GazeOnFaces#

class pymovements.datasets.GazeOnFaces(name: str = 'GazeOnFaces', *, long_name: str = 'GazeOnFaces dataset', mirrors: dict[str, Sequence[str]] = <factory>, resources: ResourceDefinitions = <factory>, experiment: Experiment = <factory>, extract: dict[str, bool] | None = None, custom_read_kwargs: dict[str, dict[str, Any]] | None = None, column_map: dict[str, str] | None = None, trial_columns: list[str] | None = None, time_column: Any = None, time_unit: Any = None, pixel_columns: list[str] | None = None, position_columns: list[str] | None = None, velocity_columns: list[str] | None = None, acceleration_columns: list[str] | None = None, distance_column: str | None = None, filename_format: dict[str, str] | None = None, filename_format_schema_overrides: dict[str, dict[str, type]] | None = None)[source]#

GazeOnFaces dataset [Coutrot et al., 2016].

This dataset includes monocular eye tracking data from single participants in a single session. Eye movements are recorded at a sampling frequency of 60 Hz using an EyeLink 1000 video-based eye tracker and are provided as pixel coordinates.

Participants were sat 57 cm away from the screen (19-inch LCD monitor, screen res=1280×1024, 60 Hz). Recordings of the eye movements of one eye in monocular pupil/corneal reflection tracking mode.

Check the respective paper for details [Coutrot et al., 2016].

Warning

This dataset currently cannot be fully processed by pymovements due to an error during archive extraction.

See issue #1346 for reference.

name#

The name of the dataset.

Type:

str

long_name#

The entire name of the dataset.

Type:

str

resources#

A list of dataset gaze_resources. Each list entry must be a dictionary with the following keys: - resource: The url suffix of the resource. This will be concatenated with the mirror. - filename: The filename under which the file is saved as. - md5: The MD5 checksum of the respective file.

Type:

ResourceDefinitions

experiment#

The experiment definition.

Type:

Experiment

filename_format#

Regular expression, which will be matched before trying to load the file. Namedgroups will appear in the fileinfo dataframe.

Type:

dict[str, str] | None

filename_format_schema_overrides#

If named groups are present in the filename_format, this makes it possible to cast specific named groups to a particular datatype.

Type:

dict[str, dict[str, type]] | None

time_column#

The name of the timestamp column in the input data frame. This column will be renamed to time.

Type:

Any

time_unit#

The unit of the timestamps in the timestamp column in the input data frame. Supported units are ‘s’ for seconds, ‘ms’ for milliseconds and ‘step’ for steps. If the unit is ‘step’ the experiment definition must be specified. All timestamps will be converted to milliseconds.

Type:

Any

pixel_columns#

The name of the pixel position columns in the input data frame. These columns will be nested into the column pixel. If the list is empty or None, the nested pixel column will not be created.

Type:

list[str] | None

column_map#

The keys are the columns to read, the values are the names to which they should be renamed.

Type:

dict[str, str] | None

custom_read_kwargs#

If specified, these keyword arguments will be passed to the file reading function. (default: None)

Type:

dict[str, dict[str, Any]] | None

Examples

Initialize your Dataset object with the GazeOnFaces definition:

>>> import pymovements as pm
>>>
>>> dataset = pm.Dataset("GazeOnFaces", path='data/GazeOnFaces')

Download the dataset resources:

>>> dataset.download()

Load the data into memory:

>>> dataset.load()

Methods

__init__([name, long_name, mirrors, ...])

from_yaml(path)

Load a dataset definition from a YAML file.

to_dict(*[, exclude_private, exclude_none])

Return dictionary representation.

to_yaml(path, *[, exclude_private, exclude_none])

Save a dataset definition to a YAML file.

Attributes

acceleration_columns

column_map

custom_read_kwargs

distance_column

extract

filename_format

filename_format_schema_overrides

has_resources

Checks for resources in resources.

long_name

name

pixel_columns

position_columns

time_column

time_unit

trial_columns

velocity_columns

resources

experiment

mirrors