pipeline.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "%matplotlib inline"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "\n",
        "# MaD DiGait Pipeline\n",
        "\n",
        "This pipeline showcases the current gait analysis pipeline used by the MaD-Lab with all required steps:\n",
        "Preprocessing -> Stride Segmentation -> Event Detection -> Trajectory Reconstruction -> Parameter Estimation\n",
        "\n",
        "This should serve as a compact example that can be copied and pasted into new projects.\n",
        "For more details on the individual steps have a look at the extended examples and the documentation of the main classes:\n",
        "\n",
        "- `Preprocessing <example_preprocessing>`\n",
        "- `Stride Segmentation (BarthDTW) <example_barth_stride_segmentation>`\n",
        "- `Event Detection (RamppEventDetection) <example_rampp_event_detection>`\n",
        "- `Trajectory Reconstruction (double Integration) <example_preprocessing>`\n",
        "- `Temporal Parameters <example_temporal_parameters>` and `Spatial Parameters <example_spatial_parameters>`\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: gaitmap in c:\\users\\sanju\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.10_qbz5n2kfra8p0\\localcache\\local-packages\\python310\\site-packages (2.4.0)\n",
            "Requirement already satisfied: joblib>=1.2.0 in c:\\users\\sanju\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.10_qbz5n2kfra8p0\\localcache\\local-packages\\python310\\site-packages (from gaitmap) (1.4.0)\n",
            "Requirement already satisfied: numba>=0.55 in c:\\users\\sanju\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.10_qbz5n2kfra8p0\\localcache\\local-packages\\python310\\site-packages (from gaitmap) (0.59.0)\n",
            "Requirement already satisfied: numpy>=1.20 in c:\\users\\sanju\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.10_qbz5n2kfra8p0\\localcache\\local-packages\\python310\\site-packages (from gaitmap) (1.24.3)\n",
            "Requirement already satisfied: pandas>=2 in c:\\users\\sanju\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.10_qbz5n2kfra8p0\\localcache\\local-packages\\python310\\site-packages (from gaitmap) (2.1.2)\n",
            "Requirement already satisfied: pooch<2.0.0,>=1.7.0 in c:\\users\\sanju\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.10_qbz5n2kfra8p0\\localcache\\local-packages\\python310\\site-packages (from gaitmap) (1.8.1)\n",
            "Requirement already satisfied: scikit-learn>=1.0.1 in c:\\users\\sanju\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.10_qbz5n2kfra8p0\\localcache\\local-packages\\python310\\site-packages (from gaitmap) (1.3.2)\n",
            "Requirement already satisfied: scipy>=1.6.1 in c:\\users\\sanju\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.10_qbz5n2kfra8p0\\localcache\\local-packages\\python310\\site-packages (from gaitmap) (1.10.1)\n",
            "Requirement already satisfied: tpcp<0.33.0,>=0.32.0 in c:\\users\\sanju\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.10_qbz5n2kfra8p0\\localcache\\local-packages\\python310\\site-packages (from gaitmap) (0.32.0)\n",
            "Requirement already satisfied: typing_extensions>=4.1.1 in c:\\users\\sanju\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.10_qbz5n2kfra8p0\\localcache\\local-packages\\python310\\site-packages (from gaitmap) (4.10.0)\n",
            "Requirement already satisfied: llvmlite<0.43,>=0.42.0dev0 in c:\\users\\sanju\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.10_qbz5n2kfra8p0\\localcache\\local-packages\\python310\\site-packages (from numba>=0.55->gaitmap) (0.42.0)\n",
            "Requirement already satisfied: python-dateutil>=2.8.2 in c:\\users\\sanju\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.10_qbz5n2kfra8p0\\localcache\\local-packages\\python310\\site-packages (from pandas>=2->gaitmap) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2020.1 in c:\\users\\sanju\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.10_qbz5n2kfra8p0\\localcache\\local-packages\\python310\\site-packages (from pandas>=2->gaitmap) (2022.2.1)\n",
            "Requirement already satisfied: tzdata>=2022.1 in c:\\users\\sanju\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.10_qbz5n2kfra8p0\\localcache\\local-packages\\python310\\site-packages (from pandas>=2->gaitmap) (2022.2)\n",
            "Requirement already satisfied: platformdirs>=2.5.0 in c:\\users\\sanju\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.10_qbz5n2kfra8p0\\localcache\\local-packages\\python310\\site-packages (from pooch<2.0.0,>=1.7.0->gaitmap) (4.2.1)\n",
            "Requirement already satisfied: packaging>=20.0 in c:\\users\\sanju\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.10_qbz5n2kfra8p0\\localcache\\local-packages\\python310\\site-packages (from pooch<2.0.0,>=1.7.0->gaitmap) (24.0)\n",
            "Requirement already satisfied: requests>=2.19.0 in c:\\users\\sanju\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.10_qbz5n2kfra8p0\\localcache\\local-packages\\python310\\site-packages (from pooch<2.0.0,>=1.7.0->gaitmap) (2.28.1)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in c:\\users\\sanju\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.10_qbz5n2kfra8p0\\localcache\\local-packages\\python310\\site-packages (from scikit-learn>=1.0.1->gaitmap) (3.2.0)\n",
            "Requirement already satisfied: tqdm>=4.62.3 in c:\\users\\sanju\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.10_qbz5n2kfra8p0\\localcache\\local-packages\\python310\\site-packages (from tpcp<0.33.0,>=0.32.0->gaitmap) (4.65.0)\n",
            "Requirement already satisfied: six>=1.5 in c:\\users\\sanju\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.10_qbz5n2kfra8p0\\localcache\\local-packages\\python310\\site-packages (from python-dateutil>=2.8.2->pandas>=2->gaitmap) (1.16.0)\n",
            "Requirement already satisfied: charset-normalizer<3,>=2 in c:\\users\\sanju\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.10_qbz5n2kfra8p0\\localcache\\local-packages\\python310\\site-packages (from requests>=2.19.0->pooch<2.0.0,>=1.7.0->gaitmap) (2.1.0)\n",
            "Requirement already satisfied: idna<4,>=2.5 in c:\\users\\sanju\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.10_qbz5n2kfra8p0\\localcache\\local-packages\\python310\\site-packages (from requests>=2.19.0->pooch<2.0.0,>=1.7.0->gaitmap) (3.3)\n",
            "Requirement already satisfied: urllib3<1.27,>=1.21.1 in c:\\users\\sanju\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.10_qbz5n2kfra8p0\\localcache\\local-packages\\python310\\site-packages (from requests>=2.19.0->pooch<2.0.0,>=1.7.0->gaitmap) (1.26.11)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in c:\\users\\sanju\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.10_qbz5n2kfra8p0\\localcache\\local-packages\\python310\\site-packages (from requests>=2.19.0->pooch<2.0.0,>=1.7.0->gaitmap) (2022.6.15)\n",
            "Requirement already satisfied: colorama in c:\\users\\sanju\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.10_qbz5n2kfra8p0\\localcache\\local-packages\\python310\\site-packages (from tqdm>=4.62.3->tpcp<0.33.0,>=0.32.0->gaitmap) (0.4.6)\n",
            "Note: you may need to restart the kernel to use updated packages.\n"
          ]
        }
      ],
      "source": [
        "pip install gaitmap"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Load example data\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "collapsed": false
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From C:\\Users\\Sanju\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python310\\site-packages\\keras\\losses.py:2664: The name tf.losses.sparse_softmax_cross_entropy is deprecated. Please use tf.compat.v1.losses.sparse_softmax_cross_entropy instead.\n",
            "\n"
          ]
        }
      ],
      "source": [
        "import numpy as np\n",
        "import pandas as pd \n",
        "\n",
        "np.random.seed(0)\n",
        "\n",
        "example_dataset = pd.read_csv('17may.csv', index_col=False)\n",
        "sampling_rate_hz = 50\n",
        "\n",
        "from gaitmap.preprocessing import sensor_alignment"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "pandas.core.frame.DataFrame"
            ]
          },
          "execution_count": 3,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "type(example_dataset)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {},
      "outputs": [
        {
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              "      <th>Angle Y(°)</th>\n",
              "      <th>Angle Z(°)</th>\n",
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              "      <th>Magnetic field Z(ʯt)</th>\n",
              "      <th>Temperature(℃)</th>\n",
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              "            Time                Device name设备名称  Acceleration X(g)  \\\n",
              "0   12:32:51.144  WT901BLE68(d8:09:5a:ca:b6:e0)              0.974   \n",
              "\n",
              "   Acceleration Y(g)  Acceleration Z(g)  Angular velocity X(°/s)  \\\n",
              "0              0.103              0.263                    0.549   \n",
              "\n",
              "   Angular velocity Y(°/s)  Angular velocity Z(°/s)  Angle X(°)  Angle Y(°)  \\\n",
              "0                   -0.244                   -0.183      21.374     -73.872   \n",
              "\n",
              "   Angle Z(°)  Magnetic field X(ʯt)  Magnetic field Y(ʯt)  \\\n",
              "0     176.891               -16.562                -89.18   \n",
              "\n",
              "   Magnetic field Z(ʯt)  Temperature(℃)  Quaternions 0()  Quaternions 1()  \\\n",
              "0                16.464           33.47         -0.09711          0.59232   \n",
              "\n",
              "   Quaternions 2()  Quaternions 3()  \n",
              "0          0.13531          0.78824  "
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        "example_dataset.head(1)"
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              "      <th>Angle Z(°)</th>\n",
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              "      <td>WT901BLE68(d8:09:5a:ca:b6:e0)</td>\n",
              "      <td>0.974</td>\n",
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              "      <td>0.263</td>\n",
              "      <td>0.549</td>\n",
              "      <td>-0.244</td>\n",
              "      <td>-0.183</td>\n",
              "      <td>21.374</td>\n",
              "      <td>-73.872</td>\n",
              "      <td>176.891</td>\n",
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              "      <th>1</th>\n",
              "      <td>12:32:51.146</td>\n",
              "      <td>WT901BLE68(d8:09:5a:ca:b6:e0)</td>\n",
              "      <td>0.972</td>\n",
              "      <td>0.100</td>\n",
              "      <td>0.263</td>\n",
              "      <td>0.183</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.183</td>\n",
              "      <td>21.368</td>\n",
              "      <td>-73.872</td>\n",
              "      <td>176.847</td>\n",
              "      <td>-16.562</td>\n",
              "      <td>-89.18</td>\n",
              "      <td>16.464</td>\n",
              "      <td>33.47</td>\n",
              "      <td>-0.09711</td>\n",
              "      <td>0.59232</td>\n",
              "      <td>0.13531</td>\n",
              "      <td>0.78824</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>12:32:51.146</td>\n",
              "      <td>WT901BLE68(d8:09:5a:ca:b6:e0)</td>\n",
              "      <td>0.974</td>\n",
              "      <td>0.101</td>\n",
              "      <td>0.263</td>\n",
              "      <td>0.549</td>\n",
              "      <td>-0.305</td>\n",
              "      <td>0.305</td>\n",
              "      <td>21.352</td>\n",
              "      <td>-73.872</td>\n",
              "      <td>176.808</td>\n",
              "      <td>-16.562</td>\n",
              "      <td>-89.18</td>\n",
              "      <td>16.464</td>\n",
              "      <td>33.47</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>12:32:51.146</td>\n",
              "      <td>WT901BLE68(d8:09:5a:ca:b6:e0)</td>\n",
              "      <td>0.972</td>\n",
              "      <td>0.100</td>\n",
              "      <td>0.263</td>\n",
              "      <td>0.183</td>\n",
              "      <td>0.000</td>\n",
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              "      <td>176.847</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>12:32:51.146</td>\n",
              "      <td>WT901BLE68(d8:09:5a:ca:b6:e0)</td>\n",
              "      <td>0.972</td>\n",
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              "      <td>0.13531</td>\n",
              "      <td>0.78824</td>\n",
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            "text/plain": [
              "            Time                    Device name  acc_x  acc_y  acc_z  gyr_x  \\\n",
              "0   12:32:51.144  WT901BLE68(d8:09:5a:ca:b6:e0)  0.974  0.103  0.263  0.549   \n",
              "1   12:32:51.146  WT901BLE68(d8:09:5a:ca:b6:e0)  0.972  0.100  0.263  0.183   \n",
              "2   12:32:51.146  WT901BLE68(d8:09:5a:ca:b6:e0)  0.974  0.101  0.263  0.549   \n",
              "3   12:32:51.146  WT901BLE68(d8:09:5a:ca:b6:e0)  0.972  0.100  0.263  0.183   \n",
              "4   12:32:51.146  WT901BLE68(d8:09:5a:ca:b6:e0)  0.972  0.101  0.263  0.549   \n",
              "\n",
              "   gyr_y  gyr_z  Angle X(°)  Angle Y(°)  Angle Z(°)  Magnetic field X(ʯt)  \\\n",
              "0 -0.244 -0.183      21.374     -73.872     176.891               -16.562   \n",
              "1  0.000  0.183      21.368     -73.872     176.847               -16.562   \n",
              "2 -0.305  0.305      21.352     -73.872     176.808               -16.562   \n",
              "3  0.000  0.183      21.368     -73.872     176.847               -16.562   \n",
              "4 -0.305  0.305      21.352     -73.872     176.808               -16.562   \n",
              "\n",
              "   Magnetic field Y(ʯt)  Magnetic field Z(ʯt)  Temperature(℃)  \\\n",
              "0                -89.18                16.464           33.47   \n",
              "1                -89.18                16.464           33.47   \n",
              "2                -89.18                16.464           33.47   \n",
              "3                -89.18                16.464           33.47   \n",
              "4                -89.18                16.464           33.47   \n",
              "\n",
              "   Quaternions 0()  Quaternions 1()  Quaternions 2()  Quaternions 3()  \n",
              "0         -0.09711          0.59232          0.13531          0.78824  \n",
              "1         -0.09711          0.59232          0.13531          0.78824  \n",
              "2         -0.09711          0.59232          0.13531          0.78824  \n",
              "3         -0.09711          0.59232          0.13531          0.78824  \n",
              "4         -0.09711          0.59232          0.13531          0.78824  "
            ]
          },
          "execution_count": 5,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "renamed = {'Device name设备名称':'Device name','Acceleration X(g)':'acc_x','Acceleration Y(g)':'acc_y','Acceleration Z(g)':'acc_z',\n",
        "'Angular velocity X(°/s)':'gyr_x','Angular velocity Y(°/s)':'gyr_y','Angular velocity Z(°/s)':'gyr_z'}\n",
        "\n",
        "example_dataset = example_dataset.rename(columns=renamed)\n",
        "example_dataset.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {},
      "outputs": [
        {
          "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>Time</th>\n",
              "      <th>Device name</th>\n",
              "      <th>acc_x</th>\n",
              "      <th>acc_y</th>\n",
              "      <th>acc_z</th>\n",
              "      <th>gyr_x</th>\n",
              "      <th>gyr_y</th>\n",
              "      <th>gyr_z</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>12:32:51.144</td>\n",
              "      <td>WT901BLE68(d8:09:5a:ca:b6:e0)</td>\n",
              "      <td>0.974</td>\n",
              "      <td>0.103</td>\n",
              "      <td>0.263</td>\n",
              "      <td>0.549</td>\n",
              "      <td>-0.244</td>\n",
              "      <td>-0.183</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>12:32:51.146</td>\n",
              "      <td>WT901BLE68(d8:09:5a:ca:b6:e0)</td>\n",
              "      <td>0.972</td>\n",
              "      <td>0.100</td>\n",
              "      <td>0.263</td>\n",
              "      <td>0.183</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.183</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>12:32:51.146</td>\n",
              "      <td>WT901BLE68(d8:09:5a:ca:b6:e0)</td>\n",
              "      <td>0.974</td>\n",
              "      <td>0.101</td>\n",
              "      <td>0.263</td>\n",
              "      <td>0.549</td>\n",
              "      <td>-0.305</td>\n",
              "      <td>0.305</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>12:32:51.146</td>\n",
              "      <td>WT901BLE68(d8:09:5a:ca:b6:e0)</td>\n",
              "      <td>0.972</td>\n",
              "      <td>0.100</td>\n",
              "      <td>0.263</td>\n",
              "      <td>0.183</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.183</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>12:32:51.146</td>\n",
              "      <td>WT901BLE68(d8:09:5a:ca:b6:e0)</td>\n",
              "      <td>0.972</td>\n",
              "      <td>0.101</td>\n",
              "      <td>0.263</td>\n",
              "      <td>0.549</td>\n",
              "      <td>-0.305</td>\n",
              "      <td>0.305</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2067</th>\n",
              "      <td>12:33:05.448</td>\n",
              "      <td>WT901BLE68(cc:c4:7a:a1:fe:ea)</td>\n",
              "      <td>0.727</td>\n",
              "      <td>-0.306</td>\n",
              "      <td>-0.268</td>\n",
              "      <td>22.400</td>\n",
              "      <td>10.376</td>\n",
              "      <td>136.536</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2068</th>\n",
              "      <td>12:33:05.500</td>\n",
              "      <td>WT901BLE68(cc:c4:7a:a1:fe:ea)</td>\n",
              "      <td>0.768</td>\n",
              "      <td>-0.367</td>\n",
              "      <td>-0.195</td>\n",
              "      <td>19.287</td>\n",
              "      <td>-18.555</td>\n",
              "      <td>144.592</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2069</th>\n",
              "      <td>12:33:05.500</td>\n",
              "      <td>WT901BLE68(cc:c4:7a:a1:fe:ea)</td>\n",
              "      <td>0.861</td>\n",
              "      <td>-0.413</td>\n",
              "      <td>-0.140</td>\n",
              "      <td>32.959</td>\n",
              "      <td>-49.744</td>\n",
              "      <td>156.067</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2070</th>\n",
              "      <td>12:33:05.526</td>\n",
              "      <td>WT901BLE68(cc:c4:7a:a1:fe:ea)</td>\n",
              "      <td>1.095</td>\n",
              "      <td>-0.373</td>\n",
              "      <td>-0.140</td>\n",
              "      <td>54.077</td>\n",
              "      <td>-95.032</td>\n",
              "      <td>152.222</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2071</th>\n",
              "      <td>12:33:05.526</td>\n",
              "      <td>WT901BLE68(cc:c4:7a:a1:fe:ea)</td>\n",
              "      <td>1.193</td>\n",
              "      <td>-0.201</td>\n",
              "      <td>-0.253</td>\n",
              "      <td>64.880</td>\n",
              "      <td>-118.225</td>\n",
              "      <td>153.381</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2072 rows × 8 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "               Time                    Device name  acc_x  acc_y  acc_z  \\\n",
              "0      12:32:51.144  WT901BLE68(d8:09:5a:ca:b6:e0)  0.974  0.103  0.263   \n",
              "1      12:32:51.146  WT901BLE68(d8:09:5a:ca:b6:e0)  0.972  0.100  0.263   \n",
              "2      12:32:51.146  WT901BLE68(d8:09:5a:ca:b6:e0)  0.974  0.101  0.263   \n",
              "3      12:32:51.146  WT901BLE68(d8:09:5a:ca:b6:e0)  0.972  0.100  0.263   \n",
              "4      12:32:51.146  WT901BLE68(d8:09:5a:ca:b6:e0)  0.972  0.101  0.263   \n",
              "...             ...                            ...    ...    ...    ...   \n",
              "2067   12:33:05.448  WT901BLE68(cc:c4:7a:a1:fe:ea)  0.727 -0.306 -0.268   \n",
              "2068   12:33:05.500  WT901BLE68(cc:c4:7a:a1:fe:ea)  0.768 -0.367 -0.195   \n",
              "2069   12:33:05.500  WT901BLE68(cc:c4:7a:a1:fe:ea)  0.861 -0.413 -0.140   \n",
              "2070   12:33:05.526  WT901BLE68(cc:c4:7a:a1:fe:ea)  1.095 -0.373 -0.140   \n",
              "2071   12:33:05.526  WT901BLE68(cc:c4:7a:a1:fe:ea)  1.193 -0.201 -0.253   \n",
              "\n",
              "       gyr_x    gyr_y    gyr_z  \n",
              "0      0.549   -0.244   -0.183  \n",
              "1      0.183    0.000    0.183  \n",
              "2      0.549   -0.305    0.305  \n",
              "3      0.183    0.000    0.183  \n",
              "4      0.549   -0.305    0.305  \n",
              "...      ...      ...      ...  \n",
              "2067  22.400   10.376  136.536  \n",
              "2068  19.287  -18.555  144.592  \n",
              "2069  32.959  -49.744  156.067  \n",
              "2070  54.077  -95.032  152.222  \n",
              "2071  64.880 -118.225  153.381  \n",
              "\n",
              "[2072 rows x 8 columns]"
            ]
          },
          "execution_count": 6,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "example_dataset.drop(['Angle X(°)','Angle Y(°)','Angle Z(°)','Magnetic field X(ʯt)','Magnetic field Y(ʯt)','Magnetic field Z(ʯt)','Temperature(℃)','Quaternions 0()','Quaternions 1()','Quaternions 2()','Quaternions 3()'],axis=1,inplace=True)\n",
        "example_dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Index(['Time', 'Device name', 'acc_x', 'acc_y', 'acc_z', 'gyr_x', 'gyr_y',\n",
              "       'gyr_z'],\n",
              "      dtype='object')"
            ]
          },
          "execution_count": 7,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "example_dataset.columns"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "{'WT901BLE68(d8:09:5a:ca:b6:e0)':                Time  acc_x  acc_y  acc_z    gyr_x    gyr_y    gyr_z\n",
              " 0      12:32:51.144  0.974  0.103  0.263    0.549   -0.244   -0.183\n",
              " 1      12:32:51.146  0.972  0.100  0.263    0.183    0.000    0.183\n",
              " 2      12:32:51.146  0.974  0.101  0.263    0.549   -0.305    0.305\n",
              " 3      12:32:51.146  0.972  0.100  0.263    0.183    0.000    0.183\n",
              " 4      12:32:51.146  0.972  0.101  0.263    0.549   -0.305    0.305\n",
              " ...             ...    ...    ...    ...      ...      ...      ...\n",
              " 1431   12:33:05.051  1.871 -0.520  0.540  152.771  -99.915 -152.588\n",
              " 1432   12:33:05.051  1.794 -0.900  0.775  142.212  -97.351 -124.390\n",
              " 1433   12:33:05.051  1.635 -1.156  0.824  123.779 -112.732  -97.717\n",
              " 1434   12:33:05.051  1.794 -1.120  0.911  107.178 -118.774  -55.054\n",
              " 1435   12:33:05.051  1.635 -1.156  0.824  123.779 -112.732  -97.717\n",
              " \n",
              " [1436 rows x 7 columns],\n",
              " 'WT901BLE68(cc:c4:7a:a1:fe:ea)':               Time  acc_x  acc_y  acc_z   gyr_x    gyr_y    gyr_z\n",
              " 0     12:32:51.170  0.896 -0.439  0.075   0.732    0.732    0.000\n",
              " 1     12:32:51.170  0.896 -0.439  0.074   1.038    0.977   -0.122\n",
              " 2     12:32:51.194  0.898 -0.438  0.076   1.160    1.038    0.061\n",
              " 3     12:32:51.194  0.896 -0.438  0.074   1.160    1.160   -0.061\n",
              " 4     12:32:51.244  0.896 -0.435  0.077   1.160    1.160    0.122\n",
              " ..             ...    ...    ...    ...     ...      ...      ...\n",
              " 631   12:33:05.448  0.727 -0.306 -0.268  22.400   10.376  136.536\n",
              " 632   12:33:05.500  0.768 -0.367 -0.195  19.287  -18.555  144.592\n",
              " 633   12:33:05.500  0.861 -0.413 -0.140  32.959  -49.744  156.067\n",
              " 634   12:33:05.526  1.095 -0.373 -0.140  54.077  -95.032  152.222\n",
              " 635   12:33:05.526  1.193 -0.201 -0.253  64.880 -118.225  153.381\n",
              " \n",
              " [636 rows x 7 columns]}"
            ]
          },
          "execution_count": 8,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Function to convert DataFrame to dictionary of DataFrames\n",
        "def df_to_dict_of_dfs(df):\n",
        "    device_names = df['Device name'].unique()\n",
        "    dfs_dict = {}\n",
        "\n",
        "    for device_name in device_names:\n",
        "        device_df = df[df['Device name'] == device_name].copy()\n",
        "        device_df.drop(columns='Device name', inplace=True)\n",
        "        dfs_dict[device_name] = device_df.reset_index(drop=True)\n",
        "    \n",
        "    return dfs_dict\n",
        "\n",
        "# Convert DataFrame to dictionary of DataFrames\n",
        "dfs_dict = df_to_dict_of_dfs(example_dataset)\n",
        "dfs_dict"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Device Name: WT901BLE68(d8:09:5a:ca:b6:e0)\n",
            "               Time  acc_x  acc_y  acc_z    gyr_x    gyr_y    gyr_z\n",
            "0      12:32:51.144  0.974  0.103  0.263    0.549   -0.244   -0.183\n",
            "1      12:32:51.146  0.972  0.100  0.263    0.183    0.000    0.183\n",
            "2      12:32:51.146  0.974  0.101  0.263    0.549   -0.305    0.305\n",
            "3      12:32:51.146  0.972  0.100  0.263    0.183    0.000    0.183\n",
            "4      12:32:51.146  0.972  0.101  0.263    0.549   -0.305    0.305\n",
            "...             ...    ...    ...    ...      ...      ...      ...\n",
            "1431   12:33:05.051  1.871 -0.520  0.540  152.771  -99.915 -152.588\n",
            "1432   12:33:05.051  1.794 -0.900  0.775  142.212  -97.351 -124.390\n",
            "1433   12:33:05.051  1.635 -1.156  0.824  123.779 -112.732  -97.717\n",
            "1434   12:33:05.051  1.794 -1.120  0.911  107.178 -118.774  -55.054\n",
            "1435   12:33:05.051  1.635 -1.156  0.824  123.779 -112.732  -97.717\n",
            "\n",
            "[1436 rows x 7 columns]\n",
            "\n",
            "Device Name: WT901BLE68(cc:c4:7a:a1:fe:ea)\n",
            "              Time  acc_x  acc_y  acc_z   gyr_x    gyr_y    gyr_z\n",
            "0     12:32:51.170  0.896 -0.439  0.075   0.732    0.732    0.000\n",
            "1     12:32:51.170  0.896 -0.439  0.074   1.038    0.977   -0.122\n",
            "2     12:32:51.194  0.898 -0.438  0.076   1.160    1.038    0.061\n",
            "3     12:32:51.194  0.896 -0.438  0.074   1.160    1.160   -0.061\n",
            "4     12:32:51.244  0.896 -0.435  0.077   1.160    1.160    0.122\n",
            "..             ...    ...    ...    ...     ...      ...      ...\n",
            "631   12:33:05.448  0.727 -0.306 -0.268  22.400   10.376  136.536\n",
            "632   12:33:05.500  0.768 -0.367 -0.195  19.287  -18.555  144.592\n",
            "633   12:33:05.500  0.861 -0.413 -0.140  32.959  -49.744  156.067\n",
            "634   12:33:05.526  1.095 -0.373 -0.140  54.077  -95.032  152.222\n",
            "635   12:33:05.526  1.193 -0.201 -0.253  64.880 -118.225  153.381\n",
            "\n",
            "[636 rows x 7 columns]\n",
            "\n"
          ]
        }
      ],
      "source": [
        "\n",
        "# Print the dictionary of DataFrames\n",
        "for device_name, device_df in dfs_dict.items():\n",
        "    print(f\"Device Name: {device_name}\")\n",
        "    print(device_df)\n",
        "    print()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "dict_keys(['WT901BLE68(d8:09:5a:ca:b6:e0)', 'WT901BLE68(cc:c4:7a:a1:fe:ea)'])"
            ]
          },
          "execution_count": 10,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from gaitmap.utils.datatype_helper import get_multi_sensor_names\n",
        "get_multi_sensor_names(dfs_dict)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "'multi'"
            ]
          },
          "execution_count": 11,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from gaitmap.utils.datatype_helper import is_sensor_data\n",
        "\n",
        "is_sensor_data(dfs_dict, frame=\"sensor\")\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "'single'"
            ]
          },
          "execution_count": 12,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "is_sensor_data(dfs_dict['WT901BLE68(d8:09:5a:ca:b6:e0)'], frame=\"sensor\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Preprocessing\n",
        "Fix the alignment between the sensor coordinate system and the gaitmap coordinate system.\n",
        "This will be different for each sensor position and recording.\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "collapsed": false
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "{'WT901BLE68(d8:09:5a:ca:b6:e0)':                Time  acc_x  acc_y  acc_z    gyr_x    gyr_y    gyr_z\n",
              " 0      12:32:51.144  0.103  0.263  0.974   -0.244   -0.183    0.549\n",
              " 1      12:32:51.146  0.100  0.263  0.972    0.000    0.183    0.183\n",
              " 2      12:32:51.146  0.101  0.263  0.974   -0.305    0.305    0.549\n",
              " 3      12:32:51.146  0.100  0.263  0.972    0.000    0.183    0.183\n",
              " 4      12:32:51.146  0.101  0.263  0.972   -0.305    0.305    0.549\n",
              " ...             ...    ...    ...    ...      ...      ...      ...\n",
              " 1431   12:33:05.051 -0.520  0.540  1.871  -99.915 -152.588  152.771\n",
              " 1432   12:33:05.051 -0.900  0.775  1.794  -97.351 -124.390  142.212\n",
              " 1433   12:33:05.051 -1.156  0.824  1.635 -112.732  -97.717  123.779\n",
              " 1434   12:33:05.051 -1.120  0.911  1.794 -118.774  -55.054  107.178\n",
              " 1435   12:33:05.051 -1.156  0.824  1.635 -112.732  -97.717  123.779\n",
              " \n",
              " [1436 rows x 7 columns],\n",
              " 'WT901BLE68(cc:c4:7a:a1:fe:ea)':               Time  acc_x  acc_y  acc_z    gyr_x    gyr_y   gyr_z\n",
              " 0     12:32:51.170  0.439 -0.075  0.896   -0.732    0.000   0.732\n",
              " 1     12:32:51.170  0.439 -0.074  0.896   -0.977    0.122   1.038\n",
              " 2     12:32:51.194  0.438 -0.076  0.898   -1.038   -0.061   1.160\n",
              " 3     12:32:51.194  0.438 -0.074  0.896   -1.160    0.061   1.160\n",
              " 4     12:32:51.244  0.435 -0.077  0.896   -1.160   -0.122   1.160\n",
              " ..             ...    ...    ...    ...      ...      ...     ...\n",
              " 631   12:33:05.448  0.306  0.268  0.727  -10.376 -136.536  22.400\n",
              " 632   12:33:05.500  0.367  0.195  0.768   18.555 -144.592  19.287\n",
              " 633   12:33:05.500  0.413  0.140  0.861   49.744 -156.067  32.959\n",
              " 634   12:33:05.526  0.373  0.140  1.095   95.032 -152.222  54.077\n",
              " 635   12:33:05.526  0.201  0.253  1.193  118.225 -153.381  64.880\n",
              " \n",
              " [636 rows x 7 columns]}"
            ]
          },
          "execution_count": 13,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from scipy.spatial.transform import Rotation\n",
        "\n",
        "# For multiple sensors, we write down the rotation matrices for each sensor into a dict\n",
        "rotation_matrices = {\n",
        "      'WT901BLE68(d8:09:5a:ca:b6:e0)': Rotation.from_matrix(np.array([[ 0, 1,  0], [ 0,  0,  1], [1,  0,  0]])),\n",
        "      'WT901BLE68(cc:c4:7a:a1:fe:ea)': Rotation.from_matrix(np.array([[ 0,  -1,  0], [ 0,  0, -1], [1,  0,  0]]))\n",
        "}\n",
        "from gaitmap.utils.rotations import rotate_dataset\n",
        "\n",
        "# We assume `data` has two sensors with the same names as in the dict above\n",
        "data = rotate_dataset(dfs_dict, rotation_matrices)\n",
        "data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Stride Segmentation\n",
        "In this step the continuous datastream is segmented into individual strides.\n",
        "For longer datasets it might be required to first identify segments of walking to reduce the chance of\n",
        "false-positives.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "from gaitmap.stride_segmentation import BarthDtw\n",
        "from gaitmap.utils.coordinate_conversion import convert_to_fbf\n",
        "\n",
        "dtw = BarthDtw()\n",
        "# Convert data to foot-frame\n",
        "bf_data = convert_to_fbf(data, left_like=\"WT901BLE68(d8:09:5a:ca:b6:e0)\", right_like=\"WT901BLE68(cc:c4:7a:a1:fe:ea)\")\n",
        "dtw = dtw.segment(data=bf_data, sampling_rate_hz=sampling_rate_hz)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "{'WT901BLE68(d8:09:5a:ca:b6:e0)':       start   end\n",
              " s_id             \n",
              " 0        29    63\n",
              " 1        69   102\n",
              " 2       108   170\n",
              " 3       170   222\n",
              " 4       285   397\n",
              " 5       397   510\n",
              " 6       510   622\n",
              " 7       622   734\n",
              " 8       734   845\n",
              " 9       845   958\n",
              " 10      958  1070\n",
              " 11     1070  1177\n",
              " 12     1177  1282\n",
              " 13     1282  1329\n",
              " 14     1394  1434,\n",
              " 'WT901BLE68(cc:c4:7a:a1:fe:ea)':       start  end\n",
              " s_id            \n",
              " 0        26   88\n",
              " 1        88  145\n",
              " 2       145  200\n",
              " 3       200  240\n",
              " 4       240  276\n",
              " 5       303  343\n",
              " 6       401  457\n",
              " 7       457  508\n",
              " 8       508  562\n",
              " 9       562  621}"
            ]
          },
          "execution_count": 15,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "segmented_strides = dtw.stride_list_\n",
        "segmented_strides"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
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              "         [  47, 1434]])],\n",
              " 'WT901BLE68(cc:c4:7a:a1:fe:ea)': [array([[ 0, 29],\n",
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              "         [ 46, 240],\n",
              "         [ 47, 240]]),\n",
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              "         [ 46, 273],\n",
              "         [ 47, 273]]),\n",
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              "         [ 45, 343],\n",
              "         [ 46, 343],\n",
              "         [ 47, 343]]),\n",
              "  array([[  0, 401],\n",
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              "         [  2, 404],\n",
              "         [  2, 405],\n",
              "         [  3, 406],\n",
              "         [  4, 407],\n",
              "         [  4, 408],\n",
              "         [  4, 409],\n",
              "         [  4, 410],\n",
              "         [  4, 411],\n",
              "         [  5, 412],\n",
              "         [  6, 413],\n",
              "         [  7, 414],\n",
              "         [  8, 415],\n",
              "         [  9, 416],\n",
              "         [ 10, 416],\n",
              "         [ 11, 417],\n",
              "         [ 12, 418],\n",
              "         [ 13, 419],\n",
              "         [ 14, 420],\n",
              "         [ 15, 421],\n",
              "         [ 16, 422],\n",
              "         [ 17, 423],\n",
              "         [ 18, 424],\n",
              "         [ 19, 425],\n",
              "         [ 20, 426],\n",
              "         [ 21, 427],\n",
              "         [ 22, 428],\n",
              "         [ 23, 429],\n",
              "         [ 24, 430],\n",
              "         [ 24, 431],\n",
              "         [ 25, 432],\n",
              "         [ 26, 433],\n",
              "         [ 27, 434],\n",
              "         [ 28, 435],\n",
              "         [ 29, 436],\n",
              "         [ 30, 437],\n",
              "         [ 31, 438],\n",
              "         [ 32, 439],\n",
              "         [ 33, 440],\n",
              "         [ 34, 441],\n",
              "         [ 34, 442],\n",
              "         [ 34, 443],\n",
              "         [ 35, 444],\n",
              "         [ 36, 445],\n",
              "         [ 37, 446],\n",
              "         [ 38, 447],\n",
              "         [ 39, 448],\n",
              "         [ 40, 449],\n",
              "         [ 41, 450],\n",
              "         [ 41, 451],\n",
              "         [ 41, 452],\n",
              "         [ 41, 453],\n",
              "         [ 42, 454],\n",
              "         [ 43, 455],\n",
              "         [ 44, 456],\n",
              "         [ 45, 456],\n",
              "         [ 46, 456],\n",
              "         [ 47, 456]]),\n",
              "  array([[  0, 457],\n",
              "         [  1, 458],\n",
              "         [  2, 459],\n",
              "         [  3, 460],\n",
              "         [  3, 461],\n",
              "         [  4, 462],\n",
              "         [  4, 463],\n",
              "         [  4, 464],\n",
              "         [  5, 465],\n",
              "         [  6, 466],\n",
              "         [  7, 466],\n",
              "         [  8, 467],\n",
              "         [  9, 468],\n",
              "         [ 10, 469],\n",
              "         [ 11, 470],\n",
              "         [ 12, 470],\n",
              "         [ 13, 471],\n",
              "         [ 14, 472],\n",
              "         [ 15, 473],\n",
              "         [ 16, 474],\n",
              "         [ 17, 475],\n",
              "         [ 17, 476],\n",
              "         [ 18, 477],\n",
              "         [ 19, 478],\n",
              "         [ 20, 478],\n",
              "         [ 21, 479],\n",
              "         [ 22, 480],\n",
              "         [ 22, 481],\n",
              "         [ 23, 482],\n",
              "         [ 23, 483],\n",
              "         [ 24, 484],\n",
              "         [ 25, 485],\n",
              "         [ 26, 486],\n",
              "         [ 27, 487],\n",
              "         [ 28, 488],\n",
              "         [ 29, 489],\n",
              "         [ 30, 490],\n",
              "         [ 31, 491],\n",
              "         [ 32, 492],\n",
              "         [ 33, 493],\n",
              "         [ 34, 494],\n",
              "         [ 35, 495],\n",
              "         [ 36, 496],\n",
              "         [ 37, 497],\n",
              "         [ 38, 498],\n",
              "         [ 38, 499],\n",
              "         [ 39, 500],\n",
              "         [ 39, 501],\n",
              "         [ 39, 502],\n",
              "         [ 40, 503],\n",
              "         [ 41, 504],\n",
              "         [ 42, 505],\n",
              "         [ 43, 505],\n",
              "         [ 44, 506],\n",
              "         [ 45, 507],\n",
              "         [ 46, 508],\n",
              "         [ 47, 508]]),\n",
              "  array([[  0, 508],\n",
              "         [  1, 509],\n",
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              "         [  3, 511],\n",
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              "         [ 10, 522],\n",
              "         [ 11, 523],\n",
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              "         [ 13, 525],\n",
              "         [ 14, 526],\n",
              "         [ 15, 527],\n",
              "         [ 16, 528],\n",
              "         [ 17, 529],\n",
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              "         [ 19, 532],\n",
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              "         [ 22, 535],\n",
              "         [ 23, 536],\n",
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              "         [ 24, 546],\n",
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              "         [ 25, 548],\n",
              "         [ 26, 548],\n",
              "         [ 27, 549],\n",
              "         [ 28, 550],\n",
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              "         [ 31, 552],\n",
              "         [ 32, 552],\n",
              "         [ 33, 552],\n",
              "         [ 34, 552],\n",
              "         [ 35, 552],\n",
              "         [ 36, 552],\n",
              "         [ 37, 553],\n",
              "         [ 38, 554],\n",
              "         [ 39, 555],\n",
              "         [ 39, 556],\n",
              "         [ 40, 557],\n",
              "         [ 41, 558],\n",
              "         [ 42, 559],\n",
              "         [ 43, 560],\n",
              "         [ 44, 560],\n",
              "         [ 45, 561],\n",
              "         [ 46, 562],\n",
              "         [ 47, 562]]),\n",
              "  array([[  0, 562],\n",
              "         [  1, 563],\n",
              "         [  2, 564],\n",
              "         [  3, 565],\n",
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              "         [ 12, 578],\n",
              "         [ 13, 579],\n",
              "         [ 14, 580],\n",
              "         [ 15, 581],\n",
              "         [ 16, 582],\n",
              "         [ 17, 583],\n",
              "         [ 17, 584],\n",
              "         [ 18, 585],\n",
              "         [ 18, 586],\n",
              "         [ 18, 587],\n",
              "         [ 19, 588],\n",
              "         [ 20, 589],\n",
              "         [ 21, 589],\n",
              "         [ 22, 590],\n",
              "         [ 23, 591],\n",
              "         [ 24, 592],\n",
              "         [ 24, 593],\n",
              "         [ 24, 594],\n",
              "         [ 25, 595],\n",
              "         [ 25, 596],\n",
              "         [ 25, 597],\n",
              "         [ 26, 598],\n",
              "         [ 26, 599],\n",
              "         [ 26, 600],\n",
              "         [ 26, 601],\n",
              "         [ 26, 602],\n",
              "         [ 26, 603],\n",
              "         [ 26, 604],\n",
              "         [ 26, 605],\n",
              "         [ 27, 606],\n",
              "         [ 28, 607],\n",
              "         [ 29, 607],\n",
              "         [ 30, 607],\n",
              "         [ 31, 607],\n",
              "         [ 32, 607],\n",
              "         [ 33, 607],\n",
              "         [ 34, 608],\n",
              "         [ 35, 609],\n",
              "         [ 36, 610],\n",
              "         [ 36, 611],\n",
              "         [ 36, 612],\n",
              "         [ 37, 613],\n",
              "         [ 38, 614],\n",
              "         [ 39, 615],\n",
              "         [ 39, 616],\n",
              "         [ 39, 617],\n",
              "         [ 40, 618],\n",
              "         [ 41, 619],\n",
              "         [ 42, 620],\n",
              "         [ 43, 621],\n",
              "         [ 44, 621],\n",
              "         [ 45, 621],\n",
              "         [ 46, 621],\n",
              "         [ 47, 621]])]}"
            ]
          },
          "execution_count": 16,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "dtw_warping_path = dtw.paths_\n",
        "dtw_warping_path"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "{'WT901BLE68(d8:09:5a:ca:b6:e0)':       acc_pa  acc_ml  acc_si   gyr_pa   gyr_ml   gyr_si\n",
              " 0      0.103   0.263  -0.974    0.244    0.183   -0.549\n",
              " 1      0.100   0.263  -0.972   -0.000   -0.183   -0.183\n",
              " 2      0.101   0.263  -0.974    0.305   -0.305   -0.549\n",
              " 3      0.100   0.263  -0.972   -0.000   -0.183   -0.183\n",
              " 4      0.101   0.263  -0.972    0.305   -0.305   -0.549\n",
              " ...      ...     ...     ...      ...      ...      ...\n",
              " 1431  -0.520   0.540  -1.871   99.915  152.588 -152.771\n",
              " 1432  -0.900   0.775  -1.794   97.351  124.390 -142.212\n",
              " 1433  -1.156   0.824  -1.635  112.732   97.717 -123.779\n",
              " 1434  -1.120   0.911  -1.794  118.774   55.054 -107.178\n",
              " 1435  -1.156   0.824  -1.635  112.732   97.717 -123.779\n",
              " \n",
              " [1436 rows x 6 columns],\n",
              " 'WT901BLE68(cc:c4:7a:a1:fe:ea)':      acc_pa  acc_ml  acc_si   gyr_pa   gyr_ml  gyr_si\n",
              " 0     0.439   0.075  -0.896   -0.732   -0.000   0.732\n",
              " 1     0.439   0.074  -0.896   -0.977   -0.122   1.038\n",
              " 2     0.438   0.076  -0.898   -1.038    0.061   1.160\n",
              " 3     0.438   0.074  -0.896   -1.160   -0.061   1.160\n",
              " 4     0.435   0.077  -0.896   -1.160    0.122   1.160\n",
              " ..      ...     ...     ...      ...      ...     ...\n",
              " 631   0.306  -0.268  -0.727  -10.376  136.536  22.400\n",
              " 632   0.367  -0.195  -0.768   18.555  144.592  19.287\n",
              " 633   0.413  -0.140  -0.861   49.744  156.067  32.959\n",
              " 634   0.373  -0.140  -1.095   95.032  152.222  54.077\n",
              " 635   0.201  -0.253  -1.193  118.225  153.381  64.880\n",
              " \n",
              " [636 rows x 6 columns]}"
            ]
          },
          "execution_count": 17,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "bf_data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "BarthDtw(conflict_resolution=True, find_matches_method='find_peaks', max_cost=4.0, max_match_length_s=3.0, max_signal_stretch_ms=None, max_template_stretch_ms=None, memory=None, min_match_length_s=0.6, resample_template=True, snap_to_min_axis='gyr_ml', snap_to_min_win_ms=300, template=BarthOriginalTemplate(scaling=FixedScaler(offset=0, scale=500.0), use_cols=None))"
            ]
          },
          "execution_count": 18,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "dtw"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Event detection\n",
        "For each identified stride, we now identify important stride events.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "from gaitmap.event_detection import RamppEventDetection\n",
        "\n",
        "ed = RamppEventDetection()\n",
        "ed = ed.detect(data=bf_data, stride_list=dtw.stride_list_, sampling_rate_hz=sampling_rate_hz)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Trajectory Reconstruction\n",
        "Using the identified events the trajectory of each stride is reconstructed using double integration starting from the\n",
        "`min_vel` event of each stride.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "from gaitmap.trajectory_reconstruction import StrideLevelTrajectory\n",
        "\n",
        "trajectory = StrideLevelTrajectory()\n",
        "trajectory = trajectory.estimate(\n",
        "    data=data, stride_event_list=ed.min_vel_event_list_, sampling_rate_hz=sampling_rate_hz\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Temporal Parameter Calculation\n",
        "Now we have all information to calculate relevant temporal parameters (like stride time)\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "from gaitmap.parameters import TemporalParameterCalculation\n",
        "\n",
        "temporal_paras = TemporalParameterCalculation()\n",
        "temporal_paras = temporal_paras.calculate(stride_event_list=ed.min_vel_event_list_, sampling_rate_hz=sampling_rate_hz)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Spatial Parameter Calculation\n",
        "Like the temporal parameters, we can also calculate the spatial parameter.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "from gaitmap.parameters import SpatialParameterCalculation\n",
        "\n",
        "spatial_paras = SpatialParameterCalculation()\n",
        "spatial_paras = spatial_paras.calculate(\n",
        "    stride_event_list=ed.min_vel_event_list_,\n",
        "    positions=trajectory.position_,\n",
        "    orientations=trajectory.orientation_,\n",
        "    sampling_rate_hz=sampling_rate_hz,\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Inspecting the Results\n",
        "The class of each step allows you to inspect all results in detail.\n",
        "Here we will just print and plot the most important once.\n",
        "Note, that the plots below are for sure not the best way to represent results!\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "collapsed": false
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "The following number of strides were identified and parameterized for each sensor: {'WT901BLE68(d8:09:5a:ca:b6:e0)': 8, 'WT901BLE68(cc:c4:7a:a1:fe:ea)': 7}\n"
          ]
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "print(\n",
        "    f\"The following number of strides were identified and parameterized for each sensor: {({k: len(v) for k, v in ed.min_vel_event_list_.items()})}\"\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "collapsed": false
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "for k, v in temporal_paras.parameters_pretty_.items():\n",
        "    v.plot()\n",
        "    plt.title(f\"All temporal parameters of sensor {k}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "collapsed": false
      },
      "outputs": [
        {
          "data": {
            "image/png": 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G4P3338f06dPx5MkTdO7cWeXH8q5du2BtbY179+6hdu3a0o//jz/+GBkZGSrt5c1Xyz+kWdLv+XPnziEnJ0ftvTQxMUGDBg0K/Ztr1KiR2o+eFypt91Fel9DOnTuFELldq9WqVROjR49Wq4syDD8lJCSUqAvQw8NDABAbN26UypKSkoSLi4to2LChVJaRkaHWpRYdHS3kcrmYPn26VFZct3ZISIjw8PBQKcvrjs6TlZUl6tatK1577TW1OF80rJLX7WlmZibu3r0rlUdGRgoA4rPPPityvUII8euvvwoAYv/+/VJZXrd137591eqXto1hw4ZJZTk5OaJatWpCJpOJOXPmSOUJCQnCzMxMZVvXrFkjDAwMxIEDB1TWtWzZMgFAHDp0SCoravhpyJAhwsXFRa1L/b333hM2NjbStuzZs0cAEN7e3mrbV79+fdG1a1e1tl9kzJgxAoBK/CkpKcLLy0t4enqqfK5Qwq7e0aNHC2tra5XhjYK+/vprYWFhIa5evapSPmHCBGFoaChu374thHj+uXFwcBDx8fFSvS1btggA4n//+58QomR/U3FxccLExER07NhRZbuWLFkiAIhVq1ZJZW3atBEAxLJly164vUII8ffffwsAYs2aNUIIIWJjYwUAsW/fPpGSkiIMDQ3F33//LYQQ4vz58wKAmDlzZqFtFfd3euLECQFAfP311yrl//77rwAgLC0tpbKuXbsKb29vtTbS0tIEADFhwoRC11+S4afCbh9//LHaEE5php8Ku9WuXVuqN2rUKOmz0LlzZ/Hbb7+J+fPnC0tLS1GjRg2V4cHU1FTRu3dvIZPJpLbMzc3F5s2bVda9a9culc9RnrzPSteuXVW26auvvhIA1P6Of/rpJ2Fra6sSe0hIiMpwkhDP9w/539v58+cLANJnPr8mTZqIZs2aCSGEUCgUwtbWVrRv316lzuPHj4WFhYUAII4fPy6Vh4SEqO2Lbt26JTp27CiWLl0qtm7dKhYtWiSqV68uDAwM1IbPCvsOHT58uDA3N1cZ1sv7Xsq/H7x27ZowMDAodLgn/+v5MusQQkivdf7tvnXrljA1NRVvv/22VFavXj1hbm4uzM3Nxaeffio2btwoPv30UwFAvPfeeypt5n0e9+zZI5WV9Hv+jz/+UNvH5OnVq5dwdnZWKx82bJgwMzNTKy9OqXtqIiIi4OTkJP0yk8lk6NOnD9avX6/WLVYWZmZmMDExwd69e5GQkFBsXVdXV7z99tvSY2trawwYMACnTp2SsnK5XC794lMoFHjy5AksLS1Ru3btF/5Sf1GceRISEpCUlIRWrVq9VJtvvfUW3NzcpMdNmzZFUFAQ/vnnn0LXm5GRgcePH6NZs2YAUOi6P/roo2JjL0kbH374oXTf0NAQgYGBEEKoHFVha2uL2rVr4+bNm1LZH3/8AT8/P/j6+qr8wnzttdcAAHv27Cnm1cj91b5x40Z0794dQgiVNjp16oSkpCS1eENCQtR+rdna2uLChQu4du1asesr6J9//kHTpk2lYTsAsLS0xLBhwxATE4OLFy+Wqr28WNLS0ood+vrjjz/QqlUr2NnZqWxzhw4doFAo1LqP+/TpAzs7O+lx3gTavPeiJH9Tu3btQlZWFsaMGaPSQzJ06FBYW1ur9UDK5XIMGjSoRNscHBwMAwMDHDx4EEBuL42xsTGaNGkCS0tL1KtXT/o1lvd//te8pBo1aoSgoCDMnTsX4eHhiImJwbZt2zB8+HAYGxvj6dOnUt2nT5+qDL/kMTU1lZaX1bBhw7Bz507s3LkTGzduxMiRI7F8+fJCe9lKauPGjVKbebfw8HBped7RI87Ozvj777/Ru3dvjBs3DitXrsSNGzewbt06qa5cLketWrXw7rvv4tdff8XatWsRGBiI999/H0ePHpXq5Q1H5/9sAc8/KwUncI4ZM6bQ2N3c3NC0aVMsWrQImzZtwtixYxEREaHW4zBw4EAIIVSGnvPeh6Leq7zlBgYGGD58OHbv3o3Q0FBcu3YNJ06cQO/evaVhpPzv6erVqyGEUJlWUL16dWzfvh0fffQRunfvjtGjR0sTjD///HOVdef/jklJScHjx4/RqlUraSgoT9u2bSGEUDm1yebNm6FUKjFlyhS13sj8r+fLrCNP8+bN0bhxY5Vt7NGjB7Zv3y7tr1NTU5Geni71Cvfs2RPff/89hg8fjvXr16t8b4aFhUEIgbZt20plJf2eL+l7mZ+dnR2ePn2qNtxVnFIN8ioUCqxfvx7t2rVTOQwzKCgICxYswO7du9GxY8fSNKlGLpdj7ty5+Pzzz+Hk5IRmzZqhW7duGDBgAJydnVXq+vj4qI311qpVC0DufANnZ2colUp89913+PHHHxEdHa2SeDk4OJQ5zr/++gszZszA6dOn1eaHlFXNmjXVymrVqqUy1hgfH49p06Zh/fr1iIuLU6mbfyw6T/6jSsraRvXq1VUe29jYwNTUFFWqVFErzz8v59q1a7h06VKRRx0UXHdBjx49QmJiIlasWIEVK1aUqI3Ctnf69Ono0aMHatWqhbp166Jz58744IMPUK9evWLXf+vWrUJPWZA3ZHHr1q1Sz/sYMWIEfv/9d3Tp0gVubm7o2LEjevfurXII+LVr13D27NkSv24F35+8nVBeAlOSv6m8c5DUrl1bpS0TExN4e3urnaPEzc1NZe5CcWxtbeHv76+SuDRs2FD60g4ODlZZZmJiIg0DldbGjRvRp08fDB48GEBuEj527Fjs27cPV65ckeqZmZkVeg6MvO72l5lDU7NmTZUjtnr27Cmdu2jw4MEICAgodZutW7dW+3vLLy/e3r17q+woe/XqhQ8++ACHDx+Wfpx88sknOHr0KE6ePCnV7d27N/z9/TF69GhERkaqtC0KDEnnfRYKfl85OjqqJUCHDh1Ct27dcPToUWnY4a233oK1tTWmTZuGwYMHo06dOi/crqLeq/zv0/Tp0/H48WPMmzcPc+bMAQB07NgRQ4YMwbJlywo9euxF7O3tMWjQIMyZMwd3795FtWrVAOQevTdp0iT8999/0ryhPIV9h+Z348YNGBgYFLvdL7uOPEXtU9LT0/Ho0SM4OztLr2Hfvn1V6vXr1w/Lly/HkSNHCm0nT0m/50vzXubJ++yVZr9aqqTmv//+Q2xsLNavX1/ocfsREREvndQAuRl/9+7dsXnzZmzfvh2TJ0/G7Nmz8d9//6kcFloSs2bNwuTJkzF48GB8/fXXsLe3h4GBAcaMGVPmw7QPHDiAN998E61bt8aPP/4IFxcXGBsbIzw8XOUXUXno3bs3Dh8+jPHjx6NBgwawtLSEUqlE586dC92ewj4opW3D0NCwRGWA6hegUqlEQEAAFi5cWGhdd3f3Ircz7/kA8P777xc5H6FgYlLY9rZu3Ro3btzAli1bsGPHDvz000/49ttvsWzZMpVeqIpQtWpVnD59Gtu3b8e2bduwbds2hIeHY8CAAfjll18A5G7366+/rjK/Ib+8xD1PSd4LTf5NAaXf6bds2RLLli1DYmKiNJ8mT3BwMFatWoXs7GwcPHgQjRs3lnpMSsvNzQ0HDx7EtWvX8ODBA9SsWRPOzs5wdXVVed1cXFywZ88eCCFUvjBjY2MB5PYCa1L79u2xZMkS7N+/v0xJzYvkxVtwQq6hoSEcHBykBDcrKws///wzvvjiC5Xkx9jYGF26dMGSJUuQlZUFExMT6Uffi3rMi7N8+XI4OTmpzaN48803ERYWhsOHDxe7c8+bUBwbG6v2fREbG6uS/JqYmOCnn37CzJkzcfXqVTg5OaFWrVro168fDAwMynzakbz1xsfHo1q1akhMTESbNm1gbW2N6dOno0aNGjA1NcXJkyfx5ZdfauT0HxWxjjyurq64cOGC2menatWqAF78/pf0ez7/e1lQbGxsoX9zCQkJMDc3L9X3TamSmoiICFStWhU//PCD2rI///wTmzZtwrJly17qV06eGjVq4PPPP8fnn3+Oa9euoUGDBliwYAHWrl0r1bl+/bral1LeOSTyuhU3bNiAdu3a4eeff1ZpPzExUeWXT2kywY0bN8LU1BTbt29X6UrL3x1cFoUNj1y9elXaloSEBOzevRvTpk3DlClTin1eUTTRRknVqFEDZ86cQfv27V/4+ha23NHREVZWVlAoFCq/fMsi7xfXoEGDkJqaitatWyMsLKzYpMbDw0Pl132evK7fsp6MzMTEBN27d0f37t2hVCoxYsQILF++HJMnT4aPjw9q1KiB1NTUl97mgor7m8rblitXrsDb21t6TlZWFqKjo186lpYtW2Lp0qXYtWsXTp06hfHjx0vLgoOD8fTpU/z999+4efMm3nnnnZdaF5D7CzXv1+XFixcRGxurMqzRoEED/PTTT7h06ZLKTjWvl6LguapeVt6J4gqeZExT8oYYCk58zcrKwuPHj6Vf0U+ePEFOTk6hUwWys7OhVCqlZXmTeAueHDHvs3Lt2jWVz8qjR4/UdoAPHz4scl0ACj2BXn5578Px48dVEpj79+/j7t27GDZsmNpznJycpB20QqHA3r17ERQUVKaeGuD5MG7ea7h37148efIEf/75p8rBJAVfp6LUqFEDSqUSFy9eLPJz9rLryFPUPsXc3FzansaNG2Pnzp3SROE8eQfSvOj8PiX9nq9bty6MjIxw/Phx9O7dWyrPysrC6dOnVcryREdHq03mf5ESz6l5+vQp/vzzT3Tr1g3vvvuu2u2TTz5BSkoKtm7dWqoACkpPT1ebcV2jRg1YWVmpdVvdv39fZaZ8cnIy/u///g8NGjSQutUNDQ3Vuk//+OMPtT9+CwsLAFA7fLUwhoaGkMlkKn+sMTExL31G3M2bN6vEdezYMURGRqJLly7SegH17uDSnJJfE22UVO/evXHv3j2sXLlSbdnTp09VjpSwsLBQe+0NDQ3xzjvvYOPGjWqHtgO5X6IlUfBQdUtLS/j4+LzwFNxvvPEGjh07pnJ4b1paGlasWAFPT88Xdh+XJBYDAwOptykvnt69e+PIkSNqJ4sDcj+fL9oRFFSSv6kOHTrAxMQE33//vcpn4+eff0ZSUhK6du1aqnUWlDdHZuHChcjOzlbpqfH09ISLiwvmzZunUlcTlEolvvjiC5ibm6vML+vRoweMjY3x448/SmVCCCxbtgxubm4q8WnC//73PwC5hxiXh7Zt26Jq1aqIiIhQea9Xr14NhUKB119/HUDur29bW1ts2rRJ5ZDl1NRU/O9//4Ovr6/0o9TNzQ3u7u44fvy4yro6dOgAY2NjLF68WOWzUth3SK1atfDw4UPs3btXpTzvzM35ewkLO6Tb398fvr6+WLFihcr37dKlSyGTyfDuu+8W+7p88803iI2NVZsTU9gh3YV9n9y7dw+rVq1CvXr1pJ6Gwr5Ds7KyVD5LeQo73Pqtt96CgYEBpk+frtbjktfmy64jz5EjR1TmHd65cwdbtmxBx44dpXXkJRMFf/j/9NNPMDIyUpk/U9gh3SX9nrexsUGHDh2wdu1apKSkSHXWrFmD1NRU9OrVS+35J0+eLPXfYol7arZu3YqUlBS8+eabhS5v1qwZHB0dERERoXIYV2ldvXoV7du3R+/evVGnTh0YGRlh06ZNePjwodr5J2rVqoUhQ4YgKioKTk5OWLVqFR4+fKjSY9KtWzdMnz4dgwYNQnBwMM6dO4eIiAiVXxhA7pe8ra0tli1bBisrK1hYWCAoKKjQORpdu3bFwoUL0blzZ/Tr1w9xcXH44Ycf4OPjg7Nnz5Z52318fNCyZUt8/PHHyMzMxKJFi+Dg4CANQ1hbW6N169aYN28esrOz4ebmhh07dpQqe9dEGyX1wQcf4Pfff8dHH32EPXv2oEWLFlAoFLh8+TJ+//13bN++XeqWbty4MXbt2oWFCxfC1dUVXl5eCAoKwpw5c7Bnzx4EBQVh6NChqFOnDuLj43Hy5Ens2rUL8fHxL4yjTp06aNu2rXTeg+PHj2PDhg0vvC7KhAkT8Ouvv6JLly4YNWoU7O3t8csvvyA6OhobN24s9SHHQO6k6/j4eLz22muoVq0abt26hcWLF6NBgwbSL5Lx48dj69at6NatGwYOHIjGjRsjLS0N586dw4YNGxATE1Ps/IqCSvI35ejoiNDQUEybNg2dO3fGm2++iStXruDHH39EkyZN8P7775d6W/OrXr063N3dceTIEXh6eqp1NQcHB2Pjxo2QyWTSWXHLYvTo0cjIyECDBg2QnZ2NdevW4dixY/jll19U5h5Vq1YNY8aMwfz585GdnY0mTZpg8+bNOHDgACIiIlSG9G7duiUdpp+3g58xYwaA3F6LDz74QCWGkydPSj3KKSkp2L17NzZu3Ijg4GC14flHjx5JbeXn5eWF/v37S483bNhQaE/D66+/Lp2/af78+QgJCUHr1q3xwQcf4Pbt2/juu+/QqlUr9OzZE0DuznLcuHGYNGkSmjVrhgEDBkChUODnn3/G3bt3VXrCgdzkb9OmTSo94o6Ojhg3bhxmz56Nbt264Y033sCpU6ewbds2tc/lJ598gvDwcHTv3h2ffvopPDw8sG/fPvz66694/fXXVeas5Z1yIDw8XKVXbf78+XjzzTfRsWNHvPfeezh//jyWLFmCDz/8UOVX/Nq1a7Fx40a0bt0alpaW2LVrF37//Xd8+OGHar1/oaGh0t9yXk/4F198gRs3bqB9+/ZwdXVFTEwMli9fjrS0NJXzSAUHB8POzg4hISEYNWoUZDIZ1qxZU+jpMI4dO4Z27dph6tSp0kReHx8fTJw4EV9//bX03sjlckRFRcHV1RWzZ89+6XXkqVu3Ljp16oRRo0ZBLpdLSdG0adOkOg0bNsTgwYOxatUq5OTkoE2bNti7dy/++OMPhIaGqvytLlmyBNOmTcOePXukZKc03/MzZ85EcHAw2rRpg2HDhuHu3btYsGABOnbsqHZpmRMnTiA+Ph49evRQ2+ZilfQwqe7duwtTU9MizxwqhBADBw4UxsbG0uG3KMMh3Y8fPxYjR44Uvr6+wsLCQtjY2IigoCDx+++/q9Tz8PAQXbt2Fdu3bxf16tUTcrlc+Pr6ij/++EOlXkZGhvj888+Fi4uLMDMzEy1atBBHjhwRbdq0EW3atFGpu2XLFlGnTh1hZGSkcmhhYYd0//zzz6JmzZrSesPDw6XD3QrGWdJDuufPny8WLFgg3N3dhVwuF61atVI7q+rdu3fF22+/LWxtbYWNjY3o1auXuH//vtprXdyZSF+2jZCQEGFhYaHWbmGHqGZlZYm5c+cKf39/IZfLhZ2dnWjcuLGYNm2aylklL1++LFq3bi3MzMzUDgt9+PChGDlypHB3dxfGxsbC2dlZtG/fXqxYsUKqk3dYY8H3XwghZsyYIZo2bSpsbW2FmZmZ8PX1FTNnzhRZWVlqdQu6ceOGePfdd4Wtra0wNTUVTZs2VTu8U4iSH9K9YcMG0bFjR1G1alVhYmIiqlevLoYPHy5iY2NV6qWkpIjQ0FDh4+MjTExMRJUqVURwcLD45ptvpLiLOwNq/veypH9TQuQewu3r6yuMjY2Fk5OT+Pjjj0VCQoJKnZIcilyYvn37CgCiX79+assWLlwoAAg/P79i23jRmb/Dw8NF/fr1hYWFhbCyshLt27cX//33X6F1FQqFmDVrlvDw8BAmJibC399frF27Vq1e3mersFv+75DCDuk2MjIS3t7eYvz48SIlJUWl3bxD4wu75R2aXNwh3ShwaK0QuadmqF+/vpDL5cLJyUl88sknIjk5WW2bIiIiVP4mgoKCxIYNG9TqnTx5Uu20Bnmv3bRp06Tv1bZt24rz588X+n13+fJl8e6770p/vx4eHmLcuHFq+5LCDunOs2nTJtGgQQMhl8tFtWrVxKRJk9T+fiMjI0Xr1q2FnZ2dMDU1FfXr1xfLli0r9GzIhR3SvW7dOtG6dWvh6OgojIyMRJUqVcTbb78tTpw4ofb8Q4cOiWbNmgkzMzPh6uoqvvjiC7F9+3a196Sow62FEGLVqlWiYcOG0vdimzZtpFOlaGIded9Ja9eulfZVDRs2VPvMCJH7PR0WFiY8PDyEsbGx8PHxEd9++61avcIO6c57fkm+54UQ4sCBAyI4OFiYmpoKR0dHMXLkyEI/o19++WWpzmadR/Zs4185np6eqFu3Lv766y9th/LSYmJi4OXlhfnz5/PaQUSkU/J6Loo7qSSRJmVmZsLT0xMTJkzA6NGjS/XcMl+lm4iI9N+sWbPw22+/qR3WT1RewsPDYWxsXOh51l6ESQ0RERUpKCgIWVlZZT7aj6i0PvroI9y+fbvQE/W9CJMaIiIi0guv7JwaIiIiovzYU0NERER6gUkNERER6YVSXSahPCmVSty/fx9WVlYvdVFIIiIiqjhCCKSkpMDV1bVMJyXVJJ1Jau7fv//CCxwSERGRbrpz5450JXNt0ZmkxsrKCkDui2Jtba3laIiIiKgkkpOT4e7uLu3HtUlnkpq8ISdra2smNURERK8YXZg6wonCREREpBeY1BAREZFeYFJDREREeoFJDREREekFJjVERESkF5jUEBERkV5gUkNERER6gUkNERER6QUmNURERKQXmNQQERGRXmBSQ0RERHqBSQ0RERHpBSY1RHpOCIGMnAxth0FEVO505irdRKQ5T3OeIupBFA7cPYAD9w7gXuo9tHBrgdCmofCw9tB2eERE5UImhBDaDgIAkpOTYWNjg6SkJFhbW2s7HKJXzq3kWzhw9wAO3juIqAdRyFJmqdUxNjDGkIAhGFJ3CEyNTLUQJRHpG13afzOpIXpFZeRkIOpBFA7eO4gD9w7gTsodleUuFi5o6dYSrdxawcXSBd+e+BaH7x8GALhZuuGroK/QulprbYRORHpEl/bfTGqIXiG3k2/jwL3nvTGZikxpmZGBERpXbZybyFRrBW8bb8hkMmm5EAI7bu3AvKh5iEuPAwC0c2+HCU0nwNXStcK3hYj0gy7tv5nUEOmwTEUmjj84LiUyt5JvqSx3tnBGS7eWaOnWEs1cmsHC2OKFbaZlp2HZmWVYe3EtckQOTA1NMazeMIT4h8DE0KS8NoWI9JQu7b+Z1BDpmDspd3KHlO4eQNSDKGQonh+5ZCQzQiOnRlIi42Pro9IbUxrXE65jRuQMnHh4AgDgae2Jr4K+QnPX5hrZDiKqHHRp/82khkjLMhWZOPHghNQbE5Mco7K8qnlVtHJrhVZurRDkEgRLE0uNrVsIgb9u/oUFxxfgScYTAEBnz84YFzgOThZOGlsPEekvXdp/M6kh0oJ7qfdw8G7uBN9jD47hac5TaZmhzBANqzaU5sbUtK1Z5t6YkkrOSsYPp37A+ivroRRKmBuZY0SDEejn1w/GBsblum4ierXp0v6bSQ1RBchSZOHEwxPSkUrRSdEqyx3NHNGqWitpboyViZVW4rz05BJmHJ2Bs4/PAgBq2tXExKCJaOzUWCvxEJHu06X9N5MaonJyP/W+lMRExkaq9cbUd6yPVtVyh5Vq2dUq996YklIKJTZd24RFJxchMTMRAPBmjTcxtvFYOJg5aDc4ItI5urT/ZlJDpCHZimycjDspnQDvRtINleVVzKpIE3ybuzaHtYluf84TMxKx6OQibLy2EQBgZWKFUQ1HoVetXjA0MNRydESkK3Rp/82khuglPEh7kDvB9+5BHI09ivScdGmZgcwADRwbSIlMbfvaMJC9epdbO/PoDGYenYlL8ZcAAHUc6mBS0CQEOAZoOTIi0gW6tP9mUkNUCtnKbJyOOy1dU+l64nWV5Q6mDrlJTLWWaO7SHDZyGy1FqlkKpQK/XfkNS04tQUp2CmSQ4Z1a72BMozF6s41EVDa6tP9mUkP0Ag/THkpzY47GHkVadpq0zEBmgHpV6klHKvna+76SvTEl9fjpYyw8vhD/u/k/AICd3A6fNf4MPXx66PV2E1HRdGn/zaSGqIC83pi8ROZawjWV5fam9tKQUrBrcKXsqTj+4DhmRs6UeqoaODbAxGYT4Wvvq+XIiKii6dL+m0kNEYC49DgcvHcQB+8dxJH7R5CanSotk0GGAMcA6QR4fg5+7JVAbvK37tI6/Hj6R6TnpMNAZoC+vn0xssFIrR2STkQVT5f230xqqFLKUebgzKMz0uUIriRcUVluJ7dDC7cWUm+MnamdliLVfQ/SHuCb499ge8x2ALlHeX0e+Dm6enXVmcPUiaj86NL+m0kNVRqP0h+p9MakZKdIy2SQIaBKgDSs5F/Fn70xpXT4/mHMipwlXXSziXMTTAyaiBq2NbQcGRGVJ13afzOpIb31IO0Bzj46i3OPzyEyNlI6JDmPrdwWwa7BaFWtFYJdg2Fvaq+lSPVHliILqy+sxsqzK5GhyICRzAgf+H+Aj+p9BHNjc22HR0TlQJf230xqSC+kZ6fjwpMLOPf4XG4i8+gc4p7GqdSRQQZ/B3+0rNYSrdxawd/BnyeRKyf3Uu9hzrE52HtnLwDA2cIZXzT5Ah2qd+CQFJGe0aX9N5MaeuUohRLRSdFSL8zZR2dxPfE6FEKhUs9QZohadrVQz7Ee6jvWR7BrME/zX8H23tmLOcfm4F7qPQBAC7cW+KrpV6huXV27gRGRxujS/lvvkxohBH8ZvuLiM+Jx7tE5nH18FmcfncX5x+dVjk7K42TuhHqO9VCvSj0EOAagjkMdmBmZaSFiyu9pzlP8dO4nhJ8PR7YyGyYGJhgcMBhD6g6BqZGptsMjopfEpKYQ5fWifHXgK5x/ch5e1l7wsvGCt623dN/SxFJj6yHNyFJk4XL8ZZx7fA5nHp3BuUfncDf1rlo9MyMz1HGo8zyJqRIAJwsnLURMJXUr+RZmRc7C4fuHAQDVLKshNCgUrau11nJkRPQymNQUorxelF7/64XL8ZcLXVbVrCq8bLzgaeMJbxvv3KTHxhtVzauyd6cCCCFwN/Uuzj06Jw0jXYq/hGxltlpdbxtvBFQJyE1iHOvBx9YHRgZGWoiaXoYQAjtv7cTcqLmIS8+d89TOvR0mNJ0AV0tXLUdHRGXBpKYQ5fWiPEh7gJtJNxGdFK1ye/T0UZHPMTcylxIcLxsv6b67lTuMDY01Fltlk5KVgvOPzz+fzPv4HOIz4tXq2cntEOAYIA0j1a1SV+evaE2lk56djmVnlmHNxTXIETkwNTTF8PrDEVInhH9jpHHRSdH4/uT3qG1fG0MChsDYgJ8xTWJSU4iKflGSs5IRkxSD6KRolaTnTsodtQmneQxlhnC3cpcSnfyJD8+gqipHmYMbiTekeTDnHp3DzaSbEFD9uBkZGMHP3u95L0yVeqhmVY09ZZXE9YTrmBk5E8cfHgcAeFp7YmKziWjm0kzLkZE+EEJgw7UNmHdsHjIUGQCAelXqYU7rOXC3ctdydPqDSU0hdOVFyVZk407KHbVk52bSTaTnpBf5vCpmVVR6dvISHidzp0qxg45Lj1OZzHvhyQU8zXmqVs/N0g31quQOIQU4BsDX3hdyQ7kWIiZdIYTA39F/45uob/Ak4wkAoLNnZ4xvMh5VzatqOTp6VcVnxCPscBj23NkDIPf6ZDcSbyAlOwUWxhaYGDQR3Wt013KU+kFX9t8Ak5oSE0IgLj0O0cnRuJn4LNlJjkZ0YrTa+VDyMzMyU+vV8bL2goe1xyvbzf405ykuPbn0fDLv43N4kPZArZ6FsQXqVqn7PImpEsBDqqlIyVnJ+OHUD1h/ZT2UQglzI3OMaDAC/fz6cbiASuXQvUOYdGgSHj99DGMDY4xuNBof1PkAD9IeYMKBCTgVdwoA0NW7KyYFTeJBIy9Jl/bfTGo0IDUrFTHJMWo9O3eS7yBH5BT6HEOZIapZVcs9EsvWS+XoLF2aP6IUStxKviXNgzn76CyuJVxT2y4DmQF8bH0QUCUA9R3rI6BKALxsvHhyOyq1S08uYUbkDJx9dBYAUNOuJiYFTUIjp0Zajox0XaYiE4tOLMLaS2sB5B5gMLf1XJWrx+coc7Dy3EosO7MMSqGEm6Ub5raei/qO9bUV9itPl/bfTGrKUbby+VBWwVth51nJ42DqUOhEZScLp3K/HlFiRiLOPT6nMpk3OStZrV4VsypSD0w9x3rwd/DnafBJY5RCic3XN+PbE98iMTMRAPBmjTcxtvFY9vZRoa4mXMWX+7/E9cTrAID3ar+HsYFjizxX1am4U5iwfwLup92HocwQIxqMwJC6Q/hDrAx0af/NpEYLhBB49PSRSq9O3v2H6Q+LfJ6ZkRk8rT3VhrM8rD1gYmhS6jiyFdm4mnD1+WTex+ekixHmJzeUo45DHZXJvM4WzpVirhBpV2JGIhadXIQ/r/0JAQErEyuMajgKvWr14s6HAOQmwOsurcO3J75FljIL9qb2+LrF1yU6/1FyVjJmHJmBbTHbAACNnRpjTqs5cLZwLu+w9You7b81ltTcu3cPX375JbZt24b09HT4+PggPDwcgYGBJXq+Lr0o2pSWnYaYpBi1w9BvpdxCjrLwoSwDmQGqWVZTn7tj4wUbuQ2A3EQqNi1W5WikS/GXkKnIVGvP09pTSmACHANQy64W5zSQVp19dBYzjs6QLkpax6EOJjebjLpV6mo5MtKmR+mPMPnQZBy6fwgA0MqtFaa3mI4qZlVK3IYQAltvbMXMyJl4mvMU1ibWmBY8DR08OpRX2HpHl/bfGklqEhIS0LBhQ7Rr1w4ff/wxHB0dce3aNdSoUQM1atQoURu69KLoomxlNu6l3FPt2Xk2UTklO6XI59mb2sPdyh33Uu/h8dPHasutTaylc8LkTebNS4SIdIlCqcDvV3/H4pOLkZKdAhlkeLfWuxjdaDQ/s5XQf7f/Q9jhMCRkJkBuKMe4wHHoU7tPmXuQbyXfwpf7v8SFJxcAAO/UfAdfNPmCw+oloEv7b40kNRMmTMChQ4dw4MCBMrehSy/Kq0QIgScZT3KTncSbuYnOs8Sn4BFJRjIj1LKvpTKZ18Pag8NI9Ep5/PQxvj3xLbbe2Aog92SNnzX+DD18epT7nDPSvvTsdHxz/Bv8cfUPAEBtu9qY23ouatiW7Ad0cbIV2VhyegnCz4dDQMDT2hPzWs+Dn4PfS7etz3Rp/62RpKZOnTro1KkT7t69i3379sHNzQ0jRozA0KFDi3xOZmYmMjOfD30kJyfD3d1dJ14UfZGenY6Y5BjcTr4NJwsn+Nn78QKCpDeOPziOmZEzpYmhDRwbYFKzSahtX1vLkVF5ufDkAibsn4CY5BgAwED/gfi04adlmlNYnKOxR/HVga/w6OkjGBsYY0yjMXi/zvtMmougd0mNqWnujnLs2LHo1asXoqKiMHr0aCxbtgwhISGFPicsLAzTpk1TK9eFF4WIXg3Zymysu7QOP57+Eek56TCQGaCfbz+MaDCCZ/nWIwqlAqsvrMaSU0uQI3JQ1awqZrScgeauzcttnQkZCZhyeAr23tkLAGjh2gIzWs4o1XydykLvkhoTExMEBgbi8OHDUtmoUaMQFRWFI0eOFPoc9tQQkaY8THuI+cfnY3vMdgC5pxwYFzgOb3i9weHVV9yDtAcIPRAqXUrjdY/XMaXZFNia2pb7uoUQ+P3K75h/fD4yFZmlOrKqMtGlpEYjfWkuLi6oU6eOSpmfnx9u375d5HPkcjmsra1VbkREZeFk4YRv2nyD5a8vh6e1Jx4/fYwJBybgwx0f4mbiTW2HR2X0b/S/6Lm1J44/PA4zIzNMD56OBW0WVEhCAwAymQx9fPtgfdf1qGlXE/EZ8Ri5eyTmHptb6JGjpH0aSWpatGiBK1euqJRdvXoVHh4emmieiKhEgl2DsfHNjRjVcBRMDU1x7MExvLP1HSw8sRDp2UVfu410S2pWKiYenIjx+8cjJSsFAVUCsKH7Brxd822t9Lz52Png166/op9vPwDA2ktr0f/v/riReKPCY6HiaWT4KSoqCsHBwZg2bRp69+6NY8eOYejQoVixYgX69+9fojZ0qfuKiF5991LvYc6xOdKciCpmVRBSJwS9a/fmYbo67HTcaUw4MAH3Uu/BQGaAoQFDMbz+cJ05V9b+u/sx6eAkJGQmwNTQFOObjEevWr0q9TCnLu2/NXbyvb/++guhoaG4du0avLy8MHbs2GKPfipIl14UItIf++7sw+xjs3Ev9R4AwEZug/5+/dHPtx/Pb6NDcpQ5WHF2BVacXQGFUMDVwhWzW83WyWt+PUp/hEmHJuHw/dx5pK+5v4ZpwdMqbFhM1+jS/puXSSAivZetyMZfN//Cz+d/li4FYmFsgfdqv4cP6nzA60lp2Z2UOwg9EIozj84AyL169sSgiTp9BJtSKLHm4hosOrkIOcrcI7Jmt5qNpi5NtR1ahdOl/TeTGiKqNBRKBXbc2oGV51biWsI1AICpoSneqfUOBvoP5DV/KljeJQpmRc5Cek46LI0tManZJHT17qrt0Ers4pOL+HL/l4hJjoEMMgwJGIIRDUbozHBZRdCl/TeTGiKqdJRCiX139mHF2RU4/+Q8AMDIwAg9avTAkLpD4G7truUI9V9SZhKmH5mOHbd2AAAaVW2E2a1mw9XSVcuRlV56djrmRc3DxmsbAQB1Hepibuu5qG5dXcuRVQxd2n8zqSGiSksIgSOxR7Dy7ErpPCgGMgN08eqCD+t+CB87Hy1HqJ+iHkQh9EAoHqY/hJHMCCMajMDguoNf+Suv74jZgbAjYUjJSoG5kTkmNpuI7t7d9X4SsS7tv5nUEBEBOBV3CivOrsDBewelsvbV22NovaHwd/DXYmT6o+C1lTysPTCn1Ry9utp6bGosJhyYgJNxJwEAb3i9gUnNJun0/KCXpUv7byY1RET5XHhyAT+d/Qm7bu+Sylq4tcCwgGE6eSTOq+Jm0k1M2D8Bl+IvAdDvq2ArlAr8dO4nLD2zFAqhgJulG+a0moMGVRtoO7RyoUv7byY1RESFuJF4Az+d+wnbordBIRQAgMZOjTGs3jA0d2mu90MKmiKEwB9X/8D8qPnIUGTARm6Dac2nob1He22HVu7yn3PHUGaIj+p/hKEBQ1/5YbaCdGn/zaSGiKgYd1LuYNX5Vdh8fTNylDkAcieCDq03FG3d2/LKzcWIz4jH1ENTsffuXgBAM5dmmNlyJqqaV9VuYBUoJSsFM47OwD/R/wDInRA9p9UcuFi6aDkyzdGl/TeTGiKiEniQ9gC/XPgFG65uQIYiAwDgY+uDYfWGoaNHR7379f2yDt47iMmHJuPx08cwNjDGmEZj8H6d9ytlEiiEwF83/8KMozOQnpMOKxMrhDUPQ0fPjtoOTSN0af/NpIaIqBSePH2CtZfW4tfLvyItOw0A4GHtgSF1h6CbdzcYG1ae85MUJlORiW9PfIuISxEAgBo2NTC39VzUtq+t5ci0707yHXx54Euce3wOANCzZk982eTLV35ekS7tv5nUEBGVQVJmEn69/CvWXlqLpMwkAICzhTMG+Q9Cz5o9YWpkquUIK97VhKv4cv+XuJ54HQDQ17cvxjYeWylfi6JkK7Px4+kf8fO5nyEg4Gntibmt56KOQx1th1ZmurT/ZlJDRPQS0rPT8cfVP7D6wmo8fvoYAOBg6oAQ/9yLZ1oYW2g5wvKnFEpEXIrAtye+RbYyG/am9vi6xddoXa21tkPTWcdijyH0YCji0uNgZGCEMY3G4IM6H7ySw3O6tP9mUkNEpAGZikxsvrYZq86vwv20+wAAaxNrvO/3Pvr56e/FMwte3LFNtTaYFjyN19MqgcSMREw9PBX/3fkPANDcpTlmtpwJR3NHLUdWOrq0/2ZSQ0SkQdnKbPx982/8fO5nxCTHAADMjczRx7cPBtQZgCpmVbQboAb9d/s/TD08FYmZiZAbyjE+cDx61+7Nw91LQQiBDdc2YN6xechQZMBObocZLWe8Ur1curT/ZlJDRFQOFEoFdt7eiZVnV+JqwlUAgNxQjndqvoNBdQe90hfPTM9Ox/zj87Hh6gYAgK+9L+a2mgtvW28tR/bqupF4A1/s/0L6rPTz7YexgWMhN5RrObIX06X9N5MaIqJyJITA/rv7seLsCpx9fBZA7sUz36zxJobUHfLKXfTwwuMLmHBggnRV6oH+A/FJw09gYmii7dBeeZmKTCw6sQhrL60FANS0q4l5rebp/DXIdGn/zaSGiKgCCCEQ+SASK8+uxLEHxwDkXjyzs2dnfBjwIWra1dRyhMVTKBUIvxCOH079gByRg6rmVTGr5SwEuQRpOzS9c+DuAUw6NAnxGfGQG8oxLnAc+tTuo7PDerq0/2ZSQ0RUwU7HncbKcyux/+5+qew199cwrN4w+FfRvYtnxqbG4quDX0lXMn/d43VMbT5Vbyc/64LHTx9j0sFJOHT/EACgrXtbTA+eDjtTOy1Hpk6X9t9MaoiItOTSk0v46dxP2HlrJwRyv4qDXYMxNGAoAp0DtRxdrn+j/8X0I9ORkp0CMyMzfBX0FXrU6KGzvQb6pOCh8o5mjpjVahaauTTTdmgqdGn/zaSGiEjLbibdxM/nfsbfN/+WLp7ZqGojDKs3DMGuwVpJIFKzUjErchb+d/N/AIB6VephdqvZr9wcIH1w6cklfHngS0QnRUMGGQbVHYRPGnyiM2ev1qX9N5MaIiIdcTflLsLPh2PT9U3IVmYDAOo41MGwgGFoV71dhZ2YLf/VpQ1kBhgaMBTD6w+HsYFu7EQro4JHnPk7+GNu67nwsPbQcmS6tf9mUkNEpGPi0uPwy4Vf8MfVP/A05ymA3ItnfhjwITp5doKRgVG5rDdHmYPlZ5djxdkVUAol3CzdMLvVbDSs2rBc1kelt+vWLkw9PBXJWckwMzLDxKCJeLPGm1odDtSl/TeTGiIiHZWQkYA1F9fg18u/IjU7FQDgbuWOIXWH4M0ab2p0+OFO8h1MODgBZx/lHnbe3bs7QoNCYWVipbF1kGY8SHuA0AOh0sTtLp5dMKn5JFibaGffqUv7byY1REQ6LiUrBesvr8eai2uQkJkAAHAyd8KgurkXzzQzMitz20IIbLmxBbMjZyM9Jx1WxlaY1GwS3vB+Q1PhUzlQKBVYdX4Vfjj9AxRCAVcLV8xpPUcrvWq6tP9mUkNE9IpIz07Hhqsb8MuFXxD3NA4AYG9qjwF1BqBP7T6wNLEsVXtJmUmYfmQ6dtzaAQBo7NQYs1rOgqulq8Zjp/Jx9tFZfLH/C2n+00f1P8LQgKHlNkRZGF3afzOpISJ6xWQpsrD5eu7FM++l3gMAWJlYob9ff/T37Q9bU9sXtqFylWiZEUY2HIlB/oNgaGBYztGTpqVmpWJm5Ez8dfMvALlHzs1uNbvCklNd2n8zqSEiekXlKHOwLXobVp5bieikaACAmZEZ+tTugxD/kEIvnpmtyMbi04ux+vxqCAh4WHtgbqu5OnnSPyqd/934H2ZGzkRadhqsjK0wJXgKOnt2Lvf16tL+m0kNEdErTimU2HVrF1aeW4nL8ZcBACYGJuhZsycG1x0MF0sXALnnw5mwfwIuxV8CALxT8x180eQLmBubay120qw7KXcwYf8E6Tpjb/m8hdCmoeX6HuvS/ptJDRGRnhBC4MC9A1h5diVOPzoNADCSGaFbjW7wsfXBklNLkKHIgK3cFmHBYWhfvb12A6Zyka3MxtLTS/HTuZ+e98a1ngt/h/LpjdOl/TeTGiIiPSOEwPGHx7H87HJExkaqLGvu0hwzWs5AVfOqWoqOKkrUgyiEHgjFw/SHMDIwwqiGoxDiH6Lxkzjq0v6bSQ0RkR47++gsVp5diZNxJzG83nC8X+f9CjszMWlfUmYSwg6HYdftXQCA0Kah6OfXT6Pr0KX9N5MaIiIiPSaEwMZrG7Hp+ias6rQKckO5RtvXpf03kxoiIqJKQCmU5dJLp0v7b/ZBEhERVQKVYdhR/7eQiIiIKgUmNURERKQXmNQQERGRXmBSQ0RERHqBSQ0RERHpBSY1REREpBeY1BAREZFeYFJDREREekEjSU1YWBhkMpnKzdfXVxNNExEREZWIkaYa8vf3x65du543bKSxpomIiIheSGOZh5GREZydnTXVHBEREVGpaGxOzbVr1+Dq6gpvb2/0798ft2/fLrZ+ZmYmkpOTVW5EREREZaWRpCYoKAirV6/Gv//+i6VLlyI6OhqtWrVCSkpKkc+ZPXs2bGxspJu7u7smQiEiIqJKSiaEEJpuNDExER4eHli4cCGGDBlSaJ3MzExkZmZKj5OTk+Hu7q4Tly4nIiKikklOToaNjY1O7L/LZTavra0tatWqhevXrxdZRy6XQy6Xl8fqiYiIqBIql/PUpKam4saNG3BxcSmP5omIiIjUaCSpGTduHPbt24eYmBgcPnwYb7/9NgwNDdG3b19NNE9ERET0QhoZfrp79y769u2LJ0+ewNHRES1btsTRo0fh6OioieaJiIiIXkgjSc369es10QwRERFRmfHaT0RERKQXmNQQERGRXmBSQ0RERHqBSQ0RERHpBSY1REREpBeY1BAREZFeYFJDREREeoFJDREREekFJjVERESkF5jUEBERkV5gUkNERER6gUkNERER6QUmNURERKQXmNQQERGRXmBSQ0RERHqBSQ0RERHpBSY1REREpBeY1BAREZFeYFJDREREeoFJDREREekFJjVERESkF5jUEBERkV5gUkNERER6gUkNERER6QUmNURERKQXmNQQERGRXmBSQ0RERHqBSQ0RERHpBSY1REREpBeY1BAREZFeYFJDREREeoFJDREREekFJjVERESkF5jUEBERkV5gUkNERER6gUkNERER6QUmNURERKQXmNQQERGRXmBSQ0RERHqBSQ0RERHpBSNtB0BEVJkolUpkZWVpOwyiEjM2NoahoaG2wyiRcklq5syZg9DQUIwePRqLFi0qj1UQEb1ysrKyEB0dDaVSqe1QiErF1tYWzs7OkMlk2g6lWBpPaqKiorB8+XLUq1dP000TEb2yhBCIjY2FoaEh3N3dYWDA0X/SfUIIpKenIy4uDgDg4uKi5YiKp9GkJjU1Ff3798fKlSsxY8YMTTZNRPRKy8nJQXp6OlxdXWFubq7tcIhKzMzMDAAQFxeHqlWr6vRQlEZ/KowcORJdu3ZFhw4dXlg3MzMTycnJKjciIn2lUCgAACYmJlqOhKj08hLx7OxsLUdSPI311Kxfvx4nT55EVFRUierPnj0b06ZN09TqiYheCbo+J4GoMK/K51YjPTV37tzB6NGjERERAVNT0xI9JzQ0FElJSdLtzp07mgiFiIiIKimNJDUnTpxAXFwcGjVqBCMjIxgZGWHfvn34/vvvYWRkJHW75ieXy2Ftba1yIyIi/bR69WrY2toWWycsLAwNGjR4qfW0bdsWY8aMeak2NGXv3r2QyWRITEws8XNiYmIgk8kgk8le+rUAILX1otdeX2gkqWnfvj3OnTuH06dPS7fAwED0798fp0+f1ulJRUREVDYDBw7EW2+9VaK6ffr0wdWrV8s3IC3SdDK1a9cu7N69+6XbiY2NrVSnVtHInBorKyvUrVtXpczCwgIODg5q5UREVLlkZ2fDzMxMOoqGXszBwQEODg4v3Y6zszNsbGw0ENGrgSdKICKiIm3YsAEBAQEwMzODg4MDOnTogLS0NISFheGXX37Bli1bpCGOvXv3SsMnv/32G9q0aQNTU1NEREQUOvw0Z84cODk5wcrKCkOGDEFGRoba+n/66Sf4+fnB1NQUvr6++PHHH0sVf2ZmJsaNGwc3NzdYWFggKCgIe/fulZbnxbV9+3b4+fnB0tISnTt3RmxsrFQnJycHo0aNgq2tLRwcHPDll18iJCRE6qUaOHAg9u3bh++++056LWJiYqTnnzhxAoGBgTA3N0dwcDCuXLlSqm3IW8dbb72FWbNmwcnJCba2tpg+fTpycnIwfvx42Nvbo1q1aggPDy912/qk3JKavXv3VqouLyKi0hBCID0rRys3IUSJYoyNjUXfvn0xePBgXLp0CXv37kXPnj0hhMC4cePQu3dvKQGIjY1FcHCw9NwJEyZg9OjRuHTpEjp16qTW9u+//46wsDDMmjULx48fh4uLi1rCEhERgSlTpmDmzJm4dOkSZs2ahcmTJ+OXX34p8ev8ySef4MiRI1i/fj3Onj2LXr16oXPnzrh27ZpUJz09Hd988w3WrFmD/fv34/bt2xg3bpy0fO7cuYiIiEB4eDgOHTqE5ORkbN68WVr+3XffoXnz5hg6dKj0Wri7u0vLJ06ciAULFuD48eMwMjLC4MGDSxx/fv/99x/u37+P/fv3Y+HChZg6dSq6desGOzs7REZG4qOPPsLw4cNx9+7dMrWvD3jtJyIiLXiarUCdKdu1su6L0zvB3OTFX/+xsbHIyclBz5494eHhAQAICAiQlpuZmSEzMxPOzs5qzx0zZgx69uxZZNuLFi3CkCFDMGTIEADAjBkzsGvXLpXemqlTp2LBggVSO15eXrh48SKWL1+OkJCQF8Z/+/ZthIeH4/bt23B1dQUAjBs3Dv/++y/Cw8Mxa9YsALnDY8uWLUONGjUA5CZC06dPl9pZvHgxQkND8fbbbwMAlixZgn/++UdabmNjAxMTE5ibmxf6WsycORNt2rQBkJvsde3aFRkZGSU+WjiPvb09vv/+exgYGKB27dqYN28e0tPT8dVXXwHIPap4zpw5OHjwIN57771Sta0vOPxERESFql+/Ptq3b4+AgAD06tULK1euREJCQomeGxgYWOzyS5cuISgoSKWsefPm0v20tDTcuHEDQ4YMgaWlpXSbMWMGbty4UaIYzp07B4VCgVq1aqm0sW/fPpU2zM3NpYQGyL0UQN5lAZKSkvDw4UM0bdpUWm5oaIjGjRuXKAYAKpcNyrvMQF77peHv769yeQ0nJyeVJNPQ0BAODg5laltfsKeGiEgLzIwNcXG6+rBMRa27JAwNDbFz504cPnwYO3bswOLFizFx4kRERkbCy8ur2OdaWFi8VIypqakAgJUrV6olPyU9ojY1NRWGhoY4ceKE2nMsLS2l+8bGxirLZDJZiYfoSiJ/+3knsSvLRU0Li7Owssp8wVQmNUREWiCTyUo0BKRtMpkMLVq0QIsWLTBlyhR4eHhg06ZNGDt2LExMTAo9D1lJ+Pn5ITIyEgMGDJDKjh49Kt13cnKCq6srbt68if79+5dpHQ0bNoRCoUBcXBxatWpVpjZsbGzg5OSEqKgotG7dGkDuJS9Onjypch6Zl3ktSHN0/y+KiIi0IjIyErt370bHjh1RtWpVREZG4tGjR/Dz8wMAeHp6Yvv27bhy5QocHBxKdejw6NGjMXDgQAQGBqJFixaIiIjAhQsX4O3tLdWZNm0aRo0aBRsbG3Tu3BmZmZk4fvw4EhISMHbs2Beuo1atWujfvz8GDBiABQsWoGHDhnj06BF2796NevXqoWvXriWK9dNPP8Xs2bPh4+MDX19fLF68GAkJCSqXDvD09ERkZCRiYmJgaWkJe3v7Er8WpDlMaoiIqFDW1tbYv38/Fi1ahOTkZHh4eGDBggXo0qULAGDo0KHYu3cvAgMDkZqaij179sDT07NEbffp0wc3btzAF198gYyMDLzzzjv4+OOPsX3788nTH374IczNzTF//nyMHz8eFhYWCAgIKNVJ7sLDwzFjxgx8/vnnuHfvHqpUqYJmzZqhW7duJW7jyy+/xIMHDzBgwAAYGhpi2LBh6NSpk8qQ1rhx4xASEoI6derg6dOniI6OLnH7pDkyocmBw5eQnJwMGxsbJCUl8ZIJRKR3MjIyEB0dDS8vr1If9UK6RalUws/PD71798bXX3+t0bZjYmLg5eWFU6dOaeQyCUDuuXjGjBlTqss1FFTc51eX9t/sqSEiIirGrVu3sGPHDrRp0waZmZlYsmQJoqOj0a9fv3JbZ3BwMBo0aIDDhw+/VDuWlpbIycmpNIk0kxoiIqJiGBgYYPXq1Rg3bhyEEKhbty527dolzS3SpGrVqkknBpTL5S/d3unTpwGU/IixVx2TGiIiomK4u7vj0KFDFbIuIyMj+Pj4aKw9Tbb1KuDJ94iIiEgvMKkhIiIivcCkhoiIiPQCkxoiIiLSC0xqiIiISC8wqSEiIiK9wKSGiIjK1d69eyGTyV7qjLZl1bZt21JdVqGi28vfrkwmg0wmk84tU1E8PT2ldWvjPdIkJjVERFSugoODERsbK13wcvXq1bC1tdVuUGX0559/qlwawdPTE4sWLdJI20OHDkVsbCzq1q2rkfYGDRqESZMmvbBeVFQUNm7cqJF1ahtPvkdEROXKxMQEzs7O2g5DI8rz6tvm5uYae50UCgX++usv/P333y+s6+joqDdXFWdPDRERFSklJQX9+/eHhYUFXFxc8O2336oNwaxZswaBgYGwsrKCs7Mz+vXrh7i4OGl5/uGnvXv3YtCgQUhKSpKGPMLCwtTWe/XqVchkMly+fFml/Ntvv0WNGjWkx+fPn0eXLl1gaWkJJycnfPDBB3j8+HGR25OQkIABAwbAzs4O5ubm6NKli3RZgjyHDh1C27ZtYW5uDjs7O3Tq1AkJCQkAVIef2rZti1u3buGzzz6TtiUtLQ3W1tbYsGGDSpubN2+GhYUFUlJSin2988t73bZv346GDRvCzMwMr732GuLi4rBt2zb4+fnB2toa/fr1Q3p6uspzDx8+DGNjYzRp0gRZWVn45JNP4OLiAlNTU3h4eGD27NkljuNVwqSGiEgbhACy0rRzE6LEYY4dOxaHDh3C1q1bsXPnThw4cAAnT55UqZOdnY2vv/4aZ86cwebNmxETE4OBAwcW2l5wcDAWLVoEa2trxMbGIjY2FuPGjVOrV6tWLQQGBiIiIkKlPCIiQrqQZGJiIl577TU0bNgQx48fx7///ouHDx+id+/eRW7PwIEDcfz4cWzduhVHjhyBEAJvvPEGsrOzAeReK6l9+/aoU6cOjhw5goMHD6J79+5QKBRqbf3555+oVq0apk+fLm2LhYUF3nvvPYSHh6vUDQ8Px7vvvgsrK6siYytKWFgYlixZgsOHD+POnTvo3bs3Fi1ahHXr1uHvv//Gjh07sHjxYpXnbN26Fd27d4dMJsP333+PrVu34vfff8eVK1cQEREBT0/PUsfxKuDwExGRNmSnA7NctbPur+4DJhYvrJaSkoJffvkF69atQ/v27QHk7pxdXVXjHjx4sHTf29sb33//PZo0aYLU1FRYWlqq1DUxMYGNjQ1kMtkLh1r69++PJUuWSHNYrl69ihMnTmDt2rUAgCVLlqBhw4aYNWuW9JxVq1bB3d0dV69eRa1atVTau3btGrZu3YpDhw4hODgYQG6S5O7ujs2bN6NXr16YN28eAgMD8eOPP0rP8/f3LzQ+e3t7GBoaSj1UeT788ENpHpGLiwvi4uLwzz//YNeuXcVub1FmzJiBFi1aAACGDBmC0NBQ3LhxA97e3gCAd999F3v27MGXX34pPWfLli349ttvAQC3b99GzZo10bJlS8hkMnh4eJQpjlcBe2qIiKhQN2/eRHZ2Npo2bSqV2djYoHbt2ir1Tpw4ge7du6N69eqwsrJCmzZtAOTuTF/Ge++9h5iYGBw9ehRAbgLSqFEj+Pr6AgDOnDmDPXv2wNLSUrrlLbtx44Zae5cuXYKRkRGCgoKkMgcHB9SuXRuXLl0C8Lyn5mU0bdoU/v7++OWXXwAAa9euhYeHB1q3bl2m9urVqyfdd3Jygrm5uZTQ5JXlH+67dOkS7t+/L23HwIEDcfr0adSuXRujRo3Cjh07yhTHq4A9NURE2mBsnttjoq11a0haWho6deqETp06ISIiAo6Ojrh9+zY6deqErKysl2rb2dkZr732GtatW4dmzZph3bp1+Pjjj6Xlqamp6N69O+bOnav2XBcXlzKt08zMrMzx5vfhhx/ihx9+wIQJExAeHo5BgwZBJpOVqS1jY2PpvkwmU3mcV6ZUKqXHW7duxeuvvw5TU1MAQKNGjRAdHY1t27Zh165d6N27Nzp06KA270cfsKeGiEgbZLLcISBt3Eq4c/X29oaxsTGioqKksqSkJFy9elV6fPnyZTx58gRz5sxBq1at4Ovrq9JrUBgTE5NC56gUpn///vjtt99w5MgR3Lx5E++99560rFGjRrhw4QI8PT3h4+OjcrOwUB9e8/PzQ05ODiIjI6WyJ0+e4MqVK6hTpw6A3F6R3bt3lyi24rbl/fffx61bt/D999/j4sWLCAkJKXGbL2vLli3o0aOHSpm1tTX69OmDlStX4rfffsPGjRsRHx9fYTFVFCY1RERUKCsrK4SEhGD8+PHYs2cPLly4gCFDhsDAwEDqdahevTpMTEywePFi3Lx5E1u3blU5j0thPD09kZqait27d+Px48dqR+7k17NnT6SkpODjjz9Gu3btVObzjBw5EvHx8ejbty+ioqJw48YNbN++HYMGDSo00ahZsyZ69OiBoUOH4uDBgzhz5gzef/99uLm5SUlAaGgooqKiMGLECJw9exaXL1/G0qVLizyiytPTE/v378e9e/dU6tjZ2aFnz54YP348OnbsiGrVqhX7mmhKXFwcjh8/jm7dukllCxcuxK+//orLly/j6tWr+OOPP+Ds7PzKniuoOExqiIioSAsXLkTz5s3RrVs3dOjQAS1atICfn580tOHo6IjVq1fjjz/+QJ06dTBnzhx88803xbYZHByMjz76CH369IGjoyPmzZtXZF0rKyt0794dZ86cQf/+/VWWubq64tChQ1AoFOjYsSMCAgIwZswY2NrawsCg8N1beHg4GjdujG7duqF58+YQQuCff/6RhnRq1aqFHTt24MyZM2jatCmaN2+OLVu2wMio8Nka06dPR0xMDGrUqAFHR0eVZUOGDEFWVpbKROry9r///Q9NmzZFlSpVpDIrKytpAnSTJk0QExODf/75p8jX6FUmE6IUx/aVo+TkZNjY2CApKQnW1tbaDoeISKMyMjIQHR0NLy8vKSF4FaWlpcHNzQ0LFizAkCFDtB2OTluzZg0+++wz3L9/HyYmJsXWbdu2LRo0aPDSZyd+88030bJlS3zxxRelet7evXvRrl07JCQkFNqDU9znV5f23/qXphERkcacOnUKv/76K27cuIGTJ09KvSUF52zQc+np6bhx4wbmzJmD4cOHvzChyfPjjz/C0tIS586dK/O6W7Zsib59+5bqOf7+/ujSpUuZ16lLePQTEREV65tvvsGVK1dgYmKCxo0b48CBAyrDG6Rq3rx5mDlzJlq3bo3Q0NASPSciIgJPnz4FkDtPqaxK20MDAP/884908kFt97S8LA4/ERFVAH0ZfqLKicNPRERERBWISQ0RERHpBSY1REREpBeY1BAREZFeYFJDREREeoFJDREREekFJjVERKR1AwcOxFtvvaXtMAAAMTExkMlkOH36dKmeJ5PJIJPJNHJNJU9PT6m9xMTEl26vsmBSQ0RElZamk6nw8HCVq5iXVVRUFDZu3KiBiCoXJjVERKRReWenrYxsbW1RtWrVl27H0dER9vb2GoiocmFSQ0RERfr333/RsmVL2NrawsHBAd26dcONGzek5XlDNb/99hvatGkDU1NTREREAABWrVoFf39/yOVyuLi44JNPPinxepVKJWbPng0vLy+YmZmhfv362LBhg7R87969kMlk2L17NwIDA2Fubo7g4GBcuXJFpZ0ZM2agatWqsLKywocffogJEyagQYMGAICwsDD88ssv2LJlizTUs3fvXum5N2/eRLt27WBubo769evjyJEjpX79wsLC0KBBA6xatQrVq1eHpaUlRowYAYVCgXnz5sHZ2RlVq1bFzJkzS902qdNIUrN06VLUq1cP1tbWsLa2RvPmzbFt2zZNNE1EpJeEEEjPTtfKrTRXx0lLS8PYsWNx/Phx7N69GwYGBnj77behVCpV6k2YMAGjR4/GpUuX0KlTJyxduhQjR47EsGHDcO7cOWzduhU+Pj4lXu/s2bPxf//3f1i2bBkuXLiAzz77DO+//z727dunUm/ixIlYsGABjh8/DiMjIwwePFhaFhERgZkzZ2Lu3Lk4ceIEqlevjqVLl0rLx40bh969e6Nz586IjY1FbGwsgoODVdoeN24cTp8+jVq1aqFv377Iyckp8TbkuXHjBrZt24Z///0Xv/76K37++Wd07doVd+/exb59+zB37lxMmjQJkZGRpW6bVGnkgpbVqlXDnDlzULNmTQgh8Msvv6BHjx44deoU/P39NbEKIiK98jTnKYLWBWll3ZH9ImFubF6iuu+8847K41WrVsHR0REXL15E3bp1pfIxY8agZ8+e0uMZM2bg888/x+jRo6WyJk2alGidmZmZmDVrFnbt2oXmzZsDALy9vXHw4EEsX74cbdq0kerOnDlTejxhwgR07doVGRkZMDU1xeLFizFkyBAMGjQIADBlyhTs2LEDqampAABLS0uYmZkhMzMTzs7OanGMGzcOXbt2BQBMmzYN/v7+uH79Onx9fUu0HXmUSiVWrVoFKysr1KlTB+3atcOVK1fwzz//wMDAALVr18bcuXOxZ88eBAVp5zOhLzTSU9O9e3e88cYbqFmzJmrVqoWZM2fC0tISR48e1UTzRESkJdeuXUPfvn3h7e0Na2treHp6AgBu376tUi8wMFC6HxcXh/v376N9+/ZlWuf169eRnp6O119/HZaWltLt//7v/1SGvgCgXr160n0XFxdp/QBw5coVNG3aVKV+wcfFKa7t0vD09ISVlZX02MnJCXXq1IGBgYFKWVnaJlUa6anJT6FQ4I8//kBaWpqUYRMRkSozIzNE9tPOcIOZkVmJ63bv3h0eHh5YuXIlXF1doVQqUbduXWRlZanUs7CweN6+WcnbL0xeT8rff/8NNzc3lWVyuVzlsbGxsXRfJpMBgNrQWFlpqu387eS1VViZpuKuzDSW1Jw7dw7NmzdHRkYGLC0tsWnTJtSpU6fI+pmZmcjMzJQeJycnayoUIiKdJ5PJSjwEpC1PnjzBlStXsHLlSrRq1QoAcPDgwRc+z8rKCp6enti9ezfatWtX6vXWqVMHcrkct2/fVhlqKq3atWsjKioKAwYMkMqioqJU6piYmEChUJR5HaRbNJbU1K5dG6dPn0ZSUhI2bNiAkJAQ7Nu3r8jEZvbs2Zg2bZqmVk9ERBpmZ2cHBwcHrFixAi4uLrh9+zYmTJhQoueGhYXho48+QtWqVdGlSxekpKTg0KFD+PTTT1/4XCsrK4wbNw6fffYZlEolWrZsiaSkJBw6dAjW1tYICQkpUQyffvophg4disDAQAQHB+O3337D2bNn4e3tLdXx9PTE9u3bceXKFTg4OMDGxqZEbZNu0tgh3SYmJvDx8UHjxo0xe/Zs1K9fH999912R9UNDQ5GUlCTd7ty5o6lQiIhIAwwMDLB+/XqcOHECdevWxWeffYb58+eX6LkhISFYtGgRfvzxR/j7+6Nbt264du1aidf99ddfY/LkyZg9ezb8/PzQuXNn/P333/Dy8ipxG/3790doaCjGjRuHRo0aITo6GgMHDoSpqalUZ+jQoahduzYCAwPh6OiIQ4cOlbh90j0yUZpj+0rhtddeQ/Xq1bF69eoS1U9OToaNjQ2SkpJgbW1dHiEREWlNRkYGoqOj4eXlpbJTpYr1+uuvw9nZGWvWrNF42zKZDJs2bdLYGYr37t2Ldu3aISEhQSOXXngZxX1+dWn/rZHhp9DQUHTp0gXVq1dHSkoK1q1bh71792L79u2aaJ6IiKjU0tPTsWzZMnTq1AmGhob49ddfsWvXLuzcubPc1tm3b184ODjg7t27L9WOv78/bt68qaGoKg+NJDVxcXEYMGAAYmNjYWNjg3r16mH79u14/fXXNdE8ERFRqclkMvzzzz+YOXMmMjIyULt2bWzcuBEdOnQol/XlDa8ZGhq+dFv//POPdLkJbfd+vEo0ktT8/PPPmmiGiIhIY8zMzLBr164KW19pzpj8Ih4eHhprqzLhtZ+IiIhILzCpISIiIr3ApIaIqAKV0wGnROXqVTnbscYvk0BEROqMjY0hk8nw6NEjODo6SqfdJ9JlQghkZWXh0aNHMDAwgImJibZDKhaTGiKiCmBoaIhq1arh7t27iImJ0XY4RKVibm6O6tWrq1yEUxcxqSEiqiCWlpaoWbOmdKgu0avA0NAQRkZGr0TvIpMaIqIKZGhoqJHzmBCROt3uRyIiIiIqISY1REREpBeY1BAREZFeYFJDREREeoFJDREREekFJjVERESkF5jUEBERkV5gUkNERER6gUkNERER6QUmNURERKQXmNQQERGRXmBSQ0RERHqBSQ0RERHpBSY1REREpBeY1BAREZFeYFJDREREeoFJDREREekFJjVERESkF5jUEBERkV5gUkNERER6gUkNERER6QUmNURERKQXmNQQERGRXmBSQ0RERHqBSQ0RERHpBSY1REREpBeY1BAREZFeYFJDREREeoFJDREREekFJjVERESkF5jUEBERkV5gUkNERER6gUkNERER6QUmNURERKQXNJLUzJ49G02aNIGVlRWqVq2Kt956C1euXNFE00REREQlopGkZt++fRg5ciSOHj2KnTt3Ijs7Gx07dkRaWpommiciIiJ6IZkQQmi60UePHqFq1arYt28fWrduXaLnJCcnw8bGBklJSbC2ttZ0SERERFQOdGn/XS5zapKSkgAA9vb25dE8ERERkRojTTeoVCoxZswYtGjRAnXr1i2yXmZmJjIzM6XHycnJmg6FiIiIKhGN99SMHDkS58+fx/r164utN3v2bNjY2Eg3d3d3TYdCRERElYhG59R88skn2LJlC/bv3w8vL69i6xbWU+Pu7q4TY3JERERUMro0p0Yjw09CCHz66afYtGkT9u7d+8KEBgDkcjnkcrkmVk9ERESkmaRm5MiRWLduHbZs2QIrKys8ePAAAGBjYwMzMzNNrIKIiIioWBoZfpLJZIWWh4eHY+DAgSVqQ5e6r4iIiKhkdGn/rbHhJyIiIiJt4rWfiIiISC8wqSEiIiK9wKSGiIiI9AKTGiIiItILTGqIiIhILzCpISIiIr3ApIaIiIj0ApMaIiIi0gtMaoiIiEgvMKkhIiIivcCkhoiIiPQCkxoiIiLSC0xqiIiISC8wqSEiIiK9wKSGiIiI9AKTGiIiItILTGqIiIhILzCpISIiIr3ApIaIiIj0ApMaIiIi0gtMaoiIiEgvMKkhIiIivcCkhoiIiPQCkxoiIiLSC0xqiIiISC8wqSEiIiK9wKSGiIiI9AKTGiIiItILTGqIiIhILzCpISIiIr3ApIaIiIj0ApMaIiIi0gtMaoiIiEgvMKkhIiIivcCkhoiIiPQCkxoiIiLSC0xqiIiISC8wqSEiIiK9wKSGiIiI9AKTGiIiItILTGqIiIhILzCpISIiIr2gsaRm//796N69O1xdXSGTybB582ZNNU1ERET0QhpLatLS0lC/fn388MMPmmqSiIiIqMSMNNVQly5d0KVLF001R0RERFQqGktqSiszMxOZmZnS4+TkZG2FQkRERHpAaxOFZ8+eDRsbG+nm7u6urVCIiIhID2gtqQkNDUVSUpJ0u3PnjrZCISIiIj2gteEnuVwOuVyurdUTERGRnuF5aoiIiEgvaKynJjU1FdevX5ceR0dH4/Tp07C3t0f16tU1tRoiIiKiQmksqTl+/DjatWsnPR47diwAICQkBKtXr9bUaoiIiIgKpbGkpm3bthBCaKo5IiIiolLhnBoiIiLSC0xqiIiISC8wqSEiIiK9wKSGiIiI9AKTGiIiItILWjujMBGVTFaOEmmZOUh9dkvLzEHKs/9TM1TLc+8rkJqRjbRMhVQvM0cBX2drNPWyR5CXPepVs4WJEX/TEJF+YVJDVA6yFUq1hKO0iUhenawcpUZiepj8CPuuPgIAyI0M0Ki6XW6S422Phu52MDMx1Mh6iIi0hUkN0TPZitwekZSMHKRlqScfKRk5SMtUIDUzOzcJKSJJSdFgIpKfqbEBLOVGsJQbweLZ/5ZyI1ia5j62KqLcUm4ImUyGM3cSEXkzHsdi4hGfloUjN5/gyM0nwG7A2FCGetVsEeRlj6Ze9mjsYQcrU2ONbwMRUXmSCR05Y15ycjJsbGyQlJQEa2trbYfzSslRKPE0W4GMbCUyshXIyFZIj58+e6xU5r7NAoAQgIBA3jufW/ZsuVSWuzy37rMyIXLvF/p89TI8qy+V5Xucv03kez7yLS/YptSGyvPzxfq8ksrzASBbIZ4lJc8TkNQCyUtmOSQiciMDWJk+TzZUkg9TI5UkRbXcEJZyY1jIDWH17H8jQ80MFwkhcD0uFZHR8TgWHY/I6Cd4mJypUsdABtR1s0FTz9wkp6mXPWzNTTSyfiLSL7q0/2ZSU06yFc8Tisx8ycXTLAUycpR4mqVAZs6zx9kKPM1LSHIUyMhSTUieFmzjWcKS+ex+jlIn3kK9oYlEJG+5sYYSkfIkhMDt+HSVJOdO/FO1er7OVs/m5DigiZcdqlqZaiFaItI1urT/rjRJjRAC2QpRZNKQUUxPR2a+Ok/z1cko0BuS/7FCS4mGqbEBTI0NYWZsCNNnN7mRAYwMZJDJcuvIIMOzfyplMlnu47z7eWQymVRXVkhZXqksX5v528h7/Hydz+qrPOd5GfI9//n6VGOUoi6wThTWpgwwNjSAhUkhvSCmqkM2r0oiUt7uJz5FVEw8jt6Mx7HoJ7jxKE2tjncVCwR55/XkOMDN1kwLkRKRtjGpKUR5vSiDwo/hWHQ8MnKUWks0chMMA5VEw9TYAGYmhjA1KlD27L6ZSW4ykr+OmYlB7v1nZWYmqs+RGxlIO3IiTXqUkomomLyenHhcfpCMgt8c1ezMpKOrgrwc4OFgzs8jUSWgS0mN3k8UzlIokZalUCmTyfA8eTA2hDx/MvEsuZDnu6+ajDwvL/r5z8uZaJA+cLSS440AF7wR4AIASErPzk1yYuIRefMJzt9Pxt2Ep7ibcA9/nrwHAKhqJX92dJUDgrzs4eNoCQMD/i1UpKdZCtxLTMed+Kd4lJIJhTQnLW/OXL75ZwXmvBWc7yYEoCw4dy7fHLeC89tEwXXlqwO1OoBSFGi30DYBQECpLEG7+bZF+axhGQADmQwGMsDAQPb8vkwGmUwGQ4O85bm9wAYyGQwNnt83kD1fnr8dmQwwLPA8AxmePVd1Pbnrhdo6DGRQrasW37N1FKhrmH+dBlCNLV9Mhs/as7MwhtxIf4901PuemrsJ6VAohUpviIkhEw0iTUrNzMHJWwmIjH6CY9HxOHMnCVkK1YnXdubG0lBVkJc9/FysYcgk56VkZCtwP/Ep7iQ8xd2EdNxNeIo78enPEsx0PE7N0naIpGPWD2uGZt4OGm2TPTUVqJqdubZDINJ7lnIjtK7liNa1HAHk7mxPS4eQP8GJWwlISM/G9gsPsf3CQwCAldwIgZ52uUmOtz0C3Gw4n6mAbIUS9xOfqiQrdxKeJy0Fj1orjJXcCNXszeFsLYehgYHaPDQDA/U5bwXnuxU21y2vByBvXl1hc+oKzm2TIa+Xoeh2Cz4fz3od1OoUaLfQ5xfYltxeG0CpFFAKkXtfCIhn9xXK5/fzL8+tX6Bu3n1lgboF2lU8e65au1IMz5eXpK54Vq54tm5RxLqVygJ1n7VroOc/6PW+p4aItC8rR4lz95JwLDp34vHxmASkZOao1DEzNkQjD1sEeTmgqZc9GrjbwtRYf7vJgdzTMcQmZUhJitTjEp/7/4PkDLxoKqC5iSGq2ZnB3c4893/73P+r2ZnD3c4c1mZG7JmmcqVL+28mNURU4RRKgUuxyYiMzp2TExUTj4T0bJU6JoYGqO9uIyU5jT3sYCF/tTqXFUqBh8n5kpb4vOQlt7clNinjhQcwyI0MCk1W8srszI2ZtJBW6dL+m0kNEWmdUilw/VEqIm8+yU10ouPxKEV1aMXQQIa6bja5Zz32tEcTT3vYmGv3rMdKpcDj1EwpSXk+nyV3mOh+4lNkK4r/ijUxNICbnZmUsKgmMGZwtJQzaSGdpkv7byY1RKRzhBCIeZKOY9HPkpyb8biXqHpCQJkM8HW2fnYIuT2aeNmjiqVc43E8Scsqck7L3YSnL7wkhqGBDK62ps97V+zMUc0+b7jIHFWt5DwqjF5purT/ZlJDRK+Euwnpz8+VczMeNx+rnxCwhqOFdAh5Uy97uNgUf0JAIQQS07PzJSvPh4jyelyeZiuKbcNABrjYFN7T4m5vDicrucYucUGki3Rp/82khoheSXEpGYiKfn4Y+eUHKWp1qtubS9eusjEzVjncOS9pSS0wYbkgmQxwsjJVTVbsns9vcbE15VFbVKnp0v6bSQ0R6YWEtCypJ+dYTDzO30t64ZFDeRyt5Pkm4T773z73f1dbU70+WRnRy9Kl/ferdSgBEVER7CxM0NHfGR39nQEAKRnZOHErAcei4xEVE4/MHOXzHpYCPS76fug4UWXBpIaI9JKVqTHa1q6KtrWrajsUIqogHAgmIiIivcCkhoiIiPQCh5+IXiVCAEoFoMh6dssu2X1lDmBuD1g6AVbOgImFtreEiEjjmNQQAci9Kl126RKFou7nZBZTpzTtFbEcGjhg0cTyeYJjWRWwdAasnHLLpHInwMweMGCHLhG9GpjUlJUQub9+898UBR4rFbk7SpXHOc9/Oec9VhZ8XJo6CvU4Cospb0eocgR/MWUq5YWVoYiy0jy/pOvXQKxCWXwCoSz+XCU6zcAYMDQBDPP+L+S+zABIfwKkPgSy04GsVCA+FYi/8YK2jQCLqs8SnmcJUF7Ckz/5sawKGGn2bL5ERKWl/0nNvnlA3CUNJBkFkgVR/FlG6VUny91JqyQIxSQNavfz/peX8nkvaq+whKUUp9gXAshMAVLjgNQHQMqDfPcf5iY9qQ9zy5/G537WU+7n3l7EzK6YxCdfb5DcunQxExUl78elIvtZT2vO8+9w6Udftup3vFDk/sjJu6989rjEZfnuq5Upng8Rq5Qpc3uDtdGOUqla1nc94N5E2+9cudH/pCZ6PxBzoOLWJzPM3dEYGAEGhs/+L/jY6FmdfI8LuxkWLDPM19azx4YFHhe1Plm+IQSVHYqskPLCygpuZyFtvLDd0q7vBeWlrauWpBRz30BPz1sikwGm1rm3Kj7F183JAtLiniU5D3MTn9S4Z4nQw3zlD3N3HE8Tcm+PLhXfrpFZvsSn4NBXvvsWjvr7PlQ0IfLt+PP9sCs0GSiYGBRXt7DEIV9dpaIcnpevLn9cll5OhrYjKFf6n9Q0HQr4dS9DAlHYrbAkosBj/gIlfWFkAthUy70VR6kEMhKfJTv5E59CeoAyk4Gcp0DirdxbcWQGgHmVfENfTkUPg5mYa2yzNSovmcjJyB3mzMnInXOVkwkonv2fk5GbQJa4Tubz5Sp18tWV2sn3nEpD9ux72jjf93r+H3qGuT8+ZQbP7xsY5D6WFVxeWFmB/1WWy/K1mX+5QSFlefcNCnmOQRFxluU5BbbN1kPbb1C54mUSiKjiZKWrDnGpJT7PytIe5XaZl5TcOt9wV8HE51lvkJG8kJ19gSSi0ISgNMlIwcRDh38Vywzz9Rrn70EukAyo3C9Y90XPMy6ibv6yF63DON8PypLU5cT2iqZL+2/976khIt1hYg7Ye+XeiqPIAdIfFxj6KmIYLCcjtwcoMxl4cq1itqOs8uZZGeW7SY9Nc3vHjExz6xmZ5quXv6w0dfKvxyTfjt+IO3/SS0xqiEj3GBrl9rJYOQMuxdQTIjeZKTjJubDeIEV2gSSisGSgYKJR2jrFJCyGJkwkiMoZkxoienXJZICpTe7NsZa2oyEiLePPBiIiItILTGqIiIhILzCpISIiIr3ApIaIiIj0ApMaIiIi0gsaTWp++OEHeHp6wtTUFEFBQTh27JgmmyciIiIqksaSmt9++w1jx47F1KlTcfLkSdSvXx+dOnVCXFycplZBREREVCSNJTULFy7E0KFDMWjQINSpUwfLli2Dubk5Vq1apalVEBERERVJI0lNVlYWTpw4gQ4dOjxv2MAAHTp0wJEjRwp9TmZmJpKTk1VuRERERGWlkaTm8ePHUCgUcHJyUil3cnLCgwcPCn3O7NmzYWNjI93c3d01EQoRERFVUlo7+ik0NBRJSUnS7c6dO9oKhYiIiPSARq79VKVKFRgaGuLhw4cq5Q8fPoSzs3Ohz5HL5ZDL5ZpYPREREZFmempMTEzQuHFj7N69WypTKpXYvXs3mjdvrolVEBERERVLY1fpHjt2LEJCQhAYGIimTZti0aJFSEtLw6BBgzS1CiIiIqIiaSyp6dOnDx49eoQpU6bgwYMHaNCgAf7991+1ycNFEUIAAI+CIiIieoXk7bfz9uPaJBO6EAWAu3fv8ggoIiKiV9SdO3dQrVo1rcagM0mNUqnE/fv3YWVlBZlMprF2k5OT4e7ujjt37sDa2lpj7b5KKvtrUNm3H+BrwO2v3NsP8DUoz+0XQiAlJQWurq4wMNDuJSU1Nvz0sgwMDMo1w7O2tq6UH+T8KvtrUNm3H+BrwO2v3NsP8DUor+23sbHReJtlwat0ExERkV5gUkNERER6Qe+TGrlcjqlTp1bqE/1V9tegsm8/wNeA21+5tx/ga1BZtl9nJgoTERERvQy976khIiKiyoFJDREREekFJjVERESkF5jUEBERkV6oNEnNnDlzIJPJMGbMGG2HUiHCwsIgk8lUbr6+vtoOq8Ldu3cP77//PhwcHGBmZoaAgAAcP35c22FVCE9PT7XPgEwmw8iRI7UdWoVRKBSYPHkyvLy8YGZmhho1auDrr7/WiWvUVJSUlBSMGTMGHh4eMDMzQ3BwMKKiorQdVrnZv38/unfvDldXV8hkMmzevFlluRACU6ZMgYuLC8zMzNChQwdcu3ZNO8GWgxdt/59//omOHTvCwcEBMpkMp0+f1kqc5aVSJDVRUVFYvnw56tWrp+1QKpS/vz9iY2Ol28GDB7UdUoVKSEhAixYtYGxsjG3btuHixYtYsGAB7OzstB1ahYiKilJ5/3fu3AkA6NWrl5Yjqzhz587F0qVLsWTJEly6dAlz587FvHnzsHjxYm2HVmE+/PBD7Ny5E2vWrMG5c+fQsWNHdOjQAffu3dN2aOUiLS0N9evXxw8//FDo8nnz5uH777/HsmXLEBkZCQsLC3Tq1AkZGRkVHGn5eNH2p6WloWXLlpg7d24FR1ZBhJ5LSUkRNWvWFDt37hRt2rQRo0eP1nZIFWLq1Kmifv362g5Dq7788kvRsmVLbYehM0aPHi1q1KghlEqltkOpMF27dhWDBw9WKevZs6fo37+/liKqWOnp6cLQ0FD89ddfKuWNGjUSEydO1FJUFQeA2LRpk/RYqVQKZ2dnMX/+fKksMTFRyOVy8euvv2ohwvJVcPvzi46OFgDEqVOnKjSm8qb3PTUjR45E165d0aFDB22HUuGuXbsGV1dXeHt7o3///rh9+7a2Q6pQW7duRWBgIHr16oWqVauiYcOGWLlypbbD0oqsrCysXbsWgwcP1ugFY3VdcHAwdu/ejatXrwIAzpw5g4MHD6JLly5ajqxi5OTkQKFQwNTUVKXczMys0vXcAkB0dDQePHigsj+wsbFBUFAQjhw5osXISFN05oKW5WH9+vU4efKkXo8fFyUoKAirV69G7dq1ERsbi2nTpqFVq1Y4f/48rKystB1ehbh58yaWLl2KsWPH4quvvkJUVBRGjRoFExMThISEaDu8CrV582YkJiZi4MCB2g6lQk2YMAHJycnw9fWFoaEhFAoFZs6cif79+2s7tAphZWWF5s2b4+uvv4afnx+cnJzw66+/4siRI/Dx8dF2eBXuwYMHAAAnJyeVcicnJ2kZvdr0Nqm5c+cORo8ejZ07d6r9SqkM8v8SrVevHoKCguDh4YHff/8dQ4YM0WJkFUepVCIwMBCzZs0CADRs2BDnz5/HsmXLKl1S8/PPP6NLly5wdXXVdigV6vfff0dERATWrVsHf39/nD59GmPGjIGrq2ul+QysWbMGgwcPhpubGwwNDdGoUSP07dsXJ06c0HZoRBqnt8NPJ06cQFxcHBo1agQjIyMYGRlh3759+P7772FkZASFQqHtECuUra0tatWqhevXr2s7lArj4uKCOnXqqJT5+flVumG4W7duYdeuXfjwww+1HUqFGz9+PCZMmID33nsPAQEB+OCDD/DZZ59h9uzZ2g6twtSoUQP79u1Damoq7ty5g2PHjiE7Oxve3t7aDq3COTs7AwAePnyoUv7w4UNpGb3a9Dapad++Pc6dO4fTp09Lt8DAQPTv3x+nT5+GoaGhtkOsUKmpqbhx4wZcXFy0HUqFadGiBa5cuaJSdvXqVXh4eGgpIu0IDw9H1apV0bVrV22HUuHS09NhYKD6NWdoaAilUqmliLTHwsICLi4uSEhIwPbt29GjRw9th1ThvLy84OzsjN27d0tlycnJiIyMRPPmzbUYGWmK3g4/WVlZoW7duiplFhYWcHBwUCvXR+PGjUP37t3h4eGB+/fvY+rUqTA0NETfvn21HVqF+eyzzxAcHIxZs2ahd+/eOHbsGFasWIEVK1ZoO7QKo1QqER4ejpCQEBgZ6e2fe5G6d++OmTNnonr16vD398epU6ewcOFCDB48WNuhVZjt27dDCIHatWvj+vXrGD9+PHx9fTFo0CBth1YuUlNTVXqko6Ojcfr0adjb26N69eoYM2YMZsyYgZo1a8LLywuTJ0+Gq6sr3nrrLe0FrUEv2v74+Hjcvn0b9+/fBwDph5+zs7N+9FZp+/CrilSZDunu06ePcHFxESYmJsLNzU306dNHXL9+XdthVbj//e9/om7dukIulwtfX1+xYsUKbYdUobZv3y4AiCtXrmg7FK1ITk4Wo0ePFtWrVxempqbC29tbTJw4UWRmZmo7tArz22+/CW9vb2FiYiKcnZ3FyJEjRWJiorbDKjd79uwRANRuISEhQojcw7onT54snJychFwuF+3bt9erv48XbX94eHihy6dOnarVuDVFJkQlOrUmERER6S29nVNDRERElQuTGiIiItILTGqIiIhILzCpISIiIr3ApIaIiIj0ApMaIiIi0gtMaoiIiEgvMKkhopeyevVq2NraFlsnLCwMDRo0eKn1tG3bFmPGjCm2jqenJxYtWvRS6yGiV1flO286Eb3QwIEDkZiYiM2bN7+wbp8+ffDGG2+Ue0x//vknjI2Ny309RPTqYlJDRGWWnZ0NMzMzmJmZlfu67O3ty30dRPRq4/ATUSW1YcMGBAQEwMzMDA4ODujQoQPS0tIQFhaGX375BVu2bIFMJoNMJsPevXsRExMDmUyG3377DW3atIGpqSkiIiIKHX6aM2cOnJycYGVlhSFDhiAjI0Nt/T/99BP8/PxgamoKX19f/Pjjj8XGW3D4KS4uDt27d4eZmRm8vLwQERGhiZeFiF5h7KkhqoRiY2PRt29fzJs3D2+//TZSUlJw4MABCCEwbtw4XLp0CcnJyQgPDweQ20uSd1XfCRMmYMGCBWjYsCFMTU2xfft2lbZ///13hIWF4YcffkDLli2xZs0afP/99/D29pbqREREYMqUKViyZAkaNmyIU6dOYejQobCwsEBISEiJtmHgwIG4f/8+9uzZA2NjY4waNQpxcXEaeoWI6FXEpIaoEoqNjUVOTg569uwJDw8PAEBAQIC03MzMDJmZmXB2dlZ77pgxY9CzZ88i2160aBGGDBmCIUOGAABmzJiBXbt2qfTWTJ06FQsWLJDa8fLywsWLF7F8+fISJTVXr17Ftm3bcOzYMTRp0gQA8PPPP8PPz68EW09E+orDT0SVUP369dG+fXsEBASgV69eWLlyJRISEkr03MDAwGKXX7p0CUFBQSplzZs3l+6npaXhxo0bGDJkCCwtLaXbjBkzcOPGjRLFcOnSJRgZGaFx48ZSma+v7wuPwiIi/caeGqJKyNDQEDt37sThw4exY8cOLF68GBMnTkRkZCS8vLyKfa6FhcVLrTs1NRUAsHLlSrXkx9DQ8KXaJqLKjT01RJWUTCZDixYtMG3aNJw6dQomJibYtGkTAMDExAQKhaJM7fr5+SEyMlKl7OjRo9J9JycnuLq64ubNm/Dx8VG5vSihyuPr64ucnBycOHFCKrty5QoSExPLFDMR6Qf21BBVQpGRkdi9ezc6duyIqlWrIjIyEo8ePZLmpHh6emL79u24cuUKHBwcYGNjU+K2R48ejYEDByIwMBAtWrRAREQELly4oDJReNq0aRg1ahRsbGzQuXNnZGZm4vjx40hISMDYsWNfuI7atWujc+fOGD58OJYuXQojIyOMGTOmQg4tJyLdxZ4aokrI2toa+/fvxxtvvIFatWph0qRJWLBgAbp06QIAGDp0KGrXro3AwEA4Ojri0KFDJW67T58+mDx5Mr744gs0btwYt27dwscff6xS58MPP8RPP/2E8PBwBAQEoE2bNli9enWJe2oAIDw8HK6urmjTpg169uyJYcOGoWrVqiV+PhHpH5kQQmg7CCIiIqKXxZ4aIiIi0gtMaoiIiEgvMKkhIiIivcCkhoiIiPQCkxoiIiLSC0xqiIiISC8wqSEiIiK9wKSGiIiI9AKTGiIiItILTGqIiIhILzCpISIiIr3ApIaIiIj0wv8DwUaSCprsA/4AAAAASUVORK5CYII=",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "for k, v in spatial_paras.parameters_pretty_.items():\n",
        "    v[[\"stride length [m]\", \"gait velocity [m/s]\", \"arc length [m]\"]].plot()\n",
        "    plt.title(f\"All spatial parameters of sensor {k}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "collapsed": false
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "for k, v in spatial_paras.parameters_pretty_.items():\n",
        "    v.filter(like=\"angle\").plot()\n",
        "    plt.title(f\"All angle parameters of sensor {k}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "WT901BLE68(d8:09:5a:ca:b6:e0)            arc length [m]  gait velocity [m/s]  ic angle [deg]  \\\n",
            "stride id                                                        \n",
            "4                6.226683             0.282567       33.329874   \n",
            "5                6.032181             0.302925       64.274687   \n",
            "6                6.299419             0.304665       47.312325   \n",
            "7                6.653279             0.333712       47.221179   \n",
            "8                5.905198             0.214911       50.191420   \n",
            "9                5.311203             0.251359       21.013918   \n",
            "10               6.186974             0.274095       43.984883   \n",
            "11               5.298685             0.287452       50.973466   \n",
            "\n",
            "           max. lateral excursion [m]  max. orientation change [deg]  \\\n",
            "stride id                                                              \n",
            "4                            0.026199                     108.377583   \n",
            "5                            0.135625                     133.969732   \n",
            "6                            0.087986                     116.042343   \n",
            "7                            0.103784                     129.310893   \n",
            "8                            0.110265                     106.145936   \n",
            "9                            0.126090                     114.050438   \n",
            "10                           0.051058                     118.070831   \n",
            "11                           0.082672                     116.759982   \n",
            "\n",
            "           max. sensor lift [m]  stride length [m]  tc angle [deg]  \\\n",
            "stride id                                                            \n",
            "4                           0.0           0.632950      -63.684604   \n",
            "5                           0.0           0.678551      -56.458234   \n",
            "6                           0.0           0.682450      -60.499639   \n",
            "7                           0.0           0.747514      -68.537551   \n",
            "8                           0.0           0.447015      -48.959273   \n",
            "9                           0.0           0.603261      -62.498613   \n",
            "10                          0.0           0.608491      -63.852883   \n",
            "11                          0.0           0.597900      -55.625450   \n",
            "\n",
            "           turning angle [deg]  \n",
            "stride id                       \n",
            "4                    36.144999  \n",
            "5                    67.702830  \n",
            "6                    61.688396  \n",
            "7                    89.434191  \n",
            "8                    53.810914  \n",
            "9                   -32.186526  \n",
            "10                   63.152327  \n",
            "11                   72.204267  \n",
            "WT901BLE68(cc:c4:7a:a1:fe:ea)            arc length [m]  gait velocity [m/s]  ic angle [deg]  \\\n",
            "stride id                                                        \n",
            "0                1.259518             0.095811       40.029608   \n",
            "1                1.503821             0.104165       39.612379   \n",
            "2                1.354846             0.142024       32.808260   \n",
            "3                0.611382             0.151417       53.855554   \n",
            "6                1.149219             0.105085       40.175022   \n",
            "7                1.865513             0.122553       37.412254   \n",
            "8                1.470445             0.104719       38.432848   \n",
            "\n",
            "           max. lateral excursion [m]  max. orientation change [deg]  \\\n",
            "stride id                                                              \n",
            "0                            0.004614                      62.323307   \n",
            "1                            0.009368                      61.742331   \n",
            "2                            0.018135                      62.565749   \n",
            "3                            0.009789                      63.873257   \n",
            "6                            0.012483                      62.336119   \n",
            "7                            0.020321                      63.714967   \n",
            "8                            0.013043                      54.071692   \n",
            "\n",
            "           max. sensor lift [m]  stride length [m]  tc angle [deg]  \\\n",
            "stride id                                                            \n",
            "0                           0.0           0.113057      -14.965575   \n",
            "1                           0.0           0.118748       -8.892956   \n",
            "2                           0.0           0.136343       -9.457794   \n",
            "3                           0.0           0.118105        7.305773   \n",
            "6                           0.0           0.111391       -7.667590   \n",
            "7                           0.0           0.132357      -14.205611   \n",
            "8                           0.0           0.111002      -10.219113   \n",
            "\n",
            "           turning angle [deg]  \n",
            "stride id                       \n",
            "0                     1.800534  \n",
            "1                     9.072232  \n",
            "2                    37.005658  \n",
            "3                    36.160448  \n",
            "6                    37.020352  \n",
            "7                    38.839901  \n",
            "8                    16.985846  \n"
          ]
        }
      ],
      "source": [
        "for k, v in spatial_paras.parameters_pretty_.items():\n",
        "    print(k,v)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "WT901BLE68(d8:09:5a:ca:b6:e0)            stride time [s]  swing time [s]  stance time [s]\n",
            "stride id                                                  \n",
            "4                     2.24            0.84             1.40\n",
            "5                     2.24            0.76             1.48\n",
            "6                     2.24            0.76             1.48\n",
            "7                     2.24            0.82             1.42\n",
            "8                     2.08            0.68             1.40\n",
            "9                     2.40            0.90             1.50\n",
            "10                    2.22            0.82             1.40\n",
            "11                    2.08            0.80             1.28\n",
            "WT901BLE68(cc:c4:7a:a1:fe:ea)            stride time [s]  swing time [s]  stance time [s]\n",
            "stride id                                                  \n",
            "0                     1.18            0.42             0.76\n",
            "1                     1.14            0.44             0.70\n",
            "2                     0.96            0.28             0.68\n",
            "3                     0.78            0.28             0.50\n",
            "6                     1.06            0.42             0.64\n",
            "7                     1.08            0.46             0.62\n",
            "8                     1.06            0.44             0.62\n"
          ]
        }
      ],
      "source": [
        "for k, v in temporal_paras.parameters_pretty_.items():\n",
        "    print(k,v)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 48,
      "metadata": {},
      "outputs": [],
      "source": [
        "x = temporal_paras.parameters_pretty_.values()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 49,
      "metadata": {},
      "outputs": [],
      "source": [
        "y = list(x)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 50,
      "metadata": {},
      "outputs": [],
      "source": [
        "temporal_left = y[0]\n",
        "temporal_right = y[1]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 54,
      "metadata": {},
      "outputs": [
        {
          "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>stride time [s]</th>\n",
              "      <th>swing time [s]</th>\n",
              "      <th>stance time [s]</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>stride id</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2.24</td>\n",
              "      <td>0.84</td>\n",
              "      <td>1.40</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>2.24</td>\n",
              "      <td>0.76</td>\n",
              "      <td>1.48</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>2.24</td>\n",
              "      <td>0.76</td>\n",
              "      <td>1.48</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>2.24</td>\n",
              "      <td>0.82</td>\n",
              "      <td>1.42</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>2.08</td>\n",
              "      <td>0.68</td>\n",
              "      <td>1.40</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>2.40</td>\n",
              "      <td>0.90</td>\n",
              "      <td>1.50</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>2.22</td>\n",
              "      <td>0.82</td>\n",
              "      <td>1.40</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>2.08</td>\n",
              "      <td>0.80</td>\n",
              "      <td>1.28</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "           stride time [s]  swing time [s]  stance time [s]\n",
              "stride id                                                  \n",
              "4                     2.24            0.84             1.40\n",
              "5                     2.24            0.76             1.48\n",
              "6                     2.24            0.76             1.48\n",
              "7                     2.24            0.82             1.42\n",
              "8                     2.08            0.68             1.40\n",
              "9                     2.40            0.90             1.50\n",
              "10                    2.22            0.82             1.40\n",
              "11                    2.08            0.80             1.28"
            ]
          },
          "execution_count": 54,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "temporal_left"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 55,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/html": [
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>stride time [s]</th>\n",
              "      <th>swing time [s]</th>\n",
              "      <th>stance time [s]</th>\n",
              "    </tr>\n",
              "    <tr>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1.18</td>\n",
              "      <td>0.42</td>\n",
              "      <td>0.76</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1.14</td>\n",
              "      <td>0.44</td>\n",
              "      <td>0.70</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.96</td>\n",
              "      <td>0.28</td>\n",
              "      <td>0.68</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.78</td>\n",
              "      <td>0.28</td>\n",
              "      <td>0.50</td>\n",
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              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>1.06</td>\n",
              "      <td>0.42</td>\n",
              "      <td>0.64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>1.08</td>\n",
              "      <td>0.46</td>\n",
              "      <td>0.62</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>1.06</td>\n",
              "      <td>0.44</td>\n",
              "      <td>0.62</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "           stride time [s]  swing time [s]  stance time [s]\n",
              "stride id                                                  \n",
              "0                     1.18            0.42             0.76\n",
              "1                     1.14            0.44             0.70\n",
              "2                     0.96            0.28             0.68\n",
              "3                     0.78            0.28             0.50\n",
              "6                     1.06            0.42             0.64\n",
              "7                     1.08            0.46             0.62\n",
              "8                     1.06            0.44             0.62"
            ]
          },
          "execution_count": 55,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "temporal_right"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 56,
      "metadata": {},
      "outputs": [],
      "source": [
        "temporal = pd.concat([temporal_left, temporal_right])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 51,
      "metadata": {},
      "outputs": [],
      "source": [
        "a = spatial_paras.parameters_pretty_.values()\n",
        "b = list(a)\n",
        "spatial_left = b[0]\n",
        "spatial_right = b[1]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 93,
      "metadata": {},
      "outputs": [],
      "source": [
        "columns = ['Elapsed Time(s)', 'Left Stride Interval(s)', 'Right Stride Interval(s)', \n",
        "           'Left Swing Interval(s)', 'Right Swing Interval(s)', \n",
        "           'Left Swing Interval(%)', 'Right Swing Interval(%)', \n",
        "           'Left Stance Interval(s)', 'Right Stance Interval(s)', \n",
        "           'Left Stance Interval(%)', 'Right Stance Interval(%)', \n",
        "           'Double Support Interval(s)', 'Double Support Interval(%)']\n",
        "\n",
        "# Create an empty DataFrame with the defined columns\n",
        "result_df = pd.DataFrame(columns=columns)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 96,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Elapsed Time(s)               0.78\n",
            "Left Stride Interval(s)       2.08\n",
            "Right Stride Interval(s)      0.78\n",
            "Left Swing Interval(s)        None\n",
            "Right Swing Interval(s)       None\n",
            "Left Swing Interval(%)        None\n",
            "Right Swing Interval(%)       None\n",
            "Left Stance Interval(s)       None\n",
            "Right Stance Interval(s)      None\n",
            "Left Stance Interval(%)       None\n",
            "Right Stance Interval(%)      None\n",
            "Double Support Interval(s)    None\n",
            "Double Support Interval(%)    None\n",
            "dtype: object\n"
          ]
        },
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              "      <th>Elapsed Time(s)</th>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "      <th>9</th>\n",
              "      <td>2.40</td>\n",
              "      <td>2.40</td>\n",
              "      <td>0.78</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>2.22</td>\n",
              "      <td>2.22</td>\n",
              "      <td>0.78</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>2.08</td>\n",
              "      <td>2.08</td>\n",
              "      <td>0.78</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "      <th>0</th>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>2.08</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.78</td>\n",
              "      <td>2.08</td>\n",
              "      <td>0.78</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>1.06</td>\n",
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              "      <td>1.06</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "    <tr>\n",
              "      <th>8</th>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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            ],
            "text/plain": [
              "           Elapsed Time(s)  Left Stride Interval(s)  Right Stride Interval(s)  \\\n",
              "stride id                                                                       \n",
              "4                     2.24                     2.24                      0.78   \n",
              "5                     2.24                     2.24                      0.78   \n",
              "6                     2.24                     2.24                      1.06   \n",
              "7                     2.24                     2.24                      1.08   \n",
              "8                     2.08                     2.08                      1.06   \n",
              "9                     2.40                     2.40                      0.78   \n",
              "10                    2.22                     2.22                      0.78   \n",
              "11                    2.08                     2.08                      0.78   \n",
              "0                     1.18                     2.08                      1.18   \n",
              "1                     1.14                     2.08                      1.14   \n",
              "2                     0.96                     2.08                      0.96   \n",
              "3                     0.78                     2.08                      0.78   \n",
              "6                     1.06                     2.24                      1.06   \n",
              "7                     1.08                     2.24                      1.08   \n",
              "8                     1.06                     2.08                      1.06   \n",
              "\n",
              "          Left Swing Interval(s) Right Swing Interval(s)  \\\n",
              "stride id                                                  \n",
              "4                            NaN                     NaN   \n",
              "5                            NaN                     NaN   \n",
              "6                            NaN                     NaN   \n",
              "7                            NaN                     NaN   \n",
              "8                            NaN                     NaN   \n",
              "9                            NaN                     NaN   \n",
              "10                           NaN                     NaN   \n",
              "11                           NaN                     NaN   \n",
              "0                            NaN                     NaN   \n",
              "1                            NaN                     NaN   \n",
              "2                            NaN                     NaN   \n",
              "3                            NaN                     NaN   \n",
              "6                            NaN                     NaN   \n",
              "7                            NaN                     NaN   \n",
              "8                            NaN                     NaN   \n",
              "\n",
              "          Left Swing Interval(%) Right Swing Interval(%)  \\\n",
              "stride id                                                  \n",
              "4                            NaN                     NaN   \n",
              "5                            NaN                     NaN   \n",
              "6                            NaN                     NaN   \n",
              "7                            NaN                     NaN   \n",
              "8                            NaN                     NaN   \n",
              "9                            NaN                     NaN   \n",
              "10                           NaN                     NaN   \n",
              "11                           NaN                     NaN   \n",
              "0                            NaN                     NaN   \n",
              "1                            NaN                     NaN   \n",
              "2                            NaN                     NaN   \n",
              "3                            NaN                     NaN   \n",
              "6                            NaN                     NaN   \n",
              "7                            NaN                     NaN   \n",
              "8                            NaN                     NaN   \n",
              "\n",
              "          Left Stance Interval(s) Right Stance Interval(s)  \\\n",
              "stride id                                                    \n",
              "4                             NaN                      NaN   \n",
              "5                             NaN                      NaN   \n",
              "6                             NaN                      NaN   \n",
              "7                             NaN                      NaN   \n",
              "8                             NaN                      NaN   \n",
              "9                             NaN                      NaN   \n",
              "10                            NaN                      NaN   \n",
              "11                            NaN                      NaN   \n",
              "0                             NaN                      NaN   \n",
              "1                             NaN                      NaN   \n",
              "2                             NaN                      NaN   \n",
              "3                             NaN                      NaN   \n",
              "6                             NaN                      NaN   \n",
              "7                             NaN                      NaN   \n",
              "8                             NaN                      NaN   \n",
              "\n",
              "          Left Stance Interval(%) Right Stance Interval(%)  \\\n",
              "stride id                                                    \n",
              "4                             NaN                      NaN   \n",
              "5                             NaN                      NaN   \n",
              "6                             NaN                      NaN   \n",
              "7                             NaN                      NaN   \n",
              "8                             NaN                      NaN   \n",
              "9                             NaN                      NaN   \n",
              "10                            NaN                      NaN   \n",
              "11                            NaN                      NaN   \n",
              "0                             NaN                      NaN   \n",
              "1                             NaN                      NaN   \n",
              "2                             NaN                      NaN   \n",
              "3                             NaN                      NaN   \n",
              "6                             NaN                      NaN   \n",
              "7                             NaN                      NaN   \n",
              "8                             NaN                      NaN   \n",
              "\n",
              "          Double Support Interval(s) Double Support Interval(%)  \n",
              "stride id                                                        \n",
              "4                                NaN                        NaN  \n",
              "5                                NaN                        NaN  \n",
              "6                                NaN                        NaN  \n",
              "7                                NaN                        NaN  \n",
              "8                                NaN                        NaN  \n",
              "9                                NaN                        NaN  \n",
              "10                               NaN                        NaN  \n",
              "11                               NaN                        NaN  \n",
              "0                                NaN                        NaN  \n",
              "1                                NaN                        NaN  \n",
              "2                                NaN                        NaN  \n",
              "3                                NaN                        NaN  \n",
              "6                                NaN                        NaN  \n",
              "7                                NaN                        NaN  \n",
              "8                                NaN                        NaN  "
            ]
          },
          "execution_count": 96,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "result_df['Elapsed Time(s)'] = temporal['stride time [s]']\n",
        "\n",
        "# Derive required features\n",
        "result_df['Left Stride Interval(s)'] = temporal_left['stride time [s]']\n",
        "result_df['Right Stride Interval(s)'] = temporal_right['stride time [s]']\n",
        "\n",
        "d = result_df.min()\n",
        "print(d)\n",
        "result_df.iloc[:,:3] = result_df.iloc[:,:3].fillna(d)\n",
        "result_df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 84,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/html": [
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              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Left Swing Interval(s)</th>\n",
              "      <th>Right Swing Interval(s)</th>\n",
              "      <th>Left Swing Interval(%)</th>\n",
              "      <th>Right Swing Interval(%)</th>\n",
              "      <th>Left Stance Interval(s)</th>\n",
              "      <th>Right Stance Interval(s)</th>\n",
              "      <th>Left Stance Interval(%)</th>\n",
              "      <th>Right Stance Interval(%)</th>\n",
              "      <th>Double Support Interval(s)</th>\n",
              "      <th>Double Support Interval(%)</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>stride id</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.84</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.40</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>0.76</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.48</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>0.76</td>\n",
              "      <td>0.42</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.48</td>\n",
              "      <td>0.64</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>0.82</td>\n",
              "      <td>0.46</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.42</td>\n",
              "      <td>0.62</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>0.68</td>\n",
              "      <td>0.44</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.40</td>\n",
              "      <td>0.62</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>0.90</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.50</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>0.82</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.40</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>0.80</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.28</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "           Left Swing Interval(s)  Right Swing Interval(s)  \\\n",
              "stride id                                                    \n",
              "4                            0.84                      NaN   \n",
              "5                            0.76                      NaN   \n",
              "6                            0.76                     0.42   \n",
              "7                            0.82                     0.46   \n",
              "8                            0.68                     0.44   \n",
              "9                            0.90                      NaN   \n",
              "10                           0.82                      NaN   \n",
              "11                           0.80                      NaN   \n",
              "\n",
              "          Left Swing Interval(%) Right Swing Interval(%)  \\\n",
              "stride id                                                  \n",
              "4                            NaN                     NaN   \n",
              "5                            NaN                     NaN   \n",
              "6                            NaN                     NaN   \n",
              "7                            NaN                     NaN   \n",
              "8                            NaN                     NaN   \n",
              "9                            NaN                     NaN   \n",
              "10                           NaN                     NaN   \n",
              "11                           NaN                     NaN   \n",
              "\n",
              "           Left Stance Interval(s)  Right Stance Interval(s)  \\\n",
              "stride id                                                      \n",
              "4                             1.40                       NaN   \n",
              "5                             1.48                       NaN   \n",
              "6                             1.48                      0.64   \n",
              "7                             1.42                      0.62   \n",
              "8                             1.40                      0.62   \n",
              "9                             1.50                       NaN   \n",
              "10                            1.40                       NaN   \n",
              "11                            1.28                       NaN   \n",
              "\n",
              "          Left Stance Interval(%) Right Stance Interval(%)  \\\n",
              "stride id                                                    \n",
              "4                             NaN                      NaN   \n",
              "5                             NaN                      NaN   \n",
              "6                             NaN                      NaN   \n",
              "7                             NaN                      NaN   \n",
              "8                             NaN                      NaN   \n",
              "9                             NaN                      NaN   \n",
              "10                            NaN                      NaN   \n",
              "11                            NaN                      NaN   \n",
              "\n",
              "          Double Support Interval(s) Double Support Interval(%)  \n",
              "stride id                                                        \n",
              "4                                NaN                        NaN  \n",
              "5                                NaN                        NaN  \n",
              "6                                NaN                        NaN  \n",
              "7                                NaN                        NaN  \n",
              "8                                NaN                        NaN  \n",
              "9                                NaN                        NaN  \n",
              "10                               NaN                        NaN  \n",
              "11                               NaN                        NaN  "
            ]
          },
          "execution_count": 84,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "another_df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 97,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Fill 'result_df' with necessary values\n",
        "result_df['Left Swing Interval(s)'] = temporal_left['swing time [s]']\n",
        "result_df['Right Swing Interval(s)'] = temporal_right['swing time [s]']\n",
        "result_df['Left Stance Interval(s)'] = temporal_left['stance time [s]']\n",
        "result_df['Right Stance Interval(s)'] = temporal_right['stance time [s]']\n",
        "\n",
        "# Calculate derived features in 'result_df'\n",
        "result_df['Left Swing Interval(%)'] = (result_df['Left Swing Interval(s)'] / result_df['Left Stride Interval(s)']) * 100\n",
        "result_df['Right Swing Interval(%)'] = (result_df['Right Swing Interval(s)'] / result_df['Right Stride Interval(s)']) * 100\n",
        "result_df['Left Stance Interval(%)'] = (result_df['Left Stance Interval(s)'] / result_df['Left Stride Interval(s)']) * 100\n",
        "result_df['Right Stance Interval(%)'] = (result_df['Right Stance Interval(s)'] / result_df['Right Stride Interval(s)']) * 100\n",
        "\n",
        "result_df['Double Support Interval(s)'] = temporal['stride time [s]'] - (result_df['Left Swing Interval(s)'] + result_df['Right Swing Interval(s)']) / 2\n",
        "result_df['Double Support Interval(%)'] = (result_df['Double Support Interval(s)'] / temporal['stride time [s]']) * 100"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 98,
      "metadata": {},
      "outputs": [
        {
          "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(s)</th>\n",
              "      <th>Left Stride Interval(s)</th>\n",
              "      <th>Right Stride Interval(s)</th>\n",
              "      <th>Left Swing Interval(s)</th>\n",
              "      <th>Right Swing Interval(s)</th>\n",
              "      <th>Left Swing Interval(%)</th>\n",
              "      <th>Right Swing Interval(%)</th>\n",
              "      <th>Left Stance Interval(s)</th>\n",
              "      <th>Right Stance Interval(s)</th>\n",
              "      <th>Left Stance Interval(%)</th>\n",
              "      <th>Right Stance Interval(%)</th>\n",
              "      <th>Double Support Interval(s)</th>\n",
              "      <th>Double Support Interval(%)</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>stride id</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2.24</td>\n",
              "      <td>2.24</td>\n",
              "      <td>0.78</td>\n",
              "      <td>0.84</td>\n",
              "      <td>NaN</td>\n",
              "      <td>37.500000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.40</td>\n",
              "      <td>NaN</td>\n",
              "      <td>62.500000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>2.24</td>\n",
              "      <td>2.24</td>\n",
              "      <td>0.78</td>\n",
              "      <td>0.76</td>\n",
              "      <td>NaN</td>\n",
              "      <td>33.928571</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.48</td>\n",
              "      <td>NaN</td>\n",
              "      <td>66.071429</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>2.24</td>\n",
              "      <td>2.24</td>\n",
              "      <td>1.06</td>\n",
              "      <td>0.76</td>\n",
              "      <td>0.42</td>\n",
              "      <td>33.928571</td>\n",
              "      <td>39.622642</td>\n",
              "      <td>1.48</td>\n",
              "      <td>0.64</td>\n",
              "      <td>66.071429</td>\n",
              "      <td>60.377358</td>\n",
              "      <td>1.65</td>\n",
              "      <td>73.660714</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>2.24</td>\n",
              "      <td>2.24</td>\n",
              "      <td>1.08</td>\n",
              "      <td>0.82</td>\n",
              "      <td>0.46</td>\n",
              "      <td>36.607143</td>\n",
              "      <td>42.592593</td>\n",
              "      <td>1.42</td>\n",
              "      <td>0.62</td>\n",
              "      <td>63.392857</td>\n",
              "      <td>57.407407</td>\n",
              "      <td>1.60</td>\n",
              "      <td>71.428571</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>2.08</td>\n",
              "      <td>2.08</td>\n",
              "      <td>1.06</td>\n",
              "      <td>0.68</td>\n",
              "      <td>0.44</td>\n",
              "      <td>32.692308</td>\n",
              "      <td>41.509434</td>\n",
              "      <td>1.40</td>\n",
              "      <td>0.62</td>\n",
              "      <td>67.307692</td>\n",
              "      <td>58.490566</td>\n",
              "      <td>1.52</td>\n",
              "      <td>73.076923</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>2.40</td>\n",
              "      <td>2.40</td>\n",
              "      <td>0.78</td>\n",
              "      <td>0.90</td>\n",
              "      <td>NaN</td>\n",
              "      <td>37.500000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.50</td>\n",
              "      <td>NaN</td>\n",
              "      <td>62.500000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>2.22</td>\n",
              "      <td>2.22</td>\n",
              "      <td>0.78</td>\n",
              "      <td>0.82</td>\n",
              "      <td>NaN</td>\n",
              "      <td>36.936937</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.40</td>\n",
              "      <td>NaN</td>\n",
              "      <td>63.063063</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>2.08</td>\n",
              "      <td>2.08</td>\n",
              "      <td>0.78</td>\n",
              "      <td>0.80</td>\n",
              "      <td>NaN</td>\n",
              "      <td>38.461538</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.28</td>\n",
              "      <td>NaN</td>\n",
              "      <td>61.538462</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1.18</td>\n",
              "      <td>2.08</td>\n",
              "      <td>1.18</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.42</td>\n",
              "      <td>NaN</td>\n",
              "      <td>35.593220</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.76</td>\n",
              "      <td>NaN</td>\n",
              "      <td>64.406780</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1.14</td>\n",
              "      <td>2.08</td>\n",
              "      <td>1.14</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.44</td>\n",
              "      <td>NaN</td>\n",
              "      <td>38.596491</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.70</td>\n",
              "      <td>NaN</td>\n",
              "      <td>61.403509</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.96</td>\n",
              "      <td>2.08</td>\n",
              "      <td>0.96</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.28</td>\n",
              "      <td>NaN</td>\n",
              "      <td>29.166667</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.68</td>\n",
              "      <td>NaN</td>\n",
              "      <td>70.833333</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.78</td>\n",
              "      <td>2.08</td>\n",
              "      <td>0.78</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.28</td>\n",
              "      <td>NaN</td>\n",
              "      <td>35.897436</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.50</td>\n",
              "      <td>NaN</td>\n",
              "      <td>64.102564</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>1.06</td>\n",
              "      <td>2.24</td>\n",
              "      <td>1.06</td>\n",
              "      <td>0.76</td>\n",
              "      <td>0.42</td>\n",
              "      <td>33.928571</td>\n",
              "      <td>39.622642</td>\n",
              "      <td>1.48</td>\n",
              "      <td>0.64</td>\n",
              "      <td>66.071429</td>\n",
              "      <td>60.377358</td>\n",
              "      <td>0.47</td>\n",
              "      <td>44.339623</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>1.08</td>\n",
              "      <td>2.24</td>\n",
              "      <td>1.08</td>\n",
              "      <td>0.82</td>\n",
              "      <td>0.46</td>\n",
              "      <td>36.607143</td>\n",
              "      <td>42.592593</td>\n",
              "      <td>1.42</td>\n",
              "      <td>0.62</td>\n",
              "      <td>63.392857</td>\n",
              "      <td>57.407407</td>\n",
              "      <td>0.44</td>\n",
              "      <td>40.740741</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>1.06</td>\n",
              "      <td>2.08</td>\n",
              "      <td>1.06</td>\n",
              "      <td>0.68</td>\n",
              "      <td>0.44</td>\n",
              "      <td>32.692308</td>\n",
              "      <td>41.509434</td>\n",
              "      <td>1.40</td>\n",
              "      <td>0.62</td>\n",
              "      <td>67.307692</td>\n",
              "      <td>58.490566</td>\n",
              "      <td>0.50</td>\n",
              "      <td>47.169811</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "           Elapsed Time(s)  Left Stride Interval(s)  Right Stride Interval(s)  \\\n",
              "stride id                                                                       \n",
              "4                     2.24                     2.24                      0.78   \n",
              "5                     2.24                     2.24                      0.78   \n",
              "6                     2.24                     2.24                      1.06   \n",
              "7                     2.24                     2.24                      1.08   \n",
              "8                     2.08                     2.08                      1.06   \n",
              "9                     2.40                     2.40                      0.78   \n",
              "10                    2.22                     2.22                      0.78   \n",
              "11                    2.08                     2.08                      0.78   \n",
              "0                     1.18                     2.08                      1.18   \n",
              "1                     1.14                     2.08                      1.14   \n",
              "2                     0.96                     2.08                      0.96   \n",
              "3                     0.78                     2.08                      0.78   \n",
              "6                     1.06                     2.24                      1.06   \n",
              "7                     1.08                     2.24                      1.08   \n",
              "8                     1.06                     2.08                      1.06   \n",
              "\n",
              "           Left Swing Interval(s)  Right Swing Interval(s)  \\\n",
              "stride id                                                    \n",
              "4                            0.84                      NaN   \n",
              "5                            0.76                      NaN   \n",
              "6                            0.76                     0.42   \n",
              "7                            0.82                     0.46   \n",
              "8                            0.68                     0.44   \n",
              "9                            0.90                      NaN   \n",
              "10                           0.82                      NaN   \n",
              "11                           0.80                      NaN   \n",
              "0                             NaN                     0.42   \n",
              "1                             NaN                     0.44   \n",
              "2                             NaN                     0.28   \n",
              "3                             NaN                     0.28   \n",
              "6                            0.76                     0.42   \n",
              "7                            0.82                     0.46   \n",
              "8                            0.68                     0.44   \n",
              "\n",
              "           Left Swing Interval(%)  Right Swing Interval(%)  \\\n",
              "stride id                                                    \n",
              "4                       37.500000                      NaN   \n",
              "5                       33.928571                      NaN   \n",
              "6                       33.928571                39.622642   \n",
              "7                       36.607143                42.592593   \n",
              "8                       32.692308                41.509434   \n",
              "9                       37.500000                      NaN   \n",
              "10                      36.936937                      NaN   \n",
              "11                      38.461538                      NaN   \n",
              "0                             NaN                35.593220   \n",
              "1                             NaN                38.596491   \n",
              "2                             NaN                29.166667   \n",
              "3                             NaN                35.897436   \n",
              "6                       33.928571                39.622642   \n",
              "7                       36.607143                42.592593   \n",
              "8                       32.692308                41.509434   \n",
              "\n",
              "           Left Stance Interval(s)  Right Stance Interval(s)  \\\n",
              "stride id                                                      \n",
              "4                             1.40                       NaN   \n",
              "5                             1.48                       NaN   \n",
              "6                             1.48                      0.64   \n",
              "7                             1.42                      0.62   \n",
              "8                             1.40                      0.62   \n",
              "9                             1.50                       NaN   \n",
              "10                            1.40                       NaN   \n",
              "11                            1.28                       NaN   \n",
              "0                              NaN                      0.76   \n",
              "1                              NaN                      0.70   \n",
              "2                              NaN                      0.68   \n",
              "3                              NaN                      0.50   \n",
              "6                             1.48                      0.64   \n",
              "7                             1.42                      0.62   \n",
              "8                             1.40                      0.62   \n",
              "\n",
              "           Left Stance Interval(%)  Right Stance Interval(%)  \\\n",
              "stride id                                                      \n",
              "4                        62.500000                       NaN   \n",
              "5                        66.071429                       NaN   \n",
              "6                        66.071429                 60.377358   \n",
              "7                        63.392857                 57.407407   \n",
              "8                        67.307692                 58.490566   \n",
              "9                        62.500000                       NaN   \n",
              "10                       63.063063                       NaN   \n",
              "11                       61.538462                       NaN   \n",
              "0                              NaN                 64.406780   \n",
              "1                              NaN                 61.403509   \n",
              "2                              NaN                 70.833333   \n",
              "3                              NaN                 64.102564   \n",
              "6                        66.071429                 60.377358   \n",
              "7                        63.392857                 57.407407   \n",
              "8                        67.307692                 58.490566   \n",
              "\n",
              "           Double Support Interval(s)  Double Support Interval(%)  \n",
              "stride id                                                          \n",
              "4                                 NaN                         NaN  \n",
              "5                                 NaN                         NaN  \n",
              "6                                1.65                   73.660714  \n",
              "7                                1.60                   71.428571  \n",
              "8                                1.52                   73.076923  \n",
              "9                                 NaN                         NaN  \n",
              "10                                NaN                         NaN  \n",
              "11                                NaN                         NaN  \n",
              "0                                 NaN                         NaN  \n",
              "1                                 NaN                         NaN  \n",
              "2                                 NaN                         NaN  \n",
              "3                                 NaN                         NaN  \n",
              "6                                0.47                   44.339623  \n",
              "7                                0.44                   40.740741  \n",
              "8                                0.50                   47.169811  "
            ]
          },
          "execution_count": 98,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "result_df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 99,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/html": [
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Elapsed Time(s)</th>\n",
              "      <th>Left Stride Interval(s)</th>\n",
              "      <th>Right Stride Interval(s)</th>\n",
              "      <th>Left Swing Interval(s)</th>\n",
              "      <th>Right Swing Interval(s)</th>\n",
              "      <th>Left Swing Interval(%)</th>\n",
              "      <th>Right Swing Interval(%)</th>\n",
              "      <th>Left Stance Interval(s)</th>\n",
              "      <th>Right Stance Interval(s)</th>\n",
              "      <th>Left Stance Interval(%)</th>\n",
              "      <th>Right Stance Interval(%)</th>\n",
              "      <th>Double Support Interval(s)</th>\n",
              "      <th>Double Support Interval(%)</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>stride id</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2.24</td>\n",
              "      <td>2.24</td>\n",
              "      <td>0.78</td>\n",
              "      <td>0.84</td>\n",
              "      <td>0.28</td>\n",
              "      <td>37.500000</td>\n",
              "      <td>29.166667</td>\n",
              "      <td>1.40</td>\n",
              "      <td>0.50</td>\n",
              "      <td>62.500000</td>\n",
              "      <td>57.407407</td>\n",
              "      <td>0.44</td>\n",
              "      <td>40.740741</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>2.24</td>\n",
              "      <td>2.24</td>\n",
              "      <td>0.78</td>\n",
              "      <td>0.76</td>\n",
              "      <td>0.28</td>\n",
              "      <td>33.928571</td>\n",
              "      <td>29.166667</td>\n",
              "      <td>1.48</td>\n",
              "      <td>0.50</td>\n",
              "      <td>66.071429</td>\n",
              "      <td>57.407407</td>\n",
              "      <td>0.44</td>\n",
              "      <td>40.740741</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>2.24</td>\n",
              "      <td>2.24</td>\n",
              "      <td>1.06</td>\n",
              "      <td>0.76</td>\n",
              "      <td>0.42</td>\n",
              "      <td>33.928571</td>\n",
              "      <td>39.622642</td>\n",
              "      <td>1.48</td>\n",
              "      <td>0.64</td>\n",
              "      <td>66.071429</td>\n",
              "      <td>60.377358</td>\n",
              "      <td>1.65</td>\n",
              "      <td>73.660714</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>2.24</td>\n",
              "      <td>2.24</td>\n",
              "      <td>1.08</td>\n",
              "      <td>0.82</td>\n",
              "      <td>0.46</td>\n",
              "      <td>36.607143</td>\n",
              "      <td>42.592593</td>\n",
              "      <td>1.42</td>\n",
              "      <td>0.62</td>\n",
              "      <td>63.392857</td>\n",
              "      <td>57.407407</td>\n",
              "      <td>1.60</td>\n",
              "      <td>71.428571</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>2.08</td>\n",
              "      <td>2.08</td>\n",
              "      <td>1.06</td>\n",
              "      <td>0.68</td>\n",
              "      <td>0.44</td>\n",
              "      <td>32.692308</td>\n",
              "      <td>41.509434</td>\n",
              "      <td>1.40</td>\n",
              "      <td>0.62</td>\n",
              "      <td>67.307692</td>\n",
              "      <td>58.490566</td>\n",
              "      <td>1.52</td>\n",
              "      <td>73.076923</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>2.40</td>\n",
              "      <td>2.40</td>\n",
              "      <td>0.78</td>\n",
              "      <td>0.90</td>\n",
              "      <td>0.28</td>\n",
              "      <td>37.500000</td>\n",
              "      <td>29.166667</td>\n",
              "      <td>1.50</td>\n",
              "      <td>0.50</td>\n",
              "      <td>62.500000</td>\n",
              "      <td>57.407407</td>\n",
              "      <td>0.44</td>\n",
              "      <td>40.740741</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>2.22</td>\n",
              "      <td>2.22</td>\n",
              "      <td>0.78</td>\n",
              "      <td>0.82</td>\n",
              "      <td>0.28</td>\n",
              "      <td>36.936937</td>\n",
              "      <td>29.166667</td>\n",
              "      <td>1.40</td>\n",
              "      <td>0.50</td>\n",
              "      <td>63.063063</td>\n",
              "      <td>57.407407</td>\n",
              "      <td>0.44</td>\n",
              "      <td>40.740741</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>2.08</td>\n",
              "      <td>2.08</td>\n",
              "      <td>0.78</td>\n",
              "      <td>0.80</td>\n",
              "      <td>0.28</td>\n",
              "      <td>38.461538</td>\n",
              "      <td>29.166667</td>\n",
              "      <td>1.28</td>\n",
              "      <td>0.50</td>\n",
              "      <td>61.538462</td>\n",
              "      <td>57.407407</td>\n",
              "      <td>0.44</td>\n",
              "      <td>40.740741</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1.18</td>\n",
              "      <td>2.08</td>\n",
              "      <td>1.18</td>\n",
              "      <td>0.68</td>\n",
              "      <td>0.42</td>\n",
              "      <td>32.692308</td>\n",
              "      <td>35.593220</td>\n",
              "      <td>1.28</td>\n",
              "      <td>0.76</td>\n",
              "      <td>61.538462</td>\n",
              "      <td>64.406780</td>\n",
              "      <td>0.44</td>\n",
              "      <td>40.740741</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1.14</td>\n",
              "      <td>2.08</td>\n",
              "      <td>1.14</td>\n",
              "      <td>0.68</td>\n",
              "      <td>0.44</td>\n",
              "      <td>32.692308</td>\n",
              "      <td>38.596491</td>\n",
              "      <td>1.28</td>\n",
              "      <td>0.70</td>\n",
              "      <td>61.538462</td>\n",
              "      <td>61.403509</td>\n",
              "      <td>0.44</td>\n",
              "      <td>40.740741</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.96</td>\n",
              "      <td>2.08</td>\n",
              "      <td>0.96</td>\n",
              "      <td>0.68</td>\n",
              "      <td>0.28</td>\n",
              "      <td>32.692308</td>\n",
              "      <td>29.166667</td>\n",
              "      <td>1.28</td>\n",
              "      <td>0.68</td>\n",
              "      <td>61.538462</td>\n",
              "      <td>70.833333</td>\n",
              "      <td>0.44</td>\n",
              "      <td>40.740741</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.78</td>\n",
              "      <td>2.08</td>\n",
              "      <td>0.78</td>\n",
              "      <td>0.68</td>\n",
              "      <td>0.28</td>\n",
              "      <td>32.692308</td>\n",
              "      <td>35.897436</td>\n",
              "      <td>1.28</td>\n",
              "      <td>0.50</td>\n",
              "      <td>61.538462</td>\n",
              "      <td>64.102564</td>\n",
              "      <td>0.44</td>\n",
              "      <td>40.740741</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>1.06</td>\n",
              "      <td>2.24</td>\n",
              "      <td>1.06</td>\n",
              "      <td>0.76</td>\n",
              "      <td>0.42</td>\n",
              "      <td>33.928571</td>\n",
              "      <td>39.622642</td>\n",
              "      <td>1.48</td>\n",
              "      <td>0.64</td>\n",
              "      <td>66.071429</td>\n",
              "      <td>60.377358</td>\n",
              "      <td>0.47</td>\n",
              "      <td>44.339623</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>1.08</td>\n",
              "      <td>2.24</td>\n",
              "      <td>1.08</td>\n",
              "      <td>0.82</td>\n",
              "      <td>0.46</td>\n",
              "      <td>36.607143</td>\n",
              "      <td>42.592593</td>\n",
              "      <td>1.42</td>\n",
              "      <td>0.62</td>\n",
              "      <td>63.392857</td>\n",
              "      <td>57.407407</td>\n",
              "      <td>0.44</td>\n",
              "      <td>40.740741</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>1.06</td>\n",
              "      <td>2.08</td>\n",
              "      <td>1.06</td>\n",
              "      <td>0.68</td>\n",
              "      <td>0.44</td>\n",
              "      <td>32.692308</td>\n",
              "      <td>41.509434</td>\n",
              "      <td>1.40</td>\n",
              "      <td>0.62</td>\n",
              "      <td>67.307692</td>\n",
              "      <td>58.490566</td>\n",
              "      <td>0.50</td>\n",
              "      <td>47.169811</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "           Elapsed Time(s)  Left Stride Interval(s)  Right Stride Interval(s)  \\\n",
              "stride id                                                                       \n",
              "4                     2.24                     2.24                      0.78   \n",
              "5                     2.24                     2.24                      0.78   \n",
              "6                     2.24                     2.24                      1.06   \n",
              "7                     2.24                     2.24                      1.08   \n",
              "8                     2.08                     2.08                      1.06   \n",
              "9                     2.40                     2.40                      0.78   \n",
              "10                    2.22                     2.22                      0.78   \n",
              "11                    2.08                     2.08                      0.78   \n",
              "0                     1.18                     2.08                      1.18   \n",
              "1                     1.14                     2.08                      1.14   \n",
              "2                     0.96                     2.08                      0.96   \n",
              "3                     0.78                     2.08                      0.78   \n",
              "6                     1.06                     2.24                      1.06   \n",
              "7                     1.08                     2.24                      1.08   \n",
              "8                     1.06                     2.08                      1.06   \n",
              "\n",
              "           Left Swing Interval(s)  Right Swing Interval(s)  \\\n",
              "stride id                                                    \n",
              "4                            0.84                     0.28   \n",
              "5                            0.76                     0.28   \n",
              "6                            0.76                     0.42   \n",
              "7                            0.82                     0.46   \n",
              "8                            0.68                     0.44   \n",
              "9                            0.90                     0.28   \n",
              "10                           0.82                     0.28   \n",
              "11                           0.80                     0.28   \n",
              "0                            0.68                     0.42   \n",
              "1                            0.68                     0.44   \n",
              "2                            0.68                     0.28   \n",
              "3                            0.68                     0.28   \n",
              "6                            0.76                     0.42   \n",
              "7                            0.82                     0.46   \n",
              "8                            0.68                     0.44   \n",
              "\n",
              "           Left Swing Interval(%)  Right Swing Interval(%)  \\\n",
              "stride id                                                    \n",
              "4                       37.500000                29.166667   \n",
              "5                       33.928571                29.166667   \n",
              "6                       33.928571                39.622642   \n",
              "7                       36.607143                42.592593   \n",
              "8                       32.692308                41.509434   \n",
              "9                       37.500000                29.166667   \n",
              "10                      36.936937                29.166667   \n",
              "11                      38.461538                29.166667   \n",
              "0                       32.692308                35.593220   \n",
              "1                       32.692308                38.596491   \n",
              "2                       32.692308                29.166667   \n",
              "3                       32.692308                35.897436   \n",
              "6                       33.928571                39.622642   \n",
              "7                       36.607143                42.592593   \n",
              "8                       32.692308                41.509434   \n",
              "\n",
              "           Left Stance Interval(s)  Right Stance Interval(s)  \\\n",
              "stride id                                                      \n",
              "4                             1.40                      0.50   \n",
              "5                             1.48                      0.50   \n",
              "6                             1.48                      0.64   \n",
              "7                             1.42                      0.62   \n",
              "8                             1.40                      0.62   \n",
              "9                             1.50                      0.50   \n",
              "10                            1.40                      0.50   \n",
              "11                            1.28                      0.50   \n",
              "0                             1.28                      0.76   \n",
              "1                             1.28                      0.70   \n",
              "2                             1.28                      0.68   \n",
              "3                             1.28                      0.50   \n",
              "6                             1.48                      0.64   \n",
              "7                             1.42                      0.62   \n",
              "8                             1.40                      0.62   \n",
              "\n",
              "           Left Stance Interval(%)  Right Stance Interval(%)  \\\n",
              "stride id                                                      \n",
              "4                        62.500000                 57.407407   \n",
              "5                        66.071429                 57.407407   \n",
              "6                        66.071429                 60.377358   \n",
              "7                        63.392857                 57.407407   \n",
              "8                        67.307692                 58.490566   \n",
              "9                        62.500000                 57.407407   \n",
              "10                       63.063063                 57.407407   \n",
              "11                       61.538462                 57.407407   \n",
              "0                        61.538462                 64.406780   \n",
              "1                        61.538462                 61.403509   \n",
              "2                        61.538462                 70.833333   \n",
              "3                        61.538462                 64.102564   \n",
              "6                        66.071429                 60.377358   \n",
              "7                        63.392857                 57.407407   \n",
              "8                        67.307692                 58.490566   \n",
              "\n",
              "           Double Support Interval(s)  Double Support Interval(%)  \n",
              "stride id                                                          \n",
              "4                                0.44                   40.740741  \n",
              "5                                0.44                   40.740741  \n",
              "6                                1.65                   73.660714  \n",
              "7                                1.60                   71.428571  \n",
              "8                                1.52                   73.076923  \n",
              "9                                0.44                   40.740741  \n",
              "10                               0.44                   40.740741  \n",
              "11                               0.44                   40.740741  \n",
              "0                                0.44                   40.740741  \n",
              "1                                0.44                   40.740741  \n",
              "2                                0.44                   40.740741  \n",
              "3                                0.44                   40.740741  \n",
              "6                                0.47                   44.339623  \n",
              "7                                0.44                   40.740741  \n",
              "8                                0.50                   47.169811  "
            ]
          },
          "execution_count": 99,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "result_df.iloc[:,3:] = result_df.iloc[:,3:].fillna(result_df.iloc[:,3:].min())\n",
        "result_df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 102,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/html": [
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              "<style scoped>\n",
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              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Elapsed Time(s)</th>\n",
              "      <th>Left Stride Interval(s)</th>\n",
              "      <th>Right Stride Interval(s)</th>\n",
              "      <th>Left Swing Interval(s)</th>\n",
              "      <th>Right Swing Interval(s)</th>\n",
              "      <th>Left Swing Interval(%)</th>\n",
              "      <th>Right Swing Interval(%)</th>\n",
              "      <th>Left Stance Interval(s)</th>\n",
              "      <th>Right Stance Interval(s)</th>\n",
              "      <th>Left Stance Interval(%)</th>\n",
              "      <th>Right Stance Interval(%)</th>\n",
              "      <th>Double Support Interval(s)</th>\n",
              "      <th>Double Support Interval(%)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2.24</td>\n",
              "      <td>2.24</td>\n",
              "      <td>0.78</td>\n",
              "      <td>0.84</td>\n",
              "      <td>0.28</td>\n",
              "      <td>37.500000</td>\n",
              "      <td>29.166667</td>\n",
              "      <td>1.40</td>\n",
              "      <td>0.50</td>\n",
              "      <td>62.500000</td>\n",
              "      <td>57.407407</td>\n",
              "      <td>0.44</td>\n",
              "      <td>40.740741</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2.24</td>\n",
              "      <td>2.24</td>\n",
              "      <td>0.78</td>\n",
              "      <td>0.76</td>\n",
              "      <td>0.28</td>\n",
              "      <td>33.928571</td>\n",
              "      <td>29.166667</td>\n",
              "      <td>1.48</td>\n",
              "      <td>0.50</td>\n",
              "      <td>66.071429</td>\n",
              "      <td>57.407407</td>\n",
              "      <td>0.44</td>\n",
              "      <td>40.740741</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2.24</td>\n",
              "      <td>2.24</td>\n",
              "      <td>1.06</td>\n",
              "      <td>0.76</td>\n",
              "      <td>0.42</td>\n",
              "      <td>33.928571</td>\n",
              "      <td>39.622642</td>\n",
              "      <td>1.48</td>\n",
              "      <td>0.64</td>\n",
              "      <td>66.071429</td>\n",
              "      <td>60.377358</td>\n",
              "      <td>1.65</td>\n",
              "      <td>73.660714</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2.24</td>\n",
              "      <td>2.24</td>\n",
              "      <td>1.08</td>\n",
              "      <td>0.82</td>\n",
              "      <td>0.46</td>\n",
              "      <td>36.607143</td>\n",
              "      <td>42.592593</td>\n",
              "      <td>1.42</td>\n",
              "      <td>0.62</td>\n",
              "      <td>63.392857</td>\n",
              "      <td>57.407407</td>\n",
              "      <td>1.60</td>\n",
              "      <td>71.428571</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2.08</td>\n",
              "      <td>2.08</td>\n",
              "      <td>1.06</td>\n",
              "      <td>0.68</td>\n",
              "      <td>0.44</td>\n",
              "      <td>32.692308</td>\n",
              "      <td>41.509434</td>\n",
              "      <td>1.40</td>\n",
              "      <td>0.62</td>\n",
              "      <td>67.307692</td>\n",
              "      <td>58.490566</td>\n",
              "      <td>1.52</td>\n",
              "      <td>73.076923</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>2.40</td>\n",
              "      <td>2.40</td>\n",
              "      <td>0.78</td>\n",
              "      <td>0.90</td>\n",
              "      <td>0.28</td>\n",
              "      <td>37.500000</td>\n",
              "      <td>29.166667</td>\n",
              "      <td>1.50</td>\n",
              "      <td>0.50</td>\n",
              "      <td>62.500000</td>\n",
              "      <td>57.407407</td>\n",
              "      <td>0.44</td>\n",
              "      <td>40.740741</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>2.22</td>\n",
              "      <td>2.22</td>\n",
              "      <td>0.78</td>\n",
              "      <td>0.82</td>\n",
              "      <td>0.28</td>\n",
              "      <td>36.936937</td>\n",
              "      <td>29.166667</td>\n",
              "      <td>1.40</td>\n",
              "      <td>0.50</td>\n",
              "      <td>63.063063</td>\n",
              "      <td>57.407407</td>\n",
              "      <td>0.44</td>\n",
              "      <td>40.740741</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>2.08</td>\n",
              "      <td>2.08</td>\n",
              "      <td>0.78</td>\n",
              "      <td>0.80</td>\n",
              "      <td>0.28</td>\n",
              "      <td>38.461538</td>\n",
              "      <td>29.166667</td>\n",
              "      <td>1.28</td>\n",
              "      <td>0.50</td>\n",
              "      <td>61.538462</td>\n",
              "      <td>57.407407</td>\n",
              "      <td>0.44</td>\n",
              "      <td>40.740741</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>1.18</td>\n",
              "      <td>2.08</td>\n",
              "      <td>1.18</td>\n",
              "      <td>0.68</td>\n",
              "      <td>0.42</td>\n",
              "      <td>32.692308</td>\n",
              "      <td>35.593220</td>\n",
              "      <td>1.28</td>\n",
              "      <td>0.76</td>\n",
              "      <td>61.538462</td>\n",
              "      <td>64.406780</td>\n",
              "      <td>0.44</td>\n",
              "      <td>40.740741</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>1.14</td>\n",
              "      <td>2.08</td>\n",
              "      <td>1.14</td>\n",
              "      <td>0.68</td>\n",
              "      <td>0.44</td>\n",
              "      <td>32.692308</td>\n",
              "      <td>38.596491</td>\n",
              "      <td>1.28</td>\n",
              "      <td>0.70</td>\n",
              "      <td>61.538462</td>\n",
              "      <td>61.403509</td>\n",
              "      <td>0.44</td>\n",
              "      <td>40.740741</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>0.96</td>\n",
              "      <td>2.08</td>\n",
              "      <td>0.96</td>\n",
              "      <td>0.68</td>\n",
              "      <td>0.28</td>\n",
              "      <td>32.692308</td>\n",
              "      <td>29.166667</td>\n",
              "      <td>1.28</td>\n",
              "      <td>0.68</td>\n",
              "      <td>61.538462</td>\n",
              "      <td>70.833333</td>\n",
              "      <td>0.44</td>\n",
              "      <td>40.740741</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>0.78</td>\n",
              "      <td>2.08</td>\n",
              "      <td>0.78</td>\n",
              "      <td>0.68</td>\n",
              "      <td>0.28</td>\n",
              "      <td>32.692308</td>\n",
              "      <td>35.897436</td>\n",
              "      <td>1.28</td>\n",
              "      <td>0.50</td>\n",
              "      <td>61.538462</td>\n",
              "      <td>64.102564</td>\n",
              "      <td>0.44</td>\n",
              "      <td>40.740741</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>1.06</td>\n",
              "      <td>2.24</td>\n",
              "      <td>1.06</td>\n",
              "      <td>0.76</td>\n",
              "      <td>0.42</td>\n",
              "      <td>33.928571</td>\n",
              "      <td>39.622642</td>\n",
              "      <td>1.48</td>\n",
              "      <td>0.64</td>\n",
              "      <td>66.071429</td>\n",
              "      <td>60.377358</td>\n",
              "      <td>0.47</td>\n",
              "      <td>44.339623</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>1.08</td>\n",
              "      <td>2.24</td>\n",
              "      <td>1.08</td>\n",
              "      <td>0.82</td>\n",
              "      <td>0.46</td>\n",
              "      <td>36.607143</td>\n",
              "      <td>42.592593</td>\n",
              "      <td>1.42</td>\n",
              "      <td>0.62</td>\n",
              "      <td>63.392857</td>\n",
              "      <td>57.407407</td>\n",
              "      <td>0.44</td>\n",
              "      <td>40.740741</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>1.06</td>\n",
              "      <td>2.08</td>\n",
              "      <td>1.06</td>\n",
              "      <td>0.68</td>\n",
              "      <td>0.44</td>\n",
              "      <td>32.692308</td>\n",
              "      <td>41.509434</td>\n",
              "      <td>1.40</td>\n",
              "      <td>0.62</td>\n",
              "      <td>67.307692</td>\n",
              "      <td>58.490566</td>\n",
              "      <td>0.50</td>\n",
              "      <td>47.169811</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    Elapsed Time(s)  Left Stride Interval(s)  Right Stride Interval(s)  \\\n",
              "0              2.24                     2.24                      0.78   \n",
              "1              2.24                     2.24                      0.78   \n",
              "2              2.24                     2.24                      1.06   \n",
              "3              2.24                     2.24                      1.08   \n",
              "4              2.08                     2.08                      1.06   \n",
              "5              2.40                     2.40                      0.78   \n",
              "6              2.22                     2.22                      0.78   \n",
              "7              2.08                     2.08                      0.78   \n",
              "8              1.18                     2.08                      1.18   \n",
              "9              1.14                     2.08                      1.14   \n",
              "10             0.96                     2.08                      0.96   \n",
              "11             0.78                     2.08                      0.78   \n",
              "12             1.06                     2.24                      1.06   \n",
              "13             1.08                     2.24                      1.08   \n",
              "14             1.06                     2.08                      1.06   \n",
              "\n",
              "    Left Swing Interval(s)  Right Swing Interval(s)  Left Swing Interval(%)  \\\n",
              "0                     0.84                     0.28               37.500000   \n",
              "1                     0.76                     0.28               33.928571   \n",
              "2                     0.76                     0.42               33.928571   \n",
              "3                     0.82                     0.46               36.607143   \n",
              "4                     0.68                     0.44               32.692308   \n",
              "5                     0.90                     0.28               37.500000   \n",
              "6                     0.82                     0.28               36.936937   \n",
              "7                     0.80                     0.28               38.461538   \n",
              "8                     0.68                     0.42               32.692308   \n",
              "9                     0.68                     0.44               32.692308   \n",
              "10                    0.68                     0.28               32.692308   \n",
              "11                    0.68                     0.28               32.692308   \n",
              "12                    0.76                     0.42               33.928571   \n",
              "13                    0.82                     0.46               36.607143   \n",
              "14                    0.68                     0.44               32.692308   \n",
              "\n",
              "    Right Swing Interval(%)  Left Stance Interval(s)  \\\n",
              "0                 29.166667                     1.40   \n",
              "1                 29.166667                     1.48   \n",
              "2                 39.622642                     1.48   \n",
              "3                 42.592593                     1.42   \n",
              "4                 41.509434                     1.40   \n",
              "5                 29.166667                     1.50   \n",
              "6                 29.166667                     1.40   \n",
              "7                 29.166667                     1.28   \n",
              "8                 35.593220                     1.28   \n",
              "9                 38.596491                     1.28   \n",
              "10                29.166667                     1.28   \n",
              "11                35.897436                     1.28   \n",
              "12                39.622642                     1.48   \n",
              "13                42.592593                     1.42   \n",
              "14                41.509434                     1.40   \n",
              "\n",
              "    Right Stance Interval(s)  Left Stance Interval(%)  \\\n",
              "0                       0.50                62.500000   \n",
              "1                       0.50                66.071429   \n",
              "2                       0.64                66.071429   \n",
              "3                       0.62                63.392857   \n",
              "4                       0.62                67.307692   \n",
              "5                       0.50                62.500000   \n",
              "6                       0.50                63.063063   \n",
              "7                       0.50                61.538462   \n",
              "8                       0.76                61.538462   \n",
              "9                       0.70                61.538462   \n",
              "10                      0.68                61.538462   \n",
              "11                      0.50                61.538462   \n",
              "12                      0.64                66.071429   \n",
              "13                      0.62                63.392857   \n",
              "14                      0.62                67.307692   \n",
              "\n",
              "    Right Stance Interval(%)  Double Support Interval(s)  \\\n",
              "0                  57.407407                        0.44   \n",
              "1                  57.407407                        0.44   \n",
              "2                  60.377358                        1.65   \n",
              "3                  57.407407                        1.60   \n",
              "4                  58.490566                        1.52   \n",
              "5                  57.407407                        0.44   \n",
              "6                  57.407407                        0.44   \n",
              "7                  57.407407                        0.44   \n",
              "8                  64.406780                        0.44   \n",
              "9                  61.403509                        0.44   \n",
              "10                 70.833333                        0.44   \n",
              "11                 64.102564                        0.44   \n",
              "12                 60.377358                        0.47   \n",
              "13                 57.407407                        0.44   \n",
              "14                 58.490566                        0.50   \n",
              "\n",
              "    Double Support Interval(%)  \n",
              "0                    40.740741  \n",
              "1                    40.740741  \n",
              "2                    73.660714  \n",
              "3                    71.428571  \n",
              "4                    73.076923  \n",
              "5                    40.740741  \n",
              "6                    40.740741  \n",
              "7                    40.740741  \n",
              "8                    40.740741  \n",
              "9                    40.740741  \n",
              "10                   40.740741  \n",
              "11                   40.740741  \n",
              "12                   44.339623  \n",
              "13                   40.740741  \n",
              "14                   47.169811  "
            ]
          },
          "execution_count": 102,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "result_df.reset_index(drop=True, inplace=True)\n",
        "result_df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 103,
      "metadata": {},
      "outputs": [],
      "source": [
        "result_df.to_csv('readings1.csv')"
      ]
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
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    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
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