pymovements.datasets.ToyDatasetEyeLink#
- class pymovements.datasets.ToyDatasetEyeLink(name: str = 'ToyDatasetEyeLink', mirrors: tuple[str, ...] = ('http://github.com/aeye-lab/pymovements-toy-dataset-eyelink/zipball/',), resources: tuple[dict[str, str], ...] = ({'filename': 'pymovements-toy-dataset-eyelink.zip', 'md5': 'b1d426751403752c8a154fc48d1670ce', 'resource': 'a970d090588542dad745297866e794ab9dad8795/'},), experiment: Experiment = <pymovements.gaze.experiment.Experiment object>, filename_format: str = 'subject_{subject_id:d}_session_{session_id:d}.asc', 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)#
Example toy dataset with EyeLink data.
This dataset includes monocular eye tracking data from a single participants in a single session. 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.
The participant is instructed to read a single text and some JuDo trials.
- 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:
- 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
Dataset
object with theToyDataset
definition:>>> import pymovements as pm >>> >>> dataset = pm.Dataset("ToyDatasetEyeLink", path='data/ToyDatasetEyeLink')
Download the dataset resources:
>>> dataset.download()
Load the data into memory:
>>> dataset.load()
- __init__(name: str = 'ToyDatasetEyeLink', mirrors: tuple[str, ...] = ('http://github.com/aeye-lab/pymovements-toy-dataset-eyelink/zipball/',), resources: tuple[dict[str, str], ...] = ({'filename': 'pymovements-toy-dataset-eyelink.zip', 'md5': 'b1d426751403752c8a154fc48d1670ce', 'resource': 'a970d090588542dad745297866e794ab9dad8795/'},), experiment: Experiment = <pymovements.gaze.experiment.Experiment object>, filename_format: str = 'subject_{subject_id:d}_session_{session_id:d}.asc', 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
pixel_columns
position_columns
time_column
time_unit
trial_columns
velocity_columns