HBN#

class pymovements.datasets.HBN(name: str = 'HBN', *, long_name: str = 'Healthy Brain Network 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]] | None = None, column_map: dict[str, str] | None = None, trial_columns: list[str] | None = None, time_column: str | None = None, time_unit: str | None = None, pixel_columns: list[str] | None = None, 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]#

HBN dataset [Alexander et al., 2017].

This dataset consists of recordings from children watching four different age-appropriate videos: (1) an educational video clip (Fun with Fractals), (2) a short animated film (The Present), (3) a short clip of an animated film (Despicable Me), and (4) a trailer for a feature-length movie (Diary of a Wimpy Kid). The eye gaze was recorded at a sampling rate of 120 Hz.

Check the respective paper for details [Alexander et al., 2017].

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:: ResourceDefinitions

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] | 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

time_column#

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

Type:: str | None

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 | None

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] | None

column_map#

The keys are the columns to read, the values are the names to which they should be renamed.

Type:: dict[str, str] | None

custom_read_kwargs#

If specified, these keyword arguments will be passed to the file reading function. (default: None)

Type:: dict[str, dict[str, Any]] | None

Examples

Initialize your Dataset object with the HBN definition:

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

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_columns`
`column_map`
`custom_read_kwargs`
`distance_column`
`extract`
`filename_format`
`filename_format_schema_overrides`
`has_resources`	Checks for resources in `resources`.
`long_name`
`name`
`pixel_columns`
`position_columns`
`time_column`
`time_unit`
`trial_columns`
`velocity_columns`
`resources`
`experiment`
`mirrors`

HBN#

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