BSC

class pymovements.datasets.BSC(name: str = 'BSC', *, long_name: str = 'Beijing Sentence Corpus', mirrors: dict[str, Sequence[str]] = <factory>, resources: ResourceDefinitions = <factory>, experiment: Experiment | None = <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)

BSC dataset [Pan et al., 2022].

The Beijing Sentence Corpus (BSC) is a Simplified Chinese sentence corpus of eye-tracking data, including word boundaries and predictability norms for each word. The sentences were selected from the People’s Daily, the largest newspaper group and an official newspaper of the People’s Republic of China. Data was collected from 60 native Chinese university students. The eye movements were recorded with an Eyelink II system running at 500 Hz.

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

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

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] | 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 BSC definition:

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

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
mirrors
experiment