Working with Local Dataset

In this tutorial, we will show how to use your own local dataset with the Dataset class. The Dataset class can help you to manage and process your eyetracking data.

Preparations

We import pymovements as the alias pm for convenience.

import pymovements as pm

For demonstration purposes, we will use the raw data provided by the Toy dataset, a sample dataset that comes with pymovements.

We will download the resources of this dataset the directory to simulate a local dataset for you. All downloaded archive files are automatically extracted and then removed. The directory of the dataset will be data/my_dataset.

After that we won’t use the python class anymore and delete the object (the files on your system will stay in place). Don’t worry if you’re confused about these lines as they are not relevant to your use case.

Just keep in mind that we now have some files with gaze data in the directory data/my_dataset.

toy_dataset = pm.Dataset('ToyDataset', path='data/my_dataset')
toy_dataset.download(remove_finished=True)

del toy_dataset

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/my_dataset/downloads/pymovements-toy-dataset.zip

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

100%|██████████| 23/23 [00:00<00:00, 345.74it/s]

Define your Experiment

To use the Dataset class, we first need to create an Experiment instance. This class represents the properties of the experiment, such as the screen dimensions and sampling rate.

experiment = pm.gaze.Experiment(
    screen_width_px=1280,
    screen_height_px=1024,
    screen_width_cm=38,
    screen_height_cm=30.2,
    distance_cm=68,
    origin='upper left',
    sampling_rate=1000,
)

Parameters for File Parsing

We also define a filename_format which is a pattern expression used to match and extract values from filenames of data files in the dataset. For example, r'trial_{text_id:d}_{page_id:d}.csv' will match filenames that follow the pattern trial_{text_id}_{page_id}.csv and extract the values of text_id and page_id for each file.

filename_format = {'gaze': r'trial_{text_id:d}_{page_id:d}.csv'}

Both values of text_id and page_id are numeric. We can use a map to define the casting of these values.

filename_format_schema_overrides = {'gaze': {
    'text_id': int,
    'page_id': int,
},
}

We can also adjust how the CSV files are read. Here, we specify that the separator in the CSV files is a tab (‘:nbsphinx-math:`t’`).

custom_read_kwargs = {
    'gaze': {'separator': '\t'},
}

Column Definitions

The trial_columns argument can be used to specify which columns define a single trial.

This is important for correctly applying all preprocessing methods.

For this very small single user dataset a trial is just defined by text_id and page_id.

trial_columns = ['text_id', 'page_id']

The time_column and pixel_columns arguments can be used to correctly map the columns in your dataframes. If the time unit differs from the default milliseconds ms one must also specify the time_unit for correct computations.

Depending on the content of your dataset, you can alternatively also provide position_columns, velocity_columns and acceleration_columns.

Specifying these columns is needed for correctly applying preprocessing methods. For example, if you want to apply the pix2deg method, you will need to specify pixel_columns accordingly.

If your dataset has gaze positions available only in degrees of visual angle, you have to specify the position_columns instead.

time_column = 'timestamp'
time_unit = 'ms'
pixel_columns = ['x', 'y']

Define and load the Dataset

Next we use all these definitions and create a DatasetDefinition by passing in the root directory, Experiment instance, and other optional parameters such as the filename regular expression and custom CSV reading parameters.

dataset_definition = pm.DatasetDefinition(
    name='my_dataset',
    has_files={'gaze': True, 'precomputed_events': False, 'precomputed_reading_measures': False},
    experiment=experiment,
    filename_format=filename_format,
    filename_format_schema_overrides=filename_format_schema_overrides,
    custom_read_kwargs=custom_read_kwargs,
    time_column=time_column,
    time_unit=time_unit,
    pixel_columns=pixel_columns,
)

Finally we create a Dataset instance by using the DatasetDefinition and specifying the directory path.

dataset = pm.Dataset(
    definition=dataset_definition,
    path='data/my_dataset/',
)

If we have a root data directory which holds all your local datasets we can further need to define the paths of the dataset.

The dataset, raw, preprocessed, and events parameters define the names of the directories for the dataset, raw data, preprocessed data, and events data, respectively.

dataset_paths = pm.DatasetPaths(
    root='data/',
    raw='raw',
    preprocessed='preprocessed',
    events='events',
)

dataset = pm.Dataset(
    definition=dataset_definition,
    path=dataset_paths,
)

Now let’s load the dataset into memory. Here we select a subset including the first page of texts with ID 1 and 2.

subset = {
    'text_id': [1, 2],
    'page_id': 1,
}

dataset.load(subset=subset)

Dataset

• definition:
  DatasetDefinition
  DatasetDefinition

  • acceleration_columns:
    None
    None
  • column_map:
    dict (0 items)
  • custom_read_kwargs:
    dict (1 items)
    • gaze:
      dict (1 items)
      • separator:
        '\t'
        '\t'
  • 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:
    None
    None
  • mirrors:
    dict (0 items)
  • name:
    'my_dataset'
    'my_dataset'
  • pixel_columns:
    list (2 items)
    • 'x'
    • 'y'
  • position_columns:
    None
    None
  • resources:
    list (1 items)
    • ResourceDefinition
      • content:
        'gaze'
        'gaze'
      • filename:
        None
        None
      • 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:
        None
        None
      • url:
        None
        None
  • 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, 2 rows)
    shape: (2, 3)

    text_id	page_id	filepath
    i64	i64	str
    1	1	"aeye-lab-pymovements-toy-datas…
    2	1	"aeye-lab-pymovements-toy-datas…

• gaze:
  list (2 items)
  • Gaze
    • samples:
      DataFrame (6 columns, 23054 rows)
      shape: (23_054, 6)

      time	stimuli_x	stimuli_y	text_id	page_id	pixel
      i64	f64	f64	i64	i64	list[f64]
      2415266	-1.0	-1.0	1	1	[176.8, 140.2]
      2415267	-1.0	-1.0	1	1	[176.7, 139.8]
      2415268	-1.0	-1.0	1	1	[176.7, 139.3]
      2415269	-1.0	-1.0	1	1	[176.6, 139.3]
      2415270	-1.0	-1.0	1	1	[176.7, 139.3]
      …	…	…	…	…	…
      2438315	-1.0	-1.0	1	1	[649.9, 633.9]
      2438316	-1.0	-1.0	1	1	[650.1, 633.7]
      2438317	-1.0	-1.0	1	1	[650.2, 633.5]
      2438318	-1.0	-1.0	1	1	[650.0, 633.2]
      2438319	-1.0	-1.0	1	1	[649.7, 633.1]

    • 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, 29660 rows)
      shape: (29_660, 6)

      time	stimuli_x	stimuli_y	text_id	page_id	pixel
      i64	f64	f64	i64	i64	list[f64]
      1788369	-1.0	-1.0	2	1	[106.2, 90.3]
      1788370	-1.0	-1.0	2	1	[107.2, 91.6]
      1788371	-1.0	-1.0	2	1	[109.9, 94.4]
      1788372	-1.0	-1.0	2	1	[113.3, 98.2]
      1788373	-1.0	-1.0	2	1	[118.3, 102.7]
      …	…	…	…	…	…
      1818024	-1.0	-1.0	2	1	[357.0, 715.0]
      1818025	-1.0	-1.0	2	1	[357.1, 714.9]
      1818026	-1.0	-1.0	2	1	[357.1, 714.9]
      1818027	-1.0	-1.0	2	1	[357.2, 714.5]
      1818028	-1.0	-1.0	2	1	[357.2, 714.0]

    • 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
• path:
  PosixPath('data/my_dataset')
  PosixPath('data/my_dataset')
• paths:
  DatasetPaths
  DatasetPaths

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

Use the Dataset

Once we have created the Dataset instance, we can use its methods to preprocess and analyze data in our local dataset.

dataset.gaze[0]

Gaze

• samples:
  DataFrame (6 columns, 23054 rows)
  shape: (23_054, 6)

  time	stimuli_x	stimuli_y	text_id	page_id	pixel
  i64	f64	f64	i64	i64	list[f64]
  2415266	-1.0	-1.0	1	1	[176.8, 140.2]
  2415267	-1.0	-1.0	1	1	[176.7, 139.8]
  2415268	-1.0	-1.0	1	1	[176.7, 139.3]
  2415269	-1.0	-1.0	1	1	[176.6, 139.3]
  2415270	-1.0	-1.0	1	1	[176.7, 139.3]
  …	…	…	…	…	…
  2438315	-1.0	-1.0	1	1	[649.9, 633.9]
  2438316	-1.0	-1.0	1	1	[650.1, 633.7]
  2438317	-1.0	-1.0	1	1	[650.2, 633.5]
  2438318	-1.0	-1.0	1	1	[650.0, 633.2]
  2438319	-1.0	-1.0	1	1	[649.7, 633.1]

• 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

Here we use the pix2deg method to convert the pixel coordinates to degrees of visual angle.

dataset.pix2deg()

dataset.gaze[0]

Gaze

• samples:
  DataFrame (7 columns, 23054 rows)
  shape: (23_054, 7)

  time	stimuli_x	stimuli_y	text_id	page_id	pixel	position
  i64	f64	f64	i64	i64	list[f64]	list[f64]
  2415266	-1.0	-1.0	1	1	[176.8, 140.2]	[-11.420403, -9.148145]
  2415267	-1.0	-1.0	1	1	[176.7, 139.8]	[-11.422806, -9.157834]
  2415268	-1.0	-1.0	1	1	[176.7, 139.3]	[-11.422806, -9.169943]
  2415269	-1.0	-1.0	1	1	[176.6, 139.3]	[-11.42521, -9.169943]
  2415270	-1.0	-1.0	1	1	[176.7, 139.3]	[-11.422806, -9.169943]
  …	…	…	…	…	…	…
  2438315	-1.0	-1.0	1	1	[649.9, 633.9]	[0.260146, 3.038748]
  2438316	-1.0	-1.0	1	1	[650.1, 633.7]	[0.265149, 3.033792]
  2438317	-1.0	-1.0	1	1	[650.2, 633.5]	[0.26765, 3.028836]
  2438318	-1.0	-1.0	1	1	[650.0, 633.2]	[0.262648, 3.021402]
  2438319	-1.0	-1.0	1	1	[649.7, 633.1]	[0.255144, 3.018924]

• 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 can use the pos2vel method to calculate the velocity of the gaze position.

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

dataset.gaze[0]

Gaze

• samples:
  DataFrame (8 columns, 23054 rows)
  shape: (23_054, 8)

  time	stimuli_x	stimuli_y	text_id	page_id	pixel	position	velocity
  i64	f64	f64	i64	i64	list[f64]	list[f64]	list[f64]
  2415266	-1.0	-1.0	1	1	[176.8, 140.2]	[-11.420403, -9.148145]	[-0.772495, -4.238523]
  2415267	-1.0	-1.0	1	1	[176.7, 139.8]	[-11.422806, -9.157834]	[-0.686663, -4.671012]
  2415268	-1.0	-1.0	1	1	[176.7, 139.3]	[-11.422806, -9.169943]	[-0.257498, -3.806023]
  2415269	-1.0	-1.0	1	1	[176.6, 139.3]	[-11.42521, -9.169943]	[1.459231, -1.557032]
  2415270	-1.0	-1.0	1	1	[176.7, 139.3]	[-11.422806, -9.169943]	[4.034446, 1.556983]
  …	…	…	…	…	…	…	…
  2438315	-1.0	-1.0	1	1	[649.9, 633.9]	[0.260146, 3.038748]	[0.268004, -3.451512]
  2438316	-1.0	-1.0	1	1	[650.1, 633.7]	[0.265149, 3.033792]	[-0.357339, -3.982536]
  2438317	-1.0	-1.0	1	1	[650.2, 633.5]	[0.26765, 3.028836]	[-0.982682, -3.982549]
  2438318	-1.0	-1.0	1	1	[650.0, 633.2]	[0.262648, 3.021402]	[-1.69736, -3.54005]
  2438319	-1.0	-1.0	1	1	[649.7, 633.1]	[0.255144, 3.018924]	[-2.233368, -2.389544]

• 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