JuDo1000#
- class pymovements.datasets.JuDo1000(name: str = 'JuDo1000', long_name: str = 'Jumping Dots 1000 Hz 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]] = <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, filename_format: dict[str, str] | None = None, filename_format_schema_overrides: dict[str, dict[str, type]] | None = None)[source]#
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
- 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:
- experiment#
The experiment definition.
- Type:
- 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
- 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 nestedpixelcolumn 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
Datasetobject with theJuDo1000definition:>>> import pymovements as pm >>> >>> dataset = pm.Dataset("JuDo1000", path='data/JuDo1000')
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_columnsdistance_columnextracthas_resourcesChecks for resources in
resources.position_columnsvelocity_columnsmirrors