pymovements.datasets.CodeComprehension

class pymovements.datasets.CodeComprehension(name: str = 'CodeComprehension', long_name: str = 'Code Comprehension dataset', has_files: dict[str, bool] = <factory>, mirrors: dict[str, list[str]] | dict[str, tuple[str, ...]] = <factory>, resources: dict[str, list[dict[str, str]]] = <factory>, experiment: Experiment | None = <factory>, extract: dict[str, bool] = <factory>, filename_format: dict[str, str] = <factory>, filename_format_schema_overrides: dict[str, dict[str, type]] = <factory>, 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 = 'ms', 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)

CodeComprehension dataset [Alakmeh et al., 2024].

This dataset includes eye-tracking-while-code-reading data from a participants in a single session. Eye movements are recorded at a sampling frequency of 1,000 Hz using an EyeLink 1000 eye tracker and are provided as pixel coordinates.

The participant is instructed to read the code snippet and answer a code comprehension question.

name

The name of the dataset.

Type:

str

long_name

The entire name of the dataset.

Type:

str

has_files

Indicate whether the dataset contains ‘gaze’, ‘precomputed_events’, and ‘precomputed_reading_measures’.

Type:

dict[str, bool]

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:

dict[str, list[dict[str, str]]]

extract

Decide whether to extract the data.

Type:

dict[str, bool]

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]

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]]

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 CodeComprehension definition:

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

Download the dataset resources:

>>> dataset.download()

Load the data into memory:

>>> dataset.load()

__init__(name: str = 'CodeComprehension', long_name: str = 'Code Comprehension dataset', has_files: dict[str, bool] = <factory>, mirrors: dict[str, list[str]] | dict[str, tuple[str, ...]] = <factory>, resources: dict[str, list[dict[str, str]]] = <factory>, experiment: Experiment | None = <factory>, extract: dict[str, bool] = <factory>, filename_format: dict[str, str] = <factory>, filename_format_schema_overrides: dict[str, dict[str, type]] = <factory>, 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 = 'ms', 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) → None

Methods

__init__([name, long_name, has_files, ...])
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
long_name
name
pixel_columns
position_columns
time_column
time_unit
trial_columns
velocity_columns
has_files
resources
extract
filename_format
filename_format_schema_overrides
column_map
custom_read_kwargs
mirrors
experiment