pymovements.datasets.BSC#

class pymovements.datasets.BSC(name: str = 'BSC', has_files: dict[str, bool] = <factory>, mirrors: dict[str, tuple[str, ...]] = <factory>, resources: dict[str, tuple[dict[str, str], ...]] = <factory>, experiment: Experiment = <pymovements.gaze.experiment.Experiment object>, extract: dict[str, bool] = <factory>, filename_format: dict[str, str] = <factory>, filename_format_schema_overrides: dict[str, dict[str, type]] = <factory>, custom_read_kwargs: dict[str, 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)#

BSC dataset [Pan et al., 2022].

This dataset includes monocular eye tracking data from a single participant in a single session. Eye movements are recorded at a sampling frequency of 1,000 Hz using an EyeLink 1000 eye tracker and precomputed events on aoi level are reported.

The participant is instructed to read texts and answer questions.

Check the respective paper for details [Pan et al., 2022].

name#

The name of the dataset.

Type:: str

has_files#

Indicate whether the dataset contains ‘gaze’, ‘precomputed_events’, and ‘precomputed_reading_measures’.

Type:: dict[str, bool]

mirrors#

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

Type:: dict[str, tuple[str, …]]

resources#

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

extract#

Decide whether to extract the data.

Type:: dict[str, bool]

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]

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]]

trial_columns#

The name of the trial columns in the input data frame. If the list is empty or None, the input data frame is assumed to contain only one trial. If the list is not empty, the input data frame is assumed to contain multiple trials and the transformation methods will be applied to each trial separately.

Type:: list[str]

time_column#

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

Type:: str

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:: str

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]

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, dict[str, Any]]

Examples

Initialize your PublicDataset object with the SBSAT 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 = 'BSC', has_files: dict[str, bool] = <factory>, mirrors: dict[str, tuple[str, ...]] = <factory>, resources: dict[str, tuple[dict[str, str], ...]] = <factory>, experiment: Experiment = <pymovements.gaze.experiment.Experiment object>, extract: dict[str, bool] = <factory>, filename_format: dict[str, str] = <factory>, filename_format_schema_overrides: dict[str, dict[str, type]] = <factory>, custom_read_kwargs: dict[str, 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, has_files, mirrors, ...])

Attributes

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