pymovements.datasets.GazeOnFaces#

class pymovements.datasets.GazeOnFaces(name: str = 'GazeOnFaces', mirrors: tuple[str, ...] = ('https://uncloud.univ-nantes.fr/index.php/s/',), resources: tuple[dict[str, str], ...] = ({'filename': 'gaze_csv.zip', 'md5': 'fe219f07c9253cd9aaee6bd50233c034', 'resource': '8KW6dEdyBJqxpmo/download?path=%2F&files=gaze_csv.zip'},), experiment: Experiment = <pymovements.gaze.experiment.Experiment object>, filename_format: str = 'gaze_sub{sub_id:d}_trial{trial_id:d}.csv', filename_format_dtypes: dict[str, type] = <factory>, custom_read_kwargs: dict[str, Any] = <factory>, column_map: dict[str, str] = <factory>, trial_columns: list[str] = <factory>, time_column: Any = None, time_unit: Any = None, pixel_columns: list[str] = <factory>, position_columns: list[str] | None = None, velocity_columns: list[str] | None = None, acceleration_columns: list[str] | None = None, distance_column: str | None = None)#

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 (19inch 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].

name#

The name of the dataset.

Type:: str

mirrors#

A tuple of mirrors of the dataset. Each entry must be of type str and end with a ‘/’.

Type:: tuple[str, …]

resources#

A tuple of dataset 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:: tuple[dict[str, str], …]

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:: str

filename_format_dtypes#

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, type], optional

column_map#

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

Type:: dict[str, str]

custom_read_kwargs#

If specified, these keyword arguments will be passed to the file reading function.

Type:: dict[str, Any], optional

Examples

Initialize your PublicDataset 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()

__init__(name: str = 'GazeOnFaces', mirrors: tuple[str, ...] = ('https://uncloud.univ-nantes.fr/index.php/s/',), resources: tuple[dict[str, str], ...] = ({'filename': 'gaze_csv.zip', 'md5': 'fe219f07c9253cd9aaee6bd50233c034', 'resource': '8KW6dEdyBJqxpmo/download?path=%2F&files=gaze_csv.zip'},), experiment: Experiment = <pymovements.gaze.experiment.Experiment object>, filename_format: str = 'gaze_sub{sub_id:d}_trial{trial_id:d}.csv', filename_format_dtypes: dict[str, type] = <factory>, custom_read_kwargs: dict[str, Any] = <factory>, column_map: dict[str, str] = <factory>, trial_columns: list[str] = <factory>, time_column: Any = None, time_unit: Any = None, pixel_columns: list[str] = <factory>, position_columns: list[str] | None = None, velocity_columns: list[str] | None = None, acceleration_columns: list[str] | None = None, distance_column: str | None = None) → None

Methods

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

Attributes

`acceleration_columns`
`distance_column`
`experiment`
`filename_format`
`mirrors`
`name`
`pixel_columns`
`position_columns`
`resources`
`time_column`
`time_unit`
`trial_columns`
`velocity_columns`
`filename_format_dtypes`
`column_map`
`custom_read_kwargs`