pymovements.datasets.JuDo1000

class pymovements.datasets.JuDo1000(name: str = 'JuDo1000', mirrors: tuple[str, ...] = ('https://osf.io/download/',), resources: tuple[dict[str, str], ...] = ({'filename': 'JuDo1000.zip', 'md5': 'b8b9e5bb65b78d6f2bd260451cdd89f8', 'resource': '4wy7s/'},), experiment: Experiment = <pymovements.gaze.experiment.Experiment object>, filename_format: str = '{subject_id:d}_{session_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)

JuDo1000 dataset [Makowski et al., 2020].

This dataset includes binocular eye tracking data from 150 participants in four sessions with an interval of at least one week between two sessions. Eye movements are recorded at a sampling frequency of 1000 Hz using an EyeLink Portable Duo video-based eye tracker and are provided as pixel coordinates. Participants are instructed to watch a random jumping dot on a computer screen.

Check the respective repository for details.

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 JuDo1000 definition:

>>> import pymovements as pm
>>>
>>> dataset = pm.Dataset("JuDo1000", path='data/JuDo1000')

Download the dataset resources:

>>> dataset.download()

Load the data into memory:

>>> dataset.load()

__init__(name: str = 'JuDo1000', mirrors: tuple[str, ...] = ('https://osf.io/download/',), resources: tuple[dict[str, str], ...] = ({'filename': 'JuDo1000.zip', 'md5': 'b8b9e5bb65b78d6f2bd260451cdd89f8', 'resource': '4wy7s/'},), experiment: Experiment = <pymovements.gaze.experiment.Experiment object>, filename_format: str = '{subject_id:d}_{session_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