Repository: bids-standard/pybids Branch: main Commit: d76047584178 Files: 797 Total size: 33.2 MB Directory structure: gitextract_hu9a0df4/ ├── .git-blame-ignore-revs ├── .gitattributes ├── .github/ │ ├── codespell_exclude_lines.txt │ ├── codespell_ignore_words.txt │ ├── dependabot.yml │ ├── release-drafter.yml │ └── workflows/ │ ├── build-test-deploy.yml │ ├── codespell.yml │ ├── docs.yml │ └── prerelease.yml ├── .gitignore ├── .gitmodules ├── .mailmap ├── .pre-commit-config.yaml ├── .zenodo.json ├── CHANGELOG.rst ├── LICENSE ├── MANIFEST.in ├── README.md ├── codecov.yml ├── doc/ │ ├── Makefile │ ├── _static/ │ │ └── theme_overrides.css │ ├── _templates/ │ │ ├── class.rst │ │ ├── function.rst │ │ └── module.rst │ ├── analysis/ │ │ └── index.rst │ ├── api.rst │ ├── conf.py │ ├── examples/ │ │ ├── index.rst │ │ ├── pybids_tutorial.md │ │ ├── reports_tutorial.md │ │ └── statsmodels_tutorial.md │ ├── includes/ │ │ ├── big_toc_css.rst │ │ └── bigger_toc_css.rst │ ├── index.rst │ ├── introduction.rst │ ├── layout/ │ │ └── index.rst │ ├── make.bat │ ├── reports/ │ │ └── index.rst │ ├── sphinxext/ │ │ ├── docscrape.py │ │ ├── docscrape_sphinx.py │ │ ├── github.py │ │ ├── math_dollar.py │ │ └── numpydoc.py │ ├── tune_toc.rst │ ├── user_guide.rst │ └── whats_new.rst ├── long_description.rst ├── paper/ │ ├── paper.bib │ └── paper.md ├── pyproject.toml ├── ruff.toml ├── schema_integration_demo.ipynb ├── setup.cfg ├── setup.py ├── src/ │ └── bids/ │ ├── __init__.py │ ├── __main__.py │ ├── _version.py │ ├── cli.py │ ├── config.py │ ├── conftest.py │ ├── due.py │ ├── exceptions.py │ ├── ext/ │ │ └── __init__.py │ ├── external/ │ │ ├── __init__.py │ │ └── inflect.py │ ├── layout/ │ │ ├── README.md │ │ ├── __init__.py │ │ ├── config/ │ │ │ ├── bids.json │ │ │ └── derivatives.json │ │ ├── db.py │ │ ├── index.py │ │ ├── layout.py │ │ ├── models.py │ │ ├── tests/ │ │ │ ├── __init__.py │ │ │ ├── conftest.py │ │ │ ├── data/ │ │ │ │ └── sample_validation_config.json │ │ │ ├── test_db.py │ │ │ ├── test_layout.py │ │ │ ├── test_layout_on_examples.py │ │ │ ├── test_models.py │ │ │ ├── test_path_building.py │ │ │ ├── test_remote_bids.py │ │ │ ├── test_rootpath.py │ │ │ ├── test_schema_config.py │ │ │ ├── test_schema_pattern_validation.py │ │ │ ├── test_schema_version_differences.py │ │ │ ├── test_schema_vs_json_config.py │ │ │ ├── test_utils.py │ │ │ ├── test_validation.py │ │ │ └── test_writing.py │ │ ├── utils.py │ │ ├── validation.py │ │ └── writing.py │ ├── modeling/ │ │ ├── README.md │ │ ├── __init__.py │ │ ├── auto_model.py │ │ ├── hrf.py │ │ ├── model_spec.py │ │ ├── report/ │ │ │ ├── __init__.py │ │ │ ├── base.py │ │ │ ├── report_template.jinja │ │ │ ├── utils.py │ │ │ └── viz.py │ │ ├── statsmodels.py │ │ ├── tests/ │ │ │ ├── __init__.py │ │ │ ├── test_automodel.py │ │ │ ├── test_hrf.py │ │ │ ├── test_model_spec.py │ │ │ ├── test_statsmodels.py │ │ │ └── test_transformations.py │ │ └── transformations/ │ │ ├── __init__.py │ │ ├── base.py │ │ ├── compute.py │ │ └── munge.py │ ├── reports/ │ │ ├── README.md │ │ ├── __init__.py │ │ ├── config/ │ │ │ └── converters.json │ │ ├── conftest.py │ │ ├── parameters.py │ │ ├── parsing.py │ │ ├── report.py │ │ ├── tests/ │ │ │ ├── __init__.py │ │ │ ├── test_parameters.py │ │ │ ├── test_parsing.py │ │ │ └── test_report.py │ │ └── utils.py │ ├── tests/ │ │ ├── __init__.py │ │ ├── data/ │ │ │ └── images/ │ │ │ └── 4d.bval │ │ ├── test_cli.py │ │ ├── test_config.py │ │ └── utils.py │ ├── utils.py │ └── variables/ │ ├── __init__.py │ ├── collections.py │ ├── entities.py │ ├── io.py │ ├── tests/ │ │ ├── __init__.py │ │ ├── test_collections.py │ │ ├── test_entities.py │ │ ├── test_io.py │ │ └── test_variables.py │ └── variables.py ├── tests/ │ └── data/ │ ├── 7t_trt/ │ │ ├── README │ │ ├── dataset_description.json │ │ ├── participants.tsv │ │ ├── sub-01/ │ │ │ ├── ses-1/ │ │ │ │ ├── fmap/ │ │ │ │ │ ├── sub-01_ses-1_run-1_phasediff.json │ │ │ │ │ └── sub-01_ses-1_run-2_phasediff.json │ │ │ │ ├── func/ │ │ │ │ │ └── sub-01_ses-1_task-rest_acq-fullbrain_run-1_bold.json │ │ │ │ └── sub-01_ses-1_scans.tsv │ │ │ ├── ses-2/ │ │ │ │ ├── fmap/ │ │ │ │ │ ├── sub-01_ses-2_run-1_phasediff.json │ │ │ │ │ └── sub-01_ses-2_run-2_phasediff.json │ │ │ │ └── sub-01_ses-2_scans.tsv │ │ │ └── sub-01_sessions.tsv │ │ ├── sub-02/ │ │ │ ├── ses-1/ │ │ │ │ ├── fmap/ │ │ │ │ │ ├── sub-02_ses-1_run-1_phasediff.json │ │ │ │ │ └── sub-02_ses-1_run-2_phasediff.json │ │ │ │ └── sub-02_ses-1_scans.tsv │ │ │ ├── ses-2/ │ │ │ │ ├── fmap/ │ │ │ │ │ ├── sub-02_ses-2_run-1_phasediff.json │ │ │ │ │ └── sub-02_ses-2_run-2_phasediff.json │ │ │ │ └── sub-02_ses-2_scans.tsv │ │ │ └── sub-02_sessions.tsv │ │ ├── sub-03/ │ │ │ ├── ses-1/ │ │ │ │ ├── fmap/ │ │ │ │ │ ├── sub-03_ses-1_run-1_phasediff.json │ │ │ │ │ └── sub-03_ses-1_run-2_phasediff.json │ │ │ │ └── sub-03_ses-1_scans.tsv │ │ │ ├── ses-2/ │ │ │ │ ├── fmap/ │ │ │ │ │ ├── sub-03_ses-2_run-1_phasediff.json │ │ │ │ │ └── sub-03_ses-2_run-2_phasediff.json │ │ │ │ └── sub-03_ses-2_scans.tsv │ │ │ ├── sub-03-test.bval │ │ │ └── sub-03_sessions.tsv │ │ ├── sub-04/ │ │ │ ├── ses-1/ │ │ │ │ ├── fmap/ │ │ │ │ │ ├── sub-04_ses-1_run-1_phasediff.json │ │ │ │ │ └── sub-04_ses-1_run-2_phasediff.json │ │ │ │ └── sub-04_ses-1_scans.tsv │ │ │ ├── ses-2/ │ │ │ │ ├── fmap/ │ │ │ │ │ ├── sub-04_ses-2_run-1_phasediff.json │ │ │ │ │ └── sub-04_ses-2_run-2_phasediff.json │ │ │ │ └── sub-04_ses-2_scans.tsv │ │ │ └── sub-04_sessions.tsv │ │ ├── sub-05/ │ │ │ ├── ses-1/ │ │ │ │ ├── fmap/ │ │ │ │ │ ├── sub-05_ses-1_run-1_phasediff.json │ │ │ │ │ └── sub-05_ses-1_run-2_phasediff.json │ │ │ │ └── sub-05_ses-1_scans.tsv │ │ │ ├── ses-2/ │ │ │ │ ├── fmap/ │ │ │ │ │ ├── sub-05_ses-2_run-1_phasediff.json │ │ │ │ │ └── sub-05_ses-2_run-2_phasediff.json │ │ │ │ └── sub-05_ses-2_scans.tsv │ │ │ └── sub-05_sessions.tsv │ │ ├── sub-06/ │ │ │ ├── ses-1/ │ │ │ │ ├── fmap/ │ │ │ │ │ ├── sub-06_ses-1_run-1_phasediff.json │ │ │ │ │ └── sub-06_ses-1_run-2_phasediff.json │ │ │ │ └── sub-06_ses-1_scans.tsv │ │ │ ├── ses-2/ │ │ │ │ ├── fmap/ │ │ │ │ │ ├── sub-06_ses-2_run-1_phasediff.json │ │ │ │ │ └── sub-06_ses-2_run-2_phasediff.json │ │ │ │ └── sub-06_ses-2_scans.tsv │ │ │ └── sub-06_sessions.tsv │ │ ├── sub-07/ │ │ │ ├── ses-1/ │ │ │ │ ├── fmap/ │ │ │ │ │ ├── sub-07_ses-1_run-1_phasediff.json │ │ │ │ │ └── sub-07_ses-1_run-2_phasediff.json │ │ │ │ └── sub-07_ses-1_scans.tsv │ │ │ ├── ses-2/ │ │ │ │ ├── fmap/ │ │ │ │ │ ├── sub-07_ses-2_run-1_phasediff.json │ │ │ │ │ └── sub-07_ses-2_run-2_phasediff.json │ │ │ │ └── sub-07_ses-2_scans.tsv │ │ │ └── sub-07_sessions.tsv │ │ ├── sub-08/ │ │ │ ├── ses-1/ │ │ │ │ ├── fmap/ │ │ │ │ │ ├── sub-08_ses-1_run-1_phasediff.json │ │ │ │ │ └── sub-08_ses-1_run-2_phasediff.json │ │ │ │ └── sub-08_ses-1_scans.tsv │ │ │ ├── ses-2/ │ │ │ │ ├── fmap/ │ │ │ │ │ ├── sub-08_ses-2_run-1_phasediff.json │ │ │ │ │ └── sub-08_ses-2_run-2_phasediff.json │ │ │ │ └── sub-08_ses-2_scans.tsv │ │ │ └── sub-08_sessions.tsv │ │ ├── sub-09/ │ │ │ ├── ses-1/ │ │ │ │ ├── fmap/ │ │ │ │ │ ├── sub-09_ses-1_run-1_phasediff.json │ │ │ │ │ └── sub-09_ses-1_run-2_phasediff.json │ │ │ │ └── sub-09_ses-1_scans.tsv │ │ │ ├── ses-2/ │ │ │ │ ├── fmap/ │ │ │ │ │ ├── sub-09_ses-2_run-1_phasediff.json │ │ │ │ │ └── sub-09_ses-2_run-2_phasediff.json │ │ │ │ └── sub-09_ses-2_scans.tsv │ │ │ └── sub-09_sessions.tsv │ │ ├── sub-10/ │ │ │ ├── ses-1/ │ │ │ │ ├── fmap/ │ │ │ │ │ ├── sub-10_ses-1_run-1_phasediff.json │ │ │ │ │ └── sub-10_ses-1_run-2_phasediff.json │ │ │ │ └── sub-10_ses-1_scans.tsv │ │ │ ├── ses-2/ │ │ │ │ ├── fmap/ │ │ │ │ │ ├── sub-10_ses-2_run-1_phasediff.json │ │ │ │ │ └── sub-10_ses-2_run-2_phasediff.json │ │ │ │ └── sub-10_ses-2_scans.tsv │ │ │ └── sub-10_sessions.tsv │ │ ├── task-rest_acq-fullbrain_bold.json │ │ ├── task-rest_acq-fullbrain_run-1_physio.json │ │ ├── task-rest_acq-fullbrain_run-2_physio.json │ │ ├── task-rest_acq-prefrontal_bold.json │ │ ├── task-rest_acq-prefrontal_physio.json │ │ └── test.bval │ ├── bids_specs_with_oligarchy.json │ ├── ds000117/ │ │ ├── .bidsignore │ │ ├── CHANGES │ │ ├── README │ │ ├── acq-epi_T1w.json │ │ ├── acq-mprage_T1w.json │ │ ├── dataset_description.json │ │ ├── participants.tsv │ │ ├── run-1_echo-1_FLASH.json │ │ ├── run-1_echo-2_FLASH.json │ │ ├── run-1_echo-3_FLASH.json │ │ ├── run-1_echo-4_FLASH.json │ │ ├── run-1_echo-5_FLASH.json │ │ ├── run-1_echo-6_FLASH.json │ │ ├── run-1_echo-7_FLASH.json │ │ ├── run-2_echo-1_FLASH.json │ │ ├── run-2_echo-2_FLASH.json │ │ ├── run-2_echo-3_FLASH.json │ │ ├── run-2_echo-4_FLASH.json │ │ ├── run-2_echo-5_FLASH.json │ │ ├── run-2_echo-6_FLASH.json │ │ ├── run-2_echo-7_FLASH.json │ │ ├── sub-01/ │ │ │ ├── ses-meg/ │ │ │ │ ├── beh/ │ │ │ │ │ └── sub-01_ses-meg_task-facerecognition_events.tsv │ │ │ │ ├── meg/ │ │ │ │ │ ├── sub-01_ses-meg_headshape.pos │ │ │ │ │ ├── sub-01_ses-meg_task-facerecognition_proc-sss_coordsystem.json │ │ │ │ │ ├── sub-01_ses-meg_task-facerecognition_run-01_proc-sss_events.tsv │ │ │ │ │ ├── sub-01_ses-meg_task-facerecognition_run-01_proc-sss_meg.fif │ │ │ │ │ ├── sub-01_ses-meg_task-facerecognition_run-02_proc-sss_events.tsv │ │ │ │ │ ├── sub-01_ses-meg_task-facerecognition_run-02_proc-sss_meg.fif │ │ │ │ │ ├── sub-01_ses-meg_task-facerecognition_run-03_proc-sss_events.tsv │ │ │ │ │ ├── sub-01_ses-meg_task-facerecognition_run-03_proc-sss_meg.fif │ │ │ │ │ ├── sub-01_ses-meg_task-facerecognition_run-04_proc-sss_events.tsv │ │ │ │ │ ├── sub-01_ses-meg_task-facerecognition_run-04_proc-sss_meg.fif │ │ │ │ │ ├── sub-01_ses-meg_task-facerecognition_run-05_proc-sss_events.tsv │ │ │ │ │ ├── sub-01_ses-meg_task-facerecognition_run-05_proc-sss_meg.fif │ │ │ │ │ ├── sub-01_ses-meg_task-facerecognition_run-06_proc-sss_events.tsv │ │ │ │ │ └── sub-01_ses-meg_task-facerecognition_run-06_proc-sss_meg.fif │ │ │ │ └── sub-01_ses-meg_scans.tsv │ │ │ └── ses-mri/ │ │ │ ├── anat/ │ │ │ │ └── sub-01_ses-mri_acq-mprage_T1w.json │ │ │ ├── dwi/ │ │ │ │ ├── sub-01_ses-mri_dwi.bval │ │ │ │ ├── sub-01_ses-mri_dwi.bvec │ │ │ │ └── sub-01_ses-mri_dwi.json │ │ │ ├── fmap/ │ │ │ │ ├── sub-01_ses-mri_magnitude1.nii │ │ │ │ ├── sub-01_ses-mri_magnitude2.nii │ │ │ │ ├── sub-01_ses-mri_phasediff.json │ │ │ │ └── sub-01_ses-mri_phasediff.nii │ │ │ └── func/ │ │ │ ├── sub-01_ses-mri_task-facerecognition_run-01_events.tsv │ │ │ ├── sub-01_ses-mri_task-facerecognition_run-02_events.tsv │ │ │ ├── sub-01_ses-mri_task-facerecognition_run-03_events.tsv │ │ │ ├── sub-01_ses-mri_task-facerecognition_run-04_events.tsv │ │ │ ├── sub-01_ses-mri_task-facerecognition_run-05_events.tsv │ │ │ ├── sub-01_ses-mri_task-facerecognition_run-06_events.tsv │ │ │ ├── sub-01_ses-mri_task-facerecognition_run-07_events.tsv │ │ │ ├── sub-01_ses-mri_task-facerecognition_run-08_events.tsv │ │ │ └── sub-01_ses-mri_task-facerecognition_run-09_events.tsv │ │ ├── sub-02/ │ │ │ ├── ses-meg/ │ │ │ │ ├── beh/ │ │ │ │ │ └── sub-02_ses-meg_task-facerecognition_events.tsv │ │ │ │ ├── meg/ │ │ │ │ │ ├── sub-02_ses-meg_headshape.pos │ │ │ │ │ ├── sub-02_ses-meg_task-facerecognition_coordsystem.json │ │ │ │ │ ├── sub-02_ses-meg_task-facerecognition_run-01_events.tsv │ │ │ │ │ ├── sub-02_ses-meg_task-facerecognition_run-01_meg.fif │ │ │ │ │ ├── sub-02_ses-meg_task-facerecognition_run-02_events.tsv │ │ │ │ │ ├── sub-02_ses-meg_task-facerecognition_run-02_meg.fif │ │ │ │ │ ├── sub-02_ses-meg_task-facerecognition_run-03_events.tsv │ │ │ │ │ ├── sub-02_ses-meg_task-facerecognition_run-03_meg.fif │ │ │ │ │ ├── sub-02_ses-meg_task-facerecognition_run-04_events.tsv │ │ │ │ │ ├── sub-02_ses-meg_task-facerecognition_run-04_meg.fif │ │ │ │ │ ├── sub-02_ses-meg_task-facerecognition_run-05_events.tsv │ │ │ │ │ ├── sub-02_ses-meg_task-facerecognition_run-05_meg.fif │ │ │ │ │ ├── sub-02_ses-meg_task-facerecognition_run-06_events.tsv │ │ │ │ │ └── sub-02_ses-meg_task-facerecognition_run-06_meg.fif │ │ │ │ └── sub-02_ses-meg_scans.tsv │ │ │ └── ses-mri/ │ │ │ ├── anat/ │ │ │ │ └── sub-02_ses-mri_acq-mprage_T1w.json │ │ │ ├── dwi/ │ │ │ │ ├── sub-02_ses-mri_dwi.bval │ │ │ │ ├── sub-02_ses-mri_dwi.bvec │ │ │ │ └── sub-02_ses-mri_dwi.json │ │ │ ├── fmap/ │ │ │ │ ├── sub-02_ses-mri_magnitude1.nii │ │ │ │ ├── sub-02_ses-mri_magnitude2.nii │ │ │ │ ├── sub-02_ses-mri_phasediff.json │ │ │ │ └── sub-02_ses-mri_phasediff.nii │ │ │ └── func/ │ │ │ ├── sub-02_ses-mri_task-facerecognition_run-01_events.tsv │ │ │ ├── sub-02_ses-mri_task-facerecognition_run-02_events.tsv │ │ │ ├── sub-02_ses-mri_task-facerecognition_run-03_events.tsv │ │ │ ├── sub-02_ses-mri_task-facerecognition_run-04_events.tsv │ │ │ ├── sub-02_ses-mri_task-facerecognition_run-05_events.tsv │ │ │ ├── sub-02_ses-mri_task-facerecognition_run-06_events.tsv │ │ │ ├── sub-02_ses-mri_task-facerecognition_run-07_events.tsv │ │ │ ├── sub-02_ses-mri_task-facerecognition_run-08_events.tsv │ │ │ └── sub-02_ses-mri_task-facerecognition_run-09_events.tsv │ │ ├── sub-emptyroom/ │ │ │ ├── ses-20090409/ │ │ │ │ ├── meg/ │ │ │ │ │ └── sub-emptyroom_ses-20090409_task-noise_meg.fif │ │ │ │ └── sub-emptyroom_ses-20090409_scans.tsv │ │ │ ├── ses-20090506/ │ │ │ │ ├── meg/ │ │ │ │ │ └── sub-emptyroom_ses-20090506_task-noise_meg.fif │ │ │ │ └── sub-emptyroom_ses-20090506_scans.tsv │ │ │ ├── ses-20090511/ │ │ │ │ ├── meg/ │ │ │ │ │ └── sub-emptyroom_ses-20090511_task-noise_meg.fif │ │ │ │ └── sub-emptyroom_ses-20090511_scans.tsv │ │ │ ├── ses-20090515/ │ │ │ │ ├── meg/ │ │ │ │ │ └── sub-emptyroom_ses-20090515_task-noise_meg.fif │ │ │ │ └── sub-emptyroom_ses-20090515_scans.tsv │ │ │ ├── ses-20090518/ │ │ │ │ ├── meg/ │ │ │ │ │ └── sub-emptyroom_ses-20090518_task-noise_meg.fif │ │ │ │ └── sub-emptyroom_ses-20090518_scans.tsv │ │ │ ├── ses-20090601/ │ │ │ │ ├── meg/ │ │ │ │ │ └── sub-emptyroom_ses-20090601_task-noise_meg.fif │ │ │ │ └── sub-emptyroom_ses-20090601_scans.tsv │ │ │ ├── ses-20091126/ │ │ │ │ ├── meg/ │ │ │ │ │ └── sub-emptyroom_ses-20091126_task-noise_meg.fif │ │ │ │ └── sub-emptyroom_ses-20091126_scans.tsv │ │ │ └── ses-20091208/ │ │ │ ├── meg/ │ │ │ │ └── sub-emptyroom_ses-20091208_task-noise_meg.fif │ │ │ └── sub-emptyroom_ses-20091208_scans.tsv │ │ ├── task-facerecognition_beh.json │ │ ├── task-facerecognition_bold.json │ │ ├── task-facerecognition_channels.tsv │ │ └── task-facerecognition_meg.json │ ├── ds005/ │ │ ├── CHANGES │ │ ├── README │ │ ├── dataset_description.json │ │ ├── derivatives/ │ │ │ ├── events/ │ │ │ │ ├── dataset_description.json │ │ │ │ └── sub-01/ │ │ │ │ └── func/ │ │ │ │ └── sub-01_task-mixedgamblestask_run-01_desc-extra_events.tsv │ │ │ └── fmriprep/ │ │ │ ├── dataset_description.json │ │ │ ├── sub-01/ │ │ │ │ └── func/ │ │ │ │ ├── sub-01_task-mixedgamblestask_run-01_desc-preproc_timeseries.json │ │ │ │ ├── sub-01_task-mixedgamblestask_run-01_desc-preproc_timeseries.tsv │ │ │ │ ├── sub-01_task-mixedgamblestask_run-01_space-MNI152NLin2009cAsym_desc-preproc_bold.json │ │ │ │ ├── sub-01_task-mixedgamblestask_run-02_desc-preproc_timeseries.json │ │ │ │ ├── sub-01_task-mixedgamblestask_run-02_desc-preproc_timeseries.tsv │ │ │ │ ├── sub-01_task-mixedgamblestask_run-02_space-MNI152NLin2009cAsym_desc-preproc_bold.json │ │ │ │ ├── sub-01_task-mixedgamblestask_run-03_desc-preproc_timeseries.json │ │ │ │ ├── sub-01_task-mixedgamblestask_run-03_desc-preproc_timeseries.tsv │ │ │ │ └── sub-01_task-mixedgamblestask_run-03_space-MNI152NLin2009cAsym_desc-preproc_bold.json │ │ │ ├── sub-02/ │ │ │ │ └── func/ │ │ │ │ ├── sub-02_task-mixedgamblestask_run-01_desc-preproc_timeseries.json │ │ │ │ ├── sub-02_task-mixedgamblestask_run-01_desc-preproc_timeseries.tsv │ │ │ │ ├── sub-02_task-mixedgamblestask_run-01_space-MNI152NLin2009cAsym_desc-preproc_bold.json │ │ │ │ ├── sub-02_task-mixedgamblestask_run-02_desc-preproc_timeseries.json │ │ │ │ ├── sub-02_task-mixedgamblestask_run-02_desc-preproc_timeseries.tsv │ │ │ │ ├── sub-02_task-mixedgamblestask_run-02_space-MNI152NLin2009cAsym_desc-preproc_bold.json │ │ │ │ ├── sub-02_task-mixedgamblestask_run-03_desc-preproc_timeseries.json │ │ │ │ ├── sub-02_task-mixedgamblestask_run-03_desc-preproc_timeseries.tsv │ │ │ │ └── sub-02_task-mixedgamblestask_run-03_space-MNI152NLin2009cAsym_desc-preproc_bold.json │ │ │ ├── sub-03/ │ │ │ │ └── func/ │ │ │ │ ├── sub-03_task-mixedgamblestask_run-01_desc-preproc_timeseries.json │ │ │ │ ├── sub-03_task-mixedgamblestask_run-01_desc-preproc_timeseries.tsv │ │ │ │ ├── sub-03_task-mixedgamblestask_run-01_space-MNI152NLin2009cAsym_desc-preproc_bold.json │ │ │ │ ├── sub-03_task-mixedgamblestask_run-02_desc-preproc_timeseries.json │ │ │ │ ├── sub-03_task-mixedgamblestask_run-02_desc-preproc_timeseries.tsv │ │ │ │ ├── sub-03_task-mixedgamblestask_run-02_space-MNI152NLin2009cAsym_desc-preproc_bold.json │ │ │ │ ├── sub-03_task-mixedgamblestask_run-03_desc-preproc_timeseries.json │ │ │ │ ├── sub-03_task-mixedgamblestask_run-03_desc-preproc_timeseries.tsv │ │ │ │ └── sub-03_task-mixedgamblestask_run-03_space-MNI152NLin2009cAsym_desc-preproc_bold.json │ │ │ ├── sub-04/ │ │ │ │ └── func/ │ │ │ │ ├── sub-04_task-mixedgamblestask_run-01_desc-preproc_timeseries.json │ │ │ │ ├── sub-04_task-mixedgamblestask_run-01_desc-preproc_timeseries.tsv │ │ │ │ ├── sub-04_task-mixedgamblestask_run-01_space-MNI152NLin2009cAsym_desc-preproc_bold.json │ │ │ │ ├── sub-04_task-mixedgamblestask_run-02_desc-preproc_timeseries.json │ │ │ │ ├── sub-04_task-mixedgamblestask_run-02_desc-preproc_timeseries.tsv │ │ │ │ ├── sub-04_task-mixedgamblestask_run-02_space-MNI152NLin2009cAsym_desc-preproc_bold.json │ │ │ │ ├── sub-04_task-mixedgamblestask_run-03_desc-preproc_timeseries.json │ │ │ │ ├── sub-04_task-mixedgamblestask_run-03_desc-preproc_timeseries.tsv │ │ │ │ └── sub-04_task-mixedgamblestask_run-03_space-MNI152NLin2009cAsym_desc-preproc_bold.json │ │ │ ├── sub-05/ │ │ │ │ └── func/ │ │ │ │ ├── sub-05_task-mixedgamblestask_run-01_desc-preproc_timeseries.json │ │ │ │ ├── sub-05_task-mixedgamblestask_run-01_desc-preproc_timeseries.tsv │ │ │ │ ├── sub-05_task-mixedgamblestask_run-01_space-MNI152NLin2009cAsym_desc-preproc_bold.json │ │ │ │ ├── sub-05_task-mixedgamblestask_run-02_desc-preproc_timeseries.json │ │ │ │ ├── sub-05_task-mixedgamblestask_run-02_desc-preproc_timeseries.tsv │ │ │ │ ├── sub-05_task-mixedgamblestask_run-02_space-MNI152NLin2009cAsym_desc-preproc_bold.json │ │ │ │ ├── sub-05_task-mixedgamblestask_run-03_desc-preproc_timeseries.json │ │ │ │ ├── sub-05_task-mixedgamblestask_run-03_desc-preproc_timeseries.tsv │ │ │ │ └── sub-05_task-mixedgamblestask_run-03_space-MNI152NLin2009cAsym_desc-preproc_bold.json │ │ │ ├── sub-06/ │ │ │ │ └── func/ │ │ │ │ ├── sub-06_task-mixedgamblestask_run-01_desc-preproc_timeseries.json │ │ │ │ ├── sub-06_task-mixedgamblestask_run-01_desc-preproc_timeseries.tsv │ │ │ │ ├── sub-06_task-mixedgamblestask_run-01_space-MNI152NLin2009cAsym_desc-preproc_bold.json │ │ │ │ ├── sub-06_task-mixedgamblestask_run-02_desc-preproc_timeseries.json │ │ │ │ ├── sub-06_task-mixedgamblestask_run-02_desc-preproc_timeseries.tsv │ │ │ │ ├── sub-06_task-mixedgamblestask_run-02_space-MNI152NLin2009cAsym_desc-preproc_bold.json │ │ │ │ ├── sub-06_task-mixedgamblestask_run-03_desc-preproc_timeseries.json │ │ │ │ ├── sub-06_task-mixedgamblestask_run-03_desc-preproc_timeseries.tsv │ │ │ │ └── sub-06_task-mixedgamblestask_run-03_space-MNI152NLin2009cAsym_desc-preproc_bold.json │ │ │ ├── sub-07/ │ │ │ │ └── func/ │ │ │ │ ├── sub-07_task-mixedgamblestask_run-01_desc-preproc_timeseries.json │ │ │ │ ├── sub-07_task-mixedgamblestask_run-01_desc-preproc_timeseries.tsv │ │ │ │ ├── sub-07_task-mixedgamblestask_run-01_space-MNI152NLin2009cAsym_desc-preproc_bold.json │ │ │ │ ├── sub-07_task-mixedgamblestask_run-02_desc-preproc_timeseries.json │ │ │ │ ├── sub-07_task-mixedgamblestask_run-02_desc-preproc_timeseries.tsv │ │ │ │ ├── sub-07_task-mixedgamblestask_run-02_space-MNI152NLin2009cAsym_desc-preproc_bold.json │ │ │ │ ├── sub-07_task-mixedgamblestask_run-03_desc-preproc_timeseries.json │ │ │ │ ├── sub-07_task-mixedgamblestask_run-03_desc-preproc_timeseries.tsv │ │ │ │ └── sub-07_task-mixedgamblestask_run-03_space-MNI152NLin2009cAsym_desc-preproc_bold.json │ │ │ ├── sub-08/ │ │ │ │ └── func/ │ │ │ │ ├── sub-08_task-mixedgamblestask_run-01_desc-preproc_timeseries.json │ │ │ │ ├── sub-08_task-mixedgamblestask_run-01_desc-preproc_timeseries.tsv │ │ │ │ ├── sub-08_task-mixedgamblestask_run-01_space-MNI152NLin2009cAsym_desc-preproc_bold.json │ │ │ │ ├── sub-08_task-mixedgamblestask_run-02_desc-preproc_timeseries.json │ │ │ │ ├── sub-08_task-mixedgamblestask_run-02_desc-preproc_timeseries.tsv │ │ │ │ ├── sub-08_task-mixedgamblestask_run-02_space-MNI152NLin2009cAsym_desc-preproc_bold.json │ │ │ │ ├── sub-08_task-mixedgamblestask_run-03_desc-preproc_timeseries.json │ │ │ │ ├── sub-08_task-mixedgamblestask_run-03_desc-preproc_timeseries.tsv │ │ │ │ └── sub-08_task-mixedgamblestask_run-03_space-MNI152NLin2009cAsym_desc-preproc_bold.json │ │ │ ├── sub-09/ │ │ │ │ └── func/ │ │ │ │ ├── sub-09_task-mixedgamblestask_run-01_desc-preproc_timeseries.json │ │ │ │ ├── sub-09_task-mixedgamblestask_run-01_desc-preproc_timeseries.tsv │ │ │ │ ├── sub-09_task-mixedgamblestask_run-01_space-MNI152NLin2009cAsym_desc-preproc_bold.json │ │ │ │ ├── sub-09_task-mixedgamblestask_run-02_desc-preproc_timeseries.json │ │ │ │ ├── sub-09_task-mixedgamblestask_run-02_desc-preproc_timeseries.tsv │ │ │ │ ├── sub-09_task-mixedgamblestask_run-02_space-MNI152NLin2009cAsym_desc-preproc_bold.json │ │ │ │ ├── sub-09_task-mixedgamblestask_run-03_desc-preproc_timeseries.json │ │ │ │ ├── sub-09_task-mixedgamblestask_run-03_desc-preproc_timeseries.tsv │ │ │ │ └── sub-09_task-mixedgamblestask_run-03_space-MNI152NLin2009cAsym_desc-preproc_bold.json │ │ │ ├── sub-10/ │ │ │ │ └── func/ │ │ │ │ ├── sub-10_task-mixedgamblestask_run-01_desc-preproc_timeseries.json │ │ │ │ ├── sub-10_task-mixedgamblestask_run-01_desc-preproc_timeseries.tsv │ │ │ │ ├── sub-10_task-mixedgamblestask_run-01_space-MNI152NLin2009cAsym_desc-preproc_bold.json │ │ │ │ ├── sub-10_task-mixedgamblestask_run-02_desc-preproc_timeseries.json │ │ │ │ ├── sub-10_task-mixedgamblestask_run-02_desc-preproc_timeseries.tsv │ │ │ │ ├── sub-10_task-mixedgamblestask_run-02_space-MNI152NLin2009cAsym_desc-preproc_bold.json │ │ │ │ ├── sub-10_task-mixedgamblestask_run-03_desc-preproc_timeseries.json │ │ │ │ ├── sub-10_task-mixedgamblestask_run-03_desc-preproc_timeseries.tsv │ │ │ │ └── sub-10_task-mixedgamblestask_run-03_space-MNI152NLin2009cAsym_desc-preproc_bold.json │ │ │ ├── sub-11/ │ │ │ │ └── func/ │ │ │ │ ├── sub-11_task-mixedgamblestask_run-01_desc-preproc_timeseries.json │ │ │ │ ├── sub-11_task-mixedgamblestask_run-01_desc-preproc_timeseries.tsv │ │ │ │ ├── sub-11_task-mixedgamblestask_run-01_space-MNI152NLin2009cAsym_desc-preproc_bold.json │ │ │ │ ├── sub-11_task-mixedgamblestask_run-02_desc-preproc_timeseries.json │ │ │ │ ├── sub-11_task-mixedgamblestask_run-02_desc-preproc_timeseries.tsv │ │ │ │ ├── sub-11_task-mixedgamblestask_run-02_space-MNI152NLin2009cAsym_desc-preproc_bold.json │ │ │ │ ├── sub-11_task-mixedgamblestask_run-03_desc-preproc_timeseries.json │ │ │ │ ├── sub-11_task-mixedgamblestask_run-03_desc-preproc_timeseries.tsv │ │ │ │ └── sub-11_task-mixedgamblestask_run-03_space-MNI152NLin2009cAsym_desc-preproc_bold.json │ │ │ ├── sub-12/ │ │ │ │ └── func/ │ │ │ │ ├── sub-12_task-mixedgamblestask_run-01_desc-preproc_timeseries.json │ │ │ │ ├── sub-12_task-mixedgamblestask_run-01_desc-preproc_timeseries.tsv │ │ │ │ ├── sub-12_task-mixedgamblestask_run-01_space-MNI152NLin2009cAsym_desc-preproc_bold.json │ │ │ │ ├── sub-12_task-mixedgamblestask_run-02_desc-preproc_timeseries.json │ │ │ │ ├── sub-12_task-mixedgamblestask_run-02_desc-preproc_timeseries.tsv │ │ │ │ ├── sub-12_task-mixedgamblestask_run-02_space-MNI152NLin2009cAsym_desc-preproc_bold.json │ │ │ │ ├── sub-12_task-mixedgamblestask_run-03_desc-preproc_timeseries.json │ │ │ │ ├── sub-12_task-mixedgamblestask_run-03_desc-preproc_timeseries.tsv │ │ │ │ └── sub-12_task-mixedgamblestask_run-03_space-MNI152NLin2009cAsym_desc-preproc_bold.json │ │ │ ├── sub-13/ │ │ │ │ └── func/ │ │ │ │ ├── sub-13_task-mixedgamblestask_run-01_desc-preproc_timeseries.json │ │ │ │ ├── sub-13_task-mixedgamblestask_run-01_desc-preproc_timeseries.tsv │ │ │ │ ├── sub-13_task-mixedgamblestask_run-01_space-MNI152NLin2009cAsym_desc-preproc_bold.json │ │ │ │ ├── sub-13_task-mixedgamblestask_run-02_desc-preproc_timeseries.json │ │ │ │ ├── sub-13_task-mixedgamblestask_run-02_desc-preproc_timeseries.tsv │ │ │ │ ├── sub-13_task-mixedgamblestask_run-02_space-MNI152NLin2009cAsym_desc-preproc_bold.json │ │ │ │ ├── sub-13_task-mixedgamblestask_run-03_desc-preproc_timeseries.json │ │ │ │ ├── sub-13_task-mixedgamblestask_run-03_desc-preproc_timeseries.tsv │ │ │ │ └── sub-13_task-mixedgamblestask_run-03_space-MNI152NLin2009cAsym_desc-preproc_bold.json │ │ │ ├── sub-14/ │ │ │ │ └── func/ │ │ │ │ ├── sub-14_task-mixedgamblestask_run-01_desc-preproc_timeseries.json │ │ │ │ ├── sub-14_task-mixedgamblestask_run-01_desc-preproc_timeseries.tsv │ │ │ │ ├── sub-14_task-mixedgamblestask_run-01_space-MNI152NLin2009cAsym_desc-preproc_bold.json │ │ │ │ ├── sub-14_task-mixedgamblestask_run-02_desc-preproc_timeseries.json │ │ │ │ ├── sub-14_task-mixedgamblestask_run-02_desc-preproc_timeseries.tsv │ │ │ │ ├── sub-14_task-mixedgamblestask_run-02_space-MNI152NLin2009cAsym_desc-preproc_bold.json │ │ │ │ ├── sub-14_task-mixedgamblestask_run-03_desc-preproc_timeseries.json │ │ │ │ ├── sub-14_task-mixedgamblestask_run-03_desc-preproc_timeseries.tsv │ │ │ │ └── sub-14_task-mixedgamblestask_run-03_space-MNI152NLin2009cAsym_desc-preproc_bold.json │ │ │ ├── sub-15/ │ │ │ │ └── func/ │ │ │ │ ├── sub-15_task-mixedgamblestask_run-01_desc-preproc_timeseries.json │ │ │ │ ├── sub-15_task-mixedgamblestask_run-01_desc-preproc_timeseries.tsv │ │ │ │ ├── sub-15_task-mixedgamblestask_run-01_space-MNI152NLin2009cAsym_desc-preproc_bold.json │ │ │ │ ├── sub-15_task-mixedgamblestask_run-02_desc-preproc_timeseries.json │ │ │ │ ├── sub-15_task-mixedgamblestask_run-02_desc-preproc_timeseries.tsv │ │ │ │ ├── sub-15_task-mixedgamblestask_run-02_space-MNI152NLin2009cAsym_desc-preproc_bold.json │ │ │ │ ├── sub-15_task-mixedgamblestask_run-03_desc-preproc_timeseries.json │ │ │ │ ├── sub-15_task-mixedgamblestask_run-03_desc-preproc_timeseries.tsv │ │ │ │ └── sub-15_task-mixedgamblestask_run-03_space-MNI152NLin2009cAsym_desc-preproc_bold.json │ │ │ └── sub-16/ │ │ │ └── func/ │ │ │ ├── sub-16_task-mixedgamblestask_run-01_desc-preproc_timeseries.json │ │ │ ├── sub-16_task-mixedgamblestask_run-01_desc-preproc_timeseries.tsv │ │ │ ├── sub-16_task-mixedgamblestask_run-01_space-MNI152NLin2009cAsym_desc-preproc_bold.json │ │ │ ├── sub-16_task-mixedgamblestask_run-02_desc-preproc_timeseries.json │ │ │ ├── sub-16_task-mixedgamblestask_run-02_desc-preproc_timeseries.tsv │ │ │ ├── sub-16_task-mixedgamblestask_run-02_space-MNI152NLin2009cAsym_desc-preproc_bold.json │ │ │ ├── sub-16_task-mixedgamblestask_run-03_desc-preproc_timeseries.json │ │ │ ├── sub-16_task-mixedgamblestask_run-03_desc-preproc_timeseries.tsv │ │ │ └── sub-16_task-mixedgamblestask_run-03_space-MNI152NLin2009cAsym_desc-preproc_bold.json │ │ ├── ds005_onsets.json │ │ ├── dwi.bval │ │ ├── dwi.bvec │ │ ├── models/ │ │ │ ├── ds-005_type-interceptonlyrunlevel_model.json │ │ │ ├── ds-005_type-mfx_model.json │ │ │ ├── ds-005_type-test_intercept.json │ │ │ ├── ds-005_type-test_model.json │ │ │ ├── ds-005_type-testnodummy_model.json │ │ │ └── extras/ │ │ │ └── ds-005_type-test_model.json │ │ ├── participants.tsv │ │ ├── sub-01/ │ │ │ ├── func/ │ │ │ │ ├── sub-01_task-mixedgamblestask_run-01_events.json │ │ │ │ ├── sub-01_task-mixedgamblestask_run-01_events.tsv │ │ │ │ ├── sub-01_task-mixedgamblestask_run-02_events.tsv │ │ │ │ └── sub-01_task-mixedgamblestask_run-03_events.tsv │ │ │ └── sub-01_scans.tsv │ │ ├── sub-02/ │ │ │ ├── func/ │ │ │ │ ├── sub-02_task-mixedgamblestask_run-01_events.tsv │ │ │ │ ├── sub-02_task-mixedgamblestask_run-02_events.tsv │ │ │ │ └── sub-02_task-mixedgamblestask_run-03_events.tsv │ │ │ └── sub-02_scans.tsv │ │ ├── sub-03/ │ │ │ └── func/ │ │ │ ├── sub-03_task-mixedgamblestask_run-01_events.tsv │ │ │ ├── sub-03_task-mixedgamblestask_run-02_events.tsv │ │ │ └── sub-03_task-mixedgamblestask_run-03_events.tsv │ │ ├── sub-04/ │ │ │ └── func/ │ │ │ ├── sub-04_task-mixedgamblestask_run-01_events.tsv │ │ │ ├── sub-04_task-mixedgamblestask_run-02_events.tsv │ │ │ └── sub-04_task-mixedgamblestask_run-03_events.tsv │ │ ├── sub-05/ │ │ │ └── func/ │ │ │ ├── sub-05_task-mixedgamblestask_run-01_events.tsv │ │ │ ├── sub-05_task-mixedgamblestask_run-02_events.tsv │ │ │ └── sub-05_task-mixedgamblestask_run-03_events.tsv │ │ ├── sub-06/ │ │ │ └── func/ │ │ │ ├── sub-06_task-mixedgamblestask_run-01_events.tsv │ │ │ ├── sub-06_task-mixedgamblestask_run-02_events.tsv │ │ │ └── sub-06_task-mixedgamblestask_run-03_events.tsv │ │ ├── sub-07/ │ │ │ └── func/ │ │ │ ├── sub-07_task-mixedgamblestask_run-01_events.tsv │ │ │ ├── sub-07_task-mixedgamblestask_run-02_events.tsv │ │ │ └── sub-07_task-mixedgamblestask_run-03_events.tsv │ │ ├── sub-08/ │ │ │ └── func/ │ │ │ ├── sub-08_task-mixedgamblestask_run-01_events.tsv │ │ │ ├── sub-08_task-mixedgamblestask_run-02_events.tsv │ │ │ └── sub-08_task-mixedgamblestask_run-03_events.tsv │ │ ├── sub-09/ │ │ │ └── func/ │ │ │ ├── sub-09_task-mixedgamblestask_run-01_events.tsv │ │ │ ├── sub-09_task-mixedgamblestask_run-02_events.tsv │ │ │ └── sub-09_task-mixedgamblestask_run-03_events.tsv │ │ ├── sub-10/ │ │ │ └── func/ │ │ │ ├── sub-10_task-mixedgamblestask_run-01_events.tsv │ │ │ ├── sub-10_task-mixedgamblestask_run-02_events.tsv │ │ │ └── sub-10_task-mixedgamblestask_run-03_events.tsv │ │ ├── sub-11/ │ │ │ ├── dwi/ │ │ │ │ └── sub-11 │ │ │ └── func/ │ │ │ ├── sub-11_task-mixedgamblestask_run-01_events.tsv │ │ │ ├── sub-11_task-mixedgamblestask_run-02_events.tsv │ │ │ └── sub-11_task-mixedgamblestask_run-03_events.tsv │ │ ├── sub-12/ │ │ │ └── func/ │ │ │ ├── sub-12_task-mixedgamblestask_run-01_events.tsv │ │ │ ├── sub-12_task-mixedgamblestask_run-02_events.tsv │ │ │ └── sub-12_task-mixedgamblestask_run-03_events.tsv │ │ ├── sub-13/ │ │ │ └── func/ │ │ │ ├── sub-13_task-mixedgamblestask_run-01_events.tsv │ │ │ ├── sub-13_task-mixedgamblestask_run-02_events.tsv │ │ │ └── sub-13_task-mixedgamblestask_run-03_events.tsv │ │ ├── sub-14/ │ │ │ └── func/ │ │ │ ├── sub-14_task-mixedgamblestask_run-01_events.tsv │ │ │ ├── sub-14_task-mixedgamblestask_run-02_events.tsv │ │ │ └── sub-14_task-mixedgamblestask_run-03_events.tsv │ │ ├── sub-15/ │ │ │ └── func/ │ │ │ ├── sub-15_task-mixedgamblestask_run-01_events.tsv │ │ │ ├── sub-15_task-mixedgamblestask_run-02_events.tsv │ │ │ └── sub-15_task-mixedgamblestask_run-03_events.tsv │ │ ├── sub-16/ │ │ │ └── func/ │ │ │ ├── sub-16_task-mixedgamblestask_run-01_events.tsv │ │ │ ├── sub-16_task-mixedgamblestask_run-02_events.tsv │ │ │ └── sub-16_task-mixedgamblestask_run-03_events.tsv │ │ └── task-mixedgamblestask_bold.json │ ├── ds005_conflict/ │ │ ├── dataset_description.json │ │ └── sub-01/ │ │ └── func/ │ │ ├── sub-01_task-mixedgamblestask_run-01_events.json │ │ ├── sub-01_task-mixedgamblestask_run-01_events.tsv │ │ ├── sub-01_task-mixedgamblestask_run-02_events.tsv │ │ └── sub-01_task-mixedgamblestask_run-03_events.tsv │ ├── ds005_derivs/ │ │ ├── dummy/ │ │ │ └── dataset_description.json │ │ ├── dummy-vx.x.x/ │ │ │ └── dataset_description.json │ │ ├── format_errs/ │ │ │ ├── no_dataset_type/ │ │ │ │ └── dataset_description.json │ │ │ ├── no_pipeline_description/ │ │ │ │ └── dataset_description.json │ │ │ └── no_type_or_description/ │ │ │ └── dataset_description.json │ │ └── other/ │ │ └── dummy/ │ │ └── dataset_description.json │ └── synthetic/ │ ├── T1w.json │ ├── code/ │ │ ├── create_synthetic_ds.sh │ │ ├── dwi.bval │ │ └── dwi.bvec │ ├── dataset_description.json │ ├── derivatives/ │ │ └── fmriprep/ │ │ ├── dataset_description.json │ │ ├── sub-01/ │ │ │ ├── ses-01/ │ │ │ │ └── func/ │ │ │ │ ├── sub-01_ses-01_task-nback_run-01_desc-confounds_regressors.tsv │ │ │ │ ├── sub-01_ses-01_task-nback_run-01_space-MNI152NLin2009cAsym_desc-brain_mask.nii │ │ │ │ ├── sub-01_ses-01_task-nback_run-01_space-MNI152NLin2009cAsym_desc-preproc_bold.nii │ │ │ │ ├── sub-01_ses-01_task-nback_run-01_space-T1w_desc-brain_mask.nii │ │ │ │ ├── sub-01_ses-01_task-nback_run-01_space-T1w_desc-preproc_bold.nii │ │ │ │ ├── sub-01_ses-01_task-nback_run-02_desc-confounds_regressors.tsv │ │ │ │ ├── sub-01_ses-01_task-nback_run-02_space-MNI152NLin2009cAsym_desc-brain_mask.nii │ │ │ │ ├── sub-01_ses-01_task-nback_run-02_space-MNI152NLin2009cAsym_desc-preproc_bold.nii │ │ │ │ ├── sub-01_ses-01_task-nback_run-02_space-T1w_desc-brain_mask.nii │ │ │ │ ├── sub-01_ses-01_task-nback_run-02_space-T1w_desc-preproc_bold.nii │ │ │ │ ├── sub-01_ses-01_task-rest_desc-confounds_regressors.tsv │ │ │ │ ├── sub-01_ses-01_task-rest_space-MNI152NLin2009cAsym_desc-brain_mask.nii │ │ │ │ ├── sub-01_ses-01_task-rest_space-MNI152NLin2009cAsym_desc-preproc_bold.nii │ │ │ │ ├── sub-01_ses-01_task-rest_space-T1w_desc-brain_mask.nii │ │ │ │ └── sub-01_ses-01_task-rest_space-T1w_desc-preproc_bold.nii │ │ │ └── ses-02/ │ │ │ └── func/ │ │ │ ├── sub-01_ses-02_task-nback_run-01_desc-confounds_regressors.tsv │ │ │ ├── sub-01_ses-02_task-nback_run-01_space-MNI152NLin2009cAsym_desc-brain_mask.nii │ │ │ ├── sub-01_ses-02_task-nback_run-01_space-MNI152NLin2009cAsym_desc-preproc_bold.nii │ │ │ ├── sub-01_ses-02_task-nback_run-01_space-T1w_desc-brain_mask.nii │ │ │ ├── sub-01_ses-02_task-nback_run-01_space-T1w_desc-preproc_bold.nii │ │ │ ├── sub-01_ses-02_task-nback_run-02_desc-confounds_regressors.tsv │ │ │ ├── sub-01_ses-02_task-nback_run-02_space-MNI152NLin2009cAsym_desc-brain_mask.nii │ │ │ ├── sub-01_ses-02_task-nback_run-02_space-MNI152NLin2009cAsym_desc-preproc_bold.nii │ │ │ ├── sub-01_ses-02_task-nback_run-02_space-T1w_desc-brain_mask.nii │ │ │ ├── sub-01_ses-02_task-nback_run-02_space-T1w_desc-preproc_bold.nii │ │ │ ├── sub-01_ses-02_task-rest_desc-confounds_regressors.tsv │ │ │ ├── sub-01_ses-02_task-rest_space-MNI152NLin2009cAsym_desc-brain_mask.nii │ │ │ ├── sub-01_ses-02_task-rest_space-MNI152NLin2009cAsym_desc-preproc_bold.nii │ │ │ ├── sub-01_ses-02_task-rest_space-T1w_desc-brain_mask.nii │ │ │ └── sub-01_ses-02_task-rest_space-T1w_desc-preproc_bold.nii │ │ ├── sub-02/ │ │ │ ├── ses-01/ │ │ │ │ └── func/ │ │ │ │ ├── sub-02_ses-01_task-nback_run-01_desc-confounds_regressors.tsv │ │ │ │ ├── sub-02_ses-01_task-nback_run-01_space-MNI152NLin2009cAsym_desc-brain_mask.nii │ │ │ │ ├── sub-02_ses-01_task-nback_run-01_space-MNI152NLin2009cAsym_desc-preproc_bold.nii │ │ │ │ ├── sub-02_ses-01_task-nback_run-01_space-T1w_desc-brain_mask.nii │ │ │ │ ├── sub-02_ses-01_task-nback_run-01_space-T1w_desc-preproc_bold.nii │ │ │ │ ├── sub-02_ses-01_task-nback_run-02_desc-confounds_regressors.tsv │ │ │ │ ├── sub-02_ses-01_task-nback_run-02_space-MNI152NLin2009cAsym_desc-brain_mask.nii │ │ │ │ ├── sub-02_ses-01_task-nback_run-02_space-MNI152NLin2009cAsym_desc-preproc_bold.nii │ │ │ │ ├── sub-02_ses-01_task-nback_run-02_space-T1w_desc-brain_mask.nii │ │ │ │ ├── sub-02_ses-01_task-nback_run-02_space-T1w_desc-preproc_bold.nii │ │ │ │ ├── sub-02_ses-01_task-rest_desc-confounds_regressors.tsv │ │ │ │ ├── sub-02_ses-01_task-rest_space-MNI152NLin2009cAsym_desc-brain_mask.nii │ │ │ │ ├── sub-02_ses-01_task-rest_space-MNI152NLin2009cAsym_desc-preproc_bold.nii │ │ │ │ ├── sub-02_ses-01_task-rest_space-T1w_desc-brain_mask.nii │ │ │ │ └── sub-02_ses-01_task-rest_space-T1w_desc-preproc_bold.nii │ │ │ └── ses-02/ │ │ │ └── func/ │ │ │ ├── sub-02_ses-02_task-nback_run-01_desc-confounds_regressors.tsv │ │ │ ├── sub-02_ses-02_task-nback_run-01_space-MNI152NLin2009cAsym_desc-brain_mask.nii │ │ │ ├── sub-02_ses-02_task-nback_run-01_space-MNI152NLin2009cAsym_desc-preproc_bold.nii │ │ │ ├── sub-02_ses-02_task-nback_run-01_space-T1w_desc-brain_mask.nii │ │ │ ├── sub-02_ses-02_task-nback_run-01_space-T1w_desc-preproc_bold.nii │ │ │ ├── sub-02_ses-02_task-nback_run-02_desc-confounds_regressors.tsv │ │ │ ├── sub-02_ses-02_task-nback_run-02_space-MNI152NLin2009cAsym_desc-brain_mask.nii │ │ │ ├── sub-02_ses-02_task-nback_run-02_space-MNI152NLin2009cAsym_desc-preproc_bold.nii │ │ │ ├── sub-02_ses-02_task-nback_run-02_space-T1w_desc-brain_mask.nii │ │ │ ├── sub-02_ses-02_task-nback_run-02_space-T1w_desc-preproc_bold.nii │ │ │ ├── sub-02_ses-02_task-rest_desc-confounds_regressors.tsv │ │ │ ├── sub-02_ses-02_task-rest_space-MNI152NLin2009cAsym_desc-brain_mask.nii │ │ │ ├── sub-02_ses-02_task-rest_space-MNI152NLin2009cAsym_desc-preproc_bold.nii │ │ │ ├── sub-02_ses-02_task-rest_space-T1w_desc-brain_mask.nii │ │ │ └── sub-02_ses-02_task-rest_space-T1w_desc-preproc_bold.nii │ │ ├── sub-03/ │ │ │ ├── ses-01/ │ │ │ │ └── func/ │ │ │ │ ├── sub-03_ses-01_task-nback_run-01_desc-confounds_regressors.tsv │ │ │ │ ├── sub-03_ses-01_task-nback_run-01_space-MNI152NLin2009cAsym_desc-brain_mask.nii │ │ │ │ ├── sub-03_ses-01_task-nback_run-01_space-MNI152NLin2009cAsym_desc-preproc_bold.nii │ │ │ │ ├── sub-03_ses-01_task-nback_run-01_space-T1w_desc-brain_mask.nii │ │ │ │ ├── sub-03_ses-01_task-nback_run-01_space-T1w_desc-preproc_bold.nii │ │ │ │ ├── sub-03_ses-01_task-nback_run-02_desc-confounds_regressors.tsv │ │ │ │ ├── sub-03_ses-01_task-nback_run-02_space-MNI152NLin2009cAsym_desc-brain_mask.nii │ │ │ │ ├── sub-03_ses-01_task-nback_run-02_space-MNI152NLin2009cAsym_desc-preproc_bold.nii │ │ │ │ ├── sub-03_ses-01_task-nback_run-02_space-T1w_desc-brain_mask.nii │ │ │ │ ├── sub-03_ses-01_task-nback_run-02_space-T1w_desc-preproc_bold.nii │ │ │ │ ├── sub-03_ses-01_task-rest_desc-confounds_regressors.tsv │ │ │ │ ├── sub-03_ses-01_task-rest_space-MNI152NLin2009cAsym_desc-brain_mask.nii │ │ │ │ ├── sub-03_ses-01_task-rest_space-MNI152NLin2009cAsym_desc-preproc_bold.nii │ │ │ │ ├── sub-03_ses-01_task-rest_space-T1w_desc-brain_mask.nii │ │ │ │ └── sub-03_ses-01_task-rest_space-T1w_desc-preproc_bold.nii │ │ │ └── ses-02/ │ │ │ └── func/ │ │ │ ├── sub-03_ses-02_task-nback_run-01_desc-confounds_regressors.tsv │ │ │ ├── sub-03_ses-02_task-nback_run-01_space-MNI152NLin2009cAsym_desc-brain_mask.nii │ │ │ ├── sub-03_ses-02_task-nback_run-01_space-MNI152NLin2009cAsym_desc-preproc_bold.nii │ │ │ ├── sub-03_ses-02_task-nback_run-01_space-T1w_desc-brain_mask.nii │ │ │ ├── sub-03_ses-02_task-nback_run-01_space-T1w_desc-preproc_bold.nii │ │ │ ├── sub-03_ses-02_task-nback_run-02_desc-confounds_regressors.tsv │ │ │ ├── sub-03_ses-02_task-nback_run-02_space-MNI152NLin2009cAsym_desc-brain_mask.nii │ │ │ ├── sub-03_ses-02_task-nback_run-02_space-MNI152NLin2009cAsym_desc-preproc_bold.nii │ │ │ ├── sub-03_ses-02_task-nback_run-02_space-T1w_desc-brain_mask.nii │ │ │ ├── sub-03_ses-02_task-nback_run-02_space-T1w_desc-preproc_bold.nii │ │ │ ├── sub-03_ses-02_task-rest_desc-confounds_regressors.tsv │ │ │ ├── sub-03_ses-02_task-rest_space-MNI152NLin2009cAsym_desc-brain_mask.nii │ │ │ ├── sub-03_ses-02_task-rest_space-MNI152NLin2009cAsym_desc-preproc_bold.nii │ │ │ ├── sub-03_ses-02_task-rest_space-T1w_desc-brain_mask.nii │ │ │ └── sub-03_ses-02_task-rest_space-T1w_desc-preproc_bold.nii │ │ ├── sub-04/ │ │ │ ├── ses-01/ │ │ │ │ └── func/ │ │ │ │ ├── sub-04_ses-01_task-nback_run-01_desc-confounds_regressors.tsv │ │ │ │ ├── sub-04_ses-01_task-nback_run-01_space-MNI152NLin2009cAsym_desc-brain_mask.nii │ │ │ │ ├── sub-04_ses-01_task-nback_run-01_space-MNI152NLin2009cAsym_desc-preproc_bold.nii │ │ │ │ ├── sub-04_ses-01_task-nback_run-01_space-T1w_desc-brain_mask.nii │ │ │ │ ├── sub-04_ses-01_task-nback_run-01_space-T1w_desc-preproc_bold.nii │ │ │ │ ├── sub-04_ses-01_task-nback_run-02_desc-confounds_regressors.tsv │ │ │ │ ├── sub-04_ses-01_task-nback_run-02_space-MNI152NLin2009cAsym_desc-brain_mask.nii │ │ │ │ ├── sub-04_ses-01_task-nback_run-02_space-MNI152NLin2009cAsym_desc-preproc_bold.nii │ │ │ │ ├── sub-04_ses-01_task-nback_run-02_space-T1w_desc-brain_mask.nii │ │ │ │ ├── sub-04_ses-01_task-nback_run-02_space-T1w_desc-preproc_bold.nii │ │ │ │ ├── sub-04_ses-01_task-rest_desc-confounds_regressors.tsv │ │ │ │ ├── sub-04_ses-01_task-rest_space-MNI152NLin2009cAsym_desc-brain_mask.nii │ │ │ │ ├── sub-04_ses-01_task-rest_space-MNI152NLin2009cAsym_desc-preproc_bold.nii │ │ │ │ ├── sub-04_ses-01_task-rest_space-T1w_desc-brain_mask.nii │ │ │ │ └── sub-04_ses-01_task-rest_space-T1w_desc-preproc_bold.nii │ │ │ └── ses-02/ │ │ │ └── func/ │ │ │ ├── sub-04_ses-02_task-nback_run-01_desc-confounds_regressors.tsv │ │ │ ├── sub-04_ses-02_task-nback_run-01_space-MNI152NLin2009cAsym_desc-brain_mask.nii │ │ │ ├── sub-04_ses-02_task-nback_run-01_space-MNI152NLin2009cAsym_desc-preproc_bold.nii │ │ │ ├── sub-04_ses-02_task-nback_run-01_space-T1w_desc-brain_mask.nii │ │ │ ├── sub-04_ses-02_task-nback_run-01_space-T1w_desc-preproc_bold.nii │ │ │ ├── sub-04_ses-02_task-nback_run-02_desc-confounds_regressors.tsv │ │ │ ├── sub-04_ses-02_task-nback_run-02_space-MNI152NLin2009cAsym_desc-brain_mask.nii │ │ │ ├── sub-04_ses-02_task-nback_run-02_space-MNI152NLin2009cAsym_desc-preproc_bold.nii │ │ │ ├── sub-04_ses-02_task-nback_run-02_space-T1w_desc-brain_mask.nii │ │ │ ├── sub-04_ses-02_task-nback_run-02_space-T1w_desc-preproc_bold.nii │ │ │ ├── sub-04_ses-02_task-rest_desc-confounds_regressors.tsv │ │ │ ├── sub-04_ses-02_task-rest_space-MNI152NLin2009cAsym_desc-brain_mask.nii │ │ │ ├── sub-04_ses-02_task-rest_space-MNI152NLin2009cAsym_desc-preproc_bold.nii │ │ │ ├── sub-04_ses-02_task-rest_space-T1w_desc-brain_mask.nii │ │ │ └── sub-04_ses-02_task-rest_space-T1w_desc-preproc_bold.nii │ │ ├── sub-05/ │ │ │ ├── ses-01/ │ │ │ │ └── func/ │ │ │ │ ├── sub-05_ses-01_task-nback_run-01_desc-confounds_regressors.tsv │ │ │ │ ├── sub-05_ses-01_task-nback_run-01_space-MNI152NLin2009cAsym_desc-brain_mask.nii │ │ │ │ ├── sub-05_ses-01_task-nback_run-01_space-MNI152NLin2009cAsym_desc-preproc_bold.nii │ │ │ │ ├── sub-05_ses-01_task-nback_run-01_space-T1w_desc-brain_mask.nii │ │ │ │ ├── sub-05_ses-01_task-nback_run-01_space-T1w_desc-preproc_bold.nii │ │ │ │ ├── sub-05_ses-01_task-nback_run-02_desc-confounds_regressors.tsv │ │ │ │ ├── sub-05_ses-01_task-nback_run-02_space-MNI152NLin2009cAsym_desc-brain_mask.nii │ │ │ │ ├── sub-05_ses-01_task-nback_run-02_space-MNI152NLin2009cAsym_desc-preproc_bold.nii │ │ │ │ ├── sub-05_ses-01_task-nback_run-02_space-T1w_desc-brain_mask.nii │ │ │ │ ├── sub-05_ses-01_task-nback_run-02_space-T1w_desc-preproc_bold.nii │ │ │ │ ├── sub-05_ses-01_task-rest_desc-confounds_regressors.tsv │ │ │ │ ├── sub-05_ses-01_task-rest_space-MNI152NLin2009cAsym_desc-brain_mask.nii │ │ │ │ ├── sub-05_ses-01_task-rest_space-MNI152NLin2009cAsym_desc-preproc_bold.nii │ │ │ │ ├── sub-05_ses-01_task-rest_space-T1w_desc-brain_mask.nii │ │ │ │ └── sub-05_ses-01_task-rest_space-T1w_desc-preproc_bold.nii │ │ │ └── ses-02/ │ │ │ └── func/ │ │ │ ├── sub-05_ses-02_task-nback_run-01_desc-confounds_regressors.tsv │ │ │ ├── sub-05_ses-02_task-nback_run-01_space-MNI152NLin2009cAsym_desc-brain_mask.nii │ │ │ ├── sub-05_ses-02_task-nback_run-01_space-MNI152NLin2009cAsym_desc-preproc_bold.nii │ │ │ ├── sub-05_ses-02_task-nback_run-01_space-T1w_desc-brain_mask.nii │ │ │ ├── sub-05_ses-02_task-nback_run-01_space-T1w_desc-preproc_bold.nii │ │ │ ├── sub-05_ses-02_task-nback_run-02_desc-confounds_regressors.tsv │ │ │ ├── sub-05_ses-02_task-nback_run-02_space-MNI152NLin2009cAsym_desc-brain_mask.nii │ │ │ ├── sub-05_ses-02_task-nback_run-02_space-MNI152NLin2009cAsym_desc-preproc_bold.nii │ │ │ ├── sub-05_ses-02_task-nback_run-02_space-T1w_desc-brain_mask.nii │ │ │ ├── sub-05_ses-02_task-nback_run-02_space-T1w_desc-preproc_bold.nii │ │ │ ├── sub-05_ses-02_task-rest_desc-confounds_regressors.tsv │ │ │ ├── sub-05_ses-02_task-rest_space-MNI152NLin2009cAsym_desc-brain_mask.nii │ │ │ ├── sub-05_ses-02_task-rest_space-MNI152NLin2009cAsym_desc-preproc_bold.nii │ │ │ ├── sub-05_ses-02_task-rest_space-T1w_desc-brain_mask.nii │ │ │ └── sub-05_ses-02_task-rest_space-T1w_desc-preproc_bold.nii │ │ ├── task-nback_bold.json │ │ └── task-rest_bold.json │ ├── dwi.json │ ├── participants.tsv │ ├── sub-01/ │ │ ├── ses-01/ │ │ │ ├── anat/ │ │ │ │ └── sub-01_ses-01_T1w.nii │ │ │ ├── dwi/ │ │ │ │ ├── sub-01_ses-01_dwi.bval │ │ │ │ └── sub-01_ses-01_dwi.bvec │ │ │ ├── fmap/ │ │ │ │ └── sub-01_ses-01_phasediff.json │ │ │ ├── func/ │ │ │ │ ├── sub-01_ses-01_task-nback_run-01_bold.nii │ │ │ │ ├── sub-01_ses-01_task-nback_run-02_bold.nii │ │ │ │ └── sub-01_ses-01_task-rest_bold.nii │ │ │ └── sub-01_ses-01_scans.tsv │ │ ├── ses-02/ │ │ │ ├── anat/ │ │ │ │ └── sub-01_ses-02_T1w.nii │ │ │ ├── func/ │ │ │ │ ├── sub-01_ses-02_task-nback_run-01_bold.nii │ │ │ │ ├── sub-01_ses-02_task-nback_run-02_bold.nii │ │ │ │ └── sub-01_ses-02_task-rest_bold.nii │ │ │ └── sub-01_ses-02_scans.tsv │ │ └── sub-01_sessions.tsv │ ├── sub-02/ │ │ ├── ses-01/ │ │ │ ├── anat/ │ │ │ │ └── sub-02_ses-01_T1w.nii │ │ │ ├── dwi/ │ │ │ │ ├── sub-02_ses-01_dwi.bval │ │ │ │ └── sub-02_ses-01_dwi.bvec │ │ │ ├── fmap/ │ │ │ │ └── sub-02_ses-01_phasediff.json │ │ │ ├── func/ │ │ │ │ ├── sub-02_ses-01_task-nback_run-01_bold.nii │ │ │ │ ├── sub-02_ses-01_task-nback_run-02_bold.nii │ │ │ │ └── sub-02_ses-01_task-rest_bold.nii │ │ │ └── sub-02_ses-01_scans.tsv │ │ ├── ses-02/ │ │ │ ├── anat/ │ │ │ │ └── sub-02_ses-02_T1w.nii │ │ │ ├── func/ │ │ │ │ ├── sub-02_ses-02_task-nback_run-01_bold.nii │ │ │ │ ├── sub-02_ses-02_task-nback_run-02_bold.nii │ │ │ │ └── sub-02_ses-02_task-rest_bold.nii │ │ │ └── sub-02_ses-02_scans.tsv │ │ └── sub-02_sessions.tsv │ ├── sub-03/ │ │ ├── ses-01/ │ │ │ ├── anat/ │ │ │ │ └── sub-03_ses-01_T1w.nii │ │ │ ├── dwi/ │ │ │ │ ├── sub-03_ses-01_dwi.bval │ │ │ │ └── sub-03_ses-01_dwi.bvec │ │ │ ├── fmap/ │ │ │ │ └── sub-03_ses-01_phasediff.json │ │ │ ├── func/ │ │ │ │ ├── sub-03_ses-01_task-nback_run-01_bold.nii │ │ │ │ ├── sub-03_ses-01_task-nback_run-02_bold.nii │ │ │ │ └── sub-03_ses-01_task-rest_bold.nii │ │ │ └── sub-03_ses-01_scans.tsv │ │ ├── ses-02/ │ │ │ ├── anat/ │ │ │ │ └── sub-03_ses-02_T1w.nii │ │ │ ├── func/ │ │ │ │ ├── sub-03_ses-02_task-nback_run-01_bold.nii │ │ │ │ ├── sub-03_ses-02_task-nback_run-02_bold.nii │ │ │ │ └── sub-03_ses-02_task-rest_bold.nii │ │ │ └── sub-03_ses-02_scans.tsv │ │ └── sub-03_sessions.tsv │ ├── sub-04/ │ │ ├── ses-01/ │ │ │ ├── anat/ │ │ │ │ └── sub-04_ses-01_T1w.nii │ │ │ ├── dwi/ │ │ │ │ ├── sub-04_ses-01_dwi.bval │ │ │ │ └── sub-04_ses-01_dwi.bvec │ │ │ ├── fmap/ │ │ │ │ └── sub-04_ses-01_phasediff.json │ │ │ ├── func/ │ │ │ │ ├── sub-04_ses-01_task-nback_run-01_bold.nii │ │ │ │ ├── sub-04_ses-01_task-nback_run-02_bold.nii │ │ │ │ └── sub-04_ses-01_task-rest_bold.nii │ │ │ └── sub-04_ses-01_scans.tsv │ │ ├── ses-02/ │ │ │ ├── anat/ │ │ │ │ └── sub-04_ses-02_T1w.nii │ │ │ ├── func/ │ │ │ │ ├── sub-04_ses-02_task-nback_run-01_bold.nii │ │ │ │ ├── sub-04_ses-02_task-nback_run-02_bold.nii │ │ │ │ └── sub-04_ses-02_task-rest_bold.nii │ │ │ └── sub-04_ses-02_scans.tsv │ │ └── sub-04_sessions.tsv │ ├── sub-05/ │ │ ├── ses-01/ │ │ │ ├── anat/ │ │ │ │ └── sub-05_ses-01_T1w.nii │ │ │ ├── dwi/ │ │ │ │ ├── sub-05_ses-01_dwi.bval │ │ │ │ └── sub-05_ses-01_dwi.bvec │ │ │ ├── fmap/ │ │ │ │ └── sub-05_ses-01_phasediff.json │ │ │ ├── func/ │ │ │ │ ├── sub-05_ses-01_task-nback_run-01_bold.nii │ │ │ │ ├── sub-05_ses-01_task-nback_run-02_bold.nii │ │ │ │ └── sub-05_ses-01_task-rest_bold.nii │ │ │ └── sub-05_ses-01_scans.tsv │ │ ├── ses-02/ │ │ │ ├── anat/ │ │ │ │ └── sub-05_ses-02_T1w.nii │ │ │ ├── func/ │ │ │ │ ├── sub-05_ses-02_task-nback_run-01_bold.nii │ │ │ │ ├── sub-05_ses-02_task-nback_run-02_bold.nii │ │ │ │ └── sub-05_ses-02_task-rest_bold.nii │ │ │ └── sub-05_ses-02_scans.tsv │ │ └── sub-05_sessions.tsv │ ├── task-nback_bold.json │ ├── task-nback_events.tsv │ ├── task-nback_physio.json │ ├── task-nback_stim.json │ ├── task-rest_bold.json │ └── task-rest_physio.json ├── tools/ │ ├── check_noqa_budget.py │ ├── prep_zenodo.py │ ├── update_changes.sh │ └── update_requirements.py └── tox.ini ================================================ FILE CONTENTS ================================================ ================================================ FILE: .git-blame-ignore-revs ================================================ # 2026-03-07 14:05:31 -0500 - markiewicz@stanford.edu - sty: Clean up trailing whitespace [ignore-rev] b1ce2c83fdc83e94ef772159d298ca7961dfe629 ================================================ FILE: .gitattributes ================================================ src/bids/_version.py export-subst ================================================ FILE: .github/codespell_exclude_lines.txt ================================================ "/RADME", # wrong filename "/CANGES", # wrong filename "/dataset_descrption.json", # wrong filename "/dataset_description.jon", # wrong extension "/participants.sv", # wrong extension "/participnts.tsv", # wrong filename "/particpants.json" # wrong filename "/participants.son" # wrong extension ================================================ FILE: .github/codespell_ignore_words.txt ================================================ complies nin nwe te ================================================ FILE: .github/dependabot.yml ================================================ # https://docs.github.com/en/code-security/dependabot/dependabot-version-updates/configuration-options-for-the-dependabot.yml-file version: 2 updates: - package-ecosystem: "github-actions" directory: "/" schedule: interval: "weekly" groups: actions-infrastructure: patterns: - "actions/*" - package-ecosystem: "gitsubmodule" directory: "/" schedule: interval: "weekly" - package-ecosystem: uv directory: "/" schedule: interval: weekly groups: uv: patterns: - "*" ================================================ FILE: .github/release-drafter.yml ================================================ template: | ## Release Notes ## CHANGES $CHANGES ================================================ FILE: .github/workflows/build-test-deploy.yml ================================================ name: Python on: push: branches: - main - maint/* tags: - '*' pull_request: {} schedule: # 8am EST / 9am EDT Mondays - cron: '0 13 * * 1' # Allow job to be triggered manually from GitHub interface workflow_dispatch: defaults: run: shell: bash # Force tox and pytest to use color env: FORCE_COLOR: true concurrency: group: ${{ github.workflow }}-${{ github.ref }} cancel-in-progress: true permissions: contents: read jobs: build: name: Build & verify package runs-on: ubuntu-latest permissions: attestations: write id-token: write steps: - uses: actions/checkout@v6 with: fetch-depth: 0 - uses: hynek/build-and-inspect-python-package@v2 with: attest-build-provenance-github: ${{ github.event_name != 'pull_request' }} test: # Check each OS, all supported Python, minimum versions and latest releases name: Test runs-on: ${{ matrix.os }} needs: [build] strategy: matrix: os: ['ubuntu-latest', 'macos-latest'] python-version: ['3.10', '3.11', '3.12', '3.13', '3.14'] dependencies: ['full'] source: ['repo'] include: - os: ubuntu-latest python-version: '3.10' dependencies: min source: 'repo' - os: ubuntu-latest python-version: '3' dependencies: full source: 'sdist' fail-fast: false env: DEPENDS: ${{ matrix.dependencies }} steps: - uses: actions/checkout@v6 if: matrix.source == 'repo' with: submodules: recursive fetch-depth: 0 - name: Download packages built by build-and-inspect-python-package if: matrix.source == 'sdist' uses: actions/download-artifact@v8 with: name: Packages path: dist - name: Extract sdist if: matrix.source == 'sdist' run: tar --strip-components=1 -xzf dist/*.tar.gz - name: Install the latest version of uv uses: astral-sh/setup-uv@v7 - name: Set up Python ${{ matrix.python-version }} uses: actions/setup-python@v6 with: python-version: ${{ matrix.python-version }} allow-prereleases: true - name: Display Python version run: python -c "import sys; print(sys.version)" - name: Install tox run: | uv tool install tox --with=tox-uv --with=tox-gh-actions - name: Show tox config run: tox c - name: Run tox run: tox -v --exit-and-dump-after 1200 - uses: codecov/codecov-action@v6 if: ${{ always() }} with: token: ${{ secrets.CODECOV_TOKEN }} test-publish: name: Push package to test.pypi.org if: github.event_name == 'push' runs-on: ubuntu-latest needs: [build] permissions: attestations: write id-token: write steps: - name: Download packages built by build-and-inspect-python-package uses: actions/download-artifact@v8 with: name: Packages path: dist - name: Upload package to PyPI uses: pypa/gh-action-pypi-publish@release/v1 with: repository-url: https://test.pypi.org/legacy/ skip-existing: true publish: name: Publish released package to pypi.org if: github.event_name == 'push' && startsWith(github.ref, 'refs/tags') runs-on: ubuntu-latest needs: [test, test-publish] permissions: attestations: write id-token: write steps: - name: Download packages built by build-and-inspect-python-package uses: actions/download-artifact@v8 with: name: Packages path: dist - name: Upload package to PyPI uses: pypa/gh-action-pypi-publish@release/v1 ================================================ FILE: .github/workflows/codespell.yml ================================================ --- name: Codespell on: push: branches: [main] pull_request: branches: [main] jobs: codespell: name: Check for spelling errors runs-on: ubuntu-latest steps: - uses: actions/checkout@v6 - uses: codespell-project/actions-codespell@master ================================================ FILE: .github/workflows/docs.yml ================================================ name: Documentation on: push: branches: - main - maint/* - rel/* - docs/* tags: - '*' pull_request: workflow_dispatch: defaults: run: shell: bash # Force tox to use color env: FORCE_COLOR: true concurrency: group: ${{ github.workflow }}-${{ github.ref }} cancel-in-progress: true permissions: contents: write jobs: docs: runs-on: ubuntu-latest strategy: matrix: include: - python-version: '3.11' steps: - uses: actions/checkout@v6 with: submodules: recursive fetch-depth: 0 - name: Set up Python ${{ matrix.python-version }} uses: actions/setup-python@v6 with: python-version: ${{ matrix.python-version }} - name: Display Python version run: python -c "import sys; print(sys.version)" - name: Build documentation run: pipx run tox -e docs - name: Upload docs as artifacts uses: actions/upload-artifact@v7 with: path: doc/_build/html - name: Deploy (on tags) if: github.event_name == 'push' && startsWith(github.ref, 'refs/tags') uses: peaceiris/actions-gh-pages@v4 with: github_token: ${{ secrets.GITHUB_TOKEN }} publish_dir: doc/_build/html force_orphan: true user_name: "BIDS Maintenance" user_email: bids-maintenance@gmail.com ================================================ FILE: .github/workflows/prerelease.yml ================================================ name: Pre-release on: push: branches: - main pull_request: branches: - main schedule: # 8am EST / 9am EDT Mondays - cron: '0 13 * * 1' # Allow job to be triggered manually from GitHub interface workflow_dispatch: defaults: run: shell: bash # Force tox and pytest to use color env: FORCE_COLOR: true concurrency: group: ${{ github.workflow }}-${{ github.ref }} cancel-in-progress: true permissions: contents: read jobs: test: name: Test if: | github.event_name != 'pull_request' || contains(github.event.pull_request.labels.*.name, 'prerelease') runs-on: ${{ matrix.os }} strategy: matrix: os: ['ubuntu-latest', 'macos-latest'] python-version: ['3.12', '3.13', '3.14'] fail-fast: false permissions: actions: write env: DEPENDS: pre steps: - uses: actions/checkout@v6 with: submodules: recursive fetch-depth: 0 - name: Install the latest version of uv uses: astral-sh/setup-uv@v7 with: python-version: ${{ matrix.python-version }} - name: Display Python version run: python -c "import sys; print(sys.version)" - name: Install tox run: | uv tool install tox --with=tox-uv --with=tox-gh-actions - name: Show tox config run: tox c - name: Run tox run: tox -v --exit-and-dump-after 1200 - uses: codecov/codecov-action@v6 if: ${{ always() }} with: token: ${{ secrets.CODECOV_TOKEN }} ================================================ FILE: .gitignore ================================================ historical/ # Byte-compiled / optimized / DLL files __pycache__/ *.py[cod] *$py.class # C extensions *.so # Distribution / packaging .Python env/ build/ develop-eggs/ dist/ downloads/ eggs/ .eggs/ lib/ lib64/ parts/ sdist/ var/ **/*.egg-info .installed.cfg *.egg pip-wheel-metadata/ # PyInstaller # Usually these files are written by a python script from a template # before PyInstaller builds the exe, so as to inject date/other infos into it. *.manifest *.spec # Installer logs pip-log.txt pip-delete-this-directory.txt # Unit test / coverage reports htmlcov/ .tox/ .coverage .coverage.* .cache nosetests.xml coverage.xml *,cover .hypothesis/ cov.xml # Translations *.mo *.pot # Django stuff: *.log local_settings.py # Flask stuff: instance/ .webassets-cache # Scrapy stuff: .scrapy # Sphinx documentation docs/_build/ # PyBuilder target/ # IPython Notebook .ipynb_checkpoints .ipynb # pyenv .python-version # celery beat schedule file celerybeat-schedule # dotenv .env # virtualenv venv/ ENV/ # Pipenv Pipfile # Spyder project settings .spyderproject # Rope project settings .ropeproject *.DS_Store *.orig # Sphinx doc/_build/ doc/auto_examples/ doc/gen_modules/ doc/generated/ # Pytest .pytest_cache/ # Vim .*.sw[op] # Emacs and others *~ .vscode # Ignore test sql database files bidsdb.sqlite fmriprep.sqlite ================================================ FILE: .gitmodules ================================================ [submodule "bids-examples"] path = bids-examples url = https://github.com/bids-standard/bids-examples.git branch = master ================================================ FILE: .mailmap ================================================ Alejandro de la Vega Alejandro de la Vega Alejandro de la Vega Alexander Ivanov Alexis Thual Ali Khan Ali Khan Ankur Sinha Antonin Rovai <81312414+arovai@users.noreply.github.com> Ariel Rokem Basile Pinsard Bertrand Thirion Christopher J. Markiewicz Christopher J. Markiewicz Christopher J. Markiewicz Chris Holdgraf Chris Holdgraf Dimitri Papadopoulos Orfanos <3234522+DimitriPapadopoulos@users.noreply.github.com> Dylan M. Nielson Dylan M. Nielson Evgenii Kalenkovich Gal Ben-Zvi Gregory Kiar Hans Johnson Hans Johnson Isla Staden James D. Kent James D. Kent Johannes Wennberg Jon Haitz Legarreta Krzysztof J. Gorgolewski Lee S. Tirrell Lee S. Tirrell Mainak Jas Matteo Visconti di Oleggio Castello Mathias Goncalves Mathias Goncalves Mathias Goncalves Michael Philipp Notter Nick Guenther Oscar Esteban Philip A Cook Satrajit Ghosh Satrajit Ghosh Steven Tilley II Steven Tilley II Tal Yarkoni Taylor Salo Taylor Salo Yaroslav O. Halchenko ================================================ FILE: .pre-commit-config.yaml ================================================ ci: autoupdate_schedule: quarterly repos: - repo: https://github.com/pre-commit/pre-commit-hooks rev: v6.0.0 hooks: - id: trailing-whitespace exclude: .gitignore - id: end-of-file-fixer - id: check-yaml - id: check-json - id: check-toml - id: check-added-large-files - repo: https://github.com/astral-sh/ruff-pre-commit rev: v0.14.10 hooks: - id: ruff-check args: [ "--fix", "--show-fixes"] - id: ruff-format ================================================ FILE: .zenodo.json ================================================ { "title": "PyBIDS: Python tools for BIDS datasets", "description": "PyBIDS is a Python library to centralize interactions with datasets conforming BIDS (Brain Imaging Data Structure) format. For more information about BIDS visit bids.neuroimaging.io.", "creators": [ { "affiliation": "University of Texas at Austin", "name": "Yarkoni, Tal", "orcid": "0000-0002-6558-5113" }, { "affiliation": "Stanford University", "name": "Markiewicz, Christopher J.", "orcid": "0000-0002-6533-164X" }, { "affiliation": "University of Texas at Austin", "name": "de la Vega, Alejandro", "orcid": "0000-0001-9062-3778" }, { "affiliation": "Department of Psychology, Stanford University", "name": "Gorgolewski, Krzysztof J.", "orcid": "0000-0003-3321-7583" }, { "affiliation": "University of Pennsylvania", "name": "Salo, Taylor", "orcid": "0000-0001-9813-3167" }, { "affiliation": "Université catholique de Louvain", "name": "Gau, Remi", "orcid": "0000-0002-1535-9767" }, { "affiliation": "Dartmouth College", "name": "Halchenko, Yaroslav O.", "orcid": "0000-0003-3456-2493" }, { "affiliation": "CEA", "name": "Papadopoulos Orfanos, Dimitri", "orcid": "0000-0002-1242-8990" }, { "affiliation": "Department of Psychology, Stanford University", "name": "Esteban, Oscar", "orcid": "0000-0001-8435-6191" }, { "affiliation": "University of Texas at Austin", "name": "McNamara, Quinten" }, { "affiliation": "University of Texas at Austin", "name": "Kent, James D.", "orcid": "0000-0002-4892-2659" }, { "affiliation": "University of Oregon", "name": "DeStasio, Krista", "orcid": "0000-0002-3959-9060" }, { "affiliation": "McGill University", "name": "Poline, Jean-Baptiste", "orcid": "0000-0002-9794-749X" }, { "affiliation": "University of Iowa", "name": "Johnson, Hans", "orcid": "0000-0001-9513-2660" }, { "name": "Kalenkovich, Evgenii", "orcid": "0000-0002-4606-4179" }, { "affiliation": "Institute for Problems of Information Transmission", "name": "Petrov, Dmitry" }, { "affiliation": "National Institute of Mental Health", "name": "Nielson, Dylan M.", "orcid": "0000-0003-4613-6643" }, { "affiliation": "Department of Psychology, Stanford University", "name": "Goncalves, Mathias", "orcid": "0000-0002-7252-7771" }, { "affiliation": "Center for Adaptive Rationality, Max Planck Institute for Human Development, Berlin, Germany", "name": "Appelhoff, Stefan", "orcid": "0000-0001-8002-0877" }, { "affiliation": "Western University, London, ON, CA", "name": "Van Dyken, Peter", "orcid": "0000-0001-7357-5815" }, { "name": "Bansal, Shashank", "affiliation": "Stanford University", "orcid": "0000-0002-1252-8772 " }, { "affiliation": "Concordia University", "name": "Hayot-Sasson, Valérie", "orcid": "0000-0002-4830-4535" }, { "name": "Stewart, Ashley" }, { "affiliation": "MRC Cognition and Brain Sciences Unit", "name": "Carlin, Johan", "orcid": "0000-0003-0933-1239" }, { "affiliation": "Montreal Neurological Institute and Hospital", "name": "Kiar, Gregory", "orcid": "0000-0001-8915-496X" }, { "affiliation": "Alan Turing Institute; Department of Psychiatry, University of Cambridge", "name": "Whitaker, Kirstie", "orcid": "0000-0001-8498-4059" }, { "affiliation": "MIT, HMS", "name": "Ghosh, Satrajit", "orcid": "0000-0002-5312-6729" }, { "affiliation": "Psychoinformatics Lab, Institute of Psychology, Otto-von-Guericke University Magdeburg", "name": "Wagner, Adina", "orcid": "0000-0003-2917-3450" }, { "affiliation": "Montreal Neurological Institute and Hospital", "name": "DuPre, Elizabeth", "orcid": "0000-0003-1358-196X" }, { "name": "Khan, Ali", "orcid": "0000-0002-0760-8647" }, { "name": "Janke, Andrew", "orcid": "0000-0002-6314-9027" }, { "affiliation": "University of Pennsylvania", "name": "Cook, Philip A", "orcid": "0000-0002-1985-8475" }, { "name": "Ivanov, Alexander" }, { "affiliation": "Australian eHealth Research Centre, Commonwealth Scientific and Industrial Research Organisation; University of Queensland", "name": "Gillman, Ashley", "orcid": "0000-0001-9130-1092" }, { "affiliation": "KTH Royal Institute of Technology", "name": "Wennberg, Johannes" }, { "affiliation": "CorticoMetrics LLC", "name": "Tirrell, Lee S.", "orcid": "0000-0001-9393-8361" }, { "affiliation": "Holland Bloorview Kids Rehabilitation Hospital", "name": "Tilley II, Steven", "orcid": "0000-0003-4853-5082" }, { "name": "Li, Adam", "affiliation": "Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD, USA", "orcid": "0000-0001-8421-365X" }, { "affiliation": "Child Mind Institute", "name": "Erkent, Alp", "orcid": "0000-0002-8820-4782" }, { "affiliation": "University of Montréal, Montréal, Canada", "name": "Pinsard, Basile", "orcid": "0000-0002-4391-3075" }, { "affiliation": "Stanford University", "name": "Mumford, Jeanette", "orcid": "0000-0002-0926-3531" }, { "affiliation": "Université de Sherbrooke", "name": "Legarreta, Jon Haitz", "orcid": "0000-0002-9661-1396" }, { "affiliation": "Charite Universitatsmedizin Berlin, Germany", "name": "Waller, Lea", "orcid": "0000-0002-3239-6957" }, { "affiliation": "Télécom ParisTech, France", "name": "Jas, Mainak", "orcid": "0000-0002-3199-9027" }, { "affiliation": "Otto-von-Guericke-University Magdeburg, Germany", "name": "Hanke, Michael", "orcid": "0000-0001-6398-6370" }, { "affiliation": "Polytechnique Montréal", "name": "Guenther, Nick" }, { "affiliation": "Stanford University", "name": "Poldrack, Russell", "orcid": "0000-0001-6755-0259" }, { "affiliation": "The University of Washington eScience Institute", "name": "Rokem, Ariel", "orcid": "0000-0003-0679-1985" }, { "affiliation": "Ottawa Hospital Research Institute", "name": "Boulay, Chadwick", "orcid": "0000-0003-1747-3931" }, { "affiliation": "CEA, Inria", "name": "Thual, Alexis" }, { "affiliation": "Berkeley Institute for Data Science; University of California at Berkeley", "name": "Holdgraf, Chris", "orcid": "0000-0002-2391-0678" }, { "affiliation": "Queen Mary University London", "name": "Staden, Isla", "orcid": "0000-0002-0795-1154" }, { "name": "Staph, Jason A.", "orcid": "0000-0002-5226-3350" }, { "affiliation": "Brigham and Women's Hospital", "name": "Drew, William", "orcid": "0000-0002-9178-8731" }, { "affiliation": "University College London", "name": "Sinha, Ankur", "orcid": "0000-0001-7568-7167" }, { "affiliation": "National Institutes of Health", "name": "Galassi, Anthony", "orcid": "0000-0001-6550-4574" }, { "affiliation": "Université Libre de Bruxelles - Hôpital Erasme", "name": "Rovai, Antonin", "orcid": "0000-0001-6781-5341" }, { "affiliation": "University of Pennsylvania", "name": "Adebimpe, Azeez", "orcid": "0000-0001-9049-0135" }, { "affiliation": "INRIA", "name": "Thirion, Bertrand", "orcid": "0000-0001-5018-7895" }, { "affiliation": "Univ Rennes, Inria, CNRS, Inserm", "name": "Clénet, Boris", "orcid": "0009-0006-7727-5100" }, { "affiliation": "Stanford University", "name": "Shain, Cory", "orcid": "0000-0002-2704-7197" }, { "affiliation": "Rutgers University", "name": "Kleinschmidt, Dave F.", "orcid": "0000-0002-7442-2762" }, { "name": "Ferdman, Emmanuel", "orcid": "0009-0004-8953-0151" }, { "affiliation": "Centre for Addiction and Mental Health, University of Toronto, Canada", "name": "Dickie, Erin W", "orcid": "0000-0003-3028-9864" }, { "affiliation": "Institute of Neuroscience and Medicine (INM-7: Brain and Behaviour), Forschungszentrum Jülich", "name": "Raimondo, Fede", "orcid": "0000-0003-4087-8259" }, { "affiliation": "Sagol School of Neuroscience, Tel Aviv University", "name": "Ben-Zvi, Gal", "orcid": "0000-0002-5655-9423" }, { "affiliation": "NIMH IRP", "name": "Lee, John A.", "orcid": "0000-0001-5884-4247" }, { "name": "Kruper, John" }, { "affiliation": "UC Berkeley", "name": "Visconti di Oleggio Castello, Matteo", "orcid": "0000-0001-7931-5272" }, { "affiliation": "The Laboratory for Investigative Neurophysiology (The LINE), Department of Radiology and Department of Clinical Neurosciences, Lausanne, Switzerland; Center for Biomedical Imaging (CIBM), Lausanne, Switzerland", "name": "Notter, Michael Philipp", "orcid": "0000-0002-5866-047X" }, { "affiliation": "Sainte-Anne Hospital Center, Université Paris Descartes", "name": "Roca, Pauline", "orcid": "0000-0003-2089-6636" }, { "affiliation": "Stanford University", "name": "Blair, Ross", "orcid": "0000-0003-3007-1056" }, { "affiliation": "University of Pennsylvania", "name": "Pati, Sarthak", "orcid": "0000-0003-2243-8487" }, { "name": "Sundaravadivelu, Suganya" }, { "name": "Asciutto, Will" }, { "name": "pierre-nedelec" } ], "keywords": [ "BIDS", "brain imaging data structure", "neuroscience", "neuroimaging", "neuroinformatics", "python" ], "license": "mit-license", "upload_type": "software" } ================================================ FILE: CHANGELOG.rst ================================================ Changelog ========= Version 0.22.0 (April 22, 2026) ------------------------------- New feature release in the 0.22.x series. This release introduces schema-based layout configurations:: from bids.layout import BIDSLayout layout = BIDSLayout('/path/to/dataset', config='bids-schema') This will use the version of the schema included in the installed version of bidsschematools_. The schema configuration will be the way to incorporate updates from new versions of BIDS (v1.11+), and will eventually be made the default configuration. Please try it out and report any issues you encounter to help us make the transition as smooth as possible. We are particularly interested in reports from users who supply custom configurations. The following dependencies have been updated to new minimum versions: * NumPy 1.25 * SciPy 1.11 * NiBabel 5.1 * ENH: Sort BIDS file name entities (#1220) * ENH: Use BIDS schema to "drive" pybids #818 (#1163) * DOC: Fix documentation deployment (#1224) * MNT: Add Python 3.14 tests (#1175) * MNT: Skip pre-release checks on unlabeled PRs (#1188) * MNT: Format with Ruff (#1231) * MNT: Dependabot updates (#1232, #1233, #1234, #1236, #1237, #1238, #1239, #1240, #1242) Version 0.21.0 (October 16, 2025) --------------------------------- New feature release in the 0.21.x series. This release fixes compatibility with ``univeral_pathlib`` 0.3. The following dependencies have been updated to new minimum versions: * Python 3.10 * NumPy 1.24 * SciPy 1.10 * NiBabel 5.0 * Pandas 2.0 Merged pull requests: * FIX: UPath protocol mismatch (#1183) * MNT: Drop Python 3.9 support (#1184) Version 0.20.0 (September 24, 2025) ----------------------------------- New feature release in the 0.20.x series. This release finalizes some deprecations that were started in the 0.7 and 0.14 series. In particular, passing indexer arguments to ``BIDSLayout`` and disabling ``absolute_paths`` mode in ``BIDSLayout()`` or ``BIDSLayout.get()`` will now error. To adjust indexing behavior, create an explicit ``BIDSLayoutIndexer()`` and pass ``BIDSLayout(..., indexer=indexer)``. To access relative paths, use the ``BIDSFile.relative_path`` property on ``BIDSLayout.get()`` return values. * FIX: Check for attribute that can be absent before assigning (#1165) * FIX: Accommodate deprecations in the upcoming Pandas 3 release (#1173) * FIX: Repaired convolution issue that arises when onset values are repeated (#1133) * ENH: StatsModels: Add `invalid_inputs` parameter to allow dropping of missing data columns to run nodes (#1145) * ENH: Add missing root-level path patterns (#1169) * ENH: Parse ``seg-`` entities from dseg or probseg files (#1172) * DOC: Update link to derivatives config file (#1132) * MNT: Address warnings produced by pybids and dependencies (#1136) * MNT: Lock test environment, update with dependabot (#1134) Version 0.19.0 (March 05, 2025) ------------------------------- New feature release in the 0.19.x series. This release adds plus signs to labels and suffixes, anticipating the release of BIDS 1.10.1 or 1.11.0 (whichever is next). Until the BIDS specification is updated and the ``bidsschematools`` package containing the updated schema is published, files with ``+`` will not validate, but can still be indexed with ``BIDSLayout(..., validate=False)``. Several small fixes resolving issues with BIDS Stats Models are included, and complex JSON metadata (lists and objects) will now play more nicely with contexts where they need to be hashed. * FIX: Catch StopIteration on directory walk (#1124) * FIX: Ensure dotfiles are always filtered (#1123) * FIX: Copy entities dict before modifying run variable type (#1120) * FIX: Allow numpy arrays to treat PaddedInt like int (#1119) * FIX: Make lists and dicts hashable (#1112) * ENH: Allow plus signs in labels (#1117) * MNT: Remove formulaic upper bound (#1121) * MNT: Use tox-uv to ease lower bound checking, test on Python 3.13 (#1109) Version 0.18.1 (December 05, 2024) ---------------------------------- Bug-fix release in the 0.18.x series. 0.18.0 was missing package data, which went unnoticed because the workflow to publish to PyPI was also broken. These have been fixed, and we no longer alter global logging state. With thanks to Fede Raimondo for the contribution. * FIX: Install package data (#1108) * FIX: Remove calls to root logger basicConfig (#1106) * MNT: Upload attested package to (test) PyPI (#1110) Version 0.18.0 (November 12, 2024) ---------------------------------- New feature release in the 0.18.x series. No actual new features, but this version moves test data out of the installed distribution and ratchets its dependencies on Python and the Scientific Python stack, justifying a minor release. * FIX: Improve compatibility with cloud paths (#1094) * MNT: Transition to src/ layout (#1099) * MNT: Update minimum Python (3.9) and dependencies (SPEC 0 + 1yr) (#1092) * MNT: Update test for BIDS-MRS (#1089) Version 0.17.2 (September 19, 2024) ----------------------------------- Bug-fix release in the 0.17.x series. * BUG: symlink dirs treated as files during indexing (#1087) Version 0.17.1 (September 06, 2024) ----------------------------------- Bug-fix release in the 0.17.x series. Mostly quiets down warnings generated by more recent versions of Pandas. * FIX: Create indices with object dtype (#1084) * MNT: Transfer validation tests to bids validator (#1081) Version 0.17.0 (July 29, 2024) ------------------------------ Feature release in the 0.17.x series. This release includes experimental support for MRS (BEP22) datasets and `universal_pathlib `__. * ENH: pathlib -> universal_pathlib to enable cloud (and other filesystem) support (#1074) * ENH: Add MRS entities, path patterns (#1075) * STY: No parentheses after assert (#1065) * MNT: Bump bids-examples from `47c4da1` to `401f4cf` (#1073) * MNT: Fix typo newly found by codespell (#1066) Version 0.16.5 (May 22, 2024) ----------------------------- Bug-fix release in the 0.16.x series. This release includes minor fixes, but reworks the testing infrastructure and resolves Python 3.12 incompatibilities. * FIX: Handle FileNotFoundError in BIDSLayout init when is_derivative=True and validate=False (#1049) * FIX: Add dwi root sbref, missing dwi entities to path patterns (#1059) * ENH: Update indexer to treat .zarr as files not dirs (#1046) * DOC: Deal with several warnings and errors in the doc build (#1061) * TST: Add bids-examples tests to cover ieeg, eeg, dwi (#1060) * MNT: Add Python 3.12 support (#1057) * MNT: Use tox to simplify testing environment setup (#1055) * MNT: Bump codecov/codecov-action from 3 to 4 (#1041) * MNT: Bump bids-examples from `1a000d6` to `eff47f1` (#1039) * MNT: Bump bids-examples from `b6e5234` to `1a000d6` (#1038) * MNT: Bump actions/upload-artifact from 3 to 4 (#1037) * MNT: Bump actions/setup-python from 4 to 5 (#1036) Version 0.16.4 (November 30, 2023) ---------------------------------- Bug-fix release in the 0.16.x series. This release resolves a bug likely introduced in 0.9.0 (when PyBIDS started using SQL), in which boolean metadata fields with values of ``False`` were read in as ``True``. * FIX: Ensure values of boolean metadata fields are preserved (#1034) * FIX: update config for PET tracer entity (#1018) * ENH: Clearer BIDSValidation error message (#1023) * STY: pacify codespell (#1029) * CI: Build(deps): Bump bids-examples from `064db11` to `b6e5234` (#1031) * CI: Build(deps): Bump bids-examples from `d039879` to `064db11` (#1028) * CI: Build(deps): Bump bids-examples from `bfcc5c6` to `d039879` (#1027) * CI: Bump bids-examples from `502c917` to `bfcc5c6` (#1026) * CI: Bump bids-examples from `1c30c6e` to `502c917` (#1025) * CI: Bump bids-examples from `e857597` to `1c30c6e` (#1021) * CI: Bump bids-examples from `c557d1f` to `e857597` (#1019) * CI: Bump bids-examples from `75968b9` to `c557d1f` (#1011) * CI: Bump actions/checkout from 3 to 4 (#1024) Version 0.16.3 (August 16, 2023) -------------------------------- Bug-fix release in the 0.16.x series. This release resolves a regression introduced in 0.16.2 when JSON sidecar files contain ``null`` values. * FIX: Drop JSON sidecar fields with null values during metadata indexing (#1016) Version 0.16.2 (August 11, 2023) -------------------------------- Bug-fix and performance release in the 0.16.x series. Notably, indexing is now significantly faster (2-2.5x). * ENH: Speed up indexing by bulk committing to database (#1013) * Include indexer in add_derivatives call (#1009) * Bump bids-examples from `8550641` to `75968b9` (#1008) * Bump bids-examples from `e96b702` to `8550641` (#1007) * Bump bids-examples from `d8455af` to `e96b702` (#1005) * Bump bids-examples from `bc36231` to `d8455af` (#1003) * CI: Use OIDC publishing instead of tokens (#1001) Version 0.16.1 (May 01, 2023) ----------------------------- Bug-fix release in the 0.16.x series. * FIX: Improve parsing of suffix/extension in non-BIDS files (#997) * FIX: Support use of Query enums in filter lists (#994) * MNT: Clean up imports in documentation scripts (#980) * MNT: update zenodo (#995) * CI: Update Python version for doc/sdist/wheel tests (#996) Version 0.16.0 (April 26, 2023) ------------------------------- New feature release in the 0.16.x series. * FIX: Adapt to SQLAlchemy 1.4+ (#985) * FIX: Fill na with zero for VIF (#976) * ENH: Only warn if missing values not explicitly set (#977) * ENH: Create run reports of whole graph / create HTML reports (#918) * ENH: Speed up Sparse to Dense BIDSVariable conversion (#969) * ENH: Update config to support motion and nirs modalities (#965) * REF: Add derivative subdatasets in a less ad hoc fashion (#954) * MNT: Update minimum dependencies, add CI job to test them (#987) * MNT: Drop jinja2/markupsafe constraints (#986) * MNT: Fix ruff issue E712 (#981) * MNT: A few Python3 updates (#971) * MNT: Drop Python 3.7 EOL (#974) * MNT: Fix repeat-word typo (#968) * MNT: Update vendored inflect to 5.6.2 (#961) * MNT: Fix flake8 issues found by ruff (#955) * MNT: Remove (large) unused PNG from doc directory (#962) * MNT: http:// → https:// (#960) * MNT: Update git submodules (#964) * CI: Drop codecov PyPI package and appveyor config (#979) * CI: Add Python 3.11 (#975) * CI: Replace custom update_submodules action with dependabot action (#967) * CI: fix errors caused by changes in examples (#963) * CI: Update parameters to gh-action-pypi-publish (#957) Version 0.15.6 (February 28, 2023) ---------------------------------- Bug-fix release in 0.15.x series. * FIX: Exclude non-Hashables from set in .get(return_type='id') (#948) * FIX: Broken pattern matching (#936) * FIX: Coerce oversampling parameter in convolution to int (#929) * FIX: Allowing custom BIDS configuration files for derivative datasets (#922) * FIX: Address change in default pandas grouping behavior (#917) * ENH: Speed up BIDSLayout.get(return_type='id', ...) (#942) * ENH: Update default anat filename patterns in config (#933) * DOC: Make clear how to ignore folders (#945) * DOC: Fix tutorial link in README (#944) * DOC: Mention conda installation in README (#928) * MNT: Update git submodules (#946 + #943 + #932) * MNT: Move package metadata to pyproject.toml, unvendor versioneer (#907) * MNT: Bump formulaic max version constraint to include 0.5.x (#916) Version 0.15.5 (November 07, 2022) ---------------------------------- Bug-fix release in 0.15.x series. * FIX: Use kwargs for DataFrame.pivot() (#913) * ENH: Add transformation history to ``BIDSStatsModelsNodeOutput`` (#905) * ENH: Update examples, add to sphinx, and convert to .md (#908) * ENH: More helpful __repr__ in modeling (#906) * MNT: Update git submodules (#911) * MNT: Bump peter-evans/create-pull-request from 3 to 4 (#904) * CI: Automatically update GitHub Actions in the future (#903) Version 0.15.4 (October 08, 2022) --------------------------------- Bug-fix release in 0.15.x series. * FIX: Use 'contrasts' in DummyContrasts, not 'condition_list' (#902) * FIX: Transformations: sync_kwargs, for non-list arguments, listify by variable length (#901) * CI: Update actions versions (#899) Version 0.15.3 (September 12, 2022) ----------------------------------- Bug-fix release in 0.15.x series. * FIX: Test and update sync_kwargs class variable on transformations (#891) * FIX: Filtering of DM columns (#892) * ENH: Enable using participants.tsv, scans.tsv & session.tsv values as grouping variables (#894) * REF: Rename incoming single contrast column to intercept (#886) * DOC: Update references to nistats with references to nilearn in docs (#882) * TST: some sort of QA for derivatives' default_path_patterns (#879) * MNT: Update git submodules (#887) * MNT: Codespell errors (#885) * MNT: Update git submodules (#884) * MNT: Update git submodules (#883) * CI: Update actions versions (#897) Version 0.15.2 (July 25, 2022) ------------------------------ Bug-fix release in 0.15.x series. * FIX: Synchronize kwargs in transformations, e.g., in Assign (#836) * FIX: Add HRF derivatives to variable collection when created (#838) * FIX: Interpolate derivative pipeline name in error message (#847) * FIX: Match only within relative path when indexer is validating (#859) * FIX: Rename intercept in DummyContrast (#866) * FIX: Add variables to correct Node in load_tsv_variables (#872) * FIX: Repair variable IO so scans.tsv is found (#869) * FIX: Restore automodel functionality (#853) * FIX: Allow ``pybids layout --derivatives`` to be a boolean flag or accept a path (#848) * ENH: Add default path patterns to derivatives.json (#605) * ENH: Rename statsmodels contrasts if they cannot be uniquely identified downstream (#861) * ENH: Make intercept-only first level models throw a NotImplementedError (#854) * ENH: Update config to support microscopy, qMRI, PET, ASL (#840) * TEST: Add bids-examples submodule and bids_examples pytest fixture (#842) * TEST: Add tests for default path patterns (#846) * TEST: Update bids-examples and remove expected failing tests (#845) * MNT: Update git submodules (#874) * MNT: Add workflow top automatically update submodule (#870) * MNT: Remove deprecated no dot config (#841) * CI: Upgrade several GitHub actions to v3 (#867) * CI: Add cron jobs to avoid sneaky failures during low activity periods (#860) Version 0.15.1 (April 04, 2022) ------------------------------- Bug-fix release in the 0.15.x series. * RF/FIX: Decompose filter construction for special queries and lists (#826) * ENH: Relax group_by rules, allowing any entity to be used (#829) * MNT: Replace deprecated DataFrame.append call (#833) * DOC: Fresh rerun of entire pybids_tutorial.ipynb (#832) Version 0.15.0 (March 28, 2022) ------------------------------- New feature release in the 0.15.x series. Downstream tools should be aware of a potentially breaking, albeit long-demanded, change introduced in #819. Run indices are now stored so that the integers that come out retain any zero-padding that was found during parsing. This release also introduces the ``bids.ext`` namespace package that allows independent packages to install modules in this namespace. This is an infrastructural change that will allow some components to be separately managed and follow a different development pace. * FIX: Allow grouping by run and session when entities are undefined (#822) * FIX: Clarify exception message (#806) * FIX: Catch UnicodeDecodeErrors along with JSONDecodeErrors for better reporting (#796) * FIX: Accept paths/strings for layout configuration files (#799) * FIX: Small typo: repeated word in docstring (#793) * ENH: Retain zero-padding in run entities while preserving integer queries and comparisons (#819) * ENH: Add bids.ext namespace package for subpackages (#820) * ENH: Handle wildcards in model X (#810) * ENH: Implement automatic detection of derivative data (#805) * ENH: Add new ``Query`` for optional entities (#809) * ENH: Add __main__ to allow ``python -m bids`` to run CLI (#794) * REF: Improve modularization of bids.reports (#617) * DOC: Link from sphinx documentation to notebook tutorials. (#797) * MNT: Test on Python 3.10, various CI updates (#824) * MNT: Avoid jinja2 v3 until nbconvert handles breakages (#823) Version 0.14.1 (March 29, 2022) ------------------------------- Bug-fix release in the 0.14.x series. * RF/FIX: Decompose filter construction for special queries and lists (#826) Includes the following back-ports from 0.15.0: * FIX: Clarify exception message (#806) * FIX: Catch UnicodeDecodeErrors along with JSONDecodeErrors for better reporting (#796) * FIX: Accept paths/strings for layout configuration files (#799) * ENH: Add __main__ to allow ``python -m bids`` to run CLI (#794) Version 0.14.0 (November 09, 2021) ---------------------------------- New feature release in the 0.14.x series. This release includes a significant refactor of BIDS Statistical Models, replacing the ``bids.analysis`` module with ``bids.modeling``. Changes to ``bids.layout`` are minimal, and we do not anticipate API breakage. * FIX: LGTM.com warning: Implicit string concatenation in a list (#785) * FIX: Take the intersection of variables and Model.X, ignoring missing variables (usually contrasts) (#764) * FIX: Associate "is_metadata" with Tag, not Entity; and only return non-metadata entries for core Entities in ``get(return_type='id')`` (#749) * FIX: Only include regressors if they are TSV (#752) * FIX: ensure force_dense=True runs to_dense only on sparse variables (#745) * FIX: get unique, with conflicting meta-data (#748) * FIX: Clean up some deprecation and syntax warnings (#738) * ENH: Add ``pybids upgrade`` command (#654) * ENH: Add Lag transformation (#759) * ENH: Use indirect transformations structure (#737) * ENH: Add visualization for statsmodel graph (#742) * ENH: Permit explicit intercept (1) in Contrasts and DummyContrasts (#743) * ENH: Add meta-analysis model type (#731) * ENH: Contrast type is now test (#733) * REF: Use pathlib.Path internally when possible (#746) * REF: Remove group_by from edges and add filter (#734) * REF: Improved/refactored StatsModels module (#722) * MNT: Make sure codespell skips .git when run locally (#787) * MNT: LGTM.com recommendations (#786) * MNT: Better codespell configuration (#782) * MNT: Constrain formulaic version to 0.2.x . (#784) * MNT: Update versioneer: 0.18 → 0.20 (#778) * MNT: Add "codespell" tool to CI checks to catch typos sooner (#776) * MNT: Disable bids-nodot mode (#769) * MNT: Send codecov reports again (#766) * MNT: Set minimum version to Python 3.6 (#739) Version 0.13.2 (August 20, 2021) -------------------------------- Bug-fix release in the 0.13 series. * FIX/TEST: gunzip regressors.tsv.gz, allow timeseries.tsv as well (#767) * FIX: run is required (#762) * MNT: Patch 0.13.x maint branch (#763) Version 0.13.1 (May 21, 2021) ----------------------------- Bug-fix release in the 0.13 series. * ENH: Improve `get` performance (#723) * STY: Fix typos identified by codespell (#720) * TEST: dataset-level model spec retrieval (#693) Version 0.13 (April 14, 2021) ----------------------------- New feature release in the 0.13 series. * FIX: Resample to n_vols for sampling_rate == 'TR' (#713) * FIX: Lazily load metadata, skip when missing data files are missing sidecars (#711) * FIX: Ensure indicator matrix is boolean when indexing in Split transformation (#710) * FIX: Correctly pair metadata file when creating association records (#699) * FIX: Resolve side-effects of new testfile in #682 (#695) * FIX: BIDSLayout -- TypeError: unhashable type: 'dict' (#682) * ENH: Add res/den entities to derivatives.json (#709) * ENH: Update datatypes (#708) * ENH: add more informative validation error message for dataset_description.json (#705) * ENH: Add flip, inv, mt, and part entities (#688) * CI: Run packaging tests on main repository only (#696) * CI: Migrate to GH actions (#691) Version 0.12.4 (November 10, 2020) ---------------------------------- Bug-fix release in the 0.12.x series. * FIX: Do not error when popping missing ``scan_length`` (#679) Version 0.12.3 (October 23, 2020) --------------------------------- * FIX: Require aligned input for logical operations (#649) * ENH: Incremental variable loading in Step setup (#672) Version 0.12.2 (October 09, 2020) --------------------------------- Bug-fix release in 0.12.x series. * FIX: Support nibabel < 3 when calculating time series length (#669) * FIX: Sanitize single derivative Path passed to BIDSLayout (#665) * FIX: Force UTF-8 encoding in _index_metadata (#663) * FIX: Explicitly convert to HTML when testing tutorial (nbconvert 6.0 breakage) (#664) Version 0.12.1 (September 09, 2020) ----------------------------------- * FIX: drop obsolete test (#652) * FIX: Convolve zero-duration (impulse) events when variable contains multiple events (#645) * ENH: Add CLI to PyBIDS (#650) * ENH: Add relpath attribute to BIDSFile and associated refactoring (#647) * DOC: Add example for using parse_file_entities from bids.layout (#639) * MNT: Remove deprecated database_file argument to BIDSLayout (#644) Version 0.12.0 (August 04, 2020) -------------------------------- New feature release for the 0.12.x series. This release includes significant speedups for ``BIDSLayout`` creation and improves handling of GIFTI and CIFTI-2 derivative files. * FIX: Remove need to eager load associations (#642) * ENH: Fetch number of time points from NIfTI, GIFTI or CIFTI-2 (#637) * ENH: Catch any NIfTI/GIFTI (incl. CIFTI-2) files as BIDSImageFiles (#638) Version 0.11.1 (July 02, 2020) ------------------------------ Bug-fix release in the 0.11.x series With thanks to Mathias Goncalves for finding and fixing the issue. * FIX: Preserve ``get`` logic when using custom config (#636) Version 0.11.0 (June 29, 2020) ------------------------------ New feature release in the 0.11.x series. One significant change in this PR is to add the configuration option ``extension_initial_dot``. Setting to ``True`` will adopt the future default behavior of returning ``extension`` entities with an initial dot (``.``). Other notable changes include a significant refactoring of the Analysis module, and a number of small improvements to error reporting that should add up to simpler debugging for users and developers. * FIX: Post-fix And and Or transformations with underscore (#628) * FIX: made _validate_file work on Windows (#627) * FIX: Scale transformation fails if passed constant input. (#614) * FIX: Certain queries involving multiple layouts are very slow (#616) * FIX: Bug in get() when passing enums as extensions (#612) * FIX: Bug in BIDSDataFile.get_df() (#611) * FIX: Make entity assertions Python 3.5-safe (#606) * FIX: BIDSLayout.build_path to respect absolute_paths. (#580) * ENH: Adds OS-level file copying instead of reading/writing via Python (#613) * ENH: Add explicit warning message when users pass in filters as a dictionary keyword (#623) * ENH: Introduce PyBIDS exceptions (#615) * ENH: Add example bids and derivatives dataset_description.json strings to error messages (#620) * ENH: Improved handling of invalid filters (#610) * ENH: Add method to generate report from list of files (#607) * ENH: Pass kwargs from BIDSImageFile.get_image() to nibabel.load (#601) * ENH: Model spec module and associated utilities (#548) * ENH: Add BIDSMetadata dictionary to report file with missing metadata (#593) * RF: Add ``extension_initial_dot`` config option to transition to extension entity with initial dot (#629) * MNT: Automatically deploy docs (#598) * CI: Drop --pre check for Python 3.5 (#621) * CI: Test on Python 3.8 (#594) Version 0.10.2 (February 26, 2020) ---------------------------------- Bug fix release in the 0.10.x series. * FIX: Add Replace as exception to recursive JSON conversion (#589) Version 0.10.1 (February 10, 2020) ---------------------------------- Bug fix release in the 0.10.x series. This release just makes available some of the latest minor fixes and improvements. * FIX: Replace ``os.path.sep`` with ``fwdslash`` because bids validator hardcodes fwd (#582) * FIX: Refactor of ``build_path`` and inner machinery (#574) * FIX: Domain entity, and slow ``__repr__`` (#569) * FIX: "strict" helptext in ``BIDSLayout.write_contents_to_file`` (#566) * FIX: typos in helpstrings and comments (#564) * FIX: Correct term "caret" to "angle bracket" in helpstrings (#565) * ENH: Extend ``build_path`` to generate lists of files (#576) * ENH: Add one parametric test of ``BIDSLayout.build_path`` (#577) * ENH: Enable partial metadata indexing (#560) * ENH: Upscale to collection sampling rate prior to resampling (#568) * ENH: Calculate default sampling rate for ``SparseRunVariable.to_dense`` (#571) * MNT: Add ``.vscode`` (for Visual Studio Code) to ``.gitignore`` (#562) * MNT: Ignore pip-wheel-metadata (#581) * DOC: Remove Python 2 support statement, now that v0.10.0 has dropped it (#561) Version 0.10.0 (December 03, 2019) ---------------------------------- New feature release in the 0.10.x series. This release removes Python 2 support. * ENH: Helpful error for db argument mismatch, and add classmethod load_from_db (#547) * ENH: Add Resample transformation (#373) * ENH: Save BIDSLayout + Derivatives to folder (with init arguments) (#540) * ENH: Adds support for NONE and ANY query Enums (#542) * ENH: Transformation-related improvements (#541) * ENH: FEMA contrasts (#520) * STY: Minor PEP8 Fixes (#545) * MNT: Various (#543) * MNT: Remove Python 2.7 support (#524) * CI: Configure Circle Artifact Redirector (#544) Version 0.9.5 (November 6, 2019) -------------------------------- Bug fix release in the 0.9.x series. Final planned release with Python 2 support. * FIX: Filter before downsampling (#529) * FIX: Copy input dict in ``replace_entities``\ ; Typos in ``default_path_patterns`` (#517) * FIX: Use string dtype for all entities when using regex search (#511) * FIX: Update Sphinx docs for 2.2 (#507) * ENH: Enable automatic derivative database caching (#523) * ENH: Raise ValueError if BIDSLayout.build_path fails to match any pattern (#508) * RF: Subclass analysis Node from object (#528) * DOC: Unify docstring format (#499) * DOC: Auto-generate stubs (#513) * STY: .sql is a misleading extension to sqlite files (#531) * STY: General cleanups (#526, #527) Version 0.9.4 (September 20, 2019) ---------------------------------- Bug fix release in the 0.9.x series. * FIX: Ignore ``default_ignore`` paths by default (#495) * FIX: Filter and sort on scalar metadata in ``get_nodes()`` (#488) * FIX: Automatically sanitize dtype of ``get()`` arguments (#492) * FIX: Check that ``default_path_patterns`` is not ``None`` before using in ``build_path`` (#485) * FIX: Add CBV and phase modalities to func datatype path pattern (#479) * FIX: Drop bold suffix constraint from echo entity (#477) * ENH: Enforce dtypes on spec-defined columns when reading in DFs (#494) * ENH: Validate built paths (#496) * ENH: Update contrast enhancing agent query name (ceagent) (#497) * DOC: Add citation information to README (#493) * DOC: Improve wording in Python notebook example comment (#484) * DOC: Finish sentence in Python Notebook example documentation (#483) * DOC: Add JOSS Badge (#472) * STY: Apply syntax highlight to Python notebook example doc (#482) * MAINT: Move setup configuration to setup.cfg (#448) * MAINT: Additional Zenodo metadata (#470) * MAINT/CI: Use ``extras_require`` to declare dependencies for tests (#471) Version 0.9.3 (August 7, 2019) ------------------------------ This version includes a number of minor fixes and improvements, one of which breaks the existing API (by renaming two entities; see #464). Changes include: * FIX: Avoid DB collisions for redundant entities (#468) * FIX: Minor changes to entity names in core spec (#464) * FIX: Make bids.reports work properly with .nii images (#463) * CI: Execute notebook in Travis (#461) * ENH: More sensible **repr** for Tag model (#467) Version 0.9.2 (July 12, 2019) ----------------------------- This version includes a number of minor fixes and improvements. EEG files are better handled, and ``BIDSLayout`` and ``BIDSFile`` play more nicely with ``Path``\ -like objects. With thanks to new contributor Cecile Madjar. * FIX: Instantiate ``ignore``\ /\ ``force_index`` after root validation (#457) * FIX: Restore ``=None`` query returning files lacking the entity (#458) * ENH: Add ``BIDSJSONFile`` (#444) * ENH: Add ``BIDSFile.__fspath__`` to work with pathlib (#449) * ENH: Add ``eeg`` datatype to layout config (#455) * RF: Remove unused kwargs to BIDSFile (#443) * DOC: Improve docstring consistency, style (#443) * DOC: Address final JOSS review (#453) * STY: PEP8 Fixes (#456) * MAINT: Set name explicitly in setup.py (#450) Version 0.9.1 (May 24, 2019) ---------------------------- Hotfix release: * Fixed package deployment issues (#446) * Updated author list (#447) Thanks to new contributors Erin Dickie, Chadwick Boulay and Johannes Wennberg. Version 0.9.0 (May 21, 2019) ---------------------------- Version 0.9 replaces the native Python backend with a SQLite database managed via SQLAlchemy. The layout module has been refactored (again), but API changes are minimal. This release also adds many new features and closes a number of open issues. API CHANGES/DEPRECATIONS: * The ``extensions`` argument has now been banished forever; instead, use ``extension``\ , which is now defined as a first-class entity. The former will continue to work until at least the 0.11 release (closes #404). * Relatedly, values for ``extension`` should no longer include a leading ``.``\ , though this should also continue to work for the time being. * The ``BIDSLayout`` init argument ``index_associated`` has been removed as the various other filtering/indexing options mean there is longer a good reason for users to manipulate this. * ``bids.layout.MetadataIndex`` no longer exists. It's unlikely that anyone will notice this. * ``BIDSLayout.get_metadata()`` no longer takes additional entities as optional keyword arguments (they weren't necessary for anything). * Direct access to most ``BIDSFile`` properties is discouraged, and in one case is broken in 0.9 (for ``.metadata``\ , which was unavoidable, because it's reserved by SQLAlchemy). Instead, users should use getters (\ ``get_metadata``\ , ``get_image``\ , ``get_df``\ , etc.) whenever possible. NEW FUNCTIONALITY: * All file and metadata indexing and querying is now supported by a relational (SQLite) database (see #422). While this has few API implications, the efficiency of many operations is improved, and complex user-generated queries can now be performed via the SQLAlchemy ``.session`` stored in each ``BIDSLayout``. * Adds ``.save()`` method to the ``BIDSLayout`` that saves the current SQLite DB to the specified location. Conversely, passing a filename as ``database_file`` at init will use the specified store instead of re-indexing all files. This eliminates the need for a pickling strategy (#435). * Related to the above, the ``BIDSLayout`` init adds a ``reset_database`` argument that forces reindexing even if a ``database_file`` is specified. * The ``BIDSLayout`` has a new ``index_metadata`` flag that controls whether or not the contents of JSON metadata files are indexed. * Added ``metadata`` flag to ``BIDSLayout.to_df()`` that controls whether or not metadata columns are included in the returned pandas ``DataFrame`` (#232). * Added ``get_entities()`` method to ``BIDSLayout`` that allows retrieval of all ``Entity`` instances available within a specified scope (#346). * Adds ``drop_invalid_filters`` argument to ``BIDSLayout.get()``\ , enabling users to (optionally) ensure that invalid filters don't clobber all search results (#402). * ``BIDSFile`` instances now have a ``get_associations()`` method that returns associated files (see #431). * The ``BIDSFile`` class has been split into a hierarchy, with ``BIDSImageFile`` and ``BIDSDataFile`` subclasses. The former adds a ``get_image()`` method (returns a NiBabel image); the latter adds a ``get_df()`` method (returns a pandas DF). All ``BIDSFile`` instances now also have a ``get_entities()`` method. BUG FIXES AND OTHER MINOR CHANGES: * Metadata key/value pairs and file entities are now treated identically, eliminating a source of ambiguity in search (see #398). * Metadata no longer bleeds between raw and derivatives directories unless explicitly specified (see #383). * ``BIDSLayout.get_collections()`` no longer drops user-added columns (#273). * Various minor fixes/improvements/changes to tests. * The tutorial Jupyter notebook has been fixed and updated to reflect the latest changes. Version 0.8.0 (February 15, 2019) --------------------------------- Version 0.8 refactors much of the layout module. It drops the grabbit dependency, overhauls the file indexing process, and features a number of other improvements. However, changes to the public API are very minimal, and in the vast majority of cases, 0.8 should be a drop-in replacement for 0.7.*. API-BREAKING CHANGES: * Changes to (rarely-used) BIDSLayout initialization arguments: * ``include`` and ``exclude`` have been replaced with ``ignore`` and ``force_index``. Paths passed to ``ignore`` will be ignored from indexing; paths passed to ``force_index`` will be forcibly indexed even if they are otherwise BIDS-non-compliant. ``force_index`` takes precedence over ``ignore``. * Most querying/selection methods add a new ``scope`` argument that controls scope of querying (e.g., ``'raw'``\ , ``'derivatives'``\ , ``'all'``\ , etc.). In some cases this replaces the more limited ``derivatives`` argument. * No more ``domains``\ : with the grabbit removal (see below), the notion of a ``'domain'`` has been removed. This should impact few users, but those who need to restrict indexing or querying to specific parts of a BIDS project should be able to use the ``scope`` argument more effectively. OTHER CHANGES: * FIX: Path indexing issues in ``get_file()`` (#379) * FIX: Duplicate file returns under certain conditions (#350) * FIX: Pass new variable args as kwargs in split() (#386) @effigies * TEST: Update naming conventions for synthetic dataset (#385) @effigies * REF: The grabbit package is no longer a dependency; as a result, much of the functionality from grabbit has been ported over to pybids. * REF: Required functionality from six and inflect is now bundled with pybids in ``bids.external``\ , minimizing external dependencies. * REF: Core modules have been reorganized. Key data structures and containers (e.g., ``BIDSFile``\ , ``Entity``\ , etc.) are now in a new ``bids.layout.core`` module. * REF: A new ``Config`` class has been introduced to house the information found in ``bids.json`` and other layout configuration files. * REF: The file-indexing process has been completely refactored. A new hierarchy of ``BIDSNode`` objects has been introduced. While this has no real impact on the public API, and isn't really intended for public consumption yet, it will in future make it easier for users to work with BIDS projects in a tree-like way, while also laying the basis for a more sensible approach to reading and accessing associated BIDS data (e.g., .tsv files). * MNT: All invocations of ``pd.read_table`` have been replaced with ``read_csv``. Version 0.7.1 (February 01, 2019) --------------------------------- This is a bug fix release in the 0.7 series. The primary API change is improved handling of ``Path`` objects. * FIX: Path validation (#342) * FIX: Ensure consistent entities at all levels (#326) * FIX: Edge case where a resampled column was too-long-by-one (#365) * FIX: Use BIDS metadata for TR over nii header (#357) * FIX: Add check for ``run_info`` to be a list, pass ``run_info`` in correct position. (#353) * FIX: If ``sampling_rate`` is ``'auto'``\ , set to first rate of ``DenseRunVariables`` (#351) * FIX: Get the absolute path of the test data directory (#347) * FIX: Update reports to be 0.7-compatible (#341) * ENH: Rename ``sr`` variable to more intuitive ``interval`` (#366) * ENH: Support ``pathlib.Path`` and other ``str``\ -castable types (#307) * MNT: Updates link to derivative config file in notebook (#344) * MNT: Add bids-validator dependency (#363) * MNT: Require pandas >= 0.23.0 (#348) * MNT: Bump grabbit version (#338) * CI: Ignore OSX Python 3.5 failures (#372) * CI: Build with Python 3.7 on Travis, deploy on 3.6 (#337) Version 0.7.0 (January 10, 2019) -------------------------------- This is a major, API-breaking release. It introduces a large number of new features, bug fixes, and improvements. API-BREAKING CHANGES: * A number of entities (or keywords) have been renamed to align more closely with the BIDS specification documents: * 'type' becomes 'suffix' * 'modality' becomes 'datatype' * 'acq' is removed (use 'acquisition') * 'mod' becomes 'modality' * The following directories are no longer indexed by default: derivatives/, code/, stimuli/, models/, sourcedata/. They must be explicitly included using the 'include' initialization argument. * The grabbids module has been renamed to layout and BIDSLayout.py and BIDSvalidator.py are now layout.py and validation.py, respectively. * The BIDS validator is now enabled by default at layout initialization (i.e., ``validate=True``\ ) * The ``exclude`` initialization argument has been removed. * ``BIDSLayout.parse_entities`` utility has been removed (use the more flexible ``parse_file_entities``\ ). * Calls to ``.get()`` now return ``BIDSFile`` objects, rather than namedtuples, by default (#281). The ``BIDSFile`` API has been tweaked to ensure backwards incompatibility in nearly all cases. * Naming conventions throughout the codebase have been updated to ensure consistency with the BIDS specs. This is most salient in the ``analysis`` module, where snake_case has been replaced with CamelCase throughout. NEW FEATURES: * File metadata (i.e., in JSON sidecars) is now searchable by default, and behaves just like native BIDS entities (e.g., metadata keys can be passed as arguments to ``.get()`` calls) * A new BIDSFile wrapper provides easy access to ``.metadata`` and ``.image`` * HRF convolution is now supported via bundling of nistats' hemodynamic_models module; convolution is handled via the ``convolve_HRF`` transformation. * Named config paths that customize how projects are processed can be added at run-time (#313) * Preliminary support for BIDS-Derivatives RC1 (mainly core keywords) MINOR IMPROVEMENTS AND BUG FIXES: * Specifying 'derivatives' in a path specification now automatically includes 'bids' (#246) * Zenodo DOIs are now minted with new releases (#308) * Variable loading via load_variables can now be done incrementally * Expanded and improved path-building via ``layout.build_path()`` * ``get_collections`` no longer breaks when ``merge=True`` and the list is empty (#202) * Layout initialization no longer fails when ``validate=True`` (#222) * The auto_contrasts field in the modeling tools now complies with the BIDS-Model spec (#234) * Printing a ``BIDSFile`` now provides more useful information, including path (#298) * Resample design matrix to 1/TR by default (#309) * Fix the Sum transformation * Ensure that resampling works properly when a sampling rate is passed to ``get_design_matrix`` (#297) * Propagate derivative entities into top-level dynamic getters (#306) * Deprecated ``get_header`` call in nibabel removed (#300) * Fix bug in entity indexing for ``BIDSVariableCollection`` (#319) * Exclude modules with heavy dependencies from root namespace for performance reasons (#321) * Fix bug that caused in-place updating of input selectors in ``Analysis`` objects (#323) * Add a DropNA transformation (#325) * Add a ``get_tr()`` method to ``BIDSLayout`` (#327) * Add entity hints when calling ``get()`` with a ``target`` argument (#328) * Improved test coverage Version 0.6.5 (August 21, 2018) ------------------------------- * FIX: Do not drop rows of NaNs (#217) @adelavega * FIX: Declare run as having integer type (#236) @effigies * ENH: MEG support (#229) @jasmainak * REF: rename grabbids to layout, closes #228 (#230) @ltirrell * DOC: add .get_collection examples to tutorial (#219) @Shotgunosine * DOC: Fix link in README to point to documentation (#223) @KirstieJane * DOC: Add binder link for tutorial (#225) @KirstieJane * MAINT: Restore "analysis" installation extra (#218) @yarikoptic * MAINT: Do not import tests in __init__.py (#226) @tyarkoni Version 0.5.1 (March 9, 2018) ----------------------------- Hotfix release: * Includes data files omitted from 0.5.0 release. * Improves testing of installation. Version 0.5.0 (March 6, 2018) ----------------------------- This is a major release that introduces the following features: * A new ``bids.variables`` module that adds the following submodules: * ``bids.variables.entities.py``\ : Classes for representing BIDS hierarchies as a graph-like structure. * ``bids.variables.variables.py``\ : Classes and functions for representing and manipulating non-imaging data read from BIDS projects (e.g., fMRI events, densely-sampled physiological measures, etc.). * ``bids.variables.io.py``\ : Tools for loading variable data from BIDS projects. * ``bids.variables.collections``\ : Containers that facilitate aggregation and manipulation of ``Variable`` classes. * Extensions to the ``BIDSLayout`` class that make it easy to retrieve data/variables from the project (i.e., ``Layout.get_collections``\ ) * A new ``auto_model`` utility that generates simple BIDS-Model specifications from BIDS projects (thanks to @Shotgunosine) * A new ``reports`` module that generates methods sections from metadata in BIDS projects (thanks to @tsalo) * Experimental support for copying/writing out files in a BIDS-compliant way * Expand ``bids.json`` config file to include missing entity definitions * Ability to parse files without updating the Layout index * Updated grabbids module to reflect grabbit changes that now allow many-to-many mapping of configurations to folders * Too many other minor improvements and bug fixes to list (when you're very lazy, even a small amount of work is too much) Version 0.4.2 (November 16, 2017) --------------------------------- We did some minor stuff, but we were drunk again and couldn't read our handwriting on the napkin the next morning. Version 0.4.1 (November 3, 2017) -------------------------------- We did some minor stuff, and we didn't think it was important enough to document. Version 0.4.0 (November 1, 2017) -------------------------------- We did some stuff, but other stuff was happening in the news, and we were too distracted to write things down. Version 0.3.0 (August 11, 2017) ------------------------------- We did some stuff, but we were drunk and forgot to write it down. Version 0.2.1 (June 8, 2017) ---------------------------- History as we know it begins here. ================================================ FILE: LICENSE ================================================ The MIT License (MIT) Copyright (c) 2015-2016, Ariel Rokem, The University of Washington eScience Institute. Copyright (c) 2016--, PyBIDS developers, Planet Earth 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. ================================================ FILE: MANIFEST.in ================================================ graft src graft tests graft doc include tox.ini # Include uv.lock so that tox can run from sdist include uv.lock recursive-exclude src README.md ================================================ FILE: README.md ================================================ # PyBIDS [![DOI](https://joss.theoj.org/papers/10.21105/joss.01294/status.svg)](https://doi.org/10.21105/joss.01294) [![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.2537312.svg)](https://doi.org/10.5281/zenodo.2537312) [![Binder](https://mybinder.org/badge.svg)](https://mybinder.org/v2/gh/bids-standard/pybids/main) [![PyPI version](https://badge.fury.io/py/pybids.svg)](https://badge.fury.io/py/pybids) [![Conda version](https://img.shields.io/conda/vn/conda-forge/pybids)](https://anaconda.org/conda-forge/pybids) PyBIDS is a Python library to centralize interactions with datasets conforming BIDS (Brain Imaging Data Structure) format. For more information about BIDS visit https://bids.neuroimaging.io. PyBIDS currently supports Python 3 on POSIX operating systems (including Mac OS). Windows is not officially supported, though most PyBIDS functionality will probably work fine. ## Installation PyBIDS is most easily installed from pip. To install the latest official release: > pip install pybids Or, alternatively via conda: > conda install -c conda-forge pybids If you want to live on the bleeding edge, you can install from main: > pip install git+https://github.com/bids-standard/pybids.git ### Dependencies PyBIDS has a number of dependencies. The core querying functionality requires only the `BIDS-Validator` package. However, most other modules require the core Python neuroimaging stack: `numpy`, `scipy`, `pandas`, and `nibabel`. The `reports` module additionally requires `num2words`. By default, all dependencies will be installed with pybids (if they aren't already available). ## Usage Get started by checking out [the documentation](https://bids-standard.github.io/pybids)! Or you can start at [our tutorial](https://bids-standard.github.io/pybids/examples/pybids_tutorial.html)! You can run it interactively without installing anything via [binder](https://mybinder.org/v2/gh/bids-standard/pybids/main). Click on the link and then navigate to `doc/examples/pybids_tutorial.md` to explore. ## Development and testing PyBIDS uses [tox](https://tox.wiki/) to manage testing environments and ensure that the same commands can easily be run locally and in CI. Install tox with: ``` pip install tox ``` To run tests with standard, minimal, or pre-release dependencies, use: ``` tox run -e [full|min|pre] ``` To run with a specific version, also specify `py3X`: ``` tox run -e py311-full ``` See `tox` documentation for more detail. ## How to cite To credit PyBIDS in your work, please cite both the [JOSS paper](https://joss.theoj.org/papers/10.21105/joss.01294) and the [Zenodo archive](https://zenodo.org/record/3363985). The former provides a high level description of the package, and the latter points to a permanent record of all PyBIDS versions (we encourage you to cite the specific version you used). Example citations (for PyBIDS 0.9.3): Yarkoni et al., (2019). PyBIDS: Python tools for BIDS datasets. Journal of Open Source Software, 4(40), 1294, https://doi.org/10.21105/joss.01294 Yarkoni, Tal, Markiewicz, Christopher J., de la Vega, Alejandro, Gorgolewski, Krzysztof J., Halchenko, Yaroslav O., Salo, Taylor, … Blair, Ross. (2019, August 8). bids-standard/pybids: 0.9.3 (Version 0.9.3). Zenodo. https://doi.org/10.5281/zenodo.3363985 ## Community guidelines Bug reports, feedback, and other contributions to PyBIDS are welcome. To report a bug, request a feature, or raise a technical (non-usage-related) question for discussion, please [open an issue on GitHub](https://github.com/bids-standard/pybids/issues). Requests for support and questions about usage (where no bug is suspected) should be posted on the [NeuroStars](https://neurostars.org) forum. For questions about the [BIDS specification](https://github.com/bids-standard/bids-specification) or [BIDS-Validator](https://github.com/bids-standard/bids-validator), please see the corresponding repositories. For general BIDS-related discussion, see the [bids-discussion](https://groups.google.com/forum/#!forum/bids-discussion) mailing list. Pull requests are always welcome. If you have any doubts about the suitability of a PR, or want to discuss implementation-related issues, feel free to open an issue for discussion. ================================================ FILE: codecov.yml ================================================ coverage: range: "0...100" status: patch: default: target: 0 threshold: 100 project: default: target: 0 threshold: 100 patch: target: 0 threshold: 100 unittests: target: 0 threshold: 100 flags: - "unittests" smoketests: target: 0 threshold: 100 flags: - "smoketests" ignore: # files and folders that will be removed during processing - "doc" ================================================ FILE: doc/Makefile ================================================ # Minimal makefile for Sphinx documentation # # You can set these variables from the command line, and also # from the environment for the first two. SPHINXOPTS ?= SPHINXBUILD ?= sphinx-build SOURCEDIR = . BUILDDIR = _build # Put it first so that "make" without argument is like "make help". help: @$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) .PHONY: help clean Makefile clean: rm -rf $(BUILDDIR)/* rm -rf generated/* # Catch-all target: route all unknown targets to Sphinx using the new # "make mode" option. $(O) is meant as a shortcut for $(SPHINXOPTS). %: Makefile @$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) ================================================ FILE: doc/_static/theme_overrides.css ================================================ /* override table width restrictions */ @media screen and (min-width: 767px) { .wy-table-responsive table td { /* !important prevents the common CSS stylesheets from overriding this as on RTD they are loaded after this stylesheet */ white-space: normal !important; } .wy-table-responsive { overflow: visible !important; } } ================================================ FILE: doc/_templates/class.rst ================================================ :mod:`{{module}}`.{{objname}} {{ underline }}============== .. currentmodule:: {{ module }} .. autoclass:: {{ objname }} {% block methods %} {% endblock %} .. include:: {{module}}.{{objname}}.examples .. raw:: html
================================================ FILE: doc/_templates/function.rst ================================================ :mod:`{{module}}`.{{objname}} {{ underline }}==================== .. currentmodule:: {{ module }} .. autofunction:: {{ objname }} .. include:: {{module}}.{{objname}}.examples .. raw:: html
================================================ FILE: doc/_templates/module.rst ================================================ {{ fullname }} {{ underline }} .. automodule:: {{ fullname }} {% block functions %} {% if functions %} .. rubric:: Functions .. autosummary:: :toctree: {% for item in functions %} {{ item }} {%- endfor %} {% endif %} {% endblock %} {% block classes %} {% if classes %} .. rubric:: Classes .. autosummary:: :toctree: {% for item in classes %} {{ item }} {%- endfor %} {% endif %} {% endblock %} {% block exceptions %} {% if exceptions %} .. rubric:: Exceptions .. autosummary:: :toctree: {% for item in exceptions %} {{ item }} {%- endfor %} {% endif %} {% endblock %} ================================================ FILE: doc/analysis/index.rst ================================================ .. _analysis: ===================================================== ``analysis``: Model specification for BIDS datasets ===================================================== .. contents:: **Contents** :local: :depth: 1 | .. currentmodule:: pybids.modeling .. _overview: An overview of the analysis module ================================== .. topic:: **BIDS models** The `BIDS Models specification`_ is an extension to the BIDS standard for describing and organizing general linear models (GLMs) or similar models fitted to BIDS datasets. A GLM can be concisely described with a json file, regardless of the software used to fit the model (e.g., `nilearn`_, `AfNI`_, `FSL`_, `SPM`_). The PyBIDS analysis module provides high-level model specification functionality for BIDS datasets. It assumes that model information is represented in line with the (upcoming) BIDS-Model specification. Note that, at present, pybids.modeling does not provide a one-stop model-fitting solution. You will need to call some other package (e.g., `nilearn`_, or a non-Python package like `FSL`_ or `SPM`_ via `Nipype`_) to handle model estimation. What pybids.modeling will do for you is automatically handle the loading and transformation of all variables, and the construction of design matrices and contrasts. .. _BIDS Models specification: https://bids-standard.github.io/model-zoo/ .. _nilearn: https://nilearn.github.io .. _AfNI: https://afni.nimh.nih.gov .. _FSL: https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/ .. _SPM: https://www.fil.ion.ucl.ac.uk/spm/ .. _Nipype: https://nipype.readthedocs.io Quickstart ========== A simple example of standard usage follows. We assume that we have a root folder containing a BIDS-compliant project in ``/bidsproject``, as well as a BIDS-Models JSON specification in ``model.json``. >>> from bids.modeling import BIDSStatsModelsGraph >>> # Initialize the BIDSStatsModelsGraph >>> analysis = BIDSStatsModelsGraph('/bidsproject', 'model1.json') >>> # Setup constructs all the design matrices >>> analysis.setup() >>> # Sample query: retrieve first-level design matrix for one run >>> dm = analysis[0].get_design_matrix(subject='01', run=1, task='taskA') >>> # Sample query: retrieve session-level contrast matrix >>> cm = analysis[1].get_contrasts(subject='01', session='retest') .. note:: For a more detailed example, please refer to the Tutorial: :doc:`/examples/statsmodels_tutorial` ================================================ FILE: doc/api.rst ================================================ API Reference ============== .. _base_ref: :mod:`bids.layout`: Querying BIDS datasets ------------------------------------------ .. autosummary:: bids.layout :toctree: generated/ bids.layout.BIDSLayout bids.layout.BIDSValidator bids.layout.BIDSFile bids.layout.BIDSDataFile bids.layout.BIDSJSONFile bids.layout.Config bids.layout.Entity bids.layout.Tag bids.layout.parse_file_entities bids.layout.add_config_paths bids.layout.index.BIDSLayoutIndexer bids.layout.writing .. currentmodule:: bids :mod:`bids.modeling`: Model specification for BIDS datasets ------------------------------------------------------------ .. autosummary:: bids.modeling :toctree: generated/ bids.modeling.statsmodels bids.modeling.auto_model bids.modeling.statsmodels.BIDSStatsModelsGraph bids.modeling.statsmodels.BIDSStatsModelsNode .. currentmodule:: bids .. _calibration_ref: :mod:`bids.reports`: Data acquisition report generation -------------------------------------------------------- .. autosummary:: bids.reports :toctree: generated/ bids.reports.BIDSReport bids.reports.parsing bids.reports.parameters bids.reports.utils .. currentmodule:: bids :mod:`bids.variables`: Variables -------------------------------------------------- .. autosummary:: bids.variables :toctree: generated/ bids.variables.SimpleVariable bids.variables.SparseRunVariable bids.variables.DenseRunVariable bids.variables.BIDSVariableCollection bids.variables.BIDSRunVariableCollection bids.variables.merge_collections bids.variables.load_variables bids.variables.merge_variables bids.variables.io bids.variables.entities bids.variables.collections bids.variables.variables .. currentmodule:: bids :mod:`bids.config`: PyBIDS Configuration utilities -------------------------------------------------- .. autosummary:: bids.config :toctree: generated/ bids.config.set_option bids.config.set_options bids.config.get_option bids.config.from_file bids.config.reset_options .. currentmodule:: bids :mod:`bids.utils`: Utility functions -------------------------------------------------- .. autosummary:: bids.utils :toctree: generated/ bids.utils.listify bids.utils.matches_entities bids.utils.convert_JSON bids.utils.make_bidsfile .. currentmodule:: bids ================================================ FILE: doc/conf.py ================================================ # Configuration file for the Sphinx documentation builder. # noqa: D100, EXE002 # # This file only contains a selection of the most common options. For a full # list see the documentation: # https://www.sphinx-doc.org/en/master/usage/configuration.html # -- Path setup -------------------------------------------------------------- # If extensions (or modules to document with autodoc) are in another directory, # add these directories to sys.path here. If the directory is relative to the # documentation root, use os.path.abspath to make it absolute, like shown here. # import os import sys from datetime import date import sphinx_rtd_theme import bids sys.path.insert(0, os.path.abspath('../')) # -- Project information ----------------------------------------------------- project = 'PyBIDS' copyright = f'2015-{date.today().year}, Developers of PyBIDS' # noqa: A001, DTZ011 author = 'Developers of PyBIDS' currentdir = os.path.abspath(os.path.dirname(__file__)) currentdir = os.path.abspath(os.path.dirname(__file__)) # -- General configuration --------------------------------------------------- # Add any Sphinx extension module names here, as strings. They can be # extensions coming with Sphinx (named 'sphinx.ext.*') or your custom # ones. extensions = [ 'sphinx.ext.autodoc', 'sphinx.ext.autosummary', 'sphinx.ext.autosectionlabel', 'sphinx.ext.intersphinx', 'sphinx.ext.coverage', 'sphinx.ext.mathjax', 'sphinx.ext.viewcode', 'sphinx.ext.githubpages', 'sphinx.ext.napoleon', 'numpydoc', 'myst_nb', ] intersphinx_mapping = { 'python': ('https://docs.python.org/3.5', None), 'numpy': ('https://docs.scipy.org/doc/numpy', None), 'scipy': ('https://docs.scipy.org/doc/scipy/reference', None), 'matplotlib': ('https://matplotlib.org/', None), 'scikit-learn': ('https://scikit-learn.org/0.17', None), 'nibabel': ('https://nipy.org/nibabel/', None), 'pandas': ('https://pandas.pydata.org/pandas-docs/stable/', None), 'neurosynth': ('https://neurosynth.readthedocs.io/en/latest/', None), } intersphinx_timeout = 5 # If your documentation needs a minimal Sphinx version, state it here. needs_sphinx = '6.2.0' # Generate stubs autosummary_generate = True autodoc_default_flags = ['members', 'inherited-members'] add_module_names = False numpydoc_class_members_toctree = False # Add any paths that contain templates here, relative to this directory. templates_path = ['_templates'] # The suffix of source filenames. source_suffix = ['.rst', '.md'] # The master toctree document. master_doc = 'index' # List of patterns, relative to source directory, that match files and # directories to ignore when looking for source files. # This pattern also affects html_static_path and html_extra_path. exclude_patterns = ['_build', 'Thumbs.db', '.DS_Store'] # The short X.Y version. version = bids.__version__ # The full version, including alpha/beta/rc tags. release = version # The name of the Pygments (syntax highlighting) style to use. pygments_style = 'sphinx' # -- Options for HTML output ------------------------------------------------- # The theme to use for HTML and HTML Help pages. See the documentation for # a list of builtin themes. # # The theme to use for HTML and HTML Help pages. See the documentation for # a list of builtin themes. html_theme = 'sphinx_rtd_theme' html_theme_path = [sphinx_rtd_theme.get_html_theme_path()] # Add any paths that contain custom static files (such as style sheets) here, # relative to this directory. They are copied after the builtin static files, # so a file named "default.css" will overwrite the builtin "default.css". html_static_path = ['_static'] # If false, no module index is generated. html_domain_indices = False # One entry per manual page. List of tuples # (source start file, name, description, authors, manual section). man_pages = [('index', 'bids', 'bids Documentation', [author], 1)] # Grouping the document tree into Texinfo files. List of tuples # (source start file, target name, title, author, # dir menu entry, description, category) texinfo_documents = [ ( 'index', 'bids', 'bids Documentation', author, 'bids', 'One line description of project.', 'Miscellaneous', ), ] # If false, no module index is generated. texinfo_domain_indices = False nb_custom_formats = { '.md': ['jupytext.reads', {'fmt': 'mystnb'}], } ================================================ FILE: doc/examples/index.rst ================================================ .. tutorials: Tutorials ========== The following executable examples are available. Note that all of these tutorials are in the MyST format (`md`), and can downloaded and opened as a Jupyter Notebook in Jupyter Lab if Jupytext is installed. See `this guide `_ to enable. .. toctree:: :maxdepth: 1 pybids_tutorial.md reports_tutorial.md statsmodels_tutorial.md ================================================ FILE: doc/examples/pybids_tutorial.md ================================================ --- jupytext: text_representation: extension: .md format_name: myst format_version: 0.13 jupytext_version: 1.14.1 kernelspec: display_name: Python 3 (ipykernel) language: python name: python3 --- # Introduction to `pybids` [`pybids`](https://github.com/bids-standard/pybids) is a tool to query, summarize and manipulate data using the BIDS standard. In this tutorial we will use a `pybids` test dataset to illustrate some of the functionality of `pybids.layout` ```{code-cell} ipython3 from bids import BIDSLayout from bids.tests import get_test_data_path import os ``` ## The `BIDSLayout` At the core of pybids is the `BIDSLayout` object. A `BIDSLayout` is a lightweight Python class that represents a BIDS project file tree and provides a variety of helpful methods for querying and manipulating BIDS files. While the `BIDSLayout` initializer has a large number of arguments you can use to control the way files are indexed and accessed, you will most commonly initialize a `BIDSLayout` by passing in the BIDS dataset root location as a single argument: ```{code-cell} ipython3 # Here we're using an example BIDS dataset that's bundled with the pybids tests data_path = os.path.join(get_test_data_path(), '7t_trt') # Initialize the layout layout = BIDSLayout(data_path) # Print some basic information about the layout layout ``` ### Querying the `BIDSLayout` When we initialize a `BIDSLayout`, all of the files and metadata found under the specified root folder are indexed. This can take a few seconds (or, for very large datasets, a minute or two). Once initialization is complete, we can start querying the `BIDSLayout` in various ways. The workhorse method is [`.get()`](https://bids-standard.github.io/pybids/generated/bids.grabbids.BIDSLayout.html#bids.grabbids.BIDSLayout.get). If we call `.get()` with no additional arguments, we get back a list of all the BIDS files in our dataset: ```{code-cell} ipython3 all_files = layout.get() print("There are {} files in the layout.".format(len(all_files))) print("\nThe first 10 files are:") all_files[:10] ``` The returned object is a Python list. By default, each element in the list is a `BIDSFile` object. We discuss the `BIDSFile` object in much more detail below. For now, let's simplify things and work with just filenames: ```{code-cell} ipython3 layout.get(return_type='filename')[:10] ``` This time, we get back only the names of the files. ### Filtering files by entities The utility of the `BIDSLayout` would be pretty limited if all we could do was retrieve a list of all files in the dataset. Fortunately, the `.get()` method accepts all kinds of arguments that allow us to filter the result set based on specified criteria. In fact, we can pass *any* BIDS-defined keywords (or, as they're called in PyBIDS, *entities*) as constraints. For example, here's how we would retrieve all BOLD runs with `.nii.gz` extensions for subject `'01'`: ```{code-cell} ipython3 # Retrieve filenames of all BOLD runs for subject 01 layout.get(subject='01', extension='nii.gz', suffix='bold', return_type='filename') ``` If you're wondering what entities you can pass in as filtering arguments, the answer is contained in the `.json` configuration files [housed here](https://github.com/bids-standard/pybids/tree/main/src/bids/layout/config). To save you the trouble, here are a few of the most common entities: * `suffix`: The part of a BIDS filename just before the extension (e.g., `'bold'`, `'events'`, `'physio'`, etc.). * `subject`: The subject label * `session`: The session label * `run`: The run index * `task`: The task name New entities are continually being defined as the spec grows, and in principle (though not always in practice), PyBIDS should be aware of all entities that are defined in the BIDS specification. ### Filtering by metadata All of the entities listed above are found in the names of BIDS files. But sometimes we want to search for files based not just on their names, but also based on metadata defined (per the BIDS spec) in JSON files. Fortunately for us, when we initialize a `BIDSLayout`, all metadata files associated with BIDS files are automatically indexed. This means we can pass any key that occurs in any JSON file in our project as an argument to `.get()`. We can combine these with any number of core BIDS entities (like `subject`, `run`, etc.). For example, say we want to retrieve all files where (a) the value of `SamplingFrequency` (a metadata key) is `100`, (b) the `acquisition` type is `'prefrontal'`, and (c) the subject is `'01'` or `'02'`. Here's how we can do that: ```{code-cell} ipython3 # Retrieve all files where SamplingFrequency (a metadata key) = 100 # and acquisition = prefrontal, for the first two subjects layout.get(subject=['01', '02'], SamplingFrequency=100, acquisition="prefrontal") ``` Notice that we passed a list in for `subject` rather than just a string. This principle applies to all filters: you can always pass in a list instead of a single value, and this will be interpreted as a logical disjunction (i.e., a file must match any one of the provided values). ### Other `return_type` values While we'll typically want to work with either `BIDSFile` objects or filenames, we can also ask `get()` to return unique values (or ids) of particular entities. For example, say we want to know which subjects have at least one `T1w` file. We can request that information by setting `return_type='id'`. When using this option, we also need to specify a target entity (or metadata keyword) called `target`. This combination tells the `BIDSLayout` to return the unique values for the specified `target` entity. For example, in the next example, we ask for all of the unique subject IDs that have at least one file with a `T1w` suffix: ```{code-cell} ipython3 # Ask get() to return the ids of subjects that have T1w files layout.get(return_type='id', target='subject', suffix='T1w') ``` If our `target` is a BIDS entity that corresponds to a particular directory in the BIDS spec (e.g., `subject` or `session`) we can also use `return_type='dir'` to get all matching subdirectories: ```{code-cell} ipython3 layout.get(return_type='dir', target='subject') ``` ### Other `get()` options The `.get()` method has a number of other useful arguments that control its behavior. We won't discuss these in detail here, but briefly, here are a couple worth knowing about: * `regex_search`: If you set this to `True`, string filter argument values will be interpreted as regular expressions. * `scope`: If your BIDS dataset contains BIDS-derivatives sub-datasets, you can specify the scope (e.g., `derivatives`, or a BIDS-Derivatives pipeline name) of the search space. +++ ## The `BIDSFile` When you call `.get()` on a `BIDSLayout`, the default returned values are objects of class `BIDSFile`. A `BIDSFile` is a lightweight container for individual files in a BIDS dataset. It provides easy access to a variety of useful attributes and methods. Let's take a closer look. First, let's pick a random file from our existing `layout`. ```{code-cell} ipython3 # Pick the 15th file in the dataset bf = layout.get()[15] # Print it bf ``` Here are some of the attributes and methods available to us in a `BIDSFile` (note that some of these are only available for certain subclasses of `BIDSFile`; e.g., you can't call `get_image()` on a `BIDSFile` that doesn't correspond to an image file!): * `.path`: The full path of the associated file * `.filename`: The associated file's filename (without directory) * `.dirname`: The directory containing the file * `.get_entities()`: Returns information about entities associated with this `BIDSFile` (optionally including metadata) * `.get_image()`: Returns the file contents as a nibabel image (only works for image files) * `.get_df()`: Get file contents as a pandas DataFrame (only works for TSV files) * `.get_metadata()`: Returns a dictionary of all metadata found in associated JSON files * `.get_associations()`: Returns a list of all files associated with this one in some way Let's see some of these in action. ```{code-cell} ipython3 # Print all the entities associated with this file, and their values bf.get_entities() ``` ```{code-cell} ipython3 # Print all the metadata associated with this file bf.get_metadata() ``` ```{code-cell} ipython3 # We can the union of both of the above in one shot like this bf.get_entities(metadata='all') ``` Here are all the files associated with our target file in some way. Notice how we get back both the JSON sidecar for our target file, and the BOLD run that our target file contains physiological recordings for. ```{code-cell} ipython3 bf.get_associations() ``` +++ {"slideshow": {"slide_type": "slide"}} In cases where a file has a `.tsv.gz` or `.tsv` extension, it will automatically be created as a `BIDSDataFile`, and we can easily grab the contents as a pandas `DataFrame`: ```{code-cell} ipython3 # Use a different test dataset--one that contains physio recording files data_path = os.path.join(get_test_data_path(), 'synthetic') layout2 = BIDSLayout(data_path) # Get the first physiological recording file recfile = layout2.get(suffix='physio')[0] # Get contents as a DataFrame and show the first few rows df = recfile.get_df() df.head() ``` While it would have been easy enough to read the contents of the file ourselves with pandas' `read_csv()` method, notice that in the above example, `get_df()` saved us the trouble of having to read the physiological recording file's metadata, pull out the column names and sampling rate, and add timing information. Mind you, if we don't *want* the timing information, we can ignore it: ```{code-cell} ipython3 recfile.get_df(include_timing=False).head() ``` ## Other utilities ### Filename parsing Say you have a filename, and you want to manually extract BIDS entities from it. The `parse_file_entities` method provides the facility: ```{code-cell} ipython3 path = "/a/fake/path/to/a/BIDS/file/sub-01_run-1_T2w.nii.gz" layout.parse_file_entities(path) ``` A version of this utility independent of a specific layout is available at `bids.layout` ([doc](https://bids-standard.github.io/pybids/generated/bids.layout.parse_file_entities.html#bids.layout.parse_file_entities)) - ```{code-cell} ipython3 from bids.layout import parse_file_entities path = "/a/fake/path/to/a/BIDS/file/sub-01_run-1_T2w.nii.gz" parse_file_entities(path) ``` ### Path construction You may want to create valid BIDS filenames for files that are new or hypothetical that would sit within your BIDS project. This is useful when you know what entity values you need to write out to, but don't want to deal with looking up the precise BIDS file-naming syntax. In the example below, imagine we've created a new file containing stimulus presentation information, and we want to save it to a `.tsv.gz` file, per the BIDS naming conventions. All we need to do is define a dictionary with the name components, and `build_path` takes care of the rest (including injecting sub-directories!): ```{code-cell} ipython3 entities = { 'subject': '01', 'run': 2, 'task': 'nback', 'suffix': 'bold' } layout.build_path(entities) ``` You can also use `build_path` in more sophisticated ways—for example, by defining your own set of matching templates that cover cases not supported by BIDS out of the box. For example, suppose you want to create a template for naming a new z-stat file. You could do something like: ```{code-cell} ipython3 # Define the pattern to build out of the components passed in the dictionary pattern = "sub-{subject}[_ses-{session}]_task-{task}[_acq-{acquisition}][_rec-{reconstruction}][_run-{run}][_echo-{echo}]_{suffix}.nii.gz", entities = { 'subject': '01', 'run': 2, 'task': 'nback', 'suffix': 'z' } # Notice we pass the new pattern as the second argument layout.build_path(entities, pattern, validate=False) ``` Note that in the above example, we set `validate=False` to ensure that the standard BIDS file validator doesn't run (because the pattern we defined isn't actually compliant with the BIDS specification). +++ ### Loading derivatives By default, `BIDSLayout` objects are initialized without scanning contained `derivatives/` directories. But you can easily ensure that all derivatives files are loaded and endowed with the extra structure specified in the [derivatives config file](https://github.com/bids-standard/pybids/blob/main/src/bids/layout/config/derivatives.json): ```{code-cell} ipython3 # Define paths to root and derivatives folders root = os.path.join(get_test_data_path(), 'synthetic') layout2 = BIDSLayout(root, derivatives=True) layout2 ``` The `scope` argument to `get()` specifies which part of the project to look in. By default, valid values are `'bids'` (for the "raw" BIDS project that excludes derivatives) and `'derivatives'` (for all BIDS-derivatives files). You can also pass the names of individual derivatives pipelines (e.g., passing `'fmriprep'` would search only in a `/derivatives/fmriprep` folder). Either a string or a list of strings can be passed. The following call returns the filenames of all derivatives files. ```{code-cell} ipython3 # Get all files in derivatives layout2.get(scope='derivatives', return_type='file') ``` ### Exporting a `BIDSLayout` to a pandas `Dataframe` If you want a summary of all the files in your `BIDSLayout`, but don't want to have to iterate `BIDSFile` objects and extract their entities, you can get a nice bird's-eye view of your dataset using the `to_df()` method. ```{code-cell} ipython3 # Convert the layout to a pandas dataframe df = layout.to_df() df.head() ``` We can also include metadata in the result if we like (which may blow up our `DataFrame` if we have a large dataset). Note that in this case, most of our cells will have missing values. ```{code-cell} ipython3 layout.to_df(metadata=True).head() ``` ## Retrieving BIDS variables BIDS variables are stored in .tsv files at the run, session, subject, or dataset level. You can retrieve these variables with `layout.get_collections()`. The resulting objects can be converted to dataframes and merged with the layout to associate the variables with corresponding scans. In the following example, we request all subject-level variable data available anywhere in the BIDS project, and merge the results into a single `DataFrame` (by default, we'll get back a single `BIDSVariableCollection` object for each subject). ```{code-cell} ipython3 # Get subject variables as a dataframe and merge them back in with the layout subj_df = layout.get_collections(level='subject', merge=True).to_df() subj_df.head() ``` ## BIDSValidator `pybids` implicitly imports a `BIDSValidator` class from the separate [`bids-validator`](https://github.com/bids-standard/bids-validator) package. You can use the `BIDSValidator` to determine whether a filepath is a valid BIDS filepath, as well as answering questions about what kind of data it represents. Note, however, that this implementation of the BIDS validator is *not* necessarily up-to-date with the JavaScript version available online. Moreover, the Python validator only tests individual files, and is currently unable to validate entire BIDS datasets. For that, you should use the [online BIDS validator](https://bids-standard.github.io/bids-validator/). ```{code-cell} ipython3 from bids import BIDSValidator # Note that when using the bids validator, the filepath MUST be relative to the top level bids directory validator = BIDSValidator() validator.is_bids('/sub-02/ses-01/anat/sub-02_ses-01_T2w.nii.gz') ``` ```{code-cell} ipython3 # Can decide if a filepath represents a file part of the specification validator.is_file('/sub-02/ses-01/anat/sub-02_ses-01_T2w.json') ``` ```{code-cell} ipython3 # Can check if a file is at the top level of the dataset validator.is_top_level('/dataset_description.json') ``` ```{code-cell} ipython3 # or subject (or session) level validator.is_subject_level('/dataset_description.json') ``` ```{code-cell} ipython3 validator.is_session_level('/sub-02/ses-01/sub-02_ses-01_scans.json') ``` ```{code-cell} ipython3 # Can decide if a filepath represents phenotypic data validator.is_phenotypic('/sub-02/ses-01/anat/sub-02_ses-01_T2w.nii.gz') ``` ================================================ FILE: doc/examples/reports_tutorial.md ================================================ --- jupytext: text_representation: extension: .md format_name: myst format_version: 0.13 jupytext_version: 1.14.1 kernelspec: display_name: Python 3 language: python name: python3 --- # Writing methods reports with `pybids.reports` This tutorial ```{code-cell} ipython3 %load_ext autoreload %autoreload 2 from bids import BIDSLayout from bids.reports import BIDSReport from bids.tests import get_test_data_path import os ``` ```{code-cell} ipython3 dataset = "synthetic" # Here we're using an example BIDS dataset that's bundled with the pybids tests data_path = os.path.join(get_test_data_path(), dataset) # Load the BIDS dataset layout = BIDSLayout(data_path) ``` ```{code-cell} ipython3 # Initialize a report for the dataset report = BIDSReport(layout) ``` ```{code-cell} ipython3 # Method generate returns a Counter of unique descriptions across subjects descriptions = report.generate() ``` ```{code-cell} ipython3 # For datasets containing a single study design, all but the most common # description most likely reflect random missing data. pub_description = descriptions.most_common()[0][0] print(pub_description) ``` ================================================ FILE: doc/examples/statsmodels_tutorial.md ================================================ --- jupytext: text_representation: extension: .md format_name: myst format_version: 0.13 jupytext_version: 1.14.1 kernelspec: display_name: Python 3 (ipykernel) language: python name: python3 --- # Executing and inspecting StatsModels with `pybids.modeling` A minimalistic tutorial illustrating usage of the tools in the `bids.modeling` module—most notably, `BIDSStatsModelsGraph` and its various components. ```{code-cell} ipython3 %load_ext autoreload %autoreload 2 import pandas as pd import numpy as np from bids.modeling import BIDSStatsModelsGraph from bids.layout import BIDSLayout from bids.tests import get_test_data_path from os.path import join import json ``` ## Load BIDSLayout Standard stuff: load the `BIDSLayout` object (we'll use the ds005 dataset bundled with PyBIDS tests) and read in one of the included model files (`ds005/models/ds-005_type-test_model.json`). ```{code-cell} ipython3 layout_path = join(get_test_data_path(), 'ds005') layout = BIDSLayout(layout_path) json_file = join(layout_path, 'models', 'ds-005_type-test_model.json') spec = json.load(open(json_file, 'r')) ``` ## Initialize the graph The `BIDSStatsModelsGraph` is the primary interface to design data, model constructions, etc. We build it from a `BIDSLayout` and the contents of a JSON model file. ```{code-cell} ipython3 graph = BIDSStatsModelsGraph(layout, spec) ``` ## Load variables from BIDS dataset We will typically want to load variables into BIDS "collections" from the BIDS project. The `scan_length` argument is only necessary here because the test dataset doesn't actually include nifti image headers, so duration of scans can't be inferred. In typical use, you can call this without arguments. ```{code-cell} ipython3 graph.load_collections(scan_length=480) ``` ## Access specific nodes Now that the graph is loaded, we can start interacting with its nodes. We'll typically start with the root node, which will usually contain the run-level model information. We can either access the `.root_node`, or use `get_node()` to get the node by its unique name (defined in the JSON file). ```{code-cell} ipython3 # Equivalent to calling graph.get_node('run') in this case. root_node = graph.root_node ``` ## Inspect `BIDSVariableCollection` +++ We can take a look at the original variables available for each node prior to running the node (i.e. applying any transformations) ```{code-cell} ipython3 colls = root_node.get_collections() first_sub = colls[0] # Collection for the first subject / run ``` ```{code-cell} ipython3 first_sub.to_df(entities=False) ``` Note that by default `to_df` combines both sparse and dense variables. We can take a look at the individual variable objects as well: ```{code-cell} ipython3 first_sub.variables ``` ## Executing a node We can "run" each node to produce design matrices for each unique combination of entities/inputs we want. This is determined by the grouping structure. When working with the API directly, this is indicated via the `group_by` argument to a `.run()` method. In this case, we're indicating that the design information should be set up separately for every unique combination of `run` and `subject`. Note that we need to include `subject` even though this is a strictly run-level model, because if we only pass `['run']`, there will only be 3 separate analyses conducted: one for run 1, one for run 2, and one for run 3. Whereas what we actually want is for the procedure to be done separately for each unique combination of the 3 runs and 16 subjects (i.e., 48 times). ```{code-cell} ipython3 # force_dense controls whether the output for run-level design matrices # will be resampled to a uniform "dense" representation, or left alone # as a sparse representation if possible. outputs = root_node.run(group_by=['run', 'subject'], force_dense=False, transformation_history=True) ``` ## Node outputs +++ The result is a list of objects of type `BIDSStatsModelsNodeOutput`. This is a lightweight container that stores design information and various other useful pieces of information. There should one element in the list for each unique combination of the grouping variables (in this case, run and subject): ```{code-cell} ipython3 len(outputs) ``` ```{code-cell} ipython3 # The first 10 `BIDSStatsModelsNodeOutput` objects outputs[:10] ``` Let's take a closer look at the `BIDSStatsModelsNodeOutput`. First, we have an `.entities` attribute that tells us what levels of the grouping variables this output corresponds to: ```{code-cell} ipython3 outputs[0].entities ``` Next, we can get the design matrix itself via the `.X` attribute: ```{code-cell} ipython3 outputs[0].X ``` Here we have a column for each of the contrasts specified in the model (with the same name as the contrast). We can access the contrasts too, via—you guessed it—`.contrasts`: ```{code-cell} ipython3 outputs[0].contrasts ``` Each `ContrastInfo` is a named tuple with fields that map directly on the definition of contrasts in the BIDS-StatsModels specification. The only addition is the inclusion of an `.entities` field that stores a dictionary of the entities that identify what subset of our data the contrast applies to. One thing you might be puzzled by, looking at the output of the `.X` call above, is the absence of any timing information. `.X` is supposed to give us a design matrix, but how come the output only has the actual values for each column, and no information about event timing? How are we supposed to know what the onsets, durations, etc. of each event are? The answer is that `.X` contains *only* the actual values that go into the design matrix, and not any metadata—no matter how important. Fortunately, that metadata is available to us. It's conveniently stored in a `.metadata` attribute on the `BIDSStatsModelsNodeOutput` object. ```{code-cell} ipython3 outputs[0].metadata ``` There's a 1-to-1 mapping from rows in `.X` to rows in `.metadata`. This means you can, if you like, simply concatenate the two along the column axis to get one big DataFrame with everything. But by maintaining a default separation, it's made very clear to us which columns are properly a part of the design, and which contain additional metadata. +++ ## Transformation History +++ To generate the final design matrix, pybids applies all transformations specified in the model for that given run. However, it's often useful to look at the intermediary outputs from each transformation to perform sanity checks, such as previewing the variable prior to convolution. Optionally, you can run the `Node` with `transformation_history=True`, and the `BIDSStatsModelsNodeOutput` object will have a `trans_hist` attribute which is a list of intermediary outputs after every transformation ```{code-cell} ipython3 ts = outputs[0].trans_hist ts ``` The first item (index=0), is the original collection. We can access the `BIDSRunVariableCollection` using the output attribute: ```{code-cell} ipython3 ts[0].output ``` We can examine the full collection at each step using either a combined dataframe: ```{code-cell} ipython3 ts[1].output.to_df(entities=False) ``` Or examine individual variables in their native representation (e.g. `SparseRunVariable` or `DenseRunVariable`) ```{code-cell} ipython3 ts[1].output.variables ``` ```{code-cell} ipython3 ts[1].output.variables['RT'].to_df(entities=False) ``` ## Traversing the graph So far we've executed the root node, which by definition required no inputs from any previous node. But in typical workflows, we'll be passing outputs from one node in as inputs to another. For example, we often want to take the run-level parameter estimates and pass them to a subject-level model that does nothing but average over runs within each subject. This requires us to somehow traverse the graph based on the edges specified in the BIDS-StatsModel document. We can do that by taking advantage of each node's `.children` attribute, which contains a list of `BIDSStatsModelsEdge` named tuples that specify an edge between two nodes. ```{code-cell} ipython3 root_node.children ``` In this case the root node connects to only one other node. We can directly access that node by following the `.destination` property of the edge: ```{code-cell} ipython3 next_node = root_node.children[0].destination next_node.level, next_node.name ``` We assign the first connected node to `next_node`, and print out its `level` and `name` for inspection (both are session). ## Passing in inputs Armed with that, we can run the session node and proceed and before. However, there's a twist: whereas the root node only needs to know about variables loaded directly from the `BIDSLayout` (which we achieved with that `.load_collections()` call earlier), the session node can't get the inputs it needs from the `BIDSLayout`, because there aren't any (at least in this particular dataset). What we want to do at the session level is average over our run-level estimates within-session. But to do that, we need to actually pass in information about those runs! The way we do this is to pass, as the first argument to `.run()`, a list of `ContrastInfo` objects informing our node about what inputs it should use to construct its design matrices. The typical use pattern is to pass one concatenated list containing *all* of the outputs from the previous level that we want to pass on. Note that we may not want to pass *all* of the outputs forward. For example, suppose that 2 out of 48 run-level models failed during the estimation process. We might not want to keep passing information about those 2 runs forward, as we can't compute them. So we can always filter the list of `ContrastInfo` objects we received from the previous node before we pass them on to the next node. (We could also do other things, like rename each `ContrastInfo` to use whatever naming scheme our software prefers; modifying the entities; and so on. But we won't do any of that here.) Let's concatenate the 48 outputs we got from the previous level and drop the last 2, in preparation for passing them forward to our `next_node`: ```{code-cell} ipython3 from itertools import chain contrasts = list(chain(*[s.contrasts for s in outputs[:-2]])) len(contrasts) ``` Notice that we're left we're 138 individual `ContrastInfo` objects. Why 138? Because we have ((3 runs x 16 subjects) - 2) * 3 contrasts. Recall that we're dropping the last two of the 48 outputs from the previous level. But each of those lists contains *three* `ContrastInfo` objects (one per contrast—`RT`, `gain`, and the `RT:gain` interaction). Hence, 138. Now we can call `.run()` on our session-level node, passing in the contrasts as inputs. We want the model specification (i.e., the part in the `"Model"` section of the node) to be applied separately to each unique combination of `contrast`, `session`, and `subject`. ```{code-cell} ipython3 sess_outputs = next_node.run(contrasts, group_by=['subject', 'contrast']) len(sess_outputs) ``` Again we get back a list of `BIDSStatsModelsNodeOutput` objects. And again we have 48 of them. It might seem odd that we have the same number of outputs from a subject-level node as we had from the run-level node, but there's actually a difference. In the run-level case, our 48 results reflected 16 subjects x 3 runs. In the subject-level case, we *have* successfully aggregated over runs within each subject, but we now have 3 sets of contrasts producing outputs (i.e., 16 subjects x 3 contrasts). This becomes clearer if we inspect the same attributes we looked at earlier: ```{code-cell} ipython3 sess_outputs[0].contrasts ``` ```{code-cell} ipython3 # Concatenate the X and metadata DFs for easy reading. # Note that only the first column is actually part of the # design matrix; the others are just metadata. pd.concat([sess_outputs[0].X, sess_outputs[0].metadata], axis=1) ``` Notice how the entities differ: the run-level node grouped on `run` and `subject`; the subject-level node groups on `subject` and `contrast`. The number of outputs is identical in both cases, but this is just an (un)happy accident, not a general principle. You can verify this for yourself by re-running the subject-level node with a different grouping (e.g., only `['subject']`). ================================================ FILE: doc/includes/big_toc_css.rst ================================================ .. File to ..include in a document with a big table of content, to give it 'style' :orphan: .. raw:: html ================================================ FILE: doc/includes/bigger_toc_css.rst ================================================ .. File to ..include in a document with a very big table of content, to give it 'style' :orphan: .. raw:: html ================================================ FILE: doc/index.rst ================================================ .. bids Welcome to pybids's documentation! ==================================== ``pybids`` is a set of tools for working with Brain Imaging Data Structure (BIDS) datasets. For more information about BIDS, please visit the `BIDS website`_. .. _BIDS website: https://bids.neuroimaging.io Contents ========== .. toctree:: :maxdepth: 2 user_guide.rst examples/index.rst api.rst whats_new.rst Citing pybids ============= To credit PyBIDS in your work, please cite both the `JOSS paper `_ and the `Zenodo archive `_. The former provides a high level description of the package, and the latter points to a permanent record of all PyBIDS versions (we encourage you to cite the specific version you used). Example citations (for PyBIDS 0.9.3): Yarkoni et al., (2019). PyBIDS: Python tools for BIDS datasets. Journal of Open Source Software, 4(40), 1294, https://doi.org/10.21105/joss.01294 Yarkoni, Tal, Markiewicz, Christopher J., de la Vega, Alejandro, Gorgolewski, Krzysztof J., Halchenko, Yaroslav O., Salo, Taylor, … Blair, Ross. (2019, August 8). bids-standard/pybids: 0.9.3 (Version 0.9.3). Zenodo. https://doi.org/10.5281/zenodo.3363985 Indices and tables ================== * :ref:`genindex` * :ref:`modindex` * :ref:`search` ================================================ FILE: doc/introduction.rst ================================================ .. _introduction: ===================================================== Introduction: What is pybids? ===================================================== .. contents:: **Contents** :local: :depth: 1 What is BIDS and why should you use it? ======================================== .. topic:: **Why use BIDS?** The Brain Imaging Data Structure provides a standard for organizing, naming, and describing neuroimaging datasets. By organizing your data in this manner, you can make sure of a number of tools that are designed to work with BIDS datasets out of the box. Moreover, many open datasets are provided in BIDS format, which means that being familiar with this structure will make it easier for you to analyze public data as well as your own. For more information on the Brain Imaging Data Structure (BIDS), visit https://bids.neuroimaging.io. What is pybids and why should you use it? ========================================== .. topic:: **Why use pybids?** Pybids makes it easier for neuroimagers who utilize the BIDS standard to query, summarize, and manipulate their data. A number of Python packages for analyzing neuroimaging data, including `Nipype`_ and `nilearn`_, are optimized to work with BIDS datasets. Installing pybids ================= Pybids is easy to install. To install the most recent release, use pip:: >>> pip install pybids If you want the "bleeding-edge" version of pybids, you can install directly from the GitHub repository:: >>> pip install git+https://github.com/bids-standard/pybids.git Finding help ============== :Mailing lists and forums: * Don't hesitate to ask questions about pybids on `NeuroStars `_. * You can find help with neuroimaging in Python (file I/O, neuroimaging-specific questions) via the nipy user group: https://groups.google.com/forum/?fromgroups#!forum/nipy-user * If you notice a bug in the pybids code, please `open an issue`_ in the pybids repository. .. _nilearn: https://nilearn.github.io .. _Nipype: https://nipype.readthedocs.io .. _open an issue: https://github.com/bids-standard/pybids/issues ================================================ FILE: doc/layout/index.rst ================================================ .. _layout: ===================================================== ``layout``: Querying BIDS datasets ===================================================== .. contents:: **Contents** :local: :depth: 1 | .. currentmodule:: pybids.layout .. _loading_datasets: Loading BIDS datasets =========================================== The :obj:`bids.layout.BIDSLayout` class requires the path to a valid BIDS dataset:: >>> from os.path import join >>> from bids import BIDSLayout >>> from bids.tests import get_test_data_path >>> layout = BIDSLayout(join(get_test_data_path(), 'synthetic')) The ``BIDSLayout`` instance is a lightweight container for all of the files in the BIDS project directory. It automatically detects any BIDS entities found in the file paths, and allows us to perform simple but relatively powerful queries over the file tree. By default, defined BIDS entities include things like "subject", "session", "run", and "type". .. hint:: To exclude folders from indexing (e.g. only index a subset of subjects to save time), you can pass a list of folder names, or a regex pattern to the ``ignore`` argument. For example, to ignore all subjects except "25": >>> BIDSLayout(bids_dir, ignore=[re.compile(r"(sub-(?!25)\d*/)")]) .. _querying_datasets: Querying datasets =========================================== Pybids layouts can be queried according to a number of parameters, using grabbit's ``get`` functionality. For example, if we want to get a list of subjects in the dataset:: >>> layout.get_subjects() ['01', '02', '03', '04', '05'] We can also get a list of all available sessions:: >>> layout.get_sessions() ['01', '02'] Or a list of tasks:: >>> layout.get_tasks() ['nback', 'rest'] .. extracting_metadata: Extracting metadata ==================== A number of ``BIDSLayout`` methods extract metadata associated with files. For example, if we want event (task timing) information for a given fMRI scan, we can use ``get_events``:: >>> f = layout.get(task='nback', run=1, extension='nii.gz')[0].filename >>> layout.get_events(f) We can also extract metadata from the json files associated with a scan file:: >>> f = layout.get(task='nback', run=1, extension='nii.gz')[0].filename >>> layout.get_metadata(f) .. note:: For a more detailed set of examples, please refer to the Tutorial: :doc:`/examples/pybids_tutorial` ================================================ FILE: doc/make.bat ================================================ @ECHO OFF pushd %~dp0 REM Command file for Sphinx documentation if "%SPHINXBUILD%" == "" ( set SPHINXBUILD=sphinx-build ) set SOURCEDIR=. set BUILDDIR=_build if "%1" == "" goto help %SPHINXBUILD% >NUL 2>NUL if errorlevel 9009 ( echo. echo.The 'sphinx-build' command was not found. Make sure you have Sphinx echo.installed, then set the SPHINXBUILD environment variable to point echo.to the full path of the 'sphinx-build' executable. Alternatively you echo.may add the Sphinx directory to PATH. echo. echo.If you don't have Sphinx installed, grab it from echo.https://www.sphinx-doc.org/ exit /b 1 ) %SPHINXBUILD% -M %1 %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O% goto end :help %SPHINXBUILD% -M help %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O% :end popd ================================================ FILE: doc/reports/index.rst ================================================ .. _reports: ===================================================== ``reports``: Data acquisition report generation ===================================================== .. contents:: **Contents** :local: :depth: 1 | .. currentmodule:: pybids.reports .. _initializing_reports: Initializing reports =========================================== The :obj:`bids.reports.BIDSReport` class requires a :obj:`bids.BIDSLayout` object as an argument:: >>> from os.path import join >>> from bids import BIDSLayout >>> from bids.reports import BIDSReport >>> from bids.tests import get_test_data_path >>> layout = BIDSLayout(join(get_test_data_path(), 'synthetic')) >>> report = BIDSReport(layout) .. _generating_reports: Generating reports =========================================== Pybids reports are then generated with the ``generate`` method, which returns a :obj:`collections.Counter` of reports:: >>> counter = report.generate() >>> main_report = counter.most_common()[0][0] >>> print(main_report) r""" For session 01: MR data were acquired using a UNKNOWN-Tesla MANUFACTURER MODEL MRI scanner. Ten runs of N-Back UNKNOWN-echo fMRI data were collected (64 slices; repetition time, TR=2500ms; echo time, TE=UNKNOWNms; flip angle, FA=UNKNOWN; field of view, FOV=128x128mm; matrix size=64x64; voxel size=2x2x2mm). Each run was 2:40 minutes in length, during which 64 functional volumes were acquired. Five runs of Rest UNKNOWN-echo fMRI data were collected (64 slices; repetition time, TR=2500ms; echo time, TE=UNKNOWNms; flip angle, FA=UNKNOWN; field of view, FOV=128x128mm; matrix size=64x64; voxel size=2x2x2mm). Each run was 2:40 minutes in length, during which 64 functional volumes were acquired. For session 02: MR data were acquired using a UNKNOWN-Tesla MANUFACTURER MODEL MRI scanner. Ten runs of N-Back UNKNOWN-echo fMRI data were collected (64 slices; repetition time, TR=2500ms; echo time, TE=UNKNOWNms; flip angle, FA=UNKNOWN; field of view, FOV=128x128mm; matrix size=64x64; voxel size=2x2x2mm). Each run was 2:40 minutes in length, during which 64 functional volumes were acquired. Five runs of Rest UNKNOWN-echo fMRI data were collected (64 slices; repetition time, TR=2500ms; echo time, TE=UNKNOWNms; flip angle, FA=UNKNOWN; field of view, FOV=128x128mm; matrix size=64x64; voxel size=2x2x2mm). Each run was 2:40 minutes in length, during which 64 functional volumes were acquired. Dicoms were converted to NIfTI-1 format. This section was (in part) generated automatically using pybids (0.5).""" .. _generating_subreports: Generating reports on subsets of the data ------------------------------------------- The ``generate`` method allows for keyword restrictions, just like :obj:`bids.BIDSLayout`'s ``get`` method. For example, to generate a report only for ``nback`` task data in session ``01``:: >>> counter = report.generate(session='01', task='nback') >>> main_report = counter.most_common()[0][0] >>> print(main_report) r""" For session 01: MR data were acquired using a UNKNOWN-Tesla MANUFACTURER MODEL MRI scanner. Ten runs of N-Back fMRI data were collected (64 slices; repetition time, TR=2500ms; echo time, TE=UNKNOWNms; flip angle, FA=UNKNOWN; field of view, FOV=128x128mm; matrix size=64x64; voxel size=2x2x2mm). Each run was 2:40 minutes in length, during which 64 functional volumes were acquired. Dicoms were converted to NIfTI-1 format. This section was (in part) generated automatically using pybids (0.5).""" .. note:: For a more detailed set of examples, please refer to the Tutorial: :doc:`/examples/reports_tutorial`. ================================================ FILE: doc/sphinxext/docscrape.py ================================================ # ruff: noqa: D301 """Extract reference documentation from the NumPy source tree.""" import collections import inspect import pydoc import re import textwrap from warnings import warn class Reader: """A line-based string reader.""" def __init__(self, data): """Parameters # noqa: D301 ---------- data : str String with lines separated by '\n'. """ if isinstance(data, list): self._str = data else: self._str = data.split('\n') # store string as list of lines self.reset() def __getitem__(self, n): return self._str[n] def reset(self): # noqa: D102 self._l = 0 # current line nr def read(self): # noqa: D102 if not self.eof(): out = self[self._l] self._l += 1 return out else: return '' def seek_next_non_empty_line(self): # noqa: D102 for l in self[self._l :]: # noqa: E741 if l.strip(): break else: self._l += 1 def eof(self): # noqa: D102 return self._l >= len(self._str) def read_to_condition(self, condition_func): # noqa: D102 start = self._l for line in self[start:]: if condition_func(line): return self[start : self._l] self._l += 1 if self.eof(): return self[start : self._l + 1] return [] def read_to_next_empty_line(self): # noqa: D102 self.seek_next_non_empty_line() def is_empty(line): return not line.strip() return self.read_to_condition(is_empty) def read_to_next_unindented_line(self): # noqa: D102 def is_unindented(line): return line.strip() and (len(line.lstrip()) == len(line)) return self.read_to_condition(is_unindented) def peek(self, n=0): # noqa: D102 if self._l + n < len(self._str): return self[self._l + n] else: return '' def is_empty(self): # noqa: D102 return not ''.join(self._str).strip() class NumpyDocString(collections.abc.Mapping): # noqa: D101 def __init__(self, docstring, config={}): # noqa: B006 docstring = textwrap.dedent(docstring).split('\n') self._doc = Reader(docstring) self._parsed_data = { 'Signature': '', 'Summary': [''], 'Extended Summary': [], 'Parameters': [], 'Returns': [], 'Yields': [], 'Raises': [], 'Warns': [], 'Other Parameters': [], 'Attributes': [], 'Methods': [], 'See Also': [], 'Notes': [], 'Warnings': [], 'References': '', 'Examples': '', 'index': {}, } self._parse() def __getitem__(self, key): return self._parsed_data[key] def __setitem__(self, key, val): if key not in self._parsed_data: warn('Unknown section %s' % key) # noqa: B028, UP031 else: self._parsed_data[key] = val def __iter__(self): return iter(self._parsed_data) def __len__(self): return len(self._parsed_data) def _is_at_section(self): self._doc.seek_next_non_empty_line() if self._doc.eof(): return False l1 = self._doc.peek().strip() # e.g. Parameters if l1.startswith('.. index::'): return True l2 = self._doc.peek(1).strip() # ---------- or ========== return l2.startswith('-' * len(l1)) or l2.startswith('=' * len(l1)) def _strip(self, doc): i = 0 j = 0 for i, line in enumerate(doc): # noqa: B007 if line.strip(): break for j, line in enumerate(doc[::-1]): # noqa: B007 if line.strip(): break return doc[i : len(doc) - j] def _read_to_next_section(self): section = self._doc.read_to_next_empty_line() while not self._is_at_section() and not self._doc.eof(): if not self._doc.peek(-1).strip(): # previous line was empty section += [''] section += self._doc.read_to_next_empty_line() return section def _read_sections(self): while not self._doc.eof(): data = self._read_to_next_section() name = data[0].strip() if name.startswith('..'): # index section yield name, data[1:] elif len(data) < 2: yield StopIteration else: yield name, self._strip(data[2:]) def _parse_param_list(self, content): r = Reader(content) params = [] while not r.eof(): header = r.read().strip() if ' : ' in header: arg_name, arg_type = header.split(' : ')[:2] else: arg_name, arg_type = header, '' desc = r.read_to_next_unindented_line() desc = dedent_lines(desc) params.append((arg_name, arg_type, desc)) return params _name_rgx = re.compile( r'^\s*(:(?P\w+):`(?P[a-zA-Z0-9_.-]+)`|' r' (?P[a-zA-Z0-9_.-]+))\s*', re.X, ) def _parse_see_also(self, content): """func_name : Descriptive text continued text another_func_name : Descriptive text func_name1, func_name2, :meth:`func_name`, func_name3 """ items = [] def parse_item_name(text): """Match ':role:`name`' or 'name'""" m = self._name_rgx.match(text) if m: g = m.groups() if g[1] is None: return g[3], None else: return g[2], g[1] raise ValueError('%s is not a item name' % text) # noqa: UP031 def push_item(name, rest): if not name: return name, role = parse_item_name(name) items.append((name, list(rest), role)) del rest[:] current_func = None rest = [] for line in content: if not line.strip(): continue m = self._name_rgx.match(line) if m and line[m.end() :].strip().startswith(':'): push_item(current_func, rest) current_func, line = line[: m.end()], line[m.end() :] rest = [line.split(':', 1)[1].strip()] if not rest[0]: rest = [] elif not line.startswith(' '): push_item(current_func, rest) current_func = None if ',' in line: for func in line.split(','): if func.strip(): push_item(func, []) elif line.strip(): current_func = line elif current_func is not None: rest.append(line.strip()) push_item(current_func, rest) return items def _parse_index(self, section, content): """.. index: default :refguide: something, else, and more """ def strip_each_in(lst): return [s.strip() for s in lst] out = {} section = section.split('::') if len(section) > 1: out['default'] = strip_each_in(section[1].split(','))[0] for line in content: line = line.split(':') if len(line) > 2: out[line[1]] = strip_each_in(line[2].split(',')) return out def _parse_summary(self): """Grab signature (if given) and summary""" if self._is_at_section(): return # If several signatures present, take the last one while True: summary = self._doc.read_to_next_empty_line() summary_str = ' '.join([s.strip() for s in summary]).strip() if re.compile(r'^([\w., ]+=)?\s*[\w\.]+\(.*\)$').match(summary_str): self['Signature'] = summary_str if not self._is_at_section(): continue break if summary is not None: self['Summary'] = summary if not self._is_at_section(): self['Extended Summary'] = self._read_to_next_section() def _parse(self): self._doc.reset() self._parse_summary() sections = list(self._read_sections()) section_names = set([section for section, content in sections]) # noqa: C403 has_returns = 'Returns' in section_names has_yields = 'Yields' in section_names # We could do more tests, but we are not. Arbitrarily. if has_returns and has_yields: msg = 'Docstring contains both a Returns and Yields section.' raise ValueError(msg) for section, content in sections: if not section.startswith('..'): section = (s.capitalize() for s in section.split(' ')) section = ' '.join(section) if section in ( 'Parameters', 'Returns', 'Yields', 'Raises', 'Warns', 'Other Parameters', 'Attributes', 'Methods', ): self[section] = self._parse_param_list(content) elif section.startswith('.. index::'): self['index'] = self._parse_index(section, content) elif section == 'See Also': self['See Also'] = self._parse_see_also(content) else: self[section] = content # string conversion routines def _str_header(self, name, symbol='-'): return [name, len(name) * symbol] def _str_indent(self, doc, indent=4): out = [] for line in doc: out += [' ' * indent + line] return out def _str_signature(self): if self['Signature']: return [self['Signature'].replace('*', r'\*')] + [''] else: return [''] def _str_summary(self): if self['Summary']: return self['Summary'] + [''] else: return [] def _str_extended_summary(self): if self['Extended Summary']: return self['Extended Summary'] + [''] else: return [] def _str_param_list(self, name): out = [] if self[name]: out += self._str_header(name) for param, param_type, desc in self[name]: if param_type: out += ['%s : %s' % (param, param_type)] # noqa: UP031 else: out += [param] out += self._str_indent(desc) out += [''] return out def _str_section(self, name): out = [] if self[name]: out += self._str_header(name) out += self[name] out += [''] return out def _str_see_also(self, func_role): if not self['See Also']: return [] out = [] out += self._str_header('See Also') last_had_desc = True for func, desc, role in self['See Also']: if role: link = ':%s:`%s`' % (role, func) # noqa: UP031 elif func_role: link = ':%s:`%s`' % (func_role, func) # noqa: UP031 else: link = '`%s`_' % func # noqa: UP031 if desc or last_had_desc: out += [''] out += [link] else: out[-1] += ', %s' % link # noqa: UP031 if desc: out += self._str_indent([' '.join(desc)]) last_had_desc = True else: last_had_desc = False out += [''] return out def _str_index(self): idx = self['index'] out = [] out += ['.. index:: %s' % idx.get('default', '')] # noqa: UP031 for section, references in idx.items(): if section == 'default': continue out += [' :%s: %s' % (section, ', '.join(references))] # noqa: UP031 return out def __str__(self, func_role=''): out = [] out += self._str_signature() out += self._str_summary() out += self._str_extended_summary() for param_list in ( 'Parameters', 'Returns', 'Yields', 'Other Parameters', 'Raises', 'Warns', ): out += self._str_param_list(param_list) out += self._str_section('Warnings') out += self._str_see_also(func_role) for s in ('Notes', 'References', 'Examples'): out += self._str_section(s) for param_list in ('Attributes', 'Methods'): out += self._str_param_list(param_list) out += self._str_index() return '\n'.join(out) def indent(str, indent=4): # noqa: A002, D103 indent_str = ' ' * indent if str is None: return indent_str lines = str.split('\n') return '\n'.join(indent_str + l for l in lines) # noqa: E741 def dedent_lines(lines): """Deindent a list of lines maximally""" return textwrap.dedent('\n'.join(lines)).split('\n') def header(text, style='-'): # noqa: D103 return text + '\n' + style * len(text) + '\n' class FunctionDoc(NumpyDocString): # noqa: D101 def __init__(self, func, role='func', doc=None, config={}): # noqa: B006 self._f = func self._role = role # e.g. "func" or "meth" if doc is None: if func is None: raise ValueError('No function or docstring given') doc = inspect.getdoc(func) or '' NumpyDocString.__init__(self, doc) if not self['Signature'] and func is not None: func, func_name = self.get_func() try: # try to read signature argspec = inspect.getfullargspec(func) argspec = inspect.formatargspec(*argspec) argspec = argspec.replace('*', r'\*') signature = '%s%s' % (func_name, argspec) # noqa: UP031 except TypeError: signature = '%s()' % func_name # noqa: UP031 self['Signature'] = signature def get_func(self): # noqa: D102 func_name = getattr(self._f, '__name__', self.__class__.__name__) if inspect.isclass(self._f): func = getattr(self._f, '__call__', self._f.__init__) # noqa: B004 else: func = self._f return func, func_name def __str__(self): out = '' func, func_name = self.get_func() signature = self['Signature'].replace('*', r'\*') # noqa: F841 roles = {'func': 'function', 'meth': 'method'} if self._role: if self._role not in roles: print('Warning: invalid role %s' % self._role) # noqa: UP031 out += '.. %s:: %s\n \n\n' % (roles.get(self._role, ''), func_name) # noqa: UP031 out += super().__str__(func_role=self._role) return out class ClassDoc(NumpyDocString): # noqa: D101 extra_public_methods = ['__call__'] def __init__(self, cls, doc=None, modulename='', func_doc=FunctionDoc, config={}): # noqa: B006 if not inspect.isclass(cls) and cls is not None: raise ValueError('Expected a class or None, but got %r' % cls) # noqa: UP031 self._cls = cls self.show_inherited_members = config.get('show_inherited_class_members', True) if modulename and not modulename.endswith('.'): modulename += '.' self._mod = modulename if doc is None: if cls is None: raise ValueError('No class or documentation string given') doc = pydoc.getdoc(cls) NumpyDocString.__init__(self, doc) if config.get('show_class_members', True): def splitlines_x(s): if not s: return [] else: return s.splitlines() for field, items in [('Methods', self.methods), ('Attributes', self.properties)]: if not self[field]: doc_list = [] for name in sorted(items): try: doc_item = pydoc.getdoc(getattr(self._cls, name)) doc_list.append((name, '', splitlines_x(doc_item))) except AttributeError: pass # method doesn't exist self[field] = doc_list @property def methods(self): # noqa: D102 if self._cls is None: return [] return [ name for name, func in inspect.getmembers(self._cls) if ( (not name.startswith('_') or name in self.extra_public_methods) and isinstance(func, collections.abc.Callable) and self._is_show_member(name) ) ] @property def properties(self): # noqa: D102 if self._cls is None: return [] return [ name for name, func in inspect.getmembers(self._cls) if ( not name.startswith('_') and ( func is None or isinstance(func, property) or inspect.isgetsetdescriptor(func) ) and self._is_show_member(name) ) ] def _is_show_member(self, name): if self.show_inherited_members: return True # show all class members if name not in self._cls.__dict__: return False # class member is inherited, we do not show it return True ================================================ FILE: doc/sphinxext/docscrape_sphinx.py ================================================ import inspect # noqa: D100 import pydoc import re import textwrap import sphinx from docscrape import ClassDoc, FunctionDoc, NumpyDocString class SphinxDocString(NumpyDocString): # noqa: D101 def __init__(self, docstring, config={}): # noqa: B006 self.use_plots = config.get('use_plots', False) NumpyDocString.__init__(self, docstring, config=config) # string conversion routines def _str_header(self, name, symbol='`'): return ['.. rubric:: ' + name, ''] def _str_field_list(self, name): return [':' + name + ':'] def _str_indent(self, doc, indent=4): out = [] for line in doc: out += [' ' * indent + line] return out def _str_signature(self): return [''] if self['Signature']: return ['``%s``' % self['Signature']] + [''] # noqa: UP031 else: return [''] def _str_summary(self): return self['Summary'] + [''] def _str_extended_summary(self): return self['Extended Summary'] + [''] def _str_param_list(self, name): out = [] if self[name]: out += self._str_field_list(name) out += [''] for param, param_type, desc in self[name]: out += self._str_indent(['**%s** : %s' % (param.strip(), param_type)]) # noqa: UP031 out += [''] out += self._str_indent(desc, 8) out += [''] return out @property def _obj(self): if hasattr(self, '_cls'): return self._cls elif hasattr(self, '_f'): return self._f return None def _str_member_list(self, name): """Generate a member listing, autosummary:: table where possible, and a table where not. """ out = [] if self[name]: out += ['.. rubric:: %s' % name, ''] # noqa: UP031 prefix = getattr(self, '_name', '') if prefix: prefix = '~%s.' % prefix # noqa: UP031 autosum = [] others = [] for param, param_type, desc in self[name]: param = param.strip() if not self._obj or hasattr(self._obj, param): autosum += [' %s%s' % (prefix, param)] # noqa: UP031 else: others.append((param, param_type, desc)) if autosum: out += ['.. autosummary::', ' :toctree:', ''] out += autosum if others: maxlen_0 = max([len(x[0]) for x in others]) maxlen_1 = max([len(x[1]) for x in others]) hdr = '=' * maxlen_0 + ' ' + '=' * maxlen_1 + ' ' + '=' * 10 fmt = '%%%ds %%%ds ' % (maxlen_0, maxlen_1) # noqa: UP031 n_indent = maxlen_0 + maxlen_1 + 4 out += [hdr] for param, param_type, desc in others: out += [fmt % (param.strip(), param_type)] out += self._str_indent(desc, n_indent) out += [hdr] out += [''] return out def _str_section(self, name): out = [] if self[name]: out += self._str_header(name) out += [''] content = textwrap.dedent('\n'.join(self[name])).split('\n') out += content out += [''] return out def _str_see_also(self, func_role): out = [] if self['See Also']: see_also = super()._str_see_also(func_role) out = ['.. seealso::', ''] out += self._str_indent(see_also[2:]) return out def _str_warnings(self): out = [] if self['Warnings']: out = ['.. warning::', ''] out += self._str_indent(self['Warnings']) return out def _str_index(self): idx = self['index'] out = [] if len(idx) == 0: return out out += ['.. index:: %s' % idx.get('default', '')] # noqa: UP031 for section, references in idx.iteritems(): if section == 'default': continue elif section == 'refguide': out += [' single: %s' % (', '.join(references))] # noqa: UP031 else: out += [' %s: %s' % (section, ','.join(references))] # noqa: UP031 return out def _str_references(self): out = [] if self['References']: out += self._str_header('References') if isinstance(self['References'], str): self['References'] = [self['References']] out.extend(self['References']) out += [''] # Latex collects all references to a separate bibliography, # so we need to insert links to it if sphinx.__version__ >= '0.6': out += ['.. only:: latex', ''] else: out += ['.. latexonly::', ''] items = [] for line in self['References']: m = re.match(r'.. \[([a-z0-9._-]+)\]', line, re.I) if m: items.append(m.group(1)) out += [' ' + ', '.join(['[%s]_' % item for item in items]), ''] # noqa: UP031 return out def _str_examples(self): examples_str = '\n'.join(self['Examples']) if self.use_plots and 'import matplotlib' in examples_str and 'plot::' not in examples_str: out = [] out += self._str_header('Examples') out += ['.. plot::', ''] out += self._str_indent(self['Examples']) out += [''] return out else: return self._str_section('Examples') def __str__(self, indent=0, func_role='obj'): out = [] out += self._str_signature() out += self._str_index() + [''] out += self._str_summary() out += self._str_extended_summary() for param_list in ('Parameters', 'Returns', 'Other Parameters', 'Raises', 'Warns'): out += self._str_param_list(param_list) out += self._str_warnings() out += self._str_see_also(func_role) out += self._str_section('Notes') out += self._str_references() out += self._str_examples() for param_list in ('Attributes', 'Methods'): out += self._str_member_list(param_list) out = self._str_indent(out, indent) return '\n'.join(out) class SphinxFunctionDoc(SphinxDocString, FunctionDoc): # noqa: D101 def __init__(self, obj, doc=None, config={}): # noqa: B006 self.use_plots = config.get('use_plots', False) FunctionDoc.__init__(self, obj, doc=doc, config=config) class SphinxClassDoc(SphinxDocString, ClassDoc): # noqa: D101 def __init__(self, obj, doc=None, func_doc=None, config={}): # noqa: B006 self.use_plots = config.get('use_plots', False) ClassDoc.__init__(self, obj, doc=doc, func_doc=None, config=config) class SphinxObjDoc(SphinxDocString): # noqa: D101 def __init__(self, obj, doc=None, config={}): # noqa: B006 self._f = obj SphinxDocString.__init__(self, doc, config=config) def get_doc_object(obj, what=None, doc=None, config={}): # noqa: B006, D103 if what is None: if inspect.isclass(obj): what = 'class' elif inspect.ismodule(obj): what = 'module' elif callable(obj): what = 'function' else: what = 'object' if what == 'class': return SphinxClassDoc(obj, func_doc=SphinxFunctionDoc, doc=doc, config=config) elif what in ('function', 'method'): return SphinxFunctionDoc(obj, doc=doc, config=config) else: if doc is None: doc = pydoc.getdoc(obj) return SphinxObjDoc(obj, doc, config=config) ================================================ FILE: doc/sphinxext/github.py ================================================ """Define text roles for GitHub * ghissue - Issue * ghpull - Pull Request * ghuser - User Adapted from bitbucket example here: https://bitbucket.org/birkenfeld/sphinx-contrib/src/tip/bitbucket/sphinxcontrib/bitbucket.py Authors ------- * Doug Hellmann * Min RK """ # # Original Copyright (c) 2010 Doug Hellmann. All rights reserved. # from docutils import nodes, utils from docutils.parsers.rst.roles import set_classes def make_link_node(rawtext, app, type, slug, options): # noqa: A002 """Create a link to a github resource. :param rawtext: Text being replaced with link node. :param app: Sphinx application context :param type: Link type (issues, changeset, etc.) :param slug: ID of the thing to link to :param options: Options dictionary passed to role func. """ try: base = app.config.github_project_url if not base: raise AttributeError if not base.endswith('/'): base += '/' except AttributeError as err: raise ValueError('github_project_url configuration value is not set (%s)' % str(err)) # noqa: B904, UP031 ref = base + type + '/' + slug + '/' set_classes(options) prefix = '#' if type == 'pull': prefix = 'PR ' + prefix node = nodes.reference(rawtext, prefix + utils.unescape(slug), refuri=ref, **options) return node def ghissue_role(name, rawtext, text, lineno, inliner, options={}, content=[]): # noqa: B006 """Link to a GitHub issue. Returns 2 part tuple containing list of nodes to insert into the document and a list of system messages. Both are allowed to be empty. :param name: The role name used in the document. :param rawtext: The entire markup snippet, with role. :param text: The text marked with the role. :param lineno: The line number where rawtext appears in the input. :param inliner: The inliner instance that called us. :param options: Directive options for customization. :param content: The directive content for customization. """ try: issue_num = int(text) if issue_num <= 0: raise ValueError except ValueError: msg = inliner.reporter.error( 'GitHub issue number must be a number greater than or equal to 1; ' # noqa: UP031 '"%s" is invalid.' % text, line=lineno, ) prb = inliner.problematic(rawtext, rawtext, msg) return [prb], [msg] app = inliner.document.settings.env.app # app.info('issue %r' % text) if 'pull' in name.lower(): category = 'pull' elif 'issue' in name.lower(): category = 'issues' else: msg = inliner.reporter.error( 'GitHub roles include "ghpull" and "ghissue", "%s" is invalid.' % name, line=lineno # noqa: UP031 ) prb = inliner.problematic(rawtext, rawtext, msg) return [prb], [msg] node = make_link_node(rawtext, app, category, str(issue_num), options) return [node], [] def ghuser_role(name, rawtext, text, lineno, inliner, options={}, content=[]): # noqa: B006 """Link to a GitHub user. Returns 2 part tuple containing list of nodes to insert into the document and a list of system messages. Both are allowed to be empty. :param name: The role name used in the document. :param rawtext: The entire markup snippet, with role. :param text: The text marked with the role. :param lineno: The line number where rawtext appears in the input. :param inliner: The inliner instance that called us. :param options: Directive options for customization. :param content: The directive content for customization. """ app = inliner.document.settings.env.app # noqa: F841 # app.info('user link %r' % text) ref = 'https://www.github.com/' + text node = nodes.reference(rawtext, text, refuri=ref, **options) return [node], [] def ghcommit_role(name, rawtext, text, lineno, inliner, options={}, content=[]): # noqa: B006 """Link to a GitHub commit. Returns 2 part tuple containing list of nodes to insert into the document and a list of system messages. Both are allowed to be empty. :param name: The role name used in the document. :param rawtext: The entire markup snippet, with role. :param text: The text marked with the role. :param lineno: The line number where rawtext appears in the input. :param inliner: The inliner instance that called us. :param options: Directive options for customization. :param content: The directive content for customization. """ app = inliner.document.settings.env.app # app.info('user link %r' % text) try: base = app.config.github_project_url if not base: raise AttributeError if not base.endswith('/'): base += '/' except AttributeError as err: raise ValueError('github_project_url configuration value is not set (%s)' % str(err)) # noqa: B904, UP031 ref = base + text node = nodes.reference(rawtext, text[:6], refuri=ref, **options) return [node], [] def setup(app): """Install the plugin. :param app: Sphinx application context. """ app.info('Initializing GitHub plugin') app.add_role('ghissue', ghissue_role) app.add_role('ghpull', ghissue_role) app.add_role('ghuser', ghuser_role) app.add_role('ghcommit', ghcommit_role) app.add_config_value('github_project_url', None, 'env') return ================================================ FILE: doc/sphinxext/math_dollar.py ================================================ import re # noqa: D100 def dollars_to_math(source): r"""Replace dollar signs with backticks. More precisely, do a regular expression search. Replace a plain dollar sign ($) by a backtick (`). Replace an escaped dollar sign (\$) by a dollar sign ($). Don't change a dollar sign preceded or followed by a backtick (`$ or $`), because of strings like "``$HOME``". Don't make any changes on lines starting with spaces, because those are indented and hence part of a block of code or examples. This also doesn't replaces dollar signs enclosed in curly braces, to avoid nested math environments, such as :: $f(n) = 0 \text{ if $n$ is prime}$ Thus the above line would get changed to `f(n) = 0 \text{ if $n$ is prime}` """ s = '\n'.join(source) if s.find('$') == -1: return # This searches for "$blah$" inside a pair of curly braces -- # don't change these, since they're probably coming from a nested # math environment. So for each match, we replace it with a temporary # string, and later on we substitute the original back. global _data _data = {} def repl(matchobj): global _data s = matchobj.group(0) t = '___XXX_REPL_%d___' % len(_data) # noqa: UP031 _data[t] = s return t s = re.sub(r'({[^{}$]*\$[^{}$]*\$[^{}]*})', repl, s) # matches $...$ dollars = re.compile(r'(? //Function to make the index toctree collapsible $(function () { $('.toctree-l2') .click(function(event){ if (event.target.tagName.toLowerCase() != "a") { if ($(this).children('ul').length > 0) { $(this).attr('data-content', (!$(this).children('ul').is(':hidden')) ? '\u25ba' : '\u25bc'); $(this).children('ul').toggle(); } return true; //Makes links clickable } }) .mousedown(function(event){ return false; }) //Firefox highlighting fix .children('ul').hide(); // Initialize the values $('li.toctree-l2:not(:has(ul))').attr('data-content', '-'); $('li.toctree-l2:has(ul)').attr('data-content', '\u25ba'); $('li.toctree-l2:has(ul)').css('cursor', 'pointer'); $('.toctree-l2').hover( function () { if ($(this).children('ul').length > 0) { $(this).css('background-color', '#D0D0D0').children('ul').css('background-color', '#F0F0F0'); $(this).attr('data-content', (!$(this).children('ul').is(':hidden')) ? '\u25bc' : '\u25ba'); } else { $(this).css('background-color', '#F9F9F9'); } }, function () { $(this).css('background-color', 'white').children('ul').css('background-color', 'white'); if ($(this).children('ul').length > 0) { $(this).attr('data-content', (!$(this).children('ul').is(':hidden')) ? '\u25bc' : '\u25ba'); } } ); }); ================================================ FILE: doc/user_guide.rst ================================================ .. _user_guide: User guide ========== .. sidebar:: **Download for offline viewing** Download the `user guide and examples `_. .. toctree:: :numbered: introduction.rst layout/index.rst analysis/index.rst reports/index.rst ================================================ FILE: doc/whats_new.rst ================================================ .. include:: ../CHANGELOG.rst ================================================ FILE: long_description.rst ================================================ PyBIDS ====== PyBIDS is a Python module to interface with datasets conforming BIDS. See BIDS paper_ and https://bids.neuroimaging.io website for more information. .. _paper: https://www.nature.com/articles/sdata201644 License ======= ``pybids`` is licensed under the terms of the MIT license. See the file "LICENSE" for information on the history of this software, terms & conditions for usage, and a DISCLAIMER OF ALL WARRANTIES. All trademarks referenced herein are property of their respective holders. Copyright (c) 2016--, PyBIDS developers, Planet Earth ================================================ FILE: paper/paper.bib ================================================ @article{Gorgolewski2016-sk, title = "A Practical Guide for Improving Transparency and Reproducibility in Neuroimaging Research", author = {Gorgolewski, Krzysztof J and Poldrack, Russell A}, abstract = "Recent years have seen an increase in alarming signals regarding the lack of replicability in neuroscience, psychology, and other related fields. To avoid a widespread crisis in neuroimaging research and consequent loss of credibility in the public eye, we need to improve how we do science. This article aims to be a practical guide for researchers at any stage of their careers that will help them make their research more reproducible and transparent while minimizing the additional effort that this might require. The guide covers three major topics in open science (data, code, and publications) and offers practical advice as well as highlighting advantages of adopting more open research practices that go beyond improved transparency and reproducibility.", journal = "PLOS Biology", volume = 14, number = 7, pages = "e1002506", month = jul, year = 2016, doi = {10.1371/journal.pbio.1002506}, language = "en" } @article{Gorgolewski2016-nw, title = "The brain imaging data structure, a format for organizing and describing outputs of neuroimaging experiments", author = {Gorgolewski, Krzysztof J and Auer, Tibor and Calhoun, Vince D and Craddock, R Cameron and Das, Samir and Duff, Eugene P and Flandin, Guillaume and Ghosh, Satrajit S and Glatard, Tristan and Halchenko, Yaroslav O and Handwerker, Daniel A and Hanke, Michael and Keator, David and Li, Xiangrui and Michael, Zachary and Maumet, Camille and Nichols, B Nolan and Nichols, Thomas E and Pellman, John and Poline, Jean-Baptiste and Rokem, Ariel and Schaefer, Gunnar and Sochat, Vanessa and Triplett, William and Turner, Jessica A and Varoquaux, Ga{\"e}l and Poldrack, Russell A}, abstract = "The development of magnetic resonance imaging (MRI) techniques has defined modern neuroimaging. Since its inception, tens of thousands of studies using techniques such as functional MRI and diffusion weighted imaging have allowed for the non-invasive study of the brain. Despite the fact that MRI is routinely used to obtain data for neuroscience research, there has been no widely adopted standard for organizing and describing the data collected in an imaging experiment. This renders sharing and reusing data (within or between labs) difficult if not impossible and unnecessarily complicates the application of automatic pipelines and quality assurance protocols. To solve this problem, we have developed the Brain Imaging Data Structure (BIDS), a standard for organizing and describing MRI datasets. The BIDS standard uses file formats compatible with existing software, unifies the majority of practices already common in the field, and captures the metadata necessary for most common data processing operations.", journal = "Scientific Data", volume = 3, pages = "160044", month = jun, year = 2016, doi = {10.1038/sdata.2016.44}, language = "en" } @article{Gorgolewski2017-cz, title = "{BIDS} apps: Improving ease of use, accessibility, and reproducibility of neuroimaging data analysis methods", author = {Gorgolewski, Krzysztof J and Alfaro-Almagro, Fidel and Auer, Tibor and Bellec, Pierre and Capot{\u a}, Mihai and Chakravarty, M Mallar and Churchill, Nathan W and Cohen, Alexander Li and Craddock, R Cameron and Devenyi, Gabriel A and Eklund, Anders and Esteban, Oscar and Flandin, Guillaume and Ghosh, Satrajit S and Guntupalli, J Swaroop and Jenkinson, Mark and Keshavan, Anisha and Kiar, Gregory and Liem, Franziskus and Raamana, Pradeep Reddy and Raffelt, David and Steele, Christopher J and Quirion, Pierre-Olivier and Smith, Robert E and Strother, Stephen C and Varoquaux, Ga{\"e}l and Wang, Yida and Yarkoni, Tal and Poldrack, Russell A}, abstract = "The rate of progress in human neurosciences is limited by the inability to easily apply a wide range of analysis methods to the plethora of different datasets acquired in labs around the world. In this work, we introduce a framework for creating, testing, versioning and archiving portable applications for analyzing neuroimaging data organized and described in compliance with the Brain Imaging Data Structure (BIDS). The portability of these applications (BIDS Apps) is achieved by using container technologies that encapsulate all binary and other dependencies in one convenient package. BIDS Apps run on all three major operating systems with no need for complex setup and configuration and thanks to the comprehensiveness of the BIDS standard they require little manual user input. Previous containerized data processing solutions were limited to single user environments and not compatible with most multi-tenant High Performance Computing systems. BIDS Apps overcome this limitation by taking advantage of the Singularity container technology. As a proof of concept, this work is accompanied by 22 ready to use BIDS Apps, packaging a diverse set of commonly used neuroimaging algorithms.", journal = "PLOS Computational Biology", volume = 13, number = 3, pages = "e1005209", month = mar, year = 2017, doi = {10.1371/journal.pcbi.1005209}, language = "en" } @article{Poldrack2017-gz, title = "Scanning the horizon: towards transparent and reproducible neuroimaging research", author = {Poldrack, Russell A and Baker, Chris I and Durnez, Joke and Gorgolewski, Krzysztof J and Matthews, Paul M and Munaf{\`o}, Marcus R and Nichols, Thomas E and Poline, Jean-Baptiste and Vul, Edward and Yarkoni, Tal}, abstract = "Functional neuroimaging techniques have transformed our ability to probe the neurobiological basis of behaviour and are increasingly being applied by the wider neuroscience community. However, concerns have recently been raised that the conclusions that are drawn from some human neuroimaging studies are either spurious or not generalizable. Problems such as low statistical power, flexibility in data analysis, software errors and a lack of direct replication apply to many fields, but perhaps particularly to functional MRI. Here, we discuss these problems, outline current and suggested best practices, and describe how we think the field should evolve to produce the most meaningful and reliable answers to neuroscientific questions.", journal = "Nature Reviews Neuroscience", volume = 18, number = 2, pages = "115--126", month = feb, year = 2017, doi = {10.1038/nrn.2016.167}, language = "en" } @article{mriqc, title={MRIQC: Advancing the automatic prediction of image quality in {MRI} from unseen sites}, author={Esteban, Oscar and Birman, Daniel and Schaer, Marie and Koyejo, Oluwasanmi O and Poldrack, Russell A and Gorgolewski, Krzysztof J}, journal={PLOS One}, volume={12}, number={9}, pages={e0184661}, year={2017}, doi={10.1371/journal.pone.0184661}, publisher={Public Library of Science} } @article{fmriprep, title={FMRIPrep: a robust preprocessing pipeline for functional {MRI}}, author={Esteban, Oscar and Markiewicz, Christopher J and Blair, Ross W and Moodie, Craig A and Isik, A Ilkay and Erramuzpe, Asier and Kent, James D and Goncalves, Mathias and DuPre, Elizabeth and Snyder, Madeleine and Oya, Hiroyuki and Ghosh, Satrajit S and Wright, Jessey and Durnez, Joke and Poldrack, Russell A and Gorgolewski, Krzysztof J}, journal={Nature Methods}, volume={16}, number={1}, pages={111}, year={2019}, doi={10.1038/s41592-018-0235-4}, publisher={Nature Publishing Group} } ================================================ FILE: paper/paper.md ================================================ --- title: 'PyBIDS: Python tools for BIDS datasets' tags: - Python - BIDS - neuroimaging authors: - affiliation: 1 name: Tal Yarkoni orcid: 0000-0002-6558-5113 - affiliation: 2 name: Christopher J Markiewicz orcid: 0000-0002-6533-164X - affiliation: 1 name: Alejandro de la Vega orcid: 0000-0001-9062-3778 - affiliation: 2 name: Krzysztof J Gorgolewski orcid: 0000-0003-3321-7583 - affiliation: 3 name: Taylor Salo orcid: 0000-0001-9813-3167 - affiliation: 4 name: Yaroslav O Halchenko orcid: 0000-0003-3456-2493 - affiliation: 1 name: Quinten McNamara - affiliation: 5 name: Krista DeStasio orcid: 0000-0002-3959-9060 - affiliation: 6 name: Jean-Baptiste Poline orcid: 0000-0002-9794-749X - affiliation: 7 name: Dmitry Petrov - affiliation: 8 name: Valérie Hayot-Sasson orcid: 0000-0002-4830-4535 - affiliation: 9 name: Dylan M Nielson orcid: 0000-0003-4613-6643 - affiliation: 10 name: Johan Carlin orcid: 0000-0003-0933-1239 - affiliation: 11 name: Gregory Kiar orcid: 0000-0001-8915-496X - affiliation: 12 name: Kirstie Whitaker orcid: 0000-0001-8498-4059 - affiliation: 11 name: Elizabeth DuPre orcid: 0000-0003-1358-196X - affiliation: 13 name: Adina Wagner orcid: 0000-0003-2917-3450 - affiliation: 14 name: Lee S Tirrell orcid: 0000-0001-9393-8361 - affiliation: 15 name: Mainak Jas orcid: 0000-0002-3199-9027 - affiliation: 13 name: Michael Hanke orcid: 0000-0001-6398-6370 - affiliation: 2 name: Russell A Poldrack orcid: 0000-0001-6755-0259 - affiliation: 2 name: Oscar Esteban orcid: 0000-0001-8435-6191 - affiliation: 16 name: Stefan Appelhoff orcid: 0000-0001-8002-0877 - affiliation: 17 name: Chris Holdgraf orcid: 0000-0002-2391-0678 - affiliation: 18 name: Isla Staden orcid: 0000-0002-0795-1154 - affiliation: 19 name: Bertrand Thirion orcid: 0000-0001-5018-7895 - affiliation: 20 name: Dave F Kleinschmidt orcid: 0000-0002-7442-2762 - affiliation: 9 name: John A Lee orcid: 0000-0001-5884-4247 - affiliation: 17 name: Matteo Visconti di Oleggio Castello orcid: 0000-0001-7931-5272 - affiliation: 21 name: Michael P Notter orcid: 0000-0002-5866-047X - affiliation: 2 name: Ross Blair orcid: 0000-0003-3007-1056 affiliations: - index: 1 name: University of Texas at Austin - index: 2 name: Stanford University - index: 3 name: Florida International University - index: 4 name: Dartmouth College - index: 5 name: University of Oregon - index: 6 name: McGill University - index: 7 name: University of Southern California - index: 8 name: Concordia University - index: 9 name: National Institute of Mental Health - index: 10 name: MRC Cognition and Brain Sciences Unit - index: 11 name: Montreal Neurological Institute and Hospital - index: 12 name: Alan Turing Institute - index: 13 name: Otto-von-Guericke University Magdeburg - index: 14 name: CorticoMetrics LLC - index: 15 name: Télécom ParisTech, France - index: 16 name: Max Planck Institute for Human Development, Berlin, Germany - index: 17 name: University of California at Berkeley - index: 18 name: Queen Mary University London - index: 19 name: INRIA - index: 20 name: Rutgers University - index: 21 name: University of Lausanne date: 19 February 2019 bibliography: paper.bib --- # Summary Brain imaging researchers regularly work with large, heterogeneous, high-dimensional datasets. Historically, researchers have dealt with this complexity idiosyncratically, with every lab or individual implementing their own preprocessing and analysis procedures. The resulting lack of field-wide standards has severely limited reproducibility and data sharing and reuse. To address this problem, we and others recently introduced the Brain Imaging Data Standard (``BIDS``; [@Gorgolewski2016-nw]), a specification meant to standardize the process of representing brain imaging data. BIDS is deliberately designed with adoption in mind; it adheres to a user-focused philosophy that prioritizes common use cases and discourages complexity. By successfully encouraging a large and ever-growing subset of the community to adopt a common standard for naming and organizing files, BIDS has made it much easier for researchers to share, reuse, and process their data [@Gorgolewski2017-cz]. The ability to efficiently develop high-quality spec-compliant applications itself depends to a large extent on the availability of good tooling. Because many operations recur widely across diverse contexts—for example, almost every tool designed to work with BIDS datasets involves regular file-filtering operations—there is a strong incentive to develop utility libraries that provide common functionality via a standardized, simple API. ``PyBIDS`` is a Python package that makes it easier to work with BIDS datasets. In principle, its scope includes virtually any functionality that is likely to be of general use when working with BIDS datasets (*i.e.*, that is not specific to one narrow context). At present, its core and most widely used module supports simple and flexible querying and manipulation of BIDS datasets. PyBIDS makes it easy for researchers and developers working in Python to search for BIDS files by keywords and/or metadata; to consolidate and retrieve file-associated metadata spread out across multiple levels of a BIDS hierarchy; to construct BIDS-valid path names for new files; and to validate projects against the BIDS specification, among other applications. In addition to this core functionality, PyBIDS also contains an ever-growing set of modules that support additional capabilities meant to keep up with the evolution and expansion of the BIDS specification itself. Currently, PyBIDS includes tools for (1) reading and manipulating data contained in various BIDS-defined files (*e.g.*, physiological recordings, event files, or participant-level variables); (2) constructing design matrices and contrasts that support the new ``BIDS-StatsModel`` specification (for machine-readable representation of fMRI statistical models); and (3) automated generation of partial Methods sections for inclusion in publications. PyBIDS can be easily installed on all platforms via pip (``pip install pybids``), though currently it is not officially supported on Windows. The package has few dependencies outside of standard Python numerical and image analysis libraries (*i.e.*, numpy, scipy, pandas, and NiBabel). The core API is deliberately kept minimalistic: nearly all interactions with PyBIDS functionality occur through a core ``BIDSLayout`` object initialized by passing in a path to a BIDS dataset. For most applications, no custom configuration should be required. Although technically still in alpha release, PyBIDS is already being used both as a dependency in dozens of other open-source brain imaging packages -- *e.g.*, fMRIPrep [@fmriprep], MRIQC [@mriqc], datalad-neuroimaging (https://github.com/datalad/datalad-neuroimaging), and fitlins (https://github.com/poldracklab/fitlins) -- and directly in many researchers' custom Python workflows. Development is extremely active, with bug fixes and new features continually being added (https://github.com/bids-standard/pybids), and major releases occurring approximately every 6 months. As of this writing, 29 people have contributed code to PyBIDS, and many more have provided feedback and testing. The API is relatively stable, and documentation and testing standards follow established norms for open-source scientific software. We encourage members of the brain imaging community currently working in Python to try using PyBIDS, and welcome new contributions. # Acknowledgements PyBIDS development is partly supported by NIH awards R01MH109682 (PI: Yarkoni), R24MH114705 (PI: Poldrack), R01EB020740 (PI: Ghosh), and P41EB019936 (PI: Kennedy), and NSF award 1429999 (PI: Halchenko). # References ================================================ FILE: pyproject.toml ================================================ [build-system] requires = ["setuptools", "versioneer[toml]"] build-backend = "setuptools.build_meta" [project] name = "pybids" description = "bids: interface with datasets conforming to BIDS" readme = "README.md" requires-python = ">=3.10" license = { file="LICENSE" } authors = [ { name = "PyBIDS Developers", email = "bids-discussion@googlegroups.com" }, ] keywords = ["BIDS", "neuroimaging"] classifiers = [ "Development Status :: 3 - Alpha", "Environment :: Console", "Intended Audience :: Science/Research", "License :: OSI Approved :: MIT License", "Operating System :: OS Independent", "Programming Language :: Python", "Programming Language :: Python :: 3", "Topic :: Scientific/Engineering", ] dependencies = [ "numpy >=1.25", # SPEC 0 + 1yr (3yr) "scipy >=1.11", # SPEC 0 + 1yr (3yr) "nibabel >=5.1", # SPEC 0 + 1yr (3yr) "pandas >=2.0", # SPEC 0 + 1yr (3yr) "formulaic >=0.3", "sqlalchemy >=1.4.31", "bids-validator>=1.14.7", # Keep up-to-date to ensure support for recent modalities "bidsschematools>=1.1.0", # Required for schema-based configs "num2words >=0.5.10", "click >=8.0", "universal_pathlib >=0.2.2", "frozendict >=2.3", ] dynamic = ["version"] [project.optional-dependencies] doc = [ "sphinx >=6.2", "numpydoc", "sphinx_rtd_theme", "myst_nb", "jupytext", ] plotting = ["graphviz"] test = [ "pytest >= 8", "pytest-cov >= 7", "bsmschema >= 0.1", "coverage[toml] >= 7", "altair >= 5", "pytest-xdist >= 3.8", "s3fs >= 2025" #for testing remote uri ] model_reports = [ "jinja2", "altair" ] tutorial = [ "nbconvert", "jinja2", "markupsafe", "jupyter_client", "ipykernel", ] # Aliases docs = ["pybids[doc]"] tests = ["pybids[test]"] ci_tests = ["pybids[test]"] dev = ["pybids[doc,test,plotting]"] [project.urls] Homepage = "https://github.com/bids-standard/pybids" "Bug Tracker" = "https://github.com/bids-standard/pybids/issues" [project.scripts] pybids = "bids.cli:cli" [tool.setuptools.package-data] "*" = ["data/*", "data/*/*"] "bids.layout.config" = ["*.json"] [tool.versioneer] VCS = "git" style = "pep440-pre" versionfile_source = "src/bids/_version.py" versionfile_build = "bids/_version.py" [tool.coverage.run] branch = true source = ["src/", "tests/"] omit = [ "*/setup.py", "*/external/*", "*/due.py", "*/*version.py", ] [tool.coverage.paths] source = [ "src/bids", "**/site-packages/bids", ] [tool.coverage.report] include = ["src/*", "tests/*"] ================================================ FILE: ruff.toml ================================================ line-length = 99 extend-exclude = [ "_version.py", # Auto-generated "tests/data", # Submodules "bids-examples", ".cursor", ".vscode", ".venv", "paper", "scripts/", "tools/", "*tests/*", "*.ipynb", "src/bids/external/", ] [format] quote-style = "single" line-ending = "lf" docstring-code-format = true [lint] extend-select = [ "F", # pyflakes "E", # pycodestyle errors "W", # pycodestyle warnings "I", # isort "D", # pydocstyle "UP", # pyupgrade "YTT", # flake8-2020 "S", # bandit "BLE", # blind-except "B", # bugbear "A", # builtins "C4", # comprehensions "DTZ", # datetimez "T10", # debugger # "EM", # errmsg "EXE", # executable "ISC", # implicit-str-concat "ICN", # import-conventions "PT", # pytest-style "Q", # quotes ] ignore = [ "D105", # undocumented-magic-method "D107", # undocumented __init__ "D203", # blank line before class docstring "D213", # multi-line-summary-first-line "PT022", # useless-yield-fixture; yield keeps fixtures/sessions alive "Q000", # double quotes "Q003", # avoidable-escaped-quote # --- Fix one group at a time; remove from ignore when done --- # 1. Docstrings (D*) "D205", # blank line between summary and description "D400", # ends with period "D401", # imperative mood "D415", # ends with period/question/exclamation ] [lint.extend-per-file-ignores] "setup.py" = ["D"] "src/bids/external/__init__.py" = ["D104"] "src/bids/modeling/report/__init__.py" = ["D104"] "*/test_*.py" = [ "S101", # assert "D", ] ================================================ FILE: schema_integration_demo.ipynb ================================================ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# BIDS Schema Integration Demo\n", "\n", "This notebook demonstrates the new BIDS schema integration features in PyBIDS, showing how the new schema-based configuration compares to the legacy JSON-based configuration." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import os\n", "\n", "from bids import BIDSLayout\n", "from bids.tests import get_test_data_path" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Setup: Using a test dataset\n", "\n", "We'll use the 7T TRT dataset that comes with PyBIDS for testing." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Using dataset at: /home/ashley/repos/bids/pybids/tests/data/7t_trt\n", "total 76\n", "drwxr-xr-x 12 ashley ashley 4096 Aug 21 08:59 .\n", "drwxr-xr-x 8 ashley ashley 4096 Aug 21 08:59 ..\n", "-rw-r--r-- 1 ashley ashley 0 Aug 21 08:59 README\n", "-rw-r--r-- 1 ashley ashley 55 Aug 21 08:59 dataset_description.json\n", "-rw-r--r-- 1 ashley ashley 482 Aug 21 08:59 participants.tsv\n", "drwxr-xr-x 4 ashley ashley 4096 Aug 21 08:59 sub-01\n", "drwxr-xr-x 4 ashley ashley 4096 Aug 21 08:59 sub-02\n", "drwxr-xr-x 4 ashley ashley 4096 Aug 21 08:59 sub-03\n", "drwxr-xr-x 4 ashley ashley 4096 Aug 21 08:59 sub-04\n" ] } ], "source": [ "# Get path to test dataset\n", "data_path = os.path.join(get_test_data_path(), '7t_trt')\n", "print(f'Using dataset at: {data_path}')\n", "\n", "# List the first few files to see the structure\n", "!ls -la {data_path} | head -10" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Part 1: Legacy JSON-based Configuration\n", "\n", "The traditional PyBIDS approach uses JSON configuration files to define entities and their patterns." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Legacy layout initialized with 339 files\n", "\n", "Available entities: ['CogAtlasID', 'Columns', 'EchoTime', 'EchoTime1', 'EchoTime2', 'EffectiveEchoSpacing', 'IntendedFor', 'MTState', 'PhaseEncodingDirection', 'RepetitionTime', 'SamplingFrequency', 'SliceEncodingDirection', 'SliceTiming', 'StartTime', 'TaskName', 'acquisition', 'ceagent', 'chunk', 'datatype', 'direction', 'echo', 'extension', 'flip', 'fmap', 'inv', 'modality', 'mt', 'nucleus', 'part', 'proc', 'reconstruction', 'recording', 'run', 'sample', 'scans', 'session', 'space', 'staining', 'subject', 'suffix', 'task', 'tracer', 'tracksys', 'volume']\n", "\n", "Layout info:\n", "BIDS Layout: .../bids/pybids/tests/data/7t_trt | Subjects: 10 | Sessions: 20 | Runs: 20\n" ] } ], "source": [ "# Initialize layout with legacy config (this is the default)\n", "layout_legacy = BIDSLayout(data_path, config=['bids'])\n", "print(f'Legacy layout initialized with {len(layout_legacy.get())} files')\n", "print(f'\\nAvailable entities: {sorted(layout_legacy.get_entities().keys())}')\n", "\n", "# Print basic layout information like in the docs tutorial\n", "print('\\nLayout info:')\n", "print(layout_legacy)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Found 6 BOLD files for subject 01:\n", " sub-01_ses-1_task-rest_acq-fullbrain_run-1_bold.nii.gz\n", " sub-01_ses-1_task-rest_acq-fullbrain_run-2_bold.nii.gz\n", " sub-01_ses-1_task-rest_acq-prefrontal_bold.nii.gz\n", " sub-01_ses-2_task-rest_acq-fullbrain_run-1_bold.nii.gz\n", " sub-01_ses-2_task-rest_acq-fullbrain_run-2_bold.nii.gz\n", " sub-01_ses-2_task-rest_acq-prefrontal_bold.nii.gz\n", "\n", "Compare return types:\n", "return_types=[, ]\n", "return_types=[, ]\n" ] } ], "source": [ "# Query example: Get all BOLD files for subject 01\n", "bold_files_legacy = layout_legacy.get(\n", " subject='01',\n", " suffix='bold',\n", " extension='nii.gz',\n", " return_type='filename', # Returns filenames instead of BIDSFile objects\n", ")\n", "print(f'Found {len(bold_files_legacy)} BOLD files for subject 01:')\n", "for f in bold_files_legacy:\n", " print(f' {os.path.basename(f)}')\n", "\n", "# Show the difference between return types like in docs\n", "print('\\nCompare return types:')\n", "bold_objects = layout_legacy.get(subject='01', suffix='bold', extension='nii.gz')[:2]\n", "bold_filenames = layout_legacy.get(\n", " subject='01', suffix='bold', extension='nii.gz', return_type='filename'\n", ")[:2]\n", "\n", "print(f'return_types={[type(obj) for obj in bold_objects]}')\n", "print(f'return_types={[type(fname) for fname in bold_filenames]}')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Part 2: New Schema-based Configuration\n", "\n", "The new approach uses the BIDS schema directly via `bidsschematools` to understand the dataset structure." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Schema-based layout initialized with 339 files\n", "\n", "Available entities: ['CogAtlasID', 'Columns', 'EchoTime', 'EchoTime1', 'EchoTime2', 'EffectiveEchoSpacing', 'IntendedFor', 'MTState', 'PhaseEncodingDirection', 'RepetitionTime', 'SamplingFrequency', 'SliceEncodingDirection', 'SliceTiming', 'StartTime', 'TaskName', 'acquisition', 'ceagent', 'chunk', 'datatype', 'density', 'description', 'direction', 'echo', 'extension', 'flip', 'hemisphere', 'inversion', 'label', 'modality', 'mtransfer', 'nucleus', 'part', 'processing', 'reconstruction', 'recording', 'resolution', 'run', 'sample', 'segmentation', 'session', 'space', 'split', 'stain', 'subject', 'suffix', 'task', 'tracer', 'tracksys', 'volume']\n", "\n", "Layout info:\n", "BIDS Layout: .../bids/pybids/tests/data/7t_trt | Subjects: 10 | Sessions: 20 | Runs: 20\n" ] } ], "source": [ "# Initialize layout with schema-based config\n", "# This uses Config.load('bids-schema') internally when you pass 'bids-schema' in config\n", "layout_schema = BIDSLayout(data_path, config=['bids-schema'])\n", "print(f'Schema-based layout initialized with {len(layout_schema.get())} files')\n", "print(f'\\nAvailable entities: {sorted(layout_schema.get_entities().keys())}')\n", "\n", "# Print basic layout information\n", "print('\\nLayout info:')\n", "print(layout_schema)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Found 6 BOLD files for subject 01:\n", " sub-01_ses-1_task-rest_acq-fullbrain_run-1_bold.nii.gz\n", " sub-01_ses-1_task-rest_acq-fullbrain_run-2_bold.nii.gz\n", " sub-01_ses-1_task-rest_acq-prefrontal_bold.nii.gz\n", " sub-01_ses-2_task-rest_acq-fullbrain_run-1_bold.nii.gz\n", " sub-01_ses-2_task-rest_acq-fullbrain_run-2_bold.nii.gz\n", " sub-01_ses-2_task-rest_acq-prefrontal_bold.nii.gz\n", "\n", "Compare return types:\n", "return_types=[, ]\n", "return_types=[, ]\n" ] } ], "source": [ "# Same query with schema-based layout\n", "bold_files_schema = layout_schema.get(\n", " subject='01', suffix='bold', extension='nii.gz', return_type='filename'\n", ")\n", "print(f'Found {len(bold_files_schema)} BOLD files for subject 01:')\n", "for f in bold_files_schema:\n", " print(f' {os.path.basename(f)}')\n", "\n", "# Show the difference between return types like in docs\n", "print('\\nCompare return types:')\n", "bold_objects_schema = layout_schema.get(subject='01', suffix='bold', extension='nii.gz')[:2]\n", "bold_filenames_schema = layout_schema.get(\n", " subject='01', suffix='bold', extension='nii.gz', return_type='filename'\n", ")[:2]\n", "\n", "print(f'return_types={[type(obj) for obj in bold_objects_schema]}')\n", "print(f'return_types={[type(fname) for fname in bold_filenames_schema]}')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Part 3: Comparing the Two Approaches\n", "\n", "Let's verify that both approaches give us the same results." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Legacy layout: 339 files\n", "Schema layout: 339 files\n", "\n", "Match: True\n" ] } ], "source": [ "# Compare file counts\n", "legacy_count = len(layout_legacy.get())\n", "schema_count = len(layout_schema.get())\n", "\n", "print(f'Legacy layout: {legacy_count} files')\n", "print(f'Schema layout: {schema_count} files')\n", "print(f'\\nMatch: {legacy_count == schema_count}')" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Legacy entities: 44\n", "Schema entities: 49\n", "\n", "Entities only in legacy: ['fmap', 'inv', 'mt', 'proc', 'scans', 'staining']\n", "\n", "Entities only in schema: ['density', 'description', 'hemisphere', 'inversion', 'label', 'mtransfer', 'processing', 'resolution', 'segmentation', 'split', 'stain']\n" ] } ], "source": [ "# Compare entities\n", "legacy_entities = set(layout_legacy.get_entities().keys())\n", "schema_entities = set(layout_schema.get_entities().keys())\n", "\n", "print(f'Legacy entities: {len(legacy_entities)}')\n", "print(f'Schema entities: {len(schema_entities)}')\n", "\n", "# Check for differences\n", "only_legacy = legacy_entities - schema_entities\n", "only_schema = schema_entities - legacy_entities\n", "\n", "if only_legacy:\n", " print(f'\\nEntities only in legacy: {sorted(only_legacy)}')\n", "if only_schema:\n", " print(f'\\nEntities only in schema: {sorted(only_schema)}')\n", "if not only_legacy and not only_schema:\n", " print('\\nAll entities match!')" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Query: {'suffix': 'bold', 'extension': 'nii.gz'}\n", "Legacy: 60 files\n", "Schema: 60 files\n", "✓ Results match!\n", "\n", "Query: {'suffix': 'T1w', 'extension': 'nii.gz'}\n", "Legacy: 10 files\n", "Schema: 10 files\n", "✓ Results match!\n", "\n", "Query: {'subject': '01', 'suffix': 'scans', 'extension': 'tsv'}\n", "Legacy: 2 files\n", "Schema: 2 files\n", "✓ Results match!\n", "\n" ] } ], "source": [ "# Compare specific query results\n", "def compare_queries(layout1, layout2, name1='Layout 1', name2='Layout 2', **kwargs):\n", " \"\"\"Compare results from two layouts for the same query.\"\"\"\n", " result1 = set(layout1.get(return_type='filename', **kwargs))\n", " result2 = set(layout2.get(return_type='filename', **kwargs))\n", "\n", " print(f'Query: {kwargs}')\n", " print(f'{name1}: {len(result1)} files')\n", " print(f'{name2}: {len(result2)} files')\n", "\n", " if result1 == result2:\n", " print('✓ Results match!\\n')\n", " else:\n", " only1 = result1 - result2\n", " only2 = result2 - result1\n", " if only1:\n", " print(f'Only in {name1}: {[os.path.basename(f) for f in only1]}')\n", " if only2:\n", " print(f'Only in {name2}: {[os.path.basename(f) for f in only2]}')\n", " print()\n", "\n", "\n", "# Test various queries\n", "compare_queries(\n", " layout_legacy, layout_schema, 'Legacy', 'Schema', suffix='bold', extension='nii.gz'\n", ")\n", "\n", "compare_queries(layout_legacy, layout_schema, 'Legacy', 'Schema', suffix='T1w', extension='nii.gz')\n", "\n", "# 1. Test with scans TSV files (these exist)\n", "compare_queries(\n", " layout_legacy, layout_schema, 'Legacy', 'Schema', subject='01', suffix='scans', extension='tsv'\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Part 5: Performance Comparison\n", "\n", "Let's compare the initialization time for both approaches." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Legacy initialization: 0.684 seconds\n", "Schema initialization: 0.679 seconds\n", "\n", "Difference: 0.005 seconds\n", "Schema is faster by 0.8%\n" ] } ], "source": [ "import time\n", "\n", "# Time legacy initialization\n", "start = time.time()\n", "layout_legacy_timed = BIDSLayout(data_path, config=['bids'], reset_database=True)\n", "legacy_time = time.time() - start\n", "\n", "# Time schema initialization\n", "start = time.time()\n", "layout_schema_timed = BIDSLayout(data_path, config=['bids-schema'], reset_database=True)\n", "schema_time = time.time() - start\n", "\n", "print(f'Legacy initialization: {legacy_time:.3f} seconds')\n", "print(f'Schema initialization: {schema_time:.3f} seconds')\n", "print(f'\\nDifference: {abs(legacy_time - schema_time):.3f} seconds')\n", "print(\n", " f'Schema is {\"faster\" if schema_time < legacy_time else \"slower\"} by {abs(1 - schema_time / legacy_time) * 100:.1f}%'\n", ")" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.10.18" } }, "nbformat": 4, "nbformat_minor": 4 } ================================================ FILE: setup.cfg ================================================ [codespell] skip = ./.git,external,versioneer.py,_version.py ignore-words = .github/codespell_ignore_words.txt exclude-file = .github/codespell_exclude_lines.txt ================================================ FILE: setup.py ================================================ #!/usr/bin/env python import versioneer from setuptools import setup setup( version=versioneer.get_version(), cmdclass=versioneer.get_cmdclass(), ) ================================================ FILE: src/bids/__init__.py ================================================ # For backwards compatibility # noqa: D104 from bids_validator import BIDSValidator from .due import Doi, due from .layout import BIDSLayout, BIDSLayoutIndexer __all__ = [ 'modeling', 'BIDSLayout', 'BIDSLayoutIndexer', 'BIDSValidator', 'config', 'layout', 'reports', 'utils', 'variables', ] due.cite( Doi('10.1038/sdata.2016.44'), description='Brain Imaging Data Structure', tags=['reference-implementation'], path='bids', ) del due, Doi from . import _version # noqa: E402 __version__ = _version.get_versions()['version'] ================================================ FILE: src/bids/__main__.py ================================================ from .cli import cli # noqa: D100 if __name__ == '__main__': cli() ================================================ FILE: src/bids/_version.py ================================================ # This file helps to compute a version number in source trees obtained from # git-archive tarball (such as those provided by githubs download-from-tag # feature). Distribution tarballs (built by setup.py sdist) and build # directories (produced by setup.py build) will contain a much shorter file # that just contains the computed version number. # This file is released into the public domain. # Generated by versioneer-0.29 # https://github.com/python-versioneer/python-versioneer """Git implementation of _version.py.""" import errno import os import re import subprocess import sys from typing import Any, Callable, Dict, List, Optional, Tuple import functools def get_keywords() -> Dict[str, str]: """Get the keywords needed to look up the version information.""" # these strings will be replaced by git during git-archive. # setup.py/versioneer.py will grep for the variable names, so they must # each be defined on a line of their own. _version.py will just call # get_keywords(). git_refnames = "$Format:%d$" git_full = "$Format:%H$" git_date = "$Format:%ci$" keywords = {"refnames": git_refnames, "full": git_full, "date": git_date} return keywords class VersioneerConfig: """Container for Versioneer configuration parameters.""" VCS: str style: str tag_prefix: str parentdir_prefix: str versionfile_source: str verbose: bool def get_config() -> VersioneerConfig: """Create, populate and return the VersioneerConfig() object.""" # these strings are filled in when 'setup.py versioneer' creates # _version.py cfg = VersioneerConfig() cfg.VCS = "git" cfg.style = "pep440-pre" cfg.tag_prefix = "" cfg.parentdir_prefix = "None" cfg.versionfile_source = "src/bids/_version.py" cfg.verbose = False return cfg class NotThisMethod(Exception): """Exception raised if a method is not valid for the current scenario.""" LONG_VERSION_PY: Dict[str, str] = {} HANDLERS: Dict[str, Dict[str, Callable]] = {} def register_vcs_handler(vcs: str, method: str) -> Callable: # decorator """Create decorator to mark a method as the handler of a VCS.""" def decorate(f: Callable) -> Callable: """Store f in HANDLERS[vcs][method].""" if vcs not in HANDLERS: HANDLERS[vcs] = {} HANDLERS[vcs][method] = f return f return decorate def run_command( commands: List[str], args: List[str], cwd: Optional[str] = None, verbose: bool = False, hide_stderr: bool = False, env: Optional[Dict[str, str]] = None, ) -> Tuple[Optional[str], Optional[int]]: """Call the given command(s).""" assert isinstance(commands, list) process = None popen_kwargs: Dict[str, Any] = {} if sys.platform == "win32": # This hides the console window if pythonw.exe is used startupinfo = subprocess.STARTUPINFO() startupinfo.dwFlags |= subprocess.STARTF_USESHOWWINDOW popen_kwargs["startupinfo"] = startupinfo for command in commands: try: dispcmd = str([command] + args) # remember shell=False, so use git.cmd on windows, not just git process = subprocess.Popen([command] + args, cwd=cwd, env=env, stdout=subprocess.PIPE, stderr=(subprocess.PIPE if hide_stderr else None), **popen_kwargs) break except OSError as e: if e.errno == errno.ENOENT: continue if verbose: print("unable to run %s" % dispcmd) print(e) return None, None else: if verbose: print("unable to find command, tried %s" % (commands,)) return None, None stdout = process.communicate()[0].strip().decode() if process.returncode != 0: if verbose: print("unable to run %s (error)" % dispcmd) print("stdout was %s" % stdout) return None, process.returncode return stdout, process.returncode def versions_from_parentdir( parentdir_prefix: str, root: str, verbose: bool, ) -> Dict[str, Any]: """Try to determine the version from the parent directory name. Source tarballs conventionally unpack into a directory that includes both the project name and a version string. We will also support searching up two directory levels for an appropriately named parent directory """ rootdirs = [] for _ in range(3): dirname = os.path.basename(root) if dirname.startswith(parentdir_prefix): return {"version": dirname[len(parentdir_prefix):], "full-revisionid": None, "dirty": False, "error": None, "date": None} rootdirs.append(root) root = os.path.dirname(root) # up a level if verbose: print("Tried directories %s but none started with prefix %s" % (str(rootdirs), parentdir_prefix)) raise NotThisMethod("rootdir doesn't start with parentdir_prefix") @register_vcs_handler("git", "get_keywords") def git_get_keywords(versionfile_abs: str) -> Dict[str, str]: """Extract version information from the given file.""" # the code embedded in _version.py can just fetch the value of these # keywords. When used from setup.py, we don't want to import _version.py, # so we do it with a regexp instead. This function is not used from # _version.py. keywords: Dict[str, str] = {} try: with open(versionfile_abs, "r") as fobj: for line in fobj: if line.strip().startswith("git_refnames ="): mo = re.search(r'=\s*"(.*)"', line) if mo: keywords["refnames"] = mo.group(1) if line.strip().startswith("git_full ="): mo = re.search(r'=\s*"(.*)"', line) if mo: keywords["full"] = mo.group(1) if line.strip().startswith("git_date ="): mo = re.search(r'=\s*"(.*)"', line) if mo: keywords["date"] = mo.group(1) except OSError: pass return keywords @register_vcs_handler("git", "keywords") def git_versions_from_keywords( keywords: Dict[str, str], tag_prefix: str, verbose: bool, ) -> Dict[str, Any]: """Get version information from git keywords.""" if "refnames" not in keywords: raise NotThisMethod("Short version file found") date = keywords.get("date") if date is not None: # Use only the last line. Previous lines may contain GPG signature # information. date = date.splitlines()[-1] # git-2.2.0 added "%cI", which expands to an ISO-8601 -compliant # datestamp. However we prefer "%ci" (which expands to an "ISO-8601 # -like" string, which we must then edit to make compliant), because # it's been around since git-1.5.3, and it's too difficult to # discover which version we're using, or to work around using an # older one. date = date.strip().replace(" ", "T", 1).replace(" ", "", 1) refnames = keywords["refnames"].strip() if refnames.startswith("$Format"): if verbose: print("keywords are unexpanded, not using") raise NotThisMethod("unexpanded keywords, not a git-archive tarball") refs = {r.strip() for r in refnames.strip("()").split(",")} # starting in git-1.8.3, tags are listed as "tag: foo-1.0" instead of # just "foo-1.0". If we see a "tag: " prefix, prefer those. TAG = "tag: " tags = {r[len(TAG):] for r in refs if r.startswith(TAG)} if not tags: # Either we're using git < 1.8.3, or there really are no tags. We use # a heuristic: assume all version tags have a digit. The old git %d # expansion behaves like git log --decorate=short and strips out the # refs/heads/ and refs/tags/ prefixes that would let us distinguish # between branches and tags. By ignoring refnames without digits, we # filter out many common branch names like "release" and # "stabilization", as well as "HEAD" and "main". tags = {r for r in refs if re.search(r'\d', r)} if verbose: print("discarding '%s', no digits" % ",".join(refs - tags)) if verbose: print("likely tags: %s" % ",".join(sorted(tags))) for ref in sorted(tags): # sorting will prefer e.g. "2.0" over "2.0rc1" if ref.startswith(tag_prefix): r = ref[len(tag_prefix):] # Filter out refs that exactly match prefix or that don't start # with a number once the prefix is stripped (mostly a concern # when prefix is '') if not re.match(r'\d', r): continue if verbose: print("picking %s" % r) return {"version": r, "full-revisionid": keywords["full"].strip(), "dirty": False, "error": None, "date": date} # no suitable tags, so version is "0+unknown", but full hex is still there if verbose: print("no suitable tags, using unknown + full revision id") return {"version": "0+unknown", "full-revisionid": keywords["full"].strip(), "dirty": False, "error": "no suitable tags", "date": None} @register_vcs_handler("git", "pieces_from_vcs") def git_pieces_from_vcs( tag_prefix: str, root: str, verbose: bool, runner: Callable = run_command ) -> Dict[str, Any]: """Get version from 'git describe' in the root of the source tree. This only gets called if the git-archive 'subst' keywords were *not* expanded, and _version.py hasn't already been rewritten with a short version string, meaning we're inside a checked out source tree. """ GITS = ["git"] if sys.platform == "win32": GITS = ["git.cmd", "git.exe"] # GIT_DIR can interfere with correct operation of Versioneer. # It may be intended to be passed to the Versioneer-versioned project, # but that should not change where we get our version from. env = os.environ.copy() env.pop("GIT_DIR", None) runner = functools.partial(runner, env=env) _, rc = runner(GITS, ["rev-parse", "--git-dir"], cwd=root, hide_stderr=not verbose) if rc != 0: if verbose: print("Directory %s not under git control" % root) raise NotThisMethod("'git rev-parse --git-dir' returned error") # if there is a tag matching tag_prefix, this yields TAG-NUM-gHEX[-dirty] # if there isn't one, this yields HEX[-dirty] (no NUM) describe_out, rc = runner(GITS, [ "describe", "--tags", "--dirty", "--always", "--long", "--match", f"{tag_prefix}[[:digit:]]*" ], cwd=root) # --long was added in git-1.5.5 if describe_out is None: raise NotThisMethod("'git describe' failed") describe_out = describe_out.strip() full_out, rc = runner(GITS, ["rev-parse", "HEAD"], cwd=root) if full_out is None: raise NotThisMethod("'git rev-parse' failed") full_out = full_out.strip() pieces: Dict[str, Any] = {} pieces["long"] = full_out pieces["short"] = full_out[:7] # maybe improved later pieces["error"] = None branch_name, rc = runner(GITS, ["rev-parse", "--abbrev-ref", "HEAD"], cwd=root) # --abbrev-ref was added in git-1.6.3 if rc != 0 or branch_name is None: raise NotThisMethod("'git rev-parse --abbrev-ref' returned error") branch_name = branch_name.strip() if branch_name == "HEAD": # If we aren't exactly on a branch, pick a branch which represents # the current commit. If all else fails, we are on a branchless # commit. branches, rc = runner(GITS, ["branch", "--contains"], cwd=root) # --contains was added in git-1.5.4 if rc != 0 or branches is None: raise NotThisMethod("'git branch --contains' returned error") branches = branches.split("\n") # Remove the first line if we're running detached if "(" in branches[0]: branches.pop(0) # Strip off the leading "* " from the list of branches. branches = [branch[2:] for branch in branches] if "main" in branches: branch_name = "main" elif not branches: branch_name = None else: # Pick the first branch that is returned. Good or bad. branch_name = branches[0] pieces["branch"] = branch_name # parse describe_out. It will be like TAG-NUM-gHEX[-dirty] or HEX[-dirty] # TAG might have hyphens. git_describe = describe_out # look for -dirty suffix dirty = git_describe.endswith("-dirty") pieces["dirty"] = dirty if dirty: git_describe = git_describe[:git_describe.rindex("-dirty")] # now we have TAG-NUM-gHEX or HEX if "-" in git_describe: # TAG-NUM-gHEX mo = re.search(r'^(.+)-(\d+)-g([0-9a-f]+)$', git_describe) if not mo: # unparsable. Maybe git-describe is misbehaving? pieces["error"] = ("unable to parse git-describe output: '%s'" % describe_out) return pieces # tag full_tag = mo.group(1) if not full_tag.startswith(tag_prefix): if verbose: fmt = "tag '%s' doesn't start with prefix '%s'" print(fmt % (full_tag, tag_prefix)) pieces["error"] = ("tag '%s' doesn't start with prefix '%s'" % (full_tag, tag_prefix)) return pieces pieces["closest-tag"] = full_tag[len(tag_prefix):] # distance: number of commits since tag pieces["distance"] = int(mo.group(2)) # commit: short hex revision ID pieces["short"] = mo.group(3) else: # HEX: no tags pieces["closest-tag"] = None out, rc = runner(GITS, ["rev-list", "HEAD", "--left-right"], cwd=root) pieces["distance"] = len(out.split()) # total number of commits # commit date: see ISO-8601 comment in git_versions_from_keywords() date = runner(GITS, ["show", "-s", "--format=%ci", "HEAD"], cwd=root)[0].strip() # Use only the last line. Previous lines may contain GPG signature # information. date = date.splitlines()[-1] pieces["date"] = date.strip().replace(" ", "T", 1).replace(" ", "", 1) return pieces def plus_or_dot(pieces: Dict[str, Any]) -> str: """Return a + if we don't already have one, else return a .""" if "+" in pieces.get("closest-tag", ""): return "." return "+" def render_pep440(pieces: Dict[str, Any]) -> str: """Build up version string, with post-release "local version identifier". Our goal: TAG[+DISTANCE.gHEX[.dirty]] . Note that if you get a tagged build and then dirty it, you'll get TAG+0.gHEX.dirty Exceptions: 1: no tags. git_describe was just HEX. 0+untagged.DISTANCE.gHEX[.dirty] """ if pieces["closest-tag"]: rendered = pieces["closest-tag"] if pieces["distance"] or pieces["dirty"]: rendered += plus_or_dot(pieces) rendered += "%d.g%s" % (pieces["distance"], pieces["short"]) if pieces["dirty"]: rendered += ".dirty" else: # exception #1 rendered = "0+untagged.%d.g%s" % (pieces["distance"], pieces["short"]) if pieces["dirty"]: rendered += ".dirty" return rendered def render_pep440_branch(pieces: Dict[str, Any]) -> str: """TAG[[.dev0]+DISTANCE.gHEX[.dirty]] . The ".dev0" means not main branch. Note that .dev0 sorts backwards (a feature branch will appear "older" than the main branch). Exceptions: 1: no tags. 0[.dev0]+untagged.DISTANCE.gHEX[.dirty] """ if pieces["closest-tag"]: rendered = pieces["closest-tag"] if pieces["distance"] or pieces["dirty"]: if pieces["branch"] != "main": rendered += ".dev0" rendered += plus_or_dot(pieces) rendered += "%d.g%s" % (pieces["distance"], pieces["short"]) if pieces["dirty"]: rendered += ".dirty" else: # exception #1 rendered = "0" if pieces["branch"] != "main": rendered += ".dev0" rendered += "+untagged.%d.g%s" % (pieces["distance"], pieces["short"]) if pieces["dirty"]: rendered += ".dirty" return rendered def pep440_split_post(ver: str) -> Tuple[str, Optional[int]]: """Split pep440 version string at the post-release segment. Returns the release segments before the post-release and the post-release version number (or -1 if no post-release segment is present). """ vc = str.split(ver, ".post") return vc[0], int(vc[1] or 0) if len(vc) == 2 else None def render_pep440_pre(pieces: Dict[str, Any]) -> str: """TAG[.postN.devDISTANCE] -- No -dirty. Exceptions: 1: no tags. 0.post0.devDISTANCE """ if pieces["closest-tag"]: if pieces["distance"]: # update the post release segment tag_version, post_version = pep440_split_post(pieces["closest-tag"]) rendered = tag_version if post_version is not None: rendered += ".post%d.dev%d" % (post_version + 1, pieces["distance"]) else: rendered += ".post0.dev%d" % (pieces["distance"]) else: # no commits, use the tag as the version rendered = pieces["closest-tag"] else: # exception #1 rendered = "0.post0.dev%d" % pieces["distance"] return rendered def render_pep440_post(pieces: Dict[str, Any]) -> str: """TAG[.postDISTANCE[.dev0]+gHEX] . The ".dev0" means dirty. Note that .dev0 sorts backwards (a dirty tree will appear "older" than the corresponding clean one), but you shouldn't be releasing software with -dirty anyways. Exceptions: 1: no tags. 0.postDISTANCE[.dev0] """ if pieces["closest-tag"]: rendered = pieces["closest-tag"] if pieces["distance"] or pieces["dirty"]: rendered += ".post%d" % pieces["distance"] if pieces["dirty"]: rendered += ".dev0" rendered += plus_or_dot(pieces) rendered += "g%s" % pieces["short"] else: # exception #1 rendered = "0.post%d" % pieces["distance"] if pieces["dirty"]: rendered += ".dev0" rendered += "+g%s" % pieces["short"] return rendered def render_pep440_post_branch(pieces: Dict[str, Any]) -> str: """TAG[.postDISTANCE[.dev0]+gHEX[.dirty]] . The ".dev0" means not main branch. Exceptions: 1: no tags. 0.postDISTANCE[.dev0]+gHEX[.dirty] """ if pieces["closest-tag"]: rendered = pieces["closest-tag"] if pieces["distance"] or pieces["dirty"]: rendered += ".post%d" % pieces["distance"] if pieces["branch"] != "main": rendered += ".dev0" rendered += plus_or_dot(pieces) rendered += "g%s" % pieces["short"] if pieces["dirty"]: rendered += ".dirty" else: # exception #1 rendered = "0.post%d" % pieces["distance"] if pieces["branch"] != "main": rendered += ".dev0" rendered += "+g%s" % pieces["short"] if pieces["dirty"]: rendered += ".dirty" return rendered def render_pep440_old(pieces: Dict[str, Any]) -> str: """TAG[.postDISTANCE[.dev0]] . The ".dev0" means dirty. Exceptions: 1: no tags. 0.postDISTANCE[.dev0] """ if pieces["closest-tag"]: rendered = pieces["closest-tag"] if pieces["distance"] or pieces["dirty"]: rendered += ".post%d" % pieces["distance"] if pieces["dirty"]: rendered += ".dev0" else: # exception #1 rendered = "0.post%d" % pieces["distance"] if pieces["dirty"]: rendered += ".dev0" return rendered def render_git_describe(pieces: Dict[str, Any]) -> str: """TAG[-DISTANCE-gHEX][-dirty]. Like 'git describe --tags --dirty --always'. Exceptions: 1: no tags. HEX[-dirty] (note: no 'g' prefix) """ if pieces["closest-tag"]: rendered = pieces["closest-tag"] if pieces["distance"]: rendered += "-%d-g%s" % (pieces["distance"], pieces["short"]) else: # exception #1 rendered = pieces["short"] if pieces["dirty"]: rendered += "-dirty" return rendered def render_git_describe_long(pieces: Dict[str, Any]) -> str: """TAG-DISTANCE-gHEX[-dirty]. Like 'git describe --tags --dirty --always -long'. The distance/hash is unconditional. Exceptions: 1: no tags. HEX[-dirty] (note: no 'g' prefix) """ if pieces["closest-tag"]: rendered = pieces["closest-tag"] rendered += "-%d-g%s" % (pieces["distance"], pieces["short"]) else: # exception #1 rendered = pieces["short"] if pieces["dirty"]: rendered += "-dirty" return rendered def render(pieces: Dict[str, Any], style: str) -> Dict[str, Any]: """Render the given version pieces into the requested style.""" if pieces["error"]: return {"version": "unknown", "full-revisionid": pieces.get("long"), "dirty": None, "error": pieces["error"], "date": None} if not style or style == "default": style = "pep440" # the default if style == "pep440": rendered = render_pep440(pieces) elif style == "pep440-branch": rendered = render_pep440_branch(pieces) elif style == "pep440-pre": rendered = render_pep440_pre(pieces) elif style == "pep440-post": rendered = render_pep440_post(pieces) elif style == "pep440-post-branch": rendered = render_pep440_post_branch(pieces) elif style == "pep440-old": rendered = render_pep440_old(pieces) elif style == "git-describe": rendered = render_git_describe(pieces) elif style == "git-describe-long": rendered = render_git_describe_long(pieces) else: raise ValueError("unknown style '%s'" % style) return {"version": rendered, "full-revisionid": pieces["long"], "dirty": pieces["dirty"], "error": None, "date": pieces.get("date")} def get_versions() -> Dict[str, Any]: """Get version information or return default if unable to do so.""" # I am in _version.py, which lives at ROOT/VERSIONFILE_SOURCE. If we have # __file__, we can work backwards from there to the root. Some # py2exe/bbfreeze/non-CPython implementations don't do __file__, in which # case we can only use expanded keywords. cfg = get_config() verbose = cfg.verbose try: return git_versions_from_keywords(get_keywords(), cfg.tag_prefix, verbose) except NotThisMethod: pass try: root = os.path.realpath(__file__) # versionfile_source is the relative path from the top of the source # tree (where the .git directory might live) to this file. Invert # this to find the root from __file__. for _ in cfg.versionfile_source.split('/'): root = os.path.dirname(root) except NameError: return {"version": "0+unknown", "full-revisionid": None, "dirty": None, "error": "unable to find root of source tree", "date": None} try: pieces = git_pieces_from_vcs(cfg.tag_prefix, root, verbose) return render(pieces, cfg.style) except NotThisMethod: pass try: if cfg.parentdir_prefix: return versions_from_parentdir(cfg.parentdir_prefix, root, verbose) except NotThisMethod: pass return {"version": "0+unknown", "full-revisionid": None, "dirty": None, "error": "unable to compute version", "date": None} ================================================ FILE: src/bids/cli.py ================================================ import json # noqa: D100 import os from copy import deepcopy from pathlib import Path import click from . import __version__ from .layout import BIDSLayout, BIDSLayoutIndexer from .modeling.report.base import generate_report from .utils import validate_multiple as _validate_multiple # alias -h to trigger help message CONTEXT_SETTINGS = {'help_option_names': ['-h', '--help']} class Either(click.ParamType): """Click type that will accept any of the types passed at initialization. Examples -------- >>> Either(click.BOOL, click.STRING).convert(True, None, None) True >>> Either(click.BOOL, click.STRING).convert('Test', None, None) 'Test' Note that the order of types can affect the interpreted type. >>> Either(click.BOOL, click.INT).convert('1', None, None) True >>> Either(click.INT, click.BOOL).convert('1', None, None) 1 The motivating use case is True or path: >>> PathOrTrue = Either(click.BOOL, click.Path(exists=True)) >>> PathOrTrue.convert(True, None, None) True >>> cwd = os.getcwd() >>> PathOrTrue.convert(cwd, None, None) == cwd True >>> PathOrTrue.convert('/does/not/exist', None, None) # doctest: +IGNORE_EXCEPTION_DETAIL Traceback (most recent call last): ... BadParameter: '/does/not/exist' does not match expected types: boolean: "'/does/not/exist' is not a valid boolean." path: "'/does/not/exist' does not exist." """ def __init__(self, *types): self.types = types @property def name(self): # noqa: D102 tpstrings = tuple(tp.name for tp in self.types) # noqa: F841 return 'any type in {tpstrings}' def convert(self, value, param, ctx): # noqa: D102 errors = [] for tp in self.types: try: return tp.convert(value, param, ctx) except click.BadParameter as err: errors.append(f'{tp.name}: {err.message!r}') messages = '\n'.join(errors) self.fail(f'{value!r} does not match expected types:\n{messages}') class PathOrRegex(click.ParamType): """A helper Type to parse BIDSLayoutIndexer ignore/force entries""" name = 'path or m/regex/' def convert(self, value, param, ctx): # noqa: D102 import re if re.match(r'^m/.*/$', value): # has form "m//" value = re.compile(value[2:-1]) return value @click.group(context_settings=CONTEXT_SETTINGS) @click.version_option(__version__, prog_name='pybids') def cli(): """Command-line interface for PyBIDS operations""" pass @cli.command(context_settings=CONTEXT_SETTINGS) @click.argument('root', type=click.Path(file_okay=False, exists=True)) @click.argument('db-path', type=click.Path(file_okay=False, resolve_path=True, exists=True)) @click.option( '--derivatives', multiple=True, default=[False], is_flag=False, flag_value=True, show_default=True, type=Either(click.BOOL, click.Path(exists=True)), help='Specifies whether and/or which derivatives to index.', ) @click.option( '--reset-db', default=False, show_default=True, is_flag=True, help='Remove existing database index if present.', ) @click.option( '--validate/--no-validate', default=True, show_default=True, help='Check for BIDS compliance when indexing files.', ) @click.option('--config', multiple=True, help='Optional name(s) of configuration file(s) to use.') @click.option( '--index-metadata/--no-index-metadata', default=False, show_default=True, help='Include metadata when indexing files.', ) @click.option( '--ignore', multiple=True, type=PathOrRegex(), help='Path (from root) or regex to exclude from indexing. ' "Regex entries need to fitted with leading 'm/' and trailing '/'.", ) @click.option( '--force-index', multiple=True, type=PathOrRegex(), help='Path (from root) or regex to include when indexing. ' "Regex entries need to fitted with leading 'm/' and trailing '/'.", ) @click.option( '--config-filename', type=click.Path(), default='layout_config.json', show_default=True, help='Name of filename within directories that contains configuration information.', ) def layout( root, db_path, derivatives, reset_db, validate, config, index_metadata, ignore, force_index, config_filename, ): """Initialize a BIDSLayout, and create an SQLite database index.""" # ensure empty multiples are set to None derivatives = _validate_multiple(derivatives, retval=False) config = _validate_multiple(config) ignore = _validate_multiple(ignore) force_index = _validate_multiple(force_index) if not (Path(db_path) / 'layout_index.sqlite').exists(): reset_db = True layout = BIDSLayout( # noqa: F841 root, database_path=db_path, reset_database=reset_db, validate=validate, config=config, indexer=BIDSLayoutIndexer( validate=validate, index_metadata=index_metadata, ignore=ignore, force_index=force_index, config_filename=config_filename, ), ) if reset_db: click.echo(f'Successfully generated database index at {db_path}') else: click.echo( f'Previously generated database index found at {db_path}. ' 'To generate a new index, rerun with ``--reset-db``' ) @cli.command(context_settings=CONTEXT_SETTINGS) @click.argument('root', type=click.Path(file_okay=False, exists=True)) def upgrade(root): """Upgrade common experimental BIDS features to finalized versions.""" click.echo( 'WARNING. This upgrade tool is EXPERIMENTAL and MAY damage your ' 'dataset. Please ensure you have a backup before proceeding.' ) click.confirm('Proceed?', abort=True) changes = False description_path = Path(root) / 'dataset_description.json' orig_desc = json.loads(description_path.read_text()) desc = upgrade_dataset_description(orig_desc) if desc != orig_desc: description_path.write_text(json.dumps(desc)) changes = True val = click.prompt('Load dataset and update filenames?', default='Y', type=click.Choice('YN')) if val == 'Y': upgrade_filenames(root, desc) if changes: click.echo( 'Upgrade complete. Please run the bids-validator ' '(https://bids-standard.github.io/bids-validator) ' 'to confirm the correctness of the changes.' ) else: click.echo('No changes to make!') def upgrade_dataset_description(description): """Upgrade dataset_description.json with recommended values""" description = deepcopy(description) # Give an opportunity to update to latest version bidsver = description.get('BIDSVersion') if bidsver is None or bidsver < '1.6.0': val = click.prompt(f'Update BIDS Version? (current: {bidsver})', default='1.6.0', type=str) if val.startswith('1.'): description['BIDSVersion'] = val else: click.echo('Expected version to be 1.x, e.g., 1.6.0. Skipping.') # Always update DatasetType if missing if 'DatasetType' not in description: val = click.prompt( 'Is this dataset [r]aw or [d]erivative?', default='r', type=click.Choice(('r', 'd'), case_sensitive=False), ) description['DatasetType'] = 'raw' if val == 'R' else 'derivative' if description['DatasetType'] == 'derivative': if 'PipelineDescription' in description: val = click.prompt( 'Convert PipelineDescription to GeneratedBy?', default='Y', type=click.Choice('YN') ) if val == 'Y': description['GeneratedBy'] = [description.pop('PipelineDescription')] return description def upgrade_filenames(root, description): # noqa: D103 dstype = description['DatasetType'] layout = BIDSLayout(root, validate=False, config='bids' if dstype == 'raw' else 'derivatives') # Rename regressors.tsv to timeseries.tsv regressors = layout.get(suffix='regressors') policy = None with click.progressbar(regressors) as bar: for bidsfile in bar: action = policy new_path = bidsfile.path.replace('regressors.', 'timeseries.') if action is None: action = click.prompt( f'Rename {bidsfile.path} to {new_path}? ([y]es/[n]o/[A]ll/[N]one)', default='y', type=click.Choice('ynAN'), show_choices=False, ) if action in 'AN': policy = action if action in 'yA': click.echo(f'Renaming {bidsfile.path} -> {new_path}') os.rename(bidsfile.path, new_path) else: click.echo(f'Not renaming {bidsfile.path}') @cli.command(context_settings=CONTEXT_SETTINGS) @click.argument('model', type=click.Path(file_okay=True, exists=True, dir_okay=False)) @click.argument('root', type=click.Path(file_okay=False, exists=True)) @click.argument('derivatives', type=click.Path(file_okay=False, exists=True), nargs=-1) @click.argument('output_dir', type=click.Path(file_okay=False, exists=False)) @click.option('--scan_length', type=click.FLOAT) @click.option( '--sub_label', '-s', multiple=True, help='The label(s) of the subjects(s) to create report for' ) @click.option( '--ses_label', '-ss', multiple=True, help='The label(s) of the session(s) to create report for' ) @click.option( '--task_label', '-t', multiple=True, help='The label(s) of the task(s) to create report for' ) @click.option( '--run_label', '-r', multiple=True, help='The label(s) of the run(s) to create report for' ) def model_report( model, root, derivatives, output_dir, scan_length, sub_label, ses_label, task_label, run_label ): """Generate a report of a BIDS StatsModel.""" entities = {'subject': sub_label, 'session': ses_label, 'task': task_label, 'run': run_label} entities = {key: val for key, val in entities.items() if val} generate_report(model, root, derivatives, output_dir, scan_length, **entities) ================================================ FILE: src/bids/config.py ================================================ """Utilities for manipulating package-level settings.""" import json import os import warnings from upath import UPath as Path from .utils import listify __all__ = ['set_option', 'set_options', 'get_option'] _config_name = 'pybids_config.json' conf_path = str(Path(__file__).absolute().parent.joinpath('layout', 'config', '{}.json')) _default_settings = { 'config_paths': {name: conf_path.format(name) for name in ['bids', 'derivatives']}, # XXX 0.16: Remove 'extension_initial_dot': True, } def set_option(key, value): """Set a package-wide option. Args: key (str): The name of the option to set. value (object): The new value of the option. """ if key not in _settings: raise ValueError("Invalid pybids setting: '%s'" % key) # noqa: UP031 # XXX 0.16: Remove elif key == 'extension_initial_dot': if value is not True: raise ValueError( f'Cannot set {key!r} to {value!r} as of pybids 0.14. ' 'This setting is always True, and will be removed ' 'entirely in 0.16.' ) warnings.warn( # noqa: B028 "Setting 'extension_initial_dot' will be removed in pybids 0.16.", FutureWarning ) _settings[key] = value def set_options(**kwargs): """Set multiple package-wide options. Args: kwargs: Keyword arguments to pass onto set_option(). """ for k, v in kwargs.items(): set_option(k, v) def get_option(key): """Retrieve the current value of a package-wide option. Args: key (str): The name of the option to retrieve. """ if key not in _settings: raise ValueError("Invalid pybids setting: '%s'" % key) # noqa: UP031 return _settings[key] def from_file(filenames, error_on_missing=True): """Load package-wide settings from specified file(s). Args: filenames (str, list): Filename or list of filenames containing JSON dictionary of settings. error_on_missing (bool): If True, raises an error if a file doesn't exist. """ filenames = listify(filenames) for f in filenames: if Path(f).exists(): settings = json.loads(Path(f).read_text(encoding='utf-8')) _settings.update(settings) elif error_on_missing: raise ValueError("Config file '%s' does not exist." % f) # noqa: UP031 def reset_options(update_from_file=False): """Reset all options to the package defaults. Args: update_from_file (bool): If True, re-applies any config files found in standard locations. """ global _settings _settings = _default_settings.copy() if update_from_file: _update_from_standard_locations() def _update_from_standard_locations(): """Check standard locations for config files and update settings if found. Order is user's home dir, environment variable ($PYBIDS_CONFIG), and then current directory--with later files taking precedence over earlier ones. """ locs = [Path.home() / _config_name, Path('.') / _config_name] if 'PYBIDS_CONFIG' in os.environ: locs.insert(1, os.environ['PYBIDS_CONFIG']) from_file(locs, False) _settings = {} reset_options(True) ================================================ FILE: src/bids/conftest.py ================================================ # ruff: noqa: D404 """This module allows you to mock the config file as needed. # noqa: D404 A default fixture that simply returns a safe-to-modify copy of the default value is provided. This can be overridden by parametrizing over the option you wish to mock. e.g. >>> @pytest.mark.parametrize('extension_initial_dot', (True, False)) ... def test_fixture(mock_config, extension_initial_dot): ... import bids ... ... assert bids.config.get_option('extension_initial_dot') == extension_initial_dot """ import os from unittest.mock import patch import pytest from upath import UPath as Path @pytest.fixture def config_paths(): # noqa: D103 import bids.config return bids.config.get_option('config_paths').copy() @pytest.fixture def extension_initial_dot(): # noqa: D103 import bids.config return bids.config.get_option('extension_initial_dot') @pytest.fixture def mock_config(config_paths, extension_initial_dot): # noqa: D103 import bids.config with patch.dict('bids.config._settings'): bids.config._settings['config_paths'] = config_paths bids.config._settings['extension_initial_dot'] = extension_initial_dot yield @pytest.fixture(scope='session') def bids_examples(): # noqa: D103 examples_dir = Path( os.getenv( 'BIDS_EXAMPLES', Path(__file__).absolute().parent.parent.parent / 'bids-examples' ) ) if not Path.is_dir(examples_dir / 'ds001'): pytest.skip( f'BIDS examples missing from {examples_dir}. ' 'Override default location with BIDS_EXAMPLES environment variable.' ) return examples_dir @pytest.fixture(scope='session') def tests_dir(): # noqa: D103 test_dir = Path( os.getenv('PYBIDS_TEST_DATA', Path(__file__).absolute().parent.parent.parent / 'tests') ) if not Path.is_dir(test_dir / 'data'): pytest.skip( f'BIDS examples missing from {test_dir}. ' 'Override default location with PYBIDS_TEST_DATA environment variable.' ) return test_dir ================================================ FILE: src/bids/due.py ================================================ # emacs: at the end of the file # ex: set sts=4 ts=4 sw=4 et: # ## ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### # """Stub file for a guaranteed safe import of duecredit constructs: if duecredit is not available. To use it, place it into your project codebase to be imported, e.g. copy as cp stub.py /path/tomodule/module/due.py Note that it might be better to avoid naming it duecredit.py to avoid shadowing installed duecredit. Then use in your code as from .due import due, Doi, BibTeX, Text See https://github.com/duecredit/duecredit/blob/master/README.md for examples. Origin: Originally a part of the duecredit Copyright: 2015-2019 DueCredit developers License: BSD-2 """ __version__ = '0.0.7' class InactiveDueCreditCollector: """Just a stub at the Collector which would not do anything""" def _donothing(self, *args, **kwargs): """Perform no good and no bad""" pass def dcite(self, *args, **kwargs): """If I could cite I would""" def nondecorating_decorator(func): return func return nondecorating_decorator active = False activate = add = cite = dump = load = _donothing def __repr__(self): return self.__class__.__name__ + '()' def _donothing_func(*args, **kwargs): """Perform no good and no bad""" pass try: from duecredit import BibTeX, Doi, Text, Url, due if 'due' in locals() and not hasattr(due, 'cite'): raise RuntimeError('Imported due lacks .cite. DueCredit is now disabled') except Exception as e: # noqa: BLE001 if type(e).__name__ not in ('ImportError', 'ModuleNotFoundError'): import logging logging.getLogger('duecredit').error('Failed to import duecredit due to %s' % str(e)) # noqa: UP031 # Initiate due stub due = InactiveDueCreditCollector() BibTeX = Doi = Url = Text = _donothing_func # Emacs mode definitions # Local Variables: # mode: python # py-indent-offset: 4 # tab-width: 4 # indent-tabs-mode: nil # End: ================================================ FILE: src/bids/exceptions.py ================================================ """Exceptions. Exceptions relating to problems with BIDS itself, should carry BIDS in their name. All exceptions should subclass from PyBIDSError """ class PyBIDSError(Exception): """Base class. Typically for mix-in.""" class ConfigError(ValueError, PyBIDSError): """Problems with config file.""" class NoMatchError(ValueError, PyBIDSError): """No match found where it is required.""" class BIDSEntityError(AttributeError, PyBIDSError): """An unknown entity.""" class TargetError(ValueError, PyBIDSError): """An unknown target.""" class BIDSValidationError(ValueError, PyBIDSError): """An invalid BIDS dataset.""" class BIDSDerivativesValidationError(BIDSValidationError): """An invalid BIDS derivative dataset.""" class BIDSConflictingValuesError(BIDSValidationError): """A value conflict (e.g. in filename and side-car .json)""" class BIDSChildDatasetError(ValueError, PyBIDSError): """An invalid BIDS dataset.""" ================================================ FILE: src/bids/ext/__init__.py ================================================ """The PyBIDS extension namespace package ``bids.ext`` is reserved as a namespace for extensions to install into. To write such an extension, the following things are needed: 1) Create a new package with the following structure (assuming setuptools):: package/ bids/ ext/ __init__.py EXTENSION/ __init__.py ... setup.cfg setup.py The important things to note are that the ``bids/`` directory must be empty apart from ``ext/`` and ``bids/ext/`` must be empty except for your extension and an ``__init__.py``. 2) Place the following (and nothing else) in ``__init__.py``:: __path__ = __import__('pkgutil').extend_path(__path__, __name__) 3) Include the following lines in ``setup.cfg``:: [options] install_requires = pybids >= 0.15 packages = find_namespace: [options.packages.find] include = bids.ext.EXTENSION bids.ext.EXTENSION.* """ __path__ = __import__('pkgutil').extend_path(__path__, __name__) ================================================ FILE: src/bids/external/__init__.py ================================================ ================================================ FILE: src/bids/external/inflect.py ================================================ """ correctly generate plurals, ordinals, indefinite articles; convert numbers to words Copyright (C) 2010 Paul Dyson Based upon the Perl module Lingua::EN::Inflect by Damian Conway. The original Perl module Lingua::EN::Inflect by Damian Conway is available from http://search.cpan.org/~dconway/ This module can be downloaded at http://pypi.org/project/inflect methods: classical inflect plural plural_noun plural_verb plural_adj singular_noun no num a an compare compare_nouns compare_verbs compare_adjs present_participle ordinal number_to_words join defnoun defverb defadj defa defan INFLECTIONS: classical inflect plural plural_noun plural_verb plural_adj singular_noun compare no num a an present_participle PLURALS: classical inflect plural plural_noun plural_verb plural_adj singular_noun no num compare compare_nouns compare_verbs compare_adjs COMPARISONS: classical compare compare_nouns compare_verbs compare_adjs ARTICLES: classical inflect num a an NUMERICAL: ordinal number_to_words USER_DEFINED: defnoun defverb defadj defa defan Exceptions: UnknownClassicalModeError BadNumValueError BadChunkingOptionError NumOutOfRangeError BadUserDefinedPatternError BadRcFileError BadGenderError """ import ast import re import functools import contextlib from typing import ( Dict, Union, Optional, Iterable, List, Match, Tuple, Callable, Sequence, cast, ) class UnknownClassicalModeError(Exception): pass class BadNumValueError(Exception): pass class BadChunkingOptionError(Exception): pass class NumOutOfRangeError(Exception): pass class BadUserDefinedPatternError(Exception): pass class BadRcFileError(Exception): pass class BadGenderError(Exception): pass STDOUT_ON = False def print3(txt: str) -> None: if STDOUT_ON: print(txt) def enclose(s: str) -> str: return f"(?:{s})" def joinstem(cutpoint: Optional[int] = 0, words: Optional[Iterable[str]] = None) -> str: """ join stem of each word in words into a string for regex each word is truncated at cutpoint cutpoint is usually negative indicating the number of letters to remove from the end of each word e.g. joinstem(-2, ["ephemeris", "iris", ".*itis"]) returns (?:ephemer|ir|.*it) """ if words is None: words = "" return enclose("|".join(w[:cutpoint] for w in words)) def bysize(words: Iterable[str]) -> Dict[int, set]: """ take a list of words and return a dict of sets sorted by word length e.g. ret[3]=set(['ant', 'cat', 'dog', 'pig']) ret[4]=set(['frog', 'goat']) ret[5]=set(['horse']) ret[8]=set(['elephant']) """ ret: Dict[int, set] = {} for w in words: if len(w) not in ret: ret[len(w)] = set() ret[len(w)].add(w) return ret def make_pl_si_lists( lst: Iterable[str], plending: str, siendingsize: Optional[int], dojoinstem: bool = True, ): """ given a list of singular words: lst an ending to append to make the plural: plending the number of characters to remove from the singular before appending plending: siendingsize a flag whether to create a joinstem: dojoinstem return: a list of pluralised words: si_list (called si because this is what you need to look for to make the singular) the pluralised words as a dict of sets sorted by word length: si_bysize the singular words as a dict of sets sorted by word length: pl_bysize if dojoinstem is True: a regular expression that matches any of the stems: stem """ if siendingsize is not None: siendingsize = -siendingsize si_list = [w[:siendingsize] + plending for w in lst] pl_bysize = bysize(lst) si_bysize = bysize(si_list) if dojoinstem: stem = joinstem(siendingsize, lst) return si_list, si_bysize, pl_bysize, stem else: return si_list, si_bysize, pl_bysize # 1. PLURALS pl_sb_irregular_s = { "corpus": "corpuses|corpora", "opus": "opuses|opera", "genus": "genera", "mythos": "mythoi", "penis": "penises|penes", "testis": "testes", "atlas": "atlases|atlantes", "yes": "yeses", } pl_sb_irregular = { "child": "children", "chili": "chilis|chilies", "brother": "brothers|brethren", "infinity": "infinities|infinity", "loaf": "loaves", "lore": "lores|lore", "hoof": "hoofs|hooves", "beef": "beefs|beeves", "thief": "thiefs|thieves", "money": "monies", "mongoose": "mongooses", "ox": "oxen", "cow": "cows|kine", "graffito": "graffiti", "octopus": "octopuses|octopodes", "genie": "genies|genii", "ganglion": "ganglions|ganglia", "trilby": "trilbys", "turf": "turfs|turves", "numen": "numina", "atman": "atmas", "occiput": "occiputs|occipita", "sabretooth": "sabretooths", "sabertooth": "sabertooths", "lowlife": "lowlifes", "flatfoot": "flatfoots", "tenderfoot": "tenderfoots", "romany": "romanies", "jerry": "jerries", "mary": "maries", "talouse": "talouses", "rom": "roma", "carmen": "carmina", } pl_sb_irregular.update(pl_sb_irregular_s) # pl_sb_irregular_keys = enclose('|'.join(pl_sb_irregular.keys())) pl_sb_irregular_caps = { "Romany": "Romanies", "Jerry": "Jerrys", "Mary": "Marys", "Rom": "Roma", } pl_sb_irregular_compound = {"prima donna": "prima donnas|prime donne"} si_sb_irregular = {v: k for (k, v) in pl_sb_irregular.items()} for k in list(si_sb_irregular): if "|" in k: k1, k2 = k.split("|") si_sb_irregular[k1] = si_sb_irregular[k2] = si_sb_irregular[k] del si_sb_irregular[k] si_sb_irregular_caps = {v: k for (k, v) in pl_sb_irregular_caps.items()} si_sb_irregular_compound = {v: k for (k, v) in pl_sb_irregular_compound.items()} for k in list(si_sb_irregular_compound): if "|" in k: k1, k2 = k.split("|") si_sb_irregular_compound[k1] = si_sb_irregular_compound[ k2 ] = si_sb_irregular_compound[k] del si_sb_irregular_compound[k] # si_sb_irregular_keys = enclose('|'.join(si_sb_irregular.keys())) # Z's that don't double pl_sb_z_zes_list = ("quartz", "topaz") pl_sb_z_zes_bysize = bysize(pl_sb_z_zes_list) pl_sb_ze_zes_list = ("snooze",) pl_sb_ze_zes_bysize = bysize(pl_sb_ze_zes_list) # CLASSICAL "..is" -> "..ides" pl_sb_C_is_ides_complete = [ # GENERAL WORDS... "ephemeris", "iris", "clitoris", "chrysalis", "epididymis", ] pl_sb_C_is_ides_endings = [ # INFLAMATIONS... "itis" ] pl_sb_C_is_ides = joinstem( -2, pl_sb_C_is_ides_complete + [f".*{w}" for w in pl_sb_C_is_ides_endings] ) pl_sb_C_is_ides_list = pl_sb_C_is_ides_complete + pl_sb_C_is_ides_endings ( si_sb_C_is_ides_list, si_sb_C_is_ides_bysize, pl_sb_C_is_ides_bysize, ) = make_pl_si_lists(pl_sb_C_is_ides_list, "ides", 2, dojoinstem=False) # CLASSICAL "..a" -> "..ata" pl_sb_C_a_ata_list = ( "anathema", "bema", "carcinoma", "charisma", "diploma", "dogma", "drama", "edema", "enema", "enigma", "lemma", "lymphoma", "magma", "melisma", "miasma", "oedema", "sarcoma", "schema", "soma", "stigma", "stoma", "trauma", "gumma", "pragma", ) ( si_sb_C_a_ata_list, si_sb_C_a_ata_bysize, pl_sb_C_a_ata_bysize, pl_sb_C_a_ata, ) = make_pl_si_lists(pl_sb_C_a_ata_list, "ata", 1) # UNCONDITIONAL "..a" -> "..ae" pl_sb_U_a_ae_list = ( "alumna", "alga", "vertebra", "persona", "vita", ) ( si_sb_U_a_ae_list, si_sb_U_a_ae_bysize, pl_sb_U_a_ae_bysize, pl_sb_U_a_ae, ) = make_pl_si_lists(pl_sb_U_a_ae_list, "e", None) # CLASSICAL "..a" -> "..ae" pl_sb_C_a_ae_list = ( "amoeba", "antenna", "formula", "hyperbola", "medusa", "nebula", "parabola", "abscissa", "hydra", "nova", "lacuna", "aurora", "umbra", "flora", "fauna", ) ( si_sb_C_a_ae_list, si_sb_C_a_ae_bysize, pl_sb_C_a_ae_bysize, pl_sb_C_a_ae, ) = make_pl_si_lists(pl_sb_C_a_ae_list, "e", None) # CLASSICAL "..en" -> "..ina" pl_sb_C_en_ina_list = ("stamen", "foramen", "lumen") ( si_sb_C_en_ina_list, si_sb_C_en_ina_bysize, pl_sb_C_en_ina_bysize, pl_sb_C_en_ina, ) = make_pl_si_lists(pl_sb_C_en_ina_list, "ina", 2) # UNCONDITIONAL "..um" -> "..a" pl_sb_U_um_a_list = ( "bacterium", "agendum", "desideratum", "erratum", "stratum", "datum", "ovum", "extremum", "candelabrum", ) ( si_sb_U_um_a_list, si_sb_U_um_a_bysize, pl_sb_U_um_a_bysize, pl_sb_U_um_a, ) = make_pl_si_lists(pl_sb_U_um_a_list, "a", 2) # CLASSICAL "..um" -> "..a" pl_sb_C_um_a_list = ( "maximum", "minimum", "momentum", "optimum", "quantum", "cranium", "curriculum", "dictum", "phylum", "aquarium", "compendium", "emporium", "encomium", "gymnasium", "honorarium", "interregnum", "lustrum", "memorandum", "millennium", "rostrum", "spectrum", "speculum", "stadium", "trapezium", "ultimatum", "medium", "vacuum", "velum", "consortium", "arboretum", ) ( si_sb_C_um_a_list, si_sb_C_um_a_bysize, pl_sb_C_um_a_bysize, pl_sb_C_um_a, ) = make_pl_si_lists(pl_sb_C_um_a_list, "a", 2) # UNCONDITIONAL "..us" -> "i" pl_sb_U_us_i_list = ( "alumnus", "alveolus", "bacillus", "bronchus", "locus", "nucleus", "stimulus", "meniscus", "sarcophagus", ) ( si_sb_U_us_i_list, si_sb_U_us_i_bysize, pl_sb_U_us_i_bysize, pl_sb_U_us_i, ) = make_pl_si_lists(pl_sb_U_us_i_list, "i", 2) # CLASSICAL "..us" -> "..i" pl_sb_C_us_i_list = ( "focus", "radius", "genius", "incubus", "succubus", "nimbus", "fungus", "nucleolus", "stylus", "torus", "umbilicus", "uterus", "hippopotamus", "cactus", ) ( si_sb_C_us_i_list, si_sb_C_us_i_bysize, pl_sb_C_us_i_bysize, pl_sb_C_us_i, ) = make_pl_si_lists(pl_sb_C_us_i_list, "i", 2) # CLASSICAL "..us" -> "..us" (ASSIMILATED 4TH DECLENSION LATIN NOUNS) pl_sb_C_us_us = ( "status", "apparatus", "prospectus", "sinus", "hiatus", "impetus", "plexus", ) pl_sb_C_us_us_bysize = bysize(pl_sb_C_us_us) # UNCONDITIONAL "..on" -> "a" pl_sb_U_on_a_list = ( "criterion", "perihelion", "aphelion", "phenomenon", "prolegomenon", "noumenon", "organon", "asyndeton", "hyperbaton", ) ( si_sb_U_on_a_list, si_sb_U_on_a_bysize, pl_sb_U_on_a_bysize, pl_sb_U_on_a, ) = make_pl_si_lists(pl_sb_U_on_a_list, "a", 2) # CLASSICAL "..on" -> "..a" pl_sb_C_on_a_list = ("oxymoron",) ( si_sb_C_on_a_list, si_sb_C_on_a_bysize, pl_sb_C_on_a_bysize, pl_sb_C_on_a, ) = make_pl_si_lists(pl_sb_C_on_a_list, "a", 2) # CLASSICAL "..o" -> "..i" (BUT NORMALLY -> "..os") pl_sb_C_o_i = [ "solo", "soprano", "basso", "alto", "contralto", "tempo", "piano", "virtuoso", ] # list not tuple so can concat for pl_sb_U_o_os pl_sb_C_o_i_bysize = bysize(pl_sb_C_o_i) si_sb_C_o_i_bysize = bysize([f"{w[:-1]}i" for w in pl_sb_C_o_i]) pl_sb_C_o_i_stems = joinstem(-1, pl_sb_C_o_i) # ALWAYS "..o" -> "..os" pl_sb_U_o_os_complete = {"ado", "ISO", "NATO", "NCO", "NGO", "oto"} si_sb_U_o_os_complete = {f"{w}s" for w in pl_sb_U_o_os_complete} pl_sb_U_o_os_endings = [ "aficionado", "aggro", "albino", "allegro", "ammo", "Antananarivo", "archipelago", "armadillo", "auto", "avocado", "Bamako", "Barquisimeto", "bimbo", "bingo", "Biro", "bolero", "Bolzano", "bongo", "Boto", "burro", "Cairo", "canto", "cappuccino", "casino", "cello", "Chicago", "Chimango", "cilantro", "cochito", "coco", "Colombo", "Colorado", "commando", "concertino", "contango", "credo", "crescendo", "cyano", "demo", "ditto", "Draco", "dynamo", "embryo", "Esperanto", "espresso", "euro", "falsetto", "Faro", "fiasco", "Filipino", "flamenco", "furioso", "generalissimo", "Gestapo", "ghetto", "gigolo", "gizmo", "Greensboro", "gringo", "Guaiabero", "guano", "gumbo", "gyro", "hairdo", "hippo", "Idaho", "impetigo", "inferno", "info", "intermezzo", "intertrigo", "Iquico", "jumbo", "junto", "Kakapo", "kilo", "Kinkimavo", "Kokako", "Kosovo", "Lesotho", "libero", "libido", "libretto", "lido", "Lilo", "limbo", "limo", "lineno", "lingo", "lino", "livedo", "loco", "logo", "lumbago", "macho", "macro", "mafioso", "magneto", "magnifico", "Majuro", "Malabo", "manifesto", "Maputo", "Maracaibo", "medico", "memo", "metro", "Mexico", "micro", "Milano", "Monaco", "mono", "Montenegro", "Morocco", "Muqdisho", "myo", "neutrino", "Ningbo", "octavo", "oregano", "Orinoco", "Orlando", "Oslo", "panto", "Paramaribo", "Pardusco", "pedalo", "photo", "pimento", "pinto", "pleco", "Pluto", "pogo", "polo", "poncho", "Porto-Novo", "Porto", "pro", "psycho", "pueblo", "quarto", "Quito", "repo", "rhino", "risotto", "rococo", "rondo", "Sacramento", "saddo", "sago", "salvo", "Santiago", "Sapporo", "Sarajevo", "scherzando", "scherzo", "silo", "sirocco", "sombrero", "staccato", "sterno", "stucco", "stylo", "sumo", "Taiko", "techno", "terrazzo", "testudo", "timpano", "tiro", "tobacco", "Togo", "Tokyo", "torero", "Torino", "Toronto", "torso", "tremolo", "typo", "tyro", "ufo", "UNESCO", "vaquero", "vermicello", "verso", "vibrato", "violoncello", "Virgo", "weirdo", "WHO", "WTO", "Yamoussoukro", "yo-yo", "zero", "Zibo", ] + pl_sb_C_o_i pl_sb_U_o_os_bysize = bysize(pl_sb_U_o_os_endings) si_sb_U_o_os_bysize = bysize([f"{w}s" for w in pl_sb_U_o_os_endings]) # UNCONDITIONAL "..ch" -> "..chs" pl_sb_U_ch_chs_list = ("czech", "eunuch", "stomach") ( si_sb_U_ch_chs_list, si_sb_U_ch_chs_bysize, pl_sb_U_ch_chs_bysize, pl_sb_U_ch_chs, ) = make_pl_si_lists(pl_sb_U_ch_chs_list, "s", None) # UNCONDITIONAL "..[ei]x" -> "..ices" pl_sb_U_ex_ices_list = ("codex", "murex", "silex") ( si_sb_U_ex_ices_list, si_sb_U_ex_ices_bysize, pl_sb_U_ex_ices_bysize, pl_sb_U_ex_ices, ) = make_pl_si_lists(pl_sb_U_ex_ices_list, "ices", 2) pl_sb_U_ix_ices_list = ("radix", "helix") ( si_sb_U_ix_ices_list, si_sb_U_ix_ices_bysize, pl_sb_U_ix_ices_bysize, pl_sb_U_ix_ices, ) = make_pl_si_lists(pl_sb_U_ix_ices_list, "ices", 2) # CLASSICAL "..[ei]x" -> "..ices" pl_sb_C_ex_ices_list = ( "vortex", "vertex", "cortex", "latex", "pontifex", "apex", "index", "simplex", ) ( si_sb_C_ex_ices_list, si_sb_C_ex_ices_bysize, pl_sb_C_ex_ices_bysize, pl_sb_C_ex_ices, ) = make_pl_si_lists(pl_sb_C_ex_ices_list, "ices", 2) pl_sb_C_ix_ices_list = ("appendix",) ( si_sb_C_ix_ices_list, si_sb_C_ix_ices_bysize, pl_sb_C_ix_ices_bysize, pl_sb_C_ix_ices, ) = make_pl_si_lists(pl_sb_C_ix_ices_list, "ices", 2) # ARABIC: ".." -> "..i" pl_sb_C_i_list = ("afrit", "afreet", "efreet") (si_sb_C_i_list, si_sb_C_i_bysize, pl_sb_C_i_bysize, pl_sb_C_i) = make_pl_si_lists( pl_sb_C_i_list, "i", None ) # HEBREW: ".." -> "..im" pl_sb_C_im_list = ("goy", "seraph", "cherub") (si_sb_C_im_list, si_sb_C_im_bysize, pl_sb_C_im_bysize, pl_sb_C_im) = make_pl_si_lists( pl_sb_C_im_list, "im", None ) # UNCONDITIONAL "..man" -> "..mans" pl_sb_U_man_mans_list = """ ataman caiman cayman ceriman desman dolman farman harman hetman human leman ottoman shaman talisman """.split() pl_sb_U_man_mans_caps_list = """ Alabaman Bahaman Burman German Hiroshiman Liman Nakayaman Norman Oklahoman Panaman Roman Selman Sonaman Tacoman Yakiman Yokohaman Yuman """.split() ( si_sb_U_man_mans_list, si_sb_U_man_mans_bysize, pl_sb_U_man_mans_bysize, ) = make_pl_si_lists(pl_sb_U_man_mans_list, "s", None, dojoinstem=False) ( si_sb_U_man_mans_caps_list, si_sb_U_man_mans_caps_bysize, pl_sb_U_man_mans_caps_bysize, ) = make_pl_si_lists(pl_sb_U_man_mans_caps_list, "s", None, dojoinstem=False) # UNCONDITIONAL "..louse" -> "..lice" pl_sb_U_louse_lice_list = ("booklouse", "grapelouse", "louse", "woodlouse") ( si_sb_U_louse_lice_list, si_sb_U_louse_lice_bysize, pl_sb_U_louse_lice_bysize, ) = make_pl_si_lists(pl_sb_U_louse_lice_list, "lice", 5, dojoinstem=False) pl_sb_uninflected_s_complete = [ # PAIRS OR GROUPS SUBSUMED TO A SINGULAR... "breeches", "britches", "pajamas", "pyjamas", "clippers", "gallows", "hijinks", "headquarters", "pliers", "scissors", "testes", "herpes", "pincers", "shears", "proceedings", "trousers", # UNASSIMILATED LATIN 4th DECLENSION "cantus", "coitus", "nexus", # RECENT IMPORTS... "contretemps", "corps", "debris", "siemens", # DISEASES "mumps", # MISCELLANEOUS OTHERS... "diabetes", "jackanapes", "series", "species", "subspecies", "rabies", "chassis", "innings", "news", "mews", "haggis", ] pl_sb_uninflected_s_endings = [ # RECENT IMPORTS... "ois", # DISEASES "measles", ] pl_sb_uninflected_s = pl_sb_uninflected_s_complete + [ f".*{w}" for w in pl_sb_uninflected_s_endings ] pl_sb_uninflected_herd = ( # DON'T INFLECT IN CLASSICAL MODE, OTHERWISE NORMAL INFLECTION "wildebeest", "swine", "eland", "bison", "buffalo", "cattle", "elk", "rhinoceros", "zucchini", "caribou", "dace", "grouse", "guinea fowl", "guinea-fowl", "haddock", "hake", "halibut", "herring", "mackerel", "pickerel", "pike", "roe", "seed", "shad", "snipe", "teal", "turbot", "water fowl", "water-fowl", ) pl_sb_uninflected_complete = [ # SOME FISH AND HERD ANIMALS "tuna", "salmon", "mackerel", "trout", "bream", "sea-bass", "sea bass", "carp", "cod", "flounder", "whiting", "moose", # OTHER ODDITIES "graffiti", "djinn", "samuri", "offspring", "pence", "quid", "hertz", ] + pl_sb_uninflected_s_complete # SOME WORDS ENDING IN ...s (OFTEN PAIRS TAKEN AS A WHOLE) pl_sb_uninflected_caps = [ # ALL NATIONALS ENDING IN -ese "Portuguese", "Amoyese", "Borghese", "Congoese", "Faroese", "Foochowese", "Genevese", "Genoese", "Gilbertese", "Hottentotese", "Kiplingese", "Kongoese", "Lucchese", "Maltese", "Nankingese", "Niasese", "Pekingese", "Piedmontese", "Pistoiese", "Sarawakese", "Shavese", "Vermontese", "Wenchowese", "Yengeese", ] pl_sb_uninflected_endings = [ # UNCOUNTABLE NOUNS "butter", "cash", "furniture", "information", # SOME FISH AND HERD ANIMALS "fish", "deer", "sheep", # ALL NATIONALS ENDING IN -ese "nese", "rese", "lese", "mese", # DISEASES "pox", # OTHER ODDITIES "craft", ] + pl_sb_uninflected_s_endings # SOME WORDS ENDING IN ...s (OFTEN PAIRS TAKEN AS A WHOLE) pl_sb_uninflected_bysize = bysize(pl_sb_uninflected_endings) # SINGULAR WORDS ENDING IN ...s (ALL INFLECT WITH ...es) pl_sb_singular_s_complete = [ "acropolis", "aegis", "alias", "asbestos", "bathos", "bias", "bronchitis", "bursitis", "caddis", "cannabis", "canvas", "chaos", "cosmos", "dais", "digitalis", "epidermis", "ethos", "eyas", "gas", "glottis", "hubris", "ibis", "lens", "mantis", "marquis", "metropolis", "pathos", "pelvis", "polis", "rhinoceros", "sassafras", "trellis", ] + pl_sb_C_is_ides_complete pl_sb_singular_s_endings = ["ss", "us"] + pl_sb_C_is_ides_endings pl_sb_singular_s_bysize = bysize(pl_sb_singular_s_endings) si_sb_singular_s_complete = [f"{w}es" for w in pl_sb_singular_s_complete] si_sb_singular_s_endings = [f"{w}es" for w in pl_sb_singular_s_endings] si_sb_singular_s_bysize = bysize(si_sb_singular_s_endings) pl_sb_singular_s_es = ["[A-Z].*es"] pl_sb_singular_s = enclose( "|".join( pl_sb_singular_s_complete + [f".*{w}" for w in pl_sb_singular_s_endings] + pl_sb_singular_s_es ) ) # PLURALS ENDING IN uses -> use si_sb_ois_oi_case = ("Bolshois", "Hanois") si_sb_uses_use_case = ("Betelgeuses", "Duses", "Meuses", "Syracuses", "Toulouses") si_sb_uses_use = ( "abuses", "applauses", "blouses", "carouses", "causes", "chartreuses", "clauses", "contuses", "douses", "excuses", "fuses", "grouses", "hypotenuses", "masseuses", "menopauses", "misuses", "muses", "overuses", "pauses", "peruses", "profuses", "recluses", "reuses", "ruses", "souses", "spouses", "suffuses", "transfuses", "uses", ) si_sb_ies_ie_case = ( "Addies", "Aggies", "Allies", "Amies", "Angies", "Annies", "Annmaries", "Archies", "Arties", "Aussies", "Barbies", "Barries", "Basies", "Bennies", "Bernies", "Berties", "Bessies", "Betties", "Billies", "Blondies", "Bobbies", "Bonnies", "Bowies", "Brandies", "Bries", "Brownies", "Callies", "Carnegies", "Carries", "Cassies", "Charlies", "Cheries", "Christies", "Connies", "Curies", "Dannies", "Debbies", "Dixies", "Dollies", "Donnies", "Drambuies", "Eddies", "Effies", "Ellies", "Elsies", "Eries", "Ernies", "Essies", "Eugenies", "Fannies", "Flossies", "Frankies", "Freddies", "Gillespies", "Goldies", "Gracies", "Guthries", "Hallies", "Hatties", "Hetties", "Hollies", "Jackies", "Jamies", "Janies", "Jannies", "Jeanies", "Jeannies", "Jennies", "Jessies", "Jimmies", "Jodies", "Johnies", "Johnnies", "Josies", "Julies", "Kalgoorlies", "Kathies", "Katies", "Kellies", "Kewpies", "Kristies", "Laramies", "Lassies", "Lauries", "Leslies", "Lessies", "Lillies", "Lizzies", "Lonnies", "Lories", "Lorries", "Lotties", "Louies", "Mackenzies", "Maggies", "Maisies", "Mamies", "Marcies", "Margies", "Maries", "Marjories", "Matties", "McKenzies", "Melanies", "Mickies", "Millies", "Minnies", "Mollies", "Mounties", "Nannies", "Natalies", "Nellies", "Netties", "Ollies", "Ozzies", "Pearlies", "Pottawatomies", "Reggies", "Richies", "Rickies", "Robbies", "Ronnies", "Rosalies", "Rosemaries", "Rosies", "Roxies", "Rushdies", "Ruthies", "Sadies", "Sallies", "Sammies", "Scotties", "Selassies", "Sherries", "Sophies", "Stacies", "Stefanies", "Stephanies", "Stevies", "Susies", "Sylvies", "Tammies", "Terries", "Tessies", "Tommies", "Tracies", "Trekkies", "Valaries", "Valeries", "Valkyries", "Vickies", "Virgies", "Willies", "Winnies", "Wylies", "Yorkies", ) si_sb_ies_ie = ( "aeries", "baggies", "belies", "biggies", "birdies", "bogies", "bonnies", "boogies", "bookies", "bourgeoisies", "brownies", "budgies", "caddies", "calories", "camaraderies", "cockamamies", "collies", "cookies", "coolies", "cooties", "coteries", "crappies", "curies", "cutesies", "dogies", "eyries", "floozies", "footsies", "freebies", "genies", "goalies", "groupies", "hies", "jalousies", "junkies", "kiddies", "laddies", "lassies", "lies", "lingeries", "magpies", "menageries", "mommies", "movies", "neckties", "newbies", "nighties", "oldies", "organdies", "overlies", "pies", "pinkies", "pixies", "potpies", "prairies", "quickies", "reveries", "rookies", "rotisseries", "softies", "sorties", "species", "stymies", "sweeties", "ties", "underlies", "unties", "veggies", "vies", "yuppies", "zombies", ) si_sb_oes_oe_case = ( "Chloes", "Crusoes", "Defoes", "Faeroes", "Ivanhoes", "Joes", "McEnroes", "Moes", "Monroes", "Noes", "Poes", "Roscoes", "Tahoes", "Tippecanoes", "Zoes", ) si_sb_oes_oe = ( "aloes", "backhoes", "canoes", "does", "floes", "foes", "hoes", "mistletoes", "oboes", "pekoes", "roes", "sloes", "throes", "tiptoes", "toes", "woes", ) si_sb_z_zes = ("quartzes", "topazes") si_sb_zzes_zz = ("buzzes", "fizzes", "frizzes", "razzes") si_sb_ches_che_case = ( "Andromaches", "Apaches", "Blanches", "Comanches", "Nietzsches", "Porsches", "Roches", ) si_sb_ches_che = ( "aches", "avalanches", "backaches", "bellyaches", "caches", "cloches", "creches", "douches", "earaches", "fiches", "headaches", "heartaches", "microfiches", "niches", "pastiches", "psyches", "quiches", "stomachaches", "toothaches", "tranches", ) si_sb_xes_xe = ("annexes", "axes", "deluxes", "pickaxes") si_sb_sses_sse_case = ("Hesses", "Jesses", "Larousses", "Matisses") si_sb_sses_sse = ( "bouillabaisses", "crevasses", "demitasses", "impasses", "mousses", "posses", ) si_sb_ves_ve_case = ( # *[nwl]ives -> [nwl]live "Clives", "Palmolives", ) si_sb_ves_ve = ( # *[^d]eaves -> eave "interweaves", "weaves", # *[nwl]ives -> [nwl]live "olives", # *[eoa]lves -> [eoa]lve "bivalves", "dissolves", "resolves", "salves", "twelves", "valves", ) plverb_special_s = enclose( "|".join( [pl_sb_singular_s] + pl_sb_uninflected_s + list(pl_sb_irregular_s) + ["(.*[csx])is", "(.*)ceps", "[A-Z].*s"] ) ) _pl_sb_postfix_adj_defn = ( ("general", enclose(r"(?!major|lieutenant|brigadier|adjutant|.*star)\S+")), ("martial", enclose("court")), ("force", enclose("pound")), ) pl_sb_postfix_adj: Iterable[str] = ( enclose(val + f"(?=(?:-|\\s+){key})") for key, val in _pl_sb_postfix_adj_defn ) pl_sb_postfix_adj_stems = f"({'|'.join(pl_sb_postfix_adj)})(.*)" # PLURAL WORDS ENDING IS es GO TO SINGULAR is si_sb_es_is = ( "amanuenses", "amniocenteses", "analyses", "antitheses", "apotheoses", "arterioscleroses", "atheroscleroses", "axes", # 'bases', # bases -> basis "catalyses", "catharses", "chasses", "cirrhoses", "cocces", "crises", "diagnoses", "dialyses", "diereses", "electrolyses", "emphases", "exegeses", "geneses", "halitoses", "hydrolyses", "hypnoses", "hypotheses", "hystereses", "metamorphoses", "metastases", "misdiagnoses", "mitoses", "mononucleoses", "narcoses", "necroses", "nemeses", "neuroses", "oases", "osmoses", "osteoporoses", "paralyses", "parentheses", "parthenogeneses", "periphrases", "photosyntheses", "probosces", "prognoses", "prophylaxes", "prostheses", "preces", "psoriases", "psychoanalyses", "psychokineses", "psychoses", "scleroses", "scolioses", "sepses", "silicoses", "symbioses", "synopses", "syntheses", "taxes", "telekineses", "theses", "thromboses", "tuberculoses", "urinalyses", ) pl_prep_list = """ about above across after among around at athwart before behind below beneath beside besides between betwixt beyond but by during except for from in into near of off on onto out over since till to under until unto upon with""".split() pl_prep_list_da = pl_prep_list + ["de", "du", "da"] pl_prep_bysize = bysize(pl_prep_list_da) pl_prep = enclose("|".join(pl_prep_list_da)) pl_sb_prep_dual_compound = fr"(.*?)((?:-|\s+)(?:{pl_prep})(?:-|\s+))a(?:-|\s+)(.*)" singular_pronoun_genders = { "neuter", "feminine", "masculine", "gender-neutral", "feminine or masculine", "masculine or feminine", } pl_pron_nom = { # NOMINATIVE REFLEXIVE "i": "we", "myself": "ourselves", "you": "you", "yourself": "yourselves", "she": "they", "herself": "themselves", "he": "they", "himself": "themselves", "it": "they", "itself": "themselves", "they": "they", "themself": "themselves", # POSSESSIVE "mine": "ours", "yours": "yours", "hers": "theirs", "his": "theirs", "its": "theirs", "theirs": "theirs", } si_pron: Dict[str, Dict[str, Union[str, Dict[str, str]]]] = { "nom": {v: k for (k, v) in pl_pron_nom.items()} } si_pron["nom"]["we"] = "I" pl_pron_acc = { # ACCUSATIVE REFLEXIVE "me": "us", "myself": "ourselves", "you": "you", "yourself": "yourselves", "her": "them", "herself": "themselves", "him": "them", "himself": "themselves", "it": "them", "itself": "themselves", "them": "them", "themself": "themselves", } pl_pron_acc_keys = enclose("|".join(pl_pron_acc)) pl_pron_acc_keys_bysize = bysize(pl_pron_acc) si_pron["acc"] = {v: k for (k, v) in pl_pron_acc.items()} for _thecase, _plur, _gend, _sing in ( ("nom", "they", "neuter", "it"), ("nom", "they", "feminine", "she"), ("nom", "they", "masculine", "he"), ("nom", "they", "gender-neutral", "they"), ("nom", "they", "feminine or masculine", "she or he"), ("nom", "they", "masculine or feminine", "he or she"), ("nom", "themselves", "neuter", "itself"), ("nom", "themselves", "feminine", "herself"), ("nom", "themselves", "masculine", "himself"), ("nom", "themselves", "gender-neutral", "themself"), ("nom", "themselves", "feminine or masculine", "herself or himself"), ("nom", "themselves", "masculine or feminine", "himself or herself"), ("nom", "theirs", "neuter", "its"), ("nom", "theirs", "feminine", "hers"), ("nom", "theirs", "masculine", "his"), ("nom", "theirs", "gender-neutral", "theirs"), ("nom", "theirs", "feminine or masculine", "hers or his"), ("nom", "theirs", "masculine or feminine", "his or hers"), ("acc", "them", "neuter", "it"), ("acc", "them", "feminine", "her"), ("acc", "them", "masculine", "him"), ("acc", "them", "gender-neutral", "them"), ("acc", "them", "feminine or masculine", "her or him"), ("acc", "them", "masculine or feminine", "him or her"), ("acc", "themselves", "neuter", "itself"), ("acc", "themselves", "feminine", "herself"), ("acc", "themselves", "masculine", "himself"), ("acc", "themselves", "gender-neutral", "themself"), ("acc", "themselves", "feminine or masculine", "herself or himself"), ("acc", "themselves", "masculine or feminine", "himself or herself"), ): try: si_pron[_thecase][_plur][_gend] = _sing # type: ignore except TypeError: si_pron[_thecase][_plur] = {} si_pron[_thecase][_plur][_gend] = _sing # type: ignore si_pron_acc_keys = enclose("|".join(si_pron["acc"])) si_pron_acc_keys_bysize = bysize(si_pron["acc"]) def get_si_pron(thecase, word, gender) -> str: try: sing = si_pron[thecase][word] except KeyError: raise # not a pronoun try: return sing[gender] # has several types due to gender except TypeError: return cast(str, sing) # answer independent of gender # These dictionaries group verbs by first, second and third person # conjugations. plverb_irregular_pres = { "am": "are", "are": "are", "is": "are", "was": "were", "were": "were", "was": "were", "have": "have", "have": "have", "has": "have", "do": "do", "do": "do", "does": "do", } plverb_ambiguous_pres = { "act": "act", "act": "act", "acts": "act", "blame": "blame", "blame": "blame", "blames": "blame", "can": "can", "can": "can", "can": "can", "must": "must", "must": "must", "must": "must", "fly": "fly", "fly": "fly", "flies": "fly", "copy": "copy", "copy": "copy", "copies": "copy", "drink": "drink", "drink": "drink", "drinks": "drink", "fight": "fight", "fight": "fight", "fights": "fight", "fire": "fire", "fire": "fire", "fires": "fire", "like": "like", "like": "like", "likes": "like", "look": "look", "look": "look", "looks": "look", "make": "make", "make": "make", "makes": "make", "reach": "reach", "reach": "reach", "reaches": "reach", "run": "run", "run": "run", "runs": "run", "sink": "sink", "sink": "sink", "sinks": "sink", "sleep": "sleep", "sleep": "sleep", "sleeps": "sleep", "view": "view", "view": "view", "views": "view", } plverb_ambiguous_pres_keys = re.compile( fr"^({enclose('|'.join(plverb_ambiguous_pres))})((\s.*)?)$", re.IGNORECASE ) plverb_irregular_non_pres = ( "did", "had", "ate", "made", "put", "spent", "fought", "sank", "gave", "sought", "shall", "could", "ought", "should", ) plverb_ambiguous_non_pres = re.compile( r"^((?:thought|saw|bent|will|might|cut))((\s.*)?)$", re.IGNORECASE ) # "..oes" -> "..oe" (the rest are "..oes" -> "o") pl_v_oes_oe = ("canoes", "floes", "oboes", "roes", "throes", "woes") pl_v_oes_oe_endings_size4 = ("hoes", "toes") pl_v_oes_oe_endings_size5 = ("shoes",) pl_count_zero = ("0", "no", "zero", "nil") pl_count_one = ("1", "a", "an", "one", "each", "every", "this", "that") pl_adj_special = {"a": "some", "an": "some", "this": "these", "that": "those"} pl_adj_special_keys = re.compile( fr"^({enclose('|'.join(pl_adj_special))})$", re.IGNORECASE ) pl_adj_poss = { "my": "our", "your": "your", "its": "their", "her": "their", "his": "their", "their": "their", } pl_adj_poss_keys = re.compile(fr"^({enclose('|'.join(pl_adj_poss))})$", re.IGNORECASE) # 2. INDEFINITE ARTICLES # THIS PATTERN MATCHES STRINGS OF CAPITALS STARTING WITH A "VOWEL-SOUND" # CONSONANT FOLLOWED BY ANOTHER CONSONANT, AND WHICH ARE NOT LIKELY # TO BE REAL WORDS (OH, ALL RIGHT THEN, IT'S JUST MAGIC!) A_abbrev = re.compile( r""" (?! FJO | [HLMNS]Y. | RY[EO] | SQU | ( F[LR]? | [HL] | MN? | N | RH? | S[CHKLMNPTVW]? | X(YL)?) [AEIOU]) [FHLMNRSX][A-Z] """, re.VERBOSE, ) # THIS PATTERN CODES THE BEGINNINGS OF ALL ENGLISH WORDS BEGINING WITH A # 'y' FOLLOWED BY A CONSONANT. ANY OTHER Y-CONSONANT PREFIX THEREFORE # IMPLIES AN ABBREVIATION. A_y_cons = re.compile(r"^(y(b[lor]|cl[ea]|fere|gg|p[ios]|rou|tt))", re.IGNORECASE) # EXCEPTIONS TO EXCEPTIONS A_explicit_a = re.compile(r"^((?:unabomber|unanimous|US))", re.IGNORECASE) A_explicit_an = re.compile( r"^((?:euler|hour(?!i)|heir|honest|hono[ur]|mpeg))", re.IGNORECASE ) A_ordinal_an = re.compile(r"^([aefhilmnorsx]-?th)", re.IGNORECASE) A_ordinal_a = re.compile(r"^([bcdgjkpqtuvwyz]-?th)", re.IGNORECASE) # NUMERICAL INFLECTIONS nth = { 0: "th", 1: "st", 2: "nd", 3: "rd", 4: "th", 5: "th", 6: "th", 7: "th", 8: "th", 9: "th", 11: "th", 12: "th", 13: "th", } nth_suff = set(nth.values()) ordinal = dict( ty="tieth", one="first", two="second", three="third", five="fifth", eight="eighth", nine="ninth", twelve="twelfth", ) ordinal_suff = re.compile(fr"({'|'.join(ordinal)})\Z") # NUMBERS unit = ["", "one", "two", "three", "four", "five", "six", "seven", "eight", "nine"] teen = [ "ten", "eleven", "twelve", "thirteen", "fourteen", "fifteen", "sixteen", "seventeen", "eighteen", "nineteen", ] ten = [ "", "", "twenty", "thirty", "forty", "fifty", "sixty", "seventy", "eighty", "ninety", ] mill = [ " ", " thousand", " million", " billion", " trillion", " quadrillion", " quintillion", " sextillion", " septillion", " octillion", " nonillion", " decillion", ] # SUPPORT CLASSICAL PLURALIZATIONS def_classical = dict( all=False, zero=False, herd=False, names=True, persons=False, ancient=False ) all_classical = {k: True for k in def_classical} no_classical = {k: False for k in def_classical} # Maps strings to built-in constant types string_to_constant = {"True": True, "False": False, "None": None} # Pre-compiled regular expression objects DOLLAR_DIGITS = re.compile(r"\$(\d+)") FUNCTION_CALL = re.compile(r"((\w+)\([^)]*\)*)", re.IGNORECASE) PARTITION_WORD = re.compile(r"\A(\s*)(.+?)(\s*)\Z") PL_SB_POSTFIX_ADJ_STEMS_RE = re.compile( fr"^(?:{pl_sb_postfix_adj_stems})$", re.IGNORECASE ) PL_SB_PREP_DUAL_COMPOUND_RE = re.compile( fr"^(?:{pl_sb_prep_dual_compound})$", re.IGNORECASE ) DENOMINATOR = re.compile(r"(?P.+)( (per|a) .+)") PLVERB_SPECIAL_S_RE = re.compile(fr"^({plverb_special_s})$") WHITESPACE = re.compile(r"\s") ENDS_WITH_S = re.compile(r"^(.*[^s])s$", re.IGNORECASE) ENDS_WITH_APOSTROPHE_S = re.compile(r"^(.*)'s?$") INDEFINITE_ARTICLE_TEST = re.compile(r"\A(\s*)(?:an?\s+)?(.+?)(\s*)\Z", re.IGNORECASE) SPECIAL_AN = re.compile(r"^[aefhilmnorsx]$", re.IGNORECASE) SPECIAL_A = re.compile(r"^[bcdgjkpqtuvwyz]$", re.IGNORECASE) SPECIAL_ABBREV_AN = re.compile(r"^[aefhilmnorsx][.-]", re.IGNORECASE) SPECIAL_ABBREV_A = re.compile(r"^[a-z][.-]", re.IGNORECASE) CONSONANTS = re.compile(r"^[^aeiouy]", re.IGNORECASE) ARTICLE_SPECIAL_EU = re.compile(r"^e[uw]", re.IGNORECASE) ARTICLE_SPECIAL_ONCE = re.compile(r"^onc?e\b", re.IGNORECASE) ARTICLE_SPECIAL_ONETIME = re.compile(r"^onetime\b", re.IGNORECASE) ARTICLE_SPECIAL_UNIT = re.compile(r"^uni([^nmd]|mo)", re.IGNORECASE) ARTICLE_SPECIAL_UBA = re.compile(r"^u[bcfghjkqrst][aeiou]", re.IGNORECASE) ARTICLE_SPECIAL_UKR = re.compile(r"^ukr", re.IGNORECASE) SPECIAL_CAPITALS = re.compile(r"^U[NK][AIEO]?") VOWELS = re.compile(r"^[aeiou]", re.IGNORECASE) DIGIT_GROUP = re.compile(r"(\d)") TWO_DIGITS = re.compile(r"(\d)(\d)") THREE_DIGITS = re.compile(r"(\d)(\d)(\d)") THREE_DIGITS_WORD = re.compile(r"(\d)(\d)(\d)(?=\D*\Z)") TWO_DIGITS_WORD = re.compile(r"(\d)(\d)(?=\D*\Z)") ONE_DIGIT_WORD = re.compile(r"(\d)(?=\D*\Z)") FOUR_DIGIT_COMMA = re.compile(r"(\d)(\d{3}(?:,|\Z))") NON_DIGIT = re.compile(r"\D") WHITESPACES_COMMA = re.compile(r"\s+,") COMMA_WORD = re.compile(r", (\S+)\s+\Z") WHITESPACES = re.compile(r"\s+") PRESENT_PARTICIPLE_REPLACEMENTS = ( (re.compile(r"ie$"), r"y"), ( re.compile(r"ue$"), r"u", ), # TODO: isn't ue$ -> u encompassed in the following rule? (re.compile(r"([auy])e$"), r"\g<1>"), (re.compile(r"ski$"), r"ski"), (re.compile(r"[^b]i$"), r""), (re.compile(r"^(are|were)$"), r"be"), (re.compile(r"^(had)$"), r"hav"), (re.compile(r"^(hoe)$"), r"\g<1>"), (re.compile(r"([^e])e$"), r"\g<1>"), (re.compile(r"er$"), r"er"), (re.compile(r"([^aeiou][aeiouy]([bdgmnprst]))$"), r"\g<1>\g<2>"), ) DIGIT = re.compile(r"\d") class Words(str): lowered: str split_: List[str] first: str last: str def __init__(self, orig) -> None: self.lowered = self.lower() self.split_ = self.split() self.first = self.split_[0] self.last = self.split_[-1] class engine: def __init__(self) -> None: self.classical_dict = def_classical.copy() self.persistent_count: Optional[int] = None self.mill_count = 0 self.pl_sb_user_defined: List[str] = [] self.pl_v_user_defined: List[str] = [] self.pl_adj_user_defined: List[str] = [] self.si_sb_user_defined: List[str] = [] self.A_a_user_defined: List[str] = [] self.thegender = "neuter" self.__number_args: Optional[Dict[str, str]] = None @property def _number_args(self): return cast(Dict[str, str], self.__number_args) @_number_args.setter def _number_args(self, val): self.__number_args = val deprecated_methods = dict( pl="plural", plnoun="plural_noun", plverb="plural_verb", pladj="plural_adj", sinoun="single_noun", prespart="present_participle", numwords="number_to_words", plequal="compare", plnounequal="compare_nouns", plverbequal="compare_verbs", pladjequal="compare_adjs", wordlist="join", ) def __getattr__(self, meth): if meth in self.deprecated_methods: print3(f"{meth}() deprecated, use {self.deprecated_methods[meth]}()") raise DeprecationWarning raise AttributeError def defnoun(self, singular: str, plural: str) -> int: """ Set the noun plural of singular to plural. """ self.checkpat(singular) self.checkpatplural(plural) self.pl_sb_user_defined.extend((singular, plural)) self.si_sb_user_defined.extend((plural, singular)) return 1 def defverb(self, s1: str, p1: str, s2: str, p2: str, s3: str, p3: str) -> int: """ Set the verb plurals for s1, s2 and s3 to p1, p2 and p3 respectively. Where 1, 2 and 3 represent the 1st, 2nd and 3rd person forms of the verb. """ self.checkpat(s1) self.checkpat(s2) self.checkpat(s3) self.checkpatplural(p1) self.checkpatplural(p2) self.checkpatplural(p3) self.pl_v_user_defined.extend((s1, p1, s2, p2, s3, p3)) return 1 def defadj(self, singular: str, plural: str) -> int: """ Set the adjective plural of singular to plural. """ self.checkpat(singular) self.checkpatplural(plural) self.pl_adj_user_defined.extend((singular, plural)) return 1 def defa(self, pattern: str) -> int: """ Define the indefinite article as 'a' for words matching pattern. """ self.checkpat(pattern) self.A_a_user_defined.extend((pattern, "a")) return 1 def defan(self, pattern: str) -> int: """ Define the indefinite article as 'an' for words matching pattern. """ self.checkpat(pattern) self.A_a_user_defined.extend((pattern, "an")) return 1 def checkpat(self, pattern: Optional[str]) -> None: """ check for errors in a regex pattern """ if pattern is None: return try: re.match(pattern, "") except re.error: print3(f"\nBad user-defined singular pattern:\n\t{pattern}\n") raise BadUserDefinedPatternError def checkpatplural(self, pattern: str) -> None: """ check for errors in a regex replace pattern """ return def ud_match(self, word: str, wordlist: List[str]) -> Optional[str]: for i in range(len(wordlist) - 2, -2, -2): # backwards through even elements mo = re.search(fr"^{wordlist[i]}$", word, re.IGNORECASE) if mo: if wordlist[i + 1] is None: return None pl = DOLLAR_DIGITS.sub( r"\\1", wordlist[i + 1] ) # change $n to \n for expand return mo.expand(pl) return None def classical(self, **kwargs) -> None: """ turn classical mode on and off for various categories turn on all classical modes: classical() classical(all=True) turn on or off specific claassical modes: e.g. classical(herd=True) classical(names=False) By default all classical modes are off except names. unknown value in args or key in kwargs raises exception: UnknownClasicalModeError """ if not kwargs: self.classical_dict = all_classical.copy() return if "all" in kwargs: if kwargs["all"]: self.classical_dict = all_classical.copy() else: self.classical_dict = no_classical.copy() for k, v in kwargs.items(): if k in def_classical: self.classical_dict[k] = v else: raise UnknownClassicalModeError def num( self, count: Optional[int] = None, show: Optional[int] = None ) -> str: # (;$count,$show) """ Set the number to be used in other method calls. Returns count. Set show to False to return '' instead. """ if count is not None: try: self.persistent_count = int(count) except ValueError: raise BadNumValueError if (show is None) or show: return str(count) else: self.persistent_count = None return "" def gender(self, gender: str) -> None: """ set the gender for the singular of plural pronouns can be one of: 'neuter' ('they' -> 'it') 'feminine' ('they' -> 'she') 'masculine' ('they' -> 'he') 'gender-neutral' ('they' -> 'they') 'feminine or masculine' ('they' -> 'she or he') 'masculine or feminine' ('they' -> 'he or she') """ if gender in singular_pronoun_genders: self.thegender = gender else: raise BadGenderError def _get_value_from_ast(self, obj): """ Return the value of the ast object. """ if isinstance(obj, ast.Num): return obj.n elif isinstance(obj, ast.Str): return obj.s elif isinstance(obj, ast.List): return [self._get_value_from_ast(e) for e in obj.elts] elif isinstance(obj, ast.Tuple): return tuple([self._get_value_from_ast(e) for e in obj.elts]) # None, True and False are NameConstants in Py3.4 and above. elif isinstance(obj, ast.NameConstant): return obj.value # Probably passed a variable name. # Or passed a single word without wrapping it in quotes as an argument # ex: p.inflect("I plural(see)") instead of p.inflect("I plural('see')") raise NameError(f"name '{obj.id}' is not defined") def _string_to_substitute( self, mo: Match, methods_dict: Dict[str, Callable] ) -> str: """ Return the string to be substituted for the match. """ matched_text, f_name = mo.groups() # matched_text is the complete match string. e.g. plural_noun(cat) # f_name is the function name. e.g. plural_noun # Return matched_text if function name is not in methods_dict if f_name not in methods_dict: return matched_text # Parse the matched text a_tree = ast.parse(matched_text) # get the args and kwargs from ast objects args_list = [ self._get_value_from_ast(a) for a in a_tree.body[0].value.args # type: ignore[attr-defined] ] kwargs_list = { kw.arg: self._get_value_from_ast(kw.value) for kw in a_tree.body[0].value.keywords # type: ignore[attr-defined] } # Call the corresponding function return methods_dict[f_name](*args_list, **kwargs_list) # 0. PERFORM GENERAL INFLECTIONS IN A STRING def inflect(self, text: str) -> str: """ Perform inflections in a string. e.g. inflect('The plural of cat is plural(cat)') returns 'The plural of cat is cats' can use plural, plural_noun, plural_verb, plural_adj, singular_noun, a, an, no, ordinal, number_to_words, and prespart """ save_persistent_count = self.persistent_count # Dictionary of allowed methods methods_dict: Dict[str, Callable] = { "plural": self.plural, "plural_adj": self.plural_adj, "plural_noun": self.plural_noun, "plural_verb": self.plural_verb, "singular_noun": self.singular_noun, "a": self.a, "an": self.a, "no": self.no, "ordinal": self.ordinal, "number_to_words": self.number_to_words, "present_participle": self.present_participle, "num": self.num, } # Regular expression to find Python's function call syntax output = FUNCTION_CALL.sub( lambda mo: self._string_to_substitute(mo, methods_dict), text ) self.persistent_count = save_persistent_count return output # ## PLURAL SUBROUTINES def postprocess(self, orig: str, inflected) -> str: inflected = str(inflected) if "|" in inflected: word_options = inflected.split("|") # When two parts of a noun need to be pluralized if len(word_options[0].split(" ")) == len(word_options[1].split(" ")): result = inflected.split("|")[self.classical_dict["all"]].split(" ") # When only the last part of the noun needs to be pluralized else: result = inflected.split(" ") for index, word in enumerate(result): if "|" in word: result[index] = word.split("|")[self.classical_dict["all"]] else: result = inflected.split(" ") # Try to fix word wise capitalization for index, word in enumerate(orig.split(" ")): if word == "I": # Is this the only word for exceptions like this # Where the original is fully capitalized # without 'meaning' capitalization? # Also this fails to handle a capitalizaion in context continue if word.capitalize() == word: result[index] = result[index].capitalize() if word == word.upper(): result[index] = result[index].upper() return " ".join(result) def partition_word(self, text: str) -> Tuple[str, str, str]: mo = PARTITION_WORD.search(text) if mo: return mo.group(1), mo.group(2), mo.group(3) else: return "", "", "" def plural(self, text: str, count: Optional[Union[str, int]] = None) -> str: """ Return the plural of text. If count supplied, then return text if count is one of: 1, a, an, one, each, every, this, that otherwise return the plural. Whitespace at the start and end is preserved. """ pre, word, post = self.partition_word(text) if not word: return text plural = self.postprocess( word, self._pl_special_adjective(word, count) or self._pl_special_verb(word, count) or self._plnoun(word, count), ) return f"{pre}{plural}{post}" def plural_noun(self, text: str, count: Optional[Union[str, int]] = None) -> str: """ Return the plural of text, where text is a noun. If count supplied, then return text if count is one of: 1, a, an, one, each, every, this, that otherwise return the plural. Whitespace at the start and end is preserved. """ pre, word, post = self.partition_word(text) if not word: return text plural = self.postprocess(word, self._plnoun(word, count)) return f"{pre}{plural}{post}" def plural_verb(self, text: str, count: Optional[Union[str, int]] = None) -> str: """ Return the plural of text, where text is a verb. If count supplied, then return text if count is one of: 1, a, an, one, each, every, this, that otherwise return the plural. Whitespace at the start and end is preserved. """ pre, word, post = self.partition_word(text) if not word: return text plural = self.postprocess( word, self._pl_special_verb(word, count) or self._pl_general_verb(word, count), ) return f"{pre}{plural}{post}" def plural_adj(self, text: str, count: str = None) -> str: """ Return the plural of text, where text is an adjective. If count supplied, then return text if count is one of: 1, a, an, one, each, every, this, that otherwise return the plural. Whitespace at the start and end is preserved. """ pre, word, post = self.partition_word(text) if not word: return text plural = self.postprocess(word, self._pl_special_adjective(word, count) or word) return f"{pre}{plural}{post}" def compare(self, word1: str, word2: str) -> Union[str, bool]: """ compare word1 and word2 for equality regardless of plurality return values: eq - the strings are equal p:s - word1 is the plural of word2 s:p - word2 is the plural of word1 p:p - word1 and word2 are two different plural forms of the one word False - otherwise """ return ( self._plequal(word1, word2, self.plural_noun) or self._plequal(word1, word2, self.plural_verb) or self._plequal(word1, word2, self.plural_adj) ) def compare_nouns(self, word1: str, word2: str) -> Union[str, bool]: """ compare word1 and word2 for equality regardless of plurality word1 and word2 are to be treated as nouns return values: eq - the strings are equal p:s - word1 is the plural of word2 s:p - word2 is the plural of word1 p:p - word1 and word2 are two different plural forms of the one word False - otherwise """ return self._plequal(word1, word2, self.plural_noun) def compare_verbs(self, word1: str, word2: str) -> Union[str, bool]: """ compare word1 and word2 for equality regardless of plurality word1 and word2 are to be treated as verbs return values: eq - the strings are equal p:s - word1 is the plural of word2 s:p - word2 is the plural of word1 p:p - word1 and word2 are two different plural forms of the one word False - otherwise """ return self._plequal(word1, word2, self.plural_verb) def compare_adjs(self, word1: str, word2: str) -> Union[str, bool]: """ compare word1 and word2 for equality regardless of plurality word1 and word2 are to be treated as adjectives return values: eq - the strings are equal p:s - word1 is the plural of word2 s:p - word2 is the plural of word1 p:p - word1 and word2 are two different plural forms of the one word False - otherwise """ return self._plequal(word1, word2, self.plural_adj) def singular_noun( self, text: str, count: Optional[Union[int, str]] = None, gender: Optional[str] = None, ) -> Union[str, bool]: """ Return the singular of text, where text is a plural noun. If count supplied, then return the singular if count is one of: 1, a, an, one, each, every, this, that or if count is None otherwise return text unchanged. Whitespace at the start and end is preserved. >>> p = engine() >>> p.singular_noun('horses') 'horse' >>> p.singular_noun('knights') 'knight' Returns False when a singular noun is passed. >>> p.singular_noun('horse') False >>> p.singular_noun('knight') False >>> p.singular_noun('soldier') False """ pre, word, post = self.partition_word(text) if not word: return text sing = self._sinoun(word, count=count, gender=gender) if sing is not False: plural = self.postprocess(word, sing) return f"{pre}{plural}{post}" return False def _plequal(self, word1: str, word2: str, pl) -> Union[str, bool]: # noqa: C901 classval = self.classical_dict.copy() self.classical_dict = all_classical.copy() if word1 == word2: return "eq" if word1 == pl(word2): return "p:s" if pl(word1) == word2: return "s:p" self.classical_dict = no_classical.copy() if word1 == pl(word2): return "p:s" if pl(word1) == word2: return "s:p" self.classical_dict = classval.copy() if pl == self.plural or pl == self.plural_noun: if self._pl_check_plurals_N(word1, word2): return "p:p" if self._pl_check_plurals_N(word2, word1): return "p:p" if pl == self.plural or pl == self.plural_adj: if self._pl_check_plurals_adj(word1, word2): return "p:p" return False def _pl_reg_plurals(self, pair: str, stems: str, end1: str, end2: str) -> bool: pattern = fr"({stems})({end1}\|\1{end2}|{end2}\|\1{end1})" return bool(re.search(pattern, pair)) def _pl_check_plurals_N(self, word1: str, word2: str) -> bool: stem_endings = ( (pl_sb_C_a_ata, "as", "ata"), (pl_sb_C_is_ides, "is", "ides"), (pl_sb_C_a_ae, "s", "e"), (pl_sb_C_en_ina, "ens", "ina"), (pl_sb_C_um_a, "ums", "a"), (pl_sb_C_us_i, "uses", "i"), (pl_sb_C_on_a, "ons", "a"), (pl_sb_C_o_i_stems, "os", "i"), (pl_sb_C_ex_ices, "exes", "ices"), (pl_sb_C_ix_ices, "ixes", "ices"), (pl_sb_C_i, "s", "i"), (pl_sb_C_im, "s", "im"), (".*eau", "s", "x"), (".*ieu", "s", "x"), (".*tri", "xes", "ces"), (".{2,}[yia]n", "xes", "ges"), ) words = map(Words, (word1, word2)) pair = "|".join(word.last for word in words) return ( pair in pl_sb_irregular_s.values() or pair in pl_sb_irregular.values() or pair in pl_sb_irregular_caps.values() or any( self._pl_reg_plurals(pair, stems, end1, end2) for stems, end1, end2 in stem_endings ) ) def _pl_check_plurals_adj(self, word1: str, word2: str) -> bool: word1a = word1[: word1.rfind("'")] if word1.endswith(("'s", "'")) else "" word2a = word2[: word2.rfind("'")] if word2.endswith(("'s", "'")) else "" return ( bool(word1a) and bool(word2a) and ( self._pl_check_plurals_N(word1a, word2a) or self._pl_check_plurals_N(word2a, word1a) ) ) def get_count(self, count: Optional[Union[str, int]] = None) -> Union[str, int]: if count is None and self.persistent_count is not None: count = self.persistent_count if count is not None: count = ( 1 if ( (str(count) in pl_count_one) or ( self.classical_dict["zero"] and str(count).lower() in pl_count_zero ) ) else 2 ) else: count = "" return count # @profile def _plnoun( # noqa: C901 self, word: str, count: Optional[Union[str, int]] = None ) -> str: count = self.get_count(count) # DEFAULT TO PLURAL if count == 1: return word # HANDLE USER-DEFINED NOUNS value = self.ud_match(word, self.pl_sb_user_defined) if value is not None: return value # HANDLE EMPTY WORD, SINGULAR COUNT AND UNINFLECTED PLURALS if word == "": return word word = Words(word) if word.last.lower() in pl_sb_uninflected_complete: return word if word in pl_sb_uninflected_caps: return word for k, v in pl_sb_uninflected_bysize.items(): if word.lowered[-k:] in v: return word if self.classical_dict["herd"] and word.last.lower() in pl_sb_uninflected_herd: return word # HANDLE COMPOUNDS ("Governor General", "mother-in-law", "aide-de-camp", ETC.) mo = PL_SB_POSTFIX_ADJ_STEMS_RE.search(word) if mo and mo.group(2) != "": return f"{self._plnoun(mo.group(1), 2)}{mo.group(2)}" if " a " in word.lowered or "-a-" in word.lowered: mo = PL_SB_PREP_DUAL_COMPOUND_RE.search(word) if mo and mo.group(2) != "" and mo.group(3) != "": return ( f"{self._plnoun(mo.group(1), 2)}" f"{mo.group(2)}" f"{self._plnoun(mo.group(3))}" ) if len(word.split_) >= 3: for numword in range(1, len(word.split_) - 1): if word.split_[numword] in pl_prep_list_da: return " ".join( word.split_[: numword - 1] + [self._plnoun(word.split_[numword - 1], 2)] + word.split_[numword:] ) # only pluralize denominators in units mo = DENOMINATOR.search(word.lowered) if mo: index = len(mo.group("denominator")) return f"{self._plnoun(word[:index])}{word[index:]}" # handle units given in degrees (only accept if # there is no more than one word following) # degree Celsius => degrees Celsius but degree # fahrenheit hour => degree fahrenheit hours if len(word.split_) >= 2 and word.split_[-2] == "degree": return " ".join([self._plnoun(word.first)] + word.split_[1:]) with contextlib.suppress(ValueError): return self._handle_prepositional_phrase( word.lowered, functools.partial(self._plnoun, count=2), '-', ) # HANDLE PRONOUNS for k, v in pl_pron_acc_keys_bysize.items(): if word.lowered[-k:] in v: # ends with accusative pronoun for pk, pv in pl_prep_bysize.items(): if word.lowered[:pk] in pv: # starts with a prep if word.lowered.split() == [ word.lowered[:pk], word.lowered[-k:], ]: # only whitespace in between return word.lowered[:-k] + pl_pron_acc[word.lowered[-k:]] try: return pl_pron_nom[word.lowered] except KeyError: pass try: return pl_pron_acc[word.lowered] except KeyError: pass # HANDLE ISOLATED IRREGULAR PLURALS if word.last in pl_sb_irregular_caps: llen = len(word.last) return f"{word[:-llen]}{pl_sb_irregular_caps[word.last]}" lowered_last = word.last.lower() if lowered_last in pl_sb_irregular: llen = len(lowered_last) return f"{word[:-llen]}{pl_sb_irregular[lowered_last]}" dash_split = word.lowered.split('-') if (" ".join(dash_split[-2:])).lower() in pl_sb_irregular_compound: llen = len( " ".join(dash_split[-2:]) ) # TODO: what if 2 spaces between these words? return ( f"{word[:-llen]}" f"{pl_sb_irregular_compound[(' '.join(dash_split[-2:])).lower()]}" ) if word.lowered[-3:] == "quy": return f"{word[:-1]}ies" if word.lowered[-6:] == "person": if self.classical_dict["persons"]: return f"{word}s" else: return f"{word[:-4]}ople" # HANDLE FAMILIES OF IRREGULAR PLURALS if word.lowered[-3:] == "man": for k, v in pl_sb_U_man_mans_bysize.items(): if word.lowered[-k:] in v: return f"{word}s" for k, v in pl_sb_U_man_mans_caps_bysize.items(): if word[-k:] in v: return f"{word}s" return f"{word[:-3]}men" if word.lowered[-5:] == "mouse": return f"{word[:-5]}mice" if word.lowered[-5:] == "louse": v = pl_sb_U_louse_lice_bysize.get(len(word)) if v and word.lowered in v: return f"{word[:-5]}lice" return f"{word}s" if word.lowered[-5:] == "goose": return f"{word[:-5]}geese" if word.lowered[-5:] == "tooth": return f"{word[:-5]}teeth" if word.lowered[-4:] == "foot": return f"{word[:-4]}feet" if word.lowered[-4:] == "taco": return f"{word[:-5]}tacos" if word.lowered == "die": return "dice" # HANDLE UNASSIMILATED IMPORTS if word.lowered[-4:] == "ceps": return word if word.lowered[-4:] == "zoon": return f"{word[:-2]}a" if word.lowered[-3:] in ("cis", "sis", "xis"): return f"{word[:-2]}es" for lastlet, d, numend, post in ( ("h", pl_sb_U_ch_chs_bysize, None, "s"), ("x", pl_sb_U_ex_ices_bysize, -2, "ices"), ("x", pl_sb_U_ix_ices_bysize, -2, "ices"), ("m", pl_sb_U_um_a_bysize, -2, "a"), ("s", pl_sb_U_us_i_bysize, -2, "i"), ("n", pl_sb_U_on_a_bysize, -2, "a"), ("a", pl_sb_U_a_ae_bysize, None, "e"), ): if word.lowered[-1] == lastlet: # this test to add speed for k, v in d.items(): if word.lowered[-k:] in v: return word[:numend] + post # HANDLE INCOMPLETELY ASSIMILATED IMPORTS if self.classical_dict["ancient"]: if word.lowered[-4:] == "trix": return f"{word[:-1]}ces" if word.lowered[-3:] in ("eau", "ieu"): return f"{word}x" if word.lowered[-3:] in ("ynx", "inx", "anx") and len(word) > 4: return f"{word[:-1]}ges" for lastlet, d, numend, post in ( ("n", pl_sb_C_en_ina_bysize, -2, "ina"), ("x", pl_sb_C_ex_ices_bysize, -2, "ices"), ("x", pl_sb_C_ix_ices_bysize, -2, "ices"), ("m", pl_sb_C_um_a_bysize, -2, "a"), ("s", pl_sb_C_us_i_bysize, -2, "i"), ("s", pl_sb_C_us_us_bysize, None, ""), ("a", pl_sb_C_a_ae_bysize, None, "e"), ("a", pl_sb_C_a_ata_bysize, None, "ta"), ("s", pl_sb_C_is_ides_bysize, -1, "des"), ("o", pl_sb_C_o_i_bysize, -1, "i"), ("n", pl_sb_C_on_a_bysize, -2, "a"), ): if word.lowered[-1] == lastlet: # this test to add speed for k, v in d.items(): if word.lowered[-k:] in v: return word[:numend] + post for d, numend, post in ( (pl_sb_C_i_bysize, None, "i"), (pl_sb_C_im_bysize, None, "im"), ): for k, v in d.items(): if word.lowered[-k:] in v: return word[:numend] + post # HANDLE SINGULAR NOUNS ENDING IN ...s OR OTHER SILIBANTS if lowered_last in pl_sb_singular_s_complete: return f"{word}es" for k, v in pl_sb_singular_s_bysize.items(): if word.lowered[-k:] in v: return f"{word}es" if word.lowered[-2:] == "es" and word[0] == word[0].upper(): return f"{word}es" if word.lowered[-1] == "z": for k, v in pl_sb_z_zes_bysize.items(): if word.lowered[-k:] in v: return f"{word}es" if word.lowered[-2:-1] != "z": return f"{word}zes" if word.lowered[-2:] == "ze": for k, v in pl_sb_ze_zes_bysize.items(): if word.lowered[-k:] in v: return f"{word}s" if word.lowered[-2:] in ("ch", "sh", "zz", "ss") or word.lowered[-1] == "x": return f"{word}es" # HANDLE ...f -> ...ves if word.lowered[-3:] in ("elf", "alf", "olf"): return f"{word[:-1]}ves" if word.lowered[-3:] == "eaf" and word.lowered[-4:-3] != "d": return f"{word[:-1]}ves" if word.lowered[-4:] in ("nife", "life", "wife"): return f"{word[:-2]}ves" if word.lowered[-3:] == "arf": return f"{word[:-1]}ves" # HANDLE ...y if word.lowered[-1] == "y": if word.lowered[-2:-1] in "aeiou" or len(word) == 1: return f"{word}s" if self.classical_dict["names"]: if word.lowered[-1] == "y" and word[0] == word[0].upper(): return f"{word}s" return f"{word[:-1]}ies" # HANDLE ...o if lowered_last in pl_sb_U_o_os_complete: return f"{word}s" for k, v in pl_sb_U_o_os_bysize.items(): if word.lowered[-k:] in v: return f"{word}s" if word.lowered[-2:] in ("ao", "eo", "io", "oo", "uo"): return f"{word}s" if word.lowered[-1] == "o": return f"{word}es" # OTHERWISE JUST ADD ...s return f"{word}s" @classmethod def _handle_prepositional_phrase(cls, phrase, transform, sep): """ Given a word or phrase possibly separated by sep, parse out the prepositional phrase and apply the transform to the word preceding the prepositional phrase. Raise ValueError if the pivot is not found or if at least two separators are not found. >>> engine._handle_prepositional_phrase("man-of-war", str.upper, '-') 'MAN-of-war' >>> engine._handle_prepositional_phrase("man of war", str.upper, ' ') 'MAN of war' """ parts = phrase.split(sep) if len(parts) < 3: raise ValueError("Cannot handle words with fewer than two separators") pivot = cls._find_pivot(parts, pl_prep_list_da) transformed = transform(parts[pivot - 1]) or parts[pivot - 1] return " ".join( parts[: pivot - 1] + [sep.join([transformed, parts[pivot], ''])] ) + " ".join(parts[(pivot + 1) :]) @staticmethod def _find_pivot(words, candidates): pivots = ( index for index in range(1, len(words) - 1) if words[index] in candidates ) try: return next(pivots) except StopIteration: raise ValueError("No pivot found") def _pl_special_verb( # noqa: C901 self, word: str, count: Optional[Union[str, int]] = None ) -> Union[str, bool]: if self.classical_dict["zero"] and str(count).lower() in pl_count_zero: return False count = self.get_count(count) if count == 1: return word # HANDLE USER-DEFINED VERBS value = self.ud_match(word, self.pl_v_user_defined) if value is not None: return value # HANDLE IRREGULAR PRESENT TENSE (SIMPLE AND COMPOUND) try: words = Words(word) except IndexError: return False # word is '' if words.first in plverb_irregular_pres: return f"{plverb_irregular_pres[words.first]}{words[len(words.first) :]}" # HANDLE IRREGULAR FUTURE, PRETERITE AND PERFECT TENSES if words.first in plverb_irregular_non_pres: return word # HANDLE PRESENT NEGATIONS (SIMPLE AND COMPOUND) if words.first.endswith("n't") and words.first[:-3] in plverb_irregular_pres: return ( f"{plverb_irregular_pres[words.first[:-3]]}n't" f"{words[len(words.first) :]}" ) if words.first.endswith("n't"): return word # HANDLE SPECIAL CASES mo = PLVERB_SPECIAL_S_RE.search(word) if mo: return False if WHITESPACE.search(word): return False if words.lowered == "quizzes": return "quiz" # HANDLE STANDARD 3RD PERSON (CHOP THE ...(e)s OFF SINGLE WORDS) if ( words.lowered[-4:] in ("ches", "shes", "zzes", "sses") or words.lowered[-3:] == "xes" ): return words[:-2] if words.lowered[-3:] == "ies" and len(words) > 3: return words.lowered[:-3] + "y" if ( words.last.lower() in pl_v_oes_oe or words.lowered[-4:] in pl_v_oes_oe_endings_size4 or words.lowered[-5:] in pl_v_oes_oe_endings_size5 ): return words[:-1] if words.lowered.endswith("oes") and len(words) > 3: return words.lowered[:-2] mo = ENDS_WITH_S.search(words) if mo: return mo.group(1) # OTHERWISE, A REGULAR VERB (HANDLE ELSEWHERE) return False def _pl_general_verb( self, word: str, count: Optional[Union[str, int]] = None ) -> str: count = self.get_count(count) if count == 1: return word # HANDLE AMBIGUOUS PRESENT TENSES (SIMPLE AND COMPOUND) mo = plverb_ambiguous_pres_keys.search(word) if mo: return f"{plverb_ambiguous_pres[mo.group(1).lower()]}{mo.group(2)}" # HANDLE AMBIGUOUS PRETERITE AND PERFECT TENSES mo = plverb_ambiguous_non_pres.search(word) if mo: return word # OTHERWISE, 1st OR 2ND PERSON IS UNINFLECTED return word def _pl_special_adjective( self, word: str, count: Optional[Union[str, int]] = None ) -> Union[str, bool]: count = self.get_count(count) if count == 1: return word # HANDLE USER-DEFINED ADJECTIVES value = self.ud_match(word, self.pl_adj_user_defined) if value is not None: return value # HANDLE KNOWN CASES mo = pl_adj_special_keys.search(word) if mo: return pl_adj_special[mo.group(1).lower()] # HANDLE POSSESSIVES mo = pl_adj_poss_keys.search(word) if mo: return pl_adj_poss[mo.group(1).lower()] mo = ENDS_WITH_APOSTROPHE_S.search(word) if mo: pl = self.plural_noun(mo.group(1)) trailing_s = "" if pl[-1] == "s" else "s" return f"{pl}'{trailing_s}" # OTHERWISE, NO IDEA return False # @profile def _sinoun( # noqa: C901 self, word: str, count: Optional[Union[str, int]] = None, gender: Optional[str] = None, ) -> Union[str, bool]: count = self.get_count(count) # DEFAULT TO PLURAL if count == 2: return word # SET THE GENDER try: if gender is None: gender = self.thegender elif gender not in singular_pronoun_genders: raise BadGenderError except (TypeError, IndexError): raise BadGenderError # HANDLE USER-DEFINED NOUNS value = self.ud_match(word, self.si_sb_user_defined) if value is not None: return value # HANDLE EMPTY WORD, SINGULAR COUNT AND UNINFLECTED PLURALS if word == "": return word if word in si_sb_ois_oi_case: return word[:-1] words = Words(word) if words.last.lower() in pl_sb_uninflected_complete: return word if word in pl_sb_uninflected_caps: return word for k, v in pl_sb_uninflected_bysize.items(): if words.lowered[-k:] in v: return word if self.classical_dict["herd"] and words.last.lower() in pl_sb_uninflected_herd: return word if words.last.lower() in pl_sb_C_us_us: return word if self.classical_dict["ancient"] else False # HANDLE COMPOUNDS ("Governor General", "mother-in-law", "aide-de-camp", ETC.) mo = PL_SB_POSTFIX_ADJ_STEMS_RE.search(word) if mo and mo.group(2) != "": return f"{self._sinoun(mo.group(1), 1, gender=gender)}{mo.group(2)}" with contextlib.suppress(ValueError): return self._handle_prepositional_phrase( words.lowered, functools.partial(self._sinoun, count=1, gender=gender), ' ', ) with contextlib.suppress(ValueError): return self._handle_prepositional_phrase( words.lowered, functools.partial(self._sinoun, count=1, gender=gender), '-', ) # HANDLE PRONOUNS for k, v in si_pron_acc_keys_bysize.items(): if words.lowered[-k:] in v: # ends with accusative pronoun for pk, pv in pl_prep_bysize.items(): if words.lowered[:pk] in pv: # starts with a prep if words.lowered.split() == [ words.lowered[:pk], words.lowered[-k:], ]: # only whitespace in between return words.lowered[:-k] + get_si_pron( "acc", words.lowered[-k:], gender ) try: return get_si_pron("nom", words.lowered, gender) except KeyError: pass try: return get_si_pron("acc", words.lowered, gender) except KeyError: pass # HANDLE ISOLATED IRREGULAR PLURALS if words.last in si_sb_irregular_caps: llen = len(words.last) return "{}{}".format(word[:-llen], si_sb_irregular_caps[words.last]) if words.last.lower() in si_sb_irregular: llen = len(words.last.lower()) return "{}{}".format(word[:-llen], si_sb_irregular[words.last.lower()]) dash_split = words.lowered.split("-") if (" ".join(dash_split[-2:])).lower() in si_sb_irregular_compound: llen = len( " ".join(dash_split[-2:]) ) # TODO: what if 2 spaces between these words? return "{}{}".format( word[:-llen], si_sb_irregular_compound[(" ".join(dash_split[-2:])).lower()], ) if words.lowered[-5:] == "quies": return word[:-3] + "y" if words.lowered[-7:] == "persons": return word[:-1] if words.lowered[-6:] == "people": return word[:-4] + "rson" # HANDLE FAMILIES OF IRREGULAR PLURALS if words.lowered[-4:] == "mans": for k, v in si_sb_U_man_mans_bysize.items(): if words.lowered[-k:] in v: return word[:-1] for k, v in si_sb_U_man_mans_caps_bysize.items(): if word[-k:] in v: return word[:-1] if words.lowered[-3:] == "men": return word[:-3] + "man" if words.lowered[-4:] == "mice": return word[:-4] + "mouse" if words.lowered[-4:] == "lice": v = si_sb_U_louse_lice_bysize.get(len(word)) if v and words.lowered in v: return word[:-4] + "louse" if words.lowered[-5:] == "geese": return word[:-5] + "goose" if words.lowered[-5:] == "teeth": return word[:-5] + "tooth" if words.lowered[-4:] == "feet": return word[:-4] + "foot" if words.lowered == "dice": return "die" # HANDLE UNASSIMILATED IMPORTS if words.lowered[-4:] == "ceps": return word if words.lowered[-3:] == "zoa": return word[:-1] + "on" for lastlet, d, unass_numend, post in ( ("s", si_sb_U_ch_chs_bysize, -1, ""), ("s", si_sb_U_ex_ices_bysize, -4, "ex"), ("s", si_sb_U_ix_ices_bysize, -4, "ix"), ("a", si_sb_U_um_a_bysize, -1, "um"), ("i", si_sb_U_us_i_bysize, -1, "us"), ("a", si_sb_U_on_a_bysize, -1, "on"), ("e", si_sb_U_a_ae_bysize, -1, ""), ): if words.lowered[-1] == lastlet: # this test to add speed for k, v in d.items(): if words.lowered[-k:] in v: return word[:unass_numend] + post # HANDLE INCOMPLETELY ASSIMILATED IMPORTS if self.classical_dict["ancient"]: if words.lowered[-6:] == "trices": return word[:-3] + "x" if words.lowered[-4:] in ("eaux", "ieux"): return word[:-1] if words.lowered[-5:] in ("ynges", "inges", "anges") and len(word) > 6: return word[:-3] + "x" for lastlet, d, class_numend, post in ( ("a", si_sb_C_en_ina_bysize, -3, "en"), ("s", si_sb_C_ex_ices_bysize, -4, "ex"), ("s", si_sb_C_ix_ices_bysize, -4, "ix"), ("a", si_sb_C_um_a_bysize, -1, "um"), ("i", si_sb_C_us_i_bysize, -1, "us"), ("s", pl_sb_C_us_us_bysize, None, ""), ("e", si_sb_C_a_ae_bysize, -1, ""), ("a", si_sb_C_a_ata_bysize, -2, ""), ("s", si_sb_C_is_ides_bysize, -3, "s"), ("i", si_sb_C_o_i_bysize, -1, "o"), ("a", si_sb_C_on_a_bysize, -1, "on"), ("m", si_sb_C_im_bysize, -2, ""), ("i", si_sb_C_i_bysize, -1, ""), ): if words.lowered[-1] == lastlet: # this test to add speed for k, v in d.items(): if words.lowered[-k:] in v: return word[:class_numend] + post # HANDLE PLURLS ENDING IN uses -> use if ( words.lowered[-6:] == "houses" or word in si_sb_uses_use_case or words.last.lower() in si_sb_uses_use ): return word[:-1] # HANDLE PLURLS ENDING IN ies -> ie if word in si_sb_ies_ie_case or words.last.lower() in si_sb_ies_ie: return word[:-1] # HANDLE PLURLS ENDING IN oes -> oe if ( words.lowered[-5:] == "shoes" or word in si_sb_oes_oe_case or words.last.lower() in si_sb_oes_oe ): return word[:-1] # HANDLE SINGULAR NOUNS ENDING IN ...s OR OTHER SILIBANTS if word in si_sb_sses_sse_case or words.last.lower() in si_sb_sses_sse: return word[:-1] if words.last.lower() in si_sb_singular_s_complete: return word[:-2] for k, v in si_sb_singular_s_bysize.items(): if words.lowered[-k:] in v: return word[:-2] if words.lowered[-4:] == "eses" and word[0] == word[0].upper(): return word[:-2] if words.last.lower() in si_sb_z_zes: return word[:-2] if words.last.lower() in si_sb_zzes_zz: return word[:-2] if words.lowered[-4:] == "zzes": return word[:-3] if word in si_sb_ches_che_case or words.last.lower() in si_sb_ches_che: return word[:-1] if words.lowered[-4:] in ("ches", "shes"): return word[:-2] if words.last.lower() in si_sb_xes_xe: return word[:-1] if words.lowered[-3:] == "xes": return word[:-2] # HANDLE ...f -> ...ves if word in si_sb_ves_ve_case or words.last.lower() in si_sb_ves_ve: return word[:-1] if words.lowered[-3:] == "ves": if words.lowered[-5:-3] in ("el", "al", "ol"): return word[:-3] + "f" if words.lowered[-5:-3] == "ea" and word[-6:-5] != "d": return word[:-3] + "f" if words.lowered[-5:-3] in ("ni", "li", "wi"): return word[:-3] + "fe" if words.lowered[-5:-3] == "ar": return word[:-3] + "f" # HANDLE ...y if words.lowered[-2:] == "ys": if len(words.lowered) > 2 and words.lowered[-3] in "aeiou": return word[:-1] if self.classical_dict["names"]: if words.lowered[-2:] == "ys" and word[0] == word[0].upper(): return word[:-1] if words.lowered[-3:] == "ies": return word[:-3] + "y" # HANDLE ...o if words.lowered[-2:] == "os": if words.last.lower() in si_sb_U_o_os_complete: return word[:-1] for k, v in si_sb_U_o_os_bysize.items(): if words.lowered[-k:] in v: return word[:-1] if words.lowered[-3:] in ("aos", "eos", "ios", "oos", "uos"): return word[:-1] if words.lowered[-3:] == "oes": return word[:-2] # UNASSIMILATED IMPORTS FINAL RULE if word in si_sb_es_is: return word[:-2] + "is" # OTHERWISE JUST REMOVE ...s if words.lowered[-1] == "s": return word[:-1] # COULD NOT FIND SINGULAR return False # ADJECTIVES def a(self, text: str, count: int = 1) -> str: """ Return the appropriate indefinite article followed by text. The indefinite article is either 'a' or 'an'. If count is not one, then return count followed by text instead of 'a' or 'an'. Whitespace at the start and end is preserved. """ mo = INDEFINITE_ARTICLE_TEST.search(text) if mo: word = mo.group(2) if not word: return text pre = mo.group(1) post = mo.group(3) result = self._indef_article(word, count) return f"{pre}{result}{post}" return "" an = a def _indef_article(self, word: str, count: int) -> str: # noqa: C901 mycount = self.get_count(count) if mycount != 1: return f"{count} {word}" # HANDLE USER-DEFINED VARIANTS value = self.ud_match(word, self.A_a_user_defined) if value is not None: return f"{value} {word}" for regexen, article in ( # HANDLE ORDINAL FORMS (A_ordinal_a, "a"), (A_ordinal_an, "an"), # HANDLE SPECIAL CASES (A_explicit_an, "an"), (SPECIAL_AN, "an"), (SPECIAL_A, "a"), # HANDLE ABBREVIATIONS (A_abbrev, "an"), (SPECIAL_ABBREV_AN, "an"), (SPECIAL_ABBREV_A, "a"), # HANDLE CONSONANTS (CONSONANTS, "a"), # HANDLE SPECIAL VOWEL-FORMS (ARTICLE_SPECIAL_EU, "a"), (ARTICLE_SPECIAL_ONCE, "a"), (ARTICLE_SPECIAL_ONETIME, "a"), (ARTICLE_SPECIAL_UNIT, "a"), (ARTICLE_SPECIAL_UBA, "a"), (ARTICLE_SPECIAL_UKR, "a"), (A_explicit_a, "a"), # HANDLE SPECIAL CAPITALS (SPECIAL_CAPITALS, "a"), # HANDLE VOWELS (VOWELS, "an"), # HANDLE y... # (BEFORE CERTAIN CONSONANTS IMPLIES (UNNATURALIZED) "i.." SOUND) (A_y_cons, "an"), ): mo = regexen.search(word) if mo: return f"{article} {word}" # OTHERWISE, GUESS "a" return f"a {word}" # 2. TRANSLATE ZERO-QUANTIFIED $word TO "no plural($word)" def no(self, text: str, count: Optional[Union[int, str]] = None) -> str: """ If count is 0, no, zero or nil, return 'no' followed by the plural of text. If count is one of: 1, a, an, one, each, every, this, that return count followed by text. Otherwise return count follow by the plural of text. In the return value count is always followed by a space. Whitespace at the start and end is preserved. """ if count is None and self.persistent_count is not None: count = self.persistent_count if count is None: count = 0 mo = PARTITION_WORD.search(text) if mo: pre = mo.group(1) word = mo.group(2) post = mo.group(3) else: pre = "" word = "" post = "" if str(count).lower() in pl_count_zero: count = 'no' return f"{pre}{count} {self.plural(word, count)}{post}" # PARTICIPLES def present_participle(self, word: str) -> str: """ Return the present participle for word. word is the 3rd person singular verb. """ plv = self.plural_verb(word, 2) ans = plv for regexen, repl in PRESENT_PARTICIPLE_REPLACEMENTS: ans, num = regexen.subn(repl, plv) if num: return f"{ans}ing" return f"{ans}ing" # NUMERICAL INFLECTIONS def ordinal(self, num: Union[int, str]) -> str: # noqa: C901 """ Return the ordinal of num. num can be an integer or text e.g. ordinal(1) returns '1st' ordinal('one') returns 'first' """ if DIGIT.match(str(num)): if isinstance(num, (int, float)): n = int(num) else: if "." in str(num): try: # numbers after decimal, # so only need last one for ordinal n = int(num[-1]) except ValueError: # ends with '.', so need to use whole string n = int(num[:-1]) else: n = int(num) try: post = nth[n % 100] except KeyError: post = nth[n % 10] return f"{num}{post}" else: # Mad props to Damian Conway (?) whose ordinal() # algorithm is type-bendy enough to foil MyPy str_num: str = num # type: ignore[assignment] mo = ordinal_suff.search(str_num) if mo: post = ordinal[mo.group(1)] rval = ordinal_suff.sub(post, str_num) else: rval = f"{str_num}th" return rval def millfn(self, ind: int = 0) -> str: if ind > len(mill) - 1: print3("number out of range") raise NumOutOfRangeError return mill[ind] def unitfn(self, units: int, mindex: int = 0) -> str: return f"{unit[units]}{self.millfn(mindex)}" def tenfn(self, tens, units, mindex=0) -> str: if tens != 1: tens_part = ten[tens] if tens and units: hyphen = "-" else: hyphen = "" unit_part = unit[units] mill_part = self.millfn(mindex) return f"{tens_part}{hyphen}{unit_part}{mill_part}" return f"{teen[units]}{mill[mindex]}" def hundfn(self, hundreds: int, tens: int, units: int, mindex: int) -> str: if hundreds: andword = f" {self._number_args['andword']} " if tens or units else "" # use unit not unitfn as simpler return ( f"{unit[hundreds]} hundred{andword}" f"{self.tenfn(tens, units)}{self.millfn(mindex)}, " ) if tens or units: return f"{self.tenfn(tens, units)}{self.millfn(mindex)}, " return "" def group1sub(self, mo: Match) -> str: units = int(mo.group(1)) if units == 1: return f" {self._number_args['one']}, " elif units: return f"{unit[units]}, " else: return f" {self._number_args['zero']}, " def group1bsub(self, mo: Match) -> str: units = int(mo.group(1)) if units: return f"{unit[units]}, " else: return f" {self._number_args['zero']}, " def group2sub(self, mo: Match) -> str: tens = int(mo.group(1)) units = int(mo.group(2)) if tens: return f"{self.tenfn(tens, units)}, " if units: return f" {self._number_args['zero']} {unit[units]}, " return f" {self._number_args['zero']} {self._number_args['zero']}, " def group3sub(self, mo: Match) -> str: hundreds = int(mo.group(1)) tens = int(mo.group(2)) units = int(mo.group(3)) if hundreds == 1: hunword = f" {self._number_args['one']}" elif hundreds: hunword = str(unit[hundreds]) else: hunword = f" {self._number_args['zero']}" if tens: tenword = self.tenfn(tens, units) elif units: tenword = f" {self._number_args['zero']} {unit[units]}" else: tenword = f" {self._number_args['zero']} {self._number_args['zero']}" return f"{hunword} {tenword}, " def hundsub(self, mo: Match) -> str: ret = self.hundfn( int(mo.group(1)), int(mo.group(2)), int(mo.group(3)), self.mill_count ) self.mill_count += 1 return ret def tensub(self, mo: Match) -> str: return f"{self.tenfn(int(mo.group(1)), int(mo.group(2)), self.mill_count)}, " def unitsub(self, mo: Match) -> str: return f"{self.unitfn(int(mo.group(1)), self.mill_count)}, " def enword(self, num: str, group: int) -> str: # import pdb # pdb.set_trace() if group == 1: num = DIGIT_GROUP.sub(self.group1sub, num) elif group == 2: num = TWO_DIGITS.sub(self.group2sub, num) num = DIGIT_GROUP.sub(self.group1bsub, num, 1) elif group == 3: num = THREE_DIGITS.sub(self.group3sub, num) num = TWO_DIGITS.sub(self.group2sub, num, 1) num = DIGIT_GROUP.sub(self.group1sub, num, 1) elif int(num) == 0: num = self._number_args["zero"] elif int(num) == 1: num = self._number_args["one"] else: num = num.lstrip().lstrip("0") self.mill_count = 0 # surely there's a better way to do the next bit mo = THREE_DIGITS_WORD.search(num) while mo: num = THREE_DIGITS_WORD.sub(self.hundsub, num, 1) mo = THREE_DIGITS_WORD.search(num) num = TWO_DIGITS_WORD.sub(self.tensub, num, 1) num = ONE_DIGIT_WORD.sub(self.unitsub, num, 1) return num def number_to_words( # noqa: C901 self, num: Union[int, str], wantlist: bool = False, group: int = 0, comma: str = ",", andword: str = "and", zero: str = "zero", one: str = "one", decimal: str = "point", threshold: Optional[int] = None, ) -> Union[str, List[str]]: """ Return a number in words. group = 1, 2 or 3 to group numbers before turning into words comma: define comma andword: word for 'and'. Can be set to ''. e.g. "one hundred and one" vs "one hundred one" zero: word for '0' one: word for '1' decimal: word for decimal point threshold: numbers above threshold not turned into words parameters not remembered from last call. Departure from Perl version. """ self._number_args = {"andword": andword, "zero": zero, "one": one} num = str(num) # Handle "stylistic" conversions (up to a given threshold)... if threshold is not None and float(num) > threshold: spnum = num.split(".", 1) while comma: (spnum[0], n) = FOUR_DIGIT_COMMA.subn(r"\1,\2", spnum[0]) if n == 0: break try: return f"{spnum[0]}.{spnum[1]}" except IndexError: return str(spnum[0]) if group < 0 or group > 3: raise BadChunkingOptionError nowhite = num.lstrip() if nowhite[0] == "+": sign = "plus" elif nowhite[0] == "-": sign = "minus" else: sign = "" if num in nth_suff: num = zero myord = num[-2:] in nth_suff if myord: num = num[:-2] finalpoint = False if decimal: if group != 0: chunks = num.split(".") else: chunks = num.split(".", 1) if chunks[-1] == "": # remove blank string if nothing after decimal chunks = chunks[:-1] finalpoint = True # add 'point' to end of output else: chunks = [num] first: Union[int, str, bool] = 1 loopstart = 0 if chunks[0] == "": first = 0 if len(chunks) > 1: loopstart = 1 for i in range(loopstart, len(chunks)): chunk = chunks[i] # remove all non numeric \D chunk = NON_DIGIT.sub("", chunk) if chunk == "": chunk = "0" if group == 0 and (first == 0 or first == ""): chunk = self.enword(chunk, 1) else: chunk = self.enword(chunk, group) if chunk[-2:] == ", ": chunk = chunk[:-2] chunk = WHITESPACES_COMMA.sub(",", chunk) if group == 0 and first: chunk = COMMA_WORD.sub(f" {andword} \\1", chunk) chunk = WHITESPACES.sub(" ", chunk) # chunk = re.sub(r"(\A\s|\s\Z)", self.blankfn, chunk) chunk = chunk.strip() if first: first = "" chunks[i] = chunk numchunks = [] if first != 0: numchunks = chunks[0].split(f"{comma} ") if myord and numchunks: # TODO: can this be just one re as it is in perl? mo = ordinal_suff.search(numchunks[-1]) if mo: numchunks[-1] = ordinal_suff.sub(ordinal[mo.group(1)], numchunks[-1]) else: numchunks[-1] += "th" for chunk in chunks[1:]: numchunks.append(decimal) numchunks.extend(chunk.split(f"{comma} ")) if finalpoint: numchunks.append(decimal) # wantlist: Perl list context. can explicitly specify in Python if wantlist: if sign: numchunks = [sign] + numchunks return numchunks elif group: signout = f"{sign} " if sign else "" return f"{signout}{', '.join(numchunks)}" else: signout = f"{sign} " if sign else "" num = f"{signout}{numchunks.pop(0)}" if decimal is None: first = True else: first = not num.endswith(decimal) for nc in numchunks: if nc == decimal: num += f" {nc}" first = 0 elif first: num += f"{comma} {nc}" else: num += f" {nc}" return num # Join words with commas and a trailing 'and' (when appropriate)... def join( self, words: Optional[Sequence[str]], sep: Optional[str] = None, sep_spaced: bool = True, final_sep: Optional[str] = None, conj: str = "and", conj_spaced: bool = True, ) -> str: """ Join words into a list. e.g. join(['ant', 'bee', 'fly']) returns 'ant, bee, and fly' options: conj: replacement for 'and' sep: separator. default ',', unless ',' is in the list then ';' final_sep: final separator. default ',', unless ',' is in the list then ';' conj_spaced: boolean. Should conj have spaces around it """ if not words: return "" if len(words) == 1: return words[0] if conj_spaced: if conj == "": conj = " " else: conj = f" {conj} " if len(words) == 2: return f"{words[0]}{conj}{words[1]}" if sep is None: if "," in "".join(words): sep = ";" else: sep = "," if final_sep is None: final_sep = sep final_sep = f"{final_sep}{conj}" if sep_spaced: sep += " " return f"{sep.join(words[0:-1])}{final_sep}{words[-1]}" ================================================ FILE: src/bids/layout/README.md ================================================ # layout PyBIDS used to depend on [Grabbit](https://github.com/grabbles/grabbit) and here we described interactions with its Layout. Since then PyBIDS grew into an independent beast and we refer the curious minds to [our tutorial](../../examples/pybids_tutorial.md) Jupyter notebook. ================================================ FILE: src/bids/layout/__init__.py ================================================ # Backwards compatibility # noqa: D104 from bids_validator import BIDSValidator from .index import BIDSLayoutIndexer from .layout import BIDSLayout, Query from .models import BIDSDataFile, BIDSFile, BIDSImageFile, BIDSJSONFile, Config, Entity, Tag from .utils import add_config_paths, parse_file_entities __all__ = [ 'BIDSLayout', 'BIDSLayoutIndexer', 'BIDSValidator', 'add_config_paths', 'parse_file_entities', 'BIDSFile', 'BIDSImageFile', 'BIDSDataFile', 'BIDSJSONFile', 'Config', 'Entity', 'Tag', 'Query', ] ================================================ FILE: src/bids/layout/config/bids.json ================================================ { "name": "bids", "entities": [ { "name": "subject", "pattern": "[/\\\\]+sub-([a-zA-Z0-9+]+)", "directory": "{subject}" }, { "name": "session", "pattern": "[_/\\\\]+ses-([a-zA-Z0-9+]+)", "mandatory": false, "directory": "{subject}{session}" }, { "name": "sample", "pattern": "[_/\\\\]+sample-([a-zA-Z0-9+]+)" }, { "name": "task", "pattern": "[_/\\\\]+task-([a-zA-Z0-9+]+)" }, { "name": "tracksys", "pattern": "[_/\\\\]+tracksys-([a-zA-Z0-9+]+)" }, { "name": "acquisition", "pattern": "[_/\\\\]+acq-([a-zA-Z0-9+]+)" }, { "name": "nucleus", "pattern": "[_/\\\\]+nuc-([a-zA-Z0-9+]+)" }, { "name": "volume", "pattern": "[_/\\\\]+voi-([a-zA-Z0-9+]+)" }, { "name": "ceagent", "pattern": "[_/\\\\]+ce-([a-zA-Z0-9+]+)" }, { "name": "staining", "pattern": "[_/\\\\]+stain-([a-zA-Z0-9+]+)" }, { "name": "tracer", "pattern": "[_/\\\\]+trc-([a-zA-Z0-9+]+)" }, { "name": "reconstruction", "pattern": "[_/\\\\]+rec-([a-zA-Z0-9+]+)" }, { "name": "direction", "pattern": "[_/\\\\]+dir-([a-zA-Z0-9+]+)" }, { "name": "run", "pattern": "[_/\\\\]+run-(\\d+)", "dtype": "int" }, { "name": "proc", "pattern": "[_/\\\\]+proc-([a-zA-Z0-9+]+)" }, { "name": "modality", "pattern": "[_/\\\\]+mod-([a-zA-Z0-9+]+)" }, { "name": "echo", "pattern": "[_/\\\\]+echo-([0-9]+)" }, { "name": "flip", "pattern": "[_/\\\\]+flip-([0-9]+)" }, { "name": "inv", "pattern": "[_/\\\\]+inv-([0-9]+)" }, { "name": "mt", "pattern": "[_/\\\\]+mt-(on|off)" }, { "name": "part", "pattern": "[_/\\\\]+part-(imag|mag|phase|real)" }, { "name": "recording", "pattern": "[_/\\\\]+recording-([a-zA-Z0-9+]+)" }, { "name": "space", "pattern": "[_/\\\\]+space-([a-zA-Z0-9+]+)" }, { "name": "chunk", "pattern": "[_/\\\\]+chunk-([0-9]+)" }, { "name": "suffix", "pattern": "(?:^|[_/\\\\])([a-zA-Z0-9+]+)\\.[^/\\\\]+$" }, { "name": "scans", "pattern": "(.*\\_scans.tsv)$" }, { "name": "fmap", "pattern": "(phasediff|magnitude[1-2]|phase[1-2]|fieldmap|epi)\\.nii" }, { "name": "datatype", "pattern": "[/\\\\]+(anat|beh|dwi|eeg|fmap|func|ieeg|meg|micr|motion|mrs|nirs|perf|pet)[/\\\\]+" }, { "name": "extension", "pattern": "[^./\\\\](\\.[^/\\\\]+)$" } ], "default_path_patterns": [ "sub-{subject}[/ses-{session}]/{datatype|anat}/sub-{subject}[_ses-{session}][_task-{task}][_acq-{acquisition}][_ce-{ceagent}][_rec-{reconstruction}][_run-{run}][_echo-{echo}][_part-{part}][_chunk-{chunk}]_{suffix}{extension<.nii|.nii.gz|.json>|.nii.gz}", "sub-{subject}[/ses-{session}]/{datatype|anat}/sub-{subject}[_ses-{session}][_task-{task}][_acq-{acquisition}][_ce-{ceagent}][_rec-{reconstruction}][_run-{run}][_mod-{modality}]_{suffix}{extension<.nii|.nii.gz|.json>|.nii.gz}", "sub-{subject}[/ses-{session}]/{datatype|func}/sub-{subject}[_ses-{session}]_task-{task}[_acq-{acquisition}][_ce-{ceagent}][_rec-{reconstruction}][_dir-{direction}][_run-{run}][_echo-{echo}][_part-{part}]_{suffix}{extension<.nii|.nii.gz|.json>|.nii.gz}", 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"[task-{task}][_trc-{tracer}][_rec-{reconstruction}][_run-{run}][_recording-{recording}]_{suffix}{extension<.json>|.json}", "[acq-{acquisition}_][rec-{reconstruction}_][dir-{direction}_][run-{run}_]{suffix}{extension<.json>|.json}", "[acq-{acquisition}_][rec-{reconstruction}_][dir-{direction}_][run-{run}_]{suffix}{extension<.json>|.json}", "[acq-{acquisition}_][rec-{reconstruction}_][dir-{direction}_][run-{run}_][recording-{recording}_]{suffix}{extension<.json>|.json}", "task-{task}[_acq-{acquisition}][_run-{run}][_proc-{proc}]_{suffix}{extension<.json>|.json}", "[acq-{acquisition}_][space-{space}_]{suffix}{extension<.json>|.json}", "task-{task}[_acq-{acquisition}][_run-{run}]_{suffix}{extension<.json>|.json}", "task-{task}[_acq-{acquisition}][_run-{run}]_{suffix}{extension<.json>|.json}", "[acq-{acquisition}_]{suffix}{extension<.json>|.json}", "task-{task}[_acq-{acquisition}][_run-{run}]_{suffix}{extension<.json>|.json}", "sample-{sample}[_acq-{acquisition}]_{suffix|photo}{extension<.json>}" ] } ================================================ FILE: src/bids/layout/config/derivatives.json ================================================ { "name": "derivatives", "entities": [ { "name": "atlas", "pattern": "atlas-([a-zA-Z0-9+]+)" }, { "name": "roi", "pattern": "roi-([a-zA-Z0-9+]+)" }, { "name": "label", "pattern": "label-([a-zA-Z0-9+]+)" }, { "name": "desc", "pattern": "desc-([a-zA-Z0-9+]+)" }, { "name": "from", "pattern": "(?:^|_)from-([a-zA-Z0-9+]+).*xfm" }, { "name": "to", "pattern": "(?:^|_)to-([a-zA-Z0-9+]+).*xfm" }, { "name": "mode", "pattern": "(?:^|_)mode-([a-zA-Z0-9+]+).*xfm" }, { "name": "hemi", "pattern": "hemi-(L|R)" }, { "name": "segmentation", "pattern": "seg-([a-zA-Z0-9+]+)" }, { "name": "res", "pattern": "res-([a-zA-Z0-9+]+)" }, { "name": "den", "pattern": "den-([a-zA-Z0-9+]+)" }, { "name": "model", "pattern": "model-([a-zA-Z0-9+]+)" }, { "name": "subset", "pattern": "subset-([a-zA-Z0-9+]+)" } ], "default_path_patterns": [ "sub-{subject}[/ses-{session}]/{datatype|anat}/sub-{subject}[_ses-{session}][_acq-{acquisition}][_ce-{ceagent}][_rec-{reconstruction}][_space-{space}][_desc-{desc}]_{suffix}{extension<.nii|.nii.gz|.json>|.nii.gz}", "sub-{subject}[/ses-{session}]/{datatype|anat}/sub-{subject}[_ses-{session}][_acq-{acquisition}][_ce-{ceagent}][_rec-{reconstruction}][_mod-{modality}][_space-{space}][_desc-{desc}]_{suffix}{extension<.nii|.nii.gz|.json>|.nii.gz}", "sub-{subject}[/ses-{session}]/{datatype|anat}/sub-{subject}[_ses-{session}][_acq-{acquisition}][_ce-{ceagent}][_rec-{reconstruction}][_space-{space}][_res-{res}][_den-{den}][_label-{label}][_desc-{desc}]_{suffix}{extension<.nii|.nii.gz|.json>|.nii.gz}", 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"[acq-{acquisition}_][dir-{direction}_][run-{run}_][space-{space}_][desc-{desc}_]{fmap}{extension<.json>|.json}", "[acq-{acquisition}_][ce-{ceagent}_]dir-{direction}[_run-{run}][_space-{space}][_desc-{desc}]_{fmap}{extension<.json>|.json}", "task-{task}[_acq-{acquisition}][_rec-{reconstruction}][_run-{run}][_echo-{echo}][_recording-{recording}][_desc-{desc}]_{suffix}{extension<.json>|.json}" ] } ================================================ FILE: src/bids/layout/db.py ================================================ """Database-related functionality.""" import re import sqlite3 from functools import lru_cache import sqlalchemy as sa from upath import UPath as Path from bids.utils import listify from .models import Base, Config, LayoutInfo def get_database_file(path): # noqa: D103 if path is not None: path = Path(path) database_file = path / 'layout_index.sqlite' path.mkdir(exist_ok=True, parents=True) else: database_file = None return database_file class ConnectionManager: # noqa: D101 def __init__(self, database_path=None, reset_database=False, config=None, init_args=None): self.database_file = get_database_file(database_path) # Determine if file exists before we create it in _get_engine() reset_database = ( reset_database # manual reset or self.database_file is None # in-memory DB or not self.database_file.exists() # file hasn't been created yet ) self.engine = self._get_engine(self.database_file) self.sessionmaker = sa.orm.sessionmaker(bind=self.engine) self._session = None if reset_database: self.reset_database(init_args, config) self._database_reset = reset_database def _get_engine(self, database_file): if database_file is not None: # https://docs.sqlalchemy.org/en/13/dialects/sqlite.html # When a file-based database is specified, the dialect will use # NullPool as the source of connections. This pool closes and # discards connections which are returned to the pool immediately. # SQLite file-based connections have extremely low overhead, so # pooling is not necessary. The scheme also prevents a connection # from being used again in a different thread and works best # with SQLite's coarse-grained file locking. from sqlalchemy.pool import NullPool engine = sa.create_engine( f'sqlite:///{database_file}', connect_args={'check_same_thread': False}, poolclass=NullPool, ) else: # https://docs.sqlalchemy.org/en/13/dialects/sqlite.html # Using a Memory Database in Multiple Threads # To use a :memory: database in a multithreaded scenario, the same # connection object must be shared among threads, since the # database exists only within the scope of that connection. The # StaticPool implementation will maintain a single connection # globally, and the check_same_thread flag can be passed to # Pysqlite as False. Note that using a :memory: database in # multiple threads requires a recent version of SQLite. from sqlalchemy.pool import StaticPool engine = sa.create_engine( 'sqlite://', # In memory database connect_args={'check_same_thread': False}, poolclass=StaticPool, ) def regexp(expr, item): """Regex function for SQLite's REGEXP.""" if not isinstance(item, str): return False reg = re.compile(expr, re.I) return reg.search(item) is not None engine.connect() # Do not remove this decorator!!! An in-line create_function call will # work when using an in-memory SQLite DB, but fails when using a file. # For more details, see https://stackoverflow.com/questions/12461814/ @sa.event.listens_for(engine, 'begin') def do_begin(conn): conn.connection.create_function('regexp', 2, regexp) return engine @classmethod def exists(cls, database_path): # noqa: D102 return get_database_file(database_path).exists() def reset_database(self, init_args, config): # noqa: D102 Base.metadata.drop_all(self.engine) Base.metadata.create_all(self.engine) # Add LayoutInfo record if not isinstance(init_args, LayoutInfo): layout_info = LayoutInfo(**init_args) self.session.add(layout_info) # Add config records config = [Config.load(c, session=self.session) for c in listify(config)] self.session.add_all(config) self.session.commit() def save_database(self, database_path, replace_connection=True): """Save the current index as a SQLite3 DB at the specified location. Note: This is only necessary if a database_path was not specified at initialization, and the user now wants to save the index. If a database_path was specified originally, there is no need to re-save using this method. Parameters ---------- database_path : str The path to the desired database folder. By default, uses .db_cache. If a relative path is passed, it is assumed to be relative to the BIDSLayout root directory. replace_connection : bool, optional If True, returns a new ConnectionManager that points to the newly created database. If False, returns the current instance. """ database_file = get_database_file(database_path) new_db = sqlite3.connect(str(database_file)) old_db = self.engine.connect().connection with new_db: for line in old_db.iterdump(): if line not in ('BEGIN;', 'COMMIT;'): new_db.execute(line) new_db.commit() if replace_connection: return ConnectionManager(database_path, init_args=self.layout_info) else: return self @property def session(self): # noqa: D102 if self._session is None: self.reset_session() return self._session @property @lru_cache # noqa: B019 def layout_info(self): # noqa: D102 return self.session.query(LayoutInfo).first() def reset_session(self): """Force a new session.""" self._session = self.sessionmaker() ================================================ FILE: src/bids/layout/index.py ================================================ """File-indexing functionality.""" import json import re import warnings from collections import defaultdict from functools import cache, partial from bids_validator import BIDSValidator from upath import UPath as Path from ..exceptions import BIDSConflictingValuesError from ..utils import listify, make_bidsfile from .models import Config, Entity, FileAssociation, Tag, _create_tag_dict from .validation import validate_indexing_args def _regexfy(patt, root=None): if hasattr(patt, 'search'): return patt patt = Path(patt) if patt.is_absolute(): patt = str(patt.relative_to(root or '/')) return re.compile(r'^/' + str(patt) + r'.*') def _extract_entities(bidsfile, entities): match_vals = {} for e in entities.values(): m = e.match_file(bidsfile) if m is None and e.mandatory: break if m is not None: match_vals[e.name] = (e, m) return match_vals def _check_path_matches_patterns(path, patterns, root=None): """Check if the path matches at least one of the provided patterns.""" if not patterns: return False path = path.absolute() if root is not None: if isinstance(path, Path): path = Path('/') / Path(path.path).relative_to(Path(root.path)) else: path = Path('/') / path.relative_to(root) # Path now can be downcast to str path = str(path) for patt in patterns: if patt.search(path): return True return False def _validate_path(path, incl_patt=None, excl_patt=None, root=None): if _check_path_matches_patterns(path, incl_patt, root=root): return True if _check_path_matches_patterns(path, excl_patt, root=root): return False class BIDSLayoutIndexer: """Indexer class for BIDSLayout. Parameters ---------- validate : bool, optional If True, all files are checked for BIDS compliance when first indexed, and non-compliant files are ignored. This provides a convenient way to restrict file indexing to only those files defined in the "core" BIDS spec, as setting ``validate=True`` will lead noncompliant files like ``sub-01/nonbidsfile.txt`` to be ignored. ignore : str or SRE_Pattern or list Path(s) to exclude from indexing. Each path is either a string or a SRE_Pattern object (i.e., compiled regular expression). If a string is passed, it must be either an absolute path, or be relative to the BIDS project root. If an SRE_Pattern is passed, the contained regular expression will be matched against the full (absolute) path of all files and directories. By default, indexing ignores all files in 'code/', 'stimuli/', 'sourcedata/', 'models/', and any hidden files/dirs beginning with '.' at root level. force_index : str or SRE_Pattern or list Path(s) to forcibly index in the BIDSLayout, even if they would otherwise fail validation. See the documentation for the ignore argument for input format details. Note that paths in force_index takes precedence over those in ignore (i.e., if a file matches both ignore and force_index, it *will* be indexed). Note: NEVER include 'derivatives' here; use the derivatives argument (or :obj:`bids.layout.BIDSLayout.add_derivatives`) for that. index_metadata : bool If True, all metadata files are indexed. If False, metadata will not be available (but indexing will be faster). config_filename : str Optional name of filename within directories that contains configuration information. **filters keyword arguments passed to the .get() method of a :obj:`bids.layout.BIDSLayout` object. These keyword arguments define what files get selected for metadata indexing. """ def __init__( self, validate=False, ignore=None, force_index=None, index_metadata=True, config_filename='layout_config.json', **filters, ): self.ignore = ignore self.force_index = force_index self.index_metadata = index_metadata self.config_filename = config_filename self.filters = filters self.validator = None if validate: self.validator = BIDSValidator(index_associated=True) # Layout-dependent attributes to be set in __call__() self._layout = None self._config = None self._include_patterns = None self._exclude_patterns = None def __call__(self, layout): # noqa: D102 self._layout = layout self._config = list(layout.config.values()) ignore, force = validate_indexing_args(self.ignore, self.force_index, self._layout._root) # Do not accept string patterns self._include_patterns = [ _regexfy(patt, root=self._layout._root) for patt in listify(force) ] self._exclude_patterns = [ _regexfy(patt, root=self._layout._root) for patt in listify(ignore) ] all_bfs, all_tag_dicts = self._index_dir(self._layout._root, self._config) self.session.bulk_save_objects(all_bfs) self.session.bulk_insert_mappings(Tag, all_tag_dicts) self.session.commit() if self.index_metadata: self._index_metadata() @property def session(self): # noqa: D102 return self._layout.connection_manager.session def _validate_file(self, f): matched_patt = _validate_path( f, incl_patt=self._include_patterns, excl_patt=self._exclude_patterns, root=self._layout._root, ) if matched_patt is not None: return matched_patt if self.validator is None: return True # BIDS validator expects absolute paths, but really these are relative # to the BIDS project root. to_check = Path(f.path).relative_to( Path(self._layout._root.path) ) # use .path then Path() to drop the uri prefix # Pretend the path is an absolute path to_check = Path('/') / to_check # bids-validator works with posix paths only to_check = to_check.as_posix() return self.validator.is_bids(to_check) def _index_dir(self, path, config, force=None): root_path = Path(self._layout._root.path) # drops the uri prefix if it is there abs_path = root_path / Path(path.path).relative_to(root_path) # Derivative directories must always be added separately if root_path.joinpath('derivatives') in abs_path.parents: return [], [] config = list(config) # Shallow copy # Check for additional config file in directory layout_file = self.config_filename config_file = abs_path / layout_file if config_file.exists(): cfg = Config.load(config_file, session=self.session) config.append(cfg) # Track which entities are valid in filenames for this directory config_entities = {} for c in config: config_entities.update(c.entities) # Get lists of 1st-level subdirectories and files in the path directory try: _, dirnames, filenames = next(path.fs.walk(path.path)) except StopIteration: # It's unclear when this gets hit, but tests will occasionally fail here. # Hopefully this only occurs when we enter an empty directory, # but even if not, there's nothing else to do in this case. return [], [] # Symbolic links are returned as filenames even if they point to directories # Temporary list to store symbolic links that need to be removed from filenames links_to_dirs = [] # Find directories in filenames for possible_link in filenames: full_path = path / possible_link if full_path.is_dir(): links_to_dirs.append(possible_link) dirnames.extend(links_to_dirs) for link in links_to_dirs: filenames.remove(link) # Move any directories suffixed with .zarr to filenames zarrnames = [entry for entry in dirnames if Path(entry).suffix == '.zarr'] dirnames = [entry for entry in dirnames if Path(entry).suffix != '.zarr'] filenames.extend(zarrnames) # If layout configuration file exists, delete it if self.config_filename in filenames: filenames.remove(self.config_filename) all_bfs = [] all_tag_dicts = [] for f in filenames: abs_fn = path / f # Skip files that fail validation, unless forcibly indexing if force or self._validate_file(abs_fn): bf, tag_dicts = self._index_file(abs_fn, config_entities) all_tag_dicts += tag_dicts all_bfs.append(bf) # Recursively index subdirectories for d in dirnames: d = path / d force = _validate_path( d, incl_patt=self._include_patterns, excl_patt=self._exclude_patterns, root=self._layout._root, ) if force is not False: dir_bfs, dir_tag_dicts = self._index_dir(d, config, force=force) all_bfs += dir_bfs all_tag_dicts += dir_tag_dicts return all_bfs, all_tag_dicts def _index_file(self, abs_fn, entities): """Create DB record for file and its tags.""" bf = make_bidsfile(abs_fn) # Extract entity values match_vals = {} for e in entities.values(): m = e.match_file(bf) if m is None and e.mandatory: break if m is not None: match_vals[e.name] = (e, m) # Create Entity <=> BIDSFile mappings tag_dicts = [ _create_tag_dict(bf, ent, val, ent._dtype) for ent, val in match_vals.values() ] return bf, tag_dicts def _index_metadata(self): """Index metadata for all files in the BIDS dataset.""" filters = self.filters if filters: # ensure we are returning objects filters['return_type'] = 'object' if filters.get('extension'): filters['extension'] = listify(filters['extension']) # ensure json files are being indexed if '.json' not in filters['extension']: filters['extension'].append('.json') # Process JSON files first if we're indexing metadata all_files = self._layout.get(**filters) # Track ALL entities we've seen in file names or metadatas all_entities = {} for c in self._config: all_entities.update(c.entities) # If key/value pairs in JSON files duplicate ones extracted from files, # we can end up with Tag collisions in the DB. To prevent this, we # store all filename/entity pairs and the value, and then check against # that before adding each new Tag. all_tags = {} for t in self.session.query(Tag).all(): key = f'{t.file_path}_{t.entity_name}' all_tags[key] = str(t.value) # We build up a store of all file data as we iterate files. It looks # like: { extension/suffix: dirname: [(entities, payload)]}}. # The payload is left empty for non-JSON files. file_data = {} # Memoizing JSON loader # Use as a function to allow lazy loading so only read JSON files # if they correspond to data files that are indexed @cache def load_json(path): with Path(path).fs.open(Path(path).path, 'r', encoding='utf-8') as handle: try: return json.load(handle) except (UnicodeDecodeError, json.JSONDecodeError) as e: raise OSError( f'Error occurred while trying to decode JSON from file {path}' ) from e filenames = [] for bf in all_files: if 'suffix' in bf.entities and 'extension' in bf.entities: file_ents = bf.entities.copy() suffix = file_ents.pop('suffix') ext = file_ents.pop('extension') key = f'{ext}/{suffix}' if key not in file_data: file_data[key] = defaultdict(list) payload = None if ext == '.json': payload = partial(load_json, bf) else: filenames.append(bf) to_store = (file_ents, payload, bf.path) file_data[key][bf._dirname].append(to_store) # To avoid integrity errors, track primary keys we've seen seen_assocs = set() def create_association_pair(src, dst, kind, kind2=None): objs = [] kind2 = kind2 or kind pk1 = '#'.join([src, dst, kind]) if pk1 not in seen_assocs: objs.append(FileAssociation(src=src, dst=dst, kind=kind)) seen_assocs.add(pk1) pk2 = '#'.join([dst, src, kind2]) if pk2 not in seen_assocs: objs.append(FileAssociation(src=dst, dst=src, kind=kind2)) seen_assocs.add(pk2) return objs all_objs = [] all_tag_dicts = [] for bf in filenames: file_ents = bf.entities.copy() suffix = file_ents.pop('suffix', None) ext = file_ents.pop('extension', None) file_ent_keys = set(file_ents.keys()) if suffix is None or ext is None: continue # Extract metadata associated with the file. The idea is # that we loop over parent directories, and if we find # payloads in the file_data store (indexing by directory # and current file suffix), we check to see if the # candidate JS file's entities are entirely consumed by # the current file. If so, it's a valid candidate, and we # add the payload to the stack. Finally, we invert the # stack and merge the payloads in order. ext_key = f'{ext}/{suffix}' json_key = f'.json/{suffix}' dirname = bf._dirname payloads = [] ancestors = [] while True: # Get JSON payloads json_data = file_data.get(json_key, {}).get(dirname, []) for js_ents, js_md, js_path in json_data: js_keys = set(js_ents.keys()) if js_keys - file_ent_keys: continue matches = [js_ents[name] == file_ents[name] for name in js_keys] if all(matches): payloads.append((js_md, js_path)) # Get all files this file inherits from candidates = file_data.get(ext_key, {}).get(dirname, []) for ents, _, path in candidates: keys = set(ents.keys()) if keys - file_ent_keys: continue matches = [ents[name] == file_ents[name] for name in keys] if all(matches): ancestors.append(path) parent = dirname.parent if parent == dirname: break dirname = parent if not payloads: continue # Missing data files can tolerate absent metadata files, # but we will try to load it anyway virtual_datafile = not bf._path.exists() # Create DB records for metadata associations js_file = payloads[0][1] all_objs += create_association_pair(js_file, bf.path, 'Metadata') # Consolidate metadata by looping over inherited JSON files file_md = {} for pl, js_file in payloads[::-1]: # noqa: B007 try: file_md.update(pl()) except FileNotFoundError: if not virtual_datafile: raise # Drop metadata if any files are missing # Otherwise missing overrides could give misleading metadata file_md = {} break # Create FileAssociation records for JSON inheritance n_pl = len(payloads) for i, (pl, js_file) in enumerate(payloads): # noqa: B007 if (i + 1) < n_pl: other = payloads[i + 1][1] all_objs += create_association_pair(js_file, other, 'Child', 'Parent') # Inheritance for current file n_pl = len(ancestors) for i, src in enumerate(ancestors): if (i + 1) < n_pl: dst = ancestors[i + 1] all_objs += create_association_pair(src, dst, 'Child', 'Parent') # Files with IntendedFor field always get mapped to targets intended = listify(file_md.get('IntendedFor', [])) for target in intended: # IntendedFor paths should use BIDS URIs # Previously (now deprecated), paths may be relative to participant sub-dir target = _resolve_intent(target, self._layout._root, bf.entities['subject']) if target is None: warnings.warn(f'Skipping association for {target}', stacklevel=2) continue all_objs += create_association_pair( bf.path, str(target), 'IntendedFor', 'InformedBy' ) # Link files to BOLD runs if suffix in ['physio', 'stim', 'events', 'sbref']: images = self._layout.get( extension=['.nii', '.nii.gz'], suffix='bold', return_type='filename', **file_ents, ) for img in images: all_objs += create_association_pair(bf.path, img, 'IntendedFor', 'InformedBy') # Link files to DWI runs if suffix == 'sbref' or ext in ['bvec', 'bval']: images = self._layout.get( extension=['.nii', '.nii.gz'], suffix='dwi', return_type='filename', **file_ents, ) for img in images: all_objs += create_association_pair(bf.path, img, 'IntendedFor', 'InformedBy') # Create Tag <-> Entity mappings, and any newly discovered Entities for md_key, md_val in file_md.items(): # Treat null entries (deserialized to None) as absent # Alternative is to cast None to null in layout.models._create_tag_dict if md_val is None: continue tag_string = f'{bf.path}_{md_key}' # Skip pairs that were already found in the filenames if tag_string in all_tags: file_val = all_tags[tag_string] if str(md_val) != file_val: msg = ( "Conflicting values found for entity '{}' in " "filename {} (value='{}') versus its JSON sidecar " "(value='{}'). Please reconcile this discrepancy." ) raise BIDSConflictingValuesError( msg.format(md_key, bf.path, file_val, md_val) ) continue if md_key not in all_entities: all_entities[md_key] = Entity(md_key) self.session.add(all_entities[md_key]) tag = _create_tag_dict(bf, all_entities[md_key], md_val, is_metadata=True) all_tag_dicts.append(tag) self.session.bulk_save_objects(all_objs) self.session.bulk_insert_mappings(Tag, all_tag_dicts) self.session.commit() def _resolve_intent(intent: str, root: Path, subject: str) -> str | None: """Resolve IntendedFor paths as absolute paths. Path may be either a BIDS URI (prefix bids:: or bids::) or a relative path from subject directory. Examples -------- >>> _resolve_intent('bids::sub-01/anat/sub-01_T1w.nii.gz', Path(), '01') 'sub-01/anat/sub-01_T1w.nii.gz' >>> _resolve_intent('anat/sub-01_T1w.nii.gz', Path(), '01') 'sub-01/anat/sub-01_T1w.nii.gz' >>> _resolve_intent('bids:named_dataset:sub-01/anat/sub-01_T1w.nii.gz', Path(), '01') """ if not intent.startswith('bids:'): return str(root / f'sub-{subject}' / intent) if intent.startswith('bids::'): return str(root / intent[6:]) ================================================ FILE: src/bids/layout/layout.py ================================================ """BIDSLayout class.""" import copy import difflib import enum import json import re import warnings from collections import defaultdict from functools import lru_cache, partial from itertools import chain import sqlalchemy as sa from bids_validator import BIDSValidator from sqlalchemy.orm import aliased from sqlalchemy.sql.expression import cast from upath import UPath as Path from ..exceptions import ( BIDSDerivativesValidationError, BIDSEntityError, BIDSValidationError, NoMatchError, TargetError, ) from ..external import inflect from ..utils import hashablefy, listify, natural_sort from .db import ConnectionManager from .index import BIDSLayoutIndexer from .models import BIDSFile, Config, DerivativeDatasets, Entity, Tag from .utils import BIDSMetadata, parse_file_entities from .validation import EXAMPLE_DERIVATIVES_DESCRIPTION, validate_derivative_path, validate_root from .writing import build_path, write_to_file try: from operator import call except ImportError: # PY310 def call(func, *args, **kwargs): return func(*args, **kwargs) __all__ = ['BIDSLayout'] class Sentinel: def __repr__(self): return self.__class__.__name__ @call class RemovedOption(Sentinel): pass class BIDSLayout: """Layout class representing an entire BIDS dataset. Parameters ---------- root : str The root directory of the BIDS dataset. validate : bool, optional If True, all files are checked for BIDS compliance when first indexed, and non-compliant files are ignored. This provides a convenient way to restrict file indexing to only those files defined in the "core" BIDS spec, as setting validate=True will lead files in supplementary folders like derivatives/, code/, etc. to be ignored. derivatives : bool or str or list, optional Specifies whether and/or which derivatives to index. If True, all pipelines found in the derivatives/ subdirectory will be indexed. If a str or list, gives the paths to one or more derivatives directories to index. If False or None, the derivatives/ directory is ignored during indexing, and derivatives will have to be added manually via add_derivatives(). Note: derivatives datasets MUST contain a dataset_description.json file in order to be indexed. config : str or list or None, optional Optional name(s) of configuration file(s) to use. By default (None), uses 'bids'. Can also be 'bids-schema' to use the official BIDS schema instead of JSON config files. sources : :obj:`bids.layout.BIDSLayout` or list or None, optional Optional BIDSLayout(s) from which the current BIDSLayout is derived. config_filename : str Optional name of filename within directories that contains configuration information. regex_search : bool Whether to require exact matching (True) or regex search (False, default) when comparing the query string to each entity in .get() calls. This sets a default for the instance, but can be overridden in individual .get() requests. database_path : str Optional path to directory containing SQLite database file index for this BIDS dataset. If a value is passed and the folder already exists, indexing is skipped. By default (i.e., if None), an in-memory SQLite database is used, and the index will not persist unless .save() is explicitly called. reset_database : bool If True, any existing directory specified in the database_path argument is deleted, and the BIDS dataset provided in the root argument is reindexed. If False, indexing will be skipped and the existing database file will be used. Ignored if database_path is not provided. indexer: BIDSLayoutIndexer or callable An optional BIDSLayoutIndexer instance to use for indexing, or any callable that takes a BIDSLayout instance as its only argument. If None, a new indexer with default parameters will be implicitly created. is_derivative: bool Index dataset as a derivative dataset. This can be enabled along with validate=False to index derivatives without dataset_description.json. If validate=True, the dataset must have a dataset_description.json with DatasetType=derivative and GeneratedBy """ def __init__( self, root=None, validate=True, absolute_paths=RemovedOption, derivatives=False, config=None, sources=None, regex_search=False, database_path=None, reset_database=False, indexer=None, is_derivative=False, **indexer_kwargs, ): if absolute_paths is not RemovedOption: msg = ( 'The absolute_paths argument is no longer supported. Use the ' '`.path` attribute of returned BIDSFile objects for absolute ' 'paths, and the `.relpath` attribute for relative paths.' ) raise TypeError(msg) if indexer_kwargs: args = ', '.join(f'{key}={val}' for key, val in indexer_kwargs.items()) msg = ( 'Passing kwargs to the BIDSLayoutIndexer is no longer supported. ' f'Pass `indexer=BIDSLayoutIndexer({args})` instead.' ) raise TypeError(msg) # Load from existing database file load_db = ( database_path is not None and reset_database is False and ConnectionManager.exists(database_path) ) if load_db: self.connection_manager = ConnectionManager(database_path) info = self.connection_manager.layout_info # Overwrite init args with values in DB root = info.root derivatives = info.derivatives config = info.config # Validate that a valid BIDS project exists at root root, description = validate_root(root, validate) if any([is_derivative, description and description.get('DatasetType') == 'derivative']): try: self.source_pipeline = validate_derivative_path(root) except (BIDSValidationError, FileNotFoundError) as err: if validate: raise err self.source_pipeline = None default_config = ['bids', 'derivatives'] self.is_derivative = True else: self.source_pipeline = None default_config = ['bids'] self.is_derivative = False self._root = root # type: Path self.description = description self.derivatives = DerivativeDatasets() self.sources = sources self.regex_search = regex_search # Initialize a completely new layout and index the dataset if not load_db: # If config supplied from user or loaded from db, use it. Otherwise, fall # back to a default config = listify(config if config else default_config) init_args = dict(root=root, derivatives=derivatives, config=config) # noqa: C408 self.connection_manager = ConnectionManager( database_path, reset_database, config, init_args ) # Do not overwrite indexer variable, so the same configuration is passed to # add_derivatives() below _indexer = indexer or BIDSLayoutIndexer( validate=validate and not is_derivative, ) _indexer(self) # Add derivatives if any are found if derivatives: if derivatives is True: derivatives = root / 'derivatives' self.add_derivatives( derivatives, parent_database_path=database_path, validate=validate, derivatives=None, sources=self, config=None, regex_search=regex_search, reset_database=reset_database, indexer=indexer, ) @property def root(self): # noqa: D102 return str(self._root) def __getattr__(self, key): """Dynamically inspect missing methods for get_() calls and return a partial function of get() if a match is found. """ if key.startswith('get_'): ent_name = key.replace('get_', '') entities = self.get_entities() # Use inflect to check both singular and plural forms if ent_name not in entities: sing = inflect.engine().singular_noun(ent_name) if sing in entities: ent_name = sing else: raise BIDSEntityError( f"'get_{ent_name}' can't be called because '{ent_name}' isn't a " 'recognized entity name.' ) return partial(self.get, return_type='id', target=ent_name) # Spit out default message if we get this far raise AttributeError( '%s object has no attribute named %r' % (self.__class__.__name__, key) # noqa: UP031 ) def __repr__(self): """Provide a tidy summary of key properties.""" n_subjects = len( [ s.value for s in self.session.query(Tag) .filter_by(entity_name='subject') .group_by(Tag._value) ] ) n_sessions = len( set( # noqa: C401 (t.value, t.file.entities.get('subject')) for t in self.session.query(Tag).filter_by(entity_name='session') if t.file.entities.get('subject') ) ) n_runs = len( set( # noqa: C401 (t.value, t.file.entities.get('subject')) for t in self.session.query(Tag).filter_by(entity_name='run') if isinstance(t.value, int) and t.file.entities.get('subject') ) ) root = self.root[-30:] s = ( f'BIDS Layout: ...{root} | Subjects: {n_subjects} | Sessions: {n_sessions} | ' f'Runs: {n_runs}' ) return s def _in_scope(self, scope): """Determine whether current BIDSLayout is in the passed scope. Parameters ---------- scope : str or list The intended scope(s). Each value must be one of 'all', 'raw', 'derivatives', or a pipeline name. """ scope = listify(scope) if 'all' in scope: return True # We assume something is a BIDS-derivatives dataset if it either has a # defined pipeline name, or is applying the 'derivatives' rules. pl_name = self.source_pipeline return any( [ 'raw' in scope and not self.is_derivative, ('derivatives' in scope or pl_name in scope) and self.is_derivative, ] ) def _get_layouts_in_scope(self, scope): """Return all layouts in the passed scope.""" if scope == 'self': return [self] def collect_layouts(layout): """Recursively build a list of layouts.""" children = list(layout.derivatives.values()) layouts = [collect_layouts(d) for d in children] return [layout] + list(chain(*layouts)) layouts = [l for l in collect_layouts(self) if l._in_scope(scope)] # noqa: E741 return list(set(layouts)) def _sanitize_query_dtypes(self, entities): """Automatically convert entity query values to correct dtypes.""" entities = entities.copy() names = list(entities.keys()) ents = {e.name: e for e in self.session.query(Entity).filter(Entity.name.in_(names)).all()} # Fail silently because the DB may still know how to reconcile # type differences. for name, val in entities.items(): if isinstance(val, enum.Enum): continue try: if isinstance(val, (list, tuple)): entities[name] = [ v if isinstance(v, Query) else ents[name]._astype(v) for v in val ] else: entities[name] = ents[name]._astype(val) except Exception: # noqa: BLE001, S110 pass return entities @property def session(self): # noqa: D102 return self.connection_manager.session @property @lru_cache # noqa: B019 def config(self): # noqa: D102 return {c.name: c for c in self.session.query(Config).all()} @property def entities(self): """Get the entities.""" return self.get_entities() @property def files(self): """Get the files.""" return self.get_files() @classmethod def load(cls, database_path): """Load index from database path. Initialization parameters are set to those found in database_path JSON sidecar. Parameters ---------- database_path : str, Path The path to the desired database folder. If a relative path is passed, it is assumed to be relative to the BIDSLayout root directory. """ return cls(database_path=database_path) def save(self, database_path, replace_connection=True): """Save the current index as a SQLite3 DB at the specified location. Note: This is only necessary if a database_path was not specified at initialization, and the user now wants to save the index. If a database_path was specified originally, there is no need to re-save using this method. Parameters ---------- database_path : str The path to the desired database folder. By default, uses .db_cache. If a relative path is passed, it is assumed to be relative to the BIDSLayout root directory. replace_connection : bool, optional If True, the newly created database will be used for all subsequent connections. This means that any changes to the index made after the .save() call will be reflected in the database file. If False, the previous database will continue to be used, and any subsequent changes will not be reflected in the new file unless save() is explicitly called again. """ database_path = Path(database_path) self.connection_manager = self.connection_manager.save_database( database_path, replace_connection ) # Recursively save children for pipeline_name, der in self.derivatives.items(): der.save(database_path / pipeline_name) def get_entities(self, scope='all', metadata=None): """Get entities for all layouts in the specified scope. Parameters ---------- scope : str The scope of the search space. Indicates which BIDSLayouts' entities to extract. See :obj:`bids.layout.BIDSLayout.get` docstring for valid values. metadata : bool or None By default (None), all available entities are returned. If True, only entities found in metadata files (and not defined for filenames) are returned. If False, only entities defined for filenames (and not those found in JSON sidecars) are returned. Returns ------- dict Dictionary where keys are entity names and values are Entity instances. """ # TODO: memoize results layouts = self._get_layouts_in_scope(scope) entities = {} for l in layouts: # noqa: E741 query = l.session.query(Entity) if metadata is not None: query = query.join(Tag).filter_by(is_metadata=metadata) results = query.all() entities.update({e.name: e for e in results}) return entities def _find_directories_for_entity_using_schema(self, entity_name, results): """Find directories using schema directory rules.""" import os from bidsschematools import schema as bst_schema # Load schema directory rules bids_schema = bst_schema.load_schema() directory_rules = bids_schema.rules.directories.raw directories = set() # Build directory paths using schema rules for each file for f in results: if entity_name in f.entities: # Build path based on schema directory hierarchy path_components = self._build_path_from_schema_rules(f.entities, directory_rules) if path_components: # Convert to full directory path directory_path = os.path.join(self.root, *path_components) directories.add(directory_path) return list(directories) def _build_path_from_schema_rules(self, entities, directory_rules): """Build directory path components from schema rules and entity values.""" path_components = [] current_level = 'root' # Traverse schema directory hierarchy while current_level: rule = directory_rules.get(current_level) if not rule: # pragma: no cover # Schema always provides valid rules; current_level only set from schema subdirs break rule_dict = dict(rule) rule_entity = rule_dict.get('entity') # If this rule corresponds to an entity, add directory component if rule_entity and rule_entity in entities: entity_value = entities[rule_entity] if rule_entity == 'subject': path_components.append(f'sub-{entity_value}') elif rule_entity == 'session': path_components.append(f'ses-{entity_value}') else: # pragma: no cover # BIDS schema only defines subject/session entity directories currently path_components.append(str(entity_value)) elif current_level == 'datatype' and 'datatype' in entities: # Special case: datatype rule has no entity field but we use the datatype value path_components.append(entities['datatype']) # Determine next level based on subdirs and available entities subdirs = rule_dict.get('subdirs', []) next_level = None # First pass: look for entity-based subdirs across all options entity_candidates = [] rule_candidates = [] for subdir in subdirs: if isinstance(subdir, dict) and 'oneOf' in subdir: for option in subdir['oneOf']: if option in entities: entity_candidates.append(option) elif option in directory_rules: rule_candidates.append(option) elif isinstance(subdir, str): if subdir in entities: entity_candidates.append(subdir) elif subdir in directory_rules: rule_candidates.append(subdir) # Prefer entity candidates, fallback to rule candidates if entity_candidates: next_level = entity_candidates[0] # Take first entity match elif rule_candidates: next_level = rule_candidates[0] # Take first rule match current_level = next_level return path_components def get_files(self, scope='all'): """Get BIDSFiles for all layouts in the specified scope. Parameters ---------- scope : str The scope of the search space. Indicates which BIDSLayouts' entities to extract. See :obj:`bids.layout.BIDSLayout.get` docstring for valid values. Returns ------- A dict, where keys are file paths and values are :obj:`bids.layout.BIDSFile` instances. """ # TODO: memoize results layouts = self._get_layouts_in_scope(scope) files = {} for l in layouts: # noqa: E741 results = l.session.query(BIDSFile).all() files.update({f.path: f for f in results}) return files def clone(self): """Return a deep copy of the current BIDSLayout.""" return copy.deepcopy(self) def parse_file_entities( self, filename, scope='all', entities=None, config=None, include_unmatched=False ): """Parse the passed filename for entity/value pairs. Parameters ---------- filename : str The filename to parse for entity values scope : str or list, optional The scope of the search space. Indicates which BIDSLayouts' entities to extract. See :obj:`bids.layout.BIDSLayout.get` docstring for valid values. By default, extracts all entities. entities : list or None, optional An optional list of Entity instances to use in extraction. If passed, the scope and config arguments are ignored, and only the Entities in this list are used. config : str or :obj:`bids.layout.models.Config` or list or None, optional One or more :obj:`bids.layout.models.Config` objects, or paths to JSON config files on disk, containing the Entity definitions to use in extraction. If passed, scope is ignored. include_unmatched : bool, optional If True, unmatched entities are included in the returned dict, with values set to None. If False (default), unmatched entities are ignored. Returns ------- dict Dictionary where keys are Entity names and values are the values extracted from the filename. """ # If either entities or config is specified, just pass through if entities is None and config is None: layouts = self._get_layouts_in_scope(scope) config = chain(*[list(l.config.values()) for l in layouts]) # noqa: E741 config = list(set(config)) return parse_file_entities(filename, entities, config, include_unmatched) def _get_derivative_dirs(self, paths): for path in paths: p = Path(path).absolute() base = p.name if Path.exists(p / 'dataset_description.json'): yield (base, p) else: yield from ( (f'{base}/{dd.parent.name}', dd.parent) for dd in p.glob('*/dataset_description.json') ) def add_derivatives(self, path, parent_database_path=None, **kwargs): """Add BIDS-Derivatives datasets to tracking. Parameters ---------- path : str or list One or more paths to BIDS-Derivatives datasets. Each path can point to either a derivatives/ directory containing one or more pipeline directories, or to a single pipeline directory (e.g., derivatives/fmriprep). parent_database_path : str or Path If not None, use the pipeline name from the dataset_description.json file as the database folder name to nest within the parent database folder name to write out derivative index to. kwargs : dict Optional keyword arguments to pass on to BIDSLayout() when initializing each of the derivative datasets. Notes ----- Every derivatives directory intended for indexing MUST contain a valid dataset_description.json file. See the BIDS-Derivatives specification for details. """ paths = listify(path) if parent_database_path: parent_database_path = Path(parent_database_path) deriv_paths = list(self._get_derivative_dirs(paths)) if not deriv_paths: warnings.warn( # noqa: B028 'Derivative indexing was requested, but no valid ' 'datasets were found in the specified locations ' f'({paths}). Note that all BIDS-Derivatives datasets must' ' meet all the requirements for BIDS-Raw datasets ' '(a common problem is to fail to include a ' "'dataset_description.json' file in derivatives " 'datasets).\n' + "Example contents of 'dataset_description.json':\n%s" # noqa: UP031 % json.dumps(EXAMPLE_DERIVATIVES_DESCRIPTION) ) # Default config and sources values kwargs['sources'] = kwargs.get('sources') or self for name, path in deriv_paths: if parent_database_path: child_database_path = parent_database_path / name kwargs['database_path'] = child_database_path if name in self.derivatives: raise BIDSDerivativesValidationError( f'Pipeline name {name} ({path!s}) has already been added to this ' 'BIDSLayout. Every added pipeline must have a unique name!' ) self.derivatives[name] = BIDSLayout(path, is_derivative=True, **kwargs) def to_df(self, metadata=False, **filters): """Return information for BIDSFiles tracked in Layout as pd.DataFrame. Parameters ---------- metadata : bool, optional If True, includes columns for all metadata fields. If False, only filename-based entities are included as columns. filters : dict, optional Optional keyword arguments passed on to get(). This allows one to easily select only a subset of files for export. Returns ------- :obj:`pandas.DataFrame` A pandas DataFrame, where each row is a file, and each column is a tracked entity. NaNs are injected whenever a file has no value for a given attribute. """ try: import pandas as pd except ImportError: raise ImportError('Missing dependency: "pandas"') # noqa: B904 # TODO: efficiency could probably be improved further by joining the # BIDSFile and Tag tables and running a single query. But this would # require refactoring the below to use _build_file_query, which will # in turn likely require generalizing the latter. files = self.get(**filters) file_paths = [f.path for f in files] query = self.session.query(Tag).filter(Tag.file_path.in_(file_paths)) if not metadata: query = query.join(Entity).filter(Tag.is_metadata == False) # noqa: E712 tags = query.all() tags = [[t.file_path, t.entity_name, t.value] for t in tags] data = pd.DataFrame(tags, columns=['path', 'entity', 'value']) data = data.pivot(index='path', columns='entity', values='value') # Add in orphaned files with no Tags. Maybe make this an argument? orphans = list(set(file_paths) - set(data.index)) for o in orphans: data.loc[o] = pd.Series(dtype=float) return data.reset_index() def get( # noqa: D417 self, return_type='object', target=None, scope='all', regex_search=False, absolute_paths=RemovedOption, invalid_filters='error', **filters, ): """Retrieve files and/or metadata from the current Layout. Parameters ---------- return_type : str, optional Type of result to return. Valid values: 'object' (default): return a list of matching BIDSFile objects. 'file' or 'filename': return a list of matching filenames. 'dir': return a list of directories. 'id': return a list of unique IDs. Must be used together with a valid target. target : str, optional Optional name of the target entity to get results for (only used if return_type is 'dir' or 'id'). scope : str or list, optional Scope of the search space. If passed, only nodes/directories that match the specified scope will be searched. Possible values include: 'all' (default): search all available directories. 'derivatives': search all derivatives directories. 'raw': search only BIDS-Raw directories. 'self': search only the directly called BIDSLayout. : the name of a BIDS-Derivatives pipeline. regex_search : bool or None, optional Whether to require exact matching (False) or regex search (True) when comparing the query string to each entity. invalid_filters (str): Controls behavior when named filters are encountered that don't exist in the database (e.g., in the case of a typo like subbject='0.1'). Valid values: 'error' (default): Raise an explicit error. 'drop': Silently drop invalid filters (equivalent to not having passed them as arguments in the first place). 'allow': Include the invalid filters in the query, resulting in no results being returned. filters : dict Any optional key/values to filter the entities on. Keys are entity names, values are regexes to filter on. For example, passing filters={'subject': 'sub-[12]'} would return only files that match the first two subjects. In addition to ordinary data types, the following enums are defined (in the Query class): * Query.NONE: The named entity must not be defined. * Query.ANY: the named entity must be defined, but can have any value. Returns ------- list of :obj:`bids.layout.BIDSFile` or str A list of BIDSFiles (default) or strings (see return_type). """ if not return_type.startswith(('obj', 'file')) and return_type not in ('id', 'dir'): raise ValueError( f'Invalid return_type <{return_type}> specified (must be one ' "of 'object', 'file', 'filename', 'id', or 'dir')." ) if absolute_paths is not RemovedOption: msg = ( 'The absolute_paths argument is no longer supported. Use the ' '`.path` attribute of returned BIDSFile objects for absolute ' 'paths, and the `.relpath` attribute for relative paths.' ) raise TypeError(msg) layouts = self._get_layouts_in_scope(scope) entities = self.get_entities() # error check on users accidentally passing in filters if isinstance(filters.get('filters'), dict): raise RuntimeError( 'You passed in filters as a dictionary named ' 'filters; please pass the keys in as named ' 'keywords to the `get()` call. For example: ' '`layout.get(**filters)`.' ) # Ensure leading periods if extensions were passed if 'extension' in filters and ( 'bids' in self.config or any('bids-schema' in c for c in self.config) ): filters['extension'] = [ '.' + x.lstrip('.') if isinstance(x, str) else x for x in listify(filters['extension']) ] if invalid_filters != 'allow': bad_filters = set(filters.keys()) - set(entities.keys()) if bad_filters: if invalid_filters == 'drop': for bad_filt in bad_filters: filters.pop(bad_filt) elif invalid_filters == 'error': first_bad = list(bad_filters)[0] msg = f"'{first_bad}' is not a recognized entity. " ents = list(entities.keys()) suggestions = difflib.get_close_matches(first_bad, ents) if suggestions: msg += f'Did you mean {suggestions}? ' raise ValueError( msg + "If you're sure you want to impose " 'this constraint, set ' "invalid_filters='allow'." ) # Provide some suggestions if target is specified and invalid. if target is not None and target not in entities: potential = list(entities.keys()) suggestions = difflib.get_close_matches(target, potential) if suggestions: message = f'Did you mean one of: {suggestions}?' else: message = f'Valid targets are: {potential}' raise TargetError(("Unknown target '{}'. " + message).format(target)) elif target is None and return_type in ['id', 'dir']: raise TargetError( 'If return_type is "id" or "dir", a valid target entity must also be specified.' ) results = [] for l in layouts: # noqa: E741 query = l._build_file_query(filters=filters, regex_search=regex_search) # NOTE: The following line, when uncommented, eager loads # associations. This was introduced in order to prevent sessions # from randomly detaching. It should be fixed by setting # expire_on_commit at session creation, but let's leave this here # for another release or two to make sure we don't have any further # problems. # query = query.options(joinedload(BIDSFile.tags) # .joinedload(Tag.entity)) results.extend(query.all()) if return_type.startswith('file'): results = natural_sort([f.path for f in results]) elif return_type in ['id', 'dir']: if target is None: raise TargetError( 'If return_type is "id" or "dir", a valid ' 'target entity must also be specified.' ) metadata = target not in self.get_entities(metadata=False) if return_type == 'id': ent_iter = ( hashablefy(res.get_entities(metadata=metadata)) for res in results if target in res.entities ) results = list(dict.fromkeys(ents[target] for ents in ent_iter if target in ents)) results = natural_sort(list(set(results))) elif return_type == 'dir': # Check if we're using schema-based config or legacy config using_schema = any('bids-schema' in str(c) for c in listify(self.config)) if using_schema: # Use schema directory rules for schema-based configs directories = self._find_directories_for_entity_using_schema(target, results) results = natural_sort(list(set(directories))) else: # Use original template-based approach for legacy configs template = entities[target].directory if template is None: raise ValueError( 'Return type set to directory, but no ' # noqa: UP031 'directory template is defined for the ' 'target entity ("%s").' % target ) # Construct regex search pattern from target directory template # On Windows, the regex won't compile if, e.g., there is a folder starting with "U" on the path. # noqa: E501 # Converting to a POSIX path with forward slashes solves this. template = self._root.as_posix() + template to_rep = re.findall(r'{(.*?)\}', template) for ent in to_rep: patt = entities[ent].pattern template = template.replace('{%s}' % ent, patt) # noqa: UP031 # Avoid matching subfolders. We are working with POSIX paths here, so we explicitly use "/" # noqa: E501 # as path separator. template += r'[^/]*$' matches = [ f.dirname for f in results if re.search(template, f._dirname.as_posix()) ] results = natural_sort(list(set(matches))) else: results = natural_sort(results, 'path') return results def get_file(self, filename, scope='all'): """Return the BIDSFile object with the specified path. Parameters ---------- filename : str The path of the file to retrieve. Must be either an absolute path, or relative to the root of this BIDSLayout. scope : str or list, optional Scope of the search space. If passed, only BIDSLayouts that match the specified scope will be searched. See :obj:`BIDSLayout.get` docstring for valid values. Default is 'all'. Returns ------- :obj:`bids.layout.BIDSFile` or None File found, or None if no match was found. """ filename = self._root.joinpath(filename).absolute() for layout in self._get_layouts_in_scope(scope): result = layout.session.query(BIDSFile).filter_by(path=str(filename)).first() # noqa: E501 if result: return result return None def _build_file_query(self, **kwargs): query = self.session.query(BIDSFile).filter_by(is_dir=False) filters = kwargs.get('filters') # Entity filtering if filters: regex = kwargs.get('regex_search', False) filters = self._sanitize_query_dtypes(filters) for name, val in filters.items(): tag_alias = aliased(Tag) val_list = list(listify(val)) if val is not None else [None] if not val_list: continue if Query.OPTIONAL in val_list: continue none = required = False if None in val_list: none = True val_list.remove(None) if Query.NONE in val_list: none = True val_list.remove(Query.NONE) if Query.REQUIRED in val_list: required = True val_list.remove(Query.REQUIRED) if none and required: # Always true, apply no filter continue # Baseline, use join, start accumulating clauses join_method = query.join val_clauses = [] # NONE and REQUIRED get special treatment if none: join_method = query.outerjoin val_clauses.append(tag_alias._value.is_(None)) if required: val_clauses.append(tag_alias._value.isnot(None)) # Any remaining values if regex: val_clauses.extend([tag_alias._value.op('REGEXP')(str(v)) for v in val_list]) elif val_list: _value = tag_alias._value if isinstance(val_list[0], int): _value = cast(tag_alias._value, sa.Integer) val_clauses.append(_value.in_(val_list)) # Looking for intersection with list of vals, so use OR val_clause = sa.or_(*val_clauses) if len(val_clauses) > 1 else val_clauses[0] query = join_method( tag_alias, sa.and_(BIDSFile.path == tag_alias.file_path, tag_alias.entity_name == name), ) query = query.filter(val_clause) query = query.group_by(BIDSFile.path) return query def get_collections( self, level, types=None, variables=None, merge=False, sampling_rate=None, skip_empty=False, **kwargs, ): """Return one or more variable Collections in the BIDS project. Parameters ---------- level : {'run', 'session', 'subject', 'dataset'} The level of analysis to return variables for. Must be one of 'run', 'session','subject', or 'dataset'. types : str or list Types of variables to retrieve. All valid values reflect the filename stipulated in the BIDS spec for each kind of variable. Valid values include: 'events', 'physio', 'stim', 'scans', 'participants', 'sessions', and 'regressors'. Default is None. variables : list Optional list of variables names to return. If None, all available variables are returned. merge : bool If True, variables are merged across all observations of the current level. E.g., if level='subject', variables from all subjects will be merged into a single collection. If False, each observation is handled separately, and the result is returned as a list. sampling_rate : int or str If level='run', the sampling rate to pass onto the returned :obj:`bids.variables.collections.BIDSRunVariableCollection`. skip_empty : bool Whether or not to skip empty Variables (i.e., where there are no rows/records in a file after applying any filtering operations like dropping NaNs). kwargs Optional additional arguments to pass onto :obj:`bids.variables.io.load_variables`. Returns ------- list of :obj:`bids.variables.collections.BIDSVariableCollection` or :obj:`bids.variables.collections.BIDSVariableCollection` A list if merge=False; a single :obj:`bids.variables.collections.BIDSVariableCollection` if merge=True. """ from bids.variables import load_variables index = load_variables(self, types=types, levels=level, skip_empty=skip_empty, **kwargs) return index.get_collections(level, variables, merge, sampling_rate=sampling_rate) def get_metadata(self, path, include_entities=False, scope='all'): """Return metadata found in JSON sidecars for the specified file. Parameters ---------- path : str Path to the file to get metadata for. include_entities : bool, optional If True, all available entities extracted from the filename (rather than JSON sidecars) are included in the returned metadata dictionary. scope : str or list, optional The scope of the search space. Each element must be one of 'all', 'raw', 'self', 'derivatives', or a BIDS-Derivatives pipeline name. Defaults to searching all available datasets. Returns ------- dict A dictionary of key/value pairs extracted from all of the target file's associated JSON sidecars. Notes ----- A dictionary containing metadata extracted from all matching .json files is returned. In cases where the same key is found in multiple files, the values in files closer to the input filename will take precedence, per the inheritance rules in the BIDS specification. """ md = BIDSMetadata(str(path)) for layout in self._get_layouts_in_scope(scope): query = layout.session.query(Tag).join(BIDSFile).filter(BIDSFile.path == str(path)) if not include_entities: query = query.join(Entity).filter(Tag.is_metadata == True) # noqa: E712 results = query.all() if results: md.update({t.entity_name: t.value for t in results}) return md return md def get_dataset_description(self, scope='self', all_=False): """Return contents of dataset_description.json. Parameters ---------- scope : str The scope of the search space. Only descriptions of BIDSLayouts that match the specified scope will be returned. See :obj:`bids.layout.BIDSLayout.get` docstring for valid values. Defaults to 'self' --i.e., returns the dataset_description.json file for only the directly-called BIDSLayout. all_ : bool If True, returns a list containing descriptions for all matching layouts. If False (default), returns for only the first matching layout. Returns ------- dict or list of dict a dictionary or list of dictionaries (depending on all_). """ layouts = self._get_layouts_in_scope(scope) if not all_: return layouts[0].get_file('dataset_description.json').get_dict() return [l.get_file('dataset_description.json').get_dict() for l in layouts] # noqa: E741 def get_nearest( # noqa: D417 self, path, return_type='filename', strict=True, all_=False, ignore_strict_entities='extension', full_search=False, **filters, ): """Walk up file tree from specified path and return nearest matching file(s). Parameters ---------- path (str): The file to search from. return_type (str): What to return; must be one of 'filename' (default) or 'tuple'. strict (bool): When True, all entities present in both the input path and the target file(s) must match perfectly. When False, files will be ordered by the number of matching entities, and partial matches will be allowed. all_ (bool): When True, returns all matching files. When False (default), only returns the first match. ignore_strict_entities (str, list): Optional entity/entities to exclude from strict matching when strict is True. This allows one to search, e.g., for files of a different type while matching all other entities perfectly by passing ignore_strict_entities=['type']. Ignores extension by default. full_search (bool): If True, searches all indexed files, even if they don't share a common root with the provided path. If False, only files that share a common root will be scanned. filters : dict Optional keywords to pass on to :obj:`bids.layout.BIDSLayout.get`. """ path = Path(path).absolute() # Make sure we have a valid suffix if not filters.get('suffix'): f = self.get_file(path) if 'suffix' not in f.entities: raise BIDSValidationError( "File '%s' does not have a valid suffix, most " # noqa: UP031 'likely because it is not a valid BIDS file.' % path ) filters['suffix'] = f.entities['suffix'] # Collect matches for all entities entities = {} for ent in self.get_entities(metadata=False).values(): m = ent.regex.search(str(path)) if m: entities[ent.name] = ent._astype(m.group(1)) # Remove any entities we want to ignore when strict matching is on if strict and ignore_strict_entities is not None: for k in listify(ignore_strict_entities): entities.pop(k, None) # Get candidate files results = self.get(**filters) # Make a dictionary of directories --> contained files folders = defaultdict(list) for f in results: folders[f._dirname].append(f) # Build list of candidate directories to check search_paths = [] while True: if path in folders and folders[path]: search_paths.append(path) parent = path.parent if parent == path: break path = parent if full_search: unchecked = set(folders.keys()) - set(search_paths) search_paths.extend(path for path in unchecked if folders[path]) def count_matches(f): # Count the number of entities shared with the passed file f_ents = f.entities keys = set(entities.keys()) & set(f_ents.keys()) shared = len(keys) return [shared, sum([entities[k] == f_ents[k] for k in keys])] matches = [] for path in search_paths: # Sort by number of matching entities. Also store number of # common entities, for filtering when strict=True. num_ents = [[f] + count_matches(f) for f in folders[path]] # Filter out imperfect matches (i.e., where number of common # entities does not equal number of matching entities). if strict: num_ents = [f for f in num_ents if f[1] == f[2]] num_ents.sort(key=lambda x: x[2], reverse=True) if num_ents: for f_match in num_ents: matches.append(f_match[0]) if not all_: break matches = [match.path if return_type.startswith('file') else match for match in matches] return matches if all_ else matches[0] if matches else None def get_bvec(self, path, **kwargs): """Get bvec file for passed path.""" result = self.get_nearest(path, extension='.bvec', suffix='dwi', all_=True, **kwargs) return listify(result)[0] def get_bval(self, path, **kwargs): """Get bval file for passed path.""" result = self.get_nearest(path, suffix='dwi', extension='.bval', all_=True, **kwargs) return listify(result)[0] def get_fieldmap(self, path, return_list=False): """Get fieldmap(s) for specified path.""" fieldmaps = self._get_fieldmaps(path) if return_list: return fieldmaps else: if len(fieldmaps) == 1: return fieldmaps[0] elif len(fieldmaps) > 1: raise ValueError( 'More than one fieldmap found, but the ' "'return_list' argument was set to False. " 'Either ensure that there is only one ' 'fieldmap for this image, or set the ' "'return_list' argument to True and handle " 'the result as a list.' ) else: # len(fieldmaps) == 0 return None def _get_fieldmaps(self, path): path = str(path) sub = self.parse_file_entities(path)['subject'] fieldmap_set = [] suffix = '(phase1|phasediff|epi|fieldmap)' files = self.get( subject=sub, suffix=suffix, regex_search=True, extension=['.nii.gz', '.nii'] ) for file in files: metadata = self.get_metadata(file.path) if metadata and 'IntendedFor' in metadata.keys(): intended_for = listify(metadata['IntendedFor']) # path uses local os separators while _suff read from json likely uses author's os separators, so we # noqa: E501 # convert _suff to use local separators. if any([path.endswith(str(Path(_suff))) for _suff in intended_for]): # noqa: C419 cur_fieldmap = {} if file.entities['suffix'] == 'phasediff': cur_fieldmap = { 'phasediff': file.path, 'magnitude1': file.path.replace('phasediff', 'magnitude1'), 'suffix': 'phasediff', } magnitude2 = file.path.replace('phasediff', 'magnitude2') if Path(magnitude2).is_file(): cur_fieldmap['magnitude2'] = magnitude2 elif file.entities['suffix'] == 'phase1': cur_fieldmap['phase1'] = file.path cur_fieldmap['magnitude1'] = file.path.replace('phase1', 'magnitude1') cur_fieldmap['phase2'] = file.path.replace('phase1', 'phase2') cur_fieldmap['magnitude2'] = file.path.replace('phase1', 'magnitude2') cur_fieldmap['suffix'] = 'phase' elif file.entities['suffix'] == 'epi': cur_fieldmap['epi'] = file.path cur_fieldmap['suffix'] = 'epi' elif file.entities['suffix'] == 'fieldmap': cur_fieldmap['fieldmap'] = file.path cur_fieldmap['magnitude'] = file.path.replace('fieldmap', 'magnitude') cur_fieldmap['suffix'] = 'fieldmap' fieldmap_set.append(cur_fieldmap) return fieldmap_set def get_tr(self, derivatives=False, **filters): """Return the scanning repetition time (TR) for one or more runs. Parameters ---------- derivatives : bool If True, also checks derivatives images. filters : dict Optional keywords used to constrain the selected runs. Can be any arguments valid for a .get call (e.g., BIDS entities or JSON sidecar keys). Returns ------- float A single float. Notes ----- Raises an exception if more than one unique TR is found. """ # Constrain search to functional images filters.update(suffix='bold', datatype='func') scope = 'all' if derivatives else 'raw' images = self.get(extension=['.nii', '.nii.gz'], scope=scope, **filters) if not images: raise NoMatchError('No functional images that match criteria found.') all_trs = set() for img in images: md = self.get_metadata(img.path) all_trs.add(round(float(md['RepetitionTime']), 5)) if len(all_trs) > 1: raise NoMatchError(f'Unique TR cannot be found given filters {filters!r}') return all_trs.pop() def build_path( self, source, path_patterns=None, strict=False, scope='all', validate=True, absolute_paths=True, ): """Construct a target filename for a file or dictionary of entities. Parameters ---------- source : str or :obj:`bids.layout.BIDSFile` or dict The source data to use to construct the new file path. Must be one of: - A BIDSFile object - A string giving the path of a BIDSFile contained within the current Layout. - A dict of entities, with entity names in keys and values in values path_patterns : list Optional path patterns to use to construct the new file path. If None, the Layout-defined patterns will be used. Entities should be represented by the name surrounded by curly braces. Optional portions of the patterns should be denoted by square brackets. Entities that require a specific value for the pattern to match can pass them inside angle brackets. Default values can be assigned by specifying a string after the pipe operator. E.g., (e.g., {type|bold} would only match the pattern if the entity 'type' was passed and its value is "image", otherwise the default value "bold" will be used). Example: 'sub-{subject}/[var-{name}/]{id}.csv' Result: 'sub-01/var-SES/1045.csv' strict : bool, optional If True, all entities must be matched inside a pattern in order to be a valid match. If False, extra entities will be ignored so long as all mandatory entities are found. scope : str or list, optional The scope of the search space. Indicates which BIDSLayouts' path patterns to use. See BIDSLayout docstring for valid values. By default, uses all available layouts. If two or more values are provided, the order determines the precedence of path patterns (i.e., earlier layouts will have higher precedence). validate : bool, optional If True, built path must pass BIDS validator. If False, no validation is attempted, and an invalid path may be returned (e.g., if an entity value contains a hyphen). absolute_paths : bool, optional Report either absolute or relative (to the top of the dataset) paths. """ # 'is_file' is a crude check for Path objects if isinstance(source, str) or hasattr(source, 'is_file'): source = str(source) if source not in self.files: source = self._root / source source = self.get_file(source) if isinstance(source, BIDSFile): source = source.entities if path_patterns is None: layouts = self._get_layouts_in_scope(scope) path_patterns = [] seen_configs = set() for l in layouts: # noqa: E741 for c in l.config.values(): if c in seen_configs: continue if c.default_path_patterns is not None: path_patterns.extend(c.default_path_patterns) seen_configs.add(c) built = build_path(source, path_patterns, strict) if built is None: raise ValueError(f'Unable to construct build path with source {source}') to_check = Path('/').joinpath(built).as_posix() if validate and not BIDSValidator().is_bids(to_check): raise BIDSValidationError( f'Built path {built} is not a valid BIDS filename. ' 'Please make sure all provided entity values are ' 'spec-compliant.' ) if absolute_paths: built = self._root / built # type: pathlib.Path # convert into a posix path for consistency with `writing.build_path` built = built.as_posix() # type: str return built def copy_files( self, files=None, path_patterns=None, symbolic_links=True, root=None, conflicts='fail', **kwargs, ): """Copy BIDSFile(s) to new locations. The new locations are defined by each BIDSFile's entities and the specified `path_patterns`. Parameters ---------- files : list Optional list of BIDSFile objects to write out. If none provided, use files from running a get() query using remaining **kwargs. path_patterns : str or list Write patterns to pass to each file's write_file method. symbolic_links : bool Whether to copy each file as a symbolic link or a deep copy. root : str Optional root directory that all patterns are relative to. Defaults to dataset root. conflicts : str Defines the desired action when the output path already exists. Must be one of: 'fail': raises an exception 'skip' does nothing 'overwrite': overwrites the existing file 'append': adds a suffix to each file copy, starting with 1 kwargs : dict Optional key word arguments to pass into a get() query. """ root = self.root if root is None else root _files = self.get(**kwargs) if files: _files = list(set(files).intersection(_files)) for f in _files: f.copy(path_patterns, symbolic_link=symbolic_links, root=root, conflicts=conflicts) def write_to_file( # noqa: D417 self, entities, path_patterns=None, contents=None, link_to=None, copy_from=None, content_mode='text', conflicts='fail', strict=False, validate=True, ): """Write data to a file defined by the passed entities and patterns. Parameters ---------- entities : dict A dictionary of entities, with Entity names in keys and values for the desired file in values. path_patterns : list Optional path patterns to use when building the filename. If None, the Layout-defined patterns will be used. contents : object Contents to write to the generate file path. Can be any object serializable as text or binary data (as defined in the content_mode argument). link_to : str Optional path with which to create a symbolic link to. Used as an alternative to and takes priority over the contents argument. conflicts : str Defines the desired action when the output path already exists. Must be one of: 'fail': raises an exception 'skip' does nothing 'overwrite': overwrites the existing file 'append': adds a suffix to each file copy, starting with 1 strict : bool If True, all entities must be matched inside a pattern in order to be a valid match. If False, extra entities will be ignored so long as all mandatory entities are found. validate : bool If True, built path must pass BIDS validator. If False, no validation is attempted, and an invalid path may be returned (e.g., if an entity value contains a hyphen). """ path = self.build_path(entities, path_patterns, strict, validate=validate) if path is None: raise ValueError( 'Cannot construct any valid filename for ' 'the passed entities given available path ' 'patterns.' ) write_to_file( path, contents=contents, link_to=link_to, copy_from=copy_from, content_mode=content_mode, conflicts=conflicts, root=self.root, ) class Query(enum.Enum): """Enums for use with BIDSLayout.get().""" NONE = 1 # Entity must not be present REQUIRED = ANY = 2 # Entity must be defined, but with an arbitrary value OPTIONAL = 3 # Entity may or may not be defined ================================================ FILE: src/bids/layout/models.py ================================================ """Model classes used in BIDSLayouts.""" import json import os import re import warnings from collections import UserDict from copy import deepcopy from functools import lru_cache from itertools import chain from pathlib import Path from bidsschematools import rules from sqlalchemy import Boolean, Column, ForeignKey, String, Table from sqlalchemy.ext.associationproxy import association_proxy from sqlalchemy.orm import backref, object_session, reconstructor, relationship from sqlalchemy.orm.collections import attribute_mapped_collection from upath import UPath try: from sqlalchemy.orm import declarative_base except ImportError: # sqlalchemy < 1.4 from sqlalchemy.ext.declarative import declarative_base from ..config import get_option from ..exceptions import BIDSChildDatasetError from ..utils import bids_sort, listify from .utils import BIDSMetadata, PaddedInt from .writing import build_path, write_to_file Base = declarative_base() class LayoutInfo(Base): """Contains information about a BIDSLayout's initialization parameters.""" __tablename__ = 'layout_info' root = Column(String, primary_key=True) absolute_paths = Column(Boolean, default=True) # Removed, but may be in older DBs _derivatives = Column(String) _config = Column(String) def __init__(self, **kwargs): init_args = self._sanitize_init_args(kwargs) raw_cols = ['root'] json_cols = ['derivatives', 'config'] all_cols = raw_cols + json_cols missing_cols = set(all_cols) - set(init_args.keys()) if missing_cols: raise ValueError(f'Missing mandatory initialization args: {missing_cols}') for col in all_cols: setattr(self, col, init_args[col]) if col in json_cols: json_data = json.dumps(init_args[col]) setattr(self, '_' + col, json_data) @reconstructor def _init_on_load(self): if self.absolute_paths is False: warnings.warn( 'PyBIDS database loaded with deprecated `absolute_paths` ' 'option. Returned paths will be absolute.', stacklevel=2, ) for col in ['derivatives', 'config']: db_val = getattr(self, '_' + col) setattr(self, col, json.loads(db_val)) def _sanitize_init_args(self, kwargs): """Prepare initialization arguments for serialization""" if 'root' in kwargs: kwargs['root'] = str(UPath(kwargs['root']).absolute()) if 'config' in kwargs and isinstance(kwargs['config'], list): kwargs['config'] = [ str(UPath(config).absolute()) if isinstance(config, os.PathLike) else config for config in kwargs['config'] ] # Get abspaths if kwargs.get('derivatives') not in (None, True, False): kwargs['derivatives'] = [ str(UPath(der).absolute()) for der in listify(kwargs['derivatives']) ] if kwargs.pop('absolute_paths', True) is not True: warnings.warn( 'Deprecated `absolute_paths` option passed. Value must be True.', stacklevel=3, ) return kwargs def __repr__(self): return f'' class Config(Base): """Container for BIDS configuration information. Parameters ---------- name : str The name to give the Config (e.g., 'bids'). entities : list A list of dictionaries containing entity configuration information. default_path_patterns : list Optional list of patterns used to build new paths. session : :obj:`sqlalchemy.orm.session.Session` or None An optional SQLAlchemy session. If passed, the session is used to update the database with any newly created Entity objects. If None, no database update occurs. """ __tablename__ = 'configs' name = Column(String, primary_key=True) _default_path_patterns = Column(String) entities = relationship( 'Entity', secondary='config_to_entity_map', collection_class=attribute_mapped_collection('name'), ) def __init__(self, name, entities=None, default_path_patterns=None, session=None): self.name = name self.default_path_patterns = default_path_patterns self._default_path_patterns = json.dumps(default_path_patterns) if entities: for ent in entities: if session is not None: existing = session.query(Config).filter_by(name=ent['name']).first() else: existing = None ent = existing or Entity(**ent) self.entities[ent.name] = ent if session is not None: session.add_all(list(self.entities.values())) session.commit() @reconstructor def _init_on_load(self): self.default_path_patterns = json.loads(self._default_path_patterns) @classmethod def load(cls, config, session=None): """Load a Config instance from the passed configuration data. Parameters ---------- config : str or dict A string or dict containing configuration information. Must be one of: * A string giving the name of a predefined config file (e.g., 'bids' or 'derivatives') * A path to a JSON file containing config information * A dictionary containing config information * 'bids-schema' to load from the official BIDS schema * dict with 'schema_version' key to load specific BIDS version (e.g., {'schema_version': '1.9.0'}) session : :obj:`sqlalchemy.orm.session.Session` or None An optional SQLAlchemy Session instance. If passed, the session is used to check the database for (and return) an existing Config with name defined in config['name']. Returns ------- A Config instance. """ # Handle schema-based config loading if config == 'bids-schema': return cls._from_schema(session=session) elif isinstance(config, dict) and 'schema_version' in config: return cls._from_schema(schema_version=config['schema_version'], session=session) # Existing JSON/file-based loading if isinstance(config, (str, Path, UPath)): config_paths = get_option('config_paths') if config in config_paths: config = config_paths[config] if not UPath(config).exists(): raise ValueError(f'{config} is not a valid path.') else: with open(config) as f: config = json.load(f) # Return existing Config record if one exists if session is not None: result = session.query(Config).filter_by(name=config['name']).first() if result: return result return Config(session=session, **config) @classmethod def _from_schema(cls, schema_version=None, session=None): """Load config from BIDS schema. Parameters ---------- schema_version : str, optional Specific BIDS specification version to load (e.g., "1.9.0", "1.8.0"). If None, uses the schema version bundled with bidsschematools. session : :obj:`sqlalchemy.orm.session.Session` or None An optional SQLAlchemy Session instance. Returns ------- Config A Config instance populated with entities and patterns extracted from the BIDS schema. """ from bidsschematools import schema as bidsschema from upath import UPath # Load schema - either default or specific version if schema_version is None: bids_schema = bidsschema.load_schema() else: # Load specific schema version from versioned URL schema_url = UPath( f'https://bids-specification.readthedocs.io/en/v{schema_version}/schema.json' ) try: bids_schema = bidsschema.load_schema(schema_url) except Exception as e: # noqa: BLE001 raise ValueError( # noqa: B904 f'Failed to load BIDS schema version {schema_version}. ' f'Check that the version exists at {schema_url}. ' f'Error: {e}' ) # Collect ALL patterns from bidsschematools (keep existing collection logic) all_patterns = [] file_sections = { 'raw': bids_schema.rules.files.raw, 'deriv': bids_schema.rules.files.deriv, 'common': bids_schema.rules.files.common, } for section_type, sections in file_sections.items(): if section_type == 'raw': for datatype_name in sections.keys(): datatype_rules = getattr(sections, datatype_name) regex_rules = rules.regexify_filename_rules( datatype_rules, bids_schema, level=1 ) all_patterns.extend(regex_rules) else: for subsection_name in sections.keys(): subsection_rules = getattr(sections, subsection_name) regex_rules = rules.regexify_filename_rules( subsection_rules, bids_schema, level=1 ) all_patterns.extend(regex_rules) # Store patterns directly - NO PARSING EVER config_name = f'bids-schema-{bids_schema.bids_version}' # Extract entities directly from schema rules entity_names = cls._extract_entity_names_from_rules(bids_schema) # Extract actual values for key entities from schema rules directly entity_values = cls._extract_entity_values_from_rules(bids_schema, entity_names) # Create Entity objects entities = cls._create_entities_from_schema(entity_names, entity_values, bids_schema) config = cls(name=config_name, entities=entities, session=session) return config @classmethod def _extract_entity_names_from_rules(cls, bids_schema): """Extract entity names directly from schema rules""" # Get entity names from all rule sections entity_names = set() file_sections = { 'raw': bids_schema.rules.files.raw, 'deriv': bids_schema.rules.files.deriv, 'common': bids_schema.rules.files.common, } for section_type, sections in file_sections.items(): # noqa: B007 for section_name in sections.keys(): section_rules = getattr(sections, section_name) for rule_name in section_rules.keys(): rule = getattr(section_rules, rule_name) rule_dict = dict(rule) if 'entities' in rule_dict: entity_names.update(rule_dict['entities'].keys()) # Add special constructs that appear in patterns but aren't "entities" entity_names.update(['suffix', 'extension', 'datatype']) return entity_names @classmethod def _create_entities_from_schema(cls, entity_names, entity_values, bids_schema): """Create Entity objects directly from schema information.""" entities = [] for entity_name in sorted(entity_names): # Get entity info from schema if available entity_spec = bids_schema.objects.entities.get(entity_name, {}) # Handle special entities that don't have BIDS prefixes special_entities = {'suffix', 'extension', 'datatype'} if entity_name in special_entities: # These entities use explicit value lists from schema rules if entity_name in entity_values and entity_values[entity_name]: # Filter out problematic values for extension entity if entity_name == 'extension': # Remove empty string and wildcard patterns filtered_values = { v for v in entity_values[entity_name] if v and v not in ('.*', '**', '*') } values = '|'.join(sorted(filtered_values)) else: values = '|'.join(sorted(entity_values[entity_name])) if entity_name == 'extension': pattern = rf'({values})\Z' # Extensions are at the end elif entity_name == 'datatype': pattern = rf'/({values})/' # Datatypes are directory names else: # suffix pattern = rf'[_/\\\\]({values})\.?' # Before extension, with optional dot else: # Regular BIDS entities - use schema format patterns directly bids_prefix = entity_spec.get('name', entity_name) format_type = entity_spec.get('format', 'label') # Get the official value pattern from schema formats value_pattern = '[0-9a-zA-Z]+' # Default pattern if format_type in bids_schema.objects.formats: format_obj = dict(bids_schema.objects.formats[format_type]) value_pattern = format_obj.get('pattern', value_pattern) # Determine separator based on entity type if entity_name in ['subject', 'session']: # Directory-level entities pattern = rf'[/\\\\]+{bids_prefix}-({value_pattern})' else: # File-level entities pattern = rf'[_/\\\\]+{bids_prefix}-({value_pattern})' entity_config = { 'name': entity_name, 'pattern': pattern, 'dtype': 'int' if entity_spec.get('format') == 'index' else 'str', } entities.append(entity_config) return entities @classmethod def _extract_entity_values_from_rules(cls, bids_schema, entity_names): """Extract entity values directly from schema rules""" entity_values = {name: set() for name in entity_names} file_sections = { 'raw': bids_schema.rules.files.raw, 'deriv': bids_schema.rules.files.deriv, 'common': bids_schema.rules.files.common, } for section_type, sections in file_sections.items(): # noqa: B007 for section_name in sections.keys(): section_rules = getattr(sections, section_name) for rule_name in section_rules.keys(): rule = getattr(section_rules, rule_name) rule_dict = dict(rule) # Get values for special entities directly from rules if 'suffixes' in rule_dict: entity_values['suffix'].update(rule_dict['suffixes']) if 'extensions' in rule_dict: entity_values['extension'].update(rule_dict['extensions']) if 'datatypes' in rule_dict: entity_values['datatype'].update(rule_dict['datatypes']) # For regular entities, we don't extract specific values # They use generic patterns like [0-9a-zA-Z]+ return entity_values def __repr__(self): return f'' class BIDSFile(Base): """Represents a single file or directory in a BIDS dataset. Parameters ---------- filename : str The path to the corresponding file. """ __tablename__ = 'files' path = Column(String, primary_key=True) filename = Column(String) dirname = Column(String) entities = association_proxy('tags', 'value') is_dir = Column(Boolean, index=True) class_ = Column(String(20)) _associations = relationship( 'BIDSFile', secondary='associations', primaryjoin='FileAssociation.dst == BIDSFile.path', secondaryjoin='FileAssociation.src == BIDSFile.path', ) __mapper_args__ = {'polymorphic_on': class_, 'polymorphic_identity': 'file'} def __init__(self, filename): self.path = str(filename) self.filename = self._path.name self.dirname = str(self._path.parent) self.is_dir = not self.filename @property def _path(self): return UPath(self.path) @property def _dirname(self): return UPath(self.dirname) def __repr__(self): return f"<{self.__class__.__name__} filename='{self.path}'>" def __fspath__(self): return self.path @property @lru_cache # noqa: B019 def relpath(self): """Return path relative to layout root""" root = object_session(self).query(LayoutInfo).first().root return str(UPath(self.path).relative_to(root)) def get_associations(self, kind=None, include_parents=False): """Get associated files, optionally limiting by association kind. Parameters ---------- kind : str The kind of association to return (e.g., "Child"). By default, all associations are returned. include_parents : bool If True, files related through inheritance are included in the returned list. If False, only directly associated files are returned. For example, a file's JSON sidecar will always be returned, but other JSON files from which the sidecar inherits will only be returned if include_parents=True. Returns ------- list A list of BIDSFile instances. """ if kind is None and not include_parents: return self._associations session = object_session(self) q = ( session.query(BIDSFile) .join(FileAssociation, BIDSFile.path == FileAssociation.dst) .filter_by(src=self.path) ) if kind is not None: q = q.filter_by(kind=kind) associations = q.all() if not include_parents: return associations def collect_associations(results, bidsfile): results.append(bidsfile) for p in bidsfile.get_associations('Child'): results = collect_associations(results, p) return results return list(chain(*[collect_associations([], bf) for bf in associations])) def get_metadata(self): """Return all metadata associated with the current file.""" md = BIDSMetadata(self.path) md.update(self.get_entities(metadata=True)) return md def get_entities(self, metadata=False, values='tags'): """Return entity information for the current file. Parameters ---------- metadata : bool or None If False (default), only entities defined for filenames (and not those found in the JSON sidecar) are returned. If True, only entities found in metadata files (and not defined for filenames) are returned. If None, all available entities are returned. values : str The kind of object to return in the dict's values. Must be one of: * 'tags': Returns only the tagged value--e.g., if the key is "subject", the value might be "01". * 'objects': Returns the corresponding Entity instance. Returns ------- dict A dict, where keys are entity names and values are Entity instances. """ if metadata is None and values == 'tags': return self.entities session = object_session(self) query = session.query(Tag).filter_by(file_path=self.path).join(Entity) if metadata not in (None, 'all'): query = query.filter(Tag.is_metadata == metadata) results = query.all() if values.startswith('obj'): return bids_sort({t.entity_name: t.entity for t in results}) return bids_sort({t.entity_name: t.value for t in results}) def copy(self, path_patterns, symbolic_link=False, root=None, conflicts='fail'): """Copy the contents of a file to a new location. Parameters ---------- path_patterns : list List of patterns used to construct the new filename. See :obj:`build_path` documentation for details. symbolic_link : bool If True, use a symbolic link to point to the existing file. If False, creates a new file. root : str Optional path to prepend to the constructed filename. conflicts : str Defines the desired action when the output path already exists. Must be one of: 'fail': raises an exception 'skip' does nothing 'overwrite': overwrites the existing file 'append': adds a suffix to each file copy, starting with 1 """ new_filename = build_path(self.entities, path_patterns) if not new_filename: return None if new_filename[-1] == os.sep: new_filename += self.filename if self._path.is_absolute() or root is None: path = self._path else: path = UPath(root) / self._path if not path.exists(): raise ValueError("Target filename to copy/symlink (%s) doesn't exist." % path) # noqa: UP031 kwargs = dict(path=new_filename, root=root, conflicts=conflicts) # noqa: C408 if symbolic_link: kwargs['link_to'] = path else: kwargs['copy_from'] = path write_to_file(**kwargs) class BIDSDataFile(BIDSFile): """Represents a single data file in a BIDS dataset. Derived from `BIDSFile` and provides additional functionality such as obtaining pandas DataFrame data representation (via `get_df`). """ __mapper_args__ = {'polymorphic_identity': 'data_file'} def get_df(self, include_timing=True, adjust_onset=False, enforce_dtypes=True, **pd_args): """Return the contents of a tsv file as a pandas DataFrame. Parameters ---------- include_timing : bool If True, adds an "onset" column to dense timeseries files (e.g., *_physio.tsv.gz). adjust_onset : bool If True, the onset of each sample in a dense timeseries file is shifted to reflect the "StartTime" value in the JSON sidecar. If False, the first sample starts at 0 secs. Ignored if include_timing=False. enforce_dtypes : bool If True, enforces the data types defined in the BIDS spec on core columns (e.g., subject_id and session_id must be represented as strings). pd_args : dict Optional keyword arguments to pass onto pd.read_csv(). Returns ------- :obj:`pandas.DataFrame` A pandas DataFrame. """ import numpy as np import pandas as pd if enforce_dtypes: dtype = {'subject_id': str, 'session_id': str, 'participant_id': str} else: dtype = None # TODO: memoize this for efficiency. (Note: caching is insufficient, # because the dtype enforcement will break if we ignore the value of # enforce_dtypes). suffix = self.entities['suffix'] header = None if suffix in {'physio', 'stim'} else 'infer' self.data = pd.read_csv( self.path, sep='\t', na_values='n/a', dtype=dtype, header=header, **pd_args ) data = self.data.copy() if self.entities['extension'] == '.tsv.gz': md = self.get_metadata() # We could potentially include some validation here, but that seems # like a job for the BIDS Validator. data.columns = md['Columns'] if include_timing: onsets = np.arange(len(data)) / md['SamplingFrequency'] if adjust_onset: onsets += md['StartTime'] data.insert(0, 'onset', onsets) return data class BIDSImageFile(BIDSFile): """Represents a single neuroimaging data file in a BIDS dataset. Derived from `BIDSFile` and provides additional functionality such as obtaining nibabel's image file representation (via `get_image`). """ __mapper_args__ = {'polymorphic_identity': 'image_file'} def get_image(self, **kwargs): """Return the associated image file (if it exists) as a NiBabel object Any keyword arguments are passed to ``nibabel.load``. """ try: import nibabel as nb return nb.load(self.path, **kwargs) except Exception as e: raise ValueError( f"'{self.path}' does not appear to be an image format NiBabel can read." ) from e class BIDSJSONFile(BIDSFile): """Represents a single JSON metadata file in a BIDS dataset. Derived from `BIDSFile` and provides additional functionality for reading the contents of JSON files as either dicts or strings. """ __mapper_args__ = {'polymorphic_identity': 'json_file'} def get_dict(self): """Return the contents of the current file as a dictionary.""" d = json.loads(self.get_json()) if not isinstance(d, dict): raise ValueError( 'File %s is a json containing %s, not a dict which was expected' # noqa: UP031 % (self.path, type(d)) ) return d def get_json(self): """Return the contents of the current file as a JSON string.""" with open(self.path) as f: return f.read() class Entity(Base): """Represents a single entity defined in the JSON config. Parameters ---------- name : str The name of the entity (e.g., 'subject', 'run', etc.) pattern : str A regex pattern used to match against file names. Must define at least one group, and only the first group is kept as the match. mandatory : bool If True, every File _must_ match this entity. directory : str Optional pattern defining a directory associated with the entity. dtype : str The optional data type of the Entity values. Must be one of 'int', 'float', 'bool', or 'str'. If None, no type enforcement will be attempted, which means the dtype of the value may be unpredictable. """ __tablename__ = 'entities' name = Column(String, primary_key=True) mandatory = Column(Boolean, default=False) pattern = Column(String) directory = Column(String, nullable=True) _dtype = Column(String, default='str') files = association_proxy('tags', 'value') def __init__(self, name, pattern=None, mandatory=False, directory=None, dtype='str'): self.name = name self.pattern = pattern self.mandatory = mandatory self.directory = directory if not isinstance(dtype, str): dtype = dtype.__name__ self._dtype = dtype self._init_on_load() def __repr__(self): return f'' @reconstructor def _init_on_load(self): if self._dtype not in ('str', 'float', 'int', 'bool'): raise ValueError( f"Invalid dtype '{self._dtype}'. Must be one of 'int', 'float', 'bool', or 'str'." ) if self._dtype == 'int': self.dtype = PaddedInt else: self.dtype = eval(self._dtype) # noqa: S307 self.regex = re.compile(self.pattern) if self.pattern is not None else None def __iter__(self): yield from self.unique() def __deepcopy__(self, memo): cls = self.__class__ result = cls.__new__(cls) # Directly copy the SQLAlchemy connection before any setattr calls, # otherwise failures occur sporadically on Python 3.5 when the # _sa_instance_state attribute (randomly!) disappears. result._sa_instance_state = self._sa_instance_state memo[id(self)] = result for k, v in self.__dict__.items(): if k == '_sa_instance_state': continue new_val = getattr(self, k) if k == 'regex' else deepcopy(v, memo) setattr(result, k, new_val) return result def match_file(self, f): """Determine whether the passed file matches the Entity. Parameters ---------- f : BIDSFile The BIDSFile instance to match against. Returns ------- the matched value if a match was found, otherwise None. """ if self.regex is None: return None m = self.regex.search(f.path) val = m.group(1) if m is not None else None return self._astype(val) def unique(self): """Return all unique values/levels for the current entity.""" return list(set(self.files.values())) def count(self, files=False): """Return a count of unique values or files. Parameters ---------- files : bool When True, counts all files mapped to the Entity. When False, counts all unique values. Returns ------- int Count of unique values or files. """ return len(self.files) if files else len(self.unique()) def _astype(self, val): if val is not None and self.dtype is not None: val = self.dtype(val) return val type_map = { 'str': str, 'int': PaddedInt, 'float': float, 'bool': bool, 'json': 'json', } class Tag(Base): """Represents an association between a File and an Entity. Parameters ---------- file : BIDSFile The associated BIDSFile. entity : Entity The associated Entity. value : json-serializable type The value to store for this file/entity pair. Must be of type str, int, float, bool, or any json-serializable structure. dtype : str Optional type for the value field. If None, inferred from value. If passed, must be one of str, int, float, bool, or json. Any other value will be treated as json (and will fail if the value can't be serialized to json). is_metadata : bool Indicates whether or not the Entity is derived from JSON sidecars (True) or is a predefined Entity from a config (False). """ __tablename__ = 'tags' file_path = Column(String, ForeignKey('files.path'), primary_key=True) entity_name = Column(String, ForeignKey('entities.name'), primary_key=True) _value = Column(String, nullable=False) _dtype = Column(String, default='str') is_metadata = Column(Boolean, default=False) file = relationship( 'BIDSFile', backref=backref('tags', collection_class=attribute_mapped_collection('entity_name')), ) entity = relationship( 'Entity', backref=backref('tags', collection_class=attribute_mapped_collection('file_path')), ) def __init__(self, file, entity, value, dtype=None, is_metadata=False): data = _create_tag_dict(file, entity, value, dtype, is_metadata) self.file_path = data['file_path'] self.entity_name = data['entity_name'] self._dtype = data['_dtype'] self._value = data['_value'] self.is_metadata = data['is_metadata'] self.dtype = type_map[self._dtype] if self._dtype != 'json': self.value = self.dtype(value) else: self.value = value def __repr__(self): msg = '' return msg.format(self.file_path, self.entity_name, self.value) @reconstructor def _init_on_load(self): if self._dtype == 'json': self.value = json.loads(self._value) self.dtype = 'json' elif self._dtype == 'bool': self.value = self._value == 'True' self.dtype = bool else: self.dtype = type_map[self._dtype] self.value = self.dtype(self._value) def _create_tag_dict(file, entity, value, dtype=None, is_metadata=False): data = {} if dtype is None: dtype = type(value) if not isinstance(dtype, str): dtype = dtype.__name__ if dtype in ['list', 'dict']: _dtype = 'json' _value = json.dumps(value) else: _dtype = dtype _value = str(value) if _dtype not in ('str', 'float', 'int', 'bool', 'json'): raise ValueError( f'Passed value has an invalid dtype ({dtype}). Must be one of ' 'int, float, bool, or str.' ) data['is_metadata'] = is_metadata data['file_path'] = file.path data['entity_name'] = entity.name data['_dtype'] = _dtype data['_value'] = _value return data class FileAssociation(Base): # noqa: D101 __tablename__ = 'associations' src = Column(String, ForeignKey('files.path'), primary_key=True) dst = Column(String, ForeignKey('files.path'), primary_key=True) kind = Column(String, primary_key=True) # Association objects config_to_entity_map = Table( 'config_to_entity_map', Base.metadata, Column('config', String, ForeignKey('configs.name')), Column('entity', String, ForeignKey('entities.name')), ) class DerivativeDatasets(UserDict): # noqa: D101 def __getitem__(self, key): try: return super().__getitem__(key) except KeyError: pass try: result = self.get_pipeline(key) warnings.warn( # noqa: B028 'Directly selecting derivative datasets using ' 'pipeline name (i.e. dataset.derivatives[] will be ' 'phased out in an upcoming release. Select instead using the folder ' 'name of the dataset (i.e. dataset.derivatives[]), or use ' 'dataset.derivatives.get_pipeline().', DeprecationWarning, ) return result except KeyError as err: raise KeyError( f'No datasets found matching {key} either as a pipeline name or as ' 'a dataset file name.' ) from err def get_pipeline(self, pipeline): # noqa: D102 matches = { (name, dataset) for name, dataset in self.data.items() if dataset.source_pipeline == pipeline } if len(matches) > 1: datasets = '\n\t- '.join(match[0] for match in matches) raise BIDSChildDatasetError( f'Multiple datasets generated by {pipeline} were found:\n' f'\t- {datasets}\n\n' 'Select a specific dataset by using ' 'dataset.derivatives[].' ) if not matches: raise KeyError(f'No match found for {pipeline}') return next(iter(matches))[1] ================================================ FILE: src/bids/layout/tests/__init__.py ================================================ ================================================ FILE: src/bids/layout/tests/conftest.py ================================================ import shutil from os.path import join import pytest from bids.layout import BIDSLayout # Fixture uses in the rest of the tests @pytest.fixture(scope='session') def layout_7t_trt(tests_dir): return BIDSLayout(tests_dir / 'data' / '7t_trt') @pytest.fixture(scope='session') def layout_ds005(tests_dir): return BIDSLayout(tests_dir / 'data' / 'ds005') @pytest.fixture(scope='session') def layout_ds005_no_validate(tests_dir): return BIDSLayout(tests_dir / 'data' / 'ds005', validate=False) @pytest.fixture(scope='session') def layout_ds117(tests_dir): return BIDSLayout(tests_dir / 'data' / 'ds000117') @pytest.fixture(scope='session') def layout_ds005_derivs(tests_dir): data_dir = tests_dir / 'data' / 'ds005' layout = BIDSLayout(data_dir) deriv_dir = join(data_dir, 'derivatives', 'events') layout.add_derivatives(deriv_dir) return layout @pytest.fixture(scope='session') def layout_ds005_deriv_dummy_vxxx(tests_dir): data_dir = tests_dir / 'data' / 'ds005' layout = BIDSLayout(data_dir) deriv_dir = tests_dir / 'data' / 'ds005_derivs' / 'dummy-vx.x.x' layout.add_derivatives(deriv_dir) return layout @pytest.fixture(scope='session') def layout_ds005_deriv_both_dummies(tests_dir): data_dir = tests_dir / 'data' / 'ds005' layout = BIDSLayout(data_dir) deriv_dir1 = tests_dir / 'data' / 'ds005_derivs' / 'dummy-vx.x.x' deriv_dir2 = tests_dir / 'data' / 'ds005_derivs' / 'dummy' layout.add_derivatives([deriv_dir1, deriv_dir2]) return layout @pytest.fixture(scope='session') def db_dir(tmpdir_factory): fn = tmpdir_factory.mktemp('data') return fn @pytest.fixture(scope='session', params=[None, 'bidsdb', 'bidsdb']) def layout_ds005_multi_derivs(tests_dir, request, db_dir): data_dir = tests_dir / 'data' / 'ds005' database_path = str(db_dir / request.param) if request.param else None layout = BIDSLayout(data_dir, database_path=database_path) deriv_dir1 = tests_dir / 'data' / 'ds005_derivs' / 'dummy' deriv_dir2 = join(data_dir, 'derivatives', 'events') layout.add_derivatives([deriv_dir1, deriv_dir2]) return layout @pytest.fixture(scope='session') def layout_ds005_deriv_name_collision(tests_dir, request): data_dir = tests_dir / 'data' / 'ds005' layout = BIDSLayout(data_dir) deriv_dir1 = tests_dir / 'data' / 'ds005_derivs' / 'dummy' deriv_dir2 = join(data_dir, 'derivatives', 'events') layout.add_derivatives([deriv_dir1, deriv_dir2]) return layout @pytest.fixture(scope='session', params=[None, 'bidsdb-synth', 'bidsdb-synth']) def layout_synthetic(tests_dir, request, db_dir): path = tests_dir / 'data' / 'synthetic' database_path = str(db_dir / request.param) if request.param else None return BIDSLayout(path, derivatives=True, database_path=database_path) @pytest.fixture(scope='session') def layout_synthetic_nodb(tests_dir, request, db_dir): path = tests_dir / 'data' / 'synthetic' return BIDSLayout(path, derivatives=True) @pytest.fixture def temporary_dataset(tmp_path, tests_dir): path = tests_dir / 'data' / 'ds005' shutil.copytree(path, tmp_path / 'ds005') return tmp_path / 'ds005' ================================================ FILE: src/bids/layout/tests/data/sample_validation_config.json ================================================ { "name": "user_sequences", "sequences": [ { "modality": "func", "session": "wave1", "type": "bold", "task": "sst", "runs": 2 }, { "modality": "func", "session": "wave1", "type": "bold", "task": "gng", "runs": 2 }, { "modality": "func", "session": "wave1", "type": "bold", "task": "react", "runs": 2 }, { "modality": "fmap", "session": "wave1", "type": "phasediff", "runs": 2 }, { "modality": "fmap", "session": "wave1", "type": "magnitude1", "runs": 2 }, { "modality": "fmap", "session": "wave1", "type": "magnitude2", "runs": 2 }, { "modality": "anat", "session": "wave1", "type": "T1w", "runs": 1 }, { "modality": "func", "session": "wave2", "type": "bold", "task": "sst", "runs": 2 }, { "modality": "func", "session": "wave2", "type": "bold", "task": "gng", "runs": 2 }, { "modality": "func", "session": "wave2", "type": "bold", "task": "react", "runs": 2 }, { "modality": "fmap", "session": "wave2", "type": "phasediff", "runs": 2 }, { "modality": "fmap", "session": "wave2", "type": "magnitude1", "runs": 2 }, { "modality": "fmap", "session": "wave2", "type": "magnitude2", "runs": 2 }, { "modality": "anat", "session": "wave2", "type": "T1w", "runs": 1 } ] } ================================================ FILE: src/bids/layout/tests/test_db.py ================================================ """Test functionality in the db module--mostly related to connection management.""" from bids.layout.db import get_database_file def test_get_database_file(tmp_path): assert get_database_file(None) is None new_path = tmp_path / "a_new_subdir" assert not new_path.exists() db_file = get_database_file(new_path) assert db_file == new_path / 'layout_index.sqlite' assert new_path.exists() ================================================ FILE: src/bids/layout/tests/test_layout.py ================================================ """Tests of functionality in the layout module--mostly related to the BIDSLayout class.""" import json import os import re import shutil from os.path import abspath, basename, join from pathlib import Path import numpy as np import pytest from bids.exceptions import ( BIDSChildDatasetError, BIDSDerivativesValidationError, BIDSValidationError, NoMatchError, TargetError, ) from bids.layout import BIDSLayout, Query from bids.layout.index import BIDSLayoutIndexer, _check_path_matches_patterns, _regexfy from bids.layout.models import Config from bids.layout.utils import PaddedInt from bids.utils import bids_sort, natural_sort def test_layout_init(layout_7t_trt): assert isinstance(layout_7t_trt.files, dict) @pytest.mark.parametrize( 'index_metadata,filters,result', [ (True, {}, 3.0), (False, {}, None), (True, {}, 3.0), (True, {'task': 'rest'}, 3.0), (True, {'task': 'rest', 'extension': ['.nii.gz']}, 3.0), (True, {'task': 'rest', 'extension': '.nii.gz'}, 3.0), (True, {'task': 'rest', 'extension': ['.nii.gz', '.json'], 'return_type': 'file'}, 3.0), ], ) def test_index_metadata(tests_dir, index_metadata, filters, result, mock_config): data_dir = tests_dir / 'data' / '7t_trt' layout = BIDSLayout( data_dir, indexer=BIDSLayoutIndexer(index_metadata=index_metadata, **filters), ) sample_file = layout.get(task='rest', extension='.nii.gz', acquisition='fullbrain') assert bool(sample_file) metadata = sample_file[0].get_metadata() assert metadata.get('RepetitionTime') == result @pytest.mark.parametrize('config_type', [str, Path]) def test_config_filename(tests_dir, config_type): # Use custom config that replaces session with oligarchy config_path = tests_dir / 'data' / 'bids_specs_with_oligarchy.json' layout = BIDSLayout(tests_dir / 'data' / '7t_trt', config=config_type(config_path)) # Validate that we are using the desired configuration assert 'oligarchy' in layout.get_entities() def test_layout_repr(layout_7t_trt): assert 'Subjects: 10 | Sessions: 20 | Runs: 20' in str(layout_7t_trt) def test_invalid_dataset_description(tests_dir, tmp_path): shutil.copytree(tests_dir / 'data' / '7t_trt', tmp_path / '7t_dset') (tmp_path / '7t_dset' / 'dataset_description.json').write_text('I am not a valid json file') with pytest.raises(BIDSValidationError) as exc: BIDSLayout(tmp_path / '7t_dset') assert 'is not a valid json file' in str(exc.value) def test_layout_repr_overshadow_run(tests_dir, tmp_path): """A test creating a layout to replicate #681.""" shutil.copytree(tests_dir / 'data' / '7t_trt', tmp_path / '7t_trt') (tmp_path / '7t_trt' / 'sub-01' / 'ses-1' / 'sub-01_ses-1_scans.json').write_text( json.dumps({'run': {'Description': 'metadata to cause #681'}}) ) assert 'Subjects: 10 | Sessions: 20 | Runs: 20' in str(BIDSLayout(tmp_path / '7t_trt')) # def test_layout_copy(layout_7t_trt): # # Largely a smoke test to guarantee that copy() does not blow # # see https://github.com/bids-standard/pybids/pull/400#issuecomment-467961124 # import copy # l = layout_7t_trt # lcopy = copy.copy(l) # assert repr(lcopy) == repr(l) # assert str(lcopy) == str(l) # lcopy = copy.deepcopy(l) # assert repr(lcopy) == repr(l) # assert str(lcopy) == str(l) def test_load_description(layout_7t_trt): # Should not raise an error assert hasattr(layout_7t_trt, 'description') assert layout_7t_trt.description['Name'] == '7t_trt' assert layout_7t_trt.description['BIDSVersion'] == '1.0.0rc3' def test_get_file(layout_ds005_derivs): layout = layout_ds005_derivs # relative path in BIDS-Raw orig_file = 'sub-13/func/sub-13_task-mixedgamblestask_run-01_bold.nii.gz' target = os.path.join(*orig_file.split('/')) assert layout.get_file(target) assert layout.get_file(target, scope='raw') assert not layout.get_file(target, scope='derivatives') # absolute path in BIDS-Raw target = (layout.root + '/' + orig_file).split('/') target = os.path.sep + os.path.join(*target) assert layout.get_file(target) assert layout.get_file(target, scope='raw') assert not layout.get_file(target, scope='derivatives') # relative path in derivatives pipeline orig_file = ( 'derivatives/events/sub-01/func/sub-01_task-mixedgamblestask_run-01_desc-extra_events.tsv' ) target = os.path.join(*orig_file.split('/')) assert layout.get_file(target) assert not layout.get_file(target, scope='raw') assert layout.get_file(target, scope='derivatives') # absolute path in derivatives pipeline target = (layout.root + '/' + orig_file).split('/') target = os.path.sep + os.path.join(*target) assert layout.get_file(target) assert not layout.get_file(target, scope='raw') assert layout.get_file(target, scope='derivatives') assert layout.get_file(target, scope='events') # No such file assert not layout.get_file('bleargh') assert not layout.get_file('/absolute/bleargh') class TestDerivativeAsRoot: def test_dataset_without_datasettype_parsed_as_raw(self, tests_dir): ds_root = tests_dir / 'data' / 'ds005_derivs' / 'format_errs' / 'no_dataset_type' unvalidated = BIDSLayout(ds_root, validate=False) assert len(unvalidated.get()) == 4 with pytest.raises(ValueError): unvalidated.get(desc='preproc') validated = BIDSLayout(ds_root) assert len(validated.get()) == 1 def test_derivative_indexing_forced_with_is_derivative(self, tests_dir): unvalidated = BIDSLayout( tests_dir / 'data' / 'ds005_derivs' / 'format_errs' / 'no_type_or_description', is_derivative=True, validate=False, ) assert len(unvalidated.get()) == 4 assert len(unvalidated.get(desc='preproc')) == 3 def test_forced_derivative_indexing_fails_validation(self, tests_dir): with pytest.raises(BIDSDerivativesValidationError): BIDSLayout( tests_dir / 'data' / 'ds005_derivs' / 'format_errs' / 'no_type_or_description', is_derivative=True, validate=True, ) def test_dataset_missing_generatedby_fails_validation(self, tests_dir): with pytest.raises(BIDSDerivativesValidationError): BIDSLayout( tests_dir / 'data' / 'ds005_derivs' / 'format_errs' / 'no_pipeline_description' ) def test_correctly_formatted_derivative_loads_as_derivative(self, tests_dir): layout = BIDSLayout(tests_dir / 'data' / 'ds005_derivs' / 'dummy', validate=False) assert len(layout.get()) == 4 assert len(layout.get(desc='preproc')) == 3 @pytest.mark.parametrize( 'dataset_path', [ Path('ds005_derivs', 'dummy'), Path('ds005_derivs', 'format_errs', 'no_pipeline_description'), ], ) def test_derivative_datasets_load_with_no_validation(self, tests_dir, dataset_path): layout = BIDSLayout(tests_dir / 'data' / dataset_path, validate=False) assert len(layout.get()) == 4 assert len(layout.get(desc='preproc')) == 3 def test_get_metadata(layout_7t_trt): target = 'sub-03/ses-2/func/sub-03_ses-2_task-rest_acq-fullbrain_run-2_bold.nii.gz' target = target.split('/') result = layout_7t_trt.get_metadata(join(layout_7t_trt.root, *target)) assert result['RepetitionTime'] == 3.0 def test_get_metadata2(layout_7t_trt): target = 'sub-03/ses-1/fmap/sub-03_ses-1_run-1_phasediff.nii.gz' target = target.split('/') result = layout_7t_trt.get_metadata(join(layout_7t_trt.root, *target)) assert result['EchoTime1'] == 0.006 def test_get_metadata3(layout_7t_trt): target = 'sub-01/ses-1/func/sub-01_ses-1_task-rest_acq-fullbrain_run-1_bold.nii.gz' target = target.split('/') result = layout_7t_trt.get_metadata(join(layout_7t_trt.root, *target)) assert result['EchoTime'] == 0.020 target = 'sub-01/ses-1/func/sub-01_ses-1_task-rest_acq-fullbrain_run-2_bold.nii.gz' target = target.split('/') result = layout_7t_trt.get_metadata(join(layout_7t_trt.root, *target)) assert result['EchoTime'] == 0.017 def test_get_metadata4(layout_ds005): target = 'sub-03/anat/sub-03_T1w.nii.gz' target = target.split('/') result = layout_ds005.get_metadata(join(layout_ds005.root, *target)) assert result == {} def test_get_metadata_error2(layout_7t_trt): """Ensure that False boolean fields are retained as False.""" target = 'sub-03/ses-2/func/sub-03_ses-2_task-rest_acq-fullbrain_run-2_bold.nii.gz' target = target.split('/') # Check get_metadata from the BIDSLayout result = layout_7t_trt.get_metadata(join(layout_7t_trt.root, *target)) assert result['MTState'] is False # Check get_metadata from the BIDSFile files = layout_7t_trt.get( task='rest', acquisition='fullbrain', suffix='bold', extension='.nii.gz' ) result = files[0].get_metadata() assert result['MTState'] is False def test_get_metadata_meg(layout_ds117): funcs = [ 'get_subjects', 'get_sessions', 'get_tasks', 'get_runs', 'get_acquisitions', 'get_procs', ] assert all([hasattr(layout_ds117, f) for f in funcs]) procs = layout_ds117.get_procs() assert procs == ['sss'] target = 'sub-02/ses-meg/meg/sub-02_ses-meg_task-facerecognition_run-01_meg.fif' target = target.split('/') result = layout_ds117.get_metadata(join(layout_ds117.root, *target)) metadata_keys = ['MEGChannelCount', 'SoftwareFilters', 'SubjectArtefactDescription'] assert all([k in result for k in metadata_keys]) def test_get_metadata5(layout_7t_trt): target = 'sub-01/ses-1/func/sub-01_ses-1_task-rest_acq-fullbrain_run-1_bold.nii.gz' target = target.split('/') result = layout_7t_trt.get_metadata(join(layout_7t_trt.root, *target), include_entities=True) assert result['EchoTime'] == 0.020 assert result['subject'] == '01' assert result['acquisition'] == 'fullbrain' def test_get_metadata_via_bidsfile(layout_7t_trt): """Same as test_get_metadata5, but called through BIDSFile.""" target = 'sub-01/ses-1/func/sub-01_ses-1_task-rest_acq-fullbrain_run-1_bold.nii.gz' target = target.split('/') path = join(layout_7t_trt.root, *target) result = layout_7t_trt.files[path].get_metadata() assert result['EchoTime'] == 0.020 # include_entities is False when called through a BIDSFile assert 'subject' not in result def test_metadata_equivalence(layout_7t_trt): """Ensure that JSON metadata is not corrupted by the layout.""" bold = layout_7t_trt.get( subject='02', acquisition='fullbrain', suffix='bold', extension='.nii.gz' )[0] root_metadata_file = layout_7t_trt.get( subject=None, acquisition='fullbrain', suffix='bold', extension='.json' )[0] assert bold.get_metadata() == root_metadata_file.get_dict() physio = layout_7t_trt.get( subject='02', acquisition='fullbrain', suffix='physio', extension='.tsv.gz' )[0] root_metadata_file = layout_7t_trt.get( subject=None, acquisition='fullbrain', suffix='physio', extension='.json' )[0] assert physio.get_metadata() == root_metadata_file.get_dict() # Verify that we cover all common metadata types types = {type(val) for val in bold.get_metadata().values()} assert types == {str, float, bool, list} assert isinstance(physio.get_metadata()['StartTime'], int) def test_get_metadata_error(layout_7t_trt): """Same as test_get_metadata5, but called through BIDSFile.""" target = 'sub-01/ses-1/func/sub-01_ses-1_task-rest_acq-fullbrain_run-1_bold.nii.gz' target = target.split('/') path = join(layout_7t_trt.root, *target) result = layout_7t_trt.files[path].get_metadata() with pytest.raises(KeyError) as err: result['Missing'] assert f"Metadata term 'Missing' unavailable for file {path}" in str(err.value) result = layout_7t_trt.get_metadata(path) with pytest.raises(KeyError) as err: result['Missing'] assert f"Metadata term 'Missing' unavailable for file {path}" in str(err.value) def test_get_with_bad_target(layout_7t_trt): with pytest.raises(TargetError) as exc: layout_7t_trt.get(target='unicorn') msg = str(exc.value) assert 'subject' in msg and 'reconstruction' in msg and 'proc' in msg with pytest.raises(TargetError) as exc: layout_7t_trt.get(target='sub') msg = str(exc.value) assert 'subject' in msg and 'reconstruction' not in msg def test_get_bvals_bvecs(layout_ds005): dwifile = layout_ds005.get(subject='01', datatype='dwi')[0] result = layout_ds005.get_bval(dwifile.path) assert result == abspath(join(layout_ds005.root, 'dwi.bval')) result = layout_ds005.get_bvec(dwifile.path) assert result == abspath(join(layout_ds005.root, 'dwi.bvec')) def test_get_subjects(layout_7t_trt): result = layout_7t_trt.get_subjects() predicted = ['01', '02', '03', '04', '05', '06', '07', '08', '09', '10'] assert set(predicted) == set(result) def test_get_fieldmap(layout_7t_trt): target = 'sub-03/ses-1/func/sub-03_ses-1_task-rest_acq-fullbrain_run-1_bold.nii.gz' target = target.split('/') result = layout_7t_trt.get_fieldmap(join(layout_7t_trt.root, *target)) assert result['suffix'] == 'phasediff' assert result['phasediff'].endswith('sub-03_ses-1_run-1_phasediff.nii.gz') def test_get_fieldmap2(layout_7t_trt): target = 'sub-03/ses-2/func/sub-03_ses-2_task-rest_acq-fullbrain_run-2_bold.nii.gz' target = target.split('/') result = layout_7t_trt.get_fieldmap(join(layout_7t_trt.root, *target)) assert result['suffix'] == 'phasediff' assert result['phasediff'].endswith('sub-03_ses-2_run-2_phasediff.nii.gz') def test_bids_json(layout_7t_trt): res = layout_7t_trt.get(return_type='id', target='run') assert set(res) == {1, 2} res = layout_7t_trt.get(return_type='id', target='session') assert set(res) == {'1', '2'} def test_get_return_type_dir(layout_7t_trt): res = layout_7t_trt.get(target='subject', return_type='dir') # returned directories should be in sorted order so we can match exactly target = [os.path.join(layout_7t_trt.root, f'sub-{i:02d}') for i in range(1, 11)] assert target == res @pytest.mark.parametrize('acq', [None, Query.NONE]) def test_get_val_none(layout_7t_trt, acq): t1w_files = layout_7t_trt.get(subject='01', session='1', suffix='T1w') assert len(t1w_files) == 1 assert 'acq' not in t1w_files[0].path t1w_files = layout_7t_trt.get(subject='01', session='1', suffix='T1w', acquisition=acq) assert len(t1w_files) == 1 bold_files = layout_7t_trt.get(subject='01', session='1', suffix='bold', acquisition=acq) assert len(bold_files) == 0 def test_get_val_enum_any(layout_7t_trt): t1w_files = layout_7t_trt.get( subject='01', session='1', suffix='T1w', acquisition=Query.ANY, extension=Query.ANY ) assert not t1w_files bold_files = layout_7t_trt.get( subject='01', session='1', run=1, suffix='bold', acquisition=Query.ANY ) assert len(bold_files) == 2 def test_get_val_enum_any_optional(layout_7t_trt, layout_ds005): # layout with sessions query = { 'subject': '01', 'run': 1, 'suffix': 'bold', } bold_files = layout_7t_trt.get(session=Query.OPTIONAL, **query) assert len(bold_files) == 3 # layout without sessions bold_files = layout_ds005.get(session=Query.REQUIRED, **query) assert not bold_files bold_files = layout_ds005.get(session=Query.OPTIONAL, **query) assert len(bold_files) == 1 def test_get_return_sorted(layout_7t_trt): bids_files = layout_7t_trt.get(target='subject') paths = [r.path for r in bids_files] assert natural_sort(paths) == paths files = layout_7t_trt.get(target='subject', return_type='file') assert files == paths def test_ignore_files(tests_dir, layout_ds005): data_dir = tests_dir / 'data' / 'ds005' target1 = join(data_dir, 'models', 'ds-005_type-test_model.json') target2 = join(data_dir, 'models', 'extras', 'ds-005_type-test_model.json') layout1 = BIDSLayout(data_dir, validate=False) assert target1 not in layout_ds005.files assert target1 not in layout1.files assert target2 not in layout1.files # now the models/ dir should show up, because passing ignore explicitly # overrides the default - but 'model/extras/' should still be ignored # because of the regex. ignore = [re.compile('xtra'), 'dummy'] indexer = BIDSLayoutIndexer(validate=False, ignore=ignore) layout2 = BIDSLayout(data_dir, indexer=indexer) assert target1 in layout2.files assert target2 not in layout2.files def test_force_index(tests_dir, layout_ds005): data_dir = tests_dir / 'data' / 'ds005' target = join(data_dir, 'models', 'ds-005_type-test_model.json') indexer = BIDSLayoutIndexer(force_index=[re.compile('models(/.*)?')]) model_layout = BIDSLayout(data_dir, validate=True, indexer=indexer) assert target not in layout_ds005.files assert target in model_layout.files assert 'all' not in model_layout.get_subjects() for f in model_layout.files.values(): assert 'derivatives' not in f.path def test_nested_include_exclude(tests_dir): data_dir = tests_dir / 'data' / 'ds005' target1 = join(data_dir, 'models', 'ds-005_type-test_model.json') target2 = join(data_dir, 'models', 'extras', 'ds-005_type-test_model.json') # Excluding a directory will disallow indexing further, even if forced layout = BIDSLayout( data_dir, indexer=BIDSLayoutIndexer( validate=False, force_index=[os.path.join('models', 'extras')], ignore=['models'], ), ) assert not layout.get_file(target1) assert not layout.get_file(target2) # To nest patterns, we must allow searching within layout = BIDSLayout( data_dir, indexer=BIDSLayoutIndexer( validate=False, force_index=[os.path.join('models', 'extras')], ignore=[re.compile('^/models/.+$')], ), ) assert not layout.get_file(target1) assert layout.get_file(target2) # Including a directory will disallow marking ignore subdirectories within layout = BIDSLayout( data_dir, indexer=BIDSLayoutIndexer( validate=False, force_index=['models'], ignore=[os.path.join('models', 'extras')], ), ) assert layout.get_file(target1) assert layout.get_file(target2) # To nest a directory exclusion within an inclusion, use regex layout = BIDSLayout( data_dir, indexer=BIDSLayoutIndexer( validate=False, force_index=[re.compile('^/models/?$')], ignore=[os.path.join('models', 'extras')], ), ) assert layout.get_file(target1) assert not layout.get_file(target2) # Force file inclusion despite directory-level exclusion layout = BIDSLayout( data_dir, indexer=BIDSLayoutIndexer( validate=False, force_index=['models', target2], ignore=[os.path.join('models', 'extras')], ), ) assert layout.get_file(target1) assert layout.get_file(target2) # To nest patterns, we must allow searching within layout = BIDSLayout( data_dir, indexer=BIDSLayoutIndexer( validate=False, force_index=[os.path.join('models', 'extras')], ignore=[re.compile('^/models/.+$')], ), ) assert not layout.get_file(target1) assert layout.get_file(target2) # With the validator, these paths will not be valid anyways layout = BIDSLayout( data_dir, indexer=BIDSLayoutIndexer( validate=True, force_index=['models'], ignore=[os.path.join('models', 'extras')], ), ) assert layout.get_file(target1) assert layout.get_file(target2) layout = BIDSLayout( data_dir, indexer=BIDSLayoutIndexer( validate=True, force_index=[re.compile('^/models/?$')], ignore=[os.path.join('models', 'extras')], ), ) assert layout.get_file(target1) assert not layout.get_file(target2) layout = BIDSLayout( data_dir, indexer=BIDSLayoutIndexer( validate=True, force_index=['models', target2], ignore=[os.path.join('models', 'extras')], ), ) assert layout.get_file(target1) assert layout.get_file(target2) def test_nested_include_exclude_with_regex(tests_dir): # ~same as above test, but use regexps instead of strings patt1 = re.compile(r'.*dels/?$') patt2 = re.compile(r'.*xtra.*') data_dir = tests_dir / 'data' / 'ds005' target1 = join(data_dir, 'models', 'ds-005_type-test_model.json') target2 = join(data_dir, 'models', 'extras', 'ds-005_type-test_model.json') layout = BIDSLayout( data_dir, indexer=BIDSLayoutIndexer( validate=False, ignore=[patt2], force_index=[patt1], ), ) assert layout.get_file(target1) assert not layout.get_file(target2) # If ignore matches a folder, it won't be indexed patt1 = re.compile(r'.*dels/.+$') layout = BIDSLayout( data_dir, indexer=BIDSLayoutIndexer( validate=False, ignore=[patt1], force_index=[patt2], ), ) assert not layout.get_file(target1) assert layout.get_file(target2) # With valid_only no path should be indexed under models/ patt1 = re.compile(r'.*dels/ds-005.*') patt2 = re.compile(r'.*xtra.*') layout = BIDSLayout( data_dir, indexer=BIDSLayoutIndexer( validate=True, ignore=[patt2], force_index=[patt1], ), ) assert layout.get_file(target1) assert not layout.get_file(target2) # If ignore matches a folder, it won't be indexed patt1 = re.compile(r'.*dels/.+$') layout = BIDSLayout( data_dir, indexer=BIDSLayoutIndexer( validate=True, ignore=[patt1], force_index=[patt2], ), ) assert not layout.get_file(target1) assert layout.get_file(target2) def test_layout_with_derivs(tests_dir, layout_ds005_derivs): assert layout_ds005_derivs.root == str(tests_dir / 'data' / 'ds005') assert isinstance(layout_ds005_derivs.files, dict) assert len(layout_ds005_derivs.derivatives) == 1 deriv = layout_ds005_derivs.derivatives['events'] assert deriv.files assert len(deriv.files) == 2 event_file = 'sub-01_task-mixedgamblestask_run-01_desc-extra_events.tsv' deriv_files = [basename(f) for f in list(deriv.files.keys())] assert event_file in deriv_files assert 'roi' in deriv.entities assert 'subject' in deriv.entities def test_accessing_deriv_by_pipeline_name_is_deprecated(layout_ds005_deriv_dummy_vxxx): with pytest.deprecated_call(): deriv = layout_ds005_deriv_dummy_vxxx.derivatives['dummy'] assert deriv.files assert len(deriv.files) == 4 def test_cant_access_nonexistant_deriv_by_key(layout_ds005_deriv_dummy_vxxx): with pytest.raises(KeyError): layout_ds005_deriv_dummy_vxxx.derivatives['foo'] def test_accessing_deriv_by_pipeline_name_via_method(layout_ds005_deriv_dummy_vxxx): deriv = layout_ds005_deriv_dummy_vxxx.derivatives.get_pipeline('dummy') assert deriv.files assert len(deriv.files) == 4 def test_cant_get_nonexistant_deriv_via_method(layout_ds005_deriv_dummy_vxxx): with pytest.raises(KeyError): layout_ds005_deriv_dummy_vxxx.derivatives.get_pipeline('foo') def test_cant_get_deriv_with_duplicate_pipeline_via_method(layout_ds005_deriv_both_dummies): with pytest.raises(BIDSChildDatasetError): layout_ds005_deriv_both_dummies.derivatives.get_pipeline('dummy') def test_layout_with_multi_derivs(tests_dir, layout_ds005_multi_derivs): assert layout_ds005_multi_derivs.root == str(tests_dir / 'data' / 'ds005') assert isinstance(layout_ds005_multi_derivs.files, dict) assert len(layout_ds005_multi_derivs.derivatives) == 2 deriv = layout_ds005_multi_derivs.derivatives['events'] assert deriv.files assert len(deriv.files) == 2 deriv = layout_ds005_multi_derivs.derivatives['dummy'] assert deriv.files assert len(deriv.files) == 4 assert 'roi' in deriv.entities assert 'subject' in deriv.entities preproc = layout_ds005_multi_derivs.get(desc='preproc') assert len(preproc) == 3 def test_layout_with_conflicting_deriv_folders(tests_dir): data_dir = tests_dir / 'data' / 'ds005' layout = BIDSLayout(data_dir) deriv_dir = tests_dir / 'data' / 'ds005_derivs' / 'dummy' with pytest.raises(BIDSDerivativesValidationError): layout.add_derivatives([deriv_dir, deriv_dir]) def test_query_derivatives(layout_ds005_derivs): result = layout_ds005_derivs.get(suffix='events', return_type='object', extension='.tsv') result = [f.filename for f in result] assert len(result) == 49 assert 'sub-01_task-mixedgamblestask_run-01_desc-extra_events.tsv' in result result = layout_ds005_derivs.get( suffix='events', return_type='object', scope='raw', extension='.tsv' ) assert len(result) == 48 result = [f.filename for f in result] assert 'sub-01_task-mixedgamblestask_run-01_desc-extra_events.tsv' not in result result = layout_ds005_derivs.get( suffix='events', return_type='object', desc='extra', extension='.tsv' ) assert len(result) == 1 result = [f.filename for f in result] assert 'sub-01_task-mixedgamblestask_run-01_desc-extra_events.tsv' in result def test_restricted_words_in_path(tests_dir, tmpdir): orig_path = tests_dir / 'data' / 'synthetic' parent_dir = str(tmpdir / 'derivatives' / 'pipeline') os.makedirs(parent_dir) new_path = join(parent_dir, 'sourcedata') os.symlink(orig_path, new_path) orig_layout = BIDSLayout(orig_path) new_layout = BIDSLayout(new_path) orig_files = set(f.replace(str(orig_path), '') for f in orig_layout.files) new_files = set(f.replace(new_path, '') for f in new_layout.files) assert orig_files == new_files def test_derivative_getters(tests_dir): synth_path = tests_dir / 'data' / 'synthetic' bare_layout = BIDSLayout(synth_path, derivatives=False) full_layout = BIDSLayout(synth_path, derivatives=True) assert bare_layout.get_spaces() == [] assert set(full_layout.get_spaces()) == {'MNI152NLin2009cAsym', 'T1w'} def test_get_tr(layout_7t_trt): # Bad subject, should fail with pytest.raises(NoMatchError) as exc: layout_7t_trt.get_tr(subject='zzz') assert str(exc.value).startswith('No functional images') # There are multiple tasks with different TRs, so this should fail with pytest.raises(NoMatchError) as exc: layout_7t_trt.get_tr(subject=['01', '02']) assert str(exc.value).startswith('Unique TR') # This should work tr = layout_7t_trt.get_tr(subject=['01', '02'], acquisition='fullbrain') assert tr == 3.0 tr = layout_7t_trt.get_tr(subject=['01', '02'], acquisition='prefrontal') assert tr == 4.0 tr = layout_7t_trt.get_RepetitionTime() assert sum([t in tr for t in [3.0, 4.0]]) == 2 def test_get_nonhashable_metadata(layout_ds117): """Test nonhashable metadata values (#683).""" assert layout_ds117.get_IntendedFor(subject=['01'])[0] == ( 'ses-mri/func/sub-01_ses-mri_task-facerecognition_run-01_bold.nii.gz', 'ses-mri/func/sub-01_ses-mri_task-facerecognition_run-02_bold.nii.gz', 'ses-mri/func/sub-01_ses-mri_task-facerecognition_run-03_bold.nii.gz', 'ses-mri/func/sub-01_ses-mri_task-facerecognition_run-04_bold.nii.gz', 'ses-mri/func/sub-01_ses-mri_task-facerecognition_run-05_bold.nii.gz', 'ses-mri/func/sub-01_ses-mri_task-facerecognition_run-06_bold.nii.gz', 'ses-mri/func/sub-01_ses-mri_task-facerecognition_run-07_bold.nii.gz', 'ses-mri/func/sub-01_ses-mri_task-facerecognition_run-08_bold.nii.gz', 'ses-mri/func/sub-01_ses-mri_task-facerecognition_run-09_bold.nii.gz', ) landmarks = layout_ds117.get_AnatomicalLandmarkCoordinates(subject=['01'])[0] assert landmarks['Nasion'] == (43, 111, 95) assert landmarks['LPA'] == (140, 74, 16) assert landmarks['RPA'] == (143, 74, 173) def test_to_df(layout_ds117): # Only filename entities df = layout_ds117.to_df() n_files = len(layout_ds117.files) assert len(df) == n_files target = { 'datatype', 'fmap', 'run', 'path', 'acquisition', 'scans', 'echo', 'session', 'subject', 'suffix', 'task', 'proc', 'extension', } assert set(df.columns) == target assert set(df['subject'].dropna().unique()) == {'01', '02', 'emptyroom'} # Include metadata entities df = layout_ds117.to_df(metadata=True) assert df.shape == (n_files, len(target) + 44) assert not ({'InstitutionAddress', 'TriggerChannelCount', 'EchoTime'} - set(df.columns)) # XXX 0.14: Add dot to extension (difficult to parametrize with module-scoped fixture) def test_parse_file_entities_from_layout(layout_synthetic): layout = layout_synthetic filename = '/sub-03_ses-07_run-4_desc-bleargh_sekret.nii.gz' # Test with entities taken from bids config target = { 'subject': '03', 'session': '07', 'run': 4, 'suffix': 'sekret', 'extension': '.nii.gz', } assert target == layout.parse_file_entities(filename, config='bids') config = Config.load('bids') assert target == layout.parse_file_entities(filename, config=[config]) assert target == layout.parse_file_entities(filename, scope='raw') # Test with default scope--i.e., everything target = { 'subject': '03', 'session': '07', 'run': 4, 'suffix': 'sekret', 'desc': 'bleargh', 'extension': '.nii.gz', } assert target == layout.parse_file_entities(filename) # Test with only the fmriprep pipeline (which includes both configs) assert target == layout.parse_file_entities(filename, scope='fmriprep') assert target == layout.parse_file_entities(filename, scope='derivatives') # Test with only the derivative config target = {'desc': 'bleargh'} assert target == layout.parse_file_entities(filename, config='derivatives') def test_deriv_indexing(tests_dir): data_dir = tests_dir / 'data' / 'ds005' deriv_dir = join(data_dir, 'derivatives', 'bbr') # missing dataset_description.json with pytest.warns(UserWarning): layout = BIDSLayout(data_dir, derivatives=deriv_dir) # Should work fine deriv_dir = join(data_dir, 'derivatives', 'events') layout = BIDSLayout(data_dir, derivatives=deriv_dir) assert layout.get(scope='derivatives') assert layout.get(scope='events') assert not layout.get(scope='nonexistent') def test_path_arguments(tests_dir): data_dir = tests_dir / 'data' / 'ds005' deriv_dir = join(data_dir, 'derivatives', 'events') layout = BIDSLayout(Path(data_dir), derivatives=Path(deriv_dir)) assert layout.get(scope='derivatives') assert layout.get(scope='events') assert not layout.get(scope='nonexistent') layout = BIDSLayout(Path(data_dir), derivatives=[Path(deriv_dir)]) assert layout.get(scope='derivatives') assert layout.get(scope='events') assert not layout.get(scope='nonexistent') def test_layout_in_scope(layout_ds005, layout_ds005_derivs): assert layout_ds005._in_scope(['all']) assert layout_ds005._in_scope('raw') assert layout_ds005._in_scope(['all', 'ignored']) assert not layout_ds005._in_scope(['derivatives', 'ignored']) deriv = layout_ds005_derivs.derivatives['events'] assert deriv._in_scope('all') assert deriv._in_scope(['derivatives']) assert deriv._in_scope('events') assert not deriv._in_scope('raw') def test_get_layouts_in_scope(layout_ds005_multi_derivs): l = layout_ds005_multi_derivs assert len(l._get_layouts_in_scope('all')) == 3 assert len(l._get_layouts_in_scope('nonexistent')) == 0 assert len(l._get_layouts_in_scope(['events', 'dummy'])) == 2 assert len(l._get_layouts_in_scope(['derivatives'])) == 2 assert len(l._get_layouts_in_scope('raw')) == 1 self_scope = l._get_layouts_in_scope('self') assert len(self_scope) == 1 assert self_scope == [l] def test_get_dataset_description(layout_ds005_multi_derivs): l = layout_ds005_multi_derivs dd = l.get_dataset_description() assert isinstance(dd, dict) assert dd['Name'] == 'Mixed-gambles task' dd = l.get_dataset_description('all', True) assert isinstance(dd, list) assert len(dd) == 3 names = {'Mixed-gambles task', 'Mixed-gambles task -- dummy derivative'} assert set([d['Name'] for d in dd]) == names def test_indexed_file_associations(layout_7t_trt): img = layout_7t_trt.get( subject='01', run=1, suffix='bold', session='1', acquisition='fullbrain', extension='.nii.gz', )[0] assocs = img.get_associations() assert len(assocs) == 3 targets = { os.path.join(layout_7t_trt.root, 'sub-01/ses-1/fmap/sub-01_ses-1_run-1_phasediff.nii.gz'), os.path.join(img.dirname, 'sub-01_ses-1_task-rest_acq-fullbrain_run-1_physio.tsv.gz'), os.path.join(img.dirname, 'sub-01_ses-1_task-rest_acq-fullbrain_run-1_bold.json'), } assert set([a.path for a in assocs]) == set(targets) # Test with parents included targets.add(os.path.join(layout_7t_trt.root, 'task-rest_acq-fullbrain_bold.json')) assocs = img.get_associations(include_parents=True) assert len(assocs) == 4 assert set([a.path for a in assocs]) == set(targets) # Get the root-level JSON and check that its associations are correct js = [a for a in assocs if a.path.endswith('json')][1] assert len(js.get_associations()) == 40 assert len(js.get_associations('Parent')) == 1 assert len(js.get_associations('Metadata')) == 39 assert not js.get_associations('InformedBy') def test_layout_save(tests_dir, tmp_path, layout_7t_trt): layout_7t_trt.save(str(tmp_path / 'f.sqlite'), replace_connection=False) data_dir = tests_dir / 'data' / '7t_trt' layout = BIDSLayout(data_dir, database_path=str(tmp_path / 'f.sqlite')) oldfies = set(layout_7t_trt.get(suffix='events', return_type='file')) newfies = set(layout.get(suffix='events', return_type='file')) assert oldfies == newfies def test_indexing_tag_conflict(tests_dir): data_dir = tests_dir / 'data' / 'ds005_conflict' with pytest.raises(BIDSValidationError) as exc: layout = BIDSLayout(data_dir) assert str(exc.value).startswith('Conflicting values found') assert 'run' in str(exc.value) def test_get_with_wrong_dtypes(layout_7t_trt): """Test automatic dtype sanitization.""" l = layout_7t_trt assert ( l.get(run=1) == l.get(run='1') == l.get(run=np.int64(1)) == l.get(run=[1, '15']) == l.get(run='01') ) assert not l.get(run='not_numeric') assert l.get(session=1) == l.get(session='1') def test_get_with_regex_search(layout_7t_trt): """Tests that regex-based searching works.""" l = layout_7t_trt # subject matches both '10' and '01' results = l.get( subject='1', session='1', task='rest', suffix='bold', acquisition='fron.al', extension='.nii.gz', regex_search=True, ) assert len(results) == 2 # subject matches '10' results = l.get( subject='^1', session='1', task='rest', suffix='bold', acquisition='fron.al', extension='.nii.gz', regex_search=True, return_type='filename', ) assert len(results) == 1 assert results[0].endswith('sub-10_ses-1_task-rest_acq-prefrontal_bold.nii.gz') def test_get_with_regex_search_bad_dtype(layout_7t_trt): """Tests that passing in a non-string dtype for an entity doesn't crash regexp-based searching (i.e., that implicit conversion is done appropriately).""" l = layout_7t_trt results = l.get( subject='1', run=1, task='rest', suffix='bold', acquisition='fullbrain', extension='.nii.gz', regex_search=True, ) # Two runs (1 per session) for each of subjects '10' and '01' assert len(results) == 4 def test_get_with_invalid_filters(layout_ds005): l = layout_ds005 # Raise error with suggestions with pytest.raises(ValueError, match='session'): l.get(subject='12', ses=True, invalid_filters='error') with pytest.raises(ValueError, match='session'): l.get(subject='12', ses=True) # Silently drop amazing res_without = l.get(subject='12', suffix='bold') res_drop = l.get(subject='12', suffix='bold', amazing='!!!', invalid_filters='drop') assert res_without == res_drop assert len(res_drop) == 3 # Retain amazing, producing empty set allow_res = l.get(subject='12', amazing=True, invalid_filters='allow') assert allow_res == [] # assert warning when filters are passed in filters = {'subject': '1'} with pytest.raises(RuntimeError, match='You passed in filters as a dictionary'): l.get(filters=filters) # Correct call: l.get(**filters) def test_get_with_query_constants_in_match_list(layout_ds005): l = layout_ds005 get1 = l.get(subject='12', run=1, suffix='bold') get_none = l.get(subject='12', run=None, suffix='bold') get_any = l.get(subject='12', run=Query.ANY, suffix='bold') get1_and_none = l.get(subject='12', run=[None, 1], suffix='bold') get1_and_any = l.get(subject='12', run=[Query.ANY, 1], suffix='bold') get_none_and_any = l.get(subject='12', run=[Query.ANY, Query.NONE], suffix='bold') assert set(get1_and_none) == set(get1) | set(get_none) assert set(get1_and_any) == set(get1) | set(get_any) assert set(get_none_and_any) == set(get_none) | set(get_any) def test_get_non_run_entity_with_query_constants_in_match_list(layout_ds005): l = layout_ds005 get1 = l.get(subject='01', acquisition='MPRAGE', suffix='T1w') get_none = l.get(subject='01', acquisition=None, suffix='T1w') get_any = l.get(subject='01', acquisition=Query.ANY, suffix='T1w') get1_and_none = l.get(subject='01', acquisition=[None, 'MPRAGE'], suffix='T1w') get1_and_any = l.get(subject='01', acquisition=[Query.ANY, 'MPRAGE'], suffix='T1w') get_none_and_any = l.get(subject='01', acquisition=[Query.ANY, Query.NONE], suffix='T1w') assert set(get1_and_none) == set(get1) | set(get_none) assert set(get1_and_any) == set(get1) | set(get_any) assert set(get_none_and_any) == set(get_none) | set(get_any) def test_query_constants_work_on_extension(layout_ds005_no_validate): l = layout_ds005_no_validate get_both = l.get(subject='11', datatype='dwi', extension=Query.OPTIONAL) get_ext = l.get(subject='11', datatype='dwi', extension=Query.REQUIRED) get_no_ext = l.get(subject='11', datatype='dwi', extension=Query.NONE) assert len(get_both) == 2 assert len(get_ext) == 1 assert len(get_no_ext) == 1 assert 'extension' in get_ext[0].get_entities() assert 'extension' not in get_no_ext[0].get_entities() def test_load_layout(layout_synthetic_nodb, db_dir): db_path = str(db_dir / 'tmp_db') layout_synthetic_nodb.save(db_path) reloaded = BIDSLayout.load(db_path) assert sorted(layout_synthetic_nodb.get(return_type='file')) == sorted( reloaded.get(return_type='file') ) cm1 = layout_synthetic_nodb.connection_manager cm2 = reloaded.connection_manager for attr in ['root', 'config', 'derivatives']: assert getattr(cm1.layout_info, attr) == getattr(cm2.layout_info, attr) def test_load_layout_config_not_overwritten(layout_synthetic_nodb, tmpdir): modified_dataset_path = tmpdir / 'modified' shutil.copytree(layout_synthetic_nodb.root, modified_dataset_path) # Save index db_path = str(tmpdir / 'tmp_db') BIDSLayout(modified_dataset_path).save(db_path) # Update dataset_description.json dataset_description = modified_dataset_path / 'dataset_description.json' with dataset_description.open('r') as f: description = json.load(f) description['DatasetType'] = 'derivative' description['GeneratedBy'] = [{'Name': 'foo'}] with dataset_description.open('w') as f: json.dump(description, f) # Reload db_layout = BIDSLayout(modified_dataset_path, database_path=db_path) fresh_layout = BIDSLayout(modified_dataset_path, validate=False) cm1 = db_layout.connection_manager cm2 = fresh_layout.connection_manager for attr in ['root', 'derivatives']: assert getattr(cm1.layout_info, attr) == getattr(cm2.layout_info, attr) assert cm1.layout_info.config != cm2.layout_info.config def test_padded_run_roundtrip(layout_ds005): for run in (1, '1', '01'): res = layout_ds005.get(subject='01', task='mixedgamblestask', run=run, extension='.nii.gz') assert len(res) == 1 boldfile = res[0] ents = boldfile.get_entities() assert isinstance(ents['run'], PaddedInt) assert ents['run'] == 1 newpath = layout_ds005.build_path(ents, absolute_paths=False) assert newpath == 'sub-01/func/sub-01_task-mixedgamblestask_run-01_bold.nii.gz' @pytest.mark.parametrize( 'fname', [ 'sub-01/anat/sub-01_T1w.nii.gz', '.datalad', 'code', 'sub-01/.datalad', ], ) def test_indexer_patterns(fname): root = Path('/home/user/.cache/data/') path = root / fname assert bool( _check_path_matches_patterns( path, [_regexfy('code')], root=root, ) ) is (fname == 'code') assert bool( _check_path_matches_patterns( path, [_regexfy('code', root=root)], root=root, ) ) is (fname == 'code') assert ( _check_path_matches_patterns( path, [re.compile(r'/\.')], root=None, ) is True ) assert _check_path_matches_patterns( path, [re.compile(r'/\.')], root=root, ) is ('.datalad' in fname) def test_symlinks_in_path(tests_dir, tmp_path): src_ds = os.path.abspath(tests_dir / 'data' / '7t_trt') src_sub = join(src_ds, 'sub-04') # This will be a normal directory with symlink contents ds_with_links = Path(tmp_path / '7t_trt').absolute() os.makedirs(ds_with_links, exist_ok=False) link_ds_description = ds_with_links / 'dataset_description.json' link_sub = ds_with_links / 'sub-04' os.symlink(join(src_ds, 'dataset_description.json'), link_ds_description) os.symlink(src_sub, link_sub) assert 'Subjects: 1 | Sessions: 2 | Runs: 2' in str(BIDSLayout(tmp_path / '7t_trt')) def test_ignore_dotfiles(temporary_dataset): arbitrary_dotfile = temporary_dataset / '.dotfile' ds_store = temporary_dataset / 'sub-01' / '.DS_Store' osx_companion_file = temporary_dataset / '._task-mixedgamblestask_bold.json' arbitrary_dotfile.touch() ds_store.touch() osx_companion_file.touch() # Default behavior layout = BIDSLayout(temporary_dataset, validate=False) assert str(temporary_dataset / 'dataset_description.json') in layout.files assert str(arbitrary_dotfile) not in layout.files assert str(ds_store) not in layout.files assert str(osx_companion_file) not in layout.files # Explicit ignores do not disable dotfile filtering indexer = BIDSLayoutIndexer(ignore=['some_ignore']) layout = BIDSLayout(temporary_dataset, validate=False, indexer=indexer) assert str(temporary_dataset / 'dataset_description.json') in layout.files assert str(arbitrary_dotfile) not in layout.files assert str(ds_store) not in layout.files assert str(osx_companion_file) not in layout.files def test_empty_directory(temporary_dataset): anat = temporary_dataset / 'sub-01' / 'anat' shutil.rmtree(anat) anat.mkdir() layout = BIDSLayout(temporary_dataset) assert layout.get(subject='01', datatype='anat') == [] @pytest.mark.parametrize( 'intent', [ 'bids::sub-01/func/sub-01_task-mixedgamblestask_run-01_bold.nii.gz', 'bids:mydataset:sub-01/func/sub-01_task-mixedgamblestask_run-01_bold.nii.gz', 'func/sub-01_task-mixedgamblestask_run-01_bold.nii.gz', [ 'bids::sub-01/func/sub-01_task-mixedgamblestask_run-01_bold.nii.gz', 'bids::sub-01/func/sub-01_task-mixedgamblestask_run-02_bold.nii.gz', ], ], ) def test_intended_for(temporary_dataset, intent): fmap_dir = temporary_dataset / 'sub-01' / 'fmap' fmap_dir.mkdir() # Create a fieldmap + populate IntendedFor fmap = fmap_dir / 'sub-01_fieldmap.nii.gz' fmap.touch() fmap_metadata = fmap.parent / fmap.name.replace('.nii.gz', '.json') fmap_metadata.write_text(json.dumps({'IntendedFor': intent})) layout = BIDSLayout(temporary_dataset) assert layout.get_IntendedFor(subject='01') def test_get_return_type_dir_with_schema_datatype(layout_7t_trt): """Test return_type='dir' with schema config for datatype entity (covers line 463).""" # Create a schema-based layout layout = BIDSLayout(layout_7t_trt.root, config='bids-schema', derivatives=False) # Query for datatype directories - this should trigger the code path # that handles non-subject/non-session entities (line 463) dirs = layout.get(target='datatype', return_type='dir') # Should return actual datatype directories assert len(dirs) > 0 # Check that these are actual directories in the dataset for d in dirs: assert os.path.exists(d) def test_get_return_type_dir_with_legacy_config_no_template(): """Test return_type='dir' with legacy config missing directory template (covers line 842).""" # Create a minimal config without directory templates config_dict = { 'name': 'test_config', 'entities': [ { 'name': 'subject', 'pattern': '[/\\\\]sub-([a-zA-Z0-9]+)', 'directory': '/sub-{subject}/', # This has a template }, { 'name': 'task', 'pattern': '_task-([a-zA-Z0-9]+)', # No directory template - this should trigger the error }, ], } # Create temporary test dataset import tempfile with tempfile.TemporaryDirectory() as tmpdir: # Create minimal BIDS structure Path(tmpdir).joinpath('dataset_description.json').write_text( '{"Name": "test", "BIDSVersion": "1.6.0"}' ) sub_dir = Path(tmpdir) / 'sub-01' sub_dir.mkdir() test_file = sub_dir / 'sub-01_task-rest_bold.nii.gz' test_file.touch() # Load with our custom config layout = BIDSLayout(tmpdir, config=config_dict, validate=False) # This should raise ValueError when trying to get directories for 'task' # because task entity has no directory template with pytest.raises(ValueError, match='Return type set to directory'): layout.get(target='task', return_type='dir') def test_bids_sort(layout_7t_trt): files = layout_7t_trt.get(task='rest', extension='.nii.gz') assert len(files) > 0 import copy import random from bidsschematools.schema import load_schema # we apply bids_sort at the model level, but just to be extra sure # we sort here one more time first_file_ents_sorted = bids_sort(files[0].get_entities()) schema_order = list(load_schema().rules.entities) + ['suffix', 'extension', 'datatype'] # collect keys from file entity then unsort them sorted_keys = list(first_file_ents_sorted.keys()) unsorted_keys = copy.copy(sorted_keys) while unsorted_keys == sorted_keys: random.shuffle(unsorted_keys) first_file_ents_unsorted = {} for key in unsorted_keys: first_file_ents_unsorted[key] = first_file_ents_sorted[key] # check order of sorted entities against schema for i, entity in enumerate(sorted_keys): for j in sorted_keys[i + 1 :]: if entity in schema_order and j in schema_order: # pragma: no branch assert schema_order.index(entity) < schema_order.index(j) assert list(first_file_ents_unsorted.keys()) != sorted_keys assert list(bids_sort(first_file_ents_unsorted).keys()) == sorted_keys ================================================ FILE: src/bids/layout/tests/test_layout_on_examples.py ================================================ """Tests runs layout on bids examples and make sure all files are caught""" """ TODO - add more 'vanilla' datasets - missing files in micr? """ import pytest from bids.layout import BIDSLayout # Values for the number of file got from a: # # find ds_name -type f | wc -l # @pytest.mark.parametrize( 'dataset, nb_files', [ ('qmri_irt1', 17), ('qmri_mese', 74), ('qmri_mp2rage', 18), ('qmri_mp2rageme', 29), ('qmri_mtsat', 26), ('qmri_sa2rage', 10), ('qmri_vfa', 19), ('qmri_mpm', 126), ('qmri_qsm', 10), ], ) def test_layout_on_examples_with_derivatives(dataset, nb_files, bids_examples): layout = BIDSLayout(bids_examples / dataset, derivatives=True) files = layout.get() assert len(files) == nb_files @pytest.mark.parametrize( 'dataset, nb_files', [ ('micr_SEM', 16), ('micr_SPIM', 26), ('asl001', 8), ('asl002', 10), ('asl003', 10), ('asl004', 12), ('asl005', 10), ('ds001', 135), # with anat and func data ('eeg_cbm', 104), ('ds000246', 19), # with meg data (excludes all files in .ds folders) ('genetics_ukbb', 96), # with dwi data ('ieeg_visual_multimodal', 148), # also with fmap data; ignore stimmuli folder ('pet001', 12), ('pet002', 20), ('pet003', 9), ('pet004', 10), ('pet005', 14), ('qmri_megre', 19), ('qmri_tb1tfl', 6), ('motion_dualtask', 645), ('fnirs_tapping', 39), ('mrs_fmrs', 169), ('mrs_biggaba', 172), ('mrs_2dmrsi', 67), ], ) def test_layout_on_examples_no_derivatives(dataset, nb_files, bids_examples): layout = BIDSLayout(bids_examples / dataset) files = layout.get() assert len(files) == nb_files ================================================ FILE: src/bids/layout/tests/test_models.py ================================================ """Tests of functionality in the models module.""" import copy import json import os import warnings from pathlib import Path import numpy as np import pandas as pd import pytest from sqlalchemy import create_engine from sqlalchemy.orm import sessionmaker from bids.layout.models import ( Base, BIDSFile, BIDSImageFile, Config, Entity, FileAssociation, LayoutInfo, Tag, ) from bids.layout.utils import PaddedInt from bids.tests import get_test_data_path def create_session(): engine = create_engine('sqlite://') Base.metadata.create_all(engine) Session = sessionmaker(bind=engine) return Session() @pytest.fixture def sample_bidsfile(tmpdir): testfile = 'sub-03_ses-2_task-rest_acq-fullbrain_run-2_bold.nii.gz' fn = tmpdir.mkdir('tmp').join(testfile) fn.write('###') return BIDSFile(os.path.join(str(fn))) @pytest.fixture(scope='module') def subject_entity(): return Entity( 'subject', r'[/\\\\]sub-([a-zA-Z0-9]+)', mandatory=False, directory='{subject}', dtype='str', ) def test_layoutinfo_init(): args = dict( root='/made/up/path', validate=True, index_metadata=False, derivatives=True, ignore=['code/', 'blergh/'], force_index=None, ) with pytest.raises(ValueError) as exc: LayoutInfo(**args) assert str(exc.value).startswith('Missing mandatory') args['config'] = ['bids', 'derivatives'] info = LayoutInfo(**args) assert info.derivatives is True assert info._derivatives == 'true' def test_entity_initialization(): e = Entity('avaricious', r'aardvark-(\d+)') assert e.name == 'avaricious' assert e.pattern == r'aardvark-(\d+)' assert not e.mandatory assert e.directory is None assert e.files == {} def test_entity_init_all_args(subject_entity): ent = subject_entity assert ent.name == 'subject' assert ent.pattern == r'[/\\\\]sub-([a-zA-Z0-9]+)' assert ent.mandatory is False assert ent.directory == '{subject}' def test_entity_init_with_bad_dtype(): with pytest.raises(ValueError) as exc: ent = Entity('test', dtype='superfloat') assert str(exc.value).startswith('Invalid dtype') def test_entity_matches(tmpdir): filename = 'aardvark-4-reporting-for-duty.txt' tmpdir.mkdir('tmp').join(filename).write('###') f = BIDSFile(os.path.join(str(tmpdir), filename)) e = Entity('avaricious', r'aardvark-(\d+)') result = e.match_file(f) assert result == '4' def test_entity_deepcopy(subject_entity): e = subject_entity clone = copy.deepcopy(subject_entity) for attr in ['name', 'pattern', 'mandatory', 'directory', 'regex']: assert getattr(e, attr) == getattr(clone, attr) assert e != clone def test_file_associations(): session = create_session() img = BIDSFile('sub-03/func/sub-03_task-rest_run-2_bold.nii.gz') md1 = BIDSFile('sub-03/func/sub-03_task-rest_run-2_bold.json') md2 = BIDSFile('task-rest_run-2_bold.json') assocs = [ FileAssociation(src=md1.path, dst=img.path, kind='MetadataFor'), FileAssociation(src=img.path, dst=md1.path, kind='MetadataIn'), FileAssociation(src=md1.path, dst=md2.path, kind='Child'), FileAssociation(src=md2.path, dst=md1.path, kind='Parent'), FileAssociation(src=md2.path, dst=img.path, kind='Informs'), ] session.add_all([img, md1, md2] + assocs) session.commit() assert img._associations == [md1, md2] == img.get_associations() assert md2._associations == [md1] assert img.get_associations(kind='MetadataFor') == [] assert img.get_associations(kind='MetadataIn') == [md1] results = img.get_associations(kind='MetadataIn', include_parents=True) assert set(results) == {md1, md2} def test_tag_init(sample_bidsfile, subject_entity): f, e = sample_bidsfile, subject_entity tag = Tag(f, e, 'zzz') rep = str(tag) assert rep.startswith(' 0 # Check that standard entities are present (using full names per PyBIDS convention) entity_names = {e.name for e in config.entities.values()} expected_entities = {'subject', 'session', 'task', 'run'} assert expected_entities.issubset(entity_names) # Check that PyBIDS-specific entities are present pybids_entities = {'extension', 'suffix', 'datatype'} assert pybids_entities.issubset(entity_names) def test_schema_entity_patterns(self): """Test that entity patterns are correctly generated.""" config = Config.load('bids-schema') # Test subject entity (directory-based) if 'subject' in config.entities: subject_entity = config.entities['subject'] assert subject_entity.regex is not None # Should match /sub-01/ or similar match = subject_entity.regex.search('/sub-01/') assert match is not None assert match.group(1) == '01' # Test task entity (file-based) if 'task' in config.entities: task_entity = config.entities['task'] assert task_entity.regex is not None # Should match _task-rest_ or similar match = task_entity.regex.search('_task-rest_') assert match is not None assert match.group(1) == 'rest' def test_schema_version_tracking(self): """Test that schema version is tracked in config name.""" config = Config.load('bids-schema') # Config name should include version assert 'bids-schema-' in config.name # Should have some version number after the dash version_part = config.name.split('bids-schema-')[1] assert len(version_part) > 0 def test_schema_version_parameter(self): """Test loading specific schema version.""" # Test loading BIDS v1.9.0 schema config = Config.load({'schema_version': '1.9.0'}) assert config.name.startswith('bids-schema-') assert len(config.entities) > 0 # Should include the version in the name assert '1.9.0' in config.name def test_invalid_schema_version(self): """Test that loading invalid schema version raises appropriate error.""" # Test with a non-existent version with pytest.raises(ValueError, match='Failed to load BIDS schema version'): Config.load({'schema_version': '99.99.99'}) def test_entity_generation(self): """Test entity pattern generation using real schema.""" config = Config.load('bids-schema') # Test that entities were generated assert len(config.entities) > 0 # Test specific entity properties we care about (using full names) assert 'subject' in config.entities assert 'task' in config.entities assert 'run' in config.entities # Test that patterns are valid regex for entity_name, entity in config.entities.items(): assert entity.regex is not None, f'Entity {entity_name} missing regex' # Test that regex compiles and works assert hasattr(entity.regex, 'search'), f'Entity {entity_name} regex invalid' # Test specific entity patterns work correctly subject_entity = config.entities['subject'] assert 'sub-' in subject_entity.pattern # Test subject pattern matches expected format match = subject_entity.regex.search('/sub-01/') assert match is not None assert match.group(1) == '01' # Test task pattern matches expected format task_entity = config.entities['task'] match = task_entity.regex.search('_task-rest_') assert match is not None assert match.group(1) == 'rest' # Test run pattern matches expected format run_entity = config.entities['run'] match = run_entity.regex.search('_run-02_') assert match is not None assert match.group(1) == '02' def test_schema_directory_query(self): """Test directory queries with schema-based config.""" import os from pathlib import Path from bids import BIDSLayout # Use the 7t_trt test dataset data_dir = Path(__file__).parent.parent.parent.parent.parent / 'tests' / 'data' / '7t_trt' if not data_dir.exists(): pytest.skip('Test dataset not available') # Create layout with schema config layout = BIDSLayout(data_dir, config='bids-schema', derivatives=False) # Test directory query for subjects subject_dirs = layout.get(target='subject', return_type='dir') assert len(subject_dirs) > 0 assert all(os.path.exists(d) for d in subject_dirs) # Should include subject directories assert any('sub-' in os.path.basename(d) for d in subject_dirs) ================================================ FILE: src/bids/layout/tests/test_schema_pattern_validation.py ================================================ """Tests to verify schema patterns match actual schema content.""" from bidsschematools import schema as bst_schema from bids.layout.models import Config class TestSchemaAccuracy: """Test that patterns accurately reflect BIDS schema content.""" def test_extension_patterns_match_schema(self): """Test that extension patterns match schema extensions.""" config = Config.load('bids-schema') # Load schema directly bids_schema = bst_schema.load_schema() # Get extension entity extension_entity = config.entities['extension'] # Test that schema extensions are matched (excluding wildcards) schema_extensions = [ ext['value'] for ext in bids_schema.objects.extensions.values() if ext.get('value') and ext['value'].startswith('.') and ext['value'] not in ('.*',) ] # Collect all failures matched_extensions = [] unmatched_extensions = [] test_files = [f'test{ext}' for ext in schema_extensions] for ext, test_file in zip(schema_extensions, test_files): match = extension_entity.regex.search(test_file) if match is not None: matched_extensions.append(ext) else: unmatched_extensions.append(ext) # Report all differences at once coverage = ( len(matched_extensions) / len(schema_extensions) * 100 if schema_extensions else 0 ) error_msg = [] error_msg.append( f'Extension coverage: {coverage:.1f}% ({len(matched_extensions)}/{len(schema_extensions)})' ) if unmatched_extensions: error_msg.append( f'Unmatched extensions ({len(unmatched_extensions)}): {unmatched_extensions[:20]}' ) if len(unmatched_extensions) > 20: error_msg.append(f'... and {len(unmatched_extensions) - 20} more') if matched_extensions: error_msg.append( f'Matched extensions ({len(matched_extensions)}): {matched_extensions[:10]}' ) if len(matched_extensions) > 10: error_msg.append(f'... and {len(matched_extensions) - 10} more') assert len(unmatched_extensions) == 0, '; '.join(error_msg) def test_suffix_patterns_match_schema(self): """Test that suffix patterns match schema suffixes.""" config = Config.load('bids-schema') # Load schema directly bids_schema = bst_schema.load_schema() # Get suffix entity suffix_entity = config.entities['suffix'] # Test that schema suffixes are matched using actual suffix values, not keys schema_suffix_values = [ obj['value'] for obj in bids_schema.objects.suffixes.values() if obj.get('value') ] # Collect all failures matched_suffixes = [] unmatched_suffixes = [] # Test all suffixes using their actual values test_files = [f'_{suffix}.nii.gz' for suffix in schema_suffix_values] for suffix, test_file in zip(schema_suffix_values, test_files): match = suffix_entity.regex.search(test_file) if match is not None: matched_suffixes.append(suffix) else: unmatched_suffixes.append(suffix) # Report all differences at once coverage = ( len(matched_suffixes) / len(schema_suffix_values) * 100 if schema_suffix_values else 0 ) error_msg = [] error_msg.append( f'Suffix coverage: {coverage:.1f}% ({len(matched_suffixes)}/{len(schema_suffix_values)})' ) if unmatched_suffixes: error_msg.append( f'Unmatched suffixes ({len(unmatched_suffixes)}): {unmatched_suffixes[:20]}' ) if len(unmatched_suffixes) > 20: error_msg.append(f'... and {len(unmatched_suffixes) - 20} more') if matched_suffixes: error_msg.append( f'Matched suffixes ({len(matched_suffixes)}): {matched_suffixes[:10]}' ) if len(matched_suffixes) > 10: error_msg.append(f'... and {len(matched_suffixes) - 10} more') assert len(unmatched_suffixes) == 0, '; '.join(error_msg) def test_datatype_patterns_match_schema(self): """Test that datatype patterns match schema datatypes.""" config = Config.load('bids-schema') # Load schema directly bids_schema = bst_schema.load_schema() # Get datatype entity datatype_entity = config.entities['datatype'] # Test that schema datatypes are matched schema_datatypes = list(bids_schema.objects.datatypes.keys()) # Collect all failures matched_datatypes = [] unmatched_datatypes = [] test_paths = [f'/{dtype}/' for dtype in schema_datatypes] for dtype, test_path in zip(schema_datatypes, test_paths): match = datatype_entity.regex.search(test_path) if match is not None: matched_datatypes.append(dtype) else: unmatched_datatypes.append(dtype) # Report all differences at once coverage = len(matched_datatypes) / len(schema_datatypes) * 100 if schema_datatypes else 0 error_msg = [] error_msg.append( f'Datatype coverage: {coverage:.1f}% ({len(matched_datatypes)}/{len(schema_datatypes)})' ) if unmatched_datatypes: error_msg.append( f'Unmatched datatypes ({len(unmatched_datatypes)}): {unmatched_datatypes}' ) if matched_datatypes: error_msg.append(f'Matched datatypes ({len(matched_datatypes)}): {matched_datatypes}') assert len(unmatched_datatypes) == 0, '; '.join(error_msg) def test_entity_format_patterns_match_schema(self): """Test that entity patterns use actual schema format patterns.""" config = Config.load('bids-schema') # Load schema directly bids_schema = bst_schema.load_schema() # Test a few key entities test_entities = [ ('subject', 'label'), ('run', 'index'), ('echo', 'index'), ] for entity_name, expected_format in test_entities: if entity_name in config.entities: entity = config.entities[entity_name] # Get expected pattern from schema format_obj = bids_schema.objects.formats[expected_format] expected_pattern = format_obj['pattern'] # Check that our entity pattern contains the schema pattern assert expected_pattern in entity.pattern, ( f"Entity {entity_name} pattern doesn't contain schema format pattern" ) def test_no_hardcoded_values(self): """Test that we don't have hardcoded schema values anymore.""" config = Config.load('bids-schema') # Load schema directly to get actual counts bids_schema = bst_schema.load_schema() # We should have more entities than before because we're not missing any assert len(config.entities) >= len(bids_schema.objects.entities) # Extension pattern should include schema extensions extension_entity = config.entities['extension'] schema_extension_count = len( [ ext for ext in bids_schema.objects.extensions.values() if ext.get('value') and ext['value'].startswith('.') ] ) # Pattern should include many extensions (not just a generic catch-all) assert len(extension_entity.pattern) > 100, 'Extension pattern seems too simple' # Suffix pattern should include schema suffixes suffix_entity = config.entities['suffix'] schema_suffix_count = len(bids_schema.objects.suffixes) # Pattern should include many suffixes assert len(suffix_entity.pattern) > 200, 'Suffix pattern seems too simple' print(f'Schema extensions used: {schema_extension_count}') print(f'Schema suffixes used: {schema_suffix_count}') print(f'Schema datatypes used: {len(bids_schema.objects.datatypes)}') print(f'Schema entities used: {len(bids_schema.objects.entities)}') ================================================ FILE: src/bids/layout/tests/test_schema_version_differences.py ================================================ """Tests for differences between BIDS schema versions.""" import json import tempfile from pathlib import Path from bids import BIDSLayout from bids.layout.models import Config class TestSchemaVersionDifferences: """Test that different schema versions handle different features correctly.""" def test_entity_parsing_version_differences(self): """Test that tracksys entity (added in v1.9.0) works in newer schemas but fails in v1.8.0.""" with tempfile.TemporaryDirectory() as temp_dir: dataset_dir = Path(temp_dir) / 'test_dataset' dataset_dir.mkdir() # Create minimal dataset with motion file using tracksys entity with open(dataset_dir / 'dataset_description.json', 'w') as f: json.dump({'Name': 'Test', 'BIDSVersion': '1.10.1', 'Authors': ['Test']}, f) motion_dir = dataset_dir / 'sub-01' / 'motion' motion_dir.mkdir(parents=True) (motion_dir / 'sub-01_tracksys-imu_motion.tsv').touch() # Test tracksys query across schema versions def test_tracksys_query(config): layout = BIDSLayout(dataset_dir, config=[config]) try: return len(layout.get(tracksys='imu', return_type='filename')) > 0 except Exception: return False # tracksys entity was added in v1.9.0 for motion datatype assert test_tracksys_query('bids-schema'), 'Current schema should support tracksys' assert test_tracksys_query({'schema_version': '1.9.0'}), ( 'v1.9.0 should support tracksys (motion added)' ) assert not test_tracksys_query({'schema_version': '1.8.0'}), ( 'v1.8.0 should NOT support tracksys' ) def test_motion_datatype_evolution(self): """Test that motion datatype (BEP029) support was added in v1.9.0.""" # Motion datatype was added in v1.9.0 according to changelog: # v1.9.0: [ENH] Extend BIDS for Motion data (BEP029) #981 config_v190 = Config.load({'schema_version': '1.9.0'}) config_v180 = Config.load({'schema_version': '1.8.0'}) entities_v190 = {e.name for e in config_v190.entities.values()} entities_v180 = {e.name for e in config_v180.entities.values()} # Motion-specific entity 'tracksys' should be in v1.9.0+ but not v1.8.0 assert 'tracksys' in entities_v190, ( 'tracksys entity should exist in v1.9.0 (motion datatype was added)' ) assert 'tracksys' not in entities_v180, ( 'tracksys entity should NOT exist in v1.8.0 (before motion datatype)' ) print('✓ Motion datatype evolution verified:') print(f' v1.8.0 (before motion): tracksys = {"tracksys" in entities_v180}') print(f' v1.9.0 (motion added): tracksys = {"tracksys" in entities_v190}') # Test specific motion-related entities that were added motion_entities = { 'tracksys' } # Could expand this list as more motion entities are identified for entity in motion_entities: assert entity in entities_v190, f"Motion entity '{entity}' should exist in v1.9.0+" assert entity not in entities_v180, ( f"Motion entity '{entity}' should NOT exist in v1.8.0" ) def test_schema_version_metadata_differences(self): """Test that schema versions have different BIDS version numbers.""" # Load different schema versions config_current = Config.load('bids-schema') config_v190 = Config.load({'schema_version': '1.9.0'}) config_v180 = Config.load({'schema_version': '1.8.0'}) # Check that config names reflect the versions assert 'bids-schema-' in config_current.name assert '1.9.0' in config_v190.name assert '1.8.0' in config_v180.name print(f'Current config: {config_current.name}') print(f'v1.9.0 config: {config_v190.name}') print(f'v1.8.0 config: {config_v180.name}') # Verify they're different configurations assert config_current.name != config_v190.name assert config_v190.name != config_v180.name ================================================ FILE: src/bids/layout/tests/test_schema_vs_json_config.py ================================================ """Tests to verify schema-driven config matches old JSON config behavior.""" import re from bids.layout.models import Config class TestConfigCompatibility: """Test that new schema-driven config matches old JSON config behavior.""" def test_core_entities_present_in_both_configs(self): """Test that essential BIDS entities are present in both configs.""" old_config = Config.load('bids') new_config = Config.load('bids-schema') # Test that both configs support the fundamental BIDS entities core_entities = ['subject', 'session', 'task', 'run', 'acquisition'] # Check core entities exist (allowing for naming differences) old_has_core = all(entity in old_config.entities for entity in core_entities) new_has_core = all(entity in new_config.entities for entity in core_entities) assert old_has_core and new_has_core, 'Both configs should support core BIDS entities' def test_schema_entities_are_superset_of_valid_old_entities(self): """Test that schema includes all valid entities from old config.""" old_config = Config.load('bids') new_config = Config.load('bids-schema') # Map old config entities to their schema equivalents entity_mapping = { 'inv': 'inversion', 'mt': 'mtransfer', 'proc': 'processing', # 'fmap', 'scans', 'staining' are invalid - exclude them } valid_old_entities = set(old_config.entities.keys()) - {'fmap', 'scans', 'staining'} missing_from_new = [] for old_entity in valid_old_entities: expected_new_entity = entity_mapping.get(old_entity, old_entity) if expected_new_entity not in new_config.entities: missing_from_new.append(f'{old_entity} -> {expected_new_entity}') assert len(missing_from_new) == 0, f'Schema missing valid entities: {missing_from_new}' def test_new_suffixes_compatible_with_old_pattern(self): """Test that all new schema suffixes would be accepted by old generic pattern.""" old_config = Config.load('bids') new_config = Config.load('bids-schema') # Get old pattern (generic) and new suffixes (explicit) old_pattern = old_config.entities['suffix'].regex new_suffixes = self._extract_suffixes_from_pattern(new_config.entities['suffix'].pattern) # Test that old pattern would accept all new suffixes incompatible_suffixes = [] for suffix in new_suffixes: test_filename = f'sub-01_task-test_{suffix}.nii.gz' if not old_pattern.search(test_filename): incompatible_suffixes.append(suffix) assert len(incompatible_suffixes) == 0, ( f'Old pattern would reject these valid schema suffixes: {incompatible_suffixes}' ) def test_new_extensions_compatible_with_old_pattern(self): """Test that all new schema extensions would be accepted by old generic pattern.""" old_config = Config.load('bids') new_config = Config.load('bids-schema') # Get old pattern (generic) and new extensions (explicit) old_pattern = old_config.entities['extension'].regex new_extensions = self._extract_extensions_from_pattern( new_config.entities['extension'].pattern ) # Test that old pattern would accept all new extensions incompatible_extensions = [] directory_extensions = [] for extension in new_extensions: test_filename = f'sub-01_task-test_bold{extension}' if not old_pattern.search(test_filename): if extension.endswith('/'): # Directory-style extensions are a new BIDS feature not supported by old pattern directory_extensions.append(extension) else: incompatible_extensions.append(extension) # Core file extensions should be compatible assert len(incompatible_extensions) == 0, ( f'Old pattern would reject these valid schema file extensions: {incompatible_extensions}' ) # Report directory extensions as expected difference if directory_extensions: print( f'Note: {len(directory_extensions)} directory-style extensions are new BIDS features: {directory_extensions}' ) # Core extensions should be present core_extensions = {'.nii.gz', '.nii', '.tsv', '.json'} assert core_extensions.issubset(new_extensions), 'Missing core extensions from new config' def test_datatype_coverage_matches(self): """Test that datatype patterns cover the same datatypes.""" old_config = Config.load('bids') new_config = Config.load('bids-schema') # Extract datatypes from patterns old_datatypes = self._extract_datatypes_from_pattern( old_config.entities['datatype'].pattern ) new_datatypes = self._extract_datatypes_from_pattern( new_config.entities['datatype'].pattern ) missing = old_datatypes - new_datatypes extra = new_datatypes - old_datatypes error_msg = [] if missing: error_msg.append(f'Missing datatypes: {sorted(missing)}') if extra: error_msg.append(f'Extra datatypes: {sorted(extra)}') # Core datatypes should be preserved core_datatypes = {'anat', 'func', 'dwi', 'fmap'} assert core_datatypes.issubset(new_datatypes), ( f'Missing core datatypes; {"; ".join(error_msg)}' ) # New should have at least as many as old (schema grows) assert len(new_datatypes) >= len(old_datatypes), ( f'Fewer datatypes than old config; {"; ".join(error_msg)}' ) def test_functional_filename_parsing_compatibility(self): """Test that both configs can parse BIDS filenames and extract core entities.""" old_config = Config.load('bids') new_config = Config.load('bids-schema') # Test core entity extraction (skip extension due to pattern differences) test_cases = [ { 'filename': '/dataset/sub-01/func/sub-01_task-rest_bold.nii.gz', 'core_entities': {'subject': '01', 'task': 'rest', 'suffix': 'bold'}, }, { 'filename': '/dataset/sub-02/ses-pre/anat/sub-02_ses-pre_T1w.nii.gz', 'core_entities': {'subject': '02', 'session': 'pre', 'suffix': 'T1w'}, }, ] for test_case in test_cases: filename = test_case['filename'] expected = test_case['core_entities'] old_entities = self._parse_filename_entities(filename, old_config) new_entities = self._parse_filename_entities(filename, new_config) # Test that both configs extract the core BIDS entities for key, expected_value in expected.items(): old_extracted = old_entities.get(key) new_extracted = new_entities.get(key) # Both should extract the same core information assert old_extracted == expected_value, ( f'Old config failed to extract {key}={expected_value} from {filename}, got {old_extracted}' ) assert new_extracted == expected_value, ( f'New config failed to extract {key}={expected_value} from {filename}, got {new_extracted}' ) # Test that new config has valid patterns assert len(new_config.entities) > 20, 'New config should have many entities' assert 'subject' in new_config.entities, 'New config missing subject entity' assert 'suffix' in new_config.entities, 'New config missing suffix entity' def test_new_config_parses_schema_compliant_filenames(self): """Test that new config correctly parses filenames with schema entities.""" new_config = Config.load('bids-schema') # Test files that use entities only available in schema (not old config) schema_specific_cases = [ '/dataset/sub-01/anat/sub-01_hemi-L_T1w.nii.gz', # hemisphere entity '/dataset/sub-01/anat/sub-01_desc-preproc_T1w.nii.gz', # description entity '/dataset/sub-01/anat/sub-01_res-native_T1w.nii.gz', # resolution entity ] for filename in schema_specific_cases: entities = self._parse_filename_entities(filename, new_config) # Should extract subject and suffix at minimum assert 'subject' in entities, f'Failed to extract subject from {filename}' assert 'suffix' in entities, f'Failed to extract suffix from {filename}' # Should extract the schema-specific entity if 'hemi-' in filename: assert 'hemisphere' in entities, f'Failed to extract hemisphere from {filename}' if 'desc-' in filename: assert 'description' in entities, f'Failed to extract description from {filename}' if 'res-' in filename: assert 'resolution' in entities, f'Failed to extract resolution from {filename}' def test_pattern_structure_similarity(self): """Test that pattern structures are similar between old and new.""" old_config = Config.load('bids') new_config = Config.load('bids-schema') # Test key entities have reasonable pattern structures key_entities = ['subject', 'session', 'task', 'run', 'suffix', 'extension'] for entity_name in key_entities: if entity_name in old_config.entities and entity_name in new_config.entities: old_pattern = old_config.entities[entity_name].pattern new_pattern = new_config.entities[entity_name].pattern # Both should be non-empty assert len(old_pattern) > 0, f'Old {entity_name} pattern is empty' assert len(new_pattern) > 0, f'New {entity_name} pattern is empty' # Both should contain capturing groups old_groups = len(re.findall(r'\([^)]*\)', old_pattern)) new_groups = len(re.findall(r'\([^)]*\)', new_pattern)) assert old_groups > 0, f'Old {entity_name} pattern has no groups' assert new_groups > 0, f'New {entity_name} pattern has no groups' def test_config_names_and_metadata(self): """Test config metadata is reasonable.""" old_config = Config.load('bids') new_config = Config.load('bids-schema') # Old config should have simple name assert old_config.name == 'bids' # New config should include version info assert new_config.name.startswith('bids-schema-') assert len(new_config.name) > len('bids-schema-') # Both should have reasonable entity counts assert len(old_config.entities) > 10, 'Old config has too few entities' assert len(new_config.entities) > 10, 'New config has too few entities' # Report entity count ratio for analysis ratio = len(new_config.entities) / len(old_config.entities) print(f'Entity count ratio (new/old): {ratio:.2f}') # Helper methods def _extract_suffixes_from_pattern(self, pattern): """Extract suffixes from a pattern like [_/\\]+(suffix1|suffix2|...)""" suffixes = set() # Look for alternation groups matches = re.findall(r'\(([^)]+)\)', pattern) for match in matches: if '|' in match: suffixes.update(match.split('|')) else: suffixes.add(match) return suffixes def _extract_extensions_from_pattern(self, pattern): """Extract extensions from a pattern.""" extensions = set() # Look for patterns with dots matches = re.findall(r'\\?\.\w+(?:\\?\.\w+)*', pattern) for match in matches: # Clean up regex escaping clean_ext = match.replace('\\', '') extensions.add(clean_ext) # Also look for alternation groups alt_matches = re.findall(r'\(([^)]+)\)', pattern) for match in alt_matches: if '|' in match and '.' in match: for item in match.split('|'): if '.' in item: clean_item = item.replace('\\', '') extensions.add(clean_item) return extensions def _extract_datatypes_from_pattern(self, pattern): """Extract datatypes from a pattern like [/\\]+(datatype1|datatype2|...)""" datatypes = set() # Look for alternation groups matches = re.findall(r'\(([^)]+)\)', pattern) for match in matches: if '|' in match: datatypes.update(match.split('|')) else: datatypes.add(match) return datatypes def _parse_filename_entities(self, filename, config): """Parse entities from filename using config patterns.""" entities = {} for entity_name, entity in config.entities.items(): if hasattr(entity, 'regex') and entity.regex: match = entity.regex.search(filename) if match and len(match.groups()) > 0: entities[entity_name] = match.group(1) return entities def _map_entity_name_old_to_new(self, new_entity_name): """Map new entity names to old entity names for compatibility testing.""" # Old config sometimes used different names name_mapping = { 'subject': 'subject', 'session': 'session', 'task': 'task', 'run': 'run', 'acquisition': 'acquisition', 'suffix': 'suffix', 'extension': 'extension', # Add other mappings as needed } return name_mapping.get(new_entity_name, new_entity_name) ================================================ FILE: src/bids/layout/tests/test_utils.py ================================================ """Test miscellaneous utilities.""" import os import numpy as np import pandas as pd import pytest import bids from bids.exceptions import ConfigError from ..models import Config, Entity from ..utils import BIDSMetadata, PaddedInt, add_config_paths, parse_file_entities def test_bidsmetadata_class(): md = BIDSMetadata('fakefile') with pytest.raises(KeyError) as err: md['Missing'] assert "Metadata term 'Missing' unavailable for file fakefile." in str(err) md['Missing'] = 1 assert md['Missing'] == 1 def test_parse_file_entities(mock_config): filename = '/sub-03_ses-07_run-4_desc-bleargh_sekret.nii.gz' # Test with entities taken from bids config target = { 'subject': '03', 'session': '07', 'run': 4, 'suffix': 'sekret', 'extension': '.nii.gz', } assert target == parse_file_entities(filename, config='bids') config = Config.load('bids') assert target == parse_file_entities(filename, config=[config]) # Test with entities taken from bids and derivatives config target = { 'subject': '03', 'session': '07', 'run': 4, 'suffix': 'sekret', 'desc': 'bleargh', 'extension': '.nii.gz', } assert target == parse_file_entities(filename) assert target == parse_file_entities(filename, config=['bids', 'derivatives']) # Test with list of Entities entities = [ Entity('subject', '[/\\\\]sub-([a-zA-Z0-9]+)'), Entity('run', '[_/\\\\]run-0*(\\d+)', dtype=int), Entity('suffix', '[._]*([a-zA-Z0-9]*?)\\.[^/\\\\]+$'), Entity('desc', 'desc-([a-zA-Z0-9]+)'), ] # Leave out session to distinguish from previous test target target = {'subject': '03', 'run': 4, 'suffix': 'sekret', 'desc': 'bleargh'} assert target == parse_file_entities(filename, entities=entities) @pytest.mark.parametrize( 'filename, target', [ ('/path/to/sub-01.ext', {'subject': '01', 'extension': '.ext'}), ('/path/to/stub.ext', {'suffix': 'stub', 'extension': '.ext'}), ('/path/to/.dotfile', {}), ('/path/to/stub', {}), ], ) def test_parse_degenerate_files(mock_config, filename, target): assert parse_file_entities(filename, config='bids') == target def test_add_config_paths(): bids_dir = os.path.dirname(bids.__file__) bids_json = os.path.join(bids_dir, 'layout', 'config', 'bids.json') with pytest.raises(ConfigError) as exc: add_config_paths(test_config1='nonexistentpath.json') assert str(exc.value).startswith('Configuration file') with pytest.raises(ConfigError) as exc: add_config_paths(bids=bids_json) assert str(exc.value).startswith("Configuration 'bids' already") add_config_paths(dummy=bids_json) config = Config.load('dummy') assert 'subject' in config.entities def test_PaddedInt_array_comparisons(): # Array comparisons should work, not raise exceptions arr = np.array([5, 5, 5]) assert np.all(arr == PaddedInt(5)) assert np.all(arr == PaddedInt('05')) assert np.all(PaddedInt(5) == arr) assert np.all(PaddedInt('05') == arr) # If the value gets put into an array, it should be considered an int # We lose the padding, but it's unlikely we would try to recover it # from an array. assert np.array([PaddedInt(5)]).dtype == np.int64 # Verify that we do get some False results assert np.array_equal(np.array([4, 5, 6]) == PaddedInt(5), [False, True, False]) def test_PaddedInt_dataframe_behavior(): # Verify that pandas dataframes are not more special than numpy arrays, # as far as PaddedInt is concerned df = pd.DataFrame({'a': [5, 5, 5]}) pidf = pd.DataFrame({'a': [PaddedInt(5)] * 3}) assert np.all(df['a'] == PaddedInt(5)) assert np.all(df == pidf) assert pidf['a'].dtype is np.dtype('int64') ================================================ FILE: src/bids/layout/tests/test_validation.py ================================================ """Tests of BIDSValidator functionality.""" from os.path import join import pytest from bids_validator import BIDSValidator from bids.layout import BIDSLayout from bids.tests import get_test_data_path # Fixture uses in the rest of the tests @pytest.fixture def testvalidator(): return BIDSValidator() def test_layout_with_validation(): data_dir = join(get_test_data_path(), '7t_trt') layout1 = BIDSLayout(data_dir, validate=True) layout2 = BIDSLayout(data_dir, validate=False) assert len(layout1.files) < len(layout2.files) # Not a valid BIDS file badfile = join(data_dir, 'test.bval') assert badfile not in layout1.files assert badfile in layout2.files ================================================ FILE: src/bids/layout/tests/test_writing.py ================================================ """Tests related to file-writing functionality.""" import os import shutil from os.path import dirname, exists, islink, join import pytest from sqlalchemy import create_engine from sqlalchemy.orm import sessionmaker from bids import BIDSLayout from bids.layout.models import Base, BIDSFile, Entity, Tag from bids.layout.writing import _PATTERN_FIND, build_path from bids.tests import get_test_data_path @pytest.fixture def writable_file(tmpdir): engine = create_engine('sqlite://') Base.metadata.create_all(engine) Session = sessionmaker(bind=engine) session = Session() testfile = 'sub-03_ses-2_task-rest_acq-fullbrain_run-2_bold.nii.gz' fn = tmpdir.mkdir('tmp').join(testfile) fn.write('###') bf = BIDSFile(os.path.join(str(fn))) tag_dict = {'task': 'rest', 'run': 2, 'subject': '3'} ents = {name: Entity(name) for name in tag_dict.keys()} tags = [Tag(bf, ents[k], value=v) for k, v in tag_dict.items()] session.add_all(list(ents.values()) + tags + [bf]) session.commit() yield bf @pytest.fixture(scope='module') def tmp_bids(tmpdir_factory): tmp_bids = tmpdir_factory.mktemp('tmp_bids') yield tmp_bids shutil.rmtree(str(tmp_bids)) # Ugly hack try: shutil.rmtree(join(get_test_data_path(), '7t_trt', 'sub-Bob')) except Exception: pass @pytest.fixture(scope='module') def layout(tmp_bids): orig_dir = join(get_test_data_path(), '7t_trt') # return BIDSLayout(data_dir, absolute_paths=False) new_dir = join(str(tmp_bids), 'bids') os.symlink(orig_dir, new_dir) return BIDSLayout(new_dir) class TestWritableFile: def test_parse_pattern_re(self): """Unit tests on the strict entity pattern finder regex.""" assert _PATTERN_FIND.findall('{extension|nii.gz}') == [ ('{extension|nii.gz}', 'extension', 'nii|nii.gz|json', 'nii.gz') ] assert _PATTERN_FIND.findall('{extension<.nii|.nii.gz|.json>|.nii.gz}') == [ ( '{extension<.nii|.nii.gz|.json>|.nii.gz}', 'extension', '.nii|.nii.gz|.json', '.nii.gz', ) ] assert _PATTERN_FIND.findall('{extension|json}') == [ ('{extension|json}', 'extension', 'json|jsld', 'json') ] assert _PATTERN_FIND.findall('{task}/r-{run}.nii.gz') == [ ('{task}', 'task', 'func|rest', ''), ('{run}', 'run', '', ''), ] pattern = """\ sub-{subject}[/ses-{session}]/anat/sub-{subject}[_ses-{session}][_acq-{acquisition}][_ce-{ceagent}][_rec-{reconstruction}]\ [_space-{space}]_{suffix}.\ {extension|nii.gz}""" assert sorted(_PATTERN_FIND.findall(pattern)) == [ ('{acquisition}', 'acquisition', '', ''), ('{ceagent}', 'ceagent', '', ''), ('{extension|nii.gz}', 'extension', 'nii|nii.gz|json', 'nii.gz'), ('{reconstruction}', 'reconstruction', '', ''), ('{session}', 'session', '', ''), ('{session}', 'session', '', ''), ('{space}', 'space', '', ''), ('{subject}', 'subject', '', ''), ('{subject}', 'subject', '', ''), ( '{suffix}', 'suffix', 'T1w|T2w|T1rho|T1map|T2map|T2star|FLAIR|FLASH|PDmap|PD|PDT2|inplaneT[12]|angio', '', ), ] def test_build_path(self, writable_file): # Single simple pattern with pytest.raises(TypeError): build_path(writable_file.entities) pat = join(writable_file.dirname, '{task}/sub-{subject}/run-{run}.nii.gz') target = join(writable_file.dirname, 'rest/sub-3/run-2.nii.gz') assert build_path(writable_file.entities, pat) == target # Multiple simple patterns pats = [ '{session}/{task}/r-{run}.nii.gz', 't-{task}/{subject}-{run}.nii.gz', '{subject}/{task}.nii.gz', ] pats = [join(writable_file.dirname, p) for p in pats] target = join(writable_file.dirname, 't-rest/3-2.nii.gz') assert build_path(writable_file.entities, pats) == target # Pattern with optional entity pats = ['[{session}/]{task}/r-{run}.nii.gz', 't-{task}/{subject}-{run}.nii.gz'] pats = [join(writable_file.dirname, p) for p in pats] target = join(writable_file.dirname, 'rest/r-2.nii.gz') assert build_path(writable_file.entities, pats) == target # Pattern with conditional values pats = ['{task}/r-{run}.nii.gz', 't-{task}/{subject}-{run}.nii.gz'] pats = [join(writable_file.dirname, p) for p in pats] target = join(writable_file.dirname, 't-rest/3-2.nii.gz') assert build_path(writable_file.entities, pats) == target # Pattern with valid conditional values pats = ['{task}/r-{run}.nii.gz', 't-{task}/{subject}-{run}.nii.gz'] pats = [join(writable_file.dirname, p) for p in pats] target = join(writable_file.dirname, 'rest/r-2.nii.gz') assert build_path(writable_file.entities, pats) == target # Pattern with optional entity with conditional values pats = ['[{task}/]r-{run}.nii.gz', 't-{task}/{subject}-{run}.nii.gz'] pats = [join(writable_file.dirname, p) for p in pats] target = join(writable_file.dirname, 'r-2.nii.gz') assert build_path(writable_file.entities, pats) == target # Pattern with default value pats = ['ses-{session|A}/r-{run}.nii.gz'] assert build_path({'run': 3}, pats) == 'ses-A/r-3.nii.gz' # Pattern with both valid and default values pats = ['ses-{session|D}/r-{run}.nii.gz'] assert build_path({'run': 3}, pats) == 'ses-D/r-3.nii.gz' pats = ['ses-{session|D}/r-{run}.nii.gz'] assert build_path({'session': 'B', 'run': 3}, pats) == 'ses-B/r-3.nii.gz' # Test extensions with dot and warning is issued pats = ['ses-{session|D}/r-{run}.{extension}'] with pytest.warns(UserWarning) as record: assert ( build_path({'session': 'B', 'run': 3, 'extension': '.nii'}, pats) == 'ses-B/r-3.nii' ) assert 'defines an invalid default value' in record[0].message.args[0] # Test expansion of optional characters pats = ['ses-{session<[ABCD]>|D}/r-{run}.{extension}'] assert build_path({'session': 'B', 'run': 3, 'extension': '.nii'}, pats) == 'ses-B/r-3.nii' # Test default-only patterns are correctly overridden by setting entity entities = { 'subject': '01', 'extension': 'bvec', 'suffix': 'T1rho', } pats = ( 'sub-{subject}[/ses-{session}]/{datatype|dwi}/sub-{subject}[_ses-{session}]' '[_acq-{acquisition}]_{suffix|dwi}.{extension|nii.gz}' ) assert build_path(entities, pats) == 'sub-01/dwi/sub-01_T1rho.bvec' assert build_path(entities, pats, strict=True) == 'sub-01/dwi/sub-01_T1rho.bvec' # Test multiple paths pats = ['ses-{session}/r-{run}.{extension|nii.gz}'] assert sorted( build_path( {'session': ['A', 'B'], 'run': [1, 2], 'extension': ['.nii.gz', 'json']}, pats ) ) == [ 'ses-A/r-1.json', 'ses-A/r-1.nii.gz', 'ses-A/r-2.json', 'ses-A/r-2.nii.gz', 'ses-B/r-1.json', 'ses-B/r-1.nii.gz', 'ses-B/r-2.json', 'ses-B/r-2.nii.gz', ] def test_strict_build_path(self): # Test with strict matching--should fail pats = ['[{session}/]{task}/r-{run}.nii.gz', 't-{task}/{subject}-{run}.nii.gz'] entities = {'subject': 1, 'task': 'A', 'run': 2} assert build_path(entities, pats, True) entities = {'subject': 1, 'task': 'A', 'age': 22} assert not build_path(entities, pats, True) def test_build_file(self, writable_file, tmp_bids, caplog): # Simple write out new_dir = join(writable_file.dirname, 'rest') pat = join(writable_file.dirname, '{task}/sub-{subject}/run-{run}.nii.gz') target = join(writable_file.dirname, 'rest/sub-3/run-2.nii.gz') writable_file.copy(pat) assert exists(target) # Conflict handling with pytest.raises(ValueError): writable_file.copy(pat) with pytest.raises(ValueError): writable_file.copy(pat, conflicts='fail') with pytest.warns(UserWarning) as record: writable_file.copy(pat, conflicts='skip') log_message = record[0].message.args[0] assert log_message == f'A file at path {target} already exists, skipping writing file.' writable_file.copy(pat, conflicts='append') append_target = join(writable_file.dirname, 'rest/sub-3/run-2_1.nii.gz') assert exists(append_target) writable_file.copy(pat, conflicts='overwrite') assert exists(target) shutil.rmtree(new_dir) # Symbolic linking writable_file.copy(pat, symbolic_link=True) assert islink(target) shutil.rmtree(new_dir) # Using different root root = str(tmp_bids.mkdir('tmp2')) pat = join(root, '{task}/sub-{subject}/run-{run}.nii.gz') target = join(root, 'rest/sub-3/run-2.nii.gz') writable_file.copy(pat, root=root) assert exists(target) # Copy into directory functionality pat = join(writable_file.dirname, '{task}/') writable_file.copy(pat) target = join(writable_file.dirname, 'rest', writable_file.filename) assert exists(target) shutil.rmtree(new_dir) class TestWritableLayout: def test_write_files(self, tmp_bids, layout): tmpdir = str(tmp_bids) pat = join( str(tmpdir), 'sub-{subject<02>}' '/ses-{session}' '/r-{run}' '/suffix-{suffix}' '/acq-{acquisition}' '/task-{task}.nii.gz', ) layout.copy_files(path_patterns=pat) example_file = join( str(tmpdir), 'sub-02/ses-2/r-1/suffix-bold/acq-fullbrain/task-rest.nii.gz' ) example_file2 = join( str(tmpdir), 'sub-01/ses-2/r-1/suffix-bold/acq-fullbrain/task-rest.nii.gz' ) assert exists(example_file) assert not exists(example_file2) pat = join( str(tmpdir), 'sub-{subject<01>}/ses-{session}/r-{run}/suffix-{suffix}/task-{task}.nii.gz', ) example_file = join(str(tmpdir), 'sub-01/ses-2/r-1/suffix-bold/task-rest.nii.gz') # Should fail without the 'overwrite' because there are multiple # files that produce the same path. with pytest.raises(ValueError): layout.copy_files(path_patterns=pat) try: os.remove(example_file) except OSError: pass layout.copy_files(path_patterns=pat, conflicts='overwrite') assert exists(example_file) def test_write_to_file(self, tmp_bids, layout): contents = 'test' entities = {'subject': 'Bob', 'session': '01'} pat = join('sub-{subject}/ses-{session}/desc.txt') layout.write_to_file(entities, path_patterns=pat, contents=contents, validate=False) target = join(str(tmp_bids), 'bids', 'sub-Bob/ses-01/desc.txt') assert exists(target) with open(target) as f: written = f.read() assert written == contents assert target not in layout.files def test_write_to_file_defaults(self, tmp_bids, layout): contents = 'test' entities = { 'subject': 'Bob', 'session': '01', 'run': '1', 'suffix': 'bold', 'task': 'test', 'acquisition': 'test', 'bval': 0, } layout.write_to_file(entities, contents=contents) target = join( str(tmp_bids), 'bids', 'sub-Bob', 'ses-01', 'func', 'sub-Bob_ses-01_task-test_acq-test_run-1_bold.nii.gz', ) assert exists(target) with open(target) as f: written = f.read() assert written == contents def test_build_file_from_layout(self, tmpdir, layout): entities = {'subject': 'Bob', 'session': '01', 'run': '1'} pat = join(str(tmpdir), 'sub-{subject}/ses-{session}/r-{run}.nii.gz') path = layout.build_path(entities, path_patterns=pat, validate=False) assert path == join(str(tmpdir), 'sub-Bob/ses-01/r-1.nii.gz') data_dir = join(dirname(__file__), 'data', '7t_trt') filename = 'sub-04_ses-1_task-rest_acq-fullbrain_run-1_physio.tsv.gz' file = join('sub-04', 'ses-1', 'func', filename) path = layout.build_path(file, path_patterns=pat, validate=False) assert path.endswith('sub-04/ses-1/r-1.nii.gz') ================================================ FILE: src/bids/layout/utils.py ================================================ """Miscellaneous layout-related utilities.""" from upath import UPath as Path from .. import config as cf from ..exceptions import ConfigError from ..utils import listify, make_bidsfile class BIDSMetadata(dict): """Metadata dictionary that reports the associated file on lookup failures.""" def __init__(self, source_file): self._source_file = source_file super().__init__() def __getitem__(self, key): try: return super().__getitem__(key) except KeyError: raise KeyError(f'Metadata term {key!r} unavailable for file {self._source_file}.') # noqa: B904 class PaddedInt(int): """Integer type that preserves zero-padding Acts like an int in almost all ways except that string formatting will keep the original zero-padding. Numeric format specifiers >>> PaddedInt(1) 1 >>> p2 = PaddedInt('02') >>> p2 02 >>> str(p2) '02' >>> p2 == 2 True >>> p2 in range(3) True >>> f'{p2}' '02' >>> f'{p2:s}' '02' >>> f'{p2!s}' '02' >>> f'{p2!r}' '02' >>> f'{p2:d}' '2' >>> f'{p2:03d}' '002' >>> f'{p2:f}' '2.000000' >>> {2: 'val'}.get(p2) 'val' >>> {p2: 'val'}.get(2) 'val' Note that arithmetic will break the padding. >>> str(p2 + 1) '3' """ def __init__(self, val): self.sval = str(val) def __eq__(self, val): try: return val == self.sval or super().__eq__(val) except ValueError: # `or` triggers `__bool__`. If this fails, it is almost # certainly an array type. Do not attempt string comparisons # and allow val to determine the return type return val == self def __str__(self): return self.sval def __repr__(self): return self.sval def __format__(self, format_spec): """Format a padded integer If a format spec can be used on a string, apply it to the zero-padded string. Otherwise format as an integer. """ try: return format(self.sval, format_spec) except ValueError: return super().__format__(format_spec) def __hash__(self): return super().__hash__() def parse_file_entities(filename, entities=None, config=None, include_unmatched=False): """Parse the passed filename for entity/value pairs. Parameters ---------- filename : str The filename to parse for entity values entities : list or None, optional An optional list of Entity instances to use in extraction. If passed, the config argument is ignored. Default is None. config : str or :obj:`bids.layout.models.Config` or list or None, optional One or more :obj:`bids.layout.models.Config` objects or names of configurations to use in matching. Each element must be a :obj:`bids.layout.models.Config` object, or a valid :obj:`bids.layout.models.Config` name (e.g., 'bids' or 'derivatives'). If None, all available configs are used. Default is None. include_unmatched : bool, optional If True, unmatched entities are included in the returned dict, with values set to None. If False (default), unmatched entities are ignored. Returns ------- dict Keys are Entity names and values are the values from the filename. """ # Load Configs if needed if entities is None: if config is None: config = ['bids', 'derivatives'] from .models import Config config = [Config.load(c) if not isinstance(c, Config) else c for c in listify(config)] # Consolidate entities from all Configs into a single dict entities = {} for c in config: entities.update(c.entities) entities = entities.values() # Extract matches bf = make_bidsfile(filename) ent_vals = {} for ent in entities: match = ent.match_file(bf) if match is not None or include_unmatched: ent_vals[ent.name] = match return ent_vals def add_config_paths(**kwargs): """Add to the pool of available configuration files for BIDSLayout. Parameters ---------- kwargs : dict Dictionary specifying where to find additional config files. Keys are names, values are paths to the corresponding .json file. Examples -------- > add_config_paths(my_config='/path/to/config') > layout = BIDSLayout('/path/to/bids', config=['bids', 'my_config']) """ for k, path in kwargs.items(): if not Path(path).exists(): raise ConfigError(f'Configuration file "{k}" does not exist') if k in cf.get_option('config_paths'): raise ConfigError(f'Configuration {k!r} already exists') kwargs.update(**cf.get_option('config_paths')) cf.set_option('config_paths', kwargs) ================================================ FILE: src/bids/layout/validation.py ================================================ """Functionality related to validation of BIDSLayouts and BIDS projects.""" import json import re import warnings from upath import UPath as Path from ..exceptions import BIDSDerivativesValidationError, BIDSValidationError from ..utils import listify MANDATORY_BIDS_FIELDS = { 'Name': {'Name': 'Example dataset'}, 'BIDSVersion': {'BIDSVersion': '1.0.2'}, } MANDATORY_DERIVATIVES_FIELDS = { **MANDATORY_BIDS_FIELDS, 'GeneratedBy': {'GeneratedBy': [{'Name': 'Example pipeline'}]}, } EXAMPLE_BIDS_DESCRIPTION = {k: val[k] for val in MANDATORY_BIDS_FIELDS.values() for k in val} EXAMPLE_DERIVATIVES_DESCRIPTION = { k: val[k] for val in MANDATORY_DERIVATIVES_FIELDS.values() for k in val } DEFAULT_LOCATIONS_TO_IGNORE = { re.compile(r'^/(code|models|sourcedata|stimuli)'), } ALWAYS_IGNORE = ( re.compile(r'/\.'), # dotfiles should never be indexed ) def validate_root(root, validate): # noqa: D103 # Validate root argument and make sure it contains mandatory info try: root = Path(root) except TypeError: raise TypeError( # noqa: B904 'root argument must be a pathlib.Path (or a type that ' 'supports casting to pathlib.Path, such as ' 'string) specifying the directory ' 'containing the BIDS dataset.' ) root = root.absolute() if not root.exists(): raise ValueError('BIDS root does not exist: %s' % root) # noqa: UP031 target = root / 'dataset_description.json' if not target.exists(): if validate: raise BIDSValidationError( "'dataset_description.json' is missing from project root." # noqa: UP031 ' Every valid BIDS dataset must have this file.' "\nExample contents of 'dataset_description.json': \n%s" % json.dumps(EXAMPLE_BIDS_DESCRIPTION) ) else: description = None else: err = None try: with target.fs.open(target.path, 'r', encoding='utf-8') as desc_fd: description = json.load(desc_fd) except (UnicodeDecodeError, json.JSONDecodeError) as e: description = None err = e if validate: if description is None: raise BIDSValidationError( "'dataset_description.json' is not a valid json file." " There is likely a typo in your 'dataset_description.json' at " f'{target.resolve()}. ' "\nExample contents of 'dataset_description.json': \n%s" % json.dumps(EXAMPLE_BIDS_DESCRIPTION) ) from err for k in MANDATORY_BIDS_FIELDS: if k not in description: raise BIDSValidationError( 'Mandatory %r field missing from ' "'dataset_description.json' at " f'{target.resolve()}. ' '\nExample: %s' % (k, MANDATORY_BIDS_FIELDS[k]) ) return root, description def validate_derivative_path(path, **kwargs): # noqa: D103 # Collect all paths that contain a dataset_description.json dd = Path(path) / 'dataset_description.json' description = json.loads(dd.read_text(encoding='utf-8')) pipeline_names = [ pipeline['Name'] for pipeline in description.get('GeneratedBy', []) if 'Name' in pipeline ] if pipeline_names: pipeline_name = pipeline_names[0] elif 'PipelineDescription' in description: warnings.warn( # noqa: B028 'The PipelineDescription field was superseded ' 'by GeneratedBy in BIDS 1.4.0. You can use ' '``pybids upgrade`` to update your derivative ' 'dataset.' ) pipeline_name = description['PipelineDescription'].get('Name') else: pipeline_name = None if pipeline_name is None: raise BIDSDerivativesValidationError( 'Every valid BIDS-derivatives dataset must ' 'have a GeneratedBy.Name field set ' "inside 'dataset_description.json', " f'here {dd.resolve()}. ' f'\nExample: {MANDATORY_DERIVATIVES_FIELDS["GeneratedBy"]}' ) return pipeline_name def _sort_patterns(patterns, root): """Return sorted patterns, from more specific to more general.""" regexes = [patt for patt in patterns if hasattr(patt, 'search')] paths = [ str((root / patt).absolute()) for patt in listify(patterns) if not hasattr(patt, 'search') ] # Sort patterns from general to specific paths.sort(key=len) # Combine and return (note path patterns are reversed, specific first) return [Path(p) for p in reversed(paths)] + regexes def validate_indexing_args(ignore, force_index, root): # noqa: D103 if ignore is None: ignore = DEFAULT_LOCATIONS_TO_IGNORE - set(force_index or []) ignore = list(ignore) ignore.extend(ALWAYS_IGNORE) # root has already been validated to be a directory ignore = _sort_patterns(ignore, root) force_index = _sort_patterns(force_index or [], root) # Derivatives get special handling; they shouldn't be indexed normally for entry in force_index: condi = isinstance(entry, str) and str(entry.resolve()).startswith('derivatives') if condi: msg = ( "Do not pass 'derivatives' in the force_index " 'list. To index derivatives, either set ' 'derivatives=True, or use add_derivatives().' ) raise ValueError(msg) return ignore, force_index ================================================ FILE: src/bids/layout/writing.py ================================================ """Contains helper functions that involve writing operations.""" import re import shutil import sys import warnings from itertools import product from string import Formatter from upath import UPath as Path from ..utils import listify __all__ = ['build_path', 'write_to_file'] _PATTERN_FIND = re.compile(r'({([\w\d]*?)(?:<([^>]+)>)?(?:\|((?:\.?[\w])+))?\})') def build_path(entities, path_patterns, strict=False): """Constructs a path given a set of entities and a list of potential filename patterns to use. Parameters ---------- entities : :obj:`dict` A dictionary mapping entity names to entity values. Entities with ``None`` or empty-string value will be removed. Otherwise, entities will be cast to string values, therefore if any format is expected (e.g., zero-padded integers), the value should be formatted. path_patterns : :obj:`str` or :obj:`list` One or more filename patterns to write the file to. Entities should be represented by the name surrounded by curly braces. Optional portions of the patterns should be denoted by square brackets. Entities that require a specific value for the pattern to match can pass them inside angle brackets. Default values can be assigned by specifying a string after the pipe operator. E.g., (e.g., {type|bold} would only match the pattern if the entity 'type' was passed and its value is "image", otherwise the default value "bold" will be used). strict : :obj:`bool` If True, all passed entities must be matched inside a pattern in order to be a valid match. If False, extra entities will be ignored so long as all mandatory entities are found. Returns ------- A constructed path for this file based on the provided patterns, or ``None`` if no path was built given the combination of entities and patterns. Examples -------- >>> entities = { ... 'extension': '.nii', ... 'space': 'MNI', ... 'subject': '001', ... 'suffix': 'inplaneT2', ... } >>> patterns = [ ... 'sub-{subject}[/ses-{session}]/anat/sub-{subject}[_ses-{session}]' ... '[_acq-{acquisition}][_ce-{ceagent}][_rec-{reconstruction}]_' ... '{suffix}{extension<.nii|.nii.gz|.json>|.nii.gz}', ... 'sub-{subject}[/ses-{session}]/anat/sub-{subject}[_ses-{session}]' ... '[_acq-{acquisition}][_ce-{ceagent}][_rec-{reconstruction}]' ... '[_space-{space}][_desc-{desc}]_{suffix}' ... '{extension<.nii|.nii.gz|.json>|.nii.gz}', ... ] >>> build_path(entities, patterns) 'sub-001/anat/sub-001_inplaneT2.nii' >>> build_path(entities, patterns, strict=True) 'sub-001/anat/sub-001_space-MNI_inplaneT2.nii' >>> entities['space'] = None >>> build_path(entities, patterns, strict=True) 'sub-001/anat/sub-001_inplaneT2.nii' >>> # If some entity is set to None, they are dropped >>> entities['extension'] = None >>> build_path(entities, patterns, strict=True) 'sub-001/anat/sub-001_inplaneT2.nii.gz' >>> # If some entity is set to empty-string, they are dropped >>> entities['extension'] = '' >>> build_path(entities, patterns, strict=True) 'sub-001/anat/sub-001_inplaneT2.nii.gz' >>> # If some selector is not in the pattern, skip it... >>> entities['datatype'] = 'anat' >>> build_path(entities, patterns) 'sub-001/anat/sub-001_inplaneT2.nii.gz' >>> # ... unless the pattern should be strictly matched >>> entities['datatype'] = 'anat' >>> build_path(entities, patterns, strict=True) is None True >>> # If the value of an entity is not valid, do not match the pattern >>> entities['suffix'] = 'bold' >>> build_path(entities, patterns) is None True >>> entities = { ... 'extension': '.bvec', ... 'subject': '001', ... } >>> patterns = ( ... 'sub-{subject}[/ses-{session}]/{datatype|dwi}/sub-{subject}[_ses-{session}]' ... '[_acq-{acquisition}]_{suffix|dwi}{extension<.bval|.bvec|.json|.nii.gz|.nii>|.nii.gz}' ... ) >>> build_path(entities, patterns, strict=True) 'sub-001/dwi/sub-001_dwi.bvec' >>> # Lists of entities are expanded >>> entities = { ... 'extension': '.bvec', ... 'subject': ['%02d' % i for i in range(1, 4)], ... } >>> build_path(entities, patterns, strict=True) ['sub-01/dwi/sub-01_dwi.bvec', 'sub-02/dwi/sub-02_dwi.bvec', 'sub-03/dwi/sub-03_dwi.bvec'] """ path_patterns = listify(path_patterns) # Drop None and empty-strings, keep zeros, and listify entities = {k: listify(v) for k, v in entities.items() if v or v == 0} # Loop over available patherns, return first one that matches all for pattern in path_patterns: entities_matched = list(_PATTERN_FIND.findall(pattern)) defined = [e[1] for e in entities_matched] # If strict, all entities must be contained in the pattern if strict: if set(entities.keys()) - set(defined): continue # Iterate through the provided path patterns new_path = pattern # Expand options within valid values and # check whether entities provided have acceptable value tmp_entities = entities.copy() # Do not modify the original query # Accept extensions with and without leading dot if 'extension' in tmp_entities: exts = [e.lstrip('.') for e in tmp_entities['extension']] # Does this pattern place a dot before the extension, or expect it inside? if re.search(r'\.\{extension', pattern): tmp_entities['extension'] = exts else: tmp_entities['extension'] = ['.' + e for e in exts] for fmt, name, valid, defval in entities_matched: valid_expanded = [v for val in valid.split('|') if val for v in _expand_options(val)] if valid_expanded and defval and defval not in valid_expanded: warnings.warn( f'Pattern {fmt!r} is inconsistent as it defines an invalid default value.', stacklevel=2, ) if ( valid_expanded and name in tmp_entities and set(tmp_entities[name]) - set(valid_expanded) ): continue if defval and name not in tmp_entities: tmp_entities[name] = [defval] # At this point, valid & default values are checked & set - simplify pattern new_path = new_path.replace(fmt, '{%s}' % name) # noqa: UP031 optional_patterns = re.findall(r'(\[.*?\])', new_path) # Optional patterns with selector are cast to mandatory or removed for op in optional_patterns: for ent_name in {k for k, v in entities.items() if v is not None}: if ('{%s}' % ent_name) in op: # noqa: UP031 new_path = new_path.replace(op, op[1:-1]) continue # Surviving optional patterns are removed new_path = new_path.replace(op, '') # Replace entities fields = {pat[1] for pat in Formatter().parse(new_path) if pat[1] and not pat[1].isdigit()} if fields - set(tmp_entities.keys()): continue tmp_entities = {k: v for k, v in tmp_entities.items() if k in fields} new_path = [new_path.format(**e) for e in _expand_entities(tmp_entities)] if new_path: if len(new_path) == 1: new_path = new_path[0] return new_path return None def write_to_file( path, contents=None, link_to=None, copy_from=None, content_mode='text', root=None, conflicts='fail', ): """Writes provided contents to a new path, or copies from an old path. Parameters ---------- path : str Destination path of the desired contents. contents : str Raw text or binary encoded string of contents to write to the new path. link_to : str Optional path with which to create a symbolic link to. Used as an alternative to, and takes priority over, the contents argument. copy_from : str Optional filename to copy to new location. Used an alternative to, and takes priority over, the contents argument. content_mode : {'text', 'binary'} Either 'text' or 'binary' to indicate the writing mode for the new file. Only relevant if contents is provided. root : str Optional root directory that all patterns are relative to. Defaults to current working directory. conflicts : {'fail', 'skip', 'overwrite', 'append'} One of 'fail', 'skip', 'overwrite', or 'append' that defines the desired action when the output path already exists. 'fail' raises an exception; 'skip' does nothing; 'overwrite' overwrites the existing file; 'append' adds a suffix to each file copy, starting with 1. Default is 'fail'. """ path = Path(path) if root is None and not path.is_absolute(): root = Path.cwd() if root: path = root / path if path.exists() or path.is_symlink(): if conflicts == 'fail': msg = 'A file at path {} already exists.' raise ValueError(msg.format(path)) elif conflicts == 'skip': msg = 'A file at path {} already exists, skipping writing file.' warnings.warn(msg.format(path)) # noqa: B028 return elif conflicts == 'overwrite': if path.is_dir(): warnings.warn( # noqa: B028 'New path is a directory, not going to overwrite it, skipping instead.' ) return path.unlink() elif conflicts == 'append': i = 1 while i < sys.maxsize: suffixes = ''.join(path.suffixes) appended_filename = path.with_name( path.name.rstrip(suffixes) + '_%d' % i + suffixes # noqa: UP031 ) if not appended_filename.exists() and not appended_filename.is_symlink(): path = appended_filename break i += 1 else: raise ValueError('Did not provide a valid conflicts parameter') if not path.parent.exists(): path.parent.mkdir(parents=True) if link_to is not None: path.symlink_to(link_to) elif copy_from is not None: if not Path(copy_from).exists(): raise ValueError(f"Source file '{copy_from}' does not exist.") shutil.copy(copy_from, path) elif contents: mode = 'wb' if content_mode == 'binary' else 'w' with path.open(mode) as f: f.write(contents) else: raise ValueError('One of contents, copy_from or link_to must be provided.') def _expand_options(value): """Expand optional substrings of valid entity values. Examples -------- >>> _expand_options('[Jj]son[12]') ['Json1', 'Json2', 'json1', 'json2'] >>> _expand_options('json') ['json'] """ expand_patterns = re.findall(r'\[(.*?)\]', value) if not expand_patterns: return [value] value = re.sub(r'\[(.*?)\]', '%s', value) return [value % _r for _r in product(*expand_patterns)] def _expand_entities(entities): """Generate multiple replacement queries based on all combinations of values. Examples -------- >>> entities = {'subject': ['01', '02'], 'session': ['1', '2'], 'task': ['rest', 'finger']} >>> out = _expand_entities(entities) >>> len(out) 8 >>> {'subject': '01', 'session': '1', 'task': 'rest'} in out True >>> {'subject': '02', 'session': '1', 'task': 'rest'} in out True >>> {'subject': '01', 'session': '2', 'task': 'rest'} in out True >>> {'subject': '02', 'session': '2', 'task': 'rest'} in out True >>> {'subject': '01', 'session': '1', 'task': 'finger'} in out True >>> {'subject': '02', 'session': '1', 'task': 'finger'} in out True >>> {'subject': '01', 'session': '2', 'task': 'finger'} in out True >>> {'subject': '02', 'session': '2', 'task': 'finger'} in out True """ keys = list(entities.keys()) values = list(product(*[entities[k] for k in keys])) return [{k: v for k, v in zip(keys, combs)} for combs in values] # noqa: B905, C416 ================================================ FILE: src/bids/modeling/README.md ================================================ ## PyBIDS: Modeling The PyBIDS `modeling` module provides high-level model specification functionality for BIDS datasets. It assumes that model information is represented in line with the (upcoming) BIDS-Model specification. Note that, at present, pybids.modeling does *not* provide a one-stop model-fitting solution. You will need to call some other package (e.g., nilearn, or a non-Python package like FSL or SPM via Nipype) to handle model estimation. What pybids.modeling *will* do for you is automatically handle the loading and transformation of all variables, and the construction of design matrices and contrasts. ================================================ FILE: src/bids/modeling/__init__.py ================================================ from .auto_model import auto_model # noqa: D104 from .statsmodels import ( BIDSStatsModelsEdge, BIDSStatsModelsGraph, BIDSStatsModelsNode, BIDSStatsModelsNodeOutput, ) from .transformations import TransformerManager __all__ = [ 'BIDSStatsModelsGraph', 'BIDSStatsModelsNode', 'BIDSStatsModelsNodeOutput', 'BIDSStatsModelsEdge', 'auto_model', 'TransformerManager', ] ================================================ FILE: src/bids/modeling/auto_model.py ================================================ from collections import OrderedDict # noqa: D100 import numpy as np from bids.variables import load_variables def _make_passthrough_contrast(level, model_type='glm', test='t'): gb_dict = dict( # noqa: C408 Subject=['subject', 'contrast'], Session=['session', 'contrast'], Dataset=['contrast'] ) block = dict( # noqa: C408 Level=level, Name=level, GroupBy=gb_dict[level], Model={'Type': model_type, 'X': [1]}, DummyContrasts={'Test': test}, ) return block def auto_model(layout, scan_length=None, one_vs_rest=False): """Create a simple default model for each of the tasks in a BIDSLayout. Contrasts each trial type against all other trial types and trial types at the run level and then uses dummy contrasts at each other level present to aggregate these results up. Parameters ---------- layout : :obj:`bids.layout.BIDSLayout` A BIDSLayout instance scan_length : int Scan length for loading event variables in cases where the scan length can not be read from the nifti. Primarily for testing. one_vs_rest : bool Set to True if you would like to autogenerate contrasts of each trial type against everyother trialtype. Returns ------- list list of model dictionaries for each task """ base_name = layout._root.name tasks = layout.entities['task'].unique() task_models = [] for task_name in tasks: # Populate model meta-data model = OrderedDict() model['Name'] = '_'.join([base_name, task_name]) model['Description'] = 'Autogenerated model for the %s task from %s' % ( # noqa: UP031 task_name, base_name, ) model['BIDSModelVersion'] = '1.0.0' model['Input'] = {'task': [task_name]} nodes = [] # Make run level block transformations = dict( # noqa: C408 Transformer='pybids-transforms-v1', Instructions=[dict(Name='Factor', Input='trial_type')], # noqa: C408 ) run = dict( # noqa: C408 Level='Run', Name='Run', GroupBy=['run', 'subject'], Transformations=transformations ) # Get trial types run_nodes = load_variables(layout, task=task_name, levels=['run'], scan_length=scan_length) evs = [] for n in run_nodes.nodes: evs.extend(n.variables['trial_type'].values.values) trial_types = np.unique(evs) trial_type_factors = ['trial_type.' + tt for tt in trial_types] run_model = dict(Type='glm', X=trial_type_factors) # noqa: C408 # Add HRF run_model['HRF'] = dict( # noqa: C408 Variables=trial_type_factors, Model='DoubleGamma', Parameters=dict( # noqa: C408 PeakDelay=3, PeakDispersion=6, UndershootDelay=10, UndershootDispersion=12, PeakUndershootRatio=0.2, ), ) run['Model'] = run_model if one_vs_rest: # If there are multiple trial types, build contrasts contrasts = [] for tt in trial_types: cdict = OrderedDict() if len(trial_types) > 1: cdict['Name'] = 'run_' + tt + '_vs_others' else: cdict['Name'] = 'run_' + tt cdict['ConditionList'] = trial_type_factors # Calculate weights for contrast weights = np.ones(len(trial_types)) try: weights[trial_types != tt] = -1.0 / (len(trial_types) - 1) except ZeroDivisionError: pass cdict['Weights'] = list(weights) cdict['Test'] = 't' contrasts.append(cdict) run['Contrasts'] = contrasts nodes.append(run) if one_vs_rest: # if there are multiple sessions/subjects, t-test run level contrasts at # session/subject level if len(layout.get_sessions()) > 1: nodes.append(_make_passthrough_contrast('Session', 'meta')) if len(layout.get_subjects()) > 1: nodes.append(_make_passthrough_contrast('Subject', 'meta')) nodes.append(_make_passthrough_contrast('Dataset')) model['Nodes'] = nodes task_models.append(model) return task_models ================================================ FILE: src/bids/modeling/hrf.py ================================================ # ruff: noqa: D404 """This module is for hemodynamic response function (hrf) specification. # noqa: D404 Here we provide for SPM, Glover hrfs and finite timpulse response (FIR) models. This module closely follows SPM implementation Author: Bertrand Thirion, 2011--2015 """ import warnings import numpy as np def _gamma_difference_hrf( tr, oversampling=50, time_length=32.0, onset=0.0, delay=6, undershoot=16.0, dispersion=1.0, u_dispersion=1.0, ratio=0.167, ): """Compute an hrf as the difference of two gamma functions Parameters ---------- tr : float scan repeat time, in seconds oversampling : int, optional (default=16) temporal oversampling factor time_length : float, optional (default=32) hrf kernel length, in seconds onset: float onset time of the hrf delay: float, optional delay parameter of the hrf (in s.) undershoot: float, optional undershoot parameter of the hrf (in s.) dispersion : float, optional dispersion parameter for the first gamma function u_dispersion : float, optional dispersion parameter for the second gamma function ratio : float, optional ratio of the two gamma components Returns ------- hrf : array of shape(length / tr * oversampling, dtype=float) hrf sampling on the oversampled time grid """ from scipy.stats import gamma dt = tr / oversampling time_stamps = np.linspace(0, time_length, np.rint(float(time_length) / dt).astype(int)) time_stamps -= onset hrf = gamma.pdf(time_stamps, delay / dispersion, dt / dispersion) - ratio * gamma.pdf( time_stamps, undershoot / u_dispersion, dt / u_dispersion ) hrf /= hrf.sum() return hrf def spm_hrf(tr, oversampling=50, time_length=32.0, onset=0.0): """Implementation of the SPM hrf model Parameters ---------- tr : float scan repeat time, in seconds oversampling : int, optional temporal oversampling factor time_length : float, optional hrf kernel length, in seconds onset : float, optional hrf onset time, in seconds Returns ------- hrf: array of shape(length / tr * oversampling, dtype=float) hrf sampling on the oversampled time grid """ return _gamma_difference_hrf(tr, oversampling, time_length, onset) def glover_hrf(tr, oversampling=50, time_length=32.0, onset=0.0): """Implementation of the Glover hrf model Parameters ---------- tr : float scan repeat time, in seconds oversampling : int, optional temporal oversampling factor time_length : float, optional hrf kernel length, in seconds onset : float, optional onset of the response Returns ------- hrf: array of shape(length / tr * oversampling, dtype=float) hrf sampling on the oversampled time grid """ return _gamma_difference_hrf( tr, oversampling, time_length, onset, delay=6, undershoot=12.0, dispersion=0.9, u_dispersion=0.9, ratio=0.35, ) def spm_time_derivative(tr, oversampling=50, time_length=32.0, onset=0.0): """Implementation of the SPM time derivative hrf (dhrf) model Parameters ---------- tr: float scan repeat time, in seconds oversampling: int, optional temporal oversampling factor, optional time_length: float, optional hrf kernel length, in seconds onset: float, optional onset of the response in seconds Returns ------- dhrf: array of shape(length / tr, dtype=float) dhrf sampling on the provided grid """ do = 0.1 dhrf = ( 1.0 / do * ( spm_hrf(tr, oversampling, time_length, onset) - spm_hrf(tr, oversampling, time_length, onset + do) ) ) return dhrf def glover_time_derivative(tr, oversampling=50, time_length=32.0, onset=0.0): """Implementation of the Glover time derivative hrf (dhrf) model Parameters ---------- tr: float scan repeat time, in seconds oversampling: int, temporal oversampling factor, optional time_length: float, hrf kernel length, in seconds onset: float, onset of the response Returns ------- dhrf: array of shape(length / tr), dtype=float dhrf sampling on the provided grid """ do = 0.1 dhrf = ( 1.0 / do * ( glover_hrf(tr, oversampling, time_length, onset) - glover_hrf(tr, oversampling, time_length, onset + do) ) ) return dhrf def spm_dispersion_derivative(tr, oversampling=50, time_length=32.0, onset=0.0): """Implementation of the SPM dispersion derivative hrf model Parameters ---------- tr: float scan repeat time, in seconds oversampling: int, optional temporal oversampling factor in seconds time_length: float, optional hrf kernel length, in seconds onset : float, optional onset of the response in seconds Returns ------- dhrf: array of shape(length / tr * oversampling), dtype=float dhrf sampling on the oversampled time grid """ dd = 0.01 dhrf = ( 1.0 / dd * ( -_gamma_difference_hrf(tr, oversampling, time_length, onset, dispersion=1.0 + dd) + _gamma_difference_hrf(tr, oversampling, time_length, onset) ) ) return dhrf def glover_dispersion_derivative(tr, oversampling=50, time_length=32.0, onset=0.0): """Implementation of the Glover dispersion derivative hrf model Parameters ---------- tr: float scan repeat time, in seconds oversampling: int, optional temporal oversampling factor in seconds time_length: float, optional hrf kernel length, in seconds onset : float, optional onset of the response in seconds Returns ------- dhrf: array of shape(length / tr * oversampling), dtype=float dhrf sampling on the oversampled time grid """ dd = 0.01 dhrf = ( 1.0 / dd * ( -_gamma_difference_hrf( tr, oversampling, time_length, onset, delay=6, undershoot=12.0, dispersion=0.9 + dd, ratio=0.35, ) + _gamma_difference_hrf( tr, oversampling, time_length, onset, delay=6, undershoot=12.0, dispersion=0.9, ratio=0.35, ) ) ) return dhrf def _sample_condition(exp_condition, frame_times, oversampling=50, min_onset=-24): """Make a possibly oversampled event regressor from condition information. Parameters ---------- exp_condition : arraylike of shape (3, n_events) yields description of events for this condition as a (onsets, durations, amplitudes) triplet frame_times : array of shape(n_scans) sample time points oversampling : int, optional factor for oversampling event regressor min_onset : float, optional minimal onset relative to frame_times[0] (in seconds) events that start before frame_times[0] + min_onset are not considered Returns ------- regressor: array of shape(over_sampling * n_scans) possibly oversampled event regressor hr_frame_times : array of shape(over_sampling * n_scans) time points used for regressor sampling """ # Find the high-resolution frame_times n = frame_times.size min_onset = float(min_onset) n_hr = ( (n - 1) * 1.0 / (frame_times.max() - frame_times.min()) * (frame_times.max() * (1 + 1.0 / (n - 1)) - frame_times.min() - min_onset) * oversampling ) + 1 hr_frame_times = np.linspace( frame_times.min() + min_onset, frame_times.max() * (1 + 1.0 / (n - 1)), np.rint(n_hr).astype(int), ) # Get the condition information onsets, durations, values = tuple(map(np.asanyarray, exp_condition)) if (onsets < frame_times[0] + min_onset).any(): warnings.warn( # noqa: B028 ( 'Some stimulus onsets are earlier than %s in the' ' experiment and are thus not considered in the model' % (frame_times[0] + min_onset) ), UserWarning, ) # Set up the regressor timecourse tmax = len(hr_frame_times) regressor = np.zeros_like(hr_frame_times).astype(float) t_onset = np.minimum(np.searchsorted(hr_frame_times, onsets), tmax - 1) for t, v in zip(t_onset, values): # noqa: B905 regressor[t] += v t_offset = np.minimum(np.searchsorted(hr_frame_times, onsets + durations), tmax - 1) # Handle the case where duration is 0 by offsetting at t + 1 for i, t in enumerate(t_offset): if t < (tmax - 1) and t == t_onset[i]: t_offset[i] += 1 for t, v in zip(t_offset, values): # noqa: B905 regressor[t] -= v regressor = np.cumsum(regressor) return regressor, hr_frame_times def _resample_regressor(hr_regressor, hr_frame_times, frame_times): """This function sub-samples the regressors at frame times # noqa: D404 Parameters ---------- hr_regressor : array of shape(n_samples), the regressor time course sampled at high temporal resolution hr_frame_times : array of shape(n_samples), the corresponding time stamps frame_times: array of shape(n_scans), the desired time stamps Returns ------- regressor: array of shape(n_scans) the resampled regressor """ from scipy.interpolate import interp1d f = interp1d(hr_frame_times, hr_regressor) return f(frame_times).T def _orthogonalize(X): """Orthogonalize every column of design `X` w.r.t preceding columns Parameters ---------- X : array of shape(n, p) the data to be orthogonalized Returns ------- X : array of shape(n, p) the data after orthogonalization Notes ----- X is changed in place. The columns are not normalized. """ if X.size == X.shape[0]: return X from numpy.linalg import pinv for i in range(1, X.shape[1]): X[:, i] -= np.dot(np.dot(X[:, i], X[:, :i]), pinv(X[:, :i])) # X[:, i] /= norm(X[:, i]) return X def _regressor_names(con_name, hrf_model, fir_delays=None): """Return a list of regressor names, computed from con-name and hrf type Parameters ---------- con_name: string identifier of the condition hrf_model: string or None, hrf model chosen fir_delays: 1D array_like, optional, Delays used in case of an FIR model Returns ------- names: list of strings, regressor names """ if hrf_model in ['glover', 'spm', None]: return [con_name] elif hrf_model in ['glover + derivative', 'spm + derivative']: return [con_name, con_name + '_derivative'] elif hrf_model in ['spm + derivative + dispersion', 'glover + derivative + dispersion']: return [con_name, con_name + '_derivative', con_name + '_dispersion'] elif hrf_model == 'fir': return [con_name + '_delay_%d' % i for i in fir_delays] # noqa: UP031 def _hrf_kernel(hrf_model, tr, oversampling=50, fir_delays=None): """Given the specification of the hemodynamic model and time parameters, return the list of matching kernels Parameters ---------- hrf_model : string or None, identifier of the hrf model tr : float the repetition time in seconds oversampling : int, optional temporal oversampling factor to have a smooth hrf fir_delays : list of floats, list of delays for finite impulse response models Returns ------- hkernel : list of arrays samples of the hrf (the number depends on the hrf_model used) """ acceptable_hrfs = [ 'spm', 'spm + derivative', 'spm + derivative + dispersion', 'fir', 'glover', 'glover + derivative', 'glover + derivative + dispersion', None, ] if hrf_model == 'spm': hkernel = [spm_hrf(tr, oversampling)] elif hrf_model == 'spm + derivative': hkernel = [spm_hrf(tr, oversampling), spm_time_derivative(tr, oversampling)] elif hrf_model == 'spm + derivative + dispersion': hkernel = [ spm_hrf(tr, oversampling), spm_time_derivative(tr, oversampling), spm_dispersion_derivative(tr, oversampling), ] elif hrf_model == 'glover': hkernel = [glover_hrf(tr, oversampling)] elif hrf_model == 'glover + derivative': hkernel = [glover_hrf(tr, oversampling), glover_time_derivative(tr, oversampling)] elif hrf_model == 'glover + derivative + dispersion': hkernel = [ glover_hrf(tr, oversampling), glover_time_derivative(tr, oversampling), glover_dispersion_derivative(tr, oversampling), ] elif hrf_model == 'fir': hkernel = [ np.hstack((np.zeros(f * oversampling), np.ones(oversampling))) for f in fir_delays ] elif hrf_model is None: hkernel = [np.hstack((1, np.zeros(oversampling - 1)))] else: raise ValueError(f'"{hrf_model}" is not a known hrf model. Use one of {acceptable_hrfs}') return hkernel def compute_regressor( exp_condition, hrf_model, frame_times, con_id='cond', oversampling=50, fir_delays=None, min_onset=-24, ): """Convolve regressors with hrf model (the main function of the module) Parameters ---------- exp_condition : array-like of shape (3, n_events) yields description of events for this condition as a (onsets, durations, amplitudes) triplet hrf_model : {'spm', 'spm + derivative', 'spm + derivative + dispersion', 'glover', 'glover + derivative', 'fir', None} Name of the hrf model to be used frame_times : array of shape (n_scans) the desired sampling times con_id : string optional identifier of the condition oversampling : int, optional oversampling factor to perform the convolution fir_delays : 1D-array-like, optional delays (in seconds) used in case of a finite impulse response model min_onset : float, optional minimal onset relative to frame_times[0] (in seconds) events that start before frame_times[0] + min_onset are not considered Returns ------- computed_regressors: array of shape(n_scans, n_reg) computed regressors sampled at frame times reg_names: list of strings corresponding regressor names Notes ----- The different hemodynamic models can be understood as follows: 'spm': this is the hrf model used in SPM 'spm + derivative': SPM model plus its time derivative (2 regressors) 'spm + time + dispersion': idem, plus dispersion derivative (3 regressors) 'glover': this one corresponds to the Glover hrf 'glover + derivative': the Glover hrf + time derivative (2 regressors) 'glover + derivative + dispersion': idem + dispersion derivative (3 regressors) 'fir': finite impulse response basis, a set of delayed dirac models with arbitrary length. This one currently assumes regularly spaced frame times (i.e. fixed time of repetition). It is expected that spm standard and Glover model would not yield large differences in most cases. In case of glover and spm models, the derived regressors are orthogonalized wrt the main one. """ # this is the average tr in this session, not necessarily the true tr tr = float(frame_times.max()) / (np.size(frame_times) - 1) # 1. create the high temporal resolution regressor hr_regressor, hr_frame_times = _sample_condition( exp_condition, frame_times, oversampling, min_onset ) # 2. create the hrf model(s) hkernel = _hrf_kernel(hrf_model, tr, oversampling, fir_delays) # 3. convolve the regressor and hrf, and downsample the regressor conv_reg = np.array([np.convolve(hr_regressor, h)[: hr_regressor.size] for h in hkernel]) # 4. temporally resample the regressors computed_regressors = _resample_regressor(conv_reg, hr_frame_times, frame_times) # 5. orthogonalize the regressors if hrf_model != 'fir': computed_regressors = _orthogonalize(computed_regressors) # 6 generate regressor names reg_names = _regressor_names(con_id, hrf_model, fir_delays=fir_delays) return computed_regressors, reg_names ================================================ FILE: src/bids/modeling/model_spec.py ================================================ from abc import ABCMeta, abstractmethod # noqa: D100 import numpy as np import pandas as pd from formulaic import model_matrix from bids.utils import convert_JSON class ModelSpec(metaclass=ABCMeta): """Base class for all ModelSpec classes.""" @abstractmethod def from_df(self, df, model, metadata=None): """Initialize from a pandas DataFrame.""" pass class GLMMSpec(ModelSpec): """Generalized Linear Mixed Model specification. s Parameters ---------- terms : list of Term A list of Term instances to include in the GLMMSpec instance. X: pd.DataFrame A pandas DataFrame containing the fixed effect design matrix (i.e., the X matrix in the typical mixed effect formulation). Each column will be internally converted to a separate Term instance. Z: pd.DataFrame A pandas DataFrame containing the random effect/grouping matrix (i.e., the Z matrix in the typical mixed effect formulation). Columns that share variance components are identified by the groups argument. groups: NDArray A binary 2d array with dimension k x v, where k is the number of columns in Z and v is the number of distinct variance components. A value of 1 indicates that the i'th of k rows is a level in the j's of v variance components. If Z is passed and groups is None, it is assumed that all columns in Z share the same single variance. sigma: NDArray An optional 2d array giving the covariance matrix for the variance components defined in the groups argument. Has dimension v x v, where v is the number of columns in groups. family: str The name of the family to use for the error distribution. By default, gaussian. link: str The name of the link function to use. Default depends on family. In the case of a gaussian (default family), an identity link is used. priors: dict Optional specification of default priors to use for new terms. """ def __init__( self, terms=None, X=None, Z=None, groups=None, sigma=None, family=None, link=None, priors=None, ): self.terms = {} self.family = family self.link = link self.sigma = sigma if priors is not None: self.set_priors(priors) if terms is not None: for t in terms: self.add_term(t) if X is not None: self.build_fixed_terms(X) if Z is not None: self.build_variance_components(Z, groups, sigma) def __repr__(self): return f"<{self.__class__.__name__}{[term.name for term in self.fixed_terms]}'>" def set_priors(self, fixed=None, random=None): # noqa: D102 raise NotImplementedError("Custom prior use hasn't been implemented yet.") def build_fixed_terms(self, X): """Build one or more fixed terms from the columns of a pandas DF. Parameters ---------- X : pd.DataFrame A pandas DataFrame containing variables to convert to Term instances. Each column is converted to a different (fixed) Term, with the name taken from the column name. """ for col in X.columns: data = X.loc[:, col].values cat = data.dtype.name in ('str', 'category', 'object') # TODO: get default prior t = Term(col, data, categorical=cat) self.add_term(t) def build_variance_components(self, Z, groups=None, sigma=None, names=None): """Build one or more variance components from the columns of a binary grouping matrix and variance specification. Arguments: Z (DataFrame, NDArray): A binary 2D array or pandas DataFrame. Each column represents a column/predictor, each row represents an observation. groups (2DArray): A 2D binary array that maps the columns of Z onto variance components. Has dimension n_rows(Z) x k, where k is the number of distinct variance components. If None, a single group over all columns of Z is assumed. sigma (2DArray): A k x k 2D covariance matrix specifying the covariances between variance components. Currently unused. names (list): Optional list specifying the names of the groups. """ if sigma is not None: raise NotImplementedError('Covariance specification is currently not supported.') if groups is None: groups = np.ones((Z.shape[1], 1)) n_grps = groups.shape[1] if names is None: names = getattr(groups, 'columns', [f'VC{i}' for i in range(n_grps)]) # Work with array instead of DF if hasattr(groups, 'values'): groups = groups.values for i in range(n_grps): z_grp = Z[:, groups[:, i].astype(bool)] # TODO: select default prior vc = VarComp(names[i], z_grp) self.add_term(vc) def add_term(self, term): """Add a new Term to the instance. Parameters ---------- term : Term A Term instance to add to the current instance. """ if term.name in self.terms: raise ValueError(f'Term with name {term.name} already exists!') self.terms[term.name] = term @property def X(self): """Return X design matrix (i.e., fixed component of model).""" if not self.fixed_terms: return None names, cols = zip(*[(c.name, c.values) for c in self.fixed_terms]) # noqa: B905 return pd.DataFrame(np.c_[cols], columns=names) @property def Z(self): """Return Z design matrix (i.e., random effects/variance components).""" if not self.variance_components: return None names, cols = [], [] for c in self.variance_components: cols.append(c.values) names.extend([f'{c.name}.{i}' for i in range(c.values.shape[1])]) return pd.DataFrame(np.concatenate(cols, axis=1), columns=names) @property def fixed_terms(self): """Return a list of all available fixed effects.""" return [t for t in self.terms.values() if not isinstance(t, VarComp)] @property def variance_components(self): """Return a list of all available variance components.""" return [t for t in self.terms.values() if isinstance(t, VarComp)] @classmethod def from_df(cls, df, model, metadata=None, formula=None): """Initialize a GLMMSpec instance from a BIDSVariableCollection and a BIDS-StatsModels JSON spec. Parameters ---------- df : DataFrame A pandas DataFrame containing predictor information (i.e., the fixed component of the design matrix). model : dict The "Model" section from a BIDS-StatsModel specification. metadata: DataFrame Optional DataFrame containing additional columns that are not part of the design matrix but may have downstream informational use and/or contain variables needed to define random effects. Rows must map 1-to-1 with those in `df`. formula: str Optional Wilkinson (R-style) formula specifying the fixed (X) part of the design matrix. All variables referenced in the formula must be present as columns in `df`. Output names will follow the conventions specified in the `formulaic` documentation. Note that only the right-hand part of the formula should be passed (i.e., pass "X1 * X2", not "y ~ X1 * X2"). If provided, will take precedence over any formula found in the `model`. Returns ------- A GLMMSpec instance. """ kwargs = {} # Fixed terms model = convert_JSON(model) formula = formula or model.get('formula') if formula is not None: df = model_matrix(formula, df) kwargs['X'] = df # Variance components vcs = model.get('variance_components', []) Z_list = [] if vcs: # VCs can be defined by variables in either the fixed predictor # DF or the supplementary metadata DF, so concatenate them. all_vars = [df, metadata] if metadata is not None else [df] all_vars = pd.concat(all_vars, axis=1) for vc in vcs: # Levels can either be defined by the levels of a single # categorical ("LevelsFrom") or by a set of binary variables. if 'levels_from' in vc: data = all_vars[vc['levels_from']].values Z_list.append(pd.get_dummies(data).values) else: df = all_vars.loc[:, vc['levels']] Z_list.append(df.values) Z = np.concatenate(Z_list, axis=1) groups = np.zeros((Z.shape[1], len(Z_list))) c = 0 for i, vc in enumerate(Z_list): n = vc.shape[1] groups[c : (c + n), i] = 1 c += n groups = pd.DataFrame(groups, columns=[vc['name'] for vc in vcs]) kwargs['Z'] = Z kwargs['groups'] = groups error = model.get('error') if error: kwargs['family'] = error.get('family') kwargs['link'] = error.get('link') return GLMMSpec(**kwargs) class MetaAnalysisSpec(GLMMSpec): # noqa: D101 pass class Term: """Represents a model term. Parameters ---------- name : str The name of the term. values : iterable A 1d array or other iterable containing the predictor values. categorical : bool Indicates whether or not the Term represents a categorical variable. prior : dict Optional specification of the prior distribution for the Term. metadata : dict Arbitrary metadata to store internally. """ def __init__(self, name, values, categorical=False, prior=None, metadata=None): self.name = name self.values = values self.categorical = categorical self.prior = prior self.metadata = metadata or {} class VarComp(Term): """Represents a variance component/random effect. Parameters ---------- name : str The name of the variance component. values : iterable A 2d binary array identifying the observations that belong to the levels of the variance component. Has dimension n x k, where n is the number of observed rows in the dataset and k is the number of levels in the factor. prior : dict Optional specification of the prior distribution for the VarComp. metadata : dict Arbitrary metadata to store internally. """ def __init__(self, name, values, prior=None, metadata=None): super().__init__(name, values, categorical=True, prior=prior, metadata=metadata) self.index_vec = self.dummies_to_vec(values) @staticmethod def dummies_to_vec(dummies): """Convert dummy-coded columns to a single integer index. Parameters ---------- dummies : NDArray 2d binary array to recode as a single vector. Notes ----- Used for the sake of computational efficiency (i.e., to avoid lots of large matrix multiplications in the backends), invert the dummy-coding process and represent full-rank dummies as a vector of indices into the coefficients. """ vec = np.zeros(len(dummies), dtype=int) for i in range(dummies.shape[1]): vec[(dummies[:, i] == 1)] = i + 1 return vec class Prior: """Abstract specification of a term prior. Parameters ---------- name : str Name of prior distribution (e.g., Normal, Bernoulli, etc.) kwargs: dict Optional keywords specifying the parameters of the named distribution. Notes ----- At present there's no controlled vocabulary of supported prior names and arguments, but users implementing new Bayesian estimators are encouraged to use the names used in PyMC3 (e.g., 'Normal', parameterized with mu and sd arguments). """ def __init__(self, name, **kwargs): self.name = name self.kwargs = kwargs ================================================ FILE: src/bids/modeling/report/__init__.py ================================================ ================================================ FILE: src/bids/modeling/report/base.py ================================================ import datetime # noqa: D100 import os from pathlib import Path from bids import __version__ as bids_version from bids.layout import BIDSLayout from bids.modeling import BIDSStatsModelsGraph from .utils import deroot, displayify, generate_contrast_matrix, snake_to_camel, to_alphanum PATH_PATTERNS = [ 'reports/[sub-{subject}/][ses-{session}/][level-{level}_][sub-{subject}_][ses-{session}_]' '[run-{run}_]model-{model}.html' ] def _build_node_dict(node, all_entities): report_node = { 'name': node.name, 'analyses': [], 'group_by': node.group_by, 'model': node.model, 'level': node.level, 'transformations': node.transformations, } for out in node.outputs_: analysis_dict = {'entities': {}, 'contrasts': []} for contrast_info in out.contrasts: cents = contrast_info.entities.copy() cents['level'] = out.node.level cents['name'] = out.node.name for key in ('datatype', 'desc', 'suffix', 'extension'): cents.pop(key, None) for key in all_entities: cents.pop(key, None) for k, v in cents.items(): if k in ('name', 'contrast'): cents.update({k: to_alphanum(str(v))}) analysis_dict['entities'] = { key: val for key, val in cents.items() if key in ('subject', 'session', 'task', 'run') and val } analysis_dict['contrasts'].append( { 'name': displayify(contrast_info.name), } ) ents = out.entities.copy() # Space doesn't apply to design/contrast matrices, or resolution for k in ['space', 'res']: ents.pop(k, None) analysis_dict['X'] = out.X # If reports were run-level if out.report_ is not None: analysis_dict['design_matrix_plot'] = out.report_['design_matrix_plot'].to_json() analysis_dict['design_matrix_corrplot'] = out.report_[ 'design_matrix_corrplot' ].to_json() analysis_dict['VIF'] = out.report_['VIF'] if hasattr(out, 'trans_hist'): analysis_dict['trans_hist'] = out.trans_hist analysis_dict['contrast_matrix'] = generate_contrast_matrix(out.contrasts, out.X.columns) report_node['analyses'].append(analysis_dict) return report_node def _build_report_dict(graph): report = { 'dataset': { 'name': graph.layout.description['Name'], }, 'model': graph.model, 'nodes': [], 'version': bids_version, 'timestamp': datetime.datetime.now(), # noqa: DTZ005 'graph_plot': graph.write_graph(format='svg', pipe=True), } if 'DatasetDOI' in graph.layout.description: report['dataset']['doi'] = graph.layout.description['DatasetDOI'] all_entities = graph.layout.get_entities(metadata=True) for name, node in graph.nodes.items(): # noqa: B007 report['nodes'].append(_build_node_dict(node, all_entities)) # Get subjects hackily report['subjects'] = sorted( {analysis_dict['entities']['subject'] for analysis_dict in report['nodes'][0]['analyses']} ) return report def _write_report(report_dict, out_dir, template_path=None): try: import jinja2 except ImportError: raise ImportError( # noqa: B904 'Jinja2 must be installed to generate reports. ' 'You can install it with pip install jinja2.' ) if template_path is None: loader = jinja2.PackageLoader('bids', 'modeling/report') template_file = 'report_template.jinja' else: loader = jinja2.FileSystemLoader(os.path.dirname(template_path)) template_file = os.path.basename(template_path) tpl = jinja2.Environment(loader=loader).get_template(template_file) # noqa: S701 model = snake_to_camel(report_dict['model']['name']) out_path = Path(out_dir) out_path.mkdir(parents=True, exist_ok=True) report_dict = deroot(report_dict, str(out_path)) html = tpl.render(report_dict) Path.write_text(out_path / f'{model}_report.html', html) def generate_report(model, dataset_path, derivatives, output_dir, scan_length=None, **entities): """Generate a report for a model. Parameters ---------- model : str Path to BIDS-StatsModels model. dataset_path : str Path to BIDS dataset. derivatives : str or list Path to BIDS derivatives. output_dir : str Path to output directory. scan_length : float (optional) Length of scan in seconds. If not provided, will be read from dataset (requires image files to be present). entities : dict (optional) A dictionary of BIDS entities to filter the layout on. """ # Initialize BIDSLayout layout = BIDSLayout(dataset_path, derivatives=derivatives) # Build BIDSStatsModelsGraph instance graph = BIDSStatsModelsGraph(layout, model) graph.load_collections(scan_length=scan_length, **entities) # Run entire graph graph.run_graph( scan_length=scan_length, entities=entities, transformation_history=True, node_reports=True ) report_dict = _build_report_dict(graph) _write_report(report_dict, output_dir) ================================================ FILE: src/bids/modeling/report/report_template.jinja ================================================ PyBIDS StatsModel Report

Stats Model Report

Dataset :
{{ dataset.name }}{% if dataset.doi %} (doi: {{ dataset.doi }} ){% endif %}
Model :
{{ model.name }}
Participants ({{ subjects|count }}) :
{{ subjects|join(', ') }}

Specification 
                        {{ model|tojson(indent=2) }}
Graph
{{ graph_plot }}

Node Reports

{% for node in nodes %} {{ node.name }} {% endfor %}
{% for node in nodes %} {% set node_loop = loop %} {% endfor %}

About

  • PyBIDS version: {{ version }}
  • Date processed: {{ timestamp }}
================================================ FILE: src/bids/modeling/report/utils.py ================================================ import re # noqa: D100 import warnings import numpy as np import pandas as pd from scipy.linalg import null_space def node_report(node_output): """Generate a report for a single node.""" # If only intercept model, don't run reports if node_output.node.level != 'run': return {} _report = { 'VIF': get_all_contrast_vif(node_output), } try: from .viz import plot_corr_matrix, plot_design_matrix except ImportError: warnings.warn( # noqa: B028 'altair failed to import, and is required for StatsModel report plots.', ImportWarning ) else: _report['design_matrix_plot'] = plot_design_matrix(node_output.X, timecourse=True) _report['design_matrix_corrplot'] = plot_corr_matrix(node_output.X) return _report def est_vif(desmat): # noqa: D417 """General variance inflation factor estimation. Calculates VIF for all regressors in the design matrix. Parameters ---------- desmat (DataFrame): design matrix. Intercept not required. Returns ------- vif_data (DataFrame): Variance inflation factor for each regressor in the design matrix generally goal is VIF<5 """ from statsmodels.stats.outliers_influence import variance_inflation_factor desmat_with_intercept = desmat.copy() desmat_with_intercept['intercept'] = 1 vif_data = pd.DataFrame() vif_data['regressor'] = desmat_with_intercept.columns.drop('intercept') vif_data['VIF'] = [ variance_inflation_factor(desmat_with_intercept.values, i) for i in range(len(desmat_with_intercept.columns)) if desmat_with_intercept.columns[i] != 'intercept' ] return vif_data def get_eff_reg_vif(desmat, contrast_def): # noqa: D417 """The goal of this function is to estimate a variance inflation factor for a contrast. This is done by extending the effective regressor definition from Smith et al (2007) Meaningful design and contrast estimability (NeuroImage). Regressors involved in the contrast estimate are rotated to span the same space as the original space consisting of the effective regressor and and an orthogonal basis. The rest of the regressors are unchanged. Parameters ---------- desmat (DataFrame): design matrix. Assumed to be a pandas dataframe with column headings which are used define the contrast of interest contrast_def (array or Series): a single contrast defined as a vector Returns ------- vif (float): a single VIF for the contrast of interest """ des_nuisance_regs = desmat[desmat.columns[contrast_def == 0]] des_contrast_regs = desmat[desmat.columns[contrast_def != 0]] con = np.atleast_2d(contrast_def[contrast_def != 0]) con2_t = null_space(con) con_t = np.transpose(con) x = des_contrast_regs.copy().values q = np.linalg.pinv(np.transpose(x) @ x) f1 = np.linalg.pinv(con @ q @ con_t) pc = con_t @ f1 @ con @ q con3_t = con2_t - pc @ con2_t f3 = np.linalg.pinv(np.transpose(con3_t) @ q @ con3_t) eff_reg = x @ q @ np.transpose(con) @ f1 eff_reg = pd.DataFrame(eff_reg, columns=[0]) other_reg = x @ q @ con3_t @ f3 other_reg_names = [f'orth_proj{val}' for val in range(other_reg.shape[1])] other_reg = pd.DataFrame(other_reg, columns=other_reg_names) des_for_vif = pd.concat([eff_reg, other_reg, des_nuisance_regs], axis=1) vif_dat = est_vif(des_for_vif) vif_output = vif_dat[vif_dat.regressor == 0].VIF.values[0] return vif_output def generate_contrast_matrix(contrasts, cols): # noqa: D417 """Generate a contrast matrix from a list of contrast definitions. Parameters ---------- contrasts : list of ContrastInfo objects cols : list of design matrix columns Returns ------- contrast_matrix : 2D array """ rows = [] ix = [] for con in contrasts: vec = cols.map(dict(zip(con.conditions, con.weights))).fillna(0) # noqa: B905 rows.append(vec.tolist()) ix.append(con.name) df = pd.DataFrame(rows, columns=cols.tolist(), index=ix) return df def get_all_contrast_vif(node_output): # noqa: D417 """Calculates the VIF for multiple contrasts Parameters ---------- node_output (BIDSStatsModelNodeOutput): Node output to compute VIF for. Returns ------- vif_contrasts (DataFrame): Data frame containing the VIFs for all contrasts. """ vif_contrasts = {'contrast': [], 'VIF': []} con_matrix = generate_contrast_matrix(node_output.contrasts, node_output.X.columns) for name, weights in con_matrix.iterrows(): # Transform weights to vector matching X's columns vif_out = get_eff_reg_vif(node_output.X, weights) vif_contrasts['contrast'].append(name) vif_contrasts['VIF'].append(vif_out) vif_contrasts = pd.DataFrame(vif_contrasts) return vif_contrasts def deroot(val, root): # noqa: D103 if isinstance(val, str): if val.startswith(root): idx = len(root) if val[idx] == '/': idx += 1 val = val[idx:] elif isinstance(val, list): val = [deroot(elem, root) for elem in val] elif isinstance(val, dict): val = {key: deroot(value, root) for key, value in val.items()} return val def snake_to_camel(string): # noqa: D103 words = string.split('_') return words[0] + ''.join(word.title() for word in words[1:]) def displayify(contrast_name): # noqa: D103 for match, repl in (('_gt_', ' > '), ('_lt_', ' < '), ('_vs_', ' vs. ')): contrast_name = contrast_name.replace(match, repl) return contrast_name def to_alphanum(string): """Convert string to alphanumeric Replaces all other characters with underscores and then converts to camelCase Examples -------- >>> to_alphanum('abc123') 'abc123' >>> to_alphanum('a four word phrase') 'aFourWordPhrase' >>> to_alphanum('hyphen-separated') 'hyphenSeparated' >>> to_alphanum('object.attribute') 'objectAttribute' >>> to_alphanum('array[index]') 'arrayIndex' >>> to_alphanum('array[0]') 'array0' >>> to_alphanum('snake_case') 'snakeCase' """ return snake_to_camel(re.sub('[^a-zA-Z0-9]', '_', string)) ================================================ FILE: src/bids/modeling/report/viz.py ================================================ import altair as alt # noqa: D100 import numpy as np def plot_corr_matrix(dm_wide): # noqa: D103 dm_corr = dm_wide.corr() dm_corr = dm_corr.where(np.triu(np.ones(dm_corr.shape)).astype(np.bool)).reset_index() dm_corr_long = dm_corr.melt( var_name='index_2', value_vars=dm_wide.columns, id_vars='index', value_name='r' ) corr_mat = ( alt.Chart(dm_corr_long) .mark_rect() .encode( alt.X('index', sort=None, axis=alt.Axis(title=None)), alt.Y('index_2', sort=None, axis=alt.Axis(title=None)), tooltip=['r'], fill=alt.Color( 'r:Q', sort='descending', legend=alt.Legend(title='r'), scale=alt.Scale(scheme='redyellowblue', domain=[-1, 1]), ), ) .properties(width=375, height=350) .configure_scale(bandPaddingInner=0.015) .configure_view(strokeWidth=0) .interactive() ) return corr_mat alt.data_transformers.enable('default', max_rows=None) def melt_dm(dm): # noqa: D103 dm = dm.reset_index().rename(columns={'index': 'scan_number'}) return dm.melt('scan_number', var_name='regressor', value_name='value') def plot_design_matrix(dm_wide, scale=False, timecourse=True): # noqa: D103 if scale: dm_wide = (dm_wide - dm_wide.mean()) / (dm_wide.max() - dm_wide.min()) dm = melt_dm(dm_wide) time_labels = list(range(0, dm.scan_number.max(), 50)) base_color = alt.Color( 'value:Q', sort='ascending', legend=None, scale=alt.Scale(scheme='viridis') ) heat = ( alt.Chart(dm) .mark_rect() .encode( alt.Y( 'scan_number:O', axis=alt.Axis(title='Time (TRs)', ticks=False, values=time_labels, labels=True), ), alt.X( 'regressor:N', sort=None, axis=alt.Axis(labelAngle=-45, title=None, ticks=False) ), fill=base_color, stroke=base_color, ) .properties( width=750, height=450, ) ) selection = alt.selection_multi(fields=['regressor']) color = alt.Color('regressor:N', legend=None) if timecourse: line = ( alt.Chart(dm, title='Timecourse (shift-click legend to select)') .mark_line(clip=True) .encode( alt.X( 'scan_number', axis=alt.Axis(title='Time (TRs)', values=time_labels, ticks=False), scale=alt.Scale(domain=(0, int(dm.scan_number.max()))), ), y=alt.Y('value:Q', axis=alt.Axis(title='Amplitude (scaled for visualization)')), color=color, ) .transform_filter(selection) .properties(width=650, height=225) ) legend = ( alt.Chart(dm) .mark_square(size=200) .encode(y=alt.Y('regressor:N', axis=alt.Axis(orient='right')), color=color) .add_selection(selection) ) resolve = alt.Resolve(scale=alt.LegendResolveMap(color=alt.ResolveMode('independent'))) if timecourse: plt = alt.vconcat(heat, line | legend, resolve=resolve) else: plt = alt.vconcat(heat, legend, resolve=resolve) return plt.configure_scale(bandPaddingInner=0.0) ================================================ FILE: src/bids/modeling/statsmodels.py ================================================ """BIDS-StatsModels functionality.""" import fnmatch import itertools import json import re import warnings from collections import Counter, OrderedDict, defaultdict, namedtuple from functools import reduce import numpy as np import pandas as pd from bids.layout import BIDSLayout from bids.modeling import transformations as tm from bids.utils import convert_JSON, listify, matches_entities from bids.variables import BIDSVariableCollection, merge_collections from .model_spec import GLMMSpec, MetaAnalysisSpec from .report.utils import node_report, snake_to_camel def validate_model(model, *, stacklevel=2): # noqa: D417 """Validate a BIDS-StatsModel structure. Parameters ---------- model : dict A full BIDS-StatsModels specification document loaded from JSON. Returns ------- True if the model passes validation. Raises an exception otherwise. """ # Identify non-unique names names = Counter([n['name'] for n in model['nodes']]) duplicates = [n for n, count in names.items() if count > 1] if duplicates: raise ValueError( f"Non-unique node names found: '{duplicates}'. Please ensure" ' all nodes in the model have unique names.' ) def capitalize(obj): if isinstance(obj, list): return [capitalize(i) for i in obj] if isinstance(obj, dict): return {snake_to_camel(k).capitalize(): capitalize(v) for k, v in obj.items()} return obj if 'edges' in model: for edge in model['edges']: if edge['source'] not in names: raise ValueError("Missing source node: '{}'".format(edge['source'])) if edge['destination'] not in names: raise ValueError("Missing destination node: '{}'".format(edge['destination'])) # XXX: May 2021: Helping old models to work. This shouldn't last more than 2 years. for node in model['nodes']: messages = [] if 'type' in node.get('dummy_contrasts', {}): node['dummy_contrasts']['test'] = node['dummy_contrasts'].pop('type') messages.append('"DummyContrasts": Contrast "Type" is now "Test".') for contrast in node.get('contrasts', []): if 'type' in contrast: contrast['test'] = contrast.pop('type') messages.append('Contrast {contrast["name"]}]: Contrast "Type" is now "Test".') if isinstance(node.get('transformations'), list): transformations = { 'transformer': 'pybids-transforms-v1', 'instructions': node['transformations'], } node['transformations'] = transformations messages.append( 'Transformations are now a dictionary with "Transformer" and "Instructions" keys.' ) if messages: new_text = json.dumps(capitalize(node), indent=2) notes = '\n '.join(messages) warnings.warn( f'[Node "{node["name"]}"] notes:\n {notes}\n' f'[Node "{node["name"]}"] reformatted:\n{new_text}\n', stacklevel=stacklevel, ) return True BIDSStatsModelsEdge = namedtuple('BIDSStatsModelsEdge', ('source', 'destination', 'filter')) ContrastInfo = namedtuple('ContrastInfo', ('name', 'conditions', 'weights', 'test', 'entities')) class BIDSStatsModelsGraph: """Rooted graph structure representing the contents of a BIDS-StatsModel file. Parameters ---------- layout : :obj:`bids.layout.BIDSLayout` or str A BIDSLayout instance or path to pass on to the BIDSLayout initializer. model : str or dict A BIDS model specification. Can either be a string giving the path of the JSON model spec, or an already-loaded dict containing the model info. """ def __init__(self, layout, model): if not isinstance(layout, BIDSLayout): layout = BIDSLayout(layout) self.layout = layout self.model = self._load_model(model) self.nodes = self._load_nodes(self.model) self.edges = self._load_edges(self.model, self.nodes) self._root_node = self.model.get('root', list(self.nodes.values())[0]) def __repr__(self): return f"<{self.__class__.__name__}[{{name='{self.model['name']}', description='{self.model['description']}', ... }}]>" # noqa: E501 def __getitem__(self, key): """Alias for get_node(key).""" return self.get_node(key) @property def root_node(self): """Returns the graph's root node.""" return self._root_node @staticmethod def _load_model(model, validate=True): # Load model info from JSON and do some validation if isinstance(model, str): with open(model, encoding='utf-8') as fobj: model = json.load(fobj) # Convert JSON from CamelCase to snake_case keys model = convert_JSON(model) if validate: validate_model(model, stacklevel=4) # Warn the caller of __init__ return model @staticmethod def _load_nodes(model): nodes = OrderedDict() for node_args in model['nodes']: node = BIDSStatsModelsNode(**node_args) nodes[node.name] = node return nodes @staticmethod def _load_edges(model, nodes): """Build edges between nodes.""" edges = model.get('edges', []) # assume we have an optional top-level 'Pipeline' field if not edges or model.get('pipeline', False): node_vals = list(nodes.values()) for i in range(1, len(node_vals)): edges.append( { 'source': node_vals[i - 1].name, 'destination': node_vals[i].name, } ) for edge in edges: src_node, dst_node = nodes[edge['source']], nodes[edge['destination']] edge = BIDSStatsModelsEdge(src_node, dst_node, edge.get('filter', {})) src_node.add_child(edge) dst_node.add_parent(edge) return edges def get_node(self, name): """Return the named node. Parameters ---------- name : str The name of the node to retrieve (as defined in the BIDS-StatsModel document). Returns ------- A BIDSStatsModelsNode instance. """ if name not in self.nodes: raise KeyError(f'There is no node with the name "{name}".') return self.nodes[name] def load_collections(self, nodes=None, drop_na=False, **kwargs): # noqa: D417 """Load collections in all nodes. Parameters ---------- nodes : list Optional list of nodes to set up. Each element must be a string giving the (unique) name of the node, Nodes that do not match will be silently skipped. drop_na : bool Boolean indicating whether or not to automatically drop events that have a n/a amplitude when reading in data from event files. """ # Use inputs from BIDS-StatsModel document, and update with kwargs selectors = self.model.get('input', {}).copy() selectors.update(kwargs) for node in self.nodes.values(): # Skip any nodes whose names don't match list if nodes is not None and node.name not in nodes: continue # Currently allowing scan_length to be passed as a hack; remove for # levels above run. node_kwargs = selectors.copy() if node.level != 'run': node_kwargs.pop('scan_length', None) collections = self.layout.get_collections(node.level, drop_na=drop_na, **node_kwargs) node.add_collections(collections) def write_graph(self, dotfilename='graph.dot', format='png', pipe=False): # noqa: A002, D417 """Generates a graphviz dot file and a png file Parameters ---------- format: 'png', 'svg' """ from graphviz import Digraph dot = Digraph( 'structs', filename=dotfilename, node_attr={'shape': 'record'}, comment=self.model['name'], format=format, ) for node, nobj in self.nodes.items(): dot.node(node, f' name: {nobj.name}| level: {nobj.level}') for edge in self.edges: dot.edge(edge['source'], edge['destination']) if pipe: dot = dot.pipe(encoding='utf-8') else: dot.render() return dot def run_graph(self, entities=None, **kwargs): """Run the entire graph recursively. Parameters ---------- entities : dict (optional) Optional dictionary of BIDS entities to use when loading collections. kwargs : dict Optional dictionary of keyword arguments to pass to BIDSStatsModelsNode.run() """ if entities is None: entities = {} _run_node_recursive(self.root_node, filters=entities, **kwargs) def _run_node_recursive(node, inputs=None, filters=None, **kwargs): """Run a node recursively, storing outputs in place as BIDSStatsModelsNode.outputs_. """ if filters == None: # noqa: E711 filters = {} # Run node run_kwargs = {**filters, **kwargs} node.outputs_ = node.run(inputs, group_by=node.group_by, **run_kwargs) # Inputs to next node contrasts = list(itertools.chain(*[s.contrasts for s in node.outputs_])) for edge in node.children: _run_node_recursive(edge.destination, contrasts, filters=edge.filter, **kwargs) class BIDSStatsModelsNode: """Represents a single node in a BIDS-StatsModel graph. Parameters ---------- level : str The BIDS keyword to use as the grouping variable; must be one of ['run', 'session', 'subject', or 'dataset']. name : str Name to assign to the node. model : dict The 'model' part of the BIDS-StatsModels specification. transformations : dict Optional dictionary specifying transformations to apply. Dictionary must include "transformer" and "instructions" keys. "transformer" indicates the specification to follow. "instructions" is a list of instructions matching that specification. contrasts : list List of contrasts to apply to the parameter estimates generated when the model is fit. dummy_contrasts : dict Optional dictionary specifying which conditions to create indicator contrasts for. Dictionary may include a "test" key ('t'), and optionally a subset of "conditions". group_by: [str] Optional list of strings giving the names of entities that define the grouping structure for all variables. The current node will be executed separately for each unique combination of levels specified in group_by. For example, if group_by=['contrast', 'subject'], and there are 2 contrasts and 3 subjects, then there will be 6 separate iterations, and the returned list will have 6 elements. Any value passed here will be overridden if one is passed when run() is called on a node. """ def __init__( self, level, name, model, group_by, transformations=None, contrasts=None, dummy_contrasts=False, ): self.level = level.lower() self.name = name self.model = model if transformations is None: transformations = {'transformer': 'pybids-transforms-v1', 'instructions': []} self.transformations = transformations self.contrasts = contrasts or [] self.dummy_contrasts = dummy_contrasts self._collections = [] self._group_data = [] self.children = [] self.parents = [] if group_by is None: raise ValueError(f'group_by is not defined for Node: {name}') self.group_by = group_by # Check for intercept only run level model and throw an error try: if (self.level == 'run') and (self.model['x'] == [1]): raise NotImplementedError( 'Run level intercept only models are not currently supported.' 'If this is a feature you need, please leave a comment at' 'https://github.com/bids-standard/pybids/issues/852.' ) except KeyError: # We talked about X being required, I don't know if we want to throw an error over that requirement here # noqa: E501 # though. pass def __repr__(self): return f'<{self.__class__.__name__}(level={self.level}, name={self.name})>' @staticmethod def _build_groups(objects, group_by): """Group list of objects into bins defined by specified entities. Parameters ---------- objects : list List of objects containing an .entities dictionary as an attribute. group_by : list of str List of strings indicating which entities to group on. Returns ------- A dictionary, where the keys are tuples of tuples and the values are lists containing subsets of the original object list. Each outer tuple of the keys represents a particular entity/value combination; each inner tuple has two elements, where the first is the name of the entity and the second is the value. Notes ----- * The passed objects can be of any type, but all elements in the list must have an implemented .entities dictionary. * Objects that do not have defined values for all of the requested `entities` will be silently ignored. Examples -------- >>> bidsfile = namedtuple('bidsfile', ('entities',)) >>> bidsfile1 = bidsfile(entities={'subject': '01'}) >>> bidsfile2 = bidsfile(entities={'subject': '02'}) >>> groups = {(('subject', '01'),): [bidsfile1], (('subject', '02'),): [bidsfile2]} >>> BIDSStatsModelsNode._build_groups([bidsfile1, bidsfile2], ['subject']) == groups True """ if not group_by: return {(): objects} groups = defaultdict(list) # Get unique values in each grouping variable and construct indexing DF entities = [obj.entities for obj in objects] df = pd.DataFrame.from_records(entities) # Separate BIDSVariableCollections collections = [obj.to_df() for obj in objects if type(obj) == BIDSVariableCollection] # noqa: E721 if collections: metadata_vars = reduce(pd.DataFrame.merge, collections) on_vars = list({'subject', 'session', 'run'} & set(metadata_vars.columns)) df = df.merge(metadata_vars, how='left', on=on_vars) # Single-run tasks and single-session subjects may not have entities dummy_groups = {'run', 'session'} - set(df.columns) group_by = set(group_by) - dummy_groups # Verify all columns in group_by exist and raise sensible error if not missing_vars = list(group_by - set(df.columns)) if missing_vars: raise ValueError( f'group_by contains variable(s) {missing_vars} that could not ' 'be found in the entity index.' ) # Restrict DF to only grouping columns df = df.loc[:, list(group_by)] unique_vals = {col: df[col].dropna().unique().tolist() for col in group_by} # Note: we can't just naively bucket objects based on the values of the # grouping entities, because an object may have undefined values for # one or more grouping variables. So we "fill in" the missing values by # Looping over objects. For each one, identify all grouping variables # with missing values. Loop over the cartesian product of the unique # values of the missing entities, combining the permutation values with # the base (present) grouping entities. Sort on this to produce the # group key. for i, row in df.iterrows(): defined = df.columns[df.iloc[i].notnull()].tolist() base_ents = [(k, row[k]) for k in defined] missing = df.columns[df.iloc[i].isnull()].tolist() records = [] if missing: product = itertools.product(*[unique_vals[col] for col in missing]) for perm in product: fill_ents = [(k, v) for (k, v) in dict(zip(missing, perm)).items()] # noqa: B905 records.append(base_ents + fill_ents) else: records.append(base_ents) for rec in records: grp_key = tuple(sorted(rec)) groups[grp_key].append(objects[i]) return groups def run( self, inputs=None, group_by=None, force_dense=True, sampling_rate='TR', invalid_inputs='error', invalid_contrasts='drop', missing_values=None, transformation_history=False, node_reports=False, **filters, ): """Execute node with provided inputs. Parameters ---------- inputs: [[ContrastInfo]] List of list of ContrastInfo tuples passed from the previous node. Each element in the outer list maps to the output of a unit at the previous level; each element in the inner list is a ContrastInfo tuple. E.g., if contrast information is being passed from run-level to subject-level, each outer element is a run. group_by: [str] Optional list of strings giving the names of entities that define the grouping structure for all variables. The current node will be executed separately for each unique combination of levels specified in group_by. For example, if group_by=['contrast', 'subject'], and there are 2 contrasts and 3 subjects, then there will be 6 separate iterations, and the returned list will have 6 elements. If None is passed, the value set at node initialization (if any) will be used. force_dense: bool If True, the returned design matrices contained in ModelSpec instances will represent time in a dense (i.e., uniform temporal sampling) format. If False, the returned format will be sparse if all available variables are sparse, and dense otherwise. Ignored if none of the variables at this node are run-level. sampling_rate: str, float The sampling rate to use for timeseries resampling when working with run-level variables and returning dense output. Ignored if there are no run-level variables, or if force_dense is False and all available variables are sparse. invalid_inputs: str Indicates how to handle invalid inputs--i.e., ones where the `X` specification contains variables that aren't found at run-time. Valid values: * 'drop': Drop invalid contrasts, retain the rest. * 'error' (default): Raise an error. invalid_contrasts: str Indicates how to handle invalid contrasts--i.e., ones where the specification contains variables that aren't found at run-time. Valid values: * 'drop' (default): Drop invalid contrasts, retain the rest. * 'ignore': Keep invalid contrasts despite the missing variables. * 'error': Raise an error. missing_values: str Indicates how to handle missing values in the data. Valid values: * 'fill' (default): Fill missing values with 0. * 'ignore': Keep missing values. * 'error': Raise an error. Defaults to 'fill' if None, but raises warning if not explicitly set. transformation_history: bool If True, the returned ModelSpec instances will include a history of variable collections after each transformation. node_reports: bool If True, report measures and plots will be included in the output filters: dict Optional keyword arguments used to constrain the subset of the data that's processed. E.g., passing subject='01' will process and return data only for files associated with subject '01'. Returns ------- A list of BIDSStatsModelsNodeOutput instances. """ inputs = inputs or [] collections = self._collections group_by = listify(group_by or self.group_by) # Filter inputs and collections if needed if filters: inputs = [i for i in inputs if matches_entities(i, filters)] collections = [c for c in collections if matches_entities(c, filters)] # group all collections and inputs all_objects = inputs + collections groups = self._build_groups(all_objects, group_by) results = [] for grp_ents, grp_objs in list(groups.items()): # split group's objects into inputs and collections grp_inputs, grp_colls = [], [] for obj in grp_objs: if isinstance(obj, BIDSVariableCollection): grp_colls.append(obj) else: grp_inputs.append(obj) node_output = BIDSStatsModelsNodeOutput( node=self, entities=dict(grp_ents), collections=grp_colls, inputs=grp_inputs, force_dense=force_dense, sampling_rate=sampling_rate, invalid_inputs=invalid_inputs, invalid_contrasts=invalid_contrasts, missing_values=missing_values, transformation_history=transformation_history, node_reports=node_reports, ) results.append(node_output) return results def add_child(self, edge): """Add an edge to a child node. Parameters ---------- edge : BIDSStatsModelsEdge An edge to add to the list of children. """ self.children.append(edge) def add_parent(self, edge): """Add an edge to a parent node. Parameters ---------- edge : BIDSStatsModelsEdge An edge to add to the list of children. """ self.parents.append(edge) def add_collections(self, collections): """Add BIDSVariableCollections (i.e., predictors) to the current node. Parameters ---------- collections : [BIDSVariableCollection] List of BIDSVariableCollection objects to add to current node. Notes ----- No checking for redundancy is performed, so if load_collections() is invoked multiple times with overlapping selectors, redundant predictors are likely to be stored internally. """ self._collections.extend(collections) def get_collections(self, **filters): """Returns BIDSVariableCollections at the current node. Parameters ---------- filters : dict Optional keyword filters used to constrain which of the available collections get returned (e.g., passing subject=['01', '02'] will return collections for only subjects '01' and '02'). Returns ------- list of BIDSVariableCollection instances One instance per unit of the current analysis level (e.g., if level='run', each element in the list represents the collection for a single run). """ # Keeps only collections that match target entities, and also removes # those keys from the kwargs dict. return [c for c in self._collections if matches_entities(c, filters)] def expand_wildcards(selectors, pool): # noqa: D103 out = list(selectors) for spec in selectors: if re.search(r'[\*\?\[\]]', spec): idx = out.index(spec) out[idx : idx + 1] = fnmatch.filter(pool, spec) return out class BIDSStatsModelsNodeOutput: """Represents a single node in a BIDSStatsModelsGraph. Parameters ---------- node : BIDSStatsModelsNode The node that generated the current instance. entities : dict Dictionary of entities/metadata applicable to the current node output. collections : [BIDSVariableCollection] List of BIDSVariableCollection instances needed too generate outputs. inputs : [ContrastInfo] List of ContrastInfo instances used to generate outputs. force_dense: bool If True, the returned design matrices contained in ModelSpec instances will represent time in a dense (i.e., uniform temporal sampling) format. If False, the returned format will be sparse if all available variables are sparse, and dense otherwise. Ignored if none of the variables at this node are run-level. sampling_rate: str, float The sampling rate to use for timeseries resampling when working with run-level variables and returning dense output. Ignored if there are no run-level variables, or if force_dense is False and all available variables are sparse. invalid_inputs: str Indicates how to handle invalid inputs--i.e., ones where the `X` specification contains variables that aren't found at run-time. Valid values: * 'drop': Drop invalid contrasts, retain the rest. * 'error' (default): Raise an error. invalid_contrasts: str Indicates how to handle invalid contrasts--i.e., ones where the specification contains variables that aren't found at run-time. Valid values: * 'drop' (default): Drop invalid contrasts, retain the rest. * 'ignore': Keep invalid contrasts despite the missing variables. * 'error': Raise an error. missing_values: str Indicates how to handle missing values in the data. Valid values: * 'fill' (default): Fill missing values with 0. * 'ignore': Keep missing values. * 'error': Raise an error. Defaults to 'fill' if None, but raises warning if not explicitly set. transformation_history: bool If True, the returned ModelSpec instances will include a history of variable collections after each transformation. node_reports: bool If True, a report will be generated for each node output. """ def __init__( self, node, entities={}, # noqa: B006 collections=None, inputs=None, force_dense=True, sampling_rate='TR', invalid_inputs='error', invalid_contrasts='drop', missing_values=None, *, transformation_history=False, node_reports=False, ): """Initialize a new BIDSStatsModelsNodeOutput instance. Applies the node's model to the specified collections and inputs, including applying transformations and generating final model specs and design matrices (X). """ collections = collections or [] inputs = inputs or [] self.node = node self.entities = entities self.force_dense = force_dense self.sampling_rate = sampling_rate self.invalid_inputs = invalid_inputs self.invalid_contrasts = invalid_contrasts self.coll_hist = None # Apply transformations and convert collections to single DF dfs = self._collections_to_dfs(collections, collection_history=transformation_history) if inputs: dfs.append(self._inputs_to_df(inputs)) df = reduce(pd.DataFrame.merge, dfs) # If dummy coded condition columns needed, generate and concat var_names = list(self.node.model['x']) if inputs: dummy_df = pd.get_dummies(df['contrast']) dummies_needed = set(var_names).intersection(dummy_df) if dummies_needed: df = pd.concat([df, dummy_df[list(dummies_needed)]], axis=1) # If a single incoming contrast, keep track of name if 'contrast' in df.columns and df['contrast'].nunique() == 1: unique_in_contrast = df['contrast'].unique()[0] else: unique_in_contrast = None # Handle the special 1 construct. # Add column of 1's to the design matrix called "intercept" if 1 in var_names: if 'intercept' in var_names: raise ValueError("Cannot define both '1' and 'intercept' in 'X'") var_names = ['intercept' if i == 1 else i for i in var_names] if 'intercept' not in df.columns: df.insert(0, 'intercept', 1) var_names = expand_wildcards(var_names, df.columns) # Verify all X names are actually present missing = list(set(var_names) - set(df.columns)) if missing: if self.invalid_inputs == 'drop': # warn caller of BIDSStatsModelsNode.run() warnings.warn( f'X specification includes variable(s) {missing}, ' 'but these were not found in data matrix. ' 'These will be dropped.', stacklevel=3, ) var_names = [v for v in var_names if v not in missing] elif self.invalid_inputs == 'error': raise ValueError( f'X specification includes variable(s) {missing}, but ' 'these were not found in data matrix.' ) else: raise ValueError( f"invalid_inputs must be one of ['drop', 'error'], got {self.invalid_inputs}" ) # separate the design columns from the entity columns self.data = df.loc[:, var_names] self.metadata = df.loc[:, df.columns.difference(var_names)] # replace missing values with 0 and warn user which columns had missing values if missing_values != 'ignore': missing_cols = self.data.columns[self.data.isnull().any()] if len(missing_cols) > 0: base_message = f'NaNs detected in columns: {missing_cols}' if missing_values in ['fill', None]: self.data.fillna(0, inplace=True) if missing_values is None: base_message += ( ' were replaced with 0. Consider ' ' handling missing values using transformations.' ) # warn caller of BIDSStatsModelsNode.run() warnings.warn(base_message, stacklevel=3) elif missing_values == 'error': base_message += '. Explicitly replace missing values using transformations.' raise ValueError(base_message) # create ModelSpec and build contrasts kind = node.model.get('type', 'glm').lower() SpecCls = { 'glm': GLMMSpec, 'meta': MetaAnalysisSpec, }[kind] self.model_spec = SpecCls.from_df(self.data, node.model, self.metadata) self.contrasts = self._build_contrasts(unique_in_contrast) self.report_ = node_report(self) if node_reports else None def _collections_to_dfs(self, collections, *, collection_history=False): """Merges collections and converts them to a pandas DataFrame.""" if not collections: return [] # group all collections by level coll_levels = defaultdict(list) [coll_levels[coll.level].append(coll) for coll in collections] var_names = list(set(self.node.model['x']) - {1}) grp_dfs = [] trans_hist = [] # merge all collections at each level and export to a DataFrame for level, colls in coll_levels.items(): # Note: we currently merge _before_ selecting variables. Selecting # variables first could be done by passing `variables=all_vars` as # an argument on the next line), but we can't do this right now # because we can't guarantee that all the variables named in `X` # in the model section already exist; some might be created by the # transformations. coll = merge_collections(colls) # apply transformations transformations = self.node.transformations if transformations: transformer = tm.TransformerManager( transformations['transformer'], keep_history=collection_history ) coll = transformer.transform(coll.clone(), transformations['instructions']) if hasattr(transformer, 'history_'): trans_hist += transformer.history_ # Take the intersection of variables and Model.X (var_names), ignoring missing # variables (usually contrasts) coll.variables = { v: coll.variables[v] for v in expand_wildcards(var_names, coll.variables) if v in coll.variables } if not coll.variables: continue # run collections need to be handled separately because to_df() # takes extra arguments related to handling of time if level == 'run': if self.force_dense: coll = coll.to_dense(sampling_rate=self.sampling_rate) coll = coll.to_df(sampling_rate=self.sampling_rate) else: coll = coll.to_df() grp_dfs.append(coll) if collection_history: self.trans_hist = trans_hist return grp_dfs def _inputs_to_df(self, inputs): # Convert the inputs to a DataFrame and add to list. Each row is # an input; each column is an entity rows = [{**con.entities, 'contrast': con.name} for con in inputs] input_df = pd.DataFrame.from_records(rows) return input_df def _build_contrasts(self, unique_in_contrast=None): """Contrast list of ContrastInfo objects based on current state. Parameters ---------- unique_in_contrast : string Name of unique incoming contrast inputs (i.e. if there is only 1) """ in_contrasts = self.node.contrasts.copy() col_names = set(self.X.columns) # Create dummy contrasts as regular contrasts dummies = self.node.dummy_contrasts if dummies: if {'conditions', 'condition_list'} & set(dummies): # warn caller of BIDSStatsModelsNode.run() warnings.warn( "Use 'Contrasts' not 'Conditions' or 'ConditionList' to specify" "DummyContrasts. Renaming to 'Contrasts' for now.", stacklevel=4, ) dummies['contrasts'] = dummies.pop('conditions', None) or dummies.pop( 'condition_list', None ) if 'contrasts' in dummies: conditions = set(dummies['contrasts']) else: conditions = col_names for col_name in conditions: if col_name == 'intercept': col_name = 1 in_contrasts.insert( 0, { 'name': col_name, 'condition_list': [col_name], 'weights': [1], 'test': dummies.get('test'), }, ) # Process all contrasts, starting with dummy contrasts # Dummy contrasts are replaced if a contrast is defined with same name contrasts = {} for con in in_contrasts: condition_list = list(con['condition_list']) # Rename special 1 construct condition_list = ['intercept' if i == 1 else i for i in condition_list] name = con['name'] # Rename contrast name if name == 1: name = unique_in_contrast or 'intercept' else: # If Node has single contrast input, as is grouped by contrast # Rename contrast to append incoming contrast name if unique_in_contrast: name = f'{unique_in_contrast}_{name}' missing_vars = set(condition_list) - col_names if missing_vars: if self.invalid_contrasts == 'error': msg = ( "Variable(s) '{}' specified in condition list for " "contrast '{}', but not found in available data.".format( missing_vars, con['name'] ) ) raise ValueError(msg) elif self.invalid_contrasts == 'drop': continue weights = np.atleast_2d(con['weights']) # noqa: F841 # Add contrast name to entities; can be used in grouping downstream entities = {**self.entities, 'contrast': name} ci = ContrastInfo(name, condition_list, con['weights'], con.get('test'), entities) contrasts[name] = ci return list(contrasts.values()) @property def X(self): """Return design matrix via the current ModelSpec.""" return self.model_spec.X def __repr__(self): return f'<{self.__class__.__name__}(name={self.node.name}, entities={self.entities})>' ================================================ FILE: src/bids/modeling/tests/__init__.py ================================================ ================================================ FILE: src/bids/modeling/tests/test_automodel.py ================================================ from itertools import chain from os.path import join import pytest from bids.layout import BIDSLayout from bids.modeling import BIDSStatsModelsGraph from bids.modeling.auto_model import auto_model from bids.tests import get_test_data_path @pytest.fixture def model(): layout_path = join(get_test_data_path(), 'ds005') layout = BIDSLayout(layout_path) models = auto_model(layout, scan_length=480, one_vs_rest=True) return models[0] def test_automodel_valid(model): try: from bsmschema.models import BIDSStatsModel except ImportError: pytest.skip('Needs bsmschema, available for Python 3.8+') BIDSStatsModel.model_validate(model) def test_automodel_runs(model): layout_path = join(get_test_data_path(), 'ds005') layout = BIDSLayout(layout_path) # Test to make sure an analaysis can be setup from the generated model graph = BIDSStatsModelsGraph(layout, model) graph.load_collections(scan_length=480, subject=['01', '02']) outputs = graph['Run'].run() assert len(outputs) == 6 cis = list(chain(*[op.contrasts for op in outputs])) assert len(cis) == 6 outputs = graph['Subject'].run(cis) # 2 subjects x 1 contrast assert len(outputs) == 2 cis = list(chain(*[op.contrasts for op in outputs])) assert len(cis) == 2 def test_auto_model_graph(model): assert model['Name'] == 'ds005_mixedgamblestask' # run level block = model['Nodes'][0] assert block['Name'] == 'Run' assert block['Level'] == 'Run' assert block['Model']['Type'] == 'glm' assert block['Transformations']['Transformer'] == 'pybids-transforms-v1' assert block['Transformations']['Instructions'][0]['Name'] == 'Factor' assert block['Contrasts'][0]['Name'] == 'run_parametric gain' assert block['Contrasts'][0]['Weights'] == [1] assert block['Contrasts'][0]['Test'] == 't' # subject level block = model['Nodes'][1] assert block['Name'] == 'Subject' assert block['Level'] == 'Subject' assert block['Model']['Type'] == 'meta' assert block['Model']['X'][0] == 1 assert block['DummyContrasts'] == {'Test': 't'} # dataset level block = model['Nodes'][2] assert block['Name'] == 'Dataset' assert block['Level'] == 'Dataset' assert block['Model']['Type'] == 'glm' assert block['Model']['X'][0] == 1 assert block['DummyContrasts'] == {'Test': 't'} ================================================ FILE: src/bids/modeling/tests/test_hrf.py ================================================ import numpy as np from ..hrf import compute_regressor def test_duplicate_onsets(): onset = [ 16, 24, 48, 79, 80, 112, 127, 144, 176, 176, 208, 216, 240, 272, 272, 304, 329, ] duration = [16, 1, 16, 1, 16, 16, 1, 16, 1, 16, 16, 1, 16, 1, 16, 16, 1] trial_types = { "Face": [0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0], "Hand": [1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0], "Swallow": [0, 1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 1], } model = "spm" resample_frames = np.arange(0, 340.0, 0.1) for name, trial_type in trial_types.items(): vals = np.vstack([onset, duration, trial_type]) regressor, _ = compute_regressor( vals, model, resample_frames, fir_delays=None, min_onset=0, oversampling=1 ) assert np.all((regressor > -0.2) & (regressor < 1.2)) ================================================ FILE: src/bids/modeling/tests/test_model_spec.py ================================================ import json from os.path import join import numpy as np import pandas as pd import pytest from bids.modeling.model_spec import GLMMSpec, Term, VarComp from bids.tests import get_test_data_path from bids.variables import BIDSVariableCollection @pytest.fixture(scope='module') def collection(): """Create a collection at dataset level.""" df = pd.DataFrame( { 'subject': np.repeat(np.arange(10), 4), 'run': np.tile(np.arange(4), 10), 'age': np.repeat(np.random.normal(40, 10, 10).astype(int), 4), } ) return BIDSVariableCollection.from_df(df[['age']], entities=df[['subject', 'run']]) def test_df_to_model_spec(collection): layout_path = join(get_test_data_path(), 'ds005') json_file = join(layout_path, 'models', 'ds-005_type-mfx_model.json') spec = json.load(open(json_file)) df = collection.to_df() data = df[['age']] metadata = df[['subject', 'run']] md = GLMMSpec.from_df(data, spec['Nodes'][1]['Model'], metadata) assert len(md.terms) == 2 assert md.terms['age'].values.shape == (40,) assert md.terms['SubjectSlopes'].values.shape == (40, 10) index_vec = md.terms['SubjectSlopes'].index_vec assert index_vec.shape == (40,) assert np.array_equal(np.sort(np.unique(index_vec)), np.arange(10) + 1) assert md.Z.shape == (40, 10) assert md.Z.columns[0] == 'SubjectSlopes.0' def test_glmmspec_empty_init(): md = GLMMSpec() assert md.terms == {} assert md.X is None assert md.Z is None assert md.family is None assert md.family is None assert md.sigma is None def test_fixed_term_init(): t = Term('dummy', np.random.normal(size=20), categorical=True) assert t.name == 'dummy' assert t.values.shape == (20,) assert t.categorical def test_var_comp_init(): Z = np.repeat(np.eye(20), 5, axis=0) t = VarComp('sigma', Z) assert t.name == 'sigma' assert t.values.shape == (100, 20) assert t.categorical ================================================ FILE: src/bids/modeling/tests/test_statsmodels.py ================================================ from itertools import chain from os import path from os.path import join from nibabel.optpkg import optional_package graphviz, has_graphviz, _ = optional_package('graphviz') import json import pytest from bids.layout import BIDSLayout from bids.layout.utils import PaddedInt from bids.modeling import BIDSStatsModelsGraph from bids.modeling.statsmodels import ContrastInfo, expand_wildcards from bids.tests import get_test_data_path from bids.variables import BIDSVariableCollection @pytest.fixture def graph(): layout_path = join(get_test_data_path(), 'ds005') layout = BIDSLayout(layout_path) json_file = join(layout_path, 'models', 'ds-005_type-test_model.json') graph = BIDSStatsModelsGraph(layout, json_file) graph.load_collections(scan_length=480, subject=['01', '02']) return graph @pytest.fixture def graph_intercept(): layout_path = join(get_test_data_path(), 'ds005') layout = BIDSLayout(layout_path, derivatives=join(layout_path, 'derivatives', 'events')) json_file = join(layout_path, 'models', 'ds-005_type-test_intercept.json') graph = BIDSStatsModelsGraph(layout, json_file) graph.load_collections(scan_length=480, subject=['01', '02']) return graph @pytest.fixture def graph_nodummy(): layout_path = join(get_test_data_path(), 'ds005') layout = BIDSLayout(layout_path) json_file = join(layout_path, 'models', 'ds-005_type-testnodummy_model.json') graph = BIDSStatsModelsGraph(layout, json_file) graph.load_collections(scan_length=480, subject=['01', '02']) return graph def test_load_collections_types(graph): # Very narrow regression test that ensures that load_collections does not # modify the types of entities in-place layout = graph.layout bold = layout.get(suffix='bold', extension='nii.gz')[0] assert isinstance(bold.entities['run'], PaddedInt) @pytest.mark.skipif(not has_graphviz, reason='Test requires graphviz') def test_write_graph(graph, tmp_path): from graphviz import Digraph dot = graph.write_graph(tmp_path / 'graph.dot') assert isinstance(dot, Digraph) assert path.exists(tmp_path / 'graph.dot') assert path.exists(tmp_path / 'graph.dot.png') def test_repr(graph): assert ( graph.__repr__() == "" ) node = graph.nodes['run'] assert node.__repr__() == '' assert ( node.run()[0].__repr__() == "" ) def test_manual_intercept(graph_intercept): # Test that a automatic intercept (1) is correct # Intercept could should be all 1s run = graph_intercept['run'] outputs = run.run(subject='01', run=1) assert outputs[0].X.intercept.min() == 1.0 # Defining both 1 and 'intercept' raises an error run.model['x'] = [1, 'intercept'] with pytest.raises(ValueError, match="Cannot define both '1' and 'intercept' in 'X'"): run.run(subject='01', run=1) # "intercept" variable from event files is loaded correctly (should not be all 1s) run = graph_intercept.nodes['run'] run.model['x'] = ['intercept'] outputs = run.run(subject='01', run=1) assert outputs[0].X.intercept.min() != 1.0 def test_first_level_sparse_design_matrix(graph): outputs = graph['run'].run(subject=['01'], force_dense=False) assert len(outputs) == 3 df = outputs[0].X assert df.shape == (86, 3) assert set(df.columns) == {'RT', 'gain', 'RT:gain'} metadata = outputs[0].metadata assert metadata.shape == (86, 7) assert set(metadata.columns) == { 'onset', 'duration', 'subject', 'run', 'task', 'datatype', 'suffix', } def test_incremental_data_loading(): layout_path = join(get_test_data_path(), 'ds005') layout = BIDSLayout(layout_path) json_file = join(layout_path, 'models', 'ds-005_type-test_model.json') graph = BIDSStatsModelsGraph(layout, json_file) graph.load_collections(scan_length=480, subject=['01'], run=[1]) graph.load_collections(scan_length=480, subject=['02'], run=[2]) assert len(graph['run'].get_collections()) == 2 def test_step_get_collections(graph): collections = graph['run'].get_collections(subject='01') assert len(collections) == 3 assert isinstance(collections[0], BIDSVariableCollection) def test_contrast_info(graph): outputs = graph['run'].run(subject='01') contrast_lists = [op.contrasts for op in outputs] assert len(contrast_lists) == 3 for cl in contrast_lists: assert len(cl) == 3 cl = [c for c in cl if c.test == 't'] assert set([c.name for c in cl]) == {'RT', 'RT:gain', 'gain'} assert set([c.test for c in cl]) == {'t'} assert len(cl[1].conditions) == 1 assert cl[1].conditions[0] in {'RT', 'gain', 'RT:gain'} assert cl[1].weights == [1] assert isinstance(cl[0], ContrastInfo) assert cl[0]._fields == ('name', 'conditions', 'weights', 'test', 'entities') def test_contrast_dummy_vs_explicit(graph, graph_nodummy): # Check that ContrastInfo from model w/ DummyContrasts # and explicit Contrasts are identical outputs = graph['run'].run(subject='01', run=1) outputs_nodummy = graph_nodummy['run'].run(subject='01', run=1) for con in outputs[0].contrasts: match = [c for c in outputs_nodummy[0].contrasts if c.name == con.name][0] assert con.conditions == match.conditions assert con.weights == match.weights assert con.test == match.test cis = list(chain(*[op.contrasts for op in outputs])) outputs_sub = graph['participant'].run(cis, group_by=['subject', 'contrast']) output = [o for o in outputs_sub if o.entities['contrast'] == 'RT'][0] cis = list(chain(*[op.contrasts for op in outputs_nodummy])) outputs_nodummy_sub = graph_nodummy['participant'].run(cis, group_by=['subject', 'contrast']) output_nodummy = [o for o in outputs_nodummy_sub if o.entities['contrast'] == 'RT'][0] for con in output.contrasts: match = [c for c in output_nodummy.contrasts if c.name == con.name][0] assert con.conditions == match.conditions assert con.weights == match.weights assert con.test == match.test def test_get_run_level_model_spec(graph): outputs = graph['run'].run(subject='01', run=1) assert len(outputs) == 1 model_spec = outputs[0].model_spec assert model_spec.__class__.__name__ == 'GLMMSpec' # Note: this implicitly also tests use of formulas, because one is defined # in the model for the interaction of RT and gain. assert model_spec.X.shape == (240, 3) assert model_spec.Z is None assert {'RT', 'gain', 'RT:gain'} == set(model_spec.terms.keys()) def test_entire_graph_smoketest(graph): # Smoke test of entire graph; should hit almost all major pieces. # We do the following: # At run level, construct a design matrix containing gain, RT, and gain * RT. # At subject level, aggregate within runs, do nothing else. # At dataset level, do one-sample t-tests separately for each gender, # but also two-sample t-tests comparing males and females. # Note that there are only 2 subjects in the graph. # Note also that there is only one session (with no session label), which # should have no effect as a grouping variable outputs = graph['run'].run() # 2 subjects x 3 runs assert len(outputs) == 6 cis = list(chain(*[op.contrasts for op in outputs])) assert len(cis) == 18 outputs = graph['participant'].run(cis) # 2 subjects x 3 contrasts) assert len(outputs) == 6 # * 2 participant level contrasts = 12 cis = list(chain(*[op.contrasts for op in outputs])) assert len(cis) == 12 # Test output names for single subject out_contrasts = [c.entities['contrast'] for c in cis if c.entities['subject'] == '01'] expected_outs = ['gain', 'gain_neg', 'RT', 'RT_neg', 'RT:gain', 'RT:gain_neg'] assert set(out_contrasts) == set(expected_outs) # Construct new ContrastInfo objects with name updated to reflect last # contrast. This would normally be done by the handling tool (e.g., fitlins) inputs = [] for op in outputs: fields = dict(op.contrasts[0]._asdict()) contrast_name = op.metadata['contrast'].iloc[0] fields['name'] = contrast_name fields['entities']['contrast'] = contrast_name inputs.append(ContrastInfo(**fields)) # GROUP DIFFERENCE NODE outputs = graph['group-diff'].run(inputs) # 3 contrasts assert len(outputs) == 3 cis = list(chain(*[op.contrasts for op in outputs])) # 3 contrasts x 2 subjects assert len(cis) == 6 model_spec = outputs[0].model_spec assert model_spec.__class__.__name__ == 'GLMMSpec' assert model_spec.X.shape == (2, 2) assert model_spec.Z is None assert len(model_spec.terms) == 2 assert not set(model_spec.terms.keys()) - {'intercept', 'sex'} # BY-GROUP NODE outputs = graph['by-group'].run(inputs) # 3 contrasts assert len(outputs) == 6 cis = list(chain(*[op.contrasts for op in outputs])) # two groups x 3 contrasts assert len(cis) == 6 model_spec = outputs[0].model_spec assert model_spec.__class__.__name__ == 'GLMMSpec' assert model_spec.X.shape == (1, 1) assert model_spec.Z is None assert not set(model_spec.terms.keys()) - {'intercept'} # explicit-contrast NODE outputs = graph['explicit-contrast'].run(inputs) # 1 group x 1 contrast assert len(outputs) == 1 assert len(outputs[0].contrasts) == 1 assert outputs[0].X['gain'].sum() == 2 model_spec = outputs[0].model_spec assert model_spec.__class__.__name__ == 'GLMMSpec' assert model_spec.X.shape == (6, 1) assert not set(model_spec.terms.keys()) - {'gain'} contrast = outputs[0].contrasts[0] assert contrast.name == 'gain' def test_expand_wildcards(): # No wildcards == no modification assert expand_wildcards(['a', 'b'], ['a', 'c']) == ['a', 'b'] # No matches == removal assert expand_wildcards(['a', 'b*'], ['a', 'c']) == ['a'] # Matches expand in-place assert expand_wildcards(['a*', 'b'], ['a', 'c']) == ['a', 'b'] assert expand_wildcards(['a*', 'b'], ['a0', 'c', 'a1', 'a2']) == ['a0', 'a1', 'a2', 'b'] # Some examples assert expand_wildcards(['trial_type.*'], ['trial_type.A', 'trial_type.B']) == [ 'trial_type.A', 'trial_type.B', ] assert expand_wildcards( ['non_steady_state*'], ['non_steady_state00', 'non_steady_state01'] ) == ['non_steady_state00', 'non_steady_state01'] def test_interceptonly_runlevel_error(): layout_path = join(get_test_data_path(), 'ds005') layout = BIDSLayout(layout_path) json_file = join(layout_path, 'models', 'ds-005_type-interceptonlyrunlevel_model.json') with pytest.raises(NotImplementedError): graph = BIDSStatsModelsGraph(layout, json_file) def test_missing_value_fill(): layout_path = join(get_test_data_path(), 'ds005') layout = BIDSLayout(layout_path, derivatives=join(layout_path, 'derivatives', 'fmriprep')) json_file = join(layout_path, 'models', 'ds-005_type-test_model.json') model = json.load(open(json_file)) # Add 'global_signal_derivative1' to the model model['Nodes'][0]['Model'] = {'X': ['RT', 'gain', 'global_signal_derivative1']} graph = BIDSStatsModelsGraph(layout, model) graph.load_collections(scan_length=480, subject=['01', '02']) # Check that missing values are filled in # Assert that a warnings is raised with pytest.warns(UserWarning): graph.run_graph() # Assert that there are no missing values outputs = graph.nodes['run'].outputs_ assert not outputs[0].model_spec.X.isna().any().any() # Check that missing_values='error' raises an error with pytest.raises(ValueError): graph.run_graph(missing_values='error') # Check that missing_values='ignore' passes through missing values graph.run_graph(missing_values='ignore') outputs = graph.nodes['run'].outputs_ assert outputs[0].model_spec.X.isna().any().any() ================================================ FILE: src/bids/modeling/tests/test_transformations.py ================================================ # from bids.modeling.variables import load_variables import math from os.path import join, sep import numpy as np import pandas as pd import pytest from bids.layout import BIDSLayout from bids.modeling import transformations as transform from bids.tests import get_test_data_path from bids.variables import DenseRunVariable, SparseRunVariable from bids.variables.collections import BIDSRunVariableCollection from bids.variables.entities import RunInfo try: from unittest import mock except ImportError: from unittest import mock # Sub-select collection for faster testing, without sacrificing anything # in the tests SUBJECTS = ['01', '02'] NRUNS = 3 SCAN_LENGTH = 480 cached_collections = {} @pytest.fixture def collection(): if 'ds005' not in cached_collections: layout_path = join(get_test_data_path(), 'ds005') layout = BIDSLayout(layout_path) cached_collections['ds005'] = layout.get_collections( 'run', types=['events'], scan_length=SCAN_LENGTH, merge=True, sampling_rate=10, subject=SUBJECTS, ) # Always return a clone! return cached_collections['ds005'].clone() @pytest.fixture def sparse_run_variable_with_missing_values(): data = pd.DataFrame( {'onset': [2, 5, 11, 17], 'duration': [1.2, 1.6, 0.8, 2], 'amplitude': [1, 1, np.nan, 1]} ) run_info = [RunInfo({'subject': '01'}, 20, 2, 'dummy.nii.gz', 10)] var = SparseRunVariable(name='var', data=data, run_info=run_info, source='events') return BIDSRunVariableCollection([var]) def test_convolve_multi(collection): # Just tests that we can convolve multiple arguments with one model output_names = ['unique_name', 'another_unique_name'] transform.Convolve(collection, ['parametric gain', 'loss'], output=output_names, model='spm') assert set(output_names).issubset(collection.variables) def test_convolve(collection): rt = collection.variables['RT'] transform.Convolve(collection, ['RT'], output=['reaction_time']) rt_conv = collection.variables['reaction_time'] assert rt_conv.values.shape[0] == rt.get_duration() * collection.sampling_rate transform.ToDense(collection, ['RT'], output=['rt_dense']) transform.Convolve(collection, 'rt_dense', derivative=True) # test the derivative exists assert collection.variables.get('rt_dense_derivative') dense_conv = collection.variables['reaction_time'] assert ( dense_conv.values.shape[0] == collection.variables['rt_dense_derivative'].values.shape[0] == rt.get_duration() * collection.sampling_rate ) def test_convolve_oversampling(collection): # Prepare a dense version of RT transform.ToDense(collection, ['RT'], output=['rt_dense']) # Test adaptive oversampling computation # Events are 3s duration events every 4s, so resolution demanded by the data is 1Hz # To resolve 1Hz frequencies, we must sample at >=2Hz args = (mock.ANY, 'spm', mock.ANY) kwargs = dict(fir_delays=None, min_onset=0) mock_return = (np.array([[0, 0]]), ['cond']) with mock.patch('bids.modeling.transformations.compute.hrf.compute_regressor') as mocked: mocked.return_value = mock_return # Sampling rate is 10Hz, no oversampling needed transform.Convolve(collection, 'RT', output='rt_mock') mocked.assert_called_with(*args, oversampling=1.0, **kwargs) with mock.patch('bids.modeling.transformations.compute.hrf.compute_regressor') as mocked: mocked.return_value = mock_return # Sampling rate is 10Hz, no oversampling needed transform.Convolve(collection, 'rt_dense', output='rt_mock') mocked.assert_called_with(*args, oversampling=1.0, **kwargs) with mock.patch('bids.modeling.transformations.compute.hrf.compute_regressor') as mocked: mocked.return_value = mock_return # Slow sampling rate, oversample (4x) to 2Hz collection.sampling_rate = 0.5 transform.Convolve(collection, 'RT', output='rt_mock') mocked.assert_called_with(*args, oversampling=4.0, **kwargs) with mock.patch('bids.modeling.transformations.compute.hrf.compute_regressor') as mocked: mocked.return_value = mock_return # Dense variable is already sampled at 10Hz, no oversampling needed collection.sampling_rate = 0.5 transform.Convolve(collection, 'rt_dense', output='rt_mock') mocked.assert_called_with(*args, oversampling=1.0, **kwargs) with mock.patch('bids.modeling.transformations.compute.hrf.compute_regressor') as mocked: mocked.return_value = mock_return # Onset requires 10Hz resolution, oversample (2x) to 20Hz collection.sampling_rate = 10 collection['RT'].onset[0] += 0.1 transform.Convolve(collection, 'RT', output='rt_mock') mocked.assert_called_with(*args, oversampling=2.0, **kwargs) def test_convolve_impulse(): # Smoke test impulse convolution data = pd.DataFrame({'onset': [10, 20], 'duration': [0, 0], 'amplitude': [1, 1]}) run_info = [RunInfo({'subject': '01'}, 25, 2, 'dummy.nii.gz', 10)] var = SparseRunVariable(name='var', data=data, run_info=run_info, source='events') coll = BIDSRunVariableCollection([var]) transform.ToDense(coll, 'var', output='var_dense') transform.Convolve(coll, 'var', output='var_hrf') transform.Convolve(coll, 'var_dense', output='var_dense_hrf') def test_rename(collection): dense_rt = collection.variables['RT'].to_dense(collection.sampling_rate) assert len(dense_rt.values) == math.ceil( len(SUBJECTS) * NRUNS * SCAN_LENGTH * collection.sampling_rate ) transform.Rename(collection, ['RT'], output=['reaction_time']) assert 'reaction_time' in collection.variables assert 'RT' not in collection.variables col = collection.variables['reaction_time'] assert col.name == 'reaction_time' assert col.onset.max() == 476 def test_product(collection): c = collection transform.Product(collection, variables=['parametric gain', 'gain'], output='prod') res = c['prod'].values assert (res == c['parametric gain'].values * c['gain'].values).all() def test_sum(collection): c = collection transform.Sum(collection, variables=['parametric gain', 'gain'], output='sum') res = c['sum'].values target = c['parametric gain'].values + c['gain'].values assert np.array_equal(res, target) transform.Sum(collection, variables=['parametric gain', 'gain'], output='sum', weights=[2, 2]) assert np.array_equal(c['sum'].values, target * 2) with pytest.raises(ValueError): transform.Sum( collection, variables=['parametric gain', 'gain'], output='sum', weights=[1, 1, 1] ) def test_scale(collection, sparse_run_variable_with_missing_values): transform.Scale( collection, variables=['RT', 'parametric gain'], output=['RT_Z', 'gain_Z'], groupby=['run', 'subject'], rescale=True, ) groupby = collection['RT'].get_grouper(['run', 'subject']) z1 = collection['RT_Z'].values z2 = ( collection['RT'] .values.groupby(groupby, group_keys=False) .apply(lambda x: (x - x.mean()) / x.std()) ) assert np.allclose(z1, z2) # Test constant input coll = sparse_run_variable_with_missing_values coll['var'].values.fillna(1) with pytest.raises(ValueError, match='Cannot scale.*1.0'): transform.Scale(coll, 'var').values with pytest.raises(ValueError, match='Cannot scale.*1.0'): transform.Scale(coll, 'var', replace_na='before').values transform.Scale(coll, 'var', replace_na='after', output='zero') assert coll['zero'].values.unique() == 0 def test_demean(collection): transform.Demean(collection, variables=['RT'], output=['RT_dm']) m1 = collection['RT_dm'].values m2 = collection['RT'].values m2 -= m2.values.mean() assert np.allclose(m1, m2) def test_orthogonalize_dense(collection): transform.Factor(collection, 'trial_type', sep=sep) sampling_rate = collection.sampling_rate # Store pre-orth variables needed for tests pg_pre = collection['trial_type/parametric gain'].to_dense(sampling_rate) rt = collection['RT'].to_dense(sampling_rate) # Orthogonalize and store result with pytest.warns(UserWarning, match='Found a mix of dense and sparse.*'): transform.Orthogonalize( collection, variables='trial_type/parametric gain', other='RT', dense=True, groupby=['run', 'subject'], ) pg_post = collection['trial_type/parametric gain'] # Verify that the to_dense() calls result in identical indexing ent_cols = ['subject', 'run'] assert pg_pre.to_df()[ent_cols].equals(rt.to_df()[ent_cols]) assert pg_post.to_df()[ent_cols].equals(rt.to_df()[ent_cols]) vals = np.c_[rt.values, pg_pre.values, pg_post.values] df = pd.DataFrame(vals, columns=['rt', 'pre', 'post']) groupby = rt.get_grouper(['run', 'subject']) pre_r = df.groupby(groupby, group_keys=False).apply(lambda x: x.corr().iloc[0, 1]) post_r = df.groupby(groupby, group_keys=False).apply(lambda x: x.corr().iloc[0, 2]) assert (pre_r > 0.2).any() assert (post_r < 0.0001).all() def test_orthogonalize_sparse(collection): pg_pre = collection['parametric gain'].values rt = collection['RT'].values transform.Orthogonalize( collection, variables='parametric gain', other='RT', groupby=['run', 'subject'] ) pg_post = collection['parametric gain'].values vals = np.c_[rt.values, pg_pre.values, pg_post.values] df = pd.DataFrame(vals, columns=['rt', 'pre', 'post']) groupby = collection['RT'].get_grouper(['run', 'subject']) pre_r = df.groupby(groupby, group_keys=False).apply(lambda x: x.corr().iloc[0, 1]) post_r = df.groupby(groupby, group_keys=False).apply(lambda x: x.corr().iloc[0, 2]) assert (pre_r > 0.2).any() assert (post_r < 0.0001).all() def test_split(collection): orig = collection['RT'].clone(name='RT_2') collection['RT_2'] = orig.clone() collection['RT_3'] = collection['RT'].clone(name='RT_3').to_dense(collection.sampling_rate) rt_pre_onsets = collection['RT'].onset rt_pre_values = collection['RT'].values.values # Grouping SparseEventVariable by one column transform.Split(collection, ['RT'], ['respcat']) # Verify names assert ( 'RT.respcat[0]' in collection.variables.keys() and 'RT.respcat[-1]' in collection.variables.keys() ) # Verify values rt_post_onsets = np.r_[ collection['RT.respcat[0]'].onset, collection['RT.respcat[-1]'].onset, collection['RT.respcat[1]'].onset, ] assert np.array_equal(np.sort(rt_pre_onsets), np.sort(rt_post_onsets)) rt_post_values = np.r_[ collection['RT.respcat[0]'].values.values, collection['RT.respcat[-1]'].values.values, collection['RT.respcat[1]'].values.values, ] assert np.array_equal(np.sort(rt_pre_values), np.sort(rt_post_values)) # Grouping SparseEventVariable by multiple columns transform.Split(collection, variables=['RT_2'], by=['loss', 'respcat']) assert ( 'RT_2.loss[13].respcat[-1]' in collection.variables.keys() and 'RT_2.loss[13].respcat[1]' in collection.variables.keys() ) # Grouping by DenseEventVariable transform.Split(collection, variables='RT_3', by='respcat') targets = ['RT_3.respcat[-1]', 'RT_3.respcat[0]', 'RT_3.respcat[1]'] assert not set(targets) - set(collection.variables.keys()) assert collection['respcat'].values.nunique() == 3 n_dense = len(collection['RT_3'].values) assert len(collection['RT_3.respcat[-1]'].values) == n_dense # Grouping by entities in the index collection['RT_4'] = orig.clone(name='RT_4') transform.Split(collection, variables=['RT_4'], by=['respcat', 'run']) assert 'RT_4.respcat[-1].run[3]' in collection.variables.keys() def test_resample_dense(collection): new_sampling_rate = 50 old_sampling_rate = collection.sampling_rate upsampling = float(new_sampling_rate) / old_sampling_rate collection['RT'] = collection['RT'].to_dense(old_sampling_rate) old_rt = collection['RT'].clone() collection.resample(new_sampling_rate, in_place=True) assert math.floor(len(old_rt.values) * upsampling) == len(collection['RT'].values) # Should work after explicitly converting categoricals transform.Factor(collection, 'trial_type') new_sampling_rate2 = 5 upsampling2 = float(new_sampling_rate2) / old_sampling_rate collection.resample(new_sampling_rate2, force_dense=True, in_place=True) assert len(old_rt.values) == math.ceil( float(len(collection['parametric gain'].values) / upsampling2) ) def test_threshold(collection): old_pg = collection['parametric gain'] orig_vals = old_pg.values collection['pg'] = old_pg.clone(name='pg') transform.Threshold(collection, 'pg', threshold=0.2, binarize=True) assert collection.variables['pg'].values.sum() == (orig_vals >= 0.2).sum() collection['pg'] = old_pg.clone(name='pg') transform.Threshold(collection, 'pg', threshold=0.2, binarize=False) assert collection.variables['pg'].values.sum() != (orig_vals >= 0.2).sum() coll_sum = (collection.variables['pg'].values >= 0.2).sum() assert coll_sum == (orig_vals >= 0.2).sum() collection['pg'] = old_pg.clone(name='pg') transform.Threshold(collection, 'pg', threshold=-0.1, binarize=True, signed=False, above=False) n = np.logical_and(orig_vals <= 0.1, orig_vals >= -0.1).sum() assert collection.variables['pg'].values.sum() == n def test_assign(collection): transform.Assign( collection, 'parametric gain', target='RT', target_attr='onset', output='test1' ) t1 = collection['test1'] pg = collection['parametric gain'] rt = collection['RT'] assert np.array_equal(t1.onset, pg.values.values) assert np.array_equal(t1.duration, rt.duration) assert np.array_equal(t1.values.values, rt.values.values) transform.Assign( collection, 'RT', target='parametric gain', input_attr='onset', target_attr='amplitude', output='test2', ) t2 = collection['test2'] assert np.array_equal(t2.values.values, rt.onset) assert np.array_equal(t2.onset, pg.onset) assert np.array_equal(t2.duration, pg.duration) def test_assign_multiple(collection): # test kwarg distribution transform.Assign( collection, ['RT', 'respcat'], target=['gain', 'loss'], input_attr=['amplitude', 'amplitude'], target_attr=['duration', 'amplitude'], output=['gain_rt', 'loss_cat'], ) rt = collection['RT'] gain_rt = collection['gain_rt'] loss_cat = collection['loss_cat'] rc = collection['respcat'] assert np.array_equal(gain_rt.duration, rt.values.values) assert np.array_equal(loss_cat.values.values, rc.values.values) def test_copy(collection): transform.Copy(collection, 'RT', output='RT_copy') assert 'RT_copy' in collection.variables.keys() assert np.array_equal(collection['RT'].values.values, collection['RT_copy'].values.values) def test_expand_variable_names(collection): # Should fail because two output values are required following expansion with pytest.raises(Exception): transform.Copy(collection, '*resp*') transform.Copy(collection, '*resp*', output_suffix='_copy') assert 'respnum_copy' in collection.variables.keys() assert 'respcat_copy' in collection.variables.keys() assert np.array_equal( collection['respcat'].values.values, collection['respcat_copy'].values.values ) assert np.array_equal( collection['respnum'].values.values, collection['respnum_copy'].values.values ) def test_factor(collection): # Full-rank dummy-coding, only one unique value in variable trial_type = collection.variables['trial_type'].clone() coll = collection.clone() transform.Factor(coll, 'trial_type', sep='@') assert 'trial_type@parametric gain' in coll.variables.keys() pg = coll.variables['trial_type@parametric gain'] assert pg.values.unique() == [1] assert pg.values.shape == trial_type.values.shape # Reduced-rank dummy-coding, only one unique value in variable coll = collection.clone() transform.Factor(coll, 'trial_type', constraint='mean_zero') assert 'trial_type.parametric gain' in coll.variables.keys() pg = coll.variables['trial_type.parametric gain'] assert pg.values.unique() == [1] assert pg.values.shape == trial_type.values.shape # full-rank dummy-coding, multiple values coll = collection.clone() transform.Factor(coll, 'respnum') targets = set(['respnum.%d' % d for d in range(0, 5)]) assert not targets - set(coll.variables.keys()) assert all([set(coll.variables[t].values.unique()) == {0.0, 1.0} for t in targets]) data = pd.concat([coll.variables[t].values for t in targets], axis=1, sort=True) assert (data.sum(axis=1) == 1).all() # reduced-rank dummy-coding, multiple values coll = collection.clone() transform.Factor(coll, 'respnum', constraint='drop_one') targets = set(['respnum.%d' % d for d in range(1, 5)]) assert not targets - set(coll.variables.keys()) assert 'respnum.0' not in coll.variables.keys() assert all([set(coll.variables[t].values.unique()) == {0.0, 1.0} for t in targets]) data = pd.concat([coll.variables[t].values for t in targets], axis=1, sort=True) assert set(np.unique(data.sum(axis=1).values.ravel())) == {0.0, 1.0} # Effect coding, multiple values coll = collection.clone() transform.Factor(coll, 'respnum', constraint='mean_zero') targets = set(['respnum.%d' % d for d in range(1, 5)]) assert not targets - set(coll.variables.keys()) assert 'respnum.0' not in coll.variables.keys() assert all([set(coll.variables[t].values.unique()) == {-0.25, 0.0, 1.0} for t in targets]) data = pd.concat([coll.variables[t].values for t in targets], axis=1, sort=True) assert set(np.unique(data.sum(axis=1).values.ravel())) == {-1.0, 1.0} def test_filter(collection): orig = collection['parametric gain'].clone() q = 'parametric gain > 0' transform.Filter(collection, 'parametric gain', query=q) assert len(orig.values) == 2 * len(collection['parametric gain'].values) assert np.all(collection['parametric gain'].values > 0) orig = collection['RT'].clone() q = 'parametric gain > 0.1' with pytest.warns(UserWarning, match='Forcing all sparse variables to dense.*'): transform.Filter(collection, 'RT', query=q, by='parametric gain') assert len(orig.values) != len(collection['RT'].values) assert len(collection['RT'].values) == 96 * len(SUBJECTS) def test_replace(collection): orig = collection['parametric gain'].clone() # Values replace_dict = {0.0335: 2.0, -0.139: 2.0} transform.Replace(collection, 'parametric gain', replace_dict) target = set(orig.values.unique()) - {0.0335, -0.139} | {2.0} assert set(collection['parametric gain'].values.unique()) == target # Durations replace_dict = {3: 2} transform.Replace(collection, 'parametric gain', replace_dict, 'duration') target = set(np.unique(orig.duration)) - {3} | {2.0} assert set(np.unique(collection['parametric gain'].duration)) == target # Onsets replace_dict = {4.0: 3.0, 476.0: 475.5} transform.Replace(collection, 'parametric gain', replace_dict, 'onset') target = set(np.unique(orig.onset)) - {4.0, 476.0} | {3.0, 475.5} assert set(np.unique(collection['parametric gain'].onset)) == target def test_select(collection): coll = collection.clone() keep = ['RT', 'parametric gain', 'respcat'] transform.Select(coll, keep) assert set(coll.variables.keys()) == set(keep) def test_delete(collection): coll = collection.clone() all_cols = set(coll.variables.keys()) drop = ['RT', 'parametric gain', 'respcat'] transform.Delete(coll, drop) assert all_cols - set(coll.variables.keys()) == set(drop) def test_and(collection): coll = collection.clone() transform.Factor(coll, 'respnum') names = ['respnum.%d' % d for d in range(0, 5)] coll.variables['respnum.0'].onset += 1 # Should fail because I misaligned variable with pytest.raises(ValueError): transform.And(coll, names, output='misaligned') # Should pass because dense is set to True and will align transform.And(coll, names, output='misaligned', dense=True) def test_or(collection): coll = collection.clone() transform.Factor(coll, 'respnum') names = ['respnum.%d' % d for d in range(0, 5)] transform.Or(coll, names, output='disjunction') assert (coll.variables['disjunction'].values == 1).all() coll['copy'] = coll.variables['respnum.0'].clone(name='copy') transform.Or(coll, ['respnum.0', 'copy'], output='or') assert coll.variables['or'].values.astype(float).equals(coll.variables['respnum.0'].values) def test_not(collection): coll = collection.clone() pre_rt = coll.variables['RT'].values.values transform.Not(coll, 'RT') post_rt = coll.variables['RT'].values.values assert (post_rt == ~pre_rt.astype(bool)).all() def test_dropna(sparse_run_variable_with_missing_values): var = sparse_run_variable_with_missing_values.variables['var'] coll = sparse_run_variable_with_missing_values.clone() transform.DropNA(coll, 'var') post_trans = coll.variables['var'] assert len(var.values) > len(post_trans.values) assert np.array_equal(post_trans.values, [1, 1, 1]) assert np.array_equal(post_trans.onset, [2, 5, 17]) assert np.array_equal(post_trans.duration, [1.2, 1.6, 2]) assert len(post_trans.index) == 3 def test_group(collection): coll = collection.clone() with pytest.raises(ValueError): # Can't use an existing variable name as the group name transform.Group(coll, ['gain', 'loss'], name='gain') transform.Group(coll, ['gain', 'loss'], name='outcome_vars') assert coll.groups == {'outcome_vars': ['gain', 'loss']} # Checks that variable groups are replaced properly transform.Rename(coll, ['outcome_vars'], output=['gain_renamed', 'loss_renamed']) assert 'gain_renamed' in coll.variables assert 'loss_renamed' in coll.variables assert 'gain' not in coll.variables assert 'loss' not in coll.variables def test_resample(collection): coll = collection.clone() transform.ToDense(coll, 'parametric gain', output='pg_dense') pg = coll.variables['pg_dense'] old_shape = pg.values.shape try: trapezoid = np.trapezoid except AttributeError: # NP1.26 trapezoid = np.trapz old_auc = trapezoid(np.abs(pg.values.values.squeeze()), dx=0.1) transform.Resample(coll, 'pg_dense', 1) pg = coll.variables['pg_dense'] new_shape = pg.values.shape # Spacing (dx) is 10* larger when downsampled from 10hz to 1hz new_auc = trapezoid(np.abs(pg.values.values.squeeze()), dx=1) # Shape from 10hz to 1hz assert new_shape[0] == old_shape[0] / 10 # Assert that the auc is more or less the same (not exact, rounding error) # Values are around 0.25 assert np.allclose(old_auc, new_auc, rtol=0.05) def test_Lag(): var = DenseRunVariable( name='rot_x', values=np.arange(5.0, 20.0), run_info=RunInfo({}, 15, 1, 'none', 15), source='regressors', sampling_rate=1, ) coll = BIDSRunVariableCollection([var], sampling_rate=1) # Forward shift transform.Lag(coll, 'rot_x', output='d_rot_x') d_rot_x = coll['d_rot_x'].values.values assert np.isclose(d_rot_x[0, 0], 5.0) assert np.allclose(d_rot_x[1:, 0], np.arange(5.0, 19.0)) # Backward shift transform.Lag(coll, 'rot_x', output='d_rot_x', shift=-1) d_rot_x = coll['d_rot_x'].values.values assert np.isclose(d_rot_x[-1, 0], 19.0) assert np.allclose(d_rot_x[:-1, 0], np.arange(6.0, 20.0)) # Half shift; don't know why you'd want to do it, but you can transform.Lag(coll, 'rot_x', output='half_shift', shift=0.5, order=1) half_shift = coll['half_shift'].values.values assert np.isclose(half_shift[0, 0], 5.0) assert np.allclose(half_shift[1:, 0], np.arange(5.5, 19.5)) # Constant mode transform.Lag(coll, 'rot_x', output='d_rot_x', mode='constant') d_rot_x = coll['d_rot_x'].values.values assert np.isclose(d_rot_x[0, 0], 0.0) assert np.allclose(d_rot_x[1:, 0], np.arange(5.0, 19.0)) # Reflect mode transform.Lag(coll, 'rot_x', output='d_rot_x', mode='reflect') d_rot_x = coll['d_rot_x'].values.values assert np.isclose(d_rot_x[0, 0], 5.0) assert np.allclose(d_rot_x[1:, 0], np.arange(5.0, 19.0)) # Forward shift -> Backward difference transform.Lag(coll, 'rot_x', output='d_rot_x', difference=True) d_rot_x = coll['d_rot_x'].values.values assert np.isclose(d_rot_x[0, 0], 0.0) assert np.allclose(d_rot_x[1:, 0], 1.0) # Backward shift -> Forward difference transform.Lag(coll, 'rot_x', output='d_rot_x', shift=-1, difference=True) d_rot_x = coll['d_rot_x'].values.values assert np.isclose(d_rot_x[-1, 0], 0.0) assert np.allclose(d_rot_x[:-1, 0], 1.0) ================================================ FILE: src/bids/modeling/transformations/__init__.py ================================================ from .base import TransformerManager # noqa: D104 from .compute import ( And_, Convolve, Demean, Lag, Not, Or_, Orthogonalize, Product, Scale, Sum, Threshold, ) from .munge import ( Assign, Copy, Delete, DropNA, Factor, Filter, Group, Rename, Replace, Resample, Select, Split, ToDense, ) And = And_ Or = Or_ __all__ = [ 'And', 'And_', 'Assign', 'Convolve', 'Copy', 'Demean', 'Delete', 'DropNA', 'Factor', 'Filter', 'Group', 'Lag', 'Not', 'Or', 'Or_', 'Orthogonalize', 'Product', 'Rename', 'Replace', 'Resample', 'Scale', 'Select', 'Split', 'Sum', 'Threshold', 'ToDense', 'TransformerManager', ] ================================================ FILE: src/bids/modeling/transformations/base.py ================================================ """Base Transformation class and associated utilities.""" import inspect import itertools import re import warnings from abc import ABCMeta, abstractmethod from copy import deepcopy from dataclasses import dataclass import numpy as np import pandas as pd from bids.modeling import transformations as pbt from bids.utils import convert_JSON, listify from bids.variables import SparseRunVariable from bids.variables.collections import BIDSVariableCollection class Transformation(metaclass=ABCMeta): # noqa: D101 ### Class-level settings ### # The following settings govern the way Transformations are applied to the # data. The default settings can be overridden within subclasses. # List all argument names that specify variables used by Transformation. # This is necessary in order to ensure that all and only variables touched # by the transformation are cloned before any manipulation occurs. # variables in 'variables' are always cloned, so only additional arguments # should be specified here. _variables_used = () # What data type to pass onto the core _transform() logic. Must be one # of 'variable' (the entire BIDSVariable object), 'pandas' (the extracted # pandas DF stored in .values), or 'numpy' (just the numpy array inside # the .values property of the pandas DF). To minimize overhead and # simplify code, it is recommended to avoid using 'variable' if possible. _input_type = 'pandas' # The data type the internal _transform() method is expected to return. # Must be one of 'variable', 'pandas', 'numpy', or 'none'. In the last # case, all desired changes must be made in-place within _transform(), as # no further changes will be committed. _return_type = 'pandas' # Indicates if variables must be aligned with one another # (i.e., onsets and durations match perfectly) before processing. # If True, will require all variables to be aligned (throw exception). # If 'force', will auto-align variables by densification. # Defaults to None. _aligned_required = None # (Optional) If alignment is required,sets which variables must be aligned. # If None, all variables must be aligned. It can also be a a string # indicating which column contains the names of columns to be aligned, # or a tuple which explicitly names the columns to be aligned. _aligned_variables = None # Boolean indicating whether the Transformation should be applied to each # variable in the input list in turn. When True (default), Transformation # is applied once per element in the variable list, with all arguments # being passed repeatedly. When False, all data (i.e., variables or their # pandas DFs or ndarrays, as specified in _input_type) are passed to the # Transformation simultaneously. _loopable = True # Boolean indicating whether the Transformation can handle groupby # operations. When True, a 'groupby' argument is made implicitly available, # and if passed, the Transformation will be applied separately to each # subset of the data, as defined by the variables named in groupby. When # False, the Transformations does not allow grouping, and will raise an # exception if groupby is passed. Transformations should set this to False # if the groupby argument cannot possibly change the returned result. _groupable = True # Boolean indicating whether the output argument is mandatory. When False # (default), transformations will be applied in-place unless output is set. # When True, the user must explicitly specify the output, or an exception # is raised. _output_required = False # An implicit 'dense' argument is always available, and indicates whether # or not to operate on dense variables. When True, the arguments listed in # _densify control which variables will be densified. Defaults to variables # named in the 'variables' argument. Note that if this value is overridden, # 'variables' will need to be explicitly included (i.e., the subclass's # _densify tuple replaces the base class rather than appending to it). _densify = ('variables',) # Allow categorical variables in the input arguments? When None (default), # any categorical variables encountered as inputs will raise an exception. # Otherwise, a tuple giving the names of the arguments whose variables will # be passed through as-is even if categorical. _allow_categorical = None # Boolean indicating whether to treat each key word argument as a one-to-one # mapping with each variable or to treat the key word argument as applying to # every input variable. _sync_kwargs = True def __new__(cls, collection, variables, *args, **kwargs): # noqa: D102 t = super().__new__(cls) t._setup(collection, variables, *args, **kwargs) return t.transform() def _setup(self, collection, variables, *args, **kwargs): """Replaces __init__ to set instance attributes because on Python >= 3.3, we can't override both new and init. """ self.collection = collection self.variables = listify(variables) self.groupby = kwargs.pop('groupby', None) self.output = listify(kwargs.pop('output', None)) self.output_prefix = kwargs.pop('output_prefix', None) self.output_suffix = kwargs.pop('output_suffix', None) self.dense = kwargs.pop('dense', False) # Convert any args to named keyword arguments in order to make sure # that operations like densification, alignment, etc. correctly detect # all named arguments. if args: arg_spec = inspect.getfullargspec(self._transform) for i, arg_val in enumerate(args): # Skip first two argnames--they're always 'self' and # 'variables' kwargs[arg_spec.args[2 + i]] = arg_val self.kwargs = kwargs # listify kwargs if synced if self._sync_kwargs: for k, v in self.kwargs.items(): if not isinstance(v, list): self.kwargs[k] = [v] * len(self.variables) elif not len(self.kwargs[k]) == len(self.variables): raise ValueError(f'Length of {k} must match length of variables') # Expand any detected variable group names or wild cards self._expand_variable_groups() self._expand_variable_names() def _expand_variable_groups(self): """Replace any detected variable groups with the associated lists of variable names. """ groups = self.collection.groups variables = [groups[v] if v in groups else [v] for v in self.variables] self.variables = list(itertools.chain(*variables)) def _expand_variable_names(self): """Filter all available arguments against collection's variables using unix-style pattern matching. """ def _replace_arg_values(values): is_iter = isinstance(values, (list, tuple)) values = listify(values) result = [] # Only try to match strings containing a relevant special character for v in values: if isinstance(v, str) and re.search(r'[\*\?\[\]]', v): result.append(self.collection.match_variables(v)) else: result.append([v]) result = list(itertools.chain(*result)) # Don't return a list unless we have to if is_iter or len(result) > 1: return result return result[0] # 'variables' is stored separately, so handle it separately self.variables = _replace_arg_values(self.variables) for k, arg in self.kwargs.items(): self.kwargs[k] = _replace_arg_values(arg) def _clone_variables(self): """Deep copy all variables the transformation touches. This prevents us from unnecessarily overwriting existing variables. """ # Always clone the target variables self._variables = {v: self.collection[v].clone() for v in self.variables} if not self._variables_used: return # Loop over argument names and clone all variable names in each one for var in self._variables_used: for v in listify(self.kwargs.get(var, [])): # Kludge: we need to allow entity variables to be passed as # names even though they don't exist as separate variables if v not in self.collection.variables and v in [ 'task', 'run', 'session', 'subject', ]: continue self._variables[v] = deepcopy(self.collection[v]) def _check_categorical_variables(self): """Convert categorical variables to dummy-coded indicators.""" # Collect variable names to pass through pass_thru = [] if self._allow_categorical is not None: for arg in self._allow_categorical: keys = self.variables if arg == 'variables' else self.kwargs.get(arg, []) pass_thru.extend(listify(keys)) pass_thru = list(set(pass_thru)) for name, col in self._variables.items(): if name not in pass_thru: if col.values.values.dtype.kind not in 'bifc': msg = ( "The %s transformation does not allow variable '%s'" ' to be categorical. Either pass a different ' 'variable or explicitly convert to a set of binary ' "indicators via the 'factor' transformation." ) raise ValueError(msg % (self.__class__.__name__, name)) def _densify_variables(self): variables = [] for var in self._densify: if var == 'variables': variables.extend(self.variables) else: variables.extend(listify(self.kwargs.get(var, []))) for v in variables: var = self._variables[v] if isinstance(var, SparseRunVariable): sr = self.collection.sampling_rate self._variables[v] = var.to_dense(sr) def transform(self): # noqa: D102 output_passed = not ( self.output is None and self.output_prefix is None and self.output_suffix is None ) if not output_passed and self._output_required: raise ValueError( "Transformation '%s' requires output names to be " # noqa: UP031 "provided. Please set at least one of 'output'," "'output_prefix', or 'output_suffix'." % self.__class__.__name__ ) kwargs = self.kwargs # Deep copy all variables we expect to touch self._clone_variables() # Make sure we don't have categorical variables we can't handle self._check_categorical_variables() # Densify variables if needed if self.dense: self._densify_variables() # Set variables we plan to operate on directly variables = [self._variables[c] for c in self.variables] # Align variables if needed self._align_variables(variables) # Pass desired type--variable, DataFrame, or NDArray def select_type(col): return {'variable': col, 'pandas': col.values, 'numpy': col.values.values}[ self._input_type ] data = [select_type(c) for c in variables] if not self._loopable: variables = [variables] i_kwargs = kwargs for i, col in enumerate(variables): if self._sync_kwargs: i_kwargs = {k: v[i] for k, v in kwargs.items()} # If we still have a list, pass all variables in one block if isinstance(col, (list, tuple)): result = self._transform(data, **i_kwargs) if self._return_type not in ['none', None]: col = col[0].clone(data=result, name=self.output[0]) # Otherwise loop over variables individually else: if self._groupable and self.groupby is not None: result = col.apply(self._transform, groupby=self.groupby, **i_kwargs) else: result = self._transform(data[i], **i_kwargs) if self._return_type in ['none', None]: continue elif self._return_type == 'numpy': col.values = pd.DataFrame(result) elif self._return_type == 'pandas': col.values = result elif self._return_type == 'variable': col = result # Overwrite existing variable if not output_passed: # If multiple variables were returned, add each one separately if isinstance(result, (list, tuple)): for r in result: self.collection[r.name] = r else: self.collection[col.name] = col # Set as a new variable else: # Either assign new name in order, or reuse existing one if self.output is not None: n_vars = len(self.variables) n_output = len(self.output) if n_vars == n_output or not self._loopable: _output = self.output[i] elif n_output == 1: _output = str(self.output) + '_' + col.name else: msg = ( 'Number of output variable names in provided ' 'list ({}) does not match the number of variables' ' produced by the transformation ({}).' ) raise ValueError(msg.format(n_output, n_vars)) else: _output = col.name # Add prefix and suffix if provided if self.output_prefix is not None: _output = self.output_prefix + _output if self.output_suffix is not None: _output += self.output_suffix # If multiple variables were returned, add each one separately if isinstance(result, (list, tuple)): # rename first output result[0].name = _output self.collection[_output] = result[0] for r in result[1:]: self.collection[r.name] = r else: col.name = _output self.collection[_output] = col @abstractmethod def _transform(self, **kwargs): pass def _preprocess(self, col): return col def _postprocess(self, col): return col def _align_variables(self, variables): """Checks whether the specified variables have aligned indexes. This implies either that all variables are dense, or that all variables are sparse and have exactly the same onsets and durations. If variables are not aligned and force = True, all variables will be forced to dense format in order to ensure alignment. """ if self._aligned_required is None or self._aligned_required == 'none': return def _align(variables): # If any variable is dense, all variables must be dense sparse = [c for c in variables if isinstance(c, SparseRunVariable)] if len(sparse) < len(variables): if sparse: msg = ( 'Found a mix of dense and sparse variables. May ' 'cause problems for some transformations.' ) # This gets called deep in Transformations.__new__() warnings.warn(msg, stacklevel=5) # If all are sparse, durations, onsets, and index must match # perfectly for all else: def get_col_data(col): return np.c_[col.values.index, col.duration, col.onset] def compare_variables(a, b): return len(a) == len(b) and np.allclose(a, b) # Compare 1st col with each of the others fc = get_col_data(variables[0]) if not all([compare_variables(fc, get_col_data(c)) for c in variables[1:]]): # noqa: C419 if self._aligned_required == 'force_dense': msg = ( 'Forcing all sparse variables to dense in ' 'order to ensure proper alignment.' ) sr = self.collection.sampling_rate variables = [c.to_dense(sr) for c in variables] warnings.warn(msg, stacklevel=5) else: raise ValueError( 'Misaligned sparse variables found.' 'To force variables into alignment by densifying,' 'set dense=True in the Transformation arguments' ) _aligned_variables = True if not self._aligned_variables else self._aligned_variables _aligned_variables = [ listify(self.kwargs[v]) for v in listify(_aligned_variables) if v in self.kwargs ] _aligned_variables = list(itertools.chain(*_aligned_variables)) _aligned_variables = [self.collection[c] for c in _aligned_variables if c] if _aligned_variables and self._loopable: for c in variables: # TODO: should clone all variables in align_variables before # alignment to prevent conversion to dense in any given # iteration having side effects. This could be an issue if, # e.g., some vars in 'variables' are dense and some are sparse. _align([c] + _aligned_variables) else: _align(listify(variables) + _aligned_variables) @dataclass class TransformationOutput: # noqa: D101 index: int output: BIDSVariableCollection transformation_name: str transformation_kwargs: dict input_cols: list level: str class TransformerManager: """Handles registration and application of transformations to BIDSVariableCollections. Parameters ---------- default: object A module or other object containing default transformations as attributes. Any named transformation not explicitly registered on the TransformerManager instance is expected to be found here. If None, the PyBIDS transformations module is used. keep_history: bool Whether to keep snapshots variable after a transformation is applied. If True, a list of collections will be stored in the ``history_`` attribute. """ def __init__(self, default=None, keep_history=True): self.transformations = {} if default in (None, 'pybids-transforms-v1'): # Default to PyBIDS transformations default = pbt self.default = default self.keep_history = keep_history def _sanitize_name(self, name): """Replace any invalid/reserved transformation names with acceptable equivalents. Parameters ---------- name: str The name of the transformation to sanitize. """ if name in ('And', 'Or'): name += '_' return name def register(self, name, func): """Register a new transformation handler. Parameters ---------- name : str The name of the transformation to handle. func : callable The callable to invoke when the named transformation is applied. """ name = self._sanitize_name(name) self.transformations[name] = func def transform(self, collection, transformations): """Apply all transformations to the variables in the collection. Parameters ---------- collection: BIDSVariableCollection The BIDSVariableCollection containing variables to transform. transformations : list List of transformations to apply. """ if self.keep_history: self.history_ = [ TransformationOutput( index=0, output=collection.clone(), transformation_name=None, transformation_kwargs=None, input_cols=None, level=None, ) ] for ix, t in enumerate(transformations): t = convert_JSON(t) # make sure all keys are snake case kwargs = dict(t) name = self._sanitize_name(kwargs.pop('name')) cols = kwargs.pop('input', None) # Check registered transformations; fall back on default module func = self.transformations.get(name, None) if func is None: if not hasattr(self.default, name): raise ValueError( "No transformation '%s' found: either " # noqa: UP031 'explicitly register a handler, or pass a' ' default module that supports it.' % name ) func = getattr(self.default, name) # Apply the transformation func(collection, cols, **kwargs) # Take snapshot of collection after transformation if self.keep_history: self.history_.append( TransformationOutput( index=ix + 1, output=collection.clone(), transformation_name=name, transformation_kwargs=kwargs, input_cols=cols, level=collection.level, ) ) return collection ================================================ FILE: src/bids/modeling/transformations/compute.py ================================================ """Transformations that primarily involve numerical computation on variables.""" import math import numpy as np import pandas as pd from scipy import ndimage as ndi from bids.modeling import hrf from bids.utils import listify from bids.variables import DenseRunVariable, SparseRunVariable from .base import Transformation def _fractional_gcd(vals, res=0.001): from functools import reduce from math import gcd return reduce(gcd, (int(np.round(val / res)) for val in vals)) * res class Convolve(Transformation): """Convolve the input variable with an HRF. Parameters ---------- var : Variable The variable to convolve. model : str The name of the HRF model to apply. Must be one of 'spm', 'glover', or 'fir'. derivative : bool Whether or not to include the temporal derivative. dispersion : bool Whether or not to include the dispersion derivative. fir_delays : iterable A list or iterable of delays to use if model is 'fir' (ignored otherwise). Spacing between delays must be fixed. Notes ----- Uses the HRF convolution functions implemented in nilearn. """ _groupable = False _input_type = 'variable' _return_type = 'variable' _sync_kwargs = False def _transform(self, var, model='spm', derivative=False, dispersion=False, fir_delays=None): model = model.lower() df = var.to_df(entities=False) if isinstance(var, SparseRunVariable): sampling_rate = self.collection.sampling_rate dur = var.get_duration() resample_frames = np.linspace( 0, dur, int(math.ceil(dur * sampling_rate)), endpoint=False ) safety = 2 # Double frequency to resolve events else: resample_frames = df['onset'].values sampling_rate = var.sampling_rate safety = 1 # Maximum signal resolution is already 0.5 * SR vals = df[['onset', 'duration', 'amplitude']].values.T if model in ['spm', 'glover']: if derivative: model += ' + derivative' if dispersion: model += ' + dispersion' elif model != 'fir': raise ValueError("Model must be one of 'spm', 'glover', or 'fir'.") # Sampling at >100Hz will never be useful, but can be wildly expensive max_freq, min_interval = 100, 0.01 # Sampling at <1Hz can degrade signals min_freq, max_interval = 1, 1 # noqa: F841 # Given the sampling rate, determine an oversampling factor to ensure that # events can be modeled with reasonable precision unique_onsets = np.unique(df.onset) unique_durations = np.unique(df.duration) # Align existing data ticks with, event onsets and offsets, up to ms resolution # Note that GCD ignores zeros, so 0 onsets and impulse responses (0 durations) do # not harm this. required_resolution = _fractional_gcd( np.concatenate((unique_onsets, unique_durations)), res=min_interval ) # Bound the effective sampling rate between min_freq and max_freq effective_sr = max(min_freq, min(safety / required_resolution, max_freq)) convolved = hrf.compute_regressor( vals, model, resample_frames, fir_delays=fir_delays, min_onset=0, oversampling=int(np.ceil(effective_sr / sampling_rate)), ) results = [] arr, names = convolved for conv, name in zip(np.split(arr, arr.shape[1], axis=1), names): # noqa: B905 new_name = '_'.join([var.name, name.split('_')[-1]]) if '_' in name else var.name results.append( DenseRunVariable( name=new_name, values=conv, run_info=var.run_info, source=var.source, sampling_rate=sampling_rate, ) ) return results class Demean(Transformation): # noqa: D101 def _transform(self, data): return data - data.mean() class Orthogonalize(Transformation): # noqa: D101 _variables_used = ('variables', 'other') _densify = ('variables', 'other') _aligned_required = 'force_dense' _aligned_variables = 'other' _sync_kwargs = False def _transform(self, var, other): other = listify(other) # Set up X matrix and slice into it based on target variable indices X = np.array([self._variables[c].values.values.squeeze() for c in other]).T X = X[var.index, :] assert len(X) == len(var) # noqa: S101 y = var.values _aX = np.c_[np.ones(len(y)), X] coefs, resids, rank, s = np.linalg.lstsq(_aX, y, rcond=None) result = pd.DataFrame(y - X.dot(coefs[1:]), index=var.index) return result class Product(Transformation): # noqa: D101 _loopable = False _groupable = False _aligned_required = True _output_required = True def _transform(self, data): data = pd.concat(data, axis=1, sort=True) return data.product(axis=1) class Scale(Transformation): """Scale a variable. Parameters ---------- data : :obj:`pandas.Series` or :obj:`pandas.DataFrame` The variables to scale. demean : bool If True, demean each column. rescale : bool If True, divide variables by their standard deviation. replace_na : str Whether/when to replace missing values with 0. If None, no replacement is performed. If 'before', missing values are replaced with 0's before scaling. If 'after', missing values are replaced with 0 after scaling. Notes ----- If a constant column is passed in, and replace_na is None or 'before', an exception will be raised. """ def _transform(self, data, demean=True, rescale=True, replace_na=None): if data.nunique() == 1 and replace_na in {None, 'before'}: val = data.unique()[0] raise ValueError( f'Cannot scale a column with constant value ({val})! ' "If you want a constant column of 0's returned, " "set replace_na to 'after'." ) if replace_na == 'before': data = data.fillna(0.0) if demean: data -= data.mean() if rescale: data /= data.std() if replace_na == 'after': data = data.fillna(0.0) return data class Sum(Transformation): # noqa: D101 _loopable = False _groupable = False _aligned_required = True _output_required = True _sync_kwargs = False def _transform(self, data, weights=None): data = pd.concat(data, axis=1, sort=True) if weights is None: weights = np.ones(data.shape[1]) else: weights = np.array(weights) if len(weights.ravel()) != data.shape[1]: raise ValueError( 'If weights are passed to sum(), the number ' 'of elements must equal number of variables' ' being summed.' ) return (data * weights).sum(axis=1) class Threshold(Transformation): """Threshold and/or binarize a variable. Parameters ---------- data :obj:`pandas.Series` or :obj:`pandas.DataFrame` The pandas structure to threshold. threshold : float The value to binarize around (values above will be assigned 1, values below will be assigned 0). binarize : bool If True, binarizes all non-zero values (i.e., every non-zero value will be set to 1). above : bool Specifies which values to retain with respect to the cut-off. If True, all value above the threshold will be kept; if False, all values below the threshold will be kept. Defaults to True. signed : bool Specifies whether to treat the threshold as signed (default) or unsigned. For example, when passing above=True and threshold=3, if signed=True, all and only values above +3 would be retained. If signed=False, all absolute values > 3 would be retained (i.e.,values in the range -3 < X < 3 would be set to 0). """ _groupable = False def _transform(self, data, threshold=0.0, binarize=False, above=True, signed=True): if not signed: threshold = np.abs(threshold) data = data.abs() keep = data >= threshold if above else data <= threshold data[~keep] = 0 if binarize: data[keep] = 1 return data class And_(Transformation): """Logical AND on two or more variables. Parameters ---------- dfs : list of :obj:`pandas.DataFrame` variables to enter into the conjunction. """ _loopable = False _groupable = False _output_required = True _aligned_required = True _sync_kwargs = False def _transform(self, dfs): df = pd.concat(dfs, axis=1, sort=True) return df.all(axis=1).astype(int) class Not(Transformation): """Logical negation of a variable. Parameters ---------- var : :obj:`pandas.Series` Variable to negate. Must be convertible to bool. """ _loopable = True _groupable = False sync_kwargs = False def _transform(self, var): return ~var.astype(bool) class Or_(Transformation): """Logical OR (inclusive) on two or more variables. Parameters ---------- dfs : list of :obj:`pandas.DataFrame` variables to enter into the disjunction. """ _loopable = False _groupable = False _output_required = True _aligned_required = True sync_kwargs = False def _transform(self, dfs): df = pd.concat(dfs, axis=1, sort=True) return df.any(axis=1).astype(int) class Lag(Transformation): """Lag variable Returns a variable that is lagged by a specified number of time points. Spline interpolation of the requested ``order`` is used for non-integer shifts. Points outside the input are filled according to the given ``mode``. Negative shifts move values toward the beginning of the sequence. If ``difference`` is ``True``, the backward difference is calculated for positive shifts, and the forward difference is calculated for negative shifts. Additional ``mode``s may be defined if there is need for them. The ``scipy.ndimage.shift`` method provides the reference implementation for all current modes. "Constant" is equivalent to the shift parameter "cval". Parameters ---------- var : :obj:`numpy.ndarray` variable to lag shift : float, optional number of places to shift the values (default: 1) order : int, optional order of spline interpolation, from 0-5 (default: 3) mode : string the `mode` parameter determines how the input array is extended beyond its boundaries. Default is 'nearest'. The following values are accepted: "nearest" (a a a a | a b c d | d d d d) The input is extended by replicating the boundary values "reflect" (d c b a | a b c d | d c b a) The input is extended by reflecting the array over the edge "constant" (k k k k | a b c d | k k k k) The input is extended by filling all values beyond the edge with the same constant value, defined by the ``constant`` parameter constant : float, optional value to fill past edges of input if ``mode`` is ``"constant"``. (default: 0) difference : boolean, optional Calculate the backward (if shift is positive) or forward (if shift is negative) difference. For the forward difference dx of an array x, dx[i] = x[i+1] - x[i]. For the backward difference dx of an array x, dx[i] = x[i] - x[i-1]. (default: ``False``) """ _input_type = 'numpy' _return_type = 'numpy' def _transform(self, var, shift=1, order=3, mode='nearest', constant=0.0, difference=False): var = var.flatten() shifted = ndi.shift(var, shift=shift, order=order, mode=mode, cval=constant) if not difference: return shifted elif shift >= 0: return var - shifted else: return shifted - var ================================================ FILE: src/bids/modeling/transformations/munge.py ================================================ """Transformations that primarily involve manipulating/munging variables into other formats or shapes. """ import re # Might not be needed since python 3.7 # See https://github.com/bids-standard/pybids/issues/423 for more info from collections import OrderedDict as odict import numpy as np import pandas as pd from formulaic import model_matrix from bids.utils import listify from bids.variables import DenseRunVariable, SimpleVariable from .base import Transformation class Assign(Transformation): """Assign one variable's amplitude, duration, or onset attribute to another. """ _groupable = False _input_type = 'variable' _return_type = 'variable' _allow_categorical = ('variables', 'target') def _transform(self, input, target, input_attr='amplitude', target_attr='amplitude'): # noqa: A002 # assign only makes sense for sparse variables; dense variables don't # have durations or onsets, and amplitudes can be copied by cloning if isinstance(input, DenseRunVariable): raise ValueError( "The 'assign' transformation can only be applied" # noqa: UP031 ' to sparsely-coded event types. The input ' 'variable (%s) is dense.' % input.name ) target = self.collection.variables[target].clone() if isinstance(target, DenseRunVariable): raise ValueError( "The 'assign' transformation can only be applied" # noqa: UP031 ' to sparsely-coded event types. The target ' 'variable (%s) is dense.' % target.name ) # Ensure attributes are valid valid_attrs = ['amplitude', 'duration', 'onset'] if input_attr not in valid_attrs: raise ValueError('Valid values for input_attr are: %s.' % valid_attrs) # noqa: UP031 if target_attr not in valid_attrs: raise ValueError('Valid values for target_attr are: %s.' % valid_attrs) # noqa: UP031 # variables must have same number of events, but do *not* need to have # aligned onsets. l_s, l_t = len(input.values), len(target.values) if l_s != l_t: raise ValueError( 'Input and target variables do not contain the ' # noqa: UP031 'same number of events (%d vs. %d).' % (l_s, l_t) ) if input_attr.startswith('amplitude'): vals = input.values.values else: vals = getattr(input, input_attr) if target_attr.startswith('amplitude'): target.values[:] = vals else: setattr(target, target_attr, vals) return target class Copy(Transformation): """Copy/clone a variable. Parameters ---------- col : str Name of variable to copy. """ _groupable = False _output_required = True _allow_categorical = ('variables',) def _transform(self, col): # We don't have to do anything else here b/c it's handled in base. return col class Delete(Transformation): """Delete variables from the namespace. Parameters ---------- variables : list or str Name(s) of variables to delete. """ _groupable = False _loopable = False _input_type = 'variable' _return_type = 'none' _allow_categorical = ('variables',) def _transform(self, variables): variables = set([v.name for v in variables]) # noqa: C403 self.collection.variables = { k: v for k, v in self.collection.variables.items() if k not in variables } class DropNA(Transformation): # noqa: D101 _groupable = False _input_type = 'variable' _return_type = 'variable' _allow_categorical = ('variables',) def _transform(self, var): # Identify non-NA rows valid = var.values.notna().values var.select_rows(valid) return var class Factor(Transformation): # noqa: D101 _groupable = False _input_type = 'variable' _return_type = 'variable' _allow_categorical = ('variables',) def _transform(self, var, constraint='none', ref_level=None, sep='.'): result = [] data = var.to_df() orig_name = var.name variableClass = var.__class__ levels = np.sort(data['amplitude'].unique()) new_cols = pd.get_dummies(data['amplitude'], drop_first=False, dtype=float)[levels] if len(levels) > 1 and constraint in ('drop_one', 'mean_zero'): if ref_level is None: ref_level = levels[0] new_cols = new_cols.drop(ref_level, axis=1) if constraint == 'mean_zero': ref_inds = data['amplitude'] == ref_level new_cols.loc[ref_inds, :] = -1.0 / (len(levels) - 1) for lev in levels: if ref_level is not None and lev == ref_level: continue name = ''.join([var.name, sep, str(lev)]) lev_data = data.copy() lev_data['amplitude'] = new_cols[lev] args = [name, lev_data, var.source] if hasattr(var, 'run_info'): args.insert(2, var.run_info) new_col = variableClass(*args) result.append(new_col) # Remove existing variable. TODO: allow user to leave original in? self.collection.variables.pop(orig_name) return result class Filter(Transformation): """Filter (remove) the values given a query to satisfy""" _groupable = False _input_type = 'variable' _return_type = 'variable' _aligned_required = 'force_dense' _aligned_variables = 'by' _allow_categorical = ('variables', 'by') def _transform(self, var, query, by=None): if by is None: by = [] names = [var.name] + listify(by) # assure ordered dict so we have consistent (if not correct) operation, # because later we ask for name_map.values # pandas .query can't handle non-identifiers in variable names, so we # need to replace them in both the variable names and the query string. name_map = odict((n, re.sub('[^a-zA-Z0-9_]+', '_', n)) for n in names) for k, v in name_map.items(): query = query.replace(k, v) data = pd.concat([self.collection[n].values for n in names], axis=1, sort=True) # Make sure we can use integer index data = data.reset_index(drop=True) data.columns = list(name_map.values()) data = data.query(query) # Truncate target variable to retained rows var.select_rows(data.index.values) return var class Group(Transformation): """Groups a list of variables.""" _groupable = False _loopable = False _input_type = 'variable' _return_type = 'none' def _transform(self, variables, name): if name in self.variables: raise ValueError( f"Variable group name '{name}' conflicts with an existing variable name!" ) self.collection.groups[name] = [v.name for v in variables] class Rename(Transformation): """Rename a variable. Parameters ---------- var : str Name of existing variable to rename. """ _groupable = False _output_required = True _input_type = 'variable' _allow_categorical = ('variables',) def _transform(self, var): # Rename happens automatically in the base class, so all we need to # do is unset the original variable in the collection. self.collection.variables.pop(var.name) return var.values class Replace(Transformation): """Replace values in the values, onset, or duration attributes.""" _groupable = False _input_type = 'variable' _return_type = 'variable' _allow_categorical = ('variables',) def _transform(self, var, replace, attribute='value'): if attribute == 'value': var.values = var.values.replace(replace) elif attribute == 'onset': var.onset = pd.Series(var.onset).replace(replace).values elif attribute == 'duration': var.duration = pd.Series(var.duration).replace(replace).values else: raise ValueError("Invalid attribute. Must be one of 'value','onset', or 'duration'.") return var class Select(Transformation): """Select variables to retain. Parameters ---------- variables : list or str Name(s) of variables to retain. All variables not in the list will be dropped from the collection. """ _groupable = False _loopable = False _input_type = 'variable' _return_type = 'none' _allow_categorical = ('variables',) def _transform(self, variables): self.collection.variables = {v.name: v for v in variables} class Split(Transformation): """Split a single variable into N variables as defined by the levels of one or more other variables. Parameters ---------- by : str or list Name(s) of variable(s) to split on. """ _variables_used = ('variables', 'by') _groupable = False _input_type = 'variable' _return_type = 'variable' _allow_categorical = ('by',) _densify = ('variables', 'by') _sync_kwargs = False def _transform(self, var, by): if not isinstance(var, SimpleVariable): self._densify_variables() # Set up all the splitting variables as a DF. Note that variables in # 'by' can be either regular variables, or entities in the index--so # we need to check both places. all_variables = self._variables by_variables = [ all_variables[v].values if v in all_variables else var.index[v].reset_index(drop=True) for v in listify(by) ] group_data = pd.concat(by_variables, axis=1, sort=True) group_data.columns = listify(by) # Use formulaic to create splitting design matrix group_data = group_data.astype(str) formula = '0+' + ':'.join(listify(by)) dm = model_matrix(formula, data=group_data) dm.columns = [col.replace(':', '.') for col in dm.columns] # formulaic output naming convention differs from patsy dm.columns = [col.replace('[T.', '[') for col in dm.columns] return var.split(dm) class ToDense(Transformation): """Convert variable to dense representation.""" _groupable = False _input_type = 'variable' _return_type = 'variable' def _transform(self, var, sampling_rate=10): return var.to_dense(sampling_rate=sampling_rate) class Resample(Transformation): """Frequency concersion and resampling of variable""" _groupable = False _input_type = 'variable' _return_type = 'variable' def _transform(self, var, sampling_rate=10): return var.resample(sampling_rate=sampling_rate) ================================================ FILE: src/bids/reports/README.md ================================================ ## PyBIDS: Reports The PyBIDS reports module generates publication-quality data acquisition descriptions from BIDS datasets. NOTE: The reports module is experimental and currently under active development, and as such should be used with caution. Please remember to verify any generated report before putting it to use. Additionally, only MRI datatypes (func, anat, fmap, and dwi) are currently supported. ### Quickstart A simple example of standard usage follows. We assume that we have a root folder containing a BIDS-compliant project in `/bidsproject`. ```python from bids.layout import BIDSLayout from bids.reports import BIDSReport # Load the BIDS dataset layout = BIDSLayout('/bidsproject') # Initialize a report for the dataset report = BIDSReport(layout) # Method generate returns a Counter of unique descriptions across subjects descriptions = report.generate() # For datasets containing a single study design, all but the most common # description most likely reflect random missing data. pub_description = descriptions.most_common()[0][0] ``` ================================================ FILE: src/bids/reports/__init__.py ================================================ from .report import BIDSReport # noqa: D104 __all__ = ['BIDSReport'] ================================================ FILE: src/bids/reports/config/converters.json ================================================ { "dir": { "i": "left to right", "i-": "right to left", "j": "posterior to anterior", "j-": "anterior to posterior", "k": "inferior to superior", "k-": "superior to inferior" }, "seq": { "EP": "echo planar", "GR": "gradient recalled", "IR": "inversion recovery", "RM": "research mode", "SE": "spin echo" }, "seqvar": { "MP": "MAG prepared", "MTC": "magnetization transfer contrast", "NONE": "no sequence variant", "OSP": "oversampling phase", "SK": "segmented k-space", "SP": "spoiled", "SS": "steady state", "TRSS": "time reversed steady state" } } ================================================ FILE: src/bids/reports/conftest.py ================================================ import json # noqa: D100 import warnings from importlib.resources import files import pytest from bids.layout import BIDSLayout @pytest.fixture(scope='session') def testlayout(tests_dir): """A BIDSLayout for testing.""" return BIDSLayout(tests_dir / 'data' / 'synthetic') @pytest.fixture(scope='session') def config_file(): # noqa: D103 # PY39: config isn't a module, so can't be used in files() # This is relaxed in more recent Pythons return files('bids.reports') / 'config' / 'converters.json' @pytest.fixture(scope='session') def testconfig(config_file): # noqa: D103 return json.loads(config_file.read_text()) @pytest.fixture(autouse=True) def _unsupported_files(): with warnings.catch_warnings(): warnings.filterwarnings( 'ignore', message=r'.*magnitude.* not yet supported.*', category=UserWarning, append=True, ) yield ================================================ FILE: src/bids/reports/parameters.py ================================================ """Functions for building strings for individual parameters.""" import logging import math import os import os.path as op import nibabel as nib from num2words import num2words from .utils import list_to_str, num_to_str, remove_duplicates LOGGER = logging.getLogger('pybids.reports.parsing') def describe_slice_timing(img, metadata: dict) -> str: """Generate description of slice timing from metadata.""" if 'SliceTiming' in metadata: slice_order = ' in {0} order'.format(get_slice_info(metadata['SliceTiming'])) # noqa: UP030 n_slices = len(metadata['SliceTiming']) else: slice_order = '' n_slices = img.shape[2] return f'{n_slices} slices{slice_order}' def describe_repetition_time(metadata: dict): """Generate description of repetition time from metadata.""" tr = metadata['RepetitionTime'] * 1000 tr = num_to_str(tr) return f'repetition time, TR={tr}ms' def describe_func_duration(n_vols: int, tr) -> str: """Generate description of functional run length from repetition time and number of volumes.""" run_secs = math.ceil(n_vols * tr) mins, secs = divmod(run_secs, 60) return f'{int(mins)}:{int(secs):02.0f}' def describe_duration(files) -> str: """Generate general description of scan length from files.""" first_file = files[0] metadata = first_file.get_metadata() tr = metadata['RepetitionTime'] imgs = [nib.load(f) for f in files] n_vols = [img.shape[3] for img in imgs] if len(set(n_vols)) > 1: min_vols = min(n_vols) max_vols = max(n_vols) min_dur = describe_func_duration(min_vols, tr) max_dur = describe_func_duration(max_vols, tr) dur_str = f'{min_dur}-{max_dur}' n_vols = f'{min_vols}-{max_vols}' else: n_vols = n_vols[0] dur_str = describe_func_duration(n_vols, tr) dur_str = f'Run duration was {dur_str} minutes, during which {n_vols} volumes were acquired.' return dur_str def describe_multiband_factor(metadata) -> str: """Generate description of the multi-band acceleration applied, if used.""" return ( 'MB factor={}'.format(metadata['MultibandAccelerationFactor']) if metadata.get('MultibandAccelerationFactor', 1) > 1 else '' ) def describe_echo_times(files): """Generate description of echo times from metadata field. Parameters ---------- files : :obj:`list` of :obj:`bids.layout.models.BIDSFile` List of nifti files in layout corresponding to file collection. Returns ------- te_str : str Description of echo times. me_str : str Whether the data are multi-echo or single-echo. """ echo_times = [f.get_metadata()['EchoTime'] for f in files] echo_times = sorted(list(set(echo_times))) # noqa: C414 if len(echo_times) > 1: te = [num_to_str(t * 1000) for t in echo_times] te = list_to_str(te) me_str = 'multi-echo' else: te = num_to_str(echo_times[0] * 1000) me_str = 'single-echo' te_str = f'echo time, TE={te}ms' return te_str, me_str def describe_echo_times_fmap(files): """Generate description of echo times from metadata field for fmaps Parameters ---------- files : :obj:`list` of :obj:`bids.layout.models.BIDSFile` List of nifti files in layout corresponding to file collection. Returns ------- te_str : str Description of echo times. """ # TODO handle all types of fieldmaps echo_times1 = [f.get_metadata()['EchoTime1'] for f in files] echo_times2 = [f.get_metadata()['EchoTime2'] for f in files] echo_times1 = sorted(list(set(echo_times1))) # noqa: C414 echo_times2 = sorted(list(set(echo_times2))) # noqa: C414 if len(echo_times1) <= 1 and len(echo_times2) <= 1: # if that's not the case we should probably throw a warning # because we should expect the same echo times for all values te1 = num_to_str(echo_times1[0] * 1000) te2 = num_to_str(echo_times2[0] * 1000) return f'echo time 1 / 2, TE1/2={te1}{te2}ms' def describe_image_size(img): """Generate description imaging data sizes, including FOV, voxel size, and matrix size. Parameters ---------- img : nibabel.nifti1.Nifti1Image Image object from which to determine sizes. Returns ------- fov_str matrixsize_str voxelsize_str """ vs_str, ms_str, fov_str = get_size_str(img) fov_str = f'field of view, FOV={fov_str}mm' voxelsize_str = f'voxel size={vs_str}mm' matrixsize_str = f'matrix size={ms_str}' return fov_str, matrixsize_str, voxelsize_str def describe_inplane_accel(metadata: dict) -> str: """Generate description of in-plane acceleration factor, if any.""" return ( 'in-plane acceleration factor={}'.format(metadata['ParallelReductionFactorInPlane']) if metadata.get('ParallelReductionFactorInPlane', 1) > 1 else '' ) def describe_flip_angle(metadata: dict) -> str: """Generate description of flip angle.""" return 'flip angle, FA={}'.format(metadata.get('FlipAngle', 'UNKNOWN')) def describe_dmri_directions(img): """Generate description of diffusion directions.""" return f'{img.shape[3]} diffusion directions' def describe_bvals(bval_file) -> str: """Generate description of dMRI b-values.""" # Parse bval file with open(bval_file) as file_object: raw_bvals = file_object.read().splitlines() # Flatten list of space-separated values bvals = [item for sublist in [line.split(' ') for line in raw_bvals] for item in sublist] bvals = sorted([int(v) for v in set(bvals)]) bvals = [num_to_str(v) for v in bvals] bval_str = list_to_str(bvals) bval_str = f'b-values of {bval_str} acquired' return bval_str def describe_pe_direction(metadata: dict, config: dict) -> str: """Generate description of phase encoding direction.""" dir_str = config['dir'][metadata['PhaseEncodingDirection']] dir_str = f'phase encoding: {dir_str}' return dir_str def describe_intendedfor_targets(metadata: dict, layout) -> str: """Generate description of intended for targets.""" if 'IntendedFor' in metadata.keys(): scans = metadata['IntendedFor'] run_dict = {} for scan in scans: fn = op.basename(scan) if_file = [f for f in layout.get(extension=['.nii', '.nii.gz']) if fn in f.path][0] run_num = int(if_file.entities['run']) target_type = if_file.entities['suffix'].upper() if target_type == 'BOLD': iff_meta = layout.get_metadata(if_file.path) task = iff_meta.get('TaskName', if_file.entities['task']) target_type_str = f'{task} {target_type} scan' else: target_type_str = f'{target_type} scan' if target_type_str not in run_dict.keys(): run_dict[target_type_str] = [] run_dict[target_type_str].append(run_num) for scan in run_dict.keys(): run_dict[scan] = [num2words(r, ordinal=True) for r in sorted(run_dict[scan])] out_list = [] for scan in run_dict.keys(): if len(run_dict[scan]) > 1: s = 's' else: s = '' run_str = list_to_str(run_dict[scan]) string = f'{run_str} run{s} of the {scan}' out_list.append(string) for_str = f' for the {list_to_str(out_list)}' else: for_str = '' return for_str def get_slice_info(slice_times) -> str: """Extract slice order from slice timing info. TODO: Be more specific with slice orders. Currently anything where there's some kind of skipping is interpreted as interleaved of some kind. Parameters ---------- slice_times : array-like A list of slice times in seconds or milliseconds or whatever. Returns ------- slice_order_name : :obj:`str` The name of the slice order sequence. """ # Slice order slice_times = remove_duplicates(slice_times) slice_order = sorted(range(len(slice_times)), key=lambda k: slice_times[k]) if slice_order == list(range(len(slice_order))): slice_order_name = 'sequential ascending' elif slice_order == list(reversed(range(len(slice_order)))): slice_order_name = 'sequential descending' elif slice_order[0] < slice_order[1]: # We're allowing some wiggle room on interleaved. slice_order_name = 'interleaved ascending' elif slice_order[0] > slice_order[1]: slice_order_name = 'interleaved descending' else: slice_order = [str(s) for s in slice_order] raise Exception('Unknown slice order: [{0}]'.format(', '.join(slice_order))) # noqa: UP030 return slice_order_name def describe_sequence(metadata: dict, config: dict): """Extract and reformat imaging sequence(s) and variant(s) into pretty strings. Parameters ---------- metadata : :obj:`dict` The metadata for the scan. config : :obj:`dict` A dictionary with relevant information regarding sequences, sequence variants, phase encoding directions, and task names. Returns ------- seqs : :obj:`str` Sequence names. variants : :obj:`str` Sequence variant names. """ seq_abbrs = metadata.get('ScanningSequence', '').split('_') seqs = [config['seq'].get(seq, seq) for seq in seq_abbrs] seqs = list_to_str(seqs) if seq_abbrs[0]: seqs += f' ({os.path.sep.join(seq_abbrs)})' variants = [ config['seqvar'].get(var, var) for var in metadata.get('SequenceVariant', '').split('_') ] variants = list_to_str(variants) return seqs, variants def get_size_str(img): """Extract and reformat voxel size, matrix size, FOV, and number of slices into strings. Parameters ---------- img : :obj:`nibabel.Nifti1Image` Image from scan from which to derive parameters. Returns ------- voxel_size : :obj:`str` Voxel size string (e.g., '2x2x2') matrix_size : :obj:`str` Matrix size string (e.g., '128x128') fov : :obj:`str` Field of view string (e.g., '256x256') """ n_x, n_y = img.shape[:2] import numpy as np matrix_size = f'{num_to_str(n_x)}x{num_to_str(n_y)}' voxel_dims = np.array(img.header.get_zooms()[:3]) voxel_size = 'x'.join([num_to_str(s) for s in voxel_dims]) fov = [n_x, n_y] * voxel_dims[:2] fov = 'x'.join([num_to_str(s) for s in fov]) return voxel_size, matrix_size, fov ================================================ FILE: src/bids/reports/parsing.py ================================================ """Parsing functions for generating BIDSReports.""" import logging import warnings import nibabel as nib from num2words import num2words from .. import __version__ from ..utils import collect_associated_files from . import parameters LOGGER = logging.getLogger('pybids.reports.parsing') def func_info(layout, files, config): """Generate a paragraph describing T2*-weighted functional scans. Parameters ---------- layout : :obj:`bids.layout.BIDSLayout` Layout object for a BIDS dataset. files : :obj:`list` of :obj:`bids.layout.models.BIDSFile` List of nifti files in layout corresponding to DWI scan. config : :obj:`dict` A dictionary with relevant information regarding sequences, sequence variants, phase encoding directions, and task names. Returns ------- desc : :obj:`str` A description of the scan's acquisition information. """ first_file = files[0] metadata = first_file.get_metadata() img = nib.load(first_file.path) # General info task_name = first_file.get_entities()['task'] + ' task' task_name = metadata.get('TaskName', task_name) seqs, variants = parameters.describe_sequence(metadata, config) all_runs = sorted(list(set([f.get_entities().get('run', 1) for f in files]))) # noqa: C403, C414 n_runs = len(all_runs) if n_runs == 1: run_str = f'{num2words(n_runs).title()} run' else: run_str = f'{num2words(n_runs).title()} runs' dur_str = parameters.describe_duration(files) # Parameters slice_str = parameters.describe_slice_timing(img, metadata) tr_str = parameters.describe_repetition_time(metadata) te_str, me_str = parameters.describe_echo_times(files) fa_str = parameters.describe_flip_angle(metadata) fov_str, matrixsize_str, voxelsize_str = parameters.describe_image_size(img) mb_str = parameters.describe_multiband_factor(metadata) inplaneaccel_str = parameters.describe_inplane_accel(metadata) parameters_str = [ slice_str, tr_str, te_str, fa_str, fov_str, matrixsize_str, voxelsize_str, mb_str, inplaneaccel_str, ] parameters_str = [d for d in parameters_str if len(d)] parameters_str = '; '.join(parameters_str) desc = ( f'{run_str} of {task_name} {variants} {seqs} {me_str} fMRI data were ' f'collected ({parameters_str}). {dur_str}' ) return desc def anat_info(layout, files, config): """Generate a paragraph describing T1- and T2-weighted structural scans. Parameters ---------- layout : :obj:`bids.layout.BIDSLayout` Layout object for a BIDS dataset. files : :obj:`list` of :obj:`bids.layout.models.BIDSFile` List of nifti files in layout corresponding to DWI scan. config : :obj:`dict` A dictionary with relevant information regarding sequences, sequence variants, phase encoding directions, and task names. Returns ------- desc : :obj:`str` A description of the scan's acquisition information. """ first_file = files[0] metadata = first_file.get_metadata() img = nib.load(first_file.path) # General info seqs, variants = parameters.describe_sequence(metadata, config) all_runs = sorted(list(set([f.get_entities().get('run', 1) for f in files]))) # noqa: C403, C414 n_runs = len(all_runs) if n_runs == 1: run_str = f'{num2words(n_runs).title()} run' else: run_str = f'{num2words(n_runs).title()} runs' scan_type = first_file.get_entities()['suffix'].replace('w', '-weighted') # Parameters slice_str = parameters.describe_slice_timing(img, metadata) tr_str = parameters.describe_repetition_time(metadata) te_str, me_str = parameters.describe_echo_times(files) fa_str = parameters.describe_flip_angle(metadata) fov_str, matrixsize_str, voxelsize_str = parameters.describe_image_size(img) parameters_str = [ slice_str, tr_str, te_str, fa_str, fov_str, matrixsize_str, voxelsize_str, ] parameters_str = [d for d in parameters_str if len(d)] parameters_str = '; '.join(parameters_str) desc = ( f'{run_str} of {scan_type} {variants} {seqs} {me_str} structural MRI ' f'data were collected ({parameters_str}).' ) return desc def dwi_info(layout, files, config): """Generate a paragraph describing DWI scan acquisition information. Parameters ---------- layout : :obj:`bids.layout.BIDSLayout` Layout object for a BIDS dataset. files : :obj:`list` of :obj:`bids.layout.models.BIDSFile` List of nifti files in layout corresponding to DWI scan. config : :obj:`dict` A dictionary with relevant information regarding sequences, sequence variants, phase encoding directions, and task names. Returns ------- desc : :obj:`str` A description of the DWI scan's acquisition information. """ first_file = files[0] metadata = first_file.get_metadata() img = nib.load(first_file.path) bval_file = first_file.path.replace('.nii.gz', '.bval').replace('.nii', '.bval') # General info seqs, variants = parameters.describe_sequence(metadata, config) all_runs = sorted(list(set([f.get_entities().get('run', 1) for f in files]))) # noqa: C403, C414 n_runs = len(all_runs) if n_runs == 1: run_str = f'{num2words(n_runs).title()} run' else: run_str = f'{num2words(n_runs).title()} runs' # Parameters tr_str = parameters.describe_repetition_time(metadata) te_str, me_str = parameters.describe_echo_times(files) fa_str = parameters.describe_flip_angle(metadata) fov_str, voxelsize_str, matrixsize_str = parameters.describe_image_size(img) bval_str = parameters.describe_bvals(bval_file) nvec_str = parameters.describe_dmri_directions(img) mb_str = parameters.describe_multiband_factor(metadata) parameters_str = [ tr_str, te_str, fa_str, fov_str, matrixsize_str, voxelsize_str, bval_str, nvec_str, mb_str, ] parameters_str = [d for d in parameters_str if len(d)] parameters_str = '; '.join(parameters_str) desc = ( f'{run_str} of {variants} {seqs} diffusion-weighted (dMRI) data were ' f'collected ({parameters_str}).' ) return desc def fmap_info(layout, files, config): """Generate a paragraph describing field map acquisition information. Parameters ---------- layout : :obj:`bids.layout.BIDSLayout` Layout object for a BIDS dataset. files : :obj:`list` of :obj:`bids.layout.models.BIDSFile` List of nifti files in layout corresponding to field map scan. config : :obj:`dict` A dictionary with relevant information regarding sequences, sequence variants, phase encoding directions, and task names. Returns ------- desc : :obj:`str` A description of the field map's acquisition information. """ first_file = files[0] metadata = first_file.get_metadata() img = nib.load(first_file.path) # General info seqs, variants = parameters.describe_sequence(metadata, config) # Parameters dir_str = parameters.describe_pe_direction(metadata, config) slice_str = parameters.describe_slice_timing(img, metadata) tr_str = parameters.describe_repetition_time(metadata) te_str = parameters.describe_echo_times_fmap(files) fa_str = parameters.describe_flip_angle(metadata) fov_str, matrixsize_str, voxelsize_str = parameters.describe_image_size(img) mb_str = parameters.describe_multiband_factor(metadata) parameters_str = [ dir_str, slice_str, tr_str, te_str, fa_str, fov_str, matrixsize_str, voxelsize_str, mb_str, ] parameters_str = [d for d in parameters_str if len(d)] parameters_str = '; '.join(parameters_str) for_str = parameters.describe_intendedfor_targets(metadata, layout) desc = f'A {variants} {seqs} field map ({parameters_str}) was acquired{for_str}.' return desc def general_acquisition_info(metadata): """General sentence on data acquisition. This should be the first sentence in the MRI data acquisition section. Parameters ---------- metadata : :obj:`dict` The metadata for the dataset. Returns ------- out_str : :obj:`str` Output string with scanner information. """ out_str = 'MR data were acquired using a {tesla}-Tesla {manu} {model} MRI scanner.'.format( tesla=metadata.get('MagneticFieldStrength', 'UNKNOWN'), manu=metadata.get('Manufacturer', 'MANUFACTURER'), model=metadata.get('ManufacturersModelName', 'MODEL'), ) return out_str def final_paragraph(metadata): """Describe dicom-to-nifti conversion process and methods generation. Parameters ---------- metadata : :obj:`dict` The metadata for the scan. Returns ------- desc : :obj:`str` Output string with scanner information. """ if 'ConversionSoftware' in metadata.keys(): soft = metadata['ConversionSoftware'] vers = metadata['ConversionSoftwareVersion'] software_str = f' using {soft} ({vers})' else: software_str = '' desc = ( f'Dicoms were converted to NIfTI-1 format{software_str}. ' 'This section was (in part) generated automatically using pybids ' f'({__version__}).' ) return desc def parse_files(layout, data_files, sub, config): """Loop through files in a BIDSLayout and generate appropriate descriptions. Then, compile all of the descriptions into a list. Parameters ---------- layout : :obj:`bids.layout.BIDSLayout` Layout object for a BIDS dataset. data_files : :obj:`list` of :obj:`bids.layout.models.BIDSFile` List of nifti files in layout corresponding to subject/session combo. sub : :obj:`str` Subject ID. config : :obj:`dict` Configuration info for methods generation. """ return _parse_files(layout, data_files, sub, config) # Indirection to allow warnings to be raised in user-written code def _parse_files(layout, data_files, sub, config, *, stacklevel=3): # Group files into individual runs data_files = collect_associated_files(layout, data_files, extra_entities=['run']) description_list = [] # Assume all data have same basic info description_list.append(general_acquisition_info(data_files[0][0].get_metadata())) for group in data_files: if group[0].entities['datatype'] == 'func': group_description = func_info(layout, group, config) elif (group[0].entities['datatype'] == 'anat') and group[0].entities['suffix'].endswith( 'w' ): group_description = anat_info(layout, group, config) elif group[0].entities['datatype'] == 'dwi': group_description = dwi_info(layout, group, config) elif (group[0].entities['datatype'] == 'fmap') and group[0].entities[ 'suffix' ] == 'phasediff': group_description = fmap_info(layout, group, config) elif group[0].entities['datatype'] in ['eeg', 'meg', 'beh', 'perf']: warnings.warn( group[0].entities['datatype'] + ' not yet supported.', stacklevel=stacklevel ) continue else: warnings.warn(group[0].filename + ' not yet supported.', stacklevel=stacklevel) continue description_list.append(group_description) return description_list ================================================ FILE: src/bids/reports/report.py ================================================ """Generate publication-quality data acquisition methods section from BIDS dataset.""" import json import os.path as op from collections import Counter from bids.reports import parsing, utils class BIDSReport: """Generate publication-quality data acquisition section from BIDS dataset. Parameters ---------- layout : :obj:`bids.layout.BIDSLayout` Layout object for a BIDS dataset. config : :obj:`str` or :obj:`dict`, optional Configuration info for methods generation. Can be a path to a file (str), a dictionary, or None. If None, loads and uses default configuration information. Keys in the dictionary include: 'dir': a dictionary for converting encoding direction strings (e.g., j-) to descriptions (e.g., anterior to posterior) 'seq': a dictionary of sequence abbreviations (e.g., EP) and corresponding names (e.g., echo planar) 'seqvar': a dictionary of sequence variant abbreviations (e.g., SP) and corresponding names (e.g., spoiled) Warnings -------- pybids' automatic report generation is experimental and currently under active development, and as such should be used with caution. Please remember to verify any generated report before putting it to use. Additionally, only MRI datatypes (func, anat, fmap, and dwi) are currently supported. """ def __init__(self, layout, config=None): self.layout = layout if config is None: config = op.join( op.dirname(op.abspath(__file__)), 'config', 'converters.json', ) if isinstance(config, str): with open(config) as fobj: config = json.load(fobj) if not isinstance(config, dict): raise ValueError( 'Input config must be None, dict, or path to json file containing dict.' ) self.config = config def generate_from_files(self, files): r"""Generate a methods section from a list of files. Parameters ---------- files : list of BIDSImageFile objects List of files from which to generate methods description. Returns ------- counter : :obj:`collections.Counter` A dictionary of unique descriptions across subjects in the file list, along with the number of times each pattern occurred. In cases where all subjects underwent the same protocol, the most common pattern is most likely the most complete. In cases where the file list contains multiple protocols, each pattern will need to be inspected manually. Examples -------- >>> from os.path import join >>> from bids.layout import BIDSLayout >>> from bids.reports import BIDSReport >>> from bids.tests import get_test_data_path >>> layout = BIDSLayout(join(get_test_data_path(), 'synthetic')) >>> report = BIDSReport(layout) >>> files = layout.get(session='01', extension=['.nii.gz', '.nii']) >>> counter = report.generate_from_files(files) Number of patterns detected: 1 Remember to double-check everything and to replace with a degree symbol. >>> counter.most_common()[0][0] # doctest: +ELLIPSIS 'In session 01, MR data were...' """ descriptions = [] subjects = sorted(list(set([f.get_entities().get('subject') for f in files]))) # noqa: C403, C414 sessions = sorted(list(set([f.get_entities().get('session') for f in files]))) # noqa: C403, C414 for sub in subjects: subject_files = [f for f in files if f.get_entities().get('subject') == sub] description_list = [] for ses in sessions: data_files = [f for f in subject_files if f.get_entities().get('session') == ses] if data_files: ses_description = parsing._parse_files( self.layout, data_files, sub, self.config, ) ses_description[0] = f'In session {ses}, ' + ses_description[0] description_list += ses_description metadata = self.layout.get_metadata(data_files[0].path) else: raise Exception(f'No imaging files for subject {sub}') # Assume all data were converted the same way and use the last nifti # file's json for conversion information. if 'metadata' not in vars(): raise Exception('No valid jsons found. Cannot generate final paragraph.') description = '\n\t'.join(description_list) description += f'\n\n{parsing.final_paragraph(metadata)}' descriptions.append(description) counter = Counter(descriptions) print(f'Number of patterns detected: {len(counter.keys())}') print(utils.reminder()) return counter def generate(self, **kwargs): r"""Generate the methods section. Parameters ---------- kwargs : dict Keyword arguments passed to BIDSLayout to select subsets of the dataset. Returns ------- counter : :obj:`collections.Counter` A dictionary of unique descriptions across subjects in the dataset, along with the number of times each pattern occurred. In cases where all subjects underwent the same protocol, the most common pattern is most likely the most complete. In cases where the dataset contains multiple protocols, each pattern will need to be inspected manually. Examples -------- >>> from os.path import join >>> from bids.layout import BIDSLayout >>> from bids.reports import BIDSReport >>> from bids.tests import get_test_data_path >>> layout = BIDSLayout(join(get_test_data_path(), 'synthetic')) >>> report = BIDSReport(layout) >>> counter = report.generate(session='01') Number of patterns detected: 1 Remember to double-check everything and to replace with a degree symbol. >>> counter.most_common()[0][0] # doctest: +ELLIPSIS 'In session 01, MR data were...' """ descriptions = [] subjects = self.layout.get_subjects(**kwargs) kwargs = {k: v for k, v in kwargs.items() if k != 'subject'} for sub in subjects: descriptions.append(self._report_subject(subject=sub, **kwargs)) counter = Counter(descriptions) print(f'Number of patterns detected: {len(counter.keys())}') print(utils.reminder()) return counter def _report_subject(self, subject, **kwargs): # noqa: D417 """Write a report for a single subject. Parameters ---------- subject : :obj:`str` Subject ID. Attributes ---------- layout : :obj:`bids.layout.BIDSLayout` Layout object for a BIDS dataset. config : :obj:`dict` Configuration info for methods generation. Returns ------- description : :obj:`str` A publication-ready report of the dataset's data acquisition information. Each scan type is given its own paragraph. """ description_list = [] # Remove session from kwargs if provided, else set session as all available sessions = kwargs.pop('session', self.layout.get_sessions(subject=subject, **kwargs)) if not sessions: sessions = [None] elif not isinstance(sessions, list): sessions = [sessions] for ses in sessions: data_files = self.layout.get( subject=subject, extension=['.nii', '.nii.gz'], **kwargs, ) if data_files: ses_description = parsing._parse_files( self.layout, data_files, subject, self.config, ) ses_description[0] = f'In session {ses}, ' + ses_description[0] description_list += ses_description metadata = self.layout.get_metadata(data_files[0].path) else: raise Exception(f'No imaging files for subject {subject}') # Assume all data were converted the same way and use the first nifti # file's json for conversion information. description = '\n\t'.join(description_list) description += f'\n\n{parsing.final_paragraph(metadata)}' return description ================================================ FILE: src/bids/reports/tests/__init__.py ================================================ ================================================ FILE: src/bids/reports/tests/test_parameters.py ================================================ import nibabel as nib import pytest from bids.reports import parameters @pytest.fixture(scope='module') def testimg(testlayout): func_files = testlayout.get( subject='01', session='01', task='nback', run='01', extension=['.nii.gz'], ) return nib.load(func_files[0].path) @pytest.fixture(scope='module') def testdiffimg(testlayout): dwi_files = testlayout.get( subject='01', session='01', datatype='dwi', extension=['.nii.gz'], ) return nib.load(dwi_files[0].path) @pytest.fixture def testmeta(): return { 'RepetitionTime': 2.0, 'MultibandAccelerationFactor': 2, 'ParallelReductionFactorInPlane': 2, 'FlipAngle': 90, 'PhaseEncodingDirection': 'i', 'SliceTiming': [0, 1, 2, 3], } @pytest.fixture def testmeta_light(): return {'RepetitionTime': 2.0} @pytest.mark.parametrize( 'ScanningSequence, expected_seq', [ ('EP', 'echo planar (EP)'), ('GR', 'gradient recalled (GR)'), ('IR', 'inversion recovery (IR)'), ('RM', 'research mode (RM)'), ('SE', 'spin echo (SE)'), ('SE_EP', 'spin echo and echo planar (SE/EP)'), ], ) @pytest.mark.parametrize( 'SequenceVariant, expected_var', [ ('MP', 'MAG prepared'), ('MTC', 'magnetization transfer contrast'), ('NONE', 'no sequence variant'), ('OSP', 'oversampling phase'), ('SK', 'segmented k-space'), ('SS', 'steady state'), ('TRSS', 'time reversed steady state'), ('MP_SS', 'MAG prepared and steady state'), ], ) def test_describe_sequence( ScanningSequence, expected_seq, SequenceVariant, expected_var, testconfig ): """test for sequence and variant type description""" metadata = { 'SequenceVariant': SequenceVariant, 'ScanningSequence': ScanningSequence, } seqs, variants = parameters.describe_sequence(metadata, testconfig) assert seqs == expected_seq assert variants == expected_var @pytest.mark.parametrize( 'pe_direction, expected', [ ('i', 'left to right'), ('i-', 'right to left'), ('j', 'posterior to anterior'), ('j-', 'anterior to posterior'), ('k', 'inferior to superior'), ('k-', 'superior to inferior'), ], ) def test_describe_pe_direction(pe_direction, expected, testconfig): """test for phase encoding direction description""" metadata = {'PhaseEncodingDirection': pe_direction} dir_str = parameters.describe_pe_direction(metadata, testconfig) assert dir_str == 'phase encoding: ' + expected def test_describe_bvals_smoke(testlayout): """Smoke test for parsing _dwi.bval It should return a str description when provided valid inputs. """ bval_file = testlayout.get( subject='01', session='01', suffix='dwi', extension=['.bval'], ) bval_str = parameters.describe_bvals(bval_file[0]) assert isinstance(bval_str, str) def test_describe_echo_times_smoke(testlayout): """Smoke test for parsing echo time It should return a str description when provided valid inputs. """ anat_file = testlayout.get( subject='01', session='01', suffix='T1w', extension=['.nii.gz'], ) te_str, me_str = parameters.describe_echo_times(anat_file) assert isinstance(te_str, str) def test_describe_echo_times_fmap(testlayout): """Smoke test for parsing echo time It should return a str description when provided valid inputs. """ fmap_file = testlayout.get( subject='01', session='01', suffix='phasediff', extension=['.nii.gz'], ) te_str = parameters.describe_echo_times_fmap(fmap_file) assert isinstance(te_str, str) def test_describe_repetition_time_smoke(testmeta): # when tr_str = parameters.describe_repetition_time(testmeta) # then expected = 'repetition time, TR=2000ms' assert tr_str == expected def test_describe_func_duration_smoke(): # given n_vols = 100 tr = 2.5 # when duration = parameters.describe_func_duration(n_vols, tr) # then expected = '4:10' assert duration == expected def test_describe_multiband_factor_smoke(testmeta, testmeta_light): # when mb_str = parameters.describe_multiband_factor(testmeta) # then expected = 'MB factor=2' assert mb_str == expected # when mb_str = parameters.describe_multiband_factor(testmeta_light) # then expected = '' assert mb_str == expected def test_describe_inplane_accel_smoke(testmeta, testmeta_light): # when mb_str = parameters.describe_inplane_accel(testmeta) # then expected = 'in-plane acceleration factor=2' assert mb_str == expected # when mb_str = parameters.describe_inplane_accel(testmeta_light) # then expected = '' assert mb_str == expected def test_describe_flip_angle_smoke(testmeta): # when mb_str = parameters.describe_flip_angle(testmeta) # then expected = 'flip angle, FA=90' assert mb_str == expected @pytest.mark.parametrize( 'slice_times, expected', [ ((1, 2, 3, 4), 'sequential ascending'), ([4, 3, 2, 1], 'sequential descending'), ([1, 3, 2, 4], 'interleaved ascending'), ([4, 2, 3, 1], 'interleaved descending'), ], ) def test_get_slice_info(slice_times, expected): slice_order_name = parameters.get_slice_info(slice_times) assert slice_order_name == expected def test_describe_slice_timing(testimg, testmeta, testmeta_light): slice_str = parameters.describe_slice_timing(testimg, testmeta) expected = '4 slices in sequential ascending order' assert slice_str == expected slice_str = parameters.describe_slice_timing(testimg, testmeta_light) expected = '64 slices' assert slice_str == expected def test_get_size_str(testimg): voxel_size, matrix_size, fov = parameters.get_size_str(testimg) expected_vox = '2x2x2' expected_mat = '64x64' expected_fov = '128x128' assert voxel_size == expected_vox assert matrix_size == expected_mat assert fov == expected_fov def test_describe_image_size(testimg): fov_str, matrixsize_str, voxelsize_str = parameters.describe_image_size(testimg) expected_vox = 'voxel size=2x2x2mm' expected_mat = 'matrix size=64x64' expected_fov = 'field of view, FOV=128x128mm' assert voxelsize_str == expected_vox assert matrixsize_str == expected_mat assert fov_str == expected_fov def test_describe_dmri_directions(testdiffimg): dif_dir_str = parameters.describe_dmri_directions(testdiffimg) expected = '64 diffusion directions' assert dif_dir_str == expected def test_describe_intendedfor_targets(testmeta_light, testlayout): for_str = parameters.describe_intendedfor_targets(testmeta_light, testlayout) assert for_str == '' fmap_files = testlayout.get( subject='01', session='01', suffix='phasediff', extension=['.nii.gz'], ) metadata = fmap_files[0].get_metadata() for_str = parameters.describe_intendedfor_targets(metadata, testlayout) assert for_str == ' for the first and second runs of the N-Back BOLD scan' ================================================ FILE: src/bids/reports/tests/test_parsing.py ================================================ """Tests for bids.reports.parsing.""" import pytest from bids.reports import parsing @pytest.fixture def testmeta(): metadata = {'RepetitionTime': 2.0} return metadata def test_anat_info_smoke(testlayout, testconfig): """Smoke test for parsing.anat_info. It should return a str description when provided valid inputs. """ anat_files = testlayout.get( subject='01', session='01', suffix='T1w', extension=['.nii.gz'], ) desc = parsing.anat_info(testlayout, anat_files, testconfig) assert isinstance(desc, str) def test_dwi_info_smoke(testlayout, testconfig): """Smoke test for parsing.dwi_info. It should return a str description when provided valid inputs. """ dwi_files = testlayout.get( subject='01', session='01', datatype='dwi', extension=['.nii.gz'], ) desc = parsing.dwi_info(testlayout, dwi_files, testconfig) assert isinstance(desc, str) def test_fmap_info_smoke(testlayout, testconfig): """Smoke test for parsing.fmap_info. It should return a str description when provided valid inputs. """ fmap_files = testlayout.get( subject='01', session='01', datatype='fmap', suffix='phasediff', extension=['.nii.gz'], ) desc = parsing.fmap_info(testlayout, fmap_files, testconfig) assert isinstance(desc, str) def test_func_info_smoke(testlayout, testconfig): """Smoke test for parsing.func_info. It should return a str description when provided valid inputs. """ func_files = testlayout.get( subject='01', session='01', task='nback', run='01', extension=['.nii.gz'], ) desc = parsing.func_info(testlayout, func_files, testconfig) assert isinstance(desc, str) def test_general_acquisition_info_smoke(testmeta): """Smoke test for parsing.general_acquisition_info. It should return a str description when provided valid inputs. """ desc = parsing.general_acquisition_info(testmeta) assert isinstance(desc, str) def test_final_paragraph_smoke(testmeta): """Smoke test for parsing.final_paragraph. It should return a str description when provided valid inputs. """ desc = parsing.final_paragraph(testmeta) assert isinstance(desc, str) def test_parse_files_smoke(testlayout, testconfig): """Smoke test for parsing.parse_files. It should return a list of string descriptions when provided valid inputs, with each string containing the description for a single nifti file (except functional data, which is combined within task, across runs). """ subject = '01' niftis = testlayout.get(subject=subject, extension=['.nii', '.nii.gz']) desc = parsing.parse_files(testlayout, niftis, subject, testconfig) assert isinstance(desc, list) assert isinstance(desc[0], str) ================================================ FILE: src/bids/reports/tests/test_report.py ================================================ """Tests for bids.reports.report.""" from collections import Counter from bids.reports import BIDSReport def test_report_init(testlayout): """Report initialization should return a BIDSReport object.""" report = BIDSReport(testlayout) assert isinstance(report, BIDSReport) def test_report_gen(testlayout): """Report generation should return a counter of unique descriptions in the dataset.""" report = BIDSReport(testlayout) descriptions = report.generate() assert isinstance(descriptions, Counter) def test_report_gen_from_files(testlayout): """Report generation from file list should return a counter of unique descriptions in the dataset. """ report = BIDSReport(testlayout) files = testlayout.get(extension=[".nii.gz", ".nii"]) descriptions = report.generate_from_files(files) assert isinstance(descriptions, Counter) def test_report_subject(testlayout): """Generating a report for one subject should only return one subject's description (i.e., one pattern with a count of one). """ report = BIDSReport(testlayout) descriptions = report.generate(subject="01") assert sum(descriptions.values()) == 1 def test_report_session(testlayout): """Generating a report for one session should mean that no other sessions appear in any of the unique descriptions. """ report = BIDSReport(testlayout) descriptions = report.generate(session="01") assert "session 02" not in " ".join(descriptions.keys()) def test_report_file_config(testlayout, config_file): """Report initialization should take in a config file and use that if provided.""" report = BIDSReport(testlayout, config=str(config_file)) descriptions = report.generate() assert isinstance(descriptions, Counter) def test_report_dict_config(testlayout, testconfig): """Report initialization should take in a config dict and use that if provided.""" report = BIDSReport(testlayout, config=testconfig) descriptions = report.generate() assert isinstance(descriptions, Counter) ================================================ FILE: src/bids/reports/utils.py ================================================ """Generate publication-quality data acquisition methods section from BIDS dataset. Utilities to generate the MRI data acquisition portion of a methods section from a BIDS dataset. """ import logging LOGGER = logging.getLogger('pybids.reports.utils') def reminder(): """Remind users about things they need to do after generating the report.""" return 'Remember to double-check everything and to replace with a degree symbol.' def remove_duplicates(seq): """Return unique elements from list while preserving order. From https://stackoverflow.com/a/480227/2589328 """ seen = set() seen_add = seen.add return [x for x in seq if not (x in seen or seen_add(x))] def num_to_str(num): """Convert an int or float to a nice string. E.g., 21 -> '21' 2.500 -> '2.5' 3. -> '3' """ return f'{num:0.02f}'.rstrip('0').rstrip('.') def list_to_str(lst): """Turn a list into a comma- and/or and-separated string. Parameters ---------- lst : :obj:`list` A list of strings to join into a single string. Returns ------- str_ : :obj:`str` A string with commas and/or ands separating the elements from ``lst``. """ if len(lst) == 1: str_ = lst[0] elif len(lst) == 2: str_ = ' and '.join(lst) elif len(lst) > 2: str_ = ', '.join(lst[:-1]) str_ += f', and {lst[-1]}' else: raise ValueError('List of length 0 provided.') return str_ ================================================ FILE: src/bids/tests/__init__.py ================================================ from .utils import get_test_data_path __all__ = [ 'get_test_data_path', ] ================================================ FILE: src/bids/tests/data/images/4d.bval ================================================ 0 0 3000 3000 2000 3000 1000 3000 3000 2000 3000 1000 3000 3000 500 3000 2000 3000 3000 1000 3000 500 3000 3000 2000 3000 1000 3000 3000 2000 3000 1000 3000 3000 0 3000 2000 3000 1000 3000 500 3000 3000 2000 3000 1000 3000 3000 2000 3000 1000 3000 3000 0 3000 2000 3000 3000 1000 3000 500 3000 3000 2000 ================================================ FILE: src/bids/tests/test_cli.py ================================================ import os import pytest from click.testing import CliRunner from bids.cli import cli from bids.tests import get_test_data_path from bids.utils import validate_multiple @pytest.fixture def runner(): return CliRunner() def test_cli_entrypoint(runner): res = runner.invoke(cli, catch_exceptions=False) if res.exit_code == 2: # click 8.2+ help_text = res.stderr elif res.exit_code == 0: help_text = res.stdout assert 'Command-line interface for PyBIDS operations' in help_text assert runner.invoke(cli, ['-h'], catch_exceptions=False).stdout == help_text # verify versioning assert runner.invoke(cli, ['--version'], catch_exceptions=False).stdout.startswith('pybids') def test_validate_multiple(): assert validate_multiple(()) is None assert validate_multiple((), retval=False) is False assert validate_multiple(('bids',)) == 'bids' assert validate_multiple((1, 2)) == (1, 2) with pytest.raises(AssertionError): validate_multiple('not a tuple') def test_layout(runner, tmp_path): def is_success(res): return res.stdout.startswith('Successfully generated database index') res = runner.invoke(cli, ['layout', '--help']) assert 'Initialize a BIDSLayout' in res.stdout bids_dir = os.path.join(get_test_data_path(), 'ds005') db0 = tmp_path / 'db0' db0.mkdir() res = runner.invoke(cli, ['layout', bids_dir, str(db0)], catch_exceptions=False) assert is_success(res) # rerunning targeting the save directory should not generate a new index res = runner.invoke(cli, ['layout', bids_dir, str(db0)], catch_exceptions=False) assert not is_success(res) # but forcing it should res = runner.invoke(cli, ['layout', bids_dir, str(db0), '--reset-db'], catch_exceptions=False) assert is_success(res) db1 = tmp_path / 'db1' db1.mkdir() # throw the kitchen sink at it res = runner.invoke( cli, [ 'layout', bids_dir, str(db1), '--validate', '--no-index-metadata', '--ignore', 'derivatives', '--ignore', 'sourcedata', '--ignore', r'm/^\./', '--force-index', 'test', '--config', 'bids', ], catch_exceptions=False, ) assert is_success(res) ================================================ FILE: src/bids/tests/test_config.py ================================================ import json import os import tempfile import warnings import pytest import bids from bids.config import reset_options from bids.tests import get_test_data_path def test_load_from_standard_paths(): # Verify defaults reset_options(False) assert bids.config._settings == bids.config._default_settings env_config = {'loop_preproc': True} cwd_config = {'loop_preproc': True} handle, f = tempfile.mkstemp(suffix='.json') json.dump(env_config, open(f, 'w')) os.environ['PYBIDS_CONFIG'] = f target = 'pybids_config.json' if os.path.exists(target): pytest.skip( 'Cannot test bids config because the default config file' ' (pybids_config.json) already exists in the current ' 'working directory. Skipping test to avoid overwriting.' ) json.dump(cwd_config, open(target, 'w')) reset_options(True) os.unlink(target) opts = bids.config._settings # assert opts['loop_preproc'] reset_options(False) def test_set_option(): reset_options(False) opts = bids.config._settings # bids.config.set_options(loop_preproc=False) # assert opts['loop_preproc'] == False with pytest.raises(ValueError): bids.config.set_option('bad_key', False) reset_options(False) # def test_get_option(): # reset_options(False) # assert not bids.config.get_option('loop_preproc') def test_extension_initial_dot(mock_config): ds117 = os.path.join(get_test_data_path(), 'ds000117') # Setting False is disabled with pytest.raises(ValueError, match='Cannot set'): bids.config.set_option('extension_initial_dot', False) with pytest.raises(ValueError, match='Cannot set'): bids.config.set_option('extension_initial_dot', None) # Setting True warns with pytest.warns(FutureWarning, match='will be removed'): bids.config.set_option('extension_initial_dot', True) # No warnings on layout construction with warnings.catch_warnings(): warnings.simplefilter('error') layout = bids.BIDSLayout(ds117) assert layout.get(extension='nii.gz')[0].entities['extension'] == '.nii.gz' ================================================ FILE: src/bids/tests/utils.py ================================================ """Test-related utilities""" from pathlib import Path def get_test_data_path(): return str(Path(__file__).parent.parent.parent.parent / 'tests' / 'data') ================================================ FILE: src/bids/utils.py ================================================ """Utility functions.""" import os import re from functools import cache from pathlib import Path # noqa: F811 from frozendict import frozendict as _frozendict from upath import UPath as Path # noqa: F811 # Monkeypatch to print out frozendicts *as if* they were dictionaries. class frozendict(_frozendict): """A hashable dictionary type.""" def __repr__(self): """Override frozendict representation.""" return repr({k: v for k, v in self.items()}) # noqa: C416 def listify(obj): """Wraps all non-list or tuple objects in a list; provides a simple way to accept flexible arguments. """ return obj if isinstance(obj, (list, tuple, type(None))) else [obj] def hashablefy(obj): """Make dictionaries and lists hashable or raise.""" if isinstance(obj, list): return tuple([hashablefy(o) for o in obj]) if isinstance(obj, dict): return frozendict({k: hashablefy(v) for k, v in obj.items()}) return obj def matches_entities(obj, entities, strict=False): """Checks whether an object's entities match the input.""" if strict and set(obj.entities.keys()) != set(entities.keys()): return False comm_ents = list(set(obj.entities.keys()) & set(entities.keys())) for k in comm_ents: current = obj.entities[k] target = entities[k] if isinstance(target, (list, tuple)): if current not in target: return False elif current != target: return False return True def natural_sort(l, field=None): # noqa: E741 """Based on snippet found at https://stackoverflow.com/a/4836734/2445984""" convert = lambda text: int(text) if text.isdigit() else text.lower() # noqa: E731 def alphanum_key(key): if field is not None: key = getattr(key, field) if not isinstance(key, str): key = str(key) return [convert(c) for c in re.split('([0-9]+)', key)] return sorted(l, key=alphanum_key) def convert_JSON(j): """Recursively convert CamelCase keys to snake_case. From: https://stackoverflow.com/questions/17156078/ converting-identifier-naming-between-camelcase-and- underscores-during-json-seria """ def camel_to_snake(s): a = re.compile('((?<=[a-z0-9])[A-Z]|(?!^)[A-Z](?=[a-z]))') return a.sub(r'_\1', s).lower() def convertArray(a): newArr = [] for i in a: if isinstance(i, list): newArr.append(convertArray(i)) elif isinstance(i, dict): newArr.append(convert_JSON(i)) else: newArr.append(i) return newArr out = {} for k, value in j.items(): newK = camel_to_snake(k) # Replace transformation uses a dict, so skip lower-casing if isinstance(value, dict) and k != 'Replace': out[newK] = convert_JSON(value) elif isinstance(value, list): out[newK] = convertArray(value) else: out[newK] = value return out def splitext(path): """Splitext for paths with directories that may contain dots. From https://stackoverflow.com/questions/5930036/separating-file-extensions-using-python-os-path-module """ li = [] path_without_extensions = os.path.join( os.path.dirname(path), os.path.basename(path).split(os.extsep)[0] ) extensions = os.path.basename(path).split(os.extsep)[1:] li.append(path_without_extensions) # li.append(extensions) if you want extensions in another list inside the list that is returned. # noqa: E501 li.extend(extensions) return li def make_bidsfile(filename): """Create a BIDSFile instance of the appropriate class.""" from .layout import models # Extract all extensions from filename (a.tar.gz -> .tar.gz, not just .gz) ext = ''.join(Path(filename).suffixes) if ext.endswith(('.nii', '.nii.gz', '.gii')): cls = 'BIDSImageFile' elif ext in ['.tsv', '.tsv.gz']: cls = 'BIDSDataFile' elif ext == '.json': cls = 'BIDSJSONFile' else: cls = 'BIDSFile' Cls = getattr(models, cls) return Cls(filename) def collect_associated_files(layout, files, extra_entities=()): # noqa: D417 """Collect and group BIDSFiles with multiple files per acquisition. Parameters ---------- layout files : list of BIDSFile extra_entities Returns ------- collected_files : list of list of BIDSFile """ MULTICONTRAST_ENTITIES = ['echo', 'part', 'ch', 'direction'] MULTICONTRAST_SUFFIXES = [ ('bold', 'phase'), ('phase1', 'phase2', 'phasediff', 'magnitude1', 'magnitude2'), ] if len(extra_entities): MULTICONTRAST_ENTITIES += extra_entities collected_files = [] for f in files: if len(collected_files) and any(f in filegroup for filegroup in collected_files): continue ents = f.get_entities() ents = {k: v for k, v in ents.items() if k not in MULTICONTRAST_ENTITIES} # Group files with differing multi-contrast entity values, but same # everything else. all_suffixes = ents['suffix'] for mcs in MULTICONTRAST_SUFFIXES: if ents['suffix'] in mcs: all_suffixes = mcs break ents.pop('suffix') associated_files = layout.get(suffix=all_suffixes, **ents) collected_files.append(associated_files) return collected_files def validate_multiple(val, retval=None): """Any click.Option with the multiple flag will return an empty tuple if not set. This helper method converts empty tuples to a desired return value (default: None). This helper method selects the first item in single-item tuples. """ assert isinstance(val, tuple) # noqa: S101 if val == tuple(): # noqa: C408 return retval if len(val) == 1: return val[0] return val @cache def entity_indices(schema_spec=None): # noqa: D103 from collections import defaultdict from bidsschematools.schema import load_schema entities = load_schema(schema_spec).rules.entities + ['suffix', 'extension', 'datatype'] return defaultdict(lambda e=entities: len(e), {elem: idx for idx, elem in enumerate(entities)}) def bids_sort(unsorted: dict, schema_spec=None): """Sorts filename entity dictionaries according to their order as defined in schema.rules.entities as well as suffix, extension. Lastly, appends datatype to the end of the sort to accommodate pybids datastructures. Parameters ---------- unsorted: dict A dictionary containing bids file entities and their values. schema_spec: str Path or version of schema to use, defaults to the version bundled with bidsschematools. Returns ------- sorted_bids: dict """ indices = entity_indices(schema_spec) return {k: unsorted[k] for k in sorted(unsorted, key=indices.__getitem__)} ================================================ FILE: src/bids/variables/__init__.py ================================================ # ruff: noqa: D104 from .collections import BIDSRunVariableCollection, BIDSVariableCollection, merge_collections from .io import load_variables from .variables import DenseRunVariable, SimpleVariable, SparseRunVariable, merge_variables __all__ = [ 'SimpleVariable', 'SparseRunVariable', 'DenseRunVariable', 'BIDSVariableCollection', 'BIDSRunVariableCollection', 'merge_collections', 'load_variables', 'merge_variables', ] ================================================ FILE: src/bids/variables/collections.py ================================================ """Classes and functions related to the management of sets of BIDSVariables.""" import fnmatch import re import warnings from collections import OrderedDict from copy import copy from functools import cache from itertools import chain import numpy as np import pandas as pd from pandas.api.types import is_numeric_dtype from bids.utils import listify from .variables import ( BIDSVariable, DenseRunVariable, SimpleVariable, SparseRunVariable, merge_variables, ) def _pandas_3_0(): """Silence pandas warnings and opt in to future behavior. This sets pandas behavior to 3.0+ defaults. Prior to pandas 3.0, the fillna() and replace() methods would convert object columns to float64 if the resulting series was all float64. In 3.0+, you need to use infer_objects() to do this. This also opts-in to copy-on-write, which previously required `copy=False` to be set. """ if args := _pandas_3_0_options(): return pd.option_context(*args) import contextlib return contextlib.nullcontext() @cache def _pandas_3_0_options(): options = [ ('future.no_silent_downcasting', True), ('mode.copy_on_write', True), ] args = [] for option in options: try: pd.get_option(option[0]) except KeyError: continue args.extend(option) return args class BIDSVariableCollection: """A container for one or more variables extracted from variable files at a single level of analysis. Parameters ---------- variables : list A list of BIDSVariables or SimpleVariables. name : str Optional name to assign to the collection. Notes ----- Variables in the list must all share the same analysis level, which must be one of 'session', 'subject', or 'dataset' level. For run-level Variables, use the BIDSRunVariableCollection. """ def __init__(self, variables, name=None): self.name = name if not variables: raise ValueError('No variables were provided') SOURCE_TO_LEVEL = { 'events': 'run', 'physio': 'run', 'stim': 'run', 'regressors': 'run', 'scans': 'session', 'sessions': 'subject', 'participants': 'dataset', } var_levels = set( # noqa: C403 [ SOURCE_TO_LEVEL[v.source] if v.source in SOURCE_TO_LEVEL else v.source for v in variables ] ) # TODO: relax this requirement & allow implicit merging between levels if len(var_levels) > 1: raise ValueError( 'A Collection cannot be initialized from ' # noqa: UP031 'variables at more than one level of analysis. ' 'Levels found in input variables: %s' % var_levels ) elif not var_levels: raise ValueError( 'None of the provided variables matched any of the known levels, which are: %s' # noqa: UP031 % (', '.join(sorted(SOURCE_TO_LEVEL.values()))) ) self.level = list(var_levels)[0] variables = self.merge_variables(variables) self.variables = {v.name: v for v in variables} self._index_entities() # Container for variable groups (see BIDS-StatsModels spec)--maps from # group names to lists of variables. self.groups = {} @staticmethod def merge_variables(variables, **kwargs): # noqa: D417 """Concatenates Variables along row axis. Parameters ---------- variables : list List of Variables to merge. Variables can have different names (and all Variables that share a name will be concatenated together). Returns ------- list A list of Variables. """ var_dict = OrderedDict() for v in variables: if v.name not in var_dict: var_dict[v.name] = [] var_dict[v.name].append(v) return [merge_variables(vars_, **kwargs) for vars_ in list(var_dict.values())] def to_df(self, variables=None, format='wide', fillna=np.nan, entities=True, timing=True): # noqa: A002 """Merge BIDVariables in the collection into a single pandas DataFrame. Parameters ---------- variables : list of str or BIDSVariable Optional list of variables or variable names to retain. If strings are passed, each one gives the name of a variable in the current collection. If BIDSVariables are passed, they will be used as-is. If None, all variables are returned. Strings and BIDSVariables cannot be mixed in the list. format : {'wide', 'long'} Whether to return a DataFrame in 'wide' or 'long' format. In 'wide' format, each row is defined by a unique entity combination, and each variable is in a separate column. In 'long' format, each row is a unique combination of entities and variable names, and a single 'amplitude' column provides the value. fillna : value Replace missing values with the specified value. entities : bool Whether or not to include a column for each entity. timing : bool Whether or not to include onset and duration columns. Returns ------- :obj:`pandas.DataFrame` A pandas DataFrame. """ if variables is None: variables = list(self.variables.keys()) # Can receive already-selected Variables from sub-classes if not isinstance(variables[0], BIDSVariable): variables = [v for v in self.variables.values() if v.name in variables] # Convert all variables to separate DFs. # Note: bad things can happen if we pass the conditions, entities, and # timing flags through to the individual variables and then do # concat/reshaping operations. So instead, we set them all to True # temporarily, do what we need to, then drop them later if needed. dfs = [v.to_df(True, True, timing=True) for v in variables] # Always concatenate along row axis (for format='wide', we'll pivot). df = pd.concat(dfs, axis=0, sort=True) all_cols = set(df.columns) ent_cols = list(all_cols - {'condition', 'amplitude', 'onset', 'duration'}) if format == 'long': with _pandas_3_0(): df = df.reset_index(drop=True).fillna(fillna).infer_objects() else: # Rows in wide format can only be defined by combinations of level entities # plus (for run-level variables) onset and duration. valid_vars = {'run', 'session', 'subject', 'dataset', 'onset', 'duration'} idx_cols = list(valid_vars & all_cols) with _pandas_3_0(): df['amplitude'] = df['amplitude'].fillna('n/a') wide_df = df.pivot_table( index=idx_cols, columns='condition', values='amplitude', aggfunc='first' ) select_cols = list(set(ent_cols) - set(idx_cols)) if entities and select_cols: ent_df = df.groupby(idx_cols)[select_cols].first() df = pd.concat([wide_df, ent_df], axis=1) else: df = wide_df with _pandas_3_0(): df = df.reset_index().replace('n/a', fillna).infer_objects() df.columns.name = None # Drop any columns we don't want if not timing: df.drop(['onset', 'duration'], axis=1, inplace=True) if not entities: df.drop(ent_cols, axis=1, inplace=True, errors='ignore') return df @classmethod def from_df(cls, data, entities=None, source='contrast'): # noqa: D417 """Create a Collection from a pandas DataFrame. Parameters ---------- df : :obj:`pandas.DataFrame` The DataFrame to convert to a Collection. Each column will be converted to a SimpleVariable. entities : :obj:`pandas.DataFrame` An optional second DataFrame containing entity information. source : str The value to set as the source for all Variables. Returns ------- BIDSVariableCollection """ variables = [] for col in data.columns: _data = pd.DataFrame(data[col].values, columns=['amplitude']) if entities is not None: _data = pd.concat([_data, entities], axis=1, sort=True) variables.append(SimpleVariable(name=col, data=_data, source=source)) return BIDSVariableCollection(variables) def clone(self): """Returns a copy of the current instance.""" # We can't simply deepcopy, because variables have non-serializable # attributes. So we shallow copy then explicitly clone collections and # more complex objects. clone = copy(self) clone.entities = self.entities.copy() clone.variables = {k: v.clone() for (k, v) in self.variables.items()} return clone def _index_entities(self): """Sets current instance's entities based on the existing index. Notes ----- Only entity key/value pairs common to all rows in all contained Variables are returned. E.g., if a Collection contains Variables extracted from runs 1, 2 and 3 from subject '01', the returned dict will be {'subject': '01'}; the runs will be excluded as they vary across the Collection contents. """ all_ents = pd.DataFrame.from_records([v.entities for v in self.variables.values()]) constant = all_ents.apply(lambda x: x.nunique() == 1) if constant.empty: self.entities = {} else: keep = all_ents.columns[constant] ents = {k: all_ents[k].dropna().iloc[0] for k in keep} self.entities = {k: v for k, v in ents.items() if pd.notnull(v)} def __getitem__(self, var): if var in self.variables: return self.variables[var] keys = list(self.variables.keys()) raise ValueError( f"No variable named '{var}' found in this collection. Available names are {keys}." ) def __setitem__(self, var, obj): # Ensure name matches collection key, but raise warning if needed. if obj.name != var: warnings.warn( f'The provided key to use in the collection ({var!r}) ' "does not match the passed Column object's existing " f'name ({obj.name!r}). The Column name will be set to match ' 'the provided key.', stacklevel=2, ) obj.name = var self.variables[var] = obj def match_variables(self, pattern, return_type='name', match_type='unix'): """Return columns whose names match the provided pattern. Parameters ---------- pattern : str, list One or more regex patterns to match all variable names against. return_type : {'name', 'variable'} What to return. Must be one of: 'name': Returns a list of names of matching variables. 'variable': Returns a list of Variable objects whose names match. match_type : str Matching approach to use. Either 'regex' (full-blown regular expression matching) or 'unix' (unix-style pattern matching via the fnmatch module). Returns ------- A list of all matching variables or variable names """ pattern = listify(pattern) results = [] for patt in pattern: if match_type.lower().startswith('re'): patt = re.compile(patt) vars_ = [v for v in self.variables.keys() if patt.search(v)] else: vars_ = fnmatch.filter(list(self.variables.keys()), patt) if return_type.startswith('var'): vars_ = [self.variables[v] for v in vars_] results.extend(vars_) return results def __repr__(self): return f'<{self.__class__.__name__}{sorted(list(self.variables.keys()))}>' # noqa: C414 class BIDSRunVariableCollection(BIDSVariableCollection): """A container for one or more RunVariables--i.e., Variables that have a temporal dimension. Parameters ---------- variables : list A list of SparseRunVariable and/or DenseRunVariable. sampling_rate : float Sampling rate (in Hz) to use when working with dense representations of variables. If None, defaults to 10. Notes ----- Variables in the list must all be at the 'run' level. For other levels (session, subject, or dataset), use the BIDSVariableCollection. """ def __init__(self, variables, sampling_rate=None): # Don't put the default value in signature because None is passed from # several places and we don't want multiple conflicting defaults. if sampling_rate: if isinstance(sampling_rate, str): raise ValueError('Sampling rate must be numeric.') self.sampling_rate = sampling_rate or 10 super().__init__(variables) def get_dense_variables(self, variables=None): """Returns a list of all stored DenseRunVariables.""" if variables is None: variables = set(self.variables.keys()) return [ v for v in self.variables.values() if isinstance(v, DenseRunVariable) and v.name in variables ] def get_sparse_variables(self, variables=None): """Returns a list of all stored SparseRunVariables.""" if variables is None: variables = set(self.variables.keys()) return [ v for v in self.variables.values() if isinstance(v, SparseRunVariable) and v.name in variables ] def all_dense(self): # noqa: D102 return len(self.get_dense_variables()) == len(self.variables) def all_sparse(self): # noqa: D102 return len(self.get_sparse_variables()) == len(self.variables) def _get_sampling_rate(self, sampling_rate): """Parse sampling rate argument and return appropriate value.""" if sampling_rate is None: return self.sampling_rate if isinstance(sampling_rate, (float, int)): return sampling_rate if sampling_rate == 'TR': trs = {var.run_info[0].tr for var in self.variables.values()} if not trs: raise ValueError( 'Repetition time unavailable; specify ' 'sampling_rate in Hz explicitly or set to' " 'highest'." ) elif len(trs) > 1: raise ValueError( 'Non-unique Repetition times found ' f'({trs!r}); specify sampling_rate explicitly' ) return 1.0 / trs.pop() if sampling_rate.lower() == 'highest': dense_vars = self.get_dense_variables() # If no dense variables are available, fall back on instance SR if not dense_vars: return self.sampling_rate var_srs = [v.sampling_rate for v in dense_vars] if len(var_srs) == 1: return var_srs[0] return max(*var_srs) raise ValueError( f"Invalid sampling_rate value '{sampling_rate}' provided. Must be " "a float, None, 'TR', or 'highest'." ) def _densify_and_resample( self, sampling_rate=None, variables=None, resample_dense=False, force_dense=False, in_place=False, kind='linear', ): sr = self._get_sampling_rate(sampling_rate) _dense, _sparse = [], [] # Filter variables and sort by class for name, var in self.variables.items(): if variables is not None and name not in variables: continue if isinstance(var, DenseRunVariable): _dense.append(var) else: _sparse.append(var) _variables = {} if force_dense: for v in _sparse: if is_numeric_dtype(v.values): _variables[v.name] = v.to_dense(sr) if resample_dense: # Propagate 'TR' if exact match to TR is required sr_arg = sampling_rate if sampling_rate == 'TR' else sr for v in _dense: _variables[v.name] = v.resample(sr_arg, kind=kind) for v in _dense: if v.name not in _variables: _variables[v.name] = v coll = self if in_place else self.clone() if in_place: coll.variables.update(_variables) else: coll.variables = _variables coll.sampling_rate = sr return coll def to_dense( # noqa: D417 self, sampling_rate=None, variables=None, in_place=False, resample_dense=False, kind='linear', ): """Convert all contained SparseRunVariables to DenseRunVariables. Parameters ---------- sampling_rate : None, {'TR', 'highest'}, float Sampling rate to use when densifying sparse variables. If None, uses the currently stored instance value. If 'TR', the repetition time is used, if available, to select the sampling rate (1/TR). If 'highest', all variables are resampled to the highest sampling rate of any of the existing dense variables. The sampling rate may also be specified explicitly in Hz as a float. variables : list Optional list of names of Variables to resample. If None, all variables are resampled. in_place : bool When True, all variables are overwritten in-place. When False, returns resampled versions of all variables. kind : str Argument to pass to scipy's interp1d; indicates the kind of interpolation approach to use. See interp1d docs for valid values. Returns ------- A BIDSVariableCollection (if in_place is False). Notes ----- Categorical variables are ignored. """ return self._densify_and_resample( sampling_rate, variables, resample_dense=resample_dense, in_place=in_place, kind=kind, force_dense=True, ) def resample( self, sampling_rate=None, variables=None, force_dense=False, in_place=False, kind='linear', ): """Resample all dense variables (and optionally, sparse ones) to the specified sampling rate. Parameters ---------- sampling_rate : int or float Target sampling rate (in Hz). If None, uses the instance value. variables : list Optional list of names of Variables to resample. If None, all variables are resampled. force_dense : bool if True, all sparse variables will be forced to dense. in_place : bool When True, all variables are overwritten in-place. When False, returns resampled versions of all variables. kind : str Argument to pass to scipy's interp1d; indicates the kind of interpolation approach to use. See interp1d docs for valid values. Returns ------- A BIDSVariableCollection (if in_place is False). """ return self._densify_and_resample( sampling_rate, variables, force_dense=force_dense, in_place=in_place, kind=kind, resample_dense=True, ) def to_df( self, variables=None, format='wide', # noqa: A002 fillna=np.nan, sampling_rate='highest', include_sparse=True, include_dense=True, entities=True, timing=True, ): """Merge variables into a single pandas DataFrame. Parameters ---------- variables : list Optional list of variable names to retain; if None, all variables are written out. format : str Whether to return a DataFrame in 'wide' or 'long' format. In 'wide' format, each row is defined by a unique onset/duration, and each variable is in a separate column. In 'long' format, each row is a unique combination of onset, duration, and variable name, and a single 'amplitude' column provides the value. fillna : value Replace missing values with the specified value. sampling_rate : float Specifies the sampling rate to use for all variables in the event that resampling needs to be performed (i.e., if some variables are sparse, or if dense variables have different sampling rates). Must be one of 'TR', 'highest', None, or a float (specifying the rate in Hz). If None, uses the instance sampling rate (10 Hz by default). include_sparse : bool Whether or not to include sparse variables in the output. include_dense : bool Whether or not to include dense variables in the output. entities : bool Whether or not to include a column for each entity. timing : bool Whether or not to include onset and duration columns. Returns ------- :obj:`pandas.DataFrame` A pandas DataFrame. Notes ----- The precise format of the resulting DataFrame depends on the variables contained in the current instance. If all variables are sparse, the output will also be sparse--i.e., the events in the DataFrame may have non-uniform timing. If at least one dense variable is present, and the user has not explicitly excluded dense variables (by setting include_dense=False), all selected variables will be implicitly converted to dense using the specified `sampling_rate` (if provided). To avoid unexpected behavior, we recommend converting mixed collections to all-dense form explicitly via the `to_dense()` or `resample()` methods before calling `to_df()`. """ if not include_sparse and not include_dense: raise ValueError( "You can't exclude both dense and sparse variables! That leaves nothing!" ) _vars = [] if include_sparse: _vars += self.get_sparse_variables(variables) if include_dense: _vars += self.get_dense_variables(variables) if not _vars: raise ValueError('No variables were selected for output.') # If all variables are sparse/simple, we can pass them as-is. Otherwise # we first force all variables to dense via .resample(). if all(isinstance(v, SimpleVariable) for v in _vars): variables = _vars else: sampling_rate = sampling_rate or self.sampling_rate var_names = [v.name for v in _vars] collection = self.resample( sampling_rate, variables=var_names, force_dense=include_sparse ) variables = list(collection.variables.values()) return super().to_df(variables, format, fillna, entities=entities, timing=timing) def merge_collections(collections, sampling_rate='highest', output_level=None, variables=None): """Merge two or more collections at the same level of analysis. Parameters ---------- collections : list List of Collections to merge. sampling_rate : int or str Sampling rate to use if it becomes necessary to resample DenseRunVariables. Either an integer or 'highest' (see merge_variables docstring for further explanation). output_level : str, optional Assign a new level (e.g., 'run', 'subject', etc.) to the merged collection. If None, the current level is retained. variables : list Optional list of names of variables to keep. If None, all are retained. Returns ------- BIDSVariableCollection or BIDSRunVariableCollection Result type depends on the type of the input collections. """ collections = listify(collections) if len(collections) == 1 and variables is None: return collections[0] levels = set([c.level for c in collections]) # noqa: C403 if len(levels) > 1: raise ValueError( "At the moment, it's only possible to merge " # noqa: UP031 'Collections at the same level of analysis. You ' 'passed collections at levels: %s.' % levels ) cls = collections[0].__class__ # Flatten all variables from all collections into a single list keep_vars = list(chain(*[c.variables.values() for c in collections])) if variables is not None: keep_vars = [var for var in keep_vars if var.name in variables] variables = keep_vars # merge_variables will automatically merge all variables that share name variables = cls.merge_variables(variables, sampling_rate=sampling_rate) if isinstance(collections[0], BIDSRunVariableCollection): if sampling_rate == 'highest': rates = [var.sampling_rate for var in variables if isinstance(var, DenseRunVariable)] # TODO: this looks like it takes first, not highest... verify sampling_rate = rates[0] if rates else None return cls(variables, sampling_rate) # For non-run collections, we may need to set a different output level coll = cls(variables) if output_level is not None: coll.level = output_level return coll ================================================ FILE: src/bids/variables/entities.py ================================================ """Data classes for internal BIDS data hierarchy.""" from collections import namedtuple from itertools import chain import pandas as pd from bids.utils import matches_entities from . import collections as clc BASE_ENTITIES = ['subject', 'session', 'task', 'run'] class Node: """Base class for objects that represent a single object in the BIDS hierarchy. Parameters ---------- id : int or str A value uniquely identifying this node. Typically the entity value extracted from the filename via layout. """ def __init__(self, level, entities): self.level = level.lower() self.entities = entities if self.entities is not None: self.base_ents = {e: val for e, val in entities.items() if e in BASE_ENTITIES} else: self.base_ents = None self.variables = {} def add_variable(self, variable): """Adds a BIDSVariable to the current Node's list. Parameters ---------- variable : BIDSVariable The Variable to add to the list. """ if self.base_ents is not None: for e, val in self.base_ents.items(): if e in variable.entities and variable.entities[e] != val: raise ValueError('Variable and node entity mismatch.') self.variables[variable.name] = variable class RunNode(Node): """Represents a single Run in a BIDS project. Parameters ---------- id : int The index of the run. entities : dict Dictionary of entities for this Node. image_file : str The full path to the corresponding nifti image. duration : float Duration of the run, in seconds. repetition_time : float TR for the run. task : str The task name for this run. """ def __init__(self, entities, image_file, duration, repetition_time, n_vols): self.image_file = image_file self.duration = duration self.repetition_time = repetition_time self.n_vols = n_vols super().__init__('run', entities) def get_info(self): # noqa: D102 # Note: do not remove the dict() call! self.entities is a SQLAlchemy # association_proxy mapping, and without the conversion, the connection # to the DB persists, causing problems on Python 3.5 if we try to clone # a RunInfo or any containing object. entities = dict(self.entities) return RunInfo(entities, self.duration, self.repetition_time, self.image_file, self.n_vols) # Stores key information for each Run. RunInfo_ = namedtuple('RunInfo', ['entities', 'duration', 'tr', 'image', 'n_vols']) # Wrap with class to provide docstring class RunInfo(RunInfo_): """A namedtuple storing run-related information. Properties include 'entities', 'duration', 'tr', and 'image', 'n_vols'. """ pass class NodeIndex: """Represents the top level in a BIDS hierarchy.""" def __init__(self): super().__init__() self.index = pd.DataFrame() self.nodes = [] def get_collections(self, unit, names=None, merge=False, sampling_rate=None, **entities): """Retrieve variable data for a specified level in the Dataset. Parameters ---------- unit : str The unit of analysis to return variables for. Must be one of 'run', 'session', 'subject', or 'dataset'. names : list Optional list of variables names to return. If None, all available variables are returned. merge : bool If True, variables are merged across all observations of the current unit. E.g., if unit='subject' and return_type= 'collection', variables from all subjects will be merged into a single collection. If False, each observation is handled separately, and the result is returned as a list. sampling_rate : int or str If unit='run', the sampling rate to pass onto the returned BIDSRunVariableCollection. entities : dict Optional constraints used to limit what gets returned. Returns ------- A list of BIDSVariableCollections if merge=False; a single BIDSVariableCollection if merge=True. """ nodes = self.get_nodes(unit, entities) var_sets = [] for n in nodes: var_set = list(n.variables.values()) var_set = [v for v in var_set if matches_entities(v, entities)] if names is not None: var_set = [v for v in var_set if v.name in names] # Additional filtering on Variables past run level, because their # contents are extracted from TSV files containing rows from # multiple observations if unit != 'run': var_set = [v.filter(entities) for v in var_set] var_sets.append(var_set) if merge: var_sets = [list(chain(*var_sets))] results = [] for vs in var_sets: if not vs: continue if unit == 'run': vs = clc.BIDSRunVariableCollection(vs, sampling_rate) else: vs = clc.BIDSVariableCollection(vs) results.append(vs) if merge: return results[0] if results else None return results def get_nodes(self, level=None, entities=None, strict=False): """Retrieves all nodes that match the specified criteria. Parameters ---------- level : str The level of analysis of nodes to return. entities : dict Entities to filter on. All nodes must have matching values on all defined keys to be included. strict : bool If True, an exception will be raised if the entities dict contains any keys that aren't contained in the current index. Returns ------- A list of Node instances. """ entities = {} if entities is None else entities.copy() if level is not None: entities['level'] = level if entities is None: return self.nodes match_ents = set(entities.keys()) common_cols = list(match_ents & set(self.index.columns)) if strict and match_ents - common_cols: raise ValueError('Invalid entities: ', match_ents - common_cols) if not common_cols: return self.nodes # Construct query string that handles both single values and iterables query = [] for col in common_cols: oper = 'in' if isinstance(entities[col], (list, tuple)) else '==' q = f'{col} {oper} {repr(entities[col])}' query.append(q) query = ' and '.join(query) rows = self.index.query(query) if rows.empty: return [] # Sort and return sort_cols = [ 'subject', 'session', 'task', 'run', 'node_index', 'suffix', 'level', 'datatype', ] sort_cols = [sc for sc in sort_cols if sc in set(rows.columns)] rows = rows.sort_values(sort_cols) inds = rows['node_index'].astype(int) return [self.nodes[i] for i in inds] def create_node(self, level, entities, *args, **kwargs): """Creates a new child Node. Parameters ---------- level : str The level of analysis of the new Node. entities : dict Dictionary of entities belonging to Node. args, kwargs : dict Optional positional or named arguments to pass on to class-specific initializers. These arguments are only used if a Node that matches the passed entities doesn't already exist, and a new one must be created. Returns ------- A Node instance. """ if level == 'run': node = RunNode(entities, *args, **kwargs) else: node = Node(level, entities) entities = dict(entities, node_index=len(self.nodes), level=level) self.nodes.append(node) # Because "entities" may have non-scalar values (such as `SliceTiming`) # we need to first create a Series to avoid expansion # From here we can concatenate node_row = pd.DataFrame(pd.Series(entities)).T self.index = pd.concat([self.index, node_row], ignore_index=True) return node def get_or_create_node(self, level, entities, *args, **kwargs): """Retrieves a child Node based on the specified criteria, creating a new Node if necessary. Parameters ---------- level : str The level of analysis of the Node. entities : dict Dictionary of entities to include in newly-created Nodes or filter existing ones. args, kwargs : dict Optional positional or named arguments to pass on to class-specific initializers. These arguments are only used if a Node that matches the passed entities doesn't already exist, and a new one must be created. Returns ------- A Node instance. """ result = self.get_nodes(level, entities) if result: if len(result) > 1: raise ValueError( "More than one matching Node found! If you're" ' expecting more than one Node, use ' 'get_nodes() instead of get_or_create_node().' ) return result[0] return self.create_node(level, entities, *args, **kwargs) ================================================ FILE: src/bids/variables/io.py ================================================ """Tools for reading/writing BIDS data files.""" import json import warnings import numpy as np import pandas as pd from bids.utils import listify from .entities import NodeIndex from .variables import DenseRunVariable, SimpleVariable, SparseRunVariable BASE_ENTITIES = ['subject', 'session', 'task', 'run'] ALL_ENTITIES = BASE_ENTITIES + ['datatype', 'suffix', 'acquisition'] def load_variables( # noqa: D417 layout, types=None, levels=None, skip_empty=True, dataset=None, scope='all', regex_search=None, **kwargs, ): """A convenience wrapper for one or more load_*_variables() calls. Parameters ---------- layout : :obj:`bids.layout.BIDSLayout` BIDSLayout containing variable files. types : str or list Types of variables to retrieve. All valid values reflect the filename stipulated in the BIDS spec for each kind of variable. Valid values include: 'events', 'physio', 'stim', 'scans', 'participants', 'sessions', and 'regressors'. levels : str or list Optional level(s) of variables to load. Valid values are 'run', 'session', 'subject', or 'dataset'. This is simply a shorthand way to specify types--e.g., 'run' will be converted to types=['events', 'physio', 'stim', 'regressors']. skip_empty : bool Whether or not to skip empty Variables (i.e., where there are no rows/records in a file after applying any filtering operations like dropping NaNs). dataset : NodeIndex An existing NodeIndex container to store the loaded data in. Can be used to iteratively construct a dataset that contains otherwise heterogeneous sets of variables. If None, a new NodeIndex is used. scope : str or list The scope of the space to search for variables. See docstring for BIDSLayout for details and valid predefined values. kwargs : dict Optional keyword arguments to pass onto the individual load_*_variables() calls. Returns ------- A NodeIndex instance. Examples -------- >>> load_variables(layout, ['events', 'physio'], subject='01') # doctest: +SKIP # returns all variables stored in _events.tsv and _physio.tsv.gz files # for runs that belong to subject with id '01'. """ TYPES = ['events', 'physio', 'stim', 'scans', 'participants', 'sessions', 'regressors'] types = listify(types) if types is None: if levels is not None: types = [] lev_map = { 'run': ['events', 'physio', 'stim', 'regressors'], 'session': ['scans'], 'subject': ['sessions', 'scans'], 'dataset': ['participants'], } [types.extend(lev_map[l.lower()]) for l in listify(levels)] # noqa: E741 else: types = TYPES bad_types = set(types) - set(TYPES) if bad_types: raise ValueError('Invalid variable types: %s' % bad_types) # noqa: UP031 dataset = dataset or NodeIndex() run_types = list({'events', 'physio', 'stim', 'regressors'} - set(types)) type_flags = dict.fromkeys(run_types, False) if len(type_flags) < 4: _kwargs = kwargs.copy() _kwargs.update(type_flags) dataset = _load_time_variables(layout, dataset, scope=scope, **_kwargs) for t in {'scans', 'sessions', 'participants'} & set(types): kwargs.pop('suffix', None) # suffix is always one of values above dataset = _load_tsv_variables( layout, t, dataset, scope=scope, regex_search=regex_search, **kwargs ) return dataset def _get_nvols(img_f): import nibabel as nb img = nb.load(img_f) nvols = 0 if isinstance(img, nb.Nifti1Pair): nvols = img.shape[3] elif isinstance(img, nb.Cifti2Image): for ax in map(img.header.get_axis, range(len(img.header.matrix))): if isinstance(ax, nb.cifti2.SeriesAxis): nvols = ax.size break else: raise ValueError('No series axis found in %s' % img_f) # noqa: UP031 elif isinstance(img, nb.GiftiImage): nvols = len(img.get_arrays_from_intent('time series')) else: raise ValueError('Unknown image type %s: %s' % img.__class__, img_f) # noqa: UP031 return nvols def _load_time_variables( # noqa: D417 layout, dataset=None, columns=None, scan_length=None, drop_na=True, events=True, physio=True, stim=True, regressors=True, skip_empty=True, scope='all', **selectors, ): """Loads all variables found in *_events.tsv files and returns them as a BIDSVariableCollection. Parameters ---------- layout : :obj:`bids.layout.BIDSLayout` A BIDSLayout to scan. dataset : NodeIndex A BIDS NodeIndex container. If None, a new one is initialized. columns : list Optional list of names specifying which columns in the event files to read. By default, reads all columns found. scan_length : float Optional duration of runs (in seconds). By default, this will be extracted from the BOLD image. However, in cases where the user doesn't have access to the images (e.g., because only file handles are locally available), a fixed duration can be manually specified as a fallback. drop_na : bool If True, removes all events where amplitude is n/a. If False, leaves n/a values intact. Note that in the latter case, transformations that requires numeric values may fail. events : bool If True, extracts variables from events.tsv files. physio : bool If True, extracts variables from _physio files. stim : bool If True, extracts variables from _stim files. skip_empty : bool Whether or not to skip empty Variables (i.e., where there are no rows/records in a file, or all onsets, durations, and amplitudes are 0). scope : str or list The scope of the space to search for variables. See docstring for BIDSLayout for details and valid predefined values. selectors : dict Optional keyword arguments passed on to the BIDSLayout instance's get() method; can be used to constrain which data are loaded. Returns ------- A NodeIndex instance. """ # Extract any non-keyword arguments selectors = selectors.copy() if dataset is None: dataset = NodeIndex() selectors['datatype'] = 'func' selectors['suffix'] = 'bold' exts = selectors.pop('extension', ['.nii', '.nii.gz', '.func.gii', '.dtseries.nii']) images = layout.get(return_type='object', scope=scope, extension=exts, **selectors) if not images: raise ValueError('No functional images that match criteria found.') # Main loop over images for img_obj in images: img_f = img_obj.path # Run is not mandatory, but we need it to behave like an int and not # a PaddedInt later when we call `dataset.get_nodes('run', select_on)` # Without this, we would see "run ==02" in the query, which breaks # pandas. entities = img_obj.entities.copy() if 'run' in entities: entities['run'] = int(entities['run']) tr = img_obj.get_metadata()['RepetitionTime'] # Get duration of run: first try to get it directly from the image # header; if that fails, look for a scan_length argument. try: nvols = _get_nvols(img_f) duration = nvols * tr except Exception as e: if scan_length is not None: duration = scan_length nvols = int(np.rint(scan_length / tr)) else: msg = ( 'Unable to extract scan duration from one or more ' 'BOLD runs, and no scan_length argument was provided ' 'as a fallback. Please check that the image files are ' 'available, or manually specify the scan duration.' ) raise ValueError(msg) from e # We don't want to pass all the image file's entities onto get_node(), # as there can be unhashable nested slice timing values, and this also # slows down querying unnecessarily. Instead, pick out files only based # on the core BIDS entities and any entities explicitly passed as # selectors. # TODO: one downside of this approach is the stripped entities also # won't be returned in the resulting node due to the way things are # implemented. Consider adding a flag to control this. select_on = {k: v for (k, v) in entities.items() if k in BASE_ENTITIES or k in selectors} # If a matching node already exists, return it result = dataset.get_nodes('run', select_on) if result: if len(result) > 1: raise ValueError( 'More than one existing Node matches the ' 'specified entities! You may need to pass ' 'additional selectors to narrow the search.' ) run = result[0] else: # Otherwise create a new node and use that. # We first convert any entity values that are currently collections to # JSON strings to prevent nasty hashing problems downstream. Note that # isinstance() isn't as foolproof as actually trying to hash the # value, but the latter is likely to be slower, and since values are # coming from JSON or filenames, there's no real chance of encountering # anything but a list or dict. entities = { k: (json.dumps(v) if isinstance(v, (list, dict)) else v) for (k, v) in entities.items() } run = dataset.create_node( 'run', entities, image_file=img_f, duration=duration, repetition_time=tr, n_vols=nvols, ) run_info = run.get_info() # Process event files if events: dfs = layout.get_nearest( img_f, extension='.tsv', suffix='events', all_=True, full_search=True, ignore_strict_entities=['suffix', 'extension'], ) for _data in dfs: _data = pd.read_csv(_data, sep='\t') if 'amplitude' in _data.columns: if ( _data['amplitude'].astype(int) == 1 ).all() and 'trial_type' in _data.columns: msg = ( "Column 'amplitude' with constant value 1 " 'is unnecessary in event files; ignoring it.' ) _data = _data.drop('amplitude', axis=1) else: msg = "Column name 'amplitude' is reserved; renaming it to 'amplitude_'." _data = _data.rename(columns={'amplitude': 'amplitude_'}) warnings.warn(msg) # noqa: B028 # Pandas already converts 'n/a' to NaN. Leaving this comment # because we used to do it manually here. # We also converted to numeric, but this is now irrelevant. _cols = columns or list(set(_data.columns.tolist()) - {'onset', 'duration'}) # Construct a DataFrame for each extra column for col in _cols: df = _data[['onset', 'duration']].copy() df['amplitude'] = _data[col].values # Add in all of the run's entities as new columns for # index for entity, value in entities.items(): if entity in ALL_ENTITIES: df[entity] = value if drop_na: df = df.dropna(subset=['amplitude']) if df.empty: continue var = SparseRunVariable(name=col, data=df, run_info=run_info, source='events') run.add_variable(var) # Process confound files if regressors: sub_ents = {k: v for k, v in entities.items() if k in BASE_ENTITIES} confound_files = layout.get( suffix=['regressors', 'timeseries'], scope=scope, extension='.tsv', **sub_ents ) for cf in confound_files: _data = pd.read_csv(cf.path, sep='\t', na_values='n/a') if columns is not None: conf_cols = list(set(_data.columns) & set(columns)) _data = _data.loc[:, conf_cols] for col in _data.columns: sr = 1.0 / run.repetition_time var = DenseRunVariable( name=col, values=_data[[col]], run_info=run_info, source='regressors', sampling_rate=sr, ) run.add_variable(var) # Process recordinging files rec_types = [] if physio: rec_types.append('physio') if stim: rec_types.append('stim') if rec_types: rec_files = layout.get_nearest( img_f, extension='.tsv.gz', all_=True, suffix=rec_types, ignore_strict_entities=['suffix', 'extension'], full_search=True, ) for rf in rec_files: metadata = layout.get_metadata(rf) if not metadata: raise ValueError("No .json sidecar found for '%s'." % rf) # noqa: UP031 data = pd.read_csv(rf, sep='\t') freq = metadata['SamplingFrequency'] st = metadata['StartTime'] rf_cols = metadata['Columns'] data.columns = rf_cols # Filter columns if user passed names if columns is not None: rf_cols = list(set(rf_cols) & set(columns)) data = data.loc[:, rf_cols] n_cols = len(rf_cols) if not n_cols: continue # Keep only in-scan samples if st < 0: start_ind = np.floor(-st * freq) values = data.values[start_ind:, :] else: values = data.values if st > 0: n_pad = int(freq * st) pad = np.zeros((n_pad, n_cols)) values = np.r_[pad, values] n_rows = int(run.duration * freq) if len(values) > n_rows: values = values[:n_rows, :] elif len(values) < n_rows: pad = np.zeros((n_rows - len(values), n_cols)) values = np.r_[values, pad] df = pd.DataFrame(values, columns=rf_cols) source = 'physio' if '_physio.tsv' in rf else 'stim' for col in df.columns: var = DenseRunVariable( name=col, values=df[[col]], run_info=run_info, source=source, sampling_rate=freq, ) run.add_variable(var) return dataset def _load_tsv_variables( # noqa: D417 layout, suffix, dataset=None, columns=None, prepend_type=False, scope='all', regex_search=None, **selectors, ): """Reads variables from scans.tsv, sessions.tsv, and participants.tsv. Parameters ---------- layout : :obj:`bids.layout.BIDSLayout` The BIDSLayout to use. suffix : str The suffix of file to read from. Must be one of 'scans', 'sessions', or 'participants'. dataset : NodeIndex A BIDS NodeIndex container. If None, a new one is initialized. columns : list Optional list of names specifying which columns in the files to return. If None, all columns are returned. prepend_type : bool If True, variable names are prepended with the type name (e.g., 'age' becomes 'participants.age'). scope : str or list The scope of the space to search for variables. See docstring for BIDSLayout for details and valid predefined values. selectors : dict Optional keyword arguments passed onto the BIDSLayout instance's get() method; can be used to constrain which data are loaded. Returns ------- A NodeIndex instance. """ if regex_search is None: regex_search = layout.regex_search # Sanitize the selectors: only keep entities at current level or above valid_entities_map = { 'scans': ['subject', 'session'], 'sessions': ['subject'], 'participants': [], } valid_entities = valid_entities_map[suffix] layout_kwargs = {k: v for k, v in selectors.items() if k in valid_entities} if dataset is None: dataset = NodeIndex() files = layout.get(extension='.tsv', suffix=suffix, scope=scope, **layout_kwargs) for f in files: _data = f.get_df(include_timing=False) # Entities can be defined either within the first column of the .tsv # file (for entities that vary by row), or from the full file path # (for entities constant over all rows in the file). We extract both # and store them in the main DataFrame alongside other variables (as # they'll be extracted when the BIDSVariable is initialized anyway). for ent_name, ent_val in f.entities.items(): if ent_name in ALL_ENTITIES: _data[ent_name] = ent_val # Handling is a bit more convoluted for scans.tsv, because the first # column contains the run filename, which we also need to parse. if suffix == 'scans': # Suffix is guaranteed to be present in each filename, so drop the # constant column with value 'scans' to make way for it and prevent # two 'suffix' columns. _data.drop(columns=['suffix'], inplace=True) image = _data['filename'] _data = _data.drop('filename', axis=1) dn = f._dirname paths = [str(dn / p) for p in image.values] ent_recs = [dict(layout.files[p].entities) for p in paths if p in layout.files] ent_cols = pd.DataFrame.from_records(ent_recs) # Remove entity columns found in both DFs dupes = list(set(ent_cols.columns) & set(_data.columns)) to_drop = ['extension'] + dupes ent_cols.drop(columns=to_drop, inplace=True) _data = pd.concat([_data, ent_cols], axis=1, sort=True) # The BIDS spec requires ID columns to be named 'session_id', 'run_id', # etc., and IDs begin with entity prefixes (e.g., 'sub-01'). To ensure # consistent internal handling, we strip these suffixes and prefixes. elif suffix == 'sessions': _data = _data.rename(columns={'session_id': 'session'}) _data['session'] = _data['session'].str.replace('ses-', '') elif suffix == 'participants': _data = _data.rename(columns={'participant_id': 'subject'}) _data['subject'] = _data['subject'].str.replace('sub-', '') def make_patt(x, regex_search=False): patt = '%s' % x # noqa: UP031 if isinstance(x, (int, float)): # allow for leading zeros if a number was specified # regardless of regex_search patt = '0*' + patt if not regex_search: patt = '^%s$' % patt # noqa: UP031 return patt # Filter rows on all selectors comm_cols = list(set(_data.columns) & set(selectors.keys())) for col in comm_cols: vals = listify(selectors.get(col)) if regex_search and any(isinstance(val, str) for val in vals): patt = '|'.join(make_patt(x, regex_search=True) for x in vals) _data = _data[_data[col].str.contains(patt)] else: _data = _data[_data[col].isin(vals)] level = {'scans': 'session', 'sessions': 'subject', 'participants': 'dataset'}[suffix] node = dataset.get_or_create_node(level, f.entities) ent_cols = list(set(ALL_ENTITIES) & set(_data.columns)) amp_cols = list(set(_data.columns) - set(ent_cols)) if columns is not None: amp_cols = list(set(amp_cols) & set(columns)) for col_name in amp_cols: # Rename columns: values must be in 'amplitude' df = _data.loc[:, [col_name] + ent_cols] df.columns = ['amplitude'] + ent_cols if prepend_type: col_name = '%s.%s' % (suffix, col_name) # noqa: UP031 node.add_variable(SimpleVariable(name=col_name, data=df, source=suffix)) return dataset ================================================ FILE: src/bids/variables/tests/__init__.py ================================================ ================================================ FILE: src/bids/variables/tests/test_collections.py ================================================ from os.path import join import numpy as np import pytest from bids.layout import BIDSLayout from bids.tests import get_test_data_path from bids.variables import DenseRunVariable, SparseRunVariable, merge_collections from bids.variables.entities import RunInfo @pytest.fixture(scope='module') def run_coll(): path = join(get_test_data_path(), 'ds005') layout = BIDSLayout(path) # Limit to a few subjects to reduce test running time return layout.get_collections( 'run', types=['events'], merge=True, scan_length=480, subject=['01', '02', '04'] ) @pytest.fixture(scope='module') def run_coll_bad_length(): path = join(get_test_data_path(), 'ds005') layout = BIDSLayout(path) # Limit to a few subjects to reduce test running time return layout.get_collections( 'run', types=['events'], merge=True, scan_length=480.1, subject=['01', '02', '04'] ) @pytest.fixture(scope='module') def run_coll_list(): path = join(get_test_data_path(), 'ds005') layout = BIDSLayout(path) return layout.get_collections( 'run', types=['events'], merge=False, scan_length=480, subject=['01', '02', '04'] ) @pytest.fixture(scope='module') def run_coll_derivs(): path = join(get_test_data_path(), 'ds005') deriv_path = join(get_test_data_path(), 'ds005', 'derivatives', 'fmriprep') layout = BIDSLayout(path, derivatives=deriv_path) # Limit to a few subjects to reduce test running time return layout.get_collections( 'run', types=['events', 'regressors'], merge=False, scan_length=480, subject=['01', '02'] ) def test_run_variable_collection_init(run_coll): assert isinstance(run_coll.variables, dict) assert run_coll.sampling_rate == 10 assert ( run_coll.__repr__() == "" ) def test_run_variable_collection_sparse_variable_accessors(run_coll): coll = run_coll.clone() assert coll.get_sparse_variables() assert coll.all_sparse() coll.variables['RT'] = coll.variables['RT'].to_dense(1) assert not coll.all_sparse() assert len(coll.get_sparse_variables()) + 1 == len(coll.variables) def test_run_variable_collection_dense_variable_accessors(run_coll): coll = run_coll.clone() coll.variables['RT'] = coll.variables['RT'].to_dense(1) assert not coll.all_dense() assert len(coll.get_dense_variables()) == 1 for k, v in coll.variables.items(): if k == 'RT': continue coll.variables[k] = v.to_dense(1) assert coll.all_dense() def test_run_variable_collection_get_sampling_rate(run_coll): coll = run_coll.clone() assert coll._get_sampling_rate(None) == 10 coll.variables['RT'].run_info[0] = RunInfo({}, 200, 10, None, 20) with pytest.raises(ValueError) as exc: coll._get_sampling_rate('TR') assert str(exc.value).startswith('Non-unique') assert coll._get_sampling_rate('highest') == 10 coll.variables['RT1'] = coll.variables['RT'].to_dense(5.0) coll.variables['RT2'] = coll.variables['RT'].to_dense(12.0) assert coll._get_sampling_rate('highest') == 12.0 assert coll._get_sampling_rate(20) == 20 with pytest.raises(ValueError) as exc: coll._get_sampling_rate('BLARGH') assert str(exc.value).startswith('Invalid') def test_resample_run_variable_collection(run_coll): run_coll = run_coll.clone() resampled = run_coll.resample() assert not resampled.variables # Empty because all variables are sparse resampled = run_coll.resample(force_dense=True).variables assert len(resampled) == 7 assert all([isinstance(v, DenseRunVariable) for v in resampled.values()]) assert len(set([v.sampling_rate for v in resampled.values()])) == 1 targ_len = 480 * 3 * 3 * 10 assert all([len(v.values) == targ_len for v in resampled.values()]) sr = 20 resampled = run_coll.resample(sr, force_dense=True).variables targ_len = 480 * 3 * 3 * sr assert all([len(v.values) == targ_len for v in resampled.values()]) run_coll.resample(sr, force_dense=True, in_place=True) assert len(run_coll.variables) == 8 vars_ = run_coll.variables.values() vars_ = [v for v in vars_ if v.name != 'trial_type'] assert all([len(v.values) == targ_len for v in vars_]) assert all([v.sampling_rate == sr for v in vars_]) assert all([isinstance(v, DenseRunVariable) for v in vars_]) def test_run_variable_collection_to_df_all_sparse_vars(run_coll): run_coll = run_coll.clone() timing_cols = {'onset', 'duration'} entity_cols = {'subject', 'run', 'task', 'suffix', 'datatype'} cond_names = { 'PTval', 'RT', 'gain', 'loss', 'parametric gain', 'respcat', 'respnum', 'trial_type', } condition = {'condition'} ampl = {'amplitude'} # Fails because all vars are sparse with pytest.raises(ValueError): df = run_coll.to_df(include_sparse=False) # Fails because no such variables exist with pytest.raises(ValueError): df = run_coll.to_df(variables=['rubadubdub', '999']) # Wide format df = run_coll.to_df() events_per_sub = 256 assert df.shape == (events_per_sub * 3, 15) assert set(df.columns) == timing_cols.union(entity_cols, cond_names) # Wide format, selecting variables by name df = run_coll.to_df(format='wide', variables=['RT', 'PTval']) assert df.shape == (events_per_sub * 3, 9) assert set(df.columns) == timing_cols.union(entity_cols, {'RT', 'PTval'}) # Long format df = run_coll.to_df(format='long') assert df.shape == (events_per_sub * 3 * 8, 9) assert set(df.columns) == timing_cols.union(entity_cols, condition, ampl) # Long format, selecting variables by name df = run_coll.to_df(format='long', variables=['RT', 'PTval']) assert df.shape == (events_per_sub * 3 * 2, 9) assert set(df.columns) == timing_cols.union(entity_cols, condition, ampl) # Wide format without entity columns df = run_coll.to_df(format='wide', entities=False) assert df.shape == (events_per_sub * 3, 10) assert set(df.columns) == timing_cols | cond_names # Wide format without timing columns df = run_coll.to_df(format='wide', timing=False) assert df.shape == (events_per_sub * 3, 13) assert set(df.columns) == entity_cols | cond_names def test_run_variable_collection_to_df_all_dense_vars(run_coll): timing_cols = {'onset', 'duration'} entity_cols = {'subject', 'run', 'task', 'suffix', 'datatype'} cond_names = { 'PTval', 'RT', 'gain', 'loss', 'parametric gain', 'respcat', 'respnum', 'trial_type', } md_names = {'TaskName', 'RepetitionTime', 'extension', 'SliceTiming'} condition = {'condition'} ampl = {'amplitude'} # First few tests assume uniform sampling rate for all columns unif_coll = run_coll.to_dense(sampling_rate=10) # Fails because all vars are dense with pytest.raises(ValueError): unif_coll.to_df(include_dense=False) # Fails because no such variables exist with pytest.raises(ValueError): df = unif_coll.to_df(variables=['rubadubdub', '999']) # Wide format df = unif_coll.to_df() rows_per_var = 3 * 3 * 480 * 10 # subjects x runs x time x sampling rate assert df.shape == (rows_per_var, 18) cols = (timing_cols | entity_cols | cond_names | md_names) - {'trial_type'} assert set(df.columns) == cols # Wide format with variable selection df = unif_coll.to_df(variables=['RT', 'gain']) assert df.shape == (rows_per_var, 13) cols = (timing_cols | entity_cols | {'RT', 'gain'} | md_names) - {'trial_type'} assert set(df.columns) == cols # Long format df = unif_coll.to_df(format='long') assert df.shape == (rows_per_var * 7, 13) cols = timing_cols | entity_cols | condition | ampl | md_names assert set(df.columns) == cols # Long format with variable selection df = unif_coll.to_df(format='long', variables=['RT', 'gain']) assert df.shape == (rows_per_var * 2, 13) cols = timing_cols | entity_cols | condition | ampl | md_names assert set(df.columns) == cols # Test resampling to TR df = unif_coll.to_df(sampling_rate='TR') n_rows = int(480 * 3 * 3 / 2) assert df.shape == (n_rows, 18) cols = (timing_cols | entity_cols | cond_names | md_names) - {'trial_type'} assert set(df.columns) == cols # Test resampling to highest when variable sampling rates diverge unif_coll.resample(variables='RT', sampling_rate=12, in_place=True) df = unif_coll.to_df(sampling_rate='highest') n_rows = int(rows_per_var * 12 / 10) assert df.shape == (n_rows, 18) def test_run_variable_collection_bad_length_to_df_all_dense_vars(run_coll_bad_length): timing_cols = {'onset', 'duration'} entity_cols = {'subject', 'run', 'task', 'suffix', 'datatype'} cond_names = { 'PTval', 'RT', 'gain', 'loss', 'parametric gain', 'respcat', 'respnum', 'trial_type', } md_names = {'TaskName', 'RepetitionTime', 'extension', 'SliceTiming'} condition = {'condition'} ampl = {'amplitude'} unif_coll = run_coll_bad_length.to_dense(sampling_rate=10) df = unif_coll.to_df() rows_per_var = np.round(3 * 3 * 480.1 * 10) # subjects x runs x time x sampling rate # Test resampling without setting sample rate (default sr == 10) df = unif_coll.to_df(format='long') assert df.shape == (rows_per_var * 7, 13) cols = timing_cols | entity_cols | condition | ampl | md_names assert set(df.columns) == cols # Test resampling to TR df = unif_coll.to_df(sampling_rate='TR') n_rows = int(480 * 3 * 3 / 2) # (Note number of volumes is 480, not 480.1) assert df.shape == (n_rows, 18) cols = (timing_cols | entity_cols | cond_names | md_names) - {'trial_type'} assert set(df.columns) == cols def test_run_variable_collection_to_df_mixed_vars(run_coll): coll = run_coll.clone() coll.to_dense(10, variables=['RT', 'loss', 'gain'], in_place=True) # Only sparse variables df = coll.to_df(include_dense=False) rows = 3 * 256 assert df.shape == (rows, 12) assert 'RT' not in df.columns assert 'respcat' in df.columns # Only dense variables df = coll.to_df(include_sparse=False) rows = 3 * 3 * 480 * 10 assert df.shape == (rows, 14) assert 'RT' in df.columns assert 'respcat' not in df.columns # Everything df = coll.to_df(sampling_rate=5) rows = 3 * 3 * 480 * 5 assert df.shape == (rows, 18) assert not {'RT', 'respcat'} - set(df.columns) def test_merge_collections(run_coll, run_coll_list): df1 = run_coll.to_df().sort_values(['subject', 'run', 'onset']) rcl = [c.clone() for c in run_coll_list] coll = merge_collections(rcl) df2 = coll.to_df().sort_values(['subject', 'run', 'onset']) assert df1.equals(df2) def test_get_collection_entities(run_coll_list): coll = run_coll_list[0] ents = coll.entities assert not ({'run', 'task', 'subject', 'suffix', 'datatype'} - set(ents.keys())) merged = merge_collections(run_coll_list[:3]) ents = merged.entities assert not ({'task', 'subject', 'suffix', 'datatype'} - set(ents.keys())) assert ents['subject'] == '01' merged = merge_collections(run_coll_list[3:6]) ents = merged.entities assert not ({'task', 'subject', 'suffix', 'datatype'} - set(ents.keys())) assert ents['subject'] == '02' def test_match_variables(run_coll): matches = run_coll.match_variables('^.{1,2}a', match_type='regex') assert set(matches) == {'gain', 'parametric gain'} assert not run_coll.match_variables('.{1,3}a') matches = run_coll.match_variables('^.{1,2}a', match_type='regex', return_type='variable') assert len(matches) == 2 assert all([isinstance(m, SparseRunVariable) for m in matches]) matches = run_coll.match_variables('*gain') assert set(matches) == {'gain', 'parametric gain'} def test_n_variables(run_coll_derivs): # Test that variables are ingested into correct collection # non_steady_state_outlier00 should only exist for sub-01 run-01 match = [ c for c in run_coll_derivs if c.entities.get('subject', None) == '01' and c.entities['run'] == 1 ] assert 'non_steady_state_outlier00' in match[0].variables.keys() # Find collections with 'non_steady_state_outlier00' matches = [c for c in run_coll_derivs if 'non_steady_state_outlier00' in c.variables.keys()] assert len(matches) == 1 # Test that collections have not incorrectly merged multiple subjects for c in run_coll_derivs: assert 'subject' in c.entities ================================================ FILE: src/bids/variables/tests/test_entities.py ================================================ from os.path import join import pytest from bids.layout import BIDSLayout from bids.tests import get_test_data_path from bids.variables import BIDSRunVariableCollection, load_variables from bids.variables.entities import Node, NodeIndex, RunNode @pytest.fixture(scope='module') def layout1(): path = join(get_test_data_path(), 'ds005') layout = BIDSLayout(path) return layout @pytest.fixture(scope='module') def layout2(): path = join(get_test_data_path(), '7t_trt') layout = BIDSLayout(path) return layout def test_run(layout1): img = layout1.get( subject='01', task='mixedgamblestask', suffix='bold', run=1, return_type='obj' )[0] run = RunNode(None, img.filename, 480, 2, 480 / 2) assert run.image_file == img.filename assert run.duration == 480 assert run.repetition_time == 2 assert run.n_vols == 480 / 2 def test_get_or_create_node(layout1): img = layout1.get( subject='01', task='mixedgamblestask', suffix='bold', run=1, return_type='obj' )[0] index = NodeIndex() entities = {'subject': '01', 'session': 1} sess = index.get_or_create_node('session', entities) assert sess.__class__ == Node sess2 = index.get_or_create_node('session', entities) assert sess2 == sess run = index.get_or_create_node( 'run', img.entities, image_file=img.filename, duration=480, repetition_time=2, n_vols=480 / 2, ) assert run.__class__ == RunNode assert run.duration == 480 assert run.n_vols == 480 / 2 def test_get_nodes(layout1): index = load_variables(layout1, scan_length=480) # scans.tsv nodes = index.get_nodes('session') assert len(nodes) == 2 # participants.tsv nodes = index.get_nodes('dataset') assert len(nodes) == 1 assert all([isinstance(n, Node) for n in nodes]) nodes = index.get_nodes('run', {'subject': ['01', '02', '03']}) assert len(nodes) == 9 assert all([isinstance(n, RunNode) for n in nodes]) def test_get_collections_merged(layout1): dataset = load_variables(layout1, scan_length=480) collection = dataset.get_collections('run', merge=True) assert isinstance(collection, BIDSRunVariableCollection) assert len(collection.variables) == 8 vals = collection.variables['RT'].values ents = collection.variables['RT'].index assert len(ents) == len(vals) == 4096 assert set(ents.columns) == {'task', 'run', 'subject', 'suffix', 'datatype'} def test_get_collections_unmerged(layout2): dataset = load_variables(layout2, types=['sessions'], scan_length=480) colls = dataset.get_collections('subject', merge=False) assert len(colls) == 10 assert len(colls[0].variables) == 94 assert colls[0]['panas_at_ease'].values.shape == (2,) ================================================ FILE: src/bids/variables/tests/test_io.py ================================================ from os.path import join import pytest from bids.layout import BIDSLayout from bids.tests import get_test_data_path from bids.variables import DenseRunVariable, SimpleVariable, SparseRunVariable, load_variables from bids.variables.entities import Node, NodeIndex, RunNode @pytest.fixture def layout1(): path = join(get_test_data_path(), 'ds005') layout = BIDSLayout(path) return layout @pytest.fixture(scope='module', params=['events', 'preproc']) def synthetic(request): root = join(get_test_data_path(), 'synthetic') if request.param == 'preproc': layout = BIDSLayout(root, derivatives=True) dataset = load_variables(layout, skip_empty=True, desc='preproc', space='T1w') else: layout = BIDSLayout(root) dataset = load_variables(layout, skip_empty=True) return request.param, dataset def test_load_events(layout1): index = load_variables(layout1, types='events', scan_length=480) runs = index.get_nodes(level='run', entities={'subject': '01'}) assert len(runs) == 3 assert isinstance(runs[0], RunNode) variables = runs[0].variables assert len(variables) == 8 targ_cols = {'parametric gain', 'PTval', 'trial_type', 'respnum'} assert not (targ_cols - set(variables.keys())) assert isinstance(variables['parametric gain'], SparseRunVariable) assert variables['parametric gain'].index.shape == (86, 5) assert variables['parametric gain'].source == 'events' def test_load_participants(layout1): index = load_variables(layout1, types='participants') assert isinstance(index, NodeIndex) dataset = index.get_nodes(level='dataset')[0] assert isinstance(dataset, Node) assert len(dataset.variables) == 2 assert {'age', 'sex'} == set(dataset.variables.keys()) age = dataset.variables['age'] assert isinstance(age, SimpleVariable) assert age.index.shape == (16, 2) assert age.values.shape == (16,) index = load_variables(layout1, types='participants', subject=['^1.*'], regex_search=True) age = index.get_nodes(level='dataset')[0].variables['age'] assert age.index.shape == (7, 2) assert age.values.shape == (7,) def test_load_synthetic_dataset(synthetic): param, index = synthetic # Runs runs = index.get_nodes('run') assert len(runs) == 5 * 2 * 3 runs = index.get_nodes('run', {'task': 'nback'}) assert len(runs) == 5 * 2 * 2 variables = runs[0].variables if param == 'preproc': # non-exhaustive match = ['Cosine01', 'stdDVARS', 'RotZ', 'FramewiseDisplacement'] sum_dense = 56 else: match = ['trial_type', 'weight', 'respiratory', 'cardiac', 'stimA', 'stimB'] sum_dense = 4 for v in match: assert v in variables.keys() assert sum([isinstance(v, DenseRunVariable) for v in variables.values()]) == sum_dense assert all([len(r.variables['weight'].values) == 42 for r in runs]) # Sessions sessions = index.get_nodes('session') assert len(sessions) == 5 * 2 target_cols = {'acq_time', 'TaskName', 'RepetitionTime', 'EchoTime', 'FlipAngle'} assert set(sessions[0].variables.keys()) == target_cols data = sessions[0].variables['acq_time'].filter({'task': 'nback'}) assert len(data.values) == 2 # Subjects subs = index.get_nodes('subject') assert len(subs) == 5 assert set(subs[0].variables.keys()) == {'systolic_blood_pressure'} ================================================ FILE: src/bids/variables/tests/test_variables.py ================================================ import json import os import uuid from os.path import join import nibabel as nb import numpy as np import pandas as pd import pytest from bids.layout import BIDSLayout from bids.tests import get_test_data_path from bids.variables import DenseRunVariable, SimpleVariable, load_variables, merge_variables from bids.variables.entities import RunInfo def generate_DEV(name='test', sr=20, duration=480): n = duration * sr values = np.random.normal(size=n) ent_names = ['task', 'run', 'session', 'subject'] entities = {e: uuid.uuid4().hex for e in ent_names} image = uuid.uuid4().hex + '.nii.gz' run_info = (RunInfo(entities, duration, 2, image, duration / 2),) return DenseRunVariable( name='test', values=values, run_info=run_info, source='dummy', sampling_rate=sr ) @pytest.fixture def layout1(): path = join(get_test_data_path(), 'ds005') layout = BIDSLayout(path) return layout @pytest.fixture(scope='module') def layout2(): path = join(get_test_data_path(), '7t_trt') layout = BIDSLayout(path) return layout def test_dense_event_variable_init(): dev = generate_DEV() assert dev.sampling_rate == 20 assert dev.run_info[0].duration == 480 assert dev.source == 'dummy' assert len(dev.values) == len(dev.index) assert dev.__repr__() == "" def test_dense_event_variable_resample(): dev = generate_DEV() dev2 = dev.clone().resample(sampling_rate=40) assert len(dev2.values) == len(dev2.index) assert len(dev2.values) == 2 * len(dev.values) def test_merge_wrapper(): dev = generate_DEV() data = pd.DataFrame({'amplitude': [4, 3, 2, 5]}) sev = SimpleVariable('simple', data, 'dummy') # Should break if asked to merge different classes with pytest.raises(ValueError, match='Variables of different classes'): merge_variables([dev, sev]) def test_sparse_run_variable_to_dense(layout1): index = load_variables(layout1, types='events', scan_length=480) runs = index.get_nodes('run', {'subject': ['01', '02']}) for _, run in enumerate(runs): var = run.variables['RT'] dense = var.to_dense(20) # All sparse values must occur in the dense variable dense_vals = set(np.unique(dense.values.values)) sparse_vals = set(np.unique(var.values.values)) | {0} assert not (sparse_vals - dense_vals) assert len(dense.values) > len(var.values) assert isinstance(dense, DenseRunVariable) assert dense.values.shape == (9600, 1) assert len(dense.run_info) == len(var.run_info) assert dense.source == 'events' def test_sparse_run_variable_to_dense_default_sr(layout1): index = load_variables(layout1, types='events', scan_length=480) runs = index.get_nodes('run', {'subject': ['01', '02']}) for i, run in enumerate(runs): var = run.variables['RT'] dense = var.to_dense() # Check that a sensible sampling rate was found assert np.allclose(dense.sampling_rate, 1) # Check that all unique values are identical sparse_vals = set(np.unique(var.values.values)) | {0} dense_vals = set(np.unique(dense.values.values)) assert sparse_vals == dense_vals assert len(dense.values) > len(var.values) assert isinstance(dense, DenseRunVariable) assert dense.values.shape == (480, 1) assert len(dense.run_info) == len(var.run_info) assert dense.source == 'events' def test_merge_densified_variables(layout1): SR = 10 dataset = load_variables(layout1, types='events', scan_length=480) runs = dataset.get_nodes('run') vars_ = [r.variables['RT'].to_dense(SR) for r in runs] dense = merge_variables(vars_) assert isinstance(dense, DenseRunVariable) n_rows = 480 * SR assert dense.values.shape == (len(runs) * n_rows, 1) for i in range(len(runs)): onset = i * n_rows offset = onset + n_rows run_vals = vars_[i].values dense_vals = dense.values.iloc[onset:offset].reset_index(drop=True) assert dense_vals.equals(run_vals) def test_densify_merged_variables(layout1): SR = 10 dataset = load_variables(layout1, types='events', scan_length=480) runs = dataset.get_nodes('run') vars_ = [r.variables['RT'] for r in runs] var = merge_variables(vars_) dense = var.to_dense(SR) assert isinstance(dense, DenseRunVariable) n_rows = 480 * SR assert dense.values.shape == (len(runs) * n_rows, 1) for i in range(len(runs)): onset = i * n_rows offset = onset + n_rows # resampled values from split vs. merged will be trivially different # at boundaries due to interpolation, so leave a buffer run_vals = vars_[i].to_dense(SR).values.values.ravel() dense_vals = dense.values.values.ravel()[onset:offset] assert run_vals.shape == dense_vals.shape assert np.array_equal(run_vals, dense_vals) def test_merge_simple_variables(layout2): index = load_variables(layout2, types='sessions') subjects = index.get_nodes('subject') variables = [s.variables['panas_sad'] for s in subjects] n_rows = sum([len(c.values) for c in variables]) merged = merge_variables(variables) assert len(merged.values) == n_rows assert set(merged.index.columns) == set(variables[0].index.columns) assert variables[3].values.iloc[1] == merged.values.iloc[7] def test_merge_sparse_run_variables(layout1): dataset = load_variables(layout1, types='events', scan_length=480) runs = dataset.get_nodes('run') variables = [r.variables['RT'] for r in runs] n_rows = sum([len(c.values) for c in variables]) merged = merge_variables(variables) assert len(merged.values) == n_rows assert set(merged.index.columns) == set(variables[0].index.columns) def test_merge_dense_run_variables(layout2): variables = [generate_DEV() for i in range(20)] variables += [generate_DEV(duration=400) for i in range(8)] n_rows = sum([len(c.values) for c in variables]) merged = merge_variables(variables) assert len(merged.values) == n_rows assert set(merged.index.columns) == set(variables[0].index.columns) def test_simple_variable_to_df(layout1): pass def test_sparse_run_variable_to_df(layout1): pass def test_dense_run_variable_to_df(layout2): pass def test_filter_simple_variable(layout2): dataset = load_variables(layout2, types=['scans']) sessions = dataset.get_nodes('session') variables = [s.variables['surroundings'] for s in sessions] merged = merge_variables(variables) assert merged.to_df().shape == (60, 9) filt = merged.filter({'acquisition': 'fullbrain'}) assert filt.to_df().shape == (40, 9) flt1 = merged.filter({'acquisition': 'fullbrain', 'subject': ['01', '02']}).to_df() assert flt1.shape == (8, 9) query = 'acquisition=="fullbrain" and subject in ["01", "02"]' flt2 = merged.filter(query=query) flt2 = flt2.to_df() assert flt1.equals(flt2) assert merged.filter({'nonexistent': 2}, strict=True) is None merged.filter({'acquisition': 'fullbrain'}, inplace=True) assert merged.to_df().shape == (40, 9) @pytest.mark.parametrize('TR, nvols', [(2.00000, 251), (2.000001, 251)]) def test_resampling_edge_case(tmpdir, TR, nvols): tmpdir.chdir() os.makedirs('sub-01/func') with open('sub-01/func/sub-01_task-task_events.tsv', 'w') as fobj: fobj.write('onset\tduration\tval\n1\t0.1\t1\n') with open('sub-01/func/sub-01_task-task_bold.json', 'w') as fobj: json.dump({'RepetitionTime': TR}, fobj) dataobj = np.zeros((5, 5, 5, nvols), dtype=np.int16) affine = np.diag((2.5, 2.5, 2.5, 1)) img = nb.Nifti1Image(dataobj, affine) img.header.set_zooms((2.5, 2.5, 2.5, TR)) img.to_filename('sub-01/func/sub-01_task-task_bold.nii.gz') layout = BIDSLayout('.', validate=False) coll = load_variables(layout).get_collections('run')[0] dense_var = coll.variables['val'].to_dense(coll.sampling_rate) regressor = dense_var.resample(1.0 / TR).values assert regressor.shape == (nvols, 1) def test_downsampling(tmpdir): tmpdir.chdir() os.makedirs('sub-01/func') import numpy as np TR, newTR, nvols, newvols = 2.00000, 6.0, 90, 30 Fs = 1 / TR t = np.linspace(0, int(nvols / Fs), nvols, endpoint=False) values = np.sin(0.025 * 2 * np.pi * t) + np.cos(0.1166 * 2 * np.pi * t) with open('sub-01/func/sub-01_task-task_events.tsv', 'w') as fobj: fobj.write('onset\tduration\tval\n') for idx, val in enumerate(values): fobj.write('%f\t%f\t%f\n' % (idx * TR, TR, val)) with open('sub-01/func/sub-01_task-task_bold.json', 'w') as fobj: json.dump({'RepetitionTime': TR}, fobj) dataobj = np.zeros((5, 5, 5, nvols), dtype=np.int16) affine = np.diag((2.5, 2.5, 2.5, 1)) img = nb.Nifti1Image(dataobj, affine) img.header.set_zooms((2.5, 2.5, 2.5, TR)) img.to_filename('sub-01/func/sub-01_task-task_bold.nii.gz') layout = BIDSLayout('.', validate=False) coll = load_variables(layout).get_collections('run')[0] dense_var = coll.variables['val'].to_dense(1.0 / TR) regressor = dense_var.resample(1.0 / newTR).values assert regressor.shape == (newvols, 1) # This checks that the filtering has happened. If it has not, then # this value for this frequency bin will be an alias and have a # very different amplitude assert np.allclose(np.abs(np.fft.fft(regressor.values.ravel()))[9], 0.46298273) # This checks that the signal (0.025 Hz) within the new Nyquist # rate actually gets passed through. assert np.allclose(np.abs(np.fft.fft(regressor.values.ravel()))[4], 8.88189504) ================================================ FILE: src/bids/variables/variables.py ================================================ """Classes for representing BIDS variables.""" import math import warnings from abc import ABCMeta, abstractmethod from copy import deepcopy from functools import reduce from itertools import chain import numpy as np import pandas as pd from bids.utils import listify class BIDSVariable(metaclass=ABCMeta): """Base representation of a column in a BIDS project.""" # Columns that define special properties (e.g., onset, duration). These # will be stored separately from the main data object, and are accessible # as properties on the BIDSVariable instance. _property_columns = set() def __init__(self, name, values, source): self.name = name self.values = values self.source = source self.entities = self._extract_entities() def __repr__(self): return f"<{self.__class__.__name__}(name='{self.name}', source='{self.source}')>" def clone(self, data=None, **kwargs): """Clone (deep copy) the current column, optionally replacing its data and/or any other attributes. Parameters ---------- data : :obj:`pandas.DataFrame` or array_like Optional new data to substitute into the cloned column. Must have same dimensionality as the original. kwargs : dict Optional keyword arguments containing new attribute values to set in the copy. E.g., passing `name='my_name'` would set the `.name` attribute on the cloned instance to the passed value. """ result = deepcopy(self) if data is not None: if data.shape != self.values.shape: # If data can be re-shaped safely, do so if data.squeeze().shape == self.values.squeeze().shape: data = data.values.reshape(self.values.shape) else: raise ValueError( 'Replacement data has shape %s; must have' # noqa: UP031 ' same shape as existing data %s.' % (data.shape, self.values.shape) ) result.values = pd.DataFrame(data) if kwargs: for k, v in kwargs.items(): setattr(result, k, v) # Need to update name on Series as well # result.values.name = kwargs.get('name', self.name) return result def filter(self, filters=None, query=None, strict=False, inplace=False): """Returns a copy of the current Variable with only rows that match the filters retained. Parameters ---------- filters : dict Dictionary of filters to apply. Keys can be either 'amplitude' or any named entity. Values must be single values or lists. query : str Optional query string to pass to df.query(). Will not be validated in any way, so must have valid column names. Takes precedence over filters in the event that both are passed. strict : bool By default, keys in 'filters' that cannot be found in the Variable will be silently ignored. If strict=True, None will be returned in such cases. inplace : bool If True, filtering is performed in place. If False, a filtered copy of the Variable is returned. Returns ------- BIDSVariable or None if no rows are left after filtering. """ if filters is None and query is None: raise ValueError("Either the 'filters' or the 'query' argument must be provided!") if filters is not None and query is None: query = [] for name, val in filters.items(): if name != 'amplitude' and name not in self.index.columns: if strict: return None continue oper = 'in' if isinstance(val, (list, tuple)) else '==' q = f'{name} {oper} {repr(val)}' query.append(q) query = ' and '.join(query) var = self if inplace else self.clone() if query: inds = self.to_df().query(query).index var.values = var.values.loc[inds] var.index = var.index.loc[inds] if hasattr(self, '_build_entity_index'): var._build_entity_index() if not inplace: return var @classmethod def merge(cls, variables, name=None, **kwargs): """Merge/concatenate a list of variables along the row axis. Parameters ---------- variables : list A list of Variables to merge. name : str Optional name to assign to the output Variable. By default, uses the same name as the input variables. kwargs : dict Optional keyword arguments to pass onto the class-specific merge() call. See merge_variables docstring for details. Returns ------- A single BIDSVariable of the same class as the input variables. See Also -------- merge_variables """ variables = listify(variables) if len(variables) == 1: return variables[0] var_names = set([v.name for v in variables]) # noqa: C403 if len(var_names) > 1: raise ValueError( 'Columns with different names cannot be merged. ' # noqa: UP031 'Column names provided: %s' % var_names ) if name is None: name = variables[0].name return cls._merge(variables, name, **kwargs) @classmethod @abstractmethod def _merge(cls, variables, name, **kwargs): pass def get_grouper(self, groupby='run'): """Return a list suitable for use in groupby calls. Parameters ---------- groupby : str or list Name(s) of column(s) defining the grouper object. Anything that would be valid inside a .groupby() call on a pandas structure. Returns ------- list A list defining the groups. """ grouper = self.index.loc[:, groupby] return grouper.apply(lambda x: '@@@'.join(x.astype(str).values), axis=1) def apply(self, func, groupby='run', *args, **kwargs): """Applies the passed function to the groups defined by the groupby argument. Works identically to the standard pandas df.groupby() call. Parameters ---------- func : callable The function to apply to each group. groupby : str or list Name(s) of column(s) defining the grouping. args, kwargs : dict Optional positional and keyword arguments to pass onto the function call. """ grouper = self.get_grouper(groupby) return self.values.groupby(grouper, group_keys=False).apply(func, *args, **kwargs) def to_df(self, condition=True, entities=True, **kwargs): # noqa: D417 """Convert to a DataFrame, with columns for name and entities. Parameters ---------- condition : bool If True, adds a column for condition name, and names the amplitude column 'amplitude'. If False, returns just onset, duration, and amplitude, and gives the amplitude column the current column name. entities : bool If True, adds extra columns for all entities. """ amp = 'amplitude' if condition else self.name data = pd.DataFrame({amp: self.values.values.ravel()}) for sc in self._property_columns: data[sc] = getattr(self, sc) if condition: data['condition'] = self.name if entities: ent_data = self.index.reset_index(drop=True) data = pd.concat([data, ent_data], axis=1, sort=True) return data.reset_index(drop=True) def _extract_entities(self): """Returns a dict of all non-varying entities for the current Variable. Notes ----- Only entity key/value pairs common to all rows in the Variable are returned. E.g., if a Variable contains events extracted from runs 1, 2 and 3 from subject '01', the returned dict will be {'subject': '01'}; the runs will be excluded as they vary across the Variable contents. """ def is_unique(s): a = s.to_numpy() return (a[0] == a).all() constant = self.index.apply(is_unique) if constant.empty: return {} else: keep = self.index.columns[constant] first_row_ix = self.index.index[0] res = {} for k in keep: v = self.index.loc[first_row_ix, k] if pd.isna(v): # Only drop NaNs if we get that on first try v = self.index[k].dropna().iloc[0] res[k] = v return res class SimpleVariable(BIDSVariable): """Represents a simple design matrix column that has no timing information. Parameters ---------- name : str Name of the column. data : :obj:`pandas.DataFrame` A pandas DataFrame minimally containing a column named 'amplitude' as well as any identifying entities. source : str The type of BIDS variable file the data were extracted from. Must be one of: 'events', 'physio', 'stim', 'regressors', 'scans', 'sessions', 'participants', or 'beh'. kwargs : dict Optional keyword arguments passed onto superclass. """ _entity_columns = {'condition', 'amplitude'} def __init__(self, name, data, source, **kwargs): ent_cols = list(set(data.columns) - self._entity_columns) self.index = data.loc[:, ent_cols] values = data['amplitude'].reset_index(drop=True) values.name = name super().__init__(name, values, source) def split(self, grouper): """Split the current SparseRunVariable into multiple columns. Parameters ---------- grouper : :obj:`pandas.DataFrame` Binary DF specifying the design matrix to use for splitting. Number of rows must match current ``SparseRunVariable``; a new ``SparseRunVariable`` will be generated for each column in the grouper. Returns ------- A list of SparseRunVariables, one per column in the grouper DF. """ data = self.to_df(condition=True, entities=True) data = data.drop('condition', axis=1) subsets = [] for i, col_name in enumerate(grouper.columns): # noqa: B007 col_data = data.loc[grouper[col_name].astype(bool), :] name = f'{self.name}.{col_name}' col = self.__class__( name=name, data=col_data, source=self.source, run_info=getattr(self, 'run_info', None), ) subsets.append(col) return subsets @classmethod def _merge(cls, variables, name, **kwargs): dfs = [v.to_df() for v in variables] data = pd.concat(dfs, axis=0, sort=True).reset_index(drop=True) data = data.rename(columns={name: 'amplitude'}) return cls(name, data, source=variables[0].source, **kwargs) def select_rows(self, rows): """Truncate internal arrays to keep only the specified rows. Parameters ---------- rows : array_like An integer or boolean array identifying the indices of rows to keep. """ self.values = self.values.iloc[rows] self.index = self.index.iloc[rows, :] for prop in self._property_columns: vals = getattr(self, prop)[rows] setattr(self, prop, vals) class SparseRunVariable(SimpleVariable): """A sparse representation of a single column of events. Parameters ---------- name : str Name of the column. data : :obj:`pandas.DataFrame` A pandas DataFrame minimally containing the columns 'onset', 'duration', and 'amplitude'. run_info : list A list of RunInfo objects carrying information about all runs represented in the Variable. source : str The type of BIDS variable file the data were extracted from. Must be one of: 'events', 'physio', 'stim', 'regressors', 'scans', 'sessions', 'participants', or 'beh'. kwargs : dict Optional keyword arguments passed onto superclass. """ _property_columns = {'onset', 'duration'} def __init__(self, name, data, run_info, source, **kwargs): if hasattr(run_info, 'duration'): run_info = [run_info] if not isinstance(run_info, list): raise TypeError('We expect a list of run_info, got %s' % repr(run_info)) # noqa: UP031 self.run_info = run_info for sc in self._property_columns: arr = data.pop(sc).values arr.flags['WRITEABLE'] = True setattr(self, sc, arr) super().__init__(name, data, source, **kwargs) def get_duration(self): """Return the total duration of the Variable's run(s).""" return sum([r.duration for r in self.run_info]) def to_dense(self, sampling_rate=None): """Convert the current sparse column to a dense representation. If sampling_rate is not provided, the largest interval able to faithfully represent all onsets and durations will be determined. The sampling rate is the reciprocal of that interval. Parameters ---------- sampling_rate : float or None Sampling rate (in Hz) to use when constructing the DenseRunVariable Returns ------- DenseRunVariable """ # Cast onsets and durations to milliseconds onsets = np.round(self.onset * 1000).astype(int) durations = np.round(self.duration * 1000).astype(int) gcd = np.gcd.reduce(np.r_[onsets, durations]) bin_sr = 1000.0 / gcd # never use a computed SR smaller than the requested one, because # when events are widely-spaced and timing is very regular, this can # result in a nasty loss of precision in the resampling step. if sampling_rate is not None: bin_sr = max(bin_sr, sampling_rate) else: sampling_rate = bin_sr duration = math.ceil( # Round up to nearest second round(bin_sr * self.get_duration(), 3) # Cut off at millisecond precision ) ts = pd.Series(0, index=np.arange(duration), dtype=self.values.dtype) onsets = np.round(self.onset * bin_sr).astype(int) durations = np.round(self.duration * bin_sr).astype(int) run_i, start, last_ind = 0, 0, 0 for i, val in enumerate(self.values.values): if onsets[i] < last_ind: start += self.run_info[run_i].duration * bin_sr run_i += 1 _onset = int(start + onsets[i]) _offset = int(_onset + durations[i]) if _onset >= duration: warnings.warn( 'The onset time of a variable seems to exceed ' 'the runs duration, hence runs are incremented ' 'by one internally.', stacklevel=2, ) ts[_onset:_offset] = val last_ind = onsets[i] if bin_sr != sampling_rate: # Resample the time series to the requested sampling rate num = int(math.ceil(duration * sampling_rate / bin_sr)) ts = _resample(ts, sampling_rate, bin_sr, num) run_info = list(self.run_info) dense_var = DenseRunVariable( name=self.name, values=ts, run_info=run_info, source=self.source, sampling_rate=sampling_rate, ) return dense_var def _extract_entities(self): # Get all entities common to all runs. The super method already does # this for entities that show up in filenames, so we just add the # ones that show up in the RunInfo tuples, as those include metadata. ent_items = [run.entities.items() for run in self.run_info] entities = reduce(lambda x, y: x & y, ent_items, ent_items[0]) base_ents = super()._extract_entities() return dict(entities, **base_ents) @classmethod def _merge(cls, variables, name, **kwargs): run_info = list(chain(*[v.run_info for v in variables])) return super()._merge(variables, name, run_info=run_info, **kwargs) class DenseRunVariable(BIDSVariable): """A dense representation of a single column. Parameters ---------- name : :obj:`str` The name of the column. values : :obj:`numpy.ndarray` The values/amplitudes to store. run_info : :obj:`list` A list of RunInfo objects carrying information about all runs represented in the Variable. source : {'events', 'physio', 'stim', 'regressors', 'scans', 'sessions', 'participants', 'beh'} The type of BIDS variable file the data were extracted from. sampling_rate : :obj:`float` Mandatory sampling rate (in Hz) to use. Must match the sampling rate used to generate the values. """ def __init__(self, name, values, run_info, source, sampling_rate): values = pd.DataFrame(values) if not isinstance(sampling_rate, (float, int)): raise TypeError( 'sampling_rate must be a float or integer, not %s' % type(sampling_rate) # noqa: UP031 ) if hasattr(run_info, 'duration'): run_info = [run_info] self.run_info = run_info self.sampling_rate = sampling_rate self.index = self._build_entity_index(run_info, sampling_rate) super().__init__(name, values, source) def split(self, grouper): """Split the current DenseRunVariable into multiple columns. Parameters ---------- grouper : :obj:`pandas.DataFrame` Binary DF specifying the design matrix to use for splitting. Number of rows must match current ``DenseRunVariable``; a new ``DenseRunVariable`` will be generated for each column in the grouper. Returns ------- A list of DenseRunVariables, one per unique value in the grouper. """ values = grouper.values * self.values.values df = pd.DataFrame(values, columns=grouper.columns) return [ DenseRunVariable( name='%s.%s' % (self.name, name), # noqa: UP031 values=df[name].values, run_info=self.run_info, source=self.source, sampling_rate=self.sampling_rate, ) for i, name in enumerate(df.columns) ] def _build_entity_index(self, run_info, sampling_rate, match_vol=False): """Build the entity index from run information.""" interval = int(round(1000.0 / sampling_rate)) def _create_index(all_keys, all_reps, all_ents): all_keys = np.array(sorted(all_keys)) df = pd.DataFrame( np.zeros((sum(all_reps), len(all_keys)), dtype=object), columns=all_keys ) prev_ix = 0 for i, reps in enumerate(all_reps): for k, v in all_ents[i].items(): col_ix = np.where(all_keys == k)[0][0] df.iloc[prev_ix : prev_ix + reps, col_ix] = v prev_ix = reps return df all_reps = [] all_ents = [] all_keys = set() for run in run_info: if match_vol: # If TR, fix reps to n_vols to ensure match all_reps.append(run.n_vols) else: all_reps.append(int(math.ceil(run.duration * sampling_rate))) all_ents.append(run.entities) all_keys.update(run.entities.keys()) self.timestamps = pd.date_range(0, periods=sum(all_reps), freq='%sms' % interval) # noqa: UP031 return _create_index(all_keys, all_reps, all_ents) def resample(self, sampling_rate, inplace=False, kind='linear'): """Resample the Variable to the specified sampling rate. Parameters ---------- sampling_rate : :obj:`int`, :obj:`float` Target sampling rate (in Hz). inplace : :obj:`bool`, optional If True, performs resampling in-place. If False, returns a resampled copy of the current Variable. Default is False. kind : {'linear', 'nearest', 'zero', 'slinear', 'quadratic', 'cubic'} Argument to pass to :obj:`scipy.interpolate.interp1d`; indicates the kind of interpolation approach to use. See interp1d docs for valid values. Default is 'linear'. """ if not inplace: var = self.clone() var.resample(sampling_rate, True, kind) return var match_vol = False if sampling_rate == 'TR': match_vol = True sampling_rate = 1.0 / self.run_info[0].tr if sampling_rate == self.sampling_rate: return self.index = self._build_entity_index(self.run_info, sampling_rate, match_vol) self.values = pd.DataFrame( _resample( self.values.values.ravel(), sampling_rate, self.sampling_rate, len(self.index), kind=kind, ) ) assert len(self.values) == len(self.index) # noqa: S101 self.sampling_rate = sampling_rate def to_df(self, condition=True, entities=True, timing=True, sampling_rate=None): # noqa: D417 """Convert to a DataFrame, with columns for name and entities. Parameters ---------- condition : :obj:`bool` If True, adds a column for condition name, and names the amplitude column 'amplitude'. If False, returns just onset, duration, and amplitude, and gives the amplitude column the current column name. entities : :obj:`bool` If True, adds extra columns for all entities. timing : :obj:`bool` If True, includes onset and duration columns (even though events are sampled uniformly). If False, omits them. """ if sampling_rate not in (None, self.sampling_rate): return self.resample(sampling_rate).to_df(condition, entities) df = super().to_df(condition, entities) if timing: df['onset'] = self.timestamps.values.astype(float) / 1e9 df['duration'] = 1.0 / self.sampling_rate return df @classmethod def _merge(cls, variables, name, sampling_rate=None, **kwargs): if not isinstance(sampling_rate, int): rates = set([v.sampling_rate for v in variables]) # noqa: C403 if len(rates) == 1: sampling_rate = list(rates)[0] else: if sampling_rate == 'auto': sampling_rate = max(rates) else: msg = ( 'Cannot merge DenseRunVariables (%s) with different' # noqa: UP031 ' sampling rates (%s). Either specify an integer ' 'sampling rate to use for all variables, or set ' "sampling_rate='highest' to use the highest sampling" ' rate found.' % (name, rates) ) raise ValueError(msg) variables = [v.resample(sampling_rate) for v in variables] values = pd.concat([v.values for v in variables], axis=0, sort=True) run_info = list(chain(*[v.run_info for v in variables])) source = variables[0].source return DenseRunVariable( name=name, values=values, run_info=run_info, source=source, sampling_rate=sampling_rate ) def merge_variables(variables, **kwargs): """Merge/concatenate a list of variables along the row axis. Parameters ---------- variables : :obj:`list` A list of Variables to merge. kwargs Optional keyword arguments to pass onto the class-specific merge() call. Possible args: sampling_rate (int, str): The sampling rate to use if resampling of DenseRunVariables is necessary for harmonization. If 'highest', the highest sampling rate found will be used. This argument is only used when passing DenseRunVariables in the variables list. Returns ------- A single BIDSVariable of the same class as the input variables. Notes ----- - Currently, this function only support homogeneously-typed lists. In future, it may be extended to support implicit conversion. - Variables in the list must all share the same name (i.e., it is not possible to merge two different variables into a single variable.) """ classes = set([v.__class__ for v in variables]) # noqa: C403 if len(classes) > 1: raise ValueError( 'Variables of different classes cannot be merged. ' # noqa: UP031 'Variables passed are of classes: %s' % classes ) sources = set([v.source for v in variables]) # noqa: C403 if len(sources) > 1: raise ValueError( 'Variables extracted from different types of files ' # noqa: UP031 'cannot be merged. Sources found: %s' % sources ) return list(classes)[0].merge(variables, **kwargs) def _resample(y, new_sr, old_sr, new_num, kind='linear'): n = len(y) x = np.arange(n) if new_sr < old_sr: # Downsampling, so filter the signal from scipy.signal import butter, filtfilt # cutoff = new Nyqist / old Nyquist b, a = butter(5, (new_sr / 2.0) / (old_sr / 2.0), btype='low', output='ba', analog=False) y = filtfilt(b, a, y) from scipy.interpolate import interp1d f = interp1d(x, y, kind=kind) x_new = np.linspace(0, n - 1, num=new_num) return f(x_new) ================================================ FILE: tests/data/7t_trt/README ================================================ ================================================ FILE: tests/data/7t_trt/dataset_description.json ================================================ { "BIDSVersion": "1.0.0rc3", "Name": "7t_trt" } ================================================ FILE: tests/data/7t_trt/participants.tsv ================================================ participant_id sex age_at_first_scan_years number_of_scans_before handedness sub-01 F 29 17 100 sub-02 F 23 6 100 sub-03 M 25 18 86 sub-04 M 26 8 100 sub-05 M 27 28 -84 sub-06 F 23 27 100 sub-07 M 29 9 100 sub-08 M 25 28 90 sub-09 M 25 37 100 sub-10 M 24 30 89 sub-11 M 30 5 100 sub-12 M 24 8 100 sub-13 F 26 28 100 sub-14 F 24 5 100 sub-15 F 22 21 100 sub-16 F 23 5 80 sub-17 F 25 41 80 sub-18 M 27 51 100 sub-19 M 25 13 100 sub-20 F 22 23 83 sub-21 F 24 48 100 sub-22 M 25 50 100 ================================================ FILE: tests/data/7t_trt/sub-01/ses-1/fmap/sub-01_ses-1_run-1_phasediff.json ================================================ {"EchoTime2": 0.00702, "EchoTime1": 0.006, "IntendedFor": "ses-1/func/sub-01_ses-1_task-rest_acq-fullbrain_run-1_bold.nii.gz"} ================================================ FILE: tests/data/7t_trt/sub-01/ses-1/fmap/sub-01_ses-1_run-2_phasediff.json ================================================ {"EchoTime2": 0.00702, "EchoTime1": 0.006, "IntendedFor": "ses-1/func/sub-01_ses-1_task-rest_acq-fullbrain_run-2_bold.nii.gz"} ================================================ FILE: tests/data/7t_trt/sub-01/ses-1/func/sub-01_ses-1_task-rest_acq-fullbrain_run-1_bold.json ================================================ { "EchoTime": 0.020 } ================================================ FILE: tests/data/7t_trt/sub-01/ses-1/sub-01_ses-1_scans.tsv ================================================ filename positive negative future past myself people surroundings vigilance images words specific_vague intrusive func/sub-01_ses-1_task-rest_acq-fullbrain_run-1_bold.nii.gz 90 0 100 100 80 100 100 95 0 100 90 30 func/sub-01_ses-1_task-rest_acq-fullbrain_run-2_bold.nii.gz 85 0 90 100 100 100 20 100 0 100 100 30 func/sub-01_ses-1_task-rest_acq-prefrontal_bold.nii.gz 75 0 75 100 100 85 15 100 0 100 95 35 ================================================ FILE: tests/data/7t_trt/sub-01/ses-2/fmap/sub-01_ses-2_run-1_phasediff.json ================================================ {"EchoTime2": 0.00702, "EchoTime1": 0.006, "IntendedFor": "ses-2/func/sub-01_ses-2_task-rest_acq-fullbrain_run-1_bold.nii.gz"} ================================================ FILE: tests/data/7t_trt/sub-01/ses-2/fmap/sub-01_ses-2_run-2_phasediff.json ================================================ {"EchoTime2": 0.00702, "EchoTime1": 0.006, "IntendedFor": "ses-2/func/sub-01_ses-2_task-rest_acq-fullbrain_run-2_bold.nii.gz"} ================================================ FILE: tests/data/7t_trt/sub-01/ses-2/sub-01_ses-2_scans.tsv ================================================ filename positive negative future past myself people surroundings vigilance images words specific_vague intrusive func/sub-01_ses-2_task-rest_acq-fullbrain_run-1_bold.nii.gz 95 0 100 0 90 100 75 100 20 100 100 60 func/sub-01_ses-2_task-rest_acq-fullbrain_run-2_bold.nii.gz 70 25 100 0 100 100 80 75 0 100 100 65 func/sub-01_ses-2_task-rest_acq-prefrontal_bold.nii.gz 100 0 100 100 100 100 20 70 0 100 80 65 ================================================ FILE: tests/data/7t_trt/sub-01/sub-01_sessions.tsv ================================================ session CCPT_avg_succ_RT CCPT_avg_FP_RT CCPT_avg_FN_RT CCPT_succ_count CCPT_FP_count CCPT_FN_count subject_id panas_cheerful panas_disgusted panas_attentive panas_bashful panas_sluggish panas_daring panas_surprised panas_strong panas_scornful panas_relaxed panas_irritable panas_delighted panas_inspired panas_fearless panas_disgusted_with_self panas_sad panas_calm panas_afraid panas_tired panas_amazed panas_shaky panas_happy panas_timid panas_alone panas_alert panas_upset panas_angry panas_bold panas_blue panas_shy panas_active panas_guilty panas_joyful panas_nervous panas_lonely panas_sleepy panas_excited panas_hostile panas_proud panas_jittery panas_lively panas_ashamed panas_at_ease panas_scared panas_drowsy panas_angry_at_self panas_enthusiastic panas_downhearted panas_sheepish panas_distressed panas_blameworthy panas_determined panas_frightened panas_astonished panas_interested panas_loathing panas_confident panas_energetic panas_concentrating panas_dissatisfied_with_self hours_of_sleep_ussually hours_of_sleep_last_night vigilance quality_of_sleep thirst liters_of_water_daily relative_water_intake caffeine_daily relative_caffeine_intake systolic_blood_pressure_left diastolic_blood_pressure_left pulse_left systolic_blood_pressure_right diastolic_blood_pressure_right pulse_right positive negative future past myself people surroundings vigilance_nyc-q images words specific_vague intrusive ses-1 500.7708333333333 507.0 n/a 96.0 1.0 0.0 1 6 4 6 2 4 4 2 5 4 6 3 5 6 6 2 1 5 2 2 4 2 7 4 1 5 1 2 4 4 1 7 1 7 2 1 5 6 1 4 2 6 2 1 1 4 2 6 1 1 1 4 5 1 5 6 1 4 6 3 5 8.0 9.5 9 5 9 1.0 7 0.5 1 108 64 66 109 69 70 0 0 75 15 5 70 0 100 0 100 95 50 ses-2 503.3333333333333 595.0 n/a 96.0 1.0 0.0 1 4 2 6 1 4 5 2 4 5 6 3 5 5 3 2 5 7 1 5 4 1 7 4 1 6 1 1 4 4 1 6 5 5 3 1 1 6 1 4 2 6 1 1 1 4 1 5 1 4 1 4 4 1 5 7 1 6 6 7 1 8.0 7.5 7 9 9 2.0 5 2.0 3 101 66 70 101 65 76 100 5 80 90 5 80 20 100 0 100 100 50 ================================================ FILE: tests/data/7t_trt/sub-02/ses-1/fmap/sub-02_ses-1_run-1_phasediff.json ================================================ {"EchoTime2": 0.00702, "EchoTime1": 0.006, "IntendedFor": "ses-1/func/sub-02_ses-1_task-rest_acq-fullbrain_run-1_bold.nii.gz"} ================================================ FILE: tests/data/7t_trt/sub-02/ses-1/fmap/sub-02_ses-1_run-2_phasediff.json ================================================ {"EchoTime2": 0.00702, "EchoTime1": 0.006, "IntendedFor": "ses-1/func/sub-02_ses-1_task-rest_acq-fullbrain_run-2_bold.nii.gz"} ================================================ FILE: tests/data/7t_trt/sub-02/ses-1/sub-02_ses-1_scans.tsv ================================================ filename positive negative future past myself people surroundings vigilance images words specific_vague intrusive func/sub-02_ses-1_task-rest_acq-fullbrain_run-1_bold.nii.gz 85 35 85 5 100 100 25 30 55 85 100 75 func/sub-02_ses-1_task-rest_acq-fullbrain_run-2_bold.nii.gz 100 0 100 5 100 100 30 100 70 90 100 85 func/sub-02_ses-1_task-rest_acq-prefrontal_bold.nii.gz 60 35 75 25 70 90 65 100 85 55 90 40 ================================================ FILE: tests/data/7t_trt/sub-02/ses-2/fmap/sub-02_ses-2_run-1_phasediff.json ================================================ {"EchoTime2": 0.00702, "EchoTime1": 0.006, "IntendedFor": "ses-2/func/sub-02_ses-2_task-rest_acq-fullbrain_run-1_bold.nii.gz"} ================================================ FILE: tests/data/7t_trt/sub-02/ses-2/fmap/sub-02_ses-2_run-2_phasediff.json ================================================ {"EchoTime2": 0.00702, "EchoTime1": 0.006, "IntendedFor": "ses-2/func/sub-02_ses-2_task-rest_acq-fullbrain_run-2_bold.nii.gz"} ================================================ FILE: tests/data/7t_trt/sub-02/ses-2/sub-02_ses-2_scans.tsv ================================================ filename positive negative future past myself people surroundings vigilance images words specific_vague intrusive func/sub-02_ses-2_task-rest_acq-fullbrain_run-1_bold.nii.gz 60 25 85 35 100 100 25 35 75 90 65 55 func/sub-02_ses-2_task-rest_acq-fullbrain_run-2_bold.nii.gz 70 25 80 10 70 100 10 15 85 90 95 60 func/sub-02_ses-2_task-rest_acq-prefrontal_bold.nii.gz 65 35 85 10 60 95 0 10 60 85 80 60 ================================================ FILE: tests/data/7t_trt/sub-02/sub-02_sessions.tsv ================================================ session CCPT_avg_succ_RT CCPT_avg_FP_RT CCPT_avg_FN_RT CCPT_succ_count CCPT_FP_count CCPT_FN_count subject_id panas_cheerful panas_disgusted panas_attentive panas_bashful panas_sluggish panas_daring panas_surprised panas_strong panas_scornful panas_relaxed panas_irritable panas_delighted panas_inspired panas_fearless panas_disgusted_with_self panas_sad panas_calm panas_afraid panas_tired panas_amazed panas_shaky panas_happy panas_timid panas_alone panas_alert panas_upset panas_angry panas_bold panas_blue panas_shy panas_active panas_guilty panas_joyful panas_nervous panas_lonely panas_sleepy panas_excited panas_hostile panas_proud panas_jittery panas_lively panas_ashamed panas_at_ease panas_scared panas_drowsy panas_angry_at_self panas_enthusiastic panas_downhearted panas_sheepish panas_distressed panas_blameworthy panas_determined panas_frightened panas_astonished panas_interested panas_loathing panas_confident panas_energetic panas_concentrating panas_dissatisfied_with_self hours_of_sleep_ussually hours_of_sleep_last_night vigilance quality_of_sleep thirst liters_of_water_daily relative_water_intake caffeine_daily relative_caffeine_intake systolic_blood_pressure_left diastolic_blood_pressure_left pulse_left systolic_blood_pressure_right diastolic_blood_pressure_right pulse_right positive negative future past myself people surroundings vigilance_nyc-q images words specific_vague intrusive ses-1 351.7291666666667 297.6 n/a 96.0 5.0 0.0 2 5 1 4 1 5 5 5 6 1 4 5 5 5 3 1 2 3 3 5 5 5 6 4 1 2 1 1 1 1 1 4 1 3 4 2 5 5 2 3 5 5 1 2 1 3 1 3 3 2 3 2 2 2 3 2 1 5 4 3 5 9.0 9.0 7 8 2 1.0 1 0.0 5 99 65 69 100 76 73 85 5 80 15 100 95 70 100 10 100 100 65 ses-2 366.3157894736842 n/a 0.0 95.0 0.0 1.0 2 4 1 3 3 3 4 3 3 1 2 1 4 3 2 1 2 2 2 4 3 4 4 4 3 2 2 1 2 3 2 4 1 4 3 3 5 4 1 2 4 4 1 2 1 4 3 3 3 1 2 4 3 3 2 3 1 3 3 2 2 8.0 8.0 3 8 4 1.0 5 0.0 5 108 75 98 106 67 85 55 25 75 55 95 100 60 100 60 80 60 15 ================================================ FILE: tests/data/7t_trt/sub-03/ses-1/fmap/sub-03_ses-1_run-1_phasediff.json ================================================ {"EchoTime2": 0.00702, "EchoTime1": 0.006, "IntendedFor": "ses-1/func/sub-03_ses-1_task-rest_acq-fullbrain_run-1_bold.nii.gz"} ================================================ FILE: tests/data/7t_trt/sub-03/ses-1/fmap/sub-03_ses-1_run-2_phasediff.json ================================================ {"EchoTime2": 0.00702, "EchoTime1": 0.006, "IntendedFor": "ses-1/func/sub-03_ses-1_task-rest_acq-fullbrain_run-2_bold.nii.gz"} ================================================ FILE: tests/data/7t_trt/sub-03/ses-1/sub-03_ses-1_scans.tsv ================================================ filename positive negative future past myself people surroundings vigilance images words specific_vague intrusive func/sub-03_ses-1_task-rest_acq-fullbrain_run-1_bold.nii.gz 80 30 50 70 40 100 15 60 100 0 65 90 func/sub-03_ses-1_task-rest_acq-fullbrain_run-2_bold.nii.gz 60 20 35 85 55 90 75 100 80 40 65 40 func/sub-03_ses-1_task-rest_acq-prefrontal_bold.nii.gz 95 5 60 50 20 45 30 100 45 55 90 35 ================================================ FILE: tests/data/7t_trt/sub-03/ses-2/fmap/sub-03_ses-2_run-1_phasediff.json ================================================ {"EchoTime2": 0.00702, "EchoTime1": 0.006, "IntendedFor": "ses-2/func/sub-03_ses-2_task-rest_acq-fullbrain_run-1_bold.nii.gz"} ================================================ FILE: tests/data/7t_trt/sub-03/ses-2/fmap/sub-03_ses-2_run-2_phasediff.json ================================================ {"EchoTime2": 0.00702, "EchoTime1": 0.006, "IntendedFor": ["ses-2/func/sub-03_ses-2_task-rest_acq-fullbrain_run-2_bold.nii.gz", "ses-2/func/sub-03_ses-2_task-rest_acq-fullbrain_run-1_bold.nii.gz"]} ================================================ FILE: tests/data/7t_trt/sub-03/ses-2/sub-03_ses-2_scans.tsv ================================================ filename positive negative future past myself people surroundings vigilance images words specific_vague intrusive func/sub-03_ses-2_task-rest_acq-fullbrain_run-1_bold.nii.gz 90 10 85 15 60 75 25 90 45 55 70 65 func/sub-03_ses-2_task-rest_acq-fullbrain_run-2_bold.nii.gz 80 35 80 30 60 75 25 100 55 75 90 75 func/sub-03_ses-2_task-rest_acq-prefrontal_bold.nii.gz 90 40 60 60 40 95 5 100 55 75 100 45 ================================================ FILE: tests/data/7t_trt/sub-03/sub-03-test.bval ================================================ ================================================ FILE: tests/data/7t_trt/sub-03/sub-03_sessions.tsv ================================================ session CCPT_avg_succ_RT CCPT_avg_FP_RT CCPT_avg_FN_RT CCPT_succ_count CCPT_FP_count CCPT_FN_count subject_id panas_cheerful panas_disgusted panas_attentive panas_bashful panas_sluggish panas_daring panas_surprised panas_strong panas_scornful panas_relaxed panas_irritable panas_delighted panas_inspired panas_fearless panas_disgusted_with_self panas_sad panas_calm panas_afraid panas_tired panas_amazed panas_shaky panas_happy panas_timid panas_alone panas_alert panas_upset panas_angry panas_bold panas_blue panas_shy panas_active panas_guilty panas_joyful panas_nervous panas_lonely panas_sleepy panas_excited panas_hostile panas_proud panas_jittery panas_lively panas_ashamed panas_at_ease panas_scared panas_drowsy panas_angry_at_self panas_enthusiastic panas_downhearted panas_sheepish panas_distressed panas_blameworthy panas_determined panas_frightened panas_astonished panas_interested panas_loathing panas_confident panas_energetic panas_concentrating panas_dissatisfied_with_self hours_of_sleep_ussually hours_of_sleep_last_night vigilance quality_of_sleep thirst liters_of_water_daily relative_water_intake caffeine_daily relative_caffeine_intake systolic_blood_pressure_left diastolic_blood_pressure_left pulse_left systolic_blood_pressure_right diastolic_blood_pressure_right pulse_right positive negative future past myself people surroundings vigilance_nyc-q images words specific_vague intrusive ses-1 426.71875 441.0 n/a 96.0 1.0 0.0 3 6 4 6 2 3 4 1 6 1 5 2 6 7 7 1 2 6 1 5 4 2 6 2 2 5 2 1 5 2 1 6 3 6 1 1 5 7 2 5 3 6 2 7 1 3 2 5 1 1 2 1 6 1 4 5 1 6 5 6 1 9.0 8.5 8 8 3 3.0 3 1.0 1 122 69 69 128 66 62 60 45 10 100 100 100 10 100 100 0 100 75 ses-2 396.0104166666667 n/a n/a 96.0 0.0 0.0 3 6 2 7 1 5 5 7 6 2 6 3 6 7 1 1 1 6 1 5 4 2 6 3 1 5 3 1 6 4 2 5 1 7 2 1 4 6 1 5 3 6 1 7 1 2 1 5 1 1 1 1 6 1 6 5 1 6 5 6 2 9.0 8.5 6 5 3 2.5 5 0.2 1 112 72 65 139 69 55 75 20 70 30 85 60 5 90 75 40 40 65 ================================================ FILE: tests/data/7t_trt/sub-04/ses-1/fmap/sub-04_ses-1_run-1_phasediff.json ================================================ {"EchoTime2": 0.00702, "EchoTime1": 0.006, "IntendedFor": "ses-1/func/sub-04_ses-1_task-rest_acq-fullbrain_run-1_bold.nii.gz"} ================================================ FILE: tests/data/7t_trt/sub-04/ses-1/fmap/sub-04_ses-1_run-2_phasediff.json ================================================ {"EchoTime2": 0.00702, "EchoTime1": 0.006, "IntendedFor": "ses-1/func/sub-04_ses-1_task-rest_acq-fullbrain_run-2_bold.nii.gz"} ================================================ FILE: tests/data/7t_trt/sub-04/ses-1/sub-04_ses-1_scans.tsv ================================================ filename positive negative future past myself people surroundings vigilance images words specific_vague intrusive func/sub-04_ses-1_task-rest_acq-fullbrain_run-1_bold.nii.gz 60 35 75 25 45 35 20 50 80 40 80 40 func/sub-04_ses-1_task-rest_acq-fullbrain_run-2_bold.nii.gz 50 50 70 55 70 70 35 65 65 60 80 40 func/sub-04_ses-1_task-rest_acq-prefrontal_bold.nii.gz 50 50 80 45 55 65 25 65 75 60 85 40 ================================================ FILE: tests/data/7t_trt/sub-04/ses-2/fmap/sub-04_ses-2_run-1_phasediff.json ================================================ {"EchoTime2": 0.00702, "EchoTime1": 0.006, "IntendedFor": "ses-2/func/sub-04_ses-2_task-rest_acq-fullbrain_run-1_bold.nii.gz"} ================================================ FILE: tests/data/7t_trt/sub-04/ses-2/fmap/sub-04_ses-2_run-2_phasediff.json ================================================ {"EchoTime2": 0.00702, "EchoTime1": 0.006, "IntendedFor": "ses-2/func/sub-04_ses-2_task-rest_acq-fullbrain_run-2_bold.nii.gz"} ================================================ FILE: tests/data/7t_trt/sub-04/ses-2/sub-04_ses-2_scans.tsv ================================================ filename positive negative future past myself people surroundings vigilance images words specific_vague intrusive func/sub-04_ses-2_task-rest_acq-fullbrain_run-1_bold.nii.gz 65 20 65 45 55 65 15 75 75 65 70 60 func/sub-04_ses-2_task-rest_acq-fullbrain_run-2_bold.nii.gz 70 30 80 40 60 55 10 60 65 65 75 60 func/sub-04_ses-2_task-rest_acq-prefrontal_bold.nii.gz 65 20 75 40 65 65 5 65 70 50 75 60 ================================================ FILE: tests/data/7t_trt/sub-04/sub-04_sessions.tsv ================================================ session CCPT_avg_succ_RT CCPT_avg_FP_RT CCPT_avg_FN_RT CCPT_succ_count CCPT_FP_count CCPT_FN_count subject_id panas_cheerful panas_disgusted panas_attentive panas_bashful panas_sluggish panas_daring panas_surprised panas_strong panas_scornful panas_relaxed panas_irritable panas_delighted panas_inspired panas_fearless panas_disgusted_with_self panas_sad panas_calm panas_afraid panas_tired panas_amazed panas_shaky panas_happy panas_timid panas_alone panas_alert panas_upset panas_angry panas_bold panas_blue panas_shy panas_active panas_guilty panas_joyful panas_nervous panas_lonely panas_sleepy panas_excited panas_hostile panas_proud panas_jittery panas_lively panas_ashamed panas_at_ease panas_scared panas_drowsy panas_angry_at_self panas_enthusiastic panas_downhearted panas_sheepish panas_distressed panas_blameworthy panas_determined panas_frightened panas_astonished panas_interested panas_loathing panas_confident panas_energetic panas_concentrating panas_dissatisfied_with_self hours_of_sleep_ussually hours_of_sleep_last_night vigilance quality_of_sleep thirst liters_of_water_daily relative_water_intake caffeine_daily relative_caffeine_intake systolic_blood_pressure_left diastolic_blood_pressure_left pulse_left systolic_blood_pressure_right diastolic_blood_pressure_right pulse_right positive negative future past myself people surroundings vigilance_nyc-q images words specific_vague intrusive ses-1 417.90625 443.0 n/a 96.0 1.0 0.0 4 2 1 5 5 4 3 1 3 1 3 5 2 2 2 1 3 3 4 2 1 3 3 1 3 3 5 4 2 2 3 4 1 2 4 3 3 1 3 3 4 3 1 3 1 1 3 2 2 1 4 1 6 2 1 3 1 6 3 4 2 7.0 7.5 5 6 6 1.0 5 0.1 1 130 74 64 110 60 68 65 20 90 30 75 45 0 100 50 85 80 40 ses-2 409.3645833333333 348.0 n/a 96.0 1.0 0.0 4 5 1 4 3 5 2 1 5 1 2 5 1 2 3 1 1 5 2 5 1 1 4 1 4 3 2 1 2 1 2 3 1 3 2 2 5 2 2 1 2 3 1 3 1 4 1 2 2 1 2 1 5 2 1 4 1 5 3 3 2 7.0 7.5 3 3 4 1.0 5 0.1 3 110 63 72 140 81 77 75 20 75 30 70 50 10 85 60 70 85 45 ================================================ FILE: tests/data/7t_trt/sub-05/ses-1/fmap/sub-05_ses-1_run-1_phasediff.json ================================================ {"EchoTime2": 0.00702, "EchoTime1": 0.006, "IntendedFor": "ses-1/func/sub-05_ses-1_task-rest_acq-fullbrain_run-1_bold.nii.gz"} ================================================ FILE: tests/data/7t_trt/sub-05/ses-1/fmap/sub-05_ses-1_run-2_phasediff.json ================================================ {"EchoTime2": 0.00702, "EchoTime1": 0.006, "IntendedFor": "ses-1/func/sub-05_ses-1_task-rest_acq-fullbrain_run-2_bold.nii.gz"} ================================================ FILE: tests/data/7t_trt/sub-05/ses-1/sub-05_ses-1_scans.tsv ================================================ filename positive negative future past myself people surroundings vigilance images words specific_vague intrusive func/sub-05_ses-1_task-rest_acq-fullbrain_run-1_bold.nii.gz 70 30 75 60 40 70 25 25 50 25 65 35 func/sub-05_ses-1_task-rest_acq-fullbrain_run-2_bold.nii.gz 60 35 65 35 35 70 30 55 65 35 65 30 func/sub-05_ses-1_task-rest_acq-prefrontal_bold.nii.gz 70 25 80 25 30 75 25 35 70 30 70 35 ================================================ FILE: tests/data/7t_trt/sub-05/ses-2/fmap/sub-05_ses-2_run-1_phasediff.json ================================================ {"EchoTime2": 0.00702, "EchoTime1": 0.006, "IntendedFor": "ses-2/func/sub-05_ses-2_task-rest_acq-fullbrain_run-1_bold.nii.gz"} ================================================ FILE: tests/data/7t_trt/sub-05/ses-2/fmap/sub-05_ses-2_run-2_phasediff.json ================================================ {"EchoTime2": 0.00702, "EchoTime1": 0.006, "IntendedFor": "ses-2/func/sub-05_ses-2_task-rest_acq-fullbrain_run-2_bold.nii.gz"} ================================================ FILE: tests/data/7t_trt/sub-05/ses-2/sub-05_ses-2_scans.tsv ================================================ filename positive negative future past myself people surroundings vigilance images words specific_vague intrusive func/sub-05_ses-2_task-rest_acq-fullbrain_run-1_bold.nii.gz 75 25 75 30 35 80 15 35 70 35 80 30 func/sub-05_ses-2_task-rest_acq-fullbrain_run-2_bold.nii.gz 70 30 80 20 25 80 20 30 75 25 75 25 func/sub-05_ses-2_task-rest_acq-prefrontal_bold.nii.gz 75 20 75 25 25 75 25 25 80 25 75 25 ================================================ FILE: tests/data/7t_trt/sub-05/sub-05_sessions.tsv ================================================ session CCPT_avg_succ_RT CCPT_avg_FP_RT CCPT_avg_FN_RT CCPT_succ_count CCPT_FP_count CCPT_FN_count subject_id panas_cheerful panas_disgusted panas_attentive panas_bashful panas_sluggish panas_daring panas_surprised panas_strong panas_scornful panas_relaxed panas_irritable panas_delighted panas_inspired panas_fearless panas_disgusted_with_self panas_sad panas_calm panas_afraid panas_tired panas_amazed panas_shaky panas_happy panas_timid panas_alone panas_alert panas_upset panas_angry panas_bold panas_blue panas_shy panas_active panas_guilty panas_joyful panas_nervous panas_lonely panas_sleepy panas_excited panas_hostile panas_proud panas_jittery panas_lively panas_ashamed panas_at_ease panas_scared panas_drowsy panas_angry_at_self panas_enthusiastic panas_downhearted panas_sheepish panas_distressed panas_blameworthy panas_determined panas_frightened panas_astonished panas_interested panas_loathing panas_confident panas_energetic panas_concentrating panas_dissatisfied_with_self hours_of_sleep_ussually hours_of_sleep_last_night vigilance quality_of_sleep thirst liters_of_water_daily relative_water_intake caffeine_daily relative_caffeine_intake systolic_blood_pressure_left diastolic_blood_pressure_left pulse_left systolic_blood_pressure_right diastolic_blood_pressure_right pulse_right positive negative future past myself people surroundings vigilance_nyc-q images words specific_vague intrusive ses-1 372.1145833333333 355.5 n/a 96.0 2.0 0.0 5 6 1 6 1 3 2 3 4 1 6 3 5 6 4 1 2 6 1 2 2 2 5 2 5 5 3 2 1 4 1 5 2 5 2 4 3 5 1 5 2 3 1 6 1 3 1 4 2 1 2 1 5 1 4 5 1 6 4 6 2 7.0 7.5 7 6 7 1.5 5 0.0 5 105 69 66 117 65 71 65 15 40 70 40 65 80 60 65 30 75 15 ses-2 369.5578947368421 646.0 0.0 95.0 1.0 1.0 5 5 1 5 1 2 2 2 5 1 6 2 4 5 4 1 2 5 1 3 2 1 5 2 2 4 2 2 3 2 1 5 2 5 2 1 2 4 1 4 2 2 1 5 1 2 2 5 2 2 1 1 5 2 3 5 1 5 3 5 2 7.5 8.0 7 4 7 1.5 5 0.0 5 112 67 74 116 68 75 75 15 75 20 20 65 10 70 70 10 25 15 ================================================ FILE: tests/data/7t_trt/sub-06/ses-1/fmap/sub-06_ses-1_run-1_phasediff.json ================================================ {"EchoTime2": 0.00702, "EchoTime1": 0.006, "IntendedFor": "ses-1/func/sub-06_ses-1_task-rest_acq-fullbrain_run-1_bold.nii.gz"} ================================================ FILE: tests/data/7t_trt/sub-06/ses-1/fmap/sub-06_ses-1_run-2_phasediff.json ================================================ {"EchoTime2": 0.00702, "EchoTime1": 0.006, "IntendedFor": "ses-1/func/sub-06_ses-1_task-rest_acq-fullbrain_run-2_bold.nii.gz"} ================================================ FILE: tests/data/7t_trt/sub-06/ses-1/sub-06_ses-1_scans.tsv ================================================ filename positive negative future past myself people surroundings vigilance images words specific_vague intrusive func/sub-06_ses-1_task-rest_acq-fullbrain_run-1_bold.nii.gz 35 35 40 45 40 35 45 35 40 40 70 10 func/sub-06_ses-1_task-rest_acq-fullbrain_run-2_bold.nii.gz 40 45 50 45 40 60 45 40 40 50 70 40 func/sub-06_ses-1_task-rest_acq-prefrontal_bold.nii.gz 40 40 40 45 45 55 40 45 35 45 40 25 ================================================ FILE: tests/data/7t_trt/sub-06/ses-2/fmap/sub-06_ses-2_run-1_phasediff.json ================================================ {"EchoTime2": 0.00702, "EchoTime1": 0.006, "IntendedFor": "ses-2/func/sub-06_ses-2_task-rest_acq-fullbrain_run-1_bold.nii.gz"} ================================================ FILE: tests/data/7t_trt/sub-06/ses-2/fmap/sub-06_ses-2_run-2_phasediff.json ================================================ {"EchoTime2": 0.00702, "EchoTime1": 0.006, "IntendedFor": "ses-2/func/sub-06_ses-2_task-rest_acq-fullbrain_run-2_bold.nii.gz"} ================================================ FILE: tests/data/7t_trt/sub-06/ses-2/sub-06_ses-2_scans.tsv ================================================ filename positive negative future past myself people surroundings vigilance images words specific_vague intrusive func/sub-06_ses-2_task-rest_acq-fullbrain_run-1_bold.nii.gz 45 35 65 55 50 60 55 5 45 55 40 30 func/sub-06_ses-2_task-rest_acq-fullbrain_run-2_bold.nii.gz 40 50 55 60 45 55 40 50 45 55 65 30 func/sub-06_ses-2_task-rest_acq-prefrontal_bold.nii.gz 45 55 45 45 40 60 40 35 55 60 60 40 ================================================ FILE: tests/data/7t_trt/sub-06/sub-06_sessions.tsv ================================================ session CCPT_avg_succ_RT CCPT_avg_FP_RT CCPT_avg_FN_RT CCPT_succ_count CCPT_FP_count CCPT_FN_count subject_id panas_cheerful panas_disgusted panas_attentive panas_bashful panas_sluggish panas_daring panas_surprised panas_strong panas_scornful panas_relaxed panas_irritable panas_delighted panas_inspired panas_fearless panas_disgusted_with_self panas_sad panas_calm panas_afraid panas_tired panas_amazed panas_shaky panas_happy panas_timid panas_alone panas_alert panas_upset panas_angry panas_bold panas_blue panas_shy panas_active panas_guilty panas_joyful panas_nervous panas_lonely panas_sleepy panas_excited panas_hostile panas_proud panas_jittery panas_lively panas_ashamed panas_at_ease panas_scared panas_drowsy panas_angry_at_self panas_enthusiastic panas_downhearted panas_sheepish panas_distressed panas_blameworthy panas_determined panas_frightened panas_astonished panas_interested panas_loathing panas_confident panas_energetic panas_concentrating panas_dissatisfied_with_self hours_of_sleep_ussually hours_of_sleep_last_night vigilance quality_of_sleep thirst liters_of_water_daily relative_water_intake caffeine_daily relative_caffeine_intake systolic_blood_pressure_left diastolic_blood_pressure_left pulse_left systolic_blood_pressure_right diastolic_blood_pressure_right pulse_right positive negative future past myself people surroundings vigilance_nyc-q images words specific_vague intrusive ses-1 443.84375 374.0 n/a 96.0 4.0 0.0 6 3 2 5 3 2 3 2 5 1 3 4 4 4 5 1 2 5 2 5 2 2 4 2 2 4 3 3 1 2 2 5 1 4 2 2 4 3 2 1 3 4 1 4 1 2 1 4 3 1 2 1 6 2 3 6 1 4 4 5 2 7.0 6.0 4 8 4 4.0 5 0.0 5 97 59 52 97 59 55 50 15 30 40 35 25 35 80 50 65 35 10 ses-2 447.4895833333333 632.0 n/a 96.0 1.0 0.0 6 4 2 4 3 1 2 2 3 1 3 4 4 4 4 1 3 4 2 4 1 4 4 3 3 4 3 3 1 2 2 5 2 4 3 3 2 2 1 1 3 2 1 3 1 2 1 3 2 1 3 1 5 2 2 4 1 4 3 4 2 6.5 5.0 3 7 3 4.0 5 0.0 5 98 59 54 99 58 54 30 55 65 60 20 45 40 20 30 50 15 30 ================================================ FILE: tests/data/7t_trt/sub-07/ses-1/fmap/sub-07_ses-1_run-1_phasediff.json ================================================ {"EchoTime2": 0.00702, "EchoTime1": 0.006, "IntendedFor": "ses-1/func/sub-07_ses-1_task-rest_acq-fullbrain_run-1_bold.nii.gz"} ================================================ FILE: tests/data/7t_trt/sub-07/ses-1/fmap/sub-07_ses-1_run-2_phasediff.json ================================================ {"EchoTime2": 0.00702, "EchoTime1": 0.006, "IntendedFor": "ses-1/func/sub-07_ses-1_task-rest_acq-fullbrain_run-2_bold.nii.gz"} ================================================ FILE: tests/data/7t_trt/sub-07/ses-1/sub-07_ses-1_scans.tsv ================================================ filename positive negative future past myself people surroundings vigilance images words specific_vague intrusive func/sub-07_ses-1_task-rest_acq-fullbrain_run-1_bold.nii.gz 85 10 60 75 20 65 5 30 95 15 65 35 func/sub-07_ses-1_task-rest_acq-fullbrain_run-2_bold.nii.gz 65 5 60 60 10 80 10 60 90 15 40 30 func/sub-07_ses-1_task-rest_acq-prefrontal_bold.nii.gz 60 15 70 15 25 35 40 80 20 65 80 25 ================================================ FILE: tests/data/7t_trt/sub-07/ses-2/fmap/sub-07_ses-2_run-1_phasediff.json ================================================ {"EchoTime2": 0.00702, "EchoTime1": 0.006, "IntendedFor": "ses-2/func/sub-07_ses-2_task-rest_acq-fullbrain_run-1_bold.nii.gz"} ================================================ FILE: tests/data/7t_trt/sub-07/ses-2/fmap/sub-07_ses-2_run-2_phasediff.json ================================================ {"EchoTime2": 0.00702, "EchoTime1": 0.006, "IntendedFor": "ses-2/func/sub-07_ses-2_task-rest_acq-fullbrain_run-2_bold.nii.gz"} ================================================ FILE: tests/data/7t_trt/sub-07/ses-2/sub-07_ses-2_scans.tsv ================================================ filename positive negative future past myself people surroundings vigilance images words specific_vague intrusive func/sub-07_ses-2_task-rest_acq-fullbrain_run-1_bold.nii.gz 75 0 25 5 0 5 5 100 5 80 95 50 func/sub-07_ses-2_task-rest_acq-fullbrain_run-2_bold.nii.gz 95 0 75 20 35 95 0 85 75 25 30 10 func/sub-07_ses-2_task-rest_acq-prefrontal_bold.nii.gz 65 15 60 25 5 70 5 60 70 30 25 15 ================================================ FILE: tests/data/7t_trt/sub-07/sub-07_sessions.tsv ================================================ session CCPT_avg_succ_RT CCPT_avg_FP_RT CCPT_avg_FN_RT CCPT_succ_count CCPT_FP_count CCPT_FN_count subject_id panas_cheerful panas_disgusted panas_attentive panas_bashful panas_sluggish panas_daring panas_surprised panas_strong panas_scornful panas_relaxed panas_irritable panas_delighted panas_inspired panas_fearless panas_disgusted_with_self panas_sad panas_calm panas_afraid panas_tired panas_amazed panas_shaky panas_happy panas_timid panas_alone panas_alert panas_upset panas_angry panas_bold panas_blue panas_shy panas_active panas_guilty panas_joyful panas_nervous panas_lonely panas_sleepy panas_excited panas_hostile panas_proud panas_jittery panas_lively panas_ashamed panas_at_ease panas_scared panas_drowsy panas_angry_at_self panas_enthusiastic panas_downhearted panas_sheepish panas_distressed panas_blameworthy panas_determined panas_frightened panas_astonished panas_interested panas_loathing panas_confident panas_energetic panas_concentrating panas_dissatisfied_with_self hours_of_sleep_ussually hours_of_sleep_last_night vigilance quality_of_sleep thirst liters_of_water_daily relative_water_intake caffeine_daily relative_caffeine_intake systolic_blood_pressure_left diastolic_blood_pressure_left pulse_left systolic_blood_pressure_right diastolic_blood_pressure_right pulse_right positive negative future past myself people surroundings vigilance_nyc-q images words specific_vague intrusive ses-1 395.3541666666667 377.44444444444446 n/a 96.0 9.0 0.0 7 7 6 7 8 7 2 6 3 2 5 1 6 3 2 4 5 6 2 6 1 6 7 5 1 1 6 2 2 1 3 5 3 2 6 1 1 6 2 2 6 1 7 2 2 2 5 1 3 2 5 1 7 2 2 1 4 2 1 1 1 5.0 1.0 4 6 1 5.0 5 4.0 2 121 73 61 131 73 60 0 0 5 0 0 0 70 95 0 0 0 0 ses-2 407.8 375.3333333333333 0.0 95.0 3.0 1.0 7 5 1 6 3 2 2 3 5 1 6 2 5 6 6 1 2 7 3 2 2 3 5 4 3 5 2 1 4 2 3 5 1 5 2 4 2 5 1 2 2 5 2 6 2 3 1 5 2 2 2 1 4 3 2 5 1 4 5 4 2 7.0 8.0 8 8 6 2.0 3 3.0 2 107 61 53 115 60 54 90 0 0 80 5 0 0 90 25 0 100 15 ================================================ FILE: tests/data/7t_trt/sub-08/ses-1/fmap/sub-08_ses-1_run-1_phasediff.json ================================================ {"EchoTime2": 0.00702, "EchoTime1": 0.006, "IntendedFor": "ses-1/func/sub-08_ses-1_task-rest_acq-fullbrain_run-1_bold.nii.gz"} ================================================ FILE: tests/data/7t_trt/sub-08/ses-1/fmap/sub-08_ses-1_run-2_phasediff.json ================================================ {"EchoTime2": 0.00702, "EchoTime1": 0.006, "IntendedFor": "ses-1/func/sub-08_ses-1_task-rest_acq-fullbrain_run-2_bold.nii.gz"} ================================================ FILE: tests/data/7t_trt/sub-08/ses-1/sub-08_ses-1_scans.tsv ================================================ filename positive negative future past myself people surroundings vigilance images words specific_vague intrusive func/sub-08_ses-1_task-rest_acq-fullbrain_run-1_bold.nii.gz 55 45 35 45 15 55 0 40 95 55 65 65 func/sub-08_ses-1_task-rest_acq-fullbrain_run-2_bold.nii.gz 70 30 60 55 35 85 10 65 75 80 80 75 func/sub-08_ses-1_task-rest_acq-prefrontal_bold.nii.gz 75 30 55 60 50 85 50 25 55 75 40 50 ================================================ FILE: tests/data/7t_trt/sub-08/ses-2/fmap/sub-08_ses-2_run-1_phasediff.json ================================================ {"EchoTime2": 0.00702, "EchoTime1": 0.006, "IntendedFor": "ses-2/func/sub-08_ses-2_task-rest_acq-fullbrain_run-1_bold.nii.gz"} ================================================ FILE: tests/data/7t_trt/sub-08/ses-2/fmap/sub-08_ses-2_run-2_phasediff.json ================================================ {"EchoTime2": 0.00702, "EchoTime1": 0.006, "IntendedFor": "ses-2/func/sub-08_ses-2_task-rest_acq-fullbrain_run-2_bold.nii.gz"} ================================================ FILE: tests/data/7t_trt/sub-08/ses-2/sub-08_ses-2_scans.tsv ================================================ filename positive negative future past myself people surroundings vigilance images words specific_vague intrusive func/sub-08_ses-2_task-rest_acq-fullbrain_run-1_bold.nii.gz 60 35 65 65 35 70 50 20 70 55 50 70 func/sub-08_ses-2_task-rest_acq-fullbrain_run-2_bold.nii.gz 65 30 75 60 30 65 65 30 80 60 60 60 func/sub-08_ses-2_task-rest_acq-prefrontal_bold.nii.gz 60 45 55 45 40 65 15 25 85 75 60 65 ================================================ FILE: tests/data/7t_trt/sub-08/sub-08_sessions.tsv ================================================ session CCPT_avg_succ_RT CCPT_avg_FP_RT CCPT_avg_FN_RT CCPT_succ_count CCPT_FP_count CCPT_FN_count subject_id panas_cheerful panas_disgusted panas_attentive panas_bashful panas_sluggish panas_daring panas_surprised panas_strong panas_scornful panas_relaxed panas_irritable panas_delighted panas_inspired panas_fearless panas_disgusted_with_self panas_sad panas_calm panas_afraid panas_tired panas_amazed panas_shaky panas_happy panas_timid panas_alone panas_alert panas_upset panas_angry panas_bold panas_blue panas_shy panas_active panas_guilty panas_joyful panas_nervous panas_lonely panas_sleepy panas_excited panas_hostile panas_proud panas_jittery panas_lively panas_ashamed panas_at_ease panas_scared panas_drowsy panas_angry_at_self panas_enthusiastic panas_downhearted panas_sheepish panas_distressed panas_blameworthy panas_determined panas_frightened panas_astonished panas_interested panas_loathing panas_confident panas_energetic panas_concentrating panas_dissatisfied_with_self hours_of_sleep_ussually hours_of_sleep_last_night vigilance quality_of_sleep thirst liters_of_water_daily relative_water_intake caffeine_daily relative_caffeine_intake systolic_blood_pressure_left diastolic_blood_pressure_left pulse_left systolic_blood_pressure_right diastolic_blood_pressure_right pulse_right positive negative future past myself people surroundings vigilance_nyc-q images words specific_vague intrusive ses-1 393.8958333333333 332.875 n/a 96.0 8.0 0.0 8 6 2 6 2 2 5 5 5 3 3 2 7 6 5 2 2 5 2 5 4 4 7 3 1 2 3 3 4 2 3 6 3 6 2 1 5 7 3 5 3 5 2 3 1 4 2 5 1 5 2 3 5 2 6 6 1 5 5 6 3 6.5 5.5 3 7 4 2.8 2 0.8 2 129 69 56 126 77 63 75 15 70 65 40 100 40 15 80 80 75 90 ses-2 386.6041666666667 336.5 n/a 96.0 4.0 0.0 8 6 3 6 3 3 3 3 4 2 4 1 6 5 4 3 2 4 3 5 2 4 5 4 1 3 3 1 4 3 3 5 3 6 2 1 5 6 1 4 4 4 3 3 1 5 3 3 2 3 2 4 4 1 5 5 2 5 5 4 2 6.0 6.5 4 6 6 2.5 3 0.7 1 123 67 62 121 74 60 80 40 65 50 40 85 45 65 80 35 85 65 ================================================ FILE: tests/data/7t_trt/sub-09/ses-1/fmap/sub-09_ses-1_run-1_phasediff.json ================================================ {"EchoTime2": 0.00702, "EchoTime1": 0.006, "IntendedFor": "ses-1/func/sub-09_ses-1_task-rest_acq-fullbrain_run-1_bold.nii.gz"} ================================================ FILE: tests/data/7t_trt/sub-09/ses-1/fmap/sub-09_ses-1_run-2_phasediff.json ================================================ {"EchoTime2": 0.00702, "EchoTime1": 0.006, "IntendedFor": "ses-1/func/sub-09_ses-1_task-rest_acq-fullbrain_run-2_bold.nii.gz"} ================================================ FILE: tests/data/7t_trt/sub-09/ses-1/sub-09_ses-1_scans.tsv ================================================ filename positive negative future past myself people surroundings vigilance images words specific_vague intrusive func/sub-09_ses-1_task-rest_acq-fullbrain_run-1_bold.nii.gz 75 5 65 60 70 80 40 75 65 0 70 20 func/sub-09_ses-1_task-rest_acq-fullbrain_run-2_bold.nii.gz 75 15 75 35 35 100 10 100 90 0 80 30 func/sub-09_ses-1_task-rest_acq-prefrontal_bold.nii.gz 80 15 90 20 60 80 5 100 100 0 80 25 ================================================ FILE: tests/data/7t_trt/sub-09/ses-2/fmap/sub-09_ses-2_run-1_phasediff.json ================================================ {"EchoTime2": 0.00702, "EchoTime1": 0.006, "IntendedFor": "ses-2/func/sub-09_ses-2_task-rest_acq-fullbrain_run-1_bold.nii.gz"} ================================================ FILE: tests/data/7t_trt/sub-09/ses-2/fmap/sub-09_ses-2_run-2_phasediff.json ================================================ {"EchoTime2": 0.00702, "EchoTime1": 0.006, "IntendedFor": "ses-2/func/sub-09_ses-2_task-rest_acq-fullbrain_run-2_bold.nii.gz"} ================================================ FILE: tests/data/7t_trt/sub-09/ses-2/sub-09_ses-2_scans.tsv ================================================ filename positive negative future past myself people surroundings vigilance images words specific_vague intrusive func/sub-09_ses-2_task-rest_acq-fullbrain_run-1_bold.nii.gz 65 25 75 65 70 70 30 70 90 15 80 15 func/sub-09_ses-2_task-rest_acq-fullbrain_run-2_bold.nii.gz 65 20 65 30 40 85 60 95 0 65 75 10 func/sub-09_ses-2_task-rest_acq-prefrontal_bold.nii.gz 95 5 95 20 20 20 0 100 15 95 100 70 ================================================ FILE: tests/data/7t_trt/sub-09/sub-09_sessions.tsv ================================================ session CCPT_avg_succ_RT CCPT_avg_FP_RT CCPT_avg_FN_RT CCPT_succ_count CCPT_FP_count CCPT_FN_count subject_id panas_cheerful panas_disgusted panas_attentive panas_bashful panas_sluggish panas_daring panas_surprised panas_strong panas_scornful panas_relaxed panas_irritable panas_delighted panas_inspired panas_fearless panas_disgusted_with_self panas_sad panas_calm panas_afraid panas_tired panas_amazed panas_shaky panas_happy panas_timid panas_alone panas_alert panas_upset panas_angry panas_bold panas_blue panas_shy panas_active panas_guilty panas_joyful panas_nervous panas_lonely panas_sleepy panas_excited panas_hostile panas_proud panas_jittery panas_lively panas_ashamed panas_at_ease panas_scared panas_drowsy panas_angry_at_self panas_enthusiastic panas_downhearted panas_sheepish panas_distressed panas_blameworthy panas_determined panas_frightened panas_astonished panas_interested panas_loathing panas_confident panas_energetic panas_concentrating panas_dissatisfied_with_self hours_of_sleep_ussually hours_of_sleep_last_night vigilance quality_of_sleep thirst liters_of_water_daily relative_water_intake caffeine_daily relative_caffeine_intake systolic_blood_pressure_left diastolic_blood_pressure_left pulse_left systolic_blood_pressure_right diastolic_blood_pressure_right pulse_right positive negative future past myself people surroundings vigilance_nyc-q images words specific_vague intrusive ses-1 485.9462365591398 n/a 0.0 93.0 0.0 3.0 9 7 2 6 2 4 3 7 7 3 3 1 6 6 5 1 1 6 1 5 7 2 7 2 3 5 2 2 3 1 2 5 1 6 2 3 3 7 1 5 2 4 1 6 1 2 1 6 1 2 1 1 4 1 7 6 1 4 5 6 2 6.0 7.0 7 7 8 1.5 5 0.5 5 126 77 65 136 74 62 55 10 60 45 65 55 90 75 15 95 100 35 ses-2 497.29347826086956 n/a 0.0 92.0 0.0 4.0 9 5 1 6 2 4 2 2 5 1 6 1 5 5 4 1 2 7 1 3 3 2 6 2 3 6 1 1 3 2 3 6 1 4 2 2 2 5 1 4 1 5 1 6 1 2 1 3 1 3 1 1 5 1 1 4 1 5 5 6 1 5.0 6.0 7 4 8 1.5 5 0.5 5 123 71 65 133 65 64 95 20 25 70 30 75 15 95 10 85 60 5 ================================================ FILE: tests/data/7t_trt/sub-10/ses-1/fmap/sub-10_ses-1_run-1_phasediff.json ================================================ {"EchoTime2": 0.00702, "EchoTime1": 0.006, "IntendedFor": "ses-1/func/sub-10_ses-1_task-rest_acq-fullbrain_run-1_bold.nii.gz"} ================================================ FILE: tests/data/7t_trt/sub-10/ses-1/fmap/sub-10_ses-1_run-2_phasediff.json ================================================ {"EchoTime2": 0.00702, "EchoTime1": 0.006, "IntendedFor": "ses-1/func/sub-10_ses-1_task-rest_acq-fullbrain_run-2_bold.nii.gz"} ================================================ FILE: tests/data/7t_trt/sub-10/ses-1/sub-10_ses-1_scans.tsv ================================================ filename positive negative future past myself people surroundings vigilance images words specific_vague intrusive func/sub-10_ses-1_task-rest_acq-fullbrain_run-1_bold.nii.gz 55 60 20 60 40 65 65 30 90 55 20 15 func/sub-10_ses-1_task-rest_acq-fullbrain_run-2_bold.nii.gz 50 35 40 60 75 55 20 35 80 25 55 35 func/sub-10_ses-1_task-rest_acq-prefrontal_bold.nii.gz 50 55 75 70 65 40 80 30 70 10 65 60 ================================================ FILE: tests/data/7t_trt/sub-10/ses-2/fmap/sub-10_ses-2_run-1_phasediff.json ================================================ {"EchoTime2": 0.00702, "EchoTime1": 0.006, "IntendedFor": "ses-2/func/sub-10_ses-2_task-rest_acq-fullbrain_run-1_bold.nii.gz"} ================================================ FILE: tests/data/7t_trt/sub-10/ses-2/fmap/sub-10_ses-2_run-2_phasediff.json ================================================ {"EchoTime2": 0.00702, "EchoTime1": 0.006, "IntendedFor": "ses-2/func/sub-10_ses-2_task-rest_acq-fullbrain_run-2_bold.nii.gz"} ================================================ FILE: tests/data/7t_trt/sub-10/ses-2/sub-10_ses-2_scans.tsv ================================================ filename positive negative future past myself people surroundings vigilance images words specific_vague intrusive func/sub-10_ses-2_task-rest_acq-fullbrain_run-1_bold.nii.gz 65 55 65 75 70 60 70 20 60 40 35 30 func/sub-10_ses-2_task-rest_acq-fullbrain_run-2_bold.nii.gz 95 30 70 80 85 95 60 55 100 30 75 60 func/sub-10_ses-2_task-rest_acq-prefrontal_bold.nii.gz 100 35 100 40 95 45 55 60 85 20 90 60 ================================================ FILE: tests/data/7t_trt/sub-10/sub-10_sessions.tsv ================================================ session CCPT_avg_succ_RT CCPT_avg_FP_RT CCPT_avg_FN_RT CCPT_succ_count CCPT_FP_count CCPT_FN_count subject_id panas_cheerful panas_disgusted panas_attentive panas_bashful panas_sluggish panas_daring panas_surprised panas_strong panas_scornful panas_relaxed panas_irritable panas_delighted panas_inspired panas_fearless panas_disgusted_with_self panas_sad panas_calm panas_afraid panas_tired panas_amazed panas_shaky panas_happy panas_timid panas_alone panas_alert panas_upset panas_angry panas_bold panas_blue panas_shy panas_active panas_guilty panas_joyful panas_nervous panas_lonely panas_sleepy panas_excited panas_hostile panas_proud panas_jittery panas_lively panas_ashamed panas_at_ease panas_scared panas_drowsy panas_angry_at_self panas_enthusiastic panas_downhearted panas_sheepish panas_distressed panas_blameworthy panas_determined panas_frightened panas_astonished panas_interested panas_loathing panas_confident panas_energetic panas_concentrating panas_dissatisfied_with_self hours_of_sleep_ussually hours_of_sleep_last_night vigilance quality_of_sleep thirst liters_of_water_daily relative_water_intake caffeine_daily relative_caffeine_intake systolic_blood_pressure_left diastolic_blood_pressure_left pulse_left systolic_blood_pressure_right diastolic_blood_pressure_right pulse_right positive negative future past myself people surroundings vigilance_nyc-q images words specific_vague intrusive ses-1 465.5104166666667 474.0 n/a 96.0 1.0 0.0 10 5 1 4 1 4 1 1 1 1 4 3 5 5 2 1 3 4 1 4 1 1 6 1 4 3 3 2 1 3 1 4 1 6 3 4 4 6 1 3 3 4 1 4 1 3 3 4 1 1 2 1 3 1 1 4 1 3 4 4 4 7.0 5.5 2 6 2 1.25 5 0.2 5 121 74 74 125 76 74 45 65 60 75 85 90 80 5 85 10 10 20 ses-2 473.4791666666667 367.0 n/a 96.0 1.0 0.0 10 6 1 5 1 4 1 2 4 1 6 4 6 6 4 1 3 6 1 4 2 2 6 2 3 4 3 4 1 1 1 5 1 6 2 3 4 6 2 4 1 5 1 5 1 3 1 4 2 2 1 1 3 1 1 5 1 3 4 4 1 7.0 7.0 2 5 4 1.25 4 0.2 5 113 70 67 110 65 65 60 45 80 50 65 75 100 25 75 20 25 30 ================================================ FILE: tests/data/7t_trt/task-rest_acq-fullbrain_bold.json ================================================ { "CogAtlasID": "trm_4c8a834779883", "EchoTime": 0.017, "EffectiveEchoSpacing": 0.0003333262223739227, "MTState": false, "PhaseEncodingDirection": "j-", "RepetitionTime": 3.0, "SliceEncodingDirection": "k", "SliceTiming": [ 1.508, 0.0, 1.55, 0.043, 1.592, 0.087, 1.635, 0.13, 1.677, 0.173, 1.722, 0.215, 1.765, 0.26, 1.808, 0.302, 1.85, 0.345, 1.893, 0.388, 1.938, 0.43, 1.98, 0.475, 2.022, 0.518, 2.065, 0.56, 2.11, 0.603, 2.152, 0.645, 2.195, 0.69, 2.238, 0.733, 2.28, 0.775, 2.325, 0.818, 2.367, 0.86, 2.41, 0.905, 2.453, 0.948, 2.495, 0.99, 2.54, 1.032, 2.583, 1.075, 2.625, 1.12, 2.668, 1.163, 2.71, 1.205, 2.755, 1.248, 2.798, 1.293, 2.84, 1.335, 2.883, 1.378, 2.925, 1.42, 2.97, 1.462 ], "TaskName": "Rest" } ================================================ FILE: tests/data/7t_trt/task-rest_acq-fullbrain_run-1_physio.json ================================================ {"StartTime": 0, "SamplingFrequency": 100, "Columns": ["cardiac", "respiratory", "trigger", "oxygen saturation"]} ================================================ FILE: tests/data/7t_trt/task-rest_acq-fullbrain_run-2_physio.json ================================================ {"StartTime": 0, "SamplingFrequency": 100, "Columns": ["cardiac", "respiratory", "trigger", "oxygen saturation"]} ================================================ FILE: tests/data/7t_trt/task-rest_acq-prefrontal_bold.json ================================================ { "CogAtlasID": "trm_4c8a834779883", "EchoTime": 0.026, "EffectiveEchoSpacing": 0.00032998944033790924, "PhaseEncodingDirection": "j-", "RepetitionTime": 4.0, "SliceEncodingDirection": "k", "SliceTiming": [ 3.915, 3.815, 3.715, 3.615, 3.515, 3.413, 3.312, 3.212, 3.112, 3.013, 2.913, 2.81, 2.71, 2.61, 2.51, 2.41, 2.31, 2.21, 2.108, 2.008, 1.908, 1.808, 1.707, 1.608, 1.505, 1.405, 1.305, 1.205, 1.105, 1.005, 0.902, 0.802, 0.703, 0.603, 0.502, 0.402, 0.302, 0.2, 0.1, 0.0 ], "TaskName": "Rest" } ================================================ FILE: tests/data/7t_trt/task-rest_acq-prefrontal_physio.json ================================================ {"StartTime": 0, "SamplingFrequency": 100, "Columns": ["cardiac", "respiratory", "trigger", "oxygen saturation"]} ================================================ FILE: tests/data/7t_trt/test.bval ================================================ ================================================ FILE: tests/data/bids_specs_with_oligarchy.json ================================================ { "name": "bids", "entities": [ { "name": "subject", "pattern": "[/\\\\]+sub-([a-zA-Z0-9]+)", "directory": "{subject}" }, { "name": "oligarchy", "pattern": "[_/\\\\]+ses-([a-zA-Z0-9]+)", "mandatory": false, "directory": "{subject}{oligarchy}" }, { "name": "task", "pattern": "[_/\\\\]+task-([a-zA-Z0-9]+)" }, { "name": "acquisition", "pattern": "[_/\\\\]+acq-([a-zA-Z0-9]+)" }, { "name": "ceagent", "pattern": "[_/\\\\]+ce-([a-zA-Z0-9]+)" }, { "name": "reconstruction", "pattern": "[_/\\\\]+rec-([a-zA-Z0-9]+)" }, { "name": "direction", "pattern": "[_/\\\\]+dir-([a-zA-Z0-9]+)" }, { "name": "run", "pattern": "[_/\\\\]+run-0*(\\d+)", "dtype": "int" }, { "name": "proc", "pattern": "[_/\\\\]+proc-([a-zA-Z0-9]+)" }, { "name": "modality", "pattern": "[_/\\\\]+mod-([a-zA-Z0-9]+)" }, { "name": "echo", "pattern": "[_/\\\\]+echo-([0-9]+)" }, { "name": "recording", "pattern": "[_/\\\\]+recording-([a-zA-Z0-9]+)" }, { "name": "space", "pattern": "[_/\\\\]+space-([a-zA-Z0-9]+)" }, { "name": "suffix", "pattern": "[._]*([a-zA-Z0-9]*?)\\.[^/\\\\]+$" }, { "name": "scans", "pattern": "(.*\\_scans.tsv)$" }, { "name": "fmap", "pattern": "(phasediff|magnitude[1-2]|phase[1-2]|fieldmap|epi)\\.nii" }, { "name": "datatype", "pattern": "[/\\\\]+(func|anat|fmap|dwi|meg|eeg)[/\\\\]+" }, { "name": "extension", "pattern": "[._]*[a-zA-Z0-9]*?(\\.[^/\\\\]+)$" } ], "default_path_patterns": [ "sub-{subject}[/ses-{oligarchy}]/{datatype|anat}/sub-{subject}[_ses-{oligarchy}][_acq-{acquisition}][_ce-{ceagent}][_rec-{reconstruction}]_{suffix}{extension<.nii|.nii.gz|.json>|.nii.gz}", "sub-{subject}[/ses-{oligarchy}]/{datatype|anat}/sub-{subject}[_ses-{oligarchy}][_acq-{acquisition}][_ce-{ceagent}][_rec-{reconstruction}][_mod-{modality}]_{suffix}{extension<.nii|.nii.gz|.json>|.nii.gz}", "sub-{subject}[/ses-{oligarchy}]/{datatype|func}/sub-{subject}[_ses-{oligarchy}]_task-{task}[_acq-{acquisition}][_ce-{ceagent}][_dir-{direction}][_rec-{reconstruction}][_run-{run}][_echo-{echo}]_{suffix}{extension<.nii|.nii.gz|.json>|.nii.gz}", "sub-{subject}[/ses-{oligarchy}]/{datatype|dwi}/sub-{subject}[_ses-{oligarchy}][_acq-{acquisition}]_{suffix}{extension<.bval|.bvec|.json|.nii.gz|.nii>|.nii.gz}", "sub-{subject}[/ses-{oligarchy}]/{datatype|fmap}/sub-{subject}[_ses-{oligarchy}][_acq-{acquisition}][_dir-{direction}][_run-{run}]_{fmap}{extension<.nii|.nii.gz|.json>|.nii.gz}", "sub-{subject}[/ses-{oligarchy}]/{datatype|fmap}/sub-{subject}[_ses-{oligarchy}][_acq-{acquisition}][_ce-{ceagent}]_dir-{direction}[_run-{run}]_{fmap}{extension<.nii|.nii.gz|.json>|.nii.gz}", "sub-{subject}[/ses-{oligarchy}]/[{datatype|func}/]sub-{subject}[_ses-{oligarchy}]_task-{task}[_acq-{acquisition}][_rec-{reconstruction}][_run-{run}][_echo-{echo}][_recording-{recording}]_{suffix}{extension<.tsv|.json>|.tsv}", "sub-{subject}[/ses-{oligarchy}]/[{datatype|func}/]sub-{subject}[_ses-{oligarchy}]_task-{task}[_acq-{acquisition}][_rec-{reconstruction}][_run-{run}][_echo-{echo}][_recording-{recording}]_{suffix}{extension<.tsv.gz|.json>|.tsv.gz}", "sub-{subject}[/ses-{oligarchy}]/{datatype|meg}/sub-{subject}[_ses-{oligarchy}]_task-{task}[_acq-{acquisition}][_run-{run}][_proc-{proc}]_{suffix}{extension}", "sub-{subject}[/ses-{oligarchy}]/{datatype|meg}/sub-{subject}[_ses-{oligarchy}]_task-{task}[_acq-{acquisition}][_run-{run}][_proc-{proc}]_{suffix}{extension<.tsv|.json>|.tsv}", "sub-{subject}[/ses-{oligarchy}]/{datatype|meg}/sub-{subject}[_ses-{oligarchy}]_task-{task}[_acq-{acquisition}]_{suffix}{extension<.json>|.json}", 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"task-{task}[_acq-{acquisition}][_rec-{reconstruction}][_run-{run}][_echo-{echo}][_recording-{recording}]_{suffix}{extension<.json>}" ] } ================================================ FILE: tests/data/ds000117/.bidsignore ================================================ run-*_echo-*_FLASH.json **/sub-*_ses-mri_run-*_echo-*_FLASH.nii.gz ================================================ FILE: tests/data/ds000117/CHANGES ================================================ Revision history for OpenfMRI dataset ds000117 1.0.0 2017-11-08 - Converted to BIDS format - Changed subject order (see README) 0.1.1 2016-09-17 - Replaced ds117_R0.1.0/sub004/MEG/run_02_sss.fif and ds117_R0.1.0/sub006/MEG/run_03_sss.fif since the original versions were corrupted 0.1.0 2014-11-03 - Initial Release ================================================ FILE: tests/data/ds000117/README ================================================ This dataset was obtained from the OpenfMRI project (http://www.openfmri.org). Accession #: ds000117 Description: Multi-subject, multi-modal (sMRI+fMRI+MEG+EEG) neuroimaging dataset on face recognition Please cite the following references if you use these data: A multi-subject, multi-modal human neuroimaging dataset Wakeman, D.G. & Henson, R.N. (2015). A multi-subject, multi-modal human neuroimaging dataset. Sci. Data 2:150001 doi: 10.1038/sdata.2015.1 Multi-subject, multi-modal (sMRI+fMRI+MEG+EEG) neuroimaging dataset on face recognition ================================================================================== Please note that the subject ordering has changed since version 0.1.x (non-BIDS) of this dataset. Additionally, three subjects have been left out. The mapping is as follows: 0.1.x: 02 03 05 14 08 09 10 11 12 15 16 17 18 23 24 25 current: 01 02 03 04 05 06 07 08 09 10 11 12 13 14 15 16 func/ ----- The experiment began at the third nifti volume (corresponding to 5 ignored scans: 3 forced siemens dummies (no files) and 2 included in the data). meg/ ---- Three anatomical fiducials were digitized for aligning the MEG with the MRI: the nasion (lowest depression between the eyes) and the left and right ears (lowest depression between the tragus and the helix, above the tragus). This procedure is illustrated here: http://neuroimage.usc.edu/brainstorm/CoordinateSystems#Subject_Coordinate_System_.28SCS_.2F_CTF.29 and in task-facerecognition_fidinfo.pdf The following triggers are included in the .fif files and are also used in the “trigger” column of the meg and bold events files: Trigger Label Simplified Label 5 Initial Famous Face FAMOUS 6 Immediate Repeat Famous Face FAMOUS 7 Delayed Repeat Famous Face FAMOUS 13 Initial Unfamiliar Face UNFAMILIAR 14 Immediate Repeat Unfamiliar Face UNFAMILIAR 15 Delayed Repeat Unfamiliar Face UNFAMILIAR 17 Initial Scrambled Face SCRAMBLED 18 Immediate Repeat Scrambled Face SCRAMBLED 19 Delayed Repeat Scrambled Face SCRAMBLED stimuli/meg/ ------------ The .bmp files correspond to those described in the text. There are 6 additional images in this directory, which were used in the practice experiment to familiarize participants with the task. stimuli/mri/ ------------ The .bmp files correspond to those described in the text. If you wish to publish any of these data, please acknowledge Daniel Wakeman and Richard Henson. The data has been published so far in: Wakeman, D.G. & Henson, R.N. (2015). A multi-subject, multi-modal human neuroimaging dataset. Sci. Data 2:150001 doi: 10.1038/sdata.2015.1 Henson, R.N., Wakeman, D.G., Litvak, V. & Friston, K.J. (2011). A Parametric Empirical Bayesian framework for the EEG/MEG inverse problem: generative models for multisubject and multimodal integration. Frontiers in Human Neuroscience, 5, 76, 1-16. Henson, R.N., Wakeman, D.G., Phillips, C. & Rowe, J. (2012). Effective Connectivity between OFA and FFA during face perception: DCM of evoked MEG, EEG and fMRI responses. HBM2012 Abstract. ### Comments added by Openfmri Curators ### =========================================== General Comments ---------------- Defacing -------- Defacing was performed by the submitter Quality Control --------------- Mriqc was run on the dataset. Results are located in derivatives/mriqc. Learn more about it here: https://mriqc.readthedocs.io/en/latest/ Where to discuss the dataset ---------------------------- 1) www.openfmri.org/dataset/ds000117/ See the comments section at the bottom of the dataset page. 2) www.neurostars.org Please tag any discussion topics with the tags openfmri and ds000117. 3) Send an email to submissions@openfmri.org. Please include the accession number in your email. Known Issues ------------ N/A ================================================ FILE: tests/data/ds000117/acq-epi_T1w.json ================================================ { "RepetitionTime": 2, "EchoTime": 0.095, "FlipAngle": 90, "Manufacturer": "Siemens", "ManufacturersModelName": "TIM TRIO", "MagneticFieldStrength": 3, "PulseSequenceType": "EPI" } ================================================ FILE: tests/data/ds000117/acq-mprage_T1w.json ================================================ { "RepetitionTime": 2.25, "EchoTime": 0.00298, "InversionTime": 0.9, "FlipAngle": 9, "Manufacturer": "Siemens", "ManufacturersModelName": "TIM TRIO", "MagneticFieldStrength": 3, "PulseSequenceType": "MPRAGE" } ================================================ FILE: tests/data/ds000117/dataset_description.json ================================================ { "BIDSVersion": "1.0.2", "License": "CC0", "Name": "A multi-subject, multi-modal human neuroimaging dataset", "Authors": ["Wakeman, DG", "Henson, RN"], "Acknowledgements": "This work was supported by the UK Medical Research Council (MC_A060_5PR10) and Elekta Ltd. We would also like to acknowledge the contribution of Andre van der Kouwe and the A.A. Martinos Center for Biomedical Imaging (MGH) for providing the Multi-Echo FLASH sequences used in this work.", "HowToAcknowledge": "Cite this paper: https://www.ncbi.nlm.nih.gov/pubmed/25977808 and consider including the following message: 'This data was obtained from the OpenfMRI database. Its accession number is ds000117'", "ReferencesAndLinks": ["https://www.ncbi.nlm.nih.gov/pubmed/25977808", "https://openfmri.org/dataset/ds000117/"], "Funding": ["UK Medical Research Council (MC_A060_5PR10)","Elekta Ltd."] } ================================================ FILE: tests/data/ds000117/participants.tsv ================================================ participant_id age sex first_ses sub-01 31 M meg sub-02 25 M meg sub-03 30 M meg sub-04 26 F meg sub-05 23 F meg sub-06 26 M meg sub-07 31 F meg sub-08 26 M meg sub-09 29 M meg sub-10 23 M meg sub-11 24 F meg sub-12 24 F meg sub-13 25 F meg sub-14 24 F mri sub-15 30 M mri sub-16 25 M mri sub-emptyroom n/a n/a n/a ================================================ FILE: tests/data/ds000117/run-1_echo-1_FLASH.json ================================================ { "RepetitionTime": 0.020, "FlipAngle": 5, "EchoTime": 0.00185 } ================================================ FILE: tests/data/ds000117/run-1_echo-2_FLASH.json ================================================ { "RepetitionTime": 0.020, "FlipAngle": 5, "EchoTime": 0.00415 } ================================================ FILE: tests/data/ds000117/run-1_echo-3_FLASH.json ================================================ { "RepetitionTime": 0.020, "FlipAngle": 5, "EchoTime": 0.00645 } ================================================ FILE: tests/data/ds000117/run-1_echo-4_FLASH.json ================================================ { "RepetitionTime": 0.020, "FlipAngle": 5, "EchoTime": 0.00875 } ================================================ FILE: tests/data/ds000117/run-1_echo-5_FLASH.json ================================================ { "RepetitionTime": 0.020, "FlipAngle": 5, "EchoTime": 0.01105 } ================================================ FILE: tests/data/ds000117/run-1_echo-6_FLASH.json ================================================ { "RepetitionTime": 0.020, "FlipAngle": 5, "EchoTime": 0.01335 } ================================================ FILE: tests/data/ds000117/run-1_echo-7_FLASH.json ================================================ { "RepetitionTime": 0.020, "FlipAngle": 5, "EchoTime": 0.01565 } ================================================ FILE: tests/data/ds000117/run-2_echo-1_FLASH.json ================================================ { "RepetitionTime": 0.020, "FlipAngle": 30, "EchoTime": 0.00185 } ================================================ FILE: tests/data/ds000117/run-2_echo-2_FLASH.json ================================================ { "RepetitionTime": 0.020, "FlipAngle": 30, "EchoTime": 0.00415 } ================================================ FILE: tests/data/ds000117/run-2_echo-3_FLASH.json ================================================ { "RepetitionTime": 0.020, "FlipAngle": 30, "EchoTime": 0.00645 } ================================================ FILE: tests/data/ds000117/run-2_echo-4_FLASH.json ================================================ { "RepetitionTime": 0.020, "FlipAngle": 30, "EchoTime": 0.00875 } ================================================ FILE: tests/data/ds000117/run-2_echo-5_FLASH.json ================================================ { "RepetitionTime": 0.020, "FlipAngle": 30, "EchoTime": 0.01105 } ================================================ FILE: tests/data/ds000117/run-2_echo-6_FLASH.json ================================================ { "RepetitionTime": 0.020, "FlipAngle": 30, "EchoTime": 0.01335 } ================================================ FILE: tests/data/ds000117/run-2_echo-7_FLASH.json ================================================ { "RepetitionTime": 0.020, "FlipAngle": 30, "EchoTime": 0.01565 } ================================================ FILE: tests/data/ds000117/sub-01/ses-meg/beh/sub-01_ses-meg_task-facerecognition_events.tsv ================================================ onset duration response_time button_press stim_file 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17.162310 EEG66 -47.459051 -63.615277 49.595017 EEG67 -30.846596 -69.850221 78.941919 EEG68 -1.357119 -74.640468 86.260043 EEG69 31.340498 -75.262114 80.961607 EEG70 45.114044 -75.206041 55.369329 EEG71 -29.643130 -78.484789 54.704785 EEG72 -2.303147 -87.170467 55.933770 EEG73 22.523638 -86.916521 57.126023 EEG74 2.855967 -83.032936 18.157925 ================================================ FILE: tests/data/ds000117/sub-01/ses-meg/meg/sub-01_ses-meg_task-facerecognition_proc-sss_coordsystem.json ================================================ { "MEGCoordinateSystem": "Elekta/Neuromag", "MEGCoordinateUnits": "mm", "MEGCoordinateSystemDescription": "VectorView: 102 Magnetometers, 204 Planar Gradiometers", "EEGCoordinateSystem": "Elekta/Neuromag", "EEGCoordinateUnits": "mm", "EEGCoordinateSystemDescription": "70 Easycap sites, roughly corresponding to extended 10-20 system: see http://imaging.mrc-cbu.cam.ac.uk/meg/VectorviewDescription#EEGlabels", "DigitizedHeadPoints": "sub-01_ses-meg_headshape.pos", "DigitizedHeadPointsCoordinateSystem": "Elekta/Neuromag", "DigitizedHeadPointsCoordinateUnits": "mm", "DigitizedHeadPointsCoordinateSystemDescription": "Headpoints include arcs of points along front of head and around nose, as well as centre of 70 EEG electrodes spread across scalp, which also help define headshape. Beware that nose points can bias coregistration with MRI if MRI image does not include all of the nose.", "AnatomicalLandmarkCoordinates": { "LPA": [-71,0,0], "RPA": [77.56,0,0], "Nasion": [0,106.36,0] }, "AnatomicalLandmarkCoordinateSystem": "Elekta/Neuromag", "AnatomicalLandmarkCoordinateUnits": "mm", "HeadCoilCoordinates": { "coil1": [14.38,-86.02,77.84], "coil2": [20.74,39.33,121.43], "coil3": [63.03,70.44,43.05], "coil4": [-68.38,-1.15,87.01000000000001], "coil5": [-6.2,107.9,59.15] }, "HeadCoilCoordinateSystem": "Elekta/Neuromag", "HeadCoilCoordinateUnits": "mm", "HeadCoilCoordinateSystemDescription": "5 HPI coils used", "AnatomicalLandmarkCoordinateSystemDescription": "For images of anatomical landmarks, see http://imaging.mrc-cbu.cam.ac.uk/meg/SubjectPreparation", "FiducialsDescription": "For images of anatomical landmarks, see http://imaging.mrc-cbu.cam.ac.uk/meg/SubjectPreparation", "IntendedFor": "sub-01/ses-mri/anat/sub-01_ses-mri_acq-mprage_T1w.nii.gz" } 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381.998 0 420198 Famous 6 meg/f096.bmp 385.106 0 423617 Scrambled 19 meg/s084.bmp 388.179 0 426997 Unfamiliar 13 meg/u139.bmp 391.42 0 430562 Unfamiliar 14 meg/u139.bmp 394.477 0 433925 Scrambled 17 meg/s140.bmp 397.601 0 437361 Scrambled 18 meg/s140.bmp 400.775 0 440853 Famous 7 meg/f129.bmp 403.999 0 444399 Famous 5 meg/f049.bmp 407.14 0 447854 Famous 6 meg/f049.bmp 410.381 0 451419 Unfamiliar 15 meg/u051.bmp 413.555 0 454910 Famous 5 meg/f140.bmp 416.562 0 458218 Unfamiliar 15 meg/u140.bmp 419.803 0 461783 Unfamiliar 13 meg/u017.bmp 423.01 0 465311 Unfamiliar 14 meg/u017.bmp 426.05 0 468655 Famous 5 meg/f033.bmp 429.158 0 472074 Famous 6 meg/f033.bmp 432.415 0 475656 Famous 5 meg/f060.bmp 435.488 0 479037 Famous 6 meg/f060.bmp 438.679 0 482547 Scrambled 17 meg/s098.bmp 441.77 0 485947 Scrambled 18 meg/s098.bmp 444.927 0 489420 Scrambled 17 meg/s110.bmp 448.135 0 492948 Scrambled 18 meg/s110.bmp 451.408 0 496549 Famous 7 meg/f140.bmp 454.533 0 499986 Unfamiliar 13 meg/u073.bmp 457.623 0 503385 Famous 5 meg/f137.bmp 460.746 0 506821 Unfamiliar 13 meg/u066.bmp 463.904 0 510294 Unfamiliar 13 meg/u078.bmp 467.095 0 513804 Unfamiliar 14 meg/u078.bmp 470.102 0 517112 Famous 5 meg/f131.bmp 473.309 0 520640 Unfamiliar 13 meg/u105.bmp 476.433 0 524076 Unfamiliar 15 meg/u073.bmp 479.657 0 527623 Unfamiliar 13 meg/u132.bmp 482.865 0 531151 Famous 7 meg/f137.bmp ================================================ FILE: tests/data/ds000117/sub-01/ses-meg/meg/sub-01_ses-meg_task-facerecognition_run-01_proc-sss_meg.fif ================================================ ================================================ FILE: tests/data/ds000117/sub-01/ses-meg/meg/sub-01_ses-meg_task-facerecognition_run-02_proc-sss_events.tsv ================================================ onset duration onset_sample stim_type trigger stim_file 25.6936 0 28263 Scrambled 17 meg/s096.bmp 28.8845 0 31773 Famous 5 meg/f148.bmp 32.1918 0 35411 Famous 6 meg/f148.bmp 35.1818 0 38700 Scrambled 17 meg/s071.bmp 38.3564 0 42192 Scrambled 18 meg/s071.bmp 41.6473 0 45812 Unfamiliar 13 meg/u005.bmp 44.7209 0 49193 Famous 5 meg/f119.bmp 47.7945 0 52574 Famous 5 meg/f145.bmp 50.9682 0 56065 Scrambled 19 meg/s096.bmp 54.2764 0 59704 Unfamiliar 13 meg/u117.bmp 57.4 0 63140 Unfamiliar 14 meg/u117.bmp 60.5573 0 66613 Famous 5 meg/f098.bmp 63.7818 0 70160 Scrambled 17 meg/s008.bmp 67.0391 0 73743 Scrambled 18 meg/s008.bmp 70.2464 0 77271 Unfamiliar 15 meg/u005.bmp 73.4373 0 80781 Famous 5 meg/f139.bmp 76.6445 0 84309 Famous 6 meg/f139.bmp 79.8682 0 87855 Famous 7 meg/f119.bmp 82.9091 0 91200 Scrambled 17 meg/s001.bmp 86.0827 0 94691 Scrambled 18 meg/s001.bmp 89.3073 0 98238 Famous 7 meg/f145.bmp 92.4136 0 101655 Famous 5 meg/f028.bmp 95.5209 0 105073 Unfamiliar 13 meg/u095.bmp 98.6955 0 108565 Famous 7 meg/f098.bmp 101.719 0 111891 Scrambled 17 meg/s037.bmp 104.976 0 115474 Scrambled 18 meg/s037.bmp 108.317 0 119149 Famous 5 meg/f089.bmp 111.408 0 122549 Famous 6 meg/f089.bmp 114.665 0 126132 Famous 5 meg/f111.bmp 117.673 0 129440 Unfamiliar 13 meg/u070.bmp 120.846 0 132931 Famous 7 meg/f028.bmp 123.987 0 136386 Famous 5 meg/f124.bmp 127.261 0 139987 Unfamiliar 15 meg/u095.bmp 130.552 0 143607 Unfamiliar 13 meg/u048.bmp 133.66 0 147026 Famous 5 meg/f074.bmp 136.916 0 150608 Unfamiliar 13 meg/u021.bmp 140.124 0 154136 Famous 7 meg/f111.bmp 143.415 0 157756 Unfamiliar 13 meg/u019.bmp 146.539 0 161193 Unfamiliar 14 meg/u019.bmp 149.846 0 164831 Unfamiliar 15 meg/u070.bmp 152.886 0 168175 Scrambled 17 meg/s026.bmp 156.177 0 171795 Scrambled 18 meg/s026.bmp 159.252 0 175177 Famous 7 meg/f124.bmp 162.459 0 178705 Scrambled 17 meg/s086.bmp 165.616 0 182178 Scrambled 18 meg/s086.bmp 168.656 0 185522 Unfamiliar 15 meg/u048.bmp 171.964 0 189160 Famous 5 meg/f132.bmp 175.071 0 192578 Famous 6 meg/f132.bmp 178.228 0 196051 Famous 7 meg/f074.bmp 181.419 0 199561 Famous 5 meg/f010.bmp 184.71 0 203181 Unfamiliar 15 meg/u021.bmp 188.035 0 206838 Scrambled 17 meg/s056.bmp 191.375 0 210513 Scrambled 18 meg/s056.bmp 194.6 0 214060 Famous 5 meg/f104.bmp 197.774 0 217551 Scrambled 17 meg/s068.bmp 200.997 0 221097 Famous 5 meg/f036.bmp 204.172 0 224589 Famous 6 meg/f036.bmp 207.279 0 228007 Famous 5 meg/f066.bmp 210.486 0 231535 Famous 7 meg/f010.bmp 213.744 0 235118 Unfamiliar 13 meg/u042.bmp 216.784 0 238462 Unfamiliar 14 meg/u042.bmp 219.992 0 241991 Unfamiliar 13 meg/u068.bmp 223.132 0 245445 Famous 7 meg/f104.bmp 226.255 0 248881 Scrambled 17 meg/s109.bmp 229.413 0 252354 Scrambled 18 meg/s109.bmp 232.671 0 255938 Scrambled 19 meg/s068.bmp 235.778 0 259356 Unfamiliar 13 meg/u027.bmp 238.835 0 262718 Unfamiliar 13 meg/u116.bmp 242.042 0 266246 Famous 7 meg/f066.bmp 245.149 0 269664 Scrambled 17 meg/s122.bmp 248.34 0 273174 Scrambled 18 meg/s122.bmp 251.414 0 276555 Famous 5 meg/f054.bmp 254.671 0 280138 Unfamiliar 15 meg/u068.bmp 257.845 0 283630 Unfamiliar 13 meg/u003.bmp 260.835 0 286919 Unfamiliar 13 meg/u138.bmp 263.976 0 290374 Unfamiliar 15 meg/u027.bmp 267.184 0 293902 Famous 5 meg/f100.bmp 270.425 0 297467 Famous 6 meg/f100.bmp 273.732 0 301105 Unfamiliar 15 meg/u116.bmp 276.989 0 304688 Famous 5 meg/f023.bmp 280.214 0 308235 Scrambled 17 meg/s119.bmp 283.304 0 311634 Scrambled 18 meg/s119.bmp 286.295 0 314924 Famous 7 meg/f054.bmp 289.418 0 318360 Scrambled 17 meg/s036.bmp 292.492 0 321741 Scrambled 18 meg/s036.bmp 295.583 0 325141 Unfamiliar 15 meg/u003.bmp 298.74 0 328614 Unfamiliar 13 meg/u012.bmp 302.014 0 332215 Unfamiliar 14 meg/u012.bmp 305.205 0 335725 Unfamiliar 15 meg/u138.bmp 308.328 0 339161 Scrambled 17 meg/s027.bmp 311.503 0 342653 Scrambled 17 meg/s092.bmp 314.643 0 346107 Scrambled 18 meg/s092.bmp 317.834 0 349617 Famous 7 meg/f023.bmp 321.108 0 353219 Unfamiliar 13 meg/u013.bmp 324.399 0 356839 Unfamiliar 14 meg/u013.bmp 327.589 0 360348 Unfamiliar 13 meg/u129.bmp 330.764 0 363840 Unfamiliar 14 meg/u129.bmp 333.937 0 367331 Scrambled 17 meg/s046.bmp 337.028 0 370731 Unfamiliar 13 meg/u137.bmp 340.335 0 374369 Scrambled 19 meg/s027.bmp 343.676 0 378044 Famous 5 meg/f045.bmp 347.001 0 381701 Famous 6 meg/f045.bmp 350.308 0 385339 Famous 5 meg/f077.bmp 353.365 0 388702 Famous 6 meg/f077.bmp 356.656 0 392322 Unfamiliar 13 meg/u141.bmp 359.68 0 395648 Famous 5 meg/f032.bmp 362.737 0 399011 Famous 6 meg/f032.bmp 365.877 0 402465 Scrambled 19 meg/s046.bmp 368.985 0 405883 Unfamiliar 13 meg/u093.bmp 372.209 0 409430 Unfamiliar 15 meg/u137.bmp 375.55 0 413105 Scrambled 17 meg/s039.bmp 378.891 0 416780 Famous 5 meg/f112.bmp 382.132 0 420345 Scrambled 17 meg/s073.bmp 385.339 0 423873 Scrambled 18 meg/s073.bmp 388.546 0 427401 Unfamiliar 15 meg/u141.bmp 391.67 0 430837 Famous 5 meg/f065.bmp 394.795 0 434274 Famous 6 meg/f065.bmp 397.968 0 437765 Scrambled 17 meg/s025.bmp 401.259 0 441385 Unfamiliar 15 meg/u093.bmp 404.433 0 444876 Unfamiliar 13 meg/u083.bmp 407.607 0 448368 Unfamiliar 14 meg/u083.bmp 410.614 0 451675 Scrambled 19 meg/s039.bmp 413.721 0 455093 Unfamiliar 13 meg/u010.bmp 416.779 0 458457 Famous 7 meg/f112.bmp 420.103 0 462113 Scrambled 17 meg/s028.bmp 423.41 0 465751 Unfamiliar 13 meg/u118.bmp 426.635 0 469298 Scrambled 17 meg/s116.bmp 429.892 0 472881 Scrambled 18 meg/s116.bmp 433.065 0 476372 Scrambled 19 meg/s025.bmp 436.306 0 479937 Unfamiliar 13 meg/u081.bmp 439.431 0 483374 Unfamiliar 14 meg/u081.bmp 442.537 0 486791 Unfamiliar 13 meg/u008.bmp 445.862 0 490448 Unfamiliar 15 meg/u010.bmp 449.136 0 494050 Famous 5 meg/f057.bmp 452.226 0 497449 Famous 6 meg/f057.bmp 455.485 0 501033 Scrambled 19 meg/s028.bmp 458.675 0 504542 Scrambled 17 meg/s020.bmp 461.899 0 508089 Unfamiliar 15 meg/u118.bmp 464.99 0 511489 Famous 5 meg/f034.bmp 468.214 0 515035 Scrambled 17 meg/s149.bmp 471.371 0 518508 Scrambled 18 meg/s149.bmp 474.628 0 522091 Scrambled 17 meg/s040.bmp 477.869 0 525656 Scrambled 18 meg/s040.bmp 481.194 0 529313 Unfamiliar 15 meg/u008.bmp 484.317 0 532749 Unfamiliar 13 meg/u125.bmp 487.491 0 536240 Scrambled 17 meg/s138.bmp 490.598 0 539658 Scrambled 18 meg/s138.bmp 493.655 0 543021 Scrambled 19 meg/s020.bmp ================================================ FILE: tests/data/ds000117/sub-01/ses-meg/meg/sub-01_ses-meg_task-facerecognition_run-02_proc-sss_meg.fif ================================================ ================================================ FILE: tests/data/ds000117/sub-01/ses-meg/meg/sub-01_ses-meg_task-facerecognition_run-03_proc-sss_events.tsv ================================================ onset duration onset_sample stim_type trigger stim_file 24.5964 0 27056 Unfamiliar 13 meg/u097.bmp 27.62 0 30382 Unfamiliar 14 meg/u097.bmp 30.8773 0 33965 Famous 5 meg/f103.bmp 34.0518 0 37457 Famous 6 meg/f103.bmp 37.3091 0 41040 Unfamiliar 13 meg/u112.bmp 40.3664 0 44403 Famous 5 meg/f130.bmp 43.5564 0 47912 Famous 5 meg/f004.bmp 46.8645 0 51551 Famous 6 meg/f004.bmp 49.9545 0 54950 Unfamiliar 13 meg/u111.bmp 53.2627 0 58589 Unfamiliar 14 meg/u111.bmp 56.52 0 62172 Famous 5 meg/f025.bmp 59.8273 0 65810 Scrambled 17 meg/s111.bmp 63.0018 0 69302 Scrambled 18 meg/s111.bmp 66.2091 0 72830 Unfamiliar 15 meg/u112.bmp 69.2827 0 76211 Scrambled 17 meg/s112.bmp 72.54 0 79794 Famous 7 meg/f130.bmp 75.7473 0 83322 Scrambled 17 meg/s115.bmp 78.9382 0 86832 Unfamiliar 13 meg/u123.bmp 82.0291 0 90232 Scrambled 17 meg/s005.bmp 85.1691 0 93686 Scrambled 18 meg/s005.bmp 88.2927 0 97122 Famous 7 meg/f025.bmp 91.5009 0 100651 Unfamiliar 13 meg/u045.bmp 94.6245 0 104087 Unfamiliar 14 meg/u045.bmp 97.8155 0 107597 Famous 5 meg/f059.bmp 100.939 0 111033 Famous 6 meg/f059.bmp 104.23 0 114653 Scrambled 19 meg/s112.bmp 107.471 0 118218 Famous 5 meg/f041.bmp 110.595 0 121654 Scrambled 19 meg/s115.bmp 113.735 0 125109 Scrambled 17 meg/s139.bmp 117.093 0 128802 Scrambled 18 meg/s139.bmp 120.267 0 132294 Unfamiliar 15 meg/u123.bmp 123.374 0 135711 Scrambled 17 meg/s076.bmp 126.531 0 139184 Famous 5 meg/f081.bmp 129.688 0 142657 Famous 6 meg/f081.bmp 132.729 0 146002 Famous 5 meg/f141.bmp 136.003 0 149603 Scrambled 17 meg/s137.bmp 139.11 0 153021 Famous 7 meg/f041.bmp 142.451 0 156696 Unfamiliar 13 meg/u120.bmp 145.742 0 160316 Unfamiliar 13 meg/u002.bmp 149.033 0 163936 Scrambled 19 meg/s076.bmp 152.124 0 167336 Famous 5 meg/f121.bmp 155.398 0 170938 Famous 6 meg/f121.bmp 158.739 0 174613 Unfamiliar 13 meg/u018.bmp 161.964 0 178160 Famous 7 meg/f141.bmp 165.17 0 181687 Unfamiliar 13 meg/u014.bmp 168.445 0 185290 Unfamiliar 14 meg/u014.bmp 171.519 0 188671 Scrambled 19 meg/s137.bmp 174.725 0 192198 Unfamiliar 13 meg/u023.bmp 177.799 0 195579 Unfamiliar 15 meg/u120.bmp 181.074 0 199181 Unfamiliar 13 meg/u041.bmp 184.181 0 202599 Unfamiliar 15 meg/u002.bmp 187.455 0 206201 Scrambled 17 meg/s125.bmp 190.663 0 209729 Scrambled 18 meg/s125.bmp 193.92 0 213312 Famous 5 meg/f146.bmp 197.01 0 216711 Unfamiliar 15 meg/u018.bmp 200.301 0 220331 Famous 5 meg/f058.bmp 203.458 0 223804 Famous 6 meg/f058.bmp 206.816 0 227498 Unfamiliar 13 meg/u146.bmp 209.957 0 230953 Unfamiliar 15 meg/u023.bmp 213.165 0 234481 Scrambled 17 meg/s143.bmp 216.372 0 238009 Scrambled 18 meg/s143.bmp 219.479 0 241427 Unfamiliar 15 meg/u041.bmp 222.786 0 245065 Famous 5 meg/f067.bmp 226.027 0 248630 Famous 5 meg/f073.bmp 229.235 0 252158 Famous 6 meg/f073.bmp 232.492 0 255741 Famous 7 meg/f146.bmp 235.633 0 259196 Scrambled 17 meg/s057.bmp 238.673 0 262540 Scrambled 17 meg/s077.bmp 241.914 0 266105 Unfamiliar 15 meg/u146.bmp 245.272 0 269799 Famous 5 meg/f055.bmp 248.463 0 273309 Famous 6 meg/f055.bmp 251.62 0 276782 Famous 5 meg/f068.bmp 254.694 0 280163 Famous 7 meg/f067.bmp 257.734 0 283507 Scrambled 17 meg/s009.bmp 261.075 0 287182 Scrambled 18 meg/s009.bmp 264.315 0 290747 Scrambled 17 meg/s031.bmp 267.439 0 294183 Scrambled 19 meg/s057.bmp 270.513 0 297564 Scrambled 17 meg/s021.bmp 273.754 0 301129 Scrambled 19 meg/s077.bmp 276.995 0 304694 Unfamiliar 13 meg/u063.bmp 280.202 0 308222 Unfamiliar 13 meg/u149.bmp 283.326 0 311659 Famous 7 meg/f068.bmp 286.566 0 315223 Famous 5 meg/f015.bmp 289.841 0 318825 Famous 5 meg/f031.bmp 292.948 0 322243 Famous 6 meg/f031.bmp 296.105 0 325716 Scrambled 19 meg/s031.bmp 299.346 0 329281 Unfamiliar 13 meg/u099.bmp 302.554 0 332809 Unfamiliar 14 meg/u099.bmp 305.677 0 336245 Scrambled 19 meg/s021.bmp 308.801 0 339681 Famous 5 meg/f024.bmp 311.908 0 343099 Famous 6 meg/f024.bmp 315.015 0 346517 Unfamiliar 15 meg/u063.bmp 318.106 0 349917 Unfamiliar 13 meg/u056.bmp 321.33 0 353463 Unfamiliar 14 meg/u056.bmp 324.521 0 356973 Unfamiliar 15 meg/u149.bmp 327.762 0 360538 Scrambled 17 meg/s066.bmp 330.985 0 364084 Scrambled 18 meg/s066.bmp 334.126 0 367539 Famous 7 meg/f015.bmp 337.166 0 370883 Famous 5 meg/f053.bmp 340.274 0 374301 Famous 6 meg/f053.bmp 343.615 0 377976 Scrambled 17 meg/s045.bmp 346.855 0 381541 Unfamiliar 13 meg/u057.bmp 349.879 0 384867 Unfamiliar 14 meg/u057.bmp 353.004 0 388304 Famous 5 meg/f082.bmp 356.144 0 391758 Scrambled 17 meg/s124.bmp 359.318 0 395250 Unfamiliar 13 meg/u069.bmp 362.492 0 398741 Scrambled 17 meg/s148.bmp 365.833 0 402416 Scrambled 18 meg/s148.bmp 368.956 0 405852 Scrambled 19 meg/s045.bmp 372.014 0 409215 Scrambled 17 meg/s108.bmp 375.087 0 412596 Famous 5 meg/f147.bmp 378.245 0 416069 Famous 7 meg/f082.bmp 381.485 0 419634 Unfamiliar 13 meg/u031.bmp 384.626 0 423089 Scrambled 19 meg/s124.bmp 387.784 0 426562 Scrambled 17 meg/s024.bmp 390.857 0 429943 Scrambled 18 meg/s024.bmp 393.947 0 433342 Unfamiliar 15 meg/u069.bmp 397.005 0 436705 Scrambled 17 meg/s018.bmp 400.145 0 440160 Famous 5 meg/f105.bmp 403.469 0 443816 Famous 6 meg/f105.bmp 406.677 0 447345 Scrambled 19 meg/s108.bmp 409.901 0 450891 Scrambled 17 meg/s099.bmp 413.158 0 454474 Famous 7 meg/f147.bmp 416.333 0 457966 Famous 5 meg/f135.bmp 419.473 0 461420 Famous 6 meg/f135.bmp 422.732 0 465005 Unfamiliar 15 meg/u031.bmp 425.888 0 468477 Scrambled 17 meg/s146.bmp 428.995 0 471895 Scrambled 18 meg/s146.bmp 432.153 0 475368 Unfamiliar 13 meg/u127.bmp 435.36 0 478896 Scrambled 19 meg/s018.bmp 438.5 0 482350 Scrambled 17 meg/s113.bmp 441.741 0 485915 Famous 5 meg/f110.bmp 444.982 0 489480 Scrambled 19 meg/s099.bmp 448.055 0 492861 Unfamiliar 13 meg/u098.bmp 451.146 0 496261 Unfamiliar 14 meg/u098.bmp 454.47 0 499917 Famous 5 meg/f027.bmp 457.527 0 503280 Unfamiliar 13 meg/u064.bmp 460.735 0 506808 Unfamiliar 14 meg/u064.bmp 463.975 0 510373 Unfamiliar 15 meg/u127.bmp 467.116 0 513828 Unfamiliar 13 meg/u089.bmp 470.241 0 517265 Unfamiliar 14 meg/u089.bmp 473.431 0 520774 Scrambled 19 meg/s113.bmp 476.705 0 524375 Unfamiliar 13 meg/u136.bmp 479.995 0 527995 Unfamiliar 14 meg/u136.bmp 483.053 0 531358 Famous 7 meg/f110.bmp 486.144 0 534758 Scrambled 17 meg/s011.bmp 489.435 0 538378 Scrambled 18 meg/s011.bmp 492.792 0 542071 Unfamiliar 13 meg/u145.bmp 495.815 0 545397 Unfamiliar 14 meg/u145.bmp 498.873 0 548760 Famous 7 meg/f027.bmp ================================================ FILE: tests/data/ds000117/sub-01/ses-meg/meg/sub-01_ses-meg_task-facerecognition_run-03_proc-sss_meg.fif ================================================ ================================================ FILE: tests/data/ds000117/sub-01/ses-meg/meg/sub-01_ses-meg_task-facerecognition_run-04_proc-sss_events.tsv ================================================ onset duration onset_sample stim_type trigger stim_file 24.3327 0 26766 Unfamiliar 13 meg/u124.bmp 27.3727 0 30110 Famous 5 meg/f125.bmp 30.5464 0 33601 Famous 6 meg/f125.bmp 33.7209 0 37093 Famous 5 meg/f012.bmp 36.8109 0 40492 Famous 6 meg/f012.bmp 40.0191 0 44021 Unfamiliar 13 meg/u044.bmp 43.2427 0 47567 Unfamiliar 14 meg/u044.bmp 46.3664 0 51003 Scrambled 17 meg/s006.bmp 49.6245 0 54587 Scrambled 17 meg/s029.bmp 52.8145 0 58096 Unfamiliar 15 meg/u124.bmp 55.9055 0 61496 Unfamiliar 13 meg/u106.bmp 58.9118 0 64803 Unfamiliar 14 meg/u106.bmp 62.17 0 68387 Unfamiliar 13 meg/u061.bmp 65.2773 0 71805 Famous 5 meg/f071.bmp 68.5009 0 75351 Famous 5 meg/f037.bmp 71.6745 0 78842 Famous 6 meg/f037.bmp 74.8491 0 82334 Scrambled 19 meg/s006.bmp 78.0564 0 85862 Famous 5 meg/f018.bmp 81.1636 0 89280 Scrambled 19 meg/s029.bmp 84.3709 0 92808 Famous 5 meg/f122.bmp 87.6118 0 96373 Scrambled 17 meg/s080.bmp 90.9027 0 99993 Unfamiliar 15 meg/u061.bmp 94.0427 0 103447 Famous 5 meg/f047.bmp 97.3173 0 107049 Famous 6 meg/f047.bmp 100.608 0 110669 Famous 7 meg/f071.bmp 103.883 0 114271 Unfamiliar 13 meg/u133.bmp 107.023 0 117725 Scrambled 17 meg/s004.bmp 110.214 0 121235 Famous 7 meg/f018.bmp 113.388 0 124727 Famous 5 meg/f007.bmp 116.628 0 128291 Famous 6 meg/f007.bmp 119.803 0 131783 Famous 7 meg/f122.bmp 122.96 0 135256 Famous 5 meg/f101.bmp 126.151 0 138766 Scrambled 19 meg/s080.bmp 129.408 0 142349 Unfamiliar 13 meg/u038.bmp 132.715 0 145987 Famous 5 meg/f108.bmp 135.94 0 149534 Famous 6 meg/f108.bmp 139.114 0 153025 Unfamiliar 15 meg/u133.bmp 142.321 0 156553 Scrambled 17 meg/s094.bmp 145.512 0 160063 Scrambled 18 meg/s094.bmp 148.635 0 163499 Scrambled 19 meg/s004.bmp 151.793 0 166972 Unfamiliar 13 meg/u121.bmp 155.067 0 170574 Unfamiliar 14 meg/u121.bmp 158.308 0 174139 Scrambled 17 meg/s095.bmp 161.432 0 177575 Famous 7 meg/f101.bmp 164.655 0 181121 Scrambled 17 meg/s061.bmp 167.813 0 184594 Scrambled 18 meg/s061.bmp 170.971 0 188068 Unfamiliar 15 meg/u038.bmp 174.045 0 191449 Unfamiliar 13 meg/u067.bmp 177.235 0 194958 Unfamiliar 14 meg/u067.bmp 180.459 0 198505 Scrambled 17 meg/s130.bmp 183.65 0 202015 Unfamiliar 13 meg/u035.bmp 186.84 0 205524 Famous 5 meg/f070.bmp 190.015 0 209016 Scrambled 19 meg/s095.bmp 193.088 0 212397 Scrambled 17 meg/s064.bmp 196.379 0 216017 Scrambled 17 meg/s016.bmp 199.503 0 219453 Scrambled 18 meg/s016.bmp 202.861 0 223147 Scrambled 17 meg/s147.bmp 206.018 0 226620 Scrambled 18 meg/s147.bmp 209.208 0 230129 Scrambled 19 meg/s130.bmp 212.449 0 233694 Scrambled 17 meg/s047.bmp 215.707 0 237278 Scrambled 18 meg/s047.bmp 218.947 0 240842 Unfamiliar 15 meg/u035.bmp 222.172 0 244389 Unfamiliar 13 meg/u115.bmp 225.313 0 247844 Unfamiliar 14 meg/u115.bmp 228.47 0 251317 Famous 7 meg/f070.bmp 231.526 0 254679 Unfamiliar 13 meg/u049.bmp 234.734 0 258207 Scrambled 19 meg/s064.bmp 237.791 0 261570 Scrambled 17 meg/s067.bmp 240.998 0 265098 Famous 5 meg/f056.bmp 244.223 0 268645 Famous 6 meg/f056.bmp 247.23 0 271953 Scrambled 17 meg/s002.bmp 250.487 0 275536 Unfamiliar 13 meg/u092.bmp 253.661 0 279027 Unfamiliar 13 meg/u091.bmp 256.918 0 282610 Unfamiliar 15 meg/u049.bmp 260.043 0 286047 Famous 5 meg/f052.bmp 263.316 0 289648 Famous 6 meg/f052.bmp 266.374 0 293011 Scrambled 19 meg/s067.bmp 269.598 0 296558 Unfamiliar 13 meg/u029.bmp 272.805 0 300086 Famous 5 meg/f114.bmp 275.829 0 303412 Famous 6 meg/f114.bmp 279.003 0 306903 Scrambled 19 meg/s002.bmp 282.176 0 310394 Scrambled 17 meg/s120.bmp 285.351 0 313886 Unfamiliar 15 meg/u092.bmp 288.592 0 317451 Scrambled 17 meg/s049.bmp 291.749 0 320924 Scrambled 18 meg/s049.bmp 294.839 0 324323 Unfamiliar 15 meg/u091.bmp 298.046 0 327851 Scrambled 17 meg/s128.bmp 301.254 0 331379 Scrambled 18 meg/s128.bmp 304.445 0 334889 Scrambled 17 meg/s106.bmp 307.552 0 338307 Scrambled 18 meg/s106.bmp 310.643 0 341707 Unfamiliar 15 meg/u029.bmp 313.733 0 345106 Scrambled 17 meg/s003.bmp 316.957 0 348653 Scrambled 18 meg/s003.bmp 320.197 0 352217 Famous 5 meg/f075.bmp 323.455 0 355801 Scrambled 19 meg/s120.bmp 326.663 0 359329 Unfamiliar 13 meg/u113.bmp 329.92 0 362912 Famous 5 meg/f091.bmp 333.111 0 366422 Famous 6 meg/f091.bmp 336.252 0 369877 Unfamiliar 13 meg/u024.bmp 339.525 0 373478 Famous 5 meg/f076.bmp 342.666 0 376933 Unfamiliar 13 meg/u016.bmp 345.84 0 380424 Unfamiliar 14 meg/u016.bmp 348.864 0 383750 Famous 7 meg/f075.bmp 352.138 0 387352 Scrambled 17 meg/s121.bmp 355.429 0 390972 Unfamiliar 15 meg/u113.bmp 358.503 0 394353 Famous 5 meg/f115.bmp 361.794 0 397973 Scrambled 17 meg/s070.bmp 364.951 0 401446 Scrambled 18 meg/s070.bmp 368.108 0 404919 Unfamiliar 15 meg/u024.bmp 371.132 0 408245 Unfamiliar 13 meg/u028.bmp 374.355 0 411791 Famous 7 meg/f076.bmp 377.714 0 415485 Famous 5 meg/f011.bmp 380.855 0 418940 Scrambled 17 meg/s017.bmp 384.145 0 422560 Scrambled 18 meg/s017.bmp 387.319 0 426051 Scrambled 19 meg/s121.bmp 390.476 0 429524 Unfamiliar 13 meg/u094.bmp 393.551 0 432906 Unfamiliar 14 meg/u094.bmp 396.807 0 436488 Famous 7 meg/f115.bmp 400.098 0 440108 Scrambled 17 meg/s059.bmp 403.205 0 443526 Scrambled 17 meg/s050.bmp 406.548 0 447203 Scrambled 18 meg/s050.bmp 409.537 0 450491 Unfamiliar 15 meg/u028.bmp 412.645 0 453909 Unfamiliar 13 meg/u007.bmp 415.902 0 457492 Famous 7 meg/f011.bmp 419.093 0 461002 Unfamiliar 13 meg/u071.bmp 422.267 0 464494 Unfamiliar 13 meg/u090.bmp 425.541 0 468095 Unfamiliar 14 meg/u090.bmp 428.681 0 471549 Famous 5 meg/f069.bmp 431.905 0 475096 Famous 6 meg/f069.bmp 435.095 0 478605 Scrambled 19 meg/s059.bmp 438.236 0 482060 Famous 5 meg/f078.bmp 441.427 0 485570 Famous 6 meg/f078.bmp 444.635 0 489098 Famous 5 meg/f090.bmp 447.892 0 492681 Famous 6 meg/f090.bmp 450.983 0 496081 Unfamiliar 15 meg/u007.bmp 454.106 0 499517 Unfamiliar 13 meg/u110.bmp 457.464 0 503210 Unfamiliar 14 meg/u110.bmp 460.688 0 506757 Unfamiliar 15 meg/u071.bmp 463.913 0 510304 Unfamiliar 13 meg/u104.bmp 467.12 0 513832 Unfamiliar 13 meg/u043.bmp 470.31 0 517341 Unfamiliar 14 meg/u043.bmp 473.401 0 520741 Famous 5 meg/f116.bmp 476.575 0 524233 Famous 5 meg/f142.bmp 479.815 0 527797 Famous 6 meg/f142.bmp 482.924 0 531216 Scrambled 17 meg/s117.bmp 486.197 0 534817 Famous 5 meg/f084.bmp 489.472 0 538419 Unfamiliar 15 meg/u104.bmp ================================================ FILE: tests/data/ds000117/sub-01/ses-meg/meg/sub-01_ses-meg_task-facerecognition_run-04_proc-sss_meg.fif ================================================ ================================================ FILE: tests/data/ds000117/sub-01/ses-meg/meg/sub-01_ses-meg_task-facerecognition_run-05_proc-sss_events.tsv ================================================ onset duration onset_sample stim_type trigger stim_file 25.3418 0 27876 Unfamiliar 13 meg/u082.bmp 28.6164 0 31478 Famous 5 meg/f088.bmp 31.6727 0 34840 Famous 5 meg/f022.bmp 34.8973 0 38387 Unfamiliar 13 meg/u011.bmp 38.0709 0 41878 Unfamiliar 13 meg/u074.bmp 41.1118 0 45223 Unfamiliar 14 meg/u074.bmp 44.1182 0 48530 Unfamiliar 13 meg/u046.bmp 47.2755 0 52003 Unfamiliar 14 meg/u046.bmp 50.3827 0 55421 Unfamiliar 15 meg/u082.bmp 53.44 0 58784 Famous 5 meg/f120.bmp 56.6809 0 62349 Famous 6 meg/f120.bmp 59.9545 0 65950 Famous 7 meg/f088.bmp 63.0955 0 69405 Scrambled 17 meg/s075.bmp 66.2527 0 72878 Scrambled 18 meg/s075.bmp 69.3264 0 76259 Famous 7 meg/f022.bmp 72.5173 0 79769 Scrambled 17 meg/s141.bmp 75.7409 0 83315 Unfamiliar 15 meg/u011.bmp 78.9318 0 86825 Famous 5 meg/f107.bmp 82.0891 0 90298 Unfamiliar 13 meg/u060.bmp 85.3464 0 93881 Unfamiliar 14 meg/u060.bmp 88.5373 0 97391 Scrambled 17 meg/s007.bmp 91.5609 0 100717 Unfamiliar 13 meg/u080.bmp 94.7518 0 104227 Famous 5 meg/f144.bmp 98.0764 0 107884 Scrambled 19 meg/s141.bmp 101.25 0 111375 Scrambled 17 meg/s091.bmp 104.441 0 114885 Famous 7 meg/f107.bmp 107.431 0 118174 Scrambled 17 meg/s133.bmp 110.572 0 121629 Scrambled 18 meg/s133.bmp 113.762 0 125138 Unfamiliar 13 meg/u148.bmp 116.886 0 128575 Unfamiliar 14 meg/u148.bmp 119.976 0 131974 Scrambled 19 meg/s007.bmp 123.067 0 135374 Famous 5 meg/f079.bmp 126.241 0 138865 Famous 6 meg/f079.bmp 129.432 0 142375 Unfamiliar 15 meg/u080.bmp 132.689 0 145958 Unfamiliar 13 meg/u107.bmp 135.88 0 149468 Unfamiliar 14 meg/u107.bmp 139.104 0 153014 Famous 7 meg/f144.bmp 142.345 0 156579 Unfamiliar 13 meg/u054.bmp 145.535 0 160089 Unfamiliar 14 meg/u054.bmp 148.76 0 163636 Scrambled 19 meg/s091.bmp 151.784 0 166962 Unfamiliar 13 meg/u087.bmp 154.89 0 170379 Famous 5 meg/f099.bmp 158.081 0 173889 Famous 5 meg/f039.bmp 161.288 0 177417 Scrambled 17 meg/s051.bmp 164.563 0 181019 Scrambled 18 meg/s051.bmp 167.87 0 184657 Unfamiliar 13 meg/u144.bmp 171.095 0 188204 Unfamiliar 14 meg/u144.bmp 174.268 0 191695 Famous 5 meg/f044.bmp 177.343 0 195077 Unfamiliar 15 meg/u087.bmp 180.633 0 198696 Unfamiliar 13 meg/u053.bmp 183.74 0 202114 Unfamiliar 14 meg/u053.bmp 186.947 0 205642 Famous 7 meg/f099.bmp 190.072 0 209079 Famous 5 meg/f113.bmp 193.229 0 212552 Famous 6 meg/f113.bmp 196.503 0 216153 Famous 7 meg/f039.bmp 199.727 0 219700 Famous 5 meg/f001.bmp 202.851 0 223136 Scrambled 17 meg/s063.bmp 205.925 0 226517 Scrambled 18 meg/s063.bmp 209.132 0 230045 Scrambled 17 meg/s033.bmp 212.373 0 233610 Scrambled 18 meg/s033.bmp 215.464 0 237010 Famous 7 meg/f044.bmp 218.621 0 240483 Unfamiliar 13 meg/u128.bmp 221.912 0 244103 Unfamiliar 14 meg/u128.bmp 225.235 0 247759 Famous 5 meg/f133.bmp 228.393 0 251232 Famous 6 meg/f133.bmp 231.484 0 254632 Scrambled 17 meg/s069.bmp 234.825 0 258308 Famous 7 meg/f001.bmp 238.099 0 261909 Unfamiliar 13 meg/u075.bmp 241.256 0 265382 Scrambled 17 meg/s060.bmp 244.364 0 268800 Famous 5 meg/f092.bmp 247.521 0 272273 Famous 6 meg/f092.bmp 250.661 0 275727 Famous 5 meg/f064.bmp 253.768 0 279145 Famous 6 meg/f064.bmp 256.875 0 282563 Unfamiliar 13 meg/u119.bmp 260.083 0 286091 Scrambled 19 meg/s069.bmp 263.274 0 289601 Scrambled 17 meg/s013.bmp 266.414 0 293055 Unfamiliar 15 meg/u075.bmp 269.538 0 296492 Unfamiliar 13 meg/u026.bmp 272.562 0 299818 Scrambled 19 meg/s060.bmp 275.803 0 303383 Scrambled 17 meg/s042.bmp 278.893 0 306782 Famous 5 meg/f003.bmp 282.134 0 310347 Scrambled 17 meg/s053.bmp 285.475 0 314022 Unfamiliar 15 meg/u119.bmp 288.665 0 317532 Scrambled 17 meg/s093.bmp 291.789 0 320968 Scrambled 18 meg/s093.bmp 294.913 0 324404 Scrambled 19 meg/s013.bmp 298.037 0 327841 Unfamiliar 13 meg/u096.bmp 301.295 0 331424 Unfamiliar 14 meg/u096.bmp 304.468 0 334915 Unfamiliar 15 meg/u026.bmp 307.643 0 338407 Famous 5 meg/f061.bmp 310.783 0 341861 Famous 6 meg/f061.bmp 313.957 0 345353 Scrambled 19 meg/s042.bmp 317.215 0 348936 Unfamiliar 13 meg/u108.bmp 320.339 0 352373 Famous 7 meg/f003.bmp 323.495 0 355845 Scrambled 17 meg/s082.bmp 326.703 0 359373 Scrambled 19 meg/s053.bmp 329.927 0 362920 Unfamiliar 13 meg/u126.bmp 333.067 0 366374 Unfamiliar 14 meg/u126.bmp 336.058 0 369664 Scrambled 17 meg/s035.bmp 339.098 0 373008 Scrambled 17 meg/s145.bmp 342.155 0 376371 Scrambled 17 meg/s032.bmp 345.363 0 379899 Scrambled 18 meg/s032.bmp 348.454 0 383299 Unfamiliar 15 meg/u108.bmp 351.61 0 386771 Unfamiliar 13 meg/u143.bmp 354.767 0 390244 Unfamiliar 14 meg/u143.bmp 357.842 0 393626 Scrambled 19 meg/s082.bmp 360.999 0 397099 Unfamiliar 13 meg/u039.bmp 364.24 0 400664 Unfamiliar 14 meg/u039.bmp 367.447 0 404192 Famous 5 meg/f043.bmp 370.705 0 407775 Scrambled 19 meg/s035.bmp 373.995 0 411395 Famous 5 meg/f138.bmp 377.153 0 414868 Scrambled 19 meg/s145.bmp 380.243 0 418267 Scrambled 17 meg/s085.bmp 383.367 0 421704 Famous 5 meg/f118.bmp 386.507 0 425158 Famous 6 meg/f118.bmp 389.865 0 428852 Famous 5 meg/f087.bmp 393.039 0 432343 Famous 6 meg/f087.bmp 396.364 0 436000 Famous 5 meg/f042.bmp 399.621 0 439583 Famous 6 meg/f042.bmp 402.695 0 442964 Famous 7 meg/f043.bmp 405.885 0 446474 Scrambled 17 meg/s012.bmp 409.009 0 449910 Famous 7 meg/f138.bmp 412.134 0 453347 Famous 5 meg/f136.bmp 415.257 0 456783 Famous 6 meg/f136.bmp 418.465 0 460311 Scrambled 19 meg/s085.bmp 421.555 0 463710 Scrambled 17 meg/s044.bmp 424.88 0 467368 Unfamiliar 13 meg/u109.bmp 427.886 0 470675 Unfamiliar 13 meg/u050.bmp 431.161 0 474277 Scrambled 17 meg/s134.bmp 434.318 0 477750 Scrambled 18 meg/s134.bmp 437.475 0 481223 Scrambled 19 meg/s012.bmp 440.715 0 484787 Unfamiliar 13 meg/u130.bmp 443.84 0 488224 Unfamiliar 13 meg/u079.bmp 446.98 0 491678 Scrambled 19 meg/s044.bmp 450.105 0 495115 Scrambled 17 meg/s089.bmp 453.345 0 498679 Scrambled 18 meg/s089.bmp 456.502 0 502152 Unfamiliar 15 meg/u109.bmp 459.76 0 505736 Scrambled 17 meg/s030.bmp 462.967 0 509264 Unfamiliar 15 meg/u050.bmp 466.107 0 512718 Scrambled 17 meg/s100.bmp 469.382 0 516320 Scrambled 18 meg/s100.bmp 472.589 0 519848 Famous 5 meg/f109.bmp 475.88 0 523468 Famous 6 meg/f109.bmp 479.021 0 526923 Unfamiliar 15 meg/u130.bmp 482.328 0 530561 Famous 5 meg/f095.bmp 485.553 0 534108 Unfamiliar 15 meg/u079.bmp 488.81 0 537691 Famous 5 meg/f017.bmp 492.017 0 541219 Famous 6 meg/f017.bmp ================================================ FILE: tests/data/ds000117/sub-01/ses-meg/meg/sub-01_ses-meg_task-facerecognition_run-05_proc-sss_meg.fif ================================================ ================================================ FILE: tests/data/ds000117/sub-01/ses-meg/meg/sub-01_ses-meg_task-facerecognition_run-06_proc-sss_events.tsv ================================================ onset duration onset_sample stim_type trigger stim_file 23.9236 0 26316 Famous 5 meg/f127.bmp 26.9136 0 29605 Famous 6 meg/f127.bmp 30.1882 0 33207 Unfamiliar 13 meg/u015.bmp 33.4955 0 36845 Unfamiliar 13 meg/u072.bmp 36.5691 0 40226 Unfamiliar 13 meg/u052.bmp 39.86 0 43846 Famous 5 meg/f106.bmp 42.9336 0 47227 Famous 6 meg/f106.bmp 46.0909 0 50700 Unfamiliar 13 meg/u036.bmp 49.2818 0 54210 Unfamiliar 13 meg/u142.bmp 52.5227 0 57775 Unfamiliar 14 meg/u142.bmp 55.78 0 61358 Unfamiliar 15 meg/u015.bmp 59.0373 0 64941 Scrambled 17 meg/s129.bmp 62.2118 0 68433 Scrambled 18 meg/s129.bmp 65.3018 0 71832 Unfamiliar 15 meg/u072.bmp 68.5427 0 75397 Scrambled 17 meg/s131.bmp 71.8173 0 78999 Unfamiliar 15 meg/u052.bmp 74.9573 0 82453 Scrambled 17 meg/s101.bmp 78.1818 0 86000 Unfamiliar 13 meg/u040.bmp 81.2555 0 89381 Unfamiliar 14 meg/u040.bmp 84.53 0 92983 Unfamiliar 15 meg/u036.bmp 87.7036 0 96474 Famous 5 meg/f080.bmp 90.7273 0 99800 Famous 5 meg/f134.bmp 93.8509 0 103236 Famous 5 meg/f046.bmp 97.0255 0 106728 Famous 6 meg/f046.bmp 100.165 0 110182 Scrambled 19 meg/s131.bmp 103.306 0 113637 Unfamiliar 13 meg/u059.bmp 106.415 0 117056 Scrambled 19 meg/s101.bmp 109.554 0 120509 Unfamiliar 13 meg/u055.bmp 112.828 0 124111 Unfamiliar 13 meg/u006.bmp 116.135 0 127749 Famous 7 meg/f080.bmp 119.276 0 131204 Unfamiliar 13 meg/u076.bmp 122.567 0 134824 Famous 7 meg/f134.bmp 125.591 0 138150 Unfamiliar 13 meg/u062.bmp 128.765 0 141641 Unfamiliar 14 meg/u062.bmp 132.039 0 145243 Unfamiliar 13 meg/u030.bmp 135.18 0 148698 Unfamiliar 14 meg/u030.bmp 138.287 0 152116 Unfamiliar 15 meg/u059.bmp 141.311 0 155442 Famous 5 meg/f014.bmp 144.535 0 158988 Famous 6 meg/f014.bmp 147.842 0 162626 Unfamiliar 15 meg/u055.bmp 151.066 0 166173 Scrambled 17 meg/s038.bmp 154.24 0 169664 Scrambled 18 meg/s038.bmp 157.431 0 173174 Unfamiliar 15 meg/u006.bmp 160.588 0 176647 Unfamiliar 13 meg/u025.bmp 163.946 0 180341 Unfamiliar 14 meg/u025.bmp 167.17 0 183887 Unfamiliar 15 meg/u076.bmp 170.411 0 187452 Scrambled 17 meg/s135.bmp 173.652 0 191017 Scrambled 17 meg/s107.bmp 176.742 0 194416 Scrambled 18 meg/s107.bmp 179.833 0 197816 Scrambled 17 meg/s022.bmp 183.124 0 201436 Scrambled 18 meg/s022.bmp 186.297 0 204927 Scrambled 17 meg/s041.bmp 189.455 0 208400 Scrambled 18 meg/s041.bmp 192.679 0 211947 Scrambled 17 meg/s104.bmp 195.719 0 215291 Unfamiliar 13 meg/u033.bmp 198.976 0 218874 Scrambled 19 meg/s135.bmp 202.035 0 222238 Famous 5 meg/f086.bmp 205.107 0 225618 Famous 6 meg/f086.bmp 208.099 0 228909 Unfamiliar 13 meg/u065.bmp 211.188 0 232307 Scrambled 17 meg/s072.bmp 214.345 0 235780 Famous 5 meg/f035.bmp 217.57 0 239327 Famous 6 meg/f035.bmp 220.694 0 242763 Scrambled 19 meg/s104.bmp 223.867 0 246254 Scrambled 17 meg/s014.bmp 227.042 0 249746 Unfamiliar 15 meg/u033.bmp 230.098 0 253108 Famous 5 meg/f126.bmp 233.239 0 256563 Famous 6 meg/f126.bmp 236.53 0 260183 Scrambled 17 meg/s126.bmp 239.57 0 263527 Scrambled 18 meg/s126.bmp 242.778 0 267056 Unfamiliar 15 meg/u065.bmp 245.968 0 270565 Scrambled 17 meg/s062.bmp 249.009 0 273910 Scrambled 18 meg/s062.bmp 252.199 0 277419 Scrambled 19 meg/s072.bmp 255.491 0 281040 Famous 5 meg/f051.bmp 258.698 0 284568 Famous 6 meg/f051.bmp 261.922 0 288114 Scrambled 17 meg/s127.bmp 265.146 0 291661 Scrambled 19 meg/s014.bmp 268.304 0 295134 Scrambled 17 meg/s058.bmp 271.545 0 298699 Famous 5 meg/f149.bmp 274.585 0 302043 Famous 6 meg/f149.bmp 277.792 0 305571 Famous 5 meg/f030.bmp 280.949 0 309044 Famous 6 meg/f030.bmp 284.123 0 312535 Famous 5 meg/f102.bmp 287.481 0 316229 Famous 6 meg/f102.bmp 290.621 0 319683 Unfamiliar 13 meg/u135.bmp 293.778 0 323156 Scrambled 19 meg/s127.bmp 296.953 0 326648 Scrambled 17 meg/s019.bmp 300.194 0 330213 Scrambled 18 meg/s019.bmp 303.334 0 333667 Scrambled 19 meg/s058.bmp 306.575 0 337232 Scrambled 17 meg/s105.bmp 309.749 0 340724 Scrambled 17 meg/s079.bmp 313.04 0 344344 Famous 5 meg/f117.bmp 316.198 0 347818 Unfamiliar 13 meg/u150.bmp 319.438 0 351382 Unfamiliar 15 meg/u135.bmp 322.545 0 354799 Scrambled 17 meg/s132.bmp 325.535 0 358089 Famous 5 meg/f008.bmp 328.693 0 361562 Famous 6 meg/f008.bmp 331.866 0 365053 Scrambled 19 meg/s105.bmp 335.091 0 368600 Scrambled 17 meg/s048.bmp 338.348 0 372183 Scrambled 18 meg/s048.bmp 341.405 0 375546 Scrambled 19 meg/s079.bmp 344.545 0 379000 Famous 5 meg/f019.bmp 347.72 0 382492 Famous 7 meg/f117.bmp 350.826 0 385909 Famous 5 meg/f005.bmp 354.035 0 389438 Famous 6 meg/f005.bmp 357.225 0 392947 Unfamiliar 15 meg/u150.bmp 360.248 0 396273 Famous 5 meg/f050.bmp 363.455 0 399801 Scrambled 19 meg/s132.bmp 366.814 0 403495 Unfamiliar 13 meg/u147.bmp 370.055 0 407060 Famous 5 meg/f085.bmp 373.212 0 410533 Unfamiliar 13 meg/u037.bmp 376.235 0 413859 Unfamiliar 14 meg/u037.bmp 379.293 0 417222 Famous 7 meg/f019.bmp 382.467 0 420714 Scrambled 17 meg/s118.bmp 385.657 0 424223 Scrambled 17 meg/s103.bmp 388.831 0 427714 Famous 7 meg/f050.bmp 392.022 0 431224 Unfamiliar 13 meg/u103.bmp 395.213 0 434734 Unfamiliar 15 meg/u147.bmp 398.336 0 438170 Famous 5 meg/f083.bmp 401.444 0 441588 Famous 7 meg/f085.bmp 404.584 0 445042 Unfamiliar 13 meg/u085.bmp 407.842 0 448626 Unfamiliar 14 meg/u085.bmp 411.065 0 452172 Famous 5 meg/f072.bmp 414.306 0 455737 Famous 6 meg/f072.bmp 417.647 0 459412 Scrambled 19 meg/s118.bmp 420.738 0 462812 Scrambled 17 meg/s097.bmp 423.928 0 466321 Scrambled 19 meg/s103.bmp 427.169 0 469886 Famous 5 meg/f029.bmp 430.294 0 473323 Famous 6 meg/f029.bmp 433.567 0 476924 Unfamiliar 15 meg/u103.bmp 436.691 0 480360 Unfamiliar 13 meg/u001.bmp 439.848 0 483833 Unfamiliar 14 meg/u001.bmp 443.124 0 487436 Famous 7 meg/f083.bmp 446.196 0 490816 Scrambled 17 meg/s078.bmp 449.287 0 494216 Unfamiliar 13 meg/u122.bmp 452.427 0 497670 Unfamiliar 14 meg/u122.bmp 455.451 0 500996 Famous 5 meg/f150.bmp 458.558 0 504414 Scrambled 19 meg/s097.bmp 461.765 0 507942 Unfamiliar 13 meg/u101.bmp 465.107 0 511618 Unfamiliar 13 meg/u047.bmp 468.247 0 515072 Unfamiliar 14 meg/u047.bmp 471.438 0 518582 Scrambled 17 meg/s054.bmp 474.579 0 522037 Scrambled 19 meg/s078.bmp 477.786 0 525565 Famous 5 meg/f016.bmp 481.077 0 529185 Famous 6 meg/f016.bmp 484.134 0 532547 Famous 5 meg/f002.bmp 487.241 0 535965 Famous 6 meg/f002.bmp 490.265 0 539291 Famous 7 meg/f150.bmp ================================================ FILE: tests/data/ds000117/sub-01/ses-meg/meg/sub-01_ses-meg_task-facerecognition_run-06_proc-sss_meg.fif ================================================ ================================================ FILE: tests/data/ds000117/sub-01/ses-meg/sub-01_ses-meg_scans.tsv ================================================ filename acq_time meg/sub-01_ses-meg_task-facerecognition_run-01_meg.fif ##-##-##T12:04:14 meg/sub-01_ses-meg_task-facerecognition_run-02_meg.fif ##-##-##T12:17:17 meg/sub-01_ses-meg_task-facerecognition_run-03_meg.fif ##-##-##T12:27:22 meg/sub-01_ses-meg_task-facerecognition_run-04_meg.fif ##-##-##T12:37:33 meg/sub-01_ses-meg_task-facerecognition_run-05_meg.fif ##-##-##T12:47:23 meg/sub-01_ses-meg_task-facerecognition_run-06_meg.fif ##-##-##T12:58:06 ================================================ FILE: tests/data/ds000117/sub-01/ses-mri/anat/sub-01_ses-mri_acq-mprage_T1w.json ================================================ { "AnatomicalMRICoordinateSystem": "NIfTI", "AnatomicalMRICoordinateUnits": "voxels", "AnatomicalLandmarkCoordinates": { "Nasion": [43,111,95], "LPA": [140,74,16], "RPA": [143,74,173] } } ================================================ FILE: tests/data/ds000117/sub-01/ses-mri/dwi/sub-01_ses-mri_dwi.bval ================================================ 0 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 ================================================ FILE: tests/data/ds000117/sub-01/ses-mri/dwi/sub-01_ses-mri_dwi.bvec ================================================ 0 0.99955850839614 0.01954407058656 0.00294903200119 0.57941150665283 -0.22696028649807 -0.88946276903152 0.81388407945632 0.25786191225051 0.94657051563262 0.48336026072502 0.31938299536705 0.42975607514381 -0.4763910472393 -0.59470033645629 -0.59882068634033 -0.83007228374481 0.88395410776138 0.2610293328762 0.09066703915596 -0.80151742696762 0.53270918130874 -0.79953873157501 0.94853943586349 0.26080328226089 -0.04598157480359 0.20106360316276 0.76368796825408 -0.13214653730392 -0.14714884757995 0.89985853433609 -0.57571458816528 -0.35732066631317 -0.33106037974357 -0.31264066696167 -0.96581315994262 -0.95102477073669 0.43446427583694 -0.75675719976425 0.73082029819488 -0.67709231376647 0.67937570810318 -0.13976696133613 -0.72053796052932 -0.07321017235517 0.5839369893074 -0.07184987515211 -0.55975395441055 0.82137787342071 0.36826944351196 -0.19602409005165 -0.73164790868759 0.43177768588066 0.52686661481857 -0.19931995868682 0.4891959130764 0.37798687815666 -0.70318156480789 0.26638954877853 -0.01923633553087 0.02821243368089 0.54735606908798 -0.30064421892166 0.73979389667511 0.28209447860717 0 -0.02049821615219 0.98761302232742 0.52286034822464 -0.80844813585281 -0.65079009532928 -0.30648133158683 0.02310398034751 0.87089902162551 0.07609922438859 -0.81609696149826 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-0.10341253876686 -0.72454214096069 -0.33900585770607 -0.58056783676147 -0.41838020086288 -0.31338959932327 0.31677860021591 0.53773391246795 0.72864228487014 -0.71746206283569 -0.52065771818161 0.79985052347183 -0.14126154780387 0.46695810556411 0.8710965514183 0.39447921514511 0.34739750623703 -0.28730210661888 0.53904712200164 -0.14077690243721 -0.68526721000671 0.99848085641861 -0.02984086796641 -0.06366975605487 -0.96898341178894 0.52985268831253 -0.22974103689193 0.72825574874877 -0.93396604061126 0.94253724813461 -0.90788620710372 -0.02007177658379 -0.24307298660278 0.0833735987544 -0.20253752171993 -0.61145150661468 -0.73070788383483 0.426168769598 -0.54575622081756 -0.61998349428176 -0.6832326054573 -0.42672303318977 -0.87086367607116 -0.13531495630741 0.38323882222175 -0.62883508205413 0.81788432598114 0.0798152834177 0.47679767012596 -0.60795408487319 0.90816700458526 -0.82300633192062 -0.29062712192535 -0.62207180261611 0.24018363654613 0.96991771459579 -0.27108195424079 0.81522691249847 0.90304642915725 -0.40197896957397 -0.05868147313594 ================================================ FILE: tests/data/ds000117/sub-01/ses-mri/dwi/sub-01_ses-mri_dwi.json ================================================ { "RepetitionTime": 8.4, "EchoTime": 0.09, "FlipAngle": 90, "SliceTiming": [4.215,0,4.34,0.1225,4.4625,0.2475,4.5875,0.37,4.7125,0.495,4.835,0.62,4.96,0.7425,5.0825,0.8675,5.2075,0.99,5.3325,1.115,5.455,1.24,5.58,1.3625,5.7025,1.4875,5.8275,1.61,5.9525,1.735,6.075,1.86,6.2,1.9825,6.3225,2.1075,6.4475,2.23,6.5725,2.355,6.695,2.48,6.82,2.6025,6.945,2.7275,7.0675,2.8525,7.1925,2.975,7.315,3.1,7.44,3.2225,7.565,3.3475,7.6875,3.4725,7.8125,3.595,7.935,3.72,8.06,3.8425,8.185,3.9675,8.3075,4.0925], "EffectiveEchoSpacing": 0.0003927297862, "PhaseEncodingDirection": "j-", "TotalReadoutTime": 0.069 } ================================================ FILE: tests/data/ds000117/sub-01/ses-mri/fmap/sub-01_ses-mri_magnitude1.nii ================================================ ================================================ FILE: tests/data/ds000117/sub-01/ses-mri/fmap/sub-01_ses-mri_magnitude2.nii ================================================ ================================================ FILE: tests/data/ds000117/sub-01/ses-mri/fmap/sub-01_ses-mri_phasediff.json ================================================ { "RepetitionTime": 0.4, "EchoTime1": 0.00519, "EchoTime2": 0.00765, "FlipAngle": 60, "IntendedFor": [ "ses-mri/func/sub-01_ses-mri_task-facerecognition_run-01_bold.nii.gz", "ses-mri/func/sub-01_ses-mri_task-facerecognition_run-02_bold.nii.gz", "ses-mri/func/sub-01_ses-mri_task-facerecognition_run-03_bold.nii.gz", "ses-mri/func/sub-01_ses-mri_task-facerecognition_run-04_bold.nii.gz", "ses-mri/func/sub-01_ses-mri_task-facerecognition_run-05_bold.nii.gz", "ses-mri/func/sub-01_ses-mri_task-facerecognition_run-06_bold.nii.gz", "ses-mri/func/sub-01_ses-mri_task-facerecognition_run-07_bold.nii.gz", "ses-mri/func/sub-01_ses-mri_task-facerecognition_run-08_bold.nii.gz", "ses-mri/func/sub-01_ses-mri_task-facerecognition_run-09_bold.nii.gz" ] } ================================================ FILE: tests/data/ds000117/sub-01/ses-mri/fmap/sub-01_ses-mri_phasediff.nii ================================================ ================================================ FILE: tests/data/ds000117/sub-01/ses-mri/func/sub-01_ses-mri_task-facerecognition_run-01_events.tsv ================================================ onset duration circle_duration stim_type trigger button_pushed response_time stim_file 4.000 .908 .534 FAMOUS 5 4 2.158 func/f013.bmp 7.273 .962 .586 FAMOUS 6 4 1.233 func/f013.bmp 10.647 .825 .546 UNFAMILIAR 13 4 1.183 func/u014.bmp 13.838 .968 .597 UNFAMILIAR 14 4 .930 func/u014.bmp 16.978 .904 .415 UNFAMILIAR 13 7 1.068 func/u016.bmp 20.219 .859 .558 UNFAMILIAR 14 7 1.207 func/u016.bmp 23.443 .804 .585 UNFAMILIAR 13 4 1.286 func/u010.bmp 26.550 .879 .526 UNFAMILIAR 14 4 1.008 func/u010.bmp 29.606 .866 .416 SCRAMBLED 17 7 1.929 func/s002.bmp 32.697 .884 .461 SCRAMBLED 18 4 1.300 func/s002.bmp 35.319 20.000 20.000 n/a 999 20000 20.000 func/i999.bmp 55.898 .974 .543 FAMOUS 5 7 2.477 func/f004.bmp 59.173 .925 .534 SCRAMBLED 17 7 1.372 func/s008.bmp 62.313 .985 .439 UNFAMILIAR 13 4 1.431 func/u012.bmp 65.587 .862 .533 FAMOUS 5 4 1.086 func/f012.bmp 68.677 .869 .461 FAMOUS 6 4 1.018 func/f012.bmp 71.750 .804 .446 SCRAMBLED 17 7 1.267 func/s007.bmp 74.774 .873 .445 SCRAMBLED 17 7 1.211 func/s011.bmp 77.881 .983 .466 FAMOUS 7 7 1.203 func/f004.bmp 81.105 .998 .468 SCRAMBLED 17 7 1.240 func/s015.bmp 84.445 .833 .583 SCRAMBLED 19 7 1.379 func/s008.bmp 87.469 .940 .434 FAMOUS 5 7 1.637 func/f006.bmp 90.676 .813 .488 FAMOUS 6 7 1.209 func/f006.bmp 93.833 .994 .570 UNFAMILIAR 15 4 1.444 func/u012.bmp 97.190 .997 .594 FAMOUS 5 7 1.352 func/f009.bmp 100.448 .869 .491 FAMOUS 5 7 1.012 func/f005.bmp 103.655 .821 .571 SCRAMBLED 19 7 1.483 func/s007.bmp 106.695 .933 .437 FAMOUS 5 7 1.228 func/f002.bmp 109.869 .818 .481 FAMOUS 6 7 1.109 func/f002.bmp 112.942 .925 .487 SCRAMBLED 19 7 1.205 func/s011.bmp 115.615 20.000 20.000 n/a 999 20000 20.000 func/i999.bmp 136.144 .980 .498 FAMOUS 5 7 .898 func/f014.bmp 139.351 .833 .458 SCRAMBLED 19 n/a 0 func/s015.bmp 142.541 .999 .591 SCRAMBLED 17 4 1.940 func/s004.bmp 145.849 .868 .548 FAMOUS 5 4 1.366 func/f001.bmp 148.906 .832 .421 FAMOUS 6 4 1.433 func/f001.bmp 151.946 .897 .445 FAMOUS 7 7 .767 func/f009.bmp 155.186 .971 .575 UNFAMILIAR 13 7 1.604 func/u013.bmp 158.393 .978 .458 FAMOUS 7 7 1.137 func/f005.bmp 161.701 .984 .564 FAMOUS 5 7 1.248 func/f015.bmp 165.008 .900 .556 UNFAMILIAR 13 4 1.239 func/u011.bmp 168.265 .828 .594 UNFAMILIAR 14 4 1.269 func/u011.bmp 171.306 .830 .447 FAMOUS 7 7 1.116 func/f014.bmp 174.312 .992 .415 FAMOUS 5 7 1.806 func/f003.bmp 177.519 .942 .442 FAMOUS 6 7 1.361 func/f003.bmp 180.777 .874 .539 SCRAMBLED 19 4 1.363 func/s004.bmp 183.984 .845 .559 SCRAMBLED 17 4 1.131 func/s012.bmp 187.058 .981 .467 SCRAMBLED 17 7 .872 func/s001.bmp 190.365 .880 .559 SCRAMBLED 18 7 1.000 func/s001.bmp 193.422 .858 .414 UNFAMILIAR 15 4 1.095 func/u013.bmp 196.027 20.000 20.000 n/a 999 20000 20.000 func/i999.bmp 216.623 .873 .554 SCRAMBLED 17 4 1.718 func/s006.bmp 219.830 .863 .558 FAMOUS 7 7 1.115 func/f015.bmp 222.937 .984 .479 SCRAMBLED 17 7 1.449 func/s014.bmp 226.178 .873 .487 SCRAMBLED 18 7 .834 func/s014.bmp 229.452 .912 .536 UNFAMILIAR 13 4 1.099 func/u002.bmp 232.559 .952 .419 UNFAMILIAR 14 4 .915 func/u002.bmp 235.733 .891 .465 UNFAMILIAR 13 4 1.023 func/u004.bmp 238.890 .947 .496 UNFAMILIAR 14 4 .953 func/u004.bmp 242.164 .824 .560 SCRAMBLED 19 7 1.068 func/s012.bmp 245.187 .922 .425 FAMOUS 5 4 1.154 func/f011.bmp 248.478 .941 .593 SCRAMBLED 17 7 1.283 func/s005.bmp 251.668 .857 .482 SCRAMBLED 18 7 1.023 func/s005.bmp 254.825 .813 .524 SCRAMBLED 19 7 1.329 func/s006.bmp 257.849 .921 .450 FAMOUS 5 7 1.221 func/f007.bmp 261.072 .903 .528 SCRAMBLED 17 7 .788 func/s016.bmp 264.163 .969 .419 FAMOUS 5 4 1.369 func/f010.bmp 267.403 .892 .516 FAMOUS 6 4 1.183 func/f010.bmp 270.627 .950 .562 SCRAMBLED 17 7 1.326 func/s009.bmp 273.901 .935 .561 SCRAMBLED 18 7 1.010 func/s009.bmp 277.025 .896 .434 FAMOUS 7 7 1.431 func/f011.bmp 279.664 20.000 20.000 n/a 999 20000 20.000 func/i999.bmp 300.109 .895 .410 SCRAMBLED 17 4 1.541 func/s010.bmp 303.233 .908 .452 SCRAMBLED 18 4 1.010 func/s010.bmp 306.373 .866 .457 UNFAMILIAR 13 4 1.137 func/u008.bmp 309.530 .880 .519 FAMOUS 7 7 1.127 func/f007.bmp 312.588 .821 .417 SCRAMBLED 17 7 1.208 func/s003.bmp 315.594 .873 .403 SCRAMBLED 18 7 .965 func/s003.bmp 318.784 .967 .543 SCRAMBLED 19 7 1.126 func/s016.bmp 322.008 .913 .495 SCRAMBLED 17 4 1.137 func/s013.bmp 325.165 .957 .477 UNFAMILIAR 13 4 1.073 func/u007.bmp 328.423 .826 .534 UNFAMILIAR 14 4 .934 func/u007.bmp 331.479 .998 .457 UNFAMILIAR 13 4 1.293 func/u015.bmp 334.770 .981 .534 UNFAMILIAR 13 4 1.075 func/u009.bmp 338.011 .920 .491 UNFAMILIAR 15 4 1.075 func/u008.bmp 341.134 .967 .442 FAMOUS 5 7 .754 func/f008.bmp 344.425 .946 .552 FAMOUS 6 7 .917 func/f008.bmp 347.632 .870 .489 UNFAMILIAR 13 4 1.286 func/u003.bmp 350.839 .990 .552 UNFAMILIAR 14 4 1.141 func/u003.bmp 354.146 .937 .542 SCRAMBLED 19 4 1.334 func/s013.bmp 356.836 20.000 20.000 n/a 999 20000 20.000 func/i999.bmp 377.298 .816 .420 UNFAMILIAR 13 4 1.415 func/u001.bmp 380.455 .837 .577 UNFAMILIAR 14 4 1.247 func/u001.bmp 383.646 .825 .572 UNFAMILIAR 13 7 1.292 func/u006.bmp 386.786 .995 .540 UNFAMILIAR 15 4 1.003 func/u015.bmp 389.993 .976 .447 UNFAMILIAR 13 7 1.536 func/u005.bmp 393.300 .906 .558 UNFAMILIAR 15 4 1.078 func/u009.bmp 396.508 .957 .526 FAMOUS 5 7 1.223 func/f016.bmp 399.264 .012 0 n/a 999 0 0 func/Circle.bmp ================================================ FILE: tests/data/ds000117/sub-01/ses-mri/func/sub-01_ses-mri_task-facerecognition_run-02_events.tsv ================================================ onset duration circle_duration stim_type trigger button_pushed response_time stim_file 4.000 .839 .543 UNFAMILIAR 13 4 1.619 func/u027.bmp 7.040 .812 .429 UNFAMILIAR 14 4 .836 func/u027.bmp 10.214 .883 .489 UNFAMILIAR 13 4 1.105 func/u025.bmp 13.321 .866 .466 UNFAMILIAR 13 7 1.128 func/u026.bmp 16.394 .929 .441 UNFAMILIAR 14 7 .801 func/u026.bmp 19.586 .969 .501 FAMOUS 5 7 1.102 func/f028.bmp 22.725 .919 .415 UNFAMILIAR 13 4 .930 func/u024.bmp 25.866 .803 .466 UNFAMILIAR 14 4 .874 func/u024.bmp 28.906 .826 .469 FAMOUS 5 7 .868 func/f031.bmp 31.478 20.000 20.000 n/a 999 20000 20.000 func/i999.bmp 52.024 .893 .514 FAMOUS 5 7 1.264 func/f024.bmp 55.164 .868 .472 UNFAMILIAR 15 7 1.249 func/u025.bmp 58.221 .930 .430 UNFAMILIAR 13 4 .887 func/u022.bmp 61.411 .938 .499 SCRAMBLED 17 4 1.522 func/s019.bmp 64.635 .967 .508 SCRAMBLED 18 4 .948 func/s019.bmp 67.943 .875 .573 FAMOUS 7 7 1.103 func/f028.bmp 71.016 .978 .425 SCRAMBLED 17 4 1.371 func/s021.bmp 74.290 .853 .532 SCRAMBLED 18 4 1.004 func/s021.bmp 77.330 .849 .406 FAMOUS 5 7 .829 func/f018.bmp 80.521 .818 .575 FAMOUS 7 7 1.039 func/f031.bmp 83.578 .874 .481 FAMOUS 5 7 1.063 func/f027.bmp 86.651 .832 .429 FAMOUS 6 7 1.297 func/f027.bmp 89.674 .992 .424 FAMOUS 7 4 1.231 func/f024.bmp 92.865 .967 .422 SCRAMBLED 17 7 .762 func/s027.bmp 96.189 .829 .589 SCRAMBLED 18 4 1.560 func/s027.bmp 99.363 .913 .580 UNFAMILIAR 15 4 1.139 func/u022.bmp 102.553 .813 .514 FAMOUS 5 7 1.009 func/f025.bmp 105.560 .987 .432 SCRAMBLED 17 7 1.221 func/s031.bmp 108.817 .971 .498 SCRAMBLED 18 7 .771 func/s031.bmp 111.540 20.000 20.000 n/a 999 20000 20.000 func/i999.bmp 132.036 .818 .462 FAMOUS 5 7 .983 func/f020.bmp 135.059 .995 .445 FAMOUS 7 7 .794 func/f018.bmp 138.266 .902 .435 UNFAMILIAR 13 7 .854 func/u018.bmp 141.440 .831 .505 UNFAMILIAR 14 7 .787 func/u018.bmp 144.463 .839 .427 SCRAMBLED 17 4 1.580 func/s018.bmp 147.637 .855 .562 SCRAMBLED 18 4 1.150 func/s018.bmp 150.694 .945 .419 SCRAMBLED 17 7 1.065 func/s029.bmp 153.918 .914 .506 FAMOUS 7 7 .958 func/f025.bmp 157.141 .858 .539 UNFAMILIAR 13 4 1.005 func/u032.bmp 160.365 .810 .599 UNFAMILIAR 14 4 .775 func/u032.bmp 163.506 .840 .560 UNFAMILIAR 13 7 1.122 func/u023.bmp 166.696 .850 .571 FAMOUS 7 7 1.083 func/f020.bmp 169.854 .841 .551 UNFAMILIAR 13 4 1.195 func/u021.bmp 172.943 .964 .474 UNFAMILIAR 14 4 .936 func/u021.bmp 176.217 .896 .550 FAMOUS 5 7 .978 func/f032.bmp 179.458 .992 .575 FAMOUS 5 4 1.327 func/f029.bmp 182.799 .973 .574 SCRAMBLED 19 7 1.002 func/s029.bmp 186.089 .984 .543 UNFAMILIAR 13 4 1.170 func/u029.bmp 189.263 .905 .426 SCRAMBLED 17 7 .952 func/s030.bmp 192.537 .838 .600 SCRAMBLED 18 7 1.347 func/s030.bmp 195.711 .949 .561 UNFAMILIAR 15 7 1.208 func/u023.bmp 198.400 20.000 20.000 n/a 999 20000 20.000 func/i999.bmp 219.013 .989 .583 SCRAMBLED 17 4 1.391 func/s032.bmp 222.221 .968 .451 SCRAMBLED 17 7 1.007 func/s026.bmp 225.377 .802 .429 FAMOUS 7 4 1.870 func/f032.bmp 228.350 .863 .411 SCRAMBLED 17 7 1.041 func/s024.bmp 231.457 .885 .431 FAMOUS 7 7 .837 func/f029.bmp 234.564 .979 .467 FAMOUS 5 7 .838 func/f021.bmp 237.788 .818 .480 FAMOUS 6 7 .324 func/f021.bmp 240.878 .918 .516 UNFAMILIAR 15 4 .952 func/u029.bmp 244.035 .947 .473 FAMOUS 5 4 1.016 func/f022.bmp 247.192 .922 .438 FAMOUS 6 4 .903 func/f022.bmp 250.332 .818 .447 SCRAMBLED 17 7 .928 func/s020.bmp 253.406 .956 .497 SCRAMBLED 18 7 .986 func/s020.bmp 256.647 .805 .514 SCRAMBLED 19 7 .816 func/s032.bmp 259.737 .922 .507 FAMOUS 5 7 1.026 func/f017.bmp 262.877 .998 .436 FAMOUS 6 n/a 0 func/f017.bmp 266.101 .880 .461 SCRAMBLED 19 4 1.094 func/s026.bmp 269.191 .886 .450 SCRAMBLED 17 7 .872 func/s022.bmp 272.332 .946 .497 SCRAMBLED 19 7 .926 func/s024.bmp 275.021 20.000 20.000 n/a 999 20000 20.000 func/i999.bmp 295.650 .982 .585 SCRAMBLED 17 7 .896 func/s028.bmp 298.974 .807 .575 SCRAMBLED 18 7 .852 func/s028.bmp 302.115 .840 .563 FAMOUS 5 7 .951 func/f030.bmp 305.171 .905 .445 FAMOUS 6 7 1.022 func/f030.bmp 308.429 .854 .577 FAMOUS 5 7 .992 func/f019.bmp 311.536 .832 .471 FAMOUS 6 7 .918 func/f019.bmp 314.542 .989 .407 FAMOUS 5 4 1.248 func/f023.bmp 317.716 .820 .405 FAMOUS 6 4 1.301 func/f023.bmp 320.790 .953 .481 UNFAMILIAR 13 n/a 0 func/u017.bmp 324.030 .998 .521 UNFAMILIAR 14 4 1.012 func/u017.bmp 327.337 .841 .538 SCRAMBLED 19 7 .810 func/s022.bmp 330.411 .880 .457 UNFAMILIAR 13 4 .859 func/u020.bmp 333.501 .880 .449 UNFAMILIAR 13 4 .895 func/u030.bmp 336.625 .876 .481 SCRAMBLED 17 7 .959 func/s025.bmp 339.732 .813 .464 UNFAMILIAR 13 7 .881 func/u031.bmp 342.889 .809 .573 UNFAMILIAR 14 7 .719 func/u031.bmp 345.996 .807 .524 SCRAMBLED 17 7 .806 func/s017.bmp 349.136 .950 .565 UNFAMILIAR 13 4 1.405 func/u028.bmp 352.326 .843 .469 UNFAMILIAR 15 4 .907 func/u020.bmp 354.916 20.000 20.000 n/a 999 20000 20.000 func/i999.bmp 375.528 .959 .582 UNFAMILIAR 13 7 .860 func/u019.bmp 378.769 .928 .502 UNFAMILIAR 15 4 .983 func/u030.bmp 381.976 .850 .513 FAMOUS 5 7 1.601 func/f026.bmp 384.999 .825 .411 SCRAMBLED 19 7 1.012 func/s025.bmp 388.140 .995 .537 SCRAMBLED 17 7 .793 func/s023.bmp 391.397 .822 .489 SCRAMBLED 18 7 1.040 func/s023.bmp 394.036 .012 0 n/a 999 0 0 func/Circle.bmp ================================================ FILE: tests/data/ds000117/sub-01/ses-mri/func/sub-01_ses-mri_task-facerecognition_run-03_events.tsv ================================================ onset duration circle_duration stim_type trigger button_pushed response_time stim_file 4.000 .859 .491 FAMOUS 5 7 1.457 func/f042.bmp 7.107 .932 .474 FAMOUS 6 7 .926 func/f042.bmp 10.431 .935 .524 UNFAMILIAR 13 4 .889 func/u042.bmp 13.705 .889 .570 UNFAMILIAR 14 4 .771 func/u042.bmp 16.895 .915 .533 UNFAMILIAR 13 4 1.026 func/u038.bmp 20.069 .870 .496 UNFAMILIAR 13 7 1.144 func/u039.bmp 23.293 .951 .569 SCRAMBLED 17 7 1.098 func/s036.bmp 26.517 .929 .514 UNFAMILIAR 13 4 .972 func/u033.bmp 29.640 .863 .419 UNFAMILIAR 14 4 .837 func/u033.bmp 32.681 .961 .414 FAMOUS 5 4 1.079 func/f035.bmp 35.387 20.000 20.000 n/a 999 20000 20.000 func/i999.bmp 55.866 .857 .434 FAMOUS 5 7 .922 func/f040.bmp 58.889 .811 .400 FAMOUS 6 7 .675 func/f040.bmp 61.912 .897 .446 UNFAMILIAR 15 4 .964 func/u038.bmp 65.103 .990 .522 SCRAMBLED 17 7 1.100 func/s034.bmp 68.444 .886 .570 UNFAMILIAR 15 4 1.252 func/u039.bmp 71.534 .824 .442 FAMOUS 5 7 .968 func/f048.bmp 74.691 .895 .553 FAMOUS 6 7 .598 func/f048.bmp 77.948 .990 .599 SCRAMBLED 19 7 1.211 func/s036.bmp 81.289 .839 .579 FAMOUS 5 7 .843 func/f034.bmp 84.379 .812 .478 SCRAMBLED 17 7 .687 func/s041.bmp 87.386 .830 .419 SCRAMBLED 18 7 1.052 func/s041.bmp 90.526 .812 .539 FAMOUS 7 4 .932 func/f035.bmp 93.533 .984 .418 UNFAMILIAR 13 7 1.214 func/u036.bmp 96.773 .822 .498 UNFAMILIAR 14 n/a 0 func/u036.bmp 99.897 .852 .523 SCRAMBLED 17 7 1.024 func/s044.bmp 103.037 .888 .533 SCRAMBLED 18 7 .936 func/s044.bmp 106.261 .955 .555 SCRAMBLED 19 4 1.065 func/s034.bmp 109.569 .923 .575 FAMOUS 5 7 .926 func/f043.bmp 112.241 20.000 20.000 n/a 999 20000 20.000 func/i999.bmp 132.871 .827 .598 SCRAMBLED 17 7 1.147 func/s043.bmp 136.028 .896 .561 FAMOUS 7 7 .740 func/f034.bmp 139.101 .922 .412 SCRAMBLED 17 7 .970 func/s042.bmp 142.275 .940 .474 SCRAMBLED 18 7 1.209 func/s042.bmp 145.565 .812 .572 FAMOUS 5 4 1.067 func/f047.bmp 148.606 .808 .464 FAMOUS 6 4 1.077 func/f047.bmp 151.629 .826 .438 FAMOUS 5 n/a 0 func/f046.bmp 154.786 .866 .558 UNFAMILIAR 13 4 .856 func/u037.bmp 157.976 .905 .555 FAMOUS 7 7 .756 func/f043.bmp 161.101 .975 .451 FAMOUS 5 7 .799 func/f041.bmp 164.291 .984 .448 FAMOUS 6 7 .718 func/f041.bmp 167.531 .851 .493 SCRAMBLED 19 n/a 0 func/s043.bmp 170.738 .965 .587 UNFAMILIAR 13 4 .954 func/u044.bmp 173.929 .986 .456 UNFAMILIAR 14 4 .734 func/u044.bmp 177.236 .968 .554 UNFAMILIAR 13 7 1.061 func/u048.bmp 180.527 .874 .557 UNFAMILIAR 14 7 .711 func/u048.bmp 183.567 .977 .400 UNFAMILIAR 13 7 .923 func/u040.bmp 186.841 .901 .530 FAMOUS 7 7 .811 func/f046.bmp 189.948 .944 .441 UNFAMILIAR 13 7 .693 func/u047.bmp 193.188 .925 .532 UNFAMILIAR 15 4 .969 func/u037.bmp 195.861 20.000 20.000 n/a 999 20000 20.000 func/i999.bmp 216.306 .813 .403 FAMOUS 5 7 .682 func/f033.bmp 219.430 .844 .544 SCRAMBLED 17 7 1.254 func/s046.bmp 222.520 .861 .472 SCRAMBLED 18 7 1.108 func/s046.bmp 225.594 .984 .447 SCRAMBLED 17 7 .827 func/s037.bmp 228.768 .861 .420 SCRAMBLED 18 7 .978 func/s037.bmp 231.858 .805 .456 FAMOUS 5 n/a 0 func/f036.bmp 234.981 .917 .496 UNFAMILIAR 15 4 .921 func/u040.bmp 238.122 .951 .465 UNFAMILIAR 13 7 1.077 func/u034.bmp 241.379 .958 .550 UNFAMILIAR 15 7 .951 func/u047.bmp 244.636 .918 .519 SCRAMBLED 17 7 1.057 func/s040.bmp 247.860 .953 .547 FAMOUS 7 7 .833 func/f033.bmp 251.101 .986 .530 UNFAMILIAR 13 7 1.000 func/u045.bmp 254.291 .845 .442 UNFAMILIAR 14 7 .854 func/u045.bmp 257.315 .913 .406 SCRAMBLED 17 7 1.193 func/s047.bmp 260.405 .898 .403 SCRAMBLED 18 7 1.068 func/s047.bmp 263.612 .972 .547 UNFAMILIAR 13 n/a 0 func/u041.bmp 266.919 .917 .562 UNFAMILIAR 14 n/a 0 func/u041.bmp 270.060 .803 .466 FAMOUS 7 7 .774 func/f036.bmp 272.599 20.000 20.000 n/a 999 20000 20.000 func/i999.bmp 293.228 .801 .592 FAMOUS 5 7 .779 func/f039.bmp 296.268 .946 .471 FAMOUS 6 7 .555 func/f039.bmp 299.392 .964 .413 UNFAMILIAR 15 7 .760 func/u034.bmp 302.532 .888 .408 FAMOUS 5 4 1.012 func/f044.bmp 305.605 .834 .414 FAMOUS 6 4 1.328 func/f044.bmp 308.746 .860 .546 SCRAMBLED 19 7 1.311 func/s040.bmp 311.803 .875 .423 SCRAMBLED 17 4 1.054 func/s033.bmp 315.026 .800 .579 FAMOUS 5 7 .664 func/f045.bmp 318.184 .874 .588 UNFAMILIAR 13 7 .932 func/u046.bmp 321.341 .838 .504 UNFAMILIAR 14 7 .542 func/u046.bmp 324.364 .815 .411 UNFAMILIAR 13 7 1.032 func/u035.bmp 327.522 .925 .585 UNFAMILIAR 14 7 .704 func/u035.bmp 330.795 .820 .579 SCRAMBLED 17 7 1.033 func/s039.bmp 333.885 .888 .486 SCRAMBLED 17 7 .904 func/s048.bmp 337.076 .833 .523 SCRAMBLED 19 n/a 0 func/s033.bmp 340.199 .997 .533 SCRAMBLED 17 7 .845 func/s038.bmp 343.423 .974 .453 FAMOUS 7 7 .725 func/f045.bmp 346.697 .939 .523 SCRAMBLED 17 7 .932 func/s035.bmp 349.821 .964 .410 SCRAMBLED 17 7 .639 func/s045.bmp 352.527 20.000 20.000 n/a 999 20000 20.000 func/i999.bmp 373.022 .932 .452 FAMOUS 5 7 1.248 func/f038.bmp 376.313 .918 .587 SCRAMBLED 19 7 .815 func/s039.bmp 379.503 .841 .511 FAMOUS 5 7 .587 func/f037.bmp 382.594 .825 .479 FAMOUS 6 7 1.381 func/f037.bmp 385.634 .801 .443 SCRAMBLED 19 7 1.044 func/s048.bmp 388.641 .853 .447 UNFAMILIAR 13 4 1.009 func/u043.bmp 391.798 .961 .543 UNFAMILIAR 14 4 .966 func/u043.bmp 395.021 .910 .495 SCRAMBLED 19 7 .971 func/s038.bmp 397.727 .012 0 n/a 999 0 0 func/Circle.bmp ================================================ FILE: tests/data/ds000117/sub-01/ses-mri/func/sub-01_ses-mri_task-facerecognition_run-04_events.tsv ================================================ onset duration circle_duration stim_type trigger button_pushed response_time stim_file 4.000 .970 .499 FAMOUS 5 7 1.062 func/f061.bmp 7.190 .922 .465 FAMOUS 6 7 .735 func/f061.bmp 10.397 .825 .410 FAMOUS 5 4 1.123 func/f058.bmp 13.472 .948 .485 FAMOUS 6 4 .721 func/f058.bmp 16.711 .827 .529 UNFAMILIAR 13 7 .910 func/u049.bmp 19.885 .875 .576 UNFAMILIAR 14 7 .846 func/u049.bmp 23.126 .836 .590 SCRAMBLED 17 7 .869 func/s064.bmp 26.266 .897 .543 SCRAMBLED 18 4 1.114 func/s064.bmp 29.423 .828 .499 FAMOUS 5 7 .731 func/f053.bmp 32.463 .904 .437 UNFAMILIAR 13 7 .952 func/u060.bmp 35.119 20.000 20.000 n/a 999 20000 20.000 func/i999.bmp 55.581 .891 .420 FAMOUS 5 4 1.102 func/f056.bmp 58.805 .893 .567 FAMOUS 6 4 .898 func/f056.bmp 61.979 .812 .513 SCRAMBLED 17 7 1.178 func/s056.bmp 65.019 .985 .464 SCRAMBLED 17 4 1.383 func/s054.bmp 68.310 .932 .549 SCRAMBLED 17 7 .902 func/s049.bmp 71.450 .932 .439 FAMOUS 7 7 .853 func/f053.bmp 74.707 .955 .562 SCRAMBLED 17 4 1.491 func/s058.bmp 77.898 .828 .455 SCRAMBLED 18 7 1.069 func/s058.bmp 80.972 .922 .484 UNFAMILIAR 15 7 .691 func/u060.bmp 84.178 .973 .504 FAMOUS 5 7 1.077 func/f063.bmp 87.419 .928 .492 FAMOUS 6 7 1.044 func/f063.bmp 90.676 .980 .562 FAMOUS 5 n/a 0 func/f060.bmp 93.883 .854 .465 SCRAMBLED 19 7 1.264 func/s056.bmp 96.957 .911 .445 SCRAMBLED 17 4 1.077 func/s063.bmp 100.147 .803 .502 SCRAMBLED 19 7 1.078 func/s054.bmp 103.304 .984 .594 SCRAMBLED 17 7 .809 func/s051.bmp 106.612 .905 .555 SCRAMBLED 18 7 1.149 func/s051.bmp 109.786 .880 .486 SCRAMBLED 19 n/a 0 func/s049.bmp 113.009 .938 .576 SCRAMBLED 17 7 2.339 func/s061.bmp 115.699 20.000 20.000 n/a 999 20000 20.000 func/i999.bmp 136.178 .979 .436 SCRAMBLED 17 7 .958 func/s050.bmp 139.518 .946 .594 SCRAMBLED 18 7 .715 func/s050.bmp 142.776 .902 .541 UNFAMILIAR 13 4 1.094 func/u063.bmp 145.883 .925 .446 UNFAMILIAR 14 n/a 0 func/u063.bmp 149.173 .883 .586 FAMOUS 7 n/a 0 func/f060.bmp 152.247 .850 .433 FAMOUS 5 n/a 0 func/f054.bmp 155.454 .873 .597 SCRAMBLED 19 7 .797 func/s063.bmp 158.544 .903 .441 SCRAMBLED 17 7 .649 func/s057.bmp 161.718 .805 .505 FAMOUS 5 4 .835 func/f050.bmp 164.758 .831 .463 FAMOUS 6 4 .706 func/f050.bmp 167.832 .947 .472 SCRAMBLED 19 7 .741 func/s061.bmp 171.039 .864 .500 SCRAMBLED 17 7 .751 func/s055.bmp 174.196 .930 .519 SCRAMBLED 18 7 .710 func/s055.bmp 177.486 .903 .597 UNFAMILIAR 13 7 1.095 func/u059.bmp 180.677 .896 .520 UNFAMILIAR 14 7 .581 func/u059.bmp 183.917 .836 .574 FAMOUS 5 7 .784 func/f051.bmp 186.924 .834 .409 FAMOUS 7 7 .907 func/f054.bmp 190.064 .990 .546 UNFAMILIAR 13 7 .888 func/u055.bmp 193.288 .981 .463 UNFAMILIAR 14 7 .702 func/u055.bmp 196.529 .966 .490 SCRAMBLED 19 n/a 0 func/s057.bmp 199.235 20.000 20.000 n/a 999 20000 20.000 func/i999.bmp 219.764 .834 .485 FAMOUS 5 7 .929 func/f064.bmp 222.888 .913 .534 UNFAMILIAR 13 4 1.035 func/u051.bmp 226.028 .852 .460 UNFAMILIAR 14 7 .781 func/u051.bmp 229.202 .943 .561 UNFAMILIAR 13 7 1.514 func/u064.bmp 232.375 .974 .456 UNFAMILIAR 13 4 .780 func/u062.bmp 235.750 .852 .576 FAMOUS 7 4 1.246 func/f051.bmp 238.857 .820 .492 FAMOUS 5 7 .851 func/f052.bmp 241.880 .898 .426 FAMOUS 5 7 .972 func/f062.bmp 245.020 .901 .480 FAMOUS 6 7 .741 func/f062.bmp 248.094 .934 .408 FAMOUS 7 7 .828 func/f064.bmp 251.318 .986 .527 SCRAMBLED 17 n/a 0 func/s060.bmp 254.475 .912 .402 UNFAMILIAR 13 7 .824 func/u054.bmp 257.749 .905 .592 UNFAMILIAR 14 7 .668 func/u054.bmp 260.872 .984 .440 UNFAMILIAR 15 7 .822 func/u064.bmp 264.163 .934 .543 SCRAMBLED 17 7 .767 func/s053.bmp 267.353 .891 .489 SCRAMBLED 18 n/a 0 func/s053.bmp 270.594 .927 .571 UNFAMILIAR 15 4 1.053 func/u062.bmp 273.267 20.000 20.000 n/a 999 20000 20.000 func/i999.bmp 293.847 .864 .551 FAMOUS 5 7 1.141 func/f049.bmp 296.903 .908 .421 FAMOUS 6 7 .694 func/f049.bmp 299.993 .890 .402 FAMOUS 7 7 .752 func/f052.bmp 303.133 .827 .483 UNFAMILIAR 13 7 .835 func/u061.bmp 306.324 .915 .586 UNFAMILIAR 14 7 .616 func/u061.bmp 309.464 .887 .465 SCRAMBLED 17 7 .935 func/s052.bmp 312.638 .967 .507 SCRAMBLED 19 7 .554 func/s060.bmp 315.895 .900 .522 FAMOUS 5 7 .838 func/f057.bmp 319.069 .928 .510 UNFAMILIAR 13 7 .734 func/u057.bmp 322.226 .804 .467 UNFAMILIAR 13 4 1.234 func/u058.bmp 325.232 .938 .428 UNFAMILIAR 13 4 .828 func/u050.bmp 328.540 .898 .599 UNFAMILIAR 14 4 .962 func/u050.bmp 331.713 .943 .505 UNFAMILIAR 13 4 .998 func/u052.bmp 334.954 .906 .532 SCRAMBLED 19 7 .816 func/s052.bmp 338.228 .947 .590 UNFAMILIAR 13 n/a 0 func/u053.bmp 341.469 .967 .520 UNFAMILIAR 14 n/a 0 func/u053.bmp 344.793 .863 .586 FAMOUS 7 7 .749 func/f057.bmp 347.966 .947 .543 FAMOUS 5 4 .960 func/f055.bmp 351.207 .922 .530 UNFAMILIAR 15 7 .919 func/u057.bmp 353.880 20.000 20.000 n/a 999 20000 20.000 func/i999.bmp 374.509 .983 .591 UNFAMILIAR 13 7 1.341 func/u056.bmp 377.699 .882 .446 UNFAMILIAR 15 4 1.000 func/u058.bmp 380.840 .857 .495 SCRAMBLED 17 7 .807 func/s062.bmp 384.030 .842 .562 SCRAMBLED 18 n/a 0 func/s062.bmp 387.053 .893 .407 FAMOUS 5 7 .726 func/f059.bmp 390.127 1.000 .402 FAMOUS 6 7 .559 func/f059.bmp 393.434 .991 .536 UNFAMILIAR 15 4 1.036 func/u052.bmp 396.625 .944 .430 SCRAMBLED 17 7 1.060 func/s059.bmp 399.364 .012 0 n/a 999 0 0 func/Circle.bmp ================================================ FILE: tests/data/ds000117/sub-01/ses-mri/func/sub-01_ses-mri_task-facerecognition_run-05_events.tsv ================================================ onset duration circle_duration stim_type trigger button_pushed response_time stim_file 4.000 .957 .431 SCRAMBLED 17 7 .921 func/s073.bmp 7.257 .910 .532 SCRAMBLED 18 7 .734 func/s073.bmp 10.531 .826 .491 SCRAMBLED 17 7 .910 func/s066.bmp 13.538 .908 .406 SCRAMBLED 17 7 .833 func/s072.bmp 16.745 .895 .522 SCRAMBLED 17 4 .897 func/s067.bmp 20.002 .992 .587 SCRAMBLED 18 4 .778 func/s067.bmp 23.260 .891 .488 FAMOUS 5 7 .703 func/f068.bmp 26.484 .821 .563 FAMOUS 5 7 .917 func/f081.bmp 29.657 .939 .569 FAMOUS 6 7 .653 func/f081.bmp 32.347 20.000 20.000 n/a 999 20000 20.000 func/i999.bmp 52.909 .800 .520 FAMOUS 5 7 1.072 func/f072.bmp 55.882 .960 .402 SCRAMBLED 19 7 .916 func/s066.bmp 59.073 .991 .466 FAMOUS 5 7 .762 func/f071.bmp 62.330 .954 .499 FAMOUS 6 7 .719 func/f071.bmp 65.588 .995 .534 SCRAMBLED 19 4 1.359 func/s072.bmp 68.794 .834 .442 UNFAMILIAR 13 7 1.174 func/u071.bmp 71.885 .854 .499 SCRAMBLED 17 4 1.018 func/s079.bmp 75.025 .911 .504 FAMOUS 7 7 .707 func/f068.bmp 78.299 .851 .596 UNFAMILIAR 13 7 .699 func/u065.bmp 81.406 .926 .490 UNFAMILIAR 14 7 .751 func/u065.bmp 84.630 .956 .520 SCRAMBLED 17 7 .902 func/s081.bmp 87.854 .935 .501 FAMOUS 7 7 .824 func/f072.bmp 91.094 .874 .535 UNFAMILIAR 13 7 .919 func/u075.bmp 94.335 .857 .600 UNFAMILIAR 14 7 .500 func/u075.bmp 97.408 .883 .443 UNFAMILIAR 13 4 .930 func/u069.bmp 100.633 .992 .585 UNFAMILIAR 15 7 1.190 func/u071.bmp 103.873 .831 .480 FAMOUS 5 7 1.313 func/f065.bmp 107.046 .825 .582 FAMOUS 6 7 1.132 func/f065.bmp 110.053 .896 .407 SCRAMBLED 19 7 1.165 func/s079.bmp 112.692 20.000 20.000 n/a 999 20000 20.000 func/i999.bmp 133.171 .968 .450 UNFAMILIAR 13 7 2.306 func/u068.bmp 136.378 .804 .479 UNFAMILIAR 14 7 .955 func/u068.bmp 139.485 .958 .519 SCRAMBLED 17 7 1.045 func/s070.bmp 142.776 .922 .566 SCRAMBLED 18 7 .878 func/s070.bmp 146.000 .839 .527 SCRAMBLED 19 7 1.093 func/s081.bmp 149.174 .828 .565 FAMOUS 5 4 1.001 func/f067.bmp 152.297 .870 .520 UNFAMILIAR 13 7 .881 func/u079.bmp 155.504 .997 .553 UNFAMILIAR 15 7 .511 func/u069.bmp 158.712 .828 .438 UNFAMILIAR 13 7 .868 func/u070.bmp 161.768 .893 .464 UNFAMILIAR 14 7 .691 func/u070.bmp 164.975 .810 .540 UNFAMILIAR 13 7 1.115 func/u072.bmp 168.082 .996 .530 SCRAMBLED 17 7 .749 func/s074.bmp 171.256 .980 .416 SCRAMBLED 18 7 .802 func/s074.bmp 174.513 .889 .515 UNFAMILIAR 13 7 .901 func/u074.bmp 177.771 .887 .598 FAMOUS 7 4 1.113 func/f067.bmp 180.894 .917 .452 UNFAMILIAR 13 4 .912 func/u078.bmp 184.035 .935 .457 UNFAMILIAR 15 7 .889 func/u079.bmp 187.142 .810 .416 UNFAMILIAR 13 7 .730 func/u073.bmp 190.148 .806 .430 FAMOUS 5 7 .946 func/f070.bmp 193.272 .964 .550 UNFAMILIAR 15 7 .838 func/u072.bmp 195.978 20.000 20.000 n/a 999 20000 20.000 func/i999.bmp 216.423 .858 .405 SCRAMBLED 17 7 .805 func/s065.bmp 219.530 .913 .474 SCRAMBLED 17 7 .896 func/s068.bmp 222.804 .946 .587 SCRAMBLED 18 7 .741 func/s068.bmp 226.012 .876 .491 UNFAMILIAR 15 4 .954 func/u074.bmp 229.235 .902 .580 FAMOUS 5 7 .936 func/f074.bmp 232.359 1.000 .465 FAMOUS 6 4 .997 func/f074.bmp 235.767 .917 .569 UNFAMILIAR 15 4 1.132 func/u078.bmp 238.957 .945 .511 SCRAMBLED 17 7 .927 func/s071.bmp 242.081 .878 .407 SCRAMBLED 18 7 .919 func/s071.bmp 245.154 .818 .421 UNFAMILIAR 15 4 1.048 func/u073.bmp 248.178 .834 .439 SCRAMBLED 17 n/a 0 func/s080.bmp 251.201 .997 .421 SCRAMBLED 18 7 .824 func/s080.bmp 254.542 .960 .574 FAMOUS 7 7 1.055 func/f070.bmp 257.832 .938 .563 UNFAMILIAR 13 7 .947 func/u081.bmp 261.106 .942 .566 UNFAMILIAR 14 7 .684 func/u081.bmp 264.247 .877 .434 SCRAMBLED 19 7 .795 func/s065.bmp 267.304 .845 .409 UNFAMILIAR 13 4 .963 func/u067.bmp 270.427 .869 .516 UNFAMILIAR 14 4 1.537 func/u067.bmp 273.033 20.000 20.000 n/a 999 20000 20.000 func/i999.bmp 293.479 .950 .410 FAMOUS 5 7 1.459 func/f066.bmp 296.686 .995 .491 FAMOUS 6 7 .487 func/f066.bmp 299.859 .800 .406 FAMOUS 5 7 1.168 func/f079.bmp 302.933 .942 .510 FAMOUS 6 7 .719 func/f079.bmp 306.190 .911 .547 UNFAMILIAR 13 7 1.045 func/u080.bmp 309.280 .944 .411 FAMOUS 5 7 .848 func/f078.bmp 312.488 .844 .498 SCRAMBLED 17 7 .831 func/s077.bmp 315.528 .849 .422 SCRAMBLED 17 7 1.018 func/s078.bmp 318.569 .853 .435 SCRAMBLED 18 7 .766 func/s078.bmp 321.708 .947 .518 FAMOUS 5 7 1.055 func/f077.bmp 324.865 .902 .450 UNFAMILIAR 13 7 1.265 func/u066.bmp 328.106 .832 .584 UNFAMILIAR 15 7 1.051 func/u080.bmp 331.179 .906 .473 FAMOUS 5 7 .936 func/f073.bmp 334.303 .882 .445 FAMOUS 7 7 .617 func/f078.bmp 337.510 .826 .559 SCRAMBLED 17 7 1.026 func/s075.bmp 340.517 .901 .412 SCRAMBLED 18 7 1.061 func/s075.bmp 343.757 .806 .578 SCRAMBLED 19 7 .776 func/s077.bmp 346.914 .849 .577 SCRAMBLED 17 7 .902 func/s069.bmp 350.088 .940 .552 FAMOUS 5 7 .856 func/f080.bmp 353.245 .808 .438 FAMOUS 6 7 .873 func/f080.bmp 356.268 .923 .440 FAMOUS 7 7 1.261 func/f077.bmp 358.941 20.000 20.000 n/a 999 20000 20.000 func/i999.bmp 379.520 .968 .538 UNFAMILIAR 13 4 1.179 func/u077.bmp 382.794 .999 .546 UNFAMILIAR 15 7 1.205 func/u066.bmp 386.052 .876 .485 FAMOUS 5 7 .847 func/f069.bmp 389.125 .863 .426 FAMOUS 6 7 .754 func/f069.bmp 392.315 .842 .555 FAMOUS 7 7 1.090 func/f073.bmp 395.372 .981 .449 FAMOUS 5 7 .681 func/f075.bmp 398.663 .884 .543 FAMOUS 6 7 1.236 func/f075.bmp 401.319 .012 0 n/a 999 0 0 func/Circle.bmp ================================================ FILE: tests/data/ds000117/sub-01/ses-mri/func/sub-01_ses-mri_task-facerecognition_run-06_events.tsv ================================================ onset duration circle_duration stim_type trigger button_pushed response_time stim_file 4.000 .940 .599 UNFAMILIAR 13 7 1.242 func/u089.bmp 7.190 .980 .481 UNFAMILIAR 14 7 .696 func/u089.bmp 10.581 .997 .542 UNFAMILIAR 13 7 .758 func/u083.bmp 13.923 .997 .585 UNFAMILIAR 14 7 .633 func/u083.bmp 17.246 .940 .555 FAMOUS 5 7 .844 func/f086.bmp 20.470 .858 .517 FAMOUS 6 7 .902 func/f086.bmp 23.594 .950 .501 SCRAMBLED 17 7 .937 func/s085.bmp 26.867 .996 .563 FAMOUS 5 7 .793 func/f083.bmp 30.125 .898 .498 FAMOUS 6 7 .690 func/f083.bmp 33.231 .946 .448 UNFAMILIAR 13 4 .907 func/u091.bmp 35.921 20.000 20.000 n/a 999 20000 20.000 func/i999.bmp 56.366 .923 .404 FAMOUS 5 7 1.169 func/f096.bmp 59.574 .980 .502 UNFAMILIAR 13 7 .711 func/u097.bmp 62.831 .846 .513 UNFAMILIAR 13 7 1.539 func/u085.bmp 65.954 .867 .509 SCRAMBLED 19 7 .638 func/s085.bmp 69.095 .880 .504 UNFAMILIAR 13 7 1.008 func/u087.bmp 72.335 .960 .587 UNFAMILIAR 14 7 .703 func/u087.bmp 75.627 .838 .568 FAMOUS 5 7 1.267 func/f082.bmp 78.816 .891 .583 FAMOUS 6 7 .813 func/f082.bmp 81.907 .944 .425 UNFAMILIAR 15 4 1.018 func/u091.bmp 85.047 .903 .431 UNFAMILIAR 13 7 1.068 func/u094.bmp 88.271 .843 .547 UNFAMILIAR 14 n/a 0 func/u094.bmp 91.361 .965 .478 FAMOUS 7 7 .652 func/f096.bmp 94.685 .803 .593 UNFAMILIAR 13 4 .952 func/u084.bmp 97.809 .852 .539 UNFAMILIAR 14 4 1.017 func/u084.bmp 101.016 .960 .593 UNFAMILIAR 15 7 1.027 func/u097.bmp 104.156 .873 .415 SCRAMBLED 17 7 2.066 func/s097.bmp 107.230 .894 .428 UNFAMILIAR 15 7 .838 func/u085.bmp 110.320 .814 .431 UNFAMILIAR 13 7 1.161 func/u096.bmp 113.427 .946 .531 SCRAMBLED 17 7 .930 func/s087.bmp 116.116 20.000 20.000 n/a 999 20000 20.000 func/i999.bmp 136.595 .803 .442 FAMOUS 5 7 .960 func/f084.bmp 139.652 .871 .482 SCRAMBLED 17 7 .825 func/s095.bmp 142.709 .863 .408 SCRAMBLED 17 7 .915 func/s091.bmp 145.866 .819 .523 SCRAMBLED 19 7 1.012 func/s097.bmp 149.006 .998 .559 UNFAMILIAR 13 4 1.345 func/u090.bmp 152.230 .924 .466 UNFAMILIAR 15 7 1.031 func/u096.bmp 155.337 .858 .417 SCRAMBLED 17 7 .869 func/s090.bmp 158.511 .987 .547 SCRAMBLED 18 7 .916 func/s090.bmp 161.851 .913 .596 SCRAMBLED 19 7 .905 func/s087.bmp 165.008 .814 .480 SCRAMBLED 17 7 .823 func/s094.bmp 168.182 .924 .589 FAMOUS 7 7 1.402 func/f084.bmp 171.289 .913 .407 UNFAMILIAR 13 4 1.057 func/u095.bmp 174.546 .937 .569 SCRAMBLED 19 7 .884 func/s095.bmp 177.754 .954 .499 FAMOUS 5 7 1.141 func/f087.bmp 180.944 .966 .457 FAMOUS 6 7 1.749 func/f087.bmp 184.118 .801 .450 SCRAMBLED 19 7 .959 func/s091.bmp 187.108 .865 .422 FAMOUS 5 4 .921 func/f088.bmp 190.332 .886 .589 FAMOUS 6 4 .800 func/f088.bmp 193.505 .936 .522 UNFAMILIAR 15 4 1.140 func/u090.bmp 196.178 20.000 20.000 n/a 999 20000 20.000 func/i999.bmp 216.707 .801 .499 SCRAMBLED 17 7 .611 func/s088.bmp 219.797 .824 .528 FAMOUS 5 7 .897 func/f090.bmp 222.921 .949 .528 FAMOUS 6 7 .698 func/f090.bmp 226.095 .946 .461 SCRAMBLED 19 7 .828 func/s094.bmp 229.318 .934 .511 UNFAMILIAR 13 7 .767 func/u086.bmp 232.425 .809 .405 UNFAMILIAR 14 7 .979 func/u086.bmp 235.666 .933 .595 UNFAMILIAR 15 4 .929 func/u095.bmp 238.773 .877 .408 SCRAMBLED 17 7 1.028 func/s086.bmp 241.880 .962 .455 UNFAMILIAR 13 7 .756 func/u092.bmp 245.120 .869 .516 UNFAMILIAR 14 7 .716 func/u092.bmp 248.227 .808 .480 FAMOUS 5 7 .907 func/f089.bmp 251.334 .891 .531 FAMOUS 6 7 .771 func/f089.bmp 254.424 .859 .420 SCRAMBLED 19 7 .823 func/s088.bmp 257.499 .890 .451 FAMOUS 5 7 .614 func/f091.bmp 260.722 .941 .555 FAMOUS 6 7 .842 func/f091.bmp 263.929 .903 .491 SCRAMBLED 17 n/a 0 func/s084.bmp 267.203 .827 .594 SCRAMBLED 18 7 .728 func/s084.bmp 270.377 .800 .579 FAMOUS 5 7 .794 func/f085.bmp 273.367 .836 .428 SCRAMBLED 19 7 .913 func/s086.bmp 275.939 20.000 20.000 n/a 999 20000 20.000 func/i999.bmp 296.535 .862 .559 UNFAMILIAR 13 7 1.132 func/u093.bmp 299.659 .911 .486 UNFAMILIAR 14 7 1.213 func/u093.bmp 302.816 .890 .483 FAMOUS 5 7 .918 func/f092.bmp 306.073 .802 .597 FAMOUS 6 7 .488 func/f092.bmp 309.230 .945 .588 SCRAMBLED 17 7 .715 func/s082.bmp 312.537 .935 .586 SCRAMBLED 18 7 .859 func/s082.bmp 315.644 .967 .403 SCRAMBLED 17 7 .941 func/s092.bmp 318.818 .932 .446 SCRAMBLED 17 7 .708 func/s083.bmp 322.025 .934 .506 SCRAMBLED 18 7 .766 func/s083.bmp 325.266 .806 .544 FAMOUS 7 4 .940 func/f085.bmp 328.289 .832 .435 SCRAMBLED 17 4 .944 func/s093.bmp 331.479 .894 .596 SCRAMBLED 18 7 1.294 func/s093.bmp 334.687 .844 .545 FAMOUS 5 7 .895 func/f093.bmp 337.777 .927 .482 FAMOUS 6 7 .741 func/f093.bmp 341.017 .974 .546 UNFAMILIAR 13 4 .860 func/u082.bmp 344.258 .837 .499 FAMOUS 5 7 .704 func/f095.bmp 347.348 .818 .473 SCRAMBLED 19 7 .852 func/s092.bmp 350.338 .959 .404 SCRAMBLED 17 7 .823 func/s089.bmp 353.595 .957 .532 SCRAMBLED 17 7 .911 func/s096.bmp 356.301 20.000 20.000 n/a 999 20000 20.000 func/i999.bmp 376.797 .882 .465 FAMOUS 5 4 1.201 func/f094.bmp 379.937 .858 .497 FAMOUS 5 7 .795 func/f097.bmp 383.011 .897 .438 FAMOUS 6 7 .816 func/f097.bmp 386.101 .985 .419 UNFAMILIAR 15 n/a 0 func/u082.bmp 389.442 .835 .593 UNFAMILIAR 13 7 1.269 func/u088.bmp 392.549 .951 .517 FAMOUS 7 7 .879 func/f095.bmp 395.305 .012 0 n/a 999 0 0 func/Circle.bmp ================================================ FILE: tests/data/ds000117/sub-01/ses-mri/func/sub-01_ses-mri_task-facerecognition_run-07_events.tsv ================================================ onset duration circle_duration stim_type trigger button_pushed response_time stim_file 4.000 .832 .414 FAMOUS 5 7 1.023 func/f101.bmp 7.075 .952 .485 FAMOUS 6 7 .813 func/f101.bmp 10.398 .990 .512 UNFAMILIAR 13 7 1.122 func/u099.bmp 13.705 .858 .542 UNFAMILIAR 13 7 .862 func/u107.bmp 16.929 .975 .591 UNFAMILIAR 14 7 .737 func/u107.bmp 20.270 .896 .585 UNFAMILIAR 13 4 .827 func/u098.bmp 23.527 .891 .590 UNFAMILIAR 13 4 1.044 func/u109.bmp 26.684 .863 .495 UNFAMILIAR 13 7 .858 func/u111.bmp 29.825 .954 .505 UNFAMILIAR 14 4 1.002 func/u111.bmp 32.531 20.000 20.000 n/a 999 20000 20.000 func/i999.bmp 53.076 .943 .514 SCRAMBLED 17 7 1.182 func/s102.bmp 56.317 .945 .530 SCRAMBLED 18 7 1.006 func/s102.bmp 59.491 .978 .457 UNFAMILIAR 15 7 1.002 func/u099.bmp 62.664 .987 .432 UNFAMILIAR 13 7 .742 func/u105.bmp 66.005 .882 .577 UNFAMILIAR 14 n/a 0 func/u105.bmp 69.095 .824 .441 SCRAMBLED 17 7 1.061 func/s109.bmp 72.152 .895 .464 SCRAMBLED 18 7 .950 func/s109.bmp 75.276 .949 .465 UNFAMILIAR 15 4 .777 func/u098.bmp 78.516 .903 .527 SCRAMBLED 17 7 .981 func/s106.bmp 81.590 .832 .412 SCRAMBLED 18 7 1.039 func/s106.bmp 84.730 .974 .539 UNFAMILIAR 15 7 1.003 func/u109.bmp 88.038 .917 .555 UNFAMILIAR 13 7 .830 func/u104.bmp 91.278 .990 .558 UNFAMILIAR 13 7 .821 func/u108.bmp 94.436 .982 .401 UNFAMILIAR 14 7 1.024 func/u108.bmp 97.726 .924 .548 UNFAMILIAR 13 7 .929 func/u113.bmp 100.900 .865 .480 SCRAMBLED 17 7 .816 func/s105.bmp 104.090 .875 .563 FAMOUS 5 7 1.038 func/f105.bmp 107.164 .905 .432 FAMOUS 5 7 1.379 func/f112.bmp 110.321 .972 .475 UNFAMILIAR 15 7 1.803 func/u104.bmp 113.043 20.000 20.000 n/a 999 20000 20.000 func/i999.bmp 133.623 .923 .547 SCRAMBLED 17 7 .980 func/s103.bmp 136.880 .942 .556 SCRAMBLED 18 7 1.697 func/s103.bmp 140.137 .932 .535 SCRAMBLED 17 7 1.158 func/s112.bmp 143.328 .998 .500 UNFAMILIAR 15 7 .673 func/u113.bmp 146.652 .824 .560 SCRAMBLED 17 7 .824 func/s108.bmp 149.825 .846 .578 SCRAMBLED 19 7 .774 func/s105.bmp 152.949 .944 .514 FAMOUS 5 4 .969 func/f113.bmp 156.191 .962 .535 FAMOUS 7 7 .917 func/f105.bmp 159.347 .819 .431 FAMOUS 5 4 .864 func/f098.bmp 162.387 .954 .460 FAMOUS 7 7 1.002 func/f112.bmp 165.627 .898 .511 FAMOUS 5 7 1.181 func/f099.bmp 168.851 .863 .565 SCRAMBLED 17 7 1.015 func/s101.bmp 171.958 .831 .472 SCRAMBLED 19 7 1.063 func/s112.bmp 175.032 .949 .476 FAMOUS 5 7 1.091 func/f104.bmp 178.189 .810 .440 FAMOUS 6 7 .851 func/f104.bmp 181.245 .947 .470 SCRAMBLED 19 7 .986 func/s108.bmp 184.369 .933 .403 SCRAMBLED 17 7 .725 func/s111.bmp 187.660 .832 .592 SCRAMBLED 18 7 .704 func/s111.bmp 190.767 .931 .510 FAMOUS 7 4 .899 func/f113.bmp 193.439 20.000 20.000 n/a 999 20000 20.000 func/i999.bmp 213.985 .928 .514 FAMOUS 5 7 .952 func/f109.bmp 217.092 .903 .410 FAMOUS 6 7 .801 func/f109.bmp 220.199 .932 .432 FAMOUS 7 7 1.180 func/f098.bmp 223.490 .902 .596 SCRAMBLED 17 7 1.155 func/s099.bmp 226.697 .988 .544 SCRAMBLED 18 7 .914 func/s099.bmp 229.904 .874 .444 FAMOUS 7 7 1.164 func/f099.bmp 232.961 .802 .409 FAMOUS 5 7 .981 func/f102.bmp 236.051 .950 .462 FAMOUS 6 7 .868 func/f102.bmp 239.308 .848 .548 SCRAMBLED 19 7 .820 func/s101.bmp 242.365 .935 .437 SCRAMBLED 17 7 1.063 func/s110.bmp 245.656 .829 .589 SCRAMBLED 18 7 .696 func/s110.bmp 248.813 .920 .559 SCRAMBLED 17 7 .793 func/s107.bmp 252.070 .901 .580 SCRAMBLED 17 7 .720 func/s100.bmp 255.277 .981 .549 SCRAMBLED 18 7 .748 func/s100.bmp 258.601 .879 .581 SCRAMBLED 17 7 1.025 func/s104.bmp 261.758 .959 .516 FAMOUS 5 4 .947 func/f111.bmp 264.965 .960 .471 UNFAMILIAR 13 7 1.225 func/u106.bmp 268.239 .977 .548 UNFAMILIAR 14 7 .613 func/u106.bmp 271.530 .974 .548 FAMOUS 5 7 1.317 func/f103.bmp 274.720 .914 .447 SCRAMBLED 19 7 1.086 func/s107.bmp 277.376 20.000 20.000 n/a 999 20000 20.000 func/i999.bmp 297.922 .908 .512 SCRAMBLED 17 7 1.088 func/s098.bmp 301.146 .857 .539 SCRAMBLED 18 7 .998 func/s098.bmp 304.303 .807 .525 FAMOUS 5 7 .852 func/f110.bmp 307.443 .811 .553 FAMOUS 6 4 .666 func/f110.bmp 310.584 .892 .558 SCRAMBLED 19 7 .803 func/s104.bmp 313.707 .830 .457 UNFAMILIAR 13 7 .594 func/u112.bmp 316.814 .853 .517 UNFAMILIAR 14 7 .709 func/u112.bmp 319.854 .813 .425 FAMOUS 7 4 .923 func/f111.bmp 322.961 .971 .527 UNFAMILIAR 13 7 .897 func/u110.bmp 326.118 .993 .410 UNFAMILIAR 14 7 .632 func/u110.bmp 329.409 .986 .529 UNFAMILIAR 13 4 .886 func/u102.bmp 332.716 .818 .560 UNFAMILIAR 14 7 .630 func/u102.bmp 335.756 .972 .457 FAMOUS 7 7 1.194 func/f103.bmp 338.913 .840 .414 UNFAMILIAR 13 4 .992 func/u101.bmp 341.987 .973 .458 FAMOUS 5 7 .954 func/f100.bmp 345.227 .877 .493 FAMOUS 5 7 1.230 func/f106.bmp 348.334 .952 .457 UNFAMILIAR 13 7 .854 func/u103.bmp 351.475 .854 .421 FAMOUS 5 7 .793 func/f108.bmp 354.081 20.000 20.000 n/a 999 20000 20.000 func/i999.bmp 374.660 .844 .545 SCRAMBLED 17 4 .986 func/s113.bmp 377.850 .991 .569 SCRAMBLED 18 4 .933 func/s113.bmp 381.074 .878 .452 UNFAMILIAR 15 4 .819 func/u101.bmp 384.181 .882 .461 FAMOUS 5 7 1.130 func/f107.bmp 387.371 .814 .538 FAMOUS 7 7 .596 func/f100.bmp 390.545 .995 .598 UNFAMILIAR 13 7 .768 func/u100.bmp 393.752 .910 .448 UNFAMILIAR 14 7 .839 func/u100.bmp 396.859 .960 .433 FAMOUS 7 7 1.039 func/f106.bmp 399.615 .012 0 n/a 999 0 0 func/Circle.bmp ================================================ FILE: tests/data/ds000117/sub-01/ses-mri/func/sub-01_ses-mri_task-facerecognition_run-08_events.tsv ================================================ onset duration circle_duration stim_type trigger button_pushed response_time stim_file 4.000 .945 .523 SCRAMBLED 17 7 1.014 func/s126.bmp 7.224 .949 .503 SCRAMBLED 18 7 .775 func/s126.bmp 10.548 .836 .514 FAMOUS 5 7 .896 func/f118.bmp 13.688 .893 .530 SCRAMBLED 17 7 .903 func/s129.bmp 16.763 .865 .418 SCRAMBLED 18 7 .577 func/s129.bmp 19.952 .923 .567 FAMOUS 5 7 .856 func/f130.bmp 23.143 .859 .487 SCRAMBLED 17 7 1.383 func/s128.bmp 26.300 .976 .527 FAMOUS 5 7 .660 func/f122.bmp 29.457 .927 .414 FAMOUS 6 7 1.146 func/f122.bmp 32.129 20.000 20.000 n/a 999 20000 20.000 func/i999.bmp 52.575 .963 .416 UNFAMILIAR 13 7 .749 func/u123.bmp 55.882 .998 .572 FAMOUS 7 7 .834 func/f118.bmp 59.123 .892 .477 SCRAMBLED 17 7 1.138 func/s118.bmp 62.196 .914 .415 SCRAMBLED 17 7 .784 func/s119.bmp 65.470 .815 .599 FAMOUS 7 7 .681 func/f130.bmp 68.594 .843 .544 UNFAMILIAR 13 7 1.240 func/u114.bmp 71.717 .816 .506 SCRAMBLED 19 7 .518 func/s128.bmp 74.842 .902 .551 UNFAMILIAR 13 7 .613 func/u124.bmp 77.965 .995 .459 UNFAMILIAR 14 7 .646 func/u124.bmp 81.289 .862 .566 UNFAMILIAR 13 7 .656 func/u128.bmp 84.396 .894 .480 UNFAMILIAR 14 7 .579 func/u128.bmp 87.569 .886 .515 UNFAMILIAR 15 7 .835 func/u123.bmp 90.661 .972 .451 FAMOUS 5 7 .792 func/f126.bmp 93.967 .979 .556 FAMOUS 6 7 .915 func/f126.bmp 97.274 .949 .560 SCRAMBLED 19 7 1.291 func/s118.bmp 100.398 .966 .412 SCRAMBLED 17 7 .730 func/s127.bmp 103.589 .906 .465 SCRAMBLED 18 7 .658 func/s127.bmp 106.812 .989 .546 SCRAMBLED 19 7 1.040 func/s119.bmp 110.170 .863 .597 FAMOUS 5 7 .948 func/f119.bmp 113.277 .821 .471 FAMOUS 6 7 .626 func/f119.bmp 116.418 .891 .551 UNFAMILIAR 15 7 .413 func/u114.bmp 119.056 20.000 20.000 n/a 999 20000 20.000 func/i999.bmp 139.502 .866 .405 FAMOUS 5 7 .902 func/f128.bmp 142.609 .833 .484 FAMOUS 5 7 .783 func/f116.bmp 145.716 .981 .514 UNFAMILIAR 13 7 .602 func/u126.bmp 149.057 .940 .592 SCRAMBLED 17 7 1.916 func/s123.bmp 152.330 .974 .560 SCRAMBLED 18 7 .925 func/s123.bmp 155.554 .987 .472 SCRAMBLED 17 7 .725 func/s115.bmp 158.828 .990 .508 SCRAMBLED 18 7 .857 func/s115.bmp 162.152 .888 .563 FAMOUS 5 7 .803 func/f124.bmp 165.376 .984 .559 FAMOUS 7 7 1.243 func/f128.bmp 168.667 .899 .536 UNFAMILIAR 13 7 .749 func/u130.bmp 171.740 .886 .410 UNFAMILIAR 14 7 .713 func/u130.bmp 174.881 .950 .491 FAMOUS 7 7 .809 func/f116.bmp 178.188 .905 .596 UNFAMILIAR 13 7 .527 func/u120.bmp 181.429 .897 .552 UNFAMILIAR 14 7 .882 func/u120.bmp 184.686 .906 .591 UNFAMILIAR 15 7 .855 func/u126.bmp 187.810 .940 .443 UNFAMILIAR 13 7 .910 func/u115.bmp 190.950 .935 .420 UNFAMILIAR 13 7 .383 func/u122.bmp 193.623 20.000 20.000 n/a 999 20000 20.000 func/i999.bmp 214.068 .956 .411 FAMOUS 5 7 .942 func/f120.bmp 217.259 .940 .464 FAMOUS 6 7 .767 func/f120.bmp 220.499 .991 .531 FAMOUS 7 7 .986 func/f124.bmp 223.690 .957 .428 UNFAMILIAR 13 7 .816 func/u127.bmp 226.880 .814 .459 UNFAMILIAR 14 7 .646 func/u127.bmp 229.938 .907 .485 SCRAMBLED 17 7 .663 func/s125.bmp 233.028 .818 .418 SCRAMBLED 18 7 .566 func/s125.bmp 236.201 .944 .524 UNFAMILIAR 13 7 .708 func/u116.bmp 239.358 .853 .441 UNFAMILIAR 15 7 .488 func/u115.bmp 242.565 .987 .590 FAMOUS 5 7 .630 func/f121.bmp 245.839 .872 .511 UNFAMILIAR 15 7 .737 func/u122.bmp 248.997 .880 .518 SCRAMBLED 17 7 .676 func/s121.bmp 252.203 .954 .565 FAMOUS 5 7 .853 func/f127.bmp 255.343 .831 .401 FAMOUS 6 7 .596 func/f127.bmp 258.484 .904 .543 UNFAMILIAR 13 7 .729 func/u118.bmp 261.574 .886 .407 UNFAMILIAR 14 7 .605 func/u118.bmp 264.681 .918 .463 UNFAMILIAR 13 7 .608 func/u121.bmp 267.871 .979 .504 UNFAMILIAR 15 7 .628 func/u116.bmp 271.145 .828 .526 SCRAMBLED 17 7 .667 func/s124.bmp 274.336 .858 .599 FAMOUS 7 7 .672 func/f121.bmp 276.942 20.000 20.000 n/a 999 20000 20.000 func/i999.bmp 297.437 .930 .454 FAMOUS 5 7 1.179 func/f114.bmp 300.544 .998 .416 SCRAMBLED 19 7 .975 func/s121.bmp 303.885 .825 .572 SCRAMBLED 17 7 .983 func/s130.bmp 307.042 .879 .565 UNFAMILIAR 13 7 .860 func/u125.bmp 310.249 .982 .562 SCRAMBLED 17 7 .691 func/s116.bmp 313.523 .919 .523 UNFAMILIAR 15 7 .656 func/u121.bmp 316.797 .952 .599 SCRAMBLED 17 7 .850 func/s120.bmp 320.054 .992 .545 SCRAMBLED 18 7 .615 func/s120.bmp 323.245 .828 .422 SCRAMBLED 19 7 .545 func/s124.bmp 326.435 .930 .591 FAMOUS 5 7 1.316 func/f129.bmp 329.542 .960 .408 FAMOUS 6 7 .863 func/f129.bmp 332.716 .979 .435 FAMOUS 7 7 .852 func/f114.bmp 335.956 .909 .489 FAMOUS 5 7 .618 func/f125.bmp 339.214 .985 .580 FAMOUS 6 7 .751 func/f125.bmp 342.521 .917 .553 SCRAMBLED 19 7 .669 func/s130.bmp 345.745 .938 .537 UNFAMILIAR 13 7 .643 func/u119.bmp 348.969 .821 .512 UNFAMILIAR 15 7 .589 func/u125.bmp 351.976 .807 .417 FAMOUS 5 7 1.078 func/f123.bmp 355.049 .912 .492 FAMOUS 6 7 .781 func/f123.bmp 358.290 .930 .567 SCRAMBLED 19 7 .871 func/s116.bmp 360.962 20.000 20.000 n/a 999 20000 20.000 func/i999.bmp 381.458 .886 .456 FAMOUS 5 7 .878 func/f115.bmp 384.632 .953 .531 FAMOUS 6 7 .692 func/f115.bmp 387.805 .866 .457 UNFAMILIAR 13 7 .676 func/u129.bmp 390.879 .969 .442 UNFAMILIAR 14 7 .444 func/u129.bmp 394.019 .875 .408 SCRAMBLED 17 7 .943 func/s114.bmp 397.193 .964 .530 SCRAMBLED 17 4 .986 func/s122.bmp 400.417 .880 .500 SCRAMBLED 18 7 .798 func/s122.bmp 403.524 .989 .454 UNFAMILIAR 15 7 .637 func/u119.bmp 406.280 .012 0 n/a 999 0 0 func/Circle.bmp ================================================ FILE: tests/data/ds000117/sub-01/ses-mri/func/sub-01_ses-mri_task-facerecognition_run-09_events.tsv ================================================ onset duration circle_duration stim_type trigger button_pushed response_time stim_file 4.000 .981 .521 SCRAMBLED 17 7 1.421 func/s135.bmp 7.224 .832 .477 FAMOUS 5 7 .762 func/f135.bmp 10.330 .982 .411 FAMOUS 6 7 .437 func/f135.bmp 13.554 .907 .470 SCRAMBLED 17 7 .799 func/s138.bmp 16.779 .823 .551 SCRAMBLED 18 4 .907 func/s138.bmp 19.918 .960 .542 FAMOUS 5 7 .733 func/f145.bmp 23.243 .824 .591 FAMOUS 5 7 .411 func/f146.bmp 26.383 .937 .536 FAMOUS 5 7 .863 func/f139.bmp 29.507 .865 .403 SCRAMBLED 19 7 .976 func/s135.bmp 32.112 20.000 20.000 n/a 999 20000 20.000 func/i999.bmp 52.675 .873 .523 SCRAMBLED 17 7 .919 func/s132.bmp 55.865 .871 .536 SCRAMBLED 18 7 .843 func/s132.bmp 58.972 .917 .453 SCRAMBLED 17 7 .675 func/s140.bmp 62.046 .855 .400 SCRAMBLED 18 7 .799 func/s140.bmp 65.270 .942 .600 FAMOUS 5 7 .762 func/f142.bmp 68.443 .861 .464 FAMOUS 6 7 .464 func/f142.bmp 71.500 .939 .420 FAMOUS 7 7 .581 func/f145.bmp 74.807 .946 .588 UNFAMILIAR 13 7 .661 func/u141.bmp 78.031 .945 .508 FAMOUS 7 7 .729 func/f146.bmp 81.138 .848 .400 UNFAMILIAR 13 7 .766 func/u135.bmp 84.213 .960 .468 FAMOUS 7 7 .393 func/f139.bmp 87.369 .813 .432 UNFAMILIAR 13 7 1.202 func/u137.bmp 90.543 .829 .593 UNFAMILIAR 14 7 .984 func/u137.bmp 93.583 .946 .436 FAMOUS 5 7 .805 func/f133.bmp 96.706 .822 .407 FAMOUS 6 7 .706 func/f133.bmp 99.830 .950 .527 FAMOUS 5 7 .751 func/f143.bmp 103.037 .893 .498 SCRAMBLED 17 7 .908 func/s146.bmp 106.194 .819 .499 SCRAMBLED 18 7 .885 func/s146.bmp 109.218 .811 .435 UNFAMILIAR 15 7 .897 func/u141.bmp 111.773 20.000 20.000 n/a 999 20000 20.000 func/i999.bmp 132.386 .904 .581 UNFAMILIAR 13 7 .509 func/u145.bmp 135.626 .910 .567 UNFAMILIAR 15 7 .809 func/u135.bmp 138.800 .979 .490 UNFAMILIAR 13 7 .614 func/u142.bmp 141.991 .861 .448 UNFAMILIAR 13 7 .767 func/u140.bmp 145.064 .978 .443 FAMOUS 5 7 .736 func/f141.bmp 148.355 .973 .544 FAMOUS 6 7 .628 func/f141.bmp 151.545 .897 .442 FAMOUS 7 7 .707 func/f143.bmp 154.769 .819 .562 SCRAMBLED 17 7 .693 func/s139.bmp 157.876 .970 .532 SCRAMBLED 18 4 .939 func/s139.bmp 161.133 .977 .507 UNFAMILIAR 13 7 .624 func/u132.bmp 164.307 .953 .422 UNFAMILIAR 15 7 .473 func/u145.bmp 167.514 .996 .490 FAMOUS 5 7 1.088 func/f134.bmp 170.738 .888 .455 FAMOUS 6 7 .903 func/f134.bmp 173.912 .873 .516 UNFAMILIAR 15 7 .639 func/u142.bmp 177.152 .999 .595 SCRAMBLED 17 7 .731 func/s141.bmp 180.510 .825 .591 UNFAMILIAR 15 7 .797 func/u140.bmp 183.533 .891 .419 UNFAMILIAR 13 7 .659 func/u144.bmp 186.740 .830 .547 SCRAMBLED 17 7 .576 func/s131.bmp 189.780 .871 .448 SCRAMBLED 18 7 .609 func/s131.bmp 192.403 20.000 20.000 n/a 999 20000 20.000 func/i999.bmp 212.882 .948 .445 FAMOUS 5 7 .752 func/f138.bmp 216.072 .801 .471 UNFAMILIAR 15 7 .490 func/u132.bmp 219.096 .930 .462 UNFAMILIAR 13 7 .457 func/u146.bmp 222.269 .963 .476 SCRAMBLED 17 7 .596 func/s133.bmp 225.577 .800 .575 SCRAMBLED 18 7 .874 func/s133.bmp 228.700 .966 .557 SCRAMBLED 19 7 .799 func/s141.bmp 231.924 .890 .489 SCRAMBLED 17 7 .491 func/s137.bmp 235.215 .810 .560 UNFAMILIAR 15 7 .807 func/u144.bmp 238.255 .954 .456 UNFAMILIAR 13 7 .725 func/u143.bmp 241.446 .849 .467 UNFAMILIAR 14 7 .728 func/u143.bmp 244.619 .805 .563 UNFAMILIAR 13 7 .649 func/u138.bmp 247.693 .902 .487 UNFAMILIAR 14 7 .554 func/u138.bmp 250.766 .919 .411 FAMOUS 7 7 .390 func/f138.bmp 253.890 .852 .438 SCRAMBLED 17 7 .533 func/s134.bmp 257.048 .882 .551 SCRAMBLED 18 7 .789 func/s134.bmp 260.204 .942 .508 UNFAMILIAR 15 7 .893 func/u146.bmp 263.394 .915 .483 FAMOUS 5 7 .534 func/f144.bmp 266.636 .973 .568 FAMOUS 5 7 .764 func/f136.bmp 269.926 .894 .547 FAMOUS 6 7 .992 func/f136.bmp 273.083 .872 .499 SCRAMBLED 19 7 .965 func/s137.bmp 275.705 20.000 20.000 n/a 999 20000 20.000 func/i999.bmp 296.251 .834 .517 FAMOUS 5 7 1.131 func/f131.bmp 299.308 .992 .458 FAMOUS 5 7 .823 func/f140.bmp 302.632 .852 .565 FAMOUS 6 7 .885 func/f140.bmp 305.789 .903 .539 SCRAMBLED 17 4 1.022 func/s145.bmp 308.996 .898 .521 UNFAMILIAR 13 7 .880 func/u131.bmp 312.086 .893 .428 FAMOUS 7 7 .642 func/f144.bmp 315.177 .865 .434 SCRAMBLED 17 7 .355 func/s142.bmp 318.200 .802 .400 SCRAMBLED 18 4 .612 func/s142.bmp 321.190 .959 .431 FAMOUS 5 7 .864 func/f137.bmp 324.447 .963 .523 FAMOUS 6 7 1.022 func/f137.bmp 327.571 .903 .400 FAMOUS 7 7 .589 func/f131.bmp 330.661 .853 .426 SCRAMBLED 17 7 1.126 func/s143.bmp 333.701 .991 .403 UNFAMILIAR 13 7 1.160 func/u136.bmp 336.892 .819 .426 UNFAMILIAR 14 7 .544 func/u136.bmp 339.915 .950 .441 SCRAMBLED 19 7 .950 func/s145.bmp 343.222 .934 .587 UNFAMILIAR 13 7 .835 func/u134.bmp 346.446 .915 .521 UNFAMILIAR 14 7 .775 func/u134.bmp 349.586 .957 .465 UNFAMILIAR 15 7 .831 func/u131.bmp 352.827 .988 .516 UNFAMILIAR 13 7 .474 func/u133.bmp 355.566 20.000 20.000 n/a 999 20000 20.000 func/i999.bmp 376.196 .836 .591 SCRAMBLED 17 7 .980 func/s136.bmp 379.253 .968 .445 SCRAMBLED 18 7 .770 func/s136.bmp 382.410 .977 .425 UNFAMILIAR 13 7 .858 func/u139.bmp 385.617 .911 .467 SCRAMBLED 19 7 .789 func/s143.bmp 388.774 .868 .473 SCRAMBLED 17 7 1.137 func/s144.bmp 391.998 .805 .590 SCRAMBLED 18 7 .773 func/s144.bmp 395.021 .875 .447 FAMOUS 5 7 .738 func/f132.bmp 397.694 .012 0 n/a 999 0 0 func/Circle.bmp ================================================ FILE: tests/data/ds000117/sub-02/ses-meg/beh/sub-02_ses-meg_task-facerecognition_events.tsv ================================================ onset duration response_time button_press stim_file n/a n/a 1.586 1 meg/u079.bmp n/a n/a 0.722 1 meg/f051.bmp n/a n/a 1.135 3 meg/f116.bmp n/a n/a 2.651 1 meg/u038.bmp n/a n/a 2.237 2 meg/f148.bmp n/a n/a 1.69 1 meg/f016.bmp n/a n/a 1.646 3 meg/f110.bmp n/a n/a 1.996 2 meg/f108.bmp n/a n/a 1.435 1 meg/f150.bmp n/a n/a 1.554 1 meg/u120.bmp n/a n/a 2.124 2 meg/f043.bmp n/a n/a 2.125 2 meg/u065.bmp n/a n/a 2.11 2 meg/u109.bmp n/a n/a 1.625 1 meg/u114.bmp n/a n/a 1.719 1 meg/u053.bmp n/a n/a 1.234 3 meg/f101.bmp n/a n/a 3.778 1 meg/u025.bmp n/a n/a 1.485 3 meg/f120.bmp n/a n/a 1.042 3 meg/f056.bmp n/a n/a 2.584 2 meg/u063.bmp n/a n/a 1.247 3 meg/f026.bmp n/a n/a 2.438 1 meg/u001.bmp n/a n/a 1.314 3 meg/f009.bmp n/a n/a 1.686 1 meg/u136.bmp n/a n/a 1.147 3 meg/f084.bmp n/a n/a 1.062 3 meg/f088.bmp n/a n/a 1.332 2 meg/u085.bmp n/a n/a 2.847 2 meg/f115.bmp n/a n/a 0.963 1 meg/u137.bmp n/a n/a 1.381 3 meg/u143.bmp n/a n/a 1.27 1 meg/u099.bmp n/a n/a 1.316 2 meg/f085.bmp n/a n/a 1.552 1 meg/u121.bmp n/a n/a 2.602 2 meg/u024.bmp n/a n/a 2.592 3 meg/f131.bmp n/a n/a 0.988 2 meg/u021.bmp n/a n/a 0.966 1 meg/f095.bmp n/a n/a 1.944 1 meg/u050.bmp n/a n/a 1.193 2 meg/u134.bmp n/a n/a 1.018 2 meg/u116.bmp n/a n/a 1.339 2 meg/u008.bmp n/a n/a 1.037 2 meg/u069.bmp n/a n/a 1.085 3 meg/f098.bmp n/a n/a 1.098 3 meg/f078.bmp n/a n/a 1.12 3 meg/f109.bmp n/a n/a 1.748 3 meg/u062.bmp n/a n/a 1.03 1 meg/f136.bmp n/a n/a 1.005 2 meg/u033.bmp n/a n/a 1.198 3 meg/u089.bmp n/a n/a 1.047 3 meg/f100.bmp n/a n/a 0.846 1 meg/u126.bmp n/a n/a 0.83 3 meg/f074.bmp n/a n/a 1.822 2 meg/u101.bmp n/a n/a 1.964 3 meg/f147.bmp n/a n/a 1.26 1 meg/f099.bmp n/a n/a 1.13 3 meg/f018.bmp n/a n/a 1.213 1 meg/u019.bmp n/a n/a 1.126 3 meg/f065.bmp n/a n/a 1.177 2 meg/f105.bmp n/a n/a 0.971 1 meg/u118.bmp n/a n/a 1.445 2 meg/f037.bmp n/a n/a 1.619 1 meg/u086.bmp n/a n/a 1.754 3 meg/f079.bmp n/a n/a 5.147 2 meg/f135.bmp n/a n/a 1.141 1 meg/u081.bmp n/a n/a 0.928 3 meg/u090.bmp n/a n/a 1.692 3 meg/f142.bmp n/a n/a 0.856 1 meg/u051.bmp n/a n/a 1.279 2 meg/u127.bmp n/a n/a 0.932 1 meg/f092.bmp n/a n/a 1.033 1 meg/u015.bmp n/a n/a 1.255 1 meg/u084.bmp n/a n/a 0.589 1 meg/u056.bmp n/a n/a 1.144 1 meg/u103.bmp n/a n/a 1.019 2 meg/u004.bmp n/a n/a 0.739 1 meg/u070.bmp n/a n/a 1.407 1 meg/f102.bmp n/a n/a 0.671 1 meg/f023.bmp n/a n/a 0.813 1 meg/u146.bmp n/a n/a 0.734 1 meg/u150.bmp n/a n/a 0.803 3 meg/f029.bmp n/a n/a 8.1 2 meg/u023.bmp n/a n/a 1.578 1 meg/u060.bmp n/a n/a 0.795 1 meg/u057.bmp n/a n/a 0.949 1 meg/u036.bmp n/a n/a 1.216 2 meg/u100.bmp n/a n/a 4.232 2 meg/u002.bmp n/a n/a 1.152 3 meg/f075.bmp n/a n/a 1.451 1 meg/f103.bmp n/a n/a 1.193 1 meg/f091.bmp n/a n/a 0.986 1 meg/u017.bmp n/a n/a 0.963 3 meg/f067.bmp n/a n/a 1.142 1 meg/u093.bmp n/a n/a 0.961 3 meg/f021.bmp n/a n/a 1.3 3 meg/f129.bmp n/a n/a 0.76 1 meg/u148.bmp n/a n/a 1.052 2 meg/u104.bmp n/a n/a 1.963 3 meg/f040.bmp n/a n/a 0.963 3 meg/f041.bmp n/a n/a 0.862 3 meg/f090.bmp n/a n/a 3.021 1 meg/f024.bmp n/a n/a 1.105 2 meg/u139.bmp n/a n/a 0.917 1 meg/f097.bmp n/a n/a 1.082 2 meg/u076.bmp n/a n/a 1.16 3 meg/f071.bmp n/a n/a 2.178 2 meg/f049.bmp n/a n/a 0.913 1 meg/u071.bmp n/a n/a 3.005 2 meg/f107.bmp n/a n/a 0.81 1 meg/f132.bmp n/a n/a 0.987 3 meg/f133.bmp n/a n/a 0.901 3 meg/f122.bmp n/a n/a 1.242 3 meg/f130.bmp n/a n/a 1.633 3 meg/u031.bmp n/a n/a 1.624 3 meg/f146.bmp n/a n/a 0.782 3 meg/f038.bmp n/a n/a 1.289 3 meg/f080.bmp n/a n/a 0.797 1 meg/f073.bmp n/a n/a 1.119 1 meg/u049.bmp n/a n/a 0.819 1 meg/f089.bmp n/a n/a 0.988 1 meg/f141.bmp n/a n/a 1.837 1 meg/u006.bmp n/a n/a 3.947 1 meg/u059.bmp n/a n/a 1.421 1 meg/u097.bmp n/a n/a 2.332 2 meg/f010.bmp n/a n/a 0.997 3 meg/f069.bmp n/a n/a 1.198 2 meg/f149.bmp n/a n/a 1.135 1 meg/u080.bmp n/a n/a 0.875 1 meg/u047.bmp n/a n/a 0.865 1 meg/u147.bmp n/a n/a 1.24 3 meg/f025.bmp n/a n/a 1.175 2 meg/f031.bmp n/a n/a 1.68 1 meg/u011.bmp n/a n/a 1.261 1 meg/u067.bmp n/a n/a 1.139 2 meg/f104.bmp n/a n/a 0.798 3 meg/f013.bmp n/a n/a 0.783 3 meg/f082.bmp n/a n/a 1.17 3 meg/f113.bmp n/a n/a 1.02 3 meg/f032.bmp n/a n/a 1.028 1 meg/f054.bmp n/a n/a 1.124 2 meg/f030.bmp n/a n/a 1.064 2 meg/f011.bmp n/a n/a 0.941 2 meg/u032.bmp n/a n/a 1.064 2 meg/u039.bmp n/a n/a 1.047 1 meg/f112.bmp n/a n/a 0.998 1 meg/u007.bmp n/a n/a 1.687 3 meg/u027.bmp n/a n/a 1.155 1 meg/u016.bmp n/a n/a 2.149 3 meg/u108.bmp n/a n/a 0.902 1 meg/u005.bmp n/a n/a 0.988 2 meg/f138.bmp n/a n/a 0.949 1 meg/u140.bmp n/a n/a 0.944 2 meg/f144.bmp n/a n/a 0.844 1 meg/f127.bmp n/a n/a 0.619 1 meg/u074.bmp n/a n/a 1.468 1 meg/f126.bmp n/a n/a 1.234 1 meg/u149.bmp n/a n/a 0.865 1 meg/u082.bmp n/a n/a 0.744 1 meg/u012.bmp n/a n/a 0.692 1 meg/u141.bmp n/a n/a 0.77 1 meg/f087.bmp n/a n/a 0.941 1 meg/u022.bmp n/a n/a 0.983 3 meg/f004.bmp n/a n/a 0.969 2 meg/f111.bmp n/a n/a 1.075 3 meg/f015.bmp n/a n/a 1.001 1 meg/u112.bmp n/a n/a 2.338 1 meg/u040.bmp n/a n/a 1.107 3 meg/f042.bmp n/a n/a 0.935 2 meg/u041.bmp n/a n/a 0.867 1 meg/u044.bmp n/a n/a 2.466 2 meg/f128.bmp n/a n/a 1.069 1 meg/f143.bmp n/a n/a 0.964 1 meg/f008.bmp n/a n/a 1.31 1 meg/f123.bmp n/a n/a 0.946 3 meg/u045.bmp n/a n/a 2.918 2 meg/f036.bmp n/a n/a 2.822 2 meg/f048.bmp n/a n/a 1.947 1 meg/u098.bmp n/a n/a 1.156 3 meg/f035.bmp n/a n/a 2.613 1 meg/f125.bmp n/a n/a 1.434 1 meg/f114.bmp n/a n/a 1.593 1 meg/u037.bmp n/a n/a 6.001 1 meg/f118.bmp n/a n/a 2.151 2 meg/u094.bmp n/a n/a 0.768 2 meg/u125.bmp n/a n/a 1.283 2 meg/u028.bmp n/a n/a 1.376 2 meg/f002.bmp n/a n/a 1.122 1 meg/u119.bmp n/a n/a 0.677 1 meg/u068.bmp n/a n/a 1.908 2 meg/u111.bmp n/a n/a 1.023 3 meg/f007.bmp n/a n/a 0.87 3 meg/f033.bmp n/a n/a 0.989 2 meg/u105.bmp n/a n/a 1.694 1 meg/f068.bmp n/a n/a 1.13 1 meg/f028.bmp n/a n/a 0.997 3 meg/f022.bmp n/a n/a 0.958 2 meg/u052.bmp n/a n/a 0.896 1 meg/f077.bmp n/a n/a 1.142 1 meg/u035.bmp n/a n/a 1.033 1 meg/u087.bmp n/a n/a 1.041 1 meg/u083.bmp n/a n/a 1.168 1 meg/f058.bmp n/a n/a 1.146 3 meg/f072.bmp n/a n/a 0.989 3 meg/f020.bmp n/a n/a 2.166 1 meg/u145.bmp n/a n/a 3.487 1 meg/f060.bmp n/a n/a 0.959 1 meg/u066.bmp n/a n/a 0.914 3 meg/f012.bmp n/a n/a 1.134 2 meg/u095.bmp n/a n/a 1.002 1 meg/u026.bmp n/a n/a 1.515 1 meg/u064.bmp n/a n/a 1.047 1 meg/f083.bmp n/a n/a 1.054 1 meg/f063.bmp n/a n/a 2.932 1 meg/u009.bmp n/a n/a 0.939 2 meg/f124.bmp n/a n/a 1.023 3 meg/f034.bmp n/a n/a 1.015 1 meg/u117.bmp n/a n/a 1.255 2 meg/f121.bmp n/a n/a 0.997 1 meg/f106.bmp n/a n/a 1.487 1 meg/u018.bmp n/a n/a 2.115 1 meg/u138.bmp n/a n/a 1.542 3 meg/f061.bmp n/a n/a 4.2 3 meg/f064.bmp n/a n/a 1.786 2 meg/f019.bmp n/a n/a 1.45 2 meg/f017.bmp n/a n/a 0.992 1 meg/u077.bmp n/a n/a 0.954 1 meg/u043.bmp n/a n/a 1.108 1 meg/u030.bmp n/a n/a 1.057 1 meg/f093.bmp n/a n/a 2.119 3 meg/f047.bmp n/a n/a 0.977 1 meg/u073.bmp n/a n/a 1.082 3 meg/f070.bmp n/a n/a 0.848 2 meg/f039.bmp n/a n/a 1.072 2 meg/f096.bmp n/a n/a 0.878 1 meg/u072.bmp n/a n/a 1.106 3 meg/f119.bmp n/a n/a 0.766 1 meg/u102.bmp n/a n/a 0.889 2 meg/u122.bmp n/a n/a 0.687 1 meg/u055.bmp n/a n/a 0.653 1 meg/u133.bmp n/a n/a 0.676 3 meg/f086.bmp n/a n/a 1.019 1 meg/u042.bmp n/a n/a 1.595 1 meg/u096.bmp n/a n/a 0.971 2 meg/u088.bmp n/a n/a 1.309 1 meg/u115.bmp n/a n/a 1.225 3 meg/f081.bmp n/a n/a 0.769 3 meg/f053.bmp n/a n/a 0.916 3 meg/f046.bmp n/a n/a 1.041 1 meg/u113.bmp n/a n/a 0.687 3 meg/f003.bmp n/a n/a 0.989 1 meg/u058.bmp n/a n/a 0.67 1 meg/u142.bmp n/a n/a 0.765 1 meg/f094.bmp n/a n/a 0.656 1 meg/u029.bmp n/a n/a 0.624 1 meg/u061.bmp n/a n/a 0.72 1 meg/u003.bmp n/a n/a 1.269 2 meg/u106.bmp n/a n/a 0.827 1 meg/u091.bmp n/a n/a 1.123 3 meg/u092.bmp n/a n/a 0.815 1 meg/u123.bmp n/a n/a 0.752 1 meg/f059.bmp n/a n/a 0.94 1 meg/f134.bmp n/a n/a 1.175 1 meg/u010.bmp n/a n/a 2.025 3 meg/f139.bmp n/a n/a 1.598 2 meg/f027.bmp n/a n/a 0.686 1 meg/u075.bmp n/a n/a 0.684 1 meg/u013.bmp n/a n/a 0.707 1 meg/u020.bmp n/a n/a 0.696 1 meg/u110.bmp n/a n/a 2.686 2 meg/f006.bmp n/a n/a 1.864 3 meg/f057.bmp n/a n/a 0.796 1 meg/u128.bmp n/a n/a 0.822 1 meg/u130.bmp n/a n/a 1.03 1 meg/u014.bmp n/a n/a 0.974 1 meg/u144.bmp n/a n/a 0.88 1 meg/u132.bmp n/a n/a 0.87 1 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30.231001 -69.561437 82.449622 EEG70 43.441176 -71.045503 55.840127 EEG71 -26.563726 -82.068458 61.233371 EEG72 -0.150288 -87.540075 59.425525 EEG73 25.532788 -84.128201 59.583589 EEG74 -0.410772 -91.595180 27.508289 ================================================ FILE: tests/data/ds000117/sub-02/ses-meg/meg/sub-02_ses-meg_task-facerecognition_coordsystem.json ================================================ { "MEGCoordinateSystem": "Elekta/Neuromag", "MEGCoordinateUnits": "mm", "MEGCoordinateSystemDescription": "VectorView: 102 Magnetometers, 204 Planar Gradiometers", "EEGCoordinateSystem": "Elekta/Neuromag", "EEGCoordinateUnits": "mm", "EEGCoordinateSystemDescription": "70 Easycap sites, roughly corresponding to extended 10-20 system: see http://imaging.mrc-cbu.cam.ac.uk/meg/VectorviewDescription#EEGlabels", "DigitizedHeadPoints": "sub-02_ses-meg_headshape.pos", "DigitizedHeadPointsCoordinateSystem": "Elekta/Neuromag", "DigitizedHeadPointsCoordinateUnits": "mm", "DigitizedHeadPointsCoordinateSystemDescription": "Headpoints include arcs of points along front of head and around nose, as well as centre of 70 EEG electrodes spread across scalp, which also help define headshape. Beware that nose points can bias coregistration with MRI if MRI image does not include all of the nose.", "IntendedFor": "sub-02/ses-mri/anat/sub-02_ses-mri_acq-mprage_T1w.nii.gz", "AnatomicalLandmarkCoordinates": { "LPA": [-69.66,0,0], "RPA": [71.69,0,0], "Nasion": [0,102.82,0] }, "AnatomicalLandmarkCoordinateSystem": "Elekta/Neuromag", "AnatomicalLandmarkCoordinateUnits": "mm", "AnatomicalLandmarkCoordinateSystemDescription": "For images of anatomical landmarks, see http://imaging.mrc-cbu.cam.ac.uk/meg/SubjectPreparation", "HeadCoilCoordinates": { "coil1": [71.23249,13.62723,91.294631], "coil2": [16.225291,-85.930645,74.58901400000001], "coil3": [-15.709732,-7.188886,132.537663], "coil4": [-12.606272,86.735755,93.681544], "coil5": [-71.436286,-2.527088,90.64806299999999] }, "HeadCoilCoordinateSystem": "Elekta/Neuromag", "HeadCoilCoordinateUnits": "mm", "HeadCoilCoordinateSystemDescription": "5 HPI coils used", "FiducialsDescription": "For images of anatomical landmarks, see http://imaging.mrc-cbu.cam.ac.uk/meg/SubjectPreparation" } ================================================ FILE: tests/data/ds000117/sub-02/ses-meg/meg/sub-02_ses-meg_task-facerecognition_run-01_events.tsv ================================================ onset duration onset_sample stim_type trigger stim_file 25.1136 0 27625 Famous 5 meg/f074.bmp 28.1709 0 30988 Unfamiliar 13 meg/u011.bmp 31.3282 0 34461 Scrambled 17 meg/s036.bmp 34.4355 0 37879 Famous 5 meg/f112.bmp 37.7764 0 41554 Famous 6 meg/f112.bmp 40.9673 0 45064 Unfamiliar 13 meg/u066.bmp 44.2745 0 48702 Unfamiliar 14 meg/u066.bmp 47.3482 0 52083 Unfamiliar 13 meg/u113.bmp 50.5055 0 55556 Unfamiliar 14 meg/u113.bmp 53.58 0 58938 Famous 7 meg/f074.bmp 56.6536 0 62319 Scrambled 17 meg/s100.bmp 59.7773 0 65755 Scrambled 18 meg/s100.bmp 62.9345 0 69228 Unfamiliar 15 meg/u011.bmp 66.1582 0 72774 Unfamiliar 13 meg/u069.bmp 69.3664 0 76303 Unfamiliar 14 meg/u069.bmp 72.39 0 79629 Scrambled 19 meg/s036.bmp 75.63 0 83193 Famous 5 meg/f039.bmp 78.9545 0 86850 Famous 5 meg/f020.bmp 82.1618 0 90378 Famous 5 meg/f101.bmp 85.37 0 93907 Scrambled 17 meg/s058.bmp 88.46 0 97306 Scrambled 18 meg/s058.bmp 91.8345 0 101018 Unfamiliar 13 meg/u061.bmp 94.9245 0 104417 Unfamiliar 14 meg/u061.bmp 98.1155 0 107927 Famous 5 meg/f105.bmp 101.356 0 111492 Famous 7 meg/f039.bmp 104.464 0 114910 Famous 5 meg/f138.bmp 107.505 0 118255 Famous 7 meg/f020.bmp 110.795 0 121874 Famous 5 meg/f064.bmp 113.852 0 125237 Famous 6 meg/f064.bmp 116.926 0 128619 Famous 7 meg/f101.bmp 120.1 0 132110 Unfamiliar 13 meg/u127.bmp 123.24 0 135564 Scrambled 17 meg/s103.bmp 126.364 0 139000 Unfamiliar 13 meg/u143.bmp 129.538 0 142492 Famous 7 meg/f105.bmp 132.762 0 146038 Famous 5 meg/f119.bmp 135.903 0 149493 Famous 6 meg/f119.bmp 139.244 0 153168 Famous 7 meg/f138.bmp 142.552 0 156807 Unfamiliar 13 meg/u074.bmp 145.742 0 160316 Unfamiliar 14 meg/u074.bmp 149.066 0 163973 Unfamiliar 13 meg/u134.bmp 152.124 0 167336 Unfamiliar 15 meg/u127.bmp 155.381 0 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meg/u138.bmp 380.634 0 418697 Scrambled 19 meg/s109.bmp 383.925 0 422317 Scrambled 17 meg/s094.bmp 386.998 0 425698 Scrambled 18 meg/s094.bmp 390.223 0 429245 Famous 5 meg/f095.bmp 393.279 0 432607 Famous 6 meg/f095.bmp 396.537 0 436191 Unfamiliar 15 meg/u141.bmp 399.761 0 439737 Scrambled 17 meg/s132.bmp 403.002 0 443302 Scrambled 18 meg/s132.bmp 406.025 0 446628 Scrambled 17 meg/s055.bmp 409.233 0 450156 Unfamiliar 13 meg/u005.bmp 412.424 0 453666 Unfamiliar 13 meg/u054.bmp 415.597 0 457157 Famous 5 meg/f022.bmp 418.738 0 460612 Famous 6 meg/f022.bmp 422.079 0 464287 Scrambled 17 meg/s095.bmp 425.253 0 467778 Scrambled 17 meg/s024.bmp 428.294 0 471123 Scrambled 19 meg/s055.bmp 431.501 0 474651 Famous 5 meg/f025.bmp 434.541 0 477995 Famous 6 meg/f025.bmp 437.615 0 481376 Unfamiliar 15 meg/u005.bmp 440.672 0 484739 Famous 5 meg/f137.bmp 443.946 0 488341 Unfamiliar 15 meg/u054.bmp 447.271 0 491998 Unfamiliar 13 meg/u107.bmp 450.445 0 495489 Unfamiliar 13 meg/u108.bmp 453.618 0 498980 Scrambled 19 meg/s095.bmp 456.809 0 502490 Famous 5 meg/f093.bmp 459.95 0 505945 Famous 6 meg/f093.bmp 463.224 0 509546 Scrambled 19 meg/s024.bmp 466.247 0 512872 Unfamiliar 13 meg/u121.bmp 469.538 0 516492 Famous 5 meg/f048.bmp 472.73 0 520003 Famous 7 meg/f137.bmp 475.886 0 523475 Famous 5 meg/f131.bmp 479.044 0 526948 Unfamiliar 15 meg/u107.bmp 482.235 0 530458 Famous 5 meg/f078.bmp 485.425 0 533968 Famous 6 meg/f078.bmp 488.716 0 537588 Unfamiliar 15 meg/u108.bmp ================================================ FILE: tests/data/ds000117/sub-02/ses-meg/meg/sub-02_ses-meg_task-facerecognition_run-01_meg.fif ================================================ ================================================ FILE: tests/data/ds000117/sub-02/ses-meg/meg/sub-02_ses-meg_task-facerecognition_run-02_events.tsv ================================================ onset duration onset_sample stim_type trigger stim_file 25.0118 0 27513 Famous 5 meg/f108.bmp 28.2527 0 31078 Scrambled 17 meg/s141.bmp 31.4427 0 34587 Scrambled 18 meg/s141.bmp 34.5673 0 38024 Unfamiliar 13 meg/u132.bmp 37.7409 0 41515 Unfamiliar 14 meg/u132.bmp 40.7809 0 44859 Scrambled 17 meg/s033.bmp 44.0718 0 48479 Scrambled 18 meg/s033.bmp 47.2464 0 51971 Scrambled 17 meg/s062.bmp 50.4364 0 55480 Famous 5 meg/f102.bmp 53.5273 0 58880 Famous 6 meg/f102.bmp 56.6009 0 62261 Famous 7 meg/f108.bmp 59.7764 0 65754 Unfamiliar 13 meg/u106.bmp 63.1164 0 69428 Scrambled 17 meg/s017.bmp 66.3564 0 72992 Unfamiliar 13 meg/u024.bmp 69.5645 0 76521 Famous 5 meg/f051.bmp 72.9055 0 80196 Scrambled 19 meg/s062.bmp 76.0627 0 83669 Scrambled 17 meg/s025.bmp 79.2536 0 87179 Unfamiliar 13 meg/u097.bmp 82.5109 0 90762 Unfamiliar 15 meg/u106.bmp 85.7845 0 94363 Famous 5 meg/f147.bmp 89.0427 0 97947 Scrambled 19 meg/s017.bmp 92.1827 0 101401 Scrambled 17 meg/s080.bmp 95.3236 0 104856 Scrambled 18 meg/s080.bmp 98.4136 0 108255 Unfamiliar 15 meg/u024.bmp 101.555 0 111710 Unfamiliar 13 meg/u098.bmp 104.829 0 115312 Unfamiliar 14 meg/u098.bmp 107.986 0 118785 Famous 7 meg/f051.bmp 111.327 0 122460 Unfamiliar 13 meg/u100.bmp 114.485 0 125933 Scrambled 19 meg/s025.bmp 117.608 0 129369 Famous 5 meg/f099.bmp 120.699 0 132769 Famous 6 meg/f099.bmp 123.705 0 136076 Unfamiliar 15 meg/u097.bmp 127.014 0 139715 Scrambled 17 meg/s071.bmp 130.171 0 143188 Scrambled 18 meg/s071.bmp 133.428 0 146771 Famous 7 meg/f147.bmp 136.719 0 150391 Unfamiliar 13 meg/u091.bmp 139.843 0 153827 Unfamiliar 14 meg/u091.bmp 143.084 0 157392 Scrambled 17 meg/s106.bmp 146.308 0 160939 Scrambled 18 meg/s106.bmp 149.415 0 164356 Famous 5 meg/f034.bmp 152.639 0 167903 Unfamiliar 15 meg/u100.bmp 155.813 0 171394 Scrambled 17 meg/s006.bmp 158.954 0 174849 Scrambled 18 meg/s006.bmp 162.127 0 178340 Unfamiliar 13 meg/u083.bmp 165.335 0 181869 Scrambled 17 meg/s015.bmp 168.626 0 185489 Scrambled 17 meg/s142.bmp 171.85 0 189035 Scrambled 18 meg/s142.bmp 175.125 0 192637 Famous 5 meg/f075.bmp 178.265 0 196091 Famous 7 meg/f034.bmp 181.489 0 199638 Unfamiliar 13 meg/u103.bmp 184.563 0 203019 Unfamiliar 13 meg/u071.bmp 187.704 0 206474 Unfamiliar 14 meg/u071.bmp 190.844 0 209928 Unfamiliar 15 meg/u083.bmp 194.051 0 213456 Scrambled 17 meg/s043.bmp 197.192 0 216911 Scrambled 19 meg/s015.bmp 200.265 0 220292 Unfamiliar 13 meg/u122.bmp 203.573 0 223930 Unfamiliar 13 meg/u028.bmp 206.897 0 227587 Unfamiliar 14 meg/u028.bmp 210.005 0 231005 Famous 7 meg/f075.bmp 213.045 0 234349 Unfamiliar 13 meg/u105.bmp 216.235 0 237859 Unfamiliar 15 meg/u103.bmp 219.343 0 241277 Famous 5 meg/f041.bmp 222.701 0 244971 Unfamiliar 13 meg/u022.bmp 225.791 0 248370 Unfamiliar 14 meg/u022.bmp 228.932 0 251825 Scrambled 19 meg/s043.bmp 232.123 0 255335 Unfamiliar 13 meg/u023.bmp 235.196 0 258716 Unfamiliar 15 meg/u122.bmp 238.405 0 262245 Famous 5 meg/f028.bmp 241.577 0 265735 Famous 6 meg/f028.bmp 244.752 0 269227 Unfamiliar 13 meg/u052.bmp 247.942 0 272736 Unfamiliar 14 meg/u052.bmp 251.166 0 276283 Unfamiliar 15 meg/u105.bmp 254.274 0 279701 Unfamiliar 13 meg/u017.bmp 257.465 0 283211 Famous 7 meg/f041.bmp 260.705 0 286775 Famous 5 meg/f089.bmp 263.829 0 290212 Famous 6 meg/f089.bmp 266.986 0 293685 Unfamiliar 13 meg/u104.bmp 270.043 0 297047 Unfamiliar 15 meg/u023.bmp 273.334 0 300667 Famous 5 meg/f149.bmp 276.491 0 304140 Famous 5 meg/f120.bmp 279.565 0 307521 Famous 6 meg/f120.bmp 282.689 0 310958 Scrambled 17 meg/s072.bmp 285.829 0 314412 Scrambled 18 meg/s072.bmp 289.02 0 317922 Unfamiliar 15 meg/u017.bmp 292.345 0 321579 Famous 5 meg/f013.bmp 295.485 0 325034 Famous 6 meg/f013.bmp 298.659 0 328525 Famous 5 meg/f004.bmp 301.783 0 331961 Famous 6 meg/f004.bmp 304.974 0 335471 Unfamiliar 15 meg/u104.bmp 308.265 0 339092 Famous 5 meg/f109.bmp 311.439 0 342583 Famous 7 meg/f149.bmp 314.779 0 346257 Famous 5 meg/f136.bmp 317.904 0 349694 Scrambled 17 meg/s048.bmp 321.161 0 353277 Scrambled 18 meg/s048.bmp 324.502 0 356952 Scrambled 17 meg/s067.bmp 327.676 0 360444 Scrambled 17 meg/s126.bmp 330.783 0 363861 Scrambled 18 meg/s126.bmp 333.974 0 367371 Unfamiliar 13 meg/u150.bmp 337.148 0 370863 Unfamiliar 14 meg/u150.bmp 340.288 0 374317 Famous 7 meg/f109.bmp 343.529 0 377882 Famous 5 meg/f080.bmp 346.67 0 381337 Famous 6 meg/f080.bmp 349.844 0 384828 Famous 7 meg/f136.bmp 353.017 0 388319 Famous 5 meg/f073.bmp 356.325 0 391958 Famous 5 meg/f065.bmp 359.566 0 395523 Scrambled 19 meg/s067.bmp 362.656 0 398922 Scrambled 17 meg/s131.bmp 365.664 0 402230 Famous 5 meg/f046.bmp 368.838 0 405722 Famous 6 meg/f046.bmp 372.012 0 409213 Famous 5 meg/f087.bmp 375.235 0 412759 Scrambled 17 meg/s144.bmp 378.56 0 416416 Famous 7 meg/f073.bmp 381.684 0 419852 Scrambled 17 meg/s085.bmp 384.791 0 423270 Scrambled 18 meg/s085.bmp 387.965 0 426761 Famous 7 meg/f065.bmp 391.155 0 430271 Unfamiliar 13 meg/u003.bmp 394.346 0 433781 Scrambled 19 meg/s131.bmp 397.454 0 437199 Scrambled 17 meg/s093.bmp 400.594 0 440653 Famous 5 meg/f132.bmp 403.617 0 443979 Famous 6 meg/f132.bmp 406.892 0 447581 Famous 7 meg/f087.bmp 409.949 0 450944 Famous 5 meg/f117.bmp 413.19 0 454509 Famous 6 meg/f117.bmp 416.28 0 457908 Scrambled 19 meg/s144.bmp 419.421 0 461363 Unfamiliar 13 meg/u070.bmp 422.645 0 464909 Scrambled 17 meg/s002.bmp 425.702 0 468272 Unfamiliar 15 meg/u003.bmp 428.875 0 471763 Unfamiliar 13 meg/u007.bmp 432 0 475200 Scrambled 19 meg/s093.bmp 435.19 0 478709 Scrambled 17 meg/s123.bmp 438.297 0 482127 Scrambled 18 meg/s123.bmp 441.305 0 485435 Scrambled 17 meg/s011.bmp 444.478 0 488926 Scrambled 18 meg/s011.bmp 447.719 0 492491 Unfamiliar 13 meg/u029.bmp 450.793 0 495872 Unfamiliar 14 meg/u029.bmp 453.834 0 499217 Unfamiliar 15 meg/u070.bmp 457.091 0 502800 Scrambled 17 meg/s010.bmp 460.298 0 506328 Scrambled 18 meg/s010.bmp 463.539 0 509893 Scrambled 19 meg/s002.bmp 466.764 0 513440 Famous 5 meg/f012.bmp 470.037 0 517041 Unfamiliar 15 meg/u007.bmp 473.245 0 520569 Unfamiliar 13 meg/u009.bmp 476.453 0 524098 Unfamiliar 14 meg/u009.bmp 479.543 0 527497 Unfamiliar 13 meg/u068.bmp 482.766 0 531043 Famous 5 meg/f054.bmp 485.941 0 534535 Scrambled 17 meg/s049.bmp 489.115 0 538026 Scrambled 18 meg/s049.bmp ================================================ FILE: tests/data/ds000117/sub-02/ses-meg/meg/sub-02_ses-meg_task-facerecognition_run-02_meg.fif ================================================ ================================================ FILE: tests/data/ds000117/sub-02/ses-meg/meg/sub-02_ses-meg_task-facerecognition_run-03_events.tsv ================================================ onset duration onset_sample stim_type trigger stim_file 27.5564 0 30312 Scrambled 17 meg/s044.bmp 30.7464 0 33821 Scrambled 18 meg/s044.bmp 33.8373 0 37221 Unfamiliar 13 meg/u055.bmp 36.9273 0 40620 Unfamiliar 13 meg/u079.bmp 40.2182 0 44240 Unfamiliar 14 meg/u079.bmp 43.4264 0 47769 Famous 5 meg/f121.bmp 46.4664 0 51113 Famous 5 meg/f033.bmp 49.7573 0 54733 Famous 6 meg/f033.bmp 53.0645 0 58371 Scrambled 17 meg/s028.bmp 56.2055 0 61826 Scrambled 17 meg/s135.bmp 59.4127 0 65354 Scrambled 18 meg/s135.bmp 62.67 0 68937 Unfamiliar 15 meg/u055.bmp 65.7609 0 72337 Famous 5 meg/f072.bmp 68.9345 0 75828 Scrambled 17 meg/s066.bmp 72.1418 0 79356 Scrambled 18 meg/s066.bmp 75.35 0 82885 Famous 7 meg/f121.bmp 78.6073 0 86468 Famous 5 meg/f063.bmp 81.7145 0 89886 Unfamiliar 13 meg/u139.bmp 84.8545 0 93340 Scrambled 19 meg/s028.bmp 88.0455 0 96850 Scrambled 17 meg/s018.bmp 91.3364 0 100470 Famous 5 meg/f076.bmp 94.4773 0 103925 Famous 6 meg/f076.bmp 97.5336 0 107287 Famous 7 meg/f072.bmp 100.575 0 110632 Scrambled 17 meg/s121.bmp 103.782 0 114160 Scrambled 17 meg/s051.bmp 106.939 0 117633 Famous 7 meg/f063.bmp 110.063 0 121069 Unfamiliar 13 meg/u140.bmp 113.17 0 124487 Unfamiliar 15 meg/u139.bmp 116.461 0 128107 Unfamiliar 13 meg/u124.bmp 119.568 0 131525 Unfamiliar 14 meg/u124.bmp 122.675 0 134943 Scrambled 19 meg/s018.bmp 125.815 0 138397 Famous 5 meg/f150.bmp 129.057 0 141963 Famous 6 meg/f150.bmp 132.297 0 145527 Scrambled 17 meg/s053.bmp 135.455 0 149000 Scrambled 18 meg/s053.bmp 138.679 0 152547 Scrambled 19 meg/s121.bmp 141.769 0 155946 Unfamiliar 13 meg/u089.bmp 144.976 0 159474 Scrambled 19 meg/s051.bmp 148.185 0 163003 Scrambled 17 meg/s052.bmp 151.342 0 166476 Scrambled 18 meg/s052.bmp 154.599 0 170059 Unfamiliar 15 meg/u140.bmp 157.89 0 173679 Famous 5 meg/f139.bmp 161.231 0 177354 Scrambled 17 meg/s138.bmp 164.238 0 180662 Scrambled 18 meg/s138.bmp 167.345 0 184079 Unfamiliar 13 meg/u128.bmp 170.469 0 187516 Unfamiliar 14 meg/u128.bmp 173.525 0 190878 Famous 5 meg/f124.bmp 176.566 0 194223 Unfamiliar 15 meg/u089.bmp 179.757 0 197733 Unfamiliar 13 meg/u044.bmp 183.048 0 201353 Unfamiliar 13 meg/u086.bmp 186.055 0 204660 Unfamiliar 14 meg/u086.bmp 189.229 0 208152 Famous 7 meg/f139.bmp 192.386 0 211625 Famous 5 meg/f091.bmp 195.443 0 214987 Famous 6 meg/f091.bmp 198.684 0 218552 Scrambled 17 meg/s105.bmp 201.958 0 222154 Scrambled 17 meg/s022.bmp 205.249 0 225774 Scrambled 18 meg/s022.bmp 208.373 0 229210 Famous 7 meg/f124.bmp 211.681 0 232849 Scrambled 17 meg/s091.bmp 214.737 0 236211 Scrambled 18 meg/s091.bmp 217.912 0 239703 Unfamiliar 15 meg/u044.bmp 221.153 0 243268 Famous 5 meg/f082.bmp 224.426 0 246869 Famous 6 meg/f082.bmp 227.684 0 250452 Unfamiliar 13 meg/u147.bmp 230.808 0 253889 Unfamiliar 14 meg/u147.bmp 233.999 0 257399 Famous 5 meg/f010.bmp 237.089 0 260798 Scrambled 19 meg/s105.bmp 240.146 0 264161 Unfamiliar 13 meg/u144.bmp 243.387 0 267726 Unfamiliar 14 meg/u144.bmp 246.511 0 271162 Famous 5 meg/f052.bmp 249.802 0 274782 Scrambled 17 meg/s035.bmp 252.975 0 278273 Scrambled 18 meg/s035.bmp 256.216 0 281838 Famous 5 meg/f090.bmp 259.374 0 285311 Famous 5 meg/f037.bmp 262.565 0 288821 Famous 6 meg/f037.bmp 265.805 0 292386 Famous 7 meg/f010.bmp 268.929 0 295822 Scrambled 17 meg/s127.bmp 272.103 0 299313 Scrambled 18 meg/s127.bmp 275.394 0 302933 Scrambled 17 meg/s128.bmp 278.501 0 306351 Famous 7 meg/f052.bmp 281.575 0 309732 Unfamiliar 13 meg/u120.bmp 284.815 0 313297 Unfamiliar 13 meg/u040.bmp 287.973 0 316770 Unfamiliar 14 meg/u040.bmp 291.214 0 320335 Famous 7 meg/f090.bmp 294.472 0 323919 Unfamiliar 13 meg/u146.bmp 297.695 0 327465 Famous 5 meg/f017.bmp 300.853 0 330938 Famous 6 meg/f017.bmp 304.044 0 334448 Unfamiliar 13 meg/u048.bmp 307.235 0 337958 Scrambled 19 meg/s128.bmp 310.458 0 341504 Unfamiliar 13 meg/u019.bmp 313.599 0 344959 Unfamiliar 15 meg/u120.bmp 316.856 0 348542 Unfamiliar 13 meg/u092.bmp 320.08 0 352088 Famous 5 meg/f066.bmp 323.271 0 355598 Unfamiliar 15 meg/u146.bmp 326.495 0 359145 Unfamiliar 13 meg/u078.bmp 329.753 0 362728 Famous 5 meg/f145.bmp 333.094 0 366403 Famous 6 meg/f145.bmp 336.135 0 369748 Unfamiliar 15 meg/u048.bmp 339.358 0 373294 Famous 5 meg/f011.bmp 342.515 0 376767 Unfamiliar 15 meg/u019.bmp 345.639 0 380203 Famous 5 meg/f106.bmp 348.93 0 383823 Famous 6 meg/f106.bmp 352.155 0 387370 Unfamiliar 15 meg/u092.bmp 355.378 0 390916 Scrambled 17 meg/s012.bmp 358.402 0 394242 Famous 7 meg/f066.bmp 361.609 0 397770 Unfamiliar 13 meg/u130.bmp 364.716 0 401188 Unfamiliar 15 meg/u078.bmp 368.041 0 404845 Famous 5 meg/f027.bmp 371.365 0 408502 Scrambled 17 meg/s041.bmp 374.623 0 412085 Scrambled 18 meg/s041.bmp 377.73 0 415503 Famous 7 meg/f011.bmp 380.921 0 419013 Unfamiliar 13 meg/u073.bmp 384.145 0 422559 Unfamiliar 14 meg/u073.bmp 387.152 0 425867 Famous 5 meg/f067.bmp 390.409 0 429450 Scrambled 19 meg/s012.bmp 393.55 0 432905 Famous 5 meg/f127.bmp 396.791 0 436470 Unfamiliar 15 meg/u130.bmp 399.981 0 439979 Scrambled 17 meg/s056.bmp 403.122 0 443434 Famous 7 meg/f027.bmp 406.379 0 447017 Scrambled 17 meg/s115.bmp 409.57 0 450527 Scrambled 18 meg/s115.bmp 412.811 0 454092 Famous 5 meg/f128.bmp 415.952 0 457547 Famous 6 meg/f128.bmp 419.159 0 461075 Scrambled 17 meg/s016.bmp 422.366 0 464603 Famous 7 meg/f067.bmp 425.506 0 468057 Famous 5 meg/f084.bmp 428.765 0 471641 Famous 6 meg/f084.bmp 432.122 0 475334 Famous 7 meg/f127.bmp 435.263 0 478789 Unfamiliar 13 meg/u065.bmp 438.436 0 482280 Unfamiliar 14 meg/u065.bmp 441.544 0 485698 Scrambled 19 meg/s056.bmp 444.651 0 489116 Unfamiliar 13 meg/u037.bmp 447.658 0 492424 Unfamiliar 13 meg/u090.bmp 450.782 0 495860 Unfamiliar 14 meg/u090.bmp 454.056 0 499462 Scrambled 17 meg/s045.bmp 457.33 0 503063 Scrambled 18 meg/s045.bmp 460.454 0 506499 Scrambled 19 meg/s016.bmp 463.578 0 509936 Scrambled 17 meg/s122.bmp 466.802 0 513482 Scrambled 18 meg/s122.bmp 469.925 0 516918 Famous 5 meg/f081.bmp 473.134 0 520447 Famous 6 meg/f081.bmp 476.407 0 524048 Scrambled 17 meg/s099.bmp 479.515 0 527466 Unfamiliar 15 meg/u037.bmp 482.655 0 530921 Unfamiliar 13 meg/u148.bmp 485.763 0 534339 Scrambled 17 meg/s063.bmp 488.936 0 537830 Unfamiliar 13 meg/u101.bmp ================================================ FILE: tests/data/ds000117/sub-02/ses-meg/meg/sub-02_ses-meg_task-facerecognition_run-03_meg.fif ================================================ ================================================ FILE: tests/data/ds000117/sub-02/ses-meg/meg/sub-02_ses-meg_task-facerecognition_run-04_events.tsv ================================================ onset duration onset_sample stim_type trigger stim_file 24.7891 0 27268 Unfamiliar 13 meg/u025.bmp 27.98 0 30778 Unfamiliar 14 meg/u025.bmp 31.1709 0 34288 Scrambled 17 meg/s146.bmp 34.3609 0 37797 Scrambled 18 meg/s146.bmp 37.4682 0 41215 Scrambled 17 meg/s143.bmp 40.7591 0 44835 Scrambled 18 meg/s143.bmp 44.0009 0 48401 Scrambled 17 meg/s087.bmp 46.9909 0 51690 Scrambled 17 meg/s114.bmp 50.2982 0 55328 Famous 5 meg/f098.bmp 53.4727 0 58820 Scrambled 17 meg/s090.bmp 56.6464 0 62311 Scrambled 17 meg/s068.bmp 59.87 0 65857 Famous 5 meg/f070.bmp 63.0445 0 69349 Famous 6 meg/f070.bmp 66.2018 0 72822 Scrambled 19 meg/s087.bmp 69.2255 0 76148 Scrambled 17 meg/s124.bmp 72.4655 0 79712 Scrambled 18 meg/s124.bmp 75.5564 0 83112 Scrambled 19 meg/s114.bmp 78.7636 0 86640 Famous 5 meg/f134.bmp 81.8209 0 90003 Famous 6 meg/f134.bmp 84.9782 0 93476 Famous 7 meg/f098.bmp 88.0682 0 96875 Scrambled 17 meg/s148.bmp 91.1418 0 100256 Scrambled 19 meg/s090.bmp 94.2327 0 103656 Unfamiliar 13 meg/u136.bmp 97.4573 0 107203 Scrambled 19 meg/s068.bmp 100.531 0 110584 Unfamiliar 13 meg/u053.bmp 103.671 0 114038 Scrambled 17 meg/s082.bmp 107.012 0 117713 Scrambled 18 meg/s082.bmp 110.153 0 121168 Unfamiliar 13 meg/u002.bmp 113.26 0 124586 Unfamiliar 14 meg/u002.bmp 116.384 0 128022 Unfamiliar 13 meg/u058.bmp 119.441 0 131385 Scrambled 19 meg/s148.bmp 122.565 0 134821 Unfamiliar 13 meg/u013.bmp 125.805 0 138386 Unfamiliar 15 meg/u136.bmp 128.946 0 141841 Famous 5 meg/f003.bmp 132.07 0 145277 Unfamiliar 15 meg/u053.bmp 135.244 0 148768 Scrambled 17 meg/s086.bmp 138.485 0 152333 Famous 5 meg/f043.bmp 141.825 0 156008 Famous 6 meg/f043.bmp 144.866 0 159353 Unfamiliar 13 meg/u014.bmp 148.14 0 162954 Unfamiliar 15 meg/u058.bmp 151.315 0 166446 Famous 5 meg/f110.bmp 154.472 0 169919 Famous 6 meg/f110.bmp 157.579 0 173337 Unfamiliar 15 meg/u013.bmp 160.686 0 176755 Scrambled 17 meg/s032.bmp 163.793 0 180172 Scrambled 18 meg/s032.bmp 167.034 0 183737 Famous 7 meg/f003.bmp 170.208 0 187229 Famous 5 meg/f030.bmp 173.499 0 190849 Famous 6 meg/f030.bmp 176.723 0 194395 Scrambled 19 meg/s086.bmp 179.83 0 197813 Scrambled 17 meg/s069.bmp 182.971 0 201268 Scrambled 18 meg/s069.bmp 186.328 0 204961 Scrambled 17 meg/s096.bmp 189.47 0 208417 Unfamiliar 15 meg/u014.bmp 192.543 0 211797 Famous 5 meg/f009.bmp 195.65 0 215215 Famous 6 meg/f009.bmp 198.975 0 218872 Famous 5 meg/f100.bmp 202.315 0 222547 Unfamiliar 13 meg/u030.bmp 205.539 0 226093 Famous 5 meg/f111.bmp 208.664 0 229530 Famous 6 meg/f111.bmp 211.72 0 232892 Unfamiliar 13 meg/u015.bmp 214.845 0 236329 Unfamiliar 14 meg/u015.bmp 218.168 0 239985 Scrambled 19 meg/s096.bmp 221.309 0 243440 Famous 5 meg/f042.bmp 224.584 0 247042 Famous 6 meg/f042.bmp 227.791 0 250570 Unfamiliar 13 meg/u093.bmp 230.981 0 254079 Famous 7 meg/f100.bmp 234.138 0 257552 Scrambled 17 meg/s039.bmp 237.363 0 261099 Unfamiliar 15 meg/u030.bmp 240.637 0 264701 Scrambled 17 meg/s059.bmp 243.661 0 268027 Scrambled 18 meg/s059.bmp 246.801 0 271481 Scrambled 17 meg/s050.bmp 250.008 0 275009 Scrambled 17 meg/s089.bmp 253.249 0 278574 Famous 5 meg/f130.bmp 256.406 0 282047 Famous 6 meg/f130.bmp 259.614 0 285575 Unfamiliar 15 meg/u093.bmp 262.788 0 289067 Unfamiliar 13 meg/u042.bmp 265.928 0 292521 Unfamiliar 14 meg/u042.bmp 269.053 0 295958 Scrambled 19 meg/s039.bmp 272.394 0 299633 Scrambled 17 meg/s145.bmp 275.484 0 303032 Unfamiliar 13 meg/u094.bmp 278.591 0 306450 Unfamiliar 14 meg/u094.bmp 281.782 0 309960 Scrambled 19 meg/s050.bmp 285.073 0 313580 Unfamiliar 13 meg/u131.bmp 288.43 0 317273 Scrambled 19 meg/s089.bmp 291.671 0 320838 Unfamiliar 13 meg/u006.bmp 294.795 0 324275 Unfamiliar 14 meg/u006.bmp 297.852 0 327637 Famous 5 meg/f002.bmp 301.194 0 331313 Unfamiliar 13 meg/u032.bmp 304.467 0 334914 Unfamiliar 14 meg/u032.bmp 307.658 0 338424 Scrambled 19 meg/s145.bmp 310.949 0 342044 Unfamiliar 13 meg/u080.bmp 314.056 0 345462 Unfamiliar 14 meg/u080.bmp 317.214 0 348935 Scrambled 17 meg/s073.bmp 320.337 0 352371 Unfamiliar 15 meg/u131.bmp 323.495 0 355844 Famous 5 meg/f103.bmp 326.668 0 359335 Famous 5 meg/f035.bmp 329.809 0 362790 Famous 7 meg/f002.bmp 333 0 366300 Scrambled 17 meg/s097.bmp 336.157 0 369773 Unfamiliar 13 meg/u008.bmp 339.315 0 373246 Unfamiliar 13 meg/u046.bmp 342.305 0 376536 Scrambled 19 meg/s073.bmp 345.362 0 379898 Famous 5 meg/f049.bmp 348.502 0 383352 Famous 6 meg/f049.bmp 351.593 0 386752 Famous 7 meg/f103.bmp 354.8 0 390280 Scrambled 17 meg/s108.bmp 357.974 0 393771 Scrambled 18 meg/s108.bmp 361.065 0 397171 Famous 7 meg/f035.bmp 364.322 0 400754 Famous 5 meg/f096.bmp 367.463 0 404209 Famous 6 meg/f096.bmp 370.62 0 407682 Scrambled 19 meg/s097.bmp 373.71 0 411081 Famous 5 meg/f014.bmp 376.867 0 414554 Unfamiliar 15 meg/u008.bmp 380.025 0 418027 Unfamiliar 13 meg/u109.bmp 383.366 0 421703 Unfamiliar 14 meg/u109.bmp 386.64 0 425304 Unfamiliar 15 meg/u046.bmp 389.714 0 428685 Unfamiliar 13 meg/u043.bmp 392.737 0 432011 Unfamiliar 14 meg/u043.bmp 395.761 0 435337 Unfamiliar 13 meg/u026.bmp 398.968 0 438865 Unfamiliar 13 meg/u133.bmp 402.109 0 442320 Unfamiliar 14 meg/u133.bmp 405.283 0 445811 Famous 5 meg/f044.bmp 408.44 0 449284 Famous 6 meg/f044.bmp 411.497 0 452647 Famous 7 meg/f014.bmp 414.638 0 456102 Famous 5 meg/f085.bmp 417.778 0 459556 Famous 5 meg/f123.bmp 420.753 0 462828 Famous 6 meg/f123.bmp 423.909 0 466300 Unfamiliar 13 meg/u115.bmp 427.167 0 469884 Unfamiliar 14 meg/u115.bmp 430.357 0 473393 Unfamiliar 15 meg/u026.bmp 433.482 0 476830 Unfamiliar 13 meg/u119.bmp 436.639 0 480303 Famous 5 meg/f057.bmp 439.713 0 483684 Scrambled 17 meg/s023.bmp 442.82 0 487102 Famous 7 meg/f085.bmp 445.927 0 490520 Scrambled 17 meg/s140.bmp 449.085 0 493993 Unfamiliar 13 meg/u114.bmp 452.308 0 497539 Unfamiliar 14 meg/u114.bmp 455.449 0 500994 Famous 5 meg/f062.bmp 458.556 0 504412 Famous 6 meg/f062.bmp 461.73 0 507903 Unfamiliar 15 meg/u119.bmp 465.037 0 511541 Famous 5 meg/f092.bmp 468.262 0 515088 Famous 6 meg/f092.bmp 471.453 0 518598 Famous 7 meg/f057.bmp 474.693 0 522162 Scrambled 17 meg/s083.bmp 477.817 0 525599 Scrambled 19 meg/s023.bmp 481.041 0 529145 Famous 5 meg/f045.bmp 484.265 0 532692 Scrambled 19 meg/s140.bmp 487.506 0 536257 Scrambled 17 meg/s147.bmp ================================================ FILE: tests/data/ds000117/sub-02/ses-meg/meg/sub-02_ses-meg_task-facerecognition_run-04_meg.fif ================================================ ================================================ FILE: tests/data/ds000117/sub-02/ses-meg/meg/sub-02_ses-meg_task-facerecognition_run-05_events.tsv ================================================ onset duration onset_sample stim_type trigger stim_file 25.0282 0 27531 Unfamiliar 13 meg/u142.bmp 28.1682 0 30985 Scrambled 17 meg/s007.bmp 31.2755 0 34403 Scrambled 17 meg/s004.bmp 34.2991 0 37729 Famous 5 meg/f086.bmp 37.3727 0 41110 Scrambled 17 meg/s054.bmp 40.6636 0 44730 Scrambled 18 meg/s054.bmp 43.6545 0 48020 Famous 5 meg/f047.bmp 46.7445 0 51419 Famous 6 meg/f047.bmp 49.9355 0 54929 Unfamiliar 15 meg/u142.bmp 53.0427 0 58347 Famous 5 meg/f055.bmp 56.0827 0 61691 Famous 6 meg/f055.bmp 59.2236 0 65146 Scrambled 19 meg/s007.bmp 62.4645 0 68711 Famous 5 meg/f097.bmp 65.7382 0 72312 Famous 6 meg/f097.bmp 69.0291 0 75932 Scrambled 19 meg/s004.bmp 72.1536 0 79369 Unfamiliar 13 meg/u051.bmp 75.3109 0 82842 Unfamiliar 14 meg/u051.bmp 78.4509 0 86296 Famous 7 meg/f086.bmp 81.6418 0 89806 Famous 5 meg/f001.bmp 84.7491 0 93224 Famous 6 meg/f001.bmp 87.84 0 96624 Famous 5 meg/f148.bmp 91.1636 0 100280 Famous 6 meg/f148.bmp 94.2373 0 103661 Scrambled 17 meg/s079.bmp 97.4445 0 107189 Unfamiliar 13 meg/u012.bmp 100.669 0 110736 Unfamiliar 14 meg/u012.bmp 103.809 0 114190 Famous 5 meg/f114.bmp 107 0 117700 Famous 6 meg/f114.bmp 110.191 0 121210 Unfamiliar 13 meg/u095.bmp 113.398 0 124738 Unfamiliar 14 meg/u095.bmp 116.689 0 128358 Unfamiliar 13 meg/u102.bmp 120.014 0 132015 Unfamiliar 14 meg/u102.bmp 123.255 0 135580 Scrambled 17 meg/s020.bmp 126.495 0 139145 Scrambled 19 meg/s079.bmp 129.753 0 142728 Scrambled 17 meg/s111.bmp 133.01 0 146311 Scrambled 17 meg/s075.bmp 136.267 0 149894 Scrambled 17 meg/s118.bmp 139.342 0 153276 Famous 5 meg/f026.bmp 142.499 0 156749 Famous 5 meg/f143.bmp 145.623 0 160185 Scrambled 19 meg/s020.bmp 148.763 0 163639 Scrambled 17 meg/s116.bmp 151.987 0 167186 Scrambled 18 meg/s116.bmp 155.278 0 170806 Scrambled 19 meg/s111.bmp 158.318 0 174150 Famous 5 meg/f029.bmp 161.509 0 177660 Scrambled 19 meg/s075.bmp 164.666 0 181133 Scrambled 17 meg/s005.bmp 167.807 0 184588 Scrambled 19 meg/s118.bmp 170.981 0 188079 Scrambled 17 meg/s139.bmp 174.138 0 191552 Scrambled 18 meg/s139.bmp 177.179 0 194897 Famous 7 meg/f026.bmp 180.486 0 198535 Unfamiliar 13 meg/u001.bmp 183.66 0 202026 Famous 7 meg/f143.bmp 186.901 0 205591 Unfamiliar 13 meg/u062.bmp 190.159 0 209175 Unfamiliar 14 meg/u062.bmp 193.383 0 212721 Unfamiliar 13 meg/u125.bmp 196.524 0 216176 Unfamiliar 14 meg/u125.bmp 199.564 0 219520 Famous 7 meg/f029.bmp 202.737 0 223011 Scrambled 17 meg/s078.bmp 205.778 0 226356 Scrambled 19 meg/s005.bmp 208.868 0 229755 Famous 5 meg/f031.bmp 212.075 0 233283 Famous 5 meg/f107.bmp 215.266 0 236793 Famous 6 meg/f107.bmp 218.474 0 240321 Unfamiliar 15 meg/u001.bmp 221.615 0 243776 Famous 5 meg/f116.bmp 224.856 0 247342 Famous 5 meg/f016.bmp 227.929 0 250722 Famous 6 meg/f016.bmp 230.969 0 254066 Famous 5 meg/f083.bmp 234.176 0 257594 Famous 6 meg/f083.bmp 237.435 0 261178 Scrambled 19 meg/s078.bmp 240.441 0 264485 Unfamiliar 13 meg/u010.bmp 243.682 0 268050 Famous 7 meg/f031.bmp 246.873 0 271560 Unfamiliar 13 meg/u059.bmp 250.046 0 275051 Scrambled 17 meg/s042.bmp 253.154 0 278469 Scrambled 18 meg/s042.bmp 256.328 0 281961 Famous 7 meg/f116.bmp 259.402 0 285342 Scrambled 17 meg/s014.bmp 262.559 0 288815 Scrambled 18 meg/s014.bmp 265.583 0 292141 Unfamiliar 13 meg/u082.bmp 268.723 0 295595 Unfamiliar 14 meg/u082.bmp 271.83 0 299013 Famous 5 meg/f023.bmp 275.071 0 302578 Unfamiliar 15 meg/u010.bmp 278.212 0 306033 Famous 5 meg/f118.bmp 281.469 0 309616 Famous 6 meg/f118.bmp 284.559 0 313015 Unfamiliar 15 meg/u059.bmp 287.584 0 316342 Scrambled 17 meg/s125.bmp 290.875 0 319962 Unfamiliar 13 meg/u033.bmp 293.898 0 323288 Famous 5 meg/f068.bmp 297.088 0 326797 Unfamiliar 13 meg/u038.bmp 300.145 0 330160 Unfamiliar 14 meg/u038.bmp 303.219 0 333541 Famous 7 meg/f023.bmp 306.394 0 337033 Scrambled 17 meg/s098.bmp 309.634 0 340597 Unfamiliar 13 meg/u137.bmp 312.825 0 344107 Unfamiliar 14 meg/u137.bmp 315.932 0 347525 Scrambled 19 meg/s125.bmp 319.023 0 350925 Scrambled 17 meg/s008.bmp 322.046 0 354251 Unfamiliar 15 meg/u033.bmp 325.186 0 357705 Famous 5 meg/f146.bmp 328.394 0 361233 Famous 7 meg/f068.bmp 331.601 0 364761 Famous 5 meg/f061.bmp 334.842 0 368326 Scrambled 17 meg/s019.bmp 338.016 0 371818 Scrambled 19 meg/s098.bmp 341.307 0 375438 Unfamiliar 13 meg/u116.bmp 344.481 0 378929 Unfamiliar 13 meg/u036.bmp 347.755 0 382531 Scrambled 19 meg/s008.bmp 350.845 0 385930 Famous 5 meg/f053.bmp 353.854 0 389239 Famous 7 meg/f146.bmp 357.11 0 392821 Unfamiliar 13 meg/u067.bmp 360.285 0 396313 Famous 7 meg/f061.bmp 363.558 0 399914 Famous 5 meg/f125.bmp 366.548 0 403203 Famous 6 meg/f125.bmp 369.705 0 406676 Scrambled 19 meg/s019.bmp 372.863 0 410149 Unfamiliar 13 meg/u117.bmp 376.087 0 413696 Unfamiliar 14 meg/u117.bmp 379.311 0 417242 Unfamiliar 15 meg/u116.bmp 382.302 0 420532 Scrambled 17 meg/s134.bmp 385.475 0 424023 Scrambled 18 meg/s134.bmp 388.633 0 427496 Unfamiliar 15 meg/u036.bmp 391.656 0 430822 Scrambled 17 meg/s119.bmp 394.73 0 434203 Famous 7 meg/f053.bmp 397.787 0 437566 Scrambled 17 meg/s021.bmp 400.961 0 441057 Scrambled 18 meg/s021.bmp 404.219 0 444641 Unfamiliar 15 meg/u067.bmp 407.394 0 448133 Scrambled 17 meg/s061.bmp 410.717 0 451789 Unfamiliar 13 meg/u111.bmp 413.775 0 455152 Unfamiliar 14 meg/u111.bmp 416.865 0 458551 Unfamiliar 13 meg/u004.bmp 420.055 0 462061 Scrambled 17 meg/s040.bmp 423.195 0 465515 Scrambled 18 meg/s040.bmp 426.37 0 469007 Scrambled 19 meg/s119.bmp 429.595 0 472554 Famous 5 meg/f006.bmp 432.835 0 476118 Famous 6 meg/f006.bmp 436.193 0 479812 Unfamiliar 13 meg/u034.bmp 439.383 0 483321 Unfamiliar 14 meg/u034.bmp 442.691 0 486960 Scrambled 19 meg/s061.bmp 445.915 0 490506 Unfamiliar 13 meg/u075.bmp 449.105 0 494016 Scrambled 17 meg/s027.bmp 452.279 0 497507 Unfamiliar 15 meg/u004.bmp 455.587 0 501146 Famous 5 meg/f024.bmp 458.811 0 504692 Famous 6 meg/f024.bmp 462.069 0 508276 Scrambled 17 meg/s057.bmp 465.226 0 511749 Famous 5 meg/f126.bmp 468.384 0 515222 Unfamiliar 13 meg/u135.bmp 471.524 0 518676 Unfamiliar 14 meg/u135.bmp 474.748 0 522223 Unfamiliar 15 meg/u075.bmp 477.905 0 525696 Unfamiliar 13 meg/u110.bmp 481.179 0 529297 Unfamiliar 14 meg/u110.bmp 484.387 0 532826 Scrambled 19 meg/s027.bmp 487.511 0 536262 Unfamiliar 13 meg/u072.bmp 490.735 0 539808 Unfamiliar 14 meg/u072.bmp ================================================ FILE: tests/data/ds000117/sub-02/ses-meg/meg/sub-02_ses-meg_task-facerecognition_run-05_meg.fif ================================================ ================================================ FILE: tests/data/ds000117/sub-02/ses-meg/meg/sub-02_ses-meg_task-facerecognition_run-06_events.tsv ================================================ onset duration onset_sample stim_type trigger stim_file 23.9691 0 26366 Famous 5 meg/f140.bmp 27.1773 0 29895 Famous 6 meg/f140.bmp 30.3845 0 33423 Scrambled 17 meg/s064.bmp 33.5418 0 36896 Scrambled 18 meg/s064.bmp 36.7155 0 40387 Scrambled 17 meg/s092.bmp 39.8727 0 43860 Scrambled 18 meg/s092.bmp 43.03 0 47333 Unfamiliar 13 meg/u041.bmp 46.1873 0 50806 Unfamiliar 14 meg/u041.bmp 49.4282 0 54371 Unfamiliar 13 meg/u099.bmp 52.7027 0 57973 Unfamiliar 13 meg/u031.bmp 55.8764 0 61464 Famous 5 meg/f071.bmp 58.9 0 64790 Famous 5 meg/f018.bmp 62.1573 0 68373 Famous 6 meg/f018.bmp 65.4655 0 72012 Unfamiliar 13 meg/u035.bmp 68.7564 0 75632 Unfamiliar 14 meg/u035.bmp 71.98 0 79178 Unfamiliar 13 meg/u039.bmp 75.2709 0 82798 Unfamiliar 15 meg/u099.bmp 78.5618 0 86418 Scrambled 17 meg/s013.bmp 81.7691 0 89946 Scrambled 18 meg/s013.bmp 85.06 0 93566 Unfamiliar 15 meg/u031.bmp 88.1173 0 96929 Famous 5 meg/f079.bmp 91.3082 0 100439 Famous 7 meg/f071.bmp 94.5155 0 103967 Unfamiliar 13 meg/u045.bmp 97.6391 0 107403 Scrambled 17 meg/s112.bmp 100.83 0 110913 Unfamiliar 13 meg/u087.bmp 104.004 0 114404 Unfamiliar 15 meg/u039.bmp 107.128 0 117841 Famous 5 meg/f008.bmp 110.369 0 121406 Famous 6 meg/f008.bmp 113.493 0 124842 Famous 5 meg/f129.bmp 116.716 0 128388 Famous 6 meg/f129.bmp 119.79 0 131769 Famous 7 meg/f079.bmp 122.948 0 135243 Famous 5 meg/f088.bmp 126.038 0 138642 Unfamiliar 15 meg/u045.bmp 129.179 0 142097 Unfamiliar 13 meg/u085.bmp 132.336 0 145570 Scrambled 19 meg/s112.bmp 135.51 0 149061 Unfamiliar 13 meg/u027.bmp 138.734 0 152607 Unfamiliar 15 meg/u087.bmp 141.992 0 156191 Famous 5 meg/f050.bmp 145.115 0 159627 Famous 6 meg/f050.bmp 148.239 0 163063 Unfamiliar 13 meg/u129.bmp 151.514 0 166665 Scrambled 17 meg/s117.bmp 154.604 0 170064 Famous 7 meg/f088.bmp 157.778 0 173556 Famous 5 meg/f021.bmp 160.918 0 177010 Famous 6 meg/f021.bmp 164.26 0 180686 Unfamiliar 15 meg/u085.bmp 167.601 0 184361 Unfamiliar 13 meg/u088.bmp 170.675 0 187742 Unfamiliar 14 meg/u088.bmp 173.832 0 191215 Unfamiliar 15 meg/u027.bmp 177.039 0 194743 Scrambled 17 meg/s037.bmp 180.28 0 198308 Famous 5 meg/f060.bmp 183.37 0 201707 Unfamiliar 15 meg/u129.bmp 186.645 0 205309 Famous 5 meg/f019.bmp 189.768 0 208745 Famous 6 meg/f019.bmp 192.993 0 212292 Scrambled 19 meg/s117.bmp 196.083 0 215691 Unfamiliar 13 meg/u060.bmp 199.257 0 219183 Scrambled 17 meg/s065.bmp 202.447 0 222692 Scrambled 18 meg/s065.bmp 205.705 0 226276 Scrambled 17 meg/s136.bmp 208.946 0 229841 Scrambled 19 meg/s037.bmp 212.104 0 233314 Famous 5 meg/f094.bmp 215.094 0 236603 Famous 6 meg/f094.bmp 218.1 0 239910 Famous 7 meg/f060.bmp 221.225 0 243347 Unfamiliar 13 meg/u018.bmp 224.515 0 246967 Unfamiliar 14 meg/u018.bmp 227.756 0 250532 Scrambled 17 meg/s034.bmp 230.78 0 253858 Unfamiliar 15 meg/u060.bmp 233.87 0 257257 Scrambled 17 meg/s110.bmp 237.127 0 260840 Famous 5 meg/f122.bmp 240.302 0 264332 Scrambled 19 meg/s136.bmp 243.292 0 267621 Famous 5 meg/f007.bmp 246.633 0 271296 Famous 6 meg/f007.bmp 249.824 0 274806 Scrambled 17 meg/s107.bmp 252.981 0 278279 Scrambled 17 meg/s133.bmp 256.021 0 281623 Scrambled 18 meg/s133.bmp 259.045 0 284949 Scrambled 19 meg/s034.bmp 262.035 0 288239 Famous 5 meg/f069.bmp 265.159 0 291675 Scrambled 19 meg/s110.bmp 268.215 0 295037 Famous 5 meg/f142.bmp 271.44 0 298584 Famous 7 meg/f122.bmp 274.647 0 302112 Famous 5 meg/f058.bmp 277.838 0 305622 Famous 6 meg/f058.bmp 280.895 0 308985 Famous 5 meg/f144.bmp 284.153 0 312568 Famous 6 meg/f144.bmp 287.294 0 316023 Scrambled 19 meg/s107.bmp 290.384 0 319422 Famous 5 meg/f056.bmp 293.541 0 322895 Famous 6 meg/f056.bmp 296.632 0 326295 Unfamiliar 13 meg/u123.bmp 299.839 0 329823 Famous 7 meg/f069.bmp 302.913 0 333204 Unfamiliar 13 meg/u081.bmp 306.07 0 336677 Unfamiliar 14 meg/u081.bmp 309.244 0 340168 Famous 7 meg/f142.bmp 312.535 0 343788 Unfamiliar 13 meg/u063.bmp 315.792 0 347371 Unfamiliar 14 meg/u063.bmp 319.05 0 350955 Scrambled 17 meg/s149.bmp 322.09 0 354299 Scrambled 18 meg/s149.bmp 325.281 0 357809 Scrambled 17 meg/s088.bmp 328.405 0 361245 Scrambled 17 meg/s084.bmp 331.612 0 364773 Unfamiliar 15 meg/u123.bmp 334.753 0 368228 Scrambled 17 meg/s038.bmp 337.944 0 371738 Scrambled 18 meg/s038.bmp 340.95 0 375045 Unfamiliar 13 meg/u112.bmp 344.125 0 378537 Unfamiliar 14 meg/u112.bmp 347.398 0 382138 Unfamiliar 13 meg/u021.bmp 350.64 0 385704 Famous 5 meg/f059.bmp 353.864 0 389250 Scrambled 19 meg/s088.bmp 357.021 0 392723 Scrambled 17 meg/s081.bmp 360.211 0 396232 Scrambled 19 meg/s084.bmp 363.368 0 399705 Famous 5 meg/f104.bmp 366.543 0 403197 Famous 6 meg/f104.bmp 369.716 0 406688 Unfamiliar 13 meg/u076.bmp 372.724 0 409996 Unfamiliar 14 meg/u076.bmp 375.815 0 413396 Unfamiliar 13 meg/u118.bmp 379.105 0 417015 Unfamiliar 15 meg/u021.bmp 382.178 0 420396 Unfamiliar 13 meg/u149.bmp 385.319 0 423851 Famous 7 meg/f059.bmp 388.595 0 427454 Scrambled 17 meg/s031.bmp 391.867 0 431054 Scrambled 18 meg/s031.bmp 394.858 0 434344 Scrambled 19 meg/s081.bmp 398.065 0 437872 Unfamiliar 13 meg/u096.bmp 401.239 0 441363 Unfamiliar 14 meg/u096.bmp 404.614 0 445075 Scrambled 17 meg/s120.bmp 407.755 0 448530 Scrambled 17 meg/s047.bmp 410.912 0 452003 Scrambled 18 meg/s047.bmp 413.935 0 455329 Unfamiliar 15 meg/u118.bmp 417.243 0 458967 Scrambled 17 meg/s102.bmp 420.484 0 462532 Unfamiliar 15 meg/u149.bmp 423.641 0 466005 Famous 5 meg/f077.bmp 426.715 0 469386 Famous 6 meg/f077.bmp 429.939 0 472933 Unfamiliar 13 meg/u057.bmp 433.179 0 476497 Scrambled 17 meg/s074.bmp 436.32 0 479952 Scrambled 18 meg/s074.bmp 439.444 0 483388 Scrambled 19 meg/s120.bmp 442.685 0 486953 Famous 5 meg/f113.bmp 445.842 0 490426 Scrambled 17 meg/s104.bmp 448.999 0 493899 Scrambled 19 meg/s102.bmp 452.04 0 497244 Unfamiliar 13 meg/u047.bmp 455.23 0 500753 Unfamiliar 14 meg/u047.bmp 458.321 0 504153 Famous 5 meg/f141.bmp 461.578 0 507736 Unfamiliar 15 meg/u057.bmp 464.735 0 511209 Famous 5 meg/f135.bmp 467.876 0 514664 Famous 6 meg/f135.bmp 471.117 0 518229 Unfamiliar 13 meg/u126.bmp 474.307 0 521738 Unfamiliar 14 meg/u126.bmp 477.465 0 525211 Famous 7 meg/f113.bmp 480.488 0 528537 Scrambled 17 meg/s070.bmp 483.645 0 532010 Scrambled 19 meg/s104.bmp 486.87 0 535557 Scrambled 17 meg/s130.bmp ================================================ FILE: tests/data/ds000117/sub-02/ses-meg/meg/sub-02_ses-meg_task-facerecognition_run-06_meg.fif ================================================ ================================================ FILE: tests/data/ds000117/sub-02/ses-meg/sub-02_ses-meg_scans.tsv ================================================ filename acq_time meg/sub-02_ses-meg_task-facerecognition_run-01_meg.fif ##-##-##T09:49:10 meg/sub-02_ses-meg_task-facerecognition_run-02_meg.fif ##-##-##T10:01:34 meg/sub-02_ses-meg_task-facerecognition_run-03_meg.fif ##-##-##T10:11:25 meg/sub-02_ses-meg_task-facerecognition_run-04_meg.fif ##-##-##T10:20:51 meg/sub-02_ses-meg_task-facerecognition_run-05_meg.fif ##-##-##T10:31:09 meg/sub-02_ses-meg_task-facerecognition_run-06_meg.fif ##-##-##T10:41:15 ================================================ FILE: tests/data/ds000117/sub-02/ses-mri/anat/sub-02_ses-mri_acq-mprage_T1w.json ================================================ { "AnatomicalMRICoordinateSystem": "NIfTI", "AnatomicalMRICoordinateUnits": "voxels", "AnatomicalLandmarkCoordinates": { "Nasion": [46,112,99], "LPA": [133,71,22], "RPA": [141,72,173] } } ================================================ FILE: tests/data/ds000117/sub-02/ses-mri/dwi/sub-02_ses-mri_dwi.bval ================================================ 0 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 ================================================ FILE: tests/data/ds000117/sub-02/ses-mri/dwi/sub-02_ses-mri_dwi.bvec ================================================ 0 0.99833703041076 0.01574797369539 -0.0582046508789 0.56115806102752 -0.29134801030159 -0.91702300310134 0.76457959413528 0.23091779649257 0.91885024309158 0.49958211183548 0.35387542843818 0.48206111788749 -0.53797882795333 -0.62921959161758 -0.5313736796379 -0.84431034326553 0.9188682436943 0.3258985877037 0.11305077373981 -0.76577430963516 0.51510250568389 -0.75088250637054 0.93137472867965 0.21081060171127 0.0322563201189 0.18845510482788 0.74945837259292 -0.20776297152042 -0.09516008198261 0.8818866610527 -0.51079857349395 -0.43126329779624 -0.25426679849624 -0.38847145438194 -0.96722930669784 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0.60764509439468 -0.22235448658466 -0.68911743164062 0.99809283018112 -0.06790404766798 -0.13946691155433 -0.95021802186965 0.55826961994171 -0.29357296228408 0.78066211938858 -0.90180605649948 0.96504563093185 -0.88519382476806 0.04232691228389 -0.1611258983612 0.02826672233641 -0.12690564990043 -0.66160529851913 -0.67505794763565 0.38300287723541 -0.55073654651641 -0.55223661661148 -0.65878963470459 -0.48739176988601 -0.87255668640136 -0.10745349526405 0.30604791641235 -0.67213213443756 0.84274733066558 0.12369664758443 0.45714077353477 -0.63560706377029 0.91296416521072 -0.85340249538421 -0.33971646428108 -0.57039356231689 0.23785927891731 0.97350424528121 -0.29369434714317 0.76403284072876 0.93126285076141 -0.44830828905105 -0.06023655459284 ================================================ FILE: tests/data/ds000117/sub-02/ses-mri/dwi/sub-02_ses-mri_dwi.json ================================================ { "RepetitionTime": 8.4, "EchoTime": 0.09, "FlipAngle": 90, "SliceTiming": [4.215,0,4.34,0.1225,4.465,0.2475,4.5875,0.3725,4.7125,0.495,4.835,0.62,4.96,0.7425,5.085,0.8675,5.2075,0.9925,5.3325,1.115,5.4575,1.24,5.58,1.365,5.705,1.4875,5.8275,1.6125,5.9525,1.735,6.0775,1.86,6.2,1.985,6.325,2.1075,6.4475,2.2325,6.5725,2.355,6.6975,2.48,6.82,2.605,6.945,2.7275,7.0675,2.8525,7.1925,2.975,7.3175,3.1,7.44,3.225,7.565,3.3475,7.6875,3.4725,7.8125,3.595,7.9375,3.72,8.06,3.845,8.185,3.9675,8.3075,4.0925], "EffectiveEchoSpacing": 0.0003927297862, "PhaseEncodingDirection": "j-", "TotalReadoutTime": 0.069 } ================================================ FILE: tests/data/ds000117/sub-02/ses-mri/fmap/sub-02_ses-mri_magnitude1.nii ================================================ ================================================ FILE: tests/data/ds000117/sub-02/ses-mri/fmap/sub-02_ses-mri_magnitude2.nii ================================================ ================================================ FILE: tests/data/ds000117/sub-02/ses-mri/fmap/sub-02_ses-mri_phasediff.json ================================================ { "RepetitionTime": 0.4, "EchoTime1": 0.00519, "EchoTime2": 0.00765, "FlipAngle": 60, "IntendedFor": [ "ses-mri/func/sub-02_ses-mri_task-facerecognition_run-01_bold.nii.gz", "ses-mri/func/sub-02_ses-mri_task-facerecognition_run-02_bold.nii.gz", "ses-mri/func/sub-02_ses-mri_task-facerecognition_run-03_bold.nii.gz", "ses-mri/func/sub-02_ses-mri_task-facerecognition_run-04_bold.nii.gz", "ses-mri/func/sub-02_ses-mri_task-facerecognition_run-05_bold.nii.gz", "ses-mri/func/sub-02_ses-mri_task-facerecognition_run-06_bold.nii.gz", "ses-mri/func/sub-02_ses-mri_task-facerecognition_run-07_bold.nii.gz", "ses-mri/func/sub-02_ses-mri_task-facerecognition_run-08_bold.nii.gz", "ses-mri/func/sub-02_ses-mri_task-facerecognition_run-09_bold.nii.gz" ] } ================================================ FILE: tests/data/ds000117/sub-02/ses-mri/fmap/sub-02_ses-mri_phasediff.nii ================================================ ================================================ FILE: tests/data/ds000117/sub-02/ses-mri/func/sub-02_ses-mri_task-facerecognition_run-01_events.tsv ================================================ onset duration circle_duration stim_type trigger button_pushed response_time stim_file 4.000 .851 .585 UNFAMILIAR 13 7 1.314 func/u015.bmp 7.056 .812 .447 UNFAMILIAR 14 7 .978 func/u015.bmp 10.364 .915 .580 UNFAMILIAR 13 7 .870 func/u012.bmp 13.604 .940 .514 UNFAMILIAR 13 4 .754 func/u007.bmp 16.828 .985 .515 UNFAMILIAR 13 7 .753 func/u013.bmp 20.119 .951 .540 UNFAMILIAR 14 7 .706 func/u013.bmp 23.476 .975 .595 FAMOUS 5 7 .977 func/f005.bmp 26.734 .882 .503 FAMOUS 6 4 1.054 func/f005.bmp 29.991 .951 .587 SCRAMBLED 17 7 1.093 func/s009.bmp 32.680 20.000 20.000 n/a 999 20000 20.000 func/i999.bmp 53.343 .867 .588 UNFAMILIAR 13 4 .902 func/u006.bmp 56.500 .996 .523 UNFAMILIAR 15 7 .809 func/u012.bmp 59.707 .994 .416 FAMOUS 5 7 .783 func/f004.bmp 63.031 .838 .565 FAMOUS 6 7 .781 func/f004.bmp 66.055 .871 .414 UNFAMILIAR 15 4 .953 func/u007.bmp 69.128 .921 .403 SCRAMBLED 17 4 .899 func/s005.bmp 72.235 .867 .406 SCRAMBLED 17 7 .997 func/s002.bmp 75.276 .945 .417 SCRAMBLED 18 7 .812 func/s002.bmp 78.566 .885 .572 FAMOUS 5 7 .705 func/f008.bmp 81.689 .993 .470 SCRAMBLED 19 4 .726 func/s009.bmp 84.863 .890 .407 FAMOUS 5 7 .936 func/f013.bmp 88.004 .945 .483 UNFAMILIAR 15 4 .803 func/u006.bmp 91.211 .826 .477 UNFAMILIAR 13 4 .901 func/u005.bmp 94.251 .922 .448 UNFAMILIAR 14 4 .673 func/u005.bmp 97.425 .812 .463 SCRAMBLED 17 7 .864 func/s001.bmp 100.515 .998 .507 SCRAMBLED 18 4 .699 func/s001.bmp 103.789 .904 .506 SCRAMBLED 19 4 1.051 func/s005.bmp 106.962 .940 .497 UNFAMILIAR 13 4 .889 func/u011.bmp 110.153 .923 .470 SCRAMBLED 17 7 1.003 func/s011.bmp 113.344 .947 .501 SCRAMBLED 18 7 .941 func/s011.bmp 116.617 .960 .563 FAMOUS 7 7 .883 func/f008.bmp 119.323 20.000 20.000 n/a 999 20000 20.000 func/i999.bmp 139.919 .880 .546 UNFAMILIAR 13 4 .694 func/u003.bmp 143.043 .932 .475 FAMOUS 7 7 .777 func/f013.bmp 146.134 .951 .400 FAMOUS 5 4 .768 func/f015.bmp 149.424 .820 .573 SCRAMBLED 17 4 .852 func/s010.bmp 152.514 .815 .497 SCRAMBLED 18 4 .826 func/s010.bmp 155.604 .844 .510 SCRAMBLED 17 7 .729 func/s004.bmp 158.778 .951 .567 SCRAMBLED 18 7 .751 func/s004.bmp 161.986 .902 .501 UNFAMILIAR 15 4 .810 func/u011.bmp 165.242 .978 .573 UNFAMILIAR 13 4 .770 func/u001.bmp 168.483 .889 .496 UNFAMILIAR 13 7 .600 func/u002.bmp 171.590 .885 .446 UNFAMILIAR 15 4 .800 func/u003.bmp 174.747 .869 .515 UNFAMILIAR 13 4 .650 func/u009.bmp 177.837 .938 .457 UNFAMILIAR 14 4 .670 func/u009.bmp 181.145 .827 .590 FAMOUS 7 4 1.055 func/f015.bmp 184.352 .986 .558 FAMOUS 5 7 .588 func/f001.bmp 187.659 .810 .559 FAMOUS 5 7 .646 func/f011.bmp 190.717 .802 .485 FAMOUS 6 7 .562 func/f011.bmp 193.255 20.000 20.000 n/a 999 20000 20.000 func/i999.bmp 213.700 .882 .416 FAMOUS 5 7 .944 func/f010.bmp 216.807 .930 .454 UNFAMILIAR 15 4 .883 func/u001.bmp 220.031 .812 .523 FAMOUS 5 7 .835 func/f014.bmp 223.105 .831 .495 UNFAMILIAR 15 7 .840 func/u002.bmp 226.295 .908 .594 UNFAMILIAR 13 4 1.478 func/u008.bmp 229.519 .889 .539 UNFAMILIAR 14 4 1.155 func/u008.bmp 232.693 .951 .404 SCRAMBLED 17 4 .758 func/s014.bmp 235.866 .832 .459 SCRAMBLED 18 4 .747 func/s014.bmp 238.873 .997 .414 FAMOUS 7 4 .815 func/f001.bmp 242.181 .872 .542 SCRAMBLED 17 7 1.082 func/s015.bmp 245.338 .877 .502 FAMOUS 5 4 .697 func/f003.bmp 248.578 .912 .588 FAMOUS 6 4 .760 func/f003.bmp 251.668 .946 .402 FAMOUS 7 7 .862 func/f010.bmp 254.809 .832 .431 SCRAMBLED 17 7 .909 func/s007.bmp 257.932 .820 .525 SCRAMBLED 18 7 .743 func/s007.bmp 261.106 .811 .573 FAMOUS 7 7 .619 func/f014.bmp 264.113 .968 .422 SCRAMBLED 17 4 .810 func/s012.bmp 267.253 .954 .408 SCRAMBLED 18 7 .926 func/s012.bmp 270.460 .890 .469 FAMOUS 5 7 .831 func/f006.bmp 273.099 20.000 20.000 n/a 999 20000 20.000 func/i999.bmp 293.629 .951 .490 FAMOUS 5 7 .819 func/f007.bmp 296.936 .857 .591 FAMOUS 6 7 .708 func/f007.bmp 300.110 .959 .540 SCRAMBLED 19 4 .837 func/s015.bmp 303.283 .895 .440 UNFAMILIAR 13 4 1.207 func/u010.bmp 306.457 .876 .513 FAMOUS 5 7 .669 func/f009.bmp 309.664 .909 .560 FAMOUS 6 7 .573 func/f009.bmp 312.805 .858 .453 UNFAMILIAR 13 7 .737 func/u016.bmp 315.928 .816 .485 SCRAMBLED 17 7 .769 func/s008.bmp 318.952 1.000 .443 SCRAMBLED 18 7 .964 func/s008.bmp 322.226 .831 .513 FAMOUS 7 7 .754 func/f006.bmp 325.249 .957 .426 FAMOUS 5 4 .760 func/f016.bmp 328.540 .979 .566 UNFAMILIAR 13 4 .890 func/u014.bmp 331.730 .949 .436 UNFAMILIAR 14 4 .863 func/u014.bmp 334.887 .906 .437 UNFAMILIAR 15 4 .845 func/u010.bmp 338.061 .938 .500 SCRAMBLED 17 7 .775 func/s016.bmp 341.185 .940 .410 SCRAMBLED 18 7 .626 func/s016.bmp 344.492 .918 .584 SCRAMBLED 17 7 1.268 func/s006.bmp 347.599 .926 .425 UNFAMILIAR 15 7 .727 func/u016.bmp 350.773 .877 .480 FAMOUS 5 7 1.383 func/f002.bmp 353.395 20.000 20.000 n/a 999 20000 20.000 func/i999.bmp 373.974 .844 .544 UNFAMILIAR 13 4 .648 func/u004.bmp 377.031 .857 .444 UNFAMILIAR 14 4 .617 func/u004.bmp 380.171 .962 .506 FAMOUS 7 4 .708 func/f016.bmp 383.479 .985 .578 FAMOUS 5 4 .870 func/f012.bmp 386.753 .894 .519 SCRAMBLED 17 7 .846 func/s013.bmp 389.943 .850 .528 SCRAMBLED 17 4 .987 func/s003.bmp 392.951 .830 .401 SCRAMBLED 18 4 .603 func/s003.bmp 396.057 .993 .506 SCRAMBLED 19 7 1.091 func/s006.bmp 398.846 .012 0 n/a 999 0 0 func/Circle.bmp ================================================ FILE: tests/data/ds000117/sub-02/ses-mri/func/sub-02_ses-mri_task-facerecognition_run-02_events.tsv ================================================ onset duration circle_duration stim_type trigger button_pushed response_time stim_file 4.000 .875 .440 UNFAMILIAR 13 7 .846 func/u031.bmp 7.123 .945 .481 SCRAMBLED 17 4 .921 func/s020.bmp 10.414 .949 .450 UNFAMILIAR 13 4 .706 func/u027.bmp 13.638 .895 .503 UNFAMILIAR 14 4 .581 func/u027.bmp 16.778 .937 .446 FAMOUS 5 4 .860 func/f024.bmp 19.935 .982 .449 FAMOUS 6 7 .757 func/f024.bmp 23.142 .951 .414 SCRAMBLED 17 7 .732 func/s023.bmp 26.300 .913 .451 SCRAMBLED 18 7 .624 func/s023.bmp 29.390 .877 .414 FAMOUS 5 7 .638 func/f030.bmp 32.613 .912 .577 UNFAMILIAR 15 4 .842 func/u031.bmp 35.269 20.000 20.000 n/a 999 20000 20.000 func/i999.bmp 55.882 .872 .557 FAMOUS 5 7 .811 func/f020.bmp 58.972 .815 .435 FAMOUS 6 7 .686 func/f020.bmp 62.129 .980 .579 SCRAMBLED 19 7 .860 func/s020.bmp 65.286 .812 .408 FAMOUS 5 4 .742 func/f023.bmp 68.443 .909 .582 SCRAMBLED 17 7 .851 func/s030.bmp 71.650 .954 .475 UNFAMILIAR 13 4 .858 func/u020.bmp 74.791 .839 .410 FAMOUS 5 7 .869 func/f022.bmp 77.948 .897 .546 FAMOUS 7 7 .742 func/f030.bmp 81.205 .933 .567 FAMOUS 5 7 .898 func/f019.bmp 84.396 .883 .468 UNFAMILIAR 13 7 1.251 func/u019.bmp 87.519 .958 .480 UNFAMILIAR 14 7 .721 func/u019.bmp 90.743 .950 .492 FAMOUS 7 7 1.431 func/f023.bmp 94.000 .941 .529 SCRAMBLED 17 4 .846 func/s021.bmp 97.241 .806 .526 SCRAMBLED 18 7 .965 func/s021.bmp 100.264 .978 .449 SCRAMBLED 19 7 1.048 func/s030.bmp 103.488 .818 .479 FAMOUS 5 7 .832 func/f025.bmp 106.545 .988 .480 FAMOUS 6 7 .700 func/f025.bmp 109.735 .920 .426 UNFAMILIAR 15 4 .764 func/u020.bmp 112.408 20.000 20.000 n/a 999 20000 20.000 func/i999.bmp 132.987 .852 .529 UNFAMILIAR 13 4 .988 func/u017.bmp 136.077 .922 .479 UNFAMILIAR 14 4 .892 func/u017.bmp 139.251 .822 .469 FAMOUS 7 7 .811 func/f022.bmp 142.308 .859 .453 UNFAMILIAR 13 4 .742 func/u032.bmp 145.532 .988 .596 UNFAMILIAR 14 4 .777 func/u032.bmp 148.856 .837 .555 FAMOUS 7 7 .737 func/f019.bmp 151.879 .821 .404 UNFAMILIAR 13 4 .738 func/u021.bmp 154.903 .992 .426 SCRAMBLED 17 7 .972 func/s017.bmp 158.143 .836 .483 SCRAMBLED 18 7 .690 func/s017.bmp 161.267 1.000 .509 FAMOUS 5 7 .734 func/f017.bmp 164.541 .935 .495 SCRAMBLED 17 4 .686 func/s026.bmp 167.698 .849 .462 SCRAMBLED 18 4 .771 func/s026.bmp 170.838 .866 .521 UNFAMILIAR 13 4 .815 func/u029.bmp 174.062 .906 .586 UNFAMILIAR 14 4 1.057 func/u029.bmp 177.269 .898 .522 UNFAMILIAR 13 4 .742 func/u024.bmp 180.426 .898 .500 UNFAMILIAR 15 4 .807 func/u021.bmp 183.650 .845 .535 SCRAMBLED 17 7 .954 func/s022.bmp 186.707 .944 .441 FAMOUS 5 7 .714 func/f032.bmp 189.864 .847 .437 FAMOUS 6 7 .684 func/f032.bmp 192.887 .929 .402 FAMOUS 7 7 .853 func/f017.bmp 195.560 20.000 20.000 n/a 999 20000 20.000 func/i999.bmp 216.022 .982 .422 SCRAMBLED 17 7 1.021 func/s018.bmp 219.179 .873 .402 FAMOUS 5 7 .919 func/f027.bmp 222.253 .988 .429 SCRAMBLED 17 7 .715 func/s025.bmp 225.610 .959 .591 UNFAMILIAR 15 4 1.003 func/u024.bmp 228.868 .928 .519 UNFAMILIAR 13 4 .968 func/u030.bmp 232.158 .999 .587 UNFAMILIAR 14 4 .748 func/u030.bmp 235.432 .975 .457 SCRAMBLED 19 7 .885 func/s022.bmp 238.756 .924 .576 FAMOUS 5 7 1.026 func/f029.bmp 241.897 .856 .443 FAMOUS 6 7 .675 func/f029.bmp 245.020 .918 .489 FAMOUS 5 7 .888 func/f028.bmp 248.128 .813 .434 FAMOUS 6 7 .785 func/f028.bmp 251.234 .926 .527 SCRAMBLED 19 4 .766 func/s018.bmp 254.391 .865 .467 SCRAMBLED 17 4 .832 func/s029.bmp 257.465 .903 .443 FAMOUS 7 7 .687 func/f027.bmp 260.739 .943 .593 FAMOUS 5 7 .845 func/f018.bmp 264.029 .992 .583 FAMOUS 6 7 .686 func/f018.bmp 267.303 .890 .516 SCRAMBLED 19 7 .936 func/s025.bmp 270.393 .855 .432 UNFAMILIAR 13 4 .842 func/u023.bmp 273.467 .997 .435 UNFAMILIAR 14 4 .604 func/u023.bmp 276.206 20.000 20.000 n/a 999 20000 20.000 func/i999.bmp 296.685 .810 .437 UNFAMILIAR 13 4 .886 func/u022.bmp 299.826 .895 .565 UNFAMILIAR 14 7 1.197 func/u022.bmp 302.916 .806 .424 SCRAMBLED 17 4 .738 func/s028.bmp 306.073 .951 .570 SCRAMBLED 17 4 .775 func/s019.bmp 309.213 .862 .433 SCRAMBLED 18 7 .822 func/s019.bmp 312.270 .865 .431 SCRAMBLED 19 4 .767 func/s029.bmp 315.494 .806 .584 FAMOUS 5 7 .856 func/f026.bmp 318.668 .888 .575 SCRAMBLED 17 7 1.209 func/s032.bmp 321.841 .934 .507 SCRAMBLED 18 7 1.245 func/s032.bmp 324.965 .804 .432 SCRAMBLED 17 4 .901 func/s027.bmp 328.072 .871 .533 UNFAMILIAR 13 7 1.668 func/u026.bmp 331.296 .890 .571 SCRAMBLED 19 4 .807 func/s028.bmp 334.403 .866 .450 UNFAMILIAR 13 4 .840 func/u028.bmp 337.493 .814 .461 UNFAMILIAR 14 4 .726 func/u028.bmp 340.617 .818 .537 SCRAMBLED 17 7 .861 func/s024.bmp 343.740 .926 .548 SCRAMBLED 18 7 .880 func/s024.bmp 346.915 .986 .484 FAMOUS 7 7 .869 func/f026.bmp 350.188 .948 .531 SCRAMBLED 17 4 .819 func/s031.bmp 352.877 20.000 20.000 n/a 999 20000 20.000 func/i999.bmp 373.456 .827 .540 UNFAMILIAR 13 7 .931 func/u018.bmp 376.597 .929 .549 SCRAMBLED 19 7 .844 func/s027.bmp 379.804 .992 .509 UNFAMILIAR 13 4 .977 func/u025.bmp 383.161 .880 .586 UNFAMILIAR 15 7 1.003 func/u026.bmp 386.252 .994 .441 FAMOUS 5 7 .669 func/f031.bmp 389.459 .821 .442 FAMOUS 6 7 .637 func/f031.bmp 392.499 .896 .449 FAMOUS 5 7 1.006 func/f021.bmp 395.205 .012 0 n/a 999 0 0 func/Circle.bmp ================================================ FILE: tests/data/ds000117/sub-02/ses-mri/func/sub-02_ses-mri_task-facerecognition_run-03_events.tsv ================================================ onset duration circle_duration stim_type trigger button_pushed response_time stim_file 4.000 .912 .455 SCRAMBLED 17 4 .860 func/s037.bmp 7.274 .864 .554 UNFAMILIAR 13 7 .774 func/u038.bmp 10.515 .805 .507 UNFAMILIAR 14 7 .619 func/u038.bmp 13.605 .998 .517 FAMOUS 5 7 1.058 func/f033.bmp 16.895 .877 .522 SCRAMBLED 17 4 .837 func/s035.bmp 20.036 .939 .497 SCRAMBLED 18 4 .681 func/s035.bmp 23.326 .935 .574 SCRAMBLED 17 7 .750 func/s033.bmp 26.600 .805 .553 FAMOUS 5 7 .683 func/f040.bmp 29.774 .904 .594 FAMOUS 6 7 .538 func/f040.bmp 33.048 .988 .596 SCRAMBLED 19 4 .900 func/s037.bmp 35.788 20.000 20.000 n/a 999 20000 20.000 func/i999.bmp 56.383 .946 .554 UNFAMILIAR 13 7 1.014 func/u042.bmp 59.591 .876 .492 UNFAMILIAR 14 4 .713 func/u042.bmp 62.781 .857 .515 UNFAMILIAR 13 7 .820 func/u047.bmp 66.005 .889 .590 UNFAMILIAR 14 7 .967 func/u047.bmp 69.078 .911 .406 FAMOUS 7 7 .838 func/f033.bmp 72.302 .968 .548 UNFAMILIAR 13 4 .746 func/u039.bmp 75.560 .851 .518 SCRAMBLED 17 7 .988 func/s047.bmp 78.750 .845 .578 SCRAMBLED 19 4 .823 func/s033.bmp 81.840 .842 .454 UNFAMILIAR 13 4 .832 func/u043.bmp 84.897 .918 .415 FAMOUS 5 4 .873 func/f035.bmp 87.987 .854 .403 FAMOUS 6 4 .786 func/f035.bmp 91.244 .932 .599 SCRAMBLED 17 4 1.194 func/s041.bmp 94.402 .858 .459 SCRAMBLED 18 4 .873 func/s041.bmp 97.625 .946 .590 FAMOUS 5 7 .733 func/f043.bmp 100.916 .997 .576 FAMOUS 6 7 .543 func/f043.bmp 104.207 .888 .527 UNFAMILIAR 15 4 .721 func/u039.bmp 107.380 .839 .509 SCRAMBLED 17 7 1.015 func/s040.bmp 110.571 .959 .574 SCRAMBLED 19 7 .808 func/s047.bmp 113.277 20.000 20.000 n/a 999 20000 20.000 func/i999.bmp 133.789 .910 .470 UNFAMILIAR 13 4 .855 func/u045.bmp 136.930 .975 .452 UNFAMILIAR 15 4 .880 func/u043.bmp 140.254 .894 .566 FAMOUS 5 7 .939 func/f047.bmp 143.444 .855 .533 SCRAMBLED 17 7 .859 func/s039.bmp 146.551 .911 .457 FAMOUS 5 7 .817 func/f036.bmp 149.808 .896 .573 UNFAMILIAR 13 7 1.474 func/u046.bmp 153.032 .875 .555 UNFAMILIAR 14 7 .650 func/u046.bmp 156.156 .971 .476 SCRAMBLED 19 4 .698 func/s040.bmp 159.396 .972 .491 SCRAMBLED 17 7 .768 func/s034.bmp 162.620 .939 .470 UNFAMILIAR 15 4 .714 func/u045.bmp 165.811 .965 .484 FAMOUS 5 7 .814 func/f045.bmp 169.085 .909 .542 FAMOUS 7 7 .882 func/f047.bmp 172.175 .997 .412 SCRAMBLED 17 7 .866 func/s043.bmp 175.382 .860 .435 SCRAMBLED 18 7 .650 func/s043.bmp 178.506 .925 .500 SCRAMBLED 19 7 .898 func/s039.bmp 181.729 .830 .529 SCRAMBLED 17 4 .955 func/s045.bmp 184.836 .836 .503 SCRAMBLED 18 4 .707 func/s045.bmp 188.027 .860 .594 FAMOUS 7 7 .980 func/f036.bmp 191.217 .803 .538 UNFAMILIAR 13 4 .880 func/u040.bmp 194.241 .816 .442 UNFAMILIAR 14 4 .752 func/u040.bmp 196.796 20.000 20.000 n/a 999 20000 20.000 func/i999.bmp 217.259 .816 .422 SCRAMBLED 17 4 1.004 func/s042.bmp 220.366 .901 .525 SCRAMBLED 18 7 .885 func/s042.bmp 223.539 .961 .516 SCRAMBLED 19 7 1.102 func/s034.bmp 226.713 .847 .437 FAMOUS 5 7 .917 func/f044.bmp 229.736 .802 .414 FAMOUS 7 7 .723 func/f045.bmp 232.843 .880 .539 SCRAMBLED 17 4 .778 func/s046.bmp 235.967 .862 .420 FAMOUS 5 7 .902 func/f046.bmp 239.024 .980 .424 FAMOUS 6 7 .660 func/f046.bmp 242.231 .806 .444 UNFAMILIAR 13 4 1.164 func/u035.bmp 245.371 .978 .566 UNFAMILIAR 14 7 1.084 func/u035.bmp 248.629 .891 .502 UNFAMILIAR 13 7 .992 func/u048.bmp 251.869 .811 .577 UNFAMILIAR 14 7 .524 func/u048.bmp 255.009 .888 .552 SCRAMBLED 17 4 .702 func/s036.bmp 258.083 .894 .414 FAMOUS 7 4 .625 func/f044.bmp 261.340 .946 .588 FAMOUS 5 7 .553 func/f041.bmp 264.497 .993 .442 FAMOUS 6 7 .687 func/f041.bmp 267.671 .871 .408 SCRAMBLED 19 4 .725 func/s046.bmp 270.878 .937 .562 SCRAMBLED 17 4 .652 func/s048.bmp 274.169 .921 .571 SCRAMBLED 18 4 .620 func/s048.bmp 276.841 20.000 20.000 n/a 999 20000 20.000 func/i999.bmp 297.454 .839 .580 UNFAMILIAR 13 4 1.304 func/u037.bmp 300.478 .942 .417 UNFAMILIAR 14 4 .617 func/u037.bmp 303.601 .890 .406 UNFAMILIAR 13 7 .645 func/u041.bmp 306.725 .815 .453 UNFAMILIAR 14 7 .815 func/u041.bmp 309.765 .861 .463 SCRAMBLED 17 4 .727 func/s038.bmp 312.906 .855 .518 SCRAMBLED 18 4 .613 func/s038.bmp 316.012 .918 .477 SCRAMBLED 19 4 .955 func/s036.bmp 319.102 .901 .408 FAMOUS 5 7 .682 func/f048.bmp 322.293 .897 .523 FAMOUS 5 7 .829 func/f039.bmp 325.450 .888 .490 FAMOUS 5 7 .949 func/f042.bmp 328.623 .881 .514 FAMOUS 6 7 .658 func/f042.bmp 331.697 .852 .419 FAMOUS 5 7 1.159 func/f037.bmp 334.804 .926 .492 SCRAMBLED 17 4 .577 func/s044.bmp 337.978 .938 .469 FAMOUS 5 7 .693 func/f034.bmp 341.185 .960 .486 FAMOUS 7 7 .770 func/f048.bmp 344.409 .937 .471 UNFAMILIAR 13 4 .845 func/u034.bmp 347.532 .894 .416 UNFAMILIAR 14 4 .674 func/u034.bmp 350.790 .963 .588 FAMOUS 7 7 1.052 func/f039.bmp 353.930 .951 .417 FAMOUS 5 4 1.045 func/f038.bmp 356.619 20.000 20.000 n/a 999 20000 20.000 func/i999.bmp 377.115 .917 .444 UNFAMILIAR 13 4 .807 func/u033.bmp 380.289 .817 .488 UNFAMILIAR 14 4 .565 func/u033.bmp 383.279 .883 .407 FAMOUS 7 4 .918 func/f037.bmp 386.402 .950 .479 UNFAMILIAR 13 4 .817 func/u036.bmp 389.676 .993 .557 SCRAMBLED 19 4 .742 func/s044.bmp 392.967 .996 .520 UNFAMILIAR 13 4 1.042 func/u044.bmp 396.241 .829 .503 FAMOUS 7 7 .728 func/f034.bmp 398.863 .012 0 n/a 999 0 0 func/Circle.bmp ================================================ FILE: tests/data/ds000117/sub-02/ses-mri/func/sub-02_ses-mri_task-facerecognition_run-04_events.tsv ================================================ onset duration circle_duration stim_type trigger button_pushed response_time stim_file 4.000 .818 .456 SCRAMBLED 17 7 .949 func/s060.bmp 6.990 .870 .413 SCRAMBLED 18 7 .576 func/s060.bmp 10.197 .842 .470 UNFAMILIAR 13 7 .769 func/u055.bmp 13.254 .817 .442 UNFAMILIAR 13 7 .656 func/u054.bmp 16.244 .898 .404 UNFAMILIAR 14 7 .820 func/u054.bmp 19.534 .952 .598 UNFAMILIAR 13 4 .646 func/u059.bmp 22.708 .810 .458 UNFAMILIAR 14 4 .674 func/u059.bmp 25.781 1.000 .483 FAMOUS 5 4 .852 func/f058.bmp 29.122 .866 .568 UNFAMILIAR 13 7 .757 func/u052.bmp 32.346 .851 .590 UNFAMILIAR 14 7 .708 func/u052.bmp 34.935 20.000 20.000 n/a 999 20000 20.000 func/i999.bmp 55.464 .860 .496 SCRAMBLED 17 7 .992 func/s061.bmp 58.655 .979 .553 UNFAMILIAR 15 4 .905 func/u055.bmp 61.828 .853 .424 FAMOUS 5 7 1.282 func/f049.bmp 65.052 .922 .592 SCRAMBLED 17 4 .816 func/s058.bmp 68.343 .823 .567 SCRAMBLED 17 4 .922 func/s057.bmp 71.450 .846 .515 SCRAMBLED 18 7 .885 func/s057.bmp 74.540 .960 .478 FAMOUS 7 4 .672 func/f058.bmp 77.797 .919 .520 UNFAMILIAR 13 4 .739 func/u058.bmp 81.038 .999 .560 UNFAMILIAR 14 4 .932 func/u058.bmp 84.262 .934 .461 UNFAMILIAR 13 7 .892 func/u060.bmp 87.419 .888 .458 UNFAMILIAR 14 7 .624 func/u060.bmp 90.659 .826 .573 SCRAMBLED 19 4 .785 func/s061.bmp 93.783 .986 .512 SCRAMBLED 17 7 .735 func/s054.bmp 96.973 .899 .444 FAMOUS 7 7 1.132 func/f049.bmp 100.097 .860 .457 FAMOUS 5 7 .695 func/f064.bmp 103.187 .925 .457 SCRAMBLED 19 4 .621 func/s058.bmp 106.478 .898 .570 FAMOUS 5 4 .955 func/f056.bmp 109.735 .880 .600 FAMOUS 6 4 .578 func/f056.bmp 112.843 .940 .451 FAMOUS 5 7 .645 func/f062.bmp 115.531 20.000 20.000 n/a 999 20000 20.000 func/i999.bmp 136.077 1.000 .503 FAMOUS 5 7 .797 func/f063.bmp 139.268 .900 .429 FAMOUS 6 7 .572 func/f063.bmp 142.475 .825 .545 SCRAMBLED 17 4 .801 func/s055.bmp 145.665 .905 .587 SCRAMBLED 18 4 .788 func/s055.bmp 148.906 .883 .558 SCRAMBLED 19 4 .896 func/s054.bmp 152.096 .925 .543 SCRAMBLED 17 4 1.179 func/s064.bmp 155.220 .855 .430 SCRAMBLED 18 7 .946 func/s064.bmp 158.344 .887 .492 FAMOUS 7 7 .815 func/f064.bmp 161.467 .965 .456 SCRAMBLED 17 4 .769 func/s063.bmp 164.641 .933 .443 UNFAMILIAR 13 4 .756 func/u056.bmp 167.831 .979 .489 UNFAMILIAR 14 4 .656 func/u056.bmp 171.072 .927 .493 FAMOUS 7 7 .887 func/f062.bmp 174.329 .807 .565 SCRAMBLED 17 4 .941 func/s062.bmp 177.453 .896 .525 SCRAMBLED 17 4 .956 func/s052.bmp 180.543 .803 .426 SCRAMBLED 18 4 .694 func/s052.bmp 183.617 .988 .497 SCRAMBLED 17 4 .695 func/s059.bmp 186.840 .831 .439 FAMOUS 5 7 .936 func/f051.bmp 189.931 .896 .498 SCRAMBLED 19 4 .853 func/s063.bmp 192.570 20.000 20.000 n/a 999 20000 20.000 func/i999.bmp 213.149 .936 .531 FAMOUS 5 4 .994 func/f054.bmp 216.356 .868 .508 FAMOUS 6 4 .558 func/f054.bmp 219.480 .985 .500 FAMOUS 5 7 .726 func/f059.bmp 222.787 .859 .566 SCRAMBLED 19 4 .930 func/s062.bmp 225.944 .813 .533 FAMOUS 5 7 .931 func/f060.bmp 229.101 .863 .579 UNFAMILIAR 13 4 .787 func/u057.bmp 232.208 .962 .478 UNFAMILIAR 14 4 .663 func/u057.bmp 235.432 .977 .427 SCRAMBLED 19 4 .866 func/s059.bmp 238.673 .951 .487 UNFAMILIAR 13 7 .811 func/u063.bmp 241.863 .985 .475 UNFAMILIAR 14 7 .833 func/u063.bmp 245.204 .889 .594 FAMOUS 7 7 1.153 func/f051.bmp 248.461 .918 .574 FAMOUS 5 7 .952 func/f052.bmp 251.718 .975 .557 SCRAMBLED 17 4 1.061 func/s056.bmp 254.942 .835 .481 FAMOUS 7 7 .680 func/f059.bmp 257.999 .961 .453 UNFAMILIAR 13 4 .854 func/u050.bmp 261.240 .868 .504 FAMOUS 7 7 1.049 func/f060.bmp 264.280 .839 .413 UNFAMILIAR 13 7 .778 func/u053.bmp 267.403 .994 .514 UNFAMILIAR 14 7 .880 func/u053.bmp 270.761 .998 .588 UNFAMILIAR 13 4 .861 func/u051.bmp 274.035 .924 .517 UNFAMILIAR 14 n/a 0 func/u051.bmp 276.707 20.000 20.000 n/a 999 20000 20.000 func/i999.bmp 297.253 .804 .510 FAMOUS 5 n/a 0 func/f057.bmp 300.260 .802 .422 FAMOUS 6 7 .514 func/f057.bmp 303.317 .939 .498 FAMOUS 7 7 .718 func/f052.bmp 306.490 .835 .460 FAMOUS 5 4 .693 func/f050.bmp 309.581 .857 .487 FAMOUS 6 4 .644 func/f050.bmp 312.671 .810 .467 SCRAMBLED 19 4 .808 func/s056.bmp 315.811 .822 .559 FAMOUS 5 7 .878 func/f061.bmp 318.968 .893 .555 UNFAMILIAR 15 4 .936 func/u050.bmp 322.092 .877 .467 SCRAMBLED 17 7 .923 func/s053.bmp 325.199 .989 .460 SCRAMBLED 18 7 1.291 func/s053.bmp 328.456 .863 .493 UNFAMILIAR 13 4 .726 func/u062.bmp 331.663 .980 .558 FAMOUS 5 7 .848 func/f053.bmp 335.004 .876 .600 UNFAMILIAR 13 7 .709 func/u064.bmp 338.161 .893 .507 UNFAMILIAR 14 7 .604 func/u064.bmp 341.385 .858 .556 UNFAMILIAR 13 4 .809 func/u061.bmp 344.592 .927 .575 FAMOUS 7 7 .835 func/f061.bmp 347.766 .839 .469 SCRAMBLED 17 4 .810 func/s051.bmp 350.923 .987 .539 UNFAMILIAR 13 4 .763 func/u049.bmp 354.247 .965 .574 UNFAMILIAR 14 4 .616 func/u049.bmp 357.521 .875 .537 UNFAMILIAR 15 4 .751 func/u062.bmp 360.143 20.000 20.000 n/a 999 20000 20.000 func/i999.bmp 380.756 .821 .576 FAMOUS 5 4 .983 func/f055.bmp 383.880 .854 .529 FAMOUS 7 7 .691 func/f053.bmp 387.053 .921 .545 SCRAMBLED 17 4 .876 func/s050.bmp 390.177 .878 .420 SCRAMBLED 17 7 1.061 func/s049.bmp 393.402 .994 .585 SCRAMBLED 18 7 .864 func/s049.bmp 396.675 .894 .511 UNFAMILIAR 15 4 .798 func/u061.bmp 399.364 .012 0 n/a 999 0 0 func/Circle.bmp ================================================ FILE: tests/data/ds000117/sub-02/ses-mri/func/sub-02_ses-mri_task-facerecognition_run-05_events.tsv ================================================ onset duration circle_duration stim_type trigger button_pushed response_time stim_file 4.000 .880 .466 SCRAMBLED 17 7 .875 func/s080.bmp 7.174 .823 .525 SCRAMBLED 17 7 .912 func/s069.bmp 10.398 .965 .520 UNFAMILIAR 13 7 .840 func/u077.bmp 13.739 .846 .584 FAMOUS 5 7 .683 func/f078.bmp 16.829 .866 .461 FAMOUS 5 4 .734 func/f073.bmp 20.003 .969 .536 FAMOUS 6 4 .587 func/f073.bmp 23.327 .930 .595 FAMOUS 5 7 .673 func/f066.bmp 26.434 .860 .413 SCRAMBLED 19 7 .823 func/s080.bmp 29.524 .977 .456 UNFAMILIAR 13 4 .630 func/u069.bmp 32.831 .820 .559 SCRAMBLED 19 7 .679 func/s069.bmp 35.404 20.000 20.000 n/a 999 20000 20.000 func/i999.bmp 55.883 .915 .442 SCRAMBLED 17 4 .859 func/s070.bmp 59.023 .947 .466 SCRAMBLED 18 4 .693 func/s070.bmp 62.297 .915 .555 UNFAMILIAR 15 7 .821 func/u077.bmp 65.454 1.000 .483 UNFAMILIAR 13 7 .807 func/u080.bmp 68.661 .898 .443 UNFAMILIAR 14 7 .748 func/u080.bmp 71.835 .972 .509 FAMOUS 7 4 .731 func/f078.bmp 75.142 .933 .555 FAMOUS 5 7 .800 func/f068.bmp 78.299 .947 .466 FAMOUS 6 7 .555 func/f068.bmp 81.473 .921 .452 SCRAMBLED 17 7 1.675 func/s066.bmp 84.613 .915 .438 FAMOUS 7 7 .812 func/f066.bmp 87.787 .966 .492 FAMOUS 5 7 .631 func/f075.bmp 91.061 .957 .540 FAMOUS 6 7 .600 func/f075.bmp 94.368 .973 .580 UNFAMILIAR 15 4 .770 func/u069.bmp 97.709 .894 .579 SCRAMBLED 17 7 1.646 func/s075.bmp 100.950 .893 .571 SCRAMBLED 18 7 .693 func/s075.bmp 104.057 .913 .438 FAMOUS 5 7 1.039 func/f067.bmp 107.164 .809 .423 FAMOUS 6 7 .547 func/f067.bmp 110.321 .895 .570 UNFAMILIAR 13 4 .636 func/u078.bmp 113.545 .844 .562 UNFAMILIAR 14 4 .650 func/u078.bmp 116.134 20.000 20.000 n/a 999 20000 20.000 func/i999.bmp 136.746 .981 .581 SCRAMBLED 17 7 1.019 func/s068.bmp 140.037 .819 .542 SCRAMBLED 19 4 .957 func/s066.bmp 143.144 .878 .532 FAMOUS 5 7 .690 func/f069.bmp 146.351 .906 .565 SCRAMBLED 17 4 .728 func/s081.bmp 149.458 .967 .421 SCRAMBLED 18 4 .572 func/s081.bmp 152.648 .805 .451 UNFAMILIAR 13 4 .819 func/u079.bmp 155.672 .849 .441 UNFAMILIAR 13 7 .941 func/u068.bmp 158.795 .960 .506 UNFAMILIAR 14 7 .807 func/u068.bmp 162.019 .883 .497 UNFAMILIAR 13 7 1.134 func/u065.bmp 165.176 .829 .511 UNFAMILIAR 14 7 .585 func/u065.bmp 168.333 .828 .555 SCRAMBLED 19 4 .763 func/s068.bmp 171.407 .824 .470 FAMOUS 5 7 .940 func/f080.bmp 174.531 .872 .534 FAMOUS 6 7 .649 func/f080.bmp 177.687 .930 .516 FAMOUS 7 7 .892 func/f069.bmp 180.961 .912 .571 UNFAMILIAR 13 4 .616 func/u067.bmp 184.235 .914 .583 UNFAMILIAR 13 7 .765 func/u072.bmp 187.509 .974 .585 UNFAMILIAR 14 7 .623 func/u072.bmp 190.817 .964 .563 UNFAMILIAR 15 7 1.101 func/u079.bmp 194.041 .836 .498 SCRAMBLED 17 4 .695 func/s067.bmp 196.613 20.000 20.000 n/a 999 20000 20.000 func/i999.bmp 217.175 .800 .531 UNFAMILIAR 13 4 .745 func/u074.bmp 220.299 .892 .564 UNFAMILIAR 14 4 .584 func/u074.bmp 223.474 .961 .517 FAMOUS 5 7 .626 func/f077.bmp 226.730 .948 .528 SCRAMBLED 17 4 .699 func/s077.bmp 229.971 .863 .524 SCRAMBLED 18 4 .657 func/s077.bmp 233.111 .823 .510 UNFAMILIAR 15 4 .699 func/u067.bmp 236.352 .839 .586 SCRAMBLED 17 7 .885 func/s071.bmp 239.475 .903 .508 UNFAMILIAR 13 4 .679 func/u073.bmp 242.649 .964 .502 UNFAMILIAR 14 4 .705 func/u073.bmp 245.806 .891 .432 SCRAMBLED 19 4 .812 func/s067.bmp 248.946 .905 .453 FAMOUS 5 7 .739 func/f072.bmp 252.137 .872 .514 SCRAMBLED 17 4 .822 func/s065.bmp 255.310 .847 .524 FAMOUS 7 7 .829 func/f077.bmp 258.467 .976 .548 FAMOUS 5 7 .878 func/f079.bmp 261.658 .881 .445 FAMOUS 5 4 1.073 func/f065.bmp 264.865 .816 .567 FAMOUS 6 4 .736 func/f065.bmp 267.939 .871 .498 SCRAMBLED 19 7 1.219 func/s071.bmp 271.012 .810 .423 SCRAMBLED 17 4 .452 func/s079.bmp 273.568 20.000 20.000 n/a 999 20000 20.000 func/i999.bmp 294.097 .874 .479 SCRAMBLED 17 7 .769 func/s074.bmp 297.170 .985 .425 FAMOUS 7 7 .769 func/f072.bmp 300.528 .880 .579 FAMOUS 5 7 .872 func/f071.bmp 303.718 .849 .539 SCRAMBLED 19 4 .799 func/s065.bmp 306.758 .964 .432 UNFAMILIAR 13 7 1.080 func/u081.bmp 309.999 .840 .516 UNFAMILIAR 14 7 1.144 func/u081.bmp 313.022 .886 .405 FAMOUS 7 7 .927 func/f079.bmp 316.146 .902 .465 FAMOUS 5 4 .936 func/f081.bmp 319.303 .948 .495 FAMOUS 6 4 .662 func/f081.bmp 322.527 .843 .497 FAMOUS 5 4 .848 func/f070.bmp 325.651 .842 .505 FAMOUS 6 n/a 0 func/f070.bmp 328.858 .973 .599 SCRAMBLED 19 4 1.027 func/s079.bmp 332.098 .882 .485 SCRAMBLED 17 4 .632 func/s078.bmp 335.222 .988 .469 SCRAMBLED 19 7 .714 func/s074.bmp 338.462 .814 .470 FAMOUS 5 7 .972 func/f074.bmp 341.453 .815 .417 FAMOUS 6 n/a 0 func/f074.bmp 344.509 .955 .470 FAMOUS 7 7 .731 func/f071.bmp 347.833 .915 .599 UNFAMILIAR 13 4 .893 func/u066.bmp 351.024 .997 .500 SCRAMBLED 17 4 .831 func/s072.bmp 354.314 .991 .524 SCRAMBLED 18 7 .895 func/s072.bmp 357.054 20.000 20.000 n/a 999 20000 20.000 func/i999.bmp 377.533 .948 .419 SCRAMBLED 17 4 .836 func/s073.bmp 380.723 .913 .472 SCRAMBLED 18 4 .766 func/s073.bmp 383.847 .992 .439 UNFAMILIAR 13 n/a 0 func/u071.bmp 387.105 .995 .501 SCRAMBLED 19 4 .911 func/s078.bmp 390.361 .831 .490 UNFAMILIAR 13 4 .658 func/u070.bmp 393.552 .933 .592 UNFAMILIAR 13 7 .926 func/u075.bmp 396.675 .902 .430 UNFAMILIAR 14 7 .867 func/u075.bmp 399.833 .859 .493 UNFAMILIAR 15 4 .793 func/u066.bmp 402.472 .012 0 n/a 999 0 0 func/Circle.bmp ================================================ FILE: tests/data/ds000117/sub-02/ses-mri/func/sub-02_ses-mri_task-facerecognition_run-06_events.tsv ================================================ onset duration circle_duration stim_type trigger button_pushed response_time stim_file 4.000 .898 .418 SCRAMBLED 17 7 .696 func/s082.bmp 7.124 .808 .465 SCRAMBLED 18 7 .550 func/s082.bmp 10.231 .869 .409 SCRAMBLED 17 4 1.004 func/s095.bmp 13.455 .905 .571 UNFAMILIAR 13 7 .789 func/u094.bmp 16.595 .845 .464 UNFAMILIAR 14 7 .553 func/u094.bmp 19.752 .960 .539 UNFAMILIAR 13 7 .723 func/u097.bmp 23.026 .815 .543 UNFAMILIAR 14 7 .537 func/u097.bmp 26.133 .859 .519 FAMOUS 5 7 .868 func/f088.bmp 29.206 .831 .413 SCRAMBLED 17 4 .790 func/s086.bmp 31.779 20.000 20.000 n/a 999 20000 20.000 func/i999.bmp 52.358 .965 .527 FAMOUS 5 4 .715 func/f090.bmp 55.682 .937 .600 SCRAMBLED 19 4 .930 func/s095.bmp 58.839 .856 .445 FAMOUS 5 7 .813 func/f092.bmp 62.030 .828 .561 FAMOUS 6 7 .679 func/f092.bmp 65.203 .959 .579 SCRAMBLED 17 4 .850 func/s089.bmp 68.377 .826 .422 UNFAMILIAR 13 7 1.576 func/u093.bmp 71.517 .825 .540 UNFAMILIAR 14 7 .568 func/u093.bmp 74.591 .894 .471 FAMOUS 7 4 .763 func/f088.bmp 77.831 .964 .578 SCRAMBLED 17 4 .841 func/s083.bmp 81.156 .831 .598 SCRAMBLED 19 4 .665 func/s086.bmp 84.229 .807 .443 UNFAMILIAR 13 7 1.292 func/u084.bmp 87.369 .999 .558 FAMOUS 7 4 .766 func/f090.bmp 90.710 .863 .567 UNFAMILIAR 13 7 .877 func/u088.bmp 93.934 .961 .594 UNFAMILIAR 14 7 .539 func/u088.bmp 97.241 .888 .579 FAMOUS 5 7 .798 func/f095.bmp 100.499 .841 .599 FAMOUS 6 7 .651 func/f095.bmp 103.539 .820 .426 SCRAMBLED 19 4 .802 func/s089.bmp 106.646 .872 .534 UNFAMILIAR 13 4 1.019 func/u090.bmp 109.753 .929 .455 UNFAMILIAR 14 4 .635 func/u090.bmp 112.425 20.000 20.000 n/a 999 20000 20.000 func/i999.bmp 133.004 .986 .526 FAMOUS 5 7 .866 func/f083.bmp 136.161 .972 .409 SCRAMBLED 19 4 .715 func/s083.bmp 139.402 .953 .466 UNFAMILIAR 13 7 1.109 func/u086.bmp 142.609 .809 .498 UNFAMILIAR 15 7 1.327 func/u084.bmp 145.666 .899 .478 SCRAMBLED 17 7 1.368 func/s097.bmp 148.773 .860 .440 UNFAMILIAR 13 4 .694 func/u082.bmp 151.880 .973 .478 FAMOUS 5 7 .919 func/f096.bmp 155.154 .981 .520 FAMOUS 6 7 .933 func/f096.bmp 158.512 .892 .596 SCRAMBLED 17 7 .902 func/s094.bmp 161.568 .809 .400 SCRAMBLED 18 7 .852 func/s094.bmp 164.708 .857 .555 FAMOUS 7 7 1.471 func/f083.bmp 167.815 .905 .471 UNFAMILIAR 13 7 .980 func/u087.bmp 171.089 .818 .590 UNFAMILIAR 15 4 .951 func/u086.bmp 174.146 .907 .476 UNFAMILIAR 13 7 .952 func/u085.bmp 177.286 .810 .454 UNFAMILIAR 14 7 .686 func/u085.bmp 180.410 .859 .536 SCRAMBLED 19 7 .906 func/s097.bmp 183.484 .906 .446 UNFAMILIAR 13 4 .958 func/u092.bmp 186.657 .853 .498 UNFAMILIAR 15 4 .868 func/u082.bmp 189.764 .868 .488 FAMOUS 5 7 1.090 func/f091.bmp 192.938 .864 .540 FAMOUS 6 n/a 0 func/f091.bmp 195.544 20.000 20.000 n/a 999 20000 20.000 func/i999.bmp 216.073 .808 .492 SCRAMBLED 17 4 .856 func/s096.bmp 219.247 .978 .599 FAMOUS 5 7 .854 func/f087.bmp 222.554 .897 .557 FAMOUS 6 7 .596 func/f087.bmp 225.778 .968 .562 UNFAMILIAR 15 4 .959 func/u087.bmp 228.985 .810 .472 UNFAMILIAR 13 4 .705 func/u096.bmp 232.042 .823 .478 UNFAMILIAR 14 4 .605 func/u096.bmp 235.115 .991 .419 FAMOUS 5 4 .820 func/f086.bmp 238.456 .887 .577 FAMOUS 6 4 .647 func/f086.bmp 241.563 .851 .450 UNFAMILIAR 15 4 .836 func/u092.bmp 244.720 .905 .541 FAMOUS 5 7 .683 func/f097.bmp 247.860 .921 .462 FAMOUS 6 7 .587 func/f097.bmp 251.017 .858 .460 FAMOUS 5 7 .797 func/f089.bmp 254.225 .848 .571 SCRAMBLED 19 4 1.106 func/s096.bmp 257.432 .860 .585 SCRAMBLED 17 4 1.012 func/s088.bmp 260.472 .867 .417 SCRAMBLED 17 7 1.002 func/s087.bmp 263.595 .837 .495 UNFAMILIAR 13 7 1.081 func/u089.bmp 266.736 .843 .530 UNFAMILIAR 14 7 .986 func/u089.bmp 269.943 .877 .595 FAMOUS 5 7 1.038 func/f093.bmp 273.133 .888 .537 FAMOUS 6 7 .663 func/f093.bmp 275.773 20.000 20.000 n/a 999 20000 20.000 func/i999.bmp 296.252 .983 .439 SCRAMBLED 17 4 .709 func/s090.bmp 299.542 .920 .542 SCRAMBLED 18 4 .592 func/s090.bmp 302.766 .867 .520 FAMOUS 7 7 .721 func/f089.bmp 305.957 .970 .559 FAMOUS 5 7 .754 func/f082.bmp 309.164 .922 .483 FAMOUS 6 7 .538 func/f082.bmp 312.404 .990 .541 SCRAMBLED 19 7 .749 func/s088.bmp 315.645 .900 .472 UNFAMILIAR 13 4 .817 func/u095.bmp 318.719 .960 .417 UNFAMILIAR 14 4 .853 func/u095.bmp 321.942 .814 .491 SCRAMBLED 19 4 1.005 func/s087.bmp 324.982 .905 .435 SCRAMBLED 17 4 .740 func/s093.bmp 328.156 .803 .492 SCRAMBLED 17 7 .755 func/s084.bmp 331.280 .941 .548 FAMOUS 5 7 1.094 func/f085.bmp 334.554 .928 .556 FAMOUS 5 7 .873 func/f084.bmp 337.711 .808 .460 FAMOUS 6 7 1.294 func/f084.bmp 340.701 .966 .402 SCRAMBLED 17 7 .973 func/s085.bmp 343.874 .898 .436 SCRAMBLED 18 7 .775 func/s085.bmp 346.965 .993 .419 SCRAMBLED 17 4 .810 func/s092.bmp 350.155 .931 .432 SCRAMBLED 19 4 .639 func/s093.bmp 352.828 20.000 20.000 n/a 999 20000 20.000 func/i999.bmp 373.307 1.000 .449 FAMOUS 5 4 .675 func/f094.bmp 376.597 .801 .531 SCRAMBLED 19 7 .785 func/s084.bmp 379.704 .848 .544 UNFAMILIAR 13 7 .645 func/u083.bmp 382.762 .801 .451 UNFAMILIAR 14 7 .923 func/u083.bmp 385.818 .909 .496 FAMOUS 7 4 .733 func/f085.bmp 388.958 .897 .455 UNFAMILIAR 13 4 .875 func/u091.bmp 392.182 .849 .562 SCRAMBLED 17 7 .864 func/s091.bmp 394.838 .012 0 n/a 999 0 0 func/Circle.bmp ================================================ FILE: tests/data/ds000117/sub-02/ses-mri/func/sub-02_ses-mri_task-facerecognition_run-07_events.tsv ================================================ onset duration circle_duration stim_type trigger button_pushed response_time stim_file 4.000 .802 .442 SCRAMBLED 17 7 .783 func/s105.bmp 6.973 .926 .403 FAMOUS 5 7 .742 func/f100.bmp 10.230 .863 .463 FAMOUS 6 7 .564 func/f100.bmp 13.421 .801 .552 SCRAMBLED 17 4 .803 func/s109.bmp 16.395 .871 .417 UNFAMILIAR 13 4 .701 func/u098.bmp 19.568 .858 .519 UNFAMILIAR 14 4 .608 func/u098.bmp 22.758 .933 .564 FAMOUS 5 7 .772 func/f102.bmp 25.982 .987 .532 FAMOUS 6 7 .598 func/f102.bmp 29.156 .824 .415 FAMOUS 5 4 .678 func/f103.bmp 32.229 .816 .473 SCRAMBLED 19 7 .864 func/s105.bmp 34.785 20.000 20.000 n/a 999 20000 20.000 func/i999.bmp 55.230 .885 .405 FAMOUS 5 4 .968 func/f109.bmp 58.438 .842 .564 FAMOUS 6 7 .530 func/f109.bmp 61.494 .934 .436 SCRAMBLED 17 4 1.107 func/s112.bmp 64.685 .882 .489 SCRAMBLED 19 4 .854 func/s109.bmp 67.892 .874 .556 SCRAMBLED 17 7 .861 func/s098.bmp 71.032 .968 .495 FAMOUS 5 7 .797 func/f104.bmp 74.256 .944 .499 FAMOUS 6 7 .598 func/f104.bmp 77.564 .868 .544 UNFAMILIAR 13 7 .808 func/u113.bmp 80.704 .887 .512 UNFAMILIAR 14 7 .698 func/u113.bmp 83.811 .833 .464 FAMOUS 7 4 .754 func/f103.bmp 87.002 .987 .600 SCRAMBLED 17 7 1.018 func/s099.bmp 90.292 .851 .522 SCRAMBLED 18 7 .564 func/s099.bmp 93.449 .890 .549 FAMOUS 5 7 .837 func/f107.bmp 96.623 .901 .517 SCRAMBLED 19 4 .895 func/s112.bmp 99.713 .910 .408 UNFAMILIAR 13 4 1.803 func/u102.bmp 102.937 .814 .545 SCRAMBLED 19 4 .696 func/s098.bmp 106.010 .931 .489 SCRAMBLED 17 4 .804 func/s106.bmp 109.151 .857 .448 SCRAMBLED 18 4 .750 func/s106.bmp 112.358 .904 .574 SCRAMBLED 17 7 .686 func/s110.bmp 115.014 20.000 20.000 n/a 999 20000 20.000 func/i999.bmp 135.626 .922 .574 SCRAMBLED 17 7 .952 func/s111.bmp 138.883 .926 .555 UNFAMILIAR 13 4 .712 func/u101.bmp 142.174 .811 .587 UNFAMILIAR 14 4 .682 func/u101.bmp 145.248 .945 .498 FAMOUS 7 7 .881 func/f107.bmp 148.438 .809 .473 UNFAMILIAR 13 7 .634 func/u103.bmp 151.528 .861 .512 UNFAMILIAR 14 7 .654 func/u103.bmp 154.685 .859 .523 UNFAMILIAR 15 7 1.395 func/u102.bmp 157.776 .930 .439 UNFAMILIAR 13 7 1.162 func/u112.bmp 161.066 .975 .593 FAMOUS 5 4 .811 func/f111.bmp 164.273 .897 .463 FAMOUS 6 4 .729 func/f111.bmp 167.347 .979 .402 SCRAMBLED 19 7 .745 func/s110.bmp 170.621 .929 .522 FAMOUS 5 7 1.070 func/f105.bmp 173.811 .893 .495 SCRAMBLED 19 4 .954 func/s111.bmp 177.069 .979 .584 UNFAMILIAR 13 7 1.358 func/u106.bmp 180.292 .963 .482 UNFAMILIAR 14 7 1.324 func/u106.bmp 183.533 .819 .504 SCRAMBLED 17 7 .900 func/s100.bmp 186.540 .909 .400 SCRAMBLED 17 7 .963 func/s104.bmp 189.647 .801 .421 SCRAMBLED 18 7 .706 func/s104.bmp 192.720 .975 .506 UNFAMILIAR 15 7 .802 func/u112.bmp 195.443 20.000 20.000 n/a 999 20000 20.000 func/i999.bmp 216.040 .881 .567 SCRAMBLED 17 4 .932 func/s102.bmp 219.179 .924 .498 SCRAMBLED 18 4 .648 func/s102.bmp 222.353 .954 .476 FAMOUS 5 7 .842 func/f099.bmp 225.643 .861 .558 FAMOUS 6 7 .604 func/f099.bmp 228.834 .820 .565 FAMOUS 7 7 .718 func/f105.bmp 231.958 .949 .528 UNFAMILIAR 13 7 .788 func/u107.bmp 235.298 .956 .575 UNFAMILIAR 13 4 1.372 func/u108.bmp 238.489 .905 .453 SCRAMBLED 19 4 .836 func/s100.bmp 241.612 .876 .442 FAMOUS 5 4 1.425 func/f112.bmp 244.737 .927 .483 FAMOUS 5 4 .810 func/f108.bmp 247.943 .965 .516 UNFAMILIAR 13 7 .889 func/u104.bmp 251.267 .873 .588 UNFAMILIAR 14 7 1.450 func/u104.bmp 254.508 .828 .595 SCRAMBLED 17 4 .772 func/s108.bmp 257.565 .968 .456 UNFAMILIAR 15 7 .714 func/u107.bmp 260.805 .845 .514 UNFAMILIAR 13 4 .705 func/u109.bmp 263.962 .929 .548 UNFAMILIAR 14 4 .700 func/u109.bmp 267.069 .977 .413 UNFAMILIAR 15 7 .711 func/u108.bmp 270.211 .803 .400 FAMOUS 5 4 .895 func/f098.bmp 273.284 .921 .518 FAMOUS 6 4 .776 func/f098.bmp 276.507 .925 .532 FAMOUS 7 4 1.307 func/f112.bmp 279.179 20.000 20.000 n/a 999 20000 20.000 func/i999.bmp 299.792 .875 .578 FAMOUS 5 4 .842 func/f113.bmp 302.982 .960 .547 FAMOUS 7 4 .719 func/f108.bmp 306.290 .909 .577 FAMOUS 5 4 .825 func/f110.bmp 309.447 .942 .470 FAMOUS 6 4 .748 func/f110.bmp 312.621 .811 .461 SCRAMBLED 17 7 .979 func/s103.bmp 315.744 .891 .546 SCRAMBLED 18 7 .735 func/s103.bmp 318.985 .947 .582 SCRAMBLED 19 4 .985 func/s108.bmp 322.175 .998 .473 FAMOUS 5 7 .709 func/f106.bmp 325.449 .847 .513 UNFAMILIAR 13 7 .706 func/u110.bmp 328.640 .975 .575 UNFAMILIAR 14 7 .660 func/u110.bmp 331.847 .999 .444 UNFAMILIAR 13 7 .772 func/u111.bmp 335.087 .818 .469 UNFAMILIAR 14 7 .667 func/u111.bmp 338.178 .813 .508 FAMOUS 7 7 .916 func/f113.bmp 341.301 .831 .549 SCRAMBLED 17 4 .755 func/s101.bmp 344.358 .990 .452 SCRAMBLED 17 4 .863 func/s113.bmp 347.599 .892 .483 SCRAMBLED 18 4 .692 func/s113.bmp 350.806 .801 .536 SCRAMBLED 17 7 .927 func/s107.bmp 353.846 .868 .483 SCRAMBLED 18 7 .789 func/s107.bmp 357.003 .802 .527 FAMOUS 7 7 .688 func/f106.bmp 359.542 20.000 20.000 n/a 999 20000 20.000 func/i999.bmp 380.121 .901 .548 UNFAMILIAR 13 4 1.033 func/u105.bmp 383.328 .814 .542 UNFAMILIAR 13 7 .612 func/u099.bmp 386.435 .938 .532 UNFAMILIAR 13 7 .730 func/u100.bmp 389.543 .872 .401 SCRAMBLED 19 4 .952 func/s101.bmp 392.599 .862 .411 FAMOUS 5 4 .702 func/f101.bmp 395.271 .012 0 n/a 999 0 0 func/Circle.bmp ================================================ FILE: tests/data/ds000117/sub-02/ses-mri/func/sub-02_ses-mri_task-facerecognition_run-08_events.tsv ================================================ onset duration circle_duration stim_type trigger button_pushed response_time stim_file 4.000 .863 .463 SCRAMBLED 17 7 .626 func/s124.bmp 7.207 .989 .562 FAMOUS 5 7 .598 func/f123.bmp 10.614 .934 .545 FAMOUS 6 4 .781 func/f123.bmp 13.738 .991 .424 FAMOUS 5 7 .640 func/f128.bmp 16.995 .936 .486 FAMOUS 6 7 .593 func/f128.bmp 20.236 .899 .538 FAMOUS 5 7 .827 func/f125.bmp 23.393 .811 .493 FAMOUS 6 7 .535 func/f125.bmp 26.516 .996 .547 FAMOUS 5 7 .778 func/f124.bmp 29.790 .852 .477 FAMOUS 5 7 .687 func/f118.bmp 32.997 .994 .594 FAMOUS 6 7 .558 func/f118.bmp 36.188 .913 .419 SCRAMBLED 19 7 .878 func/s124.bmp 38.844 20.000 20.000 n/a 999 20000 20.000 func/i999.bmp 59.289 .907 .405 UNFAMILIAR 13 7 .772 func/u121.bmp 62.430 .871 .451 UNFAMILIAR 13 7 .536 func/u125.bmp 65.670 .894 .593 FAMOUS 5 7 1.366 func/f119.bmp 68.877 .912 .541 FAMOUS 5 7 .936 func/f130.bmp 72.051 .892 .497 FAMOUS 6 7 .561 func/f130.bmp 75.292 .800 .575 FAMOUS 7 4 1.125 func/f124.bmp 78.383 .829 .512 SCRAMBLED 17 7 1.001 func/s126.bmp 81.389 .964 .414 SCRAMBLED 18 7 .598 func/s126.bmp 84.680 .957 .568 SCRAMBLED 17 4 .736 func/s116.bmp 87.853 .912 .448 SCRAMBLED 18 4 .653 func/s116.bmp 91.027 .972 .491 UNFAMILIAR 15 7 .672 func/u121.bmp 94.335 .835 .568 UNFAMILIAR 13 7 .518 func/u126.bmp 97.341 .836 .411 UNFAMILIAR 14 7 .700 func/u126.bmp 100.531 .885 .595 UNFAMILIAR 15 7 .641 func/u125.bmp 103.772 .801 .598 UNFAMILIAR 13 7 .756 func/u120.bmp 106.895 .971 .557 FAMOUS 7 7 .753 func/f119.bmp 110.053 .913 .409 FAMOUS 5 7 .781 func/f120.bmp 113.260 .903 .505 UNFAMILIAR 13 4 .935 func/u115.bmp 115.899 20.000 20.000 n/a 999 20000 20.000 func/i999.bmp 136.478 .837 .548 SCRAMBLED 17 4 .789 func/s114.bmp 139.635 .845 .550 SCRAMBLED 18 4 .582 func/s114.bmp 142.792 .912 .538 UNFAMILIAR 13 7 1.006 func/u124.bmp 146.049 .987 .582 UNFAMILIAR 13 7 .816 func/u127.bmp 149.373 .834 .558 UNFAMILIAR 14 7 .683 func/u127.bmp 152.397 .886 .424 UNFAMILIAR 15 7 .728 func/u120.bmp 155.554 .821 .505 UNFAMILIAR 13 7 .576 func/u118.bmp 158.627 .816 .470 FAMOUS 7 7 .681 func/f120.bmp 161.734 .811 .521 UNFAMILIAR 13 4 .794 func/u116.bmp 164.841 .858 .526 UNFAMILIAR 15 4 1.032 func/u115.bmp 167.965 .871 .493 UNFAMILIAR 13 7 .742 func/u123.bmp 171.105 .902 .493 FAMOUS 5 7 1.098 func/f115.bmp 174.229 .833 .462 FAMOUS 6 7 .813 func/f115.bmp 177.269 .817 .436 UNFAMILIAR 15 n/a 0 func/u124.bmp 180.326 .916 .472 SCRAMBLED 17 4 1.045 func/s115.bmp 183.466 .818 .467 SCRAMBLED 18 4 .560 func/s115.bmp 186.640 .825 .593 FAMOUS 5 4 .960 func/f129.bmp 189.697 .849 .455 UNFAMILIAR 15 n/a 0 func/u118.bmp 192.286 20.000 20.000 n/a 999 20000 20.000 func/i999.bmp 212.898 .873 .569 UNFAMILIAR 13 7 .631 func/u119.bmp 216.055 .919 .506 UNFAMILIAR 14 7 .493 func/u119.bmp 219.162 .961 .422 UNFAMILIAR 15 4 .679 func/u116.bmp 222.403 .938 .505 SCRAMBLED 17 4 .889 func/s121.bmp 225.610 .963 .485 SCRAMBLED 18 4 .787 func/s121.bmp 228.917 .902 .581 UNFAMILIAR 15 7 .776 func/u123.bmp 232.125 .859 .549 UNFAMILIAR 13 7 .723 func/u122.bmp 235.283 .957 .468 UNFAMILIAR 14 7 .479 func/u122.bmp 238.589 .950 .552 SCRAMBLED 17 7 .812 func/s118.bmp 241.846 .973 .544 SCRAMBLED 17 7 .598 func/s119.bmp 245.187 .801 .586 FAMOUS 7 4 .804 func/f129.bmp 248.311 .983 .554 FAMOUS 5 7 .657 func/f126.bmp 251.568 .841 .512 FAMOUS 6 4 .754 func/f126.bmp 254.575 .927 .400 FAMOUS 5 7 .603 func/f121.bmp 257.832 .932 .565 SCRAMBLED 17 7 .747 func/s128.bmp 260.955 .953 .427 SCRAMBLED 18 7 1.195 func/s128.bmp 264.079 .994 .403 UNFAMILIAR 13 7 1.001 func/u130.bmp 267.286 .830 .450 SCRAMBLED 19 7 1.003 func/s118.bmp 270.410 .984 .525 UNFAMILIAR 13 7 .503 func/u129.bmp 273.667 .843 .516 SCRAMBLED 19 7 .815 func/s119.bmp 276.256 20.000 20.000 n/a 999 20000 20.000 func/i999.bmp 296.802 .997 .516 SCRAMBLED 17 7 1.132 func/s129.bmp 300.009 .972 .448 UNFAMILIAR 13 7 .653 func/u128.bmp 303.350 .853 .594 FAMOUS 7 7 1.030 func/f121.bmp 306.407 .813 .433 SCRAMBLED 17 4 .733 func/s130.bmp 309.547 .953 .558 SCRAMBLED 18 4 .886 func/s130.bmp 312.738 .908 .470 SCRAMBLED 17 4 1.060 func/s120.bmp 316.011 .811 .594 UNFAMILIAR 15 n/a 0 func/u130.bmp 319.068 .966 .469 FAMOUS 5 7 .691 func/f127.bmp 322.259 .964 .464 UNFAMILIAR 15 7 .738 func/u129.bmp 325.449 .851 .467 SCRAMBLED 17 n/a 0 func/s123.bmp 328.539 .930 .480 SCRAMBLED 18 n/a 0 func/s123.bmp 331.646 .989 .415 SCRAMBLED 19 4 .839 func/s129.bmp 334.937 .918 .520 SCRAMBLED 17 7 .782 func/s125.bmp 338.111 .958 .488 SCRAMBLED 18 7 .455 func/s125.bmp 341.284 .993 .450 UNFAMILIAR 15 7 .565 func/u128.bmp 344.625 .961 .556 FAMOUS 5 7 .665 func/f116.bmp 347.782 .941 .425 UNFAMILIAR 13 4 .817 func/u114.bmp 350.939 .855 .441 UNFAMILIAR 14 4 .766 func/u114.bmp 354.046 .920 .476 SCRAMBLED 19 7 1.026 func/s120.bmp 356.719 20.000 20.000 n/a 999 20000 20.000 func/i999.bmp 377.282 .834 .534 FAMOUS 5 7 .907 func/f122.bmp 380.472 .866 .597 FAMOUS 7 7 .629 func/f127.bmp 383.662 .834 .567 SCRAMBLED 17 7 1.408 func/s127.bmp 386.786 .860 .525 SCRAMBLED 18 7 .686 func/s127.bmp 389.926 .855 .507 FAMOUS 5 7 .857 func/f114.bmp 393.100 .823 .542 FAMOUS 6 7 .907 func/f114.bmp 396.123 .804 .424 SCRAMBLED 17 4 .873 func/s122.bmp 399.130 .826 .426 SCRAMBLED 18 4 .640 func/s122.bmp 402.170 .863 .445 FAMOUS 7 7 1.135 func/f116.bmp 404.793 .012 0 n/a 999 0 0 func/Circle.bmp ================================================ FILE: tests/data/ds000117/sub-02/ses-mri/func/sub-02_ses-mri_task-facerecognition_run-09_events.tsv ================================================ onset duration circle_duration stim_type trigger button_pushed response_time stim_file 4.000 .938 .511 FAMOUS 5 7 .639 func/f140.bmp 7.157 .939 .410 FAMOUS 5 4 .819 func/f139.bmp 10.431 .937 .461 FAMOUS 6 4 .547 func/f139.bmp 13.721 .879 .578 SCRAMBLED 17 7 .728 func/s133.bmp 16.795 .855 .425 SCRAMBLED 18 7 .601 func/s133.bmp 19.985 .845 .546 UNFAMILIAR 13 7 .683 func/u144.bmp 23.126 .873 .490 FAMOUS 5 4 .902 func/f137.bmp 26.199 .869 .402 UNFAMILIAR 13 7 .744 func/u135.bmp 29.256 .935 .421 FAMOUS 7 7 .624 func/f140.bmp 31.929 20.000 20.000 n/a 999 20000 20.000 func/i999.bmp 52.491 .830 .519 FAMOUS 5 7 .732 func/f142.bmp 55.615 .873 .534 FAMOUS 6 7 .575 func/f142.bmp 58.805 .897 .510 SCRAMBLED 17 7 .781 func/s132.bmp 61.912 .986 .420 FAMOUS 5 7 .934 func/f132.bmp 65.169 .901 .504 UNFAMILIAR 15 7 .960 func/u144.bmp 68.276 .954 .438 UNFAMILIAR 13 7 .742 func/u142.bmp 71.517 .951 .514 UNFAMILIAR 14 7 1.034 func/u142.bmp 74.657 .828 .430 FAMOUS 7 7 1.166 func/f137.bmp 77.697 .810 .442 UNFAMILIAR 13 4 .778 func/u140.bmp 80.737 .833 .461 UNFAMILIAR 14 4 .766 func/u140.bmp 83.828 .866 .496 UNFAMILIAR 15 7 .868 func/u135.bmp 86.884 .841 .430 FAMOUS 5 4 1.199 func/f146.bmp 90.092 .851 .587 FAMOUS 6 4 .806 func/f146.bmp 93.215 .993 .511 FAMOUS 5 7 .765 func/f145.bmp 96.506 .842 .521 SCRAMBLED 19 7 .967 func/s132.bmp 99.546 .815 .423 SCRAMBLED 17 4 .969 func/s137.bmp 102.670 .969 .536 SCRAMBLED 18 4 .685 func/s137.bmp 105.810 .907 .414 FAMOUS 7 7 .706 func/f132.bmp 109.000 .873 .491 UNFAMILIAR 13 7 .701 func/u132.bmp 112.208 .829 .553 UNFAMILIAR 14 7 .608 func/u132.bmp 114.780 20.000 20.000 n/a 999 20000 20.000 func/i999.bmp 135.326 .961 .501 FAMOUS 5 7 .680 func/f134.bmp 138.483 .852 .405 SCRAMBLED 17 7 .775 func/s143.bmp 141.523 .868 .421 SCRAMBLED 18 7 .566 func/s143.bmp 144.563 .889 .406 UNFAMILIAR 13 4 .963 func/u136.bmp 147.703 .822 .472 UNFAMILIAR 14 4 .714 func/u136.bmp 150.827 .975 .525 FAMOUS 7 7 .749 func/f145.bmp 154.017 .979 .448 SCRAMBLED 17 7 .692 func/s139.bmp 157.308 .903 .543 SCRAMBLED 18 4 .795 func/s139.bmp 160.515 .821 .548 UNFAMILIAR 13 7 .781 func/u146.bmp 163.539 .858 .432 UNFAMILIAR 14 7 .754 func/u146.bmp 166.646 .935 .464 FAMOUS 5 4 .830 func/f135.bmp 169.886 .853 .546 FAMOUS 7 7 .699 func/f134.bmp 172.926 .829 .412 SCRAMBLED 17 4 .752 func/s145.bmp 176.117 .802 .591 UNFAMILIAR 13 7 .776 func/u143.bmp 179.157 .880 .478 UNFAMILIAR 13 7 .750 func/u133.bmp 182.214 .910 .402 UNFAMILIAR 14 7 1.052 func/u133.bmp 185.404 .906 .505 FAMOUS 5 4 .996 func/f133.bmp 188.661 .988 .583 FAMOUS 6 4 .647 func/f133.bmp 191.401 20.000 20.000 n/a 999 20000 20.000 func/i999.bmp 211.896 .890 .432 FAMOUS 5 4 1.009 func/f136.bmp 215.104 .960 .539 FAMOUS 6 4 .706 func/f136.bmp 218.344 .833 .513 FAMOUS 7 4 .747 func/f135.bmp 221.351 .855 .416 UNFAMILIAR 13 7 .734 func/u145.bmp 224.424 .850 .449 SCRAMBLED 19 4 .954 func/s145.bmp 227.448 .917 .410 FAMOUS 5 7 .673 func/f141.bmp 230.605 .800 .481 UNFAMILIAR 15 7 .641 func/u143.bmp 233.678 .935 .440 SCRAMBLED 17 7 .922 func/s138.bmp 236.919 .959 .536 SCRAMBLED 18 7 .724 func/s138.bmp 240.093 .938 .448 SCRAMBLED 17 4 .693 func/s135.bmp 243.383 .949 .571 FAMOUS 5 7 .755 func/f138.bmp 246.607 .877 .510 SCRAMBLED 17 7 1.276 func/s136.bmp 249.697 .992 .445 UNFAMILIAR 15 7 .863 func/u145.bmp 252.955 .903 .501 SCRAMBLED 17 7 1.850 func/s134.bmp 256.178 .900 .563 FAMOUS 7 7 1.088 func/f141.bmp 259.269 .901 .432 SCRAMBLED 17 4 .736 func/s146.bmp 262.392 .819 .452 FAMOUS 5 7 .648 func/f143.bmp 265.449 .829 .468 SCRAMBLED 19 4 .950 func/s135.bmp 268.589 .891 .543 UNFAMILIAR 13 7 .694 func/u138.bmp 271.780 .834 .520 FAMOUS 7 7 .688 func/f138.bmp 274.352 20.000 20.000 n/a 999 20000 20.000 func/i999.bmp 294.898 .919 .511 FAMOUS 5 7 .872 func/f144.bmp 298.139 .833 .566 FAMOUS 6 7 .583 func/f144.bmp 301.279 .849 .539 SCRAMBLED 19 7 .840 func/s136.bmp 304.369 .988 .474 UNFAMILIAR 13 7 .693 func/u141.bmp 307.626 .976 .496 UNFAMILIAR 14 7 .544 func/u141.bmp 310.884 .877 .505 SCRAMBLED 19 7 .733 func/s134.bmp 313.991 .886 .465 UNFAMILIAR 13 7 .595 func/u139.bmp 317.214 .861 .581 SCRAMBLED 19 4 .760 func/s146.bmp 320.405 .879 .556 UNFAMILIAR 13 7 .600 func/u137.bmp 323.462 .895 .417 UNFAMILIAR 14 7 .545 func/u137.bmp 326.669 .964 .537 FAMOUS 7 7 .783 func/f143.bmp 329.859 .806 .461 FAMOUS 5 4 .774 func/f131.bmp 333.000 .929 .552 FAMOUS 6 4 .660 func/f131.bmp 336.274 .913 .579 UNFAMILIAR 15 7 .834 func/u138.bmp 339.531 .866 .572 UNFAMILIAR 13 7 .787 func/u134.bmp 342.621 .946 .460 SCRAMBLED 17 7 1.305 func/s141.bmp 345.845 .896 .496 SCRAMBLED 17 4 .693 func/s131.bmp 348.952 .917 .441 SCRAMBLED 18 4 .697 func/s131.bmp 352.226 .902 .589 UNFAMILIAR 15 7 .718 func/u139.bmp 354.865 20.000 20.000 n/a 999 20000 20.000 func/i999.bmp 375.361 .918 .463 SCRAMBLED 17 7 .816 func/s144.bmp 378.501 .957 .459 SCRAMBLED 18 7 .630 func/s144.bmp 381.675 .915 .446 SCRAMBLED 17 7 .622 func/s140.bmp 384.865 .815 .504 UNFAMILIAR 13 7 .694 func/u131.bmp 387.972 .814 .531 UNFAMILIAR 14 7 .517 func/u131.bmp 390.996 .966 .443 UNFAMILIAR 15 7 .712 func/u134.bmp 394.203 .911 .456 SCRAMBLED 17 4 .785 func/s142.bmp 397.360 .999 .471 SCRAMBLED 18 4 .762 func/s142.bmp 400.567 .913 .438 SCRAMBLED 19 7 .920 func/s141.bmp 403.239 .012 0 n/a 999 0 0 func/Circle.bmp ================================================ FILE: tests/data/ds000117/sub-emptyroom/ses-20090409/meg/sub-emptyroom_ses-20090409_task-noise_meg.fif ================================================ ================================================ FILE: tests/data/ds000117/sub-emptyroom/ses-20090409/sub-emptyroom_ses-20090409_scans.tsv ================================================ filename acq_time meg/sub-emptyroom_ses-20090409_task-noise_meg.fif ##-##-##T11:05:49 ================================================ FILE: tests/data/ds000117/sub-emptyroom/ses-20090506/meg/sub-emptyroom_ses-20090506_task-noise_meg.fif ================================================ ================================================ FILE: tests/data/ds000117/sub-emptyroom/ses-20090506/sub-emptyroom_ses-20090506_scans.tsv ================================================ filename acq_time meg/sub-emptyroom_ses-20090506_task-noise_meg.fif ##-##-##T14:51:49 ================================================ FILE: tests/data/ds000117/sub-emptyroom/ses-20090511/meg/sub-emptyroom_ses-20090511_task-noise_meg.fif ================================================ ================================================ FILE: tests/data/ds000117/sub-emptyroom/ses-20090511/sub-emptyroom_ses-20090511_scans.tsv ================================================ filename acq_time meg/sub-emptyroom_ses-20090511_task-noise_meg.fif ##-##-##T09:39:23 ================================================ FILE: tests/data/ds000117/sub-emptyroom/ses-20090515/meg/sub-emptyroom_ses-20090515_task-noise_meg.fif ================================================ ================================================ FILE: tests/data/ds000117/sub-emptyroom/ses-20090515/sub-emptyroom_ses-20090515_scans.tsv ================================================ filename acq_time meg/sub-emptyroom_ses-20090515_task-noise_meg.fif ##-##-##T15:52:30 ================================================ FILE: tests/data/ds000117/sub-emptyroom/ses-20090518/meg/sub-emptyroom_ses-20090518_task-noise_meg.fif ================================================ ================================================ FILE: tests/data/ds000117/sub-emptyroom/ses-20090518/sub-emptyroom_ses-20090518_scans.tsv ================================================ filename acq_time meg/sub-emptyroom_ses-20090518_task-noise_meg.fif ##-##-##T09:33:15 ================================================ FILE: tests/data/ds000117/sub-emptyroom/ses-20090601/meg/sub-emptyroom_ses-20090601_task-noise_meg.fif ================================================ ================================================ FILE: tests/data/ds000117/sub-emptyroom/ses-20090601/sub-emptyroom_ses-20090601_scans.tsv ================================================ filename acq_time meg/sub-emptyroom_ses-20090601_task-noise_meg.fif ##-##-##T10:05:18 ================================================ FILE: tests/data/ds000117/sub-emptyroom/ses-20091126/meg/sub-emptyroom_ses-20091126_task-noise_meg.fif ================================================ ================================================ FILE: tests/data/ds000117/sub-emptyroom/ses-20091126/sub-emptyroom_ses-20091126_scans.tsv ================================================ filename acq_time meg/sub-emptyroom_ses-20091126_task-noise_meg.fif ##-##-##T15:39:18 ================================================ FILE: tests/data/ds000117/sub-emptyroom/ses-20091208/meg/sub-emptyroom_ses-20091208_task-noise_meg.fif ================================================ ================================================ FILE: tests/data/ds000117/sub-emptyroom/ses-20091208/sub-emptyroom_ses-20091208_scans.tsv ================================================ filename acq_time meg/sub-emptyroom_ses-20091208_task-noise_meg.fif ##-##-##T09:54:18 ================================================ FILE: tests/data/ds000117/task-facerecognition_beh.json ================================================ { "TaskDescription": "After the M/EEG acquisition, participants saw each of the 300 faces again, but now used three buttons to indicate whether (1) they had not seen the face before the experiment, (2) the face looked familiar, but they could not remember from where, or (3) they knew the face, i.e. could remember definite fact about them, such as their job, a movie they were in, their name etc. On average across participants, 73% of famous faces were given a rating of 2-3, and 86% of nonfamous faces were given a rating of 1. These debriefing data could be used to further refine familiarity of each participant with each face, and are available on request, but were not used in the current validation." } ================================================ FILE: tests/data/ds000117/task-facerecognition_bold.json ================================================ { "RepetitionTime": 2.0, "TaskName": "facerecognition", "EchoTime": 0.030, "FlipAngle": 78, "PulseSequenceType": "EPI", "Manufacturer": "Siemens", "ManufacturersModelName": "TIM TRIO", "MagneticFieldStrength": 3 } ================================================ FILE: tests/data/ds000117/task-facerecognition_channels.tsv ================================================ name type units description sampling_frequency high_cutoff low_cutoff notch software_filters status MEG0113 MEGGRAD T/m planar_gradiometer 1100 350 0 n/a n/a good MEG0112 MEGGRAD T/m planar_gradiometer 1100 350 0 n/a n/a good MEG0111 MEGMAG T magnetometer 1100 350 0 n/a n/a good MEG0122 MEGGRAD T/m planar_gradiometer 1100 350 0 n/a n/a good MEG0123 MEGGRAD T/m planar_gradiometer 1100 350 0 n/a n/a good MEG0121 MEGMAG T magnetometer 1100 350 0 n/a n/a good MEG0132 MEGGRAD T/m planar_gradiometer 1100 350 0 n/a n/a good MEG0133 MEGGRAD T/m planar_gradiometer 1100 350 0 n/a n/a good MEG0131 MEGMAG T magnetometer 1100 350 0 n/a n/a good MEG0143 MEGGRAD T/m planar_gradiometer 1100 350 0 n/a n/a good MEG0142 MEGGRAD T/m planar_gradiometer 1100 350 0 n/a n/a good MEG0141 MEGMAG T magnetometer 1100 350 0 n/a n/a good MEG0213 MEGGRAD T/m planar_gradiometer 1100 350 0 n/a n/a good MEG0212 MEGGRAD T/m planar_gradiometer 1100 350 0 n/a n/a good MEG0211 MEGMAG T magnetometer 1100 350 0 n/a n/a good MEG0222 MEGGRAD T/m planar_gradiometer 1100 350 0 n/a n/a good MEG0223 MEGGRAD T/m planar_gradiometer 1100 350 0 n/a n/a good MEG0221 MEGMAG T magnetometer 1100 350 0 n/a n/a good MEG0232 MEGGRAD T/m planar_gradiometer 1100 350 0 n/a n/a good MEG0233 MEGGRAD T/m planar_gradiometer 1100 350 0 n/a n/a good MEG0231 MEGMAG T magnetometer 1100 350 0 n/a n/a good MEG0243 MEGGRAD T/m planar_gradiometer 1100 350 0 n/a n/a good MEG0242 MEGGRAD T/m planar_gradiometer 1100 350 0 n/a n/a good MEG0241 MEGMAG T magnetometer 1100 350 0 n/a n/a good MEG0313 MEGGRAD T/m planar_gradiometer 1100 350 0 n/a n/a good MEG0312 MEGGRAD T/m planar_gradiometer 1100 350 0 n/a n/a good MEG0311 MEGMAG T magnetometer 1100 350 0 n/a n/a good MEG0322 MEGGRAD T/m planar_gradiometer 1100 350 0 n/a n/a good MEG0323 MEGGRAD T/m planar_gradiometer 1100 350 0 n/a n/a good MEG0321 MEGMAG T magnetometer 1100 350 0 n/a n/a good MEG0333 MEGGRAD T/m planar_gradiometer 1100 350 0 n/a n/a good MEG0332 MEGGRAD T/m planar_gradiometer 1100 350 0 n/a n/a good MEG0331 MEGMAG T magnetometer 1100 350 0 n/a n/a good MEG0343 MEGGRAD T/m planar_gradiometer 1100 350 0 n/a n/a good MEG0342 MEGGRAD T/m planar_gradiometer 1100 350 0 n/a n/a good MEG0341 MEGMAG T magnetometer 1100 350 0 n/a n/a good MEG0413 MEGGRAD T/m planar_gradiometer 1100 350 0 n/a n/a good MEG0412 MEGGRAD T/m planar_gradiometer 1100 350 0 n/a n/a good MEG0411 MEGMAG T magnetometer 1100 350 0 n/a n/a good MEG0422 MEGGRAD T/m planar_gradiometer 1100 350 0 n/a n/a good MEG0423 MEGGRAD T/m planar_gradiometer 1100 350 0 n/a n/a good MEG0421 MEGMAG T magnetometer 1100 350 0 n/a n/a good MEG0432 MEGGRAD T/m planar_gradiometer 1100 350 0 n/a n/a good MEG0433 MEGGRAD T/m planar_gradiometer 1100 350 0 n/a n/a good MEG0431 MEGMAG T magnetometer 1100 350 0 n/a n/a good MEG0443 MEGGRAD T/m planar_gradiometer 1100 350 0 n/a n/a good MEG0442 MEGGRAD T/m planar_gradiometer 1100 350 0 n/a n/a good MEG0441 MEGMAG T magnetometer 1100 350 0 n/a n/a good MEG0513 MEGGRAD T/m planar_gradiometer 1100 350 0 n/a n/a good MEG0512 MEGGRAD T/m planar_gradiometer 1100 350 0 n/a n/a good MEG0511 MEGMAG T magnetometer 1100 350 0 n/a n/a good MEG0523 MEGGRAD T/m planar_gradiometer 1100 350 0 n/a n/a good MEG0522 MEGGRAD T/m planar_gradiometer 1100 350 0 n/a n/a good MEG0521 MEGMAG T magnetometer 1100 350 0 n/a n/a good MEG0532 MEGGRAD T/m planar_gradiometer 1100 350 0 n/a n/a good MEG0533 MEGGRAD T/m planar_gradiometer 1100 350 0 n/a n/a good MEG0531 MEGMAG T magnetometer 1100 350 0 n/a n/a good MEG0542 MEGGRAD T/m planar_gradiometer 1100 350 0 n/a n/a good MEG0543 MEGGRAD T/m planar_gradiometer 1100 350 0 n/a n/a good MEG0541 MEGMAG T magnetometer 1100 350 0 n/a n/a good MEG0613 MEGGRAD T/m planar_gradiometer 1100 350 0 n/a n/a good MEG0612 MEGGRAD T/m planar_gradiometer 1100 350 0 n/a n/a good MEG0611 MEGMAG T magnetometer 1100 350 0 n/a n/a good MEG0622 MEGGRAD T/m planar_gradiometer 1100 350 0 n/a n/a good MEG0623 MEGGRAD T/m planar_gradiometer 1100 350 0 n/a n/a good MEG0621 MEGMAG T magnetometer 1100 350 0 n/a n/a good MEG0633 MEGGRAD T/m planar_gradiometer 1100 350 0 n/a n/a good MEG0632 MEGGRAD T/m planar_gradiometer 1100 350 0 n/a n/a good MEG0631 MEGMAG T magnetometer 1100 350 0 n/a n/a good MEG0642 MEGGRAD T/m planar_gradiometer 1100 350 0 n/a n/a good MEG0643 MEGGRAD T/m planar_gradiometer 1100 350 0 n/a n/a good MEG0641 MEGMAG T magnetometer 1100 350 0 n/a n/a good MEG0713 MEGGRAD T/m planar_gradiometer 1100 350 0 n/a n/a good MEG0712 MEGGRAD T/m planar_gradiometer 1100 350 0 n/a n/a good MEG0711 MEGMAG T magnetometer 1100 350 0 n/a n/a good MEG0723 MEGGRAD T/m planar_gradiometer 1100 350 0 n/a n/a good MEG0722 MEGGRAD T/m planar_gradiometer 1100 350 0 n/a n/a good MEG0721 MEGMAG T magnetometer 1100 350 0 n/a n/a good MEG0733 MEGGRAD T/m planar_gradiometer 1100 350 0 n/a n/a good MEG0732 MEGGRAD T/m planar_gradiometer 1100 350 0 n/a n/a good MEG0731 MEGMAG T magnetometer 1100 350 0 n/a n/a good MEG0743 MEGGRAD T/m planar_gradiometer 1100 350 0 n/a n/a good MEG0742 MEGGRAD T/m planar_gradiometer 1100 350 0 n/a n/a good MEG0741 MEGMAG T magnetometer 1100 350 0 n/a n/a good MEG0813 MEGGRAD T/m planar_gradiometer 1100 350 0 n/a n/a good MEG0812 MEGGRAD T/m planar_gradiometer 1100 350 0 n/a n/a good MEG0811 MEGMAG T magnetometer 1100 350 0 n/a n/a good MEG0822 MEGGRAD T/m planar_gradiometer 1100 350 0 n/a n/a good MEG0823 MEGGRAD T/m planar_gradiometer 1100 350 0 n/a n/a good MEG0821 MEGMAG T magnetometer 1100 350 0 n/a n/a good MEG0913 MEGGRAD T/m planar_gradiometer 1100 350 0 n/a n/a good MEG0912 MEGGRAD T/m planar_gradiometer 1100 350 0 n/a n/a good MEG0911 MEGMAG T magnetometer 1100 350 0 n/a n/a good MEG0923 MEGGRAD T/m planar_gradiometer 1100 350 0 n/a n/a good MEG0922 MEGGRAD T/m planar_gradiometer 1100 350 0 n/a n/a good MEG0921 MEGMAG T magnetometer 1100 350 0 n/a n/a good MEG0932 MEGGRAD T/m planar_gradiometer 1100 350 0 n/a n/a good MEG0933 MEGGRAD T/m planar_gradiometer 1100 350 0 n/a n/a good MEG0931 MEGMAG T magnetometer 1100 350 0 n/a n/a good MEG0942 MEGGRAD T/m planar_gradiometer 1100 350 0 n/a n/a good MEG0943 MEGGRAD T/m planar_gradiometer 1100 350 0 n/a n/a good MEG0941 MEGMAG T magnetometer 1100 350 0 n/a n/a good MEG1013 MEGGRAD T/m planar_gradiometer 1100 350 0 n/a n/a good MEG1012 MEGGRAD T/m planar_gradiometer 1100 350 0 n/a n/a good MEG1011 MEGMAG T magnetometer 1100 350 0 n/a n/a good MEG1023 MEGGRAD T/m planar_gradiometer 1100 350 0 n/a n/a good MEG1022 MEGGRAD T/m planar_gradiometer 1100 350 0 n/a n/a good MEG1021 MEGMAG T magnetometer 1100 350 0 n/a n/a good MEG1032 MEGGRAD T/m planar_gradiometer 1100 350 0 n/a n/a good MEG1033 MEGGRAD T/m planar_gradiometer 1100 350 0 n/a n/a good MEG1031 MEGMAG T magnetometer 1100 350 0 n/a n/a good MEG1043 MEGGRAD T/m planar_gradiometer 1100 350 0 n/a n/a good MEG1042 MEGGRAD T/m planar_gradiometer 1100 350 0 n/a n/a good MEG1041 MEGMAG T magnetometer 1100 350 0 n/a n/a good MEG1112 MEGGRAD T/m planar_gradiometer 1100 350 0 n/a n/a good MEG1113 MEGGRAD T/m planar_gradiometer 1100 350 0 n/a n/a good MEG1111 MEGMAG T magnetometer 1100 350 0 n/a n/a good MEG1123 MEGGRAD T/m planar_gradiometer 1100 350 0 n/a n/a good MEG1122 MEGGRAD T/m planar_gradiometer 1100 350 0 n/a n/a good MEG1121 MEGMAG T magnetometer 1100 350 0 n/a n/a good MEG1133 MEGGRAD T/m planar_gradiometer 1100 350 0 n/a n/a good MEG1132 MEGGRAD T/m planar_gradiometer 1100 350 0 n/a n/a good MEG1131 MEGMAG T magnetometer 1100 350 0 n/a n/a good MEG1142 MEGGRAD T/m planar_gradiometer 1100 350 0 n/a n/a good MEG1143 MEGGRAD T/m planar_gradiometer 1100 350 0 n/a n/a good MEG1141 MEGMAG T magnetometer 1100 350 0 n/a n/a good MEG1213 MEGGRAD T/m planar_gradiometer 1100 350 0 n/a n/a good MEG1212 MEGGRAD T/m planar_gradiometer 1100 350 0 n/a n/a good MEG1211 MEGMAG T magnetometer 1100 350 0 n/a n/a good MEG1223 MEGGRAD T/m planar_gradiometer 1100 350 0 n/a n/a good MEG1222 MEGGRAD T/m planar_gradiometer 1100 350 0 n/a n/a good MEG1221 MEGMAG T magnetometer 1100 350 0 n/a n/a good MEG1232 MEGGRAD T/m planar_gradiometer 1100 350 0 n/a n/a good MEG1233 MEGGRAD T/m planar_gradiometer 1100 350 0 n/a n/a good MEG1231 MEGMAG T magnetometer 1100 350 0 n/a n/a good MEG1243 MEGGRAD T/m planar_gradiometer 1100 350 0 n/a n/a good MEG1242 MEGGRAD T/m planar_gradiometer 1100 350 0 n/a n/a good MEG1241 MEGMAG T magnetometer 1100 350 0 n/a n/a good MEG1312 MEGGRAD T/m planar_gradiometer 1100 350 0 n/a n/a good MEG1313 MEGGRAD T/m planar_gradiometer 1100 350 0 n/a n/a good MEG1311 MEGMAG T magnetometer 1100 350 0 n/a n/a good MEG1323 MEGGRAD T/m planar_gradiometer 1100 350 0 n/a n/a good MEG1322 MEGGRAD T/m planar_gradiometer 1100 350 0 n/a n/a good MEG1321 MEGMAG T magnetometer 1100 350 0 n/a n/a good MEG1333 MEGGRAD T/m planar_gradiometer 1100 350 0 n/a n/a good MEG1332 MEGGRAD T/m planar_gradiometer 1100 350 0 n/a n/a good MEG1331 MEGMAG T magnetometer 1100 350 0 n/a n/a good MEG1342 MEGGRAD T/m planar_gradiometer 1100 350 0 n/a n/a good MEG1343 MEGGRAD T/m planar_gradiometer 1100 350 0 n/a n/a good MEG1341 MEGMAG T magnetometer 1100 350 0 n/a n/a good MEG1412 MEGGRAD T/m planar_gradiometer 1100 350 0 n/a n/a good MEG1413 MEGGRAD T/m planar_gradiometer 1100 350 0 n/a n/a good MEG1411 MEGMAG T magnetometer 1100 350 0 n/a n/a good MEG1423 MEGGRAD T/m planar_gradiometer 1100 350 0 n/a n/a good MEG1422 MEGGRAD T/m planar_gradiometer 1100 350 0 n/a n/a good MEG1421 MEGMAG T magnetometer 1100 350 0 n/a n/a good MEG1433 MEGGRAD T/m planar_gradiometer 1100 350 0 n/a n/a good MEG1432 MEGGRAD T/m planar_gradiometer 1100 350 0 n/a n/a good MEG1431 MEGMAG T magnetometer 1100 350 0 n/a n/a good MEG1442 MEGGRAD T/m planar_gradiometer 1100 350 0 n/a n/a good MEG1443 MEGGRAD T/m planar_gradiometer 1100 350 0 n/a n/a good MEG1441 MEGMAG T magnetometer 1100 350 0 n/a n/a good MEG1512 MEGGRAD T/m planar_gradiometer 1100 350 0 n/a n/a good MEG1513 MEGGRAD T/m planar_gradiometer 1100 350 0 n/a n/a good MEG1511 MEGMAG T magnetometer 1100 350 0 n/a n/a good MEG1522 MEGGRAD T/m planar_gradiometer 1100 350 0 n/a n/a good MEG1523 MEGGRAD T/m planar_gradiometer 1100 350 0 n/a n/a good MEG1521 MEGMAG T magnetometer 1100 350 0 n/a n/a good MEG1533 MEGGRAD T/m planar_gradiometer 1100 350 0 n/a n/a good MEG1532 MEGGRAD T/m planar_gradiometer 1100 350 0 n/a n/a good MEG1531 MEGMAG T magnetometer 1100 350 0 n/a n/a good MEG1543 MEGGRAD T/m planar_gradiometer 1100 350 0 n/a n/a good MEG1542 MEGGRAD T/m planar_gradiometer 1100 350 0 n/a n/a good MEG1541 MEGMAG T magnetometer 1100 350 0 n/a n/a good MEG1613 MEGGRAD T/m planar_gradiometer 1100 350 0 n/a n/a good MEG1612 MEGGRAD T/m planar_gradiometer 1100 350 0 n/a n/a good MEG1611 MEGMAG T magnetometer 1100 350 0 n/a n/a good MEG1622 MEGGRAD T/m planar_gradiometer 1100 350 0 n/a n/a good MEG1623 MEGGRAD T/m planar_gradiometer 1100 350 0 n/a n/a good MEG1621 MEGMAG T magnetometer 1100 350 0 n/a n/a good MEG1632 MEGGRAD T/m planar_gradiometer 1100 350 0 n/a n/a good MEG1633 MEGGRAD T/m planar_gradiometer 1100 350 0 n/a n/a good MEG1631 MEGMAG T magnetometer 1100 350 0 n/a n/a good MEG1643 MEGGRAD T/m planar_gradiometer 1100 350 0 n/a n/a good MEG1642 MEGGRAD T/m planar_gradiometer 1100 350 0 n/a n/a good MEG1641 MEGMAG T magnetometer 1100 350 0 n/a n/a good MEG1713 MEGGRAD T/m planar_gradiometer 1100 350 0 n/a n/a good MEG1712 MEGGRAD T/m planar_gradiometer 1100 350 0 n/a n/a good MEG1711 MEGMAG T magnetometer 1100 350 0 n/a n/a good MEG1722 MEGGRAD T/m planar_gradiometer 1100 350 0 n/a n/a good MEG1723 MEGGRAD T/m planar_gradiometer 1100 350 0 n/a n/a good MEG1721 MEGMAG T magnetometer 1100 350 0 n/a n/a good MEG1732 MEGGRAD T/m planar_gradiometer 1100 350 0 n/a n/a good MEG1733 MEGGRAD T/m planar_gradiometer 1100 350 0 n/a n/a good MEG1731 MEGMAG T magnetometer 1100 350 0 n/a n/a good MEG1743 MEGGRAD T/m planar_gradiometer 1100 350 0 n/a n/a good MEG1742 MEGGRAD T/m planar_gradiometer 1100 350 0 n/a n/a good MEG1741 MEGMAG T magnetometer 1100 350 0 n/a n/a good MEG1813 MEGGRAD T/m planar_gradiometer 1100 350 0 n/a n/a good MEG1812 MEGGRAD T/m planar_gradiometer 1100 350 0 n/a n/a good MEG1811 MEGMAG T magnetometer 1100 350 0 n/a n/a good MEG1822 MEGGRAD T/m planar_gradiometer 1100 350 0 n/a n/a good MEG1823 MEGGRAD T/m planar_gradiometer 1100 350 0 n/a n/a good MEG1821 MEGMAG T magnetometer 1100 350 0 n/a n/a good MEG1832 MEGGRAD T/m planar_gradiometer 1100 350 0 n/a n/a good MEG1833 MEGGRAD T/m planar_gradiometer 1100 350 0 n/a n/a good MEG1831 MEGMAG T magnetometer 1100 350 0 n/a n/a good MEG1843 MEGGRAD T/m planar_gradiometer 1100 350 0 n/a n/a good MEG1842 MEGGRAD T/m planar_gradiometer 1100 350 0 n/a n/a good MEG1841 MEGMAG T magnetometer 1100 350 0 n/a n/a good MEG1912 MEGGRAD T/m planar_gradiometer 1100 350 0 n/a n/a good MEG1913 MEGGRAD T/m planar_gradiometer 1100 350 0 n/a n/a good MEG1911 MEGMAG T magnetometer 1100 350 0 n/a n/a good MEG1923 MEGGRAD T/m planar_gradiometer 1100 350 0 n/a n/a good MEG1922 MEGGRAD T/m planar_gradiometer 1100 350 0 n/a n/a good MEG1921 MEGMAG T magnetometer 1100 350 0 n/a n/a good MEG1932 MEGGRAD T/m planar_gradiometer 1100 350 0 n/a n/a good MEG1933 MEGGRAD T/m planar_gradiometer 1100 350 0 n/a n/a good MEG1931 MEGMAG T magnetometer 1100 350 0 n/a n/a good MEG1943 MEGGRAD T/m planar_gradiometer 1100 350 0 n/a n/a good MEG1942 MEGGRAD T/m planar_gradiometer 1100 350 0 n/a n/a good MEG1941 MEGMAG T magnetometer 1100 350 0 n/a n/a good MEG2013 MEGGRAD T/m planar_gradiometer 1100 350 0 n/a n/a good MEG2012 MEGGRAD T/m planar_gradiometer 1100 350 0 n/a n/a good MEG2011 MEGMAG T magnetometer 1100 350 0 n/a n/a good MEG2023 MEGGRAD T/m planar_gradiometer 1100 350 0 n/a n/a good MEG2022 MEGGRAD T/m planar_gradiometer 1100 350 0 n/a n/a good MEG2021 MEGMAG T magnetometer 1100 350 0 n/a n/a good MEG2032 MEGGRAD T/m planar_gradiometer 1100 350 0 n/a n/a good MEG2033 MEGGRAD T/m planar_gradiometer 1100 350 0 n/a n/a good MEG2031 MEGMAG T magnetometer 1100 350 0 n/a n/a good MEG2042 MEGGRAD T/m planar_gradiometer 1100 350 0 n/a n/a good MEG2043 MEGGRAD T/m planar_gradiometer 1100 350 0 n/a n/a good MEG2041 MEGMAG T magnetometer 1100 350 0 n/a n/a good MEG2113 MEGGRAD T/m planar_gradiometer 1100 350 0 n/a n/a good MEG2112 MEGGRAD T/m planar_gradiometer 1100 350 0 n/a n/a good MEG2111 MEGMAG T magnetometer 1100 350 0 n/a n/a good MEG2122 MEGGRAD T/m planar_gradiometer 1100 350 0 n/a n/a good MEG2123 MEGGRAD T/m planar_gradiometer 1100 350 0 n/a n/a good MEG2121 MEGMAG T magnetometer 1100 350 0 n/a n/a good MEG2133 MEGGRAD T/m planar_gradiometer 1100 350 0 n/a n/a good MEG2132 MEGGRAD T/m planar_gradiometer 1100 350 0 n/a n/a good MEG2131 MEGMAG T magnetometer 1100 350 0 n/a n/a good MEG2143 MEGGRAD T/m planar_gradiometer 1100 350 0 n/a n/a good MEG2142 MEGGRAD T/m planar_gradiometer 1100 350 0 n/a n/a good MEG2141 MEGMAG T magnetometer 1100 350 0 n/a n/a good MEG2212 MEGGRAD T/m planar_gradiometer 1100 350 0 n/a n/a good MEG2213 MEGGRAD T/m planar_gradiometer 1100 350 0 n/a n/a good MEG2211 MEGMAG T magnetometer 1100 350 0 n/a n/a good MEG2223 MEGGRAD T/m planar_gradiometer 1100 350 0 n/a n/a good MEG2222 MEGGRAD T/m planar_gradiometer 1100 350 0 n/a n/a good MEG2221 MEGMAG T magnetometer 1100 350 0 n/a n/a good MEG2233 MEGGRAD T/m planar_gradiometer 1100 350 0 n/a n/a good MEG2232 MEGGRAD T/m planar_gradiometer 1100 350 0 n/a n/a good MEG2231 MEGMAG T magnetometer 1100 350 0 n/a n/a good MEG2242 MEGGRAD T/m planar_gradiometer 1100 350 0 n/a n/a good MEG2243 MEGGRAD T/m planar_gradiometer 1100 350 0 n/a n/a good MEG2241 MEGMAG T magnetometer 1100 350 0 n/a n/a good MEG2312 MEGGRAD T/m planar_gradiometer 1100 350 0 n/a n/a good MEG2313 MEGGRAD T/m planar_gradiometer 1100 350 0 n/a n/a good MEG2311 MEGMAG T magnetometer 1100 350 0 n/a n/a good MEG2323 MEGGRAD T/m planar_gradiometer 1100 350 0 n/a n/a good MEG2322 MEGGRAD T/m planar_gradiometer 1100 350 0 n/a n/a good MEG2321 MEGMAG T magnetometer 1100 350 0 n/a n/a good MEG2332 MEGGRAD T/m planar_gradiometer 1100 350 0 n/a n/a good MEG2333 MEGGRAD T/m planar_gradiometer 1100 350 0 n/a n/a good MEG2331 MEGMAG T magnetometer 1100 350 0 n/a n/a good MEG2343 MEGGRAD T/m planar_gradiometer 1100 350 0 n/a n/a good MEG2342 MEGGRAD T/m planar_gradiometer 1100 350 0 n/a n/a good MEG2341 MEGMAG T magnetometer 1100 350 0 n/a n/a good MEG2412 MEGGRAD T/m planar_gradiometer 1100 350 0 n/a n/a good MEG2413 MEGGRAD T/m planar_gradiometer 1100 350 0 n/a n/a good MEG2411 MEGMAG T magnetometer 1100 350 0 n/a n/a good MEG2423 MEGGRAD T/m planar_gradiometer 1100 350 0 n/a n/a good MEG2422 MEGGRAD T/m planar_gradiometer 1100 350 0 n/a n/a good MEG2421 MEGMAG T magnetometer 1100 350 0 n/a n/a good MEG2433 MEGGRAD T/m planar_gradiometer 1100 350 0 n/a n/a good MEG2432 MEGGRAD T/m planar_gradiometer 1100 350 0 n/a n/a good MEG2431 MEGMAG T magnetometer 1100 350 0 n/a n/a good MEG2442 MEGGRAD T/m planar_gradiometer 1100 350 0 n/a n/a good MEG2443 MEGGRAD T/m planar_gradiometer 1100 350 0 n/a n/a good MEG2441 MEGMAG T magnetometer 1100 350 0 n/a n/a good MEG2512 MEGGRAD T/m planar_gradiometer 1100 350 0 n/a n/a good MEG2513 MEGGRAD T/m planar_gradiometer 1100 350 0 n/a n/a good MEG2511 MEGMAG T magnetometer 1100 350 0 n/a n/a good MEG2522 MEGGRAD T/m planar_gradiometer 1100 350 0 n/a n/a good MEG2523 MEGGRAD T/m planar_gradiometer 1100 350 0 n/a n/a good MEG2521 MEGMAG T magnetometer 1100 350 0 n/a n/a good MEG2533 MEGGRAD T/m planar_gradiometer 1100 350 0 n/a n/a good MEG2532 MEGGRAD T/m planar_gradiometer 1100 350 0 n/a n/a good MEG2531 MEGMAG T magnetometer 1100 350 0 n/a n/a good MEG2543 MEGGRAD T/m planar_gradiometer 1100 350 0 n/a n/a good MEG2542 MEGGRAD T/m planar_gradiometer 1100 350 0 n/a n/a good MEG2541 MEGMAG T magnetometer 1100 350 0 n/a n/a good MEG2612 MEGGRAD T/m planar_gradiometer 1100 350 0 n/a n/a good MEG2613 MEGGRAD T/m planar_gradiometer 1100 350 0 n/a n/a good MEG2611 MEGMAG T magnetometer 1100 350 0 n/a n/a good MEG2623 MEGGRAD T/m planar_gradiometer 1100 350 0 n/a n/a good MEG2622 MEGGRAD T/m planar_gradiometer 1100 350 0 n/a n/a good MEG2621 MEGMAG T magnetometer 1100 350 0 n/a n/a good MEG2633 MEGGRAD T/m planar_gradiometer 1100 350 0 n/a n/a good MEG2632 MEGGRAD T/m planar_gradiometer 1100 350 0 n/a n/a good MEG2631 MEGMAG T magnetometer 1100 350 0 n/a n/a good MEG2642 MEGGRAD T/m planar_gradiometer 1100 350 0 n/a n/a good MEG2643 MEGGRAD T/m planar_gradiometer 1100 350 0 n/a n/a good MEG2641 MEGMAG T magnetometer 1100 350 0 n/a n/a good EEG001 EEG V eegchannel 1100 350 0 n/a n/a good EEG002 EEG V eegchannel 1100 350 0 n/a n/a good EEG003 EEG V eegchannel 1100 350 0 n/a n/a good EEG004 EEG V eegchannel 1100 350 0 n/a n/a good EEG005 EEG V eegchannel 1100 350 0 n/a n/a good EEG006 EEG V eegchannel 1100 350 0 n/a n/a good EEG007 EEG V eegchannel 1100 350 0 n/a n/a good EEG008 EEG V eegchannel 1100 350 0 n/a n/a good EEG009 EEG V eegchannel 1100 350 0 n/a n/a good EEG010 EEG V eegchannel 1100 350 0 n/a n/a good EEG011 EEG V eegchannel 1100 350 0 n/a n/a good EEG012 EEG V eegchannel 1100 350 0 n/a n/a good EEG013 EEG V eegchannel 1100 350 0 n/a n/a good EEG014 EEG V eegchannel 1100 350 0 n/a n/a good EEG015 EEG V eegchannel 1100 350 0 n/a n/a good EEG016 EEG V eegchannel 1100 350 0 n/a n/a good EEG017 EEG V eegchannel 1100 350 0 n/a n/a good EEG018 EEG V eegchannel 1100 350 0 n/a n/a good EEG019 EEG V eegchannel 1100 350 0 n/a n/a good EEG020 EEG V eegchannel 1100 350 0 n/a n/a good EEG021 EEG V eegchannel 1100 350 0 n/a n/a good EEG022 EEG V eegchannel 1100 350 0 n/a n/a good EEG023 EEG V eegchannel 1100 350 0 n/a n/a good EEG024 EEG V eegchannel 1100 350 0 n/a n/a good EEG025 EEG V eegchannel 1100 350 0 n/a n/a good EEG026 EEG V eegchannel 1100 350 0 n/a n/a good EEG027 EEG V eegchannel 1100 350 0 n/a n/a good EEG028 EEG V eegchannel 1100 350 0 n/a n/a good EEG029 EEG V eegchannel 1100 350 0 n/a n/a good EEG030 EEG V eegchannel 1100 350 0 n/a n/a good EEG031 EEG V eegchannel 1100 350 0 n/a n/a good EEG032 EEG V eegchannel 1100 350 0 n/a n/a good EEG033 EEG V eegchannel 1100 350 0 n/a n/a good EEG034 EEG V eegchannel 1100 350 0 n/a n/a good EEG035 EEG V eegchannel 1100 350 0 n/a n/a good EEG036 EEG V eegchannel 1100 350 0 n/a n/a good EEG037 EEG V eegchannel 1100 350 0 n/a n/a good EEG038 EEG V eegchannel 1100 350 0 n/a n/a good EEG039 EEG V eegchannel 1100 350 0 n/a n/a good EEG040 EEG V eegchannel 1100 350 0 n/a n/a good EEG041 EEG V eegchannel 1100 350 0 n/a n/a good EEG042 EEG V eegchannel 1100 350 0 n/a n/a good EEG043 EEG V eegchannel 1100 350 0 n/a n/a good EEG044 EEG V eegchannel 1100 350 0 n/a n/a good EEG045 EEG V eegchannel 1100 350 0 n/a n/a good EEG046 EEG V eegchannel 1100 350 0 n/a n/a good EEG047 EEG V eegchannel 1100 350 0 n/a n/a good EEG048 EEG V eegchannel 1100 350 0 n/a n/a good EEG049 EEG V eegchannel 1100 350 0 n/a n/a good EEG050 EEG V eegchannel 1100 350 0 n/a n/a good EEG051 EEG V eegchannel 1100 350 0 n/a n/a good EEG052 EEG V eegchannel 1100 350 0 n/a n/a good EEG053 EEG V eegchannel 1100 350 0 n/a n/a good EEG054 EEG V eegchannel 1100 350 0 n/a n/a good EEG055 EEG V eegchannel 1100 350 0 n/a n/a good EEG056 EEG V eegchannel 1100 350 0 n/a n/a good EEG057 EEG V eegchannel 1100 350 0 n/a n/a good EEG058 EEG V eegchannel 1100 350 0 n/a n/a good EEG059 EEG V eegchannel 1100 350 0 n/a n/a good EEG060 EEG V eegchannel 1100 350 0 n/a n/a good EEG061 HEOG V horizontaleog 1100 350 0 n/a n/a good EEG062 VEOG V verticaleog 1100 350 0 n/a n/a good EEG063 ECG V cardiac 1100 350 0 n/a n/a good EEG064 MISC n/a unused 1100 350 0 n/a n/a good EEG065 EEG V eegchannel 1100 350 0 n/a n/a good EEG066 EEG V eegchannel 1100 350 0 n/a n/a good EEG067 EEG V eegchannel 1100 350 0 n/a n/a good EEG068 EEG V eegchannel 1100 350 0 n/a n/a good EEG069 EEG V eegchannel 1100 350 0 n/a n/a good EEG070 EEG V eegchannel 1100 350 0 n/a n/a good EEG071 EEG V eegchannel 1100 350 0 n/a n/a good EEG072 EEG V eegchannel 1100 350 0 n/a n/a good EEG073 EEG V eegchannel 1100 350 0 n/a n/a good EEG074 EEG V eegchannel 1100 350 0 n/a n/a good STI101 TRIG n/a digitaltrigger 1100 Inf 0 n/a n/a good STI201 TRIG n/a othertrigger 1100 Inf 0 n/a n/a good STI301 TRIG n/a othertrigger 1100 Inf 0 n/a n/a good MISC201 MISC n/a n/a 1100 Inf 0 n/a n/a good MISC202 MISC n/a n/a 1100 Inf 0 n/a n/a good MISC203 MISC n/a n/a 1100 Inf 0 n/a n/a good MISC204 MISC n/a n/a 1100 Inf 0 n/a n/a good MISC205 MISC n/a n/a 1100 Inf 0 n/a n/a good MISC206 MISC n/a n/a 1100 Inf 0 n/a n/a good MISC301 MISC n/a n/a 1100 Inf 0 n/a n/a good MISC302 MISC n/a n/a 1100 Inf 0 n/a n/a good MISC303 MISC n/a n/a 1100 Inf 0 n/a n/a good MISC304 MISC n/a n/a 1100 Inf 0 n/a n/a good MISC305 MISC n/a n/a 1100 Inf 0 n/a n/a good MISC306 MISC n/a n/a 1100 Inf 0 n/a n/a good CHPI001 MISC n/a n/a 1100 Inf 0 n/a n/a good CHPI002 MISC n/a n/a 1100 Inf 0 n/a n/a good CHPI003 MISC n/a n/a 1100 Inf 0 n/a n/a good CHPI004 MISC n/a n/a 1100 Inf 0 n/a n/a good CHPI005 MISC n/a n/a 1100 Inf 0 n/a n/a good CHPI006 MISC n/a n/a 1100 Inf 0 n/a n/a good CHPI007 MISC n/a n/a 1100 Inf 0 n/a n/a good CHPI008 MISC n/a n/a 1100 Inf 0 n/a n/a good CHPI009 MISC n/a n/a 1100 Inf 0 n/a n/a good ================================================ FILE: tests/data/ds000117/task-facerecognition_meg.json ================================================ { "TaskName": "facerecognition", "SamplingFrequency": 1100, "Manufacturer": "Elekta", "ManufacturersModelName": "Neuromag Vectorview 306", "MEGChannelCount": 306, "MEGREFChannelCount": 0, "EEGChannelCount": 70, "EOGChannelCount": 2, "ECGChannelCount": 1, "EMGChannelCount": 0, "MiscChannelCount": 12, "TriggerChannelCount": 3, "PowerLineFrequency": 50, "HeadCoilFrequency": [293,307,314,321,328], "EEGPlacementScheme": "extended 10-10% system", "EEGReference": "nose", "DewarPosition": "upright", "RecordingType": "continuous", "EpochLength": "Inf", "TaskDescription": "Subjects viewed stimuli on a screen during six, 7.5 minute runs. The stimuli were photographs of either a face or a scrambled face, and appeared for a random duration between 800 and 1,000 ms. Subjects were instructed to fixate centrally throughout the experiment. To ensure attention to each stimulus, participants were asked to press one of two keys with either their left or right index finger (assignment counter-balanced across participants). Their key-press was based on how symmetric they regarded each image: pressing one or the other key depending whether they thought the image was 'more' or 'less symmetric' than average. See publication for more detailed information.", "ContinuousHeadLocalization": true, "SubjectArtefactDescription": "None", "DigitizedLandmarks": true, "DigitizedHeadPoints": true, "SoftwareFilters": { "SpatialCompensation": { "None": [] } }, "InstitutionName": "MRC Cognition & Brain Sciences Unit", "InstitutionAddress": "15 Chaucer Road, Cambridge, UK", "AssociatedEmptyRoom": "sub-emptyroom/ses-20090409/meg/sub-emptyroom_ses-20090409_task-noise_meg.fif.gz" } ================================================ FILE: tests/data/ds005/CHANGES ================================================ ================================================ FILE: tests/data/ds005/README ================================================ ================================================ FILE: tests/data/ds005/dataset_description.json ================================================ { "BIDSVersion": "1.0.0rc2", "License": "This dataset is made available under the Public Domain Dedication and License \nv1.0, whose full text can be found at \nhttp://www.opendatacommons.org/licenses/pddl/1.0/. \nWe hope that all users will follow the ODC Attribution/Share-Alike \nCommunity Norms (http://www.opendatacommons.org/norms/odc-by-sa/); \nin particular, while not legally required, we hope that all users \nof the data will acknowledge the OpenfMRI project and NSF Grant \nOCI-1131441 (R. Poldrack, PI) in any publications.", "Name": "Mixed-gambles task", "ReferencesAndLinks": "Tom, S.M., Fox, C.R., Trepel, C., Poldrack, R.A. (2007). The neural basis of loss aversion in decision-making under risk. Science, 315(5811):515-8" } ================================================ FILE: tests/data/ds005/derivatives/events/dataset_description.json ================================================ { "GeneratedBy": [{ "Name": "events" }], "BIDSVersion": "1.7.1", "License": "This dataset is made available under the Public Domain Dedication and License \nv1.0, whose full text can be found at \nhttp://www.opendatacommons.org/licenses/pddl/1.0/. \nWe hope that all users will follow the ODC Attribution/Share-Alike \nCommunity Norms (http://www.opendatacommons.org/norms/odc-by-sa/); \nin particular, while not legally required, we hope that all users \nof the data will acknowledge the OpenfMRI project and NSF Grant \nOCI-1131441 (R. Poldrack, PI) in any publications.", "Name": "Mixed-gambles task", "ReferencesAndLinks": "Tom, S.M., Fox, C.R., Trepel, C., Poldrack, R.A. (2007). The neural basis of loss aversion in decision-making under risk. Science, 315(5811):515-8", "DatasetType": "derivative" } ================================================ FILE: tests/data/ds005/derivatives/events/sub-01/func/sub-01_task-mixedgamblestask_run-01_desc-extra_events.tsv ================================================ onset duration response RT intercept 0 3 hello 0 0 4 3 yes 1 1 8 3 no 1.793 2 18 3 hello 1.637 3 24 3 yes 100 4 28 3 yes 0 5 38 3 yes 1 6 42 3 yes 1.793 7 46 3 yes 1.637 8 52 3 yes 100 9 56 3 yes 0 10 62 3 yes 1 11 74 3 yes 1.793 12 78 3 yes 1.637 13 82 3 yes 100 14 86 3 yes 100 15 90 3 yes 101 16 94 3 yes 102 17 98 3 yes 103 18 102 3 yes 104 19 106 3 yes 105 20 110 3 yes 106 21 116 3 yes 107 22 120 3 yes 108 23 126 3 yes 109 24 130 3 yes 110 25 134 3 yes 111 26 140 3 yes 112 27 148 3 yes 113 28 156 3 yes 114 29 160 3 yes 115 30 164 3 yes 116 31 170 3 yes 117 32 174 3 yes 118 33 178 3 yes 119 34 182 3 yes 120 35 192 3 yes 121 36 202 3 yes 122 37 208 3 yes 123 38 212 3 yes 124 39 220 3 yes 125 40 224 3 yes 126 41 234 3 yes 127 42 238 3 yes 128 43 246 3 yes 129 44 250 3 yes 130 45 256 3 yes 131 46 260 3 yes 132 47 266 3 yes 133 48 272 3 yes 134 49 280 3 yes 135 50 284 3 yes 136 51 296 3 yes 137 52 310 3 yes 138 53 316 3 yes 139 54 320 3 yes 140 55 324 3 yes 141 56 330 3 yes 142 57 336 3 yes 143 58 342 3 yes 144 59 346 3 yes 145 60 350 3 yes 146 61 360 3 yes 147 62 364 3 yes 148 63 368 3 yes 149 64 374 3 yes 150 65 378 3 yes 151 66 382 3 yes 152 67 386 3 yes 153 68 398 3 yes 154 69 402 3 yes 155 70 408 3 yes 156 71 412 3 yes 157 72 416 3 yes 158 73 420 3 yes 159 74 424 3 yes 160 75 428 3 yes 161 76 436 3 yes 162 77 440 3 yes 163 78 444 3 yes 164 79 448 3 yes 165 80 458 3 yes 166 81 462 3 yes 167 82 466 3 yes 168 83 470 3 yes 169 84 474 3 yes 170 85 ================================================ FILE: tests/data/ds005/derivatives/fmriprep/dataset_description.json ================================================ { "Name": "fMRIPrep - fMRI PREProcessing workflow", "BIDSVersion": "1.4.0", "DatasetType": 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}, "tcompcor": { "Method": "Mean" }, "white_matter": { "Method": "Mean" } } ================================================ FILE: tests/data/ds005/derivatives/fmriprep/sub-07/func/sub-07_task-mixedgamblestask_run-03_desc-preproc_timeseries.tsv ================================================ global_signal global_signal_derivative1 global_signal_power2 global_signal_derivative1_power2 csf csf_derivative1 csf_derivative1_power2 csf_power2 white_matter white_matter_derivative1 white_matter_power2 white_matter_derivative1_power2 csf_wm tcompcor std_dvars dvars framewise_displacement rmsd t_comp_cor_00 t_comp_cor_01 t_comp_cor_02 t_comp_cor_03 t_comp_cor_04 a_comp_cor_00 a_comp_cor_01 a_comp_cor_02 a_comp_cor_03 a_comp_cor_04 a_comp_cor_05 a_comp_cor_06 a_comp_cor_07 a_comp_cor_08 a_comp_cor_09 a_comp_cor_10 a_comp_cor_11 a_comp_cor_12 a_comp_cor_13 a_comp_cor_14 a_comp_cor_15 a_comp_cor_16 a_comp_cor_17 a_comp_cor_18 a_comp_cor_19 a_comp_cor_20 a_comp_cor_21 a_comp_cor_22 a_comp_cor_23 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}, "tcompcor": { "Method": "Mean" }, "white_matter": { "Method": "Mean" } } ================================================ FILE: tests/data/ds005/derivatives/fmriprep/sub-15/func/sub-15_task-mixedgamblestask_run-03_desc-preproc_timeseries.tsv ================================================ global_signal global_signal_derivative1 global_signal_power2 global_signal_derivative1_power2 csf csf_derivative1 csf_derivative1_power2 csf_power2 white_matter white_matter_derivative1 white_matter_power2 white_matter_derivative1_power2 csf_wm tcompcor std_dvars dvars framewise_displacement rmsd t_comp_cor_00 t_comp_cor_01 t_comp_cor_02 t_comp_cor_03 t_comp_cor_04 a_comp_cor_00 a_comp_cor_01 a_comp_cor_02 a_comp_cor_03 a_comp_cor_04 a_comp_cor_05 a_comp_cor_06 a_comp_cor_07 a_comp_cor_08 a_comp_cor_09 a_comp_cor_10 a_comp_cor_11 a_comp_cor_12 a_comp_cor_13 a_comp_cor_14 a_comp_cor_15 a_comp_cor_16 a_comp_cor_17 a_comp_cor_18 a_comp_cor_19 a_comp_cor_20 a_comp_cor_21 a_comp_cor_22 a_comp_cor_23 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================================================ FILE: tests/data/ds005/sub-01/func/sub-01_task-mixedgamblestask_run-03_events.tsv ================================================ onset duration trial_type distance from indifference parametric gain parametric loss gain loss PTval respnum respcat RT 0.0 3 parametric gain n/a 0.0835 n/a 28 17 11.17 2 1 1.203 4.0 3 parametric gain n/a -0.2665 n/a 14 7 7.07 2 1 1.123 8.0 3 parametric gain n/a 0.0835 n/a 28 18 10.18 2 1 0.9690000000000001 14.0 3 parametric gain n/a -0.2165 n/a 16 11 5.11 2 1 1.102 18.0 3 parametric gain n/a 0.3835 n/a 40 20 20.2 1 1 0.9690000000000001 22.0 3 parametric gain n/a -0.1665 n/a 18 16 2.16 2 1 1.261 28.0 3 parametric gain n/a 0.3335 n/a 38 11 27.11 2 1 1.35 32.0 3 parametric gain n/a 0.2835 n/a 36 10 26.1 1 1 2.157 40.0 3 parametric gain n/a 0.3835 n/a 40 18 22.18 1 1 1.6780000000000002 44.0 3 parametric gain n/a -0.3665 n/a 10 10 0.1 2 1 1.581 50.0 3 parametric gain n/a -0.0165 n/a 24 6 18.06 1 1 1.133 56.0 3 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14.08 2 1 1.7369999999999999 428.0 3 parametric gain n/a 0.3335 n/a 38 6 32.06 1 1 1.063 434.0 3 parametric gain n/a -0.3165 n/a 12 8 4.08 2 1 0.887 438.0 3 parametric gain n/a -0.2665 n/a 14 11 3.11 2 1 1.247 442.0 3 parametric gain n/a 0.1835 n/a 32 12 20.12 2 1 1.545 446.0 3 parametric gain n/a -0.1165 n/a 20 11 9.11 2 1 0.8490000000000001 450.0 3 parametric gain n/a 0.0335 n/a 26 14 12.14 2 1 0.9440000000000001 454.0 3 parametric gain n/a 0.1835 n/a 32 8 24.08 1 1 0.825 466.0 3 parametric gain n/a 0.3835 n/a 40 17 23.17 1 1 1.963 472.0 3 parametric gain n/a -0.1165 n/a 20 17 3.17 2 1 1.161 476.0 3 parametric gain n/a -0.0165 n/a 24 19 5.19 2 1 0.888 ================================================ FILE: tests/data/ds005/sub-01/sub-01_scans.tsv ================================================ filename acq_time meanRT anat/sub-01_T1w.nii.gz 1880-01-10T05:17:54 n/a dwi/sub-01_dwi.nii.gz 1880-01-10T05:18:54 n/a func/sub-01_task-mixedgamblestask_run-01_bold.nii.gz 1880-01-10T05:22:54 0.4 func/sub-01_task-mixedgamblestask_run-02_bold.nii.gz 1880-01-10T05:37:54 0.5 func/sub-01_task-mixedgamblestask_run-03_bold.nii.gz 1880-01-10T05:52:54 0.3 ================================================ FILE: tests/data/ds005/sub-02/func/sub-02_task-mixedgamblestask_run-01_events.tsv ================================================ onset duration trial_type distance from indifference parametric gain parametric loss gain loss PTval respnum respcat RT 0.0 3 parametric gain n/a -0.139 n/a 20 15 -32.84 4 0 2.419 4.0 3 parametric gain n/a -0.189 n/a 18 12 -24.27 4 0 1.358 8.0 3 parametric gain n/a -0.389 n/a 10 15 -42.84 4 0 1.462 18.0 3 parametric gain n/a 0.211 n/a 34 16 -22.36 2 1 2.189 24.0 3 parametric gain n/a -0.189 n/a 18 5 0.39 1 1 2.582 28.0 3 parametric gain n/a 0.011 n/a 26 13 -19.8 3 0 2.823 38.0 3 parametric gain n/a 0.161 n/a 32 17 -27.89 4 0 1.3659999999999999 42.0 3 parametric gain n/a -0.239 n/a 16 5 -1.61 2 1 1.5519999999999998 46.0 3 parametric gain n/a -0.089 n/a 22 7 -2.66 2 1 1.6119999999999999 52.0 3 parametric gain n/a 0.261 n/a 36 8 7.82 1 1 1.589 56.0 3 parametric gain n/a -0.239 n/a 16 10 -19.23 4 0 1.885 62.0 3 parametric gain n/a 0.111 n/a 30 13 -15.8 3 0 2.885 74.0 3 parametric gain n/a -0.289 n/a 14 17 -45.89 4 0 1.263 78.0 3 parametric gain n/a 0.061 n/a 28 19 -38.93 4 0 2.701 82.0 3 parametric gain n/a -0.289 n/a 14 20 -56.46 4 0 1.327 86.0 3 parametric gain n/a 0.011 n/a 26 16 -30.36 4 0 1.4480000000000002 90.0 3 parametric gain n/a 0.361 n/a 40 5 22.39 1 1 1.7919999999999998 94.0 3 parametric gain n/a 0.061 n/a 28 12 -14.27 3 0 2.3680000000000003 98.0 3 parametric gain n/a -0.139 n/a 20 16 -36.36 4 0 1.48 102.0 3 parametric gain n/a 0.011 n/a 26 5 8.39 2 1 2.448 106.0 3 parametric gain n/a 0.161 n/a 32 20 -38.46 4 0 1.296 110.0 3 parametric gain n/a -0.189 n/a 18 17 -41.89 4 0 1.24 116.0 3 parametric gain n/a -0.139 n/a 20 6 -1.14 2 1 2.8310000000000004 120.0 3 parametric gain n/a -0.139 n/a 20 18 -43.41 4 0 1.5530000000000002 126.0 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tests/data/ds005/sub-02/func/sub-02_task-mixedgamblestask_run-02_events.tsv ================================================ onset duration trial_type distance from indifference parametric gain parametric loss gain loss PTval respnum respcat RT 0.0 3 parametric gain n/a -0.1194 n/a 20 5 2.39 2 1 1.676 4.0 3 parametric gain n/a -0.0694 n/a 22 17 -37.89 4 0 1.123 8.0 3 parametric gain n/a -0.3694 n/a 10 16 -46.36 4 0 0.903 12.0 3 parametric gain n/a 0.3306 n/a 38 18 -25.41 4 0 1.237 16.0 3 parametric gain n/a -0.1194 n/a 20 14 -29.32 4 0 1.109 24.0 3 parametric gain n/a -0.2694 n/a 14 13 -31.8 4 0 0.878 28.0 3 parametric gain n/a 0.3806 n/a 40 8 11.82 1 1 1.504 32.0 3 parametric gain n/a 0.1806 n/a 32 18 -31.41 4 0 1.085 36.0 3 parametric gain n/a -0.0194 n/a 24 8 -4.18 2 1 1.9380000000000002 40.0 3 parametric gain n/a 0.2806 n/a 36 14 -13.32 0 -1 0.0 44.0 3 parametric gain n/a 0.0806 n/a 28 6 6.86 2 1 1.507 48.0 3 parametric gain n/a -0.2194 n/a 16 14 -33.32 4 0 1.255 56.0 3 parametric 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342.0 3 parametric gain n/a 0.0806 n/a 28 16 5.33 2 1 1.213 348.0 3 parametric gain n/a 0.2306 n/a 34 12 17.0 1 1 1.08 358.0 3 parametric gain n/a 0.0806 n/a 28 15 6.75 2 1 1.061 364.0 3 parametric gain n/a -0.0194 n/a 24 17 -0.08 3 0 1.023 368.0 3 parametric gain n/a 0.0806 n/a 28 11 12.42 3 0 1.258 372.0 3 parametric gain n/a -0.2194 n/a 16 9 3.25 3 0 1.229 378.0 3 parametric gain n/a -0.0694 n/a 22 13 3.58 3 0 1.047 384.0 3 parametric gain n/a 0.3806 n/a 40 11 24.42 1 1 0.97 390.0 3 parametric gain n/a 0.0806 n/a 28 8 16.67 1 1 0.826 396.0 3 parametric gain n/a 0.2806 n/a 36 16 13.33 2 1 1.254 400.0 3 parametric gain n/a -0.1194 n/a 20 10 5.83 2 1 1.146 404.0 3 parametric gain n/a -0.3694 n/a 10 18 -15.5 4 0 0.866 408.0 3 parametric gain n/a 0.3806 n/a 40 9 27.25 1 1 1.054 412.0 3 parametric gain n/a 0.1806 n/a 32 7 22.08 1 1 1.043 422.0 3 parametric gain n/a 0.3806 n/a 40 16 17.33 1 1 0.982 426.0 3 parametric gain n/a 0.1306 n/a 30 17 5.92 3 0 1.099 432.0 3 parametric gain n/a -0.0194 n/a 24 5 16.92 1 1 0.851 436.0 3 parametric gain n/a -0.2194 n/a 16 7 6.08 4 0 1.371 440.0 3 parametric gain n/a -0.0694 n/a 22 18 -3.5 3 0 1.091 444.0 3 parametric gain n/a 0.0306 n/a 26 19 -0.92 4 0 1.038 448.0 3 parametric gain n/a -0.0194 n/a 24 20 -4.33 4 0 1.155 452.0 3 parametric gain n/a 0.2306 n/a 34 6 25.5 1 1 1.198 460.0 3 parametric gain n/a -0.0194 n/a 24 16 1.33 4 0 0.91 468.0 3 parametric gain n/a -0.0694 n/a 22 6 13.5 2 1 0.955 474.0 3 parametric gain n/a -0.3194 n/a 12 7 2.08 4 0 1.109 ================================================ FILE: tests/data/ds005/sub-06/func/sub-06_task-mixedgamblestask_run-03_events.tsv ================================================ onset duration trial_type distance from indifference parametric gain parametric loss gain loss PTval respnum respcat RT 0.0 3 parametric gain n/a 0.0835 n/a 28 17 3.92 2 1 2.021 4.0 3 parametric gain n/a -0.2665 n/a 14 7 4.08 1 1 1.43 8.0 3 parametric gain n/a 0.0835 n/a 28 18 2.5 2 1 1.703 14.0 3 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0.1835 n/a 32 8 20.67 1 1 1.023 466.0 3 parametric gain n/a 0.3835 n/a 40 17 15.92 1 1 0.8959999999999999 472.0 3 parametric gain n/a -0.1165 n/a 20 17 -4.08 4 0 1.112 476.0 3 parametric gain n/a -0.0165 n/a 24 19 -2.92 4 0 1.205 ================================================ FILE: tests/data/ds005/sub-07/func/sub-07_task-mixedgamblestask_run-01_events.tsv ================================================ onset duration trial_type distance from indifference parametric gain parametric loss gain loss PTval respnum respcat RT 0.0 3 parametric gain n/a -0.139 n/a 20 15 -0.99 2 1 2.401 4.0 3 parametric gain n/a -0.189 n/a 18 12 1.21 2 1 2.399 8.0 3 parametric gain n/a -0.389 n/a 10 15 -10.99 3 0 0.9209999999999999 18.0 3 parametric gain n/a 0.211 n/a 34 16 11.62 2 1 1.099 24.0 3 parametric gain n/a -0.189 n/a 18 5 11.0 1 1 0.9790000000000001 28.0 3 parametric gain n/a 0.011 n/a 26 13 7.81 0 -1 0.0 38.0 3 parametric gain n/a 0.161 n/a 32 17 8.22 2 1 0.85 42.0 3 parametric gain n/a -0.239 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1.5719999999999998 408.0 3 parametric gain n/a -0.339 n/a 12 6 3.61 2 1 1.113 412.0 3 parametric gain n/a 0.161 n/a 32 6 23.61 2 1 0.973 416.0 3 parametric gain n/a -0.189 n/a 18 7 8.21 2 1 1.2429999999999999 420.0 3 parametric gain n/a 0.361 n/a 40 10 26.01 1 1 0.9390000000000001 424.0 3 parametric gain n/a -0.289 n/a 14 14 -5.59 3 0 1.999 428.0 3 parametric gain n/a 0.111 n/a 30 19 3.42 2 1 1.995 436.0 3 parametric gain n/a -0.039 n/a 24 18 -1.18 2 1 1.351 440.0 3 parametric gain n/a -0.389 n/a 10 12 -6.79 3 0 1.172 444.0 3 parametric gain n/a 0.361 n/a 40 19 13.42 2 1 1.23 448.0 3 parametric gain n/a -0.039 n/a 24 9 11.41 1 1 1.87 458.0 3 parametric gain n/a -0.239 n/a 16 18 -9.18 3 0 1.575 462.0 3 parametric gain n/a 0.261 n/a 36 12 19.21 2 1 1.964 466.0 3 parametric gain n/a 0.011 n/a 26 9 13.41 2 1 1.868 470.0 3 parametric gain n/a -0.139 n/a 20 12 3.21 2 1 1.4709999999999999 474.0 3 parametric gain n/a -0.039 n/a 24 14 4.41 2 1 1.307 ================================================ FILE: tests/data/ds005/sub-07/func/sub-07_task-mixedgamblestask_run-02_events.tsv ================================================ onset duration trial_type distance from indifference parametric gain parametric loss gain loss PTval respnum respcat RT 0.0 3 parametric gain n/a -0.1194 n/a 20 5 13.0 1 1 0.858 4.0 3 parametric gain n/a -0.0694 n/a 22 17 -1.78 3 0 1.9180000000000001 8.0 3 parametric gain n/a -0.3694 n/a 10 16 -12.38 4 0 1.041 12.0 3 parametric gain n/a 0.3306 n/a 38 18 12.82 2 1 1.068 16.0 3 parametric gain n/a -0.1194 n/a 20 14 0.41 3 0 1.2409999999999999 24.0 3 parametric gain n/a -0.2694 n/a 14 13 -4.19 3 0 1.275 28.0 3 parametric gain n/a 0.3806 n/a 40 8 28.81 2 1 0.745 32.0 3 parametric gain n/a 0.1806 n/a 32 18 6.82 2 1 1.355 36.0 3 parametric gain n/a -0.0194 n/a 24 8 12.81 1 1 0.993 40.0 3 parametric gain n/a 0.2806 n/a 36 14 16.41 1 1 1.764 44.0 3 parametric gain n/a 0.0806 n/a 28 6 19.61 1 1 1.226 48.0 3 parametric 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330.0 3 parametric gain n/a -0.0694 n/a 22 15 1.01 0 -1 0.0 334.0 3 parametric gain n/a 0.3306 n/a 38 8 26.81 1 1 1.2329999999999999 342.0 3 parametric gain n/a 0.0806 n/a 28 16 5.62 2 1 1.385 348.0 3 parametric gain n/a 0.2306 n/a 34 12 17.21 2 1 1.12 358.0 3 parametric gain n/a 0.0806 n/a 28 15 7.01 2 1 0.8490000000000001 364.0 3 parametric gain n/a -0.0194 n/a 24 17 0.22 2 1 1.841 368.0 3 parametric gain n/a 0.0806 n/a 28 11 12.61 2 1 1.318 372.0 3 parametric gain n/a -0.2194 n/a 16 9 3.41 2 1 1.189 378.0 3 parametric gain n/a -0.0694 n/a 22 13 3.81 2 1 1.0170000000000001 384.0 3 parametric gain n/a 0.3806 n/a 40 11 24.61 1 1 1.088 390.0 3 parametric gain n/a 0.0806 n/a 28 8 16.81 1 1 0.889 396.0 3 parametric gain n/a 0.2806 n/a 36 16 13.62 1 1 0.826 400.0 3 parametric gain n/a -0.1194 n/a 20 10 6.01 1 1 0.9179999999999999 404.0 3 parametric gain n/a -0.3694 n/a 10 18 -15.18 4 0 1.43 408.0 3 parametric gain n/a 0.3806 n/a 40 9 27.41 1 1 0.95 412.0 3 parametric gain n/a 0.1806 n/a 32 7 22.21 2 1 1.01 422.0 3 parametric gain n/a 0.3806 n/a 40 16 17.62 1 1 1.082 426.0 3 parametric gain n/a 0.1306 n/a 30 17 6.22 2 1 2.006 432.0 3 parametric gain n/a -0.0194 n/a 24 5 17.0 2 1 1.319 436.0 3 parametric gain n/a -0.2194 n/a 16 7 6.21 1 1 1.327 440.0 3 parametric gain n/a -0.0694 n/a 22 18 -3.18 3 0 2.399 444.0 3 parametric gain n/a 0.0306 n/a 26 19 -0.58 2 1 2.767 448.0 3 parametric gain n/a -0.0194 n/a 24 20 -3.98 0 -1 0.0 452.0 3 parametric gain n/a 0.2306 n/a 34 6 25.61 1 1 1.103 460.0 3 parametric gain n/a -0.0194 n/a 24 16 1.62 2 1 1.954 468.0 3 parametric gain n/a -0.0694 n/a 22 6 13.61 1 1 0.97 474.0 3 parametric gain n/a -0.3194 n/a 12 7 2.21 2 1 1.77 ================================================ FILE: tests/data/ds005/sub-07/func/sub-07_task-mixedgamblestask_run-03_events.tsv ================================================ onset duration trial_type distance from indifference parametric gain parametric loss gain loss PTval respnum respcat RT 0.0 3 parametric 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174.0 3 parametric gain n/a -0.1665 n/a 18 11 2.61 3 0 2.363 178.0 3 parametric gain n/a 0.1335 n/a 30 7 20.21 2 1 0.8370000000000001 186.0 3 parametric gain n/a -0.1665 n/a 18 13 -0.19 3 0 1.355 190.0 3 parametric gain n/a -0.2665 n/a 14 19 -12.58 4 0 1.421 198.0 3 parametric gain n/a -0.3165 n/a 12 11 -3.39 3 0 2.523 202.0 3 parametric gain n/a -0.3665 n/a 10 13 -8.19 4 0 1.494 206.0 3 parametric gain n/a -0.0665 n/a 22 16 -0.38 2 1 1.5819999999999999 212.0 3 parametric gain n/a -0.3165 n/a 12 16 -10.38 4 0 2.388 218.0 3 parametric gain n/a 0.3335 n/a 38 17 14.22 2 1 1.796 226.0 3 parametric gain n/a 0.3835 n/a 40 14 20.41 1 1 1.2429999999999999 230.0 3 parametric gain n/a -0.0665 n/a 22 5 15.0 2 1 1.248 234.0 3 parametric gain n/a -0.2165 n/a 16 16 -6.38 4 0 1.051 240.0 3 parametric gain n/a 0.2335 n/a 34 5 27.0 1 1 0.9 250.0 3 parametric gain n/a -0.3665 n/a 10 19 -16.58 4 0 1.413 254.0 3 parametric gain n/a -0.2665 n/a 14 16 -8.38 4 0 1.537 258.0 3 parametric gain n/a 0.1335 n/a 30 8 18.81 1 1 0.9740000000000001 262.0 3 parametric gain n/a 0.2335 n/a 34 14 14.41 1 1 0.715 266.0 3 parametric gain n/a 0.1335 n/a 30 14 10.41 1 1 0.695 274.0 3 parametric gain n/a 0.0335 n/a 26 15 5.01 2 1 1.284 282.0 3 parametric gain n/a -0.3665 n/a 10 20 -17.98 4 0 1.2109999999999999 286.0 3 parametric gain n/a 0.2335 n/a 34 7 24.21 2 1 0.9590000000000001 292.0 3 parametric gain n/a -0.3665 n/a 10 5 3.0 2 1 1.589 296.0 3 parametric gain n/a -0.1165 n/a 20 20 -7.98 4 0 1.032 300.0 3 parametric gain n/a 0.0835 n/a 28 20 0.02 0 -1 0.0 304.0 3 parametric gain n/a 0.2835 n/a 36 5 29.0 1 1 1.5030000000000001 308.0 3 parametric gain n/a 0.0335 n/a 26 7 16.21 2 1 1.245 316.0 3 parametric gain n/a 0.0335 n/a 26 17 2.22 0 -1 0.0 326.0 3 parametric gain n/a -0.2165 n/a 16 15 -4.99 3 0 1.0859999999999999 330.0 3 parametric gain n/a 0.0335 n/a 26 10 12.01 2 1 1.185 338.0 3 parametric gain n/a 0.3335 n/a 38 15 17.01 2 1 1.39 346.0 3 parametric gain n/a 0.0335 n/a 26 11 10.61 1 1 1.983 356.0 3 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gain n/a 0.1835 n/a 32 12 15.21 1 1 1.081 446.0 3 parametric gain n/a -0.1165 n/a 20 11 4.61 2 1 1.535 450.0 3 parametric gain n/a 0.0335 n/a 26 14 6.41 2 1 1.777 454.0 3 parametric gain n/a 0.1835 n/a 32 8 20.81 2 1 1.249 466.0 3 parametric gain n/a 0.3835 n/a 40 17 16.22 1 1 1.11 472.0 3 parametric gain n/a -0.1165 n/a 20 17 -3.78 2 1 2.617 476.0 3 parametric gain n/a -0.0165 n/a 24 19 -2.58 0 -1 0.0 ================================================ FILE: tests/data/ds005/sub-08/func/sub-08_task-mixedgamblestask_run-01_events.tsv ================================================ onset duration trial_type distance from indifference parametric gain parametric loss gain loss PTval respnum respcat RT 0.0 3 parametric gain n/a -0.139 n/a 20 15 3.06 3 0 0.9329999999999999 4.0 3 parametric gain n/a -0.189 n/a 18 12 4.45 3 0 0.7709999999999999 8.0 3 parametric gain n/a -0.389 n/a 10 15 -6.94 4 0 1.044 18.0 3 parametric gain n/a 0.211 n/a 34 16 15.93 2 1 1.0859999999999999 24.0 3 parametric 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1.236 110.0 3 parametric gain n/a -0.189 n/a 18 17 -1.2 4 0 0.8440000000000001 116.0 3 parametric gain n/a -0.139 n/a 20 6 13.22 1 1 0.8240000000000001 120.0 3 parametric gain n/a -0.139 n/a 20 18 -0.33 3 0 0.9329999999999999 126.0 3 parametric gain n/a 0.361 n/a 40 7 32.09 1 1 0.792 130.0 3 parametric gain n/a 0.261 n/a 36 6 29.22 1 1 0.773 134.0 3 parametric gain n/a -0.239 n/a 16 20 -6.59 3 0 1.0290000000000001 140.0 3 parametric gain n/a 0.361 n/a 40 15 23.06 2 1 1.16 148.0 3 parametric gain n/a -0.389 n/a 10 6 3.22 3 0 1.432 156.0 3 parametric gain n/a -0.339 n/a 12 15 -4.94 4 0 1.24 160.0 3 parametric gain n/a 0.211 n/a 34 17 14.8 2 1 1.301 164.0 3 parametric gain n/a -0.189 n/a 18 14 2.19 3 0 1.357 170.0 3 parametric gain n/a 0.361 n/a 40 13 25.32 2 1 1.912 174.0 3 parametric gain n/a 0.161 n/a 32 19 10.54 2 1 1.117 178.0 3 parametric gain n/a -0.289 n/a 14 12 0.45 3 0 1.12 182.0 3 parametric gain n/a 0.161 n/a 32 10 20.71 1 1 0.9740000000000001 192.0 3 parametric gain n/a 0.261 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n/a 0.261 n/a 36 19 14.54 2 1 1.6340000000000001 310.0 3 parametric gain n/a 0.161 n/a 32 5 26.35 1 1 0.835 316.0 3 parametric gain n/a 0.211 n/a 34 9 23.84 1 1 1.033 320.0 3 parametric gain n/a 0.061 n/a 28 7 20.09 2 1 1.185 324.0 3 parametric gain n/a -0.039 n/a 24 13 9.32 2 1 1.367 330.0 3 parametric gain n/a 0.311 n/a 38 5 32.35 1 1 0.6970000000000001 336.0 3 parametric gain n/a -0.089 n/a 22 9 11.84 1 1 1.818 342.0 3 parametric gain n/a -0.389 n/a 10 11 -2.42 3 0 1.122 346.0 3 parametric gain n/a -0.339 n/a 12 14 -3.81 3 0 1.271 350.0 3 parametric gain n/a 0.211 n/a 34 19 12.54 2 1 1.026 360.0 3 parametric gain n/a 0.311 n/a 38 7 30.09 1 1 0.722 364.0 3 parametric gain n/a 0.111 n/a 30 6 23.22 1 1 0.727 368.0 3 parametric gain n/a 0.011 n/a 26 18 5.67 2 1 1.391 374.0 3 parametric gain n/a -0.289 n/a 14 6 7.22 2 1 1.537 378.0 3 parametric gain n/a -0.239 n/a 16 12 2.45 3 0 1.048 382.0 3 parametric gain n/a -0.089 n/a 22 20 -0.59 3 0 1.047 386.0 3 parametric gain n/a -0.339 n/a 12 17 -7.2 4 0 1.285 398.0 3 parametric gain n/a -0.389 n/a 10 7 2.09 3 0 1.026 402.0 3 parametric gain n/a -0.189 n/a 18 20 -4.59 3 0 0.9440000000000001 408.0 3 parametric gain n/a -0.339 n/a 12 6 5.22 2 1 1.3630000000000002 412.0 3 parametric gain n/a 0.161 n/a 32 6 25.22 1 1 0.792 416.0 3 parametric gain n/a -0.189 n/a 18 7 10.09 2 1 1.368 420.0 3 parametric gain n/a 0.361 n/a 40 10 28.71 1 1 0.768 424.0 3 parametric gain n/a -0.289 n/a 14 14 -1.81 3 0 1.179 428.0 3 parametric gain n/a 0.111 n/a 30 19 8.54 2 1 1.186 436.0 3 parametric gain n/a -0.039 n/a 24 18 3.67 2 1 1.068 440.0 3 parametric gain n/a -0.389 n/a 10 12 -3.55 3 0 1.296 444.0 3 parametric gain n/a 0.361 n/a 40 19 18.54 2 1 0.92 448.0 3 parametric gain n/a -0.039 n/a 24 9 13.84 1 1 1.331 458.0 3 parametric gain n/a -0.239 n/a 16 18 -4.33 3 0 1.555 462.0 3 parametric gain n/a 0.261 n/a 36 12 22.45 1 1 1.36 466.0 3 parametric gain n/a 0.011 n/a 26 9 15.84 2 1 1.7280000000000002 470.0 3 parametric gain n/a -0.139 n/a 20 12 6.45 3 0 1.18 474.0 3 parametric gain n/a -0.039 n/a 24 14 8.19 2 1 1.129 ================================================ FILE: tests/data/ds005/sub-08/func/sub-08_task-mixedgamblestask_run-02_events.tsv ================================================ onset duration trial_type distance from indifference parametric gain parametric loss gain loss PTval respnum respcat RT 0.0 3 parametric gain n/a -0.1194 n/a 20 5 14.35 0 -1 0.0 4.0 3 parametric gain n/a -0.0694 n/a 22 17 2.8 1 1 2.239 8.0 3 parametric gain n/a -0.3694 n/a 10 16 -8.07 3 0 2.199 12.0 3 parametric gain n/a 0.3306 n/a 38 18 17.67 2 1 1.3780000000000001 16.0 3 parametric gain n/a -0.1194 n/a 20 14 4.19 2 1 2.562 24.0 3 parametric gain n/a -0.2694 n/a 14 13 -0.68 2 1 1.764 28.0 3 parametric gain n/a 0.3806 n/a 40 8 30.97 1 1 1.081 32.0 3 parametric gain n/a 0.1806 n/a 32 18 11.67 1 1 1.496 36.0 3 parametric gain n/a -0.0194 n/a 24 8 14.97 1 1 1.08 40.0 3 parametric gain n/a 0.2806 n/a 36 14 20.19 1 1 1.163 44.0 3 parametric 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parametric gain n/a 0.1806 n/a 32 7 24.09 1 1 0.99 422.0 3 parametric gain n/a 0.3806 n/a 40 16 21.93 1 1 1.8969999999999998 426.0 3 parametric gain n/a 0.1306 n/a 30 17 10.8 2 1 1.318 432.0 3 parametric gain n/a -0.0194 n/a 24 5 18.35 1 1 0.722 436.0 3 parametric gain n/a -0.2194 n/a 16 7 8.09 1 1 1.294 440.0 3 parametric gain n/a -0.0694 n/a 22 18 1.67 0 -1 0.0 444.0 3 parametric gain n/a 0.0306 n/a 26 19 4.54 3 0 1.686 448.0 3 parametric gain n/a -0.0194 n/a 24 20 1.41 3 0 1.39 452.0 3 parametric gain n/a 0.2306 n/a 34 6 27.22 1 1 1.338 460.0 3 parametric gain n/a -0.0194 n/a 24 16 5.93 3 0 1.0659999999999998 468.0 3 parametric gain n/a -0.0694 n/a 22 6 15.22 1 1 0.745 474.0 3 parametric gain n/a -0.3194 n/a 12 7 4.09 3 0 1.306 ================================================ FILE: tests/data/ds005/sub-08/func/sub-08_task-mixedgamblestask_run-03_events.tsv ================================================ onset duration trial_type distance from indifference parametric gain parametric 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6 31.22 1 1 0.7879999999999999 434.0 3 parametric gain n/a -0.3165 n/a 12 8 2.97 3 0 1.077 438.0 3 parametric gain n/a -0.2665 n/a 14 11 1.58 3 0 1.2409999999999999 442.0 3 parametric gain n/a 0.1835 n/a 32 12 18.45 2 1 1.242 446.0 3 parametric gain n/a -0.1165 n/a 20 11 7.58 2 1 1.374 450.0 3 parametric gain n/a 0.0335 n/a 26 14 10.19 3 0 0.9329999999999999 454.0 3 parametric gain n/a 0.1835 n/a 32 8 22.97 1 1 0.9790000000000001 466.0 3 parametric gain n/a 0.3835 n/a 40 17 20.8 2 1 1.413 472.0 3 parametric gain n/a -0.1165 n/a 20 17 0.8 3 0 1.345 476.0 3 parametric gain n/a -0.0165 n/a 24 19 2.54 3 0 1.081 ================================================ FILE: tests/data/ds005/sub-09/func/sub-09_task-mixedgamblestask_run-01_events.tsv ================================================ onset duration trial_type distance from indifference parametric gain parametric loss gain loss PTval respnum respcat RT 0.0 3 parametric gain n/a -0.139 n/a 20 15 -14.61 4 0 1.733 4.0 3 parametric gain n/a 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98.0 3 parametric gain n/a -0.139 n/a 20 16 -16.91 4 0 1.4 102.0 3 parametric gain n/a 0.011 n/a 26 5 14.46 1 1 2.131 106.0 3 parametric gain n/a 0.161 n/a 32 20 -14.14 4 0 1.609 110.0 3 parametric gain n/a -0.189 n/a 18 17 -21.22 4 0 1.034 116.0 3 parametric gain n/a -0.139 n/a 20 6 6.16 1 1 1.8019999999999998 120.0 3 parametric gain n/a -0.139 n/a 20 18 -21.53 4 0 1.001 126.0 3 parametric gain n/a 0.361 n/a 40 7 23.85 1 1 1.1540000000000001 130.0 3 parametric gain n/a 0.261 n/a 36 6 22.16 2 1 1.2990000000000002 134.0 3 parametric gain n/a -0.239 n/a 16 20 -30.14 4 0 1.161 140.0 3 parametric gain n/a 0.361 n/a 40 15 5.39 2 1 1.6669999999999998 148.0 3 parametric gain n/a -0.389 n/a 10 6 -3.84 4 0 1.1320000000000001 156.0 3 parametric gain n/a -0.339 n/a 12 15 -22.61 4 0 0.9229999999999999 160.0 3 parametric gain n/a 0.211 n/a 34 17 -5.22 4 0 0.802 164.0 3 parametric gain n/a -0.189 n/a 18 14 -14.3 4 0 1.113 170.0 3 parametric gain n/a 0.361 n/a 40 13 10.01 1 1 1.276 174.0 3 parametric 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-25.53 4 0 1.411 462.0 3 parametric gain n/a 0.261 n/a 36 12 8.31 4 0 0.866 466.0 3 parametric gain n/a 0.011 n/a 26 9 5.24 4 0 1.2109999999999999 470.0 3 parametric gain n/a -0.139 n/a 20 12 -7.69 4 0 0.9470000000000001 474.0 3 parametric gain n/a -0.039 n/a 24 14 -8.3 4 0 1.155 ================================================ FILE: tests/data/ds005/sub-09/func/sub-09_task-mixedgamblestask_run-02_events.tsv ================================================ onset duration trial_type distance from indifference parametric gain parametric loss gain loss PTval respnum respcat RT 0.0 3 parametric gain n/a -0.1194 n/a 20 5 8.46 1 1 1.3980000000000001 4.0 3 parametric gain n/a -0.0694 n/a 22 17 -17.22 4 0 1.415 8.0 3 parametric gain n/a -0.3694 n/a 10 16 -26.91 4 0 1.218 12.0 3 parametric gain n/a 0.3306 n/a 38 18 -3.53 4 0 1.4180000000000001 16.0 3 parametric gain n/a -0.1194 n/a 20 14 -12.3 4 0 1.0390000000000001 24.0 3 parametric gain n/a -0.2694 n/a 14 13 -15.99 4 0 1.035 28.0 3 parametric 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404.0 3 parametric gain n/a -0.2665 n/a 14 5 2.46 3 0 2.049 408.0 3 parametric gain n/a -0.0665 n/a 22 11 -3.38 3 0 2.169 412.0 3 parametric gain n/a -0.1665 n/a 18 19 -25.84 4 0 1.81 418.0 3 parametric gain n/a -0.0665 n/a 22 8 3.54 4 0 1.8259999999999998 428.0 3 parametric gain n/a 0.3335 n/a 38 6 24.16 2 1 1.899 434.0 3 parametric gain n/a -0.3165 n/a 12 8 -6.46 4 0 1.6269999999999998 438.0 3 parametric gain n/a -0.2665 n/a 14 11 -11.38 4 0 0.9129999999999999 442.0 3 parametric gain n/a 0.1835 n/a 32 12 4.31 3 0 1.901 446.0 3 parametric gain n/a -0.1165 n/a 20 11 -5.38 4 0 1.617 450.0 3 parametric gain n/a 0.0335 n/a 26 14 -6.3 3 0 1.8030000000000002 454.0 3 parametric gain n/a 0.1835 n/a 32 8 13.54 1 1 1.9769999999999999 466.0 3 parametric gain n/a 0.3835 n/a 40 17 0.78 2 1 2.395 472.0 3 parametric gain n/a -0.1165 n/a 20 17 -19.22 4 0 2.218 476.0 3 parametric gain n/a -0.0165 n/a 24 19 -19.84 4 0 1.635 ================================================ FILE: tests/data/ds005/sub-10/func/sub-10_task-mixedgamblestask_run-01_events.tsv ================================================ onset duration trial_type distance from indifference parametric gain parametric loss gain loss PTval respnum respcat RT 0.0 3 parametric gain n/a -0.139 n/a 20 15 3.74 0 -1 0.0 4.0 3 parametric gain n/a -0.189 n/a 18 12 4.99 2 1 2.926 8.0 3 parametric gain n/a -0.389 n/a 10 15 -6.26 3 0 2.653 18.0 3 parametric gain n/a 0.211 n/a 34 16 16.65 2 1 2.728 24.0 3 parametric gain n/a -0.189 n/a 18 5 12.58 1 1 1.7919999999999998 28.0 3 parametric gain n/a 0.011 n/a 26 13 11.9 1 1 2.478 38.0 3 parametric gain n/a 0.161 n/a 32 17 13.57 1 1 2.32 42.0 3 parametric gain n/a -0.239 n/a 16 5 10.58 1 1 1.341 46.0 3 parametric gain n/a -0.089 n/a 22 7 14.41 1 1 1.6780000000000002 52.0 3 parametric gain n/a 0.261 n/a 36 8 27.33 1 1 1.265 56.0 3 parametric gain n/a -0.239 n/a 16 10 5.16 2 1 1.358 62.0 3 parametric gain n/a 0.111 n/a 30 13 15.9 1 1 1.577 74.0 3 parametric gain n/a 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166.0 3 parametric gain n/a -0.3194 n/a 12 18 -7.52 4 0 1.755 176.0 3 parametric gain n/a -0.1194 n/a 20 9 10.24 1 1 1.686 180.0 3 parametric gain n/a -0.2194 n/a 16 8 7.33 1 1 1.755 184.0 3 parametric gain n/a -0.3194 n/a 12 20 -9.68 4 0 1.6019999999999999 194.0 3 parametric gain n/a -0.0694 n/a 22 19 1.4 3 0 2.559 200.0 3 parametric gain n/a 0.2806 n/a 36 9 26.24 1 1 1.4369999999999998 204.0 3 parametric gain n/a 0.0306 n/a 26 6 19.49 1 1 1.307 208.0 3 parametric gain n/a 0.1306 n/a 30 5 24.58 1 1 1.6269999999999998 212.0 3 parametric gain n/a 0.2806 n/a 36 7 28.41 1 1 1.7380000000000002 216.0 3 parametric gain n/a -0.1694 n/a 18 10 7.16 2 1 1.474 220.0 3 parametric gain n/a 0.1306 n/a 30 9 20.24 1 1 2.494 226.0 3 parametric gain n/a 0.1306 n/a 30 12 16.99 1 1 1.524 236.0 3 parametric gain n/a -0.2694 n/a 14 8 5.33 2 1 1.899 240.0 3 parametric gain n/a -0.1194 n/a 20 19 -0.6 3 0 1.7469999999999999 244.0 3 parametric gain n/a 0.0806 n/a 28 14 12.82 1 1 2.717 250.0 3 parametric gain 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1.2890000000000001 440.0 3 parametric gain n/a -0.0694 n/a 22 18 2.48 3 0 2.199 444.0 3 parametric gain n/a 0.0306 n/a 26 19 5.4 2 1 1.791 448.0 3 parametric gain n/a -0.0194 n/a 24 20 2.32 3 0 1.685 452.0 3 parametric gain n/a 0.2306 n/a 34 6 27.49 1 1 1.3530000000000002 460.0 3 parametric gain n/a -0.0194 n/a 24 16 6.65 2 1 1.609 468.0 3 parametric gain n/a -0.0694 n/a 22 6 15.49 1 1 1.409 474.0 3 parametric gain n/a -0.3194 n/a 12 7 4.41 2 1 1.4969999999999999 ================================================ FILE: tests/data/ds005/sub-10/func/sub-10_task-mixedgamblestask_run-03_events.tsv ================================================ onset duration trial_type distance from indifference parametric gain parametric loss gain loss PTval respnum respcat RT 0.0 3 parametric gain n/a 0.0835 n/a 28 17 9.57 2 1 2.019 4.0 3 parametric gain n/a -0.2665 n/a 14 7 6.41 2 1 1.5659999999999998 8.0 3 parametric gain n/a 0.0835 n/a 28 18 8.48 2 1 1.4609999999999999 14.0 3 parametric gain n/a 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1 2.302 282.0 3 parametric gain n/a -0.3665 n/a 10 20 -11.68 4 0 1.598 286.0 3 parametric gain n/a 0.2335 n/a 34 7 26.41 1 1 1.3769999999999998 292.0 3 parametric gain n/a -0.3665 n/a 10 5 4.58 2 1 2.166 296.0 3 parametric gain n/a -0.1165 n/a 20 20 -1.68 3 0 1.653 300.0 3 parametric gain n/a 0.0835 n/a 28 20 6.32 2 1 1.585 304.0 3 parametric gain n/a 0.2835 n/a 36 5 30.58 1 1 1.026 308.0 3 parametric gain n/a 0.0335 n/a 26 7 18.41 1 1 1.138 316.0 3 parametric gain n/a 0.0335 n/a 26 17 7.57 2 1 1.984 326.0 3 parametric gain n/a -0.2165 n/a 16 15 -0.26 3 0 2.119 330.0 3 parametric gain n/a 0.0335 n/a 26 10 15.16 1 1 1.786 338.0 3 parametric gain n/a 0.3335 n/a 38 15 21.74 1 1 1.537 346.0 3 parametric gain n/a 0.0335 n/a 26 11 14.07 1 1 2.2640000000000002 356.0 3 parametric gain n/a 0.0835 n/a 28 5 22.58 1 1 0.993 362.0 3 parametric gain n/a -0.2665 n/a 14 18 -5.52 3 0 2.696 368.0 3 parametric gain n/a -0.0665 n/a 22 12 8.99 2 1 2.162 372.0 3 parametric gain n/a 0.2335 n/a 34 15 17.74 1 1 1.396 376.0 3 parametric gain n/a 0.3335 n/a 38 13 23.9 1 1 1.6 380.0 3 parametric gain n/a -0.3665 n/a 10 8 1.33 3 0 2.787 394.0 3 parametric gain n/a -0.3165 n/a 12 9 2.24 3 0 2.176 398.0 3 parametric gain n/a 0.2835 n/a 36 11 24.07 1 1 1.5090000000000001 404.0 3 parametric gain n/a -0.2665 n/a 14 5 8.58 1 1 1.729 408.0 3 parametric gain n/a -0.0665 n/a 22 11 10.07 1 1 2.149 412.0 3 parametric gain n/a -0.1665 n/a 18 19 -2.6 3 0 1.883 418.0 3 parametric gain n/a -0.0665 n/a 22 8 13.33 1 1 1.4240000000000002 428.0 3 parametric gain n/a 0.3335 n/a 38 6 31.49 1 1 1.8090000000000002 434.0 3 parametric gain n/a -0.3165 n/a 12 8 3.33 2 1 2.009 438.0 3 parametric gain n/a -0.2665 n/a 14 11 2.07 3 0 2.1790000000000003 442.0 3 parametric gain n/a 0.1835 n/a 32 12 18.99 1 1 1.516 446.0 3 parametric gain n/a -0.1165 n/a 20 11 8.07 2 1 1.508 450.0 3 parametric gain n/a 0.0335 n/a 26 14 10.82 2 1 2.009 454.0 3 parametric gain n/a 0.1835 n/a 32 8 23.33 1 1 1.446 466.0 3 parametric gain n/a 0.3835 n/a 40 17 21.57 1 1 2.033 472.0 3 parametric gain n/a -0.1165 n/a 20 17 1.57 3 0 1.641 476.0 3 parametric gain n/a -0.0165 n/a 24 19 3.4 2 1 2.6039999999999996 ================================================ FILE: tests/data/ds005/sub-11/dwi/sub-11 ================================================ ================================================ FILE: tests/data/ds005/sub-11/func/sub-11_task-mixedgamblestask_run-01_events.tsv ================================================ onset duration trial_type distance from indifference parametric gain parametric loss gain loss PTval respnum respcat RT 0.0 3 parametric gain n/a -0.139 n/a 20 15 3.31 2 1 1.436 4.0 3 parametric gain n/a -0.189 n/a 18 12 4.64 1 1 1.9069999999999998 8.0 3 parametric gain n/a -0.389 n/a 10 15 -6.69 3 0 2.18 18.0 3 parametric gain n/a 0.211 n/a 34 16 16.19 1 1 1.158 24.0 3 parametric gain n/a -0.189 n/a 18 5 12.44 1 1 0.935 28.0 3 parametric gain n/a 0.011 n/a 26 13 11.53 1 1 1.1 38.0 3 parametric gain n/a 0.161 n/a 32 17 13.08 1 1 1.39 42.0 3 parametric gain n/a -0.239 n/a 16 5 10.44 1 1 1.236 46.0 3 parametric gain n/a -0.089 n/a 22 7 14.21 1 1 0.8109999999999999 52.0 3 parametric gain n/a 0.261 n/a 36 8 27.1 1 1 0.8220000000000001 56.0 3 parametric gain n/a -0.239 n/a 16 10 4.87 2 1 1.808 62.0 3 parametric gain n/a 0.111 n/a 30 13 15.53 1 1 1.3319999999999999 74.0 3 parametric gain n/a -0.289 n/a 14 17 -4.92 4 0 2.255 78.0 3 parametric gain n/a 0.061 n/a 28 19 6.85 2 1 2.1 82.0 3 parametric gain n/a -0.289 n/a 14 20 -8.26 4 0 2.332 86.0 3 parametric gain n/a 0.011 n/a 26 16 8.19 1 1 1.8869999999999998 90.0 3 parametric gain n/a 0.361 n/a 40 5 34.44 1 1 0.879 94.0 3 parametric gain n/a 0.061 n/a 28 12 14.64 1 1 1.028 98.0 3 parametric gain n/a -0.139 n/a 20 16 2.19 2 1 1.224 102.0 3 parametric gain n/a 0.011 n/a 26 5 20.44 1 1 1.381 106.0 3 parametric gain n/a 0.161 n/a 32 20 9.74 1 1 1.4680000000000002 110.0 3 parametric gain n/a -0.189 n/a 18 17 -0.92 2 1 2.25 116.0 3 parametric gain n/a -0.139 n/a 20 6 13.32 1 1 1.112 120.0 3 parametric gain n/a -0.139 n/a 20 18 -0.03 2 1 2.533 126.0 3 parametric gain n/a 0.361 n/a 40 7 32.21 1 1 0.757 130.0 3 parametric gain n/a 0.261 n/a 36 6 29.32 1 1 0.794 134.0 3 parametric gain n/a -0.239 n/a 16 20 -6.26 4 0 1.2790000000000001 140.0 3 parametric gain n/a 0.361 n/a 40 15 23.31 1 1 1.432 148.0 3 parametric gain n/a -0.389 n/a 10 6 3.32 1 1 0.8390000000000001 156.0 3 parametric gain n/a -0.339 n/a 12 15 -4.69 3 0 2.303 160.0 3 parametric gain n/a 0.211 n/a 34 17 15.08 1 1 1.381 164.0 3 parametric gain n/a -0.189 n/a 18 14 2.42 2 1 1.4240000000000002 170.0 3 parametric gain n/a 0.361 n/a 40 13 25.53 1 1 0.769 174.0 3 parametric gain n/a 0.161 n/a 32 19 10.85 1 1 1.429 178.0 3 parametric gain n/a -0.289 n/a 14 12 0.64 2 1 1.429 182.0 3 parametric gain n/a 0.161 n/a 32 10 20.87 1 1 1.213 192.0 3 parametric gain n/a 0.261 n/a 36 17 17.08 1 1 1.632 202.0 3 parametric gain n/a 0.061 n/a 28 13 13.53 1 1 1.092 208.0 3 parametric gain n/a 0.211 n/a 34 13 19.53 1 1 1.24 212.0 3 parametric gain n/a 0.311 n/a 38 14 22.42 1 1 1.23 220.0 3 parametric gain n/a 0.161 n/a 32 11 19.76 1 1 1.088 224.0 3 parametric gain n/a 0.161 n/a 32 9 21.98 1 1 0.805 234.0 3 parametric gain n/a 0.111 n/a 30 16 12.19 1 1 1.3359999999999999 238.0 3 parametric gain n/a 0.211 n/a 34 10 22.87 1 1 1.038 246.0 3 parametric gain n/a -0.189 n/a 18 8 9.1 1 1 0.857 250.0 3 parametric gain n/a 0.211 n/a 34 20 11.74 1 1 1.422 256.0 3 parametric gain n/a 0.161 n/a 32 16 14.19 1 1 0.945 260.0 3 parametric gain n/a -0.039 n/a 24 12 10.64 1 1 1.5019999999999998 266.0 3 parametric gain n/a 0.311 n/a 38 12 24.64 1 1 0.846 272.0 3 parametric gain n/a 0.311 n/a 38 10 26.87 1 1 1.073 280.0 3 parametric gain n/a -0.039 n/a 24 7 16.21 1 1 1.265 284.0 3 parametric gain n/a -0.239 n/a 16 13 1.53 2 1 1.7180000000000002 296.0 3 parametric gain n/a 0.261 n/a 36 19 14.85 1 1 1.881 310.0 3 parametric gain n/a 0.161 n/a 32 5 26.44 1 1 0.9059999999999999 316.0 3 parametric 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8.42 1 1 1.7280000000000002 ================================================ FILE: tests/data/ds005/sub-11/func/sub-11_task-mixedgamblestask_run-02_events.tsv ================================================ onset duration trial_type distance from indifference parametric gain parametric loss gain loss PTval respnum respcat RT 0.0 3 parametric gain n/a -0.1194 n/a 20 5 14.44 1 1 0.991 4.0 3 parametric gain n/a -0.0694 n/a 22 17 3.08 2 1 1.4040000000000001 8.0 3 parametric gain n/a -0.3694 n/a 10 16 -7.81 4 0 1.94 12.0 3 parametric gain n/a 0.3306 n/a 38 18 17.97 1 1 1.716 16.0 3 parametric gain n/a -0.1194 n/a 20 14 4.42 2 1 1.7369999999999999 24.0 3 parametric gain n/a -0.2694 n/a 14 13 -0.47 2 1 1.847 28.0 3 parametric gain n/a 0.3806 n/a 40 8 31.1 1 1 1.053 32.0 3 parametric gain n/a 0.1806 n/a 32 18 11.97 1 1 1.722 36.0 3 parametric gain n/a -0.0194 n/a 24 8 15.1 1 1 1.1420000000000001 40.0 3 parametric gain n/a 0.2806 n/a 36 14 20.42 1 1 1.013 44.0 3 parametric gain n/a 0.0806 n/a 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0.8140000000000001 412.0 3 parametric gain n/a 0.1806 n/a 32 7 24.21 1 1 1.5630000000000002 422.0 3 parametric gain n/a 0.3806 n/a 40 16 22.19 1 1 1.3019999999999998 426.0 3 parametric gain n/a 0.1306 n/a 30 17 11.08 1 1 1.811 432.0 3 parametric gain n/a -0.0194 n/a 24 5 18.44 1 1 1.155 436.0 3 parametric gain n/a -0.2194 n/a 16 7 8.21 1 1 1.659 440.0 3 parametric gain n/a -0.0694 n/a 22 18 1.97 1 1 2.011 444.0 3 parametric gain n/a 0.0306 n/a 26 19 4.85 2 1 1.764 448.0 3 parametric gain n/a -0.0194 n/a 24 20 1.74 2 1 1.459 452.0 3 parametric gain n/a 0.2306 n/a 34 6 27.32 1 1 1.4069999999999998 460.0 3 parametric gain n/a -0.0194 n/a 24 16 6.19 2 1 2.182 468.0 3 parametric gain n/a -0.0694 n/a 22 6 15.32 1 1 0.7979999999999999 474.0 3 parametric gain n/a -0.3194 n/a 12 7 4.21 1 1 2.4330000000000003 ================================================ FILE: tests/data/ds005/sub-11/func/sub-11_task-mixedgamblestask_run-03_events.tsv ================================================ onset 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2 1 2.2319999999999998 438.0 3 parametric gain n/a -0.2665 n/a 14 11 1.76 2 1 1.7830000000000001 442.0 3 parametric gain n/a 0.1835 n/a 32 12 18.64 1 1 1.037 446.0 3 parametric gain n/a -0.1165 n/a 20 11 7.76 1 1 1.624 450.0 3 parametric gain n/a 0.0335 n/a 26 14 10.42 2 1 2.549 454.0 3 parametric gain n/a 0.1835 n/a 32 8 23.1 1 1 1.804 466.0 3 parametric gain n/a 0.3835 n/a 40 17 21.08 1 1 0.888 472.0 3 parametric gain n/a -0.1165 n/a 20 17 1.08 2 1 1.5919999999999999 476.0 3 parametric gain n/a -0.0165 n/a 24 19 2.85 2 1 1.605 ================================================ FILE: tests/data/ds005/sub-12/func/sub-12_task-mixedgamblestask_run-01_events.tsv ================================================ onset duration trial_type distance from indifference parametric gain parametric loss gain loss PTval respnum respcat RT 0.0 3 parametric gain n/a -0.139 n/a 20 15 -9.09 3 0 1.501 4.0 3 parametric gain n/a -0.189 n/a 18 12 -5.27 3 0 1.371 8.0 3 parametric gain n/a -0.389 n/a 10 15 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2.5580000000000003 280.0 3 parametric gain n/a -0.039 n/a 24 7 10.43 1 1 0.9129999999999999 284.0 3 parametric gain n/a -0.239 n/a 16 13 -9.21 3 0 1.8769999999999998 296.0 3 parametric gain n/a 0.261 n/a 36 19 -0.84 3 0 1.797 310.0 3 parametric gain n/a 0.161 n/a 32 5 22.3 1 1 0.9390000000000001 316.0 3 parametric gain n/a 0.211 n/a 34 9 16.55 1 1 0.9229999999999999 320.0 3 parametric gain n/a 0.061 n/a 28 7 14.43 1 1 0.905 324.0 3 parametric gain n/a -0.039 n/a 24 13 -1.21 2 1 2.6439999999999997 330.0 3 parametric gain n/a 0.311 n/a 38 5 28.3 1 1 1.777 336.0 3 parametric gain n/a -0.089 n/a 22 9 4.55 2 1 1.931 342.0 3 parametric gain n/a -0.389 n/a 10 11 -11.33 4 0 1.444 346.0 3 parametric gain n/a -0.339 n/a 12 14 -15.15 4 0 1.9240000000000002 350.0 3 parametric gain n/a 0.211 n/a 34 19 -2.84 4 0 1.9809999999999999 360.0 3 parametric gain n/a 0.311 n/a 38 7 24.43 1 1 0.948 364.0 3 parametric gain n/a 0.111 n/a 30 6 18.37 1 1 1.882 368.0 3 parametric gain n/a 0.011 n/a 26 18 -8.9 2 1 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436.0 3 parametric gain n/a -0.039 n/a 24 18 -23.36 3 0 1.551 440.0 3 parametric gain n/a -0.389 n/a 10 12 -21.57 4 0 1.18 444.0 3 parametric gain n/a 0.361 n/a 40 19 -9.99 2 1 2.062 448.0 3 parametric gain n/a -0.039 n/a 24 9 0.32 2 1 2.074 458.0 3 parametric gain n/a -0.239 n/a 16 18 -31.36 4 0 1.215 462.0 3 parametric gain n/a 0.261 n/a 36 12 4.43 3 0 1.348 466.0 3 parametric gain n/a 0.011 n/a 26 9 2.32 1 1 1.156 470.0 3 parametric gain n/a -0.139 n/a 20 12 -11.57 2 1 2.0069999999999997 474.0 3 parametric gain n/a -0.039 n/a 24 14 -12.83 3 0 1.3559999999999999 ================================================ FILE: tests/data/ds005/sub-14/func/sub-14_task-mixedgamblestask_run-02_events.tsv ================================================ onset duration trial_type distance from indifference parametric gain parametric loss gain loss PTval respnum respcat RT 0.0 3 parametric gain n/a -0.1194 n/a 20 5 6.85 1 1 1.083 4.0 3 parametric gain n/a -0.0694 n/a 22 17 -22.73 3 0 1.679 8.0 3 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tests/data/ds005/sub-16/func/sub-16_task-mixedgamblestask_run-02_events.tsv ================================================ onset duration trial_type distance from indifference parametric gain parametric loss gain loss PTval respnum respcat RT 0.0 3 parametric gain n/a -0.1194 n/a 20 5 11.6 1 1 1.811 4.0 3 parametric gain n/a -0.0694 n/a 22 17 -6.58 4 0 1.656 8.0 3 parametric gain n/a -0.3694 n/a 10 16 -16.89 4 0 1.3940000000000001 12.0 3 parametric gain n/a 0.3306 n/a 38 18 7.74 2 1 1.48 16.0 3 parametric gain n/a -0.1194 n/a 20 14 -3.53 4 0 1.704 24.0 3 parametric gain n/a -0.2694 n/a 14 13 -7.85 4 0 1.179 28.0 3 parametric gain n/a 0.3806 n/a 40 8 26.55 1 1 1.105 32.0 3 parametric gain n/a 0.1806 n/a 32 18 1.74 3 0 1.849 36.0 3 parametric gain n/a -0.0194 n/a 24 8 10.55 1 1 1.345 40.0 3 parametric gain n/a 0.2806 n/a 36 14 12.47 2 1 1.8719999999999999 44.0 3 parametric gain n/a 0.0806 n/a 28 6 17.91 1 1 0.945 48.0 3 parametric gain n/a -0.2194 n/a 16 14 -7.53 4 0 1.354 56.0 3 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38 15 12.79 2 1 1.147 346.0 3 parametric gain n/a 0.0335 n/a 26 11 7.51 2 1 1.1159999999999999 356.0 3 parametric gain n/a 0.0835 n/a 28 5 19.6 1 1 1.0190000000000001 362.0 3 parametric gain n/a -0.2665 n/a 14 18 -16.26 4 0 1.156 368.0 3 parametric gain n/a -0.0665 n/a 22 12 1.83 3 0 1.115 372.0 3 parametric gain n/a 0.2335 n/a 34 15 8.79 2 1 1.1179999999999999 376.0 3 parametric gain n/a 0.3335 n/a 38 13 16.15 1 1 1.158 380.0 3 parametric gain n/a -0.3665 n/a 10 8 -3.45 4 0 0.955 394.0 3 parametric gain n/a -0.3165 n/a 12 9 -3.13 4 0 1.459 398.0 3 parametric gain n/a 0.2835 n/a 36 11 17.51 2 1 1.078 404.0 3 parametric gain n/a -0.2665 n/a 14 5 5.6 4 0 1.037 408.0 3 parametric gain n/a -0.0665 n/a 22 11 3.51 3 0 1.278 412.0 3 parametric gain n/a -0.1665 n/a 18 19 -13.94 4 0 1.126 418.0 3 parametric gain n/a -0.0665 n/a 22 8 8.55 2 1 1.3 428.0 3 parametric gain n/a 0.3335 n/a 38 6 27.91 1 1 0.9229999999999999 434.0 3 parametric gain n/a -0.3165 n/a 12 8 -1.45 4 0 1.2830000000000001 438.0 3 parametric gain n/a -0.2665 n/a 14 11 -4.49 4 0 0.8859999999999999 442.0 3 parametric gain n/a 0.1835 n/a 32 12 11.83 2 1 1.1420000000000001 446.0 3 parametric gain n/a -0.1165 n/a 20 11 1.51 4 0 1.268 450.0 3 parametric gain n/a 0.0335 n/a 26 14 2.47 4 0 1.2229999999999999 454.0 3 parametric gain n/a 0.1835 n/a 32 8 18.55 1 1 0.99 466.0 3 parametric gain n/a 0.3835 n/a 40 17 11.42 2 1 1.252 472.0 3 parametric gain n/a -0.1165 n/a 20 17 -8.58 4 0 1.3559999999999999 476.0 3 parametric gain n/a -0.0165 n/a 24 19 -7.94 4 0 0.991 ================================================ FILE: tests/data/ds005/task-mixedgamblestask_bold.json ================================================ { "RepetitionTime": 2.0, "TaskName": "mixed-gambles task", "SliceTiming": [0.0, 0.0571, 0.1143, 0.1714, 0.2286, 0.2857], "NullTestMetadata": null } ================================================ FILE: tests/data/ds005_conflict/dataset_description.json ================================================ { "BIDSVersion": "1.0.0rc2", "License": "This dataset is made available under the Public Domain Dedication and License \nv1.0, whose full text can be found at \nhttp://www.opendatacommons.org/licenses/pddl/1.0/. \nWe hope that all users will follow the ODC Attribution/Share-Alike \nCommunity Norms (http://www.opendatacommons.org/norms/odc-by-sa/); \nin particular, while not legally required, we hope that all users \nof the data will acknowledge the OpenfMRI project and NSF Grant \nOCI-1131441 (R. Poldrack, PI) in any publications.", "Name": "Mixed-gambles task", "ReferencesAndLinks": "Tom, S.M., Fox, C.R., Trepel, C., Poldrack, R.A. (2007). The neural basis of loss aversion in decision-making under risk. Science, 315(5811):515-8" } ================================================ FILE: tests/data/ds005_conflict/sub-01/func/sub-01_task-mixedgamblestask_run-01_events.json ================================================ { "run": 2 } ================================================ FILE: tests/data/ds005_conflict/sub-01/func/sub-01_task-mixedgamblestask_run-01_events.tsv ================================================ onset duration trial_type distance from indifference parametric gain parametric loss gain loss PTval respnum respcat RT 0.0 3 parametric gain n/a -0.139 n/a 20 15 5.15 0 -1 0.0 4.0 3 parametric gain n/a -0.189 n/a 18 12 6.12 2 1 1.7930000000000001 8.0 3 parametric gain n/a -0.389 n/a 10 15 -4.85 3 0 1.6369999999999998 18.0 3 parametric gain n/a 0.211 n/a 34 16 18.16 1 1 1.3159999999999998 24.0 3 parametric gain n/a -0.189 n/a 18 5 13.05 1 1 1.67 28.0 3 parametric gain n/a 0.011 n/a 26 13 13.13 2 1 1.232 38.0 3 parametric gain n/a 0.161 n/a 32 17 15.17 2 1 1.5019999999999998 42.0 3 parametric gain n/a -0.239 n/a 16 5 11.05 2 1 1.925 46.0 3 parametric gain n/a -0.089 n/a 22 7 15.07 1 1 1.061 52.0 3 parametric gain n/a 0.261 n/a 36 8 28.08 1 1 1.079 56.0 3 parametric gain n/a -0.239 n/a 16 10 6.1 2 1 1.4769999999999999 62.0 3 parametric gain n/a 0.111 n/a 30 13 17.13 1 1 1.599 74.0 3 parametric gain n/a -0.289 n/a 14 17 -2.83 4 0 1.8219999999999998 78.0 3 parametric gain n/a 0.061 n/a 28 19 9.19 2 1 1.405 82.0 3 parametric gain n/a -0.289 n/a 14 20 -5.8 4 0 1.206 86.0 3 parametric gain n/a 0.011 n/a 26 16 10.16 2 1 1.272 90.0 3 parametric gain n/a 0.361 n/a 40 5 35.05 1 1 0.9 94.0 3 parametric gain n/a 0.061 n/a 28 12 16.12 2 1 1.318 98.0 3 parametric gain n/a -0.139 n/a 20 16 4.16 2 1 1.367 102.0 3 parametric gain n/a 0.011 n/a 26 5 21.05 1 1 1.5659999999999998 106.0 3 parametric gain n/a 0.161 n/a 32 20 12.2 2 1 1.36 110.0 3 parametric gain n/a -0.189 n/a 18 17 1.17 3 0 1.321 116.0 3 parametric gain n/a -0.139 n/a 20 6 14.06 1 1 1.433 120.0 3 parametric 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1.2429999999999999 212.0 3 parametric gain n/a 0.311 n/a 38 14 24.14 1 1 1.112 220.0 3 parametric gain n/a 0.161 n/a 32 11 21.11 1 1 1.235 224.0 3 parametric gain n/a 0.161 n/a 32 9 23.09 1 1 0.866 234.0 3 parametric gain n/a 0.111 n/a 30 16 14.16 2 1 1.6269999999999998 238.0 3 parametric gain n/a 0.211 n/a 34 10 24.1 1 1 1.0 246.0 3 parametric gain n/a -0.189 n/a 18 8 10.08 2 1 1.526 250.0 3 parametric gain n/a 0.211 n/a 34 20 14.2 2 1 1.251 256.0 3 parametric gain n/a 0.161 n/a 32 16 16.16 2 1 2.28 260.0 3 parametric gain n/a -0.039 n/a 24 12 12.12 2 1 1.242 266.0 3 parametric gain n/a 0.311 n/a 38 12 26.12 1 1 1.011 272.0 3 parametric gain n/a 0.311 n/a 38 10 28.1 1 1 1.043 280.0 3 parametric gain n/a -0.039 n/a 24 7 17.07 1 1 1.172 284.0 3 parametric gain n/a -0.239 n/a 16 13 3.13 2 1 1.115 296.0 3 parametric gain n/a 0.261 n/a 36 19 17.19 2 1 1.75 310.0 3 parametric gain n/a 0.161 n/a 32 5 27.05 1 1 0.9179999999999999 316.0 3 parametric gain n/a 0.211 n/a 34 9 25.09 1 1 0.7340000000000001 320.0 3 parametric gain n/a 0.061 n/a 28 7 21.07 1 1 1.067 324.0 3 parametric gain n/a -0.039 n/a 24 13 11.13 2 1 1.6 330.0 3 parametric gain n/a 0.311 n/a 38 5 33.05 1 1 0.805 336.0 3 parametric gain n/a -0.089 n/a 22 9 13.09 1 1 1.39 342.0 3 parametric gain n/a -0.389 n/a 10 11 -0.89 4 0 1.806 346.0 3 parametric gain n/a -0.339 n/a 12 14 -1.86 4 0 1.6 350.0 3 parametric gain n/a 0.211 n/a 34 19 15.19 2 1 1.237 360.0 3 parametric gain n/a 0.311 n/a 38 7 31.07 1 1 0.9570000000000001 364.0 3 parametric gain n/a 0.111 n/a 30 6 24.06 1 1 0.877 368.0 3 parametric gain n/a 0.011 n/a 26 18 8.18 2 1 1.454 374.0 3 parametric gain n/a -0.289 n/a 14 6 8.06 2 1 1.9040000000000001 378.0 3 parametric gain n/a -0.239 n/a 16 12 4.12 2 1 1.486 382.0 3 parametric gain n/a -0.089 n/a 22 20 2.2 3 0 1.598 386.0 3 parametric gain n/a -0.339 n/a 12 17 -4.83 4 0 1.368 398.0 3 parametric gain n/a -0.389 n/a 10 7 3.07 2 1 1.32 402.0 3 parametric gain n/a -0.189 n/a 18 20 -1.8 4 0 2.302 408.0 3 parametric gain n/a -0.339 n/a 12 6 6.06 2 1 1.433 412.0 3 parametric gain n/a 0.161 n/a 32 6 26.06 1 1 0.9990000000000001 416.0 3 parametric gain n/a -0.189 n/a 18 7 11.07 2 1 1.071 420.0 3 parametric gain n/a 0.361 n/a 40 10 30.1 1 1 0.7829999999999999 424.0 3 parametric gain n/a -0.289 n/a 14 14 0.14 3 0 1.6869999999999998 428.0 3 parametric gain n/a 0.111 n/a 30 19 11.19 2 1 1.167 436.0 3 parametric gain n/a -0.039 n/a 24 18 6.18 2 1 1.145 440.0 3 parametric gain n/a -0.389 n/a 10 12 -1.88 3 0 1.383 444.0 3 parametric gain n/a 0.361 n/a 40 19 21.19 1 1 0.862 448.0 3 parametric gain n/a -0.039 n/a 24 9 15.09 1 1 0.8340000000000001 458.0 3 parametric gain n/a -0.239 n/a 16 18 -1.82 4 0 1.785 462.0 3 parametric gain n/a 0.261 n/a 36 12 24.12 2 1 1.28 466.0 3 parametric gain n/a 0.011 n/a 26 9 17.09 1 1 1.3940000000000001 470.0 3 parametric gain n/a -0.139 n/a 20 12 8.12 2 1 1.249 474.0 3 parametric gain n/a -0.039 n/a 24 14 10.14 2 1 1.266 ================================================ FILE: tests/data/ds005_conflict/sub-01/func/sub-01_task-mixedgamblestask_run-02_events.tsv ================================================ onset duration trial_type distance from indifference parametric gain parametric loss gain loss PTval respnum respcat RT 0.0 3 parametric gain n/a -0.1194 n/a 20 5 15.05 1 1 1.29 4.0 3 parametric gain n/a -0.0694 n/a 22 17 5.17 2 1 1.163 8.0 3 parametric gain n/a -0.3694 n/a 10 16 -5.84 4 0 1.265 12.0 3 parametric gain n/a 0.3306 n/a 38 18 20.18 2 1 1.4880000000000002 16.0 3 parametric gain n/a -0.1194 n/a 20 14 6.14 2 1 1.446 24.0 3 parametric gain n/a -0.2694 n/a 14 13 1.13 3 0 0.998 28.0 3 parametric gain n/a 0.3806 n/a 40 8 32.08 1 1 0.7809999999999999 32.0 3 parametric gain n/a 0.1806 n/a 32 18 14.18 2 1 1.189 36.0 3 parametric gain n/a -0.0194 n/a 24 8 16.08 1 1 1.189 40.0 3 parametric gain n/a 0.2806 n/a 36 14 22.14 2 1 1.102 44.0 3 parametric gain n/a 0.0806 n/a 28 6 22.06 1 1 0.863 48.0 3 parametric gain n/a -0.2194 n/a 16 14 2.14 2 1 1.9809999999999999 56.0 3 parametric gain n/a -0.2694 n/a 14 9 5.09 2 1 1.031 60.0 3 parametric gain n/a -0.1194 n/a 20 13 7.13 2 1 1.229 64.0 3 parametric gain n/a 0.1306 n/a 30 18 12.18 2 1 2.101 72.0 3 parametric gain n/a -0.2694 n/a 14 15 -0.85 4 0 1.536 76.0 3 parametric gain n/a -0.3194 n/a 12 12 0.12 3 0 1.7269999999999999 84.0 3 parametric gain n/a -0.0194 n/a 24 11 13.11 2 1 1.255 88.0 3 parametric gain n/a -0.3194 n/a 12 10 2.1 2 1 1.702 94.0 3 parametric gain n/a -0.2194 n/a 16 6 10.06 2 1 1.161 98.0 3 parametric gain n/a 0.3306 n/a 38 20 18.2 2 1 1.463 102.0 3 parametric gain n/a 0.0806 n/a 28 10 18.1 2 1 1.744 106.0 3 parametric gain n/a -0.1194 n/a 20 7 13.07 1 1 1.439 110.0 3 parametric gain n/a 0.3306 n/a 38 9 29.09 1 1 0.8059999999999999 120.0 3 parametric gain n/a 0.0306 n/a 26 12 14.12 2 1 1.041 132.0 3 parametric gain n/a -0.3694 n/a 10 14 -3.86 4 0 1.121 136.0 3 parametric gain n/a -0.3194 n/a 12 13 -0.87 4 0 1.621 146.0 3 parametric gain n/a -0.0194 n/a 24 15 9.15 2 1 1.138 150.0 3 parametric gain n/a 0.1806 n/a 32 15 17.15 2 1 1.974 154.0 3 parametric gain n/a 0.2806 n/a 36 15 21.15 1 1 2.408 162.0 3 parametric gain n/a 0.2806 n/a 36 18 18.18 2 1 1.8980000000000001 166.0 3 parametric gain n/a -0.3194 n/a 12 18 -5.82 4 0 1.222 176.0 3 parametric gain n/a -0.1194 n/a 20 9 11.09 1 1 1.403 180.0 3 parametric gain n/a -0.2194 n/a 16 8 8.08 2 1 1.328 184.0 3 parametric gain n/a -0.3194 n/a 12 20 -7.8 4 0 1.25 194.0 3 parametric gain n/a -0.0694 n/a 22 19 3.19 2 1 1.915 200.0 3 parametric gain n/a 0.2806 n/a 36 9 27.09 1 1 0.8340000000000001 204.0 3 parametric gain n/a 0.0306 n/a 26 6 20.06 1 1 1.169 208.0 3 parametric gain n/a 0.1306 n/a 30 5 25.05 1 1 0.826 212.0 3 parametric gain n/a 0.2806 n/a 36 7 29.07 1 1 0.835 216.0 3 parametric gain n/a -0.1694 n/a 18 10 8.1 2 1 1.369 220.0 3 parametric gain n/a 0.1306 n/a 30 9 21.09 1 1 0.745 226.0 3 parametric gain n/a 0.1306 n/a 30 12 18.12 2 1 1.0190000000000001 236.0 3 parametric gain n/a -0.2694 n/a 14 8 6.08 2 1 0.932 240.0 3 parametric gain n/a -0.1194 n/a 20 19 1.19 2 1 0.887 244.0 3 parametric gain n/a 0.0806 n/a 28 14 14.14 2 1 0.986 250.0 3 parametric gain n/a -0.2194 n/a 16 17 -0.83 4 0 2.204 258.0 3 parametric gain n/a 0.0306 n/a 26 8 18.08 2 1 1.084 262.0 3 parametric gain n/a -0.3694 n/a 10 17 -6.83 4 0 1.088 274.0 3 parametric gain n/a -0.3194 n/a 12 5 7.05 2 1 0.917 282.0 3 parametric gain n/a 0.3806 n/a 40 6 34.06 1 1 1.2209999999999999 286.0 3 parametric gain n/a 0.2806 n/a 36 13 23.13 2 1 1.496 294.0 3 parametric gain n/a -0.3694 n/a 10 9 1.09 2 1 2.91 298.0 3 parametric gain n/a 0.2306 n/a 34 18 16.18 2 1 1.196 304.0 3 parametric gain n/a 0.1306 n/a 30 15 15.15 2 1 1.1340000000000001 316.0 3 parametric gain n/a -0.1694 n/a 18 9 9.09 2 1 1.229 322.0 3 parametric gain n/a 0.0306 n/a 26 20 6.2 2 1 1.39 326.0 3 parametric gain n/a 0.2806 n/a 36 20 16.2 2 1 1.101 330.0 3 parametric gain n/a -0.0694 n/a 22 15 7.15 2 1 1.3319999999999999 334.0 3 parametric gain n/a 0.3306 n/a 38 8 30.08 1 1 0.878 342.0 3 parametric gain n/a 0.0806 n/a 28 16 12.16 2 1 0.9259999999999999 348.0 3 parametric gain n/a 0.2306 n/a 34 12 22.12 2 1 1.775 358.0 3 parametric gain n/a 0.0806 n/a 28 15 13.15 2 1 1.183 364.0 3 parametric gain n/a -0.0194 n/a 24 17 7.17 2 1 0.782 368.0 3 parametric gain n/a 0.0806 n/a 28 11 17.11 2 1 0.7979999999999999 372.0 3 parametric gain n/a -0.2194 n/a 16 9 7.09 2 1 0.982 378.0 3 parametric gain n/a -0.0694 n/a 22 13 9.13 2 1 1.008 384.0 3 parametric gain n/a 0.3806 n/a 40 11 29.11 1 1 1.088 390.0 3 parametric gain n/a 0.0806 n/a 28 8 20.08 1 1 2.935 396.0 3 parametric gain n/a 0.2806 n/a 36 16 20.16 1 1 1.744 400.0 3 parametric gain n/a -0.1194 n/a 20 10 10.1 2 1 1.214 404.0 3 parametric gain n/a -0.3694 n/a 10 18 -7.82 4 0 1.023 408.0 3 parametric gain n/a 0.3806 n/a 40 9 31.09 1 1 0.6970000000000001 412.0 3 parametric gain n/a 0.1806 n/a 32 7 25.07 1 1 1.3030000000000002 422.0 3 parametric gain n/a 0.3806 n/a 40 16 24.16 1 1 0.777 426.0 3 parametric gain n/a 0.1306 n/a 30 17 13.17 2 1 1.239 432.0 3 parametric gain n/a -0.0194 n/a 24 5 19.05 1 1 1.073 436.0 3 parametric gain n/a -0.2194 n/a 16 7 9.07 2 1 1.063 440.0 3 parametric gain n/a -0.0694 n/a 22 18 4.18 2 1 1.5919999999999999 444.0 3 parametric gain n/a 0.0306 n/a 26 19 7.19 2 1 1.128 448.0 3 parametric gain n/a -0.0194 n/a 24 20 4.2 2 1 1.258 452.0 3 parametric gain n/a 0.2306 n/a 34 6 28.06 1 1 1.794 460.0 3 parametric gain n/a -0.0194 n/a 24 16 8.16 2 1 0.914 468.0 3 parametric gain n/a -0.0694 n/a 22 6 16.06 1 1 1.849 474.0 3 parametric gain n/a -0.3194 n/a 12 7 5.07 2 1 1.122 ================================================ FILE: tests/data/ds005_conflict/sub-01/func/sub-01_task-mixedgamblestask_run-03_events.tsv ================================================ onset duration trial_type distance from indifference parametric gain parametric loss gain loss PTval respnum respcat RT 0.0 3 parametric gain n/a 0.0835 n/a 28 17 11.17 2 1 1.203 4.0 3 parametric gain n/a -0.2665 n/a 14 7 7.07 2 1 1.123 8.0 3 parametric gain n/a 0.0835 n/a 28 18 10.18 2 1 0.9690000000000001 14.0 3 parametric gain n/a -0.2165 n/a 16 11 5.11 2 1 1.102 18.0 3 parametric gain n/a 0.3835 n/a 40 20 20.2 1 1 0.9690000000000001 22.0 3 parametric gain n/a -0.1665 n/a 18 16 2.16 2 1 1.261 28.0 3 parametric gain n/a 0.3335 n/a 38 11 27.11 2 1 1.35 32.0 3 parametric gain n/a 0.2835 n/a 36 10 26.1 1 1 2.157 40.0 3 parametric gain n/a 0.3835 n/a 40 18 22.18 1 1 1.6780000000000002 44.0 3 parametric gain n/a -0.3665 n/a 10 10 0.1 2 1 1.581 50.0 3 parametric gain n/a -0.0165 n/a 24 6 18.06 1 1 1.133 56.0 3 parametric gain n/a -0.1165 n/a 20 8 12.08 2 1 2.2230000000000003 62.0 3 parametric gain n/a 0.3835 n/a 40 12 28.12 1 1 1.046 68.0 3 parametric gain n/a -0.0665 n/a 22 10 12.1 2 1 1.5519999999999998 76.0 3 parametric gain n/a -0.1665 n/a 18 6 12.06 2 1 1.2790000000000001 80.0 3 parametric gain n/a 0.3335 n/a 38 16 22.16 2 1 2.4530000000000003 90.0 3 parametric gain n/a 0.1335 n/a 30 20 10.2 2 1 1.328 98.0 3 parametric gain n/a -0.0665 n/a 22 14 8.14 2 1 1.008 102.0 3 parametric gain n/a -0.1665 n/a 18 18 0.18 2 1 2.222 106.0 3 parametric gain n/a -0.2165 n/a 16 19 -2.81 4 0 1.767 110.0 3 parametric gain n/a -0.0165 n/a 24 10 14.1 2 1 1.024 116.0 3 parametric gain n/a -0.3165 n/a 12 19 -6.81 4 0 1.176 120.0 3 parametric gain n/a 0.0835 n/a 28 9 19.09 1 1 2.383 126.0 3 parametric gain n/a 0.1335 n/a 30 10 20.1 1 1 1.936 130.0 3 parametric gain n/a -0.1665 n/a 18 15 3.15 2 1 1.551 142.0 3 parametric gain n/a 0.1835 n/a 32 14 18.14 2 1 1.4169999999999998 146.0 3 parametric gain n/a 0.2335 n/a 34 11 23.11 1 1 1.52 152.0 3 parametric gain n/a 0.3335 n/a 38 19 19.19 1 1 2.488 156.0 3 parametric gain n/a -0.2665 n/a 14 10 4.1 2 1 1.202 160.0 3 parametric gain n/a 0.2335 n/a 34 8 26.08 1 1 0.888 164.0 3 parametric gain n/a 0.1335 n/a 30 11 19.11 1 1 1.546 168.0 3 parametric gain n/a 0.1835 n/a 32 13 19.13 2 1 2.403 174.0 3 parametric gain n/a -0.1665 n/a 18 11 7.11 2 1 1.176 178.0 3 parametric gain n/a 0.1335 n/a 30 7 23.07 1 1 1.3519999999999999 186.0 3 parametric gain n/a -0.1665 n/a 18 13 5.13 2 1 0.8340000000000001 190.0 3 parametric gain n/a -0.2665 n/a 14 19 -4.81 4 0 0.9690000000000001 198.0 3 parametric gain n/a -0.3165 n/a 12 11 1.11 2 1 1.858 202.0 3 parametric gain n/a -0.3665 n/a 10 13 -2.87 4 0 1.403 206.0 3 parametric gain n/a -0.0665 n/a 22 16 6.16 2 1 1.136 212.0 3 parametric gain n/a -0.3165 n/a 12 16 -3.84 4 0 0.971 218.0 3 parametric gain n/a 0.3335 n/a 38 17 21.17 2 1 1.811 226.0 3 parametric gain n/a 0.3835 n/a 40 14 26.14 1 1 1.651 230.0 3 parametric gain n/a -0.0665 n/a 22 5 17.05 1 1 0.914 234.0 3 parametric gain n/a -0.2165 n/a 16 16 0.16 2 1 2.441 240.0 3 parametric gain n/a 0.2335 n/a 34 5 29.05 1 1 0.866 250.0 3 parametric gain n/a -0.3665 n/a 10 19 -8.81 4 0 0.836 254.0 3 parametric gain n/a -0.2665 n/a 14 16 -1.84 4 0 1.9069999999999998 258.0 3 parametric gain n/a 0.1335 n/a 30 8 22.08 1 1 0.9690000000000001 262.0 3 parametric gain n/a 0.2335 n/a 34 14 20.14 2 1 2.204 266.0 3 parametric gain n/a 0.1335 n/a 30 14 16.14 2 1 1.97 274.0 3 parametric gain n/a 0.0335 n/a 26 15 11.15 2 1 1.068 282.0 3 parametric gain n/a -0.3665 n/a 10 20 -9.8 4 0 1.077 286.0 3 parametric gain n/a 0.2335 n/a 34 7 27.07 1 1 1.051 292.0 3 parametric gain n/a -0.3665 n/a 10 5 5.05 2 1 1.445 296.0 3 parametric gain n/a -0.1165 n/a 20 20 0.2 2 1 1.67 300.0 3 parametric gain n/a 0.0835 n/a 28 20 8.2 2 1 0.956 304.0 3 parametric gain n/a 0.2835 n/a 36 5 31.05 1 1 1.11 308.0 3 parametric gain n/a 0.0335 n/a 26 7 19.07 1 1 2.516 316.0 3 parametric gain n/a 0.0335 n/a 26 17 9.17 2 1 1.254 326.0 3 parametric gain n/a -0.2165 n/a 16 15 1.15 2 1 1.51 330.0 3 parametric gain n/a 0.0335 n/a 26 10 16.1 2 1 0.757 338.0 3 parametric gain n/a 0.3335 n/a 38 15 23.15 1 1 1.963 346.0 3 parametric gain n/a 0.0335 n/a 26 11 15.11 2 1 0.9179999999999999 356.0 3 parametric gain n/a 0.0835 n/a 28 5 23.05 1 1 0.855 362.0 3 parametric gain n/a -0.2665 n/a 14 18 -3.82 4 0 1.391 368.0 3 parametric gain n/a -0.0665 n/a 22 12 10.12 2 1 0.966 372.0 3 parametric gain n/a 0.2335 n/a 34 15 19.15 1 1 2.406 376.0 3 parametric gain n/a 0.3335 n/a 38 13 25.13 1 1 0.87 380.0 3 parametric gain n/a -0.3665 n/a 10 8 2.08 2 1 1.271 394.0 3 parametric gain n/a -0.3165 n/a 12 9 3.09 2 1 1.15 398.0 3 parametric gain n/a 0.2835 n/a 36 11 25.11 1 1 1.564 404.0 3 parametric gain n/a -0.2665 n/a 14 5 9.05 2 1 1.695 408.0 3 parametric gain n/a -0.0665 n/a 22 11 11.11 2 1 1.11 412.0 3 parametric gain n/a -0.1665 n/a 18 19 -0.81 4 0 1.286 418.0 3 parametric gain n/a -0.0665 n/a 22 8 14.08 2 1 1.7369999999999999 428.0 3 parametric gain n/a 0.3335 n/a 38 6 32.06 1 1 1.063 434.0 3 parametric gain n/a -0.3165 n/a 12 8 4.08 2 1 0.887 438.0 3 parametric gain n/a -0.2665 n/a 14 11 3.11 2 1 1.247 442.0 3 parametric gain n/a 0.1835 n/a 32 12 20.12 2 1 1.545 446.0 3 parametric gain n/a -0.1165 n/a 20 11 9.11 2 1 0.8490000000000001 450.0 3 parametric gain n/a 0.0335 n/a 26 14 12.14 2 1 0.9440000000000001 454.0 3 parametric gain n/a 0.1835 n/a 32 8 24.08 1 1 0.825 466.0 3 parametric gain n/a 0.3835 n/a 40 17 23.17 1 1 1.963 472.0 3 parametric gain n/a -0.1165 n/a 20 17 3.17 2 1 1.161 476.0 3 parametric gain n/a -0.0165 n/a 24 19 5.19 2 1 0.888 ================================================ FILE: tests/data/ds005_derivs/dummy/dataset_description.json ================================================ { "GeneratedBy": [{ "Name": "dummy" }], "BIDSVersion": "1.0.0rc2", "Name": "Mixed-gambles task -- dummy derivative", "ReferencesAndLinks": "Tom, S.M., Fox, C.R., Trepel, C., Poldrack, R.A. (2007). The neural basis of loss aversion in decision-making under risk. Science, 315(5811):515-8", "DatasetType": "derivative" } ================================================ FILE: tests/data/ds005_derivs/dummy-vx.x.x/dataset_description.json ================================================ { "GeneratedBy": [{ "Name": "dummy" }], "BIDSVersion": "1.0.0rc2", "Name": "Mixed-gambles task -- dummy derivative", "ReferencesAndLinks": "Tom, S.M., Fox, C.R., Trepel, C., Poldrack, R.A. (2007). The neural basis of loss aversion in decision-making under risk. Science, 315(5811):515-8", "DatasetType": "derivative" } ================================================ FILE: tests/data/ds005_derivs/format_errs/no_dataset_type/dataset_description.json ================================================ { "PipelineDescription": { "Name": "dummy" }, "BIDSVersion": "1.0.0rc2", "Name": "Mixed-gambles task -- dummy derivative", "ReferencesAndLinks": "Tom, S.M., Fox, C.R., Trepel, C., Poldrack, R.A. (2007). The neural basis of loss aversion in decision-making under risk. Science, 315(5811):515-8" } ================================================ FILE: tests/data/ds005_derivs/format_errs/no_pipeline_description/dataset_description.json ================================================ { "BIDSVersion": "1.0.0rc2", "Name": "Mixed-gambles task -- dummy derivative", "ReferencesAndLinks": "Tom, S.M., Fox, C.R., Trepel, C., Poldrack, R.A. (2007). The neural basis of loss aversion in decision-making under risk. Science, 315(5811):515-8", "DatasetType": "derivative" } ================================================ FILE: tests/data/ds005_derivs/format_errs/no_type_or_description/dataset_description.json ================================================ { "BIDSVersion": "1.0.0rc2", "Name": "Mixed-gambles task -- dummy derivative", "ReferencesAndLinks": "Tom, S.M., Fox, C.R., Trepel, C., Poldrack, R.A. (2007). The neural basis of loss aversion in decision-making under risk. Science, 315(5811):515-8" } ================================================ FILE: tests/data/ds005_derivs/other/dummy/dataset_description.json ================================================ { "GeneratedBy": [{ "Name": "dummy" }], "BIDSVersion": "1.0.0rc2", "Name": "Mixed-gambles task -- dummy derivative", "ReferencesAndLinks": "Tom, S.M., Fox, C.R., Trepel, C., Poldrack, R.A. (2007). The neural basis of loss aversion in decision-making under risk. Science, 315(5811):515-8", "DatasetType": "derivative" } ================================================ FILE: tests/data/synthetic/T1w.json ================================================ { "RepetitionTime": 2.5, "EchoTime": 0.0029, "FlipAngle": 8 } ================================================ FILE: tests/data/synthetic/code/create_synthetic_ds.sh ================================================ #!/bin/bash # small bash script to create a synthetic BIDS data set # defines where the BIDS data set will be created start_dir=$(pwd) raw_dir=${start_dir}/../ subject_list='01 02 03 04 05' session_list='01' create_raw_dwi() { target_dir=$1 subject=$2 ses=$3 this_dir=${target_dir}/sub-${subject}/ses-${ses}/dwi mkdir -p "${this_dir}" suffix='_dwi' source_image=${target_dir}/../images/4d.nii.gz filename=${this_dir}/sub-${subject}_ses-${ses}${suffix}.nii.gz cp "${source_image}" "${filename}" source_bval=${start_dir}/dwi.bval filename=${this_dir}/sub-${subject}_ses-${ses}${suffix}.bval cp "${source_bval}" "${filename}" source_bvec=${start_dir}/dwi.bvec filename=${this_dir}/sub-${subject}_ses-${ses}${suffix}.bvec cp "${source_bvec}" "${filename}" } create_raw_fmap() { target_dir=$1 subject=$2 ses=$3 this_dir=${target_dir}/sub-${subject}/ses-${ses}/fmap mkdir -p ${this_dir} source_image=${target_dir}/../images/3d.nii.gz fmap_suffix_list='_phasediff _magnitude1 _magnitude2' for suffix in ${fmap_suffix_list}; do cp "${source_image}" "${this_dir}/sub-${subject}_ses-${ses}${suffix}.nii.gz" done RepetitionTime=0.4 EchoTime1=00519 EchoTime2=0.00765 FlipAngle=60 template='{"FlipAngle":%f, "RepetitionTime":%f, "EchoTime1":%f, "EchoTime2":%f, "IntendedFor": ["%s", "%s"], "PhaseEncodingDirection": "j-"}' suffix='_bold' task_name='nback' IntendedFor1="$(echo ses-${ses}/func/sub-${subject}_ses-${ses}_task-${task_name}_run-01${suffix}.nii.gz)" IntendedFor2="$(echo ses-${ses}/func/sub-${subject}_ses-${ses}_task-${task_name}_run-02${suffix}.nii.gz)" json_string=$(printf "$template" "$FlipAngle" "$RepetitionTime" "$EchoTime1" "$EchoTime2" "$IntendedFor1" "$IntendedFor2") echo "$json_string" >${this_dir}/sub-${subject}_ses-${ses}_phasediff.json echo "$json_string" >${this_dir}/sub-${subject}_ses-${ses}_phasediff.json } # RAW DATASET for subject in ${subject_list}; do for ses in ${session_list}; do create_raw_dwi ${raw_dir} ${subject} ${ses} create_raw_fmap ${raw_dir} ${subject} ${ses} done done ================================================ FILE: tests/data/synthetic/code/dwi.bval ================================================ 0 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 ================================================ FILE: tests/data/synthetic/code/dwi.bvec ================================================ 0 0.99955850839614 0.01954407058656 0.00294903200119 0.57941150665283 -0.22696028649807 -0.88946276903152 0.81388407945632 0.25786191225051 0.94657051563262 0.48336026072502 0.31938299536705 0.42975607514381 -0.4763910472393 -0.59470033645629 -0.59882068634033 -0.83007228374481 0.88395410776138 0.2610293328762 0.09066703915596 -0.80151742696762 0.53270918130874 -0.79953873157501 0.94853943586349 0.26080328226089 -0.04598157480359 0.20106360316276 0.76368796825408 -0.13214653730392 -0.14714884757995 0.89985853433609 -0.57571458816528 -0.35732066631317 -0.33106037974357 -0.31264066696167 -0.96581315994262 -0.95102477073669 0.43446427583694 -0.75675719976425 0.73082029819488 -0.67709231376647 0.67937570810318 -0.13976696133613 -0.72053796052932 -0.07321017235517 0.5839369893074 -0.07184987515211 -0.55975395441055 0.82137787342071 0.36826944351196 -0.19602409005165 -0.73164790868759 0.43177768588066 0.52686661481857 -0.19931995868682 0.4891959130764 0.37798687815666 -0.70318156480789 0.26638954877853 -0.01923633553087 0.02821243368089 0.54735606908798 -0.30064421892166 0.73979389667511 0.28209447860717 0 -0.02049821615219 0.98761302232742 0.52286034822464 -0.80844813585281 -0.65079009532928 -0.30648133158683 0.02310398034751 0.87089902162551 0.07609922438859 -0.81609696149826 -0.78027993440628 -0.53328257799148 -0.50823193788528 0.61257445812225 0.04065646231174 -0.53946721553802 0.02398272044956 -0.41599848866462 -0.91442102193832 0.48670825362205 0.79603910446167 0.26488894224166 -0.28364571928978 0.67999285459518 -0.03036121465265 -0.9791235923767 -0.642438352108 0.20882698893547 0.83522641658783 0.37077417969703 0.37174785137176 -0.00542591279372 -0.04497369006276 -0.27928236126899 -0.25846034288406 0.19096466898918 -0.89682209491729 0.62152820825576 0.30336225032806 -0.08724510669708 0.59735131263732 -0.82620531320571 0.31055691838264 0.72652155160903 -0.69059783220291 -0.48624488711357 -0.81753581762313 -0.42245289683342 -0.68479514122009 0.54096210002899 -0.67699402570724 0.76565784215927 0.5939724445343 -0.3681089580059 0.28870034217834 -0.87901186943054 -0.34432905912399 0.93346035480499 0.24267202615737 -0.96214282512664 -0.18922631442546 0.30678990483284 0.53955316543579 0.9575902223587 0 0.02150822617113 -0.15568755567073 -0.85241317749023 -0.10341253876686 -0.72454214096069 -0.33900585770607 -0.58056783676147 -0.41838020086288 -0.31338959932327 0.31677860021591 0.53773391246795 0.72864228487014 -0.71746206283569 -0.52065771818161 0.79985052347183 -0.14126154780387 0.46695810556411 0.8710965514183 0.39447921514511 0.34739750623703 -0.28730210661888 0.53904712200164 -0.14077690243721 -0.68526721000671 0.99848085641861 -0.02984086796641 -0.06366975605487 -0.96898341178894 0.52985268831253 -0.22974103689193 0.72825574874877 -0.93396604061126 0.94253724813461 -0.90788620710372 -0.02007177658379 -0.24307298660278 0.0833735987544 -0.20253752171993 -0.61145150661468 -0.73070788383483 0.426168769598 -0.54575622081756 -0.61998349428176 -0.6832326054573 -0.42672303318977 -0.87086367607116 -0.13531495630741 0.38323882222175 -0.62883508205413 0.81788432598114 0.0798152834177 0.47679767012596 -0.60795408487319 0.90816700458526 -0.82300633192062 -0.29062712192535 -0.62207180261611 0.24018363654613 0.96991771459579 -0.27108195424079 0.81522691249847 0.90304642915725 -0.40197896957397 -0.05868147313594 ================================================ FILE: tests/data/synthetic/dataset_description.json ================================================ { "Name": "Synthetic dataset for inclusion in BIDS-examples", "BIDSVersion": "1.0.2", "License": "PD", "Authors": ["Markiewicz, C. J."] } ================================================ FILE: tests/data/synthetic/derivatives/fmriprep/dataset_description.json ================================================ { "GeneratedBy": [{ "Name": "fmriprep" }], "Name": "synthetic", "BIDSVersion": "1.7.1", "License": "PD", "Authors": ["Markiewicz, C. J."], "DatasetType": "derivative" } ================================================ FILE: tests/data/synthetic/derivatives/fmriprep/sub-01/ses-01/func/sub-01_ses-01_task-nback_run-01_desc-confounds_regressors.tsv ================================================ CSF WhiteMatter GlobalSignal stdDVARS non-stdDVARS vx-wisestdDVARS FramewiseDisplacement tCompCor00 tCompCor01 tCompCor02 tCompCor03 tCompCor04 tCompCor05 aCompCor00 aCompCor01 aCompCor02 aCompCor03 aCompCor04 aCompCor05 Cosine00 Cosine01 Cosine02 Cosine03 Cosine04 Cosine05 Cosine06 Cosine07 Cosine08 Cosine09 Cosine10 Cosine11 Cosine12 Cosine13 Cosine14 Cosine15 Cosine16 Cosine17 Cosine18 Cosine19 Cosine20 Cosine21 Cosine22 Cosine23 Cosine24 Cosine25 Cosine26 X Y Z RotX RotY RotZ 22.50107527436328 4.208208113623432 8.180037590504812 n/a n/a n/a n/a 0.0016274311 -0.1054636498 0.030505627 -0.004621742 0.0092994527 -0.06387659450000001 -0.0486746709 0.0198735425 -0.0024164839 0.021243420699999998 -0.0319595135 0.0382241481 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1880-01-10T05:17:54 func/sub-01_ses-01_task-nback_run-01_bold.nii.gz 1880-01-10T05:22:54 func/sub-01_ses-01_task-nback_run-02_bold.nii.gz 1880-01-10T05:37:54 func/sub-01_ses-01_task-rest_bold.nii.gz 1880-01-10T05:52:54 ================================================ FILE: tests/data/synthetic/sub-01/ses-02/sub-01_ses-02_scans.tsv ================================================ filename acq_time anat/sub-01_ses-02_T1w.nii.gz 1802-06-04T22:54:25 func/sub-01_ses-02_task-nback_run-01_bold.nii.gz 1802-06-04T22:59:25 func/sub-01_ses-02_task-nback_run-02_bold.nii.gz 1802-06-04T23:14:25 func/sub-01_ses-02_task-rest_bold.nii.gz 1802-06-04T23:29:25 ================================================ FILE: tests/data/synthetic/sub-01/sub-01_sessions.tsv ================================================ session_id systolic_blood_pressure ses-01 112 ses-02 113 ================================================ FILE: tests/data/synthetic/sub-02/ses-01/dwi/sub-02_ses-01_dwi.bval 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anat/sub-02_ses-02_T1w.nii.gz 1814-09-10T07:05:33 func/sub-02_ses-02_task-nback_run-01_bold.nii.gz 1814-09-10T07:10:33 func/sub-02_ses-02_task-nback_run-02_bold.nii.gz 1814-09-10T07:25:33 func/sub-02_ses-02_task-rest_bold.nii.gz 1814-09-10T07:40:33 ================================================ FILE: tests/data/synthetic/sub-02/sub-02_sessions.tsv ================================================ session_id systolic_blood_pressure ses-01 114 ses-02 115 ================================================ FILE: tests/data/synthetic/sub-03/ses-01/dwi/sub-03_ses-01_dwi.bval ================================================ 0 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 ================================================ FILE: 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tests/data/synthetic/sub-03/ses-01/sub-03_ses-01_scans.tsv ================================================ filename acq_time anat/sub-03_ses-01_T1w.nii.gz 1852-10-11T23:35:34 func/sub-03_ses-01_task-nback_run-01_bold.nii.gz 1852-10-11T23:40:34 func/sub-03_ses-01_task-nback_run-02_bold.nii.gz 1852-10-11T23:55:34 func/sub-03_ses-01_task-rest_bold.nii.gz 1852-10-12T00:10:34 ================================================ FILE: tests/data/synthetic/sub-03/ses-02/sub-03_ses-02_scans.tsv ================================================ filename acq_time anat/sub-03_ses-02_T1w.nii.gz 1843-04-22T00:23:54 func/sub-03_ses-02_task-nback_run-01_bold.nii.gz 1843-04-22T00:28:54 func/sub-03_ses-02_task-nback_run-02_bold.nii.gz 1843-04-22T00:43:54 func/sub-03_ses-02_task-rest_bold.nii.gz 1843-04-22T00:58:54 ================================================ FILE: tests/data/synthetic/sub-03/sub-03_sessions.tsv ================================================ session_id systolic_blood_pressure ses-01 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tests/data/synthetic/sub-04/ses-02/sub-04_ses-02_scans.tsv ================================================ filename acq_time anat/sub-04_ses-02_T1w.nii.gz 1847-10-17T10:15:19 func/sub-04_ses-02_task-nback_run-01_bold.nii.gz 1847-10-17T10:20:19 func/sub-04_ses-02_task-nback_run-02_bold.nii.gz 1847-10-17T10:35:19 func/sub-04_ses-02_task-rest_bold.nii.gz 1847-10-17T10:50:19 ================================================ FILE: tests/data/synthetic/sub-04/sub-04_sessions.tsv ================================================ session_id systolic_blood_pressure ses-01 111 ses-02 115 ================================================ FILE: tests/data/synthetic/sub-05/ses-01/dwi/sub-05_ses-01_dwi.bval ================================================ 0 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 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-0.22974103689193 0.72825574874877 -0.93396604061126 0.94253724813461 -0.90788620710372 -0.02007177658379 -0.24307298660278 0.0833735987544 -0.20253752171993 -0.61145150661468 -0.73070788383483 0.426168769598 -0.54575622081756 -0.61998349428176 -0.6832326054573 -0.42672303318977 -0.87086367607116 -0.13531495630741 0.38323882222175 -0.62883508205413 0.81788432598114 0.0798152834177 0.47679767012596 -0.60795408487319 0.90816700458526 -0.82300633192062 -0.29062712192535 -0.62207180261611 0.24018363654613 0.96991771459579 -0.27108195424079 0.81522691249847 0.90304642915725 -0.40197896957397 -0.05868147313594 ================================================ FILE: tests/data/synthetic/sub-05/ses-01/fmap/sub-05_ses-01_phasediff.json ================================================ { "FlipAngle": 60.000000, "RepetitionTime": 0.400000, "EchoTime1": 519.000000, "EchoTime2": 0.007650, "IntendedFor": [ "ses-01/func/sub-05_ses-01_task-nback_run-01_bold.nii.gz", "ses-01/func/sub-05_ses-01_task-nback_run-02_bold.nii.gz" ], "PhaseEncodingDirection": "j-" } ================================================ FILE: tests/data/synthetic/sub-05/ses-01/sub-05_ses-01_scans.tsv ================================================ filename acq_time anat/sub-05_ses-01_T1w.nii.gz 1899-07-07T16:04:21 func/sub-05_ses-01_task-nback_run-01_bold.nii.gz 1899-07-07T16:09:21 func/sub-05_ses-01_task-nback_run-02_bold.nii.gz 1899-07-07T16:24:21 func/sub-05_ses-01_task-rest_bold.nii.gz 1899-07-07T16:39:21 ================================================ FILE: tests/data/synthetic/sub-05/ses-02/sub-05_ses-02_scans.tsv ================================================ filename acq_time anat/sub-05_ses-02_T1w.nii.gz 1868-01-31T20:57:43 func/sub-05_ses-02_task-nback_run-01_bold.nii.gz 1868-01-31T21:02:43 func/sub-05_ses-02_task-nback_run-02_bold.nii.gz 1868-01-31T21:17:43 func/sub-05_ses-02_task-rest_bold.nii.gz 1868-01-31T21:32:43 ================================================ FILE: tests/data/synthetic/sub-05/sub-05_sessions.tsv ================================================ session_id systolic_blood_pressure ses-01 114 ses-02 110 ================================================ FILE: tests/data/synthetic/task-nback_bold.json ================================================ { "TaskName": "N-Back", "RepetitionTime": 2.5, "EchoTime": 0.03 } ================================================ FILE: tests/data/synthetic/task-nback_events.tsv ================================================ onset duration trial_type weight 2.016 1 scene_nontarget_nbackparam -0.5 4.0169999999999995 1 scene_target_nbackparam -0.5 6.018 1 scene_target_nbackparam -0.5 8.019 1 scene_nontarget_nbackparam -0.5 10.02 1 scene_dist_rtparam 0.348 12.02 1 scene_nontarget_nbackparam -0.5 14.005 1 scene_nontarget_nbackparam -0.5 16.005 1 scene_dist_rtparam -0.122 18.006 1 scene_nontarget_nbackparam -0.5 20.007 1 scene_dist_rtparam 0.10400000000000001 24.025 1 faces_nontarget_rtparam 0.026000000000000002 26.01 1 faces_nontarget_rtparam -0.09699999999999999 28.01 1 faces_nontarget_rtparam -0.024 30.011 1 faces_nontarget_rtparam 0.049 32.012 1 faces_nontarget_rtparam 0.019 34.013000000000005 1 faces_dist_nbackparam -0.455 36.014 1 faces_target_rtparam -0.07400000000000001 38.014 1 faces_nontarget_rtparam -0.062 40.015 1 faces_nontarget_rtparam 0.01 42.016000000000005 1 faces_dist_nbackparam -0.455 68.01 1 scene_nontarget_nbackparam 0.5 70.01100000000001 1 scene_nontarget_nbackparam 0.5 72.012 1 scene_nontarget_nbackparam 0.5 74.013 1 scene_nontarget_nbackparam 0.5 76.013 1 scene_dist_rtparam 0.136 78.014 1 scene_target_nbackparam 0.5 80.015 1 scene_nontarget_nbackparam 0.5 82.016 1 scene_nontarget_nbackparam 0.5 84.01700000000001 1 scene_nontarget_nbackparam 0.5 86.01799999999999 1 scene_target_nbackparam 0.5 90.01899999999999 1 faces_nontarget_rtparam -0.175 92.02 1 faces_nontarget_rtparam -0.195 94.021 1 faces_dist_nbackparam 0.545 96.022 1 faces_dist_nbackparam 0.545 98.022 1 faces_target_rtparam -0.068 100.023 1 faces_nontarget_rtparam 0.018000000000000002 102.024 1 faces_nontarget_rtparam -0.019 104.025 1 faces_target_rtparam 0.156 106.009 1 faces_nontarget_rtparam -0.035 108.01 1 faces_target_rtparam -0.057 154.012 1 scene_nontarget_nbackparam -0.5 156.013 1 scene_nontarget_nbackparam -0.5 ================================================ FILE: tests/data/synthetic/task-nback_physio.json ================================================ { "SamplingFrequency": 10.0, "StartTime": 22.8, "Columns": ["respiratory", "cardiac"] } ================================================ FILE: tests/data/synthetic/task-nback_stim.json ================================================ { "SamplingFrequency": 2.0, "StartTime": 0.0, "Columns": ["stimA", "stimB"] } ================================================ FILE: tests/data/synthetic/task-rest_bold.json ================================================ { "TaskName": "Rest", "RepetitionTime": 2.5, "EchoTime": 0.03 } ================================================ FILE: tests/data/synthetic/task-rest_physio.json ================================================ { "SamplingFrequency": 10.0, "StartTime": 0.0, "Columns": ["respiratory", "cardiac"] } ================================================ FILE: tools/check_noqa_budget.py ================================================ #!/usr/bin/env python3 """Fail if '# noqa' directives increase compared to a git ref.""" from __future__ import annotations import argparse import re import subprocess import sys from pathlib import Path NOQA_RE = re.compile(r"#\s*(?:ruff:\s*)?noqa\b", re.IGNORECASE) def git(*args: str) -> str: return subprocess.check_output(["git", *args], text=True).strip() def tracked_python_files(ref: str) -> list[str]: output = git("ls-tree", "-r", "--name-only", ref) return [line for line in output.splitlines() if line.endswith(".py")] def file_content_at_ref(ref: str, path: str) -> str: return git("show", f"{ref}:{path}") def count_noqa_in_text(text: str) -> int: return sum(1 for line in text.splitlines() if NOQA_RE.search(line)) def count_noqa_at_ref(ref: str) -> int: total = 0 for path in tracked_python_files(ref): try: text = file_content_at_ref(ref, path) except subprocess.CalledProcessError: # File may not exist in one side of comparison. continue total += count_noqa_in_text(text) return total def main() -> int: parser = argparse.ArgumentParser() parser.add_argument( "--base-ref", required=True, help="Git ref to compare against (e.g., origin/main).", ) args = parser.parse_args() try: base_count = count_noqa_at_ref(args.base_ref) head_count = count_noqa_at_ref("HEAD") except subprocess.CalledProcessError as exc: print(f"Failed to run git command: {exc}", file=sys.stderr) return 2 print(f"Base ({args.base_ref}) # noqa count: {base_count}") print(f"Head (HEAD) # noqa count: {head_count}") if head_count > base_count: print( f"Error: '# noqa' count increased by {head_count - base_count}. " "Please remove added suppressions or justify and adjust policy.", file=sys.stderr, ) return 1 print("OK: '# noqa' count did not increase.") return 0 if __name__ == "__main__": raise SystemExit(main()) ================================================ FILE: tools/prep_zenodo.py ================================================ #!/usr/bin/env python3 # /// script # requires-python = ">=3.9" # dependencies = [ # "gitpython", # ] # /// import git import json from subprocess import run, PIPE, CalledProcessError from pathlib import Path from tempfile import TemporaryDirectory def decommify(name): return ' '.join(name.split(', ')[::-1]) # Users who have asked not to be cited at this time # XXX We should add a shortlog since the most recent tag and explicitly note # that a blacklisted user has contributed again recently, and verify they still # do not want to be cited. blacklist = { 'Cecile Madjar', 'Matthew Wardrop', 'github-actions[bot]', 'dependabot[bot]', } # List of repositories whose commits should be counted as contributions codependents = [('https://github.com/grabbles/grabbit.git', '0.2.6')] # Last shablona commit origin_commit = 'd72caaf5933907ed699d57faddaec7bfc836ce6f' git_root = Path(git.Repo('.', search_parent_directories=True).working_dir) zenodo_file = git_root / '.zenodo.json' zenodo = json.loads(zenodo_file.read_text()) if zenodo_file.exists() else {} orig_creators = zenodo.get('creators', []) creator_map = { decommify(creator['name']): creator for creator in orig_creators } shortlog = run(['git', 'shortlog', '-ns', f'{origin_commit}..'], stdout=PIPE) counts = [ line.split('\t', 1)[::-1] for line in shortlog.stdout.decode().split('\n') if line ] # Get additional commit counts from dependencies with TemporaryDirectory() as tmpdir: tmppath = Path(tmpdir) for repo in codependents: try: repo, ref = repo except (TypeError, ValueError): ref = None repo_dir = str(tmppath / repo.rsplit('/', 1)[1].split('.', 1)[0]) try: clone = run(['git', 'clone', '-q', repo, repo_dir], check=True) except CalledProcessError as err: raise RuntimeError(f'Could not clone {repo}') from err if ref is None: tag = run(['git', '-C', repo_dir, 'tag'], stdout=PIPE) # latest tag ref = tag.stdout.decode().strip().rsplit('\n', 1)[1] dep_shortlog = run( ['git', '-C', repo_dir, 'shortlog', '-ns', ref], stdout=PIPE ) counts.extend( line.split('\t', 1)[::-1] for line in dep_shortlog.stdout.decode().split('\n') if line ) commit_counts = {} for committer, commits in counts: commit_counts[committer] = commit_counts.get(committer, 0) + int(commits) # Stable sort: # Number of commits in reverse order # Ties broken by alphabetical order of first name committers = [ committer for committer, _ in sorted( commit_counts.items(), key=lambda x: (-x[1], x[0]) ) ] # Tal to the top first_author = 'Tal Yarkoni' if committers[0] != first_author: committers.remove(first_author) committers.insert(0, first_author) creators = [ creator_map.get(committer, {'name': committer}) for committer in committers if committer not in blacklist ] zenodo['creators'] = creators zenodo_file.write_text(json.dumps(zenodo, indent=2, ensure_ascii=False) + '\n') ================================================ FILE: tools/update_changes.sh ================================================ #!/bin/bash # # Collects the pull-requests since the latest release and # arranges them in the CHANGELOG.rst file. # # This is a script to be run before releasing a new version. # # Usage /bin/bash update_changes.sh 1.0.1 # # Adapted from https://github.com/nipy/nipype/blob/98beb0a/tools/update_changes.sh # Setting # $ help set set -u # Treat unset variables as an error when substituting. set -x # Print command traces before executing command. ROOT=$( git rev-parse --show-toplevel ) CHANGES=$ROOT/CHANGELOG.rst # Elaborate today's release header HEADER="Version $1 ($(date '+%B %d, %Y'))" UNDERLINE=$( printf "%${#HEADER}s" | tr " " "-" ) cat < newchanges Changelog ========= $HEADER $UNDERLINE END # Search for PRs since previous release MERGE_COMMITS=$( git log --grep="Merge pull request\|(#.*)$" `git describe --tags --abbrev=0`..HEAD --pretty='format:%h' ) for COMMIT in ${MERGE_COMMITS//\n}; do SUB=$( git log -n 1 --pretty="format:%s" $COMMIT ) if ( echo $SUB | grep "^Merge pull request" ); then # Merge commit PR=$( echo $SUB | sed -e "s/Merge pull request \#\([0-9]*\).*/\1/" ) TITLE=$( git log -n 1 --pretty="format:%b" $COMMIT ) else # Squashed merge PR=$( echo $SUB | sed -e "s/.*(\#\([0-9]*\))$/\1/" ) TITLE=$( echo $SUB | sed -e "s/\(.*\)(\#[0-9]*)$/\1/" ) fi echo "* $TITLE (#$PR)" >> newchanges done # Append old changes tail -n+3 $CHANGES >> newchanges # Replace old CHANGES with new file mv newchanges $CHANGES ================================================ FILE: tools/update_requirements.py ================================================ #!/usr/bin/env python3 import sys from pathlib import Path try: import tomllib except ImportError: import tomli as tomllib if sys.version_info < (3, 6): print('This script requires Python 3.6 to work correctly') sys.exit(1) repo_root = Path(__file__).parent.parent pyproject_toml = repo_root / 'pyproject.toml' reqs = repo_root / 'requirements.txt' min_reqs = repo_root / 'min-requirements.txt' doc_reqs = repo_root / 'doc-requirements.txt' with open(pyproject_toml, 'rb') as fobj: config = tomllib.load(fobj) requirements = config['project']['dependencies'] # doc_requirements = config['project']['optional-dependencies']['doc'] script_name = Path(__file__).relative_to(repo_root) lines = [f'# Auto-generated by {script_name}', ''] # Write requirements # lines[1:-1] = requirements # reqs.write_text('\n'.join(lines)) # Write minimum requirements lines[1:-1] = [req.replace('>=', '==').replace('~=', '==') for req in requirements] min_reqs.write_text('\n'.join(lines)) # Write documentation requirements # lines[1:-1] = ['-r requirements.txt'] + doc_requirements # doc_reqs.write_text('\n'.join(lines)) ================================================ FILE: tox.ini ================================================ [tox] requires = tox>=4 tox-uv envlist = py31{0,1,2,3,4}-{full,pre} py310-min skip_missing_interpreters = true # Configuration that allows us to split tests across GitHub runners effectively [gh-actions] python = 3.10: py310 3.11: py311 3.12: py312 3.13: py313 3.14: py314 [gh-actions:env] DEPENDS = min: min full: full pre: pre [testenv] description = Pytest with coverage labels = test pip_pre = pre: true pass_env = # getpass.getuser() sources for Windows: LOGNAME USER LNAME USERNAME # Pass user color preferences through PY_COLORS FORCE_COLOR NO_COLOR CLICOLOR CLICOLOR_FORCE setenv = pre: PIP_EXTRA_INDEX_URL=https://pypi.anaconda.org/scientific-python-nightly-wheels/simple pre: UV_INDEX=https://pypi.anaconda.org/scientific-python-nightly-wheels/simple pre: UV_INDEX_STRATEGY=unsafe-best-match extras = test runner = full: uv-venv-lock-runner !full: uv-venv-runner uv_resolution = min: lowest-direct commands = pytest --doctest-modules -v \ --cov src -cov tests --cov-report xml --cov-report term \ src/ tests/ {posargs:-n auto} [testenv:docs] description = Build documentation site labels = docs allowlist_externals = make extras = doc commands = make -C doc html [testenv:spellcheck] description = Check spelling labels = check deps = codespell[toml] skip_install = true commands = codespell . {posargs} [testenv:build{,-strict}] labels = check pre-release deps = build twine skip_install = true set_env = build-strict: PYTHONWARNINGS=error commands = python -m build python -m twine check dist/* [testenv:publish] depends = build labels = release deps = twine skip_install = true commands = python -m twine upload dist/* [testenv:noqa-budget] description = Fail if # noqa count increases vs base branch labels = check skip_install = true allowlist_externals = git pass_env = GITHUB_BASE_REF CI commands = python tools/check_noqa_budget.py --base-ref {env:NOQA_BASE_REF:origin/{env:GITHUB_BASE_REF:main}}