pymovements.datasets.SBSAT#

class pymovements.datasets.SBSAT(name: str = 'SBSAT', mirrors: tuple[str, ...] = ('https://osf.io/download/',), resources: tuple[dict[str, str], ...] = ({'filename': 'sbsat_csvs.zip', 'md5': 'a6ef1fb0ecced683cdb489c3bd3e1a5c', 'resource': 'jgae7/'},), experiment: Experiment = <pymovements.gaze.experiment.Experiment object>, filename_format: str = 'msd{subject_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: str = 'time', time_unit: str = 'ms', 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)#

SB-SAT dataset [Ahn et al., 2020].

This dataset includes monocular eye tracking data from a single participants in a single session. Eye movements are recorded at a sampling frequency of 1,000 Hz using an EyeLink 1000 eye tracker and are provided as pixel coordinates.

The participant is instructed to read texts and answer questions.

Check the respective paper for details [Ahn et al., 2020].

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("SBSAT", path='data/SBSAT')

Download the dataset resources:

>>> dataset.download()

Load the data into memory:

>>> dataset.load()

__init__(name: str = 'SBSAT', mirrors: tuple[str, ...] = ('https://osf.io/download/',), resources: tuple[dict[str, str], ...] = ({'filename': 'sbsat_csvs.zip', 'md5': 'a6ef1fb0ecced683cdb489c3bd3e1a5c', 'resource': 'jgae7/'},), experiment: Experiment = <pymovements.gaze.experiment.Experiment object>, filename_format: str = 'msd{subject_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: str = 'time', time_unit: str = 'ms', 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`