non_eeg_project_v01.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "9ecf2bcd",
   "metadata": {},
   "source": [
    "# Medical Signal Processing Project"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "95a599e6",
   "metadata": {},
   "source": [
    "## Based on Dataset \"Non-EEG Dataset for Assessment of Neurological Status\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fecffdc6",
   "metadata": {},
   "source": [
    "Importing libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "617abba1",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import os\n",
    "import sys\n",
    "import neurokit2 as nk\n",
    "import math\n",
    "import scipy as sc\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import glob\n",
    "import pathlib\n",
    "import re"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c4fb7ab1",
   "metadata": {},
   "source": [
    "### Raw Data Pre-prossesing"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e66a19be",
   "metadata": {},
   "source": [
    "#### For each subject read .csv file with raw data from both devices and sort them by SubjectID"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "c765c4bc",
   "metadata": {},
   "outputs": [],
   "source": [
    "def atoi(text):\n",
    "    return int(text) if text.isdigit() else text\n",
    "\n",
    "def natural_keys(text):\n",
    "    '''\n",
    "    alist.sort(key=natural_keys) sorts in human order\n",
    "    http://nedbatchelder.com/blog/200712/human_sorting.html\n",
    "    (See Toothy's implementation in the comments)\n",
    "    '''\n",
    "    return [ atoi(c) for c in re.split(r'(\\d+)', text) ]\n",
    "\n",
    "path = pathlib.Path().resolve() # use your path\n",
    "path = str(path)\n",
    "SpO2HR_files = glob.glob(path + \"/*SpO2HR.csv\")\n",
    "AccTempEDA_files = glob.glob(path + \"/*AccTempEDA.csv\")\n",
    "\n",
    "SpO2HR_files.sort(key=natural_keys)\n",
    "\n",
    "AccTempEDA_files.sort(key=natural_keys)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bcdf63bd",
   "metadata": {},
   "source": [
    "#### save data from Affectiva device to a unified .csv declaring 4 relax states for each subject"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "37bdbd34",
   "metadata": {},
   "outputs": [],
   "source": [
    "li_AccTempEDA = []\n",
    "\n",
    "for i, filename in  enumerate(AccTempEDA_files):\n",
    "    df_AccTempEDA = pd.read_csv(filename)\n",
    "    SubjectID =  \"Subject\"+str(i+1)\n",
    "    df_AccTempEDA.insert(0, 'SubjectID', SubjectID)\n",
    "    df_AccTempEDA.insert(1, 'DateTime', '2017-03-22') #2017-03-22\n",
    "    \n",
    "    relax = 1\n",
    "\n",
    "    for j in range(0, len(df_AccTempEDA)):\n",
    "#         df_AccTempEDA.loc[j,\"Label\"] = \"Relax1\" \n",
    "\n",
    "        if df_AccTempEDA.loc[j,\"Label\"] == \"Relax\" and relax == 1:\n",
    "            df_AccTempEDA.loc[j,\"Label\"] = \"Relax1\" \n",
    "            continue\n",
    "\n",
    "        if df_AccTempEDA.loc[j,\"Label\"] == \"PhysicalStress\" and relax == 1:\n",
    "            relax = 2\n",
    "\n",
    "        if df_AccTempEDA.loc[j,\"Label\"] == \"Relax\" and relax == 2:\n",
    "            df_AccTempEDA.loc[j,\"Label\"] = \"Relax2\" \n",
    "            continue\n",
    "\n",
    "        if df_AccTempEDA.loc[j,\"Label\"] == \"CognitiveStress\" and relax == 2:  \n",
    "            relax = 3                                         \n",
    "\n",
    "        if df_AccTempEDA.loc[j,\"Label\"] == \"Relax\" and relax == 3:  \n",
    "            df_AccTempEDA.loc[j,\"Label\"] = \"Relax3\"\n",
    "            continue\n",
    "\n",
    "        if df_AccTempEDA.loc[j,\"Label\"] == \"EmotionalStress\" and relax == 3:\n",
    "            relax = 4                                     \n",
    "\n",
    "        if df_AccTempEDA.loc[j,\"Label\"]  == \"Relax\" and relax == 4:  \n",
    "            df_AccTempEDA.loc[j,\"Label\"] = \"Relax4\"    \n",
    "        \n",
    "    \n",
    "    li_AccTempEDA.append(df_AccTempEDA)\n",
    "    \n",
    "\n",
    "AccTempEDA_frame = pd.concat(li_AccTempEDA, axis=0, ignore_index=True)\n",
    "\n",
    "AccTempEDA_frame.to_csv(\"AccTempEDA_frame.csv\", index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5c3462c3",
   "metadata": {},
   "source": [
    "#### save data from Nonin device to a unified .csv declaring 4 relax states for each subject"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "002f946c",
   "metadata": {},
   "outputs": [],
   "source": [
    "li_SpO2HR = []\n",
    "\n",
    "for i, filename in  enumerate(SpO2HR_files):\n",
    "    df_SpO2HR = pd.read_csv(filename)\n",
    "    SubjectID =  \"Subject\"+str(i+1)\n",
    "    df_SpO2HR.insert(0, 'SubjectID', SubjectID)\n",
    "    df_SpO2HR.insert(1, 'DateTime', '2017-03-22') #2017-03-22\n",
    "    \n",
    "    relax = 1\n",
    "\n",
    "    for j in range(0, len(df_SpO2HR)):\n",
    "\n",
    "        if df_SpO2HR.loc[j,\"Label\"] == \"Relax\" and relax == 1:\n",
    "            df_SpO2HR.loc[j,\"Label\"] = \"Relax1\" \n",
    "            continue\n",
    "\n",
    "        if df_SpO2HR.loc[j,\"Label\"] == \"PhysicalStress\" and relax == 1:\n",
    "            relax = 2\n",
    "\n",
    "        if df_SpO2HR.loc[j,\"Label\"] == \"Relax\" and relax == 2:\n",
    "            df_SpO2HR.loc[j,\"Label\"] = \"Relax2\" \n",
    "            continue\n",
    "\n",
    "        if df_SpO2HR.loc[j,\"Label\"] == \"CognitiveStress\" and relax == 2:  \n",
    "            relax = 3                                         \n",
    "\n",
    "        if df_SpO2HR.loc[j,\"Label\"] == \"Relax\" and relax == 3:  \n",
    "            df_SpO2HR.loc[j,\"Label\"] = \"Relax3\"\n",
    "            continue\n",
    "\n",
    "        if df_SpO2HR.loc[j,\"Label\"] == \"EmotionalStress\" and relax == 3:\n",
    "            relax = 4                                     \n",
    "\n",
    "        if df_SpO2HR.loc[j,\"Label\"]  == \"Relax\" and relax == 4:  \n",
    "            df_SpO2HR.loc[j,\"Label\"] = \"Relax4\"    \n",
    "        \n",
    "    \n",
    "    li_SpO2HR.append(df_SpO2HR)\n",
    "    \n",
    "\n",
    "SpO2HR_frame = pd.concat(li_SpO2HR, axis=0, ignore_index=True)\n",
    "\n",
    "SpO2HR_frame.to_csv(\"SpO2HR_frame.csv\", index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "44b00507",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SubjectID</th>\n",
       "      <th>DateTime</th>\n",
       "      <th>Hour</th>\n",
       "      <th>Minute</th>\n",
       "      <th>Second</th>\n",
       "      <th>AccZ</th>\n",
       "      <th>AccY</th>\n",
       "      <th>AccX</th>\n",
       "      <th>Temp</th>\n",
       "      <th>EDA</th>\n",
       "      <th>Label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Subject1</td>\n",
       "      <td>2017-03-22</td>\n",
       "      <td>13</td>\n",
       "      <td>48</td>\n",
       "      <td>16.000</td>\n",
       "      <td>-0.10</td>\n",
       "      <td>-0.87</td>\n",
       "      <td>0.76</td>\n",
       "      <td>30.1</td>\n",
       "      <td>0.083</td>\n",
       "      <td>Relax1</td>\n",
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       "      <th>1</th>\n",
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       "      <td>-0.11</td>\n",
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       "      <td>0.77</td>\n",
       "      <td>30.1</td>\n",
       "      <td>0.081</td>\n",
       "      <td>Relax1</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Subject1</td>\n",
       "      <td>2017-03-22</td>\n",
       "      <td>13</td>\n",
       "      <td>48</td>\n",
       "      <td>16.250</td>\n",
       "      <td>-0.10</td>\n",
       "      <td>-0.87</td>\n",
       "      <td>0.75</td>\n",
       "      <td>30.1</td>\n",
       "      <td>0.081</td>\n",
       "      <td>Relax1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Subject1</td>\n",
       "      <td>2017-03-22</td>\n",
       "      <td>13</td>\n",
       "      <td>48</td>\n",
       "      <td>16.375</td>\n",
       "      <td>-0.10</td>\n",
       "      <td>-0.87</td>\n",
       "      <td>0.77</td>\n",
       "      <td>30.1</td>\n",
       "      <td>0.079</td>\n",
       "      <td>Relax1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Subject1</td>\n",
       "      <td>2017-03-22</td>\n",
       "      <td>13</td>\n",
       "      <td>48</td>\n",
       "      <td>16.500</td>\n",
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       "      <td>-0.87</td>\n",
       "      <td>0.75</td>\n",
       "      <td>30.1</td>\n",
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       "      <td>Relax1</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>368847</th>\n",
       "      <td>Subject20</td>\n",
       "      <td>2017-03-22</td>\n",
       "      <td>17</td>\n",
       "      <td>32</td>\n",
       "      <td>52.375</td>\n",
       "      <td>0.10</td>\n",
       "      <td>-0.97</td>\n",
       "      <td>0.53</td>\n",
       "      <td>32.4</td>\n",
       "      <td>0.434</td>\n",
       "      <td>Relax4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>368848</th>\n",
       "      <td>Subject20</td>\n",
       "      <td>2017-03-22</td>\n",
       "      <td>17</td>\n",
       "      <td>32</td>\n",
       "      <td>52.500</td>\n",
       "      <td>0.10</td>\n",
       "      <td>-0.96</td>\n",
       "      <td>0.53</td>\n",
       "      <td>32.2</td>\n",
       "      <td>0.434</td>\n",
       "      <td>Relax4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>368849</th>\n",
       "      <td>Subject20</td>\n",
       "      <td>2017-03-22</td>\n",
       "      <td>17</td>\n",
       "      <td>32</td>\n",
       "      <td>52.625</td>\n",
       "      <td>0.10</td>\n",
       "      <td>-0.97</td>\n",
       "      <td>0.52</td>\n",
       "      <td>32.4</td>\n",
       "      <td>0.434</td>\n",
       "      <td>Relax4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>368850</th>\n",
       "      <td>Subject20</td>\n",
       "      <td>2017-03-22</td>\n",
       "      <td>17</td>\n",
       "      <td>32</td>\n",
       "      <td>52.750</td>\n",
       "      <td>0.11</td>\n",
       "      <td>-0.97</td>\n",
       "      <td>0.53</td>\n",
       "      <td>32.4</td>\n",
       "      <td>0.434</td>\n",
       "      <td>Relax4</td>\n",
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       "      <th>368851</th>\n",
       "      <td>Subject20</td>\n",
       "      <td>2017-03-22</td>\n",
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       "      <td>32</td>\n",
       "      <td>52.875</td>\n",
       "      <td>0.11</td>\n",
       "      <td>-0.97</td>\n",
       "      <td>0.53</td>\n",
       "      <td>32.2</td>\n",
       "      <td>0.434</td>\n",
       "      <td>Relax4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>368852 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        SubjectID    DateTime  Hour  Minute  Second  AccZ  AccY  AccX  Temp  \\\n",
       "0        Subject1  2017-03-22    13      48  16.000 -0.10 -0.87  0.76  30.1   \n",
       "1        Subject1  2017-03-22    13      48  16.125 -0.11 -0.87  0.77  30.1   \n",
       "2        Subject1  2017-03-22    13      48  16.250 -0.10 -0.87  0.75  30.1   \n",
       "3        Subject1  2017-03-22    13      48  16.375 -0.10 -0.87  0.77  30.1   \n",
       "4        Subject1  2017-03-22    13      48  16.500 -0.10 -0.87  0.75  30.1   \n",
       "...           ...         ...   ...     ...     ...   ...   ...   ...   ...   \n",
       "368847  Subject20  2017-03-22    17      32  52.375  0.10 -0.97  0.53  32.4   \n",
       "368848  Subject20  2017-03-22    17      32  52.500  0.10 -0.96  0.53  32.2   \n",
       "368849  Subject20  2017-03-22    17      32  52.625  0.10 -0.97  0.52  32.4   \n",
       "368850  Subject20  2017-03-22    17      32  52.750  0.11 -0.97  0.53  32.4   \n",
       "368851  Subject20  2017-03-22    17      32  52.875  0.11 -0.97  0.53  32.2   \n",
       "\n",
       "          EDA   Label  \n",
       "0       0.083  Relax1  \n",
       "1       0.081  Relax1  \n",
       "2       0.081  Relax1  \n",
       "3       0.079  Relax1  \n",
       "4       0.081  Relax1  \n",
       "...       ...     ...  \n",
       "368847  0.434  Relax4  \n",
       "368848  0.434  Relax4  \n",
       "368849  0.434  Relax4  \n",
       "368850  0.434  Relax4  \n",
       "368851  0.434  Relax4  \n",
       "\n",
       "[368852 rows x 11 columns]"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_1 = pd.read_csv(\"AccTempEDA_frame.csv\")\n",
    "df_1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "ec662f7c",
   "metadata": {},
   "outputs": [
    {
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       "      <th>Minute</th>\n",
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       "      <th>0</th>\n",
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       "      <th>1</th>\n",
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       "      <td>Subject1</td>\n",
       "      <td>2017-03-22</td>\n",
       "      <td>13.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>19.0</td>\n",
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       "      <td>97.0</td>\n",
       "      <td>Relax1</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Subject1</td>\n",
       "      <td>2017-03-22</td>\n",
       "      <td>13.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>87.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>Relax1</td>\n",
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       "      <th>46224</th>\n",
       "      <td>Subject20</td>\n",
       "      <td>2017-03-22</td>\n",
       "      <td>17.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>98.0</td>\n",
       "      <td>Relax4</td>\n",
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       "    <tr>\n",
       "      <th>46225</th>\n",
       "      <td>Subject20</td>\n",
       "      <td>2017-03-22</td>\n",
       "      <td>17.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>98.0</td>\n",
       "      <td>Relax4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46226</th>\n",
       "      <td>Subject20</td>\n",
       "      <td>2017-03-22</td>\n",
       "      <td>17.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>98.0</td>\n",
       "      <td>Relax4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46227</th>\n",
       "      <td>Subject20</td>\n",
       "      <td>2017-03-22</td>\n",
       "      <td>17.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>98.0</td>\n",
       "      <td>Relax4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46228</th>\n",
       "      <td>Subject20</td>\n",
       "      <td>2017-03-22</td>\n",
       "      <td>17.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>98.0</td>\n",
       "      <td>Relax4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>46229 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       SubjectID    DateTime  Hour  Minute  Second  HeartRate  SpO2   Label\n",
       "0       Subject1  2017-03-22  13.0    48.0    16.0       89.0  97.0  Relax1\n",
       "1       Subject1  2017-03-22  13.0    48.0    17.0       88.0  97.0  Relax1\n",
       "2       Subject1  2017-03-22  13.0    48.0    18.0       87.0  97.0  Relax1\n",
       "3       Subject1  2017-03-22  13.0    48.0    19.0       87.0  97.0  Relax1\n",
       "4       Subject1  2017-03-22  13.0    48.0    20.0       87.0  97.0  Relax1\n",
       "...          ...         ...   ...     ...     ...        ...   ...     ...\n",
       "46224  Subject20  2017-03-22  17.0    32.0    48.0       75.0  98.0  Relax4\n",
       "46225  Subject20  2017-03-22  17.0    32.0    49.0       75.0  98.0  Relax4\n",
       "46226  Subject20  2017-03-22  17.0    32.0    50.0       79.0  98.0  Relax4\n",
       "46227  Subject20  2017-03-22  17.0    32.0    51.0       79.0  98.0  Relax4\n",
       "46228  Subject20  2017-03-22  17.0    32.0    52.0       80.0  98.0  Relax4\n",
       "\n",
       "[46229 rows x 8 columns]"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_2 = pd.read_csv(\"SpO2HR_frame.csv\")\n",
    "df_2"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e18bf1f1",
   "metadata": {},
   "source": [
    "#### Downsample data from Affectiva device to 1Hz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "ddc3f208",
   "metadata": {},
   "outputs": [],
   "source": [
    "# df_1 = df_1.iloc[::8, :]\n",
    "# df_1.reset_index(drop=True, inplace=True)\n",
    "# df_1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d01ec293",
   "metadata": {},
   "source": [
    "#### Upsampling of Data to 8Hz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "c821dd5d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\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>index</th>\n",
       "      <th>SubjectID</th>\n",
       "      <th>DateTime</th>\n",
       "      <th>Hour</th>\n",
       "      <th>Minute</th>\n",
       "      <th>Second</th>\n",
       "      <th>AccZ</th>\n",
       "      <th>AccY</th>\n",
       "      <th>AccX</th>\n",
       "      <th>Temp</th>\n",
       "      <th>EDA</th>\n",
       "      <th>Label</th>\n",
       "      <th>HeartRate</th>\n",
       "      <th>SpO2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>41074</td>\n",
       "      <td>Subject1</td>\n",
       "      <td>2017-03-22</td>\n",
       "      <td>13</td>\n",
       "      <td>48</td>\n",
       "      <td>16.000</td>\n",
       "      <td>-0.10</td>\n",
       "      <td>-0.87</td>\n",
       "      <td>0.76</td>\n",
       "      <td>30.1</td>\n",
       "      <td>0.083</td>\n",
       "      <td>Relax1</td>\n",
       "      <td>89.0</td>\n",
       "      <td>97.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>41075</td>\n",
       "      <td>Subject1</td>\n",
       "      <td>2017-03-22</td>\n",
       "      <td>13</td>\n",
       "      <td>48</td>\n",
       "      <td>16.125</td>\n",
       "      <td>-0.11</td>\n",
       "      <td>-0.87</td>\n",
       "      <td>0.77</td>\n",
       "      <td>30.1</td>\n",
       "      <td>0.081</td>\n",
       "      <td>Relax1</td>\n",
       "      <td>89.0</td>\n",
       "      <td>97.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>41076</td>\n",
       "      <td>Subject1</td>\n",
       "      <td>2017-03-22</td>\n",
       "      <td>13</td>\n",
       "      <td>48</td>\n",
       "      <td>16.250</td>\n",
       "      <td>-0.10</td>\n",
       "      <td>-0.87</td>\n",
       "      <td>0.75</td>\n",
       "      <td>30.1</td>\n",
       "      <td>0.081</td>\n",
       "      <td>Relax1</td>\n",
       "      <td>89.0</td>\n",
       "      <td>97.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>41077</td>\n",
       "      <td>Subject1</td>\n",
       "      <td>2017-03-22</td>\n",
       "      <td>13</td>\n",
       "      <td>48</td>\n",
       "      <td>16.375</td>\n",
       "      <td>-0.10</td>\n",
       "      <td>-0.87</td>\n",
       "      <td>0.77</td>\n",
       "      <td>30.1</td>\n",
       "      <td>0.079</td>\n",
       "      <td>Relax1</td>\n",
       "      <td>89.0</td>\n",
       "      <td>97.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>41078</td>\n",
       "      <td>Subject1</td>\n",
       "      <td>2017-03-22</td>\n",
       "      <td>13</td>\n",
       "      <td>48</td>\n",
       "      <td>16.500</td>\n",
       "      <td>-0.10</td>\n",
       "      <td>-0.87</td>\n",
       "      <td>0.75</td>\n",
       "      <td>30.1</td>\n",
       "      <td>0.081</td>\n",
       "      <td>Relax1</td>\n",
       "      <td>89.0</td>\n",
       "      <td>97.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>383107</th>\n",
       "      <td>383075</td>\n",
       "      <td>Subject9</td>\n",
       "      <td>2017-03-22</td>\n",
       "      <td>14</td>\n",
       "      <td>53</td>\n",
       "      <td>55.375</td>\n",
       "      <td>-0.18</td>\n",
       "      <td>-0.64</td>\n",
       "      <td>0.97</td>\n",
       "      <td>34.7</td>\n",
       "      <td>0.561</td>\n",
       "      <td>Relax4</td>\n",
       "      <td>73.0</td>\n",
       "      <td>94.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>383108</th>\n",
       "      <td>383076</td>\n",
       "      <td>Subject9</td>\n",
       "      <td>2017-03-22</td>\n",
       "      <td>14</td>\n",
       "      <td>53</td>\n",
       "      <td>55.500</td>\n",
       "      <td>-0.17</td>\n",
       "      <td>-0.64</td>\n",
       "      <td>0.97</td>\n",
       "      <td>34.7</td>\n",
       "      <td>0.557</td>\n",
       "      <td>Relax4</td>\n",
       "      <td>73.0</td>\n",
       "      <td>94.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>383109</th>\n",
       "      <td>383077</td>\n",
       "      <td>Subject9</td>\n",
       "      <td>2017-03-22</td>\n",
       "      <td>14</td>\n",
       "      <td>53</td>\n",
       "      <td>55.625</td>\n",
       "      <td>-0.17</td>\n",
       "      <td>-0.64</td>\n",
       "      <td>0.97</td>\n",
       "      <td>34.7</td>\n",
       "      <td>0.561</td>\n",
       "      <td>Relax4</td>\n",
       "      <td>73.0</td>\n",
       "      <td>94.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>383110</th>\n",
       "      <td>383078</td>\n",
       "      <td>Subject9</td>\n",
       "      <td>2017-03-22</td>\n",
       "      <td>14</td>\n",
       "      <td>53</td>\n",
       "      <td>55.750</td>\n",
       "      <td>-0.17</td>\n",
       "      <td>-0.64</td>\n",
       "      <td>0.97</td>\n",
       "      <td>34.7</td>\n",
       "      <td>0.561</td>\n",
       "      <td>Relax4</td>\n",
       "      <td>73.0</td>\n",
       "      <td>94.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>383111</th>\n",
       "      <td>383079</td>\n",
       "      <td>Subject9</td>\n",
       "      <td>2017-03-22</td>\n",
       "      <td>14</td>\n",
       "      <td>53</td>\n",
       "      <td>55.875</td>\n",
       "      <td>-0.18</td>\n",
       "      <td>-0.64</td>\n",
       "      <td>0.97</td>\n",
       "      <td>34.7</td>\n",
       "      <td>0.561</td>\n",
       "      <td>Relax4</td>\n",
       "      <td>73.0</td>\n",
       "      <td>94.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>383112 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         index SubjectID    DateTime  Hour  Minute  Second  AccZ  AccY  AccX  \\\n",
       "0        41074  Subject1  2017-03-22    13      48  16.000 -0.10 -0.87  0.76   \n",
       "1        41075  Subject1  2017-03-22    13      48  16.125 -0.11 -0.87  0.77   \n",
       "2        41076  Subject1  2017-03-22    13      48  16.250 -0.10 -0.87  0.75   \n",
       "3        41077  Subject1  2017-03-22    13      48  16.375 -0.10 -0.87  0.77   \n",
       "4        41078  Subject1  2017-03-22    13      48  16.500 -0.10 -0.87  0.75   \n",
       "...        ...       ...         ...   ...     ...     ...   ...   ...   ...   \n",
       "383107  383075  Subject9  2017-03-22    14      53  55.375 -0.18 -0.64  0.97   \n",
       "383108  383076  Subject9  2017-03-22    14      53  55.500 -0.17 -0.64  0.97   \n",
       "383109  383077  Subject9  2017-03-22    14      53  55.625 -0.17 -0.64  0.97   \n",
       "383110  383078  Subject9  2017-03-22    14      53  55.750 -0.17 -0.64  0.97   \n",
       "383111  383079  Subject9  2017-03-22    14      53  55.875 -0.18 -0.64  0.97   \n",
       "\n",
       "        Temp    EDA   Label  HeartRate  SpO2  \n",
       "0       30.1  0.083  Relax1       89.0  97.0  \n",
       "1       30.1  0.081  Relax1       89.0  97.0  \n",
       "2       30.1  0.081  Relax1       89.0  97.0  \n",
       "3       30.1  0.079  Relax1       89.0  97.0  \n",
       "4       30.1  0.081  Relax1       89.0  97.0  \n",
       "...      ...    ...     ...        ...   ...  \n",
       "383107  34.7  0.561  Relax4       73.0  94.0  \n",
       "383108  34.7  0.557  Relax4       73.0  94.0  \n",
       "383109  34.7  0.561  Relax4       73.0  94.0  \n",
       "383110  34.7  0.561  Relax4       73.0  94.0  \n",
       "383111  34.7  0.561  Relax4       73.0  94.0  \n",
       "\n",
       "[383112 rows x 14 columns]"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_1['Index'] = df_1['SubjectID'].astype(str) + df_1['Hour'].astype(str) + df_1['Minute'].astype(str) + df_1['Second'].astype(int).astype(str)\n",
    "df_2['Index'] = df_2['SubjectID'].astype(str) + df_2['Hour'].astype(int).astype(str) + df_2['Minute'].astype(int).astype(str) + df_2['Second'].astype(int).astype(str)\n",
    "df_3 = df_2.drop([\"SubjectID\", \"DateTime\", \"Hour\", \"Minute\", \"Second\", \"Label\"], axis = 1)\n",
    "\n",
    "df_1 = df_1.set_index('Index')\n",
    "df_3 = df_3.set_index('Index')\n",
    "total_data = df_1.merge(df_3, how='outer', left_index=True, right_index=True)\n",
    "total_data.reset_index(drop=True, inplace=True)\n",
    "total_data = total_data.sort_values(['SubjectID', 'Hour', 'Minute', 'Second'])\n",
    "total_data = total_data.reset_index()\n",
    "# total_data.drop('Index', axis=1, inplace=True)\n",
    "total_data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2d56681d",
   "metadata": {},
   "source": [
    "#### Create a unified dataframe containing data of both devices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "bd7275c2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# #Find the columns that aren't in the first DataFrame \n",
    "# diff_cols = df_2.columns.difference(df_1.columns)\n",
    "\n",
    "# #Filter out the columns that are different. You could pass in the df2[diff_cols] directly into the merge as well.\n",
    "# df_3 = df_2[diff_cols]\n",
    "\n",
    "# #Merge the DataFrames\n",
    "# total_data = pd.merge(df_1, df_3, left_index=True, right_index=True, how='inner')\n",
    "# total_data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1634efa7",
   "metadata": {},
   "source": [
    "### Plotting Subject1 Signals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "84b8fa8e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x504 with 7 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df1 = total_data.loc[total_data['SubjectID'] == 'Subject1']\n",
    "# df1 = df1.set_index('Label', append=True).swaplevel(0,1)\n",
    "\n",
    "relax1 = len(df1.index[df1.Label == 'Relax1'])\n",
    "physicals = relax1+len(df1.index[df1.Label == 'PhysicalStress'])\n",
    "relax2 = physicals+len(df1.index[df1.Label == 'Relax2'])\n",
    "cogns = relax2+len(df1.index[df1.Label == 'CognitiveStress'])\n",
    "relax3 = cogns+len(df1.index[df1.Label == 'Relax3'])\n",
    "emotions = relax3+len(df1.index[df1.Label == 'EmotionalStress'])\n",
    "relax4 = emotions+len(df1.index[df1.Label == 'Relax4'])\n",
    "x = [relax1, physicals, relax2, cogns, relax3, emotions, relax4]\n",
    "\n",
    "text_values = ['Relax1', 'Physical Stress', 'Relax2', 'Cognitive Stress', 'Relax3', 'Emotional Stress', 'Relax4']\n",
    "\n",
    "df1.plot(subplots=True,figsize=(15,7),grid=True, xticks=x, y=['EDA','Temp','AccZ','AccY','AccX','SpO2', 'HeartRate'], use_index=True)\n",
    "plt.xticks(x, text_values)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8b8917bc",
   "metadata": {},
   "source": [
    "## Feature Engineering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "db7a5e56",
   "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>index</th>\n",
       "      <th>SubjectID</th>\n",
       "      <th>DateTime</th>\n",
       "      <th>Hour</th>\n",
       "      <th>Minute</th>\n",
       "      <th>Second</th>\n",
       "      <th>AccZ</th>\n",
       "      <th>AccY</th>\n",
       "      <th>AccX</th>\n",
       "      <th>Temp</th>\n",
       "      <th>EDA</th>\n",
       "      <th>Label</th>\n",
       "      <th>HeartRate</th>\n",
       "      <th>SpO2</th>\n",
       "      <th>Acc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>41074</td>\n",
       "      <td>Subject1</td>\n",
       "      <td>2017-03-22</td>\n",
       "      <td>13</td>\n",
       "      <td>48</td>\n",
       "      <td>16.000</td>\n",
       "      <td>-0.10</td>\n",
       "      <td>-0.87</td>\n",
       "      <td>0.76</td>\n",
       "      <td>30.1</td>\n",
       "      <td>0.083</td>\n",
       "      <td>Relax1</td>\n",
       "      <td>89.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>1.159526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>41075</td>\n",
       "      <td>Subject1</td>\n",
       "      <td>2017-03-22</td>\n",
       "      <td>13</td>\n",
       "      <td>48</td>\n",
       "      <td>16.125</td>\n",
       "      <td>-0.11</td>\n",
       "      <td>-0.87</td>\n",
       "      <td>0.77</td>\n",
       "      <td>30.1</td>\n",
       "      <td>0.081</td>\n",
       "      <td>Relax1</td>\n",
       "      <td>89.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>1.167005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>41076</td>\n",
       "      <td>Subject1</td>\n",
       "      <td>2017-03-22</td>\n",
       "      <td>13</td>\n",
       "      <td>48</td>\n",
       "      <td>16.250</td>\n",
       "      <td>-0.10</td>\n",
       "      <td>-0.87</td>\n",
       "      <td>0.75</td>\n",
       "      <td>30.1</td>\n",
       "      <td>0.081</td>\n",
       "      <td>Relax1</td>\n",
       "      <td>89.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>1.152996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>41077</td>\n",
       "      <td>Subject1</td>\n",
       "      <td>2017-03-22</td>\n",
       "      <td>13</td>\n",
       "      <td>48</td>\n",
       "      <td>16.375</td>\n",
       "      <td>-0.10</td>\n",
       "      <td>-0.87</td>\n",
       "      <td>0.77</td>\n",
       "      <td>30.1</td>\n",
       "      <td>0.079</td>\n",
       "      <td>Relax1</td>\n",
       "      <td>89.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>1.166105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>41078</td>\n",
       "      <td>Subject1</td>\n",
       "      <td>2017-03-22</td>\n",
       "      <td>13</td>\n",
       "      <td>48</td>\n",
       "      <td>16.500</td>\n",
       "      <td>-0.10</td>\n",
       "      <td>-0.87</td>\n",
       "      <td>0.75</td>\n",
       "      <td>30.1</td>\n",
       "      <td>0.081</td>\n",
       "      <td>Relax1</td>\n",
       "      <td>89.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>1.152996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>383107</th>\n",
       "      <td>383075</td>\n",
       "      <td>Subject9</td>\n",
       "      <td>2017-03-22</td>\n",
       "      <td>14</td>\n",
       "      <td>53</td>\n",
       "      <td>55.375</td>\n",
       "      <td>-0.18</td>\n",
       "      <td>-0.64</td>\n",
       "      <td>0.97</td>\n",
       "      <td>34.7</td>\n",
       "      <td>0.561</td>\n",
       "      <td>Relax4</td>\n",
       "      <td>73.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>1.175968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>383108</th>\n",
       "      <td>383076</td>\n",
       "      <td>Subject9</td>\n",
       "      <td>2017-03-22</td>\n",
       "      <td>14</td>\n",
       "      <td>53</td>\n",
       "      <td>55.500</td>\n",
       "      <td>-0.17</td>\n",
       "      <td>-0.64</td>\n",
       "      <td>0.97</td>\n",
       "      <td>34.7</td>\n",
       "      <td>0.557</td>\n",
       "      <td>Relax4</td>\n",
       "      <td>73.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>1.174479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>383109</th>\n",
       "      <td>383077</td>\n",
       "      <td>Subject9</td>\n",
       "      <td>2017-03-22</td>\n",
       "      <td>14</td>\n",
       "      <td>53</td>\n",
       "      <td>55.625</td>\n",
       "      <td>-0.17</td>\n",
       "      <td>-0.64</td>\n",
       "      <td>0.97</td>\n",
       "      <td>34.7</td>\n",
       "      <td>0.561</td>\n",
       "      <td>Relax4</td>\n",
       "      <td>73.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>1.174479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>383110</th>\n",
       "      <td>383078</td>\n",
       "      <td>Subject9</td>\n",
       "      <td>2017-03-22</td>\n",
       "      <td>14</td>\n",
       "      <td>53</td>\n",
       "      <td>55.750</td>\n",
       "      <td>-0.17</td>\n",
       "      <td>-0.64</td>\n",
       "      <td>0.97</td>\n",
       "      <td>34.7</td>\n",
       "      <td>0.561</td>\n",
       "      <td>Relax4</td>\n",
       "      <td>73.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>1.174479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>383111</th>\n",
       "      <td>383079</td>\n",
       "      <td>Subject9</td>\n",
       "      <td>2017-03-22</td>\n",
       "      <td>14</td>\n",
       "      <td>53</td>\n",
       "      <td>55.875</td>\n",
       "      <td>-0.18</td>\n",
       "      <td>-0.64</td>\n",
       "      <td>0.97</td>\n",
       "      <td>34.7</td>\n",
       "      <td>0.561</td>\n",
       "      <td>Relax4</td>\n",
       "      <td>73.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>1.175968</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>383112 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         index SubjectID    DateTime  Hour  Minute  Second  AccZ  AccY  AccX  \\\n",
       "0        41074  Subject1  2017-03-22    13      48  16.000 -0.10 -0.87  0.76   \n",
       "1        41075  Subject1  2017-03-22    13      48  16.125 -0.11 -0.87  0.77   \n",
       "2        41076  Subject1  2017-03-22    13      48  16.250 -0.10 -0.87  0.75   \n",
       "3        41077  Subject1  2017-03-22    13      48  16.375 -0.10 -0.87  0.77   \n",
       "4        41078  Subject1  2017-03-22    13      48  16.500 -0.10 -0.87  0.75   \n",
       "...        ...       ...         ...   ...     ...     ...   ...   ...   ...   \n",
       "383107  383075  Subject9  2017-03-22    14      53  55.375 -0.18 -0.64  0.97   \n",
       "383108  383076  Subject9  2017-03-22    14      53  55.500 -0.17 -0.64  0.97   \n",
       "383109  383077  Subject9  2017-03-22    14      53  55.625 -0.17 -0.64  0.97   \n",
       "383110  383078  Subject9  2017-03-22    14      53  55.750 -0.17 -0.64  0.97   \n",
       "383111  383079  Subject9  2017-03-22    14      53  55.875 -0.18 -0.64  0.97   \n",
       "\n",
       "        Temp    EDA   Label  HeartRate  SpO2       Acc  \n",
       "0       30.1  0.083  Relax1       89.0  97.0  1.159526  \n",
       "1       30.1  0.081  Relax1       89.0  97.0  1.167005  \n",
       "2       30.1  0.081  Relax1       89.0  97.0  1.152996  \n",
       "3       30.1  0.079  Relax1       89.0  97.0  1.166105  \n",
       "4       30.1  0.081  Relax1       89.0  97.0  1.152996  \n",
       "...      ...    ...     ...        ...   ...       ...  \n",
       "383107  34.7  0.561  Relax4       73.0  94.0  1.175968  \n",
       "383108  34.7  0.557  Relax4       73.0  94.0  1.174479  \n",
       "383109  34.7  0.561  Relax4       73.0  94.0  1.174479  \n",
       "383110  34.7  0.561  Relax4       73.0  94.0  1.174479  \n",
       "383111  34.7  0.561  Relax4       73.0  94.0  1.175968  \n",
       "\n",
       "[383112 rows x 15 columns]"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total_data['Acc']=np.sqrt(total_data['AccX'].pow(2)+total_data['AccY'].pow(2)+total_data['AccZ'].pow(2))\n",
    "main_data = total_data\n",
    "total_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "d93e58c4",
   "metadata": {
    "scrolled": true
   },
   "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></th>\n",
       "      <th>index</th>\n",
       "      <th>DateTime</th>\n",
       "      <th>Hour</th>\n",
       "      <th>Minute</th>\n",
       "      <th>Second</th>\n",
       "      <th>AccZ</th>\n",
       "      <th>AccY</th>\n",
       "      <th>AccX</th>\n",
       "      <th>Temp</th>\n",
       "      <th>EDA</th>\n",
       "      <th>Label</th>\n",
       "      <th>HeartRate</th>\n",
       "      <th>SpO2</th>\n",
       "      <th>Acc</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SubjectID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">Subject1</th>\n",
       "      <th>0</th>\n",
       "      <td>41074</td>\n",
       "      <td>2017-03-22</td>\n",
       "      <td>13</td>\n",
       "      <td>48</td>\n",
       "      <td>16.000</td>\n",
       "      <td>-0.10</td>\n",
       "      <td>-0.87</td>\n",
       "      <td>0.76</td>\n",
       "      <td>30.1</td>\n",
       "      <td>0.083</td>\n",
       "      <td>Relax1</td>\n",
       "      <td>89.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>1.159526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>41075</td>\n",
       "      <td>2017-03-22</td>\n",
       "      <td>13</td>\n",
       "      <td>48</td>\n",
       "      <td>16.125</td>\n",
       "      <td>-0.11</td>\n",
       "      <td>-0.87</td>\n",
       "      <td>0.77</td>\n",
       "      <td>30.1</td>\n",
       "      <td>0.081</td>\n",
       "      <td>Relax1</td>\n",
       "      <td>89.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>1.167005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>41076</td>\n",
       "      <td>2017-03-22</td>\n",
       "      <td>13</td>\n",
       "      <td>48</td>\n",
       "      <td>16.250</td>\n",
       "      <td>-0.10</td>\n",
       "      <td>-0.87</td>\n",
       "      <td>0.75</td>\n",
       "      <td>30.1</td>\n",
       "      <td>0.081</td>\n",
       "      <td>Relax1</td>\n",
       "      <td>89.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>1.152996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>41077</td>\n",
       "      <td>2017-03-22</td>\n",
       "      <td>13</td>\n",
       "      <td>48</td>\n",
       "      <td>16.375</td>\n",
       "      <td>-0.10</td>\n",
       "      <td>-0.87</td>\n",
       "      <td>0.77</td>\n",
       "      <td>30.1</td>\n",
       "      <td>0.079</td>\n",
       "      <td>Relax1</td>\n",
       "      <td>89.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>1.166105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>41078</td>\n",
       "      <td>2017-03-22</td>\n",
       "      <td>13</td>\n",
       "      <td>48</td>\n",
       "      <td>16.500</td>\n",
       "      <td>-0.10</td>\n",
       "      <td>-0.87</td>\n",
       "      <td>0.75</td>\n",
       "      <td>30.1</td>\n",
       "      <td>0.081</td>\n",
       "      <td>Relax1</td>\n",
       "      <td>89.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>1.152996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">Subject9</th>\n",
       "      <th>383107</th>\n",
       "      <td>383075</td>\n",
       "      <td>2017-03-22</td>\n",
       "      <td>14</td>\n",
       "      <td>53</td>\n",
       "      <td>55.375</td>\n",
       "      <td>-0.18</td>\n",
       "      <td>-0.64</td>\n",
       "      <td>0.97</td>\n",
       "      <td>34.7</td>\n",
       "      <td>0.561</td>\n",
       "      <td>Relax4</td>\n",
       "      <td>73.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>1.175968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>383108</th>\n",
       "      <td>383076</td>\n",
       "      <td>2017-03-22</td>\n",
       "      <td>14</td>\n",
       "      <td>53</td>\n",
       "      <td>55.500</td>\n",
       "      <td>-0.17</td>\n",
       "      <td>-0.64</td>\n",
       "      <td>0.97</td>\n",
       "      <td>34.7</td>\n",
       "      <td>0.557</td>\n",
       "      <td>Relax4</td>\n",
       "      <td>73.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>1.174479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>383109</th>\n",
       "      <td>383077</td>\n",
       "      <td>2017-03-22</td>\n",
       "      <td>14</td>\n",
       "      <td>53</td>\n",
       "      <td>55.625</td>\n",
       "      <td>-0.17</td>\n",
       "      <td>-0.64</td>\n",
       "      <td>0.97</td>\n",
       "      <td>34.7</td>\n",
       "      <td>0.561</td>\n",
       "      <td>Relax4</td>\n",
       "      <td>73.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>1.174479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>383110</th>\n",
       "      <td>383078</td>\n",
       "      <td>2017-03-22</td>\n",
       "      <td>14</td>\n",
       "      <td>53</td>\n",
       "      <td>55.750</td>\n",
       "      <td>-0.17</td>\n",
       "      <td>-0.64</td>\n",
       "      <td>0.97</td>\n",
       "      <td>34.7</td>\n",
       "      <td>0.561</td>\n",
       "      <td>Relax4</td>\n",
       "      <td>73.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>1.174479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>383111</th>\n",
       "      <td>383079</td>\n",
       "      <td>2017-03-22</td>\n",
       "      <td>14</td>\n",
       "      <td>53</td>\n",
       "      <td>55.875</td>\n",
       "      <td>-0.18</td>\n",
       "      <td>-0.64</td>\n",
       "      <td>0.97</td>\n",
       "      <td>34.7</td>\n",
       "      <td>0.561</td>\n",
       "      <td>Relax4</td>\n",
       "      <td>73.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>1.175968</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>383112 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   index    DateTime  Hour  Minute  Second  AccZ  AccY  AccX  \\\n",
       "SubjectID                                                                      \n",
       "Subject1  0        41074  2017-03-22    13      48  16.000 -0.10 -0.87  0.76   \n",
       "          1        41075  2017-03-22    13      48  16.125 -0.11 -0.87  0.77   \n",
       "          2        41076  2017-03-22    13      48  16.250 -0.10 -0.87  0.75   \n",
       "          3        41077  2017-03-22    13      48  16.375 -0.10 -0.87  0.77   \n",
       "          4        41078  2017-03-22    13      48  16.500 -0.10 -0.87  0.75   \n",
       "...                  ...         ...   ...     ...     ...   ...   ...   ...   \n",
       "Subject9  383107  383075  2017-03-22    14      53  55.375 -0.18 -0.64  0.97   \n",
       "          383108  383076  2017-03-22    14      53  55.500 -0.17 -0.64  0.97   \n",
       "          383109  383077  2017-03-22    14      53  55.625 -0.17 -0.64  0.97   \n",
       "          383110  383078  2017-03-22    14      53  55.750 -0.17 -0.64  0.97   \n",
       "          383111  383079  2017-03-22    14      53  55.875 -0.18 -0.64  0.97   \n",
       "\n",
       "                  Temp    EDA   Label  HeartRate  SpO2       Acc  \n",
       "SubjectID                                                         \n",
       "Subject1  0       30.1  0.083  Relax1       89.0  97.0  1.159526  \n",
       "          1       30.1  0.081  Relax1       89.0  97.0  1.167005  \n",
       "          2       30.1  0.081  Relax1       89.0  97.0  1.152996  \n",
       "          3       30.1  0.079  Relax1       89.0  97.0  1.166105  \n",
       "          4       30.1  0.081  Relax1       89.0  97.0  1.152996  \n",
       "...                ...    ...     ...        ...   ...       ...  \n",
       "Subject9  383107  34.7  0.561  Relax4       73.0  94.0  1.175968  \n",
       "          383108  34.7  0.557  Relax4       73.0  94.0  1.174479  \n",
       "          383109  34.7  0.561  Relax4       73.0  94.0  1.174479  \n",
       "          383110  34.7  0.561  Relax4       73.0  94.0  1.174479  \n",
       "          383111  34.7  0.561  Relax4       73.0  94.0  1.175968  \n",
       "\n",
       "[383112 rows x 14 columns]"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total_data = total_data.set_index('SubjectID', append=True).swaplevel(0,1)\n",
    "total_data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4ef74b3b",
   "metadata": {},
   "source": [
    "### Acc, Temp & SpO2 feature extraction functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "4145a3f0",
   "metadata": {},
   "outputs": [],
   "source": [
    "from numpy import mean, median, std, percentile, fft, abs, argmax\n",
    "\n",
    "def Mean(data):\n",
    "    #Returns the mean of a time series\n",
    "    return data.mean()\n",
    "\n",
    "def Median(data):\n",
    "    #Returns the median of a time series\n",
    "    return data.median()\n",
    "\n",
    "def Std(data):\n",
    "    #Returns the standard deviation \n",
    "    return data.std()\n",
    "\n",
    "def Min(data):\n",
    "    #Returns the minimum value of a time series\n",
    "    return data.min()\n",
    "\n",
    "def Max(data):\n",
    "    #Returns the maximum value of a time series\n",
    "    return data.max()\n",
    "\n",
    "\n",
    "\n",
    "functions = [Mean, Median, Std, Min, Max]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0725d808",
   "metadata": {},
   "source": [
    "### EDA Feature extraction functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "id": "ef06e2c2",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy.polynomial as npp\n",
    "\n",
    "def SCL_slope(data):\n",
    "    signals, info = nk.eda_process(data, sampling_rate=8)\n",
    "    coeffs = npp.polynomial.polyfit(signals.index.to_list(), signals['EDA_Tonic'], 1)\n",
    "    SCL_slope = coeffs[0]\n",
    "    \n",
    "    return SCL_slope\n",
    "\n",
    "def SCL_mean(data):\n",
    "    signals, info = nk.eda_process(data, sampling_rate=8)\n",
    "    SCL_mean = signals['EDA_Tonic'].mean()\n",
    "    \n",
    "    return SCL_mean\n",
    "\n",
    "def SCR_amplitude_mean(data):\n",
    "    signals, info = nk.eda_process(data, sampling_rate=8)\n",
    "    SCR_amplitude_mean = signals['SCR_Amplitude'].sum()/signals['SCR_Peaks'].sum()\n",
    "    \n",
    "    return SCR_amplitude_mean\n",
    "\n",
    "def SCR_peaks(data):\n",
    "    signals, info = nk.eda_process(data, sampling_rate=8)\n",
    "    SCR_peaks =  signals['SCR_Peaks'].sum()\n",
    "    \n",
    "    return SCR_peaks\n",
    "\n",
    "def SCR_rise_time_mean(data):\n",
    "    signals, info = nk.eda_process(data, sampling_rate=8)\n",
    "    SCR_rise_time_mean = signals['SCR_RiseTime'].mean()\n",
    "    \n",
    "    return SCR_rise_time_mean\n",
    "\n",
    "EDA_functions = [SCL_slope, SCL_mean, SCR_amplitude_mean, SCR_peaks, SCR_rise_time_mean]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "16ec25f9",
   "metadata": {},
   "source": [
    "### Heart Rate Feature extraction functions"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bebd95c4",
   "metadata": {},
   "source": [
    "#### Poincare plot of HR PPG signal for Subject 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "49f6a437",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SD1: 82.298 ms\n",
      "SD2: 95.566 ms\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "from matplotlib.patches import Ellipse\n",
    "\n",
    "def plot_poincare(rr):\n",
    "    rr_n = rr[:-1]\n",
    "    rr_n1 = rr[1:]\n",
    "\n",
    "    sd1 = np.sqrt(0.5) * np.std(rr_n1 - rr_n)\n",
    "    sd2 = np.sqrt(0.5) * np.std(rr_n1 + rr_n)\n",
    "\n",
    "    m = np.mean(rr)\n",
    "    min_rr = np.min(rr)\n",
    "    max_rr = np.max(rr)\n",
    "    \n",
    "    plt.figure(figsize=(10, 10))\n",
    "    plt.title(\"Poincare plot\")\n",
    "\n",
    "    sns.scatterplot(x=rr_n, y=rr_n1, color=\"#51A6D8\")\n",
    "\n",
    "    plt.xlabel(r'$RR_n (ms)$')\n",
    "    plt.ylabel(r'$RR_{n+1} (ms)$')\n",
    "\n",
    "    e1 = Ellipse((m, m), 2*sd1, 2*sd2, angle=-45, linewidth=1.2, fill=False, color=\"k\")\n",
    "    plt.gca().add_patch(e1)\n",
    "\n",
    "    plt.arrow(m, m, (max_rr-min_rr)*0.4, (max_rr-min_rr)*0.4, color=\"k\", linewidth=0.8, head_width=5, head_length=5)\n",
    "    plt.arrow(m, m, (min_rr-max_rr)*0.4, (max_rr-min_rr)*0.4, color=\"k\", linewidth=0.8, head_width=5, head_length=5)\n",
    "\n",
    "    plt.arrow(m, m, sd2 * np.sqrt(0.5), sd2 * np.sqrt(0.5), color=\"green\", linewidth=5)\n",
    "    plt.arrow(m, m, -sd1 * np.sqrt(0.5), sd1 * np.sqrt(0.5), color=\"red\", linewidth=5)\n",
    "\n",
    "    plt.text(max_rr, max_rr, \"SD2\", fontsize=20, color=\"green\")\n",
    "    plt.text(m-(max_rr-min_rr)*0.4-20, max_rr, \"SD1\", fontsize=20, color=\"red\")\n",
    "    \n",
    "    return sd1, sd2\n",
    "\n",
    "\n",
    "data_hr1 = main_data[\"HeartRate\"].loc[main_data['SubjectID'] == 'Subject1']\n",
    "\n",
    "peaks, _ = find_peaks(data_hr1, distance=500*(30/60), prominence=0.01)\n",
    "rr = np.diff(peaks)\n",
    "sd1_2, sd2_2 = plot_poincare(rr)\n",
    "print(\"SD1: %.3f ms\" % sd1_2)\n",
    "print(\"SD2: %.3f ms\" % sd2_2)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "86745427",
   "metadata": {},
   "outputs": [],
   "source": [
    "import neurokit2 as nk\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from scipy.signal import find_peaks\n",
    "from scipy import signal\n",
    "from scipy.integrate import trapz\n",
    "from scipy.interpolate import interp1d\n",
    "import seaborn as sns\n",
    "from matplotlib.patches import Ellipse\n",
    "\n",
    "\n",
    "def hr_timedomain_meanRR(data):\n",
    "    peaks, _ = find_peaks(data, distance=500*(30/60), prominence=0.01)\n",
    "    rr = np.diff(peaks)\n",
    "\n",
    "    hr = 60000/rr\n",
    "    \n",
    "    meanRR_ms = np.mean(rr)\n",
    "    \n",
    "    return meanRR_ms\n",
    "\n",
    "def hr_timedomain_STD_RR_SDNN(data):\n",
    "    peaks, _ = find_peaks(data, distance=500*(30/60), prominence=0.01)\n",
    "    rr = np.diff(peaks)\n",
    "    hr = 60000/rr\n",
    "    \n",
    "    STD_RR_SDNN = np.std(rr)\n",
    "    \n",
    "    return STD_RR_SDNN\n",
    "\n",
    "\n",
    "def hr_timedomain_RMSSD(data):\n",
    "    peaks, _ = find_peaks(data, distance=500*(30/60), prominence=0.01)\n",
    "    rr = np.diff(peaks)\n",
    "    hr = 60000/rr\n",
    "    \n",
    "    RMSSD = np.sqrt(np.mean(np.square(np.diff(rr))))\n",
    "    \n",
    "    return RMSSD\n",
    "\n",
    "\n",
    "\n",
    "HR_functions = [hr_timedomain_meanRR, hr_timedomain_STD_RR_SDNN, hr_timedomain_RMSSD]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "id": "fed55406",
   "metadata": {
    "scrolled": false
   },
   "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></th>\n",
       "      <th>Acc</th>\n",
       "      <th>Temp</th>\n",
       "      <th>SpO2</th>\n",
       "      <th>EDA</th>\n",
       "      <th>HeartRate</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SubjectID</th>\n",
       "      <th>Label</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 rowspan=\"5\" valign=\"top\">Subject1</th>\n",
       "      <th>CognitiveStress</th>\n",
       "      <td>1.054711</td>\n",
       "      <td>32.512163</td>\n",
       "      <td>96.197268</td>\n",
       "      <td>1.199491</td>\n",
       "      <td>83.573809</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EmotionalStress</th>\n",
       "      <td>1.180263</td>\n",
       "      <td>33.261690</td>\n",
       "      <td>95.644496</td>\n",
       "      <td>0.413021</td>\n",
       "      <td>78.358118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PhysicalStress</th>\n",
       "      <td>1.156814</td>\n",
       "      <td>32.514971</td>\n",
       "      <td>96.420190</td>\n",
       "      <td>0.134480</td>\n",
       "      <td>95.955048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Relax1</th>\n",
       "      <td>1.084812</td>\n",
       "      <td>31.364473</td>\n",
       "      <td>96.026656</td>\n",
       "      <td>0.086164</td>\n",
       "      <td>73.569346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Relax2</th>\n",
       "      <td>1.081296</td>\n",
       "      <td>32.517719</td>\n",
       "      <td>95.876963</td>\n",
       "      <td>1.253509</td>\n",
       "      <td>83.533162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">Subject9</th>\n",
       "      <th>PhysicalStress</th>\n",
       "      <td>1.171135</td>\n",
       "      <td>31.450974</td>\n",
       "      <td>95.113871</td>\n",
       "      <td>0.979116</td>\n",
       "      <td>99.014903</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Relax1</th>\n",
       "      <td>1.190926</td>\n",
       "      <td>29.066722</td>\n",
       "      <td>94.606414</td>\n",
       "      <td>0.639008</td>\n",
       "      <td>82.243648</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Relax2</th>\n",
       "      <td>1.155932</td>\n",
       "      <td>31.454394</td>\n",
       "      <td>95.196585</td>\n",
       "      <td>2.729339</td>\n",
       "      <td>80.380258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Relax3</th>\n",
       "      <td>1.173170</td>\n",
       "      <td>34.043274</td>\n",
       "      <td>94.606414</td>\n",
       "      <td>2.459739</td>\n",
       "      <td>75.128280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Relax4</th>\n",
       "      <td>1.177480</td>\n",
       "      <td>34.274000</td>\n",
       "      <td>95.053333</td>\n",
       "      <td>0.798933</td>\n",
       "      <td>74.160000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>140 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                Acc       Temp       SpO2       EDA  HeartRate\n",
       "SubjectID Label                                                               \n",
       "Subject1  CognitiveStress  1.054711  32.512163  96.197268  1.199491  83.573809\n",
       "          EmotionalStress  1.180263  33.261690  95.644496  0.413021  78.358118\n",
       "          PhysicalStress   1.156814  32.514971  96.420190  0.134480  95.955048\n",
       "          Relax1           1.084812  31.364473  96.026656  0.086164  73.569346\n",
       "          Relax2           1.081296  32.517719  95.876963  1.253509  83.533162\n",
       "...                             ...        ...        ...       ...        ...\n",
       "Subject9  PhysicalStress   1.171135  31.450974  95.113871  0.979116  99.014903\n",
       "          Relax1           1.190926  29.066722  94.606414  0.639008  82.243648\n",
       "          Relax2           1.155932  31.454394  95.196585  2.729339  80.380258\n",
       "          Relax3           1.173170  34.043274  94.606414  2.459739  75.128280\n",
       "          Relax4           1.177480  34.274000  95.053333  0.798933  74.160000\n",
       "\n",
       "[140 rows x 5 columns]"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped_data = total_data.groupby(['SubjectID', 'Label'])\n",
    "grouped_data[['Acc', 'Temp', 'SpO2','EDA','HeartRate']].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "8358481a",
   "metadata": {
    "scrolled": false
   },
   "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 tr th {\n",
       "        text-align: left;\n",
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       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th colspan=\"5\" halign=\"left\">Acc</th>\n",
       "      <th colspan=\"5\" halign=\"left\">Temp</th>\n",
       "      <th>...</th>\n",
       "      <th colspan=\"2\" halign=\"left\">SpO2</th>\n",
       "      <th colspan=\"5\" halign=\"left\">EDA</th>\n",
       "      <th colspan=\"3\" halign=\"left\">HeartRate</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Mean</th>\n",
       "      <th>Median</th>\n",
       "      <th>Std</th>\n",
       "      <th>Min</th>\n",
       "      <th>Max</th>\n",
       "      <th>Mean</th>\n",
       "      <th>Median</th>\n",
       "      <th>Std</th>\n",
       "      <th>Min</th>\n",
       "      <th>Max</th>\n",
       "      <th>...</th>\n",
       "      <th>Min</th>\n",
       "      <th>Max</th>\n",
       "      <th>SCL_slope</th>\n",
       "      <th>SCL_mean</th>\n",
       "      <th>SCR_amplitude_mean</th>\n",
       "      <th>SCR_peaks</th>\n",
       "      <th>SCR_rise_time_mean</th>\n",
       "      <th>hr_timedomain_meanRR</th>\n",
       "      <th>hr_timedomain_STD_RR_SDNN</th>\n",
       "      <th>hr_timedomain_RMSSD</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SubjectID</th>\n",
       "      <th>Label</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">Subject1</th>\n",
       "      <th>CognitiveStress</th>\n",
       "      <td>1.054711</td>\n",
       "      <td>1.032521</td>\n",
       "      <td>0.053131</td>\n",
       "      <td>0.730205</td>\n",
       "      <td>1.595274</td>\n",
       "      <td>32.512163</td>\n",
       "      <td>32.6</td>\n",
       "      <td>0.107279</td>\n",
       "      <td>32.2</td>\n",
       "      <td>33.1</td>\n",
       "      <td>...</td>\n",
       "      <td>95.0</td>\n",
       "      <td>98.0</td>\n",
       "      <td>1.542539</td>\n",
       "      <td>1.198265</td>\n",
       "      <td>0.106480</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0.011371</td>\n",
       "      <td>358.857143</td>\n",
       "      <td>81.105625</td>\n",
       "      <td>108.249711</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EmotionalStress</th>\n",
       "      <td>1.180263</td>\n",
       "      <td>1.183765</td>\n",
       "      <td>0.089331</td>\n",
       "      <td>0.556058</td>\n",
       "      <td>3.036281</td>\n",
       "      <td>33.261690</td>\n",
       "      <td>33.3</td>\n",
       "      <td>0.230917</td>\n",
       "      <td>32.6</td>\n",
       "      <td>33.7</td>\n",
       "      <td>...</td>\n",
       "      <td>94.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>0.743643</td>\n",
       "      <td>0.413544</td>\n",
       "      <td>0.322688</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.001525</td>\n",
       "      <td>308.909091</td>\n",
       "      <td>50.077131</td>\n",
       "      <td>73.613178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PhysicalStress</th>\n",
       "      <td>1.156814</td>\n",
       "      <td>1.141446</td>\n",
       "      <td>0.123338</td>\n",
       "      <td>0.521632</td>\n",
       "      <td>2.119929</td>\n",
       "      <td>32.514971</td>\n",
       "      <td>32.6</td>\n",
       "      <td>0.138124</td>\n",
       "      <td>32.2</td>\n",
       "      <td>32.9</td>\n",
       "      <td>...</td>\n",
       "      <td>95.0</td>\n",
       "      <td>98.0</td>\n",
       "      <td>0.043083</td>\n",
       "      <td>0.134148</td>\n",
       "      <td>0.021763</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.001048</td>\n",
       "      <td>389.333333</td>\n",
       "      <td>122.977866</td>\n",
       "      <td>133.314665</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Relax1</th>\n",
       "      <td>1.084812</td>\n",
       "      <td>1.067801</td>\n",
       "      <td>0.036434</td>\n",
       "      <td>1.016858</td>\n",
       "      <td>1.175542</td>\n",
       "      <td>31.364473</td>\n",
       "      <td>31.6</td>\n",
       "      <td>0.565531</td>\n",
       "      <td>30.1</td>\n",
       "      <td>32.2</td>\n",
       "      <td>...</td>\n",
       "      <td>95.0</td>\n",
       "      <td>98.0</td>\n",
       "      <td>0.083630</td>\n",
       "      <td>0.086176</td>\n",
       "      <td>0.002043</td>\n",
       "      <td>241.0</td>\n",
       "      <td>0.023948</td>\n",
       "      <td>346.666667</td>\n",
       "      <td>48.643830</td>\n",
       "      <td>74.447297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Relax2</th>\n",
       "      <td>1.081296</td>\n",
       "      <td>1.072474</td>\n",
       "      <td>0.024645</td>\n",
       "      <td>0.861162</td>\n",
       "      <td>1.304071</td>\n",
       "      <td>32.517719</td>\n",
       "      <td>32.6</td>\n",
       "      <td>0.102266</td>\n",
       "      <td>32.4</td>\n",
       "      <td>32.9</td>\n",
       "      <td>...</td>\n",
       "      <td>94.0</td>\n",
       "      <td>98.0</td>\n",
       "      <td>2.017218</td>\n",
       "      <td>1.254258</td>\n",
       "      <td>0.269222</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.005727</td>\n",
       "      <td>345.000000</td>\n",
       "      <td>43.023250</td>\n",
       "      <td>68.679379</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">Subject9</th>\n",
       "      <th>PhysicalStress</th>\n",
       "      <td>1.171135</td>\n",
       "      <td>1.140789</td>\n",
       "      <td>0.141528</td>\n",
       "      <td>0.578360</td>\n",
       "      <td>1.838804</td>\n",
       "      <td>31.450974</td>\n",
       "      <td>31.6</td>\n",
       "      <td>0.247588</td>\n",
       "      <td>30.8</td>\n",
       "      <td>31.8</td>\n",
       "      <td>...</td>\n",
       "      <td>90.0</td>\n",
       "      <td>98.0</td>\n",
       "      <td>0.115196</td>\n",
       "      <td>0.979297</td>\n",
       "      <td>0.054243</td>\n",
       "      <td>43.0</td>\n",
       "      <td>0.013947</td>\n",
       "      <td>599.000000</td>\n",
       "      <td>313.041531</td>\n",
       "      <td>371.792057</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Relax1</th>\n",
       "      <td>1.190926</td>\n",
       "      <td>1.193231</td>\n",
       "      <td>0.032385</td>\n",
       "      <td>0.814494</td>\n",
       "      <td>1.735627</td>\n",
       "      <td>29.066722</td>\n",
       "      <td>29.3</td>\n",
       "      <td>1.177078</td>\n",
       "      <td>26.4</td>\n",
       "      <td>30.8</td>\n",
       "      <td>...</td>\n",
       "      <td>93.0</td>\n",
       "      <td>98.0</td>\n",
       "      <td>1.110612</td>\n",
       "      <td>0.639083</td>\n",
       "      <td>0.003731</td>\n",
       "      <td>210.0</td>\n",
       "      <td>0.036615</td>\n",
       "      <td>331.428571</td>\n",
       "      <td>44.564102</td>\n",
       "      <td>80.399005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Relax2</th>\n",
       "      <td>1.155932</td>\n",
       "      <td>1.172774</td>\n",
       "      <td>0.059328</td>\n",
       "      <td>0.714493</td>\n",
       "      <td>3.080406</td>\n",
       "      <td>31.454394</td>\n",
       "      <td>31.4</td>\n",
       "      <td>0.219902</td>\n",
       "      <td>31.2</td>\n",
       "      <td>31.8</td>\n",
       "      <td>...</td>\n",
       "      <td>94.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>3.680251</td>\n",
       "      <td>2.729604</td>\n",
       "      <td>0.125620</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.002968</td>\n",
       "      <td>368.000000</td>\n",
       "      <td>97.897906</td>\n",
       "      <td>118.618717</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Relax3</th>\n",
       "      <td>1.173170</td>\n",
       "      <td>1.172263</td>\n",
       "      <td>0.016629</td>\n",
       "      <td>0.758222</td>\n",
       "      <td>1.456297</td>\n",
       "      <td>34.043274</td>\n",
       "      <td>34.1</td>\n",
       "      <td>0.305937</td>\n",
       "      <td>33.5</td>\n",
       "      <td>34.9</td>\n",
       "      <td>...</td>\n",
       "      <td>93.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>3.224622</td>\n",
       "      <td>2.460912</td>\n",
       "      <td>0.167001</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.002811</td>\n",
       "      <td>383.333333</td>\n",
       "      <td>82.733844</td>\n",
       "      <td>94.250729</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Relax4</th>\n",
       "      <td>1.177480</td>\n",
       "      <td>1.178516</td>\n",
       "      <td>0.009729</td>\n",
       "      <td>0.934666</td>\n",
       "      <td>1.295685</td>\n",
       "      <td>34.274000</td>\n",
       "      <td>34.3</td>\n",
       "      <td>0.341623</td>\n",
       "      <td>33.7</td>\n",
       "      <td>34.9</td>\n",
       "      <td>...</td>\n",
       "      <td>94.0</td>\n",
       "      <td>98.0</td>\n",
       "      <td>1.102888</td>\n",
       "      <td>0.798940</td>\n",
       "      <td>0.003163</td>\n",
       "      <td>252.0</td>\n",
       "      <td>0.031510</td>\n",
       "      <td>327.428571</td>\n",
       "      <td>47.107346</td>\n",
       "      <td>82.300263</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>140 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                Acc                                          \\\n",
       "                               Mean    Median       Std       Min       Max   \n",
       "SubjectID Label                                                               \n",
       "Subject1  CognitiveStress  1.054711  1.032521  0.053131  0.730205  1.595274   \n",
       "          EmotionalStress  1.180263  1.183765  0.089331  0.556058  3.036281   \n",
       "          PhysicalStress   1.156814  1.141446  0.123338  0.521632  2.119929   \n",
       "          Relax1           1.084812  1.067801  0.036434  1.016858  1.175542   \n",
       "          Relax2           1.081296  1.072474  0.024645  0.861162  1.304071   \n",
       "...                             ...       ...       ...       ...       ...   \n",
       "Subject9  PhysicalStress   1.171135  1.140789  0.141528  0.578360  1.838804   \n",
       "          Relax1           1.190926  1.193231  0.032385  0.814494  1.735627   \n",
       "          Relax2           1.155932  1.172774  0.059328  0.714493  3.080406   \n",
       "          Relax3           1.173170  1.172263  0.016629  0.758222  1.456297   \n",
       "          Relax4           1.177480  1.178516  0.009729  0.934666  1.295685   \n",
       "\n",
       "                                Temp                               ...  SpO2  \\\n",
       "                                Mean Median       Std   Min   Max  ...   Min   \n",
       "SubjectID Label                                                    ...         \n",
       "Subject1  CognitiveStress  32.512163   32.6  0.107279  32.2  33.1  ...  95.0   \n",
       "          EmotionalStress  33.261690   33.3  0.230917  32.6  33.7  ...  94.0   \n",
       "          PhysicalStress   32.514971   32.6  0.138124  32.2  32.9  ...  95.0   \n",
       "          Relax1           31.364473   31.6  0.565531  30.1  32.2  ...  95.0   \n",
       "          Relax2           32.517719   32.6  0.102266  32.4  32.9  ...  94.0   \n",
       "...                              ...    ...       ...   ...   ...  ...   ...   \n",
       "Subject9  PhysicalStress   31.450974   31.6  0.247588  30.8  31.8  ...  90.0   \n",
       "          Relax1           29.066722   29.3  1.177078  26.4  30.8  ...  93.0   \n",
       "          Relax2           31.454394   31.4  0.219902  31.2  31.8  ...  94.0   \n",
       "          Relax3           34.043274   34.1  0.305937  33.5  34.9  ...  93.0   \n",
       "          Relax4           34.274000   34.3  0.341623  33.7  34.9  ...  94.0   \n",
       "\n",
       "                                      EDA                               \\\n",
       "                            Max SCL_slope  SCL_mean SCR_amplitude_mean   \n",
       "SubjectID Label                                                          \n",
       "Subject1  CognitiveStress  98.0  1.542539  1.198265           0.106480   \n",
       "          EmotionalStress  99.0  0.743643  0.413544           0.322688   \n",
       "          PhysicalStress   98.0  0.043083  0.134148           0.021763   \n",
       "          Relax1           98.0  0.083630  0.086176           0.002043   \n",
       "          Relax2           98.0  2.017218  1.254258           0.269222   \n",
       "...                         ...       ...       ...                ...   \n",
       "Subject9  PhysicalStress   98.0  0.115196  0.979297           0.054243   \n",
       "          Relax1           98.0  1.110612  0.639083           0.003731   \n",
       "          Relax2           97.0  3.680251  2.729604           0.125620   \n",
       "          Relax3           97.0  3.224622  2.460912           0.167001   \n",
       "          Relax4           98.0  1.102888  0.798940           0.003163   \n",
       "\n",
       "                                                                  HeartRate  \\\n",
       "                          SCR_peaks SCR_rise_time_mean hr_timedomain_meanRR   \n",
       "SubjectID Label                                                               \n",
       "Subject1  CognitiveStress      27.0           0.011371           358.857143   \n",
       "          EmotionalStress       4.0           0.001525           308.909091   \n",
       "          PhysicalStress        6.0           0.001048           389.333333   \n",
       "          Relax1              241.0           0.023948           346.666667   \n",
       "          Relax2               14.0           0.005727           345.000000   \n",
       "...                             ...                ...                  ...   \n",
       "Subject9  PhysicalStress       43.0           0.013947           599.000000   \n",
       "          Relax1              210.0           0.036615           331.428571   \n",
       "          Relax2                9.0           0.002968           368.000000   \n",
       "          Relax3                7.0           0.002811           383.333333   \n",
       "          Relax4              252.0           0.031510           327.428571   \n",
       "\n",
       "                                                                         \n",
       "                          hr_timedomain_STD_RR_SDNN hr_timedomain_RMSSD  \n",
       "SubjectID Label                                                          \n",
       "Subject1  CognitiveStress                 81.105625          108.249711  \n",
       "          EmotionalStress                 50.077131           73.613178  \n",
       "          PhysicalStress                 122.977866          133.314665  \n",
       "          Relax1                          48.643830           74.447297  \n",
       "          Relax2                          43.023250           68.679379  \n",
       "...                                             ...                 ...  \n",
       "Subject9  PhysicalStress                 313.041531          371.792057  \n",
       "          Relax1                          44.564102           80.399005  \n",
       "          Relax2                          97.897906          118.618717  \n",
       "          Relax3                          82.733844           94.250729  \n",
       "          Relax4                          47.107346           82.300263  \n",
       "\n",
       "[140 rows x 23 columns]"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features = grouped_data.agg({\n",
    "        'Acc': functions,\n",
    "        'Temp': functions,\n",
    "        'SpO2': functions,\n",
    "        'EDA': EDA_functions,\n",
    "        'HeartRate': HR_functions\n",
    "    })\n",
    "\n",
    "features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "id": "9a345664",
   "metadata": {},
   "outputs": [],
   "source": [
    "# features.reset_index(inplace=True)\n",
    "# features.columns = ['{0}{1}'.format(a, b) if b else a for a, b in features.columns]\n",
    "# features\n",
    "#features.to_csv('features.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "id": "68e091e6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SubjectID</th>\n",
       "      <th>Label</th>\n",
       "      <th>AccMean</th>\n",
       "      <th>AccMedian</th>\n",
       "      <th>AccStd</th>\n",
       "      <th>AccMin</th>\n",
       "      <th>AccMax</th>\n",
       "      <th>TempMean</th>\n",
       "      <th>TempMedian</th>\n",
       "      <th>TempStd</th>\n",
       "      <th>...</th>\n",
       "      <th>SpO2Min</th>\n",
       "      <th>SpO2Max</th>\n",
       "      <th>EDASCL_slope</th>\n",
       "      <th>EDASCL_mean</th>\n",
       "      <th>EDASCR_amplitude_mean</th>\n",
       "      <th>EDASCR_peaks</th>\n",
       "      <th>EDASCR_rise_time_mean</th>\n",
       "      <th>HeartRatehr_timedomain_meanRR</th>\n",
       "      <th>HeartRatehr_timedomain_STD_RR_SDNN</th>\n",
       "      <th>HeartRatehr_timedomain_RMSSD</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Subject1</td>\n",
       "      <td>CognitiveStress</td>\n",
       "      <td>1.054711</td>\n",
       "      <td>1.032521</td>\n",
       "      <td>0.053131</td>\n",
       "      <td>0.730205</td>\n",
       "      <td>1.595274</td>\n",
       "      <td>32.512163</td>\n",
       "      <td>32.6</td>\n",
       "      <td>0.107279</td>\n",
       "      <td>...</td>\n",
       "      <td>95.0</td>\n",
       "      <td>98.0</td>\n",
       "      <td>1.542539</td>\n",
       "      <td>1.198265</td>\n",
       "      <td>0.106480</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0.011371</td>\n",
       "      <td>358.857143</td>\n",
       "      <td>81.105625</td>\n",
       "      <td>108.249711</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Subject1</td>\n",
       "      <td>EmotionalStress</td>\n",
       "      <td>1.180263</td>\n",
       "      <td>1.183765</td>\n",
       "      <td>0.089331</td>\n",
       "      <td>0.556058</td>\n",
       "      <td>3.036281</td>\n",
       "      <td>33.261690</td>\n",
       "      <td>33.3</td>\n",
       "      <td>0.230917</td>\n",
       "      <td>...</td>\n",
       "      <td>94.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>0.743643</td>\n",
       "      <td>0.413544</td>\n",
       "      <td>0.322688</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.001525</td>\n",
       "      <td>308.909091</td>\n",
       "      <td>50.077131</td>\n",
       "      <td>73.613178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Subject1</td>\n",
       "      <td>PhysicalStress</td>\n",
       "      <td>1.156814</td>\n",
       "      <td>1.141446</td>\n",
       "      <td>0.123338</td>\n",
       "      <td>0.521632</td>\n",
       "      <td>2.119929</td>\n",
       "      <td>32.514971</td>\n",
       "      <td>32.6</td>\n",
       "      <td>0.138124</td>\n",
       "      <td>...</td>\n",
       "      <td>95.0</td>\n",
       "      <td>98.0</td>\n",
       "      <td>0.043083</td>\n",
       "      <td>0.134148</td>\n",
       "      <td>0.021763</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.001048</td>\n",
       "      <td>389.333333</td>\n",
       "      <td>122.977866</td>\n",
       "      <td>133.314665</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Subject1</td>\n",
       "      <td>Relax1</td>\n",
       "      <td>1.084812</td>\n",
       "      <td>1.067801</td>\n",
       "      <td>0.036434</td>\n",
       "      <td>1.016858</td>\n",
       "      <td>1.175542</td>\n",
       "      <td>31.364473</td>\n",
       "      <td>31.6</td>\n",
       "      <td>0.565531</td>\n",
       "      <td>...</td>\n",
       "      <td>95.0</td>\n",
       "      <td>98.0</td>\n",
       "      <td>0.083630</td>\n",
       "      <td>0.086176</td>\n",
       "      <td>0.002043</td>\n",
       "      <td>241.0</td>\n",
       "      <td>0.023948</td>\n",
       "      <td>346.666667</td>\n",
       "      <td>48.643830</td>\n",
       "      <td>74.447297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Subject1</td>\n",
       "      <td>Relax2</td>\n",
       "      <td>1.081296</td>\n",
       "      <td>1.072474</td>\n",
       "      <td>0.024645</td>\n",
       "      <td>0.861162</td>\n",
       "      <td>1.304071</td>\n",
       "      <td>32.517719</td>\n",
       "      <td>32.6</td>\n",
       "      <td>0.102266</td>\n",
       "      <td>...</td>\n",
       "      <td>94.0</td>\n",
       "      <td>98.0</td>\n",
       "      <td>2.017218</td>\n",
       "      <td>1.254258</td>\n",
       "      <td>0.269222</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.005727</td>\n",
       "      <td>345.000000</td>\n",
       "      <td>43.023250</td>\n",
       "      <td>68.679379</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135</th>\n",
       "      <td>Subject9</td>\n",
       "      <td>PhysicalStress</td>\n",
       "      <td>1.171135</td>\n",
       "      <td>1.140789</td>\n",
       "      <td>0.141528</td>\n",
       "      <td>0.578360</td>\n",
       "      <td>1.838804</td>\n",
       "      <td>31.450974</td>\n",
       "      <td>31.6</td>\n",
       "      <td>0.247588</td>\n",
       "      <td>...</td>\n",
       "      <td>90.0</td>\n",
       "      <td>98.0</td>\n",
       "      <td>0.115196</td>\n",
       "      <td>0.979297</td>\n",
       "      <td>0.054243</td>\n",
       "      <td>43.0</td>\n",
       "      <td>0.013947</td>\n",
       "      <td>599.000000</td>\n",
       "      <td>313.041531</td>\n",
       "      <td>371.792057</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>136</th>\n",
       "      <td>Subject9</td>\n",
       "      <td>Relax1</td>\n",
       "      <td>1.190926</td>\n",
       "      <td>1.193231</td>\n",
       "      <td>0.032385</td>\n",
       "      <td>0.814494</td>\n",
       "      <td>1.735627</td>\n",
       "      <td>29.066722</td>\n",
       "      <td>29.3</td>\n",
       "      <td>1.177078</td>\n",
       "      <td>...</td>\n",
       "      <td>93.0</td>\n",
       "      <td>98.0</td>\n",
       "      <td>1.110612</td>\n",
       "      <td>0.639083</td>\n",
       "      <td>0.003731</td>\n",
       "      <td>210.0</td>\n",
       "      <td>0.036615</td>\n",
       "      <td>331.428571</td>\n",
       "      <td>44.564102</td>\n",
       "      <td>80.399005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137</th>\n",
       "      <td>Subject9</td>\n",
       "      <td>Relax2</td>\n",
       "      <td>1.155932</td>\n",
       "      <td>1.172774</td>\n",
       "      <td>0.059328</td>\n",
       "      <td>0.714493</td>\n",
       "      <td>3.080406</td>\n",
       "      <td>31.454394</td>\n",
       "      <td>31.4</td>\n",
       "      <td>0.219902</td>\n",
       "      <td>...</td>\n",
       "      <td>94.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>3.680251</td>\n",
       "      <td>2.729604</td>\n",
       "      <td>0.125620</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.002968</td>\n",
       "      <td>368.000000</td>\n",
       "      <td>97.897906</td>\n",
       "      <td>118.618717</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>Subject9</td>\n",
       "      <td>Relax3</td>\n",
       "      <td>1.173170</td>\n",
       "      <td>1.172263</td>\n",
       "      <td>0.016629</td>\n",
       "      <td>0.758222</td>\n",
       "      <td>1.456297</td>\n",
       "      <td>34.043274</td>\n",
       "      <td>34.1</td>\n",
       "      <td>0.305937</td>\n",
       "      <td>...</td>\n",
       "      <td>93.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>3.224622</td>\n",
       "      <td>2.460912</td>\n",
       "      <td>0.167001</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.002811</td>\n",
       "      <td>383.333333</td>\n",
       "      <td>82.733844</td>\n",
       "      <td>94.250729</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>Subject9</td>\n",
       "      <td>Relax4</td>\n",
       "      <td>1.177480</td>\n",
       "      <td>1.178516</td>\n",
       "      <td>0.009729</td>\n",
       "      <td>0.934666</td>\n",
       "      <td>1.295685</td>\n",
       "      <td>34.274000</td>\n",
       "      <td>34.3</td>\n",
       "      <td>0.341623</td>\n",
       "      <td>...</td>\n",
       "      <td>94.0</td>\n",
       "      <td>98.0</td>\n",
       "      <td>1.102888</td>\n",
       "      <td>0.798940</td>\n",
       "      <td>0.003163</td>\n",
       "      <td>252.0</td>\n",
       "      <td>0.031510</td>\n",
       "      <td>327.428571</td>\n",
       "      <td>47.107346</td>\n",
       "      <td>82.300263</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>140 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    SubjectID            Label   AccMean  AccMedian    AccStd    AccMin  \\\n",
       "0    Subject1  CognitiveStress  1.054711   1.032521  0.053131  0.730205   \n",
       "1    Subject1  EmotionalStress  1.180263   1.183765  0.089331  0.556058   \n",
       "2    Subject1   PhysicalStress  1.156814   1.141446  0.123338  0.521632   \n",
       "3    Subject1           Relax1  1.084812   1.067801  0.036434  1.016858   \n",
       "4    Subject1           Relax2  1.081296   1.072474  0.024645  0.861162   \n",
       "..        ...              ...       ...        ...       ...       ...   \n",
       "135  Subject9   PhysicalStress  1.171135   1.140789  0.141528  0.578360   \n",
       "136  Subject9           Relax1  1.190926   1.193231  0.032385  0.814494   \n",
       "137  Subject9           Relax2  1.155932   1.172774  0.059328  0.714493   \n",
       "138  Subject9           Relax3  1.173170   1.172263  0.016629  0.758222   \n",
       "139  Subject9           Relax4  1.177480   1.178516  0.009729  0.934666   \n",
       "\n",
       "       AccMax   TempMean  TempMedian   TempStd  ...  SpO2Min  SpO2Max  \\\n",
       "0    1.595274  32.512163        32.6  0.107279  ...     95.0     98.0   \n",
       "1    3.036281  33.261690        33.3  0.230917  ...     94.0     99.0   \n",
       "2    2.119929  32.514971        32.6  0.138124  ...     95.0     98.0   \n",
       "3    1.175542  31.364473        31.6  0.565531  ...     95.0     98.0   \n",
       "4    1.304071  32.517719        32.6  0.102266  ...     94.0     98.0   \n",
       "..        ...        ...         ...       ...  ...      ...      ...   \n",
       "135  1.838804  31.450974        31.6  0.247588  ...     90.0     98.0   \n",
       "136  1.735627  29.066722        29.3  1.177078  ...     93.0     98.0   \n",
       "137  3.080406  31.454394        31.4  0.219902  ...     94.0     97.0   \n",
       "138  1.456297  34.043274        34.1  0.305937  ...     93.0     97.0   \n",
       "139  1.295685  34.274000        34.3  0.341623  ...     94.0     98.0   \n",
       "\n",
       "     EDASCL_slope  EDASCL_mean  EDASCR_amplitude_mean  EDASCR_peaks  \\\n",
       "0        1.542539     1.198265               0.106480          27.0   \n",
       "1        0.743643     0.413544               0.322688           4.0   \n",
       "2        0.043083     0.134148               0.021763           6.0   \n",
       "3        0.083630     0.086176               0.002043         241.0   \n",
       "4        2.017218     1.254258               0.269222          14.0   \n",
       "..            ...          ...                    ...           ...   \n",
       "135      0.115196     0.979297               0.054243          43.0   \n",
       "136      1.110612     0.639083               0.003731         210.0   \n",
       "137      3.680251     2.729604               0.125620           9.0   \n",
       "138      3.224622     2.460912               0.167001           7.0   \n",
       "139      1.102888     0.798940               0.003163         252.0   \n",
       "\n",
       "     EDASCR_rise_time_mean  HeartRatehr_timedomain_meanRR  \\\n",
       "0                 0.011371                     358.857143   \n",
       "1                 0.001525                     308.909091   \n",
       "2                 0.001048                     389.333333   \n",
       "3                 0.023948                     346.666667   \n",
       "4                 0.005727                     345.000000   \n",
       "..                     ...                            ...   \n",
       "135               0.013947                     599.000000   \n",
       "136               0.036615                     331.428571   \n",
       "137               0.002968                     368.000000   \n",
       "138               0.002811                     383.333333   \n",
       "139               0.031510                     327.428571   \n",
       "\n",
       "     HeartRatehr_timedomain_STD_RR_SDNN  HeartRatehr_timedomain_RMSSD  \n",
       "0                             81.105625                    108.249711  \n",
       "1                             50.077131                     73.613178  \n",
       "2                            122.977866                    133.314665  \n",
       "3                             48.643830                     74.447297  \n",
       "4                             43.023250                     68.679379  \n",
       "..                                  ...                           ...  \n",
       "135                          313.041531                    371.792057  \n",
       "136                           44.564102                     80.399005  \n",
       "137                           97.897906                    118.618717  \n",
       "138                           82.733844                     94.250729  \n",
       "139                           47.107346                     82.300263  \n",
       "\n",
       "[140 rows x 25 columns]"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features.reset_index(inplace=True)\n",
    "features.columns = ['{0}{1}'.format(a, b) if b else a for a, b in features.columns]\n",
    "features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "id": "7a088cbf",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AccMean</th>\n",
       "      <th>AccMedian</th>\n",
       "      <th>AccStd</th>\n",
       "      <th>AccMin</th>\n",
       "      <th>AccMax</th>\n",
       "      <th>TempMean</th>\n",
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       "      <th>TempStd</th>\n",
       "      <th>TempMin</th>\n",
       "      <th>TempMax</th>\n",
       "      <th>...</th>\n",
       "      <th>SpO2Min</th>\n",
       "      <th>SpO2Max</th>\n",
       "      <th>EDASCL_slope</th>\n",
       "      <th>EDASCL_mean</th>\n",
       "      <th>EDASCR_amplitude_mean</th>\n",
       "      <th>EDASCR_peaks</th>\n",
       "      <th>EDASCR_rise_time_mean</th>\n",
       "      <th>HeartRatehr_timedomain_meanRR</th>\n",
       "      <th>HeartRatehr_timedomain_STD_RR_SDNN</th>\n",
       "      <th>HeartRatehr_timedomain_RMSSD</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.597932</td>\n",
       "      <td>0.532682</td>\n",
       "      <td>0.244454</td>\n",
       "      <td>0.538005</td>\n",
       "      <td>0.182392</td>\n",
       "      <td>0.648007</td>\n",
       "      <td>0.6500</td>\n",
       "      <td>0.052419</td>\n",
       "      <td>0.708738</td>\n",
       "      <td>0.602740</td>\n",
       "      <td>...</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.247837</td>\n",
       "      <td>0.135110</td>\n",
       "      <td>0.028562</td>\n",
       "      <td>0.052301</td>\n",
       "      <td>0.243296</td>\n",
       "      <td>0.200412</td>\n",
       "      <td>0.197052</td>\n",
       "      <td>0.165505</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.947283</td>\n",
       "      <td>0.953757</td>\n",
       "      <td>0.424384</td>\n",
       "      <td>0.357240</td>\n",
       "      <td>0.553424</td>\n",
       "      <td>0.738623</td>\n",
       "      <td>0.7375</td>\n",
       "      <td>0.129076</td>\n",
       "      <td>0.747573</td>\n",
       "      <td>0.684932</td>\n",
       "      <td>...</td>\n",
       "      <td>0.777778</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.168786</td>\n",
       "      <td>0.045120</td>\n",
       "      <td>0.087452</td>\n",
       "      <td>0.004184</td>\n",
       "      <td>0.027268</td>\n",
       "      <td>0.034104</td>\n",
       "      <td>0.089634</td>\n",
       "      <td>0.095678</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.882037</td>\n",
       "      <td>0.835938</td>\n",
       "      <td>0.593418</td>\n",
       "      <td>0.321506</td>\n",
       "      <td>0.317481</td>\n",
       "      <td>0.648346</td>\n",
       "      <td>0.6500</td>\n",
       "      <td>0.071544</td>\n",
       "      <td>0.708738</td>\n",
       "      <td>0.575342</td>\n",
       "      <td>...</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.099466</td>\n",
       "      <td>0.013079</td>\n",
       "      <td>0.005488</td>\n",
       "      <td>0.008368</td>\n",
       "      <td>0.016797</td>\n",
       "      <td>0.301887</td>\n",
       "      <td>0.342011</td>\n",
       "      <td>0.216036</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.681687</td>\n",
       "      <td>0.630905</td>\n",
       "      <td>0.161464</td>\n",
       "      <td>0.835550</td>\n",
       "      <td>0.074319</td>\n",
       "      <td>0.509254</td>\n",
       "      <td>0.5250</td>\n",
       "      <td>0.336536</td>\n",
       "      <td>0.504854</td>\n",
       "      <td>0.479452</td>\n",
       "      <td>...</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.103478</td>\n",
       "      <td>0.007578</td>\n",
       "      <td>0.000116</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.519238</td>\n",
       "      <td>0.159822</td>\n",
       "      <td>0.084672</td>\n",
       "      <td>0.097360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.671904</td>\n",
       "      <td>0.643913</td>\n",
       "      <td>0.102865</td>\n",
       "      <td>0.673938</td>\n",
       "      <td>0.107412</td>\n",
       "      <td>0.648679</td>\n",
       "      <td>0.6500</td>\n",
       "      <td>0.049312</td>\n",
       "      <td>0.728155</td>\n",
       "      <td>0.575342</td>\n",
       "      <td>...</td>\n",
       "      <td>0.777778</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.294806</td>\n",
       "      <td>0.141531</td>\n",
       "      <td>0.072889</td>\n",
       "      <td>0.025105</td>\n",
       "      <td>0.119470</td>\n",
       "      <td>0.154273</td>\n",
       "      <td>0.065214</td>\n",
       "      <td>0.085732</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    AccMean  AccMedian    AccStd    AccMin    AccMax  TempMean  TempMedian  \\\n",
       "0  0.597932   0.532682  0.244454  0.538005  0.182392  0.648007      0.6500   \n",
       "1  0.947283   0.953757  0.424384  0.357240  0.553424  0.738623      0.7375   \n",
       "2  0.882037   0.835938  0.593418  0.321506  0.317481  0.648346      0.6500   \n",
       "3  0.681687   0.630905  0.161464  0.835550  0.074319  0.509254      0.5250   \n",
       "4  0.671904   0.643913  0.102865  0.673938  0.107412  0.648679      0.6500   \n",
       "\n",
       "    TempStd   TempMin   TempMax  ...   SpO2Min  SpO2Max  EDASCL_slope  \\\n",
       "0  0.052419  0.708738  0.602740  ...  0.833333      0.6      0.247837   \n",
       "1  0.129076  0.747573  0.684932  ...  0.777778      0.8      0.168786   \n",
       "2  0.071544  0.708738  0.575342  ...  0.833333      0.6      0.099466   \n",
       "3  0.336536  0.504854  0.479452  ...  0.833333      0.6      0.103478   \n",
       "4  0.049312  0.728155  0.575342  ...  0.777778      0.6      0.294806   \n",
       "\n",
       "   EDASCL_mean  EDASCR_amplitude_mean  EDASCR_peaks  EDASCR_rise_time_mean  \\\n",
       "0     0.135110               0.028562      0.052301               0.243296   \n",
       "1     0.045120               0.087452      0.004184               0.027268   \n",
       "2     0.013079               0.005488      0.008368               0.016797   \n",
       "3     0.007578               0.000116      0.500000               0.519238   \n",
       "4     0.141531               0.072889      0.025105               0.119470   \n",
       "\n",
       "   HeartRatehr_timedomain_meanRR  HeartRatehr_timedomain_STD_RR_SDNN  \\\n",
       "0                       0.200412                            0.197052   \n",
       "1                       0.034104                            0.089634   \n",
       "2                       0.301887                            0.342011   \n",
       "3                       0.159822                            0.084672   \n",
       "4                       0.154273                            0.065214   \n",
       "\n",
       "   HeartRatehr_timedomain_RMSSD  \n",
       "0                      0.165505  \n",
       "1                      0.095678  \n",
       "2                      0.216036  \n",
       "3                      0.097360  \n",
       "4                      0.085732  \n",
       "\n",
       "[5 rows x 23 columns]"
      ]
     },
     "execution_count": 170,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = features.iloc[:, features.columns != 'Label']  #independent columns\n",
    "X = X.iloc[:, X.columns != 'SubjectID']  #independent columns\n",
    "y = features['Label']    #target column i.e price range\n",
    "X.var()\n",
    "\n",
    "def NormalizeData(data):\n",
    "    return (data - np.min(data)) / (np.max(data) - np.min(data))\n",
    "\n",
    "normalized_data = NormalizeData(X)\n",
    "\n",
    "normalized_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "id": "1d9f7e32",
   "metadata": {},
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TuUh1Ekt1Equj18nKlSsZMmRIUsfWrF7N+ttvx1qLDQQwbjfGGA6+6iq8RxzRcJzS+8bKZJ3E+5kaY5ZYa8fHO17P/JPk8/mYPn36fi396PSS6dOnU1lZSdeuXbNVPBGRtPAecQSDZs9m93vvUbt1K66+fel+1FE4PJ5sF03aQcE/SQsWLCAUCsXdFwqFWLBgAdOmTctwqURE0s/h8bR5VL/kJj3zT1JVVVVDS7+pmpqahoElIiIiuU7BP0mlpaUJR4lGR6iKiIh0BAr+SSovL8fhiF9dDoejITuViIhIrlPwT1JRURFz5syhqKiooQfA6/U2bNdgPxER6SgU/FuhrKyMyspKZs2axY9//GNmzZpFZWVlh5zmJyKSTdElfaMft9xyS7vPuW7dOubNm9fw/eLFi7niiivafd6mBg4cyPbt24FwyuBhw4YxcuRIRo8ezXvvvQfA7NmzE44TywUa7d9KXbt21ah+Eckr/qCfJTsXsc2/hRJPP8b1nIDH2b6pftH0vqkUDf7nn38+AOPHj2f8+LjT3FPinXfe4dlnn6WyshK328327dupra0FwsH/G9/4RtzXBYNBnE5n2sqVDLX8RUQkobW+Nfx82dU8tv4R/rXlJR5b/wg/X3Y1a31r0nK9gQMHct111zF69GjGjx9PZWUlU6ZM4bDDDuPee+8Fwml/r776aoYPH86IESN45JFHALj22mt54403GD16NH/84x9ZuHAhU6dOBeCLL77gG9/4BiNHjuToo49m2bJlANxwww1897vfZdKkSXzlK1/hjjvuaCjLN77xDcaNG8ewYcO4//77Y8q6efNm+vTpgzuSArlPnz7079+fO+64g02bNnHGGWcwefJkIPzo+Gc/+xmjRo3inXfe4Z///CdlZWWMHj2aH/7whwSDQYLBIDNmzGh4X3/84x8BuOOOOxg6dCgjR47kvPPOS0k9K/iLiEhc/qCfe9bcQSDkpzYUXuCnNhQgEApv9wfbvqRvdFW/6Ec0gAMccsghLF26lIkTJzJjxgwee+wx3n33XWbOnAnAE088wdKlS/nggw945ZVXuPrqq9m8eTO33HILEydOZOnSpVx55ZX7XW/mzJmMGTOGZcuW8dvf/pbvfOc7Dfs+/vhjXnrpJSoqKrjxxhupq6sDwsv/LlmyhMWLF3PHHXewY8eO/c55yimnsH79eo444gguu+wyXnvtNQCuuOIK+vfvz3PPPcd//vMfAPbu3ctRRx3FBx98QO/evXnkkUd46623WLp0KU6nk7lz57J06VI2btzI8uXL+fDDD7n44osBuOWWW3j//fdZtmxZww1Qe6nbX0RE4lqycxGWBEv6YqncuYiv9pnYpnM31+0fnT01YsQIfD4f3bp1o1u3brjdbqqrq3nzzTeZNm0aTqeTfv36cfzxx7No0SK6d++e8Hpvvvkmjz/+OAAnnHACO3bsYPfu3QCcccYZuN1u3G43ffv2ZcuWLQwYMIA77riDJ598EoD169ezZs0aevfu3XDOoqIilixZwhtvvMF//vMfvv3tb3PLLbcwY8aMmOs7nU7OOeccAF599VWWLFnChAkTgPCNUN++ffn617/Of//7Xy6//HLOOOMMTjnlFABGjhzJBRdcwDe+8Y2EjxJaS8FfRETi2ubf0tDib6o2FGCbv+1L+jYn2o3ucDgavo5+X19fn7brQThI19fXs3DhQl555RXeeecdvF4vkyZNwu+P7elwOp1MmjSJSZMmMWLECB566KG4wd/j8TQ857fWctFFF3HzzTfHHPfBBx/w0ksvce+99zJ//nweeOABnnvuOV5//XWeeeYZbrrpJj788EMKCtoXvtXtLyIicZV4+uFyxF/S1+VwU+Jp/5K+bTFx4kQeeeQRgsEg27Zt4/XXX6esrIxu3bqxZ8+ehK+JLv+7cOFC+vTp02xPwa5du+jZsyder5ePP/6Yd999N+aYVatWsWbNl2Mfli5dyqGHHgrQbFlOPPFEHnvsMbZuDd88ffHFF3z66ads376dUCjEOeecw29+8xsqKysJhUKsX7+eyZMn87vf/Y5du3alZDVZtfxFRCSucT0n8Pj6+XH3GQxje05o87mjz/yjTj311KSn+5111lm88847jBo1CmMMv//97znggAPo3bs3TqeTUaNGMWPGDMaMGdPwmujAvpEjR+L1ennooYeavcapp57Kvffey5AhQxg8eDBHH310zDE+n4/LL7+c6upqCgoKOPzwwxsGBl5yySWcffbZDBgwoOG5f9TQoUP5zW9+wymnnEIoFKKwsJC7776bLl26cPHFFzesI3PzzTcTDAa58MIL2bVrF9ZarrjiCoqLi5Oqp+ZoSd881tGXJU0H1Uks1Umsjl4nrVnSd61vDfesuQOLpTYUwOVwYzBcNugKDi8a1HCclvSNpSV9RUSkQzq8aBC/HfkHKncuYpt/KyWevoxNwTx/yS4FfxERaZbH6WnzqH7JTRrwJyIikmcU/EVERPKMgr+IiEieUfAXERHJMwr+IiKSFZ9//jnnnXcehx12GOPGjeP0009n9erVKTv/ggULGnIHPPXUU6xYsaJh3/XXX88rr7zSpvM+++yzjBkzhlGjRjF06FDuu+++uNfIZRrtLyIizaoJBHl9WTWbtgfo38fNcSOL8brbtySttZazzjqLiy66iP/7v/8Dwqltt2zZwhFHHJGKYlNeXt6wTsBTTz3F1KlTGTp0KACzZs1q0znr6uq45JJLqKioYMCAAQQCAdatWxf3Go3V19e3OyVvKqnlLyIiCS1f5+PCm1dw37MbefT1bdz37EYuvHkFy9e1L8Xsf/7zHwoLC/nRj37UsG3UqFF87Wtfi7tcbygU4rLLLuPII4/k5JNP5vTTT+exxx4DwssAz5w5k7FjxzJixAg+/vhjAB588EF+8pOf8Pbbb7NgwQKuvvpqRo8ezSeffNKwWuCLL77Iueee21CGxssAv/zyyxxzzDGMHTuWc889F5/Px549e6ivr29Y4MftdjN48OC41zj99NP56U9/yvjx4/nTn/7EkiVLOP744xk3bhxTpkxh8+bNQPwle1977bWGFQ/HjBmTMFVwW+XObYiIiOSUmkCQ6x+sYl8g1LDNX2sBy/UPVjH3uqF0aWMPwPLlyxk3blzM9sbL9W7fvp0JEyZw3HHH8dZbb7Fu3TpWrFjB1q1bGTJkCN/97ncbXtenTx8qKyu55557uPXWW/nrX//asO+rX/0q5eXlTJ06lW9+85v7Xe+kk07ikksuYe/evXTt2pVHHnmE8847j+3bt/Ob3/yGV155ha5du/K73/2O22+/neuvv57y8nIOPfRQTjzxRKZOncq0adMSXqO2tpbFixdTV1fH8ccfz9NPP01JSQmPPPIIv/jFL3jggQe45ZZbqKqqali1EODWW2/l7rvv5thjj8Xn8+HxpDapklr+IiIS1+vLqkmUAt5ay+vLqlN+zUTL9b755puce+65OBwODjjgACZPnrzf684++2wAxo0b19ANn4yCggJOPfVUnnnmGerr63nuuec488wzeffdd1mxYgXHHnsso0eP5qGHHuLTTz8F4K9//SuvvvoqZWVl3HrrrfvdhDT17W9/GwgvArR8+XJOPvlkRo8ezW9+8xs2bNgAfLlk7z//+c+GRwPHHnssV111FXfccUfD2gGppJa/iIjEtWl7INLSj+WvtWzaUdvmcw8bNqyh2z4VosvyRpfkbY3zzjuPu+66i169ejF+/Hi6deuGtZaTTz6Zhx9+OO5rRowYwYgRI5g+fTqlpaU8+OCDcY/r2rUrEL5ZGjZsGO+8807MMfGW7L322ms544wzeP755zn22GN56aWXOPLII1v1vpqTlZa/MeYBY8xWY8zyRtv+YIz52BizzBjzpDGmOMFr1xljPjTGLDXGLM5YoUVE8kz/Pm48LhN3n8dl6N/b1eZzn3DCCQQCgYZV8ACWLVtGcXFx3OV6jz32WB5//HFCoRBbtmxh4cKFrbpec0vsHn/88VRWVvKXv/yl4Zn70UcfzVtvvcXatWsB2Lt3L6tXr8bn8+137WSX8R08eDDbtm1rCP51dXV89NFHCZfs/eSTTxgxYgTXXHMNEyZMaBjHkCrZ6vZ/EDi1ybZ/AcOttSOB1cB1zbx+srV2dKLVikREpP2OG1mMMfGDvzGG40YWt/ncxhiefPJJXnnlFQ477DCGDRvGddddx/nnn8/IkSMZNWoUJ5xwQsNyveeccw4DBgxg6NChXHjhhYwdO5YePXokfb3zzjuPP/zhD4wZM4ZPPvlkv31Op5OpU6fywgsvNAz2Kykp4cEHH2TatGmMHDmSY445ho8//hhrLb///e8ZPHgwo0ePZubMmQ2t/uau4XK5eOyxx7jmmmsYNWoUo0eP5u23325YsnfEiBGMGTOmYcne2bNnM3z4cEaOHElhYSGnnXZam+s6nqwt6WuMGQg8a60dHmffWcA3rbUXxNm3Dhhvrd3emutpSd9YHX1Z0nRQncRSncTq6HXSmiV9l6/zcf2DVVhr8ddaPC6DMYZZM0oZPrCo4bhMLF/r8/koKipix44dlJWV8dZbb3HAAQek9ZrtoSV9W++7wCMJ9lngZWOMBe6z1t6f4DgREWmn4QOLmHvd0PA8/x219O/t4riRxW0e5d8eU6dOpbq6mtraWn71q1/ldODPdTnX8jfG/AIYD5xt4xTOGHOQtXajMaYv4UcFl1trX09wjUuASwBKSkrGzZ8/P8XvomOL3kXLl1QnsVQnsTp6nfTo0YPDDz88pecMBoM4nZm/IchlmayTtWvXsmvXrv22TZ48uWO0/I0xM4CpwInxAj+AtXZj5PNWY8yTQBkQN/hHegXuh3C3f0fupkuHjt51mQ6qk1iqk1gdvU5WrlyZ8u7oTHZxdxSZrBOPx8OYMWOSPj5n5vkbY04F/h9Qbq2tSXBMV2NMt+jXwCnA8njHioiISHzZmur3MPAOMNgYs8EY8z3gLqAb8K/INL57I8f2N8Y8H3lpP+BNY8wHQAXwnLX2xSy8BRERkQ4rK93+1tppcTb/LcGxm4DTI1//FxiVxqKJiIh0ejnT7S8iIvnD6XQyevRohg8fzte//vWGnPaJ3HDDDdx6663tuuZtt92GMYbt21s1U7xTUvAXEZFmBev97Nz4JlvWPMHOjW8SrPe3+5xdunRh6dKlLF++nF69enH33XenoKSJrV+/npdffplDDjkkrdfpKBT8RUQkoZrqtax54xo+XzWfHZ++zOer5rPmjWuoqV6bsmscc8wxbNy4EYBPPvmEU089lXHjxjFx4sS4aW3/8pe/MGHCBEaNGsU555xDTU14jPiZZ57JP/7xDwDuu+8+LrjgyzxxV155Jb///e8TZizMNwr+IiISV7Dez2fv30koGMCGwov42FAtoWAgvD0FPQDBYJBXX32V8vJyAC655BLuvPNOlixZwq233spll10W85qzzz6bRYsW8cEHHzBkyBD+9rfwkLH777+fWbNm8cYbb3Dbbbdx5513AvD0009z0EEHMWqUhoxF5dQ8fxERyR27tyxudknfXVuW0POgY9t07n379jF69Gg2btzIkCFDOPnkk/H5fLz99tuce+65DccFAoGY1y5fvpxf/vKXVFdX4/P5mDJlCgD9+vVj1qxZTJ48mSeffJJevXpRU1PDb3/7W15++eU2lbOzUstfRETiqq3Z2tDib8qGaqmt2drmc0ef+X/66adYa7n77rsJhUIUFxezdOnSho+VK1fGvHbGjBncddddfPjhh8ycORO//8seiA8//JDevXuzadMmIPwYoaqqilGjRjFw4EA2bNjA2LFj+fzzz9tc9s5AwV9EROJyeftiHPGX7TUOFy5v33Zfw+v1cscdd3Dbbbfh9XopLS3l0UcfBcK9Cx988EHMa/bs2cOBBx5IXV0dc+fObdheUVHBCy+8wPvvv8+tt95KVVUVI0aMYOvWraxbt45169YxYMAAKisr835dAAV/ERGJq3u/8c0u6duj37iUXGfMmDGMHDmShx9+mLlz5/K3v/2NUaNGMWzYMJ5++umY43/9619z1FFHceyxx3LkkUcC4ccDP/jBD3jggQfo378/t912G9/97ncTPrbId3rmLyIicTkLPBwy5nI+e/9OrLXYUC3G4cIYwyFjLsdR4GnzuX0+337fP/PMMw1fv/hibOLWG264oeHrSy+9lEsvvTTmmMa9BOXl5Q2DCBtbt25dG0rb+Sj4i4hIQt7iwzli4u/YtWUJtTVbcXn70qPfuHYFfsk+BX8REWmWo8DT5lH9kpv0zF9ERCTPKPiLiIjkGQV/ERGRPKPgLyIikmcU/EVEJOMyuaTvo48+yrBhw3A4HCxevLhN5+hsFPxFRKRZtf56Vrz9GW8/vZIVb39Grb++3efM5JK+w4cP54knnuC4445L2zU6GgV/ERFJaPMnX/DQL1/hzcdXsPSV//Lm4yt46JevsPmTL1J2jXQv6TtkyBAGDx6csvJ2Bgr+IiISV62/nmf/XEFdIEh9bRCA+togdYFgZHv7ewAysaSvxFKSHxERiWtt5SYSpca3FtZUbmLoMYe06dyZWtJX4lPLX0RE4qretrehxd9UfW2QXdtq2nzuTC3pK/Ep+IuISFzFJV0pcDnj7itwOelR4m33NdK9pK/Ep+AvIiJxHT62PwlW9MUYGDS2f0quk+4lfZ988kkGDBjAO++8wxlnnNHwmCCfmXxZ63jw4MF21apV2S5GTlm4cCGTJk3KdjFyiuokluokVkevk5UrVzJkyJCkjt38yRc8++cKrA139Re4nBgDUy8t48DDvnymvmfPHrp165auIndImayTeD9TY8wSa+34eMdrwJ+IiCR04GG9mHHTSayp3MSubTX0KPEyaGx/Ct0KHx2ZfnoiItKsQndBm0f1S27SM38REZE8o+AvIiKSZxT8RURE8oyCv4iISJ5R8BcRkYzL5JK+v/rVrxg5ciSjR4/mlFNOUfY/FPxFRKQFgWCI97f4ePXTat7f4iMQDLX7nJlc0vfqq69m2bJlLF26lKlTpzJr1qy0XaujUPAXEZGEPtsdYPbiTby0rpq3N+3hpXXVzF68ic92xy6401bpXtK3e/fuDa/du3cvJlHawjyi4C8iInEFgiEeXrmN2pClLhTOBlsXstSGbHh7CnoAMrWk7y9+8QsOPvhg5s6dq5Y/Cv4iIpLAiu01JEoAb4GPtrd9Vb/okr4HHHAAW7ZsiVnSd/To0fzwhz9k8+bNMa9dvnw5EydOZMSIEcydO5ePPvoI2H9J39tuu22/JX1vuukm1q9fzwUXXMBdd93V5nJ3Fgr+IiIS1xf++oYWf1N1IcsX/vo2nztbS/pecMEFPP74420ud2eh4C8iInH18hRQ6Ij/fLzQYejlaX+G+Ews6btmzZqGY55++umGlQDzmYK/iIjENbSPl0RD4wwwrI83JddJ95K+1157LcOHD2fkyJG8/PLL/OlPf0pJuTsyLewjIiJxuZ0Opg0p4eGV27CEu/oLHQYDTBtSgsvZ9vajz+fb7/tnnnmm4esXX3wx5vgbbrih4etLL72USy+9NOaYxr0E5eXlDYMI1c0fS8FfREQSOqS7myvH9+ej7TV84a+nl6eAYX287Qr8kn0K/iIi0iyX08GYfkXZLoakkG7dRERE8oyCv4iISJ5R8BcREckzCv4iIiJ5RsFfREQyrvGSvueeey41NTWsW7eO4cOHt/vc9957b8MCP61VVBQe2BgKhbjiiisYPnw4I0aMYMKECQ1Jg37729+2u4zZptH+IiLSLJ/Px4IFC6iqqqK0tJTy8vKGINlW0fS+EE65e++993L22WenoLTwox/9qN3neOSRR9i0aRPLli3D4XCwYcMGunbtCoSD/89//vOY11hrsdbicOR+uzr3SygiIllTUVHBuHHjmDlzJvfccw8zZ85k3LhxVFRUpOwaEydOZO3atUB4lb8f/OAHDBs2jFNOOYV9+/bxySefMHbs2Ibj16xZ0/D9tddey9ChQxk5ciT/+7//C4QTAt16660ArF27lpNOOolRo0YxduxYPvnkE3w+HyeeeCJjx45lxIgRcbMIbt68mQMPPLAhkA8YMICePXty7bXXNixKdMEFF7Bu3ToGDx7Md77zHYYPH8769ev5wx/+wIQJEzjmmGOYOXMmEF5K+IwzzmDUqFEMHz6cRx55JGH5M0EtfxERicvn8zF9+vT9svHV1IRX8ps+fTqVlZUNreG2qq+v54UXXuDUU08FwoH94Ycf5i9/+Qvf+ta3ePzxx7nwwgvp0aMHS5cuZfTo0fz973/n4osvZseOHTz55JN8/PHHGGOorq6OOf8FF1zAtddey1lnnYXf7ycUCuFyuXjyySfp3r0727dv5+ijj6a8vBxjvkxm/K1vfYuvfe1rvPHGG5x44olceOGFjBkzhltuuYW77rqroddi3bp1rFmzhoceeoijjz6al19+mTVr1lBRUcHu3bu54IILeP3119m2bRv9+/fnueeeA2DXrl1JlT9d1PIXEZG4FixYQCgUirsvFAqxYMGCNp872noeP348hxxyCN/73vcAKC0tZfTo0QCMGzeOdevWAfD973+fv//97wSDQR555BHOP/98evTogcfj4Xvf+x5PPPEEXu/+aw3s2bOHjRs3ctZZZwHg8Xjwer1Ya/n5z3/OyJEjOemkk9i4cSNbtmzZ77UDBgxg1apV3HzzzTgcDk488UReffXVuO/l0EMP5eijjwbg5Zdf5uWXX2bMmDFMnDiRjz/+mDVr1jBixAj+9a9/cc011/DGG2/Qo0ePFsufTllr+RtjHgCmAluttcMj23oBjwADgXXAt6y1O+O89iLgl5Fvf2OtfSgTZRYRySdVVVUNLf2mampqGgbAtUXjZ/6Nud3uhq+dTif79u0D4JxzzuHGG2/khBNOYNy4cfTu3RsIP5Z49dVXeeyxx7jrrrv497//3eK1586dy7Zt21iyZAmFhYUMHDhwv2WBG5fltNNO47TTTqNfv3489dRTnHjiiTHHNe79sNZy3XXX8cMf/pA9e/bQrVu3hn2VlZU8//zz/PKXv+TEE0/k+uuvb1P5UyGbLf8HgVObbLsWeNVaOwh4NfL9fiI3CDOBo4AyYKYxpmd6iyoikn9KS0sTtkajy+9misfjYcqUKVx66aVcfPHFQPixxK5duzj99NP54x//GLP8b7du3RgwYABPPfUUEF75r6amhl27dtG3b18KCwv5z3/+w6effhpzvcrKSjZt2gSEezmWLVvGoYceCkBhYSF1dXVxyzllyhQeeOCBhkclGzduZOvWrWzatAmv18uFF17I1VdfTWVlZYvlT6estfytta8bYwY22XwmMCny9UPAQuCaJsdMAf5lrf0CwBjzL8I3EQ+nq6wiIvmovLycG2+8Me4+h8PRsGpeplxwwQU8+eSTnHLKKUC4W//MM8/E7/djreX222+Pec2cOXP44Q9/yPXXX09hYSGPPvooF1xwAV//+tcZMWIE48ePb1gWuLGtW7fygx/8gEAgAEBZWRk/+clPALjkkksYOXIkY8eO5aabbtrvdaeccgorV67kmGOOIRQK0b17d/75z3+ydu1arr76ahwOB4WFhfz5z39OqvzpYqy1GbtYzMXDwf/ZRt3+1dba4sjXBtgZ/b7Ra/4X8FhrfxP5/lfAPmvtrc1da/DgwXbVqlUpfw8d2cKFC5k0aVK2i5FTVCexVCexOnqdrFy5kiFDhiR1bEVFBdOnTycUClFTU4PX68XhcDBnzhzKysoajmvaxZ0Ot956K7t27eLXv/51Wq+TKpmok6h4P1NjzBJr7fh4x+fsaH9rrTXGtOvOxBhzCXAJQElJCQsXLkxF0ToNn8+nOmlCdRJLdRKro9dJjx492LNnT1LHDhkyhNdee40XXniBTz/9lEMPPZTTTjuNrl277neOYDCY9Dnb4vzzz6eqqopnn302rddJpXTXSWN+v79Vv5O5Fvy3GGMOtNZuNsYcCGyNc8xGvnw0ADCA8OOBGNba+4H7Idzy78h36unQ0Vsv6aA6iaU6idXR62TlypWtapF269at4Tl7Iulu5T7zzDNpO3e6ZLLl7/F4GDNmTNLH59pUvwXARZGvLwJiMy/AS8ApxpiekYF+p0S2iYiISBKyFvyNMQ8D7wCDjTEbjDHfA24BTjbGrAFOinyPMWa8MeavAJGBfr8GFkU+ZkUH/4mISHKyOd5LUqstP8tsjvaflmBXzCRKa+1i4PuNvn8AeCBNRRMR6dQ8Hg87duygd+/e+2W1k47HWsuOHTvweDytel2uPfMXEZE0GzBgABs2bGDbtm0pO6ff7291AOrsMlUnHo+HAQMGtOo1Cv4iInmmsLAw5Ql6Fi5c2KoBZ/kgl+sk1wb8iYiISJop+IuIiOQZBX8REZE8o+AvIiKSZxT8RURE8oyCv4iISJ5R8BcREckzCv4iIiJ5RsFfREQkzyj4i4iI5BkFfxERkTyj4C8iIpJnFPxFRETyjIK/iIhInlHwFxERyTMK/iIiInlGwV9ERCTPKPiLiIjkGQV/ERGRPKPgLyIikmcU/EVERPKMgr+IiEieUfAXERHJMwr+IiIieUbBX0REJM8o+IuIiOQZBX8REZE8o+AvIiKSZxT8RURE8oyCv4iISJ5R8BcREckzCv4iIiJ5RsFfREQkzyj4i4iI5BkFfxERkTyj4C8iIpJnFPxFRETyjIK/iIhInlHwFxERyTMK/iIiInlGwV9ERCTPKPiLiIjkGQV/ERGRPKPgLyIikmcU/EVERPKMgr+IiEieUfAXERHJMwr+IiIieUbBX0REJM/kVPA3xgw2xixt9LHbGPPTJsdMMsbsanTM9VkqroiISIdUkO0CNGatXQWMBjDGOIGNwJNxDn3DWjs1g0UTERHpNHKq5d/EicAn1tpPs10QERGRziSXg/95wMMJ9h1jjPnAGPOCMWZYJgslIiLS0RlrbbbLEMMY4wI2AcOstVua7OsOhKy1PmPM6cCfrLWDEpznEuASgJKSknHz589Pc8k7Fp/PR1FRUbaLkVNUJ7FUJ7FUJ7FUJ7GyXSeTJ09eYq0dH29frgb/M4EfW2tPSeLYdcB4a+325o4bPHiwXbVqVYpK2DksXLiQSZMmZbsYOUV1Ekt1Ekt1Ekt1EivbdWKMSRj8c7XbfxoJuvyNMQcYY0zk6zLC72FHBssmIiLSoeXUaH8AY0xX4GTgh422/QjAWnsv8E3gUmNMPbAPOM/mYveFiIhIjsq54G+t3Qv0brLt3kZf3wXclelyiYiIdBa52u0vIiIiaaLgLyIikmcU/EVERPKMgr+IiEieUfAXERHJMwr+IiIieUbBX0REJM8o+IuIiOQZBX8REZE8o+AvIiKSZxT8RURE8oyCv4iISJ5R8BcREckzCv4iIiJ5RsFfREQkzyj4i4iI5BkFfxERkTyj4C8iIpJnFPxFRETyjIK/iIhInlHwFxERyTMK/iIiInlGwV9ERCTPFGS7AJK/fD4fCxYsoKqqitLSUsrLyykqKsp2sUREOj0Ff8mKiooKpk+fTigUoqamBq/Xy4033sicOXMoKyvLdvFERDo1dftLxvl8PqZPn47P56OmpgaAmpqahu179+7NcglFRDo3BX/JuAULFhAKheLuC4VCLFiwIMMlEhHJLwr+knFVVVUNLf6mampqqKqqynCJRETyi4K/ZFxpaSlerzfuPq/XS2lpaYZLJCKSXxT8JePKy8txOOL/6jkcDsrLyzNcIhGR/KLgLxlXVFTEnDlzKCoqaugB8Hq9Ddu7du2a5RKKiHRumuonWVFWVkZlZWXMPH8FfhGR9FPwl6zp2rUr06ZNy3YxRETyjrr9RURE8kxSwd8Y4zXGTDfGXGOM+YYxxhnnmK8YYx5IfRFFREQklVrs9jfGHAi8BQwEagAvsMoYM91au7jRoSXARcB301BOERERSZFkWv43AwFgsLW2CBgNfA68boz5ZhrLJiIiImmQTPA/AZhprV0DYK1dFtl2J/B/xpgr01g+ERERSbFkRvv3BLY03mCttcA1xphPgTuMMQOAR9NQPhEREUmxZIL/J0AZ8FrTHdbae4wxW4B/ApNTXDYRERFJg2S6/f8F/MAYE/dYa+3jwGnAV1JZMBEREUmPZFr+twELgSJgd7wDrLULjTFHA0elrmgiIiKSDi0Gf2vt58BzSRz3MfBxKgolIiIi6dNit78x5kBjzOPGmCnNHDMlckzf1BZPREREUi2ZZ/7/S/h5/svNHPMyUAr8LBWFEhERkfRJJvhPBe6NTO+LK7LvPuDMVBVMRERE0iOZAX+HAiuSOG4l4RTAIjF8Pl/M8r1FRUXZLpaISF5KJvjvA7oncVxR5FiR/VRUVDB9+nRCoRA1NTV4vV5uvPFG5syZQ1lZWbaLJyKSd5Lp9q8EypM47szIsSINfD4f06dPx+fzUVNTA0BNTU3D9r1792a5hCIi+SeZ4H8P8D1jzEWJDjDGfAe4GLgrVQWTzmHBggWEQqG4+0KhEAsWLMhwiUREJJl5/o8bY/4E/N0Y8xPgReAzwAKHAFOA8cAfrbVPpqJQxph1wB4gCNRba8c32W+APwGnE15meIa1Vr0OOaiqqqqhxd9UTU0NVVVVGS6RiIgk88wfa+3PjDELgZ8SnvrnjuwKAG8BZ1prn01x2SZba7cn2HcaMCjycRTwZ5RdMCeVlpbi9Xrj3gB4vV5KS0uzUCoRkfyWTLc/xpgugAt4Hvg+cGDko5u19qQ0BP6WnAn8w4a9CxQbYw7McBkkCeXl5Tgc8X/NHA4H5eXJDCcREZFUSibD31eAjwgv2fsHYA7hNL6jrLX1aSqXBV42xiwxxlwSZ/9BwPpG32+IbJMcU1RUxJw5cygqKsLr9QLhFn90e9euXbNcQhGR/GOayd0TPsCYx4DRwEXAEsKZ/O4BBlpr09Jna4w5yFq7MZIu+F/A5dba1xvtfxa4xVr7ZuT7V4FrrLWLm5znEuASgJKSknHz589PR3E7LJ/Pl7G59qFQiOrqampra3G5XBQXFyfsEcimTNZJR6E6iaU6iaU6iZXtOpk8efKSpmPmopJ55n8M8DNr7VuR71caY34Y+XygtXZzqgoaZa3dGPm81RjzJFAGvN7okI3AwY2+HxDZ1vQ89wP3AwwePNhOmjQp1UXt0BYuXIjqZH+qk1iqk1iqk1iqk1i5XCfJNL0OBP7bZNsngAEOSHWBjDFdjTHdol8DpwDLmxy2APiOCTsa2JWOmxAREZHOKKnR/oSfwWdKP+DJ8Gw+CoB51toXjTE/ArDW3kt44OHpwFrCU/0uzmD5REREOrRkg/9Lxph4g/tebbrdWtuuZX2ttf8FRsXZfm+jry3w4/ZcR0REJF8lE/xvTHspRKTT0+JOIrkjmQx/Cv4i0i5a3Ekkt+TeXCsR6VS0uJNI7lHwF5G00uJOIrlHwV9E0kqLO4nkHgV/EUmr6OJO8WhxJ5HsUPAXkbTS4k4iuUfBX0TSSos7ieSeZJP8iIi0WVlZGZWVlTHz/BX4RbJDwV9EMqJr165MmzYt28UQEdTtLyIikncU/EVERPKMgr+IiEieUfAXERHJMwr+IiIieUbBX0REJM8o+IuIiOQZBX8REZE8o+AvIiKSZxT8RURE8oyCv4iISJ5R8BcREckzCv4iIiJ5RsFfREQkzyj4i4iI5BkFfxERkTyj4C8iIpJnFPxFRETyTEG2CyDZ5fP5WLBgAVVVVZSWllJeXk5RUVG2iyUiImmk4J/HampqGDduHKFQiJqaGrxeLzfeeCNz5syhrKws28VrE93MiIi0TME/T/l8PqqqqvD5fA3bampqAJg+fTqVlZV07do1W8Vrk4qKCqZPn96pbmZERNJBz/zz1IIFC7DWxt0XCoVYsGBBhkvUPj6fj+nTp+Pz+RpuYmpqahq27927N8slFBHJHQr+eaqqqopQKBR3X01NDVVVVRkuUfssWLAg4fvpiDczIiLppOCfp0pLS3E44v/4vV4vpaWlGS5R+1RVVTW0+JvqiDczIiLppOCfp8rLyzHGxN3ncDgoLy/PcInap7S0FK/XG3dfR7yZERFJJwX/PFVUVERpaSlFRUUNQdPr9VJUVMScOXM63GC/8vLyhD0ZHfFmRkQknTTaP495vV4qKytjpsZ1tMAPNNy0NB3t73A4OuTNjIhIOin457muXbsybdq0bBcjJcrKyjrNzYyISDop+Eun0pluZkRE0kXBX0REpIlafz1rKzdRvW0vxSVdOXxsf1yezhMyO887ERERSYHNn3zBs3+uwFqorw1S4HLy1hMrmHppGQce1ivbxUsJjfYXERGJqPXX8+yfK6gLBKmvDQLhG4C6QDCyvT7LJUwNBX8REZGItZWbSJD5HGthTeWmzBYoTRT8RUREIqq37W1o8TdVXxtk17b4mUQ7GgV/ERGRiOKSrhS4nHH3Fbic9CiJn0m0o1HwFxERiTh8bH8SZD7HGBg0tn9mC5QmCv4iIiIRLk8BUy8to9DtbOgBKHA5KXQ7I9s7xyS5zvEuRNLIH/SzZOcitvm3UOLpx7ieE/A4PdkuloikyYGH9WLGTSexpnITu7bV0KPEy6Cx/TtN4AcFf5FmrfWt4Z41d2Cx1IYCuBxuHl8/n8sGXcHhRYOyXTwRSZNCdwFDjzkk28VIG3X7iyTgD/q5Z80dBEJ+akMBAGpDAQKh8HZ/0J/lEoqItI2Cv0gCS3YuwhJ/wq/FUrlzUYZLJCKSGjkV/I0xBxtj/mOMWWGM+cgY8z9xjplkjNlljFka+bg+G2WVzm+bf0tDi7+p2lCAbf6tGS6RiEhq5Noz/3rgZ9baSmNMN2CJMeZf1toVTY57w1o7NQvlkyzK9MC7Ek8/XA533BsAl8NNiadv2q4tIpJOORX8rbWbgc2Rr/cYY1YCBwFNg7/kmWwMvBvXcwKPr58fd5/BMLbnhLRcV0Qk3YxNlMQ4y4wxA4HXgeHW2t2Ntk8CHgc2AJuA/7XWfpTgHJcAlwCUlJSMmz8//j/yfOXz+SgqKsp2MVpksWzct4GQDcXscxgHB3UZgCFBVo5WalongVCAbYFw937IhnCY8JOyEndf3A53Sq6Z6zrK70kmqU5iqU5iZbtOJk+evMRaOz7evpxq+UcZY4oIB/ifNg78EZXAodZanzHmdOApIG7Tz1p7P3A/wODBg+2kSZPSVuaOaOHChXSEOnlr+xssWf9ewu73rxw8kK/2mZiSa8WrE3/QT+XORWzzb6XE05exeTbPv6P8nmRSR62TdK5R31HrJJ1yuU5yLvgbYwoJB/651tonmu5vfDNgrX3eGHOPMaaPtXZ7JsspmZPtgXcepydlNxci2ZIPa9RL8nJttL8B/gastNbenuCYAyLHYYwpI/wedmSulJJp0YF38WjgXfvU+utZ8fZnvP30Sla8/Rm1/s6xVrnsL1/WqJfk5VrL/1hgOvChMWZpZNvPgUMArLX3At8ELjXG1AP7gPNsrg5ckJTQwLv0yLWWoM/nY8GCBVRVVVFaWkp5ebmeIadIMmvUd+ZsdhIrp4K/tfZNaH7klrX2LuCuzJRIcoHH6eGyQVfEjPY3GC4bdEXan793xqDUuCUYFW0RPvvnCmbcdFJG85hXVFQwffp0QqEQNTU1eL1ebrzxRubMmUNZWVnGytFZ5csa9ZK8nAr+IokcXjSI3478Q8YH3nXWoJTNlmAgGGLF9hq+8NfTy1PAIZ4Q06dPx+fzNRxTUxMORtOnT6eyspKuXbumpSz5IrpGfbwbgM60Rr0kT8FfOoxMD7zz+XydNihlqyX42e4AD6/chgXqQpZCh+Gjfz1NfTB+WUKhEAsWLGDatGlpKU++OHxsf956In66lM60Rr0kL6cG/InkkgULFhAKxeYWgC+DUkcVbQnGk66WYCAY4uGV26gNWepC4W6HupDli03r8e/bF/c1NTU1VFVVpbws+SZf1qiX5OknLpJAVVVVQ0u/qY4elLLRElyxvSbuMkk9DhxAgdtDfSB2lUSv10tpaWnKy5KP8mGNekmeWv4iCZSWluL1xm8Bd/SglI2W4Bf++oYWf2OHHXsSxsT/V+RwOCgvL095WfJVdI36Y8qPZOgxhyjw5zH95CVv1ASCvL6smk3bA/Tv4+a4kcV43fG7vgHKy8u58cYb4+7rDEEp0y3BXp4CCh0m5gbA1aUr5b/6I8/fdBUG2zCw0uFwMGfOnA47rkIklyn4S15Yvs7H9Q9WYa3FX2vxuAz3P7eJWTNKGT4w/rS9oqIi5syZEzPavzMFpWhLMBOG9vHy8rrquPsOGjKaRUuW8OJzz+43pbIz1LFILlLwl06vJhDk+ger2Bf4cvCev9YClusfrGLudUPpkqAHoKysjMrKyph5/gpKred2Opg2pCRmtL8Bpg0poWd3t0b1Z0k6c/5LbtJPVzq915dVkygJpLWW15dVM2VC74Sv79q1q4JSihzS3c2V4/vzUaN5/sP6eHE5NfwoW3It06NkhoK/dHqbtgciLf1Y/lrLph21GS5RfnM5HYzp17EzJHYWuZbpMV3UsxErv9+95IX+fdx4XCbuDYDHZejf25WFUuWHYL2f3VsWU1uzFZe3L937jcdZkD/LIee6fMj5r56N+NTXJp3ecSOLiSwEGcMYw3EjizNboDxRU72WNW9cw+er5rPj05f5fNV81rxxDTXVa7NdNIno7Dn/tZphYgr+0ul53U5mzSili9uBxxW+CfC4DF3cjsj2xNP9pG2C9X4+e/9OQsEANhR+rGJDtYSCgfD2+tiEPnHPs28fO197ja3z57PztdcIJsgEKG3T3kyPjZeE9u+tzZkloaPleumBJQTr42fpjPZs5Ct1+0teGD6wiLnXDQ3P899RS//eLo4bWazAnya7tyxudpDlri1L6HnQsc2eo2b1atbffjvWWmwggHG72frwwxx81VV4jzgiHcXOO+3J9Ni0O73vUUEe+uUrWe9Ob1quRDpDz0Z7qOUveaOL28mUCb25+NQDmTKhtwJ/GtXWbG1o8TdlQ7XU1mxt9vXBfftYf/vthPx+bCAQfl0gQMjvb9gu7dfWTI/xutOttVnvTo9XrkTyfTVDtfxFJOVc3r4YhyvuDYBxuHB5+zb7+t0VFc32HOx+7z2Kjz8+JWVNVuMR4/6CcBd3Zxgx3pZMj6kYKJiOEfjNlaupfF/NsOP/5opIzunebzxbVj8adyEfYww9+o1r9vV1W7Y0tPibsoEAtVub7zlItVzt4k6V1mZ6bO9AwXSNwG+uXFEFLifGkPerGarbX0RSzlng4ZAxl+NwujGO8FRK43DhcLrD21uY7lfYrx/G7Y67z7jduPo233OQSrnaxZ1N7RkomM4R+M2Vy+E0HDK0hK99cygzbjopp27aGg+cXPH2ZxkZOJm/tz0iGebz+WLSBBcVdd5kN97iwzli4u/YtWVJwzz/Hv3GtRj4AbqXlbH14YcT9hx0P+qo1Bc4gXyYC99a7RkomM76bK5czgIHU747Nuda+9nKQ6CWv0gGVFRUMG7cOGbOnMk999zDzJkzGTduHBUVFdkuWgOfz8e8efO46aabmDdvHj6fr93ndBR46HnQsfQbdBY9Dzo2qcAP4OzShYOvugqHx9PQA2DcbhweT8P2TOnsc+HbIt5AQWNMUktCp7M+s7FUdXtkMw9BbtWESCfk8/mYPn36fsG0pib8D2769OlUVlZmfaGgioqKmNULb7zxRubMmZO1MnmPOIJBs2ez+733qN26FVffvnQ/6qiMBn74sis5XsDK5xHjTQcK7nJ+xpk3ndBigE13fWZ6qer2yGavklr+Imm2YMECQqH4iUZCoRALFizIcIn21/jmJHpTUlNT07A9UdkzweHxUHz88fQ991yKjz8+44Efwl3JCRJE5v2I8ehAwWPKj8TT1ZVUgG2pPutrg+1+9t24XEOPOSQnAz9kt1dJwV8kzaqqqhqCalM1NTVUVVVluET7a+nmpLq6OrMFyjHRruQClwOHMxq1DAUuR052Jee6RF3zBYUOQiHLuwtWsfSV//Lm4yt46JevsPmTL7Jc4vRpb4bF9lDwF0nAH/Tz1vY3eGrDY7y1/Q38wbYlliktLcXrjf9H7PV6KS0tbU8x262lm5Pa2o6/6mEq0gQ3XR8i0XoR0rJo1/zXvjmUMScfxjFnHgkGgnWhvMrBn81eJQV/kTjW+tbw82VX89j6R/jXlpd4bP0j/HzZ1az1rWn1ucrLy3E44v+pORwOysvL21vcdmnp5sTl6tirHtasXs3aK69ky7x57Hj+ebbMm8faK6+kZvXqpF7feFBWKBh9QJvfU/1SoXHXvLPAAcSPgp05B382Bygq+Is04Q/6uWfNHQRCfmpD4UQztaEAgVB4e2t7AIqKipgzZw5FRUUNQdbr9TZsz/Zgv5ZuToqLizNboBRKRZrgZAZlSfvk84yKpr0gmcpDoIdVIk0s2bkIG3eGOVgslTsX8dU+E1t1zrKyMiorK2Pm+Wc78MOXNydNR/s7HA7mzJmT8JFAR5CKNMH5HJgyJd9nVLQ2w2IqKPiLNLHNv6Whxd9UbSjANn/bUst27dqVadOmtadoadPczcnChQuzXbw2S0Wa4FwLTOnIiZ9t7UkaJG3TsX9jRNKgxNMPl8Md9wbA5XBT4mldatlcz+wXCIZYsb2GL/z1HHnC1zm7jxe3M/VPBLNRD9E0wfFuAJJNE5xLgSlb2eDSLfrsu+l7Uw7+9FGNijQxrPsI5tt5cfcZDGN7Tkj6XM0lzykrK0tVkdvss90BHl65DQvUhSyFDsPL66qZNqSEQ7rHz63fFtmqh1SkCY4XmJLNZpdKjQceRkV7I579cwUzbjqpQwfJjpScpzNQrYo0EggFmPXRrzBNRh4XmEKcxsllg67A40wu0UyuZ/YLBEM8vHIbtaEvQ2Nd5OuHV27jyvH9cTkdhEIh5s2b1+YWezbrIZomeP3tt2OtxQYCGLcbY0yr0gS3NZtdKuXDGgOtefbdGR9/ZJJqSjqMmkCQ15dVs2l7gP593Bw3shivO36CjLbwB/1sC2wlEIo3Atwyc9hv6OHqkfT5ksnsl80xACu21yQY1ggW+Gh7DXWfrmDlypXMnj27TS324L59PHzrrQQT5ArIRD2kKk1w48C0cOHnGW+RauDhlzrr449MUvCXDmH5Oh/XP1iFtRZ/rcXjMtz/3CZmzShl+MDUPDdesnNRwn0O4+Sj3ctaNco/1zP7feGvb2jpN1UXsmzcsYufTZ/Oj3/84/3S/kJyLfaa1atZf/vtrFyxgn0Jgn+m6iGaJrgjy/TAw1xtWXf2xx+Zonn+kvNqAkGuf7CKfYEQ/tpwsPLXWvYFQpHt8VtDrbXNv4WQjd9Sb8so/1zP7NfLU0ChI35ilUKHYfnrL7d5TYLG8+sPcrnwJMgjkAv10FFkMhvc5k++4KFfvsKbj6/IuVS7yruQGgr+kvNeX1bd7Fzt15dVp+Q6JZ5+OEz8P4m2jPLPVma/ZJfmHdrHmyCnWjjXWu32jW3uuWg8v37SAQckTIWbCxkOO4pMZYPL5jKzydDjj9RQ34jkvE3bAw0t/qb8tZZNO1KTe35czwlUsS7uvtaO8oeWk+e0ZZBbS9PlWjOq3u10MG1IScxofwNMG1LCm598pc09F43n13sLCvjtmDH8/P33w49tQiG6uFw4Xa6cyHDYkWRiRHyuDyzMtbwLHZWCv+S8/n3ceFwm7g2Ax2Xo3zs1uec9Tg8l7r64HR4sltpQAJfDjcG0apR/Y+3N7OcP+lmycxHb/FvYsWInv/vJH7DWxg3sbRlVf0h3N1eO789HkXn+vTwFDOvjxeUMt8hvvPHGuOVqqcXedH79iJ49mX/ccSzcsoVNgQBDTjiB866+WoG/DdKdDS7XW9a5lHehI1Pwl5x33Mhi7n9uE8QZm26M4biRxSm7ltvh5rcj/0DlzkVs82+lxNOXsT0ntCnwR7U1s99a3xruWXMHFste315euuzf1O/7ssu1aWBv6+wCl9PBmH6xgyajPReLFi3C6/W2quci3vz6LgUFnHbQQTg8HgZde22rR9tLZuR6y1oJgVJDtSQ5z+t2MmtGacxof2MMs2aU0iWF0/0g3APQ2tz9qdZ4cSGAjW9uxiYYmR8N7OmYXRDtUZg1a1arei5SNb9eMq8jtKyVEKj9VFPSIQwfWMTc64aG5/nvqKV/bxfHjSxOeeBPtcbd9iWefoxLsheh6eJCezftJZhgVkM0sEdnF8S7AWjPqHqHw8GZZ3+rIcfCGyv8HDfS02KOhVTNr5fM6igt62wshtOZ5MZPUSQJXdxOpkzone1iJK1xt310/MDj6+dz2aArOLxoULOvbbq4UNf+XXG6nXFvAKKB/etf/3qbn9E3x18b4sKbV7Qpx0J759cH6/3s3rKY2pqtuLx96d5vPM6C2JuHxusT9PIUMDRN6xPkC7WsOz/9JEXSoGm3PdAQzO9Zcwe/HfmHZnsAmi4udNDXDmT53+J3xUYDe9euXVs9u6Cl2QM1gSAbdwTYF/hyLEF44KXl+germHvd0LT1vtRUr+Wz9+8MPzYI1WIcLrasfpRDxlyOt/jwhuMytT5BvlHLunNT8BdJg6bd9o1ZLJU7FzU7rmBczwk8vn5+w/eF3gKOmTmBd25chA1BMBCMG9hbM7sgmWmBry+r3m+cZbCuhup1r1O7exNdex7ES+/14BvHpT5ABOv9fPb+nYSCX/Z+2FAtFvjs/Ts5YuLvcBR4kl6fQPJXrmYqzDbVgEgaNO22byyZbIEep4fLBl2x32ODA4cfSPk/zuDwVYPZu7kmYWBPZnZBstMCN20PEI2re7d8RNWr12Ox2Ho/2ws8XLn4L/R/eG7KV+bbvWVxs4mddm1ZQs+DjmXxp1tZ/q+n+WLTenocOIDDjj0JV5dwfUTXJ4g3k0Hyg9YASEzBXyQNmnbbN5ZstsDDiwbFn3b41fYPmEt2WmD/Pm427gy3+KtevZ5Q/b6G42y9n9r61q3Ml+ziTLU1W7Gh+MmbbKiW2pqtVFRU8J0LLqQ+GKQ+4KfA7eHtB//E6b+4nQOHjKYuZPnCn91sdB1NZ2olaw2A5uXvOxdJgUTPzJt22zfWmmyB6Zp2mOy0wONGFvPwWqhe93rCxxjJrszXmsWZXN6+GIcr7g2Acbios92YPn06/pq9DdvrA+HxFc/fdBXf+cuzeLt2pVcHDVzZ0NlaybmeqTDb9DBMpI0qKioYN24cM2fO5J577mHmzJmMGzeOioqKhm57t8ODyxEedOZyuHE7PPtlC6wJBHlx0Q4eeGETLy7aQU2KFilqSbKLDnndTg7q7SZUsxlbH2+p4+RyCLR2cabu/cYnXA/AGMNr721M2HNhbYi1b7+CAYb1UarXZOR6Pv+2yPVMhdmm4C/SBo2fmTde7ja6fe/evQ3d9uce/G1O6Xcq5x78bX478g8N0/yWr/Nx4c0ruO/ZjTz6+jbue3YjF968guXr4i/Ek0qtWXTI43Lwk/PG43Z3iXt8MjkEWrs4k7PAwyFjLsfhdGMc4fTNxuHC4XRzyJjLWffZhoQ9F/UBP77PNzBtSIkG+yWpM66UF81UGE8uZCrMNv1liLRBMs/M4ctu+zMHnMNX+0zcr8WfiWWKE4mm7i0qKmroAfB6vQ3bmz6//+bZ36CwMP4/0mRyCLRlcSZv8eEcMfF3HDD42/Q+dAoHDP42R0z8Hd7iw5vtuXB36cK5x4ygb1fLW9vf4KkNj/HW9jfwB+P3XEjnbCVncgnkjkgPxETaoL2pdJtrCRc6AixZ/C8G9a1pNrFNY8F9+9hdUUHdli0U9utH97IynF3it9SjWjMtsL0rFPbpUUiBE+rjxJfmFmdyFHjoedCxMdubW3So0Olk1EnD+Pmyq9uUYCkf5Xo+/7boKJkKsyXn3r0x5lTgT4AT+Ku19pYm+93AP4BxwA7g29badZkup+S3ZFLpNjeyPVFLuLTHZr4/+jkKa2DHp3UJE9s0VrN6Netvv51AQYjVh9Sxa3cBxZVzmXz6FfQaPLzZ99GaRYfaukLh8nU+Hnzp87iBH9q2OFNzNyN/e/BvPLjpr21OsJSPOkI+/7ZQpsLEcqoGjDFO4G7gZGADsMgYs8Ba2/i38nvATmvt4caY84DfAd/OfGkln7W03O3ho05oNiVuvGWK3c5avj/6OTwFdQ3b4iW2aSy4bx/rb7+dDT0DPFPuxOLEFMLgGssLm+9gPCdx+GHlLfYcJKu1KxQ2PN6ojf+IxONKvDhTSyl7E92MLN1XiV3f9gRL+agzt5KVqTC+XPuJlgFrrbX/BTDG/B9wJtA4+J8J3BD5+jHgLmOMsYn6UEVoOY1ta8VreXbp4sYY+NMffsZtj29gX+DLP6+mKXHjLVM8ut9aTKKsgI0S2zS2u6KCQEGIZ8qd1LkMB9bD1OpwgHRh8K//N6s3vcWhzfQcpFOzjzechu+eemDc9QGSTdkb72Zk2872JVjKV2ol55dc+6keBKxv9P0G4KhEx1hr640xu4DewPaMlFA6nGTS2LZFtOX52P/9lQ8XPU3/vl05vqwfbs9y/t+ED/nr0jOo2nXgfq+JjmyfMqF3zDLF/Yp24S6IP6UqmtimqbotW1h9SB0WJ4UWpu5x4OLLUU6FgA0GEvYcpFtzA/3qgpbtu2Lfb3tT9qYiwVK+Uis5f5hcajAbY74JnGqt/X7k++nAUdbanzQ6ZnnkmA2R7z+JHBMT/I0xlwCXAJSUlIybPz9+0pV85fP52tX6zR2WYN1ebKge4yjAWdgVIgEwFAqxcuVKgsHYB85Op5MhQ4bsN+Wt9XVi8e/ZAHFa7CFr2OzrTcjuP+S4V7cCencvDL/awp59QerqQ7id+ygI7Yp7LjAUenpF3tuXgj4fO/07qPFCFwtFIUOC2fFxX5+M9vye7K4Jsq26llCct+QwUFLsort3/y7/ffUhdgWCCWoBeriddClIHPwtlo37NhCyIQzgseC0EDRQaxz09w4gUS0lq/P87aSO6iRWtutk8uTJS6y14+Pty7WW/0bg4EbfD4hsi3fMBmNMAdCD8MC/GNba+4H7AQYPHmwnTZqU6vJ2aAsXLqSj10nDym9OizXhld+MNQ0D5ObNm8fs2bMTDsybNWvWft3Gra2TnRvf5PNVi+JmogvUF/De9q9RsXlIwzaPy/CjqQcxKc7SxMF6P2veuGa/xWyiHE53wmf+T937Pyw5OsSEegfDAomDYu9Dp9Bv0BlJv7eo9vye1ASCXHjziv1WBYzq4nbEXRXw1U+rWbNpT8JzfrV/NyYdWtzsddf61vD4itlM2R0ELC4MdUCho5CBo49p9yOQzvC3k2qqk1i5XCe5Ns9/ETDIGFNqjHEB5wELmhyzALgo8vU3gX/reX9+arzyWzT42lAtoUg3d6je3+4peS1pLge9u6CePt5d+20zJB7Z3lJim3hd9s4uXZh8+hUYDNVOqE0wZsA4XLi8me/u9rqdkQF9DjyucGvb4zJ0cTsSDvTr5Smg0BG/ZV7oMEml7C31HEz53gJc0PAYpBAgVNfwu9GS4L597HztNbbOn8/O114juG9fi68R6ShyquUfeYb/E+AlwlP9HrDWfmSMmQUsttYuAP4GzDHGrAW+IHyDIHkomZXfkpmS1x7N5aAP1Bewwx/uZjfOWoyBb51VFzfgNZQpkthm15Yl1NZsxeXtS49+4xqWr403+r3X4OH8eMdP+ct/70x4XmMMPfqNa9d7hbblExg+sIi51w0NT3vcUUv/3i6OG1mcsB6G9vHy8rrquPuSTdm7e8tiEqWsSzR4srHo9ElrLTYQwLjdbH34YQ6+6iq8RxzR4vXbqy31LNIaORX8Aay1zwPPN9l2faOv/cC5mS6X5J5kVn5raUpeS5npWtK933i2rH40/vPpgno+O+BzehQFKCzaRdHBa+nS58QWzxkvsU1Lo98H9R7GrOLZfLD+OQr++2+MNRhbH34MYkzCnoPWaE9A7OJ2MqXJo45EeRDcTgfThpTEvF8DSafsTeZ3I5Ho9MmQ/8veARsIYIH1t9/OoNmzW7x+e2T7xkPyQ84Ff5FktbTym8vbt92Z6VoS7ar/7P07w/+sQ7XUER509my3EF16riLaXmvrSPNkR797nB6OGngOoQFnxO05aI9kAqLDk9w1gvV+Vn70Fm9WrmXHvh4s2nQYxuneLw/CId3dXDm+Px816ukY1sebdK7+ZH43EtldUdFsj9Lu995Lqgxtkcp6FmmOgr90WM22uht1c7c1M12yGnfV1+zdxDM7FvJxYS11TR5bt2Yp38ZWbK8hmCAYBa3lo+01jOn35YjiRClx2yOZgFh8/PEtnqemei2fvn8ndbVBjju4nkB9Aad95a2GaZHRPAhd3E5cTsd+76s1kv3diKduyxZsIH6eABsIULt1K5SUtKlcLUlVPYu0RMFfOqx4re5E3dytzUzXWtGA2xM4yTea1WvuwNUor7zB7LeUb2tsq6kjmGBIa9CG96dbUgGxBdEBmjYYIPq4P5rX4Pujn+PGNy/CWldDHoT2aM3vRlOF/fph3O6479e43bj69k04nqCxWn89ays3Ub1tL8UlXTl8bH9cLQxWTEU9J9JSxkTJLwr+kjP8QT9Ldi5im38LJZ5+jOs5ocVg2dwAuWyJLuVbuXMR2/xbKfH0ZWwS7yWRffXxU+Mmuz8VkgmIwXo/u7csbvg5NF2QqLkBmgbL6L5rqdg8JO4Kf23R1t+N7mVlbH344YS9Bt2POgrefbfZc2z+5IuYVLlvPbGCqZeWceBhvRK+LqkbjzZINmOi5A8Ff8kJa31ruGfNHW1ahS0d3dztFV3KNyXnaiahDYC3MP2tt5YCYsGRJax545r9WtkbV85nZf35dOs9iONGFic1LbK5Ff7aoi2/G84uXTj4qqtiBt0ZYzj4qqtafOZe66/n2T9XUNdoWeboannP/rmCGTedlDBlblI3Hq3U3oyJ0jnpJy5Z5w/6uWfNHQRC/oaUrLWhAIFQeHtnWYc9EAzx/hYfr35azftbfASC4Ra7P+hvdt35vt5CChIkpCsw0KdLYbqL3hAQHR4Pxh1uKRq3G4fHw0E//QkbVtwXk2/BQS2HMZe/P7+OC29ewbaaHg35C5oK1BewvaZHm1b4SwfvEUcwaPZsDjj/fHqfcQYHnH8+g2bPTmq0/drKTQmfClgLayo3JXxtc/WczI1HPCu21yTI/hDOJfnR9vh5MKRzU8tfsm7JzkXYRAvatHIVtlx9rpmo2/W4gX4e3fSnZns8Gua9x4koDmOSmveeCtGAuPu996jduhVX3750P+oodu1YjN2UuDv/yJ5rqNg8hN8sKGbm1+LfxVgMq6oHJUz8kw0Oj6dNg+uqt+1taOk3VV8bZNe25oNtonpu6yj/L/z1DS39pupCli/88deTkM5NwV+ybps/Nauw5epzzea6Xf/1X6gtDGJNeNBevHXn4817L7AhjA0xtW4Lztre0CQBTLpuguIFxGSzHAbqC/nc8x0O9P+j4fFACBfWwq4eF/H3a0anLPBnM0lOcUlXClzOuDcABS4nPUpavllr7sajte8tmjEx3g1AshkTpfPRT12yLhWrsLXluWa8AJkOzXW7gsETGsY+59L9tjbt8YjOe3//oyo+e2cRRXt2MuDj93E6DWvnmf0SwGT6JqilLIfba3oA4WWN1+3qz/EnpXeAZraT5Bw+tj9vPbEi7j5jYNDY/m0+d1veWyoyJkrnk/3+UMl743pOSLjKWnNz4xs/K1+w7oNmHh3EPtf8bHeA2Ys38dK6at7etIeX1lUze/Gm/W4eUqW5bleDC6eNHf0dr8fDWRug+J4/MPydFxm4/D0K6muxgQAhv78hMUzjm6DoNetCltqQDW8Ppn5mQPd+4zEmcXf+0q3hRXSig/mig/D6DTqLngcd22zgTzROIpHGSXKiI+ab1lE6BfftY+97b3HsYB8FBVDgCv+LLXA5KXQ7mXppWcLBfsmcuy3vLdpz5HKYhjUTCh0Gl8MknTFROh+1/CXrPE4Plw26Ima0f3Nz45vODugRPIUuoT5xz9/0uWZzvQRf7KunNhhK6T/E5rpdLbUEzRcx2+P1eCSTAKbqyHEtDu5qa+KcRBrPqQ8F64EggfoCLIa/Lj2D2mB4QGJrB/O1pQcjm0lyGrfKTSDAV91etjkOouCoyfQePIBBY/u3OfBD+95bezMmSuej4C85oTVz4xvPDoiqZRtuAjiIDQpNn2s23w2f+gDZXLcrWPyOj2K2xuvxiCaA8ZtClnoGs72gmD711Yz2r8ITSQDzxcDMDe5qPK+fPRb7VABzgGVn1y68asezbOtX8IW64nEZjDGtGsyX7GOcps+/azduTCpJTqJ1BdpcF3HS8joDNRzAGhzvrmfQebNxtCPwQ/sTALUnY2Jn0ZbES51Vfr5ryUnJzo2PNzvA7/iIbsFT4h7f9Llmc93wNrI/lZpbqOa4gfDoJicWR4s9HoX9+lHV9VDuL5qKBWodLlyhWp7ufjyX+J7lwL59Mza4q6Z67X7Z86gDTrXw71p6rgrwdfMaAzyb2eHuw7Cvn8akcSWtGsyXzPS0wbs2xTz/JhSCwkKoi816GE2Ss3ydj+sfrMJai7/W4nGZ/dYVaItM9DikKwFQvmhr4qXOSsFfOpx4swOsqWVnwTx61p+P0xRgrTPhSnDNBUgT2Z9qzXW7jumTXI9Hwajx3P+Cl4D5cq58bWTe/P3dpnL8mGEM7epJ++CuaJreULDRz6AwcoUTXNjHA7jr6zh630eYkJsD6r5CF/cBrbpGS9PTdvhin3MnahVHGWMoGD2B6//4CfsCX44d8NdawO63rkBrpTMtb1Q6EgDli/YkXuqs9MBHOpzo7ICm6hzrqXbfzZElO/lq/25MGVjMleP7xzwfHtrHm2B4YVi6Rj9Hu11PPLSYMf2KGm5Ioj0eZw44h6/2mZgwDfBba/xQmCD7XaGLN1fty8jgrubS9AJw6JfBs62BL3qDFk+hw9Bl02eJUwUXFmIKCuImyXlz9T5CCQYNhoIhXl9W3eqywpet8rjlSVGrPB0JgPJFexIvdVb5dasjHVLTKXkjeo7jcebHPdaYeqYOHNJsHv3muuF7dSnI2UFQm7YHCCR4IhGopyEnfroHdzU3r59CA92+DNptDXwtTU8b+Pl/2Z2opV1XR68pU3D37x+TJGfDM581W4cbt9QArV9UKFOt8lQnAMoX7U281Bkp+EtOizfi26yDcwf+T0xmvNasnJcoQL79xtq0v6e26t/HjcdlIt3U+2uaEz+dg7uam9dPnYU9X5avrYGvuRu0aUNK8PpL2NPM8293//5xn7H32rURly2g1sT2oLhsLT13bQAObnV527seQGu0NfNgPktF4qXORsFfclZzI75fX+fhhjG/Z/nuxW1eOa+jjX4+bmQx9z+3CeK0LzOZE797v/FsWf1o4hkTnwZTEvia68EItrGlPc65gX/YgcR77mMsjHNubFNZQa3yXJbOxEsdlYK/5KyWRnx/sjPIV/ulZuW8jsDrdjJrRinXP1hFKBQiUAdOBzgchp+ff2jGcuI3ntffeBU/Yww9nRMxU2zKAl+iG7S2trS79+/LJW8+GzNjwgCX+J6l+4EntKu8bWmVt7QUsrSfy1PA1EvLYkb7G0O7Ei91ZPn3jqXD0IIksYYPLOKX5x/KDXPW4XRYgiEocMJv533arqlqreUtPpwjJqY3TW+LZWhDS7t7WRmHPfwwN2y9n/c9RzTkShjjX00XtzPjI+abTpk0DhdbVj/KIWMux1t8eEbL0tkdeFgvZtx0EmsqN7FrWw09SrztTrzUkeXnu5ac1Xhwn682SIGB+jjxP18XJKkJBPnNvE+pa1Qpgbr2TVVr6yJA0TS92dTalnbjHoNjQmuxeyI9Bm5nxkfMx5syaUO1WOCz9+/kiIm/y+jNVD4odBcw9JhDsl2MnJB//z0lI/xBP0t2LmKbfwslnn6MS+J5fOzgvviBH1qes960K9XdZyyrqkNtWuWuLe8lXV5fVt1sMpnXl1UzZULyo9VzdSXEdMqVZ/PNTZm01rJry5Ks31xJ56XgLynXNO9+vDXqm4o/uO/L/dEegESJe6KC9X62Vz3Pjk9fCY/ksUF2FJTy5qcHYBwu6qxpVYBry3tpi2RvMDZtD8Qd7Q/hZDXR6X7JaMtKiK2RzWV1W5ILI+abmzJpQ7XhtMkiaaLgLykVN+9+nDXqm2pucF+hA4b08lLkcjY7Zz36/LShG9VCHS7edJ1DvXE3DJJPNsC19b20VmtuMFoz3a8lyaTQbetsiGwvq9sRNDdl0jhcuLxK1yvpk5vZTKTDipd3Pyq6Rn08zQ/ugyKXMyYzXmNxU84CGwqGYhPk84u31G8q3ktrNL7BiN5Y1IYCBELh7f7g/su0HjeyOOHyufGm+zW3JG66BlRme1ndjqK5pZCNMfToNy7DJZJ8ouAvKRUv735UvDXqo1pK59rS4L5Ez099pifBOAldoOUA19b30hqtvcGITvfr4nbgcYXry+MydHE7YlbN+2x3gNmLN/HSumre3rSHl9ZVM3vxJj7bHX5P7a3zRJJZ5Ea+nDLpcLoxkTUajMOFw+kOb9dgP0kjdftLQm1Z9jSadz9e0Iy3Rn1US+lcW8q3n+j5aZHdidPWxr0BaCnAtfW9tEZbbjCGDyxi7nVDwz+bHbX07+3iuJHF+wX+ZJ7nt7fOE8nEIjedRS5MmUw1LZvbMegnInG1ddnTcT0n8Pj6BHn346xRH9VSOteWBp4len46oH4Fy1wnJShP8wEu2ffSnrXhk7nBSHT+5kb1N/c8PxQM8u6LrzOiyPCNHiU8scuFNYZgoQtnXS3GWs7qQ5sH+2np2dbJhSmTqaJlczsOBX+JURMIcv2DVfGXPf17FXN/nnguucfp4bJBV8QMYEsm7357FqRJlHK2kFqO9f8fb3nO22+0fzI3Fcm8l/auDd/SDYZn1zAuvGtFq8/f3PP8euPg8083UrL0NaitZWqBi/WHDcfXozdFu3Zw8CfLsQUOQrNnt2n6m5aezU/WomVzOxD9JCRGc3PJ/cEAjy1azfSvDUn4+sOLBvHbkcmtUd9UW/Ptx0s5iykALIMPHkbZIQexsjrY6puK5t5LszdJSSbcae4G47uHXM71d25s0/mjz/Pj3QA46wIU7doBteFekoL6WkpXVe53jHW62f3ee22aDpfJRW4kdwRqaltcNlcJdnKHgr/EaG4uuQ0W8tInFZx7TGlMMM92Mhxv8eEcNuFGti56gtp9n+MqOoC+E86msKgYgDH92nZej9PDV/vEriGQqoQ7iW4wFlbuxdr4C820dP5mn+dbOPiT5c2WqTXP5uPlps+VRDqSOcH6kJbN7UAU/CVG/z5uCgstdXWxI8GNs5bCot1U7ly0X0DMVDKc5jSdWx5wf4rvkYq0zS1PZcKdeDcYm7Z/0ebzxxtD4awLYCx87YV/UFDffNmSfTbfUm76bCfSkcxxFjgocKFlczsITfWTGOG54onz6noGrNxvFHpr56qnQzbmlkcT7sTT2oQ76Th/dAzFlIHFjA3uZEzFv5g65/f0+fyz/Y6zBWAPd2JHF4Q/FyT3bL5xboXoQEsbqiUUDIS31ydf5zWBIC8u2sEDL2zixUU7qAnEb0HmsmC9n50b32TLmifYufFNgq14/52B2+siQdqCvF02N5cp+EsMr9vJtLP34SioxTjD/9SNsxZTUMuBX3sGj8ux3zS3TCTDaUmiueW1hbD8iBDz3/8zb21/I6U3Iq1NuJON80fHUJw6bhBfWftBTIvflhg4xw3jCmB4QfjzOR5KLvt2i130yeSmT8bydT4uvHkF9z27kUdf38Z9z27kwptXsHydL6nX54Ka6rWseeMaPl81nx2fvsznq+az5o1rqKlem+2iZcyXy+M6KXCFx6IUuJwUup15u2xuLlPwl7jOGjGWQV+fR+/Rb9Bj8GJ6j36DQ6f+HU+fzTFT9jKRDKcl8eaWbzrQ8PfvFvD6MSHecK3gsfWP8PNlV7PWtyYl12xNwp1snz86CM/h8WDckfUMurrhBDcUmvAHRL6GrTuearHlnorc9I0HTUYfcfhrLfsCocj23O8BSGUPSEcXXTb3a98cypiTD+Nr3xzKjJtO0jS/HKRbMYnL4/Twk6GXcU9h01Honpgpe5lIhtOSpnPLawvhmXIndY26zdORlz+ZhDu5cv7oILzqd9/Et2s5tV2rqTXbwMYG2GRWlUtFbvpUr1KYDVqdb39aNrdjUPCXhJKdstfWxD6p1HRu+ZpBpplFa2zMgMX26NJCwh0IZ9xb0Sh/QWuWFE7m/Mny+zew1T6D7RaZDploalYSLfdEuRUg+dz0qRw0mS1anU86InX7S7Oio9DPHHAOX+0zMW5rOTpX3e3w4HKEu5RdDjduR2wvQbo07dauLjbUJxgsl6lHEVEt5djPlHjd04kk03JPRW76dA+azIRoD0g8Wp1PcpVa/pIS7UnskyqN55YP2FfJR3Y1tSZ24Z5MPYqA5HLstzWNbms11z3dVNOWe6Kei/bmpj9uZDH3P7eJeF0QqRg0mQmp6AERyTQFf0mZpnPVo8vJtqWru60cHg/Fxx/PpOBRvLzsagjFBv9MPYqA5nPsR5cUbktGw7Zorns6yjhcGGP2a7nXhiyzF2/ab82Fl9dVM21ICYd0d7crN310UGPTFMnGmJQMmsyEeNkl49WjSC5R8Je0+Gx3IGaRnsYBI93as8ZAKjWXY78uZNm2zw9kJvg3N0APU0DXnoPp3m/sfi33QDDEF/vqqe2Svp6LdA+azITOuDqfdG4K/pJymejqTiaVcC48imgux76lln9ve4Gv9JmckSyIzXVPOxxODh75g5hgtWJ74pSsqey5SOWgxmzpTKvzSeen4C8pl+6u7takEk6Ulz9Tmsuxb7H4zFLuWfNhyqYeNqct3dNf+OsT/izrQpYv/LGPVaRzireGg1M9Gx2Wgr+kXEtd3e0JGI1TCUelY/5+IjWBYLh7enuA/n3cHDeyGG8z3dPRHPv/XPE59bYeg4sQ4fLuLJiHNXVYHCmdetic1nZP9/IUsC3BuQodhl4e/QvJBy2t4SAdj/5yJeWa6+pub8BIJpVwuoLo8nU+fvX3/1IftNQHocAJ9z27kV9f/BWGD0zck3FIdzeD+y/hnc8347S9CJov8Ds+wpo6IPNTD1vTPT20j5dVCfYZYFgfLdbS2TWeIhplQ7VY4LP37+SIib/T2IYOSPP8JeWG9vGSYH2PdgeMbKUSrgkE+cUD/8VfGw78APXBcCKaXzzw3xbT0B7gLSFYuBJfwb/Z51zaEPghs1MPW8vtdNCrSwEuh6HQEf6pFjoMLodh2pCSjE1TlOxJ1RoOklv0lyspF+3qTkfAiKYSjiedQfSVJV9QWxf/H2BtneWVyi+aff24nhMwCW6JMjn1sC1cDtOwOuBX+3djysBirhzfPyOzNiT7lMGwc1Lwl7RovJxsKgNGtoJoxce7W9i/p9n9uZAFsT2iqwOeeGgxY/oVqcWfR5TBsHPSM39Jm2jASKWszd9P9ByjQcuZ83Jh6qFIaymDYeek4C8dTjaCaNng7ixZnXh9+bIjuyd1nmxPPRRpLWUw7JxyJvgbY/4AfB2oBT4BLrbWVsc5bh2wBwgC9dba8RkspuSITAfRk8b14oEXNxOI89zfXWg4aWzHWq+8tVMWJb8pg2HnkzPBH/gXcJ21tt4Y8zvgOuCaBMdOttZuz1zRJN953U5+deFAbpyzjmDQErLhqX6FTgezLu4YOeijlq/zxeTSv/+5TcyaUdrslEXJb8pg2LnkzKgda+3L1tpo9pd3gQHZLI9IY8vX+bhp3qc4TDjwOx3h552/uPDQDhUwawJBrn+win2BEP7acC+Gv9ayLxCKbG9+yqKIdA45E/yb+C7wQoJ9FnjZGLPEGHNJBsskeapxwAxEpucHQ1BXb7lp7qcdKmC+vqy62Tnbry+rzmyBRCQrTLLre6fkYsa8AhwQZ9cvrLVPR475BTAeONvGKZwx5iBr7UZjTF/Cjwout9a+nuB6lwCXAJSUlIybP39+it5J5+Dz+Sgq6jit1kyIVye7a4Jsq64lXsZih4GSYhfdvenu9rcE6/ZiQ/UYRwHOwq4kMQUhxo7ddXyxJ3F65V7dCujdvXC/bfo9iaU6iaU6iZXtOpk8efKSROPiMhr8W2KMmQH8EDjRWpt4ObEvj78B8Flrb23p2MGDB9tVqxIlKs1PCxcuZNKkSdkuRk6JVycPvLCJR19PlOEevnV8Xy4+9cC0lSleXvXoSOvW5lV/cdEO7nt2Y0OXf2Mel+FHUw+KWV1PvyexVCexVCexsl0nxpiEwT9nuv2NMacC/w8oTxT4jTFdjTHdol8DpwDLM1dKyUf9+7jxuOK3sj0uQ//e8ROgpELjvOrRLGs2VEsoGAhvr/fvf/y+fex87TW2zp/PztdeI7hv3377jxtZjDEJkiQZw3Eji9PyPkQkt+RM8AfuAroB/zLGLDXG3AtgjOlvjHk+ckw/4E1jzAdABfCctfbF7BRX8kU2A2Zr8qrXrF7N2iuvZMu8eex4/nm2zJvH2iuvpGb16oZjvG4ns2aU0sXtaLih8bgMXdyOyPaOM2tBRNouZ6b6WWvj9l9aazcBp0e+/i8wKpPlEokGzKbT44wxaQ+YyeZVD+7bx/rbbyfk/7InwAYCWGD97bczaPZsHJ7wnOzhA4uYe93Q8Dz/HbX07+3iuJHFCvwieSRngr9ILstkwAzW+9m9ZTG1NVupD+zCOFxxbwAa51XfXVHRbA/B7vfeo/j44xu2dXE7Y57ti0j+UPAXSVImAmbTwX2YQrB1cY9tnFe9bssWbCD+Usc2EKB2a3ZXXgvu28fuigrqtmyhsF8/upeV4ezSJatlEslnCv4iOaLx4L4GjQJ/tAcgXl71wn79MG533BsA43bj6pu9lddqVq9m/e23h29oAgGM283Whx/m4KuuwnvEEVkrl0g+y6UBfyJ5rbnBfZhCuvcbS+9Dp3DA4G9zxMTf7TfNr3tZWbODErsfdVQ6ityixmMRojcmNhAg5PfHjFEQkcxR8BfJEc0N7sPWUeDqQb9BZ9HzoGNjFlRxdunCwVddhcPjwbjdQLjF7/B4GrZnQzJjEUQk89TtL5IjXN6+SQ3uS8R7xBEMmj2b3e+9R+3Wrbj69qX7UUdlLfBD7o9FEMlXCv4iOaJ7v/FsWf0o8drJjQf3Ncfh8ew3qj/bcnksgkg+U7e/SI5wFnjCg/icbowjnDXQOFw4nO79Bvd1JLk6FkEk36nlL5JDvMWHc8TE37FryxJqa7bi8valR79xHTLww5djEZqO9jfGZHUsgki+U/AXyTGOAg89Dzo228VImVwciyCS7xT8RSTtcm0sgki+0zN/ERGRPKPgLyIikmcU/EVERPKMgr+IiEieUfAXERHJMwr+IiIieUbBX0REJM8o+IuIiOQZBX8REZE8o+AvIiKSZxT8RURE8oyCv4iISJ5R8BcREckzCv4iIiJ5xlhrs12GjDDG7AFWZbscOaYPsD3bhcgxqpNYqpNYqpNYqpNY2a6TQ621JfF2FGS6JFm0ylo7PtuFyCXGmMWqk/2pTmKpTmKpTmKpTmLlcp2o219ERCTPKPiLiIjkmXwK/vdnuwA5SHUSS3USS3USS3USS3USK2frJG8G/ImIiEhYPrX8RUREhDwM/saYy40xHxtjPjLG/D7b5ckVxpifGWOsMaZPtsuSbcaYP0R+R5YZY540xhRnu0zZYow51Rizyhiz1hhzbbbLk23GmIONMf8xxqyI/A/5n2yXKVcYY5zGmPeNMc9muyy5wBhTbIx5LPK/ZKUx5phsl6mxvAr+xpjJwJnAKGvtMODWLBcpJxhjDgZOAT7LdllyxL+A4dbakcBq4LoslycrjDFO4G7gNGAoMM0YMzS7pcq6euBn1tqhwNHAj1UnDf4HWJntQuSQPwEvWmuPBEaRY3WTV8EfuBS4xVobALDWbs1yeXLFH4H/B2gACGCtfdlaWx/59l1gQDbLk0VlwFpr7X+ttbXA/xG+ec5b1trN1trKyNd7CP9DPyi7pco+Y8wA4Azgr9kuSy4wxvQAjgP+BmCtrbXWVme1UE3kW/A/AphojHnPGPOaMWZCtguUbcaYM4GN1toPsl2WHPVd4IVsFyJLDgLWN/p+Awp0DYwxA4ExwHtZLkoumE24ARHKcjlyRSmwDfh75FHIX40xXbNdqMY6XYY/Y8wrwAFxdv2C8PvtRbi7bgIw3xjzFdvJpzy0UCc/J9zln1eaqxNr7dORY35BuJt3bibLJrnPGFMEPA781Fq7O9vlySZjzFRgq7V2iTFmUpaLkysKgLHA5dba94wxfwKuBX6V3WJ9qdMFf2vtSYn2GWMuBZ6IBPsKY0yIcO7lbZkqXzYkqhNjzAjCd6gfGGMg3L1daYwps9Z+nsEiZlxzvycAxpgZwFTgxM5+c9iMjcDBjb4fENmW14wxhYQD/1xr7RPZLk8OOBYoN8acDniA7saYf1prL8xyubJpA7DBWhvtFXqMcPDPGfnW7f8UMBnAGHME4CKPF6Kw1n5ore1rrR1orR1I+Bd2bGcP/C0xxpxKuAuz3Fpbk+3yZNEiYJAxptQY4wLOAxZkuUxZZcJ3yX8DVlprb892eXKBtfY6a+2AyP+Q84B/53ngJ/I/dL0xZnBk04nAiiwWKUana/m34AHgAWPMcqAWuCiPW3WS2F2AG/hXpEfkXWvtj7JbpMyz1tYbY34CvAQ4gQestR9luVjZdiwwHfjQGLM0su3n1trns1ckyVGXA3MjN87/BS7Ocnn2owx/IiIieSbfuv1FRETynoK/iIhInlHwFxERyTMK/iIiInlGwV9ERCTPKPiLSNKMMTdEVn+MfmwyxjxujDmsyXHnGGP+bYypNsYEjDGrjTG3G2P6NzrmMmPMc8aYHZFzTcr0+xHJVwr+ItJau4BjIh//C4wGXo3mLjfG3AbMJzy3eTrh9NF/JJzo5O5G5/kO4XTbL2Wq4CISlm9JfkSk/eqtte9Gvn7XGPMZ8AZwujHGD1wFfM9a+0Cj17xmjLmf/deR+Kq1NmSMGQ5My0jJRQRQ8BeR9lsS+TwQOA2obBL4AbDWBmm0QqK1VivAiWSJuv1FpL0GRj5/DnwVeDF7RRGRZKjlLyKtZoyJ/u/4CnAPsAd4hfCaCJ9lq1wikhwFfxFprd5AXaPvPwO+DUQXCtGCISI5TsFfRFprF3AS4SD/ObDJWmsj69wHgEOyWTgRaZme+YtIa9Vbaxdba5dYazdGl8W21tYBbwFTsls8EWmJgr+IpNJsYLwx5qKmO4wxDmPMqZkvkog0pW5/EUkZa+0zxpjbgb8ZY44FngZ8wJHAj4B1RGYDGGPGE54pcHDk5ccbY/oA66y1izNcdJG8ouAvIillrf2ZMeZt4CfAPKAL4aC/ALi10aE/ARr3ENwQ+fwQMCPd5RTJZybyuE5ERETyhJ75i4iI5BkFfxERkTyj4C8iIpJnFPxFRETyjIK/iIhInlHwFxERyTMK/iIiInlGwV9ERCTPKPiLiIjkmf8PDFMXkLDJHxEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.decomposition import PCA\n",
    "import plotly.express as px\n",
    "\n",
    "# Το PCA απαιτεί κανονικοποιημένες τιμες μεταξύ των features\n",
    "standardized_X = StandardScaler().fit_transform(X)\n",
    "\n",
    "# 3D plot\n",
    "pca = PCA(n_components=3)\n",
    "\n",
    "principalComponents = pca.fit_transform(standardized_X)\n",
    "\n",
    "principalDF = pd.DataFrame(data = principalComponents, columns = ['PC1', 'PC2', 'PC3'])\n",
    "\n",
    "finalDF = pd.concat((principalDF, features['Label']), axis = 1)\n",
    "\n",
    "total_var = pca.explained_variance_ratio_.sum() * 100\n",
    "\n",
    "fig = px.scatter_3d(\n",
    "    principalComponents, x=0, y=1, z=2, color=features['Label'],\n",
    "    title=f'Total Explained Variance: {total_var:.2f}%',\n",
    "    labels={'0': 'PC 1', '1': 'PC 2', '2': 'PC 3'}\n",
    ")\n",
    "fig.show()\n",
    "\n",
    "# 2D plot\n",
    "fig = plt.figure(figsize = (8,8))\n",
    "ax = fig.add_subplot(1,1,1)\n",
    "ax.set_xlabel('PC1', fontsize = 15)\n",
    "ax.set_ylabel('PC2', fontsize = 15)\n",
    "ax.set_title('2 component PCA', fontsize = 20)\n",
    "targets = set(features['Label'])\n",
    "colors = ['r', 'g', 'b', 'y', 'm', 'c', 'k']\n",
    "for target, color in zip(targets,colors):\n",
    "    indicesToKeep = finalDF['Label'] == target\n",
    "    ax.scatter(finalDF.loc[indicesToKeep, 'PC1']\n",
    "               , finalDF.loc[indicesToKeep, 'PC2']\n",
    "               , c = color\n",
    "               , s = 50)\n",
    "ax.legend(targets)\n",
    "ax.grid()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "id": "cdf37e83",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[t-SNE] Computing 91 nearest neighbors...\n",
      "[t-SNE] Indexed 140 samples in 0.000s...\n",
      "[t-SNE] Computed neighbors for 140 samples in 0.013s...\n",
      "[t-SNE] Computed conditional probabilities for sample 140 / 140\n",
      "[t-SNE] Mean sigma: 7.037198\n",
      "[t-SNE] KL divergence after 250 iterations with early exaggeration: 59.490383\n",
      "[t-SNE] KL divergence after 1000 iterations: 0.280365\n"
     ]
    }
   ],
   "source": [
    "#T-SNE\n",
    "from sklearn.manifold import TSNE\n",
    "from bioinfokit.visuz import cluster\n",
    "tsne_em = TSNE(n_components=2, perplexity=30.0, n_iter=1000, verbose=1).fit_transform(X)\n",
    "# plot t-SNE clusters\n",
    "cluster.tsneplot(score=tsne_em)\n",
    "\n",
    "color_class = features['Label'].to_numpy()\n",
    "cluster.tsneplot(score=tsne_em, colorlist=color_class, colordot=('#713e5a', '#63a375', '#edc79b', '#d57a66', '#ca6680', '#395B50', '#92AFD7'), legendpos='upper right', legendanchor=(1.15, 1) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "id": "341b017b",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 173,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import Image\n",
    "Image(\"tsne_2d.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b2cda263",
   "metadata": {},
   "source": [
    "###  Feature Selection using computing chi-squared stats\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "id": "3c480d46",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['AccMean', 'AccMedian', 'AccStd', 'AccMin', 'AccMax', 'TempMean',\n",
       "       'TempMedian', 'TempStd', 'TempMin', 'TempMax', 'SpO2Mean', 'SpO2Median',\n",
       "       'SpO2Std', 'SpO2Min', 'SpO2Max', 'EDASCL_slope', 'EDASCL_mean',\n",
       "       'EDASCR_amplitude_mean', 'EDASCR_peaks', 'EDASCR_rise_time_mean',\n",
       "       'HeartRatehr_timedomain_meanRR', 'HeartRatehr_timedomain_STD_RR_SDNN',\n",
       "       'HeartRatehr_timedomain_RMSSD'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 174,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.feature_selection import SelectFpr, chi2, SelectKBest, SelectFwe, f_classif, SelectFdr\n",
    "    \n",
    "selector = SelectKBest(score_func=chi2, k=23)\n",
    "new_data = selector.fit_transform(normalized_data, y)\n",
    "mask = selector.get_support()\n",
    "new_features = normalized_data.columns[mask]\n",
    "new_features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "id": "f0dbcff1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "X = X[new_features]\n",
    "\n",
    "#Using Pearson Correlation\n",
    "plt.figure(figsize=(12,10))\n",
    "cor = X.corr()\n",
    "sns.heatmap(cor, annot=True, cmap=plt.cm.Reds)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "id": "36a92410",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      AccMean  AccMedian    AccStd    AccMin    AccMax   TempMean  TempMedian  \\\n",
      "116  1.103662   1.098135  0.038082  1.024890  2.257476  30.353519        30.4   \n",
      "30   1.149028   1.121651  0.191942  0.434166  2.006639  30.676194        30.8   \n",
      "108  0.839824   0.841190  0.007347  0.786193  0.886905  29.929113        30.1   \n",
      "119  1.090346   1.087014  0.035045  0.739256  1.320114  33.065728        33.1   \n",
      "80   1.180219   1.180974  0.004733  1.133358  1.195492  30.091628        30.1   \n",
      "..        ...        ...       ...       ...       ...        ...         ...   \n",
      "37   1.149780   1.115616  0.141682  0.762496  2.850474  32.117746        32.2   \n",
      "56   1.139542   1.169188  0.053121  0.802309  1.390288  33.558520        33.7   \n",
      "31   1.196999   1.197790  0.005476  1.175287  1.261943  29.150229        29.3   \n",
      "72   1.120069   1.083836  0.158790  0.295635  2.140010  31.864404        32.0   \n",
      "19   1.165798   1.166233  0.030422  0.875785  1.848188  31.912620        32.0   \n",
      "\n",
      "      TempStd  TempMin  TempMax  ...  SpO2Min  SpO2Max  EDASCL_slope  \\\n",
      "116  0.110854     30.1     30.6  ...     96.0     98.0      0.146843   \n",
      "30   0.163961     30.1     31.2  ...     93.0     98.0      0.063923   \n",
      "108  0.960394     27.9     31.2  ...     95.0     98.0      0.257358   \n",
      "119  0.079824     32.6     33.3  ...     95.0     98.0      4.926235   \n",
      "80   0.588940     28.7     31.0  ...     96.0     99.0      0.769279   \n",
      "..        ...      ...      ...  ...      ...      ...           ...   \n",
      "37   0.310790     31.4     32.6  ...     87.0     95.0      2.002841   \n",
      "56   0.380573     32.6     34.3  ...     93.0     98.0      5.477518   \n",
      "31   0.884360     27.2     30.6  ...     95.0     97.0      0.416462   \n",
      "72   0.223404     31.2     32.4  ...     94.0     97.0      0.033097   \n",
      "19   0.104458     31.8     32.4  ...     93.0     97.0      2.621434   \n",
      "\n",
      "     EDASCL_mean  EDASCR_amplitude_mean  EDASCR_peaks  EDASCR_rise_time_mean  \\\n",
      "116     0.163658               0.001974         286.0               0.027124   \n",
      "30      0.485208               0.019785          18.0               0.006926   \n",
      "108     0.261037               0.002574         177.0               0.017961   \n",
      "119     5.662290               0.146378          86.0               0.012911   \n",
      "80      0.478682               0.003171         145.0               0.022647   \n",
      "..           ...                    ...           ...                    ...   \n",
      "37      0.934203               0.248354           4.0               0.001007   \n",
      "56      6.346697               0.107612          54.0               0.006102   \n",
      "31      0.372119               0.002428         255.0               0.024740   \n",
      "72      0.051539               0.010573          15.0               0.001880   \n",
      "19      2.683421               0.011613         360.0               0.034635   \n",
      "\n",
      "     HeartRatehr_timedomain_meanRR  HeartRatehr_timedomain_STD_RR_SDNN  \\\n",
      "116                     350.400000                           90.176715   \n",
      "30                      350.857143                           69.107429   \n",
      "108                     360.000000                           71.591433   \n",
      "119                     407.333333                           83.279983   \n",
      "80                      321.333333                           33.119313   \n",
      "..                             ...                                 ...   \n",
      "37                      397.333333                          152.029237   \n",
      "56                      336.555556                           54.180995   \n",
      "31                      366.666667                          110.409339   \n",
      "72                      439.200000                           54.105083   \n",
      "19                      330.666667                           66.319094   \n",
      "\n",
      "     HeartRatehr_timedomain_RMSSD  \n",
      "116                     91.956511  \n",
      "30                     108.049371  \n",
      "108                     99.631320  \n",
      "119                    172.083701  \n",
      "80                      51.474265  \n",
      "..                            ...  \n",
      "37                     290.285377  \n",
      "56                      88.098666  \n",
      "31                     152.808377  \n",
      "72                      55.928526  \n",
      "19                     107.703296  \n",
      "\n",
      "[112 rows x 23 columns]\n",
      "116             Relax2\n",
      "30      PhysicalStress\n",
      "108             Relax1\n",
      "119    CognitiveStress\n",
      "80              Relax1\n",
      "            ...       \n",
      "37      PhysicalStress\n",
      "56     CognitiveStress\n",
      "31              Relax1\n",
      "72      PhysicalStress\n",
      "19              Relax3\n",
      "Name: Label, Length: 112, dtype: object\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn import preprocessing\n",
    "from sklearn.model_selection import cross_validate\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.svm import SVC\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.20, random_state=27)\n",
    "print(X_train)  \n",
    "print(y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "id": "0e58d75e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "==============================\n",
      "KNeighborsClassifier\n",
      "****Results****\n",
      "Accuracy: 32.1429%\n",
      "Log Loss: 1.7700587606192972\n",
      "==============================\n",
      "DecisionTreeClassifier\n",
      "****Results****\n",
      "Accuracy: 46.4286%\n",
      "Log Loss: 18.50291592584502\n",
      "==============================\n",
      "RandomForestClassifier\n",
      "****Results****\n",
      "Accuracy: 57.1429%\n",
      "Log Loss: 1.1010563803747189\n",
      "==============================\n",
      "AdaBoostClassifier\n",
      "****Results****\n",
      "Accuracy: 21.4286%\n",
      "Log Loss: 1.581716810583687\n",
      "==============================\n",
      "GradientBoostingClassifier\n",
      "****Results****\n",
      "Accuracy: 64.2857%\n",
      "Log Loss: 1.408136760129485\n",
      "==============================\n",
      "GaussianNB\n",
      "****Results****\n",
      "Accuracy: 50.0000%\n",
      "Log Loss: 4.750609150263295\n",
      "==============================\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score, log_loss\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.svm import SVC, LinearSVC, NuSVC\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier, GradientBoostingClassifier\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n",
    "from sklearn.discriminant_analysis import QuadraticDiscriminantAnalysis\n",
    "\n",
    "classifiers = [\n",
    "    KNeighborsClassifier(15),\n",
    "    DecisionTreeClassifier(),\n",
    "    RandomForestClassifier(),\n",
    "    AdaBoostClassifier(),\n",
    "    GradientBoostingClassifier(),\n",
    "    GaussianNB()]\n",
    "\n",
    "# Logging for Visual Comparison\n",
    "log_cols=[\"Classifier\", \"Accuracy\", \"Log Loss\"]\n",
    "log = pd.DataFrame(columns=log_cols)\n",
    "\n",
    "for clf in classifiers:\n",
    "    clf.fit(X_train, y_train)\n",
    "    name = clf.__class__.__name__\n",
    "    \n",
    "    print(\"=\"*30)\n",
    "    print(name)\n",
    "    \n",
    "    print('****Results****')\n",
    "    train_predictions = clf.predict(X_test)\n",
    "    acc = accuracy_score(y_test, train_predictions)\n",
    "    print(\"Accuracy: {:.4%}\".format(acc))\n",
    "    \n",
    "    train_predictions = clf.predict_proba(X_test)\n",
    "    ll = log_loss(y_test, train_predictions)\n",
    "    print(\"Log Loss: {}\".format(ll))\n",
    "    \n",
    "    log_entry = pd.DataFrame([[name, acc*100, ll]], columns=log_cols)\n",
    "    log = log.append(log_entry)\n",
    "    \n",
    "print(\"=\"*30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "id": "46c0ffff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "sns.set_color_codes(\"muted\")\n",
    "sns.barplot(x='Accuracy', y='Classifier', data=log, color=\"b\")\n",
    "\n",
    "plt.xlabel('Accuracy %')\n",
    "plt.title('Classifier Accuracy')\n",
    "plt.show()\n",
    "\n",
    "sns.set_color_codes(\"muted\")\n",
    "sns.barplot(x='Log Loss', y='Classifier', data=log, color=\"g\")\n",
    "\n",
    "plt.xlabel('Log Loss')\n",
    "plt.title('Classifier Log Loss')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3c83789c",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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