{ "cells": [ { "cell_type": "markdown", "id": "b7bdea2b", "metadata": {}, "source": [ "# Events from a behaviour log\n", "\n", "Often, the eye tracker gets only some of the events of an experiment, for example a trigger at the start of each trial. The times of stimuli and responses, and everything else about the trials (conditions, choices, reaction times), are then in the log file of the experiment software (e.g., PsychoPy, Psychtoolbox or Presentation).\n", "\n", "This notebook shows how to get these events into the eye-tracking data:\n", "\n", "1. read the behaviour log and turn its columns into events (`Events.from_table()`),\n", "2. map the times from the clock of the experiment computer to the clock of the eye tracker (`pp.sync_clocks()`), using moments that are in both logs,\n", "3. add the events to the data (`add_events()`) and check the result." ] }, { "cell_type": "code", "execution_count": 1, "id": "c81aab65", "metadata": { "execution": { "iopub.execute_input": "2026-11-10T13:38:11.864726Z", "iopub.status.busy": "2026-11-10T13:38:11.864321Z", "iopub.status.idle": "2026-11-10T13:38:12.744499Z", "shell.execute_reply": "2026-11-10T13:38:12.743426Z" } }, "outputs": [], "source": [ "import pypillometry as pp\n", "from pypillometry import u\n", "import numpy as np\n", "import pandas as pd\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "markdown", "id": "ad56b15c", "metadata": {}, "source": [ "## The data\n", "\n", "We use one participant of the RLMW study (a reinforcement-learning task with mind-wandering probes), the same EDF file as in the [notebook on reading EDF files](importdata_edf.ipynb). The behaviour log of this participant is in the [OSF project of the study](https://osf.io/ca95r/). We download both into the folder `example_data` (only the first time):" ] }, { "cell_type": "code", "execution_count": 2, "id": "b895df29", "metadata": { "execution": { "iopub.execute_input": "2026-11-10T13:38:12.747664Z", "iopub.status.busy": "2026-11-10T13:38:12.747347Z", "iopub.status.idle": "2026-11-10T13:38:20.618199Z", "shell.execute_reply": "2026-11-10T13:38:20.617106Z" } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "\u001b[32mpp: 14:38:12\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mfetch_url\u001b[0m:\u001b[36m448\u001b[0m | \u001b[1mUsing cached copy of 'https://osf.io/trsuq/download': example_data/test.edf\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "\u001b[32mpp: 14:38:12\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36m_read_edf\u001b[0m:\u001b[36m730\u001b[0m | \u001b[1mLoading EDF file: example_data/test.edf (current log level: INFO, set to DEBUG to see eyelinkio output)\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "\u001b[32mpp: 14:38:20\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mfrom_eyelink\u001b[0m:\u001b[36m1639\u001b[0m | \u001b[1mFiltering out Eyelink triggers\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "\u001b[32mpp: 14:38:20\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mfetch_url\u001b[0m:\u001b[36m448\u001b[0m | \u001b[1mUsing cached copy of 'https://osf.io/download/658189c19513840728238876/': example_data/010_rlmw_behaviour.csv\u001b[0m\n" ] } ], "source": [ "d = pp.EyeData.from_eyelink(\"https://osf.io/trsuq/download\",\n", " cache_file=\"example_data/test.edf\")\n", "fname = pp.fetch_url(\"https://osf.io/download/658189c19513840728238876/\",\n", " cache_file=\"example_data/010_rlmw_behaviour.csv\")" ] }, { "cell_type": "markdown", "id": "7e053533", "metadata": {}, "source": [ "The behaviour log was written by PsychoPy. It is a table with one row per trial (the first line is a comment with information about the session):" ] }, { "cell_type": "code", "execution_count": 3, "id": "4966a652", "metadata": { "execution": { "iopub.execute_input": "2026-11-10T13:38:20.622006Z", "iopub.status.busy": "2026-11-10T13:38:20.621775Z", "iopub.status.idle": "2026-11-10T13:38:20.649958Z", "shell.execute_reply": "2026-11-10T13:38:20.648668Z" } }, "outputs": [ { "data": { "text/html": [ "
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subjtasktimetrialtimetrialprobetrialfixcross_durationleftrightchoicertlrewardrrewardreward
0100.0000383.60017310200150-1.000000460
1103.6041683.600156202006460.388822181818
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\n", "
" ], "text/plain": [ " subj tasktime trialtime trial probetrial fixcross_duration left \\\n", "0 10 0.000038 3.600173 1 0 200 1 \n", "1 10 3.604168 3.600156 2 0 200 6 \n", "2 10 7.208135 3.600159 3 0 200 2 \n", "3 10 10.811549 3.600154 4 0 800 6 \n", "4 10 14.414900 3.600182 5 0 200 6 \n", "\n", " right choice rt lreward rreward reward \n", "0 5 0 -1.000000 4 6 0 \n", "1 4 6 0.388822 18 18 18 \n", "2 3 3 0.966059 1 8 8 \n", "3 5 6 0.607932 18 4 18 \n", "4 2 6 0.648074 18 7 18 " ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "behav = pd.read_csv(fname, comment=\"#\")\n", "behav.head()" ] }, { "cell_type": "markdown", "id": "57cbe740", "metadata": {}, "source": [ "The columns we need are:\n", "\n", "- `tasktime`: start of the trial (onset of the fixation cross), in seconds since the start of the task, on the clock of the experiment computer\n", "- `fixcross_duration`: how long the fixation cross was shown before the stimulus (in ms)\n", "- `rt`: reaction time from stimulus onset (in seconds; `-1` if there was no response)\n", "- `probetrial`: 0 for choice trials; for mind-wandering probes, the number of the probe\n", "\n", "There are 300 choice trials and 30 probes:" ] }, { "cell_type": "code", "execution_count": 4, "id": "ff2bb1b9", "metadata": { "execution": { "iopub.execute_input": "2026-11-10T13:38:20.652466Z", "iopub.status.busy": "2026-11-10T13:38:20.652083Z", "iopub.status.idle": "2026-11-10T13:38:20.658187Z", "shell.execute_reply": "2026-11-10T13:38:20.657258Z" } }, "outputs": [ { "data": { "text/plain": [ "(330, np.int64(300))" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(behav), (behav.probetrial == 0).sum()" ] }, { "cell_type": "markdown", "id": "2ac7331a", "metadata": {}, "source": [ "## The events in the recording\n", "\n", "In this experiment, many events were sent to the eye tracker: the fixation cross (`F_`), the stimulus (`S_...`), the choice (`C_...`), the reward (`R_...`) and the probes (`P_...`, `CP_...`, `RP_...`):" ] }, { "cell_type": "code", "execution_count": 5, "id": "4fda4513", "metadata": { "execution": { "iopub.execute_input": "2026-11-10T13:38:20.660521Z", "iopub.status.busy": "2026-11-10T13:38:20.660153Z", "iopub.status.idle": "2026-11-10T13:38:20.669159Z", "shell.execute_reply": "2026-11-10T13:38:20.667641Z" } }, "outputs": [ { "data": { "text/html": [ "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
Onset (ms)Label
00.0DISPLAY_COORDS = 0 0 1279 1023
18340.0experiment_start
28360.0F_1
38560.0S_1_1_5
410560.0C_1_0_0
510576.0R_1_0
611959.0F_2
712159.0S_2_6_4
812548.0C_2_6_1
912775.0R_2_18

Total: 10 events

" ], "text/plain": [ "Events: 10 events | units=ms" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "events = d.events\n", "events[:10]" ] }, { "cell_type": "markdown", "id": "dc1c5569", "metadata": {}, "source": [ "`d.events.onset()` gives the time of a single event, e.g., the start of the experiment. It raises an error unless exactly one event has the label:" ] }, { "cell_type": "code", "execution_count": 6, "id": "42cb72b7", "metadata": { "execution": { "iopub.execute_input": "2026-11-10T13:38:20.671960Z", "iopub.status.busy": "2026-11-10T13:38:20.671578Z", "iopub.status.idle": "2026-11-10T13:38:20.678191Z", "shell.execute_reply": "2026-11-10T13:38:20.677105Z" } }, "outputs": [ { "data": { "text/plain": [ "8.34" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "d.events.onset(\"experiment_start\", units=\"sec\")" ] }, { "cell_type": "markdown", "id": "d89db050", "metadata": {}, "source": [ "To show the more common case, we keep only the fixation-cross events, as if only a trigger at the start of each trial had been sent. We keep the original events to check our result at the end:" ] }, { "cell_type": "code", "execution_count": 7, "id": "b9f981ad", "metadata": { "execution": { "iopub.execute_input": "2026-11-10T13:38:20.680684Z", "iopub.status.busy": "2026-11-10T13:38:20.680329Z", "iopub.status.idle": "2026-11-10T13:38:20.691010Z", "shell.execute_reply": "2026-11-10T13:38:20.689699Z" } }, "outputs": [ { "data": { "text/html": [ "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
Onset (ms)Label
08360.0F_1
111959.0F_2
215557.0F_3
319156.0F_4
422771.0F_5

Total: 5 events

" ], "text/plain": [ "Events: 5 events | units=ms" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "orig = d.events\n", "d = d.set_events(orig.filter(\"F_\"))\n", "d.events[:5]" ] }, { "cell_type": "markdown", "id": "ebe18163", "metadata": {}, "source": [ "## Events from the behaviour log\n", "\n", "The stimulus appeared `fixcross_duration` ms after the start of the trial, and the response came `rt` seconds after the stimulus. We compute both times for the choice trials, with `NaN` for trials without a response:" ] }, { "cell_type": "code", "execution_count": 8, "id": "fa20c4cf", "metadata": { "execution": { "iopub.execute_input": "2026-11-10T13:38:20.693787Z", "iopub.status.busy": "2026-11-10T13:38:20.693425Z", "iopub.status.idle": "2026-11-10T13:38:20.700708Z", "shell.execute_reply": "2026-11-10T13:38:20.699593Z" } }, "outputs": [], "source": [ "trials = behav[behav.probetrial == 0].copy()\n", "trials[\"rt\"] = trials.rt.where(trials.rt > 0) # -1 (no response) -> NaN\n", "stim_on = trials.tasktime + trials.fixcross_duration / 1000" ] }, { "cell_type": "markdown", "id": "58c16246", "metadata": {}, "source": [ "`Events.from_table()` turns these columns into events. The keys of the dict are the labels; the values are column names of the table or arrays of times. Times that are `NaN` give no event, so there are two responses fewer than stimuli. Other columns of the table can be kept as metadata of the events with `meta` (see the [event metadata tutorial](event_metadata.ipynb)): every event of a row gets the values of that row, and the column `row` gives the row in the table:" ] }, { "cell_type": "code", "execution_count": 9, "id": "a3ff158c", "metadata": { "execution": { "iopub.execute_input": "2026-11-10T13:38:20.702680Z", "iopub.status.busy": "2026-11-10T13:38:20.702480Z", "iopub.status.idle": "2026-11-10T13:38:20.713766Z", "shell.execute_reply": "2026-11-10T13:38:20.712646Z" } }, "outputs": [ { "data": { "text/html": [ "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
Onset (sec)Labeltrialchoicerewardrtrow
00.200038stim100NaN0
13.804168stim26180.3888221
24.192990response26180.3888221
37.408135stim3380.9660592
48.374194response3380.9660592
511.611549stim46180.6079323
612.219481response46180.6079323
714.614900stim56180.6480744
815.262974response56180.6480744
918.518260stim6291.0052305

Total: 10 events | on another clock: map with sync_clocks()

" ], "text/plain": [ "Events: 10 events | units=sec | meta=[trial, choice, reward, rt, row] | needs sync_clocks()" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ev = pp.Events.from_table(trials, {\"stim\": stim_on,\n", " \"response\": stim_on + trials.rt},\n", " units=\"sec\", meta=[\"trial\", \"choice\", \"reward\", \"rt\"])\n", "ev[0:10]" ] }, { "cell_type": "markdown", "id": "ee99e7ed", "metadata": {}, "source": [ "These times are still on the clock of the experiment computer. Its zero is the start of the task, while the eye tracker has its own zero point. We cannot add the events yet." ] }, { "cell_type": "markdown", "id": "635db158", "metadata": {}, "source": [ "## Synchronizing the clocks\n", "\n", "To map one clock to the other, we need moments that are in both logs. Here, these are the trial starts: `tasktime` in the behaviour log and the `F_` events in the recording. There is one of each per trial (including the probes), in the same order:" ] }, { "cell_type": "code", "execution_count": 10, "id": "13004815", "metadata": { "execution": { "iopub.execute_input": "2026-11-10T13:38:20.716458Z", "iopub.status.busy": "2026-11-10T13:38:20.716105Z", "iopub.status.idle": "2026-11-10T13:38:20.723167Z", "shell.execute_reply": "2026-11-10T13:38:20.722057Z" } }, "outputs": [ { "data": { "text/plain": [ "(330, 330)" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "fix = d.events.filter(\"F_\")\n", "len(behav.tasktime), len(fix)" ] }, { "cell_type": "markdown", "id": "db39d422", "metadata": {}, "source": [ "`pp.sync_clocks()` pairs them by order and fits a linear map: an offset (the different zero points) and a slope (the clocks run at slightly different speeds, so they drift apart). The units of the trial starts are given with `src_units`; the `F_` events have their own units (ms):" ] }, { "cell_type": "code", "execution_count": 11, "id": "c20c39db", "metadata": { "execution": { "iopub.execute_input": "2026-11-10T13:38:20.726275Z", "iopub.status.busy": "2026-11-10T13:38:20.726037Z", "iopub.status.idle": "2026-11-10T13:38:20.733908Z", "shell.execute_reply": "2026-11-10T13:38:20.732974Z" } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "\u001b[32mpp: 14:38:20\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36msync_clocks\u001b[0m:\u001b[36m1636\u001b[0m | \u001b[1msync_clocks(): offset=8352.59 ms, drift=-72.1 ms/h, max |residual|=17.7 ms (n=330)\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "\u001b[32mpp: 14:38:20\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36msync_clocks\u001b[0m:\u001b[36m1645\u001b[0m | \u001b[33m\u001b[1msync_clocks(): 127 of 330 residuals larger than 5 ms (pairs [0, 3, 8, 11, 12, ...]); are the times paired correctly?\u001b[0m\n" ] }, { "data": { "text/plain": [ "ClockSync(sec -> ms): offset=8352.59 ms, drift=-72.1 ms/h, max |residual|=17.7 ms (n=330)\n", " src dst residual\n", " 3.8e-05 8360 7.38\n", " 3.60417 11959 2.32\n", " 7.20814 15557 -3.58\n", " 10.8115 19156 -7.92\n", " 14.4149 22771 3.8\n", " ... (320 more)\n", " 1171.43 1179760 3.27\n", " 1175.03 1183358 -3.45\n", " 1178.64 1186974 7.76\n", " 1182.24 1190572 0.708\n", " 1185.85 1194171 -4.65" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sync = pp.sync_clocks(behav.tasktime, fix, src_units=\"sec\")\n", "sync" ] }, { "cell_type": "markdown", "id": "8e0d1999", "metadata": {}, "source": [ "The summary shows the offset, the drift (here, the eye-tracker clock loses about 72 ms per hour against the experiment computer) and the largest *residual*. The residuals are the differences between the times in the recording and the mapped times from the log. They are the check that the pairs are right.\n", "\n", "Here, `sync_clocks()` warns because some residuals are larger than the default tolerance of 5 ms. Plotting them shows why:" ] }, { "cell_type": "code", "execution_count": 12, "id": "cbe29577", "metadata": { "execution": { "iopub.execute_input": "2026-11-10T13:38:20.735959Z", "iopub.status.busy": "2026-11-10T13:38:20.735764Z", "iopub.status.idle": "2026-11-10T13:38:20.951198Z", "shell.execute_reply": "2026-11-10T13:38:20.950042Z" } }, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots(figsize=(10, 3))\n", "ax.plot(sync.src / 60, sync.residuals, \".\")\n", "ax.set_xlabel(\"time in task (min)\")\n", "ax.set_ylabel(\"residual (ms)\");" ] }, { "cell_type": "markdown", "id": "add9dba7", "metadata": {}, "source": [ "The residuals are spread evenly between about -10 and +10 ms, with no trend over time. This is not a problem with the pairing: PsychoPy logged `tasktime` at a slightly different moment than the trigger was sent, and the difference varies by up to about one screen frame (17 ms at 60 Hz). Each mapped time is therefore off by up to about 10 ms, but there is no systematic error. With a tolerance that allows for this, there is no warning:" ] }, { "cell_type": "code", "execution_count": 13, "id": "8f70d981", "metadata": { "execution": { "iopub.execute_input": "2026-11-10T13:38:20.953712Z", "iopub.status.busy": "2026-11-10T13:38:20.953437Z", "iopub.status.idle": "2026-11-10T13:38:20.960210Z", "shell.execute_reply": "2026-11-10T13:38:20.959048Z" } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "\u001b[32mpp: 14:38:20\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36msync_clocks\u001b[0m:\u001b[36m1636\u001b[0m | \u001b[1msync_clocks(): offset=8352.59 ms, drift=-72.1 ms/h, max |residual|=17.7 ms (n=330)\u001b[0m\n" ] } ], "source": [ "sync = pp.sync_clocks(behav.tasktime, fix, src_units=\"sec\", tol=20 * u.ms)" ] }, { "cell_type": "markdown", "id": "20f4c5f7", "metadata": {}, "source": [ "How large the residuals are depends on how the times were logged. Times taken at the screen flip (e.g., the return value of `win.flip()` in PsychoPy or `Screen('Flip')` in Psychtoolbox) usually agree with the trigger within 1–2 ms.\n", "\n", "This shows why it is usually a good idea to send the triggers directly to the eye-tracker from within the stimulus-presentation script." ] }, { "cell_type": "markdown", "id": "8fac02f6", "metadata": {}, "source": [ "## Adding the events\n", "\n", "Calling the fitted map on the events gives them on the clock of the eye tracker (with their metadata). `add_events()` adds them to the events of the recording and sorts all events by onset. The `F_` events have no metadata, so their values are missing:" ] }, { "cell_type": "code", "execution_count": 14, "id": "e27ee5be", "metadata": { "execution": { "iopub.execute_input": "2026-11-10T13:38:20.962686Z", "iopub.status.busy": "2026-11-10T13:38:20.962435Z", "iopub.status.idle": "2026-11-10T13:38:20.971200Z", "shell.execute_reply": "2026-11-10T13:38:20.970065Z" } }, "outputs": [ { "data": { "text/html": [ "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
Onset (ms)Labeltrialchoicerewardrtrow
08552.620039stim100NaN0
112156.677822stim26180.3888221
212545.492031response26180.3888221
315760.572609stim3380.9660592
416726.612251response3380.9660592
519963.902384stim46180.6079323
620571.822202response46180.6079323
722967.193205stim56180.6480744
823615.254219response56180.6480744
926870.474992stim6291.0052305

Total: 10 events

" ], "text/plain": [ "Events: 10 events | units=ms | meta=[trial, choice, reward, rt, row]" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sync(ev)[0:10]" ] }, { "cell_type": "code", "execution_count": 15, "id": "6e51b72b", "metadata": { "execution": { "iopub.execute_input": "2026-11-10T13:38:20.973968Z", "iopub.status.busy": "2026-11-10T13:38:20.973759Z", "iopub.status.idle": "2026-11-10T13:38:20.984199Z", "shell.execute_reply": "2026-11-10T13:38:20.983130Z" } }, "outputs": [ { "data": { "text/html": [ "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
Onset (ms)Labeltrialchoicerewardrtrow
08360.000000F_1NaNNaNNaNNaNNaN
18552.620039stim1.00.00.0NaN0.0
211959.000000F_2NaNNaNNaNNaNNaN
312156.677822stim2.06.018.00.3888221.0
412545.492031response2.06.018.00.3888221.0
515557.000000F_3NaNNaNNaNNaNNaN
615760.572609stim3.03.08.00.9660592.0
716726.612251response3.03.08.00.9660592.0

Total: 8 events

" ], "text/plain": [ "Events: 8 events | units=ms | meta=[trial, choice, reward, rt, row]" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "d = d.add_events(sync(ev))\n", "d.events[:8]" ] }, { "cell_type": "markdown", "id": "1c592482", "metadata": {}, "source": [ "Because the eye tracker also got the stimulus triggers in this experiment, we can check the result: the stimulus onsets from the behaviour log agree with the `S_` events within about ±9 ms, a few within 18 ms (the jitter we saw in the residuals):" ] }, { "cell_type": "code", "execution_count": 16, "id": "26309678", "metadata": { "execution": { "iopub.execute_input": "2026-11-10T13:38:20.986898Z", "iopub.status.busy": "2026-11-10T13:38:20.986678Z", "iopub.status.idle": "2026-11-10T13:38:21.157912Z", "shell.execute_reply": "2026-11-10T13:38:21.156897Z" } }, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "err = d.events.filter(\"stim\", exact=True).onsets - orig.filter(\"S_\").onsets\n", "\n", "fig, ax = plt.subplots(figsize=(5, 3))\n", "ax.hist(err, bins=20)\n", "ax.set_xlabel(\"stim onset (log) - S_ event (ms)\")\n", "ax.set_ylabel(\"trials\");" ] }, { "cell_type": "markdown", "id": "0e8bc021", "metadata": {}, "source": [ "## Using the events\n", "\n", "The new events work like all other events. For example, the pupil data from 2 to 2.5 minutes, with the fixation-cross, stimulus and response events:" ] }, { "cell_type": "code", "execution_count": 17, "id": "d5dd6cff", "metadata": { "execution": { "iopub.execute_input": "2026-11-10T13:38:21.161428Z", "iopub.status.busy": "2026-11-10T13:38:21.161189Z", "iopub.status.idle": "2026-11-10T13:38:21.663407Z", "shell.execute_reply": "2026-11-10T13:38:21.662439Z" } }, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(15, 4))\n", "d.plot.plot_pupil(plot_range=(2, 2.5) * u.min, units=\"sec\");" ] }, { "cell_type": "markdown", "id": "1e1c202f", "metadata": {}, "source": [ "The events from the log carry the information about their trial, so they can be selected by it, and `get_epochs()` passes it on to the epochs. Here, the mean pupil size in the second after each stimulus, in a table with the metadata of the stimulus events:" ] }, { "cell_type": "code", "execution_count": 18, "id": "61d8f0fe", "metadata": { "execution": { "iopub.execute_input": "2026-11-10T13:38:21.667178Z", "iopub.status.busy": "2026-11-10T13:38:21.666912Z", "iopub.status.idle": "2026-11-10T13:38:22.706726Z", "shell.execute_reply": "2026-11-10T13:38:22.705616Z" } }, "outputs": [ { "data": { "text/html": [ "
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trialchoicertrewardpupil
01.00.0NaN0.0832.528000
12.06.00.38882218.01018.716157
23.03.00.9660598.0887.991342
34.06.00.60793218.0855.376000
45.06.00.64807418.0963.724000
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" ], "text/plain": [ " trial choice rt reward pupil\n", "0 1.0 0.0 NaN 0.0 832.528000\n", "1 2.0 6.0 0.388822 18.0 1018.716157\n", "2 3.0 3.0 0.966059 8.0 887.991342\n", "3 4.0 6.0 0.607932 18.0 855.376000\n", "4 5.0 6.0 0.648074 18.0 963.724000" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "epochs = d.get_epochs(d.events.filter(\"stim\", exact=True), window=(0, 1000) * u.ms)\n", "pupil = d.stat_per_event(intervals=epochs, eyes=\"left\", variables=\"pupil\")\n", "\n", "table = epochs.as_pandas()\n", "table[\"pupil\"] = pupil[\"left_pupil\"]\n", "table[[\"trial\", \"choice\", \"rt\", \"reward\", \"pupil\"]].head()" ] }, { "cell_type": "markdown", "id": "meta-select-md", "metadata": {}, "source": [ "Selections work in the same way, e.g., the stimuli of the trials with a fast response:" ] }, { "cell_type": "code", "execution_count": 19, "id": "meta-select", "metadata": { "execution": { "iopub.execute_input": "2026-11-10T13:38:22.710174Z", "iopub.status.busy": "2026-11-10T13:38:22.709912Z", "iopub.status.idle": "2026-11-10T13:38:22.719787Z", "shell.execute_reply": "2026-11-10T13:38:22.718806Z" } }, "outputs": [ { "data": { "text/html": [ "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
Onset (ms)Labeltrialchoicerewardrtrow
012156.677822stim2.06.018.00.3888221.0
188440.980264stim22.04.014.00.49897721.0
2105858.858250stim26.03.018.00.49631425.0

Total: 3 events

" ], "text/plain": [ "Events: 3 events | units=ms | meta=[trial, choice, reward, rt, row]" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "d.events.filter(\"stim\", exact=True, rt=lambda rt: rt < 0.5)" ] }, { "cell_type": "markdown", "id": "83a56314", "metadata": {}, "source": [ "For event-related analyses, see the notebooks on [intervals](intervals.ipynb) and on [event-related pupil dilation](erpds.ipynb)." ] } ], "metadata": { "kernelspec": { "display_name": "pypil", "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 }