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