Preprocessing Raw Gaze Data

What you will learn in this tutorial:

• how to transform pixel coordinates into degrees of visual angle

• how to transform positional data into velocity data

Preparations

We import pymovements as the alias pm for convenience.

import pymovements as pm

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.
        

Using already downloaded and verified file: data/ToyDataset/downloads/pymovements-toy-dataset.zip
Extracting pymovements-toy-dataset.zip to data/ToyDataset/raw

Extracting archive:   0%|          | 0/23 [00:00<?, ?file/s]

Extracting archive: 100%|██████████| 23/23 [00:00<00:00, 361.87file/s]

Dataset

• definition:
  DatasetDefinition
  DatasetDefinition

  • name:
    'ToyDataset'
  • long_name:
    'pymovements Toy Dataset'
  • description:
    'Example toy dataset. This dataset includes monocu...'
    'Example toy dataset.\n\nThis dataset includes monocular eye tracking data from a single participant in a single\nsession. Eye movements are recorded at a sampling frequency of 1000 Hz using an EyeLink Portable\nDuo video-based eye tracker and are provided as pixel coordinates.\n\nThe participant is instructed to read 4 texts with 5 screens each.\n'
  • experiment:
    Experiment
    Experiment

    • eyetracker:
      EyeTracker
      EyeTracker

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

      • distance_cm:
        68
      • height_cm:
        30.2
      • height_px:
        1024
      • origin:
        'upper left'
      • resolution:
        tuple (2 items)
        • 1280
        • 1024
      • size:
        tuple (2 items)
        • 38
        • 30.2
      • width_cm:
        38
      • width_px:
        1280
      • x_max_dva:
        15.599386487782953
      • x_min_dva:
        -15.599386487782953
      • y_max_dva:
        12.508044410882546
      • y_min_dva:
        -12.508044410882546
  • resources:
    list (1 items)
    • ResourceDefinition
      • content:
        'gaze'
      • filename:
        'pymovements-toy-dataset.zip'
      • filename_pattern:
        'trial_{text_id:d}_{page_id:d}.csv'
      • filename_pattern_schema_overrides:
        dict (2 items)
        • text_id:
          <class 'int'>
        • page_id:
          <class 'int'>
      • load_function:
        None
      • load_kwargs:
        dict (4 items)
        • time_column:
          'timestamp'
        • time_unit:
          'ms'
        • (2 more)
      • md5:
        '256901852c1c07581d375eef705855d6'
      • mirrors:
        None
      • source:
        WebSource
        WebSource(url='https://github.com/pymovements/pymovements-toy-dataset/archive/refs/heads/main.zip', filename='pymovements-toy-dataset.zip', md5='256901852c1c07581d375eef705855d6', mirrors=None)
      • url:
        'https://github.com/pymovements/pymovements-toy-dat...'
        'https://github.com/pymovements/pymovements-toy-dataset/archive/refs/heads/main.zip'
• events:
  tuple (20 items)
  • Events
    • frame:
      DataFrame (4 columns, 0 rows)
      shape: (0, 4)

      name	onset	offset	duration
      str	i64	i64	i64

    • trial_columns:
      None
  • Events
    • frame:
      DataFrame (4 columns, 0 rows)
      shape: (0, 4)

      name	onset	offset	duration
      str	i64	i64	i64

    • trial_columns:
      None
  • (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	"pymovements-toy-dataset-main/d…
    0	2	"pymovements-toy-dataset-main/d…
    0	3	"pymovements-toy-dataset-main/d…
    0	4	"pymovements-toy-dataset-main/d…
    0	5	"pymovements-toy-dataset-main/d…
    …	…	…
    3	1	"pymovements-toy-dataset-main/d…
    3	2	"pymovements-toy-dataset-main/d…
    3	3	"pymovements-toy-dataset-main/d…
    3	4	"pymovements-toy-dataset-main/d…
    3	5	"pymovements-toy-dataset-main/d…

• gaze:
  list (20 items)
  • Gaze
    • samples:
      DataFrame (4 columns, 17223 rows)
      shape: (17_223, 4)

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

    • events:
      Events
      Events

      • frame:
        DataFrame (4 columns, 0 rows)
        shape: (0, 4)

        name	onset	offset	duration
        str	i64	i64	i64

      • trial_columns:
        None
    • metadata:
      dict (2 items)
      • text_id:
        0
      • page_id:
        1
    • messages:
      None
    • trial_columns:
      None
    • experiment:
      Experiment
      Experiment

      • eyetracker:
        EyeTracker
        EyeTracker

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

        • distance_cm:
          68
        • height_cm:
          30.2
        • height_px:
          1024
        • origin:
          'upper left'
        • resolution:
          tuple (2 items)
          • 1280
          • 1024
        • size:
          tuple (2 items)
          • 38
          • 30.2
        • width_cm:
          38
        • width_px:
          1280
        • x_max_dva:
          15.599386487782953
        • x_min_dva:
          -15.599386487782953
        • y_max_dva:
          12.508044410882546
        • y_min_dva:
          -12.508044410882546
  • Gaze
    • samples:
      DataFrame (4 columns, 29799 rows)
      shape: (29_799, 4)

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

    • events:
      Events
      Events

      • frame:
        DataFrame (4 columns, 0 rows)
        shape: (0, 4)

        name	onset	offset	duration
        str	i64	i64	i64

      • trial_columns:
        None
    • metadata:
      dict (2 items)
      • text_id:
        0
      • page_id:
        2
    • messages:
      None
    • trial_columns:
      None
    • experiment:
      Experiment
      Experiment

      • eyetracker:
        EyeTracker
        EyeTracker

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

        • distance_cm:
          68
        • height_cm:
          30.2
        • height_px:
          1024
        • origin:
          'upper left'
        • resolution:
          tuple (2 items)
          • 1280
          • 1024
        • size:
          tuple (2 items)
          • 38
          • 30.2
        • width_cm:
          38
        • width_px:
          1280
        • x_max_dva:
          15.599386487782953
        • x_min_dva:
          -15.599386487782953
        • y_max_dva:
          12.508044410882546
        • y_min_dva:
          -12.508044410882546
  • (18 more)
• participants:
  Participants
  Participants

  • data:
    DataFrame (1 columns, 0 rows)
    shape: (0, 1)

    participant_id
    str

  • metadata:
    dict (1 items)
    • participant_id:
      dict (1 items)
      • Format:
        'string'
• path:
  PosixPath('data/ToyDataset')
• paths:
  DatasetPaths
  DatasetPaths

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

We can verify that all files have been loaded in by checking the fileinfo attribute:

dataset.fileinfo

{'gaze': shape: (20, 3)
 ┌─────────┬─────────┬─────────────────────────────────┐
 │ text_id ┆ page_id ┆ filepath                        │
 │ ---     ┆ ---     ┆ ---                             │
 │ i64     ┆ i64     ┆ str                             │
 ╞═════════╪═════════╪═════════════════════════════════╡
 │ 0       ┆ 1       ┆ pymovements-toy-dataset-main/d… │
 │ 0       ┆ 2       ┆ pymovements-toy-dataset-main/d… │
 │ 0       ┆ 3       ┆ pymovements-toy-dataset-main/d… │
 │ 0       ┆ 4       ┆ pymovements-toy-dataset-main/d… │
 │ 0       ┆ 5       ┆ pymovements-toy-dataset-main/d… │
 │ …       ┆ …       ┆ …                               │
 │ 3       ┆ 1       ┆ pymovements-toy-dataset-main/d… │
 │ 3       ┆ 2       ┆ pymovements-toy-dataset-main/d… │
 │ 3       ┆ 3       ┆ pymovements-toy-dataset-main/d… │
 │ 3       ┆ 4       ┆ pymovements-toy-dataset-main/d… │
 │ 3       ┆ 5       ┆ pymovements-toy-dataset-main/d… │
 └─────────┴─────────┴─────────────────────────────────┘}

Now let’s inspect our gaze dataframe:

dataset.gaze[0]

Gaze

• samples:
  DataFrame (4 columns, 17223 rows)
  shape: (17_223, 4)

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

• events:
  Events
  Events

  • frame:
    DataFrame (4 columns, 0 rows)
    shape: (0, 4)

    name	onset	offset	duration
    str	i64	i64	i64

  • trial_columns:
    None
• metadata:
  dict (2 items)
  • text_id:
    0
  • page_id:
    1
• messages:
  None
• trial_columns:
  None
• experiment:
  Experiment
  Experiment

  • eyetracker:
    EyeTracker
    EyeTracker

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

    • distance_cm:
      68
    • height_cm:
      30.2
    • height_px:
      1024
    • origin:
      'upper left'
    • resolution:
      tuple (2 items)
      • 1280
      • 1024
    • size:
      tuple (2 items)
      • 38
      • 30.2
    • width_cm:
      38
    • width_px:
      1280
    • x_max_dva:
      15.599386487782953
    • x_min_dva:
      -15.599386487782953
    • y_max_dva:
      12.508044410882546
    • y_min_dva:
      -12.508044410882546

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

Preprocessing

We now want to transform these pixel position coordinates into coordinates in degrees of visual angle. This is simply done by:

dataset.pix2deg()

dataset.gaze[0]

Gaze

• samples:
  DataFrame (5 columns, 17223 rows)
  shape: (17_223, 5)

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

• events:
  Events
  Events

  • frame:
    DataFrame (4 columns, 0 rows)
    shape: (0, 4)

    name	onset	offset	duration
    str	i64	i64	i64

  • trial_columns:
    None
• metadata:
  dict (2 items)
  • text_id:
    0
  • page_id:
    1
• messages:
  None
• trial_columns:
  None
• experiment:
  Experiment
  Experiment

  • eyetracker:
    EyeTracker
    EyeTracker

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

    • distance_cm:
      68
    • height_cm:
      30.2
    • height_px:
      1024
    • origin:
      'upper left'
    • resolution:
      tuple (2 items)
      • 1280
      • 1024
    • size:
      tuple (2 items)
      • 38
      • 30.2
    • width_cm:
      38
    • width_px:
      1280
    • x_max_dva:
      15.599386487782953
    • x_min_dva:
      -15.599386487782953
    • y_max_dva:
      12.508044410882546
    • y_min_dva:
      -12.508044410882546

The processed result has been added as a new column named position to our gaze dataframe.

Additionally, we would like to have velocity data available too. We have four different methods available:

• preceding: this will just take the single preceding sample into account for velocity calculation. Most noisy variant.

• neighbors: this will take the neighboring samples into account for velocity calculation. A bit less noisy.

• smooth: this will increase the neighboring samples to two on each side. You can get a smooth conversion this way.

• savitzky_golay: this is using the Savitzky-Golay differentiation filter for conversion. You can specify additional parameters like window_length and degree. Depending on your parameters, this will lead to the best results.

Let’s use the fivepoint method first:

dataset.pos2vel(method='fivepoint')

dataset.gaze[0]

Gaze

• samples:
  DataFrame (6 columns, 17223 rows)
  shape: (17_223, 6)

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

• events:
  Events
  Events

  • frame:
    DataFrame (4 columns, 0 rows)
    shape: (0, 4)

    name	onset	offset	duration
    str	i64	i64	i64

  • trial_columns:
    None
• metadata:
  dict (2 items)
  • text_id:
    0
  • page_id:
    1
• messages:
  None
• trial_columns:
  None
• experiment:
  Experiment
  Experiment

  • eyetracker:
    EyeTracker
    EyeTracker

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

    • distance_cm:
      68
    • height_cm:
      30.2
    • height_px:
      1024
    • origin:
      'upper left'
    • resolution:
      tuple (2 items)
      • 1280
      • 1024
    • size:
      tuple (2 items)
      • 38
      • 30.2
    • width_cm:
      38
    • width_px:
      1280
    • x_max_dva:
      15.599386487782953
    • x_min_dva:
      -15.599386487782953
    • y_max_dva:
      12.508044410882546
    • y_min_dva:
      -12.508044410882546

The processed result has been added as a new column named velocity to our gaze dataframe.

We can also use the Savitzky-Golay differentiation filter with some additional parameters like this:

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

dataset.gaze[0]

Gaze

• samples:
  DataFrame (6 columns, 17223 rows)
  shape: (17_223, 6)

  time	stimuli_x	stimuli_y	pixel	position	velocity
  i64	f64	f64	list[f64]	list[f64]	list[f64]
  1988145	-1.0	-1.0	[206.8, 152.4]	[-10.697598, -8.852399]	[1.207641, -3.119165]
  1988146	-1.0	-1.0	[206.9, 152.1]	[-10.695183, -8.859678]	[1.20764, -4.072198]
  1988147	-1.0	-1.0	[207.0, 151.8]	[-10.692768, -8.866956]	[1.035119, -4.765267]
  1988148	-1.0	-1.0	[207.1, 151.7]	[-10.690352, -8.869381]	[1.207654, -4.245382]
  1988149	-1.0	-1.0	[207.0, 151.5]	[-10.692768, -8.874233]	[1.552735, -2.339263]
  …	…	…	…	…	…
  2005363	-1.0	-1.0	[361.0, 415.4]	[-6.932438, -2.386672]	[-62.062479, -20.465552]
  2005364	-1.0	-1.0	[358.0, 414.5]	[-7.006376, -2.408998]	[-61.343786, -18.073031]
  2005365	-1.0	-1.0	[355.8, 413.8]	[-7.060582, -2.426362]	[-53.501231, -14.617634]
  2005366	-1.0	-1.0	[353.1, 413.2]	[-7.12709, -2.441245]	[-41.879965, -10.276475]
  2005367	-1.0	-1.0	[351.2, 412.9]	[-7.173881, -2.448686]	[-27.710881, -6.112645]

• events:
  Events
  Events

  • frame:
    DataFrame (4 columns, 0 rows)
    shape: (0, 4)

    name	onset	offset	duration
    str	i64	i64	i64

  • trial_columns:
    None
• metadata:
  dict (2 items)
  • text_id:
    0
  • page_id:
    1
• messages:
  None
• trial_columns:
  None
• experiment:
  Experiment
  Experiment

  • eyetracker:
    EyeTracker
    EyeTracker

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

    • distance_cm:
      68
    • height_cm:
      30.2
    • height_px:
      1024
    • origin:
      'upper left'
    • resolution:
      tuple (2 items)
      • 1280
      • 1024
    • size:
      tuple (2 items)
      • 38
      • 30.2
    • width_cm:
      38
    • width_px:
      1280
    • x_max_dva:
      15.599386487782953
    • x_min_dva:
      -15.599386487782953
    • y_max_dva:
      12.508044410882546
    • y_min_dva:
      -12.508044410882546

This has overwritten our velocity columns. As we see, the values in the velocity columns are slightly different.

What you have learned in this tutorial:

• transforming pixel coordinates into degrees of visual angle by using Dataset.pix2deg()

• transforming positional data into velocity data by using Dataset.pos2vel()

• passing additional keyword arguments when using the Savitzky-Golay differentiation filter