{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "e3ef70a2",
   "metadata": {},
   "source": [
    "# Importing data from Eyelink ASC files\n",
    "\n",
    "This example shows how to read `.asc` files generated from Eyelink EDF files using `edf2asc`. Usually, it is easier to read the EDF file directly, see the [notebook on reading EDF files](importdata_edf.ipynb). However, the steps below show how to get data from any text-based format into `pypillometry`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "0d0f6616",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-11-10T13:36:18.351544Z",
     "iopub.status.busy": "2026-11-10T13:36:18.351227Z",
     "iopub.status.idle": "2026-11-10T13:36:19.195898Z",
     "shell.execute_reply": "2026-11-10T13:36:19.194823Z"
    }
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import pypillometry as pp\n",
    "from pypillometry import u\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6f527c61",
   "metadata": {},
   "source": [
    "In this example, we use data recorded with an Eyelink eyetracker. These eyetrackers store the data in binary files with the extension `.edf`. Some information about this file format is [here](http://download.sr-support.com/dispdoc/page25.html). We use a command-line utility released by Eyelink to convert this proprietary format into a more easily read `.asc` file that is a whitespace-separated plain-text format. The converter, `edf2asc`, can be downloaded for different platforms from the [Eyelink support forum](https://www.sr-research.com/support/). There is a GUI-based program for Windows and command-line programs for Linux and Mac. Binaries of the command-line tools for Linux and Mac are included in the `pypillometry` repository under [this link](https://github.com/ihrke/pypillometry/tree/master/external).\n",
    "\n",
    "On Linux, we call the program twice on an example EDF file, once for the samples and once for the events:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "5cd3d1f7",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-11-10T13:36:19.199258Z",
     "iopub.status.busy": "2026-11-10T13:36:19.198673Z",
     "iopub.status.idle": "2026-11-10T13:36:27.154216Z",
     "shell.execute_reply": "2026-11-10T13:36:27.152679Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r\n",
      "EDF2ASC: EyeLink EDF file -> ASCII (text) file translator\r\n",
      "EDF2ASC version 3.0 Linux Dec  1 2008 \r\n",
      "(c)1995-2007 by SR Research, last modified Dec  1 2008\r\n",
      "\r\n",
      "processing file ../../data/test.edf \r\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=====================Preamble of file ../../data/test.edf======================\r\n",
      "| DATE: Fri Feb 14 08:48:33 2020                                              |\r\n",
      "| TYPE: EDF_FILE BINARY EVENT SAMPLE TAGGED                                   |\r\n",
      "| VERSION: EYELINK II 1                                                       |\r\n",
      "| SOURCE: EYELINK CL                                                          |\r\n",
      "| EYELINK II CL v6.12 Feb  1 2018 (EyeLink Portable Duo)                      |\r\n",
      "| CAMERA: EyeLink USBCAM Version 1.01                                         |\r\n",
      "| SERIAL NUMBER: CLU-DAC49                                                    |\r\n",
      "| CAMERA_CONFIG: DAC49200.SCD                                                 |\r\n",
      "| Psychopy GC demo                                                            |\r\n",
      "===============================================================================\r\n",
      "\r\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processed [          ]0%\r",
      "Processed [          ]1%\r",
      "Processed [          ]2%\r",
      "Processed [          ]3%\r",
      "Processed [          ]4%\r",
      "Processed [          ]5%\r",
      "Processed [          ]6%\r",
      "Processed [          ]7%\r",
      "Processed [          ]8%\r",
      "Processed [          ]9%\r",
      "Processed [*         ]10%\r",
      "Processed [*         ]11%\r",
      "Processed [*         ]12%\r",
      "Processed [*         ]13%\r",
      "Processed [*         ]14%\r",
      "Processed [*         ]15%\r",
      "Processed [*         ]16%\r",
      "Processed [*         ]17%\r",
      "Processed [*         ]18%\r",
      "Processed [*         ]19%\r",
      "Processed [**        ]20%\r",
      "Processed [**        ]21%\r",
      "Processed [**        ]22%\r",
      "Processed [**        ]23%\r",
      "Processed [**        ]24%\r",
      "Processed [**        ]25%\r",
      "Processed [**        ]26%\r",
      "Processed [**        ]27%\r",
      "Processed [**        ]28%\r",
      "Processed [**        ]29%\r",
      "Processed [***       ]30%\r",
      "Processed [***       ]31%\r",
      "Processed [***       ]32%\r",
      "Processed [***       ]33%\r",
      "Processed [***       ]34%\r",
      "Processed [***       ]35%\r",
      "Processed [***       ]36%\r",
      "Processed [***       ]37%\r",
      "Processed [***       ]38%\r",
      "Processed [***      "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " ]39%\r",
      "Processed [****      ]40%\r",
      "Processed [****      ]41%\r",
      "Processed [****      ]42%\r",
      "Processed [****      ]43%\r",
      "Processed [****      ]44%\r",
      "Processed [****      ]45%\r",
      "Processed [****      ]46%\r",
      "Processed [****      ]47%\r",
      "Processed [****      ]48%\r",
      "Processed [****      ]49%\r",
      "Processed [*****     ]50%\r",
      "Processed [*****     ]51%\r",
      "Processed [*****     ]52%\r",
      "Processed [*****     ]53%\r",
      "Processed [*****     ]54%\r",
      "Processed [*****     ]55%\r",
      "Processed [*****     ]56%\r",
      "Processed [*****     ]57%\r",
      "Processed [*****     ]58%\r",
      "Processed [*****     ]59%\r",
      "Processed [******    ]60%\r",
      "Processed [******    ]61%\r",
      "Processed [******    ]62%\r",
      "Processed [******    ]63%\r",
      "Processed [******    ]64%\r",
      "Processed [******    ]65%\r",
      "Processed [******    ]66%\r",
      "Processed [******    ]67%\r",
      "Processed [******    ]68%\r",
      "Processed [******    ]69%\r",
      "Processed [*******   ]70%\r",
      "Processed [*******   ]71%\r",
      "Processed [*******   ]72%\r",
      "Processed [*******   ]73%\r",
      "Processed [*******   ]74%\r",
      "Processed [*******   ]75%\r",
      "Processed [*******   ]76%\r",
      "Processed [*******   ]77%\r",
      "Processed [*******   ]78%\r",
      "Proc"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "essed [*******   ]79%\r",
      "Processed [********  ]80%\r",
      "Processed [********  ]81%\r",
      "Processed [********  ]82%\r",
      "Processed [********  ]83%\r",
      "Processed [********  ]84%\r",
      "Processed [********  ]85%\r",
      "Processed [********  ]86%\r",
      "Processed [********  ]87%\r",
      "Processed [********  ]88%\r",
      "Processed [********  ]89%\r",
      "Processed [********* ]90%\r",
      "Processed [********* ]91%\r",
      "Processed [********* ]92%\r",
      "Processed [********* ]93%\r",
      "Processed [********* ]94%\r",
      "Processed [********* ]95%\r",
      "Processed [********* ]96%\r",
      "Processed [********* ]97%\r",
      "Processed [********* ]98%\r",
      "Processed [********* ]99%\r",
      "Processed [**********]100%\r",
      "Converted successfully: 0 events, 1245363 samples, 6 blocks.\r\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r\n",
      "EDF2ASC: EyeLink EDF file -> ASCII (text) file translator\r\n",
      "EDF2ASC version 3.0 Linux Dec  1 2008 \r\n",
      "(c)1995-2007 by SR Research, last modified Dec  1 2008\r\n",
      "\r\n",
      "processing file ../../data/test.edf \r\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=====================Preamble of file ../../data/test.edf======================\r\n",
      "| DATE: Fri Feb 14 08:48:33 2020                                              |\r\n",
      "| TYPE: EDF_FILE BINARY EVENT SAMPLE TAGGED                                   |\r\n",
      "| VERSION: EYELINK II 1                                                       |\r\n",
      "| SOURCE: EYELINK CL                                                          |\r\n",
      "| EYELINK II CL v6.12 Feb  1 2018 (EyeLink Portable Duo)                      |\r\n",
      "| CAMERA: EyeLink USBCAM Version 1.01                                         |\r\n",
      "| SERIAL NUMBER: CLU-DAC49                                                    |\r\n",
      "| CAMERA_CONFIG: DAC49200.SCD                                                 |\r\n",
      "| Psychopy GC demo                                                            |\r\n",
      "===============================================================================\r\n",
      "\r\n",
      "Processed [          ]0%\r",
      "Processed [          ]1%\r",
      "Processed [          ]2%\r",
      "Processed [          ]3%\r",
      "Processed [          ]4%\r",
      "Processed [          ]5%\r",
      "Processed [          ]6%\r",
      "Processed [          ]7%\r",
      "Processed [          ]8%\r",
      "Processed [          ]9%\r",
      "Processed [*         ]10%\r",
      "Processed [*         ]11%\r",
      "Processed [*         ]12%\r",
      "Processed [*         ]13%\r",
      "Processed [*         ]14%\r",
      "Processed [*         ]15%\r",
      "Processed [*         ]16%\r",
      "Processed [*         ]17%\r",
      "Processed [*         ]18%\r",
      "Processed [*         ]19%\r",
      "Processed [**        ]20%\r",
      "Processed [**        ]21%\r",
      "Processed [**        ]22%\r",
      "Processed [**        ]23%\r",
      "Processed [**        ]24%\r",
      "Processed [**        ]25%\r",
      "Processed [**        ]26%\r",
      "Processed [**        ]27%\r",
      "Processed [**        ]28%\r",
      "Processed [**        ]29%\r",
      "Processed [***       ]30%\r",
      "Processed [***       ]31%\r",
      "Processed [***       ]32%\r",
      "Processed [***       ]33%\r",
      "Processed [***       ]34%\r",
      "Processed [***       ]35%\r",
      "Processed [***       ]36%\r",
      "Processed [***       ]37%\r",
      "Processed [***       ]38%\r",
      "Processed [***      "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " ]39%\r",
      "Processed [****      ]40%\r",
      "Processed [****      ]41%\r",
      "Processed [****      ]42%\r",
      "Processed [****      ]43%\r",
      "Processed [****      ]44%\r",
      "Processed [****      ]45%\r",
      "Processed [****      ]46%\r",
      "Processed [****      ]47%\r",
      "Processed [****      ]48%\r",
      "Processed [****      ]49%\r",
      "Processed [*****     ]50%\r",
      "Processed [*****     ]51%\r",
      "Processed [*****     ]52%\r",
      "Processed [*****     ]53%\r",
      "Processed [*****     ]54%\r",
      "Processed [*****     ]55%\r",
      "Processed [*****     ]56%\r",
      "Processed [*****     ]57%\r",
      "Processed [*****     ]58%\r",
      "Processed [*****     ]59%\r",
      "Processed [******    ]60%\r",
      "Processed [******    ]61%\r",
      "Processed [******    ]62%\r",
      "Processed [******    ]63%\r",
      "Processed [******    ]64%\r",
      "Processed [******    ]65%\r",
      "Processed [******    ]66%\r",
      "Processed [******    ]67%\r",
      "Processed [******    ]68%\r",
      "Processed [******    ]69%\r",
      "Processed [*******   ]70%\r",
      "Processed [*******   ]71%\r",
      "Processed [*******   ]72%\r",
      "Processed [*******   ]73%\r",
      "Processed [*******   ]74%\r",
      "Processed [*******   ]75%\r",
      "Processed [*******   ]76%\r",
      "Processed [*******   ]77%\r",
      "Processed [*******   ]78%\r",
      "Processed [*******   ]79%\r",
      "Processed [********  ]80%\r",
      "Processed [********  ]81%\r",
      "Processed [********  ]82%\r",
      "Processed [********  ]83%\r",
      "Processed [********  ]84%\r",
      "Processed [********  ]85%\r",
      "Processed [********  ]86%\r",
      "Processed [********  ]87%\r",
      "Processed [********  ]88%\r",
      "Processed [********  ]89%\r",
      "Processed [********* ]90%\r",
      "Processed [********* ]91%\r",
      "Processed [********* ]92%\r",
      "Processed [********* ]93%\r",
      "Processed [********* ]94%\r",
      "Processed [********* ]95%\r",
      "Processed [********* ]96%\r",
      "Processed [********* ]97%\r",
      "Processed [********* ]98%\r",
      "Processed [********* ]99%\r",
      "Processed [**********]100%\r",
      "Converted successfully: 37139 events, 0 samples, 6 blocks.\r\n"
     ]
    }
   ],
   "source": [
    "!../../external/edf2asc-linux -y -s ../../data/test.edf ../../data/test_samples.asc\n",
    "!../../external/edf2asc-linux -y -e ../../data/test.edf ../../data/test_events.asc"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4e6c07ae",
   "metadata": {},
   "source": [
    "This results in two files, one containing all the samples and one all the recorded events."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "16bf06b4",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-11-10T13:36:27.157699Z",
     "iopub.status.busy": "2026-11-10T13:36:27.157387Z",
     "iopub.status.idle": "2026-11-10T13:36:27.161418Z",
     "shell.execute_reply": "2026-11-10T13:36:27.160494Z"
    }
   },
   "outputs": [],
   "source": [
    "fname_samples = \"../../data/test_samples.asc\"\n",
    "fname_events = \"../../data/test_events.asc\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e198da6d",
   "metadata": {},
   "source": [
    "The samples file contains a large table with the timestamp, x/y-coordinates of the eye position and pupil area for the left and the right eye (plus a column with status flags). Here are the first few rows of this file:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "a310bc4b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-11-10T13:36:27.163723Z",
     "iopub.status.busy": "2026-11-10T13:36:27.163455Z",
     "iopub.status.idle": "2026-11-10T13:36:27.434619Z",
     "shell.execute_reply": "2026-11-10T13:36:27.433306Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3385900\t  817.3\t  345.2\t 1707.0\t  860.6\t  375.2\t 1738.0\t.....\r\n",
      "3385902\t  817.0\t  343.5\t 1706.0\t  860.7\t  375.9\t 1739.0\t.....\r\n",
      "3385904\t  816.7\t  341.6\t 1705.0\t  861.2\t  376.6\t 1739.0\t.....\r\n",
      "3385906\t  816.7\t  340.4\t 1706.0\t  861.7\t  376.8\t 1740.0\t.....\r\n",
      "3385908\t  816.7\t  340.2\t 1707.0\t  861.6\t  376.9\t 1742.0\t.....\r\n",
      "3385910\t  816.8\t  340.2\t 1708.0\t  861.1\t  377.1\t 1743.0\t.....\r\n",
      "3385912\t  816.9\t  340.9\t 1708.0\t  860.7\t  377.5\t 1744.0\t.....\r\n",
      "3385914\t  816.1\t  342.1\t 1710.0\t  861.1\t  378.7\t 1745.0\t.....\r\n",
      "3385916\t  815.2\t  343.2\t 1712.0\t  862.5\t  380.0\t 1746.0\t.....\r\n",
      "3385918\t  814.4\t  343.6\t 1713.0\t  863.9\t  380.7\t 1747.0\t.....\r\n"
     ]
    }
   ],
   "source": [
    "!head $fname_samples"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4dae7735",
   "metadata": {},
   "source": [
    "We can read this file using `pandas.read_table()`. Eyelink marks missing x/y-coordinates with `\".\"` and missing pupil data with `0`. We convert both to `NaN`, which `pypillometry` treats as missing data. This is an explicit step: `pypillometry` does not interpret `0` as missing by itself."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "2bd62ed8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-11-10T13:36:27.437692Z",
     "iopub.status.busy": "2026-11-10T13:36:27.437435Z",
     "iopub.status.idle": "2026-11-10T13:36:28.086693Z",
     "shell.execute_reply": "2026-11-10T13:36:28.085736Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>time</th>\n",
       "      <th>left_x</th>\n",
       "      <th>left_y</th>\n",
       "      <th>left_p</th>\n",
       "      <th>right_x</th>\n",
       "      <th>right_y</th>\n",
       "      <th>right_p</th>\n",
       "      <th>flags</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3385900</td>\n",
       "      <td>817.3</td>\n",
       "      <td>345.2</td>\n",
       "      <td>1707.0</td>\n",
       "      <td>860.6</td>\n",
       "      <td>375.2</td>\n",
       "      <td>1738.0</td>\n",
       "      <td>.....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3385902</td>\n",
       "      <td>817.0</td>\n",
       "      <td>343.5</td>\n",
       "      <td>1706.0</td>\n",
       "      <td>860.7</td>\n",
       "      <td>375.9</td>\n",
       "      <td>1739.0</td>\n",
       "      <td>.....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3385904</td>\n",
       "      <td>816.7</td>\n",
       "      <td>341.6</td>\n",
       "      <td>1705.0</td>\n",
       "      <td>861.2</td>\n",
       "      <td>376.6</td>\n",
       "      <td>1739.0</td>\n",
       "      <td>.....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3385906</td>\n",
       "      <td>816.7</td>\n",
       "      <td>340.4</td>\n",
       "      <td>1706.0</td>\n",
       "      <td>861.7</td>\n",
       "      <td>376.8</td>\n",
       "      <td>1740.0</td>\n",
       "      <td>.....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3385908</td>\n",
       "      <td>816.7</td>\n",
       "      <td>340.2</td>\n",
       "      <td>1707.0</td>\n",
       "      <td>861.6</td>\n",
       "      <td>376.9</td>\n",
       "      <td>1742.0</td>\n",
       "      <td>.....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1245358</th>\n",
       "      <td>5923060</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>.C.C.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1245359</th>\n",
       "      <td>5923062</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>.C.C.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1245360</th>\n",
       "      <td>5923064</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>.C.C.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1245361</th>\n",
       "      <td>5923066</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>.C.C.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1245362</th>\n",
       "      <td>5923068</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>.C.C.</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1245363 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            time  left_x  left_y  left_p  right_x  right_y  right_p  flags\n",
       "0        3385900   817.3   345.2  1707.0    860.6    375.2   1738.0  .....\n",
       "1        3385902   817.0   343.5  1706.0    860.7    375.9   1739.0  .....\n",
       "2        3385904   816.7   341.6  1705.0    861.2    376.6   1739.0  .....\n",
       "3        3385906   816.7   340.4  1706.0    861.7    376.8   1740.0  .....\n",
       "4        3385908   816.7   340.2  1707.0    861.6    376.9   1742.0  .....\n",
       "...          ...     ...     ...     ...      ...      ...      ...    ...\n",
       "1245358  5923060     NaN     NaN     NaN      NaN      NaN      NaN  .C.C.\n",
       "1245359  5923062     NaN     NaN     NaN      NaN      NaN      NaN  .C.C.\n",
       "1245360  5923064     NaN     NaN     NaN      NaN      NaN      NaN  .C.C.\n",
       "1245361  5923066     NaN     NaN     NaN      NaN      NaN      NaN  .C.C.\n",
       "1245362  5923068     NaN     NaN     NaN      NaN      NaN      NaN  .C.C.\n",
       "\n",
       "[1245363 rows x 8 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_table(fname_samples,\n",
    "                   names=[\"time\", \"left_x\", \"left_y\", \"left_p\",\n",
    "                          \"right_x\", \"right_y\", \"right_p\", \"flags\"],\n",
    "                   na_values=\".\", skipinitialspace=True)\n",
    "\n",
    "# Eyelink codes missing pupil data (e.g., blinks) as 0;\n",
    "# set it to NaN so that pypillometry treats it as missing\n",
    "df[\"left_p\"] = df.left_p.replace(0, np.nan)\n",
    "df[\"right_p\"] = df.right_p.replace(0, np.nan)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ee6ac3ac",
   "metadata": {},
   "source": [
    "We can already use this information to create an `EyeData` object with pupil data only. We pass in the pupil area of both eyes (columns `left_p` and `right_p`) and the timestamps from the samples file. Gaps in the recording (where the eyetracker was paused) are filled with missing values:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "b0c24f75",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-11-10T13:36:28.089054Z",
     "iopub.status.busy": "2026-11-10T13:36:28.088888Z",
     "iopub.status.idle": "2026-11-10T13:36:28.278133Z",
     "shell.execute_reply": "2026-11-10T13:36:28.277408Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32mpp: 14:36:28\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfill_time_discontinuities\u001b[0m:\u001b[36m2139\u001b[0m | \u001b[1mFilling in 5 gaps\u001b[0m\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32mpp: 14:36:28\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfill_time_discontinuities\u001b[0m:\u001b[36m2141\u001b[0m | \u001b[1m[32.35   4.012  6.21   2.02   1.862] seconds\u001b[0m\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div style=\"font-family:system-ui, sans-serif; border:1px solid #ddd; border-radius:6px; padding:12px; max-width: min(600px, 50vw); min-width: 300px;\">\n",
       "  <h3 style=\"margin-top:0;\">EyeData — <span style=\"color:#555;\">test</span></h3>\n",
       "\n",
       "    <div style=\"display:grid; grid-template-columns:repeat(auto-fit,minmax(220px,1fr)); gap:8px; margin-bottom:10px;\">\n",
       "    <div style=\"background:#f6f6f6; border-radius:4px; padding:6px 8px; border:1px solid #e0e0e0;\">\n",
       "      <div style=\"font-size:0.85em; color:#666; text-transform:uppercase;\">Samples</div>\n",
       "      <div style=\"font-weight:600;\">1,268,585</div>\n",
       "    </div>\n",
       "    <div style=\"background:#f6f6f6; border-radius:4px; padding:6px 8px; border:1px solid #e0e0e0;\">\n",
       "      <div style=\"font-size:0.85em; color:#666; text-transform:uppercase;\">Sampling rate</div>\n",
       "      <div style=\"font-weight:600;\">500.0 Hz</div>\n",
       "    </div>\n",
       "    <div style=\"background:#f6f6f6; border-radius:4px; padding:6px 8px; border:1px solid #e0e0e0;\">\n",
       "      <div style=\"font-size:0.85em; color:#666; text-transform:uppercase;\">Duration</div>\n",
       "      <div style=\"font-weight:600;\">42.29 min (start 56.43, end 98.72)</div>\n",
       "    </div>\n",
       "    <div style=\"background:#f6f6f6; border-radius:4px; padding:6px 8px; border:1px solid #e0e0e0;\">\n",
       "      <div style=\"font-size:0.85em; color:#666; text-transform:uppercase;\">Eyes</div>\n",
       "      <div style=\"font-weight:600;\">left, right</div>\n",
       "    </div>\n",
       "    <div style=\"background:#f6f6f6; border-radius:4px; padding:6px 8px; border:1px solid #e0e0e0;\">\n",
       "      <div style=\"font-size:0.85em; color:#666; text-transform:uppercase;\">Variables</div>\n",
       "      <div style=\"font-weight:600;\">left_pupil, right_pupil</div>\n",
       "    </div>\n",
       "    <div style=\"background:#f6f6f6; border-radius:4px; padding:6px 8px; border:1px solid #e0e0e0;\">\n",
       "      <div style=\"font-size:0.85em; color:#666; text-transform:uppercase;\">Events</div>\n",
       "      <div style=\"font-weight:600;\">0</div>\n",
       "    </div>\n",
       "    <div style=\"background:#f6f6f6; border-radius:4px; padding:6px 8px; border:1px solid #e0e0e0;\">\n",
       "      <div style=\"font-size:0.85em; color:#666; text-transform:uppercase;\">Size</div>\n",
       "      <div style=\"font-weight:600;\">36.3MiB</div>\n",
       "    </div>\n",
       "  </div>\n",
       "\n",
       "\n",
       "    <details open style=\"margin-bottom:8px;\">\n",
       "    <summary style=\"cursor:pointer; font-weight:600;\">Signal overview</summary>\n",
       "    <div style=\"display:grid; grid-template-columns:repeat(auto-fit,minmax(220px,1fr)); gap:8px; margin-top:8px;\">\n",
       "      <div style=\"background:#f6f6f6; border-radius:4px; padding:6px 8px; border:1px solid #e0e0e0; text-align:center;\">\n",
       "        <div style=\"font-size:0.85em; color:#666; margin-bottom:4px;\">left_pupil</div>\n",
       "        <img src=\"data:image/png;base64,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\" alt=\"left_pupil sparkline\" style=\"height:60px; width:100%; object-fit:cover;\">\n",
       "      </div>\n",
       "      <div style=\"background:#f6f6f6; border-radius:4px; padding:6px 8px; border:1px solid #e0e0e0; text-align:center;\">\n",
       "        <div style=\"font-size:0.85em; color:#666; margin-bottom:4px;\">right_pupil</div>\n",
       "        <img src=\"data:image/png;base64,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\" alt=\"right_pupil sparkline\" style=\"height:60px; width:100%; object-fit:cover;\">\n",
       "      </div>\n",
       "    </div>\n",
       "  </details>\n",
       "\n",
       "\n",
       "\n",
       "  <details style=\"margin-top:8px;\">\n",
       "    <summary style=\"cursor:pointer; font-weight:600;\">Data glimpse</summary>\n",
       "    <pre style=\"margin-top:8px; background:#f9f9f9; padding:8px; border-radius:4px;\">EyeDataDict(vars=2,n=1268585,shape=(1268585,)): \n",
       "  left_pupil (float64): 1707.0, 1706.0, 1705.0, 1706.0, 1707.0...\n",
       "  right_pupil (float64): 1738.0, 1739.0, 1739.0, 1740.0, 1742.0...\n",
       "</pre>\n",
       "  </details>\n",
       "\n",
       "</div>\n"
      ],
      "text/plain": [
       "EyeData(test, 36.3MiB):\n",
       " n                 : 1268585\n",
       " sampling_rate     : 500.0\n",
       " eyes              : ['left', 'right']\n",
       " data              : ['left_pupil', 'right_pupil']\n",
       " nevents           : 0\n",
       " experimental_setup: not set\n",
       " calibration       : not available\n",
       " duration_minutes  : 42.2862\n",
       " start_min         : 56.4317\n",
       " end_min           : 98.7178\n",
       " params            : {}\n",
       " nblinks           : {}\n",
       " blinks            : {}\n",
       " glimpse           : EyeDataDict(vars=2,n=1268585,shape=(1268585,)): \n",
       "  left_pupil (float64): 1707.0, 1706.0, 1705.0, 1706.0, 1707.0...\n",
       "  right_pupil (float64): 1738.0, 1739.0, 1739.0, 1740.0, 1742.0...\n",
       "\n",
       " History:\n",
       " *"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pp.EyeData(left_pupil=df.left_p, right_pupil=df.right_p, time=df.time, name=\"test\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "709755c2",
   "metadata": {},
   "source": [
    "We can also import the eye-tracking (gaze) data from the same file. In that case, we use the `EyeData` class:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "74a8c196",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-11-10T13:36:28.280749Z",
     "iopub.status.busy": "2026-11-10T13:36:28.280457Z",
     "iopub.status.idle": "2026-11-10T13:36:28.779004Z",
     "shell.execute_reply": "2026-11-10T13:36:28.778277Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32mpp: 14:36:28\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfill_time_discontinuities\u001b[0m:\u001b[36m2139\u001b[0m | \u001b[1mFilling in 5 gaps\u001b[0m\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32mpp: 14:36:28\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfill_time_discontinuities\u001b[0m:\u001b[36m2141\u001b[0m | \u001b[1m[32.35   4.012  6.21   2.02   1.862] seconds\u001b[0m\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div style=\"font-family:system-ui, sans-serif; border:1px solid #ddd; border-radius:6px; padding:12px; max-width: min(600px, 50vw); min-width: 300px;\">\n",
       "  <h3 style=\"margin-top:0;\">EyeData — <span style=\"color:#555;\">test</span></h3>\n",
       "\n",
       "    <div style=\"display:grid; grid-template-columns:repeat(auto-fit,minmax(220px,1fr)); gap:8px; margin-bottom:10px;\">\n",
       "    <div style=\"background:#f6f6f6; border-radius:4px; padding:6px 8px; border:1px solid #e0e0e0;\">\n",
       "      <div style=\"font-size:0.85em; color:#666; text-transform:uppercase;\">Samples</div>\n",
       "      <div style=\"font-weight:600;\">1,268,585</div>\n",
       "    </div>\n",
       "    <div style=\"background:#f6f6f6; border-radius:4px; padding:6px 8px; border:1px solid #e0e0e0;\">\n",
       "      <div style=\"font-size:0.85em; color:#666; text-transform:uppercase;\">Sampling rate</div>\n",
       "      <div style=\"font-weight:600;\">500.0 Hz</div>\n",
       "    </div>\n",
       "    <div style=\"background:#f6f6f6; border-radius:4px; padding:6px 8px; border:1px solid #e0e0e0;\">\n",
       "      <div style=\"font-size:0.85em; color:#666; text-transform:uppercase;\">Duration</div>\n",
       "      <div style=\"font-weight:600;\">42.29 min (start 56.43, end 98.72)</div>\n",
       "    </div>\n",
       "    <div style=\"background:#f6f6f6; border-radius:4px; padding:6px 8px; border:1px solid #e0e0e0;\">\n",
       "      <div style=\"font-size:0.85em; color:#666; text-transform:uppercase;\">Eyes</div>\n",
       "      <div style=\"font-weight:600;\">left, right</div>\n",
       "    </div>\n",
       "    <div style=\"background:#f6f6f6; border-radius:4px; padding:6px 8px; border:1px solid #e0e0e0;\">\n",
       "      <div style=\"font-size:0.85em; color:#666; text-transform:uppercase;\">Variables</div>\n",
       "      <div style=\"font-weight:600;\">left_x, left_y, left_pupil, right_x, right_y, right_pupil</div>\n",
       "    </div>\n",
       "    <div style=\"background:#f6f6f6; border-radius:4px; padding:6px 8px; border:1px solid #e0e0e0;\">\n",
       "      <div style=\"font-size:0.85em; color:#666; text-transform:uppercase;\">Events</div>\n",
       "      <div style=\"font-weight:600;\">0</div>\n",
       "    </div>\n",
       "    <div style=\"background:#f6f6f6; border-radius:4px; padding:6px 8px; border:1px solid #e0e0e0;\">\n",
       "      <div style=\"font-size:0.85em; color:#666; text-transform:uppercase;\">Size</div>\n",
       "      <div style=\"font-weight:600;\">84.7MiB</div>\n",
       "    </div>\n",
       "  </div>\n",
       "\n",
       "\n",
       "    <details open style=\"margin-bottom:8px;\">\n",
       "    <summary style=\"cursor:pointer; font-weight:600;\">Signal overview</summary>\n",
       "    <div style=\"display:grid; grid-template-columns:repeat(auto-fit,minmax(220px,1fr)); gap:8px; margin-top:8px;\">\n",
       "      <div style=\"background:#f6f6f6; border-radius:4px; padding:6px 8px; border:1px solid #e0e0e0; text-align:center;\">\n",
       "        <div style=\"font-size:0.85em; color:#666; margin-bottom:4px;\">left_pupil</div>\n",
       "        <img src=\"data:image/png;base64,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\" alt=\"left_pupil sparkline\" style=\"height:60px; width:100%; object-fit:cover;\">\n",
       "      </div>\n",
       "      <div style=\"background:#f6f6f6; border-radius:4px; padding:6px 8px; border:1px solid #e0e0e0; text-align:center;\">\n",
       "        <div style=\"font-size:0.85em; color:#666; margin-bottom:4px;\">left_x</div>\n",
       "        <img src=\"data:image/png;base64,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\" alt=\"left_x sparkline\" style=\"height:60px; width:100%; object-fit:cover;\">\n",
       "      </div>\n",
       "      <div style=\"background:#f6f6f6; border-radius:4px; padding:6px 8px; border:1px solid #e0e0e0; text-align:center;\">\n",
       "        <div style=\"font-size:0.85em; color:#666; margin-bottom:4px;\">left_y</div>\n",
       "        <img src=\"data:image/png;base64,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\" alt=\"left_y sparkline\" style=\"height:60px; width:100%; object-fit:cover;\">\n",
       "      </div>\n",
       "      <div style=\"background:#f6f6f6; border-radius:4px; padding:6px 8px; border:1px solid #e0e0e0; text-align:center;\">\n",
       "        <div style=\"font-size:0.85em; color:#666; margin-bottom:4px;\">right_pupil</div>\n",
       "        <img src=\"data:image/png;base64,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\" alt=\"right_pupil sparkline\" style=\"height:60px; width:100%; object-fit:cover;\">\n",
       "      </div>\n",
       "      <div style=\"background:#f6f6f6; border-radius:4px; padding:6px 8px; border:1px solid #e0e0e0; text-align:center;\">\n",
       "        <div style=\"font-size:0.85em; color:#666; margin-bottom:4px;\">right_x</div>\n",
       "        <img src=\"data:image/png;base64,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\" alt=\"right_x sparkline\" style=\"height:60px; width:100%; object-fit:cover;\">\n",
       "      </div>\n",
       "      <div style=\"background:#f6f6f6; border-radius:4px; padding:6px 8px; border:1px solid #e0e0e0; text-align:center;\">\n",
       "        <div style=\"font-size:0.85em; color:#666; margin-bottom:4px;\">right_y</div>\n",
       "        <img src=\"data:image/png;base64,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\" alt=\"right_y sparkline\" style=\"height:60px; width:100%; object-fit:cover;\">\n",
       "      </div>\n",
       "    </div>\n",
       "  </details>\n",
       "\n",
       "\n",
       "\n",
       "  <details style=\"margin-top:8px;\">\n",
       "    <summary style=\"cursor:pointer; font-weight:600;\">Data glimpse</summary>\n",
       "    <pre style=\"margin-top:8px; background:#f9f9f9; padding:8px; border-radius:4px;\">EyeDataDict(vars=6,n=1268585,shape=(1268585,)): \n",
       "  left_x (float64): 817.3, 817.0, 816.7, 816.7, 816.7...\n",
       "  left_y (float64): 345.2, 343.5, 341.6, 340.4, 340.2...\n",
       "  left_pupil (float64): 1707.0, 1706.0, 1705.0, 1706.0, 1707.0...\n",
       "  right_x (float64): 860.6, 860.7, 861.2, 861.7, 861.6...\n",
       "  right_y (float64): 375.2, 375.9, 376.6, 376.8, 376.9...\n",
       "  right_pupil (float64): 1738.0, 1739.0, 1739.0, 1740.0, 1742.0...\n",
       "</pre>\n",
       "  </details>\n",
       "\n",
       "</div>\n"
      ],
      "text/plain": [
       "EyeData(test, 84.7MiB):\n",
       " n                 : 1268585\n",
       " sampling_rate     : 500.0\n",
       " eyes              : ['left', 'right']\n",
       " data              : ['left_x', 'left_y', 'left_pupil', 'right_x', 'right_y', 'right_pupil']\n",
       " nevents           : 0\n",
       " experimental_setup: not set\n",
       " calibration       : not available\n",
       " duration_minutes  : 42.2862\n",
       " start_min         : 56.4317\n",
       " end_min           : 98.7178\n",
       " params            : {}\n",
       " nblinks           : {}\n",
       " blinks            : {}\n",
       " glimpse           : EyeDataDict(vars=6,n=1268585,shape=(1268585,)): \n",
       "  left_x (float64): 817.3, 817.0, 816.7, 816.7, 816.7...\n",
       "  left_y (float64): 345.2, 343.5, 341.6, 340.4, 340.2...\n",
       "  left_pupil (float64): 1707.0, 1706.0, 1705.0, 1706.0, 1707.0...\n",
       "  right_x (float64): 860.6, 860.7, 861.2, 861.7, 861.6...\n",
       "  right_y (float64): 375.2, 375.9, 376.6, 376.8, 376.9...\n",
       "  right_pupil (float64): 1738.0, 1739.0, 1739.0, 1740.0, 1742.0...\n",
       "\n",
       " History:\n",
       " *"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pp.EyeData(left_pupil=df.left_p, right_pupil=df.right_p,\n",
    "           left_x=df.left_x, left_y=df.left_y,\n",
    "           right_x=df.right_x, right_y=df.right_y,\n",
    "           time=df.time, name=\"test\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e4fc5b91",
   "metadata": {},
   "source": [
    "This dataset is still missing the information contained in the events file which we need for analysing trial-related pupil data. The events file has a more complicated structure than the samples file:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "410c3dda",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-11-10T13:36:28.781777Z",
     "iopub.status.busy": "2026-11-10T13:36:28.781484Z",
     "iopub.status.idle": "2026-11-10T13:36:29.077820Z",
     "shell.execute_reply": "2026-11-10T13:36:29.076993Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "** CONVERTED FROM ../../data/test.edf using edfapi 3.0 Linux Dec  1 2008 on Tue Nov 10 14:36:26 2026\r\n",
      "** DATE: Fri Feb 14 08:48:33 2020\r\n",
      "** TYPE: EDF_FILE BINARY EVENT SAMPLE TAGGED\r\n",
      "** VERSION: EYELINK II 1\r\n",
      "** SOURCE: EYELINK CL\r\n",
      "** EYELINK II CL v6.12 Feb  1 2018 (EyeLink Portable Duo)\r\n",
      "** CAMERA: EyeLink USBCAM Version 1.01\r\n",
      "** SERIAL NUMBER: CLU-DAC49\r\n",
      "** CAMERA_CONFIG: DAC49200.SCD\r\n",
      "** Psychopy GC demo\r\n",
      "**\r\n",
      "\r\n",
      "MSG\t2728855 DISPLAY_COORDS = 0 0 1919 1079\r\n",
      "INPUT\t2767568\t0\r\n",
      "MSG\t2784000 !CAL \r\n",
      ">>>>>>> CALIBRATION (HV9,P-CR) FOR LEFT: <<<<<<<<<\r\n",
      "MSG\t2784000 !CAL Calibration points:  \r\n",
      "MSG\t2784000 !CAL -29.4, -23.5        -0,     -2   \r\n",
      "MSG\t2784000 !CAL -29.3, -35.7        -0,  -1544   \r\n",
      "MSG\t2784000 !CAL -32.9, -10.4        -0,   1559   \r\n"
     ]
    }
   ],
   "source": [
    "!head -20 $fname_events"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "64a5e76c",
   "metadata": {},
   "source": [
    "After a header (lines starting with `**`) containing meta-information, we get a sequence of \"events\" which have different formats. We are interested in lines starting with \"MSG\" because those contain our experimental markers. Therefore, we read the events file and remove all rows that do not start with \"MSG\":"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "8a10381d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-11-10T13:36:29.081890Z",
     "iopub.status.busy": "2026-11-10T13:36:29.081661Z",
     "iopub.status.idle": "2026-11-10T13:36:29.093040Z",
     "shell.execute_reply": "2026-11-10T13:36:29.092197Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['MSG\\t2728855 DISPLAY_COORDS = 0 0 1919 1079\\n',\n",
       " 'MSG\\t2784000 !CAL \\n',\n",
       " 'MSG\\t2784000 !CAL Calibration points:  \\n',\n",
       " 'MSG\\t2784000 !CAL -29.4, -23.5        -0,     -2   \\n',\n",
       " 'MSG\\t2784000 !CAL -29.3, -35.7        -0,  -1544   \\n',\n",
       " 'MSG\\t2784000 !CAL -32.9, -10.4        -0,   1559   \\n',\n",
       " 'MSG\\t2784000 !CAL -49.7, -23.0     -2835,     -2   \\n',\n",
       " 'MSG\\t2784000 !CAL -10.8, -27.4      2835,     -2   \\n',\n",
       " 'MSG\\t2784000 !CAL -48.3, -33.3     -2818,  -1544   \\n',\n",
       " 'MSG\\t2784000 !CAL -11.0, -34.2      2818,  -1544   \\n']"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# read the whole file into variable `events` (list with one entry per line)\n",
    "with open(fname_events) as f:\n",
    "    events = f.readlines()\n",
    "\n",
    "# keep only lines starting with \"MSG\"\n",
    "events = [ev for ev in events if ev.startswith(\"MSG\")]\n",
    "events[0:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cd29ec19",
   "metadata": {},
   "source": [
    "Our experiment sent a marker called `experiment_start` when the experiment started. Hence, we can remove all events before this marker."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "c21732d8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-11-10T13:36:29.094982Z",
     "iopub.status.busy": "2026-11-10T13:36:29.094797Z",
     "iopub.status.idle": "2026-11-10T13:36:29.098561Z",
     "shell.execute_reply": "2026-11-10T13:36:29.098069Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['MSG\\t3387245 C_GW_1_1_UD_UD\\n',\n",
       " 'MSG\\t3390421 F_GW_1_1_10_0\\n',\n",
       " 'MSG\\t3392759 C_NW_1_2_UD_UD\\n',\n",
       " 'MSG\\t3394293 R_NW_1_2_UD_UD\\n',\n",
       " 'MSG\\t3395952 F_NW_1_2_-1_0\\n',\n",
       " 'MSG\\t3397974 C_NA_1_3_UD_UD\\n',\n",
       " 'MSG\\t3399892 R_NA_1_3_UD_UD\\n',\n",
       " 'MSG\\t3400999 F_NA_1_3_-11_0\\n',\n",
       " 'MSG\\t3403206 C_GA_1_4_UD_UD\\n',\n",
       " 'MSG\\t3404640 R_GA_1_4_UD_UD\\n']"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "experiment_start_index = np.where([\"experiment_start\" in ev for ev in events])[0][0]\n",
    "events = events[experiment_start_index+1:]\n",
    "events[0:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e2426303",
   "metadata": {},
   "source": [
    "Now we can convert the events into a `pandas.DataFrame` for further processing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "766b9abe",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-11-10T13:36:29.100966Z",
     "iopub.status.busy": "2026-11-10T13:36:29.100802Z",
     "iopub.status.idle": "2026-11-10T13:36:29.112217Z",
     "shell.execute_reply": "2026-11-10T13:36:29.111429Z"
    }
   },
   "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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>MSG</td>\n",
       "      <td>3387245</td>\n",
       "      <td>C_GW_1_1_UD_UD</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>MSG</td>\n",
       "      <td>3390421</td>\n",
       "      <td>F_GW_1_1_10_0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>MSG</td>\n",
       "      <td>3392759</td>\n",
       "      <td>C_NW_1_2_UD_UD</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>MSG</td>\n",
       "      <td>3394293</td>\n",
       "      <td>R_NW_1_2_UD_UD</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>MSG</td>\n",
       "      <td>3395952</td>\n",
       "      <td>F_NW_1_2_-1_0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1065</th>\n",
       "      <td>MSG</td>\n",
       "      <td>5893078</td>\n",
       "      <td>V_UD_UD_16_UD_UD</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1066</th>\n",
       "      <td>MSG</td>\n",
       "      <td>5899076</td>\n",
       "      <td>V_UD_UD_17_UD_UD</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1067</th>\n",
       "      <td>MSG</td>\n",
       "      <td>5905073</td>\n",
       "      <td>V_UD_UD_18_UD_UD</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1068</th>\n",
       "      <td>MSG</td>\n",
       "      <td>5911072</td>\n",
       "      <td>V_UD_UD_19_UD_UD</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1069</th>\n",
       "      <td>MSG</td>\n",
       "      <td>5917071</td>\n",
       "      <td>V_UD_UD_20_UD_UD</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1070 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        0        1                 2    3    4    5    6    7    8\n",
       "0     MSG  3387245    C_GW_1_1_UD_UD  NaN  NaN  NaN  NaN  NaN  NaN\n",
       "1     MSG  3390421     F_GW_1_1_10_0  NaN  NaN  NaN  NaN  NaN  NaN\n",
       "2     MSG  3392759    C_NW_1_2_UD_UD  NaN  NaN  NaN  NaN  NaN  NaN\n",
       "3     MSG  3394293    R_NW_1_2_UD_UD  NaN  NaN  NaN  NaN  NaN  NaN\n",
       "4     MSG  3395952     F_NW_1_2_-1_0  NaN  NaN  NaN  NaN  NaN  NaN\n",
       "...   ...      ...               ...  ...  ...  ...  ...  ...  ...\n",
       "1065  MSG  5893078  V_UD_UD_16_UD_UD  NaN  NaN  NaN  NaN  NaN  NaN\n",
       "1066  MSG  5899076  V_UD_UD_17_UD_UD  NaN  NaN  NaN  NaN  NaN  NaN\n",
       "1067  MSG  5905073  V_UD_UD_18_UD_UD  NaN  NaN  NaN  NaN  NaN  NaN\n",
       "1068  MSG  5911072  V_UD_UD_19_UD_UD  NaN  NaN  NaN  NaN  NaN  NaN\n",
       "1069  MSG  5917071  V_UD_UD_20_UD_UD  NaN  NaN  NaN  NaN  NaN  NaN\n",
       "\n",
       "[1070 rows x 9 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ev = pd.Series(events).str.split(expand=True)\n",
    "df_ev"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a0600ece",
   "metadata": {},
   "source": [
    "In this table, the second column contains the timestamp (identical to the timestamp in the samples file), and the third column contains our custom markers (the format like \"C_GW_1_1_UD_UD\" is specific to our experimental design). There are more columns which seem to contain no information for most rows. Let's check what those columns are for by printing the rows where the fifth column is not empty:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "88b27887",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-11-10T13:36:29.114303Z",
     "iopub.status.busy": "2026-11-10T13:36:29.114091Z",
     "iopub.status.idle": "2026-11-10T13:36:29.121975Z",
     "shell.execute_reply": "2026-11-10T13:36:29.121347Z"
    }
   },
   "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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>209</th>\n",
       "      <td>MSG</td>\n",
       "      <td>3900393</td>\n",
       "      <td>RECCFG</td>\n",
       "      <td>CR</td>\n",
       "      <td>500</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>LR</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>211</th>\n",
       "      <td>MSG</td>\n",
       "      <td>3900393</td>\n",
       "      <td>GAZE_COORDS</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1919.00</td>\n",
       "      <td>1079.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>212</th>\n",
       "      <td>MSG</td>\n",
       "      <td>3900393</td>\n",
       "      <td>THRESHOLDS</td>\n",
       "      <td>L</td>\n",
       "      <td>56</td>\n",
       "      <td>231</td>\n",
       "      <td>R</td>\n",
       "      <td>66</td>\n",
       "      <td>239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>213</th>\n",
       "      <td>MSG</td>\n",
       "      <td>3900393</td>\n",
       "      <td>ELCL_WINDOW_SIZES</td>\n",
       "      <td>176</td>\n",
       "      <td>188</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>215</th>\n",
       "      <td>MSG</td>\n",
       "      <td>3900393</td>\n",
       "      <td>ELCL_PROC</td>\n",
       "      <td>CENTROID</td>\n",
       "      <td>(3)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       0        1                  2         3     4        5        6    7  \\\n",
       "209  MSG  3900393             RECCFG        CR   500        2        1   LR   \n",
       "211  MSG  3900393        GAZE_COORDS      0.00  0.00  1919.00  1079.00  NaN   \n",
       "212  MSG  3900393         THRESHOLDS         L    56      231        R   66   \n",
       "213  MSG  3900393  ELCL_WINDOW_SIZES       176   188        0        0  NaN   \n",
       "215  MSG  3900393          ELCL_PROC  CENTROID   (3)      NaN      NaN  NaN   \n",
       "\n",
       "       8  \n",
       "209  NaN  \n",
       "211  NaN  \n",
       "212  239  \n",
       "213  NaN  \n",
       "215  NaN  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ev[df_ev[4].notna()].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f6c2297c",
   "metadata": {},
   "source": [
    "Apparently, there are more eyetracker-specific markers in our files (in this case due to drift checks during the experiment). We can drop those by removing all rows in which the fifth column is not empty and then dropping all other columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "7a25add5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-11-10T13:36:29.124863Z",
     "iopub.status.busy": "2026-11-10T13:36:29.124668Z",
     "iopub.status.idle": "2026-11-10T13:36:29.133359Z",
     "shell.execute_reply": "2026-11-10T13:36:29.132592Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>time</th>\n",
       "      <th>event</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3387245</td>\n",
       "      <td>C_GW_1_1_UD_UD</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3390421</td>\n",
       "      <td>F_GW_1_1_10_0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3392759</td>\n",
       "      <td>C_NW_1_2_UD_UD</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3394293</td>\n",
       "      <td>R_NW_1_2_UD_UD</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3395952</td>\n",
       "      <td>F_NW_1_2_-1_0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1065</th>\n",
       "      <td>5893078</td>\n",
       "      <td>V_UD_UD_16_UD_UD</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1066</th>\n",
       "      <td>5899076</td>\n",
       "      <td>V_UD_UD_17_UD_UD</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1067</th>\n",
       "      <td>5905073</td>\n",
       "      <td>V_UD_UD_18_UD_UD</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1068</th>\n",
       "      <td>5911072</td>\n",
       "      <td>V_UD_UD_19_UD_UD</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1069</th>\n",
       "      <td>5917071</td>\n",
       "      <td>V_UD_UD_20_UD_UD</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1035 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         time             event\n",
       "0     3387245    C_GW_1_1_UD_UD\n",
       "1     3390421     F_GW_1_1_10_0\n",
       "2     3392759    C_NW_1_2_UD_UD\n",
       "3     3394293    R_NW_1_2_UD_UD\n",
       "4     3395952     F_NW_1_2_-1_0\n",
       "...       ...               ...\n",
       "1065  5893078  V_UD_UD_16_UD_UD\n",
       "1066  5899076  V_UD_UD_17_UD_UD\n",
       "1067  5905073  V_UD_UD_18_UD_UD\n",
       "1068  5911072  V_UD_UD_19_UD_UD\n",
       "1069  5917071  V_UD_UD_20_UD_UD\n",
       "\n",
       "[1035 rows x 2 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ev = df_ev[df_ev[4].isna()][[1, 2]]\n",
    "df_ev.columns = [\"time\", \"event\"]\n",
    "df_ev"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "830fc67a",
   "metadata": {},
   "source": [
    "Finally, we can pass the event markers to our `EyeData` object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "e95b7059",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-11-10T13:36:29.135545Z",
     "iopub.status.busy": "2026-11-10T13:36:29.135372Z",
     "iopub.status.idle": "2026-11-10T13:36:29.268441Z",
     "shell.execute_reply": "2026-11-10T13:36:29.267572Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32mpp: 14:36:29\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfill_time_discontinuities\u001b[0m:\u001b[36m2139\u001b[0m | \u001b[1mFilling in 5 gaps\u001b[0m\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32mpp: 14:36:29\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfill_time_discontinuities\u001b[0m:\u001b[36m2141\u001b[0m | \u001b[1m[32.35   4.012  6.21   2.02   1.862] seconds\u001b[0m\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div style=\"font-family:system-ui, sans-serif; border:1px solid #ddd; border-radius:6px; padding:12px; max-width: min(600px, 50vw); min-width: 300px;\">\n",
       "  <h3 style=\"margin-top:0;\">EyeData — <span style=\"color:#555;\">test</span></h3>\n",
       "\n",
       "    <div style=\"display:grid; grid-template-columns:repeat(auto-fit,minmax(220px,1fr)); gap:8px; margin-bottom:10px;\">\n",
       "    <div style=\"background:#f6f6f6; border-radius:4px; padding:6px 8px; border:1px solid #e0e0e0;\">\n",
       "      <div style=\"font-size:0.85em; color:#666; text-transform:uppercase;\">Samples</div>\n",
       "      <div style=\"font-weight:600;\">1,268,585</div>\n",
       "    </div>\n",
       "    <div style=\"background:#f6f6f6; border-radius:4px; padding:6px 8px; border:1px solid #e0e0e0;\">\n",
       "      <div style=\"font-size:0.85em; color:#666; text-transform:uppercase;\">Sampling rate</div>\n",
       "      <div style=\"font-weight:600;\">500.0 Hz</div>\n",
       "    </div>\n",
       "    <div style=\"background:#f6f6f6; border-radius:4px; padding:6px 8px; border:1px solid #e0e0e0;\">\n",
       "      <div style=\"font-size:0.85em; color:#666; text-transform:uppercase;\">Duration</div>\n",
       "      <div style=\"font-weight:600;\">42.29 min (start 56.43, end 98.72)</div>\n",
       "    </div>\n",
       "    <div style=\"background:#f6f6f6; border-radius:4px; padding:6px 8px; border:1px solid #e0e0e0;\">\n",
       "      <div style=\"font-size:0.85em; color:#666; text-transform:uppercase;\">Eyes</div>\n",
       "      <div style=\"font-weight:600;\">left, right</div>\n",
       "    </div>\n",
       "    <div style=\"background:#f6f6f6; border-radius:4px; padding:6px 8px; border:1px solid #e0e0e0;\">\n",
       "      <div style=\"font-size:0.85em; color:#666; text-transform:uppercase;\">Variables</div>\n",
       "      <div style=\"font-weight:600;\">left_pupil, right_pupil</div>\n",
       "    </div>\n",
       "    <div style=\"background:#f6f6f6; border-radius:4px; padding:6px 8px; border:1px solid #e0e0e0;\">\n",
       "      <div style=\"font-size:0.85em; color:#666; text-transform:uppercase;\">Events</div>\n",
       "      <div style=\"font-weight:600;\">1035</div>\n",
       "    </div>\n",
       "    <div style=\"background:#f6f6f6; border-radius:4px; padding:6px 8px; border:1px solid #e0e0e0;\">\n",
       "      <div style=\"font-size:0.85em; color:#666; text-transform:uppercase;\">Size</div>\n",
       "      <div style=\"font-weight:600;\">36.4MiB</div>\n",
       "    </div>\n",
       "  </div>\n",
       "\n",
       "\n",
       "    <details open style=\"margin-bottom:8px;\">\n",
       "    <summary style=\"cursor:pointer; font-weight:600;\">Signal overview</summary>\n",
       "    <div style=\"display:grid; grid-template-columns:repeat(auto-fit,minmax(220px,1fr)); gap:8px; margin-top:8px;\">\n",
       "      <div style=\"background:#f6f6f6; border-radius:4px; padding:6px 8px; border:1px solid #e0e0e0; text-align:center;\">\n",
       "        <div style=\"font-size:0.85em; color:#666; margin-bottom:4px;\">left_pupil</div>\n",
       "        <img src=\"data:image/png;base64,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\" alt=\"left_pupil sparkline\" style=\"height:60px; width:100%; object-fit:cover;\">\n",
       "      </div>\n",
       "      <div style=\"background:#f6f6f6; border-radius:4px; padding:6px 8px; border:1px solid #e0e0e0; text-align:center;\">\n",
       "        <div style=\"font-size:0.85em; color:#666; margin-bottom:4px;\">right_pupil</div>\n",
       "        <img src=\"data:image/png;base64,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\" alt=\"right_pupil sparkline\" style=\"height:60px; width:100%; object-fit:cover;\">\n",
       "      </div>\n",
       "    </div>\n",
       "  </details>\n",
       "\n",
       "\n",
       "\n",
       "  <details style=\"margin-top:8px;\">\n",
       "    <summary style=\"cursor:pointer; font-weight:600;\">Data glimpse</summary>\n",
       "    <pre style=\"margin-top:8px; background:#f9f9f9; padding:8px; border-radius:4px;\">EyeDataDict(vars=2,n=1268585,shape=(1268585,)): \n",
       "  left_pupil (float64): 1707.0, 1706.0, 1705.0, 1706.0, 1707.0...\n",
       "  right_pupil (float64): 1738.0, 1739.0, 1739.0, 1740.0, 1742.0...\n",
       "</pre>\n",
       "  </details>\n",
       "\n",
       "</div>\n"
      ],
      "text/plain": [
       "EyeData(test, 36.4MiB):\n",
       " n                 : 1268585\n",
       " sampling_rate     : 500.0\n",
       " eyes              : ['left', 'right']\n",
       " data              : ['left_pupil', 'right_pupil']\n",
       " nevents           : 1035\n",
       " experimental_setup: not set\n",
       " calibration       : not available\n",
       " duration_minutes  : 42.2862\n",
       " start_min         : 56.4317\n",
       " end_min           : 98.7178\n",
       " params            : {}\n",
       " nblinks           : {}\n",
       " blinks            : {}\n",
       " glimpse           : EyeDataDict(vars=2,n=1268585,shape=(1268585,)): \n",
       "  left_pupil (float64): 1707.0, 1706.0, 1705.0, 1706.0, 1707.0...\n",
       "  right_pupil (float64): 1738.0, 1739.0, 1739.0, 1740.0, 1742.0...\n",
       "\n",
       " History:\n",
       " *"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d = pp.EyeData(left_pupil=df.left_p, right_pupil=df.right_p, time=df.time,\n",
    "               event_onsets=df_ev.time.astype(float), event_labels=df_ev.event,\n",
    "               name=\"test\")\n",
    "d"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eec7d954",
   "metadata": {},
   "source": [
    "The summary shows that the eyetracker started recording at time 56.4 minutes. We can let the time start at 0 by using `reset_time()`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "0230bd44",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-11-10T13:36:29.271825Z",
     "iopub.status.busy": "2026-11-10T13:36:29.271532Z",
     "iopub.status.idle": "2026-11-10T13:36:29.283596Z",
     "shell.execute_reply": "2026-11-10T13:36:29.282318Z"
    }
   },
   "outputs": [],
   "source": [
    "d = d.reset_time()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ef0029ab",
   "metadata": {},
   "source": [
    "Now we can use all `pypillometry` functions on this dataset, e.g., plot a minute of the data. Missing (NaN) samples are masked automatically and highlighted in red. See the [blinks notebook](blinks.ipynb) for how to detect and interpolate blinks."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "ac7986a2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-11-10T13:36:29.285787Z",
     "iopub.status.busy": "2026-11-10T13:36:29.285590Z",
     "iopub.status.idle": "2026-11-10T13:36:29.844299Z",
     "shell.execute_reply": "2026-11-10T13:36:29.842884Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15, 5))\n",
    "d.plot.plot_pupil(plot_range=(4, 5) * u.min, units=\"min\");"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cda27d1e",
   "metadata": {},
   "source": [
    "## Generalize to multiple similar datasets\n",
    "\n",
    "Now that we have found a way to create our `EyeData` object from the raw files, we can wrap the code from this notebook into a function that creates such objects for any file with the same structure.\n",
    "\n",
    "NOTE: You may want to consider using a [configuration file to facilitate loading and sharing your study data](../user_guide/studies.rst).\n",
    "\n",
    "The function takes the name of an EDF file as input, runs all the code above and returns the final `EyeData` object. For convenience, we assume that `edf2asc` has already been run so that the `.asc` files are available (see above)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "1475aeee",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-11-10T13:36:29.847165Z",
     "iopub.status.busy": "2026-11-10T13:36:29.846813Z",
     "iopub.status.idle": "2026-11-10T13:36:29.856031Z",
     "shell.execute_reply": "2026-11-10T13:36:29.854857Z"
    }
   },
   "outputs": [],
   "source": [
    "datapath = \"../../data\"  # this is where the data files are located\n",
    "\n",
    "def read_dataset(edffile):\n",
    "    basename = os.path.splitext(edffile)[0]  # remove .edf from filename\n",
    "    fname_samples = os.path.join(datapath, basename+\"_samples.asc\")\n",
    "    fname_events = os.path.join(datapath, basename+\"_events.asc\")\n",
    "    print(\"> Loading '%s' and '%s'\" % (fname_samples, fname_events))\n",
    "\n",
    "    # read samples file\n",
    "    df = pd.read_table(fname_samples,\n",
    "                       names=[\"time\", \"left_x\", \"left_y\", \"left_p\",\n",
    "                              \"right_x\", \"right_y\", \"right_p\", \"flags\"],\n",
    "                       na_values=\".\", skipinitialspace=True)\n",
    "    df[\"left_p\"] = df.left_p.replace(0, np.nan)\n",
    "    df[\"right_p\"] = df.right_p.replace(0, np.nan)\n",
    "\n",
    "    # read events file, keep only messages after the experiment started\n",
    "    with open(fname_events) as f:\n",
    "        events = [ev for ev in f.readlines() if ev.startswith(\"MSG\")]\n",
    "    experiment_start_index = np.where([\"experiment_start\" in ev for ev in events])[0][0]\n",
    "    events = events[experiment_start_index+1:]\n",
    "    df_ev = pd.Series(events).str.split(expand=True)\n",
    "    df_ev = df_ev[df_ev[4].isna()][[1, 2]]\n",
    "    df_ev.columns = [\"time\", \"event\"]\n",
    "\n",
    "    # create EyeData object\n",
    "    d = pp.EyeData(left_pupil=df.left_p, right_pupil=df.right_p,\n",
    "                   left_x=df.left_x, left_y=df.left_y,\n",
    "                   right_x=df.right_x, right_y=df.right_y,\n",
    "                   time=df.time,\n",
    "                   event_onsets=df_ev.time.astype(float), event_labels=df_ev.event,\n",
    "                   name=basename)\n",
    "    return d.reset_time()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "24d31014",
   "metadata": {},
   "source": [
    "We can test this code by running the function with a filename located in `datapath`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "e5521162",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-11-10T13:36:29.858232Z",
     "iopub.status.busy": "2026-11-10T13:36:29.857910Z",
     "iopub.status.idle": "2026-11-10T13:36:31.291709Z",
     "shell.execute_reply": "2026-11-10T13:36:31.290532Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "> Loading '../../data/test_samples.asc' and '../../data/test_events.asc'\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32mpp: 14:36:30\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfill_time_discontinuities\u001b[0m:\u001b[36m2139\u001b[0m | \u001b[1mFilling in 5 gaps\u001b[0m\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32mpp: 14:36:30\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mfill_time_discontinuities\u001b[0m:\u001b[36m2141\u001b[0m | \u001b[1m[32.35   4.012  6.21   2.02   1.862] seconds\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "d = read_dataset(\"test.edf\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "61ed3f5c",
   "metadata": {},
   "source": [
    "After that, we might want to save the final `EyeData` object to a file that can be loaded back quickly. This can be done using `EyeData.write_file()` or `pp.write_file()`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "cf915e65",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-11-10T13:36:31.294520Z",
     "iopub.status.busy": "2026-11-10T13:36:31.294208Z",
     "iopub.status.idle": "2026-11-10T13:36:31.714303Z",
     "shell.execute_reply": "2026-11-10T13:36:31.713347Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "example_data/test.pd\n"
     ]
    }
   ],
   "source": [
    "os.makedirs(\"example_data\", exist_ok=True)\n",
    "fname = os.path.join(\"example_data\", d.name+\".pd\")\n",
    "print(fname)\n",
    "d.write_file(fname)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cccf34b6",
   "metadata": {},
   "source": [
    "The dataset can be read back using `EyeData.from_file()`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "bdda177e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-11-10T13:36:31.717277Z",
     "iopub.status.busy": "2026-11-10T13:36:31.717040Z",
     "iopub.status.idle": "2026-11-10T13:36:32.061552Z",
     "shell.execute_reply": "2026-11-10T13:36:32.060518Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"font-family:system-ui, sans-serif; border:1px solid #ddd; border-radius:6px; padding:12px; max-width: min(600px, 50vw); min-width: 300px;\">\n",
       "  <h3 style=\"margin-top:0;\">EyeData — <span style=\"color:#555;\">test</span></h3>\n",
       "\n",
       "    <div style=\"display:grid; grid-template-columns:repeat(auto-fit,minmax(220px,1fr)); gap:8px; margin-bottom:10px;\">\n",
       "    <div style=\"background:#f6f6f6; border-radius:4px; padding:6px 8px; border:1px solid #e0e0e0;\">\n",
       "      <div style=\"font-size:0.85em; color:#666; text-transform:uppercase;\">Samples</div>\n",
       "      <div style=\"font-weight:600;\">1,268,585</div>\n",
       "    </div>\n",
       "    <div style=\"background:#f6f6f6; border-radius:4px; padding:6px 8px; border:1px solid #e0e0e0;\">\n",
       "      <div style=\"font-size:0.85em; color:#666; text-transform:uppercase;\">Sampling rate</div>\n",
       "      <div style=\"font-weight:600;\">500.0 Hz</div>\n",
       "    </div>\n",
       "    <div style=\"background:#f6f6f6; border-radius:4px; padding:6px 8px; border:1px solid #e0e0e0;\">\n",
       "      <div style=\"font-size:0.85em; color:#666; text-transform:uppercase;\">Duration</div>\n",
       "      <div style=\"font-weight:600;\">42.29 min (start 0.00, end 42.29)</div>\n",
       "    </div>\n",
       "    <div style=\"background:#f6f6f6; border-radius:4px; padding:6px 8px; border:1px solid #e0e0e0;\">\n",
       "      <div style=\"font-size:0.85em; color:#666; text-transform:uppercase;\">Eyes</div>\n",
       "      <div style=\"font-weight:600;\">left, right</div>\n",
       "    </div>\n",
       "    <div style=\"background:#f6f6f6; border-radius:4px; padding:6px 8px; border:1px solid #e0e0e0;\">\n",
       "      <div style=\"font-size:0.85em; color:#666; text-transform:uppercase;\">Variables</div>\n",
       "      <div style=\"font-weight:600;\">left_x, left_y, left_pupil, right_x, right_y, right_pupil</div>\n",
       "    </div>\n",
       "    <div style=\"background:#f6f6f6; border-radius:4px; padding:6px 8px; border:1px solid #e0e0e0;\">\n",
       "      <div style=\"font-size:0.85em; color:#666; text-transform:uppercase;\">Events</div>\n",
       "      <div style=\"font-weight:600;\">1035</div>\n",
       "    </div>\n",
       "    <div style=\"background:#f6f6f6; border-radius:4px; padding:6px 8px; border:1px solid #e0e0e0;\">\n",
       "      <div style=\"font-size:0.85em; color:#666; text-transform:uppercase;\">Size</div>\n",
       "      <div style=\"font-weight:600;\">84.8MiB</div>\n",
       "    </div>\n",
       "  </div>\n",
       "\n",
       "\n",
       "    <details open style=\"margin-bottom:8px;\">\n",
       "    <summary style=\"cursor:pointer; font-weight:600;\">Signal overview</summary>\n",
       "    <div style=\"display:grid; grid-template-columns:repeat(auto-fit,minmax(220px,1fr)); gap:8px; margin-top:8px;\">\n",
       "      <div style=\"background:#f6f6f6; border-radius:4px; padding:6px 8px; border:1px solid #e0e0e0; text-align:center;\">\n",
       "        <div style=\"font-size:0.85em; color:#666; margin-bottom:4px;\">left_pupil</div>\n",
       "        <img src=\"data:image/png;base64,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\" alt=\"left_pupil sparkline\" style=\"height:60px; width:100%; object-fit:cover;\">\n",
       "      </div>\n",
       "      <div style=\"background:#f6f6f6; border-radius:4px; padding:6px 8px; border:1px solid #e0e0e0; text-align:center;\">\n",
       "        <div style=\"font-size:0.85em; color:#666; margin-bottom:4px;\">left_x</div>\n",
       "        <img src=\"data:image/png;base64,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\" alt=\"left_x sparkline\" style=\"height:60px; width:100%; object-fit:cover;\">\n",
       "      </div>\n",
       "      <div style=\"background:#f6f6f6; border-radius:4px; padding:6px 8px; border:1px solid #e0e0e0; text-align:center;\">\n",
       "        <div style=\"font-size:0.85em; color:#666; margin-bottom:4px;\">left_y</div>\n",
       "        <img src=\"data:image/png;base64,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\" alt=\"left_y sparkline\" style=\"height:60px; width:100%; object-fit:cover;\">\n",
       "      </div>\n",
       "      <div style=\"background:#f6f6f6; border-radius:4px; padding:6px 8px; border:1px solid #e0e0e0; text-align:center;\">\n",
       "        <div style=\"font-size:0.85em; color:#666; margin-bottom:4px;\">right_pupil</div>\n",
       "        <img src=\"data:image/png;base64,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\" alt=\"right_pupil sparkline\" style=\"height:60px; width:100%; object-fit:cover;\">\n",
       "      </div>\n",
       "      <div style=\"background:#f6f6f6; border-radius:4px; padding:6px 8px; border:1px solid #e0e0e0; text-align:center;\">\n",
       "        <div style=\"font-size:0.85em; color:#666; margin-bottom:4px;\">right_x</div>\n",
       "        <img src=\"data:image/png;base64,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\" alt=\"right_x sparkline\" style=\"height:60px; width:100%; object-fit:cover;\">\n",
       "      </div>\n",
       "      <div style=\"background:#f6f6f6; border-radius:4px; padding:6px 8px; border:1px solid #e0e0e0; text-align:center;\">\n",
       "        <div style=\"font-size:0.85em; color:#666; margin-bottom:4px;\">right_y</div>\n",
       "        <img src=\"data:image/png;base64,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\" alt=\"right_y sparkline\" style=\"height:60px; width:100%; object-fit:cover;\">\n",
       "      </div>\n",
       "    </div>\n",
       "  </details>\n",
       "\n",
       "\n",
       "\n",
       "  <details style=\"margin-top:8px;\">\n",
       "    <summary style=\"cursor:pointer; font-weight:600;\">Data glimpse</summary>\n",
       "    <pre style=\"margin-top:8px; background:#f9f9f9; padding:8px; border-radius:4px;\">EyeDataDict(vars=6,n=1268585,shape=(1268585,)): \n",
       "  left_x (float64): 817.3, 817.0, 816.7, 816.7, 816.7...\n",
       "  left_y (float64): 345.2, 343.5, 341.6, 340.4, 340.2...\n",
       "  left_pupil (float64): 1707.0, 1706.0, 1705.0, 1706.0, 1707.0...\n",
       "  right_x (float64): 860.6, 860.7, 861.2, 861.7, 861.6...\n",
       "  right_y (float64): 375.2, 375.9, 376.6, 376.8, 376.9...\n",
       "  right_pupil (float64): 1738.0, 1739.0, 1739.0, 1740.0, 1742.0...\n",
       "</pre>\n",
       "  </details>\n",
       "\n",
       "  <details>\n",
       "    <summary style=\"cursor:pointer; font-weight:600;\">Recent history</summary>\n",
       "    <ol style=\"margin-top:8px;\">\n",
       "      <li>reset_time()</li>\n",
       "    </ol>\n",
       "  </details>\n",
       "</div>\n"
      ],
      "text/plain": [
       "EyeData(test, 84.8MiB):\n",
       " n                 : 1268585\n",
       " sampling_rate     : 500.0\n",
       " eyes              : ['left', 'right']\n",
       " data              : ['left_x', 'left_y', 'left_pupil', 'right_x', 'right_y', 'right_pupil']\n",
       " nevents           : 1035\n",
       " experimental_setup: not set\n",
       " calibration       : not available\n",
       " duration_minutes  : 42.2862\n",
       " start_min         : 0.0\n",
       " end_min           : 42.2861\n",
       " params            : {}\n",
       " nblinks           : {}\n",
       " blinks            : {}\n",
       " glimpse           : EyeDataDict(vars=6,n=1268585,shape=(1268585,)): \n",
       "  left_x (float64): 817.3, 817.0, 816.7, 816.7, 816.7...\n",
       "  left_y (float64): 345.2, 343.5, 341.6, 340.4, 340.2...\n",
       "  left_pupil (float64): 1707.0, 1706.0, 1705.0, 1706.0, 1707.0...\n",
       "  right_x (float64): 860.6, 860.7, 861.2, 861.7, 861.6...\n",
       "  right_y (float64): 375.2, 375.9, 376.6, 376.8, 376.9...\n",
       "  right_pupil (float64): 1738.0, 1739.0, 1739.0, 1740.0, 1742.0...\n",
       "\n",
       " History:\n",
       " *\n",
       " └ reset_time()"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d2 = pp.EyeData.from_file(fname)\n",
    "d2"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.15.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}