Detecting Gaze Events

What you will learn in this tutorial:

• how to detect saccades using the microsaccades algorithm

• how to detect fixations using the I-DT and I-VT algorithms

Preparations

We import pymovements as the alias pm for convenience.

import polars as pl

import pymovements as pm

/home/docs/checkouts/readthedocs.org/user_builds/pymovements/envs/v0.23.0/lib/python3.9/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html
  from .autonotebook import tqdm as notebook_tqdm

Let’s start by downloading our ToyDataset and loading in its data:

dataset = pm.Dataset('ToyDataset', path='data/ToyDataset')
dataset.download()
dataset.load()

INFO:pymovements.dataset.dataset:        You are downloading the pymovements Toy Dataset. Please be aware that pymovements does not
        host or distribute any dataset resources and only provides a convenient interface to
        download the public dataset resources that were published by their respective authors.

        Please cite the referenced publication if you intend to use the dataset in your research.

Downloading http://github.com/aeye-lab/pymovements-toy-dataset/zipball/6cb5d663317bf418cec0c9abe1dde5085a8a8ebd/ to data/ToyDataset/downloads/pymovements-toy-dataset.zip

pymovements-toy-dataset.zip: 100%|██████████| 3.06M/3.06M [00:00<00:00, 26.8MB/s]

Checking integrity of pymovements-toy-dataset.zip
Extracting pymovements-toy-dataset.zip to data/ToyDataset/raw

100%|██████████| 23/23 [00:00<00:00, 368.89it/s]
100%|██████████| 20/20 [00:00<00:00, 61.30it/s]

Dataset

• definition:
  DatasetDefinition
  DatasetDefinition

  • acceleration_columns:
    None
    None
  • column_map:
    dict (0 items)
  • custom_read_kwargs:
    dict (1 items)
    • gaze:
      dict (4 items)
      • columns:
        list (5 items)
        • 'timestamp'
        • 'x'
        • (3 more)
      • schema_overrides:
        dict (5 items)
        • timestamp:
          Float64
          Float64
        • x:
          Float64
          Float64
        • (3 more)
      • (2 more)
  • distance_column:
    None
    None
  • experiment:
    Experiment
    Experiment

    • eyetracker:
      EyeTracker
      EyeTracker

      • left:
        None
        None
      • model:
        None
        None
      • mount:
        None
        None
      • right:
        None
        None
      • sampling_rate:
        1000
        1000
      • vendor:
        None
        None
      • version:
        None
        None
    • sampling_rate:
      1000
      1000
    • screen:
      Screen
      Screen

      • distance_cm:
        68
        68
      • height_cm:
        30.2
        30.2
      • height_px:
        1024
        1024
      • origin:
        'upper left'
        'upper left'
      • width_cm:
        38
        38
      • width_px:
        1280
        1280
      • x_max_dva:
        15.599386487782953
        15.599386487782953
      • x_min_dva:
        -15.599386487782953
        -15.599386487782953
      • y_max_dva:
        12.508044410882546
        12.508044410882546
      • y_min_dva:
        -12.508044410882546
        -12.508044410882546
  • extract:
    None
    None
  • filename_format:
    dict (1 items)
    • gaze:
      'trial_{text_id:d}_{page_id:d}.csv'
      'trial_{text_id:d}_{page_id:d}.csv'
  • filename_format_schema_overrides:
    dict (1 items)
    • gaze:
      dict (2 items)
      • text_id:
        <class 'int'>
        <class 'int'>
      • page_id:
        <class 'int'>
        <class 'int'>
  • has_resources:
    True
    True
  • long_name:
    'pymovements Toy Dataset'
    'pymovements Toy Dataset'
  • mirrors:
    dict (0 items)
  • name:
    'ToyDataset'
    'ToyDataset'
  • pixel_columns:
    list (2 items)
    • 'x'
    • 'y'
  • position_columns:
    None
    None
  • resources:
    list (1 items)
    • ResourceDefinition(content='gaze', filename='pymovements-toy-dataset.zip', url='http://github.com/aeye-lab/pymovements-toy-dataset/zipball/6cb5d663317bf418cec0c9abe1dde5085a8a8ebd/', md5='4da622457637a8181d86601fe17f3aa8', filename_pattern='trial_{text_id:d}_{page_id:d}.csv', filename_pattern_schema_overrides={'text_id': <class 'int'>, 'page_id': <class 'int'>})
  • time_column:
    'timestamp'
    'timestamp'
  • time_unit:
    'ms'
    'ms'
  • trial_columns:
    None
    None
  • velocity_columns:
    None
    None
• events:
  list (0 items)
• fileinfo:
  dict (1 items)
  • gaze:
    DataFrame (3 columns, 20 rows)
    shape: (20, 3)

    text_id	page_id	filepath
    i64	i64	str
    0	1	"aeye-lab-pymovements-toy-datas…
    0	2	"aeye-lab-pymovements-toy-datas…
    0	3	"aeye-lab-pymovements-toy-datas…
    0	4	"aeye-lab-pymovements-toy-datas…
    0	5	"aeye-lab-pymovements-toy-datas…
    …	…	…
    3	1	"aeye-lab-pymovements-toy-datas…
    3	2	"aeye-lab-pymovements-toy-datas…
    3	3	"aeye-lab-pymovements-toy-datas…
    3	4	"aeye-lab-pymovements-toy-datas…
    3	5	"aeye-lab-pymovements-toy-datas…

• gaze:
  list (20 items)
  • Gaze
    • samples:
      DataFrame (6 columns, 17223 rows)
      shape: (17_223, 6)

      time	stimuli_x	stimuli_y	text_id	page_id	pixel
      i64	f64	f64	i64	i64	list[f64]
      1988145	-1.0	-1.0	0	1	[206.8, 152.4]
      1988146	-1.0	-1.0	0	1	[206.9, 152.1]
      1988147	-1.0	-1.0	0	1	[207.0, 151.8]
      1988148	-1.0	-1.0	0	1	[207.1, 151.7]
      1988149	-1.0	-1.0	0	1	[207.0, 151.5]
      …	…	…	…	…	…
      2005363	-1.0	-1.0	0	1	[361.0, 415.4]
      2005364	-1.0	-1.0	0	1	[358.0, 414.5]
      2005365	-1.0	-1.0	0	1	[355.8, 413.8]
      2005366	-1.0	-1.0	0	1	[353.1, 413.2]
      2005367	-1.0	-1.0	0	1	[351.2, 412.9]

    • events:
      Events
      Events

      • frame:
        DataFrame (6 columns, 0 rows)
        shape: (0, 6)

        text_id	page_id	name	onset	offset	duration
        i64	i64	str	i64	i64	i64

      • trial_columns:
        list (2 items)
        • 'text_id'
        • 'page_id'
    • trial_columns:
      list (2 items)
      • 'text_id'
      • 'page_id'
    • experiment:
      Experiment
      Experiment

      • eyetracker:
        EyeTracker
        EyeTracker

        • left:
          None
          None
        • model:
          None
          None
        • mount:
          None
          None
        • right:
          None
          None
        • sampling_rate:
          1000
          1000
        • vendor:
          None
          None
        • version:
          None
          None
      • sampling_rate:
        1000
        1000
      • screen:
        Screen
        Screen

        • distance_cm:
          68
          68
        • height_cm:
          30.2
          30.2
        • height_px:
          1024
          1024
        • origin:
          'upper left'
          'upper left'
        • width_cm:
          38
          38
        • width_px:
          1280
          1280
        • x_max_dva:
          15.599386487782953
          15.599386487782953
        • x_min_dva:
          -15.599386487782953
          -15.599386487782953
        • y_max_dva:
          12.508044410882546
          12.508044410882546
        • y_min_dva:
          -12.508044410882546
          -12.508044410882546
  • Gaze
    • samples:
      DataFrame (6 columns, 29799 rows)
      shape: (29_799, 6)

      time	stimuli_x	stimuli_y	text_id	page_id	pixel
      i64	f64	f64	i64	i64	list[f64]
      2008305	-1.0	-1.0	0	2	[141.4, 153.6]
      2008306	-1.0	-1.0	0	2	[141.1, 153.2]
      2008307	-1.0	-1.0	0	2	[140.7, 152.8]
      2008308	-1.0	-1.0	0	2	[140.6, 152.7]
      2008309	-1.0	-1.0	0	2	[140.5, 152.6]
      …	…	…	…	…	…
      2038099	-1.0	-1.0	0	2	[273.8, 773.8]
      2038100	-1.0	-1.0	0	2	[273.8, 774.1]
      2038101	-1.0	-1.0	0	2	[273.9, 774.5]
      2038102	-1.0	-1.0	0	2	[274.0, 774.4]
      2038103	-1.0	-1.0	0	2	[274.0, 773.9]

    • events:
      Events
      Events

      • frame:
        DataFrame (6 columns, 0 rows)
        shape: (0, 6)

        text_id	page_id	name	onset	offset	duration
        i64	i64	str	i64	i64	i64

      • trial_columns:
        list (2 items)
        • 'text_id'
        • 'page_id'
    • trial_columns:
      list (2 items)
      • 'text_id'
      • 'page_id'
    • experiment:
      Experiment
      Experiment

      • eyetracker:
        EyeTracker
        EyeTracker

        • left:
          None
          None
        • model:
          None
          None
        • mount:
          None
          None
        • right:
          None
          None
        • sampling_rate:
          1000
          1000
        • vendor:
          None
          None
        • version:
          None
          None
      • sampling_rate:
        1000
        1000
      • screen:
        Screen
        Screen

        • distance_cm:
          68
          68
        • height_cm:
          30.2
          30.2
        • height_px:
          1024
          1024
        • origin:
          'upper left'
          'upper left'
        • width_cm:
          38
          38
        • width_px:
          1280
          1280
        • x_max_dva:
          15.599386487782953
          15.599386487782953
        • x_min_dva:
          -15.599386487782953
          -15.599386487782953
        • y_max_dva:
          12.508044410882546
          12.508044410882546
        • y_min_dva:
          -12.508044410882546
          -12.508044410882546
  • (18 more)
• path:
  PosixPath('data/ToyDataset')
  PosixPath('data/ToyDataset')
• paths:
  DatasetPaths
  DatasetPaths

  • dataset:
    PosixPath('data/ToyDataset')
    PosixPath('data/ToyDataset')
  • downloads:
    PosixPath('data/ToyDataset/downloads')
    PosixPath('data/ToyDataset/downloads')
  • events:
    PosixPath('data/ToyDataset/events')
    PosixPath('data/ToyDataset/events')
  • precomputed_events:
    PosixPath('data/ToyDataset/precomputed_events')
    PosixPath('data/ToyDataset/precomputed_events')
  • precomputed_reading_measures:
    PosixPath
    PosixPath('data/ToyDataset/precomputed_reading_measures')
  • preprocessed:
    PosixPath('data/ToyDataset/preprocessed')
    PosixPath('data/ToyDataset/preprocessed')
  • raw:
    PosixPath('data/ToyDataset/raw')
    PosixPath('data/ToyDataset/raw')
  • root:
    PosixPath('data/ToyDataset')
    PosixPath('data/ToyDataset')
• precomputed_events:
  list (0 items)
• precomputed_reading_measures:
  list (0 items)

Now let’s do some basic preprocessing:

dataset.pix2deg()
dataset.pos2vel('smooth')

dataset.gaze[0]

100%|██████████| 20/20 [00:00<00:00, 32.39it/s]
100%|██████████| 20/20 [00:00<00:00, 64.61it/s]

Gaze

• samples:
  DataFrame (8 columns, 17223 rows)
  shape: (17_223, 8)

  time	stimuli_x	stimuli_y	text_id	page_id	pixel	position	velocity
  i64	f64	f64	i64	i64	list[f64]	list[f64]	list[f64]
  1988145	-1.0	-1.0	0	1	[206.8, 152.4]	[-10.697598, -8.852399]	[null, null]
  1988146	-1.0	-1.0	0	1	[206.9, 152.1]	[-10.695183, -8.859678]	[null, null]
  1988147	-1.0	-1.0	0	1	[207.0, 151.8]	[-10.692768, -8.866956]	[1.610194, -5.256267]
  1988148	-1.0	-1.0	0	1	[207.1, 151.7]	[-10.690352, -8.869381]	[0.402548, -4.447465]
  1988149	-1.0	-1.0	0	1	[207.0, 151.5]	[-10.692768, -8.874233]	[0.402561, -3.234462]
  …	…	…	…	…	…	…	…
  2005363	-1.0	-1.0	0	1	[361.0, 415.4]	[-6.932438, -2.386672]	[-63.266374, -21.085616]
  2005364	-1.0	-1.0	0	1	[358.0, 414.5]	[-7.006376, -2.408998]	[-63.249652, -19.431326]
  2005365	-1.0	-1.0	0	1	[355.8, 413.8]	[-7.060582, -2.426362]	[-60.359624, -15.710061]
  2005366	-1.0	-1.0	0	1	[353.1, 413.2]	[-7.12709, -2.441245]	[null, null]
  2005367	-1.0	-1.0	0	1	[351.2, 412.9]	[-7.173881, -2.448686]	[null, null]

• events:
  Events
  Events

  • frame:
    DataFrame (6 columns, 0 rows)
    shape: (0, 6)

    text_id	page_id	name	onset	offset	duration
    i64	i64	str	i64	i64	i64

  • trial_columns:
    list (2 items)
    • 'text_id'
    • 'page_id'
• trial_columns:
  list (2 items)
  • 'text_id'
  • 'page_id'
• experiment:
  Experiment
  Experiment

  • eyetracker:
    EyeTracker
    EyeTracker

    • left:
      None
      None
    • model:
      None
      None
    • mount:
      None
      None
    • right:
      None
      None
    • sampling_rate:
      1000
      1000
    • vendor:
      None
      None
    • version:
      None
      None
  • sampling_rate:
    1000
    1000
  • screen:
    Screen
    Screen

    • distance_cm:
      68
      68
    • height_cm:
      30.2
      30.2
    • height_px:
      1024
      1024
    • origin:
      'upper left'
      'upper left'
    • width_cm:
      38
      38
    • width_px:
      1280
      1280
    • x_max_dva:
      15.599386487782953
      15.599386487782953
    • x_min_dva:
      -15.599386487782953
      -15.599386487782953
    • y_max_dva:
      12.508044410882546
      12.508044410882546
    • y_min_dva:
      -12.508044410882546
      -12.508044410882546

Detecting Events

pymovements provides a range of event detection methods for several types of gaze events.

See the reference for pymovements.events to get an overview of all the supported methods.

For this tutorial we will use the microsaccades algorithm for detecting saccades and the I-DT and I-VT algorithms for detecting fixations.

We start out by detecting saccades.

dataset.detect_events('microsaccades', minimum_duration=12)

20it [00:00, 55.11it/s]

Dataset

• definition:
  DatasetDefinition
  DatasetDefinition

  • acceleration_columns:
    None
    None
  • column_map:
    dict (0 items)
  • custom_read_kwargs:
    dict (1 items)
    • gaze:
      dict (4 items)
      • columns:
        list (5 items)
        • 'timestamp'
        • 'x'
        • (3 more)
      • schema_overrides:
        dict (5 items)
        • timestamp:
          Float64
          Float64
        • x:
          Float64
          Float64
        • (3 more)
      • (2 more)
  • distance_column:
    None
    None
  • experiment:
    Experiment
    Experiment

    • eyetracker:
      EyeTracker
      EyeTracker

      • left:
        None
        None
      • model:
        None
        None
      • mount:
        None
        None
      • right:
        None
        None
      • sampling_rate:
        1000
        1000
      • vendor:
        None
        None
      • version:
        None
        None
    • sampling_rate:
      1000
      1000
    • screen:
      Screen
      Screen

      • distance_cm:
        68
        68
      • height_cm:
        30.2
        30.2
      • height_px:
        1024
        1024
      • origin:
        'upper left'
        'upper left'
      • width_cm:
        38
        38
      • width_px:
        1280
        1280
      • x_max_dva:
        15.599386487782953
        15.599386487782953
      • x_min_dva:
        -15.599386487782953
        -15.599386487782953
      • y_max_dva:
        12.508044410882546
        12.508044410882546
      • y_min_dva:
        -12.508044410882546
        -12.508044410882546
  • extract:
    None
    None
  • filename_format:
    dict (1 items)
    • gaze:
      'trial_{text_id:d}_{page_id:d}.csv'
      'trial_{text_id:d}_{page_id:d}.csv'
  • filename_format_schema_overrides:
    dict (1 items)
    • gaze:
      dict (2 items)
      • text_id:
        <class 'int'>
        <class 'int'>
      • page_id:
        <class 'int'>
        <class 'int'>
  • has_resources:
    True
    True
  • long_name:
    'pymovements Toy Dataset'
    'pymovements Toy Dataset'
  • mirrors:
    dict (0 items)
  • name:
    'ToyDataset'
    'ToyDataset'
  • pixel_columns:
    list (2 items)
    • 'x'
    • 'y'
  • position_columns:
    None
    None
  • resources:
    list (1 items)
    • ResourceDefinition(content='gaze', filename='pymovements-toy-dataset.zip', url='http://github.com/aeye-lab/pymovements-toy-dataset/zipball/6cb5d663317bf418cec0c9abe1dde5085a8a8ebd/', md5='4da622457637a8181d86601fe17f3aa8', filename_pattern='trial_{text_id:d}_{page_id:d}.csv', filename_pattern_schema_overrides={'text_id': <class 'int'>, 'page_id': <class 'int'>})
  • time_column:
    'timestamp'
    'timestamp'
  • time_unit:
    'ms'
    'ms'
  • trial_columns:
    None
    None
  • velocity_columns:
    None
    None
• events:
  list (20 items)
  • Events
    • frame:
      DataFrame (6 columns, 93 rows)
      shape: (93, 6)

      text_id	page_id	name	onset	offset	duration
      i64	i64	str	i64	i64	i64
      0	1	"saccade"	1988323	1988337	14
      0	1	"saccade"	1988547	1988567	20
      0	1	"saccade"	1988737	1988760	23
      0	1	"saccade"	1988765	1988777	12
      0	1	"saccade"	1989014	1989031	17
      …	…	…	…	…	…
      0	1	"saccade"	2004373	2004385	12
      0	1	"saccade"	2004688	2004704	16
      0	1	"saccade"	2004879	2004901	22
      0	1	"saccade"	2005110	2005126	16
      0	1	"saccade"	2005288	2005345	57

    • trial_columns:
      list (2 items)
      • 'text_id'
      • 'page_id'
  • Events
    • frame:
      DataFrame (6 columns, 153 rows)
      shape: (153, 6)

      text_id	page_id	name	onset	offset	duration
      i64	i64	str	i64	i64	i64
      0	2	"saccade"	2008474	2008497	23
      0	2	"saccade"	2008604	2008620	16
      0	2	"saccade"	2008807	2008838	31
      0	2	"saccade"	2009202	2009221	19
      0	2	"saccade"	2009417	2009436	19
      …	…	…	…	…	…
      0	2	"saccade"	2036849	2036861	12
      0	2	"saccade"	2037161	2037188	27
      0	2	"saccade"	2037412	2037503	91
      0	2	"saccade"	2037638	2037654	16
      0	2	"saccade"	2037812	2037830	18

    • trial_columns:
      list (2 items)
      • 'text_id'
      • 'page_id'
  • (18 more)
• fileinfo:
  dict (1 items)
  • gaze:
    DataFrame (3 columns, 20 rows)
    shape: (20, 3)

    text_id	page_id	filepath
    i64	i64	str
    0	1	"aeye-lab-pymovements-toy-datas…
    0	2	"aeye-lab-pymovements-toy-datas…
    0	3	"aeye-lab-pymovements-toy-datas…
    0	4	"aeye-lab-pymovements-toy-datas…
    0	5	"aeye-lab-pymovements-toy-datas…
    …	…	…
    3	1	"aeye-lab-pymovements-toy-datas…
    3	2	"aeye-lab-pymovements-toy-datas…
    3	3	"aeye-lab-pymovements-toy-datas…
    3	4	"aeye-lab-pymovements-toy-datas…
    3	5	"aeye-lab-pymovements-toy-datas…

• gaze:
  list (20 items)
  • Gaze
    • samples:
      DataFrame (8 columns, 17223 rows)
      shape: (17_223, 8)

      time	stimuli_x	stimuli_y	text_id	page_id	pixel	position	velocity
      i64	f64	f64	i64	i64	list[f64]	list[f64]	list[f64]
      1988145	-1.0	-1.0	0	1	[206.8, 152.4]	[-10.697598, -8.852399]	[null, null]
      1988146	-1.0	-1.0	0	1	[206.9, 152.1]	[-10.695183, -8.859678]	[null, null]
      1988147	-1.0	-1.0	0	1	[207.0, 151.8]	[-10.692768, -8.866956]	[1.610194, -5.256267]
      1988148	-1.0	-1.0	0	1	[207.1, 151.7]	[-10.690352, -8.869381]	[0.402548, -4.447465]
      1988149	-1.0	-1.0	0	1	[207.0, 151.5]	[-10.692768, -8.874233]	[0.402561, -3.234462]
      …	…	…	…	…	…	…	…
      2005363	-1.0	-1.0	0	1	[361.0, 415.4]	[-6.932438, -2.386672]	[-63.266374, -21.085616]
      2005364	-1.0	-1.0	0	1	[358.0, 414.5]	[-7.006376, -2.408998]	[-63.249652, -19.431326]
      2005365	-1.0	-1.0	0	1	[355.8, 413.8]	[-7.060582, -2.426362]	[-60.359624, -15.710061]
      2005366	-1.0	-1.0	0	1	[353.1, 413.2]	[-7.12709, -2.441245]	[null, null]
      2005367	-1.0	-1.0	0	1	[351.2, 412.9]	[-7.173881, -2.448686]	[null, null]

    • events:
      Events
      Events

      • frame:
        DataFrame (6 columns, 93 rows)
        shape: (93, 6)

        text_id	page_id	name	onset	offset	duration
        i64	i64	str	i64	i64	i64
        0	1	"saccade"	1988323	1988337	14
        0	1	"saccade"	1988547	1988567	20
        0	1	"saccade"	1988737	1988760	23
        0	1	"saccade"	1988765	1988777	12
        0	1	"saccade"	1989014	1989031	17
        …	…	…	…	…	…
        0	1	"saccade"	2004373	2004385	12
        0	1	"saccade"	2004688	2004704	16
        0	1	"saccade"	2004879	2004901	22
        0	1	"saccade"	2005110	2005126	16
        0	1	"saccade"	2005288	2005345	57

      • trial_columns:
        list (2 items)
        • 'text_id'
        • 'page_id'
    • trial_columns:
      list (2 items)
      • 'text_id'
      • 'page_id'
    • experiment:
      Experiment
      Experiment

      • eyetracker:
        EyeTracker
        EyeTracker

        • left:
          None
          None
        • model:
          None
          None
        • mount:
          None
          None
        • right:
          None
          None
        • sampling_rate:
          1000
          1000
        • vendor:
          None
          None
        • version:
          None
          None
      • sampling_rate:
        1000
        1000
      • screen:
        Screen
        Screen

        • distance_cm:
          68
          68
        • height_cm:
          30.2
          30.2
        • height_px:
          1024
          1024
        • origin:
          'upper left'
          'upper left'
        • width_cm:
          38
          38
        • width_px:
          1280
          1280
        • x_max_dva:
          15.599386487782953
          15.599386487782953
        • x_min_dva:
          -15.599386487782953
          -15.599386487782953
        • y_max_dva:
          12.508044410882546
          12.508044410882546
        • y_min_dva:
          -12.508044410882546
          -12.508044410882546
  • Gaze
    • samples:
      DataFrame (8 columns, 29799 rows)
      shape: (29_799, 8)

      time	stimuli_x	stimuli_y	text_id	page_id	pixel	position	velocity
      i64	f64	f64	i64	i64	list[f64]	list[f64]	list[f64]
      2008305	-1.0	-1.0	0	2	[141.4, 153.6]	[-12.268583, -8.823284]	[null, null]
      2008306	-1.0	-1.0	0	2	[141.1, 153.2]	[-12.275749, -8.832989]	[null, null]
      2008307	-1.0	-1.0	0	2	[140.7, 152.8]	[-12.285302, -8.842695]	[-5.572617, -6.065816]
      2008308	-1.0	-1.0	0	2	[140.6, 152.7]	[-12.28769, -8.845121]	[-3.582268, -4.043733]
      2008309	-1.0	-1.0	0	2	[140.5, 152.6]	[-12.290078, -8.847547]	[-2.388085, -2.021821]
      …	…	…	…	…	…	…	…
      2038099	-1.0	-1.0	0	2	[273.8, 773.8]	[-9.071149, 6.490168]	[1.21962, 1.635403]
      2038100	-1.0	-1.0	0	2	[273.8, 774.1]	[-9.071149, 6.497527]	[1.626175, 4.497406]
      2038101	-1.0	-1.0	0	2	[273.9, 774.5]	[-9.06871, 6.50734]	[1.626186, 1.635423]
      2038102	-1.0	-1.0	0	2	[274.0, 774.4]	[-9.066271, 6.504886]	[null, null]
      2038103	-1.0	-1.0	0	2	[274.0, 773.9]	[-9.066271, 6.492621]	[null, null]

    • events:
      Events
      Events

      • frame:
        DataFrame (6 columns, 153 rows)
        shape: (153, 6)

        text_id	page_id	name	onset	offset	duration
        i64	i64	str	i64	i64	i64
        0	2	"saccade"	2008474	2008497	23
        0	2	"saccade"	2008604	2008620	16
        0	2	"saccade"	2008807	2008838	31
        0	2	"saccade"	2009202	2009221	19
        0	2	"saccade"	2009417	2009436	19
        …	…	…	…	…	…
        0	2	"saccade"	2036849	2036861	12
        0	2	"saccade"	2037161	2037188	27
        0	2	"saccade"	2037412	2037503	91
        0	2	"saccade"	2037638	2037654	16
        0	2	"saccade"	2037812	2037830	18

      • trial_columns:
        list (2 items)
        • 'text_id'
        • 'page_id'
    • trial_columns:
      list (2 items)
      • 'text_id'
      • 'page_id'
    • experiment:
      Experiment
      Experiment

      • eyetracker:
        EyeTracker
        EyeTracker

        • left:
          None
          None
        • model:
          None
          None
        • mount:
          None
          None
        • right:
          None
          None
        • sampling_rate:
          1000
          1000
        • vendor:
          None
          None
        • version:
          None
          None
      • sampling_rate:
        1000
        1000
      • screen:
        Screen
        Screen

        • distance_cm:
          68
          68
        • height_cm:
          30.2
          30.2
        • height_px:
          1024
          1024
        • origin:
          'upper left'
          'upper left'
        • width_cm:
          38
          38
        • width_px:
          1280
          1280
        • x_max_dva:
          15.599386487782953
          15.599386487782953
        • x_min_dva:
          -15.599386487782953
          -15.599386487782953
        • y_max_dva:
          12.508044410882546
          12.508044410882546
        • y_min_dva:
          -12.508044410882546
          -12.508044410882546
  • (18 more)
• path:
  PosixPath('data/ToyDataset')
  PosixPath('data/ToyDataset')
• paths:
  DatasetPaths
  DatasetPaths

  • dataset:
    PosixPath('data/ToyDataset')
    PosixPath('data/ToyDataset')
  • downloads:
    PosixPath('data/ToyDataset/downloads')
    PosixPath('data/ToyDataset/downloads')
  • events:
    PosixPath('data/ToyDataset/events')
    PosixPath('data/ToyDataset/events')
  • precomputed_events:
    PosixPath('data/ToyDataset/precomputed_events')
    PosixPath('data/ToyDataset/precomputed_events')
  • precomputed_reading_measures:
    PosixPath
    PosixPath('data/ToyDataset/precomputed_reading_measures')
  • preprocessed:
    PosixPath('data/ToyDataset/preprocessed')
    PosixPath('data/ToyDataset/preprocessed')
  • raw:
    PosixPath('data/ToyDataset/raw')
    PosixPath('data/ToyDataset/raw')
  • root:
    PosixPath('data/ToyDataset')
    PosixPath('data/ToyDataset')
• precomputed_events:
  list (0 items)
• precomputed_reading_measures:
  list (0 items)

The detected events are added as rows with the name saccade to the event dataframe:

dataset.events[0]

Events

• frame:
  DataFrame (6 columns, 93 rows)
  shape: (93, 6)

  text_id	page_id	name	onset	offset	duration
  i64	i64	str	i64	i64	i64
  0	1	"saccade"	1988323	1988337	14
  0	1	"saccade"	1988547	1988567	20
  0	1	"saccade"	1988737	1988760	23
  0	1	"saccade"	1988765	1988777	12
  0	1	"saccade"	1989014	1989031	17
  …	…	…	…	…	…
  0	1	"saccade"	2004373	2004385	12
  0	1	"saccade"	2004688	2004704	16
  0	1	"saccade"	2004879	2004901	22
  0	1	"saccade"	2005110	2005126	16
  0	1	"saccade"	2005288	2005345	57

• trial_columns:
  list (2 items)
  • 'text_id'
  • 'page_id'

Next we will detect fixations using the I-DT and I-VT algorithms.

To be able to differentiate between the detected events, we specify custom event names for each algorithm:

dataset.detect_events('idt', dispersion_threshold=2.7, name='fixation.idt')
dataset.detect_events('ivt', velocity_threshold=20, name='fixation.ivt')

20it [00:04,  5.00it/s]
20it [00:00, 100.35it/s]

Dataset

• definition:
  DatasetDefinition
  DatasetDefinition

  • acceleration_columns:
    None
    None
  • column_map:
    dict (0 items)
  • custom_read_kwargs:
    dict (1 items)
    • gaze:
      dict (4 items)
      • columns:
        list (5 items)
        • 'timestamp'
        • 'x'
        • (3 more)
      • schema_overrides:
        dict (5 items)
        • timestamp:
          Float64
          Float64
        • x:
          Float64
          Float64
        • (3 more)
      • (2 more)
  • distance_column:
    None
    None
  • experiment:
    Experiment
    Experiment

    • eyetracker:
      EyeTracker
      EyeTracker

      • left:
        None
        None
      • model:
        None
        None
      • mount:
        None
        None
      • right:
        None
        None
      • sampling_rate:
        1000
        1000
      • vendor:
        None
        None
      • version:
        None
        None
    • sampling_rate:
      1000
      1000
    • screen:
      Screen
      Screen

      • distance_cm:
        68
        68
      • height_cm:
        30.2
        30.2
      • height_px:
        1024
        1024
      • origin:
        'upper left'
        'upper left'
      • width_cm:
        38
        38
      • width_px:
        1280
        1280
      • x_max_dva:
        15.599386487782953
        15.599386487782953
      • x_min_dva:
        -15.599386487782953
        -15.599386487782953
      • y_max_dva:
        12.508044410882546
        12.508044410882546
      • y_min_dva:
        -12.508044410882546
        -12.508044410882546
  • extract:
    None
    None
  • filename_format:
    dict (1 items)
    • gaze:
      'trial_{text_id:d}_{page_id:d}.csv'
      'trial_{text_id:d}_{page_id:d}.csv'
  • filename_format_schema_overrides:
    dict (1 items)
    • gaze:
      dict (2 items)
      • text_id:
        <class 'int'>
        <class 'int'>
      • page_id:
        <class 'int'>
        <class 'int'>
  • has_resources:
    True
    True
  • long_name:
    'pymovements Toy Dataset'
    'pymovements Toy Dataset'
  • mirrors:
    dict (0 items)
  • name:
    'ToyDataset'
    'ToyDataset'
  • pixel_columns:
    list (2 items)
    • 'x'
    • 'y'
  • position_columns:
    None
    None
  • resources:
    list (1 items)
    • ResourceDefinition(content='gaze', filename='pymovements-toy-dataset.zip', url='http://github.com/aeye-lab/pymovements-toy-dataset/zipball/6cb5d663317bf418cec0c9abe1dde5085a8a8ebd/', md5='4da622457637a8181d86601fe17f3aa8', filename_pattern='trial_{text_id:d}_{page_id:d}.csv', filename_pattern_schema_overrides={'text_id': <class 'int'>, 'page_id': <class 'int'>})
  • time_column:
    'timestamp'
    'timestamp'
  • time_unit:
    'ms'
    'ms'
  • trial_columns:
    None
    None
  • velocity_columns:
    None
    None
• events:
  list (20 items)
  • Events
    • frame:
      DataFrame (6 columns, 222 rows)
      shape: (222, 6)

      text_id	page_id	name	onset	offset	duration
      i64	i64	str	i64	i64	i64
      0	1	"saccade"	1988323	1988337	14
      0	1	"saccade"	1988547	1988567	20
      0	1	"saccade"	1988737	1988760	23
      0	1	"saccade"	1988765	1988777	12
      0	1	"saccade"	1989014	1989031	17
      …	…	…	…	…	…
      0	1	"fixation.ivt"	2004132	2004331	199
      0	1	"fixation.ivt"	2004399	2004687	288
      0	1	"fixation.ivt"	2004714	2004878	164
      0	1	"fixation.ivt"	2004931	2005109	178
      0	1	"fixation.ivt"	2005138	2005287	149

    • trial_columns:
      list (2 items)
      • 'text_id'
      • 'page_id'
  • Events
    • frame:
      DataFrame (6 columns, 366 rows)
      shape: (366, 6)

      text_id	page_id	name	onset	offset	duration
      i64	i64	str	i64	i64	i64
      0	2	"saccade"	2008474	2008497	23
      0	2	"saccade"	2008604	2008620	16
      0	2	"saccade"	2008807	2008838	31
      0	2	"saccade"	2009202	2009221	19
      0	2	"saccade"	2009417	2009436	19
      …	…	…	…	…	…
      0	2	"fixation.ivt"	2036567	2036821	254
      0	2	"fixation.ivt"	2036871	2037160	289
      0	2	"fixation.ivt"	2037189	2037411	222
      0	2	"fixation.ivt"	2037504	2037637	133
      0	2	"fixation.ivt"	2037861	2038101	240

    • trial_columns:
      list (2 items)
      • 'text_id'
      • 'page_id'
  • (18 more)
• fileinfo:
  dict (1 items)
  • gaze:
    DataFrame (3 columns, 20 rows)
    shape: (20, 3)

    text_id	page_id	filepath
    i64	i64	str
    0	1	"aeye-lab-pymovements-toy-datas…
    0	2	"aeye-lab-pymovements-toy-datas…
    0	3	"aeye-lab-pymovements-toy-datas…
    0	4	"aeye-lab-pymovements-toy-datas…
    0	5	"aeye-lab-pymovements-toy-datas…
    …	…	…
    3	1	"aeye-lab-pymovements-toy-datas…
    3	2	"aeye-lab-pymovements-toy-datas…
    3	3	"aeye-lab-pymovements-toy-datas…
    3	4	"aeye-lab-pymovements-toy-datas…
    3	5	"aeye-lab-pymovements-toy-datas…

• gaze:
  list (20 items)
  • Gaze
    • samples:
      DataFrame (8 columns, 17223 rows)
      shape: (17_223, 8)

      time	stimuli_x	stimuli_y	text_id	page_id	pixel	position	velocity
      i64	f64	f64	i64	i64	list[f64]	list[f64]	list[f64]
      1988145	-1.0	-1.0	0	1	[206.8, 152.4]	[-10.697598, -8.852399]	[null, null]
      1988146	-1.0	-1.0	0	1	[206.9, 152.1]	[-10.695183, -8.859678]	[null, null]
      1988147	-1.0	-1.0	0	1	[207.0, 151.8]	[-10.692768, -8.866956]	[1.610194, -5.256267]
      1988148	-1.0	-1.0	0	1	[207.1, 151.7]	[-10.690352, -8.869381]	[0.402548, -4.447465]
      1988149	-1.0	-1.0	0	1	[207.0, 151.5]	[-10.692768, -8.874233]	[0.402561, -3.234462]
      …	…	…	…	…	…	…	…
      2005363	-1.0	-1.0	0	1	[361.0, 415.4]	[-6.932438, -2.386672]	[-63.266374, -21.085616]
      2005364	-1.0	-1.0	0	1	[358.0, 414.5]	[-7.006376, -2.408998]	[-63.249652, -19.431326]
      2005365	-1.0	-1.0	0	1	[355.8, 413.8]	[-7.060582, -2.426362]	[-60.359624, -15.710061]
      2005366	-1.0	-1.0	0	1	[353.1, 413.2]	[-7.12709, -2.441245]	[null, null]
      2005367	-1.0	-1.0	0	1	[351.2, 412.9]	[-7.173881, -2.448686]	[null, null]

    • events:
      Events
      Events

      • frame:
        DataFrame (6 columns, 222 rows)
        shape: (222, 6)

        text_id	page_id	name	onset	offset	duration
        i64	i64	str	i64	i64	i64
        0	1	"saccade"	1988323	1988337	14
        0	1	"saccade"	1988547	1988567	20
        0	1	"saccade"	1988737	1988760	23
        0	1	"saccade"	1988765	1988777	12
        0	1	"saccade"	1989014	1989031	17
        …	…	…	…	…	…
        0	1	"fixation.ivt"	2004132	2004331	199
        0	1	"fixation.ivt"	2004399	2004687	288
        0	1	"fixation.ivt"	2004714	2004878	164
        0	1	"fixation.ivt"	2004931	2005109	178
        0	1	"fixation.ivt"	2005138	2005287	149

      • trial_columns:
        list (2 items)
        • 'text_id'
        • 'page_id'
    • trial_columns:
      list (2 items)
      • 'text_id'
      • 'page_id'
    • experiment:
      Experiment
      Experiment

      • eyetracker:
        EyeTracker
        EyeTracker

        • left:
          None
          None
        • model:
          None
          None
        • mount:
          None
          None
        • right:
          None
          None
        • sampling_rate:
          1000
          1000
        • vendor:
          None
          None
        • version:
          None
          None
      • sampling_rate:
        1000
        1000
      • screen:
        Screen
        Screen

        • distance_cm:
          68
          68
        • height_cm:
          30.2
          30.2
        • height_px:
          1024
          1024
        • origin:
          'upper left'
          'upper left'
        • width_cm:
          38
          38
        • width_px:
          1280
          1280
        • x_max_dva:
          15.599386487782953
          15.599386487782953
        • x_min_dva:
          -15.599386487782953
          -15.599386487782953
        • y_max_dva:
          12.508044410882546
          12.508044410882546
        • y_min_dva:
          -12.508044410882546
          -12.508044410882546
  • Gaze
    • samples:
      DataFrame (8 columns, 29799 rows)
      shape: (29_799, 8)

      time	stimuli_x	stimuli_y	text_id	page_id	pixel	position	velocity
      i64	f64	f64	i64	i64	list[f64]	list[f64]	list[f64]
      2008305	-1.0	-1.0	0	2	[141.4, 153.6]	[-12.268583, -8.823284]	[null, null]
      2008306	-1.0	-1.0	0	2	[141.1, 153.2]	[-12.275749, -8.832989]	[null, null]
      2008307	-1.0	-1.0	0	2	[140.7, 152.8]	[-12.285302, -8.842695]	[-5.572617, -6.065816]
      2008308	-1.0	-1.0	0	2	[140.6, 152.7]	[-12.28769, -8.845121]	[-3.582268, -4.043733]
      2008309	-1.0	-1.0	0	2	[140.5, 152.6]	[-12.290078, -8.847547]	[-2.388085, -2.021821]
      …	…	…	…	…	…	…	…
      2038099	-1.0	-1.0	0	2	[273.8, 773.8]	[-9.071149, 6.490168]	[1.21962, 1.635403]
      2038100	-1.0	-1.0	0	2	[273.8, 774.1]	[-9.071149, 6.497527]	[1.626175, 4.497406]
      2038101	-1.0	-1.0	0	2	[273.9, 774.5]	[-9.06871, 6.50734]	[1.626186, 1.635423]
      2038102	-1.0	-1.0	0	2	[274.0, 774.4]	[-9.066271, 6.504886]	[null, null]
      2038103	-1.0	-1.0	0	2	[274.0, 773.9]	[-9.066271, 6.492621]	[null, null]

    • events:
      Events
      Events

      • frame:
        DataFrame (6 columns, 366 rows)
        shape: (366, 6)

        text_id	page_id	name	onset	offset	duration
        i64	i64	str	i64	i64	i64
        0	2	"saccade"	2008474	2008497	23
        0	2	"saccade"	2008604	2008620	16
        0	2	"saccade"	2008807	2008838	31
        0	2	"saccade"	2009202	2009221	19
        0	2	"saccade"	2009417	2009436	19
        …	…	…	…	…	…
        0	2	"fixation.ivt"	2036567	2036821	254
        0	2	"fixation.ivt"	2036871	2037160	289
        0	2	"fixation.ivt"	2037189	2037411	222
        0	2	"fixation.ivt"	2037504	2037637	133
        0	2	"fixation.ivt"	2037861	2038101	240

      • trial_columns:
        list (2 items)
        • 'text_id'
        • 'page_id'
    • trial_columns:
      list (2 items)
      • 'text_id'
      • 'page_id'
    • experiment:
      Experiment
      Experiment

      • eyetracker:
        EyeTracker
        EyeTracker

        • left:
          None
          None
        • model:
          None
          None
        • mount:
          None
          None
        • right:
          None
          None
        • sampling_rate:
          1000
          1000
        • vendor:
          None
          None
        • version:
          None
          None
      • sampling_rate:
        1000
        1000
      • screen:
        Screen
        Screen

        • distance_cm:
          68
          68
        • height_cm:
          30.2
          30.2
        • height_px:
          1024
          1024
        • origin:
          'upper left'
          'upper left'
        • width_cm:
          38
          38
        • width_px:
          1280
          1280
        • x_max_dva:
          15.599386487782953
          15.599386487782953
        • x_min_dva:
          -15.599386487782953
          -15.599386487782953
        • y_max_dva:
          12.508044410882546
          12.508044410882546
        • y_min_dva:
          -12.508044410882546
          -12.508044410882546
  • (18 more)
• path:
  PosixPath('data/ToyDataset')
  PosixPath('data/ToyDataset')
• paths:
  DatasetPaths
  DatasetPaths

  • dataset:
    PosixPath('data/ToyDataset')
    PosixPath('data/ToyDataset')
  • downloads:
    PosixPath('data/ToyDataset/downloads')
    PosixPath('data/ToyDataset/downloads')
  • events:
    PosixPath('data/ToyDataset/events')
    PosixPath('data/ToyDataset/events')
  • precomputed_events:
    PosixPath('data/ToyDataset/precomputed_events')
    PosixPath('data/ToyDataset/precomputed_events')
  • precomputed_reading_measures:
    PosixPath
    PosixPath('data/ToyDataset/precomputed_reading_measures')
  • preprocessed:
    PosixPath('data/ToyDataset/preprocessed')
    PosixPath('data/ToyDataset/preprocessed')
  • raw:
    PosixPath('data/ToyDataset/raw')
    PosixPath('data/ToyDataset/raw')
  • root:
    PosixPath('data/ToyDataset')
    PosixPath('data/ToyDataset')
• precomputed_events:
  list (0 items)
• precomputed_reading_measures:
  list (0 items)

This has added new rows with the fixation events to the event dataframe.

dataset.events[0].frame.filter(pl.col('name') == 'fixation.idt').head()

shape: (5, 6)

text_id	page_id	name	onset	offset	duration
i64	i64	str	i64	i64	i64
0	1	"fixation.idt"	1988145	1988563	418
0	1	"fixation.idt"	1988564	1988750	186
0	1	"fixation.idt"	1988751	1989178	427
0	1	"fixation.idt"	1989179	1989436	257
0	1	"fixation.idt"	1989437	1989600	163

dataset.events[0].frame.filter(pl.col('name') == 'fixation.ivt').head()

shape: (5, 6)

text_id	page_id	name	onset	offset	duration
i64	i64	str	i64	i64	i64
0	1	"fixation.ivt"	1988147	1988322	175
0	1	"fixation.ivt"	1988351	1988546	195
0	1	"fixation.ivt"	1988592	1988736	144
0	1	"fixation.ivt"	1988788	1989013	225
0	1	"fixation.ivt"	1989060	1989170	110

What you have learned in this tutorial:

• detecting saccades by using the microsaccades algorithm

• detecting fixations by using the I-DT and I-VT algorithms