AlMadi.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Getting started with AlMadi dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The autoreload extension is already loaded. To reload it, use:\n",
      "  %reload_ext autoreload\n"
     ]
    }
   ],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Import the tool"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "from emtk import parsers, visualization, util"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Parsing raw data from the dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "_Note_: The AlMadi dataset does not have raw data (samples). It only contains filtered fixation, saccades, and blinks."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Please cite this paper:  https://dl.acm.org/doi/10.1145/3448018.345742\n",
      "parsing file: 001.asc\n",
      "parsing file: 002.asc\n",
      "parsing file: 003.asc\n",
      "parsing file: 004.asc\n",
      "parsing file: 005.asc\n",
      "parsing file: 006.asc\n",
      "parsing file: 007.asc\n",
      "parsing file: 008.asc\n"
     ]
    }
   ],
   "source": [
    "eye_events, _ = parsers.AlMadi(sample_size = 8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        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>eye_tracker</th>\n",
       "      <th>experiment_id</th>\n",
       "      <th>participant_id</th>\n",
       "      <th>filename</th>\n",
       "      <th>trial_id</th>\n",
       "      <th>stimuli_module</th>\n",
       "      <th>stimuli_name</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>duration</th>\n",
       "      <th>x0</th>\n",
       "      <th>y0</th>\n",
       "      <th>x1</th>\n",
       "      <th>y1</th>\n",
       "      <th>token</th>\n",
       "      <th>pupil</th>\n",
       "      <th>amplitude</th>\n",
       "      <th>peak_velocity</th>\n",
       "      <th>eye_event_type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>EyeLink1000</td>\n",
       "      <td>001</td>\n",
       "      <td>001</td>\n",
       "      <td>001.asc</td>\n",
       "      <td>0</td>\n",
       "      <td>emtk/datasets/AlMadi2018/runtime/images/</td>\n",
       "      <td>1706913863030379050.png</td>\n",
       "      <td>178149</td>\n",
       "      <td>104</td>\n",
       "      <td>163.8</td>\n",
       "      <td>387.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[EFIX, R, 178149, 178252, 104, 163.8, 387.2, 4...</td>\n",
       "      <td>4163.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>fixation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>EyeLink1000</td>\n",
       "      <td>001</td>\n",
       "      <td>001</td>\n",
       "      <td>001.asc</td>\n",
       "      <td>0</td>\n",
       "      <td>emtk/datasets/AlMadi2018/runtime/images/</td>\n",
       "      <td>1706913863030379050.png</td>\n",
       "      <td>178253</td>\n",
       "      <td>26</td>\n",
       "      <td>163.7</td>\n",
       "      <td>386.0</td>\n",
       "      <td>73.3</td>\n",
       "      <td>391.5</td>\n",
       "      <td>[ESACC, R, 178253, 178278, 26, 163.7, 386.0, 7...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.01</td>\n",
       "      <td>235.0</td>\n",
       "      <td>saccade</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>EyeLink1000</td>\n",
       "      <td>001</td>\n",
       "      <td>001</td>\n",
       "      <td>001.asc</td>\n",
       "      <td>0</td>\n",
       "      <td>emtk/datasets/AlMadi2018/runtime/images/</td>\n",
       "      <td>1706913863030379050.png</td>\n",
       "      <td>178279</td>\n",
       "      <td>119</td>\n",
       "      <td>83.4</td>\n",
       "      <td>388.9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[EFIX, R, 178279, 178397, 119, 83.4, 388.9, 4170]</td>\n",
       "      <td>4170.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>fixation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>EyeLink1000</td>\n",
       "      <td>001</td>\n",
       "      <td>001</td>\n",
       "      <td>001.asc</td>\n",
       "      <td>0</td>\n",
       "      <td>emtk/datasets/AlMadi2018/runtime/images/</td>\n",
       "      <td>1706913863030379050.png</td>\n",
       "      <td>178398</td>\n",
       "      <td>30</td>\n",
       "      <td>88.7</td>\n",
       "      <td>388.4</td>\n",
       "      <td>-4.7</td>\n",
       "      <td>375.7</td>\n",
       "      <td>[ESACC, R, 178398, 178427, 30, 88.7, 388.4, -4...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.05</td>\n",
       "      <td>188.0</td>\n",
       "      <td>saccade</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>EyeLink1000</td>\n",
       "      <td>001</td>\n",
       "      <td>001</td>\n",
       "      <td>001.asc</td>\n",
       "      <td>0</td>\n",
       "      <td>emtk/datasets/AlMadi2018/runtime/images/</td>\n",
       "      <td>1706913863030379050.png</td>\n",
       "      <td>178428</td>\n",
       "      <td>125</td>\n",
       "      <td>5.5</td>\n",
       "      <td>375.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[EFIX, R, 178428, 178552, 125, 5.5, 375.1, 4181]</td>\n",
       "      <td>4181.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>fixation</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   eye_tracker experiment_id participant_id filename trial_id  \\\n",
       "0  EyeLink1000           001            001  001.asc        0   \n",
       "1  EyeLink1000           001            001  001.asc        0   \n",
       "2  EyeLink1000           001            001  001.asc        0   \n",
       "3  EyeLink1000           001            001  001.asc        0   \n",
       "4  EyeLink1000           001            001  001.asc        0   \n",
       "\n",
       "                             stimuli_module             stimuli_name  \\\n",
       "0  emtk/datasets/AlMadi2018/runtime/images/  1706913863030379050.png   \n",
       "1  emtk/datasets/AlMadi2018/runtime/images/  1706913863030379050.png   \n",
       "2  emtk/datasets/AlMadi2018/runtime/images/  1706913863030379050.png   \n",
       "3  emtk/datasets/AlMadi2018/runtime/images/  1706913863030379050.png   \n",
       "4  emtk/datasets/AlMadi2018/runtime/images/  1706913863030379050.png   \n",
       "\n",
       "   timestamp  duration     x0     y0    x1     y1  \\\n",
       "0     178149       104  163.8  387.2   NaN    NaN   \n",
       "1     178253        26  163.7  386.0  73.3  391.5   \n",
       "2     178279       119   83.4  388.9   NaN    NaN   \n",
       "3     178398        30   88.7  388.4  -4.7  375.7   \n",
       "4     178428       125    5.5  375.1   NaN    NaN   \n",
       "\n",
       "                                               token   pupil  amplitude  \\\n",
       "0  [EFIX, R, 178149, 178252, 104, 163.8, 387.2, 4...  4163.0        NaN   \n",
       "1  [ESACC, R, 178253, 178278, 26, 163.7, 386.0, 7...     NaN       3.01   \n",
       "2  [EFIX, R, 178279, 178397, 119, 83.4, 388.9, 4170]  4170.0        NaN   \n",
       "3  [ESACC, R, 178398, 178427, 30, 88.7, 388.4, -4...     NaN       3.05   \n",
       "4   [EFIX, R, 178428, 178552, 125, 5.5, 375.1, 4181]  4181.0        NaN   \n",
       "\n",
       "   peak_velocity eye_event_type  \n",
       "0            NaN       fixation  \n",
       "1          235.0        saccade  \n",
       "2            NaN       fixation  \n",
       "3          188.0        saccade  \n",
       "4            NaN       fixation  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eye_events.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Filtered fixations, saccades, and AOIs."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can visualize any filtered fixations, saccades, and AOIs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "experiment_id = '001'\n",
    "trial_id = '1'\n",
    "\n",
    "trial_data = eye_events.loc[(eye_events['experiment_id'] == experiment_id) & \n",
    "                            (eye_events['trial_id'] == trial_id)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1224x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "visualization.draw_trial(trial_data, draw_aoi = True, draw_number = True, draw_saccade=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Fixation correction through setting offset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1224x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "trial_data_copy = util.sample_offset(trial_data, -200, 100)\n",
    "visualization.draw_trial(trial_data_copy, draw_aoi=True, draw_saccade=True, draw_number=True)"
   ]
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "31b33fcf11092c8e7d239d788eaa8bd35ca0e9e7f582a9d7cc92a75d18dbc581"
  },
  "kernelspec": {
   "display_name": "Python 3.9.7 64-bit ('base': conda)",
   "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": "3.9.7"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}