{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sNtXpO8VhmFG"
      },
      "source": [
        "# Plotting - Statistical Tests"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IWLoO6svhmFI"
      },
      "source": [
        "The main library for plotting is **matplotlib**, which uses the Matlab plotting capabilities.\n",
        "\n",
        "We can also use the **seaborn** library on top of that to do visually nicer plots"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "cvUeQ2bFhmFJ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a5e370b9-6312-4cb4-d541-a8f7a8ab8545"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting tiingo\n",
            "  Downloading tiingo-0.16.1-py2.py3-none-any.whl.metadata (16 kB)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.12/dist-packages (from tiingo) (2.32.4)\n",
            "Requirement already satisfied: websocket-client in /usr/local/lib/python3.12/dist-packages (from tiingo) (1.9.0)\n",
            "Requirement already satisfied: charset_normalizer<4,>=2 in /usr/local/lib/python3.12/dist-packages (from requests->tiingo) (3.4.4)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.12/dist-packages (from requests->tiingo) (3.11)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.12/dist-packages (from requests->tiingo) (2.5.0)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.12/dist-packages (from requests->tiingo) (2025.10.5)\n",
            "Downloading tiingo-0.16.1-py2.py3-none-any.whl (16 kB)\n",
            "Installing collected packages: tiingo\n",
            "Successfully installed tiingo-0.16.1\n"
          ]
        }
      ],
      "source": [
        "import pandas as pd\n",
        "from datetime import datetime #For handling dates\n",
        "import os\n",
        "\n",
        "import matplotlib.pyplot as plt #main plotting tool for python\n",
        "import matplotlib as mpl\n",
        "\n",
        "import seaborn as sns #A more fancy plotting library\n",
        "\n",
        "#For presenting plots inline\n",
        "%matplotlib inline\n",
        "!pip install tiingo"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "NG-S8ufYhmFJ"
      },
      "outputs": [],
      "source": [
        "os.environ[\"TIINGO_API_KEY\"] = \"614c1590a592cc6696f6082f83b2666cd83882ef\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "KTG54EsfhmFK"
      },
      "outputs": [],
      "source": [
        "from tiingo import TiingoClient\n",
        "client = TiingoClient({'api_key':'614c1590a592cc6696f6082f83b2666cd83882ef'})\n",
        "start = datetime(2018,1,1)\n",
        "end = datetime(2018,12,31)\n",
        "stocks_data = client.get_dataframe('META',frequency='daily',startDate=start,endDate=end)\n",
        "stocks_data = stocks_data[['open','close','low','high','volume']]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "0jgrs31xhmFK",
        "colab": {
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          "height": 237
        },
        "outputId": "becded54-29d4-49ec-9d7a-094bd6b6546e"
      },
      "outputs": [
        {
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          "data": {
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              "                             open   close     low    high    volume\n",
              "date                                                               \n",
              "2018-01-02 00:00:00+00:00  177.68  181.42  177.55  181.58  17694891\n",
              "2018-01-03 00:00:00+00:00  181.88  184.67  181.33  184.78  16595495\n",
              "2018-01-04 00:00:00+00:00  184.90  184.33  184.10  186.21  13554357\n",
              "2018-01-05 00:00:00+00:00  185.59  186.85  184.93  186.90  13042388\n",
              "2018-01-08 00:00:00+00:00  187.20  188.28  186.33  188.90  14719216"
            ],
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              "      <th>2018-01-02 00:00:00+00:00</th>\n",
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              "      <th>2018-01-04 00:00:00+00:00</th>\n",
              "      <td>184.90</td>\n",
              "      <td>184.33</td>\n",
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              "      <td>186.21</td>\n",
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              "    <tr>\n",
              "      <th>2018-01-05 00:00:00+00:00</th>\n",
              "      <td>185.59</td>\n",
              "      <td>186.85</td>\n",
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              "      <td>186.90</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-01-08 00:00:00+00:00</th>\n",
              "      <td>187.20</td>\n",
              "      <td>188.28</td>\n",
              "      <td>186.33</td>\n",
              "      <td>188.90</td>\n",
              "      <td>14719216</td>\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "stocks_data",
              "summary": "{\n  \"name\": \"stocks_data\",\n  \"rows\": 251,\n  \"fields\": [\n    {\n      \"column\": \"date\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"2018-01-02 00:00:00+00:00\",\n        \"max\": \"2018-12-31 00:00:00+00:00\",\n        \"num_unique_values\": 251,\n        \"samples\": [\n          \"2018-08-14 00:00:00+00:00\",\n          \"2018-01-10 00:00:00+00:00\",\n          \"2018-08-27 00:00:00+00:00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"open\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 19.69649335473974,\n        \"min\": 123.1,\n        \"max\": 215.72,\n        \"num_unique_values\": 249,\n        \"samples\": [\n          208.77,\n          186.94,\n          184.93\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"close\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 19.97745158627776,\n        \"min\": 124.06,\n        \"max\": 217.5,\n        \"num_unique_values\": 249,\n        \"samples\": [\n          209.94,\n          187.84,\n          186.9\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"low\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 20.07440767348364,\n        \"min\": 123.02,\n        \"max\": 214.27,\n        \"num_unique_values\": 247,\n        \"samples\": [\n          176.4,\n          185.63,\n          160.88\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"high\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 19.424564323437952,\n        \"min\": 129.74,\n        \"max\": 218.62,\n        \"num_unique_values\": 246,\n        \"samples\": [\n          177.95,\n          187.89,\n          171.77\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"volume\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 19101434,\n        \"min\": 8855144,\n        \"max\": 169803668,\n        \"num_unique_values\": 251,\n        \"samples\": [\n          19101995,\n          10464528,\n          17921935\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 4
        }
      ],
      "source": [
        "stocks_data.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "H6xCs0q3hmFM"
      },
      "outputs": [],
      "source": [
        "df = stocks_data\n",
        "df = df.rename(columns = {'volume':'vol'})"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "3R56mLathmFM",
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          "height": 237
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            "text/plain": [
              "                             open   close     low    high       vol  profit  \\\n",
              "date                                                                          \n",
              "2018-01-02 00:00:00+00:00  177.68  181.42  177.55  181.58  17694891    3.74   \n",
              "2018-01-03 00:00:00+00:00  181.88  184.67  181.33  184.78  16595495    2.79   \n",
              "2018-01-04 00:00:00+00:00  184.90  184.33  184.10  186.21  13554357   -0.57   \n",
              "2018-01-05 00:00:00+00:00  185.59  186.85  184.93  186.90  13042388    1.26   \n",
              "2018-01-08 00:00:00+00:00  187.20  188.28  186.33  188.90  14719216    1.08   \n",
              "\n",
              "                                  gain   size  \n",
              "date                                           \n",
              "2018-01-02 00:00:00+00:00   large_gain  small  \n",
              "2018-01-03 00:00:00+00:00  medium_gain  small  \n",
              "2018-01-04 00:00:00+00:00     negative  small  \n",
              "2018-01-05 00:00:00+00:00  medium_gain  small  \n",
              "2018-01-08 00:00:00+00:00  medium_gain  small  "
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              "      <th>high</th>\n",
              "      <th>vol</th>\n",
              "      <th>profit</th>\n",
              "      <th>gain</th>\n",
              "      <th>size</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>date</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>2018-01-02 00:00:00+00:00</th>\n",
              "      <td>177.68</td>\n",
              "      <td>181.42</td>\n",
              "      <td>177.55</td>\n",
              "      <td>181.58</td>\n",
              "      <td>17694891</td>\n",
              "      <td>3.74</td>\n",
              "      <td>large_gain</td>\n",
              "      <td>small</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-01-03 00:00:00+00:00</th>\n",
              "      <td>181.88</td>\n",
              "      <td>184.67</td>\n",
              "      <td>181.33</td>\n",
              "      <td>184.78</td>\n",
              "      <td>16595495</td>\n",
              "      <td>2.79</td>\n",
              "      <td>medium_gain</td>\n",
              "      <td>small</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-01-04 00:00:00+00:00</th>\n",
              "      <td>184.90</td>\n",
              "      <td>184.33</td>\n",
              "      <td>184.10</td>\n",
              "      <td>186.21</td>\n",
              "      <td>13554357</td>\n",
              "      <td>-0.57</td>\n",
              "      <td>negative</td>\n",
              "      <td>small</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-01-05 00:00:00+00:00</th>\n",
              "      <td>185.59</td>\n",
              "      <td>186.85</td>\n",
              "      <td>184.93</td>\n",
              "      <td>186.90</td>\n",
              "      <td>13042388</td>\n",
              "      <td>1.26</td>\n",
              "      <td>medium_gain</td>\n",
              "      <td>small</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-01-08 00:00:00+00:00</th>\n",
              "      <td>187.20</td>\n",
              "      <td>188.28</td>\n",
              "      <td>186.33</td>\n",
              "      <td>188.90</td>\n",
              "      <td>14719216</td>\n",
              "      <td>1.08</td>\n",
              "      <td>medium_gain</td>\n",
              "      <td>small</td>\n",
              "    </tr>\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 251,\n  \"fields\": [\n    {\n      \"column\": \"date\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"2018-01-02 00:00:00+00:00\",\n        \"max\": \"2018-12-31 00:00:00+00:00\",\n        \"num_unique_values\": 251,\n        \"samples\": [\n          \"2018-08-14 00:00:00+00:00\",\n          \"2018-01-10 00:00:00+00:00\",\n          \"2018-08-27 00:00:00+00:00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"open\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 19.69649335473974,\n        \"min\": 123.1,\n        \"max\": 215.72,\n        \"num_unique_values\": 249,\n        \"samples\": [\n          208.77,\n          186.94,\n          184.93\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"close\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 19.97745158627776,\n        \"min\": 124.06,\n        \"max\": 217.5,\n        \"num_unique_values\": 249,\n        \"samples\": [\n          209.94,\n          187.84,\n          186.9\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"low\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 20.07440767348364,\n        \"min\": 123.02,\n        \"max\": 214.27,\n        \"num_unique_values\": 247,\n        \"samples\": [\n          176.4,\n          185.63,\n          160.88\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"high\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 19.424564323437952,\n        \"min\": 129.74,\n        \"max\": 218.62,\n        \"num_unique_values\": 246,\n        \"samples\": [\n          177.95,\n          187.89,\n          171.77\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"vol\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 19101434,\n        \"min\": 8855144,\n        \"max\": 169803668,\n        \"num_unique_values\": 251,\n        \"samples\": [\n          19101995,\n          10464528,\n          17921935\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"profit\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.8733780844045307,\n        \"min\": -9.429999999999978,\n        \"max\": 8.180000000000007,\n        \"num_unique_values\": 233,\n        \"samples\": [\n          3.530000000000001,\n          2.3500000000000227,\n          -2.140000000000015\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"gain\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"medium_gain\",\n          \"small_gain\",\n          \"large_gain\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"size\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"large\",\n          \"small\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 6
        }
      ],
      "source": [
        "df['profit'] = (df.close - df.open)\n",
        "for idx, row in df.iterrows():\n",
        "    if row.close < row.open:\n",
        "        df.loc[idx,'gain']='negative'\n",
        "    elif (row.close - row.open) < 1:\n",
        "        df.loc[idx,'gain']='small_gain'\n",
        "    elif (row.close - row.open) < 3:\n",
        "        df.loc[idx,'gain']='medium_gain'\n",
        "    else:\n",
        "        df.loc[idx,'gain']='large_gain'\n",
        "\n",
        "for idx, row in df.iterrows():\n",
        "    if row.vol < df.vol.mean():\n",
        "        df.loc[idx,'size']='small'\n",
        "    else:\n",
        "        df.loc[idx,'size']='large'\n",
        "\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "bMvNAPAEhmFN"
      },
      "outputs": [],
      "source": [
        "gain_groups = df.groupby('gain')\n",
        "gdf= df[['open','low','high','close','vol','gain']].groupby('gain').mean()\n",
        "gdf = gdf.reset_index()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "JDGnyNPVhmFN",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "outputId": "1adb3998-af1f-4c9d-d831-51cb5b7806c7"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          gain        open         low        high       close           vol\n",
              "0   large_gain  170.111111  169.610833  175.313056  174.653889  3.044808e+07\n",
              "1  medium_gain  172.305769  171.410962  175.321346  174.185577  2.774962e+07\n",
              "2     negative  171.605492  168.137747  172.566230  169.380246  2.731642e+07\n",
              "3   small_gain  171.218049  169.827317  173.070488  171.699268  2.476326e+07"
            ],
            "text/html": [
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              "      <th></th>\n",
              "      <th>gain</th>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>medium_gain</td>\n",
              "      <td>172.305769</td>\n",
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              "      <th>2</th>\n",
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              "\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-cc6b9e1c-bd43-4337-aefd-846a61502323 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-cc6b9e1c-bd43-4337-aefd-846a61502323');\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-a88a6747-d456-4ece-947e-dff9adb5ee97\">\n",
              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-a88a6747-d456-4ece-947e-dff9adb5ee97')\"\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-a88a6747-d456-4ece-947e-dff9adb5ee97 button');\n",
              "          quickchartButtonEl.style.display =\n",
              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "        })();\n",
              "      </script>\n",
              "    </div>\n",
              "\n",
              "  <div id=\"id_72baa665-78d7-48ba-98a1-407cc034346e\">\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('gdf')\"\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_72baa665-78d7-48ba-98a1-407cc034346e button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('gdf');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "gdf",
              "summary": "{\n  \"name\": \"gdf\",\n  \"rows\": 4,\n  \"fields\": [\n    {\n      \"column\": \"gain\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"medium_gain\",\n          \"small_gain\",\n          \"large_gain\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"open\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.9173623996959993,\n        \"min\": 170.11111111111111,\n        \"max\": 172.30576923076922,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          172.30576923076922,\n          171.2180487804878,\n          170.11111111111111\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"low\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.3395840130605496,\n        \"min\": 168.1377467213115,\n        \"max\": 171.41096153846155,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          171.41096153846155,\n          169.82731707317075,\n          169.61083333333332\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"high\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.4573245935867525,\n        \"min\": 172.56622950819673,\n        \"max\": 175.32134615384615,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          175.32134615384615,\n          173.07048780487807,\n          175.31305555555556\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"close\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.439453678646397,\n        \"min\": 169.38024590163934,\n        \"max\": 174.6538888888889,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          174.18557692307692,\n          171.69926829268292,\n          174.6538888888889\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"vol\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2327922.6044394746,\n        \"min\": 24763260.731707316,\n        \"max\": 30448075.777777776,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          27749616.03846154,\n          24763260.731707316,\n          30448075.777777776\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 8
        }
      ],
      "source": [
        "gdf"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ArOUcaThhmFO"
      },
      "source": [
        "### Simple plots\n",
        "\n",
        "The documentation for the plot function for data frames can be found here:\n",
        "https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.plot.html"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "l5ML30VWhmFO",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 450
        },
        "outputId": "34cc7996-6e8c-4a79-e634-5b3bd826a3ea"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='date'>"
            ]
          },
          "metadata": {},
          "execution_count": 9
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "#plot a column of the dataframe against the index\n",
        "df.high.plot()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "NIeXvRz6hmFO",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 450
        },
        "outputId": "a78292c4-aad4-485e-c194-753c01009f02"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7ff4017f4fe0>"
            ]
          },
          "metadata": {},
          "execution_count": 10
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "df.high.plot()\n",
        "df.low.plot(label='low values')\n",
        "plt.legend(loc='best') #puts the ledgent in the best possible position"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xpqT-UnhhmFP"
      },
      "source": [
        "### Histograms"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "id": "W43-9-cohmFP",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "outputId": "3d984bce-95e6-4e67-9379-e846c4f8cb90"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 16
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "#histogram for the values of a dataframe column\n",
        "df.close.hist(bins=40)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "36ftXtmAhmFQ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "outputId": "484f49a2-a92d-4137-801d-0a0462c583e1"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='close', ylabel='Count'>"
            ]
          },
          "metadata": {},
          "execution_count": 12
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "#histogram with the kernel density estimation (a smoothed function over the histogram)\n",
        "sns.histplot(df.close,bins=20,kde=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "id": "JYWJ24v1hmFQ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 523
        },
        "outputId": "26714bf7-40a7-4923-a2bb-34b5fb66510b"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.FacetGrid at 0x7ff4017ee1e0>"
            ]
          },
          "metadata": {},
          "execution_count": 17
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "sns.displot(df.close,bins=50,kde=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CLNOskFNhmFQ"
      },
      "source": [
        "### Plotting columns against each other"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "id": "HYZZFUDHhmFR",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "outputId": "ef4ce861-641c-4e66-8e7b-44196500b3f6"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            A         B         C         D\n",
              "0    0.631930  0.727462  1.015797  0.668071\n",
              "1    1.301488  0.553052  0.416444  0.841456\n",
              "2    2.495414  0.293590  0.160380  0.739603\n",
              "3    3.449249  0.243811  0.078789  0.554745\n",
              "4    3.655596  0.258616  0.073452  0.649052\n",
              "..        ...       ...       ...       ...\n",
              "95  94.612046  0.010506  0.000112  0.000078\n",
              "96  94.796924  0.010510  0.000111  0.000076\n",
              "97  95.209703  0.010449  0.000110  0.000073\n",
              "98  95.933252  0.010419  0.000109  0.000068\n",
              "99  99.169216  0.010055  0.000102  0.000049\n",
              "\n",
              "[100 rows x 4 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-93271b2c-d870-4550-87eb-b2a897f0d9f4\" class=\"colab-df-container\">\n",
              "    <div>\n",
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              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
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              "      <th></th>\n",
              "      <th>A</th>\n",
              "      <th>B</th>\n",
              "      <th>C</th>\n",
              "      <th>D</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.631930</td>\n",
              "      <td>0.727462</td>\n",
              "      <td>1.015797</td>\n",
              "      <td>0.668071</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1.301488</td>\n",
              "      <td>0.553052</td>\n",
              "      <td>0.416444</td>\n",
              "      <td>0.841456</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2.495414</td>\n",
              "      <td>0.293590</td>\n",
              "      <td>0.160380</td>\n",
              "      <td>0.739603</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3.449249</td>\n",
              "      <td>0.243811</td>\n",
              "      <td>0.078789</td>\n",
              "      <td>0.554745</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>3.655596</td>\n",
              "      <td>0.258616</td>\n",
              "      <td>0.073452</td>\n",
              "      <td>0.649052</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>95</th>\n",
              "      <td>94.612046</td>\n",
              "      <td>0.010506</td>\n",
              "      <td>0.000112</td>\n",
              "      <td>0.000078</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>96</th>\n",
              "      <td>94.796924</td>\n",
              "      <td>0.010510</td>\n",
              "      <td>0.000111</td>\n",
              "      <td>0.000076</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>97</th>\n",
              "      <td>95.209703</td>\n",
              "      <td>0.010449</td>\n",
              "      <td>0.000110</td>\n",
              "      <td>0.000073</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>98</th>\n",
              "      <td>95.933252</td>\n",
              "      <td>0.010419</td>\n",
              "      <td>0.000109</td>\n",
              "      <td>0.000068</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>99</th>\n",
              "      <td>99.169216</td>\n",
              "      <td>0.010055</td>\n",
              "      <td>0.000102</td>\n",
              "      <td>0.000049</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>100 rows × 4 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-93271b2c-d870-4550-87eb-b2a897f0d9f4')\"\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",
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              "  </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",
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              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
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              "\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",
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              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-93271b2c-d870-4550-87eb-b2a897f0d9f4 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-93271b2c-d870-4550-87eb-b2a897f0d9f4');\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-5ed9019c-7a02-42f5-b614-98e8bb2c0f42\">\n",
              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-5ed9019c-7a02-42f5-b614-98e8bb2c0f42')\"\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-5ed9019c-7a02-42f5-b614-98e8bb2c0f42 button');\n",
              "          quickchartButtonEl.style.display =\n",
              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "        })();\n",
              "      </script>\n",
              "    </div>\n",
              "\n",
              "  <div id=\"id_f7d2cc2c-b626-4db1-bb28-fbf4e9e05505\">\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('dfs')\"\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_f7d2cc2c-b626-4db1-bb28-fbf4e9e05505 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('dfs');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "dfs",
              "summary": "{\n  \"name\": \"dfs\",\n  \"rows\": 100,\n  \"fields\": [\n    {\n      \"column\": \"A\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 29.95180775268392,\n        \"min\": 0.63192962,\n        \"max\": 99.16921596,\n        \"num_unique_values\": 100,\n        \"samples\": [\n          86.32364901,\n          54.27968788,\n          73.92847274\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"B\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.09991077312231685,\n        \"min\": 0.010055408,\n        \"max\": 0.727462317,\n        \"num_unique_values\": 100,\n        \"samples\": [\n          0.011492485,\n          0.018135549,\n          0.013457308\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"C\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.11044094250893105,\n        \"min\": 0.000101675,\n        \"max\": 1.015797434,\n        \"num_unique_values\": 100,\n        \"samples\": [\n          0.000134179,\n          0.000339316,\n          0.000182968\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"D\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.17474708046824464,\n        \"min\": 4.93324e-05,\n        \"max\": 0.841455646,\n        \"num_unique_values\": 100,\n        \"samples\": [\n          0.000178233,\n          0.004374665,\n          0.000615539\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 18
        }
      ],
      "source": [
        "dff = pd.read_csv('example-functions.csv')\n",
        "dfs = dff.sort_values(by='A', ascending = True) #Sorting in data frames\n",
        "dfs"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NYtZ28u3hmFR"
      },
      "source": [
        "Plot columns B,C,D against A\n",
        "\n",
        "The plt.figure() command creates a new figure for each plot"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "id": "8Ebul5xFhmFR",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "e8932d3f-aac6-43f2-c8fc-870d8740bd22"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='A'>"
            ]
          },
          "metadata": {},
          "execution_count": 19
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 0 Axes>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 0 Axes>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 0 Axes>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.figure()\n",
        "dfs.plot(x = 'A', y = 'B')\n",
        "plt.figure()\n",
        "dfs.plot(x = 'A', y = 'C')\n",
        "plt.figure()\n",
        "dfs.plot(x = 'A', y = 'D')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "l8w1fedghmFS"
      },
      "source": [
        "### Grid of plots\n",
        "\n",
        "Use a grid to put all the plots together using the [subplots](https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.subplots.html) functionality"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "id": "lqL40yD2hmFS",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 338
        },
        "outputId": "b5bb54a9-b099-4940-ceae-8016505f1586"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='A'>"
            ]
          },
          "metadata": {},
          "execution_count": 20
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2000x500 with 3 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ],
      "source": [
        "#plt.figure()\n",
        "fig, ax = plt.subplots(1, 3,figsize=(20,5))\n",
        "dfs.plot(x = 'A', y = 'B',ax = ax[0])\n",
        "dfs.plot(x = 'A', y = 'C',ax = ax[1])\n",
        "dfs.plot(x = 'A', y = 'D',ax = ax[2])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pxV81iOjhmFS"
      },
      "source": [
        "Plot all colums together against A.\n",
        "\n",
        "Clearly they are different functions"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "id": "oKdzYp5HhmFT",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 484
        },
        "outputId": "ca4f1694-1c58-4f8f-a709-c1d6f4babd61"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='A'>"
            ]
          },
          "metadata": {},
          "execution_count": 21
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 0 Axes>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.figure()\n",
        "dfs.plot(x = 'A', y = ['B','C','D'])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uNJPPF_khmFT"
      },
      "source": [
        "Plot all columns against A in log-log scale (take the logarithm for the values in both axes)\n",
        "\n",
        "We observe straight lines for B,C while steeper drop for D.\n",
        "\n",
        "What does this mean for the B and C lines?"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "id": "hI5er5WchmFT",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 471
        },
        "outputId": "9089421a-3047-4c47-af59-e4611b662846"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 0 Axes>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.figure(); dfs.plot(x = 'A', y = ['B','C','D'], loglog=True);"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The B and C lines are a power law of A"
      ],
      "metadata": {
        "id": "onLkaviHnNN2"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7J5TqOgLhmFT"
      },
      "source": [
        "Plot with log scale only on y-axis (log-linear plot).\n",
        "\n",
        "The plot of D now becomes a line, what does this mean?"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "id": "XKhyEachhmFT",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 484
        },
        "outputId": "08057faa-3e78-4837-8ca9-75235f1d1452"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='A'>"
            ]
          },
          "metadata": {},
          "execution_count": 23
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 0 Axes>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.figure()\n",
        "dfs.plot(x = 'A', y = ['B','C','D'], logy=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The D line is an exponential function of A"
      ],
      "metadata": {
        "id": "VVqPtF06ncbJ"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UaQ1Nx1ehmFU"
      },
      "source": [
        "### Plotting using matplotlib\n",
        "\n",
        "Also how to put two figures in a 1x2 grid"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "id": "WZeNww42hmFU",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 456
        },
        "outputId": "8bc6a574-b4b4-47fc-b54d-0061525d27fc"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7ff3fb8ae1e0>,\n",
              " <matplotlib.lines.Line2D at 0x7ff3fb8ae7b0>,\n",
              " <matplotlib.lines.Line2D at 0x7ff3fb8ae900>]"
            ]
          },
          "metadata": {},
          "execution_count": 24
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x500 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.figure(figsize = (15,5)) #defines the size of figure\n",
        "plt.subplot(121) #plot with 1 row, 2 columns, 1st plot\n",
        "plt.plot(dfs['A'],dfs['B'],'bo-',dfs['A'],dfs['C'],'g*-',dfs['A'],dfs['D'],'rs-')\n",
        "plt.subplot(122)  #plot with 1 row, 2 columns, 2nd plot\n",
        "plt.loglog(dfs['A'],dfs['B'],'bo-',dfs['A'],dfs['C'],'g*-',dfs['A'],dfs['D'],'rs-')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4i0Cs_NlhmFU"
      },
      "source": [
        "Using seaborn"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "id": "t1wtsXo3hmFU",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "outputId": "e6dda114-bc20-4e90-a150-3b05cb79aa6c"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='A', ylabel='B'>"
            ]
          },
          "metadata": {},
          "execution_count": 25
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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F9NwAAJA4loeb+fPn68iRI7rnnntUU1OjadOmaePGjeEi4wMHDshuP9nB9OWXX+qmm25STU2Nhg4dquLiYm3ZskXnnHOOVT9Ct+i5AQAg8WymaQ6qRVi8Xq+ys7NVX1+vrKysuH7WdWve0uu76/TwX0/Vdy8YHdfPAgAgmfXl+9vyRfyS2clVihmWAgAgUQg3cXRyfymGpQAASBTCTRyFdwb30XMDAECiEG7iKP3EOjdsvwAAQOIQbuIoNSXUc8OwFAAAiUK4iaN0d3u4aaGgGACAhCHcxFHqiYLiJgqKAQBIGMJNHKWHF/Gj5wYAgEQh3MRRGuEGAICEI9zEEXtLAQCQeISbOGJvKQAAEo9wE0f03AAAkHiEmzgK99ywzg0AAAlDuImjtBPr3DS3BTXINl8HAMAyhJs4Cg1LmabU2mZY3BoAAAYHwk0chbZfkCgqBgAgUQg3ceSw2+RJab/FFBUDAJAYhJs4S2fGFAAACUW4ibPUEzOm2F8KAIDEINzEWajnhp3BAQBIDMJNnIV7bljrBgCAhCDcxFn6ibVuWtrouQEAIBEIN3GWmtI+LNXkI9wAAJAIhJs4C/XcsM4NAACJQbiJs5M7g9NzAwBAIhBu4oydwQEASCzCTZyd7LlhWAoAgEQg3MQZPTcAACQW4SbO6LkBACCxCDdxRkExAACJRbiJs/CwFOvcAACQEISbOEsLrXPTxrAUAACJQLiJs7SUE+GGnhsAABKCcBNn6W5mSwEAkEiEmzgL7wrObCkAABKCcBNn6ScKilv8QZmmaXFrAABIfv0i3KxcuVJFRUXyeDwqKSnR1q1be3Xds88+K5vNpiuvvDK+DTwDoZ6bgGHKHzQsbg0AAMnP8nCzYcMGVVRUaPny5dq+fbumTp2quXPn6vDhwz1et3//fv393/+9Lr744gS19PSE1rmR2ntvAABAfFkebh5++GHddNNNKi8v1znnnKNVq1YpLS1Na9eu7faaYDCoa665Rj/60Y80bty4Ht/f5/PJ6/VGPBIpxWGXy9F+m5sINwAAxJ2l4cbv92vbtm0qKysLH7Pb7SorK1N1dXW31/34xz9WXl6ebrjhhlN+RmVlpbKzs8OPwsLCmLS9L0Jr3bRQVAwAQNxZGm7q6uoUDAaVn58fcTw/P181NTVRr3njjTe0Zs0arV69ulefsWzZMtXX14cfBw8ePON291VorZsm1roBACDunFY3oC8aGhp03XXXafXq1crNze3VNW63W263O84t61kaa90AAJAwloab3NxcORwO1dbWRhyvra1VQUFBl/M/+eQT7d+/X9/+9rfDxwyjfQaS0+nUrl27NH78+Pg2+jSwMzgAAIlj6bCUy+VScXGxqqqqwscMw1BVVZVKS0u7nD958mS999572rFjR/hxxRVX6JJLLtGOHTssqafpDXYGBwAgcSwflqqoqNDChQs1ffp0zZw5UytWrFBTU5PKy8slSQsWLNCoUaNUWVkpj8ejKVOmRFw/ZMgQSepyvD8J7wxOzw0AAHFnebiZP3++jhw5onvuuUc1NTWaNm2aNm7cGC4yPnDggOx2y2esnxF6bgAASBzLw40kLV26VEuXLo362qZNm3q8dv369bFvUIwRbgAASJyB3SUyQDAsBQBA4hBuEiDUc8M6NwAAxB/hJgHS3Sd3BgcAAPFFuEmA1NAKxQxLAQAQd4SbBEgP7y1Fzw0AAPFGuEmA1BMFxfTcAAAQf6c1Ffzo0aMaNmyYJOngwYNavXq1WlpadMUVV+jiiy+OaQOTQbqLnhsAABKlTz037733noqKipSXl6fJkydrx44dmjFjhv7t3/5NTz75pC655BK9+OKLcWrqwJUami1FuAEAIO76FG5++MMf6txzz9Vrr72mOXPm6M///M81b9481dfX68svv9TNN9+s+++/P15tHbDSXcyWAgAgUfo0LPX222/rlVde0XnnnaepU6fqySef1OLFi8PbI9x666268MIL49LQgSy8zg01NwAAxF2fem6OHTumgoICSVJGRobS09M1dOjQ8OtDhw5VQ0NDbFuYBNLcoRWK6bkBACDe+jxbymaz9fgcXaWdWOfGHzAUCBoWtwYAgOTW59lS119/vdxutySptbVVt9xyi9LT0yVJPp8vtq1LEmkn1rmRpOa2oLIczMAHACBe+hRuFi5cGPH82muv7XLOggULzqxFScjlsMthtylomGr2BZXlSbG6SQAAJK0+hZt169bFqx1JzWazKc3lUENrgJ3BAQCIM8ZHEiQ0Y4qiYgAA4otwkyChtW4INwAAxBfhJkFSWesGAICEINwkCKsUAwCQGISbBAn33PjouQEAIJ4INwmSfmKtm5Y2em4AAIgnwk2CpKa0D0s1+Qg3AADEE+EmQcI9NxQUAwAQV4SbBDk5W4qeGwAA4olwkyCscwMAQGIQbhLk5ArFDEsBABBPhJsESaPnBgCAhCDcJAg9NwAAJAbhJkHYOBMAgMQg3CRIeFiKdW4AAIgrwk2CpJ1Y56a5jWEpAADiiXCTIOFhKXpuAACIK8JNgrDODQAAiUG4SZDQCsUtbUEZhmlxawAASF6EmwQJ9dxI7AwOAEA8EW4SxJNil83W/vsm1roBACBu+kW4WblypYqKiuTxeFRSUqKtW7d2e+4LL7yg6dOna8iQIUpPT9e0adP09NNPJ7C1p8dmsyktJbQzOD03AADEi+XhZsOGDaqoqNDy5cu1fft2TZ06VXPnztXhw4ejnp+Tk6O77rpL1dXVevfdd1VeXq7y8nL9/ve/T3DL+27EkFRNys9Ua5thdVMAAEhaNtM0La1uLSkp0YwZM/T4449LkgzDUGFhoW699VbdeeedvXqPCy64QPPmzdO9997b5TWfzyefzxd+7vV6VVhYqPr6emVlZcXmh+iFlhNDUXWNfuVluRU0zPDCfgAAoGder1fZ2dm9+v62tOfG7/dr27ZtKisrCx+z2+0qKytTdXX1Ka83TVNVVVXatWuXvv71r0c9p7KyUtnZ2eFHYWFhzNrfW762oFZt3quSyipd/C+vasZ9/6efbd4rH4XFAADEnKXhpq6uTsFgUPn5+RHH8/PzVVNT0+119fX1ysjIkMvl0rx58/TYY4/psssui3rusmXLVF9fH34cPHgwpj/DqbT4A/rppk/0SNVueVvae2+8LQE9UrVbP930CRtpAgAQYwNyXCQzM1M7duxQY2OjqqqqVFFRoXHjxmnOnDldznW73XK73Ylv5AkOu13rtuyL+tq6Lfu05JIJCW4RAADJzdJwk5ubK4fDodra2ojjtbW1Kigo6PY6u92uCRPaQ8G0adP00UcfqbKyMmq4sVpDa1u4x6Yzb0tADa1tGpZhXfgCACDZWDos5XK5VFxcrKqqqvAxwzBUVVWl0tLSXr+PYRgRRcP9SaYnRVmp0TNkVqpTmZ6UBLcIAIDkZvmwVEVFhRYuXKjp06dr5syZWrFihZqamlReXi5JWrBggUaNGqXKykpJ7QXC06dP1/jx4+Xz+fTSSy/p6aef1hNPPGHlj9GtoGGofNZYPVK1u8tr5bPGKmAYclk/Ix8AgKRhebiZP3++jhw5onvuuUc1NTWaNm2aNm7cGC4yPnDggOz2k1/+TU1NWrx4sT777DOlpqZq8uTJeuaZZzR//nyrfoQepbqcWjxnvKT2GhtvS0BZqU6VzxqrxXPGy31iYT8AABAblq9zk2h9mScfS83+gJx2u440tGpoukveljYVZKcm7PMBABjIBsw6N4NJmsspl9OuVZv36msPvKqXP6w99UUAAKDPCDcJlp2aomNNfn14yGt1UwAASEqEmwQ7e0R7V9qHXxBuAACIB8JNgp0zsj3c/KmmQYEgG2gCABBrhJsEOysnTWkuh3wBQ/vqmqxuDgAASYdwk2B2u+3k0BR1NwAAxBzhxgLnUHcDAEDcEG4sEKq7oecGAIDYI9xYINRz0+wPqtkfkD9g6GijT/6AoWZ/9E02AQBA71i+/cJgNKkgUxPzMvTkdcX62ea9bMsAAEAMEW4s4ElxaPm3z9H6Lfv12Ct7wse9LYHwBps3zx6nNBd/PAAA9BXDUhYpLsrRU9X7o762bss+Oe380QAAcDr4BrVIfXObvC3R62u8LQE1tLYluEUAACQHwo1FhqanKCs1+rBTVqpTmZ6UBLcIAIDkQLixiK/NUPmsoqivlc8aq4DB1gwAAJwOKlYt4nHadePF42SY0lPV+5ktBQBAjBBuLNDiD2jV5r367buHdMflk/Tmskt1rMmvnHSXPj3aLMM0rW4iAAADFuHGAg67Pby2zfef3qacdJeGZ7h1pNGngGHonbsus7qJAAAMWIQbCzS0Rs6UOtbk17Emf8TrwzLcVjQNAIABj4JiC2R6mCkFAEC8EG4sEDQMlc8aG/U1ZkoBAHBmGJayQKrLqcVzxktSxL5SC0uLdOPFY9l2AQCAM2AzzcE1Ncfr9So7O1v19fXKysqytC3N/oCcdrsaWtuU7nZq88dHtP3TY1r2rXMsbRcAAP1NX76/GZayUJrLKZfTrmEZbr2196hufnqbNn9cZ3WzAAAY0Ag3/cS44RmSpE+ONKotSM0NAACni3DTT4wakqp0l0NtQVP765qsbg4AAAMW4aafsNtt+kpBpiTpTzUNFrcGAICBi3DTj0w+EW52EW4AADhthJt+5Cv59NwAAHCmCDf9yKRQz02t1+KWAAAwcBFu+pHJBe3z9g8ea1GTL3CKswEAQDSEm34kJ92l4ZntG2Z+XMvQFAAAp4Nw089MyqeoGACAM0G46WcmMR0cAIAzQrjpZ0LhhmEpAABOT78INytXrlRRUZE8Ho9KSkq0devWbs9dvXq1Lr74Yg0dOlRDhw5VWVlZj+cPNKx1AwDAmbE83GzYsEEVFRVavny5tm/frqlTp2ru3Lk6fPhw1PM3bdqk733ve3r11VdVXV2twsJC/dmf/Zk+//zzBLc8PibmZcpmk442+XWkwWd1cwAAGHBspmmaVjagpKREM2bM0OOPPy5JMgxDhYWFuvXWW3XnnXee8vpgMKihQ4fq8ccf14IFC055fl+2TLfKnAdf1f6jzXrmhhJ9bWKu1c0BAMByffn+trTnxu/3a9u2bSorKwsfs9vtKisrU3V1da/eo7m5WW1tbcrJyYn6us/nk9frjXj0dyeLivt/WwEA6G8sDTd1dXUKBoPKz8+POJ6fn6+amppevccdd9yhkSNHRgSkjiorK5WdnR1+FBYWnnG7423SicX8KCoGAKDvLK+5ORP333+/nn32Wf3qV7+Sx+OJes6yZctUX18ffhw8eDDBrew7iooBADh9Tis/PDc3Vw6HQ7W1tRHHa2trVVBQ0OO1//qv/6r7779f//d//6fzzjuv2/PcbrfcbndM2psooQ00P65tlGGYstttFrcIAICBw9KeG5fLpeLiYlVVVYWPGYahqqoqlZaWdnvdv/zLv+jee+/Vxo0bNX369EQ0NaGKhqXJ5bSrpS2oA8earW4OAAADiuXDUhUVFVq9erWeeuopffTRR1q0aJGamppUXl4uSVqwYIGWLVsWPv+BBx7Q3XffrbVr16qoqEg1NTWqqalRY2OjVT9CzDkddk3My5DESsUAAPSVpcNSkjR//nwdOXJE99xzj2pqajRt2jRt3LgxXGR84MAB2e0nM9gTTzwhv9+vv/qrv4p4n+XLl+uf/umfEtn0uJpUkKkPvvDq49oGXT6l5yE6AABwkuXhRpKWLl2qpUuXRn1t06ZNEc/3798f/wb1A2ygCQDA6bF8WArRsdYNAACnh3DTT00+sdbN/qPNam0LWtwaAAAGDsJNP5Wf5VZ2aoqChqlPjiRPsTQAAPFGuOmnbDYbdTcAAJwGwk0/NomVigEA6DPCTT82qSBT44dnqOycfPkDho42+uQPGGr2B6xuGgAA/Va/mAqO6KYVZuuXN1+o9Vv264an3pa3JaCsVKfKZ43V4jnj5U5xWN1EAAD6HcJNP3bWsHStfn2vHntlT/iYtyWgR6p2S5Junj1OaS7+CAEA6IhhqX7M7XRo/Zb9UV9bt2WfnHb++AAA6Ixvx36qtS2gY00+eVui19d4WwJqaG1LcKsAAOj/CDf9lN1mV1ZqirJSow87ZaU6lelJSXCrAADo/wg3/VRDa5v+sKdOC0uLor5ePmusAoaR2EYBADAAEG76qUxPih5/dY+un1WkW78xIdyDk5Xq1K3fmKBFc8ZTTAwAQBR8O/ZTQcPQnK/k6a9/9qbuuHyS3lx2qY41+ZWT7tKnR5tlmqbVTQQAoF8i3PRTqS6nFs8ZL0n6++d3ymm3a2xumuadO0JXzxyjVHptAACIymYOsi4Ar9er7Oxs1dfXKysry+rmnFKzPyCn3a5mX0BOp00pDru+bGpTTrpLAcNgaAoAMCj05fubb8Z+LhReTNOhlZv2aP2W/axUDABADwg3A0CLP6BVm/fq0SpWKgYA4FSYLTUAOOx2rduyL+prrFQMAEAkvhUHgIbWNlYqBgCglwg3A0Cmh5WKAQDoLcLNABA0DJXPGhv1tb+7bJL8gaD8AUNHG33yBww1+6P38gAAMBhQhToAdFzzZt2WfeHZUj8o+4rmzyjUE5s+iTjOLCoAwGDGOjcDSGjNm4bWNqW6HDra6Ndz2w5GzKIKue3SicyiAgAkjb58fzMsNYCkuZxyOe0aluHW7979QsMyXFq/ZX/Uc5lFBQAYrPj2G6DmnTdSXzYxiwoAgM4Ysxig0lxOOe12ZaU6owYcZlEBAAYrem4GqBZ/QHuPNGphaVHU18tnjVXAMBLbKAAA+gF6bgYoh92uO154V2sXzpAkPVV9cs+pW74+Xjd8bSyzpQAAgxLhZoBqaG3TzoP1+uufvak7Lp+kN5ddKm9LmzLcTqU47WpoDchms7FzOABg0OFbb4AKrVr8yZFGff/pbSo+a6j+feF0/ey1vRG9OKx5AwAYbKi5GaA6r1p8y+xxWvvGPj32yp5wgfHwDI+mjMqSKamO1YsBAIMEPTcDVMdVi1/c8ZkumpCrv3tuZ/j18cMz9MubL9T6Lfv1d8/tpCcHADBoEG4GMHeKQzfPHqell0xQfUvkmjd3fnOS1m/Zr8deObl6sbcloEeqdksSqxcDAJIWw1IDXJqrvYA4K/XkzuE56S5dNCFXT1Xvj3oNqxcDAJIZ33BJomMNzvAMt442+lm9GAAwKFkeblauXKmioiJ5PB6VlJRo69at3Z77wQcf6C//8i9VVFQkm82mFStWJK6h/VyoBue2SyeqNRDQsAxXuCenM1YvBgAkM0vDzYYNG1RRUaHly5dr+/btmjp1qubOnavDhw9HPb+5uVnjxo3T/fffr4KCggS3tv8L1eC8/IM5aguaEbOpOrp+VhGrFwMAkpal4ebhhx/WTTfdpPLycp1zzjlatWqV0tLStHbt2qjnz5gxQw8++KCuvvpqud3uBLd2YAjtHJ6dmhLuyQn14GSlOnXrNyZoYWmRXnqvxuKWAgAQH5ZNl/H7/dq2bZuWLVsWPma321VWVqbq6uqYfY7P55PP5ws/93q9MXvv/i7Uk7PkkglqaG1TpidFH9c26K9/9qY+OdKog8eadXvZRNlsNqubCgBAzFjWc1NXV6dgMKj8/PyI4/n5+aqpiV2vQmVlpbKzs8OPwsLCmL33QBDqyRmW4ZbLaddXR2bpL84fKUl6pGq3fvTfH8owTItbCQBA7FheUBxvy5YtU319ffhx8OBBq5tkKZvNpqXfmKgff+erkqT1W/brH55/V4EgNTgAgORg2bBUbm6uHA6HamtrI47X1tbGtFjY7XZTnxPFgtIiZbid+ofn39V/bf9Mjb42Pfq98+V2snIxAGBgs6znxuVyqbi4WFVVVeFjhmGoqqpKpaWlVjVrUPnuBaP1xDUXyOWw6/cf1OrGp95Rk4+9pwAAA5ulw1IVFRVavXq1nnrqKX300UdatGiRmpqaVF5eLklasGBBRMGx3+/Xjh07tGPHDvn9fn3++efasWOH9uzZ091H4BT+7KsFWlc+Q2kuh17fXadr17yl+mYW+AMADFw20zQtrSZ9/PHH9eCDD6qmpkbTpk3To48+qpKSEknSnDlzVFRUpPXr10uS9u/fr7Fju67dMnv2bG3atKlXn+f1epWdna36+nplZWXF6scY8P544Etdv+5t1be0aXJBpv7jhpnKy/RY3SwAACT17fvb8nCTaISb7v2pxqvr1mzVkQafioal6ZkbSzR6aJrVzQIAoE/f30k/Wwq9N7kgS8/fUqrRQ1O1/2izrlpVrT2HG61uFgAAfUK4QYSzhqXr+VtmaUJehg7Vt+qO/3pXRxpa5Q8YOtrokz9gqNlP0TEAoP+ybCo4+q+CbI9+eXOp7vrVe/rnK6do/Zb9eqp6v7wtAWWlOlU+a6wWzxkvdwrTxgEA/Q/hBlHlpLv0L391np58ba8ee+XkbDRvS0CPVO2WJN08e5zSXPwVAgD0LwxLoVtup0NPVe/vcnz88AxNLcyW025XfUub/AFDdQxZAQD6CcINutXQ2iZvS2RYGT88Q7+8+UJ9erRZjb6A/v31vZp+38ua/7M39caeI7LbbAQdAIClGFNAtzI9KcpKdUYEnDu/OUnrt+zXeaOzte4P+/TYK3vCgWf9lv26fcMOanMAAJai5wbdChqGymedXDQxJ92liybk6jc7P9dFE3LDQ1ahwPPYK3vCQWh4hkdTRmXJlOjJAQAkFD036Faqy6nFc8ZLktZt2afhGW4dbfTL43TqaKNf3pZAOPD83XM7w9d17Mn5u+d2ytsS0NTCbD3w3fM0bniGWtqCSk1xyNvapixPigKGQWEyACBm+EZBj9wpDt08e5yWXDJBja1tyvCkqDUQ0LAMl7JSneHAE23oKjTLavzwDK1dOEO/2fmF8rI8WveHfUwtBwDEDcNSOKU0l1Mup105GW4FDUNXThutP+yp08LSIh1p9IWDjnRy6KrjLKtQ2Bk9NDVcpxMKQ6Gp5T/d9AnDVgCAmCDcoE9CQ1VfHG9V+UVjdU3JGL2596gWlhZJUpeenO7qdDpbt2WfnPaTfx1b/IGIVZFb2gg+AIDeYVgKfeZOceiq6aPltNt149fGKdXl0KzxubLbbHpxx2fhnhxvSyBqnU403paAGlrbNCzDLV9bUKs279Wmjw9r6SUTdNGEXHlb2uRIt1OfAwA4Jb4lcFpCAcPlPNnbEqrNaWkLqnzWWD1StTs8bNWxTidawMlKdSrN5dSBo036r+2f67fvHupSlEx9DgCgNxiWQsyEanOyU1O0eM543XbpRAUMQ3/YU6crpo4K1+lEs7C0SG/urVN+tkfrtuyLOr2c+hwAQG8QbhAXoVlW79x1mWaclaMll0wI1+nc+o0J4QLkrFSnbrt0opZcMkFOu10NrQE57fY+1ed01rlehyAEAIMLw1KIm5NDVy5JiqjTufUbE9XQ2qbME+vceFIcuvgrw+UPGBqbm3bK+pzDDa36j+pPddawNJWMHabxw9Nls9nC9Trt6/J4dNe8yZo1PlcNLX7JZpPb6ZC3pU1ZqSnyBYLK9KQk7H4AABKDcIOE6VynMyzD3f68QwdiW9DQpZPzT1mfk5Pu0vPbPtOxJr8kKTfDrdULivXqrsN6tCpyS4g39tTph3Mn66eb9mj9lpPr61Rc9hX9VfFouRyOiKBFwTIADGwMS6Ffcdik60rP0qdHm7utzymfNVb1LW1aUHqWLhyXI5fTLsM0NakgU+u37JcUuZDgrZdM1E837dGjVZHbQ3z7vJF68rX2jT+L//n/NP2+l/WzzXvlawtKOjm8xc7nADCw8L+o6Fc8LqfsbUG5nXYtuWSC7Dab1m3ZF3W21O1lX5EktbYF9XFNg7wtgS5bQpw/ZojS3c5w6AnpvIqydLJgOSfdpfkzCvXLdz7Tt6eOjLqi8pI54xU0TTnsdraTAIB+hn+B0e+4UhwqyPbIbrPphq+N1ZJLJkQMG3WeBu5Jcei8wiHyB4wuW0JMGz1E3pa2iOGtaPthdVSYk6onNn2iKaOywisqh3hbAvrtu4d048Vj9cL2z7sNP52nq7f6AwqaUorDTggCgDhjWAr9UmhaeVZqilxOu4ZluOVy2nsMA6FdzDtuCbHjs+PKSk0Jz86Suq6i3FFOuksXjhumF3d81u2MrTu/OUlPvrZXI4d4erWdhL8tqDbD1KrNn2j6fS9repQhMIlZXgAQK4QbJI3Q1hDXXXhWeEuIPx44riZfQNfPKgqf13k/rI5OtaJyX7aTcNhtOt7s1966Jj352t5uQ1CLPxCe5dW5/sffFuy29qfJ16YmX4B6IADohD5xJJXQ+jpOuz28JcRjr+7WD+dOliSt37Jfx5r8enPvUV0/q0iPVu2JuP5Io0/DM93drqjcl+0kjjb6NDTNrTHD0noMQd//+jit2vyJHqnaHXF9d8NfwzM8+q9FpXLYbXrytU/YYR0AOiHcIOl0HLoKBR1/W0A3XTxOSy+ZGK55mTU+VzZFFixfd+FZCnTa+bxjzU1ftpPISXfpeLNfbUGz2xDktNuV4rBr3ZZ9XV4LDX+dNzo7ovbnoasm6VB9q15671DUgujQz32m9Twt/oAcdjvT5AEMOPxLhaTWeSFBqX1NnJDQflidv8AXzxmvZ98+qPKLxkpSuHckYBg6cLS52/ATUj5rrAxTykl3K2AY3Yagsblp8ra2dTv89c+/+1CL5owPFz+HjofaFM26Lfu0+JLx+uiQV5me9pCVmuKQzWbr9X3ruBhid71C0Yqk24JBSbZeFU4TngDEC/+SYFALfZl2XlCw887nHVdUDhpGt+Gncwho8Qd04MSaPdFC0GVnFyjLk9Lr4a/hGW55W9p67A3ytgRU1+jX7c/u0K7ahvafx2lXTrpLQ9JcyklP0dA018nnaSkamt7+fGiaS6OGpmr9H/Z3GSYLPb9l9jg5bDa1GaaefG2vnqrer+IxOVp17QUyTPVqqKw34am3ugtJhCdg8OK/dKAb3a2oHKrD72k7idAXdKrLqXG56fr+18dJ6hqCyi8qCs/y6hgmuhv+OtLoU1Zq+5YRPQ6JpbkkmymXwy5/0JAvYOhQfasO1bf2+DPnpLv0xh2XRB0mk9p7hW76+lh9erRZv+swLHbvlV/VvrqmiGNS9KGyFn9Aqzbv7TY89WVILVpI+rvLJmn+jMIux398xRRdenZexBYc/kBQGZ4UNba2ydXheMAIyjAUccwXCEa9FkD/Q7gBTlNvtpOQ2tftCZqmbpk9vtsQtHjOeEkKfxl3N/x1rMmvP+yp0+ihaT0OiZky9fvbZ8s0TTX5g/qyya8vm/06Fv61Tccjnvt1vLlNQ9NSeiyUdtrtcjkcEUXS44anKy/To5x0o8ehsltmj9fVP6vWuvKZPYanRXPG67l3DirFYVe626l0l6P9V7dTGW6n0t0Opbuc8gWCUUPSyCGe8IrUIcVjcnT5lIIuW3D86Iqv6ptTRujJ1/eGj887r0APXTVNq15vP7d4TI6euPYCre5wTlaqU9fPKtLiORPkOUVPU296kGLRyxR6j2ZfQK4Uu8wTQ4YNoSFDeq4wiPA3HUiA1A5fKtFCUGiWV8f6n+6Gv+7/n136r0Wl3fYGdRzasdlsyjgRCgpz0nrV1tBiiN3VCDX5A2psDYRfnzg8Q42+gJp8gR6Hyo43+5WdmqK6Rt8phtR8+vfX94WH1KLproepuwUa773yq10Cj7cloBlFOV2OL/vm2RHHurs29Pz7F4/rtgenN8NvsRiiC73Hpo8P66nrZypomHp+22c6a1iaLhw3TDXeVg3PdKvJ3ya7bOEQlOK0hwNQpqe9N8rldET8HZQUEbxC53RembvjudHeu7vAFq12K2gYskldjnduH0ON6A5/K4B+onP9T0/DX2kup9qCwR57g05XtGGykMvOLlC6yym30x4OQLuPNCrD7ZQnxd7jUNmwDLd+8GdfUV6Wu8fzcjPc+urILOVmutToC6rpRHAKBSjD7H4hxmjHQz1LnbfgiHa887Hurg1Zv2W/ll4yUT956UPVNwfkctrDj+9MHamX3j/UJRSF7uvVMwvlsNn0zFufdntOb4boOg7zrV5QrEPeVr2596iumDpS67fs1+0bdsjbEtDUwmw9c0OJnny9PQStXThDazZ/El5e4LlbSrV+y75w71To/H9/fZ/WbdkXPuc3Oz/XFVNHRazM3fHczu/dU2ALLXAZqt0Kvdcvbiw5Ub+1N2r7hmd4dNe8yZo1Pld1jb6owafz874Uu4fu6+n2pnXXixYtCHZsn81mC58bCo+tbUF5UhgO7SvCDdDPdTv85TzZ89PdkNjpCC2GKKlLb0KoRqhjkfTeI0063NCqxtZAj0NlAcPQ5IIstfgD3Yan9llmph6ePy1q20zTlC9gqMkXUGaUQuyOCzR27Fmqb+k6Iy3a8c7Hurs2xNsSkLe1TZ/WNev3H9aGj+eku3TrNyZ0G4rWbdmnm2e397yd6pyv3V+lJn9QKY4TwenErykOu3IzXPrpNcVat2VfxEy6Q/UtXfZOW3rJhPBikqsXFEe8/tBVk7psNdLx/I7ndF6a4FTvHbpP0WqvOtduhd7rwLGWiKUOOrZv/PAM/fLmC8PBLVrw6fy8r+tCnUlvWrRetM7hrXNo/K9FpUpx2MI9bt+ZNkqbdh3Wt84doX9/Y29E4Hzgu+dp3PCuvVehQNXTXnedA1vHwBe6rskXCP/9OpNgZ3XvGuEGQBfRhsk69gp1LpK++8UPtOraC3o1VNZTeDrVl4fNZpMnxSHPiZlonUNSqCap4wKNu480Kju1axCKdrzzse6uDclKdSrLk6JvnD1c547Olj9gyB80NSQ1RfXNPYeihpaAAmbPs96ONfmV7k7RZ8ejF4JPys8MD/NNys+Ut6VNQcPUheOG6fYNO8LndRyu6zx0F20or7tzOi9NcKr37ixUe7X459v00FXTuixwGW2pg87v2XnT287BLNrzU60LdV3pWWpoDchhsykz1dnjbMGeetOi9aJ1/txooTFU6P/Se4fC4XH+jMKI4dDxwzO0duEMrd+yP+K/r1AB/ak2+jWlcGDrGPie33ZQ35nW3hP32u4jWrtwhlb1otctmljOgjxThBsAUXU3TV6KXiRts9lkt6lXQ2WnCk+90V1I+uJ4qxbPmRBeoHHvkSYdaWjtsiJ1qMep4/HOx6Kd09H1s4rkDwQ1f8ZZXV7rqXYpK9Wpoemu8O+7Oycv06N/X1is1jZD/qAhf8BQW9A88ashwzSVl9k+zBeaSWezSXUNkUNzHYfrJuVndlleoPNQXudjPa3M3dN7d+ZtCehok0/BoKn6lq4LXEZb6qDj+58qmHX3XOp5XaibZ4/Tn/3ba5J0ytmCN88epxn3/Z/qW9rktNvksNvktNs0LMOt3yy9qEsvWrTwFq19kvSbnZ9r0ZzxeuyV3br1GxMjevU6h7rQ/QwV0J87qmuPWiiUfXNKQcQQacfA17En7lS9bt+bWajPj7fIZrPJYWv/2e0nfs3NcOmp6v1nNMQaS4QbAKclapF0H4bKegpPvdVdSPJEOb54zgRJipjx9Pb+Y12OV/7PR3roqmnhY3e/+IGeuPaCLteearZUT7VLoWE624nf93ROYU56j/egYw/WH/bU6axh6RqTkxYRmjoO13Ueuos2lNfdOdFW5u7pvTvLSnVqeKZb1190loamuxQ0zC7v1Xmpg47v2V3o6ul5b9aFOt7cpqKcNAVM85Th7FiTXzlpLh1p8Mnf4bW8TE9EyIv2ud21T5LagmY4PI7ISo0YDu2uR6xjj9ots8dH7THLSXdpzLC0cFDqGKhCYaq3vW43zx6nbz36ho41+SNeCxX49zTEuuSSCVFfixfCDYABrbuQ1LVAu31mU8ctOPyB9mLNzscDRjDimN1m67J9R+ja7vR2+O10h+iifc7jr+7RU9fPVFvQiOhtCg3XhWqioi0v0LFeqvOx0PMrpo465bmnWrk7aJgqHT886gKX0ZY66Pj+P3/rQI/BLNrz3qwLlZvh1gtLLpJ06h63vEyPnr5xptqCpoJBUwHDUNAwZUrKz4rsRev8uT21T1I4PB7ytkQMh56qgL6nve56Cnwdr+tNr9uXTW2aOjpbuw83yjSloGEqaJoal5t+ymsbWtsi/luMt34RblauXKkHH3xQNTU1mjp1qh577DHNnDmz2/Ofe+453X333dq/f78mTpyoBx54QN/61rcS2GIAA1Folklup56mLsc79CJ1Prfz8570ZvgtFkN0Hd+j2ReQ026LGJrztgT0+Kt79MwNJbLbbHr81T1au3CGpJPLCzx3S6lstpO9Ux3PX7dlX/ic3+z8vMvK3D29d0+1V9EWuHz81T36xY0lEcc7tu/NvUe7DWbdPT/VulABwwj/mfemxy0v0xP1z6FzL1rnz+2pfZLC4XH2V/IihkO76xHrzV53PQWqjtf1ttdtXXn07+ZThcLMBM/wspmmaSb0EzvZsGGDFixYoFWrVqmkpEQrVqzQc889p127dikvL6/L+Vu2bNHXv/51VVZW6s///M/1i1/8Qg888IC2b9+uKVOmnPLzvF6vsrOzVV9fr6ysrHj8SADQLzT7A3J2mLkSNAyZal+Msbfr3ITO77LOjT+oVFf0c/uyzk2Lv316f8dzQ1OlOx8PfbZhmnpi0ycRU9R7N1tqb69nS/30xPufTlHtTzd9Ep4t5XDYej1byjDVZbZUaOHJh66aqnc/q+8S0FYvKNZ7n9fr3FHZUV+XpI23Xaz/eb8mHNhWLyjW6KFp4Zqb0HWrFxR3+x63XTqx18XUfb22t/ry/W15uCkpKdGMGTP0+OOPS5IMw1BhYaFuvfVW3XnnnV3Onz9/vpqamvTb3/42fOzCCy/UtGnTtGrVqlN+HuEGAAa+zsGtL+vc9Gaacuf378uU5tC10VaL7i40dlnn5kR4DK1z0+QLKNXlCIe6zrOlNrx98JSzpX7aIRCenC31WZfZUp1nZPU12MVrttSACTd+v19paWl6/vnndeWVV4aPL1y4UMePH9evf/3rLteMGTNGFRUVuv3228PHli9frhdffFE7d3YthPL5fPL5fOHnXq9XhYWFhBsAwIDTXegKHe/co9YxlHW+NmKdmxPX9aXXrS/ti4W+hBtLa27q6uoUDAaVn58fcTw/P19/+tOfol5TU1MT9fyampqo51dWVupHP/pRbBoMAICFTlVA39Ned12u7VA3Fvq9y+kKHzudmYyxmAUZC9Z8agItW7ZM9fX14cfBgwetbhIAAIgjS3tucnNz5XA4VFtbG3G8trZWBQUFUa8pKCjo0/lut1tud+KmnwEAAGtZ2nPjcrlUXFysqqqq8DHDMFRVVaXS0tKo15SWlkacL0kvv/xyt+cDAIDBxfJ1bioqKrRw4UJNnz5dM2fO1IoVK9TU1KTy8nJJ0oIFCzRq1ChVVlZKkm677TbNnj1bDz30kObNm6dnn31W77zzjp588kkrfwwAANBPWB5u5s+fryNHjuiee+5RTU2Npk2bpo0bN4aLhg8cOCC7/WQH06xZs/SLX/xC/+///T/94z/+oyZOnKgXX3yxV2vcAACA5Gf5OjeJxjo3AAAMPH35/k762VIAAGBwIdwAAICkQrgBAABJhXADAACSiuWzpRItVD/t9XotbgkAAOit0Pd2b+ZBDbpw09DQIEkqLCy0uCUAAKCvGhoalJ2d3eM5g24quGEY+uKLL5SZmSmbzXbG7xfaZfzgwYNMLY8z7nVicb8Th3udWNzvxIrV/TZNUw0NDRo5cmTE+nfRDLqeG7vdrtGjR8f8fbOysviPJEG414nF/U4c7nVicb8TKxb3+1Q9NiEUFAMAgKRCuAEAAEmFcHOG3G63li9fLrfbbXVTkh73OrG434nDvU4s7ndiWXG/B11BMQAASG703AAAgKRCuAEAAEmFcAMAAJIK4QYAACQVws0ZWLlypYqKiuTxeFRSUqKtW7da3aSkUFlZqRkzZigzM1N5eXm68sortWvXrohzWltbtWTJEg0bNkwZGRn6y7/8S9XW1lrU4uRx//33y2az6fbbbw8f417H1ueff65rr71Ww4YNU2pqqs4991y988474ddN09Q999yjESNGKDU1VWVlZdq9e7eFLR6YgsGg7r77bo0dO1apqakaP3687r333oh9ibjXp++1117Tt7/9bY0cOVI2m00vvvhixOu9ubfHjh3TNddco6ysLA0ZMkQ33HCDGhsbY9NAE6fl2WefNV0ul7l27Vrzgw8+MG+66SZzyJAhZm1trdVNG/Dmzp1rrlu3znz//ffNHTt2mN/61rfMMWPGmI2NjeFzbrnlFrOwsNCsqqoy33nnHfPCCy80Z82aZWGrB76tW7eaRUVF5nnnnWfedttt4ePc69g5duyYedZZZ5nXX3+9+dZbb5l79+41f//735t79uwJn3P//feb2dnZ5osvvmju3LnTvOKKK8yxY8eaLS0tFrZ84LnvvvvMYcOGmb/97W/Nffv2mc8995yZkZFhPvLII+FzuNen76WXXjLvuusu84UXXjAlmb/61a8iXu/Nvb388svNqVOnmm+++ab5+uuvmxMmTDC/973vxaR9hJvTNHPmTHPJkiXh58Fg0Bw5cqRZWVlpYauS0+HDh01J5ubNm03TNM3jx4+bKSkp5nPPPRc+56OPPjIlmdXV1VY1c0BraGgwJ06caL788svm7Nmzw+GGex1bd9xxh/m1r32t29cNwzALCgrMBx98MHzs+PHjptvtNv/zP/8zEU1MGvPmzTP/5m/+JuLYd7/7XfOaa64xTZN7HUudw01v7u2HH35oSjLffvvt8Dn/8z//Y9psNvPzzz8/4zYxLHUa/H6/tm3bprKysvAxu92usrIyVVdXW9iy5FRfXy9JysnJkSRt27ZNbW1tEfd/8uTJGjNmDPf/NC1ZskTz5s2LuKcS9zrWfvOb32j69Om66qqrlJeXp/PPP1+rV68Ov75v3z7V1NRE3O/s7GyVlJRwv/to1qxZqqqq0scffyxJ2rlzp9544w1985vflMS9jqfe3Nvq6moNGTJE06dPD59TVlYmu92ut95664zbMOg2zoyFuro6BYNB5efnRxzPz8/Xn/70J4talZwMw9Dtt9+uiy66SFOmTJEk1dTUyOVyaciQIRHn5ufnq6amxoJWDmzPPvustm/frrfffrvLa9zr2Nq7d6+eeOIJVVRU6B//8R/19ttv62//9m/lcrm0cOHC8D2N9m8L97tv7rzzTnm9Xk2ePFkOh0PBYFD33XefrrnmGkniXsdRb+5tTU2N8vLyIl53Op3KycmJyf0n3KBfW7Jkid5//3298cYbVjclKR08eFC33XabXn75ZXk8Hqubk/QMw9D06dP1k5/8RJJ0/vnn6/3339eqVau0cOFCi1uXXH75y1/q5z//uX7xi1/oq1/9qnbs2KHbb79dI0eO5F4PAgxLnYbc3Fw5HI4uM0Zqa2tVUFBgUauSz9KlS/Xb3/5Wr776qkaPHh0+XlBQIL/fr+PHj0ecz/3vu23btunw4cO64IIL5HQ65XQ6tXnzZj366KNyOp3Kz8/nXsfQiBEjdM4550QcO/vss3XgwAFJCt9T/m05c//wD/+gO++8U1dffbXOPfdcXXfddfrBD36gyspKSdzreOrNvS0oKNDhw4cjXg8EAjp27FhM7j/h5jS4XC4VFxerqqoqfMwwDFVVVam0tNTCliUH0zS1dOlS/epXv9Irr7yisWPHRrxeXFyslJSUiPu/a9cuHThwgPvfR5deeqnee+897dixI/yYPn26rrnmmvDvudexc9FFF3VZ1uDjjz/WWWedJUkaO3asCgoKIu631+vVW2+9xf3uo+bmZtntkV9xDodDhmFI4l7HU2/ubWlpqY4fP65t27aFz3nllVdkGIZKSkrOvBFnXJI8SD377LOm2+02169fb3744Yfm97//fXPIkCFmTU2N1U0b8BYtWmRmZ2ebmzZtMg8dOhR+NDc3h8+55ZZbzDFjxpivvPKK+c4775ilpaVmaWmpha1OHh1nS5km9zqWtm7dajqdTvO+++4zd+/ebf785z8309LSzGeeeSZ8zv33328OGTLE/PWvf22+++675ne+8x2mJ5+GhQsXmqNGjQpPBX/hhRfM3Nxc84c//GH4HO716WtoaDD/+Mc/mn/84x9NSebDDz9s/vGPfzQ//fRT0zR7d28vv/xy8/zzzzffeust84033jAnTpzIVPD+4LHHHjPHjBljulwuc+bMmeabb75pdZOSgqSoj3Xr1oXPaWlpMRcvXmwOHTrUTEtLM//iL/7CPHTokHWNTiKdww33Orb++7//25wyZYrpdrvNyZMnm08++WTE64ZhmHfffbeZn59vut1u89JLLzV37dplUWsHLq/Xa952223mmDFjTI/HY44bN8686667TJ/PFz6He336Xn311aj/Ti9cuNA0zd7d26NHj5rf+973zIyMDDMrK8ssLy83GxoaYtI+m2l2WK4RAABggKPmBgAAJBXCDQAASCqEGwAAkFQINwAAIKkQbgAAQFIh3AAAgKRCuAEAAEmFcAMAAJIK4QYAACQVwg2ApFFdXS2Hw6F58+ZZ3RQAFmL7BQBJ48Ybb1RGRobWrFmjXbt2aeTIkVY3CYAF6LkBkBQaGxu1YcMGLVq0SPPmzdP69eutbhIAixBuACSFX/7yl5o8ebImTZqka6+9VmvXrhUd08DgRLgBkBTWrFmja6+9VpJ0+eWXq76+Xps3b7a4VQCsQM0NgAFv165dmjJlij7//HPl5eVJkpYuXar6+no9/fTTFrcOQKI5rW4AAJypNWvWKBAIRBQQm6Ypt9utxx9/XNnZ2Ra2DkCiMSwFYEALBAL6j//4Dz300EPasWNH+LFz506NHDlS//mf/2l1EwEkGMNSAAa0F198UfPnz9fhw4e79NDccccdeuWVV/T2229b1DoAViDcABjQvv3tb8swDP3ud7/r8trWrVtVUlKinTt36rzzzrOgdQCsQLgBAABJhZobAACQVAg3AAAgqRBuAABAUiHcAACApEK4AQAASYVwAwAAkgrhBgAAJBXCDQAASCqEGwAAkFQINwAAIKkQbgAAQFL5/4S7lIIHTSQzAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "sns.lineplot(x= 'A', y='B',data = dfs,marker='o')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bYM4BQlyhmFV"
      },
      "source": [
        "### Scatter plots ###\n",
        "\n",
        "Scatter plots take as imput two series X and Y and plot the points (x,y).\n",
        "\n",
        "We will do the same plots as before as scatter plots using the dataframe functions"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "scrolled": true,
        "id": "Y040I6-qhmFV",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 432
        },
        "outputId": "f0ebdab6-3375-4bf8-e4d4-d3f098312191"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='A', ylabel='B'>"
            ]
          },
          "metadata": {},
          "execution_count": 26
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x500 with 2 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ],
      "source": [
        "fig, ax = plt.subplots(1, 2, figsize=(15,5))\n",
        "dff.plot(kind ='scatter', x='A', y='B', ax = ax[0])\n",
        "dff.plot(kind ='scatter', x='A', y='B', loglog = True,ax = ax[1])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "id": "qpsbHypvhmFV",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "outputId": "7f435dda-49e8-4a5d-fdee-be2bb2303949"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7ff3f9ce9700>"
            ]
          },
          "metadata": {},
          "execution_count": 27
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.scatter(dff.A, dff.B)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "id": "grluDCXwhmFV",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 451
        },
        "outputId": "50819de2-93a0-48d8-cff8-1f29f4f52c7e"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7ff3f9af57f0>"
            ]
          },
          "metadata": {},
          "execution_count": 28
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "fig = plt.figure()\n",
        "ax = plt.gca()\n",
        "ax.set_xscale('log')\n",
        "ax.set_yscale('log')\n",
        "plt.scatter([1,2,3],[3,2,1])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UueN8_gDhmFW"
      },
      "source": [
        "Putting many scatter plots into the same plot"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "id": "8IVUditZhmFZ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 454
        },
        "outputId": "4330558e-1dc6-4807-b5c7-e9abbbcc7e0c"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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UqrTY6LUWKOFUzZqIAFi3w7T7tTCZHbesTWYTtPu1Adkm62zqb3NzM44dO4Y777yzy3PIyclx+P3+++/H4cOH7dtyq1atwrhx4+zB2t69e7Fp0yb06dPH/mO71+BptbuLGAi5ELBTYwSNZhqUymEO15TKYdBopoVoRlEkUsoz2I73s7I1RbmGE+6/xA3N/t+ylsvlTnNyPOnVq5fb+2KxNWRoH2hdvnzZ6dhrt+SSk5ORn5+P9957DwDw3nvvYdasWfb7586dw/Tp01FfX+/wo9frcdttt/n05/AVAyEXAtFig6yk0p6oqpoJQXgIFRX3QhAeQlXVTEilkXMiI2yFe3kGNoClGJM+yP2WtWyw/7esBwwYAJVKhbKyMrS1tXW439ra6vR1ffv2RVpamsvj9YMGDQIAHD9+3H6tfeK0J7NmzcLq1auxc+dOfPPNN7j//vvt98aNG4f9+/cjLS0NMpnM4cdZnpM/MRCikLH0PQFc/zXQL/CnAmKOXB6eJ8TYAJZijCJZAVWmChKx45a1RCyBKlMFeVJgPqNlZWUwmUwYP348PvjgA+j1ehw8eBBLly7FxIkTXb7ut7/9LV599VUsXboUer0edXV1eP311wFYV4xuvvlmexL0li1b8Nxzz3k9p3vvvRdnz57FY489hkmTJiElJcV+r7S0FEajEYWFhaipqUFDQwO0Wi3mzJkD07UnYf2MgRAFXbBPUFCY8HS8f/16bpdRVNKUaKAc6bhlrRyphKYkcFvWN9xwA+rq6jBp0iQ8+eSTGD16NCZPnozq6mr87W9/c/m64uJiLFmyBH/961+RmZmJadOmQd/uM7l8+XJcuXIF2dnZmD9/PhYtWuT1nPr27Yvp06dj7969DttiAJCSkoLPPvsMJpMJU6ZMwZgxYzB//nwkJCTYt+QChQUVPWBBRf9TL1FDd1DnkDwoEUugHKkMyAkKChOVldbtMG+oVNa8JtuWniBYywOwGCQFmT8LKuqb9DA0GyAbLAvYSlCsYUFFijihOEFBYcLT8f72bNtlzCmiKCJPkqNgTAGDoDDDQIiCKhQnKChMuDre74xtu+zuu5lTREQBxUCIgioUJygojDg73u/Otm2uc4qYS0REfsBAyAXWEQqMUJ2goDBx7fF+Ww+0zghmyxDWPSKKWgyEXGAdocAJxQkKCjO24/1Tpriuhn3rre6f4alliC148XQazV2QwxwloqjHXmMUdNJ4KarmV/EEBVlpNNacn/arQ7Zq2IWF1pyg9ttjEon1vqvTY0ajtV6Rs9Wm9qfRnI279rSau7pHVTzhSBQNeHzeAx6fJwoSvd663dX+iHxLS8cg6dpg5VpqdcfgycYWRFVVOR/X/r4gWFeCXBEEHuWPAf48Pk/+54/j81wRIqLwIJd3DCxsOUXOgiRnbEUbXWlfvNHZuPaJ2J4aPRoMDISIogADISIKf86CJGe87VL9QwdslwwGz3WPPOUoEVFEYLI0UYAJghGVld9Ar2eCbcB5W7Tx5pvd35fJXNc9kkis17kaRBQVGAgRBYjReB5q9RpkZCzH1KkfQqF4G2r1GrS0XAj11KKXp6KNtiDG3Wm19kGOs7pHtkRuojA2e/ZsiEQiiEQidO/eHUlJSZg8eTKWL18Os9kc6umFFQZCRAFSVPQpdLpvHa7pdN+isPCTEM0oRrgr2tg+iPEmyLm27pEgWH93lahNFEbUajWOHz+Ow4cPo7KyEpMmTcK8efMwbdo0XLlyJWjzsFgsQX0/XzEQIgoAQTBCqz0Mk8nxUKbJZIFWe5jbZIF0bfCyfr3zIMaXIMdW94jbYdQVQS7MGRcXh+TkZAwZMgTjxo3DM888g3Xr1qGyshIrVqxw+9rly5cjMzMTcXFxuO666/D4448DAA4fPgyRSIT6+nr72NbWVohEImzevBkAsHnzZohEIlRWViI7OxtxcXFYvnw5RCIRvvrqK4f3+fOf/4z0dlvaX375JQoKCtCnTx8kJSXhgQcewMmTJ/3y18MVBkIusLI0dUVDQ6vb+wYDA6GAswUvkye7D2IY5FCghVFhzvz8fIwdOxYffvihyzF/+9vfUFpaikceeQT79u3Dxx9/DFknDgcsXLgQv//973Hw4EHMnDkTOTk5WLVqlcOYVatWoaioCIA1oMrPz8dNN92EPXv2oKqqCk1NTfjpT3/q83v7gqfGXCgtLUVpaam9DgGRL9LTE9zel8mCv7UiCEY0NLRCJpNCLufWDlHQhFlhzhEjRuCLL75weX/RokV48sknMW/ePPu1ziwKvPjii5g8ebL991mzZmHZsmV46aWXAACCIKC2thbvvvsuAGDZsmW46aab8Morr9hfs3z5cqSmpkIQBCgUCp/n4A2uCBEFgEIxACpVGiQSkcN1iUQElSotqIEIk7aJQshW2yqMmgdbLBaIRCKn95qbm3Hs2DHceeedXX6fnJwch9/vv/9+HD58GLt+KF+xatUqjBs3DiNGjAAA7N27F5s2bUKfPn3sP7Z7Dd6WxugEBkJEAaLRTINSOczhmlI5DBrNtIC/d/sj+0zajgJs+hq5vCnMGWQHDx7E8OHDnd7r1auX29eKxdawoX1TisuXLzsdGx8f7/B7cnIy8vPz8d577wEA3nvvPcyaNct+/9y5c5g+fTrq6+sdfvR6PW677TbPf7BO4tYYUYBIpT1RVTUTen0LDIaWoGxJGY3nUVT0KbTaw27HtU/a5jZZGPOmHxqFtzArzLlx40bs27cPv/zlL53e79u3L9LS0lBdXY1JkyZ1uD9o0CAAwPHjx3HTTTcBgEPitCezZs3Cr3/9axQWFuKbb77B/fffb783btw4fPDBB0hLS0O3bsELT7giRBRgcrkUBQU3BCXgcLb64w6TtsOcu9wSigwhLMx58eJFNDY24ujRo6irq8Mrr7yCu+66C9OmTcODDz7o8nW//e1v8eqrr2Lp0qXQ6/Woq6vD66+/DsC6YnTzzTfbk6C3bNmC5557zus53XvvvTh79iwee+wxTJo0CSkpKfZ7paWlMBqNKCwsRE1NDRoaGqDVajFnzhyYnPUO9BMGQkRRwtWRfXdCkbRNXgrD3BLqpBAV5qyqqsJ1112HtLQ0qNVqbNq0CUuXLsW6desgcVV0FEBxcTGWLFmCv/71r8jMzMS0adOgb/fP2/Lly3HlyhVkZ2dj/vz5WLRokddz6tu3L6ZPn469e/c6bIsBQEpKCj777DOYTCZMmTIFY8aMwfz585GQkGDfkgsEdp/3gN3nKVJUVn6DqVNdH4ltTyIRQakchqqqmQGeFXVaZaX1qLUrFRXWY/8UUH7tPu9t82DyGrvPE5GdpyP77QUraZu6wJfcEkGwJuVKJNYVI37RhidvmwdTUDEQIooStiP7Ot23DttjttWf11+/M2hJ2+QHttwSnc5xe0wisW6ryOXOk6ltXCVV24ImBktEAJgjRBRV3B3ZD2bSNvmJp9wSZ8nUNtcmVYdRdWOicMIVIaIoEooj+xRAtn5oznJLbMnUrtiSqt96C7j9dmDu3LCqbkwULhgIEUUhuZwBUFRxllvibaXdkhLX92zB0vr1zC2imMVAyIWysjKUlZUFtHYBEVGneUqm9oVK5fj/WbCxA7PZHOopkBP++PvC4/Me8Pg8EYUttbpjMnVX2ZKxuV0GwPpFq9frIZFIMGjQIPTo0cNlny4KHovFgkuXLuHEiRMwmUyQy+Udag15+/3NQMgDBkJEFLZaWqw5Pu5yhTpLELhN9oNLly7h+PHj+P7770M9FbpG7969cd1116FHjx4d7rGOEBFFFEEwoqGhlQnevrg2mbpbN+DwYeCRR7r+bIOBgdAPevTogaFDh+LKlStMlwgjEokE3bp16/IKHQMhogATGgU0nGiAbLAM8iR+sQCOQU9iYs8OjWJVqjRoNNMglXaxkm+suDaZ+oMPXNcfev11a5Bj63XlSpCbgYY7kUiE7t27o3v37qGeCvkZt8Y84NYYdZaxzYii8iJo91/dtlBlqqAp0UAaH5srHkbj+Q5BT2JiT7S2XnRaBJItQDrJ2ZaZsyRodzlGTJqmCMccIT9hIESdpV6ihu6gDibz1S8ZiVgC5UglqubHZiKqWr2mQ+VrdwThIW6TdYWn3lbucozcJU2zOjVFAG+/v1lZmigAhEYB2v1ahyAIAExmE7T7tdA3xV7ncEEwQqs97HUQBAAGQ4v9tZWV30CvZxVkn8jl1sasroIVqRRYutT5PWdd7lmdmqIQAyGiAGg44b7YnaHZEKSZhI+GhlafXzNwYG+o1WuQkbEcU6d+CIXibajVa9DScsH/E4xVngozGtr9s+qspce1rTyIIgwDIaIASB/kvtidbHDsJaKmpyd4PVYiEUGlSsPzz2+HTvetwz2d7lsUFn7i59nFMG+73NtaelybT+Rs5YgogjAQIgoARbICqkwVJGKJw3WJWAJVpiomT48pFAOgUqVBInE86ioWWxOm21Mqh+Gll37kdCvNZLJAqz3MbTJ/sXW5lzj+s2o/VWbbVvNl5YgogjAQIgoQTYkGypGOncOVI5XQlGhCNKPQ02imQakc5nBt8uQ06PUPQxAeQkXFvRCEh1BVNRMnT553+yxb/pAnzC/ygqcu94A1YnWnG6uxUGTiP7lEASKNl6JqfhX0TXoYmg2sIwRAKu2JqqqZ0OtbYDC0OBRPlEp7OpwQ87SVJpO5P03m7Kg+6xO54K7LvY2nnk5XrgRufkQBxOPzHvD4PFHoODtu722Nobw8DXbsOAazmfWJ/EIQrKfFXMnLA9atY90hChs8Pk9EEc/ZVppSOQwazTSXrzEazyMvT4Pt2486BEEA84u6xFUukc2OHTw9RhGJW2NEFLbcbaW5UlT0KXbsOOp2jMHQYn8Oe5z5QKMBZswAtm/veK/96TEWWaQIwkCIiMKeXO5dkGIr2uiJTCZlDlFnSKXAM89Yiym6wmatFGG4NUZEUcNT0Uax2FqfSC6XoqjoU69rFPHkWTve1h1yRhCAykrWHKKwEhOB0D333AOpVIqZM5kgSRTNPJ00+9GPUqDRTHPZ7uPaHCKj8TwrW1/L27pD7bE1B4WxmAiE5s2bh5UrV4Z6GkQUYO6KNt566xBs3VoIqbSnx5UjW40iX1aNYoo3dYfaY2sOCmMxEQjdcccd6Nu3b6inQURB4Kpo48cf32P/3ZsaRd6uGsUkW90hQQAqKqz/W1Xl/Og8W3NQmAt5ILR161ZMnz4dKSkpEIlEWLt2bYcxZWVlSEtLQ8+ePTFhwgTs3r07+BMloohgO2l2baXq9gnQrlaObD3O5HKp16tGMc1Td3vAc2uOLVv8OyciH4U8EGpra8PYsWNRVlbm9P7q1auxYMECvPDCC6irq8PYsWOhUqnQ3NxsH5OVlYXRo0d3+Dl27Fiw/hhEFGbkcikKCm5wedrMU42irla2ph94Sq4uKbEWY/z3v7k6RCERVpWlRSIRPvroI9x99932axMmTEBubi6WLVsGADCbzUhNTcXcuXOxcOFCr5+9efNmLFu2DGvWrHE77uLFi7h48aL99zNnziA1NZWVpYmilLsaRV2pbE3tqNXWnKBrt8ecUamsuUasUE1dFBWVpS9duoTa2loo2yXlicViKJVK7Ny5MyDvuXjxYvTv39/+k5qaGpD3IaLw4G7lqDOVrckJZ8nVrjCJmoIsrAOhkydPwmQyISkpyeF6UlISGhsbvX6OUqnET37yE1RUVOD66693G0Q9/fTTOH36tP3nyJEjnZ4/EUU2b/KNyAu25Orycs9jmURNQRYTlaV11x7bdCMuLg5xcXEBnA0RRRpvK1uTB7fd5v1YVqimIAnrFaGBAwdCIpGgqanJ4XpTUxOSk5MD+t5lZWUYNWoUcnNzA/o+REQxw1aMUSTyPNZdhWoiPwrrQKhHjx7Izs5GdXW1/ZrZbEZ1dTUmTpwY0PcuLS3FgQMHUFNTE9D3IQoEoVFA5b5K6Ju4vUBhRqMBxo1zP4YrQRREId8aO3fuHAwGg/33Q4cOob6+HgMGDMDQoUOxYMECFBcXIycnB+PHj8eSJUvQ1taGOXPmhHDWROHJ2GZEUXkRtPu19muqTBU0JRpI47m1Q2FAKgX27LH+b2ur8zF6/dXVI54gowAL+fH5zZs3Y9KkSR2uFxcXY8WKFQCAZcuW4U9/+hMaGxuRlZWFpUuXYsKECUGZn7fH74jCgXqJGrqDOpjMV48pS8QSKEcqsfT+pWg40QDZYBnkSfwv7kgiCEY0NLQ6PeIfsQ4dAnJzgVOnXI+RSKynzaqqgjcvihrefn+HPBAKV2VlZSgrK4PJZIIgCAyEKOwJjQIyns/waixXiSKD0XgeRUWfQqs9bL+mUqVBo5kWPSfXNmwA/vMf4PXXXY8RBG6Xkc+ioo5QKDFHiCJNwwkPrQza0R3UobCctVrCXUw0fZ082dqmwx1b+oQgAJWVPFpPfhXyHCEi8o/0QR5aGbRjMpug3a+FvknPbbIwZWv6eq32TV9t22S2rTOJRAyTyRx5W2ie2nAMHGhtw7F9+9VrzB8iP2EgRBQlFMkKqDJVHXKE3DE0GxgIhSlvmr4mJvbssHVmo1Kl4aWXfoSTJ8+Hf2BkS4y+tg2HRGKtPVRQ0DGXyFaBmvlD1EXcGiOKIpoSDZQjvWxlAEA2mLVawpU3TV+dbZ3ZaLWHMX78Kkyd+iEUirehVq9BS8uFAMzUT5y14VAqgfPnnSdUswI1+QkDIRdYUJEikTReiqr5VRAWCah4ogLCIgGqTBUkYonDOIlYAlWmiqtBYUyhGACVKg0SiWPxQYlEBJUqDRaLdYusfUNYd2y5RYJgRGXlN9DrWwIx7c6zteEQBKCiwvq/S5cCu3a5f1278itEncFTYx7w+DxFupa2FhSWF7K2UARqabmAwsJPnJ4a27XrGKZO/bBLz7c968SJ78PzeH5lJTB1qvsx5eXA7bfzVBl1wOPzfsJAiKKFvkkPQ7OBdYQikF7fAoOhxSFQEQQjMjKWd+m5YrG1seypU1e3zMaNG4w335yCnJzAtjHyiiAAGd6VhMC4ccCbbwI5OYGdE0UMBkJ+wkCIiMKVWr0GOt23Xm+P+SJs6hWp1dZaQ2azd+N5mox+wDpCRERRTqOZBqVymNN73vQ1dSds6hVpNNZaQ96ynSYj8hJXhFxgZWkiihS2rbNu3cS4csWMQYN647nntjs9Vu8rQXgoPPKG9HprYvTRo0BJiefxrEYd87g15ifcGiOiSNU+t2ju3OpObaNVVNyLgoIbHK6FtPeZt3lDFRWeK1ZTVOPWGBFRjJPLpSgouAFyudTpNlpiYk+IPXwLyGRXAx2j8TzU6jXIyFhur0+Unb0Se/Y0BmL6ztmKL3qeeHDmQxGPK0IecEWIiKJJ+1WigQN7dTiebyORiKBUDkNV1Uz7tTvvXI1Nm47A2bdGUJOrW1qseUBabcd77FhPP+CKEBERddB+lUgq7Ymqqpmoqfk5xo0b7DBOqRwGjWaa/XdBMGLjRudBEBDk5Gpb8cWaGuux+faUSmuCNZGX2GuMiCjG5eQko7b2Qaf1imy2bDni9hnOmsEGXE4OUFt7NZFaJnNMkBYEYMsW6xE6Fl0kFxgIudD+1BgRUSyQy7ue/GwwBDEQspHLHYMcoxGYORPYtMlxXH4+sGYNawyRA+YIecAcISIi7ytZuztuH7TTZmq18/whkQiYMoX5QzHC2+9vrggREZFHCsUA5OenYuNG51tktuRqZwGO0XgeRUWfOiRl33rrEMydOw7nzl3C//3fWUycmILJk9O6PlFBcB4EAYDFcrVjPbfJ6AcMhIiIyCtr1tzl8pTZtcnV7RUVfQqd7luHa9u3H8X27UcdriUk9EBd3YMYPjyh85NsaPA8xmBgIER2DISIiMgrtlNm11aydrfVJQhGrytct7ZeQkbGcjQ1/aLzx/DT0z2POXqUq0Jkx0CIiIh84ktSdUNDq0/PvnzZjAkT3sWnn97XuTwiW8FFV9tjwNUWHWzQSmAdISIiCqD09ASfX6PXt0KheBtq9Rq0tFwAYF1ZKi/fi7fe+gJ6fYv7B2g01hNinrBBK4GnxjziqTEioq5Rq9d0qs+ZRCLCbbddDwDYtMkxSTs/fyjWrJnhfgtNr7fWEWpsBJ5/3vU4NmiNSmy62kXsPk9E5B8tLRdcJll3lkgE/OhHQ/DMMxMgkYhhMrnJVaqsBKZOdf0wNmiNSgyE/IQrQkT+JzQKaDjRANlgGeRJ/C/xWKHXt+Dzz5uwbNnn2LbtqOcXdILTnmeeOtaXl7PydBRiIOQnDISI/MfYZkRReRG0+68msqoyVdCUaCCNZ8JqLLEFRS+9tBNffnnKb8911iwWgLXIok4HuOsWwOTpqMJAyE8YCBH5j3qJGrqDOpjMV7+MJGIJlCOVqJrPar+xSq9vwf33/wd7955wyCMSiwGzuXPPFISHYLFYsGXLEYhEIkzK6ov05x5zPE0mEsGhi6xYDEyezMrTUYKBkJ8wECLyD6FRQMbzrrcnhEUCt8limLM8IpUqDZcvm7Fx43c+P+/GGwfiiy9OOlzLzx+KD/8wCv0/3wk88ojrF9fUWBu6UkRjiw0iCisNJ9xX/N389WYYmg3MG4pR1xZrtCU+t7RcwMyZ61y29nDl2iAIADZt+g7TfmnCa5MlyHX34kcftXa1p5jAFSEPuCJE5B+eVoTaY94QXUuvb8GWLdZgaOXK/dix45jT4/jebKfJcQIC/uh+EI/URzxvv79ZUJGIgkKRrIAqUwWJWNLhnggih991B3UoLGehO7pKLpfi4YdvxMMP34h16+6BUjnM6bisrMEen6XHIOzBEPeD7r4baPFQuJGiAleEPOCKEJH/tLS1oLC80OHUmDvMGyJ3nPU8s1gsyMhY7vG12fgOe/C62zHmAYkQG/Q8RRahmCPURe0LKhKRf0jjpaiaXwV9kx6GZgOOth5FycoSl+MNzQYGQuSSq55nKlWax+KNtRiKKiighODyi1BsPIXLU6ej+87tXZ8shS2uCHnAFSGiwOFJMgoEbxOsE/A9NuDvyIGH4o7MF4pIzBEiorDnKm9IIpZAlamyB0FCo4DKfZXQN+lDMU2KMFJpT1RX/wyC8BDKy6egvHwK8vKGQCJxzEVrRW8UYZbH5x3d8nmgpkphgFtjRBRSmhJNh7wh5UglNCUaVqKmLmm/dXbffQqn/c70GIStSMNtONzxAT8wINFlarUgGNHQ0Oq6zxmFPW6NecCtMaLgsOUNta8jxErU5G+2BOtBg3pDrV6DU6cuIAHfQ8AfMAjfO4y9AhF0kCNd2NUhyDEaz6Oo6NMOBSA79DmjkGFlaT9hIEQUGswfokBrabmAu+76CNu2HUUCvsc6rMBtOGS/r0UGyvMXYk317A6vVavXQKf71qGWkUQiQlbWIMyYIUNqal8kJ8dzpSiEeGqMiCKap0rUPFFGXSWV9sTWrYX2BrBP/mk4Wvd8CRlOwYBEpKtyodFM6/A6QTA6PZVmMllQW9uM2tpmh+tcKQpvDISIKCylD0p3e182WBakmVC0s+US/fSnIzq0+HCmoaHVp+drtYdx111r8dZbU5hPFIYYCBFRWLKdKHOVI8TVIAoEV7WJ2ktPT/D5udu2/Z9DoUeuEoUPHp8norClKdFAOVLpcM12oowoVBSKAVCp0jocx/eFTvctCgs/8eOsqLOYLO0Bk6WJQs/ZiTKiUGppueD0OL6vBOEhyOVSHsMPAJ4a8xMGQkRE5Iotp6io6FO0tl70+fWrV0/D8uVfOgRU48YNxptvTkFOTrIfZxp7GAj5CQMhosgkNApoONHAVSQKikOHWpGb+y5Onbrg0+vy8oZgx45jDsfwbdLT++OPf7wdo0cP5GpRJzAQ8hMGQkSRxVk16nFDx+HNB95ETlpOCGdGsWDlyi/x//6fDufPX7Ff695dDLPZ0qHm0MSJKdi+3UOfs2tkZydhw4afMMnaC+w11kVlZWUYNWoUcnNzQz0VIvJBUXkRdAd1DtfqvqtD7su5UC9Ro6WtJUQzo6gkCEBlJaC39sF78MHR+P77+Vi/fiZ+97tbsH79TDQ1/QJK5TCHlymVwzB37jif3662tgmJicuwcuWXfpk+cUXII64IEUUOT9WoJSIJlKPYnoP8wGgEiooA7dWVR6hUgEYDSJ1vX11bo0gQjA5H6n2VmNgTNTU/x/DhCZ1+RjTjihARxRxP1ahNFhO0+7XsYk9dV1QE6BxXHqHTAYWFLl8il0tRUHCDPc/Hdgxf3Mlv4lOnLiA3910IghGVld9Ar+dqZ2cwECKiqOGpGrWNodkQ4JlQVBME60qQyeR43WSyXtd7H2hrNNMweXJap6dy6tQFZGQsx9SpH0KheBvZ2SuxZ09jp58XixgIEVHUsFWjFovc/6uN7TmoSxrcrzzC4H2gLZX2RFXVTNTU/Bzjxg3u4sSAurpm5Oa+C7V6DVpafDvBFqsYCBFRVNGUaDB51GSn9yRiCVSZKofj9EKjgMp9ldwuI++le1h5lPkeaOfkJKO29kFUV/8E/fr16OTErmpfuVoQjJg/vxp33fUhVqzY1+VnRxsmS3vAZGmiyLTn8B48+s9HUfddnf2aKlMFTYkG0nip02P27e8TuaVWW3OC2m+PSSTAxInAM89YgyF55+tXbdhwGBs3fodPPmnAl1+e6vRzsrIGob7+hMM1kQh47LGxuOmmJNx+e2rU1iZiHSE/YSBEFNlctedQL1G7bOjKU2XkUUuLNTG6/amxxETgVLugxcMpMm/p9S144416/PWv9bhwweT5BT4aPrwf/ud/7sC99yr8/uxQYiDkJwyEiKKPp2P2wiKB1ajJO3q9NSfoxReBXbs63s/PB6qr/fZ2GzYcxs6dxzBxYgpefXUPNmw4DLPZP8/u27c7/vKXfNx66/VRsUrk7fd3tyDOiYgoLHg6Zm9oNjAQIu/I5YDF4jwIAoCNG63BUhe2ydqbPDnNfsosJyfZZeNXsRg+B0hnz17Gf/2XdYVLpUqDRjMtJipYM1maiGKOp2P2PFVGPtmypWv3O8ndibNbbhnSpWdrtYdx443/wC9/uRFvvfVFVNco4ooQEcUc2zF7VzlCXA0iv3r0USAnB8jKCsjjbSfOrq1crVavcbpa5K3/+782LFly9bDBiBFS/O53t+KmmwZHxdaZDXOEPGCOEFF0amlrQWF5oVenxtjJntwSBCDDdc4ZAKB7d+DSpeDM5wctLRcwdeoH2LXruN+fnZ7eH3/84+1hnWDNZGk/YSBEFN1cnSoDnHey5xF7curOO635QO784x/A7NlBmU57en0LXnutBu+/L+DUKf8WWUxI6IHXXpsUlgnWDIT8hIEQUeziEXvyWksLMGoU0OimvcXPfw7885/Bm5MTen0Ltmw5goMHT2HJkjqYzf4LAcItwZqBkJ8wECKKTTxiTz57+23g4Ydd3w/RipArLS0XMGXK+9izp8kvzxOLgeHD++OWW1KQmNgLmZkDQ1qwkYGQnzAQIopNlfsqMXXpVJf3K56oQMGYgiDOiCJCjx7A5csdr4cgR8hbe/Y04tFH16Ourjkgzx8xQoodO2YFfaXI2+9vHp8nInKCR+ypU3bvtgY97XXvbr0epmynzgThIbz99hS/9Dpr76uvWjBgwDL85jfbw/IYftQHQkeOHMEdd9yBUaNG4cYbb8T7778f6ikRUQSwHbGXiCUO1501biWyy8qyrvz84x/WnKB//MP6e1aW9XRZZaW1wGIYksul+K//uhGnTz+B9etnorh4FHr2lHh+oZdeemkXFIq3kZi4DPff/x9s2HDYb8/uiqjfGjt+/DiampqQlZWFxsZGZGdnQxAExMfHe/V6bo0RxS5fjtgTuWQ0AkVFjn3J/NSHLBg+/FDAr3+9FQ0NrX5/dr9+3bFkSWDaejBHyIWxY8fik08+QWpqqlfjGQgRkbsj9kQeuepUr1QCVZFz+tBWsLGpqQ1ff90Cne5bvyVaA/4/dRYxOUJbt27F9OnTkZKSApFIhLVr13YYU1ZWhrS0NPTs2RMTJkzA7k7utdbW1sJkMnkdBBERAYA8SY6CMQUMgsh3gmBdCTJd0zXeZLJeD9NtMmfkcikKCm7A7NljsHjxbaipeQCC8FCH9h6dpdN9i8LCT/zyLF+EPBBqa2vD2LFjUVZW5vT+6tWrsWDBArzwwguoq6vD2LFjoVKp0Nx8Nbs9KysLo0eP7vBz7Ngx+xij0YgHH3wQf//73wP+ZyIiIgIANLhv8AuDITjzCBC5XAqd7qfIy+tabzMAMJks0GoPBz2hOqy2xkQiET766CPcfffd9msTJkxAbm4uli1bBgAwm81ITU3F3LlzsXDhQq+ee/HiRUyePBklJSV44IEHPI69ePGi/fczZ84gNTWVW2NEROQ7T+03BMFvnelDTa9vwRtv1GPp0lpcudL551RU3IuCghu6PJ+I2Rpz59KlS6itrYVSqbRfE4vFUCqV2Llzp1fPsFgsmD17NvLz8z0GQQCwePFi9O/f3/7DbTQiIuo0hcKaGC255vSVRGK9HiVBEGBdHXr11Um4fPlX+OCDGRg4sHO5PjJZcBPIwzoQOnnyJEwmE5KSkhyuJyUlodFdGfN2PvvsM6xevRpr165FVlYWsrKysG/fPpfjn376aZw+fdr+c+TIkS79GYiIKMZpNNbE6PaUSuv1KHXvvQqcOPE4BOEhLFkyCUOH9vX4GolEBJUqLeiVqLsF9d1C4NZbb4XZbPZ6fFxcHOLi4gI4IyIiiilSqfV0mF5vzQmSyaJqJcgduVyKefOyMW9eNvT6Fvz7319hz55GxMV1Q0NDq8OpM6VyGDSaaUGfY1gHQgMHDoREIkFTk+PxvKamJiQnJwf0vcvKylBWVgbTtZn+REREnSGXx0wA5IxcLsWzz050uGY7ki+TSUPWkyyst8Z69OiB7OxsVFdX26+ZzWZUV1dj4sSJbl7ZdaWlpThw4ABqamoC+j5ERESxynYkP1RBEBAGK0Lnzp2Dod3xwUOHDqG+vh4DBgzA0KFDsWDBAhQXFyMnJwfjx4/HkiVL0NbWhjlz5oRw1kRERBQNQh4I7dmzB5MmTbL/vmDBAgBAcXExVqxYgZ/97Gc4ceIEfvOb36CxsRFZWVmoqqrqkEBNRERE5KuwqiMUTtrnCAmCwDpCREQUOIJgLb4YQ4nUgcZeY37CXmNERBQwEd6QNZwFrKCi2WzG8uXLMW3aNIwePRpjxozBjBkzsHLlSjCmIiIi8kFRkbUha3s6HVBYGJr5xCCfAiGLxYIZM2bg4YcfxtGjRzFmzBhkZmbi22+/xezZs3HPPfcEap5ERETRJYoaskYyn5KlV6xYga1bt6K6utohwRkANm7ciLvvvhsrV67Egw8+6NdJEhERRR1vGrIyXyjgfFoR0mg0eOaZZzoEQQCQn5+PhQsXYtWqVX6bXCiVlZVh1KhRyM3NDfVUiCjGCY0CKvdVQt/EFYKokp7u/v7ixUBLcDuxxyKfkqWTk5NRVVWFrKwsp/c///xzFBQUeN0HLBIwWZqIQsXYZkRReRG0+68m0qoyVdCUaCCNZyJtVFCrrTlBzroYSCTWnmRVVcGfVxQISLK00Wh0W78nKSkJLYxeiYj8oqi8CLqDjom0uoM6FJYzkTZqaDSAq04JzBUKCp8CIZPJhG7dXKcVSSQSXLlypcuTIiKKdUKjAO1+LUxmx5UCk9kE7X4tt8mihVQKPPOM+zHtui+Q//mULG2xWDB79myX3dkvXrzol0kREcW6hhPuE2kNzQbIk5hIGxU85QoVFQF1dcDw4cGZT4zxKRAqLi72OCZaToyx+zwRhVL6IPdfjrLBsiDNhAJOobAWUXSVK9TaCuTmAidPBn1qsYCVpT1gsjQRhYp6iRq6gzqH7TGJWALlSCWq5l9NoBUaBTScaIBssIyrRJGqpQWYPBmorXU9Zv166xjySsAqSxMRUXBoSjRQjlQ6XFOOVEJTogFgPVWmXqJGxvMZmLp0KhTPKaBeokZLGw+tRBypFJgxw/2YnTuDM5cYE/Lu80RE5Jw0Xoqq+VXQN+lhaDZ0WPFxdqpsw4ENmLFsBrY9tS3Y06WumjDB/f0ePYIzjxjDrTEPuDVGROFIaBSQ8XyGy/t5sjyse3wd6w1FmoEDgVOnXN9PTARqapg47QVujRERRTFPp8o+a/gMM5bNYEXqSFNTYw12XDl1ypo4TX7DQMgFttggonDm6VSZ2WLGdsN25g5FmuHDrafDXnnF9ZhTp4ANG4I3pyjHQMiF0tJSHDhwADU1NaGeChFRB4pkBVSZKohF3v1rnBWpI8zly+7vb9wYnHnEAAZCREQRSlOiwS3pt3g1lhWpI4ynxGn2H/MbBkJERBFKGi/Ftqe2IU+W5/XK0OavNzNvKBKoVEBCguv79fXsQeYnDISIiCLcusfXYfIo7wrtPfLPR5g3FCl+/Wv39++5x1qIkbqEgRARUYSz1RsSFgmoeKICebI8SMSSDuNEEDn8zryhMDdwoPv7+/cDM2cGZy5RjIEQEVGUkCfJUTCmAOseX9ehIjUAWOBYNo55Q2Hu9ts9j9m4kVtkXcRAiIgoyly7QlT+YLnb8cwbClMKBZCf73ncli2Bn0sUY4sNF9h9noginTxJDnmSHEKj4HbcI/98xP7/VZkqaEo0rEgdLtasAW69FThwINQziVpsseEBW2wQUTRw1skesOYNtd8yE4vEuCX9FvYqCyeCAGS4bqcCQQDkctf3YxRbbBARkZ2zTvZAx7whW0Xq2/5wG0+UhQvbFpnIMdkdIpH1OoOgLuGKkAdcESKiaGLrZH+09ShKVpa4HCcWiTF51GRUzWfhvrDQ0gIUFgJa7dVrKhWg0QBSbmM64+33N3OEiIhiiLd5Q2aL2X6iTJ7EFYeQk0qt1aT1esBgAGQyrgT5CbfGiIhikLe9ygzNhiDNiLwilwMFBQyC/IiBEBFRjPKmV5lssCxIs6FOEwSgspL1hDqJgRARUYxy16tMIpZAlanitlg4MxoBtdp6omzqVGtStVrNths+YiBERBTjnPUqU45UQlOicTpe+6UWL/7nRWzYvyEY0yNXiooAnc7xmk5nTaomr/HUmAvtCyoKgsBTY0QU9WwnymSDZU5XghqaGzDhlQk41XbKfi0xPhE1z9Zg+KDhwZwqsbaQR96eGmMg5AGPzxMRWQ2cP9AhCLJJ6JWAlqXcjgmqykrrdpgrFRXWpOoYxoKKRETkN9ovtU6DIABoPd+K3EW5LMAYTOnp7u/LmOTuLQZCRETk0f8e+l+392u/q0VhOXNTgkahsBZUlEgcr4vF1t5kMb4t5gsGQkRE5NGE4RPc3rdYLPYCjBQkGg2gvKZtitkMbN/O02M+YCBEREQeqUarkBif6HEcCzAGka3adF6edSWoPZ4e8xoDISIi8krNszVI6JXgdgwLMAaZIADbtllXgtozmax9yVhk0SMGQkRE5JXhg4ajZWkLcoblQHRNJ3QWYAyRhgb39w1cofOEgRAREflk/S/XY8qoKQ7X3BVgpADi6bEuY/d5IiLyiTReiqr5VR4LMFIQ2E6P6XTW7TAbicSaSM3TYx4xECIiok6RJ8l9CoCERgENJxoYOPmbRmNNjNZqr15TKq3XySMGQkREFFDGNiOKyoug3X/1i1qVqYKmRANpvDSEM4sSttNjer01J0gm40qQD5gjREREAVVUXgTdQcfmoBsObIDyNSXrDvmTXG5tq8EgyCcMhIiIKGCERgHa/VqYzCaH62aLGXXf1UHxnALqJWq256CQYSDkQllZGUaNGoXc3NxQT4WIKGI1nPBwvBuAdr8W418ez9UhCgl2n/eA3eeJiDpPaBSQ8XyG1+Nvk9+GtaVrmTtEXcbu80REFHKKZAVUmSpIxBLPgwFs1W9l81Z/EwSgspJVpl1gIERERAGlKdFAOVLpeeAPtPu12LB/QwBnFCOMRmvz1YwMYOpUa80hNmPtgIEQEREFlK0Ao7BIwLih4yCCyONrpiyZwiTqrioqshZabG/DBjZjvQYDISIiCgp5khy6BTqMGzrOq/G6gzpuk3WWIFgLLJocT+vBbLZe37MnNPMKQwyEiIgoaKTxUux5fo/HLvYAYDKboN2v5WmyzvDUjLW4ODjziAAMhIiIKOjqnq+DtLd3J8MMzQYIjQIq91UyKPKWp2asBw4wefoHDISIiCjohg8aDuNfjFg/fz0euPkBt2MXVyxGxvMZmLp0KhTPKZD3hzzmDnmiUHgOhv797+DMJcwxECIiopCZnDkZKx9a6fSIvUQsQWJ8Ij5r+Mzh+nbDdsiflTMY8uTOO93fb24OzjzCHAMhIiIKOWdH7CfeMBGn2k7BbDF3GH+q7RTuWnZXsKYXme691/39HTt4lB6sLO0RK0sTEQWPvkkPQ7MBssEyGJoNmLp0qtvx5Q+W43bF7ZAnsdGoU4mJ1npCzkgkgFJp7Vwfhbz9/mYg5AEDISKi0PClPce4oePw5gNvIictJ8CzijCHDgHjxgGtra7HCEJUdqxniw0iIopoimQFbpXd6tXYuu/qkPtyLoswXmv4cOC999yP+fzz4MwlTDEQIiKisPXx4x8jMT7R6/G6AyzC2IGn02PLlgVnHmGKgRAREYUtabwU+pf1yJPleTXeZGERxg4UCuBWNytr27bFdKVpBkJERBTWpPFSbH1qK4RFAv7+wN+9eo2h2RDgWUWYuXPd33/00eDMIwx1C/UEiIiIvCFPkkOeJMcHdR9gw4ENTo/V28gGyyA0CtgibIFIJOLJsqws9/fr6qyVpqMwadqTqD811traCqVSiStXruDKlSuYN28eSkpKvH49T40REYWXlrYWFJYXQrtf2+GeRCzBbfLbAACbvt7kcC9/RD7W/L81kMZ719oj6mRnWwMeVyoqgIKC4M0nwHh8/gcmkwkXL15E79690dbWhtGjR2PPnj1ITPQu+Y6BEBFReNpzeA8e/eejqPvu6pe7KlOFy6bL2PjVxg7jRRBhSuYUVM2Pzro5HtXUAOPHu76flwesWwdIoyNQ9Pb7O+q3xiQSCXr37g0AuHjxIiwWC6I89iMiigk5aTmofb7WoQijxWJxWXvIAos9kTomt8lycwGVCtDpAJOp4/0dO4DCwqgtsOhKyJOlt27diunTpyMlJQUikQhr167tMKasrAxpaWno2bMnJkyYgN27d/v0Hq2trRg7diyuv/56/Pd//zcGDhzop9kTEVGoyZPkKBhTAHmSHA0nGjyO1+zWxO6pMo0GmDjR+T2TCdBqY64rfcgDoba2NowdOxZlZWVO769evRoLFizACy+8gLq6OowdOxYqlQrN7ZrFZWVlYfTo0R1+jh07BgBISEjA3r17cejQIbz33ntoamoKyp+NiIiCK32Qh5o5AF74+AUonlPEZvFFqRR45hn3YwyxdeIurHKERCIRPvroI9x99932axMmTEBubi6W/VDwyWw2IzU1FXPnzsXChQt9fo9f/OIXyM/Px8yZM53ev3jxIi5evGj//cyZM0hNTWWOEBFRhFAvUTtNpL6WRCyBcqQy9nKGBAHIcNO6JEpabkRFi41Lly6htrYWSuXVjsRisRhKpRI7d+706hlNTU04e/YsAOD06dPYunUrMtz8A7B48WL079/f/pOamtq1PwQREQWVpkSD/BH5HseZzNbii29teyt2t8qcOX061DMIqrAOhE6ePAmTyYSkpCSH60lJSWhsbPTqGd9++y3y8vIwduxY5OXlYe7cuRgzZozL8U8//TROnz5t/zly5EiX/gxERBRc0ngpqp+shrBIQPmD5Xjizifcji9ZWQLFcwrk/SEvNrbKGjzkUcVYccWoPzU2fvx41NfXez0+Li4OcXFxgZsQEREFha0Ao9AoYGn1Uo/jtxu2Y9hTw7DxVxuju4u9p95jMVZcMaxXhAYOHAiJRNIhubmpqQnJyckBfe+ysjKMGjUKubm5AX0fIiIKLEWyAqpMFSRiicexZy+eRe7LudG9OqRQAOPGuR9z//1AS5T++a8R1oFQjx49kJ2djerqavs1s9mM6upqTHR1/M9PSktLceDAAdTU1AT0fYiIKPA0JRooRyo9D/zBdsN2yJ+VR28w9MYb7u/v3WutKRQDQh4InTt3DvX19fbtq0OHDqG+vh7fffcdAGDBggUoLy/HO++8g4MHD+Kxxx5DW1sb5syZE8JZExFRJJHGS1E1v8qnxq2n2k7hrmV3BXhmIWIrrih2EQbEUE2hkB+f37x5MyZNmtThenFxMVasWAEAWLZsGf70pz+hsbERWVlZWLp0KSZMmBCU+bHFBhFR9Mn7Qx62G7Z7NVZYJERnJeqWFkCpjNr+Y+w11kVlZWUoKyuDyWSCIAgMhIiIokhLWwvkz8pxqu2Ux7EVT1SgYExkBgMeRXFNoaioIxRKzBEiIope0ngp9C/rkSfL8zhWNlgWhBlRqDAQIiKimCSNl2LrU1tR82wN+vXsuGIgEUugylRF57aYjaeaQjHQboOBEBERxbSctBwc/v3hDqtDypFKaEo0IZpVkHiqKSSL/tWwqC+oSERE5IltdUjfpIeh2QDZYFl0rwTZKBTW02M6nfWkmI1EYk2kjtD8IF9wRcgFFlQkIoo98iQ5CsYUeB0ECY0CKvdVRnavMo3GGvS0l5UF3HQTsGFDSKYUTDw15gGPzxMR0bWMbUYUlRc5dLlXZaqgKdFAGi8N4cy6QK8Htm0DnnwSaG29ej0xEaipAYYPD9nUOoPH5/2EgRAREV1LvUQN3UEdTOar20kSsQTKkUpUza8K4cy6aOBA4JSTkgKJicDJk8GfTxfw+DwREVEACI0CtPu1DkEQAJjMJmj3azHvX/OwYX8Ebilptc6DIMB6PUq3yRgIERER+aDhhPsj50url2LKkino/VhvrNyxMkiz8oP//V/393fuDM48goyBkAtMliYiImfSB3k4cv6D81fOo/gfxRg4fyAOnTgU4Fn5gafWVaNHB2ceQcYcIQ+YI0RERNdyliPkjrS3FMa/GAM8Kz9wlSMEWPOE9HpAGhnJ4MwRIiIiChBNiQbKkUrPA3/Q8n1LZOQNrV7t+t6pU8BddwVvLkHCQIiIiMhH0ngpquZXQVgk4KW7XvLqNf/c9c/wrzl06ZL7+9u2WVeFogi3xjzg1hgREXkycP5ArzrZ24RtzSFP3egB66rRT38anPl0AbfGiIiIgqTm2Rok9ErwerzuoA6F5YWBm1BnKRTArbe6H7NsWXDmEiQMhFzgqTEiIvLW8EHD0bK0BaOHeHeyylZzKCy3yT7+GEhIcH0/yrbHGAi5UFpaigMHDqCmpibUUyEiogix9b+3QpWp8nq8odkQwNl0klQKvPmm+zGGMJx3J7H7PBERkZ/YkqhtXewlYglUS1wHRrLBsiDOzgf9+7u/3y16wofo+ZMQERGFCXmS3N7BXpWpctmXzDZGaBTQcKIBssEy+7WQMpvd31+4EMjJiZiaQu5wa4yIiCiAnNUcUo5UQlOigbHNCPUSNTKez8DUpVOheE6B7JeysefwnhDN9gfpHqpn790LFIZhsncn8Pi8Bzw+T0RE/mDbLmu/6uOuQnXIj9ir1dZGq+5WhwQBkIfBCpYTPD5PREQURuRJchSMKXDYDnPWxd5m/f71mPza5GBO0ZFGA2RluR8TBUnTDISIiIhCwFMXewssqP2uFtInpKFp2iqVWoMhd2RhmuztAwZCLrCOEBERBZK3Xexbz7ci8zeZkdGrLAIxEHKBdYSIiCiQFMkKqDJVEIs8fxWfv3IeU5ZMQb/H+wV3dajB/aoV7r8faGkJzlwChIEQERFRiGhKNJg8yvs8oLMXz+KGZ27Ayh0rAzirdjydHqurA2bODM5cAoSBEBERUYjYCjDWPFvj9VYZABT/oxgD5w8M/OqQQgGoVIDYTbiwcWNEt9xgIERERBRiOWk5MLxi8Klx66m2U8h9OQh5rBoNMGKE+zFbtgR+HgHCQIiIiChM1D1fh8T4RK/Hn2o7FfgkaqkUmD8/sO8RQgyEiIiIwsTwQcNxcslJrJ+/Ht0l3b16zX+++A8q91UGtpP97bd37X4YY2VpD1hZmoiIQuHQiUNIfyYdFnj/NR3QatR33gls2gS0DxtEImDSJKC62v/v10WsLE1ERBTBhg8ajoZXGtCvp/f/Eb7hwAbMWDYjMBNaswaYMsXx2pQp1usRjIGQCyyoSEREoTZ80HCcfv003pnzDnp17+VxvNlixnbDdtz2h9vQ0ubn+j5SKVBVZe0vVlFh/d+lS4FduyL61Bi3xjzg1hgREYWLDfs3YOc3OyERSfDcuudcjhOLxJg8ajKq5lcFZiJGI1BUBGi1V6+pVNYTZtIQNYm9hrff3wyEPGAgRERE4UZoFJDxfIbncYsEe5NXv1KrAZ0OMLVrGCuRAEqlddUoDDBHiIiIKEp5257D0GyA0Cj491SZIFhXgtoHQYD1d6024rbJuoV6AkREROQ7TYkGM5bNwHbDdpdjFlcsxjbDNvvvmddlYttT27p2qsxT/zGDAZAHYBUqQLgiREREFIGk8VJse2ob8mR5HVaGJGIJEuMTHYIgANh/fD8GzB+AdZ+v6/wbe+o/JpN1/tkhwECIiIgogq17fF2Hxq0Tb5iIU22nXL7m7r/ejbw/5HXuZJmt/5hE4nhdIrFej6DVIIDJ0h4xWZqIiCKBvkkPQ7MBssEyGJoNmLp0qsfXJPRKwDeLv/F9q6ylBSgsjIpTY8wRIiIiigLyJLn9hJi3axyt51uR+utUbP7vzchJy/H+zWw1hfR6a06QTBZxK0E23BojIiKKMopkBTKvy/RqbNulNuS+nNu5rTK5HCgoiNggCGAgREREFJW2PbUNIoi8Hr/dsB1DnxqKPYf3BHBW4YeBEBERURSSxktR93wduom9z4I5d/Eccl/OhXqJ2vfVIUEAKisjro4QAyEiIqIolTU0C5ffvIxl9y9Dd0l3r1+n3a/FzDdmejfYaLRWms7IAKZOtZ4qU6utCdURgIEQERFRlCu9sxRNrzYhoVeC16/Z+NVGrNi+wvPAoiJru432NmywniqLAAyEXGD3eSIiiibSeCm+WfwNsodme/2aOe/MQZ/SPth4cKPzAa7abZjN1ut7wj/fiHWEPGAdISIiijYTF0/Erm92+fSam2+4GRVPVDjWHKqstG6HuXLjjcDevZ2cZdew6SoRERE5VfFEBfJH5Pv0ml3f7MKwp4Y5nirz1G7jiy/CPnmagRAREVGMkcZLUf1kNYRFAkYkj/D6dWcvnnU8VaZQAKNGuX/Rli1dnG1gMRAiIiKKUfIkOXYs3AFVpsqn12n3azHp1UnWXx55JAAzCx4GQkRERDFMGi9F1fwqCIsExPeI9/p1e4/shfQJKY5MGO1+YFZW1yYYYAyEiIiICPIkOfb9dh+6i72vN9R6vhWZ796D72+92fWg557zw+wCh4EQERERAQCGDxqOpteaMCLJt7yh2xLdnEDTasM6YZqBEBEREdlJ46U4uOggbr7BzSrPNQZd8DDg88+7NqkAYiBEREREHVQ8UeF1EnWDpzJ7f/5z1ycUIAyEiIiIqANbEnXNszUYN3Sc27H6/sDWJDcDdu0K2+0xBkJERETkUk5aDmqfr0X1gmr06+l66WfNcA8Pqqjw78T8hIEQEREReZQ/Mh+nXz+N9fPXI31QOsQixxDi+24eHlBWFrjJdQEDISIiIvLa5MzJqHm2BpNHTXa4vvU6Dy/U68Nye4yBEBEREfnElj+kna+1X9P3Bwx93L/uyJvhlzTNQIiIiIg6ZUrmFIeTZRuGuB//n0/+homLJ1r7lIUJBkJERETUaZoSDfLkeQCAjzwkTOeesnaxH/KrIY5d7EOIgRARERF1mjReiq2/3oqaZ2uw4XrA6KZDR+5JQHYaOH/lPHJfzsXtf7w95KtDMRMIff/99xg2bBh+9atfhXoqREREUScnLQefP/85FngoSC07c/X/b9Vvxcw3ZgZ2Yh7ETCD08ssv4+abvS8XTkRERL7JGpqFp15c63ZMc0/H3zd+tREb9m8I3KQ8iIlASK/X46uvvkJBQUGop0JERBTVRt5xFy4r82F2cs8M4OXajtenLJkC9RJ1SLbJQh4Ibd26FdOnT0dKSgpEIhHWrl3bYUxZWRnS0tLQs2dPTJgwAbt37/bpPX71q19h8eLFfpoxERERudP9ld87DTDEANT/B4w70fGe7qAOheWFgZ6a0zmFVFtbG8aOHYsyFxUnV69ejQULFuCFF15AXV0dxo4dC5VKhebmZvuYrKwsjB49usPPsWPHsG7dOigUCigUCq/mc/HiRZw5c8bhh4iIiHxw8qTb229u73jNZDZBu18LfVNwiy56KogdcAUFBW63rF577TWUlJRgzpw5AIA33ngDn376KZYvX46FCxcCAOrr612+fteuXfjXv/6F999/H+fOncPly5fRr18//OY3v3E6fvHixfjd737X+T8QERFRrBO7X2fJOWU9PWbo3/GeodkAeZI8QBPrKOQrQu5cunQJtbW1UCqV9mtisRhKpRI7d+706hmLFy/GkSNHcPjwYfzP//wPSkpKXAZBAPD000/j9OnT9p8jR450+c9BREQUE4xGQK22/nggc7HhIhss8/Ok3Av5ipA7J0+ehMlkQlJSksP1pKQkfPXVVwF5z7i4OMTFxQXk2URERFGtqAjQ6bwaeqi/GGiXUi0RS6AcqQzqahAQ5oGQv82ePTvUUyAiIopOggBotZ7HATAPGIDRY7Pw9aGN9mvKkUpoSjSBmp1LYR0IDRw4EBKJBE1NTQ7Xm5qakJycHND3LisrQ1lZGUwmU0Dfh4iIKCo0NHg9VHz6NNZs7Q79OwIMzQbIBsuCvhJkn0tI3tVLPXr0QHZ2Nqqrq+3XzGYzqqurMXHixIC+d2lpKQ4cOICampqAvg8REVFUSE/3fqzJBGi1kJ8BCsYUhCwIAsJgRejcuXMwGAz23w8dOoT6+noMGDAAQ4cOxYIFC1BcXIycnByMHz8eS5YsQVtbm/0UGREREYUBhQJQqaw5Qt7upnz+OSAPXRAEACKLxWIJ5QQ2b96MSZMmdbheXFyMFStWAACWLVuGP/3pT2hsbERWVhaWLl2KCRMmBGV+Z86cQf/+/XH69Gn069cvKO9JREQUkVpagMJCr3OFcPPNgJenwH3l7fd3yAOhcNU+R0gQBAZCRERE3tLrAYMBkMmA//ovYLuTCoo2NTVATo7fp8BAyE+4IkRERNQFS5YAv/yl6/uZmcCXX/r9bb39/g7rZGkiIiKKcPHx7u/v329dQQoRBkJEREQUOLff7nlMu0NTwcZAyIWysjKMGjUKubm5oZ4KERFR5FIogLFj3Y/pFrpD7AyEXGAdISIiIj955hn3969cCc48nGAgRERERIGVleX+viy4jVbbYyBEREREgWUrtiiROF6XSKzXQ1hUkYEQERERBZ5GAyiVjteUSuv1EAp5iw0iIiKKAVIpUFXlWGwxxO01AAZCLrH7PBERUQDI5WERANmwsrQHrCxNREQUebz9/uaKEBEREYWGIAANDSHdJmOyNBEREQWX0Qio1UBGBjB1qvVUmVpt7V4fZAyEiIiIKLiKigCdzvGaTgcUFgZ9KgyEiIiIKHgEAdBqgWsPI5lM1utBbsDKQMgF9hojIiIKgIYG9/eD3ICVp8Y84KkxIiIiPxIEa26Qu/t+SJz29vubK0JEREQUPGHWboOBEBEREQVXGLXbYB0hIiIiCq4warfBQIiIiIhCIwzabXBrjIiIiGIWAyEXeHyeiIgo+vH4vAc8Pk9ERBR5eHyeiIiIyAMGQkRERBSzGAgRERFRzGIgRERERDGLgRARERHFLAZCREREFLMYCBEREVHMYosND2xlls6cORPimRAREZG3bN/bnsolMhDy4OzZswCA1NTUEM+EiIiIfHX27Fn079/f5X1WlvbAbDbj2LFjyM/Px549e7r0rNzcXNTU1AT0Nb6M9zT2zJkzSE1NxZEjR2KuqnZn/l4FUrDm4+/36erzOvv6QH1uvBkXq5+bcPvMAMGZEz8zXR8bqM+MxWLB2bNnkZKSArHYdSYQV4Q8EIvFuP7669GtW7cu/w2SSCQ+P8PX1/gy3tux/fr1i6l/oQOd+3sVSMGaj7/fp6vP6+zrA/W58eW5sfa5CbfPDBCcOfEz47+xgfjMuFsJsmGytJdKS0tD8gxfX+PLeH/8maJVuP21CdZ8/P0+XX1eZ18fqM9NuP1zEU7C8a9NMObEz4z/nx1s3Bojl9hwlsh3/NwQ+SbUnxmuCJFLcXFxeOGFFxAXFxfqqRBFDH5uiHwT6s8MV4SIiIgoZnFFiIiIiGIWAyEiIiKKWQyEiIiIKGYxECIiIqKYxUCIiIiIYhYDIeq0Tz75BBkZGZDL5XjrrbdCPR2isHfPPfdAKpVi5syZoZ4KUdg7cuQI7rjjDowaNQo33ngj3n///YC8D4/PU6dcuXIFo0aNwqZNm9C/f39kZ2djx44dSExMDPXUiMLW5s2bcfbsWbzzzjtYs2ZNqKdDFNaOHz+OpqYmZGVlobGxEdnZ2RAEAfHx8X59H64IUafs3r0bmZmZGDJkCPr06YOCggKsX78+1NMiCmt33HEH+vbtG+ppEEWE6667DllZWQCA5ORkDBw4EEaj0e/vw0AoRm3duhXTp09HSkoKRCIR1q5d22FMWVkZ0tLS0LNnT0yYMAG7d++23zt27BiGDBli/33IkCE4evRoMKZOFBJd/cwQxRp/fmZqa2thMpmQmprq93kyEIpRbW1tGDt2LMrKypzeX716NRYsWIAXXngBdXV1GDt2LFQqFZqbm4M8U6LwwM8MkW/89ZkxGo148MEH8fe//z0wE7VQzANg+eijjxyujR8/3lJaWmr/3WQyWVJSUiyLFy+2WCwWy2effWa5++677ffnzZtnWbVqVVDmSxRqnfnM2GzatMly3333BWOaRGGjs5+ZCxcuWPLy8iwrV64M2Ny4IkQdXLp0CbW1tVAqlfZrYrEYSqUSO3fuBACMHz8eX375JY4ePYpz586hsrISKpUqVFMmCilvPjNEdJU3nxmLxYLZs2cjPz8fDzzwQMDmwkCIOjh58iRMJhOSkpIcriclJaGxsREA0K1bN7z66quYNGkSsrKy8OSTT/LEGMUsbz4zAKBUKvGTn/wEFRUVuP766xkkUczy5jPz2WefYfXq1Vi7di2ysrKQlZWFffv2+X0u3fz+RIoZM2bMwIwZM0I9DaKIodPpQj0Foohx6623wmw2B/x9uCJEHQwcOBASiQRNTU0O15uampCcnByiWRGFL35miHwTTp8ZBkLUQY8ePZCdnY3q6mr7NbPZjOrqakycODGEMyMKT/zMEPkmnD4z3BqLUefOnYPBYLD/fujQIdTX12PAgAEYOnQoFixYgOLiYuTk5GD8+PFYsmQJ2traMGfOnBDOmih0+Jkh8k3EfGYCdh6NwtqmTZssADr8FBcX28e8/vrrlqFDh1p69OhhGT9+vGXXrl2hmzBRiPEzQ+SbSPnMsNcYERERxSzmCBEREVHMYiBEREREMYuBEBEREcUsBkJEREQUsxgIERERUcxiIEREREQxi4EQERERxSwGQkRERBSzGAgRERFRzGIgREQxaefOnZBIJPjxj38c6qkQUQixxQYRxaSHH34Yffr0wdtvv42vv/4aKSkpoZ4SEYUAV4SIKOacO3cOq1evxmOPPYYf//jHWLFiRainREQhwkCIiGLOv//9b4wYMQIZGRn4+c9/juXLl4OL40SxiYEQEcWct99+Gz//+c8BAGq1GqdPn8aWLVtCPCsiCgXmCBFRTPn6668xevRoHD16FIMHDwYAPP744zh9+jT++c9/hnh2RBRs3UI9ASKiYHr77bdx5coVh+Roi8WCuLg4LFu2DP379w/h7Igo2Lg1RkQx48qVK1i5ciVeffVV1NfX23/27t2LlJQUaDSaUE+RiIKMW2NEFDPWrl2Ln/3sZ2hubu6w8vPUU09h48aNqKmpCdHsiCgUGAgRUcyYPn06zGYzPv300w73du/ejQkTJmDv3r248cYbQzA7IgoFBkJEREQUs5gjRERERDGLgRARERHFLAZCREREFLMYCBEREVHMYiBEREREMYuBEBEREcUsBkJEREQUsxgIERERUcxiIEREREQxi4EQERERxSwGQkRERBSzGAgRERFRzPr/YWqMYl3d0rMAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "t = dff.plot(kind='scatter', x='A', y='B', color='DarkBlue', label='B curve', loglog=True);\n",
        "dff.plot(kind='scatter', x='A', y='C',color='DarkGreen', label='C curve', ax=t, loglog = True);\n",
        "dff.plot(kind='scatter', x='A', y='D',color='Red', label='D curve', ax=t, loglog = True);"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0SS9IZLAhmFa"
      },
      "source": [
        "**Using seaborn**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "id": "6bJoIf6rhmFa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "outputId": "0a4fb77d-4475-45c2-f404-d09c1fb8652a"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='A', ylabel='B'>"
            ]
          },
          "metadata": {},
          "execution_count": 30
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "sns.scatterplot(x='A',y='B', data = dff)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NiBU9S99hmFa"
      },
      "source": [
        "In log-log scale (for some reason it seems to throw away small values)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {
        "scrolled": true,
        "id": "ImpitSmMhmFa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 471
        },
        "outputId": "15e435a4-9777-4df1-a2ca-0fcd85d019fc"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[]"
            ]
          },
          "metadata": {},
          "execution_count": 31
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "splot = sns.scatterplot(x='A',y='B', data = dff)\n",
        "#splot.set(xscale=\"log\", yscale=\"log\")\n",
        "splot.loglog()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0XUlrpkuhmFa"
      },
      "source": [
        "### Statistical Significance ###"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-C7rsDd1hmFa"
      },
      "source": [
        "Recall the dataframe we obtained when grouping by gain"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "metadata": {
        "scrolled": true,
        "id": "0Z9OlDMchmFb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "outputId": "a20a88c7-18c6-4195-868e-73c256faec55"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          gain        open         low        high       close           vol\n",
              "0   large_gain  170.111111  169.610833  175.313056  174.653889  3.044808e+07\n",
              "1  medium_gain  172.305769  171.410962  175.321346  174.185577  2.774962e+07\n",
              "2     negative  171.605492  168.137747  172.566230  169.380246  2.731642e+07\n",
              "3   small_gain  171.218049  169.827317  173.070488  171.699268  2.476326e+07"
            ],
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              "\n",
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              "\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>gain</th>\n",
              "      <th>open</th>\n",
              "      <th>low</th>\n",
              "      <th>high</th>\n",
              "      <th>close</th>\n",
              "      <th>vol</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>large_gain</td>\n",
              "      <td>170.111111</td>\n",
              "      <td>169.610833</td>\n",
              "      <td>175.313056</td>\n",
              "      <td>174.653889</td>\n",
              "      <td>3.044808e+07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>medium_gain</td>\n",
              "      <td>172.305769</td>\n",
              "      <td>171.410962</td>\n",
              "      <td>175.321346</td>\n",
              "      <td>174.185577</td>\n",
              "      <td>2.774962e+07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>negative</td>\n",
              "      <td>171.605492</td>\n",
              "      <td>168.137747</td>\n",
              "      <td>172.566230</td>\n",
              "      <td>169.380246</td>\n",
              "      <td>2.731642e+07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>small_gain</td>\n",
              "      <td>171.218049</td>\n",
              "      <td>169.827317</td>\n",
              "      <td>173.070488</td>\n",
              "      <td>171.699268</td>\n",
              "      <td>2.476326e+07</td>\n",
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              "                                                    [key], {});\n",
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              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "gdf",
              "summary": "{\n  \"name\": \"gdf\",\n  \"rows\": 4,\n  \"fields\": [\n    {\n      \"column\": \"gain\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"medium_gain\",\n          \"small_gain\",\n          \"large_gain\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"open\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.9173623996959993,\n        \"min\": 170.11111111111111,\n        \"max\": 172.30576923076922,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          172.30576923076922,\n          171.2180487804878,\n          170.11111111111111\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"low\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.3395840130605496,\n        \"min\": 168.1377467213115,\n        \"max\": 171.41096153846155,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          171.41096153846155,\n          169.82731707317075,\n          169.61083333333332\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"high\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.4573245935867525,\n        \"min\": 172.56622950819673,\n        \"max\": 175.32134615384615,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          175.32134615384615,\n          173.07048780487807,\n          175.31305555555556\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"close\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.439453678646397,\n        \"min\": 169.38024590163934,\n        \"max\": 174.6538888888889,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          174.18557692307692,\n          171.69926829268292,\n          174.6538888888889\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"vol\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2327922.6044394746,\n        \"min\": 24763260.731707316,\n        \"max\": 30448075.777777776,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          27749616.03846154,\n          24763260.731707316,\n          30448075.777777776\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 32
        }
      ],
      "source": [
        "gdf"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WrApuRlChmFb"
      },
      "source": [
        "We see that there are differences in the volume of trading depending on the gain. But are these differences statistically significant?  We can test that using the Student t-test. The Student t-test will give us a value for the differnece between the means in units of standard error, and a p-value that says how important this difference is. Usually we require the p-value to be less than 0.05 (or 0.01 if we want to be more strict). Note that for the test we will need to use all the values in the group.\n",
        "\n",
        "To compute the t-test we will use the **SciPy** library, a Python library for scientific computing."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pqp9zAwNhmFb"
      },
      "source": [
        "### The Student t-test"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "metadata": {
        "id": "3hQM8vuJhmFb"
      },
      "outputs": [],
      "source": [
        "import scipy as sp #library for scientific computations\n",
        "from scipy import stats #The statistics part of the library"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "M9srumkDhmFb"
      },
      "source": [
        "The t-test value is:\n",
        "\n",
        "$$t = \\frac{\\bar{x}_1-\\bar{x}_2}{\\sqrt{\\frac{\\sigma_1^2}{n_1}+\\frac{\\sigma_2^2}{n_2}}} $$\n",
        "\n",
        "where $\\bar x_i$ is the mean value of the $i$ dataset, $\\sigma_i^2$ is the variance, and $n_i$ is the size."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "metadata": {
        "id": "pz2xJ2kihmFb",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c798df10-245a-4e97-aaf3-11c2ca770df8"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "TtestResult(statistic=np.float64(-0.7237664320493662), pvalue=np.float64(0.4720843830832102), df=np.float64(58.686780477477306))\n",
            "TtestResult(statistic=np.float64(-0.6532701783697626), pvalue=np.float64(0.5152447681117652), df=np.float64(90.15236633446038))\n",
            "TtestResult(statistic=np.float64(-1.2743420856982142), pvalue=np.float64(0.20648370530531482), df=np.float64(74.86473858994728))\n",
            "TtestResult(statistic=np.float64(-0.12034041075217132), pvalue=np.float64(0.9045425277099464), df=np.float64(73.34821305978315))\n",
            "TtestResult(statistic=np.float64(-0.9054964354181412), pvalue=np.float64(0.36935442852925515), df=np.float64(52.30908445486685))\n",
            "TtestResult(statistic=np.float64(-0.5972302166465407), pvalue=np.float64(0.5519534365894707), df=np.float64(84.35529071251472))\n"
          ]
        }
      ],
      "source": [
        "#Test statistical significance of the difference in the mean volume numbers\n",
        "\n",
        "sm = gain_groups.get_group('small_gain').vol\n",
        "lg = gain_groups.get_group('large_gain').vol\n",
        "med = gain_groups.get_group('medium_gain').vol\n",
        "neg = gain_groups.get_group('negative').vol\n",
        "print(stats.ttest_ind(sm,neg,equal_var = False))\n",
        "print(stats.ttest_ind(sm,med, equal_var = False))\n",
        "print(stats.ttest_ind(sm,lg, equal_var = False))\n",
        "print(stats.ttest_ind(neg,med,equal_var = False))\n",
        "print(stats.ttest_ind(neg,lg,equal_var = False))\n",
        "print(stats.ttest_ind(med,lg, equal_var = False))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4ifhM-uzhmFc"
      },
      "source": [
        "### Kolomogorov-Smirnov Test\n",
        "\n",
        "Test if the data for small and large gain come from the same distribution.\n",
        "The p-value > 0.1 inidcates that we cannot reject the null hypothesis that they do come from the same distribution.\n",
        "\n",
        "Use scipy.stats.ks_2samp for testing two samples if they come form the same distribution.\n",
        "\n",
        "If you want to test a single sample against a fixed distribution (e.g., normal) use the scipy.stats.kstest"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 37,
      "metadata": {
        "id": "_vEIZhsfhmFc",
        "outputId": "24fec2ba-09fc-470c-8932-dc942b8d7b03",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "KstestResult(statistic=np.float64(0.2703252032520325), pvalue=np.float64(0.09473208642418271), statistic_location=np.int64(26266081), statistic_sign=np.int8(1))"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ],
      "source": [
        "import numpy as np\n",
        "stats.ks_2samp(np.array(sm), np.array(lg), alternative='two-sided')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 38,
      "metadata": {
        "id": "soDs0qE7hmFc",
        "outputId": "3d7d4878-fa02-4732-bdb7-bb598570ae4c",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "KstestResult(statistic=np.float64(1.0), pvalue=np.float64(0.0), statistic_location=np.int64(10464528), statistic_sign=np.int8(-1))"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ],
      "source": [
        "stats.kstest(np.array(sm), 'norm') # Check with known standard distributions (here the normal distribution)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.histplot(sm,bins=40,kde=True)\n",
        "sns.histplot(lg,bins=40,kde=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "id": "ai3mGRV04L4C",
        "outputId": "a6650b59-ce08-4dc4-fbd2-7664057bf365"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='vol', ylabel='Count'>"
            ]
          },
          "metadata": {},
          "execution_count": 39
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MhIChkwKhmFc"
      },
      "source": [
        "### $\\chi^2$-test\n",
        "\n",
        "We use the $\\chi^2$-test to test if two random variables are independent. The larger the value of the test the farther from independence. The p-value tells us whether the value is statistically significant."
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        },
        "id": "bdWPCdcBlXcj",
        "outputId": "88d6b57b-ab91-4cbd-f43b-9237747d8a60"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                             open   close     low    high       vol  profit  \\\n",
              "date                                                                          \n",
              "2018-01-02 00:00:00+00:00  177.68  181.42  177.55  181.58  17694891    3.74   \n",
              "2018-01-03 00:00:00+00:00  181.88  184.67  181.33  184.78  16595495    2.79   \n",
              "2018-01-04 00:00:00+00:00  184.90  184.33  184.10  186.21  13554357   -0.57   \n",
              "2018-01-05 00:00:00+00:00  185.59  186.85  184.93  186.90  13042388    1.26   \n",
              "2018-01-08 00:00:00+00:00  187.20  188.28  186.33  188.90  14719216    1.08   \n",
              "...                           ...     ...     ...     ...       ...     ...   \n",
              "2018-12-24 00:00:00+00:00  123.10  124.06  123.02  129.74  22066002    0.96   \n",
              "2018-12-26 00:00:00+00:00  126.00  134.18  125.89  134.24  39723370    8.18   \n",
              "2018-12-27 00:00:00+00:00  132.44  134.52  129.67  134.99  31202509    2.08   \n",
              "2018-12-28 00:00:00+00:00  135.34  133.20  132.20  135.92  22627569   -2.14   \n",
              "2018-12-31 00:00:00+00:00  134.45  131.09  129.95  134.64  24625308   -3.36   \n",
              "\n",
              "                                  gain   size  \n",
              "date                                           \n",
              "2018-01-02 00:00:00+00:00   large_gain  small  \n",
              "2018-01-03 00:00:00+00:00  medium_gain  small  \n",
              "2018-01-04 00:00:00+00:00     negative  small  \n",
              "2018-01-05 00:00:00+00:00  medium_gain  small  \n",
              "2018-01-08 00:00:00+00:00  medium_gain  small  \n",
              "...                                ...    ...  \n",
              "2018-12-24 00:00:00+00:00   small_gain  small  \n",
              "2018-12-26 00:00:00+00:00   large_gain  large  \n",
              "2018-12-27 00:00:00+00:00  medium_gain  large  \n",
              "2018-12-28 00:00:00+00:00     negative  small  \n",
              "2018-12-31 00:00:00+00:00     negative  small  \n",
              "\n",
              "[251 rows x 8 columns]"
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              "      <th>2018-01-02 00:00:00+00:00</th>\n",
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              "      <th>2018-01-03 00:00:00+00:00</th>\n",
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              "      <th>2018-01-04 00:00:00+00:00</th>\n",
              "      <td>184.90</td>\n",
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              "    <tr>\n",
              "      <th>2018-01-05 00:00:00+00:00</th>\n",
              "      <td>185.59</td>\n",
              "      <td>186.85</td>\n",
              "      <td>184.93</td>\n",
              "      <td>186.90</td>\n",
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              "      <td>1.26</td>\n",
              "      <td>medium_gain</td>\n",
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              "    <tr>\n",
              "      <th>2018-01-08 00:00:00+00:00</th>\n",
              "      <td>187.20</td>\n",
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              "      <td>14719216</td>\n",
              "      <td>1.08</td>\n",
              "      <td>medium_gain</td>\n",
              "      <td>small</td>\n",
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              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>2018-12-24 00:00:00+00:00</th>\n",
              "      <td>123.10</td>\n",
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              "      <td>123.02</td>\n",
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              "      <th>2018-12-26 00:00:00+00:00</th>\n",
              "      <td>126.00</td>\n",
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              "      <th>2018-12-27 00:00:00+00:00</th>\n",
              "      <td>132.44</td>\n",
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              "      <th>2018-12-28 00:00:00+00:00</th>\n",
              "      <td>135.34</td>\n",
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              "    <tr>\n",
              "      <th>2018-12-31 00:00:00+00:00</th>\n",
              "      <td>134.45</td>\n",
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              "      <td>-3.36</td>\n",
              "      <td>negative</td>\n",
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              "  </tbody>\n",
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              "<p>251 rows × 8 columns</p>\n",
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              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 251,\n  \"fields\": [\n    {\n      \"column\": \"date\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"2018-01-02 00:00:00+00:00\",\n        \"max\": \"2018-12-31 00:00:00+00:00\",\n        \"num_unique_values\": 251,\n        \"samples\": [\n          \"2018-08-14 00:00:00+00:00\",\n          \"2018-01-10 00:00:00+00:00\",\n          \"2018-08-27 00:00:00+00:00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"open\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 19.69649335473974,\n        \"min\": 123.1,\n        \"max\": 215.72,\n        \"num_unique_values\": 249,\n        \"samples\": [\n          208.77,\n          186.94,\n          184.93\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"close\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 19.97745158627776,\n        \"min\": 124.06,\n        \"max\": 217.5,\n        \"num_unique_values\": 249,\n        \"samples\": [\n          209.94,\n          187.84,\n          186.9\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"low\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 20.07440767348364,\n        \"min\": 123.02,\n        \"max\": 214.27,\n        \"num_unique_values\": 247,\n        \"samples\": [\n          176.4,\n          185.63,\n          160.88\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"high\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 19.424564323437952,\n        \"min\": 129.74,\n        \"max\": 218.62,\n        \"num_unique_values\": 246,\n        \"samples\": [\n          177.95,\n          187.89,\n          171.77\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"vol\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 19101434,\n        \"min\": 8855144,\n        \"max\": 169803668,\n        \"num_unique_values\": 251,\n        \"samples\": [\n          19101995,\n          10464528,\n          17921935\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"profit\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.8733780844045307,\n        \"min\": -9.429999999999978,\n        \"max\": 8.180000000000007,\n        \"num_unique_values\": 233,\n        \"samples\": [\n          3.530000000000001,\n          2.3500000000000227,\n          -2.140000000000015\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"gain\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"medium_gain\",\n          \"small_gain\",\n          \"large_gain\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"size\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"large\",\n          \"small\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 41,
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        "outputId": "2000b5ab-9742-4221-be84-d04edcee580f",
        "colab": {
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          "height": 206
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "size         large  small\n",
              "gain                     \n",
              "large_gain      15     21\n",
              "medium_gain     12     40\n",
              "negative        39     83\n",
              "small_gain       6     35"
            ],
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              "      <th>size</th>\n",
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              "    <tr>\n",
              "      <th>gain</th>\n",
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              "      <th>large_gain</th>\n",
              "      <td>15</td>\n",
              "      <td>21</td>\n",
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              "      <th>medium_gain</th>\n",
              "      <td>12</td>\n",
              "      <td>40</td>\n",
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              "      <th>negative</th>\n",
              "      <td>39</td>\n",
              "      <td>83</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>small_gain</th>\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "cdf",
              "summary": "{\n  \"name\": \"cdf\",\n  \"rows\": 4,\n  \"fields\": [\n    {\n      \"column\": \"gain\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"medium_gain\",\n          \"small_gain\",\n          \"large_gain\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"large\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 14,\n        \"min\": 6,\n        \"max\": 39,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          12,\n          6,\n          15\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"small\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 26,\n        \"min\": 21,\n        \"max\": 83,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          40,\n          35,\n          21\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 41
        }
      ],
      "source": [
        "# The crosstab methond creates the contigency table for the two attributes.\n",
        "cdf = pd.crosstab(df['gain'],df['size'])\n",
        "cdf"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "a4z8IAephmFd"
      },
      "source": [
        "We will use the [chi2_contigency function](https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.chi2_contingency.html) which compares the contigency table against the expected values as produced by the marginal distributions (see also the [chisquare function](https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.chisquare.html) which assumes uniform marginals).\n",
        "\n",
        "The chi2_contigency returns:\n",
        "<ol>\n",
        "    <li> The chi2 test statistic\n",
        "    <li> The p-value\n",
        "    <li> The degrees of freedom\n",
        "    <li> The table with the expected counts\n",
        "    "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 42,
      "metadata": {
        "id": "q7-KC539hmFd",
        "outputId": "aca96dcc-26ed-419c-96f5-a4e31fe12dca",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Chi2ContingencyResult(statistic=np.float64(8.364478767414871), pvalue=np.float64(0.03905004813922891), dof=3, expected_freq=array([[10.32669323, 25.67330677],\n",
              "       [14.91633466, 37.08366534],\n",
              "       [34.99601594, 87.00398406],\n",
              "       [11.76095618, 29.23904382]]))"
            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ],
      "source": [
        "stats.chi2_contingency(cdf)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We got p-value = 0.03905 < 0.05. Why do I get now Correlation between trading volume and gain ? Let's make a new contingency table and see what happens:"
      ],
      "metadata": {
        "id": "nc7cOK33Nfww"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "new_df = df.copy()\n",
        "\n",
        "# Calculate the quantiles for dividing the `vol` column into five categories\n",
        "quantiles = new_df['vol'].quantile([0.2, 0.4, 0.6, 0.8]).values\n",
        "\n",
        "# Define categories based on quantile ranges\n",
        "for idx, row in new_df.iterrows():\n",
        "    if row.vol < quantiles[0]:\n",
        "        new_df.loc[idx, 'size'] = 'very small'\n",
        "    elif row.vol < quantiles[1]:\n",
        "        new_df.loc[idx, 'size'] = 'small'\n",
        "    elif row.vol < quantiles[2]:\n",
        "        new_df.loc[idx, 'size'] = 'medium'\n",
        "    elif row.vol < quantiles[3]:\n",
        "        new_df.loc[idx, 'size'] = 'large'\n",
        "    else:\n",
        "        new_df.loc[idx, 'size'] = 'very large'\n",
        "\n",
        "new_df.head()"
      ],
      "metadata": {
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          "height": 237
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        "id": "584tJ-jLOBKi",
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      },
      "execution_count": 43,
      "outputs": [
        {
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              "                             open   close     low    high       vol  profit  \\\n",
              "date                                                                          \n",
              "2018-01-02 00:00:00+00:00  177.68  181.42  177.55  181.58  17694891    3.74   \n",
              "2018-01-03 00:00:00+00:00  181.88  184.67  181.33  184.78  16595495    2.79   \n",
              "2018-01-04 00:00:00+00:00  184.90  184.33  184.10  186.21  13554357   -0.57   \n",
              "2018-01-05 00:00:00+00:00  185.59  186.85  184.93  186.90  13042388    1.26   \n",
              "2018-01-08 00:00:00+00:00  187.20  188.28  186.33  188.90  14719216    1.08   \n",
              "\n",
              "                                  gain        size  \n",
              "date                                                \n",
              "2018-01-02 00:00:00+00:00   large_gain       small  \n",
              "2018-01-03 00:00:00+00:00  medium_gain  very small  \n",
              "2018-01-04 00:00:00+00:00     negative  very small  \n",
              "2018-01-05 00:00:00+00:00  medium_gain  very small  \n",
              "2018-01-08 00:00:00+00:00  medium_gain  very small  "
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              "      <th>2018-01-02 00:00:00+00:00</th>\n",
              "      <td>177.68</td>\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "new_df",
              "summary": "{\n  \"name\": \"new_df\",\n  \"rows\": 251,\n  \"fields\": [\n    {\n      \"column\": \"date\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"2018-01-02 00:00:00+00:00\",\n        \"max\": \"2018-12-31 00:00:00+00:00\",\n        \"num_unique_values\": 251,\n        \"samples\": [\n          \"2018-08-14 00:00:00+00:00\",\n          \"2018-01-10 00:00:00+00:00\",\n          \"2018-08-27 00:00:00+00:00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"open\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 19.69649335473974,\n        \"min\": 123.1,\n        \"max\": 215.72,\n        \"num_unique_values\": 249,\n        \"samples\": [\n          208.77,\n          186.94,\n          184.93\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"close\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 19.97745158627776,\n        \"min\": 124.06,\n        \"max\": 217.5,\n        \"num_unique_values\": 249,\n        \"samples\": [\n          209.94,\n          187.84,\n          186.9\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"low\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 20.07440767348364,\n        \"min\": 123.02,\n        \"max\": 214.27,\n        \"num_unique_values\": 247,\n        \"samples\": [\n          176.4,\n          185.63,\n          160.88\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"high\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 19.424564323437952,\n        \"min\": 129.74,\n        \"max\": 218.62,\n        \"num_unique_values\": 246,\n        \"samples\": [\n          177.95,\n          187.89,\n          171.77\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"vol\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 19101434,\n        \"min\": 8855144,\n        \"max\": 169803668,\n        \"num_unique_values\": 251,\n        \"samples\": [\n          19101995,\n          10464528,\n          17921935\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"profit\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.8733780844045307,\n        \"min\": -9.429999999999978,\n        \"max\": 8.180000000000007,\n        \"num_unique_values\": 233,\n        \"samples\": [\n          3.530000000000001,\n          2.3500000000000227,\n          -2.140000000000015\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"gain\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"medium_gain\",\n          \"small_gain\",\n          \"large_gain\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"size\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"very small\",\n          \"medium\",\n          \"very large\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cdf = pd.crosstab(new_df['gain'],new_df['size'])\n",
        "cdf"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "8Vv143q4PomZ",
        "outputId": "42d00159-c841-40ef-a93a-fb96948dc531"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "size         large  medium  small  very large  very small\n",
              "gain                                                     \n",
              "large_gain       5       3     11          12           5\n",
              "medium_gain     13      13      9           7          10\n",
              "negative        27      23     21          27          24\n",
              "small_gain       5      11      9           5          11"
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              "      <td>5</td>\n",
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              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
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              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "\n",
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              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
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              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('cdf')\"\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "cdf",
              "summary": "{\n  \"name\": \"cdf\",\n  \"rows\": 4,\n  \"fields\": [\n    {\n      \"column\": \"gain\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"medium_gain\",\n          \"small_gain\",\n          \"large_gain\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"large\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 10,\n        \"min\": 5,\n        \"max\": 27,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          5,\n          13,\n          27\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"medium\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 8,\n        \"min\": 3,\n        \"max\": 23,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          13,\n          11,\n          3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"small\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5,\n        \"min\": 9,\n        \"max\": 21,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          11,\n          9,\n          21\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"very large\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 9,\n        \"min\": 5,\n        \"max\": 27,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          7,\n          5,\n          12\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"very small\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 8,\n        \"min\": 5,\n        \"max\": 24,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          10,\n          11,\n          5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "stats.chi2_contingency(cdf)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ccJ60EzjP1j6",
        "outputId": "470d1870-2db3-4e6f-d800-d3e218f37cf9"
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Chi2ContingencyResult(statistic=np.float64(17.156150315137896), pvalue=np.float64(0.14381727155690543), dof=12, expected_freq=array([[ 7.17131474,  7.17131474,  7.17131474,  7.31474104,  7.17131474],\n",
              "       [10.35856574, 10.35856574, 10.35856574, 10.56573705, 10.35856574],\n",
              "       [24.30278884, 24.30278884, 24.30278884, 24.78884462, 24.30278884],\n",
              "       [ 8.16733068,  8.16733068,  8.16733068,  8.33067729,  8.16733068]]))"
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The p-value now increased!"
      ],
      "metadata": {
        "id": "K_l5Rhw_YDGE"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Exact fisher test\n",
        "\n",
        "Another statistical test used to determine if there is a significant association between two categorical variables. Unlike the chi-square test, which approximates the association using large-sample assumptions, Fisher’s Exact Test calculates the exact probability, making it particularly suitable for small sample sizes or when one or more categories have low counts. Fisher’s test is typically used for 2x2 contigency tables (two categories in each variable)"
      ],
      "metadata": {
        "id": "wodV_y4qQOZ1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_fisher = df.copy()\n",
        "df_fisher['gain'] = df['gain'].apply(lambda x: 'positive_gain' if x in ['small_gain', 'medium_gain', 'large_gain'] else 'negative')\n"
      ],
      "metadata": {
        "id": "IEHl3ZKKfZjQ"
      },
      "execution_count": 47,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_fisher"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        },
        "id": "gHN5QfSOfmdh",
        "outputId": "01be4a42-d83f-4371-9d01-1ef262d588fd"
      },
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                             open   close     low    high       vol  profit  \\\n",
              "date                                                                          \n",
              "2018-01-02 00:00:00+00:00  177.68  181.42  177.55  181.58  17694891    3.74   \n",
              "2018-01-03 00:00:00+00:00  181.88  184.67  181.33  184.78  16595495    2.79   \n",
              "2018-01-04 00:00:00+00:00  184.90  184.33  184.10  186.21  13554357   -0.57   \n",
              "2018-01-05 00:00:00+00:00  185.59  186.85  184.93  186.90  13042388    1.26   \n",
              "2018-01-08 00:00:00+00:00  187.20  188.28  186.33  188.90  14719216    1.08   \n",
              "...                           ...     ...     ...     ...       ...     ...   \n",
              "2018-12-24 00:00:00+00:00  123.10  124.06  123.02  129.74  22066002    0.96   \n",
              "2018-12-26 00:00:00+00:00  126.00  134.18  125.89  134.24  39723370    8.18   \n",
              "2018-12-27 00:00:00+00:00  132.44  134.52  129.67  134.99  31202509    2.08   \n",
              "2018-12-28 00:00:00+00:00  135.34  133.20  132.20  135.92  22627569   -2.14   \n",
              "2018-12-31 00:00:00+00:00  134.45  131.09  129.95  134.64  24625308   -3.36   \n",
              "\n",
              "                                    gain   size  \n",
              "date                                             \n",
              "2018-01-02 00:00:00+00:00  positive_gain  small  \n",
              "2018-01-03 00:00:00+00:00  positive_gain  small  \n",
              "2018-01-04 00:00:00+00:00       negative  small  \n",
              "2018-01-05 00:00:00+00:00  positive_gain  small  \n",
              "2018-01-08 00:00:00+00:00  positive_gain  small  \n",
              "...                                  ...    ...  \n",
              "2018-12-24 00:00:00+00:00  positive_gain  small  \n",
              "2018-12-26 00:00:00+00:00  positive_gain  large  \n",
              "2018-12-27 00:00:00+00:00  positive_gain  large  \n",
              "2018-12-28 00:00:00+00:00       negative  small  \n",
              "2018-12-31 00:00:00+00:00       negative  small  \n",
              "\n",
              "[251 rows x 8 columns]"
            ],
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              "\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>open</th>\n",
              "      <th>close</th>\n",
              "      <th>low</th>\n",
              "      <th>high</th>\n",
              "      <th>vol</th>\n",
              "      <th>profit</th>\n",
              "      <th>gain</th>\n",
              "      <th>size</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
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              "      <th></th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2018-01-02 00:00:00+00:00</th>\n",
              "      <td>177.68</td>\n",
              "      <td>181.42</td>\n",
              "      <td>177.55</td>\n",
              "      <td>181.58</td>\n",
              "      <td>17694891</td>\n",
              "      <td>3.74</td>\n",
              "      <td>positive_gain</td>\n",
              "      <td>small</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-01-03 00:00:00+00:00</th>\n",
              "      <td>181.88</td>\n",
              "      <td>184.67</td>\n",
              "      <td>181.33</td>\n",
              "      <td>184.78</td>\n",
              "      <td>16595495</td>\n",
              "      <td>2.79</td>\n",
              "      <td>positive_gain</td>\n",
              "      <td>small</td>\n",
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              "    <tr>\n",
              "      <th>2018-01-04 00:00:00+00:00</th>\n",
              "      <td>184.90</td>\n",
              "      <td>184.33</td>\n",
              "      <td>184.10</td>\n",
              "      <td>186.21</td>\n",
              "      <td>13554357</td>\n",
              "      <td>-0.57</td>\n",
              "      <td>negative</td>\n",
              "      <td>small</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-01-05 00:00:00+00:00</th>\n",
              "      <td>185.59</td>\n",
              "      <td>186.85</td>\n",
              "      <td>184.93</td>\n",
              "      <td>186.90</td>\n",
              "      <td>13042388</td>\n",
              "      <td>1.26</td>\n",
              "      <td>positive_gain</td>\n",
              "      <td>small</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-01-08 00:00:00+00:00</th>\n",
              "      <td>187.20</td>\n",
              "      <td>188.28</td>\n",
              "      <td>186.33</td>\n",
              "      <td>188.90</td>\n",
              "      <td>14719216</td>\n",
              "      <td>1.08</td>\n",
              "      <td>positive_gain</td>\n",
              "      <td>small</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-12-24 00:00:00+00:00</th>\n",
              "      <td>123.10</td>\n",
              "      <td>124.06</td>\n",
              "      <td>123.02</td>\n",
              "      <td>129.74</td>\n",
              "      <td>22066002</td>\n",
              "      <td>0.96</td>\n",
              "      <td>positive_gain</td>\n",
              "      <td>small</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-12-26 00:00:00+00:00</th>\n",
              "      <td>126.00</td>\n",
              "      <td>134.18</td>\n",
              "      <td>125.89</td>\n",
              "      <td>134.24</td>\n",
              "      <td>39723370</td>\n",
              "      <td>8.18</td>\n",
              "      <td>positive_gain</td>\n",
              "      <td>large</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-12-27 00:00:00+00:00</th>\n",
              "      <td>132.44</td>\n",
              "      <td>134.52</td>\n",
              "      <td>129.67</td>\n",
              "      <td>134.99</td>\n",
              "      <td>31202509</td>\n",
              "      <td>2.08</td>\n",
              "      <td>positive_gain</td>\n",
              "      <td>large</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-12-28 00:00:00+00:00</th>\n",
              "      <td>135.34</td>\n",
              "      <td>133.20</td>\n",
              "      <td>132.20</td>\n",
              "      <td>135.92</td>\n",
              "      <td>22627569</td>\n",
              "      <td>-2.14</td>\n",
              "      <td>negative</td>\n",
              "      <td>small</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-12-31 00:00:00+00:00</th>\n",
              "      <td>134.45</td>\n",
              "      <td>131.09</td>\n",
              "      <td>129.95</td>\n",
              "      <td>134.64</td>\n",
              "      <td>24625308</td>\n",
              "      <td>-3.36</td>\n",
              "      <td>negative</td>\n",
              "      <td>small</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>251 rows × 8 columns</p>\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
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              "summary": "{\n  \"name\": \"df_fisher\",\n  \"rows\": 251,\n  \"fields\": [\n    {\n      \"column\": \"date\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"2018-01-02 00:00:00+00:00\",\n        \"max\": \"2018-12-31 00:00:00+00:00\",\n        \"num_unique_values\": 251,\n        \"samples\": [\n          \"2018-08-14 00:00:00+00:00\",\n          \"2018-01-10 00:00:00+00:00\",\n          \"2018-08-27 00:00:00+00:00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"open\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 19.69649335473974,\n        \"min\": 123.1,\n        \"max\": 215.72,\n        \"num_unique_values\": 249,\n        \"samples\": [\n          208.77,\n          186.94,\n          184.93\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"close\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 19.97745158627776,\n        \"min\": 124.06,\n        \"max\": 217.5,\n        \"num_unique_values\": 249,\n        \"samples\": [\n          209.94,\n          187.84,\n          186.9\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"low\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 20.07440767348364,\n        \"min\": 123.02,\n        \"max\": 214.27,\n        \"num_unique_values\": 247,\n        \"samples\": [\n          176.4,\n          185.63,\n          160.88\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"high\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 19.424564323437952,\n        \"min\": 129.74,\n        \"max\": 218.62,\n        \"num_unique_values\": 246,\n        \"samples\": [\n          177.95,\n          187.89,\n          171.77\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"vol\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 19101434,\n        \"min\": 8855144,\n        \"max\": 169803668,\n        \"num_unique_values\": 251,\n        \"samples\": [\n          19101995,\n          10464528,\n          17921935\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"profit\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.8733780844045307,\n        \"min\": -9.429999999999978,\n        \"max\": 8.180000000000007,\n        \"num_unique_values\": 233,\n        \"samples\": [\n          3.530000000000001,\n          2.3500000000000227,\n          -2.140000000000015\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"gain\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"negative\",\n          \"positive_gain\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"size\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"large\",\n          \"small\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Create a 2x2 contingency table\n",
        "contingency_table_fisher = pd.crosstab(df_fisher['gain'], df['size'])\n",
        "print(\"Contingency Table:\")\n",
        "print(contingency_table_fisher)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lX1EYYoQgBiO",
        "outputId": "e6deccb6-9033-4fac-8a3b-75eb23b3e283"
      },
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Contingency Table:\n",
            "size           large  small\n",
            "gain                       \n",
            "negative          39     83\n",
            "positive_gain     33     96\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from scipy.stats import fisher_exact\n",
        "\n",
        "oddsratio, p_value = fisher_exact(contingency_table_fisher)\n",
        "print(\"\\nFisher's Exact Test Results:\")\n",
        "print(f\"Odds Ratio: {oddsratio}\")\n",
        "print(f\"P-Value: {p_value}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GUi1W6IvgOGl",
        "outputId": "65e8b716-3239-4255-e947-450329d38d40"
      },
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Fisher's Exact Test Results:\n",
            "Odds Ratio: 1.366922234392114\n",
            "P-Value: 0.26848533307073885\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now p-value is greater than 0.05 and so there is no statistically significant association between gain and size. This reinforces previous findings from the Student’s t-test and Kolmogorov-Smirnov test, which also suggested that trading volume does not correlate strongly with daily gain outcomes."
      ],
      "metadata": {
        "id": "nO2SCOcOhDPx"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6u3HW_pThmFd"
      },
      "source": [
        "### Error bars\n",
        "\n",
        "We can compute the standard error of the mean using the <tt>stats.sem </tt> method of scipy, which can also be called from the data frame"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 51,
      "metadata": {
        "id": "q3Gzw2MvhmFd",
        "outputId": "0ca19a88-ea21-41b2-9c29-c21d577f273f",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "3192415.794566366\n",
            "1500801.0916460739\n",
            "3272021.3263068898\n",
            "3115899.1280031265\n"
          ]
        }
      ],
      "source": [
        "print(sm.sem())\n",
        "print(neg.sem())\n",
        "print(stats.sem(med))\n",
        "print(stats.sem(lg))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RzMG7j57hmFd"
      },
      "source": [
        "Computing confidence intervals"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 52,
      "metadata": {
        "id": "WHGsBYiGhmFd",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "799d8152-cbf0-4504-fee7-602d6766874c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "18475802.95317895 , 31050718.510235682\n"
          ]
        }
      ],
      "source": [
        "#confidence interval\n",
        "conf = 0.95\n",
        "t = stats.t.ppf((1+conf)/2.0, len(df)-1)\n",
        "low = sm.mean()-sm.sem()*t\n",
        "high = sm.mean()+sm.sem()*t\n",
        "print(low,  \",\", high)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "G-g6aSPGhmFe"
      },
      "source": [
        "We can also visualize the mean and the standard error in a bar-plot, using the barplot function of seaborn. Note that we need to apply this to the original data. The averaging is done automatically."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 53,
      "metadata": {
        "id": "MmhSPBHUhmFe",
        "outputId": "6e8baaea-3d8f-4b14-9e36-6a7644f53fd6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 482
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='gain', ylabel='vol'>"
            ]
          },
          "metadata": {},
          "execution_count": 53
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "#sns.barplot(x='gain',y='vol', data = df, ci=95) #for older seaborn versions\n",
        "sns.barplot(x='gain',y='vol', data = df, errorbar=('ci', 95))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 54,
      "metadata": {
        "id": "PwS5ocmohmFe",
        "outputId": "3fe5b8ef-a241-4ac8-d0bd-f37509e02978",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 569
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/tmp/ipython-input-2493737387.py:1: UserWarning: \n",
            "\n",
            "The `join` parameter is deprecated and will be removed in v0.15.0. You can remove the line between points with `linestyle='none'`.\n",
            "\n",
            "  sns.pointplot(x='gain',y='vol', data = df,join = False, errorbar=('ci', 95), capsize = 0.1)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='gain', ylabel='vol'>"
            ]
          },
          "metadata": {},
          "execution_count": 54
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "sns.pointplot(x='gain',y='vol', data = df,join = False, errorbar=('ci', 95), capsize = 0.1)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YkCF-oZ2hmFe"
      },
      "source": [
        "### Visualizing distributions\n",
        "\n",
        "We can also visualize the distribution using a **box-plot**. In the box plot, the box shows the quartiles of the dataset (the part between the higher 25% and lower 25%), while the whiskers extend to show the rest of the distribution, except for points that are determined to be “outliers”. The line shows the median."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 55,
      "metadata": {
        "id": "lZINW_jMhmFf",
        "outputId": "c97bb91b-c75b-4ca9-b5eb-556632e92267",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 482
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='gain', ylabel='vol'>"
            ]
          },
          "metadata": {},
          "execution_count": 55
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "sns.boxplot(x='gain',y='vol', data = df)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 56,
      "metadata": {
        "id": "M5VbLGCPhmFf",
        "outputId": "56847b8c-378e-4b8f-b62e-e0d296a8a94f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 482
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='gain', ylabel='vol'>"
            ]
          },
          "metadata": {},
          "execution_count": 56
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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5qFx88cX6+eef1a9fP2VnZ2vAgAFyOp01URsAALaJiooKWAtEcnJyULZ2BEKVg8rEiRM1ZMgQ1atXrwbKAQAA+KcqB5W77767JuoAAAA4TZVPTwYAAAgUggoAADAWQQUAABiLoAIAAIxFUAEAAMYiqAAAAGNd0CX0AcA0Bw4ckNfrtbuM85aXl+f3N5i5XC5urYILRlABUGscOHBAt98xTGXHSu0u5YJlZWXZXcIFi4h06s25bxBWcEEIKgBqDa/Xq7JjpTravKcqolx2lxPSwkq80p6V8nq9BBVcEIIKgFqnIsqlipiL7C4DQDWgMy0AADCWrUFlxowZat++veLj4xUfH69u3bpp8eLFdpYEAAAMYmtQadasmaZMmaLc3Fxt2LBBvXv31sCBA/XNN9/YWRYAADCErX1UBgwY4DeclZWlGTNm6Msvv9Tll19uU1UAAMAUxnSmLS8v17vvvqvi4mJ169btjPOUlpaqtPSfpx0WFBQEqjwAAGAD2zvTbt26VbGxsXI6nfrTn/6khQsXqm3btmecd/LkyXK5XL5HUlJSgKsFAACBZHtQad26tTZt2qR169Zp1KhRysjI0LfffnvGecePHy+v1+t75OfnB7haAAAQSLYf+omMjFTLli0lSZ06dVJOTo6ef/55vfLKK6fN63Q65XQ6A10iAACwie0tKqeqqKjw64cCAABCl60tKuPHj1daWprcbrcKCws1f/58rVixQkuWLLGzLAAAYAhbg8rBgwc1bNgw7d+/Xy6XS+3bt9eSJUt0/fXX21kWAAAwhK1BZebMmXauHgAAGM64PioAAAAnEFQAAICxCCoAAMBYBBUAAGAsggoAADAWQQUAABiLoAIAAIxFUAEAAMYiqAAAAGMRVAAAgLEIKgAAwFgEFQAAYCyCCgAAMBZBBQAAGIugAgAAjFXH7gKAmnbgwAF5vV67yzhveXl5fn+DmcvlUkJCgt1lAAgiBBXUagcOHNDtdwxT2bFSu0u5YFlZWXaXcMEiIp16c+4bhBUAlUZQQa3m9XpVdqxUR5v3VEWUy+5yQlpYiVfas1Jer5egAqDSCCoICRVRLlXEXGR3GQCAKqIzLQAAMBZBBQAAGIugAgAAjEVQAQAAxiKoAAAAYxFUAACAsQgqAADAWAQVAABgLIIKAAAwFkEFAAAYi6ACAACMRVABAADGIqgAAABjEVQAAICxCCoAAMBYBBUAAGAsggoAADAWQQUAABiLoAIAAIxVx+4CAAA4XwcOHJDX67W7jPOWl5fn9zeYuVwuJSQkVPtyCSoAgKB04MAB3X7HMJUdK7W7lAuWlZVldwkXLCLSqTfnvlHtYYWggpAQdvSI3SWEPLYBqpvX61XZsVIdbd5TFVEuu8sJaWElXmnPSnm9XoIKcD6i966yuwQANaQiyqWKmIvsLgM1hKCCkHD00t+rIrqe3WWEtLCjRwiMAKqMoIKQUBFdj/9xAUAQIqgAqHXoD2M/tgGqC0EFQK3DISag9iCoAKh16JNkP/okoboQVADUOvRJAmoPLqEPAACMRVABAADGCtlDP/RItx/bAABwLiEbVOjkBQCA+UI2qHBWgP04KwAAcC4hG1Q4KwAAAPPRmRYAABjL1qAyefJkdenSRXFxcWrcuLEGDRqk7du321kSAAAwiK1BZeXKlRo9erS+/PJLLV26VGVlZbrhhhtUXFxsZ1kAAMAQtvZR+eSTT/yGZ8+ercaNGys3N1e///3vbaoKABBMuNSB/WpyGxjVmdbr9UqSGjRoYHMlAIBgwdmDtZsxQaWiokJjx45V9+7ddcUVV5xxntLSUpWWlvqGCwoKAlUeAMBQXG7CfjV5uQljgsro0aP19ddfa82aNWedZ/LkyXr88ccDWBUAwHRcbqJ2M+L05DFjxuijjz7S8uXL1axZs7PON378eHm9Xt8jPz8/gFUCAIBAs7VFxbIs3XPPPVq4cKFWrFihSy+99DfndzqdcjqdAaoOAADYzdagMnr0aM2fP18ffvih4uLi9OOPP0qSXC6XoqOj7SwNAAAYwNZDPzNmzJDX61WvXr2UmJjoe7z99tt2lgUAAAxh+6EfAACAszHmrJ9ACyvx2l1CyGMbAADOJeSCisvlUkSkU9qz0u5SICki0imXy2V3GQAAQ4VcUElISNCbc9/wXQU3GOXl5SkrK0sTJkxQcnKy3eVcEJfLpYSEBLvLAAAYKuSCivRrWKkNP47JyclKSUmxuwwAAGqMERd8AwAAOBOCCgAAMFZIHvoBULtxRpn92AaoLgQVhAS+NO0XiG3AWX1m4aw+VAeCCmo1frjMUtM/XJzVZxbO6kN1IKigVuOHyyyB+OHirD6gdiGooNbjhwsAghdn/QAAAGMRVAAAgLEIKgAAwFgEFQAAYCyCCgAAMBZBBQAAGIugAgAAjEVQAQAAxiKoAAAAYxFUAACAsQgqAADAWNzrBwAQ1MJKgvemo7VFTW4DggoAICi5XC5FRDqlPSvtLgWSIiKdcrlc1b5cggoAICglJCTozblvyOsN3haVvLw8ZWVlacKECUpOTra7nAvicrlq5E71BBUAQNBKSEiokR/HQEtOTlZKSordZRiJzrQAAMBYBBUAAGAsggoAADAWQQUAABiLoAIAAIxFUAEAAMYiqAAAAGNxHZVqVlJSIo/HU6PryMvL8/tbk9xut6Kiomp8PQAAnAlBpZp5PB6NHDkyIOvKysqq8XVkZ2dzESIAgG0IKtXM7XYrOzvb7jKqjdvttrsEAEAII6hUs6ioKFogAACoJnSmBQAAxiKoAAAAYxFUAACAsQgqAADAWHSmBS4A180BgJpFUAEuANfNAYCaRVABLgDXzQGAmkVQAS4A180BgJpFZ1oAAGAsggoAADAWQQUAABiLoAIAAIxFUAEAAMYiqAAAAGMRVAAAgLEIKgAAwFgEFQAAYCyCCgAAMBZBBQAAGMvWe/2sWrVKzz77rHJzc7V//34tXLhQgwYNsrMkAAAkSSUlJfJ4PDW6jry8PL+/NcntdisqKqrG11PdbA0qxcXFuvLKK/WHP/xBt9xyi52lAADgx+PxaOTIkQFZV1ZWVo2vIzs7OyhvomprUElLS1NaWpqdJQAAcEZut1vZ2dl2l1Ft3G633SWcF1uDSlWVlpaqtLTUN1xQUGBjNQCA2iwqKiooWyBqm6DqTDt58mS5XC7fIykpye6SAABADQqqoDJ+/Hh5vV7fIz8/3+6SAABADQqqQz9Op1NOp9PuMgAAQIAEVYsKAAAILba2qBQVFWnXrl2+4b1792rTpk1q0KBB0PZOBgAA1cfWoLJhwwZdd911vuH7779fkpSRkaHZs2fbVBUAADCFrUGlV69esizLzhIAAIDBgqozLQDYiUuqA4FHUAGASuKS6kDgEVQAoJK4pDoQeAQVAKgkLqkOBB7XUQEAAMYiqAAAAGMRVAAAgLEIKgAAwFgEFQAAYCyCCgAAMBZBBQAAGIugAgAAjEVQAQAAxiKoAAAAYxFUAACAsQgqAADAWAQVAABgrKC+e7JlWZKkgoICmysBAACVdeJ3+8Tv+G8J6qBSWFgoSUpKSrK5EgAAUFWFhYVyuVy/OY/DqkycMVRFRYV++OEHxcXFyeFw2F1OwBQUFCgpKUn5+fmKj4+3uxzUMLZ3aGF7h5ZQ3d6WZamwsFBNmzZVWNhv90IJ6haVsLAwNWvWzO4ybBMfHx9SO3aoY3uHFrZ3aAnF7X2ulpQT6EwLAACMRVABAADGIqgEIafTqccee0xOp9PuUhAAbO/QwvYOLWzvcwvqzrQAAKB2o0UFAAAYi6ACAACMRVABgCA1ceJE/e53v7O7DKBGEVSqoFevXho7dqzdZQTEJZdcov/6r/+yu4ygdeq+UlvezxUrVsjhcOjIkSN2lxJyHA6HPvjgA79x48aN02effWZPQaiSU0PlnXfeqUGDBgVs/cH82Q3qC76h5uTk5CgmJsbuMmqN2vJ+XnPNNdq/f3+lL9SEmhUbG6vY2Fi7y0AQCObPLi0qAWJZlo4fP253GZXWqFEj1a1b1+4yao3a8n5GRkaqSZMmIXXLil69eikzM1MPPvigGjRooCZNmmjixIm+6UeOHNGIESPUqFEjxcfHq3fv3tq8ebPfMp588kk1btxYcXFxGjFihP7zP//T73/XOTk5uv7663XRRRfJ5XKpZ8+e2rhxo2/6JZdcIkkaPHiwHA6Hb/jk/6V/+umnioqKOu1/zPfee6969+7tG16zZo169Oih6OhoJSUlKTMzU8XFxRf8PsFswfzZJaicp7lz56pz586Ki4tTkyZNNHToUB08eNA3/UQz2+LFi9WpUyc5nU6tWbNGhYWFSk9PV0xMjBITE/Xcc8+ddpigtLRU48aN08UXX6yYmBhdddVVWrFiRaVre/XVV5WUlKS6detq8ODBmj59uurVq+ebvnv3bg0cOFAJCQmKjY1Vly5dtGzZMr9lnHqowuFw6LXXXtPgwYNVt25dtWrVSosWLarq22a7Xr166Z577tHYsWNVv359JSQk6NVXX1VxcbHuuusuxcXFqWXLllq8eLHvOV9//bXS0tIUGxurhIQE3XHHHfq///s/3/Ti4mINGzZMsbGxSkxM1LRp005b78nv5759++RwOLRp0ybf9CNHjsjhcPi284n9Z8mSJerQoYOio6PVu3dvHTx4UIsXL1abNm0UHx+voUOH6pdffqnUa6/MvlfZ/frEj+Hs2bNVr149LVmyRG3atFFsbKz69eun/fv3V6qmYDFnzhzFxMRo3bp1euaZZ/TEE09o6dKlkqQhQ4b4tktubq46duyoPn366Oeff5YkzZs3T1lZWXr66aeVm5srt9utGTNm+C2/sLBQGRkZWrNmjb788ku1atVKN954o+/Gqzk5OZKkWbNmaf/+/b7hk/Xp00f16tXTe++95xtXXl6ut99+W+np6ZJ+/ez369dP//Iv/6ItW7bo7bff1po1azRmzJjqf9MM99///d9q166doqOj1bBhQ/Xt21fFxcW+QzJPPfWUEhISVK9ePT3xxBM6fvy4HnjgATVo0EDNmjXTrFmz/Jb30EMPKSUlRXXr1lXz5s316KOPqqysrFpqDfnProVK69mzp3XvvfdalmVZM2fOtD7++GNr9+7d1hdffGF169bNSktL8827fPlyS5LVvn1769NPP7V27dplHTp0yBoxYoSVnJxsLVu2zNq6das1ePBgKy4uzrdcy7KsESNGWNdcc421atUqa9euXdazzz5rOZ1Oa8eOHeescc2aNVZYWJj17LPPWtu3b7deeuklq0GDBpbL5fLNs2nTJuvll1+2tm7dau3YscN65JFHrKioKCsvL883T3JysvXcc8/5hiVZzZo1s+bPn2/t3LnTyszMtGJjY61Dhw6d9/tph549e1pxcXHWpEmTrB07dliTJk2ywsPDrbS0NCs7O9vasWOHNWrUKKthw4ZWcXGxdfjwYatRo0bW+PHjre+++87auHGjdf3111vXXXedb5mjRo2y3G63tWzZMmvLli1W//79T9umJ7+fe/futSRZX331lW/64cOHLUnW8uXLLcv65/5z9dVXW2vWrLE2btxotWzZ0urZs6d1ww03WBs3brRWrVplNWzY0JoyZUqlXntl9r3K7teHDx+2LMuyZs2aZUVERFh9+/a1cnJyrNzcXKtNmzbW0KFDq7RdTNazZ0/r2muv9RvXpUsX66GHHrJWr15txcfHWyUlJX7TW7RoYb3yyiuWZVnWVVddZY0ePdpvevfu3a0rr7zyrOssLy+34uLirP/5n//xjZNkLVy40G++xx57zG859957r9W7d2/f8JIlSyyn0+nbXsOHD7dGjhzpt4zVq1dbYWFh1tGjR89aT23zww8/WHXq1LGmT59u7d2719qyZYv10ksvWYWFhVZGRoYVFxdnjR492tq2bZs1c+ZMS5KVmppqZWVl+b43IiIirPz8fN8yJ02aZH3++efW3r17rUWLFlkJCQnW008/7Zt+6rbKyMiwBg4cWKl6Q/2zS1CpgpODyqlycnIsSVZhYaFlWf/cKT744APfPAUFBVZERIT17rvv+sYdOXLEqlu3rm+5eXl5Vnh4uPX999/7Lb9Pnz7W+PHjz1njv/3bv1k33XST37j09HS/oHIml19+ufXiiy/6hs8UVB555BHfcFFRkSXJWrx48TlrMsmpPzrHjx+3YmJirDvuuMM3bv/+/ZYk64svvrAmTZpk3XDDDX7LyM/PtyRZ27dvtwoLC63IyEjrnXfe8U0/dOiQFR0dXS1BZdmyZb55Jk+ebEmydu/e7Rv3xz/+0UpNTT3n667MvncmZ9uvT/6yk2Tt2rXL95yXXnrJSkhIOGdNwaJnz57Wf/zHf/iNu/nmm6277rrL+utf/2qFhYVZMTExfo+wsDDrwQcftCzLsurVq2fNmTPH7/n33Xef34/Wjz/+aI0YMcJq2bKlFR8fb8XExFgOh8N66aWXfPNUJqisX7/eCgsL831/DBs2zLrlllt80zt37mxFRkb61Vq3bl1LkvXtt99eyNsUVHJzcy1J1r59+06blpGRYSUnJ1vl5eW+ca1bt7Z69OjhGz7xvfHWW2+ddR3PPvus1alTJ9/w+QYVPruWRWfa85Sbm6uJEydq8+bNOnz4sCoqKiRJHo9Hbdu29c3XuXNn37/37NmjsrIyde3a1TfO5XKpdevWvuGtW7eqvLxcKSkpfusrLS1Vw4YNz1nX9u3bNXjwYL9xXbt21UcffeQbLioq0sSJE/X3v/9d+/fv1/Hjx3X06FF5PJ7fXHb79u19/46JiVF8fLxf02KwOPl1hIeHq2HDhmrXrp1vXEJCgiTp4MGD2rx5s5YvX37GDou7d+/W0aNHdezYMV111VW+8Q0aNPDbptVVa0JCgq9Z+eRx69evP+dyKrPvSZXfr09Wt25dtWjRwjecmJgYlPvFb4mIiPAbdjgcqqioUFFRkRITE894aPbkw63nkpGRoUOHDun5559XcnKynE6nunXrpmPHjlWpzi5duqhFixZasGCBRo0apYULF2r27Nm+6UVFRfrjH/+ozMzM057rdrurtK5gduWVV6pPnz5q166dUlNTdcMNN+jWW29V/fr1JUmXX365wsL+2TMiISFBV1xxhW/4xPfGyfv522+/rRdeeEG7d+9WUVGRjh8/Xi13Q+azy1k/56W4uFipqalKTU3VvHnz1KhRI3k8HqWmpp72xVLVMz2KiooUHh6u3NxchYeH+02rrt7948aN09KlSzV16lS1bNlS0dHRuvXWW8/5pXi2L+tgc6bXcfK4E53NTvwQDRgwQE8//fRpy0lMTNSuXbuqvP4TX4DWSXevONux7FPrqsltUJX9+mw1nqjJCpE7c3Ts2FE//vij6tSp4+vgeqrWrVsrJydHw4YN8407tY/J559/rr/97W+68cYbJUn5+fl+/aCkX9/n8vLyc9aUnp6uefPmqVmzZgoLC9NNN93kV++3336rli1bVvYl1krh4eFaunSp1q5dq08//VQvvviiJkyYoHXr1kk693fEiXEnPntffPGF0tPT9fjjjys1NVUul0sLFiw4Y3+1mlDbP7t0pj0P27Zt06FDhzRlyhT16NFDl112WaVSaPPmzRUREeH3JeX1erVjxw7fcIcOHVReXq6DBw+qZcuWfo8mTZqccx0nvhRPdqYvxTvvvFODBw9Wu3bt1KRJE+3bt++cyw5FHTt21DfffKNLLrnktO0RExOjFi1aKCIiwvcFJ0mHDx/226anatSokST5dVo7uWNtTajMvne++3Uo69u3r7p166ZBgwbp008/1b59+7R27VpNmDBBGzZskCTdc889mjlzpubMmaOdO3fqySef1JYtW/zOvmjVqpXmzp2r7777TuvWrVN6erqio6P91nXJJZfos88+048//qjDhw+ftab09HRt3LhRWVlZuvXWW/1udvfQQw9p7dq1GjNmjDZt2qSdO3fqww8/DMnOtA6HQ927d9fjjz+ur776SpGRkVq4cOF5LWvt2rVKTk7WhAkT1LlzZ7Vq1Up5eXnVUiefXYLKeXG73YqMjNSLL76oPXv2aNGiRZo0adI5nxcXF6eMjAw98MADWr58ub755hsNHz5cYWFhvi+tlJQUpaena9iwYXr//fe1d+9erV+/XpMnT9bf//73c67jnnvu0ccff6zp06dr586deuWVV7R48eLTvhTff/99bdq0SZs3b9bQoUODsmUkEEaPHq2ff/5Zt912m3JycrR7924tWbJEd911l8rLyxUbG6vhw4frgQce0P/+7//q66+/1p133unXbHyq6OhoXX311ZoyZYq+++47rVy5Uo888kiNvo7K7Hvnu1+HMofDoY8//li///3vdddddyklJUX//u//rry8PN8hxPT0dI0fP17jxo1Tx44dtXfvXt15552KioryLWfmzJk6fPiwOnbsqDvuuEOZmZlq3Lix37qmTZumpUuXKikpSR06dDhrTS1btlTXrl21ZcsW39k+J7Rv314rV67Ujh071KNHD3Xo0EF/+ctf1LRp02p8V8y3bt06PfXUU9qwYYM8Ho/ef/99/fTTT2rTps15La9Vq1byeDxasGCBdu/erRdeeOG8Q8+p+OwSVM5Lo0aNNHv2bL377rtq27atpkyZoqlTp1bqudOnT1e3bt3Uv39/9e3bV927d1ebNm38vrRmzZqlYcOG6c9//rNat26tQYMGKScnp1LHkLt3766XX35Z06dP15VXXqlPPvlE9913n9/yp0+frvr16+uaa67RgAEDlJqaqo4dO1b9jQgBTZs21eeff67y8nLdcMMNateuncaOHat69er5wsizzz6rHj16aMCAAerbt6+uvfZaderU6TeX+/rrr+v48ePq1KmTxo4dqyeffLLGX8u59r0L2a9rsxUrVpx2VeEPPvjA1/cjLi5OL7zwgr7//nsdO3ZMHo9Hb775ppKSknzzP/roo/rpp59UWFiomTNnnnb4pUOHDsrJydHRo0e1Y8cO3Xrrrdq3b5/f6acDBgzQzp07VVZW5msBnThx4hlb49atWyfLsnTdddedNq1Lly769NNPVVhYqKKiIm3evFkPP/zweb8/wSg+Pl6rVq3SjTfeqJSUFD3yyCOaNm2a0tLSzmt5N998s+677z6NGTNGv/vd77R27Vo9+uij1VZvqH92HZaJB6RCSHFxsS6++GJNmzZNw4cPr5F13H333dq2bZtWr15dI8tHcArEvgfpl19+0csvv6zU1FSFh4frrbfe8l2HpW/fvnaXhyAUap9dOtMG2FdffaVt27apa9eu8nq9euKJJyRJAwcOrLZ1TJ06Vddff71iYmK0ePFizZkzR3/729+qbfkIToHY93C6E4eHsrKyVFJSotatW+u9994jpKDSQv2zS1CxwdSpU7V9+3ZFRkaqU6dOWr16tS666KJKPTctLe2sLSMPP/ywHn74Ya1fv17PPPOMCgsL1bx5c73wwgsaMWJEdb4EGOa3TkGUpG+//VbShe17OD/R0dGnXfkZOIHP7rlx6CfIfP/99zp69OgZpzVo0EANGjQIcEUwwfHjx3/zzK1LLrlEderw/xLANHx2z42gAgAAjMVZPwAAwFgEFQAAYCyCCgAAMBZBBUBQWLFihRwOh44cOWJ3KQACiM60AILCsWPH9PPPPyshIcHvlhAAajeCCgAAMBaHfgAERGFhodLT0xUTE6PExEQ999xz6tWrl+9+NnPnzlXnzp0VFxenJk2aaOjQoX53gD310M/s2bNVr149LVmyRG3atFFsbKz69evnd1dqAMGPoAIgIO6//359/vnnWrRokZYuXarVq1dr48aNvullZWWaNGmSNm/erA8++ED79u3TnXfe+ZvL/OWXXzR16lTNnTtXq1atksfj0bhx42r4lQAIpNC+3B2AgCgsLNScOXM0f/589enTR9Kvdwlv2rSpb54//OEPvn+fuPVDly5dVFRUpNjY2DMut6ysTC+//LJatGghSRozZozvPigAagdaVADUuD179qisrExdu3b1jXO5XGrdurVvODc3VwMGDJDb7VZcXJx69uwp6dd7oZxN3bp1fSFFkhITE/0OFwEIfgQVALYrLi5Wamqq4uPjNW/ePOXk5GjhwoWSfj3b52wiIiL8hh0Ohzg/AKhdCCoAalzz5s0VERGhnJwc3ziv16sdO3ZIkrZt26ZDhw5pypQp6tGjhy677DJaRgBIoo8KgACIi4tTRkaGHnjgATVo0ECNGzfWY489prCwMDkcDrndbkVGRurFF1/Un/70J3399deaNGmS3WUDMAAtKgACYvr06erWrZv69++vvn37qnv37mrTpo2ioqLUqFEjzZ49W++++67atm2rKVOmaOrUqXaXDMAAXPANgC2Ki4t18cUXa9q0aRo+fLjd5QAwFId+AATEV199pW3btqlr167yer2+04gHDhxoc2UATEZQARAwU6dO1fbt2xUZGalOnTpp9erVuuiii+wuC4DBOPQDAACMRWdaAABgLIIKAAAwFkEFAAAYi6ACAACMRVABAADGIqgAAABjEVQAAICxCCoAAMBYBBUAAGCs/wf4dPnu87XFdwAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "#Removing outliers\n",
        "sns.boxplot(x='gain',y='vol', data = df, showfliers = False)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5Qv_aRPKhmFf"
      },
      "source": [
        "We can also use a [**violin plot**](https://seaborn.pydata.org/generated/seaborn.violinplot.html) to visualize the distributions"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 57,
      "metadata": {
        "id": "_38k3eDhhmFf",
        "outputId": "115c9283-8c6f-4008-9525-f3e6c9a61fd9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 482
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='gain', ylabel='vol'>"
            ]
          },
          "metadata": {},
          "execution_count": 57
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "sns.violinplot(x='gain',y='vol', data = df)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 58,
      "metadata": {
        "id": "7kDaDggxhmFf",
        "outputId": "0efb7f58-21f9-491c-be2a-1db865a52857",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 452
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='all', ylabel='profit'>"
            ]
          },
          "metadata": {},
          "execution_count": 58
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "df['all'] = ''\n",
        "sns.violinplot(x = 'all', y='profit',hue='size', split=True, data = df)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 59,
      "metadata": {
        "id": "VK-fX7dkhmFg",
        "outputId": "01de07c5-8d1f-4147-92bc-0fbde505c34c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 467
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='gain', ylabel='profit'>"
            ]
          },
          "metadata": {},
          "execution_count": 59
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "sns.violinplot(x='gain',y='profit', hue='size', data = df, split=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0S4jhxLShmFg"
      },
      "source": [
        "### Seaborn lineplot\n",
        "\n",
        "Plot the average volume over the different months"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        },
        "id": "rTuNN_6MmPuo",
        "outputId": "97fbacdd-209b-4837-ec8a-20be836ffb78"
      },
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                             open   close     low    high       vol  profit  \\\n",
              "date                                                                          \n",
              "2018-01-02 00:00:00+00:00  177.68  181.42  177.55  181.58  17694891    3.74   \n",
              "2018-01-03 00:00:00+00:00  181.88  184.67  181.33  184.78  16595495    2.79   \n",
              "2018-01-04 00:00:00+00:00  184.90  184.33  184.10  186.21  13554357   -0.57   \n",
              "2018-01-05 00:00:00+00:00  185.59  186.85  184.93  186.90  13042388    1.26   \n",
              "2018-01-08 00:00:00+00:00  187.20  188.28  186.33  188.90  14719216    1.08   \n",
              "...                           ...     ...     ...     ...       ...     ...   \n",
              "2018-12-24 00:00:00+00:00  123.10  124.06  123.02  129.74  22066002    0.96   \n",
              "2018-12-26 00:00:00+00:00  126.00  134.18  125.89  134.24  39723370    8.18   \n",
              "2018-12-27 00:00:00+00:00  132.44  134.52  129.67  134.99  31202509    2.08   \n",
              "2018-12-28 00:00:00+00:00  135.34  133.20  132.20  135.92  22627569   -2.14   \n",
              "2018-12-31 00:00:00+00:00  134.45  131.09  129.95  134.64  24625308   -3.36   \n",
              "\n",
              "                                  gain   size all  \n",
              "date                                               \n",
              "2018-01-02 00:00:00+00:00   large_gain  small      \n",
              "2018-01-03 00:00:00+00:00  medium_gain  small      \n",
              "2018-01-04 00:00:00+00:00     negative  small      \n",
              "2018-01-05 00:00:00+00:00  medium_gain  small      \n",
              "2018-01-08 00:00:00+00:00  medium_gain  small      \n",
              "...                                ...    ...  ..  \n",
              "2018-12-24 00:00:00+00:00   small_gain  small      \n",
              "2018-12-26 00:00:00+00:00   large_gain  large      \n",
              "2018-12-27 00:00:00+00:00  medium_gain  large      \n",
              "2018-12-28 00:00:00+00:00     negative  small      \n",
              "2018-12-31 00:00:00+00:00     negative  small      \n",
              "\n",
              "[251 rows x 9 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-d6491767-3f39-4e61-879d-d6fa7c6ce4fb\" 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>open</th>\n",
              "      <th>close</th>\n",
              "      <th>low</th>\n",
              "      <th>high</th>\n",
              "      <th>vol</th>\n",
              "      <th>profit</th>\n",
              "      <th>gain</th>\n",
              "      <th>size</th>\n",
              "      <th>all</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>date</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>2018-01-02 00:00:00+00:00</th>\n",
              "      <td>177.68</td>\n",
              "      <td>181.42</td>\n",
              "      <td>177.55</td>\n",
              "      <td>181.58</td>\n",
              "      <td>17694891</td>\n",
              "      <td>3.74</td>\n",
              "      <td>large_gain</td>\n",
              "      <td>small</td>\n",
              "      <td></td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-01-03 00:00:00+00:00</th>\n",
              "      <td>181.88</td>\n",
              "      <td>184.67</td>\n",
              "      <td>181.33</td>\n",
              "      <td>184.78</td>\n",
              "      <td>16595495</td>\n",
              "      <td>2.79</td>\n",
              "      <td>medium_gain</td>\n",
              "      <td>small</td>\n",
              "      <td></td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-01-04 00:00:00+00:00</th>\n",
              "      <td>184.90</td>\n",
              "      <td>184.33</td>\n",
              "      <td>184.10</td>\n",
              "      <td>186.21</td>\n",
              "      <td>13554357</td>\n",
              "      <td>-0.57</td>\n",
              "      <td>negative</td>\n",
              "      <td>small</td>\n",
              "      <td></td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-01-05 00:00:00+00:00</th>\n",
              "      <td>185.59</td>\n",
              "      <td>186.85</td>\n",
              "      <td>184.93</td>\n",
              "      <td>186.90</td>\n",
              "      <td>13042388</td>\n",
              "      <td>1.26</td>\n",
              "      <td>medium_gain</td>\n",
              "      <td>small</td>\n",
              "      <td></td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-01-08 00:00:00+00:00</th>\n",
              "      <td>187.20</td>\n",
              "      <td>188.28</td>\n",
              "      <td>186.33</td>\n",
              "      <td>188.90</td>\n",
              "      <td>14719216</td>\n",
              "      <td>1.08</td>\n",
              "      <td>medium_gain</td>\n",
              "      <td>small</td>\n",
              "      <td></td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-12-24 00:00:00+00:00</th>\n",
              "      <td>123.10</td>\n",
              "      <td>124.06</td>\n",
              "      <td>123.02</td>\n",
              "      <td>129.74</td>\n",
              "      <td>22066002</td>\n",
              "      <td>0.96</td>\n",
              "      <td>small_gain</td>\n",
              "      <td>small</td>\n",
              "      <td></td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-12-26 00:00:00+00:00</th>\n",
              "      <td>126.00</td>\n",
              "      <td>134.18</td>\n",
              "      <td>125.89</td>\n",
              "      <td>134.24</td>\n",
              "      <td>39723370</td>\n",
              "      <td>8.18</td>\n",
              "      <td>large_gain</td>\n",
              "      <td>large</td>\n",
              "      <td></td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-12-27 00:00:00+00:00</th>\n",
              "      <td>132.44</td>\n",
              "      <td>134.52</td>\n",
              "      <td>129.67</td>\n",
              "      <td>134.99</td>\n",
              "      <td>31202509</td>\n",
              "      <td>2.08</td>\n",
              "      <td>medium_gain</td>\n",
              "      <td>large</td>\n",
              "      <td></td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-12-28 00:00:00+00:00</th>\n",
              "      <td>135.34</td>\n",
              "      <td>133.20</td>\n",
              "      <td>132.20</td>\n",
              "      <td>135.92</td>\n",
              "      <td>22627569</td>\n",
              "      <td>-2.14</td>\n",
              "      <td>negative</td>\n",
              "      <td>small</td>\n",
              "      <td></td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-12-31 00:00:00+00:00</th>\n",
              "      <td>134.45</td>\n",
              "      <td>131.09</td>\n",
              "      <td>129.95</td>\n",
              "      <td>134.64</td>\n",
              "      <td>24625308</td>\n",
              "      <td>-3.36</td>\n",
              "      <td>negative</td>\n",
              "      <td>small</td>\n",
              "      <td></td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>251 rows × 9 columns</p>\n",
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              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df')\"\n",
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              "\n",
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              "\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 251,\n  \"fields\": [\n    {\n      \"column\": \"date\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"2018-01-02 00:00:00+00:00\",\n        \"max\": \"2018-12-31 00:00:00+00:00\",\n        \"num_unique_values\": 251,\n        \"samples\": [\n          \"2018-08-14 00:00:00+00:00\",\n          \"2018-01-10 00:00:00+00:00\",\n          \"2018-08-27 00:00:00+00:00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"open\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 19.69649335473974,\n        \"min\": 123.1,\n        \"max\": 215.72,\n        \"num_unique_values\": 249,\n        \"samples\": [\n          208.77,\n          186.94,\n          184.93\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"close\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 19.97745158627776,\n        \"min\": 124.06,\n        \"max\": 217.5,\n        \"num_unique_values\": 249,\n        \"samples\": [\n          209.94,\n          187.84,\n          186.9\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"low\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 20.07440767348364,\n        \"min\": 123.02,\n        \"max\": 214.27,\n        \"num_unique_values\": 247,\n        \"samples\": [\n          176.4,\n          185.63,\n          160.88\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"high\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 19.424564323437952,\n        \"min\": 129.74,\n        \"max\": 218.62,\n        \"num_unique_values\": 246,\n        \"samples\": [\n          177.95,\n          187.89,\n          171.77\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"vol\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 19101434,\n        \"min\": 8855144,\n        \"max\": 169803668,\n        \"num_unique_values\": 251,\n        \"samples\": [\n          19101995,\n          10464528,\n          17921935\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"profit\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.8733780844045307,\n        \"min\": -9.429999999999978,\n        \"max\": 8.180000000000007,\n        \"num_unique_values\": 233,\n        \"samples\": [\n          3.530000000000001,\n          2.3500000000000227,\n          -2.140000000000015\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"gain\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"medium_gain\",\n          \"small_gain\",\n          \"large_gain\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"size\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"large\",\n          \"small\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"all\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 61,
      "metadata": {
        "id": "1byaCLU_hmFh"
      },
      "outputs": [],
      "source": [
        "df = df.reset_index()\n",
        "#df.date = df.date.apply(lambda d: datetime.strptime(d, \"%Y-%m-%d\"))\n",
        "#df.date = df.date.apply(lambda d: datetime.strptime(d, \"%Y-%m-%d %H:%M:%S%z\").strftime(\"%Y-%m-%d\"))\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 62,
      "metadata": {
        "id": "gb1Qqv3OhmFh"
      },
      "outputs": [],
      "source": [
        "def get_month(row):\n",
        "    return row.date.month\n",
        "\n",
        "df['month'] = df.apply(get_month,axis = 1)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "zm7bq7E14mBB",
        "outputId": "81929b9a-3d1d-4a5e-84e4-d5983ff10795"
      },
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                         date    open   close     low    high       vol  \\\n",
              "0   2018-01-02 00:00:00+00:00  177.68  181.42  177.55  181.58  17694891   \n",
              "1   2018-01-03 00:00:00+00:00  181.88  184.67  181.33  184.78  16595495   \n",
              "2   2018-01-04 00:00:00+00:00  184.90  184.33  184.10  186.21  13554357   \n",
              "3   2018-01-05 00:00:00+00:00  185.59  186.85  184.93  186.90  13042388   \n",
              "4   2018-01-08 00:00:00+00:00  187.20  188.28  186.33  188.90  14719216   \n",
              "..                        ...     ...     ...     ...     ...       ...   \n",
              "246 2018-12-24 00:00:00+00:00  123.10  124.06  123.02  129.74  22066002   \n",
              "247 2018-12-26 00:00:00+00:00  126.00  134.18  125.89  134.24  39723370   \n",
              "248 2018-12-27 00:00:00+00:00  132.44  134.52  129.67  134.99  31202509   \n",
              "249 2018-12-28 00:00:00+00:00  135.34  133.20  132.20  135.92  22627569   \n",
              "250 2018-12-31 00:00:00+00:00  134.45  131.09  129.95  134.64  24625308   \n",
              "\n",
              "     profit         gain   size all  month  \n",
              "0      3.74   large_gain  small          1  \n",
              "1      2.79  medium_gain  small          1  \n",
              "2     -0.57     negative  small          1  \n",
              "3      1.26  medium_gain  small          1  \n",
              "4      1.08  medium_gain  small          1  \n",
              "..      ...          ...    ...  ..    ...  \n",
              "246    0.96   small_gain  small         12  \n",
              "247    8.18   large_gain  large         12  \n",
              "248    2.08  medium_gain  large         12  \n",
              "249   -2.14     negative  small         12  \n",
              "250   -3.36     negative  small         12  \n",
              "\n",
              "[251 rows x 11 columns]"
            ],
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              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2018-01-02 00:00:00+00:00</td>\n",
              "      <td>177.68</td>\n",
              "      <td>181.42</td>\n",
              "      <td>177.55</td>\n",
              "      <td>181.58</td>\n",
              "      <td>17694891</td>\n",
              "      <td>3.74</td>\n",
              "      <td>large_gain</td>\n",
              "      <td>small</td>\n",
              "      <td></td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2018-01-03 00:00:00+00:00</td>\n",
              "      <td>181.88</td>\n",
              "      <td>184.67</td>\n",
              "      <td>181.33</td>\n",
              "      <td>184.78</td>\n",
              "      <td>16595495</td>\n",
              "      <td>2.79</td>\n",
              "      <td>medium_gain</td>\n",
              "      <td>small</td>\n",
              "      <td></td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2018-01-04 00:00:00+00:00</td>\n",
              "      <td>184.90</td>\n",
              "      <td>184.33</td>\n",
              "      <td>184.10</td>\n",
              "      <td>186.21</td>\n",
              "      <td>13554357</td>\n",
              "      <td>-0.57</td>\n",
              "      <td>negative</td>\n",
              "      <td>small</td>\n",
              "      <td></td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2018-01-05 00:00:00+00:00</td>\n",
              "      <td>185.59</td>\n",
              "      <td>186.85</td>\n",
              "      <td>184.93</td>\n",
              "      <td>186.90</td>\n",
              "      <td>13042388</td>\n",
              "      <td>1.26</td>\n",
              "      <td>medium_gain</td>\n",
              "      <td>small</td>\n",
              "      <td></td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2018-01-08 00:00:00+00:00</td>\n",
              "      <td>187.20</td>\n",
              "      <td>188.28</td>\n",
              "      <td>186.33</td>\n",
              "      <td>188.90</td>\n",
              "      <td>14719216</td>\n",
              "      <td>1.08</td>\n",
              "      <td>medium_gain</td>\n",
              "      <td>small</td>\n",
              "      <td></td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>246</th>\n",
              "      <td>2018-12-24 00:00:00+00:00</td>\n",
              "      <td>123.10</td>\n",
              "      <td>124.06</td>\n",
              "      <td>123.02</td>\n",
              "      <td>129.74</td>\n",
              "      <td>22066002</td>\n",
              "      <td>0.96</td>\n",
              "      <td>small_gain</td>\n",
              "      <td>small</td>\n",
              "      <td></td>\n",
              "      <td>12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>247</th>\n",
              "      <td>2018-12-26 00:00:00+00:00</td>\n",
              "      <td>126.00</td>\n",
              "      <td>134.18</td>\n",
              "      <td>125.89</td>\n",
              "      <td>134.24</td>\n",
              "      <td>39723370</td>\n",
              "      <td>8.18</td>\n",
              "      <td>large_gain</td>\n",
              "      <td>large</td>\n",
              "      <td></td>\n",
              "      <td>12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>248</th>\n",
              "      <td>2018-12-27 00:00:00+00:00</td>\n",
              "      <td>132.44</td>\n",
              "      <td>134.52</td>\n",
              "      <td>129.67</td>\n",
              "      <td>134.99</td>\n",
              "      <td>31202509</td>\n",
              "      <td>2.08</td>\n",
              "      <td>medium_gain</td>\n",
              "      <td>large</td>\n",
              "      <td></td>\n",
              "      <td>12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>249</th>\n",
              "      <td>2018-12-28 00:00:00+00:00</td>\n",
              "      <td>135.34</td>\n",
              "      <td>133.20</td>\n",
              "      <td>132.20</td>\n",
              "      <td>135.92</td>\n",
              "      <td>22627569</td>\n",
              "      <td>-2.14</td>\n",
              "      <td>negative</td>\n",
              "      <td>small</td>\n",
              "      <td></td>\n",
              "      <td>12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>250</th>\n",
              "      <td>2018-12-31 00:00:00+00:00</td>\n",
              "      <td>134.45</td>\n",
              "      <td>131.09</td>\n",
              "      <td>129.95</td>\n",
              "      <td>134.64</td>\n",
              "      <td>24625308</td>\n",
              "      <td>-3.36</td>\n",
              "      <td>negative</td>\n",
              "      <td>small</td>\n",
              "      <td></td>\n",
              "      <td>12</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>251 rows × 11 columns</p>\n",
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              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 251,\n  \"fields\": [\n    {\n      \"column\": \"date\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"2018-01-02 00:00:00+00:00\",\n        \"max\": \"2018-12-31 00:00:00+00:00\",\n        \"num_unique_values\": 251,\n        \"samples\": [\n          \"2018-08-14 00:00:00+00:00\",\n          \"2018-01-10 00:00:00+00:00\",\n          \"2018-08-27 00:00:00+00:00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"open\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 19.69649335473974,\n        \"min\": 123.1,\n        \"max\": 215.72,\n        \"num_unique_values\": 249,\n        \"samples\": [\n          208.77,\n          186.94,\n          184.93\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"close\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 19.97745158627776,\n        \"min\": 124.06,\n        \"max\": 217.5,\n        \"num_unique_values\": 249,\n        \"samples\": [\n          209.94,\n          187.84,\n          186.9\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"low\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 20.07440767348364,\n        \"min\": 123.02,\n        \"max\": 214.27,\n        \"num_unique_values\": 247,\n        \"samples\": [\n          176.4,\n          185.63,\n          160.88\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"high\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 19.424564323437952,\n        \"min\": 129.74,\n        \"max\": 218.62,\n        \"num_unique_values\": 246,\n        \"samples\": [\n          177.95,\n          187.89,\n          171.77\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"vol\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 19101434,\n        \"min\": 8855144,\n        \"max\": 169803668,\n        \"num_unique_values\": 251,\n        \"samples\": [\n          19101995,\n          10464528,\n          17921935\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"profit\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.8733780844045307,\n        \"min\": -9.429999999999978,\n        \"max\": 8.180000000000007,\n        \"num_unique_values\": 233,\n        \"samples\": [\n          3.530000000000001,\n          2.3500000000000227,\n          -2.140000000000015\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"gain\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"medium_gain\",\n          \"small_gain\",\n          \"large_gain\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"size\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"large\",\n          \"small\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"all\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"month\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3,\n        \"min\": 1,\n        \"max\": 12,\n        \"num_unique_values\": 12,\n        \"samples\": [\n          11\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 63
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 64,
      "metadata": {
        "id": "rsf5mdoqhmFh",
        "outputId": "44072a05-fc77-46eb-a3a5-5c6f9934e6dc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 482
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='month', ylabel='vol'>"
            ]
          },
          "metadata": {},
          "execution_count": 64
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "#sns.lineplot(x='month', y = 'vol', data = df, ci=95)\n",
        "sns.lineplot(x='month', y = 'vol', data = df, errorbar=('ci', 95))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 65,
      "metadata": {
        "id": "JJNPsKhzhmFh",
        "outputId": "aace1462-9c78-4eb1-c173-0e4e018002ef",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 482
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='month', ylabel='vol'>"
            ]
          },
          "metadata": {},
          "execution_count": 65
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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c1yWpWRMSafPyCbWCIAjV5NawPULIp0x5XSuGLMt87WtfY/ny5ezZs4cPfOADfOITn+DrX//6pLe//vrrWbduHXfccQeKorB582Z8Ph8Au3fv5oorruCzn/0s3/3udxkYGODmm2/m5ptvnrCTU2t1E6j88pe/JBaL8Y53vGPK26xfv55PfepT1VuUUDTdctAth4A6RaDik8maNrplE1CVKq9OEITjVda0Wfsv99fkvrd++nLC/uIut7/5zW+IRqOjf7/yyiv52c9+Nvr3ZcuW8dnPfpabbrppykDlwIED/P3f/z2rV68GYOXKlaMfW79+Pddffz0f/vCHRz/2ta99jYsvvpg77riDYDBY1HorqW4Cle985ztceeWVLFq0aMrb3HrrrXz0ox8d/XsikaC7u7sayxMKpFsOhu3QEJj8oeVXZJKahWY4IlARBEGYwiWXXMIdd9wx+vdIJMKDDz7I+vXreemll0gkEliWhaZpZDIZwuHwhK/x0Y9+lPe85z384Ac/4DWveQ3XXnstJ510EuAdCz3//PPcfffdo7d3XRfHcdi7dy9r1qyp/DdZoLoIVPbv38+DDz7IL37xi2lvFwgECAQCVVqVUArdsrEdF3WKox9VkbEdh6xp04SvyqsTBOF4FfIpbP305TW772JFIhFWrFgx+vd9+/Zx1VVX8f73v5/Pfe5ztLa28uijj/Lud78bwzAmDVRuu+023va2t3Hffffxu9/9jn/913/lnnvu4ZprriGVSvG+972PW265ZcLnnXDCCUWvt5LqIlC588476ezs5PWvf32tlyLMkm46wPQN3SRJImOIhFpBEKpHkqSij1/qydNPP43jONx+++3IsvdC8Kc//emMn7dq1SpWrVrFRz7yEd761rdy5513cs0113DWWWexdevWccFQvap5wzfHcbjzzju58cYbUdW5+yASPCndQpWmf1j5FZFQKwiCUIwVK1Zgmib/+Z//yZ49e/jBD37AN77xjSlvn81mufnmm9mwYQP79+/nscceY9OmTaNHOv/wD//AX/7yF26++WY2b97Mzp07+dWvfsXNN99crW+pYDUPVB588EEOHDjAu971rlovRZglb8aPNaEj7bECPoW0YWPaokOtIAhCIc444wy+9KUv8cUvfpFTTz2Vu+++m/Xr1095e0VRGBoa4oYbbmDVqlVcd911XHnllaMFKaeffjqPPPIIO3bs4KKLLmLdunX8y7/8y7R5orUiuXN48EoikaCpqYl4PE5jY2Otl3Pc0y2bjXuH8SvytFuspu0wkjU4d1krjUGRpyIIwvQ0TWPv3r0sX768rqpRhOlN93sr5vpd8x0VYf7IV/z4pkikzfMpMqbloolW+oIgCMIMRKAilI1m2li2O2OgAiCBSKgVBEEQZiQCFaFsdNOZqeBnlF+ViWdFoCIIgiBMTwQqQtlkDGvKGT/HCqgyad3CEgm1giAIwjREoCKUTUKbueInL6Aq6JaNZolARRAEQZiaCFSEsjAsB920pxxGeCyfImFYDlmRUCsIgiBMQwQqQlnolo1hOwXvqEiShIuXgCsIgiAIUxGBilAWmulgWjOXJo/lU2QSokOtIAiCMA0RqAhloVs2SJMk0rouuJPnoQRUhYRm4ThztuegIAiCUGEiUBHKIqPbKJMEKmp2gNDAc0hmZsLHAqqcS6gVxz9ClVgG6Klar0IQhCKIQEUoi4RmTppIK+sx/In9hAZfQDbGXyACqiwSaoXqSvXB4M5ar0IQJiVJ0rRvt912W62XWBNiXLEwa6btoFkOvmMTaV0HVRvGCrag6CMEh15Aa1uL4/fmOkiShOO6ZEVCrVAtRhq0GFg6qIFar0YQxunp6Rn9/5/85Cf8y7/8C9u3bx99XzQaHf1/13WxbRtVnf+XcbGjIsyaZtoYlk3gmEBFNjPIVhbHF8aMdCEbCYKDL6BosdHbqLJMUhMdaoUq0RNgajDJUaQg1FpXV9foW1NTE5Ikjf79pZdeoqGhgd/97necffbZBAIBHn30Ud7xjnfwpje9adzX+fCHP8yrXvWq0b87jsP69etZvnw5oVCIM844g5///OfV/eZmYf6HYkLF6ZaDabuox3Slla00km3gyn6QJKzwAtTMAMGhF9Ha1mAHWwmoXuWP67pIkyXjCkK52BaYWbA0MDIQaqn1ioRqct3aBai+8OTFBiX4x3/8R/7jP/6DE088kZaWwh7D69ev54c//CHf+MY3WLlyJX/60594+9vfTkdHBxdffHFZ1lVJIlARZk23HGBioDGak5J/vyRhRTpRM/0EB19Ab11DQG0la1nolkPQp1R34cLxxdLANryJmHqy1qsRqs3MwOcX1ea+/+kI+CNl+VKf/vSnueyyywq+va7rfP7zn+fBBx/kggsuAODEE0/k0Ucf5Zvf/KYIVITjQ0a3kKVj81Nc1OwQjhqccHsr3ImSHSQ4/CJ282ridjNZwxaBilBZlublpgQaIDPsvcIWu3jCHHPOOecUdftdu3aRyWQmBDeGYbBu3bpyLq1iRKAizFpSn1jxI1lZZDuDrYYn/Rw71I6ijRAZ2YpPWU7WbERsxAsVZWnen76Q9+razIJ/8senMA/5wt7ORq3uu0wikfE7M7Is47rje1GZ5tFGmqmUt7N93333sXjx4nG3CwTmRkK5CFSEWbFsh6wxsXW+YqaRLB03MHX4YQdbUPQ4TYmX0AcD0LRavMIVKsfUvMeXGoTMiBesiEDl+CFJZTt+qScdHR288MIL4963efNmfD4fAGvXriUQCHDgwIE5ccwzGRGoCLOiWQ6GZdMY9I97v2ymgZm31u1AE7JmYx7ZAq1BaFkmghWhMvQEKD6QZMD1SpUj7bVelSDMyqtf/Wr+/d//ne9///tccMEF/PCHP+SFF14YPdZpaGjg4x//OB/5yEdwHIdXvOIVxONxHnvsMRobG7nxxhtr/B3MTAQqwqzopo1pu/iU8cGFog3jKIVtK8rhJjQtidHzAn7XgZblIIvKeaGMHAeMFCi5gFrxgRav7ZoEoQwuv/xyPvnJT/KJT3wCTdN417vexQ033MCWLVtGb/OZz3yGjo4O1q9fz549e2hubuass87in/7pn2q48sJJ7rGHW3NIIpGgqamJeDxOY2NjrZdzXDocy/LC4RiLmo5uoUuWRqR3I7bixy3gbNZxIJYxWNuu0EAa2lZC2woRrAjlY2Rg/2MQiHpHP1rc21lZ+nKQRRJ3vdM0jb1797J8+XKCwYkJ+kJ9mu73Vsz1W1wJhFnJGhYyxzZ6SyPZWdxJKn4mI8vg4KLLIQg1w+AOGNwOjuhYK5RJvjQ5v6OiBr2cFSNd23UJgjAjEagIs5LS7AnHPrKVBpdcLkDhsobtZcdH2mBoF/RvA9uc+RMFYSaWlitHzj0m1YAXuIgOtYJQ90SgIpTMsh3ShkVAHb91rmSHcRVfUV/LJ8ukjFxQogYh0gHDe6D/JW/irSDMhqlNfJ+E2FERhDlABCpCyXTLwbQcfOrRHRXJ1lGMJI4aKupr+VUZzfRa8QPeK96GBTCyF/pf9Bp1CUKpjKSXQDuWEvAavwmCUNdEoCKUTLccTMcZ1+xNNjNIdhZHKS7hzafImLaDbo/JS1H80NAFsYPQ+4LXoEsQiuW6oCWO5qfk+YJeJZDYsZsz5nDtx3GpXL8vEagIJdNMG8dl3Iwf2UwjO07RlRSqImE7LobpjP+A4oPGhZA47AUrYqteKFY+kVY9JlDJJ9Sa4jFV7/LNyzIZkVM0l+R/X/nfX6lEHxWhZFnD5piBySj6CI5S+sNKMyep9JFVaFwEiR5wbVhwqldmKgiFsDRv1yR4TAmkrIJriUnKc4CiKDQ3N9Pf3w9AOBwW09brmOu6ZDIZ+vv7aW5uRlFm1wJABCpCyVK6hW9srxPHRNETOFPM95mJKsuk9ClKkmUFmnLBSs/z0HUKBJtKuh/hOGPpXoArT/J0J8likvIc0dXVBTAarAj1r7m5efT3NhsiUBFKYjsuad0aN+NHNtNIVhY71FrS1/QrEhnDwnJc1GO3asC7qDQuHBOsnCpeCQszsyap+MlTg2KS8hwhSRILFy6ks7Nz3NA9oT75fL5Z76TkiUBFKIlu2Ri2Q8R/9CGkmBkkx5r8lWsB/IpCyjDRLRvVP8XXkGTvGCjZlwtWToNwaYGRcJzQU1PnTPmCYpLyHKMoStkugMLcIJJphZJopoNhjZ+aLOsx3Fm0I1dVCct2MKwZMsUlyStdNjPQ8xykBkq+T2Gey1f8qFPMnRpNqBVJmoJQr0SgIpREt2xcQM5vlzs2ih7D8RXXP2UCyfvaM99O8kqXbQN6t3g7LIJwLNvwclSOLU3OGztJWRCEuiQCFaEkmmEz9kRfttLIloarzC5QUSSZlGYV/gnRTsCB3uchcWRW9y3MQ5YG9jSBCohJyoJQ50SgIpQkoVkTG705RtGt84/lU2Qyuo3jzHzbUeE275Vx7xavOZwg5Fk6ONbErrRjqUHQYmIIpiDUKRGoCEVzHJeMbuMbE6goegK3yCGEk/GrMobtFHb8M1a41Wvo1fsCjOz3chMEwcziTcichpikLAh1TQQqQtF0y8Gw7aOJtK6Dog/jFjnfZzJ+RcZ0HHSrmC2VnGCzV7nR9yIM7xXBiuAFHzMleItJyoJQ10SgIhRNM20Myx0NVGQzg2xlcdTi5vtMKpf4UvSOSl6wEYIN0L8VBndR3BmSMO/ok8z4mYyYpCwIdUsEKkLRdMvBxR2t+JGtNLKt4ypTlIAWSUEiY8wiX8Af9RrBDW6HwR0i9+B4ZZvekU4hgYqYpCwIdUs0fBOKljXGV+XIRhKX8nX19KkyKd2aXbNQf9j75MGdXqDScTLMYgaRMAflhxH6Gme+7dhJyscOLxQEoabEjopQtKRuHU2kdV3U7HB5jn1yfIqMYZWYpzLuC4Ug2gHDu6F/m/cKWzh+WLoXqBSyoyImKQtC3RKBilAUx3FJj6n4kawssp3BKUMibZ5flTFtd/aBCniJktFOGNkLfVu9i5dwfDCz3p+FbMuNnaQsCEJdEYGKUBTdcjAsm0AukVYx00hW+fJTwLuuuLgYpSbUHksNeF1sY/u9iiBzmiF1wvxhZos7OxSTlAWhLolARSiKbtmYtju6oyKbKaD8k2clJLJmGZNgFZ83eTl+GPpeEK+cjweFVvzkjZ2kLAhC3ah5oHL48GHe/va309bWRigU4rTTTuOpp56q9bKEKeiWg+M6KLIXmChaefNT8vyqTFKzynvNkFVo7PJa7fdt8abqCvOTY3vlxsUEKr4gWJmjR0aCINSFmpZBjIyM8PKXv5xLLrmE3/3ud3R0dLBz505aWlpquSxhGpppj1b4SJaGYqaxlfIHKj5FyjWWc0aPmcpCVqFxESR7vB4rC07xeq8I80u+4icQLfxz1CBkY17jN3+4YksTBKE4NQ1UvvjFL9Ld3c2dd945+r7ly5fXcEXCTJKaiU/OH/ukkawsbqD8F3q/opDRDQyrzIEKeJ1K88FK7/Ow4FQINZf3PoTaMvPDCFsL/xxJ9o59jDRE2iu3NkEQilLTo59f//rXnHPOOVx77bV0dnaybt06vvWtb015e13XSSQS496E6nFdl5Ruj+lIm89PKf/DSJbBwUUrV0LtsSQZGhaBlvCGGYpmX/OLpXkjfop9bCqqmKQsCHWmpoHKnj17uOOOO1i5ciX3338/73//+7nlllv43ve+N+nt169fT1NT0+hbd3d3lVd8fNMtb1hgfmqyoo3gFJMDUCQJ0IwKtsCXJK8aSE/A8J7K3Y9QfVaJlV1ikrIg1B3JdWuX4u73+znnnHP4y1/+Mvq+W265hU2bNvH4449PuL2u6+j60T4YiUSC7u5u4vE4jY0iz6DS4hmTTfuGaY8GUF2DcM9GXEXF8UUqcn+JrEU4ILOmq8K/WyMFtgVLXy66ks4Xh5+FdL/XQ6cYlu7tsp3wMpG7JAgVlEgkaGpqKuj6XdMdlYULF7J27dpx71uzZg0HDhyY9PaBQIDGxsZxb0L1aJY9WvEjmxmkcg0inIJPkdAMB9OucCytBr1X4GJ67vzgOF7wWUrQOTpJWVT+CEK9qGmg8vKXv5zt27ePe9+OHTtYunRpjVYkTEc3HVw3N4jQTCG7DkhKxe7Pp8gYtlO5PJU8WfW2+sXFaX6w8om0JTYhFJOUBaGu1DRQ+chHPsITTzzB5z//eXbt2sWPfvQj/ud//ocPfvCDtVyWMIWUbqIquf4pegxH8VX0/lRFwnEdjHK00i+E2FGZHyzNGy5Yav6U4oesSK4WhHpR00Dl3HPP5d577+XHP/4xp556Kp/5zGf4yle+wvXXX1/LZQmTcF2XhGYRUBVwTBQ9Udb5PlOT0MvZoXYqagCyI5W/H6HyLB1cxytDL4Ua9FrpW0Z51yUIQklqPvf+qquu4qqrrqr1MoQZeDN+vJ4msplCsjLYobaK369PlknpVQpU9JQ3YbnCO0VChZVa8ZPnC0Fq0JukLJKrBaHmat5CX5gbjnaJVbxBhI7t5XZUmF+RyRgWliMSaoUC6UmvH0qpxCRlQagrIlARCqKbNrbjehU/ehx3NheCIvgUGdP2+rdUlOIDxxIJtXOd6+aGEc5ymreYpCwIdUMEKkJBdMsBXHBsL5G2gmXJY6mqlAtUqpBQKyFeRc91lj67RNo8NejlLIlJyoJQcyJQEQqS1E1UWUa20shWFlepRiKtR5K80uiKU/wioXauy5cmzza3xBf0jgHFDpsg1JwIVIQZua5LMmvhV2RvEKFj4lYx4VSVFNK6VYU7Cua61JqVvy+hMizd64kz2/wpkbMkCHVDBCrCjAzbS6T1qzKKnsStwBDC6fgUiYxuY1d6U0UNiIvTXGdp5TmuGTtJWRCEmhKBijAjzfRyRPwyKPowblX6pxzlV70OtUbFE2r93m6K2O6fu/TU7Cp+xhKTlAWhLohARZiRbnkVP35HQ7YyVUukzfMpMqbjoIkOtcJM9MTsE2nzxCRlQagLIlARZqSbDrjefB/JNnFnW/pZLClXdVrpHRXwypSzscrfj1B+luEd/ZSrSZsaBFMcBQpCrYlARZhRSrdQZQnZTHklODWgShIZo0oJtXoS7Crcl1BeluZNPi7bjkpukrIoWReEmhKBijCjlG7hUyTU7DBOtXdTcnyqTFq3K9/WQg14lSPiVfTcY+le0z65zBVpIqFWEGpKBCrCtAzLQTdtQujIVrpKgwgn8quyt5ZK56nkX0WLhNq5x8qC45R3108Vk5QFodZEoCJMS7NsdNsh4GSQbKP6+Sk5PsWr/KlKh1oQOypzkZEpfWLyVMQkZUGoORGoCNPSTQfLcgg4uR2GGuWoSBK4uCKhVpianij/tGNfKJdQK45/BKFWRKAiTEszbXBB0YZx1NrspuTJyGSNKgQqasC76Imy1LnDzg2ULFcibZ6YpCwINScCFWFaGcPC5xooZgpHqW7/lGP5VZmUblUhoTYoEmrnmnJX/IwlJikLQk2JQEWYVlKzCLlZJEvDrXKjt2P5FAnd8tr5V5TiFwm1c42lecFlJQIVMUlZEGpKBCrClAzLQTNtgm4+P6W2Dxe/omBaDkalE2rzeThiR2XusDTvz0rkUIlJyoJQUyJQEaakWzaG7RC0YjhVnJY8FVkGBxetWgm1Ys7L3GFkoVJ53mKSsiDUlAhUhCnploNt6PitVNUHEU5Hq1ZCrRYXCbVzhZGszLEPiEnKglBjIlARpqSZNoqVQra0qg8inIpfUUjpVWqlb+liu38ucGwwUpULVEBMUhaEGhKBijClrGHjs7NIrgtSmRtplcivSGimg2lXOLFRJNTOHflE2kqWz4tJyoJQMyJQEaaU0EzCVgJXUWu9lFH5DrUVz1ORciObRV5C/bP0ypUm54lJyoJQMyJQESZl2g6arhO0kzWb7zMZRZFw3CpU/kBuuz9R+fsRZsfSvKCyklVpYpKyINSMCFSESemWg5NN4Hf0uslPOUqqYkJtzBt0J9QvU6vefYmEWkGoOhGoCJPSTBvHSKNKjtdGvI74ZJl0VQKVXFmqJfJU6pqR9MrJK01MUhaEmhCBijAp3XJQjZh3/FFn/IpMxrCwnEon1Aa8/Aex3V+/XNc7niv3MMLJiEnKglATIlARJpXVNPxGog6PfcCnypi2U/lJyqJDbf2r5IyfY4lJyoJQEyJQESaVTsYJukZdJdLmqYqE5bjo1UiolRUxkK6eWZq3w1GNQEVMUhaEmhCBijCBZTsYmSQ+bJBr3zp/ci66WYVAZbR/hkiorUuWDq5dvTwqMUlZEKpOBCrCBLrl4GbjqGp9NHmbjCpVq0NtQCTU1jOrihU/ICYpC0INiEBFmEAzTMgOI/vDtV7KlHyKRFa3sSu90aEGcnkJIlCpS1rSO56rFjFJWRCqTgQqwgRGJolsZ3F89ZefkudXvQ61RsUTamXRobZeua53DFPJ1vnHEpOUBaHqRKAiTKBl4ii2iVuNBMUS+RQZ03HQqtKhVvFeuQv1xTa8HJVqPk7FJGVBqDoRqAgTaMk4qlLnD43cKJ6KlyhDLqFW5CXUHUsDu8qBCojRCoJQZXV+NRKqzbYdrOQAsr9+j33yVEkiU5WE2vx2v8hLqCuWDo5Vna60Y+UDVzFJWRCqQgQqwjhaJoFjpJADkVovZUY+1WulX/HK4XzljwhU6ouZBWqwyyUmKQtCVYlARRjHyCRxTA3FV38daY/lV2UMy8GodOmPJHvXQ3Fhqi9GuroVP3mKX0xSFoQqEoGKMI6ZTeAiIclSrZcyI5/iVf5Up0OtaPRVd/RE9fNT4OhoBZFQKwhVIQIV4SjXxUz04ypVLPecBe964VYvoVY0+qoftukdv9SqMk1MUhaEqhGBinCUmUVLJ5B89dvo7VgS3iTlihvtUFvlTqjC5Ko5jHAyahD0lJikLAhVIAIVYZStpzC1DIq//vNT8vyqTFq3K7/RoYiE2rpi1jhQ8YW8sQpikrIgVJwIVIRRRiaBaduoapUGvJWBT5HQq5FQKyuiQ209ye9sSTXKpZJVrzxZJNQKQsXVNFC57bbbkCRp3Nvq1atruaTjmpnox8CPr96bvY3hVxRMy8GoRkKtJHvb/ULtmdnaBSl5kiQSrAWhCmr+0vmUU07hwQcfHP37XHo1P6+YGmY2jq0Ga/78XwxZBgcXzbJpqPTDWQ0cTaidSz+k+ahWFT9jjU2wFo8HQaiYmkcFqqrS1dVV62UIRhozm8FWorVeSUk0o0qVP2bW64g6B/rMzFuO7ZUG1zpQyU9StjQvZ0UQhIqo+R7/zp07WbRoESeeeCLXX389Bw4cqPWSjk9GCs0wUcvYjjxluPzbE1me7q1sVY5fUUhqVaz8EXkqtWVmvUTaak5Nnkx+tILIUxGEiqppoHL++edz11138fvf/5477riDvXv3ctFFF5FMTn7uq+s6iURi3JtQHk56iKyt4Ctjo7ff7DZ4YJ/JVzZlcSpYluOvakKtIwKVWrP03DDCKs/4OZYkg+OIyh9BqLCaBipXXnkl1157LaeffjqXX345v/3tb4nFYvz0pz+d9Pbr16+nqalp9K27u7vKK56nLAMjHUOXg/jU8j0kNh7xdjl60y7P9VfuaKaqHWolSSTU1pqleSMNpJpvCHuTlLPxWq9CEOa1OviXflRzczOrVq1i165dk3781ltvJR6Pj74dPHiwyiucp4wUlp5GJ4Aql+chkTRcXhw8Gpzcv8csy9edjKJIOG6VKn/UAGixyt+PMLV6arrnE5OUBaHS6ipQSaVS7N69m4ULF0768UAgQGNj47g3oQyMNKZp4MgyZYpTeLrXwnEhmtud//Mhk5RRya5sUvUSao2s13BMqA0tUftjnzw1JCYpC0KF1TRQ+fjHP84jjzzCvn37+Mtf/sI111yDoii89a1vreWyjj/ZEQynvA+FJ3PHPq87yc+yJhnDhof3V25XxSfLpPRqBCqBXEdScWGqCccBI+nN2qkHYpKyIFRcTQOVQ4cO8da3vpWTTz6Z6667jra2Np544gk6Ojpquazji21BdoSUW75jH9tx2dTjBSrnLVK54kTv1e/v91ZuLopfkcmYFqZT4V76sgquLVrp18rojJ86GZwpJikLQsXVtI/KPffcU8u7FwCMFI6RIeP4UMtU8bN92Cauu0R8cEq7wtJGmW9t1tkx7LAnZnNis1KW+xnLp8rouolu2fj8FX5YS7K4MNWKpXmDAIPNtV7JUarfa/wmCEJF1FWOilADRhrLMjBQytY6f2NuN+XsLhVVlmgOylyw2AseKpVUqyoSluNWL6E2O1z5+xEmsnSvRFwuf7BbMjXotdIXk5QFoSJEoHK8y8YwbAnLcssXqOTyU85fdHRnI3/88+A+E9Ou0PGMC7pZjUAll1Br6ZW/L2G8eqr4yROTlAWhokSgcjxzbMgOY8hBHJyyVPwMZR12jnjBwrkLjwYq53SptIUkEobLE0cq00VWVWRSuuhQO6/pSa93ST0Rk5QFoaJEoHI8M9JgZtGlALjlyU/J76ac3CrTEjz68FJkicuW5ZJqK3T845MlMrpFpRvUehcmSyTUVpvr5oYR1kki7VhikrIgVIwIVI5nRhpsnbQll63iJ5+fcv6iiX0uLs8d/zzVazGYKX804VdlTNtFt6pQpizLoIut/qqydC8PpNbDCCczdpKyIAhlJQKV45kWx5UksqaNqsx+R8W0XZ7pPVqWfKwlDQqndSg4Ljywr/y7Kj5FxnSq1EpfDXgdSYXqsTRvxk+99FAZa+wkZUEQykoEKscr14XMEIYUxLTLk0i7ZcAmY0FLUGJly+Rf7/LlR49/3HK/+pTwEmqrsaOiBrwdKVHpUT2W7uWCyHWWowJikrIgVJAIVI5XRhrMDIYUwLScsgQq+WOfcxeqyNLkOzSv7PYRUuFIymHLQPkDCiWXp1Jx+QuTSKitHitbv0crYpKyIFSMCFSOV0YaLB0THw5uWSp+JitLPlbIJ3HxCZVLqvUpMmnDxqlKQq3oUFtVerr+Kn7GEpOUBaEiRKByvMpVKOhl6mlyJOlwMOmgSHD2gukvJvmeKn8+aJI2y/sK2a/K6JaDbldpmq3YUakePVGfibR5YpKyIFSECFSOR64LmUHwBckaFsoUxzTFeLLH2x05rUMh4p/+661tU+hukNFseORAeXdVfIqMZTtVavwWgIzoUFsVluEdtdVjIm2emKQsCBUhApXjkZkFI42rhkgbdlnyU/LTkier9jmWJEmjuyrlbqkvSeDiYlS8mQpHE2rtyk2FFnJGhxHWcaCi+L2qJJFQKwhlJQKV41Gu0ZshBTBtd9alyVnT5fl+b7v7vIWF5RC8ZpkPWYKtQzb74+XdKpeRyRgioXZesTQvIJQn9uepG5IESGJgpSCUmQhUjke5/BTDcTFtB98sM2mf7bcwHeiKSJzQWNjXag3Jo0m3f9hb3h0Jv+q10q94gYji8zrUilfQlZfvT1KGY8qKEpOUBaHsRKByPMoMgRrAtB0c10WZ5Y7K2GofqYgLyRW5nioP7DOxnPJFFaoioVvOzMc/jgU9z8Nz90D8UGl3JiF2VKrByHglwPVOTFIWhLKr41o/oSJMzXsi9YUwNAeYXYDguu6YQKW4bfnzFqk0ByRGNJeNPRYXLi7Ptr5fUUjrNrrpEFCPubgZKTi4Cfb/BQ4+6VWSgPf/V325+DtT/JCNzXrNwgz0RH0n0ub5QpAe9PqpzIX1CsIcIAKV442R9hpnBRvJGhrKLDfV9sQcBrIuAQVO71CK+lxVlrhsuY+fvWTw+z1m2QIVWfYSar0OtSokjniByYG/wJHnwB2TE+OPesFLz/Pen/5ocXemBr3Ps03vKEgoPzs3ALKeE2nzxk5SDrXUejWCMC+IQOV4YyTBdXAlhYxh4zt2x6FI+W606xaoBNTij5AuzwUqTx6xGM46tIbKsL3v2jTEXiJwcDP0b4KRfeM/3tQNSy/03hacAj97h3f0c/hZWH5RcfelBrwdFTMDStPs1y5MZGle+/xgY61XUhgxSVkQykoEKsebzDAofkzHwbCcWVf8FFOWPJmlTQpr2hS2Ddk8uM/kujWBkr6ObGYI9z9DpHcj4b6nUI3E0Q9KMnSdfjQ4aVoy/pOXnOcFKgc3Fh+oKH4vH8HMQlAEKhUxF0qTxxo7Sbnek38FYQ4QgcrxxDJAi3v5KbaD6TgEfaUfVyR0h21D3jHK+QWWJU/mihN9bBuy+f1ek2tX+wtOyFXTvUR6NxLt3Uho8AUk92hJsq1GiHecRePJr0Rd+jIINEz9hbrPgxd/AYc2lnZxkSRR+VNJc6XiJ2/sJGVfqNarEYQ5TwQqxxMj5b3yj3ZgaA62M7seKk/12DguLGuS6YyUfmRz8Qk+vv6MxsGEF/isbZ/iYenaBId3EOl9kkjvRgLJA+M+bEQXk+46j3TXeaSa1hDXHdYuaqQhMMPDfOEZXn5Jqg/iB6B5aXHfgOIDLVbc5wiFM7JeddVckd9RMTIiUBGEMhCByvHESIHrgKxiWNpsC35G81OmG0JYiIhP4pXdPh7YZ/L7Pea4QEUyM0T6n80d6Wwad6TjSjLZ1rVecLLwfMzo4tGPKYCja+iWwzR7KR5fCLrOgMNPecc/xQYq+ZJU26rvoXlzlZGcO8c+kJuk7OYmKbfVejWCMOcV9Kz661//uuAv+IY3vKHkxQgVlo2NXkg100aexVa67bhsygcqszj2ybviRC9Q2XDA5JbVCdoGNxLp3UhocAuyM/5IJ7PgbC84WXA2jn+6MERCNwvsett93tFA5bRri1u8LzgmoXaOJHzOFY7tBdhzKVCBo5OUm2u9EEGY+wq6wrzpTW8q6ItJkoRdram1QnFsy9uOVr2t6LQ+u4qfl4ZtEoZL1Adr24srS57AtTlX2cmnQo9xnv00a/54cNyHjcjC3JHO+WTb1noloAXwyTIpvYhA5YmvQ89zXoWJWkRSr+L3ypPN7NypTJkr8hU/gSLLxmtt7CRleZb/PgThOFfQM77jVGHAm1BZRsp7xR9uw7RdDMvBJ5e+o5Kv9jlnoYpSwteRrCzh/meJ9m4k3LsJ1YhzI4AMNhJG21pSuXwTM7qkpERKvyqTMS1Mx535e21eCpFOSPdDz2boPr/o+xMdaivA0udWxU+eGgIt4T0mpkvkFgRhRuJA/XhhpLxXd4oPw7CwZlnxk+9GW+gQQgA100+kN3+k8/wxRzphRtrO4nOHTudh+wy+fGYXSxpm90rUp8houolu2fj8M6xTkrxdlZd+4x3/FBuoKD7RobYSLC1XiTUH2uePpfi9AMsQgYogzFZJgcojjzzCf/zHf7Bt2zYA1q5dy9///d9z0UVF9qAQqicb91q2AoblYtml91AZzDjsjjlIwLkzBCr+2B4ajjxGpPdJAol94z529EjnPLJtp4CscuiRDLEei/v3mLz7jNkFKqoiYdkuhulAIS/IxwYqRd9Z0GvzLrb6y8vUar2C0uR3AMUkZUGYtaIDlR/+8Ie8853v5K/+6q+45ZZbAHjssce49NJLueuuu3jb295W9kUKs+TYkB0aLZU0LHtWPSny1T6r2xSag1O/0g33PcWixz+NhHd06CKjta32yoe7zp/0SOeKE31s6rF4YJ/JO04LlHSsNJYE6FaBR5eLz/JeuccPQqIHGhcWfkdqQGz1V4KemLujCcQkZUEoi6IDlc997nP827/9Gx/5yEdG33fLLbfwpS99ic985jMiUKlHRnpc51TNdJBn0ZjiySMFlCW7Lm3b7kbCIdN2Komlr/WqdALTd2992SKVRr/EUNblqV6r6EGHx1IVmZRuzXxD8Ob8LDgVep/3mr+tfWPhd5Tf6jezIlApF9f1yr7n6nC/sZOU5+r3IAh1oOiD3z179nD11VdPeP8b3vAG9u7dW5ZFCWWWH5qXq2RJ6xY+pbQzf8N2eaZv5vyU0MBzBGM7cZQAvefdSvKEV88YpAD4FYlLl3nByf17zJLWeOzXy+gWdqH54N3neX8We/yT3xkSCbXlM9da5x/LF/IGgIrHhCDMStFXq+7ubh566KEJ73/wwQfp7u4uy6KEMtMSoxdS03HRZ5GfsmXARrOgNSixomXqh0/rjp8CEF/6WuwCApSxLl/uBSqPH7GIabOrOPMrCobt5CYpF2DJud6fR57xgrtiKD5vRIFQHpbm7UbM1UAlP0lZBCqCMCtFH/187GMf45ZbbmHz5s1ceOGFgJejctddd/HVr3617AsUZslxIDM4Lj/Fsh2CgdKOVMYOIZxqJk9geDvhwedxJYXYimuKvo+TWhRWtsjsHHH4436Tvzq5tEGFkE+oddAth7C/gCTX9pUQbPZa4ve9CIvOLOLOAl6gIhJqy8PSwbUL7ptTlyRAT9V6FYIwpxX9DPD+97+frq4ubr/9dn76U+9V85o1a/jJT37CG99YxJm+UB1mJpc34TXMMiwXy3FRS0xSLSQ/pXXHzwBIdl+CFe4s6X6uPNHPzqc1fr/H5JpVhQ8qnEDy/uPtqBQQnEmyt6uy6wEvT6WoQCWXkzDm5y3Mgpmt9QpmTw1CZkhMUhaEWSj66Oc973kPLS0tPProowwNDTE0NMSjjz4qgpR6ZaS8V6aKtyth2g6u65Y05O1QwuZIykGV4awFkwcq/sR+or1P4CIxvPLNJS/7kqU+/ArsjTvsGJnd8Y8iS6S1AhNqofQ8ldGEWrHVXxZ6au7PTlJDRycpC4JQkqIDlYGBAa644gq6u7v5xCc+wXPPPVeJdQnloiW9P3Ov5rwZP6Ul0ubLkk/rUAj7Jo90Wnb+HIDUogswG0rPWYr6JV6xxLtI3b/HKPnrgNf4LW3YFNxgecm5gARDu7xXw4WSJO+VswhUZs91c6XJczQ/Jc8X9BJqDfGYEIRSFX3F+tWvfkVPTw+f/OQn2bhxI2eddRannHIKn//859m3b18FliiUzHVz+SnB0XeljdIrfmY69lHTvTQcegSAkVXXlXQfY12+3LtI/XG/iW6VPurZr8peQm2hc6hCzdCxyvv/Q5uKuzNF9ZKXhdmxjbmdSJs3bpKyIAilKOmK1dLSwt/+7d+yYcMG9u/fzzve8Q5+8IMfsGLFinKvT5gNM+s9QeYSaS3HRTMdfCVU/GRMl+cHvAv9VNOSW3bdi+Q6pDvWoTfP/rFw5gKFBWGJtAmPHiri6OYYPkXGtB10s4gjpCUlHv+MJtSK+VizYmlg63M/UAERvArCLM1qgIZpmjz11FM8+eST7Nu3jwULFpRrXUI55PNT1HzFj4NlO/jk4n/tz/RZWA4sikosbpj4+Yo2QuP+BwAYWXXt7NadI0sSl5/oXah+P4vjH+9Exi28RBmO5qkcesqr4imUGvQusuL4Z3YsHRxr7nalHcsX9DrUiuBVEEpSUqDy8MMP8973vpcFCxbwjne8g8bGRn7zm99w6NChcq9PmA09Na7awLAcr+KnhB2V0SGEi3yTVuA07/4VsmOQbTmZbPtps1v3GK9d7kMCNvfb9KRKf6JXJJmMUUTA0bkG/BEvT2JwexF3FPAusvOhYqWWzCxQ+nFfXVFDueBVHP8IQimKDlQWL17M6173OgYHB/mf//kf+vr6+O53v8ull15aegmpUBmZwdFutACG7QDFV/y4rjuaSDvZsY9spGjaex+Qy00p4+NgQURm3QKvJ8kf9pa+q+JXvVb6bqHXPlmFxed4/1/M8Y8kASKhdtaM9PzpRaP4veBVJNQKQkmKDlRuu+02enp6uPfee/nrv/5rAoHSm3EJFWRq3o5KLj8FQDdtpBI20XbHHIayLkEFTu+cePFo2vtbFCuL3nAC6a5zZ7XsyeSPf+7fa2I7pb3K9ileQq1RcC99Si9TlkVOwqzNh4qfPDFJWRBmpeir1nvf+16am5srsBShrIyUVxY5JlDJGE5Jjd7y1T7rulT8xxwbSZZG8+5fAbnclBJLn6fz8sUqUR8MZFw29xdxfDOGT5ExLbfIhNpc0DXwUnGt8dWg19lW5CSUxja9QHu+BCogJikLwiyU/6oi1Acjn5/i/YotxyVrWvhLKE3O56dMduzTeOABVCOOGV5AcvErZ7fmKQRUiUuWekmVpSbVynIJCbXRTmhZDq4Dh58u/PN8uYRaS+SplGSuDyOcTL5rcbHzowRBEIHKvJUZHlcxYVoOll18Im1cd9g25F3czzu2f4pj0bLzFwCMrHxzRXMKrswd/zx2yCKhl3b8I0kSWbPIHZnu3K7KwSL6qeRzEkRCbWnMeRio5Ccpi+MfQSha3QQqX/jCF5AkiQ9/+MO1XsrcZxneUcWYYx/DdjEdp+hmb5t6LFzgxGaZjvD4z2049Ai+7ABWoJnECa8px8qntKJF5sRmGdOBhw+U9qrUr8ikimmlD0f7qRzaSMGZuPnjL5FQW5p8u/n5lJwvq2Bb4jEhCCWoi0Bl06ZNfPOb3+T000+v9VLmByPlvZofG6jkjzyKfO6fshut64y2y4+teBNuhV/9SpLEFSd6O0SlttT3qRK6VWRCbddpRwfLDe8p/PNk5ej4AqE4ZnZ+BSl5siQmKQtCCWoeqKRSKa6//nq+9a1v0dLSUuvlzA9GysurkI8GF1oxSaQ5tuPyVK4s+bxj8lMiPU8QSB7E9kWIL3vd7NZboEuX+vDJsHPEYddI8Um1fkXBsIrsUKsGjk5QLqb6Rw2CNlL4Loxw1Hyq+Blr7CRlQRAKVvNA5YMf/CCvf/3rec1rKnt0cFzJjkyYOpsx7KI70m4bskmZ0OCXWNM2Jv/EdWnd8TMA4suvwvGFZ73kQjQGZC5YnB9UWPzxjyyD4zroVpFB29jjn0KpgVyTL5GnUhTH9vI45mWgIiYpC0Ipahqo3HPPPTzzzDOsX7++oNvruk4ikRj3JhzDNiEbG22bD2A7oJlW0TN+8sc+5y5UUMaUNYcGniMY24mjBIid9IayLLtQV+SSah/ab2DYJbwylSS0ohNqc4FK75bCm3aJQKU0ZtZLpFXnYX8mMUlZEEpSs0Dl4MGDfOhDH+Luu+8mGAzO/AnA+vXraWpqGn3r7u6u8CrnICPlvWrzH93lMGwb03FRi0ykHc1PWTh+3krrjp8CEF/6WuxA0ywXXJyzFii0hySSBjx+uPhBhT7Z61BblKYl0LjImz1z5NnCPkeSvQ7wInmyOJaeG0Y4D2b8HEtMUhaEktQsUHn66afp7+/nrLPOQlVVVFXlkUce4Wtf+xqqqmLbE1/13nrrrcTj8dG3gwcP1mDldc5Ie9vnY/JTDMvFtIur+OlPO+yNO8gSnLPw6LFPYHg74cHncSWF2Ipryrr0QiiyxGuXl95Txa/KZE0vcCtKKcc/suz1zhAKZ2legFeBxoF1QUxSFuYYw3LIFjMnrQImdvCqkksvvZQtW7aMe9873/lOVq9ezT/8wz+gKBN7cgQCAdGyfybZuHeBHMPIBX3FFFLkZ/usblNoDBz9evnclGT3JVjhzlkutjSvXe7nR1sNnu616U87dEYKv6j5FBlNN9EtG5+/iId/93mw9Zdw8Mlxgx6npeam5hZ6e2H+52+MnaRcwhRzQagm23HZ3pskoMqs6mqo2TpqFqg0NDRw6qmnjntfJBKhra1twvuFAjk2ZIfGlSUDGCVU/Dw5STdaf2I/0d4ncJEYXvnm2a11FhY3yJzeofD8gM0D+0yuP6Xw4FVVJGzH9X4mxeRrLjoTZB8keyF+CJoLOHZUA17zMkub8DsRpqDF5+exT54a8nbZzDQEavfELwiFODCU5uBImuXt0ZquQ4T088lo/5TxVTjpIit+DNtlc9/E/in5vimpRRdgNtQ2P2hsTxWn2HJPl+ITan1hWHia9/+FHv+oQZFQWwzH8R7D8zGRNk9MUhbmiP6kxp6hdF2MLKurQGXDhg185StfqfUy5i4j7VX9jCntdBzQDLuoYYTP9dtoNrSHJE5s9h4iarqXhkOPADCy6rryrrsEF3X7CKvQk3bZUuSgQlWRSeklnLkuKXKasiR7xz6ibXph5uOMn2PljwBFQq1Qx1K6xe7+FAoSIX/lRqMUqq4CFWGWtMSEXAjDdjAcB59a+K86P4TwvIUqUu7rtey6F8l1SHesQ29eUb41lyg4dlDh3uJ6qvgViaxhYRWbUJsvUz6y2XtVXAhJ9nYJhJlZmjf+YT4HKuBNUs6IScpCfTJth939KRJZi9ZIffxbFIHKfOE4kBmccOxj5IYRFlrx47ouTx7xLvz5Yx9FG6Fx/wMAjKy6toyLnp3Lc8c/fz5okjYKDzr8iuIFcMU2fmtZDuF2r3y2d8vMtwfvGCMbE91IC2HpuY7KlXsFlzFshjOljWAoGzFJWahTruuyfyhNTzxLZ0Ng9IVqrYlAZb4w0xPm+4C3o+LiFlx0cjDp0JN28cmwboEXqDTv/hWyY5BtOZls+2nlXnnJVrcqLG2U0e3iBhWqioRll9ChVpKO7qoUevyjBr3fy3yvZimHKvyMeuNZ9g9mSBvF9+ApGzFJWahTfQmdfYMZWsL+ovtuVVL9rESYHSPtvSI9JhFRLzJpNF/tc1qHQsgnIRspmvbeB+RyU+okwgZvUOHlpQwqlLxWHbpVQp5Kd5H9VNSAd1ESCbUz0xMTRj+Uk7ebYpLSLfriWu02ucQkZaEOJTSTXf1J/IpMuJjWDVUgApX5YopJvRnTRi2i4mfjMdOSm/b+FsXKojecQLrr3Nmvs8xes8yHIsFLww57Y4UHHqosk9ZKeFW9+Gwv72RkH6T6Zr69rHjHcuKiND3X9Y5DlMpV/MQyBoZp0xbxM5A0iGVrePQiy2KSslA3DMthV1+KjGHTUid5KWOJQGU+cN1cfsr4Yx/HgWwRFT9p02XLgHexP3+RD8nSaN79KyCXm1KH3UJbgjIvywVV9xeRVOtTZNKGXXzpXaABOtd6/39wU2GfIy5KM7P0iibSGrZDf1In5FfxqTKyJNETyxbfobhc1ICYpCzUBdd12TuYpi+p0dlQ2Dibaqu/K49QPDPj5ahMkp9i2g7+Ait+num1sF1Y0iCzuEGm8cADqEYcM7yA5OJXVmLlZZE//nlon4lZ4KBCvypj2A76JKMaZlR0nkrA60YqTM3SvCRltTKBSjxrkjUtIgEvqG0M+YhlDQaTBVZvldscn6Qcz5gcHM4Qz5g4tQr2hLI4EtfYP5SmPRIYN3y2nohAZT4w0l4HVHV8NGzaXsVPoUc/T4499nEsWnb+AoCRlW+uaCXGbJ23UKU1KBHTXZ7sKew4x6/ImLaDXkLX3tF+Koef9gYVzkQNeTkq5ty8KFWFpU+YUVUujgODSR2frIymWMkyhH0qvXGNTC3mmMzhScqG5bCzP8nzh+Js2j/MU/uH2TeYIpYxsEXQMqfEMga7+pOE/SpBX/0+x4tAZT7IHytM0kPFcZ2CRoo4rjs63+e8hSoNhx7Blx3ACjSTOOE15V5xWSmyxGuW5ZNqCzz+yf2oSkqo7VgFwSZvF6tv68y3V/25DrVz76JUNVa2YscgCc0kkT26m5IXCapkTYveRLb6JzCj07XnXuXP4ZEMgymdxc0hWsN+NNNhe2+Kp/aNsGnfMHsHUoykRdBS7zTTZmd/CtNyaQrV99gKEajMB5lB7xXaMQzLKbhKZ9eIw4jmElLhtHZptF1+bMWbcOdAA658S/2NPRaD2cJ2SWSk0l5NSzIsySUWF3L8I6vg2qLyZzp6umIVP8Np73hHVSb+W2gK+RlI1CixVlbm3CTleMZk/3CGppAfRZbwKTItYT+LmkO0RvwYlsOO/iRP7x8ftFh2HfRhF0Y5jsuegRRDKZ2OhvofWSEClbnOzHo7KurEoXdp3UItMAE2f+xzVpdKc/+TBJIHsX0R4steV9blVkp3o8Ip7QqOCw/uK+yi41dlUrpV2qvpJUWWKUuy6JsxHT1RkUTatGExnDGJBFRkPc6Cp79MaOD50Y/7VRlJ8vqrVD2xduwk5TnAsh32DqawbJdoYGJQORq0NIVpjfgxxwQtT+0fYXd/imERtNSFQyMZDg5n6IgGkeuo5cRURKAy1xlp70jhmB0V14WsaU/6KnIyG/PdaLsUWnf8DID48qtwjul0W8+ODio0cQuIPnyKl1BrlPLEueQc78/BHZAZnvn2agCyBdzueGQZ3tFYBRJpY2kTw7QJ+GRad/6cxoMPsXDjehTtaHJzU8hPLGswlKpyYq0ayh0Jzo0Atieu0ZvQaSugfNWnyDTngpa2iB/LdtnVn+LpfSM8tU8ELbU0lNLZM5CmIegruNCi1ubGKoWp5efIHLNzkr8AF9I6f0Rz2D7sPWG8JvgiwdhOHCVA7KQ3lH25lfTKbh9BFQ4lHV4cnPlIx6/KmJZbWkJtuBXaV3n/f/ipmW+vBsHIFj4j6HhSoWGEhu0wkPJKkiXboPHAQwAoZpLO5+4YzYmRZQiqKj0xjWw1E2vzk5TnwJFgUjPZN5QmGlCL7liqKjJNIR+LmkO0R/1YztGgZdPeEXb1JxlK6ZgiaKm4rOHlpdiuS0OwvvNSxhKBylyXHpr0lahhOVhWYTN+nuqxcIEVLTLL9nm5KfGlr8UONJV7tRUV9klc3J0bVFhAUq0kgYtbWkItFJenogZEQu1ULG3C1O9yGFuSHD3yFxQjgeVvxJUUoj1/IXrksdHbRgMqGdOiN1HFjrX5Lfc6PxJ0HJf9Qxkyuj3rpMuxQUtHQwDbddk9kOaZAyNs2jvMrv4kgyJoqQjbcdk9kGIkY9ARrf+8lLFEoDKXWbp3tu+bmJ9i2A4OhVX85PNT3tyyh/Dg87iSQmzFNeVebVXkj38eOWiSMWe+4khIZIscMzBqtJ3+Jm+Y3nREQu3UKtBLxHFgIHG0JLlx3+8BiJ94FcOrrgOg47k7UPS49wkSNIf89Cd14tVMrFX9dd9jpy+pcSSWLXvSpSJLXtDSFKIjGsRxYfdAmmdzQcvOPi9oKXp4qDCpA0NpDo1kWNAQrJthg4USgUo9cl1vFohleBc2I+1VB2RHvHyI1AAk+yBxxHuFPkkirWE54M78YLQcl6d6vUDlTdovAUh2X4IV7izrt1Qtp7QrLG6Q0Sz408GZLzh+RSZZSit9gAWneNOqtbiXqzITSfKqW4TxjEzZux4nNJOE5u2m+JIHCQ+9gItMYulrGT75OvTGpahGnI7nvzn6OX5VRnKhJ57FqlZirRr0/m3X6STlrGGzZyBNUFUKnsBeimODFteFvYNpnslVD+3oTTKQFEFLqfqTGnuG0jSH/BX9PVZKfU0emqscx3u17Dpe0yp37N+dY/4+5uO2DY7pNQ1zLO+c3rFzt3Vz1QD5z899LWyv/0KerEzajC1rWAV1GXxx0CZtwlmBQywc3oiLxPDKN5ftR1NtkiRxxXIf33le5/d7TK44cfrjBJ8qYVjeJOVAsYllsurN/tn3Z+/4p2P19LdXA6DV96vnmtATZU+kHU7rSHglyU253ZR017lYoXYA+tZ9mO4/fYyGw38iufgi0osuAKAp7GcorTOU0lnQWIV24mrQa6VvpCHUXPn7K4LruuwbSpHULBY1Va+1uiJLNIZ8NIZ82I5LxrDYP5xm31CacEClI+qnNRKgMaQSUOu3SVm9SOsWu/tTKEgTegnNFXNz1dVg6ZAeHB9gOLb3ymdscOGYuWDEzW3/jw1O8oHGmK8rSUcbW0mS90py9E0CSTn6ftU39cen4bqQNuyCIuf8EMKPh34DGqQWXYDZ0F3az6xOvGa5jzu36Lw4aHMwYdPdOPWTmV9RyOgGRimBCnjHP/lA5awbpr+tGshNuTYq1ip+zrEtb9ewjPkpacNiOO2VJEu2PppEG192xeht9JaVjKz4K1p3/pzO5/6b/e2n4vgbkGUI+RSOxDUaQz5Cle7WqfiOTlKus0BlIKVzeESjLeKv2VGBIks0BH00BI8GLQeGM+wfyhAOqLRH/LRG/TSFfCJomYRpO+zqT5HIWiysYrBZbiJQmUp2BHo2j+mW6Y4PIo59k2WQfN6fSLldDulogFFF3owfF18BpckbeyyWSAO8THsUgJHc+f1c1h6SOXehypNHLO7fa/KeM6Z+ApNlcHDRLYeGUu4sn1Dbv9Wb/huY5quoQdAHc8d1IlABvI60lg7BxrJ9yZG0iWE7NIV9RA88hmKmMEMdZBacNe52w6vfRqTX6xnUseV/6Dv7Y4CXWDuQ1OhP6Cxtq0J5fh0OrdRM78hHkaW6aa0+NmhxXJeMbnNoJMv+oTSRgEp7NEBr1E9j0Fc3a64l13XZP5SmJ56dk3kpY829w6pqcl1oWpx7WwKNC6GhC6KdEGn3SlRDzd6TbKAB/GHvYqQGvGMBeebdj0owbAfTcvAr0/9j7Us77Is7vE/9DTIO6Y516M0rqrTKyson1T6w15yxlbcEpZelNnRB81Jv9+zw09PfVla9XTlR+XOUpZe1NNmwHQZTOuHchSp/7BNfdrn3QmMMV/HTv+5DuMg0HnyYcG9uGrYEjSE/fUmNeLbE/KViqAEv96yOJikfGs4Qyxi0FtAzpRZkSSIaVFnQGKSrKYQiyRwayfLs/hGe2jfM1p44/Qmt9Iq+eaA/qbNvMENL2F90SXm9mdurFyZlWg4O7owVP08esWgnznXKBgBGVl1b8bVVy/kLVZoDEsOay6YZBhX6FYW4ZlByRWSx05Tn4CC6islX/JQpoI9nvJLkcEDFn9hHaHgrruQl0U5Ga11NbMUbAViw+T+Rc32JAj5vFk9VEmvrbJLycNrgwIh3gZsTXUuPDVpkmcMjGs8ejLH5QKz6jfzqQEIz2dmXxK/IhP1z/+BEBCrzkNdpdeYn1409Fu9Sf0cAk2zLyWTbT6v84qrEp0hcuqywniphv0JSs4hrRml3NlqmvHHmV8VqoO7LUavKyI4OiJwt24GBMVOSm/bmk2hfhh1snfLzhta8HSOyCFUbpv2F74y+vzHoYyRjMJwu8XFRKDVQN5OUzVybfNdhTl7gZEkiGlDpagzS1Rgkrds8dyjG3oHUcdMF17AcdvWlyBg2LXW6I1YsEajMQxndQpmh3FO3XHb3xXm78gCQy02ZA6+einH5ci9QeeKIxYg29ZOUokgoSAwmjdJ237tOByXgJV+P7J3+tmMTagUwkmU79hlbkixZGg0HHwYgvvyKaT/PVQL0nfUhXCSaDjxAuM87wlMUiYCqcCSWRSule3GhZMULcOuglf7hkQwDSZ22OdYQbDKyJNHRECDsU9nel+TFIwmSWn2WgZeL67rsG0zTn9TpbJi7ybPHEoHKPFNoxc/mfovreIBGKYvecALprnOrtMLqWd6ssLpVxi5gUGE0qBLLGqT0EnIS1AAsOtP7/5mOf9Sg6FCb59heAnKZApXh1NGS5IbDf0ax0hjhLjIdZ874uVrbKcROvAqAzs3/hZz7/TQEVDKGRV+iwscydTBJOZ4x2T+UoSnoL6i1wVwRCagsaAjSm9B47mCMnni2oFlgc1FPXGPfUJq2yPz6HYpAZZ4xHQfLdmccRvjMoTTvUr2t8ZFV15a94Va9uDzXR2WmQYU+VcZ2XAZLPc8e26V2OorPK2kXHWqPzvhRZ//qPW1YjOSmJMPRTrSJZZcX/NgeWnsjZngBvuwAbS/e6b1Tgoagj/6kRqKSibU1nqRsOy77htIYtkM0OPeOfGaiKjKLmkI4LrxwOM723iRaqR2p61QsY7CzP0nYr867qqf5eXU6julWbhjhNJm0ruuysOdB2qUESX8nycWvrOIKq+uSE3z4FdifcHhpePonpqhfZThjlFYBtCQXqPQ8X0AQIokdFShrxU++JDngk/HH9xAa2Y4rKSROeE3BX8NVg/St+xAAzft+R2jgOQCCPgXHgd6EVnrC9UxGJynX5nHRE8/SE9doj8z9I5/ptIT9NIf87BtKs+VwvPL5R1WimTa7+lOYljvreUz1SAQq84xpOziuizLNjsrBmMFbnd8AEF/15kk7284XEb/ERUu8f7j3z5BUG/Qr6KbNSKaEJ6+mJV6psmN6/XemMwfmu1SFpXlnlbPczdMtb0rysSXJqYUXYAdbivpa2Y7TiS27EoAFz34NyfKCzsaQj6GUxnC6QhUko5OUqx+opHSLvYOlTUaei4I+hYVNIRIZk+cPxtg/lJ7TibaO47JnMMVgSi/7PKZ6Mf8flccZb8bP9LdJb3+YJdIgMakJbfll1VlYDeV7qjy830Szpv/hhHyqN1Ok2CcuSTq6q1JInoqeqtv5LlVjlifvI5E1yRpeSbJkZQtOop3K4CnvxAx14Mv00b71+4CX9xJQVXriWfRKzJup0SRlx/GSLzOGNS9fiU9FliQ6G4MEVIVtPUm29iRKy0+rA4dGMhwcytARDc6JcvJSiEBlnskaNsp0D1bX4cz+ewF4ruNq3DK2Lq9Xp3cqdEUkMhb8eYZBhZGASsa0iGdKCCIK7aciEmo9esLL2ZkF2/EaW/kVryS54dCfUKwsRmQh2fbTS/qari9M/7q/A6B5z/8RHHwBgIagSkq36K9UYq3iq/pOW39S50gsS3tk/lSIFCMaVOlsCNAT8xJt+xLanEq0HU4b7BlI0xD04S9lBMgcMX+/s+NUxrBRp3nAKgef4ATnMAk3jLr2qiqurHZkSRodTnj/3ukDEEkCn6wwkNSLz0dYdJbX/TRxGOKHpr6d4vN2U47nhFrX9Sp+ZjlKIKGZJDWTaC6J9mgn2itmdaSU6TyLeK5J3IJnv4pkaaOJtX0JvfSJ29Pxhao6STlr2OwdTBFUlXl9kZuJT5FZ2BTEsl2ePxRnZ19qTnS0zRo2O/qS2K5LQ3B+74Ydv4/Oeci0XQzLmXrGj+vS8NJPAbhXuZzO5mgVV1dbly3zIQHP9dscSU4fgUQDaq4nR5EXDH8YunJN82aq/pGlumjwVTP5ip9Z7ugNpXQkJK/nSWwXwdhOHFklWUQS7VQGT303ZrANf7qHtm0/BLz8Bstx6IlXILFWDeYav1X++Cc/ByaeMWkOz++LXCEkSaI14qc55GPPYIoth+LESslVqxLbcdk9kCKWMWmvYM8b2UgQie3AF99fsfsoaB01vXehrHTbxnQc1CkqfkIDz9GR2UXW9bNn0fGxm5LXGZE5u8tLtrx/7/RPQIoiISExmNKLbwBX6PGP4gctVuQXn0cszWt6N4tAJWVYxDLmaDltfjclvfBC7EDTrJfo+CL0n3kzAM27f0VweJt3PyE/Q2md4UyZE2vHTlKusMGUwaGRLG3RwJweVlduQZ9CV2OIWMZk88EYB4bSM84Kq4WDw2kOjWTpbAhUJC9FNhIEhl8i3Pc0kfgOpBofU4tAZR4xLRfbdqbsodKyw9tNuce+hFO726q5tLqQP/75QwGDCqNBlVimhAZw+UDlyLPejsFU1KB39GHPzQS+WbN0cG1vUGOJRlJeSbJflZHMDA2HHgFyxz5lkuk6l0T3q5FwWfDMV5FsA1WR8CsKvXGt/Im1VZikrFvekU89TUauJ4ossaAxiF+R2dabZFtPgoxRP/9OB5I6uwfTNId8Mzb2LJasx0cDFH9iP7YawlYjZb2PktZV6wUI5eOdq04epASGtxMZfB7TVfghr+eU9uPvCeqCxSoNfonBrMszfdOfQftVGct2i++z0HoShFq9HYPeLVPfTg0c3wm1s8zP0S2HwfTRkuSGQ48gW1mM6JKyz6waOO29WIEW/KlDtL70I+/+gipJzaQ/UeZdlSpMUj44nGEoXb+TketFQ9BHRzTAoZEMzx2M0V8HibZp3WJXfxIFabS5YTmMBij9z+BP7MNWQ5jRhbi+cNnuYzZEoDKPaKY95TZg646fAfBL++Us6uqaOo9lHvMrEpcuzQ8qnDkAiQZUhtIG2WI6WEpSYcc/iv/4TqjVU6CU/kQbz5UkRwIquO6YJNrLyz6zyvE30H/GBwBo2fULAiM7vQqjgI++pFbexNoKT1IeThscHM7QOkcmI9eaL9fRVjcdnj8UZ3d/ymsBUQOm7bCrP0U8a5YtyJT1OIGhbccEKIvqJkDJE4HKPJLRJ5/x40/sJ9r7BA4S37Cv5rxF869FdqEuz/VU+cthi7g+/RNO0K+gmV4eRFEKzVOB43NHxXVzpcmlPdnmpyT7FQUkCMR2EozvxpF9JE64tMyL9aQXXUBy8SuRXIcFz34FyTYJ+hVM26E3oZWv830FJynnJyPbc3Qycq1IkkRbNEBj0MeugTRbDsdKa18wC/nk5954lgUNwVnnFcl6LBegPI0/uR/bF67LACVPBCrzhGm76FNU/LTs/DkA99vnsNtdzHkLj98nqRUtCitaZCwH/rh/5iebkE+lP6Fh2kVs+S4+2yuNHdkLqf6pb6f4IBsr/OvOF7Yxq0TaqUqSU4tejuNvLNsyj9V/+vuw/E0EEvtp2fETAJpDfgZTOiPZMlWIVHCS8pFYloGkXtEqkfks5FfoagwykjbZfGiEQyMZnCol2vYndfYNZmgO+2fVPdgLULZ6OyjJ/di+iBegqKEyrrb8RKAyTxi2jeU4Ex7EarpvNMnw69YbWdki0xo6vn/tly/3LpC/zw0qVLRhfKmeSW8bCaikDZtYMReiYBN0rPb+/9BTU99ODXo7C07992woK0sDWy8pUHHd8SXJspmuSBLtZJxAEwNnvB/wjlL9sT2oioRPljkSyxbfzXgqsgLpwbL2U4lnTfYNpmkM+ubVVN1puS445U2CzSfaKpLMi4fjvNSbKG02WBESmsnOviR+RS55J2x8gHJwzgQoecf3FWseMSwXy3ZQj3kSatn1CyTXYYvvdLa4J3L+cXzsk/fqpT58MuyJOezuTyG5Dq4aQjYmVltIEvgVmYFEkQ3glpzr/XlomuMfNVCz+S41ZWreTKQSutKmTYuRjDFaktxwcAOyraM3dKO1nVLmhU6UWvwKkosuRHJtFjz7FXAsGkM+kppVvsTacKvXMLD/pbJUhdm5Nvm65ZSlMVjWsPnl5sNs3DuMWa8zchybwMgOIj1PEhh+CTXTNzq3qRyaQj7ao0EODGd57lCMgWRlZkAZlvcclTFsWorNS3HdowFK39gAZeGcCVDyxFVrnjAs27uqjolTFG2Exv0PAHC7djUA5y8SzZ0aAxIvX6Ky4YDF/XsMll98EpJjEhjZheOf2AQvGlCJZb0GcC2FNsfqPg+e+Z63o+JYk5fhqgHvGMTMQqBhlt/VHGJpTFWdNpORlImZK0n2kmh/B0Bi2RVlT6KdysDp7yc8+ALB+B5ad/yM4dVvJRpQ6UtoNEd8RGeb/6H4IdrpHR1Kkrc7N4vE496ERk88S2fD7Nvk247Lv//hJTbt81r9R/wK5y9v4+Ur2ll3QnPZy2VL4tgEYrvwx/fi+CP4kgfwJ/bjqkGsQDN2qB3b34jji87qMeNXZRY1BRlKGzx3KMbytggntIXL9jNwXS/A7EvodDUW8btzXRQ9jpo+jC/dh+SYWMEWXHXujkkQgco8oVsO0jFP/s27f4XsGAxFV7FhcC3NAYlVrXXwRFIHrljuBSoPHvHzN6HFBF0Nf+owspnG8Y3vG+A1gPOOHJpDvsKe2zpWe8GHnvReGXedOvVtqzyIruaMtNcvpEi65TCY0onkAoHgyHYCiX04sp9Ed2WSaCdjB1sYOO1v6Xr6P2jd/hNSCy+ApmWkUxa9MY0T26OlfHvjqQEvWBneA0jQubqkKedp3WLvQIpooDw9N37wxH427RvBr8hEAyrDGYM/bu/nj9v76yNocWwCsd0E4nswQ23exTnQ5O0uWFnU7CC+dA+u4sfxN2KGOnD8Tdj+hpJ+vpIk0R4NkDEsdvYnSWgmJ3VGaSzDzlVPXGP/cJq2iL+w47oJAYqFFWye0wFKnghU5omUZuEf88QgGyma9v4WgF+FrgEkzl2oipLEnHOjQ3SGo/RnXB7fO8LFqzowIgsJxPdMCFTAawA3kjFIGRYNhfQvkBVYfA7sedg7/pkqUFF8oMVn+d3MMSVW/MSzJppp0R71nnhHk2gXv2LSnbBKSi65mOjhPxPtfZIFz36Fg6+8neagl1jbGvGXp3w0H6yM7MntrJxc1MXUcbxKkbRhsahp9tUcf3ypn/99xpthdculK7loZTvbehI8umuQv+waqn3QMhqk7D4apORJEo4vjJOrapEsDdlMEcwM4soKji+KFe7ADjTj+BuLHtYa9qsEVIX+XLn6SZ1RFjYGkUvMB4pnTHb1pwj51Jmb8s3jACVPBCrzgOm46Md0pG3a+1sUK4PecAJ3xc4AEPkpOYo2DP4Il67u4MfP9PPgtj4uXtWBFVmIP30E2cyMPqHl+VWZhOYwkjYKC1TAO/7Z87BXpnzOuya/jRo4mlBbwiu6Occ2vRyVIi8EoyXJqgqSF4hHD/8ZgPiyKyux0ulJEv1nfIDQ0AsEY7to2XUvI6v+GlWWORLPEg2q4144lEwNQKQdhnZ7wUr7qoIfJ/1JncNlmoz8Um+C/3p4JwDXnr2Ei1d1AHDKoiZOWdTEey86sbZBy3RByiRcNYitBrGDgGOimBkCsd3el/JFsIKt2MFWbH9jwSW7iiyxsClEPGvy4uE48azBie3Rorv/aqbNzv4khuWwYLojn1yA4ksd8nJwHGvOH/FMRVy55gHTcrBsh6Df226ULI3m3b8CYO8Jb+bA094MvLO7xK9bNtNIjo3WvoZXNzTx42f6R8e7L2hswAwvwp/YOyFQAYj4fQymDDoaAoQKefLJ91MZ2O6VIYeaJ94m30rfzBwfeSpm1svL8RVXRpwvSW4OeQFOw8GHvSTaxqVorasrsdIZ2aE2Bk59L13PfoXWl+4mtfB8GqPdDKY0BpM6i5rLlLCoBiHaDoO7gHywMv3FXjNt9g2m8Suzn4w8mNL53G+3Ydou5y9v5e0vWzrhNrIk1S5oKTJImbh4H3agyZsP5drIZhZf6hCB5AEcNYTlb8IOd2D7G3J5LdOvuSnkI+iTOTCYIZG1WNEZLbgk3HFc9gymGEzpdDVO8fhxXRQ9hi91eN4HKHk1TVi44447OP3002lsbKSxsZELLriA3/3ud7Vc0pykWw6W447uqDQeeADViGOGF/Ab50IATm1XiPqP72MfyTZR9ThG00lY4QV0NQY5fUkTLvDQtj4AzGgXruKfdAhXyFdkA7hwG7SdBLhweIoyZcWfq/w5TjrUWnrRU5Nd17tYkitJHptEG69iEu1kkidcSnrB2ciOyYJnvoKETdTvoyehkS7nfBg1CJE2GNrlvU3TYc5rDpYhljUKT/6egmbafPa+rcQyJsvawnzsspNnPD7OBy3ve+VJ3PnOc/nCX53GVacvpDXsJ23Y/HF7P5+5byt/850n+fIDO2ZXPTTbIOVYkoLjj2JFujAiC7GVAKo+THBgC+Hepwj3PoUvvh9FG5m29DmgKixsDpHRbZ4/FGPvQAqrgO/xcCzLwaEM7dHAxLwU10XRRggOvUio/xl86SPY/oZcFc/8DVKgxjsqS5Ys4Qtf+AIrV67EdV2+973v8cY3vpFnn32WU06pfKnhfOH9I3e9QgrHomXnLwAYWflmnjzg/eM47o99XAdfph+98QSMhu7Rd1+2ZgHPH4rz2xd6+auzlhD0N2JGFuJP7MM8dldF8hrADeaaZhU0hmDJed62/cGNsOI1Ez+ef9I/XkqU863hiwguvCnJBg25kuTg8Dbv1a4SILnkkkqssnCSRP8ZN3PCHz9AaGQ7zbt/DSuuIZ206IlpnNQRLV8c5QtBuAUGdwCSFwRPsrMymPLa5LdFZjcZ2XVdvvrQTnYPpGkMqvzz69cS8hd3jFHRnZZ8kJLYU54g5ViShOsLY+XzWmwd2cwQHHkJV5K8vJZQO06gBTvQiKuM3zWRJYmOhgBp3WJ7X5KEZnFiR2TKEvHhtMHugRQNQR8BdczPeewOSroXCQcrML9yUGZS06vX1VdfPe7vn/vc57jjjjt44oknRKBSBN20kXKbYw2HHsGXHcAKNNO36FI2b/Lq+4/ntvkAaqYfK9SO0XTSuDP+V6xo54dP7qcvofO7F3q4Zt0SzMhCfOkjSFZ2Qr+BsF9lOKMTz5q0RwvYFeg+D577MRzaBK4z+baxoh4/CbVmtugdkFjaOFqSDKO7KcnFF1U9iXYyVriDwVPfzYLN/0Xb1h+Q7jqPptBCBlMGrVGD1nAZh//5whAChnZ4P8e2k8b9PHXLZt9QGlma/WTknzx1kEd3DaLKErdeuWb6fIkClDVoGRukBFurctF2lQC2EsAGcCxkM00gvhfYi6OGsYMtWLnS57F5LZGASkCV6U1oJHNVQV2N49vgZw2bHX1JbMelIZILZFwXRR/BlzpS0wDFrINWOXVTq2rbNvfccw/pdJoLLrig1suZU1KG5f1jdp3Rdvmxk97E5kEZ04EFYYmljXXzq646RRvBVYJoLasm/CNXFZnrzvF2WH7xzGE008YJNGGGu1C12ISvJcugyjIDyQLnuyw41XslnB3xtuwnowa8QKVsA2PqWJEVP15JsjFakiwbSaKHHwVqlEQ7hcTSy8l0nInsGCx49mv4FC+5sidW5PiFQvjDXvfjwe1e+fKYib6HhrMMpWY/Gfkvuwe5+8kDANx08UmcurhpVl/vWLM6HqpBkDLxG1C954noQszIAlxZQU33EOrfTKRvI8H+Z/ElDyEbCXC9juGLmkI4LrxwOM723iRabtip7bjsHkgRy5heLkuuW3Zw6AVCfbkjnmATZqSr6t/r3pjNzY9HePxQmUZElKjmL7O3bNnCBRdcgKZpRKNR7r33XtauXTvpbXVdR9ePdgBMJBLVWmbdshwX3XTwyRKRnicIJA9iqxHiy1/Hk895Z6jnLVJnPcRqrpLNDJJtoLWfhhOY/Mn21Sd38pNNB+lP6vz+xV7edOZirMgifOleJEub8OQQDajEcw3gmmfKAVB8sOgs2P+Yd/zTvmribdTQmITa2u8QVIxjez1UighU4lmTrGnRkStJbjzwR2THRG9cjt4yyc+yViSJvjP/jqV//CChoRdp2nMfzvKrGUprDKZ0FjaV+QLjz5XQ928DJGhdzkjG5MBwmubQ7Nrk7xlI8aUHdgDwhjMWcfkpXWVY8NSK22lp5VVdBheED2NGahSkHEuScfxRb3fPdZFsDVUfwZfpw5X9udLnTuxAEy3BBjRbYd9QmqRucVJHlETW4NBIls6oH58+gi95CDXTDzjYwZYJR0rVsqnH4rOPZchYMt/bkuZtl7sll1vPVs1fZp988sls3ryZJ598kve///3ceOONbN26ddLbrl+/nqamptG37u7uSW93PDEtB9P2ApXWHT8DIH7iVdhqiCePeIHKcZuf4pgoWgyj6USs8IIpbzZ+V+UQumVjB5owIwtQtZFJbu81gPMSPAuQr/45tGnyjx8vCbX5ih+1sCde24H+pEYgV5I8Lol2+ZU1TaKdjBVZwOAp7wSgfetd+LO9RPwqPXGNTCXmwfgjEGqCgW1Yg3vYl5uMHCm0fH4SIxmDz9y3Dd1yOLO7mXe9fHkZFzyzmXdaBviXR+K86cEoX3zK5YnDJka5d6xmQ5Jw1RBWqB0zugg70IhkZwmMbCfUu4lIz0YaU7vp9iVJJVM8d3CE3QMp2qQkDSMvEup7GjXTix1swop01SxI+b9dBv/8pwwZC05tsfi3VzfVLEiBOghU/H4/K1as4Oyzz2b9+vWcccYZfPWrX530trfeeivxeHz07eDBg1Vebf3JV/w0DD9PMLYTRwkQO+kN7Is7DGRc/Aqc0XkcBiqugy89gNmwBKNx6YwXtVev7qSjIcBIxuT+F3tBkrAii3BlFSmfADpGvgFcUi+gsmNJLlDpfQEmmSd03CTUWnpuGGFhlSheSbJFJJfAGRx6EX/qEI4SJLnkVRVcaOniy68k03Yqsq2z4NmvEfbL6JZFbzw79oSmfPxRCDQwvO95kr27aJ/FkY9pO6z/7TYGUzqLm0P8w+WrazrAcFzQcuNZfOk1zfzVMoO2IKRNeGCfySf/nOW6Xyb5tyey9Re0AK7ixw62YEYXYoU7cCWHQGIfkYHNLE0/R2tiO23xbbTHnssFKM01DVBsx+Ubz2p87SkNx4XLlvn413VZGgO1DRVqHqgcy3Gcccc7YwUCgdFS5vzb8S5/btu686cAJJa+FjvQxJM93gX0zE6VoFpfrzyrQc0MYgdb0ZtPmnzOzjF8isy1Zy8B4H+fPoxhOdiBZqzwAhQ9NuH2flXGtB1i6QLObhsXQlM3uDYcfnby2ygqaPP8KNPSwGXGPhRwtCRZzpckMyaJdskrJ+1zUxckmf51t+AoAcKDz9O07/c0Bf0MJA1i2fJNQx4rJYU4oil0ZPcQzBwu6Wu4rsvXN+xiW2+SiF/hn1+/ZnTwY805NsHEHs4OHuID50T50Rsb+PKlYd600k9rUJozQQuy4rXtj3R5eS2Kj0ZrgCZ7yEvErWGAApC1XD79WJb/3e49p73jtAB/f34QXx1ECTVdwq233sqf/vQn9u3bx5YtW7j11lvZsGED119/fS2XNadopk00vovw4PO4ksLIimsA2HgcH/soegwUH3rLqqKmhL5mzQLaowGGMwZ/2OrtqpjRRYCMZE8MnqO5BnBaIWnx+eOfg1NMU1YDoMXmd0LtJDtTU8mXJOcvlrIeJ3rkMaC+kmgnY0YXMbTmBgDaX7yTkDGILEn0xLKYTnkvnrYDPTGNrBTGH24kMPwSvuShor/Or547woPb+pEl+MQVq1nSUieBoGPjj+8mGD+aOCtLEqd2qHzw7CA/fmN02qDlnm06TkW2smZJkr0OuOFOrMiColv2l9tg1uFjD6X5y2ELnwz/dEGI60+ZXXl7OdU0UOnv7+eGG27g5JNP5tJLL2XTpk3cf//9XHbZZbVc1pySMRyW7Pf6piS7L8EKd5I0XF4c9M7Ez1t4fAUqkpVFsjT05pXYweaiPtenyPx1blfl508fwrQd7EALVrgTZZIKoJBfIWvaxLIF7Krkj38ObWTSMwA16F3IyziKvu5o8YKPfWJpA8s5WpLceOAhZMdCazoJvWVlJVdZFrGTriLbugbZytK5+T9pDKrEsgaDyQLzmgo0lNYZTGo0h/w4/gYcX8gLVlKF76w8tX+YOx/bC8C7X7Gcs05oKesaSzZJkHKsmYKW7zync+uGDMPZefwCYJZ2j9jc8oc0O0ccmgIS//7qMJcsnf1QxXKqaaDyne98h3379qHrOv39/Tz44IMiSCmC7YA7vIeW/idxkRhe+WYAnuqxcFxY2ijTFa3tvp1pOaQ0qzobBY6Fqo1gNJ2IGVlY0pd47doFtEX8DKUN/rC1L7ershhwkexjAhIJgj6FgaQ+8yvlRWd4F+lUH8QOTPy4EpjfCbWO4+XnFJBImy9JDvtyQbbr0rT/fiCXRDsXSAp96z6EI/uJ9D9L86EHCPlUeuMa2TIl1mYNm8OxLEGfOtqV2vE34qiBXLByZMavcXAkw7/fv93LR1i7gKtPX1SWtc1aAUHKsY4NWj56bpCgAs/02bzv92me7i1jp+B54skjJh95KM1A1qW7UeY/L4twSvv4F7fB9BGUzECNVuipg9MnoVSGZdO+29tNSS+8ADPXcXVjT30c+1iWSyxroMgSQ2mNWMbEqtS5seviy/RjRhYWlDw7lfG7Kge9XZVgC1ao02ubfYyIXyWlW8RnaquvBmHhmd7/T3b8M98Tai2t4Nb5sYxB1rQI53qnhAa34E8dxlFDJBe/stIrLRuzYQlDa7xj7PYt36HJjZE1LXoTs0+sdV3oiWfJGhbRY6p8nEATruInMPwSarpnyq+R1Ew+85utZAybtQsbef/FJ9XHVn8JQcqxZEniypP8/NdrIyxrkonpLrduyPCd5zSsMh+/zVW/2mnwL3/OkrXgzE6Fr70mwsL8C1vHInr4URY/eiurn/0Uzbn+XLUiApU5zIgdoa33TwAMr7oW8LK2N/Uc7Z9SK7btMpLV6WoMcnJXA6sWNBINKMSzBsMpA9Mq7xaLmh3ADjSjN68EeXbblq9d20VrxM9gyuDBbX0gyZgNS/B2VcYHJLIMqiQzmNJn3jUae/wzGVkBLTmrtdctSwNr5kDFclwGUvrRkmTGJtFeXPAk23oRW/EmtJZVKFaaBZv/m6agj/7E7BNrhzMG/UndG9I4SWxhB5pwFZXg8DbUdO/Ejzsu/3b/dnriGp0NAW69cnVlphoXqwxBylhLmxT+67IIV63w4QL3bDP42EMZ+tLH71GQ7bj89zMa//W0V9lzxXIfn784TNQvoWQHad12N8v/8C4WbvoC4cEtuMjIVm1fQNXBI1MoVtqwODicwXjmR0iuQ7pj3ei5/fZhm7juEvHBKe2za6FdKsfxnkjbo0G6WyMEVJn2qJ9VCxo5uauR1qiflGExmNJHuzPOhqzHQVK85NkyXMj8qsybz/J2VX6Wz1UJtmKHOlD0ibsq0aBKPOM1gJtWPqG2Z/PkiaVqELSRyXNY5jpL90YIyNM/JpOaRVI7ukug6DGiRx4H6j+JdlKSQt+6D+PIKpG+TbT1PIIE9MZLT6zVLYcjsSyqLOObZjKyHWjGlRQvWMn0jfvYtx/dw+aDMYI+mX9+/Vqay9nmv1Sugz++m0B8b1k7zgZUiQ+dE+KfLwwR8cHWIZubfp/izwcrU4VVz7Kmy22PZvnlDu8Y+91nBPjouQEah7fQtXE9y//wLtq2/xhVG8YKNDN08v/H1nM/z8DZH6vpukWgMke4LiSyFnsGU2zrSTJ0cDtthx4CYCS3mwJHq33O6VJRa9EDwYXhjE5L2MfStvC4wX2KDC1hHys6oqzpaqSrMYBuOQwkdbK67ZWuFkmyNBQzk0uebS3bt3H5KQtoCfsYSOo8tK3f21WJLkZybXDGP8Hl8wOG0zMkSjafANEFYJtw5LmJH1cDXgAzH/NUCkgSHluSnJ+113jgQSTXQmte6ZWaz0FG4wkMn/w2ADq2/A+tcopY1mCo0IaBY7gu9Ma9mTFNoZl3Du1gC64k54KVfgB+/0Ivv3neOxL66GtWsbw9UvQ6ys518Md2EYjvxQq2VKTj7MUn+Ljj8iir2xRSJnz6sSxfeypbf2XMFTKQcfjIQ2meOGLhV+DTL3N5X+APLH34gyx57J9oOPIYkuuQbTuFnnM+wd7L72R4zdsxA7VPrhaBSp2zHRjJmOzsT7GtN8Hg8DDLdnyXMzZ+DNkxyLauJtt+2ujtn6zxsc9w2iAaUFna5u2kTEaSoCGosrw9ypqFDXS3hLBdl4GURkqzCt9QcGzU7DBG4/Jcwmv5BFRlzK6Kl6tiBduwQp2TdquNBFSG0yYpY5qEPUma/vhHnccJtXrS6xUzjWNLknEdGvflkmjn4m7KGCMr/wqt6SQUM0XXlq8TVBSvrLjIHcVY1qQ3odEYnPzIZzJ2sAUXb+r01t37+cafdgPw9pct5YKT2ov8TirAdfDHdlc0SMlbGJX58qVhrlvt7SD93y6Tv3sgzcFEBToH15FdIzZ/90Ca3TGHcwIH+UP393n7lnfT+fw3CSQP4ihBYsuuZP8l/8Whi75IaskrZ32EXk4iUKlTpuMymDLY0Zdge2+CkZRGd9/DnPX4LbTv/T8k1yG18GX0nPuPo4mYg1mHXSMOEnBuDcqS4xkTvyqzrC1CuMBx8BG/SndrmDULG1neHkWWYCitEc+Y0+d8uC6+TB9WZAFG07KKtFO/4tQumsM++pM6D2/vB1nBjC5CcmxwxgckAZ+MYduMpGYoVZ6un4okey+ZzXSZvoM64bq5QGX6ip9jS5JDA8/jT/dgq2GSS+ZOEu2kZJW+sz6EKylEe56ga/AvZE2b3rhWcGBu2i5HYlkkvMdbMexgK4dTDp976AC24/LKle1cl0sar6nRIGVPxYOUPFWWeO+ZQT5/cZjmgMSemMMH/pDmD3trO3ivUh4/bPKJh+JcqD/Gr0Kf5ufSP7Cs9/fItobe0E3/6e9j7xXfZ+DMD3rPpXXo+GqyMQfolsNIxmAgqZPSLVRZpkvbQ9cL/0MwthMAI7qEgdP/lkznWeM+d1Pu2OfkVpmWYHVj0JRmgQTL2iM0lNDRMuiTWdgUpC3qJ54x6UtqDGd0FEmiIegbPV7JU7UhbH+jl5dSoWZJAVXhzeuW8J3H9vLTpw7y6pM7IdiOFWpH1Uawwh3jbh/xqwymDDobg1PuJrF4HUgKxA9CosfrWjuWLM+/hFpL9xJppylNHp2S7Dv6Km40ibb7kvoYPjdLRtOJDK+6jrbtP6ZzyzeJX3waAymJlrB/5uGWQF9CI541aYsU3700Y7r886YACcPh5GaXj1zYVvsKnxoEKWOdu1DljisifPHxLJv7bf79SY1n+2z+7uwgYV8dVD/Nkuu6PPhiD/ZLv+WPysN0qHFwwZVkUgsvIL789d5ufK0fBwUQgUqdyBo2w2mD/pRO1rAI+lQ65Ayd2+6i8aCXi2KrIYZXv43YiVdNui139Ninult2Wd3GsB1O7IjQUsAT7nT8ikxHQ4CWiJ9E1qQ/qY+W/kaDKn5VRja8C7nesgrHV9nz9StO7eLnzxyiL6GzYfsAr1m7ADO6GDU76E0DHpMcGvarDKZ1RjIGXY1TPOn6o9B1KvQ85x3/rH3j+I+rQa9DrevOiSeQgliaN+Mn2DDlTfIlyfkpyYo2QrTnCQDiy66oyjKrYfjk64j2PE4gsY/FW79FfM1H6E1oRIPT55QlNYveuEZDQB3N3ymU7bisfzzLvrhDa1DiM+fqNMVfQvMpZc3rKko+SIntxgpVP0jJaw/JfOFVYe7ZZvD9F3Qe3Geybcjmny8MsaKlNsUIs+a6BPqfY2Tzr/nbzFOoqrc1bQZaSSy7nPiyy7FDdXDkVwQRqNSQ60JKtxhOGwymdXTTJuxT6QgrNO/5Na3bf4ySS0KMn/AahtbeiB2cPLHJsF2e6a1+fopm2qRNi2VtETqi5ZtTocoSrRE/zSE/Cc1kMOUFAKmURrMbx11walX+sQV9Cn+1bjF3/mUfP336IJes7oRQO3awFUUfGb8GyduFGUjqtEUD+Ka68Cw5zwtUDk4WqATAzCXU+udWKe6ULC0X1E3+uMyXJAfHlCQ3HngAybXJtpyM0VTdCb4VJfvoO+vDdD/yURoO/5nFC1/BAfcchlI6C6YIbi3H5XAsi+24BAs8Uh3rri06TxzxWqN/6qIwLW0NSJl+gkNb0dpOmfI5pWJcB398z5ggpfAxF5WgyBLXnxLgjE6Fz/8ly+Gkwy0PpPnbM4O8caWv9jtPBZKNFA0H/0jjnvsIpg9zAoAE+8On4Ft7FelFFxQ096weiRyVGnAciGVMdg+keKk3wZFYFr8s09EQpCP+HEsf/js6XvwuipVFa17JgVfeTv9ZH572CeWFAZusBS1BiZUt1fm1mpZDUrNY0hyaegdhlmQZmsM+TuqIsrozwmJ/kkS4m312G4msiVuFUt7XnbaQxqBKT1xjw/Z+kFWMhm5ky/AuwGNE/CopzSQxXZ+M7nO9P48841UAjaUGvQqZ+ZRQa2nTllyPTkkOHE2ibZonSbST0ZtXMLLyrwHo2nIHUTdNT1ybcmbUQFJnOK0XVOVzrIf2mdyzzcu9+Nh5IVa3eYGOFe5EsjWCQ1snHQ9RMfkgZWRXXQQpY53aofKNKyJcsFjFdOC/n9G47dEsCb2+q4L88T10PvufLL//Rjq3/A/B9GGSbogf2pdx7ylfxXjtF0kvuWjOBikgdlSqynLcKY8z1HQvHc98i2jvk95tA80Mrb2RxAmXFjRtNt+N9ryFKnIVXgFYtstIxmBRc5BFzeGKn1JIuDRYgzR0n0Rz61r6My5H4ho9CY2QT6Ex6KvYSPqgT+GadUv43uP7+MlTB3nVyWN3VWLYobbR28oyqLK3q9IS8k++Td+2AkItkB2Bvhdg0box32g+oTYDtE3yyXOQnp6y4sd1YShljCtJDvc/iy/Th61GSC1+RRUXWj3DJ7+VSM8TBJIHWLbju7yw6mb6EhpL28bvoqUNi564Rtinjk6RLtS2IYvbN3oB7/+3xs+ly8YHOla4EzXdR3BoK9n2tTiB5ll9TzOq4yAlrzEg86lXhPjVTpP/2azxl8MWO+9P8U8XhDi1o34ul5JtEj3yGE177yM0vG30/btYwl3mZWzwXcQ/vqqNk9vm6PHVMernJz+PGbZDLGPSn9RIahaKJNEY8hJEJUujdevPaN71C2THxJUUYidezfDqtxaVf/FkFaclOw6MZHQ6G4MsaY1QlYaWmSHwN0DHyUSDEaIRWNQcYiCpcziWpTehEVRlmkI+1Aos6PWnLeQXzx6iJ67xyI4BXr26E6Oxm/DAc9iu7SXI5uQbwCV1i6bQJL8PSYYl58LOP3jHP2MDFfC+lj6PEmr1xJQdaVP6MSXJQNO+3wOQPOHV8yKJdjKu4qPvrA/R/cjf03hoA4u7XkG/ehbNYf/oY8Zx4EhMQ7ds2os8Vh3MONz25yymAy9bpPLO0yf/fCvciTrmGMgJNM36e5vUHAhS8iRJ4k2r/JzSrvDZv2Q5knL42B8z3HBqgP9vjb9iL4gKoWb6adr3exr3/wFVjwHgSgr7mi/gnwcu4TFrNcubFL74yjCdkflzYDJ/vpM6lDVteuIa244k2D2QRDddWsNeoqgqQ/TQIyx96CZad/wE2TFJd6xj/6v/i8HT3lNUkHI46XAo6aBIcNaCygYqruuVD7dGApzQGp46D6Oc9KR3xNKxGoKNo+8O+hS6W8OcvbSFM7qbiAZVBlI6/QkNo8wt+kN+hWvO9Hq1/PSpg9iOixVqxwo0o+jxcbdVFQkXl6HUJN1n87pn6KeSjc2PDrWW4R39qJMHKiOZ8SXJSnaISG5XcT4l0U5GbzmZkRVvAmDxC19H0lP0xrPYuYfu2MnIxdAsl399NMOw5rKsSebWC0JT77JKEla4E9lMERzaimwkZvEdTWEOBSljrWxVuOPyCJcu9eG4Xq7PrY9kGKr2JGbXIdz/DAuf+CzL/vAeWnf8FFWPYQbbGFx9PV876Zu8uucDPGat4dyFKl9+TWReBSkgdlQqImVYjKQMBlNeJUPIp9IWCY4ej/jje+h8/puEhl4EwAwvYODU95Be+LKSKj029njHSKd1KET8FQwcXBhJGzSGvK6zU5bglpOlg5aAzrXQsGDSm/gUmYVNITobggyldXpiGgMpHcdxaQ75CZWQgDiZ15++kHufPczhWJY/7xzgVSd3YjacQHDgeezAMbsqAR/DGZMFhkXEP8k/s8XnABIM7Yb0IETGJOXm81QsDXxz40l9SvlhhJMkBmumw9AxJcleEq1DtnWtN1xynhtecz3R3ifxpw6zYs/32Lrq/bSm/USDKodjWQJjJiMXwnVdbt+YZcewQ6Nf4tMXhWcutZUkrPACfJm+ozsr/qkrtMBLos+aNiGfQkCVp044rbPE2WKFfRL/8LIgZ3Up/OdTXvnyTb9P84mXhSreq0o2UjQeeJCmvb/Fnz46CTvTcQax5a8n3nke//WsyX27vef/q1f4+OBZwZru+FSKCFTKxHW9EsLBtMZwysSwbaJ+n1dumXvcyEaCtm1307T3d0g4OEqA4VXXEltxDe4MzbCmkz/2qXS1TyxjEvDJLG2LEPJV4ezTsSHVDy3LoWXmi5YiS3Q2BOmIBohlTHriWfoTOsMZnaaQf8KU2WKF/SpvXLeYHz6xn588dZCLVnYghdqxA00oemJcsnPAJ5PQTIZTJpHWSe431AwdJ8PAS3BoE5w8JmlUDXglymZ2ngQq5qRHP/GsQda0j1aLufaYJNr5vZuS5yoB+tZ9iCV//geaDz5EZ+eFHPGfTTSokjYsOqPFHX39aKvBhgMWigT/8orQ0Wm4M5EkzPACfOlegkPb0NrW4vijk940rVskNJOmsI+0YTGUtpEliaCqEPKPCVzGBinB5jkXpORJksRrl/tZ3abwub9k2RNz+KdHMly32s87Tw+UfVRJILaLpr2/peHQI8i2N2bBVsMkTriU+PLXYTZ0kzZcPvNohqd7bSTgpnUBrlnlnzMVSsUSgcos2Q7ENYPBpMFIxsBxXRoCPprG9hPJPQG3bf0BiunlHiQXX8TgKe/ECnfO6v6zpsvz/V7lSSXzU5JZC1mB5e1RGmZ5wS9Yqg+indC+csZhdmNJkkRLxE9LxM+SVpO+uEZPXONwLEM04KMhWHrC8dWnL+SXzx7m0EiWx3YN8spVHZgN3QQHt2AHmsYlPkf8CoNpnc7GwOS7T93neYHKwY3jAxVZ8RIUzAxQoz4X5TLZ8EW8xPL+pE5QVUYD+XDfM/iyA9i+BlKLX17FRdaW1raW2IlX07Ln1yx98Q6eid5O1ozSMsVk5Kk8esjkri3ehe3vzg5yRmeR/04lCTPSlQtWtk4arCQ1k7Rhs2pBlO7WCLplk9ItklmL4YxBWrcYzjhIrkNL9gCB9F6sSNucDVLGOqFR4WuvifDNzRr/t8vkpy8ZPD9g8/8uCNFVaEA4Bck2iB5+1EuOHdk++n69cRmxE6/yJofnfoa9KYdP/jnDvrhDUIFbLwxx4eL6aXdfCSJQKZFpu8SyBgMJnYRmIiPRGPShquOfWYKDL3jzFBJ7Ae+BN3Da35LtOL0s63i2z8J0oCsi0d1QmaOYtGZhuw4ntkcnTw6thMwQ+MJeXoqv9ITKxqCPxqCPxS1e4u2hkSy98Swhn0pjqPhKobBf5Y1nLuLuJw9wz1MHecXKdqxwR25XJT5uVyXsVxlIacQyxuQ9MpacB898Hw4/NaF5HLICeqrUb7t+GJlJq9YSmklKt2gNH91JzCfRJk549ax2GOeiobU3EOndiD/Ty6q9P+LQaR+YdjLysfbEbL74hFfh86aVfl6/osRuzZKEGVmAL1cNpLWfMpovF8+aaJbN6q4oS1rCSJJE2K8S9qt0NsCJrkvGsEnrBlrPDrShvaSURnRdBl0noHq7LX5FLioAqycBVeKWc0KsW6By+8YsLw3Z3HR/io+dF+Ki7uKDBTXdezQ5Npcf5EoqqcUvJ7b89Wita8alA7w0ZPPJP2WI6a7XvO+VYVa1zo/KnumIQKVIY1vcJzULvyLTHPJPKB1Us4O0v3gnDYceAcD2RRla83avL0QRuwMzyZcln79Irci2n2baaJbD8vYwbZEqjYI30mCZsOgU74ikDMJ+laVtKl1NQQZTBgeHM/QlsvgUheawD18RlUJXnb6IXz57mIPDGR7bNchFK3O7KkMvYLtjdlUkCKoq/bkGcBO2iDtXe51q9aS3s7LglKMfUwNe+fJc71CrJyYk0rouDCYNlDElyWp2kEjvJuD4OfYZy1WD9K+7hSWP/RMtB+7HaD2ZxAmvLqj3xYjm8C9/yqBZcNYChZvWzTLIk+RcsNJLcHgb2da1DBsqtuuwdmEji5on3x2RJImITyaSOATOIdylJ6ARIGvapHWbuOYd9SU0L6diLgcuF3X7WNmi8PnHs2wbsvn0Y1muWmFx05lBAurEb0Y20/gTB/An9+NPHCCQ+1PVjw44NUMdxJddQWLpayftmfXngyZfeCKLYcOJzTKffWWYjvD8SpqdighUCpTJtbgfSOpolk1AVWiLBCb0yZBsg+Zdv/QqeWwdF4nEsssZXPM3ZS/9c12XjaNlyeXf+jMsh6RmsrQ1QmdDlcpEbQMyI9C5Bhq6yv7lA6rC4uYQnQ0BhtMGh0YyDKUNVFkquAQ0GlB5wxmL+PGmg9yz6SAvX9GOFe7ESTShGEnvCCgnElAZzugksiatxwZ6sgpLzoE9G7w8lXGBStDLUbH0We0o1ZRted/DMfkpKd0ilh1fkty4/w9IOGTaTsVs6K72SutCtuN0YstfT/Pe+1iw+Wu0bfsBiaWvJb7s8imPiE3b5dOPZunLuCyKyvy/C8PlSaaU5NwxUB/64eeQWtawtnvBlN1zAe+4cmg3DO6AcAuSL0wIr2KuNQJL3NBoEm5at4hrJlnDJuHMzcClKyrzpUvDfG+Lzj3bDH6zy2R3f5pPnTbAUucQ/uQB/In9+JMH8GUHp/w6mY4ziS1/Pemu8yZ9Eeu6Lj99yeDbz3nHeucvUvmnC0LzYh5RoUSgMg3XdUlpFoMpneG04bW496u0RwIT/yG5LpHejbRv+Rb+TC8A2da1DJz+PvTmkyqyvj0xh4GsS1CBMzrLu/1n2S7xrMHi5jALm0LVeVHvOpDsg+al0Lq8ojsJPkVmQaOXeDuY0tl6JEFaH9MddQZvOGMxv3ruCAeGMzy+e4iXr2jHaFhMcGgbtr9xdO2yDIokMZgyaAn7J35LS87zApWDG+Hsdxx9vxoALe7lqczVQMXKBVpjSsrBK0m2bffo0YZj07j/DwDEl8+/TrTFGDz13dj+Bpr2/R5VH6F1x09o2fFTMgvOJr7sStJd54xWl7muy9ee0nhh0Cbig8+8MkRjoJz/ZmT63WYixgAn+g7SEuyY+qajQcp2r5mhb2KVlyR5QYsXuPhZ4jJp4BK3TWQJ/HUcuEi2jj95iIbEfj6hHuDvuvbByH4WGwPw9OSfYwbbMBpPwGhYitF4AnrDUoyGbtxJflZ5luPy1ac0fr/HC+betNLPTesC87KyZzoiUJlCxrQYGM7QLyewbJdo0MtpmIwveYiOLd8i0u89Qq1gK4OnvIvkkosrerHNV/ucuUDFX2TXyumMNnRrCLK4JVT0ELSSJfsh0gEdq8p6PDYdWZbobAyS0Ez+//bePEzSurz3/jxr7VVdVb13V3fPvjMgzLBGURCiiCJRgkEl0eS88SCKvieiRiPGhWgu8+Y1MbicHJccxZg3AkJE3xGRTWAGhhmWgWEGZulZeu+uvepZzx9P9TZ0z/TMdFdVD7/PddXVXdW1/Orpquf5Pvf9ve9770COoK7MKYUW9qtcvbGdf9/Wy0+3HeTCZUmsYAtO9hCykZkWPYv4NEYLBtmSRfRYj894O/3Blzxh4q88TlY84baYW+lbZS9CNiWiMlGSPEUQhvqfQisOYelR8m0X1WKldYOr6IyseT8jq64nfPQJYvvvJzi4k1D/U4T6n8IMNJLpvpJ091v5j4MRfrXPO6j/9UVBuqLz+J1xYSRv4Nc1Uq3LiJoD0P+CN1Dz2Eq0Y0XKHGdUnaxw8akKuipP9NypBpJtouUOo2cP4JtI3RxAy/cjcUw/lcpuY9CN8bLTSTGcYtXyZdDQjRHpmrWKajZyhsvfPlbgmX4bWYKPnOPnmpVVSr/XGUKozEKhZDOUNwgm1Vm/GLJZILH7pzS8cg+Sa+PIKmPLrmFk1R9XxeU+OS15/v6N4w3dGsN+upLBeS+9m5XiqBdFaFpVk5LczniQoazBWMEkPkcvzrs2tvOLHUfYP1zgiVeHuWhZI0a4E//ILpwpURVVlXBKLsP5EtHAMTurUJNXfj26Dw4/DcveMvk3SfL8OouV8YqfKcJv7NiSZKZ2or0cV1m46oW9ozY/fK5Ma0hmc7vKxmZlXgX+vCKr5DouIddxCVruMLH9vyZ68DdoxSGSL/2Y+Et3crb9Bt4oX8aaDefNa08Pbx9QJuxT6WkMeVV+Tjtkj0K/BC3rJ6N8pyhSZuJY4dLhQNmaFC5jxYpwKU4KF58qn5TpeFYcCy1/FF/mwES6Rs8eRM8dRnJnbvBmaxHK0e5pUZJSOMVPXg3wg+fKOKPQ/qLM5y4KsOIkezkdzTl87uECBzMOfhU+d1FgQdL7iwUhVI6DF36c4UvgOkR6H6Txhe9PtDHOtWxiaMOfY4Y7qrK2TNnhpeFKWfJ87aQqO6iGgE5XIuiFXKuBUfAmBrdthGBtynH9mkJ3Y5Bne8eI2Oqc2vBH/BpXb2znZ095XpULlyaxQi042YPIZtYTKxXCPpXhvElL1CZ47E4rtdkTKr1bpwsV1QfFkfl6i9XHKE4L2VuOy+AxJclqYYBgvxeJTPdcuWBL+e1+k3/YVqRcmSF59x4DnwLntKhsblPZ3K7SUqfdPM1wB0PrP8TwmvcTPvJ7/Ht/SUN6F1cqT3Gl8hRGbwsZ5Q/JdF1+2pOQHQdGCiWifp2exuBks0JZ8TxjmSOA5PmpFH3eRMpMyPJrhUvJsr2J7RXhUjBszJKJxByFi2uj5fsmjK1elOQgevYQkmvN+BBbDWJEvaiIEe3yxEmkG9vXMGPE/H1rveabX33ca7//sd/k+YuNc+9zsmvI4guPFBkruzQGvMqe5fEzv7LneAihcpL4Rl+m6dnvTNS6G6F2Bjf8BYXWTVV5fdd1ebjX4l93lnBcWBKT561d8mjeIKyrdDcG8WtV2mnbpleK3LQaou3Vec1ZaI74aY15ZczHNQ1O4V0b27l35xH2DeV5ct8IFyxNYkZS+EZfwtEiEzsyv6aQLZqM5g2C+jERo9RmePbfPaEytcpH9XsHe6vsiZbFxjEzfjLF15YkT5hoG89aEJFvOy7f21nmP3d7E4TPbVVoCcpsPWoxVHR54ojFE0cseBp6YrInWtpU1jUp1YsmzhFX0TnS8iZufvY8AuWD3Bx+kHfwMHqhn8ZdPyT54o/JtV9IuudtFBs3nHTa2XG8E5V4UKencYamjrIK0TbIHPau+yKecXYBRMpMyDIEdYXgMcKlaNoUjhUurkvYGiFW7CWQ650SJelFdowZn99R/BiRVEWIdHniJNqN5U+e9LZc36Ty7SvD/MPWIo8dtrjjmTLP9Nv81fl+or7Z962/O2jy9Se8GU3L4zJf+oMgjbWq7HEdZDOPbJdqXnkohMocUcpjJHf9iOiBLUi4OGqAkVV/zOiyd4FcnZDcswMW39tR4qURLxSZ8Evc9Ib5MVqmCyaaKtPTGJq55ftC4LoV82wKEktr/mVQZImuRJDhfJmiYc+p9X40oPGOs9r4j6cPcee2g5y/JOGVdmYPIpu5aa3Ig7rKQK5MY+SYBnCtGzxRUhyBkVe86cpQMdRmPUPtYhMqjg1GbkKouC4M5aaXJONYxBbQRJsuO3zl90We6ffCKNev0fnTDZ4R0XVd9qUdnjxisfWIxa5hm/1ph/1pg5+9ZBDU4NxWT7RsalNJBmofbbEdly8/VuBw1qE5mKLtso+wT/2w1yhs//0ERncTOfwIkcOPYIQ7vFLXrsumRfZmw5uGXqYx7D/+eIypYkWSqyZSZlzKFOGSDGp0SMOYh5+BIztQ+p9FnaXSxpF1jEjqNcZWK9g0p0n1cyXqk/jClEnMTxyx+Mtf5fnMhQE2HNOMz3Vd7txl8P1Kw74LKpU9gRpU9khWCcXIIDsWthYm17ASX6Q6mYLZEELlRDgWDa/+F4mXfoJieX6BTOrNDK39U+xAsipLOJC2+Z87y96ZH+BX4brVPt6zSp+XD3Ku5D1vd2OQiL+KH4lcPwTj0LgKlPr4KMZDOp0NQV4dzNGuBeYUqn3X2R3c++wRXh3Ms23/CJuXJDHDHfhH92BMESohn8pQrkS6aNIcmSI8FN2boHzwcS+qMi5UZBVc2zPUBk4vpF91xmf8+DxPzkwlyaG+railESxfA7m2C+b15feO2tz2SIH+gotfhb86P8AbpzTkkiSJpQ0KSxsU3rfWR9ZwebrPEy3bjlqMlV0e6bV4pNf7bqyIe76WzW0qqxJKTaouvr2jzPZ+G78CX/yDIHG/jIufbPflZLsvRx97ldj++4ke+h167jBNz/8ryV0/Itd+Ceklb3tN87BxLMtltGjQHPF8aSdM+cqqF/107VmnYi84rgvpg3BkBxzdCUd3IheGmSrnXUnBiqbIBDophruQE0swot2YoZZpc7kWkvFJzOubvPb7h7IO/+PBAh9Y5+N9a71JzKbt8o9Plfj/93mVPX+0SucvNla5sse1UYwcspHHVXQsfyNWqBXbH6dYcHGOjQJXmfo4OtQpkbGXSD33dXzZgwCUYssYPOsvKSXXVOX1h4sOP3quzK/2mTiu55m5apnG+9f5SMzTGV7RsDEsh6VNIRLBKu50SmOgaN6wwRqdkc1GKhFkIFcmU7KIzVLpNZVYQOOqDW385/bD3Lm1l009CaxQK07uMLKRm3T7S6CrKgPZEomQPj21kNo8KVTO/pPJ2yVpcXaoPabi5zUlyUztRHv5vEYlp/pR2sMSt10SZEnD8Q9MEV3i0i6NS7s0HNfl5RGHrUdMth612D3isGfUYc+owY9fMIjqEpsqKaLz2pTjhvLni1++YnD3y17K4lMXBGb0LBgNSxk8+yaG1v0ZkcMPE9t3P/70K0QPPUj00IOUI12kl7yNbOotE91mDcshXTRojfpJJUNzn4YuK0AVfROuC6P74eiOCWFCcXT6fWTN67/Ufja0bURqWYem+pELJqMjhYm0Yy0yesvjCv9yRYh/errElv0mP3y+zM4Bi5vO9fPPT5fYOeBV9nz0DX6uXlG9/bBkFVHKGSTXwdYjlBOrsQKJaWlrOM4U+Cohue7inSWfyWSIxWKk02mi0ROHN+fM6AHK93wC3/4HALD0KMNrP0im+61VUeIF0+U/Xirz/71kUKqY/y7uUPnQRt+8liCWTYds2aQnGaI16q9e5sUsejuZ1rO8tE8dcmi0wPOHM7RG5zaNNF00+fAPt1G2HP7mHWvZ1JNAH9uLb3QvZmTSe+OZFcusao1MF4aZw/DTG7zP1433Toq3wrDnBUidP99vcWHJHIHD2yHWQcl02HU0gypLE+k0Nd9Hz5a/QMJl/1u/hxlqO+2XPNaPsqlN5TMXBoic5kTx0ZLDtqNetOWpPou8Ofk3WYLVSYXNbSrnt6ssazjOJOFT5NkBi1t/V8By4Mb1Pt6/fo5pQNfFN7aH2L77iRx+eGLAnaP4yHa8kaHUH9Lv66YjHqQjHqgvT47rwMg+T5gc2Ql9O73y/akouneiUxEmNK+dNUVaMGx6R7zmjrGAVtUS52PZss/gm0+XKE3x7gYqlT2bq1HZ49goRgbFLOCofqxAI1awBcsfn/GEYSBbor0hwJq2eTzGcnLHbxFRmYkX7sK3/wFcZMaWXsXI6htOugb+VLAcl/tfMfnR82XGyp5+XJNU+G9n+1jfNL//KstyyZQMUvEgLZEqihTHgvyQN2gw1lmlFz15WqN++jMlhnNlmudgrI0FNN6+oY27njnMnVsPcl53HCvYip47jGzmJ85gZRkUJIayBvHAlAZw0Q7vkjkMR7ZDzyXe7arPq4pabIbaKf1fxooGpWNLkg/8GgmXfNM58yJSxkqeH2VHZUDn+9bq3Lh+fsLncb/MFUt0rliiYzsuu4ZsnqwIl31ph11DNruGbH7wXJmEX5pIEb2hVSV0mqnZvpzD3z5WxHLgTSmVG9adxNm2JFGOr2QgvpKh9R8mcuhBYvvux5c9SOzgFmIHt5BqWIa6/hrk6GUg1zCy6dgw8mollbMD+p7zzNhTUXzQug7azvYuzavnnHoK6gpLm8LoaoG+TImgphCs1nDVY3hrZRLzlyuTmJuCEl9+Y5ClJ4j6nRaui2wVkY0MEi62HqMY7cH2J6tybDtdhFCZiQs+QungdvYF1qIuWfgprq7r8thhi3/dWeZQ1jPKtodl/nyjj0s653+Gj217OenWmJ/2hmD1Grq5LmT7vANycnnNzbPHQ1VkupMhRvNjlEwb/7EVEDPw7nM6+K/njrJnIMfTB0c5rzuBEWrDl351QqiAV9Y8YwO41GZ44S4v/TMhVPxQHlp8htpSBhQNc7wkWZssScaxiB7YAkBmHub67Bmxue3RAgMVP8qnzj+1AXFzQZElNjSrbGhW+fONMJCvRFuOWmzvtxgpufzqVZNfvWqiSF6Z6rhw6YqeXLSlYLr8zSMF0mWXFXGZ/3H+3DxTM+HoYdJLrya95B1I/S8Q2/9LGgceRx97BR79Bjx5Byy/HNa+c9IjtZA4FgzvnfSY9D372p5Bqt8zmred7UVMmlZ56eJTRFMkuhMhArrCoZEiY3mThqBWk663qajCP701xNajFhuaFGILlT50TJRyBtkq42oBzHAnVrDZK62ewxypemHxrLSaqD5yl36J0vMPs9Ba84Uhi+/uKLNryDsTbPBJvH+9j6uWaQsSinUcGMmXaYr6ScVDVKtVCgD5QfA3nPYOp1okQzodcT8Hhgt0NJz4bDMe1Hn7+lbu3nGEn27t5dyuOFaoDT1/BNks4FRaZU82gCvPLFQOTSlTltXJmTmLxVDrOF7Fj6qTLZrkSiaJ0GRUKnz0CdTyGJYvTq7t9FJaD1T8KIbtifsv/kGAnlj1vBPNIZmrlnvTig3b5blBm61HPOFyKOuwY8Bmx4DNd3eUaQ153pbz21U2Nqv4ZxheN47junztiSL70g4Jv8QX/yB43PvPlVzZxgivJPSms0Evw55fw4v3QroXXvyFd2leA2veCcve7ImF+cCxvFLmIzsnIyZmYfp9tOCkMGnfCI0r5/1gKstetNSnKhwaKTCUK5OYYWZbNdAViUs6F2A/6LrIZh7FyOJKEo7eQKlhBZY/cdx2/fWMECo14lDG5n89W+aRQ16i0qfAe1bpvHeN77TDxbPieiIlHvIaumnV7MpZynilf81rJipB6h1JkkglQgzlDDJFc9YRClO59pxOfvlcH7v7szzTO8YbuuKYwXb0zL4JoQJeA7iRvEFL1D/ZAK7tbC9HnO2D9KFJ/44keemfxYLtGWld1c9Q2kCR5GkHggkTbfdbT/lAZDsu391R5ucVg+nmih8lfJp+lNNBVyTObVU5t1XlI8DhrMPWoyZbj1jsHLDpy7vcu9fk3r0mugIbm9UJb0tbePqR8gfPlfn9YQtNhi9cEpiXKbnZooXtesb5pogP8MNZ18GG93riYdcvYN/DMPCid3n8W7DySlhzNcR7Tu7FbBMGd1eMrzs8YWIdY8rUQ55PbdxjklxetbP8eFDDr4Y5OFJgOF+uuW9lPpBsA8XIIFkGjhbCiC7BCjZWGtMt7vcmhEqVGS05/O8XyvzXXhO7Uslz5RKND673LXhjn5G8Vx7akwzN3idhIbBK3hl2y3oINVbvdeeBsE+lOxHkxaMZQj71hJ6HeEjnD9e38oudR/jp1oOck2rADLei5Q8jmYWJMxq/ppAtmYwVpjSA0wLQdpbXSv/Q1kmhstg61FolsAxyhBgrlqaVJGu5IwQHd+Aike4+tU60YyWHL/++yM6KH+VP1up8cJ78KPNJR0Tm3REf717po2i57Oy3JrwtAwWXbUe9UuhvbYdUdLLZ3FDR4c5dngD7xCY/axtPfzedLphIEixtCpM8dkSEJHnl8e3nQGEEXr7fi7Jk++D5//QurWd5aaElb5zZF2IbMPDSZFVO/wuvFSa+yBRhcrbXO6lKM71mIlDxrfi1IkfSRQKqQqia7RnmA9dBNnMoRh5XVrH9CcxEK7Yvjjtf0bA6YJH9VxYvRcvl57sN/v3FMsWK2/uCdpUPb/RVJVQ9VjDRNa+h21wamc0btgG5QUgsg1h9VviciNZYgP5smdGCQWP4xD6RP3pDJ796vo8X+7LsPJTm7FQDZqgNPbMfc0pUJaCpDGS9BnATvStSmz2h0rsV1v+Rd5vq8/L3lgHqIhhKZpXBtRkp2q8pSY4e+DUAheY3YIVaTvqpp/pRApX+KAvlR5lPAqrEBR0aF3RouK7LgYzD1iMWTx6xeH7Ipjfj0JsxJiqWAK5brfPWJaf5/3a90nBN8b778eAJtlUwAWffABvfB4ee8lJBB37veUj6ngVfFFa9DVb+oddiYLxUuP8F77s+FX8MWjdORkwSS+ruzF5TJFLxID5Nrrlv5WQYb8omOTaOFqbcsBwrkMTRY3Xt/TtVhFBZYGzH5df7TH74XJmRklfJsyIu89/O9nN2S3U2f7ZkIUvQk6wMGFsoHKtyNl32ZveAd8YUbfeqfGqRCJ4HdFWmOxlkZ28aw3JOGCJOhHSuXNfCvc8e5c6tB9nYGcMMtaHljyBZxYmBleMN4MYKUxrAdW4C7vBMhuOVPlrAq5QyC4tDqJhFSpbNcH76lGQck+iB3wCQPgUT7ZZ9Bv/4VAnD9qIVX7wkQHcV/SjzhSRJ9MQUemIK163xkTdcnu63JrwtoyWXCztUPnTWaZqnKxOQfZrMksYwsWMndx93kbInmlObPW/ZS7+El+7zfn/2373LsQTik8bXto0Q7647YTIT474Vv6rQO1JgMFciGfLX3+5qalM2dXpTNldZQKN9HTQwEUJlgXBdlyePWPzPnWUOZLxKntaQxIfO8vOmLhW5Sqq3ULawHIeljeETn02dDI5dESWVi4vXA0Tze2ddsW7whTyDnB6um86zp0pT2EdbzM/hsSLtsRN3afyjN3Tyqxf62HU0w7OH02zsbMAMtqJnezHD3uMlCXRFYTBbJhnyecbm+BIvPZYf8s5gOzd5eXtn3FDbsLBvdD4o58gYvKYkOXzkcVQjjeVPkG/dPOensyp+lLvqyI8yn4R0iTemNN6Y8prN9eVcWkLSaaWypk1AToZOr+N0qAnOvRHOucGL9O36BfQ+6UVfxoVJ+0aIdS3qs/mGoIZPDdM7WmQoVz++FckseNET1z1OU7b5x7AcRgplFFki7KvtCcHiPnrUKbuHbb63szSRQ4/oEu9fp/OO5XpVx8qXTG9gV09jiMbwaZyJO7Z3dj8hSlzvTEn1eyIk1uUZ4/SgJ0wWUxntHJEkia5kkOFcmVzZInyCyFQy7OOKta3813PjUZUGzHA7Wr4PySpN5I/DPpXRokG6ZHgN4CQJOjfD7l96B4XO8WGXkufzsa36Fn2ui1kcY6joTi9JZtJEm+6+Ys7ehGP9KDes8/wo1RL61UaWJNojp/fevOGCJWKBYyYgn/biVOi+yLvYpnf9DPs/BHSFJY2eh6+mvhXHQjGyyGYBV/Vjhdqxgs2zNmWbT0zbYSTvnRS0xvyk4kEaqtm1fAbqeI+3+Diac/hfz5b43UHPhKLJcO0qnevX+Kp+9mdaDrmyRWeD19BtzrjOZPrGKnkiZVyUaEGvB4ov7P2uBb0IyuuEqF+jKxFkd3+OoK6c8GD5nnM7+fULfbxwJMNzh8bY0BHDDLWgZw9hhr0mZ4oivbYBXGqKULnwJu/JtAAM7fU6vvpjXmRFH/8/BOrngGEb5PJ5cqZELDjVRHuY4NCzuMhkuq+Y01O9XPGjDFb8KJ+6ILAw5ZxnECecgDxfLIL2AqeKpnjDSf2aTO9oFX0rrotsFbyyYvDKimNLsf2JaX2YFgrbcRktGJi2Q3PER2ciSDKkz3sfr1NBCJV5IFN2+PELBr/Ya2A53uf58h6NP93gozlU/dChZbuMFQ3aYgE6GgKzH8Nc1yslNSuREtvyEraqD9QAhFs9p74e8g6Gqr9+Dog1oj1eMdbmDZInMNY2hn28dW0L9z/fx0+39bKhswErNENUxa8yVjTIlS0vRN9xricOxw54lReRVi/Mbhve/yrX75UvgycU1aD3d1+kEtUK1czL4ppFRjN5JDU0Y0lyoeVcrGDzCZ9nyz6D/2dbCdNZ3H6UajLnCciCEyJJ0FLpt9I7WmAoXyIe8KEsQERcsk1kI4Nsl3DVEEakCyvQVGnKtvCfecd1SRdMCqZNY9hrXZEM11cVnRAqp0HZcrlrj8FPd5Un5n+c26rw5xv9Mw4NqwaOA6OFMs0RP52JKV1nXdc70FnFysA4E5AmzZqhZvBHvIOcFqivs/Q6wqcq9CRD7Dw0hmk7aCfomPeeczvZsqufZw+neeFImnVtDVjBFrT8EUy1FQBNlbFLLkO5sidUfBFvbkn/83Bom9fHAryyUEUHKnMxXGcy8jXyinddUid9QoH49OhXFdyBmWyWbL5AONE0cZtkG0QPenOzTmSitRyX7+woTwzgO79d5dMXnDl+lIVifAJyU8QTKSecgCyYE9N9K146bV58K46FYuaQzSKuJOP445RCK72mbGp1JhW7rkumZJErmzQEdZa3hGmO+FDr8LMjhMopYDsuDxww+cGzZQaLniV6aYPMX2z0c15b7TapZ6ArkQzqdMVUNCNTOZAZlSEzuidMIh3gj06JlAQWbUVOLWiO+GiN+hnIlmiNHn+n0hzxc/maFn71Qh93bj3Il6/ZgBluR833IdnlCbd+WFcZKRi0Gn6vfDy12RMqvVsnhcqxSPKkqBzvWjtuui0OQ/aod5vq86JhgcSU//vCpO2Gx7I4SKhTuqiGj/wexchgBhrJt5w362NHSw5ffqzIs4OvDz/KfHHKE5AFcyKgKyxpmvSt+FVlWm+gOTNNnEg4WoRyvBPbF8f2xapaIZUrWaRLBhG/xrr2KM2V6FG9IoTKSbLtqMX/3Fni1TGvkqcpKPFnG3xc1qPVbIcq2SaSVSKdzdKkuaR0P7pZMbVGWj1Pw4SnpDpn1mcysizRkwwxmjcoGBbBE5gV33tuJ795sZ+dh9LsOpphbWscK9iMWuif6CXi1xWyWZPRgkFAD3hC5an/5fVUcay5d+yUKxEZX8S7PjWSlj4Ao+OeoyD4ghBITkZd9NBphZrzZYvhsVEi+nT/QnSiE+3sJtrdIzZfnOJHufWCABcLP8oJKZsOmZJJe0OQznqbgHwGocnTfSujeYN4UD+xb+V44kSPVr3hXcGwGC2YBHWFlS0R2hsCc5pjVmuEUJkje0dtvrejxPZ+72wvpMGfrPVxzcrqVvJM4DqopRFk28CRdUYMUKPNtHZ14g9FJytwatj58UwmFtToTATYO5AjoCnHNZw1R/1ctrqZX+/q586tB/nSu9ZjhjtQC31ItoFb6fQZ0FQGxxvANa70BGYpDf27vI61p4JUSe+pPm/OEkyWlpezXjM+8MyRqt8z6fobpkRdjp8CdByXTMlkOGfQnykhFcbQfQHsyt+1bC/B4eePa6Kd6kfpjMjcJvwoc6Jk2uTKFql4gPaGYHXndr0OGfet+DWVgyP52X0rM4qTFLavoSbiBLzPynhPnaWNITrigek9juqcxbPSGtGfd/jBc2Ue2G/iAqoM71qh8ydrdaILNfHyBEhmAbU0iu2PU0qsZdjUcNQAG1JJIse2xxYsGJ3xIIPZMmNF0zu7Og7vPS/Fb14aYEfvGC8dzbC6NY4VaEYtDk5EVUI+leF8iXTB9GaxdG6Cvb/x0j+nKlRmQlYq5eRTKgnGjbrZozB2EJAqaaWgl1byRyejLoo2cWbWly4yWjCxHYewCk1+F3dK+eS4iTbfugkrMH18guW4fOeZMnfv8fwoF1T8KCHhRzkhxbJNwbToigdpiwVEkLSKxAIqy5vD9I5M8a3ITt2JE/DSgqMFw+vflwjQEQ8Sm8PMsnqjpkLl9ttv5+c//zkvvfQSgUCAiy66iK997WusWrWqlssCIFOy+eHeAP91KIfpZXl4c5fKn53lf80Asarh2qjFYUCi3LAMM9LFmKlgqjbr2mPEhUipKn5NoTsZ4tlDY0R86nFNaC1RP29Z3cyWXf3cue0gX3znesxIJ2pxAMk2cRUNSQJN9hrAJUI+lNRmT6gc2gqb/3xh38yMRt2S1w23OOL1R0EmZ2uMWj4GnSB5x4fmCxMPR9E1FdnIojomtlZpaGeXZzXRjpYcvvRYkecqfpT3r9P5gPCjzIlcycKwHLqTIVqj/oX1vLuul3p0TK8q0DG963ZlDojqq1Se+as2ULAeCGgKSxI6QTvD4Eg/aBr+cENdiBMAq9ILxQFaor5KLxStLkqNT4WafrIeeughbrrpJjZt2oRlWXz2s5/liiuuYNeuXYRCC183Phv37DjM5+96mUzZ2+FubFb4i7P9rErU7oMnm3mUUhrbn6DcsAw7kCRdNCmZNmsqZihB9WmJ+mmN+hnOGzSfoF/NdeemeODFfrYfHGN3X5ZVLQnsQBNKaXiiZDfs80qVMyWT+Hizt6GXvWFxwcRCv51JJBm0IK4aJFe2yBRNRrIFisU8mj1Cg+rQrMhg+nBKAWxfA66sITnmREQlfPgxFDOHGWii0PKGiafePWLzxUcKDBZdgpX+KMKPMjeyJQvbcVjSFJocu3A6OPZrhYhteUIVANdrMKZonhDRw15loB70RExxxJtDVRoC156Sagx4P8+01LNteo0XjQKapNCRjKIme3glq1JSIjRFgzUVA7bjMlYwMGyHpoiPzrjXC0Ve5N6lmgqVX/3qV9Ou/+AHP6C5uZmnn36aN77xjTVaFSRDPjJlm1TI4v86N8LmNrV2Hz7HRi0OgaxQjq/AjKQoOgrDYwVCPpVVrRHaY0Kk1ApFluhKhhguGBQN+7gDH1tjft68qpkHXhrgzm0Hue3qdZhhL6qCY4KsoSgSEhJDuTINTXGkxpWeUDn0FKycW6O0+aBkOmRLJsN5TzTZttdpNhZLTKQZLNdFssvIdhkt24vkOoA04WmZ6ETbc6U3XgH49asG/+9Tnh8lFZH5gvCjzJnjTkCeCdedIRJieuLEu4P3fxkXIYo2xXjvn4yyKdrk7zMJD6vsRd6MgidaCiPe9dLYZBdrzT9ZfbYI5v9MwzY9P5dR8LaTPwLN3RCII/ljtMoKet7g5f4sR9JFWiL+qpf4Oq5LumhSMGySYZ01iSCNddYL5XSoq1hdOp0GIJGo4pnjDFyyopF/vLqbztwzxFpqtxbZyKGU01jBZozYUspajOG8gSQ79CRDpBLBRWWIOlNJhHQ6GwLsGyrQrvmPK2qvOy/Fg7sHePrAKC/3Z1nZnPS8KlOjKn6VsUKlAVzn5opQ2brgQsVyXLIli9FCmbGCRdm0UBWFsK5Om4A8gSThqn5s1Q++2LQ/6ZkDBEZ24Uoyma63Yjku336mxD17vIZDwo9ycozmZ5iA7FhTxMeUn25lipyEFw2RNW/sgi9aiYYEZhEhp9gSf9ysPV4i77pTxEveO8gXRytl82NesydFnUwZKb7669k0kziJ91T8WrHXCLZESOeszhh7+nMcTRdJBH1VmVI/3gslb1jE/BrLOr1eKCfq77TYqJujnOM43HLLLVx88cWsX79+xvuUy2XK5fLE9Uwms2DruWRJhN7nF+zpj49joRWHcBWdUmItRriNsRIUSiVaon66k97OarHmG89EUgnPWJspWcc1q7U3BLh0ZTO/3T3AT7cd5G/esc7rq1IYmChD1lWZTNFlJG8QSW2GHf/bM9Q69ryH0l0XcoZFtuhFT3JlCxmJoK4QCftPuW34pIn2fIakOF96sDDhR/nAOp33Cz/K8XEdJMcG2ySdKxJUbLrCPqJWHtKV+0iKd8CXNe9g7497ZmfVN4sQqdLBS5I8EaL5J9OVrusJFbNQibZkPPFSzoE94okXVZ+MutRiXtiM4mRJpRLuteLkWIK6ytr2KEFdYf9wHsNWF9S4mitbpIsGYb/G6tYIrbH67oVyOtSNULnpppt4/vnnefTRR2e9z+23384Xv/jFKq6q+shGBtXIYQRbMWI9ZAkzljWIBTTOammgJeo/Y8J5ZxJBXaU7GWLX0TRhn3rc/9Efb0rxu5cH2LZ/lL0DOZY3NmIFGlFLo1hBr6Nr2Kd6vpeWVQS0EJQzMLQHmlfPy3rLltd/YyTnpXZM2yWgKSSCvtM+nklWicjB3wLwQvIKPvrr/IQf5dYLAlwk/CizIpsFlPIYIOHIGiNFh0DAT1dzK5FI1KvEmiZCtMnf6xlJ8nwtenDyNsepCJcimHkv2lJKeyLG9irBULTJ8R0L8R5PU5wci6bILG8OE/Sp7O3PMpAp0RTxzetJpVdxZxDQFVY0e71QqhG9qSV1IVQ++tGPct999/Hwww/T2dk56/0+85nP8MlPfnLieiaTIZVKVWOJC49johWGcNUAxeQ68r4Whos2umqzojlCR3xxNOZ5PdMa87rVDufLxzXWtjcEeOPKJn63e5CfbjvI565a6/VVKQ5NRE28BnAlxkougY5zYf/D8My/wfLLvBB0rLNSpTN3LMclV/J2cmMFk5JloUoKId8sqZ1TJHz4URQrT1pv4QPbV2A4LqmIzG1/EKArKj7DMyEbORQjg6v4MKI9lP1N9OVt4i1hutoTRAJn3kRyZNlrNugLA00Qx/v8G3lPvBh5z+dSznq+F2fq2I/TqDSaZ3FyLJIk0dEQIKgp7J5H30rJtBkpGOiKxNLGMO3xwAmnuJ8p1PRduq7LzTffzF133cXvfvc7lixZctz7+3w+fL4z7wurlMeQzSJmqI1ipIdhy4dVsmlvCJBKLM6699cjmiLTnQwxmh+jZNrHFZZ/fF6Kh18e5Ml9I7wymGNZshHbn0Apj2JX+o0ENNU7I0tdgLr/YTjwmHcBz5AY7fBEy8SlG2KpaWFz14W8aZEtWgzlyuTLXllpUFdpDJ16aud4RPffD8B38pdiODIXdqjcer7wo7wG10U2c6jlLI4WoBxbihVqw1TD9GVKNCV0VrdGX18+NFnxevb4o5O32eakWdcsQGH4mEojKumi41QajYsTs+j93RedV3EyE/Fx38pAjqNjp+5bMSulxkiQigfoaAgSC76+jgk1/QbcdNNN/OQnP+Gee+4hEonQ19cHQCwWIxCozmCmWiLZBmpxGEcNUmpcz7CUJFdySIRVuiuu7cVeVvZ6IxnSaW/w0ztaoD0WnPV+nfEgf7CiiYde9qIqf/32tRiRFIHBndiVqErIpzKUKzPa8SaaLjFhcLc3UXlkvxcqT/d6l/2PTD6xJEO0HTvWTTmcIu3vYERvI+tvQ/eHaAjoCzIBdpzS4CsER3djugr/Yb+JD673ccM6XfhRpuK6yEYGxcjhaCFK8RVYoVYcLUTRsBnJlGiN+ljdFhVRVKikt2KeoABILvPml5n5yUqj4oj3eyldGc5ZqTQa98ZUSZwcS1BXWdsWJaQp7DtJ34plO14zRdehOeInlQi+br2JkuuOW8Rr8OKzbPDvf//7/Omf/ukJH5/JZIjFYqTTaaLR6AnvfzIMHTlA7/MPE27umdfnBcB1vSiKVcIMd5AJdjFoqIR8nkBpjQXmZ0KnoCbkyhbbD4yiyhIR/+w7pd6RAjf9ZDsu8M3rz2ZJwk+wfzuSlccOJAGvJDWoK6xqjU62SHddKAzB6AEY3Vf5uR93dD+SkZvxtVwkzFArRiSFEenyfka7McKduOr8lLe/NGxTfOSfuY4t/NrdTN/mz3BRx+vrzO+4uA6KkUE2Cjh6GDPciRlqmZiWO5r3+l90J4N0J0NiH3CymKXXmnUBIm1VFScz4bouR9Ml9gxksSyXxsjsZvLxXihly6Yx4iOVCNIYOvNOWk/m+F3z1M/rDckqoRZHcPQoucQK+twGJEemJxkQ5cZnCGGfSncyyItHMwT12Y21qUSQS1Y08sieIe7c2stn374GI5oiOLgT27VBUioN4EyvAdx4uFeSINQEoSbcjvMomBbZksVQtkQ5M4Q/30tD8Qih4iF82YPomYMoZhY9fxQ9fxT6tk5bhxlswYikKEe6PBETTWGEU7ja7BGhqexP29y12+DR/Vke1R4FCZrPuYqlQqR4uDZKOYNsFnD0GKXkGqxgy4RAtGyHgVyZkE9lQ1uM5nk2X75uGK80IgExPEHvunUxhFWSJM/0WvGtHE0XaY74p5URu+O9UEyLeFBndVuUxrBe9Z4s9Yg4KlYL10EpjSA5FuVoF4NqBzlHE+XGZyhtsQD9mRKjBYPG8Oy+qj8+L8Wje4Z4/NVh9g/l6Uk0YvkaUMpeF2KvARwM58o0BLSJdhOG7ZAtWQznDDJFE8O28asqoYYWlGQrBaAw/iKui2Kk0TMH0bMH0bO93s/MQVQjjVboRyv0E+p/atrazEBTRbh0TYvEOFoIx3XZdtTi57uNiUGd1ymPE5WKlINthLvPmfdtuuhwbJTyGIpdxtJjlBqXYQWbcJXJz0PBsBgrGLTGAixtCh03Aic4SSSp7vqzxEM6Gzsb2DOQ5UjFt+LXZLIli2zZIupXWdceozniFxG1KQihUgUkq4hWHMHyNTAa7maQGDG/zlnJkCg3PkPRVZmeZIidh8YwLGfWnU53MsRFyxt5bO8QP32ql0//4WrMSBf+wWexfZWoil9ltGCQLVm4QLpoMJz3OuGqskTIpxJTj3OAkyRsXwPFpgaKTdOHGyrlNFq2dyLyMi5k1PIoWnEQrThIaODpaY/Jqkl22R2UzQ5WuJ3IcgeJ1m4+bfwOcpBdcuXi6z46nzgWankMyTaxfXGK8VVYgcaJKdngnT2P5A0s12V5c4SuZPCMa9IlmJmArrC2zeu3sm+owEjBIeRTWd0apjUmqjtnQgiVhcR1vCGCrks2vIQ+pRVVD7AiHhTlxq8DGsM+2mIBDo8VaY/Nbg6//rwUj+0d4vd7hzgwnKenoRHbF0MpZ7D9ca8BXMnhwEieQtnGwSWoKTSGfad9wmj7Yti+GKXG6U0WZSNTibz0omcOIo0dQEr3ErNHiFjDnM8w56vPTj5gxPvhSiqZrstPb1GLFcdELY0iOTZ2IIkR7vQmRh9TQmvaDoO5ElG/xtrm8AlnRAnOPFRFZllTmJBPpVC2aWvwE9TF4Xg2xJZZIGSzgFIaw/DFGdA7KWpxWhsCdCVCotz4dYIsS6QSQYZyZXJla9aeBz2NIS5aluT3rwzz70/18qkrV2NGx6MqMZBkYn6dkuUQDWioC1i1M46jRykm1vK0u4qfHzZ49KiF40KUPBeEjnBtcx/n+o8QzHtpJK04BEAm9WZsX8OCr6+ekGwDpTQKuNiBJsxwO5a/cUbjZr5skS4ZtMUCEwcqwesTSZJoO84JjGAS8S2Zb1zbi6IgMxzoYVhrJR6LsFKUG78uiQU0UvEgewayBHVlVqf/9Zu6+P0rwzy6Z4jrNxXojjVVoippbH8cTZXntSnb8TBtl0d6LX7+cpndI87E7ee0KFy7spHN7a3IksQYMFb5m2wWUIsDGOGOqqyxHpDsckWgSFiBJqxwB1YgOWPay3VdhvMGLi4rmyOkEkFhkhQI5ogQKvOI110yTUFLclTtQI82sVqUG7/u6YgHGMyVGSuYJGaZerukMcSFS5M8/uow/76tl7+6chVmJIV/+HlsN1YVz0e67PBfe01+sddguOhV5GkyXNajce1KnSUNs6cqHS2IofUs+BrrAckqoZZGcWUVK9iKGe7A9sdn/R+ZtkN/tkQ8qLO8OXxcc7VAIHgtQqjMB46NWhzEdlUOa0soh9vpSMREubEAAL+m0JMM8eyhMUzbmdU0ef2mFI+/Oswjewa5fnOKrmgzTiaGYmS9FNACMV5e/JsDJoZXwEPCL/HOFTpXLdNo8AuRDSCZBdTyGK6iY0Q6sULtXprrOEahXMkiXTJJxYMsawqf8TNZBIKFQBxFTxPZyKKUM4zKSUaCnSQaW1kjyo0Fx9AU8dES9TOYLdMSndk8ubQpzPlLEjy5b4Sfbevl/75iFUakA//wi9h6dF5LLWcqLwZYEZe5dpXOm1IaWhW8MIsB2cyjlNMTc3jMUCuOHjvu/8NxXYZyZRRJYk1bhM54UFT3CQSniBAqp4pjohWGKboqA9pS9GQXa5piotxYMCOKLNGdDDKSL1MwrFkd/tdv6uLJfSM8vGeQ6zd10RlpwckeQjYyOPMQVSmaLlv2m9z1ssGhrOc/kSW4uEPl2lU66xoVIbChMocnj1LO4E7M4WnF0U/cAduwHAazJRJhneXNkVnTfQKBYG4IoXIKKOU0TilHn5zEalhCZ0urKDcWnJCGoE5nPMgrgzkC2syCYHlzmE09cbbtH+VnT/XyibeuxAh34h/Z5R0kT1FEDOQd7tlj8MtXDHKmd1tQg7cv1XnXCp3WsEjvABWBkkUtZ7G1EOX4CqxgC44entPDM0WTXNmkKxlkaVNY7BMEgnlACJWTQLJN5MIgaVsnF1pBQ2sPXY1RUW4smDOd8SAD2TJjRZN4cOYz7fdt6mLb/lF+9/IAf7wpRUe4BSd7ENnMzumMfhzXddk1bPPz3QaPHvLKiwHawzLvXqlzxRKNoCaiJ0BlDk8W2cjj6GFKiTWYweY5jxGwHS/VoykS6zpitMcCosJPIJgnhFCZC5UhgqVCjjGtGV/HSla1NItyY8FJE9AVehpDPHcoTcTnzFiiuqIlwnndcZ464EVVbrl8JWYkhT76MoqRA1cCGVxJwZU1XFkBScWVVVxZwXTk45QX62xuV8U043FcB6WcRrYKONpr5/DMhZJpM5wv0xj2sbw5TMMsAlQgEJwaQqicAMku42QHGbJ9uMl1dHYupbVBTDYVnDotER8DUR/DOWNWY+37Nnfx1IFRHtztRVXaIilsXxTJNpAcC8k2kO0iklVGsktItkE2X+De/XDPPpWhsvf51GSXt3Y6XLtcZkmDiyvbuK4EKPNqzi0aNrmSiSRLKEgEfEp9pz1c2xMoZsnrzJtcihVoOulJ0umiScGw6EmG6GkM1fd7FggWKUKoHAfXscmNDFAOt9PQsZrOlkZRbiw4bVRFpisRZKRgUDLtGQ9uK1sivKErzvaDo/zHU4f42GUrsP2JGZ/vwFCOe3ce5sGXhzBsL78TDyhcvSrEO5ZqxDUD2SoiORayVQDbQnJtwAUkXEkCSalEZFRcSYGJSM3xBXnJsMmWLXyaTCoRJOhTGcl7gxKzJQtdkQn51Kp0052G6wIOuC7SlN9xHWSrgGyb2L4GSg0rXzOHZy7YjstgroRPVVjfEaM16hfRVYFggRBH3VlwZRkn1IzWtJQVnUuIh3RRDSGYNxIhnY5YgP3DBdpj/hk/W+/bnGL7wVF+u3uA6zalaJ0SfXFcl+0HRrln5xF29I5N3L6sKcS7zu7gkuWNE/1aShMPMieiMZJjITmmd7E9ISPbJSTLQHYMMAvgWEhUjC24EyLGlVVKtkTWcNE0H51xr+tysNIjJBnSKRo2mZLpTXcumTiug19VCWoKsuxOCAmpIh7AnfY7ruO9tjv194rYGL/vBJL3mGNwJQmQPbE1MUlXxtEiFBMpr4usfPL+Mi/VY9AU8ap6hEdNIFhYhFCZhUiija4NcZLRkCg3Fsw7kjQ5ByhTsmY82K1ujXJOqoFnesf4j6d6ufktKygaNr/dPcC9O49weKwIeOXFFyxN8s6N7axti84uqGXN87Sox5kv4tgVATNFyIxfrCJGMU++kMMvOXSGFJKBMgGlDEW8SyVKEwACQFPQpaDZZIsm6YJJwXJAkvFrmhdJkuUJAeEigSR7AkNWcVEmfDdexEfxoj2SCrKMK8mMCxFXmvI8kjz5PJX7jN+GJOHK2il3+h0tGJQth6WNXqpHpIAFgoVHCJVZ8Osq/jmWJAoEp0LIp9KdDPLCkQxhnzqjIH7f5i6e6R3jgZcG0FWZB3cPkC97DdpCusJb17byjrPaZvW6nDRyRRAcc3PBsBgrGvgaFNq7/LRFFSIaYBtgW+CY3u+O7Q3jk2RAQpZkwpVLowvpos1QwWQ4b1E0HXRVJRzQ0VV1iuCQqzIy4GSwHZeBbImgrrChI0ZL1CcirAJBlRBCRSCoIa2xAP2ZMiN5g6bIa2fArGmLcnaqgR29Y9z37FEA2mJ+3rmxnbesbl7w0fBFw2a0YOBTZboTIdrjAaL+qdGf0JyfSwMao9A45Xn7MyVGCwZG0SHok4n4ZFS5vkRKwbAYLRi0Rv0sbQ4f8/4FAsFCI4SKQFBDNEWmOxliZ+8YZcvGp77WWHvjhT0cHNlFKh7gnRs7OK8nvuDlxUXDZrRooCsSXckA7Q3BefViBHSFgB6gLeYnW7YYzRscTZcYyhu4rkvEpxH0zT5tuhq4rstowcS0HZY3h+lOhmad0yQQCBYOyXXd17rQFgmZTIZYLEY6nSYanXsjLIGgnnBdlxePZjg4UqCjYW4NxhaKkmkzUvAESmvMP+8C5XhYtkO66BlwB7IlcmUbTZaI+LWqD/OzbIeBbJmwX2VFc5imiEj1CATzyckcv0VERSCoMePG2sFcmWzJJFKD1ELJ9FIxqiKRigfoaAgSC1Z3Haoikwz7SIZ9dCWDpIsm/ZkSI3mDkUKZgKYS8asLHtUYT/W0xQIsaw4TFi0JBIKaIr6BAkEdEPFrdCeCvNSXJeSrXufYkmkzVjSQZYnOeID2hgCxQO0nf/s1r2FcS9RPrpIa6quIFttxCfnUWQ3Ip4rrut7z47KyJUJXIjhj52CBQFBdhFARCOqE9gZvDtBo3iAZfq2xdj4pWzZjBRNJhrZYgI6GAA3B2guUmQhXRElHQ4B00WQk75lwB7IlJEki6ldnHfI4V0zbYSBboiGgs6yS6hEIBPWBECoCQZ2gq56x9tlDY5i2syApDsNyGC0YIEFL1E9nvH4FyrHIskQ8pBMP6aQSQcaKBoOZMkP5MqMFA7+qEPFrJ93bJFe2SBdN2hsCLG8OL3gllUAgODnEN1IgqCOawj5aon760iXaYsdpzHaSTAgUoDnqdZONLxKBMhO6KtMc8dMc8Vc8JSb9mSJjBQPDcgnpnmg5XmrIcV2GcwaS5LK6NUwqIZo7CgT1iBAqAkEdIcsS3ckQI3mDfNk67dlSpu0wkp8UKB0NARJn2DiIoK4S1FXaY34yJYvRfJm+TJnBnDc8IOzTCOnTU0NmpaqnIaixojm84Kk2gUBw6gihIhDUGbGARioeZM9AloB+ar1ExgWKCzRHfHTEAySC+hk9OE+SJGIBjVhAozMeZKxoMpQtM5grczRjoMoyUb+GaTtkSiZdiSBLGsNVL30WCAQnhxAqAkEd0hEPMJgtky6YxENzn+xr2g6jeQOH149AmQlVkWkM+2gM++gxPeNwf6bEaN5AkmBtW5SOeFCkegSCRYAQKgJBHeLXFLobgzzbO0bYPnHvENP2PCiO69IU9tERD5IMvf4Eykz4NYXWmEJL1EeubAHUpFeNQCA4NYRQEQjqlOaIn9aYF1mZbeigZTuMFkwsx6GpEkFpDPmEQJkBSZKEQBEIFiFCqAgEdYoiS3QlggznyxQMa1rZ7LhAsR2HRFgnFQ+SDPtEKkMgEJxxCKEiENQx8ZBOZ0OQVwdzBDQFx4XRgoFpOySFQBEIBK8DhFARCOqc8TlAR9MlwCUZ9pFKBGkUAkUgELwOEEJFIKhzArpCT2OIo+kinQ1BGsO6mEEjEAheNwihIhAsAtpjftqifmGSFQgErzuEUBEIFgGSJHEGNZMVCASCOSPixwKBQCAQCOoWIVQEAoFAIBDULUKoCAQCgUAgqFuEUBEIBAKBQFC3CKEiEAgEAoGgbhFCRSAQCAQCQd0ihIpAIBAIBIK6RQgVgUAgEAgEdUtNhcrDDz/M1VdfTXt7O5Ikcffdd9dyOQKBQCAQCOqMmgqVfD7Pxo0b+da3vlXLZQgEAoFAIKhTatpC/21vextve9vbarkEgUAgEAgEdcyimvVTLpcpl8sT1zOZTA1XIxAIBAKBYKFZVGba22+/nVgsNnFJpVK1XpJAIBAIBIIFZFFFVD7zmc/wyU9+cuJ6Op2mq6tLRFYEAoFAIFhEjB+3Xdc94X0XlVDx+Xz4fL6J6+NvVERWBAKBQCBYfGSzWWKx2HHvs6iEyrG0t7fT29tLJBJBkqRaL6fqZDIZUqkUvb29RKPRWi9n0SK24/wgtuP8ILbj/CC24/ywUNvRdV2y2Szt7e0nvG9NhUoul2Pv3r0T1/ft28eOHTtIJBJ0dXWd8PGyLNPZ2bmQS1wURKNR8UWcB8R2nB/EdpwfxHacH8R2nB8WYjueKJIyTk2FylNPPcWb3/zmievj/pMbb7yRH/zgBzValUAgEAgEgnqhpkLl0ksvnZORRiAQCAQCweuTRVWeLJiOz+fjC1/4wjSDseDkEdtxfhDbcX4Q23F+ENtxfqiH7Si5IqQhEAgEAoGgThERFYFAIBAIBHWLECoCgUAgEAjqFiFUBAKBQCAQ1C1CqCwybr/9djZt2kQkEqG5uZlrrrmG3bt313pZi56/+7u/Q5IkbrnlllovZdFx+PBh3v/+95NMJgkEAmzYsIGnnnqq1staVNi2zec//3mWLFlCIBBg2bJlfOlLXxJVkSfg4Ycf5uqrr6a9vR1Jkrj77run/d11Xf7mb/6GtrY2AoEAl19+OXv27KnNYuuY421H0zS59dZb2bBhA6FQiPb2dj74wQ9y5MiRqq1PCJVFxkMPPcRNN93EE088wZYtWzBNkyuuuIJ8Pl/rpS1atm3bxne+8x3OOuusWi9l0TE6OsrFF1+Mpmncf//97Nq1i2984xvE4/FaL21R8bWvfY077riDf/7nf+bFF1/ka1/7Gl//+tf5p3/6p1ovra7J5/Ns3LiRb33rWzP+/etf/zrf/OY3+fa3v82TTz5JKBTiyiuvpFQqVXml9c3xtmOhUGD79u18/vOfZ/v27fz85z9n9+7dvPOd76zeAl3BomZgYMAF3IceeqjWS1mUZLNZd8WKFe6WLVvcN73pTe7HP/7xWi9pUXHrrbe6l1xySa2Xsei56qqr3A996EPTbrv22mvdG264oUYrWnwA7l133TVx3XEct7W11f37v//7idvGxsZcn8/n3nnnnTVY4eLg2O04E1u3bnUB98CBA1VZk4ioLHLS6TQAiUSixitZnNx0001cddVVXH755bVeyqLkF7/4Beeddx7vfe97aW5u5pxzzuF73/terZe16Ljooot44IEHePnllwHYuXMnjz76KG9729tqvLLFy759++jr65v23Y7FYpx//vk8/vjjNVzZ4iedTiNJEg0NDVV5vUU9lPD1juM43HLLLVx88cWsX7++1stZdPz0pz9l+/btbNu2rdZLWbS8+uqr3HHHHXzyk5/ks5/9LNu2beNjH/sYuq5z44031np5i4ZPf/rTZDIZVq9ejaIo2LbNV77yFW644YZaL23R0tfXB0BLS8u021taWib+Jjh5SqUSt956K+973/uqNkNJCJVFzE033cTzzz/Po48+WuulLDp6e3v5+Mc/zpYtW/D7/bVezqLFcRzOO+88vvrVrwJwzjnn8Pzzz/Ptb39bCJWT4Gc/+xk//vGP+clPfsK6devYsWMHt9xyC+3t7WI7CuoG0zS57rrrcF2XO+64o2qvK1I/i5SPfvSj3HfffTz44INigvQp8PTTTzMwMMAb3vAGVFVFVVUeeughvvnNb6KqKrZt13qJi4K2tjbWrl077bY1a9Zw8ODBGq1ocfJXf/VXfPrTn+b6669nw4YNfOADH+ATn/gEt99+e62XtmhpbW0FoL+/f9rt/f39E38TzJ1xkXLgwAG2bNlS1YnUQqgsMlzX5aMf/Sh33XUXv/3tb1myZEmtl7Qoueyyy3juuefYsWPHxOW8887jhhtuYMeOHSiKUuslLgouvvji15THv/zyy3R3d9doRYuTQqGALE/fHSuKguM4NVrR4mfJkiW0trbywAMPTNyWyWR48sknufDCC2u4ssXHuEjZs2cPv/nNb0gmk1V9fZH6WWTcdNNN/OQnP+Gee+4hEolM5FpjsRiBQKDGq1s8RCKR1/h6QqEQyWRS+H1Ogk984hNcdNFFfPWrX+W6665j69atfPe73+W73/1urZe2qLj66qv5yle+QldXF+vWreOZZ57hH/7hH/jQhz5U66XVNblcjr17905c37dvHzt27CCRSNDV1cUtt9zCl7/8ZVasWMGSJUv4/Oc/T3t7O9dcc03tFl2HHG87trW18Z73vIft27dz3333Ydv2xHEnkUig6/rCL7AqtUWCeQOY8fL973+/1ktb9Ijy5FPj3nvvddevX+/6fD539erV7ne/+91aL2nRkclk3I9//ONuV1eX6/f73aVLl7p//dd/7ZbL5Vovra558MEHZ9wf3njjja7reiXKn//8592WlhbX5/O5l112mbt79+7aLroOOd523Ldv36zHnQcffLAq6xPTkwUCgUAgENQtwqMiEAgEAoGgbhFCRSAQCAQCQd0ihIpAIBAIBIK6RQgVgUAgEAgEdYsQKgKBQCAQCOoWIVQEAoFAIBDULUKoCAQCgUAgqFuEUBEIBAKBQFC3CKEiEAjOOG677TbOPvvsWi9DIBDMA0KoCASCRY0kSdx99921XoZAIFgghFARCAQCgUBQtwihIhAI5oVLL72Um2++mVtuuYV4PE5LSwvf+973yOfz/Nmf/RmRSITly5dz//33TzzmoYceYvPmzfh8Ptra2vj0pz+NZVnTnvNjH/sYn/rUp0gkErS2tnLbbbdN/L2npweAd7/73UiSNHF9nH/7t3+jp6eHWCzG9ddfTzabXchNIBAIFgAhVAQCwbzxwx/+kMbGRrZu3crNN9/MRz7yEd773vdy0UUXsX37dq644go+8IEPUCgUOHz4MG9/+9vZtGkTO3fu5I477uBf//Vf+fKXv/ya5wyFQjz55JN8/etf52//9m/ZsmULANu2bQPg+9//PkePHp24DvDKK69w9913c99993Hffffx0EMP8Xd/93fV2xgCgWBeENOTBQLBvHDppZdi2zaPPPIIALZtE4vFuPbaa/nRj34EQF9fH21tbTz++OPce++9/Od//icvvvgikiQB8C//8i/ceuutpNNpZFl+zXMCbN68mbe85S0TokOSJO666y6uueaaifvcdttt/P3f/z19fX1EIhEAPvWpT/Hwww/zxBNPVGNzCASCeUJEVAQCwbxx1llnTfyuKArJZJINGzZM3NbS0gLAwMAAL774IhdeeOGESAG4+OKLyeVyHDp0aMbnBGhra2NgYOCEa+np6ZkQKSfzOIFAUF8IoSIQCOYNTdOmXZckadpt46LEcZzTes65PP5UHycQCOoLIVQEAkFNWLNmDY8//jhTs8+PPfYYkUiEzs7OOT+PpmnYtr0QSxQIBHWAECoCgaAm/Pf//t/p7e3l5ptv5qWXXuKee+7hC1/4Ap/85CeR5bnvmnp6enjggQfo6+tjdHR0AVcsEAhqgRAqAoGgJnR0dPDLX/6SrVu3snHjRv7yL/+SD3/4w3zuc587qef5xje+wZYtW0ilUpxzzjkLtFqBQFArRNWPQCAQCASCukVEVAQCgUAgENQtQqgIBAKBQCCoW4RQEQgEAoFAULcIoSIQCAQCgaBuEUJFIBAIBAJB3SKEikAgEAgEgrpFCBWBQCAQCAR1ixAqAoFAIBAI6hYhVAQCgUAgENQtQqgIBAKBQCCoW4RQEQgEAoFAULcIoSIQCAQCgaBu+T8BLVB+HDoVTQAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "df['positive_profit'] = (df.profit>0)\n",
        "sns.lineplot(x='month', y = 'vol', hue='positive_profit', data = df) # hue Creates two distinct lines on the plot"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 66,
      "metadata": {
        "id": "YldvlTFQhmFi",
        "outputId": "32d1d2df-d50b-458b-d9ba-d668eccebb2e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       open   close     low    high       vol  profit         gain   size all  \\\n",
              "0    177.68  181.42  177.55  181.58  17694891    3.74   large_gain  small       \n",
              "1    181.88  184.67  181.33  184.78  16595495    2.79  medium_gain  small       \n",
              "2    184.90  184.33  184.10  186.21  13554357   -0.57     negative  small       \n",
              "3    185.59  186.85  184.93  186.90  13042388    1.26  medium_gain  small       \n",
              "4    187.20  188.28  186.33  188.90  14719216    1.08  medium_gain  small       \n",
              "..      ...     ...     ...     ...       ...     ...          ...    ...  ..   \n",
              "246  123.10  124.06  123.02  129.74  22066002    0.96   small_gain  small       \n",
              "247  126.00  134.18  125.89  134.24  39723370    8.18   large_gain  large       \n",
              "248  132.44  134.52  129.67  134.99  31202509    2.08  medium_gain  large       \n",
              "249  135.34  133.20  132.20  135.92  22627569   -2.14     negative  small       \n",
              "250  134.45  131.09  129.95  134.64  24625308   -3.36     negative  small       \n",
              "\n",
              "     month  positive_profit  \n",
              "0        1             True  \n",
              "1        1             True  \n",
              "2        1            False  \n",
              "3        1             True  \n",
              "4        1             True  \n",
              "..     ...              ...  \n",
              "246     12             True  \n",
              "247     12             True  \n",
              "248     12             True  \n",
              "249     12            False  \n",
              "250     12            False  \n",
              "\n",
              "[251 rows x 11 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-3fad428c-bc44-4178-a952-63e2acc87e49\" 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>open</th>\n",
              "      <th>close</th>\n",
              "      <th>low</th>\n",
              "      <th>high</th>\n",
              "      <th>vol</th>\n",
              "      <th>profit</th>\n",
              "      <th>gain</th>\n",
              "      <th>size</th>\n",
              "      <th>all</th>\n",
              "      <th>month</th>\n",
              "      <th>positive_profit</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>177.68</td>\n",
              "      <td>181.42</td>\n",
              "      <td>177.55</td>\n",
              "      <td>181.58</td>\n",
              "      <td>17694891</td>\n",
              "      <td>3.74</td>\n",
              "      <td>large_gain</td>\n",
              "      <td>small</td>\n",
              "      <td></td>\n",
              "      <td>1</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>181.88</td>\n",
              "      <td>184.67</td>\n",
              "      <td>181.33</td>\n",
              "      <td>184.78</td>\n",
              "      <td>16595495</td>\n",
              "      <td>2.79</td>\n",
              "      <td>medium_gain</td>\n",
              "      <td>small</td>\n",
              "      <td></td>\n",
              "      <td>1</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>184.90</td>\n",
              "      <td>184.33</td>\n",
              "      <td>184.10</td>\n",
              "      <td>186.21</td>\n",
              "      <td>13554357</td>\n",
              "      <td>-0.57</td>\n",
              "      <td>negative</td>\n",
              "      <td>small</td>\n",
              "      <td></td>\n",
              "      <td>1</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>185.59</td>\n",
              "      <td>186.85</td>\n",
              "      <td>184.93</td>\n",
              "      <td>186.90</td>\n",
              "      <td>13042388</td>\n",
              "      <td>1.26</td>\n",
              "      <td>medium_gain</td>\n",
              "      <td>small</td>\n",
              "      <td></td>\n",
              "      <td>1</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>187.20</td>\n",
              "      <td>188.28</td>\n",
              "      <td>186.33</td>\n",
              "      <td>188.90</td>\n",
              "      <td>14719216</td>\n",
              "      <td>1.08</td>\n",
              "      <td>medium_gain</td>\n",
              "      <td>small</td>\n",
              "      <td></td>\n",
              "      <td>1</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>246</th>\n",
              "      <td>123.10</td>\n",
              "      <td>124.06</td>\n",
              "      <td>123.02</td>\n",
              "      <td>129.74</td>\n",
              "      <td>22066002</td>\n",
              "      <td>0.96</td>\n",
              "      <td>small_gain</td>\n",
              "      <td>small</td>\n",
              "      <td></td>\n",
              "      <td>12</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>247</th>\n",
              "      <td>126.00</td>\n",
              "      <td>134.18</td>\n",
              "      <td>125.89</td>\n",
              "      <td>134.24</td>\n",
              "      <td>39723370</td>\n",
              "      <td>8.18</td>\n",
              "      <td>large_gain</td>\n",
              "      <td>large</td>\n",
              "      <td></td>\n",
              "      <td>12</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>248</th>\n",
              "      <td>132.44</td>\n",
              "      <td>134.52</td>\n",
              "      <td>129.67</td>\n",
              "      <td>134.99</td>\n",
              "      <td>31202509</td>\n",
              "      <td>2.08</td>\n",
              "      <td>medium_gain</td>\n",
              "      <td>large</td>\n",
              "      <td></td>\n",
              "      <td>12</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>249</th>\n",
              "      <td>135.34</td>\n",
              "      <td>133.20</td>\n",
              "      <td>132.20</td>\n",
              "      <td>135.92</td>\n",
              "      <td>22627569</td>\n",
              "      <td>-2.14</td>\n",
              "      <td>negative</td>\n",
              "      <td>small</td>\n",
              "      <td></td>\n",
              "      <td>12</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>250</th>\n",
              "      <td>134.45</td>\n",
              "      <td>131.09</td>\n",
              "      <td>129.95</td>\n",
              "      <td>134.64</td>\n",
              "      <td>24625308</td>\n",
              "      <td>-3.36</td>\n",
              "      <td>negative</td>\n",
              "      <td>small</td>\n",
              "      <td></td>\n",
              "      <td>12</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>251 rows × 11 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-3fad428c-bc44-4178-a952-63e2acc87e49')\"\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-3fad428c-bc44-4178-a952-63e2acc87e49 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-3fad428c-bc44-4178-a952-63e2acc87e49');\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-a2a07811-aa19-4151-bfa0-18407df5d4e4\">\n",
              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-a2a07811-aa19-4151-bfa0-18407df5d4e4')\"\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-a2a07811-aa19-4151-bfa0-18407df5d4e4 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\": \"df\",\n  \"rows\": 251,\n  \"fields\": [\n    {\n      \"column\": \"open\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 19.69649335473974,\n        \"min\": 123.1,\n        \"max\": 215.72,\n        \"num_unique_values\": 249,\n        \"samples\": [\n          208.77,\n          186.94,\n          184.93\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"close\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 19.97745158627776,\n        \"min\": 124.06,\n        \"max\": 217.5,\n        \"num_unique_values\": 249,\n        \"samples\": [\n          209.94,\n          187.84,\n          186.9\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"low\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 20.07440767348364,\n        \"min\": 123.02,\n        \"max\": 214.27,\n        \"num_unique_values\": 247,\n        \"samples\": [\n          176.4,\n          185.63,\n          160.88\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"high\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 19.424564323437952,\n        \"min\": 129.74,\n        \"max\": 218.62,\n        \"num_unique_values\": 246,\n        \"samples\": [\n          177.95,\n          187.89,\n          171.77\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"vol\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 19101434,\n        \"min\": 8855144,\n        \"max\": 169803668,\n        \"num_unique_values\": 251,\n        \"samples\": [\n          19101995,\n          10464528,\n          17921935\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"profit\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.8733780844045307,\n        \"min\": -9.429999999999978,\n        \"max\": 8.180000000000007,\n        \"num_unique_values\": 233,\n        \"samples\": [\n          3.530000000000001,\n          2.3500000000000227,\n          -2.140000000000015\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"gain\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"medium_gain\",\n          \"small_gain\",\n          \"large_gain\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"size\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"large\",\n          \"small\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"all\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"month\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3,\n        \"min\": 1,\n        \"max\": 12,\n        \"num_unique_values\": 12,\n        \"samples\": [\n          11\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"positive_profit\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 66
        }
      ],
      "source": [
        "df.drop('date',axis=1)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rw4NXVfKhmFi"
      },
      "source": [
        "## Comparing multiple stocks"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PVk4d0MLhmFi"
      },
      "source": [
        "As a last task, we will use the experience we obtained so far -- and learn some new things -- in order to compare the performance of different stocks we obtained from Yahoo finance."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 67,
      "metadata": {
        "id": "8wO8R-qGhmFi"
      },
      "outputs": [],
      "source": [
        "from tiingo import TiingoClient\n",
        "client = TiingoClient({'api_key':'614c1590a592cc6696f6082f83b2666cd83882ef'})\n",
        "ticker_history = client.get_dataframe(['GOOGL', 'AAPL'],\n",
        "                                      frequency='daily',\n",
        "                                      metric_name='close',\n",
        "                                      startDate='2017-01-01',\n",
        "                                      endDate='2018-05-31')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 68,
      "metadata": {
        "id": "TtVemypEhmFi",
        "outputId": "d7d093af-63db-4375-91e9-5756ecf0a11a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                            GOOGL    AAPL\n",
              "2017-01-03 00:00:00+00:00  808.01  116.15\n",
              "2017-01-04 00:00:00+00:00  807.77  116.02\n",
              "2017-01-05 00:00:00+00:00  813.02  116.61\n",
              "2017-01-06 00:00:00+00:00  825.21  117.91\n",
              "2017-01-09 00:00:00+00:00  827.18  118.99"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-38382eb8-a24b-45e3-a734-b605bc032148\" 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",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>GOOGL</th>\n",
              "      <th>AAPL</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2017-01-03 00:00:00+00:00</th>\n",
              "      <td>808.01</td>\n",
              "      <td>116.15</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-01-04 00:00:00+00:00</th>\n",
              "      <td>807.77</td>\n",
              "      <td>116.02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-01-05 00:00:00+00:00</th>\n",
              "      <td>813.02</td>\n",
              "      <td>116.61</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-01-06 00:00:00+00:00</th>\n",
              "      <td>825.21</td>\n",
              "      <td>117.91</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-01-09 00:00:00+00:00</th>\n",
              "      <td>827.18</td>\n",
              "      <td>118.99</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-38382eb8-a24b-45e3-a734-b605bc032148')\"\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",
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              "  </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",
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              "      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-38382eb8-a24b-45e3-a734-b605bc032148 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-38382eb8-a24b-45e3-a734-b605bc032148');\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-f339bf5b-c60c-48b9-bc16-28b460fa2c17\">\n",
              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-f339bf5b-c60c-48b9-bc16-28b460fa2c17')\"\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-f339bf5b-c60c-48b9-bc16-28b460fa2c17 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": "ticker_history",
              "summary": "{\n  \"name\": \"ticker_history\",\n  \"rows\": 355,\n  \"fields\": [\n    {\n      \"column\": \"GOOGL\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 96.04203706073258,\n        \"min\": 807.77,\n        \"max\": 1187.56,\n        \"num_unique_values\": 353,\n        \"samples\": [\n          1048.47,\n          847.27,\n          1042.68\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"AAPL\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 17.027205942941183,\n        \"min\": 116.02,\n        \"max\": 190.04,\n        \"num_unique_values\": 338,\n        \"samples\": [\n          144.29,\n          188.36,\n          155.39\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 68
        }
      ],
      "source": [
        "ticker_history.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 69,
      "metadata": {
        "id": "JYH4sywChmFj",
        "outputId": "d65e779c-462c-4c6c-c3d2-eb235599b5dc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                             META     GOOG    TSLA   MSFT    NFLX\n",
              "2018-01-02 00:00:00+00:00  181.42  1065.00  320.53  85.95  201.07\n",
              "2018-01-03 00:00:00+00:00  184.67  1082.48  317.25  86.35  205.05\n",
              "2018-01-04 00:00:00+00:00  184.33  1086.40  314.62  87.11  205.63\n",
              "2018-01-05 00:00:00+00:00  186.85  1102.23  316.58  88.19  209.99\n",
              "2018-01-08 00:00:00+00:00  188.28  1106.94  336.41  88.28  212.05"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-410c753b-8a37-4256-8076-6e151f438892\" 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>META</th>\n",
              "      <th>GOOG</th>\n",
              "      <th>TSLA</th>\n",
              "      <th>MSFT</th>\n",
              "      <th>NFLX</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2018-01-02 00:00:00+00:00</th>\n",
              "      <td>181.42</td>\n",
              "      <td>1065.00</td>\n",
              "      <td>320.53</td>\n",
              "      <td>85.95</td>\n",
              "      <td>201.07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-01-03 00:00:00+00:00</th>\n",
              "      <td>184.67</td>\n",
              "      <td>1082.48</td>\n",
              "      <td>317.25</td>\n",
              "      <td>86.35</td>\n",
              "      <td>205.05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-01-04 00:00:00+00:00</th>\n",
              "      <td>184.33</td>\n",
              "      <td>1086.40</td>\n",
              "      <td>314.62</td>\n",
              "      <td>87.11</td>\n",
              "      <td>205.63</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-01-05 00:00:00+00:00</th>\n",
              "      <td>186.85</td>\n",
              "      <td>1102.23</td>\n",
              "      <td>316.58</td>\n",
              "      <td>88.19</td>\n",
              "      <td>209.99</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-01-08 00:00:00+00:00</th>\n",
              "      <td>188.28</td>\n",
              "      <td>1106.94</td>\n",
              "      <td>336.41</td>\n",
              "      <td>88.28</td>\n",
              "      <td>212.05</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-410c753b-8a37-4256-8076-6e151f438892')\"\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-410c753b-8a37-4256-8076-6e151f438892 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-410c753b-8a37-4256-8076-6e151f438892');\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-a7e757db-2997-40f1-ac50-1e124e0ec31a\">\n",
              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-a7e757db-2997-40f1-ac50-1e124e0ec31a')\"\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-a7e757db-2997-40f1-ac50-1e124e0ec31a 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": "dfmany",
              "summary": "{\n  \"name\": \"dfmany\",\n  \"rows\": 251,\n  \"fields\": [\n    {\n      \"column\": \"META\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 19.97745158627776,\n        \"min\": 124.06,\n        \"max\": 217.5,\n        \"num_unique_values\": 249,\n        \"samples\": [\n          209.94,\n          187.84,\n          186.9\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"GOOG\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 67.31555097806508,\n        \"min\": 976.22,\n        \"max\": 1268.33,\n        \"num_unique_values\": 251,\n        \"samples\": [\n          1242.1,\n          1102.61,\n          1241.82\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"TSLA\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 28.760220040772037,\n        \"min\": 250.56,\n        \"max\": 379.57,\n        \"num_unique_values\": 249,\n        \"samples\": [\n          313.58,\n          334.8,\n          279.07\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"MSFT\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7.917806771053775,\n        \"min\": 85.01,\n        \"max\": 115.61,\n        \"num_unique_values\": 240,\n        \"samples\": [\n          91.33,\n          87.82,\n          97.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"NFLX\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 48.91920008350873,\n        \"min\": 201.07,\n        \"max\": 418.97,\n        \"num_unique_values\": 249,\n        \"samples\": [\n          361.05,\n          212.52,\n          344.72\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 69
        }
      ],
      "source": [
        "stocks = ['META','GOOG','TSLA', 'MSFT','NFLX']\n",
        "from tiingo import TiingoClient\n",
        "client = TiingoClient({'api_key':'614c1590a592cc6696f6082f83b2666cd83882ef'})\n",
        "dfmany = client.get_dataframe(stocks,\n",
        "                                      frequency='daily',\n",
        "                                      metric_name='close',\n",
        "                                      startDate=start,\n",
        "                                      endDate=end)\n",
        "dfmany.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 70,
      "metadata": {
        "id": "LrRKQtQDhmFj",
        "outputId": "40e55468-63a4-411e-c436-244dba5184e5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 414
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "dfmany.META.plot(label = 'meta')\n",
        "dfmany.GOOG.plot(label = 'google')\n",
        "dfmany.TSLA.plot(label = 'tesla')\n",
        "dfmany.MSFT.plot(label = 'microsoft')\n",
        "dfmany.NFLX.plot(label = 'netflix')\n",
        "_ = plt.legend(loc='best')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ASsmp4iGhmFj"
      },
      "source": [
        "Next, we will calculate returns over a period of length $T$, defined as:\n",
        "\n",
        "$$r(t) = \\frac{f(t)-f(t-T)}{f(t)} $$"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "38zgNQFUhmFj"
      },
      "source": [
        "The returns can be computed with a simple DataFrame method **`pct_change()`**.  Note that for the first $T$ timesteps, this value is not defined (of course): Here we calculate for $T$ = 30"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 71,
      "metadata": {
        "id": "Kw3cQ1cLhmFk",
        "outputId": "5649f6b1-5991-45e2-9301-2b4450836e05",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                               META      GOOG      TSLA      MSFT      NFLX\n",
              "2018-02-07 00:00:00+00:00       NaN       NaN       NaN       NaN       NaN\n",
              "2018-02-08 00:00:00+00:00       NaN       NaN       NaN       NaN       NaN\n",
              "2018-02-09 00:00:00+00:00       NaN       NaN       NaN       NaN       NaN\n",
              "2018-02-12 00:00:00+00:00       NaN       NaN       NaN       NaN       NaN\n",
              "2018-02-13 00:00:00+00:00       NaN       NaN       NaN       NaN       NaN\n",
              "2018-02-14 00:00:00+00:00 -0.010473  0.004413  0.005553  0.056545  0.322922\n",
              "2018-02-15 00:00:00+00:00 -0.025505  0.006504  0.053018  0.073075  0.366837\n",
              "2018-02-16 00:00:00+00:00 -0.037813  0.007732  0.066334  0.056136  0.354472\n",
              "2018-02-20 00:00:00+00:00 -0.058014  0.000209  0.057458  0.051366  0.326492\n",
              "2018-02-21 00:00:00+00:00 -0.055078  0.003975 -0.009245  0.036362  0.325348"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-e636e373-2e33-4c0a-8cb7-ed4251214131\" 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>META</th>\n",
              "      <th>GOOG</th>\n",
              "      <th>TSLA</th>\n",
              "      <th>MSFT</th>\n",
              "      <th>NFLX</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2018-02-07 00:00:00+00:00</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-02-08 00:00:00+00:00</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-02-09 00:00:00+00:00</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-02-12 00:00:00+00:00</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-02-13 00:00:00+00:00</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-02-14 00:00:00+00:00</th>\n",
              "      <td>-0.010473</td>\n",
              "      <td>0.004413</td>\n",
              "      <td>0.005553</td>\n",
              "      <td>0.056545</td>\n",
              "      <td>0.322922</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-02-15 00:00:00+00:00</th>\n",
              "      <td>-0.025505</td>\n",
              "      <td>0.006504</td>\n",
              "      <td>0.053018</td>\n",
              "      <td>0.073075</td>\n",
              "      <td>0.366837</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-02-16 00:00:00+00:00</th>\n",
              "      <td>-0.037813</td>\n",
              "      <td>0.007732</td>\n",
              "      <td>0.066334</td>\n",
              "      <td>0.056136</td>\n",
              "      <td>0.354472</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-02-20 00:00:00+00:00</th>\n",
              "      <td>-0.058014</td>\n",
              "      <td>0.000209</td>\n",
              "      <td>0.057458</td>\n",
              "      <td>0.051366</td>\n",
              "      <td>0.326492</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-02-21 00:00:00+00:00</th>\n",
              "      <td>-0.055078</td>\n",
              "      <td>0.003975</td>\n",
              "      <td>-0.009245</td>\n",
              "      <td>0.036362</td>\n",
              "      <td>0.325348</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-e636e373-2e33-4c0a-8cb7-ed4251214131')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
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              "      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",
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              "      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",
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              "\n",
              "    .colab-df-buttons div {\n",
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              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "      --bg-color: #E8F0FE;\n",
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              "      --hover-bg-color: #E2EBFA;\n",
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              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
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              "    border-radius: 50%;\n",
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              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
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              "    fill: var(--button-hover-fill-color);\n",
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              "  .colab-df-quickchart-complete:disabled,\n",
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              "    background-color: var(--disabled-bg-color);\n",
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              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
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              "      border-left-color: var(--fill-color);\n",
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              "      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",
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              "      border-color: transparent;\n",
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              "      border-bottom-color: var(--fill-color);\n",
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              "  }\n",
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              "\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",
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              "            const charts = await google.colab.kernel.invokeFunction(\n",
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              "            console.error('Error during call to suggestCharts:', error);\n",
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              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"rets\",\n  \"rows\": 10,\n  \"fields\": [\n    {\n      \"column\": \"META\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.020026092363992373,\n        \"min\": -0.05801445009365802,\n        \"max\": -0.010472935729246902,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          -0.025504954784209555,\n          -0.05507754408328025,\n          -0.037812618673032095\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"GOOG\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.002878220285611853,\n        \"min\": 0.00020866788238382838,\n        \"max\": 0.0077319587628865705,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.006503584361835735,\n          0.003974921856649827,\n          0.0077319587628865705\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"TSLA\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.034039744897087436,\n        \"min\": -0.009244671680390004,\n        \"max\": 0.06633399021041253,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.05301812450748611,\n          -0.009244671680390004,\n          0.06633399021041253\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"MSFT\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.013138724439724466,\n        \"min\": 0.036361576801087425,\n        \"max\": 0.07307469600463223,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.07307469600463223,\n          0.036361576801087425,\n          0.05613592010102164\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"NFLX\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.020095956501867112,\n        \"min\": 0.3229223653454021,\n        \"max\": 0.3668373567422578,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.3668373567422578,\n          0.32534779533128977,\n          0.35447162379030295\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 71
        }
      ],
      "source": [
        "rets = dfmany.pct_change(30)\n",
        "rets.iloc[25:35]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xMTUQjy3hmFk"
      },
      "source": [
        "Now we'll plot the timeseries of the returns of the different stocks.\n",
        "\n",
        "Notice that the `NaN` values are gracefully dropped by the plotting function."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 72,
      "metadata": {
        "id": "bMpwbNVDhmFk",
        "outputId": "598e5078-5b25-4522-871a-57700463ce78",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 414
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "rets.META.plot(label = 'meta')\n",
        "rets.GOOG.plot(label = 'google')\n",
        "rets.TSLA.plot(label = 'tesla')\n",
        "rets.MSFT.plot(label = 'microsoft')\n",
        "rets.NFLX.plot(label = 'netflix')\n",
        "_ = plt.legend(loc='best')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 73,
      "metadata": {
        "id": "GyZVSUwnhmFk",
        "outputId": "dacd1c54-33f2-4022-a672-77f0fc44caa7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.scatter(rets.TSLA, rets.GOOG)\n",
        "plt.xlabel('TESLA 30-day returns')\n",
        "_ = plt.ylabel('GOOGLE 30-day returns')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bXyRQHuYhmFl"
      },
      "source": [
        "We can also use the seaborn library for doing the scatterplot. Note that this method returns an object which we can use to set different parameters of the plot. In the example below we use it to set the x and y labels of the plot. Read online for more options."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 74,
      "metadata": {
        "id": "IIV8UtHRhmFl"
      },
      "outputs": [],
      "source": [
        "dfb = client.get_dataframe('META',frequency='daily', startDate=start, endDate=end)[['open','high','low','close','volume']]\n",
        "dgoog = client.get_dataframe('GOOG',frequency='daily', startDate=start, endDate=end)[['open','high','low','close','volume']]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 75,
      "metadata": {
        "id": "IiENM7C6hmFl",
        "outputId": "6db87923-060e-4b62-b972-9ccb349097c7",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                             open   close     low    high    volume\n",
            "date                                                               \n",
            "2018-01-02 00:00:00+00:00  177.68  181.42  177.55  181.58  17694891\n",
            "2018-01-03 00:00:00+00:00  181.88  184.67  181.33  184.78  16595495\n",
            "2018-01-04 00:00:00+00:00  184.90  184.33  184.10  186.21  13554357\n",
            "2018-01-05 00:00:00+00:00  185.59  186.85  184.93  186.90  13042388\n",
            "2018-01-08 00:00:00+00:00  187.20  188.28  186.33  188.90  14719216\n",
            "                              open    close       low     high   volume\n",
            "date                                                                   \n",
            "2018-01-02 00:00:00+00:00  1048.34  1065.00  1045.230  1066.94  1223114\n",
            "2018-01-03 00:00:00+00:00  1064.31  1082.48  1063.210  1086.29  1416093\n",
            "2018-01-04 00:00:00+00:00  1088.00  1086.40  1084.002  1093.57   990510\n",
            "2018-01-05 00:00:00+00:00  1094.00  1102.23  1092.000  1104.25  1210974\n",
            "2018-01-08 00:00:00+00:00  1102.23  1106.94  1101.620  1111.27  1003098\n"
          ]
        }
      ],
      "source": [
        "start = datetime(2018,1,1)\n",
        "end = datetime(2018,12,31)\n",
        "\n",
        "dfb = client.get_dataframe('META',frequency='daily',startDate=start,endDate=end)\n",
        "dfb = dfb[['open','close','low','high','volume']]\n",
        "\n",
        "dgoog = client.get_dataframe('GOOG',frequency='daily',startDate=start,endDate=end)\n",
        "dgoog = dgoog[['open','close','low','high','volume']]\n",
        "\n",
        "\n",
        "print(dfb.head())\n",
        "print(dgoog.head())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 76,
      "metadata": {
        "id": "J6BMW2ArhmFl"
      },
      "outputs": [],
      "source": [
        "def gainrow(row):\n",
        "    if row.close < row.open:\n",
        "        return 'negative'\n",
        "    elif (row.close - row.open) < 1:\n",
        "        return 'small_gain'\n",
        "    elif (row.close - row.open) < 3:\n",
        "        return 'medium_gain'\n",
        "    else:\n",
        "        return 'large_gain'\n",
        "\n",
        "dfb['gain'] = dfb.apply(gainrow, axis = 1)\n",
        "dgoog['gain'] = dgoog.apply(gainrow, axis = 1)\n",
        "dfb['profit'] = dfb.close-dfb.open\n",
        "dgoog['profit'] = dgoog.close-dgoog.open"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 77,
      "metadata": {
        "id": "e2XW5ecvhmFl",
        "outputId": "ff79a701-0099-4777-ca66-a1de8b27f424",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'GOOG profit')"
            ]
          },
          "metadata": {},
          "execution_count": 77
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "#Also using seaborn\n",
        "fig = sns.scatterplot(x = dfb.profit, y = dgoog.profit)\n",
        "fig.set_xlabel('FB profit')\n",
        "fig.set_ylabel('GOOG profit')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "smmLkyl4hmFl"
      },
      "source": [
        "Get all pairwise correlations in a single plot"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 78,
      "metadata": {
        "id": "Y23n_1jFhmFm",
        "outputId": "9eb06196-3f8e-4579-dfa2-9488dca977a4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.PairGrid at 0x7ff3f8c79370>"
            ]
          },
          "metadata": {},
          "execution_count": 78
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1250x1250 with 30 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "sns.pairplot(rets.iloc[30:])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YSVmqBsFhmFm"
      },
      "source": [
        "There appears to be some (fairly strong) correlation between the movement of Google and Microsoft stocks. Let's measure this."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "G8MmHHZghmFm"
      },
      "source": [
        "### Correlation Coefficients\n",
        "\n",
        "The correlation coefficient between variables $X$ and $Y$ is defined as follows:\n",
        "\n",
        "$$\\text{Corr}(X,Y) = \\frac{E\\left[(X-\\mu_X)(Y-\\mu_Y)\\right]}{\\sigma_X\\sigma_Y}$$\n",
        "\n",
        "Pandas provides a DataFrame method to compute the correlation coefficient of all pairs of columns: **`corr()`**."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 79,
      "metadata": {
        "id": "r5i4ZYtHhmFm",
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          "data": {
            "text/plain": [
              "          META      GOOG      TSLA      MSFT      NFLX\n",
              "META  1.000000  0.598776  0.226680  0.470696  0.546996\n",
              "GOOG  0.598776  1.000000  0.210441  0.790085  0.348008\n",
              "TSLA  0.226680  0.210441  1.000000 -0.041910 -0.120763\n",
              "MSFT  0.470696  0.790085 -0.041910  1.000000  0.489569\n",
              "NFLX  0.546996  0.348008 -0.120763  0.489569  1.000000"
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              "        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",
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              "\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"rets\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"META\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.28016861713651475,\n        \"min\": 0.2266797689978941,\n        \"max\": 1.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.5987760976044884,\n          0.5469963848311834,\n          0.2266797689978941\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"GOOG\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.3204818404699322,\n        \"min\": 0.21044127805125543,\n        \"max\": 1.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          1.0,\n          0.3480078376106228,\n          0.21044127805125543\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"TSLA\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.44361183315027475,\n        \"min\": -0.12076289195142191,\n        \"max\": 1.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.21044127805125543,\n          -0.12076289195142191,\n          1.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"MSFT\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.39373014294703224,\n        \"min\": -0.041910135945113274,\n        \"max\": 1.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.7900845765563286,\n          0.4895691908742243,\n          -0.041910135945113274\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"NFLX\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.4029914867135219,\n        \"min\": -0.12076289195142191,\n        \"max\": 1.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.3480078376106228,\n          1.0,\n          -0.12076289195142191\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 79
        }
      ],
      "source": [
        "rets.corr()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 80,
      "metadata": {
        "id": "KnKAHFGZhmFm",
        "outputId": "71f15bca-2ea7-47cb-a005-a93608279a49",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          META      GOOG      TSLA      MSFT      NFLX\n",
              "META  1.000000  0.540949  0.271608  0.457852  0.641344\n",
              "GOOG  0.540949  1.000000  0.288135  0.803731  0.382466\n",
              "TSLA  0.271608  0.288135  1.000000  0.042190 -0.065939\n",
              "MSFT  0.457852  0.803731  0.042190  1.000000  0.456912\n",
              "NFLX  0.641344  0.382466 -0.065939  0.456912  1.000000"
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              "\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-6088b8fc-1695-478e-aba7-c9d910c87766\">\n",
              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-6088b8fc-1695-478e-aba7-c9d910c87766')\"\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-6088b8fc-1695-478e-aba7-c9d910c87766 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\": \"rets\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"META\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.27004003727876874,\n        \"min\": 0.27160772454890103,\n        \"max\": 1.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.5409485585956174,\n          0.6413443472267002,\n          0.27160772454890103\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"GOOG\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.29560414798516554,\n        \"min\": 0.2881352351940587,\n        \"max\": 1.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          1.0,\n          0.3824663412898707,\n          0.2881352351940587\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"TSLA\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.4156515336831353,\n        \"min\": -0.065938830644713,\n        \"max\": 1.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.2881352351940587,\n          -0.065938830644713,\n          1.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"MSFT\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.3680637847832975,\n        \"min\": 0.04219040101393043,\n        \"max\": 1.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.8037310860840272,\n          0.4569124039712275,\n          0.04219040101393043\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"NFLX\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.38874263400733694,\n        \"min\": -0.065938830644713,\n        \"max\": 1.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.3824663412898707,\n          1.0,\n          -0.065938830644713\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 80
        }
      ],
      "source": [
        "rets.corr(method='spearman')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "l68OvB9AhmFm"
      },
      "source": [
        "It takes a bit of time to examine that table and draw conclusions.  \n",
        "\n",
        "To speed that process up it helps to visualize the table using a heatmap."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 81,
      "metadata": {
        "id": "MNH3OeKOhmFm",
        "outputId": "6abc1838-136b-4654-b90f-0d6761f92648",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 435
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "_ = sns.heatmap(rets.corr(), annot=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7nhb0NDQhmFn"
      },
      "source": [
        "### Computing p-values\n",
        "\n",
        "Use the scipy.stats library to obtain the p-values for the pearson and spearman rank correlations"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 82,
      "metadata": {
        "id": "Ygk5_iR5hmFn",
        "outputId": "b90049ef-5978-4a88-9dac-ccb12f27c5aa",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "PearsonRResult(statistic=np.float64(-0.120762891951422), pvalue=np.float64(0.07318882534649766))\n",
            "SignificanceResult(statistic=np.float64(-0.065938830644713), pvalue=np.float64(0.32918605296193537))\n",
            "PearsonRResult(statistic=np.float64(0.5987760976044885), pvalue=np.float64(6.856639483413281e-23))\n",
            "SignificanceResult(statistic=np.float64(0.5409485585956174), pvalue=np.float64(3.3888933351952313e-18))\n"
          ]
        }
      ],
      "source": [
        "print(stats.pearsonr(rets.iloc[30:].NFLX, rets.iloc[30:].TSLA))\n",
        "print(stats.spearmanr(rets.iloc[30:].NFLX, rets.iloc[30:].TSLA))\n",
        "print(stats.pearsonr(rets.iloc[30:].GOOG, rets.iloc[30:].META))\n",
        "print(stats.spearmanr(rets.iloc[30:].GOOG, rets.iloc[30:].META))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Oh0JMfqjhmFn"
      },
      "outputs": [],
      "source": [
        "print(stats.pearsonr(dfb.profit, dgoog.profit))\n",
        "print(stats.spearmanr(dfb.profit, dgoog.profit))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tD2AyNbAhmFn"
      },
      "source": [
        "### Matplotlib\n",
        "\n",
        "Finally, it is important to know that the plotting performed by Pandas is just a layer on top of `matplotlib` (i.e., the `plt` package).  \n",
        "\n",
        "So Panda's plots can (and should) be replaced or improved by using additional functions from `matplotlib`."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "u8__m8xahmFn"
      },
      "source": [
        "For example, suppose we want to know both the returns as well as the standard deviation of the returns of a stock (i.e., its risk).  \n",
        "\n",
        "Here is visualization of the result of such an analysis, and we construct the plot using only functions from `matplotlib`."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 83,
      "metadata": {
        "id": "cwSb1WFxhmFn",
        "outputId": "b860d13f-9dac-43af-94c8-50d5781aa43d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "_ = plt.scatter(rets.mean(), rets.std())\n",
        "plt.xlabel('Expected returns')\n",
        "plt.ylabel('Standard Deviation (Risk)')\n",
        "for label, x, y in zip(rets.columns, rets.mean(), rets.std()):\n",
        "    plt.annotate(\n",
        "        label,\n",
        "        xy = (x, y), xytext = (20, -20),\n",
        "        textcoords = 'offset points', ha = 'right', va = 'bottom',\n",
        "        bbox = dict(boxstyle = 'round,pad=0.5', fc = 'yellow', alpha = 0.5),\n",
        "        arrowprops = dict(arrowstyle = '->', connectionstyle = 'arc3,rad=0'))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MB_WM7mhhmFo"
      },
      "source": [
        "To understand what these functions are doing, (especially the `annotate` function), you will need to consult the online documentation for matplotlib.  Just use Google to find it."
      ]
    }
  ],
  "metadata": {
    "celltoolbar": "Slideshow",
    "kernelspec": {
      "display_name": "Python 3 (ipykernel)",
      "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": "3.8.12"
    },
    "colab": {
      "provenance": []
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}