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Feature/dataset sbsat (#575)
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* added from_csv (#504)

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* fix docstring error

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* cyclic import

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* added dataset gaze_on_faces

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* sbset dataset

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* bib

---------

Co-authored-by: prassepaul <[email protected]>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: David R. Reich <[email protected]>
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10 changes: 10 additions & 0 deletions docs/source/bibliography.bib
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Expand Up @@ -76,3 +76,13 @@ @article{GazeOnFaces
year={2016},
publisher={The Association for Research in Vision and Ophthalmology},
}

@inproceedings{SB-SAT,
title = {Towards predicting reading comprehension from gaze behavior},
year = {2020},
booktitle = {Proceedings of the ACM Symposium on Eye Tracking Research and Applications},
author = {Ahn, Seoyoung and Kelton, Conor and Balasubramanian, Aruna and Zelinsky, Greg},
pages = {1--5},
publisher = {Association for Computing Machinery},
address = "Stuttgart, Germany",
}
3 changes: 3 additions & 0 deletions src/pymovements/datasets/__init__.py
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Expand Up @@ -29,6 +29,7 @@
pymovements.datasets.GazeBaseVR
pymovements.datasets.GazeOnFaces
pymovements.datasets.JuDo1000
pymovements.datasets.SBSAT
.. rubric:: Example Datasets
Expand All @@ -44,6 +45,7 @@
from pymovements.datasets.gazebase import GazeBase
from pymovements.datasets.gazebasevr import GazeBaseVR
from pymovements.datasets.judo1000 import JuDo1000
from pymovements.datasets.sb_sat import SBSAT
from pymovements.datasets.toy_dataset import ToyDataset
from pymovements.datasets.toy_dataset_eyelink import ToyDatasetEyeLink

Expand All @@ -53,6 +55,7 @@
'GazeBaseVR',
'GazeOnFaces',
'JuDo1000',
'SBSAT',
'ToyDataset',
'ToyDatasetEyeLink',
]
2 changes: 1 addition & 1 deletion src/pymovements/datasets/gaze_on_faces.py
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Expand Up @@ -34,7 +34,7 @@
@dataclass
@register_dataset
class GazeOnFaces(DatasetDefinition):
"""GazeBaseVR dataset :cite:p:`GazeOnFaces`.
"""GazeOnFaces dataset :cite:p:`GazeOnFaces`.
This dataset includes monocular eye tracking data from single participants in a single
session. Eye movements are recorded at a sampling frequency of 60 Hz
Expand Down
151 changes: 151 additions & 0 deletions src/pymovements/datasets/sb_sat.py
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@@ -0,0 +1,151 @@
# Copyright (c) 2022-2023 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.
"""This module provides an interface to the GazeOnFaces dataset."""
from __future__ import annotations

from dataclasses import dataclass
from dataclasses import field
from typing import Any

import polars as pl

from pymovements.dataset.dataset_definition import DatasetDefinition
from pymovements.dataset.dataset_library import register_dataset
from pymovements.gaze.experiment import Experiment


@dataclass
@register_dataset
class SBSAT(DatasetDefinition):
"""SB-SAT dataset :cite:p:`SB-SAT`.
This dataset includes monocular eye tracking data from a single 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 texts and answer questions.
Check the respective paper for details :cite:p:`SB-SAT`.
Attributes
----------
name : str
The name of the dataset.
mirrors : tuple[str, ...]
A tuple of mirrors of the dataset. Each entry must be of type `str` and end with a '/'.
resources : tuple[dict[str, str], ...]
A tuple of dataset 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.
experiment : Experiment
The experiment definition.
filename_format : str
Regular expression which will be matched before trying to load the file. Namedgroups will
appear in the `fileinfo` dataframe.
filename_format_dtypes : dict[str, type], optional
If named groups are present in the `filename_format`, this makes it possible to cast
specific named groups to a particular datatype.
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, Any], optional
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.GazeOnFaces` definition:
>>> import pymovements as pm
>>>
>>> dataset = pm.Dataset("SBSAT", path='data/SBSAT')
Download the dataset resources resources:
>>> dataset.download()# doctest: +SKIP
Load the data into memory:
>>> dataset.load()# doctest: +SKIP
"""

# pylint: disable=similarities
# The PublicDatasetDefinition child classes potentially share code chunks for definitions.

name: str = 'SBSAT'

mirrors: tuple[str, ...] = (
'https://files.de-1.osf.io/v1/resources/cdx69/providers/osfstorage/',
)

resources: tuple[dict[str, str], ...] = (
{
'resource': '64525979230ea6163c031267/?zip=',
'filename': 'csvs.zip',
'md5': '3cf074c93266b723437cf887f948c993',
},
)

experiment: Experiment = Experiment(
screen_width_px=768,
screen_height_px=1024,
screen_width_cm=42.4,
screen_height_cm=44.5,
distance_cm=70,
origin='center',
sampling_rate=1000,
)

filename_format: str = r'msd{subject_id:d}.csv'

filename_format_dtypes: dict[str, type] = field(
default_factory=lambda: {
'subject_id': int,
},
)

trial_columns: list[str] = field(default_factory=lambda: ['book_name', 'screen_id'])

time_column: str = 'time'

pixel_columns: list[str] = field(default_factory=lambda: ['x_left', 'y_left'])

column_map: dict[str, str] = field(default_factory=lambda: {})

custom_read_kwargs: dict[str, Any] = field(
default_factory=lambda: {
'separator': '\t',
'columns': [
'time', 'book_name', 'screen_id',
'x_left', 'y_left', 'pupil_left',
],
'dtypes': [
pl.Int64, pl.Utf8, pl.Int64,
pl.Float64, pl.Float64, pl.Float64,
],
},
)
2 changes: 2 additions & 0 deletions tests/datasets/datasets_test.py
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Expand Up @@ -33,6 +33,7 @@
pytest.param(pm.datasets.GazeBaseVR, 'GazeBaseVR', id='GazeBaseVR'),
pytest.param(pm.datasets.GazeOnFaces, 'GazeOnFaces', id='GazeOnFaces'),
pytest.param(pm.datasets.JuDo1000, 'JuDo1000', id='JuDo1000'),
pytest.param(pm.datasets.SBSAT, 'SBSAT', id='SBSAT'),
],
)
def test_public_dataset_registered(definition_class, dataset_name):
Expand All @@ -49,6 +50,7 @@ def test_public_dataset_registered(definition_class, dataset_name):
pytest.param(pm.datasets.GazeBaseVR, id='GazeBaseVR'),
pytest.param(pm.datasets.GazeOnFaces, id='GazeOnFaces'),
pytest.param(pm.datasets.JuDo1000, id='JuDo1000'),
pytest.param(pm.datasets.SBSAT, id='SBSAT'),
],
)
def test_public_dataset_registered_correct_attributes(dataset_definition_class):
Expand Down
79 changes: 79 additions & 0 deletions tests/datasets/sbsat_test.py
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@@ -0,0 +1,79 @@
# Copyright (c) 2023 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.
"""Test all functionality in pymovements.dataset.sb_sat."""
from pathlib import Path

import pytest

import pymovements as pm


@pytest.mark.parametrize(
'init_path, expected_paths',
[
pytest.param(
'/data/set/path',
{
'root': Path('/data/set/path/'),
'dataset': Path('/data/set/path/'),
'download': Path('/data/set/path/downloads'),
},
),
pytest.param(
pm.DatasetPaths(root='/data/set/path'),
{
'root': Path('/data/set/path/'),
'dataset': Path('/data/set/path/SBSAT'),
'download': Path('/data/set/path/SBSAT/downloads'),
},
),
pytest.param(
pm.DatasetPaths(root='/data/set/path', dataset='.'),
{
'root': Path('/data/set/path/'),
'dataset': Path('/data/set/path/'),
'download': Path('/data/set/path/downloads'),
},
),
pytest.param(
pm.DatasetPaths(root='/data/set/path', dataset='dataset'),
{
'root': Path('/data/set/path/'),
'dataset': Path('/data/set/path/dataset'),
'download': Path('/data/set/path/dataset/downloads'),
},
),
pytest.param(
pm.DatasetPaths(root='/data/set/path', downloads='custom_downloads'),
{
'root': Path('/data/set/path/'),
'dataset': Path('/data/set/path/SBSAT'),
'download': Path('/data/set/path/SBSAT/custom_downloads'),
},
),
],
)
def test_paths(init_path, expected_paths):
dataset = pm.Dataset(pm.datasets.SBSAT, path=init_path)

assert dataset.paths.root == expected_paths['root']
assert dataset.path == expected_paths['dataset']
assert dataset.paths.dataset == expected_paths['dataset']
assert dataset.paths.downloads == expected_paths['download']

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