0N3sYBvKF44c/dh6OAn70+49clfW+401NnDwf\n4eEnA7gOIUkZ+xZd/OB/b0OGLNcOK6gsLw/xUAaoBpvinjDrIbf6Zt/xgbgPNGZB9ZnMZQIREHf0\n60JdyjYSogCWnqSoMCcpjYH+BYnbrk2DZw9f9iDfEsMNLaYKAicJgLUT4JlDoGLk+XBVztNviUGi\nPDCKQxClL4yb8zrVsJYnAgISPAEJ4OCVp4GWpAcyAKw/P16Ct3kaaO8Glu6qFoyOn0eZA+DBCigN\n5dJtviBClvW8quYC4LWzOVHs1vIEvXRkFpOro9bTWOZMNXdBeY0rvbIvnl13S5mj42cuWybSp/fv\n3DosFovlFc5zZyL83PvXs56oP/vMALvnHfzMu2fxmccDxMl4GYPjEmo1Fw4l6A9SBIMhtlY3s+eD\nIMWHPnoe737XIXjey9+qrxThh/7+DP7+1yY4eS7CwoyDQ3ts9YHl2mIFleVlgQerwNZpcT1MKISZ\ns6QcPTi2Lz0sJlbc4NYAdxG8/rwMl2XtipiZVMxlMVUk6suN9+YZoDmfBxlcLsF6vl9OpWTOBDVc\n+CJ49gioMSt/9zvitLV3y2smBTGQRI2PP8OgJJLznTksblE8lHNOJ/SMFZ2p0iEIjLyPi83aixiB\nxgAcXe7mNYDBurwXbl3Wwsa5YunPIhLRFfZAUwdkePEOQn4bfPDNwNZZEbukJAWytQhy7UBHi8Vi\nebn4xQ9sZGLKcGE1xu99dAtKKUQVrbauQ/jOb5zB/gWF//X/eAJbm+HYNikzeoMEM1dBUBkWZx0s\nztpZgpbrAyuoLC8P3QtZZHcmaIqpdGbWUv+i3BwbF0TDwZa4VNkDrN0XcwM9Kd0ulefCTXFT0his\nPND0/stL+zPiiVlERuk4DF47DoRLIDDIa+jZTTGklG6bNCHjDBWQsbcSOCEOly7dC0ccssLxJ5+3\nfs70lem9b0tzUaLQWYunuC/nzAz4DR1V3wSaCyDHewmjkV86pFyZcWWxWCyWq8Lz5yIMg/HPmJQJ\nX/hSgH/w9R088MUAwUgRAxHwqqM+ju7zcNvhBh55bFxQtZsuptr2FtNy82Bj0y0vD/GwLKaKODVJ\n+lNKtutflFK7Iltnq/drhttOur0v9tOY4bW9C+DnPgmOhpdet9vIj1MlXpiztZKjY72TSLaNxxtz\nhcmBCgyA0wicyE82b2nSaxwf4GTsOAzomHQ3/5k0SNfvALOHxUEMtuQ9cnx5XxozwMKtoKVXgRZu\nB3X2ZL1jFovFYnnlstlj0OhcRI1SjHuOOWg3VTYKEQA8Fziyx8WRvSKWfugfH0K9pkof/bWawo98\n3yEoZQMiLDcPVlBZXhScxuC158CnHwKfe2xymR2RzDUa7W8KtyS8wQiFpDqKNRM5/oRgAa8F+Wcs\nx0EaS2hEEoKXn8z3Eg/BvWX5iYMsRCNzzYLNyhlPRFTqMSK/BarNACBxxJIQzFwSPERU4Y6Z89Rp\ngmkkP/FQShtrbZRFFQFuA+T4skZOs+OYpEOTNJglDraWdCllMX1QAQt3yrGMmNKzquR1ShIM+ysT\nxKHFYrFYXoncdsBDqw6MfpnoOIw7DzPiNMZPvWsKRw/U0O54aHc83HWshn/2nZ2sFP32Y238x/fc\njTe8dgazMx7uurWNn/2p2/ENb1va+ROyWK4hl+3Hkgy2eRjAaWb+FiL6NABzl7sE4EFmfmfF6w4C\n+HUAByD/134zM58govcDeBsAY1V8HzM/+qLPxLJjcBIDLzwgIsiUzJGTRaGPMenxZCipdPUZeX3l\n7Ck9t4kc6QNKAgCp/N1rihgxYk450gMEAHO3AFtnZL29C0B/FeZDg3vL4DQGuTVw0AXVppGl8FXN\nZhoRc+S44PYBUH9FeruSaHy2keOCUwIVxFgW+T56hhyDG/Pilpm0Qb+th/nqtL0kQClNkFSm/7JQ\nDMcDzxwWRy0ZigNVmwH5LfDa87IfM8sqO7gCKQWOenKM9p4r60GzWK4j7OeUxXL51HzC2+938dHP\nxkiZEcXiQC3MAPffBbiOg1/90wDLm8gCpk4uA7/95wHe/ffyoKLbjrbx8//qrmt0FhbL9cGVFLj+\nGICnAEwBADO/1TxBRP8ZwIcmvO63AbyHmf+CiNoox7f9FDP/8ZUt2XLN2TxVFlOAiKEkKEddm2S2\n7RLiwp4IqtoUMFwbeVKG4oqYYB1eUQglMINyATmucnKxoBxg5hB4sFYSU4AOcVAuOOyD2kvIHR2W\nYIewHKEOvynPFYIjyGuBp+rAyrN6AG+SlR9m5XhBF+x6+fysScETIAlb8NsSrBHra5smWcCHvD6S\nczTXgNOxCHFSjsxfAstcq9oUOB4A0VbhPAvnJhcEBJmjxf2LoNbihHVaLNc99nPKYrkCvunNU9i3\nuIJnTgG9AbB7Hji4G2jUXRw/S7iwniIufHRFMfDs6QQnzoQ4QKfAgy6oMws1v1/K+i2Wm5TLElRE\ntB/AfwfgPQB+fOS5KQBvB/D9Fa+7C4DLzH8BAMzcHd3GcgPSW650WhD2AI/FrWEWgdBeBIUTEvoA\neTzsSVmbcssOkdeUV5kYdqWDEnQJHPy2aARSgHJw+snH8OAf/w7ac0tozy/g6P1vwq7DRyYcm0H1\nqZFSRAJ7DZ00mMj8opqeGs/5Nqh1RMgNNgoahcFpImV05hGvCXAkLyUHIK5w4fT+dPw6syvi0AxG\nVkp6wS48Djh1SRxsLWWhHhwH+nqwzLjyGhJPwQzUp8VB65r/7QrXoRgckj1E4CQQx832UVluMOzn\nlOVGIAwDbKyuIBwO4XoepufmUW9M6H/dAXzPxauOzWGms4lBEIMAdFo17F+awoc/GyGMxl+Tpoxn\nP/cF7J57FkhiwHFB9Rb8+76pPGrEYrmJuFyH6pcA/AvkpRNF3gng48y8WfHcbQDWiei/ADgC4GMA\n/mfm7K7yPUT0rwF8XD8+1khDRO8G8G4AOHjw4GUu13JVmVTCB4jD43dA7Xn5ezwEu3WJNzcDbsG5\nkHF8YLgm9/VuDcx+vl08lHlKBZHFytXDdAHEA7BTAzUXcfzBv8GJzz+Av/fTPwdOJMSBlMLyieNY\nPFT17yafDzX2uOMB5IHqI0MCSQH+lAyeDXvg4SawdhJYOQ4s3g6aP1ruQ0qG2m1isFuvDnvwGjBi\nCsjL90z6HzMDw1XQzGGZJUVKx9GnWmgWhG0sYohrUyKqlCvisDRjKwQ7PoickpgqkYRWUFluROzn\nlOW6JgyGOPfCyexzIo4jBGcGmN+1B6321RlAzsx48NENfOIzK3BdhXe8bQH33FE+VqPm4ZYD80hT\nlqpy/dkw0yZ4Lsai0x0kmKItEVMAkMTg/hbiZx+Fd/sbrsp5WCzXO5f0Z4noWwBcYOZHJmzyPwD4\n/QnPuQDeCuAnAdwP4CiA79PP/TSAO/TjcwD+ZdUOmPlXmfk+Zr5vcdGWIl0XTO/Hdkl2iPRcpTQB\nx6H0MvVXgOEmMFiTEISwJ/uoz5QEUxaUoPuGaOYQaGq/LvWTsjfur4HXToLXT8q+khD77rwLX/tP\n/hlcR8FrNFFrteE3mpg9eKwybEIONuGfP0HWVcRrAlP7wWkMnH8MvPoc8MUPAk9/DFg/BdLzokoh\nEU5d+qIcX8RU3C/P3wJ0QETVLCulxVMKhAOJMtcljVJWUd2PBU5FNHlNYLCqZ13pEA5TNpiEYB1y\nUf0O2h4qy42F/Zyy3AisXVweT2xlxtryhasSCsTMeM9/fBY/+0tfwV98egUf+eQy/uW/+zLe9wcv\nVG6vVHm24utv9zAa1EcAXIpxZ/vcyMFSJOefe5nPwGK5cbicgte3APhWIjoB4P8D8HYi+gAAENEC\ngDcA+G8TXvsCgEeZ+TgzxwD+BMDrAICZz7IQAPhNvR/LdQwzg7sXRBT57QlbUXnGVBJowaRv5s2X\nvlEf3JjT8d0VjpfygMYcyGsC9RnQ3K0SmAAlJWlJCMShzCpyXNSaLTFcOBZXTH84JYkOtDCCwqTk\nBZvlyPUizSXdm2T6s3zpRYr6wPpzcg5b54Ct83JO80eqwyaIQK4npXiDFQmccDwRhybqfJtZVswp\ncOFLoMb0WK9UpZgymIHBUV/HwevtOZXzTwIZxDyyDzbbdM+JcLRYbhzs55TluicIqkd5JEmMNE1H\nHmNcuBhgMJzUe3tpHntqC599ZB3DQPbNDARhij/68FmcvTApWTen3SD88DsbmJsSp8p1gN1zhB86\n/Gm4apvPIIvlJuSSJX/M/NOQb+lARF8D4CeZ+V366e8A8GFmnjTw5yEAM0S0yMzLkBr2h/W+9jDz\nWZKvQ94J4Isv6UwsV5/+RUnlAwPNWRFLychAPyLp3TGMPp9tp2dSeQ2gMafnUJnYOhEzo2VwaM6L\n83LxaRFEzXnArY1Hshv3hhwoUpBhunHmSBGn4N4aeOMMsP/+rPSNiED1GYkwT2P5yYQPgN4F2bfX\nBpYfyOPUL+eLxVpHYtqDLfmz42VhFjzyrSBzCqycAB58P7B4BOgsSgljycnaxkXymkA8qE4s5BRY\neVr+2LsAzN+q0wQJCLtg8uRy9FeA9q7LODGL5dpjP6csNwKO4yBOq798U4VAh498chm/8oGTCEP5\nAvDtb5nHj/3gYfjelYU+fOaRdQRB1fGAhx5dx7d+w6V/xx/a5eBf/eMmVjYZjgJmOwrBI03wehfl\nvlwFZ/fRK1qfxfJK4qVGsnwXRsooiOg+Ivp1ANA16D8J4ONE9DjkLvDX9Ka/qx97HMACgP/9Ja7F\nchVhM7ep+Au0vaTjwvXNvd8Bpg+OzGDa5sbfuC5uHejs0c6WxIJXlsGZuUozukeh1h4vocsWLB8i\nrl/LB/5qB4Y5BWYPAou3gYMtcDyUnqiwBw43wSZdz63nYgqQ/bgNEYFFV231+MTyQU5icbRISRmh\no90qpy5OVSix6zJfSq9t8xz4878PxBGwfga8dX7ckSo6aKXHXSAZypywsFd9bcw1CzbBZx4Bn30U\nfPoR8IUnkb2/k15rsdx42M8py3XB1Mzc2GcbEaE9NZ09/uCj6/gP73seW90EQZgijBh/+Tcr+L9/\n7cQVH69ZV5WFGEoRGvUJn50VEBEWphVmO/I55931FsCvA6af2XFBrWm4R19zxWu0WF4p0I00zPO+\n++7jhx9++Fov46aE0wRYe3bCkwDmbwWlCdA9O/K6WAuxEZQLLN1dLZw2z0rp3uhhWPqqxMF5Vpyw\n5rw4VGksosPxdOqfh5QJxAkoqgjtasyKKImlJJGZZWYU6bCK2oyOKXeB+rTMcto8Kw4dJ+D+BvDE\nn8hx/SYwdxR07Gv1uTkSvcFJaShwRpqIixRuyfFNgITbkOAJrwGcfAgYrAOLt4I7u0AbJ3SqoUnn\nUzJzK+zlx3DrAOnkv+UnAVf6vkipUh8Z91ekv6pkrRHQWgRN7cveH5o9Mr52iwUAET3CzPdd63Vc\nj9jPKcskmBkbayvYXFsFQGAwWu0O5pd2Z5+FP/a/PYkvfnn8M8vzCH/8/74W7eblT7s5fW6If/ov\nHkcQlu/z6jWFP3zva9FqXr6oGjuXNEF64SR42AW156Dm9074ItRiuTbs9OfUlcyhstzMkBqPNTd4\nDR3C4AL12dI8KVIuuLkgroxxVMgB5o5N/uVba8tw2sINvwzFlVpyIgUs3SXiIhpIeRonMAmC8NtA\nfQaq3hbRtXEqH5hLCqh3sqRAdmtApB0pr54LnKgn+08joDsEO56U/Ok+JPJ88NGvBhrTgOsBIPDy\nl4CpvaD6VN6PVHktSdacjbrRg3uTQEr1OntBx96abw7oEstQ+qI41elKKeD6YFIyxysaSFnmhk5G\nDDdFgFK97GU15uT5oBB4Vp8Rl9AccTSUw2KxWCwvCSLCzNwCpmbmkMQRHMeFcsqi5vzF6jJ5RxE2\nNuMrElT7dtfxYz94GL/0GyfgOjpBloF/8xO3viQxBcjMQ2e3/dLNYjFYQWW5LIhIhFH3PMacjeZC\n/tdaW4RJNJDf3F4D5Hjgzl4tUhzAa27/TZYvPUwcdnNnhdNczJEDcv28DDFLNzblal0prVMOqDkP\nbu/R+9HPpwxWhZ4kv50LDscD0lQEmOkt4jQXU3IxADBoalcpepw7iyDIcWSm1LYXdOQyFpyn7nmw\nWwPVRmJ0iwEemcCkLLCCTTR9UHAE144DC7fnpYumF6u9C9ycFwFXmwYVg0T81nUtqDgeSjAKADRm\ny2u3WCzN1KcfAAAgAElEQVSW6xylFJRfq3zuVbe18VefWx37/FAKWFq48hlP73jbIt5y3yweeXwT\nrkt4/T1TqNdempiyWCzjWEFluWyoNgUmR9ymJJIeoObC+A2tcvWw2sJrORF3JQ5EtNRn9QymqgOR\nlOT5HaB7TsIrYKZTkZ5XxUA0lPK5KqI+UOuAk0hi24vqhXWUuFcHkZL9ZsKIwEqOgWggAmY7t6m4\nbEBKFZm1GIMOw6jYcmzAb2EnpIDNM+C5o9Wzq8w1opH/ff22uHHIzwXTByQxMQv8KAzyVa44W46n\nyyV9oLMHNOl9uQ7grbPAxsn87dw4ibS1S0o/veZ4GqLFYrHcQHzvd+zDA1+QZD4jquo1hR/4h/vh\nuS+u7b3dcvG2vzP3Mq7SYrGMYgWV5Yogv6WDKK6AOCiLmjgRodRcFEdr0rEcF5jeD44D8NpxKVFr\n67K0JMyEViVpKoJspHQwwwRAkFPZJJwNI06TyQNwJwVnZOJLlyDyyLaTrCulxRMnABJg5Wnw7BGJ\njr8cwr70UbWWxEmc3g80F0GcjvdymeHASQRMH8wGDGOwJmEWzbmK9MTRXbC8B6mEdUwUfy8THA+B\n9ZMw7yenMbB2Ajj7mPzdb4P3vg40f4sVVhaL5Ybk4L4G/tN7XoXf/IMX8MTTXSzMevhH37YXX/UG\nK4gslusZK6gsV59SAELhJn2wIgLAucQ/Q+VoV4KBzl5xfZIwn29VRU33T22dmbzfNAWcS9x4E0RA\nOr6cR9FZYq4QVZyX1xWfMgLGlB46dVm/18x709LR2nkG1p8HL9xxyWZfDvtSlkgKmNon7l1rSUQR\nq9xlU752ziLZZuqglEhuvgB0LxTOW4GX7gJNiE7nJALWTxRi8Rlcn5VjxgNwmsq1Crfk3HTgBrnV\nZS6XRX9VjuTWZc0l55HlWCf/RsolZw+/+ONYLBbLNeTQvgZ+5sdvzf6+vJ7gw3/dxyBg3HuLj9sO\nujYAwmK5zrCCynL1ScwAQS2mzAcBszgpU3snu0BAHhSBFLxxEtTaJY8RAU6tsH/Idq4PzN+WO02T\nnCwdj14u+cvTBGV3EuHOjge0FsVZM2snGhdVbqPs7ARd7f6MCD8iKbeDDu5IJgxZ5ET6nC7lUvWX\nC7tWwMKt2XBjEMnrS45TTURO1JfSRjNjKztuCqwdBzt1UKMwV8yweWrkukPCSEyZZHGIMyD9cxt9\n8MwhUNUg58uEiYCN5/NER+XK9e2vIZs/tnEK3Nnz0sSbxWKxXAc89OQQv/nhHlIGkgT45OeHuPuY\njx/6tjaUFVXXJcyMv30mwiceGWIYMO6708dXv66Ommffr1cyL3UOlcVyaagwM6n4AUC6l2i70j1A\nz6fSBJvg4rBgt5a5PEyOJNjteV2W4of69IQZUXodYQ+AzIAC9FBdTqR8zuwfWnARSZ+S44mQg3Z+\nQHKOfgfwCuWQaSwx68ONisO7+tJ4E2dYyYKQJxRuRxKOiVIqBl1oYWh+spleSCUNsCim6tPA4u3S\nfxX3JDmw0KvGSSTCqYo0zK9h1cn0Vy59LhPg+izQG5nJRfo8fC04OZW1VaVRWiwWyw3EMGS8/8M9\nRLGIKQAII+CJZ0P87dMVIzks1wX/5RN9/NqHtvDkcxGOn4nxJ3/Vx7//rQ1E8Y0zpshy5VhBZbn6\n1GcgN/BV385w9aymAhQNQPO3gRbuBM3fPt4fo1wRVX4L7Hil50m5EnBRqr/TN+HmZry7DAw3pWwu\n6Mr8J0DSCEeHFDse4DTEBfMaIqzcOlCfAbn1TMQws5SltXcDjZmR40Mim2CEDZUHBY8yvAxBJQet\nfmxCnxhISRldUaB4DQmmUI78EIGTEDxcLexzm4COSwV4xBOE2OWwebp630SZ8AU5QH1q++tpsVgs\nNwBffj6Sj4oRggh44IlLfBFpuSasbSX42ENDhIXbmjAGltcSPPTkhEoUyysCK6gsVx+/LTfqlWEM\nlIcxVBF0gcFq7q4oJ+9RGoVTYPnL40dQrhYIbv5nIzCUI6V3vWVJyHNqklBYn64oGdMBE3FfSuWS\nQG7gdUAGcwpm7XYFm3posP6tSiPnmaZlgenUxlP7ZPEAYvAkR8jQXNJLLJbtFaLiJ8GpHMMIkMbc\nmGNGRBJeYVwfx68Wx8zyE/UnH++lCJ31k9vM9lKydq8BNJckVdJisVhuYKrElMHMlbJcX3zlVAy3\nojU7iIC//Ur1jDHLKwMrqCxXHyLpP6oSQsotl/SNElSk9NEEUdVfAbZOZ+V7Ge6EGHBmcaO65/Q+\nPaCXl7dxQZywEQvxIL+p51SCJOJQSgcHa7KG3nIuKnjkeNk8KIlXN0+T6QfL/pfUZYTmE3XzFHhS\nRDwAqk9JmR2nItbSuOD8celcSpjERlecKRhXKo3BYQ8c9iVgIk2y4xORhIOYcxr9L6cTdByJ6HmR\nUK0DhFXfyhLQ2Qfa+1rQwTeBHEcPPrZYLJYblzsOeZXhE74HvOVeO3/veqTdNNUnZRQBMy2bPvtK\nxgoqy85ACujsBjs1GN+ElQduL20fSFHRC0MmZMEk5ClXbuJXnwVQMVBXObqPK4cB6ZMyJWimRyoN\n5fHBKhAPwEZIRAOgtyrCKU1GhAQXkgejsotSSjDkfB1EWpyJ2OGwL71MaayFlDMetR5sTr5OROKY\nJYEWa2lhTblA5CQCx0OJIE9j6flSnggrUuD+RfBgQ4RlsCXH7F0opPnpw9VnkGXaGLFZuvCE0q8X\nUkB71+VHwFex5x7g3JNAVAw5kZll1F4qOI9ynhaLxXIj47mEH/n2DmoeUPMAzwE8F/ia19Vxx2Gb\nKXY9cvtBF/XauKRyHOBtr7NBSa9k7P+Rlh2Dwx4wXEduX4io4Nkjk2cemUjxEYh0mp/ri+i58ITs\nrzFTrc/q0zJjiUiGA4c9HcZQtdBERM3GaS0UkvEyt8xR2qakzqmVy+eKZYb69KGUxJfH/XybTJjI\nKGN5TM+M2g63KUKoMS3bhlviPLm1XGChIPY4lv0XRU6wVeno8XADNH2o/GBjJh/UDIiYDLr6erky\nNJk5i3J/qbOhyK0Db/gn4AffJ9fwyJuA6YNQXsWHVNQDMPuSjmexWCzXmjsOe/iFfz6LR5+OMAwY\ndx31sGvOOh3XK0oRfuK7p/Ef/nATm900+5Lve765jb2L9pb7lYx9dy07AjPryPFSDZzc+PdXgdZC\n9QsbsyISCq/jYmR51AMvf1mS9Lw6MHsYVXY7/DYQbIHSBLxdYbpxWvoX8qQ6VdH3w3rtVTO0HF9+\nRgUEOfqc49yB4lj2kVXnVYQ6OJ6c06TeMcPMIT0rSztmrSVz4DzRb8y+K0SrA0BjHsxcUWbCwPKT\n4OmDoMZMHhevXOlXYgZDiXANtzKBTMoF2kvj1+JFwlP7gKNvBtZPgpoLQJWYKp6XxWKx3OA0agpv\nuse6GzcKu+cdvOd/nMHp5QTDkHFotwvPtT1vr3SsoLLsDBOj0fVA1oKgYtOrlCY6ZGBO+pIg3/SA\nEyCVAAgEW+KwzM8CtQ6os6d64CERuDkv24fdsRLAEsO1imjuqhCGBEBFoAbpNMBsH5QfLw6025SK\nQIoGZaFUNSw4iSSw4hLlcuTWwJ29Ii5rHb0f43AlFWKqsF59XHK8yl4tAsRd7F8UR7G9G5QmpWHE\npJcPt7DO9u5LD242pIkuqYxFkPrl2VnMDJx/TAZC1xrgwYqIu9HrxRBhZ7FYLJaXhOm/tYOErwwi\nwv4le4t9M2HfbcvOQGqbG/rCTXMSAmsndC8R5DXNeV2uVn49EcmNs3JkH61dQHvP2O45iYC15yQo\ngkh247dlblRYiCQ3M6GMcAk2xnqHRhaunShXDx/W64t1+l9tSvcvaccojsrnkMZgOBJm4TVA25oq\nDPIuowl5aq+erVV8KV9i1lVa6O1yQFSVpKeT/sDA2nPg+gyo4tro0Hj5k9e6fDGVhGUnMh5ooV1w\nt6K+nmOl19c7B3T2gr1G5kmamA+qdS7vuBaLxWIZg4d9hA/+GdKTXwbAUHuPwXvjN0G17JdVFksV\nNpTCsjM4ft5rUyJPfmNmYP157XqY0jc9DHa7uOzpw8Du14Cm9lV/i7b6rO6p0Ql0ZphtGsi63Kb0\nGWWiQw+mrU/rBMJi4ALJY25D/qskQIK9upS8+S1xhzgFuufF7Qq6WpiVFRMDwHAT4BRE1b1iGZfp\nuJByC9sWr8V2ao2A1m55H9yGjjancthE2EUmlFRNz8aatE8WUdqcv6w1AxBRObo/Ez9viIdaXOXn\nxec+D954Qfroio7g5gulYcQWi8Vys5MyI4wuXQ7NaYrgo+8XMcVSKp6eeRbBn74PHNvAH4ulCutQ\nWXYEIgJPH9CzhGKAdfleY07cIkBumCsdIZOOVzFXqbkA2iaKm6OBdo9GHmeWSjG/Lcl4afaECBtO\ngQS5+2SG7/ptXb4o6+AkBJa/AmrMAl4NvHZBgiamD0iU+XBT4n2qSGPg/FPAnlcBSCXUoaJPijmV\nII3LpTFbEJAsqYX1qcnb+y1QS1/D9i455vkngGBd94qFBUHLQDIEeufArcXJq4p6wFYkJX/b9awB\nhcCMqv0MAZOR4daA+qw4UwW3kxxXVzbmPhXSCOhfzM7HYrFYblaSlPHBT/Txyc8PEcbAwrTCd7+j\nhbuPVc8FTM8eB/e75S8ymYE4RPL8k3CPvXqHVm6x3DhYQWXZMcjxwXPHpG+IY8BtgIquVbCN60Ek\nJX2DFd1jUwNai5L8th1plLVelXen+4vAZSfDiKkibk2CKfymFiqF/aQJsHBM+ouSUMryyAGvPiu9\nRm5NSuVKoQwE1oEOaC1If1Q0lMo8TgGovC+I9fo2TwHT+7c/12y9dXHJBmvA1jk5n+3KBUfKJJkZ\n6J2vdgVNTH3YBfwO2KtX+2DMoCSQ3rdLReNvh7kMSQj+0keA1jwwfQTYeC5/srlQnRIZ9ap70iwW\ni+Um4vc+0sNnvxgg0t9bLa+neO9/3sKP/6MpHNs3XjnCGyvVDn8cIV1fvsqrvXFJUsZHPjfEJz8f\nYhgyju1z8Z1vb2Dfok1lvBmwJX+WHYWIQH4TVJsqiylg+36lWgdU64BmDoPmbgFNH7i0mAKk9Kyi\nd4sZeYlfUbxMKi1UKp9ZVT6hQjKfmU2VyHG75yTavZhQCALrni9yPGD+GEiLOCKSa5BE4DSVmVHB\npgRBhP3Lnq1ERGC/Ax6sgU3v2eaZySVwIyIR/dWK60DA1AFg/lZg5jCwcHv+fhUECxV+AEgc/GC1\ntCczd4vDnpTpkdIR9BV4MniYP/PLwPFPAV/8ELD+ArDrtcDCHcDCnS9bgqDFYrG80ugPU3zm8VxM\nGcIY+G9/XfGZBoBmFqp/r7oe1MzS+OMWAMDvfrSPP38gQHfAiBPgyydj/MLvbWFlw5af3wxYh8py\n3UBgsFOXkrLSE0pKvV7MPpULbi0B/eVS6h45jpTyuTUZbJuMlwWOri6LUZ/0fNEGIyXhEJzq/iwz\ng8otOTqEFGP2GScSYBFu5aEZ5IDTaFyEjsBhH3zmEQnU0OfLygWUJ8LNrRk1qWd8RSJ4dL8TJxGw\ncUJKLIvnO7UPqLXFCTL6szF7iWuiCbfEMXN8KcHslkv2uLUI1GfkPUrTvHTP8YFaB7x+Clg9kZcF\nfuUT8uM3gbu+FdTZLYEVo7gN605ZLJabmrXNFK4DxBW/qs+tVP/+VnuOglrT4K0VINWfm0SAX4dz\n6M6ruNobl41uioeeisaucxwDf/FQgO/6uy9hqL3lhsA6VJbrB7cBcn1xJZQrN/VODahNX1JIbEtn\nDzB9UFwjXSqIhTuA+dvleeWIqNqOeDgxap1ZAhGYOfsBIAEVJjKd5L9kQh5IyXPmw8qpiajp7AFq\nUyByRuLUE+DCE5PXxwz0lsGnH5Tyw6LDZISU34T0owFZz5YWaukLD4mbNVzTa5fGJQZkrbWOiCkz\nYDgJQZc560kcKe1GbZ0tuHn6p3cB2DgppaBJAFYuuLmgyyFJ3LUqYRT2geVngOaCfm/MNnruVnPC\nbDOLxWJ5hfHMiT7++E/P46OfWkFvkN/Vz884SCoKL4iAg7urv1MnItTe8T1wDt8tYVLKgTpwB+rf\n9AMg9yV8Fr+COb+awKu4nEkKPH/OOlQ3A9ahslw/NGaA7lC0hjLf5tCVpcVVQEQS1NAYcbkcD0x7\n5Cbf8fL+oKif994YcRT3Jc2P8ngI5lT6lJJAxECkHSlSUh43fVDmX3EivURhT7ttM0B9FqQI8F2w\nWxOBEg1EuNVnZP7WxbXyejdPAfvurz7JqCflgZP60KJCaUex9I8T2bx3AXzhSZDfFOEZD4GaAqIB\n+P9n773DLTmrO913Vdh5n3w6R3Ury5JQIAkRBLYBG2zABhtjM9fc8YzHaYLt8czj8fUzcz2+npnr\nNL4zzuOIwQGHAQwmWBhjTBIIgYRoqaVW53DyPjtW1bp/rKodzt67u9WS6PS9z7N1zqld9dVXtVu7\n6ldrrd9K4lQIjqgvO1+zjOby2Y0G+99s11InxbRgurJ5ZNomXgiT2+0zm9xp5zdu9cxDzmYV73A4\nHFcASaL8l187xCc+u0wcK0Hg8T9+/wj/+cf2cfN1FQo54VXPL/KRzzRo92WNhwG87iXFseNKvkju\nntfTev7reOxojCfC/pw3qvOiA5ib8ofSKgE8ge1z7lp0NeAElePSwQvMIKG9Zul4fmi9op5JdOoc\nSH4CDUqw9Digtq+OmHjAM/GRdCx6s3YESvNoWtfD+mm7aQ+K5iiXiQ1NTPx4gaXKLR/qpav5ORMs\nQjfqIl5g+04iEw7NFRN/fh46td5k0+jXSGv4di2tlxpDlpo3QpiICDpzjUXFwOadq5iwaSzafJJr\nRkeJJHVMHL/nXjJka+Usaw1M1iJlmTPh9G6L3K0cGUwx9AJk7z3pTiyKBq7/lMPhuHr4u08v8w+f\nW6HVtu/2KA1H/fQvPsG7fuUWfE94w8uKTFeEv/5kg9V1Zfu8z9teU2b7ORrPPvhYxLv+to3X9wX/\n9lfnuW6Hq1vdyMyExy3XhHz5ic6AsAoCeNXzz6Pe23HZ42Sz49LC8y1CU9lslurPoZjKED8w4dYl\nTUXzA3PHC4vWG2tiJ4RlEzR+CDP7kJlrBsVUd4gEVg6hjeVeRCgomJ16WBx0pcsc/7LfUxtxmd7T\nt45nvaLGoZqmSY75XzprVjzGdEOy6FwmjURSowiF0rwJp1HbmV/5WaalFsnrGnecJ31RNBFB7v1h\n2HFnrw/V7D7k5T/qGvg6HI6rmg98bIFma/i7tdNJePRxqy0VEWKFWkPJhXBiMeZ337fO4ur4VLTF\ntYR3fbRNJ4JWp/f6nb9u0WidX7r31cb3fnOJe27NEVonD3bMe/zIt1fYMuME6NWAi1A5HADVLSZG\nmstp6p/fEwp+aJEz8c04w8uBn+tFiqLm6DGTOI0wpRef0vxoe29gIHVOxFL0SrOW1lbZArmq7W/x\ncbS6FcmVBzfPVZBO02qP1k8xKH9ksJ5IJK0ZEyAxS3jxLFLVTXFs2nuleaQ0mw43bEHejZhlgqsv\nWqVZmuDAYQZk/YHTlUYLsg1CWsIicvc/Qe/6HtuHc/ZzOBwOkmRcq5Hee4882eHP76/TiehGT46f\nifnld6/x0/90auTmnz8QMW7oh56Ief4NF377+PBjdf7u08skCi9//iQ3XVs+90aXAWEgvOWVJd58\nX5EkAd93pkhXE05QORxgQqe6xZzrTj0EG7z4xM+nNuGK+LlBERAWLT1uI0EB/ELPgW6czbvqCLe8\n9Eo2tccc+rL9JR1YOdztcaVpNEuDInzuXXDbt5gYaSzZfAMz9bDUxcQETZAzm3YUlaArpmy3MTRX\n0dT1UPKTZkKhMfh5FG8o5VAzg42oAUERTeIx9VbY8iQ1xkhiG3ejG58qFEfXzfW7DDocDsfVztff\nO8NXHq8PRak8T7hhnwmVjfVTYF/Dp5djjp2O2DY/fCvYbDHSzCJRuumFF8Jv/+lx3vvRRdodG+PD\nn1jitS+b4f98S68fYpKo+Tg9Ry6tnU7C6YUWh442Ob3YZt/uEjddW3nW9ici+O6Z31WHE1QORz+j\nemFlooLMnW8D09fAqS/1BER+yqJKWdQnLJthRdwGb0wR8MYeU15oaX9eOCKCY8542l63cZMYTn4F\nJjYjXg5CwJtDEatFqh0FTSyyVphCSrNd10TpayKsnQZaO2YRoCxala/SdSpMOuDlBlP/ErN316gF\nYXHwgiT+cIQKrEq3VbNts2MPSxYVTCIzl6hsHn2eHA6Hw9HlvhfP8HefWubBR2o0Wwm5UBARfvIH\n9xAE9n28tj463doTodYYLY5u3O3ziS9FtDcmGQDX77wwtXDoaJP//ZGemAITZ++7f5FX3TNNLoT/\n9j8O8LkvLuN5wsteNMe/+r59TE48/dT/kwsdjp/usGNzjrnp3q3uu/7iCL/9R0/RUc8y+30hDD32\n7ynxX3/yBgp5p4QcF4YTVA5HP8GIBrNBwaI7qsDwhUmKM+imW2DxcUCgunUwtS8omBCrL6AT2wbe\n08yGXPv6L3mh1W4lUSrgRlzwWmtpdCed78op5NZvSSM/aT1V7WTaxyrdPmlD/bT9VdkMrRV07YSN\nU9oESduCP2K241LeZHPRGCu3jEE7oFnr3tQiPk6QwmQqzPrFX2IRug19xVTVemJ1V+v0DCs0rV+L\nmqmYczgcDsc4fE/4j//6Gr74lRqf//IaE5WAV7xomunJngi59doch081hlzoEtWx1ul7t3rctMfn\n4SfjrqjKBfCCmwI2TV9Y+f1nvrhGPCKPsN1R/uxDi9z/0UOsrEWoWpTqY588w8En1/mdX74Dzzu/\n6FGrnfDzv3OSh77aJPAtxfH5t5b4obdt4iN/d4rfeuch2rEgXlpblkDcSvjqwXV++91H+Bffs/uC\njs3hcILK4ehD/Byanxx2pOuPtKSpbb3IkSKFabM0ry8Ope8JoH7OIl3Lh9HqFkvL0wSWn4KlJ6xe\navPXmYDyPEQCNG4yUkxloqM/GjS1PXUStPU1iQfFVG9j6NTQlTasHe1F1do1qG7rjZmfMdGWnQcR\nm39X5PSN6/tdMdUfobJpRr3+XZqYhXxrtReNGoVqz0TD4XA4HGdFRLjtxiq33Tj6IdQr7izw9w+2\nWKklXVGVC+Hb7itRyI0WKiLCW1+V45EnYx44EON7cPcNAfu3X7iXWS708D0hjgevSyLwjw+sUm8m\nA0a0UaScON3kgYeWueu2DW1PxvB7f7HAFx818ZilOX7moTp/+sEl/uq9h2m2EvxcOJTe1+4oH/zY\nGSeoHBeME1QOx0Zm9pnVeWPB/k46Pf2gaql7qhDkrX6o0zR78ZXDMLXLaqw2IGB9rEpzcPiTFuXJ\nRJp41rMqNcIQfIhblnYnfl8j3H60N58kgukd9JrlMl6sgM2/tsG4Ihm0Iyeqm/Dp309z2ebYJ3ZE\nBE3dATdeoLK0wWy5kkbbVo+hM/sGo1TZUWmMeOGw6YbD4XA4LohSweOn3jHJ/Q80efCrHSYrwivv\nLnLdrrOn0nki3Lw34Oa9z86t4j13TfC//uzEiHeERJV2ezgDJEng8NHGeQkqVeVvP10bisS1O8r7\n7l9hYXFESn8fUfQ0nGgdjg04QeVwbEDEg+m96NQeWD1q6Wdh3qI1fggoGrdNmLRWU4FyxiJT7Tpa\nGH76BVhQqTyL3vxmOPFFqJ+CwjRs+TqkuZLuu5e/rUmErp6EAx+F5bSv1bbbYd+LzOUuE3coUqgy\nGDU6y//a3oiLaHmT1WtpBMigmOo/gObygKCyHlRnvwhp1AY/Oyc+mp+wGi5/yvajCSBonKb+zd14\n1vEcDofD8fQo5j1e86ISr3nRsz/20lrCn3y0wZefiBCBO64LedPLC5SLg9Gs2amQH/jubfzi/zrW\nTYZQhe3XzNOqtzhzbJEk3miuAXt2nV/GQpJApzMiqwOoNxP27Snz0COrXTfb/uu078OL7zq/KJjD\nMQonqByOMYgIOrEN1s9Yc9tuPZP0su1yZbQtMLEtXaCW2rbRDc8LwQ+Qie0WrdpzD7r0FHrqETj1\n5a7ZhQYFS/+LO2h9AT73zp5RRtyBo5+32qjnvWkwxY8NvoRiwmVYGImJuPqZbpNf2fI8KKe26stP\nQtRhWEyl9EWyNEs9HNEseBC148ms0IMC6hfwPM+ON4nRxQNmFS8eTO44x3gOh8PheDp0IuWJYxH5\nnLBrs3/ejnZPHmnwkb8/Q7uj3Pv8aW6+btANr91R/ts7a6zWtXsp+NyjHZ46GfPv317B27Cflz1/\nkr9+IGB9rYkCE1Ml/MAnjmIOP3qESBOSVFMFvrBpvsitN02w3kj42OebPHqow6Zpn1feXWTL7KCB\nhO8LU1VhaW3wmqSqxO02t901yYGDNVrtCAnDbgZFGAiTEwHf/927nt5JdTj6cILK4TgLIh5UNqGl\nGThymoFe2KrQaSDe4FM4jTsQt9CwBAiSL1t0qbOeWoon6EN/BotPwN402pQZVcQtWD9tRhZHHxq2\nU08iWDmKrp2E4lRPRKWCZeDSVZq3FL3mskWB/DxUtyG5Crp00JYVZ6E8h3iBGWSUNtkcaidG2577\nue5ySWKriUrrwTaKSM3SEe2vXs+psIQkbVRtbtpaQzbfktaPBUhr2equvCB1/3NfUw6Hw3GhfObh\nFr//gXWr51UoFz1+6M1Vts2d3dHuzz9wgt9452GiSEkU3vvhU7zynln+1T/d0/2uf+CrHZptHXiu\nFicWtXr0UMSNewYzItYbSr3WYmKmgudZWnjUiUGEa27ZzebcMp/4zCKJCtNbppjbt5X/8BtrNBsx\nzbbSieArT0b8wxdb/MC3V7lpby/FPkmUyQos9XUxsebySm1llWp1gl/52dv4rXc+yaOP1SgUA/bu\nqnDvC2a4755ZigXn8Oe4cNydisNxHogXWK2QJpBZjSdRz8RhIBoFtFpWUzV/Q08QqMKZr6LLh+Dw\nZxOo+V8AACAASURBVGH/yyBXsXGSKK2hSmumVK3OKcjD3H4bf+EJaK7aOkuHrZ9UmNqwd+qQG0yL\nEBETTJWtg3bvmlid2OLjSHkexEdbq7DyVDr/EHIT0FreeBbMjCNqDe6D9NUnqrpiql+UZe8HuVRo\nJagKMn/jgCjVqAlLhywyV92KzOwfavTrcDgcjnNz7EzM775/faCuqNVJ+IU/WuX/+YEp/DHueYvL\nbX79Dw8PWJw3Wwkf+cQCr7p3jltTA4yjp2NaneHt4wROLCbcuGdweaXk0W60WIqt19TKwnoqemB2\nKuBt33kt9VK96ywYKSwsxyR9RhaJQjuC33nfOj/3A5ZO/vlHGvzyH5ymVh/uX9VYX8dT5e7bp7h2\nb5n/8h9uOf8TOIZGM+EfH6qzvBpz3e48N+3LP2d9sxyXB05QORznS3U7rB7GrNM9izYx/OUtImi+\nYv2U8hMD7+naYYss3flWFB+StqW9hWVk/VTPCl0T2Hoz3PK61MZcgFdAYxUe+t8wvQvqZ9DJnRaV\nCvKAD2yIaKWNezcsRMIiOn8T6gVI3DYTjqzPVlDsphmqeCbu/BCCstU+9Se/q6JJhASBZQkmkUW6\nxiBJu29ePpIvDeQq6upx9OD96dgJeA+hU48iN36Lu1g5HA7H0+TjX2gSb+wbj6XqPXoo4qa9ox9W\nffoLK6lV+WD6XKuV8LF/XOgKqm1zPrmQocbBvgebZ4YdAQNfeP0rqvzxB5ZptwYntlqL+O33LNDW\nwX6NSTycwqcKa+sxi6sJ6/WYn/+90zSbwwcqIpSqZe58XpFr9z47ZkdPHG3zf//aSeLEzmMuFPbv\nzPET79jU7f3luPq4cP9Lh+NqY2IbFKd7znpHPj9+Xc+HTTf2REA3/U0QTdDOOiw8AksH4MzDcOpB\ntDBpKXVxbMJqbh/iWcSn2+GpOAF3fYfVPHXq0FpH/QJSmLKqWi9vYsULLcVvpDlFWjvleTZmM41E\nBUUob7GoVxJZ+l9lK1LdZj2pwgJZjVjmPKjiIfRH68Z9pXiIHyJB0V6ZMBO/25dLkxh94mNpZCu2\nfSWxWcsvPPZ0PimHw+FwAKvrlq63EQVqjfEPv4JAGPUMSzwIw973/J3XhxRyg+v6HkxXPW7YPfqZ\n/etfVqU4bIZLJ4JDRxpd04juPruXUaXd6tCst2k12qyttvnslxv89d+vEsUjDjKbT+DxQ+/YO/b9\np4Oq8ou/f4Z6U2mlqY6ttnLgqTZ/88m1Z2UfjssTJ6gcjvNERJC562HLbdazqTA5us4IzPo7i+5k\nr8j6Sun6aTN/0KgnTqImnP4SmpuAfMXy0VeOoa31wXFVLeUvFWfkyj27dfHsapevIvnq6CbFA3VN\nmHBpLNsVqzDda86bK/ec+URgYzPe7vaJ1XvlqraeH5gw8/M2l+wcbKiD0syhsL3ejfSxfnq0F0YS\noae+ZE8lk/isETCHw+Fw9Lh1f0h+RBAqjuHaHcNvHDvV4eOfW6NcyXfNIfoJA4+vv9dMjDqRcnpF\n+edvKHPL3gDPg8CH510X8i/fUh4ypMgQkZEW6RmBN3gh8Hwbp9OOiDdYm7/7b1Z5/Kn2yLlmlIse\npWepPur4mQ7La8PXwnZHuf8z6yO2cFwtuJQ/h+NpImERpnajN70OPvd76PZbQdLaIdJIUtCXsqBq\nvazijhUFt1YYevQXFKC8GUTR44/CA38KJOaAt/1WuOPNtolGSNIhVsHbdCt4vQhPV8Bk8/QC0ydx\nJ61fUiTpDItAVfDT6BNpiodsuPiME47Qq/8ib/VRYKKnsZwet118VAq9DaO0dxdA3EYlz1hnQYDm\nmjVAtpHQsAzleTP0cDgcDsdI7rwhx0c/2+TY6bhbl5QL4RV3FJie6F0v6o2En/2tkxw41CZJFAHK\ns9N4qzWIOySJPQh7+7dvZ9/uEn//UMTffNZs0uMErtka8HPfX6SY92idWuD0n/0Nq7kcm17zUoJq\nZWheu7blePxIZN0/ol6qXqXksWXO5/SS0k4THwp5jy2b4UtfbQ2N0+5AvaWEAbRV0BHhOBHlH75Q\n4yV3jG58fL602hFPHFk6t7Gt46rECSqH4wKRwhS6+/mwcBByJTRXgqCIeqE15tX06V8qpkg6kJ+E\ndgM6fakBhWmY2d+NMunO22HHbfCVj8DDH4BjD5nhxO1vTNP5OnioRas0hiiyCJBPOkZPrIkXgBeg\ncQdprw4eQCb0ciVUk75SJhlhhX4eV5Ckg8Zp+l9j2URTrmQiL27buH5gY7f65rJ8GM1X7dyMeqAp\nPkztsmNNmwjTWYdaDBPbzz0vh8PhuEoJfOFHv2uCT3yxxWceblPICS+/I8/X7e/l3D34aJNf/sPT\n1Oqpg2t6DRHPpzxR4b67C2zfFPKC500xP5vjS09EvP9THRLtfWE/fkx59/0RLz/4Jzzy4z+HBJY5\nQZxwx7t/iU2vfll33U8+1GSxkac8mSOJEuq1pmUtqFJvwv5NMS+7vcyDj3WoloR7b8uRD+Hf//fG\ngElGF/EoFT2SJCGCIVG1WlN+9V0LNJrK1794Ynj780BVOXR8mWpFKRdheUN2Xy4UXn6Xa0h/NeME\nlcNxgYgITOwApBdxCtMv1KjVTfEDTEhU5u335hJ0anQjQjP7BiItWaSL619u4uQrH4InP4Xe/da0\n+a4irVUISmjShnbNolHrC0hpxowmNkbAvMDqq5IOSmJiLOlA1LZ6rMYaXTUjYg6CQm+OeAwZXmQE\naeRp7RT0N/nNjsnzIZFePZcIFGesBixqogh88S9h8TC84G1w7PN0a7XEh8pmqG7p1m2JeCgeRC00\nanejYg6Hw+EYJgyEl99R4OV3FIbe+/SX6vzPP1miXh/OQhAREvF48njMP3urPbxKEuUP3l9Dw3za\nx8nWixM4cDhm+j/+GkFzMJL0wFt+hJc98XH8aoUTCxG/+751sv699fVmr2ZKhEThPR9Z5SfeUeD7\n39ATKFGk+COKVETg+j153vZN87znQyt87pEGa+sxzVYy8Byw1VH+6P1LvPKF1dRsY5AkUb7waIuv\nHmoxMxnw4tuKVEpe3/Yx7ShGRHjj14f84XstateJLOK3f2eOb3jxM4uAOS5vnKByOJ4JE9th8aD1\nSxJSZ7rAzCu8weaJqgnUF6G6w17NpZ4hxAZEBG2vw45b4CsfhiRGkhjxcyS1M9A8A601aCxBq4bu\nfBHSXLX9omnLJ0n3m9ZN+T4UJpAktnQ7rwClKiBIsffUTpPIIlupgLEUQOimA/bjBWak0WlAoWLp\nfVEz7XsVZgdj6/RvL2mdVq5s6x5/BFo1uP//gzvfAoWSzbk8jxSnevvTZNC1MOkATlA5HA7HhfCH\n718bcujrR0RYWuk9TPvsg8ssLHWo19fRRPF9j6nZEuVqAeIOrUKVgJPd9dvlKR5560/zN3+oNBuL\nLJxYpjo7aWItTkam6CUJ/PmHl7nt+l7qfBAIb/6GKn/0gbVulEoE8qHwpldNMD0R8I43zfIO4Ht+\n4smRSRWNZsJ6I6FaHkwVb7UTfuY3Fzh2OqLZVnIh/PGHVvl33zvLvh1pGnuiCIKibJnz+MG35njk\nYEKtrlyzI+QbXjTvnGivcpygcjieASIeuuVWOP6g9ZTKao/iFpCz3lUAqAmGXLnnalfZikzsNHOK\nUcQtOPoAlKYAD4ICyZkDUD9t+2msWPRm5wsRPwelafvCj1rWWypLr8tc8/x8L/qV9q8aeQHoCpYN\nAsgPzHVPE3svLJnzX1RPBVTO0h3DglnGi58GuRJzHfSCVABtIEmg07TfOw04cxBufrU5AQ5Pru93\nHW284XA4HI5zEkXK4sqYzANSe/I44bYbe5GiP/nQCus1n+y7OI4TFk/X8HyPUg6KJw71thfhsz/w\n69TndqF4PP7FJ5naPN33sG/83E4vDc/r619YYWbC5y/vr7G4GnPd7hxvemWVbfODt7KbZgMOHRu+\n1oShUCoMh7n++hPrHD7Z6fbqMoGp/Mq7lvj5f7MJEaGQDwYuifmccPsNPiKweabsxJTDCSqH45ki\nxWl0z0usEe/S44CagAjLVuujsUVuqtv6GgF7SGZcEaUW4X1oHMHSkzC5He66AfJTaP1ML6qTqyJB\n3sSI6OCXuef3aray5V7Yixh5HnZlSIXR2AMT0Ozik6SiKrR6qLBgoqzTGJi7NfWl26A4a/RL0uk1\nIc5ElaTuhF4Ar/0p9O9/DZaPwZEvwE3fOGZOXpoeYsYZ4rmvMIfD4bgQfB+KBaHeBD/0iTvDIiYf\nJHz7q2cAiGLlxHJPTGWowvLCOjfvXCPMB8SRfccvXXMHzaktaBDSarRoNdvUltepTFXwfB8/GG80\nvXfH6MyDO28qcudNxZHvZbzlNdP84u+dHqi3yueE179iEt8fvuZ94sH6QOPjjJVazKnFmM2zlka/\nc/MET51YQfsTLUKf2cnSWefjuDpwtukOx7OAeAEysS01l/BMNDSX0KhhAsQPGRBNvl0sRMRqkFTR\nuJO+Ilg9BltuhS23IrPXIJUZQBDPR/ITSJB1ZR/dyFCCHFo7ZfvJlZGwYGInizBpYvsc9Ygwszq3\nwbr9orp27SJWIxZ3GJVXISImntZOQLtmx5SdBy8wR0E/hKzHlggUJ5FX/Shc/0rYdB2snbaRM8t5\nGLRej9oWGXM4HA7HBSEivO6lFfKh4HkeQRggnvWUygXwsjuK/NJP7mZu2h7GrdeTsZGYdrPD2952\nDVvf9Gr8sgmexvzO7kO9JLEHf7XlNVr1FklsY+VLg8IpuyZ99zdPcaHcfUuZf/bmWaYnfDwPSgXh\nja+a5E1fP3pMf1x0SRmo25ooF7h25yxzkyUmK3m2z0+wf8fsyJosx9WHe7zrcDyLSHUrmivD6lG0\n0zBhVJkfTnVLbdYhTRuUEI78Yyo4QjOqSCM43U1EAKtZskjQ+FQNAJncjtYXkPI8KmntkabpdZ26\nCZX8sJ0tqminDmHRmvbazocFVNwarGfq3zfY/BpLkJ+wnl1Ro5samK3Tf2wqPt7t34qKZ4IpiXvG\nGOL35t81+3AXMYfD4XgmfPNLK0Sx8r6Pm1FEGIS88b4qr75n+NpQKXnkQhnZRHfXlpBSMeDW3/xZ\ntr/tWzj+Zx+kXN3BgWKBOIFCKY/neSRxwuGvHmZiZoLqzASqCXG7TVjIp1/rwg++dZbNc+eujV2r\nJ5xZitk041MuDl6LXnpXlXvvrNDuKGEgZxU9L7+rxB9/aHWolmx+JmBuevA2OZ8L2DrvzCccwzhB\n5XA8y0h+AubN/EFL8xA3LeWvX3yoDugBCfNoYQoWHoNdLzDnurPWN5GOKSNlhSomSJI2euTTMLMf\nyZV7+w1LaSPdGC1M9Oq6NLEUxeWDIAFa3gT5CYRhByibj2/rbySJkck96OphKEz1nmrGrbSeKxwS\nYybC1IRXtiD7JWlbs5N+QZcbIQYdDofDcd6ICG+4b4LXvaxKvZlQKXpjxYfvC9/2qgne/cFVWn3p\ndLkA/o83znTHm3vFi5h7xYsA+NJ76jx5IiGKhT037+LgF59AgdWFVRqrNWZn8vzC/3Uzx0+bmrn1\nuiKF/PjkqceeXOf3/+wYB0/5SK5APjRnwJffVeK7XjMxMHcRIZ8794O3V72wzBcPtHj0UJs4VgJf\nCAL44e+cPue2DkeGE1QOx3OF5yOFCbQpaOs04lWsvknjtJltYTB9YtvzLP1PZEhw9egTNl1bc/uh\n9P+t6OLj0FiwFetnbN9eaHVXUctETXPF5lKYNIHTqUOSIHM3peMkg/vMULUoUuClwq//iaWYmAtL\nUJodse1ocaZneS+ryeqOLx5Ut45e1+FwOBxPi8AXJsrnbpT+6pdUKRU93vORVZZWY3ZsDvmu105y\nw57RBkHf9/oiH/xUm0890qGQq3DXd97M4wcWeOp4m8nZKvPbpvi19yd8xyvy3LL37LekX350jR/7\nmUfJV6uUp0oIQiuNKn3sc3VmJny+6d6n/6At8IUfe/sMjx/ucOBwm+mqzx03FsiFLgvCcf44QeVw\nPJfkJ1IL8g66cADZciumfgZT1lQVqR1HK/MWxYnbIKm4Es9SARVLHUw65qanlgqnqfgSIIlaUDsB\n66dsP2ljXya29WqQNGuw24F81eqi6gsmWnJlyBUYcPfTPsGU1TMlkb3ipm2Xn0hT8gASSxf0PMhV\nRqcmJlHaLyuLjKXjxpHtayg6pxCUbN5BEYrTA727HA6Hw/G14aV3lnnpnefXxDYXCK+7J8/r7jHB\ndeBozPHVHNekz9oStedv774/4todPvmziJhf+d2naLUTZqYqeN5gFKvdgQ/8w/oFCSqwaNb+XTn2\n73JtOBwXhhNUDsdzSdpvSXJ70aUnrP4nKJjYgK4xhNYXoF2HIId4OTRuQ+0MPPwRWHgCJrbAbd+K\nbNpvznpeakWORbsICijgBXmSwpT1mRKBwgxUNg274WX27plzX2Gy/026/rAi6X5II2sdOwahJ3o0\nhtYK5K0HFnGEhGkDSS9E4rgXHcvGzzZNzTHMLbBvTC/YkBIo0FpFtt52wR+Fw+FwOJ5dVtaVv/9y\nwpMnlGoJ7rnZY9/W8Sl7nz8Q0x7hqOcJPHY04eY94x+UPX7IjIjGGWOsN8ZkODgcXwOcoHI4vlbs\nvhc98ilkajfa32Mpiay+qTwLrXUTGVEDisCeO2H5sFmJH38Y7vth2HG7pfDlyminafVWccdS7JII\nrzCFludBAijOpDbphqpCYxlI+1LZ0l7D3CQy8eMXbZ2BQq+gJ6Y2oonVOYlvLn7ZvjRKe1BlTY7T\nl0gvKtVZt1RHz0vrvtL9qvbSH+NO2tvL4XA4HJcCK+vKr70/ptWxr+mFNTh8OuH51yuvvH18LdaF\nUi37LK9GRO0OYX44knTN9lG9Cx2Orw3ONt3h+Brh+SGy6x7IVU00xFGvoa0fos1VQBFiJMghhQnY\nehN8w4/BzE6rdfqH3zEhUpg0Qwo/sL9zJcQP0v5QBZjam/ao6u1f4zYsfNWET9rktxuhUqC+CM0l\nS/+rHYN4OFVPx9U4ZYifaqFUpKUCrfdEUbpRKMlqrwpVCPLWnDgoQK7Us2tXtQhYu9brY+VwOByO\ni87HvxTTbA826I0T+MevKH/5GaXZZ1yR8bxrfXIjHuUnCtduP/st6bd/8xbyeY/l08skSdLX9kPJ\nh8JbXzvxDI7G4XhmOEHlcHwNERGkOIVMbEcmdyBTu5GJHSY6SCyi07++50EQwu3fagsaS5YeJz6a\nxGhzOV2xlyahqjbe3L7UhCK2K97KYat1Ks8juYo1Hs5NQH4SChMwtSt1zlNUY7S5OHCl1CQZ2M8Q\nQRFFLZIUNdKmxv0mGlkaXxapIj3unu7rRrGyFMWsB1ZxGorzT/NsOxwOh+O54tEjo5erwvI6PHBw\n+L392zyed61P6FuaX+BD6MN3vCI4pwnEm795K6971TxEbWqnztCqNyiECS+6tchP//NZrtnu6p8c\nFw+X8udwXEyyyE15M2TiaGgVD53dnf7hQ5AzobJ61FLw8pOgCYpnkZz2OqCo+CaQoob9XtkCYWlD\n/nlizn5ZDZNfAL+NxO3UdCJCJa156jTNFCLZ6OoHeLlenyjBoltZyl7cQcNiaoueWGQtFU59thzd\nHiTWk6pvH9lKnTU0nrFIlsPhcDguKiOSGLokqjx1Rnjx9YPLRYQ3vCTkBTf6fPVwQhjArdf4VEvn\nTg/0POH7v3s3b/+2HZw802J+Nkel5G5jHZcG7l+iw3EJIJ6P5qegNVpUUV+21Lz9LzGRo1GvwW3t\nJJorI0lk4igjM4vwcyaOgvzoYt7+qFPcNlEV9CzPBaC+gC4/CXPXWq1W3E77T6Upg34eS1dM3Quz\nRryZMAuL5vaniUWqRp4EczTUJLLthlBorY22Ync4HA7H15RqWWguD6f1+R7nrJ/aNuuxbfbCkqRK\nRZ+9O0sXtK3D8VzhUv4cjksEqWweuVw7LfThD6OVTUSnDlp9U2e9J0yiOrRqaL+Y6iduQ9zqWpSP\n2fvgn7kKIl73RXEGmdplwgqFIG/CKiz2rNmjZm97L7BIWpTaqqua5fs5T4JnBhTjSM7ySNThcDgc\nXzOet0/Ib/CB8DwoFQXfgy1TF2deDsfFwEWoHI5LBc+HyjaoHbM6qLhj/ZpqC8idb4TlwwTVbXbF\nOv0otGombPIVqB2HcYJM1eRSkjrujerxFDUskpWJrg3mE+L5aGUzHDlkcyrNpU2K7T2SDT64Qlq7\nFadpiVVbnsS9+qjBGdg+s9c4XLqfw+FwXBLcsV949KjH0prSjuzSUq145EJYXe3wF59d4ffe3WLL\nrM8bXjnJ3be4qJLjysUJKofjEkKKU6jnIytPoVEbkhiZ2GxCZP4Gi0wd+UxPdMQtq5vycz3r8z7M\nU0JAQohaEPaZPYBt01o157+kDWGFLGVPoxag4Idpc2KgstWa9obltIdWB22vm8lFaoU+4OgHFpmK\nOiCpY1/ioV7YXa/nCJhFpjyGarQy8hfWtNHhcDgczy65QHjbK+DAMeHJk8pyXWh0oLYe8clPnCCO\n7Hv88XrMf//DM3zna6d4zb3Oic9xZeJS/hyOSwzJV5H5m8wFsFAxkRTkzRZ94TGLRE3vhsntlnan\nCSw/BVFnwL4Weu7jZO55nXWLJmliEbDmsi1L0ahh67Zr0F6zn40ls3RPEqS6BSlMmjufHyBBASlM\nonHbhFHWQyqzRw/SBr+N03RFUtyGqInGEZqkjYkH0gHHiKmwPNyg2OFwOBwXDd8TbtghfOMdHq1Y\nUISDB1a6Yiqj1VHe9YFlOtGY73eH4zLHCSqH41JEpNd3yfMt5S5qWs+mXNHS7YI8VDdZ1EYTOPUl\nizZpskGTSG9MBNpraO041E9Zql//mhp3e0kNELesVovU+r2n1NBOE9ZPw+oRiFuoeDY/L4DiNJqr\nMjSgxhA3U/OJjRdYNZML8emKs1wVqlsv7Fw6HA6H4zlFsR5UAEtL45uwn1qMxr7ncFzOnLegEhFf\nRD4vIu9N//64iHwhfR0Tkb8Ys90uEfkbEXlERB4WkT3p8r0i8ikReUxE3i0irjjC4ein28g2FSO1\nU8PriNdzvdPYaqnqC2lKYH+Hpw2bjd2pjHhTIFcGPzfkEqi1k7B0sBfNWj0Cy4dsKxEztchX0/qu\nc9vi9nYpMH0NTO+FmX1Ides5TDUcDnedcjguFp7AXNVSuAul0f0K41iZrLjvcceVydP5l/0jwCPZ\nH6p6r6rerqq3A58E3jNmu98D/quq3gg8H8juCn8O+AVV3Q8sAe94upN3OK5kJChYZGbtJPrgn8Ij\nH4QDH4NjX4K47ymfeD2ziczpb0PkCej1hcpV7TW8R2v2u5GwCOIPi6m4DbWTDESYNLE+WK213qip\nHfrG+q6zHDnkqibIvMAJKcfTwV2nHI6LwOKasryasLSi7LtunmtvnCMIe9/dInDHTUUqY8SWw3G5\nc153KiKyA/gm4DdHvDcB3AcMPfkTkZuAQFU/BKCqNVWti92Z3Qf8abrq7wLfekFH4HBcwWhuAr7y\n11BfxJrfKqydhiMP9q9lKXbQEy1xZOtmRVX9xVWdBpCKsGyb1Bqd4hyDkSQBLxjdv6pVG+EYCJCg\nrZXB/YpAkth+h9L/+uaJmMFGYXLsOXE4RuGuUw7HxaHVUd75MeVU+rUvIkxOFbjp6zZ3M81L1Tzf\n+wbXQ9Bx5XK+Fd6/CPw4MOqx9rcCH1HV1RHvXQcsi8h7gL3Ah4GfAKaBZVXNHrMfAbaP2rGIfB/w\nfQC7du06z+k6HFcIT34iFSL9pA1umzXIl6BdBz9tpJtPHZQEi1b5Id3UPz9I66PUekklCTRWTIxN\n7sLbdDPaWAKNekYVIwVTOouzRY7S3lOqCXRaNrckTrWU9HpXZSKR1AGwssWaCj+d9ECHw3DXKYfj\nIvDIYSXa0CJQRAhzPnuvmyeKlJ2bfaaqLjrluHI5Z4RKRL4ZOKWqnxuzyncCfzTmvQC4F/hR4G7g\nGuCfPJ0Jquqvq+pdqnrX/Pz809nU4bj8WTtuQmMjIiakOg0TTiJpo92KRXi8sOfkJ2IGFl6QCqTU\n2tzzTDzFLVg6iDZXED804RWWU8EzWtioqjn4bbQVtMmZGYUqrJ9Jxd+K1VhlDoNp1EvEQzzf3Pu8\nnNV/pXVY2l4fMbbDMYy7TjkcF4/FNYYEFdjlplgMKBU93vxyV37ouLI5n5S/e4DXi8iTwLuA+0Tk\nDwBEZA7LN3/fmG2PAF9Q1YPpU76/AO4AFoApEckiZDuAoxd8FA7HlcrMnpGNcFFN3f4Ca5pbmIby\nZqQ0jYqPrh5Blw+hzRXUC008ZdtpYpGj9rqJr/JmqGxB146hQbGXAujnTIglMapqAomsb5QicRuq\nW3rrS5rOV91mAmk9s0rve3WavfTEkZjgM4ONk2kvLIfjnLjrlMNxkdg8LYQjvtY9gbuuE37iOwts\nmXG1sI4rm3P+C1fVf6eqO1R1D/AdwEdV9W3p298GvFdVm2M2/wx2Qcoe2d0HPKx2Z/a36fYAbwf+\n8gKPweG4YpHdLzZh0x8pEg9K0z37dD9nxhETO9D1BTj9CDSWLCK0chiOP4DGnbSpbscsJLLI1ey1\nUJ5DSjMmzmrHTWD5Ya8eK4t0aWJ9o6K0mTBqxhlTe5HpvcjUHmTzLXilGWiuMrqflKaRsvO5uKpF\nthyOc+CuUw7HxeP67VDMm4DK8D0TWq+5O6CYdyncjiufZ/rI4DvYkEYhIneJyG8CqGqMpVF8REQe\nwu4KfyNd9d8C/1pEHgNmgd96hnNxOK44JF9BXvZvYOut1pspX4VrXgp7XtSLCBWmYHqfCZ+Vp1LL\n9Ay1Xk8rRyw6FRSguWSphBM7LOUuFTciYmKpU0cmtlv6YNyG1oq96gvW5yrqvy8Vm2NhEilOpY13\n0yjT6CNKe1T5IyNvklUwZww0/HU4Lgh3nXI4nkMCX3jbK4SbdkIugEIObr8G3nyvjDY0cjiutvyy\nBAAAIABJREFUQERH1kBcmtx111362c9+9mJPw+G4aGhjCdaOWbQITAz5eRBB64tw8iGrU9pIZQve\nja8HIFl6AhYfg8ldyKj0u6CATOxAV56C5rK5+YWlYYMKP2/RsbiV1nlZmqB4Ptqq9YwtBhCY2IaI\nkEQtqwHzQzy/l1+vmUlFKhalNHOBZ8vxXCEin1PVuy72PC5F3HXK4XA4Lj5f6+vU+br8ORyOi4y2\n12H1KFn0RyBNx2tZ5MkPx5hEYIIoxZveixanoX5m9LrddLy0f1RuRG8qSIVUAiQmgpKWmVz4Bdsm\naqbv91mil2YAJVl4zNL5RECVpDiNTOzqmmZ05+Hs0x0Oh8PhcFziOEHlcFwudE0eNqCJvXJVyFeG\n65e8ANl8y+A2mTV53EkjWmrOgH4I+VTE5CfMuIIR9k3dsX2r21o/1RVzWpyGqX1Ied4aDEctS+/L\nlRE/JFl6Mq2N6us/1VhCgyJS3tQTccXp0RE0h8PhcDgcjksIZ7viuHzRpGfJfTW4wSXts7wpSGUT\ncv03WYNeLzBx5AWw68VIdUtv1bhjaYNZBEk86xuVRNabKiigtZMmkjxv2BSjD00SqJ3si0SpGWKs\nHbZxg1LqQjgBXmDrt2u9psK9kWD9lFmp5ypIeT6tx3I4HA6Hw+G4tHF3LI7Lk6gJayfSP9J6m7AE\n5fmzNqO9rAnLY4SjwNQexLf/neWWN6GNZTtH5blhYdJcMrMJZMO58iBuWlph3GIg9S7Im+V5Fs0S\nz0RRY5GhqFlYNKOMfgEYtVODi1Wkm8YnaGPBxFhxFsKi2aTnJ1xbX4fD4XA4HJcNTlA5Lj9ULSoy\ncCOv1uC2sw65ysWa2XNLeZNF4wYa/QpUNnXFVHdpcWr8OFHLhNFG4SkC+CaqNi5XtSBVv+ueAusn\nB9f18zCzfzBVT9X2GTdN8Hq+mVG0VpHSHBqWQTxzg8r6T3khkr9CP0eHw+FwOBxXFE5QOS5JNGpC\nEkNY7Np6d4lbY8wXFG2tWcQjiVC/AJogQR7xN6aYXX6IH6Kz+6F2Kk2bC6A83xfxOU/O2Vh31GJB\nxRt8N+lAfgqaC71lWQ3UxjGDIhSn06HEmg3nq1Bf7ImpLgq142huv7PcdTgcDofDccnjBJXjkkLj\ntll6dxrdCIpO7kZKs+c3QKcB7RpaPwOrRwC1yp6Jncj2uy/7uhzxczC545kNkp804wpNRkSp0p+Z\nYM0+A02guYp2apCbSIWOIvkq2lwC0t5XYWGDCEpd+zYsFxHbhR+OlnBJbNEqubw/L4fD4XA4HFc+\nzpTCccmgqrDwVUvdQ3vudcuHzDI8I+27NLQ9gMZoaxVWD9sNeTbG6hH0yKe/VodyaZMrQ2Wz/Z4J\np+ynF5jYqp+xV3MFTWJ7v7logrVT740lHjJ3vTkCegFEbXq97dLPSHz7Xen91Ozds0SghiJdDofD\n4XA4HJce7vGv49KhU0/NEvpQNXODM4+ilS1WL+QFaHFmQx+ltP4GhdqJ1HWuf5wY1o6gUQsJ8s/1\nkVz6ZA6AGls0yM+ZycT6ycEarbhtxhPN5V7D4E497U0lkKtYSuXcjeYO2K7RjVZlBGnT3kwEZzVZ\n0GsMPICYMYUTVA6Hw+FwOC4DnKByXDpkN+wZqtYHSWMzm2itwOIBdGovrD7Vs+UWSV3l0hv5oRv0\nbDzQTsMJKjAHQMGiR55vUb92bYPhRYomDESSNLa/g0JqiT5lzn6dOtJZR9VjUFTJcC2UpJEqL7DP\nvbtfMbHWb/PucDgcDofDcQnjBJXj0iEsDZpNRK3BG/wsfW/xgN2ISwLtVXuvvQaVrbZ+rpLaeW9E\nGW1mcRXihXTrmwDiyNL5RiImurKPIixBZRP4eRNKYdEEUnPF1k7dAnVjlHDUyOKh4tG1Yp/YgYTF\nZ3ZsDofD4XA4HF9DXE6N46KimqCNZXTtOLRWoby5VzvTb9E9CvGsdqc017NKTxLwCwzX5gjkp5D2\n2rN9CJcn+epgjVLcPEvNklpaINg6U7uRIDWZCAp99WwbxWq6XJO+uqpsVauRUxLrcSW+vYLCMzww\nh8PhcDgcjq8tLkLluGhoEsHK4TSlTC2tD4GJnRZhatXGbxwUIXX+ExHUz9s4zUXLJivPm0CL23aj\nnp9EcuU0vSy56g0PRDx0cqc18U06dk68gIGoVfe3VBgVpqGyBclVbZnnQb9le64KjYW+faROfnHb\nhNKAqFIgSUdW1A9NmMVt1M85u3SHw+FwOByXDU5QOS4e9cVeSp+m1m+I1eLMXY8GBVg6OGwwgUDa\nuDa78RYRtN3oRlLEC6A407eJD0HRlicx+Fe3oAKzYNfJ3bD0FNA23ZQrW31VVs8mPuTKyMSOtF+U\nZ9EtP2evfuGTr5oroEaD4kljE23i0RNsgxErE18Ka0chSdDiNBRnnLByOBwOh8NxyeMElePi0a5Z\nil5rzQwnsshRcRotTFtUIyxCp5n2JPLtBr40B+IP32wnEcNpZyl+zswT4ByNba8yWquQNEA8ixgi\nSFgCst5TK+b8t/nWbu8pVC1NbyMiMLEdmkvQWCaLQiFmPKFDPcB0wMnPRJUHxNBYsv2U556b43Y4\nHA6Hw+F4lnCCyvHco2o31J11S//ychbN0NjEVDO7+cZEVX0R8CBfgdK8mSUEOauTKm+ChcdG78cP\nYVzZVV90RbN9xC1LHSxMIleryGou9X5vLYOEaFi0z6a5bIILMYFTnrf1zmY24XlIaRbNVaF2PBW5\n6fpJx8YS6Y3h57qbWp1VNrZCcxktzboolcPhcDgcjksaJ6gczx1JAo1FtNNfC+WBtKzWRtUiU0NR\nJbWb+yw6ERTsxryy1aIYQmpo4A3ebAdFYImRdOpofsIiK4uP91IMW2vQWECn9iB++Gwd+eVDJmw0\nNqe/1sKGFcSaAOfKaNS0vwszZ2vHC5og9TOAoOKD9tvhj3Za7JpWJBts2zW2CJfD4XA4HA7HJYq7\nU3E8NySxNdhNOhtuvhPAR/sc4BAfChM944J2DdrrA2JJvcCWZ8YJae2V9htVSvafMWl/uTKsHtkQ\nYbHoGeunLF3tCkGT2I5JtdsMeSRhBeLUYj7IQWvD+ZvcaZHBbmqeQu04Gs/aZxAU0jKqzD7dM5F6\nLodG0m0QNBNZ/X3IVE2I1U6hQR6K067Rr8PhcDgcjksSJ6gczz5q6Vpjb6o16ZkZeGFaE5Wmggmp\n6cGIGp32Gt2eSFkz2KzJbGZo4YeWVjiKxjJEDYYt1TGxdoWgtRPooY+nf9m50R0vxJvcObxyeQ6a\nSxZJ8vMmqqI2oJaOl6uMEDIJNM7Y59BeRRHEC+0zL81Bp95dU0RM9PaLWPG7Ak3jNCWw/31V6NTS\nn+u27uoRdO4G15TZ4XA4HA7HJYd75Ot49tHYaqbGr5A25hVz4svEVIZ4EBbQqN1tDisiXYtt61Uk\ng+OBCYD8FCMFU1gyETCWK6NOR+M2eujvTHAmkYnaJILDn0T7hE6GeD5Ut5kIiiM7t/kJE7VhZcxe\nMnOKpBtjsrQ+RdNUv4G1xQfSGjU/B15g+xUP/MB+dh0AxWrbMit9sN+TDiw/+cxPkMPhcDgcDsez\njItQOS4YTSJYPw1Rw1L4xAc/h5BAaz29WR4lVCTtHZWzOivGmBzELUg6aFBEvH43OA/NlS2SEjVN\nnHmh3X/7oRlZaJy6CHYgKEFYoJfKlkaz+ubTdQC83Fk5zGhxqOjyIWT+xt6StI5M/byZU3h+L2sS\nz/pMjRnLfvT18+qrxVI8ZEPqpXgeKiHm7Nezuoc0nTNuW8TLC9NI5Ajaa6gmLvXP4XA4HA7HJYUT\nVI6njWpiYmb9BKRCSlo16z8ElgImAuRGN2nNboi9tHaquTxmT9l2w7EuEc9qa6J2Ly3ML9iYfgiE\nlsLWTQvM8GwdTXrmCGHxyrHnzpr0bkSTbiqkqkL9TGoMkkBztSemNo4Vtzf0m8rElELcQbVp7/k5\nE0MArWVUY/BLiJ9+xaTRKOmvk+pHzGBE4xaadJxocjgcDofDcdngBJXjvNFO3SIgnYaJlrCUZn+Z\n3fXQTXB2A++HXREl4tstuSpSnLH3mqOc/ujdxCcxmt5w9xoAp7vOlUBz0K6bUBpIHRTAH0wfQ2Fy\nl5lmJG3w80hQSNfpi7hcrlS2Al8cXi4+UppHFw+mVuiJCSDPp5tqN+ozaCykY/Y36lWLDG78O4l6\nwk080HW0vNuEWWsVNEJH9Q9Lx1Cwz6XTRMPi8Hq5qhNaDofD4XA4LjmcoHKcFxq3rf+TJpijW6mX\nsqXx+A3jds+9zQvN0S1uQVA21z1Vq9fJ3PeCQi/SlJF0wDNnwIFbbE3TxwqzaH5yfKSrK8TSeXeN\nDUomDlcP9+YYFK3X1WXal0oKk+j0NbB0sBeZ8wKobreIYn/0Km6ZGA3ySDRcXwWkUb70M+8K3A1R\npsyRr9+ERGOIE1g7gaV0piYXvr9h0z6hq4oeeQBaNdhyAxrk7fMVz9JJp/Zc2ElxOBwOh+NZ4MuP\nrvHHf3WMUwst7r59ijd901Ymq1dhyxXHEE5QOc6P9TNp9KEDudIFDaFxG5afgiCH7HiBLVw8kNp7\npzf67Qj8NhSm+zZU6DSRMDWjyFRVdoPfWU97UJ0NMbOF6tbeorhj1u790ZeoYQ1pJ3Zc0DFeCsi2\nO2FiG7r0hBlHTO8104nW6vDKSRvCKhRIm/z2zoWC9QlLo5GCZ+e8v1dUJqZGohCtm0jOmvkmkdVM\n9RO30wiVwo7nQdQCxOrz5q61SKizTXc4HA7HReSD95/i//3Vg7Q7Carw2BPrvPdDJ/ntn7+dqUkn\nqq52nKBynB+dhvU2ippIrjyYjiVni+akUawkgeYyUt1qNUvNFavF6hdTtmaaKqgWqdLEbuDDEqrR\naI8LTSza1RgUBLZ7gcm9ZpaxceORTYUxoRW1rAnwZYiIQHUbUt3WXabHPz9+/VwaLfQCaC7a+fbz\nlhK4/ri58mVpkZlwVU1F0lmik2DnUFIhlvX80jhNCdTeOCJp/FHSfmQxWplHG8t4pbl0vWd+bhwO\nh8PheLp0Ogm/9JtP0Gr37lfaHWVlLeKdf3GUf/H2PRdvco5LAvfI13FeaFSH5cehdgytHTNxlSKZ\nKcGo7VTR1ho0ziAT2y3SEBYtPWzlcM8YYnArE26S9pwK80hhcnTtDX0VVfmJ4TfFQ5rLw9tqkkZC\nzGRDkxhNkl4K2jjzhMsVf8zTs/RzExGkOI1M70Nmr0OmdltE0AuRuRvoqhnPt0a/MOaz68ML055T\n0re+diNVJsaS1DgkHStL8QPEzyFhCa2dgJWnoL54wYfvcDgcDseF8tTRRl+Keo8oUv7xs0sXYUaO\nSw0nqBznRJvLcOYrPXOH+gJ06l1RZZ4UWYPdvpd49uvyE0hlK+L1DAmkG7UYQ3ZTDYiqRU5GzU3V\nrNeXn4SoPjQHAUvjywSgKrp2Al04gLZW0ahlEamsZ1PctjHHCMTLlvKWMYYbZ4kuFmeQ615Ht5my\n+KnhhFhEyw/pnetRQ4eDn3HcsrTDuDNCjCWDy8Qs1MXzeqO3VqA1xlLd4XA4HI7niGolIIpGP0Sc\nnHTJXg6X8uc4D3Thq0NpeXr6Yev3NLU7NXDQsT2n8IK0Ge8GwhI0Rgkl6UVB+peJByjJqceJP/VH\n6OmDyOxu/Oe9DpndbSYVGyIxmaMg7ZqZX6yfSWuFznbA8fiIzuVKadbqpWon7G9Va6o8uQupnWQ4\n9THtzbV0cNBswgvsPHuB1UIFResb1b9O/xgZmqVySq+p8xB9/cG8YHREsrFkn6PD4XBcwizVlE9+\nRTm6AFNleOENws45l7d8ubJpLs8N11Z4+NE1or5M90Le4y2v337xJua4ZHCCynFu2usM33Ar1E+b\nvXUljX6M6H9kruqJvbeh1krEQytboX4qradJty9vQvqNC7JIiAjJkS8R/em/7RkZLB8nOvQA/ut/\nCm/7zajq4I14FvVYP231WueTypfEaBIjl6nT3yiyuiotb057S4W9c1yatagj0P2ccyVYP9knlMTE\nU1/EyJoAC5qrpA6L2b8Rz8RyXzPmnuPg+HOqqdOfJNH4COGoHlsOh8NxCbGwpvz+R5VOZN+KSzU4\nfEZ57Z3KDTtdYtDlyn/6sRv4if/8CAcP1QkCoRMlvO2N23nJ82cu9tQclwBOUDnOTWWzNYIdZUBQ\nmrWGrH5usDdRhhdabdP6KbS8aUCkKIKUN6Ez+ywVbP2U9YUaMrzoWXbH9//Pbu1TNgpRi/jvfhPv\nrb+UWWD0NhdJ8543pJSdC004azrcZYp4PngbHBGDPFS32OenCkEeRazGDewzDEug8ZjkvtRIIokt\n+uT5afNkMMt0eumGZxFE2WenSYSME1RnNUBxOByOi8/Hv9wTUxlRDB9+EK7foWPrgR2XNlOTIb/6\nc7fy1NEGi8tt9u8pUym722iH4f4lOM6JzOxHFw6kQia7IfagNIf4eRRFPB8NCmnkKO1b5Id2Az97\nnQmmTt2iGVlqV7uGxh3IV5DCpG3b3d7QrpOcZxGtkwd6E/MDvOteglxzN7SbELVQPze+NEtkpKnf\nEF5gr3Ekkb3Otd7lhKRRpYzMuhx6PcfOeu7SOqvCJIRlS+UUxRorY9EqCcxiXRNgRA1dKpasT5gO\n9x0DKKVPAvtNLBwOh+MS4siZ0V+X7Q7UmlA9V5cPxyXNru1Fdm13H6JjkCvkbtDxXCJ+DvZ/o9VN\nrR4F1OqnCtPWGDcso8S96Ed/s1bEdEx+AjpNaK7YzXvcsXSydg00Qid3Qn4CaSx2b5LNHXCpK7A0\nX4XSFKwvgufjv/bHkeltSJjam7eWIShBfmP91cDRMF4ZpD2uJraPfoKoCg0z5OiOE5QsZe5Ku7HP\nDCX6I0Ij0jq1+9/0nMYdxGtb+h6kLn9ppDA/YamB7XVLKZRgYOxuo2i8XkpnZqvu580II+lA7SQS\nNWy7oADF2Suv5s3hcFy2lPJQbw0vVyDvvqocjisSJ6gc54UEBWTrHbD1DgCLLKW1LuKl7m/tmqV9\ndRrQu6U24ratn/UjytLCsKa9LB60cavmBqjN5bSup2+U1hr+N/5L4r/6GWTXbcj01p6YyojqaFhC\nPK9ncao9hz+ilrnNeaHV6WSiwfNN5HmhRbqCwrCoai6jnfpAWqFGdaTpW1pjp572asqbALiMRZaI\nh5Y3bbAqzxz9NgjSbgpmaiYSt/vWp3ce47Y1YUbNebGytXuOBuveksFaN8UcHOMWtPx0kYAfIFHT\nGjFXt9tnmMQmuq6k6KHD4biseOENwgc/p3T6suQDD67fAbng8r0uOByO8bg7DscFIX44GBXwc2hQ\nhKUnICyA+AM3yRrkTNhkN9siaSQrrY/KIh+rx9DZ/dCqMcoIw5vchN7+eqQ6bX2ShmdmgsZL08s6\nTbq9j9ZODrrRiW9Od2HYFYHWH+spaE7A9J7BvbfXhlLQJFvernXnSCudQ2n+8hZV5Xlz8susyjPx\nmRqIKGoiNPucvACK05buF7W6kakurdXeuqi5Dm6omdOuG+AINAbtaxIcx6iniCc2xyQyx8GMsATl\nTZf1Z+BwOC4/btwBK+vwya+AJxAnsG8rfMMd7rvI4bhScYLK8aygnTqcfMhuqDeIqS5+mDZ0zYwK\nxlhn188MGlyIRzcyoop/3w9B7RTaXkHCko3bn2bYqVstleeZkENg7fiwtbfGFvVI7dgHaK2i9UUo\nTEBzNZ33OGt4NmyfRsI66yPs3y8vpDhtUbvMal7TyKMIRG07h15otVOlWYtWFiZNyGo06LqoGxwW\nm8tQmrdaKckiin1uj+ecnJgjo3gmaJM+cQd2/utnoDz/zE6Cw+FwPA1EhBfdINy5X1muQaUIpbwT\nUw7HlYwTVI5nh8Un7EZ6ZPPYtIZGZLAOZ5xAiTvWa6jeGm2fHRYt+lGc7d2kd2/aARKIGiaq/NAi\nKd0I0gaSmCExlVE7CY0zkKSRq411P2dF01qhy1BQZaYi/3979x4kS17dB/57MrOeXV3dfe/t+547\nd16MGECAuBqkZbHQBHYAAjxI2IBAsiJWJhT8YRQWi0TIjsDSOkLesFdYoYjdZeXQmsCWNqwH2Eiy\ntIARE4aRdgYGDcwwYgaGmXtn5j773fXKzLN/nF9WZlVl9qPu7e7q7u8noud2VWVmZVb2dObp8/ud\nk3ymlYZ95r11K2teqm08Z8kvQxrHgdYNaDbb5FeBOHMe4tD6YpXq0NIULJjKqSSZu48KaA+AZ9/H\nIXLPY3cVqB9jloqIdl05EByf3eu9IKLdwICKbpEYQFCYXVBVF7xkeDnBl8KCqfKUFbAARm+Gl55z\nQ/py3qtfjc6GjqlrEFtcoK7oFTckDa5kuFSBsA1NKtH1y7Hn7N9+FXVtuF4SBPnVNCAMKttrqBtU\ngOlTkPay61EFoDLjCnoMNom2OVOZflcFPc2sMmA2m6gAIkB9qKCgpDvSwiaeZ8dTVJKdiIiIaAwM\nqOjW8II0sIhDm3uDTOCRFBpIsh9e5vY3Cm14VhRaYYHpU5BSHZoXcAGW+anN2cD0zWhkWaVyw83h\nyRDPSnznckMM4146dC+o2HsHFaiXzCVSICjnBABiQeF+EUeWkUuCFXWFI8IW+p9FpQlUZ7cXQFab\nluFKyrBrDHSX3RwrV71vJNuVE1SJZ0ML+0UxMvvZWweCKtTLDjXNbKOzmG6nvWSZ1BobMRIREdGt\nwYCKbo1S3eY9JX2jQuvTpCIWKCVzkKaO2w3twnfdHKZemokCbLmrT0D1XhSWOE/ey9W02NINfuO4\n3Xj3h/gJ0Dw7VLwi4YpKxKEFVHFo63rldO5QUiGwfhQSVK0pMZBuO6huEKxNoG5eEZCEe76z4sqX\n1wuWKyAeUKpBVKHV2TQzmZTOT/iucIkizSKpunPspQGseOnQwMgN9QvbQHkKGsfpj41YQDY4n08t\nY+aVNymvT0RERLQ1DKjo1miecUGSmxcVddOsRLaK39pVKxKQPO6ujW5LY+DGMxZ8ZbMLcBmN7E24\nYjA7Nkx8AALxAuiRO63ARPKe4ttqEkDLU5liGZ6b84XMeyVzduACghjwKpa58nw7/iRgC6ppwLVf\nDHymGwRW3eUtB1Qah8DqFQtgRGzIYBRlF0i/98v2uSVBOeCqCmY+w2z2qb+fSaYqtqAwW64+eUk8\nK5aRtX7NqlGytDoRERHdJN5N0C0hXgCdvcMa33bXBqvuZW/QNbavUt2G0g3Pq0pEXbvB1kba3ykb\npCTbAfr9sOD57nn3fkEVIl5/HREPqM5Yw2DXqyq565ahJraqQ/uNzE16v69SB1i+CG2egXjB/ixA\nkfDLbn7TJrZYgU+jHnDlW4NFJnrrQLmZZqhKVQBiQZTr+6V+2WWicn4u+hkqza/MiEzA5ZfTZaLe\nYLXBRHfNsqVEREREN6FgkgrR9olfgjROQo7cZUPmchdyc1umT9m/hVkcN26rPA00TrsmsO7HNRtM\nJaKuZT/8igViQQ0Qz2bbJDfiSUajtw7EXehmwUHkhvZVZiwA9AKb+5Xd56gLXH8GunZ18w9ogmlp\nClpQoTElWx/GuPj9/KCouwwoIOW6BbXlOrKBkXh+WnAkL1MmAtTmrZFv87bBAhOebz8jgQVqAoVA\n8zOY4o2W0SciIiIaAzNUtDMaJ4HF5zCYRRBgat4yEb0O9On/DqxfB85fsAxPdrnqjMsoqPUzqs+n\n1eKKAqG4C0Se3agD1htL1ZVZHxo61ll1hQnSeVqavYEPOxZEucwJACu0EbveVVl+CVi/Di3VIfup\nEAVgZc2XLrqGuB602rQhj9nhdACskIi/9Up/wwVAstsJyq73VFLEJDOvDW6EaFAb7EVmYzvtfK5f\nsWyi51lBkKS5cDL8UxWCNCDrl7Fww1EFLpD3WO2PiIq1O4o/e7iDR/82hAB43Q8EeOvrK6iU99Fw\nbiLaFQyoaEdIdQbaPGN9huLQMgJT88DUcWivBf2vH7f5TBoBp18F1MppRqLSBOpH7WHUsQBq9bJl\nncLWxm8ctu0Gvdq0qnBRx8qdB1XbLxHLenWWgeVL0KkTgB8AcMUzkuukFwwEU8m66vlAnFPW2/Os\nPPc+Cqg0joDr33E9nAAgtqDVL0OnT0P6lRnVsj6VRmGfsZyt5zzninX4pcHhd3EE9FoWkPaHZ0o6\nry12Jc+7ruS6m1slgGUhK9MuA2lVIkXj4veH5wqldK3HWG8VqB2FFGVUiehQimPFJ/7TOq4uxghd\nsv2hb/Twnecj/NL76vD20xxZItpxDKhox0j9CLQ2ZzfFrtADAMTPPuxKjdtVSuZuB2qzdvPu+TbX\nCS6jkDRs7a25prpbuIjFPQsW/BL6PY6664AfQCFWVCGObJvLFy3bUaoDZ37IlQvvWsamqMms56cl\n293+auQVL7/X4p5l5OKuHWt52oZkthctWBkW9SzgTIKMcn3788P8KhC1B5/LKQChSSYsbAOq0FLN\nVfEL3TlzWaRYLKiK44G5bgAgbm5dUp5f7SwPLgNYg+fscNGk5HqvBW2eZVBFRH1PPBvhxnIaTAFA\nGAFXF2M89VyEl9/O2yciSvE3Au2obLEHG152CWjOAxd+2rIEj/8XW9DNawIA9VwzXQwN0/ME8GtA\nD5msSkFFurBrQZuIZakkANS35adOQI6/AtpxFf9KdaDSTLMmfgnwBNpezN+2AskQuLTBr+vTNGmi\nnhUKyVbDay/YvoZtDDbZTSSBrAswXJ+nbVXEm70duP7U4HPJnLmwCw3Ko0Uios7ocMqgOjofL7vM\ncPDtlSAaotjQ8SYB1uplYPbc5sdFRIfC81cidHKmWXZD4OIVBlRENIi/EWhXqKoVKohDV3jAt7k0\nr/kpy2RkR5LFLkNSnsZAwKSu0S7UVfTLNHcdvlGOOq7MubjS5nYDrwAkbAOdVUilWRwEBVXIUB8s\njSN7//UFq1AX+zYEUFwhhUmsGNddwWjQqS4DVYV98DlNiYeDp6i7rYBKKg3okbuBhe8YilimAAAg\nAElEQVS5zJ2bgwW4nl6+a9wsxUM5vRIQVEYCL/XKmYISyTysnp0HkdxjsoArSisMqtoxJYF52IHe\n6AGz50dLrBPRoXO06aFcArpDQVU5sNeIiLL4W4F2R1L6fJjnW6nsETpaxjtptJtI+hR5OT/G/Ztw\n7Qdi2lsDOsvQziJ0+bniDBRgmY/aEasTF0XQ1SvA0nPAyosWAPZatj9R14K3mdv7QxUnStQteMFl\n1PI+u6TSXtRL51BBoBpDo64Fllsg1Rng+Ctt+F/J9a5KsnmdZRuG2HVDOZOMZBIkJ8Mwczcs/SF+\nGkcWLka9dFjp0M+TJv9tL6bbT44tq+uGgBLRofeaewKU/MylBPZ9uSR41V38WzQRDZrAO0A6kPo3\n5oMkKXWdZ2ToVsHwPk0rxFkj4exV0HMlsqN0PhZgwd3yJWhrg6AqqEAaJ+1GvDfUgDjqpMGK6ujr\nk2KDbIt4AXD0HpelE/SzReUGJOpY8Bp1gbAF7a4CN75rlRsXvgddfnHzsvMAxA+AmTP2eSXV/JIS\n9lHPqikGZcixey0Q6q7aisPFPVRt/lQc2bnzSlYiH7Ftx82VSyr5wa/aMuIyiGvXoXEIbS/Yfg8H\nU/YmQHtpS8dFRAdbuST4xX84hXMnPPge4HvA+ZMefvEf1lEKWJCCiAbxzyy0O4Ja4Uv98uijr8AC\nIriGroEbxpfDCzLDAGNbV7y0gEFuzyEF1i5bQYwCCrXeSXmijpvfo0BnaeslxXdTqWH7Nvz5BlZK\nXvwyMHdH+nxrwS2fUo3TkvWJ3prNO5o+5ZZJyqy76nsauc/fg1RnobOSyf4MlWNPCl701i2AS3p+\ndVYAv+lqng8GOaIx4AVWXj2OgXANCK35s8VUAkjg9n0JqpFtu7OcDtEs4oqoENHhdnzOwz99zxRa\nHfudVaswkCKifAyoaFdIULby1p2hOT0j/Y6SFTzrE5RJPPUzGwNjMJLqegLxS2kvqe6KBWCBX/we\nQD+rMVIgof/6BsPbsjf5MqHzbko1+3ySzA/UgqlKwXyv4aIQQH4lQCjQXbPhf50VC0zdeVC/DJSm\n7DSV6jYXrtJ0c6XaGDgXnp8GtJWmZY6Cmp3v2DJYyZDQ7I+C7VdkJe9LVdcwOLZgMKhBw44b4ucq\nSXplYOq4FRzprrpjyjkuvzS555KI9gQDKSLaDAMq2j3Tp4HSomVBkv5SeUpTrnfV0PNJcJP0RcrO\nsQnb6VCuXsvdSLshfuKNBmIJLygOptzrQEEGTZL/fQSozhVvY6+VG/aZZrJGI5LPJ2dYZmEwCgDt\nJWDtpcFlo44FLNVpNw9OIJVp6Nx5y2q1XcasPA00TqRzz2bPAVEX4nlu6J5nwU91ruAcuQC7s5zu\no8b28zVc5CLuAbU5e39VC5zygsfp0xv/PBARERENYUBFu0ZEgNoc1AuGJv9nCkhU5oD6EWDlBYzc\nyCdDA10wlb3x1aBqRQX6mRgnKZme3LRnb5ZVLQu2yT5rEowElbSnVbIfAFA7AilVt/gp7BE3BG5E\n2LEAJOmh5ZUwGkAWDckEsH4t/3ntQeMY4nkWVJUbFjhNn+oPExzZxaAKbZyAhm3X2LfhgqWCt0j2\nbDhADNv5Sy98z34OylMusKzZ/KtkiKhfsqIlXgCUapNZZISIiIgmDgMq2n0aF9yfC4CN5q+4wglD\nwRTgAp9yHegu5a/az4Zlh3MpELWhyy8A9SOQYDQo0rBtAZ649waAug13s0xHBWicKDrSyRa2gfXr\ng88l5cezBUE8P7+Ig3j93mG5NILVvdkgwzW8yeqsGz4IK1IRVIGoC/UH+1bZFgXQCOL5UPHt/TYa\n3hl1BvubiVivMiBdzzX6hQh0+jRkuDgGERER0RD+CZZ2X3kKhfOmqjN2I+0FaWW3JBgSV2hiZCxg\nfwOjTyXzrpLv49ANJ/Rsro5XAsJ1q/iXl21Zv+bKcVtJbkkCq5IrsuEHViBhP1pfKBjiB6S/GsQy\nNf2hj/bcRtUD+/pZwaHawxtQL3Dl0F1Z86BqmSNVe9592V5k9t0vDw7BzN0f93rcGzzukSBM0yqQ\nudUAiYiIiFIMqGjXiRcAjeMYvPGVtIAB4PogdV3p7h4Qdu2xRsVzr4ZvfpNgariiW9KrKOljZQtb\nNbhwdF5NXjbMChe45rCZCngadqDr16Fr16C9VlokY9KoujlVRcFHUn7eAlgRgfgBxC9ZKXSIq5xY\n8CtE/LRBblHz5OFdCtvW/Dnquixm7HZF3Ni+yM6lH1iFPy/o77+IAO0FYPWKDWPM+9y9wIKqsIMt\nZ806K1tbjoiIiA4tDvmjPSH1YzY3KZm/U50BytM2dK+16AoXZGSDqO4qtNIcHAKWBAheKS2RHodA\n2TWvjSV93q8U7JXrJxUUvT68uAK9lmUxanNAexlo3UD/Zr2zDJQb0Kn5iSt0oFHXBaCWbdr2/iXB\nq+cB8VCGRwFUmxa0lhsjjXZz9yeOgOWceXMaQ5OgLaiO5J4UHgQRNMrMbdMIiJLhfG6mlV8GKo00\nuBYXjGlsyxf1pdqv2UciIiLaNQyoaM9IqZYOnctav4bcghRJ1iEOge4KtNxIuh5BEAN+CVo7YvOb\nOsuwXlTuFjzpUVVpuLk/PZsv01q0773AKvXVjg69rzdSVr0fvCWiLrDwfbfPQ0PHuqvWnyrvOPeA\nquu9lS1fH4vNQXJ9u8QPXKDhjkcE0GQuVDLkTkc/W3sDABGkoPDEwH50VmxeU7mRNknOX3rDvlEK\nGS1E4XmuDHuUZiQrM5mkqNiQQVUL5vICKtUtBYNERER0uG05oBIRH8AjAC6p6ttF5CEASSfT4wD+\nWlUfzFkvAvC4e/icqr7TPf9/A/gxAEkq4udU9bGxjoIOlqKsgAjgVSxA8UqQqIWRwCvsWGls5NwM\nl+tpI972oivIkAnS1q8B9aOWbUpMHQdWXxoaQaYuG4J0/bBdkNlyBSwmJKBCdyUTTGUKbcDNL5MI\nWqpBanO231HX5jCVp+y8tJfyh8FlM1z+xhUPNeoCV57IBFEK1I6M1UzX2pPpaL+w2Hpi9fer2gT6\nc+DStwVgRS28YDSomrCsIm2O1ykiItoL28lQfRjAkwCaAKCqb0xeEJE/BPDZgvVaqvqagtf+Z1X9\ng23sAx0G5YbNhxkmPnD0brsp7q5Cw3U3XyYEgrqV6F67gv6dcjIXJ2nU2q8UJ9CkF9IABRafBZpn\n0rf0AmD6DLS9aFmvqOcCgaHhYIKc7SWvTdCNeXsRA8HUQJNk931nyT7TxsnR9afmLTvXWcZIby91\nn0neelnXnx7NKHVWLejJ5WHDZrsa21DPfpNnuLl2SSl43zJvw+cheayu2XEcZvqjWQVBZqj2HV6n\niIho123pT8IichbATwD4nZzXmgAeAPCZW7trdGhNzbsb6KGiFc0z/ZtihUCvf8e+Fp6FXvkW4tUr\no0PH2ovuxt9uyDXsQLvr6XyqYWF7tJCECKQ2B5m93Qpn5BXGGM6QZPe73NjSYe+K/n7LxoFer+WK\nN+SoHc3MQXKVGGM3RHCTUuMahfkZrqiTzunK8nxgah7SOJVbu09VXRPfGJg5lxkaKG7/k6qQ+cGu\nZp/3/LSSpBsmKFudT0d7jtcpIiLaK1sdY/MJAB+FdcAc9iCAL6hqUQfOqog8IiIPi8jwUIt/KSJ/\nIyK/KSK5dy4i8kG3/iNXr17d4u7SfiZ+CTh6N1A/ZgFMdQZonoZ0loHF70OXL0Evf9NlOZJMkQKr\nl20+TJZXsuF2Xilz067FGQ+vtHGBhqn5nKFprox64yTSYXTuq3Zksm7Ky00Ul50fktsg152f2XPW\nhHk4WFm/Bl15Ebp6BdrLW3+DIg+dVRv655ftq34MmLndAjTP689vUyAtnx510iC6twbM3g7M3QnM\n3GbDN8tThcerrgqkAhZA9dbTANGvAHN3bPz50KThdYoOvIWlLi5fzfnDHxHtqU2H/InI2wFcUdVH\nReRNOYu8Dzl/Ecy4XVUvicidAL4oIo+r6jMAPgbgJQBlAJ8E8MsAfm14ZVX9pHsdFy5c4G+QQyIt\nrQ4r7JCZ7yRxD2jMQ6OOvdaXFIvIBEtT8xDxoBhqJlydsQqD/SfFqsktPAdd+BTkZW+FTM2P7pdf\ngs7dCSxfcpkRsW1Nn7a5OLO3pzfm5bodxySpzaZDF4GNs1R5+64xEHYsjPL8TMbLBanqmuMCNiyz\nNgupHclss2TV90ayX2LNlWuzto95SlXAP2k/C+0layo8MO9JgV4bUqrYvK8kC1eZgkYh1C+5QxYX\nTMWuia/1GUPzDDSoIikTT/sHr1N00F270cW/+NdP4ltPrcDzBLMzJfzqh1+G176q4PclEe2qrWSo\n3gDgnSLyLIDfB/CAiHwaAETkGID7AfxJ0cqqesn9+10AXwLwWvf4RTUdAL/rtkOHlMYhtLua37tp\nIPAxIh4kCbgGXvDT4YJ+NTNPZuiP1pVpKz4hHlCfB47cCZk7B7n9R4H6UehTf2KZlvXrI81dpVSD\nHL0bOP4K4PgrIDO39XsuiedDKtOQanPyginAhibGPWtm3Fuzx0m/ruznLq4vWFZ3HVh5yc5Ha8EC\np362LjNUrk+B1uLA5yciwJG7MdCgWTwLgJpnN99/z7c+VOF6ft+xKAnUpD98TxWuMuQ6EHWtKEbY\ntuNHDGgI1RhYuwppLTKY2p94naIDS1Xx4X/2N3j8yWX0QkWnG+Py1Q4++uvfwouX80cSENHu2jSg\nUtWPqepZVT0P4L0AvqiqH3AvvxvA51Q19/9oEZlLhki4i9obADzhHp9y/wpsOMY3b/JYaB9SVej6\ndWDxOWvKuvIisPSc9RWyBQZLlGfl9ZMq1W2oV3XGgqb+cK+cm+TKNDBzO1CZtgDNc41rj98HnL0f\nWLtq+3P1266IxSARD5JUvlu9bJmTDct/7y1VBa5/x4IOr2TBSdiyr2ylPK8ETJ8eDI7iyBUK0f6X\nZIOukWCq/64jc6akMg2cfDXQPG3D+GbOASdfbUMJt6LSHAz+snrrAy+pxhZAagQgsmMP2zlz6NxK\n3ZVMBUfaL3idooPs8W8v4+r1DqKhvwuGkeIz//XFvdkpIhpws39Cfy+A38g+ISIXAPyCqv48gJcD\n+D9FJIYFb7+hqk+4Rf+DiMzD7nQfA/ALN7kvtB/11l0T38xdcBwCKy9CZ26zm3bx8kupD9wUi+sl\nNWPLlxuuYW3ycsE2cpraiufbnKjuarpfS89b3ysvM5wwjizgSuZwAVZqfGp+csqkZ7UWLIiCnxaU\n8Eo2/C5q2+fXPJs/56u3QZDhldK+VXlBVWfZtl2Z7j8lQcXmOW2Bdlbsc446NreqccqCwGDoM45c\nxUd3Lkb6hW38LoP7W64XL0r7Da9TtK9dudrJ/dUahoqLL7Z2f4eIaMS2AipV/RJsOETy+E05yzwC\n4Ofd918B8KqCbT2wnfemAyq3fDlcCeuu9XaqzGayI0YhVvQgqNrNfFAFpo5bs+D+QpG7x1eLH+Bn\nbrDFzbMpMFx4wpVqR3VmcN9HbtjV+lk1z9o6Uc+yHlHPMmqVaRfU7IHOov2bXJmToMovAZEbPhd2\n8vtpFfUGS7YTx8XHpbGVYi/VNmzQm7tqZxlYeBb9cx+2rbQ91D7XZK5U3HMl8jPnbVuTtrPZuJwm\nv7Rv8DpFB83L75lGmPNrqVrx8EOcQ0U0ESZwkgcdKhvdqCc3xJVpAGq9oKB20xx27Ra4fsyW8QKg\nVBvKNvlQRP05Waox4q99FvFj/wVor0BOvwLBu37NKsgNvK0W31RHPTf3KHQZnpyGsMn6GtmwwX5f\nLFdEY/rktgOLWyIORzNIIrA+TwVzzRJB1bJvw8GviJu3VvB5iQfEIbTXAlqLtp3pk5ChUvLaWbYh\nk3HPPpvaMUi1aZmpvH5hQR3oLo8OsazMwHq7bvBzlb+j6b+TOPeNiA4FVcU3v6/46pPAahs4Og28\n6Qer+PE3HMNffvUa2h373RYEgplmCW994MQe7zERAQyoaK+Vp4CWa5Q7zGVKVCNg+XkLTsSzr8rM\n4LJ586lEAPiWmYl7iL7yH6Df/ksgtJtwff4xhH/+vyF4yy+l/a00kwnJUrVgoLM0uH2/nGai0oUB\nyEBlwlRsvbGSQHBXFfdj6md2inpI+WU7/qRUvduelKo29DGoAtDRgiJxOPhZhi1g4Vno7O02lwou\nmFp9aXCdtcvWI6qgdDtEgKP3Aovfs8/eC4DZ2yGQNBAPO0PH644/6WucPNdv+uuGlw4X4yAi2iVf\ne0bx0LeA0A1+uLoM/PFXFR/46Xtw373T+KM/fQHtdoy/8/qj+Nn3nEO9tkcjHohoAAMq2lvVGSta\nEKdzXwBx5c7dXe/VJ9LgRCP3NTRfR1BQnc1lpzqr0Ce/NBT4APq3X0Z0/G4E97/H9iEoWcClmX0B\nbG5RlNPoVsT1uMpsN3DVBYvm7xQFCTutMm3zg7KS6n4iQP1YcWEIEauKGHbS+VTlun28ndXMYoPn\nQHObA6tlnpI5VWvX8pdpXbfPNq8Js/iQxgmgMfTX2bADXbtiWTO/PBQAZvmAFwzub/L9JM5/I6ID\nL1bFV55Mg6lEGAH//Ung/W87jZ982+m92Tki2hADKtpTIh505qzdlPfWXPGCZr8wgoYdoHUDwzfF\nGoeDN8RhF+qXc4IqsWFhK9dsjs9QQAWNET/xeeCBf5IsbU/Xj1rwIR5QnYH4ZbcfRQfiucDEtwzH\nhvNwttpP+xarz7sCFJlhckmlvvocZLPMjIj1gipV0+cKhgiqJq8VZMRccGqFIwo+qzgEpk4AK5cw\nkmkaCqRU1X5+uu5nqLtmwZhNnkPeEEAreDL08+IFHPJHRHui3R0NphI3VvKfJ6LJwDsH2nMiHlBt\n2tewqIPhkufaa0GkPLh83LOgKigPrt9ZBqDA1JGCIEcg83eNPluqjWYqxM8PIESA6pwrPtFNAy/x\nMBpQCFBpDG9hV4jnQ2fOWdYm6tgwyaB6c32XXMCJTFn5dNjfBkUhPJcJy8v6pRuHTB21oX+rL7mi\nE2JBVmbIpKoCKy8MDvETceXgI6QFSNIASvzS4Mg/936oHyso/05EtLOqJcD3MFIeHQBmC0ZjE9Fk\nYEBFE0NVod//CvDdL1svoCPngXvfAslW94sjYO0GcOwH3E1z1+bweL7dHffWLfDxPPd8AMCDVBuQ\nu/8H6NMPD2ZoShX4f+cfb20HS/XBOVQJv2KBwXBwoK7yXTbAKNWB8vTIJnaL9Y7KCRZvRnXGAqTO\nkp0fRGnwOjRkTzurwLWngSiErt0ATrzc5j3lBV8uUyRTxyxj6Kr49QNAVTvfnZX8YZR+Oa3kWJ1N\ne2mJa8KcFzhpDIBzEoio2MOPt/Gfv7yOxdUY5074+AdvbuCus1vso7cBzxP88MsUf/0U0MtkqgIf\neMN9/EPPbml1FU+/BCysAUcbwF0ngWqJnz9tjAEVTQx98nPAcw+nw/KuPgXc+B70vndA4pbd7HZX\ngdosoOr6RVUHtwFYViJO/sQnLoOyCP+N/whRdRr6xBeAXgeYvwPBWz8G79R9o/sS9ax4RHfFtlVu\nAFPHIJWm7UOSqQpqFiQtPZd3RHYTP33aAgy/vHcl03dauQ6U6xYaxaGVju+uW0DlV4BwHbp8Gfju\nl9y8rRhY+D7wvYegL3+rFbcYVk3LAUtSTTARR8DaFdtO7jwtp350cChjdcb2ragBcxxuXE6fiA61\nz//1Ov7wi2vousvUd54P8a8/vYiP/uws7jh98787fuReQeAp/uopoN0DmnXgTa8E7jzJG/rdsLim\n+PzjliWMFXhpAfj2JeDvvVoxXeM5oGIMqGgiaG8d+P5XR4flRT3g0qPA2R+ChUvLo/Ogkm3k9R0S\nQEs1iF+CdFYRvPkXoe/8lxDEkIK5Mrr8ArB2efDJ7iqwfhV67F5I7UiadepnOYqGt7k+T4foJl28\nAGicHHgujmPgiT+1QKj/ZA/oRsCVvwXOvBoDRUn88mDPr2Gd5bToR9JPK1dOPzG/UhBQKedPEVGh\nKFZ85kvr/WAq0e0Bf/TFNfzSB26+J5SI4IdfJrhwjyJWwPd4E7+b/r9nBrODkQJRBHzte8CPjf7t\nlaiPdw80GVav5fd0ggLrriiFeJDjr4Q++5Br2uuPDtsaqqynYQe4+iRUY7uZF88yH+d/LHc3tL1k\nmY/RV2zfVl4EZm8fnWfjV6FhKy3EkJTizivnfghJdwXaXR19QWPg+jPAfe+wjGAcWen2oAa0lyzE\nqkxDvADaXoK++HVg/Srk9IW0ImHh3DZvsElx1LMhghojt4R8UDlUgS8Rbc/yaowoyv/jzfOXb21D\ncBGBz1hqV8WquFZQ/OOlxd3dF9p/GFDRZKjNFVfGS8pra2yV3GbPQV/4OuTUD0L9susrJFCNMXL9\nWXpuMBuhNo9Grz4JOfHK0ffKNuLNM1x2PNlseWqkGqF6Jcg0S9wCsKF/RVkkv2xVHV3lPm0tAAvf\nTV9fvYy43ACeeyj9GdEIgAU/4nnQ2MNIJb9sMNVZtUbAWeL152WhVAcqOUVRiIicRr24Quux2QM6\nnPsQEQCe2FC/Yf4eFeel/YM/IjQRpDoNnLhvdMiV+MDxl6WP4why5wOQs/fbHJ2kGavnQ6pz9tg1\nd9XumpUKnzlnVfiScEtjYPHZ/B0p6h3V35/R/2X6VeaGA7E4hIYFc3UOGSnXgbnbRz8/rwTc/iP9\nhxp1gdXLsM8y83X5G+lwQa8EXb0MXbkMbS1aIYy8AhO9lvWyiqOcYMr9Wz8GTJ+y4YWs7kdEGygF\ngh//4RrKQ4nscgD8/R9jGb79TkRwft6CqixPgDtP5K9DlGBARRNDXv0e4NQPuuIDYkO/zr8eUsuM\nS68dgYhAqjOQ+jGIq/omqpDuKtA4BUwdd0O7IojnWfGKahNoHM+8W0G2pDqLgWLaSeNbje2r1Bid\nq9Uf6jfMNbAtnN9zuMir3wNMHXPV9yoWPJ94BeTc69OFOivIPTfdVXs+qFqADM/NwVoFVl4qCIQV\naC9DwzaGC6T3Xw9bt+LQiOiQePcDU3jz/XVUypa1mGl4+Ll3TONVd5c3X5km3g/dYZX9fA8IPPv3\n+Azw6tv3es9o0nHIH00M8UvAq34Seua1FhCFrcE5Ul4JmDlrwUvujbdCOktQLwCGhv+JeNCgajfz\ncQjM3Ja/E/VjNmcrSnoaDb1H+wawpMDs+czbxhvUpAjtpn2zprmHgFQawBv+CbD0PNBaAmbOQOpH\nBhcqHBboCklMnbC+ZYMruabKBUHThvg3JSLaOs8T/NQDU3jwTXV0uopaRW6ulx9NlFIgePMPAgtr\nipUWMFMHZuo8v7Q5BlQ0UcQvW+Nbz7OS6GHbhmz5FeDYvVZBrmiuFWCvJc18hykswwFA5l+R//6e\nD52/F1i/boFVb6iQgsZAawE6dTwtxx3U8t/PNmhZFAZUAFz589lzQE4xLI0jICgjt2DE7Hng6hNp\nQ+BhReXOvZI1aS6Y+3ZL+3ER0aHhe4J6lTfaB9XclGCOozhpGxhQ0eSZPg20FoDOomWUylNA7ShE\nYwuwvPIGpbI3KKHteZCj9wCz53OyHJktiAdMzVtxhDwaA+3lfpAkng+tHbH5OoMbcnPCeNHdiGoM\nLF8C2kvuvGIo2ySQqeMWcBX1jwrb9rMyEIiJDTEUz4qeDJxPBcpNVvUjIiKim8aAiiaOiAD1I/YF\nWOPWlRcG5ymVG0B3DSM30KqWdQhbGM5ySFADZu/Y0vAMDdt2k56/h5ZByz7TOA7trbkeWWoNfMW3\n4CypUkj5Vl6wYCoZuge4oCr7GSu85mno2lUbDjrAs3lzU8csaAo7FijVjlj1QMAyk40T6Tn1Kwe3\nyTIRERHtKgZUNNlUgdUXR4s+dFeB2hH7Nw7tBrk6C6xdhsCDVmeB7ko6PNArA0dftvWx7p3V3Ip+\nfdW5wcfiWYn01ZcwEMhVZ/rDDGmUagy0FpE/RNOVNNcI/Sxf/SiwFlvQlEiKlrQWgXLDGi/nZZ6S\n8uhEREREtxADKppsvfWCIXxqc6uaZwafdnOsxAuA6ly/Ip+Uatsb3iWeK8desmpyWVPzVkBDXbNf\nV7bdsh9q75k0+F2/ZtmXqXlIueF2vaiAwiEUb1CmXnygedqCJ/Ghcc8yfo0T0KgHRCGAyLbRWbJ1\nWtehQQ1SqlrJfGahiIiIaIcxoKLJtlFfKI0seIk6FsAEVctOrF3tL2Jxi9gcmu2oNi3b5PlpA1jA\nbvIbJ224Yb/QgVog111zD10wlYh7wMqL0PoxSOyGBIoHlBosiuAF9hnnFRop1SHVTPWKsG2ZQ40h\n1Rq010qHCmaFbagIpHXdhgISERER7SAGVDTZgqKAQ6AQ4MbTaQZLBFqZsb5TWeWmK1iwdeIF0Jnb\ngOXn3dA/396necaKYyQZkUQcWsPZfrn1IX7JAr8kM6WxNZsV2bEhgdpr2fyk3roFLlPH+328JoWI\nQBungOWLGPjcxAOmTw4uHFQHP6u1q8j9rJNy90mFSGapiIiIaAcxoKLJ5peA8rTNh+rfPAvUK7mq\nepIZPidWBW44YOguQ0s1oL1o62hsRS0aJ23oXpa6G/Fey6r3HbnHHkc9IOwC3TXXKDandp9I3rPu\nOMpu8zoY0HRXdySg0rDtgk2XWYu6wPILli1rnNx45V0mtVmoH1iAFHUtiG6cSAtKFK9pGcPhjGDy\nGuBeywmoVNO5WQy4iIiI6CYwoKLJVz9qQ+M6y2kw1FrEYDAFuzEump+0/PxgVcD2ItBZgR57mc23\nAmzdtctAr+2WE0hrAVqaGiw2UZ2F+Nv4XyfsAt2X3M29QCtNq/ynERC5ct/l6VubOVq9PFrIAzGw\negVanx/N4u0xKTfsvG6FKtC6YX3KkuxkHKYV/MRLP0tv6DzFEbB2xVWBhAVk5Smbbxf37LVeKy21\nXjs6URk9IiIimjwMqGjyidhNbznTZW/tMpKgZ4CGQAQArlCEiPUvGimxDgs41iYkb6sAAB4bSURB\nVG8ADTfPpruaCaaQ/juQHQMQdaGeP3qjLWJD2DTTmDbqWdCUvqkFhnGYLhO2AAh09vwWsjJbNFJa\nPN1HxL3RrEzYBdoLtr9eYNUJJ3V+V2cJGrbszLtzoF5glR6jLhBUbG5dNrACLPhaeWGwyIhGFlhH\nSWGLOH1+/bptb/r0bh0ZERER7UMb1IUmmlAa281zNpgSH4MD7uK00IFGBVX1FOitpQ+7q8ifk4PB\nEuq9FqDaryCIZC3f9TqqzgLBlGU/os7IpgaKXCTHoxGw8D1oa2lgu2PzCwIz1dGsTdixDFzYdlmz\njg2/667lb2MPadiBdldHBlaKiA0PLdXSps1Rrz88E4AFrsMVG22rQLiGfjCVfb6zAs0rmEFERETk\nMKCi/aWzDCw9PzjUqzAAUYt0yo3iZbZZrKK/3faCVZOLY+ulFEcWnK1ftyxT87QFViPD7mABTV6A\npxGwchG4/rQVlLgZjRMYnc9l1Q5lOKBqLWA0kFR7/lYEd7eAqkJXrwBLz226Txq78wG1DGQiygum\n+ivlPy8y2POKiIiIaAgDKto/wjbQWrCMwfo1C2o6K/ZvdzU/s1NpQqbmCwo/CFA/lll2GoVFJYZv\nuFVtOJhaHyRJKstBoa1F4OoTwOKz+dvabE5ObAUkbiYzIuUpYOac9dFKcnf1o6N9uwA7jjzqgpJJ\nELbckDzr8ZV7rlVd8ZBMpcVshtBPPoscRU2cVccMuomIiOiw4Bwq2j+SnkPtpUx/qsw8JL80GjiV\n3TygufPA0kU3rA+WJZo5OzhnqTQFlNbd/KPM/KzabKbBsHu/Ur0/x0iS/ehX1Ov1m/zmciXWNwys\nNALay0D9SPEym5DaLLQ6Y/s1PJ8oq6gP1NAgyj2V7TcVdYGg2q+Y2A+uuqs2pK80BdtvdQGlE9Rc\n4+fhTJXYOtENjBBvtBIkERERUQYDKto/NLICE7nzYGBBTz+gEgt63PA28QJg7rytrzHgBblFJdA4\nbhmOsNWvACfiWWW+9jKwfhXwPCCoDRU8gJsbpa4IxQaZnSQASCoSFg1hKzrObRBxpcU3UmnmDPsT\ny9hNbIU7STNVcWhFSpJCH51l+zmozlow3F9FrMDE+tW0aIcXANUjxUU8NIJGXQizVERERFSAARXt\nH0HV3fhmqujlEc96V9Xmcl7ykduXaOB9KvY1vF59DlqdBlZfyg/GFLZfGwUwUnLVBz2gMmPl23OX\n83evyl65YUFme2nwueps8Tq7rdLMFA2xcy+eb9X5Vl/MZCyBflPfsAWsX4MGtbRMvOdbH65MM2gA\nQDsnO2VvYhkxBlRERERUgAEV7R+VGaCzWvCiAFPzNm9oB4kX2DCygRv4IaUa0MvZT/GA4/fZ9y5D\npkHF9bjK8MuuofEW+zJlhZ00ANhqCXYRK5NeaVoxB88rnlO0V0p1y5h1XAn7uAv1ypbFyy0ooRZ8\nl+rWs2pqfvDl4YC4H6wPb4ZzqIiIiGhjDKho//B8YPqUBTNrV9HPUolnQ7cap3ZnP/xy2hg2SwQo\nNSC9FrQy64ooZF47cs/IfBypH4VWZux4QtdQtjJtzYO3E9Ro7EqfZ4swlO3z2up2RIDtNCzeRSIC\nNE7aZ9Vbc+e8bMP3iiRZqM6yFR/ZaPhidc41i84GZzZslMP9iIiIaCOTefdElEPby1bdD7DhaGHb\nAqnaHDA1P1oOfKf2o9wAwrar7JdRmbHmw6U6pOVBg2qaLWqchBQEK+IHQPMmg8HWjcHqdoBVuFu/\nBkwdv7ltTxAp1QaGQmp5ypr1agTANYD2K0AcA4jTeWqdFaDaLN6uX4LOnkv7ccFl7Q7QZ0dEREQ7\ngwEV7QsauuAgCRg8KxgB8SxY2YXiCdpZAVrXXfEDD+oFLvsT2w18xbOaeEEFaJyAaOyGo6nt705K\nhsIN664BdZ3g4hI3R0Sg8/cB175twY+rZqjqqi72WkBQgSY/O5XpwsyfBFXozDkLgiWwQDcpjx9H\nNgxzl4J2IiIi2j94d0ATT6Oem2eUBAziAgS1G90bz0BLNaB+bLAM+q3ch+4asHYl3QcNgd5Qw9el\n70OP3uMKIKgFgP0iGrDMSrlh83pueYCzQXPjA05qc/1AKAmsLagSC4DCjn3eyxcB8aFH7swdxqet\nBcv0JY+DKqCaOVUuA1aZObABKhEREW3fhM08JxqkURdYej7tk+T5gOdb0CK+ZQ1ELHBZeh4aFjSp\nHff9wy507cpgQKcukBtd2s3tArB6JVPkwFWm663b68sXC/o+3YSgoCJgUD34N/8aA3FvNEsZ92wo\n4EB5+whYujS4uip09bJlHzMNmhG2+k2PNflvd724xDoREREdSgyoaLKt30BafMIazWazECKSGYal\n7qb41tDOCrD8vBtOt0W9NQuW8opW2Fbtpj6ZC3ar1I+54YdJ8CD2uH7s1r7PPqEaFwet4bq9nmhd\nB7pF59h6XUnmMXprt25HiYiIaN/jkD+abAOBiVc8VyppkNtdg0a9kWp626WayTalb4JNh9CJ77JX\nmywbttOCCbeCXwKat1nwF3WsEEalufNztyaBeIBfhYat9OcjziulPkrjaLD/Vl6TZY37vcVU1ebG\nERERETnMUNFkGyoCoHk3vOmLNszryresiMXNiIrW3ygAEqs4eJPB3Ng8H6jNAo0Tth+HIZhyZOoY\n4Pk2fG+jnxHACk4khSniXvp80XpRb3CbRcMriYiI6FBihoomirYXrTBAHKa9huyVgUzB6Iruhjds\n25C65UvAkTvH3xHxUJhhErGgKYoGl0ma44oLrFoLxdvwD8Hcpt3kBZDmbdDeulU2BID2Qv6wv5kz\nA+ttzn72FJ5tb5yGy0RERHRgMaCiiaGtRdeoVQG4IXxxtsiEAqpQN5cqeYyoa8FW2HL9iDDYVHcc\nniuRnXdD7pUg06etbHp3zSr+DTeArc7YNtoL/cIGACxQEwHqR29u/2iUCKQ8ZZX4AGhtDlh6zvWV\ngn32zTOQTEAkXgANam5o6UbZzxiIQmBq/lBl/oiIiGhzDKhoIqhqpsqa5GdvytNA/ag11A3bdvt7\n7alkC4PLZnoNJQUIivoP5RER6PRpYOkigMycGfEtC+WXbPBfZYNsRbluX+oq/EUdC7KS/lm0o8Qv\nAUfusrL7GgN+OX8O3vQpYOFZpBX+csQR4AdAZXoH95iIiIj2IwZUNCE0zS4VCVuuxxOAoAbprln/\nqZEy1gJMHbeS68sv9AtbaFADmqdzexDlEb8Enb0d6K7aNrzAshvb7XUlrn8Rpra3Ht0SmxUoERHo\n3HnrM9ZZzl/I84HG6V1pIE1ERET7CwMqmhBi2Z/NgirAhtCtvmTJhKBmj5M5V6pAdRaYOg7ceGZw\ne2ELWHgWevTuLWerxPOAahNAc6yjov1BRIDGCQvCkyGCCS+wn624C439NKgnIiIiAgMqmhAiAq0d\ndXOokFNSXGyoHQCsXesXoRDxgOocNA6tUMTUCUhQgbaXbJjXMFUrLV6d2dkDon1JKk0rQNEfJmo/\ngxpHwOL3bQ5f7QgwfYrZKiIiIgLAgIomiNRmreDE+rWhYEgsE1Wdtf5C2VLXyRJe4JZzw/GiHvLn\nw8SDRSKIsvyyFR0BXGAVZQqeuJ/J1g1AI/tZVbW5fdXmtuboERER0cHBgIomilRngOqMFanordlQ\nvqAK+BWXEdiox1AmYxBU3BDAoSyViG2PaFhn1eZQ9TNPnv0MdYaynV5gwwKT5VpdoLsCbZ5l1oqI\niOgQ4p9UaSKJCKTcgFRnIUE1vVEVD/ALikJk+wOVG1ZRD0PDBr0S+wjRKFWguzI4zDT5PqhnFhQr\nUDEQOLnS/b21rb1V1LMhhERERHQgMENF+8/UMWDlJZc1cGXW/bIrHmFExCr0rV1NK7dVmsDUPLMI\nNCoOoarpnCmNLWMVDw0PLRzWp0B3fcNgXTvLwNVvAz1XdbJ+BDj2A5tWISQiIqLJxoCK9h8vAJpn\nbF5LHFow5VdGeleJ5wPTJ+2LaAPqghzA9URrLxYUNcl5LrFB9T8N28CL3xisOrl+A3jpG9DTr2OQ\nT0REtI8xoKL9SQQo1Tdfjmgr1q9bQOQFNnyvKHDSCNYzDcVVKPMsv5CzTdfwubuy8bpEREQ00TiH\nig4sVbVsA9Fmoi7QXrKqftFoFUkANs9KY2v0rLF7rDYMsHFy46F7vTXkF1QRoNfOeZ6IiIj2C2ao\n6MDRqAe98k1g5UUACp06ATlyJ8QvW4U/jz/2NKTcsB5o6zcAf4OfD1UAsStg4aoAzt0FKU9tvP3K\nLNBayM9SVVgkhYiIaD9jhooOFFWFXny4H0xh+hRk+hQQdqDdVetx1d1aNTY6RBrHAfFhFft6afZp\ngAum+g/jjedUZU2fHJ1jJR5QOwLh0FUiIqJ9jQEVHSztBRcwKRDUIFMnIJ5vZdiTZborAMtWU4b4\nJWD+XqB2xErre2U3R0/sK6gOFpRI19xSGX7xS8DpC0DjhGVI/Qowcw44ft+tPhQiIiLaZRz7RAdL\nJvsktbniMtdRB/CYGaCU+GVg9lzua6oKLDxjJfg1Tn+ujty15Qp9ElSA+Zffqt0lIiKiCcGAig6W\nynTmAQtS0K0hItC5u6y4RGfZsky1IxDOxyMiIjr0eDdAB0tlxkpQd5agrQXI1Ak3N2aIX9n9faN9\nTcQN79vCED8iIiI6PDiHig4UEYGcvR9o3gbEIXT1MlTjwVxVublhE1YiIiIioq1ihooOHPECyIlX\nAideaU/EIRB27PugymCKiIiIiG6ZLWeoRMQXka+LyOfc44dE5DH39YKIfKZgvSiz3H/OPH+HiPyV\niDwtIv+PiJRv/nCIcngBUJ6yLwZTRAcWr1NERLQXtjPk78MAnkweqOobVfU1qvoaAF8F8EcF67WS\n5VT1nZnn/xWA31TVuwEsAPiftrnvREREWbxOERHRrttSQCUiZwH8BIDfyXmtCeABALl/+SvYnrh1\n/sA99e8BPLjV9YmIiLJ4nSIior2y1QzVJwB8FECc89qDAL6gqssF61ZF5BEReVhEkovRUQCLqhq6\nxxcBnMlbWUQ+6NZ/5OrVq1vcXSIiOmR4nSIioj2xaUAlIm8HcEVVHy1Y5H0Afm+DTdyuqhcA/DSA\nT4jIXdvZQVX9pKpeUNUL8/Pz21mViIgOAV6niIhoL20lQ/UGAO8UkWcB/D6AB0Tk0wAgIscA3A/g\nT4pWVtVL7t/vAvgSgNcCuA5gVkSSKoNnAVwa7xCIiOiQ43WKiIj2zKYBlap+TFXPqup5AO8F8EVV\n/YB7+d0APqeq7bx1RWRORCru+2Owi94TqqoA/ptbHwD+EYDP3tSREBHRocTrFBER7aWbbez7XgwN\noxCRCyKSTAp+OYBHROQbsAvTb6jqE+61XwbwT0XkadhY9X93k/tCREQ0jNcpIiLaUWJ/hNsfLly4\noI888she7wYR0aEmIo+6OUc0hNcpIqK9t9vXqZvNUBERERERER1aDKiIiIiIiIjGxICKiIiIiIho\nTAyoiIiIiIiIxsSAioiIiIiIaEwMqIiIiIiIiMbEgIqIiIiIiGhMDKiIiIiIiIjGxICKiIiIiIho\nTAyoiIiIiIiIxsSAioiIiIiIaEwMqIiIiIiIiMbEgIqIiIiIiGhMDKiIiIiIiIjGxICKiIiIiIho\nTAyoiIiIiIiIxsSAioiIiIiIaEwMqIiIiIiIiMbEgIqIiIiIiGhMDKiIiIiIiIjGxICKiIiIiIho\nTAyoiIiIiIiIxsSAioiIiIiIaEwMqIiIiIiIiMbEgIqIiIiIiGhMDKiIiIiIiIjGxICKiIiIiIho\nTAyoiIiIiIiIxsSAioiIiIiIaEwMqIiIiIiIiMbEgIqIiIiIiGhMDKiIiIiIiIjGxICKiIiIiIho\nTAyoiIiIiIiIxsSAioiIiIiIaEwMqIiIiIiIiMbEgIqIiIiIiGhMDKiIiIiIiIjGxICKiIiIiIho\nTAyoiIiIiIiIxsSAioiIiIiIaEwMqIiIiIiIiMbEgIqIiIiIiGhMDKiIiIiIiIjGxICKiIiIiIho\nTAyoiIiIiIiIxsSAioiIiIiIaEwMqIiIiIiIiMbEgIqIiIiIiGhMDKiIiIiIiIjGxICKiIiIiIho\nTAyoiIiIiIiIxsSAioiIiIiIaEwMqIiIiIiIiMbEgIqIiIiIiGhMDKiIiIiIiIjGxICKiIiIiIho\nTAyoiIiIiIiIxsSAioiIiIiIaExbDqhExBeRr4vI59zjh0TkMff1goh8ZoN1myJyUUR+O/Pcl0Tk\nqcw2jt/coRAR0WHG6xQREe2FYBvLfhjAkwCaAKCqb0xeEJE/BPDZDdb9dQBfznn+/ar6yDb2gYiI\nqAivU0REtOu2lKESkbMAfgLA7+S81gTwAIDcv/yJyOsAnADwF+PvJhERUTFep4iIaK9sdcjfJwB8\nFECc89qDAL6gqsvDL4iIB+DfAPhIwXZ/1w2j+OciInkLiMgHReQREXnk6tWrW9xdIiI6ZHidIiKi\nPbFpQCUibwdwRVUfLVjkfQB+r+C1DwH4U1W9mPPa+1X1VQDe6L5+Jm8DqvpJVb2gqhfm5+c3210i\nIjpkeJ0iIqK9tJU5VG8A8E4ReRuAKoCmiHxaVT8gIscA3A/gXQXr/iiAN4rIhwA0AJRFZFVVf0VV\nLwGAqq6IyH902/nUzR4QEREdOrxOERHRntk0oFLVjwH4GACIyJsAfERVP+BefjeAz6lqu2Dd9yff\ni8jPAbigqr8iIgGAWVW9JiIlAG8H8PmbORAiIjqceJ0iIqK9dLN9qN6LoWEUInJBREYmBQ+pAPhz\nEfkbAI8BuATg/7rJfSEiIhrG6xQREe0oUdW93octu3Dhgj7yCKvXEhHtJRF5VFUv7PV+TCJep4iI\n9t5uX6duNkNFRERERER0aDGgIiIiIiIiGhMDKiIiIiIiojExoCIiIiIiIhoTAyoiIiIiIqIxMaAi\nIiIiIiIaEwMqIiIiIiKiMTGgIiIiIiIiGhMDKiIiIiIiojExoCIiIiIiIhoTAyoiIiIiIqIxMaAi\nIiIiIiIaEwMqIiIiIiKiMTGgIiIiIiIiGhMDKiIiIiIiojExoCIiIiIiIhoTAyoiIiIiIqIxMaAi\nIiIiIiIaEwMqIiIiIiKiMTGgIiIiIiIiGhMDKiIiIiIiojExoCIiIiIiIhoTAyoiIiIiIqIxMaAi\nIiIiIiIaEwMqIiIiIiKiMTGgIiIiIiIiGhMDKiIiIiIiojExoCIiIiIiIhoTAyoiIiIiIqIxMaAi\nIiIiIiIaEwMqIiIiIiKiMTGgIiIiIiIiGhMDKiIiIiIiojExoCIiIiIiIhoTAyoiIiIiIqIxMaAi\nIiIiIiIaEwMqIiIiIiKiMTGgIiIiIiIiGhMDKiIiIiIiojExoCIiIiIiIhoTAyoiIiIiIqIxMaAi\nIiIiIiIaEwMqIiIiIiKiMTGgIiIiIiIiGhMDKiIiIiIiojExoCIiIiIiIhoTAyoiIiIiIqIxMaAi\nIiIiIiIaEwMqIiIiIiKiMTGgIiIiIiIiGhMDKiIiIiIiojExoCIiIiIiIhoTAyoiIiIiIqIxMaAi\nIiIiIiIaEwMqIiIiIiKiMTGgIiIiIiIiGhMDKiIiIiIiojExoCIiIiIiIhoTAyoiIiIiIqIxbTmg\nEhFfRL4uIp9zjx8Skcfc1wsi8pkN1m2KyEUR+e3Mc68TkcdF5GkR+S0RkZs7FCIiOsx4nSIior2w\nnQzVhwE8mTxQ1Teq6mtU9TUAvgrgjzZY99cBfHnouf8dwD8GcI/7ess29oWIiGgYr1NERLTrthRQ\nichZAD8B4HdyXmsCeABA7l/+ROR1AE4A+IvMc6cANFX1YVVVAJ8C8OC2956IiAi8ThER0d4Jtrjc\nJwB8FMB0zmsPAviCqi4PvyAiHoB/A+ADAN6ceekMgIuZxxfdcyNE5IMAPugedkTkm1vc5/3uGIBr\ne70Tu4THenAdpuM9TMd6717vQA5ep3bfYfqZ57EeXIfpeA/Tse7qdWrTgEpE3g7giqo+KiJvylnk\nfcj5i6DzIQB/qqoXxx16rqqfBPBJty+PqOqFsTa0z/BYD6bDdKzA4Trew3ase70PWbxO7Q0e68F0\nmI4VOFzHe9iOdTffbysZqjcAeKeIvA1AFUBTRD6tqh8QkWMA7gfwroJ1fxTAG0XkQwAaAMoisgrg\n3wI4m1nuLIBL4x4EEREdarxOERHRntl0DpWqfkxVz6rqeQDvBfBFVf2Ae/ndAD6nqu2Cdd+vqufc\nuh8B8ClV/RVVfRHAsoj8iKua9LMAPnsLjoeIiA4ZXqeIiGgv3WwfqvcC+L3sEyJyQUSKhlZkfQg2\nBONpAM8A+LMtrPPJbe/h/sVjPZgO07ECh+t4eayTidepncNjPZgO07ECh+t4eaw7RKx4ERERERER\nEW3XzWaoiIiIiIiIDi0GVERERERERGPak4BKRP6BiHxLRGIRuZB5/u+KyKMi8rj79wH3fF1E/kRE\nvu3W+42C7Z4XkZaIPOa+/o/Ma69z231aRH5Lxq2Pu007dayZ7ZwTkVUR+UjmuWfddh/b7bKRe3S8\nbxGRp9y5/ZWdO7qRfdmpn+P7Mz/D3xCRd2Ve25Nzu0fHetDOa+767rUvuWNNPovjO3+ke3ase/K7\neLt28LOZuOuUe+9Dc63ao2M9aL/PeJ3CgTyvvE5hzN/FqrrrXwBeDmu49SUAFzLPvxbAaff9KwFc\nct/XAfy4+74M4CEAb83Z7nkA3yx4z78G8CMABDaxeGT9/XSsme38AYD/BOAjmeeeBXDsIJ3bouMF\n4MMmi9/p1v8GgPv287G65QL3/SkAVzKP9+Tc7vaxHtDzmru+ezzwXgfgvG50rHvyu3iCPpvzmLDr\n1E4eb2Y7E3Ot2u1jxcH8fcbr1ME8r7xO6Xi/i7fSh+qWU9UnAWA44FPVr2cefgtATUQqqroO4L+5\nZboi8jUM9gfZkIicAtBU1Yfd408BeBBbq9h0U3byWEXkQQDfA7C2A7s+lj043vsBPK2q33XL/D6A\nvw/giVtyQBvYqWN1yyWqAPa8csweHOtBPK9F63du8SFs2W4fK4Aj2KPfxdt1mK5TwOG6VvE6xesU\nr1O8Tt3q69Qkz6H6KQBfGz6JIjIL4B0AvlCw3h0i8nUR+UsReaN77gyAi5llLrrnJsW2j1VEGgB+\nGcC/yNmeAvgLl8L84A7s7826lcd7BsDzmcf7/ty6118vIt8C8DiAX1DV0L00yef2Vh7rgTyvm6z/\nu24YxT/f0vCC3XMrj3XSfxdv12G6TgGH61rF6xSvU9nXeZ0yvE4V2LEMlYh8HsDJnJd+VVU3bI4o\nIq8A8K8A/L2h5wNYP5HfSv4iMORFAOdU9bqIvA7AZ9y2dtQeHevHAfymqq7m/Ez/j6p6SWx86/8r\nIt9W1S9v7Wg2N4HHu2P26Fihqn8F4BUi8nIA/15E/kytMemOndtJOtZx9n879upYN1j//e68TgP4\nQwA/A+BTWzmWzUzgsU6Mw3Sdcvs2ab+7D9rvs4+D1ylep26RCfzdzevUBnYsoFLVN4+znoicBfDH\nAH5WVZ8ZevmTAL6jqp8oeM8OgI77/lEReQbAywBcwmC676x77pbYi2MF8HoA7xaR/xXALIBYRNqq\n+tuqesnt1xUR+WNYWvqWBVSTdLwAHgVwW2a5g3Bus+//pIiswsb3PrKT53bCjvUSDuB5LVo/c15X\nROQ/ws7rLblQTdix7ujv4u06TNcp934T87t7p69Vk3Ss4HVqv5/X7PvzOsXrVD7d5cll2S+MTjCb\nhU3q+8mcZf8XWETsbbC9eQC++/5O9wEc0fwJZm/bz8c6tPzHkU5+nQIwnfn+KwDest/P7QbHGwD4\nLoA7kE4KfcV+PlZ3LMkE2NsBvADg2CSc21081oN4XnPXd8d6zH1fgk1o/4WDeKzutT39XTwBn83E\nXqd24niHlv84JuhatYvHehB/n/E6dTDPK69TOt7v4l37IIZ29F2wMYkdAJcB/Ll7/p/BJnI+lvk6\nDosOFcCTmed/3q3zTgC/5r7/KdjEsscAfA3AOzLveQHAN2EVWX4bgOznYx16j48j/cV9p/sB+Yb7\nLH71IJzbouN1j98G4G/dud21493Bn+OfGfo5fnCvz+1uH+sBPa9F60/B/oL9N+6z+LdwN9wH7Vjd\na3vyu3iCPpuJu07t5PEOvcfHMQHXqt0+Vvf4oP0+43XqYJ5XXqd0vN/F4lYkIiIiIiKibZrkKn9E\nREREREQTjQEVERERERHRmBhQERERERERjYkBFRERERER0ZgYUBEREREREY2JARUREREREdGYGFAR\nERERERGN6f8HbJFSboNqkfEAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 1008x720 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AMI3SYKtBldj",
        "colab_type": "text"
      },
      "source": [
        "Pricy listings are quite scattered, though we see a cluster of them at the middle.\n",
        "\n",
        "How about which neighbourhood has the highest revenue?\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "y3U1pXz9CJQD",
        "colab_type": "code",
        "outputId": "65da9937-5a17-4b22-9902-59e1799bc224",
        "cellView": "both",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 767
        }
      },
      "source": [
        "#@title Revenue by neighbourhood\n",
        "\n",
        "pd_neighbourhood_revenue = pd_listings[['neighbourhood_group_cleansed','estimated_revenue']].groupby(['neighbourhood_group_cleansed']).mean().sort_values('estimated_revenue', ascending=False)\n",
        "print(pd_neighbourhood_revenue)\n",
        "\n",
        "pd_listings_plot = pd_listings[['neighbourhood_group_cleansed','longitude','latitude','estimated_revenue']]\n",
        "pd_listings_plot.loc[:,'color'] = 0\n",
        "\n",
        "color_value = 1\n",
        "for neighbourhood in pd_neighbourhood_revenue[0:3].index:\n",
        "  pd_listings_plot.at[pd_listings_plot['neighbourhood_group_cleansed'] == neighbourhood, 'color'] = color_value\n",
        "  color_value -= 0.2\n",
        "\n",
        "# plot\n",
        "plt.figure(figsize=(4, 7))\n",
        "ax = plt.subplot(1, 1, 1)\n",
        "ax.set_title(\"Top 3 revenue neighbourhoods\")\n",
        "\n",
        "ax.set_autoscaley_on(False)\n",
        "ax.set_ylim([47.4, 47.8])\n",
        "ax.set_autoscalex_on(False)\n",
        "ax.set_xlim([-122.5, -122.2])\n",
        "plt.scatter(pd_listings_plot['longitude'],\n",
        "            pd_listings_plot['latitude'],\n",
        "            cmap=\"coolwarm\",\n",
        "            c=pd_listings_plot['color']\n",
        "           )\n",
        "\n",
        "_ = plt.plot()"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "                              estimated_revenue\n",
            "neighbourhood_group_cleansed                   \n",
            "Downtown                            7247.666038\n",
            "Capitol Hill                        7064.079365\n",
            "Beacon Hill                         6606.983051\n",
            "Ballard                             6078.726087\n",
            "Queen Anne                          6038.752542\n",
            "Central Area                        4636.371274\n",
            "Other neighborhoods                 4409.843829\n",
            "Cascade                             4075.134831\n",
            "Seward Park                         4063.500000\n",
            "Rainier Valley                      3827.345912\n",
            "Delridge                            3641.189873\n",
            "Magnolia                            3587.819672\n",
            "West Seattle                        3370.783251\n",
            "Northgate                           2962.362500\n",
            "Lake City                           2476.432836\n",
            "Interbay                            2105.727273\n",
            "University District                 1558.557377\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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KX6feZqXUYUqpIUqp85RS+uelkZxyfG9SU2wRLRExDJh6Xj8y+sYjciBlrM+n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\n/wB3Xf8Z5W+QtgAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 288x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5zpsjqElKyl1",
        "colab_type": "text"
      },
      "source": [
        "Its clear that Downtown, Capitol Hill and Beacon Hill are the highest revenue neighbourhood. From Google Map, those looks like shopping and CBD district\n",
        "\n",
        "What are the common group size? is it 2? or family of 4? or larger? \n",
        "And if guest usually comes in 2s, is it over saturated? (supply and demand)\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6o5RFOjBJAaL",
        "colab_type": "code",
        "outputId": "5b0e8e13-3b26-40b7-c272-e1a1d3a6ed89",
        "cellView": "both",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 545
        }
      },
      "source": [
        "#@title Guests’ group size, and supply & demand\n",
        "\n",
        "def get_supply_demand_by(by_column_name, pd_listings, pd_bookings):\n",
        "  # get supply: number of listing that provide X \n",
        "  pd_listing_supply_by_pax = pd_listings[[by_column_name,'listing_id']].groupby([by_column_name]).count().sort_values('listing_id', ascending=False)\n",
        "  pd_listing_supply_by_pax.rename(columns={'listing_id':'# of listings'}, inplace=True)\n",
        "\n",
        "  # get demand base on bookings\n",
        "  pd_listing_demand_by_pax = pd_bookings[[by_column_name,'id']].groupby([by_column_name]).count().sort_values('id', ascending=False)\n",
        "  pd_listing_demand_by_pax.rename(columns={'id':'# of bookings'}, inplace=True)\n",
        "\n",
        "  # merge supply and demand\n",
        "  pd_listing_supply_demand_pax = pd.concat([pd_listing_supply_by_pax, pd_listing_demand_by_pax], axis=1)\n",
        "  pd_listing_supply_demand_pax['ratio'] = pd_listing_supply_demand_pax['# of bookings'] / pd_listing_supply_demand_pax['# of listings']\n",
        "\n",
        "  pd_listing_supply_demand_pax = pd_listing_supply_demand_pax.sort_values('ratio', ascending=False)\n",
        "\n",
        "  return pd_listing_supply_demand_pax\n",
        "\n",
        "get_supply_demand_by('accommodates', pd_listings, pd_bookings)"
      ],
      "execution_count": 16,
      "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># of listings</th>\n",
              "      <th># of bookings</th>\n",
              "      <th>ratio</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>accommodates</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>3</td>\n",
              "      <td>83</td>\n",
              "      <td>27.666667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1627</td>\n",
              "      <td>42821</td>\n",
              "      <td>26.318992</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>398</td>\n",
              "      <td>10170</td>\n",
              "      <td>25.552764</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>25</td>\n",
              "      <td>520</td>\n",
              "      <td>20.800000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>785</td>\n",
              "      <td>16041</td>\n",
              "      <td>20.434395</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>52</td>\n",
              "      <td>956</td>\n",
              "      <td>18.384615</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>184</td>\n",
              "      <td>3221</td>\n",
              "      <td>17.505435</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>332</td>\n",
              "      <td>5580</td>\n",
              "      <td>16.807229</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>15</td>\n",
              "      <td>229</td>\n",
              "      <td>15.266667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>256</td>\n",
              "      <td>3542</td>\n",
              "      <td>13.835938</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>119</td>\n",
              "      <td>1501</td>\n",
              "      <td>12.613445</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>4</td>\n",
              "      <td>48</td>\n",
              "      <td>12.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>2</td>\n",
              "      <td>19</td>\n",
              "      <td>9.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>13</td>\n",
              "      <td>98</td>\n",
              "      <td>7.538462</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>3</td>\n",
              "      <td>20</td>\n",
              "      <td>6.666667</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "              # of listings  # of bookings      ratio\n",
              "accommodates                                         \n",
              "14                        3             83  27.666667\n",
              "2                      1627          42821  26.318992\n",
              "3                       398          10170  25.552764\n",
              "10                       25            520  20.800000\n",
              "4                       785          16041  20.434395\n",
              "7                        52            956  18.384615\n",
              "5                       184           3221  17.505435\n",
              "6                       332           5580  16.807229\n",
              "12                       15            229  15.266667\n",
              "1                       256           3542  13.835938\n",
              "8                       119           1501  12.613445\n",
              "16                        4             48  12.000000\n",
              "15                        2             19   9.500000\n",
              "9                        13             98   7.538462\n",
              "11                        3             20   6.666667"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sjAjmW9dFn4l",
        "colab_type": "text"
      },
      "source": [
        "A place for 14 ranked first, but the demand is considered low (83 bookings) as compared to places for 2 or 3 person.\n",
        "\n",
        "Renting a place for 2 or 3 person will give host a pretty good regular rentals.\n",
        "\n",
        "Do these guests prefer 1 bedroom or 2 separate bedrooms?"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sqMnloeaBOgC",
        "colab_type": "code",
        "outputId": "d67c7799-9ddb-4e42-ef24-7c13654b3ee5",
        "cellView": "form",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 173
        }
      },
      "source": [
        "#@title Bedrooms configurations for 2 person\n",
        "get_supply_demand_by('bedrooms', pd_listings[pd_listings['accommodates']<=2], pd_bookings[pd_bookings['accommodates']<=2])"
      ],
      "execution_count": 17,
      "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># of listings</th>\n",
              "      <th># of bookings</th>\n",
              "      <th>ratio</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>bedrooms</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1.0</th>\n",
              "      <td>1613</td>\n",
              "      <td>40897</td>\n",
              "      <td>25.354619</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0.0</th>\n",
              "      <td>261</td>\n",
              "      <td>5376</td>\n",
              "      <td>20.597701</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2.0</th>\n",
              "      <td>9</td>\n",
              "      <td>90</td>\n",
              "      <td>10.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          # of listings  # of bookings      ratio\n",
              "bedrooms                                         \n",
              "1.0                1613          40897  25.354619\n",
              "0.0                 261           5376  20.597701\n",
              "2.0                   9             90  10.000000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rWhOATXB_oyH",
        "colab_type": "code",
        "outputId": "bbeee0c8-9983-4b95-fb60-9df783c12977",
        "cellView": "form",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 173
        }
      },
      "source": [
        "#@title Bedrooms configurations for 3 person\n",
        "get_supply_demand_by('bedrooms', pd_listings[pd_listings['accommodates']==3], pd_bookings[pd_bookings['accommodates']==3])"
      ],
      "execution_count": 18,
      "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># of listings</th>\n",
              "      <th># of bookings</th>\n",
              "      <th>ratio</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>bedrooms</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1.0</th>\n",
              "      <td>311</td>\n",
              "      <td>8214</td>\n",
              "      <td>26.411576</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0.0</th>\n",
              "      <td>53</td>\n",
              "      <td>1396</td>\n",
              "      <td>26.339623</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2.0</th>\n",
              "      <td>34</td>\n",
              "      <td>560</td>\n",
              "      <td>16.470588</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          # of listings  # of bookings      ratio\n",
              "bedrooms                                         \n",
              "1.0                 311           8214  26.411576\n",
              "0.0                  53           1396  26.339623\n",
              "2.0                  34            560  16.470588"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LJzMgw-5IZbX",
        "colab_type": "text"
      },
      "source": [
        "Majority prefers 1 bed room, less than 1% prefers 2 bedrooms (90 out of 40897). But wait, 0 bedrooms, what are kind of apartments are these?"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_bCXM69aCH-b",
        "colab_type": "code",
        "outputId": "76fd1b5c-89a7-4036-eeb1-b6feb7bc9c4a",
        "cellView": "form",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 111
        }
      },
      "source": [
        "#@title Places with 0 bedrooms\n",
        "pd_listings[pd_listings['bedrooms']==0][['room_type','listing_id']].groupby(['room_type']).count().sort_values('listing_id', ascending=False)"
      ],
      "execution_count": 19,
      "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>listing_id</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>room_type</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Entire home/apt</th>\n",
              "      <td>378</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                 listing_id\n",
              "room_type                  \n",
              "Entire home/apt         378"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8WPuQaDCI_bl",
        "colab_type": "text"
      },
      "source": [
        "All these 0 bedrooms apartments are renting their entire home, so we can assume these have 1 room. I hope they provide beds?"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7d60LtfyI-fZ",
        "colab_type": "code",
        "outputId": "468c2e9e-74dd-44e0-ea31-07631cde0935",
        "cellView": "form",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "#@title Number of beds in 0 bedrooms apartments\n",
        "pd_bed_count = pd_listings[pd_listings['bedrooms']==0][['beds','listing_id']].groupby(['beds']).count().sort_values('listing_id', ascending=False)\n",
        "pd_bed_count.rename(columns={'listing_id':'# of listings'}, inplace=True)\n",
        "pd_bed_count"
      ],
      "execution_count": 20,
      "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># of listings</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>beds</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1.0</th>\n",
              "      <td>292</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2.0</th>\n",
              "      <td>76</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3.0</th>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4.0</th>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      # of listings\n",
              "beds               \n",
              "1.0             292\n",
              "2.0              76\n",
              "3.0               8\n",
              "4.0               2"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CcIdrvH9JZZ-",
        "colab_type": "text"
      },
      "source": [
        "At least 1 bed are provided in these 0 bedrooms apartments"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HXXs50_63nXc",
        "colab_type": "code",
        "outputId": "9e49fe93-48d0-4789-abcb-6b4f730e5a8f",
        "cellView": "form",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 452
        }
      },
      "source": [
        "#@title Popular bed configurations\n",
        "pd_bed_count = pd_listings[['beds','listing_id']].groupby(['beds']).count().sort_values('listing_id', ascending=False)\n",
        "pd_bed_count.rename(columns={'listing_id':'# of listings'}, inplace=True)\n",
        "pd_bed_count"
      ],
      "execution_count": 21,
      "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># of listings</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>beds</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1.0</th>\n",
              "      <td>2201</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2.0</th>\n",
              "      <td>912</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3.0</th>\n",
              "      <td>433</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4.0</th>\n",
              "      <td>152</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5.0</th>\n",
              "      <td>73</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6.0</th>\n",
              "      <td>21</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7.0</th>\n",
              "      <td>14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8.0</th>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9.0</th>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10.0</th>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0.0</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15.0</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      # of listings\n",
              "beds               \n",
              "1.0            2201\n",
              "2.0             912\n",
              "3.0             433\n",
              "4.0             152\n",
              "5.0              73\n",
              "6.0              21\n",
              "7.0              14\n",
              "8.0               4\n",
              "9.0               4\n",
              "10.0              2\n",
              "0.0               1\n",
              "15.0              1"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nOiHT1bJSVWE",
        "colab_type": "code",
        "outputId": "52e13497-69af-49ff-f4e5-01de9e626779",
        "cellView": "form",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 328
        }
      },
      "source": [
        "#@title Bedroom configurations with highest demands?\n",
        "get_supply_demand_by('bedrooms', pd_listings, pd_bookings)"
      ],
      "execution_count": 22,
      "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># of listings</th>\n",
              "      <th># of bookings</th>\n",
              "      <th>ratio</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>bedrooms</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1.0</th>\n",
              "      <td>2417</td>\n",
              "      <td>61399</td>\n",
              "      <td>25.402979</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0.0</th>\n",
              "      <td>378</td>\n",
              "      <td>8403</td>\n",
              "      <td>22.230159</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2.0</th>\n",
              "      <td>640</td>\n",
              "      <td>10593</td>\n",
              "      <td>16.551562</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6.0</th>\n",
              "      <td>6</td>\n",
              "      <td>82</td>\n",
              "      <td>13.666667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3.0</th>\n",
              "      <td>283</td>\n",
              "      <td>3593</td>\n",
              "      <td>12.696113</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5.0</th>\n",
              "      <td>24</td>\n",
              "      <td>205</td>\n",
              "      <td>8.541667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4.0</th>\n",
              "      <td>69</td>\n",
              "      <td>569</td>\n",
              "      <td>8.246377</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7.0</th>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>5.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          # of listings  # of bookings      ratio\n",
              "bedrooms                                         \n",
              "1.0                2417          61399  25.402979\n",
              "0.0                 378           8403  22.230159\n",
              "2.0                 640          10593  16.551562\n",
              "6.0                   6             82  13.666667\n",
              "3.0                 283           3593  12.696113\n",
              "5.0                  24            205   8.541667\n",
              "4.0                  69            569   8.246377\n",
              "7.0                   1              5   5.000000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LR0fkoRUV_WD",
        "colab_type": "text"
      },
      "source": [
        "1 to 2 bedrooms configuration has most demands, or renting the entire home (zero bedrooms)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sm1EcElYC_Z0",
        "colab_type": "code",
        "outputId": "888ac593-f57a-490a-93d2-e247582e6a34",
        "cellView": "form",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 593
        }
      },
      "source": [
        "#@title Which factors (ratings) matters the most to visitors of Seattle? \n",
        "\n",
        "pd_listings_reviews = pd_listings[['review_scores_rating','review_scores_accuracy','review_scores_cleanliness','review_scores_checkin','review_scores_communication','review_scores_location','review_scores_value']]\n",
        "\n",
        "def plot_topn_correlation_matrix_with_target(df, target_col_name, k=10):\n",
        "  corrmat = df.corr()\n",
        "  top_correlated_columns = corrmat.nlargest(k, target_col_name)[target_col_name].index\n",
        "  cm = np.corrcoef(df[top_correlated_columns].values.T)\n",
        "  sns.set(font_scale=1.25)\n",
        "  hm = sns.heatmap(cm, cbar=True, annot=True, square=True, fmt='.2f', annot_kws={'size': k}, yticklabels=top_correlated_columns.values, xticklabels=top_correlated_columns.values)\n",
        "  \n",
        "  return top_correlated_columns.values\n",
        "\n",
        "top_correlated_columns = plot_topn_correlation_matrix_with_target(pd_listings_reviews, 'review_scores_rating')\n",
        "\n",
        "print(\"Top most correlated columns:\")\n",
        "for i in top_correlated_columns:\n",
        "  if(i!='review_scores_rating'):\n",
        "    print(i)\n"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Top most correlated columns:\n",
            "review_scores_communication\n",
            "review_scores_cleanliness\n",
            "review_scores_value\n",
            "review_scores_accuracy\n",
            "review_scores_checkin\n",
            "review_scores_location\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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NmjX4+vrSoEEDunTpwvnz519rW1RUxJw5c/D19cXBwYHOnTtz5swZJZuMjAyG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\nAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Dr8vcGtqJG2a",
        "colab_type": "text"
      },
      "source": [
        "Host with good communication is tends to get high overall rating, host in Seattle needs to be responsive and friendly. Good communication also directly impacts check-in rating."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xg-DBRKBC_t0",
        "colab_type": "code",
        "outputId": "4e160ea6-d2c5-4497-f0d7-fa117369de50",
        "cellView": "form",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 409
        }
      },
      "source": [
        "#@title Does good rating affects price and revenue?\n",
        "\n",
        "_ = plot_topn_correlation_matrix_with_target(pd_listings[['estimated_revenue','price','review_scores_rating']], 'review_scores_rating')"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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atGnDnDlzyMrKUq5PSUlhyJAhODo60q5dOyIiItR+r6KiorCzs2P//v107twZ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\nEZehoiciIi7j/wDTs+ChSE7DHwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "K4aXWyzYKPFH",
        "colab_type": "text"
      },
      "source": [
        "Good rating has little impact to the price set by the host\n",
        "Good rating also have very small positive correlation with revenue"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3g60RL5LDByF",
        "colab_type": "text"
      },
      "source": [
        "How about amenities, what other host are providing in their amenities?"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FHI5_CqBJW8-",
        "colab_type": "code",
        "outputId": "83c4294c-ce32-4a5e-937b-1adc4995d748",
        "cellView": "form",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        }
      },
      "source": [
        "#@title These are top 10 most common amenities\n",
        "rows_amenities = []\n",
        "rows_listing_id = []\n",
        "\n",
        "for id, row in pd_listings.loc[:, ['listing_id','amenities']].iterrows():\n",
        "  rows_listing_id.append(row['listing_id'])\n",
        "  \n",
        "  row_amenities = {}\n",
        "  amenities = row['amenities'].strip('{}')\n",
        "  for amenity in amenities.split(','):\n",
        "    row_amenities[amenity.strip('\"')] = 1\n",
        "  rows_amenities.append(row_amenities)\n",
        "\n",
        "pd_listings_amenities = pd.DataFrame(rows_amenities, index=rows_listing_id)\n",
        "pd_listings_amenities = pd_listings_amenities.fillna(0)\n",
        "pd_listings_amenities.drop('', axis=1, inplace=True)\n",
        "pd_listings_amenities['listing_id'] = pd_listings_amenities.index\n",
        "pd_listings_amenities = pd.merge(pd_listings_amenities, pd_listings[['listing_id','number_of_reviews']], on='listing_id')\n",
        "\n",
        "pd_rows_amenities = pd_listings_amenities.sum(axis=0)\n",
        "\n",
        "pd_amenities_count = pd.DataFrame({'amenities':pd_rows_amenities.index, 'count':pd_rows_amenities.values})\n",
        "pd_amenities_count = pd_amenities_count.sort_values('count', ascending=False)[:10]\n",
        "\n",
        "pd_amenities_count.drop(41, axis=0, inplace=True) # drop listing_id\n",
        "pd_amenities_count.drop(42, axis=0, inplace=True) # drop number_of_reviews\n",
        "pd_amenities_count"
      ],
      "execution_count": 25,
      "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>amenities</th>\n",
              "      <th>count</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>40</th>\n",
              "      <td>Wireless Internet</td>\n",
              "      <td>3667.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>Heating</td>\n",
              "      <td>3627.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>Kitchen</td>\n",
              "      <td>3423.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>Smoke Detector</td>\n",
              "      <td>3281.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>Essentials</td>\n",
              "      <td>3237.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Dryer</td>\n",
              "      <td>2997.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37</th>\n",
              "      <td>Washer</td>\n",
              "      <td>2992.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>Internet</td>\n",
              "      <td>2811.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            amenities   count\n",
              "40  Wireless Internet  3667.0\n",
              "19            Heating  3627.0\n",
              "24            Kitchen  3423.0\n",
              "33     Smoke Detector  3281.0\n",
              "11         Essentials  3237.0\n",
              "9               Dryer  2997.0\n",
              "37             Washer  2992.0\n",
              "22           Internet  2811.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0LL2njSTXZEs",
        "colab_type": "text"
      },
      "source": [
        "Internet, heating and kitchen are listed as necessities in Seattle.\n",
        "\n",
        "Smoke detector? I just learnt that Washington State Building Code has required smoke detectors in all dwellings since the 1973."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Fcds48WtRAOR",
        "colab_type": "code",
        "outputId": "524e9920-852c-4875-8848-af7f6c89e52d",
        "cellView": "form",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 500
        }
      },
      "source": [
        "# @title Features with most weight by Linear Regression\n",
        "\n",
        "# prep data, normalise, one-hot\n",
        "from sklearn import preprocessing\n",
        "min_max_scaler = preprocessing.MinMaxScaler()\n",
        "\n",
        "pd_model_data_x = pd_listings[['neighbourhood_group_cleansed','property_type','room_type','accommodates','bathrooms','bedrooms','beds','guests_included']]\n",
        "pd_model_data_x['accommodates'] = min_max_scaler.fit_transform(pd_model_data_x[['accommodates']])\n",
        "pd_model_data_x['bathrooms'] = min_max_scaler.fit_transform(pd_model_data_x[['bathrooms']])\n",
        "pd_model_data_x['bedrooms'] = min_max_scaler.fit_transform(pd_model_data_x[['bedrooms']])\n",
        "pd_model_data_x['beds'] = min_max_scaler.fit_transform(pd_model_data_x[['beds']])\n",
        "pd_model_data_x['guests_included'] = min_max_scaler.fit_transform(pd_model_data_x[['guests_included']])\n",
        "\n",
        "pd_model_data_x = pd.get_dummies(pd_model_data_x, columns=['neighbourhood_group_cleansed','property_type','room_type'])\n",
        "pd_model_data_y = pd_listings['price']\n",
        "\n",
        "# train and test - x and y\n",
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, y_train, y_test = train_test_split(pd_model_data_x,pd_model_data_y,test_size=0.10, random_state=789)\n",
        "\n",
        "# linear regression\n",
        "from sklearn.linear_model import LinearRegression\n",
        "lm = LinearRegression()\n",
        "lm.fit(X_train, y_train)\n",
        "\n",
        "coefficients = pd.DataFrame({'feature': X_train.columns, 'importance': lm.coef_})\n",
        "_ = coefficients.sort_values('importance', ascending=False)[:15].plot(x='feature', y='importance', kind='bar')"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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dwsSJEwEAV65cQWZmJm7fvo3Q0FBs2bKFU2Lg+0Kbm5uLkpISlJSUIC8vD7m5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\nDAaDIcX/AVgS05oKF0qRAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LpO1kf0zVc7C",
        "colab_type": "code",
        "outputId": "fe8ac877-bee0-4361-ef6b-6c191832030f",
        "cellView": "form",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 655
        }
      },
      "source": [
        "# @title Features with most weight by Decision Tree\n",
        "\n",
        "from sklearn import tree\n",
        "from sklearn.tree import DecisionTreeRegressor\n",
        "\n",
        "#Make the decision tree\n",
        "dtree = tree.DecisionTreeClassifier(\n",
        "    class_weight=\"balanced\",\n",
        "    min_weight_fraction_leaf=0.01,)\n",
        "dtree = dtree.fit(X_train,y_train)\n",
        "\n",
        "#Look at outputs\n",
        "importances = dtree.feature_importances_\n",
        "feat_names = X_train.columns\n",
        "tree_result = pd.DataFrame({'feature': feat_names, 'importance': importances})\n",
        "_ = tree_result.sort_values(by='importance',ascending=False)[:10].plot(x='feature', y='importance', kind='bar')\n"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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ISnZ2NrKzs6XfJyQkYOvWrTh9+rQSoyIKDZ0lhHy85syZo/Cxmzdv5jCSCrNn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\nAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    }
  ]
}