Sameer Chapter 1.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "N_UJtXMHZxHR"
      },
      "source": [
        "**Chapter 1 – The Machine Learning landscape**\n",
        "\n",
        "_This notebook contains the code examples in chapter 1. You'll also find the exercise solutions at the end of the notebook. The rest of this notebook is used to generate `lifesat.csv` from the original data sources, and some of this chapter's figures._\n",
        "\n",
        "You're welcome to go through the code in this notebook if you want, but the real action starts in the next chapter."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Pjxn2DLmZxHS"
      },
      "source": [
        "<table align=\"left\">\n",
        "  <td>\n",
        "    <a href=\"https://colab.research.google.com/github/ageron/handson-ml3/blob/main/01_the_machine_learning_landscape.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>\n",
        "  </td>\n",
        "  <td>\n",
        "    <a target=\"_blank\" href=\"https://kaggle.com/kernels/welcome?src=https://github.com/ageron/handson-ml3/blob/main/01_the_machine_learning_landscape.ipynb\"><img src=\"https://kaggle.com/static/images/open-in-kaggle.svg\" /></a>\n",
        "  </td>\n",
        "</table>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WgDLaTGeZxHT"
      },
      "source": [
        "# Setup"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yUFKBxRZZxHT"
      },
      "source": [
        "This project requires Python 3.7 or above:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "KT4jYrFJZxHT"
      },
      "outputs": [],
      "source": [
        "import sys\n",
        "\n",
        "assert sys.version_info >= (3, 7)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DONs8U1DZxHU"
      },
      "source": [
        "Scikit-Learn ≥1.0.1 is required:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "6nepApyRZxHU"
      },
      "outputs": [],
      "source": [
        "from packaging import version\n",
        "import sklearn\n",
        "\n",
        "assert version.parse(sklearn.__version__) >= version.parse(\"1.0.1\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mfDWa_aUZxHU"
      },
      "source": [
        "Let's define the default font sizes, to plot pretty figures:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "3cbwqT5JZxHV"
      },
      "outputs": [],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "plt.rc('font', size=12)\n",
        "plt.rc('axes', labelsize=14, titlesize=14)\n",
        "plt.rc('legend', fontsize=12)\n",
        "plt.rc('xtick', labelsize=10)\n",
        "plt.rc('ytick', labelsize=10)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0QN755aKZxHV"
      },
      "source": [
        "Make this notebook's output stable across runs:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "bxmWJjTzZxHV"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "\n",
        "np.random.seed(42)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hBLL3MzrZxHY"
      },
      "source": [
        "# Code example 1-1"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 495
        },
        "id": "VHenCRrfZxHY",
        "outputId": "75894b95-94b3-4ab5-dfa8-f57541ca6e3c"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[6.30165767]]\n"
          ]
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "from sklearn.linear_model import LinearRegression\n",
        "\n",
        "# Download and prepare the data\n",
        "data_root = \"https://github.com/ageron/data/raw/main/\"\n",
        "lifesat = pd.read_csv(data_root + \"lifesat/lifesat.csv\")\n",
        "X = lifesat[[\"GDP per capita (USD)\"]].values\n",
        "y = lifesat[[\"Life satisfaction\"]].values\n",
        "\n",
        "# Visualize the data\n",
        "lifesat.plot(kind='scatter', grid=False,\n",
        "             x=\"GDP per capita (USD)\", y=\"Life satisfaction\",  marker='s', color =\"Brown\")\n",
        "plt.title('Life Satisfaction vs GDP per capita')\n",
        "plt.axis([23_500, 62_500, 4, 9])\n",
        "plt.show()\n",
        "\n",
        "# Select a linear model\n",
        "model = LinearRegression()\n",
        "\n",
        "# Train the model\n",
        "model.fit(X, y)\n",
        "\n",
        "# Make a prediction for Cyprus\n",
        "X_new = [[37_655.2]]  # Cyprus' GDP per capita in 2020\n",
        "print(model.predict(X_new)) # outputs [[6.30165767]]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "e0yGX-VvZxHY"
      },
      "source": [
        "Replacing the Linear Regression model with k-Nearest Neighbors (in this example, k = 3) regression in the previous code is as simple as replacing these two\n",
        "lines:\n",
        "\n",
        "```python\n",
        "from sklearn.linear_model import LinearRegression\n",
        "\n",
        "model = LinearRegression()\n",
        "```\n",
        "\n",
        "with these two:\n",
        "\n",
        "```python\n",
        "from sklearn.neighbors import KNeighborsRegressor\n",
        "\n",
        "model = KNeighborsRegressor(n_neighbors=3)\n",
        "```"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "from sklearn.neighbors import KNeighborsRegressor\n",
        "model = KNeighborsRegressor(n_neighbors=3)\n",
        "\n",
        "# Download and prepare the data\n",
        "data_root = \"https://github.com/ageron/data/raw/main/\"\n",
        "lifesat = pd.read_csv(data_root + \"lifesat/lifesat.csv\")\n",
        "X = lifesat[[\"GDP per capita (USD)\"]].values\n",
        "y = lifesat[[\"Life satisfaction\"]].values\n",
        "\n",
        "# Visualize the data\n",
        "lifesat.plot(kind='scatter', grid=False, x=\"GDP per capita (USD)\", y=\"Life satisfaction\", marker='s', color =\"Brown\")\n",
        "plt.title('Life Satisfaction vs GDP per capita')\n",
        "plt.axis([23_500, 62_500, 4, 9])\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 478
        },
        "id": "FfhSSW2laRfg",
        "outputId": "541a1538-05da-4b7e-d498-787a3f91c773"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bwDQfjG0ZxHZ",
        "outputId": "e485dfeb-d008-4150-ac97-f8232602d817"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[6.33333333]]\n"
          ]
        }
      ],
      "source": [
        "# Select a 3-Nearest Neighbors regression model\n",
        "from sklearn.neighbors import KNeighborsRegressor\n",
        "\n",
        "model = KNeighborsRegressor(n_neighbors=3)\n",
        "\n",
        "# Train the model\n",
        "model.fit(X, y)\n",
        "\n",
        "# Make a prediction for Cyprus\n",
        "print(model.predict(X_new)) # outputs [[6.33333333]]\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Rsc48pAxZxHZ"
      },
      "source": [
        "# Generating the data and figures — please skip"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hr54M1qQZxHZ"
      },
      "source": [
        "This is the code I used to generate the `lifesat.csv` dataset. You can safely skip this."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Jx1JmvkQZxHZ"
      },
      "source": [
        "Create a function to save the figures:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "id": "sTolo3wwZxHZ"
      },
      "outputs": [],
      "source": [
        "from pathlib import Path\n",
        "\n",
        "# Where to save the figures\n",
        "IMAGES_PATH = Path() / \"images\" / \"fundamentals\"\n",
        "IMAGES_PATH.mkdir(parents=True, exist_ok=True)\n",
        "\n",
        "def save_fig(fig_id, tight_layout=True, fig_extension=\"png\", resolution=300):\n",
        "    path = IMAGES_PATH / f\"{fig_id}.{fig_extension}\"\n",
        "    if tight_layout:\n",
        "        plt.tight_layout()\n",
        "    plt.savefig(path, format=fig_extension, dpi=resolution)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6wHp4dSzZxHZ"
      },
      "source": [
        "## Load and prepare Life satisfaction data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ni7XukdcZxHZ"
      },
      "source": [
        "To create `lifesat.csv`, I downloaded the Better Life Index (BLI) data from [OECD's website](http://stats.oecd.org/index.aspx?DataSetCode=BLI) (to get the Life Satisfaction for each country), and World Bank GDP per capita data from [OurWorldInData.org](https://ourworldindata.org/grapher/gdp-per-capita-worldbank). The BLI data is in `datasets/lifesat/oecd_bli.csv` (data from 2020), and the GDP per capita data is in `datasets/lifesat/gdp_per_capita.csv` (data up to 2020).\n",
        "\n",
        "If you want to grab the latest versions, please feel free to do so. However, there may be some changes (e.g., in the column names, or different countries missing data), so be prepared to have to tweak the code."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Oa5rTQtLZxHa",
        "outputId": "9536f884-35a8-4075-f815-7df7da3c52ac"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading oecd_bli.csv\n",
            "Downloading gdp_per_capita.csv\n"
          ]
        }
      ],
      "source": [
        "import urllib.request\n",
        "\n",
        "datapath = Path() / \"datasets\" / \"lifesat\"\n",
        "datapath.mkdir(parents=True, exist_ok=True)\n",
        "\n",
        "data_root = \"https://github.com/ageron/data/raw/main/\"\n",
        "for filename in (\"oecd_bli.csv\", \"gdp_per_capita.csv\"):\n",
        "    if not (datapath / filename).is_file():\n",
        "        print(\"Downloading\", filename)\n",
        "        url = data_root + \"lifesat/\" + filename\n",
        "        urllib.request.urlretrieve(url, datapath / filename)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "id": "woIzBQKDZxHa"
      },
      "outputs": [],
      "source": [
        "oecd_bli = pd.read_csv(datapath / \"oecd_bli.csv\")\n",
        "gdp_per_capita = pd.read_csv(datapath / \"gdp_per_capita.csv\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NiNdncPjZxHa"
      },
      "source": [
        "Preprocess the GDP per capita data to keep only the year 2020:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "id": "dJJzkV0EZxHa",
        "outputId": "17b72233-2423-4cee-9898-00db8f5bac59"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                             GDP per capita (USD)\n",
              "Country                                          \n",
              "Afghanistan                           1978.961579\n",
              "Africa Eastern and Southern           3387.594670\n",
              "Africa Western and Central            4003.158913\n",
              "Albania                              13295.410885\n",
              "Algeria                              10681.679297"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-f95d0c0f-4361-4b0f-acd1-ef7c858b7648\" class=\"colab-df-container\">\n",
              "    <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>GDP per capita (USD)</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Country</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Afghanistan</th>\n",
              "      <td>1978.961579</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Africa Eastern and Southern</th>\n",
              "      <td>3387.594670</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Africa Western and Central</th>\n",
              "      <td>4003.158913</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Albania</th>\n",
              "      <td>13295.410885</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Algeria</th>\n",
              "      <td>10681.679297</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f95d0c0f-4361-4b0f-acd1-ef7c858b7648')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-f95d0c0f-4361-4b0f-acd1-ef7c858b7648 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-f95d0c0f-4361-4b0f-acd1-ef7c858b7648');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-a119873c-1554-4c09-9f14-c0b60e02df07\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-a119873c-1554-4c09-9f14-c0b60e02df07')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-a119873c-1554-4c09-9f14-c0b60e02df07 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "gdp_per_capita",
              "summary": "{\n  \"name\": \"gdp_per_capita\",\n  \"rows\": 224,\n  \"fields\": [\n    {\n      \"column\": \"Country\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 224,\n        \"samples\": [\n          \"Armenia\",\n          \"Heavily indebted poor countries (HIPC)\",\n          \"Lesotho\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"GDP per capita (USD)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 18838.30235782942,\n        \"min\": 731.063229715588,\n        \"max\": 110261.157353302,\n        \"num_unique_values\": 222,\n        \"samples\": [\n          42288.3480749219,\n          21608.4302710014,\n          4818.09473660531\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 23
        }
      ],
      "source": [
        "gdp_year = 2020\n",
        "gdppc_col = \"GDP per capita (USD)\"\n",
        "lifesat_col = \"Life satisfaction\"\n",
        "\n",
        "gdp_per_capita = gdp_per_capita[gdp_per_capita[\"Year\"] == gdp_year]\n",
        "gdp_per_capita = gdp_per_capita.drop([\"Code\", \"Year\"], axis=1)\n",
        "gdp_per_capita.columns = [\"Country\", gdppc_col]\n",
        "gdp_per_capita.set_index(\"Country\", inplace=True)\n",
        "\n",
        "gdp_per_capita.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6aSl-vK9ZxHa"
      },
      "source": [
        "Preprocess the OECD BLI data to keep only the `Life satisfaction` column:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 391
        },
        "id": "cltboEiZZxHa",
        "outputId": "da4e4e07-7f18-4743-84a3-4068ed42705d"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Indicator  Air pollution  Dwellings without basic facilities  \\\n",
              "Country                                                        \n",
              "Australia            5.0                                 NaN   \n",
              "Austria             16.0                                 0.9   \n",
              "Belgium             15.0                                 1.9   \n",
              "Brazil              10.0                                 6.7   \n",
              "Canada               7.0                                 0.2   \n",
              "\n",
              "Indicator  Educational attainment  Employees working very long hours  \\\n",
              "Country                                                                \n",
              "Australia                    81.0                              13.04   \n",
              "Austria                      85.0                               6.66   \n",
              "Belgium                      77.0                               4.75   \n",
              "Brazil                       49.0                               7.13   \n",
              "Canada                       91.0                               3.69   \n",
              "\n",
              "Indicator  Employment rate  Feeling safe walking alone at night  \\\n",
              "Country                                                           \n",
              "Australia             73.0                                 63.5   \n",
              "Austria               72.0                                 80.6   \n",
              "Belgium               63.0                                 70.1   \n",
              "Brazil                61.0                                 35.6   \n",
              "Canada                73.0                                 82.2   \n",
              "\n",
              "Indicator  Homicide rate  Household net adjusted disposable income  \\\n",
              "Country                                                              \n",
              "Australia            1.1                                   32759.0   \n",
              "Austria              0.5                                   33541.0   \n",
              "Belgium              1.0                                   30364.0   \n",
              "Brazil              26.7                                       NaN   \n",
              "Canada               1.3                                   30854.0   \n",
              "\n",
              "Indicator  Household net wealth  Housing expenditure  ...  Personal earnings  \\\n",
              "Country                                               ...                      \n",
              "Australia              427064.0                 20.0  ...            49126.0   \n",
              "Austria                308325.0                 21.0  ...            50349.0   \n",
              "Belgium                386006.0                 21.0  ...            49675.0   \n",
              "Brazil                      NaN                  NaN  ...                NaN   \n",
              "Canada                 423849.0                 22.0  ...            47622.0   \n",
              "\n",
              "Indicator  Quality of support network  Rooms per person  Self-reported health  \\\n",
              "Country                                                                         \n",
              "Australia                        95.0               NaN                  85.0   \n",
              "Austria                          92.0               1.6                  70.0   \n",
              "Belgium                          91.0               2.2                  74.0   \n",
              "Brazil                           90.0               NaN                   NaN   \n",
              "Canada                           93.0               2.6                  88.0   \n",
              "\n",
              "Indicator  Stakeholder engagement for developing regulations  Student skills  \\\n",
              "Country                                                                        \n",
              "Australia                                                2.7           502.0   \n",
              "Austria                                                  1.3           492.0   \n",
              "Belgium                                                  2.0           503.0   \n",
              "Brazil                                                   2.2           395.0   \n",
              "Canada                                                   2.9           523.0   \n",
              "\n",
              "Indicator  Time devoted to leisure and personal care  Voter turnout  \\\n",
              "Country                                                               \n",
              "Australia                                      14.35           91.0   \n",
              "Austria                                        14.55           80.0   \n",
              "Belgium                                        15.70           89.0   \n",
              "Brazil                                           NaN           79.0   \n",
              "Canada                                         14.56           68.0   \n",
              "\n",
              "Indicator  Water quality  Years in education  \n",
              "Country                                       \n",
              "Australia           93.0                21.0  \n",
              "Austria             92.0                17.0  \n",
              "Belgium             84.0                19.3  \n",
              "Brazil              73.0                16.2  \n",
              "Canada              91.0                17.3  \n",
              "\n",
              "[5 rows x 24 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-d8c74874-cadd-41a2-b63f-4e5adf1a04e6\" class=\"colab-df-container\">\n",
              "    <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>Indicator</th>\n",
              "      <th>Air pollution</th>\n",
              "      <th>Dwellings without basic facilities</th>\n",
              "      <th>Educational attainment</th>\n",
              "      <th>Employees working very long hours</th>\n",
              "      <th>Employment rate</th>\n",
              "      <th>Feeling safe walking alone at night</th>\n",
              "      <th>Homicide rate</th>\n",
              "      <th>Household net adjusted disposable income</th>\n",
              "      <th>Household net wealth</th>\n",
              "      <th>Housing expenditure</th>\n",
              "      <th>...</th>\n",
              "      <th>Personal earnings</th>\n",
              "      <th>Quality of support network</th>\n",
              "      <th>Rooms per person</th>\n",
              "      <th>Self-reported health</th>\n",
              "      <th>Stakeholder engagement for developing regulations</th>\n",
              "      <th>Student skills</th>\n",
              "      <th>Time devoted to leisure and personal care</th>\n",
              "      <th>Voter turnout</th>\n",
              "      <th>Water quality</th>\n",
              "      <th>Years in education</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Country</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Australia</th>\n",
              "      <td>5.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>81.0</td>\n",
              "      <td>13.04</td>\n",
              "      <td>73.0</td>\n",
              "      <td>63.5</td>\n",
              "      <td>1.1</td>\n",
              "      <td>32759.0</td>\n",
              "      <td>427064.0</td>\n",
              "      <td>20.0</td>\n",
              "      <td>...</td>\n",
              "      <td>49126.0</td>\n",
              "      <td>95.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>85.0</td>\n",
              "      <td>2.7</td>\n",
              "      <td>502.0</td>\n",
              "      <td>14.35</td>\n",
              "      <td>91.0</td>\n",
              "      <td>93.0</td>\n",
              "      <td>21.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Austria</th>\n",
              "      <td>16.0</td>\n",
              "      <td>0.9</td>\n",
              "      <td>85.0</td>\n",
              "      <td>6.66</td>\n",
              "      <td>72.0</td>\n",
              "      <td>80.6</td>\n",
              "      <td>0.5</td>\n",
              "      <td>33541.0</td>\n",
              "      <td>308325.0</td>\n",
              "      <td>21.0</td>\n",
              "      <td>...</td>\n",
              "      <td>50349.0</td>\n",
              "      <td>92.0</td>\n",
              "      <td>1.6</td>\n",
              "      <td>70.0</td>\n",
              "      <td>1.3</td>\n",
              "      <td>492.0</td>\n",
              "      <td>14.55</td>\n",
              "      <td>80.0</td>\n",
              "      <td>92.0</td>\n",
              "      <td>17.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Belgium</th>\n",
              "      <td>15.0</td>\n",
              "      <td>1.9</td>\n",
              "      <td>77.0</td>\n",
              "      <td>4.75</td>\n",
              "      <td>63.0</td>\n",
              "      <td>70.1</td>\n",
              "      <td>1.0</td>\n",
              "      <td>30364.0</td>\n",
              "      <td>386006.0</td>\n",
              "      <td>21.0</td>\n",
              "      <td>...</td>\n",
              "      <td>49675.0</td>\n",
              "      <td>91.0</td>\n",
              "      <td>2.2</td>\n",
              "      <td>74.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>503.0</td>\n",
              "      <td>15.70</td>\n",
              "      <td>89.0</td>\n",
              "      <td>84.0</td>\n",
              "      <td>19.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Brazil</th>\n",
              "      <td>10.0</td>\n",
              "      <td>6.7</td>\n",
              "      <td>49.0</td>\n",
              "      <td>7.13</td>\n",
              "      <td>61.0</td>\n",
              "      <td>35.6</td>\n",
              "      <td>26.7</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>90.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2.2</td>\n",
              "      <td>395.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>79.0</td>\n",
              "      <td>73.0</td>\n",
              "      <td>16.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Canada</th>\n",
              "      <td>7.0</td>\n",
              "      <td>0.2</td>\n",
              "      <td>91.0</td>\n",
              "      <td>3.69</td>\n",
              "      <td>73.0</td>\n",
              "      <td>82.2</td>\n",
              "      <td>1.3</td>\n",
              "      <td>30854.0</td>\n",
              "      <td>423849.0</td>\n",
              "      <td>22.0</td>\n",
              "      <td>...</td>\n",
              "      <td>47622.0</td>\n",
              "      <td>93.0</td>\n",
              "      <td>2.6</td>\n",
              "      <td>88.0</td>\n",
              "      <td>2.9</td>\n",
              "      <td>523.0</td>\n",
              "      <td>14.56</td>\n",
              "      <td>68.0</td>\n",
              "      <td>91.0</td>\n",
              "      <td>17.3</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 24 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d8c74874-cadd-41a2-b63f-4e5adf1a04e6')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-d8c74874-cadd-41a2-b63f-4e5adf1a04e6 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-d8c74874-cadd-41a2-b63f-4e5adf1a04e6');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-45c2c6e6-d1fe-4638-9997-f7119d473c75\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-45c2c6e6-d1fe-4638-9997-f7119d473c75')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-45c2c6e6-d1fe-4638-9997-f7119d473c75 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "oecd_bli"
            }
          },
          "metadata": {},
          "execution_count": 24
        }
      ],
      "source": [
        "oecd_bli = oecd_bli[oecd_bli[\"INEQUALITY\"]==\"TOT\"]\n",
        "oecd_bli = oecd_bli.pivot(index=\"Country\", columns=\"Indicator\", values=\"Value\")\n",
        "\n",
        "oecd_bli.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "x0XBkfv-ZxHb"
      },
      "source": [
        "Now let's merge the life satisfaction data and the GDP per capita data, keeping only the GDP per capita and Life satisfaction columns:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "id": "s_fOuk-kZxHb",
        "outputId": "2f38bf6c-b875-4472-86ad-73890c5f493d"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              GDP per capita (USD)  Life satisfaction\n",
              "Country                                              \n",
              "South Africa          11466.189672                4.7\n",
              "Colombia              13441.492952                6.3\n",
              "Brazil                14063.982505                6.4\n",
              "Mexico                17887.750736                6.5\n",
              "Chile                 23324.524751                6.5"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-86032f66-b872-4136-adf8-0c7c13ad0af7\" class=\"colab-df-container\">\n",
              "    <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>GDP per capita (USD)</th>\n",
              "      <th>Life satisfaction</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Country</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>South Africa</th>\n",
              "      <td>11466.189672</td>\n",
              "      <td>4.7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Colombia</th>\n",
              "      <td>13441.492952</td>\n",
              "      <td>6.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Brazil</th>\n",
              "      <td>14063.982505</td>\n",
              "      <td>6.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Mexico</th>\n",
              "      <td>17887.750736</td>\n",
              "      <td>6.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Chile</th>\n",
              "      <td>23324.524751</td>\n",
              "      <td>6.5</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-86032f66-b872-4136-adf8-0c7c13ad0af7')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-86032f66-b872-4136-adf8-0c7c13ad0af7 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-86032f66-b872-4136-adf8-0c7c13ad0af7');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-d7b1d818-52f8-4dd0-9609-e66c6aa803cd\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-d7b1d818-52f8-4dd0-9609-e66c6aa803cd')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-d7b1d818-52f8-4dd0-9609-e66c6aa803cd button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "full_country_stats",
              "summary": "{\n  \"name\": \"full_country_stats\",\n  \"rows\": 36,\n  \"fields\": [\n    {\n      \"column\": \"Country\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 36,\n        \"samples\": [\n          \"Luxembourg\",\n          \"Spain\",\n          \"Germany\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"GDP per capita (USD)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 19969.978699662497,\n        \"min\": 11466.189671582,\n        \"max\": 110261.157353302,\n        \"num_unique_values\": 36,\n        \"samples\": [\n          110261.157353302,\n          36215.4475907307,\n          50922.3580234484\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Life satisfaction\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.7758405579940837,\n        \"min\": 4.7,\n        \"max\": 7.6,\n        \"num_unique_values\": 21,\n        \"samples\": [\n          4.7,\n          6.9,\n          7.4\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 25
        }
      ],
      "source": [
        "full_country_stats = pd.merge(left=oecd_bli, right=gdp_per_capita,\n",
        "                              left_index=True, right_index=True)\n",
        "full_country_stats.sort_values(by=gdppc_col, inplace=True)\n",
        "full_country_stats = full_country_stats[[gdppc_col, lifesat_col]]\n",
        "\n",
        "full_country_stats.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GqFfY8ZJZxHb"
      },
      "source": [
        "To illustrate the risk of overfitting, I use only part of the data in most figures (all countries with a GDP per capita between `min_gdp` and `max_gdp`). Later in the chapter I reveal the missing countries, and show that they don't follow the same linear trend at all."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "id": "s2VfxQAOZxHb",
        "outputId": "0b32061c-9de3-41d5-ce9f-7a9a1da83ca5"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         GDP per capita (USD)  Life satisfaction\n",
              "Country                                         \n",
              "Russia           26456.387938                5.8\n",
              "Greece           27287.083401                5.4\n",
              "Turkey           28384.987785                5.5\n",
              "Latvia           29932.493910                5.9\n",
              "Hungary          31007.768407                5.6"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-14664d46-e9c7-4fe1-a14f-f9af4cc892e0\" class=\"colab-df-container\">\n",
              "    <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>GDP per capita (USD)</th>\n",
              "      <th>Life satisfaction</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Country</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Russia</th>\n",
              "      <td>26456.387938</td>\n",
              "      <td>5.8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Greece</th>\n",
              "      <td>27287.083401</td>\n",
              "      <td>5.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Turkey</th>\n",
              "      <td>28384.987785</td>\n",
              "      <td>5.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Latvia</th>\n",
              "      <td>29932.493910</td>\n",
              "      <td>5.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Hungary</th>\n",
              "      <td>31007.768407</td>\n",
              "      <td>5.6</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-14664d46-e9c7-4fe1-a14f-f9af4cc892e0')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-14664d46-e9c7-4fe1-a14f-f9af4cc892e0 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-14664d46-e9c7-4fe1-a14f-f9af4cc892e0');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-b696559d-419f-4d94-9f7f-63c122b1f5b7\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-b696559d-419f-4d94-9f7f-63c122b1f5b7')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-b696559d-419f-4d94-9f7f-63c122b1f5b7 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "country_stats",
              "summary": "{\n  \"name\": \"country_stats\",\n  \"rows\": 27,\n  \"fields\": [\n    {\n      \"column\": \"Country\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 27,\n        \"samples\": [\n          \"Spain\",\n          \"United Kingdom\",\n          \"Slovenia\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"GDP per capita (USD)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 9631.452318546564,\n        \"min\": 26456.3879381321,\n        \"max\": 60235.7284916969,\n        \"num_unique_values\": 27,\n        \"samples\": [\n          36215.4475907307,\n          41627.129269425,\n          36547.7389559849\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Life satisfaction\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.7656068482934606,\n        \"min\": 5.4,\n        \"max\": 7.6,\n        \"num_unique_values\": 19,\n        \"samples\": [\n          5.8,\n          6.1,\n          6.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 26
        }
      ],
      "source": [
        "min_gdp = 23_500\n",
        "max_gdp = 62_500\n",
        "\n",
        "country_stats = full_country_stats[(full_country_stats[gdppc_col] >= min_gdp) &\n",
        "                                   (full_country_stats[gdppc_col] <= max_gdp)]\n",
        "country_stats.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Full country status"
      ],
      "metadata": {
        "id": "QcMz8pL5hDXd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "all_countries = full_country_stats.copy()\n",
        "all_countries.reset_index(inplace=True)\n",
        "\n",
        "fig, ax = plt.subplots(figsize=(16, 8))\n",
        "all_countries.plot(kind='scatter',\n",
        "                   ax=ax,\n",
        "                   x=\"GDP per capita (USD)\",\n",
        "                   y=\"Life satisfaction\",\n",
        "                   color='brown',\n",
        "                   s=100,\n",
        "                   marker='s',\n",
        "                   edgecolor='black')\n",
        "\n",
        "# Annotate each country\n",
        "for idx, row in all_countries.iterrows():\n",
        "    ax.annotate(row['Country'],\n",
        "                (row['GDP per capita (USD)'], row['Life satisfaction']),\n",
        "                fontsize=8,  # Adjust font size for better readability\n",
        "                rotation=0)  # Rotate text to avoid overlap\n",
        "\n",
        "# Add title and labels\n",
        "plt.title('Life Satisfaction vs GDP per capita', fontsize=20)\n",
        "plt.xlabel('GDP per capita (USD)', fontsize=15)\n",
        "plt.ylabel('Life satisfaction', fontsize=15)\n",
        "plt.grid(False)  # Add grid for better readability\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 724
        },
        "id": "Vq-EMqmRg2k3",
        "outputId": "0482d202-0058-4428-aac9-3c5e43c3c2e0"
      },
      "execution_count": 75,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1600x800 with 1 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAABTIAAALLCAYAAADKan0LAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAADkrUlEQVR4nOzdd3yN5//H8deRTQZqp0YQFIlIiBkzrRpVVClqFtWt1UVVtdWhfKuqVdXWaNHWaoxq1Sy1SWK2SRAztoggyck4vz/I/UvIlsjB+/l4nMcj576v67o/933unHPyyTVMFovFgoiIiIiIiIiIiIgVK1LYAYiIiIiIiIiIiIhkR4lMERERERERERERsXpKZIqIiIiIiIiIiIjVUyJTRERERERERERErJ4SmSIiIiIiIiIiImL1lMgUERERERERERERq6dEpoiIiIiIiIiIiFg9JTJFRERERERERETE6imRKSIiIiIiIiIiIlZPiUwREZHbUKVKFUwmEwMGDMi0TFxcHO+//z716tWjWLFimEwmTCYTw4cPv2Nx3s1Sr9fYsWMLpP3ff/+ddu3aUapUKWxsbDCZTBQvXrxAjlXQcnI/isj9qVWrVphMJlq1alXYoYiIiOSZEpkiInJfW79+fYEmyhITEwkMDGTs2LHs2bOHa9eu5fsxcuvChQtMnDiRwMBAypUrh4ODA46OjpQvX54mTZrw3HPP8dNPP3H27NnCDrXATZ06lU6dOvHXX39x4cIFUlJSCjskuUvt3r2b9957jxYtWlCpUiWcnJxwcnKifPnytGjRgtdee43169dneY+NHTvWeD9K+3BwcKBMmTJ4enrSoUMHxowZw4YNG3IU15EjRzJs02Qy4ejoiLu7O+3bt+fbb7+1ivcnERERkawokSkiIlKAFixYwObNmwEYMGAA69atY+/evezdu5e33377jsezdOlSatasyRtvvMGaNWs4c+YMZrOZhIQETp8+zdatW5k2bRr9+vWjc+fOBRaHNfQcvHbtGqNGjQKgVq1aLFy4kJCQEPbu3cuWLVsKLa6bqReVdTtx4gTdunWjfv36fPDBB2zcuJHjx48THx9PfHw8p0+fZuPGjUyaNInWrVtTrVo1Zs2alatjmM1mzp07x8GDB/njjz/48MMPadmyJbVr12bBggV5jj0hIYGoqCj+/PNPhg0bho+PD//++2+e25O7W0H3fhcREckPtoUdgIiIyN3syJEjWe5fvXo1AOXKleP777/HxsbmDkSVsY0bN9K9e3cSExOxsbGhV69ePPbYY3h4eGBjY8OZM2cIDg7mzz//NJKv1sBisRRIuzt37iQmJgaAiRMn0rFjxwI5zp2U3f0o+WvXrl089thjnDp1CoAHH3yQnj170qxZM8qVK4ednR1nz55lz549/Pnnn2zYsIEjR44wbNiwbJP4M2bMoGHDhsD134GYmBjOnTvHzp07Wb58OXv27OHff/+lR48ePPPMM0yfPp0iRbLuo/D4448zbtw443lcXBx79+7liy++YO/evURERNC+fXsOHDhA0aJFb+/iiNVZv359YYcgIiJy25TIFBERKUAnT54EoGrVqoWaxAR47bXXjCTmn3/+SWBg4C1l2rdvzzvvvMPRo0dZs2ZNIUR556S+NgA1atQoxEjkbnTq1Ck6duzImTNnABgzZgyjRo3CwcHhlrIdOnTg7bffZs+ePYwePZq//vor2/Y9PDyoW7fuLdu7du3KRx99xLJlyxg0aBDnz5/nhx9+oGTJknz22WdZtlm8ePFb2mzYsCF9+vShTZs2bN68maNHj/LDDz/w0ksvZRujiIiIyJ2moeUiIiIFKCEhAQA7O7tCjSMqKoqdO3cC1xMhGSUx06pcuTKDBg26E6EVmtTXBgr/9ZG7z5AhQ4wk5vjx43n//fczTGKm5e3tzdKlS/nuu+9u+/iPPfYYmzdvxsXFBYAJEyYQHBycp7YcHBz48MMPjed//vnnbccnIiIiUhCUyBQREbkNGc31mHZxjb///huAv//+O90iG1WqVMmwvXXr1tG/f3+qVq1K0aJFcXV1xcvLizfeeIOoqKg8x3ns2DHj5+rVq+e5HYCrV6/y66+/MnjwYHx8fHBzc8POzo7SpUvTsmVLJk6cyJUrVzKsmzrf49GjRwGYPXv2LQuQ3DwXZHbztl26dImPPvqIJk2aUKJECSOW2rVr07VrV7755hsj4ZQ2hoEDBxrbPDw80sWQdghmdHQ0M2fO5Omnn6Z27do4Oztjb29PuXLlaNeuHdOnT8dsNufo2p07d44PPviAZs2aUaZMGezs7ChRogSNGjXizTffZM+ePUbZAQMGZHkPZXQf5XTu0WXLltG9e3cefPBBHBwceOCBB2jSpAmffvpppq8dwKxZs4xjHzlyhJSUFKZPn07Tpk0pUaIExYoVw9vbm48++ijPC8f8+OOPxjFWrVqVbflnn33WWBAnOjo63b6oqCjefvttfH19jfu0bNmyeHl50atXL2bNmsXly5dzHePu3bv5/fffges9Gt94441c1e/bt2+uj5kRT09PPv30U+N52p9zy9/f3/g59fczN26eyzUsLIyhQ4fi4eFhLCbWo0cPtm7dmqP2Tp8+zTvvvEODBg0oWbIkDg4OVKxYkR49ehhTdmQk7ftv6lykixcvpkOHDlSoUAFbW9s8zzebkpLCzz//zBNPPJFuQacaNWrQp08fFi5cSGJi4i319u3bx7hx42jXrp3xO+fs7Iynpyf9+/fP9pqkXQQKrr/nvffee9SpUwdnZ2dKlixJ69at+fnnn7NsJ7P5dlPfN1K9//77t7zX3PyecurUKaZOnUr37t3x9PSkWLFiODg44O7uzuOPP86vv/6qxdNERKRgWERERO5j69atswAWwPLee+/lun7lypUtgKV///7GtsjISKPNzB6VK1dO105cXJzlqaeeyrJOsWLFLEuXLs3Tee7atcto5/HHH89TG6latmyZ7fl5eHhY/v333zzVbdmyZbo6Wb0+Bw4csFSoUCHbNqdMmZKrGNatW2eUT32Ns3rUr1/fcurUqSyv25w5cyzFihXL8X3Rv3//XN9HGd2PacXFxVm6du2aZZsVKlSwhISEZFh/5syZRrn9+/db2rZtm2k7/v7+litXrmR5TTJy+fJli5OTkwWwDBgwIMuyZrPZUrJkSQtg6dKlS7p9GzZssLi6umZ7DZctW5brGF999VWj/o8//pjr+pl57733MrwHs3L16lVL8eLFLYClaNGiFrPZnG5/2vejzO4Li8ViiY+PN8rVqlUr17Gn/l61bNnSsmLFikzv9SJFilgmTZqUZVs5+V155plnLImJibfUTXu+M2bMsPTt2zfb95iciIyMtPj4+OTqvcNiSf8Zk9Xj7bffzvTYae+Lw4cPW6pVq5ZpOz169Mjwulgs6V+jtHLyHpf23klKSrIUKVIk2zoPP/ywJTY2NtfXWkREJCuaI1NERCSfubu7s3fvXgAGDhzIzp07adCgATNnzjTK2NvbGz9bLBa6d+9u9PB67LHH6NGjB1WrVqVIkSJs376d//3vfxw7dozu3buzadMmGjRokKuYHnroIRwdHYmPj2fp0qXMnTuXPn365On8kpKS8PLyonPnzjRo0IAKFSpgsVg4evQov/32G/PnzycyMpIuXboQGhqKo6OjUXfmzJlcvXqVdu3aERUVdcviIwDFihXLcSx9+/YlKioKOzs7hgwZQvv27SlXrhwpKSmcOHGCrVu38ttvv6WrkxrDkiVLGD16NAArV66kQoUKRhkPDw/j5+TkZBo1akSnTp2oX78+ZcuWxWw2ExkZyZw5c/jzzz8JCQnhqaeeynQxjZ9++ol+/foB4OjomC7WK1eusGfPHpYuXUpERIRR56OPPuL111/P9B6C9PdRTvTv39+4HvXq1WPEiBE89NBDXLx4kV9++YVZs2YRFRVF27Zt2bNnD+7u7pm2NWTIELZu3Ur//v3p0aMH5cqV49ixY3z22Wds2bKF7du3M27cOD755JNcxeji4kLnzp359ddfWbx4Md988026eyitP/74g4sXLwKku58TEhJ46qmnuHz5Mi4uLjz33HO0bt2aMmXKGK/d5s2bb7k3ciq1lyzAo48+mqc28kvRokVp2rQpK1as4Nq1awQHB9OoUaNct5P6ngWk+13IraioKHr37o2trS0ff/yx0ftv3bp1jB8/nsuXL/Pqq69SpUoVunTpckv9+fPn07dvXywWC1WrVuXFF1+kdu3alC5dmiNHjvDDDz+wYsUKfvjhB1xdXfn8888zjeWLL75gz549BAQE8Nxzz1GjRg0uXbqU60Wxzpw5Q7NmzYxe8W3atKF///7UqlULk8lEZGQka9euzXAF+aSkJIoVK0bHjh1p06YNtWrVwtXVlbNnz7J//36+/PJLjh49yqeffkqNGjXS9RTPSM+ePYmMjGTYsGF0794dNzc39uzZw/jx4wkPD2f+/PlUqFCBSZMm5fj8/vrrL8xmM15eXgA899xzPP/88+nKlChRwvjZcmMBtjZt2tC+fXu8vLwoXbo0sbGxHD58mO+++44tW7awatUqXnjhBWbPnp3jWERERLJVuHlUERGRwlUQPTLTyqwHTFrTp0+3ABY7OzvLH3/8kWGZixcvWurUqWMBLM2aNct1nBaLxfLiiy+m6y1Tu3Zty5tvvmn57bffLCdPnsxxO+Hh4VnuX7VqldFb5/vvv8+wTHbXLa3MXp9Dhw4Z+9L2uLxZSkqK5eLFi7dsT9u7MDIyMtP62Z3vjBkzjHZWr159y/6oqChL0aJFLYClTJkylr1792ba1rFjx27ZlpN7KFVW13X58uVGnG3btrUkJCTcUib1XuRGz66bpb1mgOWnn366pUx8fLylbt26FsDywAMPZNo7LCtLly41jrFgwYJMy/Xs2dMCWFxdXS1xcXHG9jVr1uSox2ViYqIlJiYm1/HZ2tpaAMuDDz6Y67pZyUuPTIvFYhk9enSmPURz2iOze/fuRrkPPvgg17Gn7ens5uZmOXDgwC1l9u3bZ/SSdXd3v6X36Llz5yxubm4WwDJo0KBM751Ro0YZvTv/+++/TM8XsPTr18+SkpKS6/NJK20v5vHjx2daLjY29pb3mnPnzlmio6MzrZOQkGB5+OGHLXC9h3VSUtItZdLeF4Bl3rx5t5S5fPmypV69esZ1yeh9Jrv3kpx+FqakpFgiIiKyLDNmzBgLYDGZTNm+h4qIiOSG5sgUEREpRBaLhfHjxwPw8ssvZ9q7q0SJEkyYMAGATZs2peu5l1OfffYZ7du3N54fOHCAzz77jK5du+Lu7m4s8JNZr8JUnp6eWe4PDAykc+fOAAQFBeU6zpw6ffq08XOLFi0yLWcymdL1Jsqt7M534MCB+Pj4ABmf75QpU4z5IqdPn57hStSpKlasmOc4s/P1118D1xc2mjlzZoa9OYcMGWIsBLV48WJOnTqVaXvdunXj6aefvmW7g4MDL774IgAXLlzgwIEDuY710Ucf5YEHHgBg7ty5GZa5cuUKS5cuBeCJJ55I12szp/eGra0trq6uuYotJiaGpKQkAEqXLp1l2WPHjrFv374MH2fPns3VcbOSeq2AW+YJzUp8fDw7duyge/fuLFy4EABXV1eeffbZ24rn3Xff5aGHHrple506dXjnnXcAOHnyJEuWLEm3/5tvviEmJgZ3d3emTp2KrW3Gg8fef/993N3dSUlJ4ccff8w0juLFi/PVV1+lm/8xt8LCwozf6y5duvDmm29mWtbZ2fmW95pSpUpRvHjxTOvY29sb7+1Hjx4lNDQ0y3g6depEr169btnu4uLC9OnTgetzeU6bNi3Ldm6HyWTKdq7lMWPGUKpUKSwWi/F7KiIikh+UyBQRESlEBw4c4NChQwB07949y7JpEzJbtmzJ9bGcnJz4/fff+fXXXwkICLjlj/tjx44xc+ZMWrduzaOPPsq5c+dy1O65c+eIiIhIl6RJTfDs3r0713HmVPny5Y2fUxf1KGgWi4XTp08THh6e7nxTh2BndL7Lly8HoGrVqkaC905LSkoyhkM/8sgjWSZMhwwZYtTJKqmd1dQEfn5+xs+HDx/OZbTXk61PPvkkcH34+KVLl24p89tvvxEXF5dhLGnvjZuH49+u2NhY4+fspkF4+eWX8fLyyvAxderUfIvJ2dk5w/hudvPiWk5OTvj7+7No0SKjnQULFlCmTJk8x2Iymejfv3+m+wcOHGi899y8aE9qwqtTp05ZrgBva2tLkyZNgKzfCx977DFjVfe8+v33342h1K+++upttQXXpz04duwYBw4cMN4/UtuH7N8zsxp67u/vT506dYBbr21BSklJISoqirCwMOOc/v33Xx588EGgYD8HRETk/qM5MkVERArRzp07jZ9T/zDPibQ9znLDZDLRo0cPevTowfnz59m0aRM7duxg+/bt/PPPP0ZiaOXKlbRu3ZqtW7emS5Kk2rRpE19++SWrV6825ijMyPnz5/MUZ054eHgQEBDAxo0bmTRpEitXruSJJ56gVatWNG7cmKJFi+bbsX7//Xe++eYbNmzYkGWi6ObzTUxMZN++fQA0b978tnqG3Y7Dhw8bvUKzmz8x7f7U2DNSq1atTPeVLFnS+Dmr65WVPn36MG3aNBISEli4cCGDBw9Ot3/evHnA9fkcW7dunW5f8+bNqVq1KocPH2b48OHMnTuXrl270qJFCxo2bJjruUXTSpsYu3r1ap7byU9pr3Fue5jC9Wv4+OOP88Ybb6SbHzYvPDw8KFWqVKb7S5cuTZUqVYiMjEw3L2dycrLRG/Hbb7/l22+/zdHxsnov9Pb2zlnQWQgJCQGuJ9cbN26cpzauXr3Kl19+yS+//ML+/ftJTk7OtGx275kNGzbMcr+/vz/79+8nPDwcs9l8W/d6ViwWC3PnzuWHH35g27ZtxmdHRgryc0BERO4/6pEpIiJSiPI6vDQ1KXU7SpUqZSy289dff3H27FkmTpxoDNHdv38/X3zxxS31xo4dS/PmzZk/f36WSUwgyz9u88PPP/9sJIAPHDjAhx9+SNu2bSlevDgtWrRg2rRpxMfH57l9i8XC4MGD6dSpE7///nu2Sbmbz/fixYtGb6u0vQTvtLSvU3a97cqVK5dhvZtllSguUuT/v2JmlbTJSrNmzahcuTJw6/Dys2fPGj3OnnrqqXTHg+tJp2XLlhnDm3fs2MGoUaNo3rw5xYsX59FHH2XevHl5is3Nzc0Y8pxdr+WgoCAsFovxWLduXa6PlxNpE0Vpk8g3e/zxx9m7d6/xCA8P5/z585w8eZKpU6fedhITsr+/AMqWLQukv78uXrxoDNnPjazeC29nSolUqde2ZMmSeUoKHjlyBC8vL0aNGsWePXuyveeye8/M7vqmXluLxZKraQZyIz4+no4dO9K3b1/Wr1+fbcwF/TkgIiL3FyUyRUREClHaP2qXLVuWLsmQ1ePmFWXzg7OzMyNGjEiXvLx5Fd41a9bw/vvvA9eHSk+dOpU9e/Zw6dIlEhMTjYTNu+++m+/xZcTd3Z3NmzezevVqnn/+eerUqYPJZCIxMZGNGzfy3HPPUbduXcLDw/PU/owZM/jhhx8A8PHxYdasWfz7779cvnyZpKQk43z79u0LkG6IqLUqrF6huWUymejduzcAGzZs4OTJk8a++fPnG0mvzIa4165dm7179/Lbb78xaNAgY06/uLg4Vq5cSZ8+fWjUqFGe/pmQ2tPvxIkTVtHbLLXXIEDNmjUzLVe8eHHq1q1rPDw9PdPNr5kf8np/pX0vHDx4cI7fC//6669M27SxsclTLPmpb9++REZGYjKZGDRoEH/99RfHjx8nPj6elJQULBZLunPP7j3EGn5/P/roI/744w8AWrZsyfz58zl48CBXrlwhOTnZeF8MCAgA7o73RRERuXtoaLmIiEghSptESE0yFLaBAwfy4osvkpSUxMGDB9Pt++6774DrPZ22bt2a6WIn2fXUzG9t27albdu2wPUFZlavXs306dNZu3Ythw4domfPnumSPTmVer7Vq1dn8+bNODk5ZVgus/MtWbIkRYoUISUlJcuFcwpa2l56Z86cybJs2qG6WfXuuxP69OnDJ598QkpKCj///DOvv/468P/DymvVqoWvr2+m9W1sbOjSpQtdunQB4NSpU/z55598/fXX7Nq1i127dvHss8/y22+/5Squli1bEhwcDMCff/6Z4aJHd8q1a9fYvHkzcP2fEakLTxWW7O6vtGXS3l9pf7ZYLFbxXggYw+QvXryY66Ha//33H//88w8Ao0aNYty4cRmWy8375ZkzZ7Kc4zb12t7uImeZsVgsfP/99wAEBASwdu3aW3pEp7rTnwMiInJ/UI9MERGRQlS/fn3j502bNhViJP/P3t7eSLDe3Ptn//79ALRu3TrLFZvTzv2ZkYLsVfTAAw/Qs2dP1qxZYyyuExoamqeV3lPPt3PnzpkmMS0Wi5HUupmdnZ2RkNm4cWOeeiblx7WqWrWqMRR827ZtWZbdvn278XNhJ5Pq1KlDvXr1gP9PXkZGRhoLvGS14FBGypcvz8CBA9myZYuRAF2+fHmuh77269fP+Pmrr74q1B5nM2fOJCYmBri+SE5mK33fKZGRkVy4cCHT/efOnePIkSNA+vvL3t7eWKjGWt4LAeM+SUxMzPUia6nvHwA9e/bMtFx275dp7dixI0f7PT09C2R+zIsXLxr/7HjyySczTWJeuXKFsLCwfD++iIiIEpkiIiKFyNfX11jZdfr06bc1n2NWcpNoOX78uDHctmrVqun2pQ7nzWqRk5CQkGyTZanzcCYkJOQ4rrxI7aUJeVtwIifnu2TJkix7Wz722GPA9QTPkiVLch1DflwrW1tbWrZsCcCqVas4ceJEpmVTe1vZ2trSqlWrPB8zv6QmK0NCQvj333+NhCZgDD3PLTs7O+N6JCUlZbgqelZ8fHxo3749cD0xPGnSpDzFcbsiIiIYOXKk8fztt98ulDjSslgs/Pjjj5nunzVrlvF+FBgYmG5f6j8e/vvvP1auXFlwQeZCx44djX8mZDRncFbSzvmZ1XvItGnTctzm7NmzM923Y8cOY4Gum69tTuTkvSan5/T999/nac5TERGR7CiRKSIiUoiKFCnCqFGjgOsrS/fr1y/LPyIvX77MV199levjHDhwgEceeYQNGzZkWS4+Pp6hQ4caiYbHH3883X5PT08A/vnnn1uGncP13lap80VmJXXhm0OHDuUo/oyEhoYaqxxnxGKxGAvCmEwmqlSpkutjpJ7vsmXLMhwmeejQIV544YUs23jxxRcpVqwYAM8++2yWK4FnlGBMvVaHDx++rZ5/qXGazWaeeeYZEhMTbykzY8YMY87Bbt26FeoCRal69eplJJLmzp3Lzz//DECTJk1uSbSn2rhxY4b3Zyqz2czff/8NXB+OnVXv4sx89913Rr3XX3+dDz/8ELPZnGWd/Fx8Zfny5TRt2tRYgGrkyJFG79XC9uGHH2bYG+/ff//lo48+Aq7f1ze/v7zyyis4OzsD16e4SNujMSO///47e/bsyaeoM1ajRg26du0KXF+8acKECZmWvXr1arrXOPX9A64ncDPyzTff5OofHEuXLmX+/Pm3bL9y5QrPPvsscP1zJfXn3MjJ+3Lp0qUpXrw4cH2xtYw+r3bs2HHH5kkWEZH7j+bIFBERuSE0NDTTPzbTatOmDZUqVcq34w4bNoxVq1bx22+/sWDBAoKDg3n22Wfx9/fHzc2Ny5cv899//7F+/XqWLl2Ko6MjL774Yq6OYbFYWLVqFatWraJ69eo8/vjjNGrUiAcffJCiRYty/vx5tm/fznfffUdkZCQAlSpVMuYkTNWvXz+WLVvG1atXadmyJW+//TZ+fn4AbN68mc8//5zTp0/TpEmTLIdhNm3alHXr1rFjxw4+/fRT2rdvbyT7nJyccHd3z/acQkNDGThwIA0bNuSxxx7D19eXcuXKkZiYSGRkJDNnzmTVqlXA9Z5eeUnK9evXjzfeeIOoqCiaNGnCW2+9Rd26dYmPj2ft2rV88cUXJCQk4Ovrm+nw8nLlyvHNN9/Qr18/zp49i7+/P0OGDKF9+/aUK1eOK1eusG/fPpYuXUpYWNgtSYSmTZsyc+ZMzp49y2uvvcbTTz+Nm5sbcL1nYerK3tnp2LEjTz75JAsWLOCvv/6icePGvPbaa9SqVYvo6Gh++eUXZsyYAVyfr/Dzzz/P9fUqCA8++CAtW7Zk/fr1fP3110bvyayGla9Zs4YPP/yQgIAAOnbsiLe3N6VLlyYuLo7w8HCmTZtmvF7PPPNMnoZju7u7s3z5cjp37syZM2cYM2YM33//PU899RTNmjWjbNmyODg4cOnSJcLCwvjjjz9Yvny5UT+rVd/heg/e1PkZLRYLly9f5ty5c+zcuZNly5alS+ANHTrUSBAWturVq3Pu3DkaN27MW2+9ZfTqXb9+PZ9++qkxDH7KlCm3DH0uW7Yss2fPpnv37pw6dYoGDRowYMAA2rdvz4MPPkhiYiInTpxg+/btLFy4kMOHD7Ns2TJj8aWCMnXqVLZu3UpUVBRvvvkmf/75JwMGDKBWrVqYTCaOHDnCunXr+OWXX1i0aJFxzvXr16du3brs27ePb7/9lujoaPr27Uv58uU5ceIEc+bMYeHChTRr1izHw+kbNGhA7969+fvvv+nevTuurq7s2bOH8ePHG8njF154IU/XpGnTpkRGRrJ06VK+/fZbmjVrZvTSdHV1pUyZMhQpUoQ+ffrw9ddfs2fPHpo3b85rr72Gp6cnMTExrFixgqlTp+Ls7EyFChXyvNCaiIhIpiwiIiL3sXXr1lmAXD1+++03o37lypUtgKV///4Ztt+yZUsLYGnZsmWWcZjNZstzzz1nMZlM2R7fw8Mj1+d5+PBhS4kSJXJ8jg0aNLAcOnQow7YGDhyYaT0bGxvLF198YXnvvfeMbRk5ceKEpWTJkhm2cfO1St3+3nvvpds+c+bMHJ1L06ZNLefPn78lhrT1IyMjM4zTbDZbHnnkkUzbdnJyssyfP9/Sv39/C2CpXLlypq/BrFmzLE5OTlnGmlH92NhYS9WqVXNUPrv7MS4uztK1a9csY6hQoYIlJCQkw/o5uWYWi8USGRlplJs5c2am5XLqu+++Sxejra2t5ezZs5mWT3v/ZfV4/PHHLdeuXbut2I4ePWrp3Llzjn+3KleubJkxY4YlJSUlz3GnPmrXrm1ZtGhRlvGlfS0yuy/yQ9r3uuXLl1uKFi2aYcxFihSxTJw4Mcu2li5dmun7w81trV27NtPzzY97L9WhQ4csdevWzTamdevWpasXEhKS5Xuvl5eXJSoqKtP3OYsl/X1x+PBhi4eHR6btPfHEE5bExMQMzyG7z6OQkBCLg4NDhu2mvXcuXbpk8fHxyTSGkiVLWv7+++8cf/6JiIjkhoaWi4iIWAE7OzumTp3K7t27eemll/Dy8sLNzQ0bGxvc3Nzw8fHhmWeeYeHChfz777+5bt/Dw4MzZ86wevVqRo0aRWBgIJUqVcLJyQlbW1uKFy+Ol5cX/fv3Z+nSpWzbti3TYbszZszgp59+IiAgABcXFxwcHKhcuTJ9+/Zl8+bNvPLKK9nG4+7uzvbt23nmmWeoXr260esnN3r16sWKFSt49dVXad68OR4eHhQtWhR7e3sefPBBOnfuzNy5c9m4cWO61eFzw87Ojt9//50vv/ySBg0aULRoUZycnKhevTrDhg0jODiYJ598Mkdt9e/fn0OHDvHOO+/g5+dH8eLFsbGxoUSJEjRu3JhRo0bx559/3lLP2dnZuK4PPfRQtj35suLo6MjixYtZunQp3bp1o0KFCtjb21OiRAkaNWrEJ598QlhYWKGvfH2z7t274+DgYDx/5JFHshwO/vrrr7No0SKee+45GjduTKVKlXB0dMTR0ZEqVarQo0cPli9fTlBQUKaLOOVUpUqVWLJkCSEhIYwePZpmzZrh7u5uHK98+fI0bdqUV155hVWrVnH48GEGDhyYq0Wc7OzseOCBB6hatSrt27fn3XffZePGjezfv59u3brdVvwFoWPHjuzcuZOBAwdSuXJl7O3tKVOmDE888QT//PMPI0aMyLL+Y489RmRkJBMnTqRNmzaULVsWOzs7nJyc8PDwoFOnTnz++eccOXKE1q1b35Fzqlq1qtFrv2PHjpQvX96IqUaNGvTr148lS5YQEBCQrp6Pjw+hoaEMGzaMypUrY2dnR8mSJfH392fixIls3749V73FPTw82LVrF6NGjTLeD9zc3GjRooXRwzOvCz75+PiwZcsWevXqRaVKldL9zqXl5ubGpk2b+PDDD/Hy8sLR0RFnZ2ceeughXn/9dXbv3k2LFi3yFIOIiEh2TBZLIS6zKCIiIiIid71WrVrx999/G9MASP4ZO3Ys77//PsBtzZMrIiJyL1CPTBEREREREREREbF6SmSKiIiIiIiIiIiI1VMiU0RERERERERERKyeEpkiIiIiIiIiIiJi9ZTIFBEREREREREREaunVctFRERERERERETE6tkWdgDWIiUlhaioKFxcXDCZTIUdjoiIiIiIiIiIyF3FYrEQGxtLhQoVKFIk/weCK5F5Q1RUFBUrVizsMERERERERERERO5qx48f58EHH8z3dpXIvMHFxQW4fqFdXV0LORoREREREREREZG7y+XLl6lYsaKRZ8tvSmTekDqc3NXVVYlMERERERERERGRPCqoaRu1armIiIiIiIiIiIhYPSUyRURERERERERExOopkSkiIiIiIiIiIiJWT4lMERERERERERERsXpKZIqIiIiIiIiIiIjVUyJTRERERERERERErJ4SmSIiIiIiIiIiImL1lMgUERERERERERERq6dEpoiIiIiIiIiIiFg9JTJFRERERERERETE6imRKSIiIiIiIiIiIlZPiUwRERERERERERGxekpkioiIiIiIiIiIiNVTIlNERERERERERESsnhKZIiIiIiIiIiIiYvWUyBQRERERERERERGrp0SmiIiIiIiIiIiIWD3bwg5ARERE7g4RERHExsZmur9Tp07Y29vj4OAAwNWrV2nZsiVjx47F09Mz03pHjhzBx8eHS5cu5Wu8pUqVYufOnVSpUiVf27UGOX0t7O3tiYuLo1q1avTv35/mzZtn+VoUhoJ6/fMqp9d2/vz52Npe/yr99NNPM2rUKJ5++uk7FaaIiIjIfclksVgshR2ENbh8+TJubm7ExMTg6upa2OGIiIhYlYiICGrUqJHn+uHh4Zkm0JTIzJ2CfC3utKSkJE6cOGE1icw7dW2TkpKMJKiIiIjIvaSg82v6BiUiIiLZSu2h9ry7OxXs7TMsM/H4cfqUKUP5Gz0yg2NjCblyhcj4eNatW0fXrl1p0aIFmzZtIikpidmzZ9OgQYNb2unTpw9hYWGYzWYqVqzIDz/8QLly5YyE5yuvvMLy5cuJiYnhyy+/pEOHDgAsXbqUt956Czs7Ox599NECuhKFLy+vBcCic+cIuXKFixcv8vbbb7N27VrMZjM1atTg22+/pUSJEgwYMAAHBwcOHjzI8ePHqVu3Lr/88gv29vaMHTuWAwcOEBcXR1hYGDVq1ODTTz9lxIgRREZG4ufnx9y5cylSpAjz5s1j8uTJmM1mUlJSGDduHI899hgArVq1wtvbmx07duDk5MSMGTOMGM1mM4MGDaJYsWJMnToVGxubArySt8rptW1TvDiro6MZ/uCD2BcpwuQTJziXmMjRo0d56623iIiIwGKx8NJLL/Hss88CUKVKFXr27Mm6devw9PTEYrHQqVMnevfuzdSpUxk+fDjR0dEUK1aMNm3aMHbsWJo2bUrHjh25cOECcXFx1KtXj++++45ixYoZdXv37g3AX3/9xbvvvsu2bdvuzMUSERERKQSaI1NERERyrIK9PR5OThk+bE0mFp8/z/SoKKZHRVHUxgbHIv//VeO///6jf//+7N69m5deeol33nknw2N88cUX7Ny5kz179hAQEMDYsWONfTExMXh7e7Nr1y6++uorXn31VQDOnj3LwIEDWbRoEXv27KF69epcuHChQK9FYcvutajg6Jhu20NFiwLw008/UaxYMbZv305oaCheXl6MHj3aaDc0NJRly5bx77//cubMGRYtWmTs27lzJz/++CNhYWHExsYyePBgFi5cyIEDB/j333/5448/AGjXrh1bt24lJCSEJUuWMGTIEBISEox2wsPD2bBhA2vXrjW2Xbp0iUcffZTatWvz7bff3vEkZlrZXVs/V1e8nJ3599o1PJycsDOZAPjss8+oWbMme/fuZe3atYwbN46tW7ca7V64cIFt27Yxd+5cAgMDWb16NQCrVq2iQYMG/P3331y7do3du3fTpEkTbGxsmDdvHjt37mTfvn24ubkxZcoUAF555RW++uoro+2vv/6aF1988Q5eJREREZE7Tz0yRUREJN+8+OCDVHF0BODvm4YKV69enUaNGgHQpEkTJk6cmGEb8+bN46effiI+Pp74+HhKlSpl7HN0dKRbt25GG4cOHQJg69ateHt7U7t2bQCeeeYZXnrppXw9t7td6lxC69evJzEx0UhQms3mdMPvu3btStEbSU9/f3/jGgM88sgjlChRAgBfX18cHBxwcXEBoH79+kRERAAQGRlJnz59OHHiBLa2tly8eJHIyEhq1aoFXJ9T0s7OzmjXbDbTrFkz3nrrLfr161cg55/fnixdmjGRkbS9cT0Atm/fzvTp0wEoU6YM3bp1Y/Xq1TRu3BiAAQMGYLqR9AwMDOT9998nOTmZAwcO8NFHH7F69WpsbGzw9/fHzs6OlJQUJk2axO+//05SUhIxMTE0bdoUgIcffpjhw4cTEhJCyZIl2b59O/Pnz7/DV0FERETkzlIiU0RERO4IxxsJTgAbGxuSkpJuKfPPP//w5ZdfsmXLFsqUKcPSpUsZM2aMsd/BwcFIBNnY2JCcnJzhsVLLyP87eaNHpMViYcqUKTzyyCMZlsvqdbp5X2Zln3rqKT799FO6d+8OQMmSJYmPjzfKOjs7pzumnZ0dzZs3Z9myZfTq1StdktNalba3p6mbG0Hnz2da5ub7MO15V6pUCQcHB+bOnYufnx9t27blo48+wsbGhrZt2wLXk/pr167l77//xtXVlS+//DJdL9aXX36ZKVOmULZsWQYNGmQstCUiIiJyr9LQchEREbEa0dHRuLi48MADD2A2m/n2229zVK9Jkybs2bOH//77D4AZM2ZgNpsLMtS7ys7YWLbfmP+xVatWTJo0iWvXrgFw7do19u/fn6/Hi46OxsPDA4A5c+YQHR2dZXmTycS3335LxYoV6dKlC3FxcfkaT0HpUqoUm2JiiL2RUPf39+e7774D4Ny5cyxevJiHH3440/qBgYGMGTOGwMBASpQogZ2dHQsWLCAwMBC4fh1LlSqFq6srsbGxzJo1K139vn37snLlSmbOnMmwYcMK5iRFRERErIgSmSIiImI1Hn30UWrWrEnNmjUJCAjAx8cnR/VKly7NjBkz6Nq1K/Xq1SMiIoIHHnigYIO1cl+dOMHIQ4d4LSKCv6Oj6Ve2LAD9+/enYcOGNGrUCG9vbxo3bkxoaGi+Hnvy5Ml0796d+vXrExISQqVKlXJU7/PPP8fX15cOHTpw5cqVfI2pILjY2tKuZEkjkfnGG2/w77//4uXlRevWrXnnnXeM6RQyEhgYyNGjR43EZWBgIFevXqVevXoA9OvXj2vXrlGzZk3at29PQEBAuvpFixalW7duNGvWjIoVKxbQWYqIiIhYD5PFYrFkX+zeV9DLw4uIiNzNgoOD8fPzY5yHBx5OTjmuFxkXx+jISHbt2oWvr28BRnj/0GtRcO62a5ucnIyfnx9Tpky5JckpIiIiUhgKOr+mHpkiIiIiIneZpUuXUq1aNZo0aaIkpoiIiNw3tNiPiIiIiMhdpnPnznTu3LmwwxARERG5o9QjU0RERERERERERKyeEpkiIiIiIiIiIiJi9ZTIFBEREREREREREaunOTJFREQkx6LM5gItLzmn16Lg6NqKiIiIWCclMkVERCRbLi4uAEw9efK26svt02tRcHRtRURERKybEpkiIiKSLU9PT8LDw4mNjc11XRcXFzw9PQsgqvuTXouCo2srIiIiYt2UyBQREZEcUZLGeui1KDi6tiIiIiLWS4v9iIiIiIiIiIiIiNVTj0wRERGRGyIiIjSsWO5Zur9FRETkbqdEpoiIiAjXkzw1atTIc/3w8PAskz2tWrVi+PDhdOnSJc/HuNnrr7+Os7MzY8eOzbc25d5U0Pc3wODBg+nTpw+tW7cmKCiIcuXK0bhx4zwfMyNVqlQhKCgIHx+ffGuze/fudOrUiQEDBuRbmyIiIlIwlMgUERERAaOn2vPu7lSwt8+wzMTjx+lTpgzlHRyMbVFmM1NPnsxTTzeROyUn93dsUhJTTp7kuQoVKGFnB8DuK1dYcO5cju7v77//3vg5NdmYX4nMlJSUfGlHRERE7m6aI1NEREQkjQr29ng4OWX4sDWZqODoiJutLUvOn2daVBSLzp416sbGxjJkyBD8/f3x9vZm6NChmM3mW44xb948GjVqRP369alXrx7Lli0z9rVq1YrXX3+dgIAAqlWrxrBhw4x9p06dol27dtSuXZvAwEBOnDhRsBdD7jlZ3d+udnbYmkw8VKyYsa2eszMAzz//PACXL1/Gzs6O6dOnA/Djjz8yaNAg4Pq9GxQUxIoVK1i6dCkTJkzAx8eH77//ng8++AAfHx98fHyoW7cuJpOJo0ePAjBx4kT8/f3x9fXl0UcfNbaPHTuWJ554gnbt2lG3bl1OnTqV7lw+//xzGjZsiI+PDw0bNmTLli3GvipVqjBmzBiaNGmCh4cH48aNM/b9999/NG3alDp16tClSxcuX75cQFdbRERE8ptVJjKrVKmCyWS65fHCCy9kWH7WrFm3lHV0dLzDUYuIiMj9YurJk3g4OTG+WjVeevBBY/uIESMICAhg+/bt7N69m5SUFCZPnnxL/Xbt2rF161ZCQkJYsmQJQ4YMISEhwdh/6NAh1q1bx759+1i5cqWRoHn55Zfx9/fnwIEDzJ49mzVr1hT8ycp9o5KDAzWLFuWViAgmHT/O8vPnuZyUBMC///5LQkIC69ato2HDhqxevRqAVatWERgYmK6dDh060LlzZ9544w1CQ0MZPHgwY8aMITQ0lNDQUJo2bcqgQYOoXLky8+bNIywsjC1bthAcHEyfPn2MpCnAli1b+PHHHzlw4ADu7u7pjtO3b1927NhBaGgoU6ZMYeDAgen2X7p0iS1btrBjxw4mTJjAyZMnjXrPPPMM+/fv58MPP+Tvv//O92spIiIiBcMqh5bv2LGD5ORk4/m+fft4+OGHefLJJzOt4+rqSlhYmPHcZDIVaIwiIiJyf0pISSH82jXerlz5ln1BQUFs2bKFzz//HIC4uDhsbGxuKRcZGUmfPn04ceIEtra2XLx4kcjISGrVqgVAz549sbW1xdbWFh8fHw4dOkSTJk1Ys2YNEydOBMDd3Z3OnTsX4JnK/aaIycTwihWJSkjg32vX2H3lCr+dPw9AjRo12LRpE6tXr+btt9/mtddeIyUlhbVr1zJhwoQcH2PcuHEcO3aM5cuXA9d/Z3bs2IGfnx9Aur8B4HpStGzZshm2FRISwkcffcSFCxewtbUlLCyMuLg4nJycAOjduzcApUqVomrVqkRGRuLi4kJoaKgxH6aXlxfNmzfP+UUSERGRQmWViczSpUune/7pp59SrVo1WrZsmWkdk8lEuXLlCjo0ERERkUxZLBYWLVqU7aIqTz31FJ9++indu3cHoGTJksTHxxv7044ssbGxIelGr7ib6R+3UhAqODhQwcGBtiVKMDYykoi4OBo1asTq1avZsGEDn376KV5eXsyZM4cSJUrk+Dv4jz/+yOLFi9mwYQO2ttf/DLFYLIwcOZKhQ4dmWMf5xtD2m5nNZrp162b0EL18+TJubm4kJCQYiUz9HomIiNx7rHJoeVpms5k5c+YwaNCgLL9kXLlyhcqVK1OxYkUef/xx9u/fn2W7CQkJXL58Od1DREREJDsORYpQq2hRfr9w4ZZ9Xbp0Yfz48UbCJDo6moMHD95SLjo6Gg8PDwDmzJlDdHR0jo4dGBjIjBkzgOvzZS5dujSvpyFyi4uJiYRdu2Y8v5qcTPSNe9nf35958+ZRvHhxihUrRmBgIGPGjLllWHkqV1dXYmJijOerV6/mww8/5Pfff0+XnOzSpQvTpk3j4sWLACQmJhISEpJtrPHx8ZjNZipVqgTAlClTcnSOrq6u1K9fnx9//BGA/fv3888//+SoroiIiBQ+q09kBgUFcenSJWP4R0Zq1qzJjBkzWLJkCXPmzCElJYWmTZtmOQH+J598gpubm/GoWLFiAUQvIiIi96Ln3N2JjIvjzUOH+OrGvHsAkyZNwsnJCR8fH7y9vWnbti1Hjhy5pf7kyZPp3r079evXJyQkxEjGZGfy5Mls3bqV2rVr069fP9q0aZNfpyRCssXCb+fOMeLgQUYeOsQHR47gcyPpWLt2bWJiYmjbti0ADz/8MEePHjWe36xv377Mnz+f+vXr8/333/PRRx9x7do12rdvbyz6ExUVRZ8+fRgwYACtW7emXr16+Pj4sHbt2mxjdXV1Zdy4cfj7++Pn54d9JiuxZ+THH39k+vTp1K1bl9GjR9OiRYsc1xUREZHCZbJYLJbCDiIr7dq1w97ePt1qntlJTEzkoYceolevXnz44YcZlklISEg3qf7ly5epWLEiMTExuLq63nbcIiIicncJDg7Gz8+PcR4eeNwYmpoTkXFxjI6MZNeuXfj6+hZghCJ5p/tbRERE7oTU6V4KKr9mlXNkpjp69CirV69m8eLFuapnZ2dH/fr1MxzKlcrBwQEHB4fbDVFERERERERERETuAKseWj5z5kzKlClDx44dc1UvOTmZvXv3Ur58+QKKTERERERERERERO4kq01kpqSkMHPmTPr372+sapiqX79+jBw50nj+wQcf8Ndff3H48GGCg4N5+umnOXr0KIMHD77TYYuIiIiIiIiIiEgBsNqh5atXr+bYsWMMGjToln3Hjh2jSJH/z8FGR0czZMgQTp8+TYkSJfDz82Pz5s3Url37ToYsIiIiIiIiIiIiBcRqE5mPPPIIma1DtH79+nTPJ02axKRJk+5AVCIiInKvizKbC7S8SGHS/S0iIiJ3M6tNZIqIiIjcSS4uLgBMPXnytuqLWCPd3yIiInIvUCJTREREBPD09CQ8PJzY2Nhc13VxccHT07MAohLJH7q/RURE5F6gRKaIiIjIDUrWyL1M97eIiIjc7ax21XIRERERERERERGRVOqRKSIicp+IiIjQsNJComsvIiIiInL7lMgUERG5D0RERFCjRo0819+5cyd+fn75GNGtli9fzsSJE1m/fn2BHudOu91rHx4enmkyc9asWTRu3JhatWoZz4OCgggKCsrz8VK1atWK4cOH06VLl9tuK9Xrr7+Os7MzY8eOzbc2RUREROT+oUSmiIjIfSC1N+Dz7u5UsLfPtNzCc+eIS06me+nSONnYcDIhgW+iojh58mSBJzLvVTm59hOPHweghZsb/q6uAEw+cYJziYlZ9uScNWsWxYsXNxKZ+SU5OTlf2xMRERERyQ+aI1NEROQ+UsHeHg8npwwfTjY2HLh6lVcrVqS2szMeTk64OzgA4OjoSOvWrfHz86NOnTq8+OKLpKSkANeTaYGBgfTq1QsvLy8aNGjA4cOHATh9+nSm9RITE3n++efx9PTE39+fdevWGXFmVe9uldW1tzWZ6FmmDBtjYqjg4ICHkxN2JhMAV69eZciQIfj7++Pt7c3QoUMxm818//337Ny5k1dffRUfHx9WrFgBwJUrVzJ8LQB++uknGjVqhK+vLy1atGD37t3A9dewdevWPPHEE3h5ebF9+/Z0sc+bN49GjRpRv3596tWrx7Jly4x9rVq14vXXXycgIIBq1aoxbNgwY9+pU6do164dtWvXJjAwkBMnThj7li1bhre3Nz4+PtStW5clS5bk/0UXERERkXuKEpkiIiICwJG4OMrZ2+Nie+uADRcXF5YtW8auXbvYs2cPR44cYf78+cb+HTt28PHHH7N3714CAwMZP348AMWLF8+03vTp0wkLC2P//v38888/BAcHG+1lVe9eVcnRkdrFivHHhQvptk+aNImAgAC2b9/O7t27SUlJYfLkyQwePJgGDRowadIkQkND6dChA5D5a7Fp0yZ+/vlnNmzYQHBwMB999BG9e/c2jrNt2zajXpMmTdLF0K5dO7Zu3UpISAhLlixhyJAhJCQkGPsPHTrEunXr2LdvHytXrmTLli0AvPzyy/j7+3PgwAFmz57NmjVrjDqjR4/m22+/JTQ0lD179tCyZcv8vaAiIiIics9RIlNERESylZKSwltvvUW9evWoX78+O3fuJDQ01NjfpEkTPDw8jJ8PHTqUbb01a9bQr18/7O3tsbe3Z9CgQTk+3r3qydKlWXnxIrFJSca29evXM2HCBHx8fKhfvz4bN27k4MGDmbaR2WuxZMkSdu/eTaNGjfDx8eGll17i4sWLxMXFAdC0aVNq1qyZYZuRkZG0b9+eunXr0qVLFy5evEhkZKSxv2fPntja2uLk5ISPj49xzDVr1jB48GAA3N3d6dy5s1Gnbdu2vPLKK3z22Wfs2bOH4sWL5+GKiYiIiMj9RHNkioiICABVnJw4bTYTm5R0S6/MuXPncu7cObZt24ajoyOvvfYa8fHxxn5HR0fjZxsbG5JuJOI+//xzzp49m2m9tEw3hlLntt69pLS9PU3d3Ag6f97YZrFYWLRoUY4XDMrstbBYLPTv35+PP/44w3rOzs6ZtvnUU0/x6aef0r17dwBKliyZo9f/Zje/xvv372fdunX079+fPn368Oabb+bgDEVERETkfqUemSIiIgJAOXt7Grq68t2pU1y9sdiLxWIB4N9//6VcuXI4Ojpy+vRpFixYkKM2o6OjM60XGBjInDlzSExMxGw2M3PmzBzVu9d1KVWKTTExxN54DVq1asX48eON5GB0dLTRI9PV1ZWYmJgctdu5c2fmzJnDsWPHgOu9Xnfu3JmjutHR0UYvzzlz5hAdHZ2jeoGBgcyYMQO4Pl/m0qVLjX3//fefMf/pc889x9atW3PUpoiIiIjcv9QjU0RERAxDK1Qg6Nw53ouMpIjJhPnGAjuvv/4677//PnXq1KFChQoEBgbmqL1XXnmF7t27Z1hvyJAh7Nu3j9q1a1OiRAkCAgLYtWtXtvXudS62trQrWZKF584BMGLECH755Rd8fHwoUqQItra2fPbZZ1SvXp2hQ4cyYsQIJk2alGlPy1QBAQF89tlndO3alaSkJMxmMx07dqRBgwbZxjR58mS6d+9O8eLFadOmDZUqVcrRuUyePJkBAwZQu3Zt3N3dadOmjbFv1KhRhIWFYW9vT9GiRfnmm29y1KaIiIiI3L9MltSuFve5y5cv4+bmRkxMDK6uroUdjoiISL4KDg7Gz8+PcR4eeDg55bheZFwcoyMj2bVrF76+vgUY4b1L115ERERE7hcFnV/T0HIRERERERERERGxekpkioiIiIiIiIiIiNVTIlNERERERERERESsnhKZIiIiIiIiIiIiYvW0armIiMh9JMpsLtDykjldexERERGR26NEpoiIyH3AxcUFgKknT95Wfck9XXsRERERkfyhRKaIiMh9wNPTk/DwcGJjY3Nd18XFBU9PzwKI6v6gay8iIiIikj+UyBQREblPKCFWeHTtRURERERunxb7EREREREREREREaunRKaIiIiIiIiIiIhYPQ0tFxERyUcRERFZzoXYqVMn7O3tmT9/Pra21z+Gn376aUaNGsXTTz+dr7FMmzaNadOmpdt28uRJHnjgAf777788t9uqVSuGDx9Oly5dclUvq2szadIkihYtyrPPPsvVq1dp164djzzyCGPGjMnRPJEZtb1z507MZjNNmzYFYO3atfzwww+kpKSQkJBA6dKlmTNnDjVr1sywzQEDBuDj48Pw4cOZNm0asbGxvPHGG9meZ4MGDZg4cSKtWrXKtqyIiIiIiOScEpkiIiL5JCIigho1auSobKNGjdI979u3L40aNcrXuRSHDRvGsGHDjOdHjx7F39+fL7/8Mt+OkVM5vTbTp083fl6yZAlLliwBIDw8PNNrk5vrntbRo0epVatWlm2nSnsdRURERESkcGhouYiISD5J7RH4vLs74zw8MnwUt7WlW6lSuNrYMKZyZcZ5eFDazs6oHxsby5AhQ/D398fb25uhQ4diNpsJDw83knUWi4WyZcsyatQoADZs2ECbNm2yjC0uLo6uXbvy6quv8sgjjwCQmJjI22+/jb+/Pz4+PvTo0YPo6GgA5s2bR6NGjahfvz716tVj2bJlGbabVblWrVrx+uuvExAQQOvWrdNdm7cqVqS6kxOl7eyo6uiIV7FitC5enHEeHlR0cKBv2bLUKlqUVsWLAzB37tx0PUCXL19u9Hjcv38/AA/Y2lLWzo6Wbm68UKECzjY2FC1ShHL29jR0dqZokSI4mEwEuLlR3t6eZm5uAPj6+lK/fn1q1aqFra2tkUw9dOgQP//8MwCvv/46NWrUwN/fH09PTypWrIivry+1a9dm6NCh+Pj4ULduXQYOHEhSUpIR5+nTp+nRowf+/v54eXkxevRoY1+VKlUYM2YMTZo0wcPDg3HjxmX5GoqIiIiI3O+UyBQREclnFezt8XByyvBhazLh5+qKl7Mz/167hoeTE3Ymk1F3xIgRBAQEsH37dnbv3k1KSgqTJ0+mRo0aJCQkcOzYMfbs2UPVqlVZs2YNAKtWrSIwMDDLmJ599lmqVq3K22+/bWybMGECxYoVY/v27YSGhqZLtLVr146tW7cSEhLCkiVLGDJkCAkJCbe0m125Q4cOsW7dOubPnw9AUkoKHk5OrLt0ibrFivGFpyevVqzIkfh4StjaYlekCFeSk3mkZEk6PPAAh+Lisr3ev/76KwCvVqzI556e9C5XjqbFi/NIiRK0KF6c/1Wvzss3EqcJFgvHExJoVbw4zVxdAShSpAhbt25l9OjR2NjYGMnYqKgoI3kcFBSEt7c327dvZ/v27Tz00EOMHDmSLVu2MGvWLAYNGsS+ffvo1asXu3fvNmLr378/L7zwAtu3byckJISdO3eyYMECY/+lS5fYsmULO3bsYMKECZw8eTLb8xURERERuV9paLmIiEgheLJ0acZERtK2RIl024OCgtiyZQuff/45cL0npY2NDQBt27Zl9erVREdH07dvX6ZPn86lS5dYvXo1X3zxRabH+vLLL9m1axfbtm275VgxMTEsWrQIALPZTJUqVQCIjIykT58+nDhxAltbWy5evEhkZCS1atVK10Z25Xr27ImtrS2Ojo4AXLzRW3H/1av0LlsWgJJ2dvi6uACwPjqaADc3iphM+Dg7Mz0qKttr6evryy+//MKqixdp7OZG3WLFbilTxGRiUPnyvHbwIK2LF2fP1av8dv789X1FilCzZk2uXLmCyWRi165dpKSkcOrUKTw9Pbl69SqHDx8mJiYGHx8fkpKSOHr0KLt378bNzY3k5GTs7e0BeOSRR6hatSoAV69eZc2aNZw5c8aI48qVK4SFhRnPe/fuDUCpUqWoWrUqkZGRuLu7Z3vOIiIiIiL3IyUyRURECkFpe3uaurkRdCOZlspisbBo0aIM53wMDAxk+fLlREdHM3nyZCIiIvjtt9+IiIigQYMGGR5nw4YNfPjhh2zatAlnZ+dbjjVlyhRjqHlaTz31FJ9++indu3cHoGTJksTHx+e6XGoCM1WKxZJhnKYb+zbFxGBjMrH58mUAElNSALC1tSU5Odkon/YYbdu2BaCUnR2rLl7kz4sXebNSpQyP41ikCIElSxJYsiRjIyOJiIujTp06BAQEsGLFCgCcnJyYM2cODg4OuLq6YrkRc8+ePfnqq68YPHgwDz/8MBMmTODAgQM0adIkXTymGz1sU+tt3br1luuQ0fWxsbFJNyxdRERERETS09ByERGRQtKlVCk2xcQQmyZB16VLF8aPH28ktKKjozl48CBwPWG3Zs0ajhw5Qo0aNQgMDOT999+nefPmRq/NtE6ePEnPnj2ZMWNGhonRLl26MGnSJK5duwbAtWvXjPkmo6Oj8fDwAGDOnDnG3Jk3y2m5m9V1dubvS5eut5GYSHBsLKfMZkrb2/NVjRpM9vRksqcnz1aoAED58uXZs2cPcXFxJCUlMW/ePKOtY8eOAVDfxYVeZcty8Mb5ONnYcO3Gtb2YmMjhNMPUryYnE33jGtevX5+ff/4Zk8nEvn378Pb2ZsyYMZQvXx4AZ2dnPDw82Llzp3HOLi4unD59GpPJRFxcHBEREQCsXr2aQ4cOGfVat27Np59+ahw3KiqKEydO5OgaiYiIiIhIekpkioiIFBIXW1valSyZLpE5adIknJyc8PHxwdvbm7Zt23LkyBEAypYtS9myZWnSpAkALVu2JCoqyuiReLMPP/yQS5cu8e677+Lj45PuAfDWW2/RsGFDGjVqhLe3N40bNyY0NBSAyZMn0717d+rXr09ISAiVMunhmNNyN+tbtiwH4+J44+BBpkVFUbtYMY7ExxsL8KQqc2PIdnR0NB06dKBu3bq0atUq3Srjq1evBuDrkyeZcuIEg24kIBu4uHA0IYGRhw7x+4ULrLx4kbiUFEYeOsQHR47gc6OHalBQECdOnMBkMtGoUSPq1avH0aNHjUQmQLdu3bh06RJ169Zlz549TJw4kdatWzNmzBiaNWtGUFAQXl5ezJs3j3r16hn15s6dy8GDB6lbty5eXl5069aNCxcu5OgaiYiIiIhIeiaLJZMxXveZy5cv4+bmRkxMDK43Jv8XERHJjeDgYPz8/Bjn4YGHk1OO60XGxTE6MpJdu3bh6+tbgBEWnoK8NrruIiIiIiLWoaDza+qRKSIiIiIiIiIiIlZPiUwRERERERERERGxekpkioiIiIiIiIiIiNVTIlNERERERERERESsnhKZIiIiIiIiIiIiYvVsCzsAERGRe02U2Vyg5e9mBXltdN1FRERERO5tSmSKiIjkExcXFwCmnjx5W/XvRQV5bXTdRURERETuD0pkioiI5BNPT0/Cw8OJjY3NdV0XFxc8PT0LICrrUJDXRtddREREROT+oESmiIhIPlJSLHMFeW103UVERERE7n1a7EdERERERERERESsnnpkioiI1YmIiNAwYREREREREUlHiUwREbEqERER1KhRI8/1w8PD8y2ZOWDAAHx8fBg+fHi+tCciIiIiIiJ5p0SmiIhYldSemM+7u1PB3j7DMhOPH6dPmTKUd3AwtkWZzUw9efKWnpxJSUnY2urjTkRERERE5G6nv+xERMQqVbC3x8PJKcN9tiYTFRwdCYmNZXNMDLYmE2aLxdhvMpkYM2YMK1asoFWrVvTr14/nnnuOa9euER8fT+/evRk9ejQAiYmJvPvuu6xduxaz2UyNGjX49ttvKVGixB05TxEREREREckZJTJFROTuZLGw4sIFvq5RA/siRQi7epUPjh41dtvY2LBjxw7gei/PNWvW4ODgQFxcHE2bNiUwMJDGjRszYcIEihUrxvbt2wH48MMPGT16NF9//XWhnJaIiIiIiIhkTIlMERG5K1mAcvb2TD15Ei9nZx64afj4oEGDjJ/j4uJ4/vnnCQ0NpUiRIhw/fpzQ0FAaN25MUFAQMTExLFq0CACz2UyVKlXu4JmIiIiIiIhITiiRKSIidyWTycT7Hh6EX7vGv9eusfDs2XT7nZ2djZ9HjRpFqVKlCAkJwdbWlm7duhEfHw+AxWJhypQpPPLII3c0fhEREREREcmdIoUdgIiISF7EJycTk5RErWLF6Fq6NJUdHTMtGx0dzYMPPoitrS1hYWGsWrXK2NelSxcmTZrEtWvXALh27Rr79+8v8PhFREREREQkd9QjU0RE7komk4lJJ06QkJKCCXCxscm07OjRo+nbty+zZ8+mWrVqtGnTxtj31ltvkZCQQKNGjTCZTMa2OnXqFPQpiIiIiIiISC6YLJY0y7zexy5fvoybmxsxMTG4uroWdjgiIvet4OBg/Pz8GOfhkemq5RmJjItjdGQku3btwtfXtwAjFBERERERkYwUdH5NQ8tFRERERERERETE6imRKSIiIiIiIiIiIlZPiUwRERERERERERGxekpkioiIiIiIiIiIiNXTquUiImKVoszmAi0vIiIiIiIidxclMkVExKq4uLgAMPXkyduqLyIiIiIiIvcWJTJFRMSqeHp6Eh4eTmxsbK7ruri44OnpWQBRiYiIiIiISGFTIlNERKyOkpEiIiIiIiJyMy32IyIiIiIiIiIiIlZPPTJFRETkvhUREaFpDERERERE7hJKZIqIiMh9KSIigho1auS5fnh4eK6TmevXryc+Pp5HH3000zLTpk0jNjaWN954I8+xiYiIiIjci5TIFBERkftSak/M593dqWBvn2XZnbGxhF65wmmzmb5ly/LdqVN56sm5fv16Ll26lGkiMykpiWHDhuW6XRERERGR+4HmyBQREZH7WgV7ezycnLJ87Lt6lSdLl6Z20aKcS0wEYOnSpXTp0sVoZ/ny5bRq1Qq43tuzWbNm1KtXDy8vL0aPHk1oaCjTpk1j7ty5+Pj48MEHH3DkyBGKFy/OW2+9ha+vL1999RVjx45l+PDhAOzdu5fmzZvj6+tL7dq1GTdu3B2+OiIiIiIi1kM9MkVERESycCIhgQuJiXg7O5MMLDh7Nts6X331FZ06dWLkyJEAXLx4kZIlSzJs2DAuXbrEF198AcCRI0eIiYmhTp06jB8/HoCxY8ca7VSpUoU1a9bg4OBAXFwcTZs2JTAwkMaNG+f3aYqIiIiIWD31yBQRERHJwvroaALc3ChiMuHj7Ex0UlK2dVq0aMF3333HO++8w19//UXx4sUzLWtnZ8fTTz+d4b64uDgGDx6Ml5cXjRs35ujRo4SGhubxTERERERE7m7qkSkiIiKSiSSLhU0xMdiYTGy+fBmAxJQUAGxtbUlOTjbKxsfHGz8/8cQTNG3alFWrVvHVV1/xxRdfsGLFigyPUbRoUYoUyfh/y6NGjaJUqVKEhIRga2tLt27d0h1HREREROR+okSmiIiISCaCY2MpbW/PBx4exrZtMTF8efIk5cuXZ8+ePcTFxWFnZ8e8efOMMhEREVSrVo1+/frh7+9P06ZNAXB1deXo0aM5Pn50dDQPPfQQtra2hIWFsWrVKlq0aJF/JygiIiIichfR0HIRERGRTKy/dIlmbm7ptpW5scJ5dHQ0HTp0oG7durRq1QpPT0+jzMKFC/Hy8qJ+/fr07NmTadOmAdC1a1dCQ0ONxX6yM3r0aGbOnIm3tzdvv/02bdq0ycezExERERG5u5gsFoulsIOwBpcvX8bNzY2YmBhcXV0LOxwREREpYMHBwfj5+THOwwMPJ6cc14uMi2N0ZCS7du3C19e3ACMUEREREbm7FHR+TT0yRURERERERERExOopkSkiIiIiIiIiIiJWT4lMERERERERERERsXpKZIqIiIiIiIiIiIjVsy3sAEREREQKU5TZXKDlRUREREQkfyiRKSIiIvclFxcXAKaePHlb9UVERERE5M5QIlNERETuS56enoSHhxMbG5vrui4uLnh6ehZAVCIiIiIikhklMkVEROS+pWSkiIiIiMjdQ4v9iIiIiIiIiIiIiNVTIlNERERERERERESsnoaWi4iIyD0lIiIiy3kvExMTmTlzJn/++Sc2NjbY2dlRrlw5Xn31VR5//PE7GKmIiEjuZfc516lTJ/73v/9Rs2bNdNuzmt957NixXLp0iS+++CLf4ly+fDkTJ05k/fr1+damiIgSmSIiInLPiIiIoEaNGrmuFxYWxt9//014eHiO5s1MSUkBoEgRDW4REZE7J6efc717985we04/50RErJUSmSIiInLPSO2h8ry7OxXs7W/Zfz4xka9PnuSNihUpamNjbI8ym5l68iSxsbFMnDiR+fPnk5SURJkyZfj222+pXLkyY8eOZe/evVy5coXjx4/z3Xff0bFjR1566SV+//13YmNjmTVrFgsXLmTdunUkJSXxyy+/ULduXU6fPk2vXr24fPky8fHxtG7dmi+//JIiRYowa9Ys5syZQ+nSpdm3bx8ODg7Mnz+fqlWr0qlTJ3r37m38QfrXX3/x7rvvsm3btjtzQUVExKpk9zkHMPH4cfqUKcPvFy9Szt6ekwkJpFgsnDSb+frrr9m8efMtn3Fp7d27l+eee45r164RHx9P7969GT16NHC95+a///7LtWvXOHToEOXKlWPhwoWULFmSxMREXnnlFVatWkWJEiUICAgo2IshIvcldSMQERGRe04Fe3s8nJxueSRbLJS3t6eOs3O67al/DP7xxx+EhYWxZcsWgoOD6dOnD88//7zR7pYtW/jxxx85cOAA7u7uxMTE4OfnR3BwMG+//Tbt2rWjc+fOhIaG0r9/f95//30AihcvzrJly9i1axd79uzhyJEjzJ8/32h3x44dfPzxx+zdu5fAwEDGjx8PwCuvvMJXX31llPv666958cUX78QlFBERK5bZ55yHkxO2JhMVHB1xKlKEa8nJjKtalefc3QE4evRopp9xqapUqcKaNWsIDg5m165dLFq0iK1btxr7t23bxqxZszhw4ICRDAWYPn06YWFh7N+/n3/++Yfg4OA7czFE5L5ilYnMKlWqYDKZbnm88MILmdZZsGABtWrVwtHRES8vL1asWHEHIxYREZG70RmzmZGHDvHFiRMArF+/ntWrV+Pn54ePjw+fffYZx44dM8p36NCBsmXLGs8dHR3p0qULAA0aNMDZ2ZnWrVsD4O/vT0REBHB9KPpbb71FvXr1qF+/Pjt37iQ0NNRop0mTJnh4eBg/Hzp0CICHH36YmJgYQkJCOHr0KNu3b6dHjx4Fdj1EROTe0szNDVuTyXi+ffv2TD/jUsXFxTF48GC8vLxo3LgxR48eTfeZ9eijj/LAAw8A6T+z1qxZQ79+/bC3t8fe3p5BgwYV7MmJyH3JKoeW79ixg+TkZOP5vn37ePjhh3nyySczLL9582Z69erFJ598QqdOnZg3bx5dunQhODiYunXr3qmwRURExMpVcXTkjNnM1eRkitnYUNbenk+qVWPR2bMsPn8ei8XCyJEjGTp0aIb1nZ2d0z13cHAwfraxscHR0THd86SkJAA+//xzzp49y7Zt23B0dOS1114jPj7eKJtZPYCXX36ZKVOmULZsWQYNGpTumCIiIllxvGku54EDBzJu3Lgs64waNYpSpUoREhKCra0t3bp1y/FnVlqmNAlUEZH8YpU9MkuXLk25cuWMx/Lly6lWrRotW7bMsPzkyZN59NFHeeONN3jooYf48MMP8fX1TTcUS0RERKScgwO+Li5Mj4riapp/mpotFgBatWrFtGnTuHjxInB9hfOQkJDbPm50dDTlypXD0dGR06dPs2DBghzX7du3LytXrmTmzJkMGzbstmMREZH718KFC7P9jIuOjubBBx/E1taWsLAwVq1alaO2AwMDmTNnDomJiZjNZmbOnJmvsYuIgJX2yEzLbDYzZ84cXnvttUz/o7NlyxZee+21dNvatWtHUFBQpu0mJCSQkJBgPL98+XK+xCsiIiLWbZi7O0HnzvFeZCRFTCaKFSmCzY3vGB06dMDV1dUYHp6UlMSgQYOoX7/+bR3zlVdeoXv37tSpU4cKFSoQGBiY47pFixalW7duREVFUbFixduKQ0RE7m+PPfZYtp9xo0ePpm/fvsyePZtq1arRpk2bHLU9ZMgQ9u3bR+3atY3Ffnbt2pXv5yAi9zeTxXKjC4KVmj9/Pr179+bYsWNUqFAhwzL29vbMnj2bXr16GdumTp3K+++/z5kzZzKsM3bsWGMC/rRiYmJwdXXNn+BFRETkjgoODsbPz49xHh54ODnluF5kXByjIyPZtWsXvr6+BRhh7iUnJ+Pn58eUKVO0AqyIyH3uXvycE5F7y+XLl3Fzcyuw/JpVDi1P64cffqB9+/aZJjHzauTIkcTExBiP48eP52v7IiIiIrdr6dKlVKtWjSZNmiiJKSIiIiL3PaseWn706FFWr17N4sWLsyxXrly5W3penjlzhnLlymVax8HBQZPli4iIiFXr3LkznTt3LuwwRERERESsglX3yJw5cyZlypShY8eOWZZr0qQJa9asSbdt1apVNGnSpCDDExERERERERERkTvEahOZKSkpzJw5k/79+2Nrm77jaL9+/Rg5cqTx/JVXXuHPP//kf//7H//99x9jx45l586dvPjii3c6bBERERERERERESkAVpvIXL16NceOHWPQoEG37Dt27BinTp0ynjdt2pR58+Yxffp06tWrx8KFCwkKCqJu3bp3MmQREREREREREREpIFY7R+YjjzxCZguqr1+//pZtTz75JE8++WQBRyUiIiJ3gyizuUDLi4iIFCZ9zonI/cpqE5kiIiIiueXi4gLA1JMnb6u+iIiINdLnnIjc75TIFBERkXuGp6cn4eHhxMbG5rqui4sLnp6eBRCViIhI/tDnnIjc75TIFBERkXuK/kgTEZF7mT7nROR+ZrWL/YiIiIiIiIiIiIikUo9MERERyXcREREa9iZZ0j0iIiIi9zJ91ykYSmSKiIhIvoqIiKBGjRp5rh8eHp7hl7exY8dy6dIlvvjiiyzr79y5kwkTJvDrr7/mOQYpWAV1j6QaPHgwffr0oXXr1gQFBVGuXDkaN26c6+MsX76ciRMnsn79+lv27d27l1deeYULFy6QnJyMk5MTM2fOpG7durk6ZmhoKP/99x9PPfVUruMTERER61TQ33UKQpUqVQgKCsLHx+eOHje3lMgUERGRfJX6n+fn3d2pYG+fYZmJx4/Tp0wZyjs4kGKxMOfMGcra27MxJiZP/7lOq0GDBkpiWrmc3COjIyN5p1IlnGxsjG3jjx0jNjk523vk+++/N35O/UKel0RmVnr16sWHH35I165dATh+/DgODg65PmZoaChBQUFKZIqIiNxDcvt9OFWU2czUkydv+/uwNUpKSsLW9vbTkJojU0RERApEBXt7PJycMnzYmkxUcHTEw8mJio6O2JhMlLvxJe/HH3/E398fX19fHn30UY4ePXpL24mJiTz//PPUqFGDxo0bM2LECFq1agXA+vXrjf8kHzlyhOLFixv1rly5gslkMp6bTCY++ugjGjVqZPwX+pNPPqFBgwZ4enpm2BNP8k9W9whA5Zu22aR57apUqcKYMWNo0qQJHh4ejBs3ztjXqlUrgoKCWLFiBUuXLmXChAn4+PgYCc6ffvqJRo0a4evrS4sWLdi9ezfw//eVp6cn/v7+rFu3LtPYT5w4gbu7u/G8YsWKlClTJsNjnj59mtatW+Pn50edOnV48cUXSUlJ4ezZs4wZM4Z169bh4+PDsGHDANixYwdt2rShQYMG1K9fnwULFgBw7tw5HnnkEby8vPD29mbgwIH59EqIiIhIQcjp9+HUx9STJ9PVL1WqFEeOHOHcuXNUqVKFrVu3ArBw4ULq1atHXFwcsbGxDBkyBH9/f7y9vRk6dChmsxm4/p1oxIgRtGjRgkqVKvHuu++yYsUKmjdvTpUqVfj888/THW/u3Ln4+flRvXp1JkyYYGzfuXMnTZs2xdvbG39/fzZt2gRk/F3bzc3NeG4ymXjvvfdo2LAhI0eO5NSpUzzyyCPUrl2bRx55hKeeeoqxY8fm6pqqR6aIiIgUiq9OnMDOZOJ8YiIejo7ULVaMBefOcfToUbZs2YKNjQ0//fQTzz//PL///nu6utOnTyciIoL9+/cD0KFDhzzH4ezszLZt21izZg2PP/44X331FTt37mTBggW88cYb7Nix47bOUwrOpUuX2LJlC+fPn6datWoMHDgwXXKxQ4cOdO7cGR8fH4YPHw7Apk2b+Pnnn9mwYQMODg5s3LiR3r17s3//fqZPn05YWJhxX7Vr1y7TY7/77ru0bt2axo0b07hxY7p37079+vUzPGZ8fDzLli3D2dmZ5ORkHn/8cebPn89TTz3FBx98QFBQEEFBQcY5DR06lBUrVlC+fHnOnz+Pr68vTZs2Zf78+Xh4ePDXX38BcPHixfy/qCIiImJ1SpcuzU8//USfPn34+eefGT58OGvXrsXJyYmhQ4cSEBDAd999h8ViYciQIUyePJk33ngDgKNHj7Ju3TouX75MlSpViI6OZuPGjURFRVGzZk0GDRpkJCPPnDnDzp07uXDhAr6+vjRr1owGDRrQrVs3vvvuO9q1a8c///zDE088wcGDB3MUu42NjfF9+sknn6RJkya8//77nD59Gh8fH2rVqpWra6FEpoiIiBSKFx98kCqOjiRbLPxw6hR/3UjKbN++HT8/PwCSk5MzrLtmzRqefvpp7OzsAOjfv3+64cS50bNnT+D6kPSrV68aQ3z9/f2JiIjIU5tyZ/Tu3Ru43luhatWqREZGpktkZmTJkiXs3r2bRo0aGdsuXrxIXFwca9asoV+/ftjf6B08aNAgfvjhhwzbGTFiBE8//TRr165lw4YNBAQE8MMPPxj3U1opKSm89dZb/PPPP1gsFs6ePUvdunUzHE6+efNmDh8+TPv27dNtDwsLo3HjxkyaNMnoWfHoo49mfYFERETknhEQEMAzzzxD06ZN+fHHH405OIOCgtiyZYvRuzIuLg6bNFPzdO/eHRsbG0qUKEHVqlXp1KkTJpMJd3d3SpcuzZEjR4zRTM888wwmk4lSpUrRrVs3Vq9ejYuLC0WKFDH+wdu8eXPKli1LaGgoDz74YLZxDxo0yPh5zZo1TJw4EYBy5crRqVOnXF8HJTJFRESkUNmYTPi7uDD79GkABg4cmG6YcE6kHS6elq2tbbpkaHx8/C1lHB0dr8dx4wtf2udJSUm5ikPyj6uNDVeSkymW5ov4tZsS26mvFeT89bJYLPTv35+PP/4427KZ3VepypYtS69evejVqxeVK1dm7ty5GSYyP//8c86ePcu2bdtwdHTktddey/BeTI2vTp06bN68OcP9oaGhrF69msWLF/Puu+8SEhKS7o8VERERuXsVAVLSPL/5+0JISAilS5fm+PHjxjaLxcKiRYsyXVzo5u9Lufn+lNl3odTtOfmu7ezsnOv2s6I5MkVERKTQ7b96lVI3elcuXLjQGDKbmJhISEjILeXbtGnDvHnzSExMJDExkR9//DHDdsuVK4fFYuHAgQMAmZYT6+Pl7Mya6Gjj+cZLlyiRhwniXV1diYmJMZ537tyZOXPmcOzYMeB6b8mdO3cCEBgYyJw5c0hMTMRsNjNz5sxM2/3tt99ITEwErk9ev2fPHqpVq5bhMaOjoylXrhyOjo6cPn3amPMyo7JNmzYlMjKS1atXG9tCQ0Mxm81ERkbi7OxMjx49mDJlCuHh4Vy5ciXX10RERESsU8kb34cBFi9ezNWrV43nX331FdHR0ezevZtvv/3WmKeyS5cujB8/3khIRkdH53jY981mzZoFXB+t8ttvv9G2bVtq1qxJSkoKq1atAq6PHkkdFp7b79pt2rQxjnHmzBmWL1+e6xiVyBQREZFC8dWJE4w8dIi3Dh0iymymwwMPAPDYY4/RunVr6tWrh4+PD2vXrr2l7rPPPkuVKlWoXbs2zZo1o1q1aukmGk9la2vLlClT6NSpEw0bNjQST2L9+pYty6WkJN4+dIiRhw6xOSaGp8qUyX07ffsyf/586tevz/fff09AQACfffYZXbt2pV69etSpU4dffvkFgCFDhuDp6Unt2rVp3ry5McwqI4sXL6Zu3bp4e3tTr149HBwceP/99zM85iuvvMK2bduoU6cOffv2JTAw0Ginbdu2JCQk4O3tzbBhwyhRogS///47H3/8MfXq1aN27dq8/fbbpKSksH79evz8/PDx8aFp06ZMmDAh3YT6IiIicncZf/QoL4aHG4+WNz7Xe/fuTUhICA/c+H4cHBzMxIkTmTt3LmXKlGHOnDn07duXCxcuMGnSJJycnPDx8cHb25u2bdty5MiRPMVTunRp/Pz88Pf358UXX6Rp06bY29uzePFi3nvvPby9vRk+fDgLFy7E2dk519+1J0+ezMaNG6lduzZ9+vShUaNGGX6Hz4rJYrFY8nR295jLly/j5uZGTEwMrq6uhR2OiIjIXSs4OBg/Pz/GeXgYq0/nRGRcHKMjI9m1axe+vr7Zlo+NjcXFxYXExET69OmDn58fb7311u2ELnfInbpHRERERArD/fxdJ6v8WlxcHHZ2dtja2nLhwgUaN27MnDlz0s1dnh3NkSkiIiJ3pcDAQBISEoiPj6d58+a8/PLLhR2SiIiIiIhkIiIign79+mGxWDCbzTz//PO5SmKCEpkiIiJyl9q2bVthhyAiIiIiIjnk7e1NaGjobbWhOTJFRERERERERETE6imRKSIiIiIiIiIiIlZPQ8tFRESkQESZzQVaXu5+ukdERETkXqbvOvlPiUwRERHJVy4uLgBMPXnyturLvUv3iIiIiNzL9F2n4CiRKSIiIvnK09OT8PBwYmNjc13XxcUFT0/PAohKrInuEREREbmX6btOwVEiU0RERPKdvnxJdnSPiIiIyL1M33UKhhb7EREREREREREREaunHpkiIiI5FBERoeEhIiIiIiIihUSJTBERkRyIiIigRo0aea4fHh6eZTJz8ODB9OnTh9atWxMUFES5cuVo3Lhxro+zfPlyJk6cyPr162/ZN2DAAHx8fBg+fDgAn3zyCXPmzOGvv/5i2bJlxMbG8sYbb+T6mJm5cuUKLi4uWCyWfGtTRERERETuX0pkioiI5EBqT8zn3d2pYG+fYZnRkZG8U6kSTjY2xrbxx44Rm5ycbU/O77//3vg5KCgIHx+fPCUyc+qNN95gw4YNbNiwgQceeIBhw4YV2LFERERERETyg+bIFBERyYUK9vZ4ODll+ACofNM2G5PJqFulShXGjBlDkyZN8PDwYNy4cca+Vq1aERQUxIoVK1i6dCkTJkzAx8fHSHD+9NNPNGrUCF9fX1q0aMHu3bsBSExM5Pnnn8fT0xN/f3/WrVuXZfzJyckMHjyYkJAQ1qxZwwMPPADA2LFjjZ6as2bNIjAwkF69euHl5UWDBg04fPiw0cZ7771H9erVadiwIaNHj6ZKlSrGvm+//RZPT0/q16/PpEmT0h175cqV+Pr64u3tTcuWLTlw4AAA69evp27dujz33HN4e3vj5eXFnj17GDBgAF5eXjRq1IiTJ0/m5mUSEREREZF7kBKZIiIid9ClS5fYsmULO3bsYMKECbck6Dp06EDnzp154403CA0NZfDgwWzatImff/6ZDRs2EBwczEcffUTv3r0BmD59OmFhYezfv59//vmH4ODgLI//ySefcPDgQX7//XecnZ0zLbdjxw4+/vhj9u7dS2BgIOPHjwfg999/Z9GiRYSEhLB9+/Z08e/bt4/33nuPDRs2EBISQlxcnLHv7Nmz9O7dm9mzZ7Nnzx6GDh1K9+7djWHn//33H4MHD2bPnj106dKFNm3a8Pbbb7N3714aNGjAF198kavrLCIiIiIi9x4lMkVERO6g1ARkqVKlqFq1KpGRkdnWWbJkCbt376ZRo0b4+Pjw0ksvcfHiReLi4lizZg39+vXD3t4ee3t7Bg0alGVbrVq14sCBA/zzzz9ZlkvtNZr686FDhwBYs2YNTz75JC4uLphMJp555hmjztq1a2nfvj3ly5cH4LnnnjP2bdu2DS8vL7y8vADo06cPUVFRRiK0evXq+Pn5AdCgQQOqV69OrVq1APD39yciIiLb6yQiIiIiIvc2JTJFRETyiauNDVeSk9Ntu3bTc0dHR+NnGxsbkpKSsm3XYrHQv39/QkNDjcepU6dwujGcPS1TmqHsGWnevDkLFy6kV69erFq1KtNyOY0zq+NlF0tWx8vLdRIRERERkXubEpkiIiL5xMvZmTXR0cbzjZcuUcI29+vqubq6EhMTYzzv3Lkzc+bM4dixYwCkpKSwc+dOAAIDA5kzZw6JiYmYzWZmzpyZbfstWrRg8eLF9OnTh5UrV+YqtjZt2rBo0SKuXLmCxWJhxowZ6fb9+eefnD59GoBp06YZ+xo3bszevXvZt28fAL/88gvu7u64u7vn6vgiIiIiInL/0qrlIiIi+aRv2bL8dOYMbx86hAkobmvLU2XKMDmXC9X07duXAQMGEBQUxAsvvMDgwYP57LPP6Nq1K0lJSZjNZjp27EiDBg0YMmQI+/bto3bt2pQoUYKAgAB27dqV7TGaN2/Ob7/9RteuXZk9e3aOY+vUqRPbtm3Dx8eH4sWL07JlS4oXLw5A3bp1GTt2LAEBATg7O9OtWzejXunSpZk7dy79+vUjKSmJEiVKsGDBglz12hQRERERkfubyZI6y/597vLly7i5uRETE4Orq2thhyMiIlYmODgYPz8/xnl4GCuU50RkXByjIyPZtWsXvr6+BRjhnRMbG4uLiwsWi4URI0YQFxfHN998U9hhiYiIiIhIISvo/Jp6ZIqIiEiu9OvXjyNHjhAfH0+dOnXSDSEXEREREREpKEpkioiISK789ttvhR2CiIiIiIjch7TYj4iIiIiIiIiIiFg9JTJFRERERERERETE6mlouYiISC5Emc0FWl5EREREREQypkSmiIhIDri4uAAw9eTJ26ovIiIiIiIieaNEpoiISA54enoSHh5ObGxsruu6uLjg6elZAFGJiIiIiIjcP5TIFBERySElI0VERERERAqPFvsRERERERERERERq6dEpoiIiIiIiIiIiFg9JTJFRERERERERETE6imRKSIiIiIiIiIiIlZPiUwRERERERERERGxekpkioiIiIiIiIiIiNVTIlNERERERERERESsnhKZIiIiIiIiIiIiYvWUyBQRERERERERERGrp0SmiIiIiIiIiIiIWD0lMkVERERERERERMTqKZEpIiIiIiIiIiIiVk+JTBEREREREREREbF6SmSKiIiIiIiIiIiI1VMiU0RERERERERERKyeEpkiIiIiIiIiIiJi9ZTIFBEREREREREREaunRKaIiIiIiIiIiIhYPSUyRURERERERERExOopkSkiIiIiIiIiIiJWT4lMERERERERERERsXpKZIqIiIiIiIiIiIjVUyJTRERERERERERErJ4SmSIiIiIiIiIiImL1lMgUERERERERERERq6dEpoiIiIiIiIiIiFg9JTJFRERERERERETE6imRKSIiIiIiIiIiIlZPiUwRERERERERERGxekpkioiIiIiIiIiIiNVTIlNERERERERERESsnhKZIiIiIiIiIiIiYvWUyBQRERERERERERGrp0SmiIiIiIiIiIiIWD0lMkVERERERERERMTqKZEpIiIiIiIiIiIiVk+JTBEREREREREREbF6trdTef369WzYsIFTp06RkJCQYRmTycQPP/xwO4cRERERERERERGR+1yeEpkxMTE8/vjjbNy4EYvFkmVZJTJFRERERERERETkduUpkfnWW2+xYcMGqlevzrBhw6hRowYuLi75HZuIiIiIiIiIiIgIkMdE5pIlSyhbtixbt26lZMmS+R2TSIGLiIggNjY21/VcXFzw9PQsgIjujPv1vEUKin6nRERERERE7pw8Dy3v0KGDkphyV4qIiKBGjRp5rt+/f39mzZqV63pRUVH07NmTjRs35vnYt+N2z/vrr79m+vTphIaG3rLv5nMzmUxER0dTvHjxPB9PxNrd7u9UeHi4kpkiIiIiIiK5kKdEpqenJ1evXs3vWETuiNTeU8+7u1PB3j7DMhOPH6dokSK0LF6cOsWKATD79Gki4uIwm815Om6FChUKLYkJOTtvgIXnzhGXnEz30qVxsrHhZEIC30RFcebMmUzrFPa5iRSGnL6X9ClThvIODsa2KLOZqSdPEhsbS3JyMjY2NnckXhERERERkbtdkbxUeumll1i/fj0HDx7M73hE7pgK9vZ4ODll+LA1mXi4ZEn+vXYNDycnytrbcyExMV39iRMn4u/vj6+vL48++ihHjx4lLi6OevXqsXDhQgC2bNlClSpVOHfuHEeOHEnXQ3HLli00b96cevXq4e3tzZIlSwDYuXMnTZs2xdvbG39/fzZt2nTHztvJxoYDV6/yasWK1HZ2xsPJCfcbCRh3d3eSkpJ4/vnnqVevHnXq1GHnzp0At5xbWhEREXTs2JGGDRvi7e3NV199la/nI1LYsnsvqeDoaDw/lpDAHxcuANCjRw+2b9/O559/TsOGDfHx8aFhw4Zs2bLFaLtKlSqMGTOGJk2a4OHhwbhx44x9J0+epHv37nh5eeHt7c27774LXE+wDhkyBH9/f7y9vRk6dGie/wEjIiIiIiJiTfKUyBw8eDAvv/wyLVu2ZObMmZw4cSK/4+LkyZM8/fTTPPDAAzg5OeHl5WUkTTKyfv16TCbTLY/Tp0/ne2xyf6hRtCjnEhOJTkxkc0wMdW/0zASYN28eYWFhbNmyheDgYPr06cPzzz+Pk5MTCxYs4NVXX2XHjh306dOHn376idKlS6dr++LFi3Tp0oVPPvmE3bt3ExoaSkBAAGazmW7duvHee++xZ88ePv/8c5544gmuXLlyR875SFwc5eztcbHNuLP2f//9R//+/dm9ezcvvfQS77zzTpbtJScn06tXL/73v/+xY8cOtm7dyvTp09mxY0dBhC9ilb46cYKRhw4x8tAhriUncyIhAYD58+fTpEkT+vbty44dOwgNDWXKlCkMHDgwXf1Lly6xZcsWduzYwYQJEzh58iQATz/9NH5+fuzdu5c9e/bw8ssvAzBixAgCAgLYvn07u3fvJiUlhcmTJ9/ZkxYRERERESkAeRpanjoMzmKxMHjw4CzLmkwmkpKSctV+dHQ0zZo1o3Xr1vzxxx+ULl2aiIgISpQokW3dsLAwXF1djedlypTJ1bFF0mru5saGS5fYGRtL51Kl2BATA0BQUBA7duzAz88PuJ6wS1WjRg3Gjx9PkyZN+OCDDwgICLil3S1btlCzZk1jX5EiRShZsiR79+6lSJEitGvX7vrxmzenbNmyhIaG0rx584I+3WxVr16dRo0aAdCkSRMmTpyYZfmwsDD279/PU089ZWyLjY3lwIEDNGzYsEBjFbEWLz74IFUcHQH4+9IlKjo4cDg+3tgfEhLCRx99xIULF7C1tSUsLIy4uDicnJwA6N27NwClSpWiatWqREZG4ubmxj///MPKlSuNdlL/YRIUFMSWLVv4/PPPAYiLi9PwdRERERERuSfkKZFZsWJFTCZTfsdiGD9+PBUrVmTmzJnGNg8PjxzVLVOmjBYYkXwT4ObGO4cPU87BgVJ2dsZ2i8XCyJEjGTp0aIb1goODKV26NMePH7/tGAryd+1mVZycOG02E5uUlGGvTMcbyRi4/g+N7P5JYbFYKFmyZIYLBIncrxyK/P9giNRe2OvWraNhw4ZcvnwZNzc3EhISjERmXn7vFi1adFsLEYmIiIiIiFijPA0tP3LkCJGRkTl+5NbSpUtp0KABTz75JGXKlKF+/fp89913Oarr4+ND+fLlefjhh7OcWzAhIYHLly+ne4jcrISdHT3LlqXXTT17u3TpwrRp07h48SIAiYmJhISEALB8+XJWrlzJ/v372bZtG7/++ust7TZt2pSIiAhjgZyUlBQuXrxIzZo1SUlJYdWqVQBs3ryZ06dP4+PjU4Bn+f/K2dvT0NWV706d4uqNXqYWiwXAGM6aGzVr1sTV1TXdPyUOHjxoXDeR+118fDxms5lKlSoBMGXKlBzVc3Z2pkWLFvzvf/8ztp07dw64/v40fvx4I+EZHR2tOa1FREREROSekKdEZkE7fPgw33zzDZ6enqxcuZLnnnuOl19+mdmzZ2dap3z58kybNo1FixaxaNEiKlasSKtWrQgODs6w/CeffIKbm5vxqFixYkGdjtzlWhYvjmfRoum29enThwEDBtC6dWvq1auHj48Pa9eu5dixYzz33HP8+uuvlCxZkgULFvD6668TERGRrn6JEiX47bffePvtt/H29sbX15dNmzZhb2/P4sWLee+99/D29mb48OEsXLgQZ2fnO3a+QytUoJKDA+9FRvLmoUN8eSOBmXbKhpyytbVl+fLlLF68GG9vb+rUqcMzzzxDXFxcfoctcldydXVl3Lhx+Pv74+fnh30mq59n5KeffmLnzp3UqVMHHx8fYyGtSZMm4eTkhI+PD97e3rRt25YjR44U0BmIiIiIiIjcOSZLaner2xQdHQ2Qo3kss2Nvb0+DBg3YvHmzse3ll19mx44d6VZzzU7Lli2pVKkSP/300y37EhISSLix4ALA5cuXqVixIjExMXlK2MjdIzg4GD8/P8Z5eOBxY+hmTkTGxTE6MpJdu3bh6+tbgBEWjPv1vEUKin6nRERERERE0kudLqug8mu31SNzxYoVtGvXDmdnZ0qVKkWpUqVwdnbm0UcfZcWKFXlut3z58tSuXTvdtoceeohjx47lqh1/f/9Mh9M5ODjg6uqa7iEiIiIiIiIiIiLWKc+JzFdffZXHHnuMVatWce3aNVxdXXFzc+PatWv89ddfPPbYY7z22mt5artZs2aEhYWl2xYeHk7lypVz1U5oaCjly5fPUwwiIiIiIiIiIiJiPfKUyPz111+ZPHkypUuX5ssvvyQ6Opro6GguXrzIpUuXmDJlCmXKlGHy5MnMnz8/1+2/+uqrbN26lY8//piDBw8yb948pk+fzgsvvGCUGTlyJP369TOef/HFFyxZsoSDBw+yb98+hg8fztq1a9PVERERERERERERkbuTbV4qTZ06FUdHRzZs2ECNGjXS7XN1deWFF17g4YcfxsfHh6lTp9KjR49ctd+wYUN+++03Ro4cyQcffICHhwdffPEFffr0McqcOnUq3VBzs9nMiBEjOHnyJEWLFsXb25vVq1fTunXrvJyiiIiIiIiIiIiIWJE8JTJ3795NmzZtbkliplWjRg3atGnDP//8k6fAOnXqRKdOnTLdP2vWrHTP33zzTd588808HUvuT1Fmc4GWt1b363mLFBT9TomIiIiIiNwZeUpkms1mihUrlm25YsWKYdYfbGJlXFxcAJh68uRt1b/b3K/nLVJQ9DslIiIiIiJyZ5ksFoslt5W8vLw4d+4chw4dyjShee3aNapWrUrp0qXZu3fvbQda0Ap6eXixLhEREcTGxua6nouLC56engUQ0Z1xv563SEHR75SIiIiIiMj/K+j8Wp56ZPbo0YP33nuPLl26MHXq1Fv+GDt06BAvvPAC586d48UXX8yXQEXy0/2aQLhfz1ukoOh3SkRERERE5M7JU4/MuLg4AgICCA4OxsbGBl9fX6pUqQLA0aNH2bVrF8nJyTRo0IC///4bJyen/I4736lHpoiIiIiIiIiISN4VdH4tT4lMgCtXrjBy5EhmzJhBXFxcun1OTk4MGjSITz75BGdn53wJtKApkXl7NLxSREREREREROT+ZrWJzFTXrl1j165dREVFAVChQgX8/PwoWrRovgR4pyiRmXcRERFZrmCfnfDw8NtOZvr4+LBx40ZcXFyoUqUKQUFB+Pj43FabIiIiIiIiIiKSc1Y5R2ZaRYsWJSAgID9ikbtUak/M593dqWBvn2GZiceP06dMGco7OJBisTDnzBnK2tuzMSYmXU/O5ORkbGxsch1DaGhonmIXEREREREREZG7w20nMkVSVbC3xyOT+VBtTSYqODpSxdERc0oKNiYT5W4kPYcOHUrlypUJDw9n+vTpbNmyhZ9//pnExETs7Oz48ssvadKkCaGhoQwYMMBo8/Dhw4wYMYL33nsPk8lEdHQ0xYsXvwNnKiIiIiIiIiIid1qOEpkffPABJpOJF154gZIlS/LBBx/k+AAmk4l33303zwHKveOrEyewM5k4n5iIh6MjdYsVY8G5c+zbt4+5c+dSs2ZNAKpXr85rr70GwNatWxkwYAD//fcfPj4+Rs/LdevWMWTIEAYPHlxYpyMiIiIiIiIiIndQjhKZY8eOxWQy0bNnT0qWLGk8z8n0mkpkSqoXH3yQKo6OJFss/HDqFH9dvAhAvXr1jCQmQEhICB999BEXLlzA1taWsLAw4uLicLrR23Pfvn0MHDiQ5cuX4+7uXijnIiIiIiIiIiIid1aOEpkzZ84EoHz58umei+SFjcmEv4sLs0+fBjASlABms5lu3bqxbt06GjZsaEwSm5CQgJOTE1FRUXTp0oWZM2dSt27dwjoFERERERERERG5w3KUyOzfv3+Wz0Vya//Vq5Sys+NsYmK67fHx8ZjNZipVqgTAlClTjH2xsbF07NiR999/n9atW9/ReEVEREREREREpHAVyUulY8eOcfHGsOCsREdHc+zYsbwcQu5BX504wchDh3jr0CGizGY6PPDALWVcXV0ZN24c/v7++Pn5YZ9mFfTFixfz33//MWHCBHx8fPDx8WHatGl38hRERERERERERKSQmCw5mejyJjY2NgwYMIAffvghy3JDhgxh5syZJCUl5TnAOyV1CHNMTAyurq6FHc5dJTg4GD8/P8Z5eGS6anlGIuPiGB0Zya5du/D19S3ACEVEREREREREpKAVdH4tTz0yLRZLjhb6SS0rIiIiIiIiIiIicjvylMjMqfPnz6dbyEVEREREREREREQkL3K02A/Ahg0b0j0/ffr0LdtSJSUlERYWxsqVK6lTp87tRSgiIiIiIiIiIiL3vRwnMlu1aoXJZDKer1y5kpUrV2Za3mKxYDKZGDFixO1FKCIiIiIiIiIiIve9HCcy+/XrZyQyZ8+eTbVq1WjWrFmGZe3t7alQoQKPPfaYFnG5j0SZzQVaXkRERERERERE7l85TmTOmjXL+Hn27Nk0b96cGTNmFERMcpdxcXEBYOrJk7dVX0REREREREREJDM5TmSmlZKSkt9xyF3M09OT8PBwYmNjc13XxcUFT0/PAohKRERERERERETuJXlKZF65coXDhw9ToUIFSpUqlWGZ8+fPExUVRbVq1ShWrNhtBSnWT8nI/2vvzuNruPc/jr9PlpMEWeyRCILYQoVUiX1rQ6uqdEGo7bZVXNzWUq22it4uVy2lSje0qi6toq1q1dbaCbnWJkFChNizKNnn94fm/JwmIascvJ6Px3lcZ+bznfnMyKS3787MFwAAAAAAAMXJriCDpk+friZNmujYsWO51hw7dkxNmjTRrFmzCtwcAAAAAAAAAEiSyTAMI7+DmjVrpsTERIWHh9+0rk6dOvLw8NCuXbsK3ODtkpiYKHd3dyUkJMjNza2k2wEAAAAAAADuKMWdrxXo0fLjx4+rdevWt6yrX7++tm3bVpBdwAZFRkbe8j2Y6enp+vzzz7V27Vo5ODjI3t5eAQEBmj9/vjw8PHIcM2nSJMXHx2vmzJlF1mv79u01evRo9ejRI9u6f/zjHwoJCVGHDh2KbH/AvSYvvw82bNigzz77TJmZmUpJSVHFihW1ePFi1a1bt0D7DAgI0O+//84EYQAAAABwjypQkHnt2jW5uLjcss7FxUVXrlwpyC5gYyIjI1WnTp0CjY2IiNBzzz2nTp06FXFXBfPpp5+WdAvAHa2gvw9OnDihevXqKSIiokDv1Q0LC8v3GAAAAADA3aNAQaaPj4927959y7rdu3fLy8urILuAjcm682qYt7e8zOYcay6mpWlObKzG+PiotL29JOl0aqrmxsaqbNmy+s9//qOFCxfKzs5O9913n+bOnSt3d3erbWRkZOjll1/WTz/9JEnq0KGD3n//fZnNZg0cOFBms1nHjx/XsWPH1KFDBw0dOlTjxo3TyZMn1aNHD02fPt2yrfXr1+utt97S5cuX9dhjj2natGkymUxWd2suWbJEs2bNUmpqqjIzMzV16lQ9+uijxXEKgbtGXn4fnElJ0cK4OL3o4yMnu+uvY876fdCuXTuFhIRow4YNSkhI0PPPP6+xY8dKksaMGaPNmzcrLS1Nbm5u+uSTTyx3cJpMJl2+fFkeHh6qUaOGnnnmGa1bt05xcXEaMmSIJk6ceBuOHgAAAABQUgo02U9wcLCio6M1Y8aMXGtmzZqlqKgodenSpcDNwfZ4mc3ydXHJ8ZNuGKpiNqthmTKWZVkhx9atW/X5559r69atOnDggEqXLq2XX3452/Y//vhj7d69W6GhoQoLC9OxY8esfs4OHDigH374QeHh4frtt9/09ttva926dTpw4IC++uorHTp0yFJ7+PBhbdu2Tfv379fmzZv19ddfZ9tfcHCwduzYoX379mnVqlV69tlnlZKSUgxnDrj73Oz3QQt3dzUoXVrTY2K08sIFHfrzT5Wx+/9/5Jw9e1Z79uzRjh07NHv2bMtrSMaPH6/du3crLCxMw4YN06hRo3Ldf3x8vLZv367du3frP//5j2JjY4v9mAEAAAAAJadAQea4cePk5uamMWPGqFu3blq9erUOHTqkQ4cOafXq1erWrZtefPFFubm5ady4cUXdM+5AO3fu1NNPP215T+YLL7ygdevWZav79ddfNXDgQDk5OcnBwUHPPvusVd1jjz0mZ2dnmc1mNWrUSMHBwXJ0dFTp0qXVoEEDRUZGWmqfeeYZOTo6qlSpUurXr59+/fXXbPuLiopS165d1bBhQ/Xo0UOXLl1SVFRU0Z8A4B5jZzJptI+PJvn66r4yZRRx7Zo+uCFoHDJkiEwmkypUqKCePXtars9169YpKChIDRs21OTJk2/6OHnfvn0lSRUqVFDNmjW5dgEAAADgLlegR8urVq2q1atXq1evXlqzZo3lMeAshmGoQoUKWr58uapXr14kjcL21XBxUVxqqpLS0+XqcPMfLZPJlKdt/r3O2dnZ8md7e/ts39PT0/O1z969e+udd97RE088IUkqV66ckpOT89QbgFvzcnKSl5OTOpUtq0lRUYq8di3HOpPJpJMnT2rEiBHavXu3atWqpf3796tt27a5bjs/1z8AAAAA4M5XoDsyJalNmzYKDw/XO++8o86dO6tu3bqqW7euOnfurHfffVfh4eFq165dUfYKG+dpNquZm5s+OXNGf2ZkSLoeakvXw+9ly5YpMTFRkjR//nw99NBD2bbRuXNnffHFF0pNTVV6ero+/fTTHOvyYvHixUpLS9O1a9e0ZMkSde7cOVvN5cuX5evra6m/fPlygfYFwNqltDSFX71q+f5nRoYu3xA0Lly48HrdpUv67rvv1KlTJyUkJMjR0VFVqlSRYRiaM2fO7W4bAAAAAGDDCnRHZpayZctq3LhxPD4Oi+e8vLTy/Hm9ERUlO5NJqZmZkqSuXbuqfPnyCgoKsprsJ9v4557TsWPH1LRpU0myTMxTEPXr11erVq106dIlPfbYY+rdu3e2mlmzZumJJ56Qh4eHOnbsqGrVqhVoXwCsZRiGvjt/XufT0mQ2mZQpKaBMGf36138sqFixogIDA5WQkKARI0aoZcuWkq7fJe3v76/y5curR48eJXcAAAAAAACbYzKybpm7xyUmJsrd3V0JCQlyc3Mr6XZszt69exUYGKipvr7ydXHJ87ioa9c0MSpKoaGhlnASwJ2tsL8PqlSpojVr1iggIKD4mgQAAAAA3HbFna8V6o7MLPHx8UpKSlJumSh3uQEAAAAAAAAojAIHmXFxcZo4caJWr16tixcv5lpnMpmYgAEAYPHDDz9wNyYAAAAAIN8KFGSeOXNGzZo10+nTp+Xt7a2KFSvq3LlzCgoK0vHjx3X27FmZTCYFBQXJ0dGxqHsGAAAAAAAAcI8p0KzlU6dO1enTpzV58mTFxMSoa9euMplM2rp1q86cOaNNmzapXr16MplM+umnn4q6ZwAAAAAAAAD3mAIFmWvXrpWvr68mTpyY4/q2bdvql19+0b59+zRlypRCNQgAAAAAAAAABXq0PDY2Vo888ojlu729vSQpJSVFTk5OkiRvb2916NBBy5Yt01tvvVUErcIWnE5NLdZ6AHcOfh8AAAAAAG6nAgWZf58+3cPDQ9L1gLNmzZqW5c7OzoqNjS14d7AZrq6ukqS5Bfz7zBoP4M7H7wMAAAAAQEkoUJBZrVo1nTx50vK9YcOGkqQ1a9ZoxIgRkqSrV69q69atqlKlShG0iZLm5+eniIgIJSUl5Xusq6ur/Pz8iqErACWB3wcAAAAAgJJQoCCzY8eOmjVrls6fP6+KFSuqe/fuKl26tMaOHatTp07J29tbixcv1tmzZ/XCCy8Udc8oIYQPALLw+wAAAAAAcLsVKMgMCQlRTEyMDh8+rHbt2qlcuXKaP3++Bg0apPfee08mk0mGYcjf35/3YwIAAAAAAAAoNJNhGEZRbezkyZNas2aNLl++rDp16qh79+5ydHQsqs0Xq8TERLm7uyshISHbO0ABAAAAAAAA3Fxx52t5CjIHDx6s1q1ba/DgwZKuB5ZlypRRuXLliryhkkKQCQAAAAAAABRccedrdnkpWrhwobZs2WL57uvrq7FjxxZ5MwAAAAAAAACQkzwFmY6OjkpOTrZ8NwxDRfhEOgAAAAAAAADcVJ6CTB8fH/3+++86ceJEcfcDAAAAAAAAANnkKcjs27evYmNjVbNmTdnb20uSFi1aJHt7+1t+HBwKNDE6AAAAAAAAAFjkKWWcNGmSPDw8tGrVKp06dUpRUVEqVaqUKlSoUNz9AQAAAAAAAEDeZi3/Ozs7Ow0cOFCff/55cfRUIpi1HAAAAAAAACg4m5i1/O8GDBig1q1bF3UvAAAAAAAAAJCjAr3AcsGCBUXdBwAAAAAAAADkqkB3ZJ49e1a//fabzp49a7X82LFj6t27txo2bKiHH35YO3bsKJImAQAAAAAAANzbChRkvvPOO+rQoYMSEhIsyxITE9W6dWstX75chw8f1tq1a9WpUydFRkYWWbMAAAAAAAAA7k0FCjI3bdqkBg0aqE6dOpZlCxcu1NmzZ9WnTx+Fh4dr+vTpunbtmt5///0iaxYAAAAAAADAvalAQWZsbKxq1qxptezHH3+Ug4ODZs6cKT8/P40ePVqNGzfW5s2bi6RRAAAAAAAAAPeuAgWZSUlJKlWqlOV7RkaGtm/frsDAQFWoUMGyvF69ejp16lThuwQAAAAAAABwTytQkOnl5aU//vjD8n3Lli26cuWK2rdvb1WXnp4us9lcqAYBAAAAAAAAoEBBZlBQkPbv36+ZM2fqwIEDmjhxokwmkx599FGruiNHjsjb27tIGgUAAAAAAABw7ypQkDlhwgQ5OTnppZdeUkBAgLZu3ar27durZcuWlpro6GgdPnxYzZs3L7JmAQAAAAAAANybHAoyyN/fX1u2bNGsWbN04cIFBQYGauzYsVY1P//8sxo3bqwePXoURZ8AAAAAAAAA7mEmwzCMkm7CFiQmJsrd3V0JCQlyc3Mr6XYA3EMiIyOVlJSU73Gurq7y8/Mrho4AAAAAAMi/4s7XCnRHJgCgaERGRqpOnToFHh8REZEtzKxRo4ZWrlypgICAQnb3/5544gl169ZNAwcOLLJtAgAAAACQHwSZAFCCsu7EHObtLS+zOceaaTExCqlUSVWcnHQ5LU1zYmP1WMWKWnbuXIHu5AQAAAAA4E6Up8l+7Ozs5ODgoIiICEmSvb19nj8ODmSlAHArXmazfF1ccvw4mEzycnaWr4uLmrq5ydvJSaa/3gry1FNPqWHDhpo/f36O250+fbqaNWumgIAANWvWTNu3b7esq1Gjhl5//XUFBQXJ19dXU6dOtaz7448/1LJlS/n7+6tHjx5KTEws3hMAAAAAAMAt5CllrFatmkwmkxwdHSVJPj4+MplMxdoYACC7k8nJOp2aqgN//ilJWrZsmapWrarAwEA1btxYLVq0sKrv37+/XnzxRUnSjh07NHDgQP3xxx+W9fHx8dq+fbsuXLigWrVqadCgQfL29lb//v01dOhQDRkyRAcOHND999+vvn373r4DBQAAAADgb/IUZEZHR9/0OwCgeM05dUqOJpOc7Oz0nJeXPjl92rKuUqVK6tmzp3799ddsQea+ffv01ltv6eLFi3JwcFB4eLiuXbsmFxcXSbKEkxUqVFDNmjUVFRUlV1dXhYWFWd6H2ahRI7Vu3fr2HCgAAAAAALnguW8AuAOMqFpVNZydLd9vDDIl5XiXfGpqqnr27KmNGzeqWbNmltnjUlJSLEGm8w3btLe3V3p6eo775y58AAAAAEBJy9M7Mv9u8ODB+vzzz29Zt3DhQg0ePLgguwAA3EStv4JISTp//rxWrFihBx980KomOTlZqampqlatmiRp9uzZedq2m5ubmjRpoi+++EKSdOjQIW3ZsqWIOgcAAAAAoGAKFGQuXLgwT/9Su3XrVi1atKgguwAA3ES38uUlXZ/sp0OHDnr11VfVvHlzqxo3NzdNnTpVDzzwgAIDA2XOZVb0nHzxxRf6+OOP1bBhQ02cOFFt27Yt0v4BAAAAAMivYn20PDU1Vfb29sW5CwC4683y88u2rMxfv1uXLVumpk2bWq278T3G48aN07hx4yzfx44dm2OdJO3Zs8fy53r16lnNcA4AAAAAQEkr0B2ZeWEYhvbu3auKFSsW1y4AAAAAAAAA3CPyfEdmx44drb6vXbs227Is6enpOnbsmOLi4tS/f//CdQgAAAAAAADgnpfnIHPTpk2WP5tMJsXFxSkuLi7XekdHR3Xr1k3Tpk0rVIMAAAAAAAAAkOcgMyoqStL1R8Zr1qypJ554Qv/5z39yrDWbzapQoYIcHR2LpksAAAAAAAAA97Q8B5nVq1e3/PmNN95QkyZNrJYBAArudGpqsdYDAAAAAHCnK9Cs5W+88UZR9wEA9yRXV1dJ0tzY2EKNBwAAAADgblegIPPv4uPjlZSUJMMwclxfrVq1otgNANx1/Pz8FBERoaSkpHyPdXV1lZ+fXzF0BQAAAACA7SlwkBkXF6eJEydq9erVunjxYq51JpNJ6enpBd0NANz1CCMBAAAAALi1AgWZZ86cUbNmzXT69Gl5e3urYsWKOnfunIKCgnT8+HGdPXtWJpNJQUFBTPgDAAAAAAAAoNDsCjJo6tSpOn36tCZPnqyYmBh17dpVJpNJW7du1ZkzZ7Rp0ybVq1dPJpNJP/30U1H3DAAAAAAAAOAeU6A7MteuXStfX19NnDgxx/Vt27bVL7/8orp162rKlCl66623CtUkAAA3ioyMvOl7Rbt166b3339f4eHhatSokXx9fSXd/L2ikyZNUnx8vGbOnFkcLQMAAAAACqlAQWZsbKweeeQRy3d7e3tJUkpKipycnCRJ3t7e6tChg5YtW0aQCQAoMpGRkapTp84t6/r27Zvj8oiICN5LCgAAAAB3oAIFmW5ublbfPTw8JF0POGvWrGlZ7uzsrNjY2IJ3BwDA32TdiTnM21teZnOONdNiYtTCzU0bLl9WKXt7OdvZKdDVVT9evKgnn3xSdnZ2Sk5OVt++fXN8uqBRo0aaP3++WrZsKUn6+OOPtX79ev33v/8tvgMDAAAAANxUgd6RWa1aNZ08edLyvWHDhpKkNWvWWJZdvXpVW7duVZUqVQrZIgAA2XmZzfJ1ccnx42AyqY2Hh2q5uGiQp6fer11bQX/9R7h58+Zp7969Cg0N1bfffqsdO3Zk2/bIkSM1Z84cy/cPP/xQI0aMuG3HBgAAAADIrkBBZseOHbV//36dP39ektS9e3eVLl1aY8eO1csvv6zZs2erQ4cOOnv2rLp27VqgxmJjY9WvXz+VL19eLi4uatSokfbs2XPTMZs2bVLTpk3l5OSk2rVra+HChQXaNwDg7jVlyhQ1atRILVq00IkTJxQWFpatpl+/ftq4caPOnj2rLVu2yGQyqU2bNre/WQAAAACARYEeLQ8JCVFMTIwOHz6sdu3aqVy5cpo/f74GDRqk9957TyaTSYZhyN/fv0Dvx7x8+bJatWqlDh066KefflLFihUVGRmpsmXL5jomKipKjzzyiIYOHaqvvvpK69ev1z/+8Q9VqVJFwcHBBTlMAMBdyMPDQ6tWrZKDg4N69uyp5OTkbDUuLi4aOHCg5s+fryNHjmj48OEl0CkAAAAA4EYFCjIbN26sr7/+2mpZnz591KpVK61Zs0aXL19WnTp11L17dzk6OuZ7+++++658fHy0YMECy7KsGWdzM2/ePPn6+ur999+XJNWvX19btmzRjBkzCDIB4B7lYmenq5mZVssqVaokBwcHhYeHa926dWrbtm2OY4cPH64WLVooLS1Nn3322e1oFwAAAABwEwUKMnNTrVo1DR06tNDbWb16tYKDg/Xkk09q8+bN8vb21rBhw/Tss8/mOmb79u3q3Lmz1bLg4GCNHj06x/qUlBSlpKRYvicmJha6bwCAbelQtqyWnD2rny5eVLu/Jqb7/vvvtX79etWqVUsdO3bMdWzVqlXVpEkT1alTR6VKlbpNHQMAAAAAclOkQaYkJSQkKDIyUlWrVpWnp2eBtnH8+HF99NFHevHFF/XKK69o9+7dGjlypMxmswYMGJDjmLi4OFWuXNlqWeXKlZWYmKhr167JxcXFat3bb7+tN998s0D9AQBs2yw/P0lSDWdnNXV1lSRFXbsmSVq2bJmaNm2abcykSZOsvv/555/at2+fPvjgg+JtFgAAAACQJwWa7OeXX37R4MGDtW/fPqvls2fPlqenp5o3b66qVavqX//6V4GayszMVNOmTfXvf/9bTZo00XPPPadnn31W8+bNK9D2cjJhwgQlJCRYPjExMUW2bQDAnW3evHmqV6+ehg0bdstXmwAAAAAAbo8CBZmffvqpli9fLr+/7niRpAMHDmj06NHKyMhQixYt5Obmpg8++ECrVq3K9/arVKmiBg0aWC2rX7++Tp48mesYT09PnT171mrZ2bNn5ebmlu1uTElycnKSm5ub1QcAAEkaOnSoYmJi9Morr5R0KwAAAACAvxQoyNy7d68CAgJUpkwZy7KFCxda/nfr1q0KDQ2V2WzW3Llz8739Vq1aKTw83GpZRESEqlevnuuYoKAgrV+/3mrZunXrFBQUlO/9AwAAAAAAALAtBQoyz549q6pVq1otW79+vTw8PNS7d29J12cZb9eunY4cOZLv7f/rX//Sjh079O9//1tHjx7VkiVL9PHHH2v48OGWmgkTJuiZZ56xfB86dKiOHz+ucePG6Y8//tDcuXO1bNmyAj/eDgAAAAAAAMB2FCjItLe3V3JysuX7pUuXdPDgQbVp00Z2dv+/yYoVK+r8+fP53n6zZs303Xff6euvv1bDhg01ZcoUzZw5UyEhIZaaM2fOWD1q7uvrqx9//FHr1q1T48aN9f777+vTTz9VcHBwQQ4RAAAAAAAAgA0p0KzlNWrU0LZt25SWliZHR0etWLFChmHowQcftKq7ePGiypcvX6DGunXrpm7duuW6PutR9hu1b98+2wREAIC70+nU1GKtBwAAAADYlgIFmU8//bReffVVtW3bVi1bttSCBQvk6OioHj16WGoMw1BoaKjq1q1bVL0CACBXV1dJ0tzY2EKNBwAAAADcWQoUZI4cOVKrVq3Szp07tXPnTtnZ2ek///mPvL29LTUbNmzQ+fPnNXTo0CJrFgAAPz8/RUREKCkpKd9jXV1d5efnVwxdAQAAAACKW4GCzNKlS2vbtm367bffdP78eQUEBGT7F0N7e3vNmDFDjz76aJE0CgBAFsJIAAAAALj3mAzDMEq6CVuQmJgod3d3JSQkyM3NraTbAQAAAAAAAO4oxZ2vFeiOTAC420VGRvLocj5kna+TJ0/qzz//zPO40qVLq1q1avfseQMAAAAA5B1BJgD8TWRkpOrUqVPg8REREdlCuZkzZ6p3797y9PS85fiHH35YM2bMuGMmSyvs+coSERGhQ4cOydPTUy1atJAkbdq0SaNHj1ZYWFiht/93q1ev1saNGzVjxgyr5QEBAfr999+ZFAgAAAAAbAxBJgD8TdadmMO8veVlNudYMy0mRiGVKqmKk5Nl2enUVM2Njc3xTs6ZM2eqffv2eQoy16xZU8DOS0bW8T5ZsaKWnz+f43lLSk/X7NhYOZhM6l+5sqo4Oel0SoqqmM16LyZGSRkZSkpK0sqVKxUQEGAJMotT9+7d1b1792zLiyM0BQAAAAAUnl1JNwAAtsrLbJavi0uOHweTSV7OzlbLoq5dkyT16dNHzZo10/bt2yVJkydP1unTp/X0008rICBA27ZtU/ny5RUXF2fZ16RJk/Svf/1LklSjRg1LmDZ9+nQ1a9ZMAQEBVtu0RRUdHSXlfN7cHB3lYDLJ8Ybz1srDQzVLldLVjAxJ0pYtW7Ry5Uq9/PLLcnFxkY+Pj7Zs2aL09HQ1b95cFSpUkL+/v/bs2aMrV67I3d1d7u7ukqRp06YpMDBQJpNJXbp00YkTJxQSEiIvLy95eHiocuXKqlOnjjp27KiwsDB5eHjosccek4eHh2rXrq2vvvpKHTp0sGzj2WefVWZmpsaMGWM5/23btlV4eHiJnV8AAAAAuNcRZAJAEQkoU0aS9PXXX2v27NkaNGiQJOn111+Xl5eX/vvf/yosLEwtW7ZUr169tHjxYkmSYRhatGiRBg8enG2b/fv31+7duxUWFma1zTtNNScn1S1VSpfS0/VVXJx+uHBBl9LSrGpat24tR0dHde/eXdeuXdOGDRv03nvv6Y8//tCrr74qOzs7vfDCC3r11Ve1fPlyBQUFyWQyacmSJQoPD9eGDRskSSEhIRo2bJhmzpyp5557Th4eHnr++efVsWNHVapUSUuWLFFCQoJ8fHzUvn17zZkzR2+++aa+//57hYaGSpJOnjypZcuWafz48ZbzP2zYMI0aNeq2nzsAAAAAwHU8Wg4AReR0Sook6amnnlKZMmUUHh6ua9euycXFJVvtoEGD9I9//ENjxozRpk2bVL58eTVq1Chb3b59+/TWW2/p4sWLcnBwuOk2bZmdyaTRPj4aEREhv1KlFHHtmlZduKApNWtaav78809dvHhRzZs3lyT5+fmpUaNGioqKUvfu3dWkSRNdu3ZNx44d08KFC/XMM89ox44dWrlypXbv3q3WrVtLkt577z1J0pIlS/Txxx8rOTlZS5YsUYUKFdSnTx9t27ZNzs7OCgwM1KlTpxQUFKTjx49r/Pjx2rJli6Tr571JkybKzMzU7NmzlZSUpMzMTF26dOk2nzkAAAAAQBaCTAAoAumGoa/PnZMkLVu2TLVr15a7u7tSUlJyDB2DgoKUmZmpXbt2aeHChTneaZmamqqePXtq48aNatasmRITE2+6zTuBvcmkB9zc9JSzs949cUJ7bzEzvMlkkuNfj6wPHjxYc+bMUXJyso4ePapOnTopIyNDhmFowoQJ6tmzpypWrKgDBw5oy5YtGjBggEJCQpSWlqaOHTvq9ddfl729vTIyMuTk5CSTyXS9p7+WnTt3Tjt37pSLi4ueeOIJnTt3TiNGjNDu3btVq1Yt7d+/X23bti32cwQAAAAAyFmhHi1PT0/XqlWr9Oqrr+r555/X559/bll3+vRp7d+/X+np6YVuEgBsXWpmpjIMw/J99uzZVuvd3NyUkJBgtWzQoEGaPXu2fvzxR/Xt2zfbNpOTk5Wamqpq1arluM07yaW0NIVfvWr5/mdGhs6npanSDZMClS5dWpUqVdLmzZslSUePHtWBAwdU5q9H9nv06KGDBw8qISFB/fr1U9WqVWUYhpo1a6Z58+Zp3rx5kqS0tDTt2bNHrq6ucnFxUUZGhubPn3/LHj09PeXs7CxJWrVqlVJTU+Xo6KgqVarIMAzNmTOnyM4HAAAAACD/ChxkbtmyRbVr11bPnj319ttv69NPP7U8kidJ27dvV5MmTbR69eoiaRQAbM27J05oRESERkREaNyxY2rn4SHp+jsazX+btXvkyJF69tlnFRAQYJnIp3///lq6dKk6d+6ssmXLZtu+m5ubpk6dqgceeECBgYHZtnknyTAMfXf+vC6lpem148f1Qni44tPTtfDMGRk31M2dO1cbNmyQi4uLOnTooLFjx1ruyHRyclJwcLCuXLmiQYMGycHBQbNnz9a8efN04cIFffjhh5KkgIAApaamqm7dupozZ46WL1+ugICAW/a4c+dO+fv7S5LatWunChUqqHfv3vL391ezZs0sgTIAAAAAoGSYDMMwbl1m7fDhw3rggQeUlpamF154Qa1bt9ZTTz2lgQMHWu7KTE1NVfny5dW9e3d99dVXRd54Uct6ZDMhIUFubm4l3Q6AErR3714FBgZqqq+vfPPxCHfUtWuaGBWl0NBQNW3atBg7tC1Z52uYl5fmnj7NeQMAAACAe1Rx52sFekfmlClTlJycrDVr1uihhx7KscZsNqtp06bat29foRoEAAAAAAAAgAI9Wr5x40Y98MADuYaYWby9vXX69OkCNQYAAAAAAAAAWQoUZMbHx8vHx+eWdX/++afS0tIKsgsAAAAAAAAAsChQkFmpUiUdPXr0lnVHjhzJU+AJAAAAAAAAADdToHdkduzYUV9++aU2btyoDh065Fjz3Xff6ejRoxo+fHihGgSAknI6NbVY6+825/+6A5/zBgAAAAAoDgUKMl9++WX997//VY8ePfTOO+/o8ccft6y7fPmyvvvuO40ZM0alS5fWiy++WGTNAsDt4OrqKkmaGxtbqPH3iqzjXX7+vCTOGwAAAACgeJgMwzAKMnDlypXq37+/rl69muN6Z2dnff311+revXuhGrxdint6eAB3lsjISCUlJeV7nKurq/z8/IqhI9uWdb5OnjypP//8M8/jSpcurWrVqt2z5w0AAAAA7ibFna8VOMiUpBMnTmjGjBlat26doqOjlZmZqapVq+rBBx/USy+9pFq1ahVlr8WKIBMAAAAAAAAoOJsIMn/77Td5enqqTp06Rd6ArSDIBAAAAAAAAAquuPO1PL0js3379ho4cKA+//xzSdcn++nSpYvGjRtX5A0BuHfw+DYAAAAAAMirPAWZJpNJmZmZlu+bNm1SjRo1iqsnAPeAyMjIQt3lHRERUegw8/Tp03r66af1+++/F2o7AAAAAACg+OUpyCxXrpwiIyOLuxcA95CsOzGHeXvLy2zOsWZaTIxCKlVSFScnJaana9apU+pWoYK+PX++QHdy/p2XlxchJgAAAAAAd4g8BZmtW7fW6tWr1aFDB/n6+kqStmzZosGDB99yrMlk0meffVa4LgHctbzMZvm6uOS4zsFkkpezs2o4O0uSqjo5ydFkkiRt3LhRTZs2lSQ98cQT6tatmwYOHKhPP/1U06dPl9lsVkZGhj799FM1a9ZMI0eO1Pr162U2m+Xg4KCtW7cqLi5OAQEBio+PlySFhIQoPDxcqamp8vHx0WeffSZPT8/iPwkAAAAAAOCW8hRkvv/++4qOjtbmzZu1efNmSdLRo0d19OjRW44lyARQFE6npOhKRoZ8/wo1c/PSSy/pjz/+UJUqVZSWlqaUlBT973//0/r163Xo0CHZ2dkpISFB5hzuAp05c6YqVqwoSXrnnXc0adIkzZs3r1iOBwAAAAAA5E+egsyaNWtq7969io6OVkxMjNq3b68uXbpo/Pjxxd0fgHvcnFOnZJJ0JjVV/SpXVml7+5vWd+rUSf3799ejjz6qrl27qk6dOqpZs6bS09M1ePBgdejQQY888ojs7OyyjV2yZIm+/PJLJScnKzk5WRUqVCimowIAAAAAAPmVpyBTun5npa+vr+XRck9PT7Vr167YGgMASRpRtapqODvr4JUrmhYTo+e9vCTJagKy5ORky5+//fZbhYaGatOmTXr44Yc1depU9e7dWwcPHtTmzZu1ceNGTZgwQb/99pscHP7/V+CWLVv0wQcfaPv27apUqZJWr16t119//fYdKAAAAAAAuKk8B5k3ujFAAIDboWGZMupctqx+vXxZknTw4EFJUlRUlLZs2aInnnhC6enpio6O1v3336/7779fFy5c0K5du9SpUyfZ29vroYce0oMPPqjNmzfr8OHDuu+++yzbv3z5slxdXVW+fHmlpqZq/vz5JXKcAAAAAAAgZwUKMgGgJDxesaJGRUZKkvbs2aNGjRrJ399fzZs3lyRlZGRo8ODBunTpkhwcHFSxYkUtWLBAMTExevbZZ5WWlqaMjAy1atVKXbt2VWxsrGXbXbp00eLFi1W3bl2VL19enTt3tloPAAAAAABKlskwDONWRZMnT5bJZNLw4cNVrlw5TZ48Oe87MJn02muvFarJ2yExMVHu7u5KSEiQm5tbSbcD3PX27t2rwMBATfX1zXXW8pxEXbumiVFRCg0NtcxaDgAAAAAASl5x52t5uiNz0qRJMplMevrpp1WuXDnL9zxkoHdMkAkAAAAAAADAduUpyFywYIEkqUqVKlbfAQAAAAAAAOB2yFOQOWDAgJt+BwAAAAAAAIDiZFecG//888/z9T5NAAAAAAAAAMhJsc5a/sknn2jXrl16/fXXi3M3AO5gp1NTi7UeAAAAAADcHYo1yASA3Li6ukqS5sbGFmo8AAAAAAC4NxBkAigRfn5+ioiIUFJSUr7Hurq6ys/Prxi6AgAAAAAAtoogE0CJIYwEAAAAAAB5VayT/QAAAAAAAABAUSDIBAAAAAAAAGDzeLQcAJCjyMjIW77DtFu3bjKbzXJycrIse//999WtW7dstdHR0Vq7dq2GDh1a6N4CAgL0+++/M+kTAAAAANxDTIZhGLcqsre3L9ROMjIyCjX+dkhMTJS7u7sSEhLk5uZW0u0AQImKjIxUnTp1Cjw+IiIi2ztQN23apNGjRyssLKyQ3QEAAAAAbFFx52t5uiMzD1lnrkwmU4HHAgBKRtadmMO8veVlNudaNy0mRiGVKqnKX3dknk5N1dzYWA0fPlynTp2So6OjKleurF9++UVDhw7ViRMnFBAQoGrVqmn16tXas2ePRo4cqStXrsjZ2VkzZsxQq1atFB0drYCAAI0aNUo//PCDEhIS9MEHH+jhhx+WdP2fLZcvX5aHh4fGjBmjzZs3Ky0tTW5ubvrkk09Ut27d4j9JAAAAAIDbKk9BZmZmZnH3AQCwQV5ms3xdXHJd72AyacWFC3L86z9apf71H76SkpJ0+PBhSdKlS5ckSfPmzbO6IzM1NVU9e/bUJ598ouDgYG3ZskW9evXS0aNHJUkJCQm677779Oabb2rt2rUaNWqUJci80fjx4zVt2jRJ0tKlSzVq1CitXbu2aE4AAAAAAMBm8I5MAEChjKhaVTWcnSVJUdeuaWJUlKKiojRs2DC1a9cux/BRksLDw2VnZ6fg4GBJUuvWrVW5cmWFhYWpatWqcnZ2Vs+ePSVJQUFBOnbsWI7bWbdunWbPnq2kpCRlZmZaglMAAAAAwN2FWcsBAEXum2++UZcuXbR161Y1bNhQly9fztO4G19H4uTkZPlub2+f4/uWT548qREjRmjx4sU6ePCgli5dquTk5KI5CAAAAACATSHIBAAUOZPJpO7du2vatGkyDEMxMTFyc3NTQkKCpaZu3brKzMzUunXrJEnbtm1TXFycAgIC8ryfhIQEOTo6qkqVKjIMQ3PmzCnqQwEAAAAA2AgeLQcAFMqcU6eyvSNz8ODBcnZ2Vnp6uvr376/77rtP6enp8vf3V8OGDVWzZk2tXr1aK1as0MiRI/XSSy/J2dlZ33zzjcqUKaMLFy7kad+NGjVS79695e/vr/Lly6tHjx7FdZgAAAAAgBJmMgozJfldpLinhweAO8nevXsVGBioqb6+N53s5++y3pEZGhqqpk2bFmOHAAAAAABbU9z5Go+WAwAAAAAAALB5BJkAAAAAAAAAbB5BJgAAAAAAAACbR5AJAAAAAAAAwOYRZAIAAAAAAACweQ4l3QAAwHadTk0t1noAAAAAAPKKIBMAkI2rq6skaW5sbKHGAwAAAABQVAgyAQDZ+Pn5KSIiQklJSfke6+rqKj8/v2LoCgAAAABwLyPIBADkiDASAAAAAGBLmOwHAAAAAAAAgM3jjkwAKCKRkZE8ig0AAAAAQDEhyASAIhAZGak6deoUeHxERARhJgAAAAAAN0GQCQBFIOtOzGHe3vIym3OsmRYTo5BKlVTFycmybG5srE6nphboTs6ilJGRIXt7+xLtAQAAAACAm+EdmQBQhLzMZvm6uOT4cTCZ5OXsbLXMbHf91/Abb7yhmTNnWrYzZswYTZo0SZI0adIkPf3003r00UfVoEEDdezYUZcuXZIkpaWladiwYapTp45atGihl156Se3bt5ckxcXFqUOHDgoMDJS/v79GjBihzMxMSdLChQvVoUMH9erVS40aNdK2bdtUr149GYZh6aFly5b66aefiv+kAQAAAACQB9yRCQC30ZxTp+RoMlm+x6Wm5mnczp07FRoaqvLly6t3796aP3++JkyYoI8//liRkZE6dOiQJOnhhx+2jPHw8ND333+vMmXKKCMjQ4899piWLVum3r17W7a5b98+1a1bV5JUvnx5rVu3Tg899JD27dun8+fPq0uXLkV16AAAAAAAFAp3ZALAbTSialW9XauW5eN1w2PmN9OlSxeVL19ekhQUFKRjx45JktavX69+/frJ0dFRjo6OGjBggGVMZmamxo8fr8aNG6tJkybas2ePwsLCLOtbtmxpCTEladSoUZozZ44k6cMPP9SwYcNkuiF0BQAAAACgJBFkAoANsLe3V0ZGhuV7cnKy1XpnZ2er2vT09By3c2PwOH36dJ07d047d+7U/v371bdvX6vtlilTxmpsz549tX//fu3bt0+rV6/WoEGDCnVMAAAAAAAUJYJMALABPj4+2rVrlyTp4sWLWrNmTZ7GdezYUUuWLFFaWprS0tL0xRdfWNZdvnxZnp6ecnZ2VlxcnJYvX37TbTk4OGjo0KHq3r27Hn/8cXl4eBT4eAAAAAAAKGoEmQBgA3r27Knz58+rfv36euaZZ9SiRYs8jXv++edVo0YNNWjQQK1atVKtWrUsAeSoUaO0c+dO+fv7q3///urcufMttzdkyBDFxsZqxIgRhTkcAAAAAACKnMm4cYrae1hiYqLc3d2VkJAgNze3km4HwB1m7969CgwM1FRfX/m6uOR5XNS1a5oYFaXQ0FA1bdq0QPtOSkqSq6ur0tLSFBISosDAQI0fP75A2/rmm2/00Ucfaf369QUaDwAAAAC4dxV3vsas5QBwh+vcubNSUlKUnJys1q1ba+TIkQXaTpcuXRQREaHvvvuuiDsEAAAAAKDwCDIB4A63c+fOItnO2rVri2Q7AAAAAAAUB96RCQAAAAAAAMDmEWQCAAAAAAAAsHk8Wg4AReh0amqx1gMAAAAAcK8iyASAIuDq6ipJmhsbW6jxAAAAAAAgZwSZAFAE/Pz8FBERoaSkpHyPdXV1lZ+fXzF0BQAAAADA3YMgEwCKCGEkAAAAAADFh8l+AAAAAAAAANg87sgEcNeJjIzkEW8AAAAAAO4yBJkA7iqRkZGqU6dOgcdHRETcMszctGmTRo8erbCwsALvBwAAAAAA5A9BJoC7StadmMO8veVlNudadzktTR+ePq2J1atLkk6npmpubOwt7+RMT08vumYBAAAAAECe8Y5MAHclL7NZvi4uuX58nJ1lJ1m+z42NtRpfoUIFRUdHS5Jq1Kih8ePH64EHHtCAAQOs6hITE/XQQw9p8uTJkqSff/5ZrVu3VmBgoB544AFt3LhRktStWzctWbLEMu6XX35R8+bNi+8EAAAAAABwl7HJIHPSpEkymUxWn3r16uVav3Dhwmz1zs7Ot7FjAHe7ixcvaufOnfrqq68sy2JiYtShQwf169dPr7/+uo4fP65JkyZpzZo1Cg0N1ZIlS9S3b1+lpKRo1KhRmjNnjmXshx9+qBEjRpTEoQAAAAAAcEey2UfL/f399euvv1q+OzjcvFU3NzeFh4dbvptMpmLrDcC9Z+DAgVa/V86ePau2bdvq008/VadOnSRJa9eu1dGjR9W2bVtLnZ2dnU6ePKkHH3xQo0eP1r59+1SuXDnt2rVLy5Ytu+3HAQAAAADAncpmg0wHBwd5enrmud5kMuWrHgBuZCcp84bvycnJVuvLlClj9d3Dw0O1a9fWDz/8oI4dO8pkMskwDD344INWj5DfaOTIkZo9e7YqV66swYMHy8nJqYiPAgAAAACAu5dNPlouXZ952MvLSzVr1lRISIhOnjx50/orV66oevXq8vHx0WOPPaZDhw7dtD4lJUWJiYlWHwD3rnKOjpY/r1ixQn/++edN652cnLRixQqdPn1azz77rDIzMxUcHKxff/1V+/fvt9Tt2rXL8uf+/fvr559/1oIFCzR06NCiPwgAAAAAAO5iNhlkNm/eXAsXLtTatWv10UcfKSoqSm3atMl1NuG6devq888/16pVq7R48WJlZmaqZcuWOnXqVK77ePvtt+Xu7m75+Pj4FNfhALgDPFyunCSpb9++2rdvn8qXL3/LMY6OjlqyZIkyMjIUEhKiGjVqaMmSJXr++efVuHFj1a9fXzNnzrTUlypVSj179lSrVq34nQMAAAAAQD6ZDMMwSrqJW4mPj1f16tU1ffp0DRky5Jb1aWlpql+/vvr06aMpU6bkWJOSkqKUlBTL98TERPn4+CghIUFubm5F1juA22vv3r0KDAzUVF9f+bq45Hlc1LVrmhgVpdDQUDVt2rRYesvIyFBgYKBmz56tNm3aFMs+AAAAAAAoKYmJiXJ3dy+2fM0m78j8Ow8PD9WpU0dHjx7NU72jo6OaNGly03onJye5ublZfQCguKxevVq1atVSUFAQISYAAAAAAAVwRwSZV65c0bFjx1SlSpU81WdkZOjAgQN5rgeA4ta9e3dFR0fro48+KulWAAAAAAC4I9lkkDlmzBht3rxZ0dHR2rZtmx5//HHZ29urT58+kqRnnnlGEyZMsNRPnjxZv/zyi44fP669e/eqX79+OnHihP7xj3+U1CEAAAAAAAAAKEIOJd1ATk6dOqU+ffro4sWLqlixolq3bq0dO3aoYsWKkqSTJ0/Kzu7/M9jLly/r2WefVVxcnMqWLavAwEBt27ZNDRo0KKlDAAAAAAAAAFCEbDLIXLp06U3Xb9q0yer7jBkzNGPGjGLsCMCd5nRqarHWAwAAAACA28smg0wAKChXV1dJ0tzY2EKNBwAAAAAAtoUgE8Bdxc/PTxEREUpKSsr3WFdXV/n5+RVDVwAAAAAAoLAIMgHcdQgjAQAAAAC4+9jkrOUAAAAAAAAAcCOCTAAAAAAAAAA2j0fLAdi0yMjIW77vMi0tTQsWLNDatWtlb28vR0dHVa1aVdOmTVNAQMDtabSAbnV83bp1k9lslpOTk9LS0vTUU0/pqaeeyvP7PKOjo7V27VoNHTq0KNvOpn379ho9erR69OhRrPsBAAAAANy7CDIB2KzIyEjVqVOnQGPDw8O1adOmbEFmRkaG7O3ti6C7wivI8b377rt69913JUkRERE3DTPT09MVHR2tefPmFXuQCQAAAABAcSPIBGCzsu5UHObtLS+zOceaC2lp+jA2VmN9fFTqr4DydGqq5sbGqm3btlq4cKEWLVqkcuXKKSIiQh9//LEcHBw0fvx4JSYmKiMjQ6+88oqefPJJSdLPP/+sKVOm6Nq1a7K3t9e7776rDh06SJIWLFigWbNmyTAMOTo66ptvvlGNGjVuOqawxzctJkYhlSqpipOTJOmj2Fj5ly6tXy5fVteuXeXs7Kx//vOfev755yVJNWrU0NNPP62NGzfKz89PoaGhOnHihAICAlStWjWtXr1aNWrU0MqVKy0h7/33369p06apffv2+uOPPzRo0CAlJiaqbt26unLlivr27auBAwdqyZIlmjVrllJTU5WZmampU6fq0UcfzctfJQAAAAAAhUaQCcDmeZnN8nVxyXHd2dRUVTGb5V+mTK7jd+7cqX379qlu3bqKj49Xhw4dtGbNGlWpUkUXLlxQ06ZN1bJlS6WkpGjSpEn6+eef5ebmpqNHj6pNmzaKjo7W9u3bNXnyZG3btk1VqlTR1atXJUnHjx/PdYzTX+FjYY7PwWSSl7Ozajg762Rysi6mp+tUSookadmyZapataoCAwPVuHFjtWjRQpJ08eJF7dy5UyaTSZs2bdLo0aMVFhaWp1769++vYcOGadCgQTpy5IiaNGmivn37SpKCg4PVp08fmUwmRUdHq0WLFjpx4kSejxMAAAAAgMIgyARwVzmbmqo5sbGSpEmTJqlnz55q2bKl6tatK0natm2bjh8/rq5du1qNCw8P1x9//KGjR4+qbdu2luV2dnY6efKkfvzxR/Xv319VqlSRJJUqVUqStHbt2lzH5OUdlnkx59QpOZpMcrKz03NeXvrk9GnLukqVKqlnz5769ddfLUHmwIEDZTKZ8r2fxMREhYWF6ZlnnpEk1a9fX61bt7asj4qKUkhIiE6dOiUHBwddunRJUVFRqlevXiGPEAAAAACAWyPIBHBHq+HsrLOpqfozI0Ol7e1V2WzWCG9vTYyKUmJioiSpzA13axqGIX9/f23bti3bto4cOaIHH3xQS5YsyfP+DcPI95j8GlG1qmo4O1u+3xhkSsoWWpa5yd2pkuTg4KCMjAzL9+Tk5Fxrb9x279699c477+iJJ56QJJUrV+6mYwEAAAAAKEp2Jd0AABSGp5OTmrq66uPTp/XnDeFcblq2bKmoqCj9+uuvlmVhYWFKTU1VcHCwfv31V+3fv9+ybteuXZKkRx99VIsXL9aZM2ckSVevXtXVq1dvOqa41LrhMfTz589rxYoVevDBB3OsdXNzU0JCgtWy2rVra+fOnZKu9xoeHm6pbdy4sRYvXizp+l2qW7ZssYy7fPmyfH19JUmLFy/W5cuXi+6gAAAAAAC4Be7IBHDHG+rtrZXnz+uNqCjZmUzKmpN84MCB2WrLli2rH3/8UWPGjNFLL72ktLQ0VatWTStXrlTt2rW1ZMkSPf/887p69apSU1PVpEkTLVmyRG3bttUbb7yh4OBgmUwmmc1mffPNNzcdU1y6lS+vg3/+qaeeekrOzs569dVX1bx58xxr77vvPvn7+6thw4aqWbOmVq9eralTp2rAgAGaP3++goKC5O/vb6n/4osvNHjwYP3nP/9R7dq11axZM3l4eEiSZs2apSeeeEIeHh7q2LGjqlWrVmzHCAAAAADA35kMwzBKuglbkJiYKHd3dyUkJMjNza2k2wEgae/evQoMDNRUX99cJ8PJSdS1a5oYFaXQ0FA1bdq0GDssHFs8vitXrqh06dIymUyKiopSUFCQdu/eLR8fnyLdDwAAAADg7lPc+Rp3ZAIALLZt26axY8dKkjIyMjRjxgxCTAAAAACATSDIBABYPPTQQ3rooYdKug0AAAAAALJhsh8AAAAAAAAANo8gEwAAAAAAAIDNI8gEAAAAAAAAYPN4RyYAm3c6NbVY60va3X58AAAAAAAUBYJMADbL1dVVkjQ3NrZQ423V3X58AAAAAAAUJYJMADbLz89PERERSkpKyvdYV1dX+fn5FUNXReduPz4AAAAAAIoSQSYAm3a3h3V3+/EBAAAAAFBUmOwHAAAAAAAAgM0jyAQAAAAAAABg8wgyAQAAAAAAANg8gkwAAAAAAAAANo8gEwAAAAAAAIDNI8gEAAAAAAAAYPMIMgEAAAAAAADYPIJMAAAAAAAAADaPIBMAAAAAAACAzSPIBAAAAAAAAGDzCDIBAAAAAAAA2DyCTAAAAAAAAAA2jyATAAAAAAAAgM0jyAQAAAAAAABg8wgyAQAAAAAAANg8gkwAAAAAAAAANo8gEwAAAAAAAIDNI8gEAAAAAAAAYPMIMgEAAAAAAADYPIJMAAAAAAAAADaPIBMAAAAAAACAzSPIBAAAAAAAAGDzCDIBAAAAAAAA2DyCTAAAAAAAAAA2jyATAAAAAAAAgM0jyAQAAAAAAABg8wgyAQAAAAAAANg8gkwAAAAAAAAANo8gEwAAAAAAAIDNI8gEAAAAAAAAYPMIMgEAAAAAAADYPIJMAAAAAAAAADaPIBMAAAAAAACAzSPIBAAAAAAAAGDzCDIBAAAAAAAA2DyCTAAAAAAAAAA2jyATAAAAAAAAgM0jyAQAAAAAAABg8wgyAQAAAAAAANg8gkwAAAAAAAAANo8gEwAAAAAAAIDNI8gEAAAAAAAAYPMIMgEAAAAAAADYPIJMAAAAAAAAADaPIBMAAAAAAACAzSPIBAAAAAAAAGDzCDIBAAAAAAAA2DyCTAAAAAAAAAA2jyATAAAAAAAAgM0jyAQAAAAAAABg8wgyAQAAAAAAANg8gkwAAAAAAAAANo8gEwAAAAAAAIDNI8gEAAAAAAAAYPMIMgEAAAAAAADYPIJMAAAAAAAAADaPIBMAAAAAAACAzSPIBAAAAAAAAGDzCDIBAAAAAAAA2DyCTAAAAAAAAAA2jyATAAAAAAAAgM0jyAQAAAAAAABg8wgyAQAAAAAAANg8mwwyJ02aJJPJZPWpV6/eTccsX75c9erVk7Ozsxo1aqQ1a9bcpm4BAAAAAAAAFDebDDIlyd/fX2fOnLF8tmzZkmvttm3b1KdPHw0ZMkT79u1Tjx491KNHDx08ePA2dgwAAAAAAACguNhskOng4CBPT0/Lp0KFCrnWzpo1S126dNHYsWNVv359TZkyRU2bNtWcOXNuY8cAAAAAAAAAiovNBpmRkZHy8vJSzZo1FRISopMnT+Zau337dnXu3NlqWXBwsLZv357rmJSUFCUmJlp9AAAAAAAAANgmmwwymzdvroULF2rt2rX66KOPFBUVpTZt2igpKSnH+ri4OFWuXNlqWeXKlRUXF5frPt5++225u7tbPj4+PkV6DAAAAAAAAACKjk0GmV27dtWTTz6p++67T8HBwVqzZo3i4+O1bNmyItvHhAkTlJCQYPnExMQU2bYBAAAAAAAAFC2Hkm4gLzw8PFSnTh0dPXo0x/Wenp46e/as1bKzZ8/K09Mz1206OTnJycmpSPsEAAAAAAAAUDxs8o7Mv7ty5YqOHTumKlWq5Lg+KChI69evt1q2bt06BQUF3Y72AAAAAAAAABQzmwwyx4wZo82bNys6Olrbtm3T448/Lnt7e/Xp00eS9Mwzz2jChAmW+lGjRmnt2rV6//339ccff2jSpEnas2ePRowYUVKHAAAAAAAAAKAI2eSj5adOnVKfPn108eJFVaxYUa1bt9aOHTtUsWJFSdLJkydlZ/f/GWzLli21ZMkSTZw4Ua+88or8/Py0cuVKNWzYsKQOAQAAAAAAAEARMhmGYZR0E7YgMTFR7u7uSkhIkJubW0m3AwAAAAAAANxRijtfs8lHywEAAAAAAADgRgSZAAAAAAAAAGweQSYAAAAAAAAAm0eQCQAAAAAAAMDmEWQCAAAAAAAAsHkEmQAAAAAAAABsHkEmAAAAAAAAAJtHkAkAAAAAAADA5hFkAgAAAAAAALB5BJkAAAAAAAAAbB5BJgAAAAAAAACbR5AJAAAAAAAAwOYRZAIAAAAAAACweQSZAAAAAAAAAGweQSYAAAAAAAAAm0eQCQAAAAAAAMDmEWQCAAAAAAAAsHkEmQAAAAAAAABsHkEmAAAAAAAAAJtHkAkAAAAAAADA5hFkAgAAAAAAALB5BJkAAAAAAAAAbB5BJgAAAAAAAACbR5AJAAAAAAAAwOYRZAIAAAAAAACweQSZAAAAAAAAAGweQSYAAAAAAAAAm0eQCQAAAAAAAMDmEWQCAAAAAAAAsHkEmQAAAAAAAABsHkEmAAAAAAAAAJtHkAkAAAAAAADA5hFkAgAAAAAAALB5BJkAAAAAAAAAbB5BJgAAAAAAAACbR5AJAAAAAAAAwOYRZAIAAAAAAACweQSZAAAAAAAAAGweQSYAAAAAAAAAm0eQCQAAAAAAAMDmEWQCAAAAAAAAsHkEmQAAAAAAAABsHkEmAAAAAAAAAJtHkHkXioyM1N69e3P9TJs2TfXr11fdunVVo0YNNWvWTHv27FFkZGSB9zlp0iQlJydbvg8cOFAzZ87M8/ikpCSVKVNGQ4YMsVp+7NgxNW3aVE2aNNGCBQuyjTt9+rTatGlT4L4BAAAAAABwZzAZhmGUdBO2IDExUe7u7kpISJCbm1tJt1NgkZGRqlOnToHHR0REyM/PL9/jTCaTLl++LA8PD0nXg8yAgACNHj06T+M//fRTffnll9q/f79iYmJUpkwZSdK7776r48ePa/78+dnGpKeny8HBId+9AgAAAAAAoOgVd75GCnSXSUpKkiQN8/aWl9mcbf2ZlBQtjIvTiz4+crL7/xtyT6emam5srHbu3KkBAwboypUrcnZ21owZM9SqVStFR0crICBA8fHxkqQrV67I1dVVhmFo6NChkqQ2bdrI3t5ev/zyiyTpyJEj6tSpk2JiYtSwYUMtXbpU5hx6kqTPPvtMr732mubPn6///ve/GjJkiL744gvNmDFDGRkZ2rlzp5YsWaJhw4bpvvvu0+7du+Xi4qLPP//cqq/t27dr7NixSkpKkmEYmjJlih577DGNGTNGmzdvVlpamtzc3PTJJ5+obt26RXXaAQAAAAAAUMwIMu9SXmazfF1csi2v7uysXUlJmh4To/qlS8vPxUUt3d0t68eMGaNFixYpODhYW7ZsUa9evXT06NGb7mvevHmaP3++fv/9d8sdmZIUFhamjRs3ysnJSW3bttW3336rPn36ZBt/+PBhxcTEKDg4WOnp6XrnnXc0ZMgQPfPMMzp+/Lji4+OtHlOPiIjQb7/9JkdHR0VHR1uWX7p0ST169NA333yjNm3aKDMz0xJwjh8/XtOmTZMkLV26VKNGjdLatWvzcCYBAAAAAABgCwgy7zF2JpNG+/jodEqKjly9qv9duaJVFy7oeS+v6+vt7BQcHCxJat26tSpXrqywsDBVrVo13/t6/PHHVapUKUnSAw88oGPHjuVY99lnn+mZZ56Rvb29Hn74YT3//PM6cuSI6tevn2N9v3795OjomG359u3bVbduXcs7M+3s7FSuXDlJ0rp16zR79mwlJSUpMzNTly5dyvfxAAAAAAAAoOQw2c89ysvJSZ3KltWLPj6q7eKiP65ezbHOZDJJkhwcHJSRkWFZfuPEPrlxdna2/Nne3l7p6enZatLS0vTll19q0aJFqlGjhmrXrq2rV6/qs88+y3W7We/PzKuTJ09qxIgRWrx4sQ4ePKilS5fmqX8AAAAAAADYDoLMe8yltDSF3xBa/pmRofNpaSr316Q5mZmZWrdunSRp27ZtiouLU0BAgDw9PWUYhg4fPixJ+uKLL6y26+rqqoSEhHz3s3r1atWsWVOxsbGKjo5WdHS0duzYoS+//FJpaWn52lbLli0VGRmp33//3XIsly5dUkJCghwdHVWlShUZhqE5c+bku08AAAAAAACULB4tv8dkGIa+O39e59PSZDaZlCmpjbu76pcuLUmaNm2a3njjDb300ktydnbWN998Y7kDcvbs2erWrZvKly+vJ554wmq7L730kh588EGVKlXKMtlPXnz22WcKCQmxWla/fn15e3vr+++/z9exlS1bVt99951eeuklJSUlyc7OTlOmTNGjjz6q3r17y9/fX+XLl1ePHj3ytV0AAAAAAACUPJNhGEZJN2ELint6+Ntl7969CgwM1FRf3xwn+8lN1LVrmhgVpdDQUDVt2rQYOwQAAAAAAMDdqLjzNR4tBwAAAAAAAGDzCDIBAAAAAAAA2DyCTAAAAAAAAAA2jyATAAAAAAAAgM0jyAQAAAAAAABg8xxKugEUj9OpqcVaDwAAAAAAANxOBJl3GVdXV0nS3NjYQo0HAAAAAAAAbAlB5l3Gz89PERERSkpKyvdYV1dX+fn5FUNXAAAAAAAAQOEQZN6FCCMBAAAAAABwt2GyHwAAAAAAAAA2jyATAAAAAAAAgM0jyAQAAAAAAABg8wgyAQAAAAAAANg8gkwAAAAAAAAANo8gEwAAAAAAAIDNI8gEAAAAAAAAYPMIMgEAAAAAAADYPIJMAAAAAAAAADaPIBMAAAAAAACAzSPIBAAAAAAAAGDzCDIBAAAAAAAA2DyCTAAAAAAAAAA2jyATAAAAAAAAgM0jyAQAAAAAAABg8wgyAQAAAAAAANg8gkwAAAAAAAAANs+hpBuwFYZhSJISExNLuBMAAAAAAADgzpOVq2XlbEWNIPMvSUlJkiQfH58S7gQAAAAAAAC4cyUlJcnd3b3It2syiisivcNkZmbq9OnTcnV1lclkKul2cJdKTEyUj4+PYmJi5ObmVtLtAPc0rkfAdnA9AraFaxKwHVyPuNMYhqGkpCR5eXnJzq7o32jJHZl/sbOzU9WqVUu6Ddwj3Nzc+IcQYCO4HgHbwfUI2BauScB2cD3iTlIcd2JmYbIfAAAAAAAAADaPIBMAAAAAAACAzSPIBG4jJycnvfHGG3JycirpVoB7HtcjYDu4HgHbwjUJ2A6uR8Aak/0AAAAAAAAAsHnckQkAAAAAAADA5hFkAgAAAAAAALB5BJkAAAAAAAAAbB5BJgAAAAAAAACbR5AJ3MTbb7+tZs2aydXVVZUqVVKPHj0UHh5uVZOcnKzhw4erfPnyKlOmjHr16qWzZ89a1Zw8eVKPPPKISpUqpUqVKmns2LFKT0+3qtm0aZOaNm0qJycn1a5dWwsXLszWz4cffqgaNWrI2dlZzZs3165du4r8mIE7xTvvvCOTyaTRo0dblnE9ArdPbGys+vXrp/Lly8vFxUWNGjXSnj17LOsNw9Drr7+uKlWqyMXFRZ07d1ZkZKTVNi5duqSQkBC5ubnJw8NDQ4YM0ZUrV6xq9u/frzZt2sjZ2Vk+Pj567733svWyfPly1atXT87OzmrUqJHWrFlTPAcN2KiMjAy99tpr8vX1lYuLi2rVqqUpU6boxnlduSaB4vHbb7/p0UcflZeXl0wmk1auXGm13pauvbz0Atg8A0CugoODjQULFhgHDx40wsLCjIcfftioVq2aceXKFUvN0KFDDR8fH2P9+vXGnj17jBYtWhgtW7a0rE9PTzcaNmxodO7c2di3b5+xZs0ao0KFCsaECRMsNcePHzdKlSplvPjii8bhw4eN2bNnG/b29sbatWstNUuXLjXMZrPx+eefG4cOHTKeffZZw8PDwzh79uztORmADdm1a5dRo0YN47777jNGjRplWc71CNwely5dMqpXr24MHDjQ2Llzp3H8+HHj559/No4ePWqpeeeddwx3d3dj5cqVxv/+9z+je/fuhq+vr3Ht2jVLTZcuXYzGjRsbO3bsMH7//Xejdu3aRp8+fSzrExISjMqVKxshISHGwYMHja+//tpwcXEx5s+fb6nZunWrYW9vb7z33nvG4cOHjYkTJxqOjo7GgQMHbs/JAGzAW2+9ZZQvX9744YcfjKioKGP58uVGmTJljFmzZllquCaB4rFmzRrj1VdfNVasWGFIMr777jur9bZ07eWlF8DWEWQC+XDu3DlDkrF582bDMAwjPj7ecHR0NJYvX26pOXLkiCHJ2L59u2EY1//BZmdnZ8TFxVlqPvroI8PNzc1ISUkxDMMwxo0bZ/j7+1vt6+mnnzaCg4Mt3x944AFj+PDhlu8ZGRmGl5eX8fbbbxf9gQI2LCkpyfDz8zPWrVtntGvXzhJkcj0Ct8/48eON1q1b57o+MzPT8PT0NP7zn/9YlsXHxxtOTk7G119/bRiGYRw+fNiQZOzevdtS89NPPxkmk8mIjY01DMMw5s6da5QtW9ZyfWbtu27dupbvTz31lPHII49Y7b958+bG888/X7iDBO4gjzzyiDF48GCrZT179jRCQkIMw+CaBG6XvweZtnTt5aUX4E7Ao+VAPiQkJEiSypUrJ0kKDQ1VWlqaOnfubKmpV6+eqlWrpu3bt0uStm/frkaNGqly5cqWmuDgYCUmJurQoUOWmhu3kVWTtY3U1FSFhoZa1djZ2alz586WGuBeMXz4cD3yyCPZrhmuR+D2Wb16te6//349+eSTqlSpkpo0aaJPPvnEsj4qKkpxcXFW14m7u7uaN29udT16eHjo/vvvt9R07txZdnZ22rlzp6Wmbdu2MpvNlprg4GCFh4fr8uXLlpqbXbPAvaBly5Zav369IiIiJEn/+9//tGXLFnXt2lUS1yRQUmzp2stLL8CdgCATyKPMzEyNHj1arVq1UsOGDSVJcXFxMpvN8vDwsKqtXLmy4uLiLDU3hiZZ67PW3awmMTFR165d04ULF5SRkZFjTdY2gHvB0qVLtXfvXr399tvZ1nE9ArfP8ePH9dFHH8nPz08///yzXnjhBY0cOVKLFi2S9P/X082uk7i4OFWqVMlqvYODg8qVK1ck1yzXI+4lL7/8snr37q169erJ0dFRTZo00ejRoxUSEiKJaxIoKbZ07eWlF+BO4FDSDQB3iuHDh+vgwYPasmVLSbcC3JNiYmI0atQorVu3Ts7OziXdDnBPy8zM1P33369///vfkqQmTZro4MGDmjdvngYMGFDC3QH3nmXLlumrr77SkiVL5O/vr7CwMI0ePVpeXl5ckwCAuwp3ZAJ5MGLECP3www/auHGjqlatalnu6emp1NRUxcfHW9WfPXtWnp6elpq/z5qc9f1WNW5ubnJxcVGFChVkb2+fY03WNoC7XWhoqM6dO6emTZvKwcFBDg4O2rx5sz744AM5ODiocuXKXI/AbVKlShU1aNDAaln9+vV18uRJSf9/Pd3sOvH09NS5c+es1qenp+vSpUtFcs1yPeJeMnbsWMtdmY0aNVL//v31r3/9y/IEA9ckUDJs6drLSy/AnYAgE7gJwzA0YsQIfffdd9qwYYN8fX2t1gcGBsrR0VHr16+3LAsPD9fJkycVFBQkSQoKCtKBAwes/uG0bt06ubm5Wf4lMCgoyGobWTVZ2zCbzQoMDLSqyczM1Pr16y01wN2uU6dOOnDggMLCwiyf+++/XyEhIZY/cz0Ct0erVq0UHh5utSwiIkLVq1eXJPn6+srT09PqOklMTNTOnTutrsf4+HiFhoZaajZs2KDMzEw1b97cUvPbb78pLS3NUrNu3TrVrVtXZcuWtdTc7JoF7gVXr16VnZ31v9rZ29srMzNTEtckUFJs6drLSy/AHaGkZxsCbNkLL7xguLu7G5s2bTLOnDlj+Vy9etVSM3ToUKNatWrGhg0bjD179hhBQUFGUFCQZX16errRsGFD46GHHjLCwsKMtWvXGhUrVjQmTJhgqTl+/LhRqlQpY+zYscaRI0eMDz/80LC3tzfWrl1rqVm6dKnh5ORkLFy40Dh8+LDx3HPPGR4eHlazLwP3mhtnLTcMrkfgdtm1a5fh4OBgvPXWW0ZkZKTx1VdfGaVKlTIWL15sqXnnnXcMDw8PY9WqVcb+/fuNxx57zPD19TWuXbtmqenSpYvRpEkTY+fOncaWLVsMPz8/o0+fPpb18fHxRuXKlY3+/fsbBw8eNJYuXWqUKlXKmD9/vqVm69athoODgzFt2jTjyJEjxhtvvGE4OjoaBw4cuD0nA7ABAwYMMLy9vY0ffvjBiIqKMlasWGFUqFDBGDdunKWGaxIoHklJSca+ffuMffv2GZKM6dOnG/v27TNOnDhhGIZtXXt56QWwdQSZwE1IyvGzYMECS821a9eMYcOGGWXLljVKlSplPP7448aZM2esthMdHW107drVcHFxMSpUqGC89NJLRlpamlXNxo0bjYCAAMNsNhs1a9a02keW2bNnG9WqVTPMZrPxwAMPGDt27CiOwwbuGH8PMrkegdvn+++/Nxo2bGg4OTkZ9erVMz7++GOr9ZmZmcZrr71mVK5c2XBycjI6depkhIeHW9VcvHjR6NOnj1GmTBnDzc3NGDRokJGUlGRV87///c9o3bq14eTkZHh7exvvvPNOtl6WLVtm1KlTxzCbzYa/v7/x448/Fv0BAzYsMTHRGDVqlFGtWjXD2dnZqFmzpvHqq68aKSkplhquSaB4bNy4Mcd/ZxwwYIBhGLZ17eWlF8DWmQzDMErmXlAAAAAAAAAAyBvekQkAAAAAAADA5hFkAgAAAAAAALB5BJkAAAAAAAAAbB5BJgAAAAAAAACbR5AJAAAAAAAAwOYRZAIAAAAAAACweQSZAAAAAAAAAGweQSYAAAAAAAAAm0eQCQAA7mlXr17VBx98oIceekhVqlSRk5OTXF1d1aBBAw0cOFCrV69WRkZGtnE1atSQyWSyfBwcHFSuXDnVq1dPffv21aJFi5ScnJzrfv8+3mQyyc3NTc2aNdO0adOUmppanIeNv2T9PdiC+Ph4lS9fXk8++aTV8kmTJslkMmngwIE3HZ/1cxQdHZ1t3caNG9WrVy95e3vLbDarbNmyqlu3rp588knNmTNHCQkJVvXt27e3+tm0t7dX2bJlVbt2bfXq1UsffvhhtjFZDMNQkyZN1KhRI2VmZubrHAAAANyMQ0k3AAAAUFK2bt2qJ598UmfOnJGzs7OaNWsmLy8vpaSk6NixY1q0aJEWLVqkBg0a6NChQzluo1evXipTpowMw1BiYqKioqK0bNkyff311xo/frwWLFigrl275trDjeOjo6O1fft27dmzR99//73WrVsns9lcXIePXERHR8vX11ft2rXTpk2bbtt+33rrLV2+fFlvvPFGkW538uTJlm3Wr19fzZs3l6Ojo8LDw7VixQp98803uv/++9WiRYtsY4ODg+Xp6SlJSkpKUkxMjL7//nutWLFCEyZM0AcffJAtYDWZTHr99dfVs2dPLVy4UIMHDy7S4wEAAPcugkwAAHBP2rt3rzp16qSUlBSNHTtWEydOlJubm1VNTEyMpk+frnnz5uW6nWnTpqlGjRpWy+Li4jR16lR9+OGH6tatm3788Ud16dIlT+PDwsLUvn17/fbbb/r44481YsSIAh8jbm39+vVKS0sr6TZ05swZzZ49W48++qgaNmxYZNsNDQ3VpEmT5OjoqGXLlqlHjx5W6+Pi4rR48WJ5eHjkOP7ll19W+/btrZYlJCRoxowZmjp1qgYNGqS0tDQ9++yzVjU9evRQvXr19Prrr+uZZ56RgwP/2gEAAAqPR8sBAMA9JzMzU/369VNKSoqmTJmi9957L1uIKUk+Pj6aMWOGtmzZkq/te3p6as6cOZoyZYoyMzM1aNAgpaSk5GlsQECAXnzxRUnSypUr87Vf5F+tWrVUr169km5Dn3/+uVJSUvTMM88U6XZXrFghwzD01FNPZQsxpes/q2PGjMnXOXB3d9ekSZO0cOFCSdLIkSN19uxZqxqTyaSQkBDFxsZq9erVhTkEAAAAC4JMAABwz1mzZo2OHDmiatWqacKECbesDwwMLNB+JkyYoOrVqysuLk7Lly/P87gmTZpIun5HaF5kvefRMAzNmjVLDRo0kLOzs7y9vTVy5EjFx8fnOM4wDH399dfq2LGjypYtK2dnZ9WvX1+TJk3S1atXs9VnvTcxOjpaS5YsUYsWLeTq6prr3Xw5Wbt2rbp3767KlSvLyclJPj4+6tatm7799lurut9//10jRozQfffdp7Jly8rFxUX16tXTyy+/nOPxbNq0yfIeyTNnzmjgwIGqXLmyXFxc1LRpU33xxRc3PXdZJk2aJF9fX0nS5s2brd4TeeMj1Pnt72YMw9Bnn30mV1dXPfLII/kaeyvnz5+XJFWsWLFItytJ/fr1U+vWrZWcnKxPPvkk2/q+fftKUo7rAAAACoIgEwAA3HN++uknSdKTTz4pe3v7YtuPvb29ZeKWjRs35nlcUlKSJMnJySlf+/vnP/+psWPHqmrVqnrssceUkZGh2bNnq127dkpMTLSqzczMVEhIiPr27avdu3crICBADz/8sP7880+9+eab6tChg65du5bjft5++231799fZrNZ3bp1y/Oj0C+99JK6du2qH3/8UX5+furVq5dq1aqlrVu3asqUKVa1Y8eO1WeffSYXFxd16tRJnTp1UmJiot599121bt1aV65cyXEfly5dUosWLbR27Vq1b99ebdq00YEDBzRgwABNmjTplj0GBASoV69ekqTKlStrwIABlk/r1q0L3V9ODh8+rKioKLVo0ULOzs55HpcXPj4+kqRvv/1W586dK9JtS1Lv3r0l5fzzXbNmTfn4+GjDhg25/iwBAADkiwEAAHCPadWqlSHJWLx4cYG3Ub16dUOSERUVddO6xYsXG5KMoKCgPI/v3bu3IckICQnJVy9ubm7Gnj17LMuTkpKMjh07GpKMUaNGWY157733DElG+/btjTNnzliWp6SkGEOGDDEkGePHj7ca065dO0OS4ezsbGzatClPvWX58ssvDUmGl5eXsW/fPqt1V69eNX755RerZWvWrDHi4+OtliUnJxvPPfecIcl48803rdZt3LjRkGRIMh588EHjypUrlnW7du0yypQpY9jZ2RmhoaFW47LO3Y2ioqIMSUa7du1yPZ789nczH330kSHJeOWVV3Jc/8YbbxiSjAEDBtx0O1nHf+PP1LFjxwwXFxdDkuHq6moMGDDA+OSTT4y9e/ca6enpuW4r6+9648aNN93nli1bDElGlSpVclzfq1cvQ5KxYcOGm24HAAAgL7gjEwAA3HMuXrwoSapQoUKO64cMGaKBAwdaffL7nswsWfu4fPnyTesMw9CJEyf08ssva+nSpTKZTHr++efzta8RI0ZYPQZfpkwZzZ49WyaTSZ999pmSk5MlSenp6XrvvfdUunRpLV261DIrtSSZzWbNnj1bnp6e+vjjj5WZmZltP0OGDFG7du3y1du///1vSdL06dMVEBBgtc7FxUUPPvig1bKuXbvK3d3dapmTk5NmzpwpBwcHrVq1Ksf92NnZafbs2SpdurRlWbNmzTR8+HBlZmZq7ty5+eo7NwXtLyf79++XJNWtW7dIertRzZo19f3338vHx0dJSUlatGiRnn32WTVt2lQVKlTQsGHDdObMmQJv/1Y/31nv3gwLCyvwPgAAALIwfSAAAMDfLFq0SBkZGVbL2rdvb/VocV4ZhiFJVu9hvFHW+xhvZDabNXPmTLVp0yZf+8p6zPdGDRo0UOPGjRUWFqZ9+/YpKChIe/fu1YULF/Tggw+qcuXK2ca4uLgoMDBQP/74oyIjI7MFbN27d89XX6dPn9aRI0fk4eGhp556Ks/jYmNj9f333+uPP/5QYmKiJVQ1m82KjIzMcUxAQECOgWCfPn307rvv6vfff89X70XdX06yHvkuW7ZskfV2o06dOuno0aP68ccf9csvv2jXrl3av3+/4uPj9dFHH+nbb7/Vb7/9VqAg9VY/3+XKlZP0/+/qBAAAKAyCTAAAcM8pX768JOnChQs5rk9PT7f8eejQoZo/f36B95W1j6xA5+969eqlMmXKyGQyqUyZMqpXr54ef/xxeXl55Xtf1atXz3F5jRo1FBYWptOnT0uSoqOjJUnr1q3LNYC6sf+/B1zVqlXLV19ZkxbVrFnzlvvLMn36dL388stKS0vL175udg4kWc5BYRW0v5wkJCRIklxdXXNcn5dzlhUo5lZvNpv1+OOP6/HHH5ckxcfHa+nSpXrllVd07tw5jRgxQuvWrct377f6+XZzc7PsDwAAoLAIMgEAwD2ncePG2rp1q/bt26eQkJBi3de+ffskXb8zMifTpk2zhGy3S9adg7Vr11arVq1uWpsV+t6oqCek+bsdO3bopZdekru7u2bNmqX27dvL09PTMvmRl5dXoR6HtrX+sh5Rz5rk6e9KlSolSTnOJJ/lxnU3PlafGw8PDw0dOlReXl567LHHtHHjRl29etWyr7y61c93Vkibn5ntAQAAckOQCQAA7jldu3bV3LlztXz5cr377rvFNnN5RkaGvvnmG0lShw4dimUfNzpx4oQaNWqU43JJlrs8q1atKun6+wsXLlxY7H1lzZx9/PhxGYZxyzsMv/vuO0nSW2+9pQEDBlitu3btmuLi4nIdm3WsuS0vyJ2uRdlfTipVqiTp+ozrOcn6+zp+/Hiu28haV6pUqXw9ot6xY0dJ139W4+Pj8x1k/ve//5WU+8931rszK1asmK/tAgAA5ITJfgAAwD3n4YcfVv369XXy5Em9/fbbxbaff//73zp58qS8vb3Vq1evYttPlmXLlmVb9scffygsLExlypSxTLLTrFkzubu7a/PmzbmGZ0XJy8tL9evXV3x8vJYvX37L+qzwKyvAu9Hy5cutHqP+u7CwsBzfT7l06VJJytN7Ts1msyTrVwwUVX85ady4sSQpPDw8x/WtW7eWyWSyej3A361evVqS1LJlS6tg/la9HD16VNL1Y85t8qvcfPnll9q6datKlSqlf/zjHznWHDlyRJKyTfAEAABQEASZAADgnmNnZ6cvv/xSTk5Oeu211zRu3DjLI7A3unjxYq7h0s3ExcXpn//8p15//XXZ29trwYIFlnCsOM2ePdvyqK90/XHjf/7znzIMQ4MGDZKLi4uk67Nrjxs3TklJSerZs2eOd/rFxsbqyy+/LLLeXn75ZUnSiy++aJmlO0tycrLV+xnr1KkjSfrss8+s3kF5+PBhjR8//qb7yczM1D//+U+rR61DQ0M1Z84cmUwmvfDCC7fstUKFCnJ0dNSxY8eyTfpU2P5ykjWp0+7du3Nc7+Pjo549eyojI0ODBg1SYmKi1frt27frvffekySNHj3aat1rr72msWPH6tixY9m2Gxsbq+eff17S9Qmc8vozmpCQoDfffFODBg2SJM2ZMyfXOy537dols9msFi1a5GnbAAAAN2My8vufjAEAAO4SW7Zs0ZNPPqm4uDg5OTmpefPm8vLyUnJysk6dOqX//e9/SktLU7169bR8+XI1bNjQMrZGjRo6ceKEZbIewzCUlJSkqKgoHThwQBkZGfL09NTChQsVHBycbd9Z46Oiogr9jsysbQ0fPlwff/yxOnbsKHd3d/3222+Ki4uTv7+/tm7dankXo3Q98Bs4cKC+/PJLmc1mNWnSRL6+vkpNTVV4eLgOHz6s++67T2FhYZYx7du31+bNmwvc88iRIzV79mzZ29srKChIPj4+OnPmjMLCwlS9enXLvi5evKiGDRsqLi5Ovr6+atasmS5duqTNmzerR48e2rVrl06cOGF1t+GmTZvUoUMHdevWzfL31rZtWyUkJGjDhg1KS0vTxIkTNWXKlBzP3d//L3H37t31/fffy9/fX02bNpXZbFarVq00aNCgAvV3M4ZhqFatWrpw4YLOnTuX4ztIz58/rw4dOujQoUNyd3dX69at5e7urqioKO3YsUOGYWjcuHF69913rcaNHj1as2bNknQ9gG3QoIGcnZ116tQp7dy5U2lpaapdu7Y2bdokb29vy7isv+vg4GB5enpKkq5cuaJTp05p3759Sk1NlZubm+bMmaP+/fvneFzHjh1T7dq11aVLF/300095OhcAAAA3ZQAAANzD/vzzT2PWrFlGp06djMqVKxuOjo5GmTJljLp16xohISHGd999Z6SlpWUbV716dUOS5WNnZ2d4eHgYdevWNXr37m0sWrTIuHbtWq77zRofFRVV6GPI2lZGRoYxbdo0o169eoaTk5NRpUoVY/jw4calS5dyHbtq1SrjkUceMSpVqmQ4OjoalSpVMgIDA41x48YZoaGhVrXt2rUrdM+rVq0ygoODjXLlyhlms9moWrWq0a1bN2PFihVWdTExMUbfvn0Nb29vw9nZ2ahfv77xzjvvGOnp6ZbjvdHGjRsNScaAAQOM2NhYo1+/fkbFihUNJycno3HjxsaCBQty7CenbRmGYZw9e9bo37+/4enpadjb21u2XdD+buWtt94yJBnLli3LtebKlSvGu+++azzwwAOGm5ub4eDgYHh6ehqPPfaYsXbt2hzHnD9/3vjyyy+Nfv36GY0aNTLKly9vODg4GOXKlTNatWplvPfee8aVK1eyjcv6u77x59vd3d2oWbOm0bNnT+PDDz80EhISbnpMkydPNiQZ3377bb7OBQAAQG64IxMAAOAOl9tdhfeSrDsyBwwYcFsmMCpqWXd3du7cWd9//31Jt1NohmGofv36unLliqKjo+XgwByjAACg8HhHJgAAAFDCPD099c9//lM//vijDhw4UNLtFNrKlSsVHh6uyZMnE2ICAIAiQ5AJAAAA2IBXXnlFZcuW1ZtvvlnSrRSKYRiaPHmyGjZsqIEDB5Z0OwAA4C7Cfx4FAAAAbICHh4cuXrxY0m0Umslk0r59+0q6DQAAcBfiHZkAAAAAAAAAbB6PlgMAAAAAAACweQSZAAAAAAAAAGweQSYAAAAAAAAAm0eQCQAAAAAAAMDmEWQCAAAAAAAAsHkEmQAAAAAAAABsHkEmAAAAAAAAAJtHkAkAAAAAAADA5v0fQEB1UthH5NoAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "id": "GsSgrgRzZxHb"
      },
      "outputs": [],
      "source": [
        "country_stats.to_csv(datapath / \"lifesat.csv\")\n",
        "full_country_stats.to_csv(datapath / \"lifesat_full.csv\")"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "-WwkRbFVd_gW"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 40,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 601
        },
        "id": "LosbxbhgZxHb",
        "outputId": "d7afb7a3-d2d4-4ba0-856c-326be39fe536"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "country_stats.plot(kind='scatter', figsize=(10, 6), grid=True,\n",
        "                   x=gdppc_col, y=lifesat_col,  color='blue')\n",
        "\n",
        "min_life_sat = 4\n",
        "max_life_sat = 9\n",
        "\n",
        "position_text = {\n",
        "    \"Turkey\": (29_500, 4.2),\n",
        "    \"Hungary\": (28_000, 6.9),\n",
        "    \"France\": (40_000, 5),\n",
        "    \"New Zealand\": (28_000, 8.2),\n",
        "    \"Australia\": (50_000, 5.5),\n",
        "    \"United States\": (59_000, 5.3),\n",
        "    \"Denmark\": (46_000, 8.5)\n",
        "}\n",
        "\n",
        "for country, pos_text in position_text.items():\n",
        "    pos_data_x = country_stats[gdppc_col].loc[country]\n",
        "    pos_data_y = country_stats[lifesat_col].loc[country]\n",
        "    country = \"U.S.\" if country == \"United States\" else country\n",
        "    plt.annotate(country, xy=(pos_data_x, pos_data_y),\n",
        "                 xytext=pos_text, fontsize=12,\n",
        "                 arrowprops=dict(facecolor='black', width=0.5,\n",
        "                                 shrink=0.08, headwidth=5), color='darkred')\n",
        "    plt.plot(pos_data_x, pos_data_y, \"ro\")\n",
        "\n",
        "plt.axis([min_gdp, max_gdp, min_life_sat, max_life_sat])\n",
        "plt.title('Life Satisfaction vs GDP per capita', fontsize=12)\n",
        "plt.xlabel('GDP per capita (USD)', fontsize=12)\n",
        "plt.ylabel('Life satisfaction', fontsize=12)\n",
        "save_fig('money_happy_scatterplot')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 41,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "NturVlz-ZxHb",
        "outputId": "f963ba2e-8ce9-41a2-c096-2ecf6b53a82e"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "               GDP per capita (USD)  Life satisfaction\n",
              "Country                                               \n",
              "Turkey                 28384.987785                5.5\n",
              "Hungary                31007.768407                5.6\n",
              "France                 42025.617373                6.5\n",
              "New Zealand            42404.393738                7.3\n",
              "Australia              48697.837028                7.3\n",
              "Denmark                55938.212809                7.6\n",
              "United States          60235.728492                6.9"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-e50a24e3-79e7-48f9-aa0f-d99fa270b42d\" class=\"colab-df-container\">\n",
              "    <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>GDP per capita (USD)</th>\n",
              "      <th>Life satisfaction</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Country</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Turkey</th>\n",
              "      <td>28384.987785</td>\n",
              "      <td>5.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Hungary</th>\n",
              "      <td>31007.768407</td>\n",
              "      <td>5.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>France</th>\n",
              "      <td>42025.617373</td>\n",
              "      <td>6.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>New Zealand</th>\n",
              "      <td>42404.393738</td>\n",
              "      <td>7.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Australia</th>\n",
              "      <td>48697.837028</td>\n",
              "      <td>7.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Denmark</th>\n",
              "      <td>55938.212809</td>\n",
              "      <td>7.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>United States</th>\n",
              "      <td>60235.728492</td>\n",
              "      <td>6.9</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e50a24e3-79e7-48f9-aa0f-d99fa270b42d')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-e50a24e3-79e7-48f9-aa0f-d99fa270b42d button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-e50a24e3-79e7-48f9-aa0f-d99fa270b42d');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-d36684c7-b867-41e0-b9ea-b68e0d61edfc\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-d36684c7-b867-41e0-b9ea-b68e0d61edfc')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-d36684c7-b867-41e0-b9ea-b68e0d61edfc button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"highlighted_countries[[gdppc_col, lifesat_col]]\",\n  \"rows\": 7,\n  \"fields\": [\n    {\n      \"column\": \"Country\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 7,\n        \"samples\": [\n          \"Turkey\",\n          \"Hungary\",\n          \"Denmark\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"GDP per capita (USD)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 11882.70029953928,\n        \"min\": 28384.9877846263,\n        \"max\": 60235.7284916969,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          28384.9877846263,\n          31007.7684065437,\n          55938.2128086032\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Life satisfaction\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.8420496535523418,\n        \"min\": 5.5,\n        \"max\": 7.6,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          5.5,\n          5.6,\n          6.9\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 41
        }
      ],
      "source": [
        "highlighted_countries = country_stats.loc[list(position_text.keys())]\n",
        "highlighted_countries[[gdppc_col, lifesat_col]].sort_values(by=gdppc_col)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 50,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 401
        },
        "id": "dkk9SwieZxHb",
        "outputId": "8104a37e-f9d8-4604-b5c5-07586d58d77f"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x400 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "country_stats.plot(kind='scatter', figsize=(6, 4), grid=True,\n",
        "                   x=gdppc_col, y=lifesat_col)\n",
        "\n",
        "X = np.linspace(min_gdp, max_gdp, 1000)\n",
        "\n",
        "w1, w2 = 4.2, 0\n",
        "plt.plot(X, w1 + w2 * 1e-5 * X, \"r\")\n",
        "plt.text(40_000, 4.9, fr\"$\\theta_0 = {w1}$\", color=\"r\")\n",
        "plt.text(40_000, 4.4, fr\"$\\theta_1 = {w2}$\", color=\"r\")\n",
        "\n",
        "w1, w2 = 10, -9\n",
        "plt.plot(X, w1 + w2 * 1e-5 * X, \"g\")\n",
        "plt.text(26_000, 8.5, fr\"$\\theta_0 = {w1}$\", color=\"g\")\n",
        "plt.text(26_000, 8.0, fr\"$\\theta_1 = {w2} \\times 10^{{-5}}$\", color=\"g\")\n",
        "\n",
        "w1, w2 = 3, 8\n",
        "plt.plot(X, w1 + w2 * 1e-5 * X, \"b\")\n",
        "plt.text(48_000, 8.5, fr\"$\\theta_0 = {w1}$\", color=\"b\")\n",
        "plt.text(48_000, 8.0, fr\"$\\theta_1 = {w2} \\times 10^{{-5}}$\", color=\"b\")\n",
        "\n",
        "plt.title('Life Satisfaction vs GDP per capita', fontsize=12)\n",
        "plt.axis([min_gdp, max_gdp, min_life_sat, max_life_sat])\n",
        "\n",
        "save_fig('tweaking_model_params_plot')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 52,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pPbOFYVkZxHc",
        "outputId": "d44ea3a3-6e55-4342-9ebd-e08726b0648b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "θ0=3.75, θ1=6.78e-05\n"
          ]
        }
      ],
      "source": [
        "from sklearn import linear_model\n",
        "\n",
        "X_sample = country_stats[[gdppc_col]].values\n",
        "y_sample = country_stats[[lifesat_col]].values\n",
        "\n",
        "lin1 = linear_model.LinearRegression()\n",
        "lin1.fit(X_sample, y_sample)\n",
        "\n",
        "t0, t1 = lin1.intercept_[0], lin1.coef_[0][0]\n",
        "print(f\"θ0={t0:.2f}, θ1={t1:.2e}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 60,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 401
        },
        "id": "HQpF6kOXZxHc",
        "outputId": "5dc33efa-1f19-4d9e-fa70-ed69ff00d619"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x400 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "country_stats.plot(kind='scatter', figsize=(6, 4), grid=True,\n",
        "                   x=gdppc_col, y=lifesat_col, color=\"Brown\")\n",
        "\n",
        "X = np.linspace(min_gdp, max_gdp, 1000)\n",
        "plt.plot(X, t0 + t1 * X, \"red\")\n",
        "\n",
        "plt.text(max_gdp - 20_000, min_life_sat + 1.9,\n",
        "         fr\"$\\theta_0 = {t0:.2f}$\", color=\"brown\")\n",
        "plt.text(max_gdp - 20_000, min_life_sat + 1.3,\n",
        "         fr\"$\\theta_1 = {t1 * 1e5:.2f} \\times 10^{{-5}}$\", color=\"brown\")\n",
        "\n",
        "plt.axis([min_gdp, max_gdp, min_life_sat, max_life_sat])\n",
        "plt.title('Life Satisfaction vs GDP per capita', fontsize=12)\n",
        "save_fig('best_fit_model_plot')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 61,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9aLD93-aZxHc",
        "outputId": "5568863e-c124-4784-840e-e068468ec960"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "37655.1803457421"
            ]
          },
          "metadata": {},
          "execution_count": 61
        }
      ],
      "source": [
        "cyprus_gdp_per_capita = gdp_per_capita[gdppc_col].loc[\"Cyprus\"]\n",
        "cyprus_gdp_per_capita"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 62,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "d6gU0CzwZxHc",
        "outputId": "20a46c8a-1740-4b22-9b60-0f58370e251b"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "6.301656332738056"
            ]
          },
          "metadata": {},
          "execution_count": 62
        }
      ],
      "source": [
        "cyprus_predicted_life_satisfaction = lin1.predict([[cyprus_gdp_per_capita]])[0, 0]\n",
        "cyprus_predicted_life_satisfaction"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 82,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 398
        },
        "id": "NI-bT8dlZxHh",
        "outputId": "59bbb4d9-c48c-4c91-fb1d-3e23dedef40c"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x400 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "country_stats.plot(kind='scatter', figsize=(8, 4), grid=True,\n",
        "                   x=gdppc_col, y=lifesat_col)\n",
        "\n",
        "X = np.linspace(min_gdp, max_gdp, 1000)\n",
        "plt.plot(X, t0 + t1 * X, \"black\")\n",
        "\n",
        "plt.text(min_gdp + 22_000, max_life_sat - 1.1,\n",
        "         fr\"$\\theta_0 = {t0:.2f}$\", color=\"brown\")\n",
        "plt.text(min_gdp + 22_000, max_life_sat - 0.6,\n",
        "         fr\"$\\theta_1 = {t1 * 1e5:.2f} \\times 10^{{-5}}$\", color=\"brown\")\n",
        "\n",
        "plt.plot([cyprus_gdp_per_capita, cyprus_gdp_per_capita],\n",
        "         [min_life_sat, cyprus_predicted_life_satisfaction], \"r--\")\n",
        "plt.text(cyprus_gdp_per_capita + 1000, 5.0,\n",
        "         fr\"Prediction = {cyprus_predicted_life_satisfaction:.2f}\", color=\"r\")\n",
        "plt.plot(cyprus_gdp_per_capita, cyprus_predicted_life_satisfaction, \"ro\")\n",
        "\n",
        "plt.axis([min_gdp, max_gdp, min_life_sat, max_life_sat])\n",
        "\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 87,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "Hc0qTN8JZxHh",
        "outputId": "7de0958a-b989-473b-99e3-6f666f3d94e6"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              GDP per capita (USD)  Life satisfaction\n",
              "Country                                              \n",
              "South Africa          11466.189672                4.7\n",
              "Colombia              13441.492952                6.3\n",
              "Brazil                14063.982505                6.4\n",
              "Mexico                17887.750736                6.5\n",
              "Chile                 23324.524751                6.5\n",
              "Norway                63585.903514                7.6\n",
              "Switzerland           68393.306004                7.5\n",
              "Ireland               89688.956958                7.0\n",
              "Luxembourg           110261.157353                6.9"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-6d50c64d-6260-4df8-b01e-aa506231ff7c\" class=\"colab-df-container\">\n",
              "    <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>GDP per capita (USD)</th>\n",
              "      <th>Life satisfaction</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Country</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>South Africa</th>\n",
              "      <td>11466.189672</td>\n",
              "      <td>4.7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Colombia</th>\n",
              "      <td>13441.492952</td>\n",
              "      <td>6.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Brazil</th>\n",
              "      <td>14063.982505</td>\n",
              "      <td>6.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Mexico</th>\n",
              "      <td>17887.750736</td>\n",
              "      <td>6.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Chile</th>\n",
              "      <td>23324.524751</td>\n",
              "      <td>6.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Norway</th>\n",
              "      <td>63585.903514</td>\n",
              "      <td>7.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Switzerland</th>\n",
              "      <td>68393.306004</td>\n",
              "      <td>7.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Ireland</th>\n",
              "      <td>89688.956958</td>\n",
              "      <td>7.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Luxembourg</th>\n",
              "      <td>110261.157353</td>\n",
              "      <td>6.9</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-6d50c64d-6260-4df8-b01e-aa506231ff7c')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-6d50c64d-6260-4df8-b01e-aa506231ff7c button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-6d50c64d-6260-4df8-b01e-aa506231ff7c');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-a50d870d-4a06-4bb6-9c2d-f28255de6abb\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-a50d870d-4a06-4bb6-9c2d-f28255de6abb')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-a50d870d-4a06-4bb6-9c2d-f28255de6abb button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_645884ee-67af-46e3-9666-f712545cd213\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('missing_data')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_645884ee-67af-46e3-9666-f712545cd213 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('missing_data');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "missing_data",
              "summary": "{\n  \"name\": \"missing_data\",\n  \"rows\": 9,\n  \"fields\": [\n    {\n      \"column\": \"Country\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 9,\n        \"samples\": [\n          \"Ireland\",\n          \"Colombia\",\n          \"Norway\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"GDP per capita (USD)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 37791.49262311812,\n        \"min\": 11466.189671582,\n        \"max\": 110261.157353302,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          89688.9569584859,\n          13441.4929522564,\n          63585.9035142969\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Life satisfaction\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.852936105461599,\n        \"min\": 4.7,\n        \"max\": 7.6,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          6.3,\n          7.5,\n          4.7\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 87
        }
      ],
      "source": [
        "missing_data = full_country_stats[(full_country_stats[gdppc_col] < min_gdp) |\n",
        "                                  (full_country_stats[gdppc_col] > max_gdp)]\n",
        "missing_data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 88,
      "metadata": {
        "id": "Cz3r3Q-iZxHi"
      },
      "outputs": [],
      "source": [
        "position_text_missing_countries = {\n",
        "    \"South Africa\": (20_000, 4.2),\n",
        "    \"Colombia\": (6_000, 8.2),\n",
        "    \"Brazil\": (18_000, 7.8),\n",
        "    \"Mexico\": (24_000, 7.4),\n",
        "    \"Chile\": (30_000, 7.0),\n",
        "    \"Norway\": (51_000, 6.2),\n",
        "    \"Switzerland\": (62_000, 5.7),\n",
        "    \"Ireland\": (81_000, 5.2),\n",
        "    \"Luxembourg\": (92_000, 4.7),\n",
        "}"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 91,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 401
        },
        "id": "zNAMexpdZxHi",
        "outputId": "4a754107-6c14-49fb-ced7-d5dad950b026"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x400 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "full_country_stats.plot(kind='scatter', figsize=(10, 4),\n",
        "                        x=gdppc_col, y=lifesat_col, grid=True)\n",
        "\n",
        "for country, pos_text in position_text_missing_countries.items():\n",
        "    pos_data_x, pos_data_y = missing_data.loc[country]\n",
        "    plt.annotate(country, xy=(pos_data_x, pos_data_y),\n",
        "                 xytext=pos_text, fontsize=12,\n",
        "                 arrowprops=dict(facecolor='black', width=0.5,\n",
        "                                 shrink=0.08, headwidth=5))\n",
        "    plt.plot(pos_data_x, pos_data_y, \"rs\")\n",
        "\n",
        "X = np.linspace(0, 115_000, 1000)\n",
        "plt.plot(X, t0 + t1 * X, \"b:\")\n",
        "\n",
        "lin_reg_full = linear_model.LinearRegression()\n",
        "Xfull = np.c_[full_country_stats[gdppc_col]]\n",
        "yfull = np.c_[full_country_stats[lifesat_col]]\n",
        "lin_reg_full.fit(Xfull, yfull)\n",
        "\n",
        "t0full, t1full = lin_reg_full.intercept_[0], lin_reg_full.coef_[0][0]\n",
        "X = np.linspace(0, 115_000, 1000)\n",
        "plt.plot(X, t0full + t1full * X, \"k\")\n",
        "\n",
        "plt.axis([0, 115_000, min_life_sat, max_life_sat])\n",
        "\n",
        "save_fig('representative_training_data_scatterplot')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 93,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 301
        },
        "id": "GrhSXqVHZxHi",
        "outputId": "fb819018-7c24-4fdd-d742-f5ffbd555bd3"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x300 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "from sklearn import preprocessing\n",
        "from sklearn import pipeline\n",
        "\n",
        "full_country_stats.plot(kind='scatter', figsize=(8, 3),\n",
        "                        x=gdppc_col, y=lifesat_col, grid=True, color=\"red\")\n",
        "\n",
        "poly = preprocessing.PolynomialFeatures(degree=10, include_bias=False)\n",
        "scaler = preprocessing.StandardScaler()\n",
        "lin_reg2 = linear_model.LinearRegression()\n",
        "\n",
        "pipeline_reg = pipeline.Pipeline([\n",
        "    ('poly', poly),\n",
        "    ('scal', scaler),\n",
        "    ('lin', lin_reg2)])\n",
        "pipeline_reg.fit(Xfull, yfull)\n",
        "curve = pipeline_reg.predict(X[:, np.newaxis])\n",
        "plt.plot(X, curve)\n",
        "\n",
        "plt.axis([0, 115_000, min_life_sat, max_life_sat])\n",
        "\n",
        "save_fig('overfitting_model_plot')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 94,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lls1nc0SZxHi",
        "outputId": "d241634b-899c-46b6-ca56-ee6a7d6a0cd6"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Country\n",
              "New Zealand    7.3\n",
              "Sweden         7.3\n",
              "Norway         7.6\n",
              "Switzerland    7.5\n",
              "Name: Life satisfaction, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 94
        }
      ],
      "source": [
        "w_countries = [c for c in full_country_stats.index if \"W\" in c.upper()]\n",
        "full_country_stats.loc[w_countries][lifesat_col]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 95,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 551
        },
        "id": "sK3SMjYQZxHi",
        "outputId": "7b43d29e-8578-40e8-dcd4-03308fb3379a"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                            GDP per capita (USD)\n",
              "Country                                         \n",
              "Malawi                               1486.778248\n",
              "Rwanda                               2098.710362\n",
              "Zimbabwe                             2744.690758\n",
              "Africa Western and Central           4003.158913\n",
              "Papua New Guinea                     4101.218882\n",
              "Lower middle income                  6722.809932\n",
              "Eswatini                             8392.717564\n",
              "Low & middle income                 10293.855325\n",
              "Arab World                          13753.707307\n",
              "Botswana                            16040.008473\n",
              "World                               16194.040310\n",
              "New Zealand                         42404.393738\n",
              "Sweden                              50683.323510\n",
              "Norway                              63585.903514\n",
              "Switzerland                         68393.306004"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-bb144322-ad3c-46f9-8b5a-7eb41226ca86\" class=\"colab-df-container\">\n",
              "    <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>GDP per capita (USD)</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Country</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Malawi</th>\n",
              "      <td>1486.778248</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Rwanda</th>\n",
              "      <td>2098.710362</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Zimbabwe</th>\n",
              "      <td>2744.690758</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Africa Western and Central</th>\n",
              "      <td>4003.158913</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Papua New Guinea</th>\n",
              "      <td>4101.218882</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Lower middle income</th>\n",
              "      <td>6722.809932</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Eswatini</th>\n",
              "      <td>8392.717564</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Low &amp; middle income</th>\n",
              "      <td>10293.855325</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Arab World</th>\n",
              "      <td>13753.707307</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Botswana</th>\n",
              "      <td>16040.008473</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>World</th>\n",
              "      <td>16194.040310</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>New Zealand</th>\n",
              "      <td>42404.393738</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sweden</th>\n",
              "      <td>50683.323510</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Norway</th>\n",
              "      <td>63585.903514</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Switzerland</th>\n",
              "      <td>68393.306004</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-bb144322-ad3c-46f9-8b5a-7eb41226ca86')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-bb144322-ad3c-46f9-8b5a-7eb41226ca86 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-bb144322-ad3c-46f9-8b5a-7eb41226ca86');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-defd85a9-7506-4321-bffc-5b8905fcaa3a\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-defd85a9-7506-4321-bffc-5b8905fcaa3a')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-defd85a9-7506-4321-bffc-5b8905fcaa3a button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"gdp_per_capita\",\n  \"rows\": 15,\n  \"fields\": [\n    {\n      \"column\": \"Country\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 15,\n        \"samples\": [\n          \"Botswana\",\n          \"New Zealand\",\n          \"Malawi\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"GDP per capita (USD)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 23331.713640984548,\n        \"min\": 1486.77824843475,\n        \"max\": 68393.306003945,\n        \"num_unique_values\": 15,\n        \"samples\": [\n          16040.0084730125,\n          42404.3937381567,\n          1486.77824843475\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 95
        }
      ],
      "source": [
        "all_w_countries = [c for c in gdp_per_capita.index if \"W\" in c.upper()]\n",
        "gdp_per_capita.loc[all_w_countries].sort_values(by=gdppc_col)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 99,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 401
        },
        "id": "Glu4bu3GZxHi",
        "outputId": "af02c23c-de62-43ee-9654-97b6c96253c8"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x400 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "country_stats.plot(kind='scatter', x=gdppc_col, y=lifesat_col, figsize=(10, 4))\n",
        "missing_data.plot(kind='scatter', x=gdppc_col, y=lifesat_col,\n",
        "                  marker=\"s\", color=\"r\", grid=True, ax=plt.gca())\n",
        "\n",
        "X = np.linspace(0, 115_000, 1000)\n",
        "plt.plot(X, t0 + t1*X, \"b:\", label=\"Linear model on partial data\")\n",
        "plt.plot(X, t0full + t1full * X, \"k-\", label=\"Linear model on all data\")\n",
        "\n",
        "ridge = linear_model.Ridge(alpha=10**9.5)\n",
        "X_sample = country_stats[[gdppc_col]]\n",
        "y_sample = country_stats[[lifesat_col]]\n",
        "ridge.fit(X_sample, y_sample)\n",
        "t0ridge, t1ridge = ridge.intercept_[0], ridge.coef_[0][0]\n",
        "plt.plot(X, t0ridge + t1ridge * X, \"b--\",\n",
        "         label=\"Regularized linear model on partial data\")\n",
        "plt.legend(loc=\"lower right\")\n",
        "\n",
        "plt.axis([0, 115_000, min_life_sat, max_life_sat])\n",
        "\n",
        "save_fig('ridge_model_plot')\n",
        "plt.show()"
      ]
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.10.6"
    },
    "metadata": {
      "interpreter": {
        "hash": "22b0ec00cd9e253c751e6d2619fc0bb2d18ed12980de3246690d5be49479dd65"
      }
    },
    "nav_menu": {},
    "toc": {
      "navigate_menu": true,
      "number_sections": true,
      "sideBar": true,
      "threshold": 6,
      "toc_cell": false,
      "toc_section_display": "block",
      "toc_window_display": true
    },
    "toc_position": {
      "height": "616px",
      "left": "0px",
      "right": "20px",
      "top": "106px",
      "width": "213px"
    },
    "colab": {
      "provenance": []
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}