Provo

class pymovements.datasets.Provo(name: str = 'Provo', long_name: str = 'Provo 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]] = <factory>, column_map: dict[str, str] = <factory>, 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)

Provo dataset [Luke and Christianson, 2018].

The Provo Corpus, a corpus of eye-tracking data with accompanying predictability norms. The predictability norms for the Provo Corpus differ from those of other corpora. In addition to traditional cloze scores that estimate the predictability of the full orthographic form of each word, the Provo Corpus also includes measures of the predictability of the morpho-syntactic and semantic information for each word. This makes the Provo Corpus ideal for studying predictive processes in reading.

Check the respective paper for details [Luke and Christianson, 2018].

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

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 Dataset object with the SBSAT definition:

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

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
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
column_map
custom_read_kwargs
mirrors
experiment