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* add code comprehension dataset * add code comprehension dataset tests
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# Copyright (c) 2022-2024 The pymovements Project Authors | ||
# | ||
# Permission is hereby granted, free of charge, to any person obtaining a copy | ||
# of this software and associated documentation files (the "Software"), to deal | ||
# in the Software without restriction, including without limitation the rights | ||
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell | ||
# copies of the Software, and to permit persons to whom the Software is | ||
# furnished to do so, subject to the following conditions: | ||
# | ||
# The above copyright notice and this permission notice shall be included in all | ||
# copies or substantial portions of the Software. | ||
# | ||
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR | ||
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, | ||
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE | ||
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER | ||
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, | ||
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE | ||
# SOFTWARE. | ||
"""Provides a definition for the CodeComprehension dataset.""" | ||
from __future__ import annotations | ||
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from dataclasses import dataclass | ||
from dataclasses import field | ||
from typing import Any | ||
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import polars as pl | ||
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from pymovements.dataset.dataset_definition import DatasetDefinition | ||
from pymovements.dataset.dataset_library import register_dataset | ||
from pymovements.gaze.experiment import Experiment | ||
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@dataclass | ||
@register_dataset | ||
class CodeComprehension(DatasetDefinition): | ||
"""CodeComprehension dataset :cite:p:`CodeComprehension`. | ||
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. | ||
Attributes | ||
---------- | ||
name: str | ||
The name of the dataset. | ||
has_files: dict[str, bool] | ||
Indicate whether the dataset contains 'gaze', 'precomputed_events', and | ||
'precomputed_reading_measures'. | ||
mirrors: dict[str, tuple[str, ...]] | ||
A tuple of mirrors of the dataset. Each entry must be of type `str` and end with a '/'. | ||
resources: dict[str, tuple[dict[str, str], ...]] | ||
A tuple 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. | ||
extract: dict[str, bool] | ||
Decide whether to extract the data. | ||
experiment: Experiment | ||
The experiment definition. | ||
filename_format: dict[str, str] | ||
Regular expression which will be matched before trying to load the file. Namedgroups will | ||
appear in the `fileinfo` dataframe. | ||
filename_format_schema_overrides: dict[str, dict[str, type]] | ||
If named groups are present in the `filename_format`, this makes it possible to cast | ||
specific named groups to a particular datatype. | ||
trial_columns: list[str] | ||
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. | ||
time_column: str | ||
The name of the timestamp column in the input data frame. This column will be renamed to | ||
``time``. | ||
time_unit: str | ||
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. | ||
pixel_columns: list[str] | ||
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. | ||
column_map: dict[str, str] | ||
The keys are the columns to read, the values are the names to which they should be renamed. | ||
custom_read_kwargs: dict[str, dict[str, Any]] | ||
If specified, these keyword arguments will be passed to the file reading function. | ||
Examples | ||
-------- | ||
Initialize your :py:class:`~pymovements.PublicDataset` object with the | ||
:py:class:`~pymovements.CodeComprehension` definition: | ||
>>> import pymovements as pm | ||
>>> | ||
>>> dataset = pm.Dataset("CodeComprehension", path='data/CodeComprehension') | ||
Download the dataset resources: | ||
>>> dataset.download()# doctest: +SKIP | ||
Load the data into memory: | ||
>>> dataset.load()# doctest: +SKIP | ||
""" | ||
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# pylint: disable=similarities | ||
# The PublicDatasetDefinition child classes potentially share code chunks for definitions. | ||
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name: str = 'CodeComprehension' | ||
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has_files: dict[str, bool] = field( | ||
default_factory=lambda: { | ||
'gaze': False, | ||
'precomputed_events': True, | ||
'precomputed_reading_measures': False, | ||
}, | ||
) | ||
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mirrors: dict[str, tuple[str, ...]] = field( | ||
default_factory=lambda: { | ||
'precomputed_events': ('https://zenodo.org/',), | ||
}, | ||
) | ||
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resources: dict[str, tuple[dict[str, str], ...]] = field( | ||
default_factory=lambda: { | ||
'precomputed_events': ( | ||
{ | ||
'resource': | ||
'records/11123101/files/Predicting%20Code%20Comprehension%20Package' | ||
'.zip?download=1', | ||
'filename': 'data.zip', | ||
'md5': '3a3c6fb96550bc2c2ddcf5d458fb12a2', | ||
}, | ||
), | ||
}, | ||
) | ||
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extract: dict[str, bool] = field(default_factory=lambda: {'precomputed_events': True}) | ||
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experiment: Experiment = Experiment( | ||
screen_width_px=None, | ||
screen_height_px=None, | ||
screen_width_cm=None, | ||
screen_height_cm=None, | ||
distance_cm=None, | ||
origin=None, | ||
sampling_rate=2000, | ||
) | ||
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filename_format: dict[str, str] = field( | ||
default_factory=lambda: { | ||
'precomputed_events': r'fix_report_P{subject_id:s}.txt', | ||
}, | ||
) | ||
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filename_format_schema_overrides: dict[str, dict[str, type]] = field( | ||
default_factory=lambda: { | ||
'precomputed_events': {'subject_id': pl.Utf8}, | ||
}, | ||
) | ||
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trial_columns: list[str] = field(default_factory=lambda: []) | ||
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time_column: str = '' | ||
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time_unit: str = '' | ||
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pixel_columns: list[str] = field(default_factory=lambda: []) | ||
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column_map: dict[str, str] = field(default_factory=lambda: {}) | ||
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custom_read_kwargs: dict[str, dict[str, Any]] = field( | ||
default_factory=lambda: { | ||
'precomputed_events': { | ||
'separator': '\t', | ||
'null_values': '.', | ||
'quote_char': '"', | ||
}, | ||
}, | ||
) |
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