Visualization_final.ipynb

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      "source": [
        "#### Machine Learning and Visulization for Healthcare Data: Foundtions (Part 1)\n",
        "# Python libraries for working with data (load, process, and visulize)"
      ]
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    {
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      "source": [
        "# Copyright (c) Mohamed Elgendi 2020. All rights reserved\n",
        "# Author: Mohamed Elgendi \n",
        "# Version: 1.0 (2020.11.29)\n",
        "# Email: moe.elgendi@gmail.com\n",
        "# Platform: Tested on Windows 10 with Python 3.6"
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      "source": [
        "### Objectives\n",
        " - Learn how to use Pandas to easily and efficiently analyze data\n",
        " - Review some simple statistical operations\n",
        " - Complete the exercises\n",
        "\n",
        "### Background reading\n",
        "- Pandas dataframe docs: https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.html\n",
        "- More on visualization in matplotlib can be found here: https://matplotlib.org/\n",
        "- More on visualization in seaborn can be found here: https://seaborn.pydata.org/\n"
      ]
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    {
      "cell_type": "markdown",
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      "source": [
        "## Introduction to Pandas\n",
        "\n",
        "Pandas is a python library for data analysis. At the core of Pandas, lies the concept of a 'DataFrame' - this can be thought of as powerful type of dictionary designed for data analysis.\n",
        "\n",
        "The DataFrame is designed to manage indexed and labeled data, not too different from a SQL database table or a worksheet in a spreadsheet application. Consider the following creation of a DataFrame object:"
      ]
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    {
      "cell_type": "code",
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      "source": [
        "#All libs used in the tutorial\n",
        "%matplotlib inline\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ],
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      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "\n",
        "df = pd.DataFrame([20, 60, 13, 40], columns=['Age'],\n",
        "                  index=['Subject 1', 'Subject 2', 'Subject 3', 'Subject 4'])\n",
        "df"
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              "           Age\n",
              "Subject 1   20\n",
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              "Subject 3   13\n",
              "Subject 4   40"
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        "We observe a number of important proporties of the dataframe:\n",
        "- Data itself can be provided in different shapes and types (list, tuple, ndarray, and dict objects are candidates). \n",
        "- Labels: Data is organized in columns, which can have custom names. \n",
        "- Index: There is an index that can take on different formats (e.g., numbers, strings, time information). "
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        "The following examples demonstrate several of the most import methods and properties of the Pandas dataframe object."
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        "df.index  # the index values"
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              "Index(['Subject 1', 'Subject 2', 'Subject 3', 'Subject 4'], dtype='object')"
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      "source": [
        "df.columns  # the column names"
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              "Index(['Age'], dtype='object')"
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        "df.loc['Subject 1'] # selection via index"
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            "text/plain": [
              "Age    20\n",
              "Name: Subject 1, dtype: int64"
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        "df.loc[['Subject 1', 'Subject 2']]  # selection of multiple indices"
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        "df.iloc[-1]  # selection via Index object"
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              "Age    40\n",
              "Name: Subject 4, dtype: int64"
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        "df.values # note: we extract values as an array"
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              "array([[20],\n",
              "       [60],\n",
              "       [13],\n",
              "       [40]])"
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      "source": [
        "df.Age.std()  # sum per column"
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      "source": [
        "df.Age.mean()"
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              "33.25"
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      "source": [
        "df.apply(lambda x: x ** 2)  # square of every element"
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              "      <td>3600</td>\n",
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              "      <td>169</td>\n",
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              "      <td>1600</td>\n",
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              "            Age\n",
              "Subject 1   400\n",
              "Subject 2  3600\n",
              "Subject 3   169\n",
              "Subject 4  1600"
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        "df ** 2  # again square, this time NumPy-like"
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              "      <td>400</td>\n",
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              "      <td>3600</td>\n",
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              "      <td>169</td>\n",
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              "            Age\n",
              "Subject 1   400\n",
              "Subject 2  3600\n",
              "Subject 3   169\n",
              "Subject 4  1600"
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      "source": [
        "# Adding a new column\n",
        "Adding a new feature/column to the original dataframe is so simple.\n",
        "All we need to do is put the new column between two single quotations, and then assign values as follows."
      ]
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      "source": [
        "df['A'] = [1.9, 2.5, 3.5, 4.5]  # new column is generated\n",
        "df\n"
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              "      <th>Subject 1</th>\n",
              "      <td>20</td>\n",
              "      <td>1.9</td>\n",
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              "    <tr>\n",
              "      <th>Subject 2</th>\n",
              "      <td>60</td>\n",
              "      <td>2.5</td>\n",
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              "    <tr>\n",
              "      <th>Subject 3</th>\n",
              "      <td>13</td>\n",
              "      <td>3.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Subject 4</th>\n",
              "      <td>40</td>\n",
              "      <td>4.5</td>\n",
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              "           Age    A\n",
              "Subject 1   20  1.9\n",
              "Subject 2   60  2.5\n",
              "Subject 3   13  3.5\n",
              "Subject 4   40  4.5"
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      "source": [
        "# Deleting a column\n",
        "\n",
        "We use the drop function in the dataframe."
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      "source": [
        "df.drop(columns='A')"
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              "           Age\n",
              "Subject 1   20\n",
              "Subject 2   60\n",
              "Subject 3   13\n",
              "Subject 4   40"
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    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "q1YepSDXDNYl"
      },
      "source": [
        "# Exercise 1: Create a new dataframe with 4 attributes/columns for 3 subjects\n",
        "The 4 attributes are Age, BMI, SBP, DBP, Echo"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "q_lniG03DNYl"
      },
      "source": [
        "# Exercise 2: Square the Age column\n",
        "\n",
        " Hint: access Age via df first."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8x3v1La6DNYl"
      },
      "source": [
        "# Exercise 3: Remove the Echo column\n",
        "\n",
        " Hint: use the drop built-in function."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mMn-zbtUDNYl"
      },
      "source": [
        "df2 = pd.DataFrame(np.arange(1,1000),columns=['integers'])\n",
        "df3 = df2.apply(lambda x: np.sin(x))"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "l2CwxRoHDNYl",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "outputId": "0f8ce7be-3247-472f-f09f-50bcd7a039c1"
      },
      "source": [
        "df3[0:100].plot()"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f74403f8390>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dBhjh_SiDNYl",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "outputId": "a1e5c073-9b04-49ba-ed55-71ae52105cde"
      },
      "source": [
        "df.plot.scatter('Age','A') # note that it takes columns only"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f74402e39e8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1FXQ8xAVDNYl"
      },
      "source": [
        "# Exercise #1: scatter ['s1','s2','s3','s4'] vs. Age."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9XvWwW09DNYl",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 172
        },
        "outputId": "eed109df-5662-4d58-a691-e2bc5a68b85d"
      },
      "source": [
        "df['names'] = pd.DataFrame(['Lisa', 'Bill', 'Rob', 'Depang'],\n",
        "                           index=['Subject 1', 'Subject 2', 'Subject 3', 'Subject 4'])\n",
        "df"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Age</th>\n",
              "      <th>A</th>\n",
              "      <th>names</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Subject 1</th>\n",
              "      <td>20</td>\n",
              "      <td>1.9</td>\n",
              "      <td>Lisa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Subject 2</th>\n",
              "      <td>60</td>\n",
              "      <td>2.5</td>\n",
              "      <td>Bill</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Subject 3</th>\n",
              "      <td>13</td>\n",
              "      <td>3.5</td>\n",
              "      <td>Rob</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Subject 4</th>\n",
              "      <td>40</td>\n",
              "      <td>4.5</td>\n",
              "      <td>Depang</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "           Age    A   names\n",
              "Subject 1   20  1.9    Lisa\n",
              "Subject 2   60  2.5    Bill\n",
              "Subject 3   13  3.5     Rob\n",
              "Subject 4   40  4.5  Depang"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dyUPLnnsDNYl",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 203
        },
        "outputId": "b0950fb6-150c-49d1-904a-829aeba9399f"
      },
      "source": [
        "df = df.append(pd.DataFrame({'numbers': 100, 'floats': 5.75,\n",
        "                             'names': 'Henry'}, index=['z',]))\n",
        "df"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Age</th>\n",
              "      <th>A</th>\n",
              "      <th>names</th>\n",
              "      <th>numbers</th>\n",
              "      <th>floats</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Subject 1</th>\n",
              "      <td>20.0</td>\n",
              "      <td>1.9</td>\n",
              "      <td>Lisa</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Subject 2</th>\n",
              "      <td>60.0</td>\n",
              "      <td>2.5</td>\n",
              "      <td>Bill</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Subject 3</th>\n",
              "      <td>13.0</td>\n",
              "      <td>3.5</td>\n",
              "      <td>Rob</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Subject 4</th>\n",
              "      <td>40.0</td>\n",
              "      <td>4.5</td>\n",
              "      <td>Depang</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>z</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Henry</td>\n",
              "      <td>100.0</td>\n",
              "      <td>5.75</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            Age    A   names  numbers  floats\n",
              "Subject 1  20.0  1.9    Lisa      NaN     NaN\n",
              "Subject 2  60.0  2.5    Bill      NaN     NaN\n",
              "Subject 3  13.0  3.5     Rob      NaN     NaN\n",
              "Subject 4  40.0  4.5  Depang      NaN     NaN\n",
              "z           NaN  NaN   Henry    100.0    5.75"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_aibCsicDNYl",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 203
        },
        "outputId": "9d2664db-ff9e-49bf-ac99-a06705600b01"
      },
      "source": [
        "df.join(pd.DataFrame([1, 4, 9, 16, 25],\n",
        "            index=['a', 'b', 'c', 'd', 'y'],\n",
        "            columns=['squares',]))"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Age</th>\n",
              "      <th>A</th>\n",
              "      <th>names</th>\n",
              "      <th>numbers</th>\n",
              "      <th>floats</th>\n",
              "      <th>squares</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Subject 1</th>\n",
              "      <td>20.0</td>\n",
              "      <td>1.9</td>\n",
              "      <td>Lisa</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Subject 2</th>\n",
              "      <td>60.0</td>\n",
              "      <td>2.5</td>\n",
              "      <td>Bill</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Subject 3</th>\n",
              "      <td>13.0</td>\n",
              "      <td>3.5</td>\n",
              "      <td>Rob</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Subject 4</th>\n",
              "      <td>40.0</td>\n",
              "      <td>4.5</td>\n",
              "      <td>Depang</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>z</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Henry</td>\n",
              "      <td>100.0</td>\n",
              "      <td>5.75</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            Age    A   names  numbers  floats  squares\n",
              "Subject 1  20.0  1.9    Lisa      NaN     NaN      NaN\n",
              "Subject 2  60.0  2.5    Bill      NaN     NaN      NaN\n",
              "Subject 3  13.0  3.5     Rob      NaN     NaN      NaN\n",
              "Subject 4  40.0  4.5  Depang      NaN     NaN      NaN\n",
              "z           NaN  NaN   Henry    100.0    5.75      NaN"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xwqbF41MDNYl"
      },
      "source": [
        "## Load Data Using Pandas"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7k-IFrrwDNYl"
      },
      "source": [
        "We shall now turn to collecting and performing basic summary statisical information on some time series data. This is data that is commonly used in digital health, more time series data exist everywhere, for example, monthly heart rate data. Once we understood how to apply key statistical operations to the dataframe, we shall then briefly review more advanced time series functions."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wxRjvBGnDNYl"
      },
      "source": [
        "# Now we will switch to Power Point Presenation.\n",
        "Lets go to Colab"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wMGgropLDNYl"
      },
      "source": [
        "#At this stage it is expected that you can read files "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FI07S_brDNYl",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1508585a-af49-4ef9-bc7a-4aef98502531"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "x2T3FrRaI0QH"
      },
      "source": [
        "df=pd.read_csv('/content/drive/MyDrive/07.csv')"
      ],
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "m5bAWsWmDNYl",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 417
        },
        "outputId": "f9bc9d26-2cbf-4c04-9717-b33c3bd6f50c"
      },
      "source": [
        "df.dropna()"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
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              "      <th>Elapsed time</th>\n",
              "      <th>ECG</th>\n",
              "      <th>EMG</th>\n",
              "      <th>foot GSR</th>\n",
              "      <th>hand GSr</th>\n",
              "      <th>HR</th>\n",
              "      <th>marker</th>\n",
              "      <th>RESP</th>\n",
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              "      <th>2</th>\n",
              "      <td>'0:00.004'</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>'0:00.006'</td>\n",
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              "      <td>8.763</td>\n",
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              "      <td>35.12</td>\n",
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              "    <tr>\n",
              "      <th>1048570</th>\n",
              "      <td>'35:14.052'</td>\n",
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              "<p>1048574 rows × 8 columns</p>\n",
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            "text/plain": [
              "        Elapsed time    ECG    EMG  foot GSR  hand GSr  HR  marker   RESP\n",
              "0         '0:00.000' -0.044  0.036     8.763     5.675  88   10.85  43.54\n",
              "1         '0:00.002' -0.037  0.036     8.763     5.675  88   10.85  43.54\n",
              "2         '0:00.004' -0.031  0.036     8.763     5.675  88   10.85  43.54\n",
              "3         '0:00.006' -0.023  0.036     8.763     5.675  88   10.85  43.54\n",
              "4         '0:00.008' -0.018  0.036     8.763     5.675  88   10.85  43.54\n",
              "...              ...    ...    ...       ...       ...  ..     ...    ...\n",
              "1048569  '35:14.050' -0.037  0.233    12.609     6.881  71   11.22  35.12\n",
              "1048570  '35:14.052' -0.001  0.233    12.609     6.881  71   11.22  35.12\n",
              "1048571  '35:14.054'  0.022  0.233    12.609     6.881  71   11.22  35.12\n",
              "1048572  '35:14.056'  0.011  0.233    12.609     6.881  71   11.22  35.12\n",
              "1048573  '35:14.058' -0.016  0.233    12.609     6.881  71   11.22  35.12\n",
              "\n",
              "[1048574 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2B5SgOPcDNYl"
      },
      "source": [
        "df2=df.replace({'\\'': ''}, regex=True)"
      ],
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": true,
        "id": "_JaF4W0nDNYl",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 417
        },
        "outputId": "ba9855ff-31c3-4d34-d0ab-85f6970f759c"
      },
      "source": [
        "df2"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "      <th>ECG</th>\n",
              "      <th>EMG</th>\n",
              "      <th>foot GSR</th>\n",
              "      <th>hand GSr</th>\n",
              "      <th>HR</th>\n",
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              "      <th>RESP</th>\n",
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              "    </tr>\n",
              "  </tbody>\n",
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              "<p>1048574 rows × 8 columns</p>\n",
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            ],
            "text/plain": [
              "        Elapsed time    ECG    EMG  foot GSR  hand GSr  HR  marker   RESP\n",
              "0           0:00.000 -0.044  0.036     8.763     5.675  88   10.85  43.54\n",
              "1           0:00.002 -0.037  0.036     8.763     5.675  88   10.85  43.54\n",
              "2           0:00.004 -0.031  0.036     8.763     5.675  88   10.85  43.54\n",
              "3           0:00.006 -0.023  0.036     8.763     5.675  88   10.85  43.54\n",
              "4           0:00.008 -0.018  0.036     8.763     5.675  88   10.85  43.54\n",
              "...              ...    ...    ...       ...       ...  ..     ...    ...\n",
              "1048569    35:14.050 -0.037  0.233    12.609     6.881  71   11.22  35.12\n",
              "1048570    35:14.052 -0.001  0.233    12.609     6.881  71   11.22  35.12\n",
              "1048571    35:14.054  0.022  0.233    12.609     6.881  71   11.22  35.12\n",
              "1048572    35:14.056  0.011  0.233    12.609     6.881  71   11.22  35.12\n",
              "1048573    35:14.058 -0.016  0.233    12.609     6.881  71   11.22  35.12\n",
              "\n",
              "[1048574 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZmUhHwAMDNYl",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "outputId": "053baee8-082a-4564-a0bb-175dab06834b"
      },
      "source": [
        "df2.set_index('Elapsed time')"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <td>43.54</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0:00.002</th>\n",
              "      <td>-0.037</td>\n",
              "      <td>0.036</td>\n",
              "      <td>8.763</td>\n",
              "      <td>5.675</td>\n",
              "      <td>88</td>\n",
              "      <td>10.85</td>\n",
              "      <td>43.54</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0:00.004</th>\n",
              "      <td>-0.031</td>\n",
              "      <td>0.036</td>\n",
              "      <td>8.763</td>\n",
              "      <td>5.675</td>\n",
              "      <td>88</td>\n",
              "      <td>10.85</td>\n",
              "      <td>43.54</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0:00.006</th>\n",
              "      <td>-0.023</td>\n",
              "      <td>0.036</td>\n",
              "      <td>8.763</td>\n",
              "      <td>5.675</td>\n",
              "      <td>88</td>\n",
              "      <td>10.85</td>\n",
              "      <td>43.54</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0:00.008</th>\n",
              "      <td>-0.018</td>\n",
              "      <td>0.036</td>\n",
              "      <td>8.763</td>\n",
              "      <td>5.675</td>\n",
              "      <td>88</td>\n",
              "      <td>10.85</td>\n",
              "      <td>43.54</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35:14.050</th>\n",
              "      <td>-0.037</td>\n",
              "      <td>0.233</td>\n",
              "      <td>12.609</td>\n",
              "      <td>6.881</td>\n",
              "      <td>71</td>\n",
              "      <td>11.22</td>\n",
              "      <td>35.12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35:14.052</th>\n",
              "      <td>-0.001</td>\n",
              "      <td>0.233</td>\n",
              "      <td>12.609</td>\n",
              "      <td>6.881</td>\n",
              "      <td>71</td>\n",
              "      <td>11.22</td>\n",
              "      <td>35.12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35:14.054</th>\n",
              "      <td>0.022</td>\n",
              "      <td>0.233</td>\n",
              "      <td>12.609</td>\n",
              "      <td>6.881</td>\n",
              "      <td>71</td>\n",
              "      <td>11.22</td>\n",
              "      <td>35.12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35:14.056</th>\n",
              "      <td>0.011</td>\n",
              "      <td>0.233</td>\n",
              "      <td>12.609</td>\n",
              "      <td>6.881</td>\n",
              "      <td>71</td>\n",
              "      <td>11.22</td>\n",
              "      <td>35.12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35:14.058</th>\n",
              "      <td>-0.016</td>\n",
              "      <td>0.233</td>\n",
              "      <td>12.609</td>\n",
              "      <td>6.881</td>\n",
              "      <td>71</td>\n",
              "      <td>11.22</td>\n",
              "      <td>35.12</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1048574 rows × 7 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                ECG    EMG  foot GSR  hand GSr  HR  marker   RESP\n",
              "Elapsed time                                                     \n",
              "0:00.000     -0.044  0.036     8.763     5.675  88   10.85  43.54\n",
              "0:00.002     -0.037  0.036     8.763     5.675  88   10.85  43.54\n",
              "0:00.004     -0.031  0.036     8.763     5.675  88   10.85  43.54\n",
              "0:00.006     -0.023  0.036     8.763     5.675  88   10.85  43.54\n",
              "0:00.008     -0.018  0.036     8.763     5.675  88   10.85  43.54\n",
              "...             ...    ...       ...       ...  ..     ...    ...\n",
              "35:14.050    -0.037  0.233    12.609     6.881  71   11.22  35.12\n",
              "35:14.052    -0.001  0.233    12.609     6.881  71   11.22  35.12\n",
              "35:14.054     0.022  0.233    12.609     6.881  71   11.22  35.12\n",
              "35:14.056     0.011  0.233    12.609     6.881  71   11.22  35.12\n",
              "35:14.058    -0.016  0.233    12.609     6.881  71   11.22  35.12\n",
              "\n",
              "[1048574 rows x 7 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "H9uN9sgfDNYm",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "outputId": "6fca9381-dbb6-4bf9-9872-b57535656264"
      },
      "source": [
        "df2.describe()"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>ECG</th>\n",
              "      <th>EMG</th>\n",
              "      <th>foot GSR</th>\n",
              "      <th>hand GSr</th>\n",
              "      <th>HR</th>\n",
              "      <th>marker</th>\n",
              "      <th>RESP</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>1.048574e+06</td>\n",
              "      <td>1.048574e+06</td>\n",
              "      <td>1.048574e+06</td>\n",
              "      <td>1.048574e+06</td>\n",
              "      <td>1.048574e+06</td>\n",
              "      <td>1.048574e+06</td>\n",
              "      <td>1.048574e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>-3.469524e-03</td>\n",
              "      <td>1.491908e-01</td>\n",
              "      <td>9.141651e+00</td>\n",
              "      <td>6.170585e+00</td>\n",
              "      <td>7.593876e+01</td>\n",
              "      <td>1.177907e+01</td>\n",
              "      <td>3.841916e+01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>2.150053e-01</td>\n",
              "      <td>3.429084e-01</td>\n",
              "      <td>3.107835e+00</td>\n",
              "      <td>2.147025e+00</td>\n",
              "      <td>8.188724e+00</td>\n",
              "      <td>2.236462e+00</td>\n",
              "      <td>3.303137e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>-4.142000e+00</td>\n",
              "      <td>-8.154000e+00</td>\n",
              "      <td>4.375000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>4.300000e+01</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>2.909000e+01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>-8.700000e-02</td>\n",
              "      <td>4.600000e-02</td>\n",
              "      <td>5.961000e+00</td>\n",
              "      <td>4.331000e+00</td>\n",
              "      <td>7.000000e+01</td>\n",
              "      <td>1.026000e+01</td>\n",
              "      <td>3.553000e+01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>-4.900000e-02</td>\n",
              "      <td>6.200000e-02</td>\n",
              "      <td>9.829000e+00</td>\n",
              "      <td>6.292000e+00</td>\n",
              "      <td>7.500000e+01</td>\n",
              "      <td>1.124000e+01</td>\n",
              "      <td>3.937000e+01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>2.900000e-02</td>\n",
              "      <td>1.190000e-01</td>\n",
              "      <td>1.187000e+01</td>\n",
              "      <td>7.766000e+00</td>\n",
              "      <td>8.200000e+01</td>\n",
              "      <td>1.300000e+01</td>\n",
              "      <td>4.089000e+01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>2.351000e+00</td>\n",
              "      <td>8.121000e+00</td>\n",
              "      <td>1.567100e+01</td>\n",
              "      <td>1.385700e+01</td>\n",
              "      <td>1.070000e+02</td>\n",
              "      <td>8.777000e+01</td>\n",
              "      <td>5.794000e+01</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                ECG           EMG  ...        marker          RESP\n",
              "count  1.048574e+06  1.048574e+06  ...  1.048574e+06  1.048574e+06\n",
              "mean  -3.469524e-03  1.491908e-01  ...  1.177907e+01  3.841916e+01\n",
              "std    2.150053e-01  3.429084e-01  ...  2.236462e+00  3.303137e+00\n",
              "min   -4.142000e+00 -8.154000e+00  ...  0.000000e+00  2.909000e+01\n",
              "25%   -8.700000e-02  4.600000e-02  ...  1.026000e+01  3.553000e+01\n",
              "50%   -4.900000e-02  6.200000e-02  ...  1.124000e+01  3.937000e+01\n",
              "75%    2.900000e-02  1.190000e-01  ...  1.300000e+01  4.089000e+01\n",
              "max    2.351000e+00  8.121000e+00  ...  8.777000e+01  5.794000e+01\n",
              "\n",
              "[8 rows x 7 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qFWG7IO7DNYm"
      },
      "source": [
        "## Plotting DataFrames\n",
        "In the following example, we shall demonstrate how to plot the columns of the dataframe against the time index using the matplotlib module. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jYVutnnxDNYm",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "outputId": "c2c5ee65-b38b-4a62-d519-2a386e4cdee1"
      },
      "source": [
        "%matplotlib inline  \n",
        "%pylab inline\n",
        "import matplotlib.pyplot as plt\n",
        "df2.plot(lw=2., grid=True)\n",
        "plt.xlabel('Elapsed time')\n",
        "plt.ylabel('All signals')\n",
        "plt.savefig('output.png')"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Populating the interactive namespace from numpy and matplotlib\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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zO46dlcF77v0bz+PSaW5Ohw1tmwFGvdmjD4F3PwHOc0+s+vRDrP58PYaNyvS6rclogUHnuMe79uxAREQEJkzId9m2pKQEDz30EDZs2IBhw4ahra0NW7duRVlZGfr27YuFCxfim2++wejRo2E2m3Hq1Cn7vvPnz8c777yDhoYGDBkyBPPmzUOfPn1czhEIoqYAcMwtnrAJBKBzJ2o36Ez45KldWPvyAZd1371xiPPd135b2yY8Ft2TttQRVJ9vwUeP7sDO1Wc65Hy2e2k2lmPTf96AUe/dDi8EdpdOWG+kzefgjHNkjc3W7+m3LvndTUkJdgFX1uftX53Gykd3uC3pwjcgcLbjezsfW1PQeZi5TqhfzBkhEVXuBjaPPfUIyi+U4bZF8/CvZa+ioaEBc+fOxahRozBhwgQcOcLkbjQ1N2LhH29Hbl4OZsydimMnSlBxoRwff74S73/0HxRMm4jt27dzjv2vf/0LTz31FIYNG2ZfNnv2bPtcC7W1tUhNTQXAFN4bPny4S/sSEhKQmZnJmcchWPRITWHYpFSUnayCtoH5fvFkk8/HsD1LLXXtaK7Rwmzkvqg6tREXTjSi38gEQVqIN5pr29FYqUHG6ES0Neig0xgdjjLWc11Zyh1tWQIbLHnEV02hsUoDbasB6UPignL+Y9sugVooDv92ASNu5Y5vGq0RJv1HBi8F2NaHGdXf4tg2QBWdAGbm2OBRddZzOWfAteoqIQRmkwVtDU5Cyo1pyFkjsC83U8CqddjqEh3fwR+pFwwlkd08Tx3/pB/GBX4yHrYU7GQGNjwpJa+8+Ca2btuCtV9+j4T4BDy95AmMHp2Nrz5fje07CrHgfxdg68+78PIbyzByxChs2PgdNqz9CQ/99U/47acdWHjHHxAREYEH7vsLkp1K8x87dsxjcbzFixdjyJAhKCgowLXTrsWtt9yBmHjuMSoqKqDT6TBq1Khg3AoOPVJTmLJgGAZMC+zStW1Mb/v9O0fw/TuH8dP7XG3jo0e3Y/NHx3Dgp/KAzmNj49vF+Gn5UZdOH4BHtdogsHaZt8iSYJiKvnv9IL574xCngmggeGrz+jcO4Yd3j6D+YmDF2zyhbQuS78jaOxr1Zuz4xru/wllL3bvhHCcqzIb7WH3+Y7W3uY4g3HXW/mrK7NE7W1Nw19ZQ46moIZsd23dg5jU3orVei9FDx6O+vgFNDU3Yt3835t00H5pmA66YeBWamhvRxn4uvLw3DQ0NmDRpEgYPHoxXX30VAPCPf/wDRUVFuPbaa/HZp19g1qyZdgf2119/jVGjRiEzMxMPPPAAlMrgO7F7pKbAx65vz+DQLxW4/dnxiOsV4bK+vKSB891kYB5idyq+jWM7LiH/xsDna7CFDjZWapA2iFtuOCImzG1IodSPZ4Y3soTn2Raq1p/aW43qsy1205SmWY/IuMBrQ3kq223rfJpr2pHYO5KzrqasFcd3ViJ/7kAoIzq/pn/ZkXrUlrciXGCGL999P3+4zmWZQcs/WGAEvKumwGf+cVvvi+end+7/TEYztn1+CudZwQscnxXrEeOLKjJqKRAF7Jy5L2j1llwIkhWY7U9wRt9u5PhmRowYgYMHD2L06NFISEjAzp078f7770OtdgxgBg4ciPvvvx9zrp2P4bkDUF3JOPltPgWb0Jg9ezZ69eL34/hLj9QU+Dj0C+P5X/cafwTE9+8c5nw3GYWNbPSa4JYG4CMswoNsF/rQs7YrcxKAgDsbsrB78Ot/j3Ns2sGKdxdSUM1sdO3oNn9YguPbK3Hw5+BoccFg9bIiwUKWL/adTwC4rULK0RQ8nyvMjdAUEn9f/MsFnNxTzXkH2ELBW+CTSetb4IM/CFWAx4+biLXfrQbARBbFx8UjKira7fLIyEh7J+8s8P7v//4PL7zwAk6cOGFfxi6t/cMPP9jft3NlZyGVShETwx0I5uXlYcGCBXjrrbd8u2ABiJqCE9o2IywW6jWlv6lag9SB/NMEOlN2tN6rbdugM2H1siJkjknG+NkDBLeXWqjHsEKhzmD2u9FwsQ0ZoxLdrrchpHPnMzOQIA1F5GHe6wv9uuoE4tMjkdTHYZO1aV2hNC35wwkPOQlsyo824Mf3uObKuNQIV23Rg08BAH549zDKjjoGAHzPilTO/2O11mtdtSxKodMYsXrZfiT2jsK5YlfthZ0cuf3rUjRVt+PCiUa0uMlADr1QECYVHnvkCTzy2J9RcN1EqJQqvP3aco/Lr506A3c/cCc2/fIjXvzny5gonYTY5HDIFFKMHDkSb731Fu688060trYiLi4OGRkZ+Oc//wkA+PTTT7F48WKEh4cDFoL/vLkCUqnrs/74448jNzcXTz31FKKigjelsCgUeDh3qA6xKeEuZgcOPqid+38o8yoUSvfXoLmmHUU/lnkVCs6lJjyNuBQCnhWLhXI6b95yBjzvppAXypNa7QutDVoolDK/zD171p3FDQ9ne9/QR4Jd/IzPLyB0O1+0L0oZYc0WCAC/fV3mRijsWX8OBbcPQXSiIzS2oVIDHKhFa73OLnidkTsVCizxMtGOulEf2nkXWJdscqqJVLTTIXjjYuPx8YovXHa3LQ8Ll3OyrAcOyEThpl327xYzRXubAdEJzP2aOXMmZs6cCQBoa2vjdOpfffWV/bM9+osC/3v7AixcuNC+Li0tDdXVnutr+YNoPuLh5xUl+HrpPo8hdr44xoSE6vnix+VM72iBRz1cSL+19ZMTnO981+avo5lvN19DWbVqAz5dshsf/W27941597/8Z4jjEwrufnpqobDw/jA8x3WjMV843ohPn97N0U5Ktl3Cti9O8W7vLyaj2W0CGAAoI+RI6huF+FRXP6AQbO03myxorNJ49RG6w1n7VUbyDF4C8F/Y2ucSYRYCRKHggb0bzrl9IM0+xE4HMqA06Jzq34DbQVu8aApC+vKTe7ijDR2PH8Tv55lnR29VO53xVNAtUMwmCw5udq0JxIdQuTj5lkFI7hc8dV4Izp33ueI693H6lN+sxy/APV/0uUOuJiJPeCrb4Q+qKAWjsfn5jtnMU4GaqZzfGV5hGgSFJ2QOdxaiUPBA8a8XcMCNSm8xUeGdmwCp4G5EdnjLBez57hxnGVdT8CwU/IG3wxDYibhuw+Og9iEzVdOs5zipg5lFTcAUYNu99qxLIAE/1ONXG6On9MH0P2YF2jyfkCm4r7KnhExKqRuntusysxehsGO1byU/nE1WgeLQkAKtwxTgS+T0XPJqbt2kfHKPFgoLlrqmoDtTeqCGd3lLXTuT7CMAIc+Cuwii6nOuyUzsjnHFI7+j+pyHUa4ffaiz3dftcTwce9uXp7B73RlewSGkBPOJXZVY/+YhF3U+mJnkFECztSaQL5ncdjz8sNGJqqDPP+CJk7uF25Yp5Rfo698shrqJa55wTsrsatjucdfrb3lmZevA5yEQOkUoEEIWE0KOEUJKCCFfEkKUhJAMQsheQsgZQsjXhJDApmUSQHSiClHxngP51Y38cdrh0Yrglo52V69LQDx4sOEb0fDLBGapxWzhdB4GrQkl2y7h4M8VvMcXEs742ycncfFkk0t5Bk7ilcAbYTZZYOIJTQV8k5m+3vcbHh7t2w4dBKUURp3r/dCpjdj+NXfk31jp77wCHYtEQgKu2hpyunjzbHS4UCCEpAN4GEAepTQLTJL5rQD+BeANSmkmgCYAd3dIe/y8A/t/KBPcKQlRG33pcOoqfMikDZIA4R2hWxd9+8pBfPHcXruD2ttEPL6M9p0rY/qjKTRWavDp07tdRr0E8FEqOH338rP2HhqPe16/Ag/852rc+vfQlGrwB6POjI+f3Mm7zjlarK6ie0wtSyQECekeogVFBNNZ5iMZABUhRAYgHEAVgCkA1ljXfwxgbkc0RMjowm3JXoEdiieZUHW2BQ2Valgs/Gr6hRONrqf1JVJJ+KZ2+KqONla6j+mvLWtFa50WFccbUXW2hdM+PgeexULRWq/FhZPMtWla9Nj66Qk0VbuOSp0jlUxGC+oq2nzurNpbDC5OOqbUcwBSU8CuYeFyEAnpEpnTNvhKpdggRFj9pa4C+/311zzDFx1YcaEcV147we928WEb0CxatAhr1qzh3eb111/H0KFDMXLkSIwePRr/eP4pGI3Mc/vFN5/iqun5GDVqFLKysrB+/fqgts9Gh+cpUEovEUJeBVABQAtgM4ADAJoppbZhykUA6R3RHiFRB5veL+FdHqjTU6c2Yu0rB6BQSpFzbT+X9c017fzlBHwZLfvRRL7rWvfaIZ4tufzwLlM58n+fd/hqLCZ+TeH7d4+gqUqD+U+Pw7rXDsKgNeH4zio8uHwKZ1tnh2jJtovYa41GGjPD9Z75grpJ33Gzx3Uh0wFfjSMbRr0Fa19xrb57OdNU047YZJX3DX2AT0C1txo8TrizfPlybN68GXv27EFsbCwMBgOe+/uL0Om0qKuvxZvvvIpfv/8dmSP7QK1Wo67ONfLLZDJBJgusW+9woUAIiQMwB0AGgGYAqwFc58P+9wK4FwBSUlJQWFjoVzvUajUKCwuh03oXCu5GVjt27BB0rjZ1G287Wy8yPZJBZ0b5RUfYpW1bdY1rj3W6tBTUh7LyRqPJ53t09uw5tBZ6DwM9ePAgVDyVIta8vtv+ec/uvS7rjxw5iqYq5tp2/LIfBq3jOp3b2tzMHbUe2eloV3mZ4+S239MTu3btgjzc8bK2NejsE5vwndtGYykFtQDhSdzlVdVVAIa6bM93HKO2o6SPd04dPet2XU1599ESACbxK1CohaK1yTU/wWI2469PPISiA/vQq1cqPl7xJVRKFT79chU++3IVDAYjMvpn4J03PkC4KhwP/+1+REZF4fCRQ6hrqMHfH38ON1w/F5RSPPXMY9i2Yyv69O0NuVwOrVZrb7vZbEZbWxuWLl2KH3/4EWadBC1NrZDICB5+4K8AgPPl5xAZEYWIiEj7fomJiWhra8P111+PkSNHYs+ePZg3bx4eeughznXodDqf+oDOyGi+BsB5SmkdABBC1gKYBCCWECKzagu9AfCmOlJKPwDwAQDk5eXRgoICvxpRWFiIgoICaE8d9TnW2sakiZNxcq33hKro6GgUFOS5LF+x+HcAjHI0aFAmqg8yTj7bNV061YTyrdwR+uBBg2DQmVBTzA1TdYdcJoO3e3Tsq9843zP6ZyCvoL/HbQAgJzsHyf2jcfzrQs5yHasSec7oMTi7iTsp/IjhI3BhO6N9DRs+DJf2Oia4t7XVdr4IVQR0cJiV4hNicamBEdLp6X1Qf+ICACAyMpJznXztzc/PR2SckrOuT5++aDhZwTm3M+9at7/p0Vyc2+yojZXaKxVnqhmTC1vj4DuOpkWP0+v57fgdTWJ8ChrBH1Xny4Cjo2iYPj4kx0342TFgMfPEk5wrO4vlb3+E11/6N/744EL88NMGzLtxPmZeNxsLblsEAFj26vP44utPcc+i+wAAtbXV2LjmZ9S2XsCcOXNww/Vz8ePPG3HmXCm2/7IPVKnF8OHDce+999qzmNva2kAphUajQf/0QdBpjDDrgeR+UdA2Mv7DEcNGIikxCWOvGIVp116Dm266CTfccAMA2EtgHDzIX7dNqVQiJydH8H3pDJ9CBYAJhJBwwnhgpwI4DmArgHnWbRYCCI3BzAlPlTa98dkzu71vBPeOZoOHTE3AvRPcl4zg5jLg3T/9hnf/9Bs2vF0szOTltI27fSi8RxLx7cs2x/Fd46YPWDH2TveO7e8QGhLsaIvQhezVHpzmtrZ1vXhIj5zeyy8QRLj07dMPWSOY+QpGZWWj4iIzeDh56jhm33wdrpqej2+/+wanTjsqAlx37SxIJBIMHz4cdfXMYHP33l24cfY8SKVSyIyRmDThCrvZuqWuHfo2an+2bMu3bvsV2dnZyJs0EvsP7IVUKsVXn6zFh//5BIMHD8bixYvt03QCTPXUYNEZPoW9hJA1AA6CGSYfAjPy/wHAV4SQpdZlH3VEewKJrhBaAVXdpINRbxZUwM2GQWdCM0+RMKPejNZ6/yYvv3C8Efp2k4vTM3VgDMe5aPN5W6zzznqyt3rrmL1V/iROxnaL2YKzBx2am7P/hD0NpZDQ1kBhn8PfGcBEAoM9omcTppIhJqlv/4YAACAASURBVDmcs6y9zQBNsz4o+SwKhaO8u1QqhU7H5HA8/Oj9+PiDLzBi+Eh8tfpz7NrjMCOHKRTW7SXw5NDTW2eas/23VVYtryhDWkofXH3VNZh/502YNvU6GAyMD4gQgtzsMbhmxpWYNm0a7rrrLrtgiIjwr8wHH50SfUQpfYZSOpRSmkUpXUAp1VNKz1FKx1FKMymlN1NKgzMTixeEVjr1lZFXO2bkUjfp8cN/hGTMOtjwVjG2fnrSZfnudWcFVdNMzeS/rt1rXaeujE1hXizbHAe2UcuBn8rw5T/3upTBsEO9O+q9vpxOg2yDU/y88/7swbqvQoE3PJb1mTdRkCUI3JU2EaIodJds1q5OIqvarSLcdUwbHqXwXMjSR8J4IvE0GjWSk3vBaDTi2/XfuKxXRSnsUVFSmQT54ydi/ca1MJvNqKmtxs7d22GxWFyexyeffBJ/e/wRtLQw5lFKKfTWKV+ra6pwpKQYADOr4IEDB9GvX2CBFu7o8VVSO+pdvXTKfRggAE7SUPGvFag5H9isXu5MTMd3VqGlTov0IXEYO5OZRN32aNoe5EM/V6DfiATs28g4dfmEk/UsXkfP3jpu587SWcg4h5FaWOt9HrnzbH5kywX75zMHatFrAFeYCtIUxP6+SxFMAcwXsv74X5dgxtwpSEhIRG52HjRqbrg2+/RRCUpcP/0G7Nj1O66YNg7pab2RlzsOZqNzyRSK+++/H3VVTZhx41QoFGGIjo7CmOyxGDliFFpaW/DsC0+jpqYaYWFKJKckYcWHHwTtOtn0eKFg8KEOjy8EMrPYzjWBT0TPLuPrzKXTzbh0uhlZV6ZDFaUArB2fTZU1myyCagFRAZqCd6HgdEyn7Z2nGmWfz5caSu5gD9b4OgC2IHA2lYmj/8sPiVQCi9mCvn364ffNe+zLH7j3YfvnRQvuwaIF97js+/Zr7wFwzPOhVqthsc53suy5V11PxsnOBySE4KEHHsH99zzksml0dAzWfvm9/XtkvBLhUYypyt8ITHf0eKHgaxlnIVx//0iYeeLzbbQ16rDl4xNu1wcDIf6LlY9xQ2rDwmV257dNQHiEeh+tF/3gObS18HNuqWVnC488TMqZ4e34Tv+rbDI1f9y3t/iXCqQOiMGAHEfsKfv6nAXg+cNVAAZbhUPH+xuuf2AUfvzPkQ4/b3cgLjUCTVW+l+gIxBehilRArpRygiE8JdSxz9RSx0xaJKTMPhBa5bRHF8QDgBFXpPm0PV+2rzN9sxI8jqCLf6nApVNNbtcHg0FjU3zeJ4zHRusNb5ViPRbrg6t5yMWH4OHwvicPUq/18n96n4l8stVKYgsF5/pJWmuMgpAXNBRKRUeX5+5OyBWeB0XsOZPZOD9TzvNme3r/pXIJlBFyFw1S5qUtAJNV7VyM0BPBmtKW99ghO3I3oc+weCx4IR8zHxglaPsFS/MF/SCeRtBuJ0MPInG9fI9G8LVMAAXjRBdKjICsUWdzk0fzk68uBQHmLgDYt/EcPly8HQ2VaqeQVN/OF2ouR/PVdfe5lhznOI59+BE8hZuroviFAiEEqkiHIJAppJyaSqoohdt3y93PERHDX9vTW0i6R0L42/d4oQAA0QkqKFTCwkWVEXJBDk5Pamiwpqh0x8ire/tVB0ZISWsObuvy8yPEpOV83zy9OOeP1gs+N8CMxsqOeN9n/w9lMJssOPzrBW573F1qJ/XNl6FM4K0R5fNzaSUqQel2jml3RMa7+gKdB4HBGqW3t/g/OAyGP80dolDwgYG5yQAEmFmo5xFuqKeHvHL+YBA/HlxfphgFmD7SlwQyQcLUh9E4X10lT+zbeN5eN0kIZrOFG33k5jcVNGIPQQd+OWoKzriYXny4ZoVShoS0SCiU3PfVnQYhD5NCFangnavafnoP53P37LoLZQ5E89S06H1+X4XS4x3NvmAzPXhzwlJKeR1Gmz4oQd8R8faogVDSMZqCb7kCQtRlf+f4bb1Ase/787CYLG7Pc/6wb5oFwNVcLp50rVgLIOgdfkpGtLCQZIHnHTOjHyLjlEGfP9kXxs8egL0bBJRmcfM4RSeqQCnlPtd+dqpudyOO3AJ/cCevLG5MloFOAapvNzHRg0FG1BR8oKVOWCaxREJ4be1nD9YyMf8dMMCLiPX9YeErXe0NdyW/ebcVoCl4KuvsiQs7KfZ/fx4HNpXj6Dbesll+wXZ0szOt2QhxJDpnbttI6st1Fg/ISbLnj3g9psDnKGNUErKudF90OLFPaOYhyL6mj/2zp/OzcRe4oIyQc2z9gaCK5PcnSK3atS1hLWM4E4Ri08i+Wv05/rL4YRAJwStvLMPo8UMxZcZkXHHNOKxdv8ZtGX4hwSn+0NYo3DHtC6JQ8AFvs7TZkEglnudpCKHDcuGyiQCAmKRw3PykaxE+T/gRzONb/aFuZu04vbcGXz7HX2KBjbbV4NeLP3RCL5cAh6l3DhN+AIFSwdtEUkMnpAo/pwfYWfRX3T4EudMdGbdCJ7MKtTM/NiWciSjiOU+k9f2WKaSIS41we3slEgJVlByLFz+C337agY9XfInHljxin/fAGXmYFOEx/uctdTSiUPABXxxMnrasDOEkJuxRa3K/aJ/2ZY/shEBBYfJhDt9A1eWujNd6/DwPRELvSITHuIY88oXa8jnpPT1jGaMTHdt5GKCERciC4jgNC5ehz7B4+/fUzBiOGUboVJmhKDvDfu4UShkIIS4yQSqTcExT3kJaJVKJ3dw6IGMgVEoVmpr4w8wJIZArgtPV+lI/zV9En4IPxPUK976RDQ/vQCgjB1xePh/yqnwOY6VAaZHwipuJvSO9lvvoaRBCMHxSKo7vrLIHMPCNlnOn90PWVen46G+sUu0enrHkftF2H4on/1J0QnAml6EWivZWx+Q9hBBO+4T4uIZPSoWMJ1ro3T+5lkEPBt40aa1Wi+zsbACAyWBBc0uTvVy1jZjkcGz/bRcGZAxESkqy22NJAqjGzEahktmz/ENllhI1BYGkDYpFLs/saF0NZ5W335XMggHZSeg7IiGo56KAT3beK24ZjAHZSd437I4EEAmUNzMDA3KScO3dIwC4D2dWRsiROJz5fNXtQzxGH8kEjEwz85Ix/Y9Zfjc9/8aB9s8GnZnjHJdICKd9REJw9f+6TkjEgRBIpBKMny3MpxJM+O64SqVCcXExiouL8dtPO/B/i5/irH/jjTeQM2Y0ZsydikcefNTj8WVyiV0wxCb7MLh0gq3VRXmYxS0QRE3Bjuc347p7s6B046DiPVoXsZ9HphI8uPxqzjJ3Iy9/6jX5En2UkB6JGX8aGbKRX0cTrHj1qHglZtw30rGA95YyC1NGSTD/4QIA/HML22BHYPH9RmHhMky/h0kUK/MxtNdGrwFc8yTnmSfc70RCMHxyGrZ+5q64IuwqUt71Gdi3wzG5lPMUrbXljPCJilcKir5prmm35wbZTKo6tRGtDY7AEX/KWyxevBgP//kRfPXZGix+/M+YMftat5nShBAkWpPgAjGjsk1yvuZgCEXUFITi8/vfRaSCD6RkePdBsMtnHPixzK9Z6/qPSvS+UTcgVILfU5w8twHuV3EK+fF0eHMWO2biUkULH+ywcQ5hNrJqVDmbi7rKIMmG8z0OJOfjumnXY/TIHHz22SfCdgjgXnTEfRSFghfiejHRCmz7XUK6e9t7zrS+IW2PROb+qQiPUfDaZJ2Z+D+ZLsvkYVJBL4YqUo6+IxiHYpWfDnN3cz10O3x4Q315mfnqPfH5GdhhrhIpQViEDEMnpoIQYPC4Xi7HGzKBWTbnkWwkseYl6DPU4SB2Rh4mRe+hcYLaHcaqCqCMlEMqkyAyLgzRiUqfO125kjmWc+2hYKFQyjg/itBEMHfRUX/7y//h7X+/JShE218BpFDKGE2BkJBpCYBoPrLDNgVkT+uL4l+YqfdufmosqJlaZ1JiuP6BUfh0ietUnPOfHsupkxJseg2IxvX3j7JXN71m0TD8uspRbfWOf04QlICWfU0fZI5JBiEEny7ZBYuFekzYSe4XhdpypvobIQQVx/iTuHKn98Owian4/Jk9LuvYtWZik/yzqS56aRJWPdE15jkGuAO+cDe1dDzhroOJiHU14/HWjWL9ZAuW5kMeJoVcKUP+3IGcztQWDjrlzmGYMGcAIp1s0aooBfJvGojda89ylktlEtz54kTIw6QoO1KPTR+UcNbbOm6+67FlEd/2zHi3neB9b18FncaEj590/U2VEXIoY/nLXtivS2Dfyhf5JJVJkJgegYZKDaiFumQ9A0zpaza33nyHfeY/24xnRqsJb/TIHJw6LSw50B+ZEJOkgkIls5qhIsTaRx1Bcv9o9B0RjzEz+nFeKLlC6uLlj4pTov9IV6dtREyY4wUI4m9mi0q58rYhUEUpMGZGP/QZFscx5SSkR/A+2HwQQhAVr0RkXBhufDQXiX0iMfPPTLz8VbcN5mwbGReGmQ+Odizw8MRkX9PHPoubM9csGm7/3I/n3gmBr7PsVFi/8bjZAxCfFoFpdw93s63wByIlg3kWc6f3Q/6NA5E2KBYDs10jW9hHDAuXIyxcDomE2J/f3Ov6oe+IePtsZRIJcREINvhycMwmC5QRzIifLxRSKpXYZxi89p4RvLZyhVLmNoxSppB69GO5C2ONiA2DPEwKhRv7vcv2MQpI5RJEJXCvUSKVIDpR5bUdnHM6vWMyuQQKlcwnjYYQ4tb3wDm2gim7YTuvrW+RSCV+VSwQiqgpWJFICG54iAk/O8yajYsPIiGY+eBoF4cpe0QUnRCcyIDkflG4+cmxnGUT5gx02W7crAF+Hb/XgBjMXzLO/j3rqt4gEmKf52Dhskmc7SVuOrfZj2S7dfrd/GQeJ2dCKmNe0LYG4RmZ6UNiATDmvKZqz+WvOwr2rUjqE4Xb/jHetwO40RTYzyIAThIY9/zs6B7X9flzXZ8Td/AJBU4lUZ6fnUgIrpw/GFfOZwYS1edaUH9BLcg3FQgRMWGI8CEZTKaQIiGNX4MPU8l4p9wUek5CiF/RRFEJSt6JsGKSVPbKCcoIGcKjO34gJAoFHoRmX7ru6Pg4bGIqDm6uQKvA0hjumLrIzcjTCcHOSQF4MiW5G725ExaA73MpuxxbRlBwBxPO2LXKVwcnmTEYpxeaHOYOvkgqTvIZz+/rnGQ39voMyORSDJsYnAzpyxm+1yUiJixkuQe+IJqPeEgfzDjWfHWIsl8siVSCcbMCi7eOSlAiPlVYQpm7kZA/JPd3P9JzJzAtrA4i/ybuCJW3PIibzn387AFIGxTLWTZh9kD7aGzcDR0fw+6OQM26gQpyYnU4SqTEo1AWQnSCykVyjWDXK2Ktk8klAIGLZqiMlCP/xoFuTYhs2M5r2/Z9s4KbR9OdkIdJEREbxhG+oZgVUgidL5a6IAnpkbj5yTwXG6Q3nAujGQXOmzD3rzmIiAlzcdAKqbV018uToWnWC3oRhRKfGoFbnhrLa8MnhCBvZn8U/VDGWc4uMjZ6Sh+O05JP7VZGyl0Keo2dlYHc6X1x0WlWOvaINDM3GZtxzKfr8UZYuEzY9KPOdIE4y9v+MQ7UErimoIyU49anx4FSCr3GBIuFcoQz++jzrJnAQswuzvzhlck4sasKo6c4Sqrc/EQeGqs0ITc7dSXYnb8iEoiODU5meTAQNQU3JPeL9pqtmzudG37q7PwpLaq1f77q9iGYNI8bCipXSnHF/MFIHxzH26kLCTsLj1a4VNoMBkl9o3idZ/p2E2/cO7tTksokmP7HLEhlEly9gD+LdfLNmS4OyNFTeltrynDvI9uBGWjnx4cvSYlsfJIJ7JG2QgKpTIIh43u5314gMUnhQRsQJKRHIrF3FNKHxKHPsHiO+Yj9i8elhPutmaqiFMid3o/zbCtUMvQaENMj5odgo1DKIJNLIZFzcz7CY8JAJMTv5zJQvPY6hJAIQhijASFkMCFkNiGkc1rbxci/MRMLlua7Xc9Wr/tlJSD7mr4YeRWjkisj5Ljn9Ssxyhq90V0Ij1bwOtakTqGwmWOS8ce3rsTwSfxzYKcNisM9b1zJWWbvFJxs1c4lt9mdFZ/92t1Ui3yERyv89lP4a/y58tbBuPftq3xylnY2eo1Dk/J3JrSeTGFhIWbNmsVZFpOsQlxquIswjIwNQ2LvSL/ndQgUIWf9HYCSEJIOYDOABQBWhbJR3YnoRBVGT+njogUA3KqjNi1izPX9MTAnCdfdlxXSsLJQQSTgrYzKGwvupfNwyXq1fpfKuRqE2el8s//ChMgOGd/LJVae77h8KFQyDMxJwvR7szhCKHNMsmDNK5Bfr7v99jpNaGcLvJwxmfhNk4QQt9oRe7m7/UOFEKFAKKXtAG4C8B9K6c0ARoS2Wd2LybcMQvY1rpnM3AQi5keOiAnDdfeNtDuz2TibE9IyY1226WwIIbwx3cGoA2TrKJ0zaPuM4Gbcpg2Kw4PLp+Cau1wjs3oNiOHYdZyd3jbCoxW47r6RSMuM5WgK0/+YhRFX8Gs3Lu31kF3ume4lEACHMzg6MTRF2LoiZWVlGDp0KBYtWoTBgwfjjjvuwK+//opJkyZh0KBB2LdvH/bt24f8/Hzk5ORg4sSJOHWKCeVetWoVZs+ejSlTpmDq1Kmc4+7fvx85OTk4e/YsDhw4gBkzZmDMmDGYPn06qqqqAAAFBQV45JFHkJeXh7feeqtDr1uIp4gQQvIB3AHgbuuygIp6E0JiAXwIIAuMFv4HAKcAfA2gP4AyALdQSvkLlHcT2B0l34jWmYn/k4m0wbFIyYhGVWkzhgRp8pNgQiQEfUck4Or/HYqoRCU2vFkMIDgjX75O9pq7hqP3EGFlFibNy8SA7CSse+2gfdmogt4umbrO8M1fIARfbODd3V6ePjgW19w1HMn9gu+/8sZr82d538gP/vb19163OXPmDFavXo2VK1di7Nix+OKLL7Bjxw5s2LABL774Ij755BNs374dMpkMv/76K5566il8++23AICDBw/iyJEjiI+PR2FhIQBg165deOihh7B+/XqkpqZiwYIF+Pzzz5GRkYGvv/4aS5YswcqVKwEABoMBRUVFIbl2TwgRCo8AeBLAOkrpMULIAABbAzzvWwA2UUrnEUIUAMIBPAVgC6X0JULIEwCeAPB4gOfpVNgmFZkA+2B4tMJugw9miGkwkUgIJNaKlxYzk/FqMVsQFuFfIJsqSg5tG2Oa4Os4fXHG8mlr7qbK5AgCP50D3byf9wlCSFAc492NjIwMjBzJVLAdMWIEpk6dCkIIRo4cibKyMrS0tGDhwoUoLS0FIYQz+9q0adMQH+/Qck+cOIF7770XmzdvRlpaGkpKSlBSUoI5c+ZAIpHAbDYjNdUxEJw/f37HXSgLr28ypXQbgG2s7+cAPOzvCQkhMQCuBLDIejwDAAMhZA6AAutmHwMoRDcXCmxCETXTGbDzFCRSCe54bgJAAZncP+XxivmDsflDboipNEBTVHhMGNRNerujTqGUwqDjlpnWsiaEcZ5P25MjNS41AlVW/dXf0X9PEibBQMiIPlSEhTlMpRKJxP5dIpHAZDLh73//O66++mqsW7cOZWVlKCgosG8fEcHNMUpNTYVOp8OhQ4eQlpYGSilGjBiBzZs3IyrKVQNz3r+jcCsUCCEb4WEMRSmd7ec5MwDUAfgvIWQ0gAMA/gIghVJaZd2mGkAK386EkHsB3AsAKSkpdrXMV9Rqtd/7CoVSiqg0QKZCyM/ljmBfZ+mZM2gkns0xvmA2UChjgYgUxz0y6SjCYoCodO/3reqiwwlt2zZ8IIWiAUgeSVFYWIi0iRRlTlM4GHRm3mMXFhbCpGfapOOZJM6i0Ng/6w16zn6esLDmLDh58iSq9cKKpznTEc9tVyAmJgZmsxltbW2d1ga1Wg2LxWJvg9FohFarRVtbm31dQ0MD4uPj0dbWhvfffx+UUrS1tUGn08FgMNj3bW9vR2RkJD7++GPMmTMHhBCMHz8eNTU12L17N/Lz82E0GnHmzBkMGzYMZrMZGo0mKNev0+l8emY8aQqvBtwa9+fMBfAQpXQvIeQtMKYiO5RSSgjhn2qE0g8AfAAAeXl5lC2ZfaGwsBD+7usTV3vfJJQE6zqPf7MV1EKRO34kMoI9H8K1PMuuE7br9trTaDx9EQC413mz04a3MP/Wv3kIF082YcQVaSgoYHIoWoqZZcMmpaKgYBiz4XTuZES2iV5Kfr+Enxk3ClRKJbTWKibe7rHRYMaJNYzCPXTYUAz101/UYc9tJ3PixAlIpVLeEXRHERkZCYlEYm+DXC6HSqVCVFSUfd1TTz2FhQsX4rXXXsPMmTOZYpNRUVAqlVAoFPZ9w8PDIZPJMHDgQPz444+YMWMGVq5cibVr1+LBBx+EWq2GyWTCI488gnHjxkEqlSIiIiIo169UKpGTk+N9QytuhYLVbBQKLgK4SCnda/2+BoxQqCGEpFJKqwghqQBq3R5BpMO54eHRqCxtRv+sBLQfrYdEJYUyU5gDuCtx1W1DcGz7JeSwpla1LXMuPDf9j1nY/vVpXHXbEPsydilnX6KPRItR96N///4oKXGUC1+1ahXvutOnT9uXL126FACwaNEiLFq0yL68oKDALsz79u2LY8ccJtNNmza5dP6dqQ169SkQQgYBWAZgOAB7PBql1K+ynJTSakLIBULIEErpKQBTARy3/i0E8JL1/3p/ji8SGvoMjUefofGwtBvR+Dkzh0Pvl67o5Fbx50x4IjYlHJPmDfK6DGByFjLHcEtW28qYA5ePn0hEhI2QkJH/AngGwBtgjCF3IfDyGA8B+NwaeXSOdcxvCCF3AyiHXeEX6UpYdO7nBe4M0jJjcXx7ZYedT8rSDnxyNBPejyIiXQ4hQkFFKd1CCCGU0nIAzxJCDgD4h78npZQWA8jjWTWVZ5mIiFv6j0pEeLQC8jiD942DADsySdQURC5HhIz49dbaR6WEkD8TQm4E0DWD6EV6HGEqGRYum4j0CR3TQbMTEv0OLRVjUkW6MEKEwl/AJJc9DGAMmNpHC0PZKBERX5BIJR2WMZzY27/xEBGNRiLdBCHJa/utH9VgbP8iPZmuNfVZhyNWCBW53BFSOnswIWQFIWQzIeQ3219HNC6UyDWAsUbjfUMRkWAgKgrdDqlUiuzsbGRlZeGGG25AczOTzVhWVgaVSoXs7Gz73yeffAIAWLlyJUaOHIlRo0YhKysL69czQZSLFi1CRkYGsrOzkZubi927d3fadXlDiKN5NYDlAFYA6FqhJwHQb7sUNdsPIu25iZC4qY8jIuIJsZ+/vFGpVCguZjIVFy5ciHfffRdLliwBAAwcONC+zsbFixfxwgsv4ODBg4iJiYFarUZdXZ19/SuvvIJ58+Zh8+bNuO+++3DkyJGOuxgfECIUTJTS90Lekk6C6syAKBSE07OtR/4jSpBuTX5+vtdOvLa21p7tDDAZ0bbPbK688kqcOXMmJO0MBkKEwkZCyAMA1gGwF3uhlDaGrFUiIt0BPzt6MfjINy4+sT0kxxWafGk2m7Flyxbcfffd9mVnz55Fdna2/fu///1vTJw4ESkpKcjIyMDUqVNx00034YYbbnA53saNG+2VV7siQoSCLdLoMdYyCsCvjGaR7o2oKPiHKAe6H1qtFtnZ2bh06RKGDRuGadOm2dfxmY8ApmTF/v37sWXLFixevBgHDhzAs88+CwB47LHHsHTpUiQlJeGjjz7qqMvwGSHRRxkd0ZBOQ3xbfYMVfUQp7faTx3QK4i3zic4qp2LzKbS3t2P69Ol499138fDDnmcNIIRg3LhxGDduHKZNm4a77rrLLhRsPoWujpDaRzfxLG4BcJRS2i2L1vk705aIExQ9u4MTH6MeQXh4ON5++23MnTsXDzzwgNvtKisrUV1djdzcXABAcXEx+vXr53b7rooQ89HdAPLhmG2tAMwcCBmEkOcopZ+GqG2hg11DTRQQviHeLge+CERRo+rW5OTkYNSoUfjyyy9xxRVXuPgU/vCHP2DOnDl49NFHUVlZCaVSiaSkJCxfvrwTW+0fQoSCDMAwSmkNABBCUgB8AmA8gN8BdD+hwDaB+FZkUyQI01heLsSmhKOtWYLMvGTvG7MQs5u7B2q1mvN948aN9s9a20QaTvz2G38KF7vsdldHiFDoYxMIVmqtyxoJIUZ3O3VlqIXdsfXwns1X2LeL9mz7UURMGO5+/QrI5N6znA3nWqAkgE583ES6OEJy9gsJId8TQhYSQmzzHBQSQiIA8ExY2PXRn2E1W3xJfeJykKGa/dVo+rYU1BSgmkgBmdx73SV9eSvqPzyK6THWCXp6rhwV6QYIEQoPAlgFINv69wmABymlGkppJ0826R8tm87bP1OzaD/yCdr9taymb0uh2V8Nzf7qgI7T51w/VP9rv9ftDGWtAZ2npyIGhASOP/dQSEgqBTNl5ho/2tQlkShZly3wnlGjBSAAkXVOQTRzqwGQEkhZ00Ha17XooT3egPAxKZAopKBmCiIN0XDUSSYIPQu10KDPP0AphbFKA3lKhH/7G/wfEMiIAmF6Jcx6PajJ4vm5EOdd4GBq0sFUp4VysPvpXJVKJVpaWhAVFSWGPfsJpRQNDQ1QKpXeN2bhVigQQnZQSicTQtrA7ToJcz4a7V9TOx/KnsJRgCSllKLyud2AhCD9nxND2DI35zdZUPUiM6U1X8x27fLDMDfpYWrSQ6KSovXnciQ/nANFWvCmvbB3fH6MPBrXnIbuVCN6/S2PK5ADRL39Elp+PA/lkDjAn2yaAATngKjR9s+mei3kvTwIJnHEy8GmXSU9MBphffm7kd69e+Pw4cMuzt7LEZ1O53PHLRSlUonevXv7tI/bN5RSOtn6P8rdNt0Vaqa8n91ipnZBEooRrzcsGs/+fHMTU33EUNYCQ0UbAKBtSwUSFgwXdHxKKTR7qxDWP4a3czM1aFH9ShEixvdC+Ogk9o6Cjt9exMQpF0JKqgAAIABJREFU6E41cfcPkJYfz9uP649Q8Pd3JCDISZhi/97wxQn0+ivfRIJWRKHAi6m63a1QkMvlUKvVyMvzcF+7ML4kdhYWFiInJyfELRKOkNLZAwkhYdbPBYSQhwkhsaFvWugwNbDCyazvq7Fei+Yfz8PM1wGzXmqL1hTi1jkwt+ihPdnIsdEItRFqjzUIPk/7wVo0f3cWNW8e5F1vs71r9lZzTSG+9nV+WN4opdCfb+H+ZrbD8ZjSAMDcZkDjmtMwXPIyyrReisVghqVdeCCdhHALKJpq+cMTbYgywQ2XqVVIV9qEqqV7oDvVPcvDCXlNvwVgJoRkAvgAQB8AX4S0VSFGGqVwfLGGp9YtPwz17xfR8sM5l+3ZuQxVz++B4WIb73GNde3QlzucitrjDahatg+GSv9U4KpXitCw6hh0Z1tYjXG/vb828qbVpz2u5/iW2ZqVj52dPyPz9uI61L1/BNWvFMHc5jQPs5vetvn7c2gvqkHtvw+Bmiwwtxn4I42s5qPKf+xC5XN7BEUjWQxmyIjC63ZC2tmRmNUG1H14FNpj9Z3dFDuc0PDLiPr/lsCiMaH+v8c6uyl+IUQoWCilJgA3Avg3pfQxAKmhbVZoYT+Mts8WNTNSbD/IU7nD6aWufaeYd8Re89oB1L13GKZ6LWrfO4yGT47D3KJH7duHoK9ghAU1UxiqNDBrjGjdUgFjbbv7hlo7Kf3pJscyT9FSMm6nW//fEpAgKDaUrR0FkONhbjXArDZ435BF25YK+2dn4epOa9MedtSw15e3ouqFvah9x7V4mf5MM+dZsB2PmiyMY98Ji96M6pf3Y0rq7T5dg+3Z6kxafymH/kwzGj490dlNsWNxFvKXC908oFGI189ICLkNTLVUWx1Yfr29m2BhvfDmFr3L+otPbEfCXSOgHBgLIpPw+h30Z1ugzOS3olW/WuSyrO4/h3m3bf2lHOnLJrvYH+tXOUYZ7YccgoqaKYibu89xoIOxtcf4qaNTkwV1Hx6FoawVigyH3bf1l3LHNhYKwyU15KkRgrSA5vVn0bz+LHq/dAW0JxtBZATKzDhQoxlE7jDJ2Ebs1GyBqd5hmmn9pRyqIfHMeinhaCpS689oaub+nvqzTE6KsZqZZU970qHSa4/Ug85lCRbrJdStOApDeSuir+kLc5sBRCqBclg8ICGwqI2IViS43i8PNmQSROe6v1jaO87sKRRpdFinnp9aKBo+PwF5kgox113edT99QcjTeheAPwF4gVJ6nhCSge5Y2sINmv3VUI1IdFneYFX90l+YzDsi1pbUo/HrU4gc3wsWowXho/x3oF56cgfk6ZGInZkBWVI4tEfroTvJb4/0ZOIw1bhqHcQ6V177kTpoimqgzIyFKisRknAZqMniYpe/+MR2yFMjYKxyTFVqOO8widkc2QDQ8OkJGKzmspS/joEsUQWAGXHbQmf5fAG89fFlEqQtGQ99Rav93jtjvKiGsa4dtf8uhjwlnLMuY6sUF7e6Hrfttwv2z7rTTWhYxT22/pzDNGesaQfM1H5Nrb86tBT1rkrInM7JRneiEarhrsICQNcwH/FoPr5ALRTUYA5q9JizZtvRGKs10B1rgA4IrlCQEcDk+M1b62pxZMsm5F4/B+HRMcE7T4gQkqdwHMDDrO/nAfwrlI3qSPSlzah5i9/BCgCXluwACXOdmU2zpwqAo+NQb7sYUDuMl9So++Co1+2qlu6FvE8UjBf4/RrOJJyRcDph/ekme9SO27ZUCZu72sDyn9S8foCzLjwnGcrBcdCxTV+eMFlQ+U/v89bWvMacxyDw+tnUryxxWdbwmcOcUr/C8/3nE7r243xy3H2JZ7ZGI5MgPs33vApTgxaScP8VdHdOeaHU/qcYxotq9HpiHGSxnTvCDxpsM7LRAlOjFrLk8KDnRax+fgmaa6pQV34eNz7+TFCPHQo6X6/tApg82fUBUH3XmppaqEDoTNoP1XLMXj0BfXkrwvrxhFiyhMIfXp0MhVIG3ekmNK0rRcIdw6Do7Tnq29SsR/UrRcwI9Bo/GxegtmK8yPhzdCcaEJmfxj20hcJU2850qG7MiLzJnwF0vpRSaPZVQzk4DrI4P2P8WeevemkfLBoj4m4ZjIjcFL/bxUdzDTOArDnXdafgZNM56bkACCFSQsghQsj31u8ZhJC9hJAzhJCvCfE1xENEpHOpe4/fb8TukBVW80v9yhKYm/SofacY+vJWu8NbvasS+jKHScvUrHNoOCbfOnbtsXpUvbgXhgttoIbgDGz4MrebNzLhzK0/l/HuQ80Ul/6xE1Uv7Qv4/GaNEbozTWj45Dia153xWmaEUgpjtYY/wIElk2y5QPrSEJZz6yaZ2Z0mFAD8BQA7FOJfAN6glGYCaAIzj4OISLei4XPX6B5vg/S69w6jbesF6Mta0LzhLOqWMxPEW7QmVL+036sm67Ytn56AudWA+o+PQc8Oaw4EnixwzW5mJNzmxoRquNQGUNcoLG1xLaezNlSqYWrSuT01NZpR9fwe1H9YAt0JYTkA6p2VqHnzIKqW7nXxq/BpNbaAhJ6MpzIXG+EhEp1SOtvfkxJCegOYCeAFAH8ljBFvCgBbrN/HAJ4F8J6/5xAR6Qy0R+uhr2i1Z+pSEzeCSl/eylt+RLOvChKlw3dV+24x5H74Hnjxw3TUuvUCjNUahI9M5CYs+hBuadGaoN5TxdEg2KHculNNqH37EFKfGg9NUTWa1pQCANKXTXaJpAMAvR+FBVu+d+Qd6c83I3x0MozVGpgadZDFu5qdhPrT/CFYeoKhUg0ik0Ce7D7wIRCIuwxZQshVnnaklG7z+6SErAGwDEAUgEcBLAKwx6olgBDSB8BPlNIsnn3vBXAvAKSkpIz56quvfD5/3FmChFJXJckQTqFoD/ynu5RnRnqRFBYpxaVxFuij4fJEEDMw8BdXBzYfZjmF1NjxqqdRSUEoINMH59z6KApF2+U/yUzdUAta+jPvVcavEkhN3OutybIgpcQ/Jb14cgsiIx1CRaoHwusIVE2AOoVCmwBQ62OVuYn/+TpznXtTkrIJUDXyvx8AUDvCArOcQtPLsX3vvY7zsI/d65AEkTXca68bakHSSe6xz1xnxsBNEpfnoiKzHYZMh1M7vA5IO+B6TZ6uh30PqrLN0CQDmZuZZSYFhczg+ix6Op43JAZgwG/c+3HgvVcRIYuFIcyEUXf+yWUftVpt/00lBiCqkqAtjcLCMqBLDECYVSamFzHHP3uNGdRPr/DVV199gFLKW0PEU+0jvzt9TxBCZgGopZQeIIQU+Lo/pfQDMJnVyMvLowUFPh8CKABOvLkNUdXMwylPj0TKQ9zaI7xhk2BGMdqj9Wg/UAMil0BX2sxxRMffPhS9RyUB1vm5+3pqx1TGSWdRG9F+pA6tv5aD6rgPZOxNmYgclwqLzoTK5/Zwk8dYRF6ZjshJ6ahetg/KEQlIuG0oql/e71MoIpFLkPbcRN7oi5q3D8JY6f8oShKlwMAl433er3b5YXvp6YQFwzjJV/LekYjITUHkxDQUFhbC9ixQStFWeAGtP5dzjhUzawCo3gzV6CTU8OSSpL84GURCvNateW3+LIRJVJjbj38S96STEuQsYiKRLm5yfY4G98tES4lr5rwQIiMj7ddZ/8lx6I47yplEX2L+26Kg+M4NAFeMmYjWzeWQxoYhcnIaKp9hor5SHs3jvS9sko8x70zKI7mQRClQ9fwezvqJQ8Yy4cKU4tLPO132dxYIAJBVngQdXM1Bfc+EIy5nMMytekTmp0Fb0oCmA67Z9wUFBWhaWwrNvmokLBoB5ZA4++/HvgcDSSraNzuCH/gEgu14/lL1yn6Y4TCBFRQUIPa7aiQq03Gw4RekqxQYNJ5bVJP97Fa9tA/mZj1SG1To9TdHn83XH43W9UXsrAF+t9UdnjSFo+A3H9mqpI7y64SELAOwAIAJgBJANIB1AKYD6EUpNRFC8gE8Symd7ulYeXl5tKjI80Psju0/FyJjKyNx056bCImCOwKxaE1o3VIB9Y5L9mVx/zMIEWN7+XW+zoL9wDmjO90ESimUA2IAmffJYoRAjWaYWwww1rRDd7oR7YdqoegbjcS7RoBIfR8dUzNF/cfHoBoej8gJaW6383SdfJ28RW9G5TO77N8jr+qN2BnCYtVfmz8LADA/43H3G0mA3i9ewfsyK4fEMUX8/ODSWDNyxo0BNVvsvgdnbMmQ7gY2nGZGKYKfWewUpx9qZIkqjolOmqAEkRJEXdXHawkXPuS9IpD4hyxIo11jXXRnm2GqbQc1WECUUkSO5xZ3cL7nvZ4Yi+qXHM7wr8//C3/7+ntQoxn6sla0/laB0vQm5E+ZhPpPj3NyggBA0SfKbfi1ol80ku8fzbvOG4QQ3zUFALP8OpsXKKVPAnjS2rACAI9SSu8ghKwGM77+Ckz29PpQnN+GOYy/DLUNiUqG2FkDQiKJuwqe6tn7C5FLIUtUQZaogmpEAuJuHBTY8aQESX9wsSL6dgweYScJk9p/f18qWgrGArT+VsG7yl+BAADp+6Wo3e9asoNN27aLvJn6fISk1EQHCgQAHIEAAOYGZqTuj0AAGGdz1Yt7kfJoHqjOxAkZds5lcRYKzrAFAgBEyeNdBEe/81JU7uBqXDY85eOw84SCiSfzUTnfckLIZAC3gZmRLZg8DuArQshSAIcAfBTk44uI8BKqSVxaN/O+QiGndVNZp5z3csNmSgvPSUbcvEEwNbpGRrVuvYDoq/sAgKCZ/K7v/cfgNjIECHJTEEJywEQG3QzgPIC1wTg5pbQQQKH18zkA44Jx3FBxYvtW7P72S9z0xD8R26tb1wQUERERSPuhWmhPNLj4+wAwkVWUInpKXzR9W9rxjQsBbo28hJDBhJBnCCEnAfwbQAUYH8TVlNJ/d1gLuxA/vvMamqoqsfWTFZ3dFBERkQ6ETyDYaN1cLsh/E2xstcaCjSfP30kwuQOzKKWTrYKga9V76CSopZvXxhUJCtXpl7xvJCISIpx9KcHCk1D4//bOOzyqKm3gv3PvlPReCCQQepMq2CiKuKIo9t4Ve3ftuiKuu5a1r8taVv3Agh3EhqgooKBU6V1KCCSBkN4mM3PP98edTGYykzAJacD5Pc88c++5p7xn7tz7nvq+5wE5wM9CiP8JIcZy2PpKUigaT1lsGQmX9G5rMRSKZqWhieYvgC+EEJHA2cDdQIoQ4jVgppTy+1aSUaFop0jCByQRtaecsgWNt5IbeXwacWd0Q1g0jGo3FSv3UrF8L5bEsODOng4jEq/pH2DG3A+LILe/mw6r2tISz5FJKKazyzHdb04XQsRjTjY/CBy5SqEd2MdXtA+ErhE3vmuTlEL5bznEn90DAM2mE3VMGlHHmAsYQlUKUSM6UrZwT4NxIo5OJWZsZ/R4O0IIir/bTum8QHnTnwncVxHWN4Gkq/sDpntZS1K4n5n09GdGYThcuPZVBvVuZ8uM8W4+9Mu3HgdVAPaecSRPHMDWefOgHhuDzUWnf44gf9p6f++GbUz44GRiT+lC1dYinHnlSIc74P+QfPNArEHMpTQHjVLDUspCKeWbUsqxLSKNQnEEYe1U/0MdMy4zICzoxskQNgQmXNgLS0JY7dLbhrzk1blUoxAAwvslYk2JIO1vxxLWO56U2web2dktAea/9Tg76c+MIuHCXma2YXXMU+gCe89AxWDvYSqE1kLoGu4gjqBag7B+iXT6xwiyRrhJvLb2d068pA+WpHCijksj/uweJFwUOERpz4wN2HDbXKi+WVM4REzgKlqP1HuGHjiSJrB1rn15Jl7Rt96oNWvffYk7r0dAmHQ2vPYj8crAMkRDL5MQOsF6lI2ka48KUATRJ9XKbE0zjflZEsPp8MBw0h7xN3EihAj6sqtrmrshxdkY4i/oRez4rojwwMGRiDawUtDhweEkXdUPYdGojjY3koYPTCL65MD73toopaBQNAPW1EjSnxlF1Oj0BuOl3DqY1L8eTdrfjj2gc5iYcV28x8k3D0QIgcvm/9aua/45+qR0Ok46znse1jeIi9AQRz8jhjXO2YzvC83Xxa0lISxoq1aPtgVYFRB1XHQmXtbHe5z28DFokY23ABc+IInIYalEj04n5aZA6zzRJ6YTeVzL7jtaV+hvB6ruvRdCkHhZX2JPzQyaPvW+WosUCZe27OIG5XmtKag5BUU9xI3vSliPuKCuP2sI1eRxzJjORJ+Y4ffi3zna4IRBx+DcXUbZoj1En5ThnVOwZUR7fQ13+udIQAb1GeDbW6mhxl1n8s0DKfxsC3Hn9iCse/3j/sHQbDppjxxDdU45YT0DTaik3j2UvW+sJva0TL/wtEePJeefi82TOr1wPda0kirCLOixdoxyl/dazLguhPVKoPi77Ti2FNU7f6FF1boitXaIxJISjmtv7ZCREILoMRleF7stwbqihZS5iugY0YMB1zTegpA1KZywvgkgBBGDUlpAwlqUUlAompl6NxXVY+G2Ieq+1KUFrMkRWJMjiBjs/3LwdVAjdEF9K8jt3WJJvKof1rRIhCao2lxI+MBk81pmLB3uC2onLST0GDvhMcF9OFs7RNLp8eMD00TbiDu3ByU/ZgXMpQiLRscnTkAEGdOIGWPaIE6eOAApJcXfbA+uFOoOGQXZZmSJtRN3Xg+qs0qpWJYXvHIHgUSyo2wtO8rWcsyga5qUh+/8Tkuiho8UiuamnqcqrG9CixRnSTGVUNTITiHFF0IQ3i8RS3wYeqydyOEd0OwtM2kZKlHHptHx0WOxBlGoml1HWD3yWYIrumD2q2LP7IYlJZyoEf6/S32WoaOOSSPhgl7Enp4Zstwd7g+uQMMH1g6fzdn9Tp2r7XukQfUUmoKaaFY0gB4V3L247/h4c5J6xxAcWaXYu8a2SP7tiajjO1L2y+7gCtbnZW/vEUf0yE5EB1GU1qRw05JqPQom+sQMIo/riLBpHt/WAqHB7sdMU+sJl/YhfGAS0mn4zZVoERakAbLKRdyE7ti7x+HaX0nRtH0HV+lWRikFhaKZMV8kJnETumFNj8beJabFyhNWvdHj/4cqseMysXeNxd49UAFGHm/u2bB1jSHpmvqHWmIndEeLyGpwpU9Nz0nYA1+RRoUTIYR3FZcWZcUoc2JJiSD5+gEYDjd6pLXWrPY0//TtfUpSKYWm0N7vqqJNEeEWwvokoEVaA4YuFAeHsGiE9wuyogqzB1DjYKghrEnhJFzc+BU8kcM7ULFqb8B+keTrB1C6IJuYUzMRFg3dcmiPyiuloFA0M0KIBluqipajpXxjgOl5Mf78QKdR1g6RQfdc1Es7b1Qe2ipNoVAoDjFkO59oVkqhKaiJZoVC0USy1jTsTrWtUUqhKbTz7p+idVB/A8WBMNyBZkhWfNui7ucPGqUUFAqFooX447uvAsLae2NCKQWFoomoUUTFgchaG8T2dzvXCkopKBQKRQtRVljQ1iI0GqUUFAqFooXYu/3PgDC1+kihUCgUtbRvnaCUgkKhULQUQgt8xdZnkK+9oJSCQtFEygrbj19fRfukY69gRhCVUvBDCJEhhPhZCLFeCLFOCHGXJzxBCPGDEGKL5zvQS4dC0Y4Ijw50VqNQ+NLOOwVBaYueggu4V0rZDzgOuE0I0Q94CJgrpewJzPWcKxTtlmAbkxQKP4JoBdkEZ0utSasrBSlljpRyhee4FNgAdALOptbI7DTgnNaWTaFoDIY7iAsvhcKH9r7SKBhtOqcghMgEhgCLgVQpZY2T1FygcV7DFYpWRhqqp6A4AMF6Cu1cUbSZ6WwhRBTwOXC3lLLE1+StlFIKIYL+ckKIG4EbAVJTU5k3b16Tyi8rK2ty2v0FBU1O29ocTD0PJdqinjl79rR6mUfK/YTDo64lJYE+o0uKS/zq1d7q2SZKQQhhxVQIH0gpZ3iC84QQaVLKHCFEGrA3WFop5ZvAmwDDhg2TJ510UpNkmDdvHo1Nu/y15wFITEhodNq2oin1PBRpzXrW/A9iIsJb/bc9Uu4nHB513fPDV5Tn5fiFxcRE+9WrvdWzLVYfCeBtYIOU8kWfS18CV3uOrwbarSnBvCC7FBVHHk5HVVuLoGjnSBk479Te9ym0RU9hBHAlsEYIUWNY/BHgGeATIcREYCdwURvIFhKVQbqEiiOP3RvXt7UICkWz0+pKQUr5K1CffcmxrSlLk1HWMRUeygr2E5UQ3GewQhG0U6B6CocfLekHVnFo8cYt5oinLTyc6spKhp5+FidddX1Q8waKI5FABZC3rX0PPyulEAKG4aZk377ac7VpSVGH6spKAFbM/pIVs7/0hqf16E33Yccy/Kzz0XS90flO3zCdLjFdGNFpRLPJeqQz7rNx7Cnfww8X/ECHyA7Nnr/bcJNfmY9buoNuVAs2zxAKJdUlRFujW7xRqpRCCHz98rNsWbyorcVQtDNOuuoG0vsdxY6Vy/n1o3eDxsnZuomcrZv49aN3ScroQkRsLOc+OBmLzXbA/GdumcnTS54GYPkVy7HpB05zpLE4ZzHlznJO7nxySPHdhps95XsAeOiXh5h62tRmlSe3PJczZpxBtVENwO2lQ5sl3z+L/uScWefQObozM86egV23N0u+wVB93BBQCkERjPCYGFK7dufYcy/ijukzmDp+J++ethO3LXhLLn/XTrLWruaVK8+jtCAfZ1UVhtvNjpXLef3mq9i4cD7bViylrLAAaRis21PrtSuvPK9V6iSlxO1ytkpZB0tRVRHXf389d/18FyvyVoSUptBRa8Rwed7ygOtSSn7J/oUH5j9AQVUB2aXZfLf9O4wQW/dfbP3CqxAAyvbvDyndgfgp6ycAskqzuH7O9c2SZ32onoJC0UR8W/tTVk7h3EUG45caPHrVdqwZ6fSM68lLJ71IZXExv3/+Eat//M4b/81brgnI75t/P+d3Hg50GhbG7pQqbvzhRmafP7ulquJlxjOT2bV2Fbf8bzr2iIigccqLCtn+xzIMtxuha2iaTlR8IhabjYrSYsIio0ju0hV7RGSLDnX8vOtn7/FvOb9RUFXAyE4jCbOE1Zum3Fnud/7r7l9JDk+md0JvAB785UFmbzd/59k7an/v+xfcz5LLlxBuCafKVYXLcBFli/JeX5e/jtyKXKasnNIsdauLrtUOPa7ct5IKZwUR1uD352BRSqEJhMfEtrUIinaAxVqrFJZ/+RaPzjdbk9fPMXjqkt3sLtvNeV+dz22Db2PcDbcz5uobeeXK8xpVxl+WpbIztRzNcPDCkiuprqgm+4dZYIDhdmG43bhdTlwOB0LXKc03576ik5KRhmF+pMQwDKRhjnFLwwjYY6Hput9c2X+uNVeE2yMjEUJDt1rRLRY0Xaco138zVkOMueZGSiuKKd23j279BqPpOpquI3QdTdMpLyokMb0zwqLhMKqJsptj5kLTqCoqoDB3D5qmIYSGputYw8IBWLNvNU/OfxyrZyng28veAGBg8iDuG3YfPeN7BpVnX2EOVqdAChAS/vrNbVhcGlcdPZHL+l3O91u/QwcMAVKAZkBUpQWH1eD95f/H+xs+oMxVigReO/V1UiJSefjXh9lQsMHMEzMdAAJKIpzEVFgD5BgwbYD3ON2Wzg+//sAjxz5CpDUyqNwOl8PvfHneckZ2GtkiSle0940UDTFs2DC5bNmyJqVtzC7CFy4+MyDs3o+/blK5rU172y3ZUrRmPWv+Dxf87R90GTAYgA19+vrHOVejOFKwMcN8aG8ZdAvn9zyfJHsCVWVlRMbFIw0DR2UFG375mZ/+z3ypTf9LFikFYZyyPKVV6nIwZA4aipQS3WLBVV2N01FFzpZNbS3WIcHU8TuDhmfGZPLKmFfoFtfNL/z6OdezOHexX9iDwx/kin5XNKl8IcRyKeWwYNdUT0GhaIDsDWv5ePJDnP/I38kcNNRvvL2mp1Dtrg5Id+9Ms9dw0UM6CMFrq17jtVWvMevsWZQ5y+hY6SYpPImwyCgGjzuTiNh4SlIEU3+9mezUSj4au4vrvk0lraicmEoHz52vYWgSKcCtSa4ecA3jup/ubXk/t+IF5mfPJ7rCQlm4y4yL2Wp9+NiH6ZPUh8y4bp5Wt0AIDbfbhbOqiqK8HBLTO1NWsJ+I2DjPdYGjsgKrzY7L6US3WjBcLtwuN9IwiIyLJywqKqDeNS3gs8JPYkxJX3KKs9m0fhkVYW56dzyKNftWIyT0ju1Fx4g0/shainQ4MTSz5S6k2druEt2FqooqwsLsZu9GGt5ekETitDSuMWvVrNh1Gy7DjeGovV+GkGiepr1TNzzlC4TEG15DldXtvQ6+8tae45FfNLCZaWWPonqv7SjZwdmzzuas7meRHJ7MrYNvxabbWLnP3Of70DEP8cySZwCYsXUGl/e9vNl7C0opKBQN8PFk063H509NYsDJpzL6iuu813SL+fgU5GyvN/3TU91MOVMnO9l8cM+edTYAsfZY5l80H13TEULQ+/iRTF071ZsusdTNSRt3ec9zE3VcltqH/7ndbxDdpRPndj2XTzZ9wjcFcyECyiJcATJMWvUPAJ4a+RQTuk/whr/+x6vsLNnJ8yeatpwiPMOii/YsIq88j3N7nltvvSqcFVS6Kgm3hHvDfCd7v6ycx2PXPMel31zK1iRzuMnoLJiflQ/APMxv0uorIYdRUaOYct4UthRt4fwvzwfM4RzDszxGSMnrr7pJiOvAfx7sw/ycX+uVF+D0rqcTTxS2KUsAyNxXxKanx1PhrGDW1i9ACJCSUzv/hadOfAa7bqc4fy+OinLuXzeZFXvN+r085mU2FWzitVWv+eV/79H3UuAoYOJRE4mxxYCUOCoqmDLxEr94g7fGcXn/K9nnLqBv2gAWrF9ARWQFy3KXkVhso8pmsD57LhJY+fN3FFYX0QkLYKFnTgzvZT7PjpIdDOx+bIsMHx2RSmHRpx+wZcnvFC5Z4B2vFJrnI4QZpulUlZd5WyehUO7eVl2cAAAgAElEQVQs598r/s05Pc6hQ2QHRn88GoCFly40/ySKQ5o1P33Pmp++954npGcAsOP226hvlql7Lrz4lpu7b9TZk1j7ABc7inln7TvcMPAGb5hNt9ElT3LrN24y6vzt5toeIuHKK5jx4wwe3/04AJMWTWJf5T427N8QkvyP/PoIx3Q4htTIVDbs38Cbq98E4OjUo7m0z6VsL95OkaOIm364CYDE8ERGp4/2y0NKyXc7vuOBBQ8AsPqq1czbNY9CR2HACqnhHwz3r0PW3JDkrOGXsl94YMEDfLejdoK+RiHElkn+96o5ByLLc3k25hkiTzVf0qXVpZw761zyKvzlmb19NonFkglkesOu+96g8IPPuRz4120duPZXK2n7VqH1L4bUFGKTzGG8aZ2nUe4sJ0wPQ9d0xnYeyyldTuGVFa+QHJ7MnUPvJCEsAVd+Prtvvptim42km28mYsgQRl56Nb9+OM1PlnWzvgJgLwsBc1HBKJLq+SVqw39Z9Yb32HJ8Ed36DQ7pt2wMR+ScwudPP86OlYHL0RpDYnpnwGd3sxAUOQrZW2E+zWlRHT3roSXRthg6RnX0WUEg8Cp4IWq7msI898SoPRY1Fz3leaMHj+OqrsZZVUlkfAIFhYUkJiZ60pnDBjXDA7VhwrsDV2iauQ1fCDRNxyxC1MpcI69PuJlHTb081xAIzfe6r/xmjYWm15brfV/6xKs9rVPXOq0jIdi+bRvdunevE1x7b7xZBeTh+/v5HQDw89Tah7Au9378NdLtZmP/o+qN48um1+7ksaz/+oVd3PtibhhwA6mRqbyx6g1GX/xyven7rF7F/EWLGH3iaIa+NxS3DNxEeVb3s7im/zXoQmft/rU8+uujftdj7bHcOeROftr1Ewt3LzygzLPOnsXGgo0MSBqAVbcyZ8ccnl/2vPf6e6e/x5WzrzxgPl1iurCzJPg4eu/43nw64VPvPZm5ZSaTFk1qML9bvnEzZnXtu6vD45Nw7tlD4o03onvcpFa7q7FqVkqqSxj50UgAuu+RjFqZCZg9hX57apeMhg0YQNWaNd7zrjNnYE1LQ4+LO2D9avjztNOp3rHDe9534waWzPqMX6ZPDYjbZ8SJaLpObm4uHVJTcVY7vMvf+40+GZfh4s/CP9lcuAkQDI8bSHJMGgiBBDr16sPQ8WeHLJsvDc0pHJFKYfWahbz44zOc1nMcR6cMrbNCw/BbtbE/eyfLv2m3BlsVrUhYZBRV5WXe83s//pqVzzyCferMkPPo+OuPuPcXMK9iFU+ufQGXYQ73DE0ZSnp1JFc8+HOD6au7daP/+++hx8czdd1UXl/1OhWuCgAeOfYRLu1zqV98w+GgSJazq3QX//njP/ye83vIsjaF+FLJHbZx5A3L5I01Zk/k2I0G/xr7Em/FrmTDF9O41TWKuL/exfLCVeyt2MudQ+8Mmtcr377CW/veAmB0+mheHvMyH274kF+zf+Geu4IPFcWMH0+nF18Iei27NJuNsz9k1efzgUClEBRNo9vXX2Pv1tUvWEoZdOim7oKDXsuWsnzud35K4Y5pn2ILqx12C2WRROWatey48EIA+mxYf9DDRmqiuQ6/GWtZnrCD5fvf4KZON3F1/6uJttXvhP2kq8wu/geP3EPun1sYeclVdD/6GCTgdDu55OuLvXEF+Jg7EXSNycSiW9lasAWAoSlDuKLvFaRFpYH0NaMra+1kSVnrnUlKb37STOANrrkg68RxVlZitduRwOpVqzjqqKPMK4YnX8/EnZR4vs18pTSXK9YgDQPpSWcKWyOvR9YaOaWslcfn2JtvTZ2kr7wSw3DX5u2tk88PWF9dfa95UmTt3Ennzp0DzBL7x69TjjcP74/nnwbJ/l1ZRCUmcdotd3vNVDgdVd5JZl+FEHvdNdgTEtn7fPCXEoDz82/Y99JLDOzSmenTp/PCshdYnLuYFXtXMHLWgc2n2LZtY++/nqPjM09z7VHXckrnU3hi7kPcNmkF8RdtgMdq45Z8N4fdd99N9OmnEbl+Pf957XXm9drMq565hGhrNJ9M+IRr51xLbnmuN124JZyxnceSX5nfoBKJs8dR5PCfNJ003U2ngm855fFJnHf+HDbsXknHp+8lb+Zd3LdiOZuu/D9gPikjz+SSCZdQ/NVXlOz7HlvXTPY89BARQ48m9cEHEFYrgyIGsfqq1YDZs6tcvZpLEsZyWYcz2MLIoDKVfPstJd9+S/c532HtbPbmZVUVWng46dHpROndWcX8A/7OXgyDbePHY+/bl24zZyClZNfEiRjV1XSZNg3h+U9U/PEHuZOfCEhetc7fkq6mW/wUQqiUzK7dM1H28zyiTx7T6DxC5YhTClJK74QRwBur3+Dd9e+SEZ1BtC2aq/pdxZiMMUE18eVPvYQ0DD9jZ7/n/E5hjP8O0AeHP8iOkh18sfUL/nvxe4RZwvho40e8+ser/FC9mJ9WL+O8nudxw4Ab6BhlzrSVVJcgEOhC5+ttXzM6fXSz2GXZWVRK96OPOeh82jvz5s1jdCstSbXazc1RlevW+YWn3nwLekwMe19+BVyBE74A+156CQDnziz6Jvblf6f+j0V7FjFjywzKIuYAB1YMxV98QeTIkcSeeQYZMRn8Y00fCp0rqPzgUzZ88GlA/NLZ5ph84fvvM27SY5zS+RTKXeVUfvYlEcu28MMFPwDgLi0FKdFjzPkvKSUfbPiAb7d/y5p8c1ild3xv+if154kTnkBKydysuXy08SOeHvU0SdY4Nj49EIDy3xeTfumlxORtpWa63Ll7t1cmd2EB7tJS9tz/gJ+sjvUbKHz/fbp99SVaYaH3OSz96Weyb731gL9NDX+OO42w/v2p8tyjLtOnEzF0CNLZtN3ajg0bqNq4ke3n1E6+l//+O1EjRmCUl7Pz0suCpsu6+mqKR/o/f/X1MhrCdwgr+9Zb6bloIZaEhEblESpHnFIwpEGv+F6syFnBk6Oe5NPNn7IkdwmbCzcD5qaQHnE9uKDXBYzLHEdSuP/kT13rl7tKa1eIXNnvSsqd5UzoPoFYeyx/O+5v3muX9b2MUzNP5T9//IeZW2fy6eZPmbl1JhO6TeDsHmdz+9zbqXBVeLfTZ8ZkcvfQuxmdMRqrFrj5RdH2VOXtqT25dDy6Kx+Ioc/qVTg2baJs/gKMslL2v/V28PSbNxPWqxcjOo1gRKcRbPusEgf+w0epkx6jcvkKSr75xi98z333EXvmGQAUTp8ekrzli8117rqmE7a3lN1//ycFmMMRSMn2Cy7AKK8g/eWX0KJjCOvdiyv6XRGwFt5dUkLxrFlokZGccsopnBg2gPLvF5KzdGltnELTnETO45O9YUWffe49Lpz+Ia78+odutk04iySLBeeIEWwdfWJI9atLlY/S3nnZZfRashiaaIwO8FMIALsmXk/6k/eS/Vj9PUOA6k2boaNpXl06nex76WVS/noP7rJyNHvtBkhpGLgLC7EkBppiN0pL/c6r1q0jatSoplalQY7IOQWAr+d+zZljzU1Imwo28cGGD5i5dSbhlnAqXabFS01oDE4ezJiMMYxOH02n6E7cP/9+Yu2xTDxqIpmxmTy1+Ck+3Pghtw2+jZsH3ezNX0pJ3pNPYuvShYSrr/Yre1vxNl5f9Tpzdsw5oE2VxLBETs08lTEZYzg69WivUbRvtn1DfmU+F/e+mDBLGHOz5pIele7dru823OiarjavtSCbv/kI973mkEHPc3KxhBnw8G6w+6/fl9XVbBw4KCC9pUMHenw/B+Exl5F1002Uz19A+ODBVK4016X3WDAfYbWS8+jfKPvpJ7/0seeeS4cnJpM7+QmKZ8wIyD8YHf7+BPEXXUTR5zPIedSchO7ywftY09PZeuJJfnG7z/kOW5cufmHlixaRdd1E73mvZUvZPMx/lZH32tIlZN95JxW/tdw8Rp/16yhbsIDsm285YNwOkx/HqKpi6uzPAOjtyOeklG3Y/7GW8kWLyHnoYQCSbr0FPT4B1969VP30CeV/FjdKJlv37lgSEqjwKMltyXFs9CgFYUhOX7PNL37V4MGErVxJ1CljKfvpZ7q8/x4RQ/0N6eU89hhFn37mPY895xzSnn6qyXMLaqK5DlJKFk6bxogrr/SOCdbgdDuZmzWXr7d9zcI9C70TgcHoGd+TLYXmXMGbf3mTIWWJ7LnvfpJuvQUtMopdN5hzEV3ee5eI4YEPzs6SnUzfMJ2vtn2FlJLTup5GbnkuutDpm9iXOTvmsL24dg28XbfTJaYLPeN78s02s+UYbY2m1FnbihiSMoSskiz2V+2nU1QnutKVM4acwYCkAWREZ6CJw9MGYqsrBcPN5U8M5G8fm0q9z0V7EBpw9xqI6xw0iauwkD0PPEj5L78EXLOmp+PMzgag00svEnP66QFxpJT8+umnpL31Ns6sLDNdRgbOXbsC4taLrtNh8uMUf/Y5latMg3v2nj1wbNkaNHrXmTMo/mIW1k6dCOvfjz33P4BzT20PKfr007zDU61N6qTHSLjMHLap3rEDV2EhFYuXEDliBNl33IErN9c/ga6D2823g8xVan1c+zhjwEaYbL70/YZ1di2FzydCkbliav/GSPaurF14bI104Sz3H2gR4eHEXXAByXfegR4djeFwULl8OfMevLdBpVAXe9++dP34I7BavfJsv/hiqlatpsOkx8j9xz/BMEi5714Sr2+acTylFOpQNGMmOY88QuwF55P297/X6xCltLqUhbsXMi97Hr/tMQ1u1RBpjfQa1xIIfrzwR3j9A/a/aa64iBw1yvvwC5uNxOsnknDddehBdoHWh5SSdfvXMTdrLvN2zWNrUfAHN1TCLeH0iu9Fr/hedI3tSkZ0BvFh8Vg1K4/88ggR1giGpQ6jX1I/usV2IyM6w29zEsC8XfNwGk6Gpgwlzh7H88uex6JZOLnzyfRJ6BMQv7VobaVQtOBZnv74Xa6ZayqFvpd4XpT3rIfYTgdMX7V+PTuvvQ6jOLAVWl8jAsx6nnjiiRRMncbeF18EnzHyyJEj6fD4JGwZ5v4JaRjIykqceXlY09PZc/8DlM6Z09iqNpr0118j759P+SmrDpMfJ+/pZ5AOBxHDh+MuLsax2RyyTbr1FiJ2/x+R4dvg/LdxdTiRfVOmsHfJUmzbzBeovW9fMt54nYrFi8l/4w1SH3yIqFHBJ5sB3MXFFEybRux552NN60Du5MnelnZ9SsGPZ7tCZYFfkJQgYtLg3o3ISbHkLImjeIdplC7+qitJufdeNHugSeulX81gwfvvAKBpGqf9sSWk39HSMY3U++8nrF8//hx3GgA9JuRSNvwNcic9TvRfTqHTK680yaGTUgp12HXb7ZTNrd1Ik3DddcScNg49NhZrx44Ia+AYviENthZtpbS6lMHJg3FLN8vzlrM++w+G/+NLYmOScebmelt7wdCioog9awLRp44jYtjRCEvjpnRKqkvYUriFZbnLyK/M59ye5xJtjWb53uWkRKTQJ6EPy3KXsaNkB4OcHQhLTuWjP76gLKqM9fvXs7dyb6PKA0gIS6BDZAeq3dUhKaWk8CTSItNIiUghOTzZ/I5IJt4eT3yY+Ym1xxJljWrWXktrK4UVb48m5919dPPsj/IqhUs/ht6nhZSHKz+fPQ89TPmvtcsr9dhYevyyAK0efwu+9TQqKtj371cpmDoVLSqKbt9+gzWlYZtJxbNmkfev53Dv3w+6Tvqrr5J9553eifHO77xN+W+/ET5kKAVTp1KxZEnQfBKuuYaw/v3Z+9KLuPbkEDF8OKkPP4RrfwFRo0biKihg77PPUjzrS2+LvnrXLoo++ZTYc8/B3q0brv37qc7KImLIEJjsaYUPuhTOfd1b1+NSUxEWC/aewQ3cNYbSuXPZc/8DfN3TXNwRoBTcLtA9z+RT6VBdGphJtKkUmByLlFCea0e//SfCB9S/V8VPKegW7pn+hTnhLQSOzZvZfNddpI49BefubNyFRTjz8urt/fW+cA/ak8WU//YbEcccEzDSESpKKfggDYPtF1yAY33wXaBadDThQwYTMfRotOgobJ07oyckoEdHY+3YEVd+PggNS0oyQggKP/6E3McfD8ijZmKo+w/f49q7l30vv+IdYwTQYmIIHzSI8CGD0SIiKJk9G0tiElpEBELXiDjmGOw9e2LNyECPiwsYO6xctQqjooLwIUNw5eXhzMnB3r07hsNB/qv/MScCIyKo7N6NTqNGE9a/H5XJMfwZXspW5262F28npzyHwqpCcspzsOt2zuh2BhbNwob9G9hZspPssux6h8+irFGUOcv8wnShB91QFQyBIMoWRbQ1mihbFFHWKCKtkURYIwjTwwizhGHX7Vg0CxbNglWzer9rrkdYI7Drduy6nXWr1nHc8OOw63Zsmg27xQy36Tbsur3Zh82+fu0Eur9iTqZ2evklYn73LEs+40UYPrGBlAdHk5Sf4QYf08tGRQXlvy/G1jkDe48eQPAVMVJKij75lIply4g771xc+fsJ69cXLTraq3xkdTXO3FysGRkHt3a+RikMvhzOMTf4tZSirzFo6KcUNnwFH18B574Jgy6GpzpBdVlg4uiOcO+GWnkheE/Dh2VfzWB+HaXgS916SpeLimXLqVi6lPJFi6jevh0qCojtWkHqkJIDlhcKap+CD0LT6PrRR/w+ZQrH3X47JbNnU7FiBWULFuDak4NRWkr5gl8oXxA47uuLJTkZe+/efq08a5fOaPYwMt58w7uCQFit2DIy6PLeu1Rt2kTJV19R+uNcqnfsoPyXX4KOLwMUz6p16ahFR2PNSMeakooWEYG7vIzy+QsOWFejogL7mrXkr1nrDYsDjktOZlRGBtb0TlhTe4JFp2r1GkTYFqypKVhSBmBJGYvWJZHSSI39lOEoLSaq1Il96TqiU9PRExOpTg7HUlKBNSoGa2oKxMZSYK9mn7WSvOr95Ffks7diL/mV+RQ6CimoKqCoqoji6mLKneWUVpdSWl0K5fXXoVE0YLjWIixYdSs23YZNs2HTbVg0C7rQ0TUdi/A51izmuedYF+Y3mHNODrcDd14J93nyjrb57I7X6rTcfvonGC44xb/h0Gqs/xI+uRIuehf6mbtftYiIgHXuwV7oQgjiL76I+Isvqjd7YbNh6xx8DqVJtJX/88895kZm3ggdB0MQI4cAVDSP05yGEBYLkccdS+Rxx5J8x+1m4OTWM9d/xCkFMP/IjiFDEFYrsWedRexZZ3mvOffsoWL5cipXrcaxdSuyqgrD4cBdWOg3ceXatw+Xx2+zsFrpsWA+lvj4BssN692bsN69SbnvPpw5OVQsW07Vhg24CwsRFh1rRmcQUDZvPnpcHM7du3FmZWGUluJYvyF478ZjxKsGPS4OW2YmSbffjrVjR1bM+JxuUuLYuMk7vFUje+WK0LxVWT0fAIfn02A9ga6RkXSPjkaPjkaLjUELj0ALS0WLyESLikZER+K0W6kO03DYBJVWSZUFqqySKoukyiap0iTVVqi2Sqo1SbVw4zRcONwOqlxVVLgqqHJXUe2uZl/BPsKiwqh2V+NwO7yfmnOXdOFyubwryw6WvxSav3l4twrEAh/nOL49Eilhwb/M45P/FqgwWpryfFMhAHxyVbO0MFucpvbo8taZaVP6HjhuXaQE3//FlAb29bgP9O8/9DkilUJDWDt2JLZjR2InTAi45i4qQhqG+cLOysKxbRvO3FzCBw06oEIIKCctjdgJZxI7IdBXQ9INtUbSpJS4CwrMl3l+PkZFJUZlBXpcHJEnnGBObFkstS09twt+mAQdqqBbVxzDhpHq2zV1u3Hm5ODMzjaVTm4urr37kG4X4YMGeScmaxSHu6AQo6oSLTISi2cDjZQSW3o6rn35GFVVCIsFo7wcd1ER7sJC3CUlGOXlGOXlgStA6iHc8zkgFgvCajU/Npv3uLK6mshYHWG1IayRZrjFAlYLwmJBWixIXcPQNQwNpC7McwGGJpCej6GBWxMYuvDGdetmuNQ0LAZYXJLwXz8GBNHRLny2sIPwefH7LTdugxbwc90PHKe90RSl4HbBayeYx/f/CZH1GZarhx8atrPUKPLWw9f3wF/+Dp2PNcPaqvfTRJRSaAS+uwptmZnYMjNbvEwhBJbExKAbWoKy9nP4fYr5CdIyFLqOLT0dW3p6M0taizQMUymUlOAuLcVdXIKsqsSorMKoqMAoK8VdUupVHEalqehkRYUZp7ISWVmJ4XB4e2rS5TJX2rhcSJcLWenf4rcAjhAVUDA8pvvQCPWhMB/0mM4VdYJ9Xmq+8zHSTaNcon/3MPz+XzjvfzCw/uGbejmYucLctTDrNjj1H9C1ZTZIATD3SUjoBkMurw0TDfSmDANyV0HqUeAoNZeLdhwCLh8vcs91h/u2QFQjnBQt+nfjZa+P1443v9859dDomQVBKYXDjYr8xqeproCn0qDvBLj4/YMWQWgaumfoqDn3Ykspwek0lUJ1NdLp9H4vXriQ4UOHmmEuF7La6Tl2ehWKdLmQThfS7QK3G+lyg2F+S8NthrndZnyX+S3dLo8iMs+F1Yqw29CW/pfoTlVYI+tsPvTMPbBptv9+hX2boENo1lQBUyEAzLghuFLYNh/+eB/GPwfhQax4ukM05+CqhmVvQ89TIdHTs/jkKij4E6ad2fgX26qPYOlb5iqsyAYaMvs2wS8ea6t+SqGBVvXMG2FNHTMeo+6FnuP8w3JWQ89TAHAbEl1rpZZ6yZ6gwS3pp7olOOJWHwHklVRx3FNzSYi0MTgjjrkb61+qabdoOFy1D/7QznFkJkUyY8XugLiX6D+RRDGfRl5CXknbjD2+aP0v5+nm5HdmVWjmDx6xfMCNlm88aT6gTYY6GuAosY3z9F951nUJDoIv1WxtdoQFt3UDcLHjMT62P+kX9rN7ENc6HyTKbqHcUU2myKMiqgt5pcEnNH3zD3Yfa66/6jqHF1yBSiOcKjaEXecXVjcfDYNnLW9yoWWB3/WGyh4o/mSH7EAJtb6ER2mrGa2t5mnXZWwLM01i/M81ni/dJzBA2863tnEUVdb2mq7VZ/O49T2/MmrKnOo6lY+S7mBjrrl6b0Lcdiwl2cw0RtX7m49xvMDP9nu957dW38m3xnFB43YRuZy1zTRi6F191Eh85a05B1hkv52OonZvQ/+qt3Gj0ad4I6MKTJPYBoIpXW+msQQrb8kjY0mJCWt0XnAIrT4SQpwGvALowFtSymdaopxjnzL3KOwvr25QIQB+CqEj+azMMliRFdyd3jNW08zvxyUnAY2bY6gfiR0nTiwY3kGO+qlRCI1hqFa7mUYgkc2sFCy4cKHTVGXztd20IRVFJQ+4bjpoeay4SKGQ3SQfdF7BqKsQAMboq/gvL7PC1ZO/hX0AwHuVp/AY11L3dzlLW+R3frq2mNnGsUHLiqOMDJFHMsWskL284Tbq34kPMFxs5FP73wPCo6gIEtvkeG0dH9r+SbZMYqSjdsjlPZv5mGaKWqc2dpx85blv1moXC0V/tkpzyNJXIdTlGsv3zN07lA6awTxjMK9WPQo2SHHW78LyIcuHfuf/tf2b7lXDOU1bSoSo4lP3Sd5r8+1/5QUObkjsacv//M5P1xYzXl/spxAA1oWZS5P/VVo7b2jBjYaB4TOU2F3sZrdMYri2id0yCYe0spd4nPW8npMoJp9YjnlqLjueOeOg6hKMdqMUhBA6MAX4C5ANLBVCfCmlXN9wysYTSSXj9cXoGEy2TOMV1/n017ZTLsN5330KBoLjtfWslV0plRFI4Fv7I355nOV4khRRRLTnIVonM73X+ms7qCKH/mI7bnQWGf1JEwWkCHNdu0Na+VN2rKm5N51vn00iuE6f7W3F1XCq41nCqOZqyxzedo0nQZRiw0meTOBy/Ue/uPdYPmOOexhDtS3kygR2yyRSRQEdRQFrjUyqsZIgShimbfamGSY24w5x7DsU5REryphqe46NRgaPOCfSU9tNnowngipO1ley0ujOaqMb/bSdbDbScWAlRlSQSiEbZWc6iVoXZBdZ5nvvj0TDiouh2haWGL1xoyM8EsWJUk7VlrHc6E0JEeRKf2uS39lNF5uPOq8jWybzf9Z/8aMxlBdcF9JLZFOFjZ0y1ZtfL7GLTTLDo9igi/D36BUq4/UljNdrN4NdafmRC/QF3O+8iXxiEUj6iiwm1XlpvmZ7pd48B2tb+cVyT0jlDxWbcaFzleUHLtADlzTfY/mMuyz+NpRGamu4SJ/H5+7RTLM9C0C6yOevlk+4Sv+BKa5aJy9/0WuX5l5l+cF7/ITV3+tYXer2AGqUjC8PWz8MCKthnB44WvBnWK3jn85iL2miIGidm8KlFn+jhQ3dH4Cx+grmUTvpX9ObairLwkw7T887LwSaXym0m+EjIcTxwGQp5TjP+cMAUsqn60vTZIN4rbjmV6FQtC9e2GD2FPq59nL6gE0tXt7ygo7MyzOVgobBPX0P7PEuZJo4mX2oDB91Anz3dmcDAX1mIcSNwI0AqampzJs3r9EFRRs9OVqr3/5IjkwgzdMVXG90MVtwWlaDef5ppNFdMx2UbzY60UurnXPYZnSgm+a/Mmar0RGHzzSsb5tbePoMwcrcZKTTWws0pbHO6EJ/Lbi7wxo2GBn01cyfeLXRlVRRiETQwdODAVhpdPPr2tZHqANBAslg7c8G4+wyksnQzB6Bbz2yZRLponbiPFfGkyfj0TDQkN54Dmlhm0zzlCboV+d32GBk+J3X/AbbjVScWLz3qlzaiRTmXNBmoxMSQaSo8srwp2GaR6i5z4ciq4xu2HHSR2uEET2gWurYRGi71ds7I/UdrK1IZWSv7QeO3Ax0jigi3lZBldtKj+gmLASph+muMXRswvvvQLQnpRASUso3gTfB7Ck0ZRv8W/onnP9NaM7OFYpgzLY96FUuAB+4xvKoq655C8mOMHNlTaiT/kcKQ8VmZtgnA/4Tt7PcJ3CX8/aWLdsRYzoAAAsSSURBVNyzKOxJAM9q1nm2e8jU8rzyrLNf620g3FN9Cy/ZXvMmz6yazmr79cSICu/5AfG1kVhVb6x6CTbR/P7EYxnZs5F7MkKgPSmF3YBvky7dE9bsXD+qGz3cWcrPwGFEa9dzw6QH/c47J4Tz0TnHcUxmAlrNEkjDAM9c7hkD0rhmRCbDMw/OW9Zhcz93JYPH99COZ86Ayebx2YPTOft8c5y8oboahqTabRBmbXiXuMttoAnhvSfFlU4GPfE9z184iAibzr5SB8My40mdbgePqaNtT41H+/kOc8nssTfzbOpAqLU6Y8r7tNW7tX/D309jyY4CrLogJsxKz9QotuSV0T05inCbKV9D3tZCuqeT65TfgrQnpbAU6CmE6IqpDC4B6l/3p1C0Kf4P+KgeCdCt/nX5Uy4fWu+1I5L6di6HuKNZ0wRhIZgNsej++cWGW4O/VG21r0JNEzDmUXPfTupR2NZ+Fhjfh3Cbzom9/FeyHdXJf97yUNqr0G6UgpTSJYS4HZiDuST1HSnlugMkUyjahICVV0E96LWPRRztkvpekm3mBKqOPJpmGsYLdq0tsEUHN+XdArQbpQAgpfwW+Lat5VAoDkTA6z7Y+7+drOxrn9SnFNrBC7g9EtcZ9rZOG/nw9M2oULQwAT2FAee3jSCHKvX2FNpIKTS63MNX4bernoJCcaiQHGXHu/n3rlUQn9mW4hyCtLfho3ZOKypLpRQUiiaQGhtWqxTqUwi6BU58qPX9KBwKHOREc6vi+0KOTmsrIVqtJKUUFIomEeJDOubhlhXjUKW9TzQfdLxDl3aolhWKQwA1IXqQtDOlEOr9bLM5j9YrSikFhaJJKKVwUBxKPYXuYwPjdfaY5k4b1GIS+aOGjxQKxeFMfWPz7XFOwddZkNXjNPac12H5OzDkyuBpDmHa4R1QKA4B+nps5HcKamhScSAiEuCmBXDnSv/w2JZzE9sgMR0bvn7lTNMN6EUeM+CRiTD6foju0PKyAVgjWqccVE9BoWgaJ9wFqQNqnbMrGo/v0Mt138OGL+GYG9tGlrOnwJyHYcRdwa93PxluObl1ZfLl7P/Ap9fC2MdavCilFBSKpqBboNepbS3F4UPnY9tWwcZ2govebbvyD0RST7il8V4Vm4IaPlIoFAqFF6UUFAqFQuFFKQWFQqFQeFFKQaFQKBRelFJQKBQKhRelFBQKhULhRSkFhUKhUHhRSkGhUCgUXoQ8hF0GCiH2ATubmDwJyG9Gcdorqp6HF0dKPeHIqWtb1LOLlDI52IVDWikcDEKIZVLKw95wjarn4cWRUk84cura3uqpho8UCoVC4UUpBYVCoVB4OZKVwpttLUAroep5eHGk1BOOnLq2q3oesXMKCoVCoQjkSO4pKBQKhaIOSikoFAqFwsthrxSEEKcJITYJIbYKIR4Kct0uhPjYc32xECKz9aU8eEKo51+FEOuFEKuFEHOFEF3aQs6D5UD19Il3vhBCCiHazVK/xhBKPYUQF3nu6TohxPTWlrE5COF/21kI8bMQ4g/Pf3d8W8h5sAgh3hFC7BVCrK3nuhBC/NvzO6wWQgxtbRm9SCkP2w+gA38C3QAbsAroVyfOrcDrnuNLgI/bWu4WqucYIMJzfMvhWk9PvGhgAfA7MKyt5W6h+9kT+AOI95yntLXcLVTPN4FbPMf9gB1tLXcT6zoaGAqsref6eGA2IIDjgMVtJevh3lM4BtgqpdwmpawGPgLOrhPnbMDjjZvPgLFCCNGKMjYHB6ynlPJnKWWF5/R3oI08pB8UodxPgCeBZ4Gq1hSuGQmlnjcAU6SUhQBSyr2tLGNzEEo9JRDjOY4F9rSifM2GlHIBUNBAlLOBd6XJ70CcECKtdaTz53BXCp2AXT7n2Z6woHGklC6gGEhsFemaj1Dq6ctEzFbJocYB6+npdmdIKb9pTcGamVDuZy+glxBioRDidyHEaa0mXfMRSj0nA1cIIbKBb4E7Wke0Vqexz3CLYWmLQhVthxDiCmAYcGJby9LcCCE04EXgmjYWpTWwYA4hnYTZ61sghBggpSxqU6man0uBqVLKF4QQxwPvCSGOklIabS3Y4crh3lPYDWT4nKd7woLGEUJYMLuo+1tFuuYjlHoihDgFeBQ4S0rpaCXZmpMD1TMaOAqYJ4TYgTk2++UhONkcyv3MBr6UUjqllNuBzZhK4lAilHpOBD4BkFL+BoRhGpA73AjpGW4NDnelsBToKYToKoSwYU4kf1knzpfA1Z7jC4CfpGfm5xDigPUUQgwB3sBUCIfi+DMcoJ5SymIpZZKUMlNKmYk5d3KWlHJZ24jbZEL5336B2UtACJGEOZy0rTWFbAZCqWcWMBZACNEXUynsa1UpW4cvgas8q5COA4qllDltIchhPXwkpXQJIW4H5mCudHhHSrlOCPF3YJmU8kvgbcwu6VbMiaBL2k7iphFiPZ8DooBPPfPoWVLKs9pM6CYQYj0PeUKs5xzgVCHEesAN3C+lPKR6uCHW817gf0KIezAnna85BBttCCE+xFTiSZ75kccBK4CU8nXM+ZLxwFagAri2bSRVZi4UCoVC4cPhPnykUCgUikaglIJCoVAovCiloFAoFAovSikoFAqFwotSCgqFQnGIcCDDekHiN9poolIKisMWIYRbCLHS5/OQJ3xeW2xoE0JMFUJcECT8GiFER5/zt4QQ/VpXOsUhwlQgJJMmQoiewMPACCllf+DuUNId1vsUFEc8lVLKwW0tRAhcA6zFY+xNSnl9m0qjaLdIKRfUNe8vhOgOTAGSMfc43CCl3EgTjSaqnoLiiEYI8ZoQYpmne/2ET/gOIcS/hBBrhBBLhBA9POEXCiHWCiFWCSEWeMJ0IcRzQoilHlv4N3nChRDiPx5/AT8CKUHKvwDTFtUHnt5MuG9PRghR5sl7nRDiRyHEMZ7r24QQZzVUvuKI4U3gDinl0cB9wH894U0ymqh6CorDmXAhxEqf86ellB/XifOolLJACKEDc4UQA6WUqz3XiqWUA4QQVwEvA2cCk4BxUsrdQog4T7yJnrjDhRB2YKEQ4ntgCNAb0w9AKrAeeMe3cCnlZ55dvffVmOMQ/pbbIzFNr9wvhJgJ/AP4iyfPaZjmEYKW77GJpDiMEUJEASdQa6kAwO75bpLRRKUUFIczoQwfXSSEuBHzWUjDfNnWKIUPfb5f8hwvBKYKIT4BZnjCTgUG+swXxGI+jKOBD6WUbmCPEOKnJtShGvjOc7wGcEgpnUKINUDmAcpXSuHwRwOK6vmfZ2M663EC24UQNUYTlx4oQ4XiiEQI0RWzuz1WSjkQ+AbT4FoNsu6xlPJm4G+YFi2XCyESMb1l3SGlHOz5dJVSft9MYjp9bP0YgMMjh0Fto64ly1e0Y6SUJZgv/AvBO2Q5yHO5SUYTlVJQHMnEAOVAsRAiFTi9zvWLfb5/A3NST0q5WEo5CdNaZwamQbdbhBBWT5xeQohITJegF3vG/NMwXaIGoxTT7HdTqa98xWGGx7Deb0BvIUS2EGIicDkwUQixClhHrfe6OcB+j9HEnwnRaKIaPlIcztSdU/hOSul1Di+lXCWE+APYiOn1amGd9PFCiNWYrfNLPWHPeZb6CWAupl/h1ZhDOSuEObC7DzgHmAmcjDmXkIVHsQRhKvC6EKISOL4J9XyrnvIVhxlSykvruRQwiezpYf7V8wkZZSVVoQiCMJ30DJNS5re1LApFa6KGjxQKhULhRfUUFAqFQuFF9RQUCoVC4UUpBYVCoVB4UUpBoVAoFF6UUlAoFAqFF6UUFAqFQuHl/wFCl4ohqQdzSwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dKtTtOG1DNYm"
      },
      "source": [
        "We observe that the different instruments, i.e. the columns, are scaled differently. It is therefore convenient to normalize the data by calculating the returns. We shall first perform a quick visual check that the normalized data looks 'reasonable'. We use the tail() member function of the dataframe to view the 5 most recent observations of the data. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Wbd2Oaw_DNYm",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c0ea1c0d-b5d3-48de-a275-57eaa9b83850"
      },
      "source": [
        "Cols=df2.columns.values.tolist()[1:]\n",
        "Cols"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['ECG', 'EMG', 'foot GSR', 'hand GSr', 'HR', 'marker', 'RESP']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OaifI5pbDNYm"
      },
      "source": [
        "We measure the correlation between time series. Here we shall explore VIX returns and its relation to single named equity returns in order to observe the \"leverage effect\". We shall print the pair-wise correlations in addition to producing scatter plots of the equity returns against the VIX returns."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WpQOTighDNYm",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 110
        },
        "outputId": "de9e5e64-ae0d-4ba9-deae-17de911b6dd7"
      },
      "source": [
        "df2[['ECG','HR']].corr()"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>ECG</th>\n",
              "      <th>HR</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>ECG</th>\n",
              "      <td>1.00000</td>\n",
              "      <td>-0.00469</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>HR</th>\n",
              "      <td>-0.00469</td>\n",
              "      <td>1.00000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         ECG       HR\n",
              "ECG  1.00000 -0.00469\n",
              "HR  -0.00469  1.00000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pHc9RkFVDNYm"
      },
      "source": [
        "# Exercise #2: Calcualte the correlation matrix for all attributes.\n",
        "Hint: add attributes"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OXg0MuhNDNYm",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "28bb6d10-8004-44af-bf2b-b6049088bf4e"
      },
      "source": [
        "for symbol in Cols:\n",
        "  xdat = df2[symbol]\n",
        "  ydat = df2['HR']\n",
        "  plt.plot(xdat, ydat, 'b.')\n",
        "  ax = plt.axis()  # grab axis values\n",
        "  plt.grid(True)\n",
        "  plt.xlabel(symbol)\n",
        "  plt.ylabel('HR')\n",
        "  plt.show()"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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fbiRHL87q6qGC66jf2tpaic9a1Oi+yfvLhGlZR4XJ9F7vECsrWdsZnTfrR1nkaHN43uH/87htZ70e/mxNOoJoUizTaHu8476nzsZG3HKkyvLBvOb2EzN7HXCZu1+Rvn4T8FLgdcCz3P1JM3sp8B53v3Tcunbv3u2HDx8uuP3+s81/chuakFZXV7nooouCrDtMbSm7AI95z+Px2+5uc0w95jve2E1IRcsHMzvi7ruz3ovR++grwEvM7BQzM+AS4B6gB7w2nWcfcEuIjW8mAB953U11FtCxE8Lw3+3mqFFYahU7IVQtxjmFO4CbgC8AX0pjuBZ4B/B2MzsGPAP4SLgYNrtMSjmTu/7ZtvlDbLvMfNkDoN3esu9DswaYK7tcHY9yJu/f/vrnKSFApN5H7v5u4N0jkx8AXhwhHClh8hH/1up3jHsjZI3PlL+seh8VUfb/WdX3oEhhv7BQpitr25q7qqMrmjuuXUfH5Ux3cdocdR/pgCLJZX29zHAo438Y8/y7UVKQQFd+dvMoqz5t279x411fL9bklHc/hS5cIa3bccpAtSeBu1v9rkfb9m/18c7yfS1zcniW7bUpkaimIMD4L3y5L3RzfgWTex5J1xS94GzWA6Y2XSSrpCCVGq5+N8E0PY/aqUWlDNDEeDWOUraJScHMFs3szKHXJ5vZfjO7N2xo0hRljnKaPJZQFcN4x9e2bNa8eLt7L47xxu4WM7sc+CZwl5n9iZm9nKTr6CuAN9YQn9SkyiPmGGMf5cU/WujnJ4E562zeOM1KCkXPKcz6+2hTjXTSieZ/DSy7+zEzexHwp8Br3f2/hg9NpE4LU17P0BTlAixzVXkVBdrq6qFgw7TUpU0F+ywmJYXvu/sxAHf/gpndr4Qwf2JcWFa12be7gVmb2hPK9eYps5+q+Z9OPxR1lnkbSqLJJiWFs8zs7UOvTxt+7e4fCBOW1CVcIb5O03o86+K1mGb7ovV7CykxhDfpV/u7wNPGvBbJ0aaj7jZqfPvWiGpuxynhjU0K7v7eugKRebOBEkNI3bt4Tb2F6jE2KZjZh8e97+5vqTYckWoNnxyMOXS3zJYUdE6hPpOaj44MPX8v20c2FdkmKXzrOawr2iOkXT2Mxmle8OP+F/PQ+6grJjUfXd9/bmZvG34tkicpbOtpPsoaHrsbmtd8ND7Jbu191G8K6p8nUE2gOYr8ajv3s+uCeSlMix71t7uW0EZbd/jGxtYTx0XHIpJwmtVnUKII09betrOCbcsS4eIdPVCo5rsxeSXqXdQMk040f4fNGsIpZvbt/luAu/vTQwYn9ZiXo+bRz9Ev3Ian59eMmtccM164eMN8H6aLtznfxQtnSo5tbg6bdE5B1yTMuXA/wvi/7qzP1pxCR5ptEbPsA4tptPliu7bV8aU12lb6Kt6w2hbv7NraHKakIIG07Qy24g2rbfHOrq0X27U0bBFpl7YlhfVtFz4W0eZzCrUnBTN7npndOfT4tpm9zczOMLPbzOz+9O/pdccmVWpbc4HiDanXO5R517umPnq927d9hiLLtzUhQISk4O73ufsF7n4BsAx8F7gZuBI46O7nAQfT19JabTsyVLwiEL/56BLg/7r7Q8BeoH/F9PXAa6JF1SHhLl5rW6GleENaWdmji9NaInZSuBy4IX2+5O6PpM8fBZbihNQt89wltZi2dRWpZ/9mNY2UY7pquSWiXdFsZicDrwbeOfqeu7uZZX79zGw/sB9gaWmJ1dXVUttfW1srvWwM4eLdQ1LAjBYyky426v97LGdayELLc7blGc9Hp+Utt0CSGELHXpXQMSb7anX1UMZ7ed+ZcZL/xcZG3jqbpdPlg7tHeZA0F31m6PV9wM70+U7gvknrWF5e9rJ6vV7pZWMIFW+4U3UbDThdWOjUYANiaN7+re47k8S7sDD5O7mwMFt8VZj38gE47J5drsZsPnoDm01HALcC+9Ln+4Bbao9IOqwNtYN6uRebPmFtU3XTXFwsdtGXrlCvXpSkYGanAi8DPjk0+SrgZWZ2P/AT6Wtprdl+reUKnu3rGH6MF7Z0yTvWLS/sgHiTYitaT+j1Dk3VTbOtVwHPkyjnFNz9CeAZI9O+QdIbSeaCM0vBVcURYLF7LcwW77SxVCdcvGGOvvdMnkUaIXbvI5Hg1MTQBGH+CVXUKGUr3U9BAmlbSax4wyoerwr8OJQUJJCwzTFFTa4tNCveyeY/3ipreGXGIip60ntWTUmCaj4SAWjZFcJSTNEL5y6++MLaT3o3pZlTNQUJpCHf8AL6R2pN+XHOavTIM+7nir9TixTy7t09Xu7uJ5fA2nbkPX99Ic02H/GHl4j/fShyfwOz+fs+TEtJoeOqasdsSntoWcNDJbf9s2TZ2Ih905e4O7XoOYXPfvb22vdXU753Sgoy04VU+cvatnmmeYzbxjTvl30U/bzjlimzL0PEOmpjo5p9VeYR+34KZe5vsL5eb4xNoXMKMlNbc/4FYk4/MeStf3iZcTFMiq9s/MPbX1m5cKplptlWmXimWaaKu3kNb2fW9Y3rndOkQk6KUU2h46o6+ThLQRjrBOjW7UdvdJ+o34MmKXBnv7XXLENZT+quOS8n67tISUECUakQQr8g7vVur6TZoWy3S41RNL/UfCSBbDYfjaMjymIWFvr7rJqxhMqeTF1YUGKYV6opSCPV0Sbd30ZVzTGhbS2IZ8+ms5xTWF8fn1B0TqG9lBQkkGKFVlZPjKp7+Yzr7THcHDNLgRaq58n2HjTZ+zd0j5xh43rnSHspKUgg8UqGvCPY+WqqUskrYSgpSCW2HyH6YPo0yxZ9f2vTz1ZV9Iop29ZeRffYce9tPt++gI7QpQpKClKp0YuVppm3yHrrap6IcRJ12qaY4f2rJhupmnofSWONO+oOWQjOVzOTSDGqKUhwZQrwIlcxj65/lpPTsyYEHbFL26mmIHOhSGIIvW2RNlNNQUREBpQUJJpZzhmEOjrPW++4k74i8yRKUjCz08zsJjP7CzO718xeamZnmNltZnZ/+vf0GLFJc4b4jRXDuO2px4/Mu1g1hQ8Bn3b3HwZeCNwLXAkcdPfzgIPpa4lg+I5dw0fzo6+7Km//iMyD2pOCmf0QyWheHwFw9++7+wlgL3B9Otv1wGvqjk2yVVH4zUvTS9Z+UGKQeWJe8y/TzC4ArgXuIaklHAHeCjzs7qel8xjweP/1yPL7gf0AS0tLywcOHCgVx9raGjt27Ci1bAyh4l1Z2UMyjk5eyeZsXj3bn2/4dfJ+r3eolnhDmTbe7fsr+/OHNq/7tynmPd6VlZUj7r478013r/UB7AaeBP5++vpDwL8DTozM9/ikdS0vL3tZvV6v9LIxhIq3/BBuWx958U6arymm3b/Tfv7Q9P0Na97jBQ57Trka45zCceC4u9+Rvr4JeBHwNTPbCZD+fSxCbJJh0knVvPfmsallXprBRPLUnhTc/VHgq2b2vHTSJSRNSbcC+9Jp+4Bb6o5NthvX86arPXC6/vllvsW6ovmXgI+Z2cnAA8DPkCSoG83sCuAh4PWRYhMR6awoScHd7yQ5tzDqkrpj6Tr38U06/ffKHBFnrVtH1iLNprGPJLOgHi3MzconBhFpDw1zISIiA0oKIiIyoKQgIiIDSgoiIjKgE80y9i5mItItqil0XFYvI4h7JzMRiUc1BcmlRCDSPaopiIjIgJJCx6mZSESGqflIlAhEZEA1BRERGVBSEBGRASUFEREZUFIQEZEBJQURERlQUhARkQElBRERGVBSEBGRASUFEREZUFIQEZEBJQURERmIMvaRmX0Z+A6wDjzp7rvN7Azg48Au4MvA69398RjxiYh0Vcyawoq7X+Duu9PXVwIH3f084GD6WiS4xUVYWdmDGYUfi4vJo/889ucYF0f//eHYp11WuqNJzUd7gevT59cDr4kYi3TE4iJsbADYpFkzbWz0l0/+xipUNz9HdhzD7/f155u0rHSLeYRxk83sQeBxwIHfcfdrzeyEu5+Wvm/A4/3XI8vuB/YDLC0tLR84cKBUDGtra+zYsaPsR6id4g1jZWUPSUIolxSSrzDp8g44vd6hKkIba3T/bv0c2+PY/v7mfJvxh/sMbfk+9M17vCsrK0eGWmm2cvfaH8DZ6d+zgC8Ce4ATI/M8Pmk9y8vLXlav1yu9bAyKN4yFBffkjhIb6d/ZHgsL9cQ9un83P0d2HKPvD883adkQ8TbdvMcLHPaccjVK85G7P5z+fQy4GXgx8DUz2wmQ/n0sRmzSLevrsLAAm0fMxSws9JdP/q6vVxZaIZufIzuO4ff7+vNNWla6pfakYGanmtnT+s+BlwNHgVuBfels+4Bb6o5Numl9HXq9Q6XqBv1Ctf889ucYF0f//eHYp11WuiNGl9Ql4ObktAEnAX/g7p82s88DN5rZFcBDwOsjxCYi0mm1JwV3fwB4Ycb0bwCX1B2PiIhsalKXVBERiUxJQUREBpQURERkQElBREQGlBRERGRASUFERAaUFEREZEBJQUREBpQURERkQElBREQGlBRERGRASUFERAaUFEREZEBJQUREBmLcT0FEZrSycuFMy0e4Nbu0hGoKIi2T3J9qsYJ1iGynpCAiIgNKCiIiMqCkINIyyfmA9QrWIbKdkoJIC/V6t+NO6YdIHiUFEREZiJYUzGzRzP7czD6Vvj7XzO4ws2Nm9nEzOzlWbCIiXRWzpvBW4N6h1+8HPujuzwUeB66IEpWISIdFSQpmdg7wD4DfS18bcDFwUzrL9cBrYsQmItJlsWoKvw78S2Ajff0M4IS7P5m+Pg6cHSMwEZEuM6+5K4KZvQp4pbv/gpldBPwL4KeBz6VNR5jZc4A/cvfzM5bfD+wHWFpaWj5w4ECpONbW1tixY0epZWNQvGEp3rAUb1hF411ZWTni7ruz3ouRFN4H/DPgSeApwNOBm4FLgWe5+5Nm9lLgPe5+6YR1fR14qGQoZwJ/VXLZGBRvWIo3LMUbVtF4/6a7PzPrjdqTwpaNpzUFd3+Vmf0h8Al3P2Bmvw3c5e6/FXDbh/MyZRMp3rAUb1iKN6wq423SdQrvAN5uZsdIzjF8JHI8IiKdE3XobHdfBVbT5w8AL44Zj4hI1zWpplC3a2MHUJDiDUvxhqV4w6os3qjnFEREpFm6XFMQEZERnUwKZnaZmd2XjrN0Zex4xjGz55hZz8zuMbO7zeytsWOaxujYVk1mZqeZ2U1m9hdmdm/aJbqxzOyX0+/CUTO7wcyeEjumYWb2UTN7zMyODk07w8xuM7P707+nx4xxWE68/z79PtxlZjeb2WkxYxyWFe/Qe79iZm5mZ5Zdf+eSgpktAr8JvAJ4PvAGM3t+3KjGehL4FXd/PvAS4M0Nj7dvdGyrJvsQ8Gl3/2HghTQ4bjM7G3gLsDu9uHMRuDxuVNtcB1w2Mu1K4KC7nwccTF83xXVsj/c24Hx3/7vA/wHeWXdQY1zH9nj7F/2+HPjKLCvvXFIg6eF0zN0fcPfvAweAvZFjyuXuj7j7F9Ln3yEpsBo9BMjo2FZNZmY/BOwh7QLt7t939xNxo5roJOCpZnYScArwl5Hj2cLdDwHfHJm8l2RMM2jY2GZZ8br7Z4aG3fkccE7tgeXI2b8AHyQZPmimE8VdTApnA18det2acZbMbBfwI8AdcSOZaHRsqyY7F/g68Ptpc9fvmdmpsYPK4+4PA1eTHA0+AnzL3T8TN6qpLLn7I+nzR4GlmMEU9LPAH8UOYhwz2ws87O5fnHVdXUwKrWRmO4BPAG9z92/HjidPOrbVY+5+JHYsUzoJeBFwjbv/CPAEzWra2CJti99LksyeDZxqZv80blTFeNLlsRXdHs3sXSRNuB+LHUseMzsF+FfAv61ifV1MCg8Dzxl6fU46rbHM7K+RJISPufsnY8czwY8BrzazL5M0zV1sZv85bkhjHQeOu3u/9nUTSZJoqp8AHnT3r7v7D4BPAj8aOaZpfM3MdgKkfx+LHM9EZvbTwKuAN3qz++7/LZKDhC+mv7tzgC+Y2bPKrKyLSeHzwHnpnd5OJjlJd2vkmHKl95r4CHCvu38gdjyTuPs73f0cd99Fsm8/6+6NPZJ190eBr5rZ89JJlwD3RAxpkq8ALzGzU9LvxiU0+MT4kFuBfenzfcAtER9wHskAAAMLSURBVGOZyMwuI2kCfbW7fzd2POO4+5fc/Sx335X+7o4DL0q/24V1LimkJ49+Efhjkh/Tje5+d9yoxvoxklFlLzazO9PHK2MHNWd+CfiYmd0FXAD8WuR4cqU1mpuALwBfIvkNN+rqWzO7AfhT4HlmdtzMrgCuAl5mZveT1HauihnjsJx4fwN4GnBb+pv77ahBDsmJt7r1N7tWJCIidepcTUFERPIpKYiIyICSgoiIDCgpiIjIgJKCiIgMKClIZ5nZrqyRJita93Vm9tqc996ejsD5JTP7opl9IL1AETP72XT6XekoqI0dl0vmU9TbcYp0jZn9PMlIli9x9xPpBZRvJxngbgl4F8mFR99KhzZ5ZsY6ThoarE2kUqopSNctmtnvpvcn+IyZPRXAzH7OzD6fHsl/Ih1fpl8D+LCZ/W8ze6BfG7DEb1hyn47/AZyVs713Af+8PxJrOirrVel4VmcB3wHW0vfW3P3BdP2rZvbrZnaYZFhykSCUFKTrzgN+091fAJwAfiqd/kl3/3vu3r+/wvBVozuBC0nGxelfmfuPgOeR3KPjTWSMR2RmTwd29Av6DF8EvgY8aGa/b2b/cOT9k919t7v/h6IfUmRaSgrSdQ+6+53p8yPArvT5+Wb2P83sS8AbgRcMLfNf3H3D3e9hcwjoPcAN7r7u7n8JfHbShs3s0nQIhS+b2Y+6+zrJzVNeS3Jjlw+a2XuGFvl4yc8oMjUlBem67w09X2fzPNt1wC+6+98B3gs8JWcZm3ZDaRPRmpmdm77+Y3e/ADgKnJxOc3f/M3d/H8mAgj81tIonpt2WSFlKCiLZngY8kvYKeuMU8x8C/rEl96beCazkzPc+4Jr+PX/TkU6fkj5/tpkND9t9AfBQ2Q8gUoZ6H4lk+zckd7j7evr3aRPmvxm4mGTY7a+QjGKZ5RrgVOAOM/seyUnl/wX8OXAacLWZPRv4f+m2f362jyFSjEZJFRGRATUfiYjIgJKCiIgMKCmIiMiAkoKIiAwoKYiIyICSgoiIDCgpiIjIgJKCiIgM/H+9DWEPX70vpwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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SaLhB+yJv1TXVNgVJw7CheUoMOqKqSUDSTlhTmBKeIpI0CdYUajTIgX7QZGANQdI4WFOQJJVMCpKkkkmh4aZ95ElJ08WkIEkq2dDccMP0Otpu3e5urU2pgTQxpjpMazlMOu5pLadpYU2hRuP8QI/Sk6kJ3VybGFMdprUcJh33tJbTNDEpSJJKJgVJUsmkMAMG7aHUxBusNzGmOkxrOUw67mktp2liQ3PD9btXQufy7ufbvV6TNDGmOkxrOUw67mktp2lhTUGSVDIpTAmHxZY0CbUkhYg4OyLujIg/j4iHIuKqiDg3Iu6JiEeKv+fUEVuTrbcdeJWzpKrUVVP4CPCZzPxx4BXAQ8AtwOHM3AMcLqYlSRM08aQQES8G9gIfBcjM72bms8A+4ECx2gHguknH1kRenCNpkiInfB4iIi4HbgMepF1LOAbcDDyemWcX6wTwzPp01/YrwArAwsLCFQcPHhwpjrW1NXbt2jXStuO0vLwXiOLRLYHkyJGjE4unKeXSJJbJZpZJb9NSLsvLy8cyc7HXsjqSwiLwOeCnM/PeiPgI8C3gnZ1JICKeycy+7QqLi4u5uro6UhytVoulpaWRth2nYWsCVf+7mlIuTWKZbGaZ9DYt5RIRWyaFOtoUTgAnMvPeYvpO4FXAkxFxAUDx96kaYpuoUU4NeTpJUpUmnhQy8wngaxFxaTHratqnkg4B+4t5+4G7Jx2bJM27uq5ofifwiYg4A3gUeAftBHVHRNwIPAZcX1NskjS3akkKmXkf0Ot81tWTjqVO3fc3GHQbSaqKYx81kAd+SXVxmAtJUsmkIEkqmRQkSSWTgiSpZENzA/XrkWQjtKQqWVOYMl7RLKlKJgVJUsmkIEkqmRRqNmwbgW0KkqpkQ3MDeKCX1BTWFCRJJZOCJKlkUpAklUwKkqSSSUGSVDIpSJJKJgVJUsmkIEkqmRQkSSWTgiSpZFKQJJVqGfsoIr4CfBv4HnAqMxcj4lzgk8DFwFeA6zPzmTrik6R5VWdNYTkzL8/MxWL6FuBwZu4BDhfTGlHExkOSBtWk00f7gAPF8wPAdTXGMtW6E4GJQdKgImsYtzkivgw8AyTw7zLztoh4NjPPLpYH8Mz6dNe2K8AKwMLCwhUHDx4cKYa1tTV27do16ltotOXlvUAUjwSSI0eODrTtLJfLqCyTzSyT3qalXJaXl491nKV5gbqSwu7MfDwiXgrcA7wTONSZBCLimcw8p9/rLC4u5urq6kgxtFotlpaWRtq26XrVDAb9N89yuYzKMtnMMultWsolIrZMCrWcPsrMx4u/TwF3AVcCT0bEBQDF36fqiG0WdCcAb+IjaVATTwoRcVZE/MD6c+ANwP3AIWB/sdp+4O5JxzZLMjcekjSoOrqkLgB3tZsNOB34g8z8TET8GXBHRNwIPAZcX0NskjTXJp4UMvNR4BU95n8DuHrS8UiSNjSpS6okqWYmBUlSyaQgSSqZFCRJJZOCJKlkUpAklUwKkqSSSUGSVDIpSJJKJgVJUsmkIEkqmRQkSSWTgiSpZFKQJJXquJ+Cxqj71pveVEfSTlhTmGK97sXca54kDcqkIEkqmRQkSSWTwhTr1X5gm4KknbChecqZBCSNkzUFSVKptqQQEadFxBci4o+L6Usi4t6IOB4Rn4yIM+qKTZLmVZ01hZuBhzqmPwz8Zma+DHgGuLGWqCRpjtWSFCLiQuDvAr9fTAfwOuDOYpUDwHV1xCZJ86yumsJvAf8ceL6YPg94NjNPFdMngN11BCZJ82zivY8i4s3AU5l5LCKWRth+BVgBWFhYoNVqjRTH2trayNvOMstlM8tkM8ukt1kol8gJ92mMiF8H/gFwCngR8IPAXcA1wA9l5qmIuAr4lcy8ZpvX+jrw2IihnA/85YjbzjLLZTPLZDPLpLdpKZcfzcyX9Fow8aTwgp23awr/LDPfHBF/CHwqMw9GxL8FvpiZv1vhvlczc7Gq159Wlstmlslmlklvs1AuTbpO4X3AeyPiOO02ho/WHI8kzZ1ar2jOzBbQKp4/ClxZZzySNO+aVFOYtNvqDqChLJfNLJPNLJPepr5cam1TkCQ1yzzXFCRJXeYyKUTEtRHxcDHO0i11x1OHiLgoIo5ExIMR8UBE3FzMPzci7omIR4q/59Qd66Q5LtdmEXF2RNwZEX8eEQ9FxFXz/lmJiPcU3537I+L2iHjRLHxW5i4pRMRpwL8B3gi8HHhbRLy83qhqcQr4p5n5cuDVwE1FOdwCHM7MPcDhYnreOC7XZh8BPpOZPw68gnb5zO1nJSJ2A+8CFjPzMuA04AZm4LMyd0mBdg+n45n5aGZ+FzgI7Ks5ponLzJOZ+fni+bdpf8l30y6LA8VqczcGleNybRYRLwb2UnQTz8zvZuazzPlnhXbvze+PiNOBM4GTzMBnZR6Twm7gax3Tcz/OUkRcDLwSuBdYyMyTxaIngIWawqqL43JtdgnwdeA/FKfVfj8izmKOPyuZ+ThwK/BV2sngm8AxZuCzMo9JQR0iYhfwKeDdmfmtzmXZ7po2N93TOsflqjuWhjkdeBXwe5n5SuA5uk4VzeFn5RzaNaVLgB8GzgKurTWoMZnHpPA4cFHH9IXFvLkTEd9HOyF8IjM/Xcx+MiIuKJZfADxVV3w1+GngLRHxFdqnFV9H+1z62cUpApjPz8sJ4ERm3ltM30k7SczzZ+X1wJcz8+uZ+dfAp2l/fqb+szKPSeHPgD1FL4EzaDcOHao5pokrzpV/FHgoM3+jY9EhYH/xfD9w96Rjq0tmvj8zL8zMi2l/Lv40M38JOAL8QrHaXJUJQGY+AXwtIi4tZl0NPMgcf1ZonzZ6dUScWXyX1stk6j8rc3nxWkS8ifa549OAj2XmB2sOaeIi4jXAfwO+xMb5839Bu13hDuBHaI9Ae31mPl1LkDXqGqzxb9GuOZwLfAH4+5n5V3XGN2kRcTntxvczgEeBd9D+UTm3n5WI+FXgF2n35PsC8I9otyFM9WdlLpOCJKm3eTx9JEnagklBklQyKUiSSiYFSVLJpCBJKpkUpIpFxNL6iKtS05kUpAp1XN1ay/bSsEwKUg8RcXFx74CPR8T/jYhPRMTrI+J/FPcPuLJ4/K9ikLj/uX7Fb0T8ckQciog/pT2kdOfr/p1i/R+LiCsi4rMRcSwi/kvHkBGtiPitiFilPYy3NDH+CpG29jLgrcA/pD08yt8DXgO8hfbV328HXpuZpyLi9cCvAT9fbPsq4G9n5tPF1dFExE8Bv0N7ILWTwH8C9mXm1yPiF4EPFvsCOCMzF6t/i9ILmRSkrX05M78EEBEP0L6hTEbEl4CLgRcDByJiD+0RQr+vY9t7uoZ8+AnaN3V/Q2b+RURcBlwG3NMeOofTaCeKdZ+s6D1JfZkUpK11jlnzfMf087S/O/8KOJKZP1vck6LVsf5zXa91EngR7ftW/AUQwAOZedUW++7eXpoI2xSk0b2YjaGRf3mbdZ+lfUe3Xy9OJz0MvCQiroL2MOYR8ZMVxSkNzKQgje5f0z7If4EBat2Z+STwZtr3CH8l7SGWPxwR/we4D/ipCmOVBuIoqZKkkjUFSVLJpCBJKpkUJEklk4IkqWRSkCSVTAqSpJJJQZJUMilIkkr/H+iTAn+v70hhAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AyczWW3cDNYm"
      },
      "source": [
        ""
      ],
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LDO1qUveDNYm"
      },
      "source": [
        "# Excercise #3: Plot the two attributes that are highly correlated.\n",
        "Hint: check the correlation matrix"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DJ7-mrcnDNYm",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7267c2e5-a021-48ca-bc9e-c54a68b976a7"
      },
      "source": [
        "df2['ECG'].diff() # easy way to calculate log differences"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0            NaN\n",
              "1          0.007\n",
              "2          0.006\n",
              "3          0.008\n",
              "4          0.005\n",
              "           ...  \n",
              "1048569    0.025\n",
              "1048570    0.036\n",
              "1048571    0.023\n",
              "1048572   -0.011\n",
              "1048573   -0.027\n",
              "Name: ECG, Length: 1048574, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zCrkILWBDNYm",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "57037dd7-6071-4fff-c788-f86d434349ee"
      },
      "source": [
        "df2['ECG'].diff().dropna() # can also drop NaN values"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1          0.007\n",
              "2          0.006\n",
              "3          0.008\n",
              "4          0.005\n",
              "5          0.001\n",
              "           ...  \n",
              "1048569    0.025\n",
              "1048570    0.036\n",
              "1048571    0.023\n",
              "1048572   -0.011\n",
              "1048573   -0.027\n",
              "Name: ECG, Length: 1048573, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FR_piOCCDNYm",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8b5b45de-6403-4641-d904-bdac6528dcfc"
      },
      "source": [
        "#We can create bolean vectors \n",
        "df2['ECG'].diff().head(10) > 0.006"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    False\n",
              "1     True\n",
              "2     True\n",
              "3     True\n",
              "4    False\n",
              "5    False\n",
              "6    False\n",
              "7    False\n",
              "8    False\n",
              "9    False\n",
              "Name: ECG, dtype: bool"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ppcrYNtHDNYm",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 203
        },
        "outputId": "caae7377-bf52-4f28-e068-8a1e4ad4ae15"
      },
      "source": [
        "#and we can use the vectors to chose the elements in the dataframe\n",
        "df2[df2['ECG'] > 0.03].head()"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Elapsed time</th>\n",
              "      <th>ECG</th>\n",
              "      <th>EMG</th>\n",
              "      <th>foot GSR</th>\n",
              "      <th>hand GSr</th>\n",
              "      <th>HR</th>\n",
              "      <th>marker</th>\n",
              "      <th>RESP</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>0:00.026</td>\n",
              "      <td>0.040</td>\n",
              "      <td>0.036</td>\n",
              "      <td>8.763</td>\n",
              "      <td>5.675</td>\n",
              "      <td>88</td>\n",
              "      <td>10.85</td>\n",
              "      <td>43.54</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>0:00.028</td>\n",
              "      <td>0.055</td>\n",
              "      <td>0.036</td>\n",
              "      <td>8.763</td>\n",
              "      <td>5.675</td>\n",
              "      <td>88</td>\n",
              "      <td>10.85</td>\n",
              "      <td>43.54</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>0:00.030</td>\n",
              "      <td>0.060</td>\n",
              "      <td>0.036</td>\n",
              "      <td>8.763</td>\n",
              "      <td>5.675</td>\n",
              "      <td>88</td>\n",
              "      <td>10.85</td>\n",
              "      <td>43.54</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>0:00.032</td>\n",
              "      <td>0.066</td>\n",
              "      <td>0.036</td>\n",
              "      <td>8.765</td>\n",
              "      <td>5.678</td>\n",
              "      <td>88</td>\n",
              "      <td>10.85</td>\n",
              "      <td>43.40</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>0:00.034</td>\n",
              "      <td>0.071</td>\n",
              "      <td>0.036</td>\n",
              "      <td>8.765</td>\n",
              "      <td>5.678</td>\n",
              "      <td>88</td>\n",
              "      <td>10.85</td>\n",
              "      <td>43.40</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Elapsed time    ECG    EMG  foot GSR  hand GSr  HR  marker   RESP\n",
              "13     0:00.026  0.040  0.036     8.763     5.675  88   10.85  43.54\n",
              "14     0:00.028  0.055  0.036     8.763     5.675  88   10.85  43.54\n",
              "15     0:00.030  0.060  0.036     8.763     5.675  88   10.85  43.54\n",
              "16     0:00.032  0.066  0.036     8.765     5.678  88   10.85  43.40\n",
              "17     0:00.034  0.071  0.036     8.765     5.678  88   10.85  43.40"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lkFy4YBHDNYm",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a6337c53-b041-44d9-8c52-24d84a4d4ae0"
      },
      "source": [
        "#we can also work in a similar manner with a specific column of the dataframe\n",
        "df2['foot GSR'][df2['ECG'] > 0.03].head()"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "13    8.763\n",
              "14    8.763\n",
              "15    8.763\n",
              "16    8.765\n",
              "17    8.765\n",
              "Name: foot GSR, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NeKQLhFiDNYm",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 110
        },
        "outputId": "0e6a4ed8-b955-4bef-943c-9f2a70755910"
      },
      "source": [
        "#Suppose we want to use a very specific location in a dataframe\n",
        "t = [3,4]\n",
        "#we can easily do that with the locator\n",
        "df2.iloc[t]"
      ],
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Elapsed time</th>\n",
              "      <th>ECG</th>\n",
              "      <th>EMG</th>\n",
              "      <th>foot GSR</th>\n",
              "      <th>hand GSr</th>\n",
              "      <th>HR</th>\n",
              "      <th>marker</th>\n",
              "      <th>RESP</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0:00.006</td>\n",
              "      <td>-0.023</td>\n",
              "      <td>0.036</td>\n",
              "      <td>8.763</td>\n",
              "      <td>5.675</td>\n",
              "      <td>88</td>\n",
              "      <td>10.85</td>\n",
              "      <td>43.54</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0:00.008</td>\n",
              "      <td>-0.018</td>\n",
              "      <td>0.036</td>\n",
              "      <td>8.763</td>\n",
              "      <td>5.675</td>\n",
              "      <td>88</td>\n",
              "      <td>10.85</td>\n",
              "      <td>43.54</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  Elapsed time    ECG    EMG  foot GSR  hand GSr  HR  marker   RESP\n",
              "3     0:00.006 -0.023  0.036     8.763     5.675  88   10.85  43.54\n",
              "4     0:00.008 -0.018  0.036     8.763     5.675  88   10.85  43.54"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "h5jvicDtDNYn",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 313
        },
        "outputId": "282a39c2-6a3a-40a5-f830-46e330a8f5f9"
      },
      "source": [
        "#Plotting histogram\n",
        "num_bins = 10\n",
        "plt.hist(df2['HR'].dropna(), num_bins, facecolor = 'red')\n",
        "plt.title(\"Histogram of HR \")\n",
        "plt.xlabel(\"Value\")\n",
        "plt.ylabel(\"Frequency\")"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Frequency')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 39
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AeWtUkMTDNYn",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 313
        },
        "outputId": "190f7ac8-e004-4d8d-fd7f-462a61b10bd0"
      },
      "source": [
        "#Plotting histogram\n",
        "num_bins = 100\n",
        "plt.hist(df2['HR'].dropna(), num_bins, facecolor = 'blue')\n",
        "plt.title(\"Histogram of HR\")\n",
        "plt.xlabel(\"Value\")\n",
        "plt.ylabel(\"Frequency\")"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Frequency')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 40
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hBGMDkdFDNYn",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "outputId": "d96c80fe-f79b-45b7-b991-f3cfbb343d16"
      },
      "source": [
        "#plotting boxplot\n",
        "df2.boxplot()\n",
        "plt.xticks() #try to rotate rotation=45\n",
        "plt.show()"
      ],
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mzbpL6HGDNYn",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "outputId": "dba6078c-b752-4958-a1ff-8d71492f57f3"
      },
      "source": [
        "# Lets use boxplot from Seaborn\n",
        "sns.boxplot(data=df2)"
      ],
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f74270ea860>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 61
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MXCg9mopDNYn",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "outputId": "efcc1545-2c76-4cd6-9dda-d0a30044bd88"
      },
      "source": [
        "# plotting lag plots\n",
        "#Strong autocorrelation\n",
        "pd.plotting.lag_plot(df2['foot GSR']) # try lag =2\n",
        "plt.show()\n",
        "\n",
        "#Due to:\n",
        "#phenomenon under study; or\n",
        "#drifting in the environment; or\n",
        "#contamination from the data acquisition system?"
      ],
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9-BkI2XYDNYn",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "outputId": "894fc463-6244-4052-d84b-3c9f5e4dd6c1"
      },
      "source": [
        "#moderate autocorrelation\n",
        "pd.plotting.lag_plot(df2['marker']) # try lag =2\n",
        "plt.show()"
      ],
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "x7_SgM01DNYn",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 323
        },
        "outputId": "95ccd1e5-6dc3-4b10-a436-935f5cfe3ed2"
      },
      "source": [
        "# Plotting heatmaps using Seaborn\n",
        "import seaborn as sns\n",
        "\n",
        "corr = df2.corr()\n",
        "sns.heatmap(corr, \n",
        "            xticklabels=corr.columns.values,\n",
        "            yticklabels=corr.columns.values)"
      ],
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f742ce3d320>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 45
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": true,
        "id": "OTpg_F75DNYn",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 459
        },
        "outputId": "136eb378-f649-482c-b264-01a2b7203e4e"
      },
      "source": [
        "# Plotting joint plot using Seaborn\n",
        "\n",
        "sns.jointplot(x=\"marker\", y=\"foot GSR\", data=df2[0:1000], kind=\"kde\")"
      ],
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.JointGrid at 0x7f743b262f98>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 46
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x432 with 3 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "b89qysrSDNYn",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 459
        },
        "outputId": "95e7a097-70bf-4c62-e534-ede1f97633c3"
      },
      "source": [
        "sns.jointplot(x=\"marker\", y=\"foot GSR\", data=df2[0:1000], kind=\"reg\")"
      ],
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.JointGrid at 0x7f742ce27860>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 47
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x432 with 3 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "68clae5zDNYn",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 459
        },
        "outputId": "1c350764-526b-438a-d78e-1c44cc640b57"
      },
      "source": [
        "sns.jointplot(x=\"marker\", y=\"foot GSR\", data=df2[0:1000], kind=\"hist\")"
      ],
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.JointGrid at 0x7f742a2c5c88>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 48
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x432 with 3 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hyqZ3YvGDNYn",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "outputId": "0c7c729c-05b0-4fd8-d57a-9336f248802e"
      },
      "source": [
        "#df2['HR'].plot(grid=True, style=cols[1])\n",
        "Fs=15\n",
        "df2['foot GSR'].rolling(window=Fs*60*60*1).mean().plot(grid=True)\n",
        "df2['foot GSR'].rolling(window=Fs*60*60*2).mean().plot(grid=True)\n",
        "df2['marker'].rolling(window=Fs*60*60*1).mean().plot(grid=True, style='r')\n"
      ],
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f742a216f28>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 49
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vRA5bG16DNYn"
      },
      "source": [
        "## Exercise #4: plot 1 hour and 2 hour moving averages for heart rates on same graph?\n",
        "Hint: replace the foot GSR with HR in the previous example"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YhvvlC3YDNYn",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 556
        },
        "outputId": "7a51184e-9f39-4db7-d94f-1456df065ab4"
      },
      "source": [
        "from statsmodels.tsa.seasonal import seasonal_decompose\n",
        "from statsmodels.tsa.filters.hp_filter import hpfilter\n",
        "\n",
        "decomposition = seasonal_decompose(df2['HR'][1:1000*Fs], freq=100*Fs)\n",
        "\n",
        "trend = decomposition.trend\n",
        "\n",
        "fig = plt.figure(figsize=(18,8))\n",
        "\n",
        "plt.subplot(211)\n",
        "plt.plot(df2['HR'][1:1000*Fs], label='Original')\n",
        "plt.legend(loc='best')\n",
        "plt.subplot(212)\n",
        "plt.plot(trend, label='Trend')\n",
        "plt.legend(loc='best')"
      ],
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n",
            "  import pandas.util.testing as tm\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7f7428e688d0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 50
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1296x576 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "urU49TAMDNYn"
      },
      "source": [
        "# Exercise #5: Filter ECG signal using trend."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GTm6kqqaDNYn",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 501
        },
        "outputId": "67c1aab0-6a07-4a10-d35a-1833caa7f380"
      },
      "source": [
        "from statsmodels.tsa.seasonal import seasonal_decompose\r\n",
        "from statsmodels.tsa.filters.hp_filter import hpfilter\r\n",
        "\r\n",
        "decomposition = seasonal_decompose(df2['ECG'][1:100*Fs], freq=1*Fs)\r\n",
        "\r\n",
        "trend = decomposition.trend\r\n",
        "\r\n",
        "fig = plt.figure(figsize=(18,8))\r\n",
        "\r\n",
        "plt.subplot(211)\r\n",
        "plt.plot(df2['ECG'][1:100*Fs], label='Original')\r\n",
        "plt.legend(loc='best')\r\n",
        "plt.subplot(212)\r\n",
        "plt.plot(trend, label='Trend')\r\n",
        "plt.legend(loc='best')"
      ],
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7f7426f04eb8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 60
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1296x576 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0L4XXhCjNpdN"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}