{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Analyze Eye Tracker Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preliminaries"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For parsing and converting the pupilometry data, we will use the recent fork of `python_eyelinkparser` module.\n",
    "The dataset comes from {cite:p}`mathot_pupillometry_2018` and represents the eye response to a working-memory task on digits, following the classical work {cite:p}`kahneman_pupil_1966`. The original data was captured using `EyeLink 1000` by `SR Research`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: setuptools in /home/ubuntu/projects/eye-processing/.venv/lib/python3.10/site-packages (63.4.2)\n",
      "Collecting git+https://github.com/maciejskorski/python-eyelinkparser.git\n",
      "  Cloning https://github.com/maciejskorski/python-eyelinkparser.git to /tmp/pip-req-build-ilp9dn_6\n",
      "  Running command git clone --filter=blob:none --quiet https://github.com/maciejskorski/python-eyelinkparser.git /tmp/pip-req-build-ilp9dn_6\n",
      "  Resolved https://github.com/maciejskorski/python-eyelinkparser.git to commit b0829b7a4d9a0d294f982516ec1143a88a8670db\n",
      "  Installing build dependencies ... \u001b[?25ldone\n",
      "\u001b[?25h  Getting requirements to build wheel ... \u001b[?25ldone\n",
      "\u001b[?25h  Installing backend dependencies ... \u001b[?25ldone\n",
      "\u001b[?25h  Preparing metadata (pyproject.toml) ... \u001b[?25ldone\n",
      "\u001b[?25hRequirement already satisfied: numpy>=1.18 in /home/ubuntu/projects/eye-processing/.venv/lib/python3.10/site-packages (from python-eyelinkparser==0.18.0) (1.23.1)\n",
      "Requirement already satisfied: prettytable>=3.0 in /home/ubuntu/projects/eye-processing/.venv/lib/python3.10/site-packages (from python-eyelinkparser==0.18.0) (3.3.0)\n",
      "Requirement already satisfied: scipy>=1.7 in /home/ubuntu/projects/eye-processing/.venv/lib/python3.10/site-packages (from python-eyelinkparser==0.18.0) (1.9.0)\n",
      "Requirement already satisfied: python-datamatrix>=0.13 in /home/ubuntu/projects/eye-processing/.venv/lib/python3.10/site-packages (from python-eyelinkparser==0.18.0) (0.14.3)\n",
      "Requirement already satisfied: fastnumbers>=3.0 in /home/ubuntu/projects/eye-processing/.venv/lib/python3.10/site-packages (from python-eyelinkparser==0.18.0) (3.2.1)\n",
      "Requirement already satisfied: wcwidth in /home/ubuntu/projects/eye-processing/.venv/lib/python3.10/site-packages (from prettytable>=3.0->python-eyelinkparser==0.18.0) (0.2.5)\n",
      "--2022-08-07 20:14:08--  https://github.com/smathot/pupillometry_review/blob/master/data/auditory%20working%20memory/data/awm.edf?raw=true\n",
      "Resolving github.com (github.com)... 140.82.121.4\n",
      "Connecting to github.com (github.com)|140.82.121.4|:443... connected.\n",
      "HTTP request sent, awaiting response... 302 Found\n",
      "Location: https://github.com/smathot/pupillometry_review/raw/master/data/auditory%20working%20memory/data/awm.edf [following]\n",
      "--2022-08-07 20:14:08--  https://github.com/smathot/pupillometry_review/raw/master/data/auditory%20working%20memory/data/awm.edf\n",
      "Reusing existing connection to github.com:443.\n",
      "HTTP request sent, awaiting response... 302 Found\n",
      "Location: https://raw.githubusercontent.com/smathot/pupillometry_review/master/data/auditory%20working%20memory/data/awm.edf [following]\n",
      "--2022-08-07 20:14:08--  https://raw.githubusercontent.com/smathot/pupillometry_review/master/data/auditory%20working%20memory/data/awm.edf\n",
      "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.110.133, 185.199.111.133, 185.199.108.133, ...\n",
      "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.110.133|:443... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 17685109 (17M) [application/octet-stream]\n",
      "Saving to: ‘data/awm.edf’\n",
      "\n",
      "data/awm.edf        100%[===================>]  16.87M  21.5MB/s    in 0.8s    \n",
      "\n",
      "2022-08-07 20:14:10 (21.5 MB/s) - ‘data/awm.edf’ saved [17685109/17685109]\n",
      "\n",
      "\n",
      "EDF2ASC: EyeLink EDF file -> ASCII (text) file translator\n",
      "EDF2ASC version 4.2.1.0 Linux   standalone Jun 18 2021 \n",
      "(c)1995-2021 by SR Research, last modified Jun 18 2021\n",
      "\n",
      "processing file data/awm.edf \n",
      "loadEvents = 1\n",
      "=========================Preamble of file data/awm.edf=========================\n",
      "| DATE: Wed Jan 24 05:57:43 2018                                              |\n",
      "| TYPE: EDF_FILE BINARY EVENT SAMPLE TAGGED                                   |\n",
      "| VERSION: EYELINK II 1                                                       |\n",
      "| SOURCE: EYELINK CL                                                          |\n",
      "| EYELINK II CL v4.56 Aug 18 2010                                             |\n",
      "| CAMERA: EyeLink CL Version 1.4 Sensor=BGE                                   |\n",
      "| SERIAL NUMBER: CL1-ABE24                                                    |\n",
      "| CAMERA_CONFIG: ABE24140.SCD                                                 |\n",
      "===============================================================================\n",
      "\n",
      "Converted successfully: 15156 events, 1114647 samples, 75 blocks.\n",
      "rm: cannot remove 'awm.edf': No such file or directory\n"
     ]
    }
   ],
   "source": [
    "# install the fixed repo (fork)\n",
    "%pip install --upgrade setuptools\n",
    "%pip install git+https://github.com/maciejskorski/python-eyelinkparser.git \n",
    "# download and convert the data\n",
    "import pkg_resources\n",
    "edf2asc_path = pkg_resources.resource_filename('python_eyelinkparser', \"utils/edf2ascii\")\n",
    "!mkdir data\n",
    "!wget -O data/awm.edf https://github.com/smathot/pupillometry_review/blob/master/data/auditory%20working%20memory/data/awm.edf?raw=true\n",
    "!{edf2asc_path} data/awm.edf -Y\n",
    "!rm data/awm.edf "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Parse into Tabular Format\n",
    "\n",
    "The converted data comes in the `asc` format which is essentially semi-structured text. \n",
    "\n",
    "To parse it, we use the package functions: interpolation for blinks is enabled, and the recording is downsampled by the factor of 10, from 1000 to 100Hz."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ".."
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ubuntu/projects/eye-processing/.venv/lib/python3.10/site-packages/python_eyelinkparser/eyelinkparser/_events.py:166: UserWarning: Unexpected exception during parsing of list index out of range\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "........."
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ubuntu/projects/eye-processing/.venv/lib/python3.10/site-packages/datamatrix/series.py:1476: RuntimeWarning: Mean of empty slice\n",
      "  return fnc(a.reshape(-1, by), axis=1)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "................................................................\n",
      "EDF2ASC: EyeLink EDF file -> ASCII (text) file translator\n",
      "EDF2ASC version 4.2.1.0 Linux   standalone Jun 18 2021 \n",
      "(c)1995-2021 by SR Research, last modified Jun 18 2021\n",
      "\n",
      "processing file data/awm.edf \n",
      "loadEvents = 1\n",
      "=========================Preamble of file data/awm.edf=========================\n",
      "| DATE: Wed Jan 24 05:57:43 2018                                              |\n",
      "| TYPE: EDF_FILE BINARY EVENT SAMPLE TAGGED                                   |\n",
      "| VERSION: EYELINK II 1                                                       |\n",
      "| SOURCE: EYELINK CL                                                          |\n",
      "| EYELINK II CL v4.56 Aug 18 2010                                             |\n",
      "| CAMERA: EyeLink CL Version 1.4 Sensor=BGE                                   |\n",
      "| SERIAL NUMBER: CL1-ABE24                                                    |\n",
      "| CAMERA_CONFIG: ABE24140.SCD                                                 |\n",
      "===============================================================================\n",
      "\n",
      "Converted successfully: 15156 events, 1114647 samples, 75 blocks.\n",
      "/tmp/tmpawm.edf.asc\n",
      ".."
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ubuntu/projects/eye-processing/.venv/lib/python3.10/site-packages/python_eyelinkparser/eyelinkparser/_events.py:166: UserWarning: Unexpected exception during parsing of list index out of range\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "........."
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ubuntu/projects/eye-processing/.venv/lib/python3.10/site-packages/datamatrix/series.py:1476: RuntimeWarning: Mean of empty slice\n",
      "  return fnc(a.reshape(-1, by), axis=1)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "................................................................+----+---------+---------------------------+---------------------------+-----------------------------------+-----------------------------------+----------+\n",
      "| #  | correct |     fixxlist_retention    |     fixylist_retention    |          ptrace_retention         |           ptrace_sounds           | set_size |\n",
      "+----+---------+---------------------------+---------------------------+-----------------------------------+-----------------------------------+----------+\n",
      "| 0  |    1    | [525.9 527.1 ... nan nan] | [382.9 380.4 ... nan nan] | [1810.  1810.3 ... 1902.9 1908.2] |    [1655.6 1657.1 ... nan nan]    |    5     |\n",
      "| 1  |    1    | [528.9 526.8 ... nan nan] | [386.4 387.5 ... nan nan] | [1644.6 1648.7 ... 1675.4 1673.4] |    [1717.3 1718.5 ... nan nan]    |    3     |\n",
      "| 2  |    1    | [521.4 524.2 ... nan nan] | [377.3 371.4 ... nan nan] | [1787.  1791.4 ... 1719.9 1719.3] | [1471.8 1470.4 ... 1783.2 1785.3] |    7     |\n",
      "| 3  |    1    | [523.2 520.7 ... nan nan] | [382.6 390.7 ... nan nan] | [1773.5 1774.8 ... 1755.7 1752.5] |    [1857.7 1859.1 ... nan nan]    |    3     |\n",
      "| 4  |    1    | [522.1 530.1 ... nan nan] | [373.5 378.1 ... nan nan] | [1939.2 1939.6 ... 1801.3 1802.7] | [1847.2 1849.6 ... 1940.  1938.7] |    7     |\n",
      "| 5  |    1    | [521.  515.6 ... nan nan] | [379.1 386.  ... nan nan] | [1867.4 1867.9 ... 1765.6 1768.4] | [1615.9 1619.3 ... 1872.6 1870.6] |    7     |\n",
      "| 6  |    1    | [520.8 524.2 ... nan nan] | [387.5 380.  ... nan nan] | [1675.2 1674.1 ... 1585.  1581.4] |    [1826.5 1821.8 ... nan nan]    |    5     |\n",
      "| 7  |    1    | [527.5 531.6 ... nan nan] | [396.  400.9 ... nan nan] | [1766.5 1766.7 ... 1685.7 1682.2] |    [1728.6 1726.7 ... nan nan]    |    3     |\n",
      "| 8  |    1    | [518.4 523.  ... nan nan] | [391.8 391.1 ... nan nan] | [1733.3 1736.7 ... 1733.2 1730.2] | [1636.  1637.6 ... 1730.3 1729.3] |    7     |\n",
      "| 9  |    0    | [528.2 523.1 ... nan nan] | [382.7 386.4 ... nan nan] | [1692.  1692.8 ... 1655.5    nan] | [1533.  1534.5 ... 1688.  1693.6] |    7     |\n",
      "| 10 |    1    | [528.4 534.3 ... nan nan] | [388.3 384.  ... nan nan] | [1602.2 1601.3 ... 1574.1 1575.9] | [1789.2 1793.1 ... 1599.6 1602.1] |    7     |\n",
      "| 11 |    0    | [525.  523.6 ... nan nan] | [386.7 390.2 ... nan nan] | [1742.5 1740.1 ... 1567.1 1559.5] | [1696.8 1700.6 ... 1747.5 1746.8] |    7     |\n",
      "| 12 |    1    | [503.1 505.1 ... nan nan] | [376.2 377.4 ... nan nan] | [1668.9 1668.8 ... 1556.1    nan] |    [1667.6 1667.6 ... nan nan]    |    5     |\n",
      "| 13 |    1    | [517.2 508.3 ... nan nan] | [387.1 384.7 ... nan nan] | [1609.5 1602.3 ... 1590.9 1584.3] | [1479.8 1477.9 ... 1614.1 1609.2] |    7     |\n",
      "| 14 |    1    | [507.6 505.3 ... nan nan] | [388.4 386.5 ... nan nan] | [1475.4 1473.7 ... 1484.6 1490.7] |    [1481.9 1477.6 ... nan nan]    |    3     |\n",
      "| 15 |    1    | [508.6 507.2 ... nan nan] |  [387. 389. ... nan nan]  | [1494.8 1494.2 ... 1522.9 1522.1] | [1522.4 1518.7 ... 1496.1 1495.4] |    7     |\n",
      "| 16 |    1    | [498.6 510.1 ... nan nan] | [386.1 399.2 ... nan nan] | [1550.4 1550.3 ... 1390.8 1385.9] |    [1651.6 1649.1 ... nan nan]    |    3     |\n",
      "| 17 |    1    | [510.2 508.1 ... nan nan] | [387.  382.6 ... nan nan] | [1557.6 1559.  ... 1532.2 1532. ] |    [1537.8 1534.  ... nan nan]    |    3     |\n",
      "| 18 |    1    | [521.7 519.8 ... nan nan] | [382.8 385.4 ... nan nan] | [1523.1 1521.9 ... 1409.5 1409.6] |    [1471.8 1472.6 ... nan nan]    |    5     |\n",
      "| 19 |    1    | [517.4 518.2 ... nan nan] | [386.5 384.5 ... nan nan] | [1500.1 1502.  ... 1500.5 1498.4] |    [1530.4 1531.  ... nan nan]    |    5     |\n",
      "+----+---------+---------------------------+---------------------------+-----------------------------------+-----------------------------------+----------+\n",
      "(+ 130 rows not shown)\n"
     ]
    }
   ],
   "source": [
    "from datamatrix import (\n",
    "  operations as ops,\n",
    "  functional as fnc,\n",
    "  series as srs\n",
    ")\n",
    "from python_eyelinkparser.eyelinkparser import parse, defaulttraceprocessor\n",
    "\n",
    "def get_data():\n",
    "\n",
    "    # The heavy lifting is done by eyelinkparser.parse()\n",
    "    dm = parse(\n",
    "        folder='data',           # Folder with .asc files \n",
    "        traceprocessor=defaulttraceprocessor(\n",
    "          blinkreconstruct=True, # Interpolate pupil size during blinks\n",
    "          downsample=10,         # Reduce sampling rate to 100 Hz,\n",
    "          mode='advanced'        # Use the new 'advanced' algorithm\n",
    "        )\n",
    "    )\n",
    "    # To save memory, we keep only a subset of relevant columns.\n",
    "    dm = dm[dm.set_size, dm.correct, dm.ptrace_sounds, dm.ptrace_retention, \n",
    "            dm.fixxlist_retention, dm.fixylist_retention]\n",
    "    return dm\n",
    "\n",
    "dm = get_data()\n",
    "print(dm)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualize\n",
    "\n",
    "The baseline correction is applied, with first 2 samples as the baseline. The time is clipped at 1200 which corresponds to 12s (1000Hz in the original recording was downsampled 10x)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "dm.pupil = srs.concatenate(\n",
    "    srs.endlock(dm.ptrace_sounds),\n",
    "    dm.ptrace_retention\n",
    ")\n",
    "dm.pupil = srs.baseline(\n",
    "    series=dm.pupil,\n",
    "    baseline=dm.ptrace_sounds,\n",
    "    bl_start=0,\n",
    "    bl_end=2\n",
    ")\n",
    "dm.pupil.depth = 1200"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ubuntu/projects/eye-processing/.venv/lib/python3.10/site-packages/numpy/lib/nanfunctions.py:1878: RuntimeWarning: Degrees of freedom <= 0 for slice.\n",
      "  var = nanvar(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "/home/ubuntu/projects/eye-processing/.venv/lib/python3.10/site-packages/datamatrix/_datamatrix/_seriescolumn.py:130: RuntimeWarning: Mean of empty slice\n",
      "  return nanmean(self._seq, axis=0)\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "def plot_series(x, s, color, label):\n",
    "\n",
    "    se = s.std / np.sqrt(len(s))\n",
    "    plt.fill_between(x, s.mean-se, s.mean+se, color=color, alpha=.25)\n",
    "    plt.plot(x, s.mean, color=color, label=label)\n",
    "\n",
    "\n",
    "x = np.linspace(-7, 5, 1200)\n",
    "dm3, dm5, dm7 = ops.split(dm.set_size, 3, 5, 7)\n",
    "\n",
    "plt.figure()\n",
    "plt.xlim(-7, 5)\n",
    "plt.ylim(-150, 150)\n",
    "plt.axvline(0, linestyle=':', color='black')\n",
    "plt.axhline(1, linestyle=':', color='black')\n",
    "plot_series(x, dm3.pupil, color='green', label='3 (N=%d)' % len(dm3))\n",
    "plot_series(x, dm5.pupil, color='blue', label='5 (N=%d)' % len(dm5))\n",
    "plot_series(x, dm7.pupil, color='red', label='7 (N=%d)' % len(dm7))\n",
    "plt.ylabel('Pupil size (norm)')\n",
    "plt.xlabel('Time relative to onset retention interval (s)')\n",
    "plt.legend(frameon=False, title='Memory load')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# References\n",
    "\n",
    "```{bibliography}\n",
    "```"
   ]
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "e541ff869b1997484d257fd07f51a4c7ec601914fddcc99716e9500e84535cdf"
  },
  "kernelspec": {
   "display_name": "eye-processing",
   "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": "3.10.4"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}