Repository: aeye-lab/pymovements (python)

Issue:
add provo corpus
https://osf.io/sjefs/

Pull request:
dataset: add Provo corpus
resolves #948 

Patch:
diff --git a/docs/source/bibliography.bib b/docs/source/bibliography.bib
index e709b5b..5d68486 100644
--- a/docs/source/bibliography.bib
+++ b/docs/source/bibliography.bib
@@ -239,6 +239,28 @@ keywords = {code comprehension, code-fixation attention, eye-tracking, lab exper
   publisher={The Association for Research in Vision and Ophthalmology},
 }
 
+@article{Provo,
+author = {Luke, Steven G. and Christianson, Kiel},
+address = {New York},
+copyright = {Psychonomic Society, Inc. 2017},
+issn = {1554-3528},
+journal = {Behavior Research Methods},
+keywords = {Cognitive psychology ; Computational linguistics ; Female ; Human beings ; Information storage and retrieval systems ; Male ; Psychology ; Reading ; Semantics},
+language = {eng},
+number = {2},
+abstract = {This article presents 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. Some analyses using these data have previously been reported elsewhere (Luke \& Christianson,
+2016
+). The Provo Corpus is available for download on the Open Science Framework, at
+https://osf.io/sjefs
+.},
+pages = {826-833},
+publisher = {Springer US},
+title = {The Provo Corpus: A large eye-tracking corpus with predictability norms},
+volume = {50},
+year = {2018},
+}
+
+
 @inproceedings{SB-SAT,
     title = {Towards predicting reading comprehension from gaze behavior},
     year = {2020},
diff --git a/src/pymovements/datasets/__init__.py b/src/pymovements/datasets/__init__.py
index 6b4b743..1b13bb2 100644
--- a/src/pymovements/datasets/__init__.py
+++ b/src/pymovements/datasets/__init__.py
@@ -39,6 +39,7 @@
     pymovements.datasets.InteRead
     pymovements.datasets.JuDo1000
     pymovements.datasets.PoTeC
+    pymovements.datasets.Provo
     pymovements.datasets.SBSAT
 
 
@@ -66,6 +67,7 @@ from pymovements.datasets.hbn import HBN
 from pymovements.datasets.interead import InteRead
 from pymovements.datasets.judo1000 import JuDo1000
 from pymovements.datasets.potec import PoTeC
+from pymovements.datasets.provo import Provo
 from pymovements.datasets.sb_sat import SBSAT
 from pymovements.datasets.toy_dataset import ToyDataset
 from pymovements.datasets.toy_dataset_eyelink import ToyDatasetEyeLink
@@ -87,6 +89,7 @@ __all__ = [
     'InteRead',
     'JuDo1000',
     'PoTeC',
+    'Provo',
     'SBSAT',
     'ToyDataset',
     'ToyDatasetEyeLink',
diff --git a/src/pymovements/datasets/provo.py b/src/pymovements/datasets/provo.py
new file mode 100644
index 0000000..fcd4c00
--- /dev/null
+++ b/src/pymovements/datasets/provo.py
@@ -0,0 +1,203 @@
+# Copyright (c) 2022-2025 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 Provo 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 Provo(DatasetDefinition):
+    """Provo dataset :cite:p:`Provo`.
+
+    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 :cite:p:`Provo`.
+
+    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.SBSAT` definition:
+
+    >>> import pymovements as pm
+    >>>
+    >>> dataset = pm.Dataset("SBSAT", path='data/SBSAT')
+
+    Download the dataset 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 = 'Provo'
+
+    has_files: dict[str, bool] = field(
+        default_factory=lambda: {
+            'gaze': False,
+            'precomputed_events': True,
+            'precomputed_reading_measures': False,
+        },
+    )
+    mirrors: dict[str, tuple[str, ...]] = field(
+        default_factory=lambda:
+            {
+                'precomputed_events': (
+                    'https://osf.io/download/',
+                ),
+            },
+    )
+    resources: dict[str, tuple[dict[str, str], ...]] = field(
+        default_factory=lambda:
+            {
+                'precomputed_events': (
+                    {
+                        'resource': 'z3eh6/',
+                        'filename': 'Provo_Corpus-Additional_Eyetracking_Data-Fixation_Report.csv',
+                        'md5': '7aa239e51e5d78528e2430f84a23da3f',
+                    },
+                ),
+            },
+    )
+    extract: dict[str, bool] = field(
+        default_factory=lambda: {
+            'precomputed_events': False,
+        },
+    )
+
+    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=1,
+    )
+
+    filename_format: dict[str, str] = field(
+        default_factory=lambda:
+            {
+                'precomputed_events':
+                'Provo_Corpus-Additional_Eyetracking_Data-Fixation_Report.csv',
+            },
+    )
+
+    filename_format_schema_overrides: dict[str, dict[str, type]] = field(
+        default_factory=lambda:
+            {
+                'precomputed_events': {},
+            },
+    )
+
+    trial_columns: list[str] = field(default_factory=lambda: [])
+
+    time_column: str = ''
+
+    time_unit: str = ''
+
+    pixel_columns: list[str] = field(default_factory=lambda: [])
+
+    column_map: dict[str, str] = field(default_factory=lambda: {})
+
+    custom_read_kwargs: dict[str, dict[str, Any]] = field(
+        default_factory=lambda:
+        {
+            'precomputed_events': {
+                'schema_overrides': {'RECORDING_SESSION_LABEL': pl.Utf8},
+                'encoding': 'macroman',
+                'null_values': ['.'],
+            },
+        },
+    )


Test patch:
diff --git a/tests/unit/datasets/datasets_test.py b/tests/unit/datasets/datasets_test.py
index 14cf6c4..46eefd6 100644
--- a/tests/unit/datasets/datasets_test.py
+++ b/tests/unit/datasets/datasets_test.py
@@ -45,6 +45,7 @@ import pymovements as pm
         pytest.param(pm.datasets.InteRead, 'InteRead', id='InteRead'),
         pytest.param(pm.datasets.JuDo1000, 'JuDo1000', id='JuDo1000'),
         pytest.param(pm.datasets.PoTeC, 'PoTeC', id='PoTeC'),
+        pytest.param(pm.datasets.Provo, 'Provo', id='Provo'),
         pytest.param(pm.datasets.SBSAT, 'SBSAT', id='SBSAT'),
         pytest.param(pm.datasets.ToyDataset, 'ToyDataset', id='ToyDataset'),
         pytest.param(pm.datasets.ToyDatasetEyeLink, 'ToyDatasetEyeLink', id='ToyDatasetEyeLink'),