pymovements in 10 minutes

What you will learn in this tutorial:

• how to download one of the publicly available datasets

• how to load a subset of the data into your memory

• how to transform pixel coordinates into degrees of visual angle

• how to transform positional data into velocity data

• how to detect fixations by using the I-VT algorithm

• how to detect saccades by using the microsaccades algorithm

• how to compute additional event properties for your analysis

• how to save your preprocessed data

• how to plot the main saccadic sequence from your data

Downloading one of the public datasets

We import pymovements as the alias pm for convenience.

import polars as pl

import pymovements as pm

pymovements provides a library of publicly available datasets.

You can browse through the available dataset definitions here: Datasets

For this tutorial we will limit ourselves to the ToyDataset due to its minimal space requirements.

Other datasets can be downloaded by simply replacing ToyDataset with one of the other available datasets.

We can initialize and download by passing the desired dataset name as a string argument.

Additionally we need the root directory path of your data.

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

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

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

100%|██████████| 23/23 [00:00<00:00, 305.17it/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'
        'gaze'
      • filename:
        'pymovements-toy-dataset.zip'
        'pymovements-toy-dataset.zip'
      • filename_pattern:
        'trial_{text_id:d}_{page_id:d}.csv'
        'trial_{text_id:d}_{page_id:d}.csv'
      • filename_pattern_schema_overrides:
        dict (2 items)
        • text_id:
          <class 'int'>
          <class 'int'>
        • page_id:
          <class 'int'>
          <class 'int'>
      • md5:
        '4da622457637a8181d86601fe17f3aa8'
        '4da622457637a8181d86601fe17f3aa8'
      • url:
        str
        'http://github.com/aeye-lab/pymovements-toy-dataset/zipball/6cb5d663317bf418cec0c9abe1dde5085a8a8ebd/'
  • time_column:
    'timestamp'
    'timestamp'
  • time_unit:
    'ms'
    'ms'
  • trial_columns:
    None
    None
  • velocity_columns:
    None
    None
• events:
  list (0 items)
• fileinfo:
  DataFrame (0 columns, 0 rows)
  shape: (0, 0)
• gaze:
  list (0 items)
• 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)

Our downloaded dataset will be placed in new a directory with the name of the dataset:

dataset.path

PosixPath('data/ToyDataset')

Archive files are automatically extracted into the path specified by Dataset.paths.raw:

dataset.paths.raw

PosixPath('data/ToyDataset/raw')

Loading in your data into memory

Next we load our dataset into memory to be able to work with it:

dataset.load()

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'
        'gaze'
      • filename:
        'pymovements-toy-dataset.zip'
        'pymovements-toy-dataset.zip'
      • filename_pattern:
        'trial_{text_id:d}_{page_id:d}.csv'
        'trial_{text_id:d}_{page_id:d}.csv'
      • filename_pattern_schema_overrides:
        dict (2 items)
        • text_id:
          <class 'int'>
          <class 'int'>
        • page_id:
          <class 'int'>
          <class 'int'>
      • md5:
        '4da622457637a8181d86601fe17f3aa8'
        '4da622457637a8181d86601fe17f3aa8'
      • url:
        str
        'http://github.com/aeye-lab/pymovements-toy-dataset/zipball/6cb5d663317bf418cec0c9abe1dde5085a8a8ebd/'
  • 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)

This way we fill two attributes with data. First we have the fileinfo attribute which holds all the basic information for files:

dataset.fileinfo['gaze'].head()

shape: (5, 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…

We notice that for each filepath a text_id and page_id is specified.

We have also loaded our gaze data into the dataframes in the gaze attribute:

dataset.gaze[0]

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

Apart from some trial identifier columns we see the columns time and pixel.

The last two columns refer to the pixel coordinates at the timestep specified by time.

We are also able to just take a subset of the data by specifying values of the fileinfo columns. The key refers to the column in the fileinfo dataframe. The values in the dictionary can be of type bool, int, float or str, but also lists and ranges

subset = {
    'text_id': 0,
    'page_id': [0, 1],
}
dataset.load(subset=subset)

dataset.fileinfo

{'gaze': shape: (1, 3)
 ┌─────────┬─────────┬─────────────────────────────────┐
 │ text_id ┆ page_id ┆ filepath                        │
 │ ---     ┆ ---     ┆ ---                             │
 │ i64     ┆ i64     ┆ str                             │
 ╞═════════╪═════════╪═════════════════════════════════╡
 │ 0       ┆ 1       ┆ aeye-lab-pymovements-toy-datas… │
 └─────────┴─────────┴─────────────────────────────────┘}

Now we selected only a small subset of our data.

Preprocessing raw gaze data

We now want to preprocess our gaze data by transforming pixel coordinates into degrees of visual angle and then computing velocity data from our positional data.

dataset.pix2deg()

dataset.gaze[0]

Gaze

• samples:
  DataFrame (7 columns, 17223 rows)
  shape: (17_223, 7)

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

• 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

We notice that a new column has appeared: position. This column specifies the position coordinates in degrees of visual angle (dva).

For transforming our positional data into velocity data we will use the Savitzky-Golay differentiation filter.

We can also specify some additional parameters for this method:

dataset.pos2vel(method='savitzky_golay', degree=2, window_length=7)

dataset.gaze[0]

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]	[1.207641, -3.119165]
  1988146	-1.0	-1.0	0	1	[206.9, 152.1]	[-10.695183, -8.859678]	[1.20764, -4.072198]
  1988147	-1.0	-1.0	0	1	[207.0, 151.8]	[-10.692768, -8.866956]	[1.035119, -4.765267]
  1988148	-1.0	-1.0	0	1	[207.1, 151.7]	[-10.690352, -8.869381]	[1.207654, -4.245382]
  1988149	-1.0	-1.0	0	1	[207.0, 151.5]	[-10.692768, -8.874233]	[1.552735, -2.339263]
  …	…	…	…	…	…	…	…
  2005363	-1.0	-1.0	0	1	[361.0, 415.4]	[-6.932438, -2.386672]	[-62.062479, -20.465552]
  2005364	-1.0	-1.0	0	1	[358.0, 414.5]	[-7.006376, -2.408998]	[-61.343786, -18.073031]
  2005365	-1.0	-1.0	0	1	[355.8, 413.8]	[-7.060582, -2.426362]	[-53.501231, -14.617634]
  2005366	-1.0	-1.0	0	1	[353.1, 413.2]	[-7.12709, -2.441245]	[-41.879965, -10.276475]
  2005367	-1.0	-1.0	0	1	[351.2, 412.9]	[-7.173881, -2.448686]	[-27.710881, -6.112645]

• 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

There is also the more general apply() method, which can be used to apply both transformation and event detection methods.

dataset.apply('pos2acc', degree=2, window_length=7)

dataset.gaze[0]

Gaze

• samples:
  DataFrame (9 columns, 17223 rows)
  shape: (17_223, 9)

  time	stimuli_x	stimuli_y	text_id	page_id	pixel	position	velocity	acceleration
  i64	f64	f64	i64	i64	list[f64]	list[f64]	list[f64]	list[f64]
  1988145	-1.0	-1.0	0	1	[206.8, 152.4]	[-10.697598, -8.852399]	[1.207641, -3.119165]	[690.085837, -1501.799767]
  1988146	-1.0	-1.0	0	1	[206.9, 152.1]	[-10.695183, -8.859678]	[1.20764, -4.072198]	[0.001831, -866.371365]
  1988147	-1.0	-1.0	0	1	[207.0, 151.8]	[-10.692768, -8.866956]	[1.035119, -4.765267]	[-575.06741, -57.655244]
  1988148	-1.0	-1.0	0	1	[207.1, 151.7]	[-10.690352, -8.869381]	[1.207654, -4.245382]	[-230.013049, 1328.57081]
  1988149	-1.0	-1.0	0	1	[207.0, 151.5]	[-10.692768, -8.874233]	[1.552735, -2.339263]	[690.12611, 2021.586565]
  …	…	…	…	…	…	…	…	…
  2005363	-1.0	-1.0	0	1	[361.0, 415.4]	[-6.932438, -2.386672]	[-62.062479, -20.465552]	[-1099.087619, 1477.17518]
  2005364	-1.0	-1.0	0	1	[358.0, 414.5]	[-7.006376, -2.408998]	[-61.343786, -18.073031]	[1834.348384, 2599.156806]
  2005365	-1.0	-1.0	0	1	[355.8, 413.8]	[-7.060582, -2.426362]	[-53.501231, -14.617634]	[9396.15507, 4547.960553]
  2005366	-1.0	-1.0	0	1	[353.1, 413.2]	[-7.12709, -2.441245]	[-41.879965, -10.276475]	[16194.183852, 5079.286997]
  2005367	-1.0	-1.0	0	1	[351.2, 412.9]	[-7.173881, -2.448686]	[-27.710881, -6.112645]	[16598.914618, 4193.246498]

• 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

Now let’s detect some events.

First we will detect fixations using the I-VT algorithm using its default parameters:

dataset.detect_events('ivt')

dataset.events[0]

Events

• frame:
  DataFrame (6 columns, 72 rows)
  shape: (72, 6)

  text_id	page_id	name	onset	offset	duration
  i64	i64	str	i64	i64	i64
  0	1	"fixation"	1988145	1988322	177
  0	1	"fixation"	1988351	1988546	195
  0	1	"fixation"	1988592	1988736	144
  0	1	"fixation"	1988788	1989012	224
  0	1	"fixation"	1989044	1989170	126
  …	…	…	…	…	…
  0	1	"fixation"	2004114	2004349	235
  0	1	"fixation"	2004399	2004687	288
  0	1	"fixation"	2004714	2004878	164
  0	1	"fixation"	2004930	2005109	179
  0	1	"fixation"	2005138	2005286	148

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

Next we detect some saccades. This time we don’t use the default parameters but specify our own:

dataset.detect_events('microsaccades', minimum_duration=8)

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

shape: (5, 6)

text_id	page_id	name	onset	offset	duration
i64	i64	str	i64	i64	i64
0	1	"saccade"	1988323	1988337	14
0	1	"saccade"	1988341	1988351	10
0	1	"saccade"	1988546	1988567	21
0	1	"saccade"	1988570	1988583	13
0	1	"saccade"	1988736	1988760	24

We can also use the more general interface of the apply() method:

dataset.apply('idt', dispersion_threshold=2.7, name='fixation.ivt')

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"	1988145	1988563	418
0	1	"fixation.ivt"	1988564	1988750	186
0	1	"fixation.ivt"	1988751	1989178	427
0	1	"fixation.ivt"	1989179	1989436	257
0	1	"fixation.ivt"	1989437	1989600	163

Computing event properties

The event dataframe currently only holds the name, onset, offset and duration of an event (additionally we have some more identifier columns at the beginning).

We now want to compute some additional properties for each event. Event properties are things like peak velocity, amplitude and dispersion during an event.

We start out with computing the dispersion:

dataset.compute_event_properties("dispersion")

dataset.events[0]

Events

• frame:
  DataFrame (7 columns, 264 rows)
  shape: (264, 7)

  text_id	page_id	name	onset	offset	duration	dispersion
  i64	i64	str	i64	i64	i64	f64
  0	1	"fixation"	1988145	1988322	177	0.154958
  0	1	"fixation"	1988351	1988546	195	0.291833
  0	1	"fixation"	1988592	1988736	144	0.296297
  0	1	"fixation"	1988788	1989012	224	0.271854
  0	1	"fixation"	1989044	1989170	126	0.349
  …	…	…	…	…	…	…
  0	1	"fixation.ivt"	2003929	2004090	161	2.814851
  0	1	"fixation.ivt"	2004091	2004363	272	2.819008
  0	1	"fixation.ivt"	2004364	2004883	519	2.768099
  0	1	"fixation.ivt"	2004885	2005116	231	2.805674
  0	1	"fixation.ivt"	2005117	2005298	181	2.744494

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

We notice that a new column with the name dispersion has appeared in the event dataframe.

We can also pass a list of properties to compute all of our desired properties in a single run. Let’s add the amplitude and peak velocity:

dataset.compute_event_properties(["amplitude", "peak_velocity"])

dataset.events[0]

Events

• frame:
  DataFrame (9 columns, 264 rows)
  shape: (264, 9)

  text_id	page_id	name	onset	offset	duration	dispersion	amplitude	peak_velocity
  i64	i64	str	i64	i64	i64	f64	f64	f64
  0	1	"fixation"	1988145	1988322	177	0.154958	0.110074	16.24151
  0	1	"fixation"	1988351	1988546	195	0.291833	0.206397	18.88542
  0	1	"fixation"	1988592	1988736	144	0.296297	0.209546	17.690373
  0	1	"fixation"	1988788	1989012	224	0.271854	0.192719	19.130211
  0	1	"fixation"	1989044	1989170	126	0.349	0.304362	18.616167
  …	…	…	…	…	…	…	…	…
  0	1	"fixation.ivt"	2003929	2004090	161	2.814851	2.527788	212.117446
  0	1	"fixation.ivt"	2004091	2004363	272	2.819008	2.518967	244.333244
  0	1	"fixation.ivt"	2004364	2004883	519	2.768099	2.46208	194.527643
  0	1	"fixation.ivt"	2004885	2005116	231	2.805674	2.507902	203.067333
  0	1	"fixation.ivt"	2005117	2005298	181	2.744494	2.578767	329.741947

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

Plotting our data

pymovements provides a range of plotting functions.

You can browse through the available plotting functions here: Plotting

In this this tutorial we will plot the saccadic main sequence of our data.

pm.plotting.main_sequence_plot(dataset.events[0])

Saving and loading your dataframes

If we want to save interim results we can simply use the save() method like this:

dataset.save()

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'
        'gaze'
      • filename:
        'pymovements-toy-dataset.zip'
        'pymovements-toy-dataset.zip'
      • filename_pattern:
        'trial_{text_id:d}_{page_id:d}.csv'
        'trial_{text_id:d}_{page_id:d}.csv'
      • filename_pattern_schema_overrides:
        dict (2 items)
        • text_id:
          <class 'int'>
          <class 'int'>
        • page_id:
          <class 'int'>
          <class 'int'>
      • md5:
        '4da622457637a8181d86601fe17f3aa8'
        '4da622457637a8181d86601fe17f3aa8'
      • url:
        str
        'http://github.com/aeye-lab/pymovements-toy-dataset/zipball/6cb5d663317bf418cec0c9abe1dde5085a8a8ebd/'
  • time_column:
    'timestamp'
    'timestamp'
  • time_unit:
    'ms'
    'ms'
  • trial_columns:
    None
    None
  • velocity_columns:
    None
    None
• events:
  list (1 items)
  • Events
    • frame:
      DataFrame (9 columns, 264 rows)
      shape: (264, 9)

      text_id	page_id	name	onset	offset	duration	dispersion	amplitude	peak_velocity
      i64	i64	str	i64	i64	i64	f64	f64	f64
      0	1	"fixation"	1988145	1988322	177	0.154958	0.110074	16.24151
      0	1	"fixation"	1988351	1988546	195	0.291833	0.206397	18.88542
      0	1	"fixation"	1988592	1988736	144	0.296297	0.209546	17.690373
      0	1	"fixation"	1988788	1989012	224	0.271854	0.192719	19.130211
      0	1	"fixation"	1989044	1989170	126	0.349	0.304362	18.616167
      …	…	…	…	…	…	…	…	…
      0	1	"fixation.ivt"	2003929	2004090	161	2.814851	2.527788	212.117446
      0	1	"fixation.ivt"	2004091	2004363	272	2.819008	2.518967	244.333244
      0	1	"fixation.ivt"	2004364	2004883	519	2.768099	2.46208	194.527643
      0	1	"fixation.ivt"	2004885	2005116	231	2.805674	2.507902	203.067333
      0	1	"fixation.ivt"	2005117	2005298	181	2.744494	2.578767	329.741947

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

    text_id	page_id	filepath
    i64	i64	str
    0	1	"aeye-lab-pymovements-toy-datas…

• gaze:
  list (1 items)
  • Gaze
    • samples:
      DataFrame (9 columns, 17223 rows)
      shape: (17_223, 9)

      time	stimuli_x	stimuli_y	text_id	page_id	pixel	position	velocity	acceleration
      i64	f64	f64	i64	i64	list[f64]	list[f64]	list[f64]	list[f64]
      1988145	-1.0	-1.0	0	1	[206.8, 152.4]	[-10.697598, -8.852399]	[1.207641, -3.119165]	[690.085837, -1501.799767]
      1988146	-1.0	-1.0	0	1	[206.9, 152.1]	[-10.695183, -8.859678]	[1.20764, -4.072198]	[0.001831, -866.371365]
      1988147	-1.0	-1.0	0	1	[207.0, 151.8]	[-10.692768, -8.866956]	[1.035119, -4.765267]	[-575.06741, -57.655244]
      1988148	-1.0	-1.0	0	1	[207.1, 151.7]	[-10.690352, -8.869381]	[1.207654, -4.245382]	[-230.013049, 1328.57081]
      1988149	-1.0	-1.0	0	1	[207.0, 151.5]	[-10.692768, -8.874233]	[1.552735, -2.339263]	[690.12611, 2021.586565]
      …	…	…	…	…	…	…	…	…
      2005363	-1.0	-1.0	0	1	[361.0, 415.4]	[-6.932438, -2.386672]	[-62.062479, -20.465552]	[-1099.087619, 1477.17518]
      2005364	-1.0	-1.0	0	1	[358.0, 414.5]	[-7.006376, -2.408998]	[-61.343786, -18.073031]	[1834.348384, 2599.156806]
      2005365	-1.0	-1.0	0	1	[355.8, 413.8]	[-7.060582, -2.426362]	[-53.501231, -14.617634]	[9396.15507, 4547.960553]
      2005366	-1.0	-1.0	0	1	[353.1, 413.2]	[-7.12709, -2.441245]	[-41.879965, -10.276475]	[16194.183852, 5079.286997]
      2005367	-1.0	-1.0	0	1	[351.2, 412.9]	[-7.173881, -2.448686]	[-27.710881, -6.112645]	[16598.914618, 4193.246498]

    • events:
      Events
      Events

      • frame:
        DataFrame (9 columns, 264 rows)
        shape: (264, 9)

        text_id	page_id	name	onset	offset	duration	dispersion	amplitude	peak_velocity
        i64	i64	str	i64	i64	i64	f64	f64	f64
        0	1	"fixation"	1988145	1988322	177	0.154958	0.110074	16.24151
        0	1	"fixation"	1988351	1988546	195	0.291833	0.206397	18.88542
        0	1	"fixation"	1988592	1988736	144	0.296297	0.209546	17.690373
        0	1	"fixation"	1988788	1989012	224	0.271854	0.192719	19.130211
        0	1	"fixation"	1989044	1989170	126	0.349	0.304362	18.616167
        …	…	…	…	…	…	…	…	…
        0	1	"fixation.ivt"	2003929	2004090	161	2.814851	2.527788	212.117446
        0	1	"fixation.ivt"	2004091	2004363	272	2.819008	2.518967	244.333244
        0	1	"fixation.ivt"	2004364	2004883	519	2.768099	2.46208	194.527643
        0	1	"fixation.ivt"	2004885	2005116	231	2.805674	2.507902	203.067333
        0	1	"fixation.ivt"	2005117	2005298	181	2.744494	2.578767	329.741947

      • 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
• 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)

Let’s test this out by initializing a new PublicDataset object in the same directory and loading in the preprocessed gaze and event data.

This time we don’t need to download anything.

preprocessed_dataset = pm.Dataset('ToyDataset', path='data/ToyDataset')

dataset.load(events=True, preprocessed=True, subset=subset)

display(dataset.gaze[0])
display(dataset.events[0])

Gaze

• samples:
  DataFrame (9 columns, 17223 rows)
  shape: (17_223, 9)

  time	stimuli_x	stimuli_y	pixel	position	velocity	acceleration	text_id	page_id
  i64	f64	f64	list[f64]	list[f64]	list[f64]	list[f64]	i64	i64
  1988145	-1.0	-1.0	[206.8, 152.4]	[-10.697598, -8.852399]	[1.207641, -3.119165]	[690.085837, -1501.799767]	0	1
  1988146	-1.0	-1.0	[206.9, 152.1]	[-10.695183, -8.859678]	[1.20764, -4.072198]	[0.001831, -866.371365]	0	1
  1988147	-1.0	-1.0	[207.0, 151.8]	[-10.692768, -8.866956]	[1.035119, -4.765267]	[-575.06741, -57.655244]	0	1
  1988148	-1.0	-1.0	[207.1, 151.7]	[-10.690352, -8.869381]	[1.207654, -4.245382]	[-230.013049, 1328.57081]	0	1
  1988149	-1.0	-1.0	[207.0, 151.5]	[-10.692768, -8.874233]	[1.552735, -2.339263]	[690.12611, 2021.586565]	0	1
  …	…	…	…	…	…	…	…	…
  2005363	-1.0	-1.0	[361.0, 415.4]	[-6.932438, -2.386672]	[-62.062479, -20.465552]	[-1099.087619, 1477.17518]	0	1
  2005364	-1.0	-1.0	[358.0, 414.5]	[-7.006376, -2.408998]	[-61.343786, -18.073031]	[1834.348384, 2599.156806]	0	1
  2005365	-1.0	-1.0	[355.8, 413.8]	[-7.060582, -2.426362]	[-53.501231, -14.617634]	[9396.15507, 4547.960553]	0	1
  2005366	-1.0	-1.0	[353.1, 413.2]	[-7.12709, -2.441245]	[-41.879965, -10.276475]	[16194.183852, 5079.286997]	0	1
  2005367	-1.0	-1.0	[351.2, 412.9]	[-7.173881, -2.448686]	[-27.710881, -6.112645]	[16598.914618, 4193.246498]	0	1

• events:
  Events
  Events

  • frame:
    DataFrame (9 columns, 264 rows)
    shape: (264, 9)

    text_id	page_id	name	onset	offset	duration	dispersion	amplitude	peak_velocity
    i64	i64	str	i64	i64	i64	f64	f64	f64
    0	1	"fixation"	1988145	1988322	177	0.154958	0.110074	16.24151
    0	1	"fixation"	1988351	1988546	195	0.291833	0.206397	18.88542
    0	1	"fixation"	1988592	1988736	144	0.296297	0.209546	17.690373
    0	1	"fixation"	1988788	1989012	224	0.271854	0.192719	19.130211
    0	1	"fixation"	1989044	1989170	126	0.349	0.304362	18.616167
    …	…	…	…	…	…	…	…	…
    0	1	"fixation.ivt"	2003929	2004090	161	2.814851	2.527788	212.117446
    0	1	"fixation.ivt"	2004091	2004363	272	2.819008	2.518967	244.333244
    0	1	"fixation.ivt"	2004364	2004883	519	2.768099	2.46208	194.527643
    0	1	"fixation.ivt"	2004885	2005116	231	2.805674	2.507902	203.067333
    0	1	"fixation.ivt"	2005117	2005298	181	2.744494	2.578767	329.741947

  • trial_columns:
    None
    None
• 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

Events

• frame:
  DataFrame (9 columns, 264 rows)
  shape: (264, 9)

  text_id	page_id	name	onset	offset	duration	dispersion	amplitude	peak_velocity
  i64	i64	str	i64	i64	i64	f64	f64	f64
  0	1	"fixation"	1988145	1988322	177	0.154958	0.110074	16.24151
  0	1	"fixation"	1988351	1988546	195	0.291833	0.206397	18.88542
  0	1	"fixation"	1988592	1988736	144	0.296297	0.209546	17.690373
  0	1	"fixation"	1988788	1989012	224	0.271854	0.192719	19.130211
  0	1	"fixation"	1989044	1989170	126	0.349	0.304362	18.616167
  …	…	…	…	…	…	…	…	…
  0	1	"fixation.ivt"	2003929	2004090	161	2.814851	2.527788	212.117446
  0	1	"fixation.ivt"	2004091	2004363	272	2.819008	2.518967	244.333244
  0	1	"fixation.ivt"	2004364	2004883	519	2.768099	2.46208	194.527643
  0	1	"fixation.ivt"	2004885	2005116	231	2.805674	2.507902	203.067333
  0	1	"fixation.ivt"	2005117	2005298	181	2.744494	2.578767	329.741947

• trial_columns:
  None
  None