def test_fetch_language_localizer_demo_dataset(request_mocker, tmp_path):
data_dir = str(tmp_path)
expected_data_dir, expected_files = _mock_language_localizer_demo_dataset(
data_dir)
actual_data_dir, actual_subdirs = func.fetch_language_localizer_demo_dataset(
data_dir)
assert actual_data_dir == expected_data_dir
assert actual_subdirs == expected_files
def test_fetch_language_localizer_demo_dataset(request_mocker, tmp_path):
data_dir = str(tmp_path)
expected_data_dir = tmp_path / 'fMRI-language-localizer-demo-dataset'
contents_dir = Path(
__file__).parent / "data" / "archive_contents"
contents_list_file = contents_dir / "language_localizer.txt"
with contents_list_file.open() as f:
expected_files = [str(expected_data_dir / file_path.strip()) for
file_path in f.readlines()[1:]]
actual_dir, actual_subdirs = func.fetch_language_localizer_demo_dataset(
data_dir)
assert actual_dir == str(expected_data_dir)
assert actual_subdirs == sorted(expected_files)
.. contents:: **Contents**
:local:
:depth: 1
"""
##############################################################################
# Fetch example BIDS dataset
# --------------------------
# We download a simplified BIDS dataset made available for illustrative
# purposes. It contains only the necessary
# information to run a statistical analysis using Nilearn. The raw data
# subject folders only contain bold.json and events.tsv files, while the
# derivatives folder includes the preprocessed files preproc.nii and the
# confounds.tsv files.
from nilearn.datasets.func import fetch_language_localizer_demo_dataset
data_dir, _ = fetch_language_localizer_demo_dataset()
##############################################################################
# Here is the location of the dataset on disk.
print(data_dir)
##############################################################################
# Obtain automatically FirstLevelModel objects and fit arguments
# --------------------------------------------------------------
# From the dataset directory we automatically obtain the FirstLevelModel objects
# with their subject_id filled from the BIDS dataset. Moreover, we obtain
# for each model a dictionary with run_imgs, events and confounder regressors
# since in this case a confounds.tsv file is available in the BIDS dataset.
# To get the first level models we only have to specify the dataset directory
# and the task_label as specified in the file names.
from nilearn.stats.first_level_model import first_level_models_from_bids
