5.correlation.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```\n",
    "# Copyright 2023 by the author(s) of CHI2023 Late Breaking Work\n",
    "# \"Exploring Physiological Correlates of Visual Complexity Adaptation:\n",
    "# Insights from EDA, ECG, and EEG Data for Adaptation Evaluation in VR\n",
    "# Adaptive Systems\". All rights reserved.\n",
    "#\n",
    "# Use of this source code is governed by a GPLv3 license that\n",
    "# can be found in the LICENSE file.\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tqdm\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import neurokit2 as nk\n",
    "\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "plt.rcParams['text.usetex'] = True\n",
    "plt.rcParams['text.latex.preamble'] = r'''\\usepackage{libertine}\\usepackage{fontawesome5}\\usepackage[dvipsnames]{xcolor}'''\n",
    "plt.rcParams['ps.usedistiller'] = 'xpdf'\n",
    "import seaborn as sns\n",
    "sns.set_theme(style='ticks')\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "from lbw import get_event_intervals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_ecg = pd.read_csv('processed/ecg.csv')\n",
    "df_eda = pd.read_csv('processed/eda.csv')\n",
    "df_eeg = pd.read_csv('processed/eeg.csv')\n",
    "df_acc = pd.read_csv('processed/accuracy.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>workload</th>\n",
       "      <th>ecg_rate_mean</th>\n",
       "      <th>ecg_rate_var</th>\n",
       "      <th>hrv_rmssd</th>\n",
       "      <th>tonic_avg</th>\n",
       "      <th>amplitude_avg</th>\n",
       "      <th>peaks_num</th>\n",
       "      <th>alpha</th>\n",
       "      <th>theta</th>\n",
       "      <th>beta</th>\n",
       "      <th>alpha_theta_ratio</th>\n",
       "      <th>beta_theta_ratio</th>\n",
       "      <th>adAccuracy</th>\n",
       "      <th>nbackAccuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>low</td>\n",
       "      <td>101.691743</td>\n",
       "      <td>768.889866</td>\n",
       "      <td>305.385272</td>\n",
       "      <td>174845.249756</td>\n",
       "      <td>1.584421</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.103104</td>\n",
       "      <td>6.020814</td>\n",
       "      <td>7.710586</td>\n",
       "      <td>0.847577</td>\n",
       "      <td>1.280655</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>93.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>high</td>\n",
       "      <td>82.268868</td>\n",
       "      <td>561.024381</td>\n",
       "      <td>378.258504</td>\n",
       "      <td>171962.900679</td>\n",
       "      <td>2.089351</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.395141</td>\n",
       "      <td>7.284664</td>\n",
       "      <td>11.261976</td>\n",
       "      <td>0.740616</td>\n",
       "      <td>1.545984</td>\n",
       "      <td>72.727273</td>\n",
       "      <td>83.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>low</td>\n",
       "      <td>81.405105</td>\n",
       "      <td>666.763541</td>\n",
       "      <td>363.035634</td>\n",
       "      <td>204700.507118</td>\n",
       "      <td>5.557244</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.149843</td>\n",
       "      <td>5.478949</td>\n",
       "      <td>7.111791</td>\n",
       "      <td>0.757416</td>\n",
       "      <td>1.298021</td>\n",
       "      <td>57.142857</td>\n",
       "      <td>90.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>high</td>\n",
       "      <td>84.830229</td>\n",
       "      <td>720.261262</td>\n",
       "      <td>387.400175</td>\n",
       "      <td>189605.582489</td>\n",
       "      <td>5.190685</td>\n",
       "      <td>4.0</td>\n",
       "      <td>10.723285</td>\n",
       "      <td>15.611431</td>\n",
       "      <td>33.649459</td>\n",
       "      <td>0.686887</td>\n",
       "      <td>2.155437</td>\n",
       "      <td>83.333333</td>\n",
       "      <td>85.714286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>low</td>\n",
       "      <td>101.942719</td>\n",
       "      <td>1248.513926</td>\n",
       "      <td>317.491622</td>\n",
       "      <td>185861.386674</td>\n",
       "      <td>1.555890</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.375570</td>\n",
       "      <td>12.488514</td>\n",
       "      <td>18.729955</td>\n",
       "      <td>0.510515</td>\n",
       "      <td>1.499775</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>80.000000</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>75</th>\n",
       "      <td>low</td>\n",
       "      <td>88.520034</td>\n",
       "      <td>903.015398</td>\n",
       "      <td>392.783097</td>\n",
       "      <td>220832.902538</td>\n",
       "      <td>4.291654</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.040949</td>\n",
       "      <td>5.875032</td>\n",
       "      <td>5.545755</td>\n",
       "      <td>0.347394</td>\n",
       "      <td>0.943953</td>\n",
       "      <td>93.750000</td>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>high</td>\n",
       "      <td>102.132568</td>\n",
       "      <td>1149.148648</td>\n",
       "      <td>320.594861</td>\n",
       "      <td>250662.773276</td>\n",
       "      <td>9.075797</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6.862691</td>\n",
       "      <td>10.608219</td>\n",
       "      <td>22.080246</td>\n",
       "      <td>0.646922</td>\n",
       "      <td>2.081428</td>\n",
       "      <td>88.235294</td>\n",
       "      <td>85.714286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>low</td>\n",
       "      <td>95.792852</td>\n",
       "      <td>825.593806</td>\n",
       "      <td>239.975514</td>\n",
       "      <td>266089.201377</td>\n",
       "      <td>5.371120</td>\n",
       "      <td>4.0</td>\n",
       "      <td>16.405031</td>\n",
       "      <td>5.752566</td>\n",
       "      <td>6.420085</td>\n",
       "      <td>2.851776</td>\n",
       "      <td>1.116039</td>\n",
       "      <td>93.333333</td>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>high</td>\n",
       "      <td>113.298078</td>\n",
       "      <td>242.748618</td>\n",
       "      <td>72.813290</td>\n",
       "      <td>321911.981977</td>\n",
       "      <td>19.383172</td>\n",
       "      <td>5.0</td>\n",
       "      <td>20.509668</td>\n",
       "      <td>8.313510</td>\n",
       "      <td>17.858061</td>\n",
       "      <td>2.467029</td>\n",
       "      <td>2.148077</td>\n",
       "      <td>66.666667</td>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>low</td>\n",
       "      <td>85.453865</td>\n",
       "      <td>706.068897</td>\n",
       "      <td>328.785160</td>\n",
       "      <td>311668.988358</td>\n",
       "      <td>12.785719</td>\n",
       "      <td>2.0</td>\n",
       "      <td>22.008694</td>\n",
       "      <td>18.039341</td>\n",
       "      <td>39.501176</td>\n",
       "      <td>1.220039</td>\n",
       "      <td>2.189724</td>\n",
       "      <td>84.615385</td>\n",
       "      <td>90.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>80 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   workload  ecg_rate_mean  ecg_rate_var   hrv_rmssd      tonic_avg  \\\n",
       "0       low     101.691743    768.889866  305.385272  174845.249756   \n",
       "1      high      82.268868    561.024381  378.258504  171962.900679   \n",
       "2       low      81.405105    666.763541  363.035634  204700.507118   \n",
       "3      high      84.830229    720.261262  387.400175  189605.582489   \n",
       "4       low     101.942719   1248.513926  317.491622  185861.386674   \n",
       "..      ...            ...           ...         ...            ...   \n",
       "75      low      88.520034    903.015398  392.783097  220832.902538   \n",
       "76     high     102.132568   1149.148648  320.594861  250662.773276   \n",
       "77      low      95.792852    825.593806  239.975514  266089.201377   \n",
       "78     high     113.298078    242.748618   72.813290  321911.981977   \n",
       "79      low      85.453865    706.068897  328.785160  311668.988358   \n",
       "\n",
       "    amplitude_avg  peaks_num      alpha      theta       beta  \\\n",
       "0        1.584421        4.0   5.103104   6.020814   7.710586   \n",
       "1        2.089351        3.0   5.395141   7.284664  11.261976   \n",
       "2        5.557244        4.0   4.149843   5.478949   7.111791   \n",
       "3        5.190685        4.0  10.723285  15.611431  33.649459   \n",
       "4        1.555890        4.0   6.375570  12.488514  18.729955   \n",
       "..            ...        ...        ...        ...        ...   \n",
       "75       4.291654        1.0   2.040949   5.875032   5.545755   \n",
       "76       9.075797        2.0   6.862691  10.608219  22.080246   \n",
       "77       5.371120        4.0  16.405031   5.752566   6.420085   \n",
       "78      19.383172        5.0  20.509668   8.313510  17.858061   \n",
       "79      12.785719        2.0  22.008694  18.039341  39.501176   \n",
       "\n",
       "    alpha_theta_ratio  beta_theta_ratio  adAccuracy  nbackAccuracy  \n",
       "0            0.847577          1.280655  100.000000      93.333333  \n",
       "1            0.740616          1.545984   72.727273      83.333333  \n",
       "2            0.757416          1.298021   57.142857      90.000000  \n",
       "3            0.686887          2.155437   83.333333      85.714286  \n",
       "4            0.510515          1.499775  100.000000      80.000000  \n",
       "..                ...               ...         ...            ...  \n",
       "75           0.347394          0.943953   93.750000     100.000000  \n",
       "76           0.646922          2.081428   88.235294      85.714286  \n",
       "77           2.851776          1.116039   93.333333     100.000000  \n",
       "78           2.467029          2.148077   66.666667     100.000000  \n",
       "79           1.220039          2.189724   84.615385      90.000000  \n",
       "\n",
       "[80 rows x 14 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pids = df_ecg.pid.unique()\n",
    "df = pd.merge(df_ecg[df_ecg.pid.isin(pids)], df_eda[df_eda.pid.isin(pids)], on=['pid', 'interval', 'workload'])\n",
    "df = pd.merge(df, df_eeg[df_eeg.pid.isin(pids)], on=['pid', 'interval', 'workload'])\n",
    "df = pd.merge(df, df_acc[df_acc.pid.isin(pids)], on=['pid', 'interval', 'workload'])\n",
    "df_all = df[[ 'workload', 'ecg_rate_mean', 'ecg_rate_var',\n",
    "       'hrv_rmssd','tonic_avg', 'amplitude_avg', 'peaks_num', 'alpha', 'theta', 'beta',\n",
    "       'alpha_theta_ratio', 'beta_theta_ratio',\n",
    "       'adAccuracy',  'nbackAccuracy']]\n",
    "df_all"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1100x900 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "\n",
    "df = df_all[['workload', 'ecg_rate_mean', 'hrv_rmssd','tonic_avg', 'amplitude_avg', 'alpha', 'theta', 'beta', 'alpha_theta_ratio', 'nbackAccuracy', 'adAccuracy']]\n",
    "df = df.rename(columns={\"ecg_rate_mean\": \"HR\", \"hrv_rmssd\": \"HRV\", \"tonic_avg\": \"SCL\", \"amplitude_avg\": \"nsSCRs\"\n",
    "                               , \"alpha\": \"Alpha\", \"theta\": \"Theta\", \"beta\": \"Beta\", \"alpha_theta_ratio\": \"A \\ T Ratio\",\n",
    "                                \"nbackAccuracy\": \"N-Back Acc.\",\n",
    "                                \"adAccuracy\": \"VD Acc.\"})\n",
    "corr = df.corr()\n",
    "corr[(np.abs(corr) < 0.05)] = 0\n",
    "mask = np.triu(np.ones_like(corr, dtype=bool))\n",
    "f, ax = plt.subplots(figsize=(11, 9))\n",
    "cmap = sns.diverging_palette(230, 20, as_cmap=True)\n",
    "sns.heatmap(corr, mask=mask, cmap=cmap, vmin=-1, vmax=1, center=0,\n",
    "            square=True, linewidths=1, cbar_kws={\"shrink\": .5})\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig('figures/fig5.pdf')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "chi23lbw",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.16"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "82ae58ca00847926547aabc807806e6cb01e8dbaa50ce6f12d50c344679ba919"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}